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+

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+ + + + + + + + + + + + + + + diff --git a/LightGBMLSS.png b/LightGBMLSS.png new file mode 100644 index 0000000..f52ad6d Binary files /dev/null and b/LightGBMLSS.png differ diff --git a/api/api.md b/api/api.md new file mode 100644 index 0000000..9873dfd --- /dev/null +++ b/api/api.md @@ -0,0 +1,3 @@ +# API references + +::: lightgbmlss \ No newline at end of file diff --git a/api/index.html b/api/index.html new file mode 100644 index 0000000..9e0848f --- /dev/null +++ b/api/index.html @@ -0,0 +1,15707 @@ + + + + + + + + + + + API Docs - LightGBMLSS + + + + + + + + + + +
+
+ LightGBMLSS + + + + + +
+
+ +
+
+
+
+ +

API references

+ + +
+ + + + +
+ +

LightGBMLSS - An extension of LightGBM to probabilistic forecasting

+ + + +
+ + + + + + + + + + +
+ + + + +

+ datasets + + +

+ +
+ +

LightGBMLSS - An extension of LightGBM to probabilistic forecasting

+ + + +
+ + + + + + + + + + +
+ + + + +

+ data_loader + + +

+ +
+ + + +
+ + + + + + + + + + +
+ + + + +

+ load_simulated_gaussian_data() + +

+ + +
+ +

Returns train/test dataframe of a simulated example.

+ +
+ Contains the following columns +

y int64: response +x int64: x-feature +X1:X10 int64: random noise features

+
+
+ Source code in lightgbmlss/datasets/data_loader.py + 
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def load_simulated_gaussian_data():
+    """
+    Returns train/test dataframe of a simulated example.
+
+    Contains the following columns:
+        y              int64: response
+        x              int64: x-feature
+        X1:X10         int64: random noise features
+
+    """
+    train_path = pkg_resources.resource_stream(__name__, "gaussian_train_sim.csv")
+    train_df = pd.read_csv(train_path)
+
+    test_path = pkg_resources.resource_stream(__name__, "gaussian_test_sim.csv")
+    test_df = pd.read_csv(test_path)
+
+    return train_df, test_df
+
+
+
+ +
+ + +
+ + + + +

+ load_simulated_studentT_data() + +

+ + +
+ +

Returns train/test dataframe of a simulated example.

+ +
+ Contains the following columns +

y int64: response +x int64: x-feature +X1:X10 int64: random noise features

+
+
+ Source code in lightgbmlss/datasets/data_loader.py + 
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def load_simulated_studentT_data():
+    """
+    Returns train/test dataframe of a simulated example.
+
+    Contains the following columns:
+        y              int64: response
+        x              int64: x-feature
+        X1:X10         int64: random noise features
+
+    """
+    train_path = pkg_resources.resource_stream(__name__, "studentT_train_sim.csv")
+    train_df = pd.read_csv(train_path)
+
+    test_path = pkg_resources.resource_stream(__name__, "studentT_test_sim.csv")
+    test_df = pd.read_csv(test_path)
+
+    return train_df, test_df
+
+
+
+ +
+ + + +
+ +
+ +
+ + +
+ +
+ +
+ +
+ + + + +

+ distributions + + +

+ +
+ +

LightGBMLSS - An extension of LightGBM to probabilistic forecasting

+ + + +
+ + + + + + + + + + +
+ + + + +

+ Beta + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ Beta + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Beta distribution class.

+
Distributional Parameters
+

concentration1: torch.Tensor + 1st concentration parameter of the distribution (often referred to as alpha). +concentration0: torch.Tensor + 2nd concentration parameter of the distribution (often referred to as beta).

+
Source
+

https://pytorch.org/docs/stable/distributions.html#beta

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential) or "softplus" (softplus). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score). + Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. + Hence, using the CRPS disregards any variation in the curvature of the loss function.

+ +
+ Source code in lightgbmlss/distributions/Beta.py + 
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class Beta(DistributionClass):
+    """
+    Beta distribution class.
+
+    Distributional Parameters
+    -------------------------
+    concentration1: torch.Tensor
+        1st concentration parameter of the distribution (often referred to as alpha).
+    concentration0: torch.Tensor
+        2nd concentration parameter of the distribution (often referred to as beta).
+
+    Source
+    -------------------------
+    https://pytorch.org/docs/stable/distributions.html#beta
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential) or "softplus" (softplus).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score).
+        Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable.
+        Hence, using the CRPS disregards any variation in the curvature of the loss function.
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn: str = "exp",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll", "crps"]:
+            raise ValueError("Invalid loss function. Please choose from 'nll' or 'crps'.")
+
+        # Specify Response Functions
+        response_functions = {"exp": exp_fn, "softplus": softplus_fn}
+        if response_fn in response_functions:
+            response_fn = response_functions[response_fn]
+        else:
+            raise ValueError(
+                "Invalid response function. Please choose from 'exp' or 'softplus'.")
+
+        # Set the parameters specific to the distribution
+        distribution = Beta_Torch
+        param_dict = {"concentration1": response_fn, "concentration0": response_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=False,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ Cauchy + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ Cauchy + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Cauchy distribution class.

+
Distributional Parameters
+

loc: torch.Tensor + Mode or median of the distribution. +scale: torch.Tensor + Half width at half maximum.

+
Source
+

https://pytorch.org/docs/stable/distributions.html#cauchy

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential) or "softplus" (softplus). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score). + Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. + Hence, using the CRPS disregards any variation in the curvature of the loss function.

+ +
+ Source code in lightgbmlss/distributions/Cauchy.py + 
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class Cauchy(DistributionClass):
+    """
+    Cauchy distribution class.
+
+    Distributional Parameters
+    -------------------------
+    loc: torch.Tensor
+        Mode or median of the distribution.
+    scale: torch.Tensor
+        Half width at half maximum.
+
+    Source
+    -------------------------
+    https://pytorch.org/docs/stable/distributions.html#cauchy
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential) or "softplus" (softplus).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score).
+        Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable.
+        Hence, using the CRPS disregards any variation in the curvature of the loss function.
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn: str = "exp",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll", "crps"]:
+            raise ValueError("Invalid loss function. Please choose from 'nll' or 'crps'.")
+
+        # Specify Response Functions
+        response_functions = {"exp": exp_fn, "softplus": softplus_fn}
+        if response_fn in response_functions:
+            response_fn = response_functions[response_fn]
+        else:
+            raise ValueError(
+                "Invalid response function. Please choose from 'exp' or 'softplus'.")
+
+        # Set the parameters specific to the distribution
+        distribution = Cauchy_Torch
+        param_dict = {"loc": identity_fn, "scale": response_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=False,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ Expectile + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ Expectile + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Expectile distribution class.

+
Distributional Parameters
+

expectile: List + List of specified expectiles.

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +expectiles: List + List of expectiles in increasing order. +penalize_crossing: bool + Whether to include a penalty term to discourage crossing of expectiles.

+ +
+ Source code in lightgbmlss/distributions/Expectile.py + 
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class Expectile(DistributionClass):
+    """
+    Expectile distribution class.
+
+    Distributional Parameters
+    -------------------------
+    expectile: List
+        List of specified expectiles.
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    expectiles: List
+        List of expectiles in increasing order.
+    penalize_crossing: bool
+        Whether to include a penalty term to discourage crossing of expectiles.
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 expectiles: List = [0.1, 0.5, 0.9],
+                 penalize_crossing: bool = False,
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if not isinstance(expectiles, list):
+            raise ValueError("Expectiles must be a list.")
+        if not all([0 < expectile < 1 for expectile in expectiles]):
+            raise ValueError("Expectiles must be between 0 and 1.")
+        if not isinstance(penalize_crossing, bool):
+            raise ValueError("penalize_crossing must be a boolean. Please choose from True or False.")
+
+        # Set the parameters specific to the distribution
+        distribution = Expectile_Torch
+        torch.distributions.Distribution.set_default_validate_args(False)
+        expectiles.sort()
+        param_dict = {}
+        for expectile in expectiles:
+            key = f"expectile_{expectile}"
+            param_dict[key] = identity_fn
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=False,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn="nll",
+                         tau=torch.tensor(expectiles),
+                         penalize_crossing=penalize_crossing
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + + +

+ Expectile_Torch + + +

+ + +
+

+ Bases: Distribution

+ + +

PyTorch implementation of expectiles.

+
Arguments
+

expectiles : List[torch.Tensor] + List of expectiles. +penalize_crossing : bool + Whether to include a penalty term to discourage crossing of expectiles.

+ +
+ Source code in lightgbmlss/distributions/Expectile.py + 
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class Expectile_Torch(Distribution):
+    """
+    PyTorch implementation of expectiles.
+
+    Arguments
+    ---------
+    expectiles : List[torch.Tensor]
+        List of expectiles.
+    penalize_crossing : bool
+        Whether to include a penalty term to discourage crossing of expectiles.
+    """
+    def __init__(self,
+                 expectiles: List[torch.Tensor],
+                 penalize_crossing: bool = False,
+                 ):
+        super(Expectile_Torch).__init__()
+        self.expectiles = expectiles
+        self.penalize_crossing = penalize_crossing
+        self.__class__.__name__ = "Expectile"
+
+    def log_prob(self, value: torch.Tensor, tau: List[torch.Tensor]) -> torch.Tensor:
+        """
+        Returns the log of the probability density function evaluated at `value`.
+
+        Arguments
+        ---------
+        value : torch.Tensor
+            Response for which log probability is to be calculated.
+        tau : List[torch.Tensor]
+            List of asymmetry parameters.
+
+        Returns
+        -------
+        torch.Tensor
+            Log probability of `value`.
+        """
+        value = value.reshape(-1, 1)
+        loss = torch.tensor(0.0, dtype=torch.float32)
+        penalty = torch.tensor(0.0, dtype=torch.float32)
+
+        # Calculate loss
+        predt_expectiles = []
+        for expectile, tau_value in zip(self.expectiles, tau):
+            weight = torch.where(value - expectile >= 0, tau_value, 1 - tau_value)
+            loss += torch.nansum(weight * (value - expectile) ** 2)
+            predt_expectiles.append(expectile.reshape(-1, 1))
+
+        # Penalty term to discourage crossing of expectiles
+        if self.penalize_crossing:
+            predt_expectiles = torch.cat(predt_expectiles, dim=1)
+            penalty = torch.mean(
+                (~torch.all(torch.diff(predt_expectiles, dim=1) > 0, dim=1)).float()
+            )
+
+        loss = (loss * (1 + penalty)) / len(self.expectiles)
+
+        return -loss
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +
+ log_prob(value, tau) + +
+ + +
+ +

Returns the log of the probability density function evaluated at value.

+
Arguments
+

value : torch.Tensor + Response for which log probability is to be calculated. +tau : List[torch.Tensor] + List of asymmetry parameters.

+
Returns
+

torch.Tensor + Log probability of value.

+ +
+ Source code in lightgbmlss/distributions/Expectile.py + 
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def log_prob(self, value: torch.Tensor, tau: List[torch.Tensor]) -> torch.Tensor:
+    """
+    Returns the log of the probability density function evaluated at `value`.
+
+    Arguments
+    ---------
+    value : torch.Tensor
+        Response for which log probability is to be calculated.
+    tau : List[torch.Tensor]
+        List of asymmetry parameters.
+
+    Returns
+    -------
+    torch.Tensor
+        Log probability of `value`.
+    """
+    value = value.reshape(-1, 1)
+    loss = torch.tensor(0.0, dtype=torch.float32)
+    penalty = torch.tensor(0.0, dtype=torch.float32)
+
+    # Calculate loss
+    predt_expectiles = []
+    for expectile, tau_value in zip(self.expectiles, tau):
+        weight = torch.where(value - expectile >= 0, tau_value, 1 - tau_value)
+        loss += torch.nansum(weight * (value - expectile) ** 2)
+        predt_expectiles.append(expectile.reshape(-1, 1))
+
+    # Penalty term to discourage crossing of expectiles
+    if self.penalize_crossing:
+        predt_expectiles = torch.cat(predt_expectiles, dim=1)
+        penalty = torch.mean(
+            (~torch.all(torch.diff(predt_expectiles, dim=1) > 0, dim=1)).float()
+        )
+
+    loss = (loss * (1 + penalty)) / len(self.expectiles)
+
+    return -loss
+
+
+
+ +
+ + + +
+ +
+ +
+ + + +
+ + + + +

+ expectile_norm(tau=0.5, m=0, sd=1) + +

+ + +
+ +

Calculates expectiles from Normal distribution for given tau values. +For more details and distributions see https://rdrr.io/cran/expectreg/man/enorm.html

+

Arguments

+
+

tau : np.ndarray + Vector of expectiles from the respective distribution. +m : np.ndarray + Mean of the Normal distribution. +sd : np.ndarray + Standard deviation of the Normal distribution.

+

Returns

+
+

np.ndarray

+ +
+ Source code in lightgbmlss/distributions/Expectile.py + 
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def expectile_norm(tau: np.ndarray = 0.5,
+                   m: np.ndarray = 0,
+                   sd: np.ndarray = 1):
+    """
+    Calculates expectiles from Normal distribution for given tau values.
+    For more details and distributions see https://rdrr.io/cran/expectreg/man/enorm.html
+
+    Arguments
+    _________
+    tau : np.ndarray
+        Vector of expectiles from the respective distribution.
+    m : np.ndarray
+        Mean of the Normal distribution.
+    sd : np.ndarray
+        Standard deviation of the Normal distribution.
+
+    Returns
+    _______
+    np.ndarray
+    """
+    tau[tau > 1 or tau < 0] = np.nan
+    zz = 0 * tau
+    lower = np.array(-10, dtype="float")
+    lower = np.repeat(lower[np.newaxis, ...], len(tau), axis=0)
+    upper = np.array(10, dtype="float")
+    upper = np.repeat(upper[np.newaxis, ...], len(tau), axis=0)
+    diff = 1
+    index = 0
+    while (diff > 1e-10) and (index < 1000):
+        root = expectile_pnorm(zz) - tau
+        root[np.isnan(root)] = 0
+        lower[root < 0] = zz[root < 0]
+        upper[root > 0] = zz[root > 0]
+        zz = (upper + lower) / 2
+        diff = np.nanmax(np.abs(root))
+        index = index + 1
+    zz[np.isnan(tau)] = np.nan
+
+    return zz * sd + m
+
+
+
+ +
+ + +
+ + + + +

+ expectile_pnorm(tau=0.5, m=0, sd=1) + +

+ + +
+ +

Normal Expectile Distribution Function. +For more details and distributions see https://rdrr.io/cran/expectreg/man/enorm.html

+

Arguments

+
+

tau : np.ndarray + Vector of expectiles from the respective distribution. +m : np.ndarray + Mean of the Normal distribution. +sd : np.ndarray + Standard deviation of the Normal distribution.

+

Returns

+
+

tau : np.ndarray + Expectiles from the Normal distribution.

+ +
+ Source code in lightgbmlss/distributions/Expectile.py + 
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def expectile_pnorm(tau: np.ndarray = 0.5,
+                    m: np.ndarray = 0,
+                    sd: np.ndarray = 1
+                    ):
+    """
+    Normal Expectile Distribution Function.
+    For more details and distributions see https://rdrr.io/cran/expectreg/man/enorm.html
+
+    Arguments
+    _________
+    tau : np.ndarray
+        Vector of expectiles from the respective distribution.
+    m : np.ndarray
+        Mean of the Normal distribution.
+    sd : np.ndarray
+        Standard deviation of the Normal distribution.
+
+    Returns
+    _______
+    tau : np.ndarray
+        Expectiles from the Normal distribution.
+    """
+    z = (tau - m) / sd
+    p = norm.cdf(z, loc=m, scale=sd)
+    d = norm.pdf(z, loc=m, scale=sd)
+    u = -d - z * p
+    tau = u / (2 * u + z)
+
+    return tau
+
+
+
+ +
+ + + +
+ +
+ +
+ +
+ + + + +

+ Gamma + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ Gamma + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Gamma distribution class.

+
Distributional Parameters
+

concentration: torch.Tensor + shape parameter of the distribution (often referred to as alpha) +rate: torch.Tensor + rate = 1 / scale of the distribution (often referred to as beta)

+
Source
+

https://pytorch.org/docs/stable/distributions.html#gamma

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential) or "softplus" (softplus). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score). + Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. + Hence, using the CRPS disregards any variation in the curvature of the loss function.

+ +
+ Source code in lightgbmlss/distributions/Gamma.py + 
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class Gamma(DistributionClass):
+    """
+    Gamma distribution class.
+
+     Distributional Parameters
+    --------------------------
+    concentration: torch.Tensor
+        shape parameter of the distribution (often referred to as alpha)
+    rate: torch.Tensor
+        rate = 1 / scale of the distribution (often referred to as beta)
+
+    Source
+    -------------------------
+    https://pytorch.org/docs/stable/distributions.html#gamma
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential) or "softplus" (softplus).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score).
+        Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable.
+        Hence, using the CRPS disregards any variation in the curvature of the loss function.
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn: str = "exp",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll", "crps"]:
+            raise ValueError("Invalid loss function. Please choose from 'nll' or 'crps'.")
+
+        # Specify Response Functions
+        response_functions = {"exp": exp_fn, "softplus": softplus_fn}
+        if response_fn in response_functions:
+            response_fn = response_functions[response_fn]
+        else:
+            raise ValueError(
+                "Invalid response function. Please choose from 'exp' or 'softplus'.")
+
+        # Set the parameters specific to the distribution
+        distribution = Gamma_Torch
+        param_dict = {"concentration": response_fn, "rate": response_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=False,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ Gaussian + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ Gaussian + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Gaussian distribution class.

+
Distributional Parameters
+

loc: torch.Tensor + Mean of the distribution (often referred to as mu). +scale: torch.Tensor + Standard deviation of the distribution (often referred to as sigma).

+
Source
+

https://pytorch.org/docs/stable/distributions.html#normal

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential) or "softplus" (softplus). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score). + Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. + Hence, using the CRPS disregards any variation in the curvature of the loss function.

+ +
+ Source code in lightgbmlss/distributions/Gaussian.py + 
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class Gaussian(DistributionClass):
+    """
+    Gaussian distribution class.
+
+    Distributional Parameters
+    -------------------------
+    loc: torch.Tensor
+        Mean of the distribution (often referred to as mu).
+    scale: torch.Tensor
+        Standard deviation of the distribution (often referred to as sigma).
+
+    Source
+    -------------------------
+    https://pytorch.org/docs/stable/distributions.html#normal
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential) or "softplus" (softplus).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score).
+        Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable.
+        Hence, using the CRPS disregards any variation in the curvature of the loss function.
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn: str = "exp",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll", "crps"]:
+            raise ValueError("Invalid loss function. Please choose from 'nll' or 'crps'.")
+
+        # Specify Response Functions
+        response_functions = {"exp": exp_fn, "softplus": softplus_fn}
+        if response_fn in response_functions:
+            response_fn = response_functions[response_fn]
+        else:
+            raise ValueError(
+                "Invalid response function. Please choose from 'exp' or 'softplus'.")
+
+        # Set the parameters specific to the distribution
+        distribution = Gaussian_Torch
+        param_dict = {"loc": identity_fn, "scale": response_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=False,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ Gumbel + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ Gumbel + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Gumbel distribution class.

+
Distributional Parameters
+

loc: torch.Tensor + Location parameter of the distribution. +scale: torch.Tensor + Scale parameter of the distribution.

+
Source
+

https://pytorch.org/docs/stable/distributions.html#gumbel

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential) or "softplus" (softplus). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score). + Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. + Hence, using the CRPS disregards any variation in the curvature of the loss function.

+ +
+ Source code in lightgbmlss/distributions/Gumbel.py + 
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class Gumbel(DistributionClass):
+    """
+    Gumbel distribution class.
+
+    Distributional Parameters
+    -------------------------
+    loc: torch.Tensor
+        Location parameter of the distribution.
+    scale: torch.Tensor
+        Scale parameter of the distribution.
+
+    Source
+    -------------------------
+    https://pytorch.org/docs/stable/distributions.html#gumbel
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential) or "softplus" (softplus).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score).
+        Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable.
+        Hence, using the CRPS disregards any variation in the curvature of the loss function.
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn: str = "exp",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll", "crps"]:
+            raise ValueError("Invalid loss function. Please choose from 'nll' or 'crps'.")
+
+        # Specify Response Functions
+        response_functions = {"exp": exp_fn, "softplus": softplus_fn}
+        if response_fn in response_functions:
+            response_fn = response_functions[response_fn]
+        else:
+            raise ValueError(
+                "Invalid response function. Please choose from 'exp' or 'softplus'.")
+
+        # Set the parameters specific to the distribution
+        distribution = Gumbel_Torch
+        param_dict = {"loc": identity_fn, "scale": response_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=False,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ Laplace + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ Laplace + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Laplace distribution class.

+
Distributional Parameters
+

loc: torch.Tensor + Mean of the distribution. +scale: torch.Tensor + Scale of the distribution.

+
Source
+

https://pytorch.org/docs/stable/distributions.html#laplace

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential) or "softplus" (softplus). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score). + Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. + Hence, using the CRPS disregards any variation in the curvature of the loss function.

+ +
+ Source code in lightgbmlss/distributions/Laplace.py + 
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class Laplace(DistributionClass):
+    """
+    Laplace distribution class.
+
+    Distributional Parameters
+    -------------------------
+    loc: torch.Tensor
+        Mean of the distribution.
+    scale: torch.Tensor
+        Scale of the distribution.
+
+    Source
+    -------------------------
+    https://pytorch.org/docs/stable/distributions.html#laplace
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential) or "softplus" (softplus).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score).
+        Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable.
+        Hence, using the CRPS disregards any variation in the curvature of the loss function.
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn: str = "exp",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll", "crps"]:
+            raise ValueError("Invalid loss function. Please choose from 'nll' or 'crps'.")
+
+        # Specify Response Functions
+        response_functions = {"exp": exp_fn, "softplus": softplus_fn}
+        if response_fn in response_functions:
+            response_fn = response_functions[response_fn]
+        else:
+            raise ValueError(
+                "Invalid response function. Please choose from 'exp' or 'softplus'.")
+
+        # Set the parameters specific to the distribution
+        distribution = Laplace_Torch
+        param_dict = {"loc": identity_fn, "scale": response_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=False,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ LogNormal + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ LogNormal + + +

+ + +
+

+ Bases: DistributionClass

+ + +

LogNormal distribution class.

+
Distributional Parameters
+

loc: torch.Tensor + Mean of log of distribution. +scale: torch.Tensor + Standard deviation of log of the distribution.

+
Source
+

https://pytorch.org/docs/stable/distributions.html#lognormal

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential) or "softplus" (softplus). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score). + Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. + Hence, using the CRPS disregards any variation in the curvature of the loss function.

+ +
+ Source code in lightgbmlss/distributions/LogNormal.py + 
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class LogNormal(DistributionClass):
+    """
+    LogNormal distribution class.
+
+    Distributional Parameters
+    -------------------------
+    loc: torch.Tensor
+        Mean of log of distribution.
+    scale: torch.Tensor
+        Standard deviation of log of the distribution.
+
+    Source
+    -------------------------
+    https://pytorch.org/docs/stable/distributions.html#lognormal
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential) or "softplus" (softplus).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score).
+        Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable.
+        Hence, using the CRPS disregards any variation in the curvature of the loss function.
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn: str = "exp",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll", "crps"]:
+            raise ValueError("Invalid loss function. Please choose from 'nll' or 'crps'.")
+
+        # Specify Response Functions
+        response_functions = {"exp": exp_fn, "softplus": softplus_fn}
+        if response_fn in response_functions:
+            response_fn = response_functions[response_fn]
+        else:
+            raise ValueError(
+                "Invalid response function. Please choose from 'exp' or 'softplus'.")
+
+        # Set the parameters specific to the distribution
+        distribution = LogNormal_Torch
+        param_dict = {"loc": identity_fn, "scale": response_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=False,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ NegativeBinomial + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ NegativeBinomial + + +

+ + +
+

+ Bases: DistributionClass

+ + +

NegativeBinomial distribution class.

+
Distributional Parameters
+

total_count: torch.Tensor + Non-negative number of negative Bernoulli trials to stop. +probs: torch.Tensor + Event probabilities of success in the half open interval [0, 1). +logits: torch.Tensor + Event log-odds for probabilities of success.

+
Source
+

https://pytorch.org/docs/stable/distributions.html#negativebinomial

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn_total_count: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential), "softplus" (softplus) or "relu" (rectified linear unit). +response_fn_probs: str + Response function for transforming the distributional parameters to the correct support. Options are + "sigmoid" (sigmoid). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood).

+ +
+ Source code in lightgbmlss/distributions/NegativeBinomial.py + 
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class NegativeBinomial(DistributionClass):
+    """
+    NegativeBinomial distribution class.
+
+    Distributional Parameters
+    -------------------------
+    total_count: torch.Tensor
+        Non-negative number of negative Bernoulli trials to stop.
+    probs: torch.Tensor
+        Event probabilities of success in the half open interval [0, 1).
+    logits: torch.Tensor
+        Event log-odds for probabilities of success.
+
+    Source
+    -------------------------
+    https://pytorch.org/docs/stable/distributions.html#negativebinomial
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn_total_count: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential), "softplus" (softplus) or "relu" (rectified linear unit).
+    response_fn_probs: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "sigmoid" (sigmoid).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood).
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn_total_count: str = "relu",
+                 response_fn_probs: str = "sigmoid",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll"]:
+            raise ValueError("Invalid loss function. Please select 'nll'.")
+
+        #  Specify Response Functions for total_count
+        response_functions_total_count = {"exp": exp_fn, "softplus": softplus_fn, "relu": relu_fn}
+        if response_fn_total_count in response_functions_total_count:
+            response_fn_total_count = response_functions_total_count[response_fn_total_count]
+        else:
+            raise ValueError(
+                "Invalid response function for total_count. Please choose from 'exp', 'softplus' or 'relu'.")
+
+        #  Specify Response Functions for probs
+        response_functions_probs = {"sigmoid": sigmoid_fn}
+        if response_fn_probs in response_functions_probs:
+            response_fn_probs = response_functions_probs[response_fn_probs]
+        else:
+            raise ValueError(
+                "Invalid response function for probs. Please select 'sigmoid'.")
+
+        # Set the parameters specific to the distribution
+        distribution = NegativeBinomial_Torch
+        param_dict = {"total_count": response_fn_total_count, "probs": response_fn_probs}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=True,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ Poisson + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ Poisson + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Poisson distribution class.

+
Distributional Parameters
+

rate: torch.Tensor + Rate parameter of the distribution (often referred to as lambda).

+
Source
+

https://pytorch.org/docs/stable/distributions.html#poisson

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential), "softplus" (softplus) or "relu" (rectified linear unit). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood).

+ +
+ Source code in lightgbmlss/distributions/Poisson.py + 
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class Poisson(DistributionClass):
+    """
+    Poisson distribution class.
+
+    Distributional Parameters
+    -------------------------
+    rate: torch.Tensor
+        Rate parameter of the distribution (often referred to as lambda).
+
+    Source
+    -------------------------
+    https://pytorch.org/docs/stable/distributions.html#poisson
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential), "softplus" (softplus) or "relu" (rectified linear unit).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood).
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn: str = "relu",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll"]:
+            raise ValueError("Invalid loss function. Please select 'nll'.")
+
+        # Specify Response Functions
+        response_functions = {"exp": exp_fn, "softplus": softplus_fn, "relu": relu_fn}
+        if response_fn in response_functions:
+            response_fn = response_functions[response_fn]
+        else:
+            raise ValueError(
+                "Invalid response function for total_count. Please choose from 'exp', 'softplus' or 'relu'.")
+
+        # Set the parameters specific to the distribution
+        distribution = Poisson_Torch
+        param_dict = {"rate": response_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=True,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ SplineFlow + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ SplineFlow + + +

+ + +
+

+ Bases: NormalizingFlowClass

+ + +

Spline Flow class.

+

The spline flow is a normalizing flow based on element-wise rational spline bijections of linear and quadratic +order (Durkan et al., 2019; Dolatabadi et al., 2020). Rational splines are functions that are comprised of segments +that are the ratio of two polynomials. Rational splines offer an excellent combination of functional flexibility +whilst maintaining a numerically stable inverse.

+

For more details, see: +- Durkan, C., Bekasov, A., Murray, I. and Papamakarios, G. Neural Spline Flows. NeurIPS 2019. +- Dolatabadi, H. M., Erfani, S. and Leckie, C., Invertible Generative Modeling using Linear Rational Splines. AISTATS 2020.

+
Source
+

https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.transforms.Spline

+
Arguments
+

target_support: str + The target support. Options are + - "real": [-inf, inf] + - "positive": [0, inf] + - "positive_integer": [0, 1, 2, 3, ...] + - "unit_interval": [0, 1] +count_bins: int + The number of segments comprising the spline. +bound: float + The quantity "K" determining the bounding box, [-K,K] x [-K,K] of the spline. By adjusting the + "K" value, you can control the size of the bounding box and consequently control the range of inputs that + the spline transform operates on. Larger values of "K" will result in a wider valid range for the spline + transformation, while smaller values will restrict the valid range to a smaller region. Should be chosen + based on the range of the data. +order: str + The order of the spline. Options are "linear" or "quadratic". +stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD" or "L2". +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score). + Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. + Hence, using the CRPS disregards any variation in the curvature of the loss function.

+ +
+ Source code in lightgbmlss/distributions/SplineFlow.py + 
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class SplineFlow(NormalizingFlowClass):
+    """
+    Spline Flow class.
+
+    The spline flow is a normalizing flow based on element-wise rational spline bijections of linear and quadratic
+    order (Durkan et al., 2019; Dolatabadi et al., 2020). Rational splines are functions that are comprised of segments
+    that are the ratio of two polynomials. Rational splines offer an excellent combination of functional flexibility
+    whilst maintaining a numerically stable inverse.
+
+    For more details, see:
+    - Durkan, C., Bekasov, A., Murray, I. and Papamakarios, G. Neural Spline Flows. NeurIPS 2019.
+    - Dolatabadi, H. M., Erfani, S. and Leckie, C., Invertible Generative Modeling using Linear Rational Splines. AISTATS 2020.
+
+
+    Source
+    ---------
+    https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.transforms.Spline
+
+
+    Arguments
+    ---------
+    target_support: str
+        The target support. Options are
+            - "real": [-inf, inf]
+            - "positive": [0, inf]
+            - "positive_integer": [0, 1, 2, 3, ...]
+            - "unit_interval": [0, 1]
+    count_bins: int
+        The number of segments comprising the spline.
+    bound: float
+        The quantity "K" determining the bounding box, [-K,K] x [-K,K] of the spline. By adjusting the
+        "K" value, you can control the size of the bounding box and consequently control the range of inputs that
+        the spline transform operates on. Larger values of "K" will result in a wider valid range for the spline
+        transformation, while smaller values will restrict the valid range to a smaller region. Should be chosen
+        based on the range of the data.
+    order: str
+        The order of the spline. Options are "linear" or "quadratic".
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD" or "L2".
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score).
+        Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable.
+        Hence, using the CRPS disregards any variation in the curvature of the loss function.
+    """
+    def __init__(self,
+                 target_support: str = "real",
+                 count_bins: int = 8,
+                 bound: float = 3.0,
+                 order: str = "linear",
+                 stabilization: str = "None",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Specify Target Transform
+        if not isinstance(target_support, str):
+            raise ValueError("target_support must be a string.")
+
+        transforms = {
+            "real": (identity_transform, False),
+            "positive": (SoftplusTransform(), False),
+            "positive_integer": (SoftplusTransform(), True),
+            "unit_interval": (SigmoidTransform(), False)
+        }
+
+        if target_support in transforms:
+            target_transform, discrete = transforms[target_support]
+        else:
+            raise ValueError(
+                "Invalid target_support. Options are 'real', 'positive', 'positive_integer', or 'unit_interval'.")
+
+        # Check if count_bins is valid
+        if not isinstance(count_bins, int):
+            raise ValueError("count_bins must be an integer.")
+        if count_bins <= 0:
+            raise ValueError("count_bins must be a positive integer > 0.")
+
+        # Check if bound is float
+        if not isinstance(bound, float):
+            raise ValueError("bound must be a float.")
+
+        # Number of parameters
+        if not isinstance(order, str):
+            raise ValueError("order must be a string.")
+
+        order_params = {
+            "quadratic": 2 * count_bins + (count_bins - 1),
+            "linear": 3 * count_bins + (count_bins - 1)
+        }
+
+        if order in order_params:
+            n_params = order_params[order]
+        else:
+            raise ValueError("Invalid order specification. Options are 'linear' or 'quadratic'.")
+
+        # Check if stabilization method is valid.
+        if not isinstance(stabilization, str):
+            raise ValueError("stabilization must be a string.")
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Options are 'None', 'MAD' or 'L2'.")
+
+        # Check if loss function is valid.
+        if not isinstance(loss_fn, str):
+            raise ValueError("loss_fn must be a string.")
+        if loss_fn not in ["nll", "crps"]:
+            raise ValueError("Invalid loss_fn. Options are 'nll' or 'crps'.")
+
+        # Specify parameter dictionary
+        param_dict = {f"param_{i + 1}": identity_fn for i in range(n_params)}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Normalizing Flow Class
+        super().__init__(base_dist=Normal,                     # Base distribution, currently only Normal is supported.
+                         flow_transform=Spline,
+                         count_bins=count_bins,
+                         bound=bound,
+                         order=order,
+                         n_dist_param=n_params,
+                         param_dict=param_dict,
+                         target_transform=target_transform,
+                         discrete=discrete,
+                         univariate=True,
+                         stabilization=stabilization,
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ StudentT + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ StudentT + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Student-T Distribution Class

+
Distributional Parameters
+

df: torch.Tensor + Degrees of freedom. +loc: torch.Tensor + Mean of the distribution. +scale: torch.Tensor + Scale of the distribution.

+
Source
+

https://pytorch.org/docs/stable/distributions.html#studentt

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential) or "softplus" (softplus). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score). + Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. + Hence, using the CRPS disregards any variation in the curvature of the loss function.

+ +
+ Source code in lightgbmlss/distributions/StudentT.py + 
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class StudentT(DistributionClass):
+    """
+    Student-T Distribution Class
+
+    Distributional Parameters
+    -------------------------
+    df: torch.Tensor
+        Degrees of freedom.
+    loc: torch.Tensor
+        Mean of the distribution.
+    scale: torch.Tensor
+        Scale of the distribution.
+
+    Source
+    -------------------------
+    https://pytorch.org/docs/stable/distributions.html#studentt
+
+     Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential) or "softplus" (softplus).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score).
+        Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable.
+        Hence, using the CRPS disregards any variation in the curvature of the loss function.
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn: str = "exp",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll", "crps"]:
+            raise ValueError("Invalid loss function. Please choose from 'nll' or 'crps'.")
+
+        # Specify Response Functions
+        response_functions = {
+            "exp": (exp_fn, exp_fn_df),
+            "softplus": (softplus_fn, softplus_fn_df)
+        }
+        if response_fn in response_functions:
+            response_fn, response_fn_df = response_functions[response_fn]
+        else:
+            raise ValueError(
+                "Invalid response function. Please choose from 'exp' or 'softplus'.")
+
+        # Set the parameters specific to the distribution
+        distribution = StudentT_Torch
+        param_dict = {"df": response_fn_df, "loc": identity_fn, "scale": response_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=False,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ Weibull + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ Weibull + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Weibull distribution class.

+
Distributional Parameters
+

scale: torch.Tensor + Scale parameter of distribution (lambda). +concentration: torch.Tensor + Concentration parameter of distribution (k/shape).

+
Source
+

https://pytorch.org/docs/stable/distributions.html#weibull

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential) or "softplus" (softplus). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score). + Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. + Hence, using the CRPS disregards any variation in the curvature of the loss function.

+ +
+ Source code in lightgbmlss/distributions/Weibull.py + 
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class Weibull(DistributionClass):
+    """
+    Weibull distribution class.
+
+    Distributional Parameters
+    -------------------------
+    scale: torch.Tensor
+        Scale parameter of distribution (lambda).
+    concentration: torch.Tensor
+        Concentration parameter of distribution (k/shape).
+
+    Source
+    -------------------------
+    https://pytorch.org/docs/stable/distributions.html#weibull
+
+     Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential) or "softplus" (softplus).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score).
+        Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable.
+        Hence, using the CRPS disregards any variation in the curvature of the loss function.
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn: str = "exp",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll", "crps"]:
+            raise ValueError("Invalid loss function. Please choose from 'nll' or 'crps'.")
+
+        # Specify Response Functions
+        response_functions = {"exp": exp_fn, "softplus": softplus_fn}
+        if response_fn in response_functions:
+            response_fn = response_functions[response_fn]
+        else:
+            raise ValueError(
+                "Invalid response function. Please choose from 'exp' or 'softplus'.")
+
+        # Set the parameters specific to the distribution
+        distribution = Weibull_Torch
+        param_dict = {"scale": response_fn, "concentration": response_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=False,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ ZABeta + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ ZABeta + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Zero-Adjusted Beta distribution class.

+

The zero-adjusted Beta distribution is similar to the Beta distribution but allows zeros as y values.

+
Distributional Parameters
+

concentration1: torch.Tensor + 1st concentration parameter of the distribution (often referred to as alpha). +concentration0: torch.Tensor + 2nd concentration parameter of the distribution (often referred to as beta). +gate: torch.Tensor + Probability of zeros given via a Bernoulli distribution.

+
Source
+

https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential) or "softplus" (softplus). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood).

+ +
+ Source code in lightgbmlss/distributions/ZABeta.py + 
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class ZABeta(DistributionClass):
+    """
+    Zero-Adjusted Beta distribution class.
+
+    The zero-adjusted Beta distribution is similar to the Beta distribution but allows zeros as y values.
+
+    Distributional Parameters
+    -------------------------
+    concentration1: torch.Tensor
+        1st concentration parameter of the distribution (often referred to as alpha).
+    concentration0: torch.Tensor
+        2nd concentration parameter of the distribution (often referred to as beta).
+    gate: torch.Tensor
+        Probability of zeros given via a Bernoulli distribution.
+
+    Source
+    -------------------------
+    https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential) or "softplus" (softplus).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood).
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn: str = "exp",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll"]:
+            raise ValueError("Invalid loss function. Please select 'nll'.")
+
+        # Specify Response Functions
+        response_functions = {"exp": exp_fn, "softplus": softplus_fn}
+        if response_fn in response_functions:
+            response_fn = response_functions[response_fn]
+        else:
+            raise ValueError(
+                "Invalid response function. Please choose from 'exp' or 'softplus'.")
+
+        # Set the parameters specific to the distribution
+        distribution = ZeroAdjustedBeta_Torch
+        param_dict = {"concentration1": response_fn, "concentration0": response_fn, "gate": sigmoid_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=False,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ ZAGamma + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ ZAGamma + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Zero-Adjusted Gamma distribution class.

+

The zero-adjusted Gamma distribution is similar to the Gamma distribution but allows zeros as y values.

+
Distributional Parameters
+

concentration: torch.Tensor + shape parameter of the distribution (often referred to as alpha) +rate: torch.Tensor + rate = 1 / scale of the distribution (often referred to as beta) +gate: torch.Tensor + Probability of zeros given via a Bernoulli distribution.

+
Source
+

https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential) or "softplus" (softplus). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood).

+ +
+ Source code in lightgbmlss/distributions/ZAGamma.py + 
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class ZAGamma(DistributionClass):
+    """
+    Zero-Adjusted Gamma distribution class.
+
+    The zero-adjusted Gamma distribution is similar to the Gamma distribution but allows zeros as y values.
+
+     Distributional Parameters
+    --------------------------
+    concentration: torch.Tensor
+        shape parameter of the distribution (often referred to as alpha)
+    rate: torch.Tensor
+        rate = 1 / scale of the distribution (often referred to as beta)
+    gate: torch.Tensor
+        Probability of zeros given via a Bernoulli distribution.
+
+    Source
+    -------------------------
+    https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential) or "softplus" (softplus).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood).
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn: str = "exp",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll"]:
+            raise ValueError("Invalid loss function. Please select 'nll'.")
+
+        # Specify Response Functions
+        response_functions = {"exp": exp_fn, "softplus": softplus_fn}
+        if response_fn in response_functions:
+            response_fn = response_functions[response_fn]
+        else:
+            raise ValueError(
+                "Invalid response function. Please choose from 'exp' or 'softplus'.")
+
+        # Set the parameters specific to the distribution
+        distribution = ZeroAdjustedGamma_Torch
+        param_dict = {"concentration": response_fn, "rate": response_fn, "gate": sigmoid_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=False,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ ZALN + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ ZALN + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Zero-Adjusted LogNormal distribution class.

+

The zero-adjusted Log-Normal distribution is similar to the Log-Normal distribution but allows zeros as y values.

+
Distributional Parameters
+

loc: torch.Tensor + Mean of log of distribution. +scale: torch.Tensor + Standard deviation of log of the distribution. +gate: torch.Tensor + Probability of zeros given via a Bernoulli distribution.

+
Source
+

https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential) or "softplus" (softplus). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood).

+ +
+ Source code in lightgbmlss/distributions/ZALN.py + 
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class ZALN(DistributionClass):
+    """
+    Zero-Adjusted LogNormal distribution class.
+
+    The zero-adjusted Log-Normal distribution is similar to the Log-Normal distribution but allows zeros as y values.
+
+    Distributional Parameters
+    -------------------------
+    loc: torch.Tensor
+        Mean of log of distribution.
+    scale: torch.Tensor
+        Standard deviation of log of the distribution.
+    gate: torch.Tensor
+        Probability of zeros given via a Bernoulli distribution.
+
+    Source
+    -------------------------
+    https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential) or "softplus" (softplus).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood).
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn: str = "exp",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll"]:
+            raise ValueError("Invalid loss function. Please select 'nll'.")
+
+        # Specify Response Functions
+        response_functions = {"exp": exp_fn, "softplus": softplus_fn}
+        if response_fn in response_functions:
+            response_fn = response_functions[response_fn]
+        else:
+            raise ValueError(
+                "Invalid response function. Please choose from 'exp' or 'softplus'.")
+
+        # Set the parameters specific to the distribution
+        distribution = ZeroAdjustedLogNormal_Torch
+        param_dict = {"loc": identity_fn, "scale": response_fn,  "gate": sigmoid_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=False,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ ZINB + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ ZINB + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Zero-Inflated Negative Binomial distribution class.

+
Distributional Parameters
+

total_count: torch.Tensor + Non-negative number of negative Bernoulli trials to stop. +probs: torch.Tensor + Event probabilities of success in the half open interval [0, 1). +gate: torch.Tensor + Probability of extra zeros given via a Bernoulli distribution.

+
Source
+

https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn_total_count: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential), "softplus" (softplus) or "relu" (rectified linear unit). +response_fn_probs: str + Response function for transforming the distributional parameters to the correct support. Options are + "sigmoid" (sigmoid). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood).

+ +
+ Source code in lightgbmlss/distributions/ZINB.py + 
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class ZINB(DistributionClass):
+    """
+    Zero-Inflated Negative Binomial distribution class.
+
+    Distributional Parameters
+    -------------------------
+    total_count: torch.Tensor
+        Non-negative number of negative Bernoulli trials to stop.
+    probs: torch.Tensor
+        Event probabilities of success in the half open interval [0, 1).
+    gate: torch.Tensor
+        Probability of extra zeros given via a Bernoulli distribution.
+
+    Source
+    -------------------------
+    https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn_total_count: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential), "softplus" (softplus) or "relu" (rectified linear unit).
+    response_fn_probs: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "sigmoid" (sigmoid).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood).
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn_total_count: str = "relu",
+                 response_fn_probs: str = "sigmoid",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll"]:
+            raise ValueError("Invalid loss function. Please select 'nll'.")
+
+        #  Specify Response Functions for total_count
+        response_functions_total_count = {"exp": exp_fn, "softplus": softplus_fn, "relu": relu_fn}
+        if response_fn_total_count in response_functions_total_count:
+            response_fn_total_count = response_functions_total_count[response_fn_total_count]
+        else:
+            raise ValueError(
+                "Invalid response function for total_count. Please choose from 'exp', 'softplus' or 'relu'.")
+
+        #  Specify Response Functions for probs
+        response_functions_probs = {"sigmoid": sigmoid_fn}
+        if response_fn_probs in response_functions_probs:
+            response_fn_probs = response_functions_probs[response_fn_probs]
+        else:
+            raise ValueError(
+                "Invalid response function for probs. Please select 'sigmoid'.")
+
+        # Set the parameters specific to the distribution
+        distribution = ZeroInflatedNegativeBinomial_Torch
+        param_dict = {"total_count": response_fn_total_count, "probs": response_fn_probs, "gate": sigmoid_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=True,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ ZIPoisson + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ ZIPoisson + + +

+ + +
+

+ Bases: DistributionClass

+ + +

Zero-Inflated Poisson distribution class.

+
Distributional Parameters
+

rate: torch.Tensor + Rate parameter of the distribution (often referred to as lambda). +gate: torch.Tensor + Probability of extra zeros given via a Bernoulli distribution.

+
Source
+

https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L121

+
Parameters
+

stabilization: str + Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2". +response_fn: str + Response function for transforming the distributional parameters to the correct support. Options are + "exp" (exponential), "softplus" (softplus) or "relu" (rectified linear unit). +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood).

+ +
+ Source code in lightgbmlss/distributions/ZIPoisson.py + 
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class ZIPoisson(DistributionClass):
+    """
+    Zero-Inflated Poisson distribution class.
+
+    Distributional Parameters
+    -------------------------
+    rate: torch.Tensor
+        Rate parameter of the distribution (often referred to as lambda).
+    gate: torch.Tensor
+        Probability of extra zeros given via a Bernoulli distribution.
+
+    Source
+    -------------------------
+    https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L121
+
+    Parameters
+    -------------------------
+    stabilization: str
+        Stabilization method for the Gradient and Hessian. Options are "None", "MAD", "L2".
+    response_fn: str
+        Response function for transforming the distributional parameters to the correct support. Options are
+        "exp" (exponential), "softplus" (softplus) or "relu" (rectified linear unit).
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood).
+    """
+    def __init__(self,
+                 stabilization: str = "None",
+                 response_fn: str = "relu",
+                 loss_fn: str = "nll"
+                 ):
+
+        # Input Checks
+        if stabilization not in ["None", "MAD", "L2"]:
+            raise ValueError("Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.")
+        if loss_fn not in ["nll"]:
+            raise ValueError("Invalid loss function. Please select 'nll'.")
+
+        # Specify Response Functions
+        response_functions = {"exp": exp_fn, "softplus": softplus_fn, "relu": relu_fn}
+        if response_fn in response_functions:
+            response_fn = response_functions[response_fn]
+        else:
+            raise ValueError(
+                "Invalid response function for total_count. Please choose from 'exp', 'softplus' or 'relu'.")
+
+        # Set the parameters specific to the distribution
+        distribution = ZeroInflatedPoisson_Torch
+        param_dict = {"rate": response_fn, "gate": sigmoid_fn}
+        torch.distributions.Distribution.set_default_validate_args(False)
+
+        # Specify Distribution Class
+        super().__init__(distribution=distribution,
+                         univariate=True,
+                         discrete=True,
+                         n_dist_param=len(param_dict),
+                         stabilization=stabilization,
+                         param_dict=param_dict,
+                         distribution_arg_names=list(param_dict.keys()),
+                         loss_fn=loss_fn
+                         )
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ distribution_utils + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ DistributionClass + + +

+ + +
+ + +

Generic class that contains general functions for univariate distributions.

+
Arguments
+

distribution: torch.distributions.Distribution + PyTorch Distribution class. +univariate: bool + Whether the distribution is univariate or multivariate. +discrete: bool + Whether the support of the distribution is discrete or continuous. +n_dist_param: int + Number of distributional parameters. +stabilization: str + Stabilization method. +param_dict: Dict[str, Any] + Dictionary that maps distributional parameters to their response scale. +distribution_arg_names: List + List of distributional parameter names. +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score). + Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. + Hence, using the CRPS disregards any variation in the curvature of the loss function. +tau: List + List of expectiles. Only used for Expectile distributon. +penalize_crossing: bool + Whether to include a penalty term to discourage crossing of expectiles. Only used for Expectile distribution.

+ +
+ Source code in lightgbmlss/distributions/distribution_utils.py + 
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class DistributionClass:
+    """
+    Generic class that contains general functions for univariate distributions.
+
+    Arguments
+    ---------
+    distribution: torch.distributions.Distribution
+        PyTorch Distribution class.
+    univariate: bool
+        Whether the distribution is univariate or multivariate.
+    discrete: bool
+        Whether the support of the distribution is discrete or continuous.
+    n_dist_param: int
+        Number of distributional parameters.
+    stabilization: str
+        Stabilization method.
+    param_dict: Dict[str, Any]
+        Dictionary that maps distributional parameters to their response scale.
+    distribution_arg_names: List
+        List of distributional parameter names.
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score).
+        Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable.
+        Hence, using the CRPS disregards any variation in the curvature of the loss function.
+    tau: List
+        List of expectiles. Only used for Expectile distributon.
+    penalize_crossing: bool
+        Whether to include a penalty term to discourage crossing of expectiles. Only used for Expectile distribution.
+    """
+    def __init__(self,
+                 distribution: torch.distributions.Distribution = None,
+                 univariate: bool = True,
+                 discrete: bool = False,
+                 n_dist_param: int = None,
+                 stabilization: str = "None",
+                 param_dict: Dict[str, Any] = None,
+                 distribution_arg_names: List = None,
+                 loss_fn: str = "nll",
+                 tau: Optional[List[torch.Tensor]] = None,
+                 penalize_crossing: bool = False,
+                 ):
+
+        self.distribution = distribution
+        self.univariate = univariate
+        self.discrete = discrete
+        self.n_dist_param = n_dist_param
+        self.stabilization = stabilization
+        self.param_dict = param_dict
+        self.distribution_arg_names = distribution_arg_names
+        self.loss_fn = loss_fn
+        self.tau = tau
+        self.penalize_crossing = penalize_crossing
+
+    def objective_fn(self, predt: np.ndarray, data: lgb.Dataset) -> Tuple[np.ndarray, np.ndarray]:
+
+        """
+        Function to estimate gradients and hessians of distributional parameters.
+
+        Arguments
+        ---------
+        predt: np.ndarray
+            Predicted values.
+        data: lgb.DMatrix
+            Data used for training.
+
+        Returns
+        -------
+        grad: np.ndarray
+            Gradient.
+        hess: np.ndarray
+            Hessian.
+        """
+        # Target
+        target = torch.tensor(data.get_label().reshape(-1, 1))
+
+        # Weights
+        if data.weight is None:
+            # Use 1 as weight if no weights are specified
+            weights = torch.ones_like(target, dtype=target.dtype).numpy()
+        else:
+            weights = data.get_weight().reshape(-1, 1)
+
+        # Start values (needed to replace NaNs in predt)
+        start_values = data.get_init_score().reshape(-1, self.n_dist_param)[0, :].tolist()
+
+        # Calculate gradients and hessians
+        predt, loss = self.get_params_loss(predt, target, start_values, requires_grad=True)
+        grad, hess = self.compute_gradients_and_hessians(loss, predt, weights)
+
+        return grad, hess
+
+    def metric_fn(self, predt: np.ndarray, data: lgb.Dataset) -> Tuple[str, float, bool]:
+        """
+        Function that evaluates the predictions using the negative log-likelihood.
+
+        Arguments
+        ---------
+        predt: np.ndarray
+            Predicted values.
+        data: lgb.Dataset
+            Data used for training.
+
+        Returns
+        -------
+        name: str
+            Name of the evaluation metric.
+        nll: float
+            Negative log-likelihood.
+        is_higher_better: bool
+            Whether a higher value of the metric is better or not.
+        """
+        # Target
+        target = torch.tensor(data.get_label().reshape(-1, 1))
+
+        # Start values (needed to replace NaNs in predt)
+        start_values = data.get_init_score().reshape(-1, self.n_dist_param)[0, :].tolist()
+
+        # Calculate loss
+        is_higher_better = False
+        _, loss = self.get_params_loss(predt, target, start_values, requires_grad=False)
+
+        return self.loss_fn, loss, is_higher_better
+
+    def loss_fn_start_values(self,
+                             params: torch.Tensor,
+                             target: torch.Tensor) -> torch.Tensor:
+        """
+        Function that calculates the loss for a given set of distributional parameters. Only used for calculating
+        the loss for the start values.
+
+        Parameter
+        ---------
+        params: torch.Tensor
+            Distributional parameters.
+        target: torch.Tensor
+            Target values.
+
+        Returns
+        -------
+        loss: torch.Tensor
+            Loss value.
+        """
+        # Transform parameters to response scale
+        params = [
+            response_fn(params[i].reshape(-1, 1)) for i, response_fn in enumerate(self.param_dict.values())
+        ]
+
+        # Replace NaNs and infinity values with 0.5
+        nan_inf_idx = torch.isnan(torch.stack(params)) | torch.isinf(torch.stack(params))
+        params = torch.where(nan_inf_idx, torch.tensor(0.5), torch.stack(params))
+
+        # Specify Distribution and Loss
+        if self.tau is None:
+            dist = self.distribution(*params)
+            loss = -torch.nansum(dist.log_prob(target))
+        else:
+            dist = self.distribution(params, self.penalize_crossing)
+            loss = -torch.nansum(dist.log_prob(target, self.tau))
+
+        return loss
+
+    def calculate_start_values(self,
+                               target: np.ndarray,
+                               max_iter: int = 50
+                               ) -> Tuple[float, np.ndarray]:
+        """
+        Function that calculates the starting values for each distributional parameter.
+
+        Arguments
+        ---------
+        target: np.ndarray
+            Data from which starting values are calculated.
+        max_iter: int
+            Maximum number of iterations.
+
+        Returns
+        -------
+        loss: float
+            Loss value.
+        start_values: np.ndarray
+            Starting values for each distributional parameter.
+        """
+        # Convert target to torch.tensor
+        target = torch.tensor(target).reshape(-1, 1)
+
+        # Initialize parameters
+        params = [torch.tensor(0.5, requires_grad=True) for _ in range(self.n_dist_param)]
+
+        # Specify optimizer
+        optimizer = LBFGS(params, lr=0.1, max_iter=np.min([int(max_iter / 4), 20]), line_search_fn="strong_wolfe")
+
+        # Define learning rate scheduler
+        lr_scheduler = ReduceLROnPlateau(optimizer, mode="min", factor=0.5, patience=10)
+
+        # Define closure
+        def closure():
+            optimizer.zero_grad()
+            loss = self.loss_fn_start_values(params, target)
+            loss.backward()
+            return loss
+
+        # Optimize parameters
+        loss_vals = []
+        for epoch in range(max_iter):
+            loss = optimizer.step(closure)
+            lr_scheduler.step(loss)
+            loss_vals.append(loss.item())
+
+        # Get final loss
+        loss = np.array(loss_vals[-1])
+
+        # Get start values
+        start_values = np.array([params[i].detach() for i in range(self.n_dist_param)])
+
+        # Replace any remaining NaNs or infinity values with 0.5
+        start_values = np.nan_to_num(start_values, nan=0.5, posinf=0.5, neginf=0.5)
+
+        return loss, start_values
+
+    def get_params_loss(self,
+                        predt: np.ndarray,
+                        target: torch.Tensor,
+                        start_values: List[float],
+                        requires_grad: bool = False,
+                        ) -> Tuple[List[torch.Tensor], np.ndarray]:
+        """
+        Function that returns the predicted parameters and the loss.
+
+        Arguments
+        ---------
+        predt: np.ndarray
+            Predicted values.
+        target: torch.Tensor
+            Target values.
+        start_values: List
+            Starting values for each distributional parameter.
+        requires_grad: bool
+            Whether to add to the computational graph or not.
+
+        Returns
+        -------
+        predt: torch.Tensor
+            Predicted parameters.
+        loss: torch.Tensor
+            Loss value.
+        """
+        # Predicted Parameters
+        predt = predt.reshape(-1, self.n_dist_param, order="F")
+
+        # Replace NaNs and infinity values with unconditional start values
+        nan_inf_mask = np.isnan(predt) | np.isinf(predt)
+        predt[nan_inf_mask] = np.take(start_values, np.where(nan_inf_mask)[1])
+
+        # Convert to torch.tensor
+        predt = [
+            torch.tensor(predt[:, i].reshape(-1, 1), requires_grad=requires_grad) for i in range(self.n_dist_param)
+        ]
+
+        # Predicted Parameters transformed to response scale
+        predt_transformed = [
+            response_fn(predt[i].reshape(-1, 1)) for i, response_fn in enumerate(self.param_dict.values())
+        ]
+
+        # Specify Distribution and Loss
+        if self.tau is None:
+            dist_kwargs = dict(zip(self.distribution_arg_names, predt_transformed))
+            dist_fit = self.distribution(**dist_kwargs)
+            if self.loss_fn == "nll":
+                loss = -torch.nansum(dist_fit.log_prob(target))
+            elif self.loss_fn == "crps":
+                torch.manual_seed(123)
+                dist_samples = dist_fit.rsample((30,)).squeeze(-1)
+                loss = torch.nansum(self.crps_score(target, dist_samples))
+            else:
+                raise ValueError("Invalid loss function. Please select 'nll' or 'crps'.")
+        else:
+            dist_fit = self.distribution(predt_transformed, self.penalize_crossing)
+            loss = -torch.nansum(dist_fit.log_prob(target, self.tau))
+
+        return predt, loss
+
+    def draw_samples(self,
+                     predt_params: pd.DataFrame,
+                     n_samples: int = 1000,
+                     seed: int = 123
+                     ) -> pd.DataFrame:
+        """
+        Function that draws n_samples from a predicted distribution.
+
+        Arguments
+        ---------
+        predt_params: pd.DataFrame
+            pd.DataFrame with predicted distributional parameters.
+        n_samples: int
+            Number of sample to draw from predicted response distribution.
+        seed: int
+            Manual seed.
+
+        Returns
+        -------
+        pred_dist: pd.DataFrame
+            DataFrame with n_samples drawn from predicted response distribution.
+
+        """
+        torch.manual_seed(seed)
+
+        if self.tau is None:
+            pred_params = torch.tensor(predt_params.values)
+            dist_kwargs = {arg_name: param for arg_name, param in zip(self.distribution_arg_names, pred_params.T)}
+            dist_pred = self.distribution(**dist_kwargs)
+            dist_samples = dist_pred.sample((n_samples,)).squeeze().detach().numpy().T
+            dist_samples = pd.DataFrame(dist_samples)
+            dist_samples.columns = [str("y_sample") + str(i) for i in range(dist_samples.shape[1])]
+        else:
+            dist_samples = None
+
+        if self.discrete:
+            dist_samples = dist_samples.astype(int)
+
+        return dist_samples
+
+    def predict_dist(self,
+                     booster: lgb.Booster,
+                     data: pd.DataFrame,
+                     start_values: np.ndarray,
+                     pred_type: str = "parameters",
+                     n_samples: int = 1000,
+                     quantiles: list = [0.1, 0.5, 0.9],
+                     seed: str = 123
+                     ) -> pd.DataFrame:
+        """
+        Function that predicts from the trained model.
+
+        Arguments
+        ---------
+        booster : lgb.Booster
+            Trained model.
+        data : pd.DataFrame
+            Data to predict from.
+        start_values : np.ndarray.
+            Starting values for each distributional parameter.
+        pred_type : str
+            Type of prediction:
+            - "samples" draws n_samples from the predicted distribution.
+            - "quantiles" calculates the quantiles from the predicted distribution.
+            - "parameters" returns the predicted distributional parameters.
+            - "expectiles" returns the predicted expectiles.
+        n_samples : int
+            Number of samples to draw from the predicted distribution.
+        quantiles : List[float]
+            List of quantiles to calculate from the predicted distribution.
+        seed : int
+            Seed for random number generator used to draw samples from the predicted distribution.
+
+        Returns
+        -------
+        pred : pd.DataFrame
+            Predictions.
+        """
+
+        predt = torch.tensor(
+            booster.predict(data, raw_score=True),
+            dtype=torch.float32
+        ).reshape(-1, self.n_dist_param)
+
+        # Set init_score as starting point for each distributional parameter.
+        init_score_pred = torch.tensor(
+            np.ones(shape=(data.shape[0], 1))*start_values,
+            dtype=torch.float32
+        )
+
+        # The predictions don't include the init_score specified in creating the train data.
+        # Hence, it needs to be added manually with the corresponding transform for each distributional parameter.
+        dist_params_predt = np.concatenate(
+            [
+                response_fun(
+                    predt[:, i].reshape(-1, 1) + init_score_pred[:, i].reshape(-1, 1)).numpy()
+                for i, (dist_param, response_fun) in enumerate(self.param_dict.items())
+            ],
+            axis=1,
+        )
+        dist_params_predt = pd.DataFrame(dist_params_predt)
+        dist_params_predt.columns = self.param_dict.keys()
+
+        # Draw samples from predicted response distribution
+        pred_samples_df = self.draw_samples(predt_params=dist_params_predt,
+                                            n_samples=n_samples,
+                                            seed=seed)
+
+        if pred_type == "parameters":
+            return dist_params_predt
+
+        elif pred_type == "expectiles":
+            return dist_params_predt
+
+        elif pred_type == "samples":
+            return pred_samples_df
+
+        elif pred_type == "quantiles":
+            # Calculate quantiles from predicted response distribution
+            pred_quant_df = pred_samples_df.quantile(quantiles, axis=1).T
+            pred_quant_df.columns = [str("quant_") + str(quantiles[i]) for i in range(len(quantiles))]
+            if self.discrete:
+                pred_quant_df = pred_quant_df.astype(int)
+            return pred_quant_df
+
+    def compute_gradients_and_hessians(self,
+                                       loss: torch.tensor,
+                                       predt: torch.tensor,
+                                       weights: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+
+        """
+        Calculates gradients and hessians.
+
+        Output gradients and hessians have shape (n_samples*n_outputs, 1).
+
+        Arguments:
+        ---------
+        loss: torch.Tensor
+            Loss.
+        predt: torch.Tensor
+            List of predicted parameters.
+        weights: np.ndarray
+            Weights.
+
+        Returns:
+        -------
+        grad: torch.Tensor
+            Gradients.
+        hess: torch.Tensor
+            Hessians.
+        """
+        if self.loss_fn == "nll":
+            # Gradient and Hessian
+            grad = autograd(loss, inputs=predt, create_graph=True)
+            hess = [autograd(grad[i].nansum(), inputs=predt[i], retain_graph=True)[0] for i in range(len(grad))]
+        elif self.loss_fn == "crps":
+            # Gradient and Hessian
+            grad = autograd(loss, inputs=predt, create_graph=True)
+            hess = [torch.ones_like(grad[i]) for i in range(len(grad))]
+
+            # # Approximation of Hessian
+            # step_size = 1e-6
+            # predt_upper = [
+            #     response_fn(predt[i] + step_size).reshape(-1, 1) for i, response_fn in
+            #     enumerate(self.param_dict.values())
+            # ]
+            # dist_kwargs_upper = dict(zip(self.distribution_arg_names, predt_upper))
+            # dist_fit_upper = self.distribution(**dist_kwargs_upper)
+            # dist_samples_upper = dist_fit_upper.rsample((30,)).squeeze(-1)
+            # loss_upper = torch.nansum(self.crps_score(self.target, dist_samples_upper))
+            #
+            # predt_lower = [
+            #     response_fn(predt[i] - step_size).reshape(-1, 1) for i, response_fn in
+            #     enumerate(self.param_dict.values())
+            # ]
+            # dist_kwargs_lower = dict(zip(self.distribution_arg_names, predt_lower))
+            # dist_fit_lower = self.distribution(**dist_kwargs_lower)
+            # dist_samples_lower = dist_fit_lower.rsample((30,)).squeeze(-1)
+            # loss_lower = torch.nansum(self.crps_score(self.target, dist_samples_lower))
+            #
+            # grad_upper = autograd(loss_upper, inputs=predt_upper)
+            # grad_lower = autograd(loss_lower, inputs=predt_lower)
+            # hess = [(grad_upper[i] - grad_lower[i]) / (2 * step_size) for i in range(len(grad))]
+
+        # Stabilization of Derivatives
+        if self.stabilization != "None":
+            grad = [self.stabilize_derivative(grad[i], type=self.stabilization) for i in range(len(grad))]
+            hess = [self.stabilize_derivative(hess[i], type=self.stabilization) for i in range(len(hess))]
+
+        # Reshape
+        grad = torch.cat(grad, axis=1).detach().numpy()
+        hess = torch.cat(hess, axis=1).detach().numpy()
+
+        # Weighting
+        grad *= weights
+        hess *= weights
+
+        # Reshape
+        grad = grad.ravel(order="F")
+        hess = hess.ravel(order="F")
+
+        return grad, hess
+
+    def stabilize_derivative(self, input_der: torch.Tensor, type: str = "MAD") -> torch.Tensor:
+        """
+        Function that stabilizes Gradients and Hessians.
+
+        As LightGBMLSS updates the parameter estimates by optimizing Gradients and Hessians, it is important
+        that these are comparable in magnitude for all distributional parameters. Due to imbalances regarding the ranges,
+        the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution.
+        Another way to improve convergence might be to standardize the response variable. This is especially useful if the
+        range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and
+        the standardization of the response are not always advised but need to be carefully considered.
+        Source: https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173
+
+        Parameters
+        ----------
+        input_der : torch.Tensor
+            Input derivative, either Gradient or Hessian.
+        type: str
+            Stabilization method. Can be either "None", "MAD" or "L2".
+
+        Returns
+        -------
+        stab_der : torch.Tensor
+            Stabilized Gradient or Hessian.
+        """
+
+        if type == "MAD":
+            input_der = torch.nan_to_num(input_der, nan=float(torch.nanmean(input_der)))
+            div = torch.nanmedian(torch.abs(input_der - torch.nanmedian(input_der)))
+            div = torch.where(div < torch.tensor(1e-04), torch.tensor(1e-04), div)
+            stab_der = input_der / div
+
+        if type == "L2":
+            input_der = torch.nan_to_num(input_der, nan=float(torch.nanmean(input_der)))
+            div = torch.sqrt(torch.nanmean(input_der.pow(2)))
+            div = torch.where(div < torch.tensor(1e-04), torch.tensor(1e-04), div)
+            div = torch.where(div > torch.tensor(10000.0), torch.tensor(10000.0), div)
+            stab_der = input_der / div
+
+        if type == "None":
+            stab_der = torch.nan_to_num(input_der, nan=float(torch.nanmean(input_der)))
+
+        return stab_der
+
+    def crps_score(self, y: torch.tensor, yhat_dist: torch.tensor) -> torch.tensor:
+        """
+        Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples.
+
+        Parameters
+        ----------
+        y: torch.Tensor
+            Response variable of shape (n_observations,1).
+        yhat_dist: torch.Tensor
+            Predicted samples of shape (n_samples, n_observations).
+
+        Returns
+        -------
+        crps: torch.Tensor
+            CRPS score.
+
+        References
+        ----------
+        Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation.
+        Journal of the American Statistical Association. 102. 359-378.
+
+        Source
+        ------
+        https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549
+        """
+        # Get the number of observations
+        n_samples = yhat_dist.shape[0]
+
+        # Sort the forecasts in ascending order
+        yhat_dist_sorted, _ = torch.sort(yhat_dist, 0)
+
+        # Create temporary tensors
+        y_cdf = torch.zeros_like(y)
+        yhat_cdf = torch.zeros_like(y)
+        yhat_prev = torch.zeros_like(y)
+        crps = torch.zeros_like(y)
+
+        # Loop over the predicted samples generated per observation
+        for yhat in yhat_dist_sorted:
+            yhat = yhat.reshape(-1, 1)
+            flag = (y_cdf == 0) * (y < yhat)
+            crps += flag * ((y - yhat_prev) * yhat_cdf ** 2)
+            crps += flag * ((yhat - y) * (yhat_cdf - 1) ** 2)
+            crps += (~flag) * ((yhat - yhat_prev) * (yhat_cdf - y_cdf) ** 2)
+            y_cdf += flag
+            yhat_cdf += 1 / n_samples
+            yhat_prev = yhat
+
+        # In case y_cdf == 0 after the loop
+        flag = (y_cdf == 0)
+        crps += flag * (y - yhat)
+
+        return crps
+
+    def dist_select(self,
+                    target: np.ndarray,
+                    candidate_distributions: List,
+                    max_iter: int = 100,
+                    n_samples: int = 1000,
+                    plot: bool = False,
+                    figure_size: tuple = (10, 5),
+                    ) -> pd.DataFrame:
+        """
+        Function that selects the most suitable distribution among the candidate_distributions for the target variable,
+        based on the NegLogLikelihood (lower is better).
+
+        Parameters
+        ----------
+        target: np.ndarray
+            Response variable.
+        candidate_distributions: List
+            List of candidate distributions.
+        max_iter: int
+            Maximum number of iterations for the optimization.
+        n_samples: int
+            Number of samples to draw from the fitted distribution.
+        plot: bool
+            If True, a density plot of the actual and fitted distribution is created.
+        figure_size: tuple
+            Figure size of the density plot.
+
+        Returns
+        -------
+        fit_df: pd.DataFrame
+            Dataframe with the loss values of the fitted candidate distributions.
+        """
+        dist_list = []
+        total_iterations = len(candidate_distributions)
+        with tqdm(total=total_iterations, desc="Fitting candidate distributions") as pbar:
+            for i in range(len(candidate_distributions)):
+                dist_name = candidate_distributions[i].__name__.split(".")[2]
+                pbar.set_description(f"Fitting {dist_name} distribution")
+                dist_sel = getattr(candidate_distributions[i], dist_name)()
+                try:
+                    loss, params = dist_sel.calculate_start_values(target=target.reshape(-1, 1), max_iter=max_iter)
+                    fit_df = pd.DataFrame.from_dict(
+                        {self.loss_fn: loss.reshape(-1,),
+                         "distribution": str(dist_name),
+                         "params": [params]
+                         }
+                    )
+                except Exception as e:
+                    warnings.warn(f"Error fitting {dist_name} distribution: {str(e)}")
+                    fit_df = pd.DataFrame(
+                        {self.loss_fn: np.nan,
+                         "distribution": str(dist_name),
+                         "params": [np.nan] * self.n_dist_param
+                         }
+                    )
+                dist_list.append(fit_df)
+                fit_df = pd.concat(dist_list).sort_values(by=self.loss_fn, ascending=True)
+                fit_df["rank"] = fit_df[self.loss_fn].rank().astype(int)
+                fit_df.set_index(fit_df["rank"], inplace=True)
+                pbar.update(1)
+            pbar.set_description(f"Fitting of candidate distributions completed")
+
+        if plot:
+            # Select best distribution
+            best_dist = fit_df[fit_df["rank"] == 1].reset_index(drop=True)
+            for dist in candidate_distributions:
+                if dist.__name__.split(".")[2] == best_dist["distribution"].values[0]:
+                    best_dist_sel = dist
+                    break
+            best_dist_sel = getattr(best_dist_sel, best_dist["distribution"].values[0])()
+            params = torch.tensor(best_dist["params"][0]).reshape(-1, best_dist_sel.n_dist_param)
+
+            # Transform parameters to the response scale and draw samples
+            fitted_params = np.concatenate(
+                [
+                    response_fun(params[:, i].reshape(-1, 1)).numpy()
+                    for i, (dist_param, response_fun) in enumerate(best_dist_sel.param_dict.items())
+                ],
+                axis=1,
+            )
+            fitted_params = pd.DataFrame(fitted_params, columns=best_dist_sel.param_dict.keys())
+            fitted_params.columns = best_dist_sel.param_dict.keys()
+            dist_samples = best_dist_sel.draw_samples(fitted_params,
+                                                      n_samples=n_samples,
+                                                      seed=123).values
+
+            # Plot actual and fitted distribution
+            plot_df_actual = pd.DataFrame({"y": target.reshape(-1,), "type": "Actual"})
+            plot_df_fitted = pd.DataFrame({"y": dist_samples.reshape(-1,),
+                                           "type": f"Best-Fit: {best_dist['distribution'].values[0]}"})
+            plot_df = pd.concat([plot_df_actual, plot_df_fitted])
+
+            print(
+                ggplot(plot_df,
+                       aes(x="y",
+                           color="type")) +
+                geom_density(alpha=0.5) +
+                theme_bw(base_size=15) +
+                theme(figure_size=figure_size,
+                      legend_position="right",
+                      legend_title=element_blank(),
+                      plot_title=element_text(hjust=0.5)) +
+                labs(title=f"Actual vs. Fitted Density")
+            )
+
+        fit_df.drop(columns=["rank", "params"], inplace=True)
+
+        return fit_df
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +
+ calculate_start_values(target, max_iter=50) + +
+ + +
+ +

Function that calculates the starting values for each distributional parameter.

+
Arguments
+

target: np.ndarray + Data from which starting values are calculated. +max_iter: int + Maximum number of iterations.

+
Returns
+

loss: float + Loss value. +start_values: np.ndarray + Starting values for each distributional parameter.

+ +
+ Source code in lightgbmlss/distributions/distribution_utils.py + 
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def calculate_start_values(self,
+                           target: np.ndarray,
+                           max_iter: int = 50
+                           ) -> Tuple[float, np.ndarray]:
+    """
+    Function that calculates the starting values for each distributional parameter.
+
+    Arguments
+    ---------
+    target: np.ndarray
+        Data from which starting values are calculated.
+    max_iter: int
+        Maximum number of iterations.
+
+    Returns
+    -------
+    loss: float
+        Loss value.
+    start_values: np.ndarray
+        Starting values for each distributional parameter.
+    """
+    # Convert target to torch.tensor
+    target = torch.tensor(target).reshape(-1, 1)
+
+    # Initialize parameters
+    params = [torch.tensor(0.5, requires_grad=True) for _ in range(self.n_dist_param)]
+
+    # Specify optimizer
+    optimizer = LBFGS(params, lr=0.1, max_iter=np.min([int(max_iter / 4), 20]), line_search_fn="strong_wolfe")
+
+    # Define learning rate scheduler
+    lr_scheduler = ReduceLROnPlateau(optimizer, mode="min", factor=0.5, patience=10)
+
+    # Define closure
+    def closure():
+        optimizer.zero_grad()
+        loss = self.loss_fn_start_values(params, target)
+        loss.backward()
+        return loss
+
+    # Optimize parameters
+    loss_vals = []
+    for epoch in range(max_iter):
+        loss = optimizer.step(closure)
+        lr_scheduler.step(loss)
+        loss_vals.append(loss.item())
+
+    # Get final loss
+    loss = np.array(loss_vals[-1])
+
+    # Get start values
+    start_values = np.array([params[i].detach() for i in range(self.n_dist_param)])
+
+    # Replace any remaining NaNs or infinity values with 0.5
+    start_values = np.nan_to_num(start_values, nan=0.5, posinf=0.5, neginf=0.5)
+
+    return loss, start_values
+
+
+
+ +
+ + +
+ + + + +
+ compute_gradients_and_hessians(loss, predt, weights) + +
+ + +
+ +

Calculates gradients and hessians.

+

Output gradients and hessians have shape (n_samples*n_outputs, 1).

+
Arguments:
+

loss: torch.Tensor + Loss. +predt: torch.Tensor + List of predicted parameters. +weights: np.ndarray + Weights.

+
Returns:
+

grad: torch.Tensor + Gradients. +hess: torch.Tensor + Hessians.

+ +
+ Source code in lightgbmlss/distributions/distribution_utils.py + 
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def compute_gradients_and_hessians(self,
+                                   loss: torch.tensor,
+                                   predt: torch.tensor,
+                                   weights: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+
+    """
+    Calculates gradients and hessians.
+
+    Output gradients and hessians have shape (n_samples*n_outputs, 1).
+
+    Arguments:
+    ---------
+    loss: torch.Tensor
+        Loss.
+    predt: torch.Tensor
+        List of predicted parameters.
+    weights: np.ndarray
+        Weights.
+
+    Returns:
+    -------
+    grad: torch.Tensor
+        Gradients.
+    hess: torch.Tensor
+        Hessians.
+    """
+    if self.loss_fn == "nll":
+        # Gradient and Hessian
+        grad = autograd(loss, inputs=predt, create_graph=True)
+        hess = [autograd(grad[i].nansum(), inputs=predt[i], retain_graph=True)[0] for i in range(len(grad))]
+    elif self.loss_fn == "crps":
+        # Gradient and Hessian
+        grad = autograd(loss, inputs=predt, create_graph=True)
+        hess = [torch.ones_like(grad[i]) for i in range(len(grad))]
+
+        # # Approximation of Hessian
+        # step_size = 1e-6
+        # predt_upper = [
+        #     response_fn(predt[i] + step_size).reshape(-1, 1) for i, response_fn in
+        #     enumerate(self.param_dict.values())
+        # ]
+        # dist_kwargs_upper = dict(zip(self.distribution_arg_names, predt_upper))
+        # dist_fit_upper = self.distribution(**dist_kwargs_upper)
+        # dist_samples_upper = dist_fit_upper.rsample((30,)).squeeze(-1)
+        # loss_upper = torch.nansum(self.crps_score(self.target, dist_samples_upper))
+        #
+        # predt_lower = [
+        #     response_fn(predt[i] - step_size).reshape(-1, 1) for i, response_fn in
+        #     enumerate(self.param_dict.values())
+        # ]
+        # dist_kwargs_lower = dict(zip(self.distribution_arg_names, predt_lower))
+        # dist_fit_lower = self.distribution(**dist_kwargs_lower)
+        # dist_samples_lower = dist_fit_lower.rsample((30,)).squeeze(-1)
+        # loss_lower = torch.nansum(self.crps_score(self.target, dist_samples_lower))
+        #
+        # grad_upper = autograd(loss_upper, inputs=predt_upper)
+        # grad_lower = autograd(loss_lower, inputs=predt_lower)
+        # hess = [(grad_upper[i] - grad_lower[i]) / (2 * step_size) for i in range(len(grad))]
+
+    # Stabilization of Derivatives
+    if self.stabilization != "None":
+        grad = [self.stabilize_derivative(grad[i], type=self.stabilization) for i in range(len(grad))]
+        hess = [self.stabilize_derivative(hess[i], type=self.stabilization) for i in range(len(hess))]
+
+    # Reshape
+    grad = torch.cat(grad, axis=1).detach().numpy()
+    hess = torch.cat(hess, axis=1).detach().numpy()
+
+    # Weighting
+    grad *= weights
+    hess *= weights
+
+    # Reshape
+    grad = grad.ravel(order="F")
+    hess = hess.ravel(order="F")
+
+    return grad, hess
+
+
+
+ +
+ + +
+ + + + +
+ crps_score(y, yhat_dist) + +
+ + +
+ +

Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples.

+
Parameters
+

y: torch.Tensor + Response variable of shape (n_observations,1). +yhat_dist: torch.Tensor + Predicted samples of shape (n_samples, n_observations).

+
Returns
+

crps: torch.Tensor + CRPS score.

+
References
+

Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. +Journal of the American Statistical Association. 102. 359-378.

+
Source
+

https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549

+ +
+ Source code in lightgbmlss/distributions/distribution_utils.py + 
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def crps_score(self, y: torch.tensor, yhat_dist: torch.tensor) -> torch.tensor:
+    """
+    Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples.
+
+    Parameters
+    ----------
+    y: torch.Tensor
+        Response variable of shape (n_observations,1).
+    yhat_dist: torch.Tensor
+        Predicted samples of shape (n_samples, n_observations).
+
+    Returns
+    -------
+    crps: torch.Tensor
+        CRPS score.
+
+    References
+    ----------
+    Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation.
+    Journal of the American Statistical Association. 102. 359-378.
+
+    Source
+    ------
+    https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549
+    """
+    # Get the number of observations
+    n_samples = yhat_dist.shape[0]
+
+    # Sort the forecasts in ascending order
+    yhat_dist_sorted, _ = torch.sort(yhat_dist, 0)
+
+    # Create temporary tensors
+    y_cdf = torch.zeros_like(y)
+    yhat_cdf = torch.zeros_like(y)
+    yhat_prev = torch.zeros_like(y)
+    crps = torch.zeros_like(y)
+
+    # Loop over the predicted samples generated per observation
+    for yhat in yhat_dist_sorted:
+        yhat = yhat.reshape(-1, 1)
+        flag = (y_cdf == 0) * (y < yhat)
+        crps += flag * ((y - yhat_prev) * yhat_cdf ** 2)
+        crps += flag * ((yhat - y) * (yhat_cdf - 1) ** 2)
+        crps += (~flag) * ((yhat - yhat_prev) * (yhat_cdf - y_cdf) ** 2)
+        y_cdf += flag
+        yhat_cdf += 1 / n_samples
+        yhat_prev = yhat
+
+    # In case y_cdf == 0 after the loop
+    flag = (y_cdf == 0)
+    crps += flag * (y - yhat)
+
+    return crps
+
+
+
+ +
+ + +
+ + + + +
+ dist_select(target, candidate_distributions, max_iter=100, n_samples=1000, plot=False, figure_size=(10, 5)) + +
+ + +
+ +

Function that selects the most suitable distribution among the candidate_distributions for the target variable, +based on the NegLogLikelihood (lower is better).

+
Parameters
+

target: np.ndarray + Response variable. +candidate_distributions: List + List of candidate distributions. +max_iter: int + Maximum number of iterations for the optimization. +n_samples: int + Number of samples to draw from the fitted distribution. +plot: bool + If True, a density plot of the actual and fitted distribution is created. +figure_size: tuple + Figure size of the density plot.

+
Returns
+

fit_df: pd.DataFrame + Dataframe with the loss values of the fitted candidate distributions.

+ +
+ Source code in lightgbmlss/distributions/distribution_utils.py + 
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def dist_select(self,
+                target: np.ndarray,
+                candidate_distributions: List,
+                max_iter: int = 100,
+                n_samples: int = 1000,
+                plot: bool = False,
+                figure_size: tuple = (10, 5),
+                ) -> pd.DataFrame:
+    """
+    Function that selects the most suitable distribution among the candidate_distributions for the target variable,
+    based on the NegLogLikelihood (lower is better).
+
+    Parameters
+    ----------
+    target: np.ndarray
+        Response variable.
+    candidate_distributions: List
+        List of candidate distributions.
+    max_iter: int
+        Maximum number of iterations for the optimization.
+    n_samples: int
+        Number of samples to draw from the fitted distribution.
+    plot: bool
+        If True, a density plot of the actual and fitted distribution is created.
+    figure_size: tuple
+        Figure size of the density plot.
+
+    Returns
+    -------
+    fit_df: pd.DataFrame
+        Dataframe with the loss values of the fitted candidate distributions.
+    """
+    dist_list = []
+    total_iterations = len(candidate_distributions)
+    with tqdm(total=total_iterations, desc="Fitting candidate distributions") as pbar:
+        for i in range(len(candidate_distributions)):
+            dist_name = candidate_distributions[i].__name__.split(".")[2]
+            pbar.set_description(f"Fitting {dist_name} distribution")
+            dist_sel = getattr(candidate_distributions[i], dist_name)()
+            try:
+                loss, params = dist_sel.calculate_start_values(target=target.reshape(-1, 1), max_iter=max_iter)
+                fit_df = pd.DataFrame.from_dict(
+                    {self.loss_fn: loss.reshape(-1,),
+                     "distribution": str(dist_name),
+                     "params": [params]
+                     }
+                )
+            except Exception as e:
+                warnings.warn(f"Error fitting {dist_name} distribution: {str(e)}")
+                fit_df = pd.DataFrame(
+                    {self.loss_fn: np.nan,
+                     "distribution": str(dist_name),
+                     "params": [np.nan] * self.n_dist_param
+                     }
+                )
+            dist_list.append(fit_df)
+            fit_df = pd.concat(dist_list).sort_values(by=self.loss_fn, ascending=True)
+            fit_df["rank"] = fit_df[self.loss_fn].rank().astype(int)
+            fit_df.set_index(fit_df["rank"], inplace=True)
+            pbar.update(1)
+        pbar.set_description(f"Fitting of candidate distributions completed")
+
+    if plot:
+        # Select best distribution
+        best_dist = fit_df[fit_df["rank"] == 1].reset_index(drop=True)
+        for dist in candidate_distributions:
+            if dist.__name__.split(".")[2] == best_dist["distribution"].values[0]:
+                best_dist_sel = dist
+                break
+        best_dist_sel = getattr(best_dist_sel, best_dist["distribution"].values[0])()
+        params = torch.tensor(best_dist["params"][0]).reshape(-1, best_dist_sel.n_dist_param)
+
+        # Transform parameters to the response scale and draw samples
+        fitted_params = np.concatenate(
+            [
+                response_fun(params[:, i].reshape(-1, 1)).numpy()
+                for i, (dist_param, response_fun) in enumerate(best_dist_sel.param_dict.items())
+            ],
+            axis=1,
+        )
+        fitted_params = pd.DataFrame(fitted_params, columns=best_dist_sel.param_dict.keys())
+        fitted_params.columns = best_dist_sel.param_dict.keys()
+        dist_samples = best_dist_sel.draw_samples(fitted_params,
+                                                  n_samples=n_samples,
+                                                  seed=123).values
+
+        # Plot actual and fitted distribution
+        plot_df_actual = pd.DataFrame({"y": target.reshape(-1,), "type": "Actual"})
+        plot_df_fitted = pd.DataFrame({"y": dist_samples.reshape(-1,),
+                                       "type": f"Best-Fit: {best_dist['distribution'].values[0]}"})
+        plot_df = pd.concat([plot_df_actual, plot_df_fitted])
+
+        print(
+            ggplot(plot_df,
+                   aes(x="y",
+                       color="type")) +
+            geom_density(alpha=0.5) +
+            theme_bw(base_size=15) +
+            theme(figure_size=figure_size,
+                  legend_position="right",
+                  legend_title=element_blank(),
+                  plot_title=element_text(hjust=0.5)) +
+            labs(title=f"Actual vs. Fitted Density")
+        )
+
+    fit_df.drop(columns=["rank", "params"], inplace=True)
+
+    return fit_df
+
+
+
+ +
+ + +
+ + + + +
+ draw_samples(predt_params, n_samples=1000, seed=123) + +
+ + +
+ +

Function that draws n_samples from a predicted distribution.

+
Arguments
+

predt_params: pd.DataFrame + pd.DataFrame with predicted distributional parameters. +n_samples: int + Number of sample to draw from predicted response distribution. +seed: int + Manual seed.

+
Returns
+

pred_dist: pd.DataFrame + DataFrame with n_samples drawn from predicted response distribution.

+ +
+ Source code in lightgbmlss/distributions/distribution_utils.py + 
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def draw_samples(self,
+                 predt_params: pd.DataFrame,
+                 n_samples: int = 1000,
+                 seed: int = 123
+                 ) -> pd.DataFrame:
+    """
+    Function that draws n_samples from a predicted distribution.
+
+    Arguments
+    ---------
+    predt_params: pd.DataFrame
+        pd.DataFrame with predicted distributional parameters.
+    n_samples: int
+        Number of sample to draw from predicted response distribution.
+    seed: int
+        Manual seed.
+
+    Returns
+    -------
+    pred_dist: pd.DataFrame
+        DataFrame with n_samples drawn from predicted response distribution.
+
+    """
+    torch.manual_seed(seed)
+
+    if self.tau is None:
+        pred_params = torch.tensor(predt_params.values)
+        dist_kwargs = {arg_name: param for arg_name, param in zip(self.distribution_arg_names, pred_params.T)}
+        dist_pred = self.distribution(**dist_kwargs)
+        dist_samples = dist_pred.sample((n_samples,)).squeeze().detach().numpy().T
+        dist_samples = pd.DataFrame(dist_samples)
+        dist_samples.columns = [str("y_sample") + str(i) for i in range(dist_samples.shape[1])]
+    else:
+        dist_samples = None
+
+    if self.discrete:
+        dist_samples = dist_samples.astype(int)
+
+    return dist_samples
+
+
+
+ +
+ + +
+ + + + +
+ get_params_loss(predt, target, start_values, requires_grad=False) + +
+ + +
+ +

Function that returns the predicted parameters and the loss.

+
Arguments
+

predt: np.ndarray + Predicted values. +target: torch.Tensor + Target values. +start_values: List + Starting values for each distributional parameter. +requires_grad: bool + Whether to add to the computational graph or not.

+
Returns
+

predt: torch.Tensor + Predicted parameters. +loss: torch.Tensor + Loss value.

+ +
+ Source code in lightgbmlss/distributions/distribution_utils.py + 
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def get_params_loss(self,
+                    predt: np.ndarray,
+                    target: torch.Tensor,
+                    start_values: List[float],
+                    requires_grad: bool = False,
+                    ) -> Tuple[List[torch.Tensor], np.ndarray]:
+    """
+    Function that returns the predicted parameters and the loss.
+
+    Arguments
+    ---------
+    predt: np.ndarray
+        Predicted values.
+    target: torch.Tensor
+        Target values.
+    start_values: List
+        Starting values for each distributional parameter.
+    requires_grad: bool
+        Whether to add to the computational graph or not.
+
+    Returns
+    -------
+    predt: torch.Tensor
+        Predicted parameters.
+    loss: torch.Tensor
+        Loss value.
+    """
+    # Predicted Parameters
+    predt = predt.reshape(-1, self.n_dist_param, order="F")
+
+    # Replace NaNs and infinity values with unconditional start values
+    nan_inf_mask = np.isnan(predt) | np.isinf(predt)
+    predt[nan_inf_mask] = np.take(start_values, np.where(nan_inf_mask)[1])
+
+    # Convert to torch.tensor
+    predt = [
+        torch.tensor(predt[:, i].reshape(-1, 1), requires_grad=requires_grad) for i in range(self.n_dist_param)
+    ]
+
+    # Predicted Parameters transformed to response scale
+    predt_transformed = [
+        response_fn(predt[i].reshape(-1, 1)) for i, response_fn in enumerate(self.param_dict.values())
+    ]
+
+    # Specify Distribution and Loss
+    if self.tau is None:
+        dist_kwargs = dict(zip(self.distribution_arg_names, predt_transformed))
+        dist_fit = self.distribution(**dist_kwargs)
+        if self.loss_fn == "nll":
+            loss = -torch.nansum(dist_fit.log_prob(target))
+        elif self.loss_fn == "crps":
+            torch.manual_seed(123)
+            dist_samples = dist_fit.rsample((30,)).squeeze(-1)
+            loss = torch.nansum(self.crps_score(target, dist_samples))
+        else:
+            raise ValueError("Invalid loss function. Please select 'nll' or 'crps'.")
+    else:
+        dist_fit = self.distribution(predt_transformed, self.penalize_crossing)
+        loss = -torch.nansum(dist_fit.log_prob(target, self.tau))
+
+    return predt, loss
+
+
+
+ +
+ + +
+ + + + +
+ loss_fn_start_values(params, target) + +
+ + +
+ +

Function that calculates the loss for a given set of distributional parameters. Only used for calculating +the loss for the start values.

+
Parameter
+

params: torch.Tensor + Distributional parameters. +target: torch.Tensor + Target values.

+
Returns
+

loss: torch.Tensor + Loss value.

+ +
+ Source code in lightgbmlss/distributions/distribution_utils.py + 
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def loss_fn_start_values(self,
+                         params: torch.Tensor,
+                         target: torch.Tensor) -> torch.Tensor:
+    """
+    Function that calculates the loss for a given set of distributional parameters. Only used for calculating
+    the loss for the start values.
+
+    Parameter
+    ---------
+    params: torch.Tensor
+        Distributional parameters.
+    target: torch.Tensor
+        Target values.
+
+    Returns
+    -------
+    loss: torch.Tensor
+        Loss value.
+    """
+    # Transform parameters to response scale
+    params = [
+        response_fn(params[i].reshape(-1, 1)) for i, response_fn in enumerate(self.param_dict.values())
+    ]
+
+    # Replace NaNs and infinity values with 0.5
+    nan_inf_idx = torch.isnan(torch.stack(params)) | torch.isinf(torch.stack(params))
+    params = torch.where(nan_inf_idx, torch.tensor(0.5), torch.stack(params))
+
+    # Specify Distribution and Loss
+    if self.tau is None:
+        dist = self.distribution(*params)
+        loss = -torch.nansum(dist.log_prob(target))
+    else:
+        dist = self.distribution(params, self.penalize_crossing)
+        loss = -torch.nansum(dist.log_prob(target, self.tau))
+
+    return loss
+
+
+
+ +
+ + +
+ + + + +
+ metric_fn(predt, data) + +
+ + +
+ +

Function that evaluates the predictions using the negative log-likelihood.

+
Arguments
+

predt: np.ndarray + Predicted values. +data: lgb.Dataset + Data used for training.

+
Returns
+

name: str + Name of the evaluation metric. +nll: float + Negative log-likelihood. +is_higher_better: bool + Whether a higher value of the metric is better or not.

+ +
+ Source code in lightgbmlss/distributions/distribution_utils.py + 
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def metric_fn(self, predt: np.ndarray, data: lgb.Dataset) -> Tuple[str, float, bool]:
+    """
+    Function that evaluates the predictions using the negative log-likelihood.
+
+    Arguments
+    ---------
+    predt: np.ndarray
+        Predicted values.
+    data: lgb.Dataset
+        Data used for training.
+
+    Returns
+    -------
+    name: str
+        Name of the evaluation metric.
+    nll: float
+        Negative log-likelihood.
+    is_higher_better: bool
+        Whether a higher value of the metric is better or not.
+    """
+    # Target
+    target = torch.tensor(data.get_label().reshape(-1, 1))
+
+    # Start values (needed to replace NaNs in predt)
+    start_values = data.get_init_score().reshape(-1, self.n_dist_param)[0, :].tolist()
+
+    # Calculate loss
+    is_higher_better = False
+    _, loss = self.get_params_loss(predt, target, start_values, requires_grad=False)
+
+    return self.loss_fn, loss, is_higher_better
+
+
+
+ +
+ + +
+ + + + +
+ objective_fn(predt, data) + +
+ + +
+ +

Function to estimate gradients and hessians of distributional parameters.

+
Arguments
+

predt: np.ndarray + Predicted values. +data: lgb.DMatrix + Data used for training.

+
Returns
+

grad: np.ndarray + Gradient. +hess: np.ndarray + Hessian.

+ +
+ Source code in lightgbmlss/distributions/distribution_utils.py + 
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def objective_fn(self, predt: np.ndarray, data: lgb.Dataset) -> Tuple[np.ndarray, np.ndarray]:
+
+    """
+    Function to estimate gradients and hessians of distributional parameters.
+
+    Arguments
+    ---------
+    predt: np.ndarray
+        Predicted values.
+    data: lgb.DMatrix
+        Data used for training.
+
+    Returns
+    -------
+    grad: np.ndarray
+        Gradient.
+    hess: np.ndarray
+        Hessian.
+    """
+    # Target
+    target = torch.tensor(data.get_label().reshape(-1, 1))
+
+    # Weights
+    if data.weight is None:
+        # Use 1 as weight if no weights are specified
+        weights = torch.ones_like(target, dtype=target.dtype).numpy()
+    else:
+        weights = data.get_weight().reshape(-1, 1)
+
+    # Start values (needed to replace NaNs in predt)
+    start_values = data.get_init_score().reshape(-1, self.n_dist_param)[0, :].tolist()
+
+    # Calculate gradients and hessians
+    predt, loss = self.get_params_loss(predt, target, start_values, requires_grad=True)
+    grad, hess = self.compute_gradients_and_hessians(loss, predt, weights)
+
+    return grad, hess
+
+
+
+ +
+ + +
+ + + + +
+ predict_dist(booster, data, start_values, pred_type='parameters', n_samples=1000, quantiles=[0.1, 0.5, 0.9], seed=123) + +
+ + +
+ +

Function that predicts from the trained model.

+
Arguments
+

booster : lgb.Booster + Trained model. +data : pd.DataFrame + Data to predict from. +start_values : np.ndarray. + Starting values for each distributional parameter. +pred_type : str + Type of prediction: + - "samples" draws n_samples from the predicted distribution. + - "quantiles" calculates the quantiles from the predicted distribution. + - "parameters" returns the predicted distributional parameters. + - "expectiles" returns the predicted expectiles. +n_samples : int + Number of samples to draw from the predicted distribution. +quantiles : List[float] + List of quantiles to calculate from the predicted distribution. +seed : int + Seed for random number generator used to draw samples from the predicted distribution.

+
Returns
+

pred : pd.DataFrame + Predictions.

+ +
+ Source code in lightgbmlss/distributions/distribution_utils.py + 
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def predict_dist(self,
+                 booster: lgb.Booster,
+                 data: pd.DataFrame,
+                 start_values: np.ndarray,
+                 pred_type: str = "parameters",
+                 n_samples: int = 1000,
+                 quantiles: list = [0.1, 0.5, 0.9],
+                 seed: str = 123
+                 ) -> pd.DataFrame:
+    """
+    Function that predicts from the trained model.
+
+    Arguments
+    ---------
+    booster : lgb.Booster
+        Trained model.
+    data : pd.DataFrame
+        Data to predict from.
+    start_values : np.ndarray.
+        Starting values for each distributional parameter.
+    pred_type : str
+        Type of prediction:
+        - "samples" draws n_samples from the predicted distribution.
+        - "quantiles" calculates the quantiles from the predicted distribution.
+        - "parameters" returns the predicted distributional parameters.
+        - "expectiles" returns the predicted expectiles.
+    n_samples : int
+        Number of samples to draw from the predicted distribution.
+    quantiles : List[float]
+        List of quantiles to calculate from the predicted distribution.
+    seed : int
+        Seed for random number generator used to draw samples from the predicted distribution.
+
+    Returns
+    -------
+    pred : pd.DataFrame
+        Predictions.
+    """
+
+    predt = torch.tensor(
+        booster.predict(data, raw_score=True),
+        dtype=torch.float32
+    ).reshape(-1, self.n_dist_param)
+
+    # Set init_score as starting point for each distributional parameter.
+    init_score_pred = torch.tensor(
+        np.ones(shape=(data.shape[0], 1))*start_values,
+        dtype=torch.float32
+    )
+
+    # The predictions don't include the init_score specified in creating the train data.
+    # Hence, it needs to be added manually with the corresponding transform for each distributional parameter.
+    dist_params_predt = np.concatenate(
+        [
+            response_fun(
+                predt[:, i].reshape(-1, 1) + init_score_pred[:, i].reshape(-1, 1)).numpy()
+            for i, (dist_param, response_fun) in enumerate(self.param_dict.items())
+        ],
+        axis=1,
+    )
+    dist_params_predt = pd.DataFrame(dist_params_predt)
+    dist_params_predt.columns = self.param_dict.keys()
+
+    # Draw samples from predicted response distribution
+    pred_samples_df = self.draw_samples(predt_params=dist_params_predt,
+                                        n_samples=n_samples,
+                                        seed=seed)
+
+    if pred_type == "parameters":
+        return dist_params_predt
+
+    elif pred_type == "expectiles":
+        return dist_params_predt
+
+    elif pred_type == "samples":
+        return pred_samples_df
+
+    elif pred_type == "quantiles":
+        # Calculate quantiles from predicted response distribution
+        pred_quant_df = pred_samples_df.quantile(quantiles, axis=1).T
+        pred_quant_df.columns = [str("quant_") + str(quantiles[i]) for i in range(len(quantiles))]
+        if self.discrete:
+            pred_quant_df = pred_quant_df.astype(int)
+        return pred_quant_df
+
+
+
+ +
+ + +
+ + + + +
+ stabilize_derivative(input_der, type='MAD') + +
+ + +
+ +

Function that stabilizes Gradients and Hessians.

+

As LightGBMLSS updates the parameter estimates by optimizing Gradients and Hessians, it is important +that these are comparable in magnitude for all distributional parameters. Due to imbalances regarding the ranges, +the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution. +Another way to improve convergence might be to standardize the response variable. This is especially useful if the +range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and +the standardization of the response are not always advised but need to be carefully considered. +Source: https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173

+
Parameters
+

input_der : torch.Tensor + Input derivative, either Gradient or Hessian. +type: str + Stabilization method. Can be either "None", "MAD" or "L2".

+
Returns
+

stab_der : torch.Tensor + Stabilized Gradient or Hessian.

+ +
+ Source code in lightgbmlss/distributions/distribution_utils.py + 
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def stabilize_derivative(self, input_der: torch.Tensor, type: str = "MAD") -> torch.Tensor:
+    """
+    Function that stabilizes Gradients and Hessians.
+
+    As LightGBMLSS updates the parameter estimates by optimizing Gradients and Hessians, it is important
+    that these are comparable in magnitude for all distributional parameters. Due to imbalances regarding the ranges,
+    the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution.
+    Another way to improve convergence might be to standardize the response variable. This is especially useful if the
+    range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and
+    the standardization of the response are not always advised but need to be carefully considered.
+    Source: https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173
+
+    Parameters
+    ----------
+    input_der : torch.Tensor
+        Input derivative, either Gradient or Hessian.
+    type: str
+        Stabilization method. Can be either "None", "MAD" or "L2".
+
+    Returns
+    -------
+    stab_der : torch.Tensor
+        Stabilized Gradient or Hessian.
+    """
+
+    if type == "MAD":
+        input_der = torch.nan_to_num(input_der, nan=float(torch.nanmean(input_der)))
+        div = torch.nanmedian(torch.abs(input_der - torch.nanmedian(input_der)))
+        div = torch.where(div < torch.tensor(1e-04), torch.tensor(1e-04), div)
+        stab_der = input_der / div
+
+    if type == "L2":
+        input_der = torch.nan_to_num(input_der, nan=float(torch.nanmean(input_der)))
+        div = torch.sqrt(torch.nanmean(input_der.pow(2)))
+        div = torch.where(div < torch.tensor(1e-04), torch.tensor(1e-04), div)
+        div = torch.where(div > torch.tensor(10000.0), torch.tensor(10000.0), div)
+        stab_der = input_der / div
+
+    if type == "None":
+        stab_der = torch.nan_to_num(input_der, nan=float(torch.nanmean(input_der)))
+
+    return stab_der
+
+
+
+ +
+ + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ flow_utils + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ NormalizingFlowClass + + +

+ + +
+ + +

Generic class that contains general functions for normalizing flows.

+
Arguments
+

base_dist: torch.distributions.Distribution + PyTorch Distribution class. Currently only Normal is supported. +flow_transform: Transform + Specify the normalizing flow transform. +count_bins: Optional[int] + The number of segments comprising the spline. Only used if flow_transform is Spline. +bound: Optional[float] + The quantity "K" determining the bounding box, [-K,K] x [-K,K] of the spline. By adjusting the + "K" value, you can control the size of the bounding box and consequently control the range of inputs that + the spline transform operates on. Larger values of "K" will result in a wider valid range for the spline + transformation, while smaller values will restrict the valid range to a smaller region. Should be chosen + based on the range of the data. Only used if flow_transform is Spline. +order: Optional[str] + The order of the spline. Options are "linear" or "quadratic". Only used if flow_transform is Spline. +n_dist_param: int + Number of parameters. +param_dict: Dict[str, Any] + Dictionary that maps parameters to their response scale. +target_transform: Transform + Specify the target transform. +discrete: bool + Whether the target is discrete or not. +univariate: bool + Whether the distribution is univariate or multivariate. +stabilization: str + Stabilization method. Options are "None", "MAD" or "L2". +loss_fn: str + Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score). + Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. + Hence, using the CRPS disregards any variation in the curvature of the loss function.

+ +
+ Source code in lightgbmlss/distributions/flow_utils.py + 
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class NormalizingFlowClass:
+    """
+    Generic class that contains general functions for normalizing flows.
+
+    Arguments
+    ---------
+    base_dist: torch.distributions.Distribution
+        PyTorch Distribution class. Currently only Normal is supported.
+    flow_transform: Transform
+        Specify the normalizing flow transform.
+    count_bins: Optional[int]
+        The number of segments comprising the spline. Only used if flow_transform is Spline.
+    bound: Optional[float]
+        The quantity "K" determining the bounding box, [-K,K] x [-K,K] of the spline. By adjusting the
+        "K" value, you can control the size of the bounding box and consequently control the range of inputs that
+        the spline transform operates on. Larger values of "K" will result in a wider valid range for the spline
+        transformation, while smaller values will restrict the valid range to a smaller region. Should be chosen
+        based on the range of the data. Only used if flow_transform is Spline.
+    order: Optional[str]
+        The order of the spline. Options are "linear" or "quadratic". Only used if flow_transform is Spline.
+    n_dist_param: int
+        Number of parameters.
+    param_dict: Dict[str, Any]
+        Dictionary that maps parameters to their response scale.
+    target_transform: Transform
+        Specify the target transform.
+    discrete: bool
+        Whether the target is discrete or not.
+    univariate: bool
+        Whether the distribution is univariate or multivariate.
+    stabilization: str
+        Stabilization method. Options are "None", "MAD" or "L2".
+    loss_fn: str
+        Loss function. Options are "nll" (negative log-likelihood) or "crps" (continuous ranked probability score).
+        Note that if "crps" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable.
+        Hence, using the CRPS disregards any variation in the curvature of the loss function.
+    """
+    def __init__(self,
+                 base_dist: torch.distributions.Distribution = None,
+                 flow_transform: Transform = None,
+                 count_bins: Optional[int] = 8,
+                 bound: Optional[float] = 3.0,
+                 order: Optional[str] = "quadratic",
+                 n_dist_param: int = None,
+                 param_dict: Dict[str, Any] = None,
+                 target_transform: Transform = None,
+                 discrete: bool = False,
+                 univariate: bool = True,
+                 stabilization: str = "None",
+                 loss_fn: str = "nll",
+                 ):
+
+        self.base_dist = base_dist
+        self.flow_transform = flow_transform
+        self.count_bins = count_bins
+        self.bound = bound
+        self.order = order
+        self.n_dist_param = n_dist_param
+        self.param_dict = param_dict
+        self.target_transform = target_transform
+        self.discrete = discrete
+        self.univariate = univariate
+        self.stabilization = stabilization
+        self.loss_fn = loss_fn
+
+    def objective_fn(self, predt: np.ndarray, data: lgb.Dataset) -> Tuple[np.ndarray, np.ndarray]:
+
+        """
+        Function to estimate gradients and hessians of normalizing flow parameters.
+
+        Arguments
+        ---------
+        predt: np.ndarray
+            Predicted values.
+        data: lgb.Dataset
+            Data used for training.
+
+        Returns
+        -------
+        grad: np.ndarray
+            Gradient.
+        hess: np.ndarray
+            Hessian.
+        """
+        # Target
+        target = torch.tensor(data.get_label().reshape(-1, 1))
+
+        # Weights
+        if data.weight is None:
+            # Use 1 as weight if no weights are specified
+            weights = torch.ones_like(target, dtype=target.dtype).numpy()
+        else:
+            weights = data.get_weight().reshape(-1, 1)
+
+        # Start values (needed to replace NaNs in predt)
+        start_values = data.get_init_score().reshape(-1, self.n_dist_param)[0, :].tolist()
+
+        # Calculate gradients and hessians
+        predt, loss = self.get_params_loss(predt, target, start_values)
+        grad, hess = self.compute_gradients_and_hessians(loss, predt, weights)
+
+        return grad, hess
+
+    def metric_fn(self, predt: np.ndarray, data: lgb.Dataset) -> Tuple[str, float, bool]:
+        """
+        Function that evaluates the predictions using the specified loss function.
+
+        Arguments
+        ---------
+        predt: np.ndarray
+            Predicted values.
+        data: lgb.Dataset
+            Data used for training.
+
+        Returns
+        -------
+        name: str
+            Name of the evaluation metric.
+        loss: float
+            Loss value.
+        """
+        # Target
+        target = torch.tensor(data.get_label().reshape(-1, 1))
+
+        # Start values (needed to replace NaNs in predt)
+        start_values = data.get_init_score().reshape(-1, self.n_dist_param)[0, :].tolist()
+
+        # Calculate loss
+        is_higher_better = False
+        _, loss = self.get_params_loss(predt, target, start_values)
+
+        return self.loss_fn, loss.detach(), is_higher_better
+
+    def calculate_start_values(self,
+                               target: np.ndarray,
+                               max_iter: int = 50
+                               ) -> Tuple[float, np.ndarray]:
+        """
+        Function that calculates starting values for each parameter.
+
+        Arguments
+        ---------
+        target: np.ndarray
+            Data from which starting values are calculated.
+        max_iter: int
+            Maximum number of iterations.
+
+        Returns
+        -------
+        loss: float
+            Loss value.
+        start_values: np.ndarray
+            Starting values for each parameter.
+        """
+        # Convert target to torch.tensor
+        target = torch.tensor(target).reshape(-1, 1)
+
+        # Create Normalizing Flow
+        flow_dist = self.create_spline_flow(input_dim=1)
+
+        # Specify optimizer
+        optimizer = LBFGS(flow_dist.transforms[0].parameters(),
+                          lr=0.3,
+                          max_iter=np.min([int(max_iter/4), 50]),
+                          line_search_fn="strong_wolfe")
+
+        # Define learning rate scheduler
+        lr_scheduler = ReduceLROnPlateau(optimizer, mode="min", factor=0.5, patience=5)
+
+        # Define closure
+        def closure():
+            optimizer.zero_grad()
+            loss = -torch.nansum(flow_dist.log_prob(target))
+            loss.backward()
+            flow_dist.clear_cache()
+            return loss
+
+        # Optimize parameters
+        loss_vals = []
+        tolerance = 1e-5           # Tolerance level for loss change
+        patience = 5               # Patience level for loss change
+        best_loss = float("inf")
+        epochs_without_change = 0
+
+        for epoch in range(max_iter):
+            optimizer.zero_grad()
+            loss = optimizer.step(closure)
+            lr_scheduler.step(loss)
+            loss_vals.append(loss.item())
+
+            # Stopping criterion (no improvement in loss)
+            if loss.item() < best_loss - tolerance:
+                best_loss = loss.item()
+                epochs_without_change = 0
+            else:
+                epochs_without_change += 1
+
+            if epochs_without_change >= patience:
+                break
+
+        # Get final loss
+        loss = np.array(loss_vals[-1])
+
+        # Get start values
+        start_values = list(flow_dist.transforms[0].parameters())
+        start_values = torch.cat([param.view(-1) for param in start_values]).detach().numpy()
+
+        # Replace any remaining NaNs or infinity values with 0.5
+        start_values = np.nan_to_num(start_values, nan=0.5, posinf=0.5, neginf=0.5)
+
+        return loss, start_values
+
+    def get_params_loss(self,
+                        predt: np.ndarray,
+                        target: torch.Tensor,
+                        start_values: List[float],
+                        requires_grad: bool = False,
+                        ) -> Tuple[List[torch.Tensor], np.ndarray]:
+        """
+        Function that returns the predicted parameters and the loss.
+
+        Arguments
+        ---------
+        predt: np.ndarray
+            Predicted values.
+        target: torch.Tensor
+            Target values.
+        start_values: List
+            Starting values for each parameter.
+
+        Returns
+        -------
+        predt: torch.Tensor
+            Predicted parameters.
+        loss: torch.Tensor
+            Loss value.
+        """
+        # Reshape Target
+        target = target.view(-1)
+
+        # Predicted Parameters
+        predt = predt.reshape(-1, self.n_dist_param, order="F")
+
+        # Replace NaNs and infinity values with unconditional start values
+        nan_inf_mask = np.isnan(predt) | np.isinf(predt)
+        predt[nan_inf_mask] = np.take(start_values, np.where(nan_inf_mask)[1])
+
+        # Convert to torch.tensor
+        predt = torch.tensor(predt, dtype=torch.float32)
+
+        # Specify Normalizing Flow
+        flow_dist = self.create_spline_flow(target.shape[0])
+
+        # Replace parameters with estimated ones
+        params, flow_dist = self.replace_parameters(predt, flow_dist)
+
+        # Calculate loss
+        if self.loss_fn == "nll":
+            loss = -torch.nansum(flow_dist.log_prob(target))
+        elif self.loss_fn == "crps":
+            torch.manual_seed(123)
+            dist_samples = flow_dist.rsample((30,)).squeeze(-1)
+            loss = torch.nansum(self.crps_score(target, dist_samples))
+        else:
+            raise ValueError("Invalid loss function. Please select 'nll' or 'crps'.")
+
+        return params, loss
+
+    def create_spline_flow(self,
+                           input_dim: int = None,
+                           ) -> Transform:
+
+        """
+        Function that constructs a Normalizing Flow.
+
+        Arguments
+        ---------
+        input_dim: int
+            Input dimension.
+
+        Returns
+        -------
+        spline_flow: Transform
+            Normalizing Flow.
+        """
+
+        # Create flow distribution (currently only Normal)
+        loc, scale = torch.zeros(input_dim), torch.ones(input_dim)
+        flow_dist = self.base_dist(loc, scale)
+
+        # Create Spline Transform
+        torch.manual_seed(123)
+        spline_transform = self.flow_transform(input_dim,
+                                               count_bins=self.count_bins,
+                                               bound=self.bound,
+                                               order=self.order)
+
+        # Create Normalizing Flow
+        spline_flow = TransformedDistribution(flow_dist, [spline_transform, self.target_transform])
+
+        return spline_flow
+
+    def replace_parameters(self,
+                           params: torch.Tensor,
+                           flow_dist: Transform,
+                           ) -> Tuple[List, Transform]:
+        """
+        Replace parameters with estimated ones.
+
+        Arguments
+        ---------
+        params: torch.Tensor
+            Estimated parameters.
+        flow_dist: Transform
+            Normalizing Flow.
+
+        Returns
+        -------
+        params_list: List
+            List of estimated parameters.
+        flow_dist: Transform
+            Normalizing Flow with estimated parameters.
+        """
+
+        # Split parameters into list
+        if self.order == "quadratic":
+            params_list = torch.split(
+                params, [self.count_bins, self.count_bins, self.count_bins - 1],
+                dim=1)
+        elif self.order == "linear":
+            params_list = torch.split(
+                params, [self.count_bins, self.count_bins, self.count_bins - 1, self.count_bins],
+                dim=1)
+
+        # Replace parameters
+        for param, new_value in zip(flow_dist.transforms[0].parameters(), params_list):
+            param.data = new_value
+
+        # Get parameters (including require_grad=True)
+        params_list = list(flow_dist.transforms[0].parameters())
+
+        return params_list, flow_dist
+
+    def draw_samples(self,
+                     predt_params: pd.DataFrame,
+                     n_samples: int = 1000,
+                     seed: int = 123
+                     ) -> pd.DataFrame:
+        """
+        Function that draws n_samples from a predicted distribution.
+
+        Arguments
+        ---------
+        predt_params: pd.DataFrame
+            pd.DataFrame with predicted distributional parameters.
+        n_samples: int
+            Number of sample to draw from predicted response distribution.
+        seed: int
+            Manual seed.
+
+        Returns
+        -------
+        pred_dist: pd.DataFrame
+            DataFrame with n_samples drawn from predicted response distribution.
+
+        """
+
+        torch.manual_seed(seed)
+
+        # Specify Normalizing Flow
+        pred_params = torch.tensor(predt_params.values)
+        flow_dist_pred = self.create_spline_flow(pred_params.shape[0])
+
+        # Replace parameters with estimated ones
+        _, flow_dist_pred = self.replace_parameters(pred_params, flow_dist_pred)
+
+        # Draw samples
+        flow_samples = pd.DataFrame(flow_dist_pred.sample((n_samples,)).squeeze().detach().numpy().T)
+        flow_samples.columns = [str("y_sample") + str(i) for i in range(flow_samples.shape[1])]
+
+        if self.discrete:
+            flow_samples = flow_samples.astype(int)
+
+        return flow_samples
+
+    def predict_dist(self,
+                     booster: lgb.Booster,
+                     data: pd.DataFrame,
+                     start_values: np.ndarray,
+                     pred_type: str = "parameters",
+                     n_samples: int = 1000,
+                     quantiles: list = [0.1, 0.5, 0.9],
+                     seed: str = 123
+                     ) -> pd.DataFrame:
+        """
+        Function that predicts from the trained model.
+
+        Arguments
+        ---------
+        booster : lgb.Booster
+            Trained model.
+        start_values : np.ndarray
+            Starting values for each distributional parameter.
+        data : pd.DataFrame
+            Data to predict from.
+        pred_type : str
+            Type of prediction:
+            - "samples" draws n_samples from the predicted distribution.
+            - "quantiles" calculates the quantiles from the predicted distribution.
+            - "parameters" returns the predicted distributional parameters.
+            - "expectiles" returns the predicted expectiles.
+        n_samples : int
+            Number of samples to draw from the predicted distribution.
+        quantiles : List[float]
+            List of quantiles to calculate from the predicted distribution.
+        seed : int
+            Seed for random number generator used to draw samples from the predicted distribution.
+
+        Returns
+        -------
+        pred : pd.DataFrame
+            Predictions.
+        """
+        # Predict raw scores
+        predt = torch.tensor(
+            booster.predict(data, raw_score=True),
+            dtype=torch.float32
+        ).reshape(-1, self.n_dist_param)
+
+        # Set init_score as starting point for each distributional parameter.
+        init_score_pred = torch.tensor(
+            np.ones(shape=(data.shape[0], 1)) * start_values,
+            dtype=torch.float32
+        )
+
+        # The predictions don't include the init_score specified in creating the train data. Hence, it needs to be
+        # added manually.
+        dist_params_predt = pd.DataFrame(
+            np.concatenate(
+                [predt[:, i].reshape(-1, 1) + init_score_pred[:, i].reshape(-1, 1) for i in range(self.n_dist_param)],
+                axis=1
+            )
+        )
+        dist_params_predt.columns = self.param_dict.keys()
+
+        # Draw samples from predicted response distribution
+        pred_samples_df = self.draw_samples(predt_params=dist_params_predt,
+                                            n_samples=n_samples,
+                                            seed=seed)
+
+        if pred_type == "parameters":
+            return dist_params_predt
+
+        elif pred_type == "samples":
+            return pred_samples_df
+
+        elif pred_type == "quantiles":
+            # Calculate quantiles from predicted response distribution
+            pred_quant_df = pred_samples_df.quantile(quantiles, axis=1).T
+            pred_quant_df.columns = [str("quant_") + str(quantiles[i]) for i in range(len(quantiles))]
+            if self.discrete:
+                pred_quant_df = pred_quant_df.astype(int)
+            return pred_quant_df
+
+    def compute_gradients_and_hessians(self,
+                                       loss: torch.tensor,
+                                       predt: torch.tensor,
+                                       weights: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+
+        """
+        Calculates gradients and hessians.
+
+        Output gradients and hessians have shape (n_samples*n_outputs, 1).
+
+        Arguments:
+        ---------
+        loss: torch.Tensor
+            Loss.
+        predt: torch.Tensor
+            List of predicted parameters.
+        weights: np.ndarray
+            Weights.
+
+        Returns:
+        -------
+        grad: torch.Tensor
+            Gradients.
+        hess: torch.Tensor
+            Hessians.
+        """
+        if self.loss_fn == "nll":
+            # Gradient and Hessian
+            grad = autograd(loss, inputs=predt, create_graph=True)
+            hess = [autograd(grad[i].nansum(), inputs=predt[i], retain_graph=True)[0] for i in range(len(grad))]
+        elif self.loss_fn == "crps":
+            # Gradient and Hessian
+            grad = autograd(loss, inputs=predt, create_graph=True)
+            hess = [torch.ones_like(grad[i]) for i in range(len(grad))]
+
+        # Stabilization of Derivatives
+        if self.stabilization != "None":
+            grad = [self.stabilize_derivative(grad[i], type=self.stabilization) for i in range(len(grad))]
+            hess = [self.stabilize_derivative(hess[i], type=self.stabilization) for i in range(len(hess))]
+
+        # Reshape
+        grad = torch.cat(grad, axis=1).detach().numpy()
+        hess = torch.cat(hess, axis=1).detach().numpy()
+
+        # Weighting
+        grad *= weights
+        hess *= weights
+
+        # Reshape
+        grad = grad.ravel(order="F")
+        hess = hess.ravel(order="F")
+
+        return grad, hess
+
+    def stabilize_derivative(self, input_der: torch.Tensor, type: str = "MAD") -> torch.Tensor:
+        """
+        Function that stabilizes Gradients and Hessians.
+
+        Since parameters are estimated by optimizing Gradients and Hessians, it is important that these are comparable
+        in magnitude for all parameters. Due to imbalances regarding the ranges, the estimation might become unstable
+        so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve
+        convergence might be to standardize the response variable. This is especially useful if the range of the
+        response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the
+        standardization of the response are not always advised but need to be carefully considered.
+
+        Source
+        ---------
+        https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173
+
+        Arguments
+        ---------
+        input_der : torch.Tensor
+            Input derivative, either Gradient or Hessian.
+        type: str
+            Stabilization method. Can be either "None", "MAD" or "L2".
+
+        Returns
+        ---------
+        stab_der : torch.Tensor
+            Stabilized Gradient or Hessian.
+        """
+
+        if type == "MAD":
+            input_der = torch.nan_to_num(input_der, nan=float(torch.nanmean(input_der)))
+            div = torch.nanmedian(torch.abs(input_der - torch.nanmedian(input_der)))
+            div = torch.where(div < torch.tensor(1e-04), torch.tensor(1e-04), div)
+            stab_der = input_der / div
+
+        if type == "L2":
+            input_der = torch.nan_to_num(input_der, nan=float(torch.nanmean(input_der)))
+            div = torch.sqrt(torch.nanmean(input_der.pow(2)))
+            div = torch.where(div < torch.tensor(1e-04), torch.tensor(1e-04), div)
+            div = torch.where(div > torch.tensor(10000.0), torch.tensor(10000.0), div)
+            stab_der = input_der / div
+
+        if type == "None":
+            stab_der = torch.nan_to_num(input_der, nan=float(torch.nanmean(input_der)))
+
+        return stab_der
+
+    def crps_score(self, y: torch.tensor, yhat_dist: torch.tensor) -> torch.tensor:
+        """
+        Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples.
+
+        Arguments
+        ---------
+        y: torch.Tensor
+            Response variable of shape (n_observations,1).
+        yhat_dist: torch.Tensor
+            Predicted samples of shape (n_samples, n_observations).
+
+        Returns
+        ---------
+        crps: torch.Tensor
+            CRPS score.
+
+        References
+        ---------
+        Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation.
+        Journal of the American Statistical Association. 102. 359-378.
+
+        Source
+        ---------
+        https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549
+        """
+        # Get the number of observations
+        n_samples = yhat_dist.shape[0]
+
+        # Sort the forecasts in ascending order
+        yhat_dist_sorted, _ = torch.sort(yhat_dist, 0)
+
+        # Create temporary tensors
+        y_cdf = torch.zeros_like(y)
+        yhat_cdf = torch.zeros_like(y)
+        yhat_prev = torch.zeros_like(y)
+        crps = torch.zeros_like(y)
+
+        # Loop over the predicted samples generated per observation
+        for yhat in yhat_dist_sorted:
+            yhat = yhat.reshape(-1, 1)
+            flag = (y_cdf == 0) * (y < yhat)
+            crps += flag * ((y - yhat_prev) * yhat_cdf ** 2)
+            crps += flag * ((yhat - y) * (yhat_cdf - 1) ** 2)
+            crps += (~flag) * ((yhat - yhat_prev) * (yhat_cdf - y_cdf) ** 2)
+            y_cdf += flag
+            yhat_cdf += 1 / n_samples
+            yhat_prev = yhat
+
+        # In case y_cdf == 0 after the loop
+        flag = (y_cdf == 0)
+        crps += flag * (y - yhat)
+
+        return crps
+
+    def flow_select(self,
+                    target: np.ndarray,
+                    candidate_flows: List,
+                    max_iter: int = 100,
+                    n_samples: int = 1000,
+                    plot: bool = False,
+                    figure_size: tuple = (10, 5),
+                    ) -> pd.DataFrame:
+        """
+        Function that selects the most suitable normalizing flow specification among the candidate_flow for the
+        target variable, based on the NegLogLikelihood (lower is better).
+
+        Parameters
+        ----------
+        target: np.ndarray
+            Response variable.
+        candidate_flows: List
+            List of candidate normalizing flow specifications.
+        max_iter: int
+            Maximum number of iterations for the optimization.
+        n_samples: int
+            Number of samples drawn from the fitted distribution.
+        plot: bool
+            If True, a density plot of the actual and fitted distribution is created.
+        figure_size: tuple
+            Figure size of the density plot.
+
+        Returns
+        -------
+        fit_df: pd.DataFrame
+            Dataframe with the loss values of the fitted normalizing flow.
+        """
+        flow_list = []
+        total_iterations = len(candidate_flows)
+
+        with tqdm(total=total_iterations, desc="Fitting candidate normalizing flows") as pbar:
+            for flow in candidate_flows:
+                flow_name = str(flow.__class__).split(".")[-1].split("'>")[0]
+                flow_spec = f"(count_bins: {flow.count_bins}, order: {flow.order})"
+                flow_name = flow_name + flow_spec
+                pbar.set_description(f"Fitting {flow_name}")
+                flow_sel = flow
+                try:
+                    loss, params = flow_sel.calculate_start_values(target=target, max_iter=max_iter)
+                    fit_df = pd.DataFrame.from_dict(
+                        {flow_sel.loss_fn: loss.reshape(-1, ),
+                         "NormFlow": str(flow_name),
+                         "params": [params]
+                         }
+                    )
+                except Exception as e:
+                    warnings.warn(f"Error fitting {flow_sel} NormFlow: {str(e)}")
+                    fit_df = pd.DataFrame(
+                        {flow_sel.loss_fn: np.nan,
+                         "NormFlow": str(flow_sel),
+                         "params": [np.nan] * flow_sel.n_dist_param
+                         }
+                    )
+                flow_list.append(fit_df)
+                fit_df = pd.concat(flow_list).sort_values(by=flow_sel.loss_fn, ascending=True)
+                fit_df["rank"] = fit_df[flow_sel.loss_fn].rank().astype(int)
+                fit_df.set_index(fit_df["rank"], inplace=True)
+                pbar.update(1)
+            pbar.set_description(f"Fitting of candidate normalizing flows completed")
+
+        if plot:
+            # Select normalizing flow with the lowest loss
+            best_flow = fit_df[fit_df["rank"] == 1].reset_index(drop=True)
+            for flow in candidate_flows:
+                flow_name = str(flow.__class__).split(".")[-1].split("'>")[0]
+                flow_spec = f"(count_bins: {flow.count_bins}, order: {flow.order})"
+                flow_name = flow_name + flow_spec
+                if flow_name == best_flow["NormFlow"].values[0]:
+                    best_flow_sel = flow
+                    break
+
+            # Draw samples from distribution
+            flow_params = torch.tensor(best_flow["params"][0]).reshape(1, -1)
+            flow_dist_sel = best_flow_sel.create_spline_flow(input_dim=1)
+            _, flow_dist_sel = best_flow_sel.replace_parameters(flow_params, flow_dist_sel)
+            flow_samples = pd.DataFrame(flow_dist_sel.sample((n_samples,)).squeeze().detach().numpy().T)
+
+            # Plot actual and fitted distribution
+            flow_samples["type"] = f"Best-Fit: {best_flow['NormFlow'].values[0]}"
+
+            df_actual = pd.DataFrame(target)
+            df_actual["type"] = "Data"
+
+            plot_df = pd.concat([df_actual, flow_samples]).rename(columns={0: "variable"})
+
+            print(
+                ggplot(plot_df,
+                       aes(x="variable",
+                           color="type")) +
+                geom_density(size=1.1) +
+                theme_bw(base_size=15) +
+                theme(figure_size=figure_size,
+                      legend_position="right",
+                      legend_title=element_blank(),
+                      plot_title=element_text(hjust=0.5)) +
+                labs(title=f"Actual vs. Fitted Density",
+                     x="")
+            )
+
+        fit_df.drop(columns=["rank", "params"], inplace=True)
+
+        return fit_df
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +
+ calculate_start_values(target, max_iter=50) + +
+ + +
+ +

Function that calculates starting values for each parameter.

+
Arguments
+

target: np.ndarray + Data from which starting values are calculated. +max_iter: int + Maximum number of iterations.

+
Returns
+

loss: float + Loss value. +start_values: np.ndarray + Starting values for each parameter.

+ +
+ Source code in lightgbmlss/distributions/flow_utils.py + 
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def calculate_start_values(self,
+                           target: np.ndarray,
+                           max_iter: int = 50
+                           ) -> Tuple[float, np.ndarray]:
+    """
+    Function that calculates starting values for each parameter.
+
+    Arguments
+    ---------
+    target: np.ndarray
+        Data from which starting values are calculated.
+    max_iter: int
+        Maximum number of iterations.
+
+    Returns
+    -------
+    loss: float
+        Loss value.
+    start_values: np.ndarray
+        Starting values for each parameter.
+    """
+    # Convert target to torch.tensor
+    target = torch.tensor(target).reshape(-1, 1)
+
+    # Create Normalizing Flow
+    flow_dist = self.create_spline_flow(input_dim=1)
+
+    # Specify optimizer
+    optimizer = LBFGS(flow_dist.transforms[0].parameters(),
+                      lr=0.3,
+                      max_iter=np.min([int(max_iter/4), 50]),
+                      line_search_fn="strong_wolfe")
+
+    # Define learning rate scheduler
+    lr_scheduler = ReduceLROnPlateau(optimizer, mode="min", factor=0.5, patience=5)
+
+    # Define closure
+    def closure():
+        optimizer.zero_grad()
+        loss = -torch.nansum(flow_dist.log_prob(target))
+        loss.backward()
+        flow_dist.clear_cache()
+        return loss
+
+    # Optimize parameters
+    loss_vals = []
+    tolerance = 1e-5           # Tolerance level for loss change
+    patience = 5               # Patience level for loss change
+    best_loss = float("inf")
+    epochs_without_change = 0
+
+    for epoch in range(max_iter):
+        optimizer.zero_grad()
+        loss = optimizer.step(closure)
+        lr_scheduler.step(loss)
+        loss_vals.append(loss.item())
+
+        # Stopping criterion (no improvement in loss)
+        if loss.item() < best_loss - tolerance:
+            best_loss = loss.item()
+            epochs_without_change = 0
+        else:
+            epochs_without_change += 1
+
+        if epochs_without_change >= patience:
+            break
+
+    # Get final loss
+    loss = np.array(loss_vals[-1])
+
+    # Get start values
+    start_values = list(flow_dist.transforms[0].parameters())
+    start_values = torch.cat([param.view(-1) for param in start_values]).detach().numpy()
+
+    # Replace any remaining NaNs or infinity values with 0.5
+    start_values = np.nan_to_num(start_values, nan=0.5, posinf=0.5, neginf=0.5)
+
+    return loss, start_values
+
+
+
+ +
+ + +
+ + + + +
+ compute_gradients_and_hessians(loss, predt, weights) + +
+ + +
+ +

Calculates gradients and hessians.

+

Output gradients and hessians have shape (n_samples*n_outputs, 1).

+
Arguments:
+

loss: torch.Tensor + Loss. +predt: torch.Tensor + List of predicted parameters. +weights: np.ndarray + Weights.

+
Returns:
+

grad: torch.Tensor + Gradients. +hess: torch.Tensor + Hessians.

+ +
+ Source code in lightgbmlss/distributions/flow_utils.py + 
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def compute_gradients_and_hessians(self,
+                                   loss: torch.tensor,
+                                   predt: torch.tensor,
+                                   weights: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+
+    """
+    Calculates gradients and hessians.
+
+    Output gradients and hessians have shape (n_samples*n_outputs, 1).
+
+    Arguments:
+    ---------
+    loss: torch.Tensor
+        Loss.
+    predt: torch.Tensor
+        List of predicted parameters.
+    weights: np.ndarray
+        Weights.
+
+    Returns:
+    -------
+    grad: torch.Tensor
+        Gradients.
+    hess: torch.Tensor
+        Hessians.
+    """
+    if self.loss_fn == "nll":
+        # Gradient and Hessian
+        grad = autograd(loss, inputs=predt, create_graph=True)
+        hess = [autograd(grad[i].nansum(), inputs=predt[i], retain_graph=True)[0] for i in range(len(grad))]
+    elif self.loss_fn == "crps":
+        # Gradient and Hessian
+        grad = autograd(loss, inputs=predt, create_graph=True)
+        hess = [torch.ones_like(grad[i]) for i in range(len(grad))]
+
+    # Stabilization of Derivatives
+    if self.stabilization != "None":
+        grad = [self.stabilize_derivative(grad[i], type=self.stabilization) for i in range(len(grad))]
+        hess = [self.stabilize_derivative(hess[i], type=self.stabilization) for i in range(len(hess))]
+
+    # Reshape
+    grad = torch.cat(grad, axis=1).detach().numpy()
+    hess = torch.cat(hess, axis=1).detach().numpy()
+
+    # Weighting
+    grad *= weights
+    hess *= weights
+
+    # Reshape
+    grad = grad.ravel(order="F")
+    hess = hess.ravel(order="F")
+
+    return grad, hess
+
+
+
+ +
+ + +
+ + + + +
+ create_spline_flow(input_dim=None) + +
+ + +
+ +

Function that constructs a Normalizing Flow.

+
Arguments
+

input_dim: int + Input dimension.

+
Returns
+

spline_flow: Transform + Normalizing Flow.

+ +
+ Source code in lightgbmlss/distributions/flow_utils.py + 
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def create_spline_flow(self,
+                       input_dim: int = None,
+                       ) -> Transform:
+
+    """
+    Function that constructs a Normalizing Flow.
+
+    Arguments
+    ---------
+    input_dim: int
+        Input dimension.
+
+    Returns
+    -------
+    spline_flow: Transform
+        Normalizing Flow.
+    """
+
+    # Create flow distribution (currently only Normal)
+    loc, scale = torch.zeros(input_dim), torch.ones(input_dim)
+    flow_dist = self.base_dist(loc, scale)
+
+    # Create Spline Transform
+    torch.manual_seed(123)
+    spline_transform = self.flow_transform(input_dim,
+                                           count_bins=self.count_bins,
+                                           bound=self.bound,
+                                           order=self.order)
+
+    # Create Normalizing Flow
+    spline_flow = TransformedDistribution(flow_dist, [spline_transform, self.target_transform])
+
+    return spline_flow
+
+
+
+ +
+ + +
+ + + + +
+ crps_score(y, yhat_dist) + +
+ + +
+ +

Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples.

+
Arguments
+

y: torch.Tensor + Response variable of shape (n_observations,1). +yhat_dist: torch.Tensor + Predicted samples of shape (n_samples, n_observations).

+
Returns
+

crps: torch.Tensor + CRPS score.

+
References
+

Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. +Journal of the American Statistical Association. 102. 359-378.

+
Source
+

https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549

+ +
+ Source code in lightgbmlss/distributions/flow_utils.py + 
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def crps_score(self, y: torch.tensor, yhat_dist: torch.tensor) -> torch.tensor:
+    """
+    Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples.
+
+    Arguments
+    ---------
+    y: torch.Tensor
+        Response variable of shape (n_observations,1).
+    yhat_dist: torch.Tensor
+        Predicted samples of shape (n_samples, n_observations).
+
+    Returns
+    ---------
+    crps: torch.Tensor
+        CRPS score.
+
+    References
+    ---------
+    Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation.
+    Journal of the American Statistical Association. 102. 359-378.
+
+    Source
+    ---------
+    https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549
+    """
+    # Get the number of observations
+    n_samples = yhat_dist.shape[0]
+
+    # Sort the forecasts in ascending order
+    yhat_dist_sorted, _ = torch.sort(yhat_dist, 0)
+
+    # Create temporary tensors
+    y_cdf = torch.zeros_like(y)
+    yhat_cdf = torch.zeros_like(y)
+    yhat_prev = torch.zeros_like(y)
+    crps = torch.zeros_like(y)
+
+    # Loop over the predicted samples generated per observation
+    for yhat in yhat_dist_sorted:
+        yhat = yhat.reshape(-1, 1)
+        flag = (y_cdf == 0) * (y < yhat)
+        crps += flag * ((y - yhat_prev) * yhat_cdf ** 2)
+        crps += flag * ((yhat - y) * (yhat_cdf - 1) ** 2)
+        crps += (~flag) * ((yhat - yhat_prev) * (yhat_cdf - y_cdf) ** 2)
+        y_cdf += flag
+        yhat_cdf += 1 / n_samples
+        yhat_prev = yhat
+
+    # In case y_cdf == 0 after the loop
+    flag = (y_cdf == 0)
+    crps += flag * (y - yhat)
+
+    return crps
+
+
+
+ +
+ + +
+ + + + +
+ draw_samples(predt_params, n_samples=1000, seed=123) + +
+ + +
+ +

Function that draws n_samples from a predicted distribution.

+
Arguments
+

predt_params: pd.DataFrame + pd.DataFrame with predicted distributional parameters. +n_samples: int + Number of sample to draw from predicted response distribution. +seed: int + Manual seed.

+
Returns
+

pred_dist: pd.DataFrame + DataFrame with n_samples drawn from predicted response distribution.

+ +
+ Source code in lightgbmlss/distributions/flow_utils.py + 
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def draw_samples(self,
+                 predt_params: pd.DataFrame,
+                 n_samples: int = 1000,
+                 seed: int = 123
+                 ) -> pd.DataFrame:
+    """
+    Function that draws n_samples from a predicted distribution.
+
+    Arguments
+    ---------
+    predt_params: pd.DataFrame
+        pd.DataFrame with predicted distributional parameters.
+    n_samples: int
+        Number of sample to draw from predicted response distribution.
+    seed: int
+        Manual seed.
+
+    Returns
+    -------
+    pred_dist: pd.DataFrame
+        DataFrame with n_samples drawn from predicted response distribution.
+
+    """
+
+    torch.manual_seed(seed)
+
+    # Specify Normalizing Flow
+    pred_params = torch.tensor(predt_params.values)
+    flow_dist_pred = self.create_spline_flow(pred_params.shape[0])
+
+    # Replace parameters with estimated ones
+    _, flow_dist_pred = self.replace_parameters(pred_params, flow_dist_pred)
+
+    # Draw samples
+    flow_samples = pd.DataFrame(flow_dist_pred.sample((n_samples,)).squeeze().detach().numpy().T)
+    flow_samples.columns = [str("y_sample") + str(i) for i in range(flow_samples.shape[1])]
+
+    if self.discrete:
+        flow_samples = flow_samples.astype(int)
+
+    return flow_samples
+
+
+
+ +
+ + +
+ + + + +
+ flow_select(target, candidate_flows, max_iter=100, n_samples=1000, plot=False, figure_size=(10, 5)) + +
+ + +
+ +

Function that selects the most suitable normalizing flow specification among the candidate_flow for the +target variable, based on the NegLogLikelihood (lower is better).

+
Parameters
+

target: np.ndarray + Response variable. +candidate_flows: List + List of candidate normalizing flow specifications. +max_iter: int + Maximum number of iterations for the optimization. +n_samples: int + Number of samples drawn from the fitted distribution. +plot: bool + If True, a density plot of the actual and fitted distribution is created. +figure_size: tuple + Figure size of the density plot.

+
Returns
+

fit_df: pd.DataFrame + Dataframe with the loss values of the fitted normalizing flow.

+ +
+ Source code in lightgbmlss/distributions/flow_utils.py + 
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def flow_select(self,
+                target: np.ndarray,
+                candidate_flows: List,
+                max_iter: int = 100,
+                n_samples: int = 1000,
+                plot: bool = False,
+                figure_size: tuple = (10, 5),
+                ) -> pd.DataFrame:
+    """
+    Function that selects the most suitable normalizing flow specification among the candidate_flow for the
+    target variable, based on the NegLogLikelihood (lower is better).
+
+    Parameters
+    ----------
+    target: np.ndarray
+        Response variable.
+    candidate_flows: List
+        List of candidate normalizing flow specifications.
+    max_iter: int
+        Maximum number of iterations for the optimization.
+    n_samples: int
+        Number of samples drawn from the fitted distribution.
+    plot: bool
+        If True, a density plot of the actual and fitted distribution is created.
+    figure_size: tuple
+        Figure size of the density plot.
+
+    Returns
+    -------
+    fit_df: pd.DataFrame
+        Dataframe with the loss values of the fitted normalizing flow.
+    """
+    flow_list = []
+    total_iterations = len(candidate_flows)
+
+    with tqdm(total=total_iterations, desc="Fitting candidate normalizing flows") as pbar:
+        for flow in candidate_flows:
+            flow_name = str(flow.__class__).split(".")[-1].split("'>")[0]
+            flow_spec = f"(count_bins: {flow.count_bins}, order: {flow.order})"
+            flow_name = flow_name + flow_spec
+            pbar.set_description(f"Fitting {flow_name}")
+            flow_sel = flow
+            try:
+                loss, params = flow_sel.calculate_start_values(target=target, max_iter=max_iter)
+                fit_df = pd.DataFrame.from_dict(
+                    {flow_sel.loss_fn: loss.reshape(-1, ),
+                     "NormFlow": str(flow_name),
+                     "params": [params]
+                     }
+                )
+            except Exception as e:
+                warnings.warn(f"Error fitting {flow_sel} NormFlow: {str(e)}")
+                fit_df = pd.DataFrame(
+                    {flow_sel.loss_fn: np.nan,
+                     "NormFlow": str(flow_sel),
+                     "params": [np.nan] * flow_sel.n_dist_param
+                     }
+                )
+            flow_list.append(fit_df)
+            fit_df = pd.concat(flow_list).sort_values(by=flow_sel.loss_fn, ascending=True)
+            fit_df["rank"] = fit_df[flow_sel.loss_fn].rank().astype(int)
+            fit_df.set_index(fit_df["rank"], inplace=True)
+            pbar.update(1)
+        pbar.set_description(f"Fitting of candidate normalizing flows completed")
+
+    if plot:
+        # Select normalizing flow with the lowest loss
+        best_flow = fit_df[fit_df["rank"] == 1].reset_index(drop=True)
+        for flow in candidate_flows:
+            flow_name = str(flow.__class__).split(".")[-1].split("'>")[0]
+            flow_spec = f"(count_bins: {flow.count_bins}, order: {flow.order})"
+            flow_name = flow_name + flow_spec
+            if flow_name == best_flow["NormFlow"].values[0]:
+                best_flow_sel = flow
+                break
+
+        # Draw samples from distribution
+        flow_params = torch.tensor(best_flow["params"][0]).reshape(1, -1)
+        flow_dist_sel = best_flow_sel.create_spline_flow(input_dim=1)
+        _, flow_dist_sel = best_flow_sel.replace_parameters(flow_params, flow_dist_sel)
+        flow_samples = pd.DataFrame(flow_dist_sel.sample((n_samples,)).squeeze().detach().numpy().T)
+
+        # Plot actual and fitted distribution
+        flow_samples["type"] = f"Best-Fit: {best_flow['NormFlow'].values[0]}"
+
+        df_actual = pd.DataFrame(target)
+        df_actual["type"] = "Data"
+
+        plot_df = pd.concat([df_actual, flow_samples]).rename(columns={0: "variable"})
+
+        print(
+            ggplot(plot_df,
+                   aes(x="variable",
+                       color="type")) +
+            geom_density(size=1.1) +
+            theme_bw(base_size=15) +
+            theme(figure_size=figure_size,
+                  legend_position="right",
+                  legend_title=element_blank(),
+                  plot_title=element_text(hjust=0.5)) +
+            labs(title=f"Actual vs. Fitted Density",
+                 x="")
+        )
+
+    fit_df.drop(columns=["rank", "params"], inplace=True)
+
+    return fit_df
+
+
+
+ +
+ + +
+ + + + +
+ get_params_loss(predt, target, start_values, requires_grad=False) + +
+ + +
+ +

Function that returns the predicted parameters and the loss.

+
Arguments
+

predt: np.ndarray + Predicted values. +target: torch.Tensor + Target values. +start_values: List + Starting values for each parameter.

+
Returns
+

predt: torch.Tensor + Predicted parameters. +loss: torch.Tensor + Loss value.

+ +
+ Source code in lightgbmlss/distributions/flow_utils.py + 
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def get_params_loss(self,
+                    predt: np.ndarray,
+                    target: torch.Tensor,
+                    start_values: List[float],
+                    requires_grad: bool = False,
+                    ) -> Tuple[List[torch.Tensor], np.ndarray]:
+    """
+    Function that returns the predicted parameters and the loss.
+
+    Arguments
+    ---------
+    predt: np.ndarray
+        Predicted values.
+    target: torch.Tensor
+        Target values.
+    start_values: List
+        Starting values for each parameter.
+
+    Returns
+    -------
+    predt: torch.Tensor
+        Predicted parameters.
+    loss: torch.Tensor
+        Loss value.
+    """
+    # Reshape Target
+    target = target.view(-1)
+
+    # Predicted Parameters
+    predt = predt.reshape(-1, self.n_dist_param, order="F")
+
+    # Replace NaNs and infinity values with unconditional start values
+    nan_inf_mask = np.isnan(predt) | np.isinf(predt)
+    predt[nan_inf_mask] = np.take(start_values, np.where(nan_inf_mask)[1])
+
+    # Convert to torch.tensor
+    predt = torch.tensor(predt, dtype=torch.float32)
+
+    # Specify Normalizing Flow
+    flow_dist = self.create_spline_flow(target.shape[0])
+
+    # Replace parameters with estimated ones
+    params, flow_dist = self.replace_parameters(predt, flow_dist)
+
+    # Calculate loss
+    if self.loss_fn == "nll":
+        loss = -torch.nansum(flow_dist.log_prob(target))
+    elif self.loss_fn == "crps":
+        torch.manual_seed(123)
+        dist_samples = flow_dist.rsample((30,)).squeeze(-1)
+        loss = torch.nansum(self.crps_score(target, dist_samples))
+    else:
+        raise ValueError("Invalid loss function. Please select 'nll' or 'crps'.")
+
+    return params, loss
+
+
+
+ +
+ + +
+ + + + +
+ metric_fn(predt, data) + +
+ + +
+ +

Function that evaluates the predictions using the specified loss function.

+
Arguments
+

predt: np.ndarray + Predicted values. +data: lgb.Dataset + Data used for training.

+
Returns
+

name: str + Name of the evaluation metric. +loss: float + Loss value.

+ +
+ Source code in lightgbmlss/distributions/flow_utils.py + 
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def metric_fn(self, predt: np.ndarray, data: lgb.Dataset) -> Tuple[str, float, bool]:
+    """
+    Function that evaluates the predictions using the specified loss function.
+
+    Arguments
+    ---------
+    predt: np.ndarray
+        Predicted values.
+    data: lgb.Dataset
+        Data used for training.
+
+    Returns
+    -------
+    name: str
+        Name of the evaluation metric.
+    loss: float
+        Loss value.
+    """
+    # Target
+    target = torch.tensor(data.get_label().reshape(-1, 1))
+
+    # Start values (needed to replace NaNs in predt)
+    start_values = data.get_init_score().reshape(-1, self.n_dist_param)[0, :].tolist()
+
+    # Calculate loss
+    is_higher_better = False
+    _, loss = self.get_params_loss(predt, target, start_values)
+
+    return self.loss_fn, loss.detach(), is_higher_better
+
+
+
+ +
+ + +
+ + + + +
+ objective_fn(predt, data) + +
+ + +
+ +

Function to estimate gradients and hessians of normalizing flow parameters.

+
Arguments
+

predt: np.ndarray + Predicted values. +data: lgb.Dataset + Data used for training.

+
Returns
+

grad: np.ndarray + Gradient. +hess: np.ndarray + Hessian.

+ +
+ Source code in lightgbmlss/distributions/flow_utils.py + 
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def objective_fn(self, predt: np.ndarray, data: lgb.Dataset) -> Tuple[np.ndarray, np.ndarray]:
+
+    """
+    Function to estimate gradients and hessians of normalizing flow parameters.
+
+    Arguments
+    ---------
+    predt: np.ndarray
+        Predicted values.
+    data: lgb.Dataset
+        Data used for training.
+
+    Returns
+    -------
+    grad: np.ndarray
+        Gradient.
+    hess: np.ndarray
+        Hessian.
+    """
+    # Target
+    target = torch.tensor(data.get_label().reshape(-1, 1))
+
+    # Weights
+    if data.weight is None:
+        # Use 1 as weight if no weights are specified
+        weights = torch.ones_like(target, dtype=target.dtype).numpy()
+    else:
+        weights = data.get_weight().reshape(-1, 1)
+
+    # Start values (needed to replace NaNs in predt)
+    start_values = data.get_init_score().reshape(-1, self.n_dist_param)[0, :].tolist()
+
+    # Calculate gradients and hessians
+    predt, loss = self.get_params_loss(predt, target, start_values)
+    grad, hess = self.compute_gradients_and_hessians(loss, predt, weights)
+
+    return grad, hess
+
+
+
+ +
+ + +
+ + + + +
+ predict_dist(booster, data, start_values, pred_type='parameters', n_samples=1000, quantiles=[0.1, 0.5, 0.9], seed=123) + +
+ + +
+ +

Function that predicts from the trained model.

+
Arguments
+

booster : lgb.Booster + Trained model. +start_values : np.ndarray + Starting values for each distributional parameter. +data : pd.DataFrame + Data to predict from. +pred_type : str + Type of prediction: + - "samples" draws n_samples from the predicted distribution. + - "quantiles" calculates the quantiles from the predicted distribution. + - "parameters" returns the predicted distributional parameters. + - "expectiles" returns the predicted expectiles. +n_samples : int + Number of samples to draw from the predicted distribution. +quantiles : List[float] + List of quantiles to calculate from the predicted distribution. +seed : int + Seed for random number generator used to draw samples from the predicted distribution.

+
Returns
+

pred : pd.DataFrame + Predictions.

+ +
+ Source code in lightgbmlss/distributions/flow_utils.py + 
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def predict_dist(self,
+                 booster: lgb.Booster,
+                 data: pd.DataFrame,
+                 start_values: np.ndarray,
+                 pred_type: str = "parameters",
+                 n_samples: int = 1000,
+                 quantiles: list = [0.1, 0.5, 0.9],
+                 seed: str = 123
+                 ) -> pd.DataFrame:
+    """
+    Function that predicts from the trained model.
+
+    Arguments
+    ---------
+    booster : lgb.Booster
+        Trained model.
+    start_values : np.ndarray
+        Starting values for each distributional parameter.
+    data : pd.DataFrame
+        Data to predict from.
+    pred_type : str
+        Type of prediction:
+        - "samples" draws n_samples from the predicted distribution.
+        - "quantiles" calculates the quantiles from the predicted distribution.
+        - "parameters" returns the predicted distributional parameters.
+        - "expectiles" returns the predicted expectiles.
+    n_samples : int
+        Number of samples to draw from the predicted distribution.
+    quantiles : List[float]
+        List of quantiles to calculate from the predicted distribution.
+    seed : int
+        Seed for random number generator used to draw samples from the predicted distribution.
+
+    Returns
+    -------
+    pred : pd.DataFrame
+        Predictions.
+    """
+    # Predict raw scores
+    predt = torch.tensor(
+        booster.predict(data, raw_score=True),
+        dtype=torch.float32
+    ).reshape(-1, self.n_dist_param)
+
+    # Set init_score as starting point for each distributional parameter.
+    init_score_pred = torch.tensor(
+        np.ones(shape=(data.shape[0], 1)) * start_values,
+        dtype=torch.float32
+    )
+
+    # The predictions don't include the init_score specified in creating the train data. Hence, it needs to be
+    # added manually.
+    dist_params_predt = pd.DataFrame(
+        np.concatenate(
+            [predt[:, i].reshape(-1, 1) + init_score_pred[:, i].reshape(-1, 1) for i in range(self.n_dist_param)],
+            axis=1
+        )
+    )
+    dist_params_predt.columns = self.param_dict.keys()
+
+    # Draw samples from predicted response distribution
+    pred_samples_df = self.draw_samples(predt_params=dist_params_predt,
+                                        n_samples=n_samples,
+                                        seed=seed)
+
+    if pred_type == "parameters":
+        return dist_params_predt
+
+    elif pred_type == "samples":
+        return pred_samples_df
+
+    elif pred_type == "quantiles":
+        # Calculate quantiles from predicted response distribution
+        pred_quant_df = pred_samples_df.quantile(quantiles, axis=1).T
+        pred_quant_df.columns = [str("quant_") + str(quantiles[i]) for i in range(len(quantiles))]
+        if self.discrete:
+            pred_quant_df = pred_quant_df.astype(int)
+        return pred_quant_df
+
+
+
+ +
+ + +
+ + + + +
+ replace_parameters(params, flow_dist) + +
+ + +
+ +

Replace parameters with estimated ones.

+
Arguments
+

params: torch.Tensor + Estimated parameters. +flow_dist: Transform + Normalizing Flow.

+
Returns
+

params_list: List + List of estimated parameters. +flow_dist: Transform + Normalizing Flow with estimated parameters.

+ +
+ Source code in lightgbmlss/distributions/flow_utils.py + 
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def replace_parameters(self,
+                       params: torch.Tensor,
+                       flow_dist: Transform,
+                       ) -> Tuple[List, Transform]:
+    """
+    Replace parameters with estimated ones.
+
+    Arguments
+    ---------
+    params: torch.Tensor
+        Estimated parameters.
+    flow_dist: Transform
+        Normalizing Flow.
+
+    Returns
+    -------
+    params_list: List
+        List of estimated parameters.
+    flow_dist: Transform
+        Normalizing Flow with estimated parameters.
+    """
+
+    # Split parameters into list
+    if self.order == "quadratic":
+        params_list = torch.split(
+            params, [self.count_bins, self.count_bins, self.count_bins - 1],
+            dim=1)
+    elif self.order == "linear":
+        params_list = torch.split(
+            params, [self.count_bins, self.count_bins, self.count_bins - 1, self.count_bins],
+            dim=1)
+
+    # Replace parameters
+    for param, new_value in zip(flow_dist.transforms[0].parameters(), params_list):
+        param.data = new_value
+
+    # Get parameters (including require_grad=True)
+    params_list = list(flow_dist.transforms[0].parameters())
+
+    return params_list, flow_dist
+
+
+
+ +
+ + +
+ + + + +
+ stabilize_derivative(input_der, type='MAD') + +
+ + +
+ +

Function that stabilizes Gradients and Hessians.

+

Since parameters are estimated by optimizing Gradients and Hessians, it is important that these are comparable +in magnitude for all parameters. Due to imbalances regarding the ranges, the estimation might become unstable +so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve +convergence might be to standardize the response variable. This is especially useful if the range of the +response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the +standardization of the response are not always advised but need to be carefully considered.

+
Source
+

https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173

+
Arguments
+

input_der : torch.Tensor + Input derivative, either Gradient or Hessian. +type: str + Stabilization method. Can be either "None", "MAD" or "L2".

+
Returns
+

stab_der : torch.Tensor + Stabilized Gradient or Hessian.

+ +
+ Source code in lightgbmlss/distributions/flow_utils.py + 
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def stabilize_derivative(self, input_der: torch.Tensor, type: str = "MAD") -> torch.Tensor:
+    """
+    Function that stabilizes Gradients and Hessians.
+
+    Since parameters are estimated by optimizing Gradients and Hessians, it is important that these are comparable
+    in magnitude for all parameters. Due to imbalances regarding the ranges, the estimation might become unstable
+    so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve
+    convergence might be to standardize the response variable. This is especially useful if the range of the
+    response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the
+    standardization of the response are not always advised but need to be carefully considered.
+
+    Source
+    ---------
+    https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173
+
+    Arguments
+    ---------
+    input_der : torch.Tensor
+        Input derivative, either Gradient or Hessian.
+    type: str
+        Stabilization method. Can be either "None", "MAD" or "L2".
+
+    Returns
+    ---------
+    stab_der : torch.Tensor
+        Stabilized Gradient or Hessian.
+    """
+
+    if type == "MAD":
+        input_der = torch.nan_to_num(input_der, nan=float(torch.nanmean(input_der)))
+        div = torch.nanmedian(torch.abs(input_der - torch.nanmedian(input_der)))
+        div = torch.where(div < torch.tensor(1e-04), torch.tensor(1e-04), div)
+        stab_der = input_der / div
+
+    if type == "L2":
+        input_der = torch.nan_to_num(input_der, nan=float(torch.nanmean(input_der)))
+        div = torch.sqrt(torch.nanmean(input_der.pow(2)))
+        div = torch.where(div < torch.tensor(1e-04), torch.tensor(1e-04), div)
+        div = torch.where(div > torch.tensor(10000.0), torch.tensor(10000.0), div)
+        stab_der = input_der / div
+
+    if type == "None":
+        stab_der = torch.nan_to_num(input_der, nan=float(torch.nanmean(input_der)))
+
+    return stab_der
+
+
+
+ +
+ + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ zero_inflated + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ ZeroAdjustedBeta + + +

+ + +
+

+ Bases: ZeroInflatedDistribution

+ + +

A Zero-Adjusted Beta distribution.

+
Parameter
+

concentration1: torch.Tensor + 1st concentration parameter of the distribution (often referred to as alpha). +concentration0: torch.Tensor + 2nd concentration parameter of the distribution (often referred to as beta). +gate: torch.Tensor + Probability of zeros given via a Bernoulli distribution.

+
Source
+

https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py

+ +
+ Source code in lightgbmlss/distributions/zero_inflated.py + 
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class ZeroAdjustedBeta(ZeroInflatedDistribution):
+    """
+    A Zero-Adjusted Beta distribution.
+
+    Parameter
+    ---------
+    concentration1: torch.Tensor
+        1st concentration parameter of the distribution (often referred to as alpha).
+    concentration0: torch.Tensor
+        2nd concentration parameter of the distribution (often referred to as beta).
+    gate: torch.Tensor
+        Probability of zeros given via a Bernoulli distribution.
+
+    Source
+    ------
+    https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py
+    """
+    arg_constraints = {
+        "concentration1": constraints.positive,
+        "concentration0": constraints.positive,
+        "gate": constraints.unit_interval,
+    }
+    support = constraints.unit_interval
+
+    def __init__(self, concentration1, concentration0, gate=None, validate_args=None):
+        base_dist = Beta(concentration1=concentration1, concentration0=concentration0, validate_args=False)
+        base_dist._validate_args = validate_args
+
+        super().__init__(base_dist, gate=gate, validate_args=validate_args)
+
+    @property
+    def concentration1(self):
+        return self.base_dist.concentration1
+
+    @property
+    def concentration0(self):
+        return self.base_dist.concentration0
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + + +

+ ZeroAdjustedGamma + + +

+ + +
+

+ Bases: ZeroInflatedDistribution

+ + +

A Zero-Adjusted Gamma distribution.

+
Parameter
+

concentration: torch.Tensor + shape parameter of the distribution (often referred to as alpha) +rate: torch.Tensor + rate = 1 / scale of the distribution (often referred to as beta) +gate: torch.Tensor + Probability of zeros given via a Bernoulli distribution.

+
Source
+

https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py

+ +
+ Source code in lightgbmlss/distributions/zero_inflated.py + 
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class ZeroAdjustedGamma(ZeroInflatedDistribution):
+    """
+    A Zero-Adjusted Gamma distribution.
+
+    Parameter
+    ---------
+    concentration: torch.Tensor
+        shape parameter of the distribution (often referred to as alpha)
+    rate: torch.Tensor
+        rate = 1 / scale of the distribution (often referred to as beta)
+    gate: torch.Tensor
+        Probability of zeros given via a Bernoulli distribution.
+
+    Source
+    ------
+    https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py
+    """
+    arg_constraints = {
+        "concentration": constraints.positive,
+        "rate": constraints.positive,
+        "gate": constraints.unit_interval,
+    }
+    support = constraints.nonnegative
+
+    def __init__(self, concentration, rate, gate=None, validate_args=None):
+        base_dist = Gamma(concentration=concentration, rate=rate, validate_args=False)
+        base_dist._validate_args = validate_args
+
+        super().__init__(base_dist, gate=gate, validate_args=validate_args)
+
+    @property
+    def concentration(self):
+        return self.base_dist.concentration
+
+    @property
+    def rate(self):
+        return self.base_dist.rate
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + + +

+ ZeroAdjustedLogNormal + + +

+ + +
+

+ Bases: ZeroInflatedDistribution

+ + +

A Zero-Adjusted Log-Normal distribution.

+
Parameter
+

loc: torch.Tensor + Mean of log of distribution. +scale: torch.Tensor + Standard deviation of log of the distribution. +gate: torch.Tensor + Probability of zeros given via a Bernoulli distribution.

+
Source
+

https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py

+ +
+ Source code in lightgbmlss/distributions/zero_inflated.py + 
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class ZeroAdjustedLogNormal(ZeroInflatedDistribution):
+    """
+    A Zero-Adjusted Log-Normal distribution.
+
+    Parameter
+    ---------
+    loc: torch.Tensor
+        Mean of log of distribution.
+    scale: torch.Tensor
+        Standard deviation of log of the distribution.
+    gate: torch.Tensor
+        Probability of zeros given via a Bernoulli distribution.
+
+    Source
+    ------
+    https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py
+    """
+    arg_constraints = {
+        "loc": constraints.real,
+        "scale": constraints.positive,
+        "gate": constraints.unit_interval,
+    }
+    support = constraints.nonnegative
+
+    def __init__(self, loc, scale, gate=None, validate_args=None):
+        base_dist = LogNormal(loc=loc, scale=scale, validate_args=False)
+        base_dist._validate_args = validate_args
+
+        super().__init__(base_dist, gate=gate, validate_args=validate_args)
+
+    @property
+    def loc(self):
+        return self.base_dist.loc
+
+    @property
+    def scale(self):
+        return self.base_dist.scale
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + + +

+ ZeroInflatedDistribution + + +

+ + +
+

+ Bases: TorchDistribution

+ + +

Generic Zero Inflated distribution.

+

This can be used directly or can be used as a base class as e.g. for +:class:ZeroInflatedPoisson and :class:ZeroInflatedNegativeBinomial.

+
Parameters
+

gate : torch.Tensor + Probability of extra zeros given via a Bernoulli distribution. +base_dist : torch.distributions.Distribution + The base distribution.

+
Source
+

https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L18

+ +
+ Source code in lightgbmlss/distributions/zero_inflated.py + 
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class ZeroInflatedDistribution(TorchDistribution):
+    """
+    Generic Zero Inflated distribution.
+
+    This can be used directly or can be used as a base class as e.g. for
+    :class:`ZeroInflatedPoisson` and :class:`ZeroInflatedNegativeBinomial`.
+
+    Parameters
+    ----------
+    gate : torch.Tensor
+        Probability of extra zeros given via a Bernoulli distribution.
+    base_dist : torch.distributions.Distribution
+        The base distribution.
+
+    Source
+    ------
+    https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L18
+    """
+
+    arg_constraints = {
+        "gate": constraints.unit_interval,
+        "gate_logits": constraints.real,
+    }
+
+    def __init__(self, base_dist, *, gate=None, gate_logits=None, validate_args=None):
+        if (gate is None) == (gate_logits is None):
+            raise ValueError(
+                "Either `gate` or `gate_logits` must be specified, but not both."
+            )
+        if gate is not None:
+            batch_shape = broadcast_shape(gate.shape, base_dist.batch_shape)
+            self.gate = gate.expand(batch_shape)
+        else:
+            batch_shape = broadcast_shape(gate_logits.shape, base_dist.batch_shape)
+            self.gate_logits = gate_logits.expand(batch_shape)
+        if base_dist.event_shape:
+            raise ValueError(
+                "ZeroInflatedDistribution expected empty "
+                "base_dist.event_shape but got {}".format(base_dist.event_shape)
+            )
+
+        self.base_dist = base_dist.expand(batch_shape)
+        event_shape = torch.Size()
+
+        super().__init__(batch_shape, event_shape, validate_args)
+
+    @constraints.dependent_property
+    def support(self):
+        return self.base_dist.support
+
+    @lazy_property
+    def gate(self):
+        return logits_to_probs(self.gate_logits)
+
+    @lazy_property
+    def gate_logits(self):
+        return probs_to_logits(self.gate)
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+
+        zero_idx = (value == 0)
+        support = self.support
+        epsilon = abs(torch.finfo(value.dtype).eps)
+
+        if hasattr(support, "lower_bound"):
+            if is_identically_zero(getattr(support, "lower_bound", None)):
+                value = value.clamp_min(epsilon)
+
+        if hasattr(support, "upper_bound"):
+            if is_identically_one(getattr(support, "upper_bound", None)) & (value.max() == 1.0):
+                value = value.clamp_max(1 - epsilon)
+
+        if "gate" in self.__dict__:
+            gate, value = broadcast_all(self.gate, value)
+            log_prob = (-gate).log1p() + self.base_dist.log_prob(value)
+            log_prob = torch.where(zero_idx, (gate + log_prob.exp()).log(), log_prob)
+        else:
+            gate_logits, value = broadcast_all(self.gate_logits, value)
+            log_prob_minus_log_gate = -gate_logits + self.base_dist.log_prob(value)
+            log_gate = -softplus(-gate_logits)
+            log_prob = log_prob_minus_log_gate + log_gate
+            zero_log_prob = softplus(log_prob_minus_log_gate) + log_gate
+            log_prob = torch.where(zero_idx, zero_log_prob, log_prob)
+        return log_prob
+
+    def sample(self, sample_shape=torch.Size()):
+        shape = self._extended_shape(sample_shape)
+        with torch.no_grad():
+            mask = torch.bernoulli(self.gate.expand(shape)).bool()
+            samples = self.base_dist.expand(shape).sample()
+            samples = torch.where(mask, samples.new_zeros(()), samples)
+        return samples
+
+    @lazy_property
+    def mean(self):
+        return (1 - self.gate) * self.base_dist.mean
+
+    @lazy_property
+    def variance(self):
+        return (1 - self.gate) * (
+                self.base_dist.mean**2 + self.base_dist.variance
+        ) - self.mean**2
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(type(self), _instance)
+        batch_shape = torch.Size(batch_shape)
+        gate = self.gate.expand(batch_shape) if "gate" in self.__dict__ else None
+        gate_logits = (
+            self.gate_logits.expand(batch_shape)
+            if "gate_logits" in self.__dict__
+            else None
+        )
+        base_dist = self.base_dist.expand(batch_shape)
+        ZeroInflatedDistribution.__init__(
+            new, base_dist, gate=gate, gate_logits=gate_logits, validate_args=False
+        )
+        new._validate_args = self._validate_args
+        return new
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + + +

+ ZeroInflatedNegativeBinomial + + +

+ + +
+

+ Bases: ZeroInflatedDistribution

+ + +

A Zero Inflated Negative Binomial distribution.

+
Parameter
+

total_count: torch.Tensor + Non-negative number of negative Bernoulli trial. +probs: torch.Tensor + Event probabilities of success in the half open interval [0, 1). +logits: torch.Tensor + Event log-odds of success (log(p/(1-p))). +gate: torch.Tensor + Probability of extra zeros given via a Bernoulli distribution.

+
Source
+
    +
  • https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150
    • +
+ +
+ Source code in lightgbmlss/distributions/zero_inflated.py + 
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class ZeroInflatedNegativeBinomial(ZeroInflatedDistribution):
+    """
+    A Zero Inflated Negative Binomial distribution.
+
+    Parameter
+    ---------
+    total_count: torch.Tensor
+        Non-negative number of negative Bernoulli trial.
+    probs: torch.Tensor
+        Event probabilities of success in the half open interval [0, 1).
+    logits: torch.Tensor
+        Event log-odds of success (log(p/(1-p))).
+    gate: torch.Tensor
+        Probability of extra zeros given via a Bernoulli distribution.
+
+    Source
+    ------
+    - https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150
+    """
+
+    arg_constraints = {
+        "total_count": constraints.greater_than_eq(0),
+        "probs": constraints.half_open_interval(0.0, 1.0),
+        "logits": constraints.real,
+        "gate": constraints.unit_interval,
+    }
+    support = constraints.nonnegative_integer
+
+    def __init__(self, total_count, probs=None, gate=None, validate_args=None):
+        base_dist = NegativeBinomial(total_count=total_count, probs=probs, logits=None, validate_args=False)
+        base_dist._validate_args = validate_args
+
+        super().__init__(base_dist, gate=gate, validate_args=validate_args)
+
+    @property
+    def total_count(self):
+        return self.base_dist.total_count
+
+    @property
+    def probs(self):
+        return self.base_dist.probs
+
+    @property
+    def logits(self):
+        return self.base_dist.logits
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + + +

+ ZeroInflatedPoisson + + +

+ + +
+

+ Bases: ZeroInflatedDistribution

+ + +

A Zero-Inflated Poisson distribution.

+
Parameter
+

rate: torch.Tensor + The rate of the Poisson distribution. +gate: torch.Tensor + Probability of extra zeros given via a Bernoulli distribution.

+
Source
+

https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L121

+ +
+ Source code in lightgbmlss/distributions/zero_inflated.py + 
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class ZeroInflatedPoisson(ZeroInflatedDistribution):
+    """
+    A Zero-Inflated Poisson distribution.
+
+    Parameter
+    ---------
+    rate: torch.Tensor
+        The rate of the Poisson distribution.
+    gate: torch.Tensor
+        Probability of extra zeros given via a Bernoulli distribution.
+
+    Source
+    ------
+    https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L121
+    """
+    arg_constraints = {
+        "rate": constraints.positive,
+        "gate": constraints.unit_interval,
+    }
+    support = constraints.nonnegative_integer
+
+    def __init__(self, rate, gate=None, validate_args=None):
+        base_dist = Poisson(rate=rate, validate_args=False)
+        base_dist._validate_args = validate_args
+
+        super().__init__(base_dist, gate=gate, validate_args=validate_args)
+
+    @property
+    def rate(self):
+        return self.base_dist.rate
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ + +
+ +
+ +
+ +
+ + + + +

+ model + + +

+ +
+ + + +
+ + + + + + + + +
+ + + + +

+ LightGBMLSS + + +

+ + +
+ + +

LightGBMLSS model class

+
Parameters
+

dist : Distribution + DistributionClass object. + start_values : np.ndarray + Starting values for each distributional parameter.

+ +
+ Source code in lightgbmlss/model.py + 
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class LightGBMLSS:
+    """
+    LightGBMLSS model class
+
+    Parameters
+    ----------
+    dist : Distribution
+        DistributionClass object.
+     start_values : np.ndarray
+        Starting values for each distributional parameter.
+    """
+    def __init__(self, dist):
+        self.dist = dist             # Distribution object
+        self.start_values = None     # Starting values for distributional parameters
+
+    def set_params(self, params: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Set parameters for distributional model.
+
+        Arguments
+        ---------
+        params : Dict[str, Any]
+            Parameters for model.
+
+        Returns
+        -------
+        params : Dict[str, Any]
+            Updated Parameters for model.
+        """
+        params_adj = {"num_class": self.dist.n_dist_param,
+                      "metric": "None",
+                      "objective": "None",
+                      "random_seed": 123,
+                      "verbose": -1}
+        params.update(params_adj)
+
+        return params
+
+    def set_init_score(self, dmatrix: Dataset) -> None:
+        """
+        Set init_score for distributions.
+
+        Arguments
+        ---------
+        dmatrix : Dataset
+            Dataset to set base margin for.
+
+        Returns
+        -------
+        None
+        """
+        if self.start_values is None:
+            _, self.start_values = self.dist.calculate_start_values(dmatrix.get_label())
+        init_score = (np.ones(shape=(dmatrix.get_label().shape[0], 1))) * self.start_values
+        dmatrix.set_init_score(init_score.ravel(order="F"))
+
+    def train(self,
+              params: Dict[str, Any],
+              train_set: Dataset,
+              num_boost_round: int = 100,
+              valid_sets: Optional[List[Dataset]] = None,
+              valid_names: Optional[List[str]] = None,
+              init_model: Optional[Union[str, Path, Booster]] = None,
+              feature_name: _LGBM_FeatureNameConfiguration = 'auto',
+              categorical_feature: _LGBM_CategoricalFeatureConfiguration = 'auto',
+              keep_training_booster: bool = False,
+              callbacks: Optional[List[Callable]] = None
+              ) -> Booster:
+        """Function to perform the training of a LightGBMLSS model with given parameters.
+
+        Parameters
+        ----------
+        params : dict
+            Parameters for training. Values passed through ``params`` take precedence over those
+            supplied via arguments.
+        train_set : Dataset
+            Data to be trained on.
+        num_boost_round : int, optional (default=100)
+            Number of boosting iterations.
+        valid_sets : list of Dataset, or None, optional (default=None)
+            List of data to be evaluated on during training.
+        valid_names : list of str, or None, optional (default=None)
+            Names of ``valid_sets``.
+        init_model : str, pathlib.Path, Booster or None, optional (default=None)
+            Filename of LightGBM model or Booster instance used for continue training.
+        feature_name : list of str, or 'auto', optional (default="auto")
+            Feature names.
+            If 'auto' and data is pandas DataFrame, data columns names are used.
+        categorical_feature : list of str or int, or 'auto', optional (default="auto")
+            Categorical features.
+            If list of int, interpreted as indices.
+            If list of str, interpreted as feature names (need to specify ``feature_name`` as well).
+            If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
+            All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647).
+            Large values could be memory consuming. Consider using consecutive integers starting from zero.
+            All negative values in categorical features will be treated as missing values.
+            The output cannot be monotonically constrained with respect to a categorical feature.
+            Floating point numbers in categorical features will be rounded towards 0.
+        keep_training_booster : bool, optional (default=False)
+            Whether the returned Booster will be used to keep training.
+            If False, the returned value will be converted into _InnerPredictor before returning.
+            This means you won't be able to use ``eval``, ``eval_train`` or ``eval_valid`` methods of the returned Booster.
+            When your model is very large and cause the memory error,
+            you can try to set this param to ``True`` to avoid the model conversion performed during the internal call of ``model_to_string``.
+            You can still use _InnerPredictor as ``init_model`` for future continue training.
+        callbacks : list of callable, or None, optional (default=None)
+            List of callback functions that are applied at each iteration.
+            See Callbacks in Python API for more information.
+
+        Returns
+        -------
+        booster : Booster
+            The trained Booster model.
+        """
+        self.set_params(params)
+        self.set_init_score(train_set)
+
+        if valid_sets is not None:
+            valid_sets = self.set_valid_margin(valid_sets, self.start_values)
+
+        self.booster = lgb.train(params,
+                                 train_set,
+                                 num_boost_round=num_boost_round,
+                                 fobj=self.dist.objective_fn,
+                                 feval=self.dist.metric_fn,
+                                 valid_sets=valid_sets,
+                                 valid_names=valid_names,
+                                 init_model=init_model,
+                                 feature_name=feature_name,
+                                 categorical_feature=categorical_feature,
+                                 keep_training_booster=keep_training_booster,
+                                 callbacks=callbacks)
+
+    def cv(self,
+           params: Dict[str, Any],
+           train_set: Dataset,
+           num_boost_round: int = 100,
+           folds: Optional[Union[Iterable[Tuple[np.ndarray, np.ndarray]], _LGBMBaseCrossValidator]] = None,
+           nfold: int = 5,
+           stratified: bool = True,
+           shuffle: bool = True,
+           init_model: Optional[Union[str, Path, Booster]] = None,
+           feature_name: _LGBM_FeatureNameConfiguration = 'auto',
+           categorical_feature: _LGBM_CategoricalFeatureConfiguration = 'auto',
+           fpreproc: Optional[_LGBM_PreprocFunction] = None,
+           seed: int = 123,
+           callbacks: Optional[List[Callable]] = None,
+           eval_train_metric: bool = False,
+           return_cvbooster: bool = False
+           ) -> Dict[str, Union[List[float], CVBooster]]:
+        """Function to cross-validate a LightGBMLSS model with given parameters.
+
+        Parameters
+        ----------
+        params : dict
+            Parameters for training. Values passed through ``params`` take precedence over those
+            supplied via arguments.
+        train_set : Dataset
+            Data to be trained on.
+        num_boost_round : int, optional (default=100)
+            Number of boosting iterations.
+        folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None)
+            If generator or iterator, it should yield the train and test indices for each fold.
+            If object, it should be one of the scikit-learn splitter classes
+            (https://scikit-learn.org/stable/modules/classes.html#splitter-classes)
+            and have ``split`` method.
+            This argument has highest priority over other data split arguments.
+        nfold : int, optional (default=5)
+            Number of folds in CV.
+        stratified : bool, optional (default=True)
+            Whether to perform stratified sampling.
+        shuffle : bool, optional (default=True)
+            Whether to shuffle before splitting data.
+        init_model : str, pathlib.Path, Booster or None, optional (default=None)
+            Filename of LightGBM model or Booster instance used for continue training.
+        feature_name : list of str, or 'auto', optional (default="auto")
+            Feature names.
+            If 'auto' and data is pandas DataFrame, data columns names are used.
+        categorical_feature : list of str or int, or 'auto', optional (default="auto")
+            Categorical features.
+            If list of int, interpreted as indices.
+            If list of str, interpreted as feature names (need to specify ``feature_name`` as well).
+            If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
+            All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647).
+            Large values could be memory consuming. Consider using consecutive integers starting from zero.
+            All negative values in categorical features will be treated as missing values.
+            The output cannot be monotonically constrained with respect to a categorical feature.
+            Floating point numbers in categorical features will be rounded towards 0.
+        fpreproc : callable or None, optional (default=None)
+            Preprocessing function that takes (dtrain, dtest, params)
+            and returns transformed versions of those.
+        seed : int, optional (default=0)
+            Seed used to generate the folds (passed to numpy.random.seed).
+        callbacks : list of callable, or None, optional (default=None)
+            List of callback functions that are applied at each iteration.
+            See Callbacks in Python API for more information.
+        eval_train_metric : bool, optional (default=False)
+            Whether to display the train metric in progress.
+            The score of the metric is calculated again after each training step, so there is some impact on performance.
+        return_cvbooster : bool, optional (default=False)
+            Whether to return Booster models trained on each fold through ``CVBooster``.
+
+        Returns
+        -------
+        eval_hist : dict
+            Evaluation history.
+            The dictionary has the following format:
+            {'metric1-mean': [values], 'metric1-stdv': [values],
+            'metric2-mean': [values], 'metric2-stdv': [values],
+            ...}.
+            If ``return_cvbooster=True``, also returns trained boosters wrapped in a ``CVBooster`` object via ``cvbooster`` key.
+        """
+        self.set_params(params)
+        self.set_init_score(train_set)
+
+        self.bstLSS_cv = lgb.cv(params,
+                                train_set,
+                                fobj=self.dist.objective_fn,
+                                feval=self.dist.metric_fn,
+                                num_boost_round=num_boost_round,
+                                folds=folds,
+                                nfold=nfold,
+                                stratified=False,
+                                shuffle=False,
+                                metrics=None,
+                                init_model=init_model,
+                                feature_name=feature_name,
+                                categorical_feature=categorical_feature,
+                                fpreproc=fpreproc,
+                                seed=seed,
+                                callbacks=callbacks,
+                                eval_train_metric=eval_train_metric,
+                                return_cvbooster=return_cvbooster)
+
+        return self.bstLSS_cv
+
+    def hyper_opt(
+            self,
+            hp_dict: Dict,
+            train_set: lgb.Dataset,
+            num_boost_round=500,
+            nfold=10,
+            early_stopping_rounds=20,
+            max_minutes=10,
+            n_trials=None,
+            study_name=None,
+            silence=False,
+            seed=None,
+            hp_seed=None
+    ):
+        """
+        Function to tune hyperparameters using optuna.
+
+        Arguments
+        ----------
+        hp_dict: dict
+            Dictionary of hyperparameters to tune.
+        train_set: lgb.Dataset
+            Training data.
+        num_boost_round: int
+            Number of boosting iterations.
+        nfold: int
+            Number of folds in CV.
+        early_stopping_rounds: int
+            Activates early stopping. Cross-Validation metric (average of validation
+            metric computed over CV folds) needs to improve at least once in
+            every **early_stopping_rounds** round(s) to continue training.
+            The last entry in the evaluation history will represent the best iteration.
+            If there's more than one metric in the **eval_metric** parameter given in
+            **params**, the last metric will be used for early stopping.
+        max_minutes: int
+            Time budget in minutes, i.e., stop study after the given number of minutes.
+        n_trials: int
+            The number of trials. If this argument is set to None, there is no limitation on the number of trials.
+        study_name: str
+            Name of the hyperparameter study.
+        silence: bool
+            Controls the verbosity of the trail, i.e., user can silence the outputs of the trail.
+        seed: int
+            Seed used to generate the folds (passed to numpy.random.seed).
+        hp_seed: int
+            Seed for random number generator used in the Bayesian hyper-parameter search.
+
+        Returns
+        -------
+        opt_params : dict
+            Optimal hyper-parameters.
+        """
+
+        def objective(trial):
+
+            hyper_params = {}
+
+            for param_name, param_value in hp_dict.items():
+
+                param_type = param_value[0]
+
+                if param_type == "categorical" or param_type == "none":
+                    hyper_params.update({param_name: trial.suggest_categorical(param_name, param_value[1])})
+
+                elif param_type == "float":
+                    param_constraints = param_value[1]
+                    param_low = param_constraints["low"]
+                    param_high = param_constraints["high"]
+                    param_log = param_constraints["log"]
+                    hyper_params.update(
+                        {param_name: trial.suggest_float(param_name,
+                                                         low=param_low,
+                                                         high=param_high,
+                                                         log=param_log
+                                                         )
+                         })
+
+                elif param_type == "int":
+                    param_constraints = param_value[1]
+                    param_low = param_constraints["low"]
+                    param_high = param_constraints["high"]
+                    param_log = param_constraints["log"]
+                    hyper_params.update(
+                        {param_name: trial.suggest_int(param_name,
+                                                       low=param_low,
+                                                       high=param_high,
+                                                       log=param_log
+                                                       )
+                         })
+
+            # Add booster if not included in dictionary
+            if "boosting" not in hyper_params.keys():
+                hyper_params.update({"boosting": trial.suggest_categorical("boosting", ["gbdt"])})
+
+            # Add pruning and early stopping
+            pruning_callback = LightGBMPruningCallback(trial, self.dist.loss_fn)
+            early_stopping_callback = lgb.early_stopping(stopping_rounds=early_stopping_rounds, verbose=False)
+
+            lgblss_param_tuning = self.cv(hyper_params,
+                                          train_set,
+                                          num_boost_round=num_boost_round,
+                                          nfold=nfold,
+                                          callbacks=[pruning_callback, early_stopping_callback],
+                                          seed=seed,
+                                          )
+
+            # Extract the optimal number of boosting rounds
+            opt_rounds = np.argmin(np.array(lgblss_param_tuning[f"{self.dist.loss_fn}-mean"])) + 1
+            trial.set_user_attr("opt_round", int(opt_rounds))
+
+            # Extract the best score
+            best_score = np.min(np.array(lgblss_param_tuning[f"{self.dist.loss_fn}-mean"]))
+
+            return best_score
+
+        if study_name is None:
+            study_name = "LightGBMLSS Hyper-Parameter Optimization"
+
+        if silence:
+            optuna.logging.set_verbosity(optuna.logging.WARNING)
+
+        if hp_seed is not None:
+            sampler = TPESampler(seed=hp_seed)
+        else:
+            sampler = TPESampler()
+
+        pruner = optuna.pruners.MedianPruner(n_startup_trials=10, n_warmup_steps=20)
+        study = optuna.create_study(sampler=sampler, pruner=pruner, direction="minimize", study_name=study_name)
+        study.optimize(objective, n_trials=n_trials, timeout=60 * max_minutes, show_progress_bar=True)
+
+        print("\nHyper-Parameter Optimization successfully finished.")
+        print("  Number of finished trials: ", len(study.trials))
+        print("  Best trial:")
+        opt_param = study.best_trial
+
+        # Add optimal stopping round
+        opt_param.params["opt_rounds"] = study.trials_dataframe()["user_attrs_opt_round"][
+            study.trials_dataframe()["value"].idxmin()]
+        opt_param.params["opt_rounds"] = int(opt_param.params["opt_rounds"])
+
+        print("    Value: {}".format(opt_param.value))
+        print("    Params: ")
+        for key, value in opt_param.params.items():
+            print("    {}: {}".format(key, value))
+
+        return opt_param.params
+
+    def predict(self,
+                data: pd.DataFrame,
+                pred_type: str = "parameters",
+                n_samples: int = 1000,
+                quantiles: list = [0.1, 0.5, 0.9],
+                seed: str = 123):
+        """
+        Function that predicts from the trained model.
+
+        Arguments
+        ---------
+        data : pd.DataFrame
+            Data to predict from.
+        pred_type : str
+            Type of prediction:
+            - "samples" draws n_samples from the predicted distribution.
+            - "quantiles" calculates the quantiles from the predicted distribution.
+            - "parameters" returns the predicted distributional parameters.
+            - "expectiles" returns the predicted expectiles.
+        n_samples : int
+            Number of samples to draw from the predicted distribution.
+        quantiles : List[float]
+            List of quantiles to calculate from the predicted distribution.
+        seed : int
+            Seed for random number generator used to draw samples from the predicted distribution.
+
+        Returns
+        -------
+        predt_df : pd.DataFrame
+            Predictions.
+        """
+
+        # Predict
+        predt_df = self.dist.predict_dist(booster=self.booster,
+                                          data=data,
+                                          start_values=self.start_values,
+                                          pred_type=pred_type,
+                                          n_samples=n_samples,
+                                          quantiles=quantiles,
+                                          seed=seed)
+
+        return predt_df
+
+    def plot(self,
+             X: pd.DataFrame,
+             feature: str = "x",
+             parameter: str = "loc",
+             max_display: int = 15,
+             plot_type: str = "Partial_Dependence"):
+        """
+        LightGBMLSS SHap plotting function.
+
+        Arguments:
+        ---------
+        X: pd.DataFrame
+            Train/Test Data
+        feature: str
+            Specifies which feature is to be plotted.
+        parameter: str
+            Specifies which distributional parameter is to be plotted.
+        max_display: int
+            Specifies the maximum number of features to be displayed.
+        plot_type: str
+            Specifies the type of plot:
+                "Partial_Dependence" plots the partial dependence of the parameter on the feature.
+                "Feature_Importance" plots the feature importance of the parameter.
+        """
+        shap.initjs()
+        explainer = shap.TreeExplainer(self.booster)
+        shap_values = explainer(X)
+
+        param_pos = self.dist.distribution_arg_names.index(parameter)
+
+        if plot_type == "Partial_Dependence":
+            if self.dist.n_dist_param == 1:
+                shap.plots.scatter(shap_values[:, feature], color=shap_values[:, feature])
+            else:
+                shap.plots.scatter(shap_values[:, feature][:, param_pos], color=shap_values[:, feature][:, param_pos])
+        elif plot_type == "Feature_Importance":
+            if self.dist.n_dist_param == 1:
+                shap.plots.bar(shap_values, max_display=max_display if X.shape[1] > max_display else X.shape[1])
+            else:
+                shap.plots.bar(
+                    shap_values[:, :, param_pos], max_display=max_display if X.shape[1] > max_display else X.shape[1]
+                )
+
+    def expectile_plot(self,
+                       X: pd.DataFrame,
+                       feature: str = "x",
+                       expectile: str = "0.05",
+                       plot_type: str = "Partial_Dependence"):
+        """
+        LightGBMLSS function for plotting expectile SHapley values.
+
+        X: pd.DataFrame
+            Train/Test Data
+        feature: str
+            Specifies which feature to use for plotting Partial_Dependence plot.
+        expectile: str
+            Specifies which expectile to plot.
+        plot_type: str
+            Specifies which SHapley-plot to visualize. Currently, "Partial_Dependence" and "Feature_Importance"
+            are supported.
+        """
+
+        shap.initjs()
+        explainer = shap.TreeExplainer(self.booster)
+        shap_values = explainer(X)
+
+        expect_pos = list(self.dist.param_dict.keys()).index(expectile)
+
+        if plot_type == "Partial_Dependence":
+            shap.plots.scatter(shap_values[:, feature][:, expect_pos], color=shap_values[:, feature][:, expect_pos])
+        elif plot_type == "Feature_Importance":
+            shap.plots.bar(shap_values[:, :, expect_pos], max_display=15 if X.shape[1] > 15 else X.shape[1])
+
+    def set_valid_margin(self,
+                         valid_sets: list,
+                         start_values: np.ndarray
+                         ) -> list:
+        """
+        Function that sets the base margin for the validation set.
+
+        Arguments
+        ---------
+        valid_sets : list
+            List of tuples containing the train and evaluation set.
+        valid_names: list
+            List of tuples containing the name of train and evaluation set.
+        start_values : np.ndarray
+            Array containing the start values for the distributional parameters.
+
+        Returns
+        -------
+        valid_sets : list
+            List of tuples containing the train and evaluation set.
+        """
+        valid_sets1 = valid_sets[0]
+        init_score_val1 = (np.ones(shape=(valid_sets1.get_label().shape[0], 1))) * start_values
+        valid_sets1.set_init_score(init_score_val1.ravel(order="F"))
+
+        valid_sets2 = valid_sets[1]
+        init_score_val2 = (np.ones(shape=(valid_sets2.get_label().shape[0], 1))) * start_values
+        valid_sets2.set_init_score(init_score_val2.ravel(order="F"))
+
+        valid_sets = [valid_sets1, valid_sets2]
+
+        return valid_sets
+
+    def save_model(self,
+                   model_path: str
+                   ) -> None:
+        """
+        Save the model to a file.
+
+        Parameters
+        ----------
+        model_path : str
+            The path to save the model.
+
+        Returns
+        -------
+        None
+        """
+        with open(model_path, "wb") as f:
+            pickle.dump(self, f)
+
+    @staticmethod
+    def load_model(model_path: str):
+        """
+        Load the model from a file.
+
+        Parameters
+        ----------
+        model_path : str
+            The path to the saved model.
+
+        Returns
+        -------
+        The loaded model.
+        """
+        with open(model_path, "rb") as f:
+            return pickle.load(f)
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +

+ cv(params, train_set, num_boost_round=100, folds=None, nfold=5, stratified=True, shuffle=True, init_model=None, feature_name='auto', categorical_feature='auto', fpreproc=None, seed=123, callbacks=None, eval_train_metric=False, return_cvbooster=False) + +

+ + +
+ +

Function to cross-validate a LightGBMLSS model with given parameters.

+
Parameters
+

params : dict + Parameters for training. Values passed through params take precedence over those + supplied via arguments. +train_set : Dataset + Data to be trained on. +num_boost_round : int, optional (default=100) + Number of boosting iterations. +folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None) + If generator or iterator, it should yield the train and test indices for each fold. + If object, it should be one of the scikit-learn splitter classes + (https://scikit-learn.org/stable/modules/classes.html#splitter-classes) + and have split method. + This argument has highest priority over other data split arguments. +nfold : int, optional (default=5) + Number of folds in CV. +stratified : bool, optional (default=True) + Whether to perform stratified sampling. +shuffle : bool, optional (default=True) + Whether to shuffle before splitting data. +init_model : str, pathlib.Path, Booster or None, optional (default=None) + Filename of LightGBM model or Booster instance used for continue training. +feature_name : list of str, or 'auto', optional (default="auto") + Feature names. + If 'auto' and data is pandas DataFrame, data columns names are used. +categorical_feature : list of str or int, or 'auto', optional (default="auto") + Categorical features. + If list of int, interpreted as indices. + If list of str, interpreted as feature names (need to specify feature_name as well). + If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. + All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). + Large values could be memory consuming. Consider using consecutive integers starting from zero. + All negative values in categorical features will be treated as missing values. + The output cannot be monotonically constrained with respect to a categorical feature. + Floating point numbers in categorical features will be rounded towards 0. +fpreproc : callable or None, optional (default=None) + Preprocessing function that takes (dtrain, dtest, params) + and returns transformed versions of those. +seed : int, optional (default=0) + Seed used to generate the folds (passed to numpy.random.seed). +callbacks : list of callable, or None, optional (default=None) + List of callback functions that are applied at each iteration. + See Callbacks in Python API for more information. +eval_train_metric : bool, optional (default=False) + Whether to display the train metric in progress. + The score of the metric is calculated again after each training step, so there is some impact on performance. +return_cvbooster : bool, optional (default=False) + Whether to return Booster models trained on each fold through CVBooster.

+
Returns
+

eval_hist : dict + Evaluation history. + The dictionary has the following format: + {'metric1-mean': [values], 'metric1-stdv': [values], + 'metric2-mean': [values], 'metric2-stdv': [values], + ...}. + If return_cvbooster=True, also returns trained boosters wrapped in a CVBooster object via cvbooster key.

+ +
+ Source code in lightgbmlss/model.py + 
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def cv(self,
+       params: Dict[str, Any],
+       train_set: Dataset,
+       num_boost_round: int = 100,
+       folds: Optional[Union[Iterable[Tuple[np.ndarray, np.ndarray]], _LGBMBaseCrossValidator]] = None,
+       nfold: int = 5,
+       stratified: bool = True,
+       shuffle: bool = True,
+       init_model: Optional[Union[str, Path, Booster]] = None,
+       feature_name: _LGBM_FeatureNameConfiguration = 'auto',
+       categorical_feature: _LGBM_CategoricalFeatureConfiguration = 'auto',
+       fpreproc: Optional[_LGBM_PreprocFunction] = None,
+       seed: int = 123,
+       callbacks: Optional[List[Callable]] = None,
+       eval_train_metric: bool = False,
+       return_cvbooster: bool = False
+       ) -> Dict[str, Union[List[float], CVBooster]]:
+    """Function to cross-validate a LightGBMLSS model with given parameters.
+
+    Parameters
+    ----------
+    params : dict
+        Parameters for training. Values passed through ``params`` take precedence over those
+        supplied via arguments.
+    train_set : Dataset
+        Data to be trained on.
+    num_boost_round : int, optional (default=100)
+        Number of boosting iterations.
+    folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None)
+        If generator or iterator, it should yield the train and test indices for each fold.
+        If object, it should be one of the scikit-learn splitter classes
+        (https://scikit-learn.org/stable/modules/classes.html#splitter-classes)
+        and have ``split`` method.
+        This argument has highest priority over other data split arguments.
+    nfold : int, optional (default=5)
+        Number of folds in CV.
+    stratified : bool, optional (default=True)
+        Whether to perform stratified sampling.
+    shuffle : bool, optional (default=True)
+        Whether to shuffle before splitting data.
+    init_model : str, pathlib.Path, Booster or None, optional (default=None)
+        Filename of LightGBM model or Booster instance used for continue training.
+    feature_name : list of str, or 'auto', optional (default="auto")
+        Feature names.
+        If 'auto' and data is pandas DataFrame, data columns names are used.
+    categorical_feature : list of str or int, or 'auto', optional (default="auto")
+        Categorical features.
+        If list of int, interpreted as indices.
+        If list of str, interpreted as feature names (need to specify ``feature_name`` as well).
+        If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
+        All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647).
+        Large values could be memory consuming. Consider using consecutive integers starting from zero.
+        All negative values in categorical features will be treated as missing values.
+        The output cannot be monotonically constrained with respect to a categorical feature.
+        Floating point numbers in categorical features will be rounded towards 0.
+    fpreproc : callable or None, optional (default=None)
+        Preprocessing function that takes (dtrain, dtest, params)
+        and returns transformed versions of those.
+    seed : int, optional (default=0)
+        Seed used to generate the folds (passed to numpy.random.seed).
+    callbacks : list of callable, or None, optional (default=None)
+        List of callback functions that are applied at each iteration.
+        See Callbacks in Python API for more information.
+    eval_train_metric : bool, optional (default=False)
+        Whether to display the train metric in progress.
+        The score of the metric is calculated again after each training step, so there is some impact on performance.
+    return_cvbooster : bool, optional (default=False)
+        Whether to return Booster models trained on each fold through ``CVBooster``.
+
+    Returns
+    -------
+    eval_hist : dict
+        Evaluation history.
+        The dictionary has the following format:
+        {'metric1-mean': [values], 'metric1-stdv': [values],
+        'metric2-mean': [values], 'metric2-stdv': [values],
+        ...}.
+        If ``return_cvbooster=True``, also returns trained boosters wrapped in a ``CVBooster`` object via ``cvbooster`` key.
+    """
+    self.set_params(params)
+    self.set_init_score(train_set)
+
+    self.bstLSS_cv = lgb.cv(params,
+                            train_set,
+                            fobj=self.dist.objective_fn,
+                            feval=self.dist.metric_fn,
+                            num_boost_round=num_boost_round,
+                            folds=folds,
+                            nfold=nfold,
+                            stratified=False,
+                            shuffle=False,
+                            metrics=None,
+                            init_model=init_model,
+                            feature_name=feature_name,
+                            categorical_feature=categorical_feature,
+                            fpreproc=fpreproc,
+                            seed=seed,
+                            callbacks=callbacks,
+                            eval_train_metric=eval_train_metric,
+                            return_cvbooster=return_cvbooster)
+
+    return self.bstLSS_cv
+
+
+
+ +
+ + +
+ + + + +

+ expectile_plot(X, feature='x', expectile='0.05', plot_type='Partial_Dependence') + +

+ + +
+ +

LightGBMLSS function for plotting expectile SHapley values.

+ +
+ pd.DataFrame +

Train/Test Data

+

feature: str + Specifies which feature to use for plotting Partial_Dependence plot. +expectile: str + Specifies which expectile to plot. +plot_type: str + Specifies which SHapley-plot to visualize. Currently, "Partial_Dependence" and "Feature_Importance" + are supported.

+ +
+ Source code in lightgbmlss/model.py + 
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def expectile_plot(self,
+                   X: pd.DataFrame,
+                   feature: str = "x",
+                   expectile: str = "0.05",
+                   plot_type: str = "Partial_Dependence"):
+    """
+    LightGBMLSS function for plotting expectile SHapley values.
+
+    X: pd.DataFrame
+        Train/Test Data
+    feature: str
+        Specifies which feature to use for plotting Partial_Dependence plot.
+    expectile: str
+        Specifies which expectile to plot.
+    plot_type: str
+        Specifies which SHapley-plot to visualize. Currently, "Partial_Dependence" and "Feature_Importance"
+        are supported.
+    """
+
+    shap.initjs()
+    explainer = shap.TreeExplainer(self.booster)
+    shap_values = explainer(X)
+
+    expect_pos = list(self.dist.param_dict.keys()).index(expectile)
+
+    if plot_type == "Partial_Dependence":
+        shap.plots.scatter(shap_values[:, feature][:, expect_pos], color=shap_values[:, feature][:, expect_pos])
+    elif plot_type == "Feature_Importance":
+        shap.plots.bar(shap_values[:, :, expect_pos], max_display=15 if X.shape[1] > 15 else X.shape[1])
+
+
+
+ +
+ + +
+ + + + +

+ hyper_opt(hp_dict, train_set, num_boost_round=500, nfold=10, early_stopping_rounds=20, max_minutes=10, n_trials=None, study_name=None, silence=False, seed=None, hp_seed=None) + +

+ + +
+ +

Function to tune hyperparameters using optuna.

+
Arguments
+

hp_dict: dict + Dictionary of hyperparameters to tune. +train_set: lgb.Dataset + Training data. +num_boost_round: int + Number of boosting iterations. +nfold: int + Number of folds in CV. +early_stopping_rounds: int + Activates early stopping. Cross-Validation metric (average of validation + metric computed over CV folds) needs to improve at least once in + every early_stopping_rounds round(s) to continue training. + The last entry in the evaluation history will represent the best iteration. + If there's more than one metric in the eval_metric parameter given in + params, the last metric will be used for early stopping. +max_minutes: int + Time budget in minutes, i.e., stop study after the given number of minutes. +n_trials: int + The number of trials. If this argument is set to None, there is no limitation on the number of trials. +study_name: str + Name of the hyperparameter study. +silence: bool + Controls the verbosity of the trail, i.e., user can silence the outputs of the trail. +seed: int + Seed used to generate the folds (passed to numpy.random.seed). +hp_seed: int + Seed for random number generator used in the Bayesian hyper-parameter search.

+
Returns
+

opt_params : dict + Optimal hyper-parameters.

+ +
+ Source code in lightgbmlss/model.py + 
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def hyper_opt(
+        self,
+        hp_dict: Dict,
+        train_set: lgb.Dataset,
+        num_boost_round=500,
+        nfold=10,
+        early_stopping_rounds=20,
+        max_minutes=10,
+        n_trials=None,
+        study_name=None,
+        silence=False,
+        seed=None,
+        hp_seed=None
+):
+    """
+    Function to tune hyperparameters using optuna.
+
+    Arguments
+    ----------
+    hp_dict: dict
+        Dictionary of hyperparameters to tune.
+    train_set: lgb.Dataset
+        Training data.
+    num_boost_round: int
+        Number of boosting iterations.
+    nfold: int
+        Number of folds in CV.
+    early_stopping_rounds: int
+        Activates early stopping. Cross-Validation metric (average of validation
+        metric computed over CV folds) needs to improve at least once in
+        every **early_stopping_rounds** round(s) to continue training.
+        The last entry in the evaluation history will represent the best iteration.
+        If there's more than one metric in the **eval_metric** parameter given in
+        **params**, the last metric will be used for early stopping.
+    max_minutes: int
+        Time budget in minutes, i.e., stop study after the given number of minutes.
+    n_trials: int
+        The number of trials. If this argument is set to None, there is no limitation on the number of trials.
+    study_name: str
+        Name of the hyperparameter study.
+    silence: bool
+        Controls the verbosity of the trail, i.e., user can silence the outputs of the trail.
+    seed: int
+        Seed used to generate the folds (passed to numpy.random.seed).
+    hp_seed: int
+        Seed for random number generator used in the Bayesian hyper-parameter search.
+
+    Returns
+    -------
+    opt_params : dict
+        Optimal hyper-parameters.
+    """
+
+    def objective(trial):
+
+        hyper_params = {}
+
+        for param_name, param_value in hp_dict.items():
+
+            param_type = param_value[0]
+
+            if param_type == "categorical" or param_type == "none":
+                hyper_params.update({param_name: trial.suggest_categorical(param_name, param_value[1])})
+
+            elif param_type == "float":
+                param_constraints = param_value[1]
+                param_low = param_constraints["low"]
+                param_high = param_constraints["high"]
+                param_log = param_constraints["log"]
+                hyper_params.update(
+                    {param_name: trial.suggest_float(param_name,
+                                                     low=param_low,
+                                                     high=param_high,
+                                                     log=param_log
+                                                     )
+                     })
+
+            elif param_type == "int":
+                param_constraints = param_value[1]
+                param_low = param_constraints["low"]
+                param_high = param_constraints["high"]
+                param_log = param_constraints["log"]
+                hyper_params.update(
+                    {param_name: trial.suggest_int(param_name,
+                                                   low=param_low,
+                                                   high=param_high,
+                                                   log=param_log
+                                                   )
+                     })
+
+        # Add booster if not included in dictionary
+        if "boosting" not in hyper_params.keys():
+            hyper_params.update({"boosting": trial.suggest_categorical("boosting", ["gbdt"])})
+
+        # Add pruning and early stopping
+        pruning_callback = LightGBMPruningCallback(trial, self.dist.loss_fn)
+        early_stopping_callback = lgb.early_stopping(stopping_rounds=early_stopping_rounds, verbose=False)
+
+        lgblss_param_tuning = self.cv(hyper_params,
+                                      train_set,
+                                      num_boost_round=num_boost_round,
+                                      nfold=nfold,
+                                      callbacks=[pruning_callback, early_stopping_callback],
+                                      seed=seed,
+                                      )
+
+        # Extract the optimal number of boosting rounds
+        opt_rounds = np.argmin(np.array(lgblss_param_tuning[f"{self.dist.loss_fn}-mean"])) + 1
+        trial.set_user_attr("opt_round", int(opt_rounds))
+
+        # Extract the best score
+        best_score = np.min(np.array(lgblss_param_tuning[f"{self.dist.loss_fn}-mean"]))
+
+        return best_score
+
+    if study_name is None:
+        study_name = "LightGBMLSS Hyper-Parameter Optimization"
+
+    if silence:
+        optuna.logging.set_verbosity(optuna.logging.WARNING)
+
+    if hp_seed is not None:
+        sampler = TPESampler(seed=hp_seed)
+    else:
+        sampler = TPESampler()
+
+    pruner = optuna.pruners.MedianPruner(n_startup_trials=10, n_warmup_steps=20)
+    study = optuna.create_study(sampler=sampler, pruner=pruner, direction="minimize", study_name=study_name)
+    study.optimize(objective, n_trials=n_trials, timeout=60 * max_minutes, show_progress_bar=True)
+
+    print("\nHyper-Parameter Optimization successfully finished.")
+    print("  Number of finished trials: ", len(study.trials))
+    print("  Best trial:")
+    opt_param = study.best_trial
+
+    # Add optimal stopping round
+    opt_param.params["opt_rounds"] = study.trials_dataframe()["user_attrs_opt_round"][
+        study.trials_dataframe()["value"].idxmin()]
+    opt_param.params["opt_rounds"] = int(opt_param.params["opt_rounds"])
+
+    print("    Value: {}".format(opt_param.value))
+    print("    Params: ")
+    for key, value in opt_param.params.items():
+        print("    {}: {}".format(key, value))
+
+    return opt_param.params
+
+
+
+ +
+ + +
+ + + + +

+ load_model(model_path) + + + staticmethod + + +

+ + +
+ +

Load the model from a file.

+
Parameters
+

model_path : str + The path to the saved model.

+
Returns
+

The loaded model.

+ +
+ Source code in lightgbmlss/model.py + 
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@staticmethod
+def load_model(model_path: str):
+    """
+    Load the model from a file.
+
+    Parameters
+    ----------
+    model_path : str
+        The path to the saved model.
+
+    Returns
+    -------
+    The loaded model.
+    """
+    with open(model_path, "rb") as f:
+        return pickle.load(f)
+
+
+
+ +
+ + +
+ + + + +

+ plot(X, feature='x', parameter='loc', max_display=15, plot_type='Partial_Dependence') + +

+ + +
+ +

LightGBMLSS SHap plotting function.

+
Arguments:
+

X: pd.DataFrame + Train/Test Data +feature: str + Specifies which feature is to be plotted. +parameter: str + Specifies which distributional parameter is to be plotted. +max_display: int + Specifies the maximum number of features to be displayed. +plot_type: str + Specifies the type of plot: + "Partial_Dependence" plots the partial dependence of the parameter on the feature. + "Feature_Importance" plots the feature importance of the parameter.

+ +
+ Source code in lightgbmlss/model.py + 
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def plot(self,
+         X: pd.DataFrame,
+         feature: str = "x",
+         parameter: str = "loc",
+         max_display: int = 15,
+         plot_type: str = "Partial_Dependence"):
+    """
+    LightGBMLSS SHap plotting function.
+
+    Arguments:
+    ---------
+    X: pd.DataFrame
+        Train/Test Data
+    feature: str
+        Specifies which feature is to be plotted.
+    parameter: str
+        Specifies which distributional parameter is to be plotted.
+    max_display: int
+        Specifies the maximum number of features to be displayed.
+    plot_type: str
+        Specifies the type of plot:
+            "Partial_Dependence" plots the partial dependence of the parameter on the feature.
+            "Feature_Importance" plots the feature importance of the parameter.
+    """
+    shap.initjs()
+    explainer = shap.TreeExplainer(self.booster)
+    shap_values = explainer(X)
+
+    param_pos = self.dist.distribution_arg_names.index(parameter)
+
+    if plot_type == "Partial_Dependence":
+        if self.dist.n_dist_param == 1:
+            shap.plots.scatter(shap_values[:, feature], color=shap_values[:, feature])
+        else:
+            shap.plots.scatter(shap_values[:, feature][:, param_pos], color=shap_values[:, feature][:, param_pos])
+    elif plot_type == "Feature_Importance":
+        if self.dist.n_dist_param == 1:
+            shap.plots.bar(shap_values, max_display=max_display if X.shape[1] > max_display else X.shape[1])
+        else:
+            shap.plots.bar(
+                shap_values[:, :, param_pos], max_display=max_display if X.shape[1] > max_display else X.shape[1]
+            )
+
+
+
+ +
+ + +
+ + + + +

+ predict(data, pred_type='parameters', n_samples=1000, quantiles=[0.1, 0.5, 0.9], seed=123) + +

+ + +
+ +

Function that predicts from the trained model.

+
Arguments
+

data : pd.DataFrame + Data to predict from. +pred_type : str + Type of prediction: + - "samples" draws n_samples from the predicted distribution. + - "quantiles" calculates the quantiles from the predicted distribution. + - "parameters" returns the predicted distributional parameters. + - "expectiles" returns the predicted expectiles. +n_samples : int + Number of samples to draw from the predicted distribution. +quantiles : List[float] + List of quantiles to calculate from the predicted distribution. +seed : int + Seed for random number generator used to draw samples from the predicted distribution.

+
Returns
+

predt_df : pd.DataFrame + Predictions.

+ +
+ Source code in lightgbmlss/model.py + 
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def predict(self,
+            data: pd.DataFrame,
+            pred_type: str = "parameters",
+            n_samples: int = 1000,
+            quantiles: list = [0.1, 0.5, 0.9],
+            seed: str = 123):
+    """
+    Function that predicts from the trained model.
+
+    Arguments
+    ---------
+    data : pd.DataFrame
+        Data to predict from.
+    pred_type : str
+        Type of prediction:
+        - "samples" draws n_samples from the predicted distribution.
+        - "quantiles" calculates the quantiles from the predicted distribution.
+        - "parameters" returns the predicted distributional parameters.
+        - "expectiles" returns the predicted expectiles.
+    n_samples : int
+        Number of samples to draw from the predicted distribution.
+    quantiles : List[float]
+        List of quantiles to calculate from the predicted distribution.
+    seed : int
+        Seed for random number generator used to draw samples from the predicted distribution.
+
+    Returns
+    -------
+    predt_df : pd.DataFrame
+        Predictions.
+    """
+
+    # Predict
+    predt_df = self.dist.predict_dist(booster=self.booster,
+                                      data=data,
+                                      start_values=self.start_values,
+                                      pred_type=pred_type,
+                                      n_samples=n_samples,
+                                      quantiles=quantiles,
+                                      seed=seed)
+
+    return predt_df
+
+
+
+ +
+ + +
+ + + + +

+ save_model(model_path) + +

+ + +
+ +

Save the model to a file.

+
Parameters
+

model_path : str + The path to save the model.

+
Returns
+

None

+ +
+ Source code in lightgbmlss/model.py + 
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def save_model(self,
+               model_path: str
+               ) -> None:
+    """
+    Save the model to a file.
+
+    Parameters
+    ----------
+    model_path : str
+        The path to save the model.
+
+    Returns
+    -------
+    None
+    """
+    with open(model_path, "wb") as f:
+        pickle.dump(self, f)
+
+
+
+ +
+ + +
+ + + + +

+ set_init_score(dmatrix) + +

+ + +
+ +

Set init_score for distributions.

+
Arguments
+

dmatrix : Dataset + Dataset to set base margin for.

+
Returns
+

None

+ +
+ Source code in lightgbmlss/model.py + 
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def set_init_score(self, dmatrix: Dataset) -> None:
+    """
+    Set init_score for distributions.
+
+    Arguments
+    ---------
+    dmatrix : Dataset
+        Dataset to set base margin for.
+
+    Returns
+    -------
+    None
+    """
+    if self.start_values is None:
+        _, self.start_values = self.dist.calculate_start_values(dmatrix.get_label())
+    init_score = (np.ones(shape=(dmatrix.get_label().shape[0], 1))) * self.start_values
+    dmatrix.set_init_score(init_score.ravel(order="F"))
+
+
+
+ +
+ + +
+ + + + +

+ set_params(params) + +

+ + +
+ +

Set parameters for distributional model.

+
Arguments
+

params : Dict[str, Any] + Parameters for model.

+
Returns
+

params : Dict[str, Any] + Updated Parameters for model.

+ +
+ Source code in lightgbmlss/model.py + 
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def set_params(self, params: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Set parameters for distributional model.
+
+    Arguments
+    ---------
+    params : Dict[str, Any]
+        Parameters for model.
+
+    Returns
+    -------
+    params : Dict[str, Any]
+        Updated Parameters for model.
+    """
+    params_adj = {"num_class": self.dist.n_dist_param,
+                  "metric": "None",
+                  "objective": "None",
+                  "random_seed": 123,
+                  "verbose": -1}
+    params.update(params_adj)
+
+    return params
+
+
+
+ +
+ + +
+ + + + +

+ set_valid_margin(valid_sets, start_values) + +

+ + +
+ +

Function that sets the base margin for the validation set.

+
Arguments
+

valid_sets : list + List of tuples containing the train and evaluation set. +valid_names: list + List of tuples containing the name of train and evaluation set. +start_values : np.ndarray + Array containing the start values for the distributional parameters.

+
Returns
+

valid_sets : list + List of tuples containing the train and evaluation set.

+ +
+ Source code in lightgbmlss/model.py + 
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def set_valid_margin(self,
+                     valid_sets: list,
+                     start_values: np.ndarray
+                     ) -> list:
+    """
+    Function that sets the base margin for the validation set.
+
+    Arguments
+    ---------
+    valid_sets : list
+        List of tuples containing the train and evaluation set.
+    valid_names: list
+        List of tuples containing the name of train and evaluation set.
+    start_values : np.ndarray
+        Array containing the start values for the distributional parameters.
+
+    Returns
+    -------
+    valid_sets : list
+        List of tuples containing the train and evaluation set.
+    """
+    valid_sets1 = valid_sets[0]
+    init_score_val1 = (np.ones(shape=(valid_sets1.get_label().shape[0], 1))) * start_values
+    valid_sets1.set_init_score(init_score_val1.ravel(order="F"))
+
+    valid_sets2 = valid_sets[1]
+    init_score_val2 = (np.ones(shape=(valid_sets2.get_label().shape[0], 1))) * start_values
+    valid_sets2.set_init_score(init_score_val2.ravel(order="F"))
+
+    valid_sets = [valid_sets1, valid_sets2]
+
+    return valid_sets
+
+
+
+ +
+ + +
+ + + + +

+ train(params, train_set, num_boost_round=100, valid_sets=None, valid_names=None, init_model=None, feature_name='auto', categorical_feature='auto', keep_training_booster=False, callbacks=None) + +

+ + +
+ +

Function to perform the training of a LightGBMLSS model with given parameters.

+
Parameters
+

params : dict + Parameters for training. Values passed through params take precedence over those + supplied via arguments. +train_set : Dataset + Data to be trained on. +num_boost_round : int, optional (default=100) + Number of boosting iterations. +valid_sets : list of Dataset, or None, optional (default=None) + List of data to be evaluated on during training. +valid_names : list of str, or None, optional (default=None) + Names of valid_sets. +init_model : str, pathlib.Path, Booster or None, optional (default=None) + Filename of LightGBM model or Booster instance used for continue training. +feature_name : list of str, or 'auto', optional (default="auto") + Feature names. + If 'auto' and data is pandas DataFrame, data columns names are used. +categorical_feature : list of str or int, or 'auto', optional (default="auto") + Categorical features. + If list of int, interpreted as indices. + If list of str, interpreted as feature names (need to specify feature_name as well). + If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. + All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). + Large values could be memory consuming. Consider using consecutive integers starting from zero. + All negative values in categorical features will be treated as missing values. + The output cannot be monotonically constrained with respect to a categorical feature. + Floating point numbers in categorical features will be rounded towards 0. +keep_training_booster : bool, optional (default=False) + Whether the returned Booster will be used to keep training. + If False, the returned value will be converted into _InnerPredictor before returning. + This means you won't be able to use eval, eval_train or eval_valid methods of the returned Booster. + When your model is very large and cause the memory error, + you can try to set this param to True to avoid the model conversion performed during the internal call of model_to_string. + You can still use _InnerPredictor as init_model for future continue training. +callbacks : list of callable, or None, optional (default=None) + List of callback functions that are applied at each iteration. + See Callbacks in Python API for more information.

+
Returns
+

booster : Booster + The trained Booster model.

+ +
+ Source code in lightgbmlss/model.py + 
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def train(self,
+          params: Dict[str, Any],
+          train_set: Dataset,
+          num_boost_round: int = 100,
+          valid_sets: Optional[List[Dataset]] = None,
+          valid_names: Optional[List[str]] = None,
+          init_model: Optional[Union[str, Path, Booster]] = None,
+          feature_name: _LGBM_FeatureNameConfiguration = 'auto',
+          categorical_feature: _LGBM_CategoricalFeatureConfiguration = 'auto',
+          keep_training_booster: bool = False,
+          callbacks: Optional[List[Callable]] = None
+          ) -> Booster:
+    """Function to perform the training of a LightGBMLSS model with given parameters.
+
+    Parameters
+    ----------
+    params : dict
+        Parameters for training. Values passed through ``params`` take precedence over those
+        supplied via arguments.
+    train_set : Dataset
+        Data to be trained on.
+    num_boost_round : int, optional (default=100)
+        Number of boosting iterations.
+    valid_sets : list of Dataset, or None, optional (default=None)
+        List of data to be evaluated on during training.
+    valid_names : list of str, or None, optional (default=None)
+        Names of ``valid_sets``.
+    init_model : str, pathlib.Path, Booster or None, optional (default=None)
+        Filename of LightGBM model or Booster instance used for continue training.
+    feature_name : list of str, or 'auto', optional (default="auto")
+        Feature names.
+        If 'auto' and data is pandas DataFrame, data columns names are used.
+    categorical_feature : list of str or int, or 'auto', optional (default="auto")
+        Categorical features.
+        If list of int, interpreted as indices.
+        If list of str, interpreted as feature names (need to specify ``feature_name`` as well).
+        If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
+        All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647).
+        Large values could be memory consuming. Consider using consecutive integers starting from zero.
+        All negative values in categorical features will be treated as missing values.
+        The output cannot be monotonically constrained with respect to a categorical feature.
+        Floating point numbers in categorical features will be rounded towards 0.
+    keep_training_booster : bool, optional (default=False)
+        Whether the returned Booster will be used to keep training.
+        If False, the returned value will be converted into _InnerPredictor before returning.
+        This means you won't be able to use ``eval``, ``eval_train`` or ``eval_valid`` methods of the returned Booster.
+        When your model is very large and cause the memory error,
+        you can try to set this param to ``True`` to avoid the model conversion performed during the internal call of ``model_to_string``.
+        You can still use _InnerPredictor as ``init_model`` for future continue training.
+    callbacks : list of callable, or None, optional (default=None)
+        List of callback functions that are applied at each iteration.
+        See Callbacks in Python API for more information.
+
+    Returns
+    -------
+    booster : Booster
+        The trained Booster model.
+    """
+    self.set_params(params)
+    self.set_init_score(train_set)
+
+    if valid_sets is not None:
+        valid_sets = self.set_valid_margin(valid_sets, self.start_values)
+
+    self.booster = lgb.train(params,
+                             train_set,
+                             num_boost_round=num_boost_round,
+                             fobj=self.dist.objective_fn,
+                             feval=self.dist.metric_fn,
+                             valid_sets=valid_sets,
+                             valid_names=valid_names,
+                             init_model=init_model,
+                             feature_name=feature_name,
+                             categorical_feature=categorical_feature,
+                             keep_training_booster=keep_training_booster,
+                             callbacks=callbacks)
+
+
+
+ +
+ + + +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ + + + +

+ utils + + +

+ +
+ + + +
+ + + + + + + + + + +
+ + + + +

+ exp_fn(predt) + +

+ + +
+ +

Exponential function used to ensure predt is strictly positive.

+
Arguments
+

predt: torch.tensor + Predicted values.

+
Returns
+

predt: torch.tensor + Predicted values.

+ +
+ Source code in lightgbmlss/utils.py + 
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def exp_fn(predt: torch.tensor) -> torch.tensor:
+    """
+    Exponential function used to ensure predt is strictly positive.
+
+    Arguments
+    ---------
+    predt: torch.tensor
+        Predicted values.
+
+    Returns
+    -------
+    predt: torch.tensor
+        Predicted values.
+    """
+    predt = torch.exp(predt)
+    predt = torch.nan_to_num(predt, nan=float(torch.nanmean(predt))) + torch.tensor(1e-06, dtype=predt.dtype)
+
+    return predt
+
+
+
+ +
+ + +
+ + + + +

+ exp_fn_df(predt) + +

+ + +
+ +

Exponential function used for Student-T distribution.

+
Arguments
+

predt: torch.tensor + Predicted values.

+
Returns
+

predt: torch.tensor + Predicted values.

+ +
+ Source code in lightgbmlss/utils.py + 
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def exp_fn_df(predt: torch.tensor) -> torch.tensor:
+    """
+    Exponential function used for Student-T distribution.
+
+    Arguments
+    ---------
+    predt: torch.tensor
+        Predicted values.
+
+    Returns
+    -------
+    predt: torch.tensor
+        Predicted values.
+    """
+    predt = torch.exp(predt)
+    predt = torch.nan_to_num(predt, nan=float(torch.nanmean(predt))) + torch.tensor(1e-06, dtype=predt.dtype)
+
+    return predt + torch.tensor(2.0, dtype=predt.dtype)
+
+
+
+ +
+ + +
+ + + + +

+ identity_fn(predt) + +

+ + +
+ +

Identity mapping of predt.

+
Arguments
+

predt: torch.tensor + Predicted values.

+
Returns
+

predt: torch.tensor + Predicted values.

+ +
+ Source code in lightgbmlss/utils.py + 
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def identity_fn(predt: torch.tensor) -> torch.tensor:
+    """
+    Identity mapping of predt.
+
+    Arguments
+    ---------
+    predt: torch.tensor
+        Predicted values.
+
+    Returns
+    -------
+    predt: torch.tensor
+        Predicted values.
+    """
+    return predt
+
+
+
+ +
+ + +
+ + + + +

+ log_fn(predt) + +

+ + +
+ +

Inverse of exp_fn function.

+
Arguments
+

predt: torch.tensor + Predicted values.

+
Returns
+

predt: torch.tensor + Predicted values.

+ +
+ Source code in lightgbmlss/utils.py + 
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def log_fn(predt: torch.tensor) -> torch.tensor:
+    """
+    Inverse of exp_fn function.
+
+    Arguments
+    ---------
+    predt: torch.tensor
+        Predicted values.
+
+    Returns
+    -------
+    predt: torch.tensor
+        Predicted values.
+    """
+    predt = torch.log(predt)
+    predt = torch.nan_to_num(predt, nan=float(torch.nanmean(predt))) + float(1e-06)
+
+    return predt
+
+
+
+ +
+ + +
+ + + + +

+ relu_fn(predt) + +

+ + +
+ +

Function used to ensure predt are scaled to max(0, predt).

+
Arguments
+

predt: torch.tensor + Predicted values.

+
Returns
+

predt: torch.tensor + Predicted values.

+ +
+ Source code in lightgbmlss/utils.py + 
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def relu_fn(predt: torch.tensor) -> torch.tensor:
+    """
+    Function used to ensure predt are scaled to max(0, predt).
+
+    Arguments
+    ---------
+    predt: torch.tensor
+        Predicted values.
+
+    Returns
+    -------
+    predt: torch.tensor
+        Predicted values.
+    """
+    predt = torch.relu(predt)
+    predt = torch.nan_to_num(predt, nan=float(torch.nanmean(predt))) + torch.tensor(1e-6, dtype=predt.dtype)
+
+    return predt
+
+
+
+ +
+ + +
+ + + + +

+ sigmoid_fn(predt) + +

+ + +
+ +

Function used to ensure predt are scaled to (0,1).

+
Arguments
+

predt: torch.tensor + Predicted values.

+
Returns
+

predt: torch.tensor + Predicted values.

+ +
+ Source code in lightgbmlss/utils.py + 
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def sigmoid_fn(predt: torch.tensor) -> torch.tensor:
+    """
+    Function used to ensure predt are scaled to (0,1).
+
+    Arguments
+    ---------
+    predt: torch.tensor
+        Predicted values.
+
+    Returns
+    -------
+    predt: torch.tensor
+        Predicted values.
+    """
+    predt = torch.sigmoid(predt)
+    predt = torch.nan_to_num(predt, nan=float(torch.nanmean(predt))) + torch.tensor(1e-06, dtype=predt.dtype)
+    predt = torch.clamp(predt, 1e-03, 1-1e-03)
+
+    return predt
+
+
+
+ +
+ + +
+ + + + +

+ softplus_fn(predt) + +

+ + +
+ +

Softplus function used to ensure predt is strictly positive.

+
Arguments
+

predt: torch.tensor + Predicted values.

+
Returns
+

predt: torch.tensor + Predicted values.

+ +
+ Source code in lightgbmlss/utils.py + 
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def softplus_fn(predt: torch.tensor) -> torch.tensor:
+    """
+    Softplus function used to ensure predt is strictly positive.
+
+    Arguments
+    ---------
+    predt: torch.tensor
+        Predicted values.
+
+    Returns
+    -------
+    predt: torch.tensor
+        Predicted values.
+    """
+    predt = torch.log1p(torch.exp(-torch.abs(predt))) + torch.maximum(predt, torch.tensor(0.))
+    predt[predt == 0] = torch.tensor(1e-06, dtype=predt.dtype)
+    predt = torch.nan_to_num(predt, nan=float(torch.nanmean(predt))) + torch.tensor(1e-06, dtype=predt.dtype)
+
+    return predt
+
+
+
+ +
+ + +
+ + + + +

+ softplus_fn_df(predt) + +

+ + +
+ +

Softplus function used for Student-T distribution.

+
Arguments
+

predt: torch.tensor + Predicted values.

+
Returns
+

predt: torch.tensor + Predicted values.

+ +
+ Source code in lightgbmlss/utils.py + 
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def softplus_fn_df(predt: torch.tensor) -> torch.tensor:
+    """
+    Softplus function used for Student-T distribution.
+
+    Arguments
+    ---------
+    predt: torch.tensor
+        Predicted values.
+
+    Returns
+    -------
+    predt: torch.tensor
+        Predicted values.
+    """
+    predt = torch.log1p(torch.exp(-torch.abs(predt))) + torch.maximum(predt, torch.tensor(0.))
+    predt[predt == 0] = torch.tensor(1e-06, dtype=predt.dtype)
+    predt = torch.nan_to_num(predt, nan=float(torch.nanmean(predt))) + torch.tensor(1e-06, dtype=predt.dtype)
+
+    return predt + torch.tensor(2.0, dtype=predt.dtype)
+
+
+
+ +
+ + + +
+ +
+ +
+ + +
+ +
+ +
+
+
+ + + + + + + + + + + + + + + diff --git a/assets/_mkdocstrings.css b/assets/_mkdocstrings.css new file mode 100644 index 0000000..049a254 --- /dev/null +++ b/assets/_mkdocstrings.css @@ -0,0 +1,64 @@ + +/* Avoid breaking parameter names, etc. in table cells. */ +.doc-contents td code { + word-break: normal !important; +} + +/* No line break before first paragraph of descriptions. */ +.doc-md-description, +.doc-md-description>p:first-child { + display: inline; +} + +/* Max width for docstring sections tables. */ +.doc .md-typeset__table, +.doc .md-typeset__table table { + display: table !important; + width: 100%; +} + +.doc .md-typeset__table tr { + display: table-row; +} + +/* Defaults in Spacy table style. */ +.doc-param-default { + float: right; +} + +/* Keep headings consistent. */ +h1.doc-heading, +h2.doc-heading, +h3.doc-heading, +h4.doc-heading, +h5.doc-heading, +h6.doc-heading { + font-weight: 400; + line-height: 1.5; + color: inherit; + text-transform: none; +} + +h1.doc-heading { + font-size: 1.6rem; +} + +h2.doc-heading { + font-size: 1.2rem; +} + +h3.doc-heading { + font-size: 1.15rem; +} + +h4.doc-heading { + font-size: 1.10rem; +} + +h5.doc-heading { + font-size: 1.05rem; +} + +h6.doc-heading { + font-size: 1rem; +} \ No newline at end of file diff --git a/css/base.css b/css/base.css new file mode 100644 index 0000000..2610341 --- /dev/null +++ b/css/base.css @@ -0,0 +1,325 @@ +html { + /* csslint ignore:start */ + /* The nav header is 3.5rem high, plus 20px for the margin-top of the + main container. */ + scroll-padding-top: calc(3.5rem + 20px); + /* csslint ignore:end */ +} + +/* Replacement for `body { background-attachment: fixed; }`, which has + performance issues when scrolling on large displays. See #1394. */ +body::before { + content: ' '; + position: fixed; + width: 100%; + height: 100%; + top: 0; + left: 0; + background-color: #f8f8f8; + background: url(../img/grid.png) repeat-x; + will-change: transform; + z-index: -1; +} + +body > .container { + margin-top: 20px; + min-height: 400px; +} + +.navbar.fixed-top { /* csslint allow: adjoining-classes */ + /* csslint ignore:start */ + position: -webkit-sticky; + position: sticky; + /* csslint ignore:end */ +} + +.source-links { + float: right; +} + +.col-md-9 img { + max-width: 100%; + display: inline-block; + padding: 4px; + line-height: 1.428571429; + background-color: #fff; + border: 1px solid #ddd; + border-radius: 4px; + margin: 20px auto 30px auto; +} + +h1 { + color: #444; + font-weight: 400; + font-size: 42px; +} + +h2, h3, h4, h5, h6 { + color: #444; + font-weight: 300; +} + +hr { + border-top: 1px solid #aaa; +} + +pre, .rst-content tt { + max-width: 100%; + background: #fff; + border: solid 1px #e1e4e5; + color: #333; + overflow-x: auto; +} + +code.code-large, .rst-content tt.code-large { + font-size: 90%; +} + +code { + padding: 2px 5px; + background: #fff; + border: solid 1px #e1e4e5; + color: #333; + white-space: pre-wrap; + word-wrap: break-word; +} + +pre code { + display: block; + background: transparent; + border: none; + white-space: pre; + word-wrap: normal; + font-family: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; + font-size: 12px; +} + +kbd { + padding: 2px 4px; + font-size: 90%; + color: #fff; + background-color: #333; + border-radius: 3px; + -webkit-box-shadow: inset 0 -1px 0 rgba(0,0,0,.25); + box-shadow: inset 0 -1px 0 rgba(0,0,0,.25); +} + +a code { + color: #2FA4E7; +} + +a:hover code, a:focus code { + color: #157AB5; +} + +footer { + margin-top: 30px; + margin-bottom: 10px; + text-align: center; + font-weight: 200; +} + +.modal-dialog { + margin-top: 60px; +} + +/* + * Side navigation + * + * Scrollspy and affixed enhanced navigation to highlight sections and secondary + * sections of docs content. + */ + +.bs-sidebar.affix { /* csslint allow: adjoining-classes */ + /* csslint ignore:start */ + position: -webkit-sticky; + position: sticky; + /* csslint ignore:end */ + /* The nav header is 3.5rem high, plus 20px for the margin-top of the + main container. */ + top: calc(3.5rem + 20px); +} + +.bs-sidebar.card { /* csslint allow: adjoining-classes */ + padding: 0; + max-height: 90%; + overflow-y: auto; +} + +/* Toggle (vertically flip) sidebar collapse icon */ +.bs-sidebar .navbar-toggler span { + -moz-transform: scale(1, -1); + -webkit-transform: scale(1, -1); + -o-transform: scale(1, -1); + -ms-transform: scale(1, -1); + transform: scale(1, -1); +} + +.bs-sidebar .navbar-toggler.collapsed span { /* csslint allow: adjoining-classes */ + -moz-transform: scale(1, 1); + -webkit-transform: scale(1, 1); + -o-transform: scale(1, 1); + -ms-transform: scale(1, 1); + transform: scale(1, 1); +} + +/* First level of nav */ +.bs-sidebar > .navbar-collapse > .nav { + padding-top: 10px; + padding-bottom: 10px; + border-radius: 5px; + width: 100%; +} + +/* All levels of nav */ +.bs-sidebar .nav > li > a { + display: block; + padding: 5px 20px; + z-index: 1; +} +.bs-sidebar .nav > li > a:hover, +.bs-sidebar .nav > li > a:focus { + text-decoration: none; + border-right: 1px solid; +} +.bs-sidebar .nav > li > a.active, +.bs-sidebar .nav > li > a.active:hover, +.bs-sidebar .nav > li > a.active:focus { + font-weight: bold; + background-color: transparent; + border-right: 1px solid; +} + +.bs-sidebar .nav .nav .nav { + margin-left: 1em; +} + +.bs-sidebar .nav > li > a { + font-weight: bold; +} + +.bs-sidebar .nav .nav > li > a { + font-weight: normal; +} + +.headerlink { + font-family: FontAwesome; + font-size: 14px; + display: none; + padding-left: .5em; +} + +h1:hover .headerlink, h2:hover .headerlink, h3:hover .headerlink, h4:hover .headerlink, h5:hover .headerlink, h6:hover .headerlink { + display:inline-block; +} + +blockquote { + padding-left: 10px; + border-left: 4px solid #e6e6e6; +} + +.admonition, details { + padding: 15px; + margin-bottom: 20px; + border: 1px solid transparent; + border-radius: 4px; + text-align: left; +} + +.admonition.note, details.note { /* csslint allow: adjoining-classes */ + color: #2e6b89; + background-color: #e2f0f7; + border-color: #bce8f1; +} + +.admonition.warning, details.warning { /* csslint allow: adjoining-classes */ + color: #7a6032; + background-color: #fffae5; + border-color: #fbeed5; +} + +.admonition.danger, details.danger { /* csslint allow: adjoining-classes */ + color: #7f3130; + background-color: #fde3e3; + border-color: #eed3d7; +} + +.admonition-title, summary { + font-weight: bold; + text-align: left; +} + +.admonition>p:last-child, details>p:last-child { + margin-bottom: 0; +} + +@media (max-width: 991.98px) { + .navbar-collapse.show { /* csslint allow: adjoining-classes */ + overflow-y: auto; + max-height: calc(100vh - 3.5rem); + } +} + +.dropdown-item.open { /* csslint allow: adjoining-classes */ + color: #fff; + background-color: #2FA4E7; +} + +.dropdown-submenu > .dropdown-menu { + margin: 0 0 0 1.5rem; + padding: 0; + border-width: 0; +} + +.dropdown-submenu > a::after { + display: block; + content: " "; + float: right; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; + border-width: 5px 0 5px 5px; + border-left-color: #ccc; + margin-top: 5px; + margin-right: -10px; +} + +.dropdown-submenu:hover > a::after { + border-left-color: #fff; +} + +@media (min-width: 992px) { + .dropdown-menu { + overflow-y: auto; + max-height: calc(100vh - 3.5rem); + } + + .dropdown-submenu { + position: relative; + } + + .dropdown-submenu > .dropdown-menu { + /* csslint ignore:start */ + position: fixed !important; + /* csslint ignore:end */ + margin-top: -9px; + margin-left: -2px; + border-width: 1px; + padding: 0.5rem 0; + } + + .dropdown-submenu.pull-left { /* csslint allow: adjoining-classes */ + float: none; + } + + .dropdown-submenu.pull-left > .dropdown-menu { /* csslint allow: adjoining-classes */ + left: -100%; + margin-left: 10px; + } +} + +@media print { + /* Remove sidebar when print */ + .col-md-3 { display: none; } +} diff --git a/css/bootstrap.min.css b/css/bootstrap.min.css new file mode 100644 index 0000000..4ce503d --- /dev/null +++ b/css/bootstrap.min.css @@ -0,0 +1,12 @@ +/*! + * Bootswatch v4.1.3 + * Homepage: https://bootswatch.com + * Copyright 2012-2018 Thomas Park + * Licensed under MIT + * Based on Bootstrap +*//*! + * Bootstrap v4.1.3 (https://getbootstrap.com/) + * Copyright 2011-2018 The Bootstrap Authors + * Copyright 2011-2018 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) + 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0.5rem;color:#fff;text-align:center;background-color:#000;border-radius:0.25rem}.popover{position:absolute;top:0;left:0;z-index:1060;display:block;max-width:276px;font-family:-apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;white-space:normal;line-break:auto;font-size:0.875rem;word-wrap:break-word;background-color:#fff;background-clip:padding-box;border:1px solid rgba(0,0,0,0.2);border-radius:0.3rem}.popover .arrow{position:absolute;display:block;width:1rem;height:0.5rem;margin:0 0.3rem}.popover .arrow::before,.popover 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0, 0, 0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto} diff --git a/dgbm/dgbm.md b/dgbm/dgbm.md new file mode 100644 index 0000000..0fb138f --- /dev/null +++ b/dgbm/dgbm.md @@ -0,0 +1,84 @@ +# Introduction + +The development of modelling approaches that approximate and describe the data generating processes underlying the observed data in as much detail as possible is a guiding principle in both statistics and machine learning. We therefore strongly agree with the statement of Hothorn et al. (2014) that **''the ultimate goal of any regression analysis is to obtain information about the entire conditional distribution $F_{Y}(y|\mathbf{x})$ of a response given a set of explanatory variables''**. + +
+''Practitioners expect forecasting to reduce future uncertainty by providing accurate predictions like those in hard sciences. However, this is a great misconception. A major purpose of forecasting is not to reduce uncertainty but reveal its full extent and implications by estimating it as precisely as possible. [...] The challenge for the forecasting field is how to persuade practitioners of the reality that all forecasts are uncertain and that this uncertainty cannot be ignored, as doing so could lead to catastrophic consequences.'' Makridakis (2022b) +
+ +It has not been too long, though, that most regression models focused on estimating the conditional mean $\mathbb{E}(Y|\mathbf{X} = \mathbf{x})$ only, implicitly treating higher moments of the conditional distribution $F_{Y}(y|\mathbf{x})$ as fixed nuisance parameters. As such, models that minimize an $\ell_{2}$-type loss for the conditional mean are not able to fully exploit the information contained in the data, since this is equivalent to assuming a Normal distribution with constant variance. In real world situations, however, the data generating process is usually less well behaved, exhibiting characteristics such as heteroskedasticity, varying degrees of skewness and kurtosis or intermittent and sporadic behaviour. In recent years, however, there has been a clear shift in both academic and corporate research toward modelling the entire conditional distribution. This change in attention is most evident in the recent M5 forecasting competition (Makridakis et al., 2022a,b), which differed from previous ones in that it consisted of two parallel competitions: in addition to providing accurate point forecasts, participants were also asked to forecast nine different quantiles to approximate the distribution of future sales. + +# Distributional Gradient Boosting Machines + +This section introduces the general idea of distributional modelling. For a more thorough introduction, we refer the interested reader to Rigby and Stasinopoulos (2005); Klein et al. (2015); Stasinopoulos et al. (2017). + +## GAMLSS + +Probabilistic forecasts are predictions in the form of a probability distribution, rather than a single point estimate only. In this context, the introduction of Generalized Additive Models for Location Scale and Shape (GAMLSS) by Rigby and Stasinopoulos (2005) has stimulated a lot of research and culminated in a new research branch that focuses on modelling the entire conditional distribution in dependence of covariates. + +### Univariate Targets + +In its original formulation, GAMLSS assume a univariate response to follow a distribution $\mathcal{D}$ that depends on up to four parameters, i.e., $y_{i} \stackrel{ind}{\sim} \mathcal{D}(\mu_{i}, \sigma^{2}_{i}, \nu_{i}, \tau_{i}), i=1,\ldots,N$, where $\mu_{i}$ and $\sigma^{2}_{i}$ are often location and scale parameters, respectively, while $\nu_{i}$ and $\tau_{i}$ correspond to shape parameters such as skewness and kurtosis. Hence, the framework allows to model not only the mean (or location) but all parameters as functions of explanatory variables. It is important to note that distributional modelling implies that observations are independent, but not necessarily identical realizations $y \stackrel{ind}{\sim} \mathcal{D}\big(\mathbf{\theta}(\mathbf{x})\big)$, since all distributional parameters $\mathbf{\theta}(\mathbf{x})$ are related to and allowed to change with covariates. In contrast to Generalized Linear (GLM) and Generalized Additive Models (GAM), the assumption of the response distribution belonging to an exponential family is relaxed in GAMLSS and replaced by a more general class of distributions, including highly skewed and/or kurtotic continuous, discrete and mixed discrete, as well as zero-inflated distributions. While the original formulation of GAMLSS in Rigby and Stasinopoulos (2005) suggests that any distribution can be described by location, scale and shape parameters, it is not necessarily true that the observed data distribution can actually be characterized by all of these parameters. Hence, we follow Klein et al. (2015) and use the term distributional modelling and GAMLSS interchangeably. + +From a frequentist point of view, distributional modelling can be formulated as follows + +\begin{equation} +y_{i} \stackrel{ind}{\sim} \mathcal{D} + \begin{pmatrix} + h_{1}\bigl(\theta_{i1}(x_{i})\bigr) = \eta_{i1} \\ + h_{2}\bigl(\theta_{i2}(x_{i})\bigr) = \eta_{i2} \\ + \vdots \\ + h_{K}\bigl(\theta_{iK}(x_{i})\bigr) = \eta_{iK} +\end{pmatrix} +\end{equation} + +for $i = 1, \ldots, N$, where $\mathcal{D}$ denotes a parametric distribution for the response $\textbf{y} = (y_{1}, \ldots, y_{N})^{\prime}$ that depends on $K$ distributional parameters $\theta_{k}$, $k = 1, \ldots, K$, and with $h_{k}(\cdot)$ denoting a known function relating distributional parameters to predictors $\eta_{k}$. In its most generic form, the predictor $\eta_{k}$ is given by + +\begin{equation} +\eta_{k} = f_{k}(\mathbf{x}), \qquad k = 1, \ldots, K +\end{equation} + +Within the original distributional regression framework, the functions $f_{k}(\cdot)$ usually represent a combination of linear and GAM-type predictors, which allows to estimate linear effects or categorical variables, as well as highly non-linear and spatial effects using a Spline-based basis function approach. The predictor specification $\eta_{k}$ is generic enough to use tree-based models as well, which allows us to extend LightGBM to a probabilistic framework. + +## Normalizing Flows + +Although the GAMLSS framework offers considerable flexibility, parametric distributions may prove not flexible enough to provide a reasonable approximation for certain dataset, e.g., for multi-modal distributions. For such cases, it is preferable to relax the assumption of a parametric distribution and approximate the data non-parametrically. While there are several alternatives for learning conditional distributions, we propose to use Normalizing Flows for their ability to fit complex distributions with only a few parameters. + +The principle that underlies Normalizing Flows is to turn a simple base distribution, e.g., $F_{Z}(\mathbf{z}) = N(0,1)$, into a more complex and realistic distribution of the target variable $F_{Y}(\mathbf{y})$ by applying several bijective transformations $h_{j}$, $j = 1, \ldots, J$ to the variable of the base distribution + +\begin{equation} + \mathbf{y} = h_{J} \circ h_{J-1} \circ \cdots \circ h_{1}(\mathbf{z}) +\end{equation} + +Based on the complete transformation function $h=h_{J}\circ\ldots\circ h_{1}$, the density of $\mathbf{y}$ is then given by the change of variables theorem + +\begin{equation} + f_{Y}(\mathbf{y}) = f_{Z}\big(h(\mathbf{y})\big) \cdot \Bigg|\frac{\partial h(\mathbf{y})}{\partial \mathbf{y}}\Bigg| \end{equation} + +where scaling with the Jacobian determinant $|h^{\prime}(\mathbf{y})| = |\partial h(\mathbf{y}) / \partial \mathbf{y}|$ ensures $f_{Y}(\mathbf{y})$ to be a proper density integrating to one. The composition of these transformations is invertible, allowing one to sample from the complex distribution by transforming samples from the base distribution. + +
+ +
+ + +
Image source: https://tikz.net/janosh/normalizing-flow.png
+ + +Our Normalizing Flow approach is based on element-wise rational splines of linear or quadratic order as introduced by Durkan (2019) and Dolatabadi (2020) and implemented in Pyro, since they offer a combination of functional flexibility and numerical stability. Despite this specific choice, our framework is generic enough to accommodate the use of other parametrizable Normalizing Flows. + +## Gradient Boosting Machines for Location, Scale and Shape + +We draw inspiration from GAMLSS and label our model as LightGBM for Location, Scale and Shape (LightGBMLSS). Despite its nominal reference to GAMLSS, our framework is designed in such a way to accommodate the modeling of a wide range of parametrizable distributions that go beyond location, scale and shape. LightGBMLSS requires the specification of a suitable distribution from which Gradients and Hessians are derived. These represent the partial first and second order derivatives of the log-likelihood with respect to the parameter of interest. GBMLSS are based on multi-parameter optimization, where a separate tree is grown for each parameter. Estimation of Gradients and Hessians, as well as the evaluation of the loss function is done simultaneously for all parameters. Gradients and Hessians are derived using PyTorch's automatic differentiation capabilities. The flexibility offered by automatic differentiation allows users to easily implement novel or customized parametric distributions for which Gradients and Hessians are difficult to derive analytically. It also facilitates the usage of Normalizing Flows, or to add additional constraints to the loss function. To improve the convergence and stability of GBMLSS estimation, unconditional Maximum Likelihood estimates of the parameters are used as offset values. To enable a deeper understanding of the data generating process, GBMLSS also provide attribute importance and partial dependence plots using the Shapley-Value approach. + +# References + +- Hadi Mohaghegh Dolatabadi, Sarah Erfani, and Christopher Leckie. Invertible Generative Modeling using Linear Rational Splines. In The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 4236–4246, 2020. +- Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios. Neural Spline Flows. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019. +- Nadja Klein, Thomas Kneib, and Stefan Lang. Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data. Journal of the American Statistical Association, 110(509):405–419, 2015. +- Alexander März, and Thomas Kneib. Distributional Gradient Boosting Machines, 2022b. +- Alexander März. XGBoostLSS - An extension of XGBoost to probabilistic forecasting, 2019. +- Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The M5 competition: Background, organization, and implementation. International Journal of Forecasting, 38(4):1325–1336, 2022a. +- Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos, Zhi Chen, Anil Gaba, Ilia Tsetlin, and Robert L. Winkler. The M5 uncertainty competition: Results, findings and conclusions. International Journal of Forecasting, 38(4):1365–1385, 2022b. +- R. A. Rigby and D. M. Stasinopoulos. Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(3):507–554, 2005. +- Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, and Fernanda de Bastiani. Flexible Regression and Smoothing: Using GAMLSS in R. Chapman & Hall / CRC The R Series. CRC Press, London, 2017. \ No newline at end of file diff --git a/dgbm/index.html b/dgbm/index.html new file mode 100644 index 0000000..b63a9af --- /dev/null +++ b/dgbm/index.html @@ -0,0 +1,282 @@ + + + + + + + + + + + Distributional Modelling - LightGBMLSS + + + + + + + + + + +
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Introduction

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The development of modelling approaches that approximate and describe the data generating processes underlying the observed data in as much detail as possible is a guiding principle in both statistics and machine learning. We therefore strongly agree with the statement of Hothorn et al. (2014) that ''the ultimate goal of any regression analysis is to obtain information about the entire conditional distribution \(F_{Y}(y|\mathbf{x})\) of a response given a set of explanatory variables''.

+
+''Practitioners expect forecasting to reduce future uncertainty by providing accurate predictions like those in hard sciences. However, this is a great misconception. A major purpose of forecasting is not to reduce uncertainty but reveal its full extent and implications by estimating it as precisely as possible. [...] The challenge for the forecasting field is how to persuade practitioners of the reality that all forecasts are uncertain and that this uncertainty cannot be ignored, as doing so could lead to catastrophic consequences.'' Makridakis (2022b) +
+ +

It has not been too long, though, that most regression models focused on estimating the conditional mean \(\mathbb{E}(Y|\mathbf{X} = \mathbf{x})\) only, implicitly treating higher moments of the conditional distribution \(F_{Y}(y|\mathbf{x})\) as fixed nuisance parameters. As such, models that minimize an \(\ell_{2}\)-type loss for the conditional mean are not able to fully exploit the information contained in the data, since this is equivalent to assuming a Normal distribution with constant variance. In real world situations, however, the data generating process is usually less well behaved, exhibiting characteristics such as heteroskedasticity, varying degrees of skewness and kurtosis or intermittent and sporadic behaviour. In recent years, however, there has been a clear shift in both academic and corporate research toward modelling the entire conditional distribution. This change in attention is most evident in the recent M5 forecasting competition (Makridakis et al., 2022a,b), which differed from previous ones in that it consisted of two parallel competitions: in addition to providing accurate point forecasts, participants were also asked to forecast nine different quantiles to approximate the distribution of future sales.

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Distributional Gradient Boosting Machines

+

This section introduces the general idea of distributional modelling. For a more thorough introduction, we refer the interested reader to Rigby and Stasinopoulos (2005); Klein et al. (2015); Stasinopoulos et al. (2017).

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GAMLSS

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Probabilistic forecasts are predictions in the form of a probability distribution, rather than a single point estimate only. In this context, the introduction of Generalized Additive Models for Location Scale and Shape (GAMLSS) by Rigby and Stasinopoulos (2005) has stimulated a lot of research and culminated in a new research branch that focuses on modelling the entire conditional distribution in dependence of covariates.

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Univariate Targets

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In its original formulation, GAMLSS assume a univariate response to follow a distribution \(\mathcal{D}\) that depends on up to four parameters, i.e., \(y_{i} \stackrel{ind}{\sim} \mathcal{D}(\mu_{i}, \sigma^{2}_{i}, \nu_{i}, \tau_{i}), i=1,\ldots,N\), where \(\mu_{i}\) and \(\sigma^{2}_{i}\) are often location and scale parameters, respectively, while \(\nu_{i}\) and \(\tau_{i}\) correspond to shape parameters such as skewness and kurtosis. Hence, the framework allows to model not only the mean (or location) but all parameters as functions of explanatory variables. It is important to note that distributional modelling implies that observations are independent, but not necessarily identical realizations \(y \stackrel{ind}{\sim} \mathcal{D}\big(\mathbf{\theta}(\mathbf{x})\big)\), since all distributional parameters \(\mathbf{\theta}(\mathbf{x})\) are related to and allowed to change with covariates. In contrast to Generalized Linear (GLM) and Generalized Additive Models (GAM), the assumption of the response distribution belonging to an exponential family is relaxed in GAMLSS and replaced by a more general class of distributions, including highly skewed and/or kurtotic continuous, discrete and mixed discrete, as well as zero-inflated distributions. While the original formulation of GAMLSS in Rigby and Stasinopoulos (2005) suggests that any distribution can be described by location, scale and shape parameters, it is not necessarily true that the observed data distribution can actually be characterized by all of these parameters. Hence, we follow Klein et al. (2015) and use the term distributional modelling and GAMLSS interchangeably.

+

From a frequentist point of view, distributional modelling can be formulated as follows

+
\[\begin{equation} +y_{i} \stackrel{ind}{\sim} \mathcal{D} + \begin{pmatrix} + h_{1}\bigl(\theta_{i1}(x_{i})\bigr) = \eta_{i1} \\ + h_{2}\bigl(\theta_{i2}(x_{i})\bigr) = \eta_{i2} \\ + \vdots \\ + h_{K}\bigl(\theta_{iK}(x_{i})\bigr) = \eta_{iK} +\end{pmatrix} +\end{equation}\]
+

for \(i = 1, \ldots, N\), where \(\mathcal{D}\) denotes a parametric distribution for the response \(\textbf{y} = (y_{1}, \ldots, y_{N})^{\prime}\) that depends on \(K\) distributional parameters \(\theta_{k}\), \(k = 1, \ldots, K\), and with \(h_{k}(\cdot)\) denoting a known function relating distributional parameters to predictors \(\eta_{k}\). In its most generic form, the predictor \(\eta_{k}\) is given by

+
\[\begin{equation} +\eta_{k} = f_{k}(\mathbf{x}), \qquad k = 1, \ldots, K +\end{equation}\]
+

Within the original distributional regression framework, the functions \(f_{k}(\cdot)\) usually represent a combination of linear and GAM-type predictors, which allows to estimate linear effects or categorical variables, as well as highly non-linear and spatial effects using a Spline-based basis function approach. The predictor specification \(\eta_{k}\) is generic enough to use tree-based models as well, which allows us to extend LightGBM to a probabilistic framework.

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Normalizing Flows

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Although the GAMLSS framework offers considerable flexibility, parametric distributions may prove not flexible enough to provide a reasonable approximation for certain dataset, e.g., for multi-modal distributions. For such cases, it is preferable to relax the assumption of a parametric distribution and approximate the data non-parametrically. While there are several alternatives for learning conditional distributions, we propose to use Normalizing Flows for their ability to fit complex distributions with only a few parameters.

+

The principle that underlies Normalizing Flows is to turn a simple base distribution, e.g., \(F_{Z}(\mathbf{z}) = N(0,1)\), into a more complex and realistic distribution of the target variable \(F_{Y}(\mathbf{y})\) by applying several bijective transformations \(h_{j}\), \(j = 1, \ldots, J\) to the variable of the base distribution

+
\[\begin{equation} + \mathbf{y} = h_{J} \circ h_{J-1} \circ \cdots \circ h_{1}(\mathbf{z}) +\end{equation}\]
+

Based on the complete transformation function \(h=h_{J}\circ\ldots\circ h_{1}\), the density of \(\mathbf{y}\) is then given by the change of variables theorem

+
\[\begin{equation} + f_{Y}(\mathbf{y}) = f_{Z}\big(h(\mathbf{y})\big) \cdot \Bigg|\frac{\partial h(\mathbf{y})}{\partial \mathbf{y}}\Bigg| \end{equation}\]
+

where scaling with the Jacobian determinant \(|h^{\prime}(\mathbf{y})| = |\partial h(\mathbf{y}) / \partial \mathbf{y}|\) ensures \(f_{Y}(\mathbf{y})\) to be a proper density integrating to one. The composition of these transformations is invertible, allowing one to sample from the complex distribution by transforming samples from the base distribution.

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+ +

+

+
Image source: https://tikz.net/janosh/normalizing-flow.png
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+

Our Normalizing Flow approach is based on element-wise rational splines of linear or quadratic order as introduced by Durkan (2019) and Dolatabadi (2020) and implemented in Pyro, since they offer a combination of functional flexibility and numerical stability. Despite this specific choice, our framework is generic enough to accommodate the use of other parametrizable Normalizing Flows.

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Gradient Boosting Machines for Location, Scale and Shape

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We draw inspiration from GAMLSS and label our model as LightGBM for Location, Scale and Shape (LightGBMLSS). Despite its nominal reference to GAMLSS, our framework is designed in such a way to accommodate the modeling of a wide range of parametrizable distributions that go beyond location, scale and shape. LightGBMLSS requires the specification of a suitable distribution from which Gradients and Hessians are derived. These represent the partial first and second order derivatives of the log-likelihood with respect to the parameter of interest. GBMLSS are based on multi-parameter optimization, where a separate tree is grown for each parameter. Estimation of Gradients and Hessians, as well as the evaluation of the loss function is done simultaneously for all parameters. Gradients and Hessians are derived using PyTorch's automatic differentiation capabilities. The flexibility offered by automatic differentiation allows users to easily implement novel or customized parametric distributions for which Gradients and Hessians are difficult to derive analytically. It also facilitates the usage of Normalizing Flows, or to add additional constraints to the loss function. To improve the convergence and stability of GBMLSS estimation, unconditional Maximum Likelihood estimates of the parameters are used as offset values. To enable a deeper understanding of the data generating process, GBMLSS also provide attribute importance and partial dependence plots using the Shapley-Value approach.

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References

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    +
  • Hadi Mohaghegh Dolatabadi, Sarah Erfani, and Christopher Leckie. Invertible Generative Modeling using Linear Rational Splines. In The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 4236–4246, 2020.
    • +
  • Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios. Neural Spline Flows. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019.
    • +
  • Nadja Klein, Thomas Kneib, and Stefan Lang. Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data. Journal of the American Statistical Association, 110(509):405–419, 2015.
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  • Alexander März, and Thomas Kneib. Distributional Gradient Boosting Machines, 2022b.
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  • Alexander März. XGBoostLSS - An extension of XGBoost to probabilistic forecasting, 2019.
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  • Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The M5 competition: Background, organization, and implementation. International Journal of Forecasting, 38(4):1325–1336, 2022a.
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  • Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos, Zhi Chen, Anil Gaba, Ilia Tsetlin, and Robert L. Winkler. The M5 uncertainty competition: Results, findings and conclusions. International Journal of Forecasting, 38(4):1365–1385, 2022b.
    • +
  • R. A. Rigby and D. M. Stasinopoulos. Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(3):507–554, 2005.
    • +
  • Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, and Fernanda de Bastiani. Flexible Regression and Smoothing: Using GAMLSS in R. Chapman & Hall / CRC The R Series. CRC Press, London, 2017.
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+ + + + + + + + + + + + + + + diff --git a/distributions/distributions.md b/distributions/distributions.md new file mode 100644 index 0000000..b0d13b5 --- /dev/null +++ b/distributions/distributions.md @@ -0,0 +1,25 @@ +# Available Distributions +LightGBMLSS is built upon PyTorch and Pyro, enabling users to harness a diverse set of distributional families and to leverage automatic differentiation capabilities. This greatly expands the options for probabilistic modeling and uncertainty estimation and allows users to tackle complex regression tasks. + +LightGBMLSS currently supports the following univariate distributions. + +| Distribution | Usage |Type | Support | Number of Parameters | +| :----------------------------------------------------------------------------------------------------------------------------------: |:------------------------: |:-------------------------------------: | :-----------------------------: | :-----------------------------: | +| [Beta](https://pytorch.org/docs/stable/distributions.html#beta) | `Beta()` | Continuous
(Univariate) | $y \in (0, 1)$ | 2 | +| [Cauchy](https://pytorch.org/docs/stable/distributions.html#cauchy) | `Cauchy()` | Continuous
(Univariate) | $y \in (-\infty,\infty)$ | 2 | +| [Expectile](https://epub.ub.uni-muenchen.de/31542/1/1471082x14561155.pdf) | `Expectile()` | Continuous
(Univariate) | $y \in (-\infty,\infty)$ | Number of expectiles | +| [Gamma](https://pytorch.org/docs/stable/distributions.html#gamma) | `Gamma()` | Continuous
(Univariate) | $y \in (0, \infty)$ | 2 | +| [Gaussian](https://pytorch.org/docs/stable/distributions.html#normal) | `Gaussian()` | Continuous
(Univariate) | $y \in (-\infty,\infty)$ | 2 | +| [Gumbel](https://pytorch.org/docs/stable/distributions.html#gumbel) | `Gumbel()` | Continuous
(Univariate) | $y \in (-\infty,\infty)$ | 2 | +| [Laplace](https://pytorch.org/docs/stable/distributions.html#laplace) | `Laplace()` | Continuous
(Univariate) | $y \in (-\infty,\infty)$ | 2 | +| [LogNormal](https://pytorch.org/docs/stable/distributions.html#lognormal) | `LogNormal()` | Continuous
(Univariate) | $y \in (0,\infty)$ | 2 | +| [Negative Binomial](https://pytorch.org/docs/stable/distributions.html#negativebinomial) | `NegativeBinomial()` | Discrete Count
(Univariate) | $y \in (0, 1, 2, 3, \ldots)$ | 2 | +| [Poisson](https://pytorch.org/docs/stable/distributions.html#poisson) | `Poisson()` | Discrete Count
(Univariate) | $y \in (0, 1, 2, 3, \ldots)$ | 1 | +| [Spline Flow](https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.transforms.Spline) | `SplineFlow()` | Continuous \& Discrete Count
(Univariate) | $y \in (-\infty,\infty)$

$y \in [0, \infty)$

$y \in [0, 1]$

$y \in (0, 1, 2, 3, \ldots)$ | 2xcount_bins + (count_bins-1) (order=quadratic)

3xcount_bins + (count_bins-1) (order=linear) | +| [Student-T](https://pytorch.org/docs/stable/distributions.html#studentt) | `StudentT()` | Continuous
(Univariate) | $y \in (-\infty,\infty)$ | 3 | +| [Weibull](https://pytorch.org/docs/stable/distributions.html#weibull) | `Weibull()` | Continuous
(Univariate) | $y \in [0, \infty)$ | 2 | +| [Zero-Adjusted Beta](https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py) | `ZABeta()` | Discrete-Continuous
(Univariate) | $y \in [0, 1)$ | 3 | +| [Zero-Adjusted Gamma](https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py) | `ZAGamma()` | Discrete-Continuous
(Univariate) | $y \in [0, \infty)$ | 3 | +| [Zero-Adjusted LogNormal](https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py) | `ZALN()` | Discrete-Continuous
(Univariate) | $y \in [0, \infty)$ | 3 | +| [Zero-Inflated Negative Binomial](https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150) | `ZINB()` | Discrete-Count
(Univariate) | $y \in [0, 1, 2, 3, \ldots)$ | 3 | +| [Zero-Inflated Poisson](https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L121) | `ZIPoisson()` | Discrete-Count
(Univariate) | $y \in [0, 1, 2, 3, \ldots)$ | 2 | diff --git a/distributions/index.html b/distributions/index.html new file mode 100644 index 0000000..add9b46 --- /dev/null +++ b/distributions/index.html @@ -0,0 +1,343 @@ + + + + + + + + + + + Available Distributions - LightGBMLSS + + + + + + + + + + +
+
+ LightGBMLSS + + + + + +
+
+ +
+
+
+
+ +

Available Distributions

+

LightGBMLSS is built upon PyTorch and Pyro, enabling users to harness a diverse set of distributional families and to leverage automatic differentiation capabilities. This greatly expands the options for probabilistic modeling and uncertainty estimation and allows users to tackle complex regression tasks.

+

LightGBMLSS currently supports the following univariate distributions.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DistributionUsageTypeSupportNumber of Parameters
BetaBeta()Continuous
(Univariate)		\(y \in (0, 1)\)2
CauchyCauchy()Continuous
(Univariate)		\(y \in (-\infty,\infty)\)2
ExpectileExpectile()Continuous
(Univariate)		\(y \in (-\infty,\infty)\)Number of expectiles
GammaGamma()Continuous
(Univariate)		\(y \in (0, \infty)\)2
GaussianGaussian()Continuous
(Univariate)		\(y \in (-\infty,\infty)\)2
GumbelGumbel()Continuous
(Univariate)		\(y \in (-\infty,\infty)\)2
LaplaceLaplace()Continuous
(Univariate)		\(y \in (-\infty,\infty)\)2
LogNormalLogNormal()Continuous
(Univariate)		\(y \in (0,\infty)\)2
Negative BinomialNegativeBinomial()Discrete Count
(Univariate)		\(y \in (0, 1, 2, 3, \ldots)\)2
PoissonPoisson()Discrete Count
(Univariate)		\(y \in (0, 1, 2, 3, \ldots)\)1
Spline FlowSplineFlow()Continuous \& Discrete Count
(Univariate)		\(y \in (-\infty,\infty)\)

\(y \in [0, \infty)\)

\(y \in [0, 1]\)

\(y \in (0, 1, 2, 3, \ldots)\)		2xcount_bins + (count_bins-1) (order=quadratic)

3xcount_bins + (count_bins-1) (order=linear)
Student-TStudentT()Continuous
(Univariate)		\(y \in (-\infty,\infty)\)3
WeibullWeibull()Continuous
(Univariate)		\(y \in [0, \infty)\)2
Zero-Adjusted BetaZABeta()Discrete-Continuous
(Univariate)		\(y \in [0, 1)\)3
Zero-Adjusted GammaZAGamma()Discrete-Continuous
(Univariate)		\(y \in [0, \infty)\)3
Zero-Adjusted LogNormalZALN()Discrete-Continuous
(Univariate)		\(y \in [0, \infty)\)3
Zero-Inflated Negative BinomialZINB()Discrete-Count
(Univariate)		\(y \in [0, 1, 2, 3, \ldots)\)3
Zero-Inflated PoissonZIPoisson()Discrete-Count
(Univariate)		\(y \in [0, 1, 2, 3, \ldots)\)2
+
+
+ + + + + + + + + + + + + + + diff --git a/examples/Expectile_Regression/Expectile_Regression.ipynb b/examples/Expectile_Regression/Expectile_Regression.ipynb new file mode 100644 index 0000000..e778a23 --- /dev/null +++ b/examples/Expectile_Regression/Expectile_Regression.ipynb @@ -0,0 +1,751 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Expectile Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/StatMixedML/LightGBMLSS/blob/master/docs/examples/Expectile_Regression.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T05:55:53.811155700Z", + "start_time": "2023-05-18T05:55:53.784255700Z" + } + }, + "outputs": [], + "source": [ + "from lightgbmlss.model import *\n", + "from lightgbmlss.distributions.Expectile import *\n", + "from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data\n", + "\n", + "import plotnine\n", + "from plotnine import *\n", + "plotnine.options.figure_size = (20, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T05:44:34.767607800Z", + "start_time": "2023-05-18T05:44:33.869406200Z" + } + }, + "outputs": [], + "source": [ + "# The data is a simulated Gaussian as follows, where x is the only true feature and all others are noise variables\n", + " # loc = 10\n", + " # scale = 1 + 4*((0.3 < x) & (x < 0.5)) + 2*(x > 0.7)\n", + "\n", + "train, test = load_simulated_gaussian_data()\n", + "\n", + "X_train, y_train = train.filter(regex=\"x\"), train[\"y\"].values\n", + "X_test, y_test = test.filter(regex=\"x\"), test[\"y\"].values\n", + "\n", + "dtrain = lgb.Dataset(X_train, label=y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Expectile Specification" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T05:44:34.783229100Z", + "start_time": "2023-05-18T05:44:34.767607800Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "lgblss = LightGBMLSS(\n", + " Expectile(stabilization=\"None\", # Options are \"None\", \"MAD\", \"L2\".\n", + " expectiles = [0.05, 0.95], # List of expectiles to be estimated, in increasing order.\n", + " penalize_crossing = True # Whether to include a penalty term to discourage crossing of expectiles.\n", + " ) \n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyper-Parameter Optimization\n", + "\n", + "Any LightGBM hyperparameter can be tuned, where the structure of the parameter dictionary needs to be as follows:\n", + "\n", + " - Float/Int sample_type\n", + " - {\"param_name\": [\"sample_type\", low, high, log]}\n", + " - sample_type: str, Type of sampling, e.g., \"float\" or \"int\"\n", + " - low: int, Lower endpoint of the range of suggested values\n", + " - high: int, Upper endpoint of the range of suggested values\n", + " - log: bool, Flag to sample the value from the log domain or not\n", + " - Example: {\"eta\": \"float\", low=1e-5, high=1, log=True]}\n", + "\n", + " - Categorical sample_type\n", + " - {\"param_name\": [\"sample_type\", [\"choice1\", \"choice2\", \"choice3\", \"...\"]]}\n", + " - sample_type: str, Type of sampling, either \"categorical\"\n", + " - choice1, choice2, choice3, ...: str, Possible choices for the parameter\n", + " - Example: {\"boosting\": [\"categorical\", [\"gbdt\", \"dart\"]]}\n", + "\n", + " - For parameters without tunable choice (this is needed if tree_method = \"gpu_hist\" and gpu_id needs to be specified)\n", + " - {\"param_name\": [\"none\", [value]]},\n", + " - param_name: str, Name of the parameter\n", + " - value: int, Value of the parameter\n", + " - Example: {\"gpu_id\": [\"none\", [0]]}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T05:54:36.773860500Z", + "start_time": "2023-05-18T05:44:34.783229100Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2023-08-11 12:21:09,469] A new study created in memory with name: LightGBMLSS Hyper-Parameter Optimization\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6d50788072314ea7860681950814af2f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/30 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
expectile_0.05expectile_0.95
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16.61579213.277894
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46.61579213.367647
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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Partial Dependence Plot of how x acts on selected expectile \n", + "lgblss.expectile_plot(X_test,\n", + " expectile=\"expectile_0.95\",\n", + " feature=\"x_true\",\n", + " plot_type=\"Partial_Dependence\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T05:54:38.175619300Z", + "start_time": "2023-05-18T05:54:37.727801800Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Partial Dependence Plot of how x acts on selected expectile \n", + "lgblss.expectile_plot(X_test,\n", + " expectile=\"expectile_0.05\",\n", + " feature=\"x_true\",\n", + " plot_type=\"Partial_Dependence\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T05:54:38.518306100Z", + "start_time": "2023-05-18T05:54:38.176143300Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Global Feature Importance of selected expectile\n", + "lgblss.expectile_plot(X_test,\n", + " expectile=\"expectile_0.95\",\n", + " plot_type=\"Feature_Importance\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot of Actual vs. Predicted Expectiles" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T05:54:39.632659700Z", + "start_time": "2023-05-18T05:54:38.519307400Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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tbjgRETks+XfeiVssthvOJBLzrrVsG6+rC6dYxPzhD4uSJ7RsGV1f+AIzn/gE/OIX+JUKVqkExuCFQtDdTTA4iPNXf0XP5s2LkuFYN/PlL2M/8ADh4WG8ri6CNWvmisstIOjuprF8OaHhYdxt2/B+8hPKZ51F8swzOxtcRERERETkGBc+7TS8668Hy2o3kJtvIp8xuMUiXjqNv369CtAPQHPnTryJCYzv4/T3E924Uc3YRERERERERDpEBegiIiIiIiIiIiIiS82y6Hn1qzEvfzmlH/6Q1q9+BbOzEIlgb9xI4sILCa9cedCHsWMxwip4PuTCJ59MPRzGj0SwCwXMAl9jJ5/Hj8fBdYn82Z8tUUoRETms1GrgeZiFmpbsZsJhrHIZq1ZbtEhuby99//zPtKanKV93Hf7wMDQa0N1N5MwzSZx5pprXLBLTbMKPf4w9NYUfiexVfP5QVihEa/16Ig88APk8jWuuUQG6iIiIiIhIhyWe+UwKg4N4O3cSmp6m2dWFtZ/f963p6fak9P5+3AsuWOKkRxBjKP/sZzSuvhrnrrvmPtayLKpDQzjnnUfqgguwo9HO5hQRERERERE5xqgAXURERERERERERKRDrHCYrgsugEe58cwrFGhs3w7NJk5PD5Hjj1eh2BKIbtxI5eSTsTIZQlNTNPv7IRze51rTaOAWizRXrcI/9VRCy5cvcVoRETksRCLgOFied0DLrVYLHAcTiSxyMAgNDpJ+3esW/TjyR6Wf/hS7UMCdmaG5n+LzPaxQCK+7Gzefh9/+ltb0NKHBwSVMKyIiIiIiIg9lOQ7OX/wFwfQ09tathLZto7VyJVYqNff7nWm1sKenCedyNFavJli+nJ5zz+1w8sOTaTbJf+ADOLfcgpvP4+RyOI0GGEMQCuFNTmK2bqVwww10fexj+p1YREREREREZAmpAF1ERERERERERETkCFP93e+ofec7OL/8Jfj+HyeB9PdjnXsuXS95CU53d6djHtUiL3kJ3h/+gF8s4m7bhrduHcTje60x5TLh0dH29PPeXqIveUmH0oqISKfZJ5+Muf12nKkpvGr1Yd8zHsoEAe7sLM0VKwhOOGEJU8pS8cfHsWo1rCCAA/iZLejuJlQo4BlDa3xcm+1FREREREQ6rPulLyW/bRut738fZ3ycyMgIJhTCj0SwggC3WiUIhWisXYtZvZr4FVdgx2Jz9/dyORpXXw3T0xRSKdzBQRLPe96x18DUmHbx+U9/Smj7dizPo5VO461cibEsrEqFUD6Plc3SrNcpvfWtdH/hCzipVKeTi4iIiIiIiBwTVIAuIiIiIiIiIiIicqQwhsJnP4v1rW/hFAo4uRxutdqeBOK6+N3dBBMTzNxwA/ErriCqorVFkzzrLPJ33glXX40zMkJkyxa8eJwgkQBjcCoVnHqdVlcXwdq1mFe8gsTpp3c6toiIdEjyRS+i8t//jZdI4ExO4q9fv9+p1/bUVPsv6TSRzZuXMKUsGc8DY+adfL6Xh67z/YM+vGm1qP72t/i5HDgO4fXr9XOjiIiIiIjII2FZ9L773RSWLcP/9rcJZmex8nmsVovAsmgMDkJXF8Hxx5N4z3uIrF8PQHPnTkqf/Sz2L35BoljEqtdxXBdiMSpf/Sr+k59M4g1vILpxY4cf4NIo/+xnOLfcQmj7dozj0DzuOCzHAcACSCZpDQ5ij48THhmh6brMfOUr9L7lLR3NLSIiIiIiInKsUAG6iIiIiIiIiIiIyBGi8P/+H9aVV+IMD+NUq3jd3dQHB8FxsGo13FwON5+nWalQfdvbsD73OSJr13Y69lGr961vpZBI4F95JX65jJ3LYVerYFn4iUR7KnoiAa95Dem/+qtOxxURkQ4KDQ7iP+MZUKkQ3roVMzKCv2oVVjg8t8b4PvbUFOFslsbatQRr1pA488wOppbFYvf1Yfb821er7Z8X5rN7ch6A3dv7qI/r5XKUvvlNuPFGrGIRWi2wLOqhEJVNm3AvuICu88+f2+wvIiIiIiIi87As0q97HcErXkHpe9/Du/VWKJUgHMZat47YC19I7DGPmVtef/BBqpdeijM+jjM5Sbhcxtg2tm1j+T5ePA6FArW77iL4l38h/oQndPDBLY3G1Vfj5vNYnrdX8fleLItg9Wr8eh17eprgxz/GXHzxXtdURERERERERGRxqABdRERERERERERE5AjQGB7G+uY3cUZGsJpNGscfjxWJMDcPM5HA6+/Hmp4mMj5Ow3Upf+YzRD7xiU7G3ktQq1G74w78mRnsSITw8ccTXr2607EePcsi/YY30LrwQspXX433059iFQoAmP5+rOc8h9Tmzbh9fR0OKrLIjKF2zz20xsbA83D6+4mfdhqWq7ehRB4qfdllFHbsoGkM7o4dhO6/Hy+RaBci+z5uqYSx7Xbx+dq1xD/0IRUCH6USZ51F+XOfw4vHsbNZgvkK0I0hlM/jpdP469YR3bDhUR2z/uCDVC+7DHt8HDuTwZ2ZwQoCAIJwGGtqCvPAA+R//nPSH/oQdjT6qI4jIiIiIiJyrLHjcbr/4i/gL/5iv2u8fL79O9nwMJGxMVpdXVSGhiAeJxQOY9Vq2JkMkR07aDabNN7zHtx///cj+/r5Apo7d+LcdRdOLkcrnZ7/Gohl4Q8MEB4bIygUKN98M6mzz166sCIiIiIiIiLHKO38ERERERERERERETkCVK66CrtUwi2XqR93HFYkss91ZnCQVr2OOzUFv/kNzYkJwqtWLXHavTUnJih/85tYN92EVS6D5xHYNp7rUjrlFCIXXUTyGc/oaMaDEVq2jPTFF8PFF3c6isiSMp7H7DXX4F9/PfbISHuSrjEEoRCFdBrrnHNIvfzluOl0p6OKHBbseJye//t/KX7wg/DrX+OVStjFIlarhXEcmqtXQ08P/tAQyX/6JyJDQ52OLIvE7evDf9rToFAgMjJCo1iEnp6HLzQGa2ICy/cx/f24558PlvXwdQto7txJ9dJLsbdvJzw+ThCL0Vy1CpNIYAUBVrGIm8tBPg+eR+H976fviise1bFERERERETk4UpXXok9MUF4bIzm8uUEg4PQbP5xQSxGsHYtjUSCyMQEjViM8te+Ru+739250IvMm5gAwGk08FauXPgOXV3tRmq1Gv74+CKnExERERERERFQAbqIiIiIiIiIiIjI4c8YrJ/+FDuXw0smsWKxeZf7AwOEHnwQr1Khcv31hN/4xiUK+nDV//1fGpdfjp3JYGezuLOzc9M2/WgUMhm83/2O/AtfSO/b3qZCJ5EjRFCtUrjsMuzf/hYnm8XN57FbLQCMbeP19BBMTjL7k58Q/9jHiG7c2OHEIocHJ5Wi7+Mfp75lC9XvfIfgzjuhUoFoFLNxI9HNm+l5ylP0/fAYkHjta6n95jc063Uio6O0ZmcJ+vshHgdjMLOzuJkMTq1Gc2gIf+NG0hdc8KiOVfp//w9nfJzI+DjN5csxg4MA7HmWmViM5rJluNu3E9q+HcJhyrfeekQ3CBIRERERETlcGM/D/PCHOJkMfiw29zvZPvX10ZqZwc5kMDffjP/mN+Mkk0sXdgkZ3wdj2r8DWxYLXgmxrLnrJWb3ewwiIoeDViZD+brrMCMjmGYTq7ub8JlnkjzjDF3nFREREZEjngrQRURERERERERERA5zXrGIVS7jVKt4fX0LrrdiMYJwGKtaJdi5cwkS7lv9/vtpvOc92KOjhCcn8RIJmmvWYGIxLM/DKhQIT04SFAp411xDIRIhfcklHcsrIgfIGArvehfO7be3CxUti1ZfH6arC2wbq1LByeUIb9lCs16n+va343zhC4Tm21wrcoyJHncc0Xe9q9MxpIOiGzcSfOhDNN77XhqRCE4mQ2TLFoxtY+3efO91ddFctYpg0ya6Pv5x7Gj0ER/HKxSwb7sNZ2qKViq130IHy7bx1q8nct99kM/TuOYaFaCLiIiIiIgcArU77sAuFHBnZmgewKRvv7+f6MgIQaVC9Re/IHX22UuQcuk5/f20LIsgFMKqVGChQvtyuf1nJIJ9AO+TiIgsNi+XY+aTn8T5xS+wKhXschmCABMK4f3gB+SXL8f967+m6wUv6HRUEREREZFHTQXoIiIiIiIiIiIiIoe7h0zzMLZ9QHcxltWeHOL7i5VqQZVPfxpn1y7Ck5M01qyBdBrYPW0zEsEkEu1pm9u24YyM4H/72zTOO4/I+vUdyywiC5u98Uac3/6W0PAwQTSKNzSEZdt/nFIUieD39hJMThIeG6MZDlP64hfpfc97OhlbRGTJtaanqfzgBwSTk2AM1sAAiXPOIbxqFQDx007D/uxnqX75y/CrX+FXq9BotCcjxWKYri7Mc55D9//5PzhdXY8qQ/lHP8Iql3ErFeobN847Uc5yHLzubtx8Hn73O7x8Hqu7+1EdV0RERERERNq8XA58H8v3281ZF7pDItGeDN5sEuTzSxGxI6IbN1JZtw5rcpJQPk9rcHDeScF2NouXTGISCVJnnbWESUVEHq41OUnpkktwhodxdu7ErVYJQiGMbWM3m5idO/EmJwmuuILC5CTpv/mbTkcWEREREXlUVIAuIiIiIiIiIiIicphzursxoRBBOIxdqRD098+73rRaOM0mXjSK1du7RCn3Vn/wQZx77yU0NUWzr2+u+PxhQiFa69YR3bIFv1KhctVVRC69dGnDHiEao6NUrrkG88AD7eK0ZBLn1FNJvfCFj7ooTeTRaH33u7i5HATBXPH5vpjly/GrVeypKcz//A/+W96Cs9AkIxGRo0BjdJTyZz+L8+tfQ6WCXasBYKJRql/7GqVTTyVx8cVEN21q//cv/0JreprqT39KkM2C4+CsXk3yOc/BjsUOKkswOYldq2EsCyseX3h9KoVbLIIxtHbtIqwCdBERERERkYNih8MEuwurLWMWXG/taSprWRAKLWa0zrIs3PPPx2zbhpXNYo+PE6xeve8i9FyOUKlEY/16zBln4Oh3VRHpION5lN7xDuytWwmPjOAlkzQ2bYLd195MEGDl84SmpghqNfz/+A9mV66k6/nP73ByEREREZFHTgXoIiIiIiIiIiIiIoc5y3UJTj8dP5MhPDZGo9XCmmfjmZ3NEoRCkEoRe97zljDpH9VuuAGrVGp3+R8YmHetFYvhJRLY2Szmpz+Ft71t3kknxxovn2fmwx/G+d//xa5UsGZmsIIA47qY229n9mtfg3PPpeeSS7BcXfaXxdXcuRPnnntw8nm8dHq/xed7+P39REZGCEolyj/+Md2bNy9RUhGRzqjfdx/Vt78dZ2ICZ3KyPf3IdbEAy/Pw43EoFqn94Q/4H/4wiSc9CYDQ4CDdL3vZ4oY7kJ+v9DOYiIiIiIjIIeWuW4fnOASRCFaxiFmgOZhVLBK4LkQiuOvWLVHKzkhdcAGFG26gWa8THhnBr9fxBwagq6v9+2m5jJ3NEiqVaK5ciVmxgsRrX9vp2CJyjCv9+MdY27YR3rGDVjqNWbVqr2tqlm1Dfz/NVIrw1q0EU1N4X/sanHOOrr2JiIiIyBFn/l1BIiIiIiIiIiIiInJYiG7eDD09mFAId3QUEwT7XlgqEcpm8QYG8DdtIvaYxyxt0N2CyUmsWg0/HIZweOH1qRR2vY5VLhPsnhIq0MpkmL34Ypyf/Qz3gQeIbNmCUyphNRo4hQKR++/Hve8+rG9/m/xll2E8r9OR5SjX2rULAKfRwBzINPNUqv1nvU4wNbWIyUREOq+VyVC97DLs4WEiw8OYcJjGccfROvlkmiefTP344wmiUSI7dmCPjtK8/HKa4+OLmskZHIRotD1lr1JZcL1VKhFEImBZhJYvX9RsIiIiIiIix4Loxo34J56I19dHqFDA7Jlwvg8mCHByObx0mmD16rmmZUcrOxql62Mfw/+zP6O5aRMmEiE8Nkbk7rvb/w0PYxlDY/16gqEhIh/+MJG1azsdW0SOca3vfhc7nwfLeljx+UNZkQjewABuLoc9Pk7l179e4qQiIiIiIgdPo1BEREREREREREREjgCJJz2J3FOfCo0GoW3bCD/4IK3+fkxvL5ZlQb3engRSKNDq7cUsW0bs4os7HfuRdfI3pv3H/orrjzXGMPvud+Ns2UJ4eBgvlaK1bh1WJDJ3u1cu4+7aRWjLFgAKn/0svW95SwdDy1Fv9+tUZMkYQ/XOO6nfeCMmmwXbxhocJH7++URPOKHT6UT2Ur7ySuxdu4hMTNBYsQIGBva63YrFCNasoZFIEBkfpxGLUf761+l917sWLVPi7LMpf/GLeIkEdjZLkEjsd60JAsLFIs3ly/FPOQW3rw9/nsIIEREREREROTChzZsJ7r2XIJfD3b6d1j4mmxvfx92xA8sYzOAgzvnnHxOTckODg/R8/vPMfOUrBD/+MUGhAHua1EYimEQCc8YZJF77WhWfi0jH+eUyzr334hQKtHp6FjxPB319WJOTMDtL4+c/J/GUpyxNUBERERGRQ0QF6CIiIiIiIiIiIiJHAssi/cEPUnj728FxsKemCE9OYk1MtDc3GIMfj9NYswYGBrDf/nbip53WubgDA5hoFKfRoNVqYYVC86+vVAgiEUwshjNPYdSxpHL77Tj3309odBSvp4dg9ep2s4E9LAtSKVqJBKGtW7HHxjA33ID/2tfi7Jk6LYc902oxe+ONtG66CQqFdnHtsmWEzz2X5BlnHHabTEMrVtAC/HAYq1zGdHXNf4dSqf1nNIq9bNmi55OjS/V//5fav/0bzvAw9swMVr3enioTi1G/7joqJ55I4h/+geiJJ3Y6qgim1cLceCNOJoMXjz+s+Hwvvb20ikXsTAZz8834l1yCk0wuSi63rw//qU+FQoHI8DCNbBb6+x+ePwhwR0YwjgO9vUQuvHBR8oiIiIiIiByLus45h9zPfw6+j7t9O9EHHqCeTOInEhAKYVUqhItFjOPQ2rgR/0lPovcv/qLTsZeMk0rR+5a3YC6+mPLNN+OPj2OCALuvj+SznoWbTnc6oogIAH6xCIDleZhYbMH1luNgQiFotTCzs4ucTkRERETk0FMBuoiIiIiIiIiIiMgRwo5G6f3Up5j5xjfwrr+eYGoKqlUIAgiHIRrFP/VU4n/918Qe+9iOZo2dcw6N664jCIWws1nMihX7XWuaTUKlEo316zHPfOZhV3DbKfVrrsEpFrF8H3/lyr2Lzx/Csm285cuJjIwQlEqUrruOnr/8yyVOK49G6frrMV/6Es7MDG4+j9VoABDE43g//zn5FSuIdriZxJ8Kr1pF6cQTsTIZQtPTNJYvx7Lt/a63s1m8ZBKTSpF6znOWMKkc6Uo/+hH+v/wL7uQkbiaD5XkEkQgAzuQkQTgMhQK1N78Z/8MfJnHqqR1OLMe66m9/iz0zgzszQ3PVqgXX+/39REdGCMplqr/4Bamzz160bKmLL6b8+9/TaDaJTEzgzczg9/VhEgkIAuyZGUK5HBjTLnQ47TR6nvWsRcsjIiIiIiJyzLEsej/wAQqOg/ezn0GhgJPJEC6VsGwbE4nQXLECenvxTzuN9Ic+hOUee1u8rXB4UX8/FhE5WNbua9RYVrsIfaE7GNN+H9e2scLhxY4nIiIiInLIHXtXJ0RERERERERERESOYJbr0vPqV8OrXkX5ttvwtm7FNBpYPT3EzzyT8MqVnY4IQOwxj6G6aRNks4R37qSRSGDtY1Ky8X1CIyP4sRikUsQvuqgDaQ9P9l134RQKeF1dWI4z/+JUiiAUwioU8H73O1AB+mFv5mtfw/7qV3F37iRUKGBsGz8Ww4L2tKNdu/AyGZqXXUbwvveRPIwKAUObN+Pfcw8mk8HZsYNgaGifjSOsqSlC5TKNjRsxz3gGTiq19GHliFS/7z68j3wEe3SUUD5Pa2CAoK8PKxQCwGs2sTIZImNjNFstmpdfTujLXz5svgfKsckvFMDzsIIAk0iwYDudWKy9AbXZJNg9OWmxhFevJvaRj1B/5ztpxGLYmQzhsTGsIAAgCIXw0mnMwAD+k55E+p//WQ2BREREREREDjErFKL3gx+kcu651K66Cv8Xv8DyfULhMJZl4T/hCUQuvJAeNWkVETlshfr7CXp68JNJnJkZvMHB+e9QKmH7PqRS2Js2LU1IEREREZFDSAXoIiIiIiIiIiIiIkciyyJ5xhlwxhmdTrJfsTe9icaOHbSaTaIjI7S6uwl6ezHxOHgedj5PKJ/HuC7ehg0Ez38+0RNO6HTsw4MxWNUq+D4mFlt4vWURhELgeVCpLH4+OSjl227D+Y//wBkZwalW25Ny0+m5jaVeEGBns4Smpmh5Hq0Pf5jGhg1E1q7tcPK21Nlnk7/hBlrNJqHhYez778fv6yPo7m5PQ69UcLJZnGqVxpo1BKtXk3r96zsdW44gla9/HSebJZzL0Vi/HlKpvYt5w2HMqlU0YjEi4+M0kkkq//3fhN/61k5FFsFyXczu87jl+wvfweyej2TbsLu5wmKKnXwy7pe+ROnrX8fcdBP+7CxWq9X+3hOJEKxZg3P++fS++MXH5JQ9ERERERGRJWFZJE4/nehpp7Fr2zb86WmSAwNEli/H6e7udDoREVmIZWE973n4u3YRevBBvFIJ9td81xic6WlaqRQmlaLr3HOXNusBCGo1SjfcgPf972OPj2O1WgRdXXD66cRf/GKixx3X6YgiIiIi0mF651hEREREREREREREFkX8iU8kuPxyWh/6EI1kEieTITI8PFdwFYTDeP39mIEBgrPOIv32t3c48WHEsjCRCNg2ludhDuQuvt8uYotEFj2eHJzGN7+Jk83iVio0Nm7Eisf3ut2ybczgII1olOjwMI3ubirf+haRSy/tUOK9WY5DzxVXUHzHOyAUwspkcDMZ7F27ADC2jdfdTXPVKoLVq4l/9KOEli3rcGo5UrQyGZxf/hInk6HV07P/zXsAvb14+Tx2JoP/4x8TXHwxdjS6ZFlFHiq0YQO+6xJEIljFYrvhzjzsYrHdPCYSIbR+/dJkXLaM3ksvJXjTm6j+8pf4uRy4LqGhIeKPf7wm7ImIiIiIiCwhO5nETiaJLFuG4zidjiMiIgcosXkzlauvptXbS3jHDppr10JX115rTBDgjI3h1Os0N23C/Pmf48x3rbsDyrfcQuuKK7CKRdxsFrtSAWOwQyGC0VHqP/gBlTPPJH355bruLiIiInIMUwG6iIiIiIiIiIiIyJHMGEyrhRUKHZZFQ8lnPYv6qlVUv/51zG234ddq7by7C6X9TZsIXXghPeeff1jm76Rg40aCsTFC09M0jZn/61Or4dTrtNauxdYU+cNaY3gY5667cHM5mr29MM+Ee6urCy+Vws5mMTfdRPCmNx02m3ycZJLez3yG2e98B/+66zDj49Bstm8MhQh6euC5z6Xrla/E7evraFY5stR++Uuo13GrVeorVrDQdwa/v5/w2BhBqUT97ruJn3bakuQU+VPRjRupnHgi1vQ0oUyGxrJlWPspIDBBQCiXw0un8deubRd/LyE7GiX553++pMcUERERERERERE5GoRXrqT+lrcQfPzjeEBkZAQ/GsXr7gbHwa5WcWdnMbZNc8MGghNPpPuSSzodey/ln/4U7x//EXtiglA2i3FdvFQKbBu7XicyPIwfiUCjQWF2lt5PfarTkUVERESkQ1SALiIiIiIiIiIiInKEMZ5H6cc/pnXddTgPPACehwmFCE46ifCFF5L88z/fb8FTJ0SPO47oP/4jXqFA9Ve/wszMQCRC+PjjiZ18cqfjHbZCF1xAcPfdMDmJlclgBgf3vdAY7F278OJxrESCxItetLRB5RGp/frXUK/jNBo0161buLi2t7ddXFupULvnHhJPetKS5DwQVihE98tfDi97GdU778QbG8P4Pk5fH4nTT8cKhzsdUY5AwewstFpgzLwNGvYwkQhWEIDv4xcKS5BQZP9CmzcT3HsvQT6Pu3073tBQu0nQQxjfxx0eBiAYGMC94AI14RERERERERERETmCdF1wAbOWhf+ZzxDMzmJns7jFIpYxBK5Lc+VKSKfxH/MYuj/8YZxkstOR57Smp2ldcQX22BihfJ7mqlWQTs9do/QBr9nEHR0ltG0b2DbFf/93kq9/fWeDi4iIiEhHqABdRERERERERERE5AhSf/BBqu9+N9auXbj5PPbMDFYQYByHYNcu/DvvpPCVr5D4l38hsm5dp+PuxU2n6Xr+8zsd44iReu5zKXz5yzQLBSITEzSMgYEBsO25NabVwtm5E7dapblhA/7Tn05o+fIOppaFmHIZy/Pa/xOJLHyHcLhdXBsEBOXy4oZ7tCyL+BOeAE94QqeTyFHAikQwe85zngcLNTLw/fafto0VjS5uOJEFdJ1zDrmf/xx8H3f7diL334/X1UWQSgFgVyq4xSLGdWlt2EDwlKfQc9FFHU4tIiIiIiIiIiIij1TX+efTOv10ytdei/eDH2Bns+0bHAf/iU8k8qIX0fO0px12zSfLV1+NVSwSzmapDw1hdXc/bI0VDuNv2IC1ZUu7Cfb3v4959avVeFhERETkGKQCdBEREREREREREZEjRH3LFmp///fYo6OEJybAtml1d2NCIexmE3dyEmtykmapROWSS7A+9znCq1d3OrY8SpbrEv+nf6L2939PAwjv2oXJZvFTKYzrYjUauKUSJhSiuWEDwQkn0HPZZZ2OLQuw43ECxwHAarUWLK41rVZ7c5Jt48TjSxFRpKPCJ55IIxolCIWwCgVYtmze9U6hgB+Pg+MQOeGEJUopsh+WRe8HP0ghHMa76SYoFnFyOUK7dgEQhMNzE4X8pz2N9Ac/iLX7e4KIiIiIiIiIiIgcWUIDA6Rf/3p43esIajWCRgMnlcJyD9MyHWMwN96Ik8ngxeP7LD6fY9t4y5YRHhsjKBQo//jHpF7wgqXLKiIiIiKHhcP0J1sRERERERERERER2YsxVD/wAeyxMSLj4zSWLYPBwbmu+QHgr1qFPTlJZMcOGkDpgx+k74tf7GhsOTjRk06CT3+a6uWX00ynsfJ57NlZrGYT47o0162D7m78xz6W7g99CKerq9ORZQHhxz2ORiyGHwph5fOYBSbWO4UCXjKJiUSInHjiEqUU6ZzYYx5Ddf16rMlJQrkczf7+/RbommYTt1ikuWoV/qmnElqgWF1kKViuS+/730/lBS+gfvXV8Mtf4vt++0bbxj/1VKIvehE9T33qYTf9SERERERERERERB4Fy8KOx7EP80bCrelp7FwOp1zG6+tb+A7d3TA+DqUS/n33gQrQRURERI45KkAXEREREREREREROQJUfvlL7NFRQjt30hwY2OdEWMu2MStX0gwCQjt3wgMPULv7bmKPfWwHEsuhEj3pJCJXXkn5pz+led112Nu3Q6OBSSTglFOIvuhFxB//eBWxHSFiJ59MddMm2FNc29e3/yno1Sqh2VkaQ0OYM8/ESaWWNqxIJ1gW7oUXEmzdiikWCW3bRmtoCOtPXye1GqGREfxYDNJpoi9+cWfyiuxH4tRTSZx6KkG1Smt6GozBHRjASSY7HU1ERERERJaI8X0qt95K6w9/wFSrWMkkkdNOI37qqbqWJyIiIksuqFZ3/yXAHMiUdsvC2Db4PjQaixtORERERA5LKkAXEREREREREREROQLUv/tdnEIBKwgIli1jvu2JwbJl2Pk8zMxQu+YaFaAfBSzXJXX22XD22Z2OIodA6CUvwXvgAYLZWULbtuGtXQuJxB8XGAOzs4TGxmh1d0NPD/GXvrRzgUWWWPeFF5L/3e/wjMEdHiZ6//20kklMMokxBqdUwq1U8FIp/KEhghe/mMRTn9rp2CL7ZMfjRIaGOh1DRERERESWkjEUv/ENgmuvxZ6cxCqXsXwfXJfmN79Jfe1a3Je+lK4LLuh0UhERETmGOF1dABjHwWo0MAusN0GA5XngulhqrCkiIg/hz87S2L4dWi3s7m6ixx2nRmsiRykVoIuIiIiIiIiIiIgcAaxt27BLJbxUCsu2518bCuElEtizswRbty5RQhE5UF3nnEPmjjvge9/DHR0lsm0bfjSKH49jGYNdLuO0WrR6ewlWr8b6u78jesIJnY4tcsh5uRyla67B/OpXUCphQiFYt47YhReSft/7KMTjeD/4AV6phJ3L4RQKAASxGI2VKyGRwLz85fS+4Q0dfiQiIiIiIiIibcb3yb/vfTg334wzOUloT1NRx8H2fQLXxZucJBgdpbBtG+m//3tt0hcRkSXnl0pUfv5zgnweKxQitH498VNP1fekRebPzFC69lqCm27CymbBGIK+PpxnP5vkhRfiptOLeny3t5dgaAh/chK3UKC1fPm8/+ZWLodxHOjqwj3ttEXNJiIiR4ba3XdT+/a3sW67DavVmvt4dXAQ+wUvIHXRRXMNT0Tk6KACdBEREREREREREZEjQbMJQQDugV3WNa7bnqLcbC5yMBF5xCyLnksvZaa7G+/b3yaoVrFzOexGAwA/ncbr68N0deG86U10nX9+hwPL4aa+ZQu1G24g2LULfB+rv5/o2WcTf8ITjogNgsb3KXzmM9jf+x5WpYKdy7U3KNg2we9/T/OWW6gNDZG4/HK46CKqV12F+Z//IahW2/fv7oZnP5v4i15EZO3aDj8aERERERERkT8qfOpTOD/7GaGtWzHG0FyxApNOY9k2xvexczncTIagXMa/6iqK/f30/NVfdTq2yJJr7txJ5aqrCG6/HatUglAIMzRE5IILSJ555hFxjUvkSNQYHaXyta9h3XILlMtYzSbGsmhGo9RXrsQ+/3y6X/YyrAN8P1IO3Mw3v0nw5S9jlcs42Sx2vQ5AEIlgtmyh9I1vYL3qVe2fCxbrHGhZOOedh9m6FSuTwZqexixbtu+1rRahTAYvnSZYuZLUU5+6OJlEROTIYAyFL30J6+tfx56ZwclmcapVLGMIHAd/YoJgbIyZ664jfsUVarAvchTRbwYiIiIiIiIiIiIiR4JEAlwXa3eB6kLsRgM/mcQkEoscTEQeFcui+//8H1qbN9O44Qa8m2/GKhQwto0ZHMR53vPoPv987Hi800nlMFLfsoXKJz+Jc889WOUy7p6C7EiE5ve/T31oiMjf/i2J00/vcNJ5GEP+Pe/BueUWnNFR3HIZPxIhiEah1SI8Po7ZtQuKRWqXXEL0k5+k913vgne+k6BWA9vGjkY7/ShEREREREREHqa5cyf29dfjjIxgbJvWxo1YjsOeEjLLcTCDg7TS6XaB+vg4/n/9F8FFF+kakBwzTKtF4aMfxf7Rj7AqFUL5POxpTHjffXi3305+1Sri738/0ZNO6nRckaNK5be/pfne92JPTeFMT+OWy+1m1kDgung7d2LGxsj/6lekP/pRfW86hApf+hLWf/4nzugooZkZ/EgEf/d7uE65jJPL0eruJvj3f6dQqZC++OJFy5I67zyKV15Jc3aWyPg4zVarXYQeCrUXGAMzM4R27SIIhTArVuC87GVqDCIicowrfvWr7e9lIyM45TJedzfNtWvBtqFex83ncfN5mpUK1be9Detzn1MjcZGjhArQRURERERERERERI4ET3oSwYMPEtm2Da/RwIpE9r+2WsWp1fDWrsU+7bSlyygij1hoYID4618Pr399p6PII2B8n9rvfoc3PY1lWTgrVxJ//OMXdQNW9c47aVx2Gc6uXbiTkzjNJn44DJaF3WhgXBcvk6H1rncx+4530PX85y9aloNR+PKXcW69tb3R3rZpbNzYbrKyW8P3saeniYyN0QgCau99L5FvfAM7HteGRxERERERETmsVa6+GqtSwa1UaBx/PJbj7HthKERz5Uoio6MEpRKlG26g+yUvWdqwIh1gPI/8pZfi/PrXOCMjuNUqXjyOiUbB9wnv2oWZnKSVz1P7+7/HfPzjxB772E7HFjkq1Ldto/nud2Pv2EF4cpJWKkVjaAiTSGAFARSLuLkc1swMeB6Fd7+bvk99SkXHh0D1f/8X62tfwxkexq7XaQwNQSo197X1jMErlwmNj2MND+P/f/8f5cc9juTTn74oeex4nPgVV1B761tpOA6hnTux83m8WKzdALZexwoCvJ4eglWrCDZvpnfzZlqt1qLkERGRw19jxw74z//E2bEDu9GgccIJe+9ZSqXw+vuxMhki4+M0XJfypz9N5JOf7FxoETlkVIAuIiIiIiIiIiIicgRIXHQRteuuw49GccfH8Tds2PemjyDAmZjASyQgkSD5ohctfVgRkaOUXy4ze+WVmO9/HzuTAc9rfzwUor5qFc7559P14hdjhcOH9Lit6Wka73kP9ugo4V27aPb20hocnDuOabWw83lCk5N4jQbBxz5GbfXqw26Drmm1MNdei7NzJ8DcJLiHshwHs2IFTcchvHMnze5uSt//Pt0vfnEnIouIiIiIiIgcMHPbbTi5HH48DrHYvGutri6M62Ll83i33AIqQJdjQPELX8D59a8Jbd1K4Lo0jj9+r9dKIwjajQknJmga025M+M1vqinho9DYsYPazTcTFItYoRDOhg2knv1srD0TjuWYU/n853EmJwlPTtJYvRp6ewGwABwHBgbw+vqwR0YIDQ9DOEz5f/6H5LOe1cnYR4XalVfi5PM4exrU/GmTccuCVIrmhg1EtmzBz+VofOtbi1aADhA9/nisz36W8hVXwL33QqmEVS5DEBB0dWF6ezHd3Vh/+Zf0vuIVi5Khvm0btRtvJMjlsGwba/lykuedR2jZskU5noiIPHrVPc3WZmdpHHfcvgdmWBZmcJBWvY47OQm//S3N8XHCq1cvfWAROaRUgC4iIiIiIiIiIiJyBIisX0/5zDOh0SC0dSvW1q20li/HSibbGxOMgVIJd3ISy/NoHXccwXOfS2hwsNPRRUSOCs2dOym/7W3Y27fjZDK4xSKW7wMQuC7erl2YkRHyN99Mz8c+htPdfciOXf7Wt7Cmpwnv2kVj1Sro6+OhLUisUAizbBnNeJzI8DCNRILqf/0XsY985JBlOBRKP/oRdqGAWyzSXL16/5PgADMwgMnlsLJZvOuvBxWgi4iIiIiIyGHOmpnBarXwFyg+by+28KNRrEajPW1W5CgX1Gpwww3YO3dibBtv40Ys295rjWXbmOXLabhuexp6T48aEz5C1TvuoPaVr+D8/vdQq2E3m2DbBPE4xc9+Fs45h+6/+RvsaLTTUY9uxtAYGcHPZsFxCK1ZQ2hgoGNxmjt34vzmNzjT07R6euaKzx/GtvGHhnDvvRcKBRrXXKMC9IPUmpzE+c1vcHM5vJ6efRfs7WZFIrR6enBzObjzThqjo0TWrl20bJGhISJf+AL1Bx6ges01mNHRdtPdri5Cz3gGybPPXpRzRe2ee6h+7nM4d9+NVang1Ovt97rjcSpf/zr+U55C6pJLVLAoInK4MAZz0004mUx7EMYCzaH8gQFCDz6IV6lQuf56whdfvERBRWSxqABdRERERERERERE5AiRfu97KWQyADgTE0SHhwlCofakHM/DbrVopVIE69fjP/nJ9L797R1OLCJydPBnZyn/wz9g338/4R07CMJhmitWYJJJAOyZGZxcDiufh1aL4tvfTu9nP3tIJgqZVgtuvBE7m21vYO/r2//iVIpWTw9ONgu3305rauqwmhbi3Xkn9sxMezNZT8/8iy0Lr6cHZ2YGMzxMUK1q2pWIiIiIiIgc1ozrYiwLyxjMAay3ggBj2xhXW3nl6Ff64Q9hZgZ3dpbm2rUPKz7fS18fJpNRY8JHaPYHPyD46EfbzTMzGZx6HWPb7XOSZeF1dxNkMhTvuIPuT30KJ5XqdOSjTlCvU7r+evzrr8ceGZn7eNOy8J/4RCKbN5M888z29dElVP3Rj6BSwa1W29PP52HZdrsIOp+HO+/EKxRw0+klSnr0qd99N6bVwqlWaa1cyUL/8qa3FyeXw2s2adx996IWoO8RPeEEou9856IfB6B86620/vEfcSYncaencRoNAtcFY7A9Dy8eh5kZKvfeS/DxjxM94YQlySUiIvvnl8vYs7PYtRr+/prYPIQVi+GHw1jVKsHOnUuQUEQWm65aiYiIiIiIiIiIiBwh7FiM9L/+K8VPfQpz0034pVJ7Oo7vg+tienowqRTmnHPoffObsZZg42L9gQeo/exnmJkZCIVw1q8ndc452Acy5UdE5Agx+5//ib19O+EdO2j19mJWrgTLmtssZgYH8QYGsHfsIDQ8DOEws9deS/dLXnLQx6794Q9YMzPtqeGrVi24PujvJ7JlC369TvUXv6B78+aDznComGoVy/fbG+sPYJOlCYexggAAv1RSAbqIiIiIiIgc1szKlZgHH8TNZgmMmfd3X+P7ONUqzd5erAP4fV+OTH6pROm66whuvBFraqo9PbCnB3PGGXhnnIG7fHmnIy4Z/667sGdmMLYNXV3zL95dLO3MzrYbE9Zqes9hAdX//V+Cj34Ue2QEt1Cg1dtLa2ioPW3ZGCgW20XpW7bQ8n2K73oXff/3/y55IfTRrDU9TenSS7G3bsXJZnELBexWC6DdWDSbxfvNb8iddRa9l1++JO/h7RFks9iNBsZx4ABeSyaZxC4WwRi8TEYF6AfB1GpYvt/+nwNoWGv2rPF9TK22iMmWXn3LFlr/9E/YO3YQnp6m2df3x/MUQLWKnckQGR6m0WpRfec7cb/yFT3/REQ6zHgeABa0f5Y/EJbV/hl0931F5MimAnQRERERERERERGRI4gdjdL7rnfhvfGNlK+/Hu+ee6BahXgc95RTSJ133pJMraj86lfUv/IVnPvvx6pUsJpNsCxMMsnMF76Aee5z6fnbv1WxoIgc8UyrhbnxRpzpafxodK74/GEsi2DduvZ5MZvF3zOd6SA3cQa7G41YQQDR6MJ32LOBsNVqNwc5jFixGMZxsDyvvelgga+N1WzObWSwE4mliCgiIiIiIiLyqIXOPZfg7rvbhcb5PPT17XetPT3dLgRMp4lecMESppSlMnvddfj/9m9YpRJONotdrQLthnv+9u1EvvlNquecA+95T4eTtjVGR6ledRXm1luxCwWM42CWLcN+7nNJbd6M0919UJ//UTUm3F20GVSrKkBfQO3f/x13agq3WKSxfj1WKvXHScuWBek0Xk8P9shIu4FmNEr51ltJPuMZnYx91PBLJUpvfSv2ffcRHhnBOA6t3l6Ix9uTnWdmCO/aRZDNgudRsCx63//+I6MBwJGQ8TBmp1LtCd+WhdVoQDg8/x0ajfbX3HWxjrJr4pX/+A+cqSnC09M01q2D7u69J8LH4wTr1tGcniY8Pk4zHqf0rW+RfsMbOhVZREQAJ5XChEIE4TB2pULQ3z/vetNq4TSbeOHwvL8Ti8iRQwXoIiIiIiIiIiIiIkcgN52m51Wv6sixZ669FvPpT+NOT+PumZrgurB7Sq2XShEUChTvuouuz3xGnelF5IhWue027GIRd2aG5urV82+4syxavb2EcjnMyAj1Bx8kesIJB3V8KxKBPd3kD6RL/J41tv3HySGHCfdxjyP4wQ9gYgJmZqCnZ/+LjcEpFvF7egjWrMFJJpcsp4iIiIiIiMijkTz7bIr//u+0BgYI79xJIxTC2tek51yOcCZDY9Uq/HXriJ966tKHlUU1861vYf7t33DGxggVCvjhMH4yCZaFU60S2rIFKxbDuv56CkD/+97XsSJP4/sUPvUp7Ouvx6pUcLLZdsNZIBgexmzbxuw3voH9t39L9+bNj/o4ViLxyBsTOg6gxoQLqd93H84DD+BmMrT6+7H216TYsvDXrcO9917I52lcc40K0A+RmS9+EXvLFsIjI3jd3QR/ch056OqisXw5oeFh3O3b8W66ifJznkPyjDOWJJ+zciUmFsMKAkylsmBhsz07ix+NguMQWr58STIeraKnnEIpGsVLJLDzeYIFmog7+TxePA6RCLGj6OcDL5fD+eUvcbJZWt3dME9TEzM4SFAsYmcyeD/8IeZv/gbLVdmTiEinWK5LcPrp+JkM4bExGq0WVii03/V2LtduvtLdTfQ5z1nCpCKyWOxOBxARERERERERERGRI0fll7/EfPrT7SkZu3bhdXXROOkkmiefTPMxj6Gxdi12q0V461bse+9l5rLLMLunlIiIHIn8XbugXm9vztvXpvE/lUpht1rg+3g7dx708cObNoHj4MXj2Acw0dwqFAhCIYjFcI8//qCPfyglzz4b092N19WFOzWF2d24ZJ9yOZxWC9Pfj3P++UsXUkRERERERORRsqNRwu94B2blSlq9vUSHh3G3bIFsFjMzgzU9jfvAA0QmJmiuWIFZtYr4O9+p6bJHmfoDD2A+9znsHTtwZmepr1uHd8IJmNWrMatW4R13HPVNm7B9n+iOHTg/+hEz117bmbDGUPjgB7GvvhpnyxYiW7ZgNZsE8ThBLIZTLhO5/36crVsxn/wkM1de+agP5Z5yCvT0YAVBuzHhArncYhG/uxt/0ybsaPRRH/dYULvpJiiXsXdfS5uPZdt43d24+TzO735HUK0uUcqjV1CvY/3kJ9hTU/jR6MOKz/ewQiFa69e3rx0XCjSuuWbJMibOOQeTTOIlEjjT0/OuNa0W7swMfl8f/pOfjLNAwbTMz02nMU9/On5/P6GZGUy5vP/FlQqhYhG/vx//qU8lNDCwdEEXWeXWW6FWw61U8Bc4TwF4fX24MzPYuRy1P/xhCRKKiMh8oi96EfT0YEIh3NHR/b7HayoVQpkMXn8//vr1xB//+CVOKiKLQQXoIiIiIiIiIiIiInLA6l/6EtbUVHsS8IYNmJUrIRxu32hZWN3deJs24aVShIaHce69l/LNN3c0s4jIQTGmo4cPDQzgP+Up+P39uMUiptHY71rj+7i5HF46TbBmDYknPnEJky7MjkbhvPMIVq3CCgLcbdugVttrjQkCmJoisnNnezN+fz/J887rUGIRERERERGRRyb5jGfgXH45wYYNNI4/niAaJTQ1RWR0FDebxU8maZxwAsGGDUSuuILYySd3OrIcYpVvfQuKRUIzM7TWr8fq7n54MWoiQX3dOizfx56exr/mmo5cg5q5+mqsm27C3bYNy/epH388/qZNmJUr28Xyxx9PY2gId3YWe8cOzOc/T/3++x/VsZJnn43p6TmgxoRWJoPl+wR9fbhqTLggk89Do9FuSjnPNMo9gkQCq9UCY/Dy+SVIeHSr/PSnUCzizs7iDwzM21TECoXaDQByOZzf/AYvl1uSjG5vb7sIetkyQqUSTE3tc53xfdyREYJwGHp62sVmctDir3gF9PXRSqeJDA+3G9M85BxoggByOcLDw7R6eqCvj/jLX965wIvAFIvQbIIxWPH4wneIxbB8H3yfoFhc7HgiIrKAxBOfiP/Up7ab6TQahB58EHK5ue9nptHAGh8nsn07Xk8PZvlyom94g5qtiRwl3E4HEBEREREREREREZEjQ+0Pf8DZsgU3m6XV3w+JxL4XWhb+mjU4996Llc/TvPZaePazlzSrLD3TalG++Wa8HTug2cTu7SX+rGcRWr6809FEDoq9bBl+JIKxbaxSCXp65l1vlUrtzZ6Og3uInv+xF7+Y5i9+gZ/PE96+nda6dfCnm7SaTdwdO8CyMIODOC984WH5pn7P//k/5LduBWNwduwg8uCDePE4JhoF38fdPQGmsXIlZvVqIv/0TzjJZIdTi4iIiIiIiBy41LOfTevkkyl/5zt4P/rRXoVTweAg9rnnktq8GTed7lxIWRR+qYR9yy042SytVOrh128eynVppNPECgXMyAjVO+8k/oQnLF1YY/CvvhpnehrL92mecAKW4+y9xrKgq4vm+vVEtm6lMTND9VvfIvq+9z3iw9nRKJx/Pn6hgLNlC6GtW/FWrdrrfQbj+9hTU4SzWRqrVmGWLyf1/Ocf7CM9+jlOu0HwPEX9ewmCueuG1gEUrMv8vLExrFoNyxjo6lpwvd/VhVsogDE0x8Zw+/qWICWk/vZvKf3+9zRaLcLj4wQzM3h9fe3zVBBgzcwQKhQwjoO3cSP+WWfR8+QnL0m2o130xBNpvu1tBB/9KK1QiPDkJExO4u/+HuFUqwC0+vowK1divfnNxB772E5GPuSscBiz+7xjPG/hc4/vt/+07QNqrCEiIovMskh/8IMULr0UbLv9M/uuXVjj4xjLwjIGPxajuXIlDA5i/8M/kHja0zqdWkQOERWgi4iIiIiIiIiIiMgBqf/sZ1ilEnarRdDfz3xljZZt4/X04BQKmN//Hr9cVgHhUSqo15n5ylcwP/whVi6HVauBMZhwmMoXvoD/5CeTeO1riZ54YqejijwqyTPPpLhnOlMmg7evqVV7GIObz89NID9Uz/v4aafReOlL8a+8EkZGiGzZgpdI4KdSWJaFVakQKpXwo1FaGzfin3EGfS95ySE59qFmuS69H/0ohY9+FH70I/xKBTufb08/sW2aK1ZAby/BihXE3vc+Yo95TKcji4iIiIiIiDxioeXLSb/pTZg3vpHm+DhBpYKdShFZs+awbBgnh0ZjyxasRgO3XKaxbt2C64N0GjubhWqV5h/+sKQF6JXf/hZ7dBS3UMDr73948flDxeO0UimcbBZz6634pRJOKvWIj9nz+teT376dFrQbE27bhh+JEOxpTFipYByHxpo1mNWrif/Lv7QL12Ve9po1kEi0pwVXKvtvHrxn/ewsfiyGicWWrPj5qOb7YEz77wdwfrcsa+79NeN5i5frT4SWLyf+8Y9TvewymrEYdibTLhzbXejrRyJ4g4OYvj6CP/9zei+/XN+vDqGuF7yAUjJJ6zOfwUxPQ6GAXa+3GxF0dbWviQ8MEL7kEpJnndXpuIecu3EjrViMwHWxZmagv3/e9dbMDH4sBrZNeP36JUopIiLzsaNRej/9aWa+9jW8732PYHoaqtV2c6NwGGIx/FNOIfqqV5F40pM6HVdEDiEVoIuIiIiIiIiIiIjIAQmKRZxms7054AC6zZt4vL2JAPByORWgH4X82VmKb30rzh/+gDM9jVsstqesWBYYgxePQz5P7Y47aL33vaSe+cxORxZ5xKxwGHP22QTT07hbt2Lt2oVZseLhm++MwR4dhSDADAzgnH/+Id2gl/67v6MQjeJ//evtou1sFnf3BLUgHKYxNARdXfjPfS6973rXYb050HJdet/9bpp//ddUrr6a1u23Y83OQiiEWbeO8AUX0POMZxzWj0FERERERETkQFiuS2RoqNMxZIkEewowjAH3ALZo7yn69n1Mvb644f5E6557oFZrN5xNp+dtOAsQ9PYS2bEDv9GgsXXroyqWtxyH3g9/mMKnPw033IBfqWDl81itFoRCNFevhnSaYGiI+OWXEz3uuEf12I41yRe8gPJ//AdeIoE9PU0wT7GmqdUIlcs0NmzAnHUW1oE8T2VeVl8fRCLt/6lW2xPF51tfqRDsfo/NHRhY7Hh7iR53HO6Xv0zpv/8b78YbCXK5dhG8ZWE5Dv7JJxPevJme5z5X12YXQeqZz8SccQaVW2+l8YMf4Gcy7Rv6+wk/73l0P+tZ8zcDOYIlnvxk8itX4u3ciZvN0urtxbLtfa41rRbhYpHmihX4T3gC4ZUrlzitiIjsj+W69Lz2tZhXv5ryz3+Ot2ULNJtYXV3EzjyTyAE04RKRI49+axQRERERERERERGRA2K57lxhMcYsvPlkTyEyYO/ZfCNHDeP7FC+7DOf3vyc0PIwJhWiuWgXpdHtBrYadzRIZHaVZq+H90z9R6+vTNGM5InW96lXM/vznNJtNwqOj+JUKfl8fpFIYY7BmZ3GzWWzPo7lhA/5JJ9H7ohcd2hCWRfp1r6Nx9tlUrrkG85OfEOxu8mFiMcyZZxK/6CKiJ53UXm8MfqkEltVuAHIYbhgMr1xJ+E1vgje9qdNRREREREREREQOmp1KgW1jbBsajQUnUdNstv90XewlbuBq6vX25GPLOqAiZOO6c+8NmFrtUR/Xcl16L70U7zWvoXTNNfi//CVWqYQJh2HdOqKbN7cnJh6G17IOV25fH8HTnw4zM4S3b6e1c+c+G2iaRoPwyAheMgmpFPFDff3yGJU46ywqX/gCXjyOnckQzFd4ZQxuoYDX24u/YUNHirTcdJr0G9+Ied3rqN11F34uB+Ew4aEhFY0tActxSD7rWSSf9axOR1laloV93nmYsTGsmRnc4WG8oaGHFdybZpPQ8DBBJAK9vUQuuqhDgUVEZD6W47SHD2gAgcgxQQXoIiIiIiIiIiIiInJA7LVrIZnE9n1MpYK1wIY4Z3YWPx7HxOO4/f1LlFKWSvmnP8W5+25CIyP4iUR7U9VDN7TF4wRr11Lv7ia6YweNaJTqF79I7DOf6VxokUfJTadJfPzjVN7+dpqRCHYmQ3hiAisIAAhcF7+nB29gAP/P/ozuj30MKxxelCyRtWuJvOUt8Ja3ENRq7cnrsdjc669+331Ur7oK69ZbsXZvBjbxOObP/7xdoL5p06LkEhERERERERE51sVOOol6VxdedzdOPo/f2zvverdQwI9EIBYj8uQnL1HKNjuVmisqN80m1gJNZK16HeM47SLCrq6DPr7b10f6da+D173uoD+XQOqSSyjdcw9NzyM8OkowM4PX2wuxGAQB9swMoZkZvEQCf2gI8/KXa8L8IRIaGMB/6lOhUCAyMkIjn4d9vfaNwR4bwwoCgv5+3Be+cOnDPoTlusSf+MSOZpBjS/fLX07+N78B3ye0fTuRe+/F6+4m2N2sxZ6dJVQq4cVieOvXY849l+QZZ3Q4tYiIiIioAF1EREREREREREREDkjy3HMpffWreMkk7vQ0fiKx/ykktRpuuUxjwwbMWWcd0AQVObI0v/td3Hy+PWV57Vqs/TwXrO5umr29uNPTcOedNHbs0BQNOSJF1q3D/eIXmf2v/8K78UaCfB5arfaN4TDBsmXY551H+mUvw45GlySTHYvN/d14HoWPfAT7xhuxZ2exczmcRgMDBNEowfQ09RtuoHrRRfS++c2aIiUiIiIiIiIicohZ4TA897kEk5NEtm7FLxahp2ffi+t1wsUi3ooV+I95DNGNG5cyKpGnPIV6LIYfjWJns5hVq+Zd7xQKeN3dmO5uoiecsEQp5UCFBgdJfOpTVN7xDprxOFY2Syifx261MJaFH4/TWLcOursxL3sZ6Te8odORjyqJv/kbar/9Lc16ncj4OK1SiaC/H+Lx9oKZGZxMBqfRoDk0RHD88fS84AWdDS2yxCzXJX3FFRQuvxzCYSgWcXI5QuUy0H4fozE0BF1dmAsuoPdtb9P7GCIiIiKHAe34ExEREREREREREZED4qbTmDPPxC8WCW/bRjAx0d6U9qdv/tdqhIaH8ZJJSKWIX3RRZwLLovEKBZy77sLJ5/F6erBse971wcAA4VwOr1Kh+uMfEznWptoYQ+VXv6J5zz1Qq2ElkzhPehLRk07qdDJ5hJzubtJ/93eYN7yByq9+hZ/JABBavZr4qad2bjOUMRQ++EHsm27C3b4dp16nlUq1pxwBdrlMZPt2vHgcvvUt8vU6vZdd1pmsIiIiIiIiIiJHseRLXkLl+9+nOTtLZHSUZr1OMDCA5TjtBcZg5fPEd+7Ei8cx/f1EXvGKJc8Z3biRymMf2y5U3rWL5p5p2ftSKOBWqzRWrYLnPQ8rFFrasHJAIuvWEfrqVylddx3+9ddjxsbmbjPhMMHTn078xS8m9tjHdjDl0Sm6cSP+P/8zzfe8h0Y0ipPJENm2rX2jMRjbxuvqorlmDf7xx9P1sY+1G1aIHGPsWIy+j32Myu23U7/mGvj1r/F9HwATiWDOPJPYRRcRO/nkDicVERERkT1UgC4iIiIiIiIiIiIiB6zrzW9m9t57afo+4R07MLOzeOk0JhaDIMCemSFUKuElk/hDQ/DXf73kk1tk8XnZLAB2s4k3OLjgeisSIXBdaDQwudxixzt8GMPMf/83/jXXYO/ciVWpgO9DKIQXi1HctInQK15Bz3Of2+mk8ghZrkvyjDM6HWPO7PXXY//sZ7hbt4JtUz/xxL02MPqDg3iNBqHhYdzt2/G+9z3KT3kKyWc9q3OhRURERERERESOQuGVK2l94AM0L7+cpuviTk9jZTL4sRjYNnathhUE1Lu7aS1bRuh1r+vYdaboK19J6w9/wCuVCG3bRmvFCkw6Pddw1LRa2Nks4UyG5rJlmL4+ki95SUeyyoGx43G6X/YyeOlLaYyO4hcKWKEQ4bVrcVKpTsc7qiVOPRXnc5+j8pWvwC9/iV+pQLPZbloai2F6ejBnn03Pa1+rf4tjnTEEjQZ2JHJsTvi2LBKnn07i9NMJajW8XA5sm1B/vxoziIiIiByGVIAuIiIiIiIiIiIiIgfMTadJfeYzzL797TTjcaxsFqdQwM7lMJaFH4/TWL8eurowr3oV6b/+605HlsWwZwOiZUEQLLzeGCxj2htp9kz6OcoZ3yf/vvfh3HwzzuQkbrGI7XkY28YKAvxwGGt6mmDLFgo7dpA+1qbCyyHVuvZa3EwGy/dpHnfcHydqPYQVieBt3Ej4gQcgl6Nx1VUqQBcRERERERERWQSJpz4V6+Mfp/6xj2FGR6FYxKpWsQCvq4sgnaaeSNB4xStY+8pXdjTnzBveQPCFL2B27iS8axfs2oUfjWIZg12vYxyHxqpVmFWriPzTPxFatqxjeeURsCwi69bBunWdTnJMiW7cSPSf/xkvl6Ny000E+Ty4Ls6qVaSe/WwV1x7DjO9Tvvlmmtdei3PPPeB54Dj4xx1H6IILSD3veVihUKdjLjk7FiO8enWnY4iIiIjIPFSALiIiIiIiIiIiIiKPSGj5cnq//GVKP/whre9+F7Nly9xtJhbDPPOZxF/8YqLHH9/BlLKYwitWUAuHCWIx7FKJoLd3/juUy1hBAPE4zjGykaTwiU/g7J5IbRlDa9kyTG9ve3pQqwWZDOFMBq9cJvjP/2Smr4/uzZs7HVuOQLV77sHZuhU3n8fr69tn8fmcUIhWOo2by8Hvf09zfFybuw4xL5+nfO21BLfeCjMzYNuYVasIv+AFJM86C8vV27MiIiIiIiIix4L4KacQ/6//ovqb31D73vdg1y6sIMD09uI885mYxz6WyGFwnaD7Fa9gNp3G+/d/x2QyUChgNRpgWXiDg9Ddjb9+PfG3v53YYx7T6bgiRwS3r4/uv/iLTseQw0RjdJTKu96FvWMHbj6PPTOD5XkYx8HatYvgnnsofOUrxP/5n4medFKn44qIiIiI7KXzVy5ERERERERERERE5IhjhcN0XXABXHABrUwGP5/HCocJrViBHY12Op4sMjseJ3j60/EzGcI7dtBoNvc/ucMY7EwGL5nEJJOkzj13acN2QHN8HPuGG3BGRsC2aW7ciOU4WHsWhEKYFSuop9NEtm+HiQm8r34Vc955x+SECzk4zQcfhEYDu9kkSKf/+DzbD9PTg5PN4vk+zQcfVAH6oWIMhc9/Hq66CqtcxsnlsJpNsCzMvffi//a3FD7/eSKXXUbi9NM7nVbkgHmeR61WA8D3fZz5mlyIiIiIiIjI3iyL+GmnET/ttL0+7Ps+1ampDoV6uK7nPx/z3OdS/p//ofWTn2AKBbBtWLGC2PnnE3/848Fa6KqTiIj8qebEBJVLLsEeGSE8NoYB/O5u/GQSq9XCzWSwpqZoFovU/v7v4V//legJJ3Q6toiIiIjIHBWgi4iIiIiIiIiIiMhBCQ0MEBoY6HQMWWLxF7+Yxs9+hh+LEdq+HW/DBvjTInRjsHbtwq1UaG7cSPDMZ+J0dXUm8BIqX3UVdqWCW6lQP/74/U6ktqJRmsuXE9m5Ez+Xo/STn9D1/OcvcVo54jUamCBo//0ApmaZPWuCgKDRWMRgxxBjyH/4w9jf/z726CihUgk/EiGIxcAY3KkprMlJWgMDtN71Lsof/CDJZzyj06lF5jUzM0MmkyGfz5PP5wHI5XL09fUxMDBA1zHw/VxERERERORYYrkuqWc/G5797E5HERE5apQ+9CGcHTuI7NhBs7+fYPlyLNsGwACtVauwMhkiExM0LIvq+95H9Mor1fRDRERERA4bKkAXEREREREREREREZFHLPbYx1K74AK45hrc7dsJP/AAXnc3fnc3lm1j1Wo4+Tx2q0Vz7VqCoSG6//ZvOx17adx6K3Yuh5dIYMVi8y416TRMTmIXCrRuuglUgC6PkNXdDaEQWBZWtQqp1PzrazWMbYPr4nR3L1HKo9vM1Vdj/+AHuNu2QRBQ37gRK5GYuz0IAsjnCe/aRSsIaH3oQ7S+/nVCy5YdkuPX63XK5TK+72PbNslkktgC5x6R/QmCgOHhYfL5PLOzs+RyOTKZDAClUolyuUyhUKCvr4+hoSEsbYYVERERERERERF5mPp99+Hccw+hiQla6TRm5UoediXNsjCDgzSDgPDOnTTTacq33qoGpiIiIiJy2FABuoiIiIiIiIiIiIiIPCq9b3sbecD77nehWMTJZonu2AFA4Lp46TRefz/BunUkP/5x3L6+zgZeItbMDFazSRCPL7zWsvCjUaxGA6tYXPxwctSJn346pXgcL5nEzuUIFihAd3I5vK4uTDJJ7IlPXKKURzFj8L/9bZzJSSzPo3nccVih0N5rbBv6+2mGQkRGRmj09FD+zndI/93fHdShZ2dnmZqaYnZ2lmazOVeAHolESCaTLFu2jJ6enoM6hhxbjDFs27aNbDbL6OgoQRCQTCZZvnw5xhgikQiTk5Pkcjk8z8MYw4YNGzodW0RERERERERE5LBTvfpq7NlZ7FaL1vLl864NBgchm4V8nsZ3v6sCdBERERE5bKgAXUREREREREREREREHh3LovfSS6k+85nUvvMd+PWv8X1/7uZg9Wqc88+n54UvxD6AYuyjhm23/wyCA1tvDMayMK7etjkaGGMwxmDveR4sMjedJnj60/GzWSIjI9RnZtpT0fcln8etVGhs2oR59rOxo9ElyXg0q9x+O/bEBG6hgDcw8PDi84fq7saPx7GzWbwbb8S84Q1Yj/J1PzU1xdjYGDMzMxQKBer1+txt4XCYdDpNqVRixYoVrFq16oA+p+/75PN56vU6QRAQCoXo6ekhfiydv49x2WyWfD7P6OgokUiElStXApDP5wHo7e1l2bJljI+PMzo6im3b9PT00Nvb28nYIiIiIiIiIiLyCBjfp3LrrbTuuANTrWLF44ROOYXEM56B5TidjnfUMNu2YZdKePE4zHfdGLBsGy+Vwi6VCLZvX6KEInK0aE1PU/ne9/AnJsDzsHp7iT3nOcROPrnT0URE5CignUwiIiIiIiIiIiIiS6ivr49arTb3/3fccQcnnHDCftf39vbuVVT2aP3whz/kGfN0yz/ppJPYsXty9cH4z//8T17ykpfs9/ZnPetZ/PrXvz7o43zkIx/hkksu2e/tL33pS7n++usP+jj/8A//wIc+9KH93v7mN7+ZL33pSwd9nL/8y7/ki1/84n5v//CHPzxvjgP1nOc8h+uuu26/t3/5y1+e9+t6QIzh5I0bufW73yW6YQNY1sOW3HDDDfM+Tw7UwMDAvM/b3/zmN/M+7x+Jcrm834LisbGxvV/HzWa7+NwYOJDXVRC0v05f/jLb3v9+VqxYsc9lrVaL7v0VFj9Ct912G094whP2e/vq1avnCg4PxtVXX80555yz39uf9KQncd999x30cT7/+c/z6le/er+3n3vuudx8880HfZz3v//9XHbZZQ/7uO/75HI53vCGN/CDH/wAaE+3f+ifj8Qb3/hGPvnJT+739ne+853867/+6x8/YEz7eWcM3HVX+/n0p8c1pv2fZcEdd8A997D5jjv4xje+sd/jfPrTn+bd7373I87/p572tKfxk5/8ZL+3f/Ob3+Rv/uZvDvo4Gzdu5O67797v7T/96U8577zzDvo4iUSCTCYDQPPOO7HKZWzfJ+jrwwLuq1R49p137vvOe/4dRkchldrnOfKhpqenSSaTe30sl8sxNjbGfffdx+te97p9PseMMUD7+XcgzRB++MMfkkqlaDQa1Go1jDGEQiHi8TiJRIInP/nJeJ634OdZyE9+8hOe9rSn7ff24447jomJiYM+zje+8Q02b96839vPOOMMfve73x30cT75yU/yxje+cb+3X3TRRXPnhIPxjne8gw984AP7vf3v/u7v+OpXv3rQxzn//PN5zWteg2VZrFq1Ctu292osA+A4DqtXr2b79u3MzMyQyWRUgC4iIiIiIiIiciQwhplvfhP/O9/Bnp7GKpWwfB8cBy+VojA4iL15Mz2vfOWC1y3lAOx+r8gcYFG/cRzsZhOr0VjkYCJytGhNTjL76U/j3H47VCo4lUr7PahIhMZVV1E97jhib3wj8dNO63RUERE5gqkAXURERERERERERGQJ1Wq1vQrQ9xSIzbf+UBSgBwtMYq7X63vlerT+tEjpT+0pbDtYCxXBNZvNQ3KcVqu14O2H4jjNZnPe2z3POyTHaSywacX3/UPzeByH6MaNi36chT5HEASH5DgLMcbs/ziPYAo6nndA54RD4UCOcyiOdSyce4rFIiMjI9TrdYrF4oKvswNx0OeePUXO891Wrx91556Fvl8eqnPCQwu6TaWC5fsY256bDmSA2kKv/SCAR/H9PQgCxsfHmZqaolarLfhveKB27dpFoVCYa7Zh2zae5xEKhejt7aVarS74OjsQx8I54aGW6ueRQ3WcPeex/v7+eRsXOI5DOp2mUCjMNUuKRqMHfXwREREREREREVkkxpD/4Aexfvxj3MlJ3EIBjME4TrsI3bLwdu4kmJoiv2UL6fe/X9PQD1YyiXFdnGqV/V0VNbUadj6P1WrhFgoEoRDBIbgOKyJHv/q2bVT/4R9wxsZwdu3CrVYJQiGwLOxWiyAUwspmaW7fjveOd9D1/Od3OrKIiByhVIAuIiIiIiIiIiIi0kEjIyOceOKJnY4hckTyfZ8HHngAy7KIRqP09/cTj8c7HUuOIcVikW3btjE9PU0+nz8kE6LlyGIlEuA4WEGA8f1F35RZKBTmioSXLVt2yD7v+Pg4GzduZO3atXPnUc/zKBaLTE1NHbLjyOEtCAI8zyOZTC64NplMkslk8DxPBegiIiIiIiIiIoe5wmc/i/3jH+Nu3YoVBDRXrMCk01i2jQkCrEIBd3oau1ymZQyFvj563/KWTsc+otmnnYa54w6cTAavUoFE4o83Vqs4O3fiVirg+9i+j9Ns4kUiBA8+SP51ryP6pjcRP+WUjuUXkcOXXy5Tvewy7OFhwqOjeN3dNFatgj3vk7daWNksoV278Op1go99jOqKFTqniIjIo6ICdBEREREREREREZEOev7zn7/gJGIR2bcgCNi+fTuWZRGLxchkMiSTSdatW6dCOFl0nucxPDzM9PQ0hUKB1atXH1DRphz5jDEMDw8TiUSInXwyJJMEjoOdy2EGBxf12Pl8ntnZWVzXJfHQDYsHqaurizVr1uz1Mdd16e/v1/P6GGRZ1tzfv/SlL1EulwGIRqOEw2Fe//rX7zUhXT/LioiIiIiIiIgcvrxCAa65BmfHDgCaxx+P5TjsuQJk2Tb09dHq6SG0dSvu6Cj+d7+L98pX4vb1dS74ES514YWUv/ENvEQCd2KC1qZN7YL/UonIjh3YjQZ2q4UdBFjGgDG4zSb+zAzBL39JY2SE4H3vI/mMZ3T6oYjIYaZ07bXY4+OEx8ZoDQxgVqzYe0EohFmxgkYySWR4mGY8Tu2rXyX+mc90JrAcm4yhfNttNH72MygUwLax1qwhft55RDdu7HQ6EXkEVIAuIiIiIiIiIiIichiLxWJ7FQE9Wg8tEtqXaDRKLBY76OM4C0x+jUQih+Q4rjv/5e1wOHxIjhMKhRa8/VAcJxwOz3u767qH5DiRSGTe2x3HOSTHWaj4+9EexxgzV+RmjMF1XRqNBr7vk8vlCIVCDA4OUqvVOP7447Ft+5A8noXsKYB/GN8Hz4M9hXkPLdCzrPZ/AKEQ7H6NLvR6P1SP50COc6DH+tPCw4d+7qP53JPNZqnX6+RyOdasWUMymSQUCu3zdbbna2Tb9gGd0x/tuWfPa2TP8f70WA/9uGVZx9y552DOCQ/9uoZCIbZt2zb3/O3v6aG7p4dQNkszncYCYvt67u/eRIhtg+PAAt/L9qXVatFsNud+Pljoa7sn+76eew99vgwMDOz3/nuKjn3fx7KsBV/X8zmazwn7slQ/jxyq44TDYSzLol6vzzUe+NrXvkaj0ZhbE4lEeP3rX0+tVsO2bRzHWfDrICIiIiIiIiIinVO+9lqschm3XKa+cSPWfq6dWY6Dt3o1kW3b8CsVStdcQ/p1r1vitEcPt6+P4NxzoVYjtHUroS1baPX1EZmcxK7X28XmjkOw+3q9F43SSqUw8TiRiQmazSatD32I+uc+R3TTpk4/HBE5XBhD8L3v4WSzBKEQZvny/S61Uilavb242SzceSeN0VEia9cuYVg5VpV+8hNaX/wi9s6dOMUiVqMBloVJJqlfdRWVxz+e5KWXElm3rtNRReQAWEbtyOUo8olPfIJSqUQqleJtb3tbp+OI7JPv+0xNTQGwbNmyBTdBiYiIdFqr1Zr7+0KbXUVERDpFv2vJkSQej1Or1eb+PxaLUa1WO5hI5MhQKBTYtm0bY2NjNJtNBgcHSaVScwWVnueRz+fJ5XKsWLGCgYEBTj755I5/T2gMD1P51rewfvpTrIe89r10Gs4+m8RFFxGbZ2PA4cgYw8TEBNPT0zQaDWZnZ/E8D8uySCQSJJNJkskk69evX7DA+Uh29913MzY2Rq1WY2hoaN61lUqFsbExNm3axIYNG+Yt9n20pqamGBkZYfv27SSTSVasWLHPguOJiQlqtRrr169ftCxHk0KhwPDw8Nz5xfM8wuEwxhiazSbRaJSV99/P2quuonvnTixj8FatgmRyrsmECQLsbJbQ5CStwUGC9etJfv3rhB7FtPR7772X4eFhPM9j9erVC67ftWsXQRAwNDTE4x73uLmPe57H73//e7Zv305XVxf9/f3zfh7P89iyZQtDQ0Ns3LiRFQ+ZpuF5HtPT02SzWZrN5tz5wHVd4vE4AwMD9PX1HZKGOouhUqnQbDaB9rWvY3XiexAE3HXXXYyOjmKMmXt+Pf3pT39YAfptt93GyMgI0WiUNWvW8NjHPvaw/fcVEZFjj97XEhGRI4He1xKRpZT7m7/BveUWnNlZvOOP/2Nz3P1wH3gAP5nEO/NM+v7jP5Ym5FHKeB75d7wD51e/wh4fJ5LJzE09D2wbCwgcBz8SwU8k8DZuBNvGzMwQ3bGDxtAQ/gtfSN8HP9jph9LR37VamQyNe+4hqNWwk0lip5yCk0otaQaRw0V92zbqr3kNoXvvxevvxyzwXpOp14k+8ACNE06ASy6h56/+aomSyrFq5sorMZ/7HNbkJG4uhxUEBJEIGIPTaOBHInjLlxOsWUP8E58gesIJh/T4+l1LjjRHQi2sWpGLiIiIiIiIiIiIiMhhb9euXeRyORqNBkNDQw/b3OK6LoODg4RCISYnJ0kkEuRyOQYfRYHnoRRZv57IZZcRvOUtNLZtI6jVcLq6sNeswToCJ8YaYxgeHiaTyTA5OUmpVCISiRCJRPB9n0KhgOu6LF++nEajwQknnHBAE5qPNJ7n0Ww2KZfLpNPpBdcnEgkcx6FSqSxa05FMJkM+n8d13X0Wn0N78vmqVavYtm0bxWKRTCajAvR5VKtVhoeHmZycpFAo0N/fT09Pz9y053K5zOjoKL9OJGiceCIbPI90sUhkeJggHMaPxSAIcCsVAFrLlxOsWkXofe97VMXn0C7+jUajTE9PEwTBvBPFjTFUKhXS6fTDXod7Ngu2Wq0Deo26rovrurRaLTzPm/t4vV5ny5YtlEolCoUCMzMzBEEAtDch9vT0MDs7S7FYZMOGDQc1Pf1QMsaQyWTIZDLUajV83wfak9Oj0SgDAwMMDAwcNnmXgm3b9PX1US6X2bFjB8VikZ6enn2uzefz1Ot1VqxYQX9/v4rPRUREREREREQOY9bsLFazSRCNLlh8DhDEYlitFtbs7BKkO7pZrkvvRz9K4bOfJfje93BLJWzPI7Bt/HAY47oYx8Hr6cFfsQJr9/VIq7ubVlcXTjaLue02vEIB9wDeizjaVO+4g9p//zfO7bdDqwW+T+A4zMZiBM94BomXvYzoccd1OqbIkvILBQBsz8PEYguut6JRjG1Ds4mZmVnseHKMK992G+bzn8ceGcEtl9uNufv7576/tRoNnOlpIjt20PB9qu98J6GvfU1NRUQOc0feziYRERERERERERERETmmlMtlqtUqxWKRvr6+eScrpNNpCoXCXHFtpwvQ97CjUWInnzz3/w+dFLGYfN9nZmZmbiJxLBY7qOm+k5OTZDIZduzYgeM4DA0NEXvI5gbP88jlcoyPj2OMYevWrfzZn/3ZUVecaIyZ+/NAC1Rt28YYM1eceyiVy2Xq9TrFYpHBwcF5v96WZdHb20s+n6e/v59qtUo8Hj/kmY4Gu3btYnZ2lkKhwNq1a+e+TvV6nenp6bnJ2Z7v85P163my63LyPfeQ6u8nUipht1rgODRXrIB0mmD5ciLvfCeJU0991Jn6+vrI5/NMTU0xOzu73wJhaD8vPM+ju7v7YRPO9zxvbds+4OdkEARYljX3/Gq1WmzZsoVcLsfExATRaJTly5cTj8cJgoByuTx3Pt5zjE2bNj2KR31oeZ4314RhT749RfWO49Dd3U25XCaXy3HccccdU5NTly1bRj6fZ/ny5ezatYtarfawNcYYpqenWblyJalU6rD5PisiIiIiIiIiIvvhuu3C891NGBcUBO31R2AT3cOR5br0vuUtlB73OJoXX4xTq+ElEu0GppaF5XmECgVC+TxYFl4sht/fT9DbS2RkBL9apfqrX9H1/Od3+qEsqeLXvgZf/jJOPo+Ty801esWy8JJJ/EyG2s0303z72+k655zOhhVZQvbupsLGstrn64UEAdae8/pR2DRcDi+Nr38dd3oat1SiuWkTxGI89F1rKxIhWLOGZiRCeHycZjJJ6eqr6Xn1qzuWWUQWpt8KRERERERERERERETksDY7O0u1Wp0rpFxIT08P+XyegYEBms0m4XB4UXIFQUAQBDiOc9gVWDcaDSYnJ8nn8+0C2d0F6OFwmFgsNjfd95HkDoKAqakppqensW2btWvXPqz42nVdli1bhuM47Ny5k0QiQbFYPKAp4UcSx3GA9uNtNpsLrg+CgFarheM4c9OzD6VGo0Gr1cL3fRKJxILrE4kEk5OTVCoV8vk8tm0TiUQOu+dxJzWbTYrFIrlcjp6enrni83K5zPj4OOVymWKxSLPZbE+3r1a5ddkyRk46iadUKvTdfz89gBMOY1auJHL++STOPBNr93Pn0eru7iYajdLX18fk5CShUGif/+b1ep1du3bR29tLNBp92GswFArhOA6xWIxSqbTgubVcLmOMIRaLzTWd2FOgPzExwcDAAH19fXvdJxKJkE6nmZiYYHR0FNd1552qvRSMMWzbto1sNsvY2Bi2bdPf3z/3NaxWq3NF6atXrwbghBNOmHvNH+3C4fBckwDXdcnlcnMNNx5q7dq1dHd3s2nTpkU5p4mIiIiIiIiIyKFj1qwhuOsuwrt20QiCuSmk+1wbBLjlMs3lyzG7r4/JoWHq9Xaj0l27aK5eTXjnTuxmE6vVwvZ9MKZdkN5s4lYqBKFQu2lAq3XMTS2e+fa34YtfxBkZwSmX8bq7aSxbhgmHodHAyeWIjIzQmp0luOIKyskkyTPO6HRskSURHhqiHgrhx+PYxSLBQu+dF4sYx4FEAnf9+qUJKcek+oMP4tx7L24uR6uvDx7SxP5PmYEBgkIBK5PB/9734FWvajdJEJHDkt4NFhERERERERERERGRw5rv+/i+j+M4B1QEGA6H8XdP8vAPdKLHI8iSz+fJZDJ7TYZNJpMMDAyQTqc7XsRbKpXYtm3b3OTmPYWj0C4o7OnpoVwuMzs7y4YNGw54gnehUKDRaDA7O8vq1avnvV9fXx+FQoGZmRkymcxRV4Bu2zapVIru7m6y2eyCxfyzs7PYtk0ymTygJgoHY6Hnn+d5c8W3QRDgeR5TU1OEQiEGBgbo7+8/piY+78/s7CytVotarcby5cuBdlH3+Pj4XIFyV1cXy5Ytw3Vd0uk0k5OT+NEo9yxbxsbnPpdGfz8nnXTSIS1etiyLdevWzTUcGB0dJZVK0dPTg+u6eJ7HzMzMXFH54ODgPptF2LZNb28vs7OzjI2N0Wg0iMwz+SKXy5FKpeaKyn3fJ5fLkc1mSSaTDys+f+hxVq1axdatW5mdnWV6erqjBejZbJZiscjY2BiJRIIVK1bs9ZoJh8P09PQwNTXF+Pg4oVCI6elpVqxY0bHMSy2RSHDiiScyMTFBKpV62DnFsizWrFnDypUrF63Bi4iIiIiIiIiIHDqR887D++UvMZOTWJkMLFu237V2Ntv+S28vkQsuWKKExwY7EiGwbTCGyMgItufhNBpgWQSOA44DQYDTamE1m3jhME6zSaPZPKamFvulEsEXv4gzNoZdrdLYtAlrdxGjBRCJEHR10SiVCI+M0AqFaH760/D0p6t4UY4JTiqFedrT8LNZwjt20Gg0sPZ3jjAGN5PB6+4m6Ouj+1nPWtKscmyp33YbVKvYjQamv3/+xZaFn07jZrOYqSkaIyNE1CBB5LClAnQRERERERERERERETms2baNbdv4vo8x5oAKbPcUWx5ocfWBKJfLbNu2jXq9TrFYpFQqzU1A7+rqYmZmhkQiwaZNm+Yt5FxMtVqNrVu3kslkmJ6epquri7Vr1xKNRgmCgFKpRD6fZ2ZmhiAIsCyLjRs3HtDnLpVKlMtlHMdZcMq2ZVl0dXUxOztLqVQ6FA/tsDMwMECxWGR6eppMJsPg4OA+13meN1eEH41G6erqOuRZQqEQrutiWRbVanW/x9gzvTubzVIoFDDG4HkeiUSCVCpFuVxmamqKDRs2LErOI4nnebRaLQCi0SjQLl6uVCoUi0WWLVs2Nwkc2oXLruvS399PMplkYmKCaDRKLpfb73Pj0erq6mLDhg1YlkUqlaJQKDA+Pj53fkwmk6xevZpUKsW6dev22wBicHCQTCZDMplkbGyM1atXzz3WPYIgYGpqinq9ztDQEH19fdi2TS6Xo9lsUi6XWbNmzbx5bdump6eHQqFAT08PrVarY00OMpkMxWIRy7IeVnz+UMuWLaNWq5HP50kkEixfvrzjzUWWUiQSYcOGDbRarYc9btu2GRoa6kwwERERERERERF5xJJnnkl+5Uq8XI7Izp00XBd6e/cu1jUGCgXCk5M0V6wgWL6cxJlndi70USh8/PF4joPdamH5PnYQ4IdCBH/S5DEA7FYLd3dxurtrF86mTZ0J3QGl734Xq1QiVCzSGBqaKz5/mFSK5ooVhCcnMZOTlG65hdQzn7m0YUU6JPriF9P8n//BTyQIb99Ocx+vFeP7ODt2YHkewfLlWOeei+WqhFAWTzA7i91qYWwbDqCBsYlGsT0PAL9YXOR0InIw9N1DREREREREREREREQOa4lEgng8jmVZzM7OLjhBemZmhmQyieu6h2w6a6VSYcuWLWSzWaanpwmFQnR3d+O6Ls1mk1wuRyaTYeXKlfi+z4knntiRybATExNzU8f3FKDuYds26XSa7u5uJiYmGBsbIxQKMTs7e0DFxnsm0YdCoQMqxAyHw1Qqlbn7Hsop0IeDnp4ekskkq1atYnx8HM/z6Ovrm2s+YIxhdnaWTCZDOBymv79/0aYop1KpucnNhUJhn/+elUplbnr3tm3b6OrqYnBwkI0bN+L7PsVike3btzMwMEAQBBx//PEkk8lFyXsk2NP4AphrflEul+fOL7E/2cgTBAHQbr4wMDDA5OQkhUKB6enpQ16ADu3n35/92Z8xPT1NKpXC8zyCIMC2bRzHobe3l8HBwYflfKhoNMrq1asJgoCdO3cyPDxMKpUimUxi2/Zcsw3Lsli7di09PT2sXLkSgGazSaPRAFiwIcWeNfl8fu6+nShAr1ar1Go1CoUCfX19C57H9ky1HxwcpFQqHZNNGQ70fC8iIiIiIiIiIocxyyL67nfTuPRSmkFAZGICP5vFT6cx4TBWs4lTKOA0GjSXLSNYtYroe96DdZRd0++08OrVzK5cibEsXM/DC4cfVny+R+C67enoxrQnpFerS5y2c/wf/xg3m8UPh+Eh73Hti+nthclJKBRofv/7oAJ0OUbEH/94Gi97Gf7/9//ByAjRLVvwkkn8ri6wLOxKBXdmBuO6tDZuxH/84+l9zWs6HVuOclYkAraNZQwEASw0KMD328XqgD3P+5ki0nkqQBcRERERERERERERkcNad3c3kUiE7u5ucrkcqVRqv5PNK5UK1WqVZcuWMTAwcEgK54wxbN++nVwux/T0NCtWrHhYEfzAwAC5XI6JiQksy2JkZITjjz/+oI/9SDQaDWZmZsjlcvT09OxVfP5Qtm2zcuVKtm7dOlcgfSCFlXsKW73dncgXsliT6A8XlmWxadMmfN/Htm2mp6fZvn07kUgE27ZpNpsYY+jp6WFwcJCVK1fS19e3aFkGBgaoVCoMDw+TzWbp7++fu90Yw65duygWi4yOjtLd3c2GDRtYuXLl3POkp6eHUqnExMQEtm0zMjLCySeffMwWn8ZiMSKRCK7rMjs7i2VZtFot6vU6vb29D1tfrVaJRqPYtk04HCadTpPNZhkcHKRWq81bCP5oRSIR1qxZw6pVq6hWq3PPxXg8fsANH5YtWwa0X6PVapVCoUA2m8UYQygUYnBwkK6uLrq6uti4ceMR3Uii2WzieR6e5x1w0XwQBLRaLZrN5hIkFBERERERERERWRzxxz8e8y//QvN976PR3Y2VzeLmcli+j3Ec/K4uvKEhTG8vkX/8R+JPeEKnIx+VTDg8N3n+/2fvvsMkOct7738rdk6TZ3Zmo3YlkIUASQgkg4UMIogkEPjgyME2wcY+NumA4RjbGNs4AcfG5sU2x2CMMUkgBEKAMEIEEwRICIXVpsmhp3Ou9Lx/1EzvjjbNpgm79+e6dGm2u7rqqeqnqrur6vfcmu+jggDtUddPVBBgdjphKE8p/EgE/7OfJXH11evR5DWn5/PorRZ+MtndVsej6Tp+PB5On8+vUQuF2Bhyr30tJcPA/9jH8Gs19MVFrHwelCKwbZwtWyCXw3/iE8n9+Z+jrcPA6eLCYl18MV4iEX5+lUpwkuvierkcTh+NYm/dukatFEKcDgmgCyGEEEIIIYQQQgghhBBiQ9M0rRviPHToEJOTk4yMjBxVQbdWqzEzM0MulyMej68I4J6JcrlMq9Vifn6ewcHBY1Zg1zSNvr4+PM9jfn6eZDJ5zkKnx1MqlWi327RarW6V4uMxDINMJkO5XCabzXYrJ59IMpkkmUwyNzdHs9kkHo8fd1qlFJVKhVwuRyKROG9DzLZtc8kllzAxMUE8HqfdbtNsNgmCAMuySKVSRCIRRkZGzlp/PJ6BgQGKxSJbtmxhenqaZrNJT08P8XicWq1GsVhk3759RKNRxsbGSCQS5HK5FfNIpVIMDg6Sz+fJZrNUq9Vj9vcLQSqVIhaLkc1mKRaL3SrjQLfK/TLP86jVavT395NMJjFNk2g0iud5KKVWPWjD6dJ1/Yyq1S8f1/L5PKlUCt/3u8+l02n6+/vJZDIr9mPbtrvb4WTHg+Vp7KWbm9aj+rkQQgghhBBCCCGEEBe6xJVXEvn3f6f+uc/hf+ELqPn57nPBwAD6c59L6qabMB913licPYbn4ds2+tI5Y7vVIjAMgqWBP3XfR/d9Al3Hi8VQgAZw4MC6tXnNKXVaL9NO83VCbFqaRu7Vr6b9jGfQ/PSnUV/7GkGzGT6n6/hXXkn0xS8m+5SnnHQwByHOhsRTn0qpvx8vk8HM53FzuaMGWelqNrFqNTo7dqCuuw79JNcZhRDrSwLoQgghhBBCCCGEEEIIIYTY8AYHB2k0GiilmJ6eZt++fSSTSaLRKEEQUK/XcV2Xnp4eBgYG2L59ezfseKby+TzVahXDMMhmsyectq+vj3K5TKPRIJ/Ps3UNR+t2HIdOp4Npmqta9+VgslIK13WPCtU+Wk9PD1NTUySTSRYWFti2bdtxg+XlchnP88hkMvT395/W+mwWlmWxa9cuHMdhcXGRdrtNEASYpkk2mz0quHuumKbJ7t27u5Wri8UiU1NTKKWYn59ncXER0zS56KKLSCaTbN269ZjVrDOZDAsLC1SrVRYXFy/YADqEof56vU6lUmF2drYbzD5ywAbHcVhYWCAajRKPx7vV0YMgAMLBKTbDAAzLAxOMjo7iui5KKUzTPG7F82w2i23bJJNJisXiCQPoQRBQLpfp6+sjlUqdtWPzqbJtG9M0MU2TZrN50mNes9lE0zQsy5LQvBBCCCGEEEIIIYQ4L5i5HNlXvAJ+7ddwFxbwazWMZBJrcPCsBRSdqSma3/oWqlZDi0SwL7mE+JVXSgASwHXxUykA9CAg8H0018VYCqQrTcOLRlGGgZvLhQF03wfHWcdGr62gt5cgEsGo1znp0K5KobdaeOk06iSVdoU4X0V37SL65jejXv96vFIJ5bqYPT3o0eh6N01cYDTTRHve8wjm5zH27sU8cAB/2zZ41DU2Va9jj4/jptOQThN/6UvXqcVCiNWSALoQQgghhBBCCCGEEEIIITY8TdPYuXMnpmliWRbNZpNKpUKz2UTXdTKZDNlslkgkwvbt208aFD8VrVaLRqNBKpU6aZDUNE1isRiNRoNWq3XW2rBapxJ0VadYDcIwDPr6+uh0Ohw6dIipqSmGhoZWBDODIKBUKpHP5xkaGiIWix1VZft8Zdv2SSvPn2uRSITHPOYxzM3NkUgkcBwH13W7YWjLshgZGaGnpwfTPPZlQl3XSSQStFot2u32WjZ/w+nr66NaraKU4uGHH2ZqaopKpdINmy9XvI/H4/T399Pf398NYtdqNWKxGJqmEd1EN/lomraqgLhhGPT09NBoNBgfH6dYLHbD90dSSjEzM4Omaes+IEU8HicajXar2mez2RMeM5eniUQipNPpNWypEEIIIYQQQgghhBDnmKZhDQ6GwfOzpPnDH9L6yEcwfvQjaLfRXBd0HScWoz06inHTTWRe+tILO4ieTKIsCxWJ0B4bw1hcxKxUCJYGP1W6jpfJEPT1QTyO+cgjBLEYKpFY54avHf3pT0c98ghGoYBbr6Mlk8efuFhEUwrV04N9ww1r10ghNiDNNLHO80HBxcaX/bVfo3jffeD7mAcPEnnwQdxUKvwc832MWg2j3cbNZAi2bkV79auJ7t693s0WQpyEBNCFEEIIIYQQQgghhBBCCLEpaJrGtm3bGBwcJJ/PUy6XcV23G/Ds7++np6enW5n4bAmCYEXF45MxDAOlVDf0u1YikQiRSATP82i32ycNvTYaDaLRaLfC72ps2bKFVqt1VCV627YJgoBarQbA0NAQPT09XHTRRWf9/RAnZpomo6OjjIyMUKlUuiF00zRJpVIMDAycdB66rqOUOuVBCs43ywNfGIaBaZoEQYDruszOzjIwMEAkEmFkZIRIJEJ/fz99fX0AeJ5HuVxmeHiYbDZ73LD/Zrfcx0ZGRpiZmaFer5PL5YjFYiilqNfrFItFlFKMjY2RzWbP6uAgp6O/v596vU6pVGJ+fp6hoaFjTpfP52m32wwPD9PX17cpqtgLIYQQQgghhBBCCLFeKrfcgnrf+8JA9eIixhED9AamiTc3h5qYoPDjH9PzJ3+CdpbOmSrXpf6Vr+DcdRdUq2CaaGNjxF/0IqJ79pyVZZxN2lVXEdx/P/biIh4QjI3hjI6ilq4nabreDeirdhuj2cTbsgX9yivXsdVrK3XTTdQ+9jHcdBp7chJn1y60Yw2a2mxiz87i9vQQ9PaSuf76tW+sEEKIFTTTJPfud1N65zvh7rvxKhX0YhGjVAJNI4jF8LZuhWQS7TWvIfOyl613k4UQq3B+3u0ghBBCCCGEEEIIIYQQQojzVjQaZWxsjLGxsTVZ3nLV9U6ns6rp2+02sVhszUOnPT09TE9PE4/HKRaLJ6zG7Xke1WqV0dHRUwrt67rORRddhGVZ2LZNq9WiUql0BwIYGBggnU4Ti8XYuXNntxq0OD2O4+D7PrquY9v2KYVgdV3vVp8vlUq0223q9fpxpw+CgGq1SrvdZnJykkwmg+M4Z7wOm52maWzfvp3+/n6UUui6zvT0NLZtMzAwQC6XI5fLdauGu67L5OQk0WiUdDq9rhW/zzXLsti9e3e3anqpVGJ6ero7cIFpmmSz2e422rlz57oHufv6+igUCoyNjTE5OUmr1SKXy5FcqqLTaDS6+8vo6CiZTIbBs1gFSgghhBBCCCGEEEKI803trrtQ730v+vg4ZqWCm8vhjY1BNIryPPRSCbNQgEoFgoDSu99Nz9vedsbLrXziEwT/9m9oxSJmqQSOA5qGSiZp33YbjUsvJfmWtxDZtu0srOXZkXzxi2l84hN4iQTm1BTerl2g62iGsXJCpTCnpvDjcUgkSNx88/o0eB2YPT1ov/zLBP/yL2gHDhDZuxc3lyPo6QHbRut00AsFzHIZL51GjYxg/fZvn7VBDYQQQpwZPRql913vovWTn9D6zGdQ3/42wdLANEFfH9qznkXyppuwVjFouhBiY5BvWUIIIYQQQgghhBBCCCGEECeQzWYpl8tMTEzguu4Jq4U3Gg08zyOTyax5pV/Lsshms/T19TE5OUksFusGkI/keR5TU1NEo1GSyeQpB2R1XWfHjh0MDw+Tz+epVCrdkHQsFqO/v590Or3uQdPNyvd9CoVCtwLzMsuy6Ovro6+vrxt2Xq1cLkepVGJxcZFWq0UsFus+p5Qin89TLpdxHIdyuczc3BwAi4uL/PSnP2V4eJienp6zs4KbVCKR4MorrySTybCwsMDExAQQbr92u02r1aJer1Or1YjFYoyOjjI4OEgqlVrnlp9bsViMSy65hIWFBZLJJIODg3ieh6ZpmKZJLBZjYGBgw1QR13Wd3bt3A2FAvlgssrCwwOzsbPf5TCbDyMgI6XSa3bt3Yzz65k8hhBBCCCHEBcsrlWjccQf+zAwEAfrAAIkbbsAaGlrvpgkhhBDrQyncf/gHjNlZzGoVZ+dOSCS6T2uWhRoYwOntxdq/H/PQIbw77qD9spcRXTpPdzpK738/2sc/jjE1hVkug67jRyJoQYCRzxPYNlQqNH/rt1Dvec+GqYZuDQ0RPOMZ0G5j7duHuW8f3tAQpFJh5XOloF7HmJtDd13cXbsIfu7nsEdH17vpayr7ildQqtfxP/EJgsVFjEIBe3Gx+7wXj+Ns2QK9vWivex2pG25Yx9YKIYQ4lthllxG77DIAgnYbzTRlsBAhNinZc4UQQgghhBBCCCGEEGINLQechBCbR39/PwsLC0QiEebm5hgdHT1mkNL3febn50mlUti2vS6B3dHRUer1Op7nMTs7S6VSIZfLEY1GCYKAWq1GuVzGsizGxsbo7+8nccTNYKdirSvRXwiazSb79u2j1WpRKpW64X7DMEgmk9RqNebn59mxY8cpDXDQ29vL9PQ0qVSK2dlZtm7dimmaBEHA5OQk1WqVUqlEtVqlUCgQiUS6gfUgCGi1WrTbbUZGRs7dym8Cmqaxa9cuNE0jEolQrVYpl8vUajWAbvB8OYg9eoHcFGhZFlu2bGF4eJhardYNoEcikdM+vpxLpmly8cUXs7CwQCKRYGBgANd1u89FIhH6+voYHByU8LkQQgghhBACAHd+nuo//AP6t76FVq+jN5vhE9EojQ99CP9JTyLx6lcT3bVrfRsqhBBCrLHGf/83+swMZqGAOzCwInx+JM0wcLdtI/rww3i1Gs1PfYroW996WsusffGLYfh8/350xwmDyLlcGOAGPMdBW1ggMj5Ox/dpvuUt2B/9KHo8ftrreTbl3vxmivPzoGnoU1NEDh0iME0Cy0L3PHTXxUsmcbdtw3/CE8j9wR+sd5PXnqaR+53foX7ZZXQ+8QnU/ffjdToQBGCaEIngX3klsV/8ReJPfOJ6t1YIIcRJ6NHoejdBCHEGJIAuhBBCCCGEEEIIIYQQayiTyax3E4QQpygajdLT04PjOIyPjzM+Pr4iuK2Uolarkc/n0TSNoaEhhoaG1iW4aNt2t7pvNBqlVCoxNzdHEATdxwYGBkin0wwMDEh4fANpt9vs3buXQqHA3NxcdxAD27bxPI9KpcLBgwfp6ekhCAIuuuiiVX+mmKbJ8PAwnucxOTnJoUOH6O3tpVqtUqlUmJ6exnEcNE3rBon7+/uxLKtbNR3C/tXX13cuN8OGZxgGu3fvplKpkM/nVwwEoOs6PT099Pf3E98gNzOupeXq4ZuBrusMDQ0xODhIvV7HcRwgDNOnUqkNUa1dCCGEEEIIsTG09++n+cY3YkxMYMzNYTYaKF0HQPN9vHgcSiVa996L/2d/RuKKK9a5xUIIIcTaaX/1qxjlMloQEPT2cqKzalokgpdMoheLqLvvhtMJoCuF+9GPYszNYXQ6dLZtQ3NdKBbDYHIyCbaNGh3FsSzs6WmcVIrq5z9P9hd+4bTX82zSbJuev/1bSv/3/6LuuIOgVkMrl8H3CQwDlcmgUinUM59Jz+/9Hpptr3eT103yuutIXncd7f376fzkJ9BuoyUSRK+4AvsCHzBXCCGEEGKtSABdCCGEEEIIIYQQQgghhBDiJLZt24brumiaxvz8PJOTkxiGgWmauK5LEARks1n6+/sZGBhgeHh43doai8V47GMfSz6f71Zi9n0fXdfRdb0bPt8sQdELxfj4OOVymdnZWYaGhsjlciuez2azNBoNpqam0HUdwzB43OMeh7500/vJDA8Pd/twoVBgenqaiYkJFhcX0XWdvr4+MpkM6XSa3t5eBgYGAEgmk4yPj7OwsIBt2/T29ko4l3BAmUwmg+/7eJ4HhOHl1b4fYmPQNI1UKrXezdjQnvWsZ1GtVgGIRCJEpUqFEEIIIYS4gPiVCs03vQl93z7syUm8VIr2rl1o8XhYZbXZRF9cJDI+juM4OG9/O+b/9/8R2bp1vZsuhBBCrI3FRbROhyASQVvFoLx+IoFZqRDU6yjXRbOsU1pc8wc/QJ+cxFpYINB1ogcOhFWxATQNpet4mQzBwABqYICgWERbXMS/9VZ42cu6VdLXm2ZZ9LzhDfivehX1227Du/deaLUgGsW47DLSL3whhpy37Iru2kV01671boYQQgghxAVJAuhCCCGEEEIIIYQQQgghBOC6LuVyGc/z0DSNSCRCNpuVoKUAwmq5u3fvZnp6mmg0SqfToV6vEwQBhmGQTCaJRqMMDg4yODi4qnk6jkO5XAbCEGQ0GiWbzZ6VAOtyxeuhoSEajQae56HrOtFoFPsCrpaxUTWbTer1OvPz8/T29h4VPl+WSCQYHh5mZmaGXC5HsVg8pYrkW7duJR6PE4/HcV2XSqVCu93uPh6NRunp6VlR1duyLAYGBpidnaW/v59yubyifa1Wi2Kx2K0gHYlE6O3tJRKJnN7G2GQMw8BYxY2VQmxWn/rUp5ifnwdgcHBQ+rsQQgghhLigVD/xCfTpaeypKZzBQRgcXFnZNR4n2LqVTjJJZGqKTixG4yMfIfL2t69Xk4UQQoi1dYrnirQgOBwCP41rIZ3vfAfj4EHsRgPPttE9D305gA4EhoHuugSVCs7YGH5PD2axiJqcxM3nsZYGXt0ojFSKzMtfDi9/+Xo3RQghhBBCiGOSALoQQgghhBBCCCGEEEKIC1q73WZmZoZyuUyn0+lWCI5EIkQiEfr6+hgeHpaqtgJN0xgdHWVkZIRisbgigJ5Op1c9YEGr1WJ8fJxKpUKn00EpBUA0Gl3R50435BcEAa1WC9/3MQyDeDwu/XeDW1xcpF6v43kePT09J5w2nU6zsLBAuVxmcXHxlALoAH19ffT19VGpVCgUCiSTSbZs2dINph9LKpVibm6OarXaDaAvV2Ov1+s0Gg0cx+keO2dnZ0mn04yNjUm1ZCGEEEIIIYQQm5LyfdQXv4iRzxNEInCiAQd7enArFYyFBdQ3voFfq0nVUiGEEBcEbWQElUhgLCzguS6cpKK5Xq8TRKMEfX2rqpj+aO7Xv06sWEQLAgzHITBN3EgENA0tCNBcF7Pdxvd9IhMTOH19aL4PgF+pbLgA+hlRasNUdBdCCCGEEOcvCaALIYQQQgghhBBCCCHEJuH7PrVarVvJOBaLEYvF1rtZm1qtVmPfvn1Uq1UKhQLNZhNN01BKoWkamUyGZrNJtVpl9+7dmKacVhdhNfTlEO+pqlar7N+/n0KhQKFQoN1ur+hX6XR6RZ+zTnKz1pHa7Tb5fJ6FhQVKpRLtdhtN07Asix07drB169YLpir1ZtNut2m1WsRisVUdZ1KpFK1Wi3a7fVrLU0oRiURIpVIkk8mT9uXlYLnneXieR6VSYf/+/ZRKJQqFAq7rYts2SikcxyEajdLf30+j0WD37t0kEonTaqcQQgghhBBCCLFeWj/5CXqhgFku4wwPn3T6oLeXyKFD+PU6zbvvJvXc565BK4UQQoj1FX/e82jfeiuBZaEtLKC2bDnutKrRwGw26YyNoT3zmae8LHdhgch996G7LgBeLLaiiroyjPA/38dst0HTsBYWULYNgH6cAVg3C+U41L70JdzbbkM/cADNcVCJBOrxjyf64hcTv/JKCaQLIYQQQoizTu6UE0IIIYQQQgghhBBCiA2u0+kwNzdHsVjEdd1uAN2yLBKJBAMDAyetmCuO1mq12LdvHwsLC+TzebLZLIODg0SjUZRS1Ot1CoUChw4dYmxsDE3TuPjii1dV4VqIY2m1Wuzfv5+FhQXm5ubI5XIMDw+TSCRQStFoNLp9buvWrezbt49LLrlkVX2uVCqxb98+xsfHmZycpFqt0ul0gDCsPDExQW9vL1deeSVjY2PnelXFKVJKoZRadaV6XddRShEEwWktT9O07n9KqVW9ZnlZnU6HAwcOsLCwQKFQoK+vj2w22w3OO45DsVhkcnISz/PQNI3HPvaxpzSYghBCCCGEEEIIsd78QgHleWHV1FUE1lQyGVYidRz8QmENWijOBr9SoXPwILguejZL9KKLJLwnhBCnIHrJJTQuuQRKJSKTk3SiUejtPWo61Wphj4/jZjKQSJB8yUtOeVn1z3wGTdfRlULpOloQoI5xTl0ZBr5to7sumufRSSYJslmsoaHTWseNoPnjH9N+xzvQFxcxCwX0Wi1cf9PEn5rC+da3aF16KZl3vQtTrhkLIYQQQoizSALoQgghhBBCCCGEEEIIsYbK5fJRj2Wz2eNOX61WOXDgANVqlVKpRL1e74YFbdsmm81Sq9WoVqts27ZNwtGnYGZmhmq1yuLiImNjYySTye5zmqaRSqVIpVLMzs4yOTmJbduUSiUJ+6+TIAgol8sUi8VuqDUSidDX17fivdvIZmZmqFQq3T53ZFVoTdNIJpMkk0nm5ua6fa5YLNJ7jJu1jlSpVNi7dy/33HMPc3NzuK6LruvYtk0QBCwsLDAzM8PQ0BDNZpNrr72W7du3n+O1FafCNE0sy6JSqaCUOumxvN1uY1nWGYW6o9Eo8XicarVKf3//Cad1XZd2u83Q0BC1Wg3HcSgUCoyOjh61/9m2zdDQENFolLm5OaLRKPl8npGRkdNuqxBCCCGEEEIIsdY0y0JbDrWtZgA43wdAaRraUqVVsXE1f/xjWp/8JMZ3vgOed/jxoSH0G28k9eIXY6RS69hCIcRG1pmYoPm5zxEcOgSOg5bNYj31qSSvvx7NvPDiGYk3vIHW7/4ujucRmZ7GLxbxenogGkX5PkaphFmp4CeTBNu2wStfiTUwcMrLUXfeieE4+IYBmobuut2/Hy0wTfROBx1QgPasZ6EZxpmv7Dpo/vjHdN70JoypKay5OZRh4KXTYBhojoM9NYWanYVmk+rrXkfmAx/ASKdXzkQpOocO4Vcq6LEY9rZt6NHo+qyQEEIIIYTYVC68XzhCCCGEEEIIIYQQQgixjkZGRmi1Wt1/x2Ixms3mMadtNpvdasn5fJ5UKsXY2BiRSIQgCKhWqxSLRSqVCkEQoOs6W7duXatV2dQcx+kGgbPZ7AkDzENDQzQaDSqVCvl8XgLo62BxcZHp6Wk6nQ7VahXHcdA0jVgsxuLiIvF4nO3btxNfRTWq9eI4DuVymUKhQDabXRE+f7TBwUHq9Trlcpl8Pn/CALpSikOHDnHfffcxNzeHUoq+vj4ymQzRpZuHPM9jcXGRAwcO4Ps+pmnS09ND+tE3IIl1k8vlKBQKzM/PU6/XSZ3gBmfHcajX62zfvp1cLnfay+zv76dWq1EoFKjX6yc8DhYKBaLRKJZloZSiWCySyWRO+JpsNkulUqFUKpFMJhkeHpZBUoQQQgghhBBCbBrWtm14uo4fiaBVKqgTnMsB0CoVAtNEi0axtm1bo1aKU6YUpX/8R7SPfxyjXMYoFDCaTTSlCAwDb3oaNTFB5XOfI/7udxPdvXu9WyyE2EDcuTmqf/mXGPfcg9ZoYNZqoBTKsvD/678ovf/9GL/yK2Ruvnm9m7qmohdfTPAXf0HnbW+jk0yiLy5iz86i+T5oGl4igbN1K2SzqF/7NXK/8iunvhCl0BcX0TodnEwGs93GbLUw2m28SOTwoDFL02q+j750bNd0/bQqrm8EQbtN+x3vwJiawp6dpTMyElaYXzrXroCO74fP79+PA5Tf/W563/UuAPxKhdpnPkPwhS+gLyx059uKxVDXX0/85puJ7tq1DmsmhBBCCCE2CwmgCyGEEEIIIYQQQgghxDpyXXjb2+AnPwn/vWMHXHstlMuwd28D27Ypl5P4/m6UivLQQ4cDtqmUz9BQG8cp4ro627aBrrvdirg//vHKZfX1wc/8DCwuQjoNzSbU62GBl56esC3L7XjSk+C5z4XhYbjjDpiYWFEIpstx4IEHwr+vuAJsG6pV+OlPYefOcDmpFGzfDj/6Edx//+HX7tgRzv+SS8LlP/AAzMyEzwUB3Hdf+PfVV8OLXgSXXgr798Pdd8PDD8OBA+EytmwJl1Gvw7e+Ff7/sstguZBBKhWu9xOeAP/93+Fr0+kWw8MBzWaLZnMnX/96L+PjUZpNgx07Wlx0UYsrrqjT6Wh885sZ9u/P0mw69PSkuPxyj2c9y+Sii8L5T0/DbbfBnXdCux2289FFiQcHw2161VXhNvvmN+Gee8LtEY2GzysF994bTh+LwcUXH/57ZiZcz0ePVRCLhdsOYH7+6Peopwce9zh4+tNXFoB46CH4xjfCti8uwuQkZLOwezfs2gVbt8Ijj0ChAI95DPz8z4ftXDY1Fb5+377Dy4zFwjYeOgQ/+EH42M6d8IIXhMv/1rfCx4vFo/uRaYZ95MYbw/tmjnTHHXnuuqvBwYNx2m0dXY9jmhYQEI/XeNzj5nnc46J0Oh127drNj36U5Ec/CrfnNdeE213Twn9/+9vhdm80wn53/fWQyYSPffe7MDcXLjORCLf/c58b9unZ2fD9PXAADh4MtwvAwAAs36/zuMeFy0inw/lDuK4TE+HfnY6i0dhBsznIi17UYNeuDg89lGBuLgLoR64yDz8cx3G24nku8XiCdDocYALgFa8It+c3vhHuM8Vim9nZFFNTF+P7FxGPx1EqQ6kUx/fD12zZUubii/McOnQNt9+eJJlM8f73+1x9dfi+Qbjuy+u/vD5794Z9etnISLjOR2q3w/60rLf38PaBcP/dsiV8bGQEHv/4sO3VavieP+tZ4TZbWAgff+ihcP9fWICLLoKxMXjOc+DRYwuUSvD5z4f97VjHpmWJRPheKxW+f+Xysad78EHodI5+PJ0Oj43ZbNgnfB90HSqVw9MMDYXHv1wO7ror3K+CIHxdqRQe55aKoAHh8eHii+FpTwu339e+BuPjOe6662I6na24rs/AgE29vvJAcvHFTTwP7r9fA7YRi8V59rMT2HbYT3t7w/fwrrvC6fv7w21vmmFbIpHw/0qF083O9lGt2nQ628nlSlx2mc411ziMjXXwfbjvviT33pvg+9+3CYJ+Lr1UkctpKKWYno6QTPaybZvP5Zc3GBtbufEOHYrw/e+nqdXSZLPjDAy4NBqNbmD9vvvC43GzGfYDwwjfA9MMt1kuF75XiUR4TH3a0+C//ivsH41G2B8uvTTsTx/7WHh8OdK2beE8jiUahT17wvf0oYcO78MQHouf+ER48pNXHjPn5uD228P3NhIJ38NOJ9y+11wTfuY9/HA4z2uvDY/z/+f/HH79Yx8bvkcn8sgj4bqNjcEznxn2+0gk7Et33hl+Ph+v/x7p3nvD93jZ4x9/4unz+XD9rrgCksmwfxeL4XJ1Pezf7Xb4ueU44Wue8IRw2lrtxPPu7YXLLw/fP10Pp7/ttvCzfHleEB6Hf+ZnwrbecUfYpvHx8P149H45OBi+96US/L//Fz4Wj4fv6ZF0PXx/nvGM8N/vfvfh7xjL30/i8cOfcScYS2FTC4IATdNk8AchhBBCCCFOQ2TbNuqXXoqWz2MuLOAMDh6/cqpSmIuLeNkswdAQiauvXtvGilUr/fM/o33sYxgHD2I0GniZTBiK1HW0VgujVEIvFnEaDZpveAP6P/4j9pYt691sIcQG0Bkfp/F7v4dx6BDm7CxGq4UfjaJ0Hb3TQZudDT8H3vc+SnNz5F73uvVu8pqKP+EJWP/6r9Q/+Um8O+4gKJcPn2Q0TYJrryV6883EL7/8jJcVpFJ4S5XmjXYbu9UiMAwCXUcDdM9DUwql6wSmSefxjyc1NHTGy10P9S9/GX1xEWtuLgyf9/UdNY1mGARbt+IdPIgxPQ3f/jbu/Dze4iKtt74VfX4ec3ERo1JB9zyUruMlkwT5PO0vfYnO615HZpMG9AW4Cws0v/Y1gkIBDANjdJTkM54hFe6FEEIIcdZoSh15C4IQm9vf/M3fUKvVSKVSvOENb1jv5ghxTL7vMz8/D4SVjIzjXZgQQgghNgjXdbt/W49O0QghhBAbhPzWEptJPB5fUQFd12PceOPRFdB936dWq9FsNjBNC/sEyTWlAhqNJrFYjHg8Riy2catAn4kdO8Kg9InCpqvVbrdoNhsEQR3TzJ50et/3aLXaJJNJUqkklmXyO78TBuA+8pHVL/fGG8NA8nLQf61ccw288Y1huPP22+EDH1gZUDyZPXvgD/8wDOzddx+8611wRDc+a6JR+PVfhxtuCP/9z//c4N/+zaXVaqFUQCQSwTCOHFtX4bouT33q/Vx9tcbnP38phUIfmnY40P2c58BrXgN///fwla+sXN7QUBhy/uY3j92erVvh5pvD7fXo8P+pardbNBoN2u02iUSSIy/PHCsY+Og+t3K9D2s06hQKRRqNBqZpEIvFgOMFDRXVag1d18lmM/T3D6z7Z2YiEQb9v/zllaHuI2Uy8N73Hh6c4Pvfh7/+63PTB9eT43RoNpu0Wm2CIMC2bSzLZPn99DwXx3EBRSwWJxqNnPHx3nXDYHin08F1XSxL4wUvWGBiIsW996bxPJcgUMRiUSzLxrIsWq0WzWaTZDIBaBiG4pd/eZ7HP76BUnDLLX3cfXcGCMOvzWaTSy/Vee974/T2ZvnsZ+FDHzqjZp9zN94Ir3pVGEL/yU/gT/7k2AMUnEvbt8P//t/wT/8EP/zh2i77bLv2WvilX4K3vvX4+/l6GhuDP/7jY967uSnVajUWFhaoVCrdz5pYLEZfXx+9vb2nfNz/wAc+QHFp9Jh0Oo1t27zqVa866+0WQgghzha5riWEOJtqX/4y/p/8CebevSjDwNu+He1RxxYVBBiHDmG027i7d8NrX0v2V391nVosTqS9fz/tV74SY/9+9E4HZ+dOtEhk5URKwcICkfl5Ojt24D/zmfT+5V+e9bbIda2Nw11YoP7pT6O+/nW0UgkA1d+P/sxnknzhCzGPN8qhuKAox6H0a7+G/sAD2OPjeJkM/tDQ4WOIUlAqYc3NEdg2/q5d6G96E+kXvGB9G75OlOfRfvBB/HIZLRYjsmvXWdmXijffjHnPPejtNu6uXRgTE1iVCprvo3keWhCApnWD52a7TXN0FO9Xf5XeP/7js7Bma6/wm7+J+Y1vYBYKuI95zMqRUx+t2STyyCN09uzBe/7zMb7zHbTxcSIzM/iRCH4uh4pGwfMwymXMeh03kyHYtg39jW88aX+V31obS3vfPhr//M8Y3/1ueBGz0wn7fywGmQzqGc8g86pXYaRS691UIYRYU/JbS2w2myELKxXQhRBCCCGEEEIIIYQQYg0Fweqmc10H3/cJAnXSi/iapmOaBq7r4DgmsZji+AHUzevgwbM5Nw2loN02T6PqqUYQwPved+pL/cIXTv01Z8O3vx3+99jHwr/8y6mFzyGshP23fwvveEcY5D5Xwd92G97/fvjOd8Kquf/xHwGu6xAEPvF4fEWwPKRhWTbf/Obj+OpX28TjJvG4Q/SIUf1vvz0Mtj86fA5HV/1+tImJcL3PjnCfPL1hgY+/P3uej+e5KKUwTfOE04KGbdtLYWMP13XX/YJrowGf/OSJp6lUwgD6O98Z7kMf/ODpbseNzbYjBEF4/HYcB8dx6HQ6aFpYdVzT6A5IYtv20mADZ8ayLBKJBJqmYVkWrutyyy0DKAWa5mFZFqZpYZoGiUQC74gRQMJpwPc1PvaxQR7zmIN85jP9fO97R97MFL5R4+MxvvAFmxtu2Pjhcwj72cgIPO954bF+rcPnAIcOwWtfu/bLPRe+9a2jq9RvJJOT8J//Cb/92+vdkjPjeR4HDhygWq1Sq9WoVCp4noemaSQSCWq1GjMzM+zYsYNMJrPq+b7+9a9fMXhSLBaTALoQQgghhLhgpJ75TIp3343n+5gHDhB56CG8TIYgnQZAq9exy2WUruPu3In/+MfT8/KXr3OrxfE0b7kFvV7HrNfp7N59dPgcwpMdg4O47TbG3Bx873u4c3NYm7RyrjgBpSh94ANon/gEWr2OUSigLZ0ECqJR1L591D76UbTf/E2yv/AL69xYsd5qX/oS+sQE9sQEbl8famRk5Zl4TYOeHpxkksgjjxDMzuL9+7/D859/4sDweUozTWKXXXb25/v0pxOMj2Pt3YvXbhNs306n2UQvFDCrVTTfR2kagW0T6DqBZaFGR4k++9lnvS1rQqlw0JRaDS+dPnlfisfxIxG0ahX9U59CAyIzM2Hl9N7eFa/3e3rwGw2sQ4dgchL/7/4O76lPlUE3NonGd7+L84d/iDE3h5HPYzYaKF1HUwqlaXjpNEGhQOWee0i9971YAwPr3WQhhBBCbGISQBdCCCGEEEIIIYQQQog1tNoAehAEBEGArmvHrI78aIZh4roOSqmloOKFd0PLqdB1HV0Pg7e+7x23uvQyz/MxDANNC1+7GX3tazA7C45zeq//4Q/hH/7hxIHts+WHP4Tvf9/D8/ylqsz2McLnh5mmiWHouK6L4zhEoxGODGLfcsu5b/PJGMbKPrf89/F4nreqPhfu8wBqVX1z+ZiyfKzYLO69F972trAa9fksGo1iGDqGYWDbNr7vd8PnhmGg6waRiE0ksrKPnwnLskilUt3Qe3DEB5VpGth2BMuy0LSw2nl4/NTwPK87QIrravzRH22n3V7ZB13XwzB0QOMrX4mwmQptfPjDMDgICwvr3RKxFr7xjTDwv0k/4vF9n71791IqlZiamsL3fdLpNIlEgiAIqFarFAoFent7CYKAXbt2nVIIXQghhBBCiAuWppF7xzsoGQbenXdCuYyxuIg9NQVAYNs4Q0PQ04N/xRXk/vzPj6qQLjYG5ftoX/sa+uIiXjIJJxnYLxgYILJ3L16jQf2228j9xm+sUUvFWin+zd+g3XILxvg4Zq2GF48TxOMAGEuBdDebJfj7v6fcapF9xSvWt8FiXXm33opRKKAMAzU8fNzpNNvG7e/HzOdRMzPUv/1tktdeu4YtXVvK86h95Su4X/gC2sQEmuuiUim4+moSN99MZNu2s7q8xE030fzkJ/FSKcyJCbydO2Fp33Xg8KiptRr2oUO4AwMEIyMknvzks9qOtaJ8HzwPTSlY7fcLw4BWC6tWQ/c8nN5e6Os79rSJBO7YGJFDhwiqVeqf+5wc6zaB9r59OH/4h+iHDmHPz+Nms+EgA/F4uA9Uqxj5PPa+fTieR+2NbyT7wQ+iHzF4txBCCCHEqZAAuhBCCCGEEEIIIYQQQqyhc5v31B71f3E8lmWh6zqmaeI4LrHY8U+XK6VwXZdoNNoNYW5GP/xh+N+ZuOOOs9OW1fA8jyDwCQLVDbmeiGVZdDrOEYM3rG9l70czzZV9Lho9fvvCPucRi528z4VhYJ0gUKs6viwHmjej8z18vsyybCzLxvM8PM87IoBuYlknq3J/enRdJxqNEo1GlwLo4UAmjx74wTBMDMPANC1c11nRnkeHz5VSeJ5LJBIG2CsVg//3/856088Zx4E//dP1boVYK60WFIvHvxdzo5uamqJUKjE+Pk46nWZgYADDOPw509fXR61WY3p6Ggj3+csuu2zFNEIIIYQQQohj00yTnne8g8ZznkP7M5+B730P3/e7z/uXX07kppvIPv3pF2SV283Cr1TCKtfNJu5qfvzFYgS2jdZoECwNOLCRBc0m7YcfJmg00JNJopdcIkGzE6h9+cvon/sc5oED4Pt0du8Og3tLPKXwajWsqSm0gwfxP/QhmpdfTvwJT1jHVov14tdq6Hv3YpRKeNnsSY/1QV8f2vw8VKs43/kOnKcB9MY999D50z9Fz+cxCwW0ej2svmxZBPv30/rc52hcfz25P/gDNNs+K8u0R0Zo/I//gf/Rj2Ls34+9dy9uLofq6UGZJrTbGMUiVqWC29ODGhnB/t3f3bSfz5ppomIxlGGgdTqc9MqOUmiui+77oGnojoPq7z/xa1KpsGr64iL+HXeABNA3vMaHPoQxN4c9P09n61bIZg8/qWmQyeCn06ipKexDh3CiUWq33Ubm5pvXrc1CCCGE2NwkgC6EEEIIIYQQQgghhBAb0MpQaXDC6s+wXFFZXwoMbs4bKdaSpmnd6sLNZpNOp7NUUXglpRStVgvDCIPDtn30NOLcWK7QrWmsqk9rmt4NVm/EgPWRfa7VWu5zNo8OEx/Z5wzj5H0uErExTRNNA9d1MM2jL/0opfB9n06nQ6vVQtM0fN+j2Wx027Wa6ulibZmmecz381w7WV+IRGx838PzXFqtFtFo7Kh9VKmAVqu9NOiCtdTXhdjY8vnNGUD3PI9CoUA+nycejzN8nCpcqVSKLVu2MD09TTabpVAoMDAwsMatFUIIIYQQYpPSNBJXX03i6qvxazXc2VlQCrO/H7OnZ71bJ1ZBed7hf6zy/LnStPDM3REDDmw07f37aX7iE2hf/zpaqwVBALpOJx5HPeMZJF760rNegfh84HzqU5j5PJrj4O7Zc3RlYU2DdBpnxw4i+/bhl8u0PvlJCaBfoPxKBQDN91GrGNhB0/UwDO26qFrtXDdvXTS//32ct74VY3oaa24OdB0vlSLQdfR2m8j+/fixGJ7jUCwU6HnPe9DO0rnu3KtfTbHRgM9+lqBQwCgUMBYXwyc1DS+RoLNtG/T2or/pTZu+An1w+eX409PY09N0fB/tBANKqmoVzffBslCAH43CycL/moafSqE3Gqi5ucNV5MWG5ObzGN/5DkY+j5vNrgyfH0nTCEZHUfV6OLjArbfCS14SPt5u4xWL6JFI+D1W3u8LRtBs4kxMELTbGD09RMbG5P0XQgixKhJAF+edJz3pSTzlKU/Bdd31booQx9WzdOFhuRqTEEIIsVnIdywhhBAbmfzWEpvZsfrscrVkTQPHcU4YQlUqOKJasin7wCpFIjaO4xCNRmi12niei2laS9VAFZ7n43kuuq4TicS6VdNl+66NMIBOdxCGk1V9DgIfUN3g+kZ8nyIRG9d1iESitNstPM/DsiwMIwz8ep6H63oYRtjnbPvkfc6yLCzLwrZt2u02lmWvqGgbBAGdTgff92m327iui2mauK6H47hAnXbbIBaLr6rSvBCmaWGaFtFojFarRb1exzTNbr8Lw+k+hmEQiUSX+rGxIfdJIY70gx8E7N8PpRLs3g179igeeEBjZgZGR+GKKxTL4zNMTMDDD2u4LjzlKYpc7vB82m148EF45BGNkRFIpxV33qnz8MMaN90U8NSnKiYn4cEHNarVcLpKBR7zGOjpUTgO9PRAJhOG4uv1cL6RSNgO04QDB2D5NF21WiWfT+C6ca65JoF3ZKjiUWKxGIZhkM/niUaj5I5s+CmQc4RCCCE2C/nMEkKcE9Eoxo4dACjkWLNZqGSSQNfxLSusat7be+IXuC664+BaFkEmc07e5zO9rlW//XbUe96DVi5jLC5i1mph9WFNw0ul8OfnaXzxizTf9CaSz3jG2W7+ptV++GH0Bx/EKBTCysmmGQYujyUaxU2nMfJ5gm9/m9bcHObJ+o447/imGV530DSU6x6/vyxTKgyraxqBaZ53nxN+tUrr//wfjIkJrIUFnKGhsOr7UogxANx2G2tqCmvfPgKg8P73k/mt3zprbUj97u/SeNzjcD79acyf/hTXcVBBEJ44i0bxn/IUoi9/OdFLLtn02998wQvwv/1t1Ows+vQ0wejoMQOjyvOwZ2fxUqmwv9o2WrO5ugGbNQ2UQvl+uL1WEUjd7Nt1s6p++cvozSZGo0F7eBjtJO+vm81iFov4Bw6w+O//jvrudzHvvbd7HAtSKYJnPIP4i1+MvWXLWqyCWAftBx+k/elPY3zzm2iO0328NjqKduONJJ//fPR4fB1bKMTZJ/cQis3kla98Je973/vWuxknJAF0cd6JRCIkk8n1boYQJ2ScYAQ6IYQQQgghhBCnR35rifNNWDXWwLYjtNttdN04bmXjVqt9RLVkqTK7ehrJZIJGA3TdwHVdXNfFWbrwGoaAI93K57FYbJ3be3656CLF4CBMTcH4+NE3s4SBVn2psrfXDUcPDDgsLBzdz8PgtommaRu4mrdGIpEAmui6juu6eJ6L44Q3ehiGQTQaxTQNIpEI0ehq+pxGKpXEdR1c16VWqxGPx7rbq91u4zgd2u0OSilM0ySVShKLxVFK0Wy2sCwTpSCRiGOamy+EnsnAz/zMyptsHnlEY2HhxK8bHlbMzsrI/qcjHo+jaaBpGr7vLR0/w2OnrhvdkGvYj09eFehCcu21h/vqt7516v1vZESxlHNY4WTzOtPlnshTnhIGs3/4Q41W66zOek3953+e+LNjzx7FW94ScOedGv/xHzrL96v88z/D614XcP31ikIB3vEOnenpY2/jD3xA5wMfOPb8JyfhZIOtHEuzadNs5nCcBPfcE+MXfmGOyy+vH3f6dDpNtVql0+kQBMEG/swUQgghhBBCiLNHsyz8q65CW1wkMj1N2/NOWI1XW1wkME1Ip4mco/D2mVzXavzXf8Ff/zXGxAR2qYSbStEZHQ0Dh46DUSoRPXAAp7cX/93vphGLkdjkVYDPFuenP0VrtzEcByeXO+kv8SCXwzp4ENd16Tz4IObP/uyatFNsHGZPD35PD3oyiVmp4A0MnPgFtRoEASqVQt+zZ20auYYan/88WqmEPT9PZ3QUenqO2o+0aBR3507sffswZ2Zwb7+d4JWvRD+L54oT110H111H58ABnAcfRLVaaOk0sSuuOK8Giog/6UmU9+zBaTSIHDqE5/v4g4OwfM1SKajVsGdnQSm8LVtw02nsyUmMUgk3CNBOcv5Pb7VQtk2QTEo15A1OFYvhCKiahraK69YqkUCfn8favx/vr/4KXBezVEJ3XZSm4cfjBPPzdG69lfarX0365pvXYC3EmlGKyv/7fxgf/ShmtYpRKGA0GmhKEZgm2uwswcGD1D77WWJ/8RfY27atd4uFOGvkHkKxmWyGDKwE0MV5p9PpUK/XN8UOKC5cvu8D8sVGCCGEEEIIIc4m+a0lzkexWAzf91EqrGxsGEa3IrJSqlstWdc1otEYkYiNrp/bfeCqqxQvfGHA299+fuxrmqaTTCZxHBfH6awI8GtaGIKORCIYhpxOP5s0Dd785oD+fvB9+PSnNT7xCZ2lQzkAhmGi6waWZeG6DqZpctVVNa6/vsBf/dWOFUVGfN/H933i8bBq+OkE+NbKcp9zXYdOx8H3wz63ZUubINBZXAz35VPpc7FYnFTKRSloNps0Gk10XetulzCcbxCJ2MTjCWKxGLFYrBsebrfbaFoHTQuDiRt5+z3a1q2Kt70t4NH3/O3bB29+s3HcYjSaBn/5lwH//M86d9116uur63DDDYovfWnzbKuzLRaLY9s+juNgmk53W2sa2LaNbUc2RLA1FoPf//2AP/uz9W/Lm94UcM01hzvly14G/+t/rf7zNJOBP/7jgL6+o59zHJ3vf//Y/fEFLwj4n//z8HKzWY0vfOHsbI+nP13xu78bJrGnp+F1rzs/vh8cy969Gm95i3HU4BZKwfvfrzM66vP3f3/88Pm5pdA0DcfR+Y//GGbHjgOk0/4xp9R1vVvtQQLoQgghhBBCiAuJ/aIX4X/nOwTz85gTE3jbtx87lFevYy8u4gwN4e3cSfLSS89Je073upZyHPz3vhdjZgarUqG9cycs3S+rASQS+LkcXrVKdGICZZp473kP6uqrTxi6v2C0WmjLo8qtZkBf0wy3axCgNvPIe+L0aRrccAP+zAz23r241SpaOn3MSZVSWAsLYRXqdJrks5+9xo09x5RCffGLmIuLBJEI5HLHnVTTddzBQSITE3iVCo0vf5nUC15w1psU2bmTyM6dZ32+G4amkfyzP6PxO79DR9MwZ2aIPvIIfiSCMk10x0HzPLxUCm90FO+667B/7udQ73wnanYWrVSCEwTyleNg1mp0duwguOaaNVwxcVoMIwyfK4Xi5FfzlOdhtttoQYDRbIJh4GazeLlc+Filgr1vH24mg/f+91MFCaGfRyof+Qj6Rz6CcegQZqOBl0rhbNmCMgz0ViscjGBxkU6jQesNb0B7//uxBgfXu9lCnBVyD6HYTOr14w8svlHImQRx3vne977Hgw8+yBve8Ib1booQx+T7Pvl8HoDBwUH5UiOEEGLDc123+/dyBT0hhBBio5HfWmKzO17wR9d1kskUjUYD0zRxHJd2u7Pi+UgkgmWFIelYLE4uBz/7s/D5z59Zm5QKcF2XIFBo2nJFdovnP1/nkksMkkloNs9sGRtJNBpWng4Cf8U6a9rGC2VpGlx1FXzve8d+bscOOHBg7dt1Kp72NBgZCbetZcEv/RI85Snw3vfCoUOHp4vFoigV4PsBmlbnOc+Zo7fX5ElPqvO976UA8H2PTqeNbduYpkk0Gj3jMN3oaFiZ/VyyLBvLslEqvNnxF34hRj5vc9ttp9f20dEcc3MWlmWG1Xg6HZrNFkGgiERsotEY0Wi0+7e2VMXCNC1iMY1Wq41tR/A8D9uOnHBZ/+N/wMc/flrNXOEpT4GHH4Zi8fRe//jHw1veAvH40Z/7j3kMvPjFcMstx37tE58IuZzOc54Dd9996sv+n/8TXvSisO0/+MHJp+/thULh1Jez0em6vnSuII5S4e1O2garkPLEJ8KVV+r096/ve2Ca8LjH6Rx5amXnTtizJxww4WR0Pezvw8PHPkZceSXcc8+xX3v99SuX+8xnwu23n0LjjyMahVe+Eiwr3Ae3bw8/g8bHz3zeG9XiYvheHMvb3x4OpLLWeW7D0JcGIHKXBhbR+MEPerjhhtIxp/d9n2g0imVZRKPR09pn5RyhEEKIjUyuawkhhDiezDXXULj6ajzXxdq/H33fPry+PlQuh6brqFYLvVDAKhbxcjkYHib+W791Tj5PzuS6VvVLXwoDY4UCzpYtaKnUMafTMhmc4WHsuTkoFOh861ukzlE1983EzGRQS++p3ulAPH7iFyxVidVNE6unR75fXKAyL30p9c9+Fre3l+jkJM7oaDha5BHnVZTrYk5NoTsO7tataM96FpFMZh1bffb51Srm3BxGrYa/dOw8oUwGdB29ViN46CGsl7xkbRp6nrGGhrA++EEqf/EX+P/93/jNJlqlAkFAkMmgcjlUKoX2ohcx8JrXoIKA0gc/iDc/jz0/j5NMoh2j+rwKAuyJCYJYDC2dJvWyl53wGCe/tdafvWMHQSKB0nX0ahWy2RNOb01Pg1KY7TatoSH8pcF3lo9c/sAAXqNB5NAhmJwk+OAHCa65hohUwt70OuPjmP/2bxgTE+idDp3du9FiMTSWBi7IZPAGB9Hm5ohMTuJYFu33v5/4n//52W+MUrTuvx+/UABdx9q2TfqYOKfkHkKx2XzoQx9a7yaclATQhRBCCCGEEEIIIYQQYg0drxrv8ZimSSqVotPpYBgOSgUrQn7LFbotyyKZhLe9LQzPPvww7N176u0LAp92u4PrOgRBQBCEDTYMnauvbjIwoLCsYV7zGoO//dtTn/9GY9vwZ38Gb3xj+G9dN045OLZjB8zOQrt9+DHDCOf5pS/BvfeevfYC3HQTvPzl8PrXw+Tkyude+lK44QZ4+9thbu7U533jjXDwIDzwwKm97ud/HtLp44d9j9TbC7/5m0c/vnMn/O3fhsHmT30KgoBuIFrXFc9//o9ZWJihXo9z7bWLPPTQxczNWQSBwrYtbDvCrl0x6nVjxXtxqp7zHHjVq+B974Ovf/3057NamqZz2WU6L3yhSaMB99+/MoR/pCAIlgLr2tIACeEtIn198Cd/Ap/8ZJI774zRarWoVCrouoHjdIjH49h2eJwwj1HlKKw2ry8F150TBtB/7dfCYPfkJHzrW6e/3j//8/C7vwv1OvzDP5z6vJ71LHjNa8J97Xh+6ZfCisyPHqwhk4Ff/dXw75/5GbjsMvjJT1a/7BtuCMPnmgb/63/BH//xiQPET31q2NZ3vhMeemj1y9lcVgbPBwagXAbHWb8WQViA59d/PTzW/+Zvwl//NXje0dNdfXX4Hv3RH5278PT//J9HFwTStLB973zniQd1MQz47d8O++rxPPOZ8N3vwo9/vPLx5z8fdu1a+dhFF4XHujMNob/lLUev06teFa7PyY7Dv/d7kM/Dv//7mbVh2UUXhdvoD/8QarWzM89T5R+74Pg5Z5oWpmnQ6YDnuZimxe239/DjHydJJn0OHIgyNtZh69YOj398hWazzqFDl/Cd72zh1luPHT5/5JFwO1oWHHFfqRBCCCGEEEJsbppG7p3vpPTGN4aByPl57NlZtKmp8Ee6UvixWBgs7e/HeNObSFx99Xq3+iju7bdjFgoowzhh9WEAenpQ8/NoxSLOF78IZxhA90ol/GoVPRrFGhhYEb7dLGJXX00zEsGPx9ELBYKTBND1QiGsZh2PE3vc49aolWKjsQYGMN/0Jvx3vQtX04hMTBDYNl46fbiSbLWKsizcnTsJLruM7G//9no3+6zzl6ozakGAOsa1hqNoGoFpogXByU8YihMyczl63/1unJkZGrfcgr93b3gCPpHAfNKTSD/veehLxzPNMDB//dcJ3v1ugnabyL59uL29qL4+sCxUEKCVSlj5PJpSuBddhP/0pxPds2ed11KcTPLpT6fyd3+Hl05j5vN4jxoI40hBu01kaZ/1olGCHTuOORiplkjgbN9OZN8+OrUazc98hsjv//45XQ9x7jU+9Sn0eh2zVqNz0UVosdjRE2kaangYr9PBmJuD//5v3IWF8DveWeDX69Q+9SmC225DX1gIL6JoGp6uU3/sY7Fe+ELSz372pvw+KYQQFxpNqVO93VGIjetv/uZvqNVqpFIpqYAuNizf95mfnwdkRB0hhBCbg4xeKoQQYjOQ31piMzHNOL7f6v5b12PceOPK1Nly2CcahSuuCKua+j58/vOK/fu9bgD9cMXLMJT8zGeG4XOARgO++lX40Y8gmQxDRIcOQWmpGOZLXhIGQL/0pbDSczYLP/MzLT784ToHDui4roPn+QwO1kin2+zaVeDJT27R19dLJpNhz549HDhg8ZnPwHe+s7wu8LKXhdVm77gDKpVw2a4b3gPx/e8fXsd4HH7xF8M2TUyEYWMI23/xxeF9KPfeGwZFATqdcLvs2RNeg7zvvnCbpNPhv6enw9D9smc9Cy69NJz/gw+GbfD9MFy97OabwyC3ZYXBv/e/f2Ul5IsuCtdpdBSuvTYMZe/dC62lt29wMKw2f801YdDyU58K29HXFwZU9+wJl/nFL4brcmSVZ6Vg//7w70Qi3BbXXw+f+1xYvTwWg+uuC9vwmc+E4daenrBi9DXXHN4mt90WBloNI1zmE54Qbo9qFT7/+XDdjxdqXO4XmUy4vGc8I3zvfF/xiU80uP/+gPCeIIP+/giFgrnUdoXjhAMUDAwEXHmlxo03xtB1jW98Iwx4Lnvd68J2f/3rYdB4586w7yUSx27TsokJ+MIXwhB9b6/iyivniERmaLfblMvlpaC0yU9+Msr8fB+xmM5LXpLiaU9LUSiE2/yhh46+n8nzwj6RzYbv05GGhuDJTw7Dwkv3m3L33WH/np8PA6y7d8PWreG8JyfDfWdwMNzf5ubC9br66jDI7/vwr/+6Msz/mMdAJBLuG7GYTzIJl16qeMELzG6QulqFO+8M+0xPDxw8qHjooTAY7nkehqEYGXHQNEinba64IsYLXhClvz9s8513wg9/CHfcUader+N5HqlUnJERl6mplcHyJzyhzmMe0+TKK2t8/esu992XIJ3u4RnP2MLP/mzY9267LZw2lYL//b9h+R5Lz4NvfAO++c2w/3leuI3274eFhZXbdvv2w6H6q64Kt/FTn3o4PK5UuE/feuvh48TgYDifwcGwrx75Pl1/fXhcXA3fPzwQRK0Wvgc33BDOZ5lS4eAJ//qvhx/LZsP9/8j99pJLwtDuddetvBei3Q630/33h//O58N2Q3js+LmfC6tfdzph3/zJT8JjxrL+/vC5ZDLsR4cOhQHuqanD01x2GfzWb4X76y23hNv54YfDtl90UdgnL788XM/+/nD9fuZnwnV46KGwTQAjI+HnSjodvr/Lwfvrrw/3zXodvvKVcB+s1cLBNSDsi7/0S2Hf/dGPwtclkyu3I4QDTFx1Vfh5VCqFbd23L1y/I+Xz4byW7doVfk5MTob75o4d4br5/sow8exsuL9ZFjz96eG+du+98OhBwbdsCY+Vz31u2KZl+/aF63fgQDjv/v7wuPmsZ4XvaaMRvpc//Wm4LY5lYmJlsH7XrvC1w8Phe9Fswn/+5+Hnr7su/Ly9/PJjzw/Cz44vfjEMv4+MuOzcWeKHP/SZmDDp7/f5+Z+HpzwlRyRy/MEhIGzXV74SHgOSyfCY/rSnHfveHaXCffhrXwvfU4Bt28I+dskl8MIXhtt2+Th4pOc8J3yPL7ro2O0YHw+PRQ89tDLwr+thH3zuc8NlQLjP33132N8TCXjsY8P39tvfDpc/OBh+9kWj8F//Fe4jO3aE+8gDD4R965JL4AUvCI9BpdLhz81OJ9z34vFwG3heeDw6cODUBp3Y+BTVao1Wq4Xve8Ri8eNWNe90OnieSyKRIJFIrupc3xe+ECcIDn93te0Ync4JRkwQQggh1plc1xJCCHEyynUpf/jDqC98AT2fD3/Mh6NhQiyG/4QnEHvFK4g//vHnrA1ncl2r+OIXY95zDwDBKqpH6gcPgmHgXXMNPR/72Cm3NWi3qd1+O97nP49xxCiMQTaL9uxnk3zJS7CWT4ZtEoU3vAHjjjuwp6dxdu06bhV0Va0SPXSIzs6dBC99KT1vfesat1RsNLWvfhXvr/4KrVpFy+fRGw00pVCWhd/TA5kM/hVXkH3nOzGOPLF9nvBrNWo33oj58MP46TRqePjELwgC7AcewNmyheAXf1H2oTVW/vCH4V/+BW1hAbNQQHcclGGEAwjoOl42SzA0hP+Up9DzF3+BdpLfT/Jba2Movfe9aB/7GPa+fXiZDMHo6FEXAZTrYj/4IPbSMaq+Y0d4EeMEjH37wmPZpZeSvfVWtNUMMiE2JOX7lJ//fIyf/hTN8/AfPUrxozWbRB55hM7FF6Ne/Wpyv/7rZ9wGZ2qK+hvfiH7wIPriIka5jL50wSiIRPB6e1G9vfjXXkvPO9+JZttnvEwhlsk9hGKz2QxZWAmgi/PKZtjphJAvNEIIITYbOXkshBBiM5DfWmIzOVEA/Rd+IQz4rQfHcXjwwQdZXFxkdnaWdDpNT08PsVgMpRSNRoNCoUC73WZ0dJS+vj4uueQS9FMtFy42LKUUc3Nz5PN52u027XYbpRSWZRGJREgkEhiGQbPZxHGc7m8Fy7KIRqP09/czMDBwzvqE4zgsLi5SLBbxPA9N04hEIvT19dHT07Pp+uJqfmt5nsf+/fupVCpUKhXK5TKO46BpGtFolFwuRyqVYmBggLGxsRWBw0ceeYQDBw7QaDTYtoobURcXF6nX62zfvp0nPvGJxw0vCiHOPtd1qVarHDp0iFqthqZpNJtNgiBA13VSqRSxWIxsNsv27dvlu+4ZKpXgN39z5UACq6GUwnUdHMclCAI8z8P3fUzTwDRNdN3Atm0sywTW9hjqOB2azSatVgulFLZtY5qHP1uCIMBxOnieTywWIxKxSSaTq2rnowPohhFjZqbJWSqCIoQQQpx1cl1LCCHEainPo/HNb+Lu2weOg5bJEP+5n8NeHuX1HDqjAPpNN4UBdF0n2Lr1pNPr4+NhtcknP5mej3/8lNrZGR+n8eY3o01OohcKGOUymueBruMnkwR9fahMBuMNbyD9nOec0rzXU/OHP8R5/evRDx7ErNVwhoZQPT1oS+eYle+jLy5iLSzg9vYS7NhB/J/+icj27evbcLEh+LUatVtvJfjiF9GnpkApVCyGuvpqoi9+MfHLLz+vq7kWXvEKzG9/G7NUwr3kkhOva7GIPTOD89jHYvzhH5K64Ya1a6gAoP71r+N89KPoe/eGI7AuHcNJJAgGBtBf8AIyv/IraKv4HNoov7Xae/fS/PSnUY88gtZqQSKB9sQnknzxi7EePWrueciv1Si/9rUYDz6IffBgOJhALodKJFBKYVSrmOUyeB6674NStK+88uTHpeXPvcc+lvjHP449MrIm6yPOPq9QoH7TTVgPPhgGvVdxMt964AHcgQGCX/gFet7xjjNbfrFI9dWvRn/kEezxcQLbxuvthXgcpRR6pYJVKqF0HXfXLvzrrqP3z//8vP7sFGtL7iEUm81myMLKsDRCCCGEEEIIIYQQQgixQaxnfnZ2dpZqtcrs7CzDw8Nks9nuc5qmkUwmSSaTzM/PMzU1RSQSoVAo0N/f353OcRxqtRq+76PrOolEglgstg5rI06V7/vs27ePUqlEoVCgUqkQBAGapqGUwjRNGo0Guq4Ti8XwfZ/l8W11XSebzdJoNCiVSuzevRvzHIyKb9s2IyMjjFwgNzwEQcAjjzxCsVhkcnISgFwuRzQaBaBerzMzM0M0GsX3fQC2HnHDqWmamKZJp9NBKXXSQHm73cayLEzTlPC5EGuk0WgwPz9PoVDoDjZRLBbxfZ+enh56e3uxLItCoUA0GmV4eJhOp8PFF18sN0ucgVwOnve8sFL6ajlOh1arTRAEtFqt7iAtSoWDBGha+PkYjUbQdYN4PLYiAH6u2XYE3w8A6HQcOp0OnU4HXddRShEECsMwiMfjWJZJPJ7gdEPySsH998P115/FFRBCCCGEEOIccRcWcGdmQCnMwUEJ0ogVNNMked11cN11692UU6JyOVQkglGpECh14rCOUhjNZhhMy+VOaTnOzAyN3/s99AMHsCcmUIYRzicSQfM89EqFyN69YUD7L/6CmmFsmnBp/IlPxH3d6wj+7u9wZ2aw5+Zgbg4/Fgu3WasVhqIGB1EjI1hve5uEz0WXkUqR/aVfCkeUVgrleSetHH0+MZ//fNTevWj5PFo+f9xgo/J9rPn5sEJzby8ZOZm0LpY/51o//Snt73wH6nWIRLAe8xgST33qqoLnG4W7sED1ne/EuPde9FoNvVoF30eZJsG999L4xCcIrr+e7JvfjL50Le18ZKRSpN/zHmpvfjOdSAS9UMAsFtHzeZSm4UejOFu2oNdqaK0WehCsLtir6+HJX0Cd6uitYkNRS5XGAdQq93Gl6+EVg6Vrzmei+k//hH7wIPb4OG5vL2p4uNsHNUAlEnQGBzEPHsQ6cABMk/pdd4XHKyGEEBuSBNCFEEIIIYQQQgghhBBig1ivALrv+xSLRYrFIul0ekX4/NEGBwep1+uUy2Xy+Tz9/f3U63Xm5uaoVCq4rovv+93q1MlkksHBwRPOU6y/gwcPUigUmJiYwDRNhoeHSaVS3Sq8P/nJT5iZmaHT6TA4OMill15KT08PEAahS6USlUqlW4V7z549m64i+UYzNzdHuVxmYmKCVCrF0NDQimB4Mpmkr6+PqakppqamMAyDbDZLOp0G6P49Pz9PtVolk8kcd1me51Gv1xkdHZV9VYg1sri4yMTEBNVqlQceeIC5uTkWFxeJRCL09/djGAau69LT08Pw8DALCwuMj48D4TH7oosuWuc12Nxe8hK4/XZotU4+bbvdpt1u0+m0abVauG5Y+TwcjCVA03RM08TzXDqdCLFYDKWCpbC3fe5XZkksFsMwdHTdIAhsPM9bGixGwzAMDEPHsixisRiadmaf0aXS2WmzEEIIIYQQ54RS1O68E+ezn8W4777uww5Qe+xjsV74QlI33LCpAldCHMm47jrUAw9g5PN49TqkUsedVlWraK6L6u3FfPrTT2k5tb/6K4xDh4iMj+P096OGhrrhIQX4AwP41Sr2xASupuH99V/jP/nJGEvnJze6zM03U8tkcP/xH1ELC1AqobXbAPi9vZDNEmzZQvQNbyB+1VXr3FqxYWnaBRU+B0g997mU//3fcapVIlNTOK5LMDBweDsoBbUa1swM6Dr+yAj6S16Cdg4GLharF7v0UmKXXrrezTht7vw8td/+bYwDB7CmptAdBy8eR5kmRr2Onc/jxePQalGanSX3nvec1yF0q7+f3Ac/SPW22/A+/3nUvn2Hn9R1/CuvxPV9rK9/HfPgQZTrnvRYpbXbqKX91DzFQWvExmLmcmAYBJaF3mwS9PaecHrluhiOg2fbcIbXiYNmE+1rX0NfWMCPx1eEz4+kGQbejh1EHnwQikU6t9wiAXQhhNjA5Ju8EEIIIYQQQgghhBBCbBDrldctl8s4jkO9Xl9RQfl4crkcxWKRvr4+JiYmyOfzVCoVSqUSrSOSXLZtk8vlqNVqDA0NMTo6ei5XQ5ymarVKuVxmcnKSWCzGli1bVgSd6/U6kUgETdO6le2bzSYDS1UtstksmUyG+fl5Jicnu9V6+/v712uVNj2lFPl8nmKxiG3bR4XPl5mmyejoKPv376darZLP51cE0CORCNlsloWFhaUg5NE3lyilmJ2dxbZtksmkvG9CrIFSqcT4+DgzMzMUCgUcx8GyLHbs2EFPTw/1ep18Pk+9XkcpxcjICKOjo0xPTzMzM0M0GqXVahGLxdZ7VTatVApe9CL4j/848XSu69Juh8Fzz3PRNH1pgBVFLBZF1w1838dxHAzDRClFp9NeCn5DKmWg62sXarHtCLZt47ourhsG0DUNDMPAsmwZHEYIIYQQQpz3gnab0tvfjvHf/41ZLGIUChidDgC+baPl8/g/+QnFL3yB7J//OcYJgrtCbFTJG2+k9rd/iwoCYg8+iJNOo2Ixglwu/MG7HBLvdLCnp/GyWVQ6Tfp5z1v1MjoTExg//CHm7CxuNhuGh44lncYZGyMyPk6nv5/a5z5H9ld+5Wys5ppIPfOZqOuvp3HXXXTuuAOKxTBQPDhI5LnPJfHkJ6+uaqwQFxA9FiP+539O6/d/n46uY83OohUKYdDQMNA7HQzHwU2nCcbGUM98JrlNdFwQG5BSVP/gDzD278c+dAgvk8HduRPscPDPAPCaTYyZGaylIHb5b/6Gnre9bR0bfe5plkXmppvgppvoTEzgLy6iWRbWli2YPT00f/xjnHvuITBN9GIRNTh4/JkphVUq4Q4M4F9yCcYJBrUWG59m2/hXXAH5PPb0NJ2RkRMOvqUXCgSWBek0sWc+84yWXf/qV9FqNcxqlc62bce8vt1tp2HgZrOYxSL8+Me4CwtYS/cfCCGE2FgkgC6EEEIIIYQQQgghhBAbxHplgjzPw3VdlFKrCrPFYjFc16VSqVCv16lUKtRqNXK5HENDQ9i2je/7VCoVFhcXqVarKKUwDIPh492oJtZNPp+nWq3i+z7Dw8MrLgQHQUC5XKZarZJOp4nFYlQqFVKpFO12m+hS9QBN0xgcHKTRaFAul8nn8xJkPgOVSoVOp0O5XGZkZOSEF+dN0ySTyVAqlchkMriui2VZaJrGyMgIjuPQ6XQ4dOgQvb29ZDIZDMNAKUW9XqdQKOB5Hlu3biWXyxGPx9dwTYW48CilmJiYYGFhgXq9TiaTwfd9arUafX19WJZFJBIhlUoxNzdHsVjEMAzS6TSDg4Ps27ePRqNBPp9f1aAx4jDf97vfdyzL4sUvNvnOd+DQoeO/ptPp4LouQeBjGCa+7xAEPpFItBvmNk0TXddotztEoxEMw8R1XUzTpNPpEIudu+Pqtm0wPv7oRzUsy17T6utCCCGEEEJsBMr3Kb31rRjf/jbWgQPg+3i5HN7S+UitXsdaWEDl87iOQ/nNb6bn//7fC65yrdjcqrfdhvfBD2I0Guieh+F5xAoFfNMkKJXwo1HcwUH0dhu7UMCPxQjGxtBf8Qr0Uzjv17z1VrRGA6PVwt22jRNGsNPpcICHxUX8L34RNlnQVDMMktdfT/L669e7KUJsGtGLL4a/+zsaf/Zn8MgjUKmg1+sQBPixGF5vLyqVgptuouc1r5GBHMQZaXzvexh792JNTuJlswRjY0dPFI/j79oF+/djTE3BnXfiveY1mCep/Hy+iGzdCo+6XhC//HJaO3eizc9j5fM46TQc6z4ApdCmp0EpVE8P9otetDaNFudU9MUvxv3ud1Hz8xgTEwTbtx/7WNxoYOXzuIOD+Dt3ErvssjNarj8zg9ZqoQHa0qDpJxKk09jFIp5SuNPTEkAXQogNSgLoQgghhBBCCCGEEEIIsYZuuOFHtFqq+29N07oB8MXFGhMTbeLxOLlcDuMEI1GfK2GlzBPfCKKUQinF/Pw8kUiETqfD9u3bu2FkAF3X6evrI5vNdqu8GobRDdddKNrtNo7joGkakUhYlXQjWQ6Yl8vlbjD5SI1GA8dxaDQa9Pf3E4lEKJfLtNttqtXqivdc0zRyuRyFQoG+vj6azaaEmU9Tu92m0+mglCK1ikpcqVSKcrkM0K2kDNDX14frugAUCgUKhQILCwuYponneQBkMhlGR0fJ5XJs3779nKyPEOKwUqlEp9OhWCwyMjJCoVCgXq8TjUZXfD7atk1fXx8LCwtks1mq1SrZbJZEIkGtVqNer6/jWmwu9XqdhYUFyuVytzI5hMe/N76xn899LsMPfgClEkSj8JjHwM/9HPzXf7ncdZeH6zqYponreniei2GYR1US13UDwzBwXY/+/g79/R6+32BubohodOV3q23bYGEBhodhbAzuumtle3/2Z2F0FH70I5iagosvhpe+FObn4fOfD4uxDQ7Cr/0aNJvwznee/raxLLjqqvC+s299a/WvO2IzCiGEEEIIsWFUb70V43vfwzpwgMC28XbsQDviu7tKp+kMDWGMj2MdPAimSeXjH99U1ZrFha38b/8G//RPGNPTWMUiAIFhoPk+puuiOQ5Bo0G0WKSTSuEODqL6+1EvfznZl7/8lJYVHDyIWavhR6NokciJJ9Y0/HQavdFAzcyEPxolbCrEeS+6axfRf/kXmvfeS/tzn8MfH0dzXVQqhXHNNaRf8AKMVVzfEOJk2p/5DEa5jOb7+CMjxx8URdPwh4eJ7N+P32hQu+UWcr/xG2vZ1I1F04i84hV4Bw/itVrY+/fj9vcT9PWF1bCVQjWbGPPzmI0GzvbtBLt2kbnhhvVuuTgLEk95CoUnPhHabawDB9AfeQSvrw9yOdA0VKeDvriIVSziZbOooSGir3rVmS84CE5p8iP3Z3WKrxVCCLF2JIAuhBBCCCGEEEIIIYQQayiZvBhz6cys6zq0223q9Tq+7zE/P8u+fQUSiQSRSITe3l62bNlyVMjpbItEIkQiETRNo16vkz7JaNRhe32UUriuy+jo6Iog8pFM02TLli0cPHiQdrtNoVBgaGjoXKzGhhEEAYVCgXw+T6vVIggCNE1D0zRSqRQDAwNks9n1biZAN4Tsuu4x2+R5Hp7noZQiGo1iGMsBOxff94+aPh6PMz8/360yK07P8iAPy/3mZDRN674meNTF+eHhYaLRKPF4nN7eXprNJp7noes68XicSCRCf38/w8PDq1qWEOLMFAoFKpUKpmmSSqVYWFggCAJM8+jLtvF4HMMwqNVqlMtlstkslmXh+/4xj8FipeVq8/l8vrsN2+02SikikQjZbJZ0usRznpPjt35rB7oeDsKyfCi87LIyz3nOIxw4cIDe3j7m5uaYnJxky5Yt2LaNpq0MYi9Xpt+xYzt9fX0UCgX27LmYiy++mFQqddx779/whpXzWZ7uF39x5XSXXgqPLsZWrZ7+9snl4G1vgz17wn9/8YvwgQ+c/vyEEEIIIYRYb97nP4+5uAhKHRU+X6bpOsH27egPP4yWz+Pfdhv88i9LWFZsePVvfQv+6Z/QDx7EaDZxtmwJf9iVy5j5PEazie44GK6Lb5pomoZz5ZXEXvnK06vs7XlhMG2V1yaUYaApFb7GddE22ECsQohzJ3755cQvv3y9myHOY9pPf4peLuOl02Fw+kQSCXzbRiuV8H/84zVp30aWvO46Kq95DcE//iPu3BxmoYA2P09g22i+j+77eIkEzq5dBDt3kvrLv0Q7xrUKsQlpGtl3vYvy618PhoE+P489PY02NYXS9XBAh2gUZ3gYBgfRf//3SVxzzRkvVu/vRy0NXqSaTbRE4sTNbDQIlr43WoODZ7x8IYQQ54Z8OxBCCCGEEEIIIYQQQog1tJwNbbfb3erYruuilKLRqDI/P08QBCSTSVqtFo1Gg927d5/TauiZTKYbxCoWiycMoPu+T7lcxjAMgiDAtu2TBtaj0SixWIxyuXzeB9Bd12Xfvn1Uq9VuZfHlIHYkEiGXy1GpVOjt7WX79u3nfHCBkzkycKxOoZzpaoPR4vRYloVt2yilaLfbxx3gYVm73cayLDRNW1FBeVkulyOXy1Gv16lUKvi+3w2g53I5eS+FWEOO49DpdIjFYmiahmEY6Lp+3EE7YrEYjuN0BwzxPA/TPLoCtzja+Pg48/PzTE5O0ul0yGQy5HI5AJrNJgsLCywuLjI2NkYQBOzevXvF8fDIoL9pGgSBj6aBZZndbMqRh0/LMoFwIBDDMJYGBfG7rzuR0z0Mp9Nw5ZXwgx+c2ut27oS3vx36+g4/9qQnHTuAnsk8Ac9rAaDrGrp+4s8kIYQQQggh1kP74Ycx9u3DLBbxenqOGT7v0jS83l6shQXUzAyNe+4hceWVa9fYDcIrFKjdcgvqW9+CSgVMEzU6SuT5zyfxtKedPOAl1lTn3/8dc3ERs9Ggs2sXWiwWPpHL4eVyeM0mWr0O9TqRYhFnZATjqqtOL3wOaJkMyrIwOh38IICTnIfQWy2UZaFiMQmfi81HKZr33Yfzox+hGg20eBz7CU8IQ9Vy7lyIdae1Wmi+T7AUaj0ZZdvg+9BqneOWbQ6Zl7+c+uAgnQ99CDUxAZUKmuuiNA2SSVQySfCzP0vm934Pc+n8uTg/GMkkuf/7f6l86EP4t99OUCxCoxGOiGtZEI/jX3YZsVe8gvhVV53y/JXn4ddqGIlE9/tf4hnPoPaBD+AlEhj5PMGJAuhBEP5+6+8n2LMHe3T0dFdVCCHEOSYBdCGEEEIIIYQQQgghhFhDQRCGz9rtdrc6tm3bWJbJyMgQe/bEaTQaLC4ucujQoW6Iaffu3eesTZqm0dfXR6PR4NChQ8zOzjI0NHRUKNX3faampjBNE9M0uxVEVxNejcfjdDqd87oqtu/7PPLIIxQKBSYnJzFNk56eHmKx2NIAA2Fl1GKx2A207RCt5y0AAQAASURBVNy5c13bbJomhmEQi8Wo1+tHVUG3LKsbbG6325imied5S3326KBzo9HAsiwMw8CWGw1PWyaTwbZtEokEpVKJ4eHhE05fKpXIZrPEYrEThtWTySTJZPJsN1cIcQZisRjxeJz5+fluuPx4PM+jXq8zOjoq+/JJlMtl8vk8k5OTaJrGRRddtGLbplIp+vv7mZmZYXJyEsMwWFhYYPCIChvLgwMA3YE7Hv33kTzPQ9M0dF3vfs4fOY9z5Vd/FR55JMyLrMYTnwhveQs8+uOiry+c10c+svLxa6/9Jp1OB2DV3/uEEEIIIYRYa+74OHgeuuOgTjJYJgCZDPrMDDgO/sREOLLThUIpSn//92i33ILWaKAXCmiOA5pGcP/9eN/7HqWhIaJ/8AfEn/jEY87CmZmhccstBPfcg1avh0GvXbuI3XSTBDbPgfb+/Rj3349RKOD29BwOnx8pHkfF4zAwgBsE6PU66stfRr32tacVCLee+lT8r30NZmehVILe3uNOq1wXs1bD2boVdfXVp7wsIdZT9fbbcf/zPzEOHIBmE83zwDRx4nFaO3ZgvexlpG+8cb2bKcSFLRoFXUfzPFYzlLbmuhCLHX0C9AKWvP56kk9/Oo177qHz9a9DtRoONLNtG6nnP1+C5+cxPRol91u/hfrN36R25534Bw6A46BlMkSf9jSiu3ad0vyU51H7yldwb70V44EHwjA7EGzfjrr6aoy+Prx0Gj0SwV5cpFMoHPt7pFLok5Phn729mC94wRmvqxBCiHNHAuhCCCGEEEIIIYQQQgixhpYrGjtOhyAIiMdjaFoYTFq+Ly+RSBCPx5mZmWFqaopIJEK9Xj+nYbOhoSEqlQpjY2NMTk7SaDTIZrPE43GUUtTrdcrlMpZlsXXrVmq1Go7j4DjOquavlDrvA0tzc3NUKhUmJyfJZDIMDg6uWOd4PE5PTw/T09NMTExgGAY9PT1Hhb7XkqZp9Pb2UqvVGB8fp9PpEDmigkAikcC2bVKpFJVKBdM0icViRCIRMpnMinkppbpB6Hg8TuxYN0KKVbEsi1wuR61WY3JykmQySSqVOua0CwsLeJ5HNpulv79/jVsqhDhVtm0TiUQoFosopchms5RKJUzTpFwu03dkSWqg1WqRSqUwTZPFxUVM0ySZTMr+fhL5fJ5qtYrruuzatQvjGJX7dF1ny5YtHDx4kGKxSCKRWBFATyaTRKNRbNvGcRwsy8K2ber1ereS+pHq9TqxWAxd1+l0OiSTSQzDIB6Pn9N13b4d3vMeuPVW2LsXHAdME8plmJtbOe3//t9wzTXHz4LcfDNs3Qp33QV3331Omy2EEEIIIcRZpXy/G35QhsFRX3mDIPwivPRlWC1/KQ4ClOetXUPXm1IU/uiPML76VYzxccx6HT8SIYjFIAiw5+Zgbg63UKDz5jej/vRPSTz5yYdf7jgU3/1ujDvvRGs0MIvFMKyp6wQ/+QnOf/0Xrd27Sb7jHUS2bl3HFT2/dL73PWi1MNpt3O3bj+7fj+L39GBPTxPUarQefDAcFOAUJZ/+dEr/8A94uRzW3BxOKnXsILtSGJOTKNOETIboi198yssSYl0oRem970X7zGcw5+cxS6VwEBNdRwsCAstCm5sjOHCA4oMP0vOGN8jgGkKsk+Dii/EPHcKenaUTBGgnGvBz6fPS27oV/dJL166Rm4GmkbjyShIX0sBLokuzLNLPfvYZzaMzPk7jLW9Bn5zELBTQy2V03wffh/vuQ//sZ1G6Dn19WMUimu8T37ePTj6PPzKCSibB99GqVczFRTTPw92xA//SS8k897lnaU2FEEKcCxJAF0IIIYQQQgghhBBCiDXU6XgEgY/ruktVJA9fJD/yermmaQwNDfHII49Qq9VYWFg4pwF0XdfZvXs3mqZhWRaVSoVSqUQ+n0fTNKLRKIODg6TTafr6+ujv7+fQoUOUy2WCIDhpdc9Go0EqlVoRbj6fKKVYXFykUCgQiUQYGho65nSGYbBlyxb2799PtVoln8+vawAdoL+/v9u/Jicn2bp1a7d6uaZpZLNZWq0WDz/8ML7vc/HFF3fDkMuUUszMzHTDlBKMPHPDw8NUKhXa7TbT09Nks1lyuVx3H6rX65RKJRqNBmNjY6RSKXpPUIlICLEx9Pb2UiqVWFhYoFarkU6nSSQS9Pf3Mzs7C0Aul8MwDJrNJr7vE4/HcRyHdrvN1q1bSaVS5zzUvJk5jkO1Wu0OinKs8PkyTdPo6elhYWGB/v5+arVad8CPWCxGMpkkl8uxuLiIZVmkUilKpdJRn4Ptdptms8nw8DDRaLRbqT6Xy52wqv3Z0tcHr3zl2ZnXk54U/nfffauvqi6EEEIIIcR6M/v6cEwzDJ/X6xCJQLOJXihgVipovg9AYJp4uRxYVhiOsG30C+h8SuXjH0f/6lcx9+0DoL1rF1oi0X2+EwToi4vYs7M4vo/zR39E5GMfw+zpQbkuxde/HuMHPwjD680mfixGEImguS72+HgYQq5UaP72b8Pf/R2R7dvXaU3PL0G9Hgb9NW111cwjEbQggCAgqNVOa5maaWL86q8S/O3forXbRB55BLe/n6C3F80wQClUtYq5sIDuOLi7duFfeeVphd2FWA/lD38Y7dOfxty/H91xcPr6UD09aJaFcl20YhF7cRG/VoPPfpZSNkvuN35jvZstxAUp8qIX4X3/+zA7iz43hxoZOfaESmHMzuLF45BIkJRBUcQGELTb1L/8Zbwf/hBVr6PF4+iXXkrqxhsxzuG9H2ebMz1N43d/F/3QIezJSdA03EwGWi2sWg3DddGDAKVp2PU6rm2j+z5K04hUKgT1OsqygHDAMC+bJRgYCMPn73432hpcRxFCCHH65CgthBBCCCGEEEIIIYQQa0QpcF0XzwtveHx0IEnT1Ip/G4ZBJpOhUqlQLpfPeftM0+Tiiy+mWCySz+ePCrNmMhn6+/vJZDJUq1WKxSLz8/OUy2V6enqOO996vY7jOORyufM2IFupVOh0OlQqFbZs2XLCaQ3D6Fa8zWazOI7TDXyvh2g0ysjICEEQMDU1xYEDB0in06TT6W4F91KphGVZ3cEJxsbG6HQ6wOEgtFKKsbGx8/p9XkvRaJRdu3ahlCIajbK4uMiBAwfQdR21VNErlUqxfft2stksF1100UkHghBCrL/lgSR6enqYm5vDtm1GRkZwHIfh4WEWFhao1+tYlkWxWMS2bebm5tixYwdjY2Nks1l27Nix3quxobXbbYIgoN1uH3dAmCMlk0lmZ2fpdDq02+1uAB1gcHCQWq1GrVajUqlgmiaRSITZ2Vn6+/uJRCI0Gg0KhQLpdBqlFLVajZ6eHlKpFAMDA+dyVdeNUiefRgghhBBCiLUUe8ITaPf14WUymIUCQb2OVS6j+T6666IphSIchFN3HAzHwUkmUek0iWuvXe/mrw2l8D/9acy5ObQgwNmzJwwSH0HTddTAAB3LIjI5SSeXo/aZz5D7jd+g9L73YdxzD9a+fSjLorNnD8Ri3dd6ros+P09kfJyOUjTe+lbsf/s3CZOcBVo0Csuhb89DWwrvHNdSWB1dRzviPTpVmZtuojQ3h/+xjxHMzWHm82hzcyjLCgd1UAovncbduhX/8svJ/emfSoVosSn4tRrqYx/DmJxE8zyc3bvBtlnuvZplweAgTk8P5r596BMT+B//OP7LXoaRTq9r24W4ECWf+lSKW7fiVCpEJiZwgGBwcOX3GMdBn57GaDZxdu3Cv/ZarFWcGxbiTATNJs7EBEGng5HLERkbO/xdSClK//IvcMstaOUy+tJvE6Xr8JWvUP3Qh1A33EDud35ndQMMrbPau96FMT5OZHw8HLRleBitUAgroQcBmlL4pokeBPiWhYpEaKVSRPJ5AJRphq/L5SAaJRgYQHvuc8n98i+jR6PrvHZHU45D84c/xC+V0KNRrF27iGzdut7NEkKIdSNndoQQQgghhBBCCCGEEGKNhEVHFEotVwxfeTPWsbKjkUiEVquFUgrf909YxfNs0DSN3t5eent76XQ6uK6LpmnYto11xI1t6XSaWCxGX18fCwsLRCIREkdUy1nW6XSYnZ0lm81i2/Z5G0zudDo4joNS6pjb4dGSySSFQgGlFJ1OZ10D6BBW24bwJtzlQPnU1BRKKSzLYs+ePdRqNYIgIJVKdQcpALAsi2w2263QvWvXrm5wXZyZVCrFJZdcwszMDKlUina7jeu6QBhQj0Qi9Pb2Mjw8vCYVdoUQZ07TNLZu3YrneXiex6FDh8hmswwODnYHkcjn8+TzeSzLIpfLcdFFF7FlyxaSySQ7d+5c8XksjqaU6g7UsZrPoyMH71CPSlZns1mGh4e7n4fj4+M4jkOr1eLBBx/E9/1uZfRms0ksFqO/v5+BgQG2bt0qleqFEEIIIYRYI5ppoj3rWQTT00Tuuy+sFO376EFAYBj4pokGaJ6H3WyGFTI7HVrx+IYMPJwL9bvvRp+bwyyVcIaGjgqfr5DL4efz6Pk83he/iP+yl6HdcQfG5CSBZeHv2nXUyWzNslCjoziGgT01hZNOU7/rLlI///PneM3Of/Zll+HE4wSWhVYswuDgCafXi0W8ZBIViRDds+eMlp177Wupbt2K99GPoqamoFoNA+66DqkUKp2GZz2L3Gtfe8HsS2J9Kd8naDbRY7HTHuCiduutaLUaVqVCZ/t2ON71GcvCGx0lcvAgQb1O7XOfI/srv3L6jRdCnB5NI/Gud9F43evoaBrW1BRaoYCXSqFME91xMOt1AtvG2bWL4DGPIfvWt653q8V5rPXTn9L61KfQ7r4bzXG6j9e3bsV4/vNJPfe5VP7sz9C/8Q306WnMSgWl6yjTRPM89JkZ3FSKoFKhuG8fufe8Z0N/j2o//DDG/fdjzczg5nKokREIAqy5OfROBy0IcBMJ0DQC18V0HALLQqXTtIaHsR95hMCyCOJxeN3rsHbtInHNNSf+PbJO3Pl5ah//ONqXv4y29L030HU8w6D+uMcReclLSF53nQy6JIS44MjdSEIIIYQQQgghhBBCCLFGggC+8Y1RfL/VfcwwojzlKYcA0PWjy0kGQdANRq11deNIJEIkEjnu81u3bqXT6eB5HhMTE6RSKbLZLJZl4XkelUqFarVKMplkcHCQrVu3nvMA/Xo51bDbRgxoDw8Pk06nyefzpFKpFSG8aDRKX18fQRBQKBRot9v4vg+AaZrEYjEGBgbo7e3dkOu2mcViMXbt2oXrupRKpe6gEJFIhFwut+GrniulcF0X3/cxTfOEwVmlFJVKhWazSRAEGIZBOp1e1aAOQmwmuVyObdu2AeGAJKVSiVKpBIT7QSaTob+/n3g8zvDwMNu3b6e/v1/2hVUyTRPDMNB1nXa7TfQkN2612200TcM0zWMO5rFly5bucwMDAxw8eJC5uTkymQyO42BZFpFIhNHRUbZs2UIkEmHr1q309PScq1UUQgghhBBCHEP65S+n8q//ijIMrFYLpWm4kQgsfc9XQYChaShNA01D9zzM6WmaP/oR8Sc8YZ1bf+45P/oRer2OFgSonh5OdgbPz2YxFxcJFhcp/+u/YtTrmPU6nR07jj2S6hI1OIgqFNCKRZzPfhYkgH7G4pdfTnv7dry5OaxCAaen5/hV0JtNrFqNzvbtBE97GkYqdcbLT994Izz3uTS++1063/421Otg2xgXX0z62c9GP4Mq60KshvJ96l//Os5nP4vxk5+EF9sAf9s2zBtvJPm852Ekk6uen3/XXZiFAr5tw8n2kWQS37bDsOvXvw4SQBdiXUS2b4f3v5/G//k/uOk0lMvo1Sq646BMM/x+kkrhX3EF2T/5k1M6JgixakpR+ud/Rvu3f0OvVDAKBYxmE4IAZVlos7Oo/fup/MVfoC0NlqAAZ2wMMpkwtKwU1GoY8/NY+/aF83znO+l917vWe+2Oq3nLLei1Grrj4O7cCYBWLKJ5Hobn4UUi3UC2Mk2U64bPFYv4PT24Y2PY4+P4qRRaPE7yqU9dz9U5ruaPf0z77W9Hn5tDz+cxq1W05e8c0Sja4iLej39M8TnPIfeWt2zIAL0QQpwrEkAXQgghhBBCCCGEEEKINRJWQO+gVOeIxw7frHes3G6tViORSBCNRjdcsDeVSnWrXS8H6JarZgMkEglGR0dJJpOMjY2dt9XPIawCbts2mqbRarVOWvG02WxiWVZ48XkDVbJNJBIkEgnGxsbodDoopTBNc8VABMPDwzQaDRzHQdM0bNuWCq9rwLIsBgYG1rsZq+a6Lvl8nsXFxW7VdggHM+jv76e3t7c7IIVSivn5eRYWFuh0OjSbTZRSGIZBPB7vhnCz2ew6rY0424IgoFQqUa1W8X0fXddJJBL09vYeMwC83mq12lFtzWazZ/S53NfXRywWY35+nkwmg+u63UFnTNPsVkVPyo1ypywej2PbNtlslnK5fNJjR6lUIplMYts2mUzmmNMMDg7S19dHoVAgk8nQbre7A2Ykk0nS6TTRaJTe3l56eno2/OAgZ0odPWaSEEIIIYQQ685Ip1H9/ShNI9D1MIjuOHBEVcLAMHCj0TAg4vtozSatT37yggig02yGFfwMA20Vv1mUbXcDF+q++9DKZQLLgpP9TtV1/EwGvVJBf+CBs9FyoWkYN92EOnAAValgHTiANzYGR56TVQpVrWJPTeFmMpDNknjZy85qGxJPfjKJJz/57M3zZJTCKxYJ2m2MTEbChBcoZ2aG+lvegn7gAEaxiFEuo3ke6HoY9Nu3j+pHPkLkj/+Y+FVXrWqeWqmE1ukQxGInr2CqaQTxOJrjoC0NICmEWB+RbduIfPjDNL77Xdqf/SzB3r3h97x4HC6/nPjNNxO9+OL1bqY4j5X+5V/QPvxhjEOHMOt13HQaZ3Q0HJyp1cIsFtHz+bDKue/jJpO4e/asDCprGqTT+KkU7N+POT4Od99Ne/9+ort2rd/KnYA6cACtVsOPxcC2AdBLJXTPQ2ka6sjreppGYBjovo/ebuMDpNPh76/FRbyvfhXO5nfUs6S9fz+dt7wFY2ICe2YGLx7HGR1FxeNonodWLmPNzREUi7hf+AKlSISeN75xvZsthBBrZuPdwSGEEEIIIYQQQgghhBDnqaX79Y7r0ff91et12u02o6Oj9PX1nbuGnYFMJsNjH/tYFhYWSKVSeJ6H7/vdyqM9PT0MDAyc9wHlbDaLbdskk0mKxeIJ11cpRalUIpvNEovFTlqZdT0sB3+PZzmoLi48y8HhVqtFEATdoOyR/aVcLnPw4EGazSblcpl6vd6tgJ5Op6nX68zNzbF7924ikQgHDhygUCiwuLhIpVJBKYWu6/i+j2VZ5HI5Go0Go6OjDA0NrePai7Nhbm6O+fl5Op0OtVoNz/MwDINEIsH09DS9vb2MjY1tiABvsVhkdnaWVqtFo9Hofr4tH7v7+/sZGhr6/9m78zjJsrrO+59zt9j3iNyzKquyiq6mRVCbQUEcQBuRtVtQRxyfx2fGGRFwAQVFWWRxX0ZZHRUdRR1nkKZlR6S1aQZcGm3GRrq7sqpyz4x9X+56nj8iM7qKqsrMriWzlvN+vXhRHXEz4pyIGzdu3Hu/v98lB9FjsRhHjx7Fdd3R428H3K2ti3iUx08IQaFQoNPpUK/XqVarFy2C02q1aLVao47l+g4dK3RdZ2xs7LoqBnI5tlfrzc2/wnEGwLAYiqYZwEsObmCKoiiKoiiKchHdL30JfSskbUciaJ43DJlvh6i3QuleMok/OYm+uYleqcAXv4hXq2Fkswc8g6ssGgXDQPN9ZBDsGkIXjoPc/o3keeB5w3DJHn4DB5aF0e8jXBfpeYhrsNjc9SZ1551UH3oIpERfXCR08iReNEoQjQ5v63TQHQc3kyGYnUW88pXXbQjPLZXo3H038tOfRqtWR7f7J05g3Xkn8TvuuHgHeOWG4pZKdF79arQzZ7CWlgDwUimCZBI8D73RQC+XccbHsX/2Z+FXfmVvIXRdH27Ldjtpt227Ep/alt30vFqNzj334H/5y9DpQCSCduutxO66C2tq6qCHd3M4iIIoisIwoCw+8AH0xUU022bwhCcgzj7Hn0zijY2hP/ookU4HpMTR9Yt3yRYCb3aW8COP4HW79D78YcLXaKBZuC4iCAjOmovmefA1tz32B8NO70LK4Xet5+GHQojBAFGr7ePI9677zneib2xgra9jT0/D1jklARAKIWMx7LExzNOnMc6cwf/IRxi88IWET5w40HEriqLsF/VLSFEURVEURVEURVEURVEUZZ/s1i1SiMcW6Ha7rK2tkc1mCYVC13T38FAoxOzsLNPT0+cE6KLR6DXZyfZq0HWdbDZLp9NheXl5x46rxWKRIAhIp9M3TYhNuf4FQcD6+jqVSgXbtkcBdNM0iUajxGIxpqamkFJy6tQpisXiqBhDPp9H13Ucx6HRaFCtVpmYmCAIAsLhMI1Gg6WlJTRNY3JykkQigRACz/NoNptUq1V6vR4AlmWRvdEvCL+BLS4uUiqVKJVKNJtNdF3HNE08z2Nzc5N4PM5gMKDX6/GEJzxhxzDw1ba2tsbGxgbVapV6vT4qouB5HjAsPLIdTJ+fn7+sbuimae7apVt5fAqFAuVymenpadbW1uj3++RyOSKRCAC2bVOv16nX64yPj5NMJlWBi4v4539+OUHQH/23pkWA3sENSFEURVEURVEuwn34Yej30YKAwbFjw+64jQbCcYbdoS0LmckgdB0BBOk0ocVFfN/HOXXqhg+gW9/wDXh/+ZdITRsGP3YpeKo3GvjJJEE+j8jnQdeHXYel3DWErm2H1w3j4qEb5fERgtwb30gtmYQPfxi/20WrVNAGw4JhfiqFl88jEwn0V7+a5ItffMADvjStT34S/zd/E9FqoZfL6FsBLmkYiFIJ7ytfof5nf0b8139dhT1vAq1f+RX0xUWsxcVhcYWpqXOKZ3iTk3i1GtbaGi5gv+1thD/4QbRdiv7KiQlkNIqxda7mvOrQ5ywsh11ux8aQk5NXaGbK9Ua6LvXf/E3EZz6D6HQw6nWE7yN1HfkP/0Dvgx+k/fSnk37DG9ATiYMerqIoV0Hvwx9Ga7cxOh3s48fPDZ9vEwJ0HQkIKTEGAzzXvWjhHBEK4UWjaLUawT/8w9WdwGWQ8TjSNNHbbYI9/BYQQQBSotk21kMPIbb+DeD/27/Re/BBok95yj6MfG/spSX0Bx/EKBZxM5lR+PxrCdPEPXKE8MMP47fb9D70IcI///P7PFpFUZSDcXNc+acoiqIoiqIoiqIoiqIoinIN8P3zb5NS0u12MU2TXq9Dvd6g2WzS7/fJZDKMj48zNzd3XQS5NU0jcRNfWDE5OUmj0WBiYoLNzU06nQ6ZTIZoNIqUkk6nQ61Ww7ZtZmdnSSaTKkirXBd83+fkyZPU63XK5TLtdhshBLqu47oupmmSzWZpt9t0Oh1s26bRaHDo0CFisdg5j5XL5ajX62xsbOB5Hp1OBykllmUxMzNzTtdrwzDI5XIkEgmWlpYol8uEQiEymcxlhX2Vg7GxsUGpVGJpaQkpJTMzM8RisdF72e/3qVQqo/tPnz7N8ePHD2Ss5XKZ9fV1VlZWcByHfD5PMplE13WklLTbbarVKouLi0gpMQyDubm5AxmrcmG6ro/WH8MwqFQqLC4uomkaQgh83ycSiTA7O0sqleLYsWOEQqEDHvX1Y7eiSoqiKIqiKIpyEKRtj7qdi+1O3bkcZ+++nnM0QdeHO7dBQNDvc6OLP/OZ1MbH8dbXMctlnFTq4l2kazX0wQDv8GHEC16AZprIz38ebXMT2ekgdjoGLCV6q4U3NoZ/4sSeOqYreyQE2Z/4CZzv+R66H/4w3n33IRoNEAI5Po72nd9J8sUvvm7Dj61PfpLgV34FfWUFs1rFi0bxcjmkriNsG7NchlIJp9ul8+M/TuJ978MsFA562MpVYi8toX/pSxjr63jJJHJmhvO2JlvbeQeGHUtzOdqf/CSpu+7a8bGtF7wA/4EHEJubsFtBjmoVggCZy2F+13dd7rSU65B0HGqvfS36Aw+gr6xgdLsElkVgWWiui14s4kej0O3SWFkh/e53o6dSBz1sRdmVdBzcSmVYaCOX27V4x81Meh7is59Fq1bx4nHYKnR7IcL3CXQdIwggCIaFn8bHL7p8EA6jOQ6i00H6Pt3778f5P/8H2WohQiG0+XniL34xRiZzNaa2J/o3fzPygQfQi0W8dhuSSQLTRGoauusSnLWsDAJ010Vu/QbQfB/h+2hBQKBpmKUSzo//OP077iD7hjcMf7cdsN4nPoHodtH7fdzDh8/f3ziLsCzcRAKtUkHedx/yZ37mmpjDfuj/678y+Id/gE4HwmHM224j/q3fqn7vKcpN4ubY0imKoiiKoiiKoiiKoiiKolwDguD824QQWJaF57k0m3VqtTqxWIzJyUkikQhzc3NkDvCEorJ3lmWNwm6WZVGr1VhZWUFupbSEEKTTaaampkZhN22nzhqKco04deoU1WqV5eVlQqEQMzMzxONxADzPo9FoUC6XKRaLw65hjsOxY8fOC59vy2QyuK7LqVOnME0TXde57bbbLvp5sCyLQqFAsVgkn8/TbDZVt+jrTBAEFItFisUiAHNzc+d1N98OA6+trbG6uko4HKbb7V50PbpapJSsr69TKpVwXZcjR46cUwRGCEEymSQej7OyssLq6iqmaTIxMUFYXaB1TQmHw5w4cYK1tTVisRi2bTPY6gxnWRbhcJhUKsX09LR67y5AXTOkKIqiKIqiXG+0ZBK5HageDHYMhmwvI3UdNA3tZgiKCYH+spcRbG6idTqYp07hzswgYrHRDwAZBGiVCtbmJs74ODKbJXHXXQhdp/Unf4IXj2Nubg6DNxf50SCKRYSUyFwO684793GCB8tvt+l+5jP46+tI30cvFIh+x3dgjo1d8eeypqawXvUqeNWrrvhjHxSvXsf/b/9tGO5sNLCPHIFkcnS/BJzJScT6OtbSEo6u0/qN3yD3q796cINWrqruPfeg7TEMRjaLLBaHwcCPfQx2CaDHn/1s6u97H265jLWxgW1ZiLPWt5FWC2tjA7dQIMjlSH3Hd1zWnJTrU/03fgP9gQcwFxaQpslgfn743bnF6/fRNjawTp3CkZLGm99M7nd+5wBHrCg76z/0EP0PfQhx//0IxxneqOv4T3sakZe+lOjtt6uDw1/Dq9WGAeVeD3e34jdCwNb5TuH7aLbNBS4ReWxx30cKgaxWqX/f96FtbqI1mwjXRWoaJBK0P/AB5Ld9G6nXvOZACg0lXvxiWn/8x7iJBMb6Om4shp/JYHQ6CMdBeB7SMEBKrH4fpERszS0wzWExISkJdB2934dHHgHXpTYYkHvHOw58fQs2N9F7PQLLQuyhUHGQSGCWywSDAV69fsMXRGp/9rM4f/Zn6AsLiG4XPA80DS8epzY+jnbXXaRf/vIDfx8VRbm6VABdURRFURRFURRFURRFURRln1wogA7D0J3nmRw6NMP8fBbTNCkUCuTzecyLdaFRrknRaJRbb72VjY0N4vE4juPgui7wWNgtn88zPj5+Xviy3+9TrVZxtk72m6ZJLpcjGo3u+zwUZVur1aLVarG6uko8HmdycvKc7uOGYZDP54nH43zxi1/EcRxCoRDJC12wd5ZsNsu//Mu/ADA+Pn5OwPdCUqkUpVKJVqtFvV5XAfTrTK1Ww7ZtWq0WMzMz523/zjY+Ps7CwgKdTodyubzvAfR6vY5t2zQaDaanpy+6bmqaxtTUFAsLC/R6PcrlMrOzs/s6VmV3pmkyNzfHzMzM6L3dvj2TyWBZ1gGPUFEURVEURVGUKyXy9KfTC4fxo1G0SoVgl99oeq2Gl0oRpNNEbrttn0Z5sFLf933UHn0UT0r05WXCp0/jh0IE4TAEAUa3CwyDvsH0NKG3vQ0jmwVAfsd34LfbmAsL6KdO4c3OnhPQkL6PVixiVSrYs7MEU1OknvOcA5nnfvKqVVq/+7uI++5DtNuIXg8ByHCY7u//Pv7Tnkbsv/wXwvPzBz3Ua450Xdqf/jTuvfci/+EfCD/6KGarhZ3LgeMMO1NHo7BdNE4I5NQUnuuir6/DP/wD7uYm5sTEwU5EuSrk6dOIVgs/Etk9DCYEXjKJ1umgLS3t+tjCMAi97nW4P/dzuL5P+MwZvFgMP5sF0wTXRa/VMLpd3HweOT2N9brX3TQdTpXHuOUy2mc+g76ygjRNvGPHzjk/AkAkQnDkCN7SEubSEkSj9L/ylZtm30K5fkjfp/4bv4H28Y+jNZvo1SraVsHWIBSCSgXnC1+g/63fSubNb1Yd0c8iPW/rH3LXkG0QDiM7nceW3ypUf8HHDQL0dpsgFMJcXSWoVjHrdZByGIb2fbT19eF3VLNJ85FHSL7znRi53JWa2p7oiQTceSdBt4t28iTmyZN4Y2MEuo5vmui2jS8luuOg+T4SkELgxWIgJcZggG9ZeJEIzvHjmEtLGKdOga7T+sQnSL7gBfs6n68ltgLzl2SH9/dGUP+DP0D88R9jlEoYtRqa4yA1bVhwTNPw1tcJNjaoPfQQmbe/Xe0rKcoNTH26FUVRFEVRFEVRFEVRFEVR9snFAujxeBwpJU94QownP1nsGsRUrm2WZXH48GFmZmZoNpujAHooFCKVSp13cUq/32d5eZlOp0Ov1zunO2upVCIWi53TcVpR9lO5XKbVauH7PhMTE+dfXLUlHA4Ti8XY3NxkdnaWXq+3Y/EEXdcxTZNWq7WncQghCIVCeJ6Ht32hh3LdaDabtFotDMPYdVtmGAaJRIJms0mz2dynET6mXq/TarXQdX3XsZqmSTwep9FoUK/XVQD9GmYYBoUD6EIRBAFCiItuOxVFURRFURRFuXKs6Wnat98O1SrW6ip2NntOh9JzVKsY/T6DmRnE855381woLwTZN72Jei6H9+EP47fbiFpt2P1S13EmJyGbJZiYIPzzP0/0KU8Z/Wn6J3+S+uIiAMbSEuGHH8aLxZChEHgeRqeD1DTs2Vnk7CzRX/qlG/51tZeW6P7UT6EtLqIVi5jtNhJACISUeNEo1Ov0v/xlgl/8RaLf+I0HPeRrRvOee/D/4A/Q6nWMWo3Q0hKGbaMFAXqxiCyX8S0LaRh40Sh+oQDpNAhBMD5O6NFH8bpdOvfcQ+YVrzjo6ShXg+NAEAy7v+6FrkMQIFx3TwHB2Ld8C503vxn3l34JO51GK5ex1teHnWh1HS+RwD5+HJlOY/zszxL/1m+9ApNSrjede+5BdLsY3S72sWMXX6+EIJiexvzqV6HVonf33SqArlxbpKT+y7+M9olPYJw5g97v4yaTuOPjAGjdLtbqKrJYBNumbttkf+3XEDsUE76ZGNns8HNummi9HsEOAfAgn0fWaggph2HsHfaHtUoFzXHQPA/fNDFsG2diApnNIjQNpET2ehjFItapUzhBQPMNbyD33//7vnebzvzoj1Lb2MADtLU1rLU1hO8P/7fV+VwC23FsNxRCcxx0z8M3DALLwp2aQoRCuPPzhB5+GFGp4N5zDxx0AL1QQIbDaK6LdBzELoWLtW6XIBRCWhZGJrNPo9x/rbvvRvzxH6OfPj3cZuRyBPk8wjSRQYCo1TDLZfR2GzcIqP/6r5N9wxsOetiKolwlN/bRHUVRFEVRFEVRFEVRFEVRlGvIxQLosB2uNLnBr8m7qei6TnarQ9DXGgwGuK5Lt9tleXmZbrdLuVzGdV0sy0IIgW3bo+7SvV6P+fl5UqnUPs9CuZlJKWk2mzQaDVKpFNouF/vF43E8z8O2bdrt9o4BdCEEmqYhpSTYaeP4NePZ/lvl+rJdOGCv3aZDoRDdbhff96/yyM7neR6u6xIOh/e0rkUiEbrdriqMoIxsf6fX6/XR9i0UCpHP58nn81el0JDnebTbbXzfR9M0otEoYdWhRlEURVEURbkJRX7wB3H++Z/xul1Cp0/jjI0h8/lReEY6DlqphFWr4UxOIsfHSXzP9xzwqPeZEGRe9Sq8l7+czl/9Fd799yOaTTBN5PQ0oRe/mPgzn3leqEULh8n89m/T+KVfwrvvPrxuF61WA88DTcOZmYF0muDIEaJvecsN3/Hbb7fpvu51aCdPYi0v48di2HNzkEgMX7teD61cJrS4iGPb2G98I/r73kfo8OGDHvqBa/yP/wHvfz/G2hpGvQ6ehzkYILaO/elBMOwI6nn4uo5wXYxeD7fdJpidhUgEPxRCa7fxtooiKDegRAJME81x8PcQKBe2jTQMZCy251Be/NnPxjl+nO5f/iX+Zz5D0G6P7pPxONxxB7Hv+R6smZnLmopy/Qr++Z8x6nX8UAh2ON8BgGnixuNo9TrBv/zL/gzwJtT/139lcP/9yK19F31+nsTznocWiRz00K5pnb/7O7RPfQrj9GmQksGJE+eEbINcDtt1MZaWME+fRuo6zb/8S9Lf930HOOprhxYO499+O1QqWBsb2NPTw4D4hUSjSCDQdTTfH+4rf+33mJTDolmbm8Nu0kGAkBLnCU9AWNZj3biFQMRi+EeOIFdWMBcXIRql8/nPD/fX95MQZN/2NuqHD+Pdcw9BvQ6NBlqjgdVoQBBg2vawMzZgOg6BruOFQkjTxJ6agq3rJoSu42azGNUq8pFHGJw8SejIETp/93d4Dz+MtG1EMkn46U/fl2Iekec9j8EHP4hvWWjlMnJ6+uILuy5Gs4lz+DDy6U9HmOZVH99BkK6L/0d/hL62hjYYYB8/jgiFRuum0DTI53EyGcxTpzAWF/E+9Snsl79c/d5RlBuUupRRURRFURRFURRFURRFURRln+yWsdxrIwfl+hQEAdVqlXK5TL/fx3Eczpw5Q71ex7ZtDh8+zOHDh0fBNM/zaDQabG5u4rouQghOnDhBRF1EoewTz/OQUuK67p6KH1iWRSgUot/v7xrGlVIipSQajWLb9q6P7bou/X6fsbExFaq8DmmahqZpey42sB2iPYhiA9vdqvcaft8e624FGpQbXxAEnDlzhkajQafTodFojL6/I5EI7XabjY0NDh8+fNECNY9Xv99nc3OTer2O67qj9dE0TRKJBGNjY6TT6SvyXIqiKIqiKIpyPYg+5Sl4r30twW/+Jm4ohFmpIEolgq2AjW7b+JY17NI9NUX4He/ALBQOeNQHw8hkSP/QD8EP/dCe/0YLh8m+7W3YS0v07r4b74EHoNOBUAh57BiRu+4ievvt+96R8SC0//Iv0ZaWsJaXcfN55MTEufOORgkOH8au1QitrmKHw3T+5E8IvelNBzfoa0Dnc5+DP/xD9K3uq04uh1UqASCAAJBbHeS3/99wHALPg2oVR9OGoaCtrqC47oHOR7l6jKc9Dfn5zyM2N5GtFmKH49PSdTFarWEY7PbbH9fzWDMzWD/5k8hXvpLByZPIXg8RjRI+fnzXDqjKTaDTQXjeaD9iNzIUQrNtRK93lQd28+l87nPY/+N/oC8sIDodhOOApiHjcZr//b8j77iD9CtfqYLoF2F/6EPom5sYrRZuKoW5tERgGATJJGQyoGkI08Q7ehTrkUfQymX8j3wEvvd7b4r9ur0IveQleP/4j8hiEX1piWBu7oKvjex2QQjccJjANDFbLeS//RteOo0MhRCOg9FsIlwXN5XCaLWQQuBOTl78e0cI/JkZjH/7N6jVsO+5Z/8D6FvjyPzwDyP/n/+H9qc/jfelL+F3OtidDnz5y8S+8hWsXg83HB5+PjUNL5UiKBTOK+Ih02m0UgnpOLTf/W56i4to1erw8x0EYJrYH/gAvePHCf3gDxJ/1rOu2rTCx4/Tve022N5vj8fhAvsd0vcxFxcJQiFIpQh/93dftTEdtM5nPoOo1TDqdZzpaUQodMHlhK7jHjpE+JFH8LpdenffTeg1r9nn0SqKsh9UAF1RFEVRFEVRFEVRFEVRFGWfqAD6zctxHBYWFmi32zQajVGwvFKp0Gq1OHToEL1ej3K5zMTEBEKIUffzSCTCysoKkUiEYrHI3NzcQU9HuUlsh3+FEKPu4zuJx+NEIhEGg8Gu4d1Op0M0GiWTyeB5Hp1Oh3g8ftHla7UaoVCIaDRKPp9/fBNRDlw0GiUWi1Eul3EcZ8dO6FJK2u022WyW6G5dZa6CSCRCNBqlWq3ied6u3arb7TbpdFoVRriBSCmp1+s0Gg08z0PTNMLhMPl8/qLvs5SShYUFqtUqKysruK5LMpkklUoRBAGdTodarUYmk8H3faSU5HK5yxpnvV7nzJkztNttarUa3W53dF8oFCKTydBqtZiYmGDmCnfq2sNXgqIoiqIoiqIcmOSLXkQnm8V573uRKyvQbCIGAxACLxqFeBz/ttuIvfa1hI8fP+jhXpdChw/f3MECKQk+/nGMSoXAss4Pn2/zPIjFcGMx9HIZ+bnP4bda6Mnk/o/5GmH/z/+JUS6j93rY8/OYy8torouQkgBACKSmIaVECwKkEPiGgRYE6LaNVa0yyGTQHAfPNBE38Wt5o0s8//k0f//38ZJJzI0N3HgcoevnLygl+uoq0jQvKwwmLGtfuqwq15lwGKlp6J7HXkqrCs8bbsMuEtRTLk3zL/4C+b73YRSLGNUqmusS6PowpAp4iQRBvU79K18h/du/fVN/z15I94EHsO6+m3ClQiDEsFs1w4Ivstkk2NzEKxSQhQJC0/ByOYxKBbm8TO9f/oXoN37jwU7gGhF/5jOpPvnJyMEA6/RptJMn8cbGhkFlIZC2jVapYNVqeNks/vg4QSyGPhggqlX0ZhPRbg9D2ZkMMp+HWg3HNLFqNWQmw05Rf7EV5tYbDfjnf0b6/oW/F/eBsCySL3oRvOhFo9tad9/N4Bd/EfHww9gzM8PtYDR60Q7hUtdBSszFRUSjgWbbw2B+EAy7wvs+gWUhSiW8kydp/Of/TPr//X+v2pyir3oVg4UFHMchtLSEm0wS5HLIaBR8H1GvY1WroGm48/P4z3420ac85aqN56A5996LUa8PL2DKZHZcVoTDeLEYWqWCd//9cDP/TlSUG5gKoCuKoiiKoiiKoiiKoiiKouwTFUC/Ofm+z8mTJ6lWq6yurmIYBul0mm63S2+rC8JgMGB9fX3UHXhycnL097FYjGQySb1eJ5FIMDMzs2sgUlGuBMMwME2TWCxGq9Uis8sJ5kgkgq7rJBIJ6vU6MzMzF+wK7XkexWKRmZkZ+v0+8Xic9fV1ZmdniVygO0WtVqNWqzE7O0sikVBB3+tQPp9nc3OTcDhMpVJhamrqoss2m008zyOVSlE4gC50+XyeUqmEaZpUq1XGx8cvumyr1cLzPNLptCqMcIMol8usr69j2zbtdnvUwTwWi1EsFkkmkxw+fPi8IgobGxvUajUeffRRHMchkUjQ6/UYDAZEo1EmJyfxPI+1tbXhRfSaRjweJ3SJF6O2Wi3OnDnDxsYGjUaDVCrF3Nwcpmni+z6tVotyuUyz2SQIAnRdP2ffYi9UYxtFURRFURTlehZ/xjPg6U+n+6UvYf/1XyOrVdB1xMQE0Re9SAXPlcvS/8pX0Eol9EYDd3z83B9QQTAMGVWr6LY9vM33MWybXihE+6MfJf0DP3AwAz9gg5Mn0b/yFfRqFTebRQQB+mCA7nn4uo7YDp1LOQxybXVA13wfLxzG7PcJfB9jZWX4gOk01rd928FOSrlqtEgEXvYygj/6I7SFBcxHH8WbnBwF/ZAS2e1ibG6iDwY48/P43/ANRJ/85IMeunIDEbfcgvzSl4bbLdu+aAdYABkEGO02ztQUUu1nXDGde+9Fvve9aIuLGJ0Obi5HkM8PQ61SIlstjFIJ6+RJnCCg8bM/S+4971EHN7fYi4vYP/ETRJvNYcEXTUPqOhLQpEQ4zrDQi+viOA5yZmbYmXpjA2wb99QpUAH0ISFI//Iv03zNa3B1HW1zE2tlBbG8PApM++EwztQUjI2hv/a1pJ/3PNp//de4f/VXyEceGVV29Y8cwXjhC3E//3nMT3+awDQRe7hQJAiHMbtdfN8n6HTQL9Cl+6BomQxaJII0zWGn913GJno9NMcBKdH6fYLt1y6TGX5+XRdRqWCWSvjdLv77309rbIzkd33XVRl/5Ou+Dv8XfgH3rW/F3ioeFTpzZvSeBaaJl80iCwX8b/1Wsm9841UZxzWj2QTbxg+H97Q9DaJRtF4P0Wrtw+AURTkI6go1RVEURVEURVEURVEURVGUfbJbAP2AilQrV9n6+jqNRoOVlRUymQzj4+N0u91RSGxmZoZwOEyxWGRzcxNN00gmk8RisdFjpNNplpaWcF2Xdru9axBYUa6UfD5Pu93mzJkzDAaDHcPf7XabsbExwuEwpmmyuLhIPp8nkUgghMD3fZrNJtVqFcuymJycJJ1O09o6Gb24uDgquGAYBo7j0Gg0cByHqakp0uk0hw8f3q+pP26u61Iul6nX67iuCwy7EOfzebLZLPpNvJEPhUKk02kmJiZYWlpC13UKhcJ5BQoajQabm5uMj48TDocPZFsXiUSIx+OMj4+zsrKCaZpks9nzlut0OmxsbJDL5QiFQhdcZq8GgwGVSoV+v4+UEsMwSKVSZDKZCxZxUK6OtbU11tfXKZVKNBoNdF3HsiyklJTLZcLhMGNjY/T7fW655ZZReFxKyfr6Ol/+8pdpNpukUimKxSK+76PrOrFYbFREZmpqipWVFdLpNJVKhenp6Usa68rKCpVKhUajwaFDh4hGo6P7DMOgUCiQyWRYWlpiY2MDwzDI5XLnBecVRVEURVEU5YYmBLHbbyd2++0HPRLlBuNXKkjfR/M8OOv3GL0e5uIimuOguS6a7w9DK1vB6tDmJoNf+RU6hw4RefrTD24CB8T+p3+Cfh/dtnFzOfRSCeH7CClxLQvTcQAQUiIZdmbVggCxHfzRdYTjELJt+rOzBGNjxFQA/YaW+c//merGBvJTn0JfX8daWUGuriItC+H7aK6LF4/jHDuG/8Qnkn7HO1ToVLmionfdxeAjH8EPhdA3NwkOHbroOqYVi0ghIJMhdOed+zvQG5WUOO9/P/rmJkangzM/D5HIY12ihUCkUvjJJHJpCfPMGQiH6XzhC8NiRDe5YDCg+zM/g76xgWHbIARuOAzbRc6lRPg+uuMgBgOsSgU7HEZun5cJAtg616UM6YkE6Xe/m+b734/3yU8S1OvQ7Q7390wTolH8r/96wj/0Q6PfIMkXvABe8AKk5+G32+jx+KgrePXBB4fr8W4XkWwRvj/czsCOBTEOQvRpT6MZi+ElkxjVKv4uAXR9cxOkRHcc7GwWb37+3BC+aSInJ7HTaazTpwnW1/He/37kc5971Tq/x5/xDAbvex+9P/1T5P334/d6w8+AEBAOE8zNYdx5J+m77rrx9zcMY1TwZ0+CYLj8TXwuXFFudCqAriiKoiiKoiiKoiiKoiiKsk92O3d4o5+nuhkFQUC1WqVarRKNRkeddH3fJ9jq5mJZFrquj8KOvV6Per1+TgB9O+TmeR6e5x3IXJSb03bn6lQqxcrKCrOzs+eF0LfDmZ1Ohyc+8Yn4vk8kEqFSqbCxscH6+jq6ro/CmJlMhlwux+TkJNPT09RqNYQQpNNparUa5XJ51LF3O4QbjUY5duzYJXcLvtrW1tYoFov0+30ajcYogB6JRGi1WqytrXHo0KHLCilf7w4dOkS/3ycIAtbW1kZdmy3LGhUn8DyPsbExcrkc8/PziAP6Ypybm2MwGOD7PhsbG9TrddLp9GisjUaDfr9PNpulUChw5MiRSwqKO47D0tISrVaLfr9Pt9tFSolpmiQSCUKhEJOTk4yNjV2FWSpnq1arbGxssLKyguu6TE9PE4/HR+ug67pUq1VWVlYIgoCFhQVuvfVWNE2jXC7z6KOPsrq6iqZpOI5DJBJB13Vc12VjYwPLssjn87iuSzgcPieQ/njX83a7Tb/fp1arMTY2dk74/GyGYTA9Pc2ZM2fo9/tUKhWmpqYu+7WCvV9zpCiKoiiKoiiKckMyjNFvORkEwyDcYIB1+jSabWPYNlIIAsNAahoEwfD2wQC9XMZ785vpvuMdcOTIgU5jvwXdLsLzhkEry0K4LiIICDQNDIPA80ZhfYCzf3qKrTC/4Xn4pkkwOYn+gz941QJIyjVCCHJvfCON2Vn8D32IoFaDRmO4HmkaMplEJhLIZz2LzGtfi3aRYySKcqnC8/N0v+EboNvFOn0ab3UVf2rqnG2PDAK0YhGrXMaenSWYnVXh5yuk98ADaMvLGLUa7tgYRCIXXlAI/NlZjK9+Fep17A9/WL0HQPtTn0JbXcWqVPBME8N1EZz1/SoE0jBwdR2z30dzXcxyGdeyhhctWBZaOn1wE7hGaeEwmVe9Cvlf/yvtv/kb/DNnwHEQqRSRZz2L0EX274RhYHxN0WV9fh6ZSAw7zne7cNa1AeeREr3ZJIjHCSYm0HYoGH4QtGgU+ZznEFQqhE6dwq/V4GLnRKtVrFYLqesgJf7c3EU7wItIBHd8HGtzE7m5Sff++4k/61lXbR7h+XnCb3kLfrNJ9wtfQDabCMvCmJ8n+vVff/Nc0DM1hYzF0CsVPNcdFU24ICnR2238dJpgcnL/xqgoyr5SAXRFURRFURRFURRFURRFUZR9slsAXTU5vfHU63Vs26bdbjMzMzO6/eygYrC1Yui6Tjwep9VqEY/H8TwPY6sKve/7o79T3XCV/WRZFrOzswRBwPr6OmfOnCEej5NIJNB1Hdu2aTQa+L7P7OwsmUyGW265hWq1SiQSYWxsjF6vNwqUR6NRotEoExMTozB2NpslFotRLpdH6/62cDhMoVAgl8tdsx3El5aWKBaLrK+v0+l0iEQiRKNRpJQ0m00qlQqZTAbP85BSksvlDnrIB8I0TW655RYWFhZGwfzt4PV2AYJ0Ok0kEuHo0aPnFOHYb6FQiCc84QksLCwQi8VoNBqj9VzTNOLxOJOTk0QiEY4cOUJql04OF2LbNo888giNRoNSqcRgMCASiaBp2qgTfDqdxnEcbNtmdnb2KsxU2baxsUG5XMZ1Xebm5kbfv9tM02RiYgLDMFhbWyMajdJoNMhms3z1q1+lUqlQqVS45ZZbyGQy52yvPM+jVquxubmJEALLskYFZRzHedyFNarVKu12myAISO9yAWA4HB6NtVqtPq4A+s1yHZWiKIqiKIqiKMrjZc3N4WkafjiM1mwi43GM5eVR+NyzLORZIQ3hugS6jm9ZGN0uwfo67q/8CvJd70JY1gHOZH+JcHjYFVFK5NcWWRUCPxyGwWD4n1KiSYlgGD43bBsAufW6y+//flJ33bXPM1AOhBCkf+iHkD/wA7T/+q9x//EfodOBcBj9lltIvOhF5wX6FOVKSr35zbR+5EdwpMRcXsb4t3/DSyaRponwPIxWCwB7dhY5O0v07W9XB9aukMHf/i1aq4XwfYJcjp1eVaHreKkUeq0GDzyA3C0weRPwPvpRjEqFwDSHxXOkRHNd/K859i2EwDdNdNdFcxz09XW8eBwZjRJVQf6LEqZJ8ru+67IeI/6Sl9D5wAfwolG0YpHgyJGLbj9kq4Vu23hzc2jPf/5lPe/VEv+BH6B733043S6h1VXcbhd/bGzUrV1uFWMy63UCw0DzfZxMZtg9fgcym4XNTWg0cO69F65iAH2bnkpd9vt7PYu86EU4n/kM0jTRymXkTueWmk1018XL5zGe97z9G6SiKPtKBdAVRVEURVEURVEURVEURVH2iQqg33xs22awddFcPB4f3R4KhdB1nVAoRKfTGYXPotEopVIJKeU5AfRWq4VpmliWReRiFf4V5SopFArAsABCr9ejXq9TLpeRUmIYBplMhnQ6TTwe59ixY6Og5vj4OI1Gg16vh+/7GIZBIpEgkUic9xyhUIiZmRmmp6exbZsgCDAMA+savwi3Wq1SKpVYXl5GSsmRI0fO6RA/Pj5Op9NhfX19FF6OxWLndZG/WZimyYkTJ2i1WpTLZVqtFnKrlXI0GqVQKJDNZq+JQhuRSITbbruNarVKuVwmn8+P7jNNk0KhQD6fx7yEi9iklCwsLFCv11ldXSWdTjMzMzN6LCklnU6HYrE4+g6JRCLnjEG5clqtFoPBgEajwdjY2Hnh87PlcrlRQYJSqYRlWXQ6HcrlMolE4oLvkWEYjI2NUSwWqVQqpNPp0TYg2G3n8AK2ixKEw+E9fVZisRjdbhfXdR/3cymKoiiKoiiKoijns2ZmaH/d1yEqFYzNTZx4HL3fR3ccfNM8J3yOlGieNwygRyK4hQLW5iZBPo9z332E7rjj4Cayz6wnPQknGiUwTUSthjRNpKahBwG+lKMQunQcNM9DCwKklEhNwwuH0VyXwDTp33IL2Ve96qCno+wzYZokX/ACeMELDnooyk3GyOVIvPe9tH/u53BjMWg00BoNtF4PNA1nYgKyWYLZWaLveAfh+fmDHvINQzabCMcZfm/soUBxEIlgdjr4QYDXaGBundu6GfntNvrJk+iNBt5WkQ7NdTH7fQLXPXdfBZCGgXAcNNvGsm36t96KfOYz0S+h+K6yd0YuR/DMZ0K7jXXqFN7qKv709HndwGWzSWhlBTeXQ6ZSJF7ykgMa8c6smRnct78d5+d+DtuyMEolzIcfHhZBYLgO+pEI9uws1toanhD4Fzhn/LWEpuGHQgjHQdbrV3saChB98pPpHz0KtRqh1VXsUAguUGBddjqEVldxcjlkMknihS88gNEqirIfVABdURRFURRFURRFURRFURRln6gAurLNNM1RF+larUY6nUbXdcRZVc23Q5m+71Ov10ddoqPR6EENW7mJFQoFksnkqEu57/uj+2KxGGNjY2QymXPWYSEEmUyGzOPoQCOEuK7C2cVikVqthu/7F+yaDMPiE4cOHWJxcZFUKkWpVOLQoUMHMNrHBEEw6jRvmuY579vVJoQglUqNuoYHQYAQYl/HsFeaplEoFCgUCriuOyoicLmvWb1ep9vtsrq6Sj6fPy+0LIQgkUgQiURYXFykXC4TDofJ5XLX5Ot0vWu1WnS7XYIgIJlM7risEIJ0Oj36m83NTfr9PkIIYrEYvu+f0/38bJlMhrW1NTqdzih4vlPYfacxPJ71YHt/QlEURVEURVEURblyrLvuwv+//xdZqRA+fRrh+wgpRwEbYBg+t22ElPiWhZfNQj6PLJUQlQr6Zz8LN1EAPfrkJ9M/cgSxuYlZqeDMzCBrNaQQw3C5ZYEQBKEQga5jDgYEhoEbjeIlEli1GvaJE4jv//6DnoqiKDcZc2yM7O//Pr0HHqD/oQ8RPPQQWq+HDIcJjh8ndNddxJ75zD2FpJXHwTBACMQei3iKIBh1j9a2Cn9fy5zVVQYPPojs9RCxGJFv+ibMiYkr8th+qwWA8H1kKASpFEG9jhcEGLaNHwTDfZazL06QEt11GaRSyLExov/xP16RsSg7S77mNbQefRQnCDCXljCaTbxUiiASAd/HaDTQbRs3lyOYmcH46Z/GeBznXfdb7Ju+Cf3d76b77nfDgw/idbvQ7w/vjEQgFsN/ylOwv/hFzJUVRBCwlzMYo8/3JRSFVi6BEMR+9mfp/8RP4Pg+obU1/Gp1+HvGssDz0BsNjE4HN5NBzsyg/+RPop/VkEFRlBuLCqAriqIoiqIoiqIoiqIoiqLsExVAv/lsd3CWUjIYDM4J1mYyGdrtNu12m42NDcbGxrBte9QB1zAMXNdlbW1t1GV6bGzsoKaiKOd0KXddd9Sl/FIClDeCbrdLr9ej0WiQzWZ3fB3C4TDxeJx6vU4ikWBmZuZAunxvdx5vNBqj2zRNI5fLUSgUiEQi+z6ma6Hb+V6YpnlJ3c4vpFwu02w2MQyD3AU6BmwzDIN8Pk+pVKJQKNBqtUbBfeXK8TwPz/MwDGNP66NpmqMiHK1WiyAISKfT2LZNp9O56HtkWRahUIhyucz8/DyRSOSS1qntx6lUKjsG3rd1u13C4TCWZT2u51G1DhRFURRFURRFUS4u8R3fQfXv/g58H/PBB9EdB8lW0IthSEbfKv7nhcN4sRjB2BhCCPxkEq3bxVhfP8gp7D8hMF/6UoJTp5CtFubGBr5hICwLw7aRmobUdYTvj7rJB+Ewztwc1vo6XjoN8TjR7/7ug56Johwov9Wi9/d/T9BogGUROnGC8IkTBz2sG58QRJ/6VKJPfepBj+SmoR06hIzH0dbXodeDXQp0680mfixGkE6j76Gr8kHpfvGLDP7iL9AffBAcBzwPaRh0QyH8228n/P3fT+ybvumynkPbPtejacN9E8PAPXIEcfo0nhBojoPZ7yO3DgKLIEBIiWdZ+Nks1lveQnh+/jJnquyFkcmQeOc7af3szw7X8VoNvV7H7HSQmoYfj+PNzSFTKYyf/mkSz33uQQ95V+Hjxwn/zu9gLy/T+8QnCMplALSxMWIveAHWzAy17/s+gmoVvdXCm5zc8fGkbaPbNl4shpiZ2Y8pKED4xAmCX/1VBm98I3YyiVYuYxaLaL6P1DS8RAJ7fh6SSbTXvIbkd33XQQ9ZUZSr6Oa8IkhRFEVRFEVRFEVRFEVRFOUABAF88zd/CikfS6IL8VjQ6TrJ4CmPQyaTIRQKEY1GqdfrTJ51AjUej5PL5QiCgHK5zOrqKs1mk0KhgOM4FItFOp0O4XCY2dlZCoUC2Wz2AGejKENCiMcdZLyaWq0WtVoN13URQhAKhcjlckR3uRjpcnU6HWzbxnXdXbsmA6RSKdbW1giCgH6/TywWw7ZtarUatm0Dw2BpNpu94l3ggyDg9OnTNBoNWq0WjUYD13WBYTi+3W5TLpeZnJxkamrqij63cq4gCOh0OqMw+W6drJPJJJubm3S7XRVAv0o0TUPTtFFX8t34vj9634IgwLIsYrEYoVCIRqNBNBq9aLC81+vheR7xePySi8rkcjnK5TK6rtNoNHYsYjAYDOj3+0xOTu64nKIoiqIoiqIo+88tFvFrNTBNrOnpx4JCyvVBCLK/8AvUdR339GlEvY7m+xhbx3gCTcM3TaRh4MdieEePIrZOAEhNQ0iJ8Lw9dXy8kSRf9CJqDz2E9/GPoy8tYTabaK6LFAKj30cIgQR8wyAwTdxQiNDyMkEohD83h/ye7yF0+PBBT0NRDsTg1Cl6f/ZniM99Dno9hOeBEAxCIbrHj2PedRfJF7xAVRVUbhjxF72I7p/+KV40ilYsEhw5cuEFpUS22+jdLt7MDOK5z71mPwf1P/gDxJ/8CXq1ilGtom93hgb8aBQqFdx/+ieaP/qjpP7Df7jk5zEyGYJ8Hj8eR2808MbGIBrFOX4cfW0No9MZHufePiYeBBiui5NO47761aSe/vTLnaryOJhjY2R///fp3n8/9oc/DA8+iC+He4nB2Bja859P4s47Ma6z6wRChw4ResUrLnif9p3fSbC4iFmt4rVasMN5Vr1Uwg+FhoWIXvKSqzVc5QKiT3kK1p/8Ce2778b7xCcIKpXRfTIWQ377txNT++eKclNQAXRFURRFURRFURRFURRFUZR9EgSQy33bRe9XAfQbj2mapNNpcrkcKysrxONxEmdV3R8fHx8F3zY2Nmi320gpEUIghGB2dpZYLMbY2BgzqqK3opyj2WyysrLCYDCg3W7jOA4AkUiEUqlEPB7n0KFDV62rdxAEoy7Ie+kCbxgGUkqklPR6PTY2Nmg2mwwGAwaDATAMoG9sbIy6pF+JEL2UkoWFBSqVCisrK/i+TyqVIp1OI6Wk0+mwsrJCNBrF932klExPT1/2815LpJSj7u/dbnfUNTqZTFIoFIjH4/s2lu11xvO8PXW/1jRt1HHb2+qcplxZsViMaDQ6Kg6w2/rQarWIx+OjQhyappFMJvE8j8FgwPr6OtlsllgsNuqo7rourVaLer3O1NQUsVjskovKxONxotEo+XyeYrFIOBwmFoudt5zruqyurpJIJAiHw+Tz+Ut6vguRN1tCQlEURVEURVGuEOk4tD7+cbyPfhR9YWF0ez8cRn7btxF92ctUF9vriDBNsm99K+V/+ze4915C7TbeWcdy/GgUL5eDTOacAnRav4+0LIJ4nGszHncVCUH2DW+gnkrh/+Vf4ne76KurWNXq8Jh4ECAALQiQvo8GuBMTyFyO4CUvIfuqVx30DBTlQHTuvx/37W9HK5fRy2WMTmd0gCawLESxSPDII1T/6Z/IvulNiD0cr1aUa505Nob/Ld8CrRbW6dO4a2vIqalhuFxK6HTQqlWMRgOz3yfQdYzlZewgwG+10PdQOHg/Nf/iLxB//MfoZ86g93q46TTezAzSNBGui1atYq2t4bVaBO99L61EYlhU4lIIgXj+8wnW1jAfeQSv2YRUCkIh/KNH8R0HUashHAekxKjX6WcyeE96Etkf+ZErO/HrkLO6iv3II2DbiGSS6Dd+I9pVLnotdJ34s55F/FnPQnoefrOJFokMi1RdowUVLkf8zjtp//mf42YymCsruHNz8LXnOaRElMuY9Tr24cP4X//1hOfnD2S8NzMjmyXzwz+M/P/+P+zFRYJ2Gy0SwTp8GO0KF1RXFOXapX5dKIqiKIqiKIqiKIqiKIqi7JPdGmvq+v6MQ9lfU1NTVKtVEokEJ0+eJJvNMjExQTgcRkpJKBTCNM1R0DyRSHD48GEMwyCbzVIoFK5agHab67qjQOZeApGKctAqlQpLS0tUKhVqtdros7R9n2maFAoF+v0+x48fv2A483Lpuo6+teF2XXfXz47jOGiaxmAwYGFhAcdxKJfLDAYDTNNECIHjOKMO7t1ul/n5+T11V99JsVikXq+zvLxMJBJhcnJyNG6AdDqN4zisrKywurqKpmmkUql9DWVfTYPBgFOnTtHr9Wi1WnS2Onvouk4qlRptn+fn5/dUSOBybQeSNU0bhdF3s91xW1c7CldFJpMhFAqRSCSoVCrEYrGLdqbvdDr0+32mpqZG25hkMkm73R4FvBuNBrVajVqthmmaSClHn/9EIsHExAS33XbbaF24FIcOHWIwGOB5HsvLyyQSCdLpNKFQCM/zaDabNJtNIpEIU1NTTE9PX/L3ezg8ie9vd+IR6Lq6oEhRFEVRFEVRLoWzvk779a9HO30ao1rFqNcRrgtC4Eej+KUSg7/+a/o/+INkfviHb8igyQ1JCKzv/V68tTW0f/s3nIkJiMfBsuACv8OkbWP0egwmJnC+8RsJHcCQD5wQZF71Ktzv+R46d9+Ne999uBsbaBsb6O02fiKBTCYRpgmGgf/1X0/opS8l/uxnH/TIFeVA9L78Zdy3vhVtaQmrXMZNJLDn5pCRCAQBWq2GUakgGw0IAurhMNk3vOGgh60oV0TyNa+h/eijOL6PtbyMbDZxk0nMZhO930d3XTTfR24V9pZCoP/5n9P6+McxfvqnSdxxx0FPAQC/3Sb4wz9EX1lBGwywjx9HbJ3TEgCmSRCN4uRymGfOwNoa3vveh7zjDsRWIdTHK3HXXbT/9//GyeexlpdxpqchkxnuY1oWcmIC6broKytI08SfnUV+53cOA883qc7nPof9oQ+hP/ggeN7w4g5dpxmLIZ/zHGLf//2EDh266uMQhoGRy1315zlIRjaL9upXE/zWb8HSEqFTp/Dicfx0GqnraLaNXquhOQ72zAxyZob461530MO+qQldVwUAFOUmpgLoiqIoiqIoiqIoiqIoiqIo+2S3ALq6pvDG02q1KJVK9Pt9ut0uQRBw5swZFhYWSCaTxGIxdF0nnU5z+PBh0un0KAR5OcG0vfB9n0qlQrlcxrbt0e2hUIhCoUA+n1dhR+Wa1Ol0WFpaYn19nXa7zdjYGKlUavSZ8TyPer3O2traKLj7xCc+8YoXV0gmk4TDYUKhEI1Gg0KhsOPyzWaTaDQ66o68ublJJpNhenp61EnZdV3q9TobGxu4rgvArbfeSvgSK8hLKSmXy1QqFQzDYHp6+oLBWsuyOHToEKdOnaLT6VAul2+IAPpgMODhhx8evaZCCJLJJKFQCMdxWFtbG70unudxyy23XPUQuq7rhEIhYrEYrVaL3C4XEXU6HYIgGHXpVq48TdMoFAoMBgMWFxdZW1tjYmLivHWh1Wqxvr5OLpcbdRQfDAZUq1UMwyAIAmZmZrAsi3Q6Ta/XG3WtD4fDNBoN0uk0t9xyCzMzM5c15ng8zvz8PEIIotEo9Xqd1dVV5Fbnq0gkwsTEBMlkkqmpKcbHxy/5uZ7znIXRfkIoFLpoOF9RFEVRFEVRlIvz6nU6P/mT6CdPYi0tITUNL5MhiEYRvo/WbBJaXByGLv74j6kLMQyhK9cuKRk8/DBetYo2NoYbCuGl0xi1Gm42OwxPf+2fBAHm8jJ+JALxOPoLX3gAA780XrWKV6+jhcOYExNXpLuyOTZG5hWvgFe8YnSb327T/9KX8BsNRDhM6NZbCR0+fNnPpSj7Sbounb/9W7yVlWFAPJsl9uxnY2Szl/R4/Xe/G31jA6tcZnD4MCKVArZCq4CcnMQZG8M8dQrjzBm8T36SwXd/N+FbbrlCM1KUg2MWCsR/+7fpvO51OLEYFItE1tbQXRexdeLdNwwCw8BJJtGCgNDDD+PmcvjveAdtKUk897kHPAvofPSjiHYbs9FgMDc3Cp+fJxrFnZzEWlsjqNdp//Vfk7zE/QUjl8N8wxvw3vpWXCGwVleRxSJeMgmahhgMMNttAsvCnZ/Hf/KTybzylec8ht/p0PnYx/D++q/RikUIAmQ2i3jWs4jfeSfmLufFrhtSUvut30K75x70ahWjWkUfDIZ3CYGXTBKUy/TuvRfvF36B2Ld8ywEP+MaQuvNOmr5P8O53YzebaJUK1vo6SIk0DLxUCi+fJ5icJPorv6L2CRVFUQ6QCqAriqIoiqIoiqIoiqIoiqLsk90C6Fc5b6zsIynlqDtzs9mk0WgwGAzodru4rkskEkFsVeOfm5sjkUiQz+eZmJi46sFzGAYaT506Rb/fp16v0263Rx2BE4kEnU6Hzc1N5ufnb4gQqnJj2djYoNFo0Gq1mJubOy+cbRgGhUIBy7JYX18nEolQLpeZmpq6ouMIh8MkEgkymQylUolUKjUKkn+tdrtNt9slHo9jWRbFYpHJyUnS6fQ5y5mmydjYGNFolJWVFSKRCMVikcOXeFFFq9ViMBjQbDaZmJjYMThqmiapVIp6vU4ymWR2dnZfOoJfLVJKTp06NSpGUCgUyGaz52xjfd+nXC6ztLSEEIKlpSXm96F6f6FQoN1uU6vVaLfbJBKJi86hUqmQSCQIhUJkL/Ei1YPU7/cpl8u0Wi1830fTNKLRKIVCgWQyedDDG5mcnKTT6SClZGVlhYWFBeLxOOFwmCAIaLfbuK5LLpdjbGyMo0ePYhgG8XiceDzO5OQkKysrAMzNzTEYDGi323ieh+d5tFotCoUCx44dY3x8/KLbiscjlUpx6623UiqVSCQSeJ43eo23i9uMjY1ddP26HFs5d0VRFEVRFEVR9qj1rnehnT6NtbiIm8kgp6dBiFF4MMhmGfT7WIuLsLiI/4EPMHjWswgfO3aQw1YuwO90aH/oQwQf/zja5ubwoL8QaKXSsKO972M9+iju+PgwoKVpwx9RjQZmqYQIAtxjx/Cf8xyMyyxOdrVJx6H18Y/jffSj6AsLj90ejyO//duJvexlVzwIpCcSxJ/1rCv6mIqyX4Jej+Yf/iHyU59Cq9cR/f4wRBcK0X7ve5FPfzqx//SfCB05sufHHHz1q+iPPIJZKuHk86Pw+dcSuo47N0f44Yfx2m26f/Zn2LfeivelL0GvB5EI2q23krjrrhu+q65y47FmZkj/0R/R/uQn8X/plwhKJTTPIzBN3GgUP5MhyOchHCaQEq/VwlpZwZMS79d+DfcpT8EcGzvQOXh/8zcYlQq+ZSF2O16bySA3NhC1Gu5nPgOXUbAm/uxn09F13F/+ZZxCAVGpoHU6w22TaWLPzUEyif/v/h2Zt70N7azzba2PfAT/Pe9BtFoYlQpavw9AEAohT56k++d/jvy+7yPzIz9y3VfZr7/vfYi770Y/fRrdtnHTabyZGaRhIAYD9GqV0OnTOJ0OzpvfjPZbv0XkSU866GHfEFIvfSn2N34j3Q9+EPnZzxJsrWcAweQk2gtfSOrOO9GvwnkORVEUZe+u36tGFEVRFEVRFEVRFEVRFEVRrjMqgH71SSlpNBpUKhX6/T5BEGAYBul0mkKhQOhiFeWvsOXlZYrFIisrKziOM3p+TdNot9usr68zGAxIJBJomsatt956xbszX0y32+XkyZNUKhVKpRKhUIhMJoNpmriuS6PRoFarMTY2RhAEPOEJTyAWi+3L2BRlN7Zt02q1qNfrZDKZHTuDb4ept7cJk5OTV7xz78TExChcvrS0xPj4OIlEYvQ8vu/TaDQol8vkcjlarRaWZRGNRs8Ln58tHo+TTCap1+skEglmZmbQdf1xj6/f7zMYDAiCYE8h1EQiMeqibNv2dR1Ab7Va9Ho9Njc3KRQK5PP585bRdZ2JiQl832dzc5NwOIxt21f9uyKXy7GxsUE+n2dtbY2pqanzwtie57G5uYnrukxPT5PP5/elQMmV4vs+i4uLNBoNut3uKIAuhCAej1Ov14lEIszPz+/4Od4vmqZx/PhxFhcXsSyLTqdDs9mk0+kghCCVSpFOpwmFQhw5coTUWRf7zs/P47ouQgjW19dpNBrEYrFRV/TBYDDqRJ7JZDh06NAVG3c4HObQoUNMT0/T7XbxPG8U8r/ckPt1fs2goiiKoiiKolwz/GYT8bnPoW9u4sVio/D51xKRCO7cHKGTJ/HbbXof+hDhn/mZAxixcjH20hLd170ObXkZvVzGaDTQPA8A3zTB99Edh8AwMNfXERsbw+DS1jJeKkUwNYX/7/4d6Z/9WUq12kFOZ0fO6iqd178ebWkJo1JBr9fRPA+pafixGEGxSP9jH2Pwoz9K6nu/96CHqygHzqtWab3mNWiPPIJeLGI0m8PuzFvbey8Ww69W6f3TP+G94x3Ebr99T4/b++Qn0dptNNdF7tJpWFgWbjyOubiI+Z734B4/jtFogO+DpiHvv5/On/85wb//96Rf9zq0aPRyp31TGDz6KP2/+RtkvQ66jjY7S/z5z8fIZA56aDcVLRwmfscdtH73d3ETCfxwGHe7k/jZ+1VCQCqFLQThxUWCfJ7Ohz88DEkfIFGtIgYDgmh09wOvQuBHo4jBAFGpXPZzx7/t2whuv532xz6G94lPoK2sIDxvWFDmaU8j8t3fTeTrvu6cv2l+8IPId70LfXUVs1bDN02CeBwpBHq3i16t4iUSeB/4ALVWi+zrX3/Z4zwo9tIS4n/9L/TFRYTv4zzhCbB1bF0AhEL4qRR+o0FoeRnbNOn99m8Tef/7D3TcN5LQkSOEXv96gh//cZyVFeRggJZKEZqd3fXzMlhYwF1cRLouRj5P5Bu+AXEdn9tUFEW5Vqktq6IoiqIoiqIoiqIoiqIoyj5RAfSrq9PpcObMGfr9Pq1Wi263i5QS0zRpNBoUi0VyuRyHDh26qiG+drtNuVxmZWUFIQTz8/PnhDgjkQhjY2MUi0Xq9TpBELC5ucns7OxVG9PZzpw5Q61Wo1QqMTk5eU6IDiCbzdJsNtnY2EDTNM6cOcPXfc2FB3sRBMF1FZZUrg+tVgvbthkMBkxPT++6fCaTYXNzk4mJCfr9PtErfFFdMplkZmYGKSWlUon19XV0XScSiRAEAb1ebxRyDoVCRKNRVldX99SNPZPJsLS0hOM4tFotMpdwQZuUEiklQog9fR51XR/9TbDbl9Y1brvrNrBr5/BCocCpU6cYDAZUKpU9rVuXwzAMjh49ShAEo9ByqVQimUyiaRqDwYBOp4Npmhw6dIhMJrOndeZaEQQBJ0+epFarsba2huM4xGIxLMsahf2LxSITExO4rsuJEyeumRD60aNH6fV6lMtlGo0GnuchhCASiZDP58nlcud9lkzT5MSJE5w5c4ZoNEqn06HT6RAEAaFQiLGxMSKRCJlMhsOHD19SMYnd6Lp+TXWUVxRFURRFURTlMZ2PfxzR6WB0OgyOHt25OF4kghuPo1UqyHvvRb72tYh9Klqp7MyrVum+5jVop05hLS8TmCZePo/0fYx6HbPTGYXRcRx8y8LO5ZCxGBgGJJMEmQzihS8k+8M/THAZVb+CwQChaYjLLDx2MW65TOfHfxzt9Gms5WWkpuFlMniRCHgeerNJ6NQp3FSK4F3voqlppF72sqsyluuelLQ/9zmcj3wEceYMOA7EYvDUpxK96y7C8/MHPULlCpCuS/N1r0P/ylewzpzBD4VwZmchmQQhkL0eeqVCaHERZzDAeeMb0d/znr29/+UyYjDAtyzY7ftASvReD6vTQWoa4qtfRVoWgWkiPA+9WMSLRqHToX7mDOl3vUt1lN1B94EHGPze76E//DCi20XY9jAIGY3S/qM/Qn7rt5L8sR9THeX3UecTn0C0WpjdLvbcHGKHY8oimcQPh9HKZbxPfQr+63892IqbB3y+VItGhwVjtovGSHnR12Pw1a8i3/MetKUl9E4H+/Dh0fYMwAO8bhdjZQXz9Gm8j3yE1pOeRPK7vmufZnNl9e6+G9HtYnQ62GeFz8+TTuP0epilEpw8Sf8rXyFy2237O9gbnBYOEz5+fPcFpaT18Y/j3XMP2qOPIj0PISWOYTDIZhHPex6J//AfVKEQRVGUK0gF0BVFURRFURRFURRFURRFUfaJCqBfPe12m4WFBSqVCuVyGU3TRt3FB4MBi4uLRCIRXNfFdV2OHTt2xTshbyuVSrRaLRzHOS98frbx8XFs26ZarRKLxZienr7qge1Wq8VgMKBUKjE2NnZe+HxbKpXC8zxKpRKpVIpWq7VrsE1KSavVGoU+pZQARKNRCoUC2Wz2nPn5vk+1WqXb7RIEwSg8l06nVXBduSjf9/F9H2BP3X0tyyIIAqSUeNsXAV9h4+PjGIaBZVnk83na7Ta2bSOEIJ1Ok0gkiEajJJNJ1tbWkFLuKWy73YXb87xLHrthGJimOepovltn78FggGEYCCGu6+7nAN1ul06nM/ou2IllWUQikVE3+/2QTCY5duwYuq6TTqdpNBr0ej2CIMA0TWZmZojH46TTaY4cOXJdbRdXVlao1WosLS0RjUY5dOjQOetTEASjcLqUkoWFBW677bar9r38eEWjUQ4fPszhw4f3/DemafKEJzxhFF4/+7stkUhQKBR2/fwpiqIoiqIoinJj8peX0bpdAl1HxGK7Lh+kUpjFIkG/j1suY11HBcluZM33vhd9eRlreRk3nycYG0NfWsJqtRCehxYECBh2PIZhEMZ18UMhghe9iNC3fAupO+54rKDA1vGtvRqcPEnvQx9CfO5ziE4HgCCdhm//duIvfSnWzMwVm2v7t34LbWkJa2kJN5dDTk6eE1Lz83n8bhdrcRFvZYXgve/FfeYzMcfHr9gYbgSDhx+m95a3oK2vo9dqaO02IgiQpkmwsMDgIx+h+8xnknnTm9CugcJ8yqVrfexjaA8/jLm4iJdMEnxNx1YRixHEYti1GqHVVexwmO4f/iHhX/zFXR9bbj3OXo6aaWtr6P0+mu/jC4F96BAinX4sNNrtom9uYi0sYEtJ4xd+gdxv/ualTPmG1/rkJwl+7dfQi0XMSgXNcQgMA6RE8328eBy/0aD10EPEf+d3sK5yQVNlyD95Eq3dJtB12EPxBD+dxqhWCapVvHodY5dCtVeTzOeRkQhGvU6wQ/h7uLDE6PVwx8aQhcLjeh6/08FvNBCWhZHLIS5WDHWH5+/9r/+F1mhgNpvYx47BhQo7x2J48/NYjz6KKJdxP/hBuE4D6Nx777BQQSwGkciOi8pCAa1SgVaL/sc/rgLoB0A6DrU3vQnt859Hr1YxqlU0xxnep+t4qRTBxgatz3yG6K/9GuFjxw54xIqiKDeG6/vKEUVRFEVRFEVRFEVRFEVRlOtIEMC9954gCAaj2zQtzHOe8/DWvw9qZNc33/c5ffo05XKZcrnMxMQEqVTqnCCb4zhsbm6ytLSEEIKNjY2r0k3W8zyazSb1ep1UKrVrgDObzbK6uopt29TrdXJXuVNCuVym2WwipSSdTu+4bCaToVKpjELlOwXQXddlYWGBTqdDq9Wi1Wrh+z5CCOLxOK1Wi7W1Nebn50cdoCuVCrVajWq1iu/7WJbF+Pg4yWSS8fFxJiYmVBd15Tyapo3WCd/3d+0kvL0e7rUD+KXK5XJks1kajQa1Wg3XdRFCEAqFyOfzxONxms3maLu0l+7i28ucPefHK5VKjcLV9XqdiYmJHZev1+uk02nC4TCRXS60udYFQTDqQL0XhmGM/ma/JJNJnvSkJ1Gr1ahUKvT7fYIgwDAM0uk0hUKB6IUu7rqGeZ5HtVqlWCwSiUSYnp4+L1iuaRr5fB4hBJubmyQSCZrN5q7fS9eD7fC6oiiKoiiKoijKiOcNDwwLsbfOm0IMu1IyDFgoB89vtdDuuw99cxMvFkNOTKCfOYPZamH0+8NlDAO5VSzR7PcRvk+o0SCYnITTp4m+/vWX1M1e+j71X/91tE98Aq3dRqtW0WwbgCASIVhfp3f33XRf/nIyV6C7q1sqof3936Ovr+MlEsiLHUOPxbAPHyZ86hR2u03nQx8i88pXXtZz30j6X/kKg9e+Fm11FWtjAwAvkSCwLLTBgNCpU/jhMNg29WqVzG//tgqhX8e8j34Uo1oFIc4Ln58jm8Vtt9FLJfjCF/Cq1V27Z4vxcWQkguY4SMdBXKwoq+dh1OsYjoMUAi8WOyd8DkAshn/0KHJxcVhg4p/+icHDDxM+ceLSJn6D6j34IMGv/zra0hJGvT4sxJHPD7siS4nsdtHLZaxTp3A8j87rXkf6D/9QfYb3g20PC3lo2p6+76Suj/apgq3v64Ni3HEH8qGHEKUStFpwkQLhANRqEATIXA7zO79z18eWnkfn3ntx7rkH/aGHRrcHySTccQfxl71sz0US/HYb8fnPo1cquMnkhcPn20wTN5vFqNWQCwvXZUdw6TiIZhNtMMDby/kJ08QPhRD9PrJUuurjU76GlNR+4RfQ77sP89QpCAK8bBaZSiF1HdHpoNdqWCdP4vT79H7qp9B/7/dUkSRFUZQrQAXQFUVRFEVRFEW5/knJ4JFH8MplhGFgzs5e0QrfiqIoiqIoV0oQQL+/TBA8dpJb0x4L+O2So1QuolqtMhgMKJfLTE5OXrCrt2VZzM7OsrS0RKlUIhQKMTExccUDqY7jIKVkMBiQz+d3XT4ejwNg2zb21oWDV9NgMKDf7xOPx3edu6ZpxONxer0eg8Hgost5nscjjzxyTjfb7dCr7/s0Gg0qlQpjY2OjUO7m5iYLCws0m81RiDgIAh5++OFRx/RMJsPk5OQoxF4oFEin09dMh1zlYESjUUKhEIZh0Gq1yGQyOy7farWIxWJomnbVA9VCCDKZzEXHFA6H0XWdcDhMq9XatQt6q9XCNE0sy9pTx/QLsSyLdDpNNptlfX2dRCJB7CLd1iqVCo7jjILP1ztd19F1HWePF+s7jjN6j/bTdhh7L98Z14NKpYJt23S7XY4cObLjNjubzVKtVmk2m5TL5RsigH4jUV+3iqIoiqIoinJliEwGQiE0zwPXhV1CyKLXGwWZdwsmAiAlvQceYPDpTyPLZRACMT5O5IUvJPKkJ12JKdz0Op/6FKLTweh0sI8cgXp9FD6XQgyDxGd3CDfNYSd0KbHOnMExDFp//MdkXv3qx/fEUlJ/61vR/uZvMM6cQe/3cePxUUBKb7cJnTyJl0jgf+AD1B2HzI/92OXN9SMfQXS7GL0e9vHjOy4r4nG8aBStUsH79KfhR39U/ZgEgsGA/hvfiLa6SmhtDWd8nKBQQGwX1QQ828ZYXcVcWAAhaLzznWRf//qDHbhySZzVVfSFBYxaDS+T2fUzEOTzwwIEvR7dz3yG1H/4DzsuH33+8xl86EP4loVWLiMvEiIVlQrCcdCCACcSGW4nLjQWIQhmZjC/+lVot+l96EOEf/7n9zrdm0L//e9HLxYxajWcI0fO7bQtBCIeJ4jFcNfXsZaWcKJR2h/9KKnv+Z6DGzQMg9Y3+jY4mUSaJprnIYNgtF29GGHbyK1C4cYu55KutsQLX0jz/e/Hy2SwVldxLOvC3ba7XayNDdx8niCbJXXHHTs+rlsq0f6Zn0E7eRKjWkVvNBCuC0Lgx2IEm5v07rmH/itfuad11H74YYTjYHQ6DA4fZrc1Ksjl0EolZL+P86//ev0F0C+lIPH252yruIGyfzqf/zz6/fdjnj5NYJp4R4+OtgMCIBTCz+Xwi0WslRUcy6L9u79L9i1vOdBxK4qi3AhUAF1RFEVRFEVRlOtW0OvRuvtugo99DG19fVQ53hWC9pOehHXnnSS+4ztu/APsiqIoiqJcN3Y7h3mQjZ6DIMC2bYIgGAUkrxflcpl6vY5lWRcMn28TQpDP569qx3F51snmxxOUlvt0kjoIAqSUew7ea5qGlHLH8S0tLdFoNFhZWSGTyVAoFM55/EKhQKPRYHNzk7W1NcrlMoPBANu20TQN0zQRQuB5HmtraziOg6ZpTE1NMTMzQy6XI5lM0mg0iEajzM/PX/edmZVLF4/HiUQipNNpqtUqyWTyooFh27ZptVrMzMyQyWT2PVj8tUKhEMlkkkwmQ6lUIpvNYhgXPlXn+/6oG3k0Gr1oaHwvJicnaTabDAYDVlZWyOVyZDKZ0XMPBgOq1Srtdpvp6Wni8fgV3zYehGQySSqVYnV1Fc/zLvpaA/T7fWzbJplMkjj7gkLlcet2u7TbbUKh0K77EkIIkskknU6HTqezTyNUHq96/e9xnGEhGtO00DQd+OaDHZSiKIqiKIqiXEciz3kOg//1vwhME1GpICcnL7qsDALMRgNvYgL/678efZffqN2//3vsd70LbWUFrdFAbBWRlJEI9ic/SX9+nvBP/iTRJz/5suYgfZ/u/ffjfP7zyFYLTBPt6FHiL34x5g1QxG43wdrasNOlEBCPYywsIDwP4LzwOQw7rmqehx8OExgGWqmE/+lPI3/kRx5XF/TWRz+Kdu+9GKdOgRAMbrkFEQqN7vfHx/F7PYylJfTTp/H/9/+m+9SnEvvmS//NJpeW0DodAsvauevpliCZRK/X0ep1/G4Xfavg6s2s/alPoZVKWOvrOBMTyLGx8wJ8IhTCP3oUFhbQV1eRf/M3+D/6o7t+5pVrj7fVAVdzXeRejuHGYkgA2yaoVnddPHz8ON3bboN6ndDKCnY0ChcI0mrNJqZtEwiBNE2CnYpdmiZuPI5WqxF86Uu7j/kmYi8toX/5yxiVCm42e274/GxCIKemkK0WolLB/+hH4WUv29/r06Sk84UvYH/4w2gPPTT8ngqHCY4fx3rxi4k/5zmIHY6JX4+sb/5mvL/6K6SmIapV2GkfREqMeh2vUMA/cQJtD99pV5MWjaK94hUEv/VbeIuLWAsLeKkUQSaDtCyEbaPV65jNJm46jZyawvzxH9/xPfSbTdo/8RNojzyCtbiIFAIvnUZGIuD76M0moZMncdNpgne+k6YQpF72sh3HGXS7SN8HKfe0z7K9jPB9ZK/3+F6Ua4AWDiMTCYJwGL3bxd9leem6w27p4+OIG6So8PXE/vCH0Ws1hOfhHT9+8SIU4+N4vR56sYi8/378ZhN9h2tIFEVRlN3dWHuViqIoiqIoiqLcNJz1dTo//dNoZ86gl8sY9fqwajzDk7yiUsH/8pep/u3fkn3rWx/XiVxFURRFUZSrZbcA+kHUzRkMBpRKJarVKsFZAwyFQhQKBfL5/GWHNnu9HqVSiXa7jed56LpOLBYjn8/vGBjfC9/3GQwGtNttksnkrstvd0Ludrt0u90rHrI0t/Y7TdOk3+/vGhodDAZIKbEsa/S3V5Npmpimueeg32AwIB6PXzS4ads2jUaDYrFIMplkfHz8gsul02kGgwH33nsv3W4XwzCYmZkhlUoRi8XwfZ/V1VVM06RcLo+6wadSKSYmJqhUKhSLRSYnJ/E8jxMnTlxXRRKUK2t8fHwUcl1ZWWF6evq8z0+/32dtbY14PE48HmdsbOyARnuusbExGo3GqGjDzMzMeWP3PI/V1dVRR/XLHXs0GuXo0aPAsCN6rVajUqlgmiZSSjzPIxaLcejQIdLpNMeOHTvwsP6VUCgUqFarGIZBqVRiamrqgstJKSkWi8TjcUKh0A3Tifyg+L5PEAQ7Bv7PZhgGg8HgnH0Q5dryxS9+O0HQH/23pkX4kR+5/i4mVBRFURRFUZSDEr71Vrq33IKoVDA3NrCTScSFjhlKib6yMgy1ZbNYd9214+O2PvUpgl/9VfSNDYxKBeH7BFvhZL1UIjBN3GoV+6d+Cvm2txF7+tMvafzND38Y/wMfQCuV0JrNUVdNmUjQ/dM/xX/600n99E8feGfRq0n6PmKrs6x0HPReD93z8A1j54P6Qgw7nZbL+LUanfvuGxbQ3yP37rsxSiWE7+PccgviQsdrolHc+XlCjzyCX68z+OAHLy+A7rog5TBsv5fldX342gDScS75eW8k3kc/ilGpIHUduVM4Ugj8iQlCZ87gt9t0PvpRUi9/+f4NVLkytsJvkuFxxl0/OdvLCAF7PAYb+4mfoH/qFI7jEFpZwavX8bNZZCSC8H1EvU6o0QAp8U0TJ5e7cGfls4cRCqHZNqLb3dMYbhb9v/1b6PfRBwPcubmd38+tbbxRryMXF3HW1rBmZvZlnO7mJu03vAFtYQG9XkdvNMD3QdMIVlbwH3yQ+h/8AdFf/EXCx4/vy5j2Q/wZz6A2OYm3sYFZKuGkUgjLuuCyYmMDISUyl8N8yUv2eaQXlrrrLhq9HsHv/R5OrYZerRI6fXp4pxB4sRj24cOQzSJ+4id23WdovPOd6CdPYp05g5dOE0xPn1Pt3i8U8LpdQouLeMvLBO95D87Tn451kXMlACKRQOg6UtPAcXYtRiO3zudiGIjrtAiN/LZvI9jYIHT6NJ5tn1Ps52tplQrSNCGVIvxd37WPo1S8eh39S1/CqNXwUqkL7xefxc/nCZ85g9/p0Pn0p0l97/fu00gVRVFuTCqAriiKoiiKoijKdcer1+m85jVojz6KtbSENAy8QgEZiyGDAK3VwtjcRFYq4PvUhCD3jneoTuiKoiiKohy4a60DerlcZnl5mW63S71ep9vtIqXEMIxRR9Jiscjx48cvqeO053mcOXOGVqtFp9Oh1Wrh+z5CCOLxOPV6nUgkwvz8/CWHiX1/WIt8r2E3IQS6rhMEwehvryTLsojH42QyGWq1GrlcbsdO6PV6nWg0SigUIp1OX/HxfK10Ok0qlaJSqTAYDHZ83QeDAYPBgOnp6YuObftx+v3+RcOd2/r9Pq1Wi3K5zNGjR5mamhp1St/c3GQwGOC6LrOzs7RaLRzHodfrEYlEmJycpFarsb6+jhCC06dP88QnPvGSXwfl+pbP52m320gpWV1dZWFhYdQZHaDT6dDv90mlUkxOTjIzM0P0gLtbbEulUoyPjxMEAWtra5w6dWoUkodhwY5Wq0UoFOLw4cPk8/krUigjnU5zyy23sLq6Sjqdptfr4TgOQgjC4TDhcJhMJnPBQPz1KhaLkUgkmJmZYWlpidXVVQqFAqGzLh7q9/sUi0U8z2NmZoZCobDn4PSV5LouUkp0Xb/uw//bc3D2eNH5dmGa633eiqIoiqIoiqIoOwn/8A/jLizg9fuETp/GzeUI8vlhaEpKaLUwSiU028Y5ehT/iU8k9axnXfTxBl/9Kv6v/zr60hJGvY4zNobM50dBDNd10SoVQmtrOK6L89a3Yvz+7xM6dOhxjbv+3vci/uf/RF9fx6zXAYad3IMAbWMDLxqFdpvWwgKJd70L8xopAHilafk8hELDoGerBYAIAuTFQm9BMAxuATKTQRSLCNvGX1/f83P2//Vf0U+fHnZvzeWGIchuF4IAaRiIs47XC8vCS6cxqlV44AHcYhHzIoVCdyNSKaRlDbs5+/6u4R6t1xt2S9d19D0UiL3RSd9HX1hAa7XwUqndr1GJxwkMA9Fo4D300P4MUrmirNlZHCEIIhH0Votgt6LLrRZSCGQkgj49fd7dXrVK7x//EdluI8JhrCc+kfCJEwS/+IvYb3oTdjyOVi5jrawgtk46+uEwbiSCMRjgWxbyAo/7tYTnDcexQ9DyZhQ0m2iOMyyusYfXRkajiHIZGL53+xFAd0sl2q98JdqZM5jLywgp8ZLJYUECz8MoFhGbmziNBv0f/3F45ztvnBC6EBj/8T8SbGwQ9HpYCwu44+OQyTxWDKLfRy+VMFotnEOHCA4fJvXc5x7wwB+T/oEfoHfbbfT/4i/gH/4B33GGxQMMAxkKIZ/xDKLf//2Eb711x8fx6nW0++5D39zEj8UIZmYu+J0jYjHsuTnCCwvY7Tbdu+/GevWrL/q4kSc+EScaxUsk0Gs1/F3OXWvVKn44DOEw4ac+dU+vwbUm+rKXMfj4x/EiEczFRdyjRy/c6KjVwqxUcCcn8Y8cIfrkJ+//YG9i7vo6SDnsQL+H3xwiHh8WVOr38Tc29mGEN7dgMKD3j/9I0GiAaWIdPUr4llsOeliKolxBKoCuKIqiKIqiKMp1p/X+9yNOn8ZaXMTNZpFTU6ODqAKQiQTOxATm6dMYp06BYdC57z7iO5ykVxRFURRF2Q/XUgC9UqmwtLTE+vo67XabeDzO5OTkKDRWr9ep1WpMTk4ipeTEiRPnhPZ24/s+jz76KPV6ndXVVTzPG3WX9X2fYrFIqVRiYmICz/O45ZZbLimEvh1Y03Udz/N2XX672+/VDLsVCgWazSaVSoXNzU0mJiYuGEJvtVo0Gg1mZ2dJp9P7EvrM5/Osr6+TSCTY2Njg0KFDF3wdfN9nY2ODRCKxY0fgXq9Hp9MhEolgXeSCy22bm5s4joPv+6RSqVH43LZtbNum2WwSj8dJJpOEw2FOnz5Nu92mWCySyWTI5XKjTsWJRIJ2u00ikbj8F0W5Ls3NzaHrOoZh0Ov1aDQadDodACKRCFNTU4RCIWZnZyns1GnoAMzOzqJpGoZhjAqAVKtVpJSEw2FmZmaIx+PkcjkOHz58xZ43Fotxyy230O/3qdfruK6LEIJQKEQ2m71hgudnO3r06Oi7YXNzk9OnTxOJREbfdY7jjELquVyOmX3qUAPgOA6VSoVKpYLruqPbE4kEhUKBdDq9YwGTa1UikSCRSFAqlej1ejsWfwiCgGazycTEhNqeK4qiKIqiKIpyQ4s97Wm0fuqnCH7zN3GKRYxKBatcJjire7SXTOLNzuLfeiupX/3VHYO/3T/9U/RyGbNWwz5yBBKJc7q0CtNETk5iRyKElpaw43G6f/EXhF7/+j2PufWRjwzD56dPow0GOJOTwzD19kHsXg+tWMQ6dQonCGi9/vVk/+APEAdQ2O1qiz73ufT+6I/wo9Fhl9mt9+yC4WIp0TwP3zTxYrHHOrNKOQyY7ZHz8MMwGKANBohOB7NUGoVNAQLLwstkhh22dR0/k8E8dQrP97EfeeSSA+ihZz8b9xOfQGoaWrWK3CHgI30fo9nEmZ7Gf+pTb8j3/vEK+v1hh+sgANMEzxu+77oOF3p9hEAaxnCZfn//B6xcNiOXw7/9dqhUsDY2sCcmLhxcBJASvVzGSyYhlSJ+VnfjwcMP0/3AB9C/+EUYDMB1kbrOwLLoPvGJWC99KbE/+AM6f/7nyM9+lqDbBc9DahrCMPBdF6NcxiqXka570a7QADIIMNrt4XZ9fv5KvyTXNWEYIMTwMyzl7kUkgmC0jLZPYf7W29+Ovrg4vGYukyGYmhp9N0uGx1xFqURodRVbCHpvfjPhP//zG6ZpS/LFL6a+soL/F3+BXF/H2tiAjY1hMZQgQLdt/GgU58gRgrk54r/2axf/TB6Q6FOeQvQpT8Etl7Efeoig20WLxwk/+ckYmcyeHqPzsY8h2m2MTofB0aM7nksQsRheLIZWqeB/+tPwqldddH3QIhHkd3wHQblMaGEBr9VCXKTAjOz3sWo1nMlJ/K//ekJHjuxp7Nea8Pw8vRe/GHn33ZinThF69FHcbJYgmwVNQwwGaLUaZrOJk8shJyYI/9iP3TCfqeuRBHZ99dX7sy/czU3af/ZniM9+dlioy3VB0xiYJt3jxzFf8hKSL3qRej8U5Qagfu0riqIoiqIoinJdCXo9xGc+g14s4kci54TPzyZ0HffoUUJf/SrUatgf/rAKoCuKoiiKcuCulQC667osLy+zublJr9djbm7unPB3LBYjk8lQr9fZ2NhA13WWl5c5/jgq5C8vL1Ov11laWiKRSDA2NnZOV9nx8XFqtRpra2sAnDp1iic+8YmPO2yn6zqxWIxkMkm9Xt+143hrq0PNdtD5ashkMqRSKQ4dOsTy8jK2bZPNZonH42iaNgp/NptNxsfHSaVSu3YPv1IMw2B8fHy0DiwuLpLP50kmkwghCIKAdrtNpVJBCMHExATj4+MX7QgspURKOQqT76TZbGLbNuFw+Jzl2+02g8EA3/dHXaDD4fAoWLz9nsHwta1Wq6NO6iqwePMSQnDo0CHGxsYol8ujQDUwKpqQy+WuyVC1EGIUeN5ej4OtLwhN08hkMhQKBWKx2FV5/kgkMuoWf6MzDINbbrmFpaUlwuEwg8GATqdDEAREo1GSyeRofZmZmdm3wHe1WmVpaYl+vz8qniClxDRNUqkUjUaDRCLBsWPHDmQd9jyPdruN53lomkYkEtkxSH62XC7H2toa8XicUqnEoUOHLvodsf1dk0wmr7lCEcrOtrMWiqIoiqIoiqLsXfKFL6Q7McHg/e+Hr3wFr9cD2wZNQ8ZikEoh77iDzCtegbbDbzCvWkX/whfQKxXcVAp2Oj6WTuPVamiVyjCw+OpX7/jYI1Li/cmfoG9soA8G2MePIyzr3KBHNEowN4e3uoq1uIgTjdK57z4S3/7te35NrhfW1BTt22+Hep3QmTPDDudCIHx/1Ol8m2bbAEjTxM/loN0eXs8QCg07qe+RtG1EqYTZ7yN8H23rfwBSCALHQbNtgloNd25uGHCGYYf0weCS5xp72tOozczgFYuYpRJ2InFOt/XHBijRV1aQug6ZDOG77rrk57yR6LHYMIDuOFirq3BWx83AsvByOWQu99j7JSXCdYfh9D0ee1GuPaG77sL7x38kqFQwz5zBPXLk/MCrlIi1NfR+H2d6Gvnt34629dlqffKT+L/xG+iVCnqlgtHtjg6+BKEQolzGf+ghOi96EdnXv57gx36M3gMPEDSbiFAI6wlPQDoOg//8n/FbLfTNTYJDhy46Xq1UGv4jkyH0kpdcldfkemUcOYIfiw275jabsFv352YTPxpFWhbmHjrPX67ByZPoX/4yxuoqXiqFnJk5P4SpaciJCRwpMdfXcdNpOp//PPFnPvOqj2+/ZF71KpoTE3h/+qcE5TLU68NtqabhxeMQi+E/9akkX/96zD10Sj4oZqGA+exnX9Lf+ouL6J0OUtcReziXFCSTGNUqQbOJV69jZLMXXTb2vd9L79OfxikUCC0t4Y6PE+TzjxUhknK4T7SxMXy983nCL3/5Jc3jWpF5zWuo9ft4n/40olLBqFYf21YKgReLYR8+jCwUMH/u54jdfvvBDvgmZE5MYDP8XtSaTQLLQgiBNM0LF+7qdoeFvsJhdHX+6aro/+u/0n/DG9A2N9HKZYxWa1QwK7AsRLFI8PDDVL/wBbJvf/s1VwxEUZTHRwXQFUVRFEVRFEW5rnQ++9lhBc92G/vQoZ0reOo6bjqNUa3Cgw/ilkrX9IFlRVEURVFufDsF0IXYv8K/lUqFwWBAo9Hg8OHDF+08nslksG2bcrlMPB5nMBjsqUu54zjUajU2NzeJxWIXDFdrmjbqqr25uUk8HqfVapFKpR73fLY7jpfLZWq1Grlc7oLL+b5PpVIhlUoRCoUu6bn2QgjB/Pw8QRCgaRq1Wo2NjY1RwBSGAfhDhw6RTCY5duzYJXV/v1RTU1OjzsfbXdo3NjYwDAPP8xBCkE6nyefzjI2NMb3DRTPbHagdx9nxOeXWxVKO45wXQN/ugH727ZqmoWkavu+f87rpuk48Hqfb7dLr9S7nZVBuEOFwmNnZWWZnZ5FSXlcdoyORCIcOHWJ2dhbf95FSYhjGdTWH64Gu6xw9enTUcbzb7eL7PoZhkEgkyOVyFy2ycTVUq1XOnDlDsVikXq8TjUbJ5XJomjYaY6lUYmZmBt/3OXHixL6Nr9/vUywWqdVquK47CqBblkUsFmNsbIzsDhemwfD1LhQKOI7D4uIiy8vLTExMnPM953kelUqFRqPB7OzsqJDMzabX61Eul2m1WqPXOhaLkc/nSaVSB74tUJsiRVEURVEURbnyYrffTuz22xksLND/u79DNhpgmhhHjhB/7nPR9nCMsPf3fw+DAUavx2B+ftfuf342i7W8TNDr0X/wQWJPf/quz9G5/360YhGjXscZH794J10hCKankc0molbDueceuAED6ACxH/kR+g89hDMYEFlcHAawXRdv6ze78H0010VIiRcO48VikMmgnT6Nm0gg43Fij6NgvvulLxGvVhFBgGHb+IaBFwoNg+9BgOZ5mK6L5/tw5gxuPj8Mw+s6+h67p16QEJj/6T/hr63h9fuETp3CzecJcrlhaERKZKuFUSqhOQ7u0aP4T34y6W/+5kt/zhtI78tfRltexhgM0B3nsQIFZxcNKJdxDx+GeBy5FdSR6TTGN3zDwQ5euWTxZzyD6nOeA56Hcfo0oUcewU2nkcnksEN5t4tRq6H5Ps7cHMGxY6T+y38BoPvFLxL82q+hLy1h1mq4qRSDo0chFALPQ6vXMUolZKOB95GPUI/HybzqVRcME3ef/GRot4edsVdXz+mMDQxDo6USVqmEPTNDMD1N7AYKJV8Jsec8h+Z73oOXTKKXy/ip1EUPkknbxmw2sQ8fJnjGM9D3oWBy7+670dpt9K3t7077AMH4OKJaHTZtueeeGyqADpB66UuRL3kJnb/9W9z770c2m8NiL4cPE7vzTqx9KAhwkMTWPkegaXs6kCt1/bHCFrsUqgkdOoT78z+P97a34RoGRrmMKJWGxRYAo9+HIMDLZgmmpuCHf3hP+5fXMqHr5N74Rtrf9E24d9+NfPTRYaGoIADTREYiyGc8g+gP/ADhW2456OHelIxsFjebxXQcotUqXq0GQiCFwEsmCfJ52Cp0D6BVKsP98WiU2Hd+5wGO/MZkLy4y+JmfQV9cxFpfx4vFcGZnkdEo+D5aozG8ZrtehyCg/va3k33rW9WJJ0W5jqkAuqIoiqIoiqIo1xV/bQ3R7yOkROzh4uQgmcSq1fCkxF1fVwF0RVEURVEO1E4B9P3qfg7D8Fu9Xt9TR9NcLsfCwgL9fp9qtbpjGHnbdsC93+9z9OjRHZfNZrPUarVRR+tLCYVnMplR0H1tbQ3f98lms+cE9rrdLsViEU3TKBQKTExMXNVg13bX3e0Q/nYYb7vDrWVZZLPZ80J5+0EIwdzcHNFolEgkQqFQoNfr4XkehmEQjUYJh8OMj48ztsv+cyqVIpFIUCwW6XQ6ow7mF2IYxiggfPact7uon/1+eJ6H4ziEQqHzXh9d1/E875xg+o2k3W5Tr9dHYcRwOHzNdvK+1hx0WPNSCSH2NQB9s7Is64IFUfaT67osLS1RLBZptVocPnz4vO/hfD5PqVRiZWUFXddZXV1lbm7uqo+t2Wxy+vRpWq0W9XqdTqczus80TTKZDO12ezTunT5v09PTDLYuYltfX+fMmTOEw2Esy8LzPPr9PqZpMjs7SyaTYX5+/qrP71ri+/7ote50OucE0OPx+GgfbX5+nsiFuswpiqIoiqIoinLdCx87RvjYsYveH/R6tD/xCbx//VcYDBDRKMY3fAOJ5z0P2W6D5w1DRHs4rigjkWH3P8/Dbzb3ND7n//wftGZzGIzNZncOuWsaXjqNVq+j/9//i3SciwfWr2PhJzwB/61vxXnLW+jbNpGNDQzXRet0kFvhl8Aw8C0LPxbDP3IEymXMTgd7fh757//9nsOJzvo6xj/9E7rrAuDrOsHZx1N1Hd80CVwXw7ZBCLS1NdxcDplIEH7Sky5rrok77qBRLBL83u/hFovD4GypRGAYw66GUuIlEriHDuHfdhvpX/olFSYB+l/5CvbrX48eBOjbwUAhCEwTISWa5w2LFlgW4swZ7Lk5zM1NvEQCmUiQfMELDnoKyqUSguyb3kQN4LOfhXp92D23XEbzfaSmDYNZhQL+iRMk/9t/Q0+lQErsd74TfWMDo17HPnIEEonHtrlboUe3UMA8dQp9cRH/f/9vnIuEa5NvehPtV7wCBzCXlxGNBl4yibQs8DzMZhOCAHt6Gjk9TfRtb7tw19ibmBYOI5/7XIJKBevkSeTyMv7s7LlBfkAOBphnzgzDjakU0Ze9bF/GJx99FNFq4Uciu37XCk3DSyTQ2m2ChYV9Gd9+E4ZB4o474I47Dnoo+y+ZRJommusifX/Xz7Lo95GmCUJg7KFQTfxZz6IbiWD/5m8iNzagXkfbKgzuJhLIbJYgk8H4r/+V5ItffEWmdOCEIPH858Pzn8/gkUewv/pVpG2jJZNEn/a0HbvGK1eX327TeMMbMBcW0AcDNN/H6nQIhEAaxvBz0GziZrMEs7NQrQ4LhBw5gv8t34JxkeYFyqXrvPvd6GtrWOvr2NPTsPUab+/DyEgEZ2wM8/RpjFOn8AyD3oteRPSpTz24QSuKclnU1SyKoiiKoiiKolxftk5o7tXZpzml71/58SiKoiiKojwO10IAXUqJbdsMBoMdw8LbTNMkFArR6/VGYbLddLtdOp0O4XCYUCi047KappFMJul0OueE3R4PTdM4duwYvu+jaRqbm5vUajWi0SiapmHbNq7rkkgkmJiY2FOw+krQNI2pqSkmJydpNpvYtj3qcJxOpw88cDo2NjbqHt/pdPB9f9RhfK+dV7PZLKurqySTSUqlEpFIBP0CFzkIIZBSkkgkcF33nGW2u53btj26rdVqIYQgHo+f19HecRwsy7rg81zP6vU66+vr9Pt9Op0OruuiaRqRSIT19XUymQyzs7MHvt4oinLptgu01Ov1C4bPYbi9HB8fx3Xd0XZ1Zmbmqn72O50Op06dolQqUa1WSSaTHD58mFAoRBAENJtN6vU6zWaTIAjQdZ3Z2dmLPp4Qgvn5eUKhEKZpYtv2KGRtmib5fH7U9Xxubu6mKrDh+/7/z95/x8uW1gW+/2elWpVz1c7pxG4aaGZAydBDEiW0BNMMishcuT/zFbmiMz8YEUUcvaOjtCK8BJm5joIgoiigQ3IaRwRtaGi6T9p579qVc9XK94/atc7ZfXY65+xwwvN+vXg1Z9eqWs9atVI9z/P9fnnssceo1WqsrKxg2zbRaJRQKITjOBSLRTY2NhgdHcW2bc6ePXtTBqFfQ9ecIAiCIAiCIAjXwLMsag88gPTXf43UaqE0GkiOg6couJ/5DLU/+APsqSkCw85k24a9+shse/BfWUbe5+8Lr9VCsqxB4Ow+Oq69YBC51cLxPOx6/bZNCh95xjNQHniAzvvfj/mBD+C2WqiG4QcZO6HQoBpoKDSoZtxuY0xM4E5MEH3DG/a9ns7HPobU7w8CvXUdxbJwHeeq79rTNBzXRen3kQAjHsd70YuQDyDhafJ1r6M9OYnxwQ/iXboErRZY1iDQPBYbVHZ+8YtJ/diP7fu4uq15Hr13vhN5dRWt2cQMh5EdB9Uw8FwXV9MG55NtDyrauy7hb30LOxbDnZyEV7xC7MdbnKSqZP7Tf6L1ghfQf//7kT//eRRFwYNBAgLLwnEcpGaT9p//OYk3vIHeQw8hr6yglkpYIyOwU5IKTcOamUE/dw6n06HzsY8R+MmfvHqxfJ7o7/4urV/4hUEV2GoVpdFANgw8WcbK53HTabyxMcK/8iuiiu8Okj/6o9S+9jVMx0FbWEB95JFBRftwGDwPudlEa7WwN5ONeN/3fYRuMPHHvpkmkuvi7rOv2FMUZNNEMs1Dbphw1PT77sP6xCcGwbeVCt4uz16e66LV69ijozhPfvK+7zeRpz+dyJ/+Ke0HH8T85CdxNjYGkx3SaQIvehGJF78Y6Sj79jefMz3TREkkDuR5ZyfBs2fFNfIm4Zkm9Te/GeVf/gVtfR3JdbE1DWVzHrDkuqj9Pq4sg23jVSpIsowxPo43Pk70TW865i24/Zhrayhf+QpKsYiVTPrB548nKQrW7Cz6o49CvU7/Yx8TAeiCcAsTs5QEQRAEQRAEQbilyNks3mYHotftIkUiuy4vdTq4m52d2sjIobdPEARBEARhNzsHoHvI8tFUCPE2I4Y8z0PeZ9T7cDlvn9FGjuPguu6+g+VUVcUwjBuqaB0MBrnrrruYn58nGo3Sbrfpdru4rksoFCKRSBAIBBgdHWVsbOy613M9JEkimUwe6Tr3a9i2622fLMuMjIxgmiaLi4ssLCyQz+eJRqN+AHuv16NcLhOLxZidnaVQKFCpVNB1nVgs5ldib7fbmKZJv9+nXC6TTCZJJBJbkgWYpkmn0yGXy5FIJA5iF9wUNjY2WF5eplKpUKvVcBwHXddxHAfLsgiHw3S7XTqdDmfOnCFwG1aROiyu61KtVqnValiWhSRJ6LpONpslHo8fd/OEfTIMg0ajMZikKUmEw+Fb8vsrl8t+devtgs+vlM1mmZ+fxzAMKpUKI4fYp7GyskKtVqNSqTA1NUXkir4WRVHIZrOkUimWl5dZWVlBVVWy2eyugdGSJDE1NcXY2BjlcnlLle9IJLLn+29Xy8vL1Go1FhcXiUajjIyMbHleyufzfkISgIsXL3LPPffsKynMQRPF6wRBEARBEAThaHmmSfVnfxblq19FXl1FbTZBUQZVpy0LaW0NO5FAWl1FLpdxAwHkahVvj75OpVrFCYdBVQmcObOvtkiBAJ4kITnOIAPVXj8QHAdvc5nbPYg2ePIkwXe9i97rX0/nTW9CXVpCabcH1Rhtm0CxCJKEFYthnDyJOzFB+N3vJjA+vr8VeB7eZz6DUi5jxOODKuieh9bvY2uaX710sOigv15xHCxdB8ch+v3ff2DbGr3vPqL33Uf3a1/D+Pu/x2s0QNdRTp4k/tKX3vbf9bVof+lLyCsraIUC5ugobjKJfuECNqCY5iCJgCz754lmGNiBANbICM4zn0n6//w/j3cDhIMhSUieh7q+jhuL4fR6qLaNJ8uDaq3FIrZp4lYq1P75n/GyWdR6fRBQnMmw65U2FMKORJArFezPfQ62CUAHCIyPk/ngB2n//d9jfPzjeN/4BlK/jxcI4J4+jfbKVxJ78YuRRKLbHcmhEMn/8l+ov/WtEAxCtYpaqyE1GiBJOKGQX63e+4EfIHVE56/b7Q4Kr7gucr/PfkZUJdPEUxS8PebWCbeeyFOfSnVqCrtQQCsWMWIxpO3uy56HsrICkoSXThP47u++thVJEtHnPAee85wDaff1MNfW6HzsY3if+Qxyve63y3nKU9Bf9Sqiz3++6My+jdU/9CGUhx9Gu3QJJxzGPHMGdXERtd1Gtixk2wZJQnEclF4PW9PozczgnjhB6F3vQp+ZOe5NuO10P/Up6HRQu12Mycldl5U0DTuRQKlU4H//b9xuF3mP8VFBEG5O4teDIAiCIAiCIAi3lMiLXkTrve/FjkRQymXc3TrJPQ+1VsPOZHBOnyawR4eHIAiCIAjCYWo0GiwuNrd9rdlsEosF8LzgoQcYybKMoihomkav19tzedd16ff7pNPpfVcoVVUVWZYx95lR3zRNFEXZUtHa8zy/yup+6brOXXfdRbfbpVQq0e/3/UD4ZDJJOp2+7apm3wzGxsb86uXFYpHV1VX/GLNtG8uyiEajPOlJTyKZTJJKpSiVSqyvr1Ov1wmFQliWhWmaXLx4kUAggKZpjIyM+BV4YXBMbGxsEAqFCIVC5HK549zsA1Ov11leXmZ1dZVut+sH1w8TP/T7fSqVCouLi7iuy4ULF7j77ruPJRjxVlMqlVhdXfWDly3LAiAUClGtVgmFQszMzBCNRo+5pcJO2u026+vrNJtNDMPwA5iDwSDBYJB8Pk8ul7slzgfHcTBNk263u68EGsFgEE3T6Ha7+7pfX69Op0On06FcLvuVybejKAoTExNcvHiRTqdDqVRienp6z89XVZXR0VFGR0cPuum3HMuyqFarbGxsEIlEmJiYuGoZWZbJbFaLKBQKxGIx6vU6qVTqqJsrCIIgCIIgCMIRq/3qr6J85StoFy7gqSrGzAxSPD4IqPE8aDRQCwWUbhdPknAlCa1axchmd6w+6Zkmar2OOT6O87Sn7TsIWj55EuJx5LU1vHYbaaeqvJvURgM3GsXNZlHukH6W0F13EfjkJ2n+0R/hfPrTSJUKkmEMvitdx4vF8J7/fGI/8iNo1/Cb2O31kOt15F4PO53G0XX0paVBtU/LQrIsPFnGA2TXHRwLioKnKNjf9m37D3S/BuF77yV8770H/rm3E+MTn0Cp1ZA8DzeXG1TePHUKdWlpUFjBtgcJHQBn83x1VRU7kyHz7neLYODbRO/hh7He+U7khQUC5TJWKkV/bg5CoUGG6nodtVxGOX8eHAe330fq9XCCQaR9jF25kQhyu41cqeyeHESSiD7veUSf9zwAPNsWx9g1UhIJ0r/7u3S+8AWMP/9z+NrX/Ne8YBDvvvsIv+Y1R1IhufeNb9D9yEeQH3wQbXERrVZD63bpP/II9ugopFLbHwuWhdZuD4Ll/9W/OvR2CkdMktB+5Edwlpexez30ixcxczm8bHZwPfE8aLdRikWUXg9zbg7n7FkS99133C2/Jo2PfATvgQeQ2m2UUgml2wXPw1VVKJex//mfqdx9N4l3vxtV9KHfdjzHwfvrv0bZ2MBTVdzZWSRJwjl5ErdUQqlWUYbP38NK6JqGnc8T/4M/OJTnYgHcjQ3kfh9PVQfPOHstH42ira8PEu+XSiIpgCDcosSvCUEQBEEQBEEQbilqOo33rGfhVKvo8/MYlQpsTszdwvOQl5YGGcKzWdSXv/zoGysIgiAIgrCpWCyyvLxMqaRcVUXc8zx6vR6aZnH+/DKnTp3ad2Xy65VKpWg0GqysrGDb9q6VyhuNBoqiEIvF9h38FIvFSCQSlEoler3erhVOHceh2WwyMTFBNBqlXC5TKpXodrvAoIJqLBbzg3L3E2QYDoeZEQNXR2p2dpZgMIiu6+TzeVqtFrZtoygKkUgEXdfJZDKcPXuWxx57jH/5l3+hXq9jGAamaWIYBpIkYRgGrusyMzPDqVOnmNxMIjVMKmCaJjMzM6TTaT8w/Va3trZGtVql2+0yOzt7VXXzYDDIxMQExWKRlZUVgsEgtVqNdDp9TC2+NaytrbG6usrGxgaNRgNVVQmFQnieR6FQYGNjg1wuh2EYnD59mtgeE6mFozdMvFCr1ajVavT7fSRJwvM8FEUhmUzS6XRotVqcOHHipg9CH97/h+3fD1mW8TzvqmeHg1StVmm329i2TTKZ9P9uWZafuMHzPDRNIxaLEY1GqdfrVKvVfQWgC5eVy2X6/T7dbpe5ubldl02n01SrVRqNBqVSSQSgC4IgCIIgCMJtzlhcRP7c51AXF3EDAZwTJ5Cu7COWJEgmsWIxtAsXwHFQbBs3EEC7dAlrZgYpGNz6od0ugcVFnFAI0mmCr33tvtsTe+Uraf7RH2FHo6jFIk40unOgY7OJ0u9jz8wgfdd33VEVKJVolNSP/zjem95E54tfxF5dBcdBzuWIPP/51xWM720GKeN5g36QZBJDllHX1/H6/UEg82Y/gS3LeIqCbJoY+TySSP52bKSlJeR2GzsS8c9dSddxTp/G6XSQKxXkfn9Q6VpRkA0DJxJBHh0VgcG3ke5734tSKBAolzFmZyEev/yiLEM6jZ1KIS8soM3P4wYCeNd4zZTgmq+z4hi7PpKiEH3BC4i+4AU4rRZ2pYKkqmj5PNLjxnEOhedR+93fRfrIR1AaDZRKBbnTQe31wHXR63UUw8AplbBPnIArk9F4HvLKCm4gALEY4de85vDbKxy52AtfSH19HfcP/gCrUECrVJA3NnA1DVwX2XGwo1GMkyfxzpwh8eu/vq9kFzeLxoc/jPc7v4O8tIRWr2NHItjp9ODZp9slsL6OVyhAr0fzp3+axAMP3DGJkO4UnQcfRC4WBwm1xsYu3/8kCS+fx87lsNttMAwk18VqNJBdF1VRtv6WEm4ehzjeKQjC4RK/KARBEARBEARBuOVE/v2/p/vVr2IaBvrqKlaziZvNQjSK53lIm1mDZdPEPHEC5667SL/iFcfdbEEQBEEQ7lC1Wo2lpSVWV1dpNNKbwXOXX5ckCdd16fXaVCoVFEXh5MmTh9qmXC5HuVwmEAiwtrbG5OTktkHv/X6fYrFIJpMhFAoRv3KyzC4ymQyrq6tEo1E2NjaYmZnZMTCwVCr5FdOLxSLlcpl6vU673farl8fjcer1OtFolJMnT14VoCvcHEZHR8nn89RqNZrNJo7jIMsykUiETCbjJzqQZZlQKMT6+jpLS0s0Gg1SqRSSJJHP59F1HU3T8DyPtbU1bNvGtm2i0Sizs7OkUqnbJsFAu92m1+tRq9XIZDK7Htu5XI5Go+EHI4oA9J3V63XW1tZYXl7GsiwmJye3VDl3XZd6vc7GxoZ/nN5zzz1oO1QLE45eo9FgYWGBtbU1ms0m6XSaiYkJAoEAruvSbDapVCo0m00/oHt2dvZI2uZ5Hv1+3z92gsHgvhLHKIqCJElomka/399zedd1MU0TTdN2TRRzoyzLwjRNdF1HVVVM06RYLNJqtfwkIQCaphEMBv02OY6D67qHnjTndjJMmKDrOsHHB4Y8zjABT7vdptPpHFELBUEQBEEQBEE4Lt2PfQyp00HpdunfddeOAROSomBNTBC8eBErHscLBpE7HYLnzmFHIriRCHgeSruN0u1ix2I4s7N43/d9RJ75zH23R0kkcJ//fJxGg8DFi3hLSzhTU1vb5Xl4zSb68jJWOo0XjxN71atudFfckiRVJfqCFxzIZynRKJ6u4wUCSN0uHkA8jh2LYXU6gyrbloUHeIEAbjyOvriIF48jXZFYTjhiljUIqNnu3N08N90r/uQtLQ0qYlvWkTVROFz9ixdRvv511FIJM5PZGnx+JUnCmZlBfeQRJMtC8rxBkKjj7BkYKrfbuMHgYH7UHZTs42agxGIoR5zEtvae98Cf/AnK/Dxqt4sVj2NNTOCWywTqdVTTJNDpYDkOXLiAfeYMKAp0uyiFAkq3i3HyJO5Tn0rwrruOtO3C0Um+7nW0xsYwP/CBwb2l2QTTHNyPolG8WAzvvvtI/NRPHfkxfCOMpSW83/s95KUl1Hab/qlTSJGI/7qbyWBMTKAsLxO4dAlTlmm85z2kf/7nj7HVwpDb7WKurwOg5fPXfezZCwvQ7yM5Dl4yyVV3PkmCWAxiscGzcSiEdukStutiLiygieRMh0IeGYFgEMm2odfbswq63G7j6jooClo+f0StFAThoIkAdEEQBEEQBEEQbjn6zAzOr/4qxi/+Ioauo5RK6PPzfoY8T1GwEwnsmRmcu+8m8Z//s8joKwiCIAjCsVldXaVSqdDtdslmtx/gD4XCyLLD8vIygUCATqdD5IpB1IMWDodJp9NYlsXi4iILCwtkMhlisRiyLGNZll/dNBqNks1mmZiY2Pfnq6pKLpfDsiwWFhZYXFxkdHR0S7CVaZqUy2WazSbZbJZKpUI0GqVYLKKqKolEAlVVMQyDYrFIsVhkYmIC27a56667rgoUdV2XWq1Gq9XCcRy/8nY6nd53pVnhxsmyTCaTIZPJ7LhMLpcjFosxMjLC9PQ0tm37QYyNRgPP8wiHw3S7Xf+7jEaj6LruH4u3S8Bjo9Gg2+1eVXl4O5IkkUwmaTQatNttf98IVysUClSrVQzDYG5u7qrrhSzLpNNpNE1jZWWFaDRKqVRifHz8mFosPN7KygrlcplWq8Xs7OyW+4csyySTSeLxOMvLy6ysrKBpGvl8nnA4fGhtsm2bUqlEqVTCumJysKIoZDIZcrncrkHFkiSRSCRIJBIUCgXy+fyu17JGo+Hfy/a6PhyEYWD90tISrVaLer2OYRh+8Ltt2wQCAT+hjKh+fu1c18XzvH0nFFBVlX6/j+u6ey8sCIIgCIIgCMItzfvyl1EqlUH1ZF3fdVkpEsHRdTxdx85mUcbGBhWWy2WUVgsANxjEnpyESATvda8j9cY3XnObkj/zM9QfewzT89AWFlAfeQQ7kcANBpEcZ1CN1TCwMhncyUmUN78ZdZc+QWGfJAn3Wc/CKRYJLC5iWBaSpoEkIUWjuI+r7CltbOApCiQS6C9+8TE1WvASCTxNQ2m12M+veLnfx4lE8PaZ9Fe4+fX+7u+Qul2Ufh9rdvbqILkrSLKMlUyiNpvIloWrqsjlMt7IyM5v6nZRu12MyUmkA0p4Idy8eg8/jPThD6PMzyPZNv2zZ/3nAyeVwrx4EbfdRut2CfR6KJaF/PDDuMEgimFgRyKYp07h3n03yXe845i3RjhssRe+EF7wAjpf/SrGF7+I12ggBQIoJ04Qe9nLUG7Be033ox9FarXQ6nWMxwWfD0myjDs9jXPxIvL6Ot5nP4vz4z8uqqAfF8+j+5Wv0PvoR1H+8R/BcQDoSxLO056G/qpXEX32s68pgYpnWUib4yP7qmg+XMbz8ESSn0MTfulL6X7oQzjhMHKphLvLeKFnWaiNBubkJM7Tn458iOO4giAcLhGBIQiCIAiCIAjCLSn8lKeg/P7v0/ngB/G++EWcTmeQwVOSIBjEzWSQXvpSUq9/vei4EARBEATh2DSbTfr9PtVqlXw+z8LCztV1I5EglmXRbrcplUqHGoAOMDMzg+M4SJJEuVymUCiwtra2WaHdQ9d18vk8qVSK6enpaw5+m5yc9Cu8rq+vMz8/j67rBAIBbNum1+sRDAaZmpqiVCoRDocpFouMjY2RSCS2fFY+n6dcLrO8vIwkSSwuLnLq1Cn/9UKhwMbGBoZh0Gq1/GDmaDTKysoKuVyOiYmJHauwC0cvGAwyPT3N5OQkhmHgui6KohAMBnEch2q1uiWZQDQaJZPJ3HYB17ZtY1kWqqrua9t0Xader/vvvd32x0Hodrt0Oh1qtZofZL6TWCxGOBymXq9TLpcZGxsT14mbQKvVotfrUavVyOfzOwZ1y7LMxMQEFy5c8O+dMzMzh9KmdrvNxYsX/WOr2Wz699BYLEar1fLXv1cCjlqtRrFYpFAo7Jj0wDRNSqUS6XSaUChE7BArkwQCAYLBIN1ul/n5edrtNuVymVgsRjabJRAIAOA4Ds1mk0uXLpFMJikUChiGQWiPygbCZYqioCiKX1V+L9dyfzhqmzkgBUEQBEEQBEE4KK3WoKr1HsHnwCBAeTMIXJqbI/Srv0rnox/F/cIXkDf7jdxMBumFLyTy6lcTuM6Ee0osRuK//lcab33roKpdrYZSraJ1OniShBONYs/O4iUSKD/7s8Rf+tLrWo9wtdCrX435uc/hBgKoi4vYJ05sH3TT6RAoFrHyeZzpaZJPferRN1YAQH7mM/G+8Q2Uchm704Hdxnc2g5Tt6WnkZz7z6BopHCqvWkXq93FVdc9EIgBeJAL1Oo6uY8diBDY2MEIhpG0CRT3TJLC4iBWPQzRK9DWvOYxNEG4i3T/7M5RGA7Xb3RJ8DoAk4Zw8ibu6iletona7SJ6HbNtYiQR2KgWRCO5zn0vqrW8Vc+buFJJE5GlPI/K0px13S26YZ9vwt3+LXCphRyK731MlCSefR19YwG02af/1X5P43u89usYKwCDIuPrOd6J89rMo9TpKpYJsGEieN6h8Xalgf/nLVJ75TFLveAfyLsmcrySn04PfR5KE1+1um4hgi24XV1VBllHS6QPYMmE7gfFxWk97GtRq6IuLGJEIbDM26jkO2sICrqZBMknwVa86htYKgnBQRAC6IAiCIAiCIAi3LH16Gv1tb8NpNul8/vO4lQqoKurUFJHnPEdUPRcEQRAE4dgNK3J7nkc8Hme3ApaKIhGPx6nX68TjcWZnZw+1bbIsc/LkST8As9/v0+v1/MqcwWCQWCzG6Ogo8evIjC5JEqdOnfIr0xqGQbPZxLZtdF1nZGTED1rTNI1Lly4xOjp6VfD5sK35fB7HcdjY2CAcDmMYBoFAgIWFBYrFIqVSiUajgSzLaJqGbdsUCgWi0SiGYdDtdjl16tRtUzn7diHL8lXBi4qikMvlyOVyx9SqoyPLMrIs42xmgd+L67p+gLQ4lrc3DF62LGtfiTMSiQTFYhHLsuj3+yKY9iZQq9Vot9s4jrPn/UdVVaLRKPV6nVqtdigB6N1ul/Pnz1Mul9nY2CAUCpHP5/2EKs1mk8XFRZLJJJ7nIcsyqVRq28+Kx+PE43GmpqZYXFxkaWmJTCbjJ51xHIdGo0G5XCYcDpPJZHYMUj8omUyGjY0N+v0+7XabXq9HOp2+at8Pq7GHw2Fs20aWZQqFAnNzc4favttJLBYjHo9TLBbpdruEd5n8OQz4Hxsbu67nsIMg8nEIgiAIgiAIwhEKBPBkmV07kK8gOQ6equIFAuizs+hvfjO8+c2DgB1JQjqgRFZqOk3mve+l/eCDGH/+5/DP/+z3Y7n5PPJ3fRexV70KdYffwcL1Cd97L73nPAcMA+3SJQLnzmHlcnipFJIs4xkGcqmEVqthJxJ4o6PoP/qj4ofcMYq96lW0//iPsaNR1JUVrFOntj0PPcdBW1nxA+qiIhDn9iHLg+uv5w0y9+11ProuSBL25CRuMIglSQQXFrBiMZx0GoJBsG3kWo1AvY4TDOLOzOD9u3+Htlul9ENkzM/T+1//C6/ZBF1HO3OGyLOffWD3HGHA7XaRH3wQpVLBise3T2ggSXiTkxhjY1jLywQ3NrBjMcyzZ1Ff8Qqir3oVWj5/9I0XhANgFYtI7TZKp4O1n+M4Fhs8R7fbOPPzh9/AfbJrNboPPoi7WZFeO32a8L333n7Pa55H9T/9J5TPfhb10iVk08ROJLDSaZAk5HabwNoa3sYGmCa1t76V9G/8xr7m9Uae/3xav/u72JEISrmMu1sAuuehVqvYySTuyAihJzzhADdSeLzoj/843W9+E9M00VdXset1nEwGIhE8x0Gu19GqVZAkrJMncZ73PJLf/u3H3WxBEG6AiMYQBEEQBEEQBOGWp8TjxF/5yuNuhiAIgiAIwlWG1Y11XUeWZTxv5wFFSRpUhW40Griu61fxPkySJDEyMkI+n6fZbNLtdnFdF1VVicfjNxyIKUkSU1NTjI2NUS6X/YqxsiwTiUTI5XIsLCywuLiIqqrbBp9fKZPJcPHiRXq9HuVyGYBiscji4iKSJDE5OUkkEvEDdDudDqVSiYWFBTzPQ9M0ESgn3FRCoRDhcBjP82i320Sj0V2XbzabRCIRVFVFFQm3tuU4jl+Zej/7KBAI+BOn95sIQDhclmVhmibBYHBflZ/D4TCNRgPHcQ7l3rm4uOhXLZ+YmLgqGDgej9Pr9VheXkaWZRRFIR6P79j2EydO+MdoqVTy3yfLMrZto6oq6XSaTCbD1NTUjsHsByUUCvnXlfX1daLR6LYBz7ZtUywWSafTKIqC53nUajWmpqbE9WifMpkMq6urRKNRisUi09PTOx6vpVIJWZaJxWJ3REIWQRAEQRAEQbjTeTMzuI8+SqBQwHDd7atdD5e1LJROBzObRXpcEtNDSdAuSUSf8xyiz3kOnuPgtttIur7vyoXC9Um97W3UOh1QFOSNDQLr60grK4OBBM/DCQYxx8chl0P66Z8m+rznHXeT72hqJoP3yldif/jDBC5cQDt/Hjufh1TK/86o1QgUiwDYJ07gvuxlBxIgaiwt0fvsZ3GrVVAU1Lk5oi9+MbJItLknz7Jw2m2UWOyGr5/K1BReOIzkuvuq0iq3WrihEF46jfqWt+D8xm9gxGLI5TLBxcXBMQM4oRDm2Bik03jf932k3vjGG2rn9ej+0z/R+6M/Qvn616HXQ7IskGXscJhaPo/8yleSeN3rRJGQA2KuriKZJkq3O7jO70JSFLyZGZxWC3t0FOXFLyb1pjcdUUsF4XB4prn5f7xBco+9SJKfyMl/7zHqnz9P97/9N6QHH0Tq9cA08WQZMxikNz2Nev/9JF772tsmEL31N3+D8oUvoF68iKcoGHfdhaRp/utuKoUxPo46P4926RKeotD82Mf2ValeTafxnvUsnEoFfWEBo16HHZKPS4XCIPg9m0V+2ctum/17s9Ln5nDe9S6M//AfMMJh5FKJwPIy0mZCNTcQwE6n8XI5nOc8h/Tb3y6+E0G4xYknfUEQBEEQBEEQBEEQBEEQhEMiSRKSJOEOB1pcOHPmATzPuWKZQYCaLHtbqhtLRzgAI0kSiURizwDw66WqKqOjo4yOjl71WrfbpdvtEovF9tzmQCBAKBSi0+nQbDbp9/sUCgVkWWZmZuaqIK5hpdaVlRVWV1cJBAKMjY0RFJMjhZtEOp1mZWWFWCxGpVLZkkDh8Xq9Hp1Oh7m5ObLZ7JFeI24lwwBgz/NwHGfPAOZhJWdgX8HOwtGQJAlvc5IlDI7/YRITSZLQdZ14PI6qqgd+7xweO7Is0+v16Ha7lEolstnsjpWoQ6EQ4+PjrKyskMlkqFarOwYNq6rKmTNnWFtbIxQKYZomnU4Hz/NQVdW/d42Pj5PcYSLNQctmswSDQTRNwzAMNjY2iEajfoKGbrdLq9VC0zSy2SyJRIJ2u41lWTQaDTKZzJG081anKAr5fB7TNFlYWGBpaYmRkZEtCX9M0/ST9kxPTxONRonFYsfY6u1dcXoKgiAIgiAIgnAA9Pvvx/6nf4JCAblUwtuluq1cLOJpGiQSBI+4erKkKCiH1IcsbCUHg6R/8zdp/Pf/jv2JT+AWi9DtDgYZNA1CIZx77yX4+tcTedrTjru5ApD8yZ+kWiphSRLy2hqBtTVYXcVTVSTbHlS7TiRwx8dxnvtc0v/X/3VD6+s9/DDd978f5aGHoNtF3gy6cyMRGg88gPeiF5F405tQbsJ+hePk9nq0PvlJ7L/6K5RLlwZ/lCScu+9Gu/9+Yi960ZbAuf2KfOd30vrDPxxUaS0WcXdJhuwZBlqziTE7i/e85xH/ju/AOHOGzh//Md4XvoDb7YJtg6KAquL8639N8Hu+h8iznnW9m33dmh/7GO5//a8oxSJqpYLS7+PJ8iDQXlGw19ZwCwWq//IvpN79bpGc5CDY9uC/njc4BvYiSZcTXVjW4bZNEI6Amk4DDO6f/T57dUV7loVs2xAIIO0whnNU2l/8ItY734lcLKKUSqidjt+Z7moa0vo63vw8la98hfQ733ld95ubjfnxj6OWy8iOg3H6NNI21y1JUbBPnCDw2GPIpRLOX/wFfM/37CsgOfzDP0zvy1/G7HbRl5awWi2cbBYpFALPw2u3Ucpl1HYbc3oad2qKxGtecxibKjxO+ClPQX3f+2j/8R/j/d3f4bbbg/uQJEEggHPqFOorX0nm/vtF8Lkg3AZEALogCIIgCIIgCIIgCIIgCMIhCQaDhMNhNjY2ME0Tz4Nc7ru3XVaSoN1uEwqF0HX9jgku9bxB4P1+Az8HleQ9qtUqiqLQarW2DT4fkiSJ0dFRLly4QK/Xo1QqMTU1dZCbIAjXTZZlstkstm2zsLDA2toao6OjV50P3W6XlZUVkskkoVBIVMPdRTQaJRgMoqoqjUaD9OZElZ00Gg1isRiqqqLr+hG1UthNMBgkGAyysbFBrVajXq/T7/fpdrt+lfpgMEggEPCrj0ciEQKBwA3dO1utFqVSiXq97ge/l8tl2u02hmHsWYk8Go2iaRr1ep1yubzreaooClNTU4yPj1OtVun1eriui6qqJJNJotHodW/H9dA0jYmJCRYWFkin0/T7fUqlkr8fdF0nnU4TjUbJZDLk83keffRRLMvCHk6IFPZlfHycfr8PQKFQYGFhAV3XCQQC2LZNr9cjGAwyMzNDMpnk5MmTx9bWO+RRVBAEQRAEQRBuCpHnPpfa2BhmtYq+toahKJDJbH0w9zwoFglUKhjT0zinThG+997ja7Rw6CRVJfnDP4z3gz9I58EHsc6dA9NEiscJPfe56DMzx91E4QqSopB55zup/9EfYf/5n+NWKtBsXg4kTiRwMxnk++8n84Y33NAP7/ZnP4v1K78yCAoulVB6PTxFGSQokCTsWAy3WqX+0EMkfvu3UUXyQAC6Dz1E/21vQy6XUctllGYTyXVxFQVpYwPn4YepffCDRH7t19B3CSDfjppK4T772Ti1GoFLl7DW1vDGxq7+nk0TbX4eOxyGeJzQZpCcPjeH/h/+A85P/RS9f/kX3GYTKRhEv/tuAhMTB7ULrknrC1/A/e3fRl5YQG02sdJprNlZJF3Hc12kWg2tVEI5fx4ch9ov/zKZX/mVY2nr7UTZPF9dTUPqdvH2SPziGQaS4wyCb/fowxaEW4ESj+PcdRfS5j3O2u5aegWpWsVVVYjFCD7/+UfY0q16Dz+M9Y53IC8uEiiVsBIJ+nNzEIkg2TZSvY5aqUC9Dq5L7Zd/mfQ73nGobTIWF+l8/ON4Fy4MAoOjUdRnPIPYy16GfEVi3OvVv3gR5dFHUatVrHR62+DzIUmWsTMZ1HIZb3mZ7te+RvgpT9lzHcGTJ3F+6Zcw3/Y2jGAQtVRCO3cOV1WRPA/JdbGjUcwTJ3BnZoj8xm+I5DtHKDA+Tvrnfg73x3+c7j/+I06thhQIEDhxguDddx938wRBOEAiAF0QBEEQBEEQBEEQBEEQBOGQZLNZ1tfXCYVCVCoVXHdsx2VddxB0ND4+TjabPcJWHi9FUVBVFcMw9rW8aZpEo1F6vR6O46DrOuFweNf3aJpGNBql0WjQaDREALpwUxkfH8cwDFzXZWVlhQsXLpBIJAgGgziOQ6vVotfrkUqlGBkZYWZmhkAgcNzNvmlFo1HC4TCpVIpqtepXyd5Ot9ul3W4zOztLJpPZMZGFcHiGQd+tVsuvWK9pGq7rYhgG3/zmN1FV1a9+Pqx47jgO4XDYv64/+9nPvu57p+M4XLp0iUajQavVol6vY1kWsixTqVRot9t4nsfExMSeFcmH96f93tMURbkpEkrIskw0GmViYoJYLIZpmti2jeu6wKCd0WiUVCpFNBr1g84lSRLnzTWSJIkTJ074QeeGYdBoNPxnmmFF9EQiwezs7I7Xr6NuM0iP+7cgCIIgCIIgCAdJUhRCb387/Z/9WUzPI7C2hlcqYSeTeJqGZJqo9TqS42BMTeFNThJ929uOu9nCEZEUhejzngfPe95xN0XYiyQNkga87nWDIPHN6uSEQqj33kviRS9CusHf+r2HH8b61V9FXlggUKlgptNYMzNIuj6oBNpsopZKBC5cwHQcGm95C+k/+IMbXu+trvfwwxj/9/+NsryMVijgqSp2IuFfY7VSCTY2MFstuj/1U0i/93sEJievaR3xn/gJWg8/jGnbBJaWcJtNnHQaLxTCc12URgO10cANBnHm5vBe/WpC99yz5TOUWGxwvh83z8P6/d9HKRRQWy3MkychHPZ7iCRZhkwGM5VCvXQJdX4e/v7v6T38MKEnPelYm36r0/J53DNncIpF1HIZa3R01+BbuVTCCQYhHCb8whceYUsF4eB4lkX3a1/DbTSQAgHk5z4X75vfRCoWkVZX8SYmtj8Per1BsHcuhzM3d6zJmbrveQ/K+jqBUon+zAxSInG5Vz0QwMvnMbNZtIsXUS9dwv785+k+9NC+grCvlVUs0nzXu1D++Z+ROx2kZnOQpEbT8L70JRrvfz+89rWk3vjGG0qIY126BLaNbBh7JssAcJNJ5PV1MAys+XnY57ZHnvEMlN/5HTrvfS989avY3S70+4O2h0J4sRjec55D7Md+DC2fv+7tEa6fHAoRve++426GIAiH6M7+NSkIgiAIgiAIgiAIgiAIgnCIAoEAyWSSXC7H8vIyzWYbSF61nOs6dLstUqkUuq7fUQHoiUSCRCLBysoKtm3vGmjVbrexbZt4PI5t29Tr9X0H4gYCAVGp9XEcx6HZbGLbNpIkEQwGj7zqrjAI/Dx9+jSyLKPrOs1mk0ajQafTQZIkwuEwY2NjfkXcjKhWs6eRkRE6nQ6tVoulpSXGx8cJBoP+657n0Ww2KRQKpNNpwuHwTREEfCcxTZOLFy/639MwwFxRFGKxGOvr6ywvL1OpVIjFYkxMTBCNRlE2qyf0+33K5TILCwuMjY1RLBZ5xjOecc3tcF2X8+fPU6lUWFlZwXEcEokE8Xgcx3FYW1ujUCig6zpra2sAuwahy7KM53l+5fBbRTAYRJIkYrEYwWCQ6elpP9HL8NqkaZq/fKvVQlVVdF0ndABVMu40kiQxOTnJ6OgolUrFP/5lWfavR7quH3czfd/5nW0/qYKu6yIAXRAEQRAEQRAOSeiJT8T79V+n/7a34aVSSMPqvI4zCJTMZvEyGdyxMcLvehf67OxxN1m403ge7b//e8wvfxna7UFQ9d13E33JS5Cv6HsTBtXrYy95CbzkJQf+2d33vx+lUCBQqWDMzkI8fjnATZKQEgmceBxveZnA/DxmMEjrU58i/vKXH3hbbhWebdP7pV9CWVkhsL6OMT4OmYwfdOcB5vg4UqGAvrSEIUm03vEOMn/wB9e0Hi2XI/Jbv0XnLW/BDIeRNiuty5vjUnY4jDk5Cckk7nd/N+mf/umD3tQD0/nyl5FXVgYB0Pk87JAIWpJlnJkZ9G99C7vRoPfRj4oA9AOgvvKVuN/6FlKxiLyygjs5uX2QaLOJVq1iTk7i3HuveDYQbjlWsUj7wx+GT38aqdEAxxkc67IMtRpmJkOgXMa2LNx8HiIRADzHQa5U0IpF7EgEb2QE/Yd/+IaCqW9E/7HHUB55BK1UwsxmkXYIxpZkGXt2lsC3vgWNBv2PfezAA9DNtTXaP/ETKAsLqKurKIaBEwziyTKyaSIVCtjxOO4HPkB1dZX029523fvNs23YHA/zFIU9P2W4HtcdVGS/BsGzZwn+P/8P5soK3b/9W9xKBSQJZWKC6Hd8B2oqde0bIAiCIOybCEAXBEEQBEEQBEEQBOHweB69b3wDa34ez7JQ0mnCz3gGsghSEAThDjI1NUWn08F1XT73uTbdbgdV1VCUQZCabdvYtkMqpfnVjW+GapdHJZfLUalUUFWVQqHAxMTEtoFNjuOwsbFBPB5H13VisRiNRsOvzroX13VFpdZNpmlSKBSoVquYpollWUiSRCAQIBQKkcvlyOVyIsDsCCmKwpkzZ2i1WhSLRRqNhh/AGggEyGazZLPZLQGgws4ymQydTgfP81hfX2d+fp5QKOQHoQ+TWQz369zc3E0V7Hm7syyLxx57jFqtxtraGq7rEovF0HUd27YpFAosLy/TarXo9XokEgk6nQ6yLPvV0YffYTKZ9N/fbrdJp9PX1Ja1tTVqtRpLS0tEIhFGR0f9IHeAmZkZv0J6uVxGlmUikciO52K3270qWPtWoGkayWSSVCpFoVAgm80S2ZzIBYP7RrfbRZIkFEWhWq2STCYJhUIicckNUFWVkZERRkZGjrsp2xKPAYIgCIIgCIJw9MJPeQrBP/kTWp/8JPZf/RXe/Lz/mnvmDOorX0nipS9F2mdSTuF4XNl3IcsyoVDo1u578jwaH/0ozp/8CfLGBnKzCbYNsowbj9N473vhu76L5JvedMdX2T5sxuIiykMPoZZKmOk0xOPbLyhJuFNTeI88glSpYH3iE3AHB6C3P/955EIBrVDAHB2F7ZJASxLe2Bim46CtrcGjj9L75jevqlC+F316Gu0DH6D18Y/j/OVf4q2uXn5RUXCe/nSCr30tkac97Qa36nAZf/d3yI0GkuviZjK7BxVqGlY8jlKt4n3pS4NgRNGxdENi3/mdVD/2Mcxej8DCAo5hYOfzSLHY4Fg1DORSCa1axc5k8EZGCL3xjcfd7EPhOA71eh3DMPA8D03TSKVSt1wf/K3McRza7faWJKr7TRC/m+7Xvkb/P/yHwbNFqYS6ec0BcMJhnHgctV7HjkaRHAf94kVcVfUDqT1Vxcpm8UZH4U1vIvpv/s0Nt+m6t+VTn0Jutwft2qvQgKZhJxIolQrel76EZ9sH9vzkOQ7tt74VeX6ewMICdjyONTuLtPkc6ngeXrOJtr6OfOECFlCfmSH5wz98XetTUin/O5E6HdjrebfbHdwfAgHk6wwYD0xOEnjDG67rvYIgCML1E7/0BUEQBEEQBEEQBEE4cJ7j0PzYx7A/8QmUxUUwDPA8LE2jEYvhveAFRF/3OgLj48fdVEEQhEMXCAQ4e/Ys58+fJ5uNoKoatm1hWR4goaoK4XCIaDTA7GzsjqtuHIlESCQSTE5OsrS0xPLyMrlczq+o6nke7XabYrGILMt+oJZlWUQiEWq12p6V013XpdVqkc/nCe9QpeFO0el0uHDhAq1Wi0qlQrvd9gOdVVUlmUzSbrdpNBqcPHlSBOwfsVgsRiwWw/M8HMfxgz2Fazc9PY2qqqiqimEY1Ot1TNNEkiTS6TSJRAJd15mdnSWxQyUC4XDMz89Tq9VYXl4mlUqRzWa3HOeJRIJKpcLCwgLxeJxwOIyu61SrVT+ZSCgUIp/PEwqF/OD0Uql0TQHorutSLpcplUrous74+PhViTfi8TjpdJparUapVCIej1Or1cjn81d9nmEYdLtdRkZGSN0ilRZc18XzPGRZJpfLUavV/ID88fFxer0etVrNr37tOA6lUolAIEAul9t2PwiCIAiCIAiCIAg3Rg6HSXzP98D3fA9uv4/b7aJEoyLo/BZgmibFYpFyuYxlWX7/nqqqxGIx8vk8yWTyuJt5bTyP6m/+JvLHP46yvo5aryO5Lp6qIjkOrKwMqmhWq1S/9S1Sv/Ebohr6NWq1WtRqNT9RbDAYJJPJbJu0oPc//yd0Oij9/iCgbLcPliTsVGoQFPzoo1jFItod2pdj/uVfolareLBnYKA3MoJcrUKzSe/jH7/mAHTYvI7/238LP/AD9C9dwq1WQdMIzMzcMhVavVoNyTBwAwGkfYxReOEwcq2G1O/j9nrId/hY3I2SNI34r/86rZ/5GUxFQd7YQF9YGARuShKS4+AEg5iTk3gjI6hvfeuBV1E+brZts76+TqVSwTAM+v0+MBh313WdZDLJ+Pi4n3hYOHiGYbCxsUGlUsG27S3PNfF4nJGREeI7JULZQ//iRYy3vhVlcZHA2hpOKIQ5MQGhENg2cr2OVizibt4L3WgUY2oKWi0kz8MOBCCRwJ2aQnvDG4i96EUHuenXrlJB6vdxNA328czuRiJopRKSaWLXami53IE0o/35zyNfukRgcRErlcKbnNz6rCBJSIkEVjSKdu4cytoa9p/9Gd6/+3dI15HUIfzUp1JLJAYB9dUqzh5jdEqlghWL4UWjRJ797Gte382s3+/TarX8RA3RaPSOn5ciCMLtRQSgC4IgCIIgCIIgCIJwoNx+n9ov/ALKl7+MWiqhVqvIlgWAJ8vYsRhuuUz7i18k9K53EXrSk465xYIgCIdP13We8IQnkMl0CYU8HOfywKMsS2hagFxOI5u9M7ts5+bm/MlVxWKRhYUFNE1DVVVM08R1XZLJpB/sNjY2RrfbpVQqoaoqlUpl1+qh9Xodz/OIx+Nk98o6fhszDIPz589TLpcpFArEYjEmJycJhUJ4nker1aJardJoNPxJBKdOnTruZt+RhhM4hBszPj5OLpejUqlQrVaxbRuAYDBINpsllUpdFXAsHK5ut0ur1WJ9fZ1kMrnttbvT6RAKhYjFYkSjUeLxOLHYIEGL53lIkoQkSSQSCVKpFK1Wi0aj4VcW2++5U61WMQyDVqvF5OTktsfCsDJ7JpNhcXGRWq1GOBwml8ttWd5xHNbX1wmHwwSDQXIHNFnoMFiWRblcplwuY5omMLjmJJNJYrEYU1NTzM/P8+CDDwKDIPVOp0O326Xb7RKJRJibm6Ner9Pr9Y5zU4RjtJm7RhAEQRAEQRCEQyYHgyKY9xbRbre5ePGiH0zcbDb9xJ+6rpNKpWg0GoyMjDA1NXXMrd2/+oc+hPzxj6NevIhkWVgjI3jpNJIsD34cNhqoGxso58+D61L/lV8h/cu/fNzNviU0Gg1WVlbo9Xq0220/eWYoFGJ9fZ14PM709PSWQHS3Wh1UV1UUv5rpbrxIBLlSAcAule7YAHRpfh6p1cKJx2GvpLuahh0OI7daOJcu3eCKJYInT8LJkzf2OcdBUQbBzvvsBPKuXE4kNj4QWj5P4r3vpfHAA3if/SxuqwWdzuA7CQQgHMa5915Cb3zjbRd8bpom586do9FoUC6XabfbwKAf23VdwuEwmUyGZrPJyZMnicVix9zi20+z2dzyXHNlQvNgMOg/14yNjTExMXHNn9/5rd9CWV0lsLaGMTEBj0vQ78ZimCMjqJcuIbkubjaLff/9yP0+nmkipVIEn/98wk972uBadZO4npZIB3jNND/+cdRqFQB3fHzH9kiKgj02RmB5GbdWo/XpTxN/+cuveX2SpiF9x3fgbmygnz+PUyrBTuNj1Spau41x8iTefffdNolKGo0GGxsbtFotDMPwA9CDwSCRSOSWSlotCIKwGzF7SRAEQRAEQRAEQRCEg+N51N7+dpQvfQltc0DWymTwkslBp3m3i1KpoF+4gNnt0v/5n0d+4AH02dnjbbcgCMIRkGWZcDhKLHa54qgksVlhWuI6kkrfNhRF4ezZs6ysrBAKhTAMg3a7jeu6KIriBwCOjo76wYrhcJhoNMro6CgrKysoikImk7kqgLBer1MsFhkdHSUUCt3RlY5XV1dpNBoUCgXGxsauqraTSqVIJpOsrq6yvLyMpmk0Go07ep8Jtz5N0xgdHWV0dPS4myIApVLJDxTfKSGI4zjYtk0ymcTzPDRNI5VKkclk/IkbgUDAr5puWRa1Wg3gmgLQe70e3W4XSZKIRqPbLiNJErlcDsuyaDQaLC4uEgwGmZ6eJhgM4jgOzWaTSqWCLMtMT0+Tz+cJ3KRV6crlMktLS/T7fWq1Gr1ez9/HjUaDWCxGr9fDdV1CoRDz8/NbJvRNTU3heR6maeI4DoVCAVmWmZycPO5NEwRBEARBEARBEIRj0+v1uHDhAqVSiY2NDaLRKJOTk+i67vcdlEolGo0GrusiyzJjY2PH3ew9ub0e3p/8CcrKCpJtY50+DZp2OZhJkiCZxI7HUS5dQl1YwP7CF+ifP0/w9OnjbPpNr1KpsLCw4CfOdBwHXdfxPI+NjQ0/wWG32+XMmTOEQqHBGzXtclCw5+0ddOe6/jLSTdpfdRQky0LyPNx9Bvl5ijLYd5vJG+9E0vg4XiSCUihgGcaeCQ/UZhM3HMZNpUTilAOkxGKkf/7ncX/yJ2l9+tM4S0tgWUipFOEXvvC2nGfjui7nz5+nWq2yvLxMJBJhamqKSCQCDKoM1+t1lpeXMQwDgLvuuktUQj9AnU7Hf64plUp+QvPhc80w4LbRaOB53jU/1xgLCyhf+xrqxgZWKnVV8LkvEMCamSF4/jxOv48sSaTf/vYD2sqDJY2O4oXDgyQx+7hmyq0WTjCIFwqhPG68/rp5HsrDDyPX69iJxN6B7YkE3uoq1OtY//RPcB0B6ADR7/9+Wn/3dxjdLvrqKla3i5vLwTDAvNdDLpXQ6nXMsTG8kRGiP/iD17Wum02hUGBlZYV6vU6tVvOvSYA/ttlqtRgbGxPjaIIg3PJEALogCIIgCIIgCIIgCAem/b/+lx987moa9okTSLJ8eSJCIoGTSOBUKuirqxiBAO3f+z30d7/7OJstCIJwZFwXvvSl+3Ddy4NPsqzzrGd9/o4vCDAM3BsfH6dardLpdPwA9Hg8vm2l4tnZWQzDwPM81tbWqNVqJBIJNE3Dtm0ajQa2bTMyMkI6nebEiRN3bLVjy7Ko1+tUKhWSyeRVwedDkiQxPj7OhQsX/ImRIgBdeDzXdf3JkcPB9EAgQCaTIZ1O+4HBwtHyPI9Go0Gr1fIDtaPR6E1V6b3b7dJut4lEIjsGig8rnMMg2Uiv16Pf7+84gWy4rYD/3/1wXde/z+wmmUxi2zadTod+v0+73ebcuXMEAgE8z0NVVZLJJOl0mlwud9NOIimXyywsLFAoFKjX64RCIeLxOLIs0+/3WVtbQ1VVNE3DMAxs2+bEiRP+/UKSJGKxGKlUikAgwNLSEsvLyyiKQiqV8icACoIgCIIgCIIgCMKdZhh4srGxwcTEBPF43H9N0zSCwSDpdJrl5WVWV1f9voSbXftv/mZQ4bzRwJyeZscsurKMPT1N8NFHsdttuh/9KMG3vvXI2ul5Hq1WC8uygEE/ZTQavWn6wx6v3W6zuLjI+vo6zWaTXC5HIpHw+6hM06RarbKysoLjOJw/f5577rkHRVFQpqfxwmEkz8NrtZCuONa2IzebOKEQXiBAYJsKtcN1Dft4NU0jnU7fFoGclmVhbgaQO6EQiqoiGwbOXm/0vMFyoRDskLTyThC5/356H/0ojq6jFIu4U1M7L9zpoHS7WJOTSN/xHUfXyDuIHA6TeNWrjrsZR6JSqdBut1lZWSGdTpPP57e8HgwGGR0dJRKJsLKygq7rrK+vMzc3d0wtvv0sLS1Rq9Uol8tMTU1tSeA7fK7JZDIsLi6ytraGLMtkMpl9J+bt/tVfIXU6KIaBNTu7a9VwKRTCjkaRy2W8z30O3vKWm6ri+VD4ZS+j96d/Orjf7HHN9AwDrdXCmJvDe/7zkQ5oTNVpt8F1kWwbd4/nAwAkCU/TkGwb2u3rXq+WyxF+97vp/tzPYQQCKBsb6BcuDJK+SBKybWOHwxgzM3hjY+i/8isEbtJxtGtRLpdZWVlhZWWFbrdLOp1mamrKn6dSr9epVqu0Wi0AVFUVydIFQbiliQB0QRAEQRAEQRAEQRAOjPHxj6PUakiOg33mzM4ZVTMZrHYbpViEL38Zq1hEe9zAkSAIwu3IdaFW+zKu2/P/Jsuhzf8eV6v2z3EcqtUq7XYbx3FQFIVoNHqgAaeqql41mWAnuq5z5swZLly4QDgcptls0mg0/LYNA61DoRAnT54kPMy0fQcaZt3udrt+FfmdyLJMIpGgXq/7gZf7rSgs3P4qlYpfWaJer9Pv94HBpJ9Go8HKygqTk5PkcrljbumdZWNjg42NDQzDoNPp+EHZkUgEXdfJ5/OMjo4e+8Rbx3H2DPrWdZ1gMOhX2g4EAriuu+PyrVaLcDiMoihoO02E3sYw2Nq2bf++sZNsNku73cYwDDKZDNPT03ieh6IoBINBQqEQ+Xz+pj3uDcNgaWmJ9fV1Op0OMzMzV90T8/k8q6urPProo8iyTDab5d5778VxBtNyFUXZcvxMTU1x8eJF2u02pVJJBKAfoE6nQ6lUuioZTy6XQ9+jcspR8bzjboEgCIIgCIIgXC2TydDr9XZ8/c/+7M/4ru/6rh1f/7Zv+za++c1v3nA73vOe9/CGN7xhx9df/vKX89nPfvaG1/O2t72Nt+4S5PvGN76R//E//scNr+dNb3oT/+W//JcdX//FX/xFfuu3fuuG13P//ffv2t7f/u3f5hd+4Re2fc3b5kfKTn0gz3jGM3bd/3/yJ3/Cj/zIj+zR2q1c18XzvC3rnJiY4MMf/rD/b1VVmZyc5OLFi3Q6Hf7yL//SX8+N9NeEw2HK5fKOr3/rW9/iqU996vV9uGWB4wx+BDabPPb0pxPboZ+4Lknc02zCQw/B178O73jH9a0T+PrXv86pU6d2fD2RSPjB5sPv/vHHwJUJDnfymc98huc85zk7vn7mzBlWVlb22+wd/bf/9t94zWte4/97fX2dWq1Go9FgdnaWn/zJn+SRRx7Z9r3D42pL0kXDGHwnX/valoGld8zN8e/Hxy+/1zDQGg2MmRncZz+b73v96/nkJz+55bO3O3dg9/33lre8hV/6pV/acXt/4id+gj/8wz/c8fX9+qEf+iF+//d/f8fX3/nOd/Krv/qr2772+G2THAccB8nzoFDYsuy/SaX4f5/whMt/aLdRTBM7nUZ55jN53/vex0//9E/f2MYA9957L//wD/+w4+uf+MQn+P7v//4bXs/o6CiXLl3a8fUvf/nL3Hffffv7sCuvAYuLVwV9rj372dDroS0uYiUSSNEokVe/GoDFxUXuvvvu692MLebn53cc1+r3+6TT6QNZz//+3/+bJz/5yTu+PjY2RqPRuOH1fPzjH+clL3nJjq8/5SlP4dy5cze8nve+97384C5Vhl/60pfyxS9+8YbX8453vIOf+7mf2/H117/+9XzkIx/Z9+cNz9/H31e/93u/d8u5GIvFSCQSVKtVYrEY73nPe3jggQeubyOu8NrXvpYPfehDwGA8o16vb0nS8cEPfpC3H0AV7uc85zl85jOf2fH1//7f/zs/+qM/esPrOX36NF/72td2fP1v//Zvuf/++7f8bbvnmp0Ml4vH4xSLxR2Xe+ihh3jWs541+MeV15avfGXvjfC8wf9WVyESoVKpEAqFtl20WCwyOzu792fuwyOPPLLrZ0Uika330T2umb7heNfXvw6PPsrnXvhCnv70p++4nhMnTlB43L1rR4Yx+PxyGc6f3/LS+++6i++6stq85w3uj7IMwSDPetazeOihh4YvXfsYgOchOTZvnzjF6yZmUfpdABw9hBuK0Lz7aXRe/aO89Wd/jn/4h09d44df7fWv/wXe+Mb//46vv/vd/z8++ck/uuH1vOIVP8Jb3vK7W/7mui4XLlQpFvv81V99lAcf/LMd3z88RvZ6Nn3Ws76LX/u1nT/nox/9PX77t998ja2/2tmz/5r3ve9/7fj65z73Md7+9tfd8HpyuQk++tHzO77+ta89yE/+5ItveD2KovK5zzWv+rvjQLWq8ZSnWDe8DkEQBsSMMUEQBEEQBEEQBEEQDoTTbKJ85SsolQp2IrFnllY3l0M/fx6n06HzqU+R/KEfOqKWCoIgHJ9d4udu6gD0YYXxUqmEYRi0220/KDkajbKyskIul2NiYuLIgxuDwSD33HMP9XqdUqnkZ5GGweS7XC5HOp2+pqq4tyPTNDFNE1mW91W9JBKJUK/XgUGlEBGAfnPa7wSQg1IsFllaWmJjY4N6vY6maX7QaaPRoFwuk0gkcBwHx3FEJvcjsrCwQLFYpFQq+RPgVFXFtm08zyORSNDv9+l2u5w4ceJYg9AVRUFVVbrd7o7LxONxNE0jHA5TKBSYmJjYMTh8WJF8dnaWbDZ7TduWSCQIh8Ooqkqj0dh1oqLnedi2zROe8AQ/mP/KIP9YLLbv9R6HUqlEt9ul0WgwNze37X1AURR/O4rFIvl83q/wvh1N04jH49RqNWKxGNPT03f8vfZGGYbB/Pw8nU6HVqvlJ5NQVZVEIsHGxgbpdJqZmZkj2dfD06nbnfcnWdp2YHPdoqKQIAjCQeqfP0/3L/4Cb3UVbBsplSLwghcQee5zD6wSliAIwp2g1+vtGoC+W3IzGPzG3O39+zVM5LUTwzAOZD22be/6ummaB7KeYaDvbq8fxHqGlYp3Ytv2gaxn+PtmJ47jHNp6NE0jGo1Sr9dpNBp+YscbsVdfiOd5B7I9uw5ubOoNI5UcB25gnTsFRfvr6fX2PC734ziuCf1+n2azSa1W8yuNm6a553G5oyu2wbliv3mmSWB+HjsUgkSC8Gtfi/kf/+NtdU247vU87ns3rvi35zhoa2vY0SheNEr8/vux//iPD2R79jrfD+ras9dnuK57fevZJhpRXlhAazaxo1HcmRl44xvRNscEDuzasw8HtZ79XHsOYl3ieeTg1pNOp5mfn8c0Tdrt9oFde2zbZm1tjUqlgmma/vmr6zrLy8u31PPIXtee674mPM5On9HpdCgWizz22GPbL7OP54sty+6jrUd5Tdhxmf1EcLsu9PuHc03YZv3O4/7tdTrIloUXj6OcOUP/b/7mQPbdR8I/Qi9wgoS3AXi0Ajkey9xHu5mHD8IjjxgYxo2v53Ofs1hf3/n1hx4yD2Q9X/6yyTvfufVvpmnT6eTodMIUi1lM88af6R955Or1XGl+3j6Q7VlcNHZdz9qacyDrKZf7u66nUnEPZD2SpG67Hs+TMYwYH/hA9YbXIQjCgJgxJgiCIAiCIAiCIAjCgbA2NsDzUAwDcz+V/8JhPEWBXg93l0y4giAIt5Odxg9d19kcB5SPvTrt43mex8WLF6lUKhQKBdrtNqqq+sGNhUKBaDTqTyA4derUkW+DJEmkUilSqRSe5+G6LrJ88+3LoSsrtcuyTDgcPvQg+WvdF3sNqgvHw3VdarUa5XKZTqeD53l+xfpcLneoQbCdTofl5WVWV1fp9XpMTk4SjUa3LNNutykUCqyurgLcEoG5t7q1tTWKxSKLi4t4nsfo6CjxeBxJkvA8j3a7TblcZmFhARhMdJ6enj629sbjceLxOJVKBcMwtq3mPDym2+22P2lsu2oW/X6f5eVl4vG4n3DkWkSjUcLhMMlkknK5TDQaJRAIbLtsqVTCcRwSiQRTU1MHVlXnKHieR7lcplarEY1Gd01CYlkWoVCIfr+PLMu0Wi0SicSOy0ejUQqFgl+tfj8JToTtGYbBY489Rq1Wo1AoYNs20WgUVVXp9/tUq1VCoZCfUOb06dNHFvD/+c8/Ede9PBFIlkP823+7cxIJQRAEYf+M+Xnav/7rKN/8JnKrhdTpAOAFAtif/Sy1fB71//g/iH/ndx5zSwVBEARBOCiRSIRqtbpnsNGNGiaSrVQqh7oe4do0m036/T79fp/JyckD/Wyv34d6HbnRQG00cEIhnBMncF/zGkJPetKBruu243nQaKAVCkiANT2N94pXoOzSLyaA5LoYs7OQSMAb3kDydTdeMVW49Rz2/Ww3w/5oy7IOrB2u6/Loo4/6SZfb7bb/mud51Gq1A1nP7Wa7qvVra2usra3RbrdZW1s7xtYJV/E81EJhkHAlFiP2yldi/uffPpCPtrQI/zL1mgP5rJuVZQ0SVciyJBIzC4JwxxAB6IIgCIIgCIIgCIIgHK+bNDhQEAThMOw09t1stiiXezz8cIlsNks2m90xCO6oLS8vUy6XWVxcRFVVpqam/IrHMAhKLZVKLCws4HkegUCAmZmZY2uvJEk7Vso9bpVKhfX1dfr9Pp1OB9u2kSSJSCTCysoK2WyW8fHxQxmoDAQC6LruZ7LfLpjzSp1Oh2AwiCRJN82xeKfrdDpcvHiRfr9PvV6n3W771Ynj8TjVapVYLMbJkycPpWL9xsYGzWaTdrvN3NzctoHD0WiU6elp5ufnaTQabGxsiAD0Q+Q4DhsbG6xvpvifnZ3dcv2TJIlYLEYkEmFpaYnV1VVUVWV0dPTYzutcLkehUCAUClEsFpmcnNw2QUY2m2V+fp7x8XFc16VSqSBJEqFQCNd1/erQ8Xic8fFxpqamtj0m9zI+Pk6326Xf77OwsEAulyORSPjX4X6/T6VSodVq+UkXUqnUDe+Ho2SaJo7j0Ol09gzS9zzPn7RnGAb9fn/XAHRZlv2EJSJxyfUbJvup1WqsrKyQTqdJp9NbruWmabKxscHi4iIAKysrx5pMQhAEQbhx/W99i96b34yyuoq6vo5imjjBIJ4koRgGrK1hFQq473oX9XKZ5A/+4HE3WRAEQRCEA3DYSVObzabfj2dZlv878tCJfoF9sW3bHxfQNO1AP1ut1Qj0ejihEObMDCQSeK9+Nemf+qkDXc/tRu500B55BMl1seNx3MlJ3Oc/n/RP/MRxN+2mZ991F95zn0v4e7+X4N13H/r6ut2uX5F6OHYVDocPfb3C7orFIqZpHsuYwzDoXJKkA7u/tlotKpUKKysrRKPRLePi/X7/ph2Dvtmsra2xsrLCysoKvV7vUMYthevjWRbKygpKv4956hTeffehplKI3Ar757qXCyIIgiDcKcSdXBAEQRAEQRAEQRCEA6GNjtKXZRxdR261cJPJ3d/Q7SK5LoRCyCMjR9JGQRCE47ZdALrneXQ6Her1GsvLy7RaLQqFArOzs8deYdWyLMrlMmtra2iaxtTU1FUDaZFIhHA47Ac3aprG2NiYCFp+nNXVVdbX1/0qtK7romkajuPgui6xWIxer0en0+HUqVMHPoEhlUqh6zrRaJRKpbJrdRXbtmk0GoyOjpJIJMRkiptAp9Ph3LlzVCoVNjY2/KBzRVEwTZO1tTUURWFychLbtjl79uyBTuawLIt6vU6tViOZTO4a6BsIBEgmk9TrdZLJ5LFNPLoTDKuIt9ttpqendzxXZVlmbGyMS5cu0e/3KZVKTExMHHFrBwKBAJlMBtM0WVxcZHl5mXw+v6Vy9rCNyWSSbDZLMBgkFov5iRckSSIcDjM3N0coFGJqauqaq58PJZNJpqamgMH+LJVKbGxsoOs6juNgWRbhcJiZmRkSiQSnTp069IniB204Cc913T2vC6qq+pOfTdPcM6i82+365/dBT5q+kzSbTTqdDmtra2QymW2P50AgwOTkJMvLy/4xOj4+LibuCYIg3KKcZpPuW9+KvLiIvrqKmU5j5fNIm/dV23WRqlW0QgHHMHDf9z7aMzNEn/e8Y265IAjCrefKJIx7BQgEg8E9kzbux159abquH8h69vo9EAgEDmQ9e/3e0zTtQNazV/+Rqqr+ejzPw3U9wEOStn6vj0+UNqzGOfyf4ygUCjuvp9VSCAavZXsGbfE8D1mWgEG/gaLolMtXf0eFgo3nxWm3m1f0h1x/X4Ouh/ztGSYqbLVa1Ot1DMOgUCijaYHNfODStQXpeR6SZQ4GNiQJy5Ix3e3PI7vcIAQgyXiaCvL19ymXyxI75bQcJMHVN7/3q7//x3Ndd8v3f6VaTd71WNC04DUeC9trNi8fc+WySq2mU6vpFIsysiwjSTq6Htz2vcOKsoPltvneXAccB8nzcEYmaI9M4Kkqxrc/G+llr0a7514KG5uLugF0PeifG3sdB1cud+WyhqHtut8sSzuQ/WbbgV3X0+9fXs9wPw331Y48DxwbaXj9AFQ1QC87hhtP4iSSmC/9bvR/90Y2SoNt7nTUA9keRQnuuj3N5rVee7YXCIR2XU+tJt/YerzBvgOJ3u//OXI0hgnwuHWWStKBbA/A4mKXc+fO0+126XQ6WJYFDO4bqqqg68ED6bOtVKRd952uhwgGzRteT72++7VHVQ/m2tNq7X6/A33X9Vx5Xg2uA1e+dnmZWs3hH/5hnjNnzmz7PThO4Jq2Z7BOl+E9a8g0tavuq+12m2YzTKsVwXEOZr8Zhsw3vlEmnR4nGEzT60GvN3w1SiCQIRDQr3q+uHb6AT+PbE9Vd7/21Otbrwn7vp5esTxAIBBkY2OwH3q9HufOlVhdLWPbKhMTd9FqzW+510mWBZ6L5Hl40uD5advdOJxcIct4WgCkwXm606NnucyBXXvKZWnH9cBgPXsm5vXcwSUTBsVZttnIA38e8dzN/etdPlklCSp1aHQItJu4qkZn+hTG7D3I//ZnWV+HXi+ILN/4vpOk3X+jyLJ+IOuR5d1/o8hy4IDWc/VvlCu/RknS9rmewb3zWtazdZ3qAW3P7sm8JUk5kPUoyvbPtpfXIx/Q9ogxMkE4KpIn0tELt5Hf/M3fpNVqEYvFePOb33zczRGEbQ2r0gCMjIyICbyCIAjCTW/YcQ9iMrEgCHur/NzPoXzqUwRWVzHuugtpl+uGvLiIZFk4Z88S+fCH0a4zaEQQQPzWEm4d73ynw9vfHsN1/ZFqZDnE05++xOnTTe6//yG63S6pVIrR0VHm5uaONQh9bW2N+fl55ufnmZub2xIg+HiGYXDp0iXm5uaYnZ09tuDGm1G5XGZhYYGlpSVM0ySXyxGPx/2B+3a7TblcxjRNZmZmyOfznDp1asfP6/f7NJtNHMdBlmUikQjRaHTPdiwsLLCyssLi4iK5XI5MJnPVMo7jsLKyguM4zM3NcfbsWVHB+pDs97eW53k8/PDDFItFNjY2/MQAV05ocRyHUqlEvV5ndnaWsbExTpw4cWBtrdfrnDt3jnPnzjE7O7vnpOJ+v8/8/DynT5/mzJkzt1zF6FvFuXPnmJ+fp9ls7uv7XlxcRNd1Zmdnueeee46ghdtzXZfz589TrVYpFAp0Oh10XUfTNGzbpt/vEw6HGR0dJZVKMTIyQrVapdFo+J+hqirZbJZcLncgCQ5qtRqrq6v0+33a7TaWZfmB7sFgkFQqxeTk5C3ZL2JZFl//+te5ePEiiUSCbDa747KmaXLu3Dm++tWvMjk5yZOf/OQdg/td1+XixYtks1mmp6c5c+bMYW3Cbe/ChQssLi6ysbHB6dOnd53YN7y+njx5krm5OUZHRw+tXT/+47C8DJ/8ZPiqZ9f3va/Lj/zIoa1aEAThtlf/oz+CBx5Af+wxzNFRvHx+2+W8Xg/94kWsfB77uc8l8773HXFLb01iXEsQ7lzhcJje5QghQqEQ3W73GFskHIZPfKLOBz9osLBgEQoFUZTLQQemaWKahl9l2nU9DMNAUWR0fZDgLhDQ0PWDSTYAg365VqtFt9tFVdVd+ymGyWhDocH65c0gbV3Xbzh40jAMer0e/X4Px3HQNA1V1ZBlCdf1sCwLy7JQ1UFAWzgc3lefyvd/7c08ofYlJnsX2dAnqAdGrgqeilh1xnuXqATGWIme5YGnfQhH2T245XrZtkW73d7sTwrumYjBsixM0yQSiRCLRbccL8fBsiw6nbbfH6aquz+r9Pt9wCMUChOPx3c8TiL9EmGziiNrtIMjmGrkqmVs26bdbtNutwkG91634zj0er3N8YcImnZzJhm9fA52UFVtz+Pasizo1XlG/6uctBcIegZ9NcJS4l7O5553aMeucH1s26LT6WJZJqZp4rqXg2IH1WcHldA1LUAkEjmQZI2u62KaJrZt4boekgSyrKDrgT3Pm9tFu92m1+thGAbhcNjf51eGHUmShGkaWJa1maw8ciB99Z1Oh16vu7nuyK73x16viywrhEIh4vEYN5LQBbzNa8ngPhoOh3ddut8fPHPudX2+1QzvFd1uB03T0LQAjjN4rhp+/7Iso2kanjdIUhsOhwmHQ36QdK/Xpdvtbd5Dwtsmi5lsP8obL72D0d4COadEXU3T1POYShjJc4hbVdJmEU+SWA6f4Su5l/Lpu95ypPviVjZW+zqvOv9rJMwSCbNMxG4i4WFLKk0tQ0dLcj7xr/mru35x22cGYXfdbodud3CdikQi7HXtGZ5PoVB4X3M6hKu5rrPlnryfREqGYfCBD1QZHRVzCIWb360QCyvSPQiCIAiCIAiCIAiCcGCCr3411j/+I26xiLq4iD03h7RdJ16phNZoYMzN4TzzmSL4XBCEO0atVt/276qqEg7rzMzM0O12WVlZAQYDuPF4/NiqWzYaDZrNJqFQaNfgc7hcuWf4HhGAPuB5Hmtra2xsbGBZFnNzc1d9n9FolEgkwurqKisrK+i6TqfT2RywvKzZbFIoFGi1WvT7fT8APRgMEg6HyefzuwYWTkxM0Gw2GR8fZ21tjVarRSqVIhgcVD1pt9vUajUURWF6eppMJiOCz28CtVqNfr/vB58nk8mrllEUhdHRUWzbplAoEAwGD7TyuOu6fhXl/VyPhss4joPjOAfSBuFqw8nUu1Wkv5Ku6/57jpMsy5w+fZrl5WV0XccwDD+pRigUYnx8nGAwSDqdZnp6GlmWSafTuK6LbdvIsnzg98VUKkUqlaLZbFKv17FtG0mSCIVCZDKZWzpwSdM0wuEwiUSCer2+630iEAjgui7JZBLTNHecwOF5HoXNkhyJROK6K9ALA8PJ34lEYl8VGQOBAK1Wi3a7fUQtvJpIcS8IgnADPA/3k59EKZdxdB1vl/uoFAphZTKolQreY4/R/9a3CN599xE2VhAEQRCOnm3blMtlGo2G3/8ZCoXI5XJ85Sthfud3AnQ6FpLElmBi27bo9QbBTo5jo6rqZhB2BMcZ9CeYponjDPpFJEnas897PxRF2Qw81+j3B8Hu2wc5e/T7Pf/1QEA/sD4az/Po9/sYRh/XdQmHtwZ7ybLkJ/8bBhTKsoSmaXsGb/zN6f+L3MMryLiM9RZImyXqWgZTCaJ4NgmzQsjtUA6MUQ5N8henf+5QA3gH/ZQense+AkkURfErubqux3HHnmiaiiwrqKqGaVqbfVzbfwfDvrBQKEggsPt31Qnm6AR3758Z7gfAT36wG0W5HHDqujdvR4Drun4b99t33ZFDfCX3Er4ViR5YH7pw8FzXodMZBPgNxzuuvG553iC5Rr9v+MdoLBbd1/G9PY9er49pGjiOuyUAXVEULMtCUWTC4fCxJ7M4TI7jYNs2ljXY57v1Vw7vZYNkH8aBnE+6Htj8PIt+v0cwGNr2+je85w37S28s+Hw4njX43vcz5qJpAXq93ua12rppk3RcK1VVURQFTQvQ7XaR5T6SJOE4jn+eKYqMYZj+vpJl+Yrv3sM0LSzLRFWVbYPPAVaid/E/pn+G71/6LbpGmKxbYbbzqP812lKAmpalruf5Rvp5/O2Znzn8jb+NrKeezHuf+gFOF7/Avyr+DaPdBTTXoKfGOJ94Kv8y/go24qJ/6XppWgBVNTEMA8uydx1DdBx78xll7yQ5wlae5/kJxhzH9f8uSYOxT13Xb+v7sSDcbMTZJgiCIAiCIAiCIAjCgYk885lUnvc8sCy0ixcJPPoodjqNl0ziyTJSt4tSqaB2uxgTE7gTE8R+7MeOu9mCIAhHwjRNut1BtYrtDMfOw+EwExMTLC0tkU6nKZfLh1rdcjfDSQb7DW4MBoM3RXDjzaRer2MYBo1Gg7GxsR0nQEmSxOjoKBcuXKDdblMqlbYEoBeLRZaXl6nVatRqNQzDQJIkPG8wqSqVSvkBbDMzM9tOxtA0jTNnznD+/Hl0XadWq1EoFPzA4kAgQCaTIZlMks1mmZ2dPZR9IlybYWVzVVVJJBK7LpvL5bh06RL9fp9yucz4+PiBtEFRFH+SkWVZewbjDq8BiqKIjOKHaHieD8/hvXiehyRJewa4HgVZlpmZmWF8fJxKpUKr1cJxHBRFIRqNks1mrzrOtk5iOhzxeJx4PH6o6zgOuVyOZrNJuVymXC7vGIQ+vLeMjY2haRrlchnbtkkmk1uSlVQqFUzTZHp6mng8vm1iDGH/hs9b+w180DQN13VFgg9BEIRblLG4iFwooDYa2NnsVRVEH8/LZpGLReh26T34oAhAFwRBEG5bnuexurpKsVj0k9UNE8RFIhHK5TJ/+IdzOM6g4uaVAU22bVOvNzAMA9u2UFVts4KthecNglOHSTz7/T79/iCYaq/Auv0KhYJ+YFav1/eD3wef7WFZg+C8YbI7XQ+gKMqB9aMPqgK7WJZNKBTaMdhrkMxUp9fr+wF+e/W1NMKT/L9PfDff88jb6Kpx4kaJhF1FsWxcSaGtJlgPzNHWUvz5qZ9nKf1tB7JNt69BMoBBdfEuvV6fYDB4VX++67r0+z1UVdlMWHDjfWKSdOWj594B5Vcmn7u5C/teWZH5Gt7lbX2vcPMZXNNtTNMkFApeFWQ2vI4rikyv10dRFAzDIBTavXL1TgbVbE0Mo49tOyiKjCwrfhVVwzDQdR3X9YhGI7dt0JtlWX6l2f0kZlVVbTMA09msSn9j91VV1dD1wGZylR6dTmczocxgrMlxXCzLAgbVx4dBiDdqEFw9SHByOyXpuB6hUMhPhN7pDOY2yLLiJ3Xp9QbJGQKBQYX0QEDz99kgSYyH67p73rvOJb+d3wu8i2es/ilP7X6VICYBycFDxlRCbISm+eroK3h47GU3+43opuQoOo+OvYRHx14y+MPgAfp4G3Wb0LRBooZAQPcTO213T3Bdh36/v3mOyLd0suujNkhC09lMcjL4LTVMpDTY9zamaREMBg8kqZggCHu7PZ98BUEQBEEQBEEQBEE4HpJE+u1vp2rboChI5TJqtTqYJAl4soydSGCMj+OOjxN+97sJTE4ec6MFQRCOxiBYy9mxYuSV4/GRSIRgMEitViMWix1bALosy8iyvO+JcLZtbwlUFQYB6M1mE0mS9qwmrqoqsViMer2+JdC4Wq2ytLTE6uoqnU6HdDrN1NQUmqbhOA6NRoNqtUqr1fID0id3uL8Gg0HuvvtuSqUSsVgMwzBwHAdJklAUhVgsRi6XI5VKHeh+EK5ft9ul0+kQi8X2rEik6zrBYJBOp0O32z2wNkQiEVRVJRKJUK/XCYd3n8A1XEbTNKLR6IG1Q9gqFAoRiURYXV31g7d34nkerVaLfD5PKBQ6wlbuTtM0RkdHj+0+d6dIp9NsbGwwOTnJysoKpmmSyWT8iXmO49BsNimVSqRSKebm5pAkCdd1qVQqzM/P+0lPJEkikUgwPj5OKpXi5MmTe16bhN0Nq+Xt93nLsiwikYhI8CEIgnCLchoNACTbxtvPBElNw1MUsCxotQ65dYIgCIJwPDzPY35+nlKpxPr6Ou12G13X/UDdWq2GbQc5f34Gx+ltvmeQkM+2LdrtDqZp0OsNAnYHVbLB8+zNYDiJfr9PJBIlGAzS7XY3A9hMdP0gqqCrRCLhzf+vYFkmvV7Pf12Wpc0grUHlw1AotOM4wfWwLBPLsjaDX3b/ragoKrIsbQZymPsKbK5GZnn/v/oD7ir+T5628UlGu/P+a/VAjn/Ov5RvjH0nvcDh9ykPxiwkJInNSve7B/IM+74HSRlvjv4LXQ9s9gEMEhZ0Oh2/4ix42LaD4zioqkowGCQUCh1IoOsgOFDyx3wCgd2PlWECiME+v3n7ICRJ3vyO2dxvu49Pue4god/gOBJjWTerQdXTYRVldddzQFFUVHVw7TVNlWDQu+b+SsPoYxgmvV4XSZIIh8OPG+u8XG3d86DTgVgsflv2iw4DqofXzr0oioxluf57D8JwDEOSJL/Ceq83CDofJqrVNBVV1YhEwgfyPVzrR9w6STquned5m/dMz7/vet7l71fTND+JhywPqqMPxw6GiT0Gr++9Y0rhGf504sf5tNTnidajJKQ+thygHJ1jPX7P7bdzj9NVyW4GiXGHcemqqm57vz99Gt72tqNq5K1CotWSuHhxlXK5TKVSIRKJkEgk0DQN27ZpNBp0Oh3i8TijoyPMzs6STIrjeT9M0+TcuXPUajXW19cJBDSSySSBgI7rurRaLVqtFqFQiImJCcbHx68aZ3Ycl2KxdkxbIAi3JxGALgiCIAiCIAiCIAjCgZICATK/9ms0//Ivsf7iL/DOnx9MkPQ8UFXcZBJe/GJiP/ADaPn8cTdXEAThyPR6PUxz54F3Sdr6WiwWo91uYxjGFYO2RyscDu87uNFxHNrtNuPj43sGp95JhhXhdV3f13cYDAb9SsDDgfyVlRVKpRLdbpfZ2dktmfwVRSGdTpNIJFhaWmJtbQ1VVcnn8ztOHlRVlbGxMUZHR/3M0ZIk+cHLwvYsy/LPg6PMUD6YMOvuO9BQURT/PQdFVVVSqRStVouVlRXS6fSOx0q/36derzM+Pk4ymRTZ3A9RNpulXC6jqiq1Wm3HqtYwSArgeR7xeHzX5YTbkyzLnDp1yq9AUywWuXTpkl/pzTAM/36SzWaZmpoiFouxtrZGPB7HMAy/UtswACCXyzE6OiqSzhyAaDRKNBplY2ODfD6/6z4dPE+axGKxPRPbHKaDDJQ4Kr2HH6b36U/jlcugKMhjY4Rf/nL02dnjbpogCHcYefgcLcuwj+Qjnusiue5g+QOo6iYIgiAIN6NCoUCpVGJxcRFFUZidnd2SQM+2bb71rQ69Xh9NU7HtQaJXw+hjWbb/W0lVFUKhy4ForuvS63VxHAnXHVRSDYfDm8FyFqZpHUgAOgwqtsZiUQzDwDRVXNfd7NcfBMiqqoKu62jasM/24H5Yua632X+4vynhsnzt/YeOGuSb4y/jm+MvI2g1CFhtLCU0CDo/wrGLYXDUoOKutWcAumWZaJqGoig3UbXiQXDr8NhwnEFw5bCir6IMjhVFkQkGQwdS2RfYrLypEghoGIaBpgV2GbPwtuw7Vb1Z9t3VVHWQFHn/x4SFpqmb5+WNbpeH47j+GJ4sy7dlQPJxGARFuti2s6+Eqpqm0ev1N99jX+O4hIdhmJimCUib63v89yj550y/b6Cq6mYSj9vzN5ok7T+YfLCc5L/vgFqwWd08gGkaWJa6pT9SVVV0PbD5PR/MSgd9slcm6dg9QYvj2EjSIJnF7dZHbhh9XNdD0zRCoZB/nbvyuWY4Zuk4Drbt+OfDMLHHMDB9r+us57mDZ7RwkkdSLxTj1EfAsiwMw/Cvs8MAdEkaPCfour7lXvpv/g1ckTdf2JRIxIjF5rh0yWNkZFBYotFY8p8JotEok5OjRKNRZmamyWRE8v/9unRpBdcd7M/p6exV49qjoyFMM8nS0hKNxhLxuIeuJ7dcPxwH+v1bcCBLEG5iN+8vQkEQBEEQBEEQBEEQbl2SRPyVr4RXvpL+Y49hzs+DZSGn00S+7duQ9pFNXxAE4XYzyBg/HLS/+vXHj00Pg0iBawo+PUhXBjdWq1VyudyOy9ZqNb+CtghuvGw44Wi/k/lc1/WrCkiSRK1WwzAMarUao6OjO042UxSF8fFxLl26RL/fp1QqMTExseu6hoOfws5s26ZcLlMqlTYnHw0Eg0FyuRyZTObQz83hBL8r17+bYWDiQU8KHBkZoVarkUgkWFxcZHR0lHj8coWPYYXtQqFALBYjHo8zMjJyoG0QtopGo0QiEfL5PKurqyiKQip19QSGRqPBxsYGIyMjBINBEsc8U8SyLCqVCv1+H88bTGJKpVJEIpFjbdftTtd17rrrLpaXl4lEIvR6PXq9nv8dRKNRv1pAMpkE4OTJk1iWRb1e36ykJqPrOslkUkymPUC5XI56vU6xWKRSqez4vOV5HsVikWg0iq7rZDKZI27pranzla/Qf+ABlAsXkJtNJMMAwAuH6X34w7TvvZfIz/wMwZMnj7mlgiDcKQIzM/RCIexoFLlex02nd11eqtXwZBkiEbS77z6iVgqCIAjC0XFdl42NDYrFIrIsMz09fVUgl6qqBAKjBAIBTNP0A746nQ6SJG1WwlavqtY6DMoe9rUPqp7byLKyGXBzcAkcYRDYHQqFCQY9P6gHJBRFPqLg5/0GWQyDBa/vt31fS9DXjqt/SSIQCOA4Nt3uIIBqpz5zw7jc96TrN9fY9KDCcgRdtzEME1W1/GDPYXXfYeLAgxQI6FjWsJpwj1AoeFUVcM/z6Pf7ADflvrva5WOi07EwTXPHwFHLsrBth3A4TCCwWwD+7lzXxTQHActXjv1I0qAtuh440qrxnufhOPYVx5ByLGOKB2kYlArs6zwYLjPYB9cWcGZZ1mYl4GFSkp2PC1XVkGXLD+AcBqDbto3jOMAg8HCnSsK3AkVR/LY7jr3n/cu2bRRF8ZMwHCRVVVFVdXOM3WWwf+VDCfgeJDdR0TRt8zqye3C7aQ4SXlxrko5BIPfguiFJN1/SCsex/YByXQ8SCAQ297+z5Roz/A663c7m+TAMQJc2K0Brm/foALvtx8FzmYwsK5v7XDhM/X6ffr/vJ7658h42eP7QsCybUCiIrgdJJuFFLzq+9t7sEokET3jCEyiVSkSjURzH8RNBy7JMJpMhn8+LxArXYDgmWSqVdk2qHggEmJqa4tKlS3S7XUqlElNTU0fcWkG4s4gAdEEQBEEQBEEQBEEQDlXw7FmCZ88edzMEQRCO3WACnLdjxUhZ3vpCv99H0zRkWT62iSLD4MaRkRFWVlaQJIlMJrNlMNzzPKrVqh/wHA6Hj7Ui51Cv16NUKtFut/2q1dFolFwut69qCQclGAwSiUQoFou7TnwaajabJBIJfyCyVqvRbDaRJIl4PL7re3VdJxwOU6/XqdVqewagC7trNptcunSJXq9HrVaj1Wr5ySDi8bgfbH3y5MlDDZxNJBIkEglWV1cZGRnZ9XrQbrexbdsPAD9I4XCYmZkZPM9DVVUKhQLFYtE/n3q9Ho7jkE6nyefzTE9Pi4DiIzA3N4dhGLiuS6FQoFKp+JXnbdum0Whgmib5fJ5MJsOJEyeObUKTZVksLy9Tr9fp9/v0ej1gcH+MRqMEg0FSqRShUAhZlgmFQnteM4Vro2kaJ06cwDTNLUkAVFUllUpte//WNG3XBDTCjUskEoTDYcbHx1lZWcF1XTKZzJaJi4Zh+M8Ss7OzZLPZQ68+dpPNfbwurc98Budd70Ld2EAtlZAtC2fzuqKsr+PoOjQa9C5cwPv1Xyf0pCcdc4sFQbgTyMEg3gtegFssos/PY3Q6sMNzs+e6aKUSdjKJm88Ted7zjri1giAIgnD4hgk4m80mk5OTOwaVNZsqgUAAy7KQJGkzONHFMIzNAK7L1VqHwZi27aBp2ubvp0FQuGVZV/SvHc4Pn2Hw1VEZjCHIfgXtvTiOSyCg3rIVW3Vdx7IsgsEQ/f6gTzIQ0PzvdRg453neZgVd9abtY1IUlXB48Pt+ePweZt/dMKB8EGTeo9PpbgZ4Xq5kOwwGHPbN3az77kqDY8IkFArS6/VxXQdNC2yp0GtZJrbtEAzqm9WTr69ytW3bdDqdzc+0sG3Lrx6rqiqOY2OaBuHwoHrzYRpeAy1rEAg/rEQ9CIBW0HX90NtwWAYJRQb/f1hNdjeXx16vPbmGbTubwePsq79N01Qsy8ZxXAyjj2laftCh53nIsrRZSVjbrCR8a4XrDMemNU3FNC1CoZ3b77qXq9QPrhWHd189irFyXQ9g2xaWZdLr9QmFtktI4NHvG3iet3mN1LdZ5mq2bW9Wc7e2zBUYXpevrDh9nAbHtuNXQIfh/t/+ONA0DcuycJyAH3g7fF4zTZN+v08wuP24vOM4mKbpX5dv1aQNtwrTNPyxueF9UtPUzedqb0tldICZGY+3vz2EiJ3ena7rTE5OMj4+7o+VD5+jbvVkMMdhOHbZ7XaZm5vbdVld14nFYtRqNWKxGJOTkzddUg9BuJ3cWk+0giAIgiAIgiAIgiAIgiAIt6hkMomqdnd8/cqxEMdxaDQaW6qQNptNyuUy/X4f13XRNI1kMkk2mz3UwathoJrneayvr1Or1YjH41uCGx3HYXx8nHQ6zYkTJw6tLfth2zYLCws0Gg06nQ6tVssPQI/FYn625Lm5uSOZ9JHNZikUCoRCISqVCmNjYzsu22q1sCyLZDLpB/sNB+hDodC+BszC4TC9Xm/fkw2F7bVaLS5cuECpVKJUKhEKhfxgwGHm7UqlwujoKK7rcvbs2UNLbJDL5ahUKqiqSrFY3PEYchyHYrFIPB5H13XSe1RSvB7Diu+appFOp2k2mxiblWSz2ay/7unp6W0rcQsHLxgMcvbsWS5cuEA0GqVer/vXPVmWicViJJNJQqHQoSdL2I1hGJw7d87PWt/tdtE0DUmS6PV6dDodZFkmGo2Sz+dJJpMoikIikSCfzx94QoU7XSAQ2PV+JBy9kydP+hO7CoUCtVqNaDSKoiiYpkm32yUUCjEzM0MqlWJycvJY27tTQqWbSe+b38R+97uRl5bQqlWsbBY3m0XanDhpGQZKsYi+uIhpWfR+4RdQP/ABNJFwQRCEIxB+7Wvp/83fYCUSaAsLWFNT8PjnHdNEXVpC8jzc0VHkl78cSUxcFQRBODCe59FsNv0J7jAIoEmlUqTT6Vs2MPdW1Gq1aLfbfgLRnTSbg/ugqqqb1cw9NC2wGYBu8eQn/zmGYZBIJPxqrMOEd7lcjn6/T6fTYXR0FEmSSCaTjI+PMzd3PH0OjuNSqTSBy31u16tWs1heLrC8vEw+nycSCe+4bLPZolKpMDMzw/T01FWPILcGCcsKsrRUpN1u02w2/eShgD8WEI/HicUspqamUNVbIRjlaNroeSE2NppUqzW63R7NZhPTNIHBdTAejxMOh0kkEoyPJ5HlW2PfmWaQhYUCnU6HWq3mJ78cCoVCJBIJYrEY09PTBIPXfp3vdrssLi5Sq9WoVqt+4JOmaTiOQ7vdptvtblbszDA5OXVo/Zq9Xo+lpSW63a4/HjZMYqCq6ubxHyOVSjIxcXwBWa7rbgnIGwTF7x1oaxgeFy+us7y8QjQaJZVK7rp8pVKl1+sxOTnBqVOnuZa8CaurVZaXl2k0GkxN7d3n1mq1qVarBINBkskk3W6XZrPpB7HD5aC4eDzO2NjYLTZeIlEo9NjY2GB1dZVwOEw2m0WWJWz78jY6jsPGxgaBQIDR0RFOnjzFdeZ1uIkEWFkpUqlU2NgoYFmDhM/h8OC+2u/3abVaAIyOjpJMJpmZSbDbY6PneaytrVGv1+l0Ottec6PRKIlEnPHxiWN/Bi0U6iwvL1MqlZidndlz+W53cKzMzc0yN6dvjpVqLC42qFarFAoFVFUlkUgQiUSQpEH1+OG9Ox6Pk8lkmJ2dJbzz44twgxzH4dy5SxSLRXq9HmNjY2ja1fMkLMtmfX2diQmZJzwhz9jYkwHxu2g/ZFkWidkPgGEY9Ho9NE3bV+X4aDRKuVzGcRxs2z7SJGCCcKcRAeiCIAiCIAiCIAiCIAiCIAhHIB6PI8vmZhbpq18fjicPA72HFWHD4TDf+MY36Pf7NJvNzazULoFAgEQiwdraGqOjo4cWSKbruh/cGIlEaDabNJtNut0usiyTyWT8yp0nT57c10DQYbFtm8cee4x6vc7a2hqmaRKJRPzs68vLy2iaxvj4OKZpcvbs2UMPQtd13Q+gXFpaQlVVstnsVZN92u02a2trZDKZLcHDgyoPkj9xaC/7qQQh7M7zPObn5ymXy5TLZSYnJ6+qCpxOp6nX66yvryNJEvPz8zzhCU84lPZEo1Hi8TgTExMsLS3hOA7ZbHbLudZutykWiwCMjIwwMjJyaJNUkskkiUTCD8IfTpQJBAKk02lSqZQ4Bo9YKBTinnvuoVqt+sHdQ8NJ1jc6kflGuK7L+fPnqVarrKys+ElAgsEg3W6XhYUF+v0+q6ur/gTw0dFRUqkUyWSSer3O6OjosQfcCsJh0nWdu+66i0uXLhGNRmm327TbbVzXJRgMMjIyQjAYJJPJMD09fewTEW8F3Q99CKVUIlCpYMzNQSy2ZSq/pOu4U1MY4TD66ipGJEL7Ix8h9WM/dmxtFgThzhE8eRLjx34M93d+B1ZW0BcWcHQdJx4HWUbqdtFaLRxdxzp5Eufbv530D/3QcTdbEAThttFut1lYWKDXGwReXhmAPvztOjEx4SeI3IvrulSrVcrlMoYxqIg5DGbPZrO3RPXg4+Q4g8qzewULNJuDflxZljcrXgf8/2azMqdO9SgUCkSjTdLpNLquEwwaZLMxNK1Dt9ulWCySTjuoqsoTn5jl5MkY2exRbOXVHAc2NgaJyEZG4Ea6bVw3wde/vkgqJdFoXGBycnrbfvpBEOgS09MZxsZcnvSkOLduN57GU54yR7FYpFxW6PdDfvCnoiiEQiFyudxm0KT4Db2VBIzS7cYplUpUq4ofvC9JEqlUilwut2tCiJtTgCc/eY719XUqFTDNiJ88Vdd1v/96bGzsuq/L3/zmPJlMAcsqcPbsxOPGDRQgQbersby8TCLRJRq1ueeeJx34MWgYBo8+eo5UqoxhFJiaipBMJgkGg7iuS6vVolZbQpZl0ukZ4nGH2dnZA23DftpYLBY3k+vaSJKzuR8UIpHCvxqYAAEAAElEQVQk+Xx+j2NMJxZTSadlyuUlZmfVHcfzbNvGdVc4ezbP9HSA06ev7ftNJl2CwT5raxVOnszsOb5RLtcxzXkikQi6XkTX4eTJNPF4GlVVMQyDer1OrXaRcDhOJNJjevoUiUTimtp1nO65J89jj9WYm0uwvLyM664RjSbQNM0/xgzD4MyZGBMTWaanc+Tzt3z0OQBPfOIECwsWlcogSLpWK/nPitFogNOnU8TjcZLJCCdPTqMou5/fly7Nk0qVaLWWCAZNRkeTfpBqp9Oh0biILKskk1NEIgYnT5481jG2dNohEOii6zVOnszv2pbh2HuxeAnbruM4TSQpRiaT4fTpERYX+9RqKSqVCo3GBWzb3Rz39hgdDfGEJ6RJJCKcODFOMimizw9TqVTFMJpY1gpPfvIUkYgN2P7r/X6fWq1Gq9UikWhx6VIRWT5FMBjk9OnTopK3cGT2OydGEISjJwLQBUEQBEEQBEEQBEEQBEEQjoAkSUQiO0+mkCSPdrtNuVzGNE2mp6dRVZW1tTWq1SrFYhHP84hEIsiyTLPZpFQqEYvFsCwLy7KYnp4+lLYHg0Huuecev3Jtu932B3+urFZ73EGnCwsL1Ot1FhYWiMfj/j4csm2bYrHI4uIiAPPz85w+ffrQ2zUzM+MnDlhdXaXRaJBIJPxJksPEAplMhlwux9zcnD+QO5goGaRSqfiV3HfTbreJxWLot36ZgWNTr9fp9/uUy2VGR0evCj4fSiaT2LZNqVQiHo/TbrcPbFKe67rYto0sy6iqyokTJ7BtG0mS2NjYYH5+Hl3XURQFy7KwbZt4PM7IyAi5XO5AE1J4nketVqNcLtPv9/E8D1VVSaVSTE9P3/aTqE3T9M+9m3lbZVkmm82SzWb9idvD4+e4VSoVOp0OKysrfhthMKFlaWmJZrNJp9Mhm83Sbrf9ShihUIiNjQ3q9Tqu6yLLMuPj48e8NYJweAKBAHfddRedTsdPJuG6LoqiEI/HyeVyR3odunWDEMAqFlG+/GWUUgkrmYQd7uUAZDLY1SpyuYz9qU/h/eiPIt0E105BEG5/ie/9XhqahvOe9+C2WsjlMnK7Pah4Hgj4yTPc++4j/R//o7g2CYIg7EMmk9lS8XZQgXGrZrPJhQsXqFQqlMtlv79VkiTa7TalUsnv87Fte88+lmFf5DDYbNh3omka9XqdQqFAPp8XSdV2IcsyiqJg2/auyw0roA8ScA7623U9gOuGGB+3mZycJBgM0mg0iMViZLPZLX2knudhWRblcpknPvGJW5KA3upkWWZ6ehrbtnEcx+8fTyQSqKqKZVk0Gg1arRbJZJJsNsvMzMyx9+ffKFmWGR0dZXR0lHa7jWVZwOD3tahAubdwOMzMzIx/7Az7fW+GgP3huVqv17EsC1mW/USb8Xh8x2NX0zSmp6eZmJigXq9vqTKcSqVuKICu1WrR6/UolUrkcrkdxw3C4TBjY2Osra2RSqWo1WpkMpnrXu92VlZW/AS5Y2NjJJPJLa/ruk4qlWJlZYWlpSUURSGbzR5ZUoFms8nFixdpt9t+QONwTDEcDpNKpahWq0xOTjI6Orrj5+TzeT8p9vLyMhMTE1f1jZmmycrKip+0O5/PX3N7o9Gof80YVmTeTalUwnVdarUaU1NTTE1NbTm2dF1nZGSERCLB4uIi5XLZT1Z9q5BlmTNnznDx4kUCgQDNZpNGo0G9XgcuH+fhcJjJycnr2u83K0mSmJubI5PJUCwWr/reYrEYuVxuX+PiwwRFi4uLqKrKqVOntozZDD9rdXX1/2PvzWMd2fL7vm9tZFWxyCruvPfy7t2ve94SSTOSNfJYVjyK4QWWYS1IjCBWrChIYieGEjtBYlhCYCkTQEJiQInsOAgysmLYsGFrJFtjxHGsxBNNLMuxrWW291733Xm5FIushUVWFWvLH7frTN/Xd2G/vmv3+QCD6XcvyXuqWMup8/t9v18cHp6YRmT36dtClmUyh77ofMhMvA8ODuD7Pvr9PkRRhCiKcBwHvV4PrVYLpVIJsiyj0WjA930kSQJBEJDP56FpGjmOKNfLeDyGZVkQBOHUHClJEnS7XUwmE8xmM7iuiyiKYJomdnZ2wHEcfN/HxsbGC/caCuU6EAQBuVyOPI9fVuf2fR+5XA4Mw1CjBArlmqHVCQqFQqFQKBQKhUKhUCgUCoVCuSFyuRebHtM0xWw2xXA4QKfTQbFYxPLyMorFIoIgwGg0wnA4JCLv55t/ZrMZ+v0+Dg8PwTAMZFm+tqJ0lnxRLpcBgIgB7wqe58G2bXS7XZRKpTNFijzPk593u11IkgTP885sRr1KBEHAW2+9hadPn0KSJNi2TZo1GIaBoihYXl6GKIrY3Nw81cxQrVYxHA7B8zxM07zw+3VdF/P5nKQrUT4ehmHAcRywLHtpQ1C5XIZhGLBtG9/4xjdQKpWIUDxriDuvEe2jpGkK27YxHA7hOA75Oc/zqNVq2NzchCRJkCQJvu8TIwiO44jpQLPZvFLxueM4JJ3atm2SrP3RJuqVlZV737D6PFEUwTAMDIdD0qQInDSOZWnid0HYfR4cx92pIvtwOCSNLc9fmwaDAWlCbDQaKBQKGI/HmM1miOMYmqahWq3i8PAQx8fH4DgO1WqVGmxQziUTi9i2TUwYsrlRuVy+U/OWiygUCrRR/hXxvvxlwPPAz2YIVlYufX1cqSDX6yGxLHhf/Srkb/3W6x8khUKhAFC///sR/ev/Otxf/mVE//Afgu33gTRFqihIP/MZyD/4gxAfP77tYVIoFMq94ejo6MLfz+dz7OzskETWTBz2/LOC67ro9/tkPUCSpHPFFuPxGHt7e9B1HePxGDzPQ1EUsCyL2WyGvb2TdNTMPPCm02cvo98HPv954OtfB1z39sYRBMuYzVRMp1NIkgSOO3vN5VlAM6IohCAI4HnhmXEWA1VN8G3f9m04PDyEaZrkf8ViETzPI45jHBwcwPM8bGxsYHl5Gevr6/fmOXERKpUKWbMvlUowTROHh4cAQNag2+02isUiNjY27pUQ8iLSNEWapvcwrfvuwDAMBEG47WEAOPk+j4+Poes6fN+HZVlkvV2WZZimCUmSsLm5eeHaSbaOeJUYhoHJZELWLS8iu/bYtg3DMK50LPP5nHxuuVw+dywcx2FlZQVPnz4la2Y3cZ64rounT59iOBwSE+12uw1BEIghc7fbJd8fy7LnipfL5TKazSYxd97Z2UGxWIQsy89qqydiyUKhgJWVFXJff1k0TSMC8eFwiEKhcO76umVZGI1GEEURLMtiZWXl3NeKoohGo0GOAcdxLhW33yU4jsNbb71F6la2bROzGI7jsLS09ILZy+tEqVQiNb/5fI40TZHL5V7qepnttyRJ0G63zzxWOI5Du93G7u4uLMuCruu3WuPVNA2iKELTNIxGIxSLxRfqf4PBAMPhEN1uF0dHR2i322g2m1AUBa7rYnd3F8ViEXEcY2VlBe+99x5s2yYCdJ7noWnanTZ9ft2Yz+cIguCU2D9NUxwdHcG2bei6jjAMIUkSZFlGsViE4zjY3d2FoiiI4xhbW1ukT4RCuS7K5TJkWSa9CBddD+M4hm3bxIzndXq2o1DuIne3O4ZCoVAoFAqFQqFQKBQKhUKhUF4zsia552EYBrlcDppWwoMHD5DP51Gr1cCyLA4ODqDrOlZWVs5sSpBlGWtra9jf34dhGJAk6caK0netgGMYBqbTKUmPv4hGo4GnT5+ShNHrSo5/nnw+j7fffhumaZIU+YxcLod6vY5arfaCqDVLXqjVauj3+8jlcmceC77vo9vtkoL9VTdXvUl4nofZbAZFUS4VVXMch/l8jq997WtoNBool8sIw5A0xI3HY9IQd5GDfxRFePr0KSaTCRzHeaGxznVdDAYDrK2tod1uwzCMF5JxK5XKlZ6Xtm1jZ2cHw+EQhmEQoTvDMPA8D+PxmDRRh2GIzc3NK/vbt8lkMsHOzg48zyPpLFkCerFYxGQyQb/fx/b2Nm1sXYAgCOB5HizLOtWY4vs+ZrMZLMtCqVQiDYelUgm2bSMIAriui2q1ipWVFezu7pKEIZoaR/koURRhd3cXjuPAdV3SDMmyLAqFAmzbhiiK2NraouftG0Ji28Cz5DssYjQkSWDiGGkcI3mW5EShUCg3BV8uQ/uRHwF+5EdOxOdxTNPOKRQK5ZoYDoeYzWYwDAOrq6tnPh8oioL19XXs7e3BNE30+/0zxX1BEGB/fx/9fh+2bWNlZeUFE0Lf99Hr9XB0dEQEwHfFNFLXgR/7MeC5wPhbg+dzAHywLA/fn0OSzr8PhmGIJEnB8wIKBRm+H0AQBDDMAJIkkTUDSZLgOA6GwyFJPvd9H9vb29jc3HxtBTS1Wg2yLEPXdSL8ykTpPM+jUqmg0WhcuyHrdRNFEUajEQzDgO/7AEBSYzOTQ8r9ZG9vj4gaM1MKURSRJAl0XcdgMECj0UAYhnj48OGNrvNk65wXiZMzsmu+53kIguBKx2GaJnzfh+d5l5rR8jwPVVVhmiZKpdKNGG9kRiDn3WtlWUalUsHh4SF6vR5YlkWlUjnX8HVtbY1cw7L1ZNu2AZzU3TY2NiBJElqtFlYWMCE8C4Zh0Gw24fs+ptMpDg8PsbS0BFEUkaYp5vM5oijCZDKBZVnEJHhlZeVSMbKqqtB1HZPJBOPx+F4J0DNUVYWqqkiSBJ7ngWVZcBz3xoiHeZ7/WIbEmZG0aZool8sXXjey82A8HqNarWI6nd7avYxhGNTrdXieh/39fXQ6HSwtLZF94LouRqMRDg8PYRgGNjY20G63sbW1hVwuh1qtBt/30el00Ol0yPWQpmffLgzDvFDzzgzZe70e8vk8Wq0WOU7n8zk0TSP3kDRNwbIsFEW5M6Y1lNcTWZZRKBRQrVbR7/chy/KZPRZpmqLX65H6fb1ev4XRUihvFrRiQaFQKBQKhUKhUCgUCoVCoVAoN0SSAG+//TNI0xOH+DiOkaYMCoUCmk0ODx58M6HzK1/5CklzuKghIUtH1nUd9Xr93jnoXxWTyQSTyQSyLF/aCMDzPAqFAiaTySkh+HXDMAwqlQoqlQriOCbprJeNd319Hb7vI45jHB8fwzRNIjSPogi2bcN1XZRKJTSbTayvr19Z+vFkMsFoNCKpT4IgoFKpvDYJOWeRpeYs0ojV6/UwGo2Icz/HcRBFEXEco9frYTAYoF6vYz6f46233jqzYSSOY+zu7mI8HqPT6SBNU6iqShrrJpMJ9vb2SIPPxsbGlaacn0UYhtjd3SWJYK1WC6qqnmpO8H0f/X4fBwcHYJiT69h5SSX3Bdd18eTJE5LOIkkSqtUqBEFAGIawLAvj8Zikrpz3nVK+SZaIEkURRFEkP3ccB77vIwxDtFot8nOe58GyLLlGAieNhIqikOQyKkCnPE8cx/jwww9hmiY6nQ6iKEKpVCKpHJnxS6PRQBzHN96cTLklcjk8i0JEGkVgLmuKe3a9YVgWzHPXKgqFQrlxGIaKzykUCuWaSNMUhmHANE0UCoULnwuy9S/TNFGpVDCbzV5oes/E7KZpYn19/cymeFEUsba2hr29PWLkd1cE6P/b/3Y3xOfANw1akyTGbObB9z3k83kwzPNrcyci8iCYQxTz4DgOgpBDkqSI4xiy7OH4+GQNa3t7G6ZpQpZlxHFMDO0qlQo2Nzfxmc985rUWQcmyTMRgs9mMGCsuIpq9D5imif39fQRBANM0MZvNkKYpBEGAbdsYjUbQNA2bm5s3vr1hGMIwDIxGI4TPTNEyMV61Wv1YAsY3iSxR9+DgABzHYXNz89R6YpIksCwLg8GArN+/++67N/Y9Z3WDy0xrM7LXpWl6pePIUmx5nl8oebpQKJB9FobhtaZVu65LjF7q9fq599pcLoeVlRV8+OGHyOVy+PDDD7GysgJFUc78PtvtNmq1GobDIcbjMVlz5nke1WoV9Xr9lber2WyS60mv18OHH36IIAgQBAG5l2RC9TiOoWnaQmuMLMsin89jPp+T68J9hWXZN0Z0fhX4vk/S0xc5VorFIgaDATHNuc3aU6vVguu6SJIER0dHePr0KYrFIiRJwuHhIQ4ODmAYBra3t9FoNNBqtU4dG6IoYn19Hbu7uyRd+03sXbhL5HI5iKKI8XhM7memacI0TfA8j0ajceq+5fs+NE1DuVwmpiGapsEwjGuvUVMo2XOM7/vk2CuXy8jn80jTFI7jkPnA2toaNE17wQyOQqFcPfRplkKhUCgUCoVCoVAoFAqFQqFQbogkATY3/5Mzf1erychCq+fzOebzOSaTyUJJ1qVSCf1+H9PplAiR3xTSNIVlWdjb28Pu7i6AEzFasViEpmnnNnUJgnBK4HjTcBy3cGOULMt4+PAhEflaloV+v48kSYhzfJYksba2hkql8srjc10XBwcH8DwPk8mEpHTkcjmMRiOIoojV1dXXToh+0sh60lQ0m81OFdw/immaGI/H6Ha7kGWZOPxnZMLHfr+PNE3x9OnTMxviMlOBw8NDqKqKRqNxSvxeLpfheR46nQ5JBCmVStfa6LNIIlh2DOzv78MwDMiyjHq9vnAD3l0jTVPs7e2R9KR2u/1CsbpcLsO2bfI97O/v45133rmlEd8PsmOZYRgkSUJ+njVKCoJw6px4vpHz+fMgS7GOoogkd1EoAHB0dATTNHFwcIBisYhms3nqmGo0GrBtG/1+H8DJ/ffdd9+lx9BrjvDoEUJJQsLzYE0T6SUGKaxlIZZlgOOQ296+oVFSKBQKhUKhUG4Sz/MQRREcx1lINKFpGobDIUmvfF5gniQJEbMrinKm+DyD4zhUq1UYhoFarYbJZHLrzfHTKfDrv36rQ3gBSRKfCcnxLIF2Bo7jnj27pURsKIp5CEIOhYIMhmEginlEUYStrTKCoIunT5+iVCpBkiSUy2WMx2MAwMOHD7G+vo6NjY3XWnz+PDzPv3Z1gvF4jL29PfT7fZimCVEUUSwWwbIsfN/H8fExeJ7H6uoqMaG7qef/Xq+HXq8H3/dhmiYxdBVFEY7joNvtot1u03TGc0jTFIPBAIZhgGEYknr9PFlCMMdx6PV6KBaLGI1GN2aKKggCcrkcXNddSIjueR4kSXqjjAdGoxFc10UURRdea7M0cMMwYNs2DMOA67oQBAHVahXNZvMFQXlWj1hdXSXrzFd9fm9sbJDv2LIsTKdTWJYF3/chCAIURUEUReA4DoZhvPT95L7WTigfj+fNJxY5Vp8X/161ccXLwjAMtre3SdJ9dk4Mh0NisvLgwQM0m020Wq0zzwVBEKBpGizLgqZpmM/n1MDgFqnVarAsC7quYzKZIE1TzOdzTKfTF2rh0+kUaZqiUChAVVUUCgVIkkSefagAnXLdKIqCzc1NACDH3u7uLhiGISZEqqqiVjsJ93i+P4NCoVwfb85TDYVCoVAoFAqFQqFQKBQKhUKh3DLPae9e4PnacyaKjuMYwmWJjTgpXHMchyRJbk1QfRtkydBZErOu6wjDEGmaQlEUGIaBSqVyZgNSGIYvCB/vMoqi4J133sFwOIRhGGi1WqdEmtVqFY1G41QiyMfFtm3s7OyQBqCT5s+TRtrxeIzBYIBKpYIgCLC5uXklgvfbxnEc6LpOUnIcx0Gv10MURWg0GiiXyy+IZEejEXq9HsIwxObm5gvHGcdxqNVq4Hke/X4fiqK80BAXxzERPMuyfCoJ+nkkSUK73cb+/j4qlQqGwyFWVlauZV9kiWCWZUGW5QuTGbImal3X4XkeHMe5t6YEWYr9cDhEs9k8txlcVVVEUQRd16GqKhzHee2aea+SfD4PlmVRKBTgOM6lTfaz2QzAidnFR5uRsqar226+otwdwjDEeDyGrusoFApYXl5+4TUMw5AGuH6/j1KpBNM0FzL4oZzPXT8NC5/6FMbtNqJ+H/xohHmtBuacRs90PgdvWZi324i/8zshUDEAhUKhUCgUymtJHMdI0xRJkiy03srzPBiGOdPAMktDnUwmCwkwMvNQz/MwnU5vXYD+678O3L0Q1hPjTc9jwDAskiRGGEZI0wQAg3w+D57nwXEcZFkGx/Gn3vet31qAJG0RgZRpmmAYBqqqYn19HZIkYXV1lYp/7zHz+Rz7+/vo9/twHOdM08xGo4F+v4/Dw0OwLIter3dta6jP0+l00O120ev1MJlMIEkSCoUC0jTFdDrFaDSCqqqI4xhJkqDZbF77mO4blmUhCALYto2lpaULxZqqqmI0GsE0TQyHw5cSoE8mE0ynUyRJAo7jUCwWLzQReZ5yuYzRaARd1zGdTi9cN/d9H57nYWlp6crrJ4IgIJ8/Md8IguDS5O/ZbIZ8Pg+GYRa6/70KQRDA931IknRm7S0zGsiE6rZtw3VdTKdTACd1MNd1MR6Psb29fe798rqMJU6MTUQUCgVwHIc4jlGr1UjysyzLCIIAu7u7SNMUT548Qb1ev1BUG0URfN+/kpR2yv0im7cwDAPf9y8VX/u+D4ZhwPP8nTCuYFkWW1tbmE6nGA6HZF19Pp8Tk5dGo3HhWFVVJSnFs9mMCtBvEU3TkM/nUa1W0ev1kM/nyTEnSRJ5ne/7ZN6Sz+fJdTi794ZhSM0EKDdCuVxGLpdDr9eDqqoIggBhGIJlWYiiCFEUUa/X0Ww2qcELhXJD3P7shEKhUCgUCoVCoVAoFAqFQqFQ3hAWFaBnjRkcxyFcoBswE56zLPvGJHpmIunMbd33fSiKguFwSBpXZFlGFEWI4/hUM2gURZhOp+cmO99VBEHA8vIylpaW4Hke+c5FUbwyIX3WPKPrOkajEer1OjRNI5+fJAkcx8FgMEAURaQhZ9EmrbtGmqbodDoYDAawbRuWZWEymaDX62E8HsPzPMznc1iWhdXVVdIgNJ1OMZvNcHx8TIT/54mQNU3DeDyGaZrQdf1UQ9x4PEYYhnBdF2traxeONWtczBzml5eXr6WgOp/PEYYhJpPJQk25WRP1bDbDdDq9twJ0wzDgOM4psep5lMtl8nrDMKgA/QKyZCLHcdDpdEhTpCAIEAQBYRgijmNwHIc0TWHbNorF4qnGFgCkOYnjuHtjHEK5frL7/3Q6JWkI56FpGjHXGA6HVIC+ANkt5nu/dw/zeQAAyOXy96OZh2HA/rE/hvTwELAsCDs7iDY2gI80WqeeB2F/H7EkAZoG6Yd+6HbGS6FQKBQKhUK5djIBEMMwJE37IqIoQpqmz6Vwf5NMkJ4kyUICoexvJ0lCUltvky996bZHcDYMw0CWCxDFGEEwhyCEz0zoGJLAKQg8gNPPJI8eMfju796A69aIQCozrxMEAbVaDfV6/dqFl5TrxTAMeJ4H0zSxvr5+5no0z/NYWVnBwcEBhsMhRFG8VMz8qliWhX6/j6OjI8RxjM3NzRdMYmezGbrdLo6Pj8EwJ6YJ96kucRNkIuQ0TRcy6VBVlSRTR1F06bV4OBwSE1XP88j1OxMbLy0tXbquXS6Xkc/noWka+v0+1tfXz7yuRFGEXq8HRVEgiiJqtdql2/MyVCoVdLtdkgh6nqltNhbLsrC8vAxN0269fqjrOgzDwGAwQBiGRNTdbrfRbrdhWRYODg5Qr9eRpikePXp0o7WnKIpweHiI0WgEnufxmc985kzT5zRN8fWvfx3T6RT7+/t46623zv1M0zQhCAIKhQJdj3zDUBQFgiBAVVWYpnlpHcmyLBSLReRyuTtVcyoUCigUCtjY2ICu68QU6DLxOfDNfofMBIpye7Asi/X1dURRhCiKsLOzg8lkgjAMSZ1uMplgNpuhWCxC0zQsLS2RWsDzQQiDwYAYEWQG1LVa7dy5diZaT9P0TPNpCuU8CoUCHjx4QHo1oigCy7KQJAmlUul+1KoolNcIKkCnUCgUCoVCoVAoFAqFQqFQKJQbYlEBelZ8UxQFtm2jXC5f+LmO45AC320n6NwEYRieEkm3Wi2sr69jb28PAEhx3jAM9Pt9IpLO9qOu66Th4z6m3pw0Y15P081gMIDrujAM40yBPsuy0DQNoihif38fhUIB/X4fW1tb1zKe6+b4+BjdbpcIYzVNQ6PRQKlUQrfbxd7eHn77t38bGxsbSJIEm5ub4DgOvV4P+/v7YBgGq6urqNVqF4piNU2DaZrEnTsrwmfJVyzLLvSdZsd11iBwHY2zWRPIok3UmfFF1nhwX/F9H7PZDIqiXFqwzq63nufB9/0bGuH9pdFowDAMKIqCo6MjtNttknzBcRwmk8mpY7vRaKBcLpPvITNpWF1dvfLEIMr9JjO+yOVyZzaDfpRSqYTpdEpSlSiLkc83AATP/n03BejeV74C7//6vwDLAnge7Po6Cn/wD8L5//4/IEnA7+4i/41vICyVkD6737KTCfjpFFGhgHhzE8kP/RDk7/iO290QCoVCoVAoFMq1kRk4FotFYn52EbZtQxAEiKL4wvrY8+ah8/n80jWdTMyeJWHeJqYJ/M7v3OoQLoVluWdJjNKlr93aAn78x0/+rSgKFEXB5uYm4jgGwzC3LrakXA1pmhJTOVmWLzznGIZBrVYj672maV6r6HMwGJBE2K2trTPXU2VZxtraGnZ3d+E4DnRdpwL0j5CtLfM8v9C6gyAIZB07e99ZpGmKg4MD6LqO8XhMxEsMwxAhXLlchuu6aLfbF4q5WZbFysoKoijCfD7H3t4eKpUKNE0Dz/OI4xiO42A0GoHjOCwvL6PZbF75Gn4ul4OqqqjVajg6OoIkSWeK55MkwfHxMXK5HIrF4o3UwgRBQC6Xw3g8RpIkp67BQRBgPB5D13WkaYp2uw1d18m6XnYNd10XnU4HgiDg6OgIjx49uvZxZxiGAd/3YZom1tbWzl1v3NjYwGAwQLfbxf7+PpaWls6cV9i2DcMwsLKyAkVR7q2RM+Xjkd2PXNfF/v4+TNM8t96fmWNvbGygWq3e2fmLIAjkejufzy+tIQZBAIZhqLHwHUHTNGxsbIBhGARBgPfffx+6roNlWXDcyfy72WxClmUsLS1BURTEcYz5fA7TNGEYBpIkQRiG8DyPiIEVRUGv10O5XMba2hr5ri3Lgq7rmEwmp8aR3ZM0TbuTtYa7QBzH8H2fPEMuUv+670ynU7LNgiCgWCySa2Eulztl8E+hUG4HKkCnUCgUCoVCoVAoFAqFQqFQKJQbYlEBOgDU63W4rkscqM9riozjGIZhEFHwXXJFvy6GwyFmsxkMw0C73SY/G4/H8H0fvV4PlUoFS0tLpDEtS9PVdR2O42BjYwOqqj5rZnx98H0frusS13FFURbexjiOMRqNMB6PUSwWL2yCE0WRJHuXSqVTour7wmw2w2AwQKfTQZqm2N7eJs0SWYG8WCzi8PAQOzs7p1Jzx+MxRFFEo9FAvV6/NMVEEAQizo7jmOyrNE2RpunCzSTPF+KzJKer5vkm6jAML319HMdIkuTeN5Bk38Wi28CyLHkP5WIkScLKygqSJCHGDoVCgTQo7e/vQ5ZlSJKEVquFYrFIGoPjOMbx8TFJI7qPpiGU6yNLD1z0vM1SOrL33tVGvvvAXbn0uV/+Mub/6/8KdmcH7GQCBMHJpLpQgPvX/hrwu383ou/+bkAQENk2uNEI3HiMFEAiSQi2twFFQfrH/zgq/9F/dNubQ6FQKBQKhUJ5Rf7Vv/pXp9IVWZbFJz/5SfLvarWKyWSCg4MDzGazc4VgURRhPB6jXC5DlmUUCoVTvxdFEYIgoFQqwbIsaJp24bgsyyJi9ts2D/3yly+ez/+BPwBsb9/ceD4uHHcyzs1N4Czdyn1eo6K8SJbO6bruQmnShUIBDMPA8zzMZrNrE6B7ngfXdWGaJiqVyoVCvFwuR0xKVVW9l+vp10m2tpwZdlwmSAvDkKzrXHS+Hx8fYzAY4PDwEEmSoFaroVQqkb9lWRYMwyBmhYIgXHi81Ot1smY+Ho9hmiaGwyERtPM8D03TUK1W0Wg0sLKy8rK7YiFWVlZIcm2v14PjOCiXyxBFEUmSwHEcWJYFhmGwtraGarV6I/efcrmM0WiEwWDwgrm2aZrwfR++76PdbhNxXaVSOSWgVxQF1WoVhmFAVVV4nndjdTzDMGCaJiRJeuHe/zw8z+Phw4ewLAsA8JWvfAUbGxsolUrgeR5BEMCyLMxmMzSbTSLKpLx5NBoNjEYjLC0todfrwfM8VCoVIibNDA8syyK1kWazecujPp9isUgM3jNTmIuwLAuKooDn+WszXvF9H8PhELZtn0pHrtVqVOB8BtVqFbIsI5/PE4NtVVWhqioxoS+Xy0jTFN1uF47jIEkS/PZv/zYAoNfrwbIscs+J4xi6rkOWZXieB8/z8ODBAxweHsI0TTiOQwzaARDjl+wztra26Lz9OQaDAT744AP0+33EcQyO41AoFNBqtYgh/utU10rTFKPRCLquYzabIQxDMp/K5XKo1WrXYuZDoVA+HlSATqFQKBQKhUKhUCgUCoVCoVAoN8TLCNBrtRr6/T7q9TqOj4/RbDZJ8S8jE1tnDZR3uSh9VTyfdsIwDAaDAYIgwHQ6RRiGmM1m5Of9fh+FQgHj8RhBEGAymUBVVWxsbBCX79cFx3HQ7/cxmUwQBAER1uXzeSiKgmazeWkzrGmamE6nsCxroX2jaRr29vYQhiEmk8m9SyYeDodwXRe+758SnwMnQu92uw1JkpDP5yHLMqbTKfL5PFqtFjRNg67rCIJgofPuvIY4nufB8zyiKCKF5IsIgoCM82UK8lEUwTAMTCaTUwXrWq2GXC536rW5XI4YNmQNABfhOA44joMsy7feRP0q8DwPQRAwm80Wer3v+6ShjHI5rVaLJI/5vo/xeEySgSRJQhzHyOfzkCQJy8vLiKIItm3DsiywLIu1tTXU6/XXzjSE8mpwHAee50lTyiLNyTzPg2XZ16pJ57q46715zhe+gORnfxbcYAB+NAIbhkgEAUySgInjk2Rzy0KyvQ3uL/5FhF/6EtL/9/8F86zZLVUUpJ/9LAo/9EPIv0ZzQgqFQqFQKJQ3md/ze34PPM8j/y1J0qnn/EajgeFwiHK5jKOjI5JY+vyzhOd56Ha7EAQBlUrlzHUfhmGIeWiWaJzPqwiCF58zwjDE4eEElUodUVREEMh4NiW9Fb70pfN/x3HAD/8wcI+Xd26VJEngeR4xSpMkiYqerojMAHJRI88sbTUzrrsuZrMZ5vM55vP5QsbApVIJBwcHiOMYnudRMc1zKIpCjAMcxzkz0ft5LMtCqVSCKIrnrs/O53OSUs2yLNbX119Ym88E6VlKei6XQ6VSufDcXV5ehizL6Pf7qFar8DyPGAJLkkQSZBcxS/i4iKKIhw8fgmEYiKKI8XhMjHaBEyF9uVyGpmmo1WrXWgtzHAfD4ZCIFHd2duC6LhzHwdtvv00EqrZtw3EcyLIMlmXR7/eJCPKjtatMyD6dTjEajYgR9XWSpimCIIDneQsJZRuNBpaXlxEEAcrlMuI4Jt8By7JQVRXNZhOKomB7e5uua7+hCIKAhw8fEkHlaDTC3t4euRbFcQxZlrG6ugpVVfHgwYMXanZ3CZ7nUS6XMZlM0Ol0oGnauSL06XSKyWRCTDCuWmScJAkODg4wHo8xm83gOA7CMATDMEQgL4oitra2LjSUeBORJAnvvPMOOf7G4zGWl5dRLBbBcRwMwyBhAJPJBE+fPsVgMADP81heXia1vmazCVEU4fs+DMPAwcEB0jTFl770JSiKQsxfNE0j6dVZwMBoNCKmM9n97E0mCAL8s3/2z8gxres6fN8HwzDEtHtpaQkPHz7E48ePkc/nb3vIr0ySJNjd3cVoNIJlWbAsi5j8ZNudzQMePnx4qeEFhUK5fmhXDIVCoVAoFAqFQqFQKBQKhUKh3BAvI0DneR7b29tESKzrOobDIRRFAcuy8DwPvu+jWCxiaWmJJDG/7gRBgDAMMRgMMJ/PEccxSRmQJAn1eh39fh9JkhBh73Q6Ra1WQ7VaxdraGhGhvy7C0X6/j06nA8uyTrmIA990Ep9MJlhaWnohcSMTJg+HQxiGgcPDQxweHoLjONTrdWiadu5+yhrkMvH0fSJJEozHY1iWRRz7z6JaraJSqUDTNHz44Yeo1+vkZ4PBAE+ePFmoIc62bRSLReTz+VN/S1VVFAoF8DwPy7IuTFdJ0xSWZaFer5MmgMtI0xRHR0cwDANBEJAmMJZloSgK+v0+Sd94/vPq9TomkwnG4zFs2z53+7Ljp1wuQ5Kkey1A1zQNqqpiNBrB932SQHEWWXOpqqqXCvQ/DkmSII5jMAzz2lynABAjDMMwoCgKlpeXsb6+juPjY9JcEscxdnZ2AHyzUbJcLqNer2N1dfU2h0+5g5RKJXItm06nFzaHJkkC27bRarUWagqn3G2mv/7rSH72Z8Hu74N3HIT1OpJqFYwgAGmKdDYDNxggt7ODeRxj/vnPo/L5z4PhecSOA7AsOEW5+yp7CoVCoVAoFMqVks/nsbm5iTRNwXEcut0ueJ4/lZacrbcuLy+j1Wqda7hYq9UwGAzg+xv4mZ+pwrKqz9Z8vjnHjOMIQRCAYdokTfUq9KZJkpB1UeDEjEsQhBf+/svyyU9S8fnHIUveHI1GiOOYGKRl4tZ6vX6nxWT3gWzdkuM4zOfzS18fxzGiKCLGdddFHMdE4L7IWm02lmztj/JNVFWFKIpQVZWsHZ63TzOBUrlcJoK2sxgOh/B9H9Pp9MKE1Vwuh2aziW63i1qtRhLtL0LTNGiaRsR5mQC9UCjc2Bp5oVDAJz7xCei6DkVREIYhoigi94RCoUDqGddBFEXY2dnBZDKB4zhwHAdRdHLfy/73a7/2a3j48CGazSaiKILneWAYBp1OB4IgoFaroVarvVCfyZJPM4OHm2ZRIWS5XAbP81BVFaVSCWmakvpLtn31ep2aTbzhSJKET3ziEzg+PoaiKOT8AE6uP6IoQtM0rKys3AtRaavVgmma0DQNh4eHaDQaUFX1lKjesiwMh0NUq1UUi8ULr9UfhzRN8fTpU4xGIxwfH8P3fSiKgnw+jyRJYBgGdF1Ho9FAFEV4+PDhtSWw32fa7Ta5xhqGAZZlSQ/GcDjEZDJBFEWYz+fERKDZbGI8HoNhGKRpivX1dYiiiHa7jW63i/fff59c81RVxcrKyinzoEKhgGq1im63S/oRhsPhlR8j94nsfrmzs4MPPvgAvu+jWq2i0WggSRI4joPd3V0YhoHZbIY0TfHOO+/c+3vL3t4ehsMhDg8PAQCVSoWYw/m+D9M0sb+/j1arBQCvjfCeQrnPvD5dKxQKhUKhUF6aeDLBfH8fSRCAU1WIDx7QhisKhUKhUCgUCoVCuUaSBPiX//LfQpJ8s2GCZXP41Kf+9gsCdAAoFot46623sLe3B03T4DgOZrMZ4jhGoVAgxehms/mCsPh1JUkShGGIXq9HUk+r1SoURSFNIa1WC47jkDQRURSJ2PHtt99+rZIGDMNAp9NBp9PBbDZDpVJBu92GIAgkQThLvs4c9rPkpslkgp2dHXieB9M0MRgM0Ol0MBgMkM/nEYYhSZc4yx0+a5K7j0myURQhSRL4vn+pcUOWapWlgVerVbTbbSLMvqwhznEc+L6PlZWVF/6WoiiQJAnlcpl8znnF08FgAOCkYL+I2USapnjy5AnG4zH6/T5c1yUC+CiKMBwOIUkSfN+H7/t46623yDZUq1X0+320Wi30ej3S0Pf8Nrqui36/TxLBsgLwfaVWq6HX60FRFPR6vRdE+RlRFKHf75Pv6qoa+NI0JWkxtm2Tnz/fiPc6NCvn83msrKxgeXmZNGWHYQhd1zEej0mjJMuyyOVypxolJ5MJaXqJ4xgcx5Hfl0qlNz4h4U2kXC6T5uTBYABJks69Fuu6DpZlUSqV3gjDntcd//OfB6/r4B0H8+1tQJa/KbNhGDCFApLNTYTHx8gdHGAuy5j8w3+I0vd9H7hLTGMoFAqFQqFQKC9PqOtwv/AFJF/5CuB5gCSBffddKD/wAxDOSBC/TcrlMh48eACe51GpVGDbNjzPQ5qmp9Zbl5aWsLS0dO7nnDT8P8Tf/JshfD/EfD5HEMzB8yfPJCfJyykEQUA+n4coiq8sEsgStsMwRJIkiKIIAMCyJ2JnlmWRz4sfuzH/e77nlYb3RjIej7G/v4/pdArTNMkaLMMwKJVKcF0Xuq5ja2vrUgPL1xnP8zAcDmFZFln3kSQJtVoN5XL50rXlzChCVVWYpolqtXrhOpBt22Td6Dr3O8/zRFQehuGlIvT5fE7S2V8n08mrgGEYNJtNeJ6H2WyGg4MDLC0tnarlxHEM0zRhGAaazSYkSbpQKG6aJizLIinbF5EZvjqOs5AAPUOW5VtN5Mzlcmi321heXiZCwSwV/TrHFccxPvjgA5imiU6ngyRJiNluJoa3LAu5XO5Umquu61BVFUtLSyTR9aJ1uizR/SZ4/tz0ff/S1ydJgiAIUKlUsLGxgdXVVXieR8yxJUmi69UUQi6Xw+bmJtrtNkzTJKLfrL52n4SkkiRhc3MTwMl2jUYj6LoOSZKQpil83ydC5Uqlgq2trQsNnz8O3W4Xpmni8PAQuVwODx48OLUPm80mLMvCYDBAmqZgWRbvvvvulaew3zS+75N5lCiKF86fwvDk+SRNv/k88lEqlQq53um6jqOjI3Q6HUwmE2LKxTAMGo0GJpMJPvGJT0BVVUwmExiGAY7jMBgMsLa2BgBoNBp4//33AQCiKOLtt98+c4wsy2JlZQW7u7swTROKorzRAvSvfe1r2N3dxTe+8Q1IkoR33333VD04iiJyH93Z2SE1sew8vI/Ytg3TNHF0dETqxs8fK4qiQFEUWJaFXq8HQRDQ7Xbv9TZTKK8D9AmWQqFQKJQ3EO9rX4P3d/8umF/7NTDPFjNCALNGA+wf/sMo/uAP0iYsCoVCoVAoFAqFQrkGkgTo938FSeKRn7Gs9Oz/z36Poih49913iZDY930iJC6Xy6jVam9UsxLHcRiPx4iiCK7rYmtr60xBealUwic+8QlSKFVVFWtra6+V+DxJEiIY930fm5ubpwqSmXBV0zTs7+8T0X6tVoPneXj69Cl0XSdC5KWlJURRhCiKSJJ6pVIhDuYf3Xe2bSOfzxOR6H0ia1zKGkIXIXNzz97TaDRIQ9zR0RFardapJoosbXcwGKBer0OSpDPFyisrKyT9Y39/n3xnWSOE53kYjUZwXRdra2soFovQNO3S8R4dHZEm2Fwuh42NjVPf4WQyga7rePLkCR48eIC9vT08ePAAwEnz5IMHD5AkCRiGwXA4hGEYJBEsa3IolUpotVpYWlq6tiSVm0IQBDSbTYRhiMPDQ+zt7aFWq6FUKoFlWeIynzVVLC0todVqXUnDSpYWkyXF2LaNMAzBMAwKhQImkwn6/T7W19fv/X7OeD7dXRAEbGxsoN1uk0bJrBFZlmWEYYgPPvgAk8mEJOpkAnRFUWCaJmRZxvb29pU3MlHuNizLotlsIggCHBwc4ODgAI1G41SaSRAExIhlbW0NiqLQBPQFuas9sv43vgHugw/AGwbCWg04r6GaYZCurCB1HDCjEcK///eB7/u+mx0shUKhUCgUymtO4nmwfvqnwX7pS2CmU/CmCSaOkXIc0n/+zzH9238bye/9vdD+q/8K7C0K9D6Kqqp47733YFkWDMNAEAREnFGpVFCtVhdab/3iFwuQpAhp6oHnTwz/MsNGQeAhCDwRiORyr5bWliQJXNd9JiYJEMcnCasMwyAME/h+AEEQngnfk5deA83ngd/1u15piG8clmVhb28P/X4fpmmiWCxiZWWFmKZmv6/VakjTFA8fPnzlZGTbtknKMMuykGUZlUrlzgqqkiTB/v4+TNPEdDola17Z2G3bhiiK2NzcvHTf1Ot12LZN0uZrtdqZr5vP5zAMA5VKBbIsX2viabFYhCAIkCQJlmVdatBpWRYURQHP87cqWr6rNBoNzGYzAECv18P+/j4x8Miugdm6bKVSwfb29oXHfia+W+R6mIm25/M5wjC8sm26KViWvVGTi8PDQ1iWhYODA2iahkajcUo4Vq/X0Wq18OGHHyKXy0GWZSwvLyOfz4PjOLRaLVQqlXPX6LLE3az+dFOUy2XYto1Op4Moii6cCziOA4ZhoCgKMe+lCcuUzNh7Op0ijmPwPI9isUju1YIgvJTQNk1TuK5LDEzy+fydqMeWy2XwPI98Po9yuYzJZEJE9ZlpvCzLZD3+KkmSBMPhEIPBADzPY3V19YU6L8MwxOCm1+uhVCphPB7fS2PaOI5JjTYIAvJzlmWJicfz97lsrvS80TUAYuZcqVRO7a/l5WXkcjnkcjmkaUrMW1ZXV6GqKizLguM4aLVa5D5TLBYxn89JbS4IAuTzeURRBI7j0Ov1sLW1daFAnmEYVCoVGIaBWq2G6XR6J47tm8ZxHHS7XXQ6HeRyObz11lsvGFLwPI9Go4E0TUli+Pr6OlZXV+9tj1B2jEZRhI2NjXOPFU3TMJvNMBqNoCgKCWGgUCi3w/284lAoFAqFQvnYWD//88DP/zxY2wY3GoGbTsGkKRKOQ3x8jOToCPbf+3uQf+ZnID56dNvDpVAoFAqFQqFQKJTXiiQ5/3cXhXwwDANN0xYSnb7u8DxPUuDjOL6weYjneXAcR0T7d7UR7+OSicQty0K73T63EYfneSwvL+Pw8BBBEJBC9Wg0gmEYaLfbpMEvCAJSvNc0DaZpgmEYHB8fY3t7mxSlM5F6vV5HsVi8d6LPrDiZy+Uwm80uFSNmjWdZgjhw4qA/m82Qpil6vR729vYgSdKphrg0TdFsNlGtVrG9vX1mIVhVVayurgIARqMRTNPEcDgkTcNRFEFRFGxsbEDTtFPfw3mEYQjDMNDtdpHL5bC2tgaGYRBFEXEVzxrpsnSC9957D41Gg+yLQqGAR48eYX9/H6qqwnVdsr3lchmqqpJEsOYdSzP7uKysrBDht2EY0HUd/X4fHMchjmOwLAtN01Cr1dBsNi9MQluUOI7x4YcfYjweo9PpIE1TqKoKVVWRJAkmkwn29vagaRqSJEGapuc22N53MmOV54miiKTpdLtdYnyQz+cRxzFJzskMNB49enTvrkeUV6PVapGUjsFggE6nA57nkcvlEMcxfN+HJElYX18n11DKyxHHM8Rx8OzfMQAGwO01ynv/5J+AcV2wYYj0sushwyDSNHCmifSDDxCNRuBfEyMPCoVCoVAolNsmmc1g/pk/A+4rXwF3dAR+NkMsikgEAVwYgtN1RLIMzGawjo6g/o//I7g7JMxiWRaVSmXhlNuPMp8D/+pfARx3IizKxHJJciJAZxgWgiBAEIQrSEBNMZ1OEYZzeJ4PQeAhiiIY5psLynEcIQgCeN7J3+c49qVE79/5nQB9nF6cJElwcHBAxBPr6+unBMWSJJEE9E6nA47jkMvl8O67736svzcajdDr9eD7PlzXJQL0QqGATqeDWq32QnLgbZMkCZ48eULWvObzORRFQaFQIEnWw+EQ9XodURRdKtAvl8tEeNLpdBCGISqVCkn0zMwjM7PVarV6JWt3FyEIAjRNQ6VSQbfbhaZp565LTadTTCYTrK+v32nTgNtmY2MDuVwOgiAgCAJiWpDVOIrFImRZxubm5qXCcoZhiKnsImTGszS1+mLCMIRpmhgMBlAU5VzjhVarBUmScHR0hGazSQxgh8MhETyeh2maEAQBhULhRg1Z6/U6DMNALpdDr9dDu90+83gIggC6rqNcLpPrPeXNJo5jYgodBAEmkwmpSxeLRXQ6HTQaDSwvLy90jclqH8PhEEEQEIMjnuchSRLq9Trq9fqtXq+KxSLeeecdTCYTjEYjUnPM5XJEhH4dPL+PzztHM1RVhWEYsCyLzDnuE7PZDE+fPsV0OoVpmsSEKDO/mEwmGA6HWF1dRb1ex/7+PgzDgOM4sCyL9EcIgkAMNlRVxYMHD07Vq2u1GqrVKhzHgaIoWF9fx8rKCjGgFwThBXMWVVXhOA65VzcaDcznc3Ach/l8vlCdTlEU9Pt9hGEI3/c/lgA9CALSN5IZgdwngbKu6+h2u3BdF0tLSxeOvVwuE0OAwWCA0Wh0L+vzWa+CZVlQVfXSOXGlUsHe3h6CIIBpmi9l4kGhUK4WKkCnUCgUCuUNwvrrfx34/OfB7e2Bd12EpRLmq6sAxwG+D348Bj8aYe668P7snwXzV/4K8uvrtz1sCoVCoVAoFAqFQnlt+LgCdMo3CYIAxWIRLMuC5/kLHbHjOEYURcjn8y/VaHRfGI/HcBwHPM9fWpSVZRm5XA6WZeHw8BAMw0DXdTSbzVONfZVKBY7jQJIkTCYTyLIM0zShKAqm0ykURYHv+zg+PkYulyPpFveNTEhs2zb6/T7q9fqFBU7TNCGKIkRRPNWYvLGxQZqJfd+HbduYz+dgWZaIubMUn4uaLRqNBvL5PCRJQqVSwXQ6JU0Esiwjn8+jUqmg3W4v1JxoGAZ838dsNsPm5iYYhoHneTg6OoLv+3AcB7PZjKRi2bYNWZaRJAk+85nPEBd7WZbx9ttvw3Vd8plJkpBmhUqlcqcaW6+CjY0NyLJMmniy/ZQ19YqiiGazeWWNKsfHx+S8LJVKaDabp/ZppVIh3x1wcuyWSqUbTX65Tfb398n+0TTthXO10WiQRmYA2N3dxdtvv31bw6XcEhsbG88SBXOk+Sy7hi4tLUEURZTLZaytrd3bRIjb5P/4P+pIEo/8N8tK+KN/dHZr40ktC0wYIuU4YIFmslSWwY7HAIBoPKYCdAqFQqFQKJQrwvzc58B95SsQdnaQiCKChw+BZwLcBEA0m4Hr9ZB7+hRzANZP/RSqP/3Ttzrmq+TJEyCKvvnfPM9f2/NGGEaI4wi+7yOXE84UlnMcD0ni4HkzBMEcLMs9WztYTJj0Pd9zxYN+zcmMQUejEZaXl89Ns1YUhQgus7XIl00pPj4+Rq/Xw3A4hGVZZG0uSRLEcUzWa6fTKR4+fHhnhM3dbhemaeLg4ACSJL3wTN5oNMjabJqmYFkW77333rnjZ1kWDx48IOt0uq5jd3cX+XweLMuSVNByuYx6vY52u/2C0eF10Gw2YVkWptMpDg4O0Gw2USqVyPpeHMewbRu6rqNWq5FjgnI+y8vLaDQaGI1GsCyLrPFIkoRarXahUcHziKIISZJg2/al+zyOY8xmM5TLZWpueQkfrT1chKqqGA6HxNS2VCphPp/j+Pj43ORW13UxGo2wtLQERVEWSrB/GTLTXwAvmMTIsoxyuYx2u43Dw0Ps7++jWq2iWCwSg1/LsjAajVAoFIj5BzUteLPJTIYzY4bpdIp8Pg+O4xCGITFrmM/n8H0fW1tbFx4zQRDgyZMnmEwmGI/HsG0bybMGA57noWkaXNeFaZp48ODBrd/3i8Xiwtflq2A6ncJ1XSI2vgxVVTGZTOB5HplD3Aey48AwDPT7feTzedRqNWLObNs2jo6OoCgK0jTF0dERoijC4eEhwjAk9TSGYTCbzWCaJkajEVZXV5GmKR49enRqX6RpCkmSiGl6Zv4RBAFYliUmCBmZCbDv+5jP5+QzMjPvRY7L7DxI0/Slezgcx4Gu6yRFOxOgZ/XrZrN57vz8LmHbNiaTCXiev/R4zuVyKBaLME2TPIvcRzIzgyAIFjKZEUWRzPWfP9ayeznP8/Q+TKHcELTCTqFQKBTKG8K82wU+/3mwBwdgfR/+w4dgnl+gKxYR1WpghkPkj48R8Dzcv/SXkP/Zn729QS9KmmL6z/4Zov19pFEEtlJB4bu/Gxx1l6RQKBQKhUKhUCh3DCpAf3XiOIamaRAEAaVSCcPhEFEUEVF6hud5MAwD+XyeJAonF30B95AwDDGfzyFJ0kKFNVmWSQL6SUoS80LDoyzLaDQaSNMUo9EIjuPANE1SXC6VSvA8D8ViEUtLS2i1WtA07Zq28Hqp1+skaafb7Z7rlJ81ciwvL5Pk5QyGYdBut9FsNomLfhiGYFkWoiiiVqtBVdWFvp8s9dp1XdJYl31OpVJ5qSZmx3GIkYAoivB9HwcHB7BtG6PRCLlcDqqqgud5xHEM13Wxu7sLjuPQarXw4MGDU6kdiqJcW1rBXaTRaKBer8NxHLiuS5pSisXilaaZxHGM0WhEkpnOS2WSJAmrq6vY399HuVyGYRhYXl6+snHcVTJTh16vB1VVz0zTYVmWJML3+30oigLHcWjqzBtIq9UihgRZEgjLspBlGbVa7Y0xbXgjEASAYU4m1ml68u+LiGOkz17D0OOAQqFQKBQK5UoIDg7AffnL4A8PkUgS4s3NF+dlsox4awvp3h74gwPg138dwd4e8peI1e4LX/vazf2tIAhIg/1FqeYMwxBjriTJIQyjhRIAHz8Gvv3br2y4bwSZMJbn+UtFV6qqQtd1OI6D0Wj0UgL0bN3y6OgIYRgSw8tsHXw6ncIwDOzv7yNJEvA8jwcPHrzStl0FSZLAMAwMBgMIgnCmQJNhGGiaBpZl0e12oaoqRqPRhWan+Xwejx8/xuHhIWRZJiLYJElQLpdRLBYhiiJWVlZOmYheJ4VCAevr6yRhVNd1DAYDIpqdzWbgOA7NZhOVSgVbW1un1pcpZ8PzPJrN5iuJ9avVKmzbhmEYcF33wvVt0zTJ+XyTidv3kdlshul0ilwut5BYP6t51Go1NJtNRFGEo6Mj7O7uElE6x3HwfR+WZWEymaBer6NcLmN1dfXKxp0lBVuWRYSOLMuiUqmg0WiQc3ZjY4MIKA3DQK/XQ7fbJTUyQRBQr9eJYfBNGF1Q7jZ7e3sYj8c4PDyEJEnY3Nw8dW5Mp1MMh0Ps7+8DOLmXtdvtMz8riiI8efIEo9EInU4Hoiii1WpBlmWkaYrpdIrxeAzLsrC6ugqGYfDw4cM3SnwZxzFJl1+ErA6avfe+CNAPDg5gWRZ6vR5ardYL15pisQjf93F0dIQnT54giiLwPI98Po/19fVT+0eWZVSrVfT7fRwdHYHjOPT7/XPrjM8fT5mZi2maCMPwwmcLjuPgeR5EUVxIUO553ikD9X6/D+DkO6tUKqhWq2d+z71eD8fHx7BtG6Zpwvf9U9ua9Tasr6/f+Xv688fzIucxx3FI0xRJkrx2wQuXwTAMOeZHoxE5r7Nninq9fqNmGBTKmwgVoFMoFAqF8oYw/cIXwLguBNtG8ODBafF5BsMgbTQQBgH4fh/4rd9CcHBwZ1PQ0zCE/Tf+BpIvfhHsYAD4PpCmSHI5OIUCkt/7e6H88A/f2fFTKBQKhUKhUCg3SeL7iAwDAMDXamCpg/+tQAXorw7HceB5Hq1WC3EcQ5IkUmzPRNXz+RxRFKFQKEDTNDiO81omNWcsWmBM05TsnzRNUSgUztwntVqNuJNrmkYEyo7joNFooNVqEbHseYLZ+0CpVCKJuIeHh9jd3SVpFizLwvd9mKYJ27ZRr9ehaRpWVlbO/CxBEK5sf1yF2DuOY9IMBZwkDjmOg/F4jHq9/sLnr6yskDSF4XAInufx3nvvvVFNKx8lM2h42VSql2E8HmM+n8N1XaytrV34WkmSIMsyTNOEoihYWlp67b+f4XCI2WyGIAgubTasVCrENGM4HFIB+jXwso0wt0FmSJCZElBeT7jtbaSKAqQp0skEzCXnO2vbiAsFpJKE3D2et1AoFAqFQqHcJWZf+AKY6RSc58F//Pj8ZwSGQbyyAuGDDxBNp5j+4i8i/5//5zc72Gvi61+/2s/LUtzSNAHDMOA4DizLATj5eRiG4PnLxeQ8LzwTrMeIootFIgDw7rvAn/2zl/s6UU7j+z5834eiKJc+I7Msi0KhAN/3XyqpME1T9Ho96LqOMAyxsbHxgkFloVCALMs4Pj4mQrXpdIpCofCxtuuqGI/HCIIArusSgdx5lEolYuw5HA4vFKADJ+uw29vbmM/nJIk5E99nYtabXreoVqskCbRSqWAymSAIAiKIKRaLkCQJ6+vrb5TJ521TrVZxfHyMcrmM4+NjrK2tnZmm7TgODMMgIk/6HV1MJnxbVPyZieUAYGlpiQhAM+Gg8ax2zTAMFEXB+vo6isUitra2riS9NkkS7O3twTRNTCYTIqIEToSV2fffbDbRbrfBsiwePHiAfr8PSZLQaDQwm82Qpil4nockSSgUClhaWrrW2gHlfjCbzWDbNo6Pj1EoFM6sIRYKBUiShMPDQ/T7feRyObRarTNNp/v9PmzbRqfTOXOdO5fLQdM0Yk7D8zzG4/GdF9leJTzPg2VZhGFI6t4XcTKHPtnXt50Wvyi+72MymUDXdVQqlXONLjLTnd/4jd+AIAjI5XL41Kc+deZ2MgyDpaUlBEGA8XhMrmPZ/mNZFizLQhAEeJ5H3qdpGkajEfL5PEzTJPO0JEmIID3bv0EQgOO4S+dyGcfHxxiNRgBO6k/ZPDmXy8GyLBwfH2N5efmUGY2u6+h0Ouh0OvA8D5qmYWlpCTzPIwxD8j7Xdcl1+y5fq3O5HOnVCILg0jnIfD5HkiTI5/MLGY3dRXK5HBiGQT6fx3Q6vVQ0ns31HcdBGIbkWMyO01wuB9u2MR6PUS6Xsbm5+dr2BFEotw0VoFMoFAqF8oaQ/p//JzjDQCzLwCWFhrjRIAXA2d//+8j/mT9zQ6NcnNh1Yf0X/wW43/ot8LoOzrLAPnN8BsMgUhTEhoHZP/2niP6b/waFT33qdgdMoVAoFAqFQqHcBmmK6b/8l/B/6ZfA/fqvA9mcmecRf9d3QfyBH6Bz5RvmTRagp2kK27Zh2/apVNKsOWtRRFEkxcvpdAqWZaEoCqbTKYIgQJqmUBQFxWIRPM+D53nIsgxRFF+7xqF8Pg9RFKHr+qVF9swZv1wuk0LdRcW3SqWCUqkEy7Iwm80QhiGWl5exvr6OSqWCer1+bwubz7O5uUn2xXg8hq7r6PV65PeFQgGrq6solUrY3t6+koanV8HzPJLqw3EcSqXSmedP1igQBAFmsxkR02uaduZ5EEURJElCsViEYRgol8swTfPGkoLeVDzPI9exRY6tUqmE0WiEKIoQRYulmN1nZrMZXNeFLMuXbmuWUO+6Lmaz2Q2N8PUnCAIYhgHDMEjaHsMwKJfLZ5pZUN4cbjNco/gH/gDsv/pXEZVK4HUdcbF4vlpmNoPgugi2tpB+9rM0AZ1CoVAoFArlikh+67cgjMeIZRnMJUm+TD6PSJbBjsdIfvM3b2iE10uSAN/4xvm//+QngT/yRxb7rEwE4jj2M9H4ybpptqapqip6vR46nQ5UVV0o1a3b7UJRFKysrKDdPnu9gWGA1VWgXqfi84/Ly6QOZuu2L/Mey7Lg+z5s2ybCmvM+u9Vq4enTp3BdF7quY3Nzc+G/cx1Mp1O4rguO4xYSw5dKJUwmE/i+jziOFxKI5XK5c5M7bwNVVfHuu+8SE9D5fA6GYZDL5YjpKeVmYVkW6+vrxFRxf38fxWKRrKnP53NYlgXP80hC/ToNubmUzCR6UfFnEAREMMpxHNbX16GqKnRdR7lcRhRFpN4hCAKq1SqazSbyl8wvFiFNU+zs7MAwDBwdHSGKIqiqCk3TkKYpXNdFp9OBJEnkOMlMM5aWltBqtWDb9qmajKqqt14notwdhsMhXNdFGIYXXj9YlkWj0cDBwQGCIMBoNDolqgVOBL2j0Qjj8RiyLJ9rssowDJaXl7GzswPbtjEcDt8oAXqxWISiKEjTFJPJ5EIz4qw3oVKpnGvKfhcxDGNhc2ZBEJAkCfr9PtbW1i6dQ1UqFfR6PdTrddi2DU3TTv3OcRx0Oh2SqC4IAmRZRqVSQb/fx2g0gqZpcF0XLMuS2rJpmhgOh9jc3CRzIMMwzj2ODw8Psbe3R4y3szkjwzDEGKRYLBIjrna7jSiKcHx8jH6/j/l8jq2trVO1w8wkpFwu4+DggIjy33333VszVc7mxNn8tlgsnrqHqKqKZrNJDLbL5fK5x6nneTg+PiYmLkdHR7AsC5VKBbVa7aX6bW6T7F5aLpfR7/dRr9cvPG5HoxGCIIBlWZjP5/A8D8VikRxbnueh2+1CEASsrq4iSRI8ePDgpjaHQnmjuB9XGQqFQqFQKK9E4vtgTRPsbIbouQfG82DyeSS5HJjpFPHx8fUP8GVJU1h//s+D+xf/AsLeHlKWRdhsIlVVgOPAuC7Y0Qj5vT3MfR/zv/AXwP3lvwxxe/u2R06hUCgUCoVCodwYaRjC/KmfAvtP/gk4ywI3GoF95hqc5POAYSD8tV/D6Pf9PlR+/MfBvOYitpsiTYEvfhH40peAnZ0XBecX9Zfdk5rnx2I4HKLX6yEIAkwmE9JIWSgUcHx8jEqlgtXV1YUay7J0U9d1Yds21tbWkKYpKTplbtalUgmyLOPw8BDlchmyLN968stVU61WMR6PMRgMXigSfxTXdUlzTSYsd133ws/neR61Wg2O46BYLGJjYwOPHz++4q24XbI0i+FwiEKhgHq9TowMBEGAIAgol8tYWlqCKIq3Ns5MHD+dTom7eVb4L5fLJJU+o1AoQFEUkmieNUid1eiYJAk8z0OtVkO9XieNrYZhUAH6NZMkCdI0Xbjp5fnXvUzD8n0ljuOXTtOZz+eI4/iaR/ZmMBgM0Ol0iIGF53lI0xS5XA6O42A0GtE0gdecuypCYWUZ6e///UhME7mnT5EeHiJeXQXz0eNwNoOwv4+wWASKRcg/+IO3M2AKhUKhUCiU1xBmOgXi+GSdeQHSXA6IY+C5NL2bIE2Bo6Or/9xe7+JN+a7vAr792y//HNu2sbu7iyhyEMfmM8Huye/y+TxUtYxisYggMCBJHorFBLXaIsJcA7UasL0dYWNjsW2ivByZoGU6nS70es/zUC6XX8pM0LIsTCYTMAxzqXg5Ww+3LOtOpDxma16LrunwPE/Wc15mLeiuwTAMVFW9E98B5YRs7YphGJRKJZimiV6vd8rgdWlpCYVC4U4Y0N4HMjOUfr8P13UvvD7FcQzHcbC0tHRKJKppGjRNg+/78DyP1DsURbnS8384HGI8HuPw8BD5fB4bGxunPl/TNIRhiKOjIxwdHYFlWWiaRraJYRgyVgrlLBzHgeM4UBTlUvGnLMvI5/OwbRuO47wgQLdtG0EQwHEctNvtCz8rOzZN00S1WoXv+7daw7xJNE2DKIrQNI3Uds+7bmSGzqqqLpzKfRcIggCe50EUxUvnjvP5HJIkodPpQBCES41BFEVBkiSYz+ckcTyjXq/DMAzkcjm8//77kGUZQRAgjmMMh0OkaYrhcAjDMOD7PsrlMqbTKbrdLuI4RqvVQqlUQpqmmM1mOD4+Jq/LejRmsxkGgwE++OAD8DyPIAjwzjvvoFqtnhr3bDZDt9vF4eEhGIZBoVDAfD4nNez19fVT+8b3fWLelG3f06dPUSqVLu2huEoygwld12EYBsIwRC6XI6EJoiiiUCig2WwSo+d2u42DgwNiKNFoNF74DieTCb761a9iPB6jXq8jiiL0+32Mx2PYto1ut4vl5WW0Wq0b2c5XpV6vwzRNGIaBTqeD1dXVM+uMmbFBkiRwXRf5fB5bW1unTGo0TUOj0SDHC8uy0HX9XPMDCoXy8aECdAqFQqFQ3gDSLOXw2cPlIu2pKcOcVOOy994hJr/6q+B+8zch7O0hliQkGxsAw4A8cpVKSEolBOMx8p0OglwO0//5f4b4Mz9zi6OmUCgUCoVCoVBukDTF+Cd+AtyXvgR+dxfsfI6oVEL4rLDCui5yR0dIBgMgDDEOQ1Q/97m7qzK5R/z8zwO//Msf772vq3ar0+mg1+tB13XYtk1SP7LCWKFQIKnOb7311kLuzPV6nTi6Hx0dodVqYWVl5YXC5NHREUlMuC8Ft5dBVVWIoohqtYp+vw9BEM4U2fu+j263i0qlAlEUUa/XkaYpDMOA53mnhMsfxfM84rBeLpevc3NuDYZh0Gg00Gg0MJlMiMiS4zhwHIckSTCdThGGIRRFuXGX9IODA+i6DtM0YZomwjAkvysUCphMJjBNE1tbW6SAXq/Xoes6crkcBoMBaUA4qwkjOy8zB/vJZEJS0ynXS9ZwEEXRQulOWVoMgDvRiOs4DqbT6amGzats0sz2j7egQCEMQ/IeyqvR7/dxdHSEbreLyWSCQqEATdPAsixJWfhomsBtJUhQ3ky0P/2nYf3O72CeJBD29sB//esINQ2pJAFpCs62wU+nCIvFk/rBv/fvQaSpFxQKhUKhUChXRz4PsCyY59YoLiSKAJ4/ed8NMp8D//F/fKN/EgDw9tuXv8Z1Xezs7EDXdYxGIxSLRaytrSGfzxPB3nA4hG3bCMMQURQhTdNLG+lnsxmiKIKiKFRIeY2Uy2VYloXRaHTp+urzxqAvs74aRRGiKEI+n1/omTufz2MymZAU39t8Ts/WVRdNSJ7P53dqzesqmEwmMAzjlNlpuVy+MNmScj1kybe6rqNYLJ4yrxQEAfV6HbVa7aUMIt5kyuXyM5OUkxRzSZLOXY/VdR0sy6JYLJ4p/hRF8VpFs7quYzweg2EYtNvtM69FgiBgbW0NOzs7cByHHCcUyiJk9+pFj+NcLoc4jhGd0ZM9n8/JfXOROZyiKESYOZ/P3xgBOsMwaDab8H0fs9kMBwcHaDabp+rjURRhNBrBNE0sLy9DluV7VeNO03ThuVz2ukVNq5//zI++R5ZlKIqC+XyOTqcDz/PAcRw5trJU9slkgnK5DJ7nUSwWUa1WoaoqJEnC9vY2WJbFkydPIAgCDMPA0UccwXzfh6ZpsG0b3/Ed33GmGbosy9jY2MDe3h7G4zERzluWBVEUyTmSpaLPZjPSb5JhWRZ++7d/G3Ec4zOf+cy1zo1938fR0REcx8HBwQF6vR5c1yX7MEtmVxQF5XIZrutiaWkJKysraLVa2NjYwIcffoh+v48gCMjchWEYDAYDfO1rX4Ou6yiVSpAkCUtLS+B5HtPplDxLZteWywws7gKlUgnVahVRFOHw8BC7u7vEACarQ2bG2KIowvd9yLKMtbW1M+drHMeh3W5jb28Po9EIsiyjWq3ewpZRKK83tAODQqFQKJQ3AK5QQCqKSHM5MLPZpQL0NI5PBCr5PJg7mHQ1/+VfBj8eA0mCeH39/AfDSgWh64LTdeCf/3OEgwGEjzgHUigUCoVCoVAoryPOr/wK2F/7NQg7O0g4DsHjx6cSzpNqFUEYgt/fB7+zg5Rl4Xzxiyh93/fd4qjvP7b98cXnwOspQM+Szw8PDxFFEXG+zp7jfN+HYRg4ODhAmqbY2dnBo0ePLv3cfD6Pzc1Nkhw8GAyIyznDMPA8D/P5HMViEUtLS2i1WveqsLwoDMNgfX0dYRgijmMcHh6iWCxCVVUIgoAoikjSeebuvra2RtIpisUier0e1tfXz2zsi6IIvV4PxWIR+Xz+jSjUFYtF0ozW6/UQBAFpBOF5Hvl8HvV6HY1G40YaBTudDhGCRlGEcrmMUqlEkp4ty0Kn0yHH98OHD1EsFiGKIkqlElqtFvb29jCbzV4wJ0iSBI7jwLIsNBoN4rruui6ANyNh+7bRNA2FQgE8z8O27QsT59M0hWVZqNfrKBaLN9aMmzVkPv/3dF2HruvwPO+UYYMkSSgUCmi1WleSJlAsFlEsFjEcDi9N8IiiCK7rot1u0wbBVyRLpjg+PkYQBNjc3Dy17zVNQ71eJ/f37D78Ohq9UO4urCyj9LM/C/u//C8BUQTGY/CmCcZxAIZBLMsItreBYhH40R+F9if+xG0PmUKhUCgUCuX14tEjJF/9KvIHB4jC8NTa80dJwxC862K+vo70rbeudVhJcq0fvxDFIrBIz//R0RFM08RoNMLq6uqpdRue51Gv11Eul3F4eAjP88AwDKIowmQyOfe5NzOcVBQFuVzujVjLuy1qtRq63S6KxSK63S7W19fPFGCGYYh+vw9N05DP5y9c+/koLMuCYRgkCx7Y2esYhrl1k7hSqQRFUQCcGBhelAiepils20a1WoWiKPdenD2dTrG/vw/P806ZneZyOYxGI4iiiJWVFZrKeMPk83msrq5CVVV0Oh04joM0TYn5pe/7VIC+IAzDoNVqIQgCHB4e4uDgAI1G45R5b1b7m06npCZVLBaRJAnG4zFJz02SBIIgkPXGqxTQTiYTkoh7Vprs8/A8D1VVYZomVFVFGIb0eKAsBMuyYFn2lLHFRWTGMvf9XnfbNJtNYlzc6/WIIX0+n0eSJJjNZhAEAe12G5qm3TsDXUEQkMvlEAQBkiS58HjJUsTz+TzCMLx0O7PnCkEQXrjOZcnraZoSc4UwDMk4ZrMZ4jhGrVaDqqp49913sbS0BFmWybNL9vcfP36MXq8HWZZPpa0LgoCDgwMAJ9/jRXNjnudRLpdhmiaq1Srm8znm8zkRn4dhiIODAziOQ9LuRVEEy7KkRyIMQ5TLZTx9+vTajoPpdIonT57Atm18+OGH6Ha7sG0bcRxDFEWoqgpFUcj3Oh6P4bouMSja2tqC7/tI0xT7+/sYj8cYDodgGAZBEGAwGJD9vr29jc985jOn5tae551Ki1cU5cYS31+FjY0N0utjWRZJOwdO5hqqqpIQhkKhQPbheTAMg0qlAl3XUa/XMZlMbmpTKJQ3BipAp1AoFArlTYBhkHzXdyEeDJA7OEBwSQGQHY2QchygqhB//++/wYFeTjQagfud3wE3HiMql8FcshiT1GrIP3mCeDbD9B/9I9poRqFQKBQKhUJ5Iwh/+ZfBGwaQJIgePgRzhkiNEQREW1vIvf8+2NEI4S//MkAF6K/EP/gHr/b+1yTYg5CmKUk+j6IIGxsbLzThiaKIdruNXq+HTqcDURThOA5KpdKln58VjLPio+M4pChaKpWgqiry+TwRoL+ulEolbG1tgWEYFItFmKaJ4+Nj4niuKApWV1ehKArW19eJULnVahFX6b29PdRqNZRKJbAsS4TJhmGA4zgsLS2h2Wy+EanCYRjiyZMncBwH4/GYFIiBEwGuqqqYTqcYj8d4+PDhtTYh+b6PwWCA4+NjAMDm5uap70CSJEiSBFVVcXh4iFwuh6OjI7z9LGZrY2MDQRCg3W7jK1/5CkajEUmHjuMY0+kUANBoNFAqlYgjepba9CZ837dNlkamaRoMw0ChUED+nDS4wWAAAFBVFfV6/VrH5XkedF2HaZrk+Od5HpVKBa7rkgSn7PzIrhtZklMmBH/Va2+tVkOv10OhUMBgMMDa2tq5zSGDwQCCIEBRlGvfP687uq7DdV1Mp1NsbW0hl8u98Bqe50magGEYkGUZzWbzXjVxUS7nrn+dfLmMyv/0P2H6pS8h+KVfAn7nd4Bn5impoiD93u9F4Yd+CPn19RsbUzgYIPjGN5D4PrhSCdK3fitYmjpJoVAoFArlNUT6gR9A8I//MRJBANfvI1ldPfe17GCAVBAAVYX0/d9/reNaUPtzrbz99uVz6el0itlsBsMwUKvVXjANzOB5HisrK3j69CniOIYgCDg+Psby8vIL66dRFGEwGMD3fWxsbKBarb42SdJ3EZ7nsbS0hCiKcHR0hL29PVQqFaiqCp7niehlPB5DFEU0m02srKy8lOAsM/rTdR3z+fzM5/PnyYTedyEBNVubV1WVGMeet9Y4Go2QJMmNrHldN5PJBE+fPiXCoSRJIMsyGIaB67rQdR2apiEMQ4RhiKWlpdse8htDGIbY3d3FZDKB67pwHIeI+orFIsbjMQqFAra3ty891ygnosEgCMAwDHRdx/HxMTiOgyAIiOOYCATX19ehaRq2t7dh2zb29/eJKNzzPCRJglwuB8uyoOs6qtUq1i8KI3oJPM9DEASI43ghw9KsvpamKTUkoCxMJorUdR3NZvPC+3wYhvA8D41G48y5Xy6XgyAIYBgGs9mMGLmch+u6EAQBLMu+kdetjY0Nss8yw+4oisCyLCqVChRFgaIo2NjYOLfudlcpl8vETN627QtN9rN5Z7PZXMhY3DRNYlb1UZHy3t4ebNtGmqZ4/Pgx0jRFGIaYz+fknpmJ1B8/foz19XW88847Z/4dQRCwtraGlZUVWJaF+XwO4OSZheM4PHnyZCGzLE3TiEm17/unftfpdGDbNnRdR7FYhKZpp55/oigihssbGxs4Pj6+8nTwMAzx9OlTGIaB4+Njktidz+fRarXAMAzpucjGt7q6il6vR+qa1WoVb7/9NkRRJHVRy7IQBAEsy0I+n4fneXjnnXfwLd/yLUSAnyFJEtbX17G7uwvTNDEYDG5NgB7HMZIkAcdxlz73MAyDra0taJpG5gBRFBGDIEEQIMsyRFHE06dPF7qXl0olEjCQmVRQKJSrg3YPUSgUCoXyhiD9wA9g/n//30hyOfAHB4i2ts4Wb89mEHQdYb2OZHUV8rd/+80P9gIiXQeAk4T2RdLMZflETO/7SJ65Y1EoFAqFQqFQKK8z/vvvg3v6FHxm2nRBkxnDcYgqFfCjEdInT+B/8AHEBdKnKS+SpsDf+luv9hmvW0B3VhizbRtLS0sXilmbzSYcx4HjOBgOhwsJ0IGTItJ7770Hy7JgGAaCICBu0ZVK5Y1ptNQ0DW+//TYpsMZxTEShHMehUqmg0WhAkiTynuXlZeKEbhgGdF1Hv98Hx3HkvZqmoVarodFoYHl5+Ra38GZIkgRPnjzBaDTC0dERcrncqUaQ6XQK0zRhWRZWVlYQxzHefvvtazvGDMOA53mYzWbY3t4+9xzKnOVHoxE0TYPrusQF/NGjR3AcB/P5HE+ePMFsNkMulzvVgJGlv+RyOfi+D8/z0Gq1LmxooFwdy8vLJGXn4OCAJAdkx5XneRiNRnBdF2tra9fqXJ+mKQ4PD2EYBlzXPdUUkhX4XdeF7/sol8vEvOD5NIHRaITZbAYApHHi4yIIAmq1GubzOQ4ODnBwcIBms3nqWhYEAYbDIabTKdbX16Gq6gvNF5TFieMYpmnCNE2USqULG9cYhiHpA77vX5poRqFcBwzHQfnsZ6F89rNIZjNE4zGYXA58pQLmBo1Upr/xG/D/zt8B9y/+BRBFQBwj4XnMZRnpZz+Lwh//4zcqhKdQKBQKhUK5bqR338Xs8WNgMkFubw8hwyBZWjq1Fp3GMdh+H7nxGMHGBpK33oL8Ld9ybWOKY+JHdKuco8E4RfacH0XRpc/4+XwexWIRYRgiTVNomoZutwtd11EqlcBxHIIggOM44Hkea2tr0DTtysUVlBfJBOgMw2A8HsM0Tei6DoZhSOJ1rVZDuVzGysrKS4urMwGKJEkwDOPC9dnJZIIwDEmK8G3zfEKy53lkTed5MV0YhhiNRrAsC+12G4VC4V6vR4ZhiJ2dHei6DsMwUK/XTwmh0jSF67oYDAZkvU2SpCtZ5/M8D8PhkIjvGIaBJEmo1WqoVCpvfNJuFEX44IMPYJomut0uoihCsVhEPp9HFEXodrvEiDcMQzx+/PiNFHO+LGtra8jn88jlciRpNBN/FgoFiKKIcrmMtbU1TCYT7O7uQtd1YpRbLBbBsixZF5ckCWEYIooibG9vv7IIPUkSIsZc5Bx4/lxdRMRJoQAn9+pM8JmlNJ+HYRgQRRGyLKNWq73w+8y8pVQqYTweXyhAT9MUlmWhXC6T8+1NZHl5Gc1mE6PRCLZtk2tQdg+8r7WiUqkEURRJmrMkSWd+x0mS4Pj4GLVaDTzPI01TDAYDNM/pq7dtG47jYH19HZVK5VR9ezqdYjqdot/vk94A3/fR7XbheR7iOAbP82i1WuA4jgQDXBYswHHcqfNiMplgMBiQfo7L4HkeDMOQv5/P54mZTDb/KZVKLySpZ/eAbP7c7/eRy+WwtLR0pXX9zNA5S+lO0/SF3phyuYx8Po/BYIB8Pg/HcdBqtdDpdFCv1zEej1Gv1/Ho0SOsra1hOByi3+/DdV3s7u7CdV20Wi08fPjwwv1UqVRgmiYqlQoxm78JoiiCYRgYDodkjgucGLtk8+GL7umVSoWMOQgCYniuKApc1yWmCIt8b9n9PkkSYppAoVCuDipAp1AoFArlDUH+lm+B/5nPIAoCCLu7yH34IcJ6HemzFPE0CMAaBoTxGFGphLTVgvAf/Ad3L+bk+QeCRRb70vTkfwxzaVo6hUKhUCgUCoXyOjB/+hSYz8HO50g0DZfN6BNNA6vrQBhivrtLBegfk8PDV/+M7e1X/4y7hG3bmEwmJJn7IliWhaqqcBwHtm2/1N9hGAblcvleN6ddBZmIeGVlBbPZDHEcg+M4SJJ0bkFufX0dsixDkiTU63VMp1NSjMsaFprN5p1oWrwJdF2H4zg4OjqCqqovpDcrioIoivD06VPs7e1hZWUFvV4P29vbqNfrCxsnLMpoNDrlRH8RmQN8ls6eNaYIgoBPf/rTAE6aIE3TRKPRQC6XgyRJKJfL5LVxHJMCedacQbl+NE3D6rOkOMMwMBqNoOs6BEFAkiSIooikNGiahgcPHlxbyvT+/j50XUen04Hv+1AUBeVymTQzvf/++3BdF+VyGe12+1SDLM/zqNVqKJVKODg4gK7ryOVyqFQqrzTe1dVVBEEA4KRBZH9/H/l8HoIgIIoi+L5P3P3L5TI2NzdfdTfcazKjjMxgJJ/Po1qtLtw0mzV3TKfThcQKxWIR3W6XmFdQAfqbw13sw2VlGbmbbipMU5h/9a+C+Zt/E5xpgh+NwD0z4UgZBlGphGQ4xOxXfxXhT/wElO/+7psdH4VCoVAoFMo1UvrJn8TkT/0pzNMUwtERGNM86TURBDBhCN5xkLLsifh8fR3KT/7ktfafhOHdmKd+8pOXv2Y+nyMIAoiieKFpZ0ahUMBkMiGmdZqmwbIsTKdTkhDXbrehKApKpRK2trZos/0Nsbq6StJPa7UafN8n67KiKEJRFDSbzQtFxpm4J0kS8ixfqVRIMmWj0cDh4SEx6vvoOovruuh2u6hWq+S9d4FMuJSJoTqdDhEOxXEMz/PImvZ1r3ndBNna7HA4RLvdfqEmktVJRFHE3t4eTNNEv99/JQF6kiQ4ODjAeDzGdDqFbdtkTUiWZdi2jePjY2xubl752vV9Ym9vD5Zl4fDwkJg0PF8zycSLnU4HALC7u4vHjx/f1nDvFc1mE41GA6ZpwrZtYm4syzKq1SoEQUAYhtjb28NgMMB4PMby8vILx2MYhuh2uzg4OADDMCgUClhaWnqlsQmCQO6x2T33InzfJ4mti9ybKRTgRDQuSRJarRa63S6pWT9/P0uSBMPhELZtY21tDaqqnnk8siyLarUK13Wxv78PwzDOrNOlaYput4s0TaGq6htTwz0PjuPQaDTQaDRueyhXSrvdRhAECIIABwcHqFQq0DSN1A4dx8FoNEKapvjEJz6B6XSKfD5P6nvVahWFQgEMw8DzPIzHYyJ8LpVKL9TAh8MhMchSVRWdTgeTyQSe58HzPJJcniQJJEmCruuoVCovFSwAnHxfDMOAYRiEYXipSDpLxOY4DqqqwnVdGIaBXq+H6XQKAGfOpbLfKYpCRN5BEGA0Gl3ZsZIkCQzDgGmaKBQKSJIEk8kEuVzuhXNclmUUCgU4jgNZltFoNCAIAizLgmma5DyWJAlra2vEvKVUKuGDDz5Y6J6oqiqGwyGCIIDrujciQLcsC3t7e/B9H6Zpkl4Tnuehqips24aiKHjw4MGlhgOSJL0w5syAIDNdu2ybfN8H8M05QJIkr7aBFArlFHSGTKFQKBTK60aaIp5OwXAcWFE8VcDT/uv/GuZsBnDcidN0rwem00HKsmCSBLEoYr60BDQaYH7sx1D8nu+5xQ05G2FpCT7LIs7nwToOkksW4tPpFEyaApIE5hUXJikUCoVCoVAolPtA+lyn3SKpfySVJkmQPhN4UV6eVxWgN5vA69b/E8cxoihCLpdbqHEsl8vB8zykaUqa9CgvD8uyF7rif5R6vY56vQ7HceC6Ltn3WcPqm0KaphgOhxiPx8jlci8U3h3HQbfbxXw+RxRFGI/HxHWb4ziSFry1tXUlzUlpmiKKIszn80sNHACQplbf90+5iwMnx8T29jYEQcDh4SGiKEK1WiUpWc83KjAMg3a7TUTqlJvheVOAarWK6XSKKIoAnDSZZ83DKysr19b8NhqNMBwOcXh4CJZl8eDBg1N/KwxD1Go16LoOTdMwHo9RLBZRKBROfU4ul0Oz2USv10OtViMpHB8XhmHw4MEDiKKIXC5HUt2yJpelpSViprC2tvbG3jsmkwk6nQ5msxlc1yXNxqIootvtEqODy87rrBkjTdOFxAosy4JhGJImQKG8aVi/8Atg/sbfALe3B242Q6RpCJeWAJ4HEwTgRiPkd3Ywn0wQ/sW/iNl/999B/tZvve1hUygUCoVCoVwJQqsF5S//Zbh/4S8gLBYBywJrWWA9DynLYr6yApTLiLe2UPzc55C7IL35Knj2GH2rfPrTwNra1X9ulsSqKAree+89mKaJ4XBI1lI5jiOiyo8+p1Ounyy1bzqdwnVdJEkCjuNQLBYvFEnMZjPs7OxgOp1CkiSSXC2KIo6Pj6FpGlqtFmazGUm4tCwLqqoil8shjmMiDKpWq2g0Gtjc3LxTayNra2vI5XKn1nSydNJGowFZllEqlbC+vn6v1yLTNIVhGLAsC7IsX7ieKwgCKpUKxuMxKpUKZrPZx0poTdMUOzs7MAwDx8fHCIIAiqKgUCggjmNYlkXEi3EcY3t7+400DszSWXu93pmms8A3xYtpmhKDVtd1X6rOct8JwxCj0YiYRgiCQJKVL4NhGHIdPAvDMOB5HkajEVZXV8/cr4IgYHV1FQcHBzAMgwh6X8WUolQqIZfLoVAowDTNS8V7z19fbyo19uOSpum9Nux4nWAYBtvb20Qk2+/3MRqNSP0tiiLYtg2WZYnhyvr6+rmf12q1YFkW2u02Op0OMQSWZRlpmhIT6iRJsLq6inK5fOXGM1l9PzNjoMfa7aBpGtaePVhYloXxeAzDMMAwzClBdrVaRbVaxdraGp48eQKe54mpSnatSNMUiqIQA4QHDx4gn8+f+nue58F1XTIPdRwHhmEQkTjP84jjGMPhEBzHIU1TfPDBBy9tgJAZcJVKJdi2fWkvgmVZEAQBoihibW0NOzs7UFUVX/va18AwDBRFeaGW5fs+xuMxVFVFPp8nfRCTyQSTyeTKBOjT6RRhGMK2baysrGAwGCAMwxf2bUapVEK/30cURWT+H4YhwjA88/VZ3S0zHLuM7DXP1+uiKCLPGIv27CyKZVnY2dkhBjOSJEHTNLAsi/l8juFwiOFwiJWVFcRxjMePH790jVuSJOTzeWiaBtM0LzVusiyLvEdVVZim+QpbSKFQPgoVoFMoFAqF8prgfe1rmH3hC2C//GUwngcASFUV6b/xb0D5wR9Ert0GK4qo/Pf/Pey//tcR/cqvINF1YDYDkgQQBECSEP9r/xqkf/ffhfwd33HLW3Q2XKmE+Du/ExiNkOt0EIQhmAucsbjhEJGiIFUUlP7gH7zBkVIoFAqFQqFQKLcDVy4jEgSkDIN0NgNzWUPJbIaUZQFBAHdHkjFelngygfvFLyL66lcB3wdTKID7tm9D8Q/9oRNjrhtgMnm19/++33c147hLMAwDlmURx/FCr88SXgC8Vgk9aZrCcRxSPMzlcigWi3euYF8qld4owflHmU6nCIIAtm2/UHh2HAfHx8cYjUZwHAe5XI4Uq7OUmUKhgHa7jTiO8dZbb11po2f6EvFdmWv8R2k2m+QYzJoUBoMBOI4jqSSqqqJWq6Fer2NlZeXKxk9ZDE3ToGkaXNeFZVmkIUCSJFQqlWtPXdF1HaZpIkkSrK+vv3AMTyYTBEGA1dVVhGFIkrbPakIsFosYDAaYTCYwTfOVBOjAyXG9urqKpaUlch5mZhmFQgG1Wu1eNym/KuPxGPv7+xiPxxiNRsT8JUkS0hxUr9cxnU7x1ltvXZj2kx1nPM8jCIJLG6Dn8zlpSqXJQJQ3jVDXgV/4BbAHB2DncwRvvQUmnwe5C4siYlVFbNvIHxxgLgjw/9JfgvwLv3CtyZ8UCoVCoVAoN0lueRmVz38e09/4Dfi/9EtIv/51wPOQiiLSt9+G+P3fD+3Tn76R+c9tCtAZBvie7wH+9J9e7PX5fB75fB7D4RBRFF36PDWbzZDP5yEIAjiOQ61WI4mYrypCy0TMmdBIluVLU2IpZ1MoFBY2ALBtG0+ePEGv14NpmhBFEaIoElNKWZZRq9Xgui42NjbAcRwkSYLjOLBtG47jgGVZFAoFLC8vQxRFbG1t3UmBcavVQqPRwHg8hm3bp9a86vX6a3G8+b6PMAzhOA6azealr9c0DcPhEL7vYzKZfCwBeq/Xw3g8xuHhIXK53Atmks1mk5iqZteJ9957741bvzEMA9PpFPP5nAj5zqNWq8E0Tbiui+FweK8E6EmSfKx1ujAM0el0YJomfN+H53nks7L7U6PRIAJzTdNe6p6TGf+apglJki7cp1n6c7fbRb1ef2Vj00xEnxl3FovFc//+eDyG7/tYXl5GvV6/kfqd7/sv1AEy4d5ZeJ5H9mVmXpvP58mc4E07t+8SkiTh0aNHePr0KYrFIhG6ZoY0WeJ0oVC4NAWY53k8fPgQwMkxbJom+v0+EZPyPA9N01Aul6FpGra3t6/keE3TFLZtYzgcwnEc8vNs3lmv188V1VKuj2ye1Ov1oGkafN8nc3ZRFMlcqtFogGEYPH78GL1eD7IsIwgCcl/IzIAqlQqWlpbO/C4zofN4PAbLsuj3+5AkCc1m89T1JY5j2LaNw8NDpGmKo6MjfPKTn1x4m7Jr/WQywf7+PqbT6bnz5yiKYJomKpUKCoUCSqUSqcV/+OGHODw8RK1WQ6FQAM/zCMMQrutiOp2iWCwSM6dMfB3H8cJ9K4sQRRHiOEaapmSfXnQ+CoJA5vpxHJOk8PPew3EcSYwPw/DSOXNmUM+yLCaTCWzbxuS5JiaO41CpVNBoNF55/h1FEfb29qDrOizLOtNgpl6vQ9d1HB0dgWVZHB0dYXNz86X/Vr1eh+u6GI/HxMDpLGazGSzLwvLyMjGUoVAoVwudbVIoFAqFcs9JwxDmf/vfgv3VXwXnOGBHI7DPHiQSSULS62H2hS9g+if+BMo/+qNgeB7aj/wI0h/+Ybhf/jKiJ09OBBqqCun3/B7kP8YE/6YRv//7Ef7Tf4pE18Hv7yPa2vpmamNGmoIZDMBPJphvbyP97u8GdwcLLRQKhUKhUCgUylUj/67fBbtQQFQqgR+NEF8yD+ZGI0SlEtJi8c4aUZ1H4vuwfu7nwPyjfwRmMgFv20AcI+V5pL/6q7D/l/8F+KN/FOX/8D+89ibHywToigK47tm/Y1ngj/yRqx/TbSPLMmRZRr/fh+/7lxayHMchqTB3TZz9cYjjGIPBAIZhIAgCIv7NXLqzgvDrJLa/z2QNVXEcnyp0R1GEbreL0WgE13XRarVIIlEYhiiXy2i32/A8DwcHB2AYBsfHx5c2s10GwzDkWJlOp6Sp+DyiKILv+xcKcdvtNiRJgizLqFarmM1mRHyeNRc3m80rc36nfDwURbnx5sbpdEoK8+Vy+UwDhcwlP3OOdxwHiqKc2SifpXXN5/Nznfs/DjzPo9lsLtTI+6YwnU6xv7+PXq8Hy7JQq9WgaRr5ToIgwHg8xtHREeI4xpMnT/D222+fa5IhiuKpZIDLmjyzJvnsPS9DkiSYTCaYz+ekAeguGrRQzuYlvFFeW9xf/EUwrgvBcRA8eADmvCZMVcW8Xgev60j39zH7rd+C/G3fdrODpVAoFAqFQrlOGAaFT38ahU9/+laHcZkA/Ud/FHj06Or/LsuepJ6/TA9/pVKBruvgeR6WZUHTNDiOQ0QDPM+jWCwin88jCAJMp1Nsbm6iWq2+8Fkf9xkqCAL0+32Mx2OEYYg4jsl6lKIoaDQar2woRzkbz/Owu7uLwWAAXdehaRo2NzfJ+nmW2pg9yzMMg0984hOYTCbQdf3U93JfxIcsy54yTnjdyMRMcRxfKCzMyMRG2Xr0y5IkCYbDIbmOrK6unnktKJVKRPCjqioMwzgzAfx1xnVduK4LWZYvPUc4joOiKJhMJphOpzc0wldjNpudMhbNKBaLqNfrFwrGgyDAhx9+CMuyMBwOMZvNSBJ69m9RFKEoClZXV7G6uop8Po+1tbVLE0gzsmRX13XPvId9lGKxCODkOjmdTl+4D8VxDN/3ibD3snpiliZdrVbR6XRQqVRQLpfJeZpdbx3HwfLyMhRFufbr1GQyQa/Xw2QyIeYV2Xq6KIqo1WpYWloitcM0TXFwcIDRaITpdArLssiauyRJ5PPW1tYW2seU60GSJLz77rvkfHJdF2magmVZlEol1Ov1hU3A8/k8PvGJT0DXdTIny+4VPM8T0fFVmSVEUYSdnR04jgPHccgxxjAMZFmG67oYDAZot9u0NnMLFItFFItF+L4P27ZP1XU/Wk/hOA7tdhvLy8uwLOuUMcnzdaOzCMMQuq7j/fffR5IkEASBXJOfJxMxDwYD2LYN3/fhuu5L1TUbjQaGwyGq1SqOjo7QarWgquqpbZlOp+j1ekQ4nx179XodcRyj1WphOp3C8zz0ej3yPlmW0Wg0UCgUsLS0RMaVCfevsi+DZVky5iRJiNDftu0zTcIyw3mWZZGmKWazGRqNxrnmDrIsg+M4FItF2LZ95vfxPLZtQxAE9Ho9JElCjE6y8zkzs8pSyV9lTjgajRAEAUaj0Znic+DkObXZbCKKIui6DlmW0W63F5orP0+tVoOu61heXka320UQBCiXy+TZKYoiWJYFwzBQLpehqiqWlpY+9rZRKJTzubsrDhQKhUKhUC4nTTH+8R8H9//8P+B3d8EFAcJiEXG5jDRNwU0myD95gqhYRPzX/hrM+RzlZ5bLDMeh+D3fc2LDfM8ofOd3YvTZzwJRBH5nB7n330ekaUiyRdPZDPx4DDYIMF9bQ7K2huKf+lO3PWwKhUKhUCgUCuVGYGUZ6Wc/i8QwkN/ZQTwaAecVnEcj8NMpggcPkH7v94KVpJsd7CuQeB7M/+w/A/ebvwm224UwmSDheaQ8DzYMgW4XUamE2LIw2t9H5XOfe9G46go5T1wOAOUy8Ff+CvB3/g7w9a8D//v/DjzXBwJBAF7H4OksqUCWZRiGgXa7fe5rMwFauVx+LZrQgiDAkydP4DgOTNOEZVmnioqqqpL04Mvc7im3i2VZCIIAjuMQ8flHYVkWy8vLODw8xHA4hCRJWFlZeeUU9MwBfm9v71ITB8uywPM8FEW5sMmoWq2Sz51MJqdSpEulEhV+vqF4nkeMMi4TEadpCkVR4DgOaWA4q7EgSRIwDEOPqWsmE55bloX19fUXrlH5fJ4kWnS7XUiSBMMwLmwUez5NYDQanXtN8TwPpmmSBJdFE1CiKHrBoCUTOeRyOdLomZlwUKOW14fINOF+8YtIDg9P1EGaBul7vxfSu+/e9tA+Fumv/io4w0Aky8AlKYdJrQbGMADbhv8P/gEVoFMoFAqFQqFcA1EE/KE/5Jz7+81N4PHjGxzQBRQKBciyDE3T8I1vfIOk9j0vEslS0n3fR7FYJIaWV4HjONjZ2cFkMiFpv9naZS6XQ7lchuM4aDQaWFtbo8/2V0y/34fjOBiNRlhaWkKhUDi1PiyKIkk173a7EEWRCIfL5TKSJCHio1ddf6RcDdnaBcuyJJn4IrK0TI7jwLIsfN+HYRhEWJsJxc4TDz+/Zt1uty88RxVFgSRJME0TiqK8cQL0JElIwugi8DyPKIpOibnvKsfHx+j3+3BdF6ZpwvM8knSraRosy0KpVMKDBw9e2P4kSfD06VOMx2N0Oh2USiVUq1WMx2OIogjP8xBFEUajERGDm6aJlZUVhGGI9fX1hWp52X5c9DtgGAY8z5Nk2IyPCu0zYZ8gCKjX62Qt8aNIkoStrS0AJ/e30WiE0Wh0KoVWlmWsrq5C0zQ8fPjwWs08RqMRDg4OYJomxuMxgiAAx3FkW0ulEmazGSaTCR4+fAiWZbGzswPDMNDpdBAEARRFQbFYRJqmmEwmGI/HKJfL5LryOtRYZ7MZMUXIjp1SqXTu93xXYBgG5XKZGCecJUBdFI7jsLS0hFarBdd1ielAPp8/Ny3645CZ1mbGN2maQlVVVCoVYiC7v7+PYrFIjlMqQr8dMpOKRWBZ9tyU6I8ShiH29vYwGAzQ6/XQ7XaRJAmWlpYwHA5h2zapPWfznTAMkcvliKFOZpawKLlcDpubm8SkITNlKhQKYFmWmKCUSiW0Wi0sLS2dMiRptVp45513kCQJDg8PsbKyAuCb95BSqYRKpXLK3CkIAhSLxSs1AJckCRzHQRRFOI5DTMWy56uPCsan0yl4nocgCAiCAADIte0snk+LPzg4uDAtPhODB0EAWZZxdHQEhmGgaRpEUSTn89HRERRFIefzx50XGoZBDKov26fVapX0OhiG8dLicI7j8PDhQzJ3Hg6H2NvbgyAIJB1eEATyrLS1tQVZlq807Z5CoZxABegUCoVCodxj7L/9t8F9+csQnj5FyvMIHj8Gnku5iptNxLMZhIMDYG8P8d/6W5h+53ei8KlP3eKorwCGQeUnfgJmkiDiODDjMbjRCDnDAACkHIdI0xCtryNZXYX80z8NgaZ3USgUCoVCoVDeIJR/59/B9Etfwnw2Q/74GOFshrhWA/NMlJR6HjjDgGCamC8tIW02ofzb//Ytj/rlMH/qp8D95m9CePoUSS6HYGMDKBZPks7TFLBt8P0+uCdPEAIwf+7nUPmxH7u28VyUgF6vn2hS/uSfPPnvn/u5yxN5XgcEQUC5XIbnedjf30e/3z8z8XsymeD4+Bi1Wg35fP7eO/RnxfrRaIROp0MaBmVZBgDSqLO7u4vV1VUAwKNHjxYW2MVxTMR6uVyONoBeEVmhnOM4TKdTkuBhWRYmkwny+fwpYafneaTJM2t4qdVqODw8JInDr9oUXK/X0e/3USwWSar6Wc01k8mEFGyzBubLyBzzKRQApGkPwLnXonw+D1EUTyUZpGlK3vc8URTB8zxUq9WFG2IoL08QBLBtmzQZnmWQkVGpVGBZFmk2vqhJ7KNpAvP5HJVKhQjMn08T0DQN5XJ54YaRLFkpa8LJkiCyJCTg5PpUKBSwtrYGURRRrVbRaDSQe27dm3IzXNUUIxqNYP8P/wPYL38ZjOuCnU5P3JjyeQS/+IuYPXgA8d//91H43b/7av7gTZCmYIdDML6PZIGmNUYQEIsiGM9DOhjcwAApFAqFQqFQ3jziGGDZ89th71o4dLPZxFe+8hXEcYyvf/3rAE6EooIgkERylmWhaRpWVlawtLR0Jc9Fs9kMOzs70HUdw+EQxWKRpNrGcYzJZILRaATbtpEkCViWJWuYlFcnDEOYponRaARVVS80c6tUKuT5OXuWZxjmytMbr5I0TYmwcj6fAzhZU6pUKhcmMd93RFEk6dmO41yacmvbNnieB8uy6PV6OD4+xmw2w2w2IyYUmdnfysrKCzWT6XSK6XQKhmEWEiKWSiXynWQinTcFjuPAcRxms9lCr8/2z103dzg+Psbx8TE6nQ48zyMiPZZl4XkeDMPAcDjE6uoq0jTFW2+9dWqbRqMRXNdFp9NBrVZDsVjE3t4eDMPAZDJBrVZDoVBAGIbo9XokOdc0zVNJqpcdf9nf5DiOXBMuIo5jRFFEvjcA0HUdR0dHRGg/nU6JaFFVVbiuC13X8eDBgzPHo6oqHj16hE6nA1VViRnr86njmqah3W5f6/pjJiDsdruwbRvVahWrq6tEDD+dTmEYBvb398n9V1VVIloXBOEFM4FarYbpdIrj42PyHkVR7u2a/Hw+x97eHlzXheu6mE6npwTovV4PtVoNq6ur9+J+chVjZBjmWmt53W4Xpmni4OAApVIJzWbz1BxD0zT4vo9Op4Nut0uOy/t6jN0XMqOiIAjIvKBSqaBcLl/psT+fz/HBBx+QuZuu66QPIY5jGIYBQRAQRRHCMESr1QLDMBiNRpAkidTV3YuSGs5B0zRi0p/NeTMjFVVVoWkacrkclpeXz6xnbWxsIAxDpGmKQqGAcrlMxODPXyfTNCXi9nw+f6UmHblcDqVSCeVyGbquY3NzE/l8HpqmYTQaged5UreL4xiO4xAzB9u20Wg0IIrihfPGRqMBwzBIWnyz2YSqquQ8zcxIBoMBubfNZjO0Wi3y7JChqiqCIMDR0RE5nzOB+suQpil838dsNlvo+iSKInK5HGazGTzPe6m/9fxnPH78GJ1OB4qiwPM8+L4P4OR7kGUZiqJgZWXlSk0GKBTKae7YshqFQqFQKJSFSVPEf+/vgXvWrBRub4M5q8Agy5hvbSH/4YeITRPB3/2791+ADoDheVR+8icx+cf/GPNf+iWkX/0qoiQ5EZpw3EmiyB/+w1D/zX8T3OsYJUihUCgUCoVCoVxAbmUF0ec+h+DP/3kEuRx4XYfw4YdIBOFELBFFiGUZwfo60uVl5D/3OeSWl2972AvjP3kC7stfBr+/j0QUEW9tnVbIMAygaQhLJQhPnoA7PET6K7+C+E/+SXCXJLt+HEJdR7LnQJvlMRGbiNnTDQofrbv8uT/354hTOYDXutlndXUVs9kM6+vr6HQ6sG2bNNZlhbb5fI5qtYp6vY6tra0739hzGYPBAI7joNPpkO16HlVVoaoq+v0+jo6OkMvlYBgGGhcYp6VpCtu2oes6Js+5HWTO1/V6/ULh4V1jPp+T9G2WZVEoFG59/IVCAaIoQlVVmKYJTdNIkf2shOesSJwVNQFAlmUiYF+0oe0issJ6kiQ4OjrC7u4uOX6yhq3MRb1er6NcLmNtbe2V/y7lzYPnedKQEQTBmeejpmmYTqewLAuO45CG57Ou2aZpgud5kmRNuR5s20YQBPB9nyQ8XES5XCb3G9/3z20qOStNYHd3F4IggGVZzOdz8DyPRqOBarWKjY2NhRo6oijChx9+iNFohOPjY0iShJWVFcRxTJoke70eJpMJKpUK9vb28OjRI0wmEwyHQ2xubhJzEMr9Yd7twv2xHwO3tweu1wM/nSLlOKQsCzYMkQgCmOEQ4e4u7P/0P4X6x/7YbQ/52mCAk/rFGcYdFAqFQqFQKJRX5zLDz7skQI/jGN1uF4qiwPd9NBoNMAyDKIrIs1gcx4jjGLlcDvP5/FTq36twdHQE0zRhGAZWV1dPPc/xPE9EE0dHR+h0OuB5HrVabaG1uyRJ4DgOEScukoj3ppGth89mM6ytrV26hqhpGnRdR7PZhOd5CxlP3hZZempmmJeJTUVRxHg8Rj6fx/r6OtRrqNHcNtk6veu62N/fx2w2O/e7iqII4/EYsiyj3++jWq1C13XM53OIogiGYRAEAQaDATRNw3w+x3w+P2X+l6V6cxy3kBCN53liInkfkr2vkmKxiFKpRIR8F5k+RFGE6XSK1dXVO23eOp1O0e/30el0EMcxtre3T9UZszXZfr+Pw8NDcByHfr9/av1wOBzCsixyje/1ephOp5hMJmi1WmTdMJ/P4/9n702jJFnv8s5fRGRGRu57Zu1V3dV9+17pShYMWAIjJOGREMbIkpDmDOPxMkZzZrBlGSwY2Ri8DJhjxguyLC94wGjGh8ViJCFpEBrJgDHGIBskBFxJt7trr8rKfYmM3GKdD9nxqqtrvb1UVXfH78u9pyur8s2MiDfeeP//53nS6TT9fl+I3P37V61WE+nixxEOh4lGo6TTabrd7qkG1LquoygK8XicVCpFo9Fge3ubvb09DMMglUqxsLCAoihMJhO63S7r6+vMzMzgeR43btw48n4Vi8V45plnGI/HtNttcZ/yDTLOo067t7dHu92m1+uxsrJyYG9WkiQSiQTxeJz9/X12dnYIh8MiYdY3gznKfMQ38tzY2CCTyQjjgceNyWQihLDVahXbtkUasi/O900RTNNkdXX1sRChX2Zc16XVatFsNtE07ViTWU3TWFxcZH19XaTTP47n2OOAYRhsbW0xGo3QdZ3xeIzneUQiEbGWWlhYOJOZv+u6dDqdA/X4RCJBLpcTdb319XU6nQ47OztkMhlhmN+8EwSXTCYxTZNGowEgRMuO44jzRVGU+15bpFIpnn/+eXq9Hs1m84DgPpvNks/nj+0bSafTRKNRFhcX2dvbI5FIkEwmD8yTlmVRq9UYj8esrKyQz+cPiNMfBqVSiW63S7fbZXd3l2KxKJ7jqtUqsViMaDRKq9XCsixRV71y5Yqor500l0UiEZEW79frfEE9TA3zHcchnU4LExf/fn3c31tcXGRjY4PBYEC9Xn/JvQX+8fbXomfBP0+OMjU/K6qqcvXqVSzLOtKg4aJ7TQICngYu0bZaQEBAQMCF4XnY3S7uYICSTqNc4g20gK8y/N3fRd7dJdTpYBeLR4vP7yBFItjpNEqrBb/zO9itFqHHPFEOAEki+cY3whvfyGR7G2tzE2wbOZcj+opXID3mooWAgICAgICAgICAByH2NV+D/MEPMvjn/xw+/3nswQDuuMASjUIshvPf/DfE/8pfQVtdvdjBvkSGH/0osmGgTCaMr1w5tigjyTLW3BzaxgZOv0//Yx8j48eQPyDueIzxqU9hfeITKOvrvGEjyquHMmMlxh/k38Dvz30HnfgycFiA/iM/8iMPZQyPA6FQiGeeeYa1tTU0TaPX66HrOsPhEFmWSSaTwln56tWrpyaDXHY8zxMNIZqmnZiAPTMzw3A4FAk2xwnQbdtmbW0NXdfp9Xqiec9PmPCFeXNzc2dOoL0oDMOgVqvR7XYPJDmpqiq+g4fVTPtSkSSJQqGAYRh0u12q1aoQzvrJET6tVksUcO8dryzLeJ730Br5ZmdncV0XSZJE4lGn0xFjTiaTQvy5urp6qZtQz5PBYECj0WA0GomEjHQ6TaFQeOjNBU8CqVSKcDhMPB6n2+0eWaRPpVLU63WSySSbm5vMzMwccO/38ZOx5+fnicfjwTn5CPFTJyRJOlM6TyQSwb6jyLBPUWb4aQI7OzvE43HG47FodgqHwyQSCWKxGAsLC2duyK1Wq+i6zt7eHoVCQcy51WqVTqeDrusUi0XS6bQw/uh2u/T7ffL5PJ7nce3atcd+rfA04Y7HGD/wA8i3bqFubWHH44xXV5FiMZAkPMtCbjYJV6vYoxHu+9/PYHaW+KtffdFDPx1Jws3lkCMR5MEA55SXe46DPB5jFwpIgTFHQEBAQEBAQMAj4XESoPvJs51Oh1e+8pXIsky328VxHLEP5AtLh8Mhmqaxv79/qtDvNEajEYZhiBS948ThoVCIhYUF1tbWxB7LScIE0zSp1Wq0Wi2RsOw/q0ajUYrFIsViMRCKMRXC+M/kkUjkVAG6pmnivDjtWf4iaTQabG1tUa/X6XQ6KIpCLBYTqZO1Wo18Po9lWVy5coVcLnfRQ37olEolGo0G2WyWnZ0dZmdnD+1hjMdjKpUKMN2/jEaj7O7uks1mRRIyTOsMuq4LYTpMzwV/L1pRFEKhELZtH9q7PgrTNA8kUT9NFItFqtUq0WiUer3OwsLCsXNRrVZDVVXi8fiJtZ2LptFoYBgGo9HokPjcR5IkZmdnMU2TZrNJLBZjdnZWmEuORiO63S7ZbBbHcej1evT7fWKx2CHTymQySbfbZTKZYJommUyGdrtNOp0WifEnUSwW6ff7NJtN2u32sde/bds0m00ymQzRaJRoNMqtW7fY399nNBqJZFmfaDRKJpOh1+tRqVRQFIXt7W1u3Lhx7Fg0TWPuAgzZh8Mhg8GAVqtFoVAQ37Ft23S7XbHvqigK0WgUXdfZ39/HcRzG4/GhVOp70TSNZDJJp9MhmUwyPz9/6rxwmfA8j7W1NTqdjpgT7xWKjsdjqtUqm5ubAMLcNOD+6XQ6TCYT+v0+CwsLJ742EokcOMdOmksD7g9d17l9+zbtdptGo4HnecRiMSRJEgnX2WxWrCWPSgWH6fVUrVap1+uMx2MMw8BxHBRFIZFIsLu7S6FQIJVKYRgGlUqFbDZLuVxmPB5Tq9XE3N7v90V9ul6vCzOCfD6PJEnCMP1B1haSJJHJZF6y+bAkSVy7dg3Hme7M+88iiUSCUCiEaZoMBgNUVWV5eVmstR426XSacrmM53ns7u5SrVaBr14zzWaT9fV1ZFmmWCySz+d57rnniMViXL169Uz1tUwmw/Xr11FVlWw2K8wJAHEsh8MhqqrSaDRONSiIRCIkEgk6nY4wdnkp9wz/OTUcDotxnITrukwmE7LZ7Jlq9H6fxXHnVTgcPlZgHxAQ8Gi5RNtqAQEBAQHnjd3pYHz847i//MvId1K0AZyrVwl9x3eQ/FN/CjlwBLq0mC++CKMRsm3jZrOc9jjvZjJENjdxHIfJ7dtPhgD9LiJLS0SClK+AgICAQ7jDIf1f/mXsT38auVoFx8HLZJC++ZtJvP3thIMNmYCAgIAnGu3aNbSf+AnM3V0Gn/403p1nP6lcJv5t34b6uBZmf/d3kdtt7Hgc6RTBlZRI4KgqUqeD/bnPwUMQoJu7uxg/8ANIW1uEmk1C3S6LPQnbUxgqCTKTGl9f/xSfWXoXv7/4dp72kJdwOMyzzz4ripO6rgt3Y1+kfZSDtWVZ9Hq9A0kEmUzmUhe0dV0XqQtnaWLx02gLhQKDwUC4Vfu4rsutW7dEeozneWQyGfL5PK7r0u/3hTjQFzxfVhF6s9lka2uLXq9Hp9NhNBqJn91dMJ2Zmbkw5/xSqUSn02FxcZGdnR2R+AGI8eq6Lgr7iUTigADddV0syzqQJv0wmJ+fF+Jf38Hcb0YKhULk83nK5fKZBKhPOpPJRLi2G4aBYRhCMJtKpahUKpRKJebn5y/1XHLehEIhstks/X6f3d1dUqnUoflIURRmZ2fRdR1ZljEMg+FwiK7ropnDb0ScmZkhm82yvLx8QZ/o6UCW5QOmF6c1iPgiBv93T0NVVVZXVzFN81CaQDabPXSOnPberVZLJHwVCgU8z2N/f1+kcMzMzBCNRvE8TySix2IxyuUyOzs7KIpCOBzm+eefD67fc+JBv+b+r/wK0sYG6vY2Vi6HNzd34NhJ4TDe7CxmKoW6vo5VrzP+mZ95PAToAK9/Pe7ODpFbt3BGo6nJ1zHIrRaeLEMmQ+TNbz7HQQYEBDwo7njM6ItfxO31IBIhcv066gUIFgICAi4GdzRisr6OOx6jpFJErl4NTPAvMadpcy/TofNFuqqqij3EYrEoEuxkWSYSiaCqKtVqlXa7TSqVOpPQ7yRarRaDwQDLsk41gfTNx7rdLu12+1gB+nA45NatW/T7fdrtNv1+X+z9+oaAvuHj6urqUyd+9TyPyWRyIK3afy46i4GlL6q5+/cuG4ZhsL29TaVSYTAYMDc3RzKZFOP1PI9er0etVsO2bWGset4pha7rimvMF3k+TIFmJBJhZWVF7NtWKhXq9TqJRAJJkhiNRoxGIxKJBNFolNFoxGAwoFQqHRLk+qKuWCzGxsYGrVaLeDwurlt/706WZXRdP1W01e12yWQyxGKxB963Ho1GDIdDcU4nk8lzSY++X1RVJZ/PY5omm5ub7OzsUCqVDoisJ5MJjUaDwWDA8vKySFW9jDiOQ7vdFqKx0777fD7P7u4u4/GYbrdLLpc7YFAZiUTEdTEajY4UNIZCIWRZxrZtHMchk8mwsbGBZVn0+/1TDSXy+TzVapXZ2VkqlQqWZR0QF3ueJ8yLVVWlUCgwMzMj0nB7vR7Ly8vHpten02mR0JtKpRiNRpfu+HU6HQaDAbZtk8lkcByHWq0maoqj0UjMHbFYDF3XqdVqJJNJNE07kyFnKpUSovXJZHLpvoOT6PV6DAYD9vb2yOfzRxpAaJrG0tIS29vbVKtVVFVlZmbmiVxX+MYRnU5H3Dc1TaNQKJDNZh/aesC/L0mSdKwp0d0kk0nq9TqO42Ca5rHXZMBLxzRN1tbWaDQaNJtNSqUSmUxGrFM8zxMidL9vIhqNHpobPM9jY2NDpGTrui7qKrZtU61WSSQSTCYTbt68CUzvK74hezweZ2lpidFoRCwWI5VK0Ww2sW2bXq8njPjj8TiO4/DlL3+ZZ5555iXVih4mmqZx48YN1tfXSSaT6LqOYRjimWlpaYlYLCbqlY/KmGNpaUkkmw8GA2Ek73kehUJBjCOTybCwsCDMuV5KXT+ZTPLyl78cXddFvQ6++szWbDZFoAFM7/Enrfnuvp4ty3rJ1/PdBjClUunEudgfUzKZJJ1OH/kax3FotVo0m03RkyFJEqlUilKpFBhTBwRcEgIBekBAQMBTyuC3fxvzf//fkdptlGaTUL8ProunKEjVKt7Nm3R/9meJ/YN/gPbMMxc93IAj8CYTpDvFCOksm8OhEHje9DjfefgICAgICHiy6X/mM9g/8RNIvR6hZhP5joO6p6q4a2sMPvxh3Le9jdx73vPgXcUBAQEBAZcadWEB9V3vuuhhPDQkw0CybdyzFEIkCTcSQbIsMIwHfm+r0cB4z3uQ19dRt7bwQiHsTIZ9K49nuyTtDsvDF9FDWb51+1/jSTKJxFsf+H2fBFKplCgO+Y2URxXJx+OxEKSZpinSviORCJFIhEKhIJIaLhuWZWFZlnAlP41YLCaadizLOvTzSqVCt9tla2uLVCp1KOXAb6zZ3t5mb28PWZZFY9plwv8MlUpFNMTNzMygqqpI12g2m8KcIBQKXYiQXpZlrl27BkwLtn7agq7r6LouUmtSqRSJRILFxcUDx8MX5iYSiWMLqPdLMpkkmUyKdBI/1dtvNLzs+E3I/nnuN5I9zHN1PB7z4osvigR727bF99Pv92k0GiQSCSzLwjRNrly5cmkbdy+CmZkZ0YS4s7NDoVAgk8mIBgXLskTR/9q1a0SjUdLpNLVaTTScplIp5ufnicVi4jUBj45oNIqmaeIcP23e0XWdeDwu7qlnRVXVB56T/WZKXddFmkq/38c0TXq9Hvl8XpwvkiSRTCbp9XpkMhkURWFmZkYkfPj/HnB+fNM3/Q6mOa0pqKqKJMnc0ZOciP3JTxK6I7z25uaO33uKx7HyeZRmE+9LX2J88+ZjUZdKfOd3MvzoR7ETCUJbW1irq0hHNH57hoFaq2GVSrjz84+PwD4g4CnHqlYxfv7n4bOfRer3p8aukoQTCtF/1auIvOMdJF772oseZkBAwCNifPs2w1/8RaT/8B+Q7jwHWcCwUED6tm8j+Z3fSegJTO993HlcEtBHo5EQ1PliD0CkEt6LL/Q76ndeKv5eq6qqZxKhxuNxOp0OjuMI0e7dTCYTbt26RbPZFIKWhYUFYS5mGAatVoter4fjOCKp8GnYj7EsS4iI7t73NU0T406dwje+PIl+v080GhXis8tIrVYT6ckrKyuHxuknW0YiEba2tkgkEtRqNVZWVs5lfKZpUq/XaTabIk3eN/XL5/OHxMgPQi6XQ5ZlYfbY6/XEflo0GmV2dhZN0+j1emJsJ4l3w+EwhUKBdrtNoVDAMAwSiQSpVApN08hkMjSbTZH2eRStVkuIXh8k1bvT6VCv1zEMA9M0xf60b1RYLpcvXV3CZ2lpSQi19vf32djYQNM0IcjzBct+QuqVK1cueMTHY5omnucxHA7PZILsiwLH47FIKfXrCbIsCyMMz/NEbeZe/J9JkoQsy0L07te2TsOvu/h7yLVajU6ng6ZpKIrCeDwWwvZSqcTs7Cz5fJ4/+qM/otPpEI1GTz23stmsMFlpNpsXZnJ8HLZtC/NigM3NTVEzGY/HhMNhFEURRqB+4rBpmszNzZ2pBqQoijCAOYvByf3iui6DwQDHcYhEIg/F2KLRaKDrOpIknZjc6ycI7+zsYJom7Xb7gea1y4ZvzFqv1xmNRnS7XWEYEYvF6Ha7aJrGysrKQxFj+tf2WWuM984ZAQ+Per3OcDik0WiwuLh46JnAF+JqmsbGxoaog957HlQqFRqNBltbW0iSxMLCgqgJAQwGAxqNBpubm5imyXg8ZnZ2VqzxM5kMrVZLPG+0Wi10XWcwGDAcDvE8D03T2N/fZ3Nzk2QyydzcnPjZRazxNU3jZS97Gbqu02w2GQwG4n6TTqcpFovnsoZeWFggn88LM5S774+xWIxcLkcymRTr+vvBNyjya4F++MP6+jpf/vKXuX37Nt1uVyTBl8tlId6+9zr3za3h/q7nYrFIu90mFApRq9WOXZP4z0TZbBZN046sY+q6zvr6OpPJRBi2+OvMdDpNt9slkUiwurp6qU2PAgKeBi7JtlpAQEBAwHky/K//Fetv/S3k3V3Ueh0nEsHKZiEUQjJNQp0OUqOBqeuMvu/7kD74QSKXeGPtaUVOp/H8xfRoBKdt4o5GeIoCsoz8kJugAwICAgIuH/onP4n7j/4R8u4u4XYbLxTCSSTwJAl5NCJy8yZ2IgEf/jDtbpfc3/7bgQg94KFit9vYnQ6yphEul89mmBMQEPD04nkMf//3mfyn/4Sn6xCJoKyuknzzm5GPEK95qoonSUiuy1nKIZLj4KkqPIR0YP3970fZ2kLd2sLK5/FmZ0GS6HfieDL01TyqPWJxdIuZ4QZv2v4p9MnXA49p2vwj4jgX5H6/z9raGr1ej3a7zWAwQJIkUbRMp9Micff69esPNWX6suG6Ls1mk2aziaZpx4r/VFVlaWmJ9fV1DMOg0WhcqtRhz/PY2dmh0WhgGMahJkRZloXQdWtri/39fUKhEIVC4UKKiOFwmBs3bgixsl/M393dJZlMUi6XyWazZLPZA8Xaewuoj8qJW1XVxyrpXNd1dnZ2GI1GGIYhGv00TaPRaAg3/QdtTvQ8j9u3b9Nut0VKRi6XOzBHjMdjqtUqW1tbwFebPgOmRKNRrly5gud5qKoq5h//fJtMJkQiEZ599lnh1D8cDg80UaiqSrFYpFAoPNHz82XBbzhKp9O0Wi1SqdSxjSumaQrxdy6XO/dkmLsNWnyhebfbxTCMIxNWNE2j3W7jOA62bZNOp0ViR7PZDATo50wq9Qoxf0cikTM1SFn1Osrt24Q6Hexs9tQ9J7dQQG00sIdDRr/+64+FAF1dWMB4+9vhwx8mtLZG5OZNrFwON5ebqpvGY5RWi3C3i5XN4s3Oor773cH+W0DAY8Dg934P84d/GKleR240COm6MAV3olFoNrE//3naf+bPkHvve4PrOiDgCaP3kY/gffCDyN0ucrNJyDCQPA9XUXDSadzdXfSPfxztR3+U2KteddHDDbiLx0WAbts2nudh2/aZxBD+a/znqvPkNDFCpVKh1+tRrVaZmZk59KyWyWRIp9Ps7e2xs7NDOBx+KkzFer0e6+vrDIdDut0u/X5fCPiTySSdToder4dt25TL5WOfsfy0Sf+7vYyCC9M06Xa7dDodMpnMiee0n9TpJzcvLCw88v0j/1j4aZS+Aaq/F9Hv92k2mywvL58oenwpZDIZ8Tn9BGdf2JvL5Uin03z5y19mbW3tTNeCvycyGAzQdV3soczOzoo08s3NTcrlskhbh+n502q16HQ6zM3NEY/HT02qPo7t7W1qtZo41qZpip/5ac2dTocrV66IlPbLhCzLXL9+ne3tbSKRCOPxmH6/LxLAZ2Zm0DRNJKRe5kTlu8ViZ9mfkSRJvM7/XVVVkWVZHLu7z0PHcQ7NNcM7QRe+SbQviFUU5czfVTQa5caNG8KE4m4xo2+qEIlEmJubo1gs4jgOlUqF27dvE4lEcBwHVVXJZDIHznOfUCiEpmnC5OWy4R8Hz/PY3d2l3+9TrVbRNI25uTlhFuqbC+zu7tLtdsV987QkW5jOx/5rHsXcOplMqNVq1Go1YUyjqiqKopDNZimVSmdOQfZrmf798ebNm0JMfpoYOh6PEwqF6Pf79Pv9J0qAvrm5Sb1ep1KpMBgMiEajwtSn1WpRr9cpFArYts3q6uoDG2H7JiK2bZ/pHJtMJmJ+COo/Dw/XdWm1WrTbbRKJxIlp9KqqksvlaLfbZDIZxuOxWHvZtk29XqdarSLL8pGJ3/F4nFgsxu7uLtvb28iyzMzMjPh5OBwmlUphmia//du/LWo4ruuiaRqqqmJZljChCIfD7O3t8eyzz7K3tyfMhy+Cu4MQLopoNMrS0hKLi4uidqooyiMR5u/t7bG/v8/W1hZra2vs7+9TqVRot9t0u13S6TTValWscZeWlg7c333zE7i/6zmZTIr1/NbWFtvb2weM513XRdd1Go0GkUiEYrHI3Nzcoe9C13Vu374tjLtUVSWVSqEoCpPJhGq1Sr1eZ2FhAcdxePbZZy/1Oi0g4EknuPsHBAQEPGV4lsX4R38UpVIhXK8zWViAu5p/PMCamYF6ncjuLhNZZvD3/z6Rn/qpix14wCGi3/iNDDUNJxZDbrVwT2nUVdpt7HQaN5Mh+vKXn9MoAwICAgIugvHaGu4/+SfI29uEej3MxUVIp8X93gGc4ZDQzg6h27exJYne88+T/s7vvNiBBzz2uOMx/V/5FexPfhLl9m3x714yifemN5H4zu9EvcAN54CAgMuJ8Wu/hvmhDyFvbiL1+9OkcknCSybp/eRP4r3pTWS+53sOCNG9lRW8W7cIVas4rot0UjHasggNh0xKJaSrVx9orFajgfKf/zPK/j52IiHE564nHUiBNENRdqPXWBl+iY7dR/3dj8H/9O4Heu+ngfF4zNraGvV6nXq9TiaT4cqVK2iadiAxZ3Nzk8XFRSRJ4saNG5cqMUdVVcLhMLIsMxgMTi3+DwYDwuEwoVDokKi42+0eSos96X2TySTdbpd2u30omfsi6fV6jMdj2u025XL52CbEUCjE/Pw8GxsbjMdjms3mhYmDZVmmXC5TLpfp9/tomkapVKJWq6Fp2oHU8XsLqIVC4UzJI08D7Xabzc1Nms2mEJL6x7/ZbBIKhSgWi4xGI65du0Yymbzv9+p0OgyHQyqVCqVS6ciGUU3TWFpaEk08mqZRLpcvzbVyGchkMjzzzDPs7u6SyWREopGfmB2LxUgmkywuLhKNRnFdl8lkIpzop8nIl2dOftKRJIlisSgajXd3d5mdnT3ULDIej9nd3SUej5NIJCiVShc04oOYpslkMjlT4oMkScTj8UvbRBpwGKfdBkCybbwzmIxIqoobCsFkgncnCe5xIPee99AeDLA/9SmkZpNQq4Vcr09/KEnYsRiTpSUoFJDf+14S3/RNFzvggICAUxm/+CLmD/4g8vY26v4+diyGubiIF4mA6yJ3Oqj7+9N57mMfo6NpZN99OZ/37U6H4e/8Dl6vh6SqhK9fJ/r884FgPiDgBPRPfALvn/5T5K0twr0eViKBubAwVS5PJoTabcI3b2L2+0ze9z7kD3wA7caNix52wB0cB0ajPThgWSoRjU6NQS+LTuZuMeBZEkr91/iJzQ+CLx40TfNMQqPhcEgkEjlSZGjbNp1ORxiiHSeklSSJubk5bt++LfbQnmQBuq7rYo+72WwSi8UoFouEQiFs26bb7QoxYaVSYTQasbS0dOjvWJbFzs4OkUiEVCp1aZ7l78UwDJEgfbeA6TjS6TTb29vYtn2m/fMHwTe79Y9FIpFgbm4OVVXFsdje3iabzYoE2IclnvbT1Y/ao/QT0X3x82n4158v+PTJ5/Pib9Xrdfb29lAUBU3TcBxHCIsWFhbIZDJcu3btvuaQvb09qtUqOzs7WJZFNpsVwiDfgGBvb4/BYCDGe9ECsKOQZZmVlRXm5+dpNpsHjCHi8TjFYvGxMH715+1wOMx4PD5RqAiItHq/DgVfPT/7/T47Oztks1kh4DYM40ANx/M8er0eiURC1KEajYZIkD+r4Bim+/M3btxgNBrRaDQYj8fCnCGbzZLJZJBlmWq1SqVSEUm+oVCI0WhEJBJB13UikQizs7PHvvdlTGbWNI1oNEq/30eSJOr1OvF4XKQM+/h7oMlkUhj6jkYjOp3OqULrbrcrhPxnmVteCr6Zh2EY1Ot1DMMAENePruuiNnnS/cqyLNbX1+n3+xiGga7r2LYtRPn+HDc/P3/i9RgOhw/NiY879XqdRqMhRMF+fdynXC4L0x/fgOL5559/IHOaTCZDNBpFVVW63e6h8/FuPM+j2+2Sy+VIJBKXToBumibNZpPhcHggOfleM/HLyGg0wrZtdF0/U305nU4Lgxu/hg3TtHL/31ZWVo793JIkMTMzwwsvvIAkSbRaLebnvxqkMDMzw/b2No7jUK1WsW2bQqGALMsHzrdIJEIymUSWZW7fvk04HGZmZubSnRv3i2VZNJtNDMPAcRxkWSaZTJ7JQF+SpEf6Pfj3yRdeeIHd3V1M0yQSiRzowxgOh+J8uHHjBp7nsbKyQigUEtdzPp9/oOt5dXUVx3GQJEnMX4qiIMsylmWJtbXfO3HvuthxHNbX14X4fH5+/lCvQKlUol6vs7W1hSRJ7OzssLKycl/jDQgIeHCejBk+ICAgIODM9D/zGeRmk3Cthjk3B0c5W0oSlMuYtk14fx/r5k1Gf/RH04J0wKVBnZuj/3VfB+026s4Ok2wW6bhNxVaL0GjEZGEB6c1vfvAEUs/Dc12kwEkq4LLjeRi/+ZuYv/mb0yTNUAj5yhUSb3kL4UtaoAsIeBgM/5//B7nXI9zpML569ej7QyyGdfUq6q1bSLUazkc+Am9/e9B8FnDfTLa3GbzvfUhbW4RaLZROB8lxQJJw4nHcWo3hL/0So/e8h/Tb3nbRww0ICLgkdP/v/xt+6qdQarWpWMO2cUOhabKY52Enk7idDp0XXiDz/vej3Ck4qG95C87v/R5StYrcbOKdsLaTajXccBhSKaJvfesDjXfwyU/CYDAVtF+79lVzlyPq25NQjIGSJGPWUb74WfD+SnCfPQU/Mcd3Mb67wCRJEslkkmQyyf7+Pjs7O6iqSqfTue/UjkdBMpkUabSdTufEBjrP84RDeTQaPZQAPRqNGI/HAKc2EfnvXa1WcV0X0zTPlKB0HrTbbXRdFwn2J+E34fhC+suQTp1MJvm6r/s6XnzxRbLZLLVajY2NDcLhsGiykySJTCZDsVhkfn7+Up2TF4UviN3f36fb7VIqlUin06JZ2W/y3N/fx7ZtJEniZS972X03+TUaDXq9nkgSOg5ZlimVSuzu7jIej+l0OqemG/kmA3cLsZPJ5BMrtE4kEjz77LMMh0Pa7bb43Kqqks/nD8wtsiyLNOuAi8E3ynBdl52dHW7fvk06nRbJKP1+n8FgQDKZZG5ujrm5uUP3m/NAVVWRrjQYDA40Ih91LY1GI0KhEIqiiOYXPyXoMjaRPo2cehjuNGF5knT0YvmIPyi5LsgyPOQm2UeKJJH7m38T/Wu+BuujH8V78UWYTKafORwGTcN9zWuI/tk/G9TYAgIeEwbvfz9KpYK6vz81cr+ztvTvVl48jlkuE15bg81NnA9/mMm3fzuRK1cubtD3ML59m8G//bfIv/VbSMMhWBaeLDOJRBheuULorW8l/da3PtF7FE6/z/BXfxVnf5+QJCEXi8Tf+EZCDynZNODJxNF1nA9+EGVnB8UwmKyuwt3CpmQSu1DAbrWI7O0xCYUY/B//B9pP//TFDTrgALYNv/Zr13Hdkfg3WY7y7d8+TW69LFoITdOQJEkI0U4T7/X7fWRZJhKJPPB+Xy6Xo1qtEg6HTxWzmaZJv98/NhnaT0EeDAZcOeU+KMsymUxGpGRblnUp07wfFM/z2NzcpNFo0Gq1WFxcPLSvm8lkyGazfOELXyAWi7G/v4/rujiOQyKREEashmEQjUZZWFigXC4/kHHjo8RxHJGGfJZj6r/Gdd0zGTA8CFtbW7RarWOPRSKRYDQaCbGfL1Z71EI1/+/Lsiy+u5PwPE8In+4d28LCgthzKRaL6LouBD/FYlEIWZeXl+9LkGqaJtVqlb29PVzX5cqVKwdESn46byaTYWtri3A4TCQS4eWXOBgnHA4zOzt7KWoP94OqqsRiMdLptBCPnbRP3W63iUajRCKRAwYLxWKRRqNBIpFgf39fpN42Gg1SqRSqquI4Ds1mE9u2KZVKZLNZYT5SLpeF2Pml4ifEHsXOzg7ValUkufoCetd1abfboqZm2zYLCwviuvaNUnO53KW8v+RyOXZ3d7Ftm2aziSzLx9YxJpMJk8mEK1eu0Gg0iMVibG9vn/h9dzodJpMJCwsLDz0R3DfzqNVqtFotYrEYCwsLhMNhJEmi1+uxt7cn7mGyLB8pZLYsixdffJFOp0OlUsG2bZLJJLFYDEVR6PV6jEYjYRKysrJy7LH057knJQXX8zxqtRrNZhPP81hcXDz02fw6ZCgUYmdnh2Qy+cBG3tFolEQiQS6Xo1arkUgkjl1r1ut1HMchnU5fqtR527bZ3t6m0+kwHo8ZDAZ4noeiKCSTSSKRCDMzM2cyybkoHMfBdV08zztTjdR/zb0mDL1ej16vRyQSObVuFw6HyefztFot6vU6ruuKNYZt21iWxWQyIRKJcPXqVSaTCd1ul1gsRrlcJp/PoygKlmURiUTY2dnh6tWrNJvNS/1dnwW/1tZqtRiPxwcMa/w+lVwux9LS0oWYG5imSaVSYWtri1qtRiQSEYn3rVaLeDxOt9slmUyi6zoAt2/fJh6PU6vVmJ+fp1ar4Xke6XT6gUyuFEXhxo0b7O7uEo1GMU0TwzCEuYw/p8zOzh75POmbJhwnPofpenlmZgbbtqnX60SjUebn5y/lvT4g4GngkmyrBQQEBAScF9b/+/8SarXwZBlOa64slZBbLdB1Rr/0S0FzzCUk+uf/PObnP49tGEQ2NjBLJbxCQQjDPdNErtdR223MuTnccpnkO995X+/lDof0P/EJnE99CnlnBxwHL5HA/fqvJ/aOdxB9xSse5kcLCLg/PI/B7/0e5u//PtbnPkfo859HdhxkxzmQpDn42Z/F+cZvJP3X/3rQbBLwxOGORki/9mvIzSZ2InG8OQkghcPYhQKhZhNvZ4fh7/4usa//+nMcbcCTglWrMfhrfw15fR11extPUbCzWbxoFGwbpdslcusWVjaL+xM/QU+Spk2OAQEBTzX6pz89FZ+vr6MMh5jFIl4uhxQOT02vdJ1QvU749m1wXbo/+IPkP/ABkCQSr389nZkZzHabSKXCxH/GvbvJwvOQqlXUdpvJygrOs88SfcCmF3d7G9kwpumMd4m3HOfo5o5+OEvWrCNNdOxul9BDSs94ErEsi263S7PZJJvNnthQNzMzw2AwoNfr0Wg0LpXYV5IkCoUChmGwsbFBtVo9stDqeR77+/t4nieEy0e9xm/WOAuyLIumvUfdvPdSsCwL0zTPlHALEIvFmEwmmKZ5DqM7G+FwmGeffZadnR2i0Sjj8ZjhcHiogHqUe/fTSrVaRdd1ut0uy8vLh5odQqEQhUKBSCQikpl984mXit+Yq+s6qVTq1PPMT7A3DIN+v3/sMbMsi2q1SqvVwjRNLMsCps0dkUiEYrH4RCeox2KxCxEqB7w0JElidXVVpE8YhkG326XVaiFJEpqmibSU+fn5C2v+8Rsk0+k07XabVCpFKBQiHA6LhCwfXzjvm7r4TW+j0Yh0Oh00ljwmqPPzjDQNJxZD7vVwT1uv3WmG8uJxlOXlcxjhQ0SSSH3bt8G3fds0OfmFF/DGY6RkkuirXx0YoQYEPEaMb91CeeEFQrUaZi53tJE7QDiMubyMdusWzmDA4CMfIfL933++gwXc8ZjRF7+Iq+tImkbkxg0mX/kK1o/+KEq9jtJsEjIM4RrihsNI1Sre7du0/8t/IfsjP/LgpuXniN1uY7da0zT3chn5iMZ4u9VC/8mfxPu1X0MyDEKjERLgaRrGT/4kzjd+I4n/+X8mEqQlBRxB/+MfR9L1qbnzygrScaLgfB5zMiFcrcKtW0GYwyXiNB3nZZnyQqEQmUyGXC5HpVKhUCgcmzrnG1im02k0TXvgVGFfaFQoFNjf30fTtCP3YW3bZm9vj3A4TL/fp1ar0Wg0hDFfoVAQAjn/2fM0YrEY7XYb4IkVoHe7XcbjsRDfHGcqmk6nefnLX86XvvQl8vk8yWQS0zRpNBrA1KTAF1bOzMwcSKW8bPjp3DA9rqeJAX3B9VFi6oeJL2RsNpuUy+Vjj0U0GmVubo69vT1yudyZzCofFFVVxZ6yrutkMpkTX+8nJt9tYus4Du12W6RyJhIJotEo2WxWiMg0TaNQKDyQeaSfUj0YDLh69eqxc1U0GqVUKtFqtcjlcmJfJ+DRUCqV0HVdCAfL5fKRrxsMBnS7Xebm5kin0weEjb7Bhed5VCoV6vW6MLlcW1sjlUph27YQfiWTSaLRKFtbW0Sj0QcWrR1Fq9WiWq2K5N1nnnlGmNjmcjkURcEwDFqtlqiBXbt2jVAoRK/XE0bWl6lm6OMb52qaJu7rR9UxTNOkXq8Ti8XIZrNEIhHxWbe2tiiVSqRSKTF/WpYlhPmzs7PEYrETU6zvh+3tbVqtFu12m6WlpQMi+FAoRCwWI5fLsb29TbVaRVEUstnsofvB5uYmnU6H7e1tURf1XzMcDgmHw2xvb1Or1QDY3d090uBmNBphWRbJZPJUE5/HBV3XGY/H9Ho9SqXSifdSf77vdrs0Gg1mZmYeyCx5bm6OwWDAeDxmc3OTQqEghO6AMCo2DIPFxUWSyeQBM4uLxDc18JPhR6MRmqaJ9OVarUY6ncY0TSaTCcuXdM/ZXxNJknQmY5rj1lK2bWPb9pmNvvP5PIPBQCR9+3N6p9MRxvLXrl0jkUhQq9XI5/PkcjkWFxfJZrOoqipE77Isi7n6cRagu67LrVu3aLfb7O/vMxwO0TSNcDjMcDgUpiDj8ZjxeMz169fP3Qij2WwyGo3Y398nlUrRbDaZm5sjEomQy+VEPds3MXNdF1VVRV+KaZqYpimu59PWoachyzJLS0vMz8/TbrcZDoe4riuMnU56fm02m3Q6HVRVPXXdWCgU2NjYYDwe02q1HuvzLCDgceaSbKsFBAQEBJwX0s4OsmHgpFKnuppL4TB2LIbU7+Ntb5/TCANeCrE/9sdwfuAHcP7hP8Ta3yfcbCLV67iqCp6HYpo4kQiTpSW8uTm0v//3Cd+HA13/N34D+x/8A6RuF6XZRB4MkDxvmky4t8fk13+d4dd/Pdkf+RHkoDE14CLwPPRPfhLrwx9G2d5G3twk1Woh2Tay6+JJElYsBuEwcqWCHY+DrqO/+CKJD3wAdW7uoj9BQMBDY7K2hjQaEer3Mc8g3nDzeeT9fRgOMf/wDwMBesB9of/jf4yytYW6tYVVKODNzh5YazqFArZhoG5twc4Ozgc/iP3a1wYmIAEBTzOeh/1v/g3K/j7ycMhkdRUpGhWJYkgSUjqNk0rhbW4S3tgATWPwuc8Rf81rkBSF6N/9u4z/+l/HBNT9fbxGAzudnnYSmibhXg88j8nSEu7SEsm//bcffNiWNW1cvqcx6jgBuoeMjIcse3h3hIsA73nPe4SQEabi1g984AMPPL7HGd+dfzQanVow8l3eu92uKKTdb2ryo6BcLtPpdFhcXGRnZ4fhcEg2mxXNYYPBgHa7LVzsM5nMkQ0hvjDPcZwzNWWOx2PxmsvYwHnWxFr/dZctXToUCnHlyhUWFhZotVqMRiNRQM1kMmcSPj8t+IYSfhrJSU2OyWSSRCJBu90mkUgwNzf3kptPfYd/x3HONBdIkkQ4HD4xaWk0GnHr1i36/T6tVot+vy/OTVmWSafTooHv2rVrl/KaexLp9Xo0m03RwKAoikjceNAUuMcZWZZZXV3FMAyRUuSfr77Zg2/4cFH4Bi2DwUAYtKRSKfr9vmjQ1zQNz/Oo1+sAB5rZ/EYoX6QRcD48yG1N1jS8170Ot9EgsrHBeDRCOu5+4Hko9Tp2KgXpNMlv/db7f+MLRrtxA+3GjYseRkBAwH0y/NSnkA0DxTSxSiVOmgalaBQ7HkduNvF+7dfgve89t0Rxc28P4+d/HulXfxXJMPAcB0mWsSaTqfDcdQn3+1ipFJMrV/BiMXAcpE6HcKsF3S6W69L5sR8j9xD2Sh4lnm1jfOYzmL/0Syhf+Yr49+Gd+0zsne9Ee+YZACZbWwze+17kzU3kanUqvofpHpPnTWuD3S7DL3wB58d+jNirXnUBnyjgMuN++tMozSaOqiKd0vzsFovIzeY0zOETnwgE6JeE0/Qalykks1gs0m630TSN7e1tFhcXD+0tuK4rEkILhQLFYvGh7D0tLi4yHA6xbZvd3V0hPIhEIjiOQ7/fp91uo+s60WiUcDhMtVrFNE0hgtV1HV3XmUwmDzyeJ4lms4mu62K/4iTy+TylUgnHcYjFYiwvLx9Ixs7lcpRKpQcSD58H8XiccDiMpmnoun7q/kyv1yMej6MoyiMVDt69n3fasUgmk0LA2mw2H7kA/V4T2+FweKwJpOu6tFotYUKRTqfZ3d2l0WiIlEk/lTORSBCJRCiVSszOzj6U+aLdbtPtdonH46fuK6XTaer1OoZh0G63AwH6I8Q3cV5YWGBnZwfTNMnlcuKaMk2TTqdDp9Mhl8uRyWSYO6IvrlwuI0kSsiyTz+dZX19nd3eXwWDA/v4+qqqiaRrVapXhcEi1WmV+fp75+Xkhan+Y1Go12u22SL92HIfxeEwkEhFCe/96rVarxGIxOp0OyWSSer1OPp9/KGYtj4q5uTlisRiZTIbxeCzuwaqq4roug8GA0WhENBoV5rf9fl8ISqPRKPV6XSTeep4nvh+/1viwxZC6rjMajWi1WpRKJeLx+JEC2UgkwtzcHDs7OxQKBfF6n9FohK7r7O/vk06nD9WBs9ks/X5f7Gu3Wi2i0eih+dHfO/bnuyfFEHowGIiax1nO30wmQ6PREAbgD7Lvn0wmWV5exvM8NE2j1WrRbDYJhUKihpZIJFhZWSGTyXDt2rVLUwtdW1sTpgbJZJLZ2VnxXXiex2AwoFarsX1H+6Bp2rGGHRdJNBpFlmUSiQS9Xu9Y0xyfXq8n1l53v9YXsZ/VpD4ajaJpGtlsVvQtFAoFdF2n3+8TCoXQNA3btoWBVTabPWCqn0gk6HQ6eJ5Hp9M5k4D+MrO1tUW73WZzc/OAubPPeDym0WiwtbUlXn/16tVzHWOr1aLb7WLbNq7rommaOO998yGY9orouk6j0SAej9NsNrEsi1AoxPXr1x/69awoypGBC8fheR6j0YjBYHCmeU/TNFRVZTgcMhwOH2SoAQEBD0AgQA8ICAh4ypAsa5okd8ZGTk+WkTwPLlHqVMBBkn/qTzHIZhn/i3+Bt7UFvR7SnSKPHYtBIoHz/PPEv+/70K5ff8l/3/gP/wHn7/5d5L091EYDJxyeGhgoCtJoRGRrCzcchvGYzl/7a2T/2T870nE+IOCR4Xm0/+E/RP7EJ6YJmdUq6nCI5LpTowRZxg2HkT0PK5HAyueRazXU9XVM16X/vveR+5mfeaxSHgICTsIbjQCQXPdMVv6SokzXBY4jfjcg4KVgVioon/scof197HQa7xhTDymRwFpcJLK5idvv0//Yx8i+613nPNqAgIDLgvGf/zPy/j6hdhu7VDpejCJJOEtLhL70Jeh2GX/sY8Rf8xoAos8/j/cP/yHjH/5hvEwGqdlE6ffBcUBRsEolvHwed36e+I//OOp9pOoeGk42i6eq02Z0x0G6U8Q/ToCuOQNMKUI8LKHcVTj5qZ/6KUZ33Xej0ehTL0D3i+RnTczRNI1msyl+9zIJ0GVZ5vr160Lk2ul0aDQaQiQbCoXIZrNkMpkTi3uZTEYkv3Y6nRPTJDzPo9vtks1mRdPfZcFvUup0OiJ95SQGg4FourmMhMPhwFX7FHRdx7IshsPhmZo5MpkMe3t74ndOa664F7+hSlGUA+YeJ+Gntxx1PlqWxa1bt2i1WlQqFeLxOAsLC6Lh1zAMOp0OGxsbLC0tAXDjxo0nNgn9MjAajVhfX2c0GtHr9RgMBnieRygUIpVKUa/XyWazrKysPNXHIZFIiGYwx3GQJOnc0xdOolwu0+12hUGLoijCPKVSqZDJZMSx9RtK0+k0tm2LNAdVVQMB+mNE7J3vZPyZz2AnEqgbG1grK3BPU73nuig7OyiTCebSEt4b34h8yQUWAQHnhTsaYXz2szhbW2DbSLkcsT/5Jx/Ks23AMVSrSKPRVHx6hucRN5lEabdxDQPHMFDOQWQz+J3fwfw7fwe52URuNgnp+rQWwLQPQHIcFMdhODODeyflW4Kp6rJcxiwUCK2vE1pfx/73/57R295G9BWveOTjvh+sRgP9fe9DfvFFQu02SruNbFl4koQTi+E2Gow/8xlGf+7Pkfrv/jsGP/ADyLduoW5vY8dijJeXkZJJJFmGwQC52SSysYE5HjP5wR8k9K//dXA9BRxAqtWQh0OcZPJsYQ7RKNJggFepnNMIA07jNL3DJdouI5lMMjMzg+u67O7usra2Jkz6JEliNBrR7XZRFIWlpSUymcxDE8zEYjGxHxmNRul0Ouzu7gojM1+IHolEGI1GKIqCLMvE43Fc1xVmfZIkYZomo9HoRAGtj2EYYt/3su77PSjj8VjsbZ0m5pBlmVgshmEYFItFXvWqV4ljEA6HL4246zQikQipVIpsNkutViOTyRx7fP101/n5ebLZ7LFp2g8D3+w2Ho+faa8okUgwGo3OzVShWCxSq9XIZrPs7OwwPz9/aE/Utm0qlQqO41AoFMjn89y+fZt2u02tVsMwDEKhEKFQCMuyhNmf/9mvXr36wOeRaZqMx+Mz7dcqioKmaUwmE8ygz/SR4tegPM9DURSazSY7OzviZ47joGkas7OzZLNZrl69euwcXSqVhJjVN4i9efMmrVYL0zRxXVckqRYKBYbDoUhFf5gYhsFwOBTG0/65nc1mcRyH/f19KpUK6XSaeDwuak7j8Zh0Ok06naZQKLBwidf3qqqyvLxMtVrF8zzC4TCGYdDr9YCvimN9AXo+nxffR6FQIBwOk8/n6ff7WJaFJEmUy2VisZgQET9sA1LfzMN13VNTcuPxOKqq0u12D6Qpw9SgxTcYPUqgmEwm0TSNfD5PrVaj3++TyWTodDri3PXnOdM0WV5eJp/PP9L7yHniOI6on57lnuWLw4Ezi41PIp/Piz36bDYrjpUkScRiMZGsPD8/f2m+c13XMQyDvb09MpnMoTlJkiSRFr+5uUm9XicSiVAsFg99x67r0ul0GAwGwtQllUqRTqfPZT2mKAq5XI5+v8/Ozo4w6z0K27Zpt9vkcjlisdiBuT0WixGPx4UQ/KRj5a/pr169iuM4JJNJqtUqnU6HWq1Gp9NhNBoJg5NCoUA8Hj9k+qAoCqqqirrO41yfm0wmtNttKpUK0WiUhYWFQ8df0zQWFhbY29ujUqkQiUROPF6PAtM0GQ6HhMNhBoPBoTVaIpEgFArRbreJxWJireC6LsVikWKxyOLiIvPz8+deP/S/Y8uy8DxP/P9Zzxv/czyMeS8gIOD+uByrgICAgICAc8NLJvHCYeTxGOfUF3sok8nUDTxwpbzUxL/hG4i/5jUMv/AFxp/5DF67PXVzn5sj9u3fjnbt2n39XbvVwvqxH0Pe3SXcajFZXIRMRhRcPWBsWSg7O4TX1kCS6H7wg+S+//sf3ocLCDiFzk//9FR8vrYmzBeccBjZtrEiEWTXRbEspNEI2m3McBh3ZQVrbw91a4tJLEb/s58l9W3fdsGfJCDg4SDfuWd7inI2AxnLEmJ16RG6jAc8uQw+/nGk4RBlOMQ+raCXSuFoGnKjgf3pT0MgQA8IeGoxf+M3kHs9JNfFLRROThVTFOx0GqXdhv/yX6bJXneKIbE/9sfQ/t2/w/iVX8H+5Cfx1tbu/JKE87KXEX7LW0i/8Y0PzWwo8oY3YH3iE7ihEFKzCXea/pwjmhslzyFtt6lGloj/8a8NjLpO4UEKuJexGS8cDnPjxg3RtFMsFg8I0P0GkkKhcOz4/aSGbDZLtVolkUgc2yRUr9dxHEckAV8m/LSDWq0mmmWOYzAYYJrmsanwAY8HjuMIl/uzNBSrqorneQd+76XgN8EkEgm63e6p14CfDOSLde+lWq3S7/epVCqUSqVD56zf0FatVtnZ2SEcDtNqtS7dtfe443kehmHQ7/dZW1tjMBiIJAU/6cY0Tba2ttA0DdM0sSyL69evP9ZNLg8DSZIuTSPY3SiKwvU7Bqm+QYvfKNput2k2m8zMzDAzMyOaxlqtFp1OB1VVmZmZYW5u7lKJ6p9m7ugiTkR75hnGf+Ev4PybfwNbW0Ru38ZOJKYGt7KMNByidrt4koR59SrOc8+R/V//10c/+ICAS47T79P7yZ9E+vf/HknXkQaD6Q9UleG/+Tf0v/Zrib3rXURf/vKLHegTiOd5Z5vgjv7lhzuYIxj94R9i/vAPI29todbr2PE45uIiXiSCZBhENzYIWRauLBPu9zFrNbx7xIqSouCsrBD58pexez1GH/nIpRSgO/0+/e/9XpQvfxl1cxMkCSubnZqvOw6yrhPZ2JjeVz70Idr/6T8R3tpC3d7GyudxZ2YOPmvH47jxOJNWi8jeHpNoFONDHyL3Qz90YZ8x4PIh2fb0Wj7r84QkTV/vnNp589Tj2TaebT/y/dHTthTu51HRdV10XRfGmaqqkkqlHsp+pC+SUxQFwzDodrvU63Vg+sxULpdJpVJkMpmHIiS9m2QyyXPPPUe9XieZTGJZljAy6/f7dLtd+v0+xWKRubm5A/s7pVIJwzDY3d2l2+3iOA7VavXE9D/Hcej1epTLZdLp9KV8Zn0YeJ6H53lnPlb+/oUv2Hkpz7v+Pposy4RCoQvdIy+Xy/R6Pfr9Ptvb28zOzh5KNzcMg/39fZLJJMlk8kSz1YeBd59rs/v9vZeKqqqsrKyI82V3d5dwOCzml/F4jGEYRCIRlpeXyeVymKZJq9Via2uLcDjM4uLige/ZMAwajQabm5vA1BzgbjGu4zh0Oh0hDldVlUwm88Rej086fg2qUqkQi8WEWYAvbPbrS3Nzc4eux3tRVVUkm6+vrxOJRNja2qJSqWDbtjDFsCxL7MPevHnzoaZtG4bBaDTCsqwDyerlclnU1nRdp9vt0mq1GA6H9Ho9lpeXuXr1KqVSieXl5VNF0hdNOp3mmWeeEWL6uz+rJEnCuFrTNMbjMaZpEo/HmZ2dpVwuo+u6EJdKkkQkEiGbzTIajWg2m+L4Z7PZh2L24p9XsVjsTHvu8XhcjBsQSbVbW1vs7++jquqRc44kSSwsLIi1yM2bN1lbW8O2bRRFwTRNBoMBmqaJOfEymw28VBRFEcJKXwB9EpZlHTBmfhikUilSqRTD4fDAOeYbA1y2PflGo4Gu67iue+KaQlEUSqUSe3t7lEolOp2OEFF7nkelUqHRaDCZTBgMBriui6IoJBIJYQrxqNcsMF1ft1ot0uk029vbzM3NHapfjsdjKpWKuMbvNacqFAo0Gg1RszzJvKrb7YrfWVlZodVqkUgkRGK2oihEo1FCoRCFQoFkMkmhUDhyHnAch/F4TDQaFUbejyPNZpPxeMxoNGJ1dfXYtbVv/nH79m2GwyGNRoPFxcVzHevdBhRHHRNN05ibmxOmQH5wwezsLDdu3BDm6ufFcDhkb28PXdcZj8dMJhMkSWJvb49WqyVS0E9ak3qeh2mawqAmICDgYgiuvoCAgICnjde8Bnd9ncj6OvZkgnSS612/P3URz+VQvumbzm+MAfeHJBH72q8l9rVf+9D+ZP9jH0Pq9VCbTSbLy3DXppd423AY98oVpNu3kff38T77Wdy//JeRT3E3Dgh4GDi6Dr/wC8jb2+A4mHNzaDs7yLaNEw5Pk50VBTsUIjQaoYzHhBsNzGIRb34eej3kdhvrE5+AQIAe8IQQuXaNYSaDnU5PU2XvcZ+8F6nZxFVViMXQXv3qcxplwJOEu7FBqFbDc12kdhsvFIJUCo7ZWHZSKZReD7lexzPNM6UJBQQEPHl4vR6SaeKq6jSJ6hTcaJTwcDh1ADcMlLueTeRolNTb3w5vfzuebeOORsixmBCpP0ziX/u1tJaXkWo11EYDM5mEWOxwArrnUR5t4aAw0LJob3/bQx/Lk0YkEhHu0IZhnJqoYRgG0WhUNH5eRmRZplwuUy6XhbDaH+9piUA+c3Nz9Pt9xuMx29vb5PP5A+kww+FQFOYWFxdJJpOXLhk2mUwSjUbJ5/PU63VUVT3y+E4mE9F0E4lEDrmoBzw+yLIsCvOO45zamOQ3kJ013eEoisUiuq7TbDbpdrvHNpp5nkez2SSZTIrUhrtxXZdWq0Wr1SIej594PZXLZdEg3mg0AgH6Q8K2ber1Os1mk8lkws2bN+l2u7Tbbebm5lhZWTlwfC3LolarsbW1BcDu7u65N08EnJ1wOMyzzz4rDFpKpRKVSoV2u41hGEwmEyaTCcPhkPX1dcLhMLlcjlwuJxotA84Pv8fqs5+dxXFG4t8VJcp/+99Wz/Q3Mv/T/0RXlnF+5mdwDAO52STcaIDn4YXDmDMzkMvhvOIVpH/sx4K6QsBTj9Vo0P/e70W+dQu5WiXU7wsxpOQ4ONEotNuM//APcX7oh0i8/vUXPeQnCqlQwNM0lDvGNtIpUbnSYIAbiUx/5xzM3Ef/5J8gVyqo9TqTpaWpaTjThHN/X9a7o76Ux2PUWo1JLnc48jccxkqlUFotvN/6LTzbfmjGfQ+L3j//58g3b6JubmKnUriLiwcSqd18nvFoNBWnb24SfvFFbE3DVVW82dnj/3A+j6XrKI0G3n/8jzj9/rkcu4DHAzedRlbVaQr6qS92UcZjnGwW6ZILnS4Ku92m/7GP4f1//x9ydbp29KJR3Ne8htg73vFIzC9OEqAryqnB9vf8LZtqtUqr1WIymWBZFjAV6UUiEQqFAuVy+YHFOAsLC+RyORqNBu12+0Cam280mT6iT+dhEIlERPpdv98Xoq+NjQ0hSF9cXDzyMyYSCVZWVmg0GmiaRqVSIZVKHWko6ae8h0Ih0un0uYh4LopQKISqqgyHwzO9fjQaEQqFznweeZ4n9qH6/f6B9y0UChSLxQvZK0+lUszPzwMIw8a798ANw8C2bTKZDOVymaWlpVMFsQ9KOBxGVVUMwziTKYCflh4+Zf33MMnlckiShKIo5PN5Op0Ow+FQCEh9gXkul2NmZoYvfelLIm1zcXHx0GfyDWy3t7fZ398nHA4zMzMjBHbtdluISWE6B/ji1XuNJnz8uo1hGKcaxtq2TafTEaKi4XAorvtisfjQk5kDptf+0tIS8/Pz4rv3PE8kh7/U77xWqwkz4VAoxNd93deRTCbFuTYcDmk2m2xubuK6LqFQiGv3GYp0L3cnQN89J0qSxPz8PLFYjGg0SiaTwTRNRqOREM7Pzc2xurp6al3xMlAoFNB1nXa7TT6fJxaLCaFvOBw+UB9pNpvEYjEhAJYk6YBo3bZtKpUKN2/ePNKAIJ1OMz8//0CC0JdqyuGfK71ej6985SsMBgMmkwkbGxvU63WRllwoFA4lBmuaxsrKCnt7eywuLtLv9/E8D9u2UVWVYrFINBoll8uxvLz8RJnQJpNJYrEYiqKg6zrZbPbE13e7XVKpFJFI5KHf9+9N1b6s9Ho9dF0nnU6fei4kEgkURaHf76PrOvl8Htd1uX37Nu12m3q9Tr/fF/OPb5SdTqcZj8eMx+NHXnOKRqMsLy8LUyLfmCYejyNJEqPRiNFoRDKZZHZ2lpmZmUP1S9+ou1wus7u7K9YXd68XPM+j1+tRr9eZmZkhGo2KNeRgMKDRaNDtdsX8oigK6XT62DWAb27jz8+Pc51U13V6vR6xWOzU68o/NrquH1iTnweqqhKPx4VBunlCOFU4HBY9F6ZpUigUTlwb+HO2/7uJROKBTa50XWdtbU0YyIzHY0KhEJ7n0Wq1MAxDfO/Ly8vHjk/XdWFQ/qiejwMCAk7nclUQAgICAgIeObG3vY3xJz6Bo2mEdnawr149stnfsyzClQpWKoWXSpH+ju+4gNEGXCieh/fpT6M0GjiadqT4XCBJ2OUykc1N3H6f/qc+Rfod7zi/sQY8tfQ//nEkwyCs60yuXJmmaNo2kudNBZA+koQdiRAej5EcB6nZxCuXsTIZlE4H74UXcAwD5THYjA4IOA0pFEJ605tw9/cJ37qF3e2KRrR78SYT1FYLq1TCfeYZtOeeO9exBjzeeJZF90MfIvTRjxJpNFBsG7laxZNlqFZxYjHscnkqRr8bWRaJQO5kgnJJRYMBAQGPmFAIJAnproa6E3FdvDvFjZOMK6RQ6NE2DksS6rvehbO9jT0eo66tYRUKuN4soILnEbd75MwqEXfMduwGe4VX8g1/4k88ujE9IfjNMMlkkna7fWKjiG3bdLtd5ubmHpuEjng8fl9NdfF4nCtXrgDT5gs/ITYUCuG6Lp7niYbPTCbD9evXL2Ui/PLyMpPJBMdx2NnZIZlMkslkCIfD2LYtirqJRIKZmZljm1sDzhfP8xiNRqIBWdO0M11vsViMSCRCOBym1+ud2pyo67porrnfZqhcLkelUmFmZoZqtYrjOGSz2QONJ6ZpUq1WsW2bhYUFisXiocaUXq/HZDKh3++fmqAhSRK5XI52u02hUGA0Gj3W7v6XgdFoxK1bt0Ta2u7uLtVqlWq1SqFQYDKZsLe3x2AwYG5uTjTmzc/Ps729Ta1WQ9M05ufngznkEnOvQcvq6irdbpdOp4PruozHY5H0omka8Xiccrl8auPdg+I4Dt1u91ASWHAugWXpuO5XBeiua539lyWJzF/8i0xe9zoGH/kI3q/+Kq6f5ixJOK96FZG3vY30a1/7SAykAgIeJzzLQv+BHxCJz46mMVlaQkqlQJLwRiOUZpPIzg7maIT1oz/KqFgMktAfItE3v5nJJz6BGw4jN5snCpk90yTc7zNZWcF97WtfmqrxPhh+8YvIa2uE63XMYvHQnr9kWeC6uLKME4kQHo1w/XrYEZ/DTSQINxpIkwmOrhO6REZmjmEg/fqvI1erOJp2SHzuI0WjmCsraF/5CpLnoRgG4ytXvppKfQxuPk9kcxPHMBj8x/9I6tu//VF+nIDHide+Fndn52xhDp3O9L/ZLOFv+ZbzGd9jhP6JT+B84ANI/T5ys4kyHE7nKFVFqlaZ/PqvM/yGbyD79/7eQ01FPymM/qVsH04mE27dukWv16Pdbotmd/hqOulgMKDT6fDMM888sGDVb7ZfXFwUQveXIkp+UGRZFk38/X4f13XpdruUSqUTx6BpGouLi+zu7hKPx2k0GhiGQSaTIRqN4nkehmHQ6XSQZZmlpSVyuRype+uGTxDZbPaAuOJecd3dGIaBZVnkcrkzJfZalsXt27eFeKrX600NcySJeDyOYRjUajWWlpZO3Yt7FMzOzorztlgs0u12hdDZT/T1hdN3G4/6QqjhcCgErel0+oH32Pw9u3q9jmEYJE+oG41GI8bjMfPz8+du7prNZkmlUrRaLZLJpBCQ+gJiX3C5u7srxGcnpXL6ey6bm5tMJhN2d3fRdR1d14XIx8evLRiGga7rXL9+/dD3ns/n0XWd9fX1E8/p8XjMCy+8QLPZJJvNEg6HhcFCOp2mVquRz+dZWlp6okSjlwVFUR74unddl2q1Sq1Ww3EcVlZWDtUCYrEYi4uL7O3tsbe3h6ZpDAaDh2IooSgKsiwfmwCdzWbJZrMMh0NGoxG9Xo9wOMzq6io3btw4cb69TPh1jEKhQKVSYW5u7tB90XVdMXetrKyIa+pu/BT6Xq9Hs9kU17YsyziOI0yh+/0+q6ur933vVVUVVVXpdrtnMvMYDofCVHsymdDpdLAsi/39fRqNBjAV2hqGwezs7CERYSQS4erVq1iWJQy3/XMxlUpRLBYfm2P9UvCNvLPZrLgfHFeL82tY8/Pzj7XY90Hw6+O2bZ9p/vHrSLZtC0Psra0tWq0WW1tbhMNhFhYWRG+C53kiCdy/L6uqyszMzCP9XPl8Xojg8/n8gbVUNBpldnYWTdOYnZ1l9pj9qitXrggzkkqlQqfTIZVKoaoqtm3T6/WwbVsI2K9evSqua7+XQVVVSqUSv/mbv0k0GqXf76Oq6qF5xPM89vb2GA6HrK6uMjs7+8gNhh4ljuPgOM6ZTR1UVRW/c57kcjl0XRfndb/fP7aO5RsruK5LJpMhHo8fqrP5xu31ep3RaCQMGHxjrWKxeOpz4XGMRiPW1taE6Xg2m2V+fl58x/l8nj/8wz9kY2ODjY0NZFnmypUrh97Ltm0ajYYIMTjLs1NAQMCj4fJ35gUEBAQEPFS01VUGb3gDmCbh27cJ376NXSpNxcWSNE2u7HRQ63U8WcZZXIR3vjNInXgKsbtd5HodxTCwz9DcJyWTeIoCuo7z4ovnMMKAAHB/4zdQWi0cVYVEAqndBpiKk+7d9FQUPFlGtiyUXg+7XMaLRpF6PQCcbjcQoAc8MSTe+U6MT34Ss1Qisr093VwsFL7ayOt50Ouh7u3hRKN4xSLh7/quix10wGOFOxrR+f7vR/n85wm1WoRME9l1wfOQAE+WkSwLZTRiMjcHdxU8pdEIT1XxwuFg3g0IeIqRV1bwEgnkSgWGQzjlmTPU6+HGYriFwkNtSrwfkq97Hb13vxv3gx/EqtcJNZuUjDYpJ4LiTQtMejjLfvQKW8nn+a9v+Nu84xIKgi8bsiyTz+cZDAZsbm5Sq9UolUqHmhls22Z3dxdN00gmk09Fcd1vLqlUKmQyGUajEaZpIssymqYJ5+q5ublLK8ZPJBKiMS4ej9PpdNjd3RXNu/F4nPn5eZLJJIuLixfSJBnwVWzbptls0mg0ME1TNBZJkkQ2m6VUKp3YRBCLxYjH42QyGTqdzolGEaZpHjCUuN+mbVmWuXbtmij0+8XsZDKJLMsiUdlPEcjlcszNzR05Htu28TzvTCkPsViMWq2G67pYlhUI0B8Av2mu1Wqxv79PNBoVSS3w1SZuP/lElmXRZCNJEsVika2tLSaTCa1W64lOVHuS8OeSTCbDysoKg8EAwzBE4pGf/vIo8ZsgW60WpmmKhAc/QSafzzM7O3uuKWhPIpErV4h8//fjfe/3YrfbeKZJKJcLak8BAXeh//Ivo9y6NU18TqdxFxYOPA9J0Sju4iLjdBptc5NJNMrwp36K6E/8xAWO+ski+vzzDK9dg2YTtVLBjMcPm2sCnuMQvmMSQCpF/BxMucef+hRyr4dsWbiFAod2GXzTPs8DWca9Uw8LdTpYRzUme95X62iXbM/C+PSnkXRdCPxPGp8UjeJEo6i9HpLj4EUih7+be/ASiennN03cO3XFgAC4K8whGiW0vT0NcziiydobjVCrVaxsFrdYJP2GN1zAaC8v+kc/ivv+96Ps7BDudHA0DSeZnNbpRyMiW1u4qgqTCZ33vpfc+9+P9JDW2tYJPkln3TKzbZtbt27RarXY3d0lFouxsLAgnvcHgwHtdpuNjQ0WFha4desWzz777EMRVMqyfOEJwYPBQJgRnkWs5otTY7EYpVKJ4XAo9kkA8UzlJyeurKw84k9wsRQKBfb390kkEuzv77O0tHSkWMO2barVKul0WpifnYRt29y8eZN2u83u7q5IwdU0jfF4LM7XbDYrRCMXsb9aLBbJ5XK0Wi2Rtg2I/etcLieuFc/zqNVq1Ot1sW8HCIPKRCLB7OzsfYsms9ksmqaRyWSoVqvHpsT6CZ/JZJJIJHIh35uiKJRKpRP3snq9Hr1eTwjDTsLfU+t0OjQaDZLJJJVKhUQiwdLSkthnGY1GtNtttre3xR7bc889d2D/wz+nk8kku7u7LC8vH9ofGY/HfPnLX2ZzcxNACPlkWcY0TTY3N4lGo5imiWmal9ZE92mn0+kwHo/p9XosLCwcu58vSRIzMzPcvn0bwzBoNBoPRXCYSCSIRqPC1PY4Mwg/ndkwDBYWFojH4xd+73wpKIrC1atXRR2jUqnQaDRIpVIoioJpmvR6PWRZZnl5mUwmcyh5+e7k5p2dHeLxOIuLi+I4mKZJu91mb29P7HM+++yz91W78Of0Wq1Gr9c78X41Ho/F2sk31Mlms6TTaXK5HNvb29y+fZtKpSKOr6IohwzJ/frM9evXWVlZeeTJ05eFmZkZhsMhhmGwubnJzMzMge/GT5puNpuUy2VisdgBQ5enCX8tIUnSmcW/juMgyzKyLIv7n2+k4QvL/ZqEv86Kx+NsbW1Rq9WEKPtRm6j414xvSH93GnU2m6VQKJxYp1BVlRs3bnD79m3i8bhIih8MBsiyLP5+NBrl2rVrR9ZeisUi1WqVq1evsrGxIcbjG8mHQiEsy6LZbNJsNlldXaVcLvPyx9wg0z8/rJMeKu/Ctm1hnnKe+MdnZmaGnZ0dkSReLBYPrK/G4zGdTodEIsFgMOD69evEYrFD88ra2hqdTodOp0O32xXPEr5JmWEYtFotrl+//pLvt9VqFV3XaTabB0wefPL5vHg+XF9fJxQKEY1GmZ+fBw6aQYRCIUqlErOzs8E6MiDgArmc3WgBAQEBAY+U3N/6W7TbbZAk5EoFdWcHdndxFQXZtqdJwZkM7twc3pvfTO67v/uihxxwAbijO4kmrnswSfo4JAlPUaYJhncefAMCHjndLpJp4kaj0waUux8u726euYN7J3VXurP5JN2Z8wDkx7RJ3DEMhr/3e3i6jhSJEHnZy1BPSWkLePIJl8uof+/vYf7QD2GGQoRrNaR6HScWw5MklPEYyXGm9/uFBbw//+dJ/sk/edHDDnhc8Dw6f+fvoPzu7xJeW5uuHSMRFNPEUVWxplRME9d1ieztMQmHIZ3GcxxCuo65sID3x//4pWtuDAgIOD8Sb3kLg5/9WexYDLnRwF1ePv7F/T7KaIS9tIT0rd96KeaO9DvfiTE3x+RDH8J78UVqazbjvoOHzCiUwAhn+f3iG/nPS3+Ol+eePCf2R8Xc3By6rovknH6/TzabJRqN4rouhmHQ6/WIRCIsLCxQKpVOTC55kkgkEjzzzDOMRiO63a5It9E0jVwu91iks6bTaV72spdRr9dJJpPCad4vzp5F2Bzw6PETqP0kr16vJxLQY7GYSMuZm5s7UsDtUyqV6Pf79Pt9dnZ2Djia+/gNSfF4nGQy+cCC4Vgsxo0bN1hfXyeVSqHrOsPhENu2iUQilMtlNE0TaTtHFahfatHaOyFZMeClsbu7S6/XY39/X6Rdf+UrX8GyLLLZrEgEq9VqVKtV0fzgN8jEYjHC4TCGYWAYRiBAf0zxUzbOi/F4zK1bt0QTjGEYB5IN/eacbrfLM88880Qm7Jw3UihEOLg+AwKOxP7kJwk1m3iyjLuwcOyzr5RKYWWzhBoN+PznMXd3g5rAQyT67ncz2drCMk0im5tY6TROPg/RKDjO1Mi91cKTZezVVdw3vQntuece+bi8ahVpNMLRtCOFml44PK3Tmiae607/33Gmtf8jkPt9nEgEbzik/+/+Hbguci5H7PWvJ/yIU71Ow9ncRB4M8GQZzvDMb8fjRDqdqfD8LI3CdzWoS5fUxC3gYtBWVxl+67diWxbh27dRb97EKhbxcjkkWQbTRGq1UFutab1tfp7QX/pLwXl0F5OtLdx/9s+Qt7ZQDGNqInGXeNQFxpZFaHt7Wl+SZbo/9VNkv+d7Hsr7P4wE9Fqthq7r7O7uUigUDolRU6kUqVSKWq3G7u4uqqrSaDQol8sPMPLDuK6LrutCAKFpGolE4pE327uui+u6SJJ0JjGFoiiEw2GuXLlCLpdjNBoxmUzEXpIvLPOFyU8SfsK7b96oqirJZJJyuYxt22xtbbGxsUGhUCCVSolU2l6vR6vVIhwOMzMzg6Zpp+7t7u7u0u122d7eJpPJUC6X6ff7dDodhsOh+L5v375NtVql1Wrx2te+9pDI4zw4i5jadV3W1taEuKrX6wkDTNd1UVWVXC6HYRgsLS3dlwmtLMvMz89jWRaWZbGxsSH2lvwU1G63S6fTIRwOMzc3J1LcLyO2bWPb9ktK5Ww2myiKQr/fp1gsHhIq+gKfTqfD/v4+kUiEWq3Gwl3PFuFwmPn5eRzHYXd3l7W1NTKZDOl0GkVRGI/H/MEf/AHVahXHcVheXuYVr3jFgb0d0zSpVqtsbW0hSRJ7e3sH3iPgctDr9ej3+4RCoVP35kKhEMlkkm63+9BSSBOJBLFYjEwmQ7vdJpVKHXs9GobBcDikXC5TKBQeOyFaMpnk+vXrKIpCJpOh2+2KPclQKMTMzAypVIpkMsnq6uqhe0Sr1aLf77O7u0sulzs03/pJzYlEQhh67+/vc/Xq1Zc8Vt/sJJfLUa/X0TTtyONi2zabm5siYXlhYYErV66I1+ZyOdrtNqVSiclkwv7+Pt1ul1arxcrKCvF4XNwrfbFvPB5/KkzIffL5PKM7/drVapXd3V1CoRCapuF5HoPBAEVRRHL1tWvXLu096zzwhbTdbvfU82Q8HmOapphnGo0Guq5TrVbJZDLcvn1bvDYajZLL5UQKfblcZnd3l8lkQrvdPtGoxrIsWq0Ww+FQXM/pdJp0Oi3mKcdxaDabh2r9hUJBmDHLsnzkc8jdDAYDGo0Go9EI13XFexUKBSKRCC972cvodrs0Gg36/b74Pf+6ymazx6717773u67LxsYGsVhMiIFN0xTJ7Ddu3GBubo5v/uZvfqzMQI4iHo+LedO27ROvL9u2DxihnCeqqjI7O3vAlKJWqzEajcjlcoRCIQzDQNd1ZFlmMBiwurpKPp8Xwm6YPsusra3RaDTY2dkR5108HkeSJIbDIZ1ORxivwdTM5KzzjmVZdDod2u026XT62OeShYUFXNdlNBrR7/d54YUXGI1GhEIh8ZyVTqeF+Pyo6304HIr6nuu6KIpCMpl84p4/AwIuA0/vyiMgICDgKUZSVXI/8RN0/9W/wvnUp3B7PdD1qRAtFIJUCrdUQnnHO8h913ddiub+gPNHuVMI9EIhpMmE09ppPddFtqzpOfQUN4u74zHGZz6D9elPI9XrUwF/Pk/oW76FxJ/+0yhPiTji3Lg7zRlwo9FpU43nTSvM9z7w3iNKV3o9nEQCN5Mh9Jg9cE42Nhj83M8h/cZvIA2HYNsgywzDYfqvfCWRd76TxDd/80UP86nE0XXsTgc5EiFULB6ZjnAexF/zGqR/9I8Y//iP4+3uTg0bhkMkz8NOJvFyObxMBvkv/kUy73znfb+Po+v0P/5xnM98Btmf97JZpNe9jvjb3oZ6gigl4PFk8PnPo/zO7xDe2MDVNJyVFXjxRSTPQ7EsvFAIT1WxQyFC4zFYFqH9fexkEmV7e2psk82ivf3tF/1RAgICLpBwuYzz6leDrqNubGBVKnizs4eeP73BAHV7GyubxUsmSbztbRc04sMk/sSfIPEn/gTjL3+Z33nv52htDnBklUZshVvFb8ZRpkW24BHg7CiKwjPPPMOtW7eIRCKiAaxer4sCrN94USqVWFxcvOghnzvRaPSxTliORCIsLi4yPz/PYDAQjvOxWOypbpS4LEwmkwMJ1JqmUSqViEQiOI5Dv99nb29PNBrIsiySAe4ll8vR7/dxXZe9vT3W1tZEiglMm8RGoxHJZJK5uTnm5+cfSlNsLBbj+eefp9fr0Ww2GQ6Hovkik8mI5ovjUFWVcDiMJEkYhnFqytJgMEBV1QdKKXNdVzQJjMdjPM8TaQaZTIbxeIxt28iyTDQafSJNGizLotvt0mw2SaVSZLNZXNfF8zzRLADTxptSqcTOzo5ofrg7oSEUCommmMuMaZqiuUhV1WD+uyBs2+b27du0Wi329vZIJBIH0oJ8I46trS3m5ua4desWzz33XHC8AgICHglmpYJy6xahTgc7mz21NusUCoRv3sQeDhn+6q+i/oW/cE4jffKJfe3X4v7QD2H9/b/PJB5HaTbR1te/WocKh7FzObxiEef1ryf3N/7GuY7PO+bccHM5aDRwZRnFsr56Dh1hmOQNh0RaLRxVnaal/+RPgufhRSIM/tW/wvnjf5z4X/pLaM8++yg/yvHY9lfreWfpU7hjEOPJMnK/j3dKvU/SdTxFAU1DeYr2FdzRCLvdRgqHCeVygWj6GLLvex9tXQdZRqpWCddqyHt7eLKM5Lq4kQhWuYxXLMJ3fzep7/iOix7ypWLwi7+IrOuEez3Gq6tIRzy/SuEwzpUrSLduIVer2L/yK3jvetdDSUE/xnMDOJsA3fM8ms0m7XabaDR6ouijXC6LZ4ZEInFfAnTHcZhMJmLfQtM0LMsSAmL/2RGm+xWaplEsFh9p+qKiKIRCITzPO1V4AdPnW0VRUBSF+fl5NE1jMBhg2zaSJBGJRJ44Iy/XdanX6zQaDbFn4+/lRCKRA0LfVqtFvV4XRnqO4xAKhYTRXqlUOjHFEqbPru12m0ajIc61arVKu93GMAz6/b5IGveFT/5ewzd+4zdeyn2kzc1Nms0m29vbQkyXSqWQJEkIw+v1ukhFD4fDB0SuvgCq1WoxmUzE95/L5Q4IqgqFgkhx9GsNzWZT/J27f2dmZkakgF9G/ETWs+55+Xu5sixTLpdPTMnNZrP0+33a7TaJRIK5ubkDc0y5XBYmB7qui5RMmO7xWpZFKBRieXmZ55577tA5p6oqi4uLIkU2EokwOzv7WJjqPk3cbXJwFkF3JBLBMAxxjd0vvumJJEni3moYBltbW8zOzh7Y+/XNWarVKoVCgVgs9tgKlJPJJK94xStotVpCRAqI5OVisXhsbaLRaNDtdkUa7HEkEgkymQytVotkMollWafec+5FkiQWFxfFmmRzc5NkMkk6nUZVVUzTRNd12u02g8GAVCrFZDJheXn5wBoiHA6TTCZRFIVarSYEwf78ksvl8DwPz/OIRCIsLy+LlOaniYWFBcLhMOFwmMlkQq/XE3WETCYjBNQrKytHJlc/TRSLRWEq2+v1hHj7KJrNJvF4nEgkQqFQ4HOf+xx/8Ad/wGg0wnEcRqMRnucJ4yTDMIjFYiwuLorEb13X6Xa7Rz4f2LbNzs4OnU6H8Xh8oC6ZTCbRNI3Z2VmxzvePrWmaSJJENBql0+mgaRpXrlw5ce02mUzY2NgQc+Xd5hXpdJpKpUKpVGJ+fp5sNks2mxV1Nn8tcRbK5bKYn4vFItvb29TrdVzXFeuycrnM/Pw8X/M1X3NqLfUkBoOBONf9Omg2mz33dUKxWKTZbBIKhWg2m8fWv2G6xg+FQsJo67yZm5vDcRy+/uu/nhdeeIH9/X1arRa7u7vAtFYejUZJJBLcuHGDpaUllpaWDlwn7XabbrfLzs4OsViMubm5A+eHb57QbDaFIUa1Wj2ziZBvVjAYDLhy5cqxr/PXkKZpsrW1xczMDPF4XDzPJJNJsXa897v2Tab6/b64HvyaciqVolKp4DhOYHwUEPAQCXaTAwICAp5SpFCI7Lvfjfuud2F85jPYf/RHMB5DPE74676O9Otff2GCuYDLgZJI4Ny4gVSvE2o2sY4Qg9yN1G5Pf55OE37Na85xpJeH/mc+g/WBDyB3OoRaLaTRaNqwoWl4f/RH6D/zM8h/8S+S/h/+h4se6hODNzeHF48Tqlanjcm5HF61iqsoKJaFoygHzlvZdXHCYVxVnSZpDodYCwtIb37zY2W2YfzH/4j1oz+K3GggN5uE+33RRORoGjQa2L//+3Te/nay3/u9j9Vne1zxLGs6B3z84yhf+cpX/z2ZhDe+kfg73nEhKTSxV72K2M/9HIPPfY7JL/8yXqUyNWfIZAh/y7eQfPObkc7okn0UvY99DPdf/kskXSfUbCLfKYp4qoqzscHwF3+RwXd+J9l3vzs4D58gxh/9KEq3i2RZ2KurSLKMNTODvL2N5LqERiPccBg3FMIJh1Esi9BgAF/+MhJgXb2K8zVfQ+zrvu6iP0pAQMAFk3rve+nfuoXpOKjb23jdLnY2ixeNgm2jdLuEBgOsTAZ3cRH53e++lGmJ2nPP8YVnnmP9mJ3WCwgZeawJh8M8++yztNtt6vX6oUJuJpM5sfEi4PFAluWnJr3+cWJnZ4dut3sggfpuEokEhUKBra0tKpWKSK4/Tni9tLQkGpdHo5FI8gBEw4WmaczPzz/0pDC/MH4/vxeJREin0yJp5Th84Xg+nyeRSNyXAL3b7bK1tcV4PKbb7QoBuud5ohHdF2T7IvdYLEapVDqxcfRxo91ui8YcvxHBb4ZRFOVAI6PfBNTv90kkEqJxBqaNPn7j+2XD8zw6nQ6NRkNcB4BoHisWi5dqXhwMBgeauJPJ5GOXJnQa9XodXdfZ29s7MlHET2Nvt9tUKhVUVaVWqx1IiXiaOULPGBAQ8AA4rRYAkmXhxmKcNuNKd8x4MU28dvvRD/ApI/GGNzCen2f4b/8t/NZv4YxG02RtWQZNw7lyhdBb30r+rW89t31vKZ/Hi0RQGg1sxzlUx5c0DTseR7JtQpMJLlOTcfce4aA3GhF74QUkx0GZTKDTQer18GBqXhuLQbvN6AtfwP7hH74Qo2MpkwFVRbJtME04pYYhWRbOHSFFSNcxHWd6rI7C8wg1m9iZDG6pRPwbvuEhj/6S4XkYv/mbTH7pl1B+7/fEDdyLxfDe8AZi73gH2urqBQ/yciGFQuT/wT+g9/M/j/Oxj+FVqzAYTGtr4TDEYjjPPkvkf/wfAyPwe/AsC+lXfxW52ZzORyeJXmUZu1hE3dvDabXo//t/T+rbvu2Bx3CSBu4sj4l+k3yv1zvTut9PAy0WiwwGgzMLfYfDIY1Gg3a7fUBM6nke/X4fRVHodrv0+328O9etoiik02kGgwHdbveRpV6mUik0TUNV1WNFNnfT7XZJpVJCIC9J0oWkbp8XlmVx+/ZtkWLe6/XEMZRlWRyjVCrF/Py8EEf6QiRFUYhGo8RiMWZmZkin09RqtRPfs91uM5lMMAyDlZUVarUarVaLarWKbdukUimKxSKyLDOZTFhfX2dtbQ1ZlikWizz77LOXygTAN27Y3d0lEomwsLBwYL8hFApRKBRIJpNsbW3RbDbRNE0I0Pv9PltbW4xGI3RdFwmcqqrSbrfF3/TP3ZmZGaLRKLVajWw2y2QyEaaomqYRj8eP3Ae9bMRiMeLxOLVa7cBe2FHYti2EUePx+EyfLZvNsre3h2ma9Pv9Q/uqc3NzJJNJ6vU66XQax3HwPI/t7W08zyMajfLKV77y2P1RX8C2s7ODaZq02+3HVjj8pOKLwF+KyYEsy/dliGIYhhBR++/nm6z458X+/j7b29uoqko0GsV1XQaDAZ7nUSwWKRQKrK6uvmRB9WXCn6eLxaKoCZz2fU4mE1FrOcu1nclk2NjYYDKZnCkp+ri/sby8jCRJxONxGo2GMKFQFAVVVcWcO5lMKBaLqPc8w7mui2maTCYTYf7barVEXSQWi6EoCqVSidXVVbLZ7ImCxSeZcrlMsVik3W7T6XSEKNdPyX6S11kvhVwux97eHsVikf39fZGSfPeawnEcarUag8GA5eVlYZy9ublJvV7HNE3RcyDLMqZpCsOaUqmE67qsrKwIk6ijDDcsy+LFF1+k1+tRq9UYDoeoqoqiKEwmE5GyvrW1JYx2+v2+MGnyx+g/U1iWxbVr146sFY3HY1588UU6nQ61Wg3btonH4yiKgmEYNJtNEokElmUxmUy4evWqmNvvp2Y2Ozsr7v3ZbBbHcXAcB0CYLpVKpUPX+1np9XpUKhWGw+EBA6tYLEYkEiGfzzM3N3du9b5YLEYymWR2dpadnR0kSaJQKBx4f9d1aTabdDodkX5+UWZPi4uLwhBkY2ODRqPBZDIRhubZbJb5+XnxPd5b7/bvw5IkHRKf302hUGA4HNJut4nH44eMio7DsixhpnZa/VxRFFZWVrBtm3K5zNzcHPF4nFAoRCqVIpPJHBqff+11Oh329/exLEv8zng8pt1uEw6H0TRNGJ5cxtpxQMDjRiBADwgICHjKkTWN1FveAm95y0UP5VzxHIfBb/wG5n/4D3i9HoRCyIuLxP7MnyHylG5eHEX4LW/B/dKXkGo1pP19vONSbC2LcL2Olc3ilstknsJia++XfgnvJ34CZXeXcLuNpyg48TjIMkq3i1KvY6XTuP/yX9Lp9ch+z/dc9JCfCNRv/3ac//pfkapVaLeRCgXsTGbaYDMeI5vmVGwuSUiWNTUECIdxNQ11awsrl4NUisRjlMI7/MIXsP7e30Pe3kZtNLCSScbLyxCNItk2UruNWq3idDo4H/kIHU0LzrdHjFWt0v/f/jfktTVCrRZKpzNtjJJlnFgMt1Zj+PGPM/rLf5n0AySN3zeSRPw1ryH+kM1Bej//83j/4l9M571OB0dVcRIJkCTk4RDt5k3sRALnF36Btq6T+8EfDEToTwKeh/y5z6G029ip1FcTKbJZTNNErVaRLQvZslBMEw9QHAfpjgGIdeMG7iteQeZHfzQ4HwICAgiXSiQ+8AGMH/gBzEQCqdmcGlzcMbdy4nEm169DMon8V/8qqUuUfn4vd+nIDhHUgV86siwLIZifIuMn5gSpowGPEl/0e7+NU48zvuN+s9kkk8kc27wUDodZXFxkfX2d0WhEo9E41jlckiQWFhYoFouisfru5C7/Or9MTWJ+M+RwOGRjY4NarXakON51XSqVyoFEkpdKp9NhfX2dRqNxwC1/OByyublJt9vFtm1isZhoJvaTBfr9Prqus7Ky8kSIgv3mM0VRDjRF+8217XabXC4nPms0GqXVaolENlVVReJOMpm8dE1Ytm2ztrZGr9ej1+vR7XZFOpmf5tButymVSiwuLl7YMfXT/hqNBsPhUKS4hUIhcc2Wy+Un4l7sf1Y/WeQkUYXfmOYnG57UjPOk8ZR8zICAS4HYY5MkpLM4PHge+MKAS7SWepLQnnkG7Ud+BLvVYvjbv43b6yFFIqg3bhB9/vlznyQjb3oT1mc/i1epILdaeEcY9Dnz8yjjMY7rEh6PcSQJR1XxJpNp/ajTIba3h+S64HnYmoa5sgLp9PTzDIfIzSaR7W3M0QjrR36E0fvfT/TlLz/Xzxr9lm9h/HM/hxOJIDWbx9enYSoo73Qw83lky0JyXcLr65grK4cSvj3XRdneRrJt3HIZ6du//Yk25Hf6fbp/82+ifPGLKO02SruNbFnTPbdoFLdeZ/ypTzH6c3+O7Hd/d3DjvxtJmhrKf9d3YfzWb2F95SswGiElk2iveQ3as89e9AgvJZPtbaTBAMUwsE5IixNks7C3h2QY2DdvwiMWoJ/lduk3yftiytOIRqNCiGGa5pnEB7Vajd3dXQzDEEZ9vshc13Vc16Xf73P16lUWFhaIRqN4nodhGHQ6HXq9HktLS0iSxDPPPPPQn038pDxfXO8nzh1Fr9djPB4zOztLoVA4MBbTNEWavP/8q6oquVxO7G88briuy+3bt2k2m+zs7KCqKuVyWexB+MKM9fV1sVf23HPPMRgMGAwGOI6DoigH9i18Ec9JjEYjhsMh4XAYSZKE6Mjfd7tbSOEnCFarVer1ukhCvHbt2iP4Ru4P35jPNE1xLh+FL2xqNpvk83l0XcfzPNbW1mi1WiLJPBaLIUmSMLTNZDIiydlPrvSNKsfjMf1+X+z/+kmyjwOFQoFOp0O9XheGmMfRarUO1FPOOp/5+2zHJVonk0mRojwcDnEcRxgAnMWc0xcF+QmVgQD9IK7r0m63hSjXN4XMZrPnMm/6+7CNRmPa93GCoNA3TMlmsy8podpxHNbX1+n1ehiGcSDdOR6PC1FmKpUiFAodSAmWZZlSqSSMUlZWVi6VmeeD4otET8Ovr1iWdabv3jeHuVsIeD8UCgWi0SjVapV4PI5lWTiOg6qqqKpKPB4nHA7TarWOHNfW1hY7Ozs0Gg0sy6JYLIqE23w+TzqdZjKZAIg6ydMsFLy7Th5wNLIss7q6iuM4SJJEtVql2WweEJPrui7WRtlsltnZWb70pS/RbrcZjUZC6Ho32WxWrLVkWaZareI4DpFI5FDd2PM8bt++TafTYXt7m0QiwZUrV0SNy1/Xr62t0b5jHlksFrl+/fqB9YfrunS7XWEyI0kSzz///IE6jP9e7XZbGOpms9kDrxmPx1SrVTY3N4HpvD47O/tA33MikRCi9tFoJAyV4vH4A9XRG40G29vbtNtt2u02tm0TCoWEwY1fKzUMg+vXr59bTerKlStiLqpUKnQ6HVKpFOFwGMuy6Pf7yLLMwsIC2WyWq1evnsu4jiOTyZDJZFhZWcEwDFGHj8ViaJpGOp0+cq07mUyEKdRR4u57yeVy7O7uivvyWQxQ7u618M+bk/CflQqFAleuXDn1Hr++vi6uvUwmQ6FQOHCemKbJ/v6+SIXf3t6+VM9EAQGPK49/h0BAQEBAQMBLRP/kJ7F/5meQ63XkbhfJNEGS8BIJRh/9KMYf+2Mk3/e+C0mqvWwkv/Vb6fzsz2IaBpGtLSzbximXkfxNY8+Dbpfw/j5uOIw3O4vyXd/11BWoxy++iPdP/yny1tbUWX9+flowvfM9uJ6H3e8T3t1FWl/H+bmfo/+yl5F83esueOSPP4nXv55OqYTVbKLu7zMJh3Hm55GHQ4CpCH00wpMkZMfBVRTkyQS12cTK5XAXF1G+93sJn6UIfhnwPEbvfz+h/X3URoPx8jJSOv3VNBRVxYvFMEslQuvrKJubOL/wC5hveQtqkM70SLDbbfrveQ/yrVuoW1t4kjRNbtU0cF2UbpfIrVvT5NYPfICeLJP+zu+86GE/MKMXXsD7V/9qOu8NBoxXVpCSSTHvOYAzHBLa3ia0tob9K7+C/spXkvqO77jYgQc8MI6uI5kmsmli53IHfuaVy0wiEUK1Gsp4jOQ407XCZIIbDmPncnj//X9P5nu+B/kSOe0HBARcLOrCApmf+Rn6n/409ic/iXfrlviZl0zCG99I/B3vuPTPZycJ0J+g/ocL4TKlswRcHjzPYzKZ4LquEEY+yN86KpU4Go1SLBbJ5/NPhRjdT6AejUaHGi7uJRKJkEgk6HQ6JJPJYwXod79+YWGBhYUF0VR9mcWb5XKZbrfL4uIiOzs7DAaDA810fpM4TFPec7ncmYrtw+GQZrPJeDxmPB6zsbHBaDTCcRzhPj8cDul2u6JB1G82yWazFAoFZFmm0+mIpltFUVhaWnqUX8e5cu95kc1m6ff7tNvtQ6ku3l3iPD91wG+Mv0zp8K7rcuvWLdrtNtvb28iyTCaTEfc3wzCoVCpEIhHRXLO8vHzu43Qch7W1NTqdjkj58BuOfaMFv4Hl+vXrj/392W927/V6oiH9JPxElXK5TL/fP5QWERAQ8HTijsf0f+VXsD/3uelDYSSCvLpK/K1vRT1lPXUv4fl5xqEQTjSK3Ovh3kl4PA5P16d7sfE48iV/Xn7cCeXzpP70n77oYRB/9atpLyxg1+uEazUmsRjSvaY70SiTxUXiX/kKdiiEpyiEh0PCX/kKAK4s4yoKiuNgJhJYzz9/MCk8FsNdWmKcTqNtbTHRNIb/5/9J9P3vP78PylT8P3jZy5CaTcL7+0zS6YNJyp6HNx6DbaPU6+C6uEtLTObnCe/sEFpbQ/vKV6Z1mWQST5aRDQO108EDrCtXcJ5/nuyf/bPn+rnOE3c8pvd934f8xS8S3thAcl3sdHpqzO15KLpOZH19aiL8oQ/RcRyy/8v/ctHDvnxIEolv+ib4pm+66JE8HtwRGUuuC2cRCEgSnixPDVXuNPk/KA+agP4gnGWfo16vs7Ozw97eHoZhkEwmmZ+fR1EUdnZ2aLVaVCoVrl69iuu6Ij0RpuKCdDrN/v4+Ozs7hEIh2u32I3n+nZ2dxTAMBoMBW1tblMtlUqmU+Iy2bdPpdGg2m8zMzBCPxw8ISff399nf3xcJrb6IIxKJ0Ov1qFarzM7OPrAg5rxpNBr0ej12dnZIp9OHniVTqRSpVIp6vc7u7i6qqlKr1Y5MHHwp3L2X1ul0GA6HmKZ5SHzuI0mSSFn3jSYnk8mp4uDzwt93SCaTp4qJ/IR4wzDEf31hfblcJp1Oi31bz/MYDAYiARGm+7t3J3lrmvbY7mek02mi0SjlcplKpSL2l+6eezzPo9Vq0el0KJfLDAYDLMs6U6L13efZafNZOBwWKeiJRIJ6vX5mcbQvLjtryvbTQrfbZXNzU6RU3y1A73a7VKtVyuUy84+w76tQKFCtVtE0jVardeIcres6tm2/JGNWf4/UN8bwfz8Wi+G6Lrqu02q1yOVylEolVlZWcBxHCBN9U4VCoXAo5fhpwv/cZ02r95PVJUl64DpXPB5ndXWVmZkZYeYRiUSIx+NEo1EGgwGtVuvA79i2zdbWFjdv3mRvbw/HccTxMwwD13WJxWKsrq6iaRq9Xg9Zlmm1WmKNFBBwHIlEgmeeeYb19XVSqRS9Xo/BYCBqx/Pz8yQSCVKpFFevXhUGH+PxmPn5eUzTFNeHjyRJ5HI5bNum3W6LtYpvwnA3vpnG7u4umUzm0NpQlmUxz62trQHT1Op7BcGyLJPL5QiFQuzt7ZFMJsU628dfA1YqFUql0pHPAJqmsbS0xNbWFvV6HU3TKJfLD6XGHQ6HH5oRSq/XY3t7WzwT5fN5MpmM+K79uWRzc1OYDz0K462jCIfD3Lhxg/X1dWKxmDADH41GyLLMzMwMqVRKzIcP0p/wMPHXuGc1rZhMJnieJ1LDTyMWi4nX++ZiZxmT/zzb7/fJnLLX3e/3iUajSJJ06nrdMAz6/T6VSuXIaw+m5mcLCwt0u13q9Tqzs7OnGuwEBAScTiBADwgICHgMsTsd7Hp96uhdLhM6Q4NjwJTOT/800oc+hFKpEO50AKbu6a6LUqvhRCKg6ww2N3H/yT9Bu379gkd8sUiqSvzHf5zBe97DRJYJVSqEX3wRJxLBUxSUyQRcFzuTwZ2fx33728k9AcLKl8rw3/075E6HcLfLeHX1YBMETJuAUinM1VUit27hNpuYv/ALEAjQHxgpFCLyvvdh/Y2/geU4aJub2PE4VjYLjoPkOCimieI4eHc2ISb5PO78PF4qhfK933spGofOyvCLX0TZ2CDUaGAWi0h3FcsOoKrYS0tEbt3CGQwYfPSjqH/1r57vYJ8S9Pe/H3l9HXVzEyubxZufP2DC4RQK2IMBkc1N7O1t3H/+zzG/4RtecgPiZWP44Q+jdLuEdZ3JtWtIRzmDx2JYq6uoN28i1etYH/kIBAL0xx4/jcmTpAOCD0Emg53JYBsGkq4jOQ7hRgMrk8F6xzsofN/3nfOIAwICHgdkTSP91rfCW9+K3W5jdzrImka4XD6UVnUZcRy44390JJcshDUg4LFmMpnQaDRoNpsHEoLuVyw+mUy4ffs2g8GAXq+HruvCYTuRSIji5bVr185UgH2c8Zsu/MSK0/AF6LZti+/sLDwOjWGKonD9zp6gqqqiqdoX4/rJM5lMhlwux9WrV0/8XJPJhM3NTZHu4yfHN5tNdF1ncXFR/O16vY6u6+i6zsLCApFIhL29Pfr9vmjEymQy7OzssLu7SygUolgsvqSkmctIOBxGVVVs2z7QFJ1IJMT1Xa/XcV2XTCbDaDRCVVWR3lKpVLBtm4WFBYrF4gM11JimSavVEk0U4XCYXC533w3K1WqVbrfL9vY2yWSSmZmZA+dLIpGgUCiwu7vLzs4OiqKQzWbPVeDseR7r6+sixU2SJPL5PIlEAlmWGQ6HdDodNjc3hUHFs88++1gm1vlYloVt26LJ8TTuTgJ7kLSgJwUJj9XP/GNaX74FlgWZDOHXvY7Em94UGM4FPB14Ht0PfQjvF38Rqdcj1OlMrwVZxkulGH74w/Rf/WrS73sfoXsMHI9DSSZxvuEboNlE3dlhYppIx63JPI9Qo4GdTOIlk6S+9Vsf4ocLuLRIEupf+SvYP/RD2KaJtr4+FVjn83jRKNg2crtNuNVikkrhxuPTfy8UpopMWUZZXwfADYWwXv7yg+Lzu98q/EXmEgABAABJREFUncbKZgk1GvCFL2Du7p67MWD0u78b88UXsUcjIuvrWPk8biaDrOuE2m2U8XiaeO442LEY7mhE4od/mOEv/AKEQnitFqF2W/QDuKqKVSrh5nK4r3wlmR//8Sf6ntX76Z9G/qM/Iry+jhOL4SwvI911vJ1CAWc0Iry5CRsbOD/7s4y+6ZvOPe3+ccFzHKRA/HIq8p26tRcKwXgMx9Wx7+BZFrLjQDiM9JCef04SoJ9lmzkSiRAOh5FlGcMwTm2SNwyDcDh8JnNE0zTZ3d2lUqkwHo+5cuWKePb1zchisRjlcpnJZEK/36dWq7G4uCj+hiRJzM7Osra2Rq/Xo9FoPBIBeiqVEiaCjUaDarVKrVZD0zRc12U8HhMKhZibmyOXy3Ht2jUhGNnb26NSqVCpVMRehr+nNhgM2NjYIJlMCgHqoxRTPmwajQadTodwOHyikVk2m6Xb7bK/vy+SJx9kPywUChEKhTBNU5wbsVjsWPG2aZqEQiFSqRSTyYTxeEy73b4Ugn/XdXFdF8uyDgjDj0OWZTRNwzRNarUaAM1mk8XFRZEi7yNJEolEguXlZTY2NkSS6Fne53Hh6tWrWJaF53lUq1VarZZI5fSTXv3rKhqNks/n2d7exjCMUw0I+v2+ONfOalbg778pinJmMZJlWSiKEohK76LdbrOxsUG9Xhdix0QigSRJjEYjNjY2SCQSIp3+URlXRiIR0uk05XKZ7e1tFEWhWCwemr90XWd/f59CoYCmaWcyZoXp/cFPKo3H48zMzBw4DwqFAoZhsLe3h+d5yLLM888/T6lUeqif83EnEokIoxHDMA7NhfdiGIYQ7z8sIxI/tRw4sD+sqiqapmEYBoVCAdu22dzcZGtri729PRqNBuVyGdM0MU0TwzCYmZkhGo0KoWwymWR7e5vJZEKr1QqO/2OE67q0222azaYwH/JrK/l8/sy1BMdxGA6Hou54WtJ2IpHg+eefF0bjw+FQmDf7Jhn+ddJoNOh2u8LseWdnR5hC3Us6naZSqdBoNEgmk8Tj8UPi3kajga7rwNTY+ih8owXfcNq27WOv3VQqRbvdptPpkEgkDqw3fSOkUChE7oS9RlmWKRaL7O7uMh6P6XQ6l8qwGab3g1arhWEYLC8vH6q9xeNx4vG4MN5SVRVd189tTaeqKs8++yz9fp9Go0G/38d1XWRZJpFIUCqVTk3nDoBkMilS2Nvt9onmMbZtC7PoTCZz6nzhhwrYtn2qEU6hUGBra4vJZEKz2TzVhD8gIOBkLn8HZ0BAQEDAFM/D+E//ickv/RLK7/7uNE0SQJJwXvUqIm97G4nXve6pS55+KfQ/+1mk/+v/QtnYQBmNMGdn8bJZUWy1JhOUep3I5iYTx2H4vv+fvfcOkyyt674/J1XOVV2de3qmJ21CAQEDQZYgLLIIqJgv8VJfFNMDKILy+hAVHhTlWQQfXkEeFEE2wC4CC0jOaVmXhZ2dzl3dXV05pxPu94/qc3Z6OtXMdM90z9Tnurh0p6u77lN1zn3uc/9+3+/3lbj+9V+Re2hAu5pxHzmC9M53Un3zm+G++zDqdaRqFYTAikQQ0ShWNIryq79K7Jd+6UoP97JjVqtIX/wiSi6HHgptFp+fg+R2o8diKPk84sEHaU1P4zl+/DKO9urE/4QnUHvta9Hf8Aba4TByLoc7k0EyTYSqomsakmUhVJXOxATW1BTSLbcQfP7zUQ/YBstutD7+ceRyGUnXsRIJdpzxfT4Mvx85n8f47GehL0Dfc4x8HvnLX0ZZXcUIBDaJz20kv5/2kSN4ZmZoV6vU77oL10tfegVGvDeYlQryV76Cks+jh8Oww31S0jSMeBy1WETMztJ84AG8N910GUfbZ6+RvV6sSATL50OtVDC22UQnEEAEAljNJlqhgDU0hDI5eVnH2qdPn8OJGov13KB/UNgp/Rz6Ceh9+uwVuVyOxcVFRwh5btE+EolQrVZJp9M9J/N2Oh3OnDlDoVBwUmPC4bAjgi2Xy+RyOZLJJJZlcfLkyatehH6+0/9OSOcYEm1pTHTI0TSN06dPk8vlCAQCDAwMOKYHdtNLMpkkGo3u+Jk1m00efvhhp+lV13U8Hg+5XM4RTdsJZrY4vVQqEYlEHFF5OBx2/q1er+P3+xkdHWVmZoZ6vU42mz30Keh2urTX66VYLDqNNZIkMTY25nz2hUKBcrnsNM0Ui0WEEAQCAcbGxkgkEhfdPNBut1laWqJcLjuGDNBt6ltdXXUa8C9E7G836+fzeVwu17bN3qqqMjY2xszMDJVKhWw2e1kF6Hbi+dLSEh6Ph9HR0Q3ntZ3iZqfxuVwuVldXD/V5d26i114ngV1NbHeoqtXh1Pc+hHqmgCQEwuXC+vrXKb/rXci//uuEf/mXL+9A+xwKrGaT6sc/jvHf/w2tFpLPh/IjP0LwWc86XCJQIci/4Q3In/wkyvIyarmMkGWEywWmiZJOY/h8UKtRmZsjeNttaD02THt//ufpfOlLmNks2uws+rFjm0XoQiCnUiitFp2pKcQznoF8yI1o+vRO4IlPpPKnf4r11rfSDgRQcjnc09POzy2XCyORQAwMYD3zmUT+9E9p3n8/ZrGIaLcx/vqv0ebnMYLBXaOAzYEBtDNnMBoNGp/6FK7f+q39PrwN+B73OIxXvALrrW9FX1tDTadxLSyAEEhCIFtW1yRVlpEMA7nTof2Hf4jykpeg/szP0Lzjjm5d+5xnHPP663HdeiuBZz7zUBgeXiyi00F84hMo6TRC07AmJ7dev3i9dI4dw3PmDGa5TPP22/sCdBshqH31q7Tvugv5gQeQms3ueufYMbTnPpfgz/zM9iYh1zCu4WFqY2MYq6uoxSJGMrlj/5Kcy2FpGgQCeH7qp/ZkDOd4FW6il8s+EAjg8XiIRCIUi8Udm+SFEBSLRef5fTdjq1wuR7PZpFKpbBCfQ1f4aRgGzWaT0dFRGo0GpVIJn8+3KaVNkiRnfPF4fN+SrQcHB9E0zUmbrVQq6LqOJEkkEgkCgQB+v58jR444z8nlctkRihiGwdGjRzfs0SUSCVqtFsvLy475WSAQOBQC4Xq9TqvVolQqbSmGsyzL2a9ot9vUajXy+bxjoHfs2LGL/p4ikYizJ1mpVOh0OtuaI1iWRa1WIxaLEY1GHQF6r+Lg/eZCk3uhe0xCCEqlkrMHtJPg0uVyEYvFKBaLRKNRms3moTdutPF6vZw8eZKZmRmCwSClUskRRSmK4iRD+3w+jh8/zuzsLJVKxfksthPwCSEoFApEo9ENphG7IUkSwWCQUCjk7KPvtGdSq9Wc1PS+cKuLbVyaTqcpl8uMjo5u+mxarRarq6ssLi4iSdKWIsi94siRIzSbTSzLYnl5mVKpRDgcxu12YxiGMwclEgkGBgY4duxYT0agpmmSy+XIZDK43W5GRka2PFcCgQCjo6MsLS0RiUTI5/M7Gn5ci9j1sGg0ysrKColEYltDEiEE+Xze+Q53M9a5VOLxOJVKhbm5OZrNJul0mkqlwtLSErquMzw87NxD7XWFEIJGo0Eul8PlchGNRtE0jXK5TLlc7gvQDwnFYtEReJZKJae2Yn+XKysrDA8P72iG02g0yGQyFItFx6hIlmVHcJ1MJret/cqyTDwed4TWW9U6hRC0220ajQbDw8OUSiWi0Sj5fB5Zljfd+1wuF61Wi0ajwdjYGMFgcMN6QghBpVJx5snt7n/NZtMx/7b/eyfziHA47Bgk2+t8IQS1Wo1KpUIoFNq1PuH3+1EUxUmJPkgC9Fqt5tT5bSOT7RgcHHQMu7PZ7GV/ZggGg1ftesU2+NY0jXq9vutaudFoOK/vNUFckiSSySSNRoNKpcLy8jLDw8ObTIh0XSeVSuF2uwmFQrsKyuGRBPRgMLirqZFtjlKpVKhWqz2NvU+fPttz9e6o9+nTp89VhOh0KP7VX3VFbsUiaj6P3G53BcAeD+RyGN/5DvnHP57oG9/YbzTYCiHQ3/9+lLU1lEaD9okTSG73BvGm5HZjjY/TdrlwpVJ0AgEq99xD5EUvumLDPii4RkaI/8M/0J6fp/GRj2DOzXXTHEIhtCc9ifAzn+kkol5rtB58EKndRq3VaB05srMgGLDicVzZLEa7Tfu++/oC9D0i8OQn0/mXf6F+552Y996LVS47P7NCIXj60/E885mEjh5F9njQcznqn/scVqmEpKooR48SeOITD7x7vVhbQ2o2sTyenq45y+9HqdWQ8/m+O/8+ULvnHqjXUet12lNTOzZRSIEAps+HnMth3Hsv/P7vH1rTmPaZM0idTs/znojFkNfWoNmk0xegH34kCemZz8RaXUV7+GGMeh12KEIr2Sym1wt+P/5bb72MA+3Tp0+fy8duAvR+AnqfPpdOPp9nfn6e1dVVyuUygUCAwcFBFEVxGhps12ohBKdPn961ADo/P0+xWGR5eZlEIkE8Ht9QtE8kEpRKJdLpNJIkMTs7y4033njVCg81TXPScgzD2LZpyabRaOByuZBl+apNrJFlmWQy6RTI7dRjt9vdk8mBZVlMT087ol076cFuKrfF5HYzVqFQQAiBaZobmhoCgQD5fJ52u02z2cTv96NpmpNCHwqFDrUQGLoNAJFIhEQiwdLSEj6fzxFgu1wuJicnWVlZwePxsLCwgMfjIRAIMDIywvDwMG63m4GBAcbGxi7qGm00Gpw9e9aZS5rNpuOwb5sGxONxarUax48f77nppFwu0263nQbSnVBVlXA47IgcdF2/bAnjmUyGcrmMEGLbBlDoNhE2m01yuRw+n4/R0dFDe/273W5UVUXTtJ6SwGq12gUngV3NSAgGCrMoEQ2hKCjlMlI6jRGJYL3jHRQyGWJ//MdXeph9DgjCMCi9853wn/+JVKl0zxfTRCgK4lOfovx//g/ceiuR3/mdQ7F/XXrf+5A/+Um06WmwLDrj492E1/W502g2kdNpXDMzdCyLyp//OfF//uee9oJ9j340rec9D+68E2V2FveZMxihEFY4jJAk5EYDtVhEsiw6k5NYJ08S/n/+n/0+5D4HjNBznkNzYoLGv/4rfPObmK1Wt2Yry+DxYB4/jvb85xP52Z8FScL/4z8OQOvMGVpuN7KuY/QgKpI8HoSiQLuNVSjs92FtSeg5z6GRTNL4u79DS6exFAVF1xGyTMflwlJVTL8fFAV3KoVerWK94x2YL3sZkbe+FSOfx1hdRZFltMFBtGtEvFL9r/9CLhZRy+XuHLVT7crtRg+FuubpX/oSZq2Gco1vZOmZDJVXvQrl4YdRikWUUql735IkrFQK68EHKb7nPXhf97p+vet8JAnluc9FzM0h53KQycA2Jsai1cKVz6MPDGAeP75nn+WlJqBLksTAwAD1ep1yucza2hqDg4NbClhWVlYQQhCJRHoSR+VyOUdUfv6egmEYjgjL4/GgqiqpVMp5njy/Cd/v95PNZhFC0Ol09u0ZxRYx28JqW4Bui9LPF93bz5atVoupqakt95Y8Hg9HjhxxUtwzmcyhEKB3Oh1M08Q0zU0CDdvQrtVqUa1WqVar6LpOOp12nvWbzSYTExMXJRoNBAL4fD6i0SgLCws7CrcLhYIjUI1Go6TT6Qt+v/1EkqRNCbk7YYuvFEVxklV321+BrmjfTmCt1WpXjQAdwOfzccMNNzhJr/V63flZMBhkYGCASCTiiH6q1SqlUonl5WVGRkY27eNYlsXKygrQ/dx6Ef2cy8DAAKVSiUwmQz6f3/Y7tSzLMVx0u909p2Zf7dhpvcVikSNHjmxpZuLxeJiYmGBubo58Po/P59s3AbqmaZw6dYrp6Wl8Ph+VSoVyuUytVkOWZYLBINFoFLfbzdTUVM97pMVi0Um8Hh8f33H/NhAIOCal2Wy2L0DfgoGBAQqFAh6Ph6WlJcbHxzfdc4UQrK6u0ul0GBkZ2TLNfj/GlU6nCQaDPPzww0B3beD1ep19VeiaKtRqNQYGBpwaQLFYdETybrcb0zQxdlrY9Tkw2PXbtbU1isUiqqoSCASQJIlms0mhUCAYDGIYBoZhMD4+vulv5HI5FhYWqNVqjgE54JgVVatV8vk8R48e7clIYatz3V4/2cbmIyMjmKbpmAiXSiUCgQCyLDsp5bVajbGxMQKBAEeOHNnw9+zz0zCMHdfitpheVVUURXHWk9uhaZrzc3vM9n+bptmT+FeSJCdxfaf3arVajthdCOGY+OxmrHUplEol6vU6hmHs+l3axvG2IYVtStDn0vF4PM7zgm1sttM9olAoOGu4C3l2SyaTlMtlTNNkaWmJ6elpxyzJNlaoVqt4PB7GxsYYHBzsyZzbvo56fQ7u5Xro06dPb/QF6H369Olz0BGCwqtfjfLlL6PNziIZBkY4jLG+4SfVarhWVxGZDOg6xT//c2J/+7dXtWv3xdC4/37k+XnUfJ7OwADSTgvPZBKrUEDK5TDvvht+8RcPrUhwr3FPTuL+kz+50sM4UFj1OsI0u477vbh72Y2jpoloNvd3cNcYrpERXH/wB4iXvIT23BxWrYbs9+M+dsyZE1sPP0zjve9F/vrXodlE6nRAkjC9XorJJNIttxD5jd+4elzrz527rsJEuiuNOTeHUq1iqeqOAlzn9eEwaqGAVSphViooh6CYvhVmowGW1Z33emmGP2fes66Sea+zvEz9zjuxHngAqdVCeDzIj3oU/uc/H1cPRe/Djv8FL6Bxxx3o4TDawgL60aNwfuFeCMhk0Eol2pOTmI99LK6xsSsz4D59+vTZZw6qAF3XdXK53IbmQI/HQyKR2DWxt0+fg4RhGCwuLrK2tkatVtuUnhQIBIjH447IV5ZlFhcXOb6D4Vuz2aRarbK2tkY0Gt22YSsSiSCEcJphy+XyvqdEXCmi0SiBQABVVSmVSjs2sdlpJ2NjY9fMfHIxDReFQoFGo8Hq6iqDg4PEYjEAR8gO3fM3FouxsrJCoVDANE1CodCGZlBZlp1kqHObjP1+v5MGYZrmoRUC24yOjlKtVul0OiwvL1OpVDYkwQeDQer1OrFYjBMnThCNRpmcnCQcDu+Y9LIbuq5z9uxZcrkcq6urRKNRR9Ru/7xQKLCyskKn00GSJK677rqeGhtarRbtdttJ6NoNO70Kuk3Wl0OAbjd/lkolIpHIrg080WiUpaUlOp0OpVLpQKVnXAg+nw+fz+ckB/aSBGafj70mgV1NbDXLrwzdxMj4evObEFAuo62sIM3MwO23Uzl2jNBzn3t5B9rnwCE6HQp/+qco3/wm8soKaqUCsoylqkiGgbSyghEKYZZKFObmiL3xjQe6vik6Hazbb0ddWQHLQj9+fPP+qNeLNTmJkUqhLSyA30/tS18i8OQn9/QesZe9jIIQcPfdmKUSSj6PK5UCIRCahhGPIxIJzBMnCL3lLShXaRJPn53x3nQT3je/GX1tjeY3voFVLiN5PLiuv377BOtz73O9JI4K0f2fJF3R69L3uMfRDAbpDAzgWVykGY8jAgGEx4MVjTrGFWajgWtuDiOVwnzHOzB+6qdQ43HUePyymfocFMy5OaR6HUmIrkHGLliRCK5UCrPTQV9eRjl16jKM8mBi5PNUX/pSlJkZtIUFJMvCCIexfD4wDNRsFmltDb1YpPXyl8Pf/R3eG2+80sM+UASf9zxKH/4wnVoN99ISeqeDmUw6/TnCspAKBVxra5g+H2JwEPev//qevf+lCtCh2yRfLBYZHx9naWmJer3upAJDNwm7WCwihGB8fJxoNLrrc5FlWei6TrPZ7Kmh3jYJbLfbWyZXi3Pq/vu9J2Mnru+2H9ZutzckLe/0jK6qqiN2iEQi+5bifjnQdZ3FxUXK5TLZbBZVVQmFQsiyTHO9Lr60tIQQAsuykCTpop6jR0ZGaDQa+Hw+FhcXEULg8/mcvaBms+mY4A0NDeH3+3G5XOi6jtvt3tN7oWmaFAqFDWIlO7G2FxKJBNVqldnZWeeYtsMWWGqahtfrJZ/P9/Q+9mvOF3ldLcKlc5Ne7XPL3j88FzsJ3jRNFhcXmZmZIRwOO/sajUaDUqmELMuMj48Ti8UuWNgciUTw+/2MjIywvLyMZVnEYrENc0Cr1SKdTmOapmP2eTV8D5eKZVnkcjkKhYJjNLEdiqIQj8fJ5XIkEokd03MvFZfLxXXXXeeYhJw7/3s8HgYGBojH4xe0F91oNKjX61umDG9FKBRyROu9mOVeawSDQQYHBzFNk1Qq5Qj6zhX92nvMExMTRCKRyyLk1zSN0dFRZ1ypVArTNPF4PDSbTXRdxzAMWq0WwWAQv99PMplE13XHgLdWqzki2/48cfBptVosLCywurpKtVp1xNrnv2ZlZYWlpSXHJMeulUH3Xm8bkNsJ37apgmEYlEolFhYWiMViCCE4fvx4T+vp81EUxUlwbrVaRKNRxxAjEAhQqVSo1WpYloWiKAQCAce8/MSJE5vWqvb5KcvyjqJWRVGQZRnLspxk9p3W74ZhOPOr/R72fyuKsqG+uBOGYWxrIN5sNllaWqJardJoNJwaltvtZm1tDb/fz/j4+L7UQWwjAk3TerqP2KZF0F3XXeq8YFkW5XJ5wzo2HA4f+vrqxTAwMEClUnHqn9uZU+dyORqNBpOTkyQSiQv6DiRJ4vjx48iyjMvlolwuUyqVnCRyr9frzBuDg4OM9dhXqijKBV0P9rlzLX7PffrsNf1VeZ8+ffoccCr33IPyta+hzcxgaRrGiRMbEgBEJEJ7eBh1YQFtZgahKJTvuKOf2n0e7a9+FalaRTYMRCy2c1qrJGHEYmiFAmJpCX1t7ZpxRO9z4ch+f/ealCREp4O0m3Nuu939v4qCtI9ucdcykqriOXFi07/XvvhF9De8AXltDSWbRW00HFG2UBSMlRWs1VUK3/42kbe+9UA6/EsDAwivFzmb7SnRXK7VsNxurEjkQDfuHVoMo9vA0+PGilAU55yz2m0O25ZG4/77ad1zD9b99+OemUFutZCyWcTExI5GLaLV6v4/moZ8yBsizVqN0l//NcqXv4xUr6PayReKgvjud2l8+MNUn/hEIq961a5ziFEsYqTTCNNETSbRekhH2GuEYYBlXbDphmtkhMZv/zbWu94F8/O4z55FDwaxolGQZaRWC6VQQNZ12uPjWBMTBF/2sn06ioOLWS6j53LIbjfa4GBvhg1XK0LQeugh9KUlhGmiDAzge/SjD0WyWZ8+vbBeH9mWy337E0KwvLxMJpOh1WpRKpUcAbrX66VUKuHxeJicnLyoAm2fPpebXC5Hq9WiWCwyMTGxbfJ0PB5H13Wy2SyBQGDH5lU7HUbX9V2bPiORCNls1mkkvVoF6H6/H5/PRzweZ21tzRFnno+diuNyuQgEAhecinMtkclkKJVKqKq6oaHGLs5LkoRhGHi9XieZSVVVWvYz1Dp2CoPdqGIjrjKjOY/H4xhHeDweCoUCqVTKOU5N00gmk5w8eZJ4PM6xY8f2pAktk8lQq9VIp9MMDw9vusY1TWNwcBCfz0cqlcLr9ZJOpzclXmyFEKKnpiIbSZKc37lc368tZrCb1HfD7/c7KXtbCSEOE3aDTalUchpszj+n7DlPCEE0Gu0p2fBqwj5tJbGxiU4gIWQVMB95YSSC7vPhOnsWK53G+OAHYT19t8+1S+HNb0b5xjfQpqcRqkp7YgIpFOqeF+vGBeraGsrZs+hA8e//ntgrXnGlh70t1Xvv7aYKl0p0xse33+uRJMzRUdRyGQoF2nfd1bMAHUki9opX0Hja02jefjt87WuY56jpzBMn0J73PMLPfva1vdd0HsI0aT30EGaphOR24z52DPWc9dfVijY4iHbrrc5/C8Po1iq32O91jY7S1DRMrxe5Uunu5e5Etdqtf/h8yOumq1ajQfVjH8O87z5ErQY+H8r11xO49VbUfUqxbJ05g/LQQ2jFIu2REcQ5BrAb7jA+H53xcdzz81jVKvWPfpTwb/7mvozpwNPpdE0rZLm3+7AsO6YE1nnPItca5Te9CWV2tmtmEIlgjo4inbM+1EdHkbJZXCsrtIHm//v/4vmP/7ima7BmtUr17rsx/+u/kPL5rtGMywUuF63xcbRMBu3MGUy3GyQJef0ZQo/FECMj8Fu/ReCpT92z8VyMAF0I4TwX2EINO6VR0zRKpRL5fJ61tTWg+4xoC7Kj0ShTU1O7Pu/Zz3dCiC2fY10uF5qmOaIxn8+34fnwfOr1Om6320mTPgg0Gg1HVDY8PLzr60OhEPl8HsMwaDQaB16A7na7URQFVVU3jDedTlOtVslms0SjUScRsFqtEggEGB4eZnh4mHw+TzqdRpZlQqHQBQvCI5EIExMTHD9+HNM0mZ+fd0QztqDJ6/UyPDxMMBhkbGyMfD6Px+PB6/XuiXmcruuOgWK73XbESnaieSQSYWRkZNu9W5tEIsHq6iqRSITl5WXGx8e3/J1CoUCpVHIMMO0aRy+JuPZrJEmiWCw6aZv2v4VCISd597Aba0qStK2YRpIkjh075iSh2nNdsVgEuud1MpkkFAoRjUad117o+09NTWEYBpIkkU6nKRQK+P1+ZFmm0+nQarUIBAKMjY2RSCR6miOuBdrtNqZpUqvVep430+m0kx69XwJ02Gg+IoRwhFsXuxdrz1O9Cr8URXHuf1Yv5lnXILZIT1VVqtUqxWKRlZUVoLuuSCQShEIhwuEwU1NTl03MbQvjQ6EQHo/HMag5N006kUjg8XhIJpO43W5cLhf5fJ5ms0mxWKTZbDIwMLCvKcx99ga7tlIul5mcnNzyfu7xeJiYmGBubo5isUgmk3HqZZZlsbi46Pydrf5GIBCgXq+ztLSEqqpomsaNN954UffvSCRCuVxmZWWFZDKJ3+/n+PHjlMtlfD7fhnqHbeJzww03bFk3URQFt9uN3++nWq1uu9byer14PB50XafT6TA6OrrjWslOYne5XM463xbuBwIBSqXShrqsEMJZh0P3+rcNeAKBwKZ7Ra1WY3p6mmKxSC6X21BL73Q6qKpKIpGg0WgwNTV1QWnXvWB/b73O7XZ97dzfvRgMwyCdTpPL5Zzalr2OdblcxONxhoeHrykDw1gsRj6fd4zXZmdnicVizhrONl5rtVqMjY0RDocvysxElmWOHz9OpVIhm806Bin2z2Kx2AXP+YFAgGAwyPLy8q4G7Z1OxzGC28+1U58+1wrX7g5onz59+hwS9I9+FDWXAyEwjh3bUOCykRQFY3IS15kzyNlsP7V7C0S1imQYCEXprSHD7UZadyYzy+W+AL3PtnhuuIGO14sRCKAUCli7PHTLhQKmxwNuN57HPvYyjbJP88EH0V/3OuSFBVzZLPq6eQc+X3ejolhEzeVQHn4YDIPSq15F/O1vP3DzqPtZz0L/1Ke6c1kuB4OD275WNJuo9TrtkRGkPSzc93kEKRRCuFxIut6bIUCj0U1LlyTUQyRaaf3whzTe8hbkmRnkchmlVkOrVJAsC+/qKnqthj40BNtspm6Y9x7/+Ms8+r3DrFYp/eEfojz4IGoqhdJoYHq9WJqG0myiZLOYPh80m5SWl4ncdttmEboQ1L/2NVp33ony7W87TV1twLzxRlw/93MEnva0fRXl6mtr1O64A/GZzyDnct1h+f2IJz4R38//PJ4e000iv/qrlEwT65//mXalgpLL4UqlkCwLS1UxIhGMRAJrZATfm998zaSfC8Og+ulPo3/0oyg/+MEj/+71Im6+ufsZT01dwRFeXoRhUPnIRzDvvht5fr7b/SUElqpSHBhAuuUWQi96UT+tq8+h56AloC8sLLC2tsbKygr1et0pbAohyOfzZDIZEokEhmHsS+GwT5+9JpfLUSwWe0qdjcVizMzM0Gw2yeVyjJ4jTjiXZrPpXB+7pWfYTYn1et1JLrpaGRsbo9ls0m63WVxcdJqp3W43pmlSrVbJ5/MIIThy5IhTiO6zGcMwaDabTor3ubhcLlRVJRAIUKvVnLQPOwWhWq1uSHapVquoqorb7d5QHK/X63g8HlRVvWpc2wOBANdffz3pdJpAIICu6+i6jizLaJqGz+cjmUwSj8f3pEFYCOHMMXYa9nYEg0Enfcdu5t7tc9c0DZfLhRCCVqu1axN2s9l0RAf9ZJ/9Jx6PUywWnUa3mZkZIpGI0+hiJ4FJkuQkG15oEthVgRDIbJ/isgGXC31gADWXQywu0vj2t/E97nH7O74+B5ZOKoXymc+gLCwgNA19ampjjdM2LgiF0KanURYW4OMfx3jxi1H3QCCzHxjf/CZysdgVde7yHCXJMkY4jFIqwf33O0nSveJ79KPxPfrRmNUq+soKotNBicdxjYxc4lFcXRjFItU77kB8/OPdvU7LAllGVxTMxz8ezy/8Av4f+7ErPcx9pbOyQv3OOxGf/jTyuphIBAKIJz2puxe5btgs+3yIn/xJzGwW19JSt7F6O9GiECi5HHowiAgGCTz96RRvuw3uuQepWu2e14YBioL4wheovv/9iJtvJvKylyHvst65UBof/ShytYrc6SB2M78KBjHd7u658KlPwTUqQJciEdC0rnluu+0kT29Ls4lY759Q9slI4DDQXlhA+fa3UZeXMUIhrLGxzc8ckoRIJukArnSaTixG9dOfJvTsZ1+RMV9pyv/+71jvfS9StYqayyG1Wt3PyONBhELIlQpGIoHh8SDVat2eJ5cLIhGsoSG03/otgrfcsqdjMgy4+eazwLmi7e73uNUjlp321mw2qdVqzvOnz+fD5XKhKArDw8MkEgkn2VBRFOfZNBaL9fRsahvKuVwums0m0fOutUAggKZp+P1+KpUKbrcbXdfRNG3Ts6FlWZRKJRKJBMFg8MAI0C3LcoQkvTzP2q859/cOMj6fzzEQLBQKRCIRdF2nVqs5+wTn7rNXKhUCgQBut5toNIrH42FhYYF4PE4+n78o8UYymeSxj32sI1rtdDqEQiFUVXX2hyKRCAMDAzQaDYrFIiMjI4RCoUsW+LfbbR5++GHK5TK5XI56vb5BQOT1ekkkElQqFY4fP76juEPTNCYmJhzDt/n5eQKBgJMc3+l0KJVKdDodRkZGiEQinDhxgoceesgRXu0mHimVSk4Cr2majmDXFsDa+zuBQICpqakDcx3tB7IsMzU1RSKR2JRmDThi/EsxXfV4PJw+fZq5uTlnv9NOKw0Gg4yOjjpi99HR0csm+hdCUC6XabVaCCHQNI1wOHxgxGX2fcWyrJ7mTTu91zTNTfOmEALDMBBCoKrqnoqN92Kf0jbw0HUdy7J2HV+73Xbes79HujX2fmUsFiOTyRAKhTaY1gSDQefavtxGG8lkksnJSUqlknO9ra6ukslk8Hg8JBIJwuGw893a55hhGBQKBUKhED6fr29+fMAxTZN8Pu/cT3eqfaiqSjQapVgsEo/HaTQa+Hw+x9SmWCwyNja27d/w+/0MDQ2RyWSIRqNUKpWL6m8YGBigUCigqirpdJqRkREURSEWixGLxRwzaNM0WVxcZGhoCJ/Pt62hfyKRoFqtUigUtjUGscXklmWh6zqKomw79mq1SrPZdJ4/zr12BwYGnHVYsVh01jL2msm+p8iyTKFQIBaL4XK5Nphj67rOzMwM2WyWtbU14vE44+PjznVqJ86n02nH+Of06dN4dwtkuwC8Xi8+n8+poe72t6vVKj6fb8vnol7pdDrOOrZQKFCpVJz5UpIkx+SgVCpx4sSJPT3eg4xtImSvHWxTgnQ6DXTPpXA4zPDwMKFQiBMnTlzSPTkUChEKhTBN0zFM0DTtotYsAwMD5PN5VFUlm83u+GyVy+XweDy43e7+faVPnz2gvzLv06dPnwNM6+GHUc6eRS0UMKLRLcXnNpIsY8Tj3caepSUa99+P70d/9PIN9oAjud2PuHf30uRhmgjbOWuPC+V9rjyi06F6773o996LtP7AJGIx1JtvJvCzP3tByddKMIh48pMxs1ncc3O0ajWkbX5ftNu4ikX0oSHMRz0K99Gje3I8fXan8a53oayt4cpmaR05gnTORo4kSRCPY8RiyPPzaHNz4HZT/eIXCT7lKVdw1JvxP/ax5Ccmuu766TQdrxe22OQSuo62sIARCIDfj+8FL7gCo736cf/0T6N/7GMIWUbO5xE7pGAJy0Itl+kMD2M+5jEXnDh9pWh897u0X/Uq5OVltLU1JF3H8nqxFAVVCGTDgGoV2TBoG8ZmU4RmE61Q6B73j/wI7snJK3Ice0Hp9a9HfvBBXDMzGD4f+smTSOubjhZgNJsoq6u4pqfpCEHp9a8n/uY3O78vdJ3i61+P/LnPoRSLqPk88nqaiOVyIWWzmP/93xTuvpvIX//1vghyS//6r/DP/4xUq6HkcsjNZlcM7HZjpdO07r2XxtOfTvTVr+4psSPyG79B6wlPoPHhDyO++EXMc9JRrJER5J/9WcLPe941Iy7Ws1kqr3wl8pkzqIUCSqGAZBjd1CufDyubpfXxj9N68YuJXAPNj2atRunP/xz5u99FyeVQCwVkXQdAKApGJIK1vEz5058m8Ld/e82YFPS5OtkpAV1R4HKGt2SzWTKZDIuLi0iSxNGjRzcUa+2Gm3Q67ThG33jjjQem4aZPn/MRQtBut2k2mz25UrtcLtxuN81mc1OK9LnYza29inZlWUYIcSgaYi+FYDDI0fW9Crv5Y3Z21vm5oihEIhFisRjxeLynBOhrFbvZwzTNTU2skiQRjUZpNBosLy9Tr9fx+/0Eg0GazaaTvhaLxTAMg3K5TCQScQxFoNuEZydC7EWK1UHC5XIxMTHB6Ogo1WrVEQB4PJ49NzywBQblcpmRHgR9kUiEpXXBVqVS2SQaOJ9wOIzL5cLv91MsFndNMyqVSpu+6/3GPj/dbjf1en3XlAG7yfzcBI7Lid2oajeuXgp2g43d5FgulykWi+TzeWBjWtDFJoFdDQTa2W4C7nls/pcuVjyOnE5DrUb7G9/oC9CvYWp33IFcr6M2GrROnty2xinJMsboKO6ZGcx6nepddxH97d++zKPtDcfsWtN6EpMLtxu50QDDwGo2kS8ivUsJBlF6NIy81midOUPjz/8ceWUFJZtFLZW65uKShOH1Qi6H/tWvUvj5nyf2R3904AyHLxkhKL7nPUjvf393vzef37jfu7pK6xOfoPGsZxH9sz9DUlU8L3whnS98ASubRZ2bwzh2DM7fDxACaWUFpV6nMzWF9cQnUn7DG1C+9jXkVAq1UkGoKpamIRsG8uoqRiDQNXCdnSXy9rdf1Lm+7WEuLyPV612T293WHpKEGQyi1OsomcwFGz9cLXif8hSa73kPpseDnMttSI3fhBDdXpRIBHNiAvf4+OUb6AGj/pGPINfrKM0m+pEjO677xMAAZLNI+Tz6PffANShAL7773Uj/9/+iLC2hlUqYLhfW+rWvFotImQxGLIbw+9Gvuw5lYAB0HSkaRXvqU4k8+cn7cn0aBni9W5/z528BpdNpUqmUI+I4N006nU47InOPx8Px48ed33O5XBclSrDFMqlUimQyueF5RpZlIpEIzWaT1dVVUqkUiqLg9Xo3CF4sy2J5eRlJkgiHwweqgV5RFGef7Vzh4Ha02+1Nv3fQGRgYcATnq6urqKrqJIGf+13Yye7hcNgR/tkCdlusfjECdICRkREe97jHkUgkOHPmDLqu4/f7icVijih+dXWVarVKMpkkEok4KbkXi2manD17lnw+z/LyMoFAgCNHjjjXQavVolQqOfslANddd92OovdEIuHUJ2yx/OrqqiOACYfDRKNRfD4fJ06cwOPxEI/HqVarLC0t7ShYMgyD1dVVJw00lUo5f1NRFDqdDplMhkwmw+joKIZhcPr06au+RhIOhwmHw47ZI3RFP3t13G63m9OnT1Ov18nlcjQaDUdYHQ6HSSQSl01IbM/juVyOVqtFu93ekHIajUYZHh6+bHtv22HPffZ5udue3LkCc/t3W60W2WyWfD7v7EcDzj0iFAodiL2scDjsGLBuZdh6LnYdMx6PEwgELlty92HF7/dz9OhRJiYm0HXdOUeu5Jxmm/kMDw/j8/nw+/24XC4ikQjVatUx+TkXwzCoVCrous6JEyecBPU+B5dWq4VlWU6y/W5EIhGy2axjCuPz+cjlcpRKJceweSdCoRCZTIZKpUI+n78oAbptGDQ2Nsbi4iKLi4skEokNdadqtUoul3PEqiMjI9vOo4lEgtXVVeLxOMvLy4yOjm46DtM00XUdl8uFx+OhVqvR6XQ2rGMsy6JcLrO2tuYkQZ9vhBuNRvF6vQwNDbG0tOSst23TF8uynOvI4/EQDAapVqsbjCnspPm1tTVGRkY2fYZ2+rnX62VpaQmv18va2hqTe9jrGYvFSKVSBAIB8vn8jueObSZ/9OjRizYGPncdm0ql8Pl8jI2NOd+5vQ5dWFhwvutrYV1ooygKJ0+eJJvNEggEnBAL+17idrtJJBKbnl8v9T0v9fkvEAgQCAQYHh5maWkJYNNa034uajabjI2NOaYMffr0uTT6AvQ+ffr0OcB0ZmfBMLqO3j04TVrhMPLqKrTb6HNz0BegOyinTmHZD3flMuzyeSrFImYggAgGce1UlO1z6Kh+5jPof//3yOviP2k9vUy43YgHHqDynvcg/+ZvEv6VX+n5b/pe9CJan/0seqmEe36ezvAwxGKPFE2FgEoFVyqF6fMhEgncL3rRfhxeny1oLyyg3H8/aiZDJxbbID7fgCRhHjmC+oMfQLFI56674IAJ0JEkvH/8x3T+7M8wOh3c8/Po4TBWLIbweLr3jGIRrVhEuFyYk5NYL3gB7r4oYF/wP/7xFMbHMdbW0DIZ2sGgI0jegBAoS0tdY5NYDPfzn7/nY2kvLtL68pexKhUklwv1xAkCT3ziJTVvGPk87de8BnlxEdfqKp1EAmtgAEnToNOBhx5CaTbRdB2pXofVVdoeTzf9x7KgWMS1uooRDMLAAJ5f/dU9POLLS2tmBuXrX0ddXMTw+7EmJzdvMHu9mEePIubmUBcX4Wtfoz0/3xXdC0Hhf/5PlM9+FnV2FrnT6SaEDwwgJAm5VkPNZCCbRbTblF7+cmK33banRgWl974X3vMe5IUFtHIZw+fDDIW6799o4J6bw/R4MHSdQr1O/G/+pqfzx3PqFJ6//EusRoPO4iJWq4USiXTnnQNQzLxcmJUK1T/5E5Qf/hDX/DxIEno0ivB6wbJQymXcs7MYwSDm//f/UYKrWoQuTJPSq16F8s1vos3Odj+PeBxCIYQsI9VqqLkcarFIp92m9rKXEfqnf0K9hhN2+hxudkpADwa3ng5/9Vd/1WnGAvas2LG2tkY+n8eyLI4ePbqpeCNJEpFIBFVVWVpaIhgMks1mexLd9elzJbAL40KInhuN7BRpsYVYzsZuwKnX6z39TbuQfy2kbUSjUVwuF+l0mlAoRKfTwTAMJEnC4/Hg8XhIJpMMDAwciOa1g4p9vtqpNOcTiUTI5/PEYjGy2SydTgdJkvB6vWiaRqlUwjAM2u02brebUCjkCM3t5tlAIIDX6z1QDed7iW14sJ8YhuGkWvTS0ObxeJxEIduhfyc0TSMajToN0oFAgOA2Bl1ra2sYhuGklV0uXC4XwWCQaDRKLpcjHo/vON/aiSZut3vfvx8bXdfJ5XJks1mnSRpwBCHRaPSim1FlWeb48eMUi0Wy2ewmU4ErmRZ0UHAZOyx2t0BSFISigGEgdloo97n6+eY3kQsFTJ9v6z3Tc/H7Md1upEIB6+tfhwMqQMc2u97i3r4lhtF9PeyeQNzngmgvLtJ4+cuR5+dxpVJYHg+doSHwehHtNuq6abTpdqN/4AMUNY3o7//+lR72nlJ817uQ/u3fUBYWUCsVDL//kf3eer17/F7vI/u9b3gDvh/9UVq33opx112os7O4HnoIIxzGDIe7JhGNBlqhgGSadI4cwTp2DCFJqF/9Ktr0NEKWaU9OIq1vdphCIOp11HQabXoaXQiKr33tBmPWS8Y0L0xIbq8JLAthWUiHRNS4l7iPHqX2qEdBPo9rZYV2OLytebq0uopsGBiJBMpzn3tN7emfjzh7FqlcxvR6d5+zJQkjFEKuVpFnZi7PAA8Qta98Ben970eZnUVut2lPTm7YBLWEgHIZbXkZDAPJ68X1spfh+5Ef2fex7XSLPndLp1gskkqlSKVStFotksmkk74MXUFNPp9nYWHBSWq97rrrLumZwE5pc7lcrK6uMjo6uuE5Jh6PU6vVaLVa/OAHPyAWi9HpdFAUxRHqlEolACYmJojFYruaol1O7BR3O6F6NwM5O8Va07Rtn5MPGolEgkKhwMTEBIuLi5uSwKvVKpVKBcMwGBwcdIThNnYytC2GvdjzyU4P9/l8lEolisUihUKBQqHgpDgeOXKEYDDI1NTUJac4ZrNZqtUqy8vLJBKJTQIgj8fjJIQuLy/jdrtJp9O7GlcODAw49YlgMLhh/8zj8TAwMEA8HndqHMlkklwuRyQSYXFxkeHhYYLB4IbPsdlssry8TKFQwO12Y5omQ0NDm/Yvkskk2WyWpaUlJEliYWFhg9HE1cxeis63wu/377mB5IVgGAZnz56lVCpRKBQolUrO9SaEwO/3U61WKZfLTE1NXdH5x+PxoGkaoVDIMSDdCTtN2hYWrq2tkUqlHPGcbWyqaRqRSIRisUg4HObYsWNXvK5hi4mj0SjZbBa/37/teZjNZrEsi3A4THKHEJA+GzlIhi7nmsnWajUGBwc5deoU8/PzeDwe8vm8s1ZQFIV6vc7y8jKDg4Ncd911RKPRvvnxIcA0TacW28scY7/GNgiHbjJ1q9XqyYDcNjawjUUulmPHjmEYBrIsk8lkWFpaQpZlFEVxEsqj0SjxeJzR0dEdDaBVVeXo0aPO8SwtLTlztKIotNttyuUymqZx/fXXYxgGmqY514LX68U0Ter1OkIIkskk8XicY8eObZoj7TqGruvMz8+TSqUoFAoEAgHC4TCqqmIYBsFgELfbTaVSQVEUpqenOXnyJEIIcrkchUIBv9+/o4Df7/c7CevBYJCxsbE9u4+oqkosFqPZbDI/P8/a2hrJZHLTurjVapFKpQiHw3i93osWoNvr2FQqRTwe31R7s9cthUKBlZUVXC4Xa2trl2zgdJiQJIlkMkkymaRerzvPKvZz2kGti01NTTn1wtXVVef5UlVVOp0O9XodTdMYGxsjEokwMTFxhUfcp8/VwdXfLdWnT58+hxm7oApdocZur7eLE0J0mxr6OASf/nSK73gHRjiMuraGEQo98nmdh6jVUOt12idOwDOe0VMCaJ/DQeXuu7H+9m9RlpfR8nmEomD6/SBJKMUiSiaDHolgvfOdFMtlor/3ez39Xc+JE3Re/nKst7wFfWUF18oKpNMY9t9uNJAMo5v0OTaG+I3fIPCkJ+3z0faxaXzqU0iNBkqrhT45ueNcKslyd54oFOC++zCr1QtL7hUCq9kEWUbeJydO3+Meh/kXf4HxN3+DFQig5HK45+ac+4XldmMkk4h4HOs5z+kme/TZHyQJ7cUvxkylMJpN3DMzdAYGELFYV6QtBFSrKJkMSqtF59gxzOuuI7KH13/ju9+l+b73oXzve9BsIuk6yDKGz0dhcBD5uc8l/Ku/elH3surttyNls7hWV2mPjkI8/sj143LRmZzEtbCAaLXQ2m20eh3p7Fn0aBS52UQC9FgMMTKC9JKX4P/xH9+z477cNO64A7lWQ2m3u/PIdptrkoQ5Oop25gxGvU799ttxv+IVVD/9aZQvfAF1ZgahKLRPn+6eI4AEiHCYzvAwysICrtlZdFWl/G//RuTFL96T8TcfeADe+16UuTnkVovW8eNI5xR+LaDV6aAuLKBNT4MsU/7gBwn/8i/3/B6yz4fn9Ok9Ge9hpHzbbUgPP4xrfh4jHMYaG9vQrGfG45jNJtrcHCwsYL73vbSe9CQ8U1NXcNT7R/XjH0f57nfRZmcxPR6syclHGq4B3G6MWAxpdRXXwgJtTaPynvcQe/nLr+Sw95zmgw/SefBBRKuFFAzifcITcF1Bka8wDKqf/jTGd7/bVUx7vSjXX0/wllv2NJnqWmQ3AfpWvPvd797zcVQqFSdtJJlM7thoEAgEnAa1XC7H8PDwBRWP7GQXu6js8/kObPGpz+HGLvprmkZz3UBuJyzLot1uE4vFdmyksxNfstksjUZjx3QRXdep1WocOXLkohz1DyN+v5+pqSk6nQ7lctlpxjg/eavP9miahsvlIhAIUC6XNzWFq6rK+Pi4Y66wsrJCPp9ndHTUOY9XV1eJxWKMj487DSTpdJpyuYzH42FkZITh4eEdE6X67Iwsy879y24S2gn7NbIs9yx4Hh4eplwu0263SaVSRCIRotGoI3iv1WoUCgUajQbj4+MEg8HLnmo/MDBAqVQin8+zsrLC6Ojolvf1fD5PrVZjcnKSWCx2WZoaS6USc3NzNJtNisUitVoNIQSKohAOh6lUKvj9fo4fP35JqTjRaJRoNEqn03FMgq5UyvtBw1Au8HMVomtMKMtwiUKHPoecahV0HavHa9PyeLp7m9XqPg/s4pFPnkSEQijpNEa9DjsJK4RALZcxotGumeUBaQS/Wqi9/e0oqRTuVIrO0BAimYR6HTmTQatWu/MQ4K5UcH3ve7RWVykfOUL4Oc+5wiPfG+rf/jbSv/97d7+30+nWtM95prH3e7X5edSZGZBlKnfdRegFLyD28pdTME2Mj30MSqVujWlxEUkILE3DiEa79aVjx/C8+tV0/vAPuwa7ioJ+/HhXqG4jSUiBAObUFMzNoS4swNe+RuvsWTwnTuzJsUqxGMLtRs7nu4LyXdZgUqOB5XJhBYPX9HXnefGL0R98EGPdfFZPJBCJxCOJ9+vXi1qr0ZmYwJqYIPLc517ZQV9p2m0ky+oa6fSAUBSkdhvpHIPJK4nodGg9/DBWrYbs9+M+cWLfatXtD34QJZdDaTZpnzixWbAvSRCJoLtcuKan6RSLNP/jPy6LAH2nNqlzS6X2M3ir1WJycnLDHpK9v2qaJo1Gg+9+97vceOONDA0NbRATXyh+v59oNOokLs7PzxOLxRzhu22MVqvVOHr0qPMsMjs7CzxicBaJRIjFYhw9evSix7IfqKq6IeW9Xq9vK0Kt1+tUq1Unie6gCOZ2wxb+QPd4m80mKysrjmhIlmWCwSChUIhAIMD4+PiGvQP7ewYuSYAuSRJHjx4lFAqRzWaJx+Pouu6kTWuaRjweZ3Bw8JL3jIQQZLNZisWik4C4HaFQiGq1ukGstNt36/F4GB8fZ3R0lE6ng2VZKIqy5bg9Hg+Tk5Mb9tLOTUxtNpu0Wi0kSSIWi1GpVDh9+vSWol5ZlhkcHMSyLNbW1vD5fI4JZJ/DixCCmZkZCoUCi4uLuFwuRkZGHPFUq9WiWCw6ybFCCE6fPn3JJg0XiyRJJBIJarUas7OzVKvVbQXxuq5TKBSIx+P4/X4qlQpLS0usrKw4v5dIJJzjzGQyZLNZxsfHMU2TU6dOXfEk8ZGREVqtFs1mk4WFhU0J7e12m3w+T6VSYWxsjEAgcFnML4UQF2SA3Kc3BgYGqFarFAoF5xw9cuSIk0Bcq9Wc+2exWGRwcJCpqSmmpqY4depUf0/2EKAoilNf6XQ6u+7N23vu55ol7GQkvl8oisKpU6dYXl7G4/E4QlW758Hv9+P1ehkeHu5p7R+JRJiamkKWZaLRqFPDsCwLTdMcwxzbiCOXyznCdNuY2jbD8ng8HD16dFtBvs/nIxwO4/F4cLvdjI+P43a7nTVgMpnE7XYTDocRQrC0tISqquTzedxuN7quU6lUGO0hkC8ajTI/P+/8zqU8B53P2NgY9Xqd8fFxUqkUlUqFSCTiHEu1WqVWqxEOhxkeHmZ8fPyi5oRzRfd2ov12xGIxp1YXCAQYGRm5Ju8LV9pI6ELQNI3Tp08zOzvrGJ7UajVM08TlcpFIJHC73RiGwfj4+KF55uzT56DTV9T16dOnzwFGicexVBUhy92O9mYTqdHoFs0VBSsUQvL5HHGL1GggZBmhaaiXuUntoCNpGtItt2Dl8yhnz6LMzWFOTDxSZAUQAlGt4l5cRI9GIRjE//M/f+UG3WdPaZ09i/W2tyEvLqKWy3RGRyEa3eDErVeruFIppNlZzA98gNp11xH46Z/u6e+HnvMcaj4fnbe9DSufRy4UuunqQmDEYoh4HBEMIv/2bxP5hV/YxyPtcz4il0NqtRCq2lPKiBUIoFQqIAR6JtOTAL35/e/TvOMOpK98BanV6v6daBTpGc/A/4IX7LnYLPj0p9OamKD+b/+G+MpXMNZNDpBlcLkwr78e1wteQOQZz7im0wouB8FnPINSOo317nejr62h5fPIa2tYmoZkmkiWhR4M0jl+HOv0acJvfvOefSeVu+/GetvbULJZ1PVGDySpez9TFIyVFayVFQrf/S7RN7/5ghpNhGkiPvlJlGwW0+OBrdYVoRCdo0fRUikAVF1HEgIhy+hDQxCLYcXjqC95CaFnP3tPjvlKIf77v5GLRQyfb9d5RHK7MXw+5GIR67//G4DOXXeh5vNIpol+4sSWjW+SLGNNTiKfOYOUyWD+53/Cb/7mnpwvzQ9/GLlcRq3VaJ04sWXqlORyYUxN4TpzBmltDfMjH4Ff+qX+HNIDZrWK9PnPo6TTXbH1eeJzB68XfXIS9/Q0ZrVK4/bb8bzylZd/wJcB/e67UfN5EAJzcnLrxlBJQgwPYzYaKJkM5n/9F9ZLX7pvTXGXC9HpkH/Tm1Buvx0tl0MxTYSmYcZiNOJxqj/2Y3h/7dfwPfrRl3FQgtL73491++3IhQJyuYxkGN1nx3CY8rvfDc96FpGXvhSpX8y9KHbSR/Rg2L1n1Go1Go0GlmX1JBC1xbe6rtPpdHpqriqVSmQyGarVquNqrigKLpeLgYEBBgYG+sUbcBomGo3GBpHcYSnYHTQikQjlcplUKoVhGDu6rFcqFSRJIhgM7ticFI/HWV5eJhAIOIk8W527QgjS6TQejwefz3fVJk1vh31t97k47Car2dlZarXapqYRn8/H5OSk02Dh9XoJh8MMDQ2Ry+WYnJyk1WohhKBSqTivGRoaIhQKMTQ0xMgVNLe5GvB4PCiKgsfjoVKp7NokValUHHOBXsXOHo+HqXXjKbfbTT6fZ25ubkOjeTAYZHJykkgkwvHjxy97Y4stijcMg6WlJWZmZojFYgQCASRJotlsUigUaLVajI6OEgqFGB4e3vdxlctlZmZmyGQy5PN5vF6vk37W6XSc1HLbuOH06dOX3JzYF51vpuZJwu72yI9QLHafiYNBtOuv37dx9TkEuFwXlBYurT8/c4BFH8Gf+zlq//qvGH4/ytoa5tGj2++dFQpIuo6Ix1F+9mcv70CvcjorKyjf+hbq2hp6JNIVn+dyuFdWkHQd2TCQzznvZF3Hnc3S+sM/pFSrEXnRiy7bWI1Cgdp//ifW8jJYFlIshvcZz7hkU8rWhz/cNdZuNGifPLnlnrXkcqHb+73ZLMZHPgLPfz5IErFXvpLGzTfTvP12+OY3NySOWuPjyLfeSuTWW6n8+79Dvd7dV56a2l78LUkYY2N4HnoIw973fNWrLukYbVw334zx2c/CygpSoQA7CN9Ep4NWq9E+ehTrJ39yT97/sOJ/7GOpvOIVWG99K3o6jZrLIWezWJoGloWs61iK0q3/NJtY4+M0vv51/E95yrUr3Pf7EaqK0ulgCrFrbUTqdBCKgnWF93o6qRS1//gPpM98BqlW65oBSRItnw9x8834f/EXcU9O7tn7tRcWUO6/HzWfR49Gd66Z+XwY4TBKLgdf/SpGPr/vfUzrAWhbYm8nVatVx+AqkUg44vNOp8Pq6iqNRoNms0m73cayLLLZLA899BCNRoOf+ImfuKR9ksnJSUzTRJZl8vk8a2trrK6uOsm8Ho+HRz/60UiShNfrxev1YhgGkiShKAp+v59kMkk0Gj2QZqCDg4MUi0Wi0ShLS0skk0nC4bCz52aaJqVSiWw264jv7XRb0zSdfdSDeGw2qqpy8uRJRyTZ6XSclERVVXG5XI7B2fnP9q1WC03TLsjUbifi8TjxeJxGo0Gr1XLER3bq5l7QbDYdg8pekoij0SiLi4vO7/QqVrLTcncjFoshy7KTnlkul529M7/fz+joKOl02hHC7WYmGo/HmZmZodlsksvlGBoackTw16Lo6LBTLBYpl8ssLS0RDAYZGhraMJ94PB6Gh4cJBAKkUincbjcrKyvOvt2VYGBggEwmQzKZZHl5mYGBASKRyAZxZq1WY21tDY/HQywWIxwOs7y8zMrKCq1Wi2PHjm2o74XDYQYGBkin0ywuLiLLMqurqz0JDveTWCxGa72fLpPJkE6nWVtbw+VyOYasXq/XMQI+fvz4vt0PTNMkn8+TzWadMcmy7Hx22xkB9OmdeDzOysoKiUSC5eVlRkZGCIVCTE1NUalUKJVK1Ot18vk8wWCQ48ePc91113HjjTce6HVAn0fwer2oqkooFKJcLu/am1Aul3G73bjdbucac7lcuN1uaju5/a8jhKDZbG5Yv18ssiwzPj7OyMiIU8+311HhcPiC54BIJMJNN93knM/2vCJJEpFIZMO8MjIyQi6Xo1Qqoes6kiTh8XhIJBKEw+Edz38hBPV6nXA4zOTkpPNe9ho6EAgQjUbRNM0xZbHX3kNDQ856u5f6h/0awzA27BntBfZ6enp6Go/HQ7lcplwuUyqVkCTJqZ36fD7Gx8cv+vnLXiP3uo6NxWKkUina7bYjij+s2HXlZrPpmCuEw+GrrvalaRqnTp2i0WiQzWYd8wdFUQgGg87zQp8+ffaOvgC9T58+fQ4wvsc+lmI4jKUo+GZmsFwuJMvqCsskCTIZTI8HY3AQIhGUXA4jGIRgEN9P/MQFv19nZYXGJz+JlcmAJCEPDuK/5Ra0Hhbfh4HI7/wOhe9/HywLbW4O9Yc/RA8GsXw+JMtCqVS6yabRKNb4ONIf/RGusbErPeyLQs9kqN15J9b3vw/NJni9yDfeSOAFLzjU32dnZQUjne6mDo+MoA0O9vy7tXe/G215GS2bpT0xsUF8DnRd+kMhOlNTuM+excrlaH/oQz0L0AECT30q4klPovrpT6N/8pNIa2vd6zUWQ33a0wg95znI/dSXy48sd0VuvbomnvO63VKjRadD8Q1vQP7855ErFeR8HrnT6RqFCIH4+tdpv/GNVG66CeWFLyTw/Ofv2TXoOXkSz2tfi1Es0vzWt7DKZSSPB9d11+FZd97uc3mI/PqvUx0ZQX/f+xDz810VWqfTPfcCAUQohHjqUwn/wR+g7JEKrf7Vr2L93d8hLyyglkro0Sj6+DiSx9N1TC8UuqL0SgVMk+LrX0/8jW/s+e+3Z2aQ83mUSgV9p7k2EEA/fRq9UMA7PY0eCNA5ehTpJ34C19OeRvjmmy8qff2gITUaYJqIHjfihMvVba5tNGgvLqI8+CBKPo8Rje7cvCVJGIkErnQasbZG/Wtfw3+JjXJmpYL0la+g5HLo4fCW4nPn7WUZIx5HzWYRKyvUv/GNQ51cf7mofeITSNUqWrVKe3Jy58Y0nw8jEEDO5RCf/SziZS9DusTiyEGjvbCAcuYMaj7fTTnfqUFDkjATCVxLS1jlMrXPfe5QG1aU77wT8epX419ZQWm1kE2za8xhP7tpGq3lZTrf+Q76H/8x4ec/f/8HJQSF170O6VOfQl1ZQS2VQJKwVLXbjL28jBEKYZXLFB5+mOjb3nboTQCuBBeTgL4fWJaFZVk9N7CpquoUDHcrHAohmJ+fJ5fLUS6XKRaLtNttAEdcXavVyOVynDhx4ppNChFCsLq6Sjabpd1u02w2nYK1z+fD5/MxPDx8qAuWV4JkMkk+n8flcrG6usrY2NiWBfh2u00mk3GShXdqMFQUhcHBQXRdZ2Fhgfn5eZLJpCO2hG4ak23ScOTIEWKx2CWl6/a59kgkEqyurpJIJEilUgwPD29IdQEcEw9bVB6Px51mCtM00XUd0zSdBnW7Ec5OROhzabjdbkKhENFolEwmQywW29bkwrIsisUikUgEn893QaYiwWCQ06dPs7KyQjAYpN1uO4kfdlpFPB5neHh4R5ON/UKSJKfZVlEUSqUShUKBtbU14JEGTDut48SJE5fc5LUbpmkyNzfnJK2Nj49vMnGIx+Pk83mWl5eRJImFhQVO7FHa6tVEs9mkVCo5whWv17ulGGI7LEnlVyb/Bz+69kmG24tk3KPo6tbnv7AstEwGIxzGiscJP/WpG35ulstU774b6+xZRLsNgQCuJzyBwFWyf9QLZqVC7ZOfxFxaAiGQolF8z3gG7omJKz20PUdMTmL98Ie4Vldp75JaLEwTpV6nMz4OeyiS22vUeBzzyU+GWg3XzAxicRFzbGzjfp8QUCjgWllBTyYR0SihvgB9T6nfcw9So4HSaKCPjSEVCriXl5FbLRTTxFRVdI8HIUlIQqA2m6jtNp7VVRq33UbF79+/72S9vqRns1Rvu61rWlyrITca3Z95PLQ+8AHqN92E7yUvwXvTTRf8FkY+j/LNb3b7AsLhHcWfkqKgx+No+Txifp7G/ffj+9EfBcD3uMfhe9zjMAoFOktLCF1Hjce7QtX19ar5rW+hFgqYLhfSLmsfyeXC8Pu7xqzf+c4FH9d2+J/0JIrJJHo6jZZOo/v9sMUet7AstIWFrqA6GMT7whfu2RgAzFqN+qc+hbG0BKaJHIvhe/rTD3QvQeg5z6GeSND6P/8HcfZst3bVbKIUCo5Rg9B1JMtCvfdejK9/nWIyifLLv0z4GjRTVx73OMQ3voG8ttb9rHZ43hK6jlqt0pmYgMtpNHoetc9/Hv2Nb0QuFpGzWdRKpZviLssYwSBWJkPj3ntpv+IVe7bv3r7/fuh0UFotjB7WL1YshntuDrPVovie9+A6cgQ0Ddfx43hvvHHPTZB32uK0l5vlcpl6vY5pms5zdbvdZmFhwUlv1nUdl8uFLMsYhsHZs2cxTZNkMkmn07loEZ+doJ3JZByjw3OFwx6PxzH8CgQC1Ot1R5jidrsP/L6U1+vl6NGjCCHQNI18Pk8mk3HShZvNJoqikEwmicfjDA0Nkc1mKRQKzv60LMvEYjEGBgbw+XxX8nC2RZZlBgYGeMITnuAY20WjURKJxLZJyoZhOKm+0Wh0T8dj7zvvB+fuS/Vy/nnWexUMw0DfyRHiEohEIoRCIYrFopMiCl0BSiwWQ9M0UqkUmqbtKmK0TehSqRTlcpmhoSHnZ4FAgGQySSQS6YshDwnZbNYRGZ0vPj+XYDBINBoll8sRDAbpdDpXTJClaRpTU1OOeDCbzZLNZvH7/U6auWEYRCIRBgcHGR0dRdd16vU6tVqNo0ePblmPk2WZ4eFhFhYWyOVyTm3qShsrjIyMOIJT27xV13VkWcbn8+HxeIhEIkxMTOzb3mOpVGJ+fp5Wq+WIoM8V5xUKBcLhMMeOHTuUhtumaTr7zpqmXZG9ZujuMR89ehTLsgBYWVkhm80SCoVQFAW3202r1WJoaIiJiQlisRgnT57sz7eHCFmWicfjVKtV5ufntzRhtul0OhQKBZLJJMFg0FlTxGIxSqUSuVyORqOx43qmWq066/e9SuNWFIXEDiZ3F4KqqgwODjI4OIgQwhHBbvW6oaGhDWsOy7IoFAo89NBDjmBYURRHvG7XosrlspOePjY2tqNQXpIkJ5E9kUjQbred68u+LnfCfo2dcr/X2OnVdvjBuSYEqqo6tdJL6Tk5dx273Rr9XLxeL0IIdF3ft3XsfiOEYG1tzTFYsQ0KbGPtSCTC8PDwgX3Oulh8Ph9HjhzZ9O+mafYF6H367DHXRiW3T58+fQ4xVqOB0mqhdjoIXe+KKNcL5kKSkHUdpdVCz2ZRm03ax48jbr75gkSurZkZ6u98J8q3vgWNBvK6AxdeL/X3vQ/z8Y8n8NKXHvoGGEnTiP7d31H8q7/qJiiUy8iFAmq53BW/BAIYk5OIUAjlj/+4t+YDIWh+//voqRRYFmoyifcxj7lizuBWo0HpLW9B/sIXkOp11FKpmxqhKIhvfpP6hz6E9ZSnEHnlKw+NEFqYJrV776XzkY+gPPSQ8+8dwHzUo3A9//kEb7556+KkEFS/+EXaH/wgvg9+ELXRQADu5WWsbBY9FkMkEhu+L8ntRo/FUPJ5xIMP0jp7Fs8FNC5Kqtot4B5i8dTVhjw6Cj4fsmUh6vVdm2TkSgXL60W4XDuKxYVpUnjVq1C+8hXUubmugUUg0J0L6nVkw0AxDCxZxvWlL9E6e5b6Bz+I+eQnE33lK5H36EFejUYJPvOZe/K3+lw8wac9DW6+mcZ999H60pcQlQqSpqEcO0bwllv2THgOgBC03/UulHQatVSifewYkt/vZFFJkgSJBHoshjo7izo3B1/6Ure57Ed+pKe3MCsVgK44sZfNvFgM0+vFGBlB+cVfJPoHf3CRB3dAcbtBUZB63GCUDMNJbNLn58GyunNEOLxrZpgIh5GWl6HVwlhchEsUoLenp5F0HbVep3XkyK7vb8XjyKur0GyiP/QQ9AXou2LOzSHXaghZ7klpaobDaGtrWM0m+tragW5SvBiMlRUQArnTwQoGd8/JC4W6JlutFta6wOUwUvznf0Z+05vwLi+jrhd1LVnGUpRuMyfducG3sECrXsf8+7+nFokQOE+Qsefj+qd/Qv7Up1Cnp8Gy6IyOPmLEJASiUkFbW0OZngYhLtiwpE+Xg5KAbqdyWJaFaZq7Nkbouu40Huz22oWFBTKZDIuLixiGQTQaZXh42GmAKRaLFItFxsbGEEJw+vTpfRemHTQsy2J6eppCoeAI9aH72ZqmiaqqRKNR6vU64+PjDF6Aodq1js/nIxqNMjY2xuLiIvPz88TjcYLBIJIkYRgGpVKJfD6P3+8nkUj01Ag8MjLiNOJks1mWl5eRZRlN0xxH90AgwOjoKLFYbMvCZZ8+O6GqKseOHeuahEmSY1ARCARQFIVWq0WtVsPr9XLq1Cni8fimZBc70cAwDCcN6mpzqL/SDAwMUCqVKBaLLC0tMTY2tukeZhiGI3KORqMXlbjg8Xg4duwYuq47ggZbRHAhYuD9whZC2AKBRCKBYRhOA6bL5SKRSJBMJi9L42KhUKDdbpPP5xkdHd2yeU2SJGec9rXVarUOvCjjclGr1VhZWXESHu2G3nNND0ZGRnZcB9rTUfPEn3Jj5SFGsIjpeVbV4AYTTwDRbqMtLiIJgTU8jPTc5zqicqvRoPS2tyF97nNItVrXMNGyEKqKee+9FN/xDpQXvYjwL//ynouQDgpGPk/5H/8R+YtfRKpWHxGjut00/+//pfajP4rvd38X7w03XOmh7hnuW2/F+MY3YHUVKZuFHda/8toaQlEgEsHzcz93+QZ5EURe8QrK09N0hECbn0f9wQ8wQiGExwOG0RX/GUZXfD46iuuv/mrPagF9ulgrKyj1OpbLBW432swMcqeDbFnoXm/XGBacPSnT7YZOp1uzWVrC/Md/xHr60/fMgM8oFKjedRfiU5/qnsvNJtrCAqqidN+z3cZSlK75u653BdKFAq0zZzD/8i8vyAgboP3ww91zrdGg3ctzZSyGnE4j7P3edQG6jRqLoW7XvF2rIRkGVo8Nv5bbjdzpINXrPb2+FyRFQf2d38H667/GbLdxzcw49V1cLoRlIRUKaLkckhDox49jPe1puPfIqNkoFqm8613de9h583fjve+l+uhHIz/mMYjVVUSlAh4P6okTBJ7zHJQDkJzof8IT8D/hCTQffJDGRz+K9KEPIbtcWLreNc8ElGoVuVCA5WX01VVEJkNhaYnY//gfV+19eSuCt95K5X3vQw8GUVdX0QOBrc1ThEBZWUGoKoTDePbY7KBX6l//OsZrX4u8tIQrm8UIBOiMjiLcbiRdRykUcM/OosdiWH/zN9S83gueb7ZCrBs2Q9eMedealxDIrRauH/wAUS4j/H6QZdpuN83JSZTnPY/wC16wZ+eaYWz/M/sxxjAMDMNA0zQURcGyLJaWlqhUKmSzWSc1114n+/1+Jxl9YWEBVVXxer0XLXyRJInBwUGSyaSTSmcL0EOh0IbnmQsxPjsoRCIRx6A0Go1Sq9UcgXAsFiMQCOD1elEUhZWVFer1OqVSyUmKdLvdVCoVcrkcAwMDjI+PH1gxmm1oNzExQaFQ2Pa5XghBOp1G0zQCgcBFpzheCSRJcj7/XgIfzhU07ec+hy16i8fjm362vLzsXFO7UavVWF5eRghBMBjclIBaLpfx+/0cP378mjXdPSzouk6tVqNUKvVkGhCJRJibm3NEyL0ko+4XwWCQkydPsrCwQDgcdvZxhBBO4rnb7XbqFPfffz/FYpFAILDjeWnvm6VSKec490q0eSkkEgni8biTdmvve3u9XuLx+L7ufZdKJWZmZshkMhQKBVwuF6FQCFmWabfbrKysoKoqY2NjGIbBqVOnrvieba9Uq1UymQzlcnnDfB0KhUgmkzsaNu8XdpK9LMtEIhFKpRK1Ws0RQ46MjBAMBp1k9MMo+L/WSSaTZLNZBgYGSKVSjnGLfd0IIajVaqTTafx+P9FodIPwOh6Ps7y8TCgUYmVlhcnJyS3v3+12m7W1NaLRqLPGPMhIktTz+VypVDbcj841xSiXy+TzecLhMEePHqXT6aDrOkKIngTEfr+fTCbj9I0oioLX66VSqewoyG61WszPzztmP7Yh9l7Pz3a9LRqNYpqmY6Dbi4nQhdLLOtZ+zbnr373GTiY/t0bo8XiIx+M9ieR3wu5Vsb+3crmMZVlI68Ft9jNsuVzm2LFj/cCEPn36XBR9AXqfPn36HGCKf/3X3VRuXceSZSQhMGW5W+SVpK6YqNNB6XTQ6nUaQ0NYQ0MEfu3Xen6Pxn330X7Vq1BWV1EzGZRGA2v9IU42DAyfD0ol6g8+iPXmNx/65hfZ4yH+5jfTfOABmnfeifjqV7GazW4K4PAw8rOeRfB5z0Pd5SFV6DqVO+7AvOce5KUlWBekdVSV1sAA0i23EHrRiy5rYdlqNCj+4R+iPPAASiqFWq9jejwITesaFaytYfj90GhQXFoi+r//94FvejGrVUqvfCXy/fej5vMohQLKenO46XYj5XKY3/se+U98gugb3rChWUQYBsU3vQn5059GW/881PVrSW40sDod5HYbq1Cgc+zYhoQAKx7vFmpbLdr33XdBAvQ+B4/ALbdQfe97Mfx+lEwG6+jRbV8rOh20SoX2kSNYP/VTOxo1lD/wAeSvfQ1tZgZL02idOoW2uIjSbiObJrJlYSkKkmmidDr4l5Zo1WpQr1NcWiLy9rfvrSi5z5VHkvA95jH4HvOYfX2b+ne/izw/j5rL0UkmtzVVkGQZ48gR3D/8IZTLNO+8s2cBun3uC0nauVtjHWFZXQGposDV2Oh9+jTWD36Ae2EBQ9d3TKwWuo5aq9GenEScPt1txDlnk3JX7E1oIbrmQ5eIaLe77y/EI901OyDJcrfRxzS7v7sXCIHVanVFyV7v1de0Zhjd70uWEZ0OUrMJlgWqitiqSU1RnHPC2qvP+CBx7sZ9L9/1ua/pYdP/IFL7/Ofhn/4JbyrlPLvpPp9zzZtCdNfjuo6pKHhyORo+H513vAN++qf37ZowazW4887u84oQ6CdPbjTKkiSkcBgjFEKZnUWdn4d105z++nd7ikX43vdgZaX7v9VVmJ7e/vWXc7kXDAbx+XwoikKlUtm1+FoulwkGg07awHZUq1VyuRyLi4vIsrypCcBOJMjlcqRSKad58FoT687NzTmfk6ZpjljOFkjbReJmswk8ksjSpzcmJyedBOhcLsfq6iorKyvIsoxpmmiaxsDAALFYjPHx8Z6bDyYnJx0h3sDAALVazXkfu4HLFrQf1GbXPgcbu8nKTisol8s0Gg06nQ6apnHkyBF8Ph/xeJwjR45sOs8kSdo2NaLP3hCJREgmk1iWRSqVYmZmhmAw6DT6NxoNKpUKLpeLiYkJ4vH4JaViaJp2RZtad8JuTE0kEjQaDdrtttMQaN/TLhe5XI5SqYSmaTsmeUC3UW16eppms0kul2PsKjP5uhgKhYLTJGaL+VVVdVIF7cb6Wq3GiRMndm3Mr7kHuHPyj/nF2beCpDDSnEfKyEh0hZ5ys9mtR3i96MePY/74jxN78YuBrslh6Y/+COUHP0BZWUGt1bBUFaEoyLoOKyvdxPR8nsLiIrFXvvKq2zfopFLU/uRPUObnu4aS6yZ2QpKQDQPT6+2KUX/wA8zXvIbAk598pYe8JwSe9CQKIyN01tOp27IMicSmfQApk8GVy9EeH8c8dgzfFUyS7QUlGCR0222UX/3qbgpzqYRSLCKXy93E2WgUkUh0E19f8xr8T3jClR7yVYckBBJ0zf6Kxa7IW9cx3G5HfL7xF6SuEbnLhVosYpZKVD/xCcLPf/4lj6V8xx1Y73xnV5icyyHX67hKJSTLcmpE7Xgcc2qqazLfbiPncrhTKTqtFvob3kBrcBDPddf1/J6i3e7uPUJP+732frNkWQjbhL5XXC6ELHeNantAMk2EJCH2uCk59OxnU8rnsd79bvRMBjWfR85mEbLcPS5FQY9GsQYHsX7yJ4m9+tXsXtXYnc7KCrX/8T+QZ2aQ19bQqtVuzUSSkEwTS5Jwfe978C//gh6PO5+XCIWovOc9iJtvJvJHf7ShHt+en6fz8MOITgc5EsH32MdeFtN47/XX07jtNmRNQ200MH0+OoOD3fqSJHVrPMVi1zi10YA776Q0OkrkRS/a97EdFJRgEPGc52DVaihnz6KdPYs+PIwUDD5y72o0kNNp1EaDzrFjmI96VM91t71EmCbtt7wFZXUVLZ+ndfQo0nqSt2O+EYlgVqu4FhbQFYXOW9+K+MmfRLrE61Py+7vie0Bqt2GnfpNqFffsLIqud+fuxUVnTrI0DSOdRszMkP/GN4i94Q2XPDawyzWb5yxJUpzym53iZ6+NK5UKrVaLbDZLNBrdJNCyLMtJjjUMg3w+f0kC9EfGJBEOh6+IIGy/CQaD3HDDDVSrVQqFgmMA6XK5iMfjFAoF0uk0qVSKer2Oz+cjHA6jKAr1ep1UKoXH48EwDCzLYnJy8soe0A6Mjo466YK2aWYkEkFVVUd0lc/n0XWdI0eOEIlEDpWxgMfjcYzMqtXqrsKYWq2Goii71hz2E1VVUVXVMT7YDvtcy2QyCCFwuVzOedjpdMjlcmQyGUZHR7Es65o03T1M2EmlnU6nJwGXfW4flJRTv9/P9ddf79Tjzt2Ts8V5sizTarWwLItGo7FBxLnT34WumNCuTR0EJEkiEolcVvGbYRjMzc2RyWQcQ+3z992TySRra2ssLi4iSRKpVIqJAx7YJYRwku4rlcomUxdb+B2NRjl27NhlF9SHQiFuuukmCoUC2WzWOQ9lWSYcDpNMJvv1j0OMy+ViamrKSezOZrNks1l8670yzWYT0zSJRqMkk0nGxsYIrT+3QPeePTo6immaLC0tMTs7SywWIxQKoaoquq5TKpUolUr4/X6SyeSBNie6UCqVCtPT02SzWXK5nGNIZZtipFKpDaYYlzJn2uucSCTC2toa8Xh8U02g0Wg4aeRzc3O43W6y2SzpdJpSqUQ4HGZ8fHxf1ni2QH4vcbvdqKqKpmnUarVdjYv3ex1bLBZZXl6m2WxSrVad9YfX62VtbY1gMMjExMRFGyzPzs6Sy+VYWlpCURSGhoYIBoPO+VQsFllZWXHm4ZMnT/bn3z59+lwwfQF6nz59+hxQWtPTyJ/5DGoqRScUcgSMsq6jrS8ABXSbM4TAWBcZe17/elwjIz29RyeVov0Xf4G8sIBrdRU9EsEYG+s2TEC3gJbN4p6bo93p0PzTP0W89a24x8a6wupD/CDnvekmvDfdBHSFYpKq9nw8ZrVK6c/+DOX++1GyWdRisdsoBQhFwYhEsJaXKX/mMwTe+tbLlmpZev3rUR54ANf0NKbXS/vEiQ0FP6PR6BYh7XTDN77xQKcbik6H0stfjnzffbjm5rqu9dEoxvpDj1StoqbTkM2CrlN89auJ/a//5Yhqim9+M/InP4k6M4PSaCBkuSsGcruR6BosaLqOYZpdt/wTJ2C9WOAUN02z6+Ld51CjxmKIn/opzFKp+12vriI8HpRCAbnVQloXDRpeL1qjgeH1QjiM7+d/fvs/KgTW3XejrK0hJAnj2DGUVAqlXkdtNhGShO7xOCJDrdHoztOtFq71dJTS615H/C1vuXwfRJ+rhvZnPoNcLndTthOJHdMFJE3rCgvzecRXvtIVd/Zwv3MfO0bL68UIBJCLRaxdBDxSodA1sPH7cV1//QUe0cHH+4IX0P6v/8LSNJR0Gmt8fNvXKuk0lqZBKITvBS9AGAYoSjfFqdHYuRkHkGq17nfkciHvgShNDoW6DWSKgtRqwS5NDWJdKI6mIV9i00t7YYH67bfD5z6HXKl0/77Ph3jKU/C98IV4Tp68pL9/UJDCYaRWC61SQf7hD8/5Qfdz1yMRRDIJ6+sLqV53GrS0SxCuHFSUwUF0ScJUlG4STDAIstxNAtviGUJUKt3GTbcb+YAKcXaj/YEP4FpY6DbZrq8pNhS9JAlrvQFVbbcxVBV3Ok1rbY3al760b+KC2sc+hlStds11Jic3is/PRZIwxsfxPPQQRq1G48478bzylfsypsOMEPAv/wIf/egjfd69cDnDruz0mGg0Si6XIxgMbismKpfLNJtNRkZGdk1cyWQyVCoVdF3f0YE+kUjQarWcFOqxsbFrxq3eTupJpVJ4vd5NYmVVVZ3E7vn5eXK5HB6Ph2g0etUUyfcbO5k3nU7j9XoZGBhwUkDs5Cm/38/w8PAFN+4ODQ0xODhIsVikWq06AnS/3088Hj80CRd9Di52k5XtOn/uuRuJRBgYGOgnNl9hJiYmkGUZVVWdtKRcLgfgJAwFg0HHKOBawOfz9ZSisV80m03q9fqGhrTt0DQNr9dLvV53GiyvZarVKvPz86yurlIqlYjH40xMTDjrQlsAMT8/j2VZyLLMyZMnd12TPBx/Ih+QNF64eBtF9yDDSpp4p9hNMne5aE9Ngd+P+YxnEPvzP+8+fwhB6dWvRvn+93HNzmJ6PF2BVCDQfTYUAtbFbvL0NMbHPtYVu/36r1+Oj+qyYDUa1P70T5Gnp3EtLGD6/bSPHu06Vdli1GwW99JSV4z6+tfTuu02PKdOXemhXzqShO9//k+af/IndITAtbqKyOUwwuFuYmqng1oqIZkm7bExxPg4/te85lDUHtVolPg//iONb3+b5h13IO67r2tIqChYk5MoP/uzRG655bKISq9FpFgM4Xaj6Doil+vuk6/vw235+vX9GktVu6bd+TzGpz4FlyhAL3/wg4h3vANlaQmtVOqagq//TLFNXWW5azoxP4919CiS240YHaUVDOKZn6ft81H/l3/B8+Y39378wWB3v1mWodXadb8Z+96oaUgX+KwkTU0hvvMd1HweU9edOupWCMtCrVbpDA8jjh27oPfphciv/Rq1I0do/9u/IX7wg+5eu65363B+P9bgIPLznkfsV36lew+6RCGR1Wp15++HH8Y1P4/p89GenHT2N6WFBTxra6idDkKS0Op1OsFgNylreRkjGMQslSidOUP4H/6Bxre+Ref221EefLCrkLUsLFWlHAwinvY0Ar/yKz33eFwM9W98A+X730ebn8cIBrEmJjbc+yVZhngcPRRCO3sWsbKC8YEPIF74wm4fxTVC9A/+gMLKCgJQUinc8/MIVcVyuZAMA6XdxvD76UxNYZ46ReSNb7wi9636F76AnMmgZTJ0hoYc8fkmgkE6w8O4VlYQySTVe+8l9NznXtJ7ex7zGBouF6bPh5zPY20zB4lmE/f8PGqz2a3FSxLNycnuHGZZSKUSWj7fdfs0TQqvfS3xN7zhkj9Pw4CPfzyIZT0ispNlL895TsPx7LD3kCzLol6vUygUqFarKIqy5Z5So9HA6/U6JoW5XI54PE61Wt3VLOtaJxgMbvqMyuUy6XSaH/7wh45BarPZpNlsomkakUiEeDzuCABlWSYUCh1YI087AV2SJAqFgiOwUxTFSQOPRCKMjY0RjUY5ukNYwkHETgeORqNkMhni8fi2e/5CCAqFAuFwGK/X29Pz/H4QCoUIh8OkUikMw9iyRmJZFsvLy6ysrFAul7nxxhs5efLkBlPTgYEBx3RXkiQWFhY4fvz45TyUPheAva65kHrLuUmnB4Wt5s1zsecVez9nNyRJQpZlhBA9pb9ezeRyOaeGOT4+vqXoTlEURkZGWFxcJJvNOvW+g1zrnJ+fJ5PJsLi4iK7rhMNhYrEYkiRRr9fJZDLkcjnGx8cRQnD8+PHLfs4risLAwIBTj7YsC8uyHLPpc02hE4nEnqcsX8vYBum6rjuGMsH159a9IhQKcfLkSebn54lEIo65kxCCRCJBKBTC7XYzNjZGPB7f9PuDg4NO8nWxWKRYLJLNZp2fezweBgcHiUQijI+PH9g14YVimiazs7OO+HxkZGTT2skwDNLptLMmNgzDSQdvNBq7PovU63U0TXNE1clk0hH0Ly4uMjo66gitq9Uqy8vLFAoF5ubmMAyDoaEhLMtibm4Or9dLIpGgXq9z8uTJS07rvhzYz33RaJRisUgsFtv23nnuOta+TvYSe57O5XIUi0WEEHg8HoQQ5HI5x7i60Whw8uTJC67NVatVSqUSS0tLeDyeTb0qbreboaEhQqEQi4uLuN1ulpeXOXU11CD69OlzWbl2don79OnT55DRuOMO5FoNpdWidepU14F3dRW1UkEyTaR1N16hKGCayKaJPj6Okk73/B61978feW0N1+oq7dFROP8Bz+fDmpigs7CAb3oac3GRznOfS2d0FCuZRHr2swk+//moh/yhbqcU0/MRhtEVn3/722izsyDLGIkE1rqwRqrVUPN51IcfptNuU3v5ywm96127JqpfKu2FBeSvfAV1cRHT58M8enRzcc7nwzx2DObmUBcX4ctfpr2wgPuANk2WP/hBlAceQJud7R7TkSMbEkRFKERnaAh1bq77Xagq1f/8T0K33kr9299Gvvde1NlZkCRak5N4FhdR1jcrhKpiqiqWrqO22133/tVVrHXXSrHuwIyibJss3OdwEfyDP6D6wAN0qlX88/PdzXVZRlpPBZaEwF0qYcky7YEBzJ/5GcekYivqX/ta1+G+VEIfHEQyDLRSCaXVQkgSpsfzyDUoSZiq2k27WBelqYuL8PWv05qextMvUh0ozEqF1oMPYtXryIEAnhtu6JquHCBEoYDUbmO53duLCc99vc+HXCggdTqY1SpKD8Ve2etFPPWpWJlM14imXt9WuCxME1c22zVgGRnB/xM/ccHHdNDx3nQTjeuvh2oV1/w8uiRhDQ9v+PyFaXaTvEolOpOTmDfcgPeGGxCmSSuZxIhEUPN5jHh8xwYaOZ/HCAQQgQD+PRClek6dohmNYoTDKIUC5hYb+ue/v+n1gteL52K/SyEovvOdSB/6EHKt1k3/WW90tNxurLU1Wp/4BI1nP5von/1ZT+fxQUWYJsbZswQyGWTLQj7PuMZUVSRdx1o/LyS/H61UQk8mMW+4AeUqTLbAMBDFImqrhVKpYFSr3XNelrFcLvRYDJJJ5zpQ83mMUAgRDBJ46lOv8OAvnNaZMygPPYS7VOoKzdePdSvE+vpTWk9Ep1Cg8+lPwz4J0I2vfAW1UOiaYuxyL5Ncrq7pSKGA9Y1v7Mt4Djsf+xjcddeF/97lNg0eGhpy0izn5+cZGhra0ERhmialUolsNsvg4CA+n2/HFFfTNCmXy46r9W7pmLFYjMXFRcdF+VISYg8apmlSKBQol8sYhoEsy44QOpvNUqvVnCSb7Qr3dnHXbtarVCpXZcrRfiFJEsPDwwwNDVEqlWg0Go6rfjgcviShpCRJxGKxq6Zxoc/BQ5Zl4vH4lg02fa48kiQxPj5OPB4nm81SKBSchk5JkohGowwMDPTTAC4jdmNsr81w9uusC3EKukpZWVmhWCxSKpU4cuTIpmawQCBAIBBgdXWVVCqF2+2mXC5vmaBy/sc/G3sC7xh4PNflPsez5I8Rjsx0TVxDIfjJn8T/whfiPkdQUf/Od1D++7/RFhYwAgGs89dJkgSxGJ1gENf0NPLyMsaHPoR40Yv2JAHzQmhNT9O44w7EmTNIzSbC50O68UbcN9+Me3wcJRy+qP2L6kc/ijw/300fjccRw8MbPljJ7UaMjXXFqAsLtL1e6u95zwWJUQ8ynuuuQ7z1rTT/8i8RkQhSLoeyXucUqooRj3fTwgcH8f3N3+CZmrrSQ+4dScL3uMfhe9zjgG79UFKUQyGgP+x4nvEM2nfeibWe5CytJ41v+dkLgWIYGG43RjjcFQ63WkjnNDNfDK0zZxDvfCfywgJqve4YS6gPPeTUhEy3G6XdRl03lu9ks4j1hnspFEKPRFCzWfjGN9DX1tAGB3t6b99NN1EMhR7Z793lGUbO5zF8PvB48F3gfq/vBS+g9clPYmka8i7GrLL9mUajuJ/3vAt6n14JPOlJBJ70JFpnztD62tewymUkjwftuusIPOlJe3r9Ve++G3lurjt/R6OI0dFH/n4u5yTdW+u1RlPTMP1+rMlJqFZRMpmuIbVlUfqZn0GRZdRCASWfR1lPZBWyjBEKYWUy1P/rv9Bf/3r8P/Zje3YM59K66y6UddMPc2xs+zWWpqEPDeFaWcHK56l99rMEn/nMnt5DmCZWo4Hs8x3aPX9JUYi96U2U3vUuzLvvxqpUkEqlrqpZlrvziN+P9cQnEvmzP7ti9cP2f/4nSqEAgIjHdzSqJhZDrK0h5fPon/gEXKIA3TU6SvWxj4V8HtfyMp1E4pGAi3NQV1aQWy1Uw0B3u2kPDm40XR4cpDMwgDYz0+3pcLmof+c7l3wNmJvDzx8Z0/q2ZiwWI5VKEQgEWFtbo9VqUa/XtxQZNJtNWq0W8XicaDRKKBRibW2Ner1OpVLpC9AvglQqxQMPPMDq6irhcJhsNusIzzweD41GA5fLxdDQENlslmKxSCaTOdB7dslkErfbzerqKrFYjGaz6QipvF4vHo+HRCLB8PDwgRK69srAwAClUolCocDS0hJjY2Ob6gSWZbGysoJpmsTj8V1Nb/eTZDJJPp9HVVXW1tYYHR3d9JpKpUKz2WRxcZGRkREikYizTy+EoNFoYBiGk4CZyWTw+/202+0rluzeZ2dcLheSJOHxeKjVarvu4dXrdaArbDxM36m23uOqaRqtVmvX49R1HdM00TRt1/re1Y59T/F6vbt+bolEgsXFRTqdDoVC4YrOaTthG94uLi6iKMoGA0ro7gEODAywsrLiJOJms1mSVzAUQAhBKpUil8vRbrepVCqYpomiKM5+ZTQa5ciRIwda+H/QabVapNNpCoUCuq6j6zqSJOFyufB4PAwMDDA4OLhn65JAIMCNN95IpVIhn8/TbrcRQqBpGrFYbFdD9tHRUfx+P2tra8RiMef3ZVl2zHDsNOerBdsUI5fLOQbI52MnxC8uLpLJZHC5XKiqSjgcplAo7Ph5CCEoFotEIhF8Ph9erxev1+uIypeXl5mdnSUQCKAoCqlUipWVFfL5PLFYjGPHjpFIJBgdHXX6PlKpFKZpcvbsWa6//vpDcV8ZGBigUqk46d8jIyObROhCCFZXVzEMg1gstudzfqVSYWlpieXlZRqNBslk0km6h67RQD6fdz7f6enpC/58s9msY7K/0zOHz+cjHo+Tz+eJRCI0m81DYSbQp0+fg8PBn/n79OnT51rlq191BEjS+kaXdeQIHcNAKhaR1gWyQtOwIhFcZ88iNxron/88PPvZu/55q9FA+vznkTMZ9EBgs/icrghXm51FabWQTBOt0UBZWqJTrWItL2MtLVH90IdQX/lKgk972p4e/kGlcs893YapuTlMn69bRJakR4p6bjdGLIa0uoprYYGOplF93/uI/smf7Ou46rffjlyvbzAs2BJJwhgdddIN67ffjvvlL9/XsV0UQmDdcw9KJuMkRmx1TJKiYBw7huuhh5ByOfSPfhRuvZX2HXcgF4vI7Tad06eRZLmbbqAoyIaBuZ54L1wuLMtC6XQQ5TJt00RSlK74zu0Gjwf3ox99BT6APnuNlkyivuQlyL/xG1iKgtLpIJumk0YhbDfcdQd2Zmawms1tE0r0M2eg0UA2DEQ0iry21jUDsSx0r3dzuquioLTbSIAZi6EtLXUTRu+4o58wekBonTlD44MfRPrSl5DabcT6fNDxehFPfjK+F70Iz4kTV3qYwHoShZ0S1QvnpJ5fSFKF7xd+gda996KHw2hzc3TGx7spCuec36LdRltY6M6pQ0MoP/dzV22TZei1r6X6e79HB9CWlpCKRYxgEKFpSLqOWq0iFIXO5CTW0aOEXvtaoHuvkp79bKylJdRiESmd7jYbb0U+j1at0j56FPHkJ+9J85Kkqt33X13F/fDDmPn8lus+ABoNtGKRzsgI5o/+6EWb1BT//u/h9ttR5udR63UMvx8zEumumep13HNzmF4vhmFQbDaJvfa1h/a8Kf7t36J+61tIut691oTA8Hi6aw7LQtZ1tGYTwzRxz81h+P0gBCIeR7v11is9/D2n+oUvYLzhDciVCrJhoJgmcqOBkGWEJGF1OsjtNka1inHsGFI2i1Kr0Tl+HPHUpyJfwYTFi6X19a9DvY5smljr682dsDQNxTZAqtUw15M19wOpVkPS9e66todrzHK7kVstpFpt38Z0mPnYxy7u97bzfXnjG9+IYSei0S0g/sVf/MXFvck5xONxmuvN7el0mlQq5aRDCyGo1WooisLQ0BCxWIypqakdi1edTgchhNNouRu2ALjdbtNeb6i+GlhdXWVtbc1phrCd4v1+P5lMhlQqha7rhEKhXYuBdqOqnbDbF6BfOLYYNLrPZn99+vS59vD5fBw5coSJiQnnPq2q6qFsED/sqKrqNNPuhhCCdrtNOBx2GnGvVZrNppNwnkgkdmweGhwcpFqtUqlUyGazWwrQt8JUPTw4fAuJp9zCU3YpLThiN8PAHB/f9lqSNA19eBjX0hJWsbgn6Zy9YuTzlF/3OpT77kOu1ZBKJaRmE6XVQv6P/4DXvpZGOIxx6hQ885mbRPY70Vlexvzbv8Vz9ixKu41Vq2EuL2MNDDh1PxspHEaPRFCyWfjmNy9IjHrQ8d50E+4PfpDqxz+Occ89iNlZ52fmiROoz30ukWc9C9njuYKjvHSupYTgK433hhtonDiBlMvhnZ4GukLeTQjh7MEIVcVKJJDz+T0ZQ+PDH0Yul9EqFdrHj3dTyJvNbu3JMDA1DWQZ0+NBbTaRdR01l0M/p4nVSiTQzp7FaDapf/7zRF70op7eW3K54BnPwEqncU9PYxYKsI0oT9RqaMUi7fFxzMc/Hm1o6IKO03P6NPUbboBKBff8PB1FwRoa2mAYjhBI2SyutTXao6NYo6P4n/SkC3qfC8Vz6hSe/UxpEgLj7rtRczmEomwUnwuBtraG3G4jCYHu9aLoOrJhdPf2DQNCIcxgEDE/j+eHP0QoStcgUgiMWAwjEkEoSvd+UyjgnpmhU6/T+Yu/QHn72/f+2IRA/s53UIpFjF4MVaJRWFmBYhH9y1+GHQToQtepffrTdO6+G+WHP3TqUebx46jPfS7ByzS/W40GtS98AWttDSQJZXiYwE//9EUZ2kiKQvSlL8V68Yup3HMPxne/C9UqeDzIp04ReP7z0a6gaAdASqeRm00Mv3/j9bjliyVMvx+p0UC6gDCLnfD8yq+gf+c7GOUy2uws+sgIrNeAAESrhVosorXbmIqC5fFgbdHEL8ky+uQk7oceglKJ1l13XbIAXde3/5l9q1YUhUQiQavV4uGHHyafz9PpdDath5vNJplMxkltjEQiKIqCqqqYpom5k9q9z5bU63W+//3vk0qlaDQaTtqppmlOIn0qlSIcDiOEwOfzUSwWnT3vgyyOCIfDhMNhGo3GBgNTn89HJBI51PsKkUiEgYEBLMtiaWmJ6elpIpEIfr/fSeAslUrIsszExATRaPSKiht9Ph/hcJixsTEWFxdZWlra8HwshHBEV6qqOoaEdgJlsVh0RLvQFddnMhncbjeDg4OMjY1dsWPrsz2KohCJRIhGo6ysrJBIJHas1RQKBQKBAG63+1DVGTRNc+YVe37caX4plUq43W48Hk/P+z5XI6Zp0ul0qNfrPdXkfD4fiqJQq9VonBdAcJDIZDKUSiVM09xWsC3LMqOjo8zOzlIsFgkEAldsjrYsi+npaQqFAqurq9TrddxuNy6Xi2az6aTOt1otWq0Wp06d6ovQL4JqtcrMzAzlcplCoeAYbkD3fIhEIs565fjx43v6GYdCoU0p3r0SiUQcQWy9XneMCYLB4KEyCukVe83hcrl2FJJLkkQikWBpaQnDMPB6vcRiMebm5shms1uKpW1BtRDCWcfZjI2NIcsyiqLQbDYpFovMz8+Tz+ep1Wpcd911Tg1+cH1v2k7Ptg2XvF4vuVyOoQvcY7oSxGIxisUipmmyuLjIzMzMhnVsvV6nVCohhGBiYmLT57UXrK6uUiwWqdfrTE5ObjqfVVVlcHAQt9tNOp3G5/ORz+edz383LMtyku3D4fCu13Q0GiWfz9NoNCgUCluaNfXp06fPdvSrYH369OlzQJErFaROB+t8xz1VRQwMcK7kTKIrFqDTgWKxp79f/+pXkWo1tGqV1pEjm12RdR3X9DRKq9UVoK8LaixVxfR4UOp1tHyeTjyO+frXU1MUAj/90xd/wIcE8+67UdZFIrb4fBOShBge7qbnZjKYn/404vd/f18TO8T3vodcLGL4fJsalzYNz+3G9PmQi0Ws++/ftzFdCo1vf3tDuvROghlJljGiUdRCAXH2LI3vfQ/5619Hyee7LuTrTYdGONxt8Gi1uoWC9QctU9PQ1k0WpGIREQziKhTQh4Ywb7ihn059lWC1WhjvehdyNIparaL7fN3z6pyUct3nQzIM1EYD68EHKf3d3xHbRgQk2m0ky+qKKWUZpVZDNgwsWd4y9VSccw5Lqorh93evwfvu25fj7XNhlO+6C/H2tyMXiyi5HGqt5jTJGMEgZjZL67OfRX/FKwjecsuVHi7S6CjC50NZXUVvt3ed9+VqFcvrRYTD25oqbIVnaor2H/0R1tveBqkUnoUFTJcLMxgESUJuNFDrdUyfD/34ccynPIX4L/3SpR7egUUbHCRw221UX/OabpJwsYhcLiO3Wl3h+fg4RCKYJ04QeuMbNzQghX7xFyl/4hN0mk1cS0sYzWa32SYQ6M5DjQZyNotWLtMZHkYMD+P/jd/Ys7EHf+EXKH/kI7TjcdyLi3Tq9W6j4PraRJgmcj6PlslghEIwMIDn13/9ot6r9uUvI915J8rsLJJp0jpxAumc804ArU4HbX4edWYGQ5ap/NiPETqEYuzWD3+I/LGPoa4L6i2Xq5tq1OlgahqWpmGuJ16r7TZC11GaTZrXX4958iThZzzjSh/CntJ84AGM178eeWEBV6mE6XZjqSrKunGWJUnIhoGs69377X33ITSNzvg41vg4wd/6rSt8BBeHqNWQDIMeLUEQ6+ZVAsCytk1L35OxaRpCkpB7TIG01zZc46KdrSgWYXX1wn9PkuCGG7b+2Rvf+EZHKA7g9Xr3RIAO3cKhpmlomka73aZcLqOvd1+OjIwQDAbxer0cPXp018RosW54I3o1voED3UxnGAa5XG5TcvZOTYDz8/NkMhnS6TTVahVVVfF4PM7f0jSNSqVCpVLh9OnTu45BlmXcbjedTsf5Xvr06dOnz8FCkqRrXsh8pYlEIpRKJVKpFIZh7Ng0XK1WsSyLYDB4TTfTQjf5qNFobCmgOR+74bBUKhGJRJymvr1E/va3UQoFjFBod7FbOAzLy13Dv69+9ZLTOXvByOep/P7vo0xPoy4tobRasG4mJ6+bjQJdc718nnYmQ/Oee2j8yq8Q/d3f3bZuYTUalP7mb5A/9Sm8Z86gNRpYsoxWLqPU65DPoweDmEeObPhcrEQC99mzmI0Gjc99jvBVtM8me72EX/hCeOELsVotrEYDJRC47En3fa4ePL/3e+hzcxipFK56HUwTS1W7tT8hkAwDxTC6gl+vFz0UAr8fZWkJIxpFJBIX/d5Wo4H0xS+i5HLowWBXfA6O6lES4hFBvCRhalpXoNzpdEWsdmOxbWbc6SB6rO/bBH/pl6jdey+dWg13KkWn2eymq5+z3yvlcrgzGfRoFAYG8F3kfm/wNa+h9nu/R1sIXEtLUChghMMIlwsMA7VcRjJN2qOjiLExfK973aFNv7ZpLyygLC6iFovosdjG+b5c7t4nDAPT5UKSZSxNQ2s2MS0LqVTqfheShJlIoK6uIiQJwzRpPepRSOtrTAnA7caMRDALBdypFG1No/6//zee227b0+OxWi2kTgdJ1x/pPbEspEKha+i+fu4KTcOMRhGxGJbLhaTriEpl27/bmpmh8apXIS0voxYKyOsJ60JRuka8Dz1E6X3vw/emN+G57ro9PSYbPZOh+p73IH3uc1CtIq2bIZoeD6V/+AfEM59J+MUvRrkIEYTs83WNIXo0h7isWNYG8+ldkeVuivseCab9P/ZjlH73d7H+6Z9geRlXKoVYXcVcrwVphQLael3A8Hppj44+Mleeh6RpGOEwSj4PX/0qVqt1YaYFQlD/xjdofeQjSDMz/MbXNP5DdOhs8dJzHylGR0ep1WpMTk6ysrJCJpMBuuaethC60+kQCoWIx+MMDw+jqipCCCet+/zkvD67c/bsWUqlEul0mqmpqU3Jm7aAO5vNAjgCYF3XaTQaB1qAbuPz+Xbdcz+MHDlyBEn6/9l77zjZsrLc/7t2qhw75z5hImFAQJSM5MyA8RL9yTWBoIhwFTMg4aIoXhQwXESuosAAQxocuGICL0Fghknn9OlYnSrn2rXT+v2xu/Z0z+l05vRJQ30/n2GY7uraoXatvfZ63+d5BKqq0mg0qFQq1Go1wBcl9ZIc0+k0x48fv+Q1gmPHjgVpr/l8nsXFxSAB2rZtVlZWkFIyMzNDNpsllUqxuLhIq9WiXq/TbDbxtp4HVVWl3W4zPz9PKBRibGysL4i8TBkeHqZcLhMOh8nlckxOTu66npTP5wMR2MDAwBU3ng8PD1Ov1ykWi/umWXc6HUqlUpAaHL7CTd/Oh9732fO8Q39/VVVFShn87V44joNpmnieFxiCX4wx0DRNms0mlUqFTCaz73EJIchmsxSLRQYHB2k2mwemwF8IVlZWKJfLLC4uYhgGx44d23FddrtdCoUCS0tLACwsLHCy37d7IK7rUq1WsSyLTqfD4uIi3W6XUqlEKpVidnaWcDiM53nBPbxerzM1NYWiKFx1mYTx9OgldT+Q8TwP0zRptVqHEuzHYrHAFKN335qcnCSXy9FqtchkMsH8szcuOI4TCKrvGzIwPj4ejAnhcJh6vU6n0wmSz9Pp9K73jEwmQ61WC8ws7juXvxwRQnD8+HHAH9d7aejFLQ2IrutkMpmgX+TEiRNHOi/oGQaXy2Wy2ey+Zgo9c5lKpUKhUDi0AN1xHKSUwfPrfliWheM4uK5Lp9Pp96r06dPnnOkL0Pv06dPnMkWqql8IOqRYICgwHbIxzqtU/EK4lLsWe5SVFRTT9MW6uo6nquimidQ0ZDKJk81i12qElpexhcB++9txH/GII0nqvFwx5+ZQzpxBK5dxMpn9C3pC4AwN+Ykd1SrNf/kXEhdQZCRaLXAcv9h/CLxQyC8wXqbphvbSEnS7KFsu8QchUymUfB7pOHS+8x1U10U1TaxtDSTe0BBetYqraf51HQohNQ0UBQkI10WpVlHzedxIBDk4iPEAavD6fqf5T/+Eks/7CQzT037zR6eDdBx/ISQc9sdPKeHMGdTVVeQ//zPOL/4i2i5ut0oqhdx6vex2g0L/rikbbEtWB1DVoDFHbHOZ7HNpqN9yC/I970FZXkavVrGTSd+YxTCQluWnTywsYFeruO98J81o9JIbrkSf9zw6H/sYbiiEms/jTU3t/eItkXh3fBz51Keec8p06sYbqes67nvfi9doIAoFlE7Hv94Ng+7YGMTjeM96Ftk3vOGKTbE+LMb4OAN/+Ze0vvUtzJtuwrvrLkS7jYxGkddfT/jGG0k/4hFnnQc1kSD27nfTev3rsQwDdWOD0MJCMC4Iz8OJxejOziLHxgi99a2EpqePZJ97+6qUShiVCsLziK6tIdfXsaNRpGGgWBZSUbAHB5FjY4jXve5+p0x0P/5x1HIZtdvFuvrqXRuJhWFgnziBcffdiEIB55OfhCtQgN76+MdR63XUTgfz2msRjQah1VV/7LBtVNu+d+yXEuF5eOEwztQUqXe+8wGXjNX+sz9D3djAKBQwZ2chFkNfWEC2WkFTpvA8hJQopomj63Smp/Guuorou951yVNj7jfhsN+5JgRSUVBdF2eflwspkVKieB4yEoEL6Aosjh1DJhJopRKube//rCglWr2OPTyMd0TjzwOJhYX793c/9EN7hpBdcEZGRhgaGqJUKgVFRiEE4XCYwcHBfZ20t9MT34VCIdrt9oENCb0mC8MwLivhnuu6QWNDt9ul1WoFzSDxeJxwOMzo6OhZjTqbm5vk83mWl5eRUjI1NUUsFgt+b9s2xWKR06dP47ouuVzuUMVWz/P6jap9+vQ5cmzbplQqUa/Xg4b4WCzG0NDQAzIhos8Dm6GhIYrFIoZhsL6+zuTk5K73V8dxyOfzQVPU97sA3XEcbNsOGusPIhwOB6IBx3GOtIleOg6i0/HTz7fNn/ZECDzDANtGNhpHth9776Ck9pu/iTo3hzE/j7NlEqh2OoHw3NE0hOeheB6qZRG5+26swUHcv/1bKq5L5hd/8ay39dptKr/0S6i3344+P4/e6fgGz0IgXBfdtn0TU89DOX0a+8SJQIy4XYzqnaMY9UpCCYev+LTzPpee2CMfSeN//A+6v/qrKFvfNb3TuTd9Vwg8TcPTdZxEAm9mBmo130RwcBDtPGq23TNnEKaJ2mr5pqRb7KgPbTNxk5rmi389D9FqIbeex+WW+TtCnLMZgz4ygvGWt2D9xm/Q1XU/kbtUwg2FfPPYbhepqtgjI8jRUdQ3vIHIQx5yv47XGB8n/r730XzTm7ASCSiXUWs1lHYbqSg4g4PIgQG8kRGib3vb4dO7e+foMqwpOFuNyIptn9VHoWwJ7hHCrzNDYDApPG9H/LJareIpim+GoGl7rwdns1jtNlo+D7fdhnnmDOETJ47seIL7jKL4/QGlEsb6OsJx/Fp8T5SsKKitFnJjA1wXN52GPZ4juktLtF/3Ot+MNJcDRcFOpXwTBNtGK5UQ+TxWtUrnl38ZLkCyu3n6NO03vtHva8nn0ep1/56L/3104nG8+XmqN92E8trXEn/CE9CPOMnsUiHTaaRhoFUqeAcJ0aVEabdxEwnkESa8pl/yEuqJBO4HPoBXrSJKpXsNAKJRvw7gOHSPHfPT0ffBi8XQNzdxXRenWMQ4ZLqweeYMrd/9XdTFRdRqFaVe52Q5hCJ39lj1zs72r6CiKFx99dUoisLs7CxAkEAI/jx5eHgYwzAYHx8PGvmbzWaQzP1AFBlfSCzLolwuUyqVgjXq3Z7xotEoQ0ND5PN5YrFYsN58LgapfY4eIUQg1i4UCkFKfY9kMsnQ0NBl80yuqirXXHMNKysrhMNhLMui2Wziui6aptHtdikWi4yMjDA8PMzi4iK1Wo18Po9hGAwMDBAOh5FS0ul0KJfLbGxsMDIywtzcHFdfffWhBVeu61KpVOh0OkFNope82edo6SU792o1Z86cIZVKkUgkEEJgmmZQL5uamiKdTjM2Nnapd/ucyWQyJBIJJiYmyOVyWJbFwMBAINh0HIdqtUqxWAxS4cfHxy/xXl9aeutdmqZhWbvZ1OzE8zwsy0LTtD3X15rNZpBA3hOqK4qCYRgMDQ0xNDR0Qc0qut1ukOx+GDF5IpFgY2MDy7IwTfOiC9B7tYu1tTUMw2B6evqscTQUCjE5Ocnq6iqrq6uEw2Ha7XZ/zrUHtm2zvr5OqVTCsiy63S6rq6uBgPXEiRMMDQ0F9XpVVUmn06RSKVZXV8nlchiGEZiT9rl49IwtevOSw6CqajDO9EwDDMMI5ii99+zNM3qi6hMnTuw6ZwmHw0xOThIOh+l2uziOw8mTJw80K0mn0+TzeUZGRuh0OlfE91NRFE6cOMHAwACFQoFMJhPMY4UQQR01m80euaC+Xq9jmiaWZZE5xPN4JpOhUCjQ7XYxTfNQ5jGH6UvpGQd0t57ZV1dXA4OvwcHBS2JK0qdPnyuTB1a3c58+ffo8gJDj43hLS6i1Gs5BRSPbRut06I6MIA5ZjOmJJICzRO6y20VvNlEty09tNIydr+mJpVIprKkpQsvLdIeGaH7mM6T+2387l8O8onDW1vymIMtCHkYUnUz6heZuF3dj48LuXDjsu/of0pFKOI4vnr1Mm3yk6waNB72UyH1f37smpUTsdQ4iEazJSUIrKyCEn0RqWXiK4jeUdbuEKhW6IyN4U1PIl7yExBOfeIRH1edS4tx8M2qxiFTVIHmAaPTsa0sInNFRwvPzuI0GzZtvJv2KV5z1fuHHPpbO+9+PG4mgFIv3jot7mIYoto2nqni67jerbG765gf9BvBLimeauO99L8rqKlq9TvfECYjFgutCRCJ4qRRmo0FocRFb07De8x7k4x53SYWjoZkZmg97GFSrhJaWsEIh5C7CTdntYiwt+eku8TjxF7/4fm0v+dzn4j7xiTRuvhn3i1/E29hAOA5eOg2PexyxF7+Y0MwM0nGwt8xAtGz2gdtIKgSxRz7ynAXaodlZlA98gPr734/813/FbTah3fZ/GQ5DNIr76EcT/7mfI3Ts2HnvpnRdKu96F8oXvoBarfrpEa6LspXULIXwDVk8j+7EBGJgAG9yEv3nf/5+3/+stTXUb38brVTCTqeDxJ3dEKqKMzCAVi4jz5yhc8cdRPaKCb4MkY6D8m//hrKVWiYMAwYG6BoG2sYGst32GxG3zWd008ROJlGe+MQjb3RzymWcchklHEYfHb3oY5R5+jTqHXeg5fNYAwOIVMrfrxMncAsFPwHHsgLDFs008QwDZ2yM9F/8Bdp9HHevJEIPfShWNIpjGCie588FXNefm++C4jj+HEAI5PAwkec854LtW/SFL8T8whfwdB2xuYnc71mxVALPw8tm0a9AQ4gLzfz8ub1e1+Hxj4dXv/rC7M9hURQlaHK4v+i6TjKZJJPJkM/nGRwc3Fc0XalUiEajhEKhQxXRLgaO43Dq1CkqlUqQbGEYBqqqYlkW6+vrpNNput0u3W6XqS0BgZSSjY0NCoUCnucxOzt7VrOIruuMjY2xsrLC/Pw81WqVRqOxr7t0rwkgGo1+XydO9OnT5+iQUrKyskKxWMQ0TRqNRiAkjcfjbG5ukk6nmZmZOXQzS58+l5poNEomk2FycpKlpSWWlpYYHBwkFoshhMB13SDtyTAMRkZGGB8fv+wTNy40QgiEEAemM/XwPC84Z/udu3/5l4fhup3gdaoa5glP+O7++6KqvhGnEH494nA75IvjLkIqdvu730W94w705WU/yVfXUWs1tE4HqSiBiBMA0wQp8XTdN2ZcWMD96EdpP+YxRB/2sB3vW337233x+ZkzvvgzEkHvdPAMA6mqOFtidr3jn0+5tIS7Pc3pHM2m+/T5fibx9KfT+tCH6P7YjyHW1oJE6p7Ju2cYOAMDyKEhpGkSWl3FSaeRySTJZz/7fm9XmiawVQ/a/oy4NW54ioLSS2SHe8eSnuB8C1Gp+PsajaIdcj3Yzuex19cRioIxNYX63vfS+sM/hLvv9tOft9abnXAYUim8qSlCr341scc85n4fL4AxOUnmwx+m9S//QveTn4Q77vDTlFUV96qr0J/3PFLPeMaBQnrz1Cnan/gE4qtfRdTrSE3DGx9HfeYzSTz/+ZeN0X1vbVUC0vN21BLFlsmmFGL3/o1tayZatbrDoFpKuef91hsawiiXcVotOl/4AuHXvOaoDgehaXhTU7hra+j5vC8MtixUx/HNGrauY8VxUG0bV1VRXRe720XZ7dqUkubv/z7qygqhlRWsoSHk6OiO82GPjyMKBUKrq3SFoP07v0P47//+yAwH7ELBF5/PzWGsrODG43RnZxHxOLLTQVtdJZTPo6yv4y4v4/78z9O87jq8H/5hIj/xE0R/4AeOZD8uFdqTn4z87ncR+TyyXg/W43dDNpuo3S7O7CzqERtsJ5//fOQzn0njlluwb70VUS7721xYwM3l0FqtA8XnZ3HIa8Q8fZrO616HurKCvrbmGz3H4zji7GdeVdqobhdV3VmX7wlUFUVB0zTuueeeID1Q13XS6TTpdDp4jpZSUiwWSaVSl9Xa55VCqVQKDLOSyWQg7NuN3mdQLpcDwW5/PePyIJFIkEgkAtEjcGgDtIuNoijMzMwwMTFBqVQKTGlVVQ3EWUIIyuUyrVaLfD5PKpU667utaRqZTAbP89jc3KRarVIqlRjcFoiyG67rsra2RrFYxLIs2u02nueh6zqxWIxoNMr4+DipfcbwPudOr76zPeW0JxDePrYnEglOnjx5WV67B6EoCidPngzGx0KhwOLiIpqmIYTAcRx0XWdkZIRsNsvs7Oz3vbBMURQSiQSpVIpiscjQ0NC+62D1eh1FUYjH47vW+1ZXV1lfXw/SpNtbz2GqqpJKpWg2mxQKBU6ePHnBkpzlluk9HCw+3P6a7X93MenVLtrtNseOHdt3n3tmH73zODMzcxH39Mqg2+1y6tSpYH282WziOA4rKysUCgWSySStVovFxUWmpqZ21KOFEIyPjzM3N0e9XqdQKDwgBejdbneHWXM0Gr1sxsJzNcWQUmLbdjAnDoVCXHfddYFhU898pfeePTOtg/o6wO+j6M2PDtO3EA6HcV0Xz/NwnP2iOS4vhBBkMhkymQzdbjc474ZhXFADb8dxgqCIw8w5dF0PPsvevw9C0zRUVSUSidBsNnd8n23bZmVlhXa7TaPRoNFo0Ol0KBQKSCnJ5/PcfffdjIyMBHOoPn369NmPK+/pqU+fPn2+T1Cf+Uzk6dOohQJOvQ77LDiKfN4XNSaTRA4pFtCPH8cKhfyGmWoVOTIS/E7pCZQ8D3trcq04ju/cLoSfwtAjlcI1DESxiPP5z8MDWIB+XlzghRt51VV4d9yBsbRE17bvdTLf7bW2jdZs0p2ZgauvvqD71cMpl2l++tN4p04hu11EPI726EeTePrTd91XdWDAbxJRFL9Z4oAFd9Fu+00amoZ2/Dge4BmGnySwfYEkm6WrKOirq3i27TcJbD2oeZpGZ2ICeeIEyk//NOl++vkDBylR5uZQ6nWcVOrAwrWIxXyhVr2Oc+edu74mND3tC4CLRYy1NZxIBKlpqKaJc58GEmHbwXjqZLNI10VrNrGGh3GzWeyNDfSRkcsy5eGBTuMLX4BqFb1cxpqchD1cpkUigTU+jrG+jiyVaHzpSySf+cyLvLc7ib/+9bTn5ug6DqHVVdxq1b++wmFwXdRqFa1Ww43H/XSXV77y0IkBu6EmEqRf8hJ4yUvO+p15+jTlt78d8ZWvILaaZ1FV3B/8QcI33kjs0Y/uX99b6ENDDPzWb/kGF7fcgru2BoAyOEjsaU87ugRoKam84x2Iz30ObX4epdvFSSaxZmawhEBUq4QKBYSUuOEwMhxGe8tbSD/xief1WVmnTiEdB7XTwZ6YONBAxstkUDY3odvFvueeK0qA7lQqftKRaWJvF3YmEjiJBE67jahW703DCYfx8nm8VAq5lS53vkjLonHLLTg334xy6tS9P49GkU99qm8OcQRmBoehc+utiFYL1bKwh4bu/ewVBTkygj0yAo0GdLsI/LkhQqB5HsoV4Ii7H9FHPhJzchIrlyOay+GqKlq36zf63reY43m+AB3oDAzgHT9O9OEPv2D7Fr7uOlrXXgu1mm9YoqpnNYMiJVQqhNbWsEZHkYODJJ761Au2T1cqZ87s/btEAp7xDBgfh5ER/3F5evqiaGYuGkNDQ0FawtraGhMTE7s2BtRqNarVapAccbkkoJ85c4Zyuczy8jKxWIxjx44FBVQpJY1Gg3w+j7klIgiFQgwPDwcu0NVqlYmJiX2TCo4dO0ahUKBSqbC2travAL1UKhGJRAiHwwxcwQYcffr0OX+klLRaLewtM8VQKHTOaQFSSk6fPk25XGZ9fZ1Wq0U4HEbXdUzTpFgsEg6HGRsbwzRNrrnmmiuysbLP9yezs7NBc06hUCCXy22Jn9XAZCGdTjM4OMj4+PiBzd/fD4TDYSKRCJ7nHSohqNlsEolEUFV137lbq3Uaz+sE/60oh2ieFQJ3ZgaxuopWqWCPj++75iA7HX9tNZFAOX784Pc/T8ybbvJTbG0bZ3SU0Nycbz4sxE7xOeAaBnqngws48Th6vY5brdL52Md2CNCt1VWUf/1XtJUVvHAYZ3oa9a678FTVF+GrKkJR8EIhsCw00wRFwW23fdPSSgWpKMhIBK3fXNunz6GIPeIR1P7oj2i//e3oc3NonQ5OOIybzSKzWYRloeRyaNWqn4Y8OYnycz+Hch4iAGXrec/TNOh2/YUB/JRpO5FA2DZqt+vXz4XYYVAZCNalRCsWcVIpvKEhYo9//N4blJLGrbdiffrTqLffHvy4C7g33IDxEz+BPjFB+7OfRa6vg5SIgQFCz3gGsUc84kjW5s3Tp2l/4hPw9a+jNBrIUAh3dhbjBS8g9axnHSg8l5ZF5a1vRfnKV1AaDZRyGWFZvmB/eRk5N0f9wx9G/ZVfueS1FwBjZgZLUXAjEdRaDW9bnVpuGUsKKQMD/16tWW4znpZbQvVArM7+whARCuHqOqLTwdvcPPJjUp71LOTtt2O0Wnhb5u9OKHRvijvg4dc0NdMEITAqFeytZOjttL/7XdTTp9HX1rAGBpC7JYdumW9arou+vo6dydD8938nvt+1fg40/uZvUFZWMFZWsEZGYGTEX5OuVgmtrKDYNgJQPA+13cbrduG227ArFayvfQ3r//v/SL/ylUeyL5eCxHOfS+2v/gonncZYXcWKRHb/Hto2+uoqdiKBTCRIvOAF9/5OSjrf+x720pJf1xkYIPboR+94H2nbmKdO4TUaKLEYoZMnzxo/hWGQfP7zYVufUuUv/xL5gQ+gnDqF7HQQB4y5SrOJFw4jdf1QZrXScWi/+c0oPROEwUG8kRGEqpJvx5Dte2Cb/ZGQHk+c/yCa9ku7vt/s7CzNZpNYLEa5XN5VEOo4Dmtra7iuy9DQEMPDw9/35lfnSqfTCdLjVVWl0Wjs+7wSi8XI5XIMDQ2haRqJy8SkpI9PT+RyJaBpGiPbeiLh3nvy8vIyQgjq9fqexhLtdhspJVNTU0GydKFQ2HcNomeK23tts9lEURQURcFxnEDU3m63mZ6ePi8T4T47EUIwPT1NOp2mUCgE43nPCCgSiTA0NMTAwMCBorzLGcMwuPbaa8nlckSjUbrdbnCtGoZBNBolFosxMTHRHz+3GBoaolarkc/ng/Te3XAcJxDkhsPhs+YEa2trQXp0p9MJ1iZVVcU0TarVKpVKhfHxcaSUXHvttRgXoGjcExwqioJpmgcKKDudDkKIS2YaUq/XqdfrQX10PzRNIx6PB2LJPjtxXZfTp09TKpVYXV0lHo8zNTWFaZo4jhMk3K+vrwf3qmPHju343BVFIZVKBennvXvTA4Fms8nGxga1Wo1utxsI0Hv1t54w+1IihDhnUwxghymGqqqMjY0xOjpKs9nEsiyEEBiGcU5C+978BPxra79+iN5r7vt3VxqhUOiCis630ztPUsogwX4/XNcNroXDnl8hBAMDAzQaDZaWluh2u4RCIRzHYWlpiVqtRqFQQFVVkslkYEIRi8VoNpssLS0FRsWT59Fb3KdPn+8PHhizhT59+vR5ABJ/3vOo/83fYA8M+IUzRQkK2AFSQqmEUSrRnZrCveaaQwt3og99KJ2ZGcTGBlq5jDU0hNiasArT9J3ZFcUXTUjpu8brOk40ujM1WwjcZBKl2URdWbk3neEBiDY6iqMovti/2dxTqNhDNBq+aD8UQjkqQdkeRF70Iqx//mekrqPskm4oPQ9RLqM0m77buuMgul2MZzzjgu6X22hQ+6M/QvzrvyKaTdR6HTzPd7T/8pepvv/9iB/7MdIve9mO6yb22MdS2xJwqcUi7n4CdCnRSiWcdBpvaIjUk55E+aabEMUi2uYm1thYcG0DkE5jp1JQraKWy37TiRBY2Szer/0a6Ze97IoXQvXZidftQi+R9DCLZUL4jUOu66fb7EH45S/Hvu02nFYLrVwOmkhUy/KbCLclCDihEJ5h4CWT6Pfcg9ZqITY24Otfp/XjP443MYH6vOcRf/7zUS8Tt8XvB5x/+ie0Usk3sDjIzTObRW5sIMpl7C98AQ7ZBOWZJvbmJnge2uDgkSV4hGZmkH/4h7Tf9Ca68ThKsYi+uekLG4XAicWwZmYglUK+7GVkXvGKI9nuDqSk8r73If7xH/2msWIRtZcIZRhQLGJ/9auUfvAHybz1refV1PdAQ00kSP3Yj12w92997Wsot9yCNj8PUmJde+2O5CyZStGZmkJdWUFtNlF0Heeuu+A80y880/THWtgz/XkHvTHZ84LkoCuGg8yFolFfCL79Z4XCgaL8w2KtrdF44xtRFxZQSyXUSgXFtv2UtmgUL5+n89nPYv7sz5K6CAZRXqmEapp4mrZ3w2kiAYkEEnCFQN/cxPU8nFLpvAwyLjlCoL7oRXjLy9jlsp+WJ4QvTtA0P0FoK/FP33LRdcJhnJMnCb/+9Rf82Sn+5jfTevWr6XoeRi6HrFT8tK9QCGFZqLUaqmVhjYzgTUwQ+q3f2tfM6vuV/RLQH/pQePnLj3Z7tm1Tq9UCwVU4HA6KQZeCVCpFOp1mamqK5eVlFhYWyGazxONxFEWh0+lQqVRoNBqMjo6SSqUYHx+/JPt6X3rNCblcjmQyydh9mqKFECSTSaLRKIuLi+TzeUKhEENDQzQajaAx7KBCbc8x2zRNFhcXmZ6e3rWBslgsUqvVmJqaCtKS+vTp8/2H67oUCgUKhULQAAN+w0g0GmVoaIjBwcFDjfsrKyuUy2UWFxcxDGOHyQaAZVlBCs7MzAzz8/NcfZEMKftcudi2TbPZDJqzYrHYJblnKYrCVVddxdraGpFIJEjJ6SU8xWIxIpEIY2NjZLPZi75/lyPZbJZcLkc8HqdUKu0r6Oh2uzQaDWZmZhgYGNh1zDnf6af27GcjT51C5PNQqcBen5OUqPk8bjQKsRixF77w3l85Dt3Tp3HrdZRolNCJE0ezfv+976FUq77gvdNBsSwU18XV9bMPXFH8RGPHQbEsnGQStVSCr30Nt1ZD3apftG66yTdna7cxr756hxhV63b99bKtxFzXMFDbbYTrohSLeFNTvhg1nUYODBB/8pPP/xj79Pk+IfXCF1JtNnH+4i9wSiW0UonQxgZsbADgRqNYExMwMACvehWpG288r+2FTp6kPTCAm0yilcs42xqXvcFBZL3um4GbJk4o5KdMb9XcZTKJ9DzUpSWE4/iCyWc/+97Ebdum8aUv4dx2G3Q6SF1HfutbqJubaJUKaqmE0u36daxQCFEs4n7721iPfvQFWYP3TJPKW96C+m//dq9wfKsGoSws4N1zD5W/+itCv/EbxH7oh3Z9D+k4lH/t11D/3/9DW1hA7Xax43G8eBw8z1+bKhSwBgbw3v526q5L8jnPOdLjOFe0TAb30Y/2+zByOSzb9tfXt9ICFcdBcxyUdttf99uqD0opA1N1Ab4ZgJR4qnqWucmu9FIJe2vsR0jiBS+g+du/jacoaI6DFQr5tbHtSOmv7/eaz3Ud+9Zbz6qHmZ/6VGDi4g0P77vm7Y2MIEolqFTofvrTRyJA90wT8eUvoxQK/tyhJx6q1wktL6N0u6i2jaeqWJEI6pbZgWLbGLkcbqWC+1d/RTUSIf0TP3He+3MpUKJRxMtfjvfnf46Yn8c4fdo3qR4cBF1H2jZKuYxeLPr16ZkZ+ImfQMtkkI5D/aabcD7zGdSlJbAskBJH16mk0/C0pxF+ylMwv/xluPVWlC2xA4AZiSCf/GSiP/ZjhE+c2HP/Ys96Fu2//VvcaBS1UMCbnt77YGwbrVbDmplBPvaxKIdI3Wt86Usoa2u++H54GDk6igD2qtoIJA8rfgnXfDlwdt+JruscO3YsEASsr69TLBZJJBKBqKvZbBIKhZiZmSGbzTI6OnrgfvbZibc1tqVSKVRVZW1tjVarRWyPvivTNGm1Wpw4cYJMJrOnGMZxHEqlUiC87BmVXcr19D6XP8PDw5RKJRzHoVgs0u12dxWBu65LuVwmHo8TCoUYHR2lWq0yODhIp9PZVYQvpdxhihuJRJiamgqudcdxqNVqQRow+GLifhL60ZJMJkkmk9i2jWVZQfr8YdJdrxQ0TWN2dpbJyUnK5TKmaSKlDAwOztXo9IFOOp0mFosxOTnJysoKjuMwODgYrHlKKYM0aMMwGBoaOqumaJom6+vrrK6u4jgOJ06c2GHoGA6HSafTlMtl1tbWUFWVlZUVTuwzb7q/xGKxYOyoVCoHjiHVapV4PI6u6/uaaF8oXNfFdd1Dm5fruo5t21dUwvLFIp/PU6/XWV1dZXBwMBBT91LQI5EIExMTQTq6ruuUSqWzTBdisRjVahUgSNe+0ikWiywtLQVGEOa2PjRd10mn0zSbTZrNJjMzM7vOFXvnbXsdIplMMjQ0dKQ1ku2mGIVCgeE99AUHmWL0xOz3l0gkQigUCgyi0gf0z/ZMpHo9LH32JxaLEQ6HUVWVer1+4Pmt1+vEYjFUVT2n621oaIh8Pk88HieXyzE9PU2pVKLZbJLP50kmk2Sz2aAHaWxsjKGhIUZHR1leXmZlZQVVVclkMns+n/Xp06cP9AXoffr06XPZoiYSqL/8y3jveAeO5xGan8eJxXAzGaSm+YWzSgW126U7Po6cmCD2xjcefgNCoD3/+cj5eahU0ObncY4dQ6iqX1jsiWqk9MVkQiB1HXcX9y+pqr7Lt5S+m/cDVLAQvuoq2rOzOBsb6OUy9vDw3oViKf30+mQSmUwSP09B10FEb7iBzjXX+IXVxUUsIfDGxhBCINbXMbYaAtQtcwFX11HLZezf+R3KT30q6de+9lDFvHPBqVSovfa1qHffjbq2htZq4em6f73YNmJtzReNVyqUV1bI/sZvBOdTiUSQT30qXrFIaG4OL59H7vGQLTY2/GTXmRnEs56FUFWMF7wA97vfhXwedWkJb3Z252clBGQyuEKgtVp0TpzAfdrTGPi5nzvSc9Dn8kAJhUDTkJqGsKw9i88BUqLYti9C3+eBOvaIR1B73evw3vMe7FDIbxjyPDTXRW6ll0lV9cXnoRB2Ok3kzjv9ZkNVRTFNjKUlpKbhrK8jFxepfexjRN/xDsLXXHN0J6DPnojNTUSngxuPH6rxx41GEZ2O3zR7AJ3vfY/Oxz+O+Pd/95M8eu/x8Idj3HgjiSc84by7eMPXXov2oQ/RuOkmnM99Dq9YDIxgpK4jH/tYwi9+MdEbbjiv7exF+b3vRfzjP6IuLqK1WtjxuJ8ELQRKq+ULHTc2wLKo/Nqvkf3jPw4a6fpcWMxPfMIXJFsW3Wuu2XVuJhQFb3oa5fRplHwe5wtfQP73/35en5GaTuNpGlJREKa507RoNzod/3ugaYgrrKiuZbNIw8ALhVBaLbwDHHKlZfkC8XAY5Txd7J1ymebrXoc6N+ffRxQFJ532jaJcF7VWIzQ/j51M4v35n1MTgtRP/dR5bfMgArOfg4T5u3GFuuJuJ/WjP0rpe9+jKyXyzjsJNZtIQHUcNMvyn5UAV1VxIhHMEyfQ/+f/3JGUd6EIzcwg/+RPaP+P/4GVTCJKJdRqFVGr+YYFqRTO4CAykyH0e79H9JGPvOD7dKXR6cD6+t6/P8qegV7jQqVSwbIsbNtGCBG4MA8NDTEyMnLR3aSFEBw/fjxo4CuXy+Tzeda3TowQgng8zszMDMlkkpMnT142BcdeEd7zvD3TDMBv1BkeHmZtbS1IfN/eDHFQs6KmaUxOTgYpovPz80GDk6qqQZK64zhMTEyQTqeZ3q/5tk+fPg9YLMvi9OnTNBoNyuUytVotEKD3mgN7vzt58uS+aQO9Jtn19XUMw2B6evqs8cowDCYmJoLGuEgksm+Dd5/vb1qtFpubm1SrVWzbDhIvek1WIyMjF71BUQjBxMQEY2NjVCqVoPFLVVVSqVQ/xek+qKpKNpul3W6zuLjI5ubmrumM3W6XlZUVEolEYHxxrhzm8S/x3OdS+9CHsIeHfYGQEL4J5Lb9kZ6HsraGVq9jHT+O+6hHYUxOYufzND/+ceQtt6BsNUMCmIaBlUqh1Ou+gFtV/drBYx5D7MUvJnTI5HCxJf72DAPR6fgGplLuSILd+QciEOY56TThpSVcx6G7sBA828nvfhelUsGJRoOkz54Y1bNtVNPEDYd9saIQQTK62mzC0pIv5JuZQTzzmf01tD59zpH0S19K69prMT/6UfjGN3AsyzcZVlUwDNxHPpLIT/4k0Uc96ry3JVQV8cxn4q2uop86hVMq+eJ2gEQCa2AAY8us1Wi3EVLi6rpfFyoU0LbWZOzjx3Ef+lAyL3sZ0nWp/vVfw803+4bi9To4Dlo+j2pZqJaFq+tYIyPYY2N+HavRQN/YQBYKYNtUfv3Xyf7hHyIOYwx6CKRtU/nVX0X95jfRFhdRu10/RT4SQbguWrGI2NzEHh7G/vVfp/W2txF7zGPOep/qhz6E+o1voJ85g6frmNdeu8PA0pESp17HWFnB8TzcP/ojrBtuuOSGlZEf/VGsr34Vr1hEm5/Hnp1F3dxEr9dReolfnheIrz1AtW3UM2dwZmf9cbwnJBcC9wCzHLn1OTvhMOICGNt7polMJv19UVU028Zz3XvNMz0vOC4nHEZK6Ztuf/ObWGtrGNuNBufnUep13Hj8wL4QoSg48ThKo4G3uHgkx9L6939H1Ov+3GFqyv+hlOirq74xuG1jb9WGATwpUW0bNI3u+DjGxgZyZQX3L/4C95nPDIxkrjTSL3kJlUoF9x/+Aa9QQCuVUPL5YM7iGQb2wAByeBjv+c8n+6pX4bXbVN70JpRvfQutWEQrl1F6NW1FwUml8Obm8N72NpSREZRGA61eR7iu//tEAq9QwPziF7F+5Vf85PNdMCYmaDzykX4i/dIS3Vjs3nFyG9J10RcW8MJhSKUIv+hFhzp2++abUTc2EN0u2DYil0OGw3ips5OLwRegR5wG8v99Dl6wu2FvOp0OnsEzmQzVajUQNOu6HohHs9nsnmKVPvujaRq6rhOJRFAUhWw2Sz6fJ5VKkUwmA9GT4zg0Gg3W19fJZDLE4/GzBIDgC9l6pny95N+eSCgejxMOhxkfH2dgl2uvT59oNEo6nWZ4eJilpSUqlcqO52LP82i321QqleA5e2BggFgsRrFYxPM8LMvaVYDeS/ndyxRX0zQGBgaIx+MsLi5SKpWIRCJ9AfoFQtf1QwtezwfLsgJT6VAodFHreL36Vp/9EUJw8uTJwHBmc3OT+fl5DMNAVVUsy0JKGYwNu91DCoUC7XY7MEjZ69rKZrN0u10KhQLxeBzLso48BV0IwdDQEM1mk4WFBUql0p73vFqtRqPRYHZ2loGBgQMTji8EvbT2brd7qNf3UoD32tdWq0W9Xt9hoPr9YD4jpaRQKFCpVAiHw/smeSeTSdrtNvV6PVj/3T429RKPgQfEeatWqywtLbG2tka9XieTyTA+Ph4kQdfrdcrlMvV6Peh5mOo9TwKdToeFhYXgnLVarWBumU6n2dzcZGBggOnp6SMZ43umGBMTE+RyOVzXZWBgIBgrpJQ0m002NzfRNC0QCx/1Z5VIJALzjFKpRDKZ3PP4bNumWq0yPj5OOp1+QJgWXGgSiUQwzzvo/PaMz6ampshms+d0nfWefTzPI5fLcfr0acrlchC2oGkaq6ur2LbN0NAQ8XickZERNE1jenqaubk5ms0mhUKhXz/u06fPvvRH/j59+vS5jEk+61nUHQfvPe+hOzCAUiigb2z4LtqqipNK4czO4o2MEPmDPyB81VXn9v4vehHlr30NHAd9YYHQnXfipNPgOL7juOuitFr+tsJhPzU6s61oU6/7zsnlsl88X16m8eUvk3jqU8+7OcZrt2l89rM4X/oSolz2xXTDw+jPeAaJZz5z72TFc0BaFo1/+iecu+8Gy0IkEhg//MPEHvGI3UWBQqA+73nIuTkolVCWl3235vu+VkrExgZqp4M1MYF8ylOOXNy9G4nf+z2av/ALdAF9ZcV3E/c8vzHBcVBcF9lravI8VNNE3nUXbr1OZX6ezB//8dHtp5TU3vxm1Lvuwpifx41GMU+cQPQeTqSEahV9YwPl9GlsKalOTJB+5SvvPZ6XvYzGv/4r3U6H0Ooqdk/c1UueazRQi0XUVgtrehpvaorUj/84APGnPIXypz4F3S76/DzKqVO+sCadRigKstVCLZXQazWskRHk2Bixvvj8gYsQuA96EGxsYGxs0J2YuFcotwuyV0hPp9F+4Af2fevUC19IM5vF+sAHaKfThO6+G9U0UbcSIDxFwdN1XEUhtrwcJANLRaEzNQWRCHS7aJUKSqGA1WjQ+ZVfQbzvfYSOHTuyU+CZJubdd+M1GoholPDVVx9ZEjeA1+nQuOUW3NtuQ7bbiFgM7WEPI/70p1+U8e9+c65Cya2mjX3/TkrKf/iHKDffjFKr+YkknY7/q61UcPdb36L0Az9A+m1vO+/Eey2TIfMzP4N85Svpzs3hViqIcJjQ8eOoF7ApvP2Nb6B8/OOo8/MI18W85hrENudFb2CAruuiLS2hnzkDikLtIx/ZMc73uTDYhYLfFFgq4aRS+zeACYEzOIixtoZXLtP8yldIPPWp93vbkYc9DCseD5LA3AOcO9VSCScWg0iE6B4JOZcrQtOQj3kMbqGAsbR0bwrOHiiFAq5hQDxO5D6JMedK/Y//GGVhAWNxETuTQU5M7JiPugMDuO02+sICLC/jffCDWI9//AVt2lTGxiAWQ3EcaLfhAEd1pV7Hi0SQoRD6IYUO0nGwcjmkaaIkkxhjYxc8PfzQCEH2t3+bSjKJretYhQLG2hp6pwOq6s/BDQPPMGi/8IWkfvd3L2oTbfjECUL/5//QuOUWnJtvRp46FfzOm5hAee5zST7/+Uc6N3ggsbCw/++PcMrGXXfdFRQ/W61W8HNFUUilUrTbbWq1GlddddVFbw5QVZWrrrqKfD5PLBZjaGgoaBLQNA3DMIL0n8sp1bvX7JVKpQ4s0vVSjRqNBrVaDUVRUBQlEIYeRDQaZWxsDF3XmZ6eDlJMeiK5dDodFJNPnjx55M0mffr0ufxxXZfTp09TKpXI5XIYhsHw8HCQRtNsNqlUKlQqlaDx5eqrr96zoaSX0NRqtTh27Ni+jSe9NIXLqYHA87ygMVPTtAdEk9OVTKlUYmlpiVqtFgi9e4TDYTKZDLVajfHxcca3C5AuEoqiMDAw0BcPHIKJiQmazSbT09OsrKwEySXhcBjXdWk0GjSbTeLxOOPj40xMTFww8yAlGkV/85txfvM3saUktLKCu7mJm0ohdR1hmujVKgKwZmfxjh0j+aY30f7GN+j+9m8jikXUYhGtVkO4LmJLVCakRACuEKCquIaB9e1v0/7Up2g94xlk3vSmA2tUMhLxDfS2komDEWgf02G2EoxRlHvXB7dEWwC0Wv668vb1gUQCO5uFUgnVNNHbbVxNQ6qqn6Lruuj1OkiJfewY7oMeROanf/p+nvFDICVep4PX6aAmEkdSa+tzedO54w46n/0scn3dr19ms4Se9jRiP/zDl8+6yhERe+QjiT3ykVhra5jf/S6y2UTE44RvuGGnePYISPzoj1L/zGewGg1Cq6tYpumbYeg6cmICS0pCm5soW8nYwvMQjoNimlhjY75J9sMfTvoP/gChaZTf+EbUr33NN+So1fzPxrbROx0Uz8NTFDAMfyzcWveXqRTdsTG0xUX0+XnQNOo333zeCe89Kn/2Z6j/9V++cNwwdgjHJb6ol3IZY20NW0qs3/99Qn//92jbegmk4yA/8xnU9XVfvHr8+Nn1OSEglcJSFELz83gDAzQ/8Qmyr3vdkRzH/SX6qEdhPve52J/+NPr8PNHbboMtkXbPbLKXuCx7BuuOg9ZsIhYWsAcHA1N44Xl48fi+SeHb14+jz372kR+PedttEIv5BrJba0qKbQfHI4XANQykquJGItijo4QXF3Eti+53vrPzO7SVmH1WgvpeqKrff9Izaz5PvGLR7+3wPGQi4X8O9TrKlgmsYxiB+By2whS29lmoKvbUFKHFRbxmk8anP0365S8/kv26FGRe8xoa11yD9dGPIk+f9tfme+Yb0SjusWPoP/7jpJ/zHJCSypvfjPr1r/t1OyFwBgbwtsyBRKuFtrFBJJdDqiqhSgUzm8UaG0OGQv4YVi775reZDN4f/iGNcJjE05++677FX/Ma2nfdhWXbhFZXcSoV3IEBZDTqj2XVKnql4hu1nziB94xnHMqw1bz7bvTPfIZwPo8EQlum4VJVkWvrDFtnJ5NLBHG7irtw+77vnUqleOhDH0q5XKZQKOxIkk2n0wwNDe0qNu1zONLpNIlEIjAT660FVKtVarVaIOJzHAdFUQiFQkxOTnL99def9bziOA733HMP1WqVfD5Pq9VC1/VAQCiEIJ1O0+126Xa7l+QZts+92La94/sUiUQuizWg2dlZNjc3GRsbo1arBQIvVVWDxN14PE4mkwnWI7Ynqe5FzxRXSrmvKW4oFGJwcJBKpUI2mw2e069kemnFQggMw7joZsoXG8/zKJVKFAoFOls9QXDvGlb/vnH5YRgG1157LcvLy0SjUTqdTmBgout6IMQcGxs7S9grpaRUKlGpVEgkEgfW+LLZLPPz83Q6HcrlMqOjZ89RzpehoSGKxSLj4+Osra3R6XQYGBgIrjvTNKlUKlSrVUZHR0kkEhdkPw5DLBYjHo9TKpWwbXtfY4je+uXY2NhZdYxqtcr6+jrtdptOpxPMGyKRCOFwmOHh4V3NOB8otFotLMuiVqud9VkahkEoFMLzPLrdLqFQiGQyST6fx7Is2u32jvtMq9UiHA4jhLgoRh1HgZSSer0emL4LIYhEIgwMDJDL5SgWi4HZwvb5o6ZpZLNZUqkUKysrrK6uBuYdoVCITqfDPffcQ6VSYXNzE8/ziMfjaJpGt9tlYWGBSCSCbdtYlsVVV1113tdYzxSjZ6KQz+c5c+YM4XAYRVGwLAvXdQNTjNHR0ftlInsYhoeHA1OHXC7H+Pj4WeLybrdLLpcjGo2SSCT6xieHRAgRnN9Go8HKygrj4+NnfedarRZra2skk0ni8fj9Or9jY2NIKRFCsLq6ytraGrlcLqip9UTnkUiEqampYB96BtS9+9vU1NQlMSq5P/SOt0+fPhePvgC9T58+fS5zks97Ht0bbqD18Y8jv/QlvGYz+J03MoLy3OeSuvHG+yUyE6pK5g/+gMrv/A4YBlQqqKUSaqfjO1xLibuV7Gin077YGqDRQM/lUCwLYdvopomraajNJs5b30rlz/8c7ed/nuSznnW/jrn2sY/h/eVfIhoN3z19q6ldRiJ43/kOlQ9+EO01ryF5P8U70rapfvCD8PnPI6pVlEbDdyDXdeyPfYzy1BTaS1+66/4nX/hCyv/0T2BZ6AsLKPfc4wubew56jYbvLm2agSg6eSGbhbZhjI8Tf9/7aLz5zZBIoJ4+TahSQbVtpBBYoRDSMHAjEZzZWbAs1I0N9NOn/YLjW97CwNvedv82LiWdO+/EXlnxr5t8HuX229EXF30n7PsmMG0lkFvxOPrcHOrqKu7HPob3kz8ZiFX1kRGi73wnnTe+kW4ohJrPE7qP+sNOJLBOnMCbnib27ncHDuVCVUm/851Uf/VXQdNQNjcx1tcRuVywfScWozs7ixwZwfj93yd89dX379j7XBGEbrwR59vfho0NlPV1X6y3C9Lz0Dc2cBIJZCJB8jnPOfC94094Ajz+8bS/+U1aH/842mc/i7rVFAkgpMSoVv0GFCGwEgmsq6/eIdZ1hocRhQKhXI6uqtJ817sI/fmfn/dxW2trtP7hH+DWWxHNpj/WKQpWKIT3hCcQ+/EfP6+0dek4VN//fvjc56BWQ9k6bqmqeF/8IrUPfACe9zzS//2/H1nqxlEis1lkKIRWq/nNUvstSEiJ2m7jDAwg90mqKL/rXSg334w6P4/W6WAnk9ijo/emgq+vIzc3wTSp/tqvkf2TPzmSRk+hquf1WZ4rnY9/HLVaRTVNP2F7l2MQqoo7O4s4dcpP2P7sZ5Eve9lleS08kLCWlvzrtdPB2aeo3UOm04iVFeh0cJeXz2vbSjiMfMpT8AoFQnNzuOUy7PV9qdfRazW6MzO4P/zDaFegiCD84hdjfeUreKEQ6uKi30C52/VdrWKUSnQnJ3Ef9CDCJ0/e72065TLKv/876vo6Tjx+lvg8IBrFnp0lNDdHt9G44E2bsec8h9bf/i1ONIpSKODtkzgnu130RoPu7CzeE55wYEqOlcvR+vjH/XtZoxH8vDkxgfKc55B4wQsuC+G0UFWyv/qrWD/5k7Q++Um6//IvWIUCstPBS6XgiU8k9XM/R2wfF+oLun+G4SfSPP/5eKaJW6+jRKPnbYTy/cD8/P6/P378aLYjpSSfz1MoFEin0xw7doxwOIzneUES7sLCQuCqvV9hs1d8rVQqQfG154J+PuJwIQQjIyOMjIzQaDR2NI2lUqkLXojqCRV7yUMHNS95nhf8zWGElr2mKMdxcByHVCpFPB5nY2ODdrsdCET3olarMTY2xujoKIODgxSLxaBRDQjc5c/VsbpPnz4PHDY3N6nVauRyOTKZzFkNBNlslkwmw9raGisrK2iaRqlU2jPFomeyEQ6HDxSPappGIpGgVqtRr9eP7JjuD41Gg0KhQLVaRW6Jd/qNmZeWarXK4uIi6+vr1Gq1oKlJ1/UgHWRzczNooO4nK13eaJrG1VdfzdzcHKFQiFqtRrVapVQqIYQgGo0GCY4TExP7Np4eRe9O/LGPpfn7v4/9trfRHRjwReX1OkJKPFXFGR1FZrN4x48Tf8c7cDY26P7Gb6CsrGBsbOBGIlgjI+gbG4Qsa0farAA810W4LvrSEs76ut/YW6mQfec7912Dktdcgzc3h5HLYQ0OBuLB3vrpzhdLFM/DDoXwolGUTgdvqwlP2Wa+JyMRpKqi3Edg501NYQsBpRKe46A4ji+S2zINtkMh7Ouuw33IQ0i/4x0oF2AcdCoVmp/6FN7nP4+yuen/UAjchzwE44UvJP6kJ13S1HW3Xqfx6U/j3XorolDwhY3ZLOJHfoTEjTdeketGlxrz7rtpv/vdKKdOodTriC1jERkKYX/pS5THxzF+4ReIP+lJl3ZHLwDG+PiRC87vizYwQPitb6X7pjfR1TTfcLtUwg2H/VpAt+unfUci/vgxOoocGvK/dw97GMaLXkT6CU8AISi/7W2oX/0q+tycX8+dnIRUCv3UKTzb9oW7gNrtQrmMpevIrSRNoao4x45h3H03olDA+fSn4QgE6F6ng7jlFpS1NaSm4Z44cfYahBAwMOALx1dW6GYyND/96R0mtM1//meUUgmtVsMaH9/XHJpEAicW89cWb70V+epXX9JxCSD7xjdSFgL7Ix9BazTQLMu/fwmBgi8+74nRnS2hud5uo5omWq1Gd3AQvV7HVRT0XA77xImD14+vv/6cQwcOg2y1wHGQmkbnqqv8ZOtyGbV3zxICJxrFHRyEVCq47nBdvG3GRADE40hNQ2008A6xbdHtIkOhe03mz5ft15HngaqiViqIrX2WB103iQSuYSCKRZxbb4UrWIAOkHja0+BpT6Nzxx10v/UtZKuFiEYxbriB6A03BBO6xpe+hPqNb6DPz+OFwzizs4jtJjy6DpubSFVFsyyscBgZicDAQPAaL53GbDYJLS5i53I473kP3uMfv+vcJXTsGN4730nn13+d7lbdwMjlArN2NxTCGR5GDgzgPuUpZN/0pgOPtfXVr2L99m8T2UpuF0Lgbb2fkBJXVRlwNxGcbSSuSBfhdM76+VmvUxQGBwf3TZPsc/9IpVKEQiHS6TSNRoOJiQlKpRKxWCwQioMvnqrX60QiEa6//npOnDhx1nvNzc1RqVRYXl4mFosFa+ngrwnX63WKxWIgCA2FQn0zs0tAo9Egn89Tq9XwPM834FIUDMNgaGiIoaGhSypuUVWVa6+9llKpRLFYJJPJIIQIUlEjkUhw7WS2DHZ6Zge947gvveuvWq0eyhQ3nU5TKBRotVpUq9UrUoAupaRSqVAoFGhu62l9oK/1dbvdIC20Wq3uEEPGYjHq9TqFQoHJycl9jQj6XHw0TeP48eNYlkWpVKLT6exIOe6ZpNwXx3FwXZdOp3OoeUIoFELXdUzTPHTq935IKalWq9Tr9UB0HY1GOXbsWFA3LRaLLC4uBmNPL+Fa13VarRZDQ0NsbGwwMDBw0Q1qBwcH2dzcJBQKUSgU9jWHKZfLKIpCIpHYIbjd2Nggl8tRLpepVCrYto2qqsFxptNp2u02zWaT48ePPyBFibZt47ounuedNbYmEgl0XScajVKtVhkZGSEUCiGlxHXdHTVrx3Go1WpMTEyQyWSuCLFpuVwOjBaazSb21vNANBrl1KlTVCoVGo0G4+Pje9bLVFVlfHycM2fO0Ol0KBQKTExMMDc3F7z/brV8y7LY2NhgcXERgNXVVSaPIHCiZ4qxtLRENBoNErDBnzsnEglCoRBjY2MXtB4zNDQU1H5WVlaYm5sjmUwSjUaDJPaeUc7ExARjY2NX5JzlUjE0NERjq9ctl8tx5swZ4vF4cJ32+m4ymQwjIyNMT0/fb8Pg8fFxUqkUtm2Tz+exbZuZmZngXtAzYrjvd77XEyOlDMbWyxHXdYO5+26GcQf18/Tp0+f86QvQ+/Tp0+cKIDQ9Tej1r0f+0i/RXV5GdjooySShqanz7gBSwmEG3vEOWl/7GuanPgVf/7qf7nXnnajtNhLoTk3dKx6q1TCWllAsC3Wr2Qfwi+m2jXHnnb7j8dvfTq1aJfVTP3VO+1P5q79CfOhDqLkcWqWC1HXcrUmhViwiNjexs1n//Tudc3Zx9zodKq9/Pep3vuO7yG81XEpVRXEcPE3D2djAW16msrRE5ud/fsffC10n9a53UXvDG0DXEfk8+uYmyuqq/z6K4ifTT07izcwQe/e7L2pjjDE5ycCHPkT9i19E/uzP+m7lmoYdi+HF437RuCfQCYVw43GYn0dbWoJ/+ze6CwvnlLosHYf6Jz+J+5nPoCwuBmkfai6HVquhOA7WfZoSpOP4BWAhEIaBMzaGsbKCV63SuOUWUi98YfDa8HXXobz//TQ++EHkf/wHbrMJvUW5cBiZTCKf9CSSP/uzZ51nNZEg8973UvvIR3A+9zm8QgFM008mMQxkNIp87GOJvuIVhHcpWvV5YBF74hMpz85iNRoYS0s4noc3MuKbb4Df0NZsoq+v+0kEk5Pw/OcfvtlPCKKPehTRRz0KemPqTTeh3HEHytISimmidjq0p6dhYmLXRh05PIxlWejr63DHHZinTp2XMULz3/8d+y1vQZRKKIUCWr3uF/WFwInHcQsFzC9/me5rXkPqR3/0nN9f2jblN7wB9etfv3c8VRQ8TUPYNmJtDSeZxK1UKC8skH3b2y55s9B9UZ/8ZOSdd6IUCtBowH5mLlviem9gAH2PhOjW176G8tnPos3Pg+ftSAQB8LJZuhMTqEtLQSJJ9cMfJvOqVx31oV1QnFIJ9etfRy0W/YTt/QT0iuInbG9s4G1s0PqP//BNG/pcOBzHv9dJuTPBaw/EljkG7Tb2TTdR/uIXodOBSAQe9CAiL34xkQc96NCbj//UT9H60pewmk1CuRxWp+M3V/bScWwbpVhELxaxBgaQQ0PErtAGr+gNN2D+yI/g2Db6mTMY99xzb2KIoiA6HZRSCb3RwBoeRo6PE33Na85rm83PfAZaLbRWi+6JE/s/C8RiftNmsYh3663w2tdesGQrfWQE9wd/EKpVQgsLWPm8n/p0X2wbfWEBJxKBZJLYAfef+mc/i/dHf4So11EKBdRm00+20TSctTXk4iK1T3yC6NvfTvjaay/IsZ0rxsQExmteA+f5WZ8LbrNJ80tfwtvYAEAZGSH+1KfuKcxXwuHA9KnPwewnQM9k/H+OAiklxWKRqampHQW7Xvp5MpkMBImGYQTisPtSLpdZXV3FNE0ajcaO4uvGxgapVIrp6Wk0TaNcLlMul4PX9BI3kj2DtX1IJBIkLpL5Q6vVolAoUC6XA6Fir5FgaGhoz/3oHcP2ZtSD6LmMK4pCNpsll8sRj8cpFApM39dYbRu1Wg3btkmn04yPjzM0NMT4+DiWZQUNa1eKe3yfPn38saBcLgfNZ710ruT9MADt0RvnK5UKoVBoz2YRIUTQAFOtVikUCns2tLmui+u6hx5feg1v7pZh3sXG8zwWFhaCRqBqtYq1JXgJh8OBMH10dJSJPYwD+xw9vaaiQqFAvV7fNR2kl4C+tLTExsZGkBhy3xSMK4le49R2s54LlQJ+KdB1nWuvvTZoAs9uM4c7qibwbhcqlUO++MGPx/3gx+h+/tMot34efX2rniIE9nUPgee8kPDjnkhLVTHf+usYK+uE1jdpj08hM1n0+TlCjUYgPvfYSp3trWmwFVBu20Rv/x51qbH2Zx8m+tK9zYGtp9yI9i9fRRPryI6FVFQ8RUHYNp6x81pQuyaeoiCFipUaxFhZwkpm6GZGkQMn6Wydh870dYRj3yVaLGG2LYSxzfxpdAorkUUrF9C31mn1TgvHCNM8fg3u63+H0BN+hLqnwmHP6yEx/+2fUf/4nSj1KmqxiNpqgvSfrb2NIuY3v0tz9iNov/dOtOGL3xjfvvkm9A99AKVRRykXUUxfKOQZIdx7zlD5m7/D+tH/RvRlPwOAW6v6KcTJ1GW33ny5YH7nW6hveTPq+ipqYRPVtnFCIRACrVtErq5hreXpvPl3qf/CLxN5zgsv9S5fmcw8DOutf4r7v96NcfoeRKMn9JfI9CBeKo07PIL7U69Ef/BDwXVRsoOoiQQOUKmCvZpDfP5WtIUlXEWje+ykL9JutDBMC8VxcXUDT9PRzA5K10IrlGhnR7YJmVW6qQH0Uhl5aoHN/7wL45rrzuvQOp/7InqpjlGt05mcBncf8VQ8g6IXkPkS3U9+nsrzXxGMza1v3Emk2kS6EjuRRTj7rzW4ySyh/AZeuUHhjhz69Oz9PoatkjXg62rvHwLx399I+xu3oa5uoHYtutGovzYgPZStejdS3puMLiVCghsKYx6/lvrEFOHTdxNaPIN+1z1YmQG8VAZUBWF2UMsljGadzuAI7uA47stec/j76zlgyjgaOoYUeJbEGxjFyY74aVGui9S0e9c8XKBjI6XAQcfyYnjb9qnz4EcT+q/b0fNFnHoH9mvwNbsYzTbt4XHM6x+FOIJj6yQm0NUwuqLiVRrI7ABG1wbXwxMKEoXt+mPFcZFbP3fUELgKMppAaZu4a8ULcr4vCeMP8v/BP/wu0K3e++vOP9xMuFBGOh7miWMIT2G7g4BoNDBMC+F6uKqG8DzUao3uiLfTOCGcoDM6SXh1hfZAnfV//ALR579o932afAjuH/017Y//PcZXbkVp1H0jBCEQmoZ1zYMQz7uR8JOeSrWx//hgnTmNePPvoS2voHYtvy9HUfHCEX8i6LmotkPMbe0qQHdQaNo2p06dwjAMBgYGLtraZh+f3nqHZVmYpsn6+jpDQ0MMDw8H69idTodGo8HAwAAnT55keHj4LHFLrVaj2WySy+VIpVJnGWopikI6nSYej7O4uEihUCAUCpHNZh+QQrTLlVwux8bGRmCU2zMD6K2xNZtNCoUCV1111SV9Fk+n00xPT+N5HpVKhcHBwWAtsJfWK4JnTl9onU6niUajuz5P98R9juMc6rh6dYOeKe6Vhm3bzM3N0Wg0AvO77TWp3lrfQeZ3Vxr21v2kXC6Ty+XQNI1MJhMkD/cSZOPxeFCb6ovQLz8Mw2Bsy9jrQrF9/DgfCoUC6+vrdLtdGo1GIECPxWJBynUsFtth6tJqtahUKoH4WFEUNjY2gppDNBplenr6ognRw+EwqVSKsbExlpaWUBTlLCMSz/Mol8uUSiUmJiaCpGWAUqlELpcjl8sFSe+pVApN04LvXalUCkSemqYxs09owpXKjh7s+1xXPcP43jxru7HJ9r9zHIdcLkc4HCYej1+wVO3D0ru/9u4hiqIExvq9e23PfKBXY5NSEgqFcF2Xzc1NGo0GlmXR6XR2NS/ajmEYxONxqtUq5XKZaDRKp9NhfX2dkZGRHevo2/9mamqKpaWlwEhhbGzsSES6uq5z8uRJut1uYKC03RSjZ5BzIRFCcPz48WBe0mw2qVQqlEolACKRCLOzs0QiEcbHxy/42PlAY/v51TQtMB7qmQ30jILD4TDT09PnbZwVi8WYmpqi3W4HY6GqqmfNTU3TxN5aPOv1tMDB96yeCYa2fR3pIlCpVFhaWqLb7VKpVGi324EBS7VapVgsks1mmZmZ6YdB9OlzAelX5Pr06dPnCkLo+oURygpB7DGPIfaYx+A2Gtj5PK3PfhblH/4BfWEBfWMD2/OQ0Sih5WVU00SxbRACT1FwIhGcRAL3+HGoVNDX13FtG+/976d1/DixRz/6ULvR+upXEX/zN6gLCyidDt2ZGUQyGRSpPSmhUsFYW8NxXbz3vhfz2msJX3f4Inrld38X9b/+C/3MGaSmYU1N+S7eQiBd13dhLxTwWi3c//N/qI2MnCVy1zIZMu97H/WPfhT3c59DbmyAZd0rbM5k4OlPJ/nSl6IdlRrhXBACd3UVMTaGV6vRnZ72RWS6Dvd96BUCZ3KS8D334LRatD7xCUJveMOhNuM2m1Tf9CaUb38btVhE23KaBtDabdhKMDHm57GPHfPFUuUy6jaXdKmq2L3U8koF5z//E7YJ0MEX8Qz83u/hVCq0vvxlX0guBMrICImnPW3f1EYlHCbzqlchX/lKmv/xH7jLy0jLQslmiT/xiWj7pBj3eWAhVJXEO95B85d+CUtR0FdX0e++GzcSwdM01G4XxbJw4nGc48dxH/c4svcxoDj8xu4dUwHKL385fO1reLUa7JVUu4UcHkYpl6HRoP3JTxI+hNt7DyuXo/WpT+GdOYO3ukro619HtSz0ZhMnHseamIBQCBwHpVz23elTKbz3vpd6OEzyuc89p8OsvPOdqF//up/Koap0p6fvHbOlhFoNbWMD9fRpbKDyx39M9pDjy8Ui8YIXUP/wh7FTKfRcDuvkyV3F1LLbxVhdxUmnIZUi/qxn7fp+5ic+gVqpoJgm1rXX7prqKxQFb3bWT57Z3MT5/OeRP/3TV1QqeHdhAVwXrdOhe5iE7UwGsboKnQ7O/Dz0BegXFHVgAFsIPF1HabeRB7iOynodtdMhtLqKaLf9xUHXBVXFu+ceul/6Eu2HPpTU7/3eoUx1jIkJnLe8he6b30xX132jnlIJdytxV91K/7HHxpCjo2i//uuEr7nmSI79UpD5zd+k0u1iKwqiUPDnQ1si4J7hR/fYMeTwMMbv/M45ifl3w1taQmk2/aS1QxQDvWQSrVTCq9dxKpULOvdJvPa1NO+6i67jYORyeNUqTjbrNyA6Dkqthlat4oXDuMeP473whfvO4+tf+ALe//yfKMvL6OUyTiyGPToKmoYwTbRKBVEsYtXrdF7/esT73oc2PX3Bju9yxC4UaPzFXyC+8hVEo4EwTQBkOEz9z/8c+cQnknjVq9C/H5oapMQ8fRpn6/unTUwc2bPzfgL0o0o/B7+Q02uM2w0hBGNjY8zNzQXJCfcVoPeKr9tTZXsNL/l8nnA4zPDwMLlcLijY1mo1LMsKhFflcplIJMLMzMwlb8LcLojrFTq73W5wXLVajXK5TDabZXZ29qxCkhCCSCRCPB6nVqsdmEhgmiaWZRGLxYhGo6iqyuDgIKZpsrS0xOrqKqOjozvEdlJKarUaGxsbDA0NEQ6Hg8J0T9Tfp0+fKwfP81hdXaVYLGJZFq1WK3BPj8ViRCIRxsbGdm1AOYh6vU6326Vare6b7gH++JHJZILG106ns2tTq6qqKIpy6AbVXqPZpXCul1Jy5swZisUiKysrgWlHKpUKhMArKytEIhFc10VKeU4JEr3Emp7jva7rZDKZ74sUBtd1EULc74aKWq2GaZqUy2VGRkb2bJDWdZ3x8XGWlpYYGhqiVCpdkc2zjuOQz+eD73nPkKH3PR8eHr5f3/HLESEE2WyWbDaLbdtBc2ovre18+frX/X8OTwJ4KfBS1EwX3TOxtBheXoP/DfxvGK1+j1fcked4o0Jen6acHye+WuZ4qx6kZdpoSAQCiSpdXKliKmFUPDTHAldi37bCd//g//LBf3oZntijHUL+IC/LPZSTrsPk5hwOBoYnCdsmXUfgCg1FSgxponrQVOPU7AHapyyGu5J5Z5ovLP8Y//XKe9d6M+2f4GdX/y8zZpHuHVU2IrP3WQuOAkMI1WXUWiIuqsyHHsL/Tr2H9Q89BD50LufzcBwrfo0fu+d/MmSuMmBt0FKi1PVRXKFhtE3S5RIslFm/S7L2r2/ibx/6HjrGxatrPWzlJp6x/EGGOytk7CIdJUxTTQMQdZtEVudpahlKt3+Y/B/8X0LSIuy2ALAVgzszj+XbY89lM3X9Rdvny51It8SrvvNOxlsFRrqbrBqjVIxhPNdfqxaKS9IuMbyySn1dYf2X/4K//eBJNtIPvsR7fqVyNfBBBkJneEj1FrJyHUW6NN0MdylPZEn+IPz93jWhx83fwo/kJLMtm/nYg7FP+/fhhNVmsqOiuoIWEaSjoEqVqNWi6yrk7+hQDd1raKS7YxxvlZm7W+djv7zJ6eHzE6A/5fQST151GDHDzK2Pwcb+DaWZ7hipeonFeoM/eUkdU/drv0+a03hKLsSoGWbu1MFrmTHbYrxT5PQ9Uf7yl11K57Es4Xn3znnP57YX6Zb4pW+WmWlG2TQGqarDCCSOouMaGnG7QsYqEJUNEKAJm5DXZcWe4q/bv8qpxlN5Ru3d3OD+X9JWgUyugL5cBkAiqGgpKsaDqDZG+ITzZtbfdgLdLWGrURzt6NJCI9bDefWZDDPWAPZcm83I/ulpw50CbS/D0nyaP/vLR9L6yPZz8mJes/xppq0iyl0FVmLXIMUuJ1l6TLZzdGSapZVh3v+VH6d+TnOH3RHyh/jZpVmusipEFxos5SeZboVIOxqK9Oh09G2vhZhn0hFh6t0US8v+2tBgJ0LIE5y5PcqfvOL89+lyJ9It8dpvzjHTbFHQximePvs5abS9gWaBIhU6IkrUatN1VTbuNKkb9xGlyAjHO2Uqd7a57bf+lb/7xB4CdACGgdehGr/AmPo9ol4VVzEoR6eplKb9+c+HDj6G59z1MX5ws8FUp0xBGSbmNgk5Jp22juwNUVISEl2Q1o6/FUhqSpL1zAi1+XlCoRDFYpFIJML09PQlXwP9fmJwcDAwoisUCuTzeTzPQ9d1PM/Ddd0gGTuTyXB8lwX4QqEQJGrvlwSpaRqDg4Pk83kGBwep1+uktnqT+lxYVldXAzPdbrdLKpUKRIamaVKtVqlUKoyPjyOl5Nprr901TfxioCgKg4ODQbKnpmm7CvGklKyvrwd1nL3Eer1n7XMxxe297kozSHAcZ4cIu2f+EA6HcV2XRqPBysoKsVgsOMYHigg9l8tRq9XI5XIMDg6eVXtKJpN0u11WVlZYW1sLzk2/XnRl0xPa6bpOPp8PjKl79YNEIkE6nQ5qiI7jYNs2hmGcl0H1ysoKGxsbwTbBF8O6rsvGxgbxeJzh4WHS6TTXXXcdjUaDzc3NQHjY20fLsrAsi2KxGNwjTdPk5MmTF20uNDs7G6zfr62tUa1WSSQSGIaB4zjU63WEEEEqd09ILKVkdXWVfD6PaZrMzs7uuG/0vmPJZJLl5eXAGGK/te4rlVAohKZp6LpOo9E4a1wZGhqi1WoxPDxMPp9nc3MTz/PwPA/Hcdjc3KRarRIKhZicnNzV7OdiUigUWFtbo9vtUq/XAwF6JBIhn8+TSCRIJpOB+YBpmgwPD5NMJoN7brfb5fbbb2d1dZVEIkEul+P48eP73lcjkUiQot6bW/bMRPZCCMHQ0BArKytYlkW5XD5S8X4oFLqkxsyKonDs2LHg2kkkEoEQudczMTg4eMV/p6SUgdnQxUQIwezsLCMjIxQKBSqVSlDb7RkuDAwMHJnps67rhMNhdF1H1/VgzPQ8L+ix2V4f633/hoeHdz03vftHsVgMROtCiGCefz4G7oehXC6zsLDA5uYmlUoFwzBIJBIIITBNk1wuh2EYWJaF4zicPHnyiptb9+lzpdAXoPfp06dPnx2oiQRqIkH4ta+l4nk4N92EWFvD2NhA6XRQHQfVcfAUBU9VcUMh3GgU59gx35F9YAA7GsWYm8MqFjH/7u8OLUA3//7v/fSFdpvuVVch7rvwJgRks1ihEKEzZ+iWy7Q++lHCv/d7h3r/zu23o37ta+gLC77w5dixHc0/QlX9BOJUCmNuDm9tDffDH0Y+73lnpSgo4TDpV74SXvEK2t/8JnYuh5ASZXCQ2A//8K6iw4uJ+8UvEp6b8wVly8vBz51YzE9B3yZUEKEQTjSKUi7jfeMbh3p/6ThU/8f/QP3GN/wUXyGwBwaQWw8SkTvvROt0UKVEttto3/0unmGguK7vBr/1cOqpKopto1gWbqXiJxDvgZbJ3K+UZgChaSSe+MT79bd9HjgYk5PE3/c+Gm97G9x+OzQaiK20GScSQWYyyEQCnvMcsq997ZEJgsXqKkqrhZtIHJw8axi44TCi1ULmcod6fzufp/7Od6J+85uIVgutXsdYX0exLFTXxYpG8ZJJP56zZ+iRStFtt9EXFiCXw/3TP8V90pP2NXTYjrW6inLrrWiLi0hNwz5xwr8HBActIJ3GTibR5+ZQl5bg85/H+emfPpSA9byQkvZ3v4ubz4MQ6JOTfiruLudeTSQQL3853vvfj1hYIHTqFHY2izcw4Jt2dLso5TJGpYIbjeJNTqK86lUouzT+24UC6je+gVYq+UL1/e4DQvip4OvrePk8zX//9ytrjOqZrkiJVNWDE7YVxT//nofcHjtyBWPedRedW27BKxYBUMbGiD73uYRmZy/tjgHhkydpT0zgrK2hlss4Q0N7jz22Tfj0aYTnoZkmslrFSST869e2CS0u4uk6tFrUf/EXSf7Znx3qOxx91KNQ/uRPaL3nPXD33f54u+Vs74TDkEziHj9O+DWvIfbIRx7l4V90hKaR/YM/oPapT+F8+tPI+Xk/Bs7zkLoOsRjeE55A/KUvJXTs2Plv0HEQUvopb4dAqmow75KWdcCrzw9jYoLoH/4h7Te+ESsaRSkUMDY2EFsL1m406puhZDJ4z3se2V/5lT3fy200cP/4j1FzObRaje7x47Ct8ClTKezhYUShQGh1la6m0XzXu0j/r/91QY/xcsI8c4b2r/0ayvIyyuYm+n3m0U48jlsq0fj614m++92Er7rqEu3phUVaFvVPfxr3M59BWVyErSYWR1FonTyJ9rznkdzlee6wOA4sLe39+6P4Wm9nv6Im3FtA76VsOI4TFKGq1Sq5XI7V1VU6nc5ZxddeIfS2225D0zRc1yWZTGIYBpFIBCkl+Xw+aMizLIsTJ05c0qa85eXlQFRvmibJZDIo5HY6HdbW1oJ0ECnlrsXkoaEhGo0GxWKRer2+b/GrWCwSjUYJh8OBs/TExETgnry6usrc3BzxeDxwVW80Griuy/DwMIODg5w4ceKSCDv79Olz/riuy+nTp6lUKuTzeZrNZiDWtm0bVVVJp9N0Oh1M0zxQRH5feg1eUspDpYrEYjHy+Tyu62JZ1q4C9FgsRiKRCIS0+zXr9lJARkdHL1qqyXby+Tzlcpnl5WVCoRCzs7M7xst0Oo1lWUEjkaIopFKpA5vfLMtiZWWFarWKaZp0Oh2klBiGERiKTExMPOCazFutVtCk0mvi1XU9aAI6l8btUqkUNPcddJ5698lKpUIymbziBOimaXL69GkajQaVSiUQLYDfuJnJZKjX6wwODnLs2LEHVINKr9HocsFVQ7jq2Y3XD938IjG7iiadQFiZ6eYJeSYCiYeC3FqJ8p90BQoeurRpK3Giwn/+zDgl0t1NZov/j/mhx+6+E0LwyWt/i5ff/noAJtpnCHu+qVfcbeCi+tsUKm01hqWEkAiGu6usRY5RDE/yvbFn73jLSnSaU6lHEnJbzLTuwTMFxfDkDhG8Il2y5hoJp0IuepJc9CRDzXmuKf4binRp6WlODz2ecmz2/p/gLQynxfPn3s2gmSNj5clFTtLSUjvWiUpygkEzx1T7NJ5QeMqZP+Oz1735vLd9GEbqd/H05b9kvD1PxG2yEjlJW0vu2L+Q0+R46w5GujlOKLfTUhOIravAUsKku3luKP0z3x14Mv909a/gKZfPdX6upNo5blj7LCdr3yLstnAUg/XoCb4z+mxWMj9wcG1hi4ev3Uy6m2eku8xq+DhNY6ephhQqNWMYU4kz076bdjfBD+X+kU/1BejnRSlxgq8kXn3Of3d15f+RtIq01QS2em8T726ftouKJxQ0aRF3KjsE6D3xr5ASRbrnvB/3xXBNFOniCu1Q154j9GC7mtuFra+iqcZxhY4qHRTp7G0KsoXudXEV/zVd7fIwEkq3VxFIIl6LQngCS905L2/qWZp6FiE9VOkggZPN22hpKeJ2DU9ofOGaN7GQfgSP3PgME63TaF4XIT1cRaerxrgj/Rgq4VGetPS/GW+fCd57IzLLf408m7tGnnreYvSOkeGO7GNJWgUmOvNU9GEsbffkcsPtkLZLrEdmuSf9aFrhnU31nVCWbww/C90zmWndzXTrbgqhiXvHcCmJuA2GzDV0abEUu4bbBp5EPXo0zfRSqPzX8LMYNNdI2SVGzCVsRcdDQWerfiUEQkLY843xbWFQMe79zkTdJh0tTkN/YBgPHUTS3AQg5HUoqLt/DobXRcHFRfO/+4CCi+7tUlcQgqaWJOK2yFibh9oHVzHIZX/gfu1/pFvm+spXyVqbVPVBmlqaqfYcIWmiyy6WCAX71VVC4DZ3/L1E0BBR1q95GGq7TalUQlVVBgYG6Ha7HD9+/Cyjzz4XjvHxcUKhEOFwmKGhIZrNJo7jIIQgHo8HCfWTk5NnGWdJKanX64GY/CBjrWQyyebmJs1mk2q1+oBbG7gc6XQ6wZq+53mcOHFih5AmHA6TTqcpl8usra2hqmogFLtUjIyMUKlUmJycJJfL0Wq1yGQyRKNRpJS0Wi3K5TKe5zE1NUU6nd4zmVLTNDRNC9K/D6r5dDodHMcJjC+vJHomngsLC2SzWcbHx3eso6RSqUCEvbq6Gqz1XWnHeV9s2w7WjxOJxJ7Gxz1x58LCAqZpUiwWL6mwsM/5I4RAVVUqlQqrq6tkMplA3K3rerBO31ufrVQq6LpONBq932aX+XyejY0NlpeXcRyH0dFRkslk8F3rdDoUi0UWFxeZmZlheXmZyclJLMsKBMcjIyOkUqlgLd5xHKrVKhsbG8H99/rrr78oRiCapnHNNdcwPz8fCIDr9TqdTgdFUYLji0ajnDhxIhBX99b+q9UqY2Nje+6roihMTEwwNzdHu92mUCgwNTV1wY/rQtFqtSiVSnS7XcBf381ms0Sj0cDAOJvN7pgPaZrG9PQ0y8vL6LrOqVOnkFJSKBQIhUJEIpHgmhgaGrqk56dnWJPP56lWq6iqGhjrF4tFdF1nZGSE733ve6iqSqfTOct8APzxdnh4mGKxSKFQoNPpHGg8JKVECIEQgmazSaPR2PHd2otYLIamaYFxzaVOj78QxGIxjh07xuzs7A7z3isZx3EolUoUCoXg+ySECEyFLqYhWc8EbXp6OrgOLwSZTIZwOEwkEqFSqTAyMoLjOORyueCar9frgSF4sVhkbGyMSqXC/Pw8J0+eDD73QqHAysoKnU6HarVKu93G8zwMwyCVSlGpVEilUkHK+1FjWRaLi4tsbGxQq9WYnJw8yzjDcRzW19dZXl5GURQ2NjYYGxs78n3p06dPX4Dep0+fPn32Qggyv/Ir1Kancf/+7/HW1gjffjs4Dq6m+WJNVcVJp3EnJnYKDyMR7EwGrVSC73yH7tISoZmZfTfXXVpCve02tFIJO5M5W3y+nVgMO5VCLRaR//EffprjIZLGOzfdhFKroVgW1okTe07eRSiEPTqKsb6OLBRo/vM/k3ja03Z/UyGIPupR8KhHHbj9i4KUVN73PsJf+QpGo4HiuoitxHEpBMK20Vot3EIB+9ixQIjhhcMoloVoNvd794DG5z+P+u1vo8/P+wnSMzOgKH5DhJRIXcezbfA89HYbqSioto1UVTxNQ25dL4rjoHY6ICVGrYa5n7qjT58jwBgfZ+B978M8dYr2Jz+JPHPGFwrG46iPehSJF7zgUOPJoZES0TNdOGTEglQUXzx1iAQza3WV5i/9EuriItraGmqng6eqqN0uquMghUC1LMTamt/ovj2BPRrFnpwktLSEV6/T/OxnSf3kTx5qH5sf/zhKq4XabmNeffXOe8A2hKLgTEwQOnMGt9Wi8clPknnVqw61jXPFa7epf+xjeJ/7nJ8+7PiNPq6m0ZqZ8QVoN954lkFI+qUvpVKv+/e6YhG1VMIoFILfu+Ew9ugocnAQ+YpXkH7xi3fdvr24CFKidjo4h00Fz+Wg08FdWIArSICuZrN+wram+aLig8QLW+M8hoFyhRf229/5Dp33vQ/1nnsQjQaaaYIQyGiUzj/+I80bbiD2y798ZKm79wshUJ77XOTiIkqphFhfR46N7dooqJ06hb61uNqNx7Guv37Hd6Rr2yjr6xjz81hA7Xd/l4E//dND7Ub4uusIf/CDmPfcQ/uzn0Xm8+C6iKEhws98JtEbbjiSw70sEILUjTfCjTfS+d73sE6fBttGTaeJ/tAPoR6l02cqhTQMVNvGdt0DzVKUdtsXwguBehEaqMJXXYX2139N4xOfwPn8532TBs/zrz9FwX3kIwm/+MXEHvOYfd+ncfPNiHodvVSiOzOzQ3weIAQyk8EtFoncfTfu8jK1b38bb3QU+3GPw3n5y1H3Sb+4knEqFV98PjeHsbKCG4vRnZ299zw1GijFom/aZVm03/hGtL/8ywtvAnORcSoVam94A+pdd6EWi2jlMsqW0YlnGIjNTeQ991D+p38i/a53od6PwtHyMrj79G0f9XB/mPSDaDRKpVIB/EaXXuFnfX2dcrlMu93etfjaE+JJKbnnnnsYGBhgampqR8NLz/F4c3MzSFN90IMedEnSR6rVKvl8npWVFYQQO4pcQFAYX11dZWlpCSHEDoF6j2w2y+rqKoODg6ytrQGcJULvJcQ3m01mZ2fJZDKBQEtRFK666ip0XScUCtFqtajVakEzxMDAAKlUinA4zPHjx78vknb79HmgMj8/T6lUCgTSU1NTgVC7Nz4Wi0U6W8ZKoVBoz2bTi8Xg4CAbGxtEIhEKhcK+TYzlcnnPsfJiUCgUKJfLQRPYbk3ihmEwPT3N3NwcjUaDQqGwb+OHaZqcOnWKarUaNBYZhoEQAsuygsSKTqfDzMzMng2hVxKe57G0tES5XA4a6C3LQghBJBKh0WiwsbERpJccBtu26Xa7RKPRQzW7RKNRLMsKUg6uFGzb5tSpU5RKJdbW1giHw4yOjhKJRPA8j2azSalUCkT9QgiOHbXbUJ8DyZprRNwWbTWGJzR0r0vcraPiQSA77iHwtpLQFTwQvrgs5HUQCLLdDcYbd+0tQAeakRE+/JA/4oX3vI22lmK4s8RYdxlNClTpboncQZM2nqegKTYr0avIR2b42PW/j6WdvSb2hat/leztawgk450F0o3v0tBS2CKEJm2SThVXaKyFj6G5FqPmEs9beC9ht4VAYisGT1r9OxYSD+bfpl/Oevoh9/t8Xr/+ReJ2lUFrg1zkOC09fdZrpFAohKfQpMOwmeO6ylf5v2aRdvjCj5mPWP0UCbtM3KmxELsOWz1biJB0KiDBkBa6ayEk5KLHkQiiTpOJzhm63QiaZ2G4HT5z/W8ixZVlCKU6Jk+f+2MeWvoXwm6TpFX2rzmhMN28i+sr/8FmZIabr/n1A40JFM/mYYVbSVt52mriLPH5drpalIo+SMbKc3XtmyQ6GzQiD4xEwiuJmFNHlxYdZacQ2BXa1pjnj0eOUECAh4KChyZ31o/CTgsPFUfotPb53A+LqcZwhYbmWQjp7Z5uvQ3DMwPB6vaxcTH7SDprH8UWBimrSCW0zzUmJWm7SE3LUghP0gxfHut6Ki492xPJ3udBCgVHGIEZqECi9D4nIbh79GncPfo0BhtzDLYW0LwuHS1Fy0jzwtPv4gdKt5KyiiTtMiouLipZc42J9mmesPp3fPya32Izdf15Hcu3xl/Ig8v/Rt2pMt05xUZomqZ+r3k1UhJ3qoyaSzS0FHV9gK+P755s/a8nfo5UNw8IhjrLTHbO4AoVWxjo0kaVNk0tzVr4OKdTj+DWq153Xvt+X/5r4kVcU/kaqrSZap9G8Vx/piAlhuyCFBhbKdhtJUZTS1E3/Oe3kNMk4rVYN45x++CTj3S/rmREMPM5HB4KcDSmFwdxvPQ1DLdNzK2zGLqerhbFVMOoOETcNgKJJUK+ce8uzzQSwbcGn851Vx1nZqaL67rUajXy+XwgvLruuuuu+DS/K4mBgQEGBgao1+vUajUcx0FRlECkt5dwoSeQsG2bZDJ5oGhDUZQgVdXdb6G/z5FRKBRotVp0Op2zxOfbyWazgSg3Ho9j2/YlM03TNI2rtkycDcOgXC6zsbERGNZpmkY6nSaTyZBOpw9MVOwlqi8sLNDpdPYVXJdKpcDw9v4KVC82lmWxubnJf/7nf5LbCtXopSxnMpkdAr7eGuvCwkJgpjg9PX0pd/+8KZVKmKZJu90+cN0oHA4Ti8Uol8vE4/GzRPqXCz3D1e1J3pfjfl5qqtUq9Xo9EDbXajUmJibQNI1Op0OtViMajQZJ3pZlMTo6Sjwev1/GC1JK1tfXg/nK7OzsWWNqJBJhamqKtbU1crkcoVCIubk56vU61WqVmZmZs7bdSz8Ph8OsrKwERriTk5PndX4Oi67rXHPNNTSbTQqFQmD0rSgK8Xh81yTdSqVCo9FACHGgUFTXdeLxOLVajUqlckUI0HtzG/D3v91us7y8TLvdptlsYpq+YaZhGBSLRbrdbnAtrK6unlVvMQyDmZkZ7rnnHjKZDCMjI4yOjjIwMBCY0PaM7C8V5XKZ9fV1VlZWsG2biYkJ4vF4MPb0BMOnT5/Gtm0ajQY33HDDnj0LhmEwODjI+vo6tVqNeDy+rwC91WoRiUTQdT0wbDisyFrTtO+LuaUQ4ooXnoM/fiwuLgYmFj3xtK7rQRL4hRRP78eFvNf2xOHZbJa1tTUikQjlcjkwIFFVNehx6c1HpZRUq1VKpRIAV199NaVSiaWlJdbW1lhbW8NxnKAO67ouGxsbpNNppqencV2Xq6+++kCTsHOlV5uvVCrMzMwQjZ5tpqhpGpOTkywuLlIoFIL6X58+fY6eK//O0KdPnz59LiipF78YXvQiSh/4APY734myvo6VSuFlMngDA4g9kk+9gQGMUgnHNDG/8Y0DBejmf/0XdLuopolziMVGL5MhtLiIa5qY3/se8cc//sC/Ef/5nyjlMnYi4Ysl63VfTaCqvnhu+wNEJgPr61CpYH3lK7CXAP0yo/ze9yL+8R/Rms1AfOpuPXgLz0O1bdgShjM/j3PypC8cd12koiAPIXgAcG6+GbVUAilxZ2bOSj52w2GULQGs2Nq2pyg4kciOop+raXi2jdHp4LkuxqlTWGtrGOeYItWnz7kSvvpqwm9604XfkBDIZBJpGIhO5+DSvZQo3S5uOg0HCHWl49B84xtR5ucxlpZwkkns6WnUjQ3UbhfFdXHCYV+AbpoYxSLdcBi2N1wnk7i6jigWcb/4RTikAJ2vfx2lXMaNRhEHLZTHYrihEKJUwvvP/4QLIEC3Nzepv+ENqKdPoxYKaNUqypaA39M0lPl55H/+J6X3vx/t1a8m+exn70gxz/ziL9K8/nq6//APyDvuwOl2770/hEK4D30ooZ/8SeKPe9ye+yBt+9xSwbc19MhDmA1cToSvuYb28DDO6qqf+H5AE79SKuFEoxCJEH3CEy7SXh49za98Bfstb0Hd2EDP51Fs27/HSolq27ihENRqdObm8N7xjksqsE7eeCPVz3wGq9PBWFrCNU3cwUFfnCoEstNBXVsjXKsB4BoG9lVXnWXQgK7jTU3hSIm+sgLf/S7mqVOEr7760PsSvuYawtdcc5SHd1kTefCDiTz4wiVGhZ/8ZKxPf9o3gCgWYR/DC+l5aLUa1tgY7sMfjnKRmqe0TIbMq16F/OmfxrzrLtxyGWEYGMePox9SBOPecgtasYhnGLBH4Uusr/uGIY6DZtsozSbceSesr+MsLdH6/Ofp/MzPkH7JS47y8C4LGn//9yi5HMbKCvbQEPK+C+fJJF4ySXdzE2NlBSscpv6Rj5B93dE2el5KPNOk9qu/inrbbegLC6Ao2AMDsCX+FfW6b4hWKoFlUX3jG8n+6Z+ecxL6/Pz+v78UoRzbm+p6/261WrTbbSqVCgMDA3sWX0ulEoqi4Lou8Xgcy9qZYKQoSiC+XllZIZFIHChovFAUCgXq9Tq2bXP8+PFdC5yqqjI5Ocn8/HwgPryvqFJVVY4fPx40i62trVEoFIKEHMuyqNVqKIrCzMwM6XT6rGYERVE4fvw4nU6HQqFAtVoNBPrRaJTBwUEymUy/IadPnyuYZrNJrVYjl8sRi8XOagbsjY/xeDwoXvcaQw/73e+lDwshaLfbBzZKtdttdF1HVdU9x/VQKEQmk8E0zaABYHh4eMeY6XkepVKJYrHI5OQk0Wj0ojcW1ev1oLljeHh43+aDXlNvr+ljrwZkz/OYm5ujXC6zurpKKpVibGwsMHPpCQq2JxqEQqGLmmRw1EgpmZ+fp1gssrKygmVZJBIJMplMIKBeWFggmUwGTfkXIqFcynMTplwurK6uUqvVgu/JfRu7w+Ew2WyWlZUVVlZW0DSNgYGBS9qIdzlwkXuutgRM0hcPAbprbomh5Nb/7mEqvHVZOkInTAcE6J6J7poHbrMZGeEjN/wJY7Xv8bCNz3Fd+T+Ybt1DxG1jCQNT89PPTTWOqcW4J/WD/N/jP08zsvv3yzTS/N1D3s3z7n47phonatdIOmVC0sQVKmuRY5hKlIy1SUtLMtGeI+HUcIWCREGTNl0lQtIqMtO8i0+dfAOnh5907icTeHj+FlJWga4SpqWl936hEJSMMY617yDstHjIxhf4f7MvO+ftha0qD964hePVbxFxW9jCYC1+FbeNPecs4XTEqvCgyn+QtgrU9Oyu4vOIU2fA2iQsTTwUPBQcRdtKio5QN4bIe5NMdOaZap/CEwqLa5/jtonnn/O+9xhqnGKi+j1CbouuGmM1/WAKicOvg50rqtvlx+54Myfr32a8PU/Ya9FSE3SVCKp0GDWXGDZXiNsVXnr7r/GRB7+LcnxvkcNQ4zQJu0LKLrMePrjWWjWGyba+h+GZzFa+ye2R5x7l4fU5BI7QkQgUdjYut7UkjqJjSx1DWjjcOx+SZxlyQMbOU9OzNIwsa+dhXNFjMf0DtDY/A0IQt8s0jH1qAFKSsktUjGFWYyd3CNA3ktezET1GprvOgLVBQ88gpCTktRFS4gqNjhZHCpWUXcDwTGrGEN8aec6hktcvBm0jAwgcYRB2W5gHJLOH3BYAthLa1fijmDhJMXESgGxrkZfe/mu+AYq5hCN0anoWSwmhexYpp8ygtc5maIr/dueb+ciD3kEhef/X2vPJa/n8sVfz3Pk/ZdDMMWYu4XVztFT/mGJuE1U6VIwhCuEpvjj7c2ykd1/r9oTGZ67/LQqLf8ujNj9LzKkTsyuo0sUTKk09TUdN8K2hZ/Afx/8/XOVozQ1dLczHr38LL77zd3CUEEkzz6x5GgWPmNfCRsdSwthCp6mlWI2eAASqZzFuLtLQMnTVGN8be9aR7tflSj3sz1u6SoSo26Ctn12DdhQNiYKCjbplnuChYu/x2fVS5Jv7zTGOiJhdDZLYu1tzhtXoVUy37gb8ZHfDs3CEFswjt+Oh8OWJl/EQbR3w1wuz2SyhUIiVlRWi0Sibm5vMHNDT1OfoSSaT5/TMpSgKzWaTzc3NYI1XCBGkaieTybPWHHqitv7a7YVHShkYu8Xj8QMF5dlsNkiHLpfLF2QN47CEQiGuu+468vk8sVgsSIkEf60qEokwNDTE4ODggaKaoaEhNjc3SaVS5HI5pqamzjK4kFKyubkZmOJms9krQuTVEyGtr6+Ty+VYXFxkYGAAx3GIx+M0m02i0ShTU1PB5x8KhYjH41QqFZLJJFNTU1f097Hb7dLpdNB1/VDGJYlEgnK5HIgVL6fPuVwuk8/nabVaOI6DlDJYix4cHGR4ePiSGUNcbliWxcLCAo1GA9u2ufrqq4P/H4lEgjT09fV17r77biKRCNdccw2ZTOZ+11grlQrdbpdqtcr4+Pi+187IyAinT5+mUqnQ6XRwHId0Or2v8D0ejwffzZ5BwlGLBvejt/3DYNs2tm0TDocPNX5EIpHgur6c6RlzlMvlYM293W5Tr9eRUgbH0DvuUqnE5uYm6XQa0zRJpVLUajXm5uZIp9OBoXK73aZarRIOh3nc4x5HLBZjdnYWRVEIh8OXxfd6fX2dUqmEZVkcO3bsrOtb0zRGRkao1WrccccdB+5zOp2mUqmQSCTI5/Nks1lc191VUNxut+l0OoyPjzM0NMTa2hqKohzacNdxHDRNu+hi5T7nTi/Ne3Nzk3K5TDgcJpFIoKoqpmmytrYWCJcvlHj6UjI+Pk6j0cCyLO6880663S71ep10Ok02m6XT6VAul7FtmxMnTpBKpQiFQiwvL6NpGrlcjmKxyOnTp1leXg76YVqtFlJKDMMgEomwublJrVbjhhtuYH19/Uh7i6SUFAoFKpUK0Wh0V/F5DyEEg4ODrK6uMjw8TLVa/b6v7/XpcyG4fJ5m+vTp06fP5YsQhK65Bmd8HK9cxp2agmh0X3Gd6C2yOQ7yEKnast0O0n6lYRwo3JNbgi9cF+8w7+84iFYLxTRBSrQ770R4nv8eQiAVBSeZxBsehkgEFAUvFEJYli9UvwJof+c7KJ/4BOrCAlII7HAYzbKQquofI+DpOoplBYmt7sYG3ugoWqOBNTaGnJ09cDvdpSWUU6d80WE2u2vysZPNorZa6KaJBP/zFMIXum9fMJAS4bq4mobiuniRCK1PfhLj1a8+orPSp8+lR/7wD+PlcoQWFjAtC7FfgmSl4o9N2Sz6AYnYjVtvRSwuYiwvY2ezyIkJBKDYtm/6oGlITcNWFPROB6XbRd/c9MVZvQVZIXATCZROB29z8/AH1WyCbeMdUszohcMI24ZG41CvdyoVmjffjHvnnWCaEIuhPfzhJJ7zHJT7LCS4zSaN178e9c47MZaW8DQNe2QEGQ6jFIsY5TJGvY6QEmdtDefOO6ndcAPyKU8h+TM/E6TBxp/0JOJPehLm3Bzdb38b2ekgolHCj3gEoUMkXp1zKni77X8OhoFyEVKJjxQhUJ79bOTKCkq57CfM7lUQrtfRKxW6U1O4P/iD6Feou6B56hT2W9+KsrSEUSxiDQ4ih4Zga5Hb7nZR83lCS0tYto355jej//VfH1pse9Qo0Sjxd7+b5i//MpamoW5sEFpcDNIfhOuC5+HqOorj0D5xArHXIp0QeGNj6HffDY0G7U98gvCv//rFPaA+AdGHPxxzdhZncxOjUMBKJGC3z05K1JUV/zPPZDBuvPGi76tQ1fsnxpcSdXUVpdXC2TJNuC9KLodeLAaGK74QARACrd0mVKngptN4738/lU6HzAUwP7lUSMdB3nILaqGAGw7vPf4CjIzgNhoohQLerbcif+EX9p+HXEHUP/pR1LvuQl9YwI3H8aand1wrMhbDHhlBWVryBeq6Tv2mm0j9+I+f03b2E6BHInDUt7V2u71v0QR8kWSv0NwrttZqtaAIvZebt2madDod6vU6o6OjmKaJaZpBkXQ78XicWCy2o+ngYjYE9Ypf5XKZdDq9b1OFoihks1lKpRIDAwO0Wq2gwN4jmUxy8uRJFEUhnU5TrVZpNpuBm/no6CjJZJJ4PH5W0vp2IpEI09PTV3xCR58+fc4mn8/TaDRwHIfR0dE9xzxd14Mm0sHBQWq1GulDPs/1CvepVIpyubyvEFpKSaVSCZrE9msUm5mZCVLZc7kcc3NzxOPxIOGrlwwyOTlJJpPhxIkTh9rfo8Q0TbpdP2XuMALwRCIRpFB3u91dm4tKpRKtVovV1VUGBwfPSjfvCQqi0SiLi4tEo1HW19dJJBI0Go2guQn8z/VKEBrn8/mg0beXFr/9njUwMIBpmuRyuaBpKpFIHDi3MAyDUChErVbD87wDG2x6Bgp7GSNcjjiOQ6VSCZIc9koVUxSFycnJICmoUChc9tfFhWZ6Gv7rvy7e9ppGFlsJkbJ981uxJTzv4f+32PHfIPCCcVsAEiH9JNruLgnluyIE6+mHsJ5+CF/Y+tFw/W6uKf4rcauCJ1Qq4THuHHnaoRJ5O0aGf3zouxip38UN65/jWP02DM/EUkKsxa5iuv49dK/LZOcMbS3OcvRqOmochC+MS1sFRs1lQl6HF869m4+EBllPndvztSIdhs0VYk6dhpY+UMhpaREsJUzUqTPcXjznbT3hzAd4ZP4Wwm6LhFVCkw6eULi69k3+f/b+O8y2tCzzxz/vSjvnXbtyOrn70A00SI5KkqBoI8zgqBiHnzOjo6KC8x0FwziMzuiMoiJpdAgqiGQkgwQbmoZuOp1YOe2cw4rv749de/WpPlV1qk+fUAfqvq6+urtq1VrvWutN63me+76fnP8Y5+KP5xPHfg3NszlR+DyztbuYaJ0iZ63RVKLEnBoShZ4SomYM0daTpKwCmrRRpUtLiRDx2ijS65PbN8lnnqKzEj7CbOsBUmaeJ258lO+MvewRE1cPF77MU9Y+wET7DIbb8QmUphpmJXKMO8ZewfncpYWwHymeNfc2Dje+zVT7FD01xPnwLTjKQ0LRBemSNPOMd+cRwCtOvYm3P+HteGL7b5ag00JIDwUXW7m0w5q9eS3Vswk6e4vdH+DKohiaZLL1IFlrnYL0YNNpXAqFmpZB90wiXhvds3CEjiYdukrYJ2ACpMwNQm6HfHCabw+9YMf+8UiwkH0y1YUcNTPLkLlOW0/ueN6UtYEmHepGlruGHyZiIARfHX8VI+05UlaRk41v4KDhoiJE31HcQ+AKFUNarIQOUw6O8eDI/hGGr4SnKQQnyfRWSVkFavoQ7LJfStpF2mqcnkFwOJYAAQAASURBVBbhfPbpO59YSl525n8w1FtirLdAPjBBzchtmb8qcpS4XWa0t4gUgh8+82be/oR3PCpy/v2jL8ZSw7xw/i+p2FXiVpmA1wGgbAzTMDK09DSfmfl5Tg0/b9dzeULja7M/zTcm/w3HC59nqnEvAaeDqYVZjd3MqeEf2CJIcKXRM5L83a1/xE0bn+YJ+U9g10OMd84T9Lro0sYRGoXAKDVjBM2zidsbpOwiphJiPTTDF8dfTddIXbX27Sd0Axnm4o8lYRUZMlcpB0aRYithoqGlidl1Al6PoOziCA1XqLT0i5+R4XT6LvL6LA9krvz6+HC4m2Id8NB+0FYCLEZOMNaZwxEamnTQpAPbyMG7ig6aSi63lVQSiUSIxWLUajUqlQoTExMHRJJ9DNu2OX/+PBsbG7Tbbcrlsk8GDofDtFotAoEAExMTfhyl2+1i2/YlyQoHuDKwbRvP8+j1ehfFabZDMBhE0zQ/bnS9oaoqo6OjjIyM0Gq1sCwLIQSGYeyZJAn4MRvP81hfX2d+fp5IJOILJPR6Per1OlJKpqamSCaT10UE+JGiUqkwPz9PPp/n3LlztNttYrEYo6OjWJblOy4PDQ3hed4Wt+ZoNEqxWMR1XWzbvqFiSg/HgCS615yZEML/m/0i6iilZH5+nlKpRK1Wo1qt+sTLgTtyq9WiXC5z9OjRy3Lv/m7DgKhfqVS47bbbqFarNBoN3z23VCoB+PHsVCqFpmkcOnToEc0fF6Jer9NsNv0Y724YONnm83k/vrsXUY9kMsnq6iqO4/hjej9CCIEQwhcbvxQ8z/P/Zj9CSsnS0hKlUolWq+WLDTiOQz6fx3VdTNNkamqKY5vmIIP8ymAPFI/HMU2T0dFRbNumWq1SqVSAh9zA4/E4qVTqujg774Zms+m7GWez2V3rAGKxGEIIv3Zgp/1FMBgkFAqRzWaZn5+n3W5vS0A3TdMXNA4EAmSzWRqNBvF4nFKpRC6X27bftFotarUapVKJ9fV1Xxw4nU4Tj8f3bV/7XoZt2ywsLLCxsUG9XmdycvKi+TiXy7GxscHS0hKKorC6unqRUcF+h5QS27aRUl4kjBAOh/2c8OrqKuvr69TrdVRV9XPH0WiUbDZLLBZjcnISVVWZn5+nUqn45PVz584RDocpFAooikIoFEJRFNrtNrVajXA4TL1e5/Tp04RCIUZHR68Ykd+2bX+N2sv3xeAdd7td2u3293x+7wAHuBo4IKAf4AAHOMB1Qr0uqFavvYPE5cIthEmZOpqn0Gt5eGL3hguzh+4pdC2daiXCxqndz+9Vo6RsA91T6DUcZHD3YKPSctE9hY5lUK3EUS9xfqRKar1GqNZACtEnaXoPqdx5igamg1dt0smN46SyhG2wbY1aM0DxUuffB1De+iES+SaRVo/6+FFi6wsoOGDaeMZDJFFXD6K7XYRpI0o1bFtDcxU6wSzVx738kvfq3b1BsiNI9By6egKvu01fCGYx3GU8IVDlZpGYJ5EueJsfF1JKNNtCcT2sQAikxCu1aP/jZ8h//y/uG2X7A+wOadvIr30W4wsfI7gyj7AtvHCUzsnbcJ7/o6jHr54L7O4Nk3itJrLTQkRiKNHrFyR1H3c7qX/6DGghmFuhM3Fo28lfmD0iK3k6sQwdI0t78vms7TIetb/5GPH1Gpqn0E5OQrc/ZsK2QPNAuBKl00ORLsKT6K6FUqtj33+G3vAEXqRP6jNsFdUWtBuQ3+F6F4o86joEe0HiroZ0JOZ2c8DDEOxJPF2n2Q1S2OWeZK+L8n//lOjXv4DSaaE2awjPQ6oa1if/heb/fhfN574M/s0vIDafoXzvu0neO0d0cYlOLI01MoGwLcIL59HMLsJxwZMI6aF3uqg9E+cb36a3WmX1M9+g9ev/A3XqQvvSI3DLkYf+1wT2sgbI4wRDo0Qj6+j5Ku1wbtd5LLBRpWdE6cgItdFnsX4DrDMXwrvlFUTinyA41COyvozZtLDSOWRoM3lvW+iVIqFaiXZyCDM6Su2Zr7kh1tPtoPzpu4kvl4nmy9THD+HFEuDQ/weAMGRnUNUokZUVOkqMwp99AH78F69jqyfwfu2tqH//10S+8SXUThOl2wEknhEgvLaIKnvYkSTd0BB0dztXCGlEcYsNmnec2XUcH+BqQ+D+wM+SenCZYMNEPztPLzWEncqCpoOUKM0GgWoeYZo0Jg/TGb8VO/esvc1l+wES4k1J2BbYho79sHVGrVeJFisoPROQmIEwmmXiqSqmEaUxNoPSbRMvruGdWaT3V+9mLfE41FueeH3u5wrDvecuUqt1YpUGndw4bm/30J4azRHZWKK33uDBf/gGyhOfcY1aehUhJcH3fZLoSgnQ6WRnobfdmqtC7hDRuQexVyu0/t/HWJ19EeJLnyBwxxdQ62UQCnY6h/XMF8DTX4DyMHGdU7uMm9nZK//JUqlUdi2EcxyHer3O+Pg4qVTKTyC5rusnUXdK0nY6HT95nclkqFar/t9u9zeJRIKNjQ0cx6Hb7V7TAr1er+cn18fGxi55fDweJ5/PY1kW3W73IgL64JhbbrmFSqVCsVj0yZqDYpqhoaGDBNQBDvBdBNd1KZVKVKtVHMdBCOEXkwzcsS5EvV6nXq+TSCQuWYgzmHOazSa1Wm3PBPSB8nqr1WJhYYFSqbRtslxKycbGBq7rkkwmGRoa2vW8qqpy/Phx5ufnCQaDtFotGo0GpmmiKIovsjEoNBg4hF9LeJ7nF4ftpdBpcIyUcseismKxSK1WQ9f1XYsOgsEgmUyGSqWCYRh+4VGr1aLX6zsjG4ZBuVwmGAwyOTm5o5jL9YSU0iegD0jS2xVwDO5hfn6eVqtFsVi8pItfJpOhXC5TKBR894Od0G63sSyLZDJJZlPQ70bAoIhv4Cy2GwYFm9VqlUQisa1Yz/cSXvhC+MQnrt31Hsw+i5srXyHXWyFqV7HVAB7qFsLRQ+gT1F0U3xlYwUEBXCHoqHGWko+77LYU4icoxE9c/s0A+fhNfDp+05af3bT+KR5T+TITvfNU9Syl4MSWDwtXMSgHx2lrcaY6Z+kpYZ6+9B4+cMsfPqJrK+5m8Th9h+G9wEVFINE2HUb3dB3p8PL738iJ2h2MdBaIunVMJYSpBAh4LhlrA1MJETOLPKnwMepGDs0zme6cJulUUHHRXQvPVXHQCCktYk4dW9ExvB66Z2EpxgU5SYkitzpFS6FSDgyTNdcZ6i0zWr+P9b06QEvJ0+ffyTPW30/KzJO0ixjeQ+QXSwmQMjeYaJ/mq61X8JXZn33EH4LJzjIz5TsJOk1cRScfPcJS+vsIOC0eX/oMw70lLCXIaujolnML6RKzyoTdFuBxuHUfEbvGE+ffwzdmf3LbdthKALlJYFblpZ2bBq7bUijY4trvEfYKRTpMlb9JqruKIl3aepL57FMx9a15n1RniZMbnybVW0dIj7aR4sHss1lL3rpvc453D7+II/W7yFmrxO0yDeOhvV8lMELCqaBKj6DXRYoeIHFQqWlZQk6TlJUn6tRZC81SCE3x7fGXX5F2SaFyx9grSJl5wk6T6fYp8oFJOlrcf5aKZ5M2N8jYBVZDs5SD45zJPeeic50fegbVhSyGZ6JKl6DsPkxQROCg0lEjtPQk7z/xxqtKWn6kiPYKmEqAlF0kYVeINv6VthqjrmeoGUNbnL2jdpWkXWE5fIQHUk+jZyR3PO9o7V5GOvMM91YoGSPUAtsQRISgYWRRpUPOXKWpZ5guf4PF7JMf1T2dzT2H89mnc6T4ZW4pfJaEVUQgaRhZ7h36Ac4OPQtX3fuc4Ggh7h97CfePveRRtWsARToEzRqq7NciONrOpCdXMbhv7KXcN/oS0p1FHrf6YZ65/v6+IIpTY8jaYMjaAPru30VjjHpgiK+N3s6dU//2irT3RsG3Rl7K4fq3yFgbjHXnWAsd9tcMgJaexjFX8DwFQ1o0RZC6nr5IfEKRDmO9eVpaAlOL8J3RF1/1tldCE5hqCE+oROwaLaMvKOUoAZaiNxFw2yStIkG3098neO2LzvHkJzcxjIuJf8lkkuXlZWzbptVqXbfvwYGA3MCtOxwO78lZ93sFjuNw+vRpqtUqtVoN0zTxPM936m00GlSrVd/tcnp6mlAoRLlcJhKJEAgEbqhv2BsVA3JWo9HY4jC5Gwbk3P1CzIV+mx4tCXMQqxoI6FarVUqlElJKP5YVj8eJxWK7iuLuFziOw+LiIvl8nkajwfj4OIFAgEqlQiQSIRKJ+O6zhUIBVVXZ2NhgYmIC6MdaBu94rwTS/QpN09B13SdEXerd9Xo9dF1HCLFvCKALCwsUCgWWlpbwPI9UKkUsFkNRFJ8UurCwwMjICFJKbrrpphtaNODRQkpJuVz2RbtjsRjRaNR3002lUliW5TvIDxzIh4eHH9XYdl0Xx3EwDGNP5FbDMHwxD13X9/TODMNASunnmfcrgsEgwWCQUqm0p3HXbDZJJBL7di81GIPLy8uYpkk8HiebzVIsFrFtm8XFRYaHh2m329x9991Eo1E8z0NKiaIoeJ7H4uIiJ06c8Pvjw2P6sViMoaEhksnkviNHNxoNP09zqdz8gCC7trbm1zns9P5HRkYoFAokEgn/+WazWQKBAI7j0Gg0aDQaRKNRRkdHmZycRNM0hoaGqNfrfv7lwnyTbdusrKzQ7XZpNpssLi6iKAqlUomRkRHOnDlDJBK5brm3A+yMUqnkr2lTU1Pb1o+oqsrY2BhLS0sUCgVCoRBjY2P7Zr3eDaZpUiwWKZfLOM5DPJhoNEoul/PHfjweZ2JigsXFRTY2NsjlcoRCId/sIhAI+Pm+Qc4xnU6Tz+cxTdOvwVEUhWw2SyQS2TKndLtdf25eXl5mYmKCarV6xb69BvvGwbffpTDYb12Ylz7AAQ5wZbG/v1wPcIADHOC7GPfcY/Ce9yj7Nd99EQL2zfzHxTjTdgZ3scNGeHeVvHSvgnSizK+leMdHH0/pi7ufP979Pl67kmTGitM+36YUusCFREqCbouQ20aRLq7QiNo1ekqGhaUkb3n3rZh/v/v5Z4v/ym8s1ZCOQMPGJICtxvBQUPDQHQvNtvAUibK4QWVDxzAdluwsn//mcb78G3t7TtcLhtPmP935LQLNBk0lR748yqRdJ44gZHWwbRdLMfyiAEdCyOqAY2G0NpgPHWN+4wjveP8z4QO7X2uyovHquRDHexorqyFMbftgzaSbYVT28HBRkAgpkZaHY3uo0kWjX/zSUmL0nDA1LU28VGO5Z/Inv9rG0i9PAfIA1w7D9Qf4kTN/SMrME7fKaG4dIT0codO5q0Dnff/K2cRtfOzEb12zYhHDaXPTxmd4Qv7jDPVW/J+vhw9x1/CLOZX7Adwd+uzVw3FeVP9BnuAKphqn8eqLlAMjtLQkCAVFOsStEllrg6oSZ0U9wseav8D9v7XzVl11TV53xxyBVpuqNkpp4aHii/F2iBGrjIqDh4JAIuh/UCueh1Fr4DSWqBkZ8sEZpto2XS3Bg/NjvGOHuc7zHvqAVxT4oXM38+TmHMPmMgumsaVA4eFQpMORpslaaIyvnbqZT+5wDc3p8qr7Xs9s814CvSWiTh1bCWALHcNroskCHS1F7YEPcv/7inzspt8C4LV3/gtao47jxlhSjyDmPKbb55GuheaauEKlp0RxUYnQxpUqomGiNRcoLCms/NQb+dvH/u8r4HIguK3wCn6wWeRQ635qD9aoBke3PTJs15nstlgMHeYble/nY2++Pi7Zjw5x0t4f828av0VORMlW1gkX53CEgRQC3TMxlSAbxmGqnRE+yOuZe9vN17vRl4WQVeU/fPPbBFoNWsow+eIwFHc6eoJJq40112TxbV/mL7/983jKxW591w4Z4A0YyV/iuPUFkvo6wnNpaRmeof49s+69eB2Vwtyl58XhbgyB5ME7JX+9z/dF3/14NrfIX+MHrb8g5W6QXi9grJZxhIGCi5AeFS1FOXiMxfpJ3t/4A3q/eYN8dAAg+A/LIxxpr6G0XNaaW/vnZLtOyJaoEtpKHM+EqGdieTrVVoTVpSgQxZApDlUeoNjt8o03fJKP3vzdQUA/nm/xijmNwx2FhVIWp7J7IkvxshztrrIyp/HB/93ige2Xpj1BkQ4zpa9zvPxl3wWwHBrn3uEXUY5dOzfV8eo9/Lv76xxptVgPTNJY2F15P2mPk1kv0CnfR/0rt6MgSVhFDK8HQmAqYRqfPk1Tfw+fnP2Pe3a1O3To0sc8EgghfKLYdmTDQdImGAwSjUa3HCOEQFEUXNdFSrltInmQZBkovg8SSDslnQ3D8JPZ17LoYOCU0Wq1sG17T0lxIQTdbpdisUg4HPZVxx9eJKaqKkNDQwwNDfmkxhshgXiAAxzgkWFjY4P19XVM06Rer/tuRaFQiGq1SjAYZGZmxp8jBvOj4zjbFiA8HIqi+AUrFyb194Lh4WGq1SoTExOsrKzQarV8l27oF+pWKhUcx/GJ0HtRdNc0jaNHj9JutykWi1uK1iORyHUX2RgUZkK/6PJShV+9Xg9VVVEUZdtiItM06Xa71Go1UqlLf8snk0mWlpZoNBrEYjE8z8N1Xb/IolKpkM/nSafTmKbJzMzMvitMbzabvkNWLpfb1T0gEAj4LvKD4pLd1rt4PE4wGPSLSgKBwLbORpZlsb6+7ruD7LdntBssy8I0TX8uuBSi0Sj1eh3oF5nt90Lwq4nxcXjDG+BDH7o21zs/9AwaC1mqRpZhc5ml8DFsNYDlGQSkxcAr10Og4m1S0EU/9wEY7qZQmQiyEDvJyqMgoF8tPCH/MRJWCRf1IvL5hehpMSrGEGkrz+HG3SQ7y9TCe3dgcdQgtmLgKDqGt6vqYB/Sw5BmP7av7X3NePrcOzlRu4Pp9ikcdBbCN21xnlc9i5HOPMc692ELnfHePJp0UaWDitsXi96MXet4qJ6DFD1sqWN4NgIPUwn6x7DplPxwNLUUI70lDLfHUHt+zwT0x698kGeu/QPjnbMEvS4VPUfdyOIqBqpnkbBKZKwNok6dZ6y9n46e5FuTr9jTuceq9/D0lfdxqHEPhttFkzYeCj01QjU4QkUfImQ3iDp1FsPHt/SFVG+d7KartCYthPQwpMWwtcpPnnsTxxrf5BNHf41KZGbLNSvRGVyh0VbjxJwKHX13El3c6seTTDVMPnZ0T/d1LaE6PZ648n5uK3yKhFXA8HoIKbHVAL2FCPenn8m/Tv5bVFyed/4tHGp8h6DTIui2EUgsJcgTC5+kEJzk8zM/x2LmSdf7li7C3NDTqS0OU7TGGOkt4QmVlpYCIfAUneXQUSa7Z1FwCHldPBTCXoep7hkE0NQSLEWOUwhO8/6bf3dXwvMjxT1jP0S2swB5GO4tMdE9t9lfgijSI+y2sJQAK6HDFEJTvP+mN21LWn7m+beRsfL+2O0qYST4uWMFD0V6uELDQzxMbOQ6QkqesvC3PGvt7wk6TT+OFPbaBLweYbdF1tqgaIzSUpMknQIpu0QhMEElMModU6/e9fSPy3+SqF1FlQ7V7cjnF6Bm5MhY68TsCo/Lf+JRE9ABPEXnzPD3c2b4+x/1ua4UEp0VHrv2MR5b/jxhpwn05WbOxx/Ht0ZeykL2yRe5dvsQgkpkhs8f+2XuH34+T179AMdrX0f3TFTPwRMKjhJgPvYY7hz7UeazT72Gd7Y/cD77dE7nn4wmbSbbZ5ht3U/FyNHSk0gEQbfVF7iVLrbQCUizX0vj2XiKvpnPLpOx8jhCYz10iK+O3k43cPW/CxazT6IxP0RdS5OxNvqu7Besm6YaIR/q7z+EZ/LhRocNbYh7E0/hM+Ov4ZZbOvzQD5W2PfeAeDX4TrzW6Ha75PN5KpUKtm373/K6rhOLxXzywPc6FhcXqdVqLC0tMTo6Sjgcplqt0mw2fXfPgVsvwNLSEvF4nHa7zfT0NOl0+nv6u+5qQkpJqVTy3YEHBJ5ms0mr1fIJLYO40IWwbRvbtgkEAtv+/kbHwE2yVCoRjUbpdrt0u108zyOZTDI9PU0mk9l3xMDtUCqV6PV6Pomr2+1Sr9e3iCMLIUin0ziO4xNzB0TBbrfrk2G3e9edTgfLsvzf7yVGe72QSqWIRCJomkatVts1duu6LvV6nZGRERKJxBVzA300aDablMtllpeX0TSNycnJLe3SdZ14PE61WmVjYwNd11ldXWV2dvY6tvr6otfr+UIxo6P9RLsQgkwmQyaTodVqYZqm7z4bCoWYm5sD+vH+y3VAH+RyH4nr94B0N8gZX6rPDfY+iqLsi/65EwbkbE3TqFQq5HI719gN8svJZHJPuZVrjUqlQqlU8h2XByIknueRz+cxDIPp6Wnq9Tr3338/4XCYUCjk72MURSEajWLbNvfccw9PfepTOXnyJEKILfPofhaNGKwdD3dr3g7BYJB4PM7S0hLdbndHYf3BsQNB2Xg8TiQSYWNjw6+PiEQiTExMEI1GmZyc9Gstkskk4XCYsbExVldX8TyPdDqNlJLFxUXq9Tqrq6vU63UCgQAjIyMkk0mKxSKlUomxsTEcx+H48eMHJPR9hGKxSLVaJRQK7bqvGIiHDwQhqtXqvpw7LkSxWGR5eZlOp0O1WqXT6fhrUCKRoF6v+yJHuq6jqiojIyPUajWGh4f9eqFAIEA0Gr1oLxqJRDBNk0qlQqVSQVVVhoeHt83tDhzPV1dXaTabrK2tceLEoxP1vRCDOUJVVX+O2w17Me44wAEO8OhwMLIOcIADHOAAe4Kpx7g/9QwSZpHx7nkqzgjWDmrTimeTsotUjGGWI8cpxY5se9yFaITGOJd4PAmryLC5TN0bwhYB4naZtJUn4HZRcem7G3gEvR5VPU0+MHWRwv127fnB+T/3k9YeKpYS8F0fPBQcVUOVLiGvTcATTHbP09QS9LQo94686BE/r2uNRHcVzbMJuW2qxgwA66FZjPYpQBLyuhieiY2BJxQU6aHhILweG4FxiqFJPnTst/bkANAKDoEQ2EqAsNPE1C4OlA1IyD0MQnTxEP0EpfQwsPAQmCKIIwy6apjl8FGiTt0nyaqPwGHjex1CusyUvs5k/TsE3TaWGmI1fjPnh55xkSL5lcRw/QF+/IHfIttbIddbwRMKDS2Fp6gYXo+J7jls08Bwu0Tv/Q3ee8sf76pQfyUwWruX20//PgmrRMIqEnNqvmhFqrfORPsMz1z9O95/4o3XlCQF8Jlj/5mwU0cKhUxvjdHuIoIFXKGiSQdb6JT1YarBEb40/mruv4RavOG0ANBwsJQLPu6lJOR2UPAQfkGmgoeGiotEoEubiNdCsSS6ZxJyW2yEZrg/86w93889Iz/ITdV/JWeukLTyVAM7s9lSZh5XaLT0FHeP7HxfLz7zR8w27mG6c5qeEmIxfIKeGunPS1ISdhoMmatMt08hgOr8O1mKP5a4VSbhVFgPTIMQJM0iIbdDyO1gKQamCIIABFiegSEtuiJMVc8y1p3vF6GtfIAvH/r5Pd//Trhv9MU8Mf9xAm6H8e5cv2jRGPaLLjXPJGkWyNgFisYotcAwX5u8cR0WKpEZ/vaWP+G583/NidodBJ02QbcNm8V9PS3GYvRmvjTz06wnHnO9m3vZmK58E8PrEnEbLAQuHSSr6kOM9haJ2jWG6w+ynrr1GrRyd1hahHvHXrrlZ48tfAZPqOjS3OGvtkL3TEw1jKleOwfcA+yMe8deSj2Q41nL72a8fZaQ00STNhJBV4vR1uJ8O/t8vjbzU1d9/b0aOJV6KkPdJaY6Z1E9y3cSMtwuYbeFLi0sEcATAs2zEYAjdBr6QwQkRw1S0zOkrCInanfwWbN8TYrhrjakUJCbe3axB0eIQZG+hAsc4x45juc/x3OX/i9Js0DMrqB75mZ/i/Lk/MdYit7Ep4780kXF71cDyd46ujRRpUN7D8SItpZgqnseo9cjYZcJeF1sJUBbjSKQZKw8OXOVipHj9rN/yEfdDg+OvvCS570aBPQBIbHZbJJMJgmFQnieR7PZ9JOaExMT5HK5LQULg+Qz9BPr27lzDAoVpJQ0Gg1CoRCqqu5Y2DUgf18rV4ZareYXpnU6HVZWVlhdXfVdXrdzLIZ+on51ddV3C1YUhUaj4T+TkZGRbQly+8lt4gAHOMCVw/Lysk9AbzabGIbhz6WFQoF8Pk82m8W2bY4cOUI8HvfnFiHEnou5XNfdIuaxVwyI4tAvAqpWq6yvr29xB08mk6RSKZLJJEeOHHlERagDx6H9hkQigWEYhMNharUaIyMjux5fq9X8v9mOLDwojHMcZ08FPYNiaE3TaLfbnDhxgmQy6a8DnufRaDTI5/P++hcMBvfVs+x0OvR6vT25gkD/ma+srOB5HqZp+iIH20EIwfT0tO/ctLi4SCwWI5lMYhgGjuNQr9ep1WpEo1FGRkYuSWrfr3gk42k/ua9dbzzhCWAY0L2Aw6zr8Du/A7LbQbnjs6gbSwjHxouncZ70XMTo1GVeTUN88d8Sefca+lqTE937cCM6XleDXp9cruKi0iekeYqCp2kENBfVbmNg4aoa1lCWidfdzu88e58V8pc3GH79/URb65jpFNPZS+RBrDjJhVXEcIVffdrncX/4p/Z8KWk7hP94iuCdZ8lUNghNDIG6i+BpvUzIsUgfCvHUn388T3nKHq7RbjL06/9EVD+LEvVoTUwxoqoge4heC6NWRjM7hGQNDbufo1I1XE1HuBLpCX+sKXh4QkEoElfVCDk9EH1yqmE4KK4DQmAEPdIzOml9azxLSpfgWYfx0Q63v6zOj7xsr+1/O5HAWXSvTXPyCDE9QAwJDM6fwbGjJJbPEQ6cZdx4By993QsRkd3zkuJz/8TQ3/8ZuigSlCU0p4cUAiH7OcqekkLt9NC8LjKiMDyrg+j38cDGEiGzjCbMTUK+wFMVcCUhr8OIs8Iz9X/hSd05Sq/9n4hDF8YrEwT+4jaMf1khvbFIaCSFDOwQF3IdYosrmOk0uZND/MLvPQb20ZDx6lUSf/obROQDGMoGAaeGMtireQJTi3KMVV5a+iiKlGhak5DcQPN62EYQhECzeniuSk9b5Im9cxSf/SvI5+yhc1xTaMgHfoXYn70eL9/lcOUMNkGsaApP11EsiyA2etfFNMJ4qk47O4pnBPECIZRwFOO2ZzH0Iz/Lf0rnkJ0m9DoQjiFClxfHlZUC6p1fRKlX4DEh1PnHoM4rmN0seqNCxLGRQscKT+FF4gRnTpD+mdfzS9vM/d7CGUb/4B8IK6cRcYXS2G1o3TZGs4bYHNeuHsDTdCLVPIdTG/xG+k9p/9afP9oHi+M8RCTVtEdOptD/35+SXvonQuoCAbOJHQ/i9FxwQHVsYl4DTwoSvSqOptONpzGTk0SyoyT+/W/yS7fsvhbG/+uDJNsllGCQyQkX2J34qq6FGBUlnjNzmsf9ziO+nX0P9ePvIfPhd6DKFoZSRPc6ICWupnNELPMD3a/S0k/S/I9/gJK4lAjVzcBvQ3kDcf9diE4bGQiiHjnJTZOHuela3NC+hEB230Dkz/4L7oMORq3IRGMF1Z4HQCoKvaEkDRnB6DQwo0lynTJj9pq/hrmajplOQ3qY2NOey/N/5jU8/5qsHRraPz6H0EcXiC8VOW6cpjs8hXj4N4ltEl45jxvVWE8GCP7ED/Di56WIxeKwg7jF4Pvycr7xHy1qtRpzc3M0m00qlQrt9kPO7YFAgFQq5RMXJyf3LkT03QbTNKnVauTzeeLxOGNjY1viOQOyViQSQdd15ufnicfjjI6O+t//38vP72rC8zzOnz9PpVLx3ekrlQqtVssncHW7XRqNBpOTkxfFeKrVqi+Cl06nd7jKjY2BSOAgrqNpmk8MXV5ept1u70jo2U94OIlLVVVfOKDRaGwRiEwmk6yurvpjN5PJUK/XfUL+IJ40iNcVi0XfMRr68dFQKMTQ0BDZbHbfkWIjkQjhcJh0Ok2xWCQajW77/qSUrK+vo6oq8Xh8W+Hp64GBeKlt20xNTe34fFOpFN1ul3K57JM1v1eJXIO9gud52z6DaDR6EclcCIHrunvONzQaDarVKo7j+LFpVVUJh8Pk8/k9iboOxuJA0GYw7i71N+Fw2B93+xXhcJhoNMrw8DArKysYhrGtQE+n02F1dZVUKkUgELjma0un09niQv9wp2Doj8FarYbrukxPT/tz4iD23263/fx9uVym3W4zNjbmi0VYlkWz2fTH8fnz55mdnWV0dPSGEXMZCB44jrOjsP4Agz3fILe027GdTgfP87j11luJxWJomsbIyIgvyqAoCqlUilwutyVfIoTg8OHD/vjb2NigUqlQq9VoNBqsr68TCAQYGxtjeHiYRCLBxMQEUkrK5TIrK32DrLm5OW666Xv3a3M/wXEcbNum3W7vaR6IRCIoikK73abT6VyDFl4+isUii4uLrK2t0Wg0iEajZLNZf9915swZv097nsfx48f9v9U0jVQqtae5YjAftVotxsbGdl2DBsT3QqHgCy5dKei6TigUIpFI+PvK3VCv19E0jXA4fF2F2Q9wgO9mfG9+ERzgAAc4wAEuC3eN/TC3lr9I004x2T3DenCm74RwwYddwOkw1pvDFgY1I8edYy/f8/m/MXY7R+vfIuZUmWqdwlICRDYJJsamwr/YLDDyUAm5HQ637uFJi+/hG9M/vuN5jxS/TMIqEXC7dNQwmnQIyQ5dGfZJ6ACuUDEJEvY6eAjy6gTn4o9/RK4S1wuK1w/ECjw8+oEJRwmwFDnOROccLhq6tNGkjSadTT9klY4SpmyM8N6Tf7hnQm41PMVq5Agpc4OUVeirsgsFRbrE7DIpq0jA6xLwegSwUJDYaH1CuhLeJMwIWlqMqp6joyVACIJuG1sE8IRySVGBAwBS8rjVD/Hk9Q+SMguEnQaqdPCESluL01jIcufIy/jm5Ct3VoO/TKiuye2nf59sd5kRc5mN4CR1fWjLXJD3bIZ7y0x3TiOB75/7Sz597FevaDsuxHD9Af7tg/8fQ91lcuYKlhKkrqVxFB3ds0jZJYasdSJ2jR+///X8v1v+mGpk+qq15+FwFYMPnXwTT1z6e56Y/zhxq0TIaaHg4gqdrhZjIzTD1yZexdnccy59PrX/Ue+hoMqHHNdSVh5dWiibDkDQ9wWSm0l1iYIlDASSkNciZLWpahl6WpR7Rl+y5/tZTH8fpdA4cbvMaG8RV2g09OxWEQspSVoFstYGq6FZNsKzO5KQ0605bqr+K+PdOTpqlLXQ4a3nEoKOnmBJizLVPs1wd4Hvy3+cjhZH90wU6dLR+0GDlF1El1Z/LhmQzzfhCI2A10Mg6ehxArJHysrzuOJn+drMT/kEx8uFpUV4/82/y6vu/y+4QidrrjHTeRBXaEgUNGnRU8KshWapBEb4x2O/RSV6hdlr1xit0DAfvfm/8vlekZsLnyXZ20BIl7ae4sHcc2/4+wMI2k20TWEUaw/ka1MNoeCiSIfQpkvHfsRS/DFMtx5gqnNmC8F3O2ieScRtUgqMsxi7ccUEUu1FHrv+ccZaZzC8Lj01ykL8Vr4z+hI6wf2tYLodljJP4t2ZJ5FrnOJw+Q7CTgNbGJQj05wees4NSTwf4NujL+WJhU/SU0KMdudZDR9FCoWg20aRHop0cZQwQnoEZQ9LGNiK0XeIuoAo0tDTTHdOo3o2w82zLHwXENCroXFcoWMLg4hTo6buTuCKOHUcoeMoBrXQ2GVd87blD/D8pbeT7a2SsotIBKYSQkiPIWsdSwkQtavk7l3ifTf/PoX4lVO03Q5Cun69oNxDdXxo06VKFTYeguXwsS3fsXkpiTpVRnuLqNLhpfN/Rj529JJr2KMloD/vec/zk9DQT55ks1mCwSDVapVyuewX3AyI1PF4nFwud1HB3CCRnkgk/GKQhydgw+EwgUAAz/OoVCocO3aMZDK5Y6J2oIysquolixqklNi27SsrPxJCmud5zM/PU6lUfHJbt9v1ifjnz58H+gWZExMTfnHHoHinUqkwNzdHu91GCEE0GqXRaPgkzk6nQ6fTOSgyPMABvgdQLpfZ2NhgaWkJ13WZmpraUkQipaRer29xPHjMYx6DrusEg0HC4TDNZvOSRVmmaWJZ1hbxj0eCQCDATTfdRD6fJxqNksvl/Pl+0JahoSGGhob2XYHl5ULXdd/xamVlhVgstiO5u1Kp0Ov1GBsbY2hoaNt16kLRAMuyKJVKNBoN/zkOCtDi8TiKolAul+l2u0gpOXz48EUFCoqikEwmCQaDLCwsEIlEyOfzHLrSajOPAp7n+f12L/1iIDoz+NtLIR6P+05G0WiUarXK0tKS//tQKOQX+U9MTOybotm9YuD64nnengo22+22f8yNUrh3raEoMPvZNyM+/3lEswntdv9bzDDgi+/AfexjCf3czxF+7GMf+cmf8DJq6RK8613IYhGjVMJwTaQiUL1BjJNN73OJ6tno3R6KlDiahh2LIv79T3Dbr7509+tcB3TvL2NGHQxpIlMGgahzib/QUNYEEa1LKFEi/YRLX6N35gydD3wA8aUvIfJ5ArVVtF4Hbe7bmLOzsJ2DpmWh11dxswnC00ke/++fw160bWvv+QgoNQJmHXN2llhMQqeBtryM2ushHAfVslBc1xdQ010H1XPxNA1HM1BtG8XzkEKgSA8kqIrE1TX0ze8lzemheh5OIIBMRIimNOBhz67dRtUk4aTKocdGSezhWdXf+1GkUiPQrWDOzBCNqxefFwAVqY4SWVhAU2oklj9C8sd3zkm2vvhFnI//KUppAa3RwE6nsbNTiEAA6XmISoVwqYTeaoKUeJoGsU3yW7GI0S6hWt0+0S8UQG5+X7mWQDoOUlNJlhYwgw7xd/8myfe9D+WCPU/nl34U6+wXoBsmmT+LNT2NeNi6K00TfW0RDDAmM+R+5mUcfeL+YZ9Lx6Hyi7+JuvZt9LV5pK7jTI3iDr5hWy30cpnQ+hmE5/mxICeRwB6fQCSTIASO66KUy8Ty6ziiQ+xj/wv9OWNEnrCHDnIt8YTbaB/9Xaw3vQmnEkcplYg2iuB5SFXFzSXpZY/gJpPI5z3PF+BRRkaIvuAFADQ//GG8d30CZXXVP6179Cjay15G7EUvQtkDqal37hztt78d9etfh04HTBOEQIZCyEQU5/ghlMgtCNMEw0Cbnib88pczsUuBdeXT/4giqwScNr3jx4kFVEjFoatDp4NwXRRNQ8ZiuLpFrLlOqPgdgvGzBDeFmy4Xtv3Qfz/S5bz1+c/j3PVBtMI5wMU5cRgRDmM7Dt76OlqthmPbaL1ePwdlqIiJIYwXPJ/Uz/0c03toeyXWRdMd0EC95HoASghwHFKRDoevUxd2qlXan/scXrEIioI6Nkb0ec9DeZRkldrf/A18+m0ohUX0ZhMnEsEdzfSv0ekQr60i62tEtTa5//vLJP/qr7bMeztjBF6w99zn9wbCyKf8EY0PfhDnIx9BLi3hWFZ/HtV11GQSnv98mJ5G/fjH4fRpnE4HHAdUFSIR1KkpYrffzsTLX74nc4MrBe/kT1DN34EXMAktLBBauQ8nmURurnFKvY7WaOAGAnRyI9ROniT6tCyx2O7iDvV63XeV3E2460qj2WwyNzfHxsYG1WqVeDzO9PQ0hmHgui6NRoNisUi9XkdKiaqqjI1dXnz/RsfAebnb7frPIBaLMTExgRCCeDxOo9Gg3W77TvaGYZBKpchmsxw5cuSGFFC7ETA/P++7uA7cI3O5HMvLy37Mv9PpkM1mkVIyMzPj72NarRaVSoXR0VFisdi+J2BfLtbW1lhbW9uS+wC25DIqlQqzs7Pbkin3AzzPw7IsOp2OT+QJBoM+GahcLvv5J+jH5XRdp9frYZomGxsbSClJJBJ+PMl1Xc6dO0etVvP/sTc3bwMSU6vVolwu++6d+wnj4+N0u11M02RhYYFMJkMymUTXdaSUftsHJO9EInHdSFBSSrrdrp/DK5VKVKtVEonEJQnl6XSa+fl53wV1N9fp72Zc6L5qX/iRsQMGpNq95Eur1Sqrq6v0ej2fUDwg26qqSqlU8onI4+PjO56n0Whg2zbT09O4ruvPsclkcsf3bFkWtVqNsbExv//uZ0xPT/sO2IP906Ddg71Tq9UilUoxPDy8hdx9NTEgIQ+IlxeKaQQCAT/Xo6oq3W6XVqtFtVollUptad9AsMA0TYQQ1Go1wuEwwWCQYDCIEALDMDAMg2g0SigU4v7776fRaHD27Flc1/X7ZyAQIJPJ7Fs37oGQB+wsrD/AgDg+MjKC4zgUCgVGR0cverftdtsXH4jFYn7ur9Pp+AL2A3H+7RAMBjlx4gTz8/NEo1FKpZI/X2YyGSYnJwkEAiSTyS0O0kNDQ0gp2djYIBKJ0Gq1LhKkOMDVw2B9GsydA+GJQU5MSrnnPOsgp7ZX4ZDrAcuyWF5eZm1tjU6nw+zsLL1ez8+rmqaJpmnUajW+8Y1vUCgUsCyLI0eO+CJI7Xb7knvOwXcV9PeBe5lLQqEQlmXhui69Xu9K3K6PoaEhms0mpVKJcrm8Iwndtm1KpRLpdNoXLhmIyBzgAAe4cjggoB/gAAc4wA2EaDfPY/KfIttdRvEcunqcM5mns5j+vmuS3CnGj/OpmX/Pixb+klx3qe9wrARoqXEkgrDbIuS1aWpJ1kOz3DHyQ5wdevaez7+Svo3PTb6GH1h+F4ea32HYWkPFQwIOKgML244IIFFwhcpQb5UfnvsTnrj+ETpGEolCLTDMvbnns5B9Mp7QuKXwWWJWGU/RWArMMN6bR3iSsNvGEX1itESg4KHTJ25aIoiCx6eP/NLVepxXFF0jCYCjGAS9Nl36gUtbCTIfOUnEqZK2ioTdFgMXeTxYC07z0dlfohg79oiu963hlzDZOkXayjPSXaShpxnrzaF7A5K7jSo9VFwEoCJpK0HORm9FKhoeypY+K6RLwqmwEZjiTOKJeMr+Dmpdd0jJC8/8Tx5f/DRZc42kXQbp4QodFQck1PU0CavISOscH7/pDVfUDf1Y4YskrCLD5grrwSkaxsVFp56isx6aZbx7luHeMreWvsC/zPwMvc2+eiUhpMsPn30z2d4qw+YKa8EZmnp6Sx+rBEZJ2EVGe4t4QuGlZ/6I//e4P7u2iXGh8Y3pH+ebU6/iUPGrjLTOors9elqUhdQT+uTsPbbHUsNUAiMM9ZaJ2xWqxjAIQcoqoEsLF42+I5CHioO3Sc7yELhCAQkRaeJsikN8dfT2XV1hg1adsF3DFRqdUA5XDfDhY2/g393/GwgkI71lsuYGdT2NrRjonkXCLqNJh7XgDMXgFB859ps73t/j1z9GyGkQ8Hosh4/ueJwUKvngJNOd04ScJrPVb21tp9NC90x0aV1EPt8ONSPHWHeeiF1lqnIX89mn7v4HD38udp2wWUZISTuYpacnqIan+Ntb/oRnL7yDk5WvEHTbBNw2ArCUAKYa4Xz8cXxp+jUU48cveY0bBe3gEHdO7eLmLiWj9fuYqt2N4Xax1SAr8cewknr8NR2HlwNXNZD0A5JCukixe3ByIAohUXD28Xo2IPiaSpCh3gobodnt34WUDPVW6CnhvljF2P4r4L4UQlaVHzzzPzlav4ug0yLq1FCkiys0jtbv4pnr/8A92R/gc4f/A666P5Mwu6EQP3HVCb/XGpXoIe7JPBfNM5lqn2a6/SAlYxThufSpBhJN2hjSxEXDFEGKgXGEeMg9DfDHoIKH7l3ZIPf1QjF6lI3wLOneOmmrSN3I7TwvSY+0VaCmZykGJ9mI3/yIrzdd+jrPX3oHY53zhN0WG4FJWnrav6bi2aStPOPdORTp8IpTv8s7H/+X9PTEo7nNXdE0hnAUHYlC2G3RUnZ3PcqZq4jNb6CV0CE6D2+bELT0NEsiwEznFE07xRPWPsxnjv3KjudUVZi6XFPHTXzkIx+56Gee51EqlYhGo37CckCySyaTDA0NbVukMkhwdrtdFhYWWFlZYXR0dEtRwcABeJDwGSgcb4dOp0Or1WJmZmZXhwnLsigWi5RKJT+hDvjtT6VSuyqRSymZn5+nUCiwtLSE53l+4jYajTI/P8/c3Bye5/mkuIEjQ6FQoFKp+IVshw8f5vDhw4yPj/uFBgNleOgXPQ0PD+/YlgMc4AA3PvL5PJVKBcdxmJmZuaiwSgjhF1wtLy8Ti8UoFouMjY2RzWZpNpuUy+VLFoiUSiVCoRChUOiSSus7YVA4Pjo6SrPZxLIsv4Bot2KbGxG2bdNsNtE0Dc/ziEajLC8vk8lkSKVS/nsaFEk0Gg3Gxsb8tWQ7BAIB3+3jvvvuI5lM+gV6g9+3Wi10XWd4eJj19XXa7Tajo6O7vrNgMEgymfSLMG3b3jeFd5qmoWkaUkosy8IwdhfQGxSaDP52L0ilUgSDQQqFAvF4HMdx/OIsTdNIJpPkcrkrWkAlpdxSBBYMBq+Km9JArCcSiVCpVHYlbnieR71eJ5fLEY/Hv2fdnS4Jy0L5wAdQ8nm0RqMfTxAC4bq4wSBUKpgPPID7hjcQe97zHvHpkz/90zQPHcJ6z3uQp05h1+sYi4uopolqWX3CqRAIz0OVEk9RsAIBnEiE3q/8CtnXv/4q3PSjhxjMKZttvySk7P8jBOIS4x4pqf7VXyHe9z6UZhOlVELtdFAGzkudDuqpU1iJBM7Ro6BpfTJ0tYqezyN1HW9sDPXHfxyxx37vfvvbaNUqnq5DNIpstwnMzaFYlv+ehJQ++XzwZaBKidy8f/93F/xbcRycYBA5aLttYxsGUtNwd1gblHIZJxyGYJDwM56xp/Y7X/wiWrmMaxhwqfU3FsM1DESphPuFL8CAgL75fnxIifVXf4W6vo7WbGIdPgzhsH/vQlEgm8VOp1Hvvhu910NxXcx2GyIRtFIJxbIQUuKEQg/Lo0lQFLxAAGt4GGN1FSsep/HRj5J81av848KPfSzWa16D+653IRcXCZ47hxsK4cZiIARKp4PWauGGQjhHjuA+5zlkXvGKPT2za4XmP/8z6v33o8/P40ajeFNT/XEwOCAWw4vFcB98kHC5vOnKq0GrhdZuI9fWsFMpGBpC5nJYkQjG3BxWoUDvb/92/xHQgchTnkLgve+l9eEP437848h83v+dNzmJ+rKXkfqhH7qIdNv++tex3vQmRKWCWiqhNpuITeK6yOeRp05Re/e7Cf/3/07wyJEdr9/++texfvu3UTc2UIvF/nPcfOZSCJx4HKVWwztyhNif/in6XsgnUiK++EWUUgk7Gu3PY5UKarmM1ulsmeNQFOxwGLXbxWm16HziEwR/+Zcv93E+apjvf39/PJom1vHjPoNdaBre5CTW+DhUq1jtNoFCATsex33iE8m++c17voaMx5G6jtposJfyZqXXwwuFLj1fXQVYKys03/Y2lK9+FdFq9UUIAC8Uov6WtyCf9zziP/uzaDvEmXZD79w5eOc7Uc+f75OHw2GEbaNVq7iRCDKXwxwbQ11e7o9jRaH2F39B+nWvu9K3+V0Jp1zG3thAaBr6yAhqIoHQNBKvfCX82I/Rve8+7KUlpG2jZjJEnvzkh/YcL385vQcfpHfnnchWCxEOo99yC8nbbrsueT0lGCT5v/4Xtde/HoLB/rxXLqNUqwC4oRDW5CQkk3Sf9CSsF7yAWrlMOpPZcU9v2za1Ws13MrzUd9aVxPLyMuVymWq1epGAnqZpfmx1aWmJtbU1VFXdl0SidrvtE8QHZLtUKkUqlbpionqDWHUoFNryjmKxGEeOHPFFBAYxgYET+vDwMCdOfHflzq4XBm66A0dW6Is3DMTrAoEA4+Pj/jtvNpuMjo4yPz9PsVhkfX2dqakpVFVlYmKCarVKvV73Y0MTExPX8/YeMQYil8VikXa77T+bgcv1ILa3sbHB6uoqKysrdLtdkskkIyMjaJrmu1SurKyQTqeRUnL06NF9Exfs9XqUSiWf0HTmzBny+TyTk5M+cTmXy9Hr9bAsi/X1dRKJhO84K4TwhYaTySRTU1PEYjGSySRSSs6dO0epVGJ5eRkhBLFYDNd1cRyHbrfL8vIyhUKBQ4cOIaXk+PHjW5zTG40GlmUhpcQwDF+E8lphIBoC/dxbpVKhVCptEYRMJBKMj4+TSqWui8il4zh+/m5ACLNt2xeOOPowwaTBs/c8zxemDgaDKIriC7J+r2IQr4zFYtRqNRKJ3XPgtVrNF5rdLY6az+dZXl72Sa5SSl9IPJ/P+2ubaZp0u11UVSWXy13U15vNJmtra2SzWX8+XlhYoNFosLy8zNjY2EX7h16vx8rKCpFIhFgsdkOICwSDQY4dO8a5c+eIRCJbxNwVRSEajTI7O0soFGJmZmbHHPiVhOu6nD9/nmq1epGYhqqqW8Q0jh49Sq/Xw3EcLMu6aL4fEJoHDui2bTMyMkKlUsHzPBzH2fIeo9Eouq5z7tw53018QBgNBoOsr68Tj8eZnJzcdyIvyWTSF9YvlUq++/R2aLVaBINBYrEY4XCYXq/H2bNnSSQSBINBX3Ch2+2SSqUYGRlhZmbG3zPuJIS8HQKBACdOnKDdbnP33XeTSqXo9XrMzs6SSCR2FHTIZDJUKhVarRbFYvGAgH4N0Ol0WF1dpdFo+MRrIQTBYJCVlRUSiQSO46Bp2p7Wr8EeZJCH268oFot0u10ajQazs7PUajXK5TKNRoNms+nPh4Mc19mzZ33BrsGYq9VqlySg12o1UqkUzWaTeDxOp9PZUre0HQbrlGEYj2jc7QWZTIaNjQ3GxsZYW1vDtm0ymYz/XTLYGxYKBT93Pzo6ekXbcIADHOAh7N9Z8gAHOMABvocQtGqc3Pg0461TGG6XnhZhKX4rDw4/D1sLE+6VeN75t3Ci9nUCbpuw00LgYSsGtxU+RTU4whenfoozuede9bbeM/7DWEqQFy38FRVnlIRVJOh1QEJXi7Kuz2BqUb489krumP6JR5x8+ubUq5BS8toHf6XvTi4dbDQU0SdO2oqOJYKsh2bQ3R4z3dNk7DwT3fOU9BEQgp4a5qbqv1JbyPHJQ/+JpJUn6HVoq3FaRoZVBGO9eSxhYEiLgDQBiUTBFkafrik05qKPoRna3VFwv6ARHPWJKCm7RNUYeejZC0FbT9PW0/1kPx7Z7gphr0MlOM6Z3N5FAgY4NfI8bi18Bt2zmGnfz6i5iESgSQdPKHgoA1NCJKDgokmH6c4ZFiInQLlATU5KhruLeKg0jTTfHr3xiG3XGk9Z+FtuK36KyfYZFNxNIlCqTwTadHDMmutMt08hkDTPp/nikf9wxa5/W/4TJKwSzsD1eicIQckYY6ZziqDb5jEbn+KbU6/a+fjLxEzp66R7G+TMFTaCkzSNbYqJhaBu5NA8m1xvhZaeZqTxABuJk1e8PZeCJzTO5Z7NucsYez6E4Fu5HyTXXSRt5Yk4dSQCY5N83VOCCCQBr4eySUQH0HDAk0ihYol+sM1UQ9w7/KKLLqFIhyOFf+G2jY8z1XrA/7mtBPlO9jl8e/RlvPfm/8YrTv0eLT1F3CwSd2p9V3dUqkaOupGlEhjl/Te9cVfH+SO1bxK3KzS1+BYXZs3rEXQ6KHh4QqWjxeipUUwlRMyqkOmtYSt9cnDIafadjzaLGl3l4k8ddZM8LBE4IoCtGgg8dM8iapX39uylZKpyJ4/f+DjHa3cifHd5wbnEbXxr5KUsZJ7MJ0/8Jl+0foHj+S+QNNdRpEdLT3Em92xq4e8tB84T+c/ylNX3M9xdIui0UKWDJ1S6WpRScJxvjP4I946+ZN8S0YuRWSw1hCMM4naFWmB38lrMrtJVInhCpRKZuTaNvAxUooc4m3gCAbfNVPsMdOcpBcdxlIeSFppnMtRbIeo2WAyf4FTyyVTDj5JxeY0RMsv8u3tfx1jnHCPdRQJel44awxE6QbdD1lqno8Yw3C7J3jr/ePL3b0gS+ncjPnPsPxN2GkihkOmtMWouYngmIa+DhgueiakEsESAYmCMhnHxnsRwe/R3vyo97bsk0SMEdw2/mLH2WZJ2kbHuedZChy8ioQvpMdKdQ5EetcAw3xq5vHn2aat/R9IsEHabLIZvwla3Jig9RacUnMBSAoz0luhocU6uf4q7pl75qG5zN6ykH09LT1HX06SsAi0tueO96U6HmFsHoKuG+99EO8DUIjS0FEmryC3lL/El5xewtO2TI7feClcj96QoCrlcjlwu56ukD9SaL0WAGx0dpdVqIaVkeXmZc+fO+c4hUkqazSadTodcLodlWTiOg+M4W847SM5sbGyQSqUIh8M7kv/K5TKLi4t0u12q1SrtdtsvMkwkEtTrdaLRKEeOHNmxeHNQHLC0tIRhGExMTPgJ5rGxMUzTJBgMcv78eU6dOsWRI0eIRqMkk0kKhQLnz5+nXq9z/PhxMpmM31ZVVX2F84WFBTY2NnyH+QO3mwMc4LsTrVaLTqdDrVYjnU7vWiAQjUYJh8PUajVKpRKjo6Nks1nW1tZIp9Osrq4yMTFxUYJcSkmxWKTZbDI9PU0ymXzUxemDxP93IzqdDhsbG37R1WDdabVa2LbN+vq67+AC/WLmcDjM5OQkyWRyV4eyQeGjaZosLS1RrVaJRqN+AVaz2aRWqxGLxWi1WqysrKAoCplM5pLFP8lkkvn5eZ84n07vvHe4lhgUVAWDQWq12iWLAgfFkIFA4BEVmIVCIaanpxkfH/eLqBVF8Qv4rxRs2/aLYG3b3uLunkqlyOVyV7RIRdd1UqkUrVaLpaUlwuHwtkU2UkrW1tZQFMUvHD/ADvA8jPPncYNBzJkZxCaxVZomSrFIYGUFq9vF+cM/pDM0dFlO6LFnPxue/Wx6p0/T+/rX6ebzuHfeib64CJ0OWqmEsKw+ITcYxHza04j9wR+Q3YVgeb1hTEzQNQzccBilXse7VDFsowGAjERQp3eOrQJU//IvEe99L+r8PFq7jR2LYY+MYLkuwZUV9F4P1XEIVqu43/oWdjyOYtt9l+hUCm9sDHn77SRf+Qi+5dptcBw8XUc6DsbCAoppolkWjq4jhUC3LITr4gmBIuVDJHTX9UnoAwg2Sdaui9rr9cnSUvZl6FQVc2xse+Jlo4Feq2FOTeE+9anoexy7olJBmGaf0Hmp72Uh8EIhhGUhz56l/Gu/hnLffYhuFxkI4B09iv5DP4QSi6GsrqKVy9i5HOzgpCoUBWt0FH1+HuF5KGtreLkcimWh2DZuIHARsV1xXVxdxwmHkbkcXqXSJ8R/5CPwylduOT75Mz9DLRDAe/vbMdttlGIRtdXqO64HApiHDkE0iveDP0j6139938Vl7Q9/GK3cj9cPyOcXYWODYK3WzwcAquMgOh0AXE3rv6tKBXN6GhGLYadS/XPefTfm0hKBR6sqtwucahWnUABFQR8dRd1j8bGWSpF8zWvgp34Kp1LBXFjAOn0axfNAVTHn5gg95jH+8e1vfhPrt34LZWUFI5/HNYy+S7quI0wTrVxGFApYjQad//yfEW95C4Ft5pLeuXNYv/3bKAsLGPk8djLZH2/hcJ8g3migFosY585hOQ7N172O5F//9SVd1d1mE9HtopomTiqFsrKCXi4jHAfVtvvjfZOg4wmB0uv1HdELBZyFBdx6HTUev+b9s3f+POr996OWy9jp9Pb26YoCmQwyk8HcdOnWzpzBWlnB2COJTnnKU5Df+Q5qsYjdbiN22fvIbhe108HJ5fCOHcOpVNCu0T61e++99F7/etS1NdRCAW1T6A82xQliMbxymcaddxL9X/8LYxdnxu6999K74w5ks4kIBtFvvpneF75A4NQpQuUyjmGgb85VCNGft0ol7Hgcb3ISb24OZWMD+ZnP4P3iL+7RBf17D9J1aX3uc1gf/jDqvff6P+8pCu6TnkTwR36EyFOeAkIQuuUWQrfcsuO5gjfdRPCmm65Fs/cENZEg/ed/7t+fvOD+0DTcpz6V4I/+KLkjR6ifPUuj1WJxcZHx8fGLvosGxKuBg++1JF4NYhjlcpmhoaEdndc1TWN8fJy5uTmfDLqb++m1RK/XY2FhgXa77d+PlNJ3aQ0EAoyNjV2R5+p5nk+IfDg0TSOTyZDJZPzjqtUqnU7nmjraXy04jkOpVKJer/uEknA4TDabveLEjofDdV3K5TLFYnGLi6FhGAwNDVGr1ajX67iuy9jYmB/XHzgtN5tNhBC4ruu7iC4tLTE9Pc309DQTExPE43EOHTp0Q72rbrfL+fPnffJPs9n0+2c8HqdSqRCNRpmamvLdzy3L4tChQ1viKpqmEYlEaLfbLC8vo2kagUCAkyevff3ShZBSsrKy4jv51mo1TNNkfX2dRqOBlBLbthkeHiaVSvnkHsMwaDQa1Go1hBCsra35xOuZmRk/1ieEoFQqUavVWF5eRtd1VFWlWq1uITnruk6tVuPOO+/ktttu8x2N8/k8xWLxomMNwyCbzfoE/2uBgSjI2toayWTSJ5ZCP742cD4eGRm5puR46PfTs2fP+k7L9Xrdd7IvlUo0Gg3fUTkej/uirAPnbiGET7j+XiaeDzAQAW+1WszPz+8qYOs4DpVKhUwmQzgc3nGurtfrrKyssLq66uduLxRSsCzLF9n2PA9FUajVajQaDZ9IOBDjtiyLbDbL0NAQhw4d2kIWXF1dZW5u7iLH6W63SywWY2xsjPHx8RuGMBsOhzl58qQv5m5uCmPBQ3uCoaGhayLYI6Vkbm7OF9OAvghqNBpFCEGn06FarVKtVn2hlYH4PHDRvDAYkwPiaCKR8NeNwR7nQhSLRSzL8udoz/PI5XK+gG0wGCSbzdJutzl69CihUAjTNHFd119zdiOTXk0IIXwRkwuF9R+ef7hQXCGXyxEOh31holqtRrVa9Z3Nx8bGCAaDTE9PP+qcTiQSIZ1OU6vVfBL/blBVlWg0SqvV8oXxD3D1UK/XmZubo1qt+kI5A/Fm13WJRqNks1kqlYqfT8tms7v290ajgaqqRCKRS4qMXC9IKX3Bkkgk4otbbGxs4Lou8XicWCzmE9ArlQrnzp1jaWnJ34+k02nfHGKn77RCoYBpmgwPDyOl9HO6A1L6djBNk3K5TCQS8YVNoD93DdzpdV2/7DlHURSOHj3qr4eFQoFqtUooFEIIgWmaSCl9Ee2BANABDnCAq4MDAvoBDnCAA1xHqE6P759/K48rfpasuULG2kD3bDyh0FGj5M9N8vXcSznUvIfh7iK53gohr42lBPFQMKSJZImquUbqbJ7PmCW+NfljV73dD46+kPNDz+Cmjc/w2OJnSJl5QNLSU9yXeTbfGX3xrk66l4IuLZbDRzne/DaWMHyCOCh99wPhkestE/S6SMQmCUUnrDQx1RA5s++CXDJHeOXpN/VzlEjk5v61ZaQ5r0VJWkWSdglN2v61e0oYIT16SghbDT2q53RNIQTfGn4x4+2zZKwNsuYqpcD4xUl5IQg6bVJOifXQLOcSt9EI7ez4shM8ofFPN7+RH7vvDUy1TyGAgOzhoqBIiYLsiwUQwPBMBJKI10KRHqPdeVYjx0BKwk6DjLVBwOuyHD7GfOxWllJPfPSPQ7pEeiUMr0tXi9E10vuugOZyEbCbPG3jg4x0FxFIFiM3bXU333RwbGsJJttnGO3M832FT3DnxCtpB69A0aSUjLfPEnXqNPRLP1dTi2IpQSJ2ndHWmUd//W3w+PwniNtlJGJ3QjxQDQyTsfJE7BqPXf/EdSGgXyncP/pCnrvyt/SUEMdad2+uCxYqLg4alhLAU8KEvTYSiYuKLXRMJYgnVByhE/I6FIOTpHorNMMPKb+FzAq3P/hGJluniFtlEnYJ3bOQQtBVIySsAk8s/DNfHH81f/34t3G8+AWekP8Eo505/xyrkaPcNfwSzuSec0kyZ9Bto3k25iapLWLXSFl5Im4TRfadbiUCTyg0tDQuCpq06epRDGwaeoqUVaCm79LHpcSQFpYwsNQgphpC8Z2qhe8SvxtU1+QHz/wxJytfJmZVSNlFAl4XAFMESVhFjtXu5MHUU/nY8dfTNVLcPfmjlzzvdy2k5Fnn38pTNz5E2uwLpGjSwhMqinRxhE62t8ZQd5nR5mk+fexX9+VcvRG/mXxoinRvjbRVoG5kkWIHIoRnk3TK5AOTnEk8kVZwfysFf+L4r5P4TgEQjHQXONy6l44awxYGurQIu016SpjF8AmWoyf45LEbzFVESl5+6g8Ya59jqnOGup5iOXx0i9CF4XTJmUtMt08B8Nzzf8lnj/3na9K8oF3naPHLxMwiAPVAjrNDz8bSb4wE39WGqxh86OQbecLSP/B9+Y8Tt4pE7RpT3bMEvC4uGg0tRSkwsiOpOGmXaGpJelrksty/9yseGH0Rx8tfQ/UcJrtnOdS6l5qeoaP1CWxhp0nSLiERLIePshC9CelJnjb/LjwU6sERzg09E1vbvagn2zzHVOsUKbtARR++iHx+IRrGECmrSMoq8ITCJ7hr8scua05XpEPAbgJg6rGt+9xNuIrBt4deQMrMM9N+kLS5TiW4zTeNlIz2FvoCNAgKxtgl21TXM0x2z6F7PXLNM6ykHn/RMULAj16D5T0UemTfo0IIn6xnGIaffG00GgghiEQivrvI4PcLCwsEg0FCoRBSStrtNo7jkEgkUFUVKSWLi4u+Sns6nUZRFMrlMvPz8z6xMBKJ+E7pgwRTsVhkYmICz/M4ceLEtkU/xWKRer2OlHIL+Rz6hQKTk5NIKTl27Bhnzpzh3Llz1Ot1YrEY6+vr1Ot1jh49SjqdZmJi4iIiqKZpjIyMsLKygmmaVCqV604k6/V6/n0PigZDoRDZbJZkMnndkv0HOMCNjkHRlG3beyoOSCQSFAoF30EkGAwyOzvrF/EsLS0RCoWIx+Ooqoppmn6h3sTEBIlE4pJFJ9/LqFQqvstKpVKhs0kGg37BgZTSd9JJpVK+M8HAgXy7Of1CVKtVbNumXq+jadpFTnCZTIZOp0M+n2dpaYlOp0M6nWZoaOiS8+ygyGng/r1fEAgEiMfjpFIpNjY2iMfjOxLLB0W74+Pjl73uDQRlrgba7Tbnzp2j3W5vKYKFfnFXo9GgXC4zPj5+RV0KxsbGaDQajI6OsrGxQbPZ9F3fB4U0lUoFKSVTU1OkUqlLOkF8L+Gtb30rlUoF5957CX/pSwTW1nASCbyJiS3jSgQCyIkJzHicwMICZihE9+1vJ/xnf3bZ1w4eP07w+HH//71Oh9bnPoezttYn5Q4NEfn+7yd+HYqKvE6H5sc/jvP5zyOqVVAU3FQKZmYwjh1DjcUInDyJPtwXM1TCYeQznoFbLBJYXKRnmoidimKlRCsUcOJxSCSIvuAFO7aje++9iL/7O9T5eYRl0Tt2DLE5Rwigl8lgrawQzOdRHacvFqAoWBMTkEjg5XKor341yQtcsKXjYOfzeL0eWjq9vattKASqinAclHIZxbb75HPD6BNgbdsnlw7asuW/N8nlg/+XF/5ciL6jtW0jVbVPRu/1cC98ZraNKJXQi0WsTAaZyxH5yZ/c+YU9HI9QnEo4Dlq9jtpuY5dKqPU6wnWRioK3soL3ne/gVqsI20Z4Hl4ms2vEWeZyuMvLfWGASoVuIIBwnP6zeFjbBs9SahpuNgtC4CYS6MUi3HUXlbe+FePECSLPeIbvYJ/88R/Hfv7zaX3wgzif/SxKsdgnoMfj8MxnEn7FKwgePvyInsG1gLW2hnr6NFqlgpNKbfsdLwoFjLU1FNf1+4632WekoqA4Dnq3i+O6BBYWsA4fxstkMMplnG6X3te+duUJ6J5H5W/+Bu8jH0E7fx4MA0IhegNC5O2379l5vfvAA3Tf/W6UO+5A9Hr9caAomMEgnUOH0H/0R4m96EWYv/d7qKur6Pk85sQEXPC8JGCPjiLyeQLLy5iKQusP/oDAX//1Rddrv/OdqBsbGPk85tQUXLj+CQGJBG48jlxZwVhYwAoGaX784yRuv333G7lg/Cu1Glq3i9rr9QWEVbWf83ddhJSonocwTQSgra7Sfde7aH7zm32X8B/4ASK3374tef5qwHrwQbAsnzh/KchUqk9Wt23MBx7YMwE99iM/QvO978WJRtFXVrCPHEFsMy/JXo/AmTMopklgcRH5yU/S+tzncA8dQnvZy4i9+MUojzCGtFdYq6t03/AGlPl5Amtr2PE4vUOHfLK8bDTQBuIEtk3rda8j+Y53XEQMb372s9jveQ/K+fOIdrs/p6kqtmEQu+ceFMfpr3uWhaco/X7neei23f9/18W1LOyhIQLLy3jNJs1PfYrEj/zIVbnvGxlus0ntDW9AvftutIFDuGX1hVQCgT6h/1//lcpzn0vqt3/bXzNuJAhVJfaCF8ALXoBTLuOUSn3Bj5ER1AuEakZHR32Bqfn5eUKhkE8Ca7fbW4hXY2Nj11QcbkD0c133kt8cgUDAd/mMxWLXlIA+IHMPSMYD8nMoFGJhYYFqtUqhUMBxHMLhMIqi0Gw2KRQKJBIJbNvGsqxH7W49cCJsbIoz7QRFUVAUBcuyUFX1hhYiHZAGBwSQQSxZCEE0GvXdQmdnZ68Kya7dbnP+/Hlf7LHRaPh9IBaL0Wg0WFxcxPM80um0/6wHIoCNRsMXmwsEAmSzWTY2NrBtGyklUkrS6TRHjhx5xHmQ64lut8vp06epVCpsbGz45N2Bu+ba2hqqqjI+Ps76+jqaptFsNpmdnd1R1C8SiZDL5SiXy77T5PV0QV9cXCSfz/uk2Gg06hO5Op0O58+f9wmSnuf57pPFYpFYLOY7u2cyGQ4fPsyRI0eYnJxkbGzM7yeFQsEXrPQ8z89lmaaJqqoIIXAcB8Mw6PV63HvvvWiaRqVS8WONF8aSBu9hEGc6evToNXP7jUajHDt2jF6vR61W25LzuV75HtM0OXPmDOVymfX1dYLBIMPDwwSDQV8cYiDQOBiTgUDAd5EdIBQK+a7Ow8PDj1qI9Vqj0+lQqVSwLMsX+r4wfvxIMTQ0RD6fZ2hoiJWVFYaHh0kkElvymq1Wi42NDQKBAOl0mpGRnU2v1tbWKJfLdDodpqenL2qXYRg+IXdwzkwmg6IoNBoNOp2OPyenUikCgQCHDh3yRQMG11ZVlV6vR7VapdVqAX2i8+joKMFgkPHxcYaHdzfg2G9QVZXh4WGGh4d9QrWiKNecUF2pVKhWqywvL/vP8sLrG4bhC6qvrKyg6zqKovjznGmaFwmThEIhwuEwa2trZDIZ30198HcDdLtdFhcX/X1PNBplYmLC33PZtk2lUmFlZYVOp0OhUCCVSm3pr7quMzQ0RDabvaLCs3vFyMiIL6y/srLC+fPniUajvqv5wM15ICpw+PBhksmk7zIej8d9Mv9gr5HNZq+YCMmFeay9QNO0LS70B7g66Ha7zM3Nkc/n/b3TxMSE34e73S6VSoXFxUXi8bi/hy0WizsSrm3bplQqkUqlCIVC13UfthsGIt/tdptkMukLPHmex+jo6Ja+rygK2WyWZrOJaZqsrKyQTqcJh8NMTEz4c8PgZ/DQutnr9ZicnCSVShGPxykUCtTrder1up+DH6xZruv6+zgpJalUirGxMVzX9et6LmzTYDxfzv4/GAxy4sQJlpaWiEajdDodP/ecSCR8cZbx8XEymcvnLh3gAAe4NG68SOIBDnCAA3y3oNfh39z/Jm4t/wtTvXPoXl8xcJAWTjg1Mlaek81v0lJiCDzqRob10Kxf+C+kS8yukDNXCXhdnr/0TmrBMeaGnn7Vm29pEe6ZeDn3TLz8ip/75vKXyfWWUaWLq2h4UkGTDuCgShdDmqjS7TviovhES1sNsB4+gpAeMbvCcG8JDQfD7WEpAWJO3VfMdhWDcnCccmAMVdoo0sMVKh4qs50HMNUwje1clPcxHhh+Pk9c/wgBt8NE5xyG16NiDNNTo32VcM8iZRVIW3mqRo5qYJivTP27y76eqce4Z+j53Fr5F2LdBk0ljrdJyJMoOIpGTc8ipMdobxFFeoRlhwlzgYRd9h1WG1qSjcg0C7HH8KGbfvtRkQ8jvSK3rn+cxxc/Tcyu+j/Ph6b41vBLeGD4+TjajZPA2A43b3yakNMk5lRZDB/flpQDIIXKRmiG2fb9BJ02t6x/gjtmf+pRXz/gNIlbRRJ2mahTJ25XsRWdhp6moWe2JWU6QkNIF93rbXPGR49cd5GQ06SpJS5y/3w4PKHR1mKEnQa57uJVac+1wtHil4nYNcJeByElIbq+G3dA9gi6PUBgC42OEsEWBiV9BE/R6GkRPKkw23kAgUTxHiqqNpw2r7r/DUw3H2Ciew6JoKZn6aphFOkSc+pMdc7SUuM8d+XdAHx95ie4f+wlqK6J4Xaw1PAjchC2lQBSKCieQ7a3TMbMo0sLQ1ooctA2gSNUdGmjeyYlRrDVEHcnn0Byk4BmC6PviCP6BHVLbLZBSoJeD4GHIwyq+hAIQdSu4woNWwlQv4QYh5AuLzn137ml8iUmOmfRpU1Ny1A2hgFByGmSM5fJWOto0kY95fChk2/ccYx+L+C2lQ/wtI1/YqJzBsMzKRsjNIwM3uacELfLZMw8EaeOQNLWk3z10M9e72ZfDCH45sjLGO3MkXAqTHTOsho6jKdsDcJrnslE5xw9JUTDyPCtsZddpwbvHT09wXtv/Z+86MwfY9a+QdBpEXOqKNKlp4QpBsfpqVEeTD6Ffz7+uh2dgPcrRuv3MdV6kLHuHDU9QyF4sWOSpYVYUY8x0TnDSHeRx5c+y1dnfoqucfUK16O9Ak9f+BtOVr9CyGlhuH0hC0sN0V18G/dlnsVXpn6STnB3UZXvBXhC487pV3PX1CuZLf4rI60zPHn9Q8y0HyDuVNkITGHuQNgP2XViTo2l8HG+k3nuJcnWNxI8ofHhm/4r8tQf4NR04laZpF0ia+UBMJUgZWOUjhbFAyba55ht3ofh9fcGphqiu/BXfCf7XL42/RM79veZ6jcJuG0Mr0fNuDR5qWZkyZobpMw8qe4y1fDei7mHmmd43NrHeFzpcwTcDp6i0lVj3J95Jt8efSnF2LEtx981+QpOVv4F3TMZ784RdltUjdwmCV8ScRqkrAJxt46pBNGlTSVwaQKToxiARJEuutO96PfDw/Da18JlGDheEyiKwuHDh2m32xSLRRKJBN1ul3q97ruKzszMYJqmTyzr9XqYpokQwj9+4PhaKpUoFou+08EgOVUqlXzi1szMzEVJomw2S7FYZHl5GUVRWF5eZnZ2dssxvV7Pd194eNJ7gGAwyMzMDIVCASkl+XyeaDSK53m+ivPk5CRDQ0M7FhRFo1E0TfOV0K8XAd3zPBYXF30i5qAIbyAOUKvVCAaDHDp06Kq71xzgewdSSt/le6D+res66XSaTCZzQxfiPhyu6/rK53u5L13X/UKQAck4mUxy6NAhFEUhmUz67hRSSjRNI5vNEo/HiUajHDly5LoU5dwIaDQaLCwssL6+7rtwDw8Po+u678pSrVbxPI9QKEQ0GmVkZMQvotvLc83n83S7Xd+lrlKpsLa2RjAY9IveTdPEcRyazSbJZBIp5Z4I1YP+MChc308YHR2l2Wz6BWZDQ0O+aAz0nXEqlQq1Wo3R0VFfIGY/odfrcfbsWd+ZIRgM+u9/0D9WV1eJRCJIKVEU5YoVQQYCAY4ePer/96AIb1CopmkayWSSVCpFOp3m0KFDV+S63y149atfTT6fx73jDmKKgh4KYU9M7BzLj8ex0mm0YhHuuQdzcfGKEQeVcJj4y65/zKX293+PfOc7Ec1m32W41UJtt1E3CdOepmGPj+NkMrhPfCKhV72K8Pd9H6Ef+zHMz38eJxJBn5vDPnToIhK69DzU5WUU08SZnEQ+//m7Og133/9+lE1itHn8+EXnE6qKnJ6mMzZG4IEH+uTuSAT3Fa8g8MxnEnn2s33So7WyQvsDH4DPfAbRbPrncI8eRf/hHyb2ohchNgvPlZtvRn7lK6jr64hCAcWykIqCvHAu3+wjygVE1C33qihIIRCe13dD3oQTDIKiYHseQlWxJifR8nn006dxdb2f87IsPF3HGhuDkRG0N7yB4IkTe3h7m9ceGUGGw+iFAtZm7nDHYy2LQLHYJze3WgjPw4nH+67ojoNeKEA+j6coaN0uTii0LZF0C1QVKx4nWK0iPA+jWOwT2oV4qC1Sotg2qm3jBAK4wSCEQijLy+jFIlqvh1hdpfeWt+AkElTTacSLXkTiNa9BCYfRczlSr30tvPa1SNcFKfc92dApFAD6/Wk7FzbbxtjYQLUsxAUiBmw+N6nruLqONxBEEAJtZQXn6NH+MbaNV6tdsfZK26b2vvfBf/tvhNbW+oIMjoPwPKSiYEciUCxif+UrVF74QlK/+Zu7voPGP/8z7h/9Ud+1vlTa4jbtGQZiYwPv3DlKf/d3aMUiej7fHwPbOXwJgRwZwXIc9LU1OHWK3oMPbnEztgsFlE9/Gn15uS/64HlIx7m4jULgTUwgWy1EqYTzkY/01fl2GTdqPI4MhfBUlUCl4v/cMQw0y/LH/cDF3r8UEKrXUe+6Cyubxcvn6X74w3R/6qdI/vRPX3URW9nrwea3gtS0S0oXD+Y84Xn9v90jtHQaXvYy3G4X/dw5jLNnsYeH+wIAQvTH68oK4dXVvqiFqvb7/9mzSFVFbGwgz5yh9r73EX7zm6+KoETr//5flLU1AmtrmOPj8DBhDbEpTuCtrWEsLmIFgzQ+8AGSF4iBVN/+dsTf/i1qPo9WqfTHtqoiPA+l20WzbRQp8YTACof77vKbcD0P1bLQuv0Ynadp/WfTauHOz1/x+91vcMplmv/0T3hf+pIvdEMuh/rCFxJ7yUsuIvpLy6L2G7+B+s1vos/Pg5Q4ySRONNpfT5pNjNVVvGKx787pumR+//f3pTD0XqFlMmg7FJePj4/7MYIB8WpQoH4h8WpsbGxXgtjVwMCtMhgM7imGEQ6Hfee5gTPt1cbGxgb5fN6P4Q7Iz6FQyHd09jzPd0G+kPAwIOAtLS0BfRLjoyEBxONxn/zQbrd3jZ0O4gAD8cAbERc6qq6vr9NqtQgGgwQCARzHYWVlxRdeNU2T48ePX1Gy7cA5uVQqkc/nfQKdYRjYtk2j0WBjY8OPcQ/e7YDcMiCsJhIJn3Q8IC4P+kkymcR13RsqNjl4L4MYVC6XI51ObxmPw8PDFItFlpaWcF0Xy7J84bvdMBDKbDablMvl60Z8KpVKFAoFlpaW/FzTgHQcCoX897exsQHg5zbC4TDT09O+OHGn0+H48ePMzMzwtKc9bUt8rdPp0O12KZVKtNttpJSUy2Wi0egWcvCgr9XrdVZXV+l2u5w4cYJWq0UgEGBkZIRIJIIQwiebLSws+CIdN9100zVzQgf8+NaVhmVZFItFKpWKn08axFAzmcy297iyskK9Xmd9fd2foy9s58zMDJqmcc8999BsNtE0zRe9HMznjuP4bt+GYVCpVG4Y8eSBEEa73abdbvsO7sFgkPX1dd/N+JHOm7quc/jwYT9eWSwWKRQKW/rhQOB7eHiY0dHRHd1X2+22746dTqd3JcVns1l/To3H42QyGRqNhh/PDAaDDA0NEQ6HqVQqzM3NbRFVHRCaY7GYT2Ye9KHrRTzeDo7jbMlh7VUo4Fo4ne+EYrFIbTO28HDy+YXI5XL+PDWYS2OxGNVq9SLn+XQ6TSgU8sfdQLRX0zS/z1qWxblz5/z3OT4+7ouhDKDrOsPDw9i2zbe+9S2fZGoYBp7noaoq8XicVqtFPp/n8OHD13ztEUJw+PBhdF1H13Vf9KbdbiOEIJlMkkwmCQQCzM7O+vu6gTDK7OysLz5wNeangZjRheLKu8GyLAzDuKZrz6XQ7XYpFot0Oh3/vScSiStK1L/WWF9f9wWMJyYmLhpDoVCI8fFxn5w9EG8arKOZTMYfS57n0Wg0KBaLBINBMpnMFRVEvtKQF8TNB9+GnU6HkZGRHd/nYL5pNBo4juMfq+s6lUqF9fV1P1euKAqJRILR0VESiQRHjx4ln89z88038/Wvf514PE6n02FtbQ1N01AUBdu2UVWVaDRKq9VidnaWXq/n70UGgkMDEfJms0mxWGR0dJSxsUdumjjI8fV6PUqlEr1ez8/hp1IpEonEDbNfOcABbmTcmCvIAQ5wgAN8F+CJX/nvpPgUcXMRkAiFftGHoiKQCNcl6HURQNDt0o7G8W4eIYYELiyEj4I5xdDCOWKJeX5t7G3wJ0+7oRNF+g/dR6q4jlBcdCw8XcPVdPoPSaL3uqgDQqIi8AQYqk0q0iZ0ePBsItCaYHx5jl4qhfBaBFptjg1v4MWTO1zZQTTLRFZaqEeneNJPP4Vn/PA1uOErhgDO+u+g/+6vIpcsUsUNcu37kV6/gEdxHdxQEHN8mPjQMOov/hK/86y9F+NsB/lrHyFSM7FrMazRCYRtIeRmsj0YJre+jNFuououwuo7EShIYrKBi0Y3lSV0Yobss17I8E+8lqeGL58YZH3pM0T+6o8QSh1dL6Jbzc2EvMYJfY6ncx+O9zdYv/rf0A4fv/QJd7pnKXFdFyHEdUmIeK//DNHaKrqqMHFIBXExMeZC6PM6h4MrjBz+LD/xZ3snoNu27Sd9BgFH6wv/TOhdf06CsxiiDQgioo1EISMrSHeebm4UL/0QqUNKSeRsk3A2zLNfAD/w+su67V2hvqZO6IEunq4TG939eQDoyzaoHk95Qo2nXL75znWF9cH3En3PX6HHVgjaJooAR2pojo0CaHibrh8KiiYhFcWdnCGtXqB2VykRLHocOmrxK2+Kom1OB/Zf/AXR1bsJt85hDSewRidJAsnNvxMigttLkV6eJ2k8wOHQO3j1rz5pc1wFNv95ZPDeOELkXwzCqwtIoaIpPRTPw9U0XC3YL0KUHqpjYzhNBJIhrciTn2nx1N+4Hf31n4H5GqNrSyiyh/AkQbeLGujPfapjIxSJHQiihjQys1EyokN4bhknHWXmZJzX/8Wt7GBq3X/mn/o40fXPEqqdxkvqmJNHSKgaD6WuA0g3SXhlgRPWvUynLJ714g+hv/QVO590n8ItF3E//VGU+bMI20JGoniPfzL6s56P2GMCwmu3MP79OwiETqPRoTtzhKxukMUG7M2jYuCGiCyc4yb9FLPx9/Kq3/0h1Mz1dSbdFvIlOH/4FdSvd4gunSdt3YUZiGKHwggERreN0WlhJyOYk9OMvuj5/Oa//77r3eo9Iga8CXvhPHzyn9DO3I9i9vBCYbRjJxEv/lGmJmd44fVu5mXAefPfEy2sEXK6KEdnias7FwCKTobI4jlCEyX+23P/EePVP3d12nTuNIE/+A00YwVDLaCbrf4chwQX7GCUm/UVfoQvY73uf6DNHr0q7bjxoAHPBJ6J/cD3EXzTr8CSx4nWA3TTI7jJDEJRkEjsXg+jViFSK2OOZxmdDPPD//2l3H6Fja2uP0Igfw/rS59G+/SH0c88wCCdrAGx7DDZ5XmEY2NUzqF32pvrqQQHrECMk/oqt6tfwf6tP0Ybv/gB2e8oEPuHJsFVl0PH+oJgu0G0JJHlDkeOdPmd11XRT176oUvHwf7ff0Dio39HsJhH73UQcpMQqOo8Vv8WPxb9IO3veyna/+91FxRAJ3CW3ojx+7+OXHWJlQtkW6fB2SwQFgpWNoGjDmHUq3gOHJpuQmB3MSqlUSe06jB7xOKXX6+i3/bQ7yKR7eu59yMikQiqqmLbNrZtEwqFfEK4lBLDMDAMg3A4jKqqJJNJotEo6+vrPgFskGxTVRXLssjn88TjcdbW1uh2u3S7Xaanp7dVKBZCkMvl/OKYgZryhYUMA1X6gTL7TjAMg4mJCbLZrJ8Ys22bbrdLMpnckwNxKBTyVaGvB6SUnDt3jkql4hdLRaNRAoEAnuf5hV25XA7HcTh69Oiuz+QAB9gLBiTggUtSd7Ng3zAMarUaq6urjI6OXvMi66sFVVVRFAXP8/ZUwGrbtl/8eOGxqVTKd5IauIUMEI1GyeVy18295kbB8vIypVKJWq120TqhqirZbJZkMsnS0hKWZaFpGqOjo3t20el0Or6j0czMDIqioOu6XxQ/cM3QdZ2xsTHfKS6ZTFKr1S4p8tFoNLask/sJ0WiU6elpfy2vVCoUCgUCgQBSSkzTJBQKMTExQTKZ5OjRo/uumHtpaYlarcb6+jqjo6MXuf1FIhEymQxLS0u+c1gqlbpiLkvhcJibbrrJF7WxLMsnUui6TigUIpfLkc1mD8b5NnBLJQLz82jV6o6uxBfCGxrquw13OnQ/9zkCP/Mz16ilVx/Vt74V8e53o66soFWrsEmQE56H6jh9UqwQBBoNzHgcSiWsb3wD8yd/ktTP/Ry9f/NvcN/7XtT5eYKnT2PHYn1XalVFtNvomwWs1qFDeCdOkPiFX9ixLW6zifjqV1FLJZxEYmdHdUDoOtb0NMbyMoyMoD/pSUS///v939f/8R+Rf/7niGazT35tt33XbVEo4J06RfXv/o7o//gfGBMTRH/4h2m/5z04wSCBeh3heX1y+CYe7uK9bZs2n5VUFNgko0pFwQ2H+6TrkRHsxz0OMTwMd9+N024jOp0+qS4YhFgM9+hRgr/4i3t2lx5Af9GL8O66CzY2oFKBXYhR+pkzKJ4Hnkcnm8U9cgSxuZeQ0CewFwqEFhb6x7dal/iCpk9wVdU+gXrTtVu17b4rdK/Xz6dtEtKdQAAvGMQeGcE4dw51051Z8Tz0bhe5uAiahpNI4OXz1O64g9j//J/oFwhwXZIQv08waKcUok+ye/jvSyVwXRTXxVOU/nu5EJ7nuyl7gGaaSFXFbjb7IuWKgrhCJC23Xqf2a79G4KMfRa/X+6TqC/q0FAKt00E/cwYrmewTPj2PzP/3/207h7bvuAPvzW9GXVxEr1Swk0l6o6MQCoHjoFQqaJUKsl5HPXcOXLc/di5B6pPDwyiVCjQadP7pnwjedBPSdWm8//04/+f/ED59uu8YbxioS0tIVe0LLAwPw4XPSgicdLrfhvn5vrjIzMzOFxYC+axnIe+7r/9OhNhCPlc8r/+sBoc//M9dF61WQ9TrOLEY7jvfSdV1Sf38z+/p/VwulHgcZ5PoLHo9uFROpNfrv09NQ9nGQdmt12l96lO46+v982ezRF7wAvShIZL/6T9RzeexhUBZXcVYWYHVVTxNQ+12++Ob/rPxNA1zchJ0HdHroVWriGIRq9Wi8yu/gvIXf7Fn9/VBjGSwl95u3+M2m4gvfQm1WMSOxXbuZ0Igx8bwmk1EqYT7sY/BT/wECMHGe9+L8ra3YSwtoXa7WNksDA0hdB3ZbhO6/35foEQCim33XboHUBTcYBC110M1TWj1Y+lICRd8q33XQco+cf9970O022ilEopp9sdLKIR84AHq73gH6i//MvEf/EH/z+rvfS/q3Xejz83hhsO409P+WgXgJZOYIyNoCwto58+DqtL8zGf6buL7BG6rResTn8A9dw5pmoh4nMDTn07kyU++rPqn0dFRstkspVKJeDzuf+MbhnFdiVeP5lvjWnynDByQB253qqpiGAau63L27Fmazabv2Ppw8jn0v11nZmaYn5+nXC4TDocfFQE9nU6zurpKLBYjn88zPT2947duPp9H0zSi0eh1EyJ9tFhfX6dUKrGwsICmaczOzm4hSjqO4wuqeZ7HuXPnOHny5BXrG/Pz81QqFfL5PCMjIxd9tw8Eau+8807a7Tbr6+sMDQ35wqvVapXh4eEtMZVwOEw8HiebzZJOp6lUKsTjcfL5/J7i+/sBA8fhgQPydn1aVVVGRkbwPI8HH3zQz5NcCqqqEgqFME1zSyzyWqNQKPiikQ8fZ5FIxHfYXV5eZmlpyRc5HjhdttttWq0WU1NTjI+Pc/jw4YvEHQdxmEqlgqZplMtlMpnMRcRHXdd9ocpvfOMbuK6L67rceuutF8WzB2TEcrnM6uqq7xg9scd90X7FysqKLwQycIgfELgajQZra2tMTk5uEZ+0LItareYLVG9HgB7ESHVdZ3l5maGhIcbGxshms3ieR7vd9sn/9XodRVFYW1tjbm5u38fLarUac3NzVKtVSqUSjuNgGAZSSizL8sna7Xabo0ePPmIx6Hg8zrFjx1hYWCCZTPqu8QPX14Er7NjY2K5jfzCf2LZ90Ry7HZLJJPV6naGhIaanp9E0zRfjFUKwvr7O6dOn/Zj5oE26rpNMJonH4yQSCU6ePLnvYsXNZtPfb1xIrgyHwwwNDZFOp/edSCz0RVYHz3sv+aLBXiaXyxEOh0mn0ywsLFCv17cI5gwc7QdrZa1WY2hoyCfKDnIgpmliGAaxWIxQKISu6xftx5rNpi9me/78eSYmJnjsYx+LpmnYtu2Pl1wuh5SSY8eOXXOB9IFw/sjICMVikWq16sfqB+N1tz5wNfvzQACpXC5jmuauYge2bdNutxkaGtoX7tmWZbGwsECz2fTX5gFJd1DrMTQ0xMTExL6e0x+OQb8tl8t+bctOyGazNBoNvxZjenqafD7vC6uoquqvq8lkkqGhIcbHx0nv42KgwRgf7J8sy0LX9R0FVQZ7p3A4jOd51Ot1UqkUJ06cIJ/PE4lEyOVyvpjFQPwjm80yPDyMqqrkcjlKpRInT55kbm6OWCxGOp3Gsiy/T0F/vpmamvLH5Pnz53Fdl0QiQTqdxvM8Wq0Wy8vLhMNhXyjlckjo0BdfudH3eQc4wI2MAwL6AQ5wgANcB9jnz5O440vE1xcR0vOLB6Sq9pN0gCclWruNtpk4D3bbOGYNsU3ikkAAZzhLqFxCK85hlO8m/PjHX5W2D1T4pJS+GtqVTMpIx6G9Po/i9dXh7XC4X3TjH7Ap+k0/2SqkhypBaiqqBoHABYUGgQhuI4rumahmDzcVJ1xYxooHti3CkaaJUVjGTScwslFmXvMS1But5ntyHOdv/5L6W96C/NKXsFst2CwScgMBiERQHvMYIr/wC4w87nGXfRnbtikVi3iL9yGaFcxIEDeioeshNFUFx0E/ewZlMzmueN6WAgJFSnAdgs0qcmOe1E++lNCxyy/qbH3+8zhv+32U9RX0Ugk3HMYZGQJNQ5gmgUqFUK2AZdYJ/9GvEn7LW3YvitgGAxfBQcAd+oGMTCazq+PelUbFq6NJEyJBAkEJD/k7AH1yw4A8LqXEMRRUp0PYqZK5RO7G8zzK5TLFYtEvTod+Ejb29a+T/pu/QVlbQ7P64hjCc/FUBdW10RwLV9NQCytYwkYOgv71Oiou6lCCzA88gfhVyB9VchHUORW910ENeLsfLCWa08WNJgmPRC/5TPYjeqdO0fvgW1HW51G7HczjRyAcRpRKhOfn4UJijQDP0BHJCMGwAmw+HynRGkXcTBJ9Msvs8072j+10qH/rs6iVNWQ4gDIzTlDILYFeIQQEAriBGYJnzoBVR//qB0g/579c9j01X/0SnHu/gr5g9gtHhMAOBRGK0u9rAChILYDs9gvIVOmSqMyTuSWF+Rf/g/brXocd1dEWFtCrVRTPRe008RQFT9NwDQMvFEIemiWoSZSlRTTPwhvNEr79haRmdg9Mlr/2IbRmAVV4uEdnCSgXPE8fCvLIDOrp0wSbBbSvfojMa2+/YURh3EaDypvfjPqVryA6HZRms+94ouvwzc8i//EvEa98Jcl/9+8ueU/1v/sY0qoTaNfozcwQiGpc/LwAFLzpcQLnz4PTJHDnh656AdnlQSD/z++x8V//K+3PWVCvo9dqBGotBOAEAjQnRtFSKfRXvoJDv/RLF1fM7XdMHoZnvg7oB6Tr9bpfhBYKNYhvtwfd56gs34vWqeLFIwjNwXF6/h5SUVV0TXsoKREIIQsGgU4FdenbV2V9sAsFmn/y6yjr5wmsrmLHYlhHZhGDRE6rhVIqEV0+h4VJ6E9/k/jb3953wznAQ5i8lXr3PyH/5E9w19eJlNahvN53AZMS0ekgFQVndBh1bIzYf30Do0+fvfR5b0gI+IkXwk+8EGttDXttrf9jKTHe+EYUp4WxtoadSGCPH+ozqKWEZhOtVCKwfBZLmHh/8usk3v521IclxKojBiIAqvAwdGdLweK2aFsITRAIeCRmgwQvNY6kpPja/0DmH/4BvdVCdV0k+MXHqucSy6/glTcImHXcUJfMG9/40Bo0OYvz7rfSeNe7EJ/7HHajAabZ/10gAKkU3uMeh/OFL/D/Z+/PwyTNyzJf/PNuse8Rue9VWUsvQLNpC9ogIKuyiDgo4MoZFRn1CA7qOePoNeACR2fcBkZRB53zUw+iqIiyOYgC0ihN792VmZWZkZGZse/7u/3+eOP9VuSeVZXVVYV5X1ddNBkR7/pdn+e571tbXsbXKGJHpg69JLlZxIqG8Ma8jLzoTpSbnyO8JrTbbS5duiRcDtbX1zEMQxSOuGrP0WiU8+fP4/P5yOfzhEIhUcw3OTm5I5nabDbJ5/Osr6+jKArhcPhIYl4qlWJ1dZVut0upVLouoqtbKOEqttfr9WMXHbhr2ZuVRN3a2qJSqbC+vo7H49njnDw2NibIeOVyWTjGu0S4VCp16op+iquCWzhSKBQolUrIsiycOOr1uiBXu6SDr4UkbSgUEoU2tVrtyCKBWq0mYpq7C0c0TWNiYoKJiQkMw8CyLKHmforD4RY0lctlRkdH9xUpAadAYmpqisuXL9PtdikUCsKh6Ci0Wi263a4o0HMV/KvVKtVqVYiNSJJEOBxmdnaWxx9/nGAwSKFQYGRkZF8ys2maVCoVLl++TCKREAWKtxrc4lu/308ikRBOPm5Rlt/vJxqNMjMzc1MdYPZDt9sVDgeJROLAAktXfGZ1dVW4Yl1rIcpBx5+ZmWFqako4P7gOUqcCMIfDKpUAhxymBwJHhj0kN+fV72MNOd/e7mh+9rMO+fzyZeRej34qhbdYBEC2LIyBO7es69gDN2xpYwOzWsX80IeoBQLEf+RHqEgS5h//MWajgVwsomWzSLaN5fFgjI1hJxKYd9xB9Fd+BeWQttlPp5F0HaXdpn8c8kY0ipROQ6u1wz229hd/gf3f/htyOo1WrWIEg+ijo05+rtNBLRaR8nn6rRbNn/gJwu9/P9rICOZ992EXCvjK5Sv370KSBqJ7O+HGZnb+0XFBtgb/7anXsTQNO5nE85a3EHnVq+itr9P+2Mewsllsy0JKJvG/7GX477rr6PveB+Fv+RYqH/gA+sgInq0t+poG+8W/ikW89TrgODdb587t3VtIEoyNoZfL+CoV8TuGyAACluXkWSoVx93aJeDKskN4tW2HXK2qTu5YVTH9fvTJSTzr6yjdLmrPiaMbmoYRCtG/cAGpXkctFlGXlugbBvWf+iniH/gA8g3OXVmWRaPR2DGeXs/+RZucpCdJWD4fSr2Otcu9VK1WHZKqoojco/s2XLd43HyGbSPbNlqrhb20hO31QiCAduHC4OPju8gahkGxWBSuSeg6ofe+l8QXv4i3VEIeOFQbXq/zHk3TERQATI8HtdGA5WX45CdpvuAFhO67b+cJbJvub/0W6vY2aqVCb2EBwmFxb7bHQz+ZpB0K4U+n8TUaSLZNd2Tk6PyDpmEEAkiNBvbaGla3S+Vnfgb5S1/Cu7Tk5HFN02lX/b7zbHUdq16nPze3o1/YgQByLuc8k2LxyFxr4Du+A/PXfg1wHMJdd3jZsrBkWbifi/sc+m/FMLB8ProzM3hyOVhbw/yjP6Jz7734n/a0I97YtcP/nOfQ8PsxgkHkchnriAJypVTCCIWwAwH8Q0IYej5P4/3vR/rHf0RqNpHdvKvPR+uDH8S6916CP/RDJH7xF6n83u9hfPSjWJUK1GrQ76O025iqimyatMfGsBYWxLu2AX18HCmbxZtO05NlGu97H8lf//UDr9OyLCqVCoVCgVarJf6uqiqpVGrPOrn1uc8hNZuojQa9o3LqkoSZSKAWCtjZLJv/9E+Uw2GC/+N/ENzYQGq1qExPI/l8yIaBR5bxlMvYrggHg9oBw8DStB0u6ACm14vWbmPqOoplgceDdJs6Gx8H5V/9VeSPfhR5fR2t0cD0+bAGeyu1UkHK5zGSSaxf+iVq3S7R173OEd342MdQ83lsRcGan993fJM0DePMGTxPPIFUKtH/6EfhFiCgW+021d/6LaTPfAapXhf5STQN/aMfpTw9jfo937ODcH8YZmdnd9Q6+P1+4cZ9K8Dj8eDz+SgWi8cS0Wu1Wvh8vqdkj5rL5cjlcqTTaSzLYnR0VMQxXTf5fD5PtVpldnaWTCbD/D5jhDu2FItFUqkUjUbjmgk5Lvmh3++zvr7O+vo6o6OjO/ZvbmzBJcZHIpHbMp5qWRb5fJ58Po+iKMzOzu6JR6mqyujoqCCFBoNBqtXqgU67VwNXXCCfzwsBwf0Qj8dJJpOUSiVBUq3VatTrdeGIPYxer4dlWcJNvd1uUy6XCYVCTE1N3RYxt0KhQH2wJzgq7phKpUSsplarHTvmdTPRbDaF+2wikdh3XJqamhKkV8MwWFtbo9ls0mg0BAkpkUiQTCaZmZk5tE02Gg08Hg9er/fQsSEYDIo6Nr/ff6iwRDKZpNVqUS6XCQaDTE5O3hZtaz+sr6+TzWbZ2tqi1WoRCAQIBALYtk2r1aJUKhGPx0VdoPtcyuUy3W6XTqdzoItrKBRClmVBgK3X65TLZeGg3W63hUN9KBQSIphPPPEEHo+HZzzjGbfkc+10OqyurpLL5SiXy2IMc8lx/X5f5N/cveudd9551TXPoVCIu+++m1qtRqlU2kH2TiQSxyJMu3kZVVWP5ULs8/koDWJyhmGgaZroo1tbW2xubrK5uSneWSqVQpZlOp0O+XyeYrHI9PQ0lmVx/vz5W+L92bbNxsYG+Xyeer2+L3HedSbenVO9FdDv97EsC13Xj7XeCAaDop43Fothmibj4+NC9D2RSODxeJBlmfHxcba3t9ne3haETlVVabfbeDweMbY2m01isRi2beP1enfUc1mWxfb2NpVKBUmSCAQCQnjJnaPdZ+yKwK6vr3PnnXfemAd2BHw+HzMzM7eUKE0qlWJ7exu/3y8Ec/bbX9m2TS6XE7mZmy2A1O/3eeKJJ6hWq2SzWXRdF4YEriN6MBhE13V6vR5nz569bUjotVqNfr9Pu91mbGzsyO/H43GKxSILCwuirsU1WbAsi0QiQSgUwufzMTk5eV2CWU8FZFkmEokQi8VYWVlBVdVD94fNZhNFUQgEAvj9fvr9vnAsn5ycZGJigkajIcSPNE0jEonsaA9er5eFhQUxzuRyOVqt1o651efzceHCBVRVxTAMKpUKfr9/zzosFovR7/fZ2Nhgc3MTRVGIRqO35Z7tFKf4t45TAvopTnGKU9wE2B//OJ7tbaSBsrfh9+9JpIFTKHeFaG2jbWxgHFBIYScSTsK30aD3+c+fOAHddeRqt9v0BkX9qqqiaRrxeJzx8fEDiwqvBs3//b+dZPNAhX73c5EG6kcS7FS2HxTm7IaZSOBbX6c3Pg4+H5Jp4l1aQo/HsRIJh5jQ6yGXy3gqFUy/H2tmBvn7v//Qop5bGWoiQfI//SfMH/sxRyF6YwNMEykex/+Sl+BbXLzmY9u2TSaTcQjKrRYT7TZew6BvWejdriCqhTMZR5m93XZ+h/PO3CSyNSg60no9yGTovuY1SH/5l/jOn7/qa7LabfT3vhd5c/NKUcZwMQRgjI4iFQp4Mxl6ikLzV34F7/vff+x7Xl9fp1Qq0Ww2hbIoOMlKV41xYmLiRIshD8SggG0HyRjnPnu9HoauY1oW1iDQrPT7WJJEo92msba2b5IKnGDs8vKySCrUajVM00SWZaKlEnd88IOY29t4ul26U1N4ikXUTsdxLPD7wTQd14JuF08uRzcQgGAQbXsbIxKBaJTQK195Y57JvfdiLS2hLS2hdzpIh41FzSZKv4+RSKA873k35npuMFof/jBKrYbWaNBbXERyE3hjY3R7PTyFAmq3i+H1oui6UzhULjsFg4MggZTNIvf7GKkU8itfKf7e+Nu/RarXnWPPzx9auCT5fOjhMEqxiP0P/4D1jndcczFb6IUvpKrrWKqK1u2ie717SW62jazrTiGjx+M4CzUaNL/wBULPfz7K7/4u9T/8Q/qf+AT2Aw+g1euOI4IkYakq/VgMK5FAKpfRymWwLPpnzmDeeSfx7/7uQ6+v++STKMvLqOUyRiJxKAFPkmWMZBK1WMROp2k/9BCBZzzjmp7LUwXDMMhdukTvne/Eu7aGv1BA63YxNQ1JVVENA2lrCyMWw6pUKGcyJH76pw9tH8Y//ANqqeQc46iCgmAQ0+dDLpXQP/c5uCUJ6JAvl9l6wxuonzmD+qlPkXrsMZTBusT0+Wg897lIr3gFM8973m0jOrAbrVaLbDZLtVoVwUdZlvH5fEJhdnR09LYJQEutFlavR9/jodPtiiSsS0DXFQVFlvH5/ciShKVpSIbhOGndADT+6I+Qt7bwbm7SHx/HHh3dWXAdDmOFw/RzOTyZDH2fj8Yf/zHxH/3RG3I9tzOir30tzViM/m/9FnYuB+Uy0qD4uh+NYkWjKAsLBN7xDgLPfe7NvtynBJ7JSTyDtWj5Pe9B3t7Gs7VFb2pqp0ORJEEkghkOY21v40mnnbb2kY8Q+77v23FM9Y47MINBbFlGqlSOdNRSqlWn4NbvxzM3d+Q1lz7wAYJ/+qdozSayaWLIMqaqij2DapqiONn/5JO0//Ivqd97745CQzWZJPHOd2K9/e00P/tZrHweJAllfJzoC16A5PFQ+tEfxaxU0LJZeqnUgY6AdrPprIEWFrBf8II9hPzbBbqus7S0JNwe2u020WiUUqlEJBJhYmICXdeF0MilS5coFAqoqkq9XufcuXP7Fqa4hSYrKysUi0X8fr8oNjoIPp8PTdPodDp0u90dn6kDERC3cPEoQa9OpyPcSb1eryj+P4owYJomzWaTycnJp0w0bBiWZVEoFIT7zn4JakmSME2TdrvN5cuXicViNBoN4vE4wWCQYrFIMBhkfn7+ptzDKW4v9Pv9HYVW4+PjRKPRHe2u3W6zvb1NOp0WxSe3sqr7cRAKhQgEAsRiMeGidFDxlltQOTY2dqRry3EKwE5xBaVSiUajIZTtD4NLTqtWq5TL5WMX45qmKYQh3fejaRojIyOMjIyINb+iKEiSRLfbFWIDmqaRTqeZmpoScWxd1ykWixQKBba3t0UsrNFo8PDDD5NIJJicnLyl2kIkEuHOO++k2WxSLpd3EP6SyeQtRzx3USqVRG7hqGIyn89HIBCgWq2eOAHdhSzLx3IZ+rcMy7KQJOnKODnoB7YkIe12HN4Pti2EJo90j72N0PuTP0EtFlE6HXqLi3hWV5H7fRTDQPf5wHVvVlUnRuvxYEQiKJ0OdjqN+fu/j/GKVxD/kR+h+6IX0f6zP3Niq0PrZfPuu/G89rVEX/QipCPGH3vgUiJyakdhQArHtsVv9Xwe67d+C2VjA6XZpLu4eEUsD7DjcfSJCaStLTzr6/QVhcav/RqJX/ol4u96F+WHHsJYX8fTaqH1ek5uQpadeO4gdubC4gq51M0Z2ZaFbNuOOPeAgK30erTjcayJCZIDYpx3bg7vCcZIJFXF8453YPzcz2GYJt61NYxQCDORwNY0h9hfLuMplbAUBcU06RxA6HOhz87irVSQAM/mpuP2OwzTRF1ZQWm3HSL5gEjtth1F17ElCdmy0DXNIRgmkxAMOu+n30ft9TAH12N5vRjJpNNOEgmMeBx5YwPP6ip9r5fGX/0V0e/8zut6TqZpivyYS5ILh8OEw2GKxaJw3XHnI1VVCQQCjI6OkkwmrzqOqCaTmM9+NhSLeLa36Y2PI7ljyCBHIA3I3rYkObEDEITm3Y7o7mfeToe+JNENhymNjFB/4AExzgWDQVKpFPF4fN89rkv+6PV6Im4a+od/IPrwwwSyWUcEXJYxh8hWtixjqKozPvT7GD4fSquFUS7T+8hH9hDQW//6ryjpNGqxSH90FIZiEr1eD13XsWwb0zDoJZNMViqOgF+zSafbxef1HvqsbZfs3e9T+fmfR/nCF9AuX0budDAH44zu94tnrA5iqZ71dfpnz8Lg3naIFR+HLHH+PLVoFK3RQDIM1MG4Y0uSQ9S3rB3kc7giYyvZNpJhoDQa9M+cwffkk5i1Gu0/+7MbSkBXEwns5z8fs1jEu75Or9HY8T7AMRIAoF7H68aS7rtPxJK6Kyu03/lO5HQaJZdDdZ27B/dlBAJQqdB+8EHMX/xF4m99K/b3fi+NT34S44EHML/0JeR2G61SobO4CPvt1yQJe2IC3TBQt7fhgQfora3tKwrQ6/VYXl6m2WwKkqCb0w8GgzSbTXK5HPPz82JvaJVKjsu4bWOHQkcKz9ihEPL2Nt12m61HHqFeLDKbzeKt1agkEvQty3FuVxSnbTUajtjGoG3Ktu0IdOx2QR/cqyXLKP2+I24TDOL/5m8+8l3ejqj/7d8if/SjjkO5adI9e3bHnGzZNtRqeDIZRzDtN36D7l13YRaLyLkcSqVCf2xM9FV5PxK6omDE4yjlMvYjj9BLp/HOzj5l97gbZr1O7cd/HPmxx5C3ttAajrg4ioKk69iKgpHNYmUyVDKZY4lYF4vFPQT0WwnJZFLEYyuVyg732t3odrsihnGjSRGWZZHNZsnn8zSbTUKhELlcTjjU2bbN1tYW/X6fRCJBPp/H6/WK7+6G63DdarWui4AOjjueW6OTy+XIZDIoioKmaZimSb/fF/HTeDzOmTNnrvlcNxOVSoVer0e9Xmd6evrQ2HsymaRSqVCr1cjn8ydCQC8WiyKuc9jxFEVhYmKCra0t8vk8mUwGcGL4+xGCarUafr8fj8dDJBLB4/GQyWTQdZ1ms3lbiKC7DqLhcPhI8qbH4yEYDNJoNKhWq0ce2zRNOp0O8Xj8pokhuvEaXdcPfB+uKEKhUMA0TTY3N0mlUkQiEcLhsBAfmJycPDDeommacM0slUr7ClgMo16v4/F4qNVqhEIhWq3Woe0lkUiQyWTo9/vU6/XbMu5TKpXI5/NsbGwAcObMmT2xvlarxdbWFqZpiv1MIBCg1+vR7Xb3FV0dht/vR5ZlxgZrBldoyyXmuXUhrtBlJpNheXkZv99PIBDg3LlzR/YDy7LodrtYloWiKPh8vhtaY5LNZgWZfmZmZg+hzePxMD4+js/nI5vN4vP5KBQK1xxzjEajR8a/D4Isy8iyfKV25Yjn4q7b3d+6cPM87nh65syZHWNIJBJhZGRE5IIURSGbzT41ta1HYGNjQ1x7r9cjGo0K4rwrspDP55mfn8eyLC5evHjLube7uNp2rWkai4uLgmxfLBZZWVkRJPt63TEHuXjxonAI93g8QujDtm18Ph/r6+t0u13GxsYIBoM73n29XhemI64IumVZYk3nIhKJoOs6hUKBaDR63eu1ryV4PB6SyaQQQEqn03tEl10BpE6nw9zcHPF4/Kbm8W3bZnl5mUqlQiaTIRaLkUqlduS2ut0u2WyWtbU1wJkPbgehHHDGQtet+zjP2efzYRgGiqIwMzMjBMDcuUlVVWKx2B7S9a2MkZERqtUqkiRRrVYPXOe48ctYLLZDyGz4PiVJOtYaPBaLsbi4KISG6vW62Gu7giHuWq3VaqFpGlNTU/s+U4/Hw+zsLCsrK4JzsbDwtWomc4pTfO3i1qmYOMUpTnGKf0PQ7r8fb7MJgKWq+xeFDCVwbZzkuKfZRDdNpH021JKmOcfSdexa7cSu1bZtVldXKRaLVCoVqtWqWMgDO4KmCwsL1x286//d36FpmnBFkEwTe2gTJLlFSyBcAVyCvrVP8lXyeJzEaCKB/rzn4bn/fqxiEaVUwjNwpQCHOKaPj2OnUthvehPKq14litlddadbQQHwaqBEo0S/67tO7Hi2bXP58mWKxaJQ2UwqilN40+/TGij++w0Dudl0yMk478aWJCwc9XJLkjA1DdPjwdPpoHW7sLFB+53vxPtnf+a8s6tA/a//GqlWw1Ms0pud3d+hQpKwB6rI2vY2PPoo3SefxDdwOTjqnguFAhsbG/T7fSKRCMlkEtu2aTabrK+vEwwGdyiLDhd+nnS7sWdmsEIhPJkMvcF4YOMkcwxdFyqL8qDAy9tuU4tGKfj9mFtbdLtdzp07tyMw1+12uXTpEqVSie3tbTRNIxqNCmKH/8/+DKtYxNNoUJ2ZwRuPo7RaSJaFOiiMszwedJ8PrdtF0nW0TMZJDAPW9DS89rU3zGkj9PrX0/7zP8cIBNAyGfSzZ/clCNumiba1hREOY4dCRF7zmhtyPTcSZrOJ/LnPoRSL6OGwKPpxYY2NYdXrGLaNOhAfUHQdq9/HrteRZBm5UEBttejPzmLNzhJ9wxuuHH9tDbnVcgphjhFUtKJRtO1tpG4XfXsb7zUGBSRFwRodhSeecIrnDEO4vLhFrbJpOq4uPh+m14tkGFCt0v/EJ+D5z0eNx0n8+I9j/8iPUP/0p2n8+q+jra05Dj3tNp5aDWo1LE3DSCSwUynHReh970M+ovCgf/ky6Dpyv499jCSCFY0ib29Dr4exvg63MAG9Xq9zeWUFzy/8AsFLlwhub9PzeqmOjCAFg6iq6pBSej28+Tzy8jLG3/wN1elpYm95y4HHlWo1JF13XBmOEaiz/H7o95GPkYS9GSgWi0KFsRUKEf+BH0AOBtH6fXTDoGYYVOt1ArKMNCh6uNnKpleLSqXC6uoqtVpNJBhdKIpCLBYTRSJnzpy55ddFtm3TtCz8gNXt0ut2kWUZSZaxbZt+r+cQGT0eLNsm4PejGgam3499A+Yrq9tF+sxnkAsFjEAAe3T04C+PjWHW68jFIsYnPoH9Qz90rILOpxL9fl+Qczwez00JyIde+EJ4wQto/uM/0vvEJ6BUwrZt2oEA9otexMwrXoFyiz23pwJmo4H0D/+AUig4a4WDitIGRarWwG3P+NjH4Hu/d8eYHfrGb6QyOoqxvY1aKKDH4weLsLRaqM0mvTNnsF/0oqPXfbaN+v/8PyjtNrJp0tU0UfDsXoOpKMimiWcgqONZWqLzkY/APk43ss9H5OUv3/dU3je+EePhhzHrdTzLy+jT086ewb1X24ZyGe/2Nno8DvE4gess0D8Ofu/3fk+4tIJDovvBH/zB6z5uJpMhn8+ztraG3+/H5/OxublJMBjcUfwfDofJ5XLE43EKhQLdblc4ThwE1xVhc3MTwzCOVcAjD8bd4SJ1cGIJXq+XWCxGpVI58jiVSoVwOCxcS1dWVsjlctRqtUN/W61WBUHisGLOG4VyuUyv16PRaDA9Pb3veLm1tUW5XKbZbFIsFun3+yiKQjAYpFKpoKoqY2Nj9Ho9zp8/f8sVzZ7i1oIrHlkqlZidnd1XOTwQCDA3N8fq6iqlUolgMHjbE9ABxsbGRDHo+vo6ExMTOxyXLMuiXq+TzWZJpVIEAoHbbs1+q0MfxKX8fv+xCsICgQDNZlMo7B8HrngJOGvS3YW5u8+r6zp+v1+882q1KuZIVVXZ3NykUqlQHMSJ4/E43W5XFMm3Wi1qtRrnz5+/5YjdoVDotnLsdotgvV7vsVxrQqEQ9XodXdevyqH2FNeHdrtNoVCgXC4LsQfXXcceGcHWNEy/H7lexzqC4GDX685aOxhEOYDUpOv6DvEKWZaFW9PNKrw/DN2lJZTHHkMtldATCUfcuN9H1nVHMHNoDLIHuRNJ151Y7MwM3vV1rEaD5kc/Suz7vx/fhQv4/q//C+sd70DP5bB1HTWZRL0K8ojiEgU1DTqd/fMjQ7B7PUdAwONBHqyjmx/5CFKjgVap0DtzZgfRTUCSsCcnMXQdZWsL/vmf0fN5tNFRYh/8IK1nP9shBpvmDpECQ9NQdV0QzId7so2TP1IH/+3+fwnoqSptv5/SD/wAYcviRo3Aofvuo/GzP4vx3vc6Md1iEc/GhiMgLssY4TBGMIjaatELBpFSKazB/kbCESMdvicpGET3+1G7XbR2m/4uF3RlbQ2l1UJtt5EHDuZmIEDv4kWHYLeygq9UQhq4UeuaBsEgtmk6zt+9nhPzN01MTcNWVaxk8so1SBLWzAz2E08gFYsYf/VX8IY3XJNYpm3bbG5uUigU6Pf7op+6ZK9sNovH40FRFOF6CFeKD93c8dXGEZvNJqV77yX42c86f1hepjs7i+b3owzEE4YxLGggD4Tf7aHzuSIIkm1jWxbdrS3WL1+m0ensIODWajV8Ph9nz57dsYbOZDJsb2+ztbVFo9FwRFL8fkb/9V/x53JgWdiDd8nu+UqSsDwepG4X2TCwZBm1WISvfpVWOo05cCtTVZXu3/0dSq3m9KFUStxXp9sVuT/TNJ0cj6qiKwqaaeLpdKgN8oIBv3//+XIg6mAEg1jtNurnP4+2uoqlafRHR/FtbKAMnMltVXXI/YaB2uthyzLK1hbmQNxcqdUwBzF/zzHdyazpacx8HsW2sU3TIRrbNtJAPACGyOcDMjI4JHXZMFDrdazZWfRoFKVUwv785zEbjRsqHBh405vofP7z6I0GnrU19LExzHgcfeBah66j1WoEymVa8ThWJEL4jW+kXq+TW1lB+4//Ed/aGqFcDj0UwlhYQAqFnL7YbiMXCnjX1uj3evR+9mdRf+d38ExPE3nVq+BVr6L8pjdh+3yYwSAcMSeYo6NoTz6J0WrR/uhH8f7ET+z4XNd1Ll26RLlcJpPJiCJrr9eLaZrUajVWV1eduX7w7BOJxI45bXe/2w+2bWMNns92oUDiX/+VQLeLrKooqRQ+wDQMdMPAsixHBMW2HeGIobZhG8ZeAjpOG1FNk57fj3nxIr6LF4+8ptsR+oc/jFooIPf79M+fvyLA4UKSIBajr2l4V1boVSq0/7//D3tuDrNeR9V1WpoGA5FdSZYdMa5Brs+FFY3iKRQwLAt9be3mEdBtm+rP/izKww/juXwZ0+dzBNLDYWftYZrIpRJqoYDVbmP+4R9SHxsj8upX35zrPSG4RMFUKiVIcPvt7XRdZ3Nzk3A4LPa1LrrdLpVKRbgse71eEonEdTmEVqtVut0uKysreDweOp2OIDCBQ/potVrU63VGR0eFE2+1Wt33+t39u2maO+Lg14qFhQVBoOj1ejSbTTGXh0Ih8QxmZmZuWZLaUWg0GsKt8Kj9viRJRKNR6vU6zWbzRPbNruBBKBQ68hm6Tr/ZbJZ0Os3ExAS2be/Zx9VqNdrtNuPj44TDYVRVxev1Ytv2ibWNpwKWZQkX9+MgFothWRbtdvtIAd5arSZyGDfLfdO9PzhckNMlLYfDYXRdZ2pqSgg/RKPRI9ttMBgUuQ93P3EYKpWKWKuHw+E95Mnd8Pv9wmX4auKNtxJcYVnTNJmfn9/3fQSDQWZmZoQLfaFQYG5ubk8e7iCYpkkkEmF8fJxWq4Wu66JfTk5OEg6HCYVCRKNR4dJdr9dZX19ndnaW7e3tAwmLrsNuqVQSbQqc2NLIyMgeMuRJQNd1KpUKpVLpSDfVWCxGrVYTopcTExNPeczRdaMFhLDFYWg0GgQHtVtun7Ftm5WVFdLpNFtbWwfuu2VZZnJykvX1dSEufjPueRgu6dAVLzl79iyKoogcaavVwrZtyuUyW1tb3HHHHQQCgVuKpOi6lWuaRqvVOpIM2263hci6K1Jy8eJFMpkMoVCIbrcrhNynp6cpFov0ej2nzlnTSCaTQgDFnXcrlQp33HEHoVCI0V21R9VqVawn/H6/IKzu995jsRiFQoFGo0GxWDwloA9hdnZWrLVd0rab23AdxIcFkI4SVbnRqNfrQqAkkUjsaRfgkLJnZ2dJp9NiHh4fH78t1s7DbfgokwT3O8O/0zRtXxOG2wnRaJRIJMLs7CwPPvggmUyGYDAo5j3TNGk0GkL8yRX32N7eZmRk5JoFEiKRCE972tOo1WpifAInBpxIJJBlmeXlZba2to6cY1xeQqVSEfdyO7S/U5ziFFfwb68S9xSnOMUpbgGouZyj6G3bTlHIfthvEWZZ0GrtX0AySNQiy46r9wlhfX2dQqHA+vo6tm2TSCQEGbvf7wvFrEQigW3bnD9//roK36RCAVTVIdNLkqMKL0lXXCPgyrMZqKK7hSkdTcMzKH5wYeu6o6CuKARf/3rs172O3p/8CfaDD2L0emCazrG9Xoy776bzspdRO3uW7pNPCtVNd/OfSqUYGxu7rqTN7YxsNkuxWGRtbQ1VVVlYWMB49rMxWy1C2azjxmDbaPm84x5qWUIkwHWtAKdgwBwQ0vteL55uF7XbRXniCRqf/CSRb/3Wq7ou82//VrjscgQx1B4dRS6XodGg/dGP4nvXuw79frFYpFgssr6+LhQIhwOh8XicXq9HOp3mscceY319XSgLwhWCxsjIyI6i5+uB79WvRv/c57C3tpDzeeyJCbqDApRur4ciy3h9PhRZRi2XHbJdMknl676Oyvo64PTrYdXn9fV1KpUKW1tbjI2N7Sg8t2o1xtbXCXQ6tHw++qqK3euhLCwgraw499nvo7Xbjhq5baN1u6j9Pp3RUfT5eawXvpDkv//3J3L/+8EzPU3zW74F+n205WW0pSWMsTGnPbgFSZUKWj4PgDk7C699Lco1qpHeTPRWVpB6PZRB0eIeeL305+fxrK1hyDJyr4dsmmitFtLSErbHgxEK0T9zBmtujuD73rezSMcwnOcly8crShsuBhoSJ7kWKLqOHgqhdDogy8iG4YwlOGO8W1Bn+nwYCwsoGxtInQ52obDjOJLHQ/SVr8R+6UtpfPzj6H/5l9hLS84cOrhma34e5Vu/lcRrX3s80YsB+R0GTiFHfF2Q84achG5FNBoNR/3ys59l5vHHieRy9EIhOmNjjguHaaLrOh6Ph7bPB2fO4L18GXlzE+NP/xT7jW/cW/ziwh0rj+OIBcIRy74FyaKWZZHJZMjlcnQ6HRYWFq4kQ4NBFMAHxJNJEaR1lR9vlyBZo9FgdXWV7e1toYI5MTEhitAajQalUol6vY5t28iyfMu7B2xtbVEbH8e6dIlotUrP49lBRrZtG90w6A8Co91qFX+ngzE9jXQDitfa//zPSPU6ar2+//i9C2YyiWdzE6tSof0v/0Lw3ntP/JquFq7jlUtUdaFpGqlU6uYQBCSJ0H33Cdco0zTp53Lis3+LaP/jPyI1m6iNhlOsdxgkCSOZxJPNYuXzdB55ZIeDlKQoyK95DWY2i1Kvo62soM/O7nQQt23sRgPPxgZ6NArRKIHv+I4jr7P6p3+Kt1hE1XV0RcEcFEHuLtS2ZRkd8Og6Wq9H+wtfoLu0hO/cuWM/k9A3fROV7/kezA99CDuTwZNOO2uKQMAhwrdaYFnoqRT25CTyO95xVce/VvyH//Af9jjxXA8BvVKpkM/neeCBB8hms3S7XWzbpl6vo2naHudtv99PKBSi0WgAjmOC1+ulVqsdSERVFIVAIICmaULF+DDyt+s+o2navgUlIyMjNJtNce37JULde2u1WszPz5NMJgUJ3i3SVFV13zhErVajUCgwPj6O3++/KUlzlwx7UNFgqVSiXC6TzWYBmJ+fxzAMYrEYU1NTWJYl4i6WZbG8vMxdd911ywvBnOLmwHWOKZfLhEKhQwut3PWq67TVarUIBoP0ej1arZYgF4VCoVuSALgfkskk7UGRvevq4fF48Pv9WJYlCpfcQrczZ878m43x3Wgct9gROLDw6SCEw2G8Xq+Ys44SEahWq0QiEcbGxhgfH2dra4tUKkWhUGBlZUUUao+NjZFKpZienmZkZIRWq0W5XGZtbY3p6WmWlpa44447bps93q0IV5DmuO97+HunBPRrh67rOwijB8GyLNbX14UojitELEkSPp9PrP1m77qL8VoNbzpNr9fbuTcYhm2jFgoYkQh2OEzkZS/bc76NjQ1KpZJw+Buee7a3t0kkErdcAVLvX/8VOh2HBJxMomxvOyKZsG9My1JV1IEjOLLsiGoWi5if/jR8//eL78k+H965uWu6Ju/MDM25OaRsFq1cRh8dPXQ/LBcKmD4fBAL4X/hCZ0/3yU+iDATzDhUllSSs0VG8S0uYrRbNv/or4m99q+NY/fa30/mDP8C/soLh8zkCgIM9nd1qoXW7wnndJaODE/d1o4eWLKOrKh7DoOf18tjznoc3GLzhY3D4pS+ld+ECrQ9/GPvTn8Zqt6/EKv1+jEIBo1DANk1a7Tb2cLxTktBUFU3TxBrdiMdR8nlMVcW7tYVZKmEkk2AYaNUqaqeDhBPvNn0++uPj4PE4fzt3ji7gKxaRLYvA2hrdAaFI6XRQByRm0+PB8njozc7uS07UEwm0Ugk7nXb20OfPX9Uzcfce5XKZXC5Hs9kUxINut8v6+jqGYWDbNqlUijvvvJNAIIBlWTSbTcrlsnDYkiSJs2fPHnlOXde5fPmyQ7waGcF3/jyjrRaxXA7/0hK9SAQ9EsHDQLig3xeuyHtczy1rh6CBDRiyjGQY6K0WzU99CuUFL8Dv92MYBpVKhUKhwNjYGKZpcuHCBSFck81m2djYwDAM5ufnHVLLpUvE6nVC3S7mgAhuDK5DHggzD78PS1Udd3uvF7nZpNNqsfyZz2DffbfzbhWF0KVLpJpNZ5wYtPVev+/k/rpdh1w4yP0B9AMBtGYTzTTR63WIROh0uwT2EyxrNJB1HTuRwO52oVpF0nWMs2dBUbC3tzFVFVnXMRVFxOlN03Suu9XC7HaxFQW1VqM/OYn5nOegHocgJUnYqRT6yAhSNuu8E9N0BDosy6lvgB3CAdLAzd6SZaeewrLAMBzC7MYGZr9Pf23thrqg+y5coP9TP4X1K7+C7vGg5PPIW1vYHg+KaaL1+5iyTDUSoZ9IkH3d6yg++iijhQJ8+MNMrK/j396mHo1ijIwgSxJav+8IGgUCWHNz9EolvJub9Px+mn/4hyR+9medk9s28uYmcquFOSygeNAj9nqddtNoYA7ywMNIp9NUq1UymQypVGqHOCE4hIN2uy0IILIsE4lEUGdmMHw+bEVBqlYPFrl0r6NaRVdVerqONTpKUlHQLAtjIF4qAbKmoQzGEZOB8IgkOXUEloVs205/dmteBvOGpOti/LP8fnxDc+jV4lZeV3YefRRleRm1XHaEbg7brwaD6OGwI1j+mc+w+fznk+r18FkWnV4PW9eRAGXQl/uy7AiFuf1sWJjzJpI/W1/6EsqDD6KtrWGEQlhzczvavKQojslANIp3eRlrexvjD/8Q+1Wv2te043bC7OysYzBgGGxsbBAOh/eIQ1SrVXw+H5OTk0xOTjr52kF/bTQadDodZ00+WLNvbm4Si8WYnp6+plhOrVYTc7E6aDuuqKckSbTbbarV6g43a5cUuh9cgvGwkNz1YmJigrGxMSqVCvV6XbgauoT+kyY1PtUwTRPTNI8dr9I0TZBrXJfh64EbzznOOBkMBolGoxiGIVx4XUdEj8cjBIx6vR4jIyM7CHIuiViSpNsmzu2uhY9Lavb5fHi9XgKBAJubm8zNze3bPtvttsiN3KwcBly5P3AEBI8SYXSd0icmJlhYWDg0Dr0bo6OjRCIRTNMUBOD9UK1W6ff7BAIBQZi6mjn8Vp3vD0Or1aLT6VCtVonH44eOaT6fj3A4LAhc09PTor5V13UMwzj09y4ZLxqNCiOqmZkZQqEQPp9vR99UFIVEIoEkSeTzeXw+HxMTE3v6b6FQIJ1O02q1qFQqQhxDURSi0SjNZpN8Ps/i4uKJ1VACQsCy3W4zNjZ25PdjsRjZbJbx8XHa7fZVtd+TgDvfR6NRSqUSoVDowPba6XRoNpsiPwpO/W42m+Xhhx9mY2MDWZaFcIEr6jhMMpQkiWQyyebmJqOjo6J93Sy4ZOdut8viQOhsbW2NdrstDDFM00SSJJrNJk888QTtdlsYRt0K8Pl8BAIBIbLu9o+DUC6Xhch6dFCr6vP5WFxcpNfrUS6Xxfzi8Xh41rOexebmJqVSSYjnunnkXq+HqqqMjo6K/P9uUmm/36fb7RIIBET+X5blfcd2VxTEXVeeFGzbFuPAcMw3FovdNnO/LMssLi6SyWSEAJLbPt052+fzkUwmmZmZuen3VSgUqNfrAIeK8suyzMjIiDBjK5fLN0Ss2xVIabfbwnE8EokwMjJyTXsVd20lyzKNRkP0pYPQaDTwD4Qab6Yz/UnCjbG6IhOPPfYYa2trhEIhZFkWfS0WixGNRhkfHxemei4Z/XrOHYvF9q0N2t7eptfrYVnWsday4XCYTCaDbdv0er0TXROc4hSnuPG4vaMupzjFKU5xu0KSROLEApFcRZKuFOgNCjDsIeIuOI7g+2KwebBDIZTB5vx64TombWxsoKrqno2SG1CKRCJsbGzg8XjIZDJcvA7Sjj0o6uiHw07xwIDMaimKQ0oHQWYbLlLpapqTiDdNFFkWwTClXMYIhbC9Xnx33okSjRJ6/vPpra3RfeAB7GYTORhEfdrTyA4SOaV0mlqttiPoHYlEROHhuXPn/s05jtm2TT6fp1AoIMsys7OzyLJM95u/GeMrX8FSVfz5PN2JCfy6jqrr2DjvyBy0a9m2sXDavA5gGJiShDL4npzL0f7IR66agH41SXg8Hgy/H6nZxN7YOPLY+XyeSqUCcGCgwHX0W19fx+fzsb29LZL3wWCQer1OsVhkcnLyRFTUgl/3dZRnZ9ErFbyZDG1Joq2qdPt9J5Hs9YJloTSbBEslWskkRipF6uUvp5fNsrW1hdfrFUmDdrtNs9kkl8sJMseO+7t8Gbnfx9ft0hkbQ9d1VFV1inUWF7E2NlAbDaeQyDDEmKX7/RgjI/DmN5P8kR+54SSw+LveRXngViVvbeHZ2IBMBktVkQeJcyMaxZqcxHrRi0j86I/e0Ou5Fhyr6GEQcJQsa6f7wDBCIfqLi8jb22iNBnKzialp9ONxrNlZ7FAI6777iPzwD6PtCiBJsRgM3MXtfl+Qs3e704jvt9tCSOVYxUaHwJYkbE2jH4lgeb3IhQJKt+vMh5KE7vPRi8UgFsOjaRyVPpVU1VHAf/Wr6a6sYGxvO8Wv4+P4Fhevqk0qiQSWpjnk81braKGXVssplvJ4UG6RAPhu2LbN6uoqxWKRwGc+Q7DfR5UkWuPjqIO2pWmaQ9AdFPN1JQl5fBxPJoNVqdD4xCcOHLPtiQmsYBDP1hY9yzrYMde5GJRGA2NkBHty8kbc7nXBdS6tVquHFm24yqCZTIaRkZEbFqS9EchkMqIwdG5ubsc6xw1ORiIRMpkMGxsbQsn3qU7GHRemaZLP58k97WksPvAAcqOBr1JBH3ofkiTh0TQkoN/rESqX0X0+CAYJvf71J39NpRL0+874fYznZgeDznd1HXMwv91MdDodlpaW6HQ6VCoVsVZ2nSUajQa5XE6o+57i5sFta9g2dih0pGgKgYCzxzRN57e7EH3zmyk//DC6baOuruJ78kmnGNDvF+O30uuhx+NYMzNIb387vuMUln/kI3gH69e+qu5xjXMhAbamYes6smWhFAr0L126aoJ4/K1vpRaNYn7wg1iNBlKphNTtgiyjj45iJ5NYiQSen/xJQi984VUd+2bDsixWV1cpl8tsb2+zvr4unF07nQ6dToexsTG2t7f3KFi7yR1w9jWdTkeorO+XiJMkiXA4TCQSodFoHOlOUq/XReJ6v2RUKpWiWCwyNTVFJpOh2+2SSCQIBoOiqNElQbkOKePj40iSxPT0NPl8HsuyePDBBwmFQkxPT+P1etF1nVqtJu49kUjcNGV8N6m3X/Gfq+BfrVaFs4TrGgSIZOzIyAiKorC9vU0wGKRcLt8UN/dT3PpwC5cbjcax4g/RaJR8Pk+32yWbzWLbNrVaTZAV3cLDaDQqnG1udczMzAjRi16vR61Wo9/vI8uyKG70+XzMz8/fFvdzu8Hr9eLz+SgWi6LQ4TA0m03hXnZcuMWViUSCXC5HNBo98PfNZlMU6KVSKcbHx8Xc0263icVitFotzp8/z8zMzI7CTtc1oFgsigIjV9TkFNcGTdNETPI47aPdbqNpGoqi3PTirdsNhmEI8bDhwkGv18vIyAjJZHJHEbJt21y+fJlisSgKz8LhMNFoVJBJi4N9qfT0p5P86ldRAwG01VX0hYW9JHTbRs5kULpd+mfPYr/4xchDRUSWZbG0tESlUmF7e1s4BWmaJpyy/H6/WJeeP3/+liGhW82myNFJHg+SrjtE16Gcn4thMWXbtsGyMEMh5G4X6yT3+ZKE+m3fhr2yglQoIG1vY09M7B/7bDTQymX609OY99yDd24Os9lELpWQ222MQ8SlBAIBx9G52cQaIjoG3/AGOh//OL122yGoTk87ArGGgfL445iDgnZ54BZtyTJ9RUEakExNVXXizKaJZZq0zp5Ffu5zxRicz+dvqEuNd24O7zvfifX2t9N98knsVgspEMB34QKF//AfMD75SbRu1xnDBsRrJAlFljEHzkdenw9NVcGy6CUSSLaNOTuLXCyi5XJozaYjugrofj+2qtIbH4dhIS5JckjopomvWsXweoV7tWRZWJKEHghgaRr6zAzSAY73diiEnM2CZWHk83CVBPSNjQ0hxKJpGrOzs6IYcGtri0gkwtLSkhA9arVaRAbXkkgkiMVibG5ukk6nUVWVVCp1aGGorus88cQTYlwwDIPwd34nkiRhP/ggaq1GsNHAX60i67pDYrZt+l6vQ0pl4HA+lFMXwrmAKcs0IxHo9wl0OpxfWqL1xjeK84+OjlKr1chms0iSxMrKCnfffTe5XI5KpUKv1+PMmTNi7JQLBSTTdNqzojhk2cG59svzWIqCOnApx7LodbtkNzaoDnIrsixzvtEgqusouu7k22QZvd/f4Ww7fFw9EMButbAliVi1SnngwLZ7jrVNE8/WFnokgh0MolYqTu48GhXiBXo87hB8Ox3kfh/L4xHu7Uq77ZDACwXUTsfpp/E43m//9mO3J+nFL8ba2EDa3hb5JNPrRdZ1lIEo8jD53PkPySE+D9dFKIp4r9YJFsYPo9vt0ul0nP34N3wDyrvfTfPXfg3D74dKBanbRdI0uvE4RihEM5nksfvuY7ndxru8zOOPPsor/uEf8NZqGJpGOxLB6nZRFEU4hPvdeEsyiV6rIRcK2J/9LOaP/RhKKIRtmkKEn+OugQYC4ewSRO73+1SrVfL5/KHFxoFAgKmpKdLpNIlEglKpxOjznkdlZAQjGkUtFjESiQPzarZpopVKNKJRChMTjD3jGUh///eOScAucSp5EJfvaRreft8Rp/b7kU0Ta/BdIVrClb5tSxKGz0fnJS9h5Bu+4XjPBWfdUSqVxB7AeVwy0WiUkZGRA/eFlmWh67owK3gq1iL9hx5yhG76fexj5BatRALP2hq9Wo1GPk/QNIkAPtPE8vmwLAvDNDF0Hc3jAdsmEAg4a+t22xm/FAX5ACHKpwLdv/gLlGoVyTQxd4lnDkPyeumPjeHJZrGzWVr/+I+3XQx3N4LBIGfPnkWSJAKBAOVyWRThgyMeOjY2RjQaZWJigvHxcer1OisrK8LltdfroWkatm1jGIYgYTebTc6fP3/VRItms0mpVKJWqwmBtuG2r2kaY4N6kXw+z9jYGKVSSYho7t631et1cX8nmU+UZZlkMnnLkNBOErIsoyjKsV3BXQd497fXCzcW2Gq1jvV9d/03Pj5Oo9GgXC5z+fJl4dIaCoVIJpMEg0FBjgWnbfh8PlRVvW1q7yKRiMiVH0Xs7XQ6wr3azamvrKwQj8cdkZcBkb1arVKv14nH4ySTSWZnZ5/CO9oJlxjpOuUeReJ1Be01Tbtq0tDU1JRw3y4Wi9i2TSQSEW2h2+1Sr9fp9XpMTEywtbVFLBbDMIwjifHNZhNZlvF4PLeNqOowWq0W3W4XXdePJNaBE993xYs7nQ6JREI42lYqlQNrVVRVRVEUyuUy8XgcTdP2dVEGBDksEokQi8WEsMTuPJVr7rO1tUWj0SAUCjE1NYWiKKK9VyoVxsfHsSyLixcvnhgh0DAMQaI/qo2437EsC9M0jz3eniQkSWJkZIROp8Pa2hqbm5uMj4/vGVdc0ZlYLEYgECCRSIh42tbWFul0mkwmQzweR9d1fD6fIPpPT0/vEMV2/3vYaftmwDAM0RbcvPH6+jr1ep3cwGTAFUW2LIt+v8/q6iqqqvLggw/yzGc+85apRRkZGaFer4s4xkGuv+4+xM1R7J6vvV7vvvGus2fPihxeKpUSRmqqqpLJZGi1WsiDGvmDYFkW7XabRCJBKBQ6UOBGGtR2X42w8EGwbZtcLkcul9sheO2uC7xeL6OjoyLff6vDrZOfnJykWCzuIdQnk8lbRui60WjQaDSEueBhCAaDqKoqfnOStY29Xo/Lly8Lkf5msylqHqLRKNlsVqx7rmb96uZ3o9Eo5XKZSCRyYBtyx5rJyUlisdhtL1I1DFVVefazn02j0aDf75PP5wkGg/h8PhRFwe/34/f7xRzt1rC6z+5GwLZtIUJ6nH7t8i3gilP9KU5xitsHXzsj6ilOcYpT3CawbZtmMsn7LYv32jY0m8f/sWHAo4/uSO69MBbj/3fnnSj5PEYkArEY4Ze/XHz+wQ9+kB/7sR+75mt1/507d44PfehD+34vGAyyvLzMD/zAD1y3QumIpvHQ5CRKvU5vagrv1ha2YTiJ716PL9s2L95vs9tuYz/88I4/Sc5NOM8rl4Ndm3U3yWjbNpcuXaJUKoliCNu2eetb37rjWcAVV56r2QBfvnz5wOLEXq93YoGRL37xizzjGc848PPJyUlRyH4tsAaFQf/lv/wX4XoqXbxI8/x55F6PcCaDnMlwX7PJ0vA72v2+LAv2Kwyo17E/9CH4kz9BkiQ+8IEP8D3f8z0HXs/LX/5yPve5zznHGrits7Z2nBtxvvuBD8Dv/z6/8Au/wE/91E/t+Vqz2aTT6fBLv/RLfOlLXzrwcMP9BHaq9A4HZt7whjfwvve978AiqXe961385m/+5tHX7xxYEIuo1Zw/DT56udfL73o8eEyTZjKJPjpK5Qd/EGmgfvhbv/VbfOQjHxHtePj6923XA2K5ZNvYQ4mm54ZC/NU992CeOYPZ6yEXi0idDpgm3mqV/+X38+NPPAHvfKfz7xqxuLjIQw89dODnn/70p3n1q1995Q+GIYp9drQ9SXKEOra24IEH4Fd/dcdxwuGwCGbuh0ceeYSv+7qvu6Z72I1isUggEMAwDIrFIsViURSD1ut1XvnKVx48xrjv3rLgwQcPJVF/8dnPZnZ0FPnxx+klk/Re8AI8r3kN4Ze/HCUcJhQK7d3EDx///vvZM9oOXAoELIu/mp3lmRcvHkhAP3v2LNvb24c+E+DKeQfFJLvxq5EIL/V4UPt9zH4fX7uNmUyKAr3nP//5PPDAA0ef5wj81//6X/mhH/qhHX8LPPe5VKJRjGiU71le5tNHuZq7be+LX4RXvnLfr/z0T/80P/dzP3fgId72trfxP//n/7yaS98X3//9389v//Zv7/l7tVql2+3yPz7wAT7+t397pSBv11y6G5IkOe/pi1+E171OODC84hWv4CMf+Yj4nvfbvg3jC1/A3t5GLhT4Pcvi/758ef+D2rbzr1p1xvE/+IMDz//MZz6Tz3/+8wd+/hd/8Re86U1vOvQejoPJyUmWl5cBh4Beq9V2uKs++OCDvO1tb9v3t+54epz1gqIownl2P1y+fJm77757x7FdXG0wPp1O71tg1mq1KJVKvOpVrzrWMY+6vy9/+cvcddddB/5+dHSU5tWsfw/Axz72MV70ohft+1m5XOYNb3iDo/g8KGKU6nXY3Nw7rsEOpx1KJRgQWyVJ4oMf/CDf/d3ffeB1vOQlL+ELX/jC0Rdsms4cNRhfd+P/np/nbVNT4v8PF1uiKLzpTW/iL/7iL44+zxH4sR/7MX75l3/5wM/f8Y538P73v3/P3/db7wx/Nvw3WZb5d//u3/EHh/Tl9773vfz8z//8tdzCDrzwhS/k4x//+IGff+hDH+JHfuRHrvs8Fy9e5Ctf+cqBn//d3/0d334VhbcHIR6Ps7m5eeDnDzzwAM9//vMPP4hpOsWmtu2M1Qdg7Ru+Ac9QYN9ta+Ao1O5xJ3PXWLbtrKmGfydJ0Gg466y3vMX5N8CTTz7JzMzMnvNL2SwYBiFw1vPHLRwuFrG//dv3ONx87nOf4znPec6BP5ufnyefzzv/Z1DIu+MeVledYt1Pf/rQ03/4wx/mVa961YGff/3Xfz0PHzGXuhh2P78erK6uUigUWF9fFyrmqVRKrG/deczj8QhFf2VIbEaWZfr9vnBFME1TFCrsB1eZX9d1isUis7Oz+84HvV6PQqFAIpE40LlDURTODcZcVVX3FFpKkkQoFGJubo5IJMK5c+cwTZONjQ3hptcdFHJXKhVyuRx+v5+RkRHi8TiTk5MEAgHOnj1705STVVVFlmVRtDz8rNzEYLPZJJlMIsuycO2BnUWDiURCkNULhcIpAf0UgDMHu6J33W6XVqvFxsYGlUqFyWMIO6mqKlxL6vW6KEwbLkDy+/3EYjFqtRozMzP7FqHdahgfH2dkZES4wRuGIUg7LvnpdihsuR2RTCYpFAqoqkqlUjl0rHLJPRMTE1ddNO4WNjebTdbX14VAgvteLcsSRJtUKkUwGBRt13UHyWaztNtt4vH4vmsVF27xvusgMzY2dtp+rhGJRIJ8Po+qqlSr1UPfu2EYNJtNZmZm9ohlnuJwNJtNVlZWhHhYvV4XZJBwOEyj0WB7e5vFxUUR48jn85RKJdbX1/F4PMzOzu4oykqlUvzwD/+wKBAtDYSr/ACFwt745PA6+4EHnJzWYP/37ne/m9e//vWUy2XW19fxer0sLCyIted/+k//ib//+7+/rnwMwNvf/nbe+973Hvj5O9/5Tv77f//vV3VMYOfe/gtf4LUeD38wuFcbrghLD2Jdv97r8R7ThEYD++GHr8RVs1m4ivXpfffdx9/93d/t+btlWdRqNf4ok+Fd99/vxBKqVXBjcMPPzY2/SRI8/jgsL8Pv/q7z2SDHslir8blDiAZ/X6nw5scec+6/VIJf/uWdcXb3+dg2DIR9XffaYUSBh6JRGskkWiBwZX9nmoTX1/mXYJBXPvQQ9k/91FXF2fZDuVw+cG+TzWZFrusw2LoOpunkSA7IsX1icZEFAEUh1GzSW1jAeNWrUBYWMP7mbxj/y78U4toAuPvBZtN5F/th4PrMIJeJbfMpReGu6WnHiXif53HPl79M3s0duXGBl7zk+CTWAd7znvcwOzuL1+tlZogQaBgG9Xqd973vfTti/ge9n6Pe32/8xm/w1re+lcuXL1OpVNjY2CAWiwkRrndsb/PPy8tO2x56foLQuntffUCx9DtUle8dHydQrRJst7HTaZrVqiPIC/ziL/6iiC8NX/OBubOhvJlAp8ObVZVf83r3igcPxoj/0uvx65aF/dhj2O9+9873YhgO0du2sY+INb44GuUPFYWezweShGZZxLNZuskkfUXhf1Uq/OfV1SvtAJxzZbNXYiKSBMNC3e7zNU0Ro3mWLPO/JQm528WXzaJHIhhnz2K+9KXEnvc8PvKRj/CWoRjQgRjOO7lw2zUwAzw6+J40EKiwBoRqW5b5vGXx6vvvv3I/n/88fNM3XbXwtcfjOTBPXqlUuP/++3nNa16z/48HbVCcURo4tksS9he+cKWtAL9gmjweDCKNjODz+zEtC73fp9vr4QNKts3T3Dije09bW5BKXWkTvZ7z91wOVlYOvKf//cxncsHvR+r1sBMJR/hjCGNjY3Q6nWO7fu/ps4N44EeCQZ6fTmPNzu4VXDFNnnf//awbBna9jr21Bd/8zULwUrJt7HJ53/OJPjTIF/yuLPMGRcH0+a6ID0gSkq6DovAySeJf/vzPr2oOHY5pu//7lre8hTe96U2iaH1YYKLdbvPGN76RT33qU3vyMVc7D/2f/+f/yXve854DP/+Jn/gJfud3fufKH4bjul/+8nFuDmybb11d5btf/GKmolH0ep1Ao0EnEkFRFDRN4ze2tvhNNy7KUJ5PkuALX4DnPe9Y9/OiF72Ij33sYwd+/vu///u8/e1vP9axBNy2DvDP/wzAxUCAv3/mM/d81U4kIJvlk5kM3/fSl8IR5M/bAdFolDvvvJNcLkcoFMIwDEHQcd1iXTG9TqfDysoK+XyeYrFIIpEQInzg7LHL5TIbGxuYpsnS0hJ33nnnVYknuKIr3W53j5ApXDHuSKVS1Ot1SqUSyWRSXPMwXPGHaDQqXF5PcTRCoRChUIhsNnukELgrJBmPx4Wg6/XCdUt0yXKHxdRdEuOFCxeIx+Ok02ls26ZarTI+Po7H40HTNOLx+A7ij0sIctv2cciitwJGRkYoFouoqkoul2NqKKc7DMuyRJ+empoikUhQKBSo1Wo79tOAENUNh8MsLCzcVLFMt5+6ztDRaPTAfVS9XhdO06lU6qrbnt/v54477qBWq5HJZFBVlVKpJGqX3LxQMpkU7r3BYJB+v38kYblSqYhxJ3KAWNetDMuyxHM4DlnOrW91f+eOIclkkmw2e6AASDgcptvtChK2x+M5cLxpt9uYpkkoFGJiYoJcLker1RI1t+D063Q6LeKt8/PzO96V6xRdrVbZ3t5GURTS6TTnr1Io7SDIsizaoSusexjcZyzL8k0TvZyYmBAO8RsbGywvLwtypSsG2el0iMfjjI2NMTc3x9bWFsVikXQ6ja7rJBIJTNMUbvSNRoN8Pi+I3cPvYXiPezPJfi6JutPpkEwmyefzNBoNstmsEJ0dHlOi0Sj9fp9KpcLm5ibxeJxwOHxLkEmTySSlUomZmRnS6TSrq6uC6A3OerRcLtPpdHYIbxwXrlDByMgIzWZT9FlVVZmfnxeC9BsbG0xPT+94Ju4aznW9D4VCB9an27YtBOKvl0jtGuEUCgWKxSLVahVJktA0DcMw2N7eJhqN0u12abfbnDlz5rbJtaiqekuLE7t92zRNAoGAI0Y2ENB0RUd2Q9M0TNMUJn0ngW63y5NPPkm1WiWbzWIYBqFQCFVVxX7F7/ej6zr9fp9z584duw24faLdbrO6usrm5iaTk5N7xnFd19nY2BBi1reLcdDVQFVV7r33XqLRKKurq2xvbyNJkljLKYpCqVSi3W6TSqVIpVLMz8/fsP6mqqoQRnNN8Q5Dt9sV482tMJ6f4hSnuDqc9tpTnOIUp3gKYVkWKysrZJ7zHHqf/zzXlPYYThYDPctCXltD7vfRZ2fhpS9FHgriGIZxIgkW/Qiynd/vdxxSrxNdrxc7kXAICbJM99w5lGIRtVbDMAy6uk7nICW+g1TYbNtJkO5SLVxeXmZkZATDMKjVamxsbIiAWTab3eEOciNxUgmwo1ToXPe768WODYIk0Xrb21De9z6QJHyFAj3bvra2Dc67Grzfo9pcr9fbeT+7+saR5zFNGLiD7YdOp0Ov10PX9RNpC9VqVSiz7qfiq+v6ibwfyzBoBQKUEgn0WIzqD/4g/osXAae4QlGUa7+f4bFn+Fl7vVhugqXTwex2McNhOoXCtd6GwFHKm66S65Fwr/eAoMlRm1k3AHpScIPCvV6PSqUijt1ut4+vNnpEm7dtG7laRQ+HsRYWCP/CL+xwJHVdHA49/jH+ZkYiaAcV5XClqPyqsM95ZE1DkiT6vR5qo4Fp2+ihEIFXvOLaz7MP9hsTJE2Db/kWrFyO/uYmneMG5G17bxHcALvHOMuyKJfLQv3TDURfLw6am91Ep2UYdA8TDdkN9/Ndc+vu84Se/3zKk5MYpRKerS2MQODo53aQQMkQjhq/TNM8kec23A/dwOdwkt0Nml0vjir8OKm54TBUKhWazeaJrOPgqVuPHDZ+uQm5Pfd0nPY9KIxzkc/nDy3Q27MeOQ72uXZj17VJtZrjNqRpeBYW6Pf7T8ma/qTa3FH946T2KDdjTNgPx16PHIGjCieuej1yjPlKrtUwvV6QZbQhd4Vjn2d4PbLPOHLkmHC8s+zEPuc5Kll+5NhzTIX7oxJwJ7UeOS4qlQrlcpl0Oo3H42F0dJRMJkOj0UDTNILBIPF4nEAgQKlUQpZl4agBV96Pz+fDNM0rJP1D4LqgzM7OYpom6XSaZDIpEuru/rpUKhEIBEgmkwcWY4GzR7lw4QLZbJZgMEiv1xPjt+tSkUgkmJiYQNd1lpaWxPHb7TY+n0/s2bxeryg8GBsbY2pqilQqdVMTV+GB8JNt20J13EW73RbtJRgMOoKFzaZw+9idMHXdJdyCn1vFCfQUNwfNZpO1tTU6nQ71ep1OpyOczEulEpcuXWJubu5QArBLIOp2uyI5HIvFmJiYQBs4ebpOkG4xlKIot4XDlKIojI6O3haE+a8lBINB4biWzWbx+Xw7nFZc9Pt9MpkM4XAYn8931aIakUiE6elpbNsmn8+ztbWFqqpiLdVqtZAkidHRUZLJJGfOnNnhelSv1+n3+9Trdaanp488XywW2+HYsd89neJohEIh4daTz+cJBAL7xkhN0ySTyeDz+QgGg1+TBUI3Cq1Wi6WlJYrFIrlcDp/PJ4oH3TG9XC4zOjqKbducP3+eQCAgCOiyLDM9Pb1v8e3DDz+8Z/8lVr2H5WUGMXgX3W6XUqnE9vb2HmIrOHPTScRbnpK9r2XRlyQsQDVNxxWbK+Rzm8Ge1P3+7vjbVZx/9zMxTZNsNiuETbPFIt3hvcpw/G433D3cPs+5d8R+x7LtK/G9A/Ju+55vFzySRGVkBI/Pd4V8rusEt7exJYl+JEI3lzu+UNkh6Ha7O8Rodu8NTiJuDWCYJnqvR6BUwtQ0CIcJveUt+C5cwP6+76Oze09y2Ds65Jy63+8Q7g6IU3VNc2cM9hjx1n2PM9jb7i4GbjQaIlZ6VD87DgzDEI5I29vbxGKxHQW9uq7TO4HztAIBLNvGDgSQB0LL9hAB/cTGniGS6/Bzkwb9xHDzp7a9x6l6B45oF7ph4NF1atPT6D4fmmmiZrNEslmkYhF7dzsAQaDecY4jztO3LBTAVBR6kQjm+fOYL30piZ/5GZCkk4u7AbLrIj8QOjQ1Da3Xw/B60QMBOsOCppYlcslXg/1iOrZtk06nxZryRNoBIFsW5mBtqsgystdLd5Bz1ofHUheH9dUj8oBUKg4xPh7H84IX7Pi82+2ezD2Nj6OUSiiPP44Rj2OHQkL01VOt0rOsK217v7nhmPl7XdNoz8wgKQpStwumidxqIUsS/XPn6G1v08lmr/t+ALGHmJ6exjAMEZvKZrPUarUTcYa8rvXIVRCDepZF9MwZWs0mvmaTyOYm/VoNc0D4tWR5/zzgPmu1Q89zo2Pxg+vqHnDvkixjeTzYuk5H1w8fR28jeL1eZmdnmZqaotVqCUfrQCCwYx+bzWaFCOFuV1Nw4ruTk5P4fD62trbw+XwUCoVjE2Usy6LX6wln52GRzGGEw2Gq1SqpVIpHH32UcDi8p230+322trawLItUKsXo6OhtQy662XDdi0OhEIVCgUAgcOCzq1arGIZBNBo9sX1zMplkc3OTcDhMNpvdI07mwt2PBINB/H4/586dY2JignA4zPLyMpIkCbLd8PW78SCPx0MkErmt9vuBQEDEo9bX18lkMqRSqR15vXa7TT6fxzAMpqenGRkZYXp6mlgsJkihhmFgWRaKoqCqKolEgrGxsVvCCX58fFyQy9fX1xkdHSUajYoYgWEYgkQ/NjZGKBS65nc4NTXF3XffjWVZNBoNJicnRb7KjVOHQiF6vR6JRAJZljFNk83NTaampvbtF26d5+TkJCMjI7fluKMoihh7XeHow9Dv94VJjvu76elpWq2WIOBFo1Hi8TherxfTNIXQJsDs7CyFQkG4pu5Gr9ejWCwSDofxeDyEw2HhWj+cl3VjE9Vqlbm5uUMFrl231nA4TKfTOZG27/f7xX6/Xq8fKWZZr9dFDu6kXNivFpIksbi4iKIoeDwems0m1WqVWq2GJEn4/X4xr8/OzqJpGtVqlY2NDfx+P7Ozs1y+fJlqtUq/3xfCC8FgkFwuh6Zp5PN5Zge5/36/L8jLNzNnOlw3YJqmcBD3+/0H5p18Ph+RSESYrBSLxVuCCCxJEmfPnhUE33K5TD6fF2J9Lhl0bGxMiKxf67N3xSWG0Wq1dggYhMNhkWtutVrUBrX28Xh8Rx5/N5rNJoZhEIlErttEbXNzU4jmu2uBUCgkxA+azSaFQoG1gcGYx+M5VBz4FMeHKzKh6zpbW1sUCoUd/c0VIRhuB+6+46SEOGzbZmVlRQhGJJNJEonEjvml1+uRy+VEG8hkMlfVBsbGxqjVaszOzrKxscHS0hLRaJRAICDaWKPRwO/3Mz09LcSrrxWuIYPbnyRJEjnQmykcBM47vXDhApqmMTU1RbVapdFoUCgUkGWZSCTC2NgYgUCAM2fO3NDrdQ0s/H4/lUrlyDG6UqkIDsXNmodPcYpTXDtOCeinOMUpTvEUYm1tjWKxyKWFBTqaduwi98MgN5soqop+5gzm059O/Id/+ASu9OpxUoXPkteLFY+jJxJo29voZ85gzczQnZykW6k4yuTHcdI9BtbX16lUKtRqNUzTRJIkJiYmbssg5FEwTfNI8sdxsfv5SKEQ9Z/+aXp//MdEv/xlrFLpqpKiB6F8gBr6UwXX8f2koOs6rVaLQqEgAnw3ArrfT25ujifOnKH59V9PNBTirK4L1bATIykc8GykUgnT7xeuyKfYi0KhQKFQIJvNUq1W8fv9IuDWGnKZv250u2jlMvrEBObTnraDfH6SsGZmCL/0pTfk2MOQZRmvxwO9HuFqlUY8TjcaJfnc597wcwNE3vxm6n//91hPPCHcGE4Ctm2L4F+v16PZbGJZFvV6/cTOsR9cRVHvjUpmShK+n/kZeu98J33TRE2nb8x5nkKc5JxwHPT7fS4f5Bp/gtB1/UQKRm8lnKRCa7VaZXV1lYWFhadsjWhbFlq5jJFIYJ09i28gJHM74aQEDU5xY2GbJlqlgjE6innXXXjn5p6yc5tzc1j33/+Une+k4DoMugm53UmRp1o1XgjKWBbT09NYliWIFb1eD0VRkCRJEMybzSYej4dYLIYkSTSbTRRFIR6Pi3Vou90+NAFeLBbFmDg2NiZcyyVJQlEUUaSYTCZJJpPMzMwItf2D4Bb8Tk5OUqvVRNGiS4Z1RbSWl5cpFotks1lisRhjY2Pi+bfbbXK5nCDYezwe4Zp3MxGLxfD5fMRiMQqFAsFgUFyTaZqi2EySJBGbOEiJ3lUid397s+/tFDcP9Xqd5eVlSqUSxWIRcIi/Ho9HENK73S4+nw/DMA5M9LrOuD6fj2g0ysLCwo6iNkVRRPtNp9PkcjlUVSUej980Z5BT3PqYnZ0VZL+NjQ3C4TCxWAyPxyNED6rVKoFAgMnJSaampq7JWWNsbEwUpLrua71eD0mSGB8fF44xc3NzeworDMMQ7g/HKW5wHSNM0/ya2zs91Zienqbb7dLv91lfXycWi4m50jAMGo0GpVIJSZKEiMatUIR9u2BtbU0UO05OTu5xGUwkEtTrdba2tpBlmdXVVfFOqtUqY2NjN3x8dwV42u32U7rPvlHQJQndtpFtx93XlGXc6JFi29yIp+mKMlWrVcrlMrVaTawHrheSZWGb5hVS+A2CZNvE8nmsSMRxb+508Lbb6B4PzelpmvPzsLR0Iud67LHHhKiyJEnEYjHh/nSS8Nk2we1tNMOgc+YM8nOfi+/CBee8J/g8zVAIpVBwSIU3sP+4jpe7HWtc8vlJolAoiILnqxWlOS5MTXOKam1bPDfpCDLHtUDaL35s28i6jjX475OA0u3SSySwgkEK//k/4/nc5wh89rM0Gw0ChoHVbMIJ5JpsWcaSJAy/n+499xD4+Z8ndu+9N6ztWQNHccPjQdF1LFnGVlWsaBSOIZh3LchkMmSzWTY2Ng50R78WuGIkLlzXt36/79znSZ1H19G2tzFiMaxkkuiLXnRixx6Gee4c+sgIUrGIWi4jDwTITZ+P/vg4dqdzTaIAuyHrOnK7je31gmmiNpvYikL/3DmsM2dQVBVOgIAeDoc5d+4cuVyO9fV1AL785S8jyzKZTOa2cc92Ycsyoac9jd7sLP2HHqLZ7RLK5+m22/Svk0RyGFxSWrvdxrKsYwlbXjdM82u2BkFRlAPdenVdF0Kk0Wj00HWMu+avVCqEQiHGxsaOteZ2iUe2bQuX2rGxsX2vc3R0VAiDttttKpUK2WwWWZbpdru0Wi38fj/z8/OkUql9j3NcuI7alUoFXddF7DuVSh3qDn67QlEUUqkU3W6X9fV1NjY2hJu4C1fUvlAoMDExQSAQuG7C2PD5x8fH0XWddDrN2toaqVSKSCSCLMvYti2EEABBsnadzl0H1kwmw/b2No1GQ5Dh2u02jUaDQCAgCEFH5Q1uNZw5c0bEhXK5HKurq4K46+bbXVfzYWHeaDRKNBql1+uJMVNRFOEIeqsgFAoxOzuLbdsUi0UKhQL5fB6fzyccclVVZWJigng8ztmzZ6/ZLXdYeGpjY4NKpUIgECAQCKAoCrIsU6vV0DSNu+66S4w5mUyGlZUVYrHYHpdhwzCYmZkhGo3etqKkkUgEr9eLz+ejWq0eeR/VapVoNIqqqsJMIRgMcvbsWUFiLpfLrK6uit+oqkosFiMej9NoNIQbuW3baJomREhcEp8rJumO5bqu7yDKg5M3dEnEw6YO+yGRSAhjjlKpdCxx0KPgil7G43HK5TKxWOzA+Jau61SrVSYmJohGo9ft+Hw9kGWZs2fPilrSSCQi8szuXOeKbC8vL4scouv464py1+t1EQP0+/2CrB0MBoWQgft+3HzPzYL7vDVNI5fLCROGycnJfb/vOqaHw2Fs26ZSqRAOh28JAjo4/en8+fMUCgVUVcXj8dDr9ZBlWZBi3bXQSY/3CwsLQszEFTAolUqAM+/Mzs6KGoKDYi26rpPL5UR8/nrWE7quCwK+oijMzs7u6IeSJAmSfDqdZmtrC03TGB8fv6n98GsJrsBINpslEomI/qNpmhgrwuEwU1NT9Pt9+v3+vuIG14parUa73WZra0uMX7vhCtKur6+Ty+Xwer1MTk4eu+bBFe+QZRlN00S+sdFoAAjiubsPutY5xrZtMpkMhUKBbre7oz7INX4IBAIsLCzc1PxVJBLhrrvuEuu24fylmzt11+o3Eh6Ph2g0SiKRYGtri0gkcuB6oFgsous6sVjsthKjOsUpTnEFt84O9hSnOMUpvsbRaDQol8tsbGzg8fmoPOMZ8M//fN3HtX0+9DvuwHzmM4m95z073M9vV6g//MNY730vZr+PtrREJxym7ffTsSx6J0Dad5HL5cjlcui6TqPR4Ny5c7d9wdNudLtd4WByUmSI/Qh4kt9P/wd+gPwb3kDv277tRMiZrsLzzQq4n7TqoxvQi0Qie5xdThLl8+e59BM/Qa/bpZTN4u12KZfLIhh8Uu3AvX4bJyGp6zp0u4SLRdqjo3THx+HJJ0/kXF9ryGQywrFxbm5ux4b7JIs8tXQaIxbDHhnB95a3nNhxdyP4oz/qOITfYEidDr52G1+rRSsapRGJUPuO7yBaqdywwrRhqMkkgfe+F+6778QI6LZts7y8TLlcJpfLiQSPqqo3vMBFjAU3cN4L3HMP9rvfTf/nfx6j24XblITu8Xjw+XxCzfmpWCtYlsXS0hKVSuWGnwv2istcD24F4vFJEvLcQG0wGLyuIpnjwjZN1LU1kCTskRGUV7/6hp/zRuAkHGJOcWNhmyba2hooCnYyifaa1zyl51de8ALan/wkDApWbxesra3x5JNPYpomsiyjqiqhUIjR0VE0TTsRV6njot/v02g0hEKwq1AdDoeJRCJC3TgQCNBqtQgGg1QqFbxeL+vr68iyTD6fJxQKkUgkME2TiYkJQeDz+Xw75gfTNMnlcrRaLebm5oRDsutC0W63hcq764A7MTFxVYUMsiwfmNx2XX6y2ey+hCo3yVcqlSgUCoTD4aty+blRcIn6rvjP+vo6Y2NjgojuqpEXi0WazSYjIyNCzX833MQmnOxcd7vAJSzClfXZvxW4iuXNZpNer8fq6iqdTgfDMJibm9vhRKNpGoFAgCeeeEIoyO9XVGQYBul0Gp/PR6/X20M+H4bX62V8fJzNzU1SqRSVSuW2cEHfD61Wi3q9LsbxYDC4Zzw5xfXBLXJ0/9sVKnHjiX6/n4mJCSKRCFNTU9e1xnYdFNxCfLcY3ePxiALpk8BwLO12ih0/VfvXq0EkEmF+fh5w5u6DimATiQSpVOqGinh+raFer9PpdMjn84yMjBw4tkUiEQzDoFAoEI1GyeVydLtdLMs6sT5zGHq9nnAh+VqYy5VeD32wpvOYJiaALKPZNpr7/08QbiyxVCqxsbGBz+djamqKiYmJkzm+JKFsbGDNze0bM7RPKOZjyzKmouBrNJBwyMH1iQmsUIjqc55D95u+CT72sRM518rKinAB8vv9oiB9fHz8RPM/0VwO2eulPD6OOT/P/M///IkdexhWPI6SzWJtbmJPTd2w2K5pmnvI5zcKrVaLRqNx4wk4koSn1UL3+RyX+huwnpUtC1XXsTwe593YNnKvh2Ka2JJEz+c7EZKuLct0JiYovfzlyFNTGN/1XVx63vPwfOlLTNTrWMvL8OlPX/d5LFmmE4nQP3+e6B/8AZ4DSAEnARvAsrBUFUXXHSK6z4ceCiHfILFe16XUFdU7CfKLC0NV8TWb6EOFyEKM7gTFCz3pNFYwiDU1hfI934N0g/pQ5Cd+AqnVwvjrv8YeFgiRJMx77kH68IdPxECgn0iAqiL3+w7xfGYGYjHMixeJvPvdSK973XWfw4WiKExOTrKxsUE6nd4x7t1upNZeMons94PfT+ltbyP527+N5fHgq1SIptP4TlhEyzAM1tfXhQulK6x9UoI4lmXR6XTQPB7U4fhXu43S72PfZoTVk0C9Xqff79NqtVhYWDjy+67b8tjYGJ1O50gyoIvhuG6j0aBYLArn4WG4MZXR0VFGR0eZmJgQIm8ej4fR0VF8Ph8jIyPXVRfjxhN6vZ54Bu6arlgsEggE9tRafC1gamqKdrsNOE6iKysrQhio2WySy+WEcE+/32d8fPxEa0wmJibEsy4Wi+TzebLZrCCluo6uIyMjpFKpHfOnSzZTVZVWqyXEA8CJL7rv63oIQTcTiqJw4cIF0uk0fr+fbrdLo9HAsiyCwaAgD4+MjOzr0u31ep+yNfa1IpVKoWkaXq+XZDJJo9EQMflUKkUoFCIYDDI7O3vdfW92dhZJklBVlZGRESqVCr1eD9u2BSEtHA6TSCSYmZlhbW1NuEBXKhUKg/ziMKE6EokIV+nbAZ1Oh06nIwiCoVBIuBC7BMKDYieNRkPMC6lUakd7i0ajXLx4kWw2Szgcpt/vi/7rOpSOjIwIUWvTNMkORH7c47htORgMMj4+TjQapVqtIsuyuE4XvV6PTqdzLBKjqqr4fD46nc6J1hOMjo7SbDap1WpsbGwwNTW1Z3/Z6/XIZDKCpH2rCBUEg0GCwSDz8/PinezOz9ZqNSE44I758Xicdrst+oSbXw2Hw0Lsu16vEwgEqFQqgnR/M8chj8dDMBgkFovx5JNPivq4g67JFVD2eDzifnu9HpZl3TIiyY1Gg1qtJv6/+/76/T5jY2PE4/EbEutwBVxHRkYoFAqUy2WRx1AUhZmZGbrdrhBMSSaTRCIRISbvClv6fD7GxsaEsMG1olQqCdOdhYWFA48lyzLj4+Osrq4KR/uTimt+raHb7VIoFIQQkyzL+Hw+UqnUHmfxjY0N+v0+9XqdUqmEoihCiKo7qB13BabctXwoFBLz/UnANU6QJOnQY0qSxOjoKOl0ml6vR6lUuqrxWFEUzp07R61Wo1Ao7Mh/y7JMIpFgZGTkmtcptm1z+fJlisUiq6ur5HI5kXeUJAnbtolEImLNfP78+Zu6H9E0jYmJCcbHx+n1esLEwOv1PqX5wcnJSZGjSqfTJJNJYrGYIL93Oh1KpRLNZlOIBNyutQanOMW/dZwS0E9xilOc4ilCoVCg0WgI5yH73Dm8X/4y8jEcGm1Jwh5sGCTLYnhZqE1MoP3arxF79rP3LTRQVfWaVZZs2xYb04OKUF1YliUW2bsDIVcDv99P5Fu/le1sFvP970fRNDzVKtFikbBtM2Ka+HGeCQDuRspN1to2tiRhSZKTrB5cy36b2sXFRS5dusT29ja9Xk8URzo/kw4MbrjFe9dzn7vvefjYbkGme+zjnmP391zHTpeUoCjKjvfofv+4wQPXEfwwUoUUDiMlk3hbLcdJ4CpgSxK2qsJAOdeyLEql0oGFqG5QFABdd9rAMKFy+HkMK83LMqiqaDsHBXkikQgej+fQJID7TNzN5WHtwk1AuG5Ju/uUpmnH6qvD53RubWebUf1+GCTeAoEA9Xp9R/DYMAyxwXSPsfuYe2DbSLou3BtsScIry+iG4SQATBNqNULFIh2vl5bfT21xEd+XvrTvMd1xZXdbHxY3cK/vqEJE13nyenHUMdxk5vXCtm1RUDU/P7/v/bnt7dA2NXDV2dG2d8FIJLBmZ5F+9EcJ3nvvjs9M08Tn8+0oot4z/tg2kmle6Ve770WWkVQVZRDIsSxLFKy6TnnuOzzOsxPnNwykwTmHHUNC1SpSOEx9YoJOKMRX77sPKRxGHgRKj3ueo3BY4Nd34QKBZz0L/6c/vWPecS528I7cMeaIsVvTNNLpNKVSifX1deH86SZlkskkXq93xzu5lnnnoPnbVS8G8Gma89wHc+hhkIbH0qGx8aDzBO+9F88f/RHqj/84/s3Nve1VUZx/x7yvo5IhiqKcSDsY7psuySaXy1Gr1QRJ56h1wnHm14OSn+VymWazST6fP/Sed/fb4TZy3Lbi8/lE0uaw37jnGj6fe4/Df1tbW+Puu+8+sE34/f4TcSg/7PmGQiH8fv+h9zR8Ly7262OKolAsFvH7/YyOju5bLHBVbW6/NQuOc5GUyeCpVrEVBf3MGcyv+zoSAwK6x+M5kbZ9lLLp7vXI8Hs/bpuybfvI9n89e5Rh7Nc/Op0OvV5PuAqd9JgADvG3WCxSrVZ3EPiud29wouuRft9pZ7vnqQG8TzwBmua0tW/8RmIve9lVXctxcdAzib3+9WR/+7fxXyUB3fJ4kPcZO49qc36//7ruye0HpVKJpaUlMX54vV7hCFMul5Fl+cixB/af16/2+txCnF6vt4NkHY/HqdfrQgTP6/XSbreRZVmQ8/x+P81mUyQYs9kslmURjUaZnp4mm81SLBYJh8MoiiISlqqqMjc3RzweZ25uDkVRmJqaolwu0+l0sCxLFPucZLGuaZqUy2XK5TLhcPhQsqhbFHW1Lj83Em6xJ8D29jYbGxtCsKBWq7G9vS1cC8Lh8IGFmbVajXA4LFxV/i3A7XeFQkG4wwCCODwyMkIikbjp7/hGod/vs7KyIvaQbvGvm8Sfn58X7WK46CgWi3HmzBkuX74syPrDCfhWqyViYbIsi+LBw+ASgmq1GqVS6bZLCrtuXO12m3a7LcY/d35wi6ZPcTKIRCLceeedQujEMAzxzBVFIRqNMjY2diLOCpIkkUgkSCQSx/6Nu/9RFIVms3mkWEqz2RR76Fu9UHh3URJcccxJJpO3hJtIIpHA6/XucOIwTXNHEezo6Oip+8FVolgsUq/XsW37SLeaWCwmvq/ruihuPWxNPRyvhIPX+UfFrtzC5oPa4nA/270f3X3cw2JlV7v3dc83fOz9sDt+KUsSqmWhDtYoIcPAxiFS6qqKbNv4TFPk9Nxcny3LVxXDcZ9JqVSiXq+TyWSIRqNiH+AWlQ1jOO571H7Jtm1k08SjaajNJtbyMvroKNLAodzWdaRyGe/2Nn4396JpTnzwKuA6AquKQn5qiohloWmaQ0B/1rPoffM3I509i3zpkrif/URAhv9397PbHb9KJBLCRazZbLK1tSXi4kfFJvaLiYh4uW3vyNU2JyfxBQJkL17E92M/hhIO7ziW3+93XFsNw/n97hzW3pM7/2QZXDIzwMwMfa8Xz/o6VrOJmUhgh0LOO6zV8A9yqMiy8+8axn1XWMzYRxBc0zQ8Ho9w9nK/Dwf3/8NipaqqYpompmnuG5d3yS+7j+fcotOP6PX2xNmGn60NaJKE1O3ibbepT05Sf85zkIaOu3vsce9nOE++5/ptG2kQU3fPpkoSkmniGZDGJNtGsm10RaHt9WLG43gLhZ3XtzsvY1lI7rixq525eW/Z76f00pdiDsi4tm3T6PdJfcu34JmdJfqpT+H//Of3PE/3ukXMyLmxnR+73wF8lkU9kaC2uEip2WSsVtuxH3dj8cP3sN88sadtWJYTIx20c68kYcsy2LYg69uyjGQYaI0GiqZdGXuG+8NVYne82hX56na7nD17lu3t7T3tYHcMeXdb3v3+3PG0HQoxUq+jJxIiHy0N/mHb+GTZ+W93PJBl7EF/FccDkbc5KBdoRqOYs7PY3/VdxF7/+j2fD7+f4+ybD8pLKppG9LWvhde+ll46jZHPI8ky2vQ02ugo/k9/Gn+1un++edBXRHvefR/uu1UU+MZvxJAkpE4H2++Hu+/G9+3fTuAZz4ChteJx7uOw+x4eb1KpFPfff7/Ix91zzz37jj1HPaP9cC3rkd39Y9/5YugzG2BYHOLiRYrvehf+P/9zIo8+SqfTwcxm8Q2JHdtuHpOj127D8Hq96LrOk08+SbVaJZfL0el08Pv9yLJMp9MR/ew4z8iyLDBNZ8wbyk9qOHFPwzTxejzOMW0bOZ/HDASQryEffDNd6U4Cuq6Lefk4e1Kfz4dpmqI25DhQVRVJkpiYmBCinvl8nnQ6TSgUctZspkmr1cI0TUZGRvB4PNx5552MjY2J9ZamacLN7nr2z7lcjo2NDbG/dWsO3FihS1jtdrucO3fuxJwbbwVIkuPsmE6n0TSNTqdDNpvl8uXLYp/v/t11Li4Wi8zMzJzYc3CJ4n6/n1QqJWJpiqIIMvzY2NieWi5JkpiZmSGVSgkynJsTVhSFZDIpBFhvVyiKwsLCAtPT0xSLRdEnFEUhEomQTCZv+9i969her9d3CD66JLWTIllJksTs7CzJZHKP+7MkSWIsCQ/2VouLi5TLZQqFAslkckfbckUvUqnULUNKPQxuzqPVaqHrutiDaZqGz+cjFAoRDodJp9OMjo4SiUTEfZmmSaVSoVgsMjo6KvIku+H3+0VbrdfrgoDu9/vFMwV4znOeAzhzTTgcFnlBV4A7kUgQCAQwDINisSickt19wXFiKQdhPwOka0UqlRJxsY2NDZaXlwmHwwQCAVGn12w2CYVCTE5OMjExseM53CrYr/26c7mu6zti4NFolHK5zOjoKPl8nn6/TywWw+v1oiiKIB8riiLyRk8l0ddwa0pte8f6emRkhHq9jmVZVKvVA+uK3LyYm0dwhViAW4aAvrm5yfb2thABGK6pdttetVplfn7+qvIXVwNXkGd2dlbE2N15aG1tDa/XS7lcplgsks1mxb5V0zQh8Ds1NXXdsfhqtSrq6Y6a5936TldY4ZSAvhO2bbO+vk6pVKLValGr1ej3+8J4oF6vs7m5KWo2Go0GuVyOarWKpmnccccdVCoVDMMgEokQDAaJx+MUCgUhwhEKhbjnnntIpVInJpDg5rEjkciR80EgEEBVVRqNBs1m85rys+56xa3FlyQJTdOue2zIZrNsbW3x5S9/GcMwRF/u9/sillytVqlWq1y4cIGVlRXuuuuumz4mHafO/kYiEAhw5swZgB3jjqZp2LaNYRgEg0EhrO862Z/iFKe4/SDZJ7mKP8UpbjJ+9Vd/lUajQTgc5h3veMfNvpxTnELAsiy++tWvsra2JpLy9XqdZrOJ8rGP8Y3/8i9EqlUC3S6ybWMDHUWhnkhgTEzQeNObqN5zD9lslgsXLnDhzBn8qooSCt1Q59KNjQ3W1tZYX1/nzJkzhyYIstksnU6HhYUF7r777utKJlSrVVZWVqj80z/h+7u/Y3R9HbpdjF4Pw7bB4yE/MUHlBS9Au/deJiYmyGQyQsmwXq8LF4hQKMSzn/1s4vE4wWCQmZmZHQvtRqPBP/3TP9FqtXj605/O+fPnD70227Z54oknmJub49y5cyfiplYsFtnY2KDb7VKtVkVhuqve5yrN7aeQehAajQZLS0tks1lRpB+LxfB4PBiGQa1Wo1arEQqFmJqaYn5+/lAHYdu2eeihh9jY2KDT6TA/P3/gBuArX/kKG5//PN/38Y8zVqvh6/exgb4sY8syMqBYFjZgShKybbOVStGan6f/678Omsby8jLJZJIzZ86IjclhsLpdKj/1Uyj/8i/I2Sxqtepct6IgGwaWqmLE49jj49j/7t8Rf/vbj/UcL1++TDqdZnNzk5mZmR0kirW1NR577DFyuRx+v59KpcLIyAiTk5OMjo4Sj8d3FMLX63VyuRznzp275j5SLBZZWlpiZWVF9Mlut8vq6iobGxsEAoEdReCtVotCocDc3Bzz8/Ni3FlcXOSOO+4gEAhgWRYPPfQQmUyGVqvF/Pz8voEF+zOfIfqhDxEslwm3WqAo9Lxep2Cw20WxLDrhMO1kkqUXvIDi859PLBZjbGyM2dlZRkdH6Xa7PPbYY2xubtJsNpmcnNyTBOv3+2xtbWFZFnNzcywsLHzNFJxalsWDDz5IeuACfZSqc7VaJZ/Pc+7cOc6fP7/Hhai7vEzrV38V5dFHodFAarcdEQ6vF2IxrPFxtB/6IcIvecmO3xWLRdbW1uh0OtRqNZFo2NzcFMShZzzjGaIt2ek08j//M0qzia2q2FNT1J/+dDKlEouLi0xNTQnBiOGCQE3TRJLnOAXOjz32GKurqxiGwVSxSPDTnyZ06ZIQPzAliZ4ss3n2LFtf93VkBgWryWSS8fFxfD4f8Xgcj8cj1GpvpEuBUS7T+sQnsFwl4NFRQi9/OeoxiRi9Xo9HHnmEtbU1kVw7SLBhfX0dTdOYmZnh6U9/+okE/zY3N1lbW2N1dZUzExNM/tzP4d/YIFCp0BwbwwyHxRrDNE16nQ7xRoNAp4O+uIj5speRfM97ruqctmHQGxRHyoEA3oWFG+bIcZKwbZsHHniAJ554gnQ6vaNoQpZlotEo8Xgc27ZJp9PEYjEmJyd52tOeds3Bsscff5x0Ok21WuXs2bM72oZhGGxsbIh1z9bWFu12m9HRUaanpwURKxKJcO7cuSPnm36/z8MPP8za2ho+n2/P2sa2bVZWViiXy2xvb9Pv91lcXGRyclIUFti2TbFYpFQqMT09zfj4OBcuXLimez8JmKbJQw89RDqdFs6gw89weXmZjY0NWq2WCNCfPXuWubm5PWNtrVYjm81y7tw5zp07d1VOvvvBarep/MzPoPzrvyKVyyilEvJAOd7yejESCUgkMO+9l/i73418k4swnnzySS5fvkyv12NmZubI77sEwfn5eZ71rGc9BVfowC06ymazZDIZDMMgHo/j9XqFA+bo6Oh1j5+2bZPJZCgUCmIeddWGA4EAkUhEJPRvtlOOWa9Tfde7UB56CKlUQimXnbYmSVg+35W2dt99JP7zf0Y6QnDsRiD9i7+I+nu/R2J9HXmoqNotNpQH6wpLkuj4fFx68YsJ/NIvcddddz1l19hut3nyySfJ5/PCVdstrjBNU5C8XUeSVqvF3XfffSBB2i18kGWZ2dlZnv70p19zMVK9XufJJ5/kySefZH5+fkchZS6Xo1AokM1m2djYoNfrkc1mReIxEAgQi8UIh8MYhiGUkaenp0kkEoyPj4u9g0vMiEajwtXgzJkzN9aZbhcqlQqXLl1ieXl5z75sP7hFsBcuXODixYs3vT+62NraIpfL0e/3qVar9Pt90uk0tVoNRVE4c+YM09PT+xZbuET1xcVFFhYW/k0QZQ3DYGVlRSRxq9XqjiJLt2AmFotx9uzZ27awTx9yIhveN/X7fZ544gkqlQrb29tYlkUoFCKTybC9vS0Kb9wivOG9fLPZJJPJsLGxwebmJuPj4ywuLhIIBOh0Oui6TiQSEWSLkZGRYxV4bG1tIUkSCwsLT+lYfL3Y2tpia2uLUqkk2pGiKKJIKRqNkkqlGBkZYX5+/mtW0OBmwbIsWq0WhmGINdOtQIJ+5JFHxL5qYWHhwPduWRaXL18mHo8zOzvLxYsXn+IrPR7cdWo+nxfON65Dlev46/V6mZmZuaXibK7IzXD7+FoiDjyVePTRR1ldXcU0zWM5yW1ubiLLMuFwGFVVWV1dZXFx8cD+OSwItLW1xcjICMlkUhRLw/Hm5yeeeELsMQ9yuO/1eqytrVEul1ldXSUcDot4tvt5pVIR7k0jIyPccccdVz222LZNNpulUCjQ7XYdgtaABOuSLSYmJkgkEpimySOPPEI+nyefz2PoOuG//3sWPv95ArZNOJ8n2ukg2zYyDmnQliR0VaWtaTSDQYxYjNpLX8rjz3kOmc1N4S4UDAbx+XzCXewwB8PD4kW77+2LX/wi2WyWWCzGfffdd+D63bZt5znLMuf+1/8iubWFvL2N2mg4xDZZvpJjSSaxx8bgrW8l9j3fc+CzHS4Ezufz5HI5ut2uIK6k02lq1SpGq8Xdd95JamZmT65ze3ubQqHAI488gqIoTE9Pc+7cOWzbptlsous68XicsbEx5ufnSSaTrK+vk8lkWFtbE+SW3XBF5BRFYW5uTuRJ9sOlS5dYXV2l1WoxNzd35Zm1Wiif+xzeJ55A7naxvF76Cwvkn/EMGrLM/Pw8z372s/eeu1Kh8Z3fibKyglqt0p+dhV1xKGwbaWsLrVymv7CA+S3fQvJXfkV8rOdy1N/5TpTlZaRCAbVSQR4Uo7txJSuZxPr6ryf+nvc4jrgHoN1u73HJAmfv4c4l58+f39GPDcNgeXlZFC0nk0k2NjZEbmxyiARpmibLy8uMjY0xNzfH4uLivtfx6KOPsra2hq7rzMzMYNv2DjLJ7na+vr6Oz+djYWGBO+64g/Kv/Aryn/0ZnpUVjEgEa1d70nWdXq1GYGMDy+ejMztL9md/Fnl+fu87Gtzf9PQ0nU5HxNA1Tdt3r6ZUKpz9wz8ksLlJrNEg2e2iDpFsLUXB1DR6Xi+Pfv3Xo//7f094aOyrVqti3e66CwJkH36YC0tLTD/xBGqng2LbKJpG1zConD+P8fKXE7/vPnEcl4hz7tw5FhcXjxQiKb/3vUgf/Sja5cvI/T56LEY/GKRvmtBu46nV8PR6VONx6iMjXPo//g+iFy6IuLf7npvNJktLS5RKJfL5PJIkCXGsXq9Hq9XC7/czNTVFKpXaMW710mla/+//i/TZzyI1m1CpoBYKKP0+pqJgBQLY4TB2MAjBIOZzn0vox38c7zHik8Pv3sXuOcK2bb761a+STqeRJImpqSl0XWdjY4N2uy0Kp4f37KqqMjU1xeLiInfffTemabK0tMTm5qYgCW1sbHA2m+XrPvMZEtkslizTnpwEVXXqPwYEXZ/HQyCbRWm3ac7Nsf3a11J/4Qt3rOF8Ph/xUIjQxz/O6L/+K75OB6nRcAjKmgbhMNb0NOqb30zkVa/a9xnYts0jjzzC5uYm1WqVubm5fWMOtm2zublJt9tlcXGRCxcuXPWazI3Jb21tUa/XmZmZEbEqu9FA+exnifzjP6IUCvT7fTxeL0osRvDVryb4Hd+Bd2icvR7Yts1XvvIV1tbWCIVCh9Y/gNPv/+Zv/oZyuUw8Hhd5DXddE41Gd8yh9Xqdra0tkbu93nzBfqj+wR/A7/8+8uYmaqWCrSiYfj+SbaMMYpD66CjNSITHX/c6aufPM7/PmGaXStj/9E801tboGwar7TZbCwtEBv3Y3SeEw2HGx8ePtYZ8/PHHKRQKbGxsEIlESKVSO9qUK6wMCCLM7pxTrVZjeXmZ7e1t6pkMz/3AB4jm8/iaTerj4/S9XrE/8Ho8zr9i0Zkbz5zBfMlLdsyN/xaQz+dZWlpidXV1z9y8H9rtNuvr61y8eJELFy4cm2B3+fJl1tfX2djYEOYK7XZbCJkOExcbjQbRaJQ777zzuuvDdqNer7O0tEQmk6HT6TA6Oorf76ff7wtxiWG3xNHRUe68885bItZwLTBNk1wuBzhCpsPvt9frsbS0xOOPP065XKZWqwnyfSAQoNvtoqoqqVSKeDzO2bNnDxVvvRbUajXh8u26ZUaj0WPHz4bXdac4xWFwRTNcYuRhdRbdblcQPd2c1+0Ad//tipwPC0hKkkQkEiGRSDhrJY9H5CCHRTg6nY7o94lEgsXFxesiUtu2zWOPPcbW1haXL18mHA6TSqXEGsjdg+dyOVRVZXZ2Vriuu3jwwQdZX1/Htu0j1xOWZbG0tMTExAQLCwv7rmGu515c0maz2aRardLr9cTzi8fjItYzvHe91eHWli0tLTE6OrpjnNd1nfX1dXG/riB5LpcjFAoxMzPD+fPnicfjLCwsHLlXPC4O22u568FqtbpDZMA1T0okEiwtLfHoo4/y1a9+lXq9zsWLFwmFQkiSJOLFjUaDRCIhRNHdvdLc3BzPfOYzb3oex83Bu3n4eDwuHMZ1XReE7FgsJnJ0Jz1HHwfFYlEIR7k1oa6YXDAYZHx8/ET2NI888girq6tC0OgoZLNZTNNkYWGBpz3tadd9/q8VuPV5xWKRTCZDr9cjFAoJMc1Go7EjJrqwsEClUmF9fZ1sNsvU1BSbm5tC0BwcYrBrOLC+vk4oFOLOO+/kOc95DhcuXDgREvDwPjwcDh9LxHx9fZ1gMMiZM2c4d+7cdV/DScCyLO6//36+9KUvUalUBHna6/WKWJdlWSKnAo6Qy+Li4m0n3H4cuLFj15jFFXZ31yD7tZ1WqyXaYKfTod/vi3nY6/UK0Yunau922F7rFKe4FXE7cGFv/Wr/U5ziFKf4GoCrVtjv94lEIhiGgWVZmKbJ5jOfyRdf9jLiDzzA6KOP4m00sBWFoiRReeYz8b/85YzPz+MduMqapokpy3vU828ERkZGyOfzBINBMpkMc3Nz+xapuEGxmZkZIpHIdSUXdF1ndXXVUeUaGWHiP/5H6p0Olc9/ntrWFnVdx/O0p7EtyxiGwUS1SqlUAhCqfX6/n1gsRrfbZXJyEkmS2N7eFsGv+fl5sYD1+XxEIhGhDnlUIX2r1UKSJOGMfb1wVYNdMoCrZClJEu12m9XVVaEkaRjGsQNvq6ur4p+r/FssFolEIkSjUVE4lU6nd6gdHrTAliSJsbExut0u6+vrpNNpQfh04aps1mo1Qnfeyd8kErzhwx+Gchmt28VjWdiWJRzqJRwl/XQ8TjuVYvN7v5fxwXtxXQ+Gi14Og+zzkfiv/5XqBz+I+Td/g1Uug5uEV9UrSfjv/m4i3/ZtxzomwMTEBLVajU6nw8bGBqlUilgsRqfTYXt7m3K5LIqVYrEY9XpdkLq73S6tVovp6WlkWabb7Yp2d61kDdcJAq4o9buqgKlUSmyWYrEYiqLsUBDLZrP0+32hfOwG4GVZZnJyEl3XSafTrK2tkUqliEQi6LpOuVwmm81Ssm0SX//1fMMDD6AbBt56HTodbKAXi2HG4xihENXXvpbUi1+Mt9FgZWWFQqFAsVjkwoULGIZBs9mkXq8f6P7t8XiYmZlhdXWVcrl8oFrr7QjXHbzZbDI1NXXk9yORCNvb23Q6Hdrt9h5SpG9xEd/730/30iXaf/VX2FtbjlJ8PI7nJS8h9Pzn73HSWF5eFoIDjUZDJGNCoZBwhEulUsIFc2RkBGl2Fn1yEjd8rKoqIUCuVNja2hLumJVKhWaziW3bKIpCOBwWSYj5+XkRbD1O4Eq65x7a99xDK5dDXl2l32iQbTYpjYxQNQwR4KhUKkQiEXK5nLiX6elpkskk5XJZEKRuRPBATSSIftd3XfPvi8WiKG49rGDUNE2hbhyLxSiVSnvUxK8F4XCYTqdDt9vl8bU1jDe+kYUPfQiAUC6HWSrRD4UwFQWl1SLaboOi0JiZoT8+jvzmN4vE13EhqSq+s2ev+9qfarTbbVqtFt1ul263y1e+8hU8Hg+hUIhQKESpVMI0TXw+H9PT04yOjjI7O3vsIG2v1xNFZa5rbrvdFsmI4bZhWRaZTEaQolVVZXp6mnK5zNjYGNFolH6/z+rqKtPT09i2zR133HHovOPxeATpJpPJ4PV6dySeWq0WvV7PKbA2DKFMP/wdSZIYGRkRgbtQKES73b5pyV5FURgZGaHf77O2tibWLV6vl1KpRDqdZnV1FcuysCxLKKuPj49jGMaO5+WOxW6RzfUmX+RAgOR/+280PvUp+n/1V9gPPXTlQ0nCvOcevK95DbFv/uYbKjR1XLjOVrVa7VhOOd1uVzhh3Qg0m01KpZLoM24hYD6fF2SItbU1DMOgVCqhaRrRaFT87ty5c9es+mrbNpcvX6ZYLLK5uUm73cbv94tkj0tEGB0dRdf1m+66oUQiJH7zN2l84hPof/mX2E88ceVDScJ81rPwvu51xL7pm25aW5v6j/+RS+vrGJ/5DNHNTfz9vuMGNXAIsyUJQ1FoBgJcfu5zkX72Z09EAOxqsLGxIfaKux1EXIe0WCzGF7/4RUqlkiCPHARJkkilUqTT6R1FctcCtwBH0zTRHl2MjY0Jl9lwOMz999+PYRg0Gg1isRiRSIRwOEwwGGR6eloUFHo8HiHiE4vFhOO0qqpPicDPQXBdfmzbPpZjkd/vF8VKw0UQNxuueEupVBJCUNFolEqlQrVa3XfN6qrj53I5MQd/LSYwd8OyLCEsuLGxgSRJQljQdatwiWeuQMq5c+duesHJSeLy5ctUKhUymQyJRIJkMolhGNTrdarVqli3uOSfra0tQeRzC4skSaLX6xEIBMT/RqNRIVDoFpAeF8d1zbuVUCgU2NraEsKPyWRSFM9ZlkW9XhcFaa7TzHFEd05xfLgE11sNo6Ojwj1ia2uLiYmJPXs4y7LY3NwEEK5LtyrW1tbI5/OiQN/dr4Kzhi6Xy0SjUbEHOom4wknAFYs6xfXDJWIcNwamKAqmaRIMBoWTWbVaPbSduwVwPp+PXC6Hz+cjFouJ9aE7P5fL5QPnZ1VV6XQ6LC0tidikx+MRa1RZlimXy7RaLUqlkigMHo6Ler1ekZvIZrP4fD4KhcJVFQ+791IqlUTh9/BzcfMlbuxM0zS63a7Y8+XzeZbvvpvNO+5g6tIlol/9Kv5cjtFeD1+vR9vjwfD50Px+TEUhHY+T/4ZvoHnxImazSTabpdfroeu6yAdFo1Ha7Tb1ep3FxcU9++perydIwfF4/NA5udVq4fP5qFQqTE5OHhrPcNdZxWKR8k/+JMF//Ee8//APWI3GlRyLx+PkWObn0b73ewm/+MV7nme9XqdQKAjHMXByh5ZlieJhd2/R6XSoDxxuHlpe5rws7yhSrw7yfo8//jiWZXHx4kXOnDkj7mFsbEy4mVuWJYrYXGeVUCh0YFt25/qVlRVRFH1Q3k3TNFRVFY5dwgU9GMR6xSvovOIVO77fyWTwDPZo+547Hkf5yZ/E+uVfxrBtvKurGIEAVjSKrapI3S5qpYJk2/Tn5rDm54nsKjLSxsaI/87vUP/zP8f82MewNzexB4QwZBnz4kW017yG2MtfjnTIeLC1tcX29rYoUu90Oti2jWfgNOvGWOLx+I59uLs3dJ04XedN103JhTuHut8/bGxxXfZWVlaQJIlWq7XD+TAcDpNIJATxrN1uMzY2JtYX0be9jdpDD9G3LLS1NdTHHkOPx7EDATBNvLUa/lqNjsdDLZmk/tKX7ks+Bydn6/a9YDAo8mftdlvkmQOBgGgLejTKV974RuS//muevr39/2fvTX5kSbc8oZ+bu7m5mQ9mPs8ec9whb+arl/WGous9dQvoQkWpkRoBi2JVtBB0iQUSzYb/ABaNxAoBC1bseoNYdEMJgaim6vGqX9WrHG+MPs+D+Wxu5gOLyHPSPcIjwuPeuPdGZvpPemp15Y0Idxu+7zvn/AaMAQQlCfZ+HzYAc1nG5Hd/F/n/4D9A1TQhSxKWT0WU9ERposCVOcvY40H9j/4I7T/8QzizWfRKJYgeDzqqCkckgng8Dj+u3r12u416vc5i0k16l/7/4r9Ax7Iw/ef/HGi1IDSbkOt1SIsF5gAMrxf9WAyzSASD/+Q/gTsQWHm/7XY7QqEQzwAbjQai0eiNXrZpmqhUKsjlcrDZbFAUBeFw+Koedjoh/KN/BOef/AkcX36Jua5jLoqYzmaYn51h0enAZrdjEghg8POfY+ByYV6vw9ZorAgX3pQoTTPoyWTCCZokNq/X65xiurOzw4T90WiEWq0GTdNQqVSQSCTgdDqhKAoGgwF/FuunP0W304H029/CXSrBl81i4vFgIssQLQtKvw9lOITNbscglUJ7dxfnz57BKBRY1Ed172WxCM/f+TuY/Dv/DsKnp1AbDSwmE9h8Pki/93tw/+xnd/b1SFw/mUxgGAbPgFVVhd1u57/TbDZhWRYymQwbED4UZOxBKehEPGcRxi9/iYtXrzDOZqHM57C5XIj+6EeQfT5IwyFC9fqjpMbS+kHCirtgmibOzs7Q6XTQaDTgcDjYONLlcmE0GqHZbCKZTPI18Xq9LH41DOOtPutt0P7kT9DVNMz+p/8Jc12HrdWCbTLBdD6HEQphpqqwwmG0//iP4cxkMM7nYRjGjZ7kTFWR/egjdFMpFAoFdLtdHO3vY3d3l03pyuUy97fpebkN3W6XyeQkeLgOj8cDWZaRy+VQr9chSRIikQi/H2Tk3Gw20ev1sPPxx+j/R/8R5P/5f4atVoNWLsN0uTDxeGDOZrBPp3CNRnAIwtXeuLcH3z/5J497wb8DILK+3W7n/u5d6PV6kGX5wSl44XAY7XYbLpeLRYa6rvPeTL/P4XBA+Ob89rb8sHWoVCrodDoYDocIhUJctxNPRhAEDn4pl8twu91oNpsfJD3TNE20Wi0WdNFM6iEC7btAZ2m32w3TNLG/v89rOPAtR6tarXLowMuXLx/1ntD3eVNsRR5bbApBEDbmnmySsPsUQf27fD6PxWLBRtGUVt3pdJDNZhGLxZivFQwG0ev1eNYVDAaZ85fJZN56Zmez2XB4eIjZbAZJknje3e/3WWw3m82gaRoi39Qi13t5fr8f3W4XhUIBlmXdKSrr9Xordd5jwmazYXd3F5FI5IbZmsPhYKO6x9633jVoHizLMgaDwcqaLIoidnd3Ua/X4XK5YFkW15mZTAYHBwdIp9NrA4LeBQqFAmq1Gvr9PjqdDvcaRFGEpmno9/toNBocotTtdtlskWrtxWLBYV0ej4fN4LrdLmKx2KPtsW8Dy7LY+Hg6nd7gm1NKsqZpyOfzzCn4EAL0UCiEUCiEfr+P4XDIfU+fz/eofC5BECB8w+ffBLPZjH9mi29Rq9XQarU4qOjg4GBlTY1EIhgMBiiVSmzguFgs0Ol0uJbf3d1FsViEoigYDocYjUa8Nh8cHGA4HLIJ2WNdf5vNBpvNBkEQNuZmUNDHUzortlotnJ+fo1qtQhAEBINB7mkAV/XsaDRCu92Gw+HAdDpFsViEz+f7XvE35vM5Li8voes6hyMtB7N0u12USiXs7e3d4LO73W4cHx9jPB6z0Q7VkYFA4L2GXGyxxRbvBtu3eIstttjiPYOIF3ToBoCZy4XOL36Bzi9+wf8un88jEAgg/U3DbN3PvWu4XC7E43HMZjMUi0Wcn59DVVUmAk0mEyYmxONxqKr61iRNSk1st9vfNsp8Pgj/+r+OYbGIcqGAaCCAqCShUqmsEExo4EyOftSMiEaj6PV6aDabEEVxZfggiiKCwSCnRxmGcWfDhQSNj9EUGI1GnEblwyrXAAEAAElEQVQ1Ho+xs7Nzo7CfTCYol8vswL6JQ1gul8Pnn3+O8/NziKKIdrvNxBEi1EQiEQQCASQSCRZW3yd+iEajnOxXqVRweXkJWZYhfZOCTSSJo6MjDAYDmOEwPv83/g0k//v/Hv4//3M4RyOI3wyFTLsdHVmGrihouN346g/+AAffDDQXiwUsy4LD4XhQgWlzOOD/T/9TLP7RP0L/z/4M0y+/BCYTwO3eaAi/DpRgSdeP3OVIVF0oFGC32xGPx5FMJhGJRJgQTs8mEVR0XUckElkpSh8KarxQ44CIhR6PB4ZhIBqNotFooN/vw+12w7IsdDodAFdp2wcHB/D7/TdS5SORCAu5Wq0WarUaPv/8c4xGI4xGI4zH46sG7LNn+NXPfoZ4Ngv3r34Fud+HpqqYaxpGP/855r//+1g4nWh943w2n8+RzWbR6XS4Id7v9+H3++8cBtjtdgSDQTSbTW6CPUWyMnDVECOHN4fDcef6TI3tTcmggiDAZrMxOfg2uI6P4bpn2G5ZFj777DN2nNR1nZ/F6XTKjuEulwvT6ZTTKmRZvnVNpEaB0+mEruvweDxIJpOw2Wyo1Wo4OTlh9/NsNsuO25RYfr2hQMYe/X6fCX22aBQTv58/Mwm7SGzo8XjYKEEURYiiyEQgSiWZTqcbucG/b1CKo6Ioawdpy4mqlOz72WefYTgc4mc/+9kbk6sG35Bcu90up4zVajU0JAnlv//38emf/RksVYWo63AMh7BNp5jabNA1DVOfD6N0GvU/+RPI7TZqoxE0TUMmk/nOusrfh/F4jJOTExiGwQN+n8/HJiPVahWGYSAQCPC5hQwXCJQS1u/3uYlODqNEql4+5wmCwM2762tlt9vle+h2uxEMBmGz2Zj8R2cf2q8cDgeq1eq9TtPpdJqHDZVKhcXvkiRxCk+9XsfOzg7C4TDC4fDa5zYUCnEDstls3ppm9j6QTCZ5oFUqlXB2dsbDtouLC2SzWU746Pf7fB7sdrtIp9M8eLTZbA82xrkXNhu8f/AHwB/8Aax6HdNvDGQcsRjEJyZqocF3tVrFYDC4cz+2LAuDwQA7OzuP5p5NGA6HyOfzbARA5D6n04m//uu/xmg0gmmaKwKIYDDIyXiXl5dMnnv+/PkbrVm1Wg3NZhPZbBYOhwN7e3sr7+h8Poeu62ziIggCXr169UH3H5vDcZWA9Ed/BLNcxrReh81uh5hIwPEEBiB2hwM7//Sf4vS//W8x/hf/AmKpBHenA8dshoUgYOJ0Qo9E0Pg3/024//iPEU8k3uvgZjwe83pGhI613+MbM6rLy0tomoZOp3OnUF5RFDgcDgyHQwyHwzf+fLIs81r8+eefM+nT4XDA6/Wy+U+hUMDu7i663S4ODw8RDoeRyWT4XSHBj9/v51pHlmW8fPnyyQx9r5ux3Pe5aF+l8/RTgt1u52Qe4GpNIifzUqmE8/NzuN1uSJLETuaLxQLRaBTBYPA7nfT9EDQaDXS7XeTzeXg8HsTj8ZV7KcsygsEgSqUSm8I1m80nLQ59CAaDAZ/5/H4/Py9Ut8/nczYRWiwWaDabkCQJk8mEzzA04B0Oh1AUBfF4HJFIBKIoIhAIIBAI4Pz8nGv2+8TlNFS/nm72lDGfz1Eul1Gr1WCaJvb29lbOAIIg8FmH+nMOhwPRaPQ78x23eHOEw2E2saC+t6Zp3JsdDofQdR2CICCTyfB78xTRbDbRaDSQz+chCAIODg5WnuFQKMQprqVSiXvM35V0qC02g8PhgCiKGAwGG/378XgMt9sNt9sNu92OQCCAarUKr9d7a9+WhHnz+Rx7e3s3DBVpfybjD4fDgUajgUgkgsViwSRUSgCiJCOXy4XBYACn04lYLMY9q/l8DkVRoCjK2hpT0zR0u110Op2154W7kMvl0Gw2kcvlYLfbkUgk4PV6YbPZMJvNeJ5E53XqA5EBJs0wtHAYjd/7PXy2u4tqtXolsOp24Tw9RVhR4A2FsDg6wlfd7hWZu9HgfTyRSHD6EZmmZbNZnncdHx+vfB8i71mWda8p03Q6ZWEh9X/vApkgz51OuP70T+H7z/9zDP75P8f07AwwTdhUFdIvfwnlRz+6MWOxLAtnZ2ds6tHr9bgXNh6P+bq9fPmSBWiz2QxfffUVcrkcyuUyTNNkcjSZqGazWUwmE3z88cdsILMMj8eDTCaDy8tLqKqK09NTTCYT/j13QRRFNqb2+Xy3GklSTVWr1bhXdhto9nFfT8T3h3+I/mKB6T/9p5iHQhCaTThaLWCxwMLhwDQcxiIYxHx3F57/+r+GuGZmJ8gytP/wPwT++I9hfP01rHodNkGAmEpB2tu787sD4P2AzPW8Xi/fm/F4DF3X0Ww24XQ68cUXX0AQhCuj2m/ufTgcxmAwgCiKyGazSCQSbOhI94DmUrSH3jVPDYfD+Pzzz1Gv11GpVCBJ0opZ53A4RK/Xg8/nw3w+ZzNAOvvbvV74/rv/Dt3/8r8EZBm2Vgv2dhtCu42FzYaZomC8s4Ox04nLn/4U+o9/jMhgsFLjk4k3pd2TgJbeVTI+bDQaLExdLBbcn3L84R/i/7Is/L7bDX8wCLvDAVs0CvWXv4TN6UT/7Ay9XA61Wg2hUIhrOjIfJ1NS4GouIEkSJEnC3t4eypoGx/4+at98BvEb01+328094UQigUAgcKfR7TJsdjsC/9V/hf4vfoHJP/tnMP7f/xfmeIy5ZcGpKJj5fOj9nb8D6+//fTg1DWFcrbmUqEVmTuPxGI1GgzkC10GG07lcDsViEc1mE9FolM3iBEGAJElwRSIIf/QRIpEIf/7pdIqLiwtOvut9Y45qs9n4u5dKJezv7z/K/LDZbHKy4/Is3ul0wjRNGIbBZiUkuPf5fGxW0mq10Ov1EIvF4JQkzP6z/wyd//F/xOI3v4G914PU7cLT7V4JKiQJ03gcls+HViaDL//BP4DN4cDBzs5KvUK9zWKxiGK9DuHjjxF6QJIxgVI0gasZMJkWUM97sVjA5/MhmUxyMuKbIhAIcG1JhnuVSoXF4KZpYh4MomezwePxoNpqwdZuQ5Zl9Pt9lEol7OzsvNW52263c0+fklHXgdIi2+0214yKokDTNDYT7/V60DSN0zzJNGaT2e3bQv2H/xDzP/xDDP73/x29/+1/w6RSwXSxwMjjgf7Tn2L+ox/BKUkoFgoYjUYol8vY2dlZ6RnV63UMBgO+B3t7ewgGg5z6RQnx+XwexWKRDXhuqxOoV7NYLO7sv1Dvq1gsct1F71Sv14NhGGy27XK5sPj930dtPkf4f/lfYKoqnLoOuduFMp/DBDDUNNgjETiOj+H5b/6bDzpD+VDGfD6fj2cfZC522+eYTqcs0NI07UGzEBIDTqdTXvMDgQD3uk3TZN4HGY/cN398KKgn32632fSa3kday2zfrCFerxe6rsPr9cLj8SAWi723+2NZFpvXUlI8CdCppko8wjyjWq2yiC+Tyay8n5RKbJomBEHAF198AcMwoKrqDT7QFlts8eHR7/fZcJeM0ZbrUDKT1HUdlUoF6XQamqYhGAyuJKVLkoRQKPSoZtGSJOH58+ecRku1PJ0TvV4vJEniGcN1hMNhNBoNuFwulMvlG9+NQMZ+1Au6Llp7LCiKgp2dHWQyGcy+MWz7Ls/VBEFAIBBAr9dDsVi8IfJ3OBxIJBKIRCLo9XrI5/PY29vDzs4Ofu/3fu+98S0LhQLK5TKKxSIMw+Dnd7nX0Gq1kEqlMJ/P8aMf/Yi52nRetNvtbDJD5oLSNxxxQRDuNbh7X6DAmX6/j729vVsFlbIsIxqN8nPf7XY/iAgd+Nb8711BlmUoisKmQXe9c1RvXQ8/+6FjsVigXq+j2WxCEIRb11IyIc/lcnA6nZhOpxiPx/xuSJKEg4MDNp2kwCxBEOB2u1Gv1xEKhfhs/VggA7p6vY5oNHrnuXw0GmE6nfI5/qkgn89zvfPRRx/d2KeoJ+VyudgEgLg4m3BYvguYz+c4PT1Fu91GsViEaZp8RpnNZtw3DoVCmM1mODw8XLufy7K8UbDEFlts8d3DVoC+xRZbbPEeIIoiu/hQ8oDT6YQoirDb7RgOhyuHMNM0MZvN+N8AV8MYEhMTuVYQBB4MvSt3vkQiwYfjXq/HCVPA1YFa0zR2nDs8PHyropBIsyTIW26W+Xw+JvcQoV/TNHz99ddwOBxwuVzIZDKw2WxM0F4WiZMjP6WMRyIRLnSXxbDlchmHh4driwESx+/u7iIUCr11wUBDv8FggL29vbX3UJIkJtF0Oh3UarU7hxStVgt/8zd/w6nqlAAvCAIXlW63m5O0SUBGRJu7BOg2mw17e3v8XBqGwaJiamZ5vV7IsozRaHT1eRsNWH/6p/izTz+F6//+vxHKZuETBCxEEXW3G/9fIIBuKoWoJCH8DanL5XJhNpvB4/G8UbPRJorw/eEfAtfSJ94Umqbh+PgYxWIRmqbhyy+/ZOfVly9fwrIsFh9S0eR2u9FoNDhdfjKZwGazvXUjjAbw5PpM6W8kfLYsi8kbRJSl5/3FixfQNA37+/trB42UfLicfqjrOmazGSKRCLxeLw8zf+PxYPSv/WuIRCL4+OOPmXg1n81QyOfR7/eZRDgej5HNZtH+hkBAKQBEGLoNPp+PhZ1EgnoqoBRCEvsT7nNMpYajw+FgIsN1UDIlEY/IVfZtmuGz2Qynp6f8v06ng729PXg8Hsznc4xGI1SrVei6jul0Cp/Px0Sxdru9ttFjWRYqlQoTFF68eMHi9cI3ZAe73Q7LsvD69WtomoZGo4EXL14wAe16Cm0gEGCiR7/f5+ej0WgwCTMcDkNRFBaSOp1OHB4eIhaLsbt4KBRCJBLBZDLhZI1isYidnZ03vobvAmRecP1ZIZEEJQnQQJ0SpMg4pdFo4PDw8EHEAXLJ1HWdSaBkLNJqtdACcP5v/Vs4KBRweHYGpV7HYj7HXBBQT6XQ++UvMf/kE8znc1S/MSGJRCIYjUZ49uzZW4s0ZrMZO8RT6suHFo3lcjl0Oh3U63UcHx9DkiR0Oh0mXJPzfKPRgCiKTAABrtaKcrmMer0O0zRZgA5cGRBMJhM2ryAiMCWmtFqttWn3nU6H1wUSn5MZCe33wJXAgAx9PB4PEonEnecWSZJwfHyM09NTyLLM33k2m6FQKGAwGCCRSCAejyMcDt+aUOdwOFZcVD8kyK2bzi2UyFOv11fWCnIsNQwDpVKJ9+nd3V0mUxJx+100i8VIZC2Z+KnA7/dzwjMl3K1bd2jtosHSYw7+er0eJ9E0m01Mp1O4XC7YbDbkcjk+oweDQXz88cf8ntE7qaoqCztFUUSlUnmwOcJisWABOomQrj8PNPwVBAHVahU+n++t0q0fG85EAs4HJAK+KWazGVqtFp8ryJH9NjKE4nbj2T/5J7j8h/8Q+v/z/6D1t3+LWb+PuSTBPDqC6xe/gLZkuPHll1/y71UUBaFQ6J0RFFqtFobDISzLulNAQCl+kiSx6OK+pPbHMLagdKbBYMBO/kQqpHOc3+9nl/VEIoFXr17B7/fzOi6KIqdeAlf7R7FYxGQyQbvdfjKJpMspP3elNxKIsE/CgKcMm83G7uVUZ+u6zmSicDgMVVXhcrmwv7//QRLoPwTomXY4HLeKySiZ6/z8nOuzTfefXq/H6U2UyviUhqD0bk+n05X3kPYem83G50dN05jQc90AgwQ6mUwGh4eHN5KgiLxGf++uupvEZN8lF/dOp8Pmmclk8tbaiYwFLy4uYBgGGo3GnalvW3w/QPWCzWaDKIrodrtMnKA0aDKSDAQC2Nvb++D16W2o1+vodDqYz+c3RCcEWZaRTqdxeXl5b+LvFt9N+P1+rpnG4/Gd+5phGDAMg4Vmbrcb/X4fo9EIuVzuhomqaZpoNpvI5/NcZz179uzW/TmRSODi4oKF4eFwGBcXF2g2m6hWq/x/p/4x7WGaprFZIiWbh0IhrjNv+97lchmxWAzD4XAjwhqJnorFIkRRvEHmI/GVx+NBLpfjlGiHw7FCEiXTIxK62Ww2mKYJORBAcXcXPZ8P0WgUoVAIvUIBwJXhcCqVgt1uhyzL3Ncnsl2lUkGhUOBkp+VzL6dvf9MLug/LZocPhaAo8P27/+69/456v+12m0l3lMhYrVZRKBRgmiaOj4+h6zpUVYUsy7Db7Xjx4gX3eU3TRKFQYFFqp9OBIAg4OjpCKBRi09PrcLlc8Hq9bOJJs9hNDDY8Hg8byJGh3XXQOdzv96PRaNxqJPrQnoj33/63Yf3u72Lwz/4Zpv/iX2D+zcwVACeYq3/wB7Dd1/e12eB68QKuFy/u/b4EMtQsFouc0rX8nbxeLxsOnZ6ewuPx4OLiAt1uFz6fj/uYZGj70UcfsWnC2dkZAHCymd/vRzAYvFfMSuvWdDpFuVyGqqqIxWJcH9Xrdf69kiThpz/9KferCI5gEIH/4X9A///4P2D9r/8rFl98wf9tIcsQ/u7fxeTnP4fP44FRr7NxpyiKmM1mmEwmLFLw+/2QZRnZbJaTvkVRhMPhgKZp3CcArmYaNHOdz+dQfud34H758sYaTObpzWYTlUqFn+nrzzXNAyKRCDweD8++y+UyJEniGfhkMsF0OuX1WpZlHBwcPKymsdng/bt/F+arVyj/xV+g9KtfIaxpcEUiwPExbA4Hlj+dKIqIx+MoFAqYTCY4PT3FaDRiY6zbQHX5b3/7WzaeXj6rkKkfffeDgwMsFgucnJyg0+nws0rrB/Wdzs7O4HK5cHJywnP7cDi8MXGZer1kijwajXimulzv0HtNZhQejweTyYRnsJFIBOPx+Cq5fjwGcFVn2UQRk3/8j1H97W/h/LM/g/zb3wIAXJIEyeWC/Xd/F/Uf/xjFZBKzbhd76fTaMxzxBEiMX6/X32heGovFIEkSFEVBMBhksjf17SRJQjgcRjwef+seeDAY5Dm51+vlXm2pVMJ8PodhGGxCL8syFosFC7I0TWNR/NvUnT6fj+/Zcvr2MkjUWSqV2ETH7/fz9VVVFaPRiA27BUHA4eEhLMviFMd3ndwluFzo/vjHqMbjaHxj/g5cvY/zVgvT6RSGYUAQBBSLReZ+eL1e9Ho9fP3117i8vMRoNEI4HIYoivD7/SvPGgngzs/Pucd42zy11+uh1+tBVdV7nxMyOSLhMN1PMmsg3gTjl79E/eVLiP/n/wnfX/wFxG/m72PDwHBnB/Z/8A/w6j/+j+/fGx8Zi8UC3W6XOQE0i6MzrqZp761ODIfDvFYWi0UkEokb6waJoiVJemNeyu7uLn9PEorVvjFRpu+eyWTg8/kevvdsAKoPKpUKPB4PWq0WLMviMwpxYnq9HhvXnJycIBaLwTCM99LfM00Tr1+/hq7rPHOkXvRkMmFDyclkAtM03ziZfTqdMj/gekopzZFN02Txe7/fx+vXr3k+e1t/YIsttvgwoL3ENM1beanAVb+fDI68Xi8bSr9rOJ1OHB0dsTkXCRapDvL7/bfueTQHJgOTy8tL5mBQGi5xlIjn/NgGJutApkTfB4TDYebUFgoFZDKZG9+NuC1UR6ZSqffGtRwOh6h/U2svFgscHh6ufD7av2u1Gv7Vv/pXzD/+8Y9/DK/XywE3ZIajqiocDgeHe41GI+zs7LDA9UOj3W5D13U2yLsLVJdQCM2HEqC/a1D4md1uR6fTuZNj0Ol0HoVH/X1Dt9vFZDJhrsldNRdpKzqdDqbTKRaLxY1/T6Lh69B1nXmGjwnqfVGK+23PAAntPR4PJEl6UjPnRqOBXq/H9dRtsNvtUFUVlUoFNpsNo9GIA4m+6yiXy+h0OsjlcswxWF7Po9Eout0uKpUKAHAwy/dlv91iiy3ux/Zt32KLLbZ4DyAxQr/fRy6XYwL4ZDKB1+tFt9uFx+PhA2iv12OCtaqqME0Tp6enPFwGvk0woERCTdOws7PzTg5yqVQKqqqiXq9DVVV2frfb7RBFkYe6b5t+alkWJydeL0AEQYDf74dlWZxe5PV6YRgGE2Dn8/lKcsZwOFw51Kuqimq1yqJOatBRs8zn82E2m+Hi4gKaprGLNaX8EjmIyA9vg9lsxmJ+SlS/DZRA0m63EQwGMbjmzE8Yj8dMiGq1WojFYizKX/43zWZzJd3Y7Xbz8OM+2Gw2pFIpRKPRG8ISShzwer3o9/s4PT2FZVn4zW9+A300QmN3F1YyyWYCNChKfiMMq9Vq/GwFg0Em9jwFeDwePH/+HM1mk9MuIpEIVFVFt9tFt9vl4ReRcILBIM7OzjgJ8Sc/+Ql8Pt8bN5Km0ymKxSI/i4VCgd0fgauGrs/nY2MIMgKIx+M4OjrCRx99dG9jMRgMYj6fo9FooFqtIhaLIRaL8UCLTAzoftH643K52NmNGgkOhwPhcBixWAyVSoUTZolMWC6XWai4DkTce9cO8g+FZVk4Pz9nkT0JUYmkFgwGEQqFcHx8fKNJRqkVPp8Puq6vDAkmkwmazSYPHCzL4gTkQqGAeDy+kozxEJRKJTQaDZydnbHz3PIa6/V6oWkadF2HaZqoVqvY399Hr9eD1+tl1+xlFAoFvjf7+/twuVyYz+coFArodruoVqucQE6JKJqmMdGAyPwvXrzgfYtIY5S45HQ64XA4rtItvvksHo9nRYCYSCS46RYKhVgo2+/3sbu7y6nNLpcLyWTySTU7llMSlkGNnGq1itlsxmkbdrsds9kMw+EQFxcXSKfTWCwWePbs2UbPRa/XQzabRaVSQa1Wg8/nQywWw+HhIXRdR6FQQLPZxGQyQeMnP8Hgl7+E025Ht9mE5PPh4PAQqW8GRMDVM9tqtZDP5zGfz3F2doYXL168EdFiPB5zigylTFHSld/vRzQaRSwWu3Oo9S5AybSNRgOhUIjXb3J2pnRtEo6R6y6ZJSyTq4mo7HA4UCgUOPlcVVUkEgkcHR2xKVG73eZG3cnJCX72s5/BZrPBMAxMJhM2aKBrQcQ2SZJWCKOBQIBFhJTUcRco7bbZbMLj8SAajfJ5r1wuc/rJfeQNQRCwWCyexNpts9mws7MDRVFQrVZhs9mQTqeZmC4IworogsRrDocDzWYTsVgMvV6PnWCfkhnK+wKJ+yzLgmmayGaznDxGaWq9Xg+tVosFL6FQ6NEEn5PJhM0DyEV1mSQ3n8/R6XSg6zpCoRAqlQpUVb1xtg6FQjwwVxQFyWRy7dq5WCz4jL7sJE+GQiRgu74vUtIEpeqQeM3j8TwZAfr7ANVpNBhbTqZqNptwu93Y3d298Xy4XC68+OgjDHd3mdRAJkuUBliv12EYBv9eEqCTydH+/v6jkctmsxn/zXa7zWYh67BYLDAcDrlGaDQasNvtLJZZJxZeLBYwTZPJtm+CdrvNBNJWq8Wpkbqus5EUmVcVCgUMh0OkUin4/X7s7u7eWrt7PB44HA4+T901HKb9evl+kAnDY4NSYzZJ+aFam2rTd2XW95ig/SoWi7EpH70/siyzCP2pCh8fG4PBgIX4ywl462Cz2bhfQgY8dwmO6vU6rydkfESmhpSU9BTIH8PhEP1+Hx6PZ2WdoBrJ4/GwYJw+/3A45HMhodfrcQ9yXR+JDBn9fj8qlQqLCq/DMAxUq9Un16u5D+RST9fsLpAYQ9d1dDqdrQD9BwK73Y6joyM2YrietEgEqPdB8nxTDAYDNgJd7hOuA/WkOp0OvF4v0rcInbb4biIYDKJUKsHr9aJSqdwqNpjNZizmoNQsh8PBQqNms8k9cOr3TSYTnsF0u128fPnyzh6bzWaD3+9notnl5SWazSay2SwcDgd+/vOfo1wuo9/vo9Fo8Lknn89zjSkIAg4ODjiR9TZIkoTFYsFGj5uATMwmkwkODg5uPb+KoohoNIpcLgfgKpklEAjwdydxLJk+kSGfoigrvZHhcAjDMGCz2RAKhWBZFjweDxsuL1+3WCyGs7Mz9Ho9NBqNlfWHej509r/LmIgMUAVB4HTluzAcDrlv9RCDyXw+z31Fv9+PcDgMQRC4/qN5iK7rLI4j8w+73Y69vT0oioIvvviCZwnLotxoNIrZbIZsNstC0WVDaeBq7lcqlZhovemZeRMhP/VEaH5GJHpN0/jvUU9ksViwCHaTnogYjcL/p38K/ON/jGm7jfl4DLuqwv6Oe0/1ep2Ng8kI6zoEQcDOzg5M02SjM5pNkvFsMpnE8fExDMNgUzNKlqP3IRKJIBAI3HlPer0earUaZrMZFEXBJ598Al3X0e12IUkS182Uuk39Z1qzyECAjaW+MaeedjqYtlqwOZ1wxmKwOZ0IAixUox4rfWYSewcCAaTTadRqNQiCwMJyVVXh8/lW6mDgW7H9dDpFs9nk63cdqqpC0zSk02kUCgVcXFzwvu10OtFsNrFYLDAejxEIBOD1enm+5HA4kMlkeFataRoikQh2d3fZ8OBt6sV2u42uJMF89QquexLU3W43nE4nWq0WC1Luq5/IHJ5E3oeHhzg8PAQAnjOfnp7CZrPhxYsXbMhMZFcyT6c+ZL1e5/X19evXbCbx0UcfodVqwe12Y29v7973kGrJXq+Hr776CqIoYjQa3ZroSAR6WZYxn8/x9ddfc09SlmU2FRAEAfV6HcPhEHa7HYaqwvijP4L3j/4IUYcDQb8f8Z/8BKYsw/jqK+hnZzcEwdchiiKniauqeiPxcFP4/X74/X4MBgN0u10mR8uyDL/f/6j9HJfLhXQ6zWtotVrFaDSCYRicCry830QiEQwGA5TLZSbOU6rYm4DuGfUArvM6qJ9Gfy/xjWnn9bOEoijw+/3I5XJsWEVBEqFQaCPDk7dBvV5HtVpFPp/HdDrlmT29p6PRCI1GA4VCAR6Ph9d36lOWSqUVo2MynKSZKoESm0kYti5pjzgcFKJxH8joazqdsgkBcCXcnUwmkGX5xt+wBYOY/vv/Ptr/3r+Hha4Dpon6aATT4cDu7u57F5/TbIJE3zS3FAQBXq8XnU4HTqcTe3t7LLB7U5ARg2masNlsnPq5vCZTqul8Pkc+n8fZ2Rmbc9B8YjAYQJZl5vO8iUBLEATs7+/D5/Oh0WggGAyy8QydFSkV/V30XSloga7HYrFgAyeC0+lkEy0yQSJT7XeNxWKBs7MzTgT0+XyIx+N8Leh82Gg0YBgGADD/4aHo9/uwLAvD4XBllkl8MNpf6X2bz+e4vLzkumMymeD4+Hhb92+xxRMAJYbSef4+3pLf78fl5SUMw0C73b4RWvAu4XK53kgcnslk+CzVbrdRr9dZmEchUtFoFJqmvTdR/fcJiqJwj6RQKOD8/ByqqsLr9cJut3PdNZlMkEwmoWnaexH5E6jXYBjGivh8NBqhVqtx+BBxIb/88ksMh0N8+umnHALWarW4T6/rOubzOQdY7e7uQtM0HBwcvLfvdBfoTLtJPUA116bc7O8qvF4v94LK5TIbfF4HcbJisRhznX8oIONN4Oo8e/2MRkEvi8Vio+vi8/lQLBb592xixkTn03dhqKaqKhRFQSKRYAO66zOryWSCWq2G6XSKZDKJcDj8ZM6qpmlymjzNHO7qkXk8HliWxfXLU/keb4P5fM7aElEUbzWFJVO6YrEIVVXXBiwRqI8AfKsH2GKLLb7beDoKjC222GKL7znC4TATwyuVChKJBLueErmdXGsHgwGnis9mM/z617/GcDjkpExBEJjc0mw2IcsyO7o+e/bsnQjsvF4vvF4vLMtiwZXdbl9JS3tbkFCJkk+vIxwOs6ij0WigVCqx8FPTNBQKBdjtdqTTaei6zkNUKqxcLhcWiwUf/IFvRSbPnj0DcCXWoqZfo9EA8K1jVfIb8fTR0dFbX2MiOpMQ4D6QG95kMsFoNFpLnKVE9X6/z27e1++NLMuIx+MolUrsJk7D24e4momiyOLkdfB6vTg8PGShEAlDRFFkB/NIJAJZlhGNRuFwOHBxcQFd1xEMBvHy5ctbHck/JCaTCTweD/x+PzfMer0epw+QGJ0IZssEDL/fj4N7yBu3YbFY4Pz8HK1Wi1OfKYWCBIJE1AkEAuy8/vHHHyOVSnGayCagxh8JDReLBZrNJsrlMrsskxMeEb88Hg+Li0nsGw6HV1LZiIhDTU+v13tn2hI5yNvt9idToM9mM05dILIBNRtoPb68vGTy/qeffor9/X3+eRr2kRCfROjk2EmkD8MwmLBExM56vc5DwocQSygNglJf1wkPZrMZTNNEIBBgklS73eZE8/F4vEJotCyLk8WpyQ2AE7ur1SrcbjeTF0j8BFytDU6nkz9PtVrlNZCEoWRGks1mYbPZMBgMMBwOEY1G0W63cX5+zmYPyyJASgsaDAYYj8ewLAuhUAjZbJbF0u9zMHMfyI2SzDcEQUCv12MBP6U9kvB8Pp+zENtutyOfz8Nut6NWqzE55i5cXl7i5OQE2WwW4XAYo9EI4/GY3+Fnz57h4OAAFxcXfA8URUEolcLe3t6N506SJCQSCYiiyAkRnU7nwQP0RqOBfD6PwWCAUqmEXC4H0zRhGAZEUYTH44HP58Pz58/h9/uRTqffaEj/EMxmM7TbbXz22WfIZrOo1+vY39/ndEwiwV1/l2ggQsYNy+TqdDrN7qPLAkFKWtF1HalUipM2RqMR8vk8N19jsRgTdSj9Gfg2fYHIf8sCaUVR+Nyz6SDD4XAgFoshGo3yWjSfz/l3bSLuJEOOp2T40O124XK52HSByFfFYpGdlIErR3ESGZO4qtlsQtM0TtK+DbRuE3mV3i3ar7/LCIVC/Ay1Wi0eGtP6ZLPZoKoqQqEQgsHgg9PF7wKdbxuNBtLp9Mp7Z1kWD0j29/cxHA5ZILHujOr3+zl9jc6cy6AEPDJtoO8syzK7dhMpmTCfz/ndp7Wr3+9D13V89dVXnID2Q0iaoPWS0gFJxEgmAQ6HA9FolM8z69YTIpa2220+R5+cnPD7NR6PoSgKJEnCbDZDtVpFvV5HJBKBZVk4Pj5+K+KnYRio1Wpot9uYTqfI5/Mol8ssaA0EAiu/3zAMfmbo/NFsNqGqKhuiKYqCVCq1siYOBgPM53Pe4x6KyWSCbDbLSU2/+7u/i3a7zUY/REwrFotcW5NRUjqdvvcseV86e6vVQqVSgWEYGA6HK0YDxWIRoVAIiUTi0Wu55ZSfUqm09m+QaReZF3zXDCAkSUIymfzBC18nkwksy8JsNtso8Z3O1NPp9FbCx2KxYPEbETRpD1ssFnA6ndxDIpLuhwS9g9fPU2Q4NxwOV9YnEmMsv7fT6ZT7L7IsrzXSEQSBazASd/l8PibBERmIEs7C4fANo8OnDErndLlcG31mIgERAeRNQD1HIow/1lpI++l1I5bvw1nzQ4PER4FAAKZprhD+39Zs9X2ARLTT6fRBpCQSuW2aEvquQIlOy8Qnv9//gyKePRaonqdz7OXlJUKhEPcxSAjRbDYhCALi8Tj35QFwwi+dGweDAb8PsixjPB5zr2YTIxKPx4NarQbLslAqlVhUnslk2AyuWCxCkiSMRiMMh0MAV0aaiqKwafDOzs6d69xyyvem+xP1ZUkEft/3EEUR3W4X8/kclmXB4XCwuJrEuT6fjxNxe70e9wUEQWBDvMViwSL1eDwOTdNu7BOCILBRKIlalwXvJGKt1WoIBoO39l7cbjcMw2AztrvedZqRUe9j05qK+ifVahW+b9LeCSSAohQbEqy63W4+VwDfiu6J2EfPLJlz0ryUSGqyLLPYk8SLDodjZY5Ave779kf6mwDu7GEFAgFYlgWbzYZ2u82zQ3qvaH5IxrQP7onYbHC8p6SfZWNsVVXvfP5tNhvi8Tj3WHw+H4LB4EryK5k0A1f3nK6Ty+Xa2CCuXq/z+ebnP/85Go0Gm2YPh0MsFgu43W68evUKzWYToijC5/OxWXmr1VoRm7lcLha+u9asVbT2tdvtlXeVzlV0Tex2O/r9Pq9jfr+fa/h1/cFsNguPxwOn03nrs7e/v8/PKvXWyPyUBKGpVAputxupVOqG8NayLAQCARweHuLg4OBOw7iHgHrHsixvtI4qigLDMPh+39cDJiHCcDhkQWO1WsV4PMZoNOK9Rtd1/PrXv+Y5J5kbUGrsYrFgE+xms4nRaARZltHtdiHLMk5OTuD3+xGJRDCZTDbiS1CKoN1uR6lU4u93HYZhQJIkxONxTKdTiKKISqWCxWIBTdOYkGuaJkRRZKFqu93GYrG4MsdTFIS+uXc2mw16s8lm1Juce0iAPp/PMRqN3so47bYUtHcBElvQer5YLG41QaJU5cvLS6iqyv3gN4HP54PX60UqlUI+n8dsNkMwGMR0OoWu68hmsygWi8jlcgiHwywmp7VFURROEyajTVoTaO/q9Xp4/fo1EonEG6cb34XFYoFKpYJ6vY7pdIrd3d0bz7SiKJxGRsbgs9kMoihiMBgAABvESJLE4nSaMy330D0eDxqNBubz+VrzmuVZ7KYi3+Xz0INgs8FGa3itBnwAoQ4lXHc6HZTLZQDgkIXhcIivvvqKQzG++uor7O7uIhqNIhwOP6iWmc1mqNfraDQaXNcBYLPBcDi8wvsh8yRRFJlvQAJkEgySwfTbiM5sNhvC4TD3Y4kfRnPSdznroD2Y5gOZTObWv+f1euF2u9kIfFNDqrcBzXlLpRKCweCNQAI6z7vdbmSzWbRaLciy/Eaz7dlshtlsxucvWsNarRaq1SoWiwXC4TAUReFkWRLrnZ+fQ5ZlXFxc4Ojo6LG+/hZbbPGGIP7bZDLZqKficrnYHP27Ili12WzY39/ntTkcDt8wAfZ6vYjH4z9I0//HQCqVYrOmXq/H9T1wtX+S6Nzj8eDw8PC99bav9xrozEpGMb1ej2dyoijCZrOhUCgAuDqD7uzsIBqNMldqOBxyvel2u7m3dFvAwBZPB3t7e2wgVK1WeUbpcDgwnU45iCkWiyEQCGBvb+9Df+RHAfXdlwMuvF4v94OJ90vcWeDbeW8kEuH6nIKg1nH+18HhcEAURdjtdkiShE6nc+8e0+l0oCgKnE7noxuyU71CM9JqtYp2u81m65PJBOPxmPlTgUDgSXEiZrMZ6xsAMDfpNtjtdhiGwb3vp6ZzeBO022027UylUnc+hxQg0el0uAZdBhnmtdtt5i9QEGM4HH60cJEtttji/ePpMMK32GKLLb7noOYKCR5yuRzS6TQnoxUKBRQKBRackWDo17/+NUajEebzOTRNw0cffbQydDZNE41GA9lsFru7u7i8vNyogUw/NxwOMZvNOE19ecC+DqIovrMGBTUJ7Hb72gYapW+TA5csy8jlcpjNZnA4HIhEIlAUhQX51HgnweU6gTUJ//x+P/b39yEIAjv50oBiOcHusVy3qGCkQc19oH9zWxo0ieUoUX0+n/Ng+3ohQIMhSlCdTCbQNO3RCauU2ksCUxLljkYjJrB5PB42EaCkw0AgAI/H806GtW8LEmUuu8DRsJFSLmk4SEkvJJw9ODh4YzEekYwLhQJisRji8TharRaazSZKpRJcLhckSYIoivj666+hqir29vaQTCaRTqc3Jo3RMK/b7SIcDmM6nbIwWtd1JoS0Wi30ej24XC4mre7t7bGYJhQK8XNH7xEJhYLBIBqNBrrdLrxeL7vdX4eu65AkCS6X68mQToncks1mMRgM4HK5WHQNgAn62WyWSQlOp3NlwBsKhdhtnwRaRFxot9tMXhwMBjg4OIDH44Hb7cbl5SXS6TROT0/x/PnzjZsWy40BVVVZMAiADQVIOETP93Q6xcnJCXw+HwKBwIo5CH1Om80GSZJWvhsNue12+4qojxpdNByJx+Oc8ux2u1fEQ4FAgIUg3W4X5+fnyOfzTMByOp1M/IhGozeaYXRP6PtRY4iSrJ8SyJyjXq9zUhkNrWezGYvPgaskGLvdzuI3WZbZ7ZhIWHc1fWq1Gr744gucnp4y8ZX2RGp8ElHik08+wWeffcbmBS9evLhz3w+FQmy+sS4x7i60Wi3kcjmUy2VUKhVOW5pMJvw+VatVHqa/ePGCxTHXB/qPhUajgWKxyKlG+Xyez0zj8ZgNWNY1Yj0eD8bjMSdbVSoVTqihZ5zeE5fLhf39fTSbTTQaDTidTl4PbTYbnj17xolcX3311VoHaHKeNE0ToVDoBnH5IcYy10EEc+CKmDmZTJDL5XgPJIHT9cb3eDzGZDK5lYz5IUCDDCJvEzG41WohGAyi2WxiNpuxO7Sqqizq/PLLL+H3+xEKhe506ywWi0wQIvIqmRRUKhX4/f7vvAA5kUhAlmUoisImCcskXUmSEIlEEI1GH9WcatnY4ToxkgY4RMC12+18/dcRjdxuNw/3r9catVoNhUKB12S73c7JIZ1OZ0UQPRqN4Ha7+ZxEeyMJAafTKQ+YSCxwfHyMnZ2dW5v0JLBeTg2XZRmhUOg7MYSv1Wqo1+ucDJjJZFbOn9PplBNB5vM5zs7O8NFHH62sWdPplMnjpmliPB7z/Wi32/B6vWxGQqCah9KKzs/Pb/zeTdHpdHB5eckGAoPBgNdoSiHu9/tMSjUMA7lcjtPu6F4v79GdTgeRSGSFJEqkQk3TOL3voaD9odPpYHd3l4WlZPpFQpVkMsnnbkmSEIvFNkqOsiyLDVKuo1QqoVKpoNlsMvGZyK3z+ZzP98PhEIeHh4+67kUiEU7oKhQKnPJD7/ZwOGSDl0wmw3vjFt9tbLKnLP+b284/hUIBjUaD16lIJMKCPCJv0rtFhm7v2vToLlAC6TohNCXKapqGer3OSVSyLPM7Nx6Pkc1meZBP5+pAIHCj90M1nyAILKSj6wRcnXFTqRS8Xi92dna+U+8ViRFvM9S4DkpNfeg+QueFRqPBJH/ginRMIpi3MUipVquoVqtc29NZgc6aJNB8SgZQ31XcJRp7qljuFW/aY6Z6btN3412AzlJkOjgejwGAjdVIPPFdWnOeAhKJBO8dlFRBJodkXkHmYZFI5AbBS1VVqKrKYhYie0qShPl8DsMw2DDyPtC/GQwG3OeLxWK8xjq+SbKkc+1yjTkajThZ8b53knrUNMPZBNQH3cTohmqzwWAAp9OJbreLUCgEv9/PtUOj0WDT0UAggEqlwjOX2WzGfT3qGYXDYXg8nlvPGiSyps+6/G5HIhE2UC4WizfMroCrfalWq8HpdPIz0Wg01vY1SNw2m814z9oUZBJmGMaNZ2k6nWI6nbLA2+PxrJgyruvpUiIapV83m002YaREx+Xes2manNJNz/Z8Pmfi212GQvP5HLqu85nwvvUzGo2yuXggEMB4POYetizLLHqORCJP2qhnMplgsVhgNBptJEqjPowoikilUixIJwPm4XCIbrfLoreH9i9M00S322UDBJo3mKaJTqfDYgXqPSWTSfT7fVQqFTaFoPkGiRpUVcVgMEC1WsXR0RGvC7TnkRAyFAqtFXBTD5QMvIksmUwm2Vw3FAqtvL+UOByPx1dmY9chCAIODw9ZZE+m65S83u12Odnt+to3Go1QqVQQDAb53z82Nuklz+dzdLtdDAYDGIbBhj3LiczLoJS7brfLxlnlchlOpxPtdvtGknKxWMRwOITX64XP58Mnn3zC/42MxCqVCgRBQCKRgCRJKBQKnOCtaRon4Z6dneH4+Jj3ncVigel0yvUeGSyHw2EkEgmUy2WebyiKAkEQYFkWmwZTumw0GkWr1QIANqIAgOPjY3i9XjSbTXz++ed8Zqc+JZmYv379GqlUauUstkn9Qf+GuAWPAUrVJTMNt9v9TmqK67Pnu3pFtJZQP3jZhPohWCa+00zsV7/6FfMRcrkcms0m13+0x5OpI91bCmagvZwM6kn8WygUWCD62AmTnU6HzV6SyeSt98ZmsyEajbJBqmVZkCQJR0dHGAwGmEwmzB0iM/VqtQrg6nrTeZvEEcDt64Hb7YbH44Gu6/fO6AzDYC7I8pnL6XTC5XJxquV995d68e+7Tru8vOTzVjAYRDAYhCAIbB4iSRLa7TY+//xz+P1+NBoNfPzxx7ynEe/oLpimidPTUzasoL0N+FbETPP0o6MjiKIIQRBwcHCAfr+Per0OVVX5fgmCwGT+t+lBXIfb7d7o3PxYoP46mV/dty7R3k7Jg++6hqR+O50nboMoigiFQmg2mwgGgytm2JtiOByiUqnwfNbhcKBUKvEcfdnQg863uq7D4XCg1Wrhz//8z/Hs2bONuARbbPF9wWKxWDEsWq4TtmKnN8N4POZE6uUZdjgcht/vX9nvbDYb18a9Xm/lHvh8vo37NlvcjnQ6zbMh6q0QN1iSJJ5jv09eChksjMdjnnsbhoFSqcT8Uk3TmI9Dn9lutyOXy7GRysuXL2EYBj9rZGZ1PUH5KYD4rJvwEOfzOcbj8TvhZj81yLKM4+NjnJ2dcd1AAR7ExSLT7IODg0c9s34IWJaFWq3GvL3ldHMKPKEwOTKNIJ4S/fd2u83m3zQbpj7Cfedg0zS5TpVlGfl8nsPi1oE4yjR3eRcaEJfLhefPn+Pi4oL5NdR3IE0H6TDS6fSTOp/SnFySJAiCgE6nA5fLdWtdR5oHr9d7b31M4XLERVjeG5/S+YT41RS4cx8oWG0ymfB7vlgs+FmkWQCFTrpcLvT7fTQaDcRisSdlQLDFFltsji0jZYstttjiPSKdTrPgr9/vc0JJr9eD0+lkl2E6cJOTGwmLfvzjH984dDudTiSTSZTLZRSLRbhcLk5AWofZbIZcLodOp8MJfSTg9nq9PMAmR+l2u41Wq8WHQCL+PKS4p8QBGureVrxQOq/P51ubSgh8m4gQCoVwenrK/yYSiSAej3NBZZomJpMJi11HoxEPlEncRqm4mUwGPp+PiQHBYJAFVPS5qNnxWHA4HCzaIoLBXaDi87Y0aCJoDQYDJJNJtNtttNttDIfDtcXAcpEPALu7u49OWBiPx/ycp9NpJis3m01MJhN2NZvNZkzQIeLsunv/lHB98CrLMjKZDEzTZDKwIAgYj8cwTRORSOStmmE0yALAwyFqOhiGwYJ+GmQ5nU7IsoxkMvkg0hiJJkm4WygUmERHafX0PpD7dLVahcfjwddffw1JkuDxeFYKbxJqi6KIcDiMWq3Ggnoi5l1//ieTCdrtNiKRCJNOPzRIjNtsNtHr9SBJEiqVCkRRRCQS4TWb/t3Z2RlcLhc+++wzTogHrt79w8NDJnR88cUXTJiitQcAN91isRj8fj9qtRqnArVarY3Ft+PxGOPxmF0PyR17Pp+jVqthPB4zWQgAOyIqioJOp4PPP/+cE4doHQ+FQpwuSOYAhmEwGX4d2YiIC/TuBAIBFvl2u90V8RU1Vmq1GkzT5KZaOp3m5GlqxF3H8rtps9mY2EnXdJPUz/cFn88HWZYRiURQrVZZ1EjXkJoy5AYbiURW0mOI0EpirNsG6oZh4K//+q9RqVRQqVQQi8WYLCeKIr9v/X6fyR6CIKBWq2GxWNxrAEHJx0Q8o4bSfZjNZsjn87wmkCvneDxGNBqFx+OBzWbj1GGXy4Xz83M4HA4eaj12Ske1WkWxWES1WmWyEV0PagZ7vV4W2V1/Bol8SUlMzWaT9wZJkpgwQUlPRAYpFosYj8fQdZ3PgrIsY39/H6enp7DZbPj666/h9Xo5FaZcLvM5glKIrjeRh8MhE+OI8PsmCIfDqNfrkCQJZ2dnnHxGoPeR0s08Hg+7ED8F0BmKkr+Aq/MUEf1sNhunn9FaXqvVYBgGDg4OkE6nObniOojMSKZHlO5FYkzaP+neHx8fP3lhEA2Rl8mH9Hz5/X4ekiynRLndbqiq+ujDAXK3JnfV++B0OjmZqtlsrr1n6z4jmfyUSiWMx2MkEgl2JAauziWGYbATd6FQwP7+PsrlMrrdLv+/drud9xhJkmCaJp8ViDxmGAYODw/5dy8WC5TLZdRqNU7ToeG9oihotVpQFAV7e3tPZuhwHSRuaDQaWCwW2N3dvbEPkFmY3W5HpVKB2+1m8xXgajh3cnLCpBBKRCoWi6jVaiwmqNVq/EwCYJKX3W7nM+ny790UvV4Pl5eXqFarvNan02kkk0mcnZ3h7OwMuVwO0WiUneWppidyMhEn6W/T+ZUInWRGQd8nHA4jFos9+L0h8xFyEqbnwm63M6mAkucoAeXs7AyKoqDZbCKRSNz5N2kv93g8NwhxzWYT5XIZhUKBaxwS8NLPNptNZLNZHpodHBw86PvdBSIP095GZyQS57hcLn5/Q6EQdnZ2Hu1vb/F+QeRSm82G0Wh073l0NBpxnbCOvGEYBpsM2e32FXMg4GqNIsOPXC6HRqPBBhEfavDt9Xrh9XpRKBRukAwcDgfvi3RubzQayGQyGAwG+Prrr1GpVGCz2eD1ejEYDLg/VC6XoaoqUqnUyvmQ0t5qtdrKeZeuayAQeGsR9YcApbi22+2NyBqDwYDFT5tiOp3i7OyMhSm6rrNgU5ZlNip500F6Lpdjo5dutwuHwwGn07ly1oxGoxiPx3j27NmTqXe3eH+w2+38bE8mk3uf38lkwnXuh6pNhsMhTk9Poes66vU6LMvis9NkMmHDjMlkgp2dnUdLeP2hYGdnh016w+Ewzw3I2NDlciEajd7ZM6Z9aBm6rrNJ0Wg0urdfS/sziYNv6zEpirKyv8RiMfzN3/wNJElCo9G4QWZeBpnI0Ll0k/WbUplKpRKf6ylZnHo110E9IJrltNttBINBeL1eRKNR1Go1VKtVnuXRdVo21TRNk4Ufbrcb6XT61ndw2VT4+lmE6nMyZTo/P4emadxHG41GLOh69eoVut0ufD4fG6b5/X6+3iRoms1myGQynOJ9H0iId3l5yXXy9TPYdRMYmnVSf2TdNRYEAa1WC4IgsAHq9fSU2WzGqUGLxYJNvcmYyuVycd+MRHLrri+l0hPJbxPQMzIajW4kZ98mvn1quN47vw/LvQv6WZorNBoNmKbJQnyq1cPh8MbCLzL6MwxjJYXX6XSuNRCYTqdsXkLvu9frZXMLEq63Wi0kEglMJhMEAgGe2QFXZ2lKK72+ZhiGgdevX0PXddRqNYxGIzbwIAO0QCDARr5kCFGr1Vjsd9+etSzIIFFuMplENptlYWi5XGYiMBlrDodDTjHe2dl51FQlIs7Te7Xu2ZjP52g0GmzeR59NFEW0Wi1YloVgMHijX74sPHY6nej1epxarqrqSoourQPn5+dsLEdryGKxYKM/6ofTNfB4PBiNRvwZBEFY6T3JsszmgstrEplupNNpNJtNpNNpjEYj/jv07+jv0DtPps8ulwvT6ZTX3dPTU8TjcTQaDe7bU38PAMrlMur1OkKhEEajEafAAWBDsbtASfEksqR9VpKkB68/g8EAtVqNUwjp2ouiyKld0+l0xWSVzFPeBGQ2NJ1ONzIcp31rNpvBMIw3rkNFUcTz589RKBTQarX4mbAsi9OJF4sFotEo18zD4ZDXNjLIIZMNMnPe29tjA5hSqYRCoQBBEOD1em8YrNC8iEz4ad66ifCEZlR0D+4CmUy1Wi3MZjP84he/gGEYKBaLKJfLXA+7XC7E43FOoiQjSwDMpaHftw5kSN1sNtnw9zaQ2cb1WVEwGESr1UKtVmMx0m0YjUYwDAOpVOq9zpsoTKFarULTND4r0P5Xr9cxmUyYw9Dr9bBYLHBxccF95fl8jqOjo1vfz9lshtPTU7RaLRQKBbhcLsRiMRZ6DwYDnluRMOPZs2e8JtF5nRLrATAP4LsOMpenveYuowIyG1ZVFU6n850noBMfi57d+663z+dDrVZj055NBejz+RwXFxcsaNJ1nU3cB4MBBoMBotEoBoMBVFVlM18S3LZaLYRCITQaDTYuOD4+fq9puFts8SEwHA5xcXHB58/rRlWNRgOapq2dY75r0BotSdJGMw/DMDCbzSBJ0gcTrM7nc2SzWTb9ouRiOuN2u124XC7s7e2t/T4+n+/JhM1830DnAMuyMJlM2Cz8Q3EJ1vUaWq0WhsMher0ep5svQxRFaJoGTdNQq9WgKArq9Tqbvz11kIkcGdfdVbPous71wlPhUL1LKIqCV69eod1uM2efQP1qqt2/yxiPxzg9PWUDw36/v2IM5fP58OWXXzL3hxLPw+EwmzqWy2UO9AK+ndUSv+i+54VMPqlHMp/PUSwWuR9G7x31yzqdDkKhEHw+3zsNhpMkCS9evMBwOFwRXC/3Dt/X3jYcDln7smxAv66X63Q6IUkSmxRSD5QSzgmUeF8qleD3+6Gq6p3z3/F4jIuLC+4pExeKBOgf8nxyHWR6uOnnsNvtK8Fmdrsd2WwW9XodhUKB6+ZgMMjPfT6fhyzL3I8indIWW2zx3cHTZj5vscUWW3zPYLPZsLOzg8lkglarxQfTZ8+eYTKZYDAYwG6389Cx1WpxAfL8+fM7G9iRSARnZ2cYDAZoNBprydaz2QyvX79Gp9NBtVrlJCSHw8FiE0VRMJlMUKlU4HQ6YVkWD8aAb9NlS6USUqnUneQMIuTrur7SbCDXp3VN+XA4zIVZp9O5MxHO4XDg1atXAK6K1+WBs9Pp5MMpkdMvLi74PtCAL5FIQFXVFSEIcFXwvsvGDF13Eu3dlxKq6zoPN6836ChpicQrJHLzeDz8nF0v2og0Qc+KJEmPRuabzWao1WrI5XLI5XIolUqYzWY8FNE0jQciNHCl4anT6eRkkKcIImJQE+86QcXpdK4U/8VikVPI3rRAtCyL3cCW35lwOAy32416vb7y3C8WC3S7XWQymQf/zfl8vpI+TesJFcsERVHg9/thWRankwYCARiGsTIoNk0Tg8GAiQmhUAi9Xg/hcBhfffUVD78I9Nnr9Trcbjf8fv8K8ehDgkglNKgjQez1VBO73Y5IJML/NpvNIpPJrFwXRVHY7Y9S1cncgwT+RLKixiq9M2QIsCkxjlKtKDWcyD30XJFbHxkXUMJzr9cDcEXeIZMIv9/PCbRerxe9Xo9FqERGoVSa659hMpmspCcoioLFYsFkrevweDzweDycNAGAXdtJvLZuiDwej+F0Ojl5wLIsFAoFJmzRHhuLxT64eMJms2F/f5+//+XlJZO4yAiARGBEpE0kEvzzHo8H8/kcpmneSPJdRqFQQLVaxcnJCRPISCgpCAInppDTot1uZ0Ltun1wNpux+G557aH7MZ1ON1p7qMFH+xuRaa6ns9Ja0O/3EYlE0Gg0VoTOj4V+v49iscjN2FQqBY/Hg3K5jHa7jVQqhdFoxGnZywlHBErMzOfzbA5CBDCHwwG73c7kJXr+KC13OByupCUCV8ZFy4k45IxLLv6Hh4f8rqRSqRsN+na7DVVV154dHgKXy8Uk6lwuB9M04XQ6WRimKAo3jUOhEJ4/f45IJPLkhdayLGNnZ4dJyURmAq7WqFQqhWQyiXQ6fWvznchruVyO09uW3xvaCyhF9PLyktNGnhosy0I2m0Wv1+PncXkIQCZVmUyGn7t3DSIXLRaLtQMIIsrQsymKIouX6b3z+/0IBoMQRZENSa4P66vVKhN49vb2Vs5TlLhFJL3T01NuhhNp3TAMuFwutNttSJIEVVXhcDh4r6HEOxJRFAoFZDIZAODme7VaZVMeGjKVy2U+V5imiePj4w++d60D1YrdbhfxePzOPSAQCLARUaPR4EEDGTkUCgW43W4275jNZrwftdtt3jOvD9E1TUOr1Vr5vQ8BOfB2Op0b6e1+vx+7u7uoVqucdjSZTGC329FsNtmAo1qtIplMYrFYQNM09Ho9mKaJ0WiE169fs4tvKBRCNBrl/z0U5B5Me+Z1kOB0+fPXajU25iGxzDqQuN3r9UKSpBWDMjJLIJHY3t7ejXWeEo1KpRIbJw2Hw0dNyLHb7Tg6OuIz8fIZe3lw+10gJ2xxO8gsSdM0dDqde88xVCtLkrT2eaNzyng8xuHh4a2kBkmSEIlEUKvVmFT9oZJ3w+EwGo0Gm6tcH1x7PB5OOaIzqNPphN1uZ1MdMi6y2+1oNBqwLAuyLLNRxfHx8cpaSoYn6XR6xYhGUZQnf667DZQ05XA4oOv6nX2vXq+H6XQKTdM27o/NZjOcnJyg3W6jUCjwOiTLMubzOQaDAfeFqM/1EBF6rVZDrVZDPp/HfD7nGoUwmUzQbDaRy+V4P33x4sXGv3+L7wd8Ph8bUeq6fu/5gkhJH4qUSCK+VqvFNUYgEOB1hpJV6/U69zpI7LPF5iBjIiI0LQvI3lQoS717r9cLXdfvNbPtdDpcG1FPZJO/S5+T+i2FQmFt6qdlWSgWi3A6nfD5fBuZsJIhTb1eZ+ET9UR9Ph/8fj+SyeTK/j+dTlkgalkWp/fOZjM2qKLa4Pz8HJVKBePxmM/TiUSCDQHo/btLfA5c9ahkWWYx4HXQ7M9ut7NhYqfTYRIdEXh9Ph9++tOfIpvNcip4vV7nPYlmNdT7v+ucBFztlZVKhec62WyWTW9EUVwRn0qSxMmgy6Y2ZHC6DNozSZBLQr+dnR02ZSODWzLGofMNGQt+/PHHCIVCnBQ6mUxQLBb5WlDCer/fR7vdhmmaLLq/bzZ3HddNE75LoOeOjI3vq9WWyZk0Rz47O0O/3+fankiGND8gU4hMJnPvO//QBGiaRTmdTqiqilevXt2YRQQCAbRaLXz++efcE3W73Ww4LkkS+v0+arXaikHQfD7H2dkZ2u02yuUy/H4/dnZ2UK/XmTTd6XSQzWaZfFosFuHxeBCJRBAMBrG/v/8gUReJBmjNPj8/h8/nQ6fTQaPRYMGzx+NhY8K9vb1Hr1GCwSDa7TZqtdraGmg+nyOfz6Pf76NUKqFUKiGRSHDKUa1WAwA2Hl/uVZCRMSXh0aw4Eonc6C0KgoBgMIh6vQ5d1zEej3kNoFlUtVqFqqpot9tcq5DJCXDVu6AEMV3Xef5OYr1lI2ZaD4LBID7++GNOaLQsC4FAgJ9PMmY1TZNnZNVqFYlEAuFwmA0TR6MRTk9P4fP50Gq12EiZeuj0vlEvcTqdYjqdwu12cw13FxqNBqbTKbLZLF8v4NuE3XA4vNHzRya8tH/QjI96TSSOWp7P0XfyeDw4ODh4sHiRruWyoP8ukJiafvZtQFyMUCiESqWCeDyOSCSCdruNXq/Hs0vLsvj86ff7eZ66PL+itZCMX4CrPfni4oLfBdpT5vM5qtUqGo0GJpPJinkJGd8cHR3dOQOnZ8TpdN67ni4WC+7PBgIBuN1uNiYm8wc6u1HyIBklk7FQt9tFNBqFqqq39ncDgQDK5TIikQhKpRIA3DCmnU6n/E7s7u4iGAyunHvInDsQCKBarcLpdK7dV8lQhmZc71OsQzOG6XTK/QES9ZJ5dzKZhCiKLACnWavdbkc+n4fdbue9Zh3q9Tp6vR4KhQKb/i2DzjDVahWFQgGiKKLRaNyo9wRBuFW8Qe/Rd1GUHggE4PF42KCMzuzLINE1/Tu/33/jnPnYoHM0vZv3gQzj6H3eBIvFAufn52g2m7xea5rGhhgkAvV4PGg2mzAMg03Oi8UiVFWF3+9HKBSCZVkol8uwLIv34efPn39n+3xbbHEXhsMh92nJCJrSlg3DQC6Xg8vl4tr++Pj4vYofiafk9/tRLpcRCoXufBcpNIT2l/eN5dktBYvQDHA2m7GhOBnkHR4ebsXmHwB3hYCtA80TlhOpyWzqbQIt6FkmziEl3PZ6vRUzdYJpmmwO4ff7MRwO2Xj9PiP1p4JQKMRn1XK5jEwms/ZsQIErFGz0Q3lPKEQgFAoxF5l6g98HWJbFZlLEN0ulUpBlmUW21Ceez+eIRCL4yU9+svL9yVC/WCyyqZmmaVAUhbkvPp/v1nec0tSJW62q6kqfOpvNcj+e6pVEIgFN03BwcPBeZkNut/tReSIPwWg0Qj6fZ+4baRScTieazSZcLhfS6fQNrkkoFMLe3h5z16kXRr1p4m0CV2teKBTC4eHhrRqX8XiM169f8/lksVjw+YQ4zRRA+SHOJ9dBcxzSBNz3WSzL4j3Abrej1Wqh0Wggn89DEAQcHh6unHf8fj9M01x57n0+34P781tsscWHxbajsMUWW2zxAUCkBk3TOJnM4XCw0zI5X52fnyObzWKxWNybKOFwOODxeNDr9Vg4eB2Xl5fodDrI5XJwu904ODhYKX4Nw0C9Xsdnn33GBz9Jkvh3A2BXY1VVMZvNMJvNbgxOSHxFjuKUtr5YLCBJEjRNY4LwwcHBSnFFw6Z4PM7N8GUS2mKxYGduEmhS4nA+n2dRJg12d3d3OXGbSKSxWAzpdJpF17FY7L0XuFSwDAYDXF5eotfr3dpkMAwD7XabE06Xmz7NZhOVSgXtdhuVSgWNRgPAt8NzOrCrqgqPx8OJnJ1OB81mE6lUColEAolE4lEGDVRgk0kBCbNIbEcpoP1+nxNFCbPZDNVqlUWRDwG5G9N3f1cuacFgEOVymUUwd7nBURr0zs4OgsHgGzfISGiz7IDf6/XQbrcxHo+ZFE6EtEAggMVigdlstlbYexeIEEjik36/z0KqZfh8PnZCJ+ExORaSUIyEmtRMoAEzJc5qmobBYIBcLseNJlonAoEAQqEQ0un0g5pvk8mE3esBMNHvMZ7t8XjMjudEJg+FQrfeV5/Ph3q9jvPzcwQCAUynUyYSOZ1OFq+nUik0m03EYjFIksRCv3XCPhJghEIhFrvdB0o+n8/nLBw/PT3lROfrf8uyLDZSSKfTME2TSTREwKRGajabxeXlJZO3brsWJPijhFwAGw+AKQ2EnnWn08nPVLvdXklBIoKMzWZjcm29Xl9ZF4GrfUbXdezt7d1pcvI+oCgKjo+PcX5+zilFlUoFvV4PLpeL74/L5eKUwuVU9GKxyGJwu91+IxWKyJZfffUVi/goiRX49uwwHo/h8XgwHA5hGAaGwyG7Y9psNk4XogYmQZZl+P1+PrcAm5EFgSsBuq7rPHjv9/s8RLsOIjdTqgqRmEkI/Rig1PPRaHRDgErpFx6PBw6HA5VKhV0yiaRIZBgaprRaLQwGA0768ng8nKxFztAEURRhWdYN8xVK/E2n0wgEAryXFotFTurKZDJrifiUwBCPxxEOh99qSGOaJicSTadTDAYDJrXYbDaUSiWYpsl/S1GUN0p3fBOQCQ89H2QMEAqF+FlaTqSmZ53gcrmwv7/PSTVEiCQi8ieffHIrQWg6nfI5TBTFtcRaSZKQSqVQLpdRLBbhcrnudUD+ELAsC19//TV0XUelUoFlWSw2I2KVLMswTROTyQRHR0fvpelNaUEAbrwfwNUaRklKlFbbarX43rtcLk4VT6fTPLgkUSNwtb9TMiw5106nUxSLRSanUpp0v98HAE7qDofD7D5Pfz8QCGA0GjGBNBwOs1GIaZrI5/MQRRHxeJwJzPl8Hjab7YaBAZ3Zy+UyD/pfvXr15Nyg+/0+7/X3CYNsNhtUVeWfmc/n6HQ66PV6KBaL8Pv9XB/ous7nzUQiAcMwmJRISb+3/d7bErvWodfrwTAMtFotRKPRG+9nLBZjYnC1WkUul2NjG1q7i8UiRFFELBbj9drr9XI9UCqVuPZMp9OIxWJvbLK0nP61yRmXSDY0gKzX65jP50yIJND1nU6nbDS3/KxdNxq47W9TEtmyOd67GCwSCXM6nfLZn8S3W3z3YbPZEA6HMRwOcXl5yUSmdWg0GjAMA4lE4tb6jMQx1BO5C6qqMgm40+l8MAG6LMtQVRWJRAK5XA7FYvEGCYgS38kow+VyseDNNE28ePECoVCIvzMlthaLRU7aevny5Y33mQj63we43W4oioJwOIxKpcJigeugOiwYDD6I1FepVKDrOvL5PFRVRTQaXXkGyck/n8+jWCxyzbbJWZCMbur1OsbjMZuRLZshSpKEZDLJIhJKXP+hkJi2uAL17fx+PyqVClRVvbVfRMKqaDR6Z0/rXaLRaGA4HHL///o7KQgC/H4/ZFlGNpuFoiioVqtbAfobgM7Ij7Wm22w2BAIBTi+6a38mIUQ8HockSdynI3LrXaAEQ1mWEQwGUalUOHmY6qXhcMgGXqlUisVSd6FSqaBUKiGXy+Hk5ISNkylRlq5Xo9HAixcvuC5pt9sQRZETT4kYVavV0G63eU5QKBT4M5EwLRAIsPEUJTxT//02UO2ZSqUQCATW1n9UPwYCAdTr9RvfnfY++vnnz5+zOIfqWODqLEHJQ6FQ6M5ak1JLOp0OC7gbjQaazSYbglJPkXrINMPo9/vcx1s3+9F1ne+B2+3G2dkZv/vUY6NnmXqlZHw1GAxwdHTE9bcoitjf3wdw1RNotVq4vLyEzWbjOlHTNP6MBwcH73QtpF4ameR9aBK1JEmcdtvpdO4V81F6tCRJUBQFp6en3AujpFYS9/d6PdTrdXQ6He4pL9ft67BsTrHO8Pk6qtUqm9V99NFHt+53ZHqbz+fh8/nw/Plzfk9GoxGy2SwLJ2azGTKZDPd/y+Uyp3EB4NkpJafruo5yuczP9dHREaf+vY1Zo8fjwUcffYRms8np8svX6bbU9odgsViwCcTyWkRnh2AwuFYMWiqVUKlU8PXXX6PdbiMUCsFut68YbORyOU5pJLNl4KrvRgJ1Wv8lSbr1WhGPwOPxQNd19Pt9jEYjXF5e4vz8nBPQe70eHA4HnE4nRqMRPB7PirhZVVV8+eWXzK+YzWaIx+MrQlIyziMDzJ2dHeZm0PNBRgA0D9Y0jY01PB4PgsEgE8Q/++wzLBYLfP3114hEIlAUhRPGqWdNnAsS1tPeWiwW2SxhHchgmJ7L8/Nz3lO9Xi8GgwHq9Tr29/fvrAXIiKVYLGI4HLJhPAA2lyiVSpBlGYlEAplMBoIgoFQqweFwIB6Pw7IsPH/+/EFCFxJgEgfmvn4W7Rf0s2+Ler2O4XAIVVVxcHAAQRAQCARQKBRweXmJer2OwWDABvlUU8/ncyQSCbjdbjb79/l86Pf7XPtRjdlut6FpGiaTCZuYknliLpdDt9tlQwYy0cjlcjg4OMDx8THfW7pGi8WC961NRPjUezYMA16vlxNWFUWBqqpMsKd32+v1svH6cDhkUwdVVW81QKc09+l0ytfsL/7iL3geRHt9v9+Hw+HAzs4O/H4/UqnUyu+h/cE0TUynU16rlxOsu90udF2H2+1GPB7nxPn3heFwiOFwuGLI12632ehv+fMIgsAmgJZlIZPJ4PLykt/z630C4OpaNhoNtNtt5gvchlgshtFoxLON+wzHKHSk0+mw4Jnm/psaVXxoOBwO5oqQUTKtTTQnH4/HsCwLXq8XPp+PwznedZIi7TObvpsAHmTAAVyt+e12m5MBP/30U5RKJe4R0ZpkWRYLWex2O+r1OoLBIBKJBOLx+Io5oqIoXMfQ2rPFFt8nzOdznJ+fs1FINBrlPZv4qvP5HIVCAcViEa9evWKj+veJcDiMdrsNp9OJUqmEdDq9dm3QdR26rrMw7kOs3RTSQML9TCazshdHo1F0u11UKhUAV+vjq1evtgYXTxSz2Qy5XI7fBzJGonqiVquxCdubnL+pb6ZpGprNJnNJx+PxWl4tcVHJNE4URZTLZcRiMeZlPXUs81+LxSIuLi6gaRqbcRJHhYygKOjhh4jlcJvvC6hnSeZc12eKPp8PXq8XxWIR3W4Xu7u76Pf7N2Yh9Bydn59zaBC9B+PxGNlslgOLqKaYTqdotVrcp6Aeut1ux/Pnz1EqlTiAkEz4iKfs8/mQTCafHF/tsTEYDHB6esoGjyTAFwSBz88khM5kMis1cCgUQq1Ww/Pnz9kokmYi0+mUeZzD4RCSJOGTTz651YSTjKXI7HK5X09Y5iABV2a87/t8sgzqTRGv/C6eBHFqyTxMEISV3vBte4rT6UQmk8H5+TmbUX9feAlbbPFDwfbEv8UWW2zxAUGDn2XYbDZujJMD2G0NGnKAI9Fmp9OBKIprhxSj0YhFwR6PZ60wiFJwqTFHCQnXBXrD4RCVSgXFYhHAFaFleaCZz+fZjZdIAySAHI1GaLfb8Hg82NnZwXw+X3FustvtODw8ZAclEjHTUJaIIpqmIRKJIJFIQNd19Ho9nJyc4OTkhF25ydl5sVjA5/Mhk8nA5/NxYuh1R+b3DUpyjUQiKJfLME0TmqatJL4QaYMaNctChWKxyKLzTqeDVquF6XSKUqnE7tw0/BiPxyvGBOPxGIFAAKlUCvv7+4+SMk0JAZReSCIMcncjN+1KpcJD/uXkvG63C4fDAZfLtVEzabFYsHCHhCbAt6l/4XAY2lJi+GOABEvhcJify3XEZEpgoWdx07Tq+0CNK13XMRgM0O/3WXBBjUEidLyJK6TX64XL5cJ8Pue/se6zExmF0tEp9d7tduPi4oLTAagIp4YHAB62lkolhMNhJpXSe+rz+Zg8uKk4eDQaoVwuo9vtwjAMJue4XC4Ui0UEAgF2In9T0LCfBEgej+fWxisJ+GgQfX5+zkmRlUoFmqZhZ2eHGxPBYBCHh4f3fgZFUVCv1zk5fBMBuqZpnDwuCAKTWshIYXkPogSRxWKBWCyGTCbDRERas4kMGAqFUK1WoSgKKpUKIpEIBEHgJBQiQhEZ0eVyMdmOUi5ov7uLNEWOq8tu32TWQqK1YDDISRTU4CEiB7ntxmIx7Ozs8D5IQjmHw/HBScxutxuvXr1CqVRiohuRLUjg7fV6ublDRLtms8nNe0VRYLfbIUkSi5WBq2RdSl+gtF5K/ZnNZhiPxyy6pmtK6Vg+n48dcEl4ToQvMr5wu90s3Nzd3X2Qwy41O6n5PhqNbt2LyFTGNE1e50gI/SaOzySIo+8ym81YPKJpGp/DltO9aBhIBLh+vw+Px8PP9ddff80CfhKd+/1+JmUNBgNO1KC0C/rstyX9jEYjfm93dnbg9XphWRZOTk6wv7+PUqmEWq3GjtNkYkD3KxKJMEmp1WpxmuhDQanYwNW+LQgCptMpX79kMsn7Bp273gcajQbK5TILPEnYrygKarUafD4fdnZ2+B5qmsbJyNev9XJ6ValUgtfr5aTiZdCaNhqNUKvVUKlUUK/X8fHHH9+550YikRUx5odsGK8DETgLhQIbjSzXH5PJBLVaDblcDjab7b01vWltk2UZ3W73hgsvOcRSqq1lWQiFQvB6vXwWKZfLCAQC6Ha7kCSJCX20h5OAnUQOs9kM+Xye11sS79IQ1LIsboT3+30EAgHe4zRNQ7/f5+GRKIq8TpRKJUQiEdTrdTafaTQaaDQaWCwWa5vvdrudSb3VahUej4eJvk8JRNimhKT7QENn+tlGo4Fut8tp7wRKR6LrQjVCvV7nd325diITJfrZTQfkrVYLvV6P65XrIPdjSuE7OTlBNpvFbDZDOBzmvZqIx7SXmabJom0yIgmHw/jkk0/easi6TMDdxGyKSKs01Kb0HUquokT38XgMRVE4mYiEjgQifl9PWF8HOl9RwuO7xH0Cni2+u6BEsmQyiVKphNFohEAgwPs1EaTH4zFSqdSdyaeWZcE0zY3MEOhdJ9HKh8Te3h7XttVqFRcXF5BlGQ6HA5ZlwTAMKIqCjz76CIFAAKIoolAowDRNHB0d3eirSJKEeDwOl8vFoqFGo3Gnsd73Abu7u5hMJpxAt5z+TEZbw+EQfr8f4XAYOzs7GydwUk1GAqx1IKOi8/NzFobs7u7e+/spOeHzzz+H3+/nHiydjYjMZLPZEIlE0O12eVC/FaD/8BCLxdDr9TAcDpHL5RCJRKCqKj/L0+mUe8fRaJTPJe8bi8WCewwk0r0NZAZBZiCbCBO3ePcIhULQdR1er5d7n5SyCXy7P49GI96fk8kkzs/POa37vn4vpe+mUim43W64XC4MBgNO1wLAvWNKHr4ubLqOXq+HUqmE09NT5HI5Ts91OBzcA5vNZiiXyxiPxzAMAz//+c+xWCzQarWQTqdZUK4oCrLZLAv+Li4uMJ/P4fV6Icsy3G43IpEIwuHwijjG6XRiNpvxXrDu3EKpJ9SLve89pV46nXUoqfr6uyIIAptgkcEaADb6vA8kgKGeKgkuY7EYNE3DyckJWq0WG9Q6HA7EYjEWobZaLQBXfa7rqce0d8ViMViWxYKBQCDA553BYIBmswlFUXh+4Xa7MZ/PIUkSi3+pJ6mqKp4/f45iscj/hkwtQ6EQGxfGYrF3RoDt9/tsckB9MpoDUB35GKBrQ4IKURShadoNw7FlUNJ8q9W69VkErt5nXdeRSCTYnIHmHuvItbRuk7iVRNN3PWOU0EuC+PvOMGTGqijKrb0R6lUbhgHDMHBwcACv18vizmAwuJK0Y7PZ4PP5+PvR575+zcjU1uv1wuPx8FwiGo1yz+BtQe9ONBrl2YYgCHC5XG/1+0mM2Gq1VsRytM6Q0IBM9nK5HPdSR6MR/uZv/gYXFxc8twCuzhZkxrhYLNhk94svvmDzX+qj0vtK/aC7egrj8Zjf83q9jlarxUnojUYDvV6P33USTFAiLM3w2u02Xr9+jZOTE0ynU066J6FFKBRijgEZk9hsNt53HA4HvvrqK4iiyEbdZI5MqVdUb1HPQ1EULBYLvH79GovFAh6PB61Wi2dg8/mchaaBQACTyYR7hGQsXigU+LNRn4N6il9++SUnSR0eHnLvdjqdQtd1XFxcIBwOYz6f4+joaO01JgPFarWKyWSC/f19nhXkcjm+/5lMBoZh8P0iLkej0eB35vLyEsfHxxs/gzT3dTqd0HX93t6mruvw+XxMkn8bkDlKp9NZMXAmAj+dEYCrd91utzNfAQCb4wUCAXi9XiwWC55H0PtACfE0s2q1Wmi1Wshms2i32ysCZTJsqdfrqNVqLOiOx+NwOp1ot9tsFNNsNrnOSyaTd8525vM5hsMhRFFcWXfJ4JsS3mkNo7NCr9fjs1Emk7nVvMgwDJydnTHfiGZRoijCNE3kcjk4nU6kUik+o/l8Puzv76/dj3w+H/b29gCA19R8Ps//XZZlxONx+Hw+Nsp8n6D1hD473VtK8bzei7Tb7WxmQcYStVoN4XCYk+aX0e/3WZS0CUeHTIfoeVy3t1JASLPZ5D2cjJbIOLtarSKVSt0peH8KsNls8Pv9ePbsGf72b/8WqqryPSBTfOKnkRE/cXLWzYxpP6LZKRk0BINBnr9sCvp7brcb3W733tqGBPRkkHgfyJyg1WpBFEUkk0nYbLYVPiBxRcLhMEqlEizLwng8xu7uLmKxGHNvADDXMBgMwufzoVarsdn0uxbrb7HF+wTNCWq1Gtei5+fnK/wWCnUYjUb47W9/i16vh2g0+tZnjYeA6hIyYCHB6nJSe6fTwXA4ZPOj92X6vww6+9XrdYiiiFQqtbYeod4jmSlR33GLp4XZbIaTkxO0221Uq1WeBzscDpimyUEEVEM/e/bsjUTo4XCYe3flcnnFCGcZo9EI/X4f4XCYTaMp6Gg2m/HPfRcQiUS4xiZT606nA+DqPOPxeDhQZG9v74NzE7d4HBC3ttlscr/4OobDIUzTZHOGwWBwq+k5mZoRXzGTyfDaX6/XUa1WUavVmM9N9TqZlBwdHfE7K4oidnd3kUql2HCM+sWBQOAHMV+xLAtnZ2doNpuo1+s3+izLeoxlfjm9n06nE3t7e8w3qtVqbL5K9a9pmkgmkzg6OkIoFLp176PAjWq1ikgkstYI1OFwIJlMolAo8H1OJpMfjP9C/RsysltX/xKoD0JGdqPRiMN//H7/nXsJPff0Xmzrky22+G5hy9DbYosttnjCoDSA64l/pmmiVCpxIi8JgHRdZwJMOBzG7u4uD9JIpGtZ1p2CESKAdbtdZDKZtYW92+1m997r5Eb6/xcKBR6kjcdjjMdjdvmWJAn1eh3tdhuffvopE0wIsiwjGo2iUqmsiAzpZynVgr4HiU7sdjuazSZKpRLm8zlkWWYB7IsXLxCLxXB0dPRkiilRFHF4eMgDrGXiqiAIGI/HPKQKhUIIBoNoNpvI5XJoNBqo1Wosxkkmk/D5fKhWq6hWq9A0jRMdiaAci8VYINJoNJBKpfDy5cuNHW7JFOA2kHijUChAVVXEYjHUajUWu5KTOiWk0PA+FothOp1ymrGiKPcK0KfTKc7Pz6HrOqfyLqde+/1+6LqOQCCA/f39RyUSpdNpdjWrVCo8XJIkiUlkuq7zfSGC912g4hQAu6gTyJl4Pp/jyy+/hCzLLLiioo8GT/1+H/V6HcDVO//xxx8/6LvJsszDZxLW3PbZqVlGLmZnZ2ew2WwYj8dwu90s4ozFYjeIQ8PhEEdHR4hEIojH4yyMFUURfr//QeYQvV6Pn4VWq8WCVmpOejwebmAeHx+/8ftPAm5KfL+NOEUkmEajwSIdwzCg6zouLy95fRuPxzxwpsEH/Z3bCEXUuCQR+CaggWYgEECj0WACI6Vb12o1vibNZhOTyQSZTAa7u7uIx+Ow2+0olUoIBoMrhApJkhCJRHg4QiYabrcb/X4fbrcbnU4HlUoFs9kMqqpCURTk83kIgoB+v49IJMKOputgWRZyuRxarRa63S7m8zlOTk4gSRKLhtvtNhM4SKRsGAZkWYYgCPD5fAiFQkzso30hn88zeeXly5cbXct3CUEQkE6n+drpuo7Dw8MbyaOVSgXtdpub15IkQRAETCYTnJ6ewu/3M1EoHA7j8vISjUYDgiAglUpB13VONhYEgYfyzWYTuq7z/i8IAjd4vvjiC0iShG63y/+e3gUSaM5mMx5ebwoicNAwAcCdDTQikdEghN7Fh8AwDFxeXnJSFqXvjsdj6LqOer2OFy9e8H5HYg4SIC2nyTUaDViWhWq1iuFwiHq9zu/v/v4+Wq0WqtUq/9+8Xi/cbje++OILFvCSQJYITNfX23a7DVVVIUkSp1Y4nU68ePEC5+fnTNgiwgHwbZqI3W5Hp9OB0+nExcUF7HY7EzpJcLAJxuMxE1MCgQCfJbrdLsbjMV8rSoWhdYQI4+8KpVIJ5XIZ9Xoduq6zAQMNRGmNGo/HOD4+RjgcRrfbZSEPkTaugwSWZOSwTPbL5XIsJh0MBigUCkz0oPt5W9q8w+GAx+NBr9dbMQS6C6PRCI1Gg5PiyGjmMYnKwBXZaTAY8FltHeGJzC0KhQIqlQqcTufapjelwtNaQyTZUCi04sq7KeiZpTTHYDDIJFMawvf7fSiKgv39fVxcXMCyLCYj+3w+NqnyeDx4/vw5fD7fitCOzsY2mw2iKKJWq/Ez73a7EQwG+XNTYgQlrudyOf77giCg2Wzy+klpJIIgwOl08t7tcDg47RsAp0nf1Xz3+/1ot9u8Tj01ATrVrCR0vu8+T6dT/r5kSkNrxvLPkhv2bDZjYme322XjCdM0EQwG+TxKzxz97KaYTCaYTCZ8H9fB7XZjb2+PxTSWZWE+nzMBks5ay4Njp9PJZD4y6VkWgr0pJEnixCUaRN4FXdf5Xdrb20OtVoPH48FgMOB9UFEUrlkopf36fZxOp5hOp5AkaaN3mYiNdM7/0El/W3z34HA4cHR0xHUiJXcvG8/5fD6uN5cH7NdBz9+mJjlEGP7Qz63dbsezZ884ZYRqfqrTE4kECxgSiQQ+//xzdDodqKp651nP7/dzH8Xj8SAWi33w7/ouIcsyjo6OOFV12cCRjGb29vbYhGPTM2yn0+H96T7hoyiK8Pl86HQ68Hq9SKfTd+79pmnir//6r3F2dsapidQPcDgc8Pl8bEqYTCbZcf8hZ80tvl8gMhsJvIkkSv0e6pXF43H4/X4cHBx8kPQkMn7q9XobiR40TUOr1eIa8zGFElRPkJEZmYxujW3uhsPhwN7eHnq9HiRJunV/3t3d5WTg5cRlMse77TqTUMbv9yMSibCQu9FowOfzrQh5qQbfRMhRq9XQaDRweXnJvctEIgFN0zjh0ul0wuFwoFAocAr5ixcvmMxHsyiXy4Xnz59jOBzi7OyMU3ojkQgODg5uiFjIqJaSVIlYRWJ8So+mekeWZaRSKcRisY3TaB5iCPnQFKnFYoFSqcRmfLu7u7y2qKrKYigSA1ECIxnCkhjr7OwMwNU5xOFwoN1us5lsNBpFIBBAtVpFs9lk0SalwnS7XSZqEoHN7XYjlUrBNE3s7Oys9Ooty+J+iiRJbF5ss9lgmibi8TibqT425vM5Cw77/T4/08C3NRKlGb2NkIBSkIbDIQaDAQsqiLxXLBYRj8fX9nhIFJdMJvnZXDZ6siyLk+4DgQCTCMkclmau60DJTZSkXK/XWWC4DnSfB4MBstnsnTUuXU+Hw4FoNHon+XEwGGCxWPDsiZ5PAiXtkHErmU/clezjcDgQCoUQCoWws7ODk5MTTi167LM0iQUfA9Vqlc0jOp0OxuMx11iqqqLb7cLr9eLw8PCGGPTy8hJffPEFv5uxWIxNFFwuFxPrVVXFZDLB8fExqtUqgG9N0BOJBPL5PBOfiYC9DpZlYTQasQC/3+/j9PQU6XSahXiqqiISibD5eqVSYUEircuTyQQXFxeoVquw2+2Ix+M8T/f5fGyES2Ynl5eXzI2guRP1e1utFs9N6Pn0er2YTCYrQhzDMHh+QIZ8qVSKDaGBb40KK5UKAoEAcrkcMpkMnz9EUWQS+/IZrlqtcj346aef3iA2a5qGwWCAYrHIM59Xr17deC6pb6/rOjKZDM+hDcPAaDRCs9lkUVS/30e73YbP5+MEQlpPyuUyFEXBaDTaeJ+iuaTf7+c9/TZiMc0dKLH+bd8vMhUnE1/g24QxMhJdTor2eDxsnCAIAhuJ0PzLNE02GKZ52DKRnXgLhUKBhcI0J4pEIpBlGTabDcPhENlsFtVqFd1ul88bZIQOXK0FtPd9/vnn+J3f+Z1b967hcIjZbMZzqOVrT/Wq0+lEr9dDuVzGYrFApVJBOBxGJBLB3t4eNE3D/v7+2mv4+vVrTm2z2+1s4NbpdFAsFjEej+Hz+TCfzxGLxdjQ4S7QOYjMhKnfupwsH4lEPohQx263w263YzweAwAb/Uwmk7X7hGVZEEWRz4A+nw+VSoWNuK8L0GnGScLk+6AoCs8xbjNYpHlwsVjkNHA6P5NpEpku0X16yiBjyng8jvl8zv3sZTGbpmmw2+2oVCqIx+NsYLIMSg4nrgady2RZ5vchk8msFYPcBjq7kDnLbfeQzKzI4JjOT9PplLlGdrudjZgBsJE5mQDRGhiLxdggAwAHwZimifF4DI/Hg3g8jkAgwM8omTcTnygYDOLi4gKGYaDZbN4woN1iFWQeQ/vnsngrFAp9kJ7KFreDZtMOhwNOpxP5fJ45FBR4QXPMfr/PvYFf/epX+P3f//332gva3d1lMTx9xkajAWC1p+LxeD4Yv/V63/uu8xitvbquo9FoPIoAnebw3W6Xe/iyLCMcDn/woKnvIqhHkcvl4Ha7cXBwsHIWN00T9Xqd03ez2ezGPOJlkGFuOp3G559/jmazyVxx4rTRTIO4FGQyRLyv72JSdiwWg8fjQa1W47ADEq46HA4EAoG3NruYTqf8PlC/gPhjW7x/dLtdfp5vmw2apsm8pXA4zM+9YRhrnwUKxaGagwzKnE4nQqHQSiBLOByG2+2G1+tFJpNZ+/uo/vwhgjQy1Pu8zmMlPpfL5eJeZLVaXak7NU3D4eEh1766rq/00zRNY4NTCoG47bP0ej2eZdwG4qZeXl5yUNSHqtdsNhtisRgMw+D+M3FMac0hE1nqfbndbvj9frRaLX72N+EPk0kwBWi9SwH6siHnFlts8fbYTtK32GKLLZ4wqFhotVo8uJhMJsjlcuj1euxWTgNMEqg0m01OSKV08X6/f6srL4GabSSMpOb3OldhaupfTyFZFl/Ksszu4CScIaGZLMsYDof427/9W05EJnEZOTrR5xwMBjBNk1N+qEgnYU61WmUyQTQahWmaXOhNJhM4nU5OFnwq4nOCx+NhQjENicnBVlVVTr0lQQ8NQy8uLlh0ubu7i36/j1AoxANPEkULggDLspgUMJ1OUa/XcXR0hP39fRwdHd362Ujc2Wg0eMBGpNZwOHxj4LecEEBFkN/v5+SCWq3GqZQk9iPB8zIx6b6G5PWkdZvNBu2blOfFYoHhcMhEJWpSHRwcPFrjxel0MnlZlmUW/tHfUhQFyWSSBz23DW8WiwULiQaDwcp/o/fUsixOjqnVajg9PeXkkOX0eOBKjKKqKl6/fo3hcMhDp4ciFotx0gqlJvl8Pm7yzWYz9Ho96LqOZDIJTdN46E6Ncvo9y6ItApG/dnZ2kEwmN0r+ug2GYeD8/Bz1eh3NZhN+v3/FBX48HnMjNZlM4vT0FC9evHgjt056xux2O4t1rkPX9RXxOaWoxONxpFIphEIhFqzRcF/XdRZS0YBxWXC3/FmJlFcsFqEoCmRZhqqqNwRb15FMJtmog4SDbrcboVCIn7HhcAiHw4GdnR34/X4cHx/D5/OxmGw6nd5IAEylUtzgXBZFN5tN5PN5/i7AlZCy3W7zsAIAm22s++ymaeL169fQdZ0NF6g52+12oes6u/lT8g59Tkpgl2UZgUAAwWBwZXCsKArC4TCbXgwGgweTL98VqPFI6wqtpUQ0IrKnLMu879HeR4JJuieUAt/r9RAMBqGqKg9LHQ4HD8SpudZsNtlZ/cWLFwiFQmi1WsjlcphMJjg8PLzRIHI6ndzoNk2Tk+U3gSiKcDqdGAwGvP9T05QIBPQ5FUXhhEd6XkjwuCmIJNPpdFg0T2vteDxGsVhEr9djclAmk4HNZuPzEKVh9Ho9To0hQho1FrvdLj7++GNeJ0RRRLfb5eePRFLlcpkTr2i9ooYcgdKL1hGt7HY7jo6OOKkoFArxNSMzGDKZuJ4aQc3RTCYDSZL47yyTVIPBIO8vlJZAydL099c1R4lM0e120Wg03pkAvdVqoVKpMCmc9tzrzU4iwZ+dneHFixfQNA3pdJodxUm4SuYp5Cgei8Wgqirv35Q6T43n8XgMWZaZmDqZTDgBm5xO161rTqeT04vugmVZuLy8ZGE4kWooUZhIetfPAW8K2rPIuOI23Nf0LpfLnKDT7XY5qYwG+x6PBx9//PGDCWORSASdTgfdbhe5XA6pVIpJriQEPjg4wOnpKUKhEKcKlctl9Ho9HgoQ6WBnZ2eFGEIGJovFgomguq7zMOc6NE1jkx3an8bjMfx+P5PmJElCKBRaW3c4nU428KA1bJNroqoq12lUVz0VuN1uTrMiUupd6Ha7bOZBAhLTNG8MxxRFgaIobGxGhKl2u83mTUTg6/V6GI1G2N/fX1kPNsUmolQyVqLPMZ/PmaB5V41JCblkvDKbzThhxe/38x5IRgj33dtlgj69G7eljJumiXa7zalzgUAAgUAAg8GAa7z5fM7i+LuIUyTG3XQ4syzg3Q7At3hTSJKEFy9esCkIDS0BcLonOYvftSe6XC4oisKpCndhOp3CMIxb1/H3Dbvdjv39fU5rIlI5GdPQe9tut2FZFtfa98Hv96NarSIajW60dn/X4Xa78dFHH7Fb+3KvhMRE4XD4QcNtIpIDm4n5vF4vE/1N07zVUGg2m+H09BSNRoOTrJYFinQG7fV6vCanUik2ACWTr+8aUWuLtwetB3TO6Pf7/KyHQiF4PB643W7s7Ow8qqHVQ0A9JjoP3QcycnoTA7rbMBqNUCqV0Ov1YBgGJpMJE/eKxSKLZp86mZyM0WhP8Hg87y21S5IkPH/+nMVZk8lk7f4ci8V4f47FYhiPxyy2InImYTqdck8xHo9DlmXupVA9ubOzw/0Zh8MBm83GQpy7ztDUG8tmsxBFkQ0Q3G43hsMhz7HIDJieBUq2DYfDODw8vHF93W439+Nns9mtCZ+USEPJZovFAgcHB0w0p37O8kzhbcXJjwnqR3c6HUSj0ZV3lxK9yaCR5ouBQID/fSQSQb1eZ3IxpfWScWgqleIebqFQQLPZ5CRiup6hUIiNOMmMkOqMeDwOURR53xuPxzg9PeX5KQmRgav3n8jrvV7v0cn8i8UCFxcXnBJsWRYTsEn8RXMXIiSn0+kH/53RaISTkxPuH0+nU7jdbhY0kojUsiyYpnmDCCkIAhs9ORwO7uXbbDaeRUqSxIl7+/v7bNw2Go3uFJQD35rl0FxwZ2dn7blk+R41m02e+yWTSQSDQT6XkSCe5j+j0ehWkdiyMNTr9fJZ7bbPSfNLmi3OZrONzoNkMrduf7rLbPp9o1qtolAocC/a4/Gw+YJhGCx2TqVSmM/neP78OV6+fMkpyZQ+NJ1Okclk8Pz5c6iqyt9peVZDwloSU9IMk8xQLy4u8Otf/5rrmeug2TXVbuFwGPl8HsPhkEXoROqfTCaw2+1sAL27u8u9ShIX0tqeTqdxcHDAfAcysabnn0TRnU4Hg8GAZ6GJRAKhUAjVahXlcpnrSOrLKYoCfSnJm5J5KSWeCOHLIKPCVquFTqeD2WyGTqfDibWFQoHnVfQMKYrChqu/8zu/c+v8nPaOWq0GTdPWmsOSuaUkSSufjXqss9mMf8btdqPdbnOKMu3ZoVAIuq5zX2mT2pMQi8XYMGN59kw9I9ofms0mwuEw98kokXFTsft1LPf8aI3LZrPcdyejJhKBUd3tcrl4naD7GYlE2Jh4OcCB7pfdbuc5MQkLKpUKm+XSd6VeXDAYRLlc5pmOqqp4+fIlP1fj8RilUgmGYaBUKkEQBHzyySc31lQyN6E9+XrvzuPxYHd3d6UebrfbiEaj2Nvbw9HREXZ2dm41ZyGThlKphEgkgkAgwN+FggDa7Tbq9TqnQG4iqgauZlaU0EfrAxkeb3Ien06nfP2Wf/ZtezqqqsLn863Mz+h/60w7x+Mx8wiAb81R6Weu47F7141Gg0Ms7Hb7DcOxUCjE/IJyucxmfJvepw8BMge2LAv5fB6DwQCBQIDXjclkwqZO4XAYfr//hgAol8uhVquxEQvNG2ge3Wq1oGkan8vu6xkSaH/w+/0oFot8hl/GbDbjeRnNeSeTCWq1GhuhLRsuLM8q6fxyfW60bL5lGAYnxRuGgXA4zHUUgepFj8cDTdMgSRJcLhevxVvcjk6nw/wIMiYHwHP/SqWCWCy2FfE/ERBHcDAYwOv1olQqcb1N/bBl+P1+mKaJfD6PYDCIXC73RmLbN4XNZsP+/j5UVeX9m0xJaJYdDAYRjUY/WG+K+GOCIGzU9ybD7MlkwpyGNwEZutH5dFlwSyEeLpcLBwcHb3w2/KGBDFgqlQob+V2H0+lEKpXiAAgyv3pon9Fms+Hg4ADz+RxHR0cYj8eoVqv4+uuv4fV6sVgs4HK5uCdHnA/gqj9AITMfqm/9NqDeJZmqzWYz5rq9DafDMAxUKhV0Oh02MKLQEjJlWK4ztng/WBaX32VERFAUhUOX1gUBAuvrh2AwiEAgsNJDpt51KBT6Tr4r7xpk0Kx/E+J4lwiaDN6va18Iqqrik08+YUOi5V4SzWx7vR5++9vfcv/C7XYjHA5D0zQIgsDhSJuEtbhcLjidTg5T+pAIh8N8/q3X6yiVSryWLRv8EMf5+fPnAMDzaWCzwJDl5/5dCMTJ0IYMu+n7hMNhNtfcYost3gxbAfoWW2yxxRNGOBxGrVaDy+VCvV5HIpFggRSRNihFrd1uc2PZ5XIhl8txwubOzg6Tce5qUJELtGEY7FJ81+GOXIhIwCgIArrdLrvmj0YjGIbBjuDLJBBysK5UKjg7O8Pe3h5UVUWpVEKlUsHFxQVarRYAsAiTBLXZbBY2mw1ff/01i3n29vb4ULgsvCWRgiAIKBaLePHixWPdnkeDoih48eIFD7+XHVTdbjc7uFOSMAAmrMiyzEI6y7JYFEHJp5QeP5/PmfyTSCQQjUaRSqVuLbR6vR4uLi64qU5EHUptIufz/f19Fu+ReGuZ/OF0OpFIJFiY3Gg0YLfbeWhMqTfUoI9EIvc6DJPQPZ/Ps8h7uUhTFAXBYBClUolFsH6//0HOxfdBlmW8fPkSjUaDEy5p6EiC+Egkcmszdj6f4/z8nAdjy6JcMpqg4pSSMfL5PCetknN2LBZbeafb7TYPj+bzOVqt1q1CuNtA97VUKqHdbqNUKqHb7TKphMgd5DqeyWRY8KNpGhRFQbPZRLPZhGmaXERTY9OyLKRSKaiq+tZEOmqAFgoFJBKJFfILAE6hbzabKJVKcLlcaDabb+S6SoYggUCAE1qWxT7z+Rzdbhe9Xg+DwYAHzfP5nJMqKTG2XC7jN7/5DVRV5bRdKnCpiU/vSjwe57TUbDbL6RP5fB6iKLJrdjwev3UwSkQv4IqwRYQuckq32WzcuIpGo3jx4gU/V3elrttsNuzt7TGJmcScsizjiy++wGg0gt1uRzKZRCQSYWduSkYYjUYYjUbodDo86KEkDCIK9vt9BINBTtrVdZ2vP7neut1uxGIxDIdDJpft7u5CFEUEg8FbBYREJGm3209GcBEMBtFoNDhp2DAMBINBTvppt9tQFAXj8RihUGglnZdSI7LZLHq9HhtRkBEAJZFQCgPdB1EUOQW8Wq2yiOfg4ACGYXACFAn+RFHkQR6lBgFg4hqJc+8DEdSazSYGgwGnMLpcLiYuOxwODAYDbt4SKdPlcnEa+yYgETKtaaFQCIFAgBtksixjOp3i888/R7FYhN1uR61WY3FtMBhksTaZvMxmMyQSCRweHqLT6ayYoQDfkiOq1Sr/NyKv0dnu7OwMkiRx4oiiKCwoHA6H7Ma5zpnUZrNx6s5oNIJpmkzAIxK93++Hqqqw2+2wLIuHS61WC+fn5ytEpuXkm3K5jGg0ikQigdFohOFwCFmW7x0W0nM4HA7fKXmhUqmg2WzCsizs7u7e+FyiKDLRvVQqQZZl6LqO/f39lTNSvV5HpVLhz+71erGzswOv14ujoyPeTy4uLphs4PV6kUgkVvYaSqUsl8sAVlOPl0EDgbsarZZlsVFCuVzGdDpdMaUpFAoQRRHJZBKmaeLZs2dr7wsRjq83wcmcaBmDwYANjO4bgLlcLm6kk9EK8C2Rp1KpoNfrsTCY3NGBq/con8/jRz/6EQ4PDzcemnu9XiSTSSwWC5TLZVxeXsLlcrEpBxngkHM0JWbPZjP4fD7+O7qu816xvH97PB5IkgRRFFEul1fSltZhMBggkUjAMAxIkoRqtQpZluHz+dho6foaSEZONLCge0Bkgk3OaaIoch3w1ATofr8fkiQxKY+I7+tA5gR0VqZ/R6YZy6Dkm8lkwmdcMt+h9U8URTQaDSY07O7ubkwUIzidTkiSxIO/+4YhRBoj3HUvqGYpFApQVRWWZfF3oXPzshs6/T76frcNPUKhEGq1GqLRKCqVCl9TWg+IRFer1VjMsWwYQYPwh4DIvvV6nUnod4GIs9vBzRZvC0pRTCQS6Ha73OuhdWeTASaZ9ZHA5a7nv9Pp8DlzXR3xoUC15G2YTqdMXNjkvaOewV2Eh+8byCwxFotxz0UQBEiS9EYEHTLY3PRnyXAEuJs8Tgl49Xqdz4HLvTt69smQkwR8tJeQwHeLHyZUVWVzHurZU7p3MBj84ORMMiEk4fB9oATSx0q/6Xa7XN+1220WHJHo0OPxYDQaod/v4/j4+EkYkVxHr9fjxE7TNLk2cDqd8Hq9iEajt5oTPSZu25+dTifPq5YRDAa591Cr1VAsFuFwOHg/Go1GcDgcSCaT8Pv92N/fh67rPCsBwH0+RVFQrVZXzGfJKC8SidzYB4fDIQstHQ4HFEVZ6d0s17gkvjs/P4fD4YDdbseLFy/W1t0kqKe+5l11AdXk/X4fu7u7SCQSbCRDIAFJOBze6Ky+bDjyLommNL8gk8brIDENACbkvX79mnuZpmkinU7zvab0RuBqTwuHw5zGaFkWJEmCw+FgsxYiDpKBL9VV1Id3u90oFovY29uD3+/H6ekpms0mKpUKPB4P0un0inFxu91GNpvlFLfnz58/Wn3daDTQarWQz+chSRJ2d3dXfremadzbKRQKbAr6EKO+2WzG5syUXHv9+RsOhyw8JhPl63Wy0+nE8+fPUSqVoCgK9xMXiwWcTidcLhd8Ph8SiQTcbjf3qe12+0ZnTRIKLxYLvq+Efr+PXC4HwzDY2JGSbgDg5OQEbrebhbD08zQfCAQCt86tDcPgmUcgEOC69LZ7TH2M+XzOc5JN9ifaN2h/WiwWnFx43Wza6/XyfXqfIMFquVyGYRg3zPNoDarVavw8lkolZDIZZDIZPk84HA6k02mkUqkbhqTU86ZZZCqV4jnT3t4ejo+PAXyb0B0Oh7kvvZxiTs+Ay+XiNYESwUmoYLfbUa/XYRgGz8mIdE1rOpk9uVwuFvJRf4+eHUqjppmnx+OB1+tl0Wa73UYikUAsFuO1jwzK6G9Wq1UWcgcCAQiCwCaZpmmuJNmug9/vZ5Nb+hsej4d5AjSHsdls3G92OBz3zjVVVUW9Xkev10Oz2bwhQKf3/DpBnub7FAAAXJ3ZXC4XixwIJKIgsf1DoKoqUqkUmyBXq1Wek5DRFgDu3btcLlxcXHCqu6IoG3EIroP2c0EQmMdAZs6UAOl2uzEejzm9k+bzVGdRojD1L6/PSzudzkqyqq7rAK7eQ8uyOITBsiyeIdP5J5fLYTabQdd1/L2/9/fg8/l4v9U0DYFAAL/+9a95tmVZFpsLk8mpaZpMoLbZbGx8v3yecLlcbCpOArFkMom9vT387Gc/u/UsQfeaZofrrj/NmOfzOarVKidVP6T3SOaYm2KxWKBYLKLRaLDZEK3LXq8X1WoVmqZhZ2fnjURwZA5AawatWXa7fcVIlUyZae+ktZ5MnYH1xHuauwqCwJyCu0BnZTr3X0e9XufExHQ6vXYNcrlcyGQyuLi4YIPs+0xlPjSI4yYIAtdwtVqN/zud9Xw+3w0xIhl4k0n3/v7+yj4YiUQwHA5RLpdXBCOb1MyiKGJ3d5fXg0KhwEmkNCunPTSTycDv90NRFHz11VdsQr98XqD50rKo7DZTVzKVc7vdPEOkUJPlfz8YDNBqteD3++F0OrmvtDyb2mI9Wq0WstksmwVcNwhot9vwer3ci70r9XKL9wN6nufzOc+mibe4bi+iFFISJZGB0fs2jqIQDTKemM/nPJf40Mai1PfetFam3hr97Jv+zdPTUz7zGIbBM3wyZKrX64hGo5hOpzg+Pn7SRipPBRRwYhjGrUnNhEgkgrOzMw4DexPDPIfDgWfPnkFVVXS73RU+O3EmafZG5ynq8cRiMWia9uRNQe8C8a0eA71eD+fn59znX66/7HY7VFXFcDhEt9vF4eHhB183fqi4bUZI4XrAt4Zld/Vvx+Mx163L9Qv1i993T2kZxDGlsC673Q6fz7dRwvX7xng8XllT7oOmaTx/IGOrZQiCwLwdgmVZuLi44BlNr9djw1O3241utwuXy4X9/X2ew29ak9J++hTO6ul0mtftUCjE/f7JZMIhLzRfy+fzaDQaHAwDgIPY7gKZQy+/L4+FUqmEarXK2iGqIyeTCYbDIZ8p7tsbt9hii/XYCtC32GKLLZ4wiJgej8eRzWZxdnaG8XjMDsWCIDCBdjQaYXd3l4V3hUIB1WqVB/M0CL+LWLpusH5XgUpFP4k8iKBQLpdZRJtIJG40B2RZhizLsNlsuLy8ZCEhJQl+8cUXnJxJgsJlN/L5fI5/+S//JSd+/uQnP1khPUiSxAMEr9cLu93Ow9bhcPhkm1CUHriML7/8ErqucwJLMBhkcU8oFEIikUC73Ua1WgVw9d3T6TQTVEm4R8mMh4eHkCSJ05jXodfr4ezsjN2SSXROJgDlcplJZrPZDMfHx+zSO51ObxQPJECmApQcxUkoQmS1TVxiaTBNCb7XxecEQRCQTCZxcXHBjuWPKUAHrpoFsVgM0WiUvw8Jxu8qihaLBc7PzzkJY7FYQNM0LnzPz89RKBR4KJRKpTgVhUhtJycn/HcTiQSni8xmM0SjUS70KIX0oSm0e3t7qFQq/PfI/ZmIZpS+HYvF4HQ6USqVOFmWxDQkoiwWiwDAybqUdvIQ8dl1mKaJQqGAv/zLv2RyDJEFacCrqio/G6FQiAd6ZBjwJkgkEtjZ2YGu68jlcnC5XAgGg5x2PBwOUalUoCgKQqEQTNNk4eryPSAyQavV4ueICCuz2QyXl5fcUC8Wi2zYYBgGYrEYv0emaaJarbKweDKZ3FoYUyosDccsy4Isy8hkMvzMapqGcDi8MkgYDAYQRZFJoetApCAifp6enrLAXJIkFju7XC7E43Ee+NNgHrhq6vT7fRiGgWazycSBYDDI6UA0DKKBkGEYKBQK8Hq9fE1I4EUGG7ftYUSWW05pegqgBBoATHjK5/PI5/NoNpt8n1RVhdvtRiaTWXmPXC4XNE1j4xVqPFMaejweR7VahdPp5DQYGgbRv49Go3j27Bk3x/1+PwKBAMbjMQaDATeAZFlmkW0gEFhJrNlkGBEKhVCpVCAIAgqFAju/+/1+3gtpkGsYBiKRCKcZ03uwKRlc13U2vQmHwzf2P0VR+PnsdrtotVoQRXHlfSCxnmEYOD8/5wSVTz75BBcXF0x8IyQSCRbG1ut19Pt9FpC43W5O7qDB4mg0wuvXr1kIvbu7C5/Ph6Ojo3sbbYqiwOl08p5nWRb29vZWng26bi6XC7/61a/4DJZKpfhM2ev10Gg0OCWJ9nZqIm8CIl2+q4YopYd0Oh1EIpE7r00wGISu6yzgCQaDODo64pQ2WqdJfL/sKE5D5n6/j36/z/vccpOaxJiUdkPnJkovWb5mZAQUi8XuPIdSmkg+n4emaQiFQivfkdKAstksdnd3kc1mcXh4yP99PB4jl8ux0zulRouiiFarxc/5skCexEqbvk8khKa6odFooFarIZ/PYzqdcnOYagPgiiBFYlkyqnj+/PnGAph4PL7SxKbkZjIpicViLKohchgJcp1OJ9xuN87PzzmlfhlEKtI0DRcXF3x2WGekMZlMMB6P+azl9XrR6XQQi8UgyzJCodDafafb7UJRFP5vHo8HoihiPB6zSOw+wYBlWfzzT0l8Dly995FIBJPJBNlsFvl8HvF4fIWssVgsoOs6arUaJ+H0+320220Ui0UMBgPUarWVQRURM202G0ajEWRZhiiKEEURsixz7TSbzXBxcYFEIsFmCg8B7WG1Wg29Xu/OczORcjOZDEzTRLfbRa1WWysKbTabqFarODk5QbPZRKfTQTqdZiM2XdfhdDp5bSHRgSzLfD0SicSK2J3gcDhweHi4YvLVbDbZHZ5I936/H5FIBKlU6sH1wHVQsovL5UKr1Vr7uQj9fp/fwYcaAmyxxW0QBOGNB+xkUKdpGsrlMtdA10Fpa5Rc9KGFmg/B8j6yiZnGstHYD5GY8hiiUtqTSLB0H0mRzL3oZ9eBRHZk+qiqKorF4g3nfUEQEA6HUSwW2dyFzMqIoELJi8u9kS1+OHiqBCAyjPJ4POh2u/eKUvr9/gpB/k3Sdwjj8RgXFxeo1WpotVoIBAJIpVL8Po7HY7RaLeRyOaRSKZyenuLFixdP6uxdrVb5vScDR4IkSSxoSyaTd57VHhMP2Z9TqRScTif3W3q9Hp/3A4EAG1z6fD6cn5/DMAwmNS0WC0iSxIaVdHamayBJEqd/0vmX1r7ZbMbpf5ZlQRRFtNttTht0u93w+/3c/6bELqo5xuPxWoEw9Vcty9roffN6vSx0dbvdSCaT/LfIKOK+543MhpcF2fS7KV3lsdd8+o4ul+vW351MJtmAlRJk6ZkkwngwGOSUZTJOoPePzOum0yn8fj8LBiRJQqPR4ITK6XTK6TSUWkJms/l8HqVSCaPRCJVKhRNaCSTwJOPXYrEIp9OJRqOxESFxE5D4y2az8Uz2OkRRRCaTYRIxJU1vilarxSai4XAYoiiygFWWZU423tnZQTab5TpxXV3mcDiws7ODVCrFa8r1JJ/ruO/5IhPiVquFRqPB6WaJRIKTgS8uLtBsNtFqtbBYLPj8QubBJMojsRSZdnq9XsTjcdTrdRSLxbV9wWVTdcMwIAgC/+w6LIttaf7c7XbvXdfIsJtMLE5PT7kHSclkwLfGQZeXl2yyQcbj13t+j416vY7RaITBYHBDfE6w2WyIxWJsWqwoCpLJJM8lyJDgvrqIxOqj0Qjz+ZwNc6kXpOs6RFFk4WS5XObfT4JxMnb1+XwIBoM4OTlBIpFAs9lk432Hw4FWq8WGIjQnCQaDqNVqbM5C88HRaHTjrEGzum63y0aqkiSh1+sxsZb6ppSWFQ6Heb8ArgTk9Xqd911N02CaJvd5HA4HX+/5fM7muiQqJL4GiZOXe5DXeQJk6O92u++t22idI4HJplhelzeBw+G4N8BgHUjITCKJ5b9Lhp6U6iZJEorFIs+uRFHkGWK328Xe3t7G+53H42GDnFKpBAC89gaDQTZSJwH0dDrFwcEB+v0+m2xKksSmp5PJBKqq8j2mRLNUKgWv18uz2+l0isFgAEVR2Gi52Wwy2ZnWy3a7DbvdDlmWkcvl+LkmSJKEn/70p/jLv/xL7rvTrI7605qmscC8UChgPp+jWCwiFovdqAepnxkOh5HJZLC/v3/ntSRjDRLiExaLBc9WaE9fnhOSgf+7APEsyIxlMBjwOWQ6nfLeF4vFYBjGrSa+d4HOq5ZlIZvNol6vs1kwJdiRaappmkgkEmymSvMby7LY+HQ8Hq+kV5OhhaZpzMO46/u2221omsbn02UMBgPuNYdCoTvfZdpbSUBLnKKniuWUYJrx0XNHZwW/38+zz2XQuWw2m601sgau1tx0Oo1sNsui/N3d3Y0+G5lmEXeBROU0j6PeoqqqCIfDLFLpdDoc8OB0OjGbzXjGMJlM2ESF9o91/SN6tmOxGC4vL1eMzQ3DwGAw4HO1pmlIpVI8uzVNE6qqPun7/iFhGAZyuRwqlQr6/T6SyeSNc6RhGByQQcYFj81D2+JhoAAcu93OhtV2u/3WfWixWGA6nfIZjvhJHyrRnvizTwl2u50DDjaZNZimyfvPm/bRSqUSBwK4XK4bfMJoNIpOp4NqtcpngVevXv0gZxsPAYkj3W73vfxMqlXJpPFNQaEcv/zlL/Hb3/4W5+fnEAQBsVgMgUBgZQ+aTCYoFotsgrcu5OH7BurtkSkVcamXz+3j8Rjn5+eo1+toNBpQVRW7u7ts7kgci2UTpmXO0BbvFlR72Gw2DIfDtT0n4gYqioJGo8FG2LfNEakmkCTpycymF4sFKpUKm36NRiPMZjPuj1DN9ZTOQcS9ms/nGxnL0L9Z5r/d9/tPTk7YLGU+n8Pn83HoEZlTRqNR7h/QfroJiBN21146nU65z0Rz6neFaDSKYDCIv/qrv+KezXJYEhkIttttRKNRjMdjNnqmFPq70Ol0oKoq7z+PhXw+j2q1ilKpxIFLNL+ZzWbI5XJQFIW5kltDqS22eDi2HYUttthiiyeOnZ0dGIaBnZ0d/NVf/RUKhQI6nQ4CgQAf7sltqdPpIBqNYj6fIxKJ4PLyEpPJhAXrHo+HXVzXHVSpMUcH4HA4fCepazmh1G63c9IauXwTAeI2hEIhnJ+f89DN6XSiWCyiVCpBkiTU63UoioJAILDiDD2fz9lteW9vD06nc63jNEHTNCblUCrfUwSlmi+LdQaDAcrlMoLBIJNEltPsHQ4HIpEIk+ckSUIkEsHh4SEGgwF0XYfD4eAkx/39fQSDwVubcCTgIBEDDcyWQX8vl8vBZrOhWCyuEDLWJS15vV5uVOm6zuJREqM+f/58o4KIrk+3211JTVwHSpAgd9NNU3kfCiL3bIpWq4V2u81uyIlEgu/HYDDgZi4RKmgwHY1GUa1WkUgkmBhPQ6RgMMgiXFmWkUqlODVkmYz4EBwdHUEQBHzxxReYzWacYE1CGWpskQM/JVmGQiGoqsoixuXngchOdz2Dd2E2myGfz+M3v/kNisUizs/POamXiKter5eFd8vp76qqIpvNcjqQLMuckLBc8M7nc7TbbR6o03AiEAhAVVV8/PHH7H6Xy+Wg6zpkWWbiiyAIiMfjmM1mUBSF7w01USlpltz8X758ySSoL774Ap1Oh0l3hmHw57fb7SweisfjCIVCvOZS8gPwbXLMOvh8Pnz66adMdul0Okgmk9zMvb4v0LWgofb1xgC5HNbrdXS7XeTzeRZvkfGBJElMJqW12u12Y3d3F4PBAJeXl2g2m5xeMx6Pef2ha1ur1TAcDpHJZPhdoxRem83GInwi/xPh4j481VQ4URTx7NkzTmBQFIXdmkksTOmv69ZNTdPw/7P3XcuNXVmWC95770GbVlJPV3dFTX/BfO98wLxNzLQtlVRKpSEJ79298B7zkLO2LkiQBDOZTsKK6OiKVCYJXHPOPnsvk8vlhABD4iH3iEQiIaJat9stRK9isQi/34/j42O8ePECb9++xXQ6xdnZGdxutyRDMOFlOp2K0z+JoEyzuq9+ACBrCYd8rVYLdrsdOp1OSDkcljOhvdlsyrv4ECOJVqslDru7mp90k/Z4PPKOMnn+erIL8P59zGQyyGQytz5rJHFyYEJyFcmcR0dHksTO9ZWfhcTcWCy2NwmA5KV+v49sNrvz2VitVmg2mzAajSgUCnj69Kk4CRPj8Rj1eh3FYlFSaEwm096JJvP5XPauT4F+vy/OqvcRvJnCQwIk1/RwOIxwOCw1BRuzu0wrSHReLpc31lafz4dutwuz2SzPSqPRkOQRbW1EAjJJi1rM53NJ+xkMBqhWqzfE7oTRaJQBeLVahdVqFRHGeDzG27dvJd2dpkB6vV72Cg7TF4uFCGb3ManSgsRzPrOsQWksxEQ+ChG4d9BQgwRdJsnt6ygfiUTg9/uFQEYSDQ2ZfD6fiOO5R9yVcnX9Z49GI+TzedTr9Z0ESrp9U5AYDodxdXUFl8slhiu7xMs00WBtwHU9FosJkXI4HN5LOCcxVnvtPzem06kYHhkMhq1EkFgshtFohM1mg0qlgsvLSzgcDnGm5/7hcrnQ7/dhsVhQLBbFLIlnu9VqJQl1o9EIdrsd6/UamUxGDIgoLrHb7WLmc3Z2htFohHA4jG63+6ABNVO6SVjm83Mdi8UClUpFhsD8/YVCAeVyecutfTgcolgs4u3bt6jX64jFYpIqR1IZ63mHw4Fut4u//OUv8Pl8GA6HYmrCoccugbvD4cCTJ0+Qz+dl/+I+7HK54HK5YLVakUgkHmXwZ7FY4PF4EA6HUSqVJIHy+nlMe35lGtgBB2jTSb8EWLvxncrlcnJ+5uCXfYpYLAafz/fNDRspODIajRgMBveKZvr9vphWfKiY848O9kWsVqv0Y28DxTc027qt/0WhpaqqSKfTaDQasNlsUBTlxs8nCeHi4kLS74LBoBBZvF4vFEX5aAPAAw54TDClut/v7zRX0GI8HuPVq1ciErbZbHIGYm/xIftKrVYTwzn2VrRgP5WmiBaLBZ1O56shPlIwWy6XMR6PtwT0TPWg+R3w/uz4NRoBsXfc7XahKMqWoWswGJRkWwpGSGjT6/Vi6EphBhN5gffPS7FYhNVqxXK5lDMEADFpZS9dK5qjUNDpdMLr9cJoNN5Ia77trKz9Ofv0ufm8ak37tEbK94HvBAUmFIRRwMta/Z//+Z8fLW0JgMzc7hMassel1+thsVgQi8UkVZZpxgC2hOfEbDYTkQ2NxarVKi4vL4WYS2NFANIPyGQyYnBWKBQwn88lJfeuc0goFBLjXZrVfqxwn718RVEQDofvfCYociTJ7jZx0S60Wi3UajX0ej0AkORFihI5j2CiWLPZFMOH2/poBoPh3vWCxo00Ob3+3C4WC1SrVYzHYzEB5bsaDofFIFxRFBFfRSIReDyeLaORfr8vAlGTyYRYLCYCY/ZXx+MxTCYTarXaltkEry17XEyysVgst4pPtEk7fr8fBoNBkmHvEqx0Oh14PB6YzWYxDyqVSgAgvbD1eo1isYiLiwusViu4XC7k83kxzuW5OZVK7T0r4/yEgk+KY3d9xm63K8/4feuM3+9HqVTCfD5Hr9dDMBjEeDzGaDSS/v1gMLj1vaJ4lobodrsdk8kEP//8s/Sraeo4mUwwGo1kj7XZbCJENplMUFUVg8FATFoNBgOePn0qIqFKpYJIJIJ+vw/g/VmTQpR6vQ632y0kWBKAr39/GkqOx2PZN2nkp31/uT+43W4h/bMmYeo5rxFNTvx+P1arlTwnFBVorxVF4larVYwzbwNnN/s+IxRJ75p9UQh7vc/Pc4WWEE3jn+sCEQAyE9q3Dlsul7i8vJSZVL/flx4u9xhyXZxOp5gBhMPhLYFFr9cT4bper99bJEpy9HA4xOvXr+X68Mzscrmk5zkejyXdy2KxoFqtijA8kUjIemu32+F0OsVsw+/3w+12IxKJYDgcQqfTyfpst9sxGo1kRjUajWQmS7MKk8mE8XgMRVFQKpWQTCZvBC98//33yOfz8Pl8CIVC8txcr41NJpM8M41GA81mcyu5dzabiaHHPmIBPudOp1PSv3kvtAR9CoFpUD8ej/e6Px+CcrksCcVGoxHZbHaLpzGfz9FqtZDP55HJZHB5eYknT548+Pckk0nhWDDdrlqtSsAA99x4PA6n04nlcilm4vl8XkxUnE6nBIxEo1HhLrAe4d64ax602WykZ857fx3j8VhMjfYxtXG73RKQQDP8rxlagcNoNMJkMpEUQZpHXAfrBfZh7ppXMrm81+vB7XbfmiC/Cz6fDy6XC51OR9YS4DeOUigUgtPpxC+//CIGtel0eotvYTKZZJb1888/b+2JNOK+Dj73pVJJ3n3yBrnPuFwumM1mJBIJ+X3kvrhcrkc9L/ye0Gw2MRwOoaoqMpnMTg6Y1WpFOp1GLpdDt9uF0+n8qoRXf1TwuX7z5o30EW4743Ed8Xg8Ylj3KfetL4XFYiGJrBRK7gu32y29mPuMuzebjZh4cf7/UKxWK7TbbTQaDZjN5hvnPABiXqjX61Gv1+FyudDtdm8NXjrgPTgT27fnQHOUfYSY9yEYDOL4+FiCd2jco92XWGfSTPMhBn3fGhjoxt6mNk2a5/ZoNIpwOIxKpSI8glQqtVWvGQwGMa2qVCoolUrCVzrs758HNCTzeDxi7HQd2uC4q6srHB0dwWaz7eRGtdttMdPbVe+vVitZz41G42cxnF6v17i4uBDuGc0vDQaDnIu9Xi8mkwmm0+lOLsuXgF6vlz7DvoJy/rvb+i7kRjOMg/zwYDB4I+QrHA5DVVUJEpzNZnA6nWg2m/f2i0ejEZbLJdxu985nioFCqqpu9Xw+pTEt8J6XSF5vNptFNBrdOl9FIhE50wKQkIpyuQy3233reZP9EXJnH8tQhqEoNOc7OTkRo00A0rfj+kke58eGeRxwwB8NBwH6AQcccMBXDqPRiCdPnuDq6kqGkovFAo1GQ0SUFGlRCLNYLJBOp0UIUalUoNfrUa1WUSwWoaoqUqkU/H7/1gCLA0MKNJkkfBsoDuQQdrVayeejC/BdYJLEdDqV5GCmW9AN/vrBi8SMUqkk6dz/9V//JYdoumCTMMRraLPZZLD2JcDholbUygOA1WoV1zoK+oD3gh4KyrSDVC0hg/D5fKhUKpjNZlAUBaFQSIQHdFbOZrP3EkmYsszUr10HGoPBgHg8jlKphGazKaJikouGw+HOZjyHLRSD6XQ6nJ2d3UjwvQuz2Uyez30GYky0oKnC1+Ac2mw2oSgKAGwJpAFsuSM/e/ZM0u6B34R0dCvm803nYr6v2gPlxwhsaWrx7NkzlEolGVzSAazVakFRFOh0OllPOFyhS38qldpKzviYlLF+v49ff/0Vb968EfE5BwFM76XAkNeQaSOKoqBSqaBarYrzPN34KCZLp9PodrvS7LsuQGeqRjwex3//7/8dZrMZpVJJmqQUsPIddjgciEajNxLX6UDNAyzdP9+8eSMmGaPRCEajUURKdMr2er3Q6/UiIPV6vXC73bDb7cjn85IqEgwG72wqUHyRy+WwXC63iF7Eer0WshfNBbSYTqe4uLjAeDxGr9fDmzdv0Ov1cHV1hfV6DavVikwmIwYibB6bzWYZglqtVrRaLQDvh/fPnz+XhCaSdQwGg7h+s2HK55oNu+VyidFoJMQXmjLcBZLlrFbro6TfPTYMBgMSiQTi8Tjy+Twmkwnm87lc07sGN1wbSBxiOvZyuUSj0YDD4ZD9gYLX4XAog/VUKgW73S6EWP4M7SCgUqlgtVphMBhIelOv15OUErfbjUwmc+994GDd5XLh+fPn0rh88+aNkMT4+cfjMeLxOGw2GxKJxIPS3IbDoQjub3s3AoEAhsMhvF6vkES8Xq8Q6zabjQhrvV4vXC6XEFTptH89WcRkMuHo6GjLfZ7N4WQyiR9++AEvX75Ev9/fIhvxXX8ISN62Wq237nVsis5mMyFd8DsTdrsdmUwGuVxOksJdLhdarRbG4/GdDpVM9UilUp9suMHrR0LpfeCzz8+nfXdoznQX+v2+kHSvv3dmsxkulwvBYBC1Wk2a2VzvfD6fGHm0Wi0kk0lJzmESR6vVwnw+x2QywZs3b9BoNDAej/HixQtp2u+qkUKhkKzBrVYL8XgcFxcXkvoRDAa3iDWbzUYEviTX2mw2SZlzuVyo1+uS7H0bZrOZiPNoLESCtc1mk6QGGiJp3zcmhpB0GI1GUa1W9yYLAr+Rj0nGInnuPlEIRfckdl5HIBAQoxO6q5OgvNlsJKmcyTg0bqjX60gmk+h0OpKGwmSZ+XwuYu1QKLR13UlI6vV6shfelWSkKAoWi4WkWX8oeE7ggMpms+2Visq1bzAYSG1CwaLD4UAkEoHP58Pp6akkGjKhieRVDlSazSYcDgdqtZqk15lMJlknX716hcVigUwmI+YRfr9fBEc0tWJ6Eesgm80mZOaHCoV0Op0kmvM+cnBLQixTzKxWK2KxGBKJhJgB0QyH5i9Go1FcdWnoxUQrr9crzssk8o7HYyyXS1SrVTFToON7uVyGXq+Hx+PZuV7ZbDY8e/YMo9FIzo2su/1+/6MPmtLptJDdOfwm6X61WonJEs3Tjo6OvqrU0AM+L2azGVqtlpiUAJBUIAoNPif8fr8Q1WlUV6vVtkir8Xgcdrsdp6enX0Xv4CFwOp2yzlAQc9u+slwuRdDMd/iAh4MEE7/fj1qtJsm9u9BoNLBer3eerbVgz2uz2cDj8UgfkykD2rPgcDjEcDhErVbDZrPByckJAoEAstmsiK6azaakzX5I6tsBB3wKUIjodDpRLBa3CPFEvV7HL7/8ImdQbe/N7XZjMBig0Wjg7OxsLxMNGo3QZPGuc2AwGBQDVaZpf2mwR8f+GA15CbPZLCSjQqGAer0u9eDXWIvRTPk6SXgymdxIm+OZbzAYwOl0ot1uYzKZiNiEoh6/34/FYoFyuYxSqQS9Xg+32y2zrVqthul0ikajAQBb6yGN3SaTCaLRKAaDAaxWq8zHbqunab6n0+kwm83uXWO1QteHrMdMMf33f/93NJtNFItFDAYDeQaMRqP0O1KpFFqtFv75n/8ZJycnj3IWsFgskkzNfsxtIFkvm81Kquo+oMASgMyvNpsN/va3v6Hdbsufswcxn8+l/0IC3H/9139JAtFf/vIX6VUwEYb1Ht9/r9cr5D8Snj8G3LP3nVvR7JNnw33mY4vFAvV6HW/evJFZiHbeyT7VaDSSd6xer2M0Gt0pQN8Hfr8flUpF0m60Irn5fI5CoYB+vy/mujRRXy6XYv5ULBYxnU6xXC5viK+A3+aHVqsV+Xxe0sW1JFaHwwGfz4dUKoVisYh8Pi8GwDqdDiaTSUSurI3vWsdp1mMymZDNZjEejxEMBlEul3eacy8WC+lnME203++LEDEej0vyd7lclsSlcrks/TudTid9IK49Z2dnd67XNJak4JPiUpPJhGAwiHA4vPUMcYY6Ho/32sco0CFxGPgtFZt9DEVR7hTmk8zp9XoxGo2g1+vFfA/4rXfNe8PzSjgclmuRy+VgMpkQDofx3Xffba2tnC0nEgk0Gg151pg2zST1eDyOWCwGi8UihrrXa3SKcCgQ7nQ6su5f72nTOMdqtaJWqyEcDsNisUi/aL1eY7lcol6vy7Wx2+346aef4HQ6pZfK/pHVakWlUsFkMkE6nYbP57vz3jPNinPi+zCZTCRp7TpouNvpdLb6/Ha7fatXR84J8P7Z0F6T+Xwuz9U+4pDlcimzw0qlImRqpj8z1GC5XMrafnx8fOPdCwQCQmAulUowGo0IBoN7r93hcFj6sb/++iucTqfMEx0Oh5jKlstlpNNpWXto8m6xWFCr1cT0kmazNLsNBAI4OjqSma/NZpN5wXK5hKqq0ucMBoMyJwUgaZCr1UpMjGu1Go6OjuTzcybC3iR7hbwP2v3e4/Hg7OwMJpMJPp9Pfi/Nu1OplIhbOI+9C9o5oaqqUlOxB6gVzdDUIR6P32tc86HgXIX1biaTuVHvUHTLxDPOjR+61+t0OiGrU8gPvF/vOp0OAIj5Ee/P1dUVBoMBwuEwYrEYUqmU1Kg2m03W8GQyKc9SKpVCqVSSPjjns+PxWBK80+m0GJNfh9YUaZ8ak39H++++FZB/ch9obr5YLPaaVXo8Htm3uY7uC6PRiEgkgkgkstM0hO8K14/bZqwmkwnRaBSlUgkOhwNGoxHdbldMiXf9XqfTKbP44XAoczmr1SrzDX4W9okomLrPuPKPiPV6jU6nIwY+d/WGed6mcc9oNPpqQ3D+KAiFQlAURUytbjMKpXkRZ4xms3nLJO5bh5aXoBXVGwwGmdvt00fj2uP9/2FPt9WXAGT+Q+ONDwH7fuSZ3NXL4Gdi3+6PIECnuS2vEwAxAL9vzm8wGKDX6x+UvmsymR6tn5jNZqUPRiNA8kwtFosYCfEc93uFVszbaDQwGAyEz8f5qcfjES4HA2B8Pt+tdYlOp0MsFsPFxQVUVRXTtAM+PWiQxICLRqOxc9/x+/24uLiQPon2fAlA6v3hcCiGuVzTNpvNlgEpwbNwKBT6pLPdQqGATqeDYrEIo9GIZDIpvSPutdxrNpuNzCe+NGgY6HA4JMjsLvT7feGzX6/TV6sVarWahJQNBgNcXl5KYAr7Zdr1ktz+zWaDRqMhZ2OdTod2u31rj269Xgt/3Ww2bxlAbjYbFAoFtNtt6b1qQ8q8Xq/wqXl+fSzM53NcXFzg119/xXw+RywWQ6VSgcfjkf4HuR/kTKXTaQn4KZfLCAQCW9xFmtp1u13h1T/mDI790/l8juPjYxiNxhtmBGazGalUCldXV2LsfBCgH3DAw3BgnBxwwAEHfAOgGJvDLKaNkYDPBNN+vy8E2nK5jMViIYMOr9eLbreL5XKJi4sLDAYDxGIxIdmy+JzNZuKYdZdglASOWCwGr9crhxomNzMJ+C70+305/FIISjfwcDi886DEQn0+n6PT6Qgpk47SFosF4/FYXIRZHHLo/yGC3NVqhW63KwQnElbYQLtLjMV7QGKoNjXQ5XKh0WiIG5qqqluO0cViEaPRCG63G4VCAalUSsSSJHqw+UPn8slkcsOdk46U+xCoKSomWeU6KPRcrVYwGo1oNBqIRqMyuCPZ+b6E6263KwOOhxBftPdwHwLVPkLsxWIhhzQKahwOB4LB4KOTzsfjsTgu8/BFULg3GAzE2d/tdkNRFHnGSGqLRCJwOByoVCribK4l/VDgTHLCh4CDVYorSRahYNhisSAYDAqZbxepjcLVj0W/38fFxQVevXqFy8tLqKqKaDQqadmqqkpKBZ+7RqMhzbrRaIRGoyEDaQ7VOp0OTCYTQqEQ3rx5A4/HIwkLXGtJ7KJYdzwe4+TkBC6XC2/fvpVEHg6HHQ4H0um0DKZSqZQcorX3mOYdTJ6ez+fo9/uSHm0wGNBut9Fut2Ug4Pf7MZ/P0e12YTKZpJnMVKN6vY5QKCTp47chEAhAURRJlL+4uIDH45HBGJ9RvV6PdDqNQCCwJSSezWZ48+YNut0uqtWqiJ0WiwVcLheGw6E0X4D3qaixWAyz2Qz1eh2qqopIVqfToVar4cWLF4hGo9JcJRnC5XJhsVjg1atXso5x+M73la7hmUxGTEqYCHwbeA2ZwvO1QqfTicslh2H3NYt4Dfmc81rQJGQwGKBWq8nf55AaAFKpFF68eCF/TiMEYjqdolwuy4CDgw2+KyRi+Hw+LJdLTKdTaSRd39NXq5Wksh8fH8NmsyEcDqNer2OxWAgxi6m0TO94qJM49w2Ki26D3W4XgkC32xWjB64pJOn4/X6EQqEt53MS0QqFws7UIq3QudfrwWQy4ezsTIR5jzHoXywWW47Bu9Dr9SRBxG63i9jyOgwGg6Qkcd1xOp1oNBpIp9M7n0EmMTC15VM1l9nAJFHnvlqAQlv+24eC5K7bGtPRaBTT6RTRaBStVkvcaS0WCwwGgxjuJJNJ+Hw+HB8fo9/v4+rqCpPJBM1mE5eXl5hMJvJuWSwWEeDSafO6MJzrFw0k2u22pNezNtdCp9PB6XQim80il8uh0+lIIhjXFyaZ895Np9MtATFFwjabTcyHms2muDNzv+U+tuveOJ1OqKoKs9ksxMxkMvlgMRRdzpmceB8JnuQ8rie7kE6npeZfrVZb7wdF+hTA8+xlMpmQTqdxdHSE0WiEarWKq6srAJBkXavVinq9Lue3fr8Ph8OBXC6H0WiE4XAoZzQSHAm66TK1y2q1flCaNMVpJOszZZBpuaFQCNFodOc9azQaW+u+dk8gkXEwGCAajSKVSiGTycj7oKqq/C69Xi+EqW63KwIZricejwdXV1e4urrCL7/8IskRTMniGTAej8NoNCIcDsNoNAoBwm63iyCHw/eHwOPxIJvNyrvS7XaRz+flv1utVhFqxuNxGSKen5+jVCqJaIUife6hHBImEgkxZSiXyxgOhzCZTDg9PZW1w+l0ol6vI5vNwmKxIJ1OS0KUluS4C/uS7z4WZrMZ5+fnuLi4ENMJnl957eLxOKxWK46Ojg6D7j8warUaqtUqJpPJ1iDUbDaj3++j3W4jGAwinU5/Fqd2ggT1VqsFl8t1I60rFAohGAx+syLdcDgsKT0UzVz/LiQ+Uzz9NRACvmUwpXU2m6FUKsHn88Hn88nZh/v8ZDJBKpWCy+Xau/Zn74fnomazKURkvV6PcrmMarWKXq+Hly9f4vT0FE+fPpXzQCgUQqPRQLFYlL//EOOfrwHaHiT7S4ck928f7JVtNhvUajUUCgXYbDYRGvPPhsMhHA4HvF4vnjx5Ar1eL6LGbreLeDyOzWaDp0+f3ku2UlVVenq3kZC18Pl8aLfbCIVCmEwmX9yUhHW4oihIJpO3fl+KFsrlMkKhkNTd3wpIwFRVFdlsdutsRDKewWDAy5cvUa/XMRwO0el0pE42mUxIpVLI5XJyjrLZbCgWi5JEStNMkppojNxut7HZbCQNkimEnMPsAs+ITqdTTBnvQq/X2/o3+2CxWODdu3f429/+hmKxiLdv34rISZte6Ha7sV6v8euvv0pPl8mIH4tgMIhWqwWj0Yher3fnM0UhqNfrfRAJnORmo9GI6XQqiaM+n0/OTlpBDwVT/ExMWGbNSVHufD6XM6zJZJK5VzQaFREYU4s/Ftq51T5EP/Z4HjKzpOlgu92WdGCmy1O4SANMXiv+t49NLzObzZJEXi6XxbCS702/3xfDORoFJpNJqfdpjs765q7+Jc/5tyXEZ7PZrbkAzx7sF+r1eqRSKQyHQ5kv7MJwOES/30c6nYbf75ceNfuN1WoVzWZTZnXT6VTEXTSJnU6n6Ha7YjLMs02j0YCqqqhWqzAYDHj27Bk6nY7M82OxGFqtFgqFAgAgn8/j5ORk5+esVquoVqtCMp1MJgDe14qcjbfbbZycnMjawmdK+//vO3dp3y0AWxwBmgO0223odLob691msxFTvFgshvF4LIa6FBEsl0v0+308e/YMs9kM1WoVbrdbjMP5u4bDofAP4vG4mODyu+l0OkltJjF/sVgI3yAej+Pp06dSQ7IHc33d5bVhX5MJmNffR7vdjlAohM1mg1arJYnY7IU0Gg0xXj4/P8e7d+9gMBigKArevXsnAmeuY91uV/aZzWZzY058HT6fD71eT3q/d7072uSxXWeOQCCASqUCt9uNarWKTCYjvb1erwez2YxOpyN8Eu6D2mvXbDZltrdPjVEsFqEoCgqFgnAqtGtkJBJBo9HAX//6VyEo3/bO0rT54uICw+FQyNn7wGg04vT0FL/88ouYY9RqNUlGWywWYnoxnU6h1+vFHIQ1qM1mw8nJCaxWK5LJpJhF2u12OesBv82KuHZ1Oh0xAtaeFWkYazKZpE9rt9sxmUyE62G328XYj6ne5McwJdFmsyGdTiMej4sIweVy4eXLl1BVFe12W86UvN/cO/YB92ea63c6HfT7fdhsNjFQWCwW0ovgPOa2Ne1joSX/s5d7GyhGfOjzogXF9ky55uzTarVKz2swGMgswev14sWLF/B4PCIiAd7XdKVSSYySPB4PXC4Xzs7OhM/BdEHu2eQi+Xw+eDwenJ6e7pxxcb0GsCVouQ3kgrFO+D3iQ0T5nCN/jAiVa4oW3W4Xg8FATAnvAmdYrHs8Hg8KhQKCwSA8Ho98F9Yf/X4fL1++3OKTcT7LM8xms5H5AsXy0Wj00RIGf0+gqcZwONwrxdPj8aDZbMqZ8nMJ0Pep6/6IINcpEong1atXMlPTrgE0gwDe11h+vx+Komyto98y5vM53r17h+FwCEVRtvZlu90ua0EqlbpX6EVh63Q6xXg8FgN2bbL8YrFAp9OBoihiLvmhnBfyaZlefx94DqLw8XO9E1/i/VMUBcVi8Ubfg7xsq9UqRjW7QI4DQ4fuetbZi6fQ9DFAE/hQKIRWqwW3271V69AY4WsMjXlM7BLzas/vFPNOJhM4HA7M5/O9UqUNBgM8Hg8URYHX65U5ygGfHtFoVHr15XJZAkpYg3Et9vv98Pv9stdcXV1Br9dL74EcLrfbLeaEi8VCtB3kYpBz43A4MBwOhbv3KUw4uF9ylpxMJrfWPvaInE4n8vk8ms0mzGbzvWFZnwPkjpNHdhd3mNwwn88Hu92+dV5kT157BqZ2hHsp+0G71kz20JbLJRaLBeLxuBheXechTKdT1Ot1MbsMh8Nb7zGvcbFYFIMt3vfJZIJ2u41Op4NkMonNZoPz8/NHuQ+tVgs//fQT8vk8CoUCfD4fqtWq8I7ZY6fug5qRyWQixsgmk0k+H884NANMJpPwer04PT19tLkvewW9Xm9L9L4L1A/w797HMz/ggAO28e2fng444IAD/iCoVCoifspkMuISR5cknU4Hu92OTqcjyTqVSgV2u11S1SiE1Ov1KJVKKJfLSCaT4sTFQVc2m8VsNsNisYCqqluJoWxSU5Dp8Xi2iFsUpNbrdSnQdoGCC6YQ0zl5NBrBbrff2lygQ5RerxcxNwd2TMPgwZoDAo/Hg+l0Crfb/eCCVVVV5HI5IXmRvMGhDxNSj46ObjTqF4sFXr9+LclaFDAxlYIp7mazGcPhEOl0GuFwWAh3k8kE/X4fnU5HivVsNisFu8VigaqqcqgwGAwyIGeyV7/f39ttcr1eC4niuiicqejD4VBITkwxZAJiJpMR8RKTB00mkwhweICmOCQejz/48ElBNa/PfcPCyWQCg8Egh5rr37dUKknqOwXoOp0OLpcLzWYTbrcb2Wz20Q460+lUSE3XPztFo/P5XN4bpmTyXXe5XJJWweEyXRG10Lq2fozww2Aw4PT0VFz1roueXC6XCIg+VRNhtVrh6uoK1WoVlUpFxDp0kOe7TddzDuGm0ylarRaazaYQO6LRKNLpNE5OTuQ97Ha7+Nvf/gaj0YjxeIxsNrvV7AMgw2kKkSwWC1KpFNxut4iGkskkqtUqyuUyfD4fIpGIPPcEyWxMQ7HZbNIYJ0nqhx9+kBRJp9MpxMh+vy9CX5qKMNEZgKy7bEDdNcRkigDF60x26vV68v3C4bCYCxwfH2+R1PL5PBRFQbVaRSgUEsE5CRJMUKDwkqlrdN7jsJ5pJNpGDq+XTqeTxFEmyBSLRQSDQUl8GI1G8n3H4zH0er3sLSRt7Xp32RiKx+P3Olh/DaDTud1uh6qq935eJhF7PB7ZF5joM51O4XA4ZB3XigJPTk5wcnIie4XH44HH40GpVBKyDFOoer2epJMA79daOgS6XC5cXV0hl8vB7/dLk5RDb5KqxuMxVquV7H9OpxPRaBTHx8dSDxiNRln3LRaLGLYMBoO9ReiskfZx9mUTrFarSVI1U61Z33CIo33HKMS12WyoVCpIpVI7G/vT6RTNZhOBQABWq/Wjkoduw22kVTY2SUTgcOq2tZtrymw2k/2mWCyiUCggFApJrQJAUrjn8zkymcxWY/2xYbfbxYRin4Qqin2ZyvxQXHcevg4Sutl8p1CWSUPcC+x2O05OTsQIioRFDrqZTD+dToXcxrSQZDKJ+Xx+Q6RnNBoljajVaqHX68k6exs4yKNZEGs+pjSVy2VMJhMxNCBZVCsuPz4+FhIMCTlM7KKJzm3kFX5mj8cjQl1FUW4djGjTvCi65zUIhUJot9uwWq0ol8u3vndc85PJJNxu950DzKOjI0ynU1gsli1zFrPZDK/Xu7WvNxoNbDYbPHnyBOFwGOPxGH//+99xdHSE4XC4VYMGg0Eh2kajUSHBMyXcbDajWq1iPB5L2td6vcZgMBBird/vx+np6YOHhhRS93o9EYHSmItkN6bTXCexdTodlEolVCoVjEYj+P1+MQygOJ4EKt5fpkwlk0kkk0n5WcViEd1uF51OB6lU6sa7G4lExMgsn8/LYIvpR7FYTJJlr5sWbDYbuaZMF/8QcG1uNBpwu91YLBZCXmfaZjgc3qqtDQYDstksEokE2u02RqPRFik0kUhgNBqJKQPT+sbjsbwrNEDS1idcs+4i3X8pWCwWPH/+fKfr9u9BwHvAx6NSqaBSqaBarW4Z1el0OoxGI5TLZUmIWq/XW6lenwNWqxWpVAqJRELOZRQ7fOnB+MciGAxCVVUxOeNepq2dmOrKIfbBKOLjYLfbJd2V55Vutys9HOB93ySTycgg/a7nTNvzYq2byWRQLpdhNpsxHo+3RO00LQqHwzg+Pt4SpLCGYMqVzWb7IOOfL4HxeCzJllwrSI73+XxbZ9EDvk3QmIdJ2L1eD8PhUMirFMc8ffp0K/mIZNZOpyOivlKpdK/AZbFYYLFYSPLofeA+xWTlLw2mxmoNBG8Dk3xZq38rAnQSvihs065VNA9gH9RisUiPnIahPOdpiUN+vx+FQgGqqkKv14vQjsnDdrtdRB4UiprNZsTjcSyXS2QyGbhcrlvPkCS0DQYDFIvFO5M1+/0+hsMhstksgsHgXoKP1WqFt2/folgs4vLyEtVqVQwmSYharVaYzWZicMJ0WJLDtck5Hwom44bDYVQqlVvnDdPpFNVqFV6vFxaL5UHmkbwXNBaloUs4HJaeOGsZJlrSkDkajcJgMGA8HuOnn37CeDyWPoHWlJk9QZ6ttHOOx6gBr4u/7ltrKHDcV/y12WxQrVahKAqm0yni8fjWNTYYDDCbzSIsbbVaIijeNT/6ECQSCVmry+Uy3G43DAYDRqORGO/RqJYERL4T4XAYV1dX6Ha7CAQC9xL3vV4vcrkcFosFBoPB1v3S6/U4PT1FvV4XEazWONxgMKBer2Oz2Yg42+v1bpkEK4oiRiMul0tm3BT4GQwGeL1eKIoiYiSaENLQMRAI4O3bt1AURfa16z+fQi/2pWkWDgDJZFLOTjQXv95PZXpvuVyWnlE4HIZer8d8PoeiKMjlcohGo9KjstlsmE6nqFQqMoPmDJ0p4tefB84KtP1Tt9sNj8eDbreLSCQitS2JxU6nU9KdaQ7z/PlzMUznmmcymcRsnc+GXq+XZ9Vms4kYksYQo9FITKf5zHEuRNBYmuJwpk1TtMK1PRAIiDje5/OJke5kMpF35MmTJ8KP6Pf7NwQ57OlQiExBMM2D4/E47HY7ms0mOp0OfvnlF0wmE3i9Xul98vvxuo3HYwQCgRuG8tdB0bjL5UKtVkMmk9m5ZlBQzX1r19pPoQOF+VdXV5KQy1nI5eWlmJl5vV4xrOT3Ho/H0v+/TyTCVCvySbQm6gRnlA6HA+/evUMqldqae+76Dtwr3G43MpnMvXvqcrkUfk0kEhFzf7fbLSaW8XhcerU2mw2TyQSdTkeMNU5PT5HNZjGfz+V+m81mBAKBG3t/OByWZLNKpYJeryfrsPYdH4/HWCwWsFqtIoAnt4Um4aznmPJJoeN4PEahUMBms4HX60WlUhGDnkQiIfeN7/zHgEL7//zP/4TVahWjiev7nMfjQafTwevXrxEKhaRf/NgYDAZisnzfDIpG/+y7PhTL5RLv3r0TA5hAICDzTr7v7XYbb968kZn/06dPZa3Wfj7uIRSS0BjRZDLhyZMnIpAPhUIyj+czw17rbc+62+2W66Eoyr2GW+w1m0ymr34+/qGgcQKwnyifM4jHqpmu/2xyKPY5A/DvMdWWvaZWqyVmJovFAna7XQSHf/7zn1EoFOTZzOfzMJlMMBqNmM/nEugSDoclrf2Am+CskzXlfWBwyGOYPd33uWhGw3qK69tdxi1/NOh0OpyenqJQKGA0GuHy8lIMDynmWy6XsNlsCAaD8Pl80vshn/FbxmKxkACRcrksfFb2DQaDAQqFghis63S6e/tFwWBQzK9oVs45ymq1wmQykXmwz+e71ShlH5DvsO8azDk1/+2nEtxuNhtJlmaYll6vF56klkv9KdBut1EoFNBqtcT4jHt3s9lEo9FAIBDAYrHA0dHRTn42hd/sh92VMt5ut8U08bH7iTRNYiAA990/Amgcw56attcN/GYu53A4UCgUUK1WsV6vhaNyH+x2u9S6DI054NPDYDCImZTZbJa9mrMEzje8Xq/cy8FgIFwRhuwx9Ccej8NkMmG5XG6t5xR7WywWSR7P5XLweDyyDj62CL3VamE0Gu3kqGnBnhfD09iP/9LgmZzmG/F4/MY8cT6fC0+bPWhis9ng4uJCuFKcqdvtdulhMByD/WYGTBA6nQ5utxuj0QjxeFwCuhqNhgQ9GAwGOSswuCUQCCAej8vPYX+RRuPkuxMMcKrVaiiVSmIe97H1fq1WQ6VSQS6Xw+XlpYQh0TCDPZnVaiVGW/zO5POen5+jXC4LH41cShrpud1uCWN8LFAjRV3KfXC73fLeTiaTgwD9gAMegK+fbXLAAQcc8AfHcrlEpVLBX//6V5RKJQBAuVyWwyOHXAaDAU6nE263G8PhEG/fvkWv14PX60U6nRb3dw4NE4kEKpWKiDPp/ux0OuUwpCgK6vW6EBW1gjEW3xSNEbFYDH6/X4SZ8/kcbrdbmiCz2Uyc5f1+PyaTiRyiAIig/jZQaKgoipAQKZBhkUpxN0XSi8UCm80Gbrf7QQcdVVVxeXkpQySKqTlkLpfLMJlMSCaTWC6XODs722qQXF1dodfroVQqSeqD9hBAs4BXr14hlUrdEA25XC5JsOLvb7fbIsDw+/2o1+viaKcVh08mE4xGI/T7fSFX3ffdtQ067efs9/tCHtc6KgLvCXA6nQ7v3r2TZFWdTodSqYRisSipCBaLBVarFTqdDuv1GqlU6oZ5wT7gEI3pifeR7jhEo8u89rteXFxIgvN4PIbNZpPDPN3eo9EoZrMZnjx58miCj4ckuGuTMCgmVVVViDq7QNJmJpNBIBD46OYSU0aZgsuBvNls3qvZ9bFgyg8b4qqqylCWKb4U9qxWK/T7fVgsFtjtdiGdkFRIISCfb5ISzWYz3r59i2g0Ki59WtDFvlgsolqtSqIQG+qJRALj8Rh/+9vfhOh511B/PB7DaDTCZrNhPp9LemskEpG1kk0LDpDYsGLjiAJvDkPY1GJa731gYyIQCKDVat34vBxa8c85OCPhvV6vCxmw2WyK6HA+n8vnZnKpzWaTdHimAvV6PWnYJ5NJeb9IvAPei1doLMK0M6Ym0gV3MpnIGlkqlUSw32w2MZ/PRUivJWWNRiNEIhH4fD6k0+l7r9WXBp8nuglyWLQLJDQxATkcDksyMQV1sVhMyNeDwQCTyQRHR0fyLPNecBhhsVhQr9cRCAQwnU6ltuCayiRzOm1S2JjL5cT93+/3w+v1iqnN6empDGSZvABAkpV3NUgpdvyQQS4Hh2y03Sf6iMViIshxuVzyfgUCgZ2GG1arFaFQCMvlEoVCAblcDoFAQIiYfPaY/hsMBreEmY8Bs9ksxLpd4DXjYGw6nYrT6i5oU5K4vtJQolKpQK/Xw2w2S91BcYzf7//ghC2aWbAuvW7iAUDIbWwEagXJu37eZDJBLBb74CEZ1612u33rs0MROsWYgUAAsVgMkUhEjBe4VpOwxMQgGt6wVuIeTvdaEuWB98NdLVGPBDWu+6yt7wNd+bmPOJ1OEcePRiP89NNP4qLLITRF4MFgUEhug8FA1uzZbCYEzruasqzNOZzelVbN1CS6PHNPMxgMktYdCoUkgWi5XKJYLOLq6krcadlUphGNw+EQYvJPP/0El8slCZqsr4hMJoPpdAqn0ykmUFpS5a4k00wmI+SGVquFVqslhgKVSkX2wkwmA4/Hs0VK7na7ePv2rZiNcC9nKhjPUddFZftgMBggn8+jVquJwzL3XJqKdTod5PN5WRtOT0/lPpTLZUm5Z/Jcu90GAHlGQ6HQlgAoFArdWFe4TzDx77b6ncnEer0evV4PNpsNZrMZqqoiEokIqfM6qVtRFCwWC1QqFanno9HoB4mgHQ4Hjo+PJbmHAnSHw3EnUY17BwAx+WBCmtaUgSZv6/Vaaj4K7/nM9Pt92QddLhd6vZ4YhX0NAnRg+3zAffn3nJpzwP4YDAao1Wpyvjk+Pt56d5gUVS6XUSgUZC/8FA7t90FL1vm9gEZjfB+Hw6HUJwBkSO50OhGJRPZK8/ncWCwWaLfbIvQhWU67f35t8P7/dOZqtSpmeFqhK0V48Xj83v6MNmVQURQ4nU4YDAZkMhmMx2OpFdkHtdvtOD8/x9HR0a09iGAwKMaanU7nqycak0xBEsNoNJL/RjJVt9uV5JQDvl2w59TpdERETFGNqqr47rvvbjXJCAQCYvjDVJi7epQ8t2hNW+8C6/FdCXZfAuzH7nsesNvtksL4LWA+n+Pq6gqvX7/G5eUlotGoEH5Inl2tVpjP5/LeO51OqKoqIkJtveFyudBqtaS2phCHP2uxWEgSK8+vNIni+ePly5cIh8P3poJFo1FJBS+Xy1upNvxdNCeJRCJwOp33/kyiVqtBVVX8+uuvYuzIdMxYLCamcNoeQygUElJXPp9HMBh8lDqL5+TVaoVarSa9QZ5fVVUVg7tIJIJMJvPgmQTFakz/pACI8835fI5KpYJut4vFYoHj42NEo1FZJwKBgJx1Ly8v8eTJE6l5AEiaIYWHiqLA5XIJgf5j4fF4YDab4XA45B4RNO3juclisUBRFPk3+9SkTFseDofS290Fg8GAcDgsYnWeZx/DuMVms0nPgD2sX3/9Fe12W0wU3W63/J+W6EchZKlUkn7EXSIPnn2Xy+XOPjCN6qLRqMyMWTvSZODy8lKEm+12W+7zbDaD0WgUsz8aLRJutxvfffed/NvpdCo/2+fzIRgMinE0jeu0fQ5FUWRNicfjsufYbDZ0u10xqLZarYhGo7i4uMBoNEKr1dqaVfCZr1armM/nN8x2aW6qTVqn4IyJh5PJBNVqFbFYTGY0rVZL7pX2M/NZZE0ZCoWkl8YkUJ1OJzMfrQBjtVohnU7D5XIhm82iVquJYJz9xuumlX6/H6qqiqGG2WzGfD6XvmW5XBaR93Q6xWw2wz/+4z/eeF+5LtlsNplP0QQilUoJAV9RFBHNKIoiM+EnT54gFArh/Pxckutpcq8F73s+n5fnieLN8XiMWCwmsypVVUX4R3E1e+j8vrFYDKPRSJKj7kr95jtfKpWQz+e35g6cidLInO/Fbf2ZcDgsxsSqqop5AH8OayyaPJLsTGE2xeeZTGbnz9eC7w/787eBadJGoxGz2UyIzLeBYn8aMGjnqNp/NxqN0Gg0ZKa0Xq+F08Brwf/NWQRFflrziUQigaOjIzx79myvXqPNZpM0vFKphG63K2s3+4JM5abhAsMZ1us17Ha79LVpqAG8XxO0syqv14v5fC58oX/913/F06dPxTB2H7L1PggGg3j16hWMRiPK5TKePXu2s/9Pg1Ca3ppMpp3GGh8L7gv78hPMZjNGo9EHiUObzSaGwyFqtdpWLQm8N2WJRCKyRr9+/RqRSARer/fW94P9DXJXmEKq1+tFFEyhBz/7Pvs3zYF8Ph9qtZrwYnaB624sFvsqEgo/Fcir4Mz0vvqbdRnFJI+Jh5rl8p54vV6cnJzAZrPB7/eLIYtOp4PNZoPVaoXX60UqlYLJZBKBB41d+NzTxIx1x75nkT8iDAaD9AAWi8W96xc5iDSi/hSgkHo6nQrHhXUp+XIul+uGGOmPCrPZjL/85S+YTCYYj8fyvpA/xLCgQCAAv9+PfD4vJndfg2DuY1Aul6GqKsrlMgKBwI01nj3rUqkkHDqeR+8Ceaw0nx4MBlgsFtDr9QiHw2JilMlkPoo7yPeI79V9+9N8Ppf37lOJmKfTKS4vL4VPwYAsg8Egs1uHw4GTk5NPkt49Go2EnzgcDsXkjdeGQtRGoyFcCqvVemPtosgvHo/LTO56TcuwH0VR5Fz32PuhFn80gTSNvKbT6Z0GxRTzkgdODvd90L4zv9fa7muF2WzG06dPxUwqHA5vmUldD+khb1ArQPf5fFvvBNfzUqkk2gztffV6vZhMJiiXy6jX62Lw95j8bfaUuG/eBc4wGRj4NeynNpsNqVQKm80G9XodxWJR6gBqX3gGZ1igtn/e7XYlQd3j8QhHr9PpYLPZCKfVZrOh2WyKwcd1Dih7NgaDAU+fPkU+n4fD4RDeNesT1iKhUOhG2nyz2RTu3snJyc71U6/XIx6PI5fLCXf3+nPzEPT7fTHDZEgJANEpbDYbjMdjtNttmaPR5NdoNMr5hz2368Ej5N5+ir2T/RAAe9XGvJ7af3fAAQfsh8Pp84ADDjjgK8ZkMsG7d+9EzNvtdmUQTTcgivx8Pp+k6I3HY7x9+xaLxUIIGLVaTYQCdNB69uwZisUiNpsNnE4n4vG4uD4pioJisQi/378lOg4EAkKITKVSNw4O4XAYp6enaDQaIlav1+vSrGXxHIlEhNzBQp4u2yRjXC+aKVYslUqYTCYwGo1YLBZbDlpa1+N8Pg+r1Yper4fT01NJUdgHy+USV1dXaDQakkR3feC5XC7loKLX61Gr1YTAq6oqCoUCfv75ZyEFDIdDeDweeL1eSbZkA4gJDloXXqZLjMdjSTu32WwIh8MyqFyv16jVavj111/R7/dFiF6tVrFYLBCJRGQ4P51O72wS8XobDAZx5CYhQVVVGYJzCMoUpU6ng8FggGQyKWQaHnhbrRZKpRJsNhsCgQDS6TTOz89FgPjQxhKTcUajEfL5PLrd7q2HV4p/ksnkDVJquVyWREiLxYKjo6OtYRxJz8ViEQBweXmJp0+fPuiz7oLJZILBYIBer8dkMtkijPDgYzKZMJ1OJalAp9OJ+FZRFElmVBQFs9lMft5wOJShdSwWE6HpY0IrOp9MJkIK1opzHttpl6mubL4wzQOAEEomk4k0eJk6uV6vxQWYjnUOh+OG66eiKCKEIzlnl7hfr9cjGo0il8thOp2KGQTwmwgrHo9js9mIKyXTPAk2XUajkZgbWCwWeTevN4RI8litVkI6YNN9F/nqQ5xC6YBPQTLwW+raLrTbbTG7CAQC8hk48DeZTGJ+wc8xmUwwGAzgcDhkeDCfz4WYT8IMUwX4HOfzeWnChcNh+XNeAyZi8H7RNZJDQSYFMNmdzqFHR0ciuPxWRCdMZhgMBiiVSmJEogWJEE6nUww+nE6npN2TAFmr1bYaPhQ4xuPxLTECh9rz+RyFQgG//PKLpES6XC7Zt7hfMPGEhHEmEdntdkmlaLfbSKVSWK/XiMVi8ozw57DW4OBGK0Dm4ORDHOhDoRAURUGj0UCn07mVfLvZbNBsNiXl9vvvv9/7d6VSKSFvkaDI+mez2chwnwSUj02euI5gMIh+vy/v6PXnQzuEIymJiaS7wH2ee0w6nYbNZpO6TTtkczqdMJvNCAaDSCQSD1qDKDZuNpuS4kgBOhuP0Wh0y6QiFAphOp0in8+jXC4jFovdaB7SPCcQCEgN8iHgs9NsNu91bB2NRshkMjg6OsLLly9vrOl8xjmAoGO2zWaD0WiUdX21WglhnEMKGhGFQiEZwPK/U4izL/GK67DWzMFisSCbzeLdu3dwOBwYDAZwOp1yzymOJpmi0+ng8vISiURC1naKg+4i1AyHQxF+7xJzrFYrXF5eCtGPwmIA4u5L05GzszNZT/R6PRRFkVSdTqeDTqcjyUbj8Vjeb+5VJE0yuYrGMruSTK+urkSwAkAItdeTTM1mMxKJhJwH3rx5c6vrLfC+9guHwzCbzSiXy/D5fCJ4pzAyGAx+sIM6yfmKoiCTyWzteXSgdblckopF52UOzmezGWq1GtbrtZiW8FpyaMb9fjKZiJvw9UQj1g3D4fBe8w2r1YqzszPkcjmp35lWpq0vh8MhKpUKptPplkiBBN5cLod6vS4Gaw8FXak/BNp9Yzqd3vpzeE9JBGNahtZIiD+LYqmvEZ+SYHXAt4dmsyn9o+Pj451nCqPRiFQqJWZFzWbziwjQf6/Q6/U4Pj5GOByWupb7FxOUaOTyNYFGkqyXmG7KRBGn04lUKvXVCo4dDgfOzs5EMMFzAcWT+9bHTNPd1fOy2+2w2+1Sw6mqKgKTu2pdq9Uq9RBTa75W9Ho96aewx0lTzcViAVVVJQGS9cjXQGg54MNB4yDWjz/++KOch+7r4fv9flxdXWE6naLT6dzZA6UZhF6vx2AwuPdnD4dDMeP9GlIPeHbaV7zwUJHDlwT7881mE7VaDY1GQ+ZHDocDo9FIDJyA34jF7AHsIgexNuVZZTQaIZvNSqKpXq8Xoy0m/hqNRrhcLjF1PD093TLmvA0WiwUnJydirtjpdNDtdqUGms/nsFgsSCQSkgimPbcvl0v5Nzw7knzIuQsNANfrtQjztT2SQCCARqMhJHun0ym9a4ohP+RMpIXNZpNUH84oms2m9KKdTifS6TQcDgfS6fSta7PWpIXiTSIYDApZr1QqYbFYyPyLs5pGo4F2uy3p9Nr7wzkKZ4OcU7FnQEKmxWJBpVLBfD4XYfZj9IcpTh4MBiiXy3C5XLBarXImp/iRfbn5fI4ffvhBkpXvA8mPnM1WKhWMRqOdwjRe20qlstP4+kPBe8IEcwp5mSytNY/W9n2B98+q3W6XOdZoNLpTYKo1I7yrjrot4ddms+H8/FzMwWl6CUDMfhwOB1Kp1M5ryDnkXecUJmReB8Wk7I1rP6v23wIQUVi/37+RzksjjcFggKOjo1vnNR6PR0xcWDuyz8r1gN+d5rD8/ZzJsJfDXiAA6csuFgvk83nZazl7WSwWkuTm9Xrx/Plz+Syr1UruL++llq/Aa8A+qdFoxNnZmbwPJNfO53MxG+n1euh0Oshms9J/JifCYDDg5cuXsmaWy2WkUinYbDZks1k0m00xrOTfefr0KeLxuBjzWiwWST+n4FdrgjUcDlEoFFCr1QC87w2yJ0quwH/8x3+IaJhJ5Ha7XcQDTHbfbDZisj0ajSSZ6zZEo1FZO2k6ohUl0KwjEAggHA7fKzyOx+NwOp1oNBrwer3Skz4/P0e73cZms4HD4RCDBYvFgmg0CqvVCr/fv1fqOPD+XRgOh1KH3QXWXJPJZMsE/zo2mw263S4KhYI8azRgczgcMoNhuANNrZg0z/eTpgvPnj27sUdGIhGEQiHkcjmcnJzg6OgIf/rTn/bqu2t5E7PZDMFgEBaLBc1mE69fv0az2ZQ+2mazERH6ZrOBoigIh8NoNBoIhUJYLBbSjzcajVBVFev1WgwAWAvRSCAYDKLT6cj1dLlcjzKr5/e2WCzyfl439OOMfrVa4ejoSEzTOA/UgoaoWpF1IBDY+7NyX9hXUK6dcT0EnFvRJHVXf5fPXTQaRbvdhtPpxGQyuTPBlX13roHXn78PNY2JxWLo9/sYj8coFApiSsTPQSPcTqeDaDQqfI7fK2hi7fP5UK/X4Xa7bz3XkbdxdHT0SUT55NZ0u929zNDG47GIQmmswRm61uyQ5ksEzQsTiYRwCrVC5V3m6gdsg7Naj8cDRVHuTcTmXmK1Wj9JCvloNMLbt28l1EZr3L5YLFCtVmE0GpFIJLBcLvH06dPDnAbvDXC/++47eL1e/PLLL9IP5/rK+jefz4sBSDQa/aaTmFkrN5tNSebeBRo05XI5+P1+tNvtvQxryOfl7J5m8wxoeIy+GeuWer2O4XB45zvF9NVgMCjmPI+N2WyGN2/eoNfroVqtykzdZrNJvddsNhGPx+X9e+zwHppj9ft9ZLPZG9dZm4pcKBTgdDrRbDZ3GuFks1kJgaBZHYN1aMhuNBqRTqfh8/lwdHT0qN/ljw6tmPc+o3maxlCwTgOzuzAajSQQ7Gsxsv8jQWsmNR6Pt8Tl1++d2Wy+0wyINXuz2dwy1bwOm82GZDKJfD4Pn8+393q+L8gf3udcwu+6WCz2Csv6XAiHw8It5R7GvoDZbJZZQTgcvsFfouEd7y3BcyV7+A6HAy6XC6qqivGglrfIAC+j0QiTyYSzszPpwdFIk2Z6u8zuZrOZ8KCuGxVcB8Mj2A+8z/jzLnD/GY/HSCaTwpXU/i7yB2u1GkajkWhraBCj5a7bbLbPZohPg0KaWd43E2Eq+4HvdMABD8dBgH7AAQcc8JViNptJQ5GuehRfjkYjdDodjEYjaUDT8dJgMODy8lJEF0wAZIolh7H8WX6/H41GQ5xSeXhgwpCiKDcGQX6/Hz6f70Yjh2K00WgEp9O5NYg1m80ycGUDg4JsCt3pNtXr9dBoNG6INykkbDQaGA6HQpyw2+3odDqSkkdB6mw2w3/+53/i+++/F/Fps9mUw89dYg46c3c6HaRSqZ0FKRu6hUIBzWZTXMu73S7+9V//FYVCAfV6HcFgELVaDSaTSUQzLpdLCv5EIoHBYCDJsiTd+Xw+uV+bzUZSIAeDgQjYcrmcpFiSyJJOp6X5RVIITQmePHlyK6FmuVyKQxUPpyR9TqdThMNhjMdjOWiy4cEk+l6vhydPnuCf//mfJUV8PB7Lz6Rw2OVy4cmTJx/cxI1EInJwYXo5Ey8ByMGGB6HriWp0b6zVajCbzUilUjeeA6YY6nQ6SVN8DLKW0+mExWKBx+ORJFyCrsoku3k8HiGdNBoNAO8FKuPxWEgUFKzw/5hA63a7cXZ29kmaS6vVCrlcDqqqitCYQ36n04lutyvpkR/rVsbnfjQaweVySTOA8Hg8mEwmCAQCkibDpCQ+k4vFAslkEpFIRIYLWnAt8vl8kii0WCx2XjsaRqiqKmRvLVKplHzGcrmMd+/eybq3Wq0wGAwkLWK9Xoszsdls3iKS0pmapMtutyvJowCEBKt9bincZNr7Q3GX6FwLvtN2u10Ek8R6vRYHRCb3mkymGwm3FFHRVINrXKFQwHA4xGAwQKvVEiKCx+OByWRCPp8X4aDD4UC73cZyuYTb7YbD4UCj0YDdbsfZ2Rk6nY6Qz0iq0uv1MJlMCAaDCIfD31TzlQTOzWaDRqMhaR18nkma9Xq9iEQikmwIvN/Pnz17hkajAZfLJYQinU4njvvhcHjnICeRSKBarWK9XqNcLqNcLgsxwmw2i/iY6edM8kgmk+j3+0KSCQaDCAQCaDabYtrCAQ1TGR0OB6bTqRBq2IDnv1VVVRyWH0rAoMFNPB5HpVLBer2+kf49m81Qr9fl81Pouy+YOEnyGZOiSZTlteD7uQ/R4CHw+/0ol8vwer2o1WpIp9NbazCbmgaDAZVKBefn5zCbzbfua4PBQIjF/Dt0x6cZgnbt9/v9D27IrddrSSPSDi2B30hS3NPPzs7k2sXjcTEYKZfLuLi4kGEM3Z5pkhEOh+8kad4Hr9crRg00b6BDLcFBIwXoPp9P6tFOpyNpKaVSCYqiiCETk7J9Pp843Q4GAwwGA2lGz2YzqRmZjkJyOfekSCQixPp9mvrr9VrunfYZZGpbr9fDy5cvRXBNgxmmgA+HQ5RKJTGNItnl73//OwwGg6RxXgeNlkKhEMxmM5bLpZhiAZCUMpoXsUnOlHvWur1eT4YAT58+RTablXQkv9+PbreLYrGI5XKJWq0mQ2vW2UxP8vl8KBaLOD8/l8Y9xcoej0eSTJlmrXUP5xnE7XZjMBiIUZD2OSPRv9vt3uvg6vV6oSgKjEYj/H4/Xr58+dEDc57JaLpxW/3P1LB3797J/utyudDtdlGv14Ww0Gg0ZM3ns8bai6YU3LOvC9AplNtsNnvVhmazWcQBfFb5zNPEq1AoYDAYoN/vi6FCMpkU04HLy0uEw2FsNhucn58/StrbvuD+RWKZFloTlcViAbPZLCISilO0957iy0O6+AHfArhPMcnprr3XYDDA5/MJSfo28cwBHw6n0wmn0ymmQgDEAOZrw2KxwJs3b6CqKmq1mhhaLZdLGeS7XC6k02k8f/4c5+fnX/gT3w6KMz4GsVgMiqJIz2s0GsHn84mYjkZLk8lEzNXuE9Nqk5+/Vmw2GxSLRUmZu06sM5lMsNvtUsM1m005z37LJNEDfgNrRvZ57wMFllph3W2g4EDbj71tPWTaZzweh9fr/SpqMJLaKQq7ay3nfOZTpUg8Jnq9nhhHsffkcrkkfZDJKU6nUwQU7K1oTQqvrwEkDgEQcRLPQ0+fPoXb7UalUkG73d7qXUWjUYRCIRwfHyMQCOyV7gpAZh1MRRmPx/IZaCLtcrlEDEnU63XUajXMZjMR8fKzFotF1Ot1TCYTuN1umWlNp9Mb4m4Ke5ncwjkfxaGc1X0sHA4HXrx4gXa7jVardYOAFwgEdpLP2dsmAU77DHs8HhFI6fV6nJ2dyc+r1+sol8totVoiqJpMJtLj1yb8sl/KpBWHw4FWqwWv1ytzBcJkMmE+n2M0Gsm587HAxNtAIIB3795hOp1KAizFeDTtTqfTaDabePLkyV4/m4nE4XBYBN+tVkv68dp1gf0lpgo7nU4xwv2QWnA0GqFer0NVVcznc+krAxDjA4qadDqd3C+n0ynGkDzP22w26SneBfaBOQd4CPgObjYbxONxGAwGSdoBfhM7fqwhExOudTodJpOJPGfcz66fh24735P0f72n1+l0xHT5PsK5z+fDu3fvYDKZUKvVkEqlkMlkUKlU4PV6Razs8XgwGAzkc7Ln7vV64fP5bhCW0+m0XLdGoyFCJ5PJhPV6LQTaWCwGv98Pk8mEq6srIXwCtyew8TlQFEV6e+v1Wsy3OR9wOBwoFovCa6DQnkaSTAL0er04OztDpVJBpVJBLpcTQ3XuifV6HRaLBd9//z1CoRD+/Oc/b60BJycn8nzX63URhs/nc9mvRqMREokEotGoGJCoqorXr1+jVquh1+uJCJzp37z3NL1VVVXWKxpv3YdUKiWGVMFgUMjK3APYI97X3I2iRs4iSXx2Op0ivB0MBlt/HgwGHySsoUnMfbUUnykA8u7uAmcB+Xxe9m+DwQCTySRCKK/Xi0qlInMq8lA465nNZvjpp5+wXC6Ry+Ww2Wzw8uXLrfVguVxKkmQmk0EsFoPZbJaUL/YqOdskptOp9Fc56zCZTGLswL2ZM36a43MuPZ/PcXV1Jd+V66rZbJaZmtFoxOnp6dY1JZeABiFM6WRP9zHAfmsmk8FqtZI+sdbkVmt2zdm9tkYfDAYoFovCjdGagrTbbdhsNkncvAsOhwNOp1NCCO6qdzkrCofDD95LmETOc8FtoBmJw+EQHs94PL71e7Buvc3E5EPhcDiQyWSw2WxgNpvR6XSEu7TZbDCbzWA2m5FMJsVU91uakX8IotEo+v2+JMiGw+Et0/PVaiXG6aFQ6JOJ8gOBgAiIVVW986xLoZnX692q+R9iTEDT4wMeDqYCD4dD5HK5O7lh8/lcZmMul+vRAw84P6eBezQa3TLGASDmo0w0LhaLB9Ho/8fx8bGchdhnpHFNu93Ger2G1+tFKBQSAfq3jE6ng+l0ivF4fO8zYLVaYbfb0ev14HK59hYs0vzuQw3/7wM5CB6PB61WS0ycdqHT6WC9XkuN9ylwdXWFXq+HcrmMUCiEQCCw9f653W7kcjn827/9G8LhMCqVCv7bf/tvOwWEHwLO2mj2ddd5kD0n/v1kMnnj2plMJjx58gS5XA52u104eeyLhMNh+Hw+uFyuW42lPxVozM5a92ucX30sOOfa16whGAyiXC5LKMRdws3lcol+v49kMgm/33+Yk3xh8Mz+oaCRGc2R7oLNZhOT0McWoOv1euG07YP1er0zfORLIxgMwuv1ik6DZ1AGUYRCoRvnSWosFEW5YeDEs2iv18NkMpH1t1qtSi+Wtf5qtUK/30cikdjqOzGdfR/QmHU2m+0VQEetDnvIH4LZbCZGfuQ9UuvC70wwyIaBZKFQSM7fn8KcaR+Qp+V0OmWudBd6vZ78my/1mQ844FvFl5+cH3DAAQccsBMUqdRqNYTDYRFRcNAXCASEdE8ho8FgQLFYlKIvHo/j2bNnUiCt12vU63U0Gg30+30R8K1WK1xdXUmqbjqdFjcgn8+3F+lruVzi4uJCiA0kEDNpwe/3S2FsMpnQ7/dhNBqFVEonQyY6rFYrVKtVEXIx2ZDfj2Jzt9stAp1+vy+ObiT58PeS/E93QhK3gsEgIpHIjUM4U5dtNtudxSgb0eVyWUwDmNx7dXUFg8EgYtHpdIpqtSoNHSY+BINBuWbaNCIeTEOhEOr1Onq9HgKBwBZ5mALdxWKB8/NzSYTiYcntdksqrk6nw+XlJV68eHHj0DcajXBxcSGO5NVqFY1GQ4ZrTEsCIGYGiqKIUNBoNMo1HAwGMmSkmJcurE6nc4ugsi/6/b48WyQ0aZNFSqWSDOroPknSwtnZ2VaDrd1ubx3Y7/osTD4dDAbiMPcxuD64aLfbW4QAioxVVcXV1ZW4pFEIrxX7j8djRKNR2O12hMNh+P1+WCwW+P1+xGKxTyY+f/v2LbrdrhygeRBbLpeoVqswGAyIxWKYz+d4+vTpR5EdeU8pWKLjvpYwFQ6HxY2MiRCqqkrqic/nQyqVQiAQ2OkOyOE4k8T4Z7ddP4vFIuSF6zAYDHjy5Amurq5gtVq3UunprkvHOQpvl8ul/G6+//wOJDPM53PU63Xo9Xpxi7uemEN3dYvF8ska/8Bv4nG+7yQZ2Gw2jMdjuN1u+P1+ua6dTkeEeiRoMHWeieWr1QrNZhOtVktIkScnJyKWWC6X+PXXXzEej6HT6RCJRMRJkMNik8kkxEJVVSUlLxgM4ujoSIguTFr5FhEKhaTxHgwGt8ipFNtbLBYkk8kbRCOr1YpMJoNkMimpCiQn3UYaajQaqFQqkshK1342q0jiCwaDkkbVbDaRSCRkOKAlUfDeMflAr9dLDXJ5eSkpBxSastFDwsZkMkEoFJIEoYdAp9Ph9PRUkgjr9Tq63S6cTqcQjyiazWQy8Pv9H+zASOIsDXPoam8ymbBarVCv19Hv91EulxEIBIQI+bEwGAxIp9MypMnlcvB4PFInLRYLMfRgutdtA1UOYD0ej5D0icccMF5dXW2JjYPBoJBmKRbO5/PyObmn8H4yJZ2pJVxrte/D0dHRvYKcu6DT6YR8CLx/Lzqdzlb9ORgMYDQaRXwej8dxdXUFRVGEiLter1EsFtHv92E2m4UENpvN0Gq15OfTTIJ1K8mh/G/9fl/uYyaTkVRoNkSZZNTv9yUhCHhfM/J50JoL2Gw2MbX69ddfJfnmroGG0+kUgbLL5UI0GhW3eKYzeb1eea7X67UQ71irbDYbOJ1OmM1mSaliKlipVBIXVG2Nxr/bbDZRLpdhMplQr9clSc7pdOLdu3colUqYTCbodrtIpVKSIMOG//Pnz2Vw4HK50Gq1pKbic8YB/OnpKebz+VaS6Wq1wmKxEAI990QKoLhH0cBpPp/vlTTm9XrRarVkjf1Yx/her7dlTnIXmJREA6TNZoP5fI5SqSRngV1JNBSPNhoNWCwWSZi6Dg6pgLtrLILvg8FgQDweR7fbxWAwwL/9279J0g5rLO06RaJIIBAQcZ7BYEA+n8eLFy8ecPU+DqzFPB7P1rkbgBj4UHgOQFLJSF7V7sv8jhTdHXDA1wwarkwmk72IjjQPYR32OQXoPKcoirKVHhQMBh9VhPQ1gD22rxWbzQYXFxfo9Xq4urrCYDCQdCgSAyeTiQhwVFXFZDLBDz/88KU/+icDhTwAhDhOIzDgt3TAWCwGq9WKZDJ5Z19rvV5vOe1/rej1etK/jcfjt9ZC/O6VSgWBQEB6pQd8+9AKMPbt29Bc4T7xhk6nQygUwmg0Qj6fR6VSQTwev/F7FosFSqWSJJh9LamATOlgbXnXGZdGoR6P56t+N5bLJfL5vPRIKBrU6/VCBucZpdvtClmz3++LUSxNKq/XyVrikMlk2kob1Ol0coakuR6fH23ScDqd3nt+sV6vMR6PsdlssFqtREjodrvFyO/6mlapVFCtVlGr1dDv98Vgjp+f59T5fC4muRSB7no/LBYLRqMRgPc1mdfrFeHfY5qPsJcUiUQwnU6lv2i1Wnd+rsVigYuLC/T7fSiKIrUX8J5w5/V6xZAom83CbDbjyZMnqNfrcDgc0jtgr0Kv1yMajUrvkSDhnmdDu92ON2/eoFwuS9+Y12YwGIg5N1MOHws2mw3Hx8eSflqv19HpdMQwdrPZwO12i3mGTqdDtVqF2Wy+VzRKIqfBYEAikRAhP00d2fNmv4N9dFVV5e8w9WcXwfI2dDodFAoF+Tna+WW/30c+n5d0du0ZwGq1ymwyGo3C6/XC6XSKWcJdAnSS7R/SB95sNuh0OvLuLJdLbDYbMcUOBoNbicG7MBqNZD5B0S33Au1nmM1mWCwWYjDndrtFwAH81gO5/h0Hg4H0wrSETc5Lte8QDTem0+le5xPOxmguyZ5aMpkUInCv10O/38d8Pke1WkUgEMB0OsXp6Sl8Ph+SySSm06mYYnPGfHZ2hnK5DIvFIn1QchI4j+Lv5DtHE0LO61m/jsdjOfd1Oh3UajUxbeY8dDabSWpyPB6XPnSz2RRjXNaK6XRaxEO8Trz+7BtVKhW5RhReOhwOnJ2d3RCLORwOnJ+f4/LyEm63W/pPNEE0mUwiXk8kEtL7dDqd0vucz+dwuVyIRCIYjUayPwAQg/ZYLIZOp4PFYiEmq/uA14LXjPMa9mo/BFpzUC0+lLzOvYu9RaPRKM/UbaDQiKn2t9WBtVoN7XYbhUIBwWAQp6enOD09FWJ8v9+XmRZNc7///vut87DFYsE//dM/4dWrV7i6ukKpVMJ4PJbUWJLGGUBAo2Ea13BGz+fM7/cjHA7DaDRKqES1WoXVapXnNBgM4s2bN/jXf/1X6YsyxdxqtcoseTKZoFKpSG+bXBh+juFwiEwms1PkSCNfhkv0ej14vd5bzdY/BNFoVLg3FNLzGaQxK0n8xWIRAKQGURQFV1dX6HQ66HQ6IlgH3q8F5H3MZjMcHR3dyUti6IPNZkOr1bqRVqdFt9uVvfehdT35CJvN5s59SJtoN5vNbuUPENPpVOanj30+ZyK21WqF3+8XwSXXYAom0un0o4tlv0bQyBB4z/tqNBpbovzJZCK1rd/vx8nJySfpn9EM7bqx9HUsFgvU63XZW/c1FDngcREKhdBqtRAKhVAulxEOh7eMDzebDYbDoZxXOBN8bFDYRPH5rhkfOVk0BmEa6u/dXGIfGAwGnJ+fo1wuw2q1CjeQ4S4MEOHZ9lsHTY+4B9wHipW5130NiZ9MmZ3NZsjn8ygWi4hEIjdMgtrtNhRFQTKZhN1u32v+/lAMh0MxYQsGg1vrMXmvND0bj8d49eqV9Bl4NrmvX08TK54JgN+M0niOJrdnH4MEr9cr5vw0YLoOk8mE8/NzqKoqdSuF3+Rtfy7xHbkbDKUhyIMLBoNfvanmQ0Ajuof059g7YgDArh7vcrmUPrbT6fxq+tgHfDjILfvS6zm5Uoqi3Dvv51q1q//9NcBoNEpC/T7gWW65XN6o2clrsNvt6Ha7iMViwl+8bixJA+2PeTev823vg7aX8qFmZzRQZe+N8xWHwyHfWft7nE4n6vU6VqsVKpXKB5+/HwvsLbCXd5ehFM1Y0uk0AoHAV1EPHXDAt4Svl3FywAEHHPAHxmw2g6qqaLfb4mZ3dXUlKegnJydCTKGbPwfqTGi0WCxbjr5sPGobFSQhrNdrVKtVmEwmhEIhTKdTnJ+f711YaUWppVIJwHtXyU6nI01a/m4ektgoJWGyUqlgPp/D4/Gg0+lIkiWHtsD7VAg2X7SiezqFWywWSRt1uVxCbCGxhIMm4H3R7fF4MBqNoKrqlmP0crnEfD6XVOT7QMFAp9ORAeRwOEQgEIDD4dhyqOXwnIIViviYAHy94ZBMJuXe0mGeg/7hcIjLy0tMp1P84z/+I9LptLgZau+dz+cT53heF23Dia7cTD43m804Pz9HPp+X5BmDwYBWqyXJShaLRUTGdNO02+0oFosyXAcgxGEAIvpjQvU+zcBOpyNpHxTRcJhJh/BUKoXFYoH5fC6EBg5mtEkYBI0bLBbLvYdfEkgogH8MhMNhdLtdJBIJVCoVMWhg+jkHj7lcThIy4vG4NJj4bPH6U2wei8WQTCY/qbi2WCyi1+uhUCjIkFJ7fVerFbrdLsrlMgDg8vISz58//+Dfdz0RgmQINnCB9/coGo2Kq3u/3xcnMyYcHB0d3SraIbmHbvn8vbeBg4nb/o7BYMDZ2RlGoxGazSa8Xq80bplOwHft//yf/yMOrVarVdJ+KDAMBAIiXB2PxyJk8/v9UBQFDodDxNvj8RiZTAaBQOCTkso5TCepUafTwev1Yjweo1qtSlovCVYknJBA0u12YTQaEY/HRaheLpdhNpsxHo+lQcO0pl6vh81mIyklZrMZdrsdfr9fUmh9Pt9Wk34wGEijLRgMwmq1fvTQmMk4WlLPlxjgBQIB+Hw+dLtddDqdG8kt97maasWsd+E6EZYESr1ej2q1KkQZ/jxed6vVKuSYRqMhyTYkgQLvk7rZ6NHr9RgMBvLcut1ueVfH47GQHEmu1A5y+b7z/bLZbHemcFssFjx9+hS5XA5OpxPD4RDD4RDr9Rp2ux3RaBQ2mw2BQOBBRGNCm+g0GAzkPaf42GazyTtLAu14PMZwOMTZ2dmjvLeBQEBIi6yfSPahOUs6nd4iCl1PYp9Op2JaQ+fvT+FUyoRUDkWui42Z2qkoCur1OkwmE6rVqgij9Xq9kHqbzSY8Ho88C1arFaFQ6NFcfu12O548eSLkw36/L+Qdo9GIZDIpRL9UKoV3795BURQ0Gg1MJhNYLBZZ/0iWZF3KRBDWNTQ14vVZLpciHieB+OjoSMTu2WxWxPuDwQA///wzFEWBwWCQ5jDwfo0geW46nSIcDkNRFBQKBVmjS6WSpCQZDAaEQqFbiUherxflchmLxQI2mw3hcBgvXrzAX//6V+TzeVnPAQg52e/3w+PxiGFJNpvdauS2Wi1JiL/+PGgRDodFYO5wOBCLxbBYLPD27VsUi0VcXl7il19+gdVqFUOv6XSKYDAo6fXhcBitVgvD4RCJRALBYHDrOTs+PpbfZzabZd3hmsT7x72QzwlFDKlUSswg9iWTMRUewJ0ktX1BgQYTue4DyY40JGOymaqqODs72zl0piHFer2WfX7XZ6fo2mazScrTXWCdbjKZEI1GMRgMRMSRy+VQqVSkzuPnWK/X8Pv9MoB1u91YLpeyPvT7/c8qKo3FYojH40KMdTgccnalI7U2gWy5XCISicBkMsnn5LMbi8UQCoW+OtfoAw64Dq3AaZ9ekpa49zlTmZk2SoMNCtBtNhu63S5sNtud58ePwfW66wBIXcXzgdlsRqvVkl4MjWsGgwFyuRzm8zlMJhP8fv/eLvHfImw2G549eyaEVpo7Ar+Z8oxGIxE/3YV+vy8CjE9BynssdDodqKoqRNC7wN4ZewBfs8j2gP1BM1wa9dz3vLJ/TxLMfWDi6Hq9RqlUwrt37+D1emGz2YREPhgMhLQdjUa/mtQDzocCgQDq9foN8SIxmUxQr9cl6XWfucZ1cCZC8SXNIx8bTHTh+YkzB/ZTaT7Kvmqj0RARG2dGfr//xufTEod8Pp+kzg6Hw63zAI1/1+u19DA7nQ58Ph9sNtveRNdut4tisSgp5jy30HCTsxYtYZim06VSCYvFAplM5kbdYTAY8NNPP2E8HovpFns4y+Xyxj3hWYE9AKvVKmLlT0GeYu10F1arFd69eyfmwQaDAT6fT677cDjcMm4EgKOjI+mzsHdMkevV1RUcDsdW75tJTzRRHAwGODk5kfmJ0WjEeDxGrVYD8NseyuTUSCRybxL3Q8HZGUWFz549EwGu2WyGxWKR571er8u50Ov13nl257/t9XpIJBLSu3E6nWJsrf0us9lMTG/fvHkjtcBwOESz2ZQ5wl1QVRX5fB61Wk3medFoFGazWVKoaYxBQ2iabQ+HQ7RaLUm95xzXbDbD6XSi0+kgGAzeMKGiAJPv/z4E0fV6LcLG60YHOp0OHo8Hw+EQ3W4XZ2dnN3oSTLdmr1gr7HW73Wg2m5IW3e12xagbgCQq0lwyGAxKP5x9a6PRCEVRMJ/PEQqFthKUaNoYjUZvNeTapxdA8xHun/w3NMxstVqyjkynU+lNeDweMXgul8tbKUl85mi2GY/H0el0bgiftb09rq2bzUaMhpn8zfXd5/Oh1WqhVqtJorqiKJIE3uv1pH/I3q7X68VgMBAjV5PJhEgkgqOjo635OwAxR9cmpRNMZAoGg7eujQ6HA999952YgbB/OJvNkM1mcXx8fGP9W6/X2Gw20vfi+0ii9vVzIOddfEYeOmdyuVwfXKPQaFg7d/1YMDGd7zx7g+S5KIqCaDR6K+mXZjI0v9+1LrNmePPmDaxWK46OjraEPXxOZrMZLi8voSgKXrx4sfM7Wq1WPH/+HOv1WpK3eRblWslk281mg//5P/+nfKbNZiP3ksYDnU5HxKzValUM4wmmMKdSKRSLRZmFaFPBaGLpdDpxdnYmhuMMKeBc9TZBqtFolL3U4XBAVVVZEx5jjkneBbkI/X5f5l6cfbPvybRtGmpOp1PkcjnZ88PhMDwej7yDq9UKqqqi2WzK2n2bQBd4vzaxxisUCnLNtfeas7pWq4VEIiHr3UOgXXvv6llxVsz5i9aIdRe4xt31HT8GbrcbL168kPkFe140TP0jCM+1CIVCYgZL01ryifx+v7xnmUzmkxpjptNp6esUCgW43W54vV6ZCfX7fXQ6HRHIUOxxwOeH2WzG8fGxnIe5v9ntduh0OtnjWacmk8mPMkG/Da1WS3pk9/38YDCIXC6H6XSKdru9V0Lm5wLnpMD7veRz9sUNBgMymQwSiYQYsTOwwu12w+fz/W5mbhSoPcTMkXsbn/WvATS832w2qNfrKBaLUoNo65pkMgmfzydGRI8N1v+r1WqrppvP58IRoHjbarXKGv7TTz8hm80Kd/X4+Hjn56MZP82/eP6yWCzodDoi/GT9uS/HAMBWj2sXGKbFz07BJJPQySs/Pj7+JGsb8F6senl5KaE8DFRgeAJ7RQzf+j2AvIBOp3Mv92q5XGIymQjXxGw2o1KpoN/vw+fzwWq1Sh+BwT/JZHJLH3DAt4uvZT1nknW73b6XY8NQKp5df0+4LuJm8NhisUCtVkOtVhO+Mo1DWY8xUIZ97Q+B0WiU3r6Wc3sbJpOJcNM+tN+zWq3kmWLtdv07BwIB+SwGg0FmINPpFE+ePIHD4fhke8g+iEQi6Ha7YihFcxeCgXHtdlvMT/cJVjjggAO2cRCgH3DAAQd8heh2u+JSGI/HhVzCgpWO7dfdsdfrtQi3OeTlf2u32xgOh2i327BYLMhms+K+P5vNkEwmYTQaUSgUAACFQmFLfHEXKAIoFosysNbr9UgkEtJ8Ad6LmmazGeLxOH744Qch2QPvSWCNRgPv3r1DOp2WdO1gMAij0SjDOwp2SSCJxWLweDySDDybzUTgTCJauVzGd999h1gsBpvNJgSXbreLXC4nQrPz83MR5AMQYsQ+MBgMMgBcrVbSyLwuwqCTZrFYFHdoDmTNZvON32c0GpHNZnFxcYFIJIJQKASHwwGDwYDlcilCmpOTEyQSiVsPoT6fTw6GJOQQ5XIZqqqiXq8jHo/j7OwM+Xweo9FInkUekvgMxuNxSeumMIoO+q1WS/6eFm63W5Lu+/3+DSLhcDjcSphk+hFFWGwIU7zn8XjkIEBiC68xn4ld4AB738ENReCPRUYyGo04PT2VBPd2u72V4E6iQiKRQKvVkqR7irI4CKOYudlsAoCIOT8VOHCv1WpwOBw704EpWAOAarUqCaAfI/jxeDzweDziaO71eiVpU0uy4JoXDoclMdPj8Qgx7zZYLBbYbDYUCgUhbt32bKxWKxGr3TekdTgctybI8tk/PT3FarVCoVBAp9OR9Fe6ctvtdjgcDkmBKxQKkl7W7/fx9u1bIfFQjHiX2/tDQMIpTR/MZrOkyFBcS+MJCqIdDgcajQYikYi4QZNQ4PV6sVqtRAB7dHSEbDaLXC6HfD6ParWK8/NzIXhSUOx2u0XIzAa/w+GQ5AA205ksAbxvqmjTWHYRM/cFG9yKomC5XAq5iuteJBL54GbRh4Ii1U/lfq6q6hYRNpvNijCbjUOHw4HxeIzRaIRqtSokI71eLynNzWYTwWAQo9EIuVwONpsN8XgcDodDhnzz+RzZbBaTyQSqqsq9JlEWeJ84zd8dj8exWq3wyy+/bBmT6HQ6OBwOlMtlBIPBW/dDitCZaEOSDIeN2mfqIZjNZnj79i2q1SoKhQIqlYoI0fV6PbLZLLLZLHw+HwKBAEajEdrtNvL5PDabDS4vL6UG+VhwuEByJBuEbEw+ffoUqqoK6Y/mMnQ3ZhIvBY+fqtHWarXQ7/exXq8Rj8dv/e40uKA5USqV2mqW2mw2ZDIZZDKZT/I5tb/nxYsXUFX1RhqS2+1GOByG3W7H69evxZCJiTZc1zho//XXX4UEFgqFttYQs9mMbDYrTWmz2YzpdCppWVw/vV4vstms1I2hUAivX78Wkp3ZbIbD4YDNZoNOp5P6bD6fw263Y7lcIpFIoFQqYTAYYDAYyPNgtVrFgCiRSOwkNFIMxu+UyWTEJMfj8eDy8hK1Wk2ETk6nU+oc/ly32y1EiOl0itFoJOks970Lfr8flUpFhPS1Wg2FQgH/63/9LwwGAzErmkwmMjzt9/u4vLzEcDgUommn04HH44Hf74fX60Wn04HL5dqZDqMoyhbx2u12y/emYLtaraLf72Oz2YipAIWVu9ak9Xot5iaDweDORLuHgtdwX3dbbe2u0+kwGo2EFHWfCNPr9eLy8hLBYBDD4fDGfzebzfB4PFLD+f3+W2stNvxZ47fbbRmWAO9JtvF4HC6XCz6fT5JleKbVXmumyvP88zkF6G63Gy9fvpT19tdff0U0GpX3bzAYSF0xmUwkVc3j8WC1WqHVaqHX6yEWix1cyw/4ZsBBqE6nE9HYXSCx5nOmMheLRRH6MMGOyUf1eh3NZhOhUAjz+RxnZ2cfXedrSc8UqlCAoU3p+yOj2Wyi3++j2+2KOYfH47khmrTZbDCbzXj37h1KpRIuLy9FePh7hclkQiqVQiKRQL/fFzK+xWLB+fk53r17h4uLCzQajVvJpfP5XIQ+TCL9WsGeLkm994F7P+unA7596HQ6+Hw+9Pt9VCoVEQvcBkVRJEViH6G1Xq/H6emppCHTuJapnBSeO51OSVj+WqDT6ZDJZCTNhMagFKzyPDIYDCRtKZPJPKjHMBgM0Gg0ZKbC/pfRaBRy/WMKVtrtNnq9nqT0AO/nF6qqwuVyiZCehCUaFK9WK7x69UoENBTQ8hxBQ2SXy4WzszO8ffsWHo8H3W4XLpfrxjWh+SmvYSQSEcHUPt8hn8+j2Wyi1+vJ8wi8T1LudDrSl+G8CHjf5+Lc4/j4eOdz7vV6kUgk8OOPP26lb/b7fYxGoxtntPl8DoPBAFVVkUgkMJvNhAz2pdb+er0uxGr2ULXXn6mypVIJxWJRCG2cW+n1eumPezweTKdTXF5eIpfLIRgMSrouTeP6/b7M1yjWYyKats9AkRzTqweDgRi/cl7D/t2HEDdpVGe1WvHDDz9Iz4AEQK3QXK/Xo1gsIhgMChH5NgQCATH8VFVVCPckHXNPbDQaYizn8/mQTqdFaM/Ecq/XK6nY18W7WpRKJZkpZrNZIRcul0vplcfjcRHdtttt6eFSaF+v17fSYdnXczqdIsTk3j8ej+V8HI/HZfZ7H3K5HFqtlphw+nw+ed8nkwl6vR7y+Tzi8Tg2m42YAgDv3503b96g1+uhVqtJwtF4PEav18NwOJRzDgX4g8EAi8VCEu+azSbm8zmurq4Qj8fhdDplnkizQ6ZB8ZkcDAbSH1ZVVYw0CT6fBoNBek18jijOdzgc8lyPRqOtWa0WNHAOhUIYDAZiINzv9xGJRDCfz6X3qp0xrtdrSV3u9/s4OztDOBy+s1esrTnb7bZ8Rq7vZrMZr169gk6nQ7PZhKqqSKVS8Hq9YjY5mUzg8/nEFNHv94tp92q1EjEvTXB3PcPsFyeTSTFV2cfkiOC74ff7YbFYxLz69PR057pATgENRClC0aayX//57FdeN7F/CGgIPhgMRFTOtfW6GLfT6YhAnKCJ68ckPa1WK1xcXIj5w3UDiF6vh9FohP/6r//CX/7ylztnLxTWz2YzFItFeZ5WqxVyuRyurq4wnU7xD//wD/D7/TvrEgrJOdfcxUXgd/+Hf/gH/PTTT2IO7/V6UavVoNfrRUz8+vVrMXllAn0mk4HFYkG/30e5XJY1Y7Vawefz3bifNEtyu90wGo0y3/V4PGLmzVlsMpmEw+FAtVoVc4JkMgmz2YxqtXon74H/jeub1iTwY+H1emVPX6/X8Pl8t9bf7I1T3Eejkm63i1QqdeMMbzAYxHC8WCzKnDmbzd76eTKZjJj+lMtlXFxcwOVyyb1noEAikfhggRq5GUyevI3szz2Nxr/z+fzWfli73cZ8Ppek+E+JjzGq+L3B5/PB6/VCVVX0ej0RoFOU/zlm/G63WzgjTqdTwh4Iq9WKcDgs5iX7pN1+LCgUYQAA8JvZ/R89AdDtduP8/ByFQkHqNopig8GgGGYmk8lPZohIA3u3233v+sXAhuFwuGWU9CXBmbSiKPJnrAmvz8U/NWjscB/W67XU8DRr+laMZHnWms/neyXgTqdTmM3mvQ3UPyey2SwsFgvMZrMEd5HrRQMzl8uFbDb7KGaJPBdr3zO+f06nU67lZrNBuVwWDpTL5YLH45GzrtFolKCIfD4PAGI6pEWn00E+n0e73Uan0xH+JwDhSgUCAZhMJqmpGZBzF8iHpBHhLozHYwmmajQa0lPT8hy0/ZbT01OYzeYtTozL5foogxRtuFm5XBbDOIqq+/0+crkc3G631LG/h/l4MBiEoihyBr7LdLXT6cBkMsHr9eL4+FgM+nq9HprNpvCWbTab9DDD4fCj8US/NMhB5HPvcrn2Ngn9PeBD1nO+k4+5nvv9fhHu8oy8a++mEWQymYTL5foqE9AfCiaaM7zj+vcmhwD4zZhWURTpSTNUixzufbU3u2C320UDQR7dXej1enA6ncLN+hBozWwXi4UEAJLT0W63UavVYDabYTKZhHdqNpvx7Nkz+Hw+nJycfFGTH4vFguPjY2w2GxiNRnQ6HTSbTekhsHaORqPw+/04OTn5XTy7BxzwufF1nSIOOOCAAw4AABFR82AxGo3EMV+v14vowFZ7jQABAABJREFUgYdO/p3NZoN+vy+iZOC35hEH5haLRYZfVqsVvV5PDvUcxjJlex/3pNVqJYNLm8221RBn2uZ6vUYmk8F8PsdyucTR0ZEUooTRaEQikcBisUC1WkU0GhVy0Hw+l4E7B+jr9RqpVGprwEV3aqY+qqoqhwLtoIpJc263W0R2JpMJvV5vKz3VYDDsRWRcr9cirtfpdIhGo0JuaDQa4nKv/f0+n08OIK1WC0aj8daBqdFohN1ux7Nnz+BwOJBOp8XxjCmO9yU/6fV6uFwujEajrcYzm3btdhs+n08OINlsVkhgFO0HAgEMBgM4nU4ZyhgMBjQaDTgcDiwWCyFPjcfjG0NEppNfdzBXFAXValUaCYvFAoqiQFVVdLtdWK1WnJ+fi9s9U2lI8spkMjfu8V3gYWnfdEkmqj7moMVqteLp06ci5taSZs1ms5AzHA6HEGN4MOI7wGbgZrORJtTPP/8sJgokNH2ooPI66FY2mUzuPSAHAgFx8G+1Wh9FdCNhjEJ7DuTpVMuGEx1HKej785//DEVRxMWeAsvr4ACc1/8ut1uaTHysEIgp6SaTCbPZTIgiy+VSDAjYbKZzuqqqMJlMQh6czWYYDAbIZrPw+/1wuVw4OTn56Od0NBqhXq+L0yiffzaUAoGAJJXU63UR30UiEUlArVarsNlsGI1G8Hq9cLlcMqR59uyZmJAYjUbk83m4XC60Wi1J9NY2mBVFEXdZq9UqJEteF2IymUgqx2KxkLUK+HAhX7PZRKlUkqG0lpzDBA5FUZBOpz9Z45t7GVOtKfj/lAMvCu5nsxmOjo5kryaBw+VyiYCTjSSSZ+mMS8Lber3GeDxGuVyG3+/HfD5HJpORxHTgfcN9tVrJ0F+bGux0OvHkyRMMh0MxPalWq5KcQTMRuvmS4DccDnF+fn5nksljiVUWiwV++ukn/PzzzyiVSpjNZkLYm0wmUmuQnAhASNTVahWlUglms1lIR9ehNXygMQ7X9tsGPDTASKVSW0kUNLAZDofI5XKSCkyClN1uRywWE7LDpyLcUwysKIqYVdwFv9+PXC4nSSdfatCk0+mEuHwdm80GrVZLxOc+n+/GQNvtdot4ajabCdEkFottXQO9Xg+Px4NoNCrrn8ViwcnJCc7Pz/Hs2bOdaU10uNU+96wVKQ6nIJvDUL/fj2QyidVqJfUe6xDui5lM5kZ9yjpOp9PJZ4/H4/gf/+N/4K9//SvcbreIOSheYMIX9yzug/z86/Uai8ViL+KBw+HAZrPBYrFAo9FAtVrFjz/+iNlshtFoBKPRCIvFguVyKfUejZV6vR6i0agYZFxeXmK5XOLZs2ey9vV6vS1C7WazQbFYFDGzlngNQNKtfD4fisUiGo2GpJ7tIjsuFgtJ+aSIhOeBfD6PVColtcBtINmeNd/19YAGO0y3uu9MNxwOYbPZhFhtsVjEDGY0Gt1ZyzF9lYlmu0CzgH6/j0KhgGQyeePvTqdTVKtVSaWz2WxoNBool8uwWCz4l3/5F1xcXCCfzyMUCiEUCkmNpKqqpKhxjeC6N5lMZKDwOXF8fIzhcChJYjwn2Ww2xGIx2O12Ie3zLGqz2aAoCmw2m5CwT09PvzoSyAEH7ILD4ZCzqqIo954BFUWRf/M5hFEk1pRKJaxWKxE0Eev1Gr1eD/V6XWqn2xLT9sFwOMTl5SWm0ykURcFwOJQzvdvtlkHwycnJY33Fbw7sYXa7Xeh0OqiqKvvpLrjdbvj9fvT7fTQaDbRard+1AJ1gmt91uFwupFIpFAoFLJdLBAIBeaYpMmOvNhgMIh6Pf+ZPfsABD0coFJIeZ61W20q21GIymaDdbouYb9+1gP04Gotp3y2KX0Oh0L0GTF8CHo8HR0dH0Ol0cLlc6PV6KJfLYiTldDqF7JXNZu8lJGnBmpvCsev9L84xHrNHMJ1OMR6Pt4QxVqsVsVhMeh8062QfdLFYYLPZIJFIiGlWo9EA8P5MyVROEp1oXjkajcRMLBaL3XimlsslyuWykLP2MeKbTCYoFouo1WoYDodiXnD971QqFZRKJTFOpDCAc6jb6gyXyyXmCq1WC6FQCMlkEm/fvhXDWt5jGteu12vZK6fTqSSrfgkCFfvxTO+6TURjMBiQSqVwcXEh+9b13hj7X61WCzqdDvV6XQTLJIzRyKDb7YqZGuediURCrjNFgL1eD4vFAuVyWfqQfO5NJhMURcHV1RVCoRCi0SgsFsveNSGNwJfL5Y1E1uugyWqv14PL5bpTGOF2u2G1WhEIBNBoNMR4kMncACSNLZ/PI51OiyEm+0QejweTyUSeSZoq7vqM/X4f0+lUzGW1z5GqqpjP5xiNRjg6OoLVasVkMkGr1ZL6hMIuv98vole/348XL16IARTNp3lep3kpTUj3EV8piiKET4vFcsOUlGeNbreLarUKk8mEarUqRpZXV1fo9XoolUryTvGcDrx/Ri8uLjAej2VN+ctf/gKHwyHvP80vbDabzCS4ZvHckUql4Ha74XK5kM/nMZ1O0Ww2pYfBWSfNj2mM0el0UKlU4PF4ZL23WCxQVVWuk91ul1k4DRt2gT1lXuvNZiPi+Xa7LfsHe4C8/8ViUcyOnj17dueskca5k8kE+XwelUoF0Wh0ay+ZTCYoFArSF3Y6ndDpdGJomM1mxVSa/RE+M1ojB5Jvd5lIar/zx+7p5GvY7fZbZw3aRGyr1SqJV+l0eue/WSwWks7OlPmHYLPZoFqtotVqyZyQAmSn04lqtYpAIIBUKoXpdIqLiwtMJhPpy1Fo43K5MBgMUKvVcHJy8mAx2GazwcXFhZisU7jPuoxzGl7D//t//y/+4R/+Yev8P5vN0Gq1MJ1O8U//9E8YjUYyX65UKmKsWa/XYTAYkE6n7xRnskfJ9ESa8+wCe+NOpxOJRALL5RJer3fLOMJsNmO1WgkHgp83lUohHo8jEAjg73//u6yZt5nkco4dDAZljsp1lGn0Xq9XjG7j8Thev34t7wLXaRLAr2M4HMJut8vn5Rr/WP3EUCiEVqsFi8WCarWKVCq1c6ZDAzSv1ytGJLlcTozX76rZaRDDPZHBGbtgMBhwfn6Oq6srEQaoqorJZAK9Xo9wOAy32w273f4gMvlisUCr1UK73Ra+EtfhH374AR6PZ+fZxO/3Y7VaIRaLiSmUtl8/nU5lJppIJGTWfsDnw12zvc8Fmmo0Gg24XC6Zm3ENoGnL5zAOaDabqFQqUotp03dVVUWlUkEqlfpkRvjfApxOJ168eCEz+tlsJqYUfr//k6Znkxd2W1jOdDoV8yzWOgaDQcywvyTW6zVyuZwE1/D5Yg1Lk55QKCQhQV8a0+lUeEo09ee8MxAIIBwOf/XiR5/PB4fDAaPRKAZOt2G1WkFVVeHjfg33QAuGIUUiEZmjk69ms9kQCoU+2hyRJmIMQQIgAnfWapy9E+RYsGehrd8NBgPW6zVMJhPS6bTwBCwWyxYHZDweo1AooFariRmY9h6QH9doNGC32zEajWRmcl+fSlVV2O124RbvQj6fF94lw3y08Hq9GA6HKJfLGI1GKJfLiMfjsl+xtqEx24ecs9h7vM2o0Ov1YjqdolQqieiVph/fMlgDhMNh1Ot1+V5abDYb4bGwx+jz+cTcjWc/rWkCe5hfs+nwvuCzORqNtvY4s9kMt9uNaDT6hzBXotkgTUbvWs/X6/WWmepjrOfz+Vw4b6qqyhmGvUFqKubz+ZYxpdfr/eSBMZ8LRqNR1vl6vX7D4A94b3RmsVjELIABagx3sVqtYgr6MRxunU4nASBM7L6tPuc5NJPJbGlfHgqn0ynm/YqiyDqdSCRgNpthNBqxWCwwHA6lp55MJpHJZJDNZnFycvIoGoWPhdvtxpMnT6TfOxgMUK/XAUDWE4fDsdMk74ADDtgPBwbjAQcccMBXBrrQv337VkQbTH0zm82SNMuBp8lkwnQ6leEWReCz2QxGoxGKosBqtYr4W3s40el0GAwGCIfDMoh3OBwyjGy32/e6pFEgPhwObxVB0+Gabnj9fh92ux2hUOjGASgSiWA0GsHj8cDpdCIej4ujKNOQhsMhHA7H1jB0NpvJIHOxWMjw32KxYDabodls3mjK6HQ6xGIxXFxcCPGMRTjJG71eD8Fg8M6DGu8TRftutxsGg0GSL3aJpSjiZnI6ide7Bs1My8pms0JWAN4LaSqVypZIn+LEXcWxwWDAYrHYajzzWZpMJltkVKPRiFQqhWKxKGlJFIJQgOhyuSSVW9uIZYojRaRa8bZWzAr8JvBk6gkbV4VCAY1GAzqdDmdnZ1siNQ5fmHZbLpdhMpkQjUb3GiLy2WGD5q5mFZt8kUjk0Q8cbEAmEgkh7fDPmfQeCoXg8/mEYGcwGG4Iqa1WK/L5PBRFgcfjgcPhkAYnk8ODweBHN/GZokBi111gw2wwGOxMwnwoZrMZrFYrarUaXC6XDNKZ2DoejzEYDGCxWHB0dITz83NEo1Fks1kUi0Xk83mUSiXE4/EbZBQ2ZbRCTW0KKPBbs49r4vX150Og1+txfHyMxWIBl8uF//2//7c0IkKhkCR+cF3zeDxitnBycoJWqyVpHk+ePHmUZlu73UaxWBTzBy3hlMYZHJzHYjEUCgURgXFoy9RzmkoEg0HY7XacnZ0JyUorpIpEIsjn89JEHQwGkmrEJmylUpH0GpJVtOsYiRpsxm02G4xGI0m4/ZCGdKfTQbFYRKVSwXA4lEY6k6VonDEYDOTd3NX8o6ii1+ttpZQEg8E71yutMcd4PN4arFgsFoRCoRui1Y8FiZc//vgjCoUCHA6HkG+ZWk5xp6IoaLfbYgrABiTXZ7/fj2AwKAN9OrpuNhv8+7//O4rFIhaLBbxer6SAA7+JZdfrNcLhMJxOp5gdDAYD/P3vfxfSTTAYhMfjkWvAdKl8Pi9r5vPnzx/t+tyGy8tL/PLLL3j37h3W67UQZdbrtRgCkeD1+vVruUZmsxnRaBTv3r1Dv9+X+60oiiS81Ot1EYQy8YoNz1qthlAohEQiITWeVjQKQARO1+F0OvHy5Ush97KJrzUu+Rj34vvA/W46ne5FYLBardDpdJjP519dwiGHjXz26vW61HXXHVGZ3kNCKA0Xut3u1nXg8DcQCIgpRzwex9OnT/Hdd9/tJF7RpMXpdMpaNBwOhcRrMplwfHyM8XiMXC6HwWAgtQHwfp+lo7vVahWyHY2S+PcI7rnXB5lmsxl//vOfcXZ2JkMiLWmX6184HH40Y596vY63b99KvcFEAJJTh8MhDAaD1ApMPuI5iKZJ9Xodk8lEUr+1IEmC5NDb1m8Ok8vlsjjBev9/sjoT1kj2Ho/HUFUV4/EYs9kM7XYbkUhE7lG/38fx8fGNdX42m6FSqUgyl9FolMQwJmbp9XoEAgFUq1XY7XZ0u907RWfT6RTD4RDZbBaBQECeUaPRCJ/Ph3a7LX92/b6ReEnS+m1EZybekySby+Vgs9m2EgLH47Gc/5jC1e/3MZvNcHJyIsYJNptNamLgvSCByfQkhLOOo3nVvknwj43vv/8eHo8H7969Q7ValbOXxWJBJpPB6emp1CmhUEjEE1arFR6PB6lU6psfrB/wx4F2EJrL5aCq6q1rwmg0gqqqSCaT8Pl8n8VkodFooNPpYLlcIpvN3vidXDuNRiOq1aqYWuyTkHIdw+FQ0iSazaasjxzMM209kUhgtVrdmn76ewcNbXiOmEwmdwqN9Hq91IW9Xg/dbhfpdPqLptFsNhv5HgaD4bOS9k5OTkRcwNRPg8EAvV4vKY1+vx+BQADJZPKDkxU/F2gy1+v1bvRkdoE9gE95bjng84PkOvakc7kcAoEAXC4X9Ho95vM5er2eiM74fD8UHo8HHo9HSDLA+2fwa0+38vv9sNvtkrZ0nRzt9/sRDocfRMalaKxSqch7db3/xTnLcrncO3H+Lmhr8+vvOvdLptzQrJb7dCaTkcQrkrw2mw0sFov0a1OplJxTo9EoVFVFOp1GqVTCxcWFED8BSA/UZDLh6OgIoVBoL+Jms9nEYDDYaVBG2Gw2ZLNZEbg2Gg34/X4xor1eJ9FckX1GCpJfv36NeDwOv98v6drFYlF6D0wCjsViMlujCcHnSE/cBVVVMZvN0O/37zUuoCiW859dYlKPx4N/+Zd/gcPhEAM9mu7S/K1arUpvk+Izm812436uViuUSiV5ttgfYE+5Vqvhxx9/hNlsFlOKaDQKr9eLUCh0bw+evWv28+4DzTT36XfR5Hu1WqFYLEpiL1PJSqUSLi8v4ff7kclkEIlEbpzhKfIulUqSqr6r3qW58GazufGsktTIVJ9EIiGJorVaDQCkfzCbzSTFLZvNwufz4ezsTBKZaUgLQMx3d5E7bwONAtfr9Q3xuRZ+v19Svh0OB5LJpMxearUafD4fNpsN2u22GIGazWYRoNLEnClQZrNZyJZcO/hv2C/nHHuz2WA6nUKv16PRaKDb7aLT6WC9XiMYDIpxYLFYxHQ6FUN6zv15L+LxOHQ6HbrdLrrdLrxer5gQKYoCn8+H1Wq1F9GT8xSj0YjZbIZYLHajVmQ/knN5i8WCRqNxrxl6PB4Xw3yuuxTfsi9HISDT1ym+o3Glx+NBp9PBZDJBv98XgR57JsD7ZxTAnQL0j8V0OkWv1xMj3Otm8wTPKeQGcJ2oVqtwu92SnsjZOcXUiUQCp6enD6o/NpsNrq6uxGBNK3RdLpdiGM/PvlqtoCiKiKo9Hs9WymG320UoFMJ6vcb5+fmD5uBMOyuVSrDb7fKMEhaLBX/605/wt7/9DZVKBfP5HL/88osYSq5WK1kvM5mMpPx1u104nU5Z7yiw6PV6iEQiexlrMQlba4a/C/y8pVIJNpsNtVpNer80Z0gmk/B6vRiNRmKGYjAYZM5pMpmQz+dhNBpRLpeRyWS2+uU0IqVJLJ/XYDCIQCAgMz+mULpcLjHSp5Cezxh78loMh0MsFgtJHO/1ehJi8VimSvxsi8VC5sKBQABut1vMMsjzsNvtiEajSCQSstfu4hCxH619vz0eD4rFIpbLpcxlb4PRaMT5+bncF+1a4XA4pKbb94ze7XaRz+dlljcajTAYDGTd+o//+A8xdtGuA0yR5p7rdDplVqk1z3Y4HMhkMvB4PDg7O/tdpUuPx2O0Wq0bCansdR/wG3g+YCgMn1nOYmiu9SlRq9VkvkRDG64Vg8EAnU5Hzsqr1eqD+qK/J5AT9zmhNRHRnhEURUGv18N0OpVzDGdm3W4Xx8fHX3RtYY3CHuVqtdoS6pLbaLFY5Ny7b8DMp0Kv10Mul8NoNEKv1xPeD7kcDMU5Ojr6ogYW98HpdApPtNVqwW637+xRbDYbqUNpevO1wmAwIBwOP+pnXK/XKBQK6Ha7GA6HUBQFi8VCOAU0ZhqPx3KWJch5BnDjfEwuh9FoFEOucrksoRXkCtfrdfT7faiqurOPRBNai8WCQqGw9bt9Pt+tZx6aiVAEuuvvkQ/RarWEY7UL5KK+evUKXq8X7XZ763xpt9slpCWbzT5ofSQnpt1uS724C1arFalUCrlcDn6/H+12+5OFZnwu6HQ6HB8fSyARA1fIrVgsFmK2wPvI9dFgMCASiQiHhJxDq9X6u5mLlEol1Ot16flreToU8vb7faRSqa963XoMMNyJ/Byame4CzQz2NVO9D5VKBfV6XUzVaXiwWCxgNpulduR5lVxQh8OxxXf5PSAcDsvzWC6Xd5qjkcvn9Xrh9/sRjUalh/aYfEuaViaTSZTLZUynU5kRAb/1rTqdjhhiAu/3Di2fdl/wXM4wN+5LDEj0+/1QVRWj0UhMYLPZLL7//ns8efLkUb7zY8Fut+PJkyey//EZjcViYmx9wAEHfDj+eMymAw444ICvFMvlEpeXlxgMBjKEbjQaIrJjOiUAOVhSSDEYDGT4rU0EdLvd4oI0m83EiQiANDs46A0EAiIMdjgcUFUV/X7/3s/NtAr+u+vgcJ2CAori2bTWFugUzdK9P5PJwOfziQDdbDbj6dOnQjJrt9vSAB8MBjKsohBuuVzCbDZjsVjg6upK3Ie10Ol04rzOdFaz2YxwOCzCrE6nc6tAarlcSmPgevofB1y1Wg2bzWYr1YJp7kzzZXJJv98Xx/fJZCL3LplMwuPxIBaLYT6f4/LyEqVSSUgabIw5nU4R+CcSia0DDZNAtNd8Pp9jOp3eSBMG3jeXbDabiEOYvKwVFNP1is+K0+nEYrEQZ1PCYrHIwJfJs6qqolQqoVwui1DV4/HIvRyNRpJuodfrUSwWJekFeN+Ai8ViuLy8lMPCfeQH4P1Ql6K+Vqt1J1GRSVyPdWDfhevC1fV6jXw+L0S0uwb+TLQYDAZQFAXhcFgEOkwosNvtWCwWWCwWODk5+WBCNN9ZPlNsxFO4yDXA5/OJKG0ymaDRaOD169cioKVA6q7vxSTZZrOJ6XQqablMOaHhg7a55vF44Ha7kUql8PTp0y2jhs1mg0qlgouLC3mumdK8WCxkSO1wOGS44PF4pNlHk4lEIgGfz4ejo6MPuoa7cHR0hNFohEwmg19++UUIIGazGTqdTtI16KjPlJB3794hHA4jFos9yuBLURQUCgVUq1VJao1GozCZTJI432630e/35T3MZDJotVqo1Wqo1WrydzebjRDz3G63rF8UqmufQZ1OJwYPXGtIRCWhh4R6kom4J6zXa0ynU3F0dTgcQk5ar9fw+Xz3GogQJAeS5Eln2slkgqOjo61mmdFolO9Hkw66vWqbN71eD5VKBZPJRNKvdTqdJLq63W6k0+kbjbh6vY5yuSwEHl4HXlu32y373enp6aMME6vVKur1uqReVatVSbWnCJ9JKxxqMrGXQgfg/R5AowGmLul0OgQCARgMBlxdXQmBY7PZCBlSp9PJu8b6hekfo9EItVpN9gSTySTknuv3Vq/Xo9lsSrquoijIZDIIBoOfpOG5WCzw6tUrST6nCIQJ08FgEHq9fivVplqtIpFIIBqNilnH1dUV6vU6EokEyuUy6vU6arUaFouF7N1HR0eIRqMwm83odDqwWCyo1Wr46aefttyBDQYDgsHgveQO7m23DZgOuB9swtOE4fXr12g2m0LMHAwGcLlciMfjQhZgDbRcLuXvdjodIe3SUImJNTQkYK3OGo6mAwDE9Kjb7QrhnM7y2oRst9stg81utwuLxSLrKkluLpdLBgf83yT8sU5cLpdirkQCqRZMu2HqGtdWk8kkKQ7XYTabodfrpTa+z1BoPB7Lz2GqGo0rmDILvK+phsOhrKGsHUwmkwxHNpuNDBBUVUUmk7nxGemyvsuRWvuZRqORGAiUSiXEYjEEAgEMBgMUi0UEg0FUKhUoioJOpyOGFYqi4Pj4WK4PjTloVkNcXl7ip59+Qrfb3UoJs1qt4izrdDpxdnYGi8UiBPlisSju7dcxn89RLpdloMVBOIlijUYDq9UKtVpN3Ht9Ph9sNhsmk4kI+FmX3zVMSSQSQqDkXsB7ZbVaEYlEJPUwHA7jp59+kuGI9gzFOpfgesYUy+FwKLURzcE+pbCTRNJut3vDbMbn84nTcKvVkv2PJBYm9dGQgqZzgUDgdzWoO+CPg3A4jHa7jWg0imq1KoNQrg0UsrXbbfj9fnGu/9SgMEtRlHsFLR6PR5IGWq3Wg4mW6/VaBAHNZlPODVqEw2E0m00Ui0XpdWjX+z8KeHZarVayt99XA6zXazgcDkkeIsHrc4Mkd21SCpNxQqHQZ/lMRqMRT548QaVSgc1mE7Mw/jeesePx+EeLRT8HAoGAvHf9fv/OcwqFsKyRD/h94ejoSETV7XYb9Xod1WoVOp0Om81G6kWS8T6mJ8ba9FuC1WoVQ1POi7SpZw/BarVCuVze6n9p5wPsf/l8PhQKhb3ez32gJdRr63rC6XTi9PQUg8EAqqpiOp1iNpshEong+fPn+OGHH2S2RUI+a+jr4nu9Xo+zszNcXl7CZDJBVVXpgQ4GA0kN57maM6G7ROgkzTKh8641n+mqTDy3WCyyb7AeIVFMVdUtcT6TVDweD9rttiS7kyxer9dRqVREbM21/+TkBKFQSBKXvwRms5ns1fuYuDqdTpklzWaznedKn8+HaDQKg8GAUqkEAFs9CbfbjUKhIGmyu87Ai8UC+XwezWZTzJMjkYgI6EqlkvQn8vk88vm8mPel02kxpbvP+GIfIxWCa9tdpmlaEcTx8bEI5kmEpHku+1AnJyeIRCK37v9Mab4reZ0mkFar9QZZkbMmPvucXVQqFSGgTiYTMctMJBJwOBzwer04OzuTOTRJ1A/BeDyWszfnvhRj30eq9Pv9KBaLmM1mMgNiTWE0GlGv1yUNJxaLbQnPs9ms/G6tQTpTjygyoGEwRZRcu6xWK3K5HJrNJtbrtaQ4s59DASONpjn/sFqteP78ufT4KDAdjUZoNptot9soFAo4OTmR+9Lv9++s/dgHW6/XMoO8y6iISUEUBd8l9AfeP9Ps3VOozzMgTY5TqRQmkwnW6zVGoxFsNpsIVdlPpyEu61yautP0k+/YpzCAUlVVxN3lcllMR4D3686u/Yb3hSltfBbYh+W7zmuXTqcRj8dxdnb2oM9WqVTkWTEYDDfSmmiWWigUtpJQuZ9rEQgEoKqqpAteXV3h5cuXe19T9rmYVLnr3+l0Ovzwww9YrVaYTCbwer3w+Xxi/hGLxWCz2SQxjWtGLBaT95PXrF6v39vf43PHYIe7wPmi1WrFarUScvVqtcKrV69Qq9XQ6/Vgs9mwWCzE/J093YuLC6xWK1lPxuMxvF6vvLuc49FInelxTNbk/s1ABm09wUAJ8noo7O71evJ3N5uNvJs+n09myqPRCEdHR/IuPRQMqOA6RHOIVColfc92u41yuSx/j+IEh8MBq9WK2WyGWq0Gm80ms0z+PO5ZNGXV9lINBoPMRO8zDyA4b/kYdLtdXF1dodFoyLWmyTYFaJxRttttnJ+fw2g0isGs0WjE06dPZZ0iz4WpvzT29/v9N7g73zLIB+J+SuNf1qqNRgM+nw/ZbParN/r63KAZuBb7PvMfg36/j0qlIsLI64YZAGQmX6lUpOb82FCGAx4Ot9sNt9uNarWKUCiEZrMpXA4td4Tm1Ayz+JLgrLZYLMJischsnHC73QiFQiiXyyiVSiK2fUxh93Q6RafTEfGVlkN7HaqqCj+D5yKu0RSDXl1dCef2/Pwc4XD4q0sMJxKJhOzJhUIBgUAAXq8XRqNRwjva7Tbm8znS6TQ8Hs8fIk2Y0Jo4lUolzOdzObeRS3B1dSVivfV6jfF4LPwIcgCuhwXN53PMZjMEAgF5zsiLJpc7FAphsVhAURQ5u97VR6KRHg0RzGYzCoWCCBu1oKEaOVq3cVrb7TYGgwFWq9WdZ0WGOLFWOz09lT4UAzRoYAm8N8nbt+/U7XaFB3M9+OA6yLdm7XvbWeNbAvkbrJMURREzPQYCeL1e2O32W8W8v0exZLVaRa1WQ6lUwmKxkOugfeYajQaGw6Hwjb6FWdfHIJFICMeKfUktV4ZmV5PJBJlMRriZH4NCoSAzGHKnHQ6HhIORt2+1WsX0+no4xu8JLpcL4XBYTEwvLy/l3K4NIFqv13j+/DkCgQCePHnySeZMBoMBZ2dncjZnsBjBfchgMMg+xVqM/Ur20/dFJBKBqqoYDocoFouIRqPy3TnroCbg+fPniMVi967rXxJ2ux3JZFLO4bsMWw844ICH4yBAP+CAAw74CrBarfDmzRshDEynU0ly4BCu3W5LGjqHm06nU4jqJO86nU68efMGiURCxNWvXr1Cs9kU8gQH0cvlUhzCtGQMOk/v0+her9cyKNsFEic4WGUKHf8tCch0FwTeO6/ye56ensrPslqtklJBoUyz2ZT0d6a+22w2+TzBYFBEE3QZv950sdlsaLfbACDOXXTpooOUdtjH78OhoclkEqf6er0u5It4PC7fiY0sOh+Ox2NJvIrFYnA6nfB6vZLEDrw/RHg8HiSTSbhcLkkGeP36tZAyp9OpPAN6vV4OfaFQaCvZiw7T1xtAdxFbeJDkQN/j8dxo6jCJhIkLFNS3221xOqbomMQMpsK+efMGnU5HUjnYQBkOhyI+SiaT6Ha78vxeJ7mZTCYRaCmKspcAncN/HtYrlQoikcgNx2omESQSCREIfW7suj98Z7rdrgz2crmcENu132U2m6Fer6NQKEhy5T7u8LtAUqaiKMjlciI6Ymo4SW5s0JOUQTEWiQIOh0OSJq4Le/md2fylwIrCW5JS6AQdi8WwWCxkuB2JRGQIo6oqQqEQstmsiFY5DB6NRiJWoltbJBKRYbmiKEJC1Ov1krpgt9txcnLyqGRys9mM8/NzXF5eIhKJSOISr4vVaoXL5ZIEH65Bj3kYZhoFG8/XHVeZPu7z+VAsFlGtVmXvYGI77zMbK1xHKUa77fOazWYZyMfj8RsJcj6fDz/++CMmkwkWi4X8nmq1Ks7gTqdTiD+qqoqAkIOsu8C1lA13AJJCoSgKnj17dqsAjKTnSqWCYDCIXq8n5HcKSuj6zzSmzWaDZrMJi8WCcDiM8XiM8/NzISx1u12Uy2VUKhWMx2MZDlEkyeFQPp+XQb52n/wQFItFETxTME+iVKVSkaQMCqr9fj8MBoPUJUwsT6VScq/D4fCNoRUJMq1WC0ajEYlEAsFgUL47n3Xug0yBdzqdIvBerVbIZDLodDqYTqdCeqvVakLymc/nqFarMmTbbDZoNBpIJpOPbiRSKpUk3Z3pBa1WS64XaxEm8hYKBSiKgkqlImIrRVHQaDRgNBqRy+UA/EYkI8EpGAyi2WzC4XAgGo3C5XLh559/FnOffD4vAzGn04nhcIhmsynJQv1+X9ZqDjU4oNhsNkIC1K7nNI14SHLavuA6RlOb+8gLJCexSfo1gEYN5XIZk8lEiHKTyQQWiwWdTkdSWUgspRlDrVaD0WjEer2WdOdyuYzFYiHGPxaLBePxGDabDeFwWNbTZrOJfD4vCXSbzUYMNJbLJX744Ydbh/ZMI2Fzer1eYzKZSKOVaWrD4RD1el2eHaaWBYNBzOdzVCoVSRm7751ikvN9IEnC6/UKMf+uQWK32xWRwGKxEJK12+0WIYLT6RQxA41VnE6nGNn0+33Z31wul5CMR6PRDVMmNu5tNtuNz9Xr9US8MJ1OodPpxHxrs9kglUohnU6jUqngP/7jP8Q4xe/3Y7lcYjweC9k3Ho/D7XajXC6jWCyKccBms8GPP/6I169fi9iNifUmkwmbzQbv3r1DPp/Hn/70J2w2GzHjGQ6HSCQSqFQqUFVVzlF8/gaDARwOB+LxOJLJJCwWC+bzOVqtFpbLJWazGRaLxVZtTMInk8pprpJIJO4luUSjUQQCATH5ui4YYYodDYSm0+nWOVVrjjCbzWRNsNvtYpIym83gcrlkT9CmLDw2SPrhfZ3P53I9KDanWVA4HEYqlZKURhKEfk9grc26n2vj723weMDtoFEQn/FWqyWmJ8D79VQrZjs6OvoktcZ1DIdDEentQ8bzer1CVtOasOyDXq+HyWRyq/gceC+Gi0ajWK1WsqY+9Pf8HsBzr81m20tMQHK61rX9rp7Wp8Bms0GhUECn0xExDUmNTErhvf8cyRwGg0GEqCSG0VTN6/V+M0RCCusajQY6nQ7y+bzURT6fbyu5juR/9nK+9mT3Ax4OvV6P09NTNBoN2O12IVzRuJYpDCS+/FGhTVj9UJD8qSgKksnkrfuQxWJBLBZDqVSSpKPrJr/7Yjweo91uS43AJFGeAwidTiekexK6zs7OcHx8LHXmfSJgggmZvV4P9Xodw+FQxGaDwQCTyURSgIvFohjW7DIn43dYrVYYjUZ7fQb2iijK5h5GAnKn0xGjae4pTIDnWZRmoDRem06nCIfDktLq8/mE8MaZypckZGpNt/YhCe8jwqaolb2tVqslibM6nU7MqI+PjzGfz7FYLNBut6UvwLTr8XgMn8+H4XCIZ8+ewel0YrlcolgsQlVVNJtNGI1GnJycoNvtwmq1YjKZoFqtimgdgNx7Gjyz/qX4sdvt3jk3JcbjsZgNEOv1Wnqo3NtpiBcKhfDs2TM0m024XC4sl0sxEGWS98nJyb3EZKfTKeK/2673bffuNmNB1r7BYFDmNUajUXpKwWBQhMMPTesaDAZCdKdQmebHtVoNJycnd6bCAb+RtVnjD4dD9Pt9uFwuKIoCVVWxXq9FLEPx62Qy2SJXUrihNY4zGAwimPZ4PIhEIvB4PCiXy9IL5Lw+mUzC6XSKQJnX0263o1Kp4NWrV2IA+I//+I9imDAYDCQRHYAkBJ+fn0vdNxgM0G634XQ6b70W7XYbw+FQhN37rBU+nw/lchnz+Vx6bHeBhoo0Du/1enLdNpsNvv/+e1itVhH9KIoiJp2dTkf6XUxi9Xq9SCQSYm7M+TFN/B4TfJeYMsoZJQ0ao9EohsPhjbMe58d2ux0mk0nmOh6PR5K8ue/Y7XZ8//33eP78+a3v2a5UZ6bQK4oCo9GIdDp9Y42x2WxIpVK4vLxELpfDZrPBixcvbr3PFN00m014PB6oqrqXEIzz4Xa7LaJto9GI5XIpMzztee/09BTValWSLO12u3AHrtcAAG6YgJI/wVnbbdfN7XZDVVUMBgMJC7gNqqpuGULW63X5LpyXzudzEY47HA4EAgG0221cXl7CZrPBarXKPGg4HEqyq9lsxnQ63erBTqdTTCYT5PN5WK1W6btStE1wXpPJZOQ7cW4+nU7x5s0buTacX/NnNBoNMfy+bzZ6HfP5XAzvR6OR7D/sT1ssFqm/DQYD+v2+3HMazthsNkni5c8hfwP4jd/AfZ/7JPd/CrwqlcpWehqvCQMZ+HcDgcBO7spDwFT3RqOBfr9/Q5D78uVLBAIBXF5eotVqQVVV/Prrr2IAHY1GxYybQmuaC9GUnOfWT2mQ+rmxWCzw+vVrKIoiZtoOh2MrIIH7zHw+x/n5+aE//BWA/JnxeHzDfIxwOBySPEvR1x/53P2lEAqF0O12YTAY8Ne//lXmcpzpspbm/sBakSnNH2vM8SFoNpvo9XrQ6XS3GhYZjUYkk0lcXV1JGNBjCNCn0ylKpZKY0E4mEwDv9wqawadSKXmWN5sN8vk82u228Iq0az8FVovFAn//+9/h9XpRrVbx9OlThEIhhEKhr85MxOPxIJPJYDQaYTKZIJfLYbVaiRk78L6mYE39RzPDpRlQoVCA2WxGOp3e2peDwSAmkwnK5bLsZXq9Ho1GY4vDpp1l0VSHXAjts0xjfPLPGIwzHo/34iuxLvf7/bDZbBgOhygUCsKn2Gw2MgMnR5TnzF0gb8HhcNzK26PZeqfTEZGpw+GQvYIcFpqtmUwm6HQ6vHz58t7vA/x2fuAZ4D643W7hozFc4FuHy+XCy5cv0el0hO9NsH73+Xx/mJqJHPtarYb1eo2jo6Ot95KcSo/HI8ao5KF+64YEd8Hr9Qr3nZwzciyp0XA6ncIFPDk5+ajfR04+zT6Pj4+3OHmhUAjxeBzValX61aenp1/c+OZTg/fAYDCI8RvPtTRCYFDaYwVH3QaLxYKnT5+iVqvBbrdLL5p9UdYsFKizZ+J2uzEajaCqKs7OzvZeR10uF5LJpHBtK5UKjEaj7D/UddAY1u/3/+44TwcccMD9+P10uA444IADvmFQwFQoFOD1enFyciJJdd1uF5FIBKvVCrlcDu12Gw6HA6enpyKsjsfjSKfTSCaTePfuHdbrNTabDZ4/fy4DUbpLkwDBA3o8HsfTp0+3Pg8dM/c51BqNRphMJjn0Xx+e6PV6SbWgYzH/DgVSHFqTTNZsNqHT6VAqlUTwSYI+U3m1TUN+TgqlKAAJBoNwuVwyhG21WiJo1BbVu4gtJLtx8NhsNmWgqNPpZGDs9XqF7EMxtaIocLlcMBqNyGQyqNfrsFgs4ua32WywXC4lqfBPf/oTgsGguOcvl0sRsJAwRPclis/L5bIkBzSbTaxWK4RCIQQCARmGA++H5el0GvV6XZpR2qEjxZ80JdBeF5KiOVAjKY3pkRR4MGW70+kAeD+0pYuWlrw1mUzw5MkTOZiNx2P0er0bybR8lujWrRVEMQVRC4q0OHTcB+l0WohB1WpVkrH5LHM4nUwm5Z28DiYAGwyGR20AMQHUarViNBptpTlxgD2dTlGr1cR9nwPeXq+Hq6srpFIpEbClUilJaeYw9EM+LxNdcrmcOG5qxeDL5RK1Wk0MLGq1mgiGeOhcLpdyMI1Go5jNZnjy5MnWQZSNI4rmKWSlyzid1SmApfkGxWQUlHk8HkmlPTs7QyQSEbKLNjk9GAwiEAhI6gbFpHTtBN4Tstjsu62RReH6YrEQYgWJoExduQ1Mp5nP5+JIpzVv8Pl8W6mxfP8orP9YME2o0+lIIscuUGxWKpUwnU5hNBrx3XffiWCfpBKHw7G3y2EgEEC325VECbfbvfXvYrGYkPuHw6H8Xp1Oh//H3nv0Rppm2cEnvPfeMuiTWVndjXHdYyDok8FgfoAACdpIgNb6GbMQoKWW2kgCBK0EYfYjYAaa6e4Z1ZTLrMxkkOG99z7iW1Dn1hvBiCCZyazKquIBCt1VmSSDr3me+9x7zGQyEedeo9GIdruNXC6HWCyGRCKx1eCAWK1WSKVSkmrbarWEcFepVDAcDgHcuLyaTKad948OtlwjKYLP5XIiIvf7/Wu/F5v3+Xwei8UCyWQSz58/h0ajQbFYFCFwIpG4Rb61Wq2wWCwoFAqSynMfAe8u8Nrncjksl0skEgkxCqGhCxMcSKqjMDkWi+Hly5coFouw2WxCBON6zQQu/s65XE7IR4FAAMFgUEh5SjgcDlSrVVQqFXkH5vO57Ksul0uae36/H/l8fm0vZHLPYrEQwwybzSZGFg9N1tkHEuRInisUCtIEXiwWa0RTEpKYTD2fz+XZq1arIh4h0ZWmEzQhqlarQnKeTCYwGo3IZDJC4mSqdq1Ww9XVFWKxmKRxMPWDYhSTyQSLxQK73S5ifppzKAXotVrtgyQU8Pu7XC6Uy2X4fL69DVmKiQ0Gw0fhaEuBNt+bo6MjaDQaDIdDdLtdWK1WWK1WSevTaDRoNBpS77BBT1LWfD5fMx1h+oHP50OtVhPH0K+//hrJZBKlUgm1Wk0SJUiUo6jvxYsXW9cEJqbTHZjPKEGiFI0ySBYwGo2Sdt3v98UhNBAIPKozvc/nQ7fbRavVQqlUQigU2rrnKtdIp9OJ5XKJyWQi9UKn05H7oTS9MhgMQqDm+8dEKyan87x0nzV1Pp/jzZs3yGQy8o6ORiOo1WpMJhPo9XoxV5nP5wiFQkK4pakPDXB0Ot0acTUSieDrr7/GN998I3Xfl19+KelZiUQC0WhUEmB7vZ4Ifv/+7/8ev/rVr5DNZnF8fIzz83NcXl7Kes6kMOBmD+NAOhKJIBAIYDab4c2bN5K+NRqNJLWHa9B0OpW6hEQWEhHvIwLjGXSfKRLFB8p0KOBm6Fav18Xwio69vLe856xLWYN+iHRUkhNpgsJ1frlcIpPJiIkIkxMolPN6vfD7/R+ds26n01kjNjPt5z7rf7fbFVdsmgZRGMTfORQK/WSG5j91WCwWPHv2TEzqKGxhWgPr2X2EmMcGyT7bzu7j8Vh6Cax/uU/yax8C9k9Y7+yD1+sVEnm9Xn9ns7gfMtjHSiaTIhbbdcalMIXGZwC+U+IRz5DValXSoux2Ozwej5iFZTIZWCwWOXvcVxz5vqCpzg8Rk8kEyWQSw+FQRBgkoXLPtFqtCAQCGI/HaLfb8u/RaPRpb/mRQqVSIRgMIhAICIFYeVb9WIzJfuhgb4UGr/tAsVGv1xOy1UOwmZDI3kir1cLLly/FzGqzZqQ5lsvlknPJu4DCT/aSu92uiNj5PXnWbzQaso4fHh7e+l4k+1GUdRf4d5bLpSRgGgwGXF1dYbFYoFqtYjKZSP3NnidT1r1e75r4ejqd4uLiQs6vJKmyZ/K+QqzHgE6ng16vF0PZuwyHKKRQqVR793adTofz83MUCgVJ+uPZX6fTwWKxiGGzVqtFu91Gv98HACGZ03wzkUjIc88eCwWY7D1RYMczc61WQy6Xk5pyMBhIYhV7bsr0d85yKZLdfF4mkwkGgwH8fr+cpZm8xlmDsldnt9slAez4+Fj6sIvFQgw6+CzdBaXof5uYU6/Xw2g0ol6vSx29eX/ZP+c9NBgMkmJMzGYz/MM//AOazSbUarXMZWgO7PP57qyZW60WUqmUmOJxtk1jgHq9LmY18Xj83nsE+2LstfZ6PTGCBb6dwQDfziApmOR8WHntKP7U6/UyRy6Xy5JSP5/P4ff7RZi8OYMhOZTGbg6HAzqdTubAnI3OZjNZfyhOZ7r89fU1hsMh0uk0fD7f2kyC60qn00EwGES325Vz+11g2jJnt/eF2WzGwcEBDg4OUKlU5KzFZOZgMCh98kwmIzNjp9MpvTumxbPupoA1HA6v3a/HABO/aIbrcrkQDoel11upVFAsFuFyueQecB2hOYbP54PZbJZePddC7h3xeFzSqWlQq8R8Psf19bXsl51OR/o0TLivVqv4/d///Z11MNecXq93r1rZ6XSK2J2C8n1YLpcolUpIJpO4vr5GJBJBuVzGYDBYe6b4OZT7t8ViQSQSefDZxev1inHxplG9Ei6XC5eXl2KKv0+EU61W4fF45P+zn0XhGteAer0OnU6H6XQq5hW8N5xDcg7EpLi3b9/KehePx3F0dCS9kOvra5jNZjGfyeVyODw8lP2vVqthuVzC6XTi5OQEvV5PCO5v376VtESdTicp6+R5hEIhuFwunJycPMicYTwe4+3btzKrmk6nMBqNsg9yf5zNZvD5fGIm3Wg0EI/H4fV64XK5YDKZZOb5+vVrMTqaTqciIKIo2+PxyF5MERXXeoZwXFxcYLlcikkPzXKBm3WJhoc0h34XsP/darW2pkGrVCpZb7755ht0u114PB4cHR2tzXGUNSrNy3/MuLq6QqvVQi6XE/MZZZ0znU5RrVaRyWQA3BihJxKJ7+nTPgG4qQM6nQ7a7TacTufeNYI1Hffkfb26J3wY0IjZbDbjr/7qr8RcXCnwodkHzb15f3O53C3+54dGv9+X8wvrvF3g2skkaKXR9LtgMBjg8vIS7XZbjN84D2aAkcfjwWg0wtHRkTzb5J2FQqG1tb/dbovYn9yJYrEo6zrPjcfHxx+V8We9XkelUhGjbqZ4j0YjCamhKdhPbVZH3kO9Xodard7ZT6aRUzqdljPveDyWOYBarZZZEc1W5vM5wuGwnN0Izux5VlGK0e+znnKNdrvdcLlcGA6H8Pl8a2a4drtd1vNYLLZm5r7tGgDbzeSIdrstQnXOhrdxqRmeUKvVYLfb0e1279Wr2zbz3wflZ/2uTYg/JBjmwnRlniN/Su8kQdPIXq93S3yuBPk05XIZXq8X7Xb7o+CMfUj4/X4YDAaYzWZ4PB4xYiR/g9yXXYYvD0GlUpH1bNd9oJFLKpWS3tKPXYDOs7zL5ZLzsHItYj/+rmCVx4JOpxMz8larhW63i2QyKb2FQCAgZ3KKxJvNJtLptPBOz8/P7/28MPyOQVWsiVqtlhjqM0SKoV8+nw9+v/8nuZ494Qk/RTwJ0J/whCc84XvGbDYTAZjVapWBI5MM6PhF4napVEKpVMJqtZIhNhOyKXA+OjoSokUoFBJxCgejdOs7PT0V0RJB13QlGZiO+0q3Xgo31Wq1CDbH47EQBpRDPQ7cKFB2u91YLBaSbNPtdmEwGCSpUJkQ7vF4sFgscH5+Lo0xn8+H4XAoSbB09aNjtMFggM/nQ71el8ZMKBRCvV6XYZdyuMnUCwBrzU06SGWzWVgsFoxGI0njczgcImjgZ1KpVOj3+0in0zLYpIsnfy5d/WazGeLxOF68eIGLiwsAN0PeXC4n95cNqUqlIkKPwWAgqZQUpS+XSxSLRUk4cLvdWC6XaDQa0lhVqVQ4ODi45TrldrtRKpVgMBjQarVuuS36/X4cHh7i5cuX0Ol0kkTOgbnNZsPZ2RmSyaS4Zrnd7jUC7mg0kpTbyWQCjUaDL7/8UkTLm4dSElAoilKpVLDZbNK43iS8cLD7kAOMRqPB2dkZUqmUCL273a4c2MPhsDT1j4+P5blg+mOtVhMTBD7jj+lG6PF4JNl8PB7DaDRiOBwin8/Lu0iBFYdAy+VSxP8UkhqNRqjVani9XmmMKlOaH/qZqtUqlsslrq6ucHR0BIfDsXYvSI64vr7GZDKB1WrFH/3RH62JkRaLBRqNhgj3kskkPvnkE6hUKgyHQxHm6nQ6xGKxtetpMBhEwPOb3/wGOp0OhUIBBwcH8Pl8QujioZcJPgBwfn6OWCwmLnXbQLd4CvD4bO1rFtAUgM/neDwWQ43VaiWDV5o/bA5hgW9JkEwoPT4+FoHXtiYsyaEUer8vms0mut2ukJz2gUJEus0zvfBdG3xcu91uN8rlshBSCDZgp9MpPvvsMzFXcDgcQvKig2yv10MkEkE8HscvfvGLvU32dDotKeUkc1DwMR6PJfm10+nIYHxbo02lUgnhkSmupVIJzWYTg8EAiUTi1tCMQkODwSAOhY1GAwaDQYgOfr9/5/BXpVIhFArh8vIS/X4ftVrtncU7bCbOZjMcHh6KkQU/G9dbpv7w+eb+r9FoEA6HMRqNcHx8LMkUTAzi52UdwdTko6MjER8Ph0MZ7FF8QxMKGgywAc/3ye12o16vI5lMQqvViuGF1+uFRqORhJRgMIh4PI5SqYRisSif77GSNmk2QIMUEpCGw6E4+CoT+0wmk+w1vNblchmz2QwOh0Pcj0lWpaDY5XIJ4f7zzz+XJqbdbkez2RSRFElBarUaf/M3fyPNeNY8vBc0Oen3+2K8olar18w+aHAQiUQwm81wenr6qI1CCn7r9ToKhcLOYR8NKFjDfAyCzWq1KmZHJycnsjbodDpxoWY6eLFYRLvdhtFoFIIp37vJZAKHwwGLxSL3gSRMr9eL+XwOk8mERCKBZrMpCde9Xg9GoxGBQABqtRqNRgPX19dSz2o0GknxUoJ7GdfObcMsmtRoNBoZnpIMZ7PZhIRFwc9jwuPxoNFoiHFOKpUSd23WCEyVjUQi4tT+2WefCcmaqTpOp1PIciR79vt96PV6GbjTOIMJUbweJCgq9yIaNvV6PSHcfvXVV8hms5L0QbI8k36sVisuLy+xWq3wx3/8x9BoNFJLcQ/gmrhJmGUyOYUdvV5P7n08HodKpZLEH4/HI2sByYdv3ryRtDyDwYDnz5+j0WigWq2unYFY95AcC9wYozWbTSSTScznc/nMXKdoUsak73w+j4ODAwQCAXkmHwM0m9HpdBiPx/L5tFqtpMeTGMy9gjUTrx0Nj3i+Zuq6UgjLc4tyODKfzyW9jPXlJmg6kM1mRZhC8XmhUIBOp8NyucSbN29gMBiQy+UQj8fh8XjQ7/fRaDRwenr6UYinaJYxHo/R7/fFgM1isSCfz8Pr9SIcDu+8t7VaDdlsVgwOptOpnFGBGxIEDSw+tAP0Ez4eGI1GnJ2dickVzzV87+4jinlMsF9AQodKpVoz2SP5h0ZiyjPgQ59Z9qqUic27YDAYpB+hFPD8lODxeCQ55urqCpVK5VaNwT7MeDxGIBCQHhMNqr4rNBoN1Ot1ZLNZaLXatToQuBE7TKdT5PN55HI5OXu8q1jyp4DJZII3b96g2WyiWCxKSpLZbEav1xN3fafTiVQqhaOjI4RCITgcDkSj0b1n/if8OECB4sdMaJrNZiIAZQ35QyHnM137vmJVs9mMTqfzIBNYYHdCImdBuVwOjUYDiUQC0+kUBwcHa0mXJpNJ0lbeh9BFY9JSqSR9QGWNa7VapYeezWahVqvhdDpv9Tx5duT57y6wX8jnYzKZwGQy4c2bN/K9otHo2p5isVhgMBhQr9cxm83EyNFsNq+laX4MYvNtIAnaarWKMdcusD/mdDqFRLkPNH2ORCJSH7A/7HQ6RfRZr9dlHsivo5FuOp0WgeVisRCxhslkWrvfVqtV6sXpdIpYLIZkMonLy0sRwStNTdlby+VyYkgYjUbld2IfgqnrxWIRZrNZZq39fh+Xl5dy3qToXKPRyJy4Wq0iEolgPp/j2bNnUmcwYbbb7WI6nUrfiudkGlHyeRmPx9DpdHL23oTH45GEJRohEOxhdjodjEYjmUFuismn0ym+/vpr1Go1+e+9Xg8ApHfDPjh7HZsYDAZIpVKSSO31eqUHQMNzzs149j48PNxaw1NAzn4MzQFpWLxcLmEymTAcDrFcLmVt57ySogGmTi0WizVjaPY5gZveIXt27XZb5t9M7W61WjIrUoK9YAr2CafTCafTKfMn3tdQKIRmsynPdyQSAXCz3lUqFVQqFZkXcu2hYbvT6UQ6nV4zp9wFpWDhXdccElf5ewI3/S/gW+PI0WgknAEmKWs0GklfWi6Xcq9tNtujGjDNZjMUi0UUi0XMZjMRlwIQngU/M2f4Wq0WiURCTGZNJhN+//d/H4vFAlarFb1eT2YfNNExGo3w+XyIRqO3riXNLlutFgqFgpjy0sA0mUwK76FarYrp3zZQtM4+6D5wBs95/j6Mx2NcXl6iUqnImjQYDMTQm+mQ5CIwIY2prNuMXe4Dl8u1xpPRaDQiXOY8mLO1QqEgZuLkTXCWPZ1OZb10OByy71N8HwgExHRAp9Oh0+ms9fKUPY5QKCRmxzRFn81m0kdPJpMIBoNroQOc4avVaklTNxgMQuBn2jo5OuR8BINB1Ot1eDwe5PN5MTPX6/Uwm81Sw1itVsTj8QfNv1arFZLJJJrNppy9NlNB+/0+KpUKstksOp2OzE7JM1KCtQvXOtYDTEzvdruSnNZoNGQ9pkERU2ktFosIIQaDgczR+bvRxNrj8ch8813m5fV6Xd7ffTWx2WzGJ598gqurK0SjUYRCIUQikZ9kn5MmDKypt5lpcLaYz+dRKpVgNBoRDocfZIzwhMcFTf9Go9HWe7YJh8MhNdJgMHhUM+oPBYaMKOcbP+Q0xng8jv/9v/+37B80c2cdqtPphJOnPIc4nc417sV3gfF4LPPY+4iyaZC/WCzESIXYZo61C7PZDMlkEo1GA6VSCW63G7FYTHoyNC4i3xIAnj17JgEVPG8RrPPq9bqI+w8ODlAqlWAymWSP8/v9WK1WwmH9PrFarZDJZFCtVsVkkAZt/J25p7Ov8lMDuXs04to3xzYajVJjzmYzMaFKp9Ni0s6AAAqx7Xb7WgAGTY1sNpvMl5Vi9G3BXptgr4khOlarFY1GQ2awwM1e6/F44PF47qxHGGxDg75t6HQ6a/wI5fltEwwUYfjUfeYfd4WbbYIGEsDDZ3Q/FLAn8VMFjTdp8L8PdrsdlUoF3W4XzWbzRy9AByAzEQbhkdPH3uVjPDvkNbbbbbjd7r3vJfvQrVYLdrsdsVjsO53Pfl+gKRDNs4Gb9exDcHzYf+Y+vu1cr9FopIdsNBpRrVYRi8XW+rHs8dhsNgnWYQjmQ2aqHo8HLpdL+izk5yrN5Mm/Uj6rm7PzJzzhCT9OPL3lT3jCE57wPYMDq+FwuDZ4o9gauGlysAE2nU4xHo8xGo0QCoVEqEoSws9//nN0Oh3YbDa0Wi0kk0kRGul0OrRaLbhcLkl32CR21ut1ce1m8mKxWBTRGcUGq9VK3LMpTimVShgMBnC5XNDr9QgGgzIg63a7uL6+FjLhZDIRInowGBRXZAoI6RbMFGQmeft8PnzyySf47LPPhDRBATqFVRQQ0NGJaYrT6RSdTkd+N+BbgkkgEBBRhRJ6vR4nJyfigKlMXCEBiQ1Quvz5fD7k83n4fD4RlFAUT3fmZ8+eIRwOSxpAv9/H1dUV+v0+2u02Op2OEKMtFoskO7MZwwOB3+8X0lez2RRiA68ZRVuffvopXC7XWvoAny2bzQav14tisSgpfQQPrQaDAel0WgR0JPry84VCIdRqNZydnYm4mWLzQCAgX6PX61Gr1aTRtq1pRRI2h5harVbc2nnPlAdpJhM8dICg0WhwcnKC0WgkrunKBGefz7d2LXiPmLjEAQYH6ByanpycvPcww+fzoVwuw2azIZ/PIx6Pi4hNaaDAgS2dzGw2mzh3UsgMfJtSw6HguwjQh8OhJB2YTCZJeVDeCxJ++J45nc5bzUYKitVqNUqlEqxWqzgUMm1kMpng5ORkZ/OXgqrLy0scHx8LAY8gIadSqYiYvVqtPshxXKVS3dmwYfoGBTedTkfWdDaeNRoNjEYjDg8PEQgE4Pf7cXZ2duu6+Hw+EUX93d/9Hex2uxB71Gq1ONzrdDpJ/GR67vuCzv0mk+lewjEKrpWE0/l8fov053K57hy6qFQqxGIxzGYzzOdzpNNpEeOxAb1cLtFut3FwcIDlcinJu6PRCN1uV9Z5j8eDZ8+e4fz8fK+TerfbFRMEmoQo7zUTtOnsr9Fo5Jpvg3IQRsEJjR72NZ3YqKEjs9VqRb/fx2q1urNJr9FoROj9LslTAETostlMZNI0B1QkRlEcQ8MAEpW5T0wmEyEBGo1GEVIrwZRAGqjQLZKk4+l0KgJ+kk04WLFYLGvvhNfrlVR1rtl8frl/AutunIPBALVaDfF4/J2u2eb1y+fzIj63Wq1CGiXhqVKpQKPRYLFYwOVySVMYgDy/zWYTVqtVUo/4GVUqFdrttqwhTFwi2dtkMsHj8YgbcSQSEQJrqVQS4lu/30c4HMbh4aEkDTWbTXz55ZdiSPTJJ5/cInpS3JnNZqWm2GfgoQTT55UDsE2BLdO25/M5stksrq6u4HK5YLVaoVarhTA2Ho8RDodht9s/iqEoh9UkR2+KjuiIzuE+iWO8v7z+TBrWarWypzN11WQyCSktHA7LfUyn05jNZiLG57MUCARgMBjwzTffIJvNwmg0wmKx4OzsbO3zUSDNxB2NRnNrD1GpVJJsXigU4PV6EYlEEI1GZYjJlBMlKAxTpvKYzWZJfb8PVCoVjo+PZQ9hXUtRoEqlEkKT3W7H6empkDeZfD6dTsUB3OFwCNncYDCIidNkMhHn8nq9jsFgAI1Gg0QisZaypITb7RbDMBIS8/k8vv76a4zHYyFxU3RMEvJ0OkWhUMCvf/1rnJ6ewu124/DwEGdnZzuvQ7fbRblcFhd9EueMRqMkgNL0BrjZD5xOJ9xutzxnlUpFjKsikQjcbje8Xi98Ph8mk4kk8BgMhrX3kumCn3/+OabTKex2O9LpNADI78+afj6fo1ar4fz8HE6nE36//1GTXWh4Q0K6kkDo9/vlHlFA2mq1ZO0nIdXj8cj78dVXX4khGGsVvV6PRqOB3/72t+IirTwP2Gw2SZ0nuYfPXLVaFYKM0qSmWCyKuQgAHBwciHGLwWDAeDzG9fU1otEoVqsVLi4uvtcBTKFQkMS4drstJiUU6dpsNozHYwwGg61GJDTq4bmTRhs0BKBhHEntNMF7wk8HRqNRRBDfJ1inqdVq2Vd7vZ78wzWFJPZ+vy+GPw8RETLFgc/7fUBRwH2EHz9GaLVanJ+fo1QqYTgcIpPJYDKZwOPxQKVSYTabod/vS29pMpnA7XbDbrffMlD80KhWq2IAs2mWRzBd9erqSlK8nwTou5FOp4W8wL2bxkPNZlPc/JnEqNFocHBwAL/f/1GlDD3hp4nBYCDrAkWQ7N+xPn5Xs8CPGe8iPNyVkBiJRJDL5eD1epHJZPDq1SvUajUhjtN8yuPxyJn0fUBCtlqtRiQS2fq7aLVaxGIxXF1dSdLqJqmRQkKbzYZ2u33nfe50OjAajdDr9TIfefXqFYCbevzi4uKW2e5gMBBRPg20Wq3WVsHixwjeu36/L+bOu4wkKpWKmIM+RExKw8pdf6Y0I2Pvlj1knnsAiFHkcDi81XfSaDQiPObXUHzIOafb7UYoFIJKpUKv18Pr169RqVSk3iwWi7DZbHC5XPB4PGi1WjIT43sQCATETI7i82AweEvo4/f7xQhNrVYjm83i6OhI+qez2QzpdBqVSkWEJ/wzs9ksZlBOpxP9fh+xWGwnaZdmgCaTCfl8XuahwLdkc6vVKmdfq9Uq/T/gpn/29u1bZDIZMZ2Ix+Mwm82SZlupVNbO8tv6texxtttt+XrlfXa5XIjH48hmsyK2brfbW9eLZrMppuIul0tEoqlUSp4F4FvjRhohktQ5m82kH8a5CHvrw+EQlUoFnU5HxCo8Z1BswBk2eyus/5XkT84bea7YBPuGBEXy/NpQKCQmil6vF71eby0li2npfr8fb9++FcPGu+aZvV5PBAP3mX3SvJEzNhqp6nQ6mM1m1Go1mbMOh0NEo1ERKrLXzr44BfTslWm1Wklc3lYPzmYzNBoNjEYjuY7b5pSb4FxvW/oazZBpxlIqlXB5eSlze4fDIemEgUAA8XgcvV4P9Xpd3kOlocRgMEAymZTv5/V6odfrkclk0G63kclk4HA44PP51j4HyeGcneTzeZkBb7sHWq12zXBtH8j72HcuVZpHZbNZDIdDSYZcLpdoNptChuYzuFwupZeqUqlQr9fx4sWLOz/PJtRqNQ4PD+X9zOVyMvOez+fyM2imqdPp4Ha7xVCiVqvJ9zKZTJJUGQ6Hkcvl0Ol0sFqtEAwGhfdgMpmEp+B2u8XEhrNyrnez2UwEQDqdDsViEcPhEBqNRp51JrKOx2NYLBYx0eh2u2LMHgwGYbFYJM375ORE9nyas3POQ4Pt8Xgsn4HP+kOI8NPpFG/fvsVXX32F6+tr+Xo+16xPaKCRTqdxdXUF4FtBhHIWrlKppIes1WpxdnYm/VM+I6FQSGZovEaz2QyFQgHD4RAGgwGLxQKlUgnpdFqMdBOJBCwWi+zVq9UKnU4HlUpF1huj0fggISCN4IfD4b3EtUajEQaDQebWP1XBELkc8/l8b+2mUqng9/txfX2N0WiEer3+Ucz4fqpQpu/eZx7Bv/ND6FlyHk/DfeXZ3Gq1wu/3/yCFajSgZ3K31+uVsyEFaDQhorFprVbDYDBAs9n8ToXRytr1PudV/h1+HQMXOGPmOYTJovvMkfv9PsrlMkKh0K21nKmdZrMZ2WwWZrNZ5sTck5Xm7aVSCY1GA4PBAOFwWPZUh8MBtVotyZ6FQgEajQbpdBrPnz+/1zVS7h2PuX8UCgUxiVkul/B6vVJzktPQbDblbMrU3J8ShsOh8Hzv0082GAx4/fq1mJeTL8Wzn8lkEk6o0+lEIBBYe6Z5VlZea9Z/5PzcdabqdDryjvNraQT/LnA6nXA4HPLObOtjkdfA84rb7d55vdRqNYxG41pIy30+A38XmjztAvmBFPj/VGuuHztmsxlms9m9jVEZevZQY9QfOhj69yEwGAzkmt7HANhut8t+w8CPnwr2mXK8L1qtlgRlsDZirefz+eDxeNZqIXIOaWS3794FAgHRotRqtQebepMzxb2QeiUayXKO0Ww2kUqlhHd6dnb2g5hhPOEJT3h3PAnQn/CEJzzhewbFapupr8BN4U5xBEXTFotFxAixWExEnhSbWK1W/PKXv0S5XBZXZgpcSPog4ePg4ECIFovFQojn/L7tdhvFYhGVSkVS5IxGo6TwDYdDGSBzCPz5559Lkvt4PBbHKxJtAEiCd7fblUETh6Z0MGYBncvlUKvVYDKZhIzRbDbx7NkzfPnll9DpdCL0bjQa4q7N1G8Oefn5RqORNDIWi4WIU5lUuQsc7u2DXq8X4QwFi7VabS3ZlUmFXq8XBwcHsFqtGI1GkmxA8kEwGIRerxeBfD6fF6dnJrwCEKKLwWCATqcTssxisZABvNvtRiQSQSQS2dqcjUajGAwGmM1myOfzQohhAm6j0UCr1RL3Kg4oKOB5+/YtxuMxjo6OZDC6XC5lwK18RpmCS5fdbZ+HQhM6YTPRnQcT5ddQlP9QgpISdP3fh8FgsJY+wWdGq9ViNpvJYTAcDmM8HuP4+FhIPe9yoKIzNB39v/rqqzVR63K5RL1eF/MEi8UCj8cDvV6P+XwuYkgKMwGsJTO8C2q1mjh+RqNRNBoNFAoFGAwGSaAmsYOfxeVySTLFJki4UpL56Mp6V5JZs9kU4hgThbc1pShSbjabsFgsQh7YRL/fl6GUSqUSssA+cXe/319L37BYLEIkHI1GmM/nGA6HkgRP8RTNBTaT4SkUW61WKJVKuLq6EjMGuuexkXNxcSFGIo+Bhz6jSvLTfD5HoVCQNBgSapjQTkLAPoIPzTHowtdsNkXwBty8o7/4xS/E2IDCXBL//H4/nj17JoYnd73PNJygkHOzYU1hIv9hejHFZ5vXgkQbmrHkcjkhxd0Fp9OJZrMJn88nAgsmINwFo9Eo14Eil+l0inq9viagM5vNa+m2xGAwwHg8vtVMZIqCVqtFuVyGw+HAarWC2WxGs9mUemE4HEq6gFarlQQGkth6vR6m06kMpnU6nbiRms1m+P1+qFQqjEYjvH37VgYUzWZTxJVcW/g7djodSYcfjUbQ6/XI5/PweDxrCbmDwQAmk0kEt0yZabVakpjyPim98/kcX375pZh6qFQq2esASDOQqSmhUEgIUQ6HQz5LLpeT5BgOrPgcsYk3Go1kcMxU3cFggEAggGq1Cr1eD6/XK0RWprBzPVapVCiXy0JGo6mLx+PB559/jkgksnUwRBJsNptFtVqVhIJ9A57ZbCZ1A40tAMi75PF4EAqFpPakUYRarRaDFZLB1Go1HA4HQqEQbDYbTk9PP4oUOdZyynqSoODZ+f/St/nekUTFBJNWq4XFYoFwOAybzSZ763w+R6PRQD6fh9VqlbRxrVaLly9fYrVaSTq6EiSxxuNxXF5eolariRhc+Rn5XLJhzdpxG7i3v3jxApFIBD//+c+37svcs+gez7pMrVYLEZmf7T73j8Q0mhX5fD4ZInEv9Hq98Pv98nlisZikg5dKJfh8PgQCATkT0HWf9atGoxGCGtNwvV4vWq2WCLI390XWxW63G8lkck0gx31amTpGMst0OkW1WpU18/j4eO8gZrVaCQF7PB6L6GI4HEKn08Fms4mIkilaFEvy3ee9eP36tQyMms0mDAYD4vH4XiJivV7H1dUV6vU6NBoNzGYzLi4uMBqNxLSE5GuXyyW1vslk2mv88q7w+Xzo9XqS3q48q4XDYXHEZkoW15iTkxOYTCYZxF9fX8t5lvUJxQdfffUVgJthttlsRjwel/P3b3/7W3G4t1gsiMVisreUy2V0Oh2cn5/Ls9jr9dDpdFAul6HX69eMUUjGPTg4kPdcq9WiVCrd29zjsUGjuWw2i/l8LoN8fuZ+v78mHtdqtbeIzYVCAY1GQ0zPlP0M7o0WiwWFQkHclUnyf8ITvkvY7XaYTCbY7Xa8fPkSVqtVDH/4Z8DNu1qtVlGtVuF0OtHr9R5EKmZ/jOaRd4FJGtzvf6pwOBz4gz/4A4xGIxgMBjHBJOmftc9wOITb7UYikfjOE5H7/b7sg5vD/k3QrKTVakkK3sdQx35sGAwGQhJ1uVxrNa7ZbJaUX6VpZCwWu2UW+YQnfB+o1+vIZrNiesvUT+Dm+aXhYCwW+87NMh4CihTvY2xIc6F958ht2JeQqFarRVBqsViQz+cllTkUCsFut8NsNiMUCr23YIBp0CSw7uuF0jCRRs40NVN+bppCZzKZtf77JiaTCVqtFgKBAOx2u5w52UfmWbTb7cJgMGC1WomYzOVywel0wmaziWFrp9P5QaQPAjeJwe12G8FgEMViUQx+2Jdg72oymSAej8Nut38wIjzvN2s6irUBiMEm+yRKcMbFPsFwOJQkcppLs45kGix7k6PRCG63G1arVVLEC4WCEMfj8TiOj4/h8/nW0tx3ic/5uYPBIBaLBcrlshhEd7tdjEYjfP755ygWi5JA5nK5YLfbZW5DA9B0Og2PxyN9FyWYHl+r1cQ8czQa4W//9m/h8/kQj8fhcDhgt9vXzHlsNpsY6g4GA7x9+1YM3WiQrSS5m81m6UHn83loNBq5XgQNxZvNphAbN0Ehud1uF9EyTTuVYL85Ho/D7XZDo9GIITBNcYfDoTwPwM38mKbsyWQSXq8XDodj7VngTL1er2M8HkOv18NisUivhj0U4GY9PDk5wWKxQKVSQa1Wk/4Zz9LKc8c2AfomKC5Xfq3H44Hb7RbhPvt0RqNRhM7AzTyKs4l9wh6KRtnT30d8Vpqz8PPz3vp8PrlfX3zxhaTPK4XkPp9PnuFKpSIp8ldXV2Jabbfb4fP5bpmtc77NnhpFJuxNGI1GHBwc7NxP2Gfblb7mdrthMBjQaDTkHjscDrhcLhwdHcFsNiMQCMjMj2lTRLVaRalUwmQyEXMCCp2Zqtnr9cSYfJvJozJlu16vw2w27xSQcN3aNYffBPe6fedSCuSLxaLwSGj4qewZsU+k0Wjk73399ddrPc53gd1uF+Nyo9GIr7/+Wvp93JspULFarVgul/I7hUIhMYPQ6/VwOBwIBoPodrvQ6XTyNePxWJKwKVxkQAQNKzqdDhKJBKxWqwQiWK1WMdsdDofI5/Ni0kvjBc6cmRynrCf4/JDvokxvJebzOa6urmSOojTTo1iIRPSTk5O913m1Wolg7c2bN/J99Xq9hE3Q1J3nbpp+JJNJEXjyH84baMw8Go2QSCRkj2LYgFqtFpNe9sHz+bwYJ3s8HuEV0VSEpvhM2GT9RLNag8GATCYDq9WKarX6oB4116mHmgjuMgn5qaDX66Hb7cJms93ZLzMYDHL/9qWuvg9ogErjEZrmcL9/wg2UolfO9feBtbIytfdjRLvdRiqVkrO58jkjV6nb7Ur9/UMB18herwePxyNink0BusVikRk7+QrkZ3yXoChLpVJJT3cfaJC/Wq2QSqUkqIh7hjKkp1Ao4Ojo6FYvcLVaSS1OM+tdMJvNUvdylsvrSJA71Ov14Pf7186Gyv4Bw33q9boYsSiF7EqwZqvX62v3hNxfZdjRu2A6naJSqaBYLEKlUuHw8HDtfaUAn8FP5DnSePenAp7rVSrVnft9v99HPp+Xe+Z2u6HX6yXIhWZu7XYbv/zlL9f4hLPZDJVKBZPJBIlEAh6PR9Zag8EgZ/RKpSLBM9swnU7RbrelDnuMuYLH40GhUIDdbke5XF4zVleCwm+XywWj0XhnL448p/vAYrFIz7Rer8NisewU4lerVQCQs98Tfty4b13N9/intH59aCiNhu5T7/Hv/BAMin4IWC6XSKVSaDQaUsuyJ0mTY+pelGd8hgH1+/07z7/U4bD/Nh6PHxyqVyqVJERls2dNXqfdbkepVEIulxNu8Pv0+ReLhfT3yEF/l3DCJzzhCR8OP11m0xOe8IQnfCRQphBug9FoRDQaxWw2w2AwgNlsRjAYRDQaxfn5uYhJ6Z7HBofNZsNwOBSSXjAYRDabxWw2w3g8xnw+lwYTG2kcBrpcLrhcLmSzWeRyOUynU0nWnEwm0kTr9XoyvKWzFckMhUIBb9++hdfrxcnJCcLhsBSg5XIZyWQSwI2wkIMqi8UiqRYsQj0eD7LZrIjSSHKg43mn0wEAIeY0m00sl0uEw2GoVCoZaJbLZUynUzgcDmQyGRHj0y2dg8n3hd1ux9nZGbLZrDSfWAzb7XbY7XYYjUbEYjG5HhzaVqtVcd1Wgted7u8cxLEhpFKpRLDf7XYxGAywXC5FkHFycrJX2MDUbrpDUxRH4Xm73ZbGDsnZTGktlUoimLRYLHjx4sXWYp/PHcWQg8FAkr42SVpMPHS73ZIeOxqNYDKZ1oYj/X4fxWIRXq/3Xs2vd8VqtcL19bWIX7YRgFwuF9LpNH7zm9/A7XYjk8lI046JkyR23BfBYFAIEvxdG42GNO+1Wi08Ho8Mpvn97Xa7JAdQGE1i+/s4qpJwfXp6KoJOJm1yDSMBx2g0ihCSCZ2bINmfCdYUmU6n0733kinJg8EADocD8/n8VlKpEi6XC+VyWQSxyve81+shl8uJEQiNK4xGIyqVCmw2m4iNNsGESyZzcyhPt3WSPph+wfeTpKXJZII/+7M/E+JUOp0WYSkJDnTC5bBfrVZL8k0sFns0l0MSTpk4clfjfTAYwGazQaVS4c2bNyIa5ZpKYguNTwaDgTTZd8Hr9cJkMqFarcJut2M2m8ln4bDY6/UK+TwQCAi5odvtCkF1MBjgq6++kqTYzQGzMr1Y6eCvhMPhEJGoVqsVIkev17v17qfTaaRSKYRCobX9hiRUt9u9ZjSw7drz+WXD9L4mEWzo0RW6VCrJcJECdO7RNLI5PDyUtULZFFQOGCjyYMI6kyHVarXsufP5XFKIx+Mx6vU6SqXSWuKjci9jKgVw0yhj0nUgEBDHaaYQk7hjs9ng9/vh8Xhgs9lk8FepVBCLxYRUQ0IBh+Gj0QjT6RSBQGBtLWHDa7lcYjKZSP1BUgIJwPsa1kzLePPmDa6vr8UpGrgROMbjcSF3snYqFAooFAqo1WpYrVYIBAJQq9UixFN+DpIO1Wo19Ho9rFYrVqsVptOpiJf1er0kBZEUVa/XJVlmNpthOp1K4mir1UKv15N9FIA06lwul6y7m+sj8G19sVmHbcN4PJbkl3q9jn6/v+Z2brVaMRwO0el0cHp6CoPBIOQtvV4vSeAmk0nEWxRwbqa9fJ8guYjPjBIU7fP3rtVqsoa0221JQppOp/iDP/gDLJdLGU7z+dBoNELcobM1yTPcZ7eBztsulwuNRgPpdBrBYFDWXZK7e72eCIdHo9FWQRSJDSSKKcXem2DaCokQFImTfGc0GhEMBjEajXB+fr51SKr8LCQQ0sWbJHumVXPvUSIajeLk5ESSB0ulEubzOcxmswxpF4uFfL1Go0EoFJJ1m+8Y15F6vY5PP/107WcwiWs2myGVSkmiGe8Z30XW/Uy3U6vVGI/HGI/H8h4x1clisci7zJQaEmS73a6Q8rnXKU2NaDrAAThF56PRSAy5XC4XZrOZ3B+32y3GK7ucbZmE3el0cHx8LM8Pz3j9fh8+n0+G60yvMhqNKBaLQu6lIN/lcr2X2YfH40G1WpUULKbesv4xm82YTCbodrt4/vw5XC4Xzs7OJBV1NBrhm2++kf0xGo2ukXLn8zkcDgdevXoFk8kEv98vxOpcLidnnfF4jFAohF6vh+PjYzF4mM1mIoZRqVQiPloul2vic6vVKmk/k8lE6hkO2O8y9/gQWK1WKBaLqFarmM/nW8kGSvF4Pp+HwWBYO7fxbMC9YdfAR6VSIRQK4fLyEr1eD7Va7Uk4+ITvHExd4lqVzWYRCATW0k9XqxUmk4mQ02lUQtL1fUFhdKFQQCAQ2Pt+k3hts9m+UzH1x4hQKIQ//dM/xWeffQan0ylJcewnkIRPw6vj4+PvlGgyGo0wmUwwn8/v1TukCIpGmE8C9NvgmYFCg23Q6XQi/Li8vJR+wFOq/BO+T7RaLWQyGRSLRUmHTCQSMmPpdDryZzwvfKzpUh6PR4STuwRsBFOjaf54X9yVkKica7hcLrx9+xZ+vx9OpxNnZ2ePliLPfvJisbjX97TZbKjX61gsFhiPx7eEyX6/X4wYc7kcAoGAiMmAm7qCCc8UeVJ8z3ONzWYTQjKvEQA58xgMBklDff36tZwtHwuTyWTtDMd+1GPBYDDIHIHGnUrDUZVKJemzm0mvd4GmcvV6Xc5gPIduM+EklAZynU5HTDf5eTbBPoderxdjTj5Lfr9/7XrxPWLtqCT1MQV4Op2iVqtBp9PBYDDA7/eLiJbiCRrZ7IPX68Xr169F+KnRaCQ9nYl+q9VK5kKRSATj8VgMyDweDyKRiDx/NEqcTqcolUrSA2Qyb6fTkeRiGjW5XC44HA651jQh73Q6knhsMBjgdDrxySefbH3vtFototEoksmk9MaUf6/T6chzuqsep/B9sVhgMBgglUpBp9MhEAjAZDJhNBqJITYJ/PxeVqtVzNVbrRZarRbC4bD0wRaLhZhFchbFXj5FmsC3ZslMjBuPxyIqoYkg56kkfAYCATmb0KiBM7xqtYrJZIJisYhgMLhzhsFrxN7MpkjG6XTuFeL4/X50u120223kcjlEo9FbP4dJkAD2moCvViuk02lJV2y322uztm63i1qtBofDIeLt6+trESEoP7fZbEa320UikRChh16vh91uRzQaRSAQuPV7McWZc3aKnDUaDYbDIdRqNVwuF+bzOWKx2NbkQJqp7ktfo+Ey3zH2gi8uLvZ+XS6XQ7lcRrVaFTNJ9vir1ar0w5l2dX5+vvX7MN2Z6dnsz217v9i74fxkH2gsve3aEqPRSPY1l8sFt9st5+d+v79meElyM023uffSULper7+zCNBut+OTTz7B3/zN38BkMkGv1+P8/Fy4C263W2YznOv7fD4Mh0PhL9hsNpmRFQoFAJA+fKPREDNhCsppzMG0dwqugZteqlqtFvG4RqMR00madjqdToTDYQSDQZnP9Ho9OesAEN7P6enpVi7BarUS09BcLifidd5nmqXRAAkAzs/Pd+7rmUxGxN9XV1fodrtiwMp132aziWDL6XRiuVxKUjl7o9zfiFarhel0Cr1eL+8T5xwUFDqdTkmmJ+dBq9XC7/fj6OgITqdThJQ0K/J6vSiVSmi1WiKc4+9Gs0Gl+fR9Z0k0atDpdPeqsbiXf8j0ux8COFveZXK7Ce6p7xqQoAR76sPhUGobnU4ncxiKE9iXCwaDawZYjwH2hwBIjfhDgNVqFUMMJuvuA/cXpu9+jOj1emI+TGHxwcGBhLxQsKM8m98VOPOuWCwWEhJDc+f3mfkw7IL80E3exSZofESjpO9anMdgJYYc3fXMMEylXC7D6/WiUChIz5X7D3sVXq8Xi8VCjIgIGkJ1Op17vedOpxOpVEqE4AaDAd1uV/6cnBimyisxHo/XAlQcDgdSqRRevXol6etqtXqNU0lzBJ5HGETCWX+n04HFYsHJycne9ZRnKwap0NQegJhgDQYDHB0d7Xzm9Hq9nD9ogrePv/RjA/dtnrd29enH4zHy+TxqtRo6nQ58Ph+Oj4/FaO309BRfffWVnJX/9m//VkzKKQSk6ZXb7b4VJhMIBNDpdNDtdpHJZBAOh7cGiJRKJVitVjgcjndOPN+EWq1GKBTCbDZDNptFKpWCx+ORsyZnGAymokHCrnpnsVhgNBrJu78JBlPREI3BQi6XS/bwTCYjJm/8OcPhEI1GA8PhEPF4/IPMzabTqbxPNIl8wvcDBjL1er07/y73WafT+WDu9RN2g4bqACR4Yx+m06lw+Z7enfdHKpVCtVpFNpuVc3coFJJ6rtVqodFoIBaLSa+IxnPkx95V0/PvVKtV2QcfIuJeLBaStq4MZdoE+UA0t6vVau80E1ssFqKPmEwmEvLBYE+73Y5wOPzRnk+e8ISfEn66XbEnYLFYIJvN4vLyEslkEslkEul0WobcL168wJ//+Z+/0/f+4osv8Jd/+Zd48+YNGo0GdDodPB4Pfud3fgf//J//80dL7HzCE36omEwmMoDl8JoCqF3NQDr5kPDi8/nudDEym81rLuC/+MUvcHV1JckT3W5X3P0pMLdarTg6OsLV1ZUQKg4PD6UJ02w2ZVh+cnKCcrmMdDotjREKyQGI43+/38fz588RiUQwmUzw2WefCQHTYDCIazrXis3UG41GI6l7wA3BgkTg+XyOV69eSYKxzWaT9HAKD9h0SyaTaykDbFJ6vd5HTe2zWq14/vy5pMZNp1MRznD4yftM4Wu1WpUGzzYYDAaEw2F88cUXQkrYFI+w8Wyz2STRkQPYu2Cz2XBxcSHu6nT7H41GCAaDODg4ECIHm/QkVXz55ZcAIMkdBNPbOXAkOHg2mUwijNw8GITDYUnOZZoqm6btdhudTkeSDn0+Hw4PD/cSpAaDgRDeKJYmCeSuwVCn05H0iW0D+PF4jFwuJ+SjV69ewev1ot/vw+PxCPEokUg8WCTP5OharSYuqhTUVioVGaZtprCYTCb5PADk9+Y7/i7gcJCkKZ/Ptya0obu50WiUJuJdies6nU4Ss5Vkr32ECKWzusFguHOAQoEXRZlEq9USJ7lms4n5fA6j0YjVarWWKkzRvfIZHY/H4rbudruxXC5FBMpDfr1eh8lkwsHBgQxamGicTCZhNBrx2Wef4Ze//KUIxgqFAiKRCM7OzsRMgk6/TDdZrVbweDyyHj4GPB4PGo2GpAXsGzhQqB+NRuWzXV5ewmg0wu12C/GMQm8Spkg23Cc4slgsODw8RDQaFWIKRcHcg9hoZ/M5m83CaDRKckipVIJGo4HdbpeEpZOTE3nP+QyMx+OdAjyStpgwRMMA3lvgW2OKN2/eyDpFoWm9Xheyw3g8xmQy2UmSY0o58G3KG51x9w2b5vO5pO+SHMhnkiJnj8eD5XIphJdQKITpdIrz83Po9fq1piBJIYTT6UQgEEC73YbP55NkDmXSst1uRzAYlOd0NBohlUrB6/Wuraur1UpMMVgH8M8pxOt2uzg6OsJ8PketVsNwOITD4UAsFhMyEhMYmIy8SRCliJUmBiQZEHTSBiCE8E6ns5bQoNPpJF15W8JEMplEu93Gy5cvkclkMBwOZWDFNPpOpyMkTuDm/SqVSjKoUqlUCIfDUKvVMvgGIGJfEjVHo5EI4g0Gg5CfuT/TdZ4NeYpTWeOZzWY4HA4RT7ZaLRG0jsdjDIdDSRfmPdwm5CCpjd93G9GQ6wDXMavVumaSQYJlOp1GKBSS/YzDc+4hOp0O8/kcXq8X0Wj0TkOA7wNKExgmoihhtVoRjUbFZKVer0vdT9JyPB5HKBSCz+cToif3Qbr2ss6jGQaNhXY1g2lERYF7uVzG9fU1jEYj1Go1hsMhRqORnA10Oh2WyyWurq4kkYckHT7LNEsKh8Nbf2apVEK1WkUmk4Farb5ljDKdTlGv15HJZLBcLpFMJnFxcSF/TpF6rVYTIYBarRYSOtOY76pbXC4XotEoxuMxvvrqK2m6U7DmcDhENMukbta1fO4mkwnevHkjRiIkTm7+HNZlNNug8Qoxn8+h0WhkgG00GjEej6HRaKSp73a78eWXXyKRSAhpgGcFDmNJZvB4PPI9AaztQ3q9Hnq9XmptEgRJ5PX7/UKqpSMtB6pMRtgECfMkWSqhNEDw+/1Cms3lcrBarfI+k4DLdKlAIPDOJC+1Wo3T01P5niRZApA602Qy4fT0FA6HQ4gARLValdSERCKx9v7QgI1pqtPpFNlsFpVKBd1uF9VqFd1uV/bmdrstRg8ej0cGPiTJ03CH6S/Kc4mS7Mv/dblcIqpvt9vfuSiJRiTtdhvhcHgniUClUiEYDK4JAvjs8BywWCzuJACwfuB13Nf3eMITPhSYdmqxWNBsNqWGNhgMUnvRwMPpdErvo1arIRgM3vuZZZKiVqvdK17n+YUknQ9lqPdDgsvlwh/90R8JsZzkftbD7EUEg8FHFcjdB8oexX1+tlKA+FNORtsH1lwWi+VO8j6Nutg//ylhPB5L70WtVktv6QnfD1arFbLZLKrVqiRMKGtMCo1o7FupVCQt8kOvWzz3c75Ls9Z975fb7UY+n4fT6USxWEQ8Ht965hyPxyiXy5L++pAE7vsmJKrVavj9fjnTvE8Pexs2e8+bmM/ncibluX/z65QwGAw4PDwUAnmtVkO1WpVaeTQaYbFYwOVywe/3IxqNyrvL70fj0/l8vpb0xjmgsifCfvlj7CmdTkfOO0wg1Wg0MpvbTHt7H1gsFjx79kwI1Dz/sk9sMBhkb78vcXE0GuHq6krM2zj3oLCcPafN9Dfg5jpybpNKpTAYDISMvlgs1sjonG3RUNpgMEhqO+d7ShE/z8gUQFJAzPkVhTDxeBylUkkM0AjOCbcZ722CMzL2VcLhsJAALy4uMB6P0Wg0kM/n0Wg0cH19LZ95sVhI72M2m+Grr74SY0waYA8GAwQCAYRCIcTjcZl/FYtFETx4PB4EAgHodDqoVCpMp1Mx06WItt1u45NPPtlrHEPBfavVgt1uRzwel/tG4SdrwV2gGWQsFsPV1RU6nY7MX9hjSiQSsNlst8SdFKCz18x7SIEvTUSMRqOQLSk+LBaLQoLW6/ViRALczL1IqA+Hw9JvoeiDpqTsO2i1WiF0UmBZqVRk9uF0Om+ZHtAULhAISBrwQ+B2u0U8msvlkEwm4XA4ZO0dDAZiysXk+G3zlNVqhcvLSxSLReRyOSyXy7W/2+/3kcvlYDKZEI1Gxayd94HpSMC3MwqmXPr9ftmDDg4OEAqFbj1PnLfxmbfZbDg8PJT9bDqdimEF98hte9mu60fRD8UTAGQewITdfTPmWq2GcrmMbDaL+XwuXAz+PPZP37x5IzyCXaIYCrvZA+Q/uz43hYbsu25bZznbMpvNMBqNO00GaPgxm83WelgqlUrWLfaMOLdl34zGDzSpbzQaOwXo5FVQoMw1wuv1ynUZDofSr/vd3/1dqbMo/ikWi+j3+9L7oyib/eZNc0rg234BU0S5D3u9Xtl/K5WKmKnz/aMJBvf7er0u+4rVapVggkgkstZjt9lsKJVKYowSi8UQDod38jloUpLL5WCxWISUThgMBjFj5ztFQ/1t36tarSKXy2E2myESiYgJOGsVBlxwn1LyJGjOzvvpdDrh8XigUqnQbDZRq9Xg9XqFU6E0rLbb7WIAruSbmM1mfPLJJ7LWc2+lYR971dVqVdKMle+w0+lEJpOR/vR9hUqc4TgcDlQqlVvr7CY6nY7wSx5SD//YoFarJSzjPpjP5+8t8JpOp0in0zLP5JpPQ3IKHsjLoXEda8/3Tb5erVYSVME6nzMLrp3kr32s4BnD7XajUCjsTRBknR2NRuF2uz9aw4VcLif10+Z8kmbZTqcT2WwWhUIBGo0GXq/30c46AISX0Gw2hb9E/oXb7Ybf77+XMGgTSv4IeV/71jaleRJn0d8l+Pv2ej1ks1nZH7eBHFTO2vn3N41cfT4fOp2OmCGp1Wp8+umna2cFBpzc557y78znc1nDWXPQ7JoBBkrwnacQluemTqeDRqMhplnkJ9HcC4Dw0Ngn0mg0YvZVq9UQiUSwXC7x7NmzW/UfzccbjYactQDIOYIziHa7DbPZfOc14H7KhNWfkgDd4XDImZ7cp21gvVsqlRCNRiWMA7ipGTweD371q1/hiy++QKvVgtlslsAInU6Hg4MDSfhOJBK39gTWhKvVCuVyGZlMRgymVquVcIAcDoeYEz1mXywQCMh5tdlsyr7G4KjpdCom9DRt3wWaarE3oES1WhV+Ek34aTRmsVhE4M5wLAY60ODGarXi4OAALpfrQUaF+0DzefLQCCYD+/3+JzHj9wCGOPHcuO955zNns9l28jyf8HCQV2kymdDpdO4UJrNPus2s5QkPA7lG2WwWer0e0Wh0bd/gbKdSqSCXy0Gr1aJSqdzi7t2Hc6OciTx0TVWmrd91pqLhHLlPd/GeNzGbzcRckn1UAKKB4TXp9Xo4PDx84lU84QnfMz7OLsETPjh+/etf4z/+x/8ozoyPheFwiP/0n/4T/vqv/3rtvzMxOZPJ4C/+4i/wr/7Vv8K/+Bf/4lF/9hOe8LFjuVyiUqngq6++wuXlpTj/zmYzzGYzETAcHh7ubOJS2ETX84dCp9Ph2bNn0njv9Xoy+LHZbOLITrF3q9WCx+ORZhfJML1eDxaLRQZtSkHiycmJCA+z2SwGgwGm06m4ztNJkt+DLvMcxG1rzFM0piRkuN1u/Mmf/An0ej3S6TRGoxG8Xi9CoRBsNpt8XxIeSJ7n/9rtdsRiMUnU+BC4D1GKB+n5fH5nYUxxZ71eR6PRkEO1cgBOIibTUl+8eHFvYqLBYMDBwQGi0SguLy8RCAQwHo9xdna2do0CgYCIw+kO/erVK2kgORwOcYckUZJEILq4M9HW5XKh2WyKmz/vPwVayWQSOp0OPp8Ps9lMTBusVitCoRBMJhOOjo52NrBnsxmur69FONfr9UTkQ1dVt9u9972r1WrodrtyUFKCzoxsMHPQZLPZhHCSSqVEpHx8fPzg581qtSISicjAnQPQYDCIQqGAXC4niTsESWls0jFdgENoAPJukvB1V3OAwxI2JZlWq2zQ6nQ6SXmgwGnfQJFpp/x+FGv1+/07m94kyBkMhgcPUYbDIVKpFEqlkjSZnU7n2sCi2WyiUCgI4f358+eyFlIcxiRypir0ej0YDAbU6/U10ZzZbMZwOBRR5mg0Qi6Xg9vtlkRzitBOTk5E8My0aTYAXC4XBoMBCoWCuKY+hqsg0wFICiDpahMkzjmdTnmnvvnmG7nP1WpVCDButxsejwdWqxXpdFrSUXelNyjBIeguUHTGIUCtVoPJZJLUll6vh0KhgOvra4RCIUwmE7x48eLeAz6+XxwUFgoFWCwW+P1+2bOTyaTscR6PBxcXFwiHwyL8pQkTa22+L5vgXshEXg6hGo3GTtHneDzG27dvkcvlYLfbcX19LYRDmpVwSHBwcCDXKpfLSVLCxcUFbDYbDAaDJFErPx+HI5FIRIwDOFyjgYvFYkGr1RJho9FoRK1Ww29/+1u4XC74fD4R8XP9I7GEYhy6STP5hder2WzC5XIJYQm4STnO5/Mwm81otVryjkwmEwwGA9TrdRnueTyeW4I6JnLQFIHkbO6bJCSS5MTfEfg2YaLRaCCVSqHb7Uq6BklWl5eXQkSjAQmfJ5VKBbfbjfPzc/zhH/6hpKN5PB6k02l5bwBIispgMFgTOM9mM0minc/nQvA0mUyy74zHY9RqNbmOJO9Mp1MZprCWIjnCYDDIkHYXOLhW1mFKUORZKBTg9XpvNf2ZDlOv1/H555+LMJU1BEmdJO7p9XpUq1UcHh7u/EzfF0wmEwwGg5Bjt9VuVqsVJycn6HQ6GA6HiEajQqwejUb45JNPpDYzm83w+/2YTCZSVxkMBlQqFQwGAyEHA3eLnVjLd7tdIV4zdW00GgkJLxAIiPu/1WrFYrFAuVyWdAjWmzabDScnJzvJiZVKBdVqVdLBN/+eXq+XZOdisSgJRna7HePxGJeXl+j3+yK65vPFZJh2uy0i6n2NcJVKhdPTUyyXS3kXSZBlqplKpcJ8Psfp6akYa9EgiU76fF9msxlqtZqk3SjBofHf//3fizFTIBCAWq0Wk4HlcinEZe4ZNG0iUalUKqFWq8HpdGIwGMj357tEJ1em3TFhfTgcrp2X9Ho96vW6EL4okOa6TUIra8B0Og2bzYZKpYKjo6O1342prqPRCAaD4c5hhdVqxeeffw6LxYJ0Oi3PLgkU5XIZDodDSBtKU7SHQK/X49mzZ/IMsc4kKZckonA4vLbP04233W7DZrPdGpgpa7lIJIJMJoNqtSpE8tVqhcPDQ9hsNkwmE2SzWZTLZej1enF+p4GCTqeT/WI2m92qYUmYV66jVqtV0paVBkkfGtwnvvrqK2QyGbRaLWg0GkwmEzidzq0kZ61WC5vNJuJx5e81m83E1OYuGI1GMSmg6OEJT/guMRqN4HK5oNPpcHR0JOdOEmCcTiesVisMBgNCoRDm8znK5TL8fj96vd69E5fZ24pGo8hkMlgsFvB6vbIOrVYrMaljUtr3Iaj+WMEez2w2W0vIMBqNcDgc35t5hTLhbBsZcRM0YXtKAtgNpund9/rQZGjXmeTHBp6zWRtSBEdTKqYsPuHxMJlM0Gw2pe9JI1llz5G9iGaziXA4vNcgLBQKIZ/Pw+v1otlsfjBy3HA4RKFQQLfbFTNSrpvsz1FYtAmKCtkTSKfTcDgccDgc0tdut9vSd2af5yFrsdLQ9D5gf+AxEhKV0Ol0Iiwfj8diWEpDL+WsXKvVCgGXwuxtcDqdOD09hU6nEzEU0zO9Xi/sdjsMBgOi0egaIZf3gn10t9u9tw+qTHN83xo6l8uhUqnI783Py94WBdbHx8ePZnZhMBiQSCQQjUbR6XTkHXuXvX00GuHNmzdoNpsol8siHOOZJp/PQ6fTIRKJYDab4ezs7NY+4/P5xAw6l8vB4/GIwVu324XH4xGjWfZWaA7Hc6bH40G9XgcAESJSXEBjApVKJclmg8FAxCculwuLxeKW4JpznPvsi0y6a7fbOD8/h81mE9H38fExJpOJ9IxpBk2jTqPRKCbdFJANh0NUKhW0Wi30ej0JEeC7SwH02dkZ3r59K6LW8/PztbMwUx8rlYqkKCn7ZhSvApA0TZVKBavVimKxKH0UXgMSIznn2lUv0xRRrVaj0+kgFAohGo2KsJNGw5uGo/w8JpMJFxcXaLfbKJVK6Ha7ci4IBoOSVFev16UHw3S4breLcDgs/fjZbIZKpSLmxMvlEmazGV6vV+anFDnSGKtarYqwkyZ//J2/+OILBINB6YUof89yuSwG2+9aExweHkpvlmIUzqQMBoOYQ7pcLhwdHd16XzkfevXqFYrFoqQYk//gcDjgdDoxnU6Rz+eRy+Xkv9EAdzOBjrMp8gnIN1CpVGvGjESr1UK/35eZ2aZQgunnFosF+XwezWYTpVLp1rrLHpOyT9dut1GpVETIzD7ieDyW78E9xO12i5CCoLikVqthPp8jkUjcWsfZPy0Wi0gmk/D7/ej3+1vXYF4bGkpzXqwEDS65llMIl06nZW/ie0WuyHK5RCKRELOMbRiNRhiNRvJeARCjFRrN0LSA33u5XMr7YbFYxHiaxuXK9W48HosxCI0wKKK32+0olUrw+XyIRqMyswdu1nSuDaPRCNlsVpLhmVTPMIPFYoGzszP0ej2cnp5KAAJ7yBTOt9ttEeXwDAIAoVBIzH0pCqIxN58nJtauVisR2PD527yXNKdkYvu+WomzV6ZX7to7g8EgRqMRms2mvEebf5ffa7FYIJFI4OrqSuoewmw2IxgMIp/Po9friTCKqfLsCU6nU3lnXC4X3G63zMr1ej0Gg4HsIQDEdIP/zl6sx+NZI6bTIIbCZQAyh+YzonyHubbfZYq/DT6fTxLaKpXKThPB6XQqJqQmk+kHawrG+pfvh16vX+NH3AdWqxU2mw3FYvGWYHQTNOsJBoPvLKKjeXCr1RIDd4ZOVKtV2Qf5Pp+cnKDf74uRCwDZi98Fi8VCzMn5j7KeIWei0Wjg9PT0o+5503ie3N1AILBWj5NzVqlUxJTisdJ3Hxs0Amk0GvD5fDuFg1qtFpFIBNfX1xiNRqjVao8WmNVsNsUYgdeV5wCr1SqiuoODgwcbEJNbwLWG56hdZ1NyNu12u9QK3zWCwSBarRb8fj8KhQKGwyFcLpes4+PxWJKj/X6/hCqYzeadnBg+n+QnNZtNqXt5lgFwr7Wff4emZLPZDHa7HcVicS2sSLlvzudz1Ot1ERjTZKrT6aBSqQiP0WazYTaboVwui/B2Op3CYrHg4ODg1vrn9XrFSEOtViObzeL4+Fj+fDgc4vLyUsIneNYD1tedcrksoUR3gX0i1t4/JZBH43a7JZBps+4ivyefz0ti/Da+otFoxB/8wR/gq6++knf04OBATNw3zwObIPeA3DFlmJPNZoPT6ZQ+0ofo+0ajUZhMJpjNZgm9IY/3+PgY2WxWelCc/26CZ4hAICChU0Q+n0epVJK+E7lIi8VCAgxZO/j9fng8HjmXq9VqmM1m6PV6eDweRCKRR5mrTKdTJJNJWauVxmLkrjSbTQSDQUQikff+ee8LpseTV0dDWo/H84OfMynNP8mVoylOqVRCLBbb+v5sPnM/1Dr8Y4Rer4fD4YDL5UKpVJK+4Tb0ej30+30cHh7C4/E8zbTfE+xrrFarW+JzABJ8MZvN0Gw2ZXYRCoWkd8aAorveCRqLAFg7d98H0+lUdBb3MR0wm82o1WoAbnrF9/15NJdsNptiYqk0mJrNZmi1WiiXy3It9Hr9k3nIE57wPeLj7Xw84YOCYtHHxHw+x5//+Z9LEi4AHBwcCGns1atXkjD6X//rf8ViscC//Jf/8lE/wxOe8LFiPB7j66+/xps3b1AqlXB1dSVEFxKxSfhfLBY4PDzcWoBVKhUZhL1rs2GxWIhzkjLNQQkKVWaz2RrJnIOz4XAIt9stAzgO6ZgcZTQaEQwG1xyHK5UKXC4Xzs7OoNPp4Pf7RXRz1+edzWZrZE+Cg+9EIoGvv/4ah4eHt9ycmQat0+nE9dxoNCIej0tT/fsERXDKoe0uuFwuGeaWy2Wcn59L+gCNPjiUZRI1k7N9Pt+9i3qNRiMN2G0CfY1Gs9bEDAaDyGQyMqAzmUzIZrNCyuCAWKfTiQssG8v9fh8ulwudTgeXl5dymFUaB1Ao5PF45LOQNKJ0Pd/EbDbD69ev0Wq1UCqVxDCBzzHFWAcHB6jVavj93//9rd9rNBrJgW3zfSEZtF6vw+v1wmazoVwuA7g5tMXjcQwGA2keazQa/OxnP3vwQZwJOqPRSBp4RqNRjAhIEiDhajqdiqgsm81itVqJ+3K9XketVpP1ht//LodmJqSWy2URW22CrpWlUgmr1QrBYHDnAXS1Wgmpi7+Tx+NBr9dDKpWS92ITOp0OGo1GBFJ3Je8Mh0NpYLFBWi6X0el00G63cXBwcKspT6KMwWBAsViEyWSSNBHgW8ENSYjT6VTWTCZmL5dLGa7rdDpxZlssFvD7/ahUKjLQ5jtL8SvTrLvd7lq9NhgMYDAYkEqlhLDxWI3neDwu4jM6DVMISkF+p9OBxWKBw+FAKpVCpVJBoVCA2WxGsVgUMxG6//b7fUQiESHbulwujEajBzk9dzodeXeZema325FKpYT0FYvFYLVa0ev10Gg0MBqNoFarMZ1O8cUXX4jz9B//8R/LOkgh6K6Bt8PhEBG30+nEcrmU5ASSDdVqNXw+H37xi1/IAILktEAgIOsxCYtut/sWqabX68ngUZlmks1mJY1bue7Q8Y91BAWnTEsGgEwmg3Q6LUlWz549kwZgsViExWIRESiHLUxf5PVh4oXdbkev15NBczgcFvOPdDqNwWAgyV9cR0hyGo1GsNlsOD4+htvtltQZul+/efNGjAxINiM5yOVyieMs4XQ60e12xaBgPp8LObHdbiMajcpQhinFSjAZiGnto9FoLT1mOp2i1WohlUohGAxitVrh2bNnknDDhAkOQnO5nJC+EokETCYTMpmM1GrdblfS6Xgm+uUvf4l//I//saxFv/71r+F2u5FMJkWISqI3yaMc+iwWC0kZJqmT7xeTNICbPatQKKDT6WA0GglZjwQirt1qtVqEsfucLlerFabT6U7xzGq1Qr1el7pjH4mDNd3V1RW8Xi9+93d/d430xv00nU4L+eWxiADvg9lsJoYSFBKbTCY0m03Z/zfButhut+PZs2cAbgZGjUbjVgOYtaoSFMWZzWbodDosFgvZW/bVEBqNRoabyiQqkjSZ5MK6ggTno6MjqUOtVqsIbHc9F2xyMwVh38CLaxRTZI1GI96+fYtGoyHpWYFAYK3+q1Qq8s6qVCrEYrGd3x/4VqRMsXYwGJT3yel0wuVyyT6Vz+eh0WiEDEfCPc8EL1++hMVigdPpvGWCQNMHv9+P+XyOyWQCj8ezlmBOQQNJeHT3pot5pVKR9yYejyMajYpZAAmzo9FICPNutxvBYFDIgMo1crFYiMCN4nMmw1BIqXymSNBg+oey7p3NZrDZbEJCZ/LaLnD4yWGDMl1qtVphMBiskbxI3H4XaLVaxONxRCIRIfZSgOFyubYOwJmU0+/3t64jw+FQakk+Zxx+U4jN2p818mQyQb1eh8vlwnQ6FRE6jR+2geJ1Cih5HnA4HN8pyWO1WiGfz4tpUjabRbFYxHg8FsMLkrW2kZOY9sd0F64rNAW6D7ifAbuTxZ7whA+JyWSCxWIhZoQ0y+MaQNIt330KXbmePwRHR0dCYqlWq0ilUmLWMJvNsFgsJH2NAvQnrIMEvo8F7GlZLBZJxtyHdrst+8nTAHw72Odlr+QuMBHzh060ug8oQGu1WlL78MzEFEjWshSOPeHdoTxD06AIuKl/SIKLRqMwGAySbkWDnn2wWq3QarVot9trRrIPARN6Op2O1JMUTxiNRnQ6HRGYMkWMZkrsIfBZOTs721o3u91u2QtJbs5kMvLn7D/abDZEo9F7Jx0S7CV8lwmJ28C0XavVilarBa1Wi1wuJ705Ze+RKVZ2ux2TyWQvucpms+HFixciLlCKedxu99ZzNedTDocD7Xb7ThNW7js0w3tXFAoFlEol5HI56XeS/DyZTNBut5FOp+H3+7FarXB+fv6oaTZarfa9hBDKlGMmiLvd7lsCgUqlgkwmA5VKhVwutyYmACDpyqvVSpJ4mYzMtVej0UjyrcfjkV53qVQSQfd4PEY2m8VisUC9XhcDC5PJhGq1Ku+oMsXVZDKJAR7/mU6nIqqjceA+KI0TrFarJLqTTKh8d9xut5iv0QiSIop6vY7Ly0v83u/9HjweD1wul6whk8lEehcajUZmLwaDAWdnZ7i+voZGo5FrAUAE7gBE0M7zIvczZXKfRqOR+lvZm6CwAoCYbrLPepdhrUajwenpKfx+P87OzmQmtMs4g+bgAPCrX/1K0rPL5TKGw6E8IxaLBVqtVgSRnFHM53NYrVYxFWAPgsZ14/FY6v9AICBprBTGqtVqEa6kUimsViskEglJQR6NRigWi6hUKqhUKohEItJXVM77jo+Pd85I73qWuL6wr0xjd4Iiis21hz0Gmu6nUikxei6VSmJoweRpGo5fXV2JITsTodlDU6lU8hxxRsG+f71el8RrJqzzc9ZqNUkl3nd+oJCBiY+DwWCtTlemrw2HQ0wmE5RKJRH98P1RmhHQBHK5XOLw8BDVahU+n0/MNFlbtNttBIPBvRwAn88nIrZms7mzzgiHw5KM2Gq1RHiv0+kkRGCxWEgSsNFolJ5suVxGuVxeM4PlPfZ6vXv7rzRF2NyftVqtzMYNBoPMpU0mk/Q1w+GwcBu2YTwer4lL+W6xP1mv10XMzRkSZ/bsk89mM+RyObk/drsdXq8Xer1e1r/lcolcLifr0PPnz2V+Q0No5cx5PB5jsVjIbGu1WsFoNMJkMmGxWCAYDN7aw4fDoRgmXF9fw+VywWw2bxU1s95wu92yf2zDcDiU54hJ4/vAlDQmWCqfJQo2GUCh1+sl8Y5p9YRer8dwOBQzSV4Hm80mn0Or1YooksLV8XgsP4fvJWsYpRk251zstxM066QpKM0KuEbQ3EgJ3lOlGPG+oBFvNBqVHq7H45EaiDPFWq0mYq1dQsmPGTRZrNfra4aoNOFxu90IhUL3Mvz3+XxoNpvQaDSo1+t7BcrkDd01O9wFpRCBZzO73Y7lconhcCjhATQnAm7qFa5/6XRaTGHe5+fT5J1za+4ffJ+43wHA+fn5RyvIoRiWvQWaTPN5Z/3j8Xjg9Xpl9v0xotlsCsfyrvAPzvJpMPIYc+d2u41UKoVyuSwmyNFoVIzcOp0O8vk8HA7HrZr6PmBdHw6Hkc1m5YwRDAa3npWr1SpGoxGOj4/vFMB+KBgMBpycnAC4WVsajQaur6/XehQWiwWxWEzMvfP5/K3z2ibsdjtqtZpwEVnv8ZzA/fSu/ivN21mvhMNhOQNeX18LN5F8qX6/j06nI2sK61zWiyaTCeFwGIFAQM65fr8fl5eXyOfzmM/nuLi42MpFUorg+Q4yJXQymeDy8hL1eh3lchlWq1VEw+RSsE9EvgvN3sghMJlMW9fzuzghP2aEQiH0ej0MBgNkMhkx2uL7xOtdq9VwcXEBk8m0s/+iUqkQDofR6XQQiURwcXHxoMAc9jIajYbM64FvzysfWmjMn8EAJyUP7/DwENlsVsJenE4nHA6HcFDb7baEi7jd7rU03Hq9jlKpJPVUNBoV0zngphah8QIDDI6Pj9HtdjGfz6FWq8Xc6bF+//l8jrdv36LZbAo/xOVySW3PgA3W98DNs8IzK/uwDxVsvivK5bKY7WzeGwaBRCKRj7bO2Ab2hGq12prZG7mQoVBI6nsGKTgcDmi1WjFG7ff78Pl8cLvd7xx48ITdCAaD6Ha7sj4GAoG1QAyaAlJ7wPCmJ7w7+I63Wi04nc61d5ocK4aTzGYzDAYDlEolMSh6/vy5nLG38Q+VWCwWaLfbopF5aP9QWTM8JG39oSD3jPXzJm+CWiOr1YpsNishaKenp+/0857whCe8P54E6D9x0KGd/3z22Wf4i7/4i3f6Xv/jf/wPEZ/r9Xr8+3//7/GP/tE/kj+fzWb4b//tv+F//s//CQD47//9v+PFixd48eLF+/8iT3jCR4zpdIqXL1/iiy++wNXVFdrttoiXnE6nOMpRdLlYLDAej/HJJ59I0UfH/cFggHg8DpvNdu/0JeCmuOt0Omuu0MDNkN/r9d4SJ/Nwp1ar1waiytS28XgsCSwejwftdlt+FkF3eavVKp+fgjmn0ynD2H3FabfbFcLHtqYsHXMtFos4s1M8wYH/YDAQ99tAIIBqtSr35qEgWYSih/c92PN63afJx2ao0+nEaDRCMpmUpiKduDkQ5Ofk8Hk2m+FnP/vZvRsjHOTeZ6jgcrnWXD01Gg16vZ44QbMxR0JDLBaD0+kUR/vpdIrDw0Np4vb7fahUKnHnNplMiMfjcDqda254d4EEPA6U+bkqlYqI0JlI0Ov10Ov18E//6T+9RQJgKtPmvea1bzabQlQHvhX38d5aLBaEQiEUi0VJfH/okMvlcslQhIQhtVqNaDQqJEE2mvn/KfKIRCIizGTasJK8CEAIrNVqFRaLRVJOlI1SOnBWq1URKCtJOBTaMRGAa1m325V3k+55VqsVzWZzjXjPn0HX0Xw+j3g8fqtZazQaxXVTo9GIC+YuNJtNGWzzOeW1dDqde59xEgHZTKGz/Kbghs8+ny2LxSLvJJ9VpasuxUxMlFCr1XJfS6US6vU6er2eNNfUarU461MgVKvVkEqlHo2Ub7PZJMWCydb5fF6eY71eL056nU4H6XQaL1++hNVqlSEG3ef52fkMBoNBMT24D1kfgIjz+TuTZGA2m9euA/eASqWCRqOBbrcrYj/gpjnz+vVrSRx49uyZ7EH1en2vM6PL5ZKhsd1ux+HhoRAT+d79/Oc/X9ubOHyeTCZr+zv3SQ6gmGTvcDjWGnTxeFy+Lp/Po9PpiBHAcrlEMplEPp/HeDzG4eEhAoEAWq0WisXi2jUnwTkUCknaDAmkJEvY7XYEg0Eh2mazWQQCAZjNZiFFGgwGVKtVcX+mKP3Vq1col8vI5XIihD4+PkY8HketVkOxWMR8Ppdh3/HxMVwulySJq9VqZDIZZDIZlEolSUKlccdwOMR0Ol1LF9RqtdBoNAgGg3C5XCIudblcUKvVkqLudDpvreMkhClFuJvkPP5unU5HEluy2SzOzs5QrVaFOBQOh5FMJiVxGICQdfhsDQYDWK1WRCIRWUfOz89xcXGxtg9zOMOhRrfblfQLCoObzSZGoxHsdrskUzD9gwQmZUORA7FsNiskPmWDmIMSrlPAzVq3ax1lWsmu2rPf7wtReR/xZj6fi3hiPB7D7/cLcYifi87suVwOpVIJOp3uTnLeffCQWksJEtdI1h2NRgBu6udKpYLFYiEmQCS08OsoAKB5jtlsXltT7wLfZZvNJoYPo9ForyiYDvdqtRpHR0fyc/v9vtTRHHSTGLNYLBCJRIQYYDAY7lVjca3VarV3Jleo1Wo4HA50u110u10RdxSLRfh8PjGXaLfbMlw8PDxEoVBAPp8XA4+7yANMtA2FQtDpdBiPx0KGU6vVYnJTrVZl7+U6x+eTIvi//du/lesUiUTWDJDUajX8fj9KpZKQKSkEHw6HYh7ABjyfc9YvRqMRuVxOhOcc8AIQh1vuY1wb6AA9m81EBG21WtHv98VAYzAYoNlswu/3IxgMbiU2OhwOpNNpMcxRmnwANzUr9/NWqwWTybT1/RsOhyiXyxgMBohGozg7O1sjtDDhgc9utVqV5Kz3OTvx7HofcK0kmWoTJLxoNBox/Fkul0Lw2BSKsKbT6/ViWuB0OpFKpdbInDqdDpPJRIyiqtWqEEhoMKDX68XYZF+61GOCBin5fF7WMhJWptOpJMxTYL55nbkmKckpJN3OZrOdxlFKMDGJZlJPeML3CZ1OJ4Kdu0Bx0EOg1Wpxfn4uLt08vyyXSxEt0rQqGAz+5FJHfojgmYoDcNat29But8UIatNQ7AnfgiZaTHHdt4+w1tkmgPqxodVqCdFvMBjA7XYjFouJ2IKmXTQColHPE94N/X5f0uz4LPI8xHqc4s7T09O1PsV93m2LxbIm8HgIKBKj8RfJmDS14pmHBsFut1sI7wBkXpBOpxGNRnF5eYmLi4ut9TgTdKvVKux2u5iwcDbERFmDwYBKpbL3czMliXved52QuAsajUYItslkEtVqFcvlErVaTfZmzpNKpZKIZTivuEv4yuT4+4KiMIqfd9Ukg8EA7XZb+ksPOTtwJtjpdDAYDES8bTKZ1kzogG9rI5JvKdA/Pz+/98/70OD+SnPcbcJ9JhzmcjmUy2UYjUZEIpFbfQ6Hw4GTkxN5vu12O66urjCfzzEYDBAIBOByuURMwF5GPp8Xo0qa+PJdmU6nqNfr6HQ6skawj0jharVaFQPXwWCA8/NzqTP5DBUKhb3vSr/fR7fbRb/fx9HRkfQGKPicTCZiAM3fBbgRrfp8Prx+/Rq1Wg3D4RDHx8ci2GSf5+LiAhqNBuVyWcRlyr4iZ5StVkuENpvg2Xk2myGZTMo6RqNNANLzotCX75jyXtnt9jUzPWVC5yam06kIAPi73gWmitMcPJFIwGq1IhAISOI1Sefs+V5fXwvhMxqNyudiP0mv16PX64lZHsmiTPg1mUxidpvL5WSGFAqFcHp6CpPJhEgkIjN1rVaLYDAoAjqdTodIJAK/3w+j0YhEIvHg9K/5fC5mFEymm8/nYpwYi8VwdnYmwvttyOfzkthdKBQwmUyQSCTEuIN9vkAggNVqhYODA5lBUhxD069dwsVGoyEJ8TQMNBgMGI/HyOfzMu/nOr3v+SCcTqc8/+12+5YAvVgswuFw4Pr6Wojyo9EIfr8fs9lMZgwUDVPUXKvVJAWdqW6Hh4fyrgK4k9NB04tsNotWq4V4PL719+E6l8lkcHp6CqvVKn0Z9j8dDgdMJhNOTk5EsKY8lyqT9NivCwaDe6+fVquFTqeThC6auNHAmsbU5I1wf2WyOgAx7OacB7gtLuW8ZNPIuVwuI51OA4A8Q8o5S7PZlCRcCuIJmuEok92MRqOItCuVihj6k7szGAzkDM95Ck06gG+FdSaTSeZXNJ/jM8J6nfNLg8EgfUCalxgMBuj1+r3PB4VwynnbPlitVpRKJTFPVV4LhtTM53OpW2gcznpBKaxVq9WoVquIxWIiNne73dLT4PfnGk9DCXI9jEajzJYHg4GYuet0OplxKc15p9MpqtWqzFjZT+Wez7nsZj3LvVer1b6TAdzx8bG8F7VaDdlsVtK0aUbvdrvh8XgQjUZv9dQ/dkynU1xeXgo3gu8x8G0fn+bgp6endxoQ2Ww2EW/mcjnpJSv3DPJvBoMBEokE3G73O/XAW60WBoMBisWiBFNQKKrVauXdCQaDKJVKaLfbMBgMcLvdYpBBPhr3ioegXq/fMidXrpVGo1FMH3K5HLRarYiEP1ZQ2MjwDxpHABBBJsNcPub+i7KOu8+sgeuJ0pj9XbFarZDNZsUEZzPsgvXyeDxGJpMRA8b71CqEWq2WcziNvNvtNvL5PKxWqxi1sAYmJ8Dn84kp+vcBq9WKZ8+eibETk8CBm/eFHIjpdIpcLicmKHeBIR6j0UjuH82kaUDu8Xj2JsQzLMNsNss1ZK1P7hxFkjSusNvtIj7XarUYj8ey/3P9VO6z5DHSpJthF7vWP4/HI6ETNMMpFovodDoolUoIhUK3ehEulwtOp1MEwjT/Ojw8lJkn+bVut1t6K4vF4lYq/WODZr40qfmYRLIMjAAgZlKsAZfLJcrlMvr9vtzvu8z3TSYT6vU6AOxMCic4q1Z+P61Wi0AgsNfE5UOD74ESNHlRqVTodrtisgJ8WzMcHBzAarXi6OhorWag0c18Pkcikbh1luTvrNVqZSY9mUw+aOo436dcLge3232Ln84ZYTabRSqVkjpQq9VK3cme4bbwrscEfz55LNyrabTIfWU4HOL09PSjer92gYYag8FA+pO8rqw/yW1jkrOSp0r+WCKREB7zY/eLnwB5rxmgUa1WZX0kT5m9KbfbjePj43fmtpDny9pAp9PJ+fKnBIabTCaTNU4X+/Ltdnut76FSqdBqtZBOp4WfbTQa4fF4xBApEAjcqjPJZ2Rw4Lvwy9n3UalUwoPdB/Z9+LX3BetqtVq9d280m80SmulwOMTA5wlPeMJ3jycB+k8Uv/M7v4P//J//861N5c2bN+/0/drtNv7X//pf8u//7t/9uzXxOXBTMPzbf/tvUa/X8dd//ddYrVb4L//lv+A//If/8E4/8wlP+KEgk8kglUohk8lgsVjg4uJC/owJ3i6XC5eXl9DpdDL4mkwmCAQCknhIR1Kn04nj4+N7//z5fI6rqysRfbTbbWn6sNFaqVQQjUalgKNDMMVPyrRMCiE7nY6ILIDtoh6SCex2OxaLBfr9vggjOp2OHFp2NeGn06mImE0m09YDBx0fs9ks3G43JpMJarWaHEaZUmg2mxEMBmG320WA/hAMBgPUajW0Wq01kgTdwR9iCKAEyRiNRuPOVEvgpjFGZ91SqSTDG4/HI8lBTDjhYO///t//i1AohHq9jl/96lf3GoyRIHMfojUHPEx8vLq6wnA4FPH5arWSgf5qtUIgEBBXtHw+j5OTExHvkaBGF9hYLCYu5fxZ98FgMJAkaRKHksmkCBDZ8NTr9ajX6/irv/or/OpXv4Lf78fPfvazW80/ikmU4DvAFDWC6ePK78HnjoRGJalsuVyK6IWJqZvvk9frRalUkvsej8eh0WiEZMIGHZOFzWYz7HY7fv7zn0syDt8/DsC8Xq8ITYvFIr7++mtMp1P4fD5kMhkkEglJB3Y4HLDb7ZhOpzKopOCcxA8263mNh8MhMpkMxuOx/D56vR6tVkvIECcnJzCbzWvCrkgkguVyKU1zuo8r01im0ykSiYQ40e4aFFWrVYzHY4TDYSF+d7tdGXLfZwDodDpRKpUQDAYxHA5hsVgkqQaApK50Oh1JOgGwls7J9B4SQbLZrCSmf/755yLeoei22+3KUEZpGkLTBg5umfb9WO7TTqcTFxcXqFarsNlsa2YkTENoNBrI5XKyp9FlnK7YRqNRXKRJNqUgeDab3SvxKJvNolKpyGCX34dDoH6/LyYEZ2dnMiQol8uYzWYi6tZoNBiPx+I0zCEW1yam70Yika3PD9NIPvnkEwSDQfj9fiyXS7x9+xbL5VKSdTfh8XhkAMaEEgAi6iIRg0TGeDwuDRGdToezszMkk0lJyiEJjM09NlSePXuGdruNTCYj6d3c98LhMKrVqqx9iURC1hiSX4CbtSkajcqw9Msvv1xLKWg0GpjP5zCbzWIwkkqlZJiu0+kQCASENDOZTHBycoKzszMUi0UMh0Mh1XFfOTk5EaLvaDTCdDqF1+uF1WqFTqeTtYyEyclkIi6nwE1dwzXkH//jfyxEFtY2SjDltVaryRpiMpn2JsOQKFWr1WCz2dBut2UPYyqPXq+HxWJZS4Ww2Wyw2WwioA4Gg4hGo5JqFYvFbtUxJLLGYjFxb72+vhaBHsk4/P8cGJrNZiGNUaxOwhufNY1GI4njNOohXC4X+v0+MpmMGDtse5YXiwVqtZqYeGyrHyi+Xa1We9civk/L5VKGV9vWA5oMpFIpEdpurtWsK5RuyJv3kzVko9GQdZlpW5vGS9swmUzw9u1bcVQfDoeSJk6CAp/D9v9L99ZqtfLnBoNBBsNHR0ciDJjNZvdat+nWThGsx+NBJpORofc2sxwafZDwx7QvEq53EWPy+TyOjo7Q6/XuPbTh/nDfuoziiOl0KsnxFOh++eWXGAwGcp+4xgWDQSGr1Wq1e5PH9Hq9nGkCgQB6vR5GoxHy+Tym0ym0Wq2sAay/uUbxmZvP5/jss89gt9tFyM6UJeDG/frrr7+GzWZDvV4XETMAIT+ZzWYxBjIajQiFQpKaxv2I/53fl9eHiVndblfe92fPnuHVq1dCOOQgl8npy+USoVAIwWAQ5+fnW88mSlOczaQY1p+hUEia+8ViUYwLeEYZjUa4vr4WEXw4HN55DtJoNJJiQCOj90m8ewhIEuQ7uSlqozkATal6vZ6I7zeJAMC3BEebzYbpdCoEfL1ej0KhAJfLBZPJBKvVKskDTC8lmTQcDkOtVmM0GuHq6krMiEhq+FCgED6bzUr93Wq1oNPphLy3XC5FLM8+gZI40Ov1hMBMkJxtNpvRbDb3mpCMRiMMh0MRYz7hCd8H9Hq9EP6Gw+GdtcBoNFoj1T8UGo0GiURCkgppMkURD02UnvDDQTAYFFOxXC4Hr9e7Zn7FfbzVaiEUCsFqtX5UKe4fG5R9rnK5jHg8vvWd4Lma4o4f8zVlmijFTYlEYm2tYq/UZrMhl8uJWRNFaE94GCaTCZLJpBggulwuRCIRWfMnk8masSnw7gkODwXNgyqVylpKF8/oNNFisuTh4eGts5zJZEI0GkWtVhPRbKPR2PkOWSwWHB4eIhqNSr+bPQiuc5tnCCV4Bud5Sgmetz50QuJdCAaD0jtLp9NCdOU1oTBPr9fj2bNn0rtNp9P49NNPH9VEiWKYaDQqRpMUpwLrxnbsY24zGdvEYrEQsmalUpGzJkWc7XYbh4eHqNVqYgyoBFNN6/W6nF83+xc8Q6nVahGDcm6yL236fUFzbSZW7YPP50MqlRIi/7YZgN1ux6effir9Pu7vFE+q1WrU63XUajWo1WpMJhN4vV7pU7I/2e12Ua/X5czDlCoKmX0+H+LxuBCYKWKyWq3I5/P4nd/5HfnMjUZD0ih3na/Ys6QggobAarUai8VCzqMUyPL3mEwmuLq6kkRmCqRMJpOknfM6B4NBeUZpGqjsG1KATqHt5nNEUQN/1mw2E7E/nyeKlAuFAqbTqfSElX0mlUoFn88n11WZlKbEZDIRYRiJ4e8K1nc0cuY8AgAKhYL0s9kDVKlUcg24f1BcQ4E9DeYtFoukJJJUGgqFMJvNcHFxgXA4LOlym5/FbDYjlUpJj+b09PROERPnNpzvmEwmLJdLvHz5Eq9fv0axWBTRLuF0OmX+8//9f//f1neNnAKuXbzeXq8XGo1GCOTNZlOeTa7/NFg1GAzQarVotVpbz0WlUkkE6NfX15J+3ev10G63YbVaZZ9mj/k+hFONRrNzTqXVasXwu1gsIpvNrpmnX15eolAoYDQawev1wuFwIBqNYjKZSA8XuJltsX/EHh7JuvvgdDrFGLLZbGI+n++cX7TbbRwcHIhRL+d0NJf2+Xxrxop+v1/El1yjuMdvGjDugsvlQqPRQKVSESNGztj0ej06nY7sY1xDKWQEbt7Tfr+PRCKx1gNrtVoYDocoFovw+/1b+2MUQmYyGUl4pbk98K35PUXIyhnMeDxGpVIRwwMKOCaTiSTLOp1OhEIhpNNpWXeUgpzlcinPI42h9Hq9iOLNZjOGwyE6nQ4ymQzy+TyWyyWCwaDMuMjj6Ha7cDgcsu55vV7hCT02ttWty+VSepxcaxwOB+r1OlwuF2q1GpbLpZjSkBhvsVhkVse9bTabCfeIPQ8afnJuwGsJ3NQPrEvK5bKkymo0GjGxZ93h9/uxWCwkBICG+JwlKt93pTky53cPBd9/vs/sk7OHZLFYZO76QxOfL5dLXF5eytpuNpvXEklpVJzNZqVeuk+S69HRkcxzKPy2Wq3QarViIGc0GnFwcACXy/XOpmlKIYLH4xFjBaUhLACpD2u1GpxOJ3q9ntRJNNJlf/uhP5/GyZvic0KlUiEQCMjzyoCNj9mMkIm2nU5nTQTE9N2HCKV/iiDXiUEAu+a9nP8Vi0W43W60Wq2tJlq74Pf7Ua/XcXR0hFQqBbvdLkEDnEfStDoYDOLg4AC/93u/972n1huNRpycnEiflGsFeQY6nQ5XV1fC/7kPlBwI9teBm2vUbDZhNpuRzWYRiURu9cfm8zny+byESCjPCpFIRPqSRqNR0ki73a7UNORBcD8nt49mLJvz0clkIrNG7lG7zid6vR4GgwGDwUD6D9lsVkyv+Pk3rxP5QAyZ6HQ6a3NQmtv0+334/X54vV60Wi0xCXzsfgvDicgH5Wek4PddzGE+BLxeL3Q6nYj2+v0+ptOpzICMRiO63S4ODg7uPFso66xt6yV5y+RaARBDG5/P9+jvKc83nFu/zxrOuUa1WoXD4RAeEA0W3G43AoGA8BYBCL+Mz/u+d9vj8aDVagkH50PNjBeLhZg+7ktsZk3+61//GjabTdYD3mOuXZ1OB4FAALFY7NH3yGKxKHN8ALfS44fDIWq1GtLptBjSPkQ38H1gOp3izZs30qMkb5S8wk6ng+vra3me2BuhvoBrPcMJ/H7/d2Lo/yFAc9rVaiVrzccGag4MBoOcW7k+kstpNpsRj8ffaU0nb5NcfpofGQwG6XEykPGnAKXxPd/zyWSCQqEgwVsMAyRfm6FF9XodNptNOI+xWAz5fB79fl8Casgfa7fb0Ol0iMfj72zu7XQ6pcfZbDb38gnZ92Yv6L7vLMNT2KO/a41lf2o8HqPVan3U5l9PeMKPGU8C9J8oHrsx+pd/+ZdSGEQiEfzpn/7pzr/7b/7Nv8H/+T//B8vlEq9fv8bV1dVHXxQ/4QnvCh6wU6kUAIhQWSkoBG6EFsfHx8hkMtDr9TK8ooiVww6mntyX7MKhAkWnFBAyhbTX6yGdTktqKnAj1OAghWIKkgv43+hyTbfl0WgkiczK4rHf78NgMMgAiU7vTECmcHw+n8Pr9coha7lcSkKryWSCx+PZSbhg8yyRSMjAnyR6ig7sdjvcbrc0jHaRiCkK4oGdpPt8Po9qtSpua1zv9Ho9nE6nDMmZjvsQuN1uEU5uEjk2QdHsixcvMJlMoFar0el0ZBBK8R9JDzzEslFKEVo0GpXrtQsGg0GcVO8C0xVMJhOGwyGazaaI/UajkTSkmBJAUTOdSa+urlAsFhGNRkWIq9PpoNVqRXjz0EZCrVZDv98XJ9TLy0sRljC9hcNKs9mMer2OL774QgwFlMkNTqcT7XZbEiM5vJxMJuKkxXeSBCW6Pyths9kwGo3k+WHTsV6vrxH49Ho9vF6vOKkCN00vuuxns1mk02l4PB7Y7XYYDAYcHBygXC6j3W4jGAwiGAwiEong5OQEXq8X9Xod3W4XuVxOUo/5eYvFIgaDAXQ6HcrlsqRsdLtdJBIJdDod+Hw+ceak6K9arWKxWEhjnKmpFL7x7yaTSRGQ8/fnO6vT6XB8fLz2LAaDQWlg8tmlwyFTVH/+85+jVqvBarVKEjjTUVUqFTqdDsrlMsbjsTTnSHAkyYDP+V0wGAwyNOfXkURlNpvx8uVLGYRTgEmnbx6SabyRTqeF+MmhA5tMwM2Qm8YRdKElSYjPBhMFKEqu1WqPmnZFMkckEllLHqd48O/+7u8k1YLrM5sUk8lEHAuZJsHkb6UAeR9KpZIka8/nc2lMsKnS7Xbx2WefoVKpyNCO4sTlcnnLjZbmI6vVSlyLdTqdEIRyuZw8P3Q65mfgUIbpdfw9KULc1RhUqVSIRCKy1pDsxcQLs9ksA7B4PH6LVGMwGHBxcSGfdzAYALghFpEc8umnn8Jut+Py8lKSb0Kh0FoizXK5xHg8FrIISSUUJhOBQEB+TqVSwdu3byUJgYMEuvzPZjM0Gg1J3WUiDwlgJADF43GEw2FcXV3dGh5wGO92u2Vop9PpZJih0+kwnU4lZaDdbsv9YGqrzWaT9Cc2l5rNpuw/FJYyqYdJ7iR70rRmVyIhHRPpGs0ELzZTXS6XkIr6/f7aes8BBAf4vV4PTqdTEnuUINmDA6jJZIJmsymCWa5pq9VKBC96vR7RaFSMgkajEfr9/pppjMlkQjAYFEIx09IJm80m5E/gpl7bHATxeQBuBrj7CBP87/vI+ByCcn3btx7Q5KPT6ci+BtzUadw3+ezQHMnpdMLn88Fms6FUKqFYLErDkURophBVKhUEAoGtKU3ATV1DUk6hUIDdbsfh4aG88/P5HO12G5VKRd6NUCgk90Cv1wvZjcNri8WCq6srjEYjfP755/D5fFLjc9hCKIWaPp8PRqMRZ2dnGA6HQnSMRCIy1B4MBpKyZbVacXJyItcLwE6TDZVKhWAwKEQJi8VyZ9oNweu+T4CweU1VKpUI66vVqgj72UTnWYRnnkqlIoYLdrt9pyjpPp+VpAC1Wo0XL17AaDTKvSU5ymKxiDM9DV8uLy+RTCbhcDjg9/uFcNBqtYRwrVKpxGCI6z4NjrgGG41GWS9p1kKn2YODA7k+er1eREesY1gTajQafPrpp8jlciiVSkK8Z0L68fExfD4fXrx4sVNUohxyb15Lfp5YLCbmIST2ci1lLUhCUjAYRDwe3ztooCEM96rvSoBuNptF5Nlut28NrPhs1et1LJdL9Pt9EddsPtvcT0kGJoGEyWUqlUrMe0ajkTgTLxYLSYQLh8NrhAKHwyHPZq1Wg8vlemdDs7tQrVaFlH1wcCDiNSVJ2Ov1IhQKIZ/Py3mOZ+Fut4vZbAan07lGBGDChM/nQzabhU6n2/rsMZ2MAvYnAfoTvi+YzWapx9jD2Qe6Z+9KI2PK5aawfDOtTafTPQ0/fyRgYiyN5khgYv3AtORYLAaHw4HT09N3Ir//VMB9g+LObDYrZBaC5KrpdIqDgwN4PJ4ftZs9yXqtVkvSpreBYhmeL2q1GmKx2Hf8aX/4qFQqcj5Unq8Ig8GAYDAIo9Eo5EOmTjSbzXulpg0Gg1vnvbtAU6BsNit9LuV7MZvNUC6X8ebNGyyXS3i93r3EL5/PJ6bEtVrtThMH9swegkajgUwmg9FoJCmFJJNThDibzVCtVj9oQuJdoPHpy5cvhZDb7/dFMAXcrPXsT1HE4vP50Gw2H9UAQ61W4/T0VMyv6vU60uk0NBqNiORobOdyuW4lS21isVgISS2bzaJYLEpiOIXAJA7SiI39u81+EPts7PFSkEkxNs0F2+229KlIROR52Ov1SrLVY4HCIRoh7wMFwJwP7QJFCC6XC6enp/jFL34hIqrJZLKW3sZeoslkQqFQkJRbzriUhjTsx5DUzs9LwnmxWJT7QRGi1WoVo1AKGJUzU6boKee8FJhzltdqtYRkyNp3Op1K4t9oNJI+J8+GdrtdTHPb7baYllLozb6Asqep7EFu6wWy96fT6fD69Ws5EyvvG+ev7B0fHBxIX0gJirWZGH55eSlnS/6uFKGFw2GEw+EHGRgajUaZz7GGp4El+xetVgtff/013r59KymWZ2dnayKF8XgsxpVqtVoMUymSHY1GsrZ4PB5Mp1M5N1itVrx48WLrZ1R+Fs7gmXq4C5tzR/Y3ut0u0uk0crmcCEmZ5su0du5BR0dHmM/n+LM/+7Nb/ZRqtSoGu6FQCKVSCQDW+kw002Qqp9FohM/nk7/jdDpl7cvn84hEInL96/W6GAc3Gg243W4cHh4iFotJMjXJ9+FwWHpzm8/Prn1a2U/ehM/nk14e53DffPONzBkGg4EIx2jIzjXGarXi8PAQ+XwerVYL1WoVZrP53v1TilydTqcIpUKh0NoMimYvvV5PksXPzs72/r7Ke8L5/LuApGOn0ynmUJyROp1OVKtVGAwGWCwWOSPRAJW/D7knyv2U4lKaVOyCWq2Gz+dDPp+H2WyWPjPNQGjMQ2HJcrlEvV6XeZHVapV6hGYtrHlOTk4wHo9xcHCAQqGATqcDm80m5uKcrUWjUTidTjx79gy9Xk/ex+vra2SzWdTrdUynU8TjcTHqZkp4o9FAPp8Xs1Om03P921f3UMTGGftd5yEml5NrpAS/D8+v3L8ikYg8Q3zeW60WyuWyzKEpJONMazAYCA+HBu12u11mYTSh9ng8CIVCaDQaaLfbYuobDAYxnU4xn8/FgIVCToqaMpkMhsMhyuUyAAgXgf2cfr+PYrEoSepms3mrMco+0LiThiGcT7KHHwqFEAqFfrBiiHq9LiYuTFNWgiaknMMaDAYxiNsHvV6P8/NzpFIpMetmSAZrQa4PiUTinYycuM/TuEG51m8TrnJtGI1GGAwGMi+nwf19Z1nEcDiU843H47lzjXW5XKhWq/D7/bcE8h8j3ndf+L6hrOO2CXQ3QVOE9xVmApCZJmvpfbDZbNBqtSL2f4gAnSYO5Ejl83nhZer1euEZ8Sxzfn4uCc8fA/R6/c6eOA0+uN7edU8mk4kYQClrOJpkr1YrOaNZLBZYrVaoVCoRupFP5/V6b82n+A7wzM+9pVwuw+VyyfpVLBblrMQU2M00ZZ6NyPlScgN3gXwP8qOurq6QTqcRDAYlDIKicdaF3W5XeC8Uoil5O4PBYM1oipyzYDAooTKPgeVyiUwmg0ajIedGcjIo6KcR4ocQ7b4LHA6H8HfIoaUhi9vtRjablaCBfWByM2f7SpCnMhqNpPYhf4Q8aK/Xi3g8/l7XZLlc3uKTATf1HgMZ3lXoyuANhuhw/sWZ/ya63a70Au8z66bRHbmnj9k7IlqtFqbTKfr9/t66Zjweo1QqYTab4fr6GrFYDEdHRzCZTFgsFmKwwDRitVq9k2P0LlgsFqhUKiiXy1CpVBJGpQSFv4VCAYVCAQaD4VGDij4Estks2u229LY2aymXy4XBYCAGIeTBRKNR4ZlrtVqYTKYfpJG3kp/a7/fXxMZmsxk+n+/O+vK7BtdHGoxxfWRt/65mIr1eD8lkUkJK5vO5rE0M+vB4PJhMJjg8PPzBGX69C3h+1el0Ev7InlO320UgEFh7v8fjsfSUPR6P8HgikYgkorNPz7M7+WkOhwNer/eda0S1Wg2v14vBYIB0Oi3nnU0waI718UPmKOSwbQsV2QadTic85fuEoD3hCU/4MHgSoD/hUfDrX/9a/v8/+Sf/ZG9x5PP58Omnn+KLL76Qr30SoD/hxwqmso5GI6jV6r2NZhIMnE4nJpOJiH3sdrskGD/00E2iSSaTgd1uRyAQWDuYOZ1OIYQXi0URd9rtdiHmsmFFASI/Zy6XA/CtCNfr9a6lG3AoR1EfhQrKBFQezqvVKlKplBSIFL25XC54vV5Eo9GdBwwKn71eryQ4kFRB8oXycN5sNmVQysYHG3RsGJD4w9Tr2WyGUqkkwlmKpzn0qtfriMViWC6XODk5edABkdeaQhtlsrISdF03mUywWCzweDxYLpfo9XoIBAKYTqe4urpCv99Hr9cT4ZPD4ZDUzEqlgq+++koa64lEYufn8nq96HQ6Mpja9+y2221oNBocHx+LKDYWi8Htdss9JtmC9xSAfH6mYFLAy4E8BXP9fh+xWEwExBRxKknemwddXgfeo0ajIcQxNgL5GUajkTjFvnz5Eqenp5JQCNzsWxyol0olaY7SkUwp9mw0GpJMuknWo4B2tVqh2WwinU6Ls3Sv15PnzmaziZDz+PhYGsCRSEQO+PV6XZpQFB8x0ZAELzbRVqsVarUams3mGvmdAs92u41GowGDwYCTkxNJVyX5r91uC4lE6eZ6fHwsSRUk61FgxDQHJhlw6MjPwyYlUyk5QCcopK1Wq+Lmy+EDmxu/+7u/i1QqJQIwDoiZ8sHhAglA+Xwefr9fBuJ8r+5aV/m5laQUHrAzmYwkVJNQQ5c1DjaWy6UIItlo5ztBIRtJYCSHHR4eYj6fSxOXhHY2P9Vq9VpK2GMK0InNodlsNsPl5aU8L+FwWMgaBIf/HDAYDAaUSiVJeuU/+651uVxGuVzGYrFAIpG4lXrCJJ5CoYBarSai/OFwuDUBhV+n1WrFkc/j8QihQqvVotlsolwuC9mV+5XVapXkQwoQad7BYdAuqNVqRCIRSUMfj8cIBoOSYMYB167nj27udHRn2i2FcXa7XUg1g8FAHEOV0Ov1MlSjoNpoNMq7SlBw//btW2SzWQyHQ0ynU0lDWCwWMmyjaM1iseD58+cyqJpMJuh0OiiVSjLko7N9q9WC1+u91QTX6XRrqSJ81vnZE4kEarUajEYjxuMxMpmM3Lvz83M4nU4R9LndbhQKBbjdbjHX4fvNJpXZbJZ9Vq/Xiyv4NnGuTqcTchAHZUooUyFI5uNwj6BDpc1mE1dWpVC70+lI0hpNLsLhsJBTua7b7XZJ1E2lUojH4yJYTSaT8l6QpEyCDF39aVgxmUywWCwwnU5l3ybZ1Gg0IplMitEQax+r1YpQKASfz7dzSGw0GqVm6vf7O4fnNPFQutXuI1Yx1Z4NQxqa0AyINRtwUwu6XC40m025vhwEKwfNw+FQUkn4WbatnyRaFwqFrcNorVYrTfZMJgO/3w+z2SxmSaxfWM9RVMMkgUwmI4JTm82GRqMBu92OUCgkqU1Op1MIeTqdDp9++imm0ykMBgMKhQJevnwJtVothDG9Xg+fz4dEIoGjoyMsl0uk0+l7pdi4XC4RO5PMfRd4XSuVCsbj8d5GMM1DXC6XrLmvXr2CWq1GLpcTUhn3xE6nI3U5hw0HBwf3Gvju+qxMCrRYLLDb7VJ3UZjMOpfGK/zMHAZzWMx7PBqNxGyiXC5LIgz3f579SIxiPUdzHa4vvHabNRD311evXsk6whqGQ2Q+dzT4ikQi+MUvfrFX6MYkII1GI4YrfCcpsm+325IwxkQnDvpoNtbpdKDX63FycnJngi9rN6UB0IcGUwWm0ykmkwkqlQrsdvtabc5zrtFoFNF1KBQSs6rBYCBnWxJNjUaj1KccELrd7rWENRpzNZtNqFQq+P1+BINBqVVYlwaDQVitViyXS3nmP4QAfTAYYDgcot1ur5FlKBLhGZRnF5vNhm63K6Y8NKui4G/TQCAWiwmpIJ/PiwGL0WiUwTzXlVAohFgs9oN1KH/CjwMkkbH+31XfsI/H9EflXjqfz5HNZsWckOQBnp8KhQL8fr+Y0/zYwZqWPRr2PmnA+WODw+HA+fk58vm8CGAoMmM/j2ZnH2OSwccGpswBkCRNklBZO1itVhGkfojex8cEmpGtVqs73x/u5a1WS0yEnnB/MAWHiYr7rjdJkbzWrAfvOjv1+33M53M4/1+y8n2wWq1QLpfF9HOzLwZA5jNmsxnJZBKhUOjOz8LzstfrvfP89lA0Gg2kUikxRrVYLJKCy3RaCpZp3vWhEhLvA5pMVioVmV1wHacxNA1qtVqtfN56vf6oAnTgpo/67NkzFAoF6a2RmExBsdLYbhdmsxnevn2LdrstZHWaJFCETEMxnls2+3dKUEROc+PxeIzLy0sMBgO0Wi20Wi0UCgUhCgPfmlPRqJT11unp6aM9b8vlci3t7i7QFHRf/3YTNJuZzWZot9uSfs21mWaOq9UK6XRaBJbAtym+vV5PDCsoqqQ55Wq1knnrcrmETqdDtVrF4eEhAIjoF4DMTDk3YZ/AbrdDp9NJKjYAmaHl83nM53NMp9O11OHZbCYiLuDbpGWLxYLhcAiz2Sxp9pwhMfWP4kAlhsOhnJ133Q+dToflcini03q9Luk3SrNWvV6PUCgk6YKsJwn2VEjMZF9wMBhI8l8ikYDJZEIkEnmQ8ZPb7RbhRrvdvrVWT6dT5PN5ZLNZZDIZpNNpmUUojcCVhsJKI2HOVsfjMWw2m/RjmUAUDAbR7/fvvUeQZL5PQNftdnF1dSWiE57JObMiWVSn0yEQCMg8hinMNPxNJpPQ6XT4zW9+g3/2z/6ZfH+mGbVaLZmzK80XNvt2DocD1WpVzGsByOyf4uVsNiuzF6PRiFwuh0wmI6RbJg6zp0NDwXw+L3Mk9u94L3lG41zI6XRKCiTXxl1zKhoJFgoFADfPYLFYFPN7isJpCMO5Mef0brcbjUZD0sA5x+O7dtc9DgaDMmNOpVIwGAzSV+N8JZFIwOl04ujoSL72Q5891Wo1QqGQ7AupVEpMMykAp2DHaDRK/4d9fKPRiEgkglAoJM+JUlx6H3MRhjZwXqrVaiXIgP1cGorXajUxXVgul3C73TI/7Pf7It4IhUKwWCw4Pz/H9fU1TCaTJInyPQ8Gg8LVOT4+ljmo3W5HrVZDsViUz5ZIJNDv9zEYDOT+sM/m8XjQ6XRgt9ths9nkWeaf7YLFYhHODjku+0AzPaaUKUFOisFgkB4wry0DMHjGoLE902LVajXa7bYY+NB8jWJI9v7YQ6SpAudk7Lc7nU5JOfR4PCiVSmg2m1IfUsAP3PS/l8slUqkUKpUKwuGwmOlS9A7crDXj8RiFQgGlUkmE1vvm0avVSuY0/PmsCYFv+UdMR/2hgom0DDrYBafTiX6/L0bFSmOQXaAIncYg7BGT6/I+wjfgW34G13ngW7MTGjYpwTVfuR+vVivhWzxUBD+ZTKQvcR/BjdVqFQHbD/mZ+aHA4/GgVqvJ3r/P9HY8Hovp92MYJHMvpOnBPnCONB6Pt/IN7oLH45Gam6EPvV5PBNkejwdOpxOhUAiRSORdf6XvHOQ+cL66r5/B8CKGVmxecxpdqdVqMXXsdDoyO6epHwW/m+h2u7i+vpazG88UnKvl83nhJ6xWKxwfH8Pv98Pv99/ak/k5yN+76zzKRFPyGvv9PtLpNOr1OvR6vXB7eMZm8EqlUpEzfzQaFf5epVKR2kKn04mhk9lsxu/93u/B7Xbv5Yg+BKvVCqlUCrVaTWpAhjMAEGGp0WgUTsZj/ezHgJIbANw8Z19++SXsdrtwS3eBQQXhcBhOp3Otb1coFFAsFlEoFCTEglwBGsoprwl7AQ/FdDqV57LdbqPT6WA2m4khD/mmsVhs7/5/F+5rUkkeELlsd4HnZ37thxCgj8djjEYjjEYjNJtN5PN5qVPMZjNcLpfwx1jvMSmdQQJKU61cLiezC571HgPkD5EXvate4bkkmUxK7fWxzkrIHaxWq7fM7ZVgzUmjpEajAb/f/2gmGd8XFosFrq+vZT+iGQLwbf+UIu/j4+MP8vy/Dzj/eAxQy1CtVlGv1+H1etfWzcViIc8KrxF7RT9m8KxJw3ybzYZOp4Ner7e1X8UerVarlTMyZ1UvXryQOf3mu8YZx7sknwMQPh0NVVQqlTzbyvM2e7VqtRrxeBxut/tBJles7baZS+7CfUPQnvCEJ3w4fFy71xN+kJhOp3j79q38+6effnrn1ygF6F9++SX+9b/+1x/s8z3hCd8n2Cxis/uuJjvTxNVqNfx+vxRl7wK6O1O8FQqFtv49DgKvr68lWYZiXza5stmsiEkp7jIYDCgWiwBuiOdKQfd4PEatVoPBYJB0biaFcLCo0+mEtGkymTCZTESUSiKV0WhEOBy+8xr4/X5xKGQq7rYCk67uoVBISMS5XA6VSkVSNpmuSkdcCimDwSBOTk7WDn50bCoWi8hms1Cr1ZI2+hCQcDmdTkUs5HK5pOnCIRoLdZ/PJ8PPeDwOp9OJzz77DMPhEPV6Xa7xYDAQsS+FJRwCazSaW8lCSrjdbjnkF4tFxOPxrQ0Uuv1RhOhwOCShlCJ5NrmUBgXADdGp3+9LEikdpDk0otCV94Tu2IPBAOPxGFqtVgh3TDmmUcFyucR0OkW1WkWj0YBKpYLP57sleKCQl0MKOnq322243W75HHTrpFCTTQA+I2zskYTg8XhuNYZGo5G4l5GYxwTNUCgkRKJ2u43r62sEAgEsl0ucn5/LAOPw8HDNJZ7vDH8Po9EoJHmi3+9Lg0f5bJIsyUM+G6w8QFJEXiqVcHV1JQSD09NTaDQaScVwuVwiVH3z5o0kd/zqV7+CWq1Gq9US0iXFsmq1Gi9fvkSpVMKbN29wenoqSa8Uy1ksFkk2GI/H4matNBA4Pz8X93qtVot8Pg+DwSBkDK1WKyLo0WgkDqtMa+92u3euLzzgq9XqtXcgGAxiuVyKMziT4DUaDfL5vPweBoMBi8UCk8kE1WpVkmy1Wq0QF3Q6HT777DMhv5JYtfnZ+PNIblKaF3zoQ/VyuUS1WkWz2RRCF01WptPp2rtls9mEdMK11e12rznTbwObq71eD7FYbG29VQ6MKpWKiCtpeEDS0jZMJpM1g5dut4tOp4OzszO5JxT/D4dDIUZQVEYhZDgcxmQygU6ng9lsFgd/mkvQiGHznTSbzTg5OcHBwcEaKei+4FqzuX6xUcmB5T6XU+6/bPJy7R+Px3j9+jV+85vfIJPJYDabCRmQZBylYzGFhEajURKD+f2NRiNqtRqq1aoI7Zh0TOHz5ucLBAJotVrodrvIZrPi0MjfOxQKweFw4O3bt4hGo4hEIjg5OcHh4eHa9WATdDAY4KuvvhIjD4/Hg6OjI1itVrx9+1b29ul0KjUM38VN0GiEZjSTyQSZTEZEtGzsqdVqIaiQMMxE3aOjIxwcHKwJuPv9PlKpFMbjsSSo0zgjl8shHA7j+fPnsFqta4OX8XgMn8+H6XSKw8NDeWYptNZoNDLU4bumUqkQi8UQiUTkvaE4KhKJQK/Xi7iD6/NyuYTJZJLETxL9doF7MAlQuwTPbP51u10hEd1lSEFR/Xg8xtu3b9FoNKSeDAaDsr72ej0RCHPAZbPZ1lLLAYhRSKFQQD6fF4HU5rrEeoCGJ7tA4Xun04Hb7ZZkDiVmsxnevHkje2G/34fT6RSSAI1GXC4XMpmMJFgFg0EcHByImDgQCOCP/uiP4HK54PP5hEDK+8Va4uTkBA6HA1dXV5jNZvcixlgsFiHd35cA4fV6xTjirsRFmhORmPrZZ5/Jsx8MBtfMGYCb95lp73QIb7Va9/pc20AR/2g0WkvIoXmH1WqV9Zuuu6PRSAjSFK37/X4cHh4K2Y5ES7vdjjdv3sg5wmazCcHfYrHg5OREkiS4bvO+GgwGzGaztZqRKTK8zmq1GuVyWerhaDSKo6MjIWlTbF0ul/c25KfTKVqtlpAQSGwgYZPrul6vRzweRzabxXw+l3d8uVyKIMLhcAhh/T6EHSa8f2iH6n6/j1KphFKphEqlgmq1Ktfu9evXODs7w89//nNJOifZk0Yiy+USkUgEnU5HzKl4llKmjdEoq9vtyhobjUZFfD0ajbBcLqUOqFarACD1LcmtsVhM6iSn03mrnnoMML1nNpvdGi4Fg0ERuJCYq9PpxDX57du3klqmfP6V0Ol0ODs7QzKZhNFoFKIDz18WiwUHBwfizv7Yop0nPOGh8Hq9aDQaCAQCeP36tey9NGCgWdxoNEI0GhUjR2I+n8uaz/WFaz77DzabTdbLw8PDH+3wczKZ4OrqSkzgaKBEonGr1RLCDgAh/W32hX6IoECB5CUmmOr1+h99Qvdjg70PJihOJhPpH1H8QAO3aDT6g3927gLFd6w57gIFEUqhxBPuh16vh9lsJuS+u+B0OlEsFhEIBDCbzeB2u1Eul6U/sgn2kHkGuu98h72XTqeDUCi0k4BGcwYae5JwtAsUyvFrHwuz2UxMb5nGqjzn00SSRqo8fzmdTvR6vUdNSLwv2K+Mx+OIRqPodrtrImq73b728202m9TUHwI6nQ6JRALRaBTNZlNS7DaN7XZhtVpJikw2m0Wz2YTBYJBeDg0QmZZnNpvFpJr9u8FgsLN3wPl/s9lEoVCAXq/HdDqF1WoVIzbOfpj0yhkXf/7FxcVeo7T7gn2v+655/Jzv8jzpdLq1swtF3TQk7fV6YvLMXrGyj6gUPlFcqdVq0e12MZvN5CyvFIUDNz3K8/NzMesbj8fSq+P7QyHCcDgUYrzdbpf57NXVlbxby+VSTOxoNgpAjAlMJpMYQKxWK1itVvmZuwif8/kc3W5XTMO37c3KM3E0GpXfnb1Y4GYuRzI+DYOn06mIE5VQq9U4Pj5Gv99HtVqFxWIRYibNK+8yE9kGzhY9Ho+YlvJdoIFks9nE5eUlstksrFar1LLVahUej0eMk5neyMRVzjIymYzMea+vr0UgxV5VNBq99/pCY8Bd9VC/30cymUStVpOZGQVPWq0W19fXyOVy6PV6IoKgMTXPSJyh+Hw+ZLNZETrxnvC5HY1G0ifhzI68ASW4J3D+PRgMpOf8/PlzSZTvdrsi8FHuKey7UVhEqFQqhEIh6UUsFgtcXV3JTJcGk6zR+/2+zDso0Nm1Ny+XS5nzeb1eWc8GgwECgcDaezEcDtFsNjEej1Eul3F0dAS73Y5KpYLJZCIzGavVKkb2u+4fn+fnz5/LO8lZFvklwWBQBBDxePw7J6r7/f41k3T207guME1bo9Gg2+3KWZlz0GAwuDZnUCYZ33efYKq3Wq2G2+1GJpMRwxB+LxowpNNpOW+Q6A7c9GO73S6m0ymur6+F23B2diZiDtYpnPNtM1jT6/XS9xqNRri4uIDVapX5f61WQygUkjnSYrFAPp8Xrgb7jaxJ2Vfe1hukmR45DLvMNmq1Gsbj8RoPRgma2zCpmfsHcHPOPT4+lntHs3qK1vV6vZik04CT8yj2O1l/cI+02Wxwu92yJ5FjdHBwgD/6oz+S3gGFf41GQ8xYaeBfLBbR7/dxcHAgn50zS71ej9FoJPwm9vK73a68c7s4O4VCAZVKBblcDovFQowuaKJEc/7hcCj983cl7n9fYA17lziXoLiK+/F90wdpavPYUIYC8B3XarUyo+eeogTfW34tzU9Yq74rHtKLYG3whA8Lmnh4vV6ZG2/jvc3nc1lXjEbjvd6Fu6BMMb0PeE54V3B9YnKo8szodrvh9Xof5bz1XYImxjQrMpvNO+saztf2pWmGQiG43W7U63UxVwYg9YLP59t6tqEZSb1eR7PZRDweh8ViQbvdRrPZhN/vx3Q6xXA4xNdffy2mFrFYbGeAj81mEyF5MBjcex4l74n3d7FYwO12Q6fT4fDwEJPJRITErGPcbrfUiAw1YD+aYv1arSb8sdlshkAgAL1ej9PT00cT9TUaDTQaDTnvHBwcrN1Dmp7n83nkcjmo1WqZlX+MYAANwwzK5TICgcCt93w+n4uIXGlODkD4KrlcDvP5HEdHR2vnI5qz0eSMtdJD16X5fC68GabNbhqDM6SMz8GHns1y76Up1F3r43w+X9vnPwRomlWpVCTJnQFT7ClotVqp+2w225qBjRJarRbRaFTM3h5T/M3AI51Od2etwr4Qv+ZjBQ2+x+PxneYo7HNRhPs+hgkfA1arFa6vr1Gr1ZDL5bBareByueTeDgYDMfWLxWJYrVY4Ozv7Qaa83wfVahX9fh+1Wk0MU5RgqrfRaEQ2mxUDv3fh1f7QQM4f91L2CDbP+t1uF6PRCKFQCE6nU9Z8mr8MBgMJ1aEJKQDhxb8r6vU6isWi1BzsYWu1Wpn1kDvL2ThDEh9qrsIeDw1U7urvspe+K+TxCU94wneDJwH6E94bhUJBBgQqlepeBYAy8Tyfz3+wz/aEJ3zfYJr2fQ/a/HvAt0KddwXTJXq9nogYdoEuyEz9iEQi8Pl8ImAuFosyhOPAmA7TFGx4vV5JdJtMJpJ24fP5ZNBqMpnWRAparVYINhQak0xBR/L7wOPxoNFoIBaLIZfL4fr6Gm63Ww5wHFJNp1NEo1E4HA4EAgHk83lpAE0mEzidTnFxnkwmePXqFfL5PBaLhbgnWyyWtfui0WgQiUSQSqVETPzQA7HT6ZSkCbPZjFarJa7NvE4Upft8PsRiMbRaLRlAX19fo1QqIZPJyOFMrVZjNpuhXq9Dq9XK8/TixQsReFar1Z0CdLVaLQmrFHqSxEGiT7vdxnA4hM/nE7dMEpfooL/rGR6Px+h2uygUCjAajULuVv7O/Perqyt8+eWX4hBLwS2H2W63W9L3wuGwuJjP53Mh+FDQvwsU5FE4yOeapgV0HFSpVGi1Wvjtb38rTScSz30+H5xOpzSxmWgL3Ay8BoMB4vG4fKZOpyOCjM3ngYQctVqNdDqN58+fy5/7/X74fL61JHiK6La9M9PpVBIyeL+ZOLCZ/AnckEIajYasXxaLRcjkR0dH0iQn4UH5czqdDr755hscHR2J6JcOhYVCQUSf/X4f5XJZ3Mir1SoCgQCePXsGh8MhZg8k/yvXBeUzpVarEQgE5O/qdDpxmQ8Gg5IETKIdG9qLxULEkhwobwOf823upaPRCOFwGMlkEufn5yJQ6na7GAwGqNfrMvis1WoiHKNrOEUOJNGwQcwhWSQSEZIMmwbj8ViIPWwyvI8o7yEgIZPp8kzINBqNaLVat94xNobm8zlcLhc8Ho8I6XaBZg+bzVWaqTBFhokyNOKgQcC2IQ5TQUjGoyEDRQKBQACDwQBOpxONRgPxeByBQGDtftNUI51OI5FIiJs/zUuWy6UID2iWwJT74XC4lgL4PiCxmcQYuhZTNJ9Op+H3+4WkQgIkADHCYRIJiQPlchnX19dIpVKyxpJMSnGsw+GQZFMSVFUqFbLZLGw229oa5vF4JEW91WrdKUykq+lqtUKpVEI6nZZkEDoBk4gTDocRDAb3NvPpRAvcvGvxeHzNLZH3NBQKoVKpiDv6Zhr9fD7HZDIRshSJJplMBuFwWBLSaVZgsVjkHe10OnC5XEgkEri4uEAgEJA6rNfr4fLyEo1GA7VaTQZLTPqp1+t49eoVarUaAoGAOGzPZjN59ykYjcfjKJVKcq+VyS4UPR4eHuLo6EgMTQiDwQCfzwePxwOtVovRaIR6vS6DFzYt3W73vYi6bI62220UCgWEw+GtIkUSqSi43gUSxZj4ksvl0Ol0UCqVpLG6+Rx5PB789re/RavVglarxdnZ2dZmo16vRywWQyqVQrvdRrVaXfssFIxRVH4XmCTHr9msAekAms1m4XA48Cd/8ifyPJE0xrovFotJHXNwcHDr/XE6nfjDP/xD/OxnP0Mmk5H0VQBrLqK8j8D9CBD8Ow8hQLD+YnpSoVCQ/ZigoQ2NhngWoIttIBBYE/UpYTabEQqF8M0330jq+7uKEJxOJ/R6/ZqbMI2vzGYzms2m7NtMb2GNxeQWCtb7/T68Xi/8fj8cDgcuLy8xHA6F2J5MJiU1yuVyieCEawyNVkjGpPh/E1arFT6fD41GQxJ6mKBOwgQT0Pv9Pt68eSOmYaFQ6FZ9PxgMUCwWxR0XAJLJpBCylssl9Hq9fG61Wo1EIiFJjyRD63Q6eS4LhQLq9TouLy+hUqlESLL5s3k2JPF5H8bjMer1+lpait1uv7VGb0Oz2cT19TVev36NQqGATqcj7xkN4f7hH/4B19fXuLi4gN/vFxLeycmJkEtpxsFEO5vNhmAwKCJKuvc2m02Ew2FJOGfiAU0MeH4jEZdrqzIZkMl5zWYTy+US4/H40QXorOcB3HqHdDodDg4OkMvlYDAYxFCr1+vJesYE36Ojo50kS4PBgIuLCzSbTanXCCZzvG/izROe8FigMKTZbMqQNJfLyYCUaXKHh4ew2+04PT1dW3+4r2cyGVit1lskosFggGq1ikwmAwBiqvhjw2QyEYOdYrGI1WoliYWz2QzFYhFv376V9NZIJCL9G+6Lfr//o3P2fyhozPaE94NKpZI0RCZ1skZhGuCHFKN+jLhvXc7z349dmP8hQMEikzLvAoVgy+USwWAQer0es9kM6XRaEp50Ot2akanVapV+wH1JZN1uV0QSu8jMwLepWkpSzr65ExMh+LXvg+VyKemy1WoVxWIRlUoFn3zyyVaTORJdmQTIPYBi2MdKSLwvKAihQeFd536elz60iIRGhw8FzUmZwkThkM/nk7WT9XgymZTnMxAIrPXvlH1Yiq0pSl0ulygUCjLvqNfrKJVKMBqNOD4+xmq1EqNdg8GAo6Mj9Pt9FItF6HQ6lEqlRyHmcoZBIvi+92owGGA+n98rtYdneuDmbLPPLJGCtslkIsalnAGxJ8meDGelb968wWw2k76+snerFKoTnG1EIhE0Go01AbrdbofL5UKxWJRE7lqtBp/PJyYGdrsd2WxWEjg5WxkMBpJetlgsYDabJRmd51Cr1SrzDbvdLum4rNmWyyWKxeL/z9579Eh2bdnBK7z33kfayjLkM92vW5A0EL4GNFUPNBHQ0FS/Rj9APRMgCdJMYwEtCBq0Wup+/d4jiyyTLrz3N7z9Bom1eSMyIjLKssiXCyBYZKWJuHHvOfvsvQyMRqP0LbZhOp1KihmTUa1Wq6SIApDeKtfQq6urBxNDaSDY6XRgsVgkMX00GqFcLkNRFNhsNunfH4JkMimmbfl8XsSN7Lt/9913klz35MmTteRrktUpwLTb7RiNRqjX6/JemWqkFgfxTEIT39FohNlstpcYulqtoCiKGHZsAwXzaqFzLpdDp9PBeDyG1+tFuVyWn0czrcFgIEaX8XhcTGRpCH57e4tf/epX8n0Ee+0Oh0POQRSpcn1XvycaVEYiEVmjjo+P4ff7Ua/XxYxgPp/DaDTi+Ph4a5+JIPl+Npshl8thtVrhD3/4g8yiKMDo9Xpi+mwymfDnf/7ne+dUnC2zP+T1ejGdTqXXowafo2azKTNg7ivce30+n/RPaWaz+XM4D/b7/bBYLPjqq69kvd0mhP6Q3hFnWWpTSrfbfbAYKRaLSY/X7/fLehuLxTCZTDAYDOQ50ul0Ijbi2qfGNnHpQ6CRZyqVEiNVioQo3KKQWavVytmen8t4PEahUEC73UYgEEC5XBax1p/+6Z/C5/O9Uzone4msKWhSv1gsUKvVUCgUxACTxsH5fB4GgwEnJydiAnF5ebnWNwyFQmv3fiAQkPlBPp/HeDxeEzLQDIEzFbPZvHONDofD0uvOZrNiPsH60uFwYDabyXl3tVpJzUleyPX1Nfx+vxhRDwYDWe94j/FZZwgDa+lyuSxCKxogW61W3NzcwOPx4ObmBt999x2m06msR4lEQnrXTEWjgVksFhOROz9/XnfWfJvCrslkgmq1imKxCAA4OjpaO3OS/+B2u5HNZmXP+qkJ0MkN4X70ECwWi4grvwSTMwYDMBGQn6Pb7cZwOEShUFgzUmLNQz4bw1ooyntX0SM5JzqdDsPh8MG1dzgcQqPRiFHGIz49WMdREOpwOGQmx/Wg0+lIf5YGGR8Ko9EoYrlDMBwOpWfwvuBa9jEE9F8CmOTLmXwmk5G9jz0L8vYYXEGuzy6YTCbEYjHEYjE5wz/UT+x2uzKX5BwQgAilh8PhWs2UyWSkH7QLHo8HlUoFAIRfug009NPpdGi1Wnjy5AlsNpusbePxWMyMGIxBY2zubwDE8DWZTEqwinruXKlUhLv7MVOFa7WaPAO7+l56vR6JRALX19drachfKiKRiBiPFwoFCRXg/sizKPmtm/VarVYTDt/JycnWZ57XhPziWq32zs91Pp8X8y5yU9W9PnKb8/m8cE5pAvapQL4Ur9O+niZwd/6h8PhTGGgwWKTf76NYLGKxWIjhJLl/NDlkD8fpdMqzvW3tYJAaDSo+lgCdtdqhpsY0hfqYBqMfG+PxGMPhUPoDD4HmU+TU/pTB0CkaVWzy5qibKJfLyOVy0Ov1wg0EIEaim1yZn2IqPGvxVqslho67QFM49jUf6pH9HEDT3kQige+//150MzqdDqvVSnQVNIBUB4NwvdjsK5tMpo9ikF4qlVAqlVCr1SSMz2g0wuVyCRfK4XCIsS6NoILB4MEmZmqQ/9br9VAoFB4M7aC+6aEQtEc84hGfFj9tls8jvgiwMQvg4AOLuuFNcutPrWH7iEccAr1eLw0enU6HwWCw91BAJyK6Bn9IUTgajeRwdshBhCmvi8UCs9kMJpNJXAr1er045FJU+vXXX8Nms0kyN0WSFDUZjUYZIrNgVjspb16nXQKUQ0AHa/6sdruNRqMhjT0K6OimdXZ2hul0imq1ikKhgOVyKYnWxHw+x2KxQLPZFDFAt9tFPB6XhiO/noUwk6QVRXmwobIJv98Ps9ks6SiTyUSGyiS+hEIhaT4xFTSXy2EymaDZbMJoNCIej69dYyYQV6tVEX9FIhF0Oh0hm6ibJ9PpFI1GA81mE7PZTMS0NpsNi8VCyMVsUoVCIXHFttlsYhxQrVbR6/V2ru0UN/T7fXz99ddrjcZ+vy+D0sFggJcvXyKfz2O5XMLpdCIej0vzmWK8V69eSYOHjR+tVivCkn3PAElVHo8H1WpVUs2cTicqlYo494dCIUn+1mg0sFgs6HQ6QpwmgYjCWwpTl8slqtWquG67XC5J+t011Hc6nfJZuFwuIbkQGo1GktreBxTuTiaTBxuabKovFgv58zayIodvbHSRqDQcDpHL5VCpVCQhje7oTBit1+vyfn/1q1+hWCzC7XZLagSb+nQzpiAcuDtQM7lBr9fjq6++WiMYkRA1HA5FXGCxWOB2u9HtdpHP54UQrwaJCFardavTIgla4XAYoVBIGqlsFpHUQjIrxVlMK/D7/SLkBiBipFqtBq1Wi3K5vHat+T0cfF9eXq45dX5qwjGbrdwTVqsVvF6vEE8bjcaau9xoNBLH3kAggEAggEgksvd3kJil/vzonEcRv9VqlXR0NowpZqU4lWsa13CbzSYEOxpJqIdXTFZn06bdbqNerwOAED74+ReLRRwfHwuB9ttvv5UEWwqAdTqdPC9Mq+50OjCZTEKCYZqbum7mWgRAkqnV8Hg8kp5SKpUkrXc8Hku673Q6hdfrhcvlkoGmyWSSVBgO2km+oPvmcDiEw+EQwwoS9qxWK3q9npArmPbDpAWv17uWiEoiFJOeLRaLDOl3nRHC4bCIKv1+vxgMAJC1l/f9QwIPJjyMRiP5nAiXyyVkON6/dGzcTADodDpy/3EdarVakmLtcrkkJZyiS5fLhVKpBJfLhWQyiSdPnuDrr7+WtWU+n+P6+loMFWh0wMSWcDgsayXJPAaDQYguNAbo9/vybJEUSOIP6wb++8mTJzg6OpIhLgmr29JoDkl92wW/349OpyPr8fX1tRDXaCTQbrclOWa1Wm0dMDJlqdlsykDZ6XQim82iXq+LoHcXKABOJBIyZN0GkrLb7Ta8Xi9Go5EQcLiHHTroYZqHmkRLUPhaKpXgdrvFtCWdTouZhtPpRDKZFAd3Elr3ORyTBEShMJ83Xj+DwYBqtQqNRoPhcPjg+xiNRkKMeZdGfiKRkLqgUCjg6upKGr3z+VwS2sPhMLxeL05OTvDq1SshbT5EbFSnJPFabhrRHAKNRoNUKoVyuSxGQvV6XdbF8Xgs5xmSQLVarRD52NgvlUprIkIK6AqFAn71q1/h+vpa1m4m2asT69RkvOl0CqfTCa1Wu5MAoSgKjo+PEYvFcHp6KuYmPCcSdrsd8XhcSP+FQgFGo1GuL8n04/EYFotFjFuCwaC8BgpluB/EYjFJBnK73WLqNBgMUKlUZG+8vb3FZDKBy+WSOpcJ7nyNjUZD0tV21ZwkWqhF42qCfalUEsLits9fURTc3t7id7/7HUqlEsbjsewfJAJdX1+j1+sBgJBEmCZfqVRQqVRk7WENarPZMJlMMBqNRMjRaDQkvef09BQ+n2+tviGxw+Vy4ezsDOPxWPZ2mg6oB438M88MHxus2wCIKZwaJHXTbIA1XiKRQDKZRCqVOsgAQE2AoqBLq9XKGvmIR3wJGAwGuLy8RKfTwWq1kv4VkyuBu72HSSM0RSNoJFcsFuFyubaebZh+xjRYdR/h5wQSpAqFAnw+H3w+n6xnNC2r1Wq4vr6G2WxGPp+X1EK32y09n7Ozs8e08EcIfm5k2vcBeypqQ5t96Pf7kjj8IVALKtUpWjxv/ByhTpA9RFisrtMoOtVoNFJDZTIZ+XuLxYJIJCJn9ne5p0kaeqiGYoIFiZL83l33jKIosFgsIr57X5TLZXz//feYzWaSpttoNKDValEqlaAoCsLh8NrroNCc/Tbg7gzzUI/wU4GpiawHHqpVORf6Uk1TeLblrIL96M3zNknJ5XJZxIIU86oTuAFIP4pnexJzWUvV63X0+32Ew2EReDFBmn3LcDiM0WgkfVkasn4IAoGAELRrtdrO9NflcinGy0ajcavJAEmQ7COqUzUtFgsCgcBacqzRaJS+LpPfLy4uRPymTtjjzI1nMSb7sTfCGUC5XBZR0zawJ7gN4XAYnU4H8XhciPEejwfxeBzVahVWq1V6m+xbs4fZbrcxn89RKpVkBupwOJBOp6Uu5gyFon+Hw4FOp4Nms4nVaoVUKrVmAL4P7CkHg0ExOSZxkQakmyL8XWCNzd4pry/FjpwzTyYTnJ+fHyS2o6kUn5t2u403b96IuYZGo5FUbRoO5vN5EZszadDhcMButyMcDiObzYrJOWfSFJsDd/cJwx1oGttsNnd+3gCk77or+VFRFDEYpSE0Z2aj0Qh+vx83NzcYj8eS7kujH4PBgHa7DYPBIEJqr9eLxWKBfr8vyWrcL1mj0GSUBvGLxQLffPMNbDabzGf52inUubi4uGdeQKMI9T62XC4fNNYH7u4vmiLQTFSr1Yr5o8FggMVikfdxcXEBRVH27kE2mw2Kooj54Pn5uRjyUhyvBoWw0+lUZq4AxHwyHo+LOTn7p+RKUCDP+ysQCKyZgIfD4b33xbuAZiJqY07WfVarVeauh8yceQZUFEVev06ng9VqFbEW+0KcD20DjXTtdruQjCncp1h080xOs2m32w2Px4NsNiu9Lq1Wi2azufZafD6fCMNbrRZarRYKhYL8t8FgEHMECudPT08PrpdYuzEIAIDUfuzN93o96UmORiMxOB2NRjAYDGtcO4vFAo/Hg06nIz05vrfT01OZlbdaLVQqFRHYkVPCGsfpdOLbb7+V/Z81QqvVkrky16ByuYxqtSqC3dFoJP1tmmpwfsr9oFarQVEUOT+QP9Pv96HVauF0OmUeop4bz+dzVKtVAOs1uMPhwIsXL/D73/8eGo0GOp0Oy+USZrNZ5ujsM47HY4zHY+j1ehwfH98TI9rtdhFdVSoVMVtW30ucNw+HQ5ycnOzci81mM4LBIOr1Onw+n/AefipQz8IPOfN8iandNL9uNBrCKaWhvMfjQb1eXztrcM1hiuJyuRQDrHftUdvtdlmL2u32g6LJVqsl4ud35Yk94v1AjhoAMZsuFApyL9NwlP1fg8Fwjxv3PvB6vTJn3ORwbaLf72M2m4np+yN+QDAYlPq4VquJETkNRSaTCSwWC1KpFNxuN05PTw9+jmkkx9Am4AfOj/rzbzQa6PV6wr3bBGsk/rndbmMwGOD6+nrn58m9Kh6PY7FYoN1uC18NuKvJut2uJATTFDcajUptRjN37p9WqxVOpxPdbldqAYPBgMFgIPszBcBqwz8aAR163joUDJDodDpSf++CTqcTTobH48FkMvli5wE6nQ5nZ2dyXqWJRaPRAAD5nBwOx1oABfBDsA33i318C/U18fl8a2YqD2E+n8v6sy8cy2w2i9Cd9dynNNJ1u92oVCpwuVxoNptrZhKbYGBFNBr9ZMns19fXwpX2eDxidk+wh8CEan7m5Dft6gGy3qBo/GP0CmkCdehzymf7c/XsuWap5wabtfW271EbZTwErVYre/fn4L5+StTrdenHxWKxre+F/MDb21u0Wi1Zu6vVqoSk8bzGs63VapW+4k8F7D8rirK2/qjNYdXweDy4vb0VvupP6b2+D9Rn/HQ6jX6/j3w+j0wmA4PBIL3+SCQCm82GRCKxxj3lz/jYa0Gr1UKpVJKE9XA4LIZ1wJ0BVLPZFFMZnU6Hp0+fHmxquAuBQEB4Zfl8Xkzo1FitVqhWqxiNRkin02vchEc84hGfH1/mxPQRPymwYQ7gYKeyTecRRVEeBeiP+FmCojUOnnq93s7GwXw+F6FlJBJZa2a9D1iwH9pA5TAH+IFUpdFoEI/HJc2t3W5jNpvJ8O+rr75CsVjE27dvRVDhcrng8XgkPXA8Hkta5YeImx4C0y7ZaKHDNN8bhfEUxedyOQwGA3EfVB+OO50Obm5uUK1W5TO5ubmRQyKTIRKJhDT9mL5Al873GSzY7XacnZ1hMpnI0JZivs3GtcvlgkajQbfbRbfbhc1m2yoqowt8tVoVMYDFYpEUcbqFA3cksnK5jNFoJGYDwA9p9xQrszFjMBhkKM5DDgcrbrcbtVoNVqt1a3Ot0+kgl8vBbrfLsBi4ay4zgbLVaiGXy6FYLEpyaCQSgdPphM/nkyQJ4G6gyzR1OodT4MHEhV3o9XpC2jMajSIKo1gunU4LWYViv9vbWxSLRRHSMSW22WyiXC5jOp2i1+uJ0N5gMODo6Aj5fF7MAh66R5i0wEFJOp3eaeKwD3xvWq0Wg8EALpdL0iT4ntUYj8dySNXr9dI4YzLQLkLjYrGQhhNwt6aR1MBUSh6Qeb3q9TqCwaCkEQDA3/zN3yCRSMiQmq+Fr4MEhkQigVAohFarJQJSpjRug9VqRSgUQrVaxdnZGYxGI1KpFAqFAq6vryWZk66cFORGo1HEYrF7zyAHtxSW+3w+zOdzHB8fI5fLievr69evMZvNhIDG1Bmj0QitVotqtSrp6Ux0IGmFh2US1ux2u5DY+P8pCv7UKUEcvJDY1ev1pCHM54xpGbxPdDodkskkkskkLi4uHtyPNgfh4/EY9XpdDArURgHqFIrJZCJCkE6nA4/Hg8FggE6nI8mbdAIcDodwOp1y31NsVywW4XQ6JW1KnXzDZFyv1yvkDO6LOp0Or169ElIgyVJ0H2ZaUL1ex//8n/8THo9H7mV+fmazWdKT1aB4nzUy03fn8zmKxaK4Y7L5xGRfEvCYpsPnl58fBfzz+RzNZlPIJfzMSExmDUJRnN1uR7fblc+x1WrJZ6Bez4xGIwaDgZDnWBPsI/0Fg0ExcnE6nWv7D0kyh5hccU+0Wq331koSd4xGoyQy8/Ml6Qe42xOYIsE0HYvFgrOzMySTSVQqFUlI531H4xmn04lnz57B5/Ph4uJiba2kY2WtVkMsFrtHVHG5XHj+/DlisRi+//57eeaTyaTcw8PhUGqubDYrAlIOlygibTQaiMfjkopCstenwqYREEn4HMiZTCYkk0lYrVZMJhNotVp0u13Z+5lEQ6IkyZJMIKHT977BGBPrWc92u9295kZOpxP1el2eaV4f9RDgUFLOruRBkg/m8/naAE2j0ayRK5fLpYjV+JztOi+Q8N1oNFCr1USgq9PpMJ1OUSgUZEg1HA5liLkPdJI1mUzvRKDS6XQ4Pz/Hzc0NzGazkDkHg4EIiJxOJywWC05OTmC1WtdSnubzuTi4b7uu9XpdyJ61Wg1/+MMfJLEmEAgcPIwF7lzLnz17ht/+9rcy4GYi1WQyEUIik5+m06nUnVqtVkhpTJphnTgej1GpVNDv9+WZNZlMQnRrtVry2SwWCxlcMp1uW+3OxvlkMpGE7V01Iw0yaLIynU4RCAQk3Qq4O6NYLBYUCgWMx2MEAgGpswi9Xi+fVyaTQaPRgMlkwtnZGfL5vAgJyuWyECe73a4I2nn+abVaCIVCWCwWkr7W7/eRTqfh8Xgk6Yt1DI2LmKLLs5TVapVkEZ7tZrMZJpMJTk5O7j1rhUIBNzc3kuTFWov7lcPhQCKRQCaTEdE8E4lSqRT+/M//HDc3N3j16hWur68xHo+RSCRE/DMej4WgG4lEEI1G8eTJE0Sj0TVRPJPmb25ukM1mMZ1ORcSvHsyowddCp/WPDdY8NM7aRlzeNLYKBoNIJpP49a9//V5D3nc1s3jEIz4HZrMZrq6uxKjQ6/UimUxCr9dLncEEUZKsNwkk6n19X0KpVqtFMBhEPp/HZDJBq9X6ZESaHwMUqvE6qt8bTWlo3kJTi2AwiEQiIcSe29tbIWht1syPeMQfM2hKSHPVfWsH1653FTir0e/3kc1mMRqN5LkF7s70rVZL0qN+TmsYwQQWpkexZ7QLNCdVm1parVYxh2MKL3t17i3JlYdA3QPdB547SEreR+qbzWbodruIxWLweDzvvebe3t6iWq1KyrC696bX61EqleDz+URw2e/3ZZZE0Mxq08Tpc8Ltdssch+nxu0AjrkQi8cEpXZ+CQMnXx/7KeDwW8dnm7+L8pVKpoFqtyvlMnUhKgzumv7GfTbFtvV5HPp9HrVaDyWRCpVKBxWKRc71WqxVTslAoJEJdGic+9Jw9BCb+RKNRIQkzUZoYDodisp1IJBAMBiXNWC02uby8lN5Vp9NZS+V2u91QFAWtVgunp6fSV6FYlIJFPktMl+FspNVqAbgj5PX7fVitViEKRiIREVyTgPk+KUachV5eXorpWrValTMs16VwOAyNRoPxeIxcLidCXJIaOddiLWyz2TCfz2UWrNPpRKjJa+P1euHz+ZBKpXa+PpPJJEm+g8EAZrNZ1sZtGAwGDyaG1ut11Go1EZMlEom1vtB8PhfR0XK5xNXVFZ4/f34QEdHtduPi4gLValXWBD4bdrtdDBZ5D/t8PiFU02SbM1EagyiKgnQ6LSmy9Xpd+t008m61WmJW0ul0oNVq76XXAXezVPayuQdtgjOBxWIhnz/NHGmawkQxdd+fPRj2piwWi/SXSXgeDAZiQMV6v9lsotFowGazYTgcYjabyV6Yz+dlhjifz+X9/MVf/AX8fv89oy9Cp9PBYrFIb+4QkJgN3CUY00SBnwvX3kQigWg0itlshmAwKGL1bWCvzmAwiLCapO/hcLjG25jNZpIuT4F6p9OBxWKB0WiU+eZm/5RzYxp00TSYad0fG/P5HJeXl2tiOfaieO71er0YDodIJpMH1340V9jEu65rjUZDxPE0tLVarTIrCoVC0Gq1YgppMplE7HR8fCym8fwMut2uhBHw3m61Wmg2m7i9vcV0OkUkEkEymYTRaEQul4OiKHj9+jXOzs6wXC5xcXFxUG9LXbup93q/3y9iSBqTr1YrFItFuTftdrukj+v1etkvaRYDYK23bzAY8OTJEzFZGY/HMqPiDI8m17e3t8IFubq6EjPbbreL6XQqr5N7PU28NRoNfD4f9Hq9zFhoSsX7Wv051+t1vHnzRgzqQ6GQ1MDhcPjevcCE6G3chHq9Lns1zVHV/EGaxt/c3GCxWGydsRFMG8zlcvD7/Wg2m2uzolarhU6nc88kfBtcLpcI7jlH+anAZDJJD56G4ftAAwGj0SgcHAZl7Hoeer2erCtq8/V9M413AY3EOS9crVZwuVyIxWLyzDFxVqfTIRwOYzAYIJPJwGKxIJ1Ow+/3v3cQSiAQkHMN9+JtoFl8JBJZMzF6xKcH5/K1Wg0Oh0NmvpzJsxYcDAa4urqSPSQajb73DMPr9aJQKMDtdqNcLiOVSm1dS6bTqQgy+XsfsQ6KjTgn43zW7XYjGo1K7cs+/iGYz+eoVCryc9XhDzQoi0QiMBqNYkBNY6x9sFqt8Pl80Ol0EnISCATWZtz9fh+1Wk24Gfw7zu5ZM+h0OrlfyX/gvcr06nK5vBbm5nA4xHyOYQxut1vOFoT69wL4JOvReDyW+v+Qtd7hcKDRaGCxWIiB+JcKg8GAi4sLtFotMaAhWLvT8EuN0WgkRj77ZkcE64vpdIrhcHgw54Gz6sFggKOjo71fSyOfdrsNu93+SQXoPMuOx2NkMhnk83lEIpG1dZa1RblcluCdT2FEqyiKcLojkQhms5mcXVkLkcfSaDRQLpcxn8+l5/QQr0bNH+KfP+Q5ozaAIWH79ib2IHjW+JRYLBZrayl7JJtr6a4QOr1eL99ziPknDdx+yjUUDa7IWd33Xmh2WKvV4Pf78c0332A8Hovpkjr4SM2THo/Ha4EZXzJo1sb3Qn4Ve2PkWpMnreYCPzQb+bmAZ3yLxYJ2u43pdAqDwSAzHH725AUTvV5Peq0Wi0XqJ5oPfwg3tFwuo9FoYDqdIp1O31uTjEajrO+NRkPW/g9dk6xWKyKRCJbLpZz72XPSarWiZVmtVkgmk1Ir7gN7GjSfpVnfIx7xiI+DR3bPIz4YJDYDOEgYsu3r1D/jEY/4OYGpd7FYDJlMRsQUFB4Sq9VKhhderxepVOqgpsQ+0PWQhTmHvZ1OB71eT5zmORhngiq/Vw2j0YhoNLq1cAsEAggGgyLapSiBCaGRSAR2ux2np6efRSAZDAYRDAaluOYBWN00XK1WaDab0mhRF8udTgelUgn1el3IIuFwWAR5nU4Ho9FISAbpdFoGOMSHOgRTLL8PLOCZsksxzSaWyyV6vR48Hg9ms5kMmTZRLBZRKpUkrYTEGOAHB0um4VLEYTab7x2YNRoNYrEYptOpDDl9Pp8IH9lUoggmlUqJAJOpE9VqVdzi2YilsQHvL41Gg1AohFgshnq9LiJHJiROp1P4/X5J4iiVSmsO5BRk93o9EdYzFZCNaArm1J8t05IByP1AUUy/30ez2ZTGkMlkEndspmEwVd7r9e5ttHS7XbTbbTSbTRF58mc6nU6Ew+GDB3ccyLtcLrTb7QcNX3q9nghX1QdEtePgNqibQXSp5/UtlUqw2+1yL/I68/v8fj8URUG5XMZkMsHr16+RSqVwdnYmDb7FYiFusKwbjEYjms2mJLM8RGxwOp0yiEylUnJoJ2GSAmCz2SyEnmg0ulWsw8O8wWAQkhHJWkx4Z4KuyWSSoSgPx4vFQlKtgB+c72n4YDKZEA6HheBisVig1Woxm81Qq9Xk+fR4PJ/FldxsNsNisQhhaTAYCKGNKdlMHCHRyO/34/j4GP/0n/7Tg4ZCZrMZVqsVpVIJi8VC1tvRaHQvpd7r9Yrr3WAwQLVaxZs3b9DpdIRc6HA4pBnj9/sl4ZYmFgCEgMGEUjZG1MmxNI8hgYMEVbvdjvl8LgMwdYouCZEUz11dXcl6QLIj75nVaiXEGt7bald1r9eLdDqNbDYL4O75evLkCRqNhqSfuFwuqS0qlYoMfJhEazabUa/X8ezZM/j9fhlwca01mUwiXF8sFmvPvlpYy8YvRb61Wk2clQmaSZDU4Xa7DyIt6fX6D071IKlvW6Ob13SxWKBcLqNUKokJy2w2Q6vVQjabRaVSEVOF5XKJUCgEv9+PaDSKcrkshFya7FxdXcHv9yOdTuP58+dCbtq85+l2ajabdza1tFotvF4vvv76a5TLZSSTSYTDYVkHHQ4Hksmk/Dy6oJrNZqxWKxH60P309PRU1hR1mtOnaNzToZMpypvDGbvdjqdPn6JarUpCB/dLGrZQdEuyr8fjQSaTWSO+7ALNkyhoZUN7l/EF/z8F4ARrOqY5P7SvMBFnm8kC7xOHw7H3dZCUa7VahSC56z1eX1+L+DwcDovouN1ui/EFSbk0RyBRj1DXH41GA8Ph8L0dQukGPhgMUK/X1wyUrFYrAoEA3KrUcu6FZrMZDocDrVYLFotFSG40ZKnVakJAJvnu9vYWHo8HiqKg2WwK4fmQ+zkQCKBer+P4+FhqRKa3MH3P4XBAr9ejWq1Cp9PJvkeXfda2Nzc3ItJRFEVIozqdTgbGBoMBoVBIiPxms1nOHPV6XYjVnU4HlUpFnMDZOF8ul0gmk5KStonFYiEGLUxbI3F0MpnAZrMhFAqJYzfFSYVCAdFodOfnbDQaxWHZYrHg7//+77FardBoNPD69Wt0u125p00mk9Q8nU4HVqtVHOF9Ph9KpRLC4TBSqZSI4L/77ru13g/FVRxiulyuNfde4K4+qlarkippNpvXXOV5TsnlcvI8BYPBe+9Ro9EglUphtVphOp3Kc8616uTkBEajEeFwWIQIo9FIBhLJZFKGHC9evFh7nYvFAjc3N7KW8Vmgg7uiKNIT2FwnWJsbDIZ3MlU4FGqRQKlUgsvl2jl04ms+OjoSocIjHvFzQa1WE9H0ZqobTfH8fr+IcSwWCyqVCtLptHwdzTdo/LIPNAhk0tmPKd782II3tRB/U0jW6XTkOjPtlEl2TBp0Op1oNBpixlKtVj8p+ekRj/gpwWq1Clm/WCzKPr6J8XiMUqkEs9kstSh7miTDPoRer4erqyshVK5WK6lFSJh1u90yVzhELMweA/DlG9Kwx82+y760dyZWBQIBqYP5M46Pj6UnTAGvzWY7eE667XXZ7XZUKpW9CUgajQYej0fOkmqTcDVms5nsa5uJs++CcrmMer2ObDYrwgqKPhaLhRiLlstlSZxkSiQNCtkn1uv1Ypb3oYLk9wH70jTP3ZXMulqtUCqVYDKZ5PW+CyhU5pkb+CEdm/fSh4J9ntlsJr12jUaztU9sMpng8XjEGJOGinytPIuybvF6vTCbzfjDH/6Afr8vBGee5SmSHQ6HaDQacLlc0mtcLpfSW+T5S21E8CE4OTmRnkOlUsHV1ZWYR/b7fUynUzidThwdHckz8vLlS5lDLJdLVCoVAD+YEvv9fjkjDYdDtFotlMtlOBwO3NzciKCE/Vea7lG4S7CPxDMvz+ScWVCgCtzVUzSxe9/n0mg04uLiYi35bLFYiJlJs9lEMBiE3+/HP/7jP8JsNqPZbMo8mCZv4/FYZivq9OZIJILz83MkEgkR4LO3vU0krQZ7/Ore9r6vpxCbM6xNrFYrVCoVESem0+l7fVe9Xi9m8OVyGTabDa1W62BCO80EKQ5morrT6YTZbEar1ZI5u91ulxrbarXKvGY6naLVaompKQ2H+XnxuiuKgnw+L/0as9mMp0+fiiEC+/wU0tIAy+fzSVr7JvhZ0tBPURQxXfT5fGKuDmAtWZJ9fBqaABDy+3Q6lR6jzWYT0/J+vy9GxP/rf/0vMZlkwiIT47vdroj6OcM6Pj7eS0qlOL7ZbD6YKMd1mr19Jod3Oh10Oh1ZdxaLBRwOB1wul5xl6vX6XhGY1+uVvVPdJ2QPirOgbrcrwkybzSbz40gkIvc0r7m6f0qTaRp2c939VMlN19fXMvtgGiJrDPJGaL4JQOZ+nxq5XE6SJdn/83g8Itzmde71ejAajVgul4jH4yLq4vVstVpSy3Ld4Z5GQ9RsNotSqQSNRoOzszOZWU2nUxHk2Gw2FAoF6HQ6FIvFtX7ALtjtdthsNjE2V+/vnDPShHU+n4vZvtvtxtOnT9fucbPZjHA4DLfbjVwuh3q9LucBPvc6nQ6RSAThcBjdbhevXr2C3W7HYDAQwYzVasVyuUS/38fLly/FOJVGA+xXM3WV6xQF35xb0kAuEomg3W6jWCzKe3I6nVJ/qJ8XzrN8Pt/W/iP7oJsm/dyfmY6eTqfvnWnYX9fpdKhUKkilUnvrZZvNBrPZjHa7LUmlBPfAQ8wSKOT7mPXM5wKTuGn+vW/+w7kSjUZYU3I+7HK5xBAbuNt3rq+vZYZOExTO5mlsw577+0Kn0+Hk5ER4J6wHOG9oNBqyH9OkKxKJSM1PM9737YsFAgE0m02ZrYxGI3i9Xpnps3YcjUaIx+P3ZnGP+DwwmUxIJBKIxWKoVCpi0sRADu51AGTN7PV6OD8/fy8hrFarRTweF04CZ5ec89AErt1uS2JpLBb74OT1nxPm8zkymYyYmhuNRqlDdDqdmL9fXFy8k6BpOp3i7du36Ha7aLVaMjcHfhC6DQYDdLtdnJ2dyfcdukZEIhERqq5WK9ze3sJoNEo9ulgsJDjA7/fDZDKJeJPBR+yJsA6sVqtr7zEQCGAwGMhcezAYiIk8zedprvDkyRO4XK69+xkDaj5kLd4E+WAADqpf+TWsQb500JDH5/PJ/s95+67zifqaHPKsq6/Ju3D9xuMxhsMhDAbDQfxrcjHUvPFPhUgkIve5OgTIZDJhuVzKGZUhV8fHx5/k9XBGv1wuxWh/Op2iWq1KoAg5KTwHLxYL6f/t69exH1Sv1/Hdd99JX85oNAon8V2fNb/fj1qtJtzXfVw5ckhcLtcnEe8Ts9kMl5eX6HQ6aDabspayPmTvpNvt4vT09F49ztT5arWKXq+392xJ/mU8Hv9g888fG+TszGazg84Zdrsd5XIZ+Xxeei5arVZ4SzSw7Ha7aDab0l82GAw/CcNg8v2HwyEuLy+lVzUcDkWUTh45zUzV3/fHAp1Oh6OjIzEYKBaLwrWLRqP31vr5fI52uy06B/Kd2d/T6/XC0/P5fO90DlIURWYEm6avm+BspVKpSF/gQ0FdEjm55NJR+0P9idPpFNPYbdg00OD1ocn0dDpFPB7/pCFOj3jEHwMeBeiP+GCQVALcF63uwubmpP4Zj3jEzwk8GHDoqHbUpwhhMpmgWq1CURQEg0GcnZ0hGo1+cGHmdrvFjbjdbmO5XIqTFA/2bGp1u13UajWcn5+LAORQRKNREXW6XC5xftdqtXLoCQQCnz3RyGaz7U2bXy6XGI/Ha4dyumEyUVntTEmnOZ/PJ41CrVaLcrmMdDotxBIO8T41jEajkD4MBgO63a4InNWJ5SSghUIhDIdDIUFwQMTv5aF2Pp/j6Oho7QDj9/vlkFOv19cSYbeBjTgAIvRngg+H6hT4Op3ONddBkkhCoRDK5bI4FjudTkkoYVPHYDBIEk8+n5fPlKKRZ8+e4fe//z3cbjcKhYIMGSmcXK1W8vuZTkm3/8VicU+8woONOhmIYhkeVlqtlgwyjUYjzGazELycTif+9m//VshrdL3bBEV5iqKgUqlIU4ECJo/Hg16vJykeD0Gj0Uij+ubmRj4LkljUjUYSO0gKASCDF352uw5wFGQvl0sxOyABnY6FdF8DIKQ1igqZPk/XV5omEDqdDl6vF3a7HZlMBs1mU1Kbx+PxQaIdHtLpynp6eirN+1arJQdyks/2NQhJhvF4PGISQGIlE7Q5RKAgjAJhmo4wlQSAJGlMp1MYjUb0ej0Mh0Mxb2AzmS7lbMZQBP+pYbfbYTabcXx8jMvLS/h8PjSbTRl6OBwOSQRhAvXp6alc40PAQa5OpxMH+l6vJ2RQDiWY+OrxeOBwONBsNuFwOFAoFMRwJBgMwmg0wu/3w+12YzAYoFarIRgMwmKxyBrYarXkH5/Ph3g8vnY93W43JpMJarWaPDskQTGBnOkQ6vuVwybg7n7K5/M4PT2Fz+dDMBjEdDrF69evUSgU0O/34fF4cHJyIqI6iur5TPZ6PRHqfv3115Je8vbtWyGtcY1kQn0kEkEqlUIkEpH7ig19AGIKQvdR9XqgXvvUiTUkFq9WK/h8PiiKIunxFotljaRAR+pP3QBXg81IPsubCIfDMvxRFAWZTEaS4/m+NBoN6vU6gLthkdfrRbFYRLvdRiKRgNPpFDIgU97ZRI7FYohGo/fWSRJaKKp+CA6HA6VSCYPBQO51gjUVG2y8N4C7/ddut8NqtYowN5fLra1vJKoHAoH3SlnaB/5sj8cjYl3gB1dx4O6Zur6+hsPhQCaTETIV991wOAybzYZwOIzpdIpXr16h1WqJUc2ue0mr1UKj0YhRB7DfEIiE2s26jfsPzXL27QM0NNpFkuXefSiBgXXSrvuXgli1+LxWqwm5VX0vABAH/jdv3qBWq8n1URPADQYDTk9PxdzmfbGv7laDZDSmMpO82Wg05GuYAkMXZ7fbDY/Hg3A4jPl8jkKhAJPJhOFwiFKpJAR6phFtO0PRJT8ejyOXy8Fms8Hn88kQn4lfJKFQkKLRaNDv9+Hz+TCbzST1nMk6brcbOp1OyFw04RqNRmi320in07BYLLKukgzOa+33+4UYC0CI6XzvDocDl5eXQkpnQ5xpba1WC91uV9Y1uuB6PB5Jntfr9fjmm2/EUOGhmsHpdCKfz+P169difgNA0gz5GprNppCT1clQ/X4fs9kMT548kfe/WCyQzWbRbrdlkMtnVlEU1Ot1pNPprcImi8WCZDKJXC6HSqUirrpcZ5nexKHbPvEQTWiazaakB3B/BCDJhjzX8j5krRoIBOD3+9fWjMViIQnuhUIB8/kcDocDi8UC1WoV1WoV7XYbPp8P8/kcqVRKamESbGl08qkE3+FwWNLLstmsGEPwGnJQReMY1lGPeMSHYrVaodvtSnIez/WfwmxhH5bLJRqNBlqtFmw2216yg91uF+M2p9OJeDwuzzxr5UPrp01Rx+fCarUSAYE69Y9iMZfLdfB6s1gsxJSJiZQ8k5L4rQZT5ONv62kAAQAASURBVHU6nQy2LRaLmIoRfr8fg8FATCEjkcgfFaHgEY/Yh2QyidFoJCSJTqcDt9stawp7+JPJRAio2WxWSPfFYhFerxfxeHxn/46mSvV6HY1GA8FgEG63e438OBgMUKlU5Ey3K3EV+CH1Ti1uA+7OtcFg8J3Wnc+JQCAgPax8Po9YLHavp8/zh8FgEOHwJphK8THAJDW73Y5GoyFnkm1gX+D4+BgAcHV1JSaRFB3RUDcej29NoTwEy+VS6trVaiX3Fut59paYSvj69WshzanPfuz9NxoNuN1u3N7eStrL54RGo0E0GpWeRSaTEfISie40PVssFkilUiKsOhQUPZBkzlRUg8EAp9Mps5kPJdvy3tBoNDI3oFCWZzc12Fcn4b3RaEiSN0W1NN46OTlBuVxeOydEo1Ex9eH9R5EtDRfZa/lUZqlMhqGIkgaTFMrx/q9Wq+h2u5LOzL47++00CP7lL39577Ol+Pj6+lqEuyTdcZ4xHA5RKBTE9JNzKaYjL5dLMTkm2d9sNku6eKfTQSKRkH76+4JJn6FQCIPBYC2BejAYoFAo4LvvvhPhAtcrJuFyPsT+G8/2v/rVr3B8fIxf/OIXktpEY+pDEQgEZE5XKpUQjUa3rme1Wk2MLXYJ25lW0+l0EA6H9z43Xq9X5gr1ev2gnrSiKLi5uZHf0W635XpxnkrDOf48GpKQnMlZIwWjBoMB8Xgcw+FQDK+4z3IGoigKarUaEokEOp0Onj59KqZXnIGq07FPTk52nqXYv1AbRKtnXdPpVESBFMir62/1nw0Gw9pzMhwOpf/v9/vh8/nEiPn6+hp6vR4ejwdfffUVTCYT+v0+qtUqFosFotGo9LAdDoekk+6CmnzfaDT29ip51tTr9dIz48/w+/0iAlFfl/F4LCLyfXA4HIjFYuj3+5IayBSyXC4ngg8aFfJ3VatVfPXVV3C73Vtfu7p/+qFGYTz3DQYDLJdLMUzYXFNoJpnP5+FwOO4FDej1evj9ftjtdmSzWZl78dxMrgHP9B8rsbJWq6FaraJQKMieyP4U98xut4tisSh90T/7sz+TpL18Pi97Envm7HkzvTmXy8Hn88m9wprI4/HIs0yTSq1Wi1AoJOYxNpttrR+w7drzzH19fS1ciBcvXty7x7nH3t7eikD3/Px851pGs49qtSqvf7MOp5EpDX4CgQDC4fC959pmsyGTyWAwGCAUCsFqtUqPmhylq6srpFIp6PV6pFIpmEwm/OEPf0Cn00EsFhPTUTX/YrVaweFwwOfzIRqNSjqZ0WhcMw5Vg2nE2/qg5C/1er0HzzE02iD3al+PyW63Yzgc3gtwUJtuH4LPYQL/qRAIBESIWSgUtopgV6sV8vk8stms1OZXV1ciquJZkAbwwWAQb9++lT2eeyD5VJVKBbVaDdFoFPP5HBcXFx/EY7Db7Tg/P8fNzQ2cTqdwMTQajZiP0NAlHA6L2Q7Fnx8CrVYrIlWDwSBGRZzHUJwfDofhdDpxdnb2KDL+DNi2h06nUzFSpliOBu+8D2gYkM1mEY/HcXl5iadPn77XZ+b3++UZUQeLECaTCaFQCC6XS/hsj7jDfD6XUIlSqYTpdComMrPZDIPBQNbtm5sbXFxcHHRuXiwWuLy8RLPZRKFQkJAGir3Vnz9nkeTAbPazdmEymeDo6Ej4fzRnYi1GsW0oFJLfEY1GhUumrtmMRiO+++476bUQer0eiUQCwN26w/4EgyHYa+PX7kv/5LyWgR0fC+SnMVzjoXkPZ+o6ne6z84Y/FGqOyz6o05u39WM2wX4rv+9QvGvittqk8FPXMxqNRngJPFNyNq3VasWow2w24+jo6JOl0A4GAzGGokkycLcud7tdMdoB7jhcTqdT+iLxeHzntZ3NZsjlchLUlc/nZa0iP48G2O/CuWEIUCgUQj6flwCnzdfBMI9wOCzGmp8Cy+USV1dXaDabyOfzEgzAtXQ0GqHVaiGXy8n7vLi4WLvnyZ0i58Jqte40/6xUKsKP+alzE9TP2KHPKPvT3W5Xgho2uZperxcOhwPZbBbValX4zF/ivEUNzobZ0+McgcEc8/lcQtlCoRCazSbsdrtwfv9YMJlMUCgU0Gg0sFgsMJ/PcXNzI3oj8nbMZrNoN5gor9VqJWxN3QN0u91ydud56RCwH0kT1m3gGsC5WqlUEm0Hg5I+5N6MRqNwu93C41cb13CdUJv0bWI+n4sZUaPREN3McrlEp9MR/nm/38fJycljIvojHvEB+GlV9Y/4IqE+aLHB8hA23UE/ptPaIx7xpSEYDGIymeA3v/kNXr58iVKphFqtJk154E6Mc35+jkgkgl/84hf3XMzpFk5nPaZn7ivYKPDw+Xz49ttvJaF2NBpJIi/di29vb6HT6dDtdt/L3YcD9NlsJmQPivS+9AOPGrzGvV4PsVhMhmHqYTSHahSh2Gw2ISbwIPQ5itPFYoFIJIKbmxscHx8jl8uh3W4jm82KKG65XMqgmM1OkhVTqZQID2q1mgjYdxGfzGYzUqkUrq+v15zrdyESicBsNsNms8Hv92M4HK4lYprNZnHX1mg06HQ6QtLhYLVYLEoiBdMrSILh4J8DJTrsabVajEYjzGYzST2g2GY4HGI4HIp43+FwSKqj1+tdSwjcPNCyAd5oNNDv92G1WoV41O/30Wq1pGEaDAaFfMRUTz4HkUhEEihardY9giLJn9VqVYRh4XAY0WgUkUhEROm1Wg2NRgPJZFJI5PuGd8FgEK1WSxyaKdBjIoDZbJZ7gMITvjZeBwpqdxHD6QDO58Nqtcpgn4NsGhswaZrENw6LeZj2eDwYjUb3hsJ8HYFAQNy639XEhq5m19fX8r0mkwmxWEyeiUPJ78FgEIqi4M2bNxgOh1gsFrJGkJzV7/dxe3uL4XAogyeK/G02m/wuOuWqE5M0Go0MFABIGoLFYsFgMEA6nZbE1s8BGhmwUREOhzEYDNZeI00IwuEwkskkfvnLXx788ymcDQaDyOVycg9QZM4BDh336Lb+9OlTIYlNp1NoNBpx65/NZigUClAURZxqmSANQAY+8/kcoVBoa+OFafTFYlFeJ1NxmXQO/GBuQsIKXUFXq5WQrSaTidzvBoNBxKAWi0WIkkxQ9/v9KBaLyOVya7UynQpJ9LBYLCKCpnnEcDhEIBAQ0uN0OhUSKRtFbPT7/X68ffsWer1eDA7U4HrNn8PBltoEhfcAyWlnZ2e4uLiAz+fD6enpZ6sFrFYrbDYbKpWKfBZqaDQaSbNoNBpiKjIcDoWsFQqFkEql0O/3YTQaMZ1OUSqV4PP5sFqtkE6n4Xa7xciChKdEIgGTybT1HuLzwfqIfyZ5UW1YwfVjnxjZ4/HA7XbLUJtnK5PJBL/fD5vNJnvoYDAQkSO/hqJ7Jkg/tOZRQMb7QK/Xy4BoF3YN5Gw2G54/f456vY5CoSDrWiQSkb1HnSx6dHQkhC6moGy7xlardc3wY59hCgCp2wwGwz1SWDAYRLPZhM1mQy6Xk89WDaa7Mf1TTZKlQI0i20OSMWgUpV5D+HmSONhoNNDtduX607CGAhTW/sAP4ni64dLdmp8hh1dMe/6TP/mTz7KXxGKxtb2fSRS8XjT0YZ3HZCu16Y7f78fvfvc7XF9fy/1C4pndbhfysnqtB+7uJRpwrVYrSeag66lOpxNh+mw2g1arlXRur9crhM1utytEK56l3G43gsEgTk5OYLVakclkxNgqkUhgOp1isVjAbrcjnU7D5XIhEomg2+0KgZUiIxKB6RBO0TRrb+6BHPqxbqNQiERrEhTOz88l6eHQVBeuiYqi4E/+5E/EqKXb7cLhcKDf78v1Wi6X8m/gbn1i6hZTsFhHk+xuMpkwn89xeXmJXC6H6XSKaDQqw7Vtr4m1wWQykSQ3AJJ0Rzw0kDKZTGK8wrOreq0ieZh/x7XZYrFs3cvy+bycwbg/Mr2BqaRarRa1Wk0+U5LLWUt96qGq1WqV1CY6qNfrdTFeYVoCE9VOT0+/6MTSR3zZmE6nIlrk8HMymYibOdOyIpHIZ3OzpxiHfZ6H4Ha7kc1mJbWb52KtVgudTndw75sGbp9TWD0ajdYSqJhMwL2k0+lIAtU+EtRwOJS+pXq4q04j3FwnxuOxGC6p+5U0ANxMN6HJUzAYRK/X+8mnGzziER8LFosFZ2dnIvajqSfXFK5BJHlSTMl6kiKLfr+P8/PzrXs6hYj1eh2JROJejchUqFQqhdvbW7TbbVQqlXvP6Wq1QjabFSIHzZoACMmMgpl3Ibp8LtDQjWJNtYAbgPS/mBQaCAQ+mtB8F3hOG4/HyGazKBaL98SWPJ9XKhW8ePECs9kMoVBIUiG63a4YgMTjcdjtdklcex9QzEyjQ3UfljOher0Oj8cjPbt+v490Or32O2ezGfx+P0KhEFarlSTKUvj9OeH3+8VgJZvN4vLyUszjmGhFwwGv14tUKvXgz1wul2g2m7i5ucHV1ZWIjElMZzr21dWVJOY1m0385je/ee+zOHv1FosFiqIgHA6j0WhIn3/zjDEej+FyuSSJGICkqAYCARgMBni9XoRCIZjNZrx580bS0X0+n7xOGqXyHMUE6GazKcRdnU4nwqBDCduHwmAw4Pj4GK9fvwYAqc9Wq5WkzNJYcjKZIBaLiYkZnx32TXO5HFKplPQ+s9ksFEVZM6ijoRxNO9k35dpB4qROp0MoFJJ1kEJ2t9stZ0GK95PJpKyNHwKKLSkk5DzB7Xbj5uZGDJPZh57NZuh0OkJiVBRFCIzL5VJSoefzORKJBM7Pz9/7+fT5fNKLyufzuL6+htfrXROAtNttTKdTJBIJuFyunYmhiqJIktohIni32y2pShR77gKfS87l9Hq9JLhTZN3tdqHVanF7eyvzPuCHlLPxeCxpzDTlpLGmzWa7N0fVarVyncfjMUqlEr7++ms5I5EcCUBSix5Kx+bMkz0mnl2AH2YWTqdTEtVpQMOfyVqd/TK+hvl8Dq/Xe29u/Kd/+qcA7moKEsZvb2+lz2CxWKTfEY/HxXzQZrOtGQFuguT7YDCIQqEAvV6/NbGJzzINJbfNqtlfVZ/f2A96KHmRYjHeE0xYYuI6n/PZbIbZbIZWqwWv1yvmislk8sEe3PvOS0jSZz+YxkXkZPC8zZpFnQC4TwhhNpvh9/vRbrfh9XqFH8HzH2EymcSg8X3Xh9VqhXK5jFqthvl8jvPzc8znc5RKJUkm5T7Dz5Fm1KenpygUCiiVSigUCvL5h8NhMV4bDAZyDqX5C+fOyWRybaahKIrMCGg4nclkMJlM0Gq17u2lo9EIV1dXYlTP9Wk8Hsufk8nk2jNDovP19bW8rtlshmw2C51OJwIS9TNG41nOcbbV4fV6Hc1mU8w6N+8pdUrefD5Hq9VCv9+X8zr7NEajEW/evIFWq4XP50MikRARDnlGdrtdknzVzw/Nvq1WKxaLBdrtNvL5vNQ+AMR8bjAYIBaLbU2I7na7Yma3r7fL9Y2ckMFgsPde2/Wc0Rim3+8/aFTCtfpjiJk/JWiMwCRyXiePxwOfz4fFYoFcLofr62u43e61/bjRaEjCPcMcfD6fmOzQWKTT6WCxWODq6go6nQ6FQkHm7uo9KhQKoV6vI5/PAwBub29xfn7+Qe/PZrPhxYsXMitUzyopNv9UCXY0QKpWqyIA4ZnIYDDIHC8UCv3khJU/JQyHQ9TrdZnZARAuFmdE2WwW5XIZ1WoVsVhMepV8drlPNhoNMbpuNBrvnVofDodhtVpRrVbhdrvljElDY653j8KWdWQyGXQ6HZmpJZPJtWdnPp+jXq8jm80CuBOhH7KGNBoNKIqCQqEAr9d7bw/n599ut1Eul2E0GmG32+F2u1GpVFAsFmEymSSgaDN5nWE/iUQCx8fH8Hq9aDQaa2ZADofjXt2u1Wq37jXL5VJ6N9yfCZPJhHQ6LXNEr9cr6x6vl8ViEc7dtvVvMpmgXC7D7XbDZDJ9VPM/JkizPnhoLlur1QBAztqHpnd/SSAnlHWx2WyW8CoAEvTlcrnQ6XQefO553VirHgoaCM5mswfPugDkegOHJbPvA3nr5FqYTKZ7s3OtVoujoyNEIhExKyXnwmw2S2/1U87MyNnkuuJwOJBOp9fOo3wPNMxkcNB4PN56jpvP5/j9738vxkbkuPBrB4MBbm9vxaB+uVzuNYfYBEPPVquVBKPQzFB93UOhkJzPPxX/jiYu+XxeTHbUsFgsiMVisNvtKJVKMBqNqFQqa71TtfnnZDJBJpOB3+8XDhg5LY1GA7PZTHpkm+vuTw0GgwE6nQ46nU646fvAHjrXw2g0uvPZMBgMCIVCKBaLCAQCsjd8ydBqtRKkUS6X8fz583s8II/HIyEPq9UKR0dHsNlsP+tkamoXaPZXrVbR7/elptDr9VAUBd9++62cPYrFovQE2IfX6/Vi4MggLj5bDMhkzXwoZ5ec/c3QKoIhWjR7VZs1ejweqWc+lGvEwCXy7GhS+NBZa7Va4erqSkwyTCaTGGjwPqQRCTmcFxcXP+v77RGP+JR47H484oOhPhQeKgLb/LqPebBkEfK5E2Ye8Yh9oPCMhc2bN28wGAzgcDjEOTiRSOD58+dCAKEIg4ncHGTyIM8h32ZKsxqhUEgEEN988424ZbFwUhQF/X4fzWYTWq1WSAAul+ud3OwJiouJh4a4PxaYCkGxHd8rhRV6vR4mkwlWqxWDwUCEuS6Xa21gXS6XJRl3tVrh6dOnMrj7HGvQarVCJBKRZiMP9qvVStKK6aDpcrmQy+VEtGuz2cS9vt1uo9FowOFw7H3tHN7ya4fD4d5hm9PphNPpRL/fR6/Xk4GXzWZDNBpFJpPB1dWVCOh6vZ7c841GQw4tvN7D4VCIi4vFQv6biQK9Xm8tteH169fQaDQIBoPiaq/X60UYxmRuJlifnJwIKUJNQuDgmwTNUCgk75uptjqdTv7MBEge9tXPAQcNdNtSFEWemfl8LinLFJ9TNKbX60UMREKX3W5HoVDAxcUFjEajpIPuOhgdHx8LEYHOr71eD9fX15KQYrVa0Wq1YDQacXt7C7vdjkqlIglmTOPdtm8zvZyNpEqlgn6/LwQwXgc+U2azWYT+6s+Dr5/Cy233o81mQ7lcFuIEn+WHnrtGo4FXr17BZrNhNBpJPcJ0GKPRiGQyebADLFOcLBYLrq+vsVqtkEwmJZmKgrZAIIDRaCQJGHwGgR/uD96bTqdTiDVsIhF8BnnPcYjxOdYbCvBarRaOj4+RyWREVMLhNRMkXC4Xnj17hpOTE3leDwXTvvx+P377298in8+LsJTkX14nDg57vR6SyaSQLK1WqzxLGo0GTqcTiUQCNpsN6XRayBAkrrHpzbWGA0I2nSkcMRqN0tADIKn26mRpdSIryYrBYFCa0Wxgk4iq0+lwcnKCer0Ot9uNdru9Riah0Ui9Xsd8Pkc4HBZnb6/XK2nTlUpFElc4bLq5uYHP58OzZ8+EyHd0dLS2JiWTSeTzeVxeXoq75iaZjzUGU3Qmk8maAN1sNotwkIShSCSCFy9eIBQKfbY9EbhrUDKBnfvdNlgsFjEHUNdX8XhczDMajQYuLi5gt9slkUar1aJUKiGRSMjew/uNqci73iv/P4cHdPXu9/sizOKwjmQODiR3/UwKJTfBIXuhUMBwOFwzxRgOh8jn82KIMJ1OcXJysrXhSFJ4rVbDdDqVJBHWSU6nE5FI5L3TmmiswvpJPfjie2ZiTLValdpg3+cK/JBgsasOZdM1Ho/LOq6+xnQnn8/nKBaLkkDNa93tdtHpdKDX63F8fAyXywWfz4dKpSLkFwAoFotoNBpSa+yqmTqdDorFIjKZjBihML2MpJZUKiWkNpvNhm63i2azKWZWkUhkreFKsh7vgeFwCK/Xuza4mEwmGA6HsNls7+Qi/yGg6QUNNhwOByKRiDw79XpdjEQGg4EYBiUSCRkY5vN5GAwGlMtlNBoNxONxpNNpESaTuDedTu8Rj46Pj+Vz4HPLdY2GLk6nE4qioFwur9VWdHw1Go3wer1YLpeSAM7UI71eD6/Xi1wuJwRN1tysMb1eL2Kx2Fry2Gg0wtu3b+FwOFCv11Gv18UNejKZCAGMRITlcgmfz3ePQOF0OuFwOFAul5HJZET4zMQnCv33YTabyXvl3pnP51EsFoWQzsE66z3WAiSYm81mKIqCfD4Pk8mEfD6PYDB4bxBms9kkPY7nUbPZLGuBel2iwQcJF1wHmOpFURbP0bvAvX65XGI+n8u+vIlDzrWz2UzI1jQDAO6GgJFIRM4+NPD4/vvvEYlE0Gw24ff7hVx+dHQk6UOfCqwBSGZQFEVS3BaLhYjl7Xa7GGs9GkU+4l2gKIrUL7lcDp1ORwwQmWDCetrj8Uiq1bskAbwvWO9waPrQs8avITlik9TYaDQedJdn4il7Bw/9TvXfvy/5Zjwe4/Xr10Kio+ic58xyuYxyuYxoNIrJZCJ9hE2USiWUy+W1JDkS991utxideL3etfQennUoKOF7ohHdtnprPp+LaOJ9epCPeMTPFWazGRcXF1IT8lnr9/soFosi7GItwdqJNdXt7a2YET158mRtveIZj/2TfXM0rVYLl8uFZrMJl8u11sME7oQEtVoN+Xwes9lMhK18LRRHJZNJzOdzEdZ/SUgmk9L/oYCbojuTySTn3UAggHg8/ll6G+FwGIqiIB6Po1gs4s2bN5K0S1PNxWIh9fXp6anU6Zu9HK7V7IO9D0hYoikCe6nL5VKItRqNBsViUfYBpj7xd6rFyTRfKhQKcLvdqFar7034f19wdtDv9yX5dDabiViVxo+xWEz6cA+dMa6urtBut/H73/8e+XxeEmSNRiOq1ar0z0jQ5nPYaDTw53/+5++dRuZyuWTuQ3Gv2+1GuVyGRqMRY4B+v7+W8kRDT4/HI0lFDodjTbDKWQdnZG63W1LPuR6wbuEZkH1q9uVsNpsQ8T/m83Nzc4N2uy3mdUwT12g0ePXqlZzpedaigIqfAVOYKL5LJpPIZrOSXOh2u3F6egpFUeBwOBAOh6WfGwgEUCgUxHSZhqL8vD0ej4hWE4kEvF6v1I7RaBQ2mw0Oh+ODzoEUELOHQjAFKRAI4Pj4WHo+oVAIyWRSZk2NRgNGo1FS3cfjsdTKJpNJzsnff//93pTeh5BOp+Vc3G63Ua/XpY9FUW8kEoHD4RAx/rbrwZ41icoPXTOtVivC/H1iZ6Yk0uw5HA6LEYlWq0WlUpH5O0VKr1+/FnMPk8kkvSgK+ZxOpwiyx+Mx3G731n4C5w40dKUY4/j4eM2kmM/sQ++bJuTlcllEokyXoikVjSs1Gg1yuRxarRZMJhPG47Gsh41GQ8zrOXs6Ojq6V8sz8TUUConwnoICJt5x/6SQ5+bmRoyumAi5DYlEAoPBAJFIREzB1eT7Xq+H6XSKYDAoQnCKsHd9zgQTox+aV7hcLuj1eoRCIQyHQxHT+nw+Ickz9ZHE79PTU5yfn+Po6EhM4j82lsslrq+v0Wq1RFhOKIqC1WolBirn5+eIx+NotVoiKH3oPnI4HCgWi3j58iX+8Ic/wGq1ys+1WCzy3DLl6vT09L1EAq1WS1KzotEoNBoNDAYDUqmUcBY4Q6F4JRAIwGg0isllNpsVgrp6jbJarfjFL34BvV4vyWCcxdKInteg3+/L58ske64X5BRs3j9v3rxBq9WSfqjT6ZSUZ9a/nU5H9iDWmHyuW60W/H4/SqWS9DIpXiR/gHuxyWTCaDTaOnNimiTNLTfXGZqMlEolEbyzvgyHw7L+cF6vKAoymQzsdrv0NNU9V71eL8YDs9lM9k9e+3A4vJbUzs8HwNqMlmn3m88g13kanO8CDQlYo8xmMxEQbYOiKGIErv59Ho9nLcV+H+GcPevVaoXhcIjb21vpG3Pm9mNDbUoxGAzEDMhms8FoNMLv90sSHTk5FAOyN8O6/tmzZ/dqc5okFAoFvHnzRkwGeR7Ztrb4/X4sFguUSiWpEz+GqIjco2341Ge1UCgkJoqsvygy5n3wIa9B/b2PXNcfQNOwVqslaxZniLPZTIJJdDqdzAx1Oh0ajYashZzPEVwDeGZ7yIhiH2w2G46PjyUJW20AQT7V4+f5A8bjMVqtliSUc73ZrDODwSAWiwUKhQKMRuNBawhN28mz2nXdacrEpNFms4lGo4FMJiPGbDQqYSDAfD5HoVCQ8y2TNwOBwL10+0POKwRNjmjooa5peJ5nKEIul8Px8TFOTk5wdnaG29tbVCoV3NzcCB+UYloaqjPwgkLYj3kv0jgrk8lsNd0CIMYQuVxOxHFv376VcIBQKLQzYfVLQbfbRbFYXONqAnd7Y6FQEA42zQd7vR5ubm7QarV2vjeaycfjcTidznfqCzgcDpm17OPMAD+EH4TD4YPOArtAM4NtvHUaaW7y1tmb4BmOtZPdbn/v13EouCera2iDwYBoNCqGJWoRPesjs9mMTCYDp9Mpxg3swxaLRbRaLeFin52drfF+aM7PcBn2iQ6deel0Opyengp3l8+woihy5nC73TCbzTg5OYHZbP5k15BrqVarRSAQ2Pl71KakNEFTc70YOsV1tlqtytmEdZTX60UikYDb7UYymfzJ75fk6Kt59fvQaDTkjBGLxR5cC3hmIcf/S18/ySkaj8cIBoPodDpi3qSGzWZDoVCQc6DT6Vy7DuyBMpmbXGmfz/fFGR7vA4NlhsOhzFByuRz6/T5GoxFSqRR+9atfQVEUeb/kfLLH9OLFCzHBpIH+Jl/M7XbD5XIJ/1Cr1UoY30PgGX4bf4qzDJ47bTab6GgsFov0nhKJBGazGS4uLj7K56MWsj+0RpC/QJMj1v88v5pMJgSDQej1epRKJej1euTzeZycnHzw63zEIz42OIP7kvEoQH/EB0N9iKQj+ENot9tr/31Iwf2f/tN/wn/5L/9l79coioJ/82/+Df7yL/8S1Wr1oNfyiEd8TgQCAZjNZni9XhEb0VHZZDJhOp3KvUuHNEVRZJBEaLVaERORTL6taKOzkVp4e3NzIwP/4XAoTugcAnzzzTci5tt0m/w5QS1iYWImST9MB2Xjg8l9ZrNZxGdMKczn87BYLDg6OsLNzY1cr8/hjsQB7Gg0klRHOmmTWGO322UozXQpn88niSR0GSchsNVqPXjdarUa7Ha7CKUOAV3eAAiJmykYr1+/loQJEkPors5DDdNdSB5hQ4gNYCZ8AXdDLR48K5UKfD4f3G63kCrowsifWa/XkUqlJEmAggwe1jnML5fLCAaDmEwmkszNxg9hsViQy+WEgLB5cCapcTab4erqSkRJACSNkKnvjUYDwN1Qm27qvBYUzsbjcSHAM9kilUrt3FcpdLJarTg+PpZrymEMDTFub2/FbY0Cw+l0itlshm+++UaIUnTXpLDG7/djuVzCYrGIuF1RFCFHzudzWCwWERTTRXIwGKDf78swmAkV/Cy2gU7HJAbQ2W3Xs9dsNpHL5ZDL5eS6dzodcS/nsPT6+hqhUAh+v3+ru+0m+PqNRiNcLtfa4JbNpVAoJKkMtVpN3j/XC4qoKThdrVZi3BAMBqWJTMLmZDJBPB6Xe/Fz1jxMsCJZo9PpyECNDU+v1wu32w2LxfJer40EZwBCGGQD2OFwyLozmUyQzWYxGAxgtVrl7/k16rQPq9UqiSQkJvFajkYjcS2l4JyNaXUznERkiljorr/Nua9QKKBarWI8HmM4HMq60Ol0UK1W0W63JZWd4v1yubw1HZBEXp1Od08QB0BS45l6xN+p0+mE3Mv0MV5X9fcaDAYEAgG8efNGBn8AhBzKoelkMpH7lmR1CrZJCopGowiFQnj27Bm0Wq0kDn1ODIdDGAwGMTihwJiYzWZ49eoVSqUSBoOBuJ9znaMb73g8xtXVFVKplOxf+XxeiIv83JksQEHSrrqJ+9Lr169hMBgk+Zifm0ajWSMFsdaLxWLv9Bz1+31cX19LkzYSiQihCIC4XFcqFXz77bfiGrk5oFoul+JC3mq1oCiK7GGr1QomkwkulwvZbFZSrd8F19fXyOfzUg9TyLsNFKxdXV0JuW8Ty+USlUoFGo0GNptN9lJ1mtpisVhLDWHdxPSazd9JMyKtVotms4nLy0v0+335HVarFTc3NwDuHNsXiwW63a4Mrrg/spZ/9uzZPRdwEn9vbm7kOePrH41GYgDD4War1RITFhINI5HI2jkBuLsvKTxm4mswGBTyPEnFnU4Hr1+/RiwWw2AwQDqdlnta3cS3Wq0iHvxQsC5rNBoiuvZ6vUKKzefzGA6HMJvNSKVSYnDQarXEVbVarcpr4b+5D7RaLZTLZQDAb3/7W5ydna39fqPRiGg0ikgkIk1orne8v8fjMRwOh9ScJM/rdDoEg0EZBDQaDRkqFgoFSe8CIGcJmlVwKMkGvBrZbBa1Wg2FQgEWiwVer1fWo/F4jNlshlwuh2KxKISBUCi0s0YymUxCPGD9xmtHYxA+w5umQvV6Xe4dmhbxPqWQdLFYiNCIjry8Z3Q6nXw+0+lU6j5+hsDd/t3pdPD999+j2WxitVrhH//xH+H1enFzcyPrCddBl8sFk8kk96R6aKsoytrA2Gaz7TXIoqEG08mYBrcLq9VKEoy4NrMOJnmexiSbn4fT6cR0OpWamQQFikRISOj3+2sp7p8SHMZls1l0u10Mh0OMx2NJ/CMZ3Gw2v9fa/og/TlQqFVSrVSiKIomf7XYbGo1GEqIGg4EQx25ubmS97nQ6nzxNlqm8vV5Path9YFo6h/jqNED2hm5vb/f2QsrlshDQF4vFZzmvvX37VvoJ7A+wZmKiabvdFjMMRVFwenq69jOq1aoI1SkKt9lskmSfzWZRrVYxm81weXmJxWIBp9MJo9EoZmE8x5LwRjHPdDpdWydZl3GP4WD7EY94xA8g0Ys9q1arJYILpjxunqNsNhsGgwFevnwp4kY1GWk0GkndGQwGH+wDU9hssVjEDBe4mwNmMhnk83nodDohmbEWpjFTtVrFN998g2Qyiel0eo+Y+yXAarVitVrJOs8zG899Xq8XJpNJen2fA06nE51OBy6XS9ZXEjxJ7tHpdHC73WKA5nQ6YbPZ5OsotmFf9n1BctFoNFqb//LP8/kcZrMZV1dXGAwGkiRaLBZF2EURLMmpNE68vb092NT8Y2E6neL29lb2ehqikphqsViE3MHnZR+Wy6WQi9+8eSNiSaatlkolKIqCyWQiKZQulwvlchlv3rxBOBxGJpPBkydPcH5+/s4mUMvlUsTD3333HUKhkJyZSqWSpLur92eKJHQ6nZirqecdAETkySQeGgyof2epVJKem7rHPp1ORZAfj8dlHflYGAwGuLq6Qi6Xg16vRyQSkdRmzhdGo5GcmTUajaS4NhoNdLtdxGIxSeaez+cYDAYiGPT7/bLWdrtd2O12OYPGYjFUKhXY7XbMZjMkEgnpDzARj2R64IekLfZt2Z/e1hc4FP1+H5lMBqPRSMzj2K+z2+1wuVywWCxIJpMy+4lGowgEAqhWq2JiXS6XJaGbiTqTyUSMWL/99lvE43F0Op0PIuLxWZhMJmsGsDQ9s9lskni0CzQOpyHKQ2I/9t0519vVq200GiJk5bNDUjnNqIrFopgJ93o9mM1mtNttMQKnCYfT6YTf78f19bXMRBwOh8whtoGic6fTiUajgVwu997iPD4Do9EIv/vd7xCNRmV2QXMqrte9Xg8ej0fmsJzF8j63WCySBh2JRLZyfxaLu6Tj5XIpzxNwR7g2mUyS5kdDa+CuBimVSrI/7YPb7ZazYr/fl7mJem/W6XSIRqOo1WqSAr0vfZEiJp5NHjqjUVyrKAq63S58Pp+EAnAmV6/XpV7T6/WYTqf45ptvpJ/FGdrHQi6XQ71eR6FQgE6ng8vlEnHdZDJBt9sV0cLl5aWYETcaDTGX3nd9Go0Gvv32WzEh47rFWsjhcKDf7+P29lbWFJJ3aapAk4OH3kc2m4WiKPfOiQBkLyam0ymKxaLMmwqFgsz11fN5ggTrer2ON2/eiJEyZyQUY9M8gbUIX4eiKCIq5b68Wq3w5s0bNBoNVKtVBAKBtfUolUrBbDaLIR97xDQ27ff7MkcvFAro9/tifjebzUTgW61WEY/HxYSTRpKb92utVhPDCRqCqtHpdNBoNMTIlb1QAFIPEDSjvLy8hNVqxdu3b2Vv4z6yD0xXDwaDwjHg7wQg/22xWODxeGQupgbXeYrldoE8A86knU7nzv1jOp2iXC5Lorv6GnLvB4Dvv/8esVhsaw3W7/dlJqUoCprNpohLaTxD0dWPIUQnz4lrOWt+3lucR/h8PjEzp9E4z4iLxULmUhRe7lorrFYrXr16Jb9733wCuDtPNRoNWQfj8fjHfPtfBNScg48JcoT+2LFarXB7e4t2u41SqYTZbCamxqyJG42G1GLq55Lcx1qthkAgIJwMNTKZDHQ6ncztPhY4K9439/5jRbFYRLFYRK1WQzqd3ruGkI9yyBpC3iNTdg85a71+/VrSQikKu7m5EV4Qjal0Op1wEl0uFwwGw0d7RtXnjpcvXyIajW49u9BMOh6PCz+PPRabzYZms4l8Pi9fb7FY5FxosVju7YMfCwzrePXqlQjeuJ9Wq1Xc3t6KKcBoNJLzldFohNvtFvOdj21SzLkuA3TInfV4PO/Ef6ZZQrvdlrOP0WiUz81kMsHn84k5gF6vl3nr69ev4Xa74Xa75R7m2kAuKvt37/rZ0FSLHIBtM3EakI5GI/h8Pvnvh8D5F0MpJpOJnJHJMSd28dYHgwFqtZrU+qydyOWkMc6nwmQyEUNmo9G49TMnL4g1ejAYFNNCBkdwXmU0GsVQarVaIZVKrZ1z1bDb7cjlcmLilE6n3+m1ky9Dwye+BovFIhzIT8kpoIg+m81KkNc+MHSF563Nz5V7bL/fl7Mcee5ms1meT56DgPc36P6SwDP4fD7faRJBA0zeK9vOxdswmUxkVvO+ITGfC2/fvhXOCUPBLi8vYTKZpO4iX5cBLdPpVHrx5PG1220x2mKfwm63S2hIJBL5IkzBdmG1WqFQKKDZbArnmNzhVquFfr8vnFD2dj0ejxj8sp9NswK9Xo9utysp6LvuG4bdcP6yyQfYBs5JOp0O8vm89NjG4zHy+TxqtZqY9zKBndx/v9+PRqOBly9fCp89Fot91Gv5EK6urlAsFtHr9fb2wNjDvLm5wWw2g9FofAzmeMQXh7/8y7/EX//1X//YL2Mvfp6qwkd8Vqg3CroNPrQgq4fNJBQ/BB5QHsIm+f4Rj/iSwObnLmdWggQcui3SGdhkMon4tN1uI5/Py9CVjsFqcCButVolWZIOzHQJBiCu/xxyc9jMAvTnKEL3+Xxot9swGAyoVCpCVGGyLPDDYXi1WiEajWI4HAq5g4I8Drvp9NfpdHB9fY3j4+OP4uT70HvgwGs8HuPJkyew2+0Yj8ci4KUYg4LReDyOZDIpSTij0Ui+luv3voMZ7wW6X78v9Ho9YrGYDMBev34tJAKLxQKbzbaW8jyfz0U8zeRHNZmAAm02xEhm5nANuEu1oWCbTY1gMIjZbCZiqEQigWw2i3q9juFwKA7v/X5fnE7VYBo4U9VJFqGgeJNYSeI3kw8KhYI4FrK5RdHPcDiE0+kUMgjFT+p0dT7ber0e5XJZhlwnJydbByU6nQ7hcBjBYBCvXr0SR3m6inc6HRHWqkVGNpsNZ2dnGI1Gcg1IsmFjMRAIwGKxIJ1OYzQaCTGdAkYmw/t8PmkE8BnRaDSSOk+xltVq3UkYoUCbRgTqROFkMnmP1DEYDFCpVHB7eyviIbvdLs5ydK7M5XIwGo1IpVI4Pz+XYZXP59vaBOW1slgsOD09lTVWDbqgq1Pem82muF5SfLxcLhEKhaTZzuc6n8/LOsTXHQwG4XK5kE6nP3sjRafTiXkBSV9qOJ1O+Hy+D0qws1qtSKfTePnypQiqmdqp1+tht9slVYTpKWxapVKptaQQkmS2NSrH47EIUSaTiQwO9Hq9JIFZLBZxFmSyB5NuSdJS7xsEiXX9fh+z2Qy1Wg0mk0mISBx6kHhBl3k2OtSw2Wzy+TNxYvP30WF0NBoJ+cJisSCRSODp06c7nQUtFguePn0qAsW3b99itVohm82K2IQkbJKmQqEQtFqtNADp9MyhUjqd/lGbnclkUpryFECxBmIqVbfblSERm2Ncr10ulxCreZ8Hg0FJFZ5Op+j1etIsHg6H0Gg0QrDbBb/fL0YUdC/lOqj+PCeTCd6+fSsGHfuEkdvQaDRkoJNKpXYOVSKRiBCiSIBVg6YtND8IBoNCICMJVz0kUBNsDwEF2g+JwADIIIqJnnQEZyIPBd5arRZfffUVxuMxTCYT2u22pPkAkLqCgqzhcIjr62sAPxDTSAQGIGttLpdDuVwWITGNYfg8/8M//IMQsT0eD5xOp+x5TENgjXN0dASbzQadTofZbCZ702AwwPHxMWKxmKQAcRDJuqDdbkOv10s6Vb/fh8Ph2Lq+sVYl6ZpryOZ9GggEJOmdzzoFzzS7IbmzVCqJ8PlD9h4mr2o0GpRKJTSbTWQyGYzHYxHsq9MczWYzstmsrPk0fQmFQiLw44BVp9MhEAjIwJZ13Oa9yVQdnU6HfD4Pj8eD4XAoa7Db7ZYagc72AKQBzfR0l8slKWs8t43HY1gsFnGct1qtMgyu1WqSekaQIFqtVmXwypqOgg0KAobDIarVqhAidg2tuF9xX2OiDX8uBzs8K4ZCIenhkKDP16LRaIR4OhqNxLiA5Cr+w/2Tv5dDXRKCAci6THO1VqslSfFMPiqXywgEApKuR7Kjy+WSum/TQdvj8cBisYgpzq61mEIXrVaLUCgEvV6/97zGRDk+SzRk4L49Ho8xGo22nhEAiFELzwV6vR6TyQRerxdPnjzZu2d8KpBoQiKo0WgUMzwKU3K5HJxOp5z1HkXoj9gHCp7L5bKcx3nm5Fpaq9XkzBCLxSTRlSIBtVD6U8BsNkOn08FkMkFRlAdrDxLBN83NeA7w+/3S194UwJC0PxwOJcHicwwt1SI5Eos3odVqxYywXq/DZrNhNBrJe+QZnntrMplce+0UDPKssFwu8Xd/93c4Pz+HzWaDxWKBVqsVopXBYEC73YbJZBKCnho0xGR9tQvqvfBTkEIf8YgvHazLWJuTPLFvbQkEAiLC3OzbsL/I2u0hUFBEMhRBgcNyuRSRzCa0Wi3C4fDaufNLFKADEAHmcrmUM4Fer//spDcKJrneUnTEpCL2Hnw+nyRMqKE2gP1Y4AxpV8IEUzeZ9ERTP/bVmOwJ3J2nuDdwH/qcAnQa7bG3YDabEYlE1shUFD+xVt9MNdpEs9lEr9dDsVhEp9OR/vZwOBTjS5JSW62WPEtOp1NSTHimVRQF5+fn75SGbrFY5DOoVCooFouS4ETSdaVSwXQ6lfNpMBiU/jWFhDSs2ARNDkgApKjU7Xaj3W6jWCwCgJytKMhln9tut3/0hPtGoyFrnM/nEwNCg8EgdQONkEjsoyCO0Gg0cDgcItjt9XqShLLtjKieG5pMJjSbTZkDqJPqZrOZzNnOzs5wfHyM4+Pjj9Yn5XyS/Qez2QyXyyXkwEqlgslkAo/Hg3q9DqfTKSaEFotlbRbkdDrh9XpFmExRhdFolB5gqVQSsvX7vgeNRgOfzwefzye9KgAy/znkHGC1WuVzoaHFPiiKIqmv+/Y6Pr+bPU0KmX0+H+r1upgPzudzMSRwOp1otVpwu91icujxeKTvQTLoLpA4SlGsxWJ573krTZ845+31eqhWq2tp9tPpFKPRCO12W0z1aMw5HA7FeCqRSMjcyuVyHSQWdDgc0nvj3GYb1GnuwF1/ot1uo9vtSr+HpHWHw4Hz8/OtMyjugzxHdjodIeTy7LcNNI6lseNDCAaDkrDKlCcaGnAtDwQCck4xmUyS+sxzS61Wg9frlZTvDwHnFRRSBAIBMdjlZ891qlwui/Gu2WzeanqsBsX5vV5PjOI569Vqteh2u2KqzTR4irZoYA5ATNl9Pp+Yeez6fYvF4h4fgb1H1mJcJ4xGo5hCc2bDufg20Gx4OBwin8+vib9pimEymeD3+2Gz2RCLxeS+YS+YYvXRaAStVitJ5PV6XQzf1NBoNAiHw/D5fLi+vpbXGAqFpA/OszETGInpdCozAADCWRmNRrDb7TAYDGKqS9I95x40ItoEe3yc4TkcDqlHNu8FrVYLv98v/e5mswmPxyMzcwqpNn8+n1vO/8kToHkSRe9msxmJRGIvH5AGppzV7qq9aKhNk7xdzxWNAGmoYbfbMZ/P5Z4xGAyIRCJYLpcol8vI5XJibs7eR6/XQ6lUkjqY/5+hHhQDsNbj8/I5wYRKzi7UMzzuMxQl8mxxfHwsiXsAZFZKodE+6PV6GAwGlMvlg+pV1ofknDziEe8Kni2Zgh2LxaDX69eM4S0WC3Q6nfRLAEiNabPZxKiGfCz2IDkPUfcAHvHpQf4i1/N9IMdnl9BTDda8FJA+BHJ82JvgHs5ZHtNHgbu1NR6Pi9H7x+xrkZ9Bw4RsNivmglqtVnoU8/kc4XB47Xzt8XhgNpslZXc+n4vQl+e9QCDwyTirJpMJR0dHUtvTZJYGEbVaTeb5fr8f5+fnYuTGfiVFvXq9/qD6/BDQzJ77M/dsq9WKSqUCj8dzL6l5GwaDAYrFIqrVKvr9vvDf+H2sS8vlMhaLBTKZDM7Pz+Wa6HQ6ERuy5iWvijPwZDL5XnOOcDgs5n+FQgFOp1PCtHi+73a7Empjt9uh1WpRLBbXkr89Ho/0SRaLBcrlsvS1hsOhmG5Np1P0+33E43HhrfMcuclb93g8KBaLYsKhNmmhgRbDiRKJxCeZC9KciWYRuwwOVquVCHndbjeOjo7Q7XbF3JP3DudprPf39RjYx+VZ4BANjRrkd6iNgrRa7WfjhDKUgeZ1D4H3L89Q22C326VfTX4Uz60GgwHVahWVSgVms1mu7yZn63OC5yfWzu+ql6AWIRwOS+AIeSzkpzEgIBwOo9lsyn126OtTByp9qWDgB2fl5PdwbebaoNfrEQ6HxeTc4XAIJ4cmRJy5G41G4Xw2Gg3YbDYxcfkxuNOHgma1DBByu91rswia2dNYLRaLCU+dZ65yuYzVaiV9xn6//2C/ncbUNKHcd84m3G43SqUS7HY72u221NTsp0wmE0QiETGWGY/HCIfDcDqd0Gg0CAQCmE6naLfbYmr5uc7I0+lUeqKHmJe63W5ks1mp/T5WHfKIR/wx4eenKHzEZ0csFhMRymq1ws3NDS4uLvZ+D4n+wOFOl3Tg3gdFUfD69Wv89//+3/Hv/t2/O+jnPuIRnxskpAJ3gpptjY3r62sRtTx79uyeaMLv9yOZTKJcLqPf7yMcDkuDQA0eCkOhEM7PzzGZTPD69WtJRQyFQohGo1JgMvnEZrOh3W6LGGMzOfBdwSJ4sVjIcOJLgdPpRC6XE1EAzQGYgj6fz/HVV19JOhwbFXT6DgQCSCaTePbsGYbDobhO9Xo9JBKJtcMoXYXpnP6hCAaD8hlTIPbixQv0ej1xvuZAkoSJYDAoCddMHGPaBwluFHJsuzeZ2hcMBhGNRg8yENmFUCiEQCCAbDaLYrGIRqOBVqslrn4ApKlMB0hed95LTFkuFosYj8eSSklHOZLe1aJRtSO/wWDAcrnEYrFAJBKRRB4K4zwej5A1KHgjmACiTkdg4jyb1dsEGxaLBfV6HW63WwTI9Xod9Xod+Xxe3ls6nZamY7VaxXA4RDKZlLRhHp7cbjdOTk7Q7/fF+X42m+11U6zX6yJY/dM//VMAd+uOzWZDt9tFNBoV4S+HxgBE8GcymXBxcSHNLoPBAL/fL+7YAHBxcSFNcj5Pi8UCLpcLqVRq7ZDJZgob+y9evJB1bvNQSJfjbrcrZA+Sw9rtNq6vr3F0dLTmDklTAQqOKLpik4uJtUxVZ4P+7OwMHo9H3vPR0ZEMbheLhTjkV6tVpFIpWK3WNREx06KZQECxFRtVvHYUnQcCAVxcXMBqteLv//7v4fF4EA6HZchAgkIgEEA4HP6sw2w1YcpgMEgzg+YbAB4kdb0LJpMJzs7OoCiKNMYpFFaDCcydTgfhcBiJROKgVJZeryefO007OITgdZ5Op5IyZrVa4ff7Ybfb8eLFC8TjcZTLZcxmM2i12nv7L5ND+OfZbIZAICAp261WS5pqNDGgcG9z3WC6mdFoFJLEvrWXAo3T01McHR1J/byrwRIMBuHxeBCPx+H1eiWRlmJdNrfi8bg0eieTiTR+I5EILBYLAoGApI/82AgGg7i+vkYqlUKn0xFiPHBXQ5F0wuEzCd3E6empuLCS/OZ0OsWEw263y+fEgUskEsH5+fnO55Ki0nA4jNevX+P4+BjBYHDt63kvplIpaDQauN1uMUs5pGE6m81QKBSg0WiQTCYfPDvpdDo0Gg0x/CCZtN/vC5GWg9XN3x8KhTAej5HL5USU/i6kYhIjl8vlQeJGGiiRMK12jtfr9UgkEmIWEg6Hkc/nZShNciufa4pZKfzl33H/8vl88hkMBgM0m02kUilUq1VJBDaZTFgsFvjuu+8kDe7k5ATHx8drz2cikcDvf/97vHr1Cre3t6hUKkgmkzCZTKhWq+j1etBqtfizP/szeDyee/tjIBBAt9tFqVQSYnCz2RQBbCgU2lpX0+jE4/Hg7OxMTJPsdrvc6yT/8/OgGJkDSofDAbPZjNlshslkAqfTKaLok5OTD2rih0IhpFIpXF5eCgFEURTU63VUq1V4PB4xfphOpyIc5j56cXGxRnB1uVxC/CNphAktGo1m671J4Ti//uzsTIgqWq1WBsf1eh03NzciHl4sFvD7/TLQdblcaLVaaLVaQug2GAzI5XLw+/1CsGOKPQ1CWFMy9dxkMiGVSqFUKslZQqPRIBgMwmAwSLJZpVIREXEoFNppbGaxWFAoFESYz1StyWQCq9UqRgOsU0hEX61WCIfD6PV6GI/HGAwGsq+MRiN4vV7Zv3jN+/2+1LXdbhcGgwHD4VDMK3j9S6USWq0WMpmMkPKZssL7drVaiYiJ5wCSUvv9Pp4/f45UKrX2mVKs8e2338rAZZNUQeIgDWy+/vprnJ+f7xw88zVSAM8z3GQyEfMB/v0uoyI1IpEI/H4/FEVBOp3+ZIP1h5DNZqHT6dDv92Wf2HwdTGMiQV1NQnjEI9SYzWYoFouyrtDUcLVaIZFISE1IYTNwV+MfHx/LGddut0vS16cEzSdY9+06s/Bcn06nEY/H752pnU4n3rx5I2fAVqslP4/JcwaDAS9evIDH48GTJ08OOh9tnrXeFf1+HwaDAS6XC8fHx3vPiU6nE1dXVyJO43qay+VgNpthMBjw5MmTtedeURQho1N40O/3xdCCvS+mkLZaLQyHQyH+08BGDUVRkEgkEIlEcHp6em8tYrIch9gUyzJBNhwOf1A/6o8JalESSUs2mw2BQED6wSRektSo1WrlDPwo+P8yUCwWYTabYbPZkE6nHzx30+CHPSSuCw6HQxIRHQ7Hgya5NKPz+XwIh8MiouG9k0wmHyTf6nQ6Mb5guupPFewhkzT1seo5prex/mbSkMlkkr5qLBb7UYgwPC8xUVV9FuB9xYS5crksPTX2zCkIoFCAJnIOhwNGo1HOfp+jNq7VajAajRgOh0ilUlt7JtFoVATlFJ/t63Wwb0TR3WAwgMFgkF5ut9sVU4hyuSzmssfHx4hGo3JNK5UKjEYj4vG4JGkeCs5Y/H6/CEz5OrRaLdLptJxhk8kk/H4/jEajiG+9Xq+Q9QjOD5lgT1MzCrX589lro/ibZO7T01M4HI4HBfzviuVyiTdv3qBYLIopLesok8kkyXBM4LLZbHIupsma+jOieI3PczgcljWKKV806WIqzWKxQK1WkzqW5qLsoUQiESHfHR0dPSjmehd89913AO729qdPn2K1WokAn4JXrrkk8gaDQUl7UxRFEqVPT0/FlHc2m0mPkETyo6MjNBoNIct/bCOBd0EgEJAUs/F4vLb+bIIGEkdHRzg6Otr5ukm8n06n8nyo63YaUrpcLjEKVpvS0cyNe+MvfvELSZj//vvv5dzNmkoNGpnrdDqEQiF4PB5J1n7X6zwajXB5eSk9GM7faSxJ09nJZCIGgNPpFF6vV/oa7PGfnZ1J8hgFt+QAbYLGiNyvVqsVnE4n3G731n2evTDOR/gc8QzFeRpr1clkgqOjI+nZcB6nNtZXw+l0SqI2BZhEv98XTsazZ88QDAZxdHR00PUNhUKo1Wpi7sA1gPMCtTiVhubkdlAYb7fboSjK1jPPu4DnNbPZjKOjIzEA4WyJSdvA3fqVy+Wk38Z+567eVS6Xg1arRblcFuOyZ8+eyf3IUATORubzuZzpKdCm+TLvq16vJ6bdm6DoRKvVwuv1SoI2jRtoesPzH9OaaQBRr9cRj8f3mlF4vV5Je+TPHI/HMvsmz2Ozt06BP41Jm80mgDuhL7kWu0xbCI/Hg+vra+n/tVot5PN5xONxxONxMfdQizpodDcej2Wu7Xa7JTyBfWGe6UajEWazGUajEer1Ok5OTtbeh0ajQSaTkXuVJrZMU9vsO9BcQKfTwWg04unTpyJAGQ6HUjv0+30xAGVgRrPZlPrJ4/FgPB5Do9GIMSkF9Mvl8t7rVH9e5GU8NC8aj8cicKPIikamNARut9tYrVawWq1YLpfo9Xoy+wgEAvD5fAiFQvD7/RL2wDkiADHLC4VC6Ha78Hg8SCQSYnTN+oAzFb7vQ9eWj4H5fI5SqSSz2mQyee958/l8iMViKBQKUsNv9nkLhQK63S7cbvdBCXW8Vxm48lCdx/vB7Xb/qLXETwGHcAj/mMC03tVqBY/Hs2byQAFzs9nE2dkZDAYDrq6uxBhcbdxNc22uGVxfKADjevBT7lP8lEBTAeAw42POEB9aQ5haTOOoh4ST3N+1Wi2eP3+OcDgshoCcXbIOoCH6V199JaY7HxPBYBCFQkHmh+12W/YjvV6PeDwuNfbp6em9dTedTmM+n8sslbPVT83JZX3lcrnEsIyiTdadLpcL5+fniMVia68nHA5jNpshl8uJKcrHWPeKxaL01HifGQyGNS6n1WpFp9PB+fn53rnN5eWlnD2++uqrrfdUOBxGu91GtVoVIyyeYfL5vJgGcw+m0ZnVakUikXhvkzeeId++fYvb21uZ5zJYRKvVIhKJ4NmzZ2LU0Wq1MBgMpH9itVrX6s1MJiOzds6aOp2OJPTy9RoMhjUTmmg0inK5LMLtarUqARs0qWYQC2u0fr8Pr9eL6XSKZDL5XtfgIcznc1itVglg8/v9a8/OeDxGrVYT061IJIJUKgUA0ifhjGQ4HKJQKODq6uqeafM2WCwWZDIZ4YD+lGZYNpsNnU4HtVpNeB/7sFwuxdxwWz9jsVjg9evXAO7W80AggPPzc7knyeOlURiNCbheeL3ezyYsJle41WqticEZEPUuRup+vx9v375FKBSS3ivP5hqNBl6vF6enp3A6nTg+Pka1WkWn03lwX6RRWjQaRTwe/6Jra57pyMvjeY8mhFyLqNPweDwol8vCw2SfgRyks7OztXWY5vfsa89mMwni+5JAzs1wOJQzJXmYTqcT7XYbT58+Ff4CcPesbJ7LTCYT8vm8rGN2ux3xePxBzjjXI4/HI2vSQ2BIHQMaGf4C3NUs/Hw6nQ5isRh8Ph+Ojo5kTzUajSgUCjL7eRej3Q/BcDiE1+tFvV4X0x41GMoH/DDX6vV6cDgcj2fVR3yR+A//4T/82C/hQTwK0B/xwTAajTg/P5eC8eXLlw8K0DkkBYCvv/76oN/zV3/1V/irv/qrvV/z7//9v5cD3B97Q+4RPw1sS6Wge1y73d7qoqxGLBbD9fW1NMN2kZAoClwsFiLSTCQSW9OZOfS2Wq1CCKbb9buCJMd6vb5GqFUPeH7MZ5XiGb1eL2JtulkyoYFuTHQ0ZpMPuCvUz87OcHp6KoIes9mMcrksBwW/3y/XQJ3o8bHEek+ePAFwdyirVqsyhKQbdqfTEYdhHsBevXol6YeTyUTIM81mE0+ePJGDdTQahdVqFfFRp9PB7e0tFosFKpUK4vE4HA7HBzUvg8EgTCYTXr9+jUKhIIRlg8Eg5JdOpyPOlyQ90cVsNpvh//yf/4Nerwe73S5i7MFgII01RVFQrVYljSKdTt9rLt3e3sp/h0IhES0Xi0VxkOOwlES5drst6ah0ASTRQ6PRyH2zCUVRcHx8LEnRJIVOJhNJxvX7/Tg+Pl5r0gEQ8ZlWq5XPYjAYiFvkeDxGu92G2+0WF/dtILGYTdBcLieHxGg0KgkROp0OhUJBhszD4RAGg0FcOZmGvC0NVafT4fz8HKvVCrFYDP/v//0/zGYzcYlnw4oO3BxEM/WeyZTEarXC7e0tMpkM3rx5I9/H5Hkmt2g0Gtzc3AiJh0m9TDbhzwyFQtDpdKhUKkLMYWObpjoUqrPJz1QKNodIuufnzn84UG80GkL2GY/HknZMASVJmBaLRYbdVqsV0+lUDBE49CJR4FMnBO6Cusmmvq+ZTvuxf1en05EUCZPJhEqlglarJdcOgDSqKaAj2e+hNZXpPuPxGFdXV1gsFkKMZTIACRQmkwmJREJEWIlEQhJNu90uwuGwkMvUzRoaLuTzeVitVpycnMBqtcJkMklS73w+Rz6fRzKZxHg8lqHR5usfDAZwOp1C5KFgeNt1n81msncz5ahcLgO42ye8Xi8CgcC97724uBDx67Nnz/DmzRu5t202mxiP+P1+uFwu2b+Ojo4+Kun6Y8HhcODrr79Gu91GvV4XZ2z1tfjqq69we3sra7X6urvdbgSDQbTbbVQqFTidTnFcpaOoVqsV199oNCoi0V2gwyTTgBeLBYrForid0vyEhFiDwSDCbhqxPAQmSXKo89Cz4Ha7xXSD6Q0AJD1jsVisNQs3QYMw1jvj8fjBFCL199IAgNdzF+gUfX5+DqvVCo/HI9eLLs0Wi0XuUY1Gg+fPn8s+wYEnU5cnk4k8Rx6PRxKeS6WSuDYDwPHxsRBw2u02jo6O1kgI4/EYHo9HBhHL5VJSXnktOXCjS6hGo0E+nxdB8GKxkFqCz9MmvF4ver2e1Bo6nQ7tdluIUJvfwxQUnU4nQx8mgAF36zYTPMbjMXq9HvL5vBCvmU7LNBaKgGmUQ5fseDwujuYknNIc6BBXdTrG07m22Wzim2++kWdltVoJsRH4wT2dzWAO0pnSpb4OJNRRILztujocDiHxFQqFtWEgrxVrYv4/ury6XC5EIhH4fD4ZaC+XS0nMoemBxWKB2WyG1WrF0dERrq+vpeYmCX04HIqjLx2PaaK0uecHg0Gp4SjWV9c2avB+n06nSCQS+M1vfoNGoyFpcRaLRdahwWCAarUqA1he32w2Kw62TAuaTCbw+/1SkzJNmARFDrHpEM31tVqt4uXLlyiXyxgOh7L28WczgYKO4Hq9XtaYWq2G6+tr+Hw+TKdTqeEImo30+31cXl7i1atXMgDR6XQYDAaSwmc0GvHs2TP4/f6d6ySTmenaTvEAjRdZ4zEB+VCCIX//+zhmfwzM53MhCzidzrW0JTXsdjtSqZQQDtrt9s6v/VBw7dxFIn/El41arSZ1Cs+Ug8EAdrt9jYRht9uxWCzEdZqpjFyPer3eJx9ARiIRScrkHrZJFCF5lySabUkYDocDz549w/X1NdxutxAHuG6RiONwOHBycnLws77rrHUoJpOJkDQOSY+hsRINp1gnkQyg7v/R/ITkN5vNhl//+td48+aNpEFcXV2JKzrdwl0uF87OzmS9UT/jtVpNhujbzv3ZbBa1Wk1ME1nzrVYrGI1GqW0SicTjUHgPlssl8vm8iKZInKJAjfWGwWCQmpnkD4rUm80mHA4H0un0oxnJjwzumRT3PgSbzSa9Kp6n+P+NRqMkzjwkjOx2uyJ8p+CCPX4SFR9at1wuF2q1mohKfopzOyZ7d7tdSXfS6/Xw+XxrZg7v+7Ovr68l0Xi1Wsm5mgRqt9stvcpdBkqfChTfORwOMWgEcK/vzf4mTV7T6TQ8Ho/0whaLBbrdrpzpWq0WUqmUCNY+ByhONhqNe4njNGXudDprCdKb6Pf7MrugsdZisUAsFpMEE9b+7J/y7LNcLoX8HAqFhFSXyWTWTHcPBQlf9XodlUpF6hOfz4d0Og232w2Xy7W2HzscDuTzeUkaovEXwRTT0WgkZy6Hw4HZbIZYLCaGzRT3abVapFIpPHnyRGaPH/uzrdVqKBaLqNVqIpA1mUxi4sYZ3HK5hN/vl3RpGo7O53MhyFNwTFIx379Op0Or1cLNzQ20Wi1ubm7k3Mq+m/q9A3fzV4fDgaOjI+nf01T2Y4Fpss1mU+5f9stpgkRx6mq1wvX1tfQq2a8g+Z29QxrO8f20220xCK7X65LA82Mbjup0OhEpZDIZlEolRCKRtfuVAs1qtSrXfrN3ANzN3ykoLhQKKJfLcl3YQ2OP4/j4GJVKRcxYZ7MZDAYDPB4Pzs/PJUGZpM9gMIhqtYrT01Pc3NyICS2NrNn/5SyDhkAOh2OvKfguzGYzmYOVSiVYrVY8f/4cy+USzWZTyNMUs4VCIeHQMI2ORg0+n08E1Ol0Wta32Wy2s7/ndrsxGo2Qy+WEg7DZnyM6nY6YqjD5slwuS0+JaxBTxoPBoBhEkzS6az2Jx+OyBlWrVZTLZZn3cfbK9+X3+3eKoneBphKdTkf6ZVqtFqVSCeFwGMViEW63G36/f+01jsdjVKtVMQ0vl8sfRIhWFAWKosDlcsk9S5HVZj3n9/slIazX6yEUCqFarSKZTN6bC7F32mg0MBgM4Ha7ZVbG68Qkznw+LwI/ri00HqE5PMXvq9UKmUwGZ2dn92ZHPDPX6/W1hMxer4fBYCBrKw1SFUVBKBRaEx9SmM7ZwLZZGQ3p+TM6nY7M9ngOUqNer+O7776TtPBCoSDp8kyv4wxyU0ilhsVigd1uFzM+7hunp6colUpwu91oNptiOk0EAgHk83kJDjg9PUWtVoPf70c2m5XPQq/Xy8y00+mgWCzCYDCsndnsdjv0er1cr/F4LMYp267VaDQSM2TuJzToLRaLMk9dLpeYTCZot9vodDqYTqdi2OH1elGtVhEIBOByuURIX6vV0Gg0EI1GsVqt8OTJk3vXTqfTwe/3YzqdIpPJoFarIRgMrj2rXOeHwyGePn2KxWIBn8+HbreLTCYjX6c24iJvg3u6w+EQrszJyYkELdRqNbTb7bU+5c3NDVarFaLRKE5PT++tQayt8vk8yuUyzGbze/Op3gc0NRoOh3vP6zqdTvhks9kMrVZrTdCg5jYcsjaydud1euh7yE3bNvt+xG4c+nn8nMF+oKIoCIfDa+tGv9/HaDQS7g9nSwwEYDgJcLcmk4/EYB8Awn8gj+7nfr1p7vJjv0/Ox9iTfgjsBe+qMQkKri0WC0aj0d5z2GAwkFo4nU7LTJ8zWZqhL5dLpNNpVCoVmSV/rOs3nU7FxITnUQYnsK5jQvAh3FqdTvdRjd8eAnmj7JuwT7hcLsXAptfr4Z/9s3+2s/dC053r62ssFgsYDAZcXFy89/ugmW2xWMRsNkMkEpG5LgCpd8lzyGQywrXd9tp4n/j9/r33E40Der0eWq0WotEo7HY7nj59ivF4LKEfwB0f2+v1fpQ+AU0eNRqNmCfRKIvcl1qthkAgIK+NfREmejOQ6PXr17Db7ahUKsJrMxgMwtNgbcPAH7WhNLmg19fXyGQy8vfHx8drNadOpxN+WT6fF0OmSCTy0bmGAMRwj3zQbDYrgTDkGttsNpycnIjImfeKTqdbm1eSC0AexEPrAL+GnLIfe919F5C/arPZMBqNHjRnGQ6HwtPctpeyP1ksFoVbDEC4+eqEd55HxuOx3B+cm35oMMZDKJVKEgBIfg33H3UPcduZYBssFgueP3+OSqUCh8OByWQiegEamNLQkPM4GqDsu+bNZlOCc7b1u74kkMPFmQBr23A4jHA4LGd+fq7kwRmNRjH7oiB9W7ipw+GAzWZDLpdDrVaTc/6Pwb/ZBwZTTafTNTM29funQR7rXrfbfe9cSaNbPhO8ng9xaziT2MaJ3YVEIiEmYjTfaLfbokWoVCqYzWZiLBCPx9f2bpfLhXK5LP3Pz3Wfvst75ddwjeb3PuIRXxIYevcl48tacR/xk8U/+Sf/RATof/M3f4N//a//9c6vrdfr+MMf/rD2vY94xCN+QKfTwXg8xmg0etBBkQKNTqcDj8cjA3CCBdV8PhcxClMBtpE02dwC7obRmUxGBCfv6jzX7XaFnEEXOx4qXC6XCONOT08fFKh8Kmi1WpycnMhwyeFw4PXr1zIEIbGATlNMaxmPx+h2u4hEItIIJJiCSJERh5Ik69JxkNegXC5/0DUwm824uLhAJpOB3W4Xt/tWqwWdTieDPB5o/+///b8Yj8fSgKUAnGKely9fSiJzsVhEJBLBYDCARqMRMaXf70ev15ODnNfrRTKZfG/RwHw+RzqdFjfu5XKJSCSCRCIhCT9MeyQpiOIxHsqYFP0v/sW/kOeHzS2r1Yp+v496vQ69Xi/u6LtAAhVwJ0AplUpCVmfqOHDnHM3kEKYo00WapMxNULwTj8eRTCZxdHQkLpL1eh2BQADlchknJycimKGpgNVqlWvMQzcHqQSFcOPxGK1Wa+saQpJJp9ORge5oNEKv10M0Gl0zvaDAvVqtimA6m81KU/709FQSFLallQN3zaD5fI7f/OY3+N3vfifN3VKpJMMOo9Eow1q3243ZbLZ2QJ3P53j58iXy+Tzevn2LTqcjJhEk3/JATqduNhh4/7bbbUns5WC/XC6La7zP55NmzfX1tTQej4+PcX19jb/927+VhEu1UQiFYM1mE7/+9a9hMpmEUE+yH9PESOp68uQJ0uk07Ha7pGCoGyEkQ9psNiSTyT+6xDESBUejEc7OztBqteD1etfIFFwr6PBIgtxDDSUSX5rNJmq1GsLhsKQFRKNRSTzm50syTTgcRjabhc1mk6bF0dGR7AevXr2C2WyWNZV73GQyQTQaFcJPo9GQFFadTgdFUfDmzRshZGwTGbORQzFIuVwWoSSJMrz3mURBQSYd8AEI4ahWqyEUCiEWi8kzy5SfUCiEZDKJ58+fo1qtrqXb8141mUwIBoMIh8NfnPBcDaZ1MOliuVzCbDbLfsnUR4PBgPF4vNbMNZvN4tSqKApyuRyAu+HVYrGQJCimHdMdWA0O19hMJiGDaR1MCKbBCBNRaIbh9Xrx9u1bEaceIkAnQYbv/yFwn+L14Z9JIOJ9uQ8UsZNg9tDQjKnjdLTmPrdP+NDtdrFYLBAKhYR4qB7KktS1CavVKvUVf0+r1ZIUnc2B1nw+R7FYRDabFVE8Bch+v3/tHiHptVKpYDgcijs6xWgejwedTgeVSgXNZlOEtBaLRUQa2WwW7XYbgUAAy+VybwOW15nJGxSC0a0UgAwCmfBBY57ZbLbWPKX4jo673ENns5kYTrRaLfndqVRKiPH5fF7Sbmu1mhiGkBxIR2yKJB5KhKHLL9f4m5sbGAwGtNttMRxQ3zsk75KIRzL1tjqaruasNbYhHo8LaYBrKAc8dLZnQhrX+16vJ+sGCe+KoqzVft1uVwgPvG8oIuCaPp1OhQjP10hTMabRqMFkWaal04CFKQ1qLJdLGZT5/X4cHR3BarXKWu5wOGR9IrHv5OQEs9lMhovqWtJoNEJRFEwmE0m8pMC02+0KoZCpARqNBsPhEKVSSdbhV69eoVqtCoHQZrPJusgUIg4TOVgymUwyUCFxcLlc3iMpGo1GfPXVV/J3lUoFlUoFxWJRSJlMqz89PcXx8THOzs62rm9Mly+Xy9BoNPeMIbRaraQNDAYD2dc7nc5e47jlcinGNYe4C38KcP3r9/v3Up03wWExn+ePKUBnX0JtEAPcrdkkIDyK0X8aaDabaLfbsi4tFgtJFtiEw+FAu92WdBnWlPP5XNLuPiVcLpeYTRUKBdzc3Kz1cobDoYgfmOa7SxRvtVrx4sULdDoduY+Xy6X0ugKBwHunV7wvWAMeOlznoJr1H1PGxuPxPWNLktpJ+ufZNBwOCzmOLuokiz1//hzdblcM7liLUMREcxR+LmoUi0VUq1XkcjkRj3GPpZEGBQrAD8StR6xjuVzi8vISrVZLzF+Y0kqxisVikTRno9Eo50g+z61WS2rP8XiMJ0+efFYy4yPWQTIW+6APQW1soT6385ysKAqy2ayIzbaBaZQ0kOTaxjSjzZ+977UTh77+dwHr0MlkIkJhj8fzUQidi8UCNzc3cg4gsZbkIJqevm/qyHQ6xfX1Ner1OhqNhiSq8JqxLlb3ZywWy2dNz6HYcjQa4fb2Foqi3Nsr2Oei0RYFTux5mEwmOBwOeL1eaDQaSVg9PT39qOnQ+0CxXqfTOcj4xuv1IpPJ7DxzAZBEZp6r2bthfTOZTGCxWOQ60LySvfTFYiEiPCaf1Ot1RCKR95rHmc1mJBIJ6W1Op1Ocnp7ufBaYan1zcyOGsWpBUiAQkITRRqMhxi/qBGCTyYTxeIxMJoNwOIxEIiEGxzScdTgcH2X/GAwGyOVyQlRNJBJSjwAQEVar1ZJzHd8Pezxut1vqPgrnWJNy/kVSerPZxGKxwHQ6FQI6z9RMUCd5m/eVwWDAV1999UlMWyhQWK1WMJvNyGazaDQaGA6HCAaDa2s5n6v5fI5CoYBwOIzLy0sxLabxc7PZlIRYGiSyh1EoFGCxWJDP55FKpeT8/mMhEonIuZsmBD6fD3a7XQyj+R58Pp/M19Sg4cdgMECr1RKRY7vdRjgclt5hKBSScylNR9krpanD0dHRPcExyckvXrxAo9GQ595isaz12NmTYTo9TRw3DeMfQqlUEoNJmvARLpcL8Xgc7XYb//AP/yDihNPTU6kRRqORrFcUJxuNRsTjcUynU0mo3FUneDwemRNqtVoRoDN9j9eGtSfNS5fLpQiU0+n02uyL/YJKpSL33PPnzx8857DPazQape/BvnE4HIbZbIbH48Hx8fF7kUop5OCzRSP7XC4Hj8eztQbg2TKbzaJSqcBkMiESibw3IZq9dJqcsj+5bR/l+sx/+Blls1mkUqm1njjP6a1WSz63baYTJOfS9JTG/DTQZd/T5XKhVCohn89Ln3ibQJ5J499++y3MZjPq9frafA2ACL+bzaaIZyaTCcrlsszXKLYxmUzwer2IRqNyfuTcJRQKIRwOiyH0YDAQEa1Go5GfSa4bDWW5rkynU1QqlTUDL64bu2ZkFOj2ej0xuHc6nXIOpwEe+xI873L/PDs7w2QygdVqlVTUzX7ZarXC5eUlbm9vpQ9Ng0wKza1WK8rlMgKBwL35BsE9jobB/X4fpVIJAMQ8XVEUNJtNFAoFLJdLWbNsNtuaWOP09FQM5olgMCjGthqNBtlsFqenp/deRzKZxHA4RCqVQrFYxOXlJVwul/TPOQ+iCXE4HBZTQ9ZV/Kz4Z6ZOcvbB+VAwGMRyucT5+bnM4WOxmBgb0Gzi6uoKsVhs53Or0WgQiURkb3mf+u190e120e12xZx5H4xGo/R5mVRHcAa+WCyk7/8QaGr40NmOZonhcPgxXfoR7wyaJK1Wq3vzFJrkkOMG3PG2KEBqt9trRtNms1nObsAPRr2hUEjWmZ8jeMbj+Qi4288ZVvBj8I94/m21Wve4rZuYz+eShvtQr91sNstctdlswul0otfridk+jQZcLhe63a7cK5u9I86Lyd3i7FOdAv0hoFkozwqbAnT2UhjYw5nwl4Rer4ebmxtUq1U0m02pCXitXr16JT1l8j/UtSd7XJPJRGpQ7hcMnkkmk+/UQ1itViiVShKMlU6n7+3dFosFiUQC5XIZhUJB+Fzb9qder4fxeIzxeHwvhXYb3G639PTG47G8X7PZfND3vys6nQ4ymQx6vR60Wq3wTgHIvJ11NAWcqVRqLUCBQVVv3ryR+fXFxYW8Xn4uDOLQ6XQolUpot9ty7uUaq9VqYTQaUSwWsVgs8Otf/3qnqJzGOFdXV8LB+RSpxeRK53I5mM1mjMdj6WWRE0pDOfbRdoFrAYCDzIam0+newKovGexbejwe6bnsew/NZhMulwsmk+ler3q5XEoABc8UwA/p8+TUUjRM4zaz2YxoNIpSqSScrc3zxcdEqVRCoVBAsVgUATh7jaPRSAxjE4kElsslnjx5ctDnynC7aDSKbrcrnEE+P6xfvF4vCoUCfD4fSqUS4vH41j4uucn8+x9LZ/EuYPBEpVJBMBhcO8tuPnOKomC5XMJiscg5Zzgc7j1bsd9ze3srGoLPbdz7EMhrIw+UUF8L9ugcDocYIHS73Xu9MuDu/qG5xXA4fLA+GgwGMBgMsk4fAq1WKzwqBmSRP2exWODxeMQMPxqNbt1HOcf8FLPAXeC1sVqtUBTlwWvD4Dmz2fw4937EI94TjwL0R3wU/H//3/+H//pf/yvG4zGKxSL+x//4H/iX//Jfbv3a//gf/6NsihcXFzg5OfmcL/URj/jiQQcgJp0+BKvVKiIRpioQdHyiuHg0GmEwGMBsNu9MbbDZbOJuzAEb038PBQf5dDVmmidTUjqdjrg784D2YzXONBoNAoEAAoGApAFGIhF8++23IuJmQ7HT6UhCpsvlkkTNzYOR2+1GoVCQ5jYPBl6vd61JycPPh14Dk8mEJ0+eiFiayd+RSEQSi9rttojmy+Uylssl4vG4DNl++9vfotfrodfrwWw2o1qtwuVy4erqSggRahdlg8GAm5sbWK1WTCYTjMdjnJ+fvxcJhUQCEtTUiY5erxfL5VIGlNVqVdwWSQRhE/gv/uIvhCjLwTEdTu12uwiHbDabiNmBHw5cdCwkESOXy8nvur6+lhRculKaTCbE43F5nki+r1arkr5BzOdzaQhQuM6Dr8PhgMPhQCqVwu3traQGkPxUr9dFPM1rRfIwcHcY4v9n0gATGbZhNBoJ8c1qtQrBYLFYrB0Ml8ulED3YEGDzWKfTySCXbpnAnUhtE3q9Hk+ePEE+nxfSUqVSEdETxQfxeFwS00ajEbLZrIgHi8UiXr58icvLS8xmM5ydnUnCKgfbbrdbDrpM5yTZXavVIp/PYz6fw+FwCBGGjUY283k92XBmI2g+n6PZbEoCt91ux2q1wmQyQalUEgG6oihrAgwaWZBsQOJaq9XC+fn5VqES7xUSqLgukKijNvOw2+0fnLT0JYJ1IgWNTGemA+Z8PofdbhdBts1mEwGqmmS9DdwLG42GpG6Q+MWBlNPphNvthtfrRavVEiGx3++XAR0/Dw5HuFcwNZxDs2QyiWaziWAwKD+LxB/eY5PJBHq9HrVaDb/+9a/XXi8F5Ux4oGjabrej1Wrh9vZWvpbpLHq9XpKlbDabPNfD4VCeq/l8jsVice+Z1Wg0Imx/8eKFOPby2ttstnuJvF8SVquVkGzp+Guz2UR8qxbnsFk9GAzETEXdLObnz1RWJpJ1Oh1EIhFJCp5Op0in0/K9i8UC1WpV7g82k5k03O/3cXFxAaPRKAIcDjQdDsfa8IfioH0CWjXUro6TyeRBx2CSsNRpvBTYT6fTg0gh3Dtns9leAVm/30elUpF7nukXo9EI//AP/yAJWZv3ljqZmY7KGo3mnZNSa7UaFEXBYDDA8fHx1gYrk0tvb2+FWMr1nEQ5rh/9fh+5XE6EADabTQh4VqsVDodDBLAkp/FnORwOxONxOBwO5HI5MYEpl8s7U5RJAjo6OhI36cFggOvraxmC8LlmghLJ7tyb9Ho9zGYzbm5uxBwqEomISQVwJ8imeQudrMvlMo6Pj6WJ/+2338q1oYkDUwYmkwkKhQIMBgMSiQTm8znOzs4Oqg/r9bqsYdw31eCe6nQ60W63AUAIItsGPWox9S5oNBokk0m4XC7U63UxEMjn87Db7Wg2m1gulyKUIEmNhDp1UlwwGAQAGYhQaKw+c1HcT0IhXbC1Wq28J9Ywm+BnqE5v6/V6sFqt954H1iU0F+EQigY9XIsosK/X61AUBVarFWazGX6/Hz6fD41GAzqdDvF4XEjZfI2sq5hyzvuLwnKuIzwTMdk9HA5LkhWF+8APie0k0A4GA0lWttlsOD4+FrIR/58aZrMZf/qnf4pwOIzvvvtOSIMUCAUCAYRCIUQika3JxwTXbkVREI1GMZlM5D5Xf5Yk25bLZZhMJtzc3OAXv/jFzvuNCX1Op/OdSeUfCzwDHEpacTqdQgRSn50+9DWQWNDtdoXMy7qW5Pkf06TuEYeDhl0knfOfbQNF9rdIEATwTuLFj4FEIiGkDwqh2UtTn0f9fv+DhOFNAcKPDe6drDsfAoULfK75WdAwkRgMBmIe5PF41tYOnU4naXck7WSzWSQSCVgsFvzyl7+EoihiOMHvoRjG4XDg7Oxsbd2cTqeSVLLNBESv14sgnSnpRqNRUj0e8QPy+TxarRay2SxMJtM986XpdIq3b98KicftduOXv/zl2to7n8/RaDRQKBQAAFdXV3j27Nnjtf6RQHOYxWKxVzROKIoiaV6bezh7rB6PB/l8HpFIBA6HY+2zZdoIRUhqkpc60Xmz/7kNTCZjGunHwmw2k1qTZqR8fSRicb15HyyXS1xdXclzsFgs4HK54HK5RBieyWTgcDjk7LJJhpvNZtJTIQFGXccyYZiEy01iDPsWJpMJmUxGRHmfU4AO3AmCSNz+7rvvcHt7K+etxWKBbDaLTqcjgiSmlFCsZjabpUfldrtxc3MDp9OJ6XT62WpjpnDN5/ODamGumZzV7QLvBfafDAaDnMVWq5WQlkejkfxMjUYjicgkyjP9S6fTod1uS9/gfWYt3H9pYKsmQG/CZDLBarWKyYKaHG21WqUXmsvlcHt7u5akzgTUXq8nPcvVaoXXr19LOjh7Ri6XC+Fw+INMeihwo1Epz52EXq9HJBIRkzW/349ms7mWRO92u9HtdhEMBpHP56EoCs7OzkS8ksvl0O/30Wg05HwCQPoxvL85o6JwmObC7XYbs9nsk4hYeB/ZbDbpOwwGA0QikXvrsEajgcvlknM369/BYCAzjMFgIH0W/tnv96/1xDkHZX/0UNDYlf1Zpju9b7IZzT7UQiSNRiOpWF6vV2pEpq1t3mv9fh9XV1eo1+uo1+tCwmePnQlxFotFamLObtiLCYfDODs7k9nUNiQSCQwGAzx79gw6nU5eN58bnq9DoZCIBlOp1D1hxkPgjKnZbMJut281ZGL/iOnXR0dHOD4+RiwWExHibDYT82QaYcRiMZmJ5fN5OJ3Oncaf/Pd0OsWbN2+kT0XCvUajwXw+h8fjgdfrlfqSz9RmD4VGOXq9HqVSSWY2h5DNI5EIQqGQzMI5zzAajWIQ8bEEALVaDb1eD8vlcu8+RqOPTCaDyWRy8HvZ9bNIMp7NZlAUZa8RF9PBmRDNve/29lbEX5ybcsbk8/kwHA63/tzFYiGm0RR4cx1Wr8VqQW6v15OaUw0K53U6ndx7fA7UoCCexu1ms1kMRL799ltJk1utVjIfrNVqiEQiCAQCmE6nsNlsMBqNePbsGbLZrPzOcrks587VaoV6vS5i/fPzc6nh2DPw+/1YLBZot9vIZrOyts7nc8Tj8XtnJPVshn1c4K43y8+G5uiVSkWuHetYs9kMn8+HarUKj8eztVccCATkzH55eYnVaiVrCY1EOYNk71j9LK9WK+nXWq1WMQGoVCpyXUg4Z582nU6L+Qb/cTqdyOVyYpjPOoa/S6vVCr+BZvfbhM4UCl1fX8t76PV6UBRFejCcCavX+clkIgI+itYBIJ1O31ur+TyUy2WMx2NUq1V5bQCkV86kZPYr1aBwlT1NAGL463K5PpsAnQYih/IDdplA8p6xWCxirLALvE6sD5vN5prRuRqr1QrVahUWi0XMVh7xiHcB9xjWE2qo0zIJdQLrfD6X5F+tVrv2dTRoosju53pvFotFMVBnABLwQ1gBg1EeEl5+bAQCARF/12q1veJcmrJz5n3Iz26323j9+jWKxaJwxngf2e126TtPJhN4PB74fD6pV8kLUK+T5A6yl7CNG3ooVqsVrq6u0Gq1UCwWJVmaHItCoQC9Xo9wOIzFYiFcki8Jy+USt7e3YmywrY8VCoXEoJCBTOQ+dDodlMtldDod6ZWxFiEHh4nAz58/P5g/S+4Jv3/fLDMUCuHy8lJCO7b1DLlfkmv0ELjH8ns/JZbLpcxF2IvZdo589eoVMpkMVqsVnj9/fm9+QnOser2Oy8tL6U8RvAZq3jrDkNxuNwaDwdpnT7Mgcvf2gbySj2XssAs6nQ5HR0eIxWLC7aGRtMPhuGcotwsOh0PWok6n8+B92el05Hs+t1H1x0AwGJTPJp/PI5FI3DtHs85jGE8gELi3NvK53Ow1kjNC82o+Y+SlOxwOrFYrxONx3N7eStDHpxCgD4dDMaWYz+f3OGScF9Mwm7ypfYFnmyAXft/fHx0dyXk7l8uJYQpnvjTQjEQicLvdD4YMfCngvlyr1VCtVneGMM7nczE4ZzAQTcWKxSI8Hs+92RXBtYt6hy9JgE6e53Q6vTfLIUfMaDRiOBxKUCKN0zb3EkVRpI9Co5F2u713jWHwEM2l36XnyEDHwWCAUqkk/XU+i5wPbOuxMXCMRmeboF6BX8Pe1IeCnIVer4disSj6hl1otVoyd/tSuB6PeMRPDY8C9Ed8FLjdbvyrf/Wv8N/+238DAPz1X/81rFYr/vk//+fyNfP5HP/5P/9n/O///b/l//3bf/tvP/trfcQjvnRsa5juw74mFwfudNzjgWVbgdXtdtHpdES8WqlURLj5kKBPDXXTqdlsbm06MeWQaReZTAZPnz49+Hc8BA583nWYbLfb8atf/Qp/93d/h0AggKurKxF7kijGFBI6VE8mE2nQEnq9HuVyWZqax8fH9xpXPp8P7Xb7o16DwWAgznF0cq9Wq5hOpzJgZrIGRcy9Xk+athQbkxTSaDQAQJzKT05O4Ha78dVXX8Hj8YhraiaTAQDkcrn3OuhStMJDj06nE9fqbrcrgkGSm1erFWazGU5PT+XwrybaMJGRJCE2uBwOB0qlkqSyeL3eewcuNou0Wi3S6TQikYi4kvN1hkIh+Hw+2Gw2IWcwxbxYLMJut2M0Gkk6KR3gmaJMp32LxYJ+v4/BYIDFYiFpjfP5XBzLAYjxAYVrNptNGi8kCakTKLgmbK4Ly+USrVYLr1+/xtu3b4UISKcynU639syQyNRqtcR53ul0CinJ7Xbj+PgYxWIR+XxeDnPbUh31ej2Ojo4Qj8cRi8WELDabzUQ4xe9tNpvIZrOSHvv27VuUSiWUSiVYrVYcHR0J+VVtIlCv14Vcxc9wtVrB7/cjk8lguVxiMBjIs8p0ch6SicViAYvFIuR9mgKMx2MYDAYR9HW7XQB3JMxCoYDr62tUq1URxp+eniIYDK4lTJNMQXIDHeWJyWQiCd9ut1sEWplMRu4DElvYGCUBIZVK/eQcNHdBp9OhUqmI+FGj0YgAfT6fS9rMcrlEIpGAyWQSI4mHmsRMiwEga0YwGJQ0cpIaDAaDJG7MZjOkUim5T3u9Hl69eoVer4d2u722HxDz+Rxer1eScMrlMrxeL8xmM+bzuXxWFNm1Wi30+30hEo5GI7RaLSiKImRwJv1aLBYhHLP5pNPphMRE59VYLLbWRPL5fJhOp/LMMgV3H1GJBhlfOhaLhRAI6WJO0bnFYpFUBL1eD61WK8+31+uVz7ter99z3uR6x6RumkJwLSCJ/vLyUvaky8vLNZEq6zkSlLrdLm5ubvDixYu9g1XWbJtr8z4w6cLlcqHdbj9ISu90OkKQ5teq16xD679d+w7Bdb3dbos4gM+1oiiYz+f45ptvUC6XJVmRiRT9fl/c2NPp9HsNdjk45jXZNywk0bFer0Ov16Pf70ttwkRzrhPc51kjLhYLIcOZTCbc3t6iUCisJdOQEMehKkkOHEZx4LztPZB09+TJE8zncxlC0A2YJhFMAp1MJsjlcuKw63K5ZO/rdrsIBAIwm82SikljDwCw2WyYzWbSuKbgl2vlYDCA1WrFb37zm3trxHw+R6VSEUFTuVx+0OGbTu+r1Ur2Tj7PdFCl4Q/fA8nU2+7z5XKJfr+PWCx20HCPQpLRaITf/va3YsJFIxuS91OplNQVL1++lO+j+G08HouIgiYxavBZXq1W8nzRKOTNmzdCat52j2o0GjEWA+7uVf4+9XlwtVpJmiWJwwQH6pPJREwNKOrm8PWXv/wlfvGLX8jnV6/X5fV6PB4Mh0NZQ5kwyUE1a5pgMCh7Ip2hR6MRrFarEP9I1lgul/K+VquVmD8xucbtdiMUCkmCIsX72wbYOp0OqVQKqVRKSDSsG6xWK7xe74Oip1arhVwuJ2JJ9fVnggJ/t81mQzAYFFJiNptFKBRae23T6VRqj2QyCbvd/qMloHM/OnSIoxYSfwyH4vF4jDdv3kgar16vlxQikjfVBm3n5+ePIvSfGIxGozyrDw3rZrMZRqORpP59LsRiMdnnHQ6H9I60Wq3UGz/F+46pXkxq3bfWUQjHZDgAcoYyGAwiKAAgRoTL5XJtPyUBifuz3++XWptJf+FwGPV6fc3ES6fTwWQyiQnkZo1br9cloWNfOqHRaEQwGESlUkEgENiaqvfHDCaa0iRlG5nUYDAIwVFRFKRSqXv7A0mPer0exWIRFovl8Vr/iGAaBk2S9q1VNKBlOuvm528ymZBOp0UcSqE5n3OKHB0Oh4i51IZHVqtVBKU0hNwHnil49vkYmEwmePv2LTqdjoi4DQbDWgqn1+vFaDRCOp3e2/fYBZ73aKgZiUTW1iXW/4VCQQxh2Tdl0hAJYuq0aKvVimAwCI/Hg0ajgXa7Leapu0CBZbvdlvf1Oc18dTodzs/PodFokE6nxTCy3++LuI+1u16vR7fblXvIZDKh3+/LHsFEG/YvPtf7UD8Hh9S2aqOcXX0ImvTyWni9XhGrs6fHfuF4PMZisRDh/mAwkLMuBa8ajUYMt5bLpfSgD8VqtUKr1cLl5aX0wmn2xDMrZ1ub72ObIAm463tzXkRCvvqsZLfbkUwm0e/3sVqtxAhZLdpnOkq325Uz9btiMBjg6uoK3333HQCIqeFkMoHL5ZJ7jcaOVqtV5pvsfZC8mUwmkclkpC/M3hhTxGw2mzzLFMvS2I2fSyAQkD4N7+V2uy3J3LvMBT8EFHmuVisx86Kx7zbw3o1EIlgul3A4HJjP57i6ukI+n5e0L85nKBakAZ/P50M2m5Uz+CFiC0VRUCgU5IzM3gF72U6nE4lE4p3q/0qlgkKhgG63Kz1FGiCwb7parWCz2UQYvu115nI5tFotSa7i7JAzIyb30HCZfXMKTOv1+s5kLzVMJhPOz89F6OJ0OlEoFNBqtWQdXCwWaDQaCAQCUvO+q2CR+0u/39/5vfP5HL1eD/1+X8Sz/Cz9fj8sFosYEvp8PsxmM9zc3ECn0+FP/uRPRJRVKBTg9Xrh8XjknEEDivF4LETZ1WqF77//XmYVJpMJbrcbfr8fR0dHYnRIE4h9/W4aH7CGOJRszr6uum7ZZyLyvuAcyel03kuk5nrPfZ/CIgpp35c4TzFHuVyW1O5+vy/rL+cgNptNTCG9Xq8Y5dCUksmw5IzQmN/n82EwGMBoNEJRFFkzCc6xl8ul3Mc0Zd2cydHAod1uw+PxYDqd3ut1RqNR/Pa3v5W6iSIkzuUG/z97b/IbWZpdh5+Y53megwwyyczKqq6uktS2YPsHw5AWXhuyPC0EtLUybHhhA94Y8NYLr2TAsLQR4IX1PxjeqCGppW51zZlMDjHP8zxH/Bb0ufWCjCAjx8qq5gGEVlWRjIgX733f/e49w3Ao5gUmk0lS6TgnpQE9zU8Gg4GYqfC+n06n+NGPfiQC2+PjYxFAkwzfarVkTe31ejg8PES/3xdCN8nuFLKr1WrpY6XTaajV6lvi2vF4LL1rmlsrDYbdbjdMJhNarRasVqv0Z9VqNVqtFiwWi4iEFovFTiNeo9GIaDQqxgDVahWVSkWE+aPRCAAkBbbT6ci5ju9zuVyKEQ3J8cqkXO7t7CU/ffpUDF+NRiPy+byY6DC5nCYkN2tbr9eLi4sLjMdj1Ov1rcINzj76/T7q9fotIyefzweXy7Xx3LHHQAMMks13nVFsNhvy+Ty++OILeL1etNttMaymmS9Nh2nAy2tBwR2fP84hadxHov67EBvxfe07w2M9t60OdDqd8Hg8wlvYNhNer9cy+6IRQKvVQqFQkFqLYLLldDqV2fr7JqJ8wPsPpeH+TXNc9rS4djE4g3sXTazz+TxsNhva7baYol1cXEivzO/3v3FzN/Lnut2urBE09npXBv/sURSLReE28LWHwyHy+bzwVVar1TsVspG7GgqFkM1mUSwWhf9C0ECg1+shkUjAbrfv1a+noFWv1yOdTuPq6goOh0Pm3blcTnifrJncbreYiDC4hL1wjUYjhtvL5fK1k6x5JuBZ9PDw8FagTbPZvGUE+j6h3W5LymwoFNq633GWytm6xWKRM0i5XJYeHg3zeM6kUXu1WkW9XketVsM//If/cK+eCGs59lTuglqthsPhEC7qrp/hzFrJw9wF1ir83bcJnsFarRbC4fDWszUNJyheZa28bX9ncA/XyJt1p7J/ZrFY0Gq1MB6Pb6XL0tiM5n73gTXX6xo77AO9Xo9wOPzKv6/VaqWndXZ2hn6/L+YUPPPyexgMBjJr8Xg8b/1+eBtwOBzwer3CQ7q4uIDT6RQjchqbrFYrxONx4XHcBINa1uu13CsM0WE/S1kf8kwCQGacHo9HAk16vd4b51fUajXhaKZSqa1zVYY9kItpNpsRDoff6HfrcDgkDITzlnq9LnuRw+GQc/HR0dE7nam/Dux2uxiscg9m6AwAOSt//fXXkj7PmUq9XofJZBJevc1mQyQS2dpH4vn8bRuAvA5uziJoZMs6lSZ/20Cuv9PplN7tarVCJpORELxtr0cNCDnsrwL2WlerlZydDw4O7lyzGd7Hmo9oNpuo1Wpiqkj9Ek0jA4HAa58ZfT4fms2m9CpisditfhUNNDg3/L6u1Q94wPuABwH6rzH+y3/5L+LaSjDRCrg+zP67f/fvbv3ef/7P/3nrxvVP/+k/xbNnz/DFF19gNpvhv/7X/4o/+7M/QyqVwnw+x9dff73xev/8n/9zPH369A1+ogc84IcBg8EgxfY+iSZMNN/lvhcIBIR4RvEtD4Vs2NZqNbRaLUlDNhqNIgTh4HFf7NN0Aq6NK0gU2ZVk9zKgEJyuS8C3TmJ+v39vEvFyuRSnKY/Hg5OTE0ne1mg08Pl8kr7Y7XbRaDSk4cCitdPpiNBsm/iccLlccv1f9xrQyZpiarqP0z3SYDBgOp3Ka+h0OnS7XRFuzmYzIQbz9zOZDNRqNWKxGDweDyaTiYijOLCNRqPSxGbj5mUPBHT/I2HG4/GI0xQH2hRdMWmciV16vV6GW0oBnNPplLR6pmJwUEjB5nq9RqlUwnq93nngMhgMOD4+liTjbDYrjWkOGik2p9uh0WiU4TCTmSl8sVqtiEajmEwm+Oabb8TZq16v48WLF+LuyYaOwWAQckKxWJThQDwelyE+RZYAhNxA8RuhTBusVCqoVquoVqsArps3FPSQeMKD/mAwkO+IaXFKwgZFbUo3+7uaPjqdbqerHeHxeGCz2dBoNFAul8WogeLdg4MDSTQmSKpgOgnJjt1uV9JitFot+v2+PKeLxUJIqcqDPI0ceI9QhM90VH7/FJcBkCHNixcvpDEyHA5FZEmDgkePHmE6naLVauHy8hKTyQRHR0cy8KEAMBwOIxgMiiCN14Cp9RQGNJtNmEwmTCYTTKdTPHr06AchQmejj83LVColzxTXOl6rSqUCq9Uqe+d9xGeSPumAzYYnSZ9MHeI+QqMDNsbG4zHy+bwIV+x2uwyI2Pj54osvMJ1OhYjKZ7Xf76NWqwmhhE6pfK7q9Tr+5m/+BsFgUN4Xk3m5DgHX9zvra2VyaLlcxmKxwGQyQTwe39og1ev1GwnPTP7+PmM2m4lTMQlDJF3Q3ISmF3QLpTiH+/lisUC5XJZkH5JKc7mcpKqHQiFEIhEhM3JwlsvlxI1arVaj1+uhUCjAYDAgHA7Lvs6kzcFggFKpBLvdjmQyudM0odvtCil732Y6U34Hg4F8x7sEIiRlRaPRjRqGQ302c++7Pzg8uLnvEL1eD5lMRvZJl8uFSCQCvV4v5gxMxxqNRvjyyy9FJMFrZLFYcHBw8MpDBSZvjUajvRxpbTYbyuWy3EfKBDeazthsNoTDYZhMJjGFqVQqQiJn4jqJRzSR4V5F91Km1ZAEuss5udfryf5msVjwm7/5myJeqFQqQuh2OBxSU3755Zfodrvwer0ivi2VSpLQwsE5SVZGo3GjZqUwvtfrSToOa+Jms7lVfA5cP3eRSATZbFaSU0Kh0J2NW9aidHWvVqvQarXodDri+s7amt8J6/JtJCumV9lstpdqqA8GA+h0OnGaJaHW6/UKIZCp5+VyGeVyWYiBWq0WR0dH8Hq9sFgsiMVit9ZhGlfwOQOuG+E0PLjPOZtibRqV8AzBlG8amvT7fXi9Xmg0GtkXmZZIMwQAQtLkMF9pukHHcb1ej1arJYZQNCvjQMBmsyEej4txxHQ6hcfjEQOAs7MzEbdrtVoZsKxWK0wmExlSMy1oMBjAYrHI2SQQCODg4AB6vR5nZ2cb4v27YLfbX3rN4H5SKBQwGAykHuAQmYRNGiGRzF+pVOQck8/nRZQ/mUzkvBUMBmG325FKpV7qPb1J8CxFIsB9NSPJpcDLG7ttQzqdRqfTQalUQiAQuOW47/V6xehDpVIhnU7jgw8+eO3XfcDbg06nk34CSXTD4VCMzJQ9GIp17HY7tFotWq2W7GOvIkJ6HRiNRsRiMcRisQ1h9Pc51VmZHlOpVLYmsAHX30OlUpFENV57jUYDu90Op9O5kRDExHqKSgiSbCj0ImhaQ7GXz+fDaDQSUxKef3Zdawrb7hJTEXa7HdVqVUzBHkTR36LRaEhtvYsM0Ov1MJvNpLZUEtduggaauxIMH/BuwD71aDRCNptFpVK5ZaAGXPf/CoWCiAJ21cJut1t6ZSTRMUHcbDYjFArBaDQiGAzeui+U504KmHat5SSVkXj3JmqK1WqF8/NzETzYbDYcHBzIPIXGNrVaTcSPNCrdF6yReXbblezHPSWdTmM4HEp6UiaTEWM6XlcAYjjJMyWN1/YhQrIvyRr6XQrQgeta8tGjR9BqtWg2m0IWVqvVYnxrs9lgt9vh9XrFyI+k9mazKedlk8kk9XKj0ZD78WXBRA2K4O8C5wIkud43K6K43mAw7NyTOJugkImEJRq+MpWU75UCrclkAqfTCZPJBKfTidFoJEnxFEID+wuKgOuZ1uXlpZz7KpUKZrOZmM5yptPtdm+Rr5bLpRg23oRKpZJ+TK1Wk+9WSRLjvLFWq2E+n4vQnUIQGlEPh0MxJlCr1XImoXnbrmd0MBjgF7/4BbLZrJirsGYYDoeIRCIioKU40Ol04urqSsxoOQ+jSG+5XMLj8cBut4vJ2NnZmRjHJpNJESAwjWWxWAip3Wq1iomy2WyGy+VCs9mUdeltCNCZHENxNM2cdoG9OqfTKQJGtVqNQqGAWq0mczia4PEMzbVuOBxKz4CGyHcJdNrttqRTUbzI369Wq1J7DofDW2Zj/F5IrjWZTGKIVygURNROsQp7akzxplnoLuI6ZwkUffNes9lsImhkz97pdMosqNPpwGAwoFKpQKvVwu/3IxgM3ntuMJlMePr0Kfx+P7766isEAgGZz3OeTzEMxQHffPMNwuHw3mej2Wwm68uu9YziyNFoJKZNwPU8MJ/Pi/hjOBxCpVJhMpmgUqnIusZUOJ1Oh2aziWazKT0Cmter1WpJYeZzRQNx9mP5O3wvq9Vqr34NRejT6fRek613Dc4Ib9Ye7XYbi8VC5gRM2+S/U4qQXxZer1d6tLVaTYw7mRDLOQJ7eJyXORwOdLtdMbEGIGsacP1dZrNZTKdTScFuNpuYz+eyLtBMdT6fY71eQ6/Xy+xz1+zCbDaj2WzKa9zcpzlvSafTePTokYjO2TtlSjtnCEajEblcTvZPJk3SQIP1Ced3zWYTJycnWK1WMpNWq9WyhvzFX/yF1CbValXqlW63i8lkgmaziePjY1n3bDab9DA7nY4YArVaLSFM0zS7Wq2KMYrH4xE+gVKITwNpzvOU4qtAIID5fI7BYCDzgF2wWq04PDyUvZ99W5q20hCCezWfY4r9LRaL1MyclT9+/FjO+c1mE9VqFdlsFslkUjgofB557uT75HVinat8bmlgx7Ww3W6LuSlT3vnzSmPsfUVJNABlEvw2rNdr5HI5zOdzVKtVDIdDJJNJHB4eiiED6wfyUGjywTlAo9GQ2QHrYfJWHA4HXrx4gePj47du7G0ymWC1WiWx8a77hGa9fr9/6xkiHA5Lj4BnSfKA1us1+v2+7O2xWExmhpyJpdNpMQ/mfMBisSCZTMLlcr2zVPgH/LDAnoZOp0On09mokZxOJzqdDtRqNQaDgZhvcx7InvNwOESj0UC1WkUgEIDP50M4HJafe5P3JjkFDKNQCtAtFouIcQ4ODl7ZpII9UP5do9EIj8ezcV6leXo+n8dqtUIqldrYg10uF+bzOYrFInK5nMzi3yVXJJlMSh1bKpVwcXEh5zXy1nQ6HRKJBFwu195nK5qJjUYjhMNh6HQ6MfFmncR9l/VFr9eT2X+v15Pzk1qtlvNfuVyG3+9HuVzGdDp9JeEfjc1Ya28zC2UQjkqlQqlUkjPYd2VovQ2NRkNCrXa9LxpE0MiL3AdyImhwz0Cicrkse5pOp0O/38ezZ8+wXq/xV3/1V/jt3/7te+cEFB5SFHwfDAaDiOK3zUrJ+dRoNOj1evfyz8jhUKaFvy00m010u12pu7aBRkzsIXBmd/PnyVsxGo2YTqcb5y7OfgCICSb7yDzbKMHnFsBe57Y3YXr+LqFSqVCpVDAej5HJZOSsYjKZJMSAAmr2HO7q17zvoDEJuW886/J873Q6JQH56OhoZ7//5vfM8+S2fpbS9Im/Z7fbhSfL4IU3BfKOKP697771eDwoFAqYTqdyHnyTYG+y0Wig0Whs8LJpePamZivvEqlUSkw6arUa0um07BGz2QzValV0AcvlcsPMgD1lmhiuViskEolb6zx1Hu+biJhrptFoxHA4vKW9cblcYm5YqVTEJJQ8Y+B6fysWi1CpVHC5XPD5fNKvjEQiYjZEgwIAMrObz+fCN34Z3c82+Hw+dLtd6Q/sSprna4fDYTHxXK/XSKfTYsTMmgCA8PnI531dMzcaWC6XSxQKhQ0DDfK5uWcnEgk4HI7XMih5wAN+3fEgQP81Rj6fR61W2/nfJ5MJ0un0rX+/yy1Gq9XiP/2n/4T//t//O372s58BALLZLLLZ7K2f+/3f/3383u/93mu8+wc84IcLl8slpCiKpXaBQ81wOCykjpvw+XwyvC8UCnj+/Dmq1ao42Q8GA0nwtFgsiMfj6Pf7UuhzoFQqlfYquuhgflfTSflZm80mBoMBGo3GK4uvKRqjIzWJXRTu0vFp20HkJpgeq9Fo8JOf/ATL5VIE0hSysTDX6XQi6le6ARYKBRnC3kcYUF6Der3+yteAwuPpdCrfE4fPw+EQDodDGkYc2lJUxIPdwcEBGo2GiPHMZjPW6zWi0Sji8Ti63S6m0yl6vR78fr84Qvv9flxdXYmQ+mXdP9mgcLvdKBaLcDgciMfjQswi8UStVouYiSmVrVYLtVpNmpR0qvV4POj3+/D7/ajVaiKep+iYBEgmKNMFnt+dTqeDy+WSploikZBmfqPRwJdffikHXTbc2BAwm83ihg5ABBZ0Nc/lcqhWq5KOOxqNUCwW5R4mKZ9NSjYq2+02+v2+CB0pJqPTIHB9/1osFhgMBml4MBWICVgAZPAMXDd5Wq2WJLeGw2ExpyAhZLlcyiGMKa0k6TO9kMmyb4IMonShZLI8Uxp2HTaZMEsSJgXBHF47HA4Ui0URiG9LsOE9x8PncDgU0Rv/+2KxuHXopdiMz2+lUgEASSYym80IBoOwWq2wWCxIp9MYDAbi8EjxBhM+Q6EQwuEwLi4u0G63kc1mRZylvLaz2Qy1Wk1qrWw2i8PDw9e69u8CTCNWEoEpgCVhl0mpFHCzWULSDMXTTNp9+vQpbDbbraEPhd29Xg+LxQJXV1eSeGgwGGSQYTKZRGBOgqxKpRIjlnA4LMYMdLxlWgqh0WgQCASQSqXQ7/eRz+dlSKXT6TAYDKDRaCSVa7VaiUEFiSBMOuc67Xa7dxp7MOUY+Pbe7XQ6IkacTqeyfijrAz4TnU4HHo/nnSdnvUksl0ucn5+j0WigUCjAaDRK+iyF41xrudYz6aNarSIajUoSkEqlEoJ4o9FAqVRCs9mEVqvF8fExjo+PEY1GZThDV8put4tSqSTP9HQ6veX0ytTr09NTAMDZ2RlcLhfsdvvW4QNdrl0ul5iX7AuKWYPBICqVCqbT6cZ+xuePtZHdbr/VIPR6vSJin0wmdw5TW62WDOK2NS7p6N3tdpFMJjf+llqtRiQSgdfrxTfffCNC30QiIQJjr9crhIFXhTIlap+mPH9GrVYLAbNarUoieygUgsFgQL/fl7qIz+9gMEAmk5GBHp1IaXgRCoXgcrlE0MDEQYfDgdlstjWVh8lRdOCkGPbk5ESSKZkwvVwu0Wq1hBB+dHQkRNLVaiUEVbPZLHUUcD3AUaZrA5DPnslk5HPk83lxXy+Xy/irv/oruN1uEbbSyVatVsPr9cogpt1u39nY5mCB78VkMkl9R5MDACIOb7fb8twqSfk0dup0OpLI/TLDqHq9LmZaOp0OlUoFHo8HWq1WHFk5kDYYDDIQ59rOBFgm/91Ep9OR3+FzTSMMCpkpHlfu98vlUmrGk5MTqelpvHF5eQkAIjhPJBLi9E9heqVSkf3V7XZvfN8cGFAkWCgUkEqlcHBwIMQYnq9oUsakMK51g8FAzj46nU6IanwvJEsUi0X5XofDoTw7TPehuRdwPeCkgztJ2Urx/pvEYrHA+fm5CKD5+hyMDIdD1Ot1IaYAkKTuYDAo7386nQphZTwey/7N5HiKr78Ld2qn0ynGN91u987hKEm3PFu97tBuMBgIqdfr9e58bafTieVyiWq1KoL/94lg84BNeDweIcBPJhM4HA4R3rEW4f7d7/clIZL3l7I2+67wNtaT7wJGoxFutxuz2QzZbBa5XA4ejweLxUL2UJ4XgOv1zefzbew1FKXU63VUKpWdxnE01/B4PHsJxc1m896GkPP5HLPZbK+fp4hwV/306wym97FG3wYa4LEXzH/eBpJR2Ud726kkD9iNSCQiKZf5fF7EgEajUYizo9EIDocDoVAI8Xj8TlGu0+mUZLBGo4HpdCo9UbfbDY/Hs7O/5/P50Gg0hOgymUyk/wVATA263a4Y8ewiprwsKBYqFApSfyuh1WrFQCqTyYjQ5dGjR3u/RqfTkSTDXeJzgueCdrsta1O1WpV0+GAwKDUkE3x7vZ70Y5TivLvAXtLrCtheF+x/BgIBmSlkMhlcXl5Co9EglUphPB7L2YcJvTS0oiCSpieXl5coFApigHsfSNymkItg+iWNuG6CZxSn04lqtSrnvG1gkvh9acc0Y/B6vdLvdzqd8p1ShKkUfvEcy3OG2WxGtVoVMTtnUMD+dcp6vcbl5SUajQby+byYgg2HQwSDQTk79no9eQ4TiYSIwGkedtdcionCo9FIZo003nr27JmY+B0cHGy8b/a7LRYLfvGLX0gaMM3kmBDdbDZFRKdct0ajEc7Pz1GtVlEoFDCfz5FKpaQ3XC6XJcWQyTEULpPYTsMOiqdo0Ox0OuFyuZBMJqUnwlTz5XKJdDqNSqUCtVqNXC4H4Hr+oOzVNRoNuRbsA3Gd3rVuTCYTqV0oXtsHLpdLjDCVgtNtIHmcSbH1eh1WqxXBYFASfzm/5PejfL88/waDQQwGAyGk78JwOEQ6nUa5XEan05HkWb4/zp8KhYL0cj/44APp4TSbTflMnDe5XC7pY0ynUzGmIzQaDYLBIFwuF3K5HGq1mswZb55fOX+lwJxQqVSIxWJyL9J0kntAs9lEMBiEw+FAIBBAIBDYm0Su1WoRDocRCoXQaDTw85//XIwH1+s17HY7HA6HmEc4nU7MZjPMZrM3Rn7kjA+AXLvhcIhf/OIX6Ha7MlvhTFOv12M0GqFer+Py8hInJyewWq2IRCIyj+V9wL2VQuujoyMRfynT6WlAwv4iCbj79BhoGsHP8j4J0Dmvpogpn89jNBqJETXfN80M+b0fHx8DuD6bKWdWPCvfZcbhdruRTqcxnU6Ry+VkPmY2m0X4zP6sRqNBPB4XoRWNWigoj0QiMidPp9OwWCxirthut8VULhKJSH+badycY1FAftOUjFCKDbath+xdh0IhRKNR6bvz9ygAMxgMIpDnuscUUqY5NhoNqFQqEQK7XC5oNBoRm/FMxFnr2dmZpIWzX86zUalUQq/Xk3keza8pZuRcjWskhcr8nXq9jsVigWg0KqZsTG9rNBq3nm9yAYBvDVU5J2i1WrKOsi7l/sG6hkJ8ZQ3x6NEjMVnW6XQ4OzvD+fk5Xrx4AaPRKAY4NFjmvnR6eoof//jHG98nDf/tdjtGoxFGo5GsjQBk7eYzTeENRcu8tjQiTafTMJlM8Hq98p2Sr1IulzfMr9brNTqdjhisUazudDrh9Xpv7Z/j8Rij0ejWjEOJRqMhRuFWqxUej0fqAeB6LjYajfDZZ5+JgfPR0ZGcbdgzDofDt0IAOE+u1+tQq9V4+vTpW+05Md2NwQ13mfDSgMRut2/9OaYpskZlQi/BNEaPxwOXyyU/m8vlpG9Pzg1nHTRgiMVi750Y5AHfD5Bv5Xa7N3gd/G8MBOEchhw4PtfVahWTyQTj8RjRaBSxWAwnJyeyNr+OuOUmeB5rNpsoFosYj8eyfywWC6TTaTFcbjQa+OSTT14qeb3dbkvfpd/viyG12WxGuVwWbp1er0etVhNOXyqV2roO6XQ6MdKjed+7FKBrNBqcnJxIv2Y0Gsm8kaYunO3ePF/uwnA4RK/XQ6lUwtHRkSSKDodDAN+amXCN43z/m2++AXA9vwoGg7f4MgaDQc4GKpUK2WwWJycnL/2ZWQ9z1n9Xj4kJo6zX36f5GJ8pJT/xJpxOp8yuWSvzLKassZS8vng8LuEsfr8fL168wNXVFYxGI/x+Px4/fnznXkITn317ZTzDANsTy+12u3DSWq0W7Hb7ztdneEo0GoXb7X7r4lCGrdxl8stzoEajgdFolHPmTfD3yb3g79KEj4EVnDMAkJ6W8rUXiwXG47HwOfcRl7PPsK9pgBLdbhe9Xk++R/Jm31a9QfM28i00Go3UqEojMLvdLkZ0qVTqrZsRvE3QjJJ78E0TBrPZDL/fL7qDbaCRgUqlEu4nz8nK8wNB0072hIBvuVrsKb9J8DxK05L7wLphMpnInPVNgzzpUCgk70+j0bx2KvR3Ca1Wi5OTE+TzeZhMJuEH8do7nU45W3/wwQdiXsJgr3A4LKJnmtsr5wfz+Rzj8VjMR983ULuQyWQwnU431gWbzQa32y3GnWdnZ/I7wDUXr9vtQq/XIx6Pw+PxyM8fHR0BuH7OWCdy7dVoNHA4HHC73XA6nXJ2ex2Q2xqLxZDL5WT2wL2IfEBykJ1OpzxX2WwWtVpNwp9cLpfwCclxzmQyEtR1enr6WrwRBhJpNBoJcmSQJI0sDg4O5Np830wdHvCA9wk/DJbVA94bWCwW/Mf/+B/xu7/7u/i///f/4vnz55Ko5vV68eMf/xi/8zu/I2laD3jAA25DmWiSyWRQr9e3DgMWi4UkbN6X7kcxO5uNJFsoD2t+vx/xeFyc2Jg+QZdlJlyT1LWLRKF0O76vgNVqtTCbzZIC/SoolUoi3u33+0IeYco7rxGT3g4PD+98X2zmUaA2n8/h9/ul4UKCDUV9er0eg8FAUhbY3NTpdAgGg/c2OZRkT+U1oHCe/06r1cLtdsPlcm39m0oXOH4+inl2kVHoLEc3cR5a9Xq9uHAxgcJoNIrbJoepbESTONTr9dDv9zdeg0IoNhUpMrnZAAoGg5JakcvlROzAhupwOMTXX3+NQqEg19/j8YgA/OLiArFYTNLNPR6PuNbqdDpxz2RjiInmNpsN0+kUo9FIRPQceLKpGIlEYDKZRHRMcWG324XD4RDSE4emHLBWq1XodDosFgv4/X4xLKhUKsjlclgulyIAosibbuMk8BuNRiSTSdRqNblP+P3SqILNHg5O4/H4RpptJpMREwm/3y/OfByGMx3t4uJCDu1MT61UKvKdTSYTlEoldDodIfdQbG6z2WSAOZvN3hgZhEIoHlpv3l83wURQAHJd2Bx79uwZ7HY70uk0Dg8PRcBEUj4NPfh90Jme5IxKpYL5fC7iHSWY7kBRGe8fi8WCYDAoDUcOxR0OB7788ku43W6513U6HTwej/wMU2mKxaIk7d6EXq9HNBpFoVBAuVwWMtj72syczWYoFosi7mOjm2upy+XCbDaTpPpkMilkAF57fja73Y5Go4Hz83NJ4lFeIxKyKcokmadSqaDb7aJeryMUCm2Qr6xWK0wm08ZwiWYcdMOjgzu/722w2+0IBAJ49uyZiLcASLoCCUoajUZcUulW6XA4EAwG5Rnbl6AwHo8xmUwk/YsNFJpYKB3r+R5J1h2NRt9bATq/z0KhIM+TEnq9Hk6nE6VSCfl8HgcHB9BoNAiHw8hkMigUCvD5fIhGo5IEyXVYrVYjHA5Do9Hg448/ht/v39rocjgcGI1GuLi4kERmpSCz0+kIeYqEJrPZjHQ6LXu78nseDAbigO3xeF6apM8UZhJyGo0Grq6uNhJq9Ho9gsEg3G43Dg8Pb33/Pp9PkjpIBN+2rtBlORaLwel03mp+DwYDjEYjtFoteL3enY1Cg8GADz/8EOfn50KOPTw8hNFofCPDIhqmaDQaTKfTe+93kilJPCoWi0LyUq5DTL1mostqtZImOUnKRqNRrpNKpRKzFtZdJEZwP705lOv3+yiVSkJwVtb7drtdGvYOh2MjJUav1+Po6AiNRgNGo1EIhaVSSYjGTHqKRCIifGCttl6vJcWd5FESK0mGu7q6Qr/fF9MeDhXX67WsOd1uF91ud6ur683viN81k1ZjsZjUnCTXaTQatNtt5PN5PHv2DF9++SV6vZ4MFcbjsaTZsEZ7GdCEhCQ5CgWYNEgRMfDtOUan04lgnbXgNvC8wPRH5b19cHCAer2OTqeDL774Aufn5yKI4OCWJicUq7OO8Hg84orP+p0phExsIYl6NBoJOZTgeYAkbQrOW60W/H4/vF6v7FE2mw3pdFrqHd5HTJhjcg4AIT9Go1HUajVJWGN9zXqcRiwcmFDYaLFYNs5xk8lECNFvg3BBojzNP5SfDYCQM6vVKqrVqqRYDQYDBAIBxONxJJNJIazwOWKawng8RqlUEuMHDjb2wXq9xmw2k3X9VcmLVqtVyLI0f9tVM1arVREc3tVn2BeNRkPERveRmfj+SNp8nwg2D9iE1+tFuVyG1WpFsVhEIpEQ0ybguldjMpmg0WhEVEQiLk0UmcD2fcR4PEa/399I79xXaP02kEgk5Fz7/PlzWY/YF+IznUwmxdxMCYfDAYfDIQPkbDaL+XwuCWrcB3u9niTc3lwflMShVwHJS/uSSbYRnt4ESKbjuktzwO8LmEJyXwKaMr2WPdNdYO+Tv/swlP9uoNFo8OjRIxFbMoGEvTKKgkne3CdFlf3Vl01B0Gg0OD4+luek2WyKQBOAJObyrHh0dPTGyFKsm2kAtQs052i1WnA6nfeaqykxHo+lv7gPMZ2JLDxv9no9JBKJjbMnr7XT6UQ+n0e5XMZ6vRYx132gqRkFVe8LGo2GzJhoDELzkel0imq1Kuc0k8kkswrg+uzHJAomj95Ve/Z6PVxdXWE8HgtZWSliZj/l6Ohoo3c7Ho9Rr9flrF6pVNBut3F6eipnE2K9XqNcLmM2myESicDn8925z/j9fkSjUbRaLfz85z9Hu93GcrmUGQHF8tyHzWazJLNYrVZcXV3JWYOiNs4E9u3vNptNOScbjUZ8/PHHyGazyOfzYkBms9lEOKVWq6VPQ+ESk47vw01jGRrqDgaDneIA9mM1Gg3Ozs7g9/thNptF/FYsFqHVahEKhTCfz3FyciLrBUWWzWYTPp9PUqUo6NDr9cjlcigWiyL2dzgcQpTmOtRsNuV6UgjCe46G1kajEQcHB7i4uJCzOMnF/L64f9JogPeMEtvmpuwr0xROOccjyf6+86Hb7ZZU0qurK6xWK0kZv/lajUZDzs/8vth3icfjyGQy0j9uNpvS06CZCQ3t1Go15vM5KpUKIpHITvE1+7OdTufW2gdcz+cCgYCkiZtMJpydnWE6nYr4gL1TPs80tJxOpzg+Pt65hxgMBgSDQTEw3maAOJ1OhfB8c/3U6XRIJpMbsxXgen2yWCw4OjqC0WhEMBjcMBvdF+yv2Ww2dLtdnJycwO12y+eh4S3TrIFv96+7oEyko6nCTdw0FRgMBhgMBpjNZqhUKiKEV86gKY7S6XTIZrM4ODjA4eEhEomEGF6qVCrMZjMxDFfev8rUYkKj0aBWq4nQis/effU1U9aB7cKQ7xJMvCsWi2L03mw2pZ/PntlwOJR+k9frRbfbRSaTkdk/01HZY6LB+OHh4a1nmyJzvV4vzxv/Pb9rmv9R/KHT6aQOMhqN8t1QTM55u9KggYKtUqmETCYDu90uCeSj0UiMYFKpFNxu9859kmdDAPfWgLyewO0zHp+N+XwuJHUK7Vqtlqzpyn4tRcpWq1VIx+PxGLlcDl9++aUQoCms5l7G9ZJr32AwENMaPic0beD3R/L8YDCQ14xGo/B6vWJiRLPibDYLtVoNv9+/cU9TaF2r1RAMBmVGxb46Z3hMceU1bTabwrFpNptYLpf48MMPxQSZiMfjqFar8Pl8Iro3mUwwm81y9uUc7WayIPcrs9kMk8mETqcjRjKsSdk/V96n5XJZ+Cc016Xpp0ajEVNirVYr718ZIuDxeMTIvVKpSJgGOSuVSkXmejfTEu9KYCT5XKvVwufzbXBrCLPZjCdPnuAv/uIvsFqtRABHI4lwOLzx/fEeiMVicDgc0stsNBpvzIBrG2geEggEUCgUoNFo4HK5bj2PynuL4vBtsNlsOD09Rblchs1mE7M/CjaZvKi8f5PJJCKRiPR9ubazrrxZF7LuGg6H0kujIcuDyd0DtiEUColhImsOCkFDoRBGoxEajQYuLy8RCoUksbpQKMjzbjabxeic68abNrXh3DWbzcpslqEmnKEOBgN8+eWX8Pl8qNVq+M3f/E1Eo9F790gKdpgauV6vJYSjWq3KmjwajZBMJqVG2GWSTdDMmrXzaDR6q31t5fyZ8/pUKiUBN6xFWBP5fL6X4q7wb5CXxzMu5+80idPr9QiFQvjLv/xLMdJg2vrN12PIBXl+PLu8SrAD58N6vf7e3yWvh7X7+wTum3et2eQ/OBwOpNNpSX5VniFYs3LvYugD8K1BQj6fR7FYlH7HXX1L9ikqlcpevT/ON8iBvQmGMNFgqlAoIBwO33qmyM2yWq1y375t7PMd8HxwnxCcISo0ZlCCa8RkMkG5XEa73RZzK2W/imcKBihwr7/rWrDnGgqF9upDEY1GQ75jrhkqlUr4BF6v91ad9rpgHzGXy0GtVuPTTz/FYDCQWRlN+6fTqYQ10QTjhwDODBl+A0DumftA7pgyfI91ukajwXw+l2eVfRE+l/z7b4IbsQvKnt6+dSh/7k2L4be9zvvK+X0VaDQaJJNJRKNRNBoNMSijINlgMCCVSsn9oTQyYRBdqVQSLYdyP2AfhvXe+wbyLq1WKwqFAhKJxMa9HAwGJRSm1+ttmHvp9XpEIhHho83nc/zqV7+S36VpLs+E5NOxX3jz7PY6UKlUODo62jDwZGiNMnyL3I/Dw0MJEqRhrlarvWVOxv2TISY6nQ6FQkEE9q/6XmOxGFwul/QiWYcyXOnw8BA+n++96zU+4AHfNzwI0H+N8Sd/8idv7W9//PHH+Pjjj9/a33/AA37oCIfD6PV6Iizs9/twuVxC0qELu16vRywWg8/n23mApVs5nbFnsxlcLpeIVJbLpTjR8m+2Wi1YLBYkk0nYbDas12sp5v1+/0YKodvt3hAVseGxb5HGYdSrHNAojC8UCpLgrWwmuVwuLBYLFItFZLPZvUj0dGEcjUa4vLyUAp3u2xSlBAIBKdo5EKPImemz+4oK2PyhW3c2mxXBpDLNncNUkp+UoLO1Wq3GeDyWxFvl8Bn4lkTAAwCdIA0GwwbZdDabYblcwmg0yuHHYDBArVZvdUjU6/UbCSzr9RrFYlEEjiRmk7xbKBTg9/sRCoXEoTWZTMpnqdfrqNVqMBqNWC6XuLi4kIFUJBLBkydPZJhrNptRKBQ2BEpMn0kmkxvNMAqCT09PodfrxSygUChgMBjI9VoulzCZTOJGlkqlJK2HhDW/349IJCJNk5v3nlarFTFoo9GA1+tFpVIRgmEymYRWq8WLFy/EPCEQCMBsNuPy8lKGIpFIRA5ctVpN7nefzydkKD7fkUgETqdTyOQc7HJIzoM4020nkwna7bYMvavVqojYOMygsQIH2mz+MOEmHo9v3MP7OFruC/49k8kElUolpKNdzRYmUw8GA1gsFvk9t9uN4+Nj9Pt9pNNpEdXwmab4mA0tCspJRqNJAa/TNhIZm5tOpxNmsxnr9VqueSQS2Wgi0L0/mUzi0aNHWxshTGqZz+f3CjP8fj8uLy+FUHlTTPA+gIk1TFkdDoeyT6xWK1gsFvT7fSE/0cU+m82Ki2ipVJLBFBvKFLJxPQCu75uLiws0m02USiURWHNIXi6XRZQ5nU5FIKrX6zcGfSR00PWe97bJZBJh6jY4nU6YTCZJzCUxextpeTgcijMoSah+v/+lBxSdTgcXFxeSWsZmLAnAw+FQkseUCTRvwy30XWG9XqPRaEiTaBcJUqVSIRwOy7qqTHUql8tC9mXjmgYUHIg9ffr0XvMso9GIXq8nCdcctoxGIyH4LRYLIU/pdDrUajUx96EYdTKZSBIY98hXcR03m804PT2VoZfSiVVJ1qXw/ibYzFwulyiVSkin0yI0UqvVmE6n6HQ6ksrjdDq3XiOSSfiZ7gLNT5i48SYH3RS4MnHmvvdCAS6HZdy7SXIiKLolqR64fqZo9qTX61EqlWRvMpvNaLVaIjY3GAyo1+siYOd7Zb2vFGv6/X4cHBzc2v/MZjNOTk4wHo/RbDY3SGgejwd6vR75fB6tVkvqWyalx2IxEQD3+30Ui0X0ej1J1VKmgYRCIYRCIVSrVRGYL5dLcavl52y32/K+KGjudrtSv9KF/CY4kKZYns/zTfMrukl3Oh2pS1kf0lghHo9jPp/fm/h4E0rX5dlsJslOs9kMzWZT7kvePyQfULjFuoi1APd9Gtw0Gg1JVri5Xmm1WvzGb/yGpDUwRY0kVBL0TSYTIpGIpEinUimpRYFv69/VaoUvvvhCRKGLxUKSpG/eQ0wyZWqE0WiUsyZJpCqVCqFQSASIl5eXQvgPBAJi1kPDonq9LmL54XAogm6LxSL/S1LyYDAQAsFkMhGBisFgkMRsXr/Hjx+L0P1NgolcjUYDgUBAhl79fn8jiUKr1Qqpj6QnJg55vV4xKqCZFU2qSMTsdDrI5/Mitj8+Pr5zCD2dTlGv19FoNDbOaRaLRcw6XnYwEg6HxbQmk8nA6/WKQAOAGBaMx2PE43FJx3pdcD8k+fcukGQ0HA7lLPyA9xMkbHE/uLq6gsvlgtfrxWq1gk6nQ7lcFnKB1WpFIpFANBqF2WzG0dHR93KITtI0TQRJniNJPBAI3HlWeFugoIjnJzqfsz9nNBrlfE0Cu7IOVKlUSKVSGwNkJrtVq1X0+33p/9EgTVmvkbgeCoVeWshK0Nyw2+3eK/BZLpcYj8fweDxv7D5qtVobhEWS11mPkHD4vkOZyLgLNCqjGSLJn7vA+5y/+4DvDkyNoLGe8mxDE2iKNN82DAYDTk9PUSqVYLFYJHEX+Jbo4nK5biUEvg4oIux0OnuttUxEo0hoXwE6zwb7mlyo1WqMRiMYDAaMx2OEw+GdpGKa4p2fn8ta0+127yUndrtd6HQ62W/eF/R6PRGa0HSNBlk803AmQMHQfD6Hy+XCxx9/LCIlrkVMuruJwWCAi4sL6cvzzE6jyG63i1arJfX8yckJDAYDMpmMmN72ej05P1arVUlNSaVSMJlMImwHrsVaFIzdBY1Gg08++URSts/Pz7Fer+H3+4VQScEcBdJ+vx+TyQS1Wg1erxcHBwdy7uC12TdlGfjWlAGAnLt5fWiwaLfb4fF4MJ1OhdC2Xl8nvbM39CopgK1WS2YYBoNBkuR4bWw2G0qlkhDumVRosVjkMy6XSyGjAUA6ncbJyYn07Ov1OsLhsIgput2uGDXztS0Wi5iD8Tuj6QN70Pw9kpw5G6DJmslkQjqdhtlslvkZU9E5++Ie2Gg05HpxjaDo8+b9S6MFCrs7nc7GDIvmfR6PB8lkcuc+q9FoRKTPPk+5XMbh4aHs6cPhEJ1OB2q1Gl6vF263W0SgfK1Op4NoNIrZbAabzSbXWWngqtPpRBzAROxsNovJZIKDg4ONGpBnXYpc7xJUOJ1OORdns1mZebFHybNzt9vF+fm5mCHdFFPehNVqhVarlXVgVy26az0nCZKfBbiubSORCI6Ojl7p7E1QXE9C/M05ENOOzWYzMpkMGo0GTCbTvf1gJZm73W7v7O+ydqX4gwaaBoNBrvliscBoNBJz6maziePjY+nV1Ot1IfPzHJFOp6VXdt9+7HQ60Wg0ZD9gSvCu/iz7quzZ32dK/V3A5/Oh3W7j888/x3K5xGQykXOSEpw/UXx7eXkpBFyaIpBjQDOOYDCI2WyGk5OTjbql2+2K2Mzj8WC1WqFWq6FSqUi/jcJr9pXT6TS8Xq+s8zQl1el0KBaLMm+lGJjiHpqbMpWahtEUCvHZBL6dySqfr+VyKWc6Zc9JCZqIMEmQa+rNZ20+n2M2m4lRxmQyQTweFyNgt9stc1QAIpCIx+NYr9cYjUYoFosYDofIZrMolUpYr9c4PDzcEAdns1kxa5xMJvB4PGKu0e/3N2bgFFbTnJYiNxou+f1+hMNhuSaJRELMR4rFIrrdLux2O3Q6naSBLpdLMQxOpVJot9vQ6XS4vLyE0+mUz8hrSZNho9Eo+8GTJ09urR2LxULE3TRP4XnB4XCgWq3KusMeLefXAMTInd8ZU9iHwyHsdjusVqsYOvOM3mw24XA4xCiXvQnOw2jOOp1OEQwGodVqxQA/l8thPB7jq6++QrPZRD6fF1GCWq1GqVTC+fk5QqEQjo+PMZ/PcXh4COB6LafIcxvIaxgMBvD5fHI22HYOdjqdcLvd6Ha7uLy8hM/nw2AwuBWuwD4+TeYjkYiY51it1rcqQAeuTW2Zjsj+G9/LYrGQ7ysQCIgZ9V1nG5rxxGIx+V3O4WlGcfPf22w2hEKhO98nDZ7YOyfXij1gmsm8TP1LLBYL6eEwBfV1EvMe8H7B4XAgGo3KbLBWq6FarcJgMGC9Xgtn6MmTJ5jP56hWq8jn82KIYDKZRLAznU5RLBbR7/dxfHz8xoz5V6uV9E5ZU87nc2SzWQwGA3S7XeGYLJdLPH/+XIzrBoPBne+l2+2KCHc0Gm0I8IHrOrzVaiGfz8tZgFzBfdYfmmZwHvqmBeicu9Xr9Y05D8OVaJDzMgFmyjM9z5s8U3S7XTkXEDqd7lYv12QyIRQK4cWLF8J7I8eDplzkkFHg7/V6RQhXr9clCGdf0CB2374y98Z9E73fFXg97wqX4jyZMyPWn+QrqdVqlMtlCeZiiItyf6XZOTlitVrtznmDx+NBqVQSE8C7uHI05HI6nXfysfx+v8zP8/k8Li4uxNif9wiNe8LhsMy83jbYb73rjGQwGKQPy5p8l3jX5XIhk8kIT0nZTyan1O/3o1arIZ/Py2fU6/WoVqtoNpvQ6XRIpVJS+xWLRTEhuAmuxUyZ31cwykCeRqOBTqcjzxOfLavVivF4LOvqmzDuXK/XqFQqqNfrWCwWwu+lOQHNxABImAPPcj80bFtL74NGo5GeVC6XE44Wn28KzgFIn8Jms230FzqdjnBXtt1Pr/uZ+L+TyeTenihrbp45HvDyYL8DgIS/MBhC+czyfOD1elGr1cS8s91ui97FZDJJP5x8lvcxdMlgMCAcDmO5XKJQKODy8lKMHZT6C/Y+NRoN4vG4GKTQ6Hg+n0t4g7IGYnhOLBbbOLfvExr5suBcslwui/Ew6wFyHx0OxwZ3gPP22WyGo6Ojnb1Vu90uxlJWqxWz2ey155sUtytD6WhQe7PueMADHvBqeBCgP+ABD3jAewitVovj42NxtSLpnodXk8kkgzufz7ezwdXv95HL5fD111+LYOT/+//+P4xGI2lWUsjBpmyn00EgEEAwGITNZkMikUCxWMSXX36JYDCIZrMphT8FvNVqVdIeeNjaN9F8MpnA7Xa/0gGNyYyj0QipVGrr32Cj9+LiAul0Gu12W4paJnErDzKj0UgGXBSfkYDLhkm1WhU3aw6yKL6moy+TTve9Bi6XC5PJBC9evECr1RLhCYkDg8FAHLkpiuVAZ7VaCUFqOp3i/PwcH3zwgSQgmkwmcXrkQZ7pkhzI0IWa5FoSU0j8InYdUJi2QLLLxcUFWq2WXCsOYZl4weTx8XgsAy+mwDIFkYSldrsNu92OxWKBw8NDHB8fy3tyuVwiqFkul0Im46FEo9HA6/VKkmUkEkE0GkUikZBknlwuJ2J1HkjZsGfqKQA8fvxYBt3z+RzHx8d3ithsNhtMJhPa7bYYOdCpjIkgvG+m0+mGMyaTojudjpBQFouFCPgpdpzNZnJfRKNRWK1WHB0dybOwK23QaDRKWjSFkMvlEvP5HJ9//vmGUJKNdd7fdrtdUuzUajXy+bwMy5m++abA4TYbpHQZ30X+4wCJbqnKpvGTJ08wHA5FSLZcLoX8N5lMYLPZJJnA4/HAYrFArVaLqyU/37bvnM8Q12g+pxzA37wm/Lm7Uov6/b4YE9x3TSko7fV67w0hh0QDivdyuZwk2drt9g3DkMlkgmaziaurK2k0ffrpp5JAQjMKPn8cAFFM7vP5bqXv0Glap9NtvBYNBNbrtaQtk0BSqVRgt9vh9/ths9lQq9VkcJhMJmEwGDCZTO512NRqtUIcYzLizebIcrmU/cvv98s/K1MM9gX3+m63i0ajIW6GXGO4f1gsFjFL4Pt5G26h7wrdblful0gkcufPqlQqEdV6vV4hcPE7pUkKiQ4ApIG3D6mdzzHJmblcTuqj9XotRBwOgFkncI0l8ddsNotANBwOvxLhgqBTKNc8mhLQPOi+BqySsEuSXLFYBPDtPe50OmGxWHYmEZEgRlOc+2A2m9Fut0UI86ZS3VQqFbxer5iQ3CVCp5ghFAoJ0Sifz0OtVkuijc1mk+cLuG6Qu1wuSQeiqz2HgHQmHQ6HACDrIWsmrVYrQxiSJrjfsGmcTCbvrDlMJtPOgaoyiQK4rk/K5bIkZ6vVaqTTabjdbmni8/4FICYD3W4XxWIRGo0Gk8kEjx8/FiLqZDIRIRNd2dvtttx/7XYbuVwOT548gV6vh8/nk0Qg4Pp+tdvtcLlcqFarQqQlSNAj8bHT6chgms+xwWCQNY0k2pcB3z8FDBTMTiYTqFQqBAKBjb9JMh6Nb7in89ym3OM4UCGJcJsAR6/X47d/+7dht9vxi1/8Ap1OB1arVcQHTC8qFouYzWZIJBJwOp1bCV5qtRo+nw/T6RSff/65iG64pnDd63a76PV64pxLUuBgMIDX68VkMpG1gql8i8UC33zzjRCWeZaaTqciXPZ4PFLTcp2jgYPX65VzjtvtFjFKJpMR4TufPyZhN5tNSWF4G4QLnqEGgwFisRjK5TLcbrece5VDR9biTEH/rd/6LRiNRjGQKpVKmM1mODw83DibcqA6HA7F5ddgMOCDDz7Y+p7q9Try+TxGo5GYeZBM7HA4RKT0smmiSsMQJZmcBiwc1HPNSaVSb2RA9bImdSTBf19Ncn6dwMGpSqVCt9uV54lnS5fLhVgsBovFIrUN94D3Kb11X5RKJZRKJbTbbdnnCJ1OB5fLhcFggGAw+M5NwZbLJS4vL6HX60WglEgkpE+j0WjkzNFsNnFxcYHHjx9vrCFMV+YAmYkxPGsy4c7r9W7UkkwvpaHbqzrNe71e9Ho95PN5ZDIZIYObTKZbBJB2uy1r8us626/Xa2QyGdTrdbmPSShhr4bmCk+ePPlOk+73AY1eeAbYtk8w8a5Wq2E0GiEajd5J0mb9bDabH5LJ3gMozwpK0yIa9r1LMCkqGo3KHsB/zzPSmwT7aUzc2ef90ZBhn5RxQmlQRwOxu0DxPc1c7yPF0SCt1WpBrVZLMueuc/pisUCr1YLL5dop0P6uwDqA5mxXV1dCJmU6GIUvNPlyOBwbycyxWEzOA9VqVYRExHq9FrEjycQ3RX5ut1tMYlQqFa6urqDVaqW/P5vNROTKMx+NPDudjszCPB6PEMFSqdRe9avBYMBPfvITFItF1Go16eEHAgH4fD4xT+z1evLMsm9HE1uNRoNGoyGmifuaEY7HY4xGIzFl4BoQDocl6a3RaAgZf71ei+F1KBRCKpWC0+nEwcHBXq93ExQrzGYzXFxcCHGOZxfgWqROoZrJZJJZJKHRaBAIBEScxHNTs9lEr9cTgiETUGu1GkKhkBgrr9drJBIJMYFg8rff74fFYkGj0cDV1RUCgYCYuBFutxvT6RSFQgH5fB71el1e75NPPsHl5aWIb0mY557I+aBWqxXBgtfr3ZjVLpdLnJ+fSzKMWq2W55i9hHq9jlarJWfdm/e/EsFgEPP5XATZV1dXmM1mIj4lKdPhcMi9nMlkxCDO7XajXC5LzRUIBOS8WalUYDAYMJ/PZZY0GAxEcEhT5dlshkePHsk6xBnVvkIX9jxIVjw+Pt5Yz00mExwOB9rtNr766it4PB4R2u8C+w6cQ9wE0w6ZXrtrr+R9MxqNYDQaRST5OmB9qdVq7/xbOp1O+iXsYd5lTELBxmg0kkTtm3/fYrFAp9NhOByKQFKj0QgZlTUizatIZF0ul7KmsJfOOW+n08FiscDV1RWq1SpWqxVcLhfcbvedptFcf4DrvgQFscozIY05hsOhCGpphvl//s//gU6nkxkq69LXMQd4Hdjtdmi1WlgsFnz99de3zCc4j87n8xgOh3A4HLi8vMRyuUSlUkEsFkMqldqo05fLpawV6/Ual5eXePLkidyvtVoNz58/x2q1QjAYlJ4c9xaayS+XS/mOaIjSaDSgVqtlVkHz3E6ng0QiAZvNJkJf9sJ0Op2cCXO5nBhPUvTHfSWXy8l78Xg8QqymYeMuYY/D4YDBYIDNZkOr1bqT8E+RD81rlfUSE5QJ9juV5tc0v2E/vF6vyzpDcSDNYjqdDux2O/r9vhgLdzodEdLT4DUYDIrQJRgMIplM4vDw8BbnBPiWqH15eSkEcvIfaJbicDhgMpmQSqXEqLpcLsvZPRKJwG63w2azyZrf6/WQy+VkDmu322/NDzqdjqSpHR0dYTweyz1J43TeU6wXlAJ0zrdpzsL7h3U1jU1oXkHz93a7LdeDxjcknVutVjGmKZfLiMViMBqNiMfjuLi4wM9//nN0u10AEJMCpbmyXq9HsVjEYDDAJ598IkaATqcTDocDjUZja/qp0iyLvey7Aj5SqRR++ctfYrVa4dmzZxiPxzg6OpJ9hN8hZxqRSERSa3kWfxPE+btgNBrx6NEjXFxcwGKxiGHCYrGARqORGZ7RaMTR0dHevYybe9ZsNkM+n5fPRQ4JTRDY69u2v3I9azabqNVqYl7F3i9wvR7w2WJYxX0Yj8eoVqtot9tSu5BTYrPZ4Pf77zWifsD3A8pQGAaH0ASBosrZbIZKpYJ+v49SqQS/3y88IY/HI6bW2WxW5mmPHz9+I/0Kmrn0+31Eo1GsVivk83l0u13UajUx0OeM7OrqCsPhEM+ePZO9b9d7obHEcDgUnooS5GXN53P88pe/hM/nk3T0feoj5V75pucwg8EAl5eXmEwm6HQ6klZMM/putwuLxbL3fIsif64Vq9VKesY0f2fAzD6g6f5sNkMymYROpxMBJAD53sxms/TCafC+Lw9WCc6f9+1LcU173+Yn7EUyHGdX/W+1WqVu8Xq9mM/nqNVqIkxeLpdikERenhL8Hmw2m/Cp2GfZBvYgx+MxMpmMGATefA4GgwGKxaIEUdx1PlOpVDg8PJS6mDVhu92W82cgEBB+xutwel4GrL2YIrxtb2fNzrCUw8PDnUbj5AyHQiHpL3Ft0mq1Ynjc7/fR6/WgUqmEM8TnhCYCkUgEL168kIAL9k74GjR7sFqtItrfZx2u1+sol8vI5XJYLBa3zDh43s1kMtI/fJ30XKLX64mhKedkhNlsvnXtLy4u9jJM+HVCKBSSnit7euxHkPPDHiHPV+ypTyYTtFotBIPBrQELrwvyNpxO54YR2y60220Rwz/UmK8PmqxMp1PhrisRjUbFuJdBaZ1OByaTSfZGJl07/1/41/uKUCgktRn73DTAZZDi6ekpLBbLBo9ssVjg/PxcwhnJg6fZGDk+wWAQ6/UaJycnb12Er9FoEI1GEQ6HpdfAGYDL5dqo6VarlXzWXcaESrhcLlxdXW08+28CnPORO/2ABzzgzeH7qTbYgrOzM5ycnHzXb+MBD3jAA94YmCLChrzP5xPhGAWpdCjeBTrOVSoVETW5XC5xNTaZTFLIUxTIBLt+v49EIiGJtXR0jMfjMuxnA8dkMol7OR370+n0vYMVCmO3DcXuA9ME2+32nUl46/Vahvz1eh1ut1vcy2w2GwqFAnw+HyKRCEajESqVCsbjMbLZLBKJhJCAFouFJEEybZzkXbojHh0dyZCs1WrtdQ2YDGqxWGRI0mg0ZDDCpsl6vRYiCEkUBoNBnOnr9TrOzs5QLBbRbrdxdXUlZCy73Y5KpSIDTLoCJpNJzGYzEeCSWEviIl3UechWJpYoDwZ0yGdzMJfLodVqIZPJQKfTCeFc+b3X63VkMhkA1wNDCiHsdjuePn2KXq8nDXsmUNvtdjx58mTj+rEhwCEyU+MpVAa+FZUcHx/D7Xbj9PT03uQBpmharVbk83kRuvKaL5fLvdIA6e5PoTgPxBx8knShFGQ4/186sZI0xe8RgCTRPn36FNFoFGq1WgSnXq93oxHNJqHZbL51mCNJolaryTOoVqtRqVTk3ub3RgGl2WyWlEemoen1enS7XSQSiY3P9ibgcDhgs9mEPEiSFF3wlE3j1Wol4rpHjx4J6YGw2WxIpVKSvs0keTb68/k82u02YrEY7HY7HA6HuOWuVis4nU7MZrOtjX6SLpT/zAHstp/nfUCSyjaQ7LPv8IsOn9+1E+54PEYulxNnYBKH2u02Op2ONJOV94nRaJTh/GeffSbrO9dfilVIaGMSHc0QSNoDrq8bkx/YFFc2CjUaDYLBoDRpvv76a3FQVKlU4rJPYurh4aHse3TwYzPoLgSDQRHJq1QqEdEzEWI6nUo6GZMuSFZUkjD3QTabxWg0EuGcwWDYuK9I6KtUKmi1WtBoNLBYLEJCuy9l630F9y8Kt3dhsVhIA5Pu/MvlEgcHB4hEIuK6SMEzxeKXl5c4Ozvb673wHlOpVEIgZ/rIzVQEh8MhRHMOnGOxmAywXC7XGyXPMZ30VRAIBGCz2YQMqxxAG43GewVkrFn3HVwra443LZzw+XzSgC2Xy5JkwjWW5EqmCFLYa7FYEAqFUCwWxbiE5jBspsZiMahUKtlza7WaENbphE2jnnq9Ls10ktaYRkYis9frxaNHj0TAdfMeehXodDpJ+TIajWJOxLWTKUbr9RrZbBbNZhPlchmBQEDMgCiI5rXjelav1yXxwuVyidCAJi90t+f6mUqlMBgMUC6XkUgkZK8OBALodrsYDAZCBOGQkyTwq6srdLtdJJNJRKNRJJNJGI1GSd3m6+RyuVci0JOURfE5TQMo1FeCwnQOlS0Wi5C56HQNXIvpuK7crImVWCwWYpRDUgyNSVqtFiwWywahzeVy3fkZw+EwRqMRIpEIarUaarUatFotRqPRhvEGHXZJUCVoBKEEzchoTDQcDpHL5aDVaiX5JxQKwWw2y35EYwVeA+7dnU5H0s5rtZqcN10ulyQgTKdTmM1mEQUwZeUuE4lXwWQyEdIIP8NkMsFisZBzJAmXrFFqtRo++OAD2Gw2HBwcCLmRRlO76jcm/bGW3UYqbzQayGazKJfL8to+n0/Ike12W8QpTHd8GbKU3++XMwYTAefzuaTE6PV6eDweRCKRN0ZwIQFw38HOdDoV8vQD3n/EYjExb3K5XJL0ye/dYrGIkdm7SOR9W6jX6ygWiygUCmKkSNER14ZWqyXJGEo393f1/vr9PorFIoLB4E5RdrfbRalUgsFgQKVSuWVoqVarEYlEZIDsdDpxdXWFVqu1YaBD0jkH5jS5C4fDr0zgXK1WKBQKkopAsZfRaJS1iSaEjUYDwWDwjQgxs9ksarUacrkclsulmMBxr8xms3jx4gX8fj9KpRL+zt/5O++MVPYq8Pl8qFarMBqNqNfrW8267Ha7iIaYGLDLeItCo/uSYR7w3YB9/O8aNAB922BNvu85kyY4/J19QaEGjYHvu/fb7TbMZjPm87kYot4HmhauVis4HA7kcjmEQiFJEifG4zFKpZKYyd2XyP2uwcRiCgFo5lksFkV8bjAYMBwO4Xa7ZT6h7FNQtN9qtWC322+J/klo4pl+Vx+Lhln1eh2j0UjSUMxmM+LxuNSWgUAAh4eHyGQyKBQKksh9cHAgZxGlmHsfmEwmPH78GJ3/l8g9HA4xGo3EGJPn3fF4LMlB4/FYyE88QzmdThwdHe1dhyuNf5T7oV6vRyKRQD6fF+E0ifqLxQI2mw2xWExmWq9K5GTiJtPdmTbIngn7AqPRCD6fT3qe264t5w2cHQ0GA7x48QKdTkfOnyQ9D4dD6PV6MaNjH3Y4HKLdbuPg4ECuBw2zaTZ9U4BsMBgkvV6ZrkQTNs4HSArk3EqlUkmtl8/n5flV1lbValWSCx0OBwKBwMZr80xWLBaRy+XEnPCu8y5TbZhOVqvVxNSC5zkKILPZrMzP+F6Z4sp5bywWk+TD2WwmM6bxeCxEdJ4hK5UK1us1Li4u8PTpU2g0GjnT84yr0+nErHgbKBKnwfOuWZLFYoHD4ZDnfxe5XoldzywN5vh93HV9J5OJCEpvGkFQdNtoNMS4nDMKClSUUBpmO53Oe9cU1mfD4VBM5+5CKBQSY1YmetJ0lIJdisqNRiO63S4ODg7QarU2rjvTKU0mk4iWeN6haJe90Ha7jfF4jEKhIEa4pVJJ3seu90zBDddb9pNCoZA8m/l8Hr1eD6VSSf52Op1GuVyW5G29Xi9pSoFAANFoFD6fD+Fw+N51k+RlrVb72n1WGq+QPA9A1tvFYiHm+RRC8b7/+uuv4fF4tpq5ajQaEcqUSiURk9L8UpkeHIlEkEqlpJ82Ho9lT1mtVnImJS/Cbrfj6uoKi8VC6gqmUAHA06dPEYvFxLifQh+KVwaDgfT9XC6XEIkpHuMZrVQqyQwsHo+LsPomOJ+x2+3weDxiBrat3tJoNOh2u2KsS7L1LnDv5VpPY7Futwuz2YzRaITZbIZqtYrZbCa9WJqL8rPz2WYyudK0mIbSnKW3Wi0sFguZT28Toet0OpyengrPhPcHe3HK2mO5XOKLL76QZ2s+n6NSqUhPl/USherNZlOMt28aunY6HfT7fZkZMtCAZ2yuSxROswcwHA7lXMy5RblclnuGv2c0GqVv+fz5cxSLRVgsFhFicXZQLBZl1utyubBcLuU5p4CNItKrqysxyvH5fGKYwd55r9cTk6EXL16IANrr9aJUKon5bjwe3/geVquVnAuazabsmdv2hPl8LpwdJt7TsIYJq8FgEJFIRAyIlWItnlPehbGmyWTCBx98IAIEzjP433w+39Z7chf6/b70lzjHajQa0Gq1mE6nUksB188n75dOp4NUKnXrdXK5nPS6KViwWCxSK7K/MxqNAFzXZffNMzudDtLptPSjlP1mg8Ega1IgEHipZGXgeu5MHhVFziaTCV6vd2MvH41GaLVa8jPsWb1pcdQDruFyuaQub7fbwsFhgiENwZ8/f46TkxOEQiFZVwir1Yp2u41yuQy9Xi/rxOuCc0Qa7rBO5Jz4Zq/N7XZjOBzCZrOhWCxKjX3zXuWMstVq3bq3GPgyGAxktsXannWO1WpFIpG4871zhrlvWMFyudwwG9vVixoMBjg/P5fUeprSsHdQr9dRq9UQiUSwWCxwenp6Z18rk8nIeaPz/5KP2RPS6XRibrSvIRbwrakh9xolN5LBIwwzophLo9Fs9JheBuSm1mq1rSYpSqxWK/R6Pfj9/nsTed81fD6fcI9pIrNtfyF/9PHjxyLUzWQywrNkLc9a8ybYq7553991n8ZiMYxGI8TjcRQKBeE/G41G4fqS3+Dz+XBwcHDv3qhWq3FwcAC/3y81Nfd2Goh5vd532pdlvWO321EqlSSVWwnW7fP5HEajEe12W0zslJhMJiiXy0gmkzLH55mGZmHk69IEmHxomtqzjnU4HNDpdBK0ZjKZxCSS14w1otVqRSQS2Wu+wnMQzR63fV6LxQKz2SyzO/YgX3dmxN7TarXamxvMs8SbDNr4PkOv12+E7zWbTTEfnUwmEnYXDofhcDjEyIXnWO6jr8p1uw8+n09q0Wq1unMPYa8xFAqJkdoDXg/37aUGgwGJRAKFQgFmsxn9fh+NRkNmDeR1U7T9vqfS01CuWq3C4XCI+Q85oh6PB36/f2M/+fLLL3F+fo6Liwvh4NEcj4bprG0ZOHNTU/G2oFar7zXPJZeXZsT3gbX7LnPRBzzgAe8ffjBMvsePH+Mf/IN/gH/9r/81/sk/+ScPG/0DHvCAHwQ4cPT7/VJEU+CxT/IGiaUmkwmj0UiKPyaBjsdjAJAGIYnCHOZfXl5Kk7TVaglZhoQy5bAyk8lApVIhEokIQaNUKiEej28d5rIxysPZvqkOxGQykfe6i+C2Xq/FYZSOp+v1Wga+1WoVVqtVHPqZrDudTuF2u6URSNhsNrhcLhSLRUl593q9SCQSODk5wcHBgRCuSXLa5xpweGc0GlEqlRCLxW41E5kamEgkkE6n5QBsNBrx1VdfCTmHzeZCoYBKpYJQKAS9Xi+Oy71eD8D10GkwGMDhcEiaIpNKmBxJUlKn04Hb7Ua32xWHWWWDhSJkikouLy+lYU5RlhIUMudyOSHQkSTE740JM+VyGZeXlygUCgiHw3j+/Ll8DyR0hEIhGSZbLBacnZ0hk8lIAqNTkQ5+fHwMnU4n6SZ6vf5OgiTd9Futlrwuh8L7kBS0Wq0M3mazmaS5E0wn0ul0mE6ncr+RrLRYLOBwOBAOh8URWukIGQqFoNPpdrqY8dC6672azWYkk0khlHAwP5vNcHp6ivV6vfHZeYAHIENFCvR/9KMfvXEStsvlgsFggMPhQL/fl6EuExesVit0Op24CY9GIzx69AipVEocSQk67DIRLhgMYrlcikB6NptJ0gMJ+Xq9HoFAQAinbLpSrG80GiUpnSYJJOIzWWOby3pHke65qyFNV9uXccLlZ/uuMBwOcX5+LoNuEkar1aqsv263G4VCQZpzStCZeTgcolqtIplMyvqh1Wpv7RMkWZJ4AkCEESTQbSN2aTQazOdz2O12lMtlMQHR6/WS2uNwOIRA8PHHH8Pv90uSE0lyd+3DarVaBtJ6vV7WDg7FbDYb9Hq9kBlpWFKpVHB5eYnZbCapEXd9p3RebbVaSCaTsiffbKrTpKJWq4nY/fHjxyLy/yFiPB6LCIhEiXK5DJPJJKndND+4CT53JFjdN+AjyVqv16Pdbsv6cFd94vV6xaXX4/G8tcb568JsNkuiHeskCq2azeaGmYzH49kgWxoMBphMpr3THgaDAUwmk6TUvUlotVpJqNBqtajX67i4uBDzCdY//D6SyaQ8Q0zzI2GazzK/c6PRKKSBarUKnU4nDvsOh0MIlEz7YA1BYwSS8EejkSQv2+32O9OddmG9Xm+cGQwGw0ZvRKfTIRAIYD6fI5fLIZ1OS5J4IpFANpuFTqdDv98HcP0dMmGIAnxeM5IcWWtwGE/iULfblZqR5EeSE5koxrQyvgemxVarVRQKBUmJpjBgNpshlUrJM8M91mg0IhaLoVgsSk3Lc84uKEXYarVahME0h6AxDQ1SlCABgAYDJFkzTY37BLD92biJ6XSKFy9eoNfrCRGSDvez2UyGuEzRozDwrudEpVIhlUqh1+uJw3Sr1RKX/pvJ3hSbl0olOQso/z5TF7VaLf7u3/27yOfzQninSQKJJKwpgev1Ua/XywCbxEWao3BQ3m63cXx8DK/Xi2AwKAYRFB56PB5JNalWq2/cXZpmXzxDzOdzSWShsBSAEEP9fr8k2lssFqTTabTb7b3MmBwOh5BNmfpFzGYz5HI5IVgfHBxs/D2mMjYaDRQKBWg0GhQKhZc2XKBJBs8pNCDjevam68n7UoiUmE6nGI1Gkl70gO8HaN5yM4XEZrP9IGrN1WqFYrEoJPWbRhM0lnI6nchkMiiXy9BqtWIe8bZBUjeF4Hf1OHi2ZsrcLrMJ9g1dLhf8fr+QezudjqTjAdd7cCAQgMPhQCgUemXRfS6XQ7ValXqDyad6vV5SUxqNhuxfgUAAbrf7XjLlfRgMBmg0GsjlckI2LJfLGI/HIuZYr9eYTCZ49uwZRqMR1us1Pv3003eecr8vDAaDmCnxfmTSrhKsZ61Wq/T7lKBggUQgk8n00r3bBzzgTcNgMEjt3uv17hXn8VxjMpleKgHCaDTK+aZSqcBms+2sXxqNhhjsvozIZL1eS7+IJk2sxVmnUwhps9lkjX0fn8N2uy39Jpp00fix3+9vGCVoNBocHBzc6rM4HA5ks1khKit7hkzkpCnzXXC5XCIyBa7NlyKRyK3+oM1mw4cffohgMIhSqYRgMIhYLPZSAn+eIebzuSQcRyIR6Q8o+/JqtVrS6SgMzWQycLvdiMfjCAaDcDqdiMViL01e3mWyoNfrcXh4KLM9poYtl0sR4adSqVc2AFyv19JTz+fzCIfDYrRJ0d/5+Tmq1aoY8vFZ2fYZee/3+310Oh3kcjnZh5jiy7qgUCggl8thtVrJHkXxB0UEFFH2+30Eg0E55w2Hw1v333A4FMNpmixzFkHxMpPCAUgqei6Xk1S3RCKBTz/9VOoq1mfNZlNE8tug0WgQiURweXkpSYn3nYNCoRB+93d/F59//jlKpZL04WlU3Gg0sF6vJfmRhlHANWG92WwiEomIyVC/35ceCs/n4/FYUp2Z9qXRaGRNpBDx6uoK5XJZ+mHsQ9HM4SZfh9eR5nO7wFkARWk0d9gGmh14vd6t/CCHwyFnXZrEblvTF4sFSqUSrFariPaI8XiMy8tLjMdj9Ho9MZtmP8xut8scgecE9pc5t7kPNPDic3ofSPAHID3pcrm8IYpIJBKyHnItUM6Fef6mkYzFYpEzHZOhp9OpmLJzZsJ/puEhv8tdxotMrQ4EAmImXC6XkU6npU/barVQq9UkzZjGV6VSSYwxAeCLL76Q+b3P58NHH32EZDKJTz75ROpdzpiZvtdoNMRcnfeEz+eD3W5/5TWQyasU//L6UiS+Wq2kL6rX69FoNFAul+X5WS6XW+cBHo8HrVYLvV5PjFIp+OZaRaEyhTsUOiiv99dffw21Wi38CJoLUMBNLgENxk9PT3FwcIDBYCCm7sC1UDccDkuP9Mc//jFarZakAfIsBVyvTalUCgcHB9JfJyiU5vUBrnuGl5eXYkBGUw7eJ4PBAM1mU7gnNMUaDAZb1w9yVijk5z3E17JarSgWi9LvY1+TZlzcT2u1GjQaDZbLJSKRiMwNB4OBmGNrtVqUSiUsFgsJe2AvsVgs7kyatNls8t5vGqLwOj179kxMUWhUNBqNUCwWhfPDeSYFXLzflGKX+Xwu+x4FkEoDYIPBID9bLBaxXq+Ry+XkeTs9PYXT6YTH45G5Rq1Wu2Uu6Xa7USqVMJ/PYTKZ0O12EYvFUC6XMRqNpFamSJszAZr7tdttSW2r1WpoNBpQqVRSVxBMP1c+Q9lsFqlUSswalHOPTCYjZog0i6fQhXX1tvkozW2Zsg5AuBPsbdOE3uPxIBgMbvwN7scA3viMaxfYy6B4kvfBy7y+8nmk4c9kMpH7nDyzx48fw263Y71eyzNKsxOVSiX7EgARr5NXRaMLgrW91WoVM1bO1nb10vr9Pq6urlCtVsWMIZFISKIu107OrTQazdZUxZtYr9coFAoiDmW/HICEiNCUu9vtSt3J2Q2DLBwOhxjzPuDNgvfLtvPg1dWVzOvuOt+4XC7pN1osljdiwMszn1arlT5ar9cTw/mb0Gg0ImY0Go0b70X5fHS7XTHzUp4NFouFpBE3m01Mp1PhBzWbTSyXSzidTlxcXNxrUE/uE9fWXRgOh1KrKQVjFJ4qTdTX6zWurq5EfM5zrhJ+v1/mWyqVCtlsdmPtUIL9eOXZj6FKDB7gWQ24NgTalnytBLmh/MxmsxnRaFRm9uxfKNd29oZpVP2yoGjYYrGgVqvt5DAB11wn4Ppc9r4Zger1evj9frkPM5mMzPdvfic6nQ7xeFyM6Bn0k8vlZOa77RrMZjOZD1osFnlG79vTtFotHj16hMvLSxiNRuEp0CCC4meDwYDDw8O9xHAEgz2SyaTsd+9qj78J7muLxQL5fB5XV1fCM2HPqdvtotfrwe12IxwOY71e4/LyUs7FNMAfjUaw2+0i/qWpBbkcXNtCoRDsdrvMkgAIB+Xmd2ixWPDkyRPU63UxEFYaO3DWtK84nIZ0DLrZ9fzRIPDi4kJMDV9HgL5er9Fut5HP54UPTbO9XWdIBgYBeBCgK2A2m3F6eirhLbPZDJFIBLlcDtPpVIzRlssl8vk8ptOp9Cc8Hg8SicRbM+NgjygWi0lIEXuCwLemVAxQ4MztAa8PnU4nnOhdvBGj0YhUKiV6D5/Ph2g0KkFD7Om8TTBEkDUX37vb7d7a87wL7AWwDuEaS845sV6v8c033+Cbb77B+fk5gOszEPs3NMKigehqtUK1WpXe0OsGUDG4h2cho9H4Sr2zNx049IAHPOD9w/efAabAn//5n+PP//zP8W//7b/Fv/yX/xI//elP8eGHH37Xb+sBD3jAA94IXpYo1e/3RYRHQR9/n0UexcRM1qAYuFarQa1WYzQaCeGDDaFisShO/xxoLpdL5HI5SXFk0ymTyWw0nejc3Ov10Gw2RUQRCoVeukGjdCredXCvVCrodrsol8vQaDTweDwIh8OIRCLwer3SLM1kMpLUOBwOEQwGN0QWysYEh+TFYhF6vR6PHz/G8fExPvjgg42hnc/n27gGXq9XrjNFK3QMDgQCMswxm813Hp55kHn27Bn0ej3q9boMk/j5OKArFotoNBriKk93azpbm81maf5SiOL3+0V0RTIU//t0OpVGHwc5HOLE43FJvKTb+l0NfrVajWAwiHQ6jclkgkajgVAoJE2cVquFs7MzdDodZLNZVKtV9Ho9cdJkGiAHA3QLYzPM6/UiEomIw60yHZzpmEwZvQ8ul0vEoXTav0/YDUCIUtVqVb73VqslA3M2Wm02mzTBXC7Xxv3MYRYbeY1GQxy56RJ9F5jwcV/aIEk3tVoNBwcHmE6n+Oijj1AqlYQ0R1IJ8K3AbTweo9ls4kc/+pEIkN4keJ/MZjNMJhMRidMggW7n/X4fKpUKT58+RTAYxEcffbTRaGAyBwlVH3zwgVwXCsZJlqA4nc1PPqelUkkSXShKNxqNIkQ0GAwIh8OYTqfI5XJIJBIbZCYOYyuVCqxWq6Se7ILVahVC3X2OrjS9YMrqd4H5fI6LiwvU63VUq1V4PB643W5xXuYepGxUkNxBcD1stVrizH9Xw0gp9OIQj8PxXenyFCGv12tJqmUCbCwWw3K5FCKZy+XC4eEhQqEQXC4XJpMJKpUKDAYD2u32nYYL8/lcHP35nVB0TjIgG0BffvmlkGMmkwkKhYKYfxSLxTsdYDncX6/XSCaTQl5lco1S9GMwGDAej5FOp5FKpWC3299pKuObBhv9KpVKEjmIVqsliTEkUXKft1qtqFQqmM/nYhxzE1qtFk6nEy6XC5VKBV6v985nkOTo4XAoiQi7ms8U55IMQhLG+96s1mg0Qvy6uroSIiIbrQaDQUiC8XhcCGPlchlGoxGtVuvO+43kRqXR0ZuG2WzGyckJ8vk8rFar7GMAxEiHg2bl2kM37GQyCZ1OJzUSHZzpxEtHd41GI+ln4/F4w5RjNBrJ8JoCJpVKJQ72+Xxe3Pv/0T/6Rzg6OtrrszE1pF6vizkHB6A2mw1+v1+G/JFIRATqrVYLjUYDlUpF6nSj0Si18MnJiSQ70PCF6+TV1RVqtZoQ7yjGZor5eDyGw+GQpDnuoSRwF4tFAJD3qNfrhbBFIR/J1zR2OT4+FrLrtuZ5IBDA2dmZkEiUREei1+uhUqmg3+/LtaLwliTYg4MDvHjxQshrvHZMgaKIwmKxYLlcwuv1SvKaRqN5qeSExWKB8/NzNJtNSYk5PT2VRCgSO8bjsaSsB4PBvc5OarUap6enaLVaOD8/F2IlRZJOp1NMIli3krhdKBRgs9lgMpkklY/CdKfTiUePHqFSqSCdTgvp12w2b7wvJt2fnp5iNptJXcW6dDabyRD76dOnOD4+htlsluutFKsD3w763G43xuPxS52N7/sOCoWCECM56FQSVQGIsKHf70uNwD1kNpthOp3uNdDmGrHNwZfuzd1uF8lkcuvATaVSwefzYTaboVarybr1Ku7OPCO+bZBgwz14l0HbarWSBIX7RLQPeD+xS8zxfQeHy91u987njfU/yRMUI7xtcO3qdDp7CedcLhfy+Txms5mce+6C0+nE48ePheDLxDqK6ex2OwKBwCsP+8vlMiqVigjbT09PMRwOMZ1OheCRzWah1WoRj8dFWHZ0dHRvEuZ9YMIqiS/FYhHtdhvtdlvEL6yBcrmcnNWYjrMPkfm7QCKRwGQyQSKRQLFYlB4ak+P6/T40Go0YHmm1WlxeXsJisYg4hefMQCAAj8eDVCr1nZHrHvAAgukKvV4PhUJBEhO3gaaaTDN/WWObcDgs5xr29p1Op/QGJpOJJLdEo1ExKtynjwdc18rs+8ZiMen50/wCuD7DhsNhSQJ83/oGNBrjed5qteKjjz7CcrlEuVyWNECemynOoNGpEtxbeeZUgn//Zjr8NrBvz57v06dP7/wdpm6TpLvPPsqZiPL8PR6PZabhcDhwdHQk5wqNRiPmCcD1vl0qlTCZTJBMJnF0dISDg4NXSs0iMV6r1YphnBI0NWZvkqmhh4eH8Hq9r0UG63a70Gg00udwOp0b+zJTuWnuS9Ku2WzeSfam+JZGs5xLMt3Y4XCg1WqJcWEmk5GEQZPJBL/fD5/PJ/PGZrOJxWKBYDCIQqEgYjyC599qtSr9vEAgIN/Zer0WQTXnd+v1WmaHVqsVR0dHCAaD+PTTTzd6EBTudrvde+sFJojSrG8fE0ebzYYf//jHcLvd0qPj2kFBtMFgQDQa3ZjZ8G8zqTefzyOXy6HVaiEQCEg/ORQKwWKxSOI6APkuK5UKCoUCfD4fqtWqGKwVi0UYjUYxwxkOh4jH4xtnd6bHc8a4CxSZarXae+d4FL4xTfkmVCoVotGoGGRmMhkhD7M2qlQqqNfr0Ol0ODg42DDMHo/HODs7Q6vVQiaTkRkdydKZTEZMFvL5PP7e3/t7G8IX9tv2wcuYXwPX56/Hjx+jVquJSRP7ilqtVowwnz17tpHqSiE+UyNJKPV4PCIkpYEkTYgePXok14QG9+PxGC6XC+12W2aRqVRq4z0qU+VJ2Kah8HQ6lWtbqVQQCATELJ89I5pTKA1Eef4ZjUYisGg0Gvjkk0/Q6XQkoaxarcr+Q4Nv9v1pFp5KpV5JSMRE7lgshmAwKNeWwtRoNIpgMAiDwYBqtQqLxSLG1pVKBVqtVsyJlVCpVMLBYP9MrVZjOByK8El5fzBVknNO4HoGdnFxIXNTh8Mhs3z+Xc5V0um0zL0XiwXi8biIsdn3bzQaMJvNCIVCcLvdcDqd6HQ6MJlM8Hg8mM/nGyYUyWRyQzA1Go1wcXEhfXIKgtrttvSnWdPV63VcXV1Bp9MhFAohHA7DZDKJCbNarZZ5j3JtWS6XqNVqYha7Wq3gdrvFPIOCcpqx0YSA97SyB0qhCwVgfC75PmjMy7mw0+lEt9uV3/N4PKjX6/j000/vnJlv24PT6TSKxaKILz799FOptzjH7vV6GI/HcLvdMBqNkgzH2RT3fGUKN3kHDE+giJp8HfJoms2mGFWwB3t6eipn5Wg0KunvzWZT9kQGadRqNSQSCXg8HhFrA9eifq/XK0ntXJ+Gw6FwjbrdrvTdDw4Odj6XNBEuFouYzWbCG3E6nQiHwyIIrtfrqFQqYhDIvdJsNsNisQgHQAnWIN1uF9lsVrgmBoMBuVxO7hGmF3O+o0S325U9mPschfg0eKYw1eFwvHFi/svsIcR8Psfz58/RbrdRKpVkTx2NRphOp8jn83C5XMJL4h5Ow1HeszqdDp1OR9a1er2+wRna9b40Gg0CgYD0qe7qpbHebrVat0xPaATpcrkkBIMCpvvqqnQ6jXq9Lgbv5OWtVisJBTGZTPjyyy9hs9kwHo9lZrVer8WAgfwzzjke8PbBe4yBKvc9U8qeaLfbfW1uE/eY+XwuIQeDwWDn36XxNACpu9ifvSk0588q10MmO1cqFZhMJjFKGY1GcjZkgAj7gNvq48FggF6vh2g0emdYAflR3Be47tGMmsZMR0dHYojE8A8+K6xnuG7o9Xoxe2eNtE18xvqCvYWbidUajUYM3VljsE9zl3C70WhIoBLNI2goswuDwUDWxvv66Nug1Wrh8XgwnU7FcJ11L0GznlarhUgkArPZ/NaFda8C9r9UKpVwTSuVisxTNRoNXC6XGMlzPU8mk2Kc0Gw2txoK8d7hGsw5PM8Z90Gn0+Hk5ETuS4YzMYCHRurlchmtVkt4zC+D90FUrDzzcY5Rr9flvxsMBgQCARwfH4vAcTAYiFE9Te8DgQBMJhNisZjwmxuNBmw2m5wDgG/NELbVLcr+CqHT6RAOhxEKhSQNnDyMlz13kTNIs+u7oNVqYbPZJCH4VdHtdpHL5ZDNZjfubxp38frevHfIzQHenQnR9wV6vR7xeByRSATdbhfz+RzHx8fCd2fPgGatNAWMRqOv9V3eB5VKhaOjI+F+NxoN5PN5qFSqDd4Uw6XC4bCcn2jCwDPnA14OZrNZwvTa7fbOuY2yP/L06VPE43F89NFH72QtrtVqG6ZpNNLTarXIZrMSUkJO8H2cUoLGattAI5+zszMx9Dk8PBQOGw3CyMtjCFij0UC/30e9Xn9lAfp0OkWlUpHQL67v7IszRGTf9U2r1cos6KbZ8K7XXywWG+foBzzgAe83fnC7Hx2I/uiP/gh/9Ed/hN/8zd/EH/7hH+L3f//3Hxo8D3jAA36tsFwuZai9rTBjQUvhMcXmFF/4/X4pVrVaLbrdLrrdLgqFghB+OPACrhsLV1dXIiIhIYCiFg6vSLqha3IoFHqpZAlCq9VuJEhua0hykM4hOofTLPgpNMrlcnjx4oU0Wk5PT4X4yvQOulmSqNlut8Wp9+Dg4FajJRaLSTFOZ3NeAybMOZ1OeL1eOBwOaDQaVKvVvcSAJF2QVEBCCNMplWKJYrEoSY4ejwfxeFwGgnQCj0QiMjRlwnUikYBKpcLZ2Rl0Oh3y+TwsFosQcYrFoog8OZBOJpM4Pz9Ht9u9l0ACQBq7TAXVaDTiSn12diYOXr1eT8gcGo0Gz549g9/vF7FoPB6XhpPFYhGy2OHhIWKx2K39f7FYyDXYhxzPn5nP5zAYDDCbzdBoNLcGAcR6vRZXfqYlA9hwnqULOJv6JFI0Gg34/X45OHq93g2iC/87B6T3gcM/kqopAGajjwIYuuhzDTAYDIhEIjKE4fdQKpWgUqlEMEfhr91uRzKZfCuNtEAgIA1OJkvQgZbrTyAQQDAYRCKRwG/91m/duvf4mZn8xfXpJnFpuVzi7OwMq9UK4/FYCIRMuqOgtVwui1kAm0o0QWDTdjqdits9BbKLxUKG2rFY7E6xuM/nE/E73UGB6/u31+vJfcVBEgl835UTLu+xarUqLql8v2y2cphEQWqz2RSiJ1Mh+L0uFgtJhNg2LGSTPBKJwOFwyHfOvW9XY4cJLr1eD8lkEtPpFK1WC263G9FoVIg/er1eDDKq1SpcLpeQZL1erxDqtg0lFouFpNWQyEHBGvDtmpLL5VCpVDaGiG63G2azWUhTdrtd/vs2suJ0OsVkMoHZbBZSIcWgNOQgmYiJuhxYHRwcvDHx3ncBkjhIPmUjrdls4uLiAtVqFZPJZMPsgUk4TGgCrtfmbd+jz+dDu91Gs9lEoVBALBbb2sikmDKZTKJcLoshzzbhKwnwi8UCDocDJpNpQ+z4thql3PNY073qWt3pdCQdiSRe3s/1el1EwySU2O12IXMVCgUYDIatg9npdLohdn2bgkOj0Yjj42MZQitJP0zP2fY7TG7i+6Q5S7lclhqlUCgI2Y6mOTabTeoGDuxsNpuQ2JTpGfP5XNKM2cTVaDRIJpN3kiam0ynOz88xGAzQarXQ7XblDMBrTnMe/q1kMgmr1QqLxSLfGYeQTqdT6pUPP/wQarUa4/EY2WwWjUYDgUAAhUJBiKJMMeIax0Qii8UCt9sNk8kkwux+v49Go4FIJIJYLIZMJgOn04l6vS5mO16vF263W4hETNjQ6XR49OjRnaIDDhdZW95ErVZDPp+XIatygMrzAgX3kUgEzWZTkl2UBE+mkU+nUxmkDwaDW4LlfVCv16Ve8Hq9G/e/0lWdSVDca3m97gPdpEn+/uijj26t/XStX6/X+OyzzxCNRhGNRmG1WkU8w3+2WCySts17plQqoVKpCHmELvfj8Rgejwd+vx+Hh4eSIDedTjdS56xWKxKJhNTFu2C320XkoTRXex3QBESZCsh6gITC+Xwuhi40+/J4PHA6na8k+r4LJCybzeZ7P5/H45FapdlsvtemMiSHzudzSUbw+XxCIgcghlzL5RLJZHIvcuADHvCuwAQwZSrtLnBwzr3mXQjQ5/M5FosFVqvVXmujyWSS+nAbSWgbSGyPRqMi6lOr1XIOeVWQvM4Es2QyKXtsu90WocNqtZKemtFoxMnJyWvvA0wSbLfbcDgcYu5FIf/NXlIsFgNwvXe8ePECBoNBTITeN2i1WpycnODq6gomk2mjt6ZWqxEKhaT2pgEpBTzT6VTO70xGOTw8fCeGJQ94wD6gqZHVakU+n0c0Gr3V32XvaT6fixHty4o7WKMC1+cqmncpRQbsoTscDkSjURFMtdvtO/tzSjEe60oaq94Up7jd7r0JTO8arVZLiIKHh4eSguhyueB2uzGbzYQIVa1W0Ww24XA4pL+uBPcjCgSV4Jl53++QokSaGt0FEtk4h7gPPH9TwEZDLQDShzo7O4Narb6VYkfwPPno0SPEYjE8evTolcVH/IxOpxOVSkV6fzSRU/ZMAYjgUpmAS4Kn0+l8qb4UCc4ajQbRaFT618rX475NIRhFA7v4GkyIn06nmE6n8Pv9skexF+DxeKDVakXMQFL6er1GPB7H8fGx9BRuisIICshplFosFkWc2W63cXh4KP1Cj8cjRtesgdhvPD4+xo9//GM8fvz4Vt+WhmPr9Xovw1ir1SpiPiYZ3gemiw0GA1k7gG9NrG8SxJlUPBgMoFKpMBwO0el0JGHKYrFApVJJiuy2tcdiseCbb74R0zy32y0Gn4PBAD6fD8PhENVqVe7Fg4MD6HQ6MXhm6g9nKABEJErwPuV1YR12EzSM9Xg8d5qNuFwuMQvsdDrodDpC0KSxgMVikfWg1WqJaO3y8hLNZhOXl5dirMhUcc7VarUaPvvsM6llf/KTn4iRkdVqRbfbvVeswnRlCor3BXsVoVAIg8FABCE0UJ5MJgiHw5IAzHkNUwdNJhPsdvut2rpUKsFgMGAymSCVSm30PzhzpFmpw+EQ0eVwOJS6kSZzFotFUuXZc/P7/Xjx4oUYTFMQz5qVf4/POQn/NMLmftztdvHzn/8ckUgE5XIZ0WgUxWIRtVoN3W4Xi8UCbrcbLpcLBwcHIlSu1+uIxWJYrVZ49OjRS/flybFQzo19Ph/m87kQhAmKXtiHValUIl7Yds/qdDqZl3EPJAlX2cu8eR8oxb6z2UxmJ/V6XVJ9aUZJ8RXXgkwmA4fDAbVaLfwAfhf9fh/hcFjmTDT893g8MkcNhUIol8uSckiMx2O8ePECzWYTlUpF1rbVaiVztOl0CpPJJKbONKmgSJcpipVKBb/85S8ldTMYDMo9To6By+XCaDSS92c0GtFut7FaraTWmEwmYv6jBHviHo9HzASYIq3X6yX4YTgcwm634/j4GL1eT3qKXq9XZsysG3/yk58gkUjsdX/xPMbZAEV15NRQqEWBGhMw1+s1Go0G2u22zMyBb4UvTCRlH5/7Qz6fR7PZRDablT2d91yhUEC/34fP55M5LM/IDocDFotFzo1ca5gsOpvNYLfbZd7LRHllD5D3LM3+AYiwcbVa3TJauAm+Jvdy/g3uOeyr0hydszflXrvtfNBqtTAcDsVwNZlMSo1js9kwHA4RDocxGo3QbDbljMD5I0X00WgUPp9PZirD4VBSRVnjM/34vn74u0Amk0Gn0xGhudfrFSNtjUaDVCol4nPgWgBLMzum1V9cXEj6ONc1csv24Qzxmeceva2XRpPxRqMBn8+3s0fBfZF9c+XsaRuq1SoajQay2awY9Sn7POSL/epXvxJx5Y9+9KONOmk2m6HVam0YTD958uS9PMP90MDzA2u0+0ABz01jqleFzWaTWp/rEQ1KboJhFay7eIbZ9l7Ig+S6rFKp5BmguFspLKd4jvvw1dUVrq6usF6vxQAcuD4HsQ6m+HbXXKlUKqFQKKBYLG4kJatUKgl7aTabiEajWK1WOD09lZCcUqmEQCAgZg0ANhLf2Qvh2t9oNBCNRjdev9FoYDKZYDAY3BKfK6HVanFwcIBqtQqHw4F0Oi3mhcp1nkZyvV5PUrlpXlAqlYT7eRMUwjudzp08h30Qi8VEvFYsFsUIlIZYN03Aj46O3sv0UJVKhcPDQxGI+nw+2b951jebzQgGgxtGDIFAYMMg/vnz50gkEvKsjEYjMVvyer0iOOS+tO+1oPmSw+HAer0W83fuazRi4/3ImcfrJsa+a0QiEVgsFlSrVbjdbkynU1kHGIQSDAblPFWv12G326VHotPphJPA9UGr1SIYDCIYDEoID2vzVwFr+tcBeXJ6vX6v92EwGMQw4mX6iAQ5vo1GA6VSSXi1NE1qtVqw2+2Yz+cSYkN0u13YbDYYDIaH/X8HyIckYrGY8DRuGjW9q2dSp9Ph9PQUpVJJzjjKmp19ivV6jWKxKMFZwPWctVgswuVyIRKJvHHOyA8ZnD/S4KbRaOzkCNKIIplMwuPxvBPxOQ3Ty+Uyer2e1G3NZlMC9DjzyGazODo6QqlUgsfj2VlP7INGo4FGo4F0Oo3pdCoGmYTD4RDueq1Wk3XWarWKId2rYDgcynmOhnTcL9RqtYQxdbtdHB8f77XGqdVquFwu4cH5fL47f08ZQPamw+8e8IAHvB38YKqdjz/+GJ999hmAb91C1+s1/vqv/xp/8zd/g3//7/89/tk/+2f46U9/it/4jd/4Dt/pAx7wgAe8G3AoRqGRSqXacIRm8iPJCf1+X4Y6VqtVGkQkH3g8HnEk7Pf78Pv9QrRks/Ty8lIEDI8fP0YymYTZbJaUCjY8OGxmY/FVYDKZhESjdNMl2u22EFUikQj6/T4MBoMM/AmVSoVgMIjz83NpxtDZn4J0unHz/ZvNZiSTSej1ekkEvwk23pSDdTYnmMxrNBoRCARgNBrl+u9zIO33+zLEslgs8Hg8sFqt8v2QeMyivNVqiTjo8PBQEvM49DYajeh0OpJy3uv1cHl5CeB6LzWZTPjwww+hUqk2DvmRSEQE8CTlcKhKgfZ9Q0I2VEngyefzOD8/F8JLMpkU8Q8d76xWKwaDgQh76/W6HLh6vZ6ItkKh0NZBA93qAGwMwndBObi02+2YTqfQ6XQ4OzvbEKySNFOtVtFut5FOp9Fut5FKpXB0dASDwYB0Oo0XL14gnU4LKSmZTIrItFKpbIi82aDl36Rxw80G/C54PB4Ui0XYbDZ8/fXX8mwPh0Nx/jebzajVahgOh5LGwrQY3uuNRkMETCSwkFwxGo3keXhbiMfjIoxnE4IDPA6SeV22NRs4TFCr1XeuORqNBj6fD7/85S/h8XjERZgNL6/XC4/HIwJ4s9ksRAwKDUhOJaGShBC6mXOgfN/aZzabYbPZJEFvPp9LKgvTOhaLBYbDIWazmQyAv4vEwfV6jXq9LgIBpXOd8nkjUcZms6HVaqHX66HdbmM2m0kjhKJ/vV6PYrEozsx8vuguSDGlw+HYGBzTaVpJtCCYzMvviw77JKfdNENheofH4xHiaiwWEzOBYrGITqcDl8slKXFMOjAYDDg+PsZ8Pke9XhcyERN36YCt1+uFQBKLxRAIBJBKpYT4Uy6XUSgUpHF00xXwJgmX62alUpF9ng1Qp9MJm80GjUaDRCLx0ulf7xvUarW4yTMZYrVa4euvvxYxIQnP5+fnMrSLx+PQ6/XI5XLimLwtEctutwtpKJfL4fLyEi6XS4gyvBen06mQROgyTvdqkkgASHrDcrlEMBiE1WoV8TywmUTxpjAejyU9WZm043Q64fP5XsrVeTweI51OS/LlzfQ3Dq1pqqBSqfD48WPEYjEh+ZTLZXlm9Hq9JCuyXgiHw+LW/rZhNBr3FvAC1/sQ67hKpSIDd7qqk1zLz8U1zGAwyLBzOp3K/1L8Y7FYNtK5TCYTyuUyGo0GLi4uJHFJue/TvZTJN9wjSADw+/0wGo1Sn3N95r5PcwQOFvv9/gYRKxqNolKp4OLiQhKdeZbg//n9fuTzeUmUIWFCp9PBZrOh3W7DYDAIIc5gMEgCwHK5lEQIi8WCdrst37/SGZrvj3XYfD7f655lbXmzxmu1WsjlcigWixgOh3C5XIhGoxu1KBvqHo8HkUgEjUZDTGQmk4kMmmazGXQ6naQ3UUCwrws7odw/70t7drvdYoJ183rdhfF4vPE9tVot+Hy+W88ZzwE0BwuHw0gkEmJuRSKt8txzcnKCi4sLmM1mdDodSR1jyl4oFILJZMLBwQGcTqcM4Ejmo1mBSqXC8fHxvTUM15v1ev3SQv9dyGazGAwGGA6HiEQikrjEMwSF+/V6HRqNBl9//TU8Ho+YaxB6vR4GgwHD4fBeM6DlconJZCJrofLf81neZ4/m0HM8Hgtx5X1GJBKRmqRer6NYLEriAIliXMNonPausVgsNlysOei+STrs9XobA2iHw/FAKPiBg0TAfYUgZrN5wyH8bePmmes+KNfQlyXhMH30TYGGQP1+H4lEQp4lg8GAYDAIv9+P2WyG1WqFcDiMcrkMu92Obrf72gJ0nrG59pN0eDPBlWBPkQKbyWQixjrvIyhu5D7Gs4harYbVaoXP55MeH88NFOlw72fN/z4SHh/w6wumfK1WK5RKJRGIKM/qTGamaeqrGvV4PB7odDpJf6U5Ed8HjTmj0Sh0Op0Iy8rlMgwGw9bzy2w22zBgY+3InuWrzkq+C9TrdXQ6HYRCITEco0Ela2OmTfJ60ej3JjqdjiRb3Zy18Jy1L4lqPB7LnGcfUMx8n9B9sVjg/PwczWZzg8zPnjAAXFxcbAjstolmSaQPhUKvnUIOQMSw2WxW5gDs3dOsgWRvitcoTuSMiMJGt9uNUCh0b19muVzK3zs8PMR8PketVhPCJmdCNHkdDocwm83ynGzDfD6XPi3nE36/X8zpeOYCIHOv+XwupDi3241AICDPlDItkCnAOp0Oy+VSEn9pPFCr1VCr1TAajaTvHAgEEAgE5BmncYROp0Oj0YDJZMJHH32En/zkJ1vnX8qabJ/vWPkz+9RzSihT7neB6d0kT1qtVjx9+hR+vx9nZ2colUoyC6QhwbYzDs/Pk8kEBwcH8Pl80nNg6rTP55MkY4PBgGazCafTiUKhAKvVKun25+fn8lnZR3C5XNKHCgQCSKfT0Ov1qFQqYnSp1WolobvX68n5PB6P33mtfT4fTCaT9M2vrq6g1Wqh1+sxmUykX9fv95HP56XnDFyLREjCZ1qmcvYZDAaRzWZRLpehVqvh9/tljR8MBri6utoQZt8EBaQkrL+KsOXm/JZg0vByucTjx4/FwMVut98ynqBBFVNBXS7XVpEfUxw502RvkYRUs9ksJpTr9VrEmMqeWaVSQaFQQLfbRblchsViQa/XE9MJq9WKYrEIADJn8/l8IlamWQDN+WiCQuN0niHYx6P5VCKRgM/nQ7VaRTabhUqlEtHRy4D3Ko1nOp0OBoOBmPwrQWMNik4NBoOcO7bdF/P5XIRnPOuRhE8+xy5C/mKxEAMIipV5dqGInHMK9mTITchmszCZTGIa2e/30el0sF6vpRd2E0rOCbkLXEOBa2Fru91GuVyW/YWC3PV6jdPTU5nz8xxGITjrAp/PJ2eS3/iN38CzZ89QrVbxzTffyNphs9nEWCuRSCAcDsPv98PhcIgBP3ufDodDapWb6/d6vZZ7lWYJBwcHInbudruS5klRDvucwLdGAC9evMBsNhMj2kePHt3bN2INtVgsJBWX/Xqew/kduN1uSYRdLpfyOjT74Z7HfbfZbMo5kAlkX3zxBZ49e4ZOpwONRiNzb71ej1AohOVyiRcvXsBqtaJWq+Ef/+N/jIODA8TjcXQ6nY1ENKaxp9NpzOdzmWUoje9u3rdMj+Z3QJN8Cv/vO+/TkIHcHSUCgQD8fr9wT3gdDw4OpCeZzWbFdE2tVovIOJvNYjqd4vHjx2JCzr7BeDwW0xMGDZCf0O12Ua/XJU3UaDTi7OwM7XYbtVpNzJx5XlEKxuLx+DsxL9wGGraXy+WNOTmT77h2kA/WbDZhNps31i6GbnBdo6EtRWP71uQGg0F+ZxuYmLtcLu/tnXMtpjnkrr4Nv3eK6+Px+K37SaVSifnY2dkZPvzwww3TD+D6fqQpBpOp6/U6QqHQXp/9Aa+Ol+2JKmvPl4XS7It7CEXJNHzh39/2Xjg/s1qtcDqdG+/l5vtRGhTzDNfpdGT/vFkr0rBZr9eLIDydTqPb7eLzzz+XPsNsNhORqcvlwuHh4da5HIM9isUi5vP5LTMiq9UKj8cjpt8ajUYE61dXV5hMJigUCjIvp0G/Xq+Hy+XCZDJBPB6XumM0Gt16D6yvuKYqzfa5NxI2mw2RSASDwQAWiwWNRgPNZhN2u13qQppqxONxOJ1OPHr0SD4fTZO9Xq+cp1lDNZtN4YXuO5fdBrVajePjY2SzWQmKoqG8RqMR7qXFYrllhPE+wu/3w+fziRkPP4fdbt96NlWprpOGaYBPkS+DoJRGrxTSuVyujb7Zy4JGO5VKBY1GA2q1Wvb1TqeDarUKj8eD2WyGw8PD7x13igZq3Du1Wq30L5R1FPsFyWRSZgP3iSNvPmPfFV6G1wtAenrb1tX7QC4UDQgPDw9hs9lQrVYxn88RDAYlNEq5F/DsO5lMEIlEvrPAoO8rGI71XUKj0SAWiyEcDgv/j2dktVqNYrGIdrst3HutVov1ei17OvuLjx49eu/X7vcJDOWKRqOSNM4+CwAJdOEZnMYabxvD4RDFYhHFYhHT6RTJZBJGoxHFYlHqEACy/9FsLBgMij7mVUwOgWszwXa7LT2YbX+D53UGSLZaLQnWeBX+1HQ6xcXFhYQr2u12qdHIN2RPmVqak5OTvdZY9vUNBsOdoU+dTkeCIBl48oAHPOD9xw+GHfe3f/u3+Nu//Vv8z//5P/G///f/lrQrCrj6/T7++I//GH/8x3+Mjz76CH/4h3+If/Ev/sVLke4f8IAHPOD7BIvFIoI3JmDTlQmANCm1Wi1qtZqQDubzuQgnKW6kKJtDJr/fL2QA5euRzFgoFODxeHB6eoqnT5+i2+3eajq9rluZ0g3r6uoK7XZ74/2wwCf5gU7NbGApYTAYYDAY0O12pblpsViQSqUkgU5JHCZJ/j4neorbA4GANIR5DShIWiwWaDabGA6H8s93gQQHNmgp9CHBiwcckgroTtjv91GpVNBsNhEOh28la9Clv16vC9mCB2o69HFYxGEe00A5TGk0Gsjn85KcsVwuhZy7K6mFP8PGbaVSgdfrRaVSgd/v33CYm81mIj6nkzwFTnTO53CVBIRt4IGfaZ33uS5yEG8wGISEREFXtVoVEaxer5eE8cvLS3S7XTx69Aherxd+vx9qtRrNZhOJREJ+V6vVolKpIB6Py3WtVCrIZrOw2WwisjcajQiFQnA6nUgmk3s/PyTqXFxcSHIt7xkKrsrlsgzCmXpOAgLXgkgkgsVigX6/v5GiQuME3m+vgvV6Lc0ailIoOlYSndnE7nQ60ownKZrJJ7vAtC+73X7nIZgDehJl+J2Gw2EEg0GMRiOk02khA1E4PBwO5f8/OjpCLBYT11R+Jp1OB4/HA5fLtbfb3cHBgZABf/nLX8r1V6bBqFQqcRCm0//Ne5/ED+U1e5n3cR8oyGbjRwnl+jEajUSE/+LFC0mppVu9Wq2WdJr5fA6r1SopLGwW05ghGo3CbrcjlUptNNBJKCIpjOmxdrtdiEaj0Qg+nw/r9RrD4VBSH26Cqbx0V+awj47DZrMZrVZLXHP5+oFAQMggvV5PUuptNpsYOTDBdTAYCNGL4jylmDyRSODq6kqGIDdJPlyj+/2+XCOj0YhkMinu09xXtFotOp2OJBz8EBAIBNBut+F2u/GrX/1K9sdmswmbzYZKpSKGBtFoFL1eT4hhwWAQtVoNbrdbyIo3kUgksF6voVarJQWJzsgk/UQiEdhsNhwfH0s6L1+HDpH8edY/VqsV0WgUzWZT1q43PcQpl8solUpC/lSK6fhZHA6HGMjcBzp5tlotxGKxrcRDr9cLo9GIfD4v5iZM8zo/P4fJZEK73Ua9Xpe9hMkYVqtVkjTeRxiNRjx69AgXFxfQaDT44osvUCwW0e/3xSGe6U9MbapUKqhWqxiNRkKe1mq1Ui8bDAb4/X4Eg0FZjylWpzNzJpOByWRCIBDAer3G5eWlDDFY27XbbfT7fQSDQTgcjo1al0KJfD6PXC4nplLKOoLJU8RyuUQ+n8dgMMAvf/lLBINBqFQqtNtt6HQ6GeCTCEdSHB1XSQLiuq/VasWZn3s+m992u10GdfP5fGszmYPQ+2pk5fu/OVhkcku9Xsd4PEYymdwYAJE463A4kMvlxFDl7//9v4+LiwshujLViHsL6xWK0/V6/Uutr4PBQEi9+wxM3G43isWiiAf2eS0SAkmsrFarKBQKcDgcMrigWYnNZoPdboder4fP5xNzm10wGo148uQJWq0W6vX6BklEr9eLiYCSmEKzH5/PJ3UNyZX3gTXVtsE376mXqW3G4zH6/T6q1SqSyaQYStRqNVitVrkW3P/Pz8/h9XqRSqXgcDg2zp7cS0iuuasu7na7cpZSCoxYS2wjTu4CCZIvK1D4LsAUIhKG/H6/rBckwXJdDIVC71TsOJ/PUSwW5TxCcRPNnex2u6TJsTakWIeEXrfbLUTDB/wwoVKp7hzeKmtvJny9K6Kp0WiUPhPTA+/CYDCQn/+u71mKvLal4QLX6xzfI8nDrGXfFNmAsyOaDd3Vb2EvRK/Xo91uSx/lfQbrlrtgMpm+E+OPBzzgVcEzilqtlhqO/SSS/ylUPDw8fK3+l91ul0RXJkuy98IEViIej0vCF00LSZqhARuF1jRg+76mo9AkczAYSLJsoVAQURN7pWazGdPpFIFAQFJyb9b+7GfS3O9mj9flcol45L5am2s5a/l9MJ/Pxcz5rhqUfZYvvvhCRC2NRkMMyzQajZC9PvvsM5kpcL8iKWw8Hktf83XFRkyd45mKicUU7RkMBphMJlxdXUmvoN/vA4AIQ4Fvxb/KNJO7hF+clc1mM4RCITGsHY1G0qsAIEK2YDAoBPJdPfxarQa9Xi/9E5VKBbfbLX3iSqUi/QuK09hjYL9NafLFa0DBcSAQkPkMDWc5V2KCLkXUNNKj+PDmZ1epVIjH44hGozvXF87ueF3u24tHo5H8/JteF5bLJbLZLKrVKgaDgZAnges5KmdXHo9HxK3lchkHBwcbf4fCs/F4DK/XK2dizkeAb0XFZrNZZsU0krXb7bBYLGJYQOMHXi+m3DmdTjFqpMiPhgPZbFbeD/urNpttb7NVq9WKw8NDEdhynVYm9PFZpfB2PB5jNBpJanQwGLz1fKhUKnkW+v0+stmspMNXKhW43W4UCgURlihBEfd8PhfC+pua2wAQc/lMJiOJyuFwGM1mU4zg2COjgHu9XiMYDGKxWOysuTmjBa7Xonq9jqurK+m1AJDZsM/n2zDXLJVK+Oyzz/CLX/wChUIB0+lU3ieN+fj/O51OTCYTeYb5rNKcpFarSb+b8z8m5JH0S2GoVqtFqVRCMplEKBTCbDZDo9GA2WxGJBJ5qetuNpulP5rL5YRPcVMQxnWFiat6vR5Wq1X2tPF4vLE+0PSOPAwiFArB4XBsCPspkCJ4L9Esw+12i1m8y+WStUVpCNPr9bBYLOB0OsUIhOaXTNZiv+Y+Y4Sb4lEKymq1mhhb5fN5OXuFw+GNRHTOdJxOJ7xeL3K5nJiExGIxPHnyRPra2WxWnk0anbrdbpnNUiTG92U0GlEul6HT6eD3+yUN0263i0EK+0GLxULSWWOxGI6OjjAej7FarTCbzXBwcCC8hlarhel0Cr/fv5F0xpngN998A6fTicvLS5ycnNx5/WjU63K5xPx0NBptrWeMRiP0er2YXDscDtjtdozHY1xeXoogkqL8YrGIaDQKr9cLi8WCL774As+fPxfDTZrxUBBHXhFFATz//s7v/M5OcTiNAJgAzfubgk32si0Wi/x3h8Ox0QeYTqeSKnmfeIPm7LznaCJOqFQqOJ3OW3sD33uz2RSuBtMys9msCI4cDoeIkcmboFlIuVwW7s1yuZR9nntJIpFAJpNBo9EQ3ouyBuK5oFaryZyf/cd3DRodz+fzDXNczmuUpk404yNXRrl2mc1mtNttAN8KXF9WNEYD5V1r8Ww2w3Q6lR7YfbBYLDJ/2WUyRYMOzme2vTbnF/P5HC6XC/P5HJ1OZ6uZMIX47Bfx3PqAtwfu+XyO7zOvGAwGUm/vIxJbrVZb51+sU30+HwKBgNxH+XxeTLj497kG9no9OXuw7tVoNBtGW4RS2E6RLo2gbgbATKdT2VedTicsFgtOT0+lR8zzOo3H2CfcNusnaIoyGo1weHi49XxCjhLrKabDNhrw+QxuAAEAAElEQVQNrFYrJBKJjfdKLlGtVsNqtUKhUBAR37Z1goawGo0GtVoNnU5nw2yfez/rIe4xGo0G4XBYTEgoFgyHw3KePDw8hFarRTwel/Md1/dSqSRne41GA5fLBY/Hg3A4/NrCUrVajYODA4TDYeGTLBYL6Z2Qz/p9Aa/7vnNxtVqNn/zkJ8LlPD8/x3w+F+NH8sX6/b6YSb5qEM5gMMCXX36J58+fo9VqiYHYeDwWwxWGCyiDFb7rWck+WCwWyOfzwv9h+BDrjNFoJDxlJVQq1ffOSJthBvV6HbPZ7N57gfXmqxgX12o1MeBKJBIwmUxiHF8qleRMEwqFUCqVxABtNpuhUqmI4d33yVj0AZvQaDQb9d1oNMLZ2Rmq1SqazSbcbreY0ALXPBMaXUSjUVxcXODx48dvtJ/zQ4ZGo8Hx8THW6zW0Wq1oDoj1eg2LxYJ4PA6Hw4Gjo6N3IkzmWjAcDnF4eAi9Xo9qtYpOpyM1Ao3qSqWS8KtUKhWy2axw/FKp1Eu9br/fF5N0t9stNcw2aLVa4VtbrVYJY3yVNb5cLosxJHUJBIP67HY7yuUy8vk8dDodWq3WXmsdzZoXiwVyuRyurq4k9IkmeO12G6PRSF5730C+BzzgAd89vl9V5T345JNP8D/+x//Af/tv/w1/9md/hj/5kz/BX/7lXwLYTEX//PPP8W/+zb/Bf/gP/wG/93u/h5/+9Kf47d/+7e/yrT/gAQ94wBuHyWQS50kO7diYczqdkixIYVSn05EGEYm1FGnQyZwFKx3hlZjNZtBoNAgGgzJkr9Vq4hT4pt0Cx+MxxuMxCoUC+v0+0uk03G63uASzEUBHawq+diULGo1GdLvdjSQNiqm8Xi/m87k0GXU6nQiC9iGD0J3c5XJhOByKIImDMQDSIFoul3jy5MnOYcRyuZQhh8lkgsPhwHw+R7PZRKfTwWQyEWdIYjKZwGw2i2Mfk1eVBCeSoSho4RCfAvJdn3M6neL58+ci+qtUKkIIpmB8PB5jMBhI6izBgTxd69nw55CTDW8K7OmoDEDEnBwuNxoN+e48Hs+d5C2dTidCTw7+dh3C6MbqcDiEcDIcDtHr9YQ89M0330gqBAcIq9UKp6en0oDg36drOIVQl5eX8twxlXK1WuHk5AQWiwWRSEQIIA6HA6FQ6F6y0k3M53Np3pP4tFqtsFgsRCAGfEs0oyFDq9XaEGzTMVP5dwuFAvx+v6ScvqyAst1uI5fLYTqdotfrSVq4wWAQcwG60PI98lnaF6vVSgYX9z2vtVpNhjdKQlQoFJJn4aOPPkK5XMZgMNh4jmq1mgwfSGwgaWkXeO+2220ZbjAB1el0QqfT4ejoCFdXVxtiZoq4SYygo2mr1cLl5aU0x1utFsrlsiTNKJPMSLAjmet1wHuJgyslSGZRNj/a7baszYlEQlzuKKp3u90iHmSalN1uF5dkuiMryV6r1Qr5fF6MNJgSy/WOJALeDyTerFYrWCyWrfcUn43lcrkx0LJarXj8+LEI1Lg/qFQqERkzXaFWq2G9XkvThuJ/rpFspgUCgVtCNABCFKLJyk2irdvtRqvVQrVaFSGs8torCWJs3jidznvvze8LtFotYrEYnj9/DovFgvPzc1mfzWbzRvLLeDyGyWRCsVhEIpGARqNBs9nEer3GZDLBkydPbhkzUCxHMwKXy7VxD1Gk6fP5oNFo4Ha7cXJyIiJkt9st9w5FPi6XS2qtbrcLv98Pu93+RhvRpVIJxWJRXC9Z/3CPrVQqqNfriMViWK1WOD4+vvP1F4sFWq2WkHrv2odINFIKckwmEz744AM0Gg0xXyBI4PH7/Xunin5XMJvNePLkCT777DMh26RSKbnXWHc2Gg0R40+nU0kGs1gs0ihnvcZhFEERNkXdbCyXSiURW5HgxHRVGnWQTLBYLJBMJjfMDaLRKC4vL4XYtEtYNZlMcHV1JemXpVJJzHZWqxUGgwEGgwGOj4+FdMV1zefzIZ1OYzqdotvtSnoIiZ6s4UajESqVCpLJpIhngd3pBGazGVarFY1GQwbRu0CSpsVi2biurJHb7TZCodBOYglJCOl0Wgbm0WhUkr/4mSmWtdvtQoSMx+NC7OS15NpP8iuJe3yG5vO5mDvtc//TafauJBAlhsMhvv76a2QyGQCQvZoD/H6/L7VMIBDYMLYCrp/9bYOc9XotSWMUD/t8PnGOZ4LSfSAZBLhOCiSZmMZnN8FUGb7P6XSKer2OZrO5YbbidruFyHMXGo2GEOwSiYScb/V6PbrdrhhBkaBGQrxKpUIqldo4r/l8PhFEcJ/Zdr4g8ZbPvvJZpMhi21l7G5bLpRBSvy+kAZLyadBGoqZGoxGDu3c9mJ1Op3jx4gW63S4ajYYIc2kOx1r0q6++gt1uFzKZkhRFAlin08HR0dH3iiT0gP1gNBphNBqltlc+/+PxGNVqVcjvNMPyer2ScBuPx99qncPzlsvlQr1e39iPboI9DqfTCaPR+NrmkK8LJizuS+4ymUwbvbTXAQnR7H+wDtt17WazmZgo0iyRKeoPhOLvDygU5VmcaYkP+P4hEAjIWd1ms8m6wD6r3+9/o2vcPmYOZrMZx8fHYrbWbrclVZj/naTn99mAbR+wFuL/9no9mM1mEXFbrVY4HA5UKhUx3lUmTALfnt8qlQocDgdsNtvWa8JUeYvFgkqlsjMtg0arNJ1UqVR3Jg7z/TOp9a77ZbVa4cWLF/jqq6/EZJgmAyTGK5NkVSoVKpUKfvazn+GDDz6Q+tLhcIhBwvHx8WvV8TRKrdfr0Ol0CIVCCIfDYgxAA0ySuMbjMQKBgPSpotEoLBaLzDharRay2awY6Dx+/HjnuY77Hs9LTCajGGm1WmG1WknvbbFY4OLiQuZaSsxmM9RqNQyHQyQSCbm+NBLg++33++j1enLGHgwGkm7MM7ry/uEaXygU0Ol0EIlE5AzeaDREiAtARAk+n09mRZVKRVINlfdQvV4Xo7K7BAg0LGYv/K77kAlsNEl9FbL0XSAputPpIBqNbtRdynRFivU6nQ5sNpv0Ugkml69Wqw1DYAr12QOieJiG0VarFT6fD7PZDB6PR0y9AMjzPJlM5F7mjGCxWCAej0v/jKITmrZxXsW02X1B8Xu/30c8Hr8lsKaYs1qt4osvvgAAmacpr9dN0FCJCdQ87yaTSVkXCoUC9Hq91B80gtbpdIjH43C5XG/cxIpp3xaLBdVqFaFQSOqgbreLUqkkhi7NZnND6MGZ4i6Ew2E5K9FE2OVyIRwOw2w2i/lcIBCQdWM2m+EXv/gFzs7O0Gg0RAihVqtFyEkhDp93Gkoo93quP0yYYto3zc45A2NSLgXOZrNZZiwej0cE8K1Wayd/YBei0ShGoxF6vR6+/vprdDod6Y1ReE6j60AggOl0KrNH8jCUcycAMq+w2+0ba0wgEMDp6SmazeZG6rrZbIZGoxHxNL8zu90Ok8kkc7ebfQqaOiyXS2QyGZlx93o9+U5LpZKYgbjd7nvJ3lz3+V2zx7JYLES0ORgM0O/3YTKZNvYECr3YW0kkErdmDHq9HrFYDJFIBB988AGazaYIXvV6vawJN80wfT4farUanj9/LkENNCFrNBpiJktjfp6v9Xo9wuEwAoGAnPUpSiA3ZTQaibG78kzhdrtFPJ7P52E0GreahyvBvY/cB5vNhm63K2boN3+WvIrFYoHHjx/DZDIhkUgIR4NzW7PZLCJ6tVot4joa5/l8PiwWC1nbaLZoNptlFs3Z8nQ63dnb8Pl8wiv667/+azidTozHYxHrTqdTfPXVV8KZoNjM6XRiMBhIIjjvwZuJ6crP3mq18OzZMzGBZkiC2WyG3++Hx+PZeUb3+/3Q6XSyBrBmIk9GrVYjEolsiEsACE9Aq9ViPp+j2+3KXufxeBCLxWAymXBwcCCzkEqlgnA4fEuYx/rCZDIhk8mg0+mgUql8JwJ01o1ms3lrXaqc2ZBfwDP1LvDaK0Vj9wmxaSLgdrt37rOv03fZ9bs0yuC6uw1cqyaTCfx+v3CTds2qnE6n8MDG4/F7P/f8vkPZE200GvB4PHf2eMj7IvfqLjARkntTt9sVPg/Deer1OiKRiPCFaJxGM3fg27MGexRMj7yrP6tSqeD3+zGZTJBOp8XshvMKYjweC1+FcwzgW6NJcmoikYjUCG63+877kqJ7ngnu2/9pxsN6noYWN58pGoFTZMZQCaaU3wR5FK1WS3hG7OdpNBrYbDYxTI/FYsJTUgq8eWYkf8fn821ca876rFYrTCaTPOP8HQYSKc2q3gQMBsOvrcBKrVbjo48+EhMxnvEpAjebzfJcJBKJV7rus9kMP/vZz3B1dYVcLic1JWtnms6FQiHE43Fks1kJ43rfDVoXiwXOzs4kvGQ0GonpEI37HA4HptMpptPpe/957gPDqxj+c9dZWWlg9rLnOtaXXPdYi3i9XjljVatVCeFYrVYSaMAwI5/PJ+YWD/hhgDyfZrMpgSpKmEwmRKNR1Ot1MWVtNpuvbVby6wSDwYDT01OUy2Xhvin5OuRXhsPhd5Iuv1gsxOiYfV3+u0ajIboY1ppKI02atxWLRRiNxls91ftADvVisZD+NnnF22oxs9kstdF4PEYkEnnpICryQPl579L20FyWBo/7mm0oA/lo1FWv1wF8K3BPJpOwWq04Ojp6J9/zAx7wgDeDH2TFYzab8Qd/8Af4gz/4A3zzzTf44z/+Y/yv//W/0Gw2AXw7EB2NRvjTP/1T/Omf/ilOT0/xh3/4h/hX/+pf/WAEGQ94wAMe4Pf7Jfk7l8uJiHY8HktSgE6nk5RzDhiZysikiNlsBrfbjUajAbvdjsViIQnXHHr1+31JVCCpgokWb/KAvV6vkc1mN1LD2QQsFAqoVqtyKKEI7eDgAA6HQ4bh20Bxy3K53EpMYrIZACneQ6HQSwlh2+020um0NEqVYnc67HI4zoTdm6CIiYNjpnhPp1NJHrh58OCw0O12S8PYYrFsFWm/jEvlcrnE+fk5ms2mpBqcnJyImz8JCiR+AdgQRLVaLSEW0BmZSfTK6890SZI0KLaPRqMyyG+324jH40gkEiIGvAs+nw/tdhvtdhv5fB6xWOzWfbparVAul4XUQCfJxWKBJ0+eYDabSUperVZDs9kUkavP54Pf7xeiBWGz2RAOh4Xk/OLFCyE4eL1eBAIBnJycSMIAiRUchLwsKHBer9eIRCLSyKdbO9PEdDqduGwzGaTf76NYLCIUCt0abrdaLTx//hzT6VQIXl9++SUsFgt8Pp8IiO9CvV5HNptFvV5Hq9XacE2ngQVJEMlk8pWdInnwvy89dTabYTgcotPpwGg0ilCTCafKvxcMBpHP5+F0OuHxeBCJRJDJZMTVfh+USiVUKhURsTDVk6luBoNB0jg5uH/06BEMBoOQKCmcNhqNGAwGQmQol8swGAwolUpoNBpCztJqtSI+o4Cm3+8jmUwCgCSVv8o15nW+SZgBviVb9Pt95HI5zGYz9Pt9RKNRSV9drVao1WoAIEL+brcrIrPDw0NxpL5pisG1iELsTqcDrVaLXq+HdDotCegUB3EdZDLBLuGFkvxy8xlgonY4HN4Q95tMpo37xe/3i2O72+2WNBAanvDeVq4NN8GEYN6jyn3M5XJJ043rz7a1gulb3Ptftun+PoNuo+VyGfF4HJ1OB2q1WojNOp0OdrtdhIokhzAZRdm022XMQOMLDmtoaEHhMcHEDBJ9lKQKrVa78d0xDeommex1MRwOUS6XUSgUMJ/PxZWTIBG/VCohl8tBrVYL+XAXOLAZDAZIJBL3vgen0ymvPxgMYLfbodFoEAgEEAgEMB6PxbRh37SC9wUkuJIgRMEsDS4uLi6EkBuPx7FcLvH8+XMRiWo0GsznczGIoKkGcL3mTKdTWCwWSWZhI5YD9kKhIEQ6kq8tFguSySQmkwkqlQpUKhXK5bKQGwBILXFXfT6ZTHB2doZ2uy2mMCT4FotFzGYzcfj/2c9+JmYbzWZTEtKZfkETJzapmU7G1InhcCgkN6VQfhu8Xi+q1SqMRiMajcad9ypJhzcNPVqtlqTP3SdCMRqNQkRcrVaIRCIoFoti7MVaZTqdolqtot1ui5FEMBhEv9/HF198gUwmI8RSkv04hLRarTg4ONhIar8rCWQwGGA6ncr+uW2vvYnhcIgXL15Iok+328XR0RGcTqcMUWmkQVGw0+mUtEJge5p4vV6X+kW5//G+9Xq9YsRzF9brtZjGDIdDXF1doVAobKRYkbzH8ymNZRwOh7wPJl7S2EKv16PRaCCXyyEUCuHw8HDnexiNRpJiptVqYbVakUwm0Wg0YLFYROCorD+sViuCweCt54eCj+VyiXw+j6urKzidTiHPkOzPJCOv17vxjAKb5zC6pd+1Pna7XXmuXuZM+j6AddF33X9V1pCFQkHuAT7nPO/96le/gkqlwmw2QzweRygUkmQNmlvkcjk5X5+enn4vkhIesD88Ho+cNbrdruxvg8EAhUJBEm1phMiavdVqoVgsYjweI5VKvVUysc/nE7Mapitu63GUSiX0+31oNBo0Gg18/vnnGyS8Ny14ug88K+0rKGdf6E0IvjUajZgTZrPZe2tS9j1JblX29B7wfoPGC7VaTcjiFF7pdDrpx31fk6h/nUHSbiQSkX6XMm3vu4DVasWTJ09Qr9eldmQPlmKTm6mm30fMZjOUy2W8ePECy+VS0uDZ86bZIonfrVZL0q1YQ5GIT3FpIpHYSoRnWinTjzOZDDwej5j5MUWSIrLj42PU63V0Oh1cXFwgHo/DZrNt7XlS/OhwOO7szaTTaeTzeeTzeVitVukLMRGJKfckTjFF2OPxYL1eIx6Pi0kn15zXnZlVKhWZMzHRmvMOCv2AawJbsViU896jR49ufVYS62l6ZzAYUK1Wd5LidTqd9Hyq1aqcv9kvMRqNMvc5Pj7G+fk5ut2upA+zT71YLISQl0gkxKCQCaQUs+r1ejHZpiFMKBSSFHOKKDudjojqh8Mh+v0+FosFEomEEM5brZbUEhRTLxYLMS7g2ZuJeZ1OB6lUSoSAvV4PiURCRHm7QKPKwWCAXC4nAvNtYOoaCYuvi16vJ4a3wLX5da/XE0G8EhSVcKZLszD2tpS1IWdhZrN5a5paOBwWI1WKCGhYx/58oVCQZ6BWq0lqNU3MS6USzs7OYDQacXh4CL/fj4ODAzFJZ7obBSEv+xyROM770Ol0bjUTYqKjWq1Gs9kUg2eLxYJutwsAMkdT9k5oYM4eMufzqVRK7ol2uy19F61WK0Rmp9OJg4ODN26MYzAYNvoV7HGxDmLiNY2xaZTR7XZRrVZRLBYRDAYlcZmCH67XbrdbzJNtNhui0agkXjqdzlvX9vnz56hWq8jlcrJGcjbscDiwWq3EMBCApHGTiMu1hd8lk9xpak3D4ePjY+kV8bxDoSENKq1Wq8wB9zECvAmr1YrDw0PM53MxSCwUCtKf5LViorNGo0G5XEa5XJbZKs8gSuJvJBIR8S/hcrngdrvx0Ucf4euvvxaD28lkIoJ9r9eLwWAg3znvW51Od+u+oknmbDYTQ4TBYCCzEoPBAL/fD4vFguVyee/aRG4FzWgBiDE2n1XyU0qlkgj1ORelAJxn6UQiITMGPvvs6bKe2jf4IBQKIZ/PQ61WS9+bRmjL5VKE2eztOp3OjT4j7zcK3ihGb7VaMlu82W/m79CguNfroV6v39kToIkP66rhcCj7rN/vl/kuDR1ZBzgcDiGcU8in7IvTcGO1WuHZs2fIZrO4uroSESON2JgiazKZJBmdojtyW6rVKg4O/n/23izE9jU9C3/WPM/zXOOu2lOfw+kmgzFKwMbgAFEQjCCKxqSJV4oX8cI7QZIbQUGEThzuciNBIigRbPFvkxiTPqfPtPeuWlW15nme5/W/KJ93/9aqVatqz3ufrgdCn5xTw6rf8H3v977PsLvx89NUALjcd9LpNGw220pC4Gw2w+effw61Wo2HDx+i3+/LPaYZ0LNnz+B0OlGv10WQz/dkPB4jk8lIr9tkMslMhuEDTK7c29u7dj3lvI+9nOl0KvukwWBAOBzeeDbkvKPdbsNoNIoBQSKRwMHBgTzXyWQSzWZTgiSug8FgEJNxp9N5xYD8bUBpuK2Ecm41Ho9l7kwB/voso9frCbeC146zJQakbOtfkzNE05pN0Ov1MiO6TSgC15Nt4lka2yjNM9ahNAM2Go2yP103zzEajWLG9DrME3+SQONjpWkgAxu2gc8aZ2E0RlpHqVTCeDyWJOttfYPpdIqTkxOZG9DkhLPgbre7cjaMx+OIxWIYDAYwGo1IJpNotVpi3EwTI64vrEVpprwJnDdS6JjNZtHr9aT3MRgMMB6PYbfb4Xa7ZeZMzOdzGI1GhMPha9fu6/52prhvC30hzGaznA35PZytbarVHQ6HGHsvFgt89NFHG9dKcovIb6EwWa1Wy7yN55fZbIbJZCJmIC9SJ6hUKgnq4TlIKVr/0Hs37yPUajX29/fR7/dRrValhgcgXB2l4dmLYDqd4quvvkI2m8XJyYnwnml01u/3V2pRmkQqTY/eZ97K2dkZms2miOZ3d3eldlgul/JuZTIZAJfX80M2v+QZMRAIIJPJCP9kff3u9XrI5/MS1vWis2fyHXq93srsXqVSiXkSgwp6vR60Wq2YPIbDYfj9fuzt7b01s2fulzR/IreCvc27+fSrg33Ber0Op9O5dS/w+XzSh2MIwR1uDxoyRiIRNJtNCXKiic7bNHXg+zQYDIRTyx4JDV6VNabVapVe6XA4RDAYRDKZlP3tRUxAeH4ALjkJ7MnW63XpTyrBmUi9Xhc9wIvygFutFiaTCXq9nvDGrwNNS5vNJjwez8oZ8abvY+gTdQDUHZGr5vF47uazd7jDB4hvpABdiQcPHuBf/st/id/8zd/Ef/pP/wm/8zu/gx/84AcAVlPRnzx5gn/8j/8xfuM3fgN//a//dfzKr/wKfuEXfuFdfvQ73OEOd3hlMCGWSXSnp6ci2K5UKhiNRmg0GpJIsb+/D7fbDb/fD7VaDY/HI8MfEiv1er0M3ljccpDu9XoluSyXy8nw8HUdBpbLJc7Pz0W4SmMROpaTFK7T6bBcLmUA6/F4tiZHUwTFwV+pVLo2tW4+n6NYLIqLqHKIty31iCntxWJRyCYc2s/nc9RqNXz++ecYDAZ49uwZlsulJKYQo9EI+XxeDll0uOZhYNMQZTKZCPnJZDLJIYUH4FchQFcqFXS7XeRyOSGKAZfNHSYZ0BWd6QXNZhNerxf1eh2NRgPRaHTFrZ73br2pR4K/Wq0Wx3AeOklu2dnZgdfrRTwev5FoSNHUfD5HLpdDMpmUBjLfkXa7DbVajWg0ilqtJqRopYje7XYLSSYajeJHP/oRWq0W3G43YrHYxsOWw+GAyWRCo9GQRGiSJZm04fP5XsvBqlariUDo8PAQBoNBhtYkPzJ9cT6f4+zsTAa28Xgc2WwWyWRSCFXL5RKpVEqc75kQmU6nZbjL63FwcHDt39Dv95HJZFAoFNDv96Wxq6zN6FpO4Y/JZHppl2gm8tJtcNPzQbFdt9uV1AAmbq+DKY1MDiXB5baJpJlMBqVSCcViUdz+2QQkKdrr9YoYkSSCbcMhEnoajQZmsxksFgsKhQImk4m4DPO96vV6KBaLyGQysFqtePbsmRAKaSLwIgmUJFrRYW99rXU4HDIg++qrr5DP52WQPpvNUK/XJW2SAjiSC3O5nAzMrmuWpFIpSekxmUz49re/jUqlIkm/fJa4RzA94N69e7Db7eI0vQ6mU2xLBryNmNHn80kqF0VtTLCNRCIrSe6bwPdoUyKGSqVCLBYTt/VUKgWXyyXkuXa7La7cHEKQfPRNANMTWq2WkBEvLi6gUqngcrkkyQC4XFe+/vprtNttMcxhYoherxezAw4ONqWCXyfwV4KpUhQDc7jMnzUajST9Ix6PSw32usC9eTAYYH9/f+M6zBSHVColyS/bEgiYvgHgRqdzfg3Fu5vWxLctbHqdUA6g1p1umRhCQ41cLgeLxSIkvn6/L6kfdrsdRqNR7j3NdPR6PabTqSRqcK/hUJTJTsBzwTrwPI1vuVwKAXu96Wu1WtFqtQDgSn2+XC5xdnYmQjm6NJO0z70ol8uhWq0K6UOv18Pn8yGZTIoJi9VqXUkP5hnCYDAI4ZYOsUoi8iaRXLPZRL1eR6lUQq/XE7Lb+rPNtLBKpSINeGWNy2tlMplWnnOm3ZBoBjwnUTHBiyTJWq2GVCq1QgY0mUyIxWJyjiqXy/jTP/1TFItFpNNpdLtd2UdMJpMkZH/00UcYj8digKTT6YQgqvxsjUZDBi001+r1ekilUnA6ndcmb83ncySTSTGh8fv9kv5EEyqaFM1mMzgcDiH0KJML19/3bDaLUqkkzwVwuUcpE3qUded1dcRyuZSkPqb2sSakyYPZbEalUhECHs0AnE6nmKrk83l55kgCz+fzkpTj9/vx9ddfr+x7FGxTXEAiCWE0GhGNRjGbzUSsoEwl30ZIDoVCcu6iCJTnVQBS89tsNuzu7m48h9EkiwL768xpaKri8XjESO0OLw7WoBRBrNeaer1e1vHPP/8c4XD4yr7NhEKahBgMBuTzeezv77/tP+cObxA8DzNxgalXuVwOjUZDROk6nQ61Wg337t2TpAXlmfb+/ftvzL2b5hYkJZ6dnYlxBgDpA9ZqNSFlM1WQhDru8Ts7O2+NaKXcI9mjuA6LxQK9Xk9MxF4HfD4fGo0GDAaDpA1uIjL3ej10Oh0EAgFYrVYxsHrXQtc73IzFYoGLiwtJSFTWfewFM/nm8PDwLp3sAwUNNd8X6PV6RCIRhEIhDAYDmT2YTKYPnkjTaDRwcnKCTqeDH/3oRygWi7I+2mw2WCwWqc9HoxGsViv8fr+cd2azGVqtFmazmZg8abVaSTm+DkyvVqlUqNVqKJfLKJVKkphKMZrBYMBwOIRWq8V4PEahUBDhltfrFVMxpmDT5NZsNl87J6EhcqVSEVJ7pVIRo7BAIAC9Xi/Ju8vlErFYDL1eTwxELRaLmNG8qriUhOIvv/wS+XxezFMpPuRMT2msyZRUknWvg8vlQq/XEwPjcDgsn5dnpOl0KsaHw+EQmUwG/X4fWq1WZg9MUqU55Hw+l/5/PB4XozaDwSAzMpfLJYaOzWYT5XIZvV5PniuLxSLiRJ67EokEPv30UxEk1Go1MXzUaDQiaM9kMiiXy3L2dDgcqFQqWC6XkpjocrnEMLFUKon5EPtsi8UCLpcL3/rWt6ReugmhUEgMKtnXYLLncrmU/Yf9gXXR4IuCfRuKDSnszGazaP2/ZOZ1cRvTjWlwXalUoFKpZO4IQAzhKpUKPB6PzLQ2wWg0IhQKIRQKycz28PBQ+i8A8NFHH6FcLkOj0aDT6aDT6YgxABOwOUOLxWIrBgcv0kOlsJli+PF4LL2dfD4Pl8uFi4sLmWu5XK6VGpDkynQ6LeuPz+eTd4IkdM5laDrJ66zspzqdTjx+/FiMFNgroyESe2NvCuFwWGpt9itoSM35qtVqxWAwQDweh0ajkdlvLpcTIqjRaEQgEIDFYoHRaJQUST4TBwcHMnO57p4kk0mZcx8cHIgYm+sIzeDNZrOsqcPhUN5Pi8UiYu1arSaiVppRTCYTlMtljEYjMRCkEFyn00mqFME54DZjyG3gvaXp/GAwgN/vl1mhRqOB0WhELBZDs9mESqXC+fk56vU6LBYLHA7HipAhGo3C6XReMamnUIf9yIuLC/R6PTGWJF8gGAzKOtvpdKT3tQlcW4PBoPSWer0eIpEIDg4OAEBmbJx7XweKR202m3AFKNrmO0NDlE6nI+ncrN/YQ9br9SiVSkK25vnsNmac14H7jVarFcMYfkaaRbAvGo1G4XA4JL2a8+LBYCDvOA06aO6t1WqvvL80HOA7TiPkbeA9YH/b7XaL0QL7TZ1OR9bqfD4vovHRaIRKpSJ9Ab/fL7NwvV6Pfr+PJ0+eoF6vI5VKiVGGxWKB2+2Wd4SzTqa3zedzmSOQVL/+d/J+VqtVqRX4ntKYiPyC8/NzWUsXiwVCoZDMSPj3ud1ufPnll7BarSIQp+FPPp+XfqvX68X+/j5+/ud/XgwUaLrKZ57P8XWw2+2yXlFsQAOb63o3NE8CnifnHh8fyzlyNBrJdbmNcNPpdAqXgz3ttwk+p+s9b66ZJpMJrVZrZSZFIxBiNpuh3W6LcSzfa/bS/H4/yuWyGIcrwb2ayZIWi+XaM7nb7UalUoFWqxW+0XUYjUYYDAYIBAJbayvOqChE31Sncx7Ev59fo6wZhsMh2u221FPlchmRSOTa33uHVSwWC5TLZVSrVYzHYxGg63Q6GAwGOJ1OhEKha2fLLpcLHo8H8/lceqIUi5H31Ww2MZvNZJ53kyAzl8tJba3kwRE0vs9msygUClCr1Xj06BH+7J/9s7Ke02CDpi4mk0nWNZ4ZtxlQqVQqHBwcyP41Go2EK8mzl8/ng16vFwMVYjQaYTKZwGq1vnCfa7lcyv/e9uzK36fX6+H3+1EqlVCv1yWoYR0Wi0XM4sjnVIJ7DoOBYrHYSh1isVhEBFUqlWA2m6HT6fDgwYOXDmEgt+RDM3t+Fcznc7TbbekdUOz7tnrdrEV3dnZem+ks38lCoQCdTofDw8OVvZVntXK5DOC5AQFnOP1+/42aCL8KOp2OnNHIN1ZCpVKJmRCDgLj+fMjzi0QiIXVaPp8XvjX5Cd1uF8PhEB6PRwzsXnS2xTqAe48SarUa4XAYHo8HzWYTvV5PuMp+v1+MiF63kdx1GAwGODs7k9qx1+vJWYa9LpfLhZ2dnbf2mb6JUNZ16+/aJrhcLjQaDfh8vndiKvVNAPva7xLK3pCSX93v94XzpQTfMfZd2CO8zfl/HeyHARDDzNlshkKhgGKxCJfLtVLTsbfHORRrwhcBe2icW90Es9ks6eXT6fSF+A6ssVjbcd2yWq13a9Ud7vCB4v2ZyL9h6PV6/PIv/zJ++Zd/Gefn5/j+97+P//gf/6MMIil8G4/H+N3f/V387u/+Lvb29vAP/sE/wN/9u3/3Vs3BO9zhDnd4HxGNRmV4QLJFrVYTh/PZbIZOpwMA+Jmf+RlpqNGd+fz8HL1eD+fn5zIwI1mKpAE6kDNtkE1JYLP7K5vwdMCnk/dtEpNrtRo+/fRT9Pt9cQOm4MHlckkDmam6/NnbhveNRkOaoXTOTaVS4tzOATSTNDQaDcLhMLxeL1qtlgy5KZxmkomy8CcxudVqIZFIrBTuTAX96KOP8MMf/hD5fB6VSgVnZ2fw+XzyGcbjsYgm2bTmZx+PxxtdPEl0YvMjFAqhUqmI8/jLNs8ocKLDt7IxHolERHDG6zUajVAoFCSlkIncTqcTbrdbUlhJ9CW5RQmr1SrO2zQ8mM1mCIfDSCQSePjw4Qsd4ik61Wq16PV60qAGIM+D3W7HYrGA3+/HxcWFuBsSHDQygWEymeBHP/qRELmvqx84CKCAOZFIYH9//4ow6lVB4W273UYmk1kRnbtcrhWiENMrO50O4vE4zGYz9Hq9CLN6vZ4860piCgcDvLf1eh2xWAzL5RJHR0cbm3vlclkapExoUYLJMwaDAalUCjabDZVK5VbErk1gGhyFTJuGjRSn0bGcrrzbHKz5LJMAsc0hm6jX65LwsFwuV1xJgediuGKxKGLF+Xx+q0Gl0+nExcWFkHVUKhV2dnauNElJful0Okin03C5XGg2m0J4URKGbtOgoOtzp9MRZ1Hl91HEx6b3bDYT0ka325WUUyahUGzFfarX68k7v47hcCjpvGazWX5PLBZDuVyWd7TT6aBcLmM+n8NutwtJKpFIbGyKTKdTNBoNMVZ5VcGsTqdDOByWZHemrd4mZXBbEjsAcQhkck+hUMBXX30lQlclwapSqSAUCm01TPmQQGEsiQSj0Qh2u11MBpTo9XqYzWYolUqw2+24d+8eFosF3G43wuEwXC6X7P9sDr6MeIykTeByD6xUKiiVSjAYDJKKyWHaJjLZq4CEUKZcbyO2c6BbLBbh9/tX0jw3fS2bfrch4/FrlN9309eTMP4+u0oDz8X4rPlYr5B04/V65f3q9/tot9vy9zEl3GazSeoZ17h+vy+EMOB5A5ZkJY1GI99DsN5WPj9MhmEi720GIsBlg3owGKBYLMLn88nvCYVC8Hq9aDabsj6T4Mha4dNPP0W32xWSMt+9TqcjdYdGo8HOzo7U6ySTmEwmHBwcXKmXWq0W0uk0xuOxvOc8r5ydnSGdTiMejwuhk0JhkpxcLtfKdVGaHRFMiiHJlfdSp9PJP7tcLnzyySeyx0wmEyFjkXjrdDoRiURQqVTw+eef4+nTpygUCrBarSI0HI1GQlhfLBb40z/9U3z729+WtCwSJWj+slgsVhJ1mWpQrVblrFKr1TCdTrG3t3eFuNVoNIR4uL+/j2q1KkQCs9ksSSTBYBDFYhGNRgM6nU4S6knwVr6/FHhkMhm51na7Xa4znXy5hup0umvTx2lkkE6n0Ww2ZRhHo5jFYiGJFUzEevDggSReNJtNZLNZLBYL7O/vYzwei7kNcPluZDIZfP3113C73QiFQjJkN5vNYkpUq9WwWCw2ispZ2yjR6XREdHodIpGIkEU5GGKNqNPp4Ha7EQgErt377XY7HA4HotEoMpkM0um01CIARLBTr9dhtVrh9XrvyGyvAKYrbDM6ajQamM/nYkbBemL9OeD6VqvVYLPZxBxove+xKQHumwAlefx938tfBjxXMV0zk8mg2+2iVqtJgmCj0cB0OoXf74fNZkMsFpNzRDabFWMNZYLC68bOzg5UKpWIeSi4YW0yHo9hNpulV8E9ejKZIJvNyqB6Op3i3r17b2XoS+ONcrl8o6iBf4vdbn9tRASbzQa324379+/jj/7oj8RgIBgMQq1Wi0ndYDCAx+OB1WqF3W5HPp/Hzs7OK4nE7vB2kEqlpO4ALus7GkSxj5VKpaRuPj4+fmNGEXf4yYNarf7GGAUtl0t8/vnn+OEPf4hyuYxsNotarSbplBSZkeS/WCyk30BxqdVqlbM/ZzputxtGoxH5fF6SqTaBaRkWi0VSWynup8lUq9USgVqn05F9g30Zp9MJh8MhSVp6vR6xWAwul2trb6bZbMqZzuFwiFhtOBzC6/XKOZ5zoVqtJkbBLpdLenavSqii4dZgMEChUBCz43g8jmQyCavVimAwKGZ2BBPGLy4u4Pf7MRqNtvYiaS5NEzSj0SiC7Ol0Kum+pVIJ5XJZRB0Uqz179kwESzyrTiYTxONxJBIJ7O7urpjjMulS2Q+h0KRcLksPFVg1hlwul8jlcpKcTqEen0eKMvjPpVJJUgA5iyTBmMRdvV6PwWAgzxjF4aVSCV6vV4wVjo6ObmV6odfrcXh4KP/caDRkJgA8n4GEw2E4HI5XIi3XajWk02nUajVJkOO1KpfLaDabmM/n0k9RPgMej0fOxRTx9/t96HQ6aDQaMYuLxWJoNBqw2+0YDoc3Cqb7/b70Q2kcyL4HRUR8TtgLoDH6bDaD1+t9adPOSqUiwmb2rBqNhiSj1et17O/vo9VqwWKxyFw5FoutzGqcTqeITXU6nYiICKPRKCI3u90uzxhwuQesp6P7/X7pgb3t/jwN8ZT9Cn5OAFKLV6tVjEYjSYAcDAb4/PPP4Xa74XK5JH2J6yHNhz0eD4xG49aZc7PZFPNvnkcoAqEwnGKz+XwuwlkasDMhnabGfE5pYlGtViV9k/Mv8gP4Oyh8BZ7PyW7q9dwEs9mMjz/+GBaLBZ9++umKwF2tVqPT6WA0GgmvwmAwYG9vT4wlaYROA5fd3d2N9ajFYsHR0ZGYjbXbbTHl0Gq1YrLOZNAf//jHqNfr0s9cn8NScLS3twev1yv3JRqN4vHjxzAajfjqq6+gUqlQKpXE7GUdNAPw+/3odrtIpVKYTqfIZDKoVqtYLpcIBAJiPklzfGWyJXuhw+FQ5to881Eg+rLgmmaxWPDgwQMx5FUmOvMZYV0xGAyws7OD2WyGdDot/VomktOInSmDys/Hr3O73bBYLDAYDBgMBteandE0hBwDnlFoGKzRaDAcDnF2diZmzLPZDPF4HHt7e3j06BEikQi63a6kIAKXzyXPq1arFYFAQN7/UCiEfD4Pu90uRHbl38Ak8MFgIEYKvBfApfCFRgF8vi4uLtBoNLBcLhGPx8XwgfWaz+dDLBaTZ+ng4ED4AW63W1LfT09PMZ/PcXJyAqPRKNwRruH9fh/RaBSPHj3C8fGxPNdutxtOpxP5fF4SMilQug1MJhMsFovMy91u97XPHY1znU7nFcE09zyuYTdBp9OJicS7SMumCJ8ieF5PjUYjBhkM17BarZL2yVqTQQ86nU7CHwiVSoW9vT3MZjM5G9TrddjtduGo8XnmjHYbD4RCWo/Hg3K5DKPRCKvVKvMdzuBoDkkR3rb+EY2OuFZvel4MBoOYPVWrVXi9Xtk3eH1YC4zHYzQaDQAQgWAikXjhJNafJNBMmRwjzlQ5z+M9pzH1dXUGQxzI0Wo2m6jVagCez3qcTqeEaGzrX89mMzEJVhqrrMNgMCAajeLi4gLD4RDVahXRaBTf+c534PF4sLu7K/1Z7od6vV44iE6nE/v7+1v3OLVajb29PZkhq1QqZDIZWb+d/y/pe72OYSDC+lz5NuDP4jp+UxADxUvK+QdDfUqlElwu10pYwGAwQL1ex2KxEJ7d+jWoVqvCgeR1NBqNK+stjbQ6nQ6y2SyOjo4kbOAO2zGZTGQ2PJlMhItEcZ3X60UgEHirc57XMYdgcAWF12q1+oqZPflTBoMBpVIJFotFuL6bwkDeJ1SrVeFebNOxaLVa+P1+CRxgXfOhQqfT4d69e0gmkzCZTGi322KARI5rOByG0WjcyJm4DZRn99lsJmEb5PrQkE6n04npPfuUm9awN4V+v4+TkxMxTCQvh3zydDoNo9Eo7/W2sIA7bMdsNpP7fhseutFolLX0XdT0d3g9ICeXnCWj0Sh7wybuJTllSi4v65cX3U/sdjv0ev2KARifKQaZcaY/n89RLpdht9slPf5luAdKDt1tepTKr3nZfqbRaLwzaLjDHb4h+IkRoCuxt7eHf/Ev/gX++T//5/j93/99fP/738cf/MEfSEOKg7+zszP803/6T/HP/tk/w1/9q38Vv/Zrv4bvfve77/jT3+EOd7jDi4PiEToCkyDDpBcWqtPp9ErBHI1GZaCVTCbR7Xbl8J7NZgFAGqR0ya7X6yuNSaLT6aBYLK4MgFUqlQz6/H4/AoHAtUVqpVLBV199hWazCZ1Oh1KpJA5QTEzWarUIhULodrvixF6r1URotZ56SPFBNBqVpA6z2YxGoyFCUQrQOVz1er0wGo1oNBoijOn3+0JmabfbqNfrcDqd2N3dlQEb05fXCT4kQlGAkclkoNFohMDV6XRgMplw7949eL1ecbBmEiSbYcrGAUWsg8EAKpVKPjNTeF/E0Z2DPQ6faNjCf8fkZIKizlwuB5PJJA6AFINGo1GEQiGYzWYcHBxgNpuhXC4LwYfkbbfbfeVZ4KB5Z2dHElWOjo6uOFfeBhSpklRhs9lWhEl2ux0+n0/ENCRsM+0EgCS1sLFvMBhgtVpRr9dvdLeeTqeYzWaSzlKtVuXnGgwGEeS+LKbTKU5PTyWFZDQayXPCpGqTyYRoNCpDUCavLpdLHBwcyMDa4XDIfSRJ48GDByufz2KxyNCPhgKlUumKKIaNXyZabrtvJKxQsLduAHBbcIAeCoVQKBSwXC6vJFpMJhMRaNEleTqdygBkvTGnfFbm8zn6/f5W92WiXC6LkIXu+EpwXdZqtXj69CkASJLxTTAajRgMBlCr1ZhOp7h///6VNZ0C5VqtJo1Hkvd4rWu1GiKRCObzOY6Pj2+VzuT3+yW1IZPJIBqNrtxbptyyCerxeLC3tydDIJvNJm7USnAIfV1zplqtinCQZgj8fevCSY/Hg0wmI4QkrVaLwWAAnU63cn97vR5KpZJ83U0u2C8CJlGwUZ3L5W4Ud9Dhf51ACkDIVkxg6PV6GAwG0Ov14gTLfYoEr1qtBp/Ph8ePH8Pv979Wcj3TR5SOhQ6H440N+5SOjFwver0eWq3WClmCLvp0Tuf7xCQqpkNaLBbk83lJlCAx6UVBsSOHdL1eT5zxWfO43e6XXtOuA9M6BoPBlb15E2w2m4g2t6X60jlcr9ej0+ncOKymyQKNgDaBxFOayhAchlOc/b6BQpVKpbLyjHFtc7lc8Pl8MJvNaDabQkQ1Go2y/jI1plgsyvrDfXixWODg4EAMlFKpFIDLd4vmSgRr4MVisZLCaTQaxcBEicFgIO/7+rrOoeUmp3mdTrdCkI3FYjg7O4Pf7xfy2n/9r/9V0ux0Op0Qfvr9PrxerxDCA4GACKH7/T4eP34Mq9W6IvRtNBqSjs0zhcViQTwex8XFxYqYYTAYwOv1CmHDZDJhZ2dn5ZkCIO8j999er4dCoSDCPLVaDaPRCJVKhU6nI3sFa+VAICD7BvcZnU4Hl8sl70WpVMLTp0/RarUQDocRiURkbSfpgkY+FosFT548kWfG6XTK8xEOh1EoFNBqtVbInYPBQPY1rlVca4+OjlbWKiaU8zMy6YvPn9frleEJTQtUKpUk7JjN5pUalte7XC5jNpttrF9ItmMyOUmK62vAZDKRlB4ObijkoDCXpGMK42kKYDQaxTF4Mplgb28Po9FIyOPtdhtGo1HI6q1WC6lUCoVCAUdHR1gulwiFQmLqYDKZhODZ6/W2ioOm06kYL9xEXHQ4HELEJsmSDr63Wdf29vZEmFOr1eT+qNVqecd8Ph/cbjfi8fhLE/LfFZbLJdrtttRwNPTg2etNpr4pwQSNdrt97b42m83EMCkQCAg5pdfrbXwOXC4Xzs/PUavV8Md//MdSm5MIYTKZJJkkFAp98EJ01qFMpyHMZrM8o98kcoPNZsP+/r70BT799NMVcZbBYIDNZsN8PsdoNMJ4PIbVahWzxkajAZvNhnA4/MbIWxTKs8fB9YHmkiaTCYPBAA8ePLhCjKFRE0W62WwWiUTijXxOJex2u5gasme0iUza7XYlwZbik9eFnZ0d6cupVCrkcjkRubBnGQ6HpadQrVZXBJB3eH9B0Wsmk4Fer0c0Gl157pnyp6zN8/n8tSY6d7jDTwLYr+Y5lfXKF198gc8++wzPnj2T1G2bzSZC1Hq9jlKphMFggL29PTHaOjk5wXA4hEajERJ+IpGAVquVHnEqlYLP58NyuRSzwFwuJ6ZbrH9DoRB8Ph98Pp+k/FDIRUFzNpvFZDKB2+1GLBaTWqXRaEhCh1arxcHBAQKBAOx2+0bzTiWazSaGw6GImev1uqS0rguL+O9arZbsVxSsNxqNlza7p7i4Xq+j0Wig0Wig0+mg3++jVqtJfdrpdMT8k2cKEuUohLqJAMfEWfbFmVDdbDZRLBZRqVRQq9XEUJSzonQ6LXOqbDYrqacUbfR6PQCXhNltJFj2dCloS6VS8Hq9K+nxNMqdTqdIJBIIBAIizGASFQnDJycn6HQ6cLlcOD4+RrVaFdMR9mSePXuGbrcLjUaDw8ND2O12Eb8HAgGEw2GMx2MRy1arVYTD4VvdO7PZjPv376NYLMJqtWIymUgvkwI/r9eLYDD40jVir9dDOp1GoVBAt9uF3++X9HngMpFwNpuhWCzCZrOJIEx5Pg2HwyI4Z6qyRqOBVqsVAzqTySS1UL1evzJfUUIpzmNy6HQ6XekRUzDD+SlTFjUaDQqFwkpP7UXAxHv237rdLtrttpj5KcWUdrtd0m5pKrmzsyMpyRT79Ho9mM1muVfAZX+DvXan04lKpYJwOIzRaCTmGtv6DO/iPMh+BT/7YrGARqPByckJRqMRstksvF6vvLtGoxHRaBSj0Qj9fl9mYJPJBLVaDeVyGQaDAYeHh/B6vYjH41t/f6/XEwNCztGYvM71lSnh4/EYWq1Wkus5LyW/gfes2+3CaDSi1+vBZDJhMpmIKTq5A0xnplkvibmtVgtarVb6dq96bdkDYl1JgzaSdGliYDKZ5JrRGInn6Jt6ImazGY8ePZK+Ou8j5zDsS19cXODBgwf43//7f4tQJR6Pizi90+lgPB6L6SHnKjs7O9jd3ZX7s7u7i+l0isViIWmmTDhm742JyLVaDcFgUATo3KspEuG75HA4pI9N8G+IRCISCNDr9cSo4EXTW5U/l4Y5wWBQjC1arZYIb/k5ptOp7BnRaBR+vx/BYFB6R9zHptOp9B7XwwiAy7oBgPS8R6ORrMfKvY/95G63K+s0+ys/+tGPsLe3h729PTidTjE77Xa7YjYdDAYRDofhdDoxmUxEDE0DLs6glMnNFPAYjUaMx2Po9fpr1yn2dik+5JrQbDZxcXEhZy2aSjBEYnd3F2q1WsxkOKfkfTw4OECxWEQsFpNrTNhsNuzt7cmMgmlvXLPUajXu3buH4+NjBIPBK2IqtVqNSCSCZDKJTqeDarX6Qu82jZNarRay2ayEfCjBJHYAMgta/wwk8t9GdEDB+rsyZ/Z6vSgWizCbzSiXyytzdp/Ph36/L1yZbDYLg8EgfXXOIDQaDeLxuKxpSlgsFqn77HY7Wq2WrEkajQYulwsOh0OejW17/mg0EpGV0WjEkydPxAh3sVhgOp3KXMFqteLBgwcIhUJbjQDYk6Kp6Kb+PUW8NJje2dmRUBQaVNLoR6PRiLkNz0c0F7gLvbqK5XKJs7Mz1Go16duEQiExU6EhTi6Xk/790dHRxj1BrVZjf38fbrcblUoFbrdb5nvsy/t8vlsJBWlC1e/3Rdh+HVjvkcvEueD+/j48Hs/GdYgGXOtG2ttgsVjw8ccfi6FRrVaD1+u98rPn8zkqlQr6/b6YH7zorF2r1Qpvh6YL265Zs9mUukGj0SAUCgkPtNFooFQqCcdvNptJwjDnB5vOVTTV4ppE3iavN8VX3W5X9jOTyfTeJle/TxgMBjg9PZV9nOnJAGRm3O/30Wq1cHh4eCue2PsCzuCHwyHcbreEk3D23+12MZlMRBza7Xbx7NkzMeJaNxB738DejPKsfx34nvAs/CEL0IHLuvTBgwdi6MG6nP/tVblFFotFeE00QmGIwWAwWOnhkB9usVg28vfeFBaLBc7OzmQ99Pv9V/aR6XQqMz6VSoV8Pv9GDbG/ySB/OJ/PS6/MbDbD5XJtXBdpngO8m17P60av1xPuoV6vl17iNx0MI+C5hWEc153ter0eDAaD8NkAyL7zovuJXq+Hw+GA2+1GoVCA3W4X3rVOp8N0OpX+z2AwgNvtRiKRwP3791869IhGWwzXuWk949ycn/cOd7jDTzbeP0b3W4RGo8Ev/dIv4Zd+6ZeQz+fxW7/1W/jX//pfXxEnTqdT/N7v/R5+7/d+DwcHB/hH/+gf4e///b//QR2y7nCHO9yBw6j1JMTFYoHT01Mh9lQqFREJAs8JEvV6HUajUQS2FDQzJTkYDMJqtYrIy+l0rjhIVyoVZLNZNBoNNJtNccHk4NXhcMjQeJPTZ6fTQS6XE7GCRqNBMBhcGUTM53MRywOXzU4SROnyb7fbpTDngCMSicDlcmFvbw8Gg0Fcjj0ej6QPczBGJyYO0+r1urj6cihOp99oNIrZbCbDb4qN10EBDJ2pQ6EQ5vO5fBalw14sFpMhNMVPn332Gcbjsbh8TqdTERwzWZEO5nSt0mq1t2q+kARGASOJAEyFoHB7PcHeaDRif39fkgvZUOagdnd3dyX5mQP+fr8vDepNSdUcMjL9nESOVyEfWywWETaRoKR0vC8Wi0KmYGICRdocijNlmQ66xWJRkpn5DLEhwWel0WhgsVigUCiIexoF6EajEdVqFWazGYlE4oWH69PpFM+ePRNiGMW5JJlRzOJwOFCv17G7uyspysrGKofKi8VCSCh0Jt/0/KhUKgSDQYzHYxEjh0KhK+JeDgFvk8zqdDqRTqcxm83Q7/df6l6rVCrs7+/LsKNcLq8MEinK4v0juYxr1ibXaxINVSoVut0u1Gr1Vhdk/u1M01C6+2+C2WyG1WqVZ+k2LokkRTEpd33QS1EBiZJMveC64PV64fF45PnQaDTI5XI3DteAy/coEolguVxKCo/ZbIbFYhExGUlANNO4f/8+AGxdj0gIu645w0b7daKudeEkEyXohlqtVuVd4+fkWh0IBBCJRF6ZfARcdRJmoi2HmCS/rBMMuPYyVYR76nw+l+eTQ1CavNRqNUmroMM7nbdPTk6gVqsRj8cxHA6xu7srIpVXAV3ulZ8HeJ4eQdHG67iWSrDZB0DWUCZQMO2djXc25LimcY9Rvitcw5LJJHq9nohLXgZMbSBhRSk+u+n9vwk0maEpDM1QOEwnYeUm8PrdZEpjMpmEMEWDnW3vrPKZ3dR4nEwmco2ZSqZ0Ke52uygWi9jd3b31M0OznHa7vVK3kTT5OrBYLFAul0WUxH0EuGywklDncrkQDAZhsVjkOvE9++KLL/Dll1/i/Pwc/X5fCNQUmDM9ORaLSYINa4D1a04XVKPRuGIMQGKj8p4yUdrn8wlJTYler4dut7uSaH0dWAuTFM7Env/zf/6PmHqQlMfni2T3dDot6bHBYBA2m22lIU5yealUQrvdRjgcXhloxGIxVKtVPHv2DNPpVN6nYDAohI3FYnFFgM69TaPRoFwuo9PpoF6vo9friaBbpVJhNpshk8lgPB5jMpmI8DcWi4nRltPpxM7Ozsp6zQEUBdebSCw0YikUCnLNObggGSaTyeDLL78U0hJTnNrttqxPTqcT8Xgcg8FA0mpzuRyOjo7kXvNMxZrJ4/Gg3+8jGAxKWqHFYpF05vPzc+j1ekka6Xa7Io7QarVi4NFut7caZ2xaQ9fFizS0olkK7wcNziaTiZybXC4XGo0Gdnd3odfr5fmYz+dCYuc5qtPpIBgMwmQyoVKpyLtAkwQOyFQqFQ4PD8WEjGIQigs2Ef2WyyVKpRIMBoMQgm8DZTrfi0Cj0eDevXsoFAowmUxCruYZjvVVKBT64MTng8EA5+fnGA6HK0ldTNVhCs7e3t4bJ12S7MTz+nVfA1yerVwul9SJ19XlBoNB6n0SPEjE5mCSfY9er/dKCYfvGp1OR+4lRR00E7DZbFKXHhwc3GjQ9SHB4XDA5XLh5ORE9je/3y/7Offgfr+PUqmExWIBn88nZh80MHjT7y6J9HTpbzabmE6nuLi4wM7OzkbTBZL9c7mcEPoikchbMQRSihry+TwajQYcDocINNhXIVF0d3f3tZIdmCbEcymJwhz4m81m2Gw2aLVaFItFSYoPBoN3SdnvOWiyRAOd69ZcCiNY328yaL3DHb7JoFCUBkEEReNqtRpff/01Tk9PJXWSiZw2mw31eh1WqxXlcllMEkn4zefzUKvVcLlcuH///hWDTZqe5nI5LJdLSc9mb4tmTjabDaFQCNFoFA8fPhQRJQDpBRUKBSwWixVhbTgcht/vFzPUXC4Hi8UCvV6PBw8e3KpOoXmn2WzGfD5HoVDAbDa7tl+k0+mkd+JyuaQftW4Sd1t0u90VcTFrC+5BNNl68uQJTCYT9Hq9EO+57tH8mL3xbSD5fDQaIZ1OYzKZSJ3ONVKtVsPhcIhwl6a1NPiy2+0yw6GIz2g0IpfLQavVIpPJbE249Pv9Miep1WqSYq/T6TCfz7FYLGC32xGJROB2u+Vncc5DnJ2dSX8iHo+jUqlgPB6vXL92uw2Hw4Fnz55Jb5vCBp1Oh3A4jHv37kGlUiGdTqNarcJoNCIYDF4xhCbJcP0so9frkUgkxOybKUoGgwEWiwXNZhPZbFbORna7XQzabgMmk3Y6HUmTVYImmKPRSOq8Vqu1UhOqVCrpAXQ6Hfh8PukR0CzIZrNJXakU563Xi+viPJ71gM3JdkxCJ5jws97bug0qlYoYJFPsxZkJr0MqlZLentVqRSQSQafTQa1Wg1qtRrlcRiwWE2L5ZDJBLBYTs3DOj9gfazQaSKVSMiOlcHI9Ffd9Ag0GyuUykskkfvSjH+HZs2cwGAzIZrNi6rGzswODwSD1eD6fRzqdhsvlEmGtw+GARqPB7u7ujaIfzklNJpOYofLZI0HXaDSK0Um9Xpc1iX1XzktouMjkbqbTUxCczWbR6XRgsVjE2JGfvdvtinkK5+3Ke9Xv98VkjaKCTe8j+RCVSgXVahW5XE7mM1qtFl6vV3pI5BSQT+FwOPCzP/uzL3X/VCqV3IPrQJOt4+NjGAyGK71om80mCd00Fuf7rlwb1sWjpVIJyWRS6gWr1YrFYiGk9EKhIFwKl8uFQqGAdrstaa0kc9PUgQEA7INyHeeasbe391LiOaLVaslcn4a97F1w3eT8YjKZyN/idDrx8z//89Dr9SJkpBD84uJC+prKNY1zv8FgIH1kq9WKSqUi4m0+R+TMNJtN6btyhtVutwEAz549Q6lUEoMVANITJTEegHAwDAaDrLlM9GQwBb+XPVTu5fl8Hm63+9p6iEaMrMH0ej0uLi5QKpWECxQIBFAsFqVfNplMUCqV5BpsMveiKSyT3pVwOp24f/8+yuWyGAGyhisWizg8PBSDuE2gmSvTz1/kbOd2u+H1esVMJ5lMwuFwSG3BFEytVivCznUeC+cmVqsV7Xb7RnNuhmusJ/u+LdBQaDKZIJVKIZPJwO/3S/IxjesBiJFAs9lcMdFxOBxiQrwJVqsVjx49EmMopRm2xWKRuc5153Wus5wZMPGvVCqJETP3Cq4xNptN6tOb0vyCwSCGwyH6/T7S6bTM9giaBEwmEzGwODg4QC6XE/N31pb1eh3hcBiBQAAejwetVuuKifodnoNmwzyjhcPhK3wzchBzuRz0ej0KhQIODg6u/ZncHzn3AS6f8xfpT3N+xfncTbDZbGJyRANh4LnZkpIDtsnE7LZg/5Iz8FKphFqtBrvdDo1Gg8lksrJGuVyulxYe+nw+MXAqlUrXmukOBgM0Gg3hh3G+x7MXw09oRsI6sNlswuVyIRqNXumt8jpyP+V+Z7FY0Ov10Gw2ZT2yWCzwer2oVCpvNYX4Q8VkMsHp6ancV6vVing8LiZ0vV5PTOLj8TiSySSOjo4+mOs6Go3EOAe4fN9oUjAYDGR2xz2DJjrk24xGo/f2/EYuz7ZelBJK86v3OdX9RUA+NDkV5Ba9jvkVeaKdTgdPnz6V8CzW5cr+EXkYNMi4zXyINaSSw/QiPR8AEgJTLpeFL7IO8tRpymcwGN6oIfaHCJrkd7td6U9YLBbhFM7nc6RSKeFicn9nmA37o5t40CaT6dbPxPuI5XKJarUqWgT25rRaLfR6PbxeLwKBwHsZIvO6oFar4fF4JBCQhoc0p5jNZvL3s8+g5N+Rp7bJqO42CIVCaLfbGA6HyGQyElLo8XiEV0nO8ePHj7G3t4dHjx699LycHGSGV24ToFOTQx7o3fz0Dne4wzd3N3gB/OAHP8D3v/99/N7v/d7GxZj/brlc4vT0FP/wH/5D/NZv/Rb+zb/5N/jFX/zFt/1x73CHO9zhlaFMHSMRr1QqYTqd4vz8HPV6XURZygTu+/fvo9Pp4ODgQNzjSDwhKpWKDMA4WCUZIZfLod/vw+PxiHiJyeUc0lPQuy547PV6QqS4LhGApCiNRiNkX5fLJeRbumzTJZlDEZPJhIODAzkEejweOVCsHzrNZjO++OILIe1EIpFrRZ4c+s3nc0wmE3EFVoLDxVKpBACIRCIYjUbodruIRCI4PDxEv99HsVgUkwCHwwG/34+f/umfFlH3yckJUqkUGo2GJGJywHtwcACfzwev14tarSZipZuGHM1mE+fn56hUKpJASDd2NjboaK5Wq4V8RnCQRMGdRqORVIlms4mnT5/C4/FISgUFlPl8XgQnfJaY5stEqmaziUQiAZ/Ph2Aw+FrIxyQ3rKNerwuBgOkPfG57vR76/b4MpClwarVa+PTTTyV9WNlQ1+v1QqTj85HNZjGfz+X3KwkKo9EIBwcHL5QgnEql0Gw2JUl+NBoJGYEGDePxGCcnJ3C5XGg2m4jFYmg2mzg+Pr7StKSDWrfb3So8JDweD3K5nIiVlEQIitCAq2K6TeChlQT/l4XJZMLx8THOz89hs9nQ7XYlodVoNOLRo0fI5XLodrtCMKdDPEVrvAccelDUXKvVEA6HYTKZtt4nJjzMZrNbiQ68Xi/Oz8+FuHiT2xwFdyqVaiMpptlsyvrChGamPC8WCxnC+nw+SSg1m823SlIGIE02jUYjYhS6j+p0OsTjcUkuoUh+27vLxEgmB24Ch1C3HdYxWYGkJpPJJIlJJO6wyRKJRF5Lop3SSZiCR6UI/euvv0a320W320UikZBkDiaxkyTFJPbZbIaTkxMZujM99MmTJygUCuj1evD5fCIo1Ol0cDqdCIfDiMViKJVKqFQqshfyvm8jLG0DkyGYutHr9aDVaqFWqyVVhG7wsVjstSbK8xmu1Wry93CQy2vq8Xhk7WACNJMY3G43AoHAynNIYnOr1Xot4iDWIK8Di8VC9qPRaCQDazZ9zWazEGpuMySja6nSdOU6MP2h0+kgm80iEolcee9owkNjmE2O/jRIaTQaQkSnUJ3pJjQHYBr4tvuwXC4lmZnpLRw4Wa1WlEolOJ1OSVh7WSyXS1xcXKBerwtxbTKZSDN5NBqhWq1KY3mxWCAQCKDT6SASiSAUCuHo6AgHBwci5i8WiyI+tlqtQnAm0bpUKklKmF6vvyJ4ZKKyw+FYcX6fTqcwGo1Sc/Ia8VpvWte4x24iYYzHY9k/gMt6ic7OXDstFoukk5jNZjGN6XQ6WC6XIh6gCJiEA4qICRKJWq0WEonExs9DQtL5+Tk8Hg8CgYCIr68DzTh8Ph++/PJLAJd7ptLMajabIZ1OI5fLrdRkNPAYj8cyqJtMJrh3756IS5lWTsOU6949mm7RpKdUKsHj8eDjjz+GSqWCVqvFkydPpPZkyo7D4ZA0okgkItfc4/GIsGE4HMJkMsl6R6EG71k8HheXZgoYSLjt9Xqw2+1CyhoMBnKuODw8FHGp0r33OvC5JHloHc1mUwj3xWJRRPRMIzQajbDZbGIKwLODVqtFuVyW2jkcDqPT6ch+6vV6xbiLg1yr1Yr5fA6z2SykFqZlsQ64uLiA1WrFaDTCxcUFfD7fihHDerIeza/eNHi2CofDkrjIczKJph8aBoOBrP/lchkqlWqFHEUxSjQaxXQ6xdHR0RsdiitNbG4SNSidrZXftw4atLEeOTw8XOl70HyIfQ+NRnNFhPUhoNvtIplMipET+w7KNZHno8ViIYT3bwK63a4YabhcLuj1euzu7kKn08l6xZqAvQuTySSJqcqz8NsABdW5XA6DwUDEMNeBZ8GLiwtJGryNadyrwmKxiCENhU31el16cVarFaFQSMjrbyJVQqVSIZFIwOl04quvvpL0RdY//X4fBoMBPp8PDodDzMru8P6ChIhmsynCqG1wuVzy7Debzbt0sju8FSyXS+l9kaDGeczLJGW8DEhAprCv3W6LKMlsNgvhKpvNyhlqPB5L74n9a61WC4PBgEqlIoYrFotFiI/xePzaddNqtcLtduP/+//+PxF30MSHyWalUgn5fB7lchmDwQAPHjyQs3e1WkW320Wv18Pu7q7UXoPBQMhyRqMRh4eHkuBIQdJthQjsMSyXS5yfn0uy4jqWy6X03pmMU6vV5L+9DDaJiyl8Y8ovBc3n5+ew2+1YLpdiNtvtdpHP54VE+fDhw62/r9frQaPRoNlswmAwSBKsVqsVEym32w2HwwGn04lisSjiOZvNJrMi9gBsNhuOjo6g0+mQzWblXMre9HVgygvP8v1+X/o9NL6mSHpTfc66lKIgilSNRiO63a78bhLSKaIcDAbyjHs8HknaBC579fl8XkwNaDxZqVSk/wFAiJler3eFkMf5JfBcpM29R2nOxblEKBS6sTc9Ho8loZ7i0XXQUJIpcO12W8zzlNeO9d/u7i4ikQgeP3585dq6XK6t4rzBYIBWqwWdTodEIiGzVhrrjUajG2u54XAoQv4XITTSPK5SqYj5DImZg8EA4XBYiOScuTK1mj2RVqsFs9mMyWSCRqOBYrEoBuYkgJ6ensLj8UgPgqZmGo0G1WoV3/nOd+ByuV6ollAmkrP38jrOw0xrAyDiXuDyXp+enuLs7Ax/+qd/iouLCzHiYf+B4tq9vT0Eg0ER6rLX6vP5YDAYMJvNEAwGbzWf0Wg0ItKjOQeNgWnsRT5CvV6XPYCz+VqthuVyKUbfXq8X1WpVDDaCwSCi0Sj6/T78fj9yuZyYyAOQuf9wOMTXX38tCcrhcFjI1jSKZx+WfXefzwe/3y/3ZTabiclFu93GV199JUb4JKdToO52u+Hz+bCzsyNGtWazGZ1O542JEVUqlZyduK4Ui0UAkL4bnzOKzwOBgMynlLBarbh//z4+/fRTTCYTmSVQ0Mz71Ov18PHHH6/08vV6PZ4+fYo/+qM/QrValbV/NBqhVquJGcFyuZSenE6nw9nZGeLxOAC8Ul3OWdFyuZReYyKREHGz8h3hOa/RaCAWi8Fqtcp7Pp1OYbfbxUyGXJonT54gGAxiPp9LTz4UCsFmsyEajcosUMmZaTQayGQyyOfzsqfGYjExWalUKnj69KmI61gHcA/jGqXRaMToVGmIp1arZXbR6XRwdHSE6XSKVCqFer0u8xsmRn/66adIJBIrvJv5fI56vS6GtYFAAIFAQAx9mErL/iTNYTwej+w15XJZ3vn1vclkMq0kkK7DaDSKcUu/30c2mxWy/zbhKWE2m9FoNADghQToAMTwjoYsrMGWyyUMBgOCwaDMdHZ2dq7skxQtdLtdZLNZjEaja3vJvM5Op/NGfsGbRCwWE6FuOp3G//2//1dE09PpVPoiNHs/PDyUOp3G/DfNl5na7Pf7pTem0Wi27nXL5VLMf1jTTSYT9Pt9WW9pLBSPx1eMjOfzOQaDAarVKrRarawnm+D1euXdLpVKyGazwrOiiQ33Q76XX3zxBfr9PhqNBrxer/QRaF7LPcPlcglnp1Qq3QnQ10Cu4mKxuFbgDEBmqo1GA3a7XWZ12/Cy5sDAi5/b1kPO1vEqovN12Gw2mU3SEIE8Ea1WK6ber2ry63Q6YbfbEYvFkMlkkE6nV8y8J5OJ8Bzdbjf8fj9msxlqtRrq9bqcC30+HzqdzooAnf8Xj8c3isaVZvvsFe/v78s6oOytkwNDQ6uXPXOvYzqdSugC59rXnbU+JBSLRXQ6HTGKUQpYOa+j4RDXQq5zHwLYI1KawSSTSZhMJrTbbeFJ8L+Ta1UqlRCJRKROfx9FdXxPrkvh3QSKnT8UA4EXgVqtfu3pu36/H/l8HsPhEOfn58IJsVgscg1pVsM+HI3LrttvWq0WisUiBoOB9H/Jn6bR3235zqyHyBO4DiqVCl6vF9lsVvoar4Pz+E0ADQtHo5FwhWlmYjAYpHdF3iPrSBpGtdttGAwG6VvHYjFoNBox0WYgz4co0J7P50gmk3Leo9gaeM5vYu17eHj4we+H2+D3+1Gr1WRNYI9Cr9dLL5iGODabDVarVXq9PIPa7faXeu/MZrPoZfR6vQRqUKfA3gL5JzyXAc85bUqdi9Vqhc/n22re5vV6MRgMcHFxgXK5vJHPy0AqBjTerSl3uMMdgJ9gAXqlUsG///f/Hr/927+N8/NzAM8Pr/xnjUaDv/AX/gKePn2KdDoN4HJDXS6XSKVS+Mt/+S/jN3/zN/FP/sk/eWd/xx3ucIefXMxmM0mxVaaGrSdRr2M6neLs7AzdbncllXM+n2M4HEpq12w2E3cui8WCTz75RFIGxuMx9Ho93G63HOp50Oj3+4jFYuKeDQC5XE7EYXRMJ3hQsdlsyOVy4l4aCARWCmAOEZiesa1Z6nA4UK1WhUjwZ/7Mn8FsNhNhHNd7FtrXOcttEj2WSiWMRiPU63UZQK6DyVHpdBqVSkWaERyOs2lKIQRd8iKRiIi7CTbBmAxht9tRrVYlbekv/aW/BLPZDJ1Oh3w+L6Qck8kEr9cr7ssej0dSqf1+vwxOrwOdjsvlMlqt1hWhvdVqleFlKpWCXq+X3wlACNe8D/P5XJIbdDodcrmckBMWiwV2d3fR7Xbl2iSTSUkJLxaLMoxigsfu7i4ODw9vRcJ5FfDQyJTWRCIhDWd+dpVKhVarJU3ver0OrVaLfr+P6XQqB87FYiGDbAAiWtfr9fB4PCuEWDYoSqWSkBAfPHhwqyYa0+lLpRKi0SiGwyFyuRyq1aoIviiEHo/HSKVSYrZAd+ZPPvlk5WeyuT4ajW7VYCa5YjQarThZA88H4fw7b2q+sKmh/L6XhdFoxIMHD9DtdoVIolw/A4GAOM+bzWZEIhFJR6To0Gw2o16vY7FYCAmGybabnNyVoMBvUxLKdZ/XaDTCYrHIe3jd9VoulzJ4WywWG5ucFDzz5/IzUUijXAc9Hg8uLi5kvdsmVFAiEAgIubJer68IZN1uN/b395HL5XB6eipDqev+nmq1CqvVKs2+TWDT/LaNdroQUrzG93K5XMLlcslQ2O12v5Zm/LqTsM1mQyKRgNlsxs7ODpLJJLLZLE5OTkRwyGE/01/9fr/sq8Bleg9JKnTjLBQKWC6XQhL4+uuvMRgMoNFoEI1GJdXC7/cjkUggl8tJEpVOp8NgMMC9e/eg1WphMpluLWxbLpc4OztDs9lEJpORv4vOrxQCcu8ELoe+rysJ3el0ijDw9PR0RWSq1+vRbDaRz+dlvRuNRrJ3hEIhIYvw2rHJrNfrpel8kzv+28J8Psfp6SlarRbq9brUDsBz0wm3241KpQKDwbD1/SKazaaIom66JyShLZdL5HI5nJ2dwWq1yrPS7/fR7/dhtVoRDofl2q4jk8mg1WrJM+jz+VauLxu2+Xxe1srHjx9fm0qcSqVkOEjzAxL2q9UqTCYTAoEAhsOhEJ5fBvV6HfV6Hel0Gh6PR+oxuprSfZSfZTqdolarSVIqG7Vmsxnf/e53EQgEkE6nUSwWYTAYpDGtUqlQKBQk5ZNpYbVabYVoRfj9fiFhV6tVcex1uVySUERDn1gsJgTxdXDgryR9USDc7/fFNZxE2FarJeK7w8NDEUdXKpUVQ6JOp4NkMrliTjIej4XoT4Lm/v6+7K8kam6rU7mntFoteDweIaxuQyKRwGAwgFarlYHzfD6Xd//8/FyE5oFAQJIEmPQRj8fRbDalN5TJZLC7uyvrLg0ubhKyms1mVKtVMSkiIUKZ5sJzFPc+k8kkplrK94X77WAwkPeZ74pWqxWxBXC5X8ZiMTE+4ZmPZNPd3V0cHR3B4/Gg0Wjg/PxcCJT8WTcZxxBGo1Gey3VMJhNJ+Gk0GpIC1O/3Jd2b9zMQCECv14s5AkXws9lMkooojiCJutvtYjqdYjKZiEB/uVzCZDKhWCzi/Pwc7XZbkt2Uxl0GgwGlUmklWW8+n8Nms0my3nVJLm8KJC1+6GC90Gg0UCqVhICnfJ5msxnK5bK8Y9lsdmsq4quCyVN8tjaZK+h0OqhUKqkp1Go1dDrdtWSCTCaDbrcLq9WKvb29K4mCPBfm83k5D9PQ40MBDVlqtRpqtRqi0egVgqzX60W9XkehUIBarUYqlbrRKORDAQU74/EY4XBY0rCV53QSzvP5vKSGkDD/tsSE6+B5f30f2QSuhxTyvS3YbDY8evRIhC/rvZNNIq43AYfDgU8++QTlchmNRgPT6VTOCDSq8vv9r+08c4fXi263K0IzGjp1Op1b7aUUjlGscYc7vEnwvFqtVjGZTOTc0Wq1xFCXAlSuf2+CvLZuEKcUCVFU9od/+Icy4zCZTLDb7VLnc/+jqJY9AKUQ12aziXHYdSIjAJJqSXOphw8fwmq1ylmE5L8f//jHkj66v78vwpRarSY1PM1h2VsDngsIHQ6HGC4zteYmGAwGmbeoVCo4nU7pRShFl6PRSMxqmW5rsVhQLBbl7PGioJEojfrYz6UA22azIZPJiJiPSb0mk0nWPqfTiWw2K/OKQqFwrQkS+8smk0lmcR6PB1arVVJdG42GmPOqVCoMBgOMRiOo1WpEIhExxuOZTafTYTKZQK/XIxQKIZlMCinuJtNTPv/sdZCsy37otrqKZ9LhcCgJPUoTP6baU1hFgfF0OkU+n4derxdTUMJqtYopH5/JbrcrAlTOccxmM7rdLkqlkhiZKTGfz1fMRXu9HnQ6nZhzlUolOBwO6WVuSy7sdDrydTR5GI1GYiTHWQTP2SaTSUxLSabmO9ZoNCSJdN1sWgkK7jQajczPyuWyiPMofFGm01PE9uTJExHxWiwWOByOK/ex0WhAo9EI0ZKkzpsSworFIjKZDJLJJPx+v/Qcut2u9D8BiCCm1WqhUqlIf9PhcIipMvt73W4Xh4eHODg4kHWGn4miQvajaBLhdrtF2HwTut0uKpUKWq2WCDg1Gg20Wi08Hg/8fv+tib00EuFskcbZSjNomofVajU8ffoUT58+FYGk3++Hx+NBoVBAsVhEt9sVo9yDgwMEg0HpXdK4+fDwEBcXF5jP52IEuA00lnry5AlUKhWq1SqCwaDcG86tvF4v/H4/BoMBfv7nfx5qtRqBQABff/21mEq4XC45n3u9XrRaLTHmYCqew+EQAQH3o36/Lz3bSCSCRCIBi8UiBrs0TlTOJUm2rtfrODw8hE6nQzKZRL1eF2I/r+/R0REMBoP0GI1Go5hu2O12HB8f4+LiQgzs3rQY0e/34xd/8Rfx5ZdfCh+BcyMaetBcm6YR6yAZnTNx5fyFicmDwQDHx8fS6+Zz6/V6EYlEsLu7i88++wyTyQTVahUqlQrtdhvlclmEhVyz+c46nU5J0X4d4PphMpmws7OD4XAoxjsApEfI+Viz2UQymRRhqTJJmQas/Pdut3tlbw4EAhiNRpIwbjQa4fF4sFwukc/nhTuxzpnRaDTCe3j69KkkrrlcLpRKJZjNZiwWCxEGsmbLZrPSI2avqdfrCZ9hPp/LfjUcDoVrQSPYk5MTlMtlhMNhqNVq9Ho9VKtVeDwexONxPH78WExY6vX6FRI758tcw2gAwfp23diOe9NN/QmNRgO73S7rFk1+3iRUKhV2d3clUdfpdK4IGmkmsO3dpeGR3W5HNpuV/VaJyWSCQqEgaZ7v0gBNrVbj8PAQX375pRi+s+5jcjfX71gsBqfTiUgkIv2jF8VNwnMim82iXC7LXMzlcsFisSCdTsNisUhoArk2nEly72M4Ck3pt+2nNIHQarViSMMZBOtbngUGgwF+/OMfo1gsYjqdYjabSf1DgyT2hLlf0ihomyHBTxpms5lwFdeNqjeBAvTRaIRms/lGDTNpNEqjoZvOccPhUPqVb0N0ZrVa8fjxY0lmZaKz0oT8NoYWPJPxeacokvzJ/f19MV2o1WrI5XIAIOu9yWRCOByGw+HA3t6e8EBSqRRyuRzC4bCkCtOkgrw1u90u54518BpqtdqVFHu+W+T1cFazWCwk3OVVr/9kMkE+n5danyY57AnQYOZDNIiez+doNBqScLqttx0IBKRGqFarH4wAnbXcZDJBKBRCtVrFbDbDxcUFPB7PFU4Ka0Cfzyfv+rZ+ybuG2WyW3sxNHFkGWdDI6w43w2KxCO+ZNT9ngTTTm06nUgfz+SG/bx2lUgm5XE7Mm2i0yfM5wwV6vR729/e37oM02ez1ercKULFYLNBqtej1emIg+pMO9ra579G4hXxAh8OB09NTzOdzCUl6/PixzLrJTWq322LsptPp4Pf7kc1mZY/4EE2Nl8slksmk8NE0Gg28Xq+EXpGHdHFxIX3H4+Pj99Ks43VAr9fLzEGj0Qgfgue3fD4vnDv2LIxGIxqNBqrVqvRXXrbX43K5xFje5XLJuR6AaEJoEmQ0GmWfY4geNT/sg9dqNdFZbKqTmLoei8WQy+XQ6/Xgcrmk98B6gKZeDHu8wx3ucIefOAH6f/tv/w3f//738V/+y38RQRGwmnIeDofxK7/yK/iVX/kVGX7+9//+3/Fv/+2/xX/+z/9Ziszlconf+I3fwM/93M/hZ3/2Z9/Z33SHO9zhJwt0FaLjIp3xWTiS7LqpCcTUVIobAEjSKx3N6/W6OPvScZxkz29961siHGo0Gjg7O4NGoxGhhdVqxc7ODpxOJw4ODqBWq9HpdDAajYR0e90gQq1WIxwOI5lMymBLSfCnYAXAjSJcDsA7nQ5qtRrS6TSm06k0SC0WCwKBwI1i/U2gm7rJZNo6TKerXCaTkftUq9XQ7/dXSMftdhvNZhN7e3vy72gEwAEwAHEjpyiHyQ4mk0mETCcnJ8hkMiJYtNvtsNlskrxRqVSEnBMKhbb+nWxWNxoNxOPxK4MxDpsSiQQuLi4kEcPj8Uizmokn4XAYtVpNhGb7+/uYzWbI5/NIp9NCxmc6KZ+9ZDIpbsZ0Jzcajdjf30cgEHjj4nPgsllE1/BAICCfh/9rtVrlXSMZncQMCiuYnsBh6nQ6FcHRdDrFwcHBFZEV02MMBgNyuRwsFouIum4CB9eTyQT7+/soFApwOp348ssv5feThBYIBFAsFrFYLFAoFBCNRjEejyXFYNMA8DbvDP/W5XJ5xWmW5EEKz24atDGRg6Kk1wGbzbZx8MKhxWw2Q6lUEsEXh4yVSgUXFxeSDBeLxXB4eAiTybTxPq6DQ9XlcnmrgRXd60icyWaziEajVxo58/lc7iMFokwF5XrJRtl6yghFL+vrKokxw+HwionATSCBjckRwGqtXa/XEQwGUSwWRfitbKhOp1NJMybp5bphNNfGcrksazwAET5TLGA0GjEcDnF6eir3lOYYdGnX6/WYTCbiersJJOCSbMLB2XX3nkS1TU7CDocDR0dHcDgcKJVK8jczoYP7czQaFeJPp9NBt9tFLpcTImsul5MEocFgIM3qdruN+XyOcrkMAGKksbe3JwI/APjxj3+MUCiETqcjxjJms1lIZ9vANS+bzcrnLRQKGI/HGI/HQjhRqVRIJpOSRPu6BBs0g2CN4Pf7Zeg8GAxkjz07O0Ov15OEgsPDQ0nwVg5Xec4jQXab4/rbBJu+9XodmUwGOp1OEqU4bKhWqygUCnC73ZKYU6vVrh0KMpGIw+HbGJzE4/EVIXGz2US32wVwSaTY3d2F0WhEOBzeWGeQuFGpVGC3269twLPOyefzktK06WuZapROp0Vcq6xXJpOJ1IEUPx4fH9/4d25CpVJBs9kEABwcHKDVakGlUqFer6NcLouAVafTIZ1OSwrI48ePr7z7ZrMZjx49gslkQjwelxQNmhbZbLaVlE+uxTqdDuVyeSU1TqfTIRaLSVL0+fk5BoOBCGgNBgMcDgc8Ho8QBq4zXbLZbCiXy5JCkU6nJZGe4mO1Wi0GOTqdToyIfD4fWq0WyuUy6vU6vF6vpKwxPczlciESiUiiKY1ceA6JRCLSxL5NvUPDKboU31Qj8LlyOp2w2WwiBKdb7Hg8hlqtRigUwt7eHmw2GyaTiRARp9MpotEocrkcSqUSjEYjIpGI1Dqs75Wi703g/kKinfLf2Ww2xONxDAYDMRYj2WUTlMN0/l6edZgg6/V6V+4512GDwYCdnR1YLBY0Gg1Eo1G4XC4sFgs5O6RSKYRCIXmWbmsEdB2JkCTLTqeDYrEoqUler1eI9hyMcP8hqV2r1UKtVsvPpJHBaDQS8hcAIYUorxn3e4oERqORkOmY+L6/v49YLAa/349eryd/A41wtiXr3eFmsHai+HwTEZ8mbrlcDuVyGSaTCZFI5I0NMLVarZiEFIvFjcIuukPbbDZ8/fXXQgzeJBjnXjyfz+H3+6/df1UqFYLBIJLJpIgN6Gb9IYDkKwoFriOzeTweMRWwWq23Mgp538E9odVqyVpBI6f1OopC5U6nA5vNhnq9DqPRKOfgtw0aod32d2u1WkkMeZvQarUIBoOSrsvznV6vf6vrL9PWIpGIDOspzvimEYVJPlSeb51O5wdljAFcnpOz2awIVihALxaLqFar0Gg02N/f/8bdvzt8mBiNRpI4TlOmUqkkNSvXPSb5dbtdFIvFjSLWV0Uul0O73UYul4PH45GzA/uio9FIZlFMTzYYDCJcWzfsIymcabU0yDKZTFuJlL1eD6VSCc1mE1qtFjs7Oyv9KI1Gg0AggOl0ikKhgEajIXW92WxGKpVCu92Gy+XCH/7hH64QpLiXMf1qOByi0+nA4XDceHYjaK5DcTc/C8+/vAacqbHfcXR0JCk560Lm26Ldboup4jqh1e12o9vtSp3tcrkQDofFFHc6nYrBZzweF9FhJpOB3++/Usctl0sUCgXM53MRgZJ0tlwuUSwWxUyMhFm1Wo1ut4tms4nBYCC9NJ5RaW5gNBqxs7MDk8kEi8UiNdVtzv4A5Hz8IiCJFHguYqCglYmjnF1SODQYDFCpVOD1ehGLxWC321f66Dyf0uTbbDYjm82K0JUGB91uF+fn53C5XHKtlO/v+fk5Go0G0um0mHmyTmNfn/0O4LLndl0PjcIYPn88c5PIzLOtwWAQ4jTNAZ49ewaz2SyiCs6zotHo1vVGpVJhZ2dH0qdp7sn/RnGezWbDfD5HJpMRM+BUKoVWqyXmXJVKBW63W9af8/NzJJNJeL1eqNVqnJ+fS5/DaDSu9H6JwWAgBq/n5+eoVCrQ6XRy3mq32yu9NJrgBYNB1Go1MVU1m80ifOcz7XQ6RazABOCTkxPpx/LZiUQiYnoTCAS2mgYQxWJR7hfPzLyGVqsV3W4X9Xode3t7W5//ZrMpqWqdTkfEwOyZsBfPhMiLiwsUi0XkcjkxDmfvg+skDfmYFMjrw+RxjUYjvSUaBW4zGSE4R9nb20MqlYLRaESxWBQxBc0xeb72+Xx4+PAhfvqnf1p6h5lMRgxFdDod3G43UqmUCBNpGg1AZssUtJPvwO87ODiA3W4XA+FsNisJUEz4JNn6/Pxc1iwmjmYyGTHlZCI1DRacTifK5bJcY4/HI+suv9/hcKzMtF4E7Ht3Op0Vc45N6VY6nQ4ff/wx2u22pM0q/xvNPq6bD3DtT6fTsNls2NnZEUEDZ61er1eM+3K53EoPmqJVvmsUa/d6PUlRNRqNcn+47kYikVfmADCpTGnEQdBcRgmmi7EfzHRXfi6lITaDHJhmyGvJazYajcRIYnd3V9ZepXjyOs6MVqvFvXv3JAmeprjtdnvF4GKxWIjR6XK5xHw+x2g0kveZBuPsY1gsFjEf/fjjj+FyuXBxcYFWqyUGLEyGp6H4n/tzfw7Hx8crZPb1moZJw41GQzhLNHzkZyaYKP0iBht6vR4GgwHL5RLD4fDG3ka/35ef/bJ9RT7bTLfnz7pNf5xzn+VyiXK5jEwmI7walUolezSNH5iY/S5BU1ebzSaG+FznaUrLfclsNmM4HN6qNmNdU6vVxOCY+9E2k0Gml+dyOahUKklyZm0znU5xeHgItVotZjKDwUDeQY1Gg3A4jLOzM/lZN+3NTGumUIz1FRNKuTbVajUUi0VUKhUJ8FCr1SJCWX+vaaTOAIm7vsQlWDfQSPgmkCujnIW9KbCmoBn3NvHtYrGQde42QvrXBdYr/GwvYqa/WCyQSqXQbDbR7/dlz2PNR/4WTatZq9GgDHhubORwOBAKhSSchGFA2WwWyWRSggN4Pufawdpg03pOIb3T6USlUoHP51tJf15/xyiKetkzNzEajXByciL1Eg3Nya8jF7Lf72Nvb2/FQJXP5/uMVqslM9h4PL71a1UqFVwul8yYaZ4xHA5Rr9evJMPfxvDgbYDz/1KpJBwlnvEYHMDnh2d/v9+Pvb09BAIB4c4oOXZvCsrzOrlqN+0PPp9P9tR2u711H67VajLL+CaYnL8NUCBuNBoRj8dht9vR7XaFp242m2Gz2YQLTD41TeyV63+9XpcANho7MnhsuVxKTZVKpWQms21OzN4m6+zbgOLqtz3jex9RLBalJp/P52JaRE4zw1HYS7FYLDg8PBThP58FzruXy+WKdsJutyMajSIYDN5oyvc+gn12BnWt82HsdjvsdjtqtZoEHJVKpVv1vT5U2Gw2HB0dIZPJyLnl9PRUeO4ApG4aj8dIJpNYLBYIh8Nyjn6VmbbNZsP9+/clDHI6nUoN5PF4ZC+bzWZ49uwZms0mCoUCptOpcPJns5mklofDYYzHYxwfH2/kwty7dw+np6cScEMOJADpxdrtdni93jcaHnGHO9zhw8JPhAA9l8vhd37nd/Dv/t2/E8ElD98sJADgL/7Fv4jvfe97+Ct/5a9cORh+97vfxXe/+12cnJzg13/91/E//sf/kO/9V//qX90J0O9whzu8FVBATvHOujM+cNlUIfF+ncCRy+VkKOp0OuH3+1cOwXTYzefzsFqtsFgsePjw4Uqjw2QyoVAowG63i8Mbm+9McqabJQARJy2XyxvdnUjSpcu4UoBOAaqS+LINTGviAYhDczZvOp0OzGbzCxEQZ7OZNOVu+v38zBQT8X4tl0txUKxUKuI8TXd4Ds8okFGCyZKz2Uyc03ndjo+PoVarEQwG5XfRfZsNQBI5SJ65DsvlUpykzWbztcRTl8uFwWAAq9WKbDYLp9OJbreLTqcjyReRSERISST48zPHYjEh2yiTFfx+vzhr09l4PB7DbDaLW/r+/v5LuSq/CJj8wBTS5XKJL774QmqIdZEC3V9NJhMcDgdisZgk0rKuUKvVWCwWODk5EdHNtuEniThK44GbBhf9fl8c4nU6HSKRCEqlEiwWC87Pz0WwO5lMRHQDXCZXhEKhFZITh8JKp9nhcHgjGZlusCRzKEFyCAkYLpfr2gbueDwWgSRJMm8SBoMBh4eHsqaxYQRcktaYUGuxWCTx/EXSsjlk0el0Nw6sAMjASqfTSQLH2dmZPBckTbTbbWg0Gnm36WjaaDTkHrLepSMwACFG0EF+HWq1eqOJwItgvamiUqmwv78v5LdyuYxarQabzbYiPiPp3+12byUjktjC9dRqtaJer0sqDgfE8/kcFxcXMuCmIQYbL61WC2dnZ/D7/Vgul7h3797Kcz6fz8XdfzKZiJu0TqcTghTTXZTfw9Tk65yErVarmGp88cUX0Ov1QuJxu91XxPkkCC0WC/h8PiGHNJtNaTjX63X0+31ZX4xGI+r1uvztJIaRTDIajWSYFolEhIhIA4a9vT0Ztq6Dn4eGGhx4KAmOvE4A8PTpU1gsFsTj8dfSfC2Xy2LG8uDBA3HgZlIYk3EajQb6/T7sdjtCoRAODw8xGAxwdnYmAx4Sk+lm6/f7USgUtoq43xaYTpLJZETken5+Ls83Pzf3F7PZLCTK8Xi8kpLF2qBer8Plcsmg97aIRqOS4GCz2YQkoNPphJB53ZpOksVwOLzxd9IUgilH60TbxWKBcrmMSqUClUqFRCJxZR2mGYZarUY+n4fJZEKn03lht9F+vy9DR76TXPdJLKHQBYCk+pJ8bLPZJL2JpAWr1YqHDx+KWQ+TuWiO4fP5sLOzI5+VqTzpdBrn5+ciSieRmeJ+1rYOhwPhcBh6vV6awsoh/TooIKfzL0kHJO8wLYxENg4U6H7O5DAmvtKso9lsotfrIRQKQaPRoNFoSCqa3++H2WyWhFyeKThouwlcVyiquw0sFgt2dnZQr9fhcDjESKtWq4mL7b1794T0wM+hFOH5fD6cn5+LANPtdkOlUkltRaLfdecLpmrSgV35ddwzSdJ7WbGmz+eTdb3ZbK4Qx3lP1Go1/H4/isWi7F2VSgWTyQTdblfE2kzMZVKH1+vdSibg4HSTOQsN1LiPBQIBOSuRzEb39nq9DuDy2d7d3ZWvoalM6/+lQwJYea5Zt2g0Gnmver2eGCzR0GixWKDVaqHdbovAnM81U+JI3r1tnXeH61GtVqVevekc7fV6cXFxISTZN5lm4vf7Za3KZDKSdqOE0+lEMpmERqPBaDSCzWbbODSkkMJisSAcDm8lHFGYROHNhwQa91DAvw0ej0fWnFqtdiOx6X0Hz07D4RC7u7uYz+fSv+JZRgmr1SoJCeVyGXt7e7Db7W+8j7EJNPK4LUGTQ+p3SZx7F9dpHRRjfxOx6XxLIjiTUSKRyBtPZHwdaDQauLi4QL1eF8EDDY6UJlLsmVzXz6LQ1mAwvHGS4R1+cjGdTnF6eop6vS4Jy7PZDBaLBf1+X8wgmKhLwdd1ItZX/Szsp9lsNhG9zGYzpFIp9Ho96WnRcEWn060Y61arVQQCgZWfuy5Mp9HuJqI4kU6nhYx+cHCwcf8hyVmr1QoJ3Ww24/j4WM7UyWQSVqtVyIQ+n0/OJBSeFwoFEVofHBzc6lqxn+v1elEul7G7uwuVSiWfh/Uhk9Wj0Sji8bjcQ4/HI73DF4Wyt7mpTs1ms7DZbKhUKiKs7vV6sFqtKBaLYkAWCoUwHo9RLBbRbrfx2Wef4ejoaEVgyQSgWCwmayIFldlsFq1WS/6P/VAamZTLZcxmM6TTaTH7Go/HaLfbkmKrUqkQiURgMBgwnU5vfY5XgjMn9hopBtwEmpgBl887RYg897HnxBS41v9LoKb5GAX9StBUlmJEiq8DgcDK56AwIZ/PA4CYDep0OkmpzuVysFqtK8JoANJPslgsSKVSqFQq0Ov1V9LaCPamFosF0um0iK5pQsw9kedc9kHYd6LZnNFolGTw2+7/VqtVTKqVCcJ8hznP5oy40WhAr9ejWCyi0WjAaDTC6XRiOp1KAmapVJKEIfa0aRpK09XBYCDvYafTwdnZmaQxMY2URHH2InU6nZD6NRoNgsGgpAjpdDoxUKQpsVJEFI1G5f56vV643W75vDR64BrHPv9NKJfLK4R0h8MhvTfOxDKZjCQm37t3b+MaQhE712v+Hey1cHZFwTWvE+cHoVAIJpNJzCKVhho845Dwypn6fD5feb/Yl76tcTWToflznU6npM6zp8MZuNfrFeGE1+tFv98XAQmF3hQR0+SS3zubzeD1eoXsH4lEJCmdRqoOh0MMB64jW7O/y7mDVqtdMVUMhUIolUqYTqcrqb5Mbc9ms+j1eiJOAC55Fr1eT96TF6k9F4uFpF1TzKg0UazVarBardjd3V35uTSHcDqdWCwWIlq/qQdLM5FyuSz9DoJrMvA8KZPiVJq9AJc9XLvdjnv37smsJRaLieHApr1ud3cXgUDglY3saPBttVrRaDS2/rz5fI5CoQCj0Sg8mfF4jGg0euX9CwaDMJlMyOfzMhdUq9Vi2mCxWBCNRmEwGFZMJJrNJjqdDlQq1Y1rrV6vF9OWarUqe6eyFiQfg6Z3NJ3n/NJoNIroD4D8HXa7HeVyGR6PBx6PR9ZRzhLC4TAODg7w8OFDfPzxxzCbzWg0GphMJhtNSp1OpxjZdrtdOBwO4fOwr64MZiBn6LZzN5fLJX3jer2+dWZMMShJ8q/KK7htWvc6OIvRaDTSHyMfQ6/Xi/mMsif9LsF1pdvt4lvf+tbGd2U6nSKdTqNQKMjfte369vt9mcvzWVUa3xWLReEGre8h1WoVvV4Po9Fo5XzAnwNcznooVGVPVika5LvPGfptrrNGo7nREMBgMCAUComZksVi2fqM8AxFc5c7XMVteShKnvebBPlx5KoxBGjT5ykWi9BoNLDb7e/USOJFxOenp6cSWMTAFdZ4zWZTTIBms5kY+vOZ51mM77HyjK80i+C5hzM4cq84E43FYluvl8/nk89SLpev5RSw3mfN+LLck/l8Lr0acmFpug88758Ui0WpG5XcCwBiwvW+9lKn06mYM93GpJZcV3IHM5mMzI7JRRsOh9BoNDAYDAiHw2Jk9q5mCqw/aDrNZ5Gm/5wZ05je6/UiGAzi3r178Hg8OD8/x3g8RrPZvNWZ7mVAM3/ucRTKGwwG2Gw2CdLaBLfbjXw+D5/PJwnM6187n89RqVTQ7/eRSCTgcrm+sSnFrxs049Fqtfjoo49WQgqA56bm5Jfo9XrkcjkJxuFZjz1Hmgru7OxcOZcxTCybzSKbzUr4yHXvJutopTZgG8jde9k69puE+XyOUqmEUqmE5XIpxmAEex/kT3KWnU6npb/Ffr3JZMJ4PMZ8Pke1Wl0JbgsGg1f6ex8KyOFQqVRbwxi8Xq8YkVgslivGC980cO7Q7/dRr9cRCoUkAFGtVmM2m2E+n0vvym633zq87LYgv/86MDAwnU7D4XAIB5qYzWaoVqtIp9Py9YeHh1d+jk6nw/HxMer1uhizKnt9rHO/qfPyO9zhDi+Hb6wAfT6f4/d///fx/e9/H3/wB38grp/AagKj3+/H3/t7fw+/+qu/eit3jnv37uEP/uAP8J3vfAefffYZAOCHP/zhm/oz7nCHO9xhBefn51I4rjvjLxaLFQdZAEKmBy6LykajIUKS64jcPCRkMhkZxiq/1mw24+DgQIbgSpcll8t15fDKRhYFAzeBblF0YuP3sGHI/74psZdg80+r1aLT6YjIxGQyifC7XC4jHA5jNpvh+Pj4VuRWihwoargJlUoFw+EQw+EQZrMZ0WhUSBYej0eSS0ulEobDoQhw6Ci8Ts5nCpXysxA8+NCBnKQ14LnTo9FolHTDbaBzbK/X2zr840BxZ2cHn332GS4uLkRAVKvVsFgs5BAfCASuCDDVajU8Ho80hUmOolMmHeABCBl2uVyi2Wziq6++kiH1mxoyKAnmBwcHOD8/h9Vqxfn5uRg9kLBDF3wSfu7fv78iune73dI8HI/HGI1G+Oyzz2AwGFCr1SQlgkSo9eucz+dFlHZT45zXiUKxxWIhg8JwOAyz2SzmEcrkZ5vNho8//hiFQkEEf4FAQN5vJiW0Wi14PJ6t173ZbMpzvOkAGggERNSUyWQ2OgH2ej0Ui0VJEHhTTd51mM1m3L9/H9VqFbVaDYFAQAgcFA96PJ5rm+d8HkjgoOCcP9tsNkuiL1MaNqHb7WIwGCAQCGBnZ0fuKYc1lUoFAKQZabfb4XQ6sbe3h/Pzc/j9fmSzWWi1WkkQZxIKCZIkyZEAogRJhjqd7tYumi9yjY+OjkRM3+l0JJ1Hp9MhkUjIddrZ2dm6d3BtDwQCSCaTklbPBCD+LRyKMF324OBg5RnmesbBeSqVwsOHDwE8Jwg3m03UajUR7nG90+v1cLvd0shXppVTUL9tsM2m5v3791Gr1RCJRMRdfh3tdlscbZkq3G63hTRE8b7ZbIZer8disZDUIjqpGo1GIauMx2P4fD5JuTGbzSgWi6jX65hOp0JYOTg4kPqAzXPWHawF2u22OApTsM7GG0lWzWYTqVQK4XAYjx8/fvGHRwGmFHW7XTx+/Fic42mEQmG0wWAQMx0m5g6HwxVXUrr/Mh2nXq9jMBhIMg8Fx+8K5XIZ5+fnyGQyMJlMK+QLklhJGqYLb7lcxkcffYROp4OLi4sVl269Xo9gMAiXy4Xd3d0XTjg0GAyIRqOIRqNXztjEpmSI4XCIfr8vQtObsF7PcLCpFI6S3L1tnfD7/fK1XPdeBNwzp9PpyveyvqKAkKRTJlvodDoUi0URypLU53a7EQgEMJlMUKlUYDAY5L0k0W44HOLs7AxOpxM7OzvY3d0VMubp6amYbCjF1263G5FIBMFgEEdHR0JgdLlcsNvtN66lJFk+e/YM/X4fnU5HEmLa7TZms5k8c5FIBC6XSz4TDVxIFjw5OUGpVEKr1RJxO4UHoVBIEuHUarUk9rD+4XN6k0EUn68XGZ5xiEtzE7o/s17ivk1wHeGaD1w+/0xw6fV6iEQiUmN/9dVXMJlMaDQaCIVCK+8FyX6ZTEZEHLx+JJtR7EeC3k1NfD6bJJUTLpdLRAaFQgGz2UxqUSaD6vV6lEolqFQqmM1mDAYDISyyPqSD8XQ6RS6Xg0ajweeff45vf/vb194frr8ccihRrVYBQFJDKNQfDAby80iIrVarqFar0Ov1sh8yCclsNkttzLqGYEr6YDDAZDKRWp57tsFgkBqHqYftdlv2pwcPHtzKaOwOLwamw9FRfBuMRiMMBgN6vR76/f4b/Vw875DArTR64lmdJgX379/HcDgUcyqSi4fDoRi7OJ1OSei8CUojBQpBPgTQSIZmWNtAM43hcCik9A8Z6ymaFBb1+32USiX4/f6Vsx3XnVKphHA4DIfDcW1y5ZsGk7tKpdIVgdQ6mJbI77nDNw9KMdj6+ZZ7q9vtxmAwQCKReOdmXNvQ7/eRSqVQKpVEZOBwOFZIWaenp8hkMiIQ3dvb29iHoSBlUz/2Dnd4Xcjn82i320IYVfZLrFYrdnZ2xKxxNBqJmSyNFJji/TpMEur1OkajkQg5AUhSWafTQblcljMFjRvZVyTBqlqtwmKxrPR02QvQ6/XQarUYDAaSen1dD7RWq4noaZOAgyZZvE4ej2flM5GMxTM4U1yVn4tC2VqthtPTU7hcLhFj3QaJREJIhpVKBRqNBgcHByJ2Zd91d3dXRFLZbBYWiwVer/eKkHkbaC47n89l1sdeg7L+oiHjeDyGRqMRUiUTZW02G8LhMAwGg4gwgcveCsnXNP5ib5OmsWq1WpKYKpWK9P9Y61I4zP8OQAyWgUthHsWWBoNB0mEAiNHHbWc6y+VS5pqsU9gn55wtEAhceb6UBoA07AMu54+7u7uo1+swmUxypqaxgc/nw/7+/sb5B83U2IflO7IJFB+m02m4XC7UajWEQiHp6c1ms61mW1qtFoFAQNaLdXM5guJcJtH3ej0YDAYRMxNutxu9Xg/JZFIEmUdHR9Kzo9HCy4CGcetQzrMtFgt+5md+Rsj2rVYLnU4H6XQa6XRahMR6vR56vR6PHj0S0T2N7mq1mpAmKcpn4jkNbiORCHq9HqLRqJhTsCddLpcRiUSEROz1eqXXxetAU2UKrNVqNS4uLvDw4UOpL5QppwT786wltoEpwxQxr9cmNFfo9/vIZrMALuu3+/fvi9ksABGc08CbZ5TJZIL9/X0RAddqNTGWDgQCSKfTSKVS0itkT4hGPiqVCnq9HpPJBBaLBcPhcMWAksnI/L7FYgGLxXLr54fpgo8ePUIqlUKxWJSfMZlMUK1WZWZBEUk6nRbTdKZA8xmfTCYijuf9pzAbAOLxOB48eLCSAkWDXuByjs/95Cay9WAwQDablbMhv3499IRQq9WSlma322X+pfy6F5lvLxYLJJNJNBoNMd7ne8N0K61WK6Yjx8fHG2sGtVp961piU72g/Dw0vOaMgubEzWZT5vsajUbEGhTyXZd+vVgscHFxIcaWr3om1Wg0MrtLp9Ni7KPEdDpFvV5HMplEr9eDXq+HWq1GOp3G3t7eRsGoTqfD3t4eAEg/mfN04Dk/x+PxrKzFnDEYjcZb3Xuz2Sz9Ua5PTHKnIKzX64kQkCbF8/kcw+FQ5kWHh4eoVqtipGexWGTvoBnueDyWmey9e/ews7OzYtaz7fOyPnW73ajX69fOlJkcyPt/mwRt4Op9bDQaG/fExWKBfD4v7967ThWnAUa9XofNZpMeNs/cnM2+a3Q6HRGTbDN+0Ol0iEajYhzK2mYTer2eiFxZK3L2wf6qxWKRd4LvE/C89qPho1qtlvlSPp+Xn8dnkmsPjW+VPQwafrzOxGzW/ZxD3NTT5Z6hnDHdAXIe4D26aW6rNNZ40+/NYrGQc7jNZkMqlYLH45GwDKal0tQxHo/D4XC8t8JjJcgDS6fTkvKrXLOXy6XwPfjcPn78WNbhm6BWq8VEplqtSn0JXN5zprbfJIhl2mosFhOxF0NCgMvauN1ui/GOx+N5JeEf+6OFQkH2byV0Op1wZ7/++mtYrVbk83nZx9hL5JntJn7Tu8CLmjewXh2NRkgmkzLPZj+Z/A1yPtPpNA4ODuDz+RAIBK7MyN8WwuGwhG+k02nM53Ph2rEP0u12xbA+Go1Kv4bi3lfdM64z6OIZkKbYSiExnyHyXjb1CtRq9UptWiwWUa1WpRdOwbRGo0E8HofT6fzgTaHfJmiKBVzOby0Wy9YZAfeDdYOZdruN0WgkRgbXnbt4/kwmk+j3+6hUKishbetfa7PZJIXa7/dvfb/6/b7U/T/pMz7u1d1uF/F4fGPdRj4duZfsUdMsD4CcxxgCYjabJdDn/v37r2yc9q5Ac8BmsynnyW1wuVzI5XJyrX4S5mhKEfiDBw+Ec8geMQ3oKNB+W3uf0myXQUzrYH8IuKwBydnY1L9Tq9XSFx8Ohyt6IOU6NhwOxbSAvHgGCr7Nv/8Od7jDu8c3ToB+fn6O3/7t38Z/+A//AeVyGcDVtPPlcolf+IVfwPe+9z38tb/2115YVKNWq/Grv/qr+PVf/3UAkN9zhzvc4Q5vEhRU5XK5jYUjh95WqxWpVEqGwV6vFyqVSlIi+v3+lSHhOjgsZurypuYGSQw3gevvumD6OrAZw+9VfqZYLIZsNguNRoNisQiXy7WScjCbzdDpdJBMJkUwmkgkcHh4uNLg8/v94uoJXO4d9+/fv/Gz8SB6G1c5Cv7p4P7o0SMR7dTrdUmHZbJ7Op2G3W5HJBKRgn698UphpfKzKGE0GnF0dIThcCippyTjuN3uWxH+gefidjrZXweVSiUu00xpZnO2WCwiEAjAbrcLwSsWi11ptJIEPR6PRVzd7/dxcnIiQ6nlcinkAA6lzGazmBvs7++/kQOM0ryG4ms2l61Wq6SKMImR6WderxcOh0NSdfn3EySDkBDQ6XTkfeaghWIxkj3YuGLDaxuYAFAsFkWsVywW0ev14PP5xIWPB2EOdRKJhDQ2K5WKkAp4kPb5fGLoUC6XryVIdbtdtFotxGIxuWYU5/HwSSd2ACiVSsjlcpK0BUBIWE6nE4FAYGtK1ZuATqdDOBxGKBQSJ1Q681431KKQkO8eAHE3dzqd8Pv9sp5SqJBOp6+I7ymsJOnNYrGImMHtdl8Z1gCXQ1sK4phqzEFcLpdDq9USUuByuUQqlYLL5YLT6YTb7d6Y0sjmtMVieSMudiaTCQ8fPpTmDNO5SUbhs3ob7O3tSXOHqUkc7KvVapRKJXl/9Hq9uPuvP8MkAbFR3+12YbVakUwmUa/XJQVD+TyS8EpyMAAxdJhOp+KiexsCFk1SlMJdJTikns1mMlCnyzCThbRa7YqQlSn2Op0ODocD4/EYmUxGvo6DIBI/5vO5PDv9fl+IgqPRCJFIBIPBQNLZuR7RaKXb7UqakXKtJ/nFYrFIKl0ul8OjR49eae1mKjVTH9xutzSE+bzz5/Md4Zr74x//GCqVCs1mE36/X+4nBdK7u7tCwiuXy/Jcvgtx2GAwwFdffYWzszP0ej2kUilx27VYLGJkwc/IFKVHjx6h1WqJcJmEZYo3mbL0qkn0m8i4TEdVfg3J3RxY3wb8umaziS+++EL+Dqafc73dlnjBn2O329HpdIR8+CLg2sm/RQkO3ZWD91KphIuLC3E2Vv69VqsV3W4X6XQai8UC4/FYxMhMTh+Px7IvRqNRcZk3Go0rz7VOp4NOp5OfT3d6k8mE09NTOBwOLJdLEXdfR8omDg4OMJ/PYTKZ8OWXX6JcLsPn8wmxmmRQEjzD4TD6/T5yuZw0oo+Pj3F+fi7pQSSEa7VaIeHzHMPPTSL2dDqVdOF2u33jQIyJRUw/uQ3MZjM0Gg2sVis6nY4QEphCsV738pllcnuxWBRBvsvlEnMRn8+HXq8n6zjTbmlsMZvNUCqV0Gw2cX5+LkTzarUqe2yj0UAgEJAkrWw2u5LYoQTXaKbNL5fLFUKEWq3GwcGB1HrlchmNRgMqlQqVSgX5fF7I0jSB0mg0qFar8Hg8QpTh7+G9rlarKBaL+PLLL/Htb3/7yvvQ7XZF8EGXeYLrKxOo5vM5Li4uJE1rPB5LjcX9q1arifEZk7uYlDWZTHB6eiqmWbxWvN71eh3L5RKj0UjOUP1+X/ZEpji2Wi0Rk/R6PVQqlfcideY2oKnVYrEQYcX7ONShwQPrvNuAxP+3kQpDQzONRiMkaZ5B9Xo9/H4/9vf3xSih3++LgQ3w3PU+kUjIOfU2fWal2d77Rkbahpe5l/yeDx3KlL/JZAKbzSaJhJVKBblcTgRAACSlbGdnB/F4fGt6xesGUzxobkDSZKVS2ZhWq/w+mlYaDIYrZLs7fPhYLpciWslkMjCbzZLSCzw/3/KMDkDOku8jKDznfr5+7mZS5nA4xPn5uST3rb8DTPRVGiyxfr1LQ7/D6wJTk0mQdrlcYnao0+lWyIM0laTxIEWsTqcTtVrthcTM14GmMkoxY6/Xk+Rcu90Ot9uN8XgsgmWmrPr9fiEcF4tFSTkDLt8nEq94/uM58zqwptdqtZI2p0Sn0xFDQb/fLyISzmeUBrY887FWXu8t0vyVxozrou7rYLVasbe3J+JBi8WCZDIJvV6PbrcLACIsGo1Gkpju8XiE+HsTms0mSqWSGCKyR1epVFAoFKDVarGzsyNn+lqthkwmg/l8Dp1Oh8ViIX8zjYyZvMqzVjwel2dw/dxjtVrh8/ngcrkkAZY9P/YVYrGYnPfNZrP0PWmAyV54s9mUBBim1ZdKJej1egyHQ3z00Ue3ImdSiMjfSfM/4HlqFE0cDg4OVn4mZ2Ldbhe5XE7EjHwOgsGg9ATPzs7QarUQCARw//79jeYrNARg2vhyubzRpIWEVM5ZQ6GQmIva7fYba2oKtbrdLtrt9kbRB0X+NJBwu90bycgkDRqNRsznc9jt9lvPeF8GJPbncjkxQwCeJ+nRnGE8Hot543A4RDAYxCeffLLy7vJeW61WZDIZFAoF6HQ6ERlXq1XEYjGMx2Pk83m0Wi3pMYRCIcznc6TTaREuK80YSCBmj87j8SAajSIejyObzYqJKQUK153dGo2GiKVv6mcpk6uV7/Q6WNN/+umniEajYgTG9aVQKKBSqWA8HmNnZweZTEYSwinet1gsqFQqqFQq0j/mtR2NRkJKr1QqIkRlb5LzWvZz2EujQD6RSMh6QuHpbUCzDvZwYrEYms2mGLT4/X7pxTocDrlWNCnodrsYjUYoFAqIRqNiIsd1h8/2cDjEzs4OPB6P9A35dwUCAbhcLpmBcn52037gdrvx9ddfyxrLn6vT6cTwUNnfAi5nPq1WS+4nE0FNJhM0Gs0L8dRyuRwajQZSqdSV9R24rDVqtRqy2azU/Q8ePLj1z9+Efr8vxhbre6pGo5E1kT0Os9m80YTO5XKJSV25XMbe3t6VnzebzVAoFKTPyb40BRQajebWPRcluPaGQiGZlXNPGAwGODk5QblcRrvdFvNf9ok41+71elcMCtRqNbxeLyqVCvx+Pw4ODoSzsq3Ps8ms4DpQTM693W63o1qtynsynU4xGAykFmK6OPe7wWAg8yCagdOsg+IrziSNRiP0ej2i0Si+/e1vXxETMB29UqlIPaREJBKRGQLXFLvdLmKodruN4XAoouvlcokf//jHwrO4ibTP+xgOh1EoFNDtduU+0qiEpk3xeBxut/udJbSNRiNUq1UxFKLRy97e3mtLw3udIJ9JOT+gUQVNmPlMG41GWdeYBLiO+XyOs7Mz1Go1eT9ocESMRiOUSiUxljGbzTKz53PEmSZnAMPhEOVyWfr+7FXY7XYxJFnnb72JxGzWvFar9VZzrE6nIzP5D1Wc9CZA43fWi5u4cUrQjIwG028Cg8FghYczGAzEDLxUKqFarcrcArg8h0YiEQmIeN+xWCzkveT8edOZge9roVAQE40XNVU1m81IJBJIJBLXinG3QaVSYX9/Xzif1WoV2Wz2ioGm8sz9Kr3LarWKZrN5bT+cicacIzYaDezu7sLv90Or1YpBba1WkzXsTXEYXxZKI7her3fj2sUeTTabhdPpxPn5ufDYyAdkCmytVoPT6USj0cC9e/cwmUzkjPK2r4Hb7ZbfX61WUSqVUCgUhB/Jvgb5mMq+yMs8q0Sn0xHzPq4RNJ93u92o1WooFApiHkYDQK1WK0bplUpFjBL5fK/DZrPh8PBQeEGtVkvCG8jdoOh4f3//gzG+fh/Asw2AW5mGc369Xvs3m010Oh2oVKob1yWlaSLnzNfB5/PJc1Kv16/tR7EWdzgcd0a7eD730el0G+sw9haazSYGg4GsDeT2MmCCJpsmk0nChLRaLYLB4Add39Hkk9zDm2CxWOTrb9JNfFNBkxwAt54rvAlUq1UJ+tq2dgCX6wfNLqrV6o3mJCaT6cq5dbFYIJVKodlsSpgN6wCLxYJWqwWj0Yi9vb2X4vdzVqPRaG40KrrDHe7wfuAbI0D/3d/9Xfz2b/82/uf//J8bCeJsVP+dv/N38Gu/9ms4PDx8pd+nFG/eRgx2hzvc4Q6vCuWgYluDT6vVwufzoVQqSeKgy+XCcDjEYDBYIRVtA8Ufk8lkhRz9omAzvVKpXCuiUIKDV4PBsFKku91uuFwuxONxlEolGSgzUZzDW6aTz2YzPHr0CPfu3bvy2TkUZEKl1WoVofA2UIxpt9vRbDZF3L8JFMGNRiOEw2G43W5JHzUYDJKwSkco3leSPjY1C1qtliT/bbuOJpPplYQTHN4yUXDbddFoNIhEIqhWq5LuzKYYG+5OpxMOh2PjtWLTRilMODs7E5EzSU/Ke0hSSiqVAnB5wL3OYflVwAaTSqXCZDIR4gaH3MrDNO8tGxFMfaA4ch1sHqpUKhHdAJdEBZvNJk3faDS6sXFFgQyNDCim8nq9aLfbIvDkz+KzRgGl3W6Hx+PBfD5HNpsVUT1w+fzQpVEpgqX4mUYQJOQwdZcujq1WS1KnjEYjvvzyS0kp5uGTh9Xlcgm/3y9rFUkJfG4MBgNisdg7I76rVKpbidqazSZ+9KMfoVAoCAETuByuBAIBhMNhNBoNRCIRhEIhhMNhWX+YSsDr0e/3oVKpRIS2v7+/kqCeSCQQi8Uk0bNWq60I/IHLAQKf3f39fRFd0rGVZBESqtYHDOPxGLVaDW63W1IGXqc7OMGm66uS2fk8abVaEUcqU4E5uHY4HAgGg6jX6zAYDHA6nVf2Q5J5maoym81k+MnvV4LNf5q/GI1GFAoFHB0dAbgUp7RaLUlWZ9r8dYJC/m+j0UCxWBQjEZ1OB6fTKe8QBfVMdyVZiUN2pVEK114mYVBIub+/L6LRZrMJo9GIUqkEs9mMaDSKnZ0dIS4Cl6ZbSodPXjuSvkaj0dYkauX6RmHDqzS81xM2gEsiE0UDNI6g+NVms6Fer8NqtYoYk+83DQQo0GE6skqlQi6Xg9frfakB66uA5KpPP/1U1greZ4fDIXVMv9+XwR5wuffW63WUSiXs7u5iZ2cH0+l05VlSkm5f5+e9uLiQoYrSudloNMo6xBrhNs3Y0WgkafTT6RStVkuGzcViEY1GAzqdDtlsVtbM60CTBj4367+bvwPAFUId3y3uddv2hVarhWw2K2Qnv9+PUCgk72a73UYqlZJkHZPJhMPDwyvrMAkN6XQaKpUKX331lRDQY7GYGPHwbzEajSIkevLkidSTTFNRkrIPDw833n+9Xo/9/X384Ac/wHw+X0kp4zVgrbZYLFbqil6vh263C4/Hg/v370saMJ3nWUNvEvGwrhqPxyIWSKfT6Pf71zanJ5MJGo0G/H7/C6V0aTQacRDP5XJCzmfNp+ztjEYjXFxcrBA5SYijCcTZ2Rl2dnbg9XpRrVbxrW99C3/yJ38ipkm5XA56vV4Ef8ViERaLBbu7uzg+Psbu7i6m06mQ+xeLBVwuFywWC8xmM7LZLBKJhNTmXMNJ8KrVaggEApLi5/P55HxiMplwdHSEVColay/J8iaTCdFoVIjLarUa2WxWhmqbcHBwIImC1WoVn3/+OcLhMHQ6HebzuYhCKDDY29u7Ur/PZjNMp1McHBxI8tjJyYmYLjgcDuj1eoxGI3keWO+EQiHYbDYkEglZG3Q6HZ49e4ZarYazszMxxJpMJkKWY5IZrwkJpExxMBqNGI/HiMfjkmixbqTyvoEDZgqAeP6lUdZ6CvO7Bg0FSKS4DZgi+aKk4pdFNBqVs47dbl8RS9MYiSYvNI5g75nnPpJvMpnMFcL5JvR6PSH3vk9EpJug1WpF7HEbjEajWxPEWbu/r6QYJqsqUzSDwaCsK0xaYq0zHA4RjUYRjUZxeHj4VswtaKZFYxeCKWl+vx/FYlHmJPP5XPp9i8UC1WoVs9kM0WgUXq/3vb0Xd3h5tNttdLtdZLPZG8+3TJYj4fR9A8/aNP7cVA/SsGtnZwdffvklnj17hul0KgR1prYVCgXpQxuNRiSTSRESKVMt7nCHVwGFcb1eDzs7O0LS4fqsrAeUplkUrZrNZrRaLUkafNX6QVnLKD8je5rs1/B87HA40Ol0MBqNsFwuJeGS5x72YkqlEhwOByaTiRD1KGKn+GwdFJiwf7N+DqSRp1JYTaEzPyN7akx94rVWnu3Z/wqHw9I7ozhqG5hE1Gw2sVwuMR6PMR6PcXBwIOaT8/lcTC+tVquc6cLh8K3W0EKhgEKhIEnNPO+3Wi2cn5/LdS4UCohEIpjP55hMJjIfWy6XItQzmUxiYFer1TCfzxEMBqW/a7PZ5EzKvg3XPMLj8aBUKonhM5OT1Wo1hsMhJpOJ1Bus4dxut/Q0O50O4vE4jEajrLl2u13OvjTOvgk02qY4yeVyiQiK9zOVSiEYDGK5XOL4+Hjl7wgEArJPZDIZMUzmc69SqaS/sr+/LzMqChqUz0C5XBZSHc+22/YGpvKNRiOUy2VotVpJK6O56E1gX1OZxLUJer0eJpMJo9FI7sn6u0aDFZqKqtXqrcZErwrOrmaz2Uo/leJGXp/pdCqiDbPZjN3d3Wvn12q1GsFgEBcXF9IPHQ6HIkA2Go2oVqsijOP9djqdqFar8vzRvLdSqUg/ir0rmjfu7u7C7Xbjiy++QDabxfn5OQwGAyKRiMzB2KfjXOZb3/rW1pkxUa/X0Wq1NhI6gecCl1arhcFggFKpJDO+cDgMp9OJfD4v82deT661SvGmSqWC1+sVUxP+expMDAYDZDIZ9Ho9LBYLSS6eTqdiWMs1N5PJSM+k3+/jiy++gFqtxne+8x25frcFzc3Z03K5XDg5ORHz+GAwKGsIcHlWZMplv9+HXq9Hs9kUE2v2Hdfn/Q6HQ56/xWKBYrEopmFer1dM/abT6a3mgGazWb5euYc6HA559trt9sozr+SqcQ/pdrtIJBLweDy33s8pLqeZB830lCABnqIxi8WCTqfzSoms7KVvur+8lkqjc/IK+PcqnznOe9VqtRi40UyRBsd6vR6JRAIWiwWDwQCff/65CLm497tcrq0pzevQ6/XCDTQYDKjX65LaViwWhbTNxFONRoNMJgOj0SiJ7sDzlEIl7HY7SqWSGLbcJHg2Go0wGo0ye7ypV8JahvwEmpqUSiUx8GGCGfv4TAq32WwiRgIuz0YUqKvVaoTDYfj9fuExUExAUdY6PB4PyuWyvH/rszKTyYR4PC4mr+xbMlnbZrPB5/OhVqvJNaCwk7UvTQD29/evrI+8j9wbm82mGBYAkBmq0+mEx+O5MYzjTWA2m4mB9GAwkGtAATr7jru7u2+t53kTlssl0uk0nj17JvdDmf7J9zIcDss6abFYJBVvE3hGqFQqCIVCG2thJi+nUinUajWYTCYEAgFZM7hmc0/iHsa9q91uyxxgW7+51+uJMdbrAtN+2VvZZHpFzOdz1Ot16Vn8pKeQrsPn86Fer8NoNCKdToupNgA5P2i1WlSrVTlHu93uN9KvzOfzUnPRGEnJPed8g4Jj8hl9Pp/sve87Go2G1CGbxOdKMGWXJpWvwo942Wuj1WpxdHQkiZ2TyQSDwQCLxUJM4V7kzH0daPy3XsMpQS4W+x3sLTKVmHtcPp9HOp2W8KjbnHnfFihGpanAtvWIZxwGXTx9+hR6vR6lUgmVSkVEZuRn2Ww2OYtWKhX5OUaj8ZV5nNyXuP/fpnYOBoPQarUoFApyZmNwidFoFAMTv98vz+dgMAAAMb6/LcgV6PV6aLfb6HQ6ct5mKAHPYo1GA9PpFHt7eyt1gF6vlxlkJpOR/oHb7d74/thsNjx+/BiNRgPValU+u1qtlnnm3X7z4jCbzcLF7nQ6N76/nU5HeC3KupXP7Drn/ToYjUbpnWzjTzmdTpjNZkQiEeRyOTH8Vz5Lg8EA5XJZeLjKZ/wnFbwf19VqjUZD+s5ut1vCUJQ8bfI7S6USTCaTcL2VpuV3+MnEu+R3kJ9nsVhuPFty3tLtdqW/8SJYLBY4PT0VLcJkMpE+rNLgyO/3Yzab4d69e7cSofOsxGAfgjW2x+N5b87Nd7jDHa7iG/N2/q2/9bfELXQ97fznfu7n8L3vfQ9/42/8jdeWWHC3sN3hDnd422i1Wrd2xme6K53xXS7XigP1baB073wVAbrX6xVyPwfHxHg8FodEJtl1Oh381E/91JXDPIdr9+7dk6EayUJMeuWBj0P3SCSydbBKolSv10O1Wr3VgNnv94ur/jZXuW63Kw1IEiCASwOTVquFZrMJj8cDk8kk94vplMqfyeQ3DqvC4bC4178p0KTAbrej1WrdKP7tdDpwOp3Y39+H3+/HxcUFZrOZpLlvA8WMFDZxKMV04k3DWoPBgHg8jnQ6jUqlImnar/tgz0Mah54kllssFoTDYRFpc+BA0giFPXQ1VN6r5XIpfyPF4xSnARA3/3a7jUAggOVyCYPBII0rrVaLp0+fotfrodfryfCSTY8vvvgCvV4PpVJJnC5JACRBw2QyXUm5N5lMcq2vc15nmibJBhzSK/82pkM7HA5YrVYRZJOgp9frsVgsMJlMZJjIdBWSEwHIu+DxeN77hk0+n8f/+l//S1LvSFwjKYDOqjs7OyuDfaZv0qmSA9JgMAi73Q6TyYREIrFxXVKr1RiPx7i4uBCzC2WTgKnrNG9gQgYAEd6RqKN8PikKrtfrkkISiURu7cb/rkCChM1mw4MHD4SEyHNBrVaDTqdDKBSC0+lELpfDcDhEq9W6QrjnOz4YDDAajUTgRVOA60DSUqPRgMvlQrPZRD6fRzablWaJxWKBXq+HwWCQZHtlo5ON5fPzcxmikdhL8kQ+n5d0EtYCNIqhGQTd35WERq1WK0Ytg8FABu4mk0lMEIbDoTjRE0w5cTgcYj5hNBpRLBZxeHgoCUuVSkWSfq/DaDQSUjBJsq8iQOffPxqNhIjDM9p1w6FoNCrO5UzMqlQq8t66XC5MJhMhtAIQMovNZnsrAvTJZCLCoVqthmQyia+++grNZhNutxuDwQAGg0HWVY1GI2sy9yKHwyHJHM1mE/v7+2/0My+XS0k0YPqWw+GQVFAmNZOc1+l0xBTiOjAdWafTod1uSwoticJGoxGTyQTlclkGKXR73gQOJ5X7MpuhfEeVwjcmbrjdbthsthXB2XX14nw+R6lUQiaTgVarRTwex+7u7kpiucViEXfQfr+P+/fvb/x5Wq0WkUgE6XRahMskOSrT5Xj9s9ksptMpFosFms0mEokEnE4nYrEYRqORpOOw3jg+Pr7SG1kul0ilUvIZmeyu1+tFYAlADDo4WKEAWUmGNpvNYqykfG8ooOaeR1MODrHdbjfm8zk8Hg+y2azsicpagHWO2WyG0+l8YbI0Cegc5AaDQTHMqlarQox79uyZpGZwgG4wGIRoTOLzyckJ7t27JzXST/3UT+HJkyeSBkNS8GAwkBTcRCKBg4MDuaYkA2ezWTEK8vv9SKVS+OKLL0QYXiwWhWTYbrfhdDplfcpms+I6z2fEaDTi+PhYiNk8EwKXZIO9vT1J2Fsul1cG0sPhUO6zyWTCt771LRlkMFFcOUiPRqPibLv+s5S1hMViEaKsXq9HKpVCo9FAqVQC8DzBrd1uw2AwIBgMIhQKYXd3F5PJBOl0WkwDrFYrRqORkD94TqL5EQmwer1ehJY8P47HY6lNdTqdmL0Nh0MZiiwWCzQaDSG2L5dLaLVaOJ1OIX6+LfBdL5fLYvTBYQxNfWg0cHBw8FLuwm8KdrsdNpsNpVLpxnMbnysSd94WlEZP3K+YEEfQOGk6na7UaCRAffHFFzCZTGg0GluJNUy1DIVCN56X3zc4nU60Wi2k0+mNiVdKsLal6GMTmApVr9dXBvput1vSMt8XMA2ZJiYkfAQCAbkuZrNZ0kqZLMZ38/z8HB6P540JedvtNs7PzyXBj2sa9zCr1Sqk11wuh88//xw6nQ5qtVrEc36/H/fu3RMzpjt881CtVtHpdADgxvOtx+NBo9EQAcP7tK8AEJNDpqVeh1AohOVyib29PWQyGTSbTTx9+lTIESTUj8dj6HQ6lMtlqFQqEXEykWtvb++9FOLf4cXB88x6bfcyhBYKapTGom63e+MZj6Jl9o0pTGU/Yh0mkwn9fh/L5RKz2QwOh0MEv7PZ7JUNh3iWm0wm0ltmYrHNZlvpF/L6UOynFDjqdDoR2zSbTTgcDjidTnzyyScitgUuU1tTqZSkPNFAmGlNdrsd4/EYFotlpVbkTGs2m4khLEX7rBWZkkuzGCbTWSwWTKdTEVIOBgM4nU7Y7Xa5jtsEK6y/aQxNEwH2d5n4S0MPCuT4TN123aQwlgIWit9IbDQYDMjn8+h0OiIAICGWvQOmnrhcLhGTl0ol5PN5WcfsdjsqlYr0IbaZHHM2ZDabcXp6KjUvgJX6NZVKIZ/PA4CIgur1OhKJBGw2GwKBgLxX7NEeHx/LM70NnU5HjPYoWlY+Gzwv8Cyo0+lQKBRWBGcmk0l68lqtFuVyWUTHAOQsfHx8jMFgALPZjNFohGQyKX3W8Xgs4tRQKCQGB/V6fePnnk6nKJfL6PV6GI/HYthrMBjw5Zdfolwuy3t3G9CIcdsZis8J+zC5XG7FiICiChoY6/V6ORPfJtnrZcDnlX3pdWi1Wpk7jkYjmVPcFPpAoUOpVJK1gUZPNO1kqmq1WoXb7RZhyvn5uezzw+FQ+g6BQACj0UjWLz7jXq8X+/v7mM1m0uOhGI69IdZViUQClUoF4XD4xnn6ZDKRWcEmMH2LJhA8TzG1u9lsolgsArh83o6OjkSMM5/Pr/QpSIjv9XrQaDRiWMC5ldVqFXN3h8OBbre7QsImWZvifJVKhVKphGaziePjYwyHQ0Qika337TpwZkPT9n6/j6Ojoyt1qkajgdvthsViwfn5uRhAWCwWVKtVEQnzbBkOh+HxeMTApd1uo16vQ6PRIJFIwO/3iwHii0JpYsk5i1J4UywWJVEduNz/lUnhmUxG9nCr1XrrxCzWGxTgbfser9crzycFr0osl0tZo9jXWO+/Kv9ejUazcb/kjIlcAhp6aDSaK7Nxzs85G6WxDlMiuc7T4J3mk+uzT5KmG40GEonErYVdJpMJ9+/fR6lUgsViwWQyQSqVgtPpxHw+x/7+PqLRKEwmEzKZjDxzer0e5XIZpVJJxGTKuo1G4DQwuQkejwfValXmONs+f6/Xw2QyQSAQkL2AHCGbzSaiqn6/L4YLRqMRs9lM9nk+d0qsCyrZM9VqtSvJj5uuIQV2uVxORGxKmM1mxONxfPXVV4hGo/B4PGJqqlKphLNE4bjyM5O0zprq+Pj4ilCE95FmE8pro9VqYTabhaj+tsUAs9kMJycnIoyfTCYwm83Q6XSYTqfIZrPQ6XTCbTo6OnqjnFP2tOv1+hXuhdvthkqlwnQ6xfn5OQqFgqTWRyIRqQ/Zf3Y6nVgsFohEIrfq1VarVbRaLej1+q31HgNDisUi/H6/mJZrtVqUSiWMx2Pk83kRUNE0xmg0Sr01HA7FrH3dlLXf72M0GiESibx2Eajf70etVoPdbkcmk1kR6BOTyQSFQkH6nG/T4PxDgcVigdvtRi6Xk54muQsMoFksFrBardjd3ZXE8deNfD6PfD4v/BHyAdRqtRgeMBlUo9Fgf38fFotlxfTnQ0C/35d68Da8SLvdLjzON3VmuAkajQaxWAzhcFgMoF7mzL0NPMfP5/NrBYqsR5bLJaLRKNLp9BWTMI1Gg2g0ivPzczSbTVSr1fdKgE5jqn6/j1QqJbO69Wd4Pp8jl8tJIAZDq8hhXCwWiMfjsjeQ9zgej9FoNODxeLBYLFAul8Us/0X5fsvlUq7hOgfO4/HA5/PdGLrl9Xrxne98B1arFaenpwiFQis82PXPxOCKF0mM5t7faDSQz+exWCyEJ0kj/1qtJudHnt2u2//1ej2CwaCEYzQajWufIe6h/O/vMoX3mwKTyQSr1QqXyyXhH9fdKxrj0iBOWX8oDchuA6XWads9VKlUODg4kF5SuVyWMxYNstm7CgaD8Pl8byTI60MDz2ubzmo03aRpKo3m13vTwGUvigZvNBdQBn19qGAvTq/Xo9/v38h7YaCWTqd7bTq8O7wcODO57ZmSnL7b9qOVIN8tnU6L8Zzy9wYCAbRarRXe5qNHj27kA52dnYnBbKfTkX4qw1WKxSL29/fvTFXucIf3FN9IFTWblX/7b/9tfO9738ODBw9e++9wuVz483/+z7/2n3uHO9zhDptAh+nZbHarJtomZ3wlqeg2gvLRaCRpj6/STOTwkQMpvV4Pq9Uq6XkkPw6HQ3Q6HSF0c3ClPNxEIhEMBgM8ePBAilum09A5nGJLkku2/Z1srvZ6PUn3uAlMJYxGo+IqxyEkcLkHdbtdGVDabLaVphDThDweD8bjMRwOhziWDwYDcbWjOL/RaIijptvtRrPZxP3791/ybtwefr9fXPVJON2E6XSKer0Ol8sl4mOKOvv9/o2EejZEmBCSTCbRarVEbHkd1Gq1PFO8VtuST18GHDDzftKZnIkGHMYClySCyWQin1mlUgn5pNPpiMHAfD4XMTiH+soUZibKVKtVOZhNp1McHx/DYrHg7OxMCCYUZphMJkynU5ycnEhi2Ww2w/3794WUwLRni8UiqRsULO3s7IgzGXA51NbpdNBqtVcEnDqdDkdHRyKEI3mSBAH+HpPJJIOa0WgEv98vpC02v+r1OtLpNKLRKPR6Pe7fv//BNUWbzSb+6I/+CBcXF5LA6/V6xfG/UChApVLB5XLh2bNnQtagoIKEbibbqFQqSYDZZp4xHA6RTCYlUdXhcCCRSAiBmu8u02YMBgOOjo4wm83w8OFDIYXWajURzXOIoNVq4fP5YLVaodVqkclkVsitJO68T80kEqJmsxl2dnak0cFm7enpKfL5/IqIjo6km4jqfA5JUiVh6Kbnk2kqXDdJeu33++h0OvD5fJKI5HQ6MRwOJcmYwxwmmlPASsLNcDjExcWFCEzr9TqCwaCIIZn8MB6P4XK5kM/nZSDDBAE64JKI4XK5RAhKweP6umswGNBqteQd5z7kdDoxGo3g9XphMpkwGAzg9XpFcLMu5GViMMk5JAi8LPiM53I5lMtlIRFzKLCp0Vur1aRp3O/3MZlMxAwAgKy3XOdNJpMMiYbDoRDK3yQ6nQ7Oz8/R7/fRbDbx9ddfo1KpoFarYTwe4+nTp1CpVGLOwYEvhW+lUknuldPplMH4mx461Wo1IQUbDAaEw+GV2pF1YDabRSqVglarRSqVwsOHDzc2REnyHgwGUtft7u6u/EwOTygyZZObjuPrP6/b7QpREbh8v5PJpJBouLcDkJR4kp339/fh8XhkKHtdcgwNQZrNJh4+fHglxY5gIjMT13q93samKQcXyWRSTHoSicSVurzb7UoqkcViwf7+vhCJea2sVqvUD0yxTiQSVz5/r9dDuVyG0+lEOBzeOGS1Wq0igDaZTCJAVn4umqzQ/GQymUj6FUXdwOVQQ6fTyXU2m83wer3SmFaSw0kQZrpRIBBAJBJ5YSEQDV74OwqFAqbTqazVT548ETL4YrFAMBjE7u6uiOTH47GY7TA1k0304+NjFAoFOV+QYERH9f39fezv71+pWbVaLWKxGJLJJIrFoohR5vM5ptMpTk9PpS4cj8dwu9148OABXC4XwuHwSjI619+DgwN5/iwWizz7JH5RRD8ajVaS4JXgXkkDBrVajXv37gkJRzm4pBHLdYm5FPKStEPzKg7ySqWSkLB5Tex2Ow4PDxGNRkXgf3Z2hkqlIiLKn/7pnxZTKq6Xw+EQtVpNklFIbOUelM/nodPpEAwGhXzN9Ubpat3pdHBxcSECd4oTdDqdCD9cLteNZ87XhXw+j2KxKPWC0+lEKBQSYyKm/9HEahNR813B5/Oh2WzKfbpOKEhTG5oQ3GSE9iagNNy4DlzjlODey/2OifTrGI1GyOfzcgZ8F3/jq8Dn86FSqcBkMqFYLG5MXQMu689yuQy73Q6DwbDxrF4oFFAsFjEYDFYEXQaDAZ1OB7VaDV6vVxK03gdsStFkekggEIDP58PFxQUymYyIx2gcRlNGo9EoZ/DXhU6nI+tjrVYT0rVWqxVxX6PRgMlkwunpKQwGA9RqNSqVipwBSUDu9XpwuVzvzTW/w+vDfD4XMdhtz7dMmeR+/bYxGAykbiQJmVCSJbb1J9RqNSKRCHQ6HXq9ntSQFosFuVwOi8UC/X4foVBopXc1mUwkAZ519dHR0XtljHGHFwP7KoPBQMTIFKU6HA7k83l5Pm7zs5gYS2EDcPmc8vwSi8U2mjIpiYjbZjTrhqY8jwC4lcDpJnDGodFoxPQYuHy31usco9EIvV6Po6MjlEolMXxi34zGqB999BFcLhc++ugjPHz4UFKXMpkM1Go1Go0GGo2GCEDZvzg+PoZarZYUUqVoaP1/K5WKCP3i8bh8RofDAbvdDqPRiPF4jNlsJgZjFPmHQiExRKX51TZkMhmUSiXpb7NG5H2nIUA8HkcsFsPR0ZGIem57PlgsFsjn8yJK3t3dxXg8Ri6XQ71eF6HW/fv3kcvl0Ol08Pnnn4vxYjgcXjEa5bmLvTn2/YbDIcxmM9RqNZrN5q3mfdFoFE+ePIHD4UCxWESpVILNZpO6l0mr7DHyPNXpdDAej6HValGr1TAajcRMgeeycDh84z5EU1D2e6+7pux/1et1WK1WxGKxlV4TUxLZ1+TnAyBGh0ajET6fT3qGvMfL5RJGoxFOp1PI4PP5HGdnZ5I0rryWo9FI1oZWqyWmmRQg2e12DAYD6UveJETi7MVisWwlgc7ncwQCAXmX2SvjzJPzBqUAoFqtyve+CTEJ9+nb1A80jdRoNLciQir71ZynEH6/X3pRlUoF2WxW+hp6vR7tdhvD4VAExDTvczqd0udgnc6U7slkIvPrVqsla4zFYpHzTq/Xg9frRaVSkbSfl+kT8Nmh2DUajaJSqcBgMMj50e12o91u4+zsDA6HA41G48YzrMFgwGAwgF6vlzlnv99HvV7HbDaTWVW/3xehaLFYlGRlm80Gs9ks4lP2vUajEWw2mxiJvCwoIler1VvfCxotcGbNhD+aDoRCIUnlpckrTTJoFuLz+cRsiwJd1ok39VAoOAcg6yn//0AgIAa/7IHabDZJe6aYejqdwuv1YjAY4Pz8XDgDPp9va03L/i8NBLaB+zK/h2DPpVqtykybfS72YJgYSdBQkMYa6+dYch0Gg4EIoiORiHzdfD4XARGv/9HR0RVhLAWa7FtTUGG32xGPx8XwptVqIZfLyfPG77sNdDodYrEYIpEIarWa7J9+v19MZPl5KLpn3cBZRbPZXDkPULTG2e9NYI/W5/NJn35Tb3s0GqFQKMDpdMrfx+f64OBA1kCumTRMtNvtaDQaYv5Bc5bZbHbFDIH9fRLXs9ksfD4ffD7ftYaDsVhMuCyFQkEEwzT45Drp9/sRjUalT6LRaJBOp9Hv95HJZOByueDz+VbWSfZbOFc/OzvDw4cPr3wGmu+Ew2FJ7OZe8C7Papy7p9NpqUeU15BmJul0GgBwcXGBw8PDN/JZ6vU6stms8GdY9xgMBjnjGwwGqU++/vprEZywlwRAzOg4WwUuZw782k1r1mw2k7CFbTwfwmazyRmA/Sju4zTK8fl8sgZwn+SMod/vo1wuw+Fw4PDwUNYGvkMul+uN9F+Z4E6RpZIDp/xsNGXgu3WHVbBm5XvMGQe5ECaTSQJLzGYzDg8PX7txA8UtuVxODEmU7y5rHz6nFKa/isH+uwI5p7et/bVarewz3OveFTQazRsTc6vVatmPNp1FOEOl2Sz7MptMwvhsNJtNqffepz5iMBhEu92W0Ama2JlMJjEoarVa0Gg0UnM3Gg3M53MxJ10sFit1mMViETOZer0Oj8eDWCyGbrcrovQXuXeciTNRnAYQ7Ot0u11UKhXEYrEbz9M+nw/1el36I6FQaOMaUqvVxOSJ/YPbIJvNotVqIZPJwOl0Xkmb9nq96HQ6+JM/+RNMJhM5i20DOXrknNz22t3Ncl4PAoEAOp0OOp0O0uk0IpHIlXvGc4/VapVAASWUoQc3mXgDl+c8JV93GwwGA46Pj5FKpWC1WsU0brFYCNeYZ+ZwOPziF+AbCN6PZrO5sYdHY7lgMCjn0+vuG+vWWq0Gj8cjc9gPGQaDAVarFU6nU/os23pZjUZD+mB3Zs3vFqxf2MO4CXyuX7Sem8/nqNVqonuIRCJX9hzy4mlYyb7EdXvYYDDA6enpys+lmcdsNhMTSb/fj+VyicPDww/e7OEOd/gm4hslQGfa1K/92q/hb/7Nv/lGk4g++eQT/OAHP3hjP/8Od7jDHZRQEm1uSzBad8anMEutVt/Y9KcbXyAQ2CjkuQ2Gw6EUuT6fTxzYU6kUarUa2u22kEjowE8Rjk6nw6effopWq4Wf+qmfWhH10ilZr9fL4Z8NJ6vVKqJ2EopuAhunt72uSlc5lUqFSqWCs7MzGI1GIf8vFgtJy2QS8aafQ6dhOkcul0tUq1X88R//MUajkSRGMBGCKYTJZBIejweJROKNiR3cbrcM7CnMUSbBUMxF8gNFqRRL9Xo9XFxcrDi9r4PJXOFwGA6HQxJ4u93urQ6qFDX2ej0Rlr1ucODp8XjEbVmj0aDT6cDtdgtRtdFowO/3i+DryZMnK4PXbDYrJEQ6szIBhO8KB3UqlUqI62dnZwiFQv8/e++53Gi6XQcvZLzIOUemZqczOi7LkmW5ylX2DfgqdEm+jO+/y1WussuydOzjkaZnpgMTQOScX2Tg+0GtPQAJgOjATsP1R6ozbBJ4w/PsZ+8VYLfboaoq+v0+crmciBtWD4aqqor49dmzZxiPx4hEImg0GuICTdIDBTNMoGXzieJMDnI2vUN0b41EIjK4XE1oNhgM+OWXX9BoNNDv9yWhh6kMfFf5s4VCAYqiSLrvt4LpdIrXr18jn8+jVCrh4OAA4XAYg8EAnU5HiHZMFPf5fPjf//t/S6oDBUOrzqT7olAoiGNqLBZbI7TShd/hcKBQKEiigt/vl2HAyckJisUiFEXBeDyWZ5ICLZLgmKRLUpXZbEav10Oj0ZDv8DWAyTNGo3FtaMC9S6fTQa/Xy5BdURT0ej0haOh0ujVxWTabhcPhEAE4Rcn3gaYt2WwWFosFy+VS9lESkB0Oh6RxUOyaSCRQKpXQaDSELBqJRO6QtkhAzWazMBqNkgBMYozFYpHEaa4ZdLnnsNbj8aDf78NoNGI4HMJoNMrP3vf+sWnUaDQwGo3Q6XTg8/mEtHZ2doZOp7OWiMF9kALVaDQq5L4PqS2WyyWur6/F8ZhGC8ViEeFwWETDDocD0WhUGm6Xl5c4Pz/HcrlEo9FArVYT8pDD4RBXfaYHsyag4yf/9kOCDo8kgZF8NBwOhfRbrVYlUZzJXqxhSKadTCY4Pj6WgR+f84dMU6hWq2IKxOt+G0wEZ12jqioymYwYbvDfkNBSKpXgdDqhquqdxG/gN0GM3W6X2sHlcm0Uc1NcTlLifD7H2dmZEHFI9OOex+R4prsBN+9pq9VCMBhEoVCAqqpwu91C8GRC1vX1tazrm94pJqxpNBpJtOt0OltdO202m1wvOmffxqrzus/nw3g8liQo7kXAjcCANQvTqlevK1PdjEYjwuGwEH5ugwOFVYffZDK5Rip2u91ot9u4urpCp9MRkiIJ2LzfJDxMp1N4PB4xsmG9z3eaScNut1uGZ9FoVIY6JNY3Gg0RH7OuCgaDd54fmjgwcYsEZI/Hg3q9Lu97KBTC4eEhgJt9v9lsYjQaIRwOw+fziXDa4XCg1WrB6/UikUggGo2KWYfFYkEul8Pp6enO+paJ4hxaejwe2aPb7TYASNLf06dP4fP54Ha75bvRfITPNOvQ2wgEAmK8QEf2TeQR3qtwOAyn0ynkbOC3pOjnz59LWuJ9wwqmIjmdTqk1+e75fD48ffpUiLHL5VLqlMPDQ/zVX/0VlsslXr16JeLz1cSVeDwuKeU//fQTXr16BeDmPKMoivwtrukejwfD4RDz+RyhUEgItCRhUoRzcXGBWq0mDvV2u11qhlwuJ+lMs9kMx8fHDzrgHw6HQmZjiuvqus79vN1uC4E1n8/j6OjowT7T+8DhcMBmsyEajeL6+hqLxUKS9whVVVGpVOScdJus8S0gHo/LsL9QKMizTgJOr9eT81EoFEIymfzo9NLPDZI2p9MpstksstmsrIfsV1E8rtfrEQqF5Nyzinw+j2KxiEKhgMFgALvdLmuWqqqSpkUy92qS5ZeEoijyWWg2RUE+ACFg05TJ6XTKwJSmKj6fT0jjHyPSIJbLJTKZjPQC4vH4nUGsy+VCtVrFn//8ZxgMBvR6Pfj9fgQCAej1etkLuI5ls1kxuHrE9wPu4+9zvtXr9WJI97mwWCxQr9dRq9WEBMTPs5pox/QK/ptdewb7UJFIBOl0GrFYDNPpFKPRCJPJBKlU6o45CoWGdrtdenrFYvGr2Vsf8X5QVVVSiWi+wTlJv9+XsyWFPav7Dkm0/X4fi8UCo9EItVptrd/CcxETLT0eD0ajEdLptKylnGfM53MxEqTYcpMAlMZlwM3z3+/3pf77FMRvm80Gi8UCj8eDWq0mZ9vbSZnso1CcGQqF5JyyXC7l3YrFYvD5fPiLv/iLtdRr4OY8TcGV1+sVsy29Xi9mXQaDAVarFaVSScjlZrNZRKkajQa5XA7T6RTJZBIul2vt2rIfajQahbzp9XpFGK3T6eB0OuHxeKCqKnQ63c60lna7LeJVANIvHQ6HMn+aTqdyrjo/P0e5XMYPP/yAfD6/dl7dBfZd2+22nNGZftztdhEMBmGxWLBYLGCxWHB+fi59dCa7GwwGpFIpXF9fS0+XAiKHwyHpNExcZj/N6/XuJG/xPFYqlcRAr1aryXmZhqjsr/n9fmSzWTEQ4L+bzWZCaOz1epjP5/eS40g2a7VacDqd9z7zHo8HV1dXQnC/TQB2Op148eIFOp0Oms2m9Ig5n3M6ndBoNGKqTGOz1XQ9mgv1ej0xb+h2u/IczmYz5HI56dnbbDYR5jqdTlitViHw09QsFArtnAvU63VJMt/1c9yzXS4XEokEOp0OrFarCCg4t+HZuFaryTV9KCEJiZD71BCr6cH7nAHZw+C/WV2zdDodEokECoWCmM31+30xsqEggv3JVdH5am9w9fcdHR3h4uJC1q6DgwM4nU4xZKZoo16vi7Dw+voaqVTqzmefz+fSd6TJDU1Q9Xr9mtFuLBaTvr/D4ZA9QaPRIBAIIJfLSS87EonINV/dPwiK9TmPAm5mDdlsFk+fPoWqqjJ/5j2z2+3y3oZCIRwdHeGHH34AAHm+y+Uy+v0+qtXqB59teE06nc5eJkk08DabzdIbGg6HODw8XDN0HAwGmM/nYn7BXuJqujrJ6C6XC+12+97UZJqRsS6l6IT/WyKREIPWXq8n94hGF6xhe70etFotGo2GzIuq1ao8W9uuE2d4+8BgMIh4E7hZo2gAT+N7/jcma/PZPD4+lvMCk2k5V6aZCeFyuWRGTAN1i8UCp9OJ6+trMUSORCLweDw4OjqC2WxGJBJBJBKR95dGO69evRIOCfcYwmg0Su1yfX2NSqUiHIz36R+x1lEUBQaDYY1Azfkck9aZOM53gYmW/Hs01FcUZe93IJlMSl+Sez5rGPJF+v0+HA4HgsEgksmkiN+urq7QarXg9/vhdrvlb2u1WgSDQTQaDeGo+P1+NJtNGI1GtFotTCYT2O12ER3TWENVVfm/iqKg3+/jl19+wcHBwZ3vpCiK9EBZjzDNHICYzNjtdrnHAES4Vy6XxVj2Nmi8vFwuxfRmmxEwf/5T9FT2wWQykX4fn4fVWpfzUvb1Nxla8T3QaDTS96bJxz7gGYTmPBT93Ta0r1ar8n60223o9XqZ2+RyOZTLZZhMJuh0ujWzHIvFgn6/j7OzMxweHkofnWcmmh/TnCQcDm+cf62KVfdZr1YNaLge1Go1GI1GEUDyrEajbu4bNOXlDPHq6krqb84Jg8HgnYS+TwWfzyef3+v1ot1uyxnJaDQinU7DbDaLIcMj1jGbzXB2diYzt1gshng8vhbWwvRts9ksHIxPDfINWEtsem61Wi0ikQiy2SwajYaYm31rgk/2IKbT6V7G9dPp9JP2IL5WsJ9D/tBtAy0als9mM1gsFjGzNhqNG/sJdrsd1WpV+o5fkwBdp9PJPm40GqUuJK+Ha5bD4ZCzFOs88sRYd9z+vT6fT9bpyWQivJtut7s3F46J4gyhYJ1nMplk7c9ms2tGALtE6DabDYFAQIJALi4u1njTo9FIaqRYLAan07mXKSUA6QdVKhUJTdgE8pB+/fVXxGKxnVxt1oG1Wg2lUkn2FXJDH1IH84gbuFwuhEIhLJdLlEolXF1dQVEUWK1W4WiNRiM4nU6Ew2HEYrE7fTWfz4dKpQKTySShMttAw0K32733e2I0GiUsoFarrfWunE6n9K4ASO+aPW2GqP2eDKh9Ph8ajYb0W1Z7taw5gBseS61Wg91ux2Kx2Gg6qdFoZP775MkTOed/6/D7/dKDLRaLGwXGACT8IZVKSTDXI74c7HY7HA4HstnsvWYXs9kMg8FgI3/gPjSbTUwmE/T7/XsDA8jhZJDHtnUtk8mISTHNplZBA1mGvej1erx48eJ3s2494hHfCr4bAfrf/d3f4e/+7u/wF3/xF1/6ozziEY94xCcHG15Wq3WvBg0JdKvO+Dz4uFwuXF1dCfmGrsAUcwBApVKBVquF0+m81zVwFcvlUkiCHJ4BEDI/E+iazSbOz88lmTkUCsHr9cJkMgn54vXr1zIo/8u//EspgLVaLdLpNMLhsLiic9BCJ3aHwyHuuPeBwt/3GTzQVe76+hpWqxXD4VDSBJxOpxBLRqORDD5WG4sUOtKhmmm2JFVVKhWUy2VMp1M4nU7EYjF4PB5xHONQZbFY4ODg4EEKbBIr2IRgYvaq0H4+n8ugcDUlMxAIiJt5qVSSpEYeduhSXq/X4XK5pDnD52VfoRzTjG+TTD4lzGYz0um0JOK8evVKxBEkCwG/uYszKfHs7ExI+EympYtvs9mETqdDOp0Wgh1F2GxODIdDceJ0Op2S3lIqlaTptQq6+ut0OgSDQSGtMd1Bp9Mhn8+L6JVDX4rMdDodFosFisUigBsR033v/jZn+V6vJ0lAJG5yQERXXQ6OSaBjI/VbEqDX63WUy2VUq1VxNGWTnk6TfKY5uDKbzXj9+jV0Oh1CodAHvbvj8Vjc3kj+3AQOw5jwW6vV1txoaSLQarXWmpIkAhaLRfT7fVitVthsNiHtZDIZuFwuGQbv24R/SGg0mp3XkmkPTLhZFRLP53Nks1l0Oh0hWjSbTUn/o/BsX3f0ZrMJ4Ibc/PLly7Wkp3K5LKmxdMdkQg9JgoPBAKenpxuHUSaTCalUSpzgTSYTWq0WlsulkFLoAEhiIwc8/Bur6yvJqP1+X8TLt0GR+irhl+TB2Wwma8hPP/0k15jJlSSskeQVjUaFwKMoyr3JLJuQzWZRqVTEvT8QCMBoNArxgoSNUCiExWIBt9uN169fo1gsyhrc7XYBQMxLKH5lY8tsNgsJg0JUfu6HRDabFQFlPB4HgLUUErPZLGR3s9kspBASpqbTqaTcp9NpGZ4BD+t6zPWt3W7f22jW6XRIJpNi0mA2m1GtVlEul2EwGCR52Gazwel0ot/vI51Ob3w2mY48Go1QKpWEqEUHcuCmLm2329K4NJvNcLvdkjCdy+VEBLgKs9kMj8eDYrEoYl6v14vDw0N5Hur1uqTUADe1zXA4lHrgduIWsbr+WCwWcdHdBY1GI4TJ20P+6XQqCX52u33NqZ3i7lWQoDeZTNacs1cTMWOxGMbjMbrdrvze27Db7Wi1WmJWdDup2OfzoVQqwWQy4ccff5TUV4oT+P6rqorz83MsFgtZD0OhEBwOh5DDG43GmtmNx+NZc9+9nehBgyPW95VKBdFo9E5dQzF7q9USo5irqyvYbDbk83m0Wi1ZB0gUMJlMIsgmwYSkyFqtJteAw3aS31nv7kKhUMB0OkUul5PaOhKJYLlc4qeffsL5+bmY2FCcf3voFYlEcHFxIfv+JuGg2+2Gy+VCPB6XRD8mhTOhhM70gUAAJpMJi8UC7969k/pAURQoioJUKrW1DrmNVZOqy8tLSTYm9Ho93G63pAMMh0OkUikx3KpWqxiNRnfE54TH44Hdbkej0RBRRiAQkLVGr9evEdqZGKbRaDAYDNBqtRCNRkWIcnZ2JmL3TaYws9kMpVIJ2WxWiIMPWRPVajU5Xx4eHm49K7lcLoxGIxE88Lz9NeDw8FDSTMrlMi4uLqAoCnQ6nayFJGr4/f6vxujofcBk0LOzMyiKgna7LesYiaGpVErSb7619HMikUiImUq1WkU+n5dakb0Zmr4Eg8E7xGKSjnO5HBaLhRBbCbfbjdlsJsZLJBV/LdeL6+/tFM1+vy/kMwB49uzZWo3Bc2+lUhFDw+fPn+9MudsHJAzX63WEQqGtw2OmGeZyObx48QLPnj27Q6gbDofI5XJyP0m2f8T3gdWzwb49NO6bn2uwP51OpYfRarXESAr4TTzHMztrUIPBgE6nc+8a0ev1xGgxGo3i4uJCzNE2CSAIq9UqRmwOh+Or2lu/FbCHTbLI58Z8Psf5+TkajQZKpZIIdlbXt+FwiHK5jOvra0lV8vv9yOVych4hKTCfz8t5KZlM4vj4WPoF8/lchMsU2DKN0ePxSP+XiZ6shWhQQywWCzFbpVESDaasVusnu47RaBTD4RCj0Qj5fF5StCl64ll/tWdCUvZ0OoXFYkEsFsPR0RFevHixUwTk9Xrh9XolWZokPqbq+nw+6fNXKhU0Gg3Y7XaYTCY0m02Uy2W0222cnJwgEAiICBKA9JjYawsGg2Kcyb74Kkm6UCjILIz7Jucz3W4X8/kc19fXcu85kyuXy9J7mk6nQuDkv1ksFpjNZkgmk9I3u6+mpdCc4qBOp4PJZCI9FvYHaerY7XZRKpVgtVoRDofl3rAPd3V1JYmtvLfVahXBYBAnJydC6KJw/smTJzs/XyAQwPPnz2XW6PP5ZA/hu0LjL7/fL33r1XkN09opdBiNRqhUKmJovAnsk4zH471qwNWZ1TZxu0ajERH2LtAsYRvsdjsURYHb7UatVoPVahVxn6qqqNfrcLvda3sa5zsUrb59+xYOhwO//PIL/vjHP955dxaLhZgEJpNJMTne9Zn4mcfjMUKhkJCm+d0J9pG5njxUrWexWGCz2VCv1xEMBnfWEqu9xPtq48ViIYaJNKW9PTPX6/VIJpNQVVXmqc1mE0dHRzg4OMDLly+RyWRQKBRgtVqRTCZ3/t3hcAiTyQStVovDw0O8fPlS/s4q2TgajSKfz6NcLos4mvsDjfZoyt7r9SSV2OVyoVaricC12+3K2t/tduVdW31OjEYj/H4/Xr9+LXNEJq+u9kYJzqiZ+F2v1+XeZzIZOBwO6TPTLNNiscDtdiMSicDr9eLJkyeIx+NrAmTOul0u117pbpvAWo9mFfeBhguz2Uzmp9xDOffs9XqoVCpiaKTRaMQY8bbgatWosVgsikD0NlbJ1uFwWBJbec8dDgesVitisZj0CtkH5fWfTqcIh8MIBAIwGAyYTqfodDq4urqCz+eTZKdNvTbyLPY1hmKdS7OGi4sLNBoNMawOBAKyBw6HQ7RaLWQyGbmGp6enYvASCATEsCCbzSIQCNwxIm00GjCZTHj58qX07WlobLPZpB9yWziz+v7zbMv+8DaRjdlsRjgcRqFQgNfrFVPQ9wHXD71ef+e5YwLzam/cbDbLLI5zsclkIgb2Vqt1b4EZRdzsE7VaLTFIouCXxHD2jpgkH4vFpGZzu91wOBxiqk2zWPYF/H4/JpMJfD4f3r59i+VyKeJ3rjWsoRj6wB4scLPenpyc3OkxWK1WPHv2DNVqFTabTfgdnEtwXr767zi3mkwmMvvaBo/HIzXZ6lz7S4BrCblI5MzQcCoYDEJRFPl+4/H43u/n9/txfn6OwWCAWq2GRCKx8+fn8zkymYzMPW4L0JvNJiwWCw4ODjCfz5HL5VAoFMTYmveh0+nAZDJBr9cjk8nIfQdu+nycX5dKJej1eqTTaTlzsL9ODsTJyclWc5xVg5v75l4A5J3iGsfPOh6P4Xa75Tu9efNG1jNFURAOh6W/Btys0blcTng9NMB66P6r1+uFy+VCo9EQ4SXnWG63G36//7F/sAUUfhcKBTEaBH4z8SZvhc/lYDBAuVy+9515H3BtpenVtjqGRg2z2QwXFxdiTPwt8ZqA30yCAcgZZRs4X6dp2fcsMmMggNvtRrlclt4EsVwu185VrNWNRuPGcyOv1eq/+5pgMBjw5MkTCWzgGZ98K4fDAb/fD71ej7OzM+E2kauw7dzE3stoNMJgMBA+2aZU+W2gqXMulxO+4mpdzvknzY05h9q1znJfpvE49zIAws8i5+7o6Gjvflu9XsdoNBJezi7Q3JxmT5t6Euz1kO/DWSp7iOQgJJPJRwHeA4McH71eLyYF5J9brVZEo1GYTCbE4/GNxpNms1mS0fP5vMwRb2M4HKJYLErgxfvWK4qibN0T2X+o1WrS0141UjKZTAgGgzvF8d8LaMAaCARQKBSEjwJgLTSn1+tBo9EgGAzCYDBguVwil8tBURTpP3AWTM7YbDbDmzdvoNVqZZbwLdZ95OfQWPPi4kLO8RqNRrhXNAzaxK97xOeH3++X0MBqtbqTp1StVqVu2ccwdxXkJ7OnfR8YGqSq6kbDo16vB1VVxbB/W5/e4XBgPp+jWq3C6XSKaeUjHvGIrwffjQD9v/yX//KlP8IjHvGIRzwofD6fJM9uE4MQ9XpdhgGrzX8OBzhIJJnCarUKKZpDz0QiIQQgEv4pwnW73XeKSrpns2lDchLwG0mQ5Cm6+vHzMWXWbrfDaDQiGo3i6upKCADBYBDPnj1b+3tmsxnxePzOIIdp3Uwy2HW4o2PxajLwPuD3YLIoACGNsUllt9vx6tUrTCYTVKtVaQStCvSZ6MghJon3FLz4/X5YrVY4nU5xDHc4HCgWi2tE7Pc9HOwLRVHw5MkTZLNZ2Gw2SflYLpdwOBwydI1EIncIHsfHx/K81Go1NJtNIUJxqBsMBuHxeJBOp0WIDECI6/dhdSj1kKRol8uF4+NjmEwmWK1W/Prrr6jX62IawGG7oigoFAqSuslkFZIamY5SqVTw9OlTmM1mHB4eYjgciouvqqoAbohZBwcHcuCi+Hc6nW4c6JFcRXE5ha0ulwuxWExcDkkEYOObhBQmJWg0GiQSCbjd7g8emnQ6HZRKJZTLZVlbSOhjGnGj0UCz2UQwGJThjsfj2Zj487WiXq+j2WxCq9VKgnSz2ZTkGpIqtFqtCB6n0ykuLy/h9/uRz+fvHURvQqvVwmg0wnA4FDLINvAZJLGVjVEKJrRa7Vojs91uS3oqDS64hs5mM0kpogBYo9GI6ceXBNNGxuPxRqITXfYsFgvq9boIQ1RVxZ/+9Cdx4GOCk81mE0EnE6ffvn2Lf/Nv/s3OoQOTW41GIwKBgAzu6GzOxCoSdflOp9NpWK1WjEYjESVtA1NcMpmMEG10Oh1UVZVkoVgsJmlTTFskGYV/12AwwOfzIRKJoN1uSxrpKpjcSKdjptGwSaTRaGSfd7vduLi4kARj/hwF6Fxzut2uEKj3dXElKPDks8chUL1eF6IFRV7NZlPSnrVarRBgVgVTuVxOyLgejwflchnRaBRms1lMMvieHB8fP+ggmQ24Wq0Gv98vey4ASeZiYhIJYR6PR4iOXBOYCFGtVsXIgPvRQ4GmR0wNug8kxQaDQdhsNhgMBhnck2BlNBpRKpWErLoJ0+kULpcLvV4P4XAYrVYL2WwWWq1WElN7vR4WiwVCoZAkrAC/rd8kZG0CTTwuLy9lsPfkyROcnp6iUCjAZrPJdwcg5J1Op4NgMLjV7XaVgDMaje69XiRtG41GTKdTjMfjtd+9KcmS13KVtENwfeF6SXAAzDqjXq8L2WuxWMgaQJCwzKSnVTdlAEJaKxaLknYfj8cl6Wc+n6PX66HT6YhImLVOpVKRlKf7yOG3Ez1ocMH19vz8HB6PR8j/t/dN7oPcC1+8eIH/8T/+hwx1FUWR68+zC8XnvLZMCGINt4rVNXMXeC2YQsb0QJ7d7HY7vF6vONIrioJGo3FHVE+SX6vVEjL2bTKARqORVHcaEJ2dnQnJzGw2S1ogE0+r1aqc4TjA6Ha7ePv2LVwuFw4ODvYioAQCATQaDSFLDodDGWABWCOHh8NhSSkBbghJNBbYttZQuOF0OjGbzWCz2cS8g6ZhfBbG4zFUVcX19bWYszx9+hRerxfNZhPD4VAMQTatQ3q9HrFYDNlsVpK3NqU8fwowdbLVasm6uQsej0fS2SjQ+xpgMBhwenqKbDYrghimB/GsyxpqG+H7W4DJZMKzZ88kYXW13qE5xbc6hCa0Wi2Ojo5QKpXEKI33kgNIs9ksZgK3Ua1WhUy6zVBBr9cjHo9Lule1Wv1qBOgAxCil2+2i0WhgMpng8vJSzJueP39+513luZAicCbIfax5Rb1eR7fblbPfJqiqKknSJA202+07JDlFURCJRJDP52U9fKh+0yM+P0icslgs6PV69w7rVVWVZMDPkbDCmoTmQjyzWiwWSRghMZg1sKIoUvswOXQTKOaMRqNwuVxYLpfo9/simLpvz3G73ZLK1W63dwrWH3GD+XwuZNbVcw9Fqh6P57P1/1jb0Txr07NP0l4mk0GtVhMD3Xa7jXK5LMJsGomUSiVEo1EsFguUy2U5vzD9TlEUMa4tl8s4ODiQ+YjP50Mul5P90m63o91ui4nocrlErVYTUYnb7Ua9Xhcj2PcxC74PTqdTyIrNZlP6w/V6XWYfJCY5HA5Eo1G0220xjHv+/DkSiQT+8Ic/7H0/bTbbxvo6Eongr/7qr2AymZDP50V41Ov1AEAStq1WK/7whz/c+R0UA7fbbfkbt2uH5XKJSqUivTPucRSFUshKY9V2u702L2NKGBPQwuGwkIGLxaKYejUaDTlvs87dBpq30pi43W6LUeQmMjtneWazGc+fP0exWJREr+FwKIZtTAA2Go1wOBxC6Do9PRUTNpfLdW/ypsfjEfHOZDJBIBAQQwmTySTmfdVqVeYlFKfSJIzicJpDMiW0UCjI7/hU+FxniEgkguFwiOFwiEwmA5/PJ+8QDbbL5bJcD6aAjsdjjEYjGI1G9Ho96HQ6ZDIZ2Gw2Mf7hcwjcEJ9d/5JqvgsUuLMHzmTWTdeDyd9MUXso+P1+MeRgH3MbuL7RfHEXWq0WNBqNfHY+z7eNRYCb989isYhY6OnTp0gkEjg5OcFoNMJ4PN5qynr7b3ItYlr6JvBzXV1dSRoZjQUp/qWZBkXRmUxG+iJMn2Wvmmd6GhCs/l2XywW32y3Gs7FYTN5pCu55zVVVxXQ6lSTDf/7nfxaz+FgsJsYjnDsNBgMsl0tYrVY4HA6kUinpNU4mk7XPwT0MuKm3PkSAzuf0fUySKNTd1IMZDocoFAoYDAaYTCZQVVVE9d1uF2azGbFYTEjnNH+Px+PI5XK4vLyUtY9ka14bkq0TiQSazSZyuRym06nUCuzF0oz35cuXYtCpqioSicSd54d7aj6flz1nU7ITORTka+xKWqM5CkXAjUZDZkAUDK/+fhomNxoNFItFGI1GlMtlmV+Gw2Gp5zgbpHCb+4/FYsHf/u3figEl7yl5IPukbHHv12q1984c7Xa79OLfx0B6HzAMwOv1ijEpha/Ab33xarUKRVHg8Xjem4Bvs9nw7NkzWbNX+/TsVwcCAbnPq4mlBoNBjGE5b9doNCIQ4zPO+S3FxgBwdnYG17+k1ns8HozHYwA3M0yHwyEmOkwdzWazeP78+Z3Pz+T4SCQixkJarRZms3njOkDSOlPnd4Ezd5oWfSmUSiUUCgXhPa0GX9AwpNlsIp1Oo9/vo9vtwmq13vv9aDrEefIu8IzcbDZRKBTEEMhoNGI2m4lJfigUwnQ6hdFoRLfbFWPn1fl+qVRCs9nEfD7Hy5cvcXl5KbMtGo2cn5+jWCwin8/L7NpgMGA+n2M6naJYLEri4arJyu3vt3rGue/dpNGK1WqVnn+j0UAmk1kTvHQ6HXQ6HTidTsznc5ntRSIRSUhn3cue4i4ji08JJrV/ynPa9w6eeZvNJsxm89pzwt4VYTKZ0O/30Wg0YLPZxET4U4CGAaqqbkyp7/f7qNfrwiUg54LGRKenpw82E3oIUKjrdDrFSGtbH63RaIjRz++hNxwIBKTneH19jXg8Lmsc67vlcolCoSCp3NsShIfDoQTpfAkTxn2g1Wrh9/vh9/vFYI/BVTwb0eyJARcGg2Fnva6qqoRa8N9sq9c3YTabodlsijHXtt4r65zVM9em95cg/9HlckmwzKqhgNlsFhOM91lbeMYwmUz3JiCzH9hoNDYGaXU6HRSLRamF2+02QqEQnE4nFosFcrmcmPrNZjMcHh5+s/PTbwXhcFj4MuTYATf8F5/PB6/Xu/P9TiQSMlspFotiPEAOU7fbFUOpYDCIVCr1yfa2xWIh/QfyO2igNJ1OUSqV4HQ6hRuZSqW+++cpnU5jPB5LH5+hGZ1OB2dnZ2JIm0gk1tKYVVWFqqp3QvHIKSXHRKvVwmazoVKpwOl0IpVKfbXr/yZoNBocHBxIGAp1JTQmo9lHJBKB3W7H8fHxN/X9vlewDxyJRJDNZlEoFMTskJhOp7IOpFIpOJ3O9z4f8SzIMBie+7f1Usk/AyDGF6vg3Gk+n28MU1kFDTu73a6EHD7iEY/4evC4EzziEY94xDcCr9eLYrEIr9eLQqGw5lpLLBYLVKtV9Pt9pFIpIbcCN4OSdruNxWKB8XiM2WyGWq2GUqkkQmESil6+fCmEh59++kkOnnRsJdGFaXCLxUKSRJj06vF4RORCkmA+nxdibDgcRiQSkXTC1cYTxcm5XE7IK4PBYGfyAOHz+VAul6EoCiqVCuLx+MbDMglHJFLu0zhlc4AOtqupwRwq+f1+KXiZfJjJZHB9fQ29Xi8iplXXfKZpFotFaUj6/X74fD65T6uCiVgsJoKoarX6oE1fs9mMJ0+eiAhjNBoJYWZXY9ViseD09BS5XA42m00a8/yuJFTEYjEZKHEwz6HUfeJEuvQxofkhYbfb8fz5c3S7XbjdbnH75HvB+wfcEOLoGEiSLAVkkUhEXOqdTifsdrukUFut1rVkJ4rwPB4PBoMBer3e1kHAasopCcVMUdfr9ZLuk8/nhUxyfn4u7z/FY06nEx6PBwcHBx/cZCLBgwlw4XD4TtOVQvhyuSzEe6YifgsC9NlsJgYWJEM3m010u11MJhNxhSUURYHFYhGSTrVaRaVSkWTR98FkMpFU633S8oxGo6TRcwANQEwuVpujFINMJhMcHBxsfNbMZjMSiQQuLy+FZPGlBeher1cSq2lssAq60dOxsVQqwev1olqtStosxZdcnxwOhzRscrkc+v0+stmsiPY2oVAowGg0YjwerwlJmLpCogKJp6FQCJVKBQ6HQ1La92mYOBwOmEwmRKNReL1e2QN8Ph+KxeKak73VasUf//hHLJdLEcTw2SERgA2i1X12Pp+jUqnAaDRKmhEAIUSYzWYoioJyuSymIS9evBDx4up7TAEn979gMHhvmtAmMCGGxIdVt382dsPhMLxeL/L5PJrNppgT+P1+ETUHAgFJDuNgqNlsQq/Xi0EO96FSqYRIJCLDp4dCvV5Hv98XwiUAGeRNp1NpVtMxmmRnCqxJJprP55hMJtDpdMjlcvh3/+7fvbfQ/31x2/n7PvBnHA4Hnjx5Im7hbNzzeVsul8hms2uDzPl8jk6ng1arJeQYJncriiKCKhoP+Hw+MQg6PDyExWIRwVu73b7XxINiXhJlSO47PDyUhJPJZCIkdJvNhmKxuJM4xOGExWJBPp8XF91tIHGJ141Dx13o9XqSlLRvA3mV7KnRaBCLxWQ4TOEFTVWm06kMpJPJpJjd3EY4HMZPP/0khPvpdComAayfXS6XuEBTfMx0oPuG0oPBYGOiBwfhwM1aValUJJ2X6/s2GAwGJBIJ9Pt9GAwGWCwWIZkCWHO7psiJBMRNYnODwSAkrU1icKLVaonRUyAQkEEg8FvNAdykaZVKJXnm/X7/nffOYrGg0WgAwNa/SdJivV4XgbbZbMZ8Phfnbn6XRqMBVVVlKOr3+2GxWKCqKi4uLhCPx7FYLIQEuQv8u7yWjUYD+Xx+bQ2x2WxIJpNwOBySJsmUvMFgsPP+TadTIaDx2Wcqaq1Wg9FoxHw+l3qbotnlcolAICDi53fv3qHVasm6sg0k1TIF80MSl/YB0xRHo9Fea7rRaITBYBBC/dcEvV6Pw8NDjMdjOeNzPWDC9bdwFrgP3IN8Pp+QQmiA9L0M8jUaDSKRCEKhENrt9lqSKWvbTd91MplITexyuXYOqWmg1mq14PP59u4JfU7wu1Lo0ev17t3baVRGU4n76pH7wKQN1kGb0Ol0xG2cNc22dFAaXVCA+HsgGf5eoNFo4PV60ev1kMlk7hWukNz7qcWB28BkuVwutzFFwWw2i9CRohcKLXu9HnK5HKLR6J3ahz0qEtoDgcCaidM+9TJNvdhXecRuDAYDnJ+fS93McyvFRL1eD6VSCYeHh59lXacomb3sbaBRDMU8FotFCPGpVApmsxlnZ2dwOByyr7MXbDAY1p5Zi8Ui4iUSFQ0GAyKRiPQOC4WC/B6j0SipKjTyC4VCMJvN6Ha76Ha7iMViIkj/lKAxTrFYFMGGoigYDAbweDxwu90inF8ul5jP55KcS7HKp6rfKH6iOSZ7QPy7iqKIucF8PpcUKaZuDwYDnJycSM++1WrJfIoCwtlshkQiIUT6q6srMYzt9Xpi5sg0Pp75NBoNotEoVFVFv9+H1+uVnq7T6USv18NgMBBzPqbHl8vlnQJ0nsF5JppMJhiNRtDpdCIUBiApRUwX5SzAYrGIwSX7FUwKXCwWmEwmkp6eTCbFXJpCChqcrYImg3w+maCayWQklZbfnT3+8XiM6+tr6V0dHh7K2YiGWtPpVJICmXBL4d5tsJYymUwYDAb3Gh5yZsX36aHhdrulD8JzbT6fR71elx4szYFDoRCSyaS8J7PZDHq9HhcXFzIbtNvtslbq9XoxA7XZbHulsGk0GoRCITGozGazYvxIzGYzMWGNRqOSbv1QcLlcUBQFwWAQ5XJZTAZX69X5fC7pbhTvdDodMZi5DfaWKSiLRCJiis464HYdzvWWYhuKwyi4LZfLkvq7DZz70ChvF9gz7/f76Pf7ACCmGplMRlJlQ6EQyuUyKpUKBoMB2u22GEfzvZxMJrDb7fIcrYKGz1x/Wq0WDg8PRXRMkTLFiHa7HePxGGdnZ8jn8zCZTCLI53pAk2ye15jqGovFUC6XsVwu7whOVg1rPzTh0WAwyByDvfld6Pf7sv7dXr8GgwHOzs6ELD2bzaTHTkGRVqvF69evEQqFEA6HZR4K3PQsWq0W6vU6yuUygN/I1uFwWHpVrVZLDJNYH7JnajQapce2XC5xcHCAy8tLRCKRrT0mm80msyK3270x2YmzH6vVimq1upX7AEBSHWlgXywWhSh8W3y+Cq/XC1VV0Wg0YDQa4fV6pbeZTqeFZDyZTNb6+axZXC6X7DUfApp0cJZ6H2jqvO1suwucG9GkcnVOq9PppN+p1WrRbDal9qHRPte0QCAgBhDvC4PBgHA4LOv3qoh705pkMplwenqKSqUCm80m5xqmV6/WTOfn59LbTSQS0nunUSw5QDQLNplMiMVicj4MBAJC8Kbp8Sawl3sfuH7sWzPqdDpMJpO9TSk+NThPoIEq12G9Xi/mZvl8Xu47P+u+JhycF9z3/XK5HJrNJrLZLCwWy5ogErjhhjSbTRGM8z2nwAm4ufb1eh2qqqLT6YiBksViQalUEoGAoig4ODjAbDaTOUU+n5f3ebFYiFlsKBTaacoWCAREMMz+3ybwXEEjHafTKfOeer0us87pdIperyfrPutil8sFq9UqRr5OpxMajQbJZBI2m23jWX86nUJVVelhco72iIfDZDKRMzkNbebzucwd9zHPdblcyOfzsv98qhqW7+CqkT5Bow8a2JDD1Gq1ZK22Wq3o9/s4Ojr6Jp4jnhnIc9x0ZphOp/LuRqPRB+lBfI2w2+0IhUJYLBbI5/O4uLgQYzsaz3S7XSyXSzx//hx2u31rr7TVaonx0z7G/V8a24xHafZNkyKv1yt8IiaiE4vFAoPBAIqiCAd2NbBlHzBgilznXaCROjkF0Wj03tqRcxyK6Lhvf6hRCfmf+wjseR2r1eqdRFgKghuNBvr9vnAxotGohIbRBIYhVTw7POJhwVr9Q0zeTSYTnjx5grOzM1gsFrT/JcSN5yebzYZ0Og1FUYTb/6lwfX2NRqOBbDYrZvpc52nKW6vVkMlkhOv8sSbZXzvMZjNOTk5wfn4Om82Gy8tLvHr1SrgKy+VS+rEGg0HMvhkOw/f99evXYpjMPhONoci54dnuyZMnH2TQ96VADg+NC6hX4H8zmUzw+XzCY/pQsK9PY3fg5l3zeDwSYPiI/ZFKpYQLVCwWcX5+LroCnutMJhNSqRRcLtfG/ZXrAut0s9ksfYHRaIRSqYRsNis9NoYEWq1WCZ9aBWcRALaaRpLTcN+zxD1PVdWNBi6PeMQjviweV+xHPOIRj/hGoNPpkE6npRGay+XEQZZiCDrjk0RDd+pWqyXEHZ1OhxcvXkhiMoXBvV4Ps9lMhKsUn9VqNfT7fXHMJ0GColi6n3c6na3u2WazWZK7z8/PRVy0ayjFxDpVVVGtVtFoNPZqTnEoSlLM9fU1AoHAWuNoOByiWq1iMpkgmUxKYusuzOdzGVzXajX0ej1JNuE1cbvdUFUV8Xhcho4sgK+urvDLL7+IKMnr9cohgANLm822Jmg3Go0Ih8My8K1UKuI8yVRKr9eLfr//4AJsRVHeO63ZbDbj+PhYCEWTyURMDOggfxt+v1+cq5jeuAmLxQK1Wk2cWj9X45uNyZOTk7UkHx685/M5Tk9PRbS/KloaDociiGTyKYUqwWDwzrCOqTM8GNJtbhNWhUokO9xOjFgVwQI34tdEIoFUKiUi/kAgAJfLJekAwM07tSp+uw8UBvb7fZycnGx8t+iqWi6X0Wq1hJDxrTgEcx1mo6Ver8NgMIhL5aamCAkoHNhXq1Wcn5/j5OTkvRpPvA/7EHr6/T6urq5kveOzQWFIv99HuVyWwRjJStu+A0HSLgfHu0R1nwPcC5ksz0HzcrnEdDrFYrGAy+XCaDQSoiaF2hxae71eMRLR6XTS4CdpuFKp4OLiQlKfCBKMKN4nMbLT6YjRCAm8rn9J8iWRczabSUowcCMM2aehxu+g0Wjg8Xjw8uVLPH/+HH//93+PYrEoaU4kXc5mM5hMJiSTSUQiEVitVkynUxmeco8nMXc6nUqKbCgUgtvthtlsFqMF17+kADscDmSzWbTbbUniaTQaaylZdDimICcUCmG5XN6b3HMbHCpzsJvJZCS9djWFikYCiUQC7969g1arRTwex8HBgYiZuYeupuAyXYgDKzom+/1+BAIBBIPBB33GOey1WCyyztMkwGQyoVqtCqHC6XQKAU5RFDidTmi1WgwGA8TjcUn9ICH4oQXodPDWaDQYjUb31mp8D7lvWa1WaTRz7+DeSWf7QCCA0WiEXC4nNStNaFYTzykGTaVSImanMRDXTta/TPK5DzabDbVaTYjbbLYajcaNwz3WbLtEcm63G+VyWdxCtxG4OKzgPTSbzSgWi2vOpNw/DAaDDECGwyEikcgdYi8ASQ1ZdTEHNidiplIp5PN5IVHwfEASE3AjMn/+/PnG/XswGMDv9+P6+lpMsTjU5f7JdAMKUZjk0e/37zU3oVvr7USPVVgsFiSTSTFNoenHLlC4r6oqLBaLECsp5u31elBVVZKsSITiurwKrpeKoqDVam0kZk2nUyEbAzeDaIvFsnU/cDgcqNVqcLvd6PV6H2wCQ8Mrv98PRVGQyWREqE83WQ78XS4XVFWFoihCPjWbzahWqygWizg9PYXVat1rQEmyZKlUgtVqXXsn+Vx6PB6Ew2F5Rne55N4G08ssFovUwqt1t6Io8m4OBgN0u10xfeIeyGdwn/MFyQB0u38IAfqHkri/ZtBI5/cAkrW/V9B8cF9x6upetU+CsMPhkETS4XD41QnQiV6vJ8T3fYjnLpcLxWIR4XAYw+Hwo56RTQYot0Ei/yqRfdfawnr4azOxeMTHw+/3i4lcLpdDPB6/I0KnYaeqqkgmk5/FHISk+GazKUlmm0Dzi4uLC7TbbTEyWiwWKBQKuLi4kHM8AKlhHQ4HwuEwotEobDab9OneJ2mTxl/fi5HIQ0FVVbx790762EajEU6nEzqdTs7ZlUpF0sOfPHnyoKl0TOXsdrs7RcCEw+HA9fW1CEEjkYgQWSlU6Pf7UkNXKhXU63Xo9fo7JCmXy4WrqyvpZ5Cse3R0BODmfNdsNtFqtTAcDtHr9VCr1aAoCtxut/RldDqdpDXtY/r0IWC/m4LZ8/NzMcliD6rdbkNVVZkvPX/+fE2E+6k/SyAQENENz2i8VjxHtFotAJAzzF//9V9L32o0GqFer6+R5VwuFzweDxwOB46OjmT2RaO0eDwuiZSz2UzSyGkGzfOS2Wy+c/7kvep2u3Id2WNdTRknqYtGvePxGEajEa1WC/P5HKPRCJVKRUigNL2gaWKj0RCxDOdT7IVNp1M4nc61eon9pNW1ked/9oqBmzWOs45VQpdOp5M5TjKZRLFYxOXlpRDLaY7Ic2+j0YDdbpe038lkIu9TMBgUM17Of2iMcpuYzz4q+yd+v3/nXtRsNkVA9zlMUwBI342CTL7HNAdgcvztfVSv10taJ+/lqsEoAHkHtpk6bQLT64DfhHOrCcUk0jL5+ejo6EFnMRqNRgyHAaBcLkt6GMVknU5H1ji+I51OR9LjnU6nCHw7nQ6m0ymCwSA8Hg/S6bT0/W4LRVbP/EzcjUajCAaDUqv7/X60Wi1Zx7ellrJGodHdPnsJ53ns+9VqNRSLRej1eiQSCWi1WumHarVa9Ho9dLtdFAoF6UEyZZvz9k3zX/aPnE4nOp0OLi8vYbPZRBRSqVSk38/0QZ1OJybgNF7hzJCGJ+wtk0y6WjfdfmZ4lmHS4YeA/VzOsvYxSeI6s/q+z2YznJ+fi3jc5/NJX4dr+XQ6FQMYpgFHIhHpUTCNfhPZmnOCbreL6+trFItFdLtdJBIJPHv2DKPRCKPRSFKbFUVBp9NBs9mERqO599nh2XdbspNerxdeQSaTQaFQuGN+xh57s9lENBqFVqtFv9+XRONd4nNew8VigTdv3oiJEIWhfr9f5tjNZhOdTkeeYYvFAp/P995m2x+Lj6mJPB4PCoWC1BebzK9SqRRKpZKIrWicGAqF7phEf+z32LcepgFQOBxGr9eTffyHH36QRG6amAM3/TeTyQSv14tSqYTXr18jGAyK+SvNSG4b2q0mjTUajY82IecsaF/S+HQ6/WLJsbPZDIVCAaVSSczaV98zzlBcLheur6/FBJSi+X1Ac6pd+zBFoAzT2CSuowEmADE5mUwmeP78uXBUut0ucrmcGAZRNGi325HL5QDcnOEcDgesVitOTk7w9u1bEVfSfIuGR4lE4l6+kMfjQalUErOV2WwGt9u9Vg8Nh0MxqOH6Ui6XRYRHPoPrX5JqWXMxxZM8BdbuTGVkGn2j0VirrQaDASqVioi/KDgmvysYDH6WtPTfE5bLJfL5vJji8NoDN2cPchT2Mb5UFEU4H/wdnwKr81UaoAA3fd5yuYxqtYrhcCjiUZobezwezOdzZDIZmVnxnP+1g/N29v42nRmMRuPameFz9cFoTMDPZzAYhKPyORCLxYT/yYAlChMZ0EEuzzYjIPZNUqkUvF7vN8N924ZIJIKrqysxKqeBiqqqa6EWND/SarXwer3CDyAHYB/QZJemiPfBbrdLz4emU/vgU83rWKfQAGjXe8I6yul0otFoYDQayXckJ4ZGXDSiouEYP3M8Hkcmk0G9Xhejt8ce9V1w9kijGUVRvth7aDab8ezZMzSbTdRqtTXOFvvIDFH5VBiPx2g0GhKcc3utYg1osVhwfX2NcrksfMXvXfhrsVjw4sULvHr1SmrAyWQi9eNkMsF4PEaxWMR4PMZ8Ppe5wHA4xE8//STGfoFAAKlUam2tmkwmqNfryGazWC6XuLi4wOnp6Rf8xu8P9mZ8Ph9UVZX5MIMKb685/M40fKVBj8/n28hTq9fryOVymEwmaLVa8vuNRiM6nQ6KxaL07h6xH3Q6HZ48eYJMJiMaG6aLkyvKvmEqlVp7z2ezmYRCMgAJgHA4zWYzWq0W2u02ut0uSqWSzDiMRqNwzql5AG7q/263C5/Pd8eshiCnYd+1mSF/3yNf6hGP+NbxfVcOj3jEIx7xncHpdOLw8FAa7qspIruc8dmsH4/HSKVS0lBhqjKHhIPBQIQ9uVxOEhRXD04k39RqNYzHY0nnZHrotgHmYrGA1+sV4td9zWEmLDAx4H0crOPxuBxUKpUKMpmMJMFRVMB0PY/Hg2Qyee/vvLy8lIR3Nl3Z8KEAv9ForCXFsynLwY7H4xEHeqPRCLfbveYU7vF4JA2+3+/DZDLB5XKJ+IWCSTY4KTIcDocPLkD/GJjN5r0FBqtDqVKphPl8LkOpxWKBbreLSqWCYrGI+XyOeDwuqZKfc7it0+kQCAQQCAQk0ZoO16lUSoa6LpdL3PA0Gg0uLy9RqVTQbDbR7/cxnU63EiqazSacTqekqrJhvQkUelI4xjXh9oGNhLxut4tnz54hFArhxYsXMJlMMBgMqNVq+OWXX9ZE9UxJppvdrkPgcrkUQajBYLiXyOt0OpHP5xGPxz+aeP85wSEp04xIpmND5TYWi4WkXphMJuh0Orx+/RrtdluEXYFAYK/3eDVBYRcZZzAYIJ/Po1Qqod1uw2KxSOI6kwebzaaQtf1+vzi+7vO+Op1ONJtNzGYzqKr6RQXowM26r6qqpJzTsZOEcjri0fyAIkSr1SoJ1xSL2+12NJtNVCoVhMNhIUAtl0v89NNPSCaTWC6XYhKxKsykuDKXy8lAnAT/QCAg6eMUNJJ4wSb4PmlqJALRWRC4uR//6T/9J5yfn4sok+8UvxeFg3zfzs/PYTabUS6X5XnM5/OSbkLBczAYxHw+R6lUwnQ6RTQahc/nQ6/Xk2c/kUisObZ2u100m01pHNlsNhwfH0NVVRGhvw+YZFQul4XwwxQZJvWStMj0bEVRxDCHwlASc5mSzLTsyWSCfD4vwy2TyQSLxYJwOIxAIPDZRHKbxKskLbbbbQSDQUwmE/j9fjETISGZZGedTifJYBSKPiTo4m2z2dBqte4VhjE5mu/AxcWFpJWtNplns5kIuZ1OJ+r1utRaBoMBDodDBp2FQgH9fl/IV4eHh3sRQt93MHffc8skGofDgVKphEQisXFtNBqNGAwGSCaTkp5GwjL/Tq/XQ7VahcFgEIIjkyqur6/h9/slgdtqtcJqtSKfz4uhBkULt8G0VaPRuEZk3JaIeXh4uGYKwPqi1+vh5OQEh4eHW00OSAbh+0/xII0DrFbr2mCGJJLZbHbvWjibzdBqteR52lUDsiZvtVpwuVx3Um1uw263o91uS0Od15hO9zQZcTgcshcfHx9vJOIxYYtpnfw9t6/TcrlErVaTc9zqveO+odFoxKiC12nT+Wh1T953bz4+PhY3+1qthrdv38o75/F4xGBlsVjI+ZD1ZrPZxK+//oper4d//+///V5O4CRoRKNRqYX4eV0u1x1hAddwvV6/Uwyp1+tFHE+Tin6/D41GA4vFIukOHKDw2QiFQmLmkc1mAWCn8dMqSEhhvf8QYE3PwdF94laa43yu9L9HPOJ9sJqat89wke/hprS9rwnz+Vxqy32IGgaDQYas8/n8o+o1rn37ELk1Gg3G47EIf7aBaZyPA93vDyaTSdL8isUirq6uYLPZYLfbodVqMRqN0Ol0sFwuEY/H4XK5PstZiCYOnU7n3gQXjUYjtR1FU6uCBp7RgZt+ZDgchtlsRiwWE3EZaysmlN4nqKCJ06r7/yM24/LyUvopTMPkHIFrymAwwI8//oiTkxPo9Xq8ePHiwT4P6759EwkpNOfMIxQKyblxsViIwRHrM9b5TEJcPRuxDiNJnbDZbHj69CnK5bKkFjH5g3U+zwRM+XO5XHcSDz8E9xF8LBYLnjx5glAoJCJAvlPssTEhze/34+Tk5JMTOZfLJa6vr8UUudPpiPiRPdHpdAqdToeTkxMAEBGKz+eD1+tFp9MRciefAZ1OJwI6ihyq1SoqlQq0Wi2SyaR8F97fVbF5v98XI+LbQlXeX7vdLuZaFotFyHTsB9dqNRFg8Cw3mUxEJJrJZCQNedUoC4CQIafTqRg7M3Hd7XbLc7a6dzNRJBAIiBEEwXM51/7z83MxRltNZaKoh+SyZDIp6/V8Ppd3eDAY4Oeff5b1stFoyLXkOddqtSIej4sx6mry86ZkOAqE6/W6mABset5IhIvH4/eaqn5quN1uuN1umaWyj9PtdnF4eCgilk1wOBxilMn9jP3eDyWSJxIJmfPQ6JmGjX6/H1arFXa7Hclk8rPM0rimXFxcwG63o9PpyFmdQny73Q6bzYbDw0NMp1NJpOJ95bPqdDrh+pdU9dVru1oHUChCcwqTyYREIgGLxYJgMLhmmLE6F8jn81gsFneMRMbjMSqVCubzuRgx7PN8zWYzmM1m6HQ61Ot1jEYjDAYDpNPptWc4GAzCarWKuNPn8+H//b//B71ej8VigVAoJKmum8DETY1Gg2g0KkJ2g8Eg5ubpdFoEq6qqipEye6004WW613w+l2TvVqslsz6bzSb9cmK5XEqvzWKxfFSN5PP5UKlUJP02FovdEZ5wNjAejxEKhe6Iemq1mggMI5GI9IYbjYbMyPr9vqTCdzodXF1dYTAYwOVyibDy+fPnGAwGGI/HYgZCce5yuUQul0O1WpV0Rn7v1X7fZDKRvWw+nyOZTN77XnPN3WUExrnOcrlcS7diL51GwzQlMJlMePPmjZhOAjd1idfrvbMGcJ5IAxf2cSlAXTWzXt1LPyWYSs650n3XjHODD6mP2IP0+XzI5XIbDSZ4djo/PxcifDqdhtPphNvt3kiq/1zQaDR3zjBerxexWAztdlt66+QpaLVaeV99Pt+9Zy4+jzRW+FjQYIim/rvWCz7L8Xj8o4XvHwI+/91ud41TdRvcW9jTp2CR/IhtYBJdPB7fyQmgQI37x65nzePxIJvNotfrYTwey3qoqiq63S7y+byY9XDt596p0WjWTG6tVquIsX0+H4LBILRaLcrlMlwuF7xe7739Zp1Oh8PDQ+HGUNhgs9mk9qQYnjNYt9uNV69eoVQqwW63w263o1gsyjXlvNPlcolIgs86zwgUq/P3E9VqFblcTkzOV7k2q/PsVCr12YyMfg+4urpCrVZDoVAQs2ea3VerVeTzefT7feTz+TWh5SbwjPOpTQFpMETeJe9/tVpFu93GcDhcM0put9vyrB0eHsqzZTQaN5pqfa3g7NpoNMp6xzNDIBCAxWKBw+FAMpn8LPMlGn/QEJu1jtFohKIosNvtksb+0KAhUa1Wg91ul3kEDePJVyyXy/B4PGsJoc1mE91uF6FQCHa7/bsQ0CUSCRSLRTQaDcznc1SrVeEMce8bj8eSJM4+gk6nE+Owfeu0fUx2V7H6c19ihkFjCoZ97Hr/yVuKRqPQ6/XIZrOw2WxwOp2oVCrSGwFuaibOrVe/I8X95XIZgUBAhOqPuAHrn3q9jvl8Ln0EnU4nhn9foqev0+nk7ETeDLnwD3GOoKnkcDjE4eHh1r+h0+kQCoUk9KXRaHwXa9Z96Ha7wquLRCLSk2m322JO2Gg0cHV1hWaziXa7Da/XKzxachFPT0/v9CeNRqOYfNHo7FuqDW7DYrFs7WluMxkiH4f1UjqdlvquWq3i+vpaDKEYhAbcnOvZF+OsPRKJfLbv+q2DZ6/RaCTzFPKPOcO5vf6RL8yQsna7LXspuVarpoNGoxGJREK45QzgI1+MPOxGoyGmmtt6RuQ0MGTzPtC05Xs3yXjEI75FPL6Vj3jEIx7xjcHpdOLFixfi8r06+NnkjN/v96GqqiQtrzZ4KFhZxY8//oh2u43JZIIXL17cIdTQic1msyGTyQD4LU0yEAhsPcCStMPD22oC0yaQiEtXdf5eOv6qqiqEDJIPeMDTarU4Pj6WhjXTCufzuZAsSOzYRlxZRaFQwK+//oqzszOYTCaEw2H57HQ29Hq9sNlsyGazaDQaIvxhekalUkG1WkU6nZbBOJNO6IQPQMhF3W5X3COZcu90OiWxhff9eyMFa7VaHB0dySGGQymNRoN6vS6kJJowqKoKVVXx888/w+fzIZFIfPaBL80NOPS4TYwgmQuApJ40Gg10Oh0sFouNZgyNRkNcLrvdrriE1ev1jS7MJHMNBgMUCgXMZjOk0+mNjfhV59VkMgmHw4HpdIq3b9+i0+nI4XKVoOd0OtHv99FsNnF8fLy1UTybzWC1WsVQgY2FbfdEo9FIUvMuQtbXBh7Ug8GgOOBXq9WtDm5MubBYLJhMJtBqtcjlcpKQ7fF40Gq1EI/Ht6ZtEHTmt1qtktBwG8vlUoxHKpWKiARXHWYpKCuVSmJAwud0H8HV1yYGURQFR0dHGAwGktpTrVZlryDJVKPRCOlCp9NhuVyKo+eqMF+r1UpTkU7WJLGfnZ1hPB4LEYkiQJI9+b8z0ctisWA0GmE4HCKZTEpjhIQwq9UqbqxMld+FTqcjg5zVhqXBYMDTp0+RSqWEYMB9kiQ2DgqtViuePXuGarUqgjZ+fg7vV5NAmPSRSCRk6EEXeg5NVhOpLBbL2nPBz9tqtdYSnPYF18Bmswmj0SikQCa4ADfkqW63i2q1Cr1ej3q9Dr/fL4m6FARVq1W43W4RLqwKf91uNwKBgCToptNpPHny5MH3FRID2djj32N6st1ul9qHayWTajqdDmazGex2u9Q4JJZ+DnMaDm76/T6y2Sy63e5WYhCdy8PhMLRaLa6urlCtVsXEiN+t1WqJ8+liscDf//3fw+v1CqF/9bknkfvo6EiI3ZlMBi9fvtxY39HZmsKC+64R3bbZWL3vWkSjUcznc+TzeVxdXUlq/aqRQLvdlhQoJjBeXFxIKimNLGhOsprWyO/ANY6GDLy2JAZsEieQXLQtyZKJmHa7Hfl8HrFYTJ4/XnO64s9mM0SjUXg8nq17xmoyEmvwXQSBVUHiPgTD+XwuQv77QFfx6XQqz8w2kFinqio0Gg1OT0/X7r3L5cJwOJRn12w24+XLl1v370gkIoYd+XweLpdL3m2ubcViEaPRCEdHRyICI3gOUBQF3W53LTnvNhaLxdp7si85RKPRIJ1Oi6iA6Sg0HyJpmmSC4XAoz6tGo8HFxQUA4PXr15hOp5Joz3rearXKkxDVAgABAABJREFUuWgVFO3s8/lIVKxUKlsNkZg+RBMzh8Mh5haxWEySNyeTiZiPHBwc4PT0FOl0GrVaDRaLBbPZbG9TmMVi8eCpPNyvSBikoc02tFotIYY/kuYe8bWBhjkUQt8nfCFh9EslX+0LklkoHLzvs3IYvylF8H3hdrvRbrelFtjUY+NAl4ITm822k8hNQ6ZvxSDuEe8HDv61Wq30i5noQwEnSXSHh4cPnn4OQESVi8ViL0Kp1WpFtVqVs3gsFoPf70etVkOz2VwTXHi9Xvh8vrXnmTVIr9dDPp+H3+/f+bw3Gg0RXm0yeXrEDSgUrlarQqxjMkK32xUhFw0rX716hel0ilgs9mCkydVZwT5JaTQDo8j49nrO38danLXnYDDAYDBYE6Cv9kRur/UmkwnJZBKxWAydTkdEJZPJRNKbODvYR+SxC0zfbTQaInomYY5J7rfhdDrx8uVLNBoN1Ot1MTlh6pzP58PBwcGDrA/ZbBaVSgW5XA7j8VjmPjQQpPlgJBKBwWBAOp2GwWBY6w/RNJY1P4A1YhsAMVfu9XqIx+Nr98hsNksfq91uS6+bRk+3vzdN5QwGg9QD/Bka556dnaHdbstMgH13jUYjhm//83/+T5llMXWG9348HqNQKNy5XrVaDR6PR/opqqqKuUar1RKR7eq5QFVV6eVpNBq8e/cOzWYThUJBTLpoQNbtdpHNZuFwOMT0T6PRSC+K6am8Tz/++CP0ev1ar4VzstWZJU0DmBK9CQ6HAx6PB7PZDNlsFpeXl5LSSvESRUQ0FftcBpK3QfPKTCaDbre7dk82od/vC6GPhuCrM6SPQTAYhN/vl4Ri1p0Ukn3udE+mTLHeuN2v9vv90gMxGAx4/vy5JIStGiwpigK/33+nD6XRaBCPx+Hz+UQEuDrX8ng8CAQCG2tk9uY0Go0IlK1Wq6TYMtmHMwcam+5a+9gzCYVCMuftdrtbTWxsNhtsNpvMy0ne5fln2/ycAv1EIoFYLCbCzlVRHf89+0OXl5di5mkwGDCZTFAsFuV78ppZLBbYbDY0m020Wi0x9FEUZe3zUJzvcrk+WozMuQNF5plMRpLaV02StFqtzCdW33cm1fPzUkg8Go1QKpXk2WO/nDNSps7RaJnvCvvsm647BUY0OdoEGgBcXV1Br9fLXOk+3GcEptVqcXJygmw2C5PJhOFwKO85+9LkhFitVuRyOZkBlstluf/dbnfNZF9VVeRyObRaLakX7Ha7vJ80hQ2FQlgsFnjy5MmDrCUejwfNZlME/rvI+jR4pyD2Q0Djhul0Kon2nBfN53MhY+v1evyrf/WvEAwGkUqlPvDbfR6Qw7MJqwLOffGp+Cc07rXZbCiXy0gkEhv3vOVyKUmMVqv1QYwO7kOj0UC73YaiKPc+506nE9VqVfYNk8mEarW6sx6p1WoyF971/VZN7e8TTNGopd1uo1wuw+FwiDiba9poNJK5N/tCo9EIfr9/TaTA38caz+Vyod1uy7xq33titVrx5MkTXF1dyayANSj7/GazGcFgEJFIBKVSSWoicrxcLhfOzs7EuAaA8KVoNkOBvtVqhclkErMQPrv1eh3X19diYs3vwNRaGrNTvE5OzCM+DpVKBbVaDdlsFjqdDgcHB2s9YJfLJSKZYrEoM95t/dRerydGzZ9aQOj3+9Hr9VCv12U+RFG2y+WS89hwOESv15O9kXv92dkZer2eCJa/FYRCIQQCgY1nhs8p1JxMJnj37p0kn9ZqNVmD9Ho97HY7PB4P+v0+Dg4OPovg1mKxIJlMIpFISH9Rr9djNBrh7OxM6qKrqyvZy9jPjMfjcDqdODo6+i766jxjdzodnJ2dIZ1Oi3iwXq9Dr9fL+tlqteD3+7FYLPD06VO43W7E4/G9/xbnF+yJ3TdfGY1Ga3vD5wb7Ji6XC5VKZac4jmEl7PHwemUyGeRyOVQqFQSDQTlzrXJCVsEABHJkHwXoN2i322IsRn4D+VY01K/Val/caIYcpYdEp9NBp9MRk7JdUBQFJpNJ/s3vQYBOA1DyQ/jO+nw+CRxwu92SIm8wGORcDNzUnZvE56vw+/1ot9vfZG2wD5bL5UaTIYYG1ev1tdnQ8fGx8KMLhQKGwyHi8fidfstwOEShUEAulxNtyWNN/n4wm8177bucUzQaDRSLRSiKIqFdDPf4+eefpTd5cnKC09NTlMtllMtlNJtNeDweOJ1O6bctl0vhasZiMTlHbgLNxdnD3NU/J886HA4/zmUf8YivEF8vc+sRj3jEI36HYJI4kxKYkn2blKvX6xEMBhEMBiVtbZszfqfTEZHGanE+m83WyF8UVAI3ww8KBQgSTigep2CiUCiIkG8XSZANIw5jVhOWN4FiOAoOdDodMpmMkGpJVGDj0WQyIRQKyaGYxINIJIJGoyEiCH5Pr9d7byNKVVVcXV3h7OwMFxcXKJfLCIfDKJVK0ogmgQG4GZL7/X7U63V4vV4RYZGYpCgKksmk3AeKHenS3e121wT3FPYTTOUdj8cyWPnaidgfAkVRcHp6iqurK3GNvLi4QLvdxmAwgN1uFwISjRAURRES2C4nv4fCquMu38dNIDnc5/Ph9evXcvDmu8MUgkwmA6vVKkLT+XyOer2ObreLP/3pTzg9Pb1j+MBnjm7Vw+FwrZkxmUyENBIOh2Gz2ST9+vz8HI1GA7lcDnq9HoFAYM1tjqJ0Hla3NVXobMZUk9XEmds/T/GW3W4Xctu3BDqLejweEYQ1m00ZSuv1enFxPjs7E5JtMBiUAzQTLEhu4Fq8izDBazUYDHB9fQ2z2XynUcpB7sXFBfx+P3w+38br63Q6hYi6KpybTCb3riskQ9Il/WsAU6iYrEyzFCaM87uSLGa322E2myVJl8Sy2WwmCWx0fnc6nbBYLKjVakJScrvd8Hg8klxEIpTNZpMEG77zpVJJktFTqZSIBEmcdLlcmM/nuLi4QKfT2dpMowmL1+uFxWLZuO8qioJEInHv9aK4gI6mFEg6HA6Mx2Pk83nUajVJQT84OIDf78fBwcFaojivPQDZiwOBACaTiawJJNByjXrfNVqv14vb+Hg83ji4Y0NYr9fj/Pwcs9lMnlM2u9joHQwG8qwwoZjpKUyESaVSCIfDn6Up7PF4xDCCRjPAzT0iibbb7YqZAYkjTGCxWCxCypnP55L0+7kQDAbRbDYRDAal0bxav45GI6nFIpEI9Ho9VFVFpVKR5PJVA4/lcol+vy8EhG63i1KphFAoJKRDConH47EkH3g8HtTrdXGr37SWMn3dbrdLGvg2cADodrv3IhwBN3sDia40dWk2m/LfLRaLpPCEw2Eh0A+HQ3G3puCHBE8ONTweD/R6vRiX8PtznWN9uFwuoaqqnA2Gw6EQ2qPRqPzt2zCZTIjFYlgsFigUClKH2Ww26HQ6IcEuFgskEol7EzG5FxoMhntF38DN3mUwGHYSbInVtOl99iD+DF2vd/1eJrXx+anVapI+SOh0OjFK+sMf/oDpdLp1rbBYLDg8PARw0/gnMYFkKCZjRSIRGI1GxGKxtTVSp9MJIadUKqFarUKj0ci1Iph+pNFodrrKboNGo0E4HBYCF1NF7Xa7pFA1Gg24XK615FwSCvr9Pn788UdUKhUReVGAznQLv98vgoX3BQd2TA3ctMaRgLRcLmVdd7vd6Pf7kppA6PV6JJNJxONxPHv2DE6nE+fn52Io43A4ZB3Z9Xm73a6I7B9yv6BrsMlkQj6fl+TA26BhFPevx5TWRwAQ8RV7TFar9YuldFmtVukLtdvte9OsuFcbDIYvkny1L2w2mwjPOp3OvQT4TqcjZkKKomwVXu0Dn8+HUqkEm82GUqmERCJxZ31wOBwiyAiFQjuv53g8hqqqCAaD38QZnfU9BaPsTfr9fjidzi/2rH/t8Pv9IlSy2+1r/VmSaj5not/7mmtu+lysZVfTTXchEAiIgWgul9to4ETzJZrJ3U7afMQ66vU6Op2O1LgkHDGlwOPxyPm80+mgUqnAbrfj//yf/4P/+B//44Nc21Xx3T7rM+cHRqPxTl+GBkw0AWOdxbr39lrOnq7ZbN5KpKE48qHAZDkaHo5GI6kHut2u9Gw3GbrqdDoRcvMdHQwGePPmjYgOeTZdFRZ/DFjvX19fi0nt6pmH61OxWESxWEQqlRLx2SbsMkGjKFSv19/5GRpAMYl3MpnAYrGg0+mIWQbBlHGfzydnXp71+P/ncjlJFeZ1pYh6OBzCYDDg//7f/4vpdIpCoSDJtPV6Xc41o9EIXq8XiqKIeJwpv6PRCIvFQuYKnU5H+sUkjK+ex2mqQfOEdruNQqEAn893R7TmcDgwGo2Qy+XExPTly5d3DFF5rmXaut1ulz7ttjM7UzF3PTupVArL5VKSW1utFqrVqlxbp9OJVCoFu92Oo6Ojz5LQtwkmk0nM+2hssg2cs3ImYjQaP6oW3AStVrvxfn4paDQaeDyevda71YQwrq06ne5ewwv2wxOJxJoAfReMRqOI46xWq4j9lsul9FbNZjNMJhNUVcXl5aUYRG9DrVaTHqLP50Or1cJsNrv32TSZTDCZTEilUshmszJbG41GMgddNQ/qdrvSw+E77vP51kxMaMBJVKtVmY2Q8MnUPa/XC6vVKsbfw+EQRqMR+XxeiKher1f2AibzMqH4U+xlfF61Wq3MVNiHZI+cokXOJwjOZ7rd7tqciyYV/X4foVBI1nu73S5G/BqNBolEAldXV5J8tq2/Q+NwGijcBs0yyW1g3VOr1fD06dN7r4Gqqvcmemu1WqTTaUQiEemb8xzEcyYNVmi2S6HLdDoVAQxwk2qpKAqKxaJcb/b8vF6v7PE0hi2VStBoNLi6usKzZ8/u/T7AzayxXq+LUTTFo5uMKl0ulwiZKELe1E+eTCYol8vS5//Q589sNuP4+BgajUYCA/L5/Fryl8fjkdTwfUxQv2ZQ9LJq7rENTJHmPOJjodfrRYSQyWRwfX0tKbsEZ0E8e7lcrs9umgLcPF/j8Xgv03ytViu1GdfJbDaLYrF4x8xqNpuhVquh0+lIOMCuv8H5x77X32azQVVVTKdTXF5eSgiHXq/HcDjEaDQSzlWz2USpVJIEZIvFgmKxCK/XC7PZLHNi4OaMx73PYrG8l+GDoih49uyZcKrYn+d7vsrVIg9FURRYLBYxeHe5XMhms5Kwq9PpZKY/Go1weHgoSeoAxFSFZzQK+4bDIdLp9Nqawh7pcDjE9fW1zIxfvHjx2Ev6CJAbU6/XAWCj4QTPDayvQqGQ8Kc2/b5msylrwqc2Xvd6vahWq4jFYsjn83J+XiwW0i/j2dHhcMBms8nnpGFBp9O5YzD/LeBLnxkWiwXOzs6QzWbx+vVrEcCzhtVqtVKfsNf35MmTz5KEDtycYVZreIvFgqdPn6JQKIiBFM1zWcu73W5EIpF75+HfCmhePp1OYbPZcHl5CY/HA6PRCK/XC1VVMZlMoNPpEI/HEY1G8fTpUwQCgfc2LXS5XLBarcIJ2vVcLhYLdDodCef6Er1aztTJEctkMggEAmt8l9FoJEZYyWQSfr8fiURCOJLkUGo0GoRCIYTDYbm+2/4mDas+dQ/hW0Wv18Pl5SUqlYr0usi3mE6n6HQ6yGazcLvdvwujGfYD9l2DTCaT/JvvHcPhUMw6N51xvF6vzK25Fi2XS+FyarVa4evtAnsFrB2+N9xnMrRcLtHpdFAul4UrarVa0ev10O/379TjBPUUq32Z7/ld/ZIoFoti/BMIBO70U5bLpcxfuEaYzWbhC9TrdVQqFXlHrq+v0el0EI1GcXp6CrfbjaOjo601sdfrRaFQED1IMpncWC/QoNJqtcJsNn81Pe5HPOIRv+HrUIs84hGPeMTvHJPJBIVCAa1WSwglwG8keg6RNw1a7nPG55CeSVPz+RzValUIMxw0Me0zl8uhXq+LkzVwc2gvFouYTqfo9XoiQNdoNOIQZjab8fz5862fQ6PRiIjt8vJyTTRzG8vlEu12GwBErMCBdrVaxWAwgNFolGQ6Dhp57VaHgKtkqfdBv98Xx6dff/1VHE91Oh1UVUW73ZaEvNlsJqQbDuz7/b4MazmsJFmG4PVlQi6dnZi4eBu81/P5XARRXzsR+0NhNpvx9OlTcWtmQkAqlRIiNBsCk8kEpVIJ2WxWnNA2Case+vPymez1eluJbzqdDolEAo1GA9FoVMRLFDvN53MhiQ0GAxgMBoxGI+h0OiwWC4zHY9TrdUynU6TTaRwcHKw9K2yEHx0dYbFY4OLiQpJbOQC97bxaqVTQ6XSQy+XgcDjuCHqY1Fkul5HL5WAwGLYKf3hP/H4/Op0OQqEQqtUqrq+vhdhGQhzf/+VyuXV9+5rhcrlgs9lwdHQkicsGg0GSsTmgL5VKYnzx7NkzHB4eymCC19vtduP6+hrlchmqqooAUKPRyEB3tdFMZ/5IJCLi9dU01fPzc1xeXgoZheLJTXC73ajX65LATXH1fY2zdrstosfPMejhvkAXvNWkFBIVMpkMOp0OAOBv/uZv5L1h6vlsNsMvv/yCf/qnfxLxKcXRTEkzGAwYDAZQVVWc7km0ZQOl2WzC6/UilUpJeny73UYqlcJkMsGvv/6KZrMpSR3BYFDSiply3el0ZF+kOILutqVSCdPpFG63ey1pvt/vo1KpCIn0YwXG8/kcZ2dn4i4YiURwcnKyZo7S7XahqqoQrVOplHwmJn3pdDpxjidIyl5Fv9+Xn/+QwdtsNpPaYxcJy263SzOT34WkdzY2uT+bzWZJsiLBnGRzr9cLv9//WYbFTqdTBpO8x7xGgUAAsVgMzWZTTB+CwSAcDgfC4bCQJJgCyLQtl8v12dy16U4P3NSu9Xodl5eXa4lfVqsViURCSHQU26dSqTvPikajgd1ux/Pnz/Hf//t/l/R6m82GWq0m9RQNhRRFQTweh9lsFpdemgHdhlarhdfrxWAwQCaTEUOHTcjlchgMBggEAu8lwAqHw3I/XC6XJHox4Yv1MNdO7u23CTibRD8OhwPPnz9Hr9dbS3c8PT1Ft9sVsTnXS35nis7tdjuOj4+3Phusl5kQz4Qqkj05fCG5excBimQhGgIoiiLEeCYq8TlfLBZotVpwuVywWCx7DW84lJ1MJrJ2T6dTIXuu/o5Vl/pdA2+exXQ6HVKpFMbjsSTv0HBlsVhIokksFsNyuYTVat1JLnE4HDg9PUWpVBIi6HQ6lf2pWCxK/b/pXOfz+dDv9+H3+/H27VupN0gIo+gYAOLxuDw/HwKKt71eL2KxmFzfcrkMr9d759xBUggFWjTIicfjYgCzWCxEuDAYDHYOHbbB6XTCZrMhGo3i+voas9lMjHz4ubln8fMXCgUMBgN4PB5YrVYhAoxGI9jtdiQSCaTTaRlYGI1GIVeazWZJitw20KCwijXYQw7CrFYrvF6vpP9dXV1JiqBWqxUDim63C7/fD7fbvbcA7xFfFhTpATdr1O1340MSqvjvKPwbDoeYz+eyx9A4z+/3f5Zk41UwzbPf7+Pq6mqn8VK/35dktk9Fen4oWK1W2fOazSacTufWz6uqKnq93pqY9WMIQzQ5m06nuL6+xtXVFXw+n6wPNOKq1+uyhjudzo33fj6fo1QqyUD3S6SN7YvpdIqLiwsxmep2u2J8QkEJTWD2NeNYNQUFIKagX0rM9tBgLRONRjGbzYTc/rnXBQBCfKd50n2mLqqqiqjxQ888FJbN53Pk83lcXFzA4XAIUZDmS/P5XOqr24LLR6xjOBxiOBxCr9dLDT2bzRCNRtfuk9Vqhcvlwng8Ri6Xg8/nQ7lcfrDry1Q/Jk9v23cWi4WslfP5/M7awTMpzRu41nPuslrHs3fP883nIkivgn3cUqkkxpk2m03es1wuJ6aM8/kcBwcHW3/XaDRCNptFt9tFLpfDaDSSRN96vQ6z2YxYLPbRxiWcWc1mMxweHm5cj5jwenV1JWLkD9mvODPbtsZTFOP3+3F5eSkJ6AaDQcQ9fKasVitarZak6+p0OhGWU7BeLBbFkG61rrNarZIi9qc//UkSqmkkS/NKCtw9Hg9arZYI1dPpNPx+P5bLJc7OzqR3FgwGYbfbEQqF5Gy8emajaJvmsU6nc2diqsFgwLt374Rg/+zZs7V3hIIdini3/S4+O91uF+VyWQx5tkGr1UrSXbVaFYEticNGoxE+nw+BQOCLJ8yRbG40GlGpVBAIBO58N5oMAzd9Aq4dj+Ymm7GP8Hzbv9sXFKGrqrpx7sCUul9++QV+vx+FQgHL5RI+n2+t3p9Op6hUKlBVFclkUmpI9q52mSGuYrFYyDrBmUKxWFwz6rHZbCJePDo6Wuuz7zJ9oOEB029pKqnT6dBsNsVcWaPRYDabrZlSMY2KvS/OtThn/FTPsM/ng9PpRK1WkzkowTnKNuE3/y/Xdc5ae72enG9WrxNNimmoQtE7a5RtwufxeCzXafV/556wagIwmUwkbX0wGOysB9jXdLlce+2nrOWJ0WiEX375BeVyGcvlEul0Wp4FGtmWy2XYbDYUi0UxN2X/s91uw+v1ot/vyyx3lb/BHnetVoPD4ZCf24bZbIbr62sRiXJmqtPp7oQKtNttST1vt9toNpsSiMD6gomYNHxlLR+LxT7q/GK1WvH06VNUq1XYbDZMp1O5f5x9biJlf4vgM86gh13n5MFggNls9skMJgAgFothPB5LHfrzzz/DaDTKOz0ej2G1WmUN/drT5gmuBatGvcViEefn52LCOJ1OhfPB75dOp3f+XtYHq+vgLnB91uv10n/nGkCRNftVDodDzl9WqxXRaFTObzQl5B7GOSSFBB/Sm6NJzy5MJhMx3uZnjsVikpqeyWTQarVgMpnkc9GcO5lMCp+GCXkej0f6O1xLts2lFUVBMBhEpVKBx+NBp9N5TJX9CNBAhzyhbTWCx+PBYDBAsVjE1dUVFEW5YwbMc9RyuYTb7X6QPqVWq8Xx8bH0zV+9eoV8Po/RaCR9VZ457Ha79NDIZZzP58KdZG/gEfuBXJoff/wRWq0WOp1OOHMAhAep1+txcXEBs9mMUqkkfIQvAaPRiHQ6jXg8vrGH/KXPpA8Bm80mCbbsR3S7XTHXNxgMsNlsUBQFz58/RyqV+qAeGOcrHo9HAkG21SqVSkXOJO/LA/6UCAQCwu2uVqsoFovQarUwGo3SQ1qtbZi4nEgkJFme/Y1QKHSvuG6Vb/K41kDqg3q9jmaziUQisfbs0ZBUVVUxhNTr9fjDH/7wTZmFvA/et/+wqaf9vYIahclksnU2oNfrxZglGAwin88LJ7TX6+09JzQajej3+9+dUcSqyRCN/G4/O+TA6nQ65PN54cz1+33hRG4Dw7ioh7jvzPqI98dsNkOz2USj0YDdbt941mctz/eEfOhYLAaj0Qi9Xo/ZbIZ+vy89AybWh8NhpFKpne8K+a7bOA3kfLF/HovF4Pf7v8sa6xGP+NbxWI0+4hGPeMQXxnA4xNnZmZA9VhMLZ7OZNFt7vR6Ojo7eO1ltVbTM4q3X68nQkwIONisajYYMBOngRXF8p9ORNCEKPujYZbfbcX5+jtPT062HdSa9UnhRKpXk4EHMZjM0Gg2oqgqtVotkMolWqwWHwyHO7+l0ek1s0O/3ReQK3DQSdrnQ3weK8i4uLiShgmI0Nn0oFiqXy/I3PR6PiBk5pAR+E+Vy+MF7yOu0msY7Go1QLpcxnU7h8XjWrs1iscByuRTRysHBwVdPxP5YUATSbrdxenq6caBtNBoRj8eRzWZRr9ehKApCodBnbRo5HA4oigKn0ykDvNt/n8PxVdKDy+VaI/gVi0XM53P0ej2k02mEQiE5mEWjUbx9+xbZbFbElO12G8FgELPZTFyd//jHP4rbGEU2wM076nK5EA6HkUgk5B2q1WpotVrQ6/U7Ba2hUEgGlFarFcFgcGM6js1mg9vtFhc0o9EoKQO9Xk/EVn6/H6PRSIaX35p7nUajEaH/v/7X/xqLxQJv3rwR0RuHTX6/H7PZDKenpzg6OoKqqpjNZnI/gBuyiKIoePXqFUKhEBqNBiwWi6SGFotFuN1uSeQyGo04OTnB2dmZOPNXKhVpJNJ5kennu9ZD3jMSZwwGA/L5PJxO59bhAO8lRRMPTVBnmjsHlrVaTfYoj8eDYDAog3gaqGwiIrHJTxHccrlEr9eT55Ru2+PxGPP5fM19nS7GgUBAhvitVgvD4VASd1aTSqbTKcbjMd6+fYtkMgmz2YzhcCjvbzqdRjKZhM/nE/Hr4eGhCKfpKs/nYDQaYTabiXA1Eol8NLGXSYH5fP6Ow/VqinO320W1WsVyuUS5XEY8HgcASVZdFdpsW3cpLmViy/s2CnmfSC7YRRqnSY3BYICiKHj79q2Yu1Ds3+v1MBgMJA2k1+vB7/fj4uICL168QCKRgNfr/WxDK41Gg3g8jslkgslkgkwmA7fbLfdbURS43W6USiWEw2ExriABmQ7KNEmIx+Nr5J3PAZvNhtPT0zXnbw5eV52/g8Eg3rx5Iyk7u54Fpvfw/vNdJyjmZjoKPwcTHbaBbvo08aBAlQmkxWJRxOehUAj1eh02mw2j0QiBQGCvwSkTeWhKQvIqU1xvf89dSeKbsIm0Q4OMVVI28JvJFMWG99WNdAxnIubqcMRms4m4dZ86KxAIIJ/Po1AoIJfLyTrBASzFejTlYDLPfaDzsKIouLy8hNlsxnQ6FXK/qqqScsHkbCay7yJHqqoq6/rR0REajcYaSYwJRnz3Vs2lptPpzma6oig4ODjAdDpFu93GbDaThJ10Oo3Ly0tx6b5dj+n1esTjcUlJs1gsGA6HuLq6ArD+Lng8HhweHn5wHcznQ6/XCwG11+tBr9dvXFNY33KgXq/X8fTpU0QiEUm9GwwGqNVqyGQyIl74EBLh4eGhiAur1Squrq7E2ITpzkwkYj12dnYmNQKFRDQn83g8a+8er5nT6cRwOEQ0GkWhUMBsNoPX65V3d9UUhsPIjzWF2QfJZFKMdTal/9lsNiQSCdjtdhweHu6V1POILwe+F61WS2p4mqRYLBZxYud/oxh4n9p7Pp/j/Pwc7XZbTEm4J9CUhEK84+Pjzy6uDQQCUrMXi0U58/MdI4mPZjIOh+Oj+jufC8FgUFITs9ksIpHImgkYzx6lUglut1uI7J8C0WhUjE1ofFUul6UXqNfrkU6nMRwOMZ1OJfGVNdR8Pke320Wj0YBWq0UsFkMwGPxqhdfT6VTqyWKxKEYnFFJWq1VUKhWEw2HMZjM8efJkZ805Ho9RKBTQbrfXTEFNJtOaKej3Sna4z+Drc8DhcIghWLPZvLf3TWEvDcU+FKvCKxr6kNxMwgsJ+kdHR19EnP8tYblcYrFYCPlkNBpJ6t5tcM+rVqtiihcOhx+kl+v1elEqlRAIBFAqlTCfzyW5hGA/frlcIhwOy/dgj4bweDwivC6VSvB4PBiPx/I8ApAUaya4fgky7GAwQKFQQKFQwHg8XpulEPycNHRluvim30XzwnK5jEajIf0ZJpK43W5MJhMkEokPFgWwP0OS2X2iZI/Hg3q9LsY275uER1LmNjIg985arYbxeIyLiwtMp1Po9Xr8+uuvYtQIQEwcWauzfxCNRjEajWSN3faMj8djuN1uOXNwJhQKhdDr9aS/bzAY0Gw2ZW+PRqN4+fIlDg4OhDhWKpXQaDSwWCwk8anf76+ZaiQSCbhcLjHgolDnNgaDgcwgVFVFp9PBmzdvJCGapl58tti/uby8vCO0VFVVjJEoZOW5jvVgPB7fSETUaDTwer3wer3S76FJHlOhvwa4XC4kEgk0m038/PPPuLy8lKRGGpv0+325t3a7XcxgP3WS4yPeHxSwrYL9bPbQ6/U6xuMxXr9+LeJXk8mEyWSCwWAAs9ks4oJEIiG/12azIZ/PYzab7ezJce8JhUKIx+MiwggEAtLzoGkPzWnfx+SVYjySf+12u5gH2u12DAYD+Ts0Xna5XEgmk2JQCED681yn37x5IynAJI1+KNhvIXcB+M1knuZdm8D/fXVd51oxHA7vnCc5aycXA4DUoVw3t61HtzEYDJDP52WdpMEocDNX4Zr5z//8z/jLv/zLjXX3bDYT00pyEd4XNDdVVRWHh4fyrLEvwM9Hnsf19TU0Go2QeoHfBPZer/dOrQT8Zmbd7/elX74J0+kU7969E4P54XAIk8kkoQKVSgUOhwO9Xg8//fST9IZpKjadTkXI9/btW7hcLrkfnBE4nU4hIn8sjEYjYrEYIpEIer2e9GwVRfnmjNN3wefzyXNWLBaRSCQ2rkkMfmB9+7FzSKLRaGAymUBVVRFPDQYD4c0kk0nY7XZ4vV7E4/Evtr9znVVV9d6f5RyCaebxeByZTAZmsxmqqsozbTAYZF13OBxrBhHbYLfb4XA4pH7a9SwuFgvUajW4XC4xC+eMyGKxiGmETqdDuVyWmfZ8PkcoFJJZONNtKSIgL81isSCVSj1or5nr623DlZcvX2I+n2MymaDX60n/sNfryX7LuUSxWJS5tc/nw8XFBdrtttSMu8Brzd7Y9yZAp5Bj1RT5U9ef/Bs///wz3r17J33J4XAIt9t9Z191OBxwOp1IpVK4vLxEJpORlFEAEgij1WoRj8cfTIAO3Lz3nG+3Wi1MJhPpS9PMlfPFSqWC8XgsvXZyDVwu19rz+4j78fbtW1xeXorpGute9qo5HzGZTNDr9bi6upK5+5dOGNfr9V9U+Py5Ybfb8eLFC6n/eC4AILy/VV7VhyIajUJVVTFBZE3K38uZFkOpyMX4kmBPhNeh1+vJHNtqtYqRYDweX+tz6XQ6eDwe+Hw+DIdDdLvdewXonU4HBoNB9vbfOyiS5Kxx24yAZ3f2cclZ+x6hKIoIfm/zqm6DpnPkSH3vWCwWMm/fZ60i744cr1095NuYzWYfbKj4NaPT6WA8HqPT6SAcDu98vux2OxRFQalUEj3KPnWcw+EQM0L22R7x6cA6l2aEmzCdTjGZTGRv63a7sNlsmM1m8Pv9Mh/r9XpYLBawWq1i4n18fLzXfC8Sici5pFarCaeB4naeV8l5fwzdeMQjvk58v4q1RzziEY/4BjCbzXB+fi4u5hw6sIBm0kGhUMBkMoFGo8HTp0/fq6HGFMHlcol3797J4MhsNiMSicjvms/naLfbePv2LSaTiTgXMvmNqXu3D+10HRqNRsjn81L8bQIH2ScnJ+j3+xgOh/jzn/8Mr9cLu92O2WyGbreLyWQiaZZMP8nn8yJUXQUJUky/rVar0tz5kOHUYrHATz/9hFevXuHq6gpWq1Ua3MvlEpPJRMjCbFK0220ZwvHQqdFopKjm93a5XEKMYlI1Bf1MyKBr2qpjJEHh8nK5xPPnzzemVX9vICmfKS3bQOJisVgUodGnGoruC7/fL4nBhUIBkUhE3LlIqByNRshkMpjP5wgGgwiFQphOpyLAmkwmmM1m8t9uf8cnT57A4XDgzZs3kpan0+lgNpsRCoVEOJVMJlGv18U1kw0REmKIVQLaPk1yt9uNarWKQCAgJJXbCAQCIhLmkK/VagnRAoA0RjudDvx+vxBxvjUwIcNisYgoLJfLyffhOmc0GnF4eIjxeCyCPoPBIAJaJvy0221JrqTIqFKpQFEUIY+cnJzAaDTCbDbj6dOna41+rj16vR7tdhsej2cjgfA2NBqNuEeTIJvL5eD3+9dMQlYTBvx+P2w224OLQVRVxbt371Cv13F2doZ+vw+DwYDFYoHpdIpMJiODUq61uxwrgRsyEYUtnU4Hi8VC3rfBYIB+vy/ifwrWRqMR5vM5hsMhDg8PodVqcXZ2Jmm4qw0Pvh/1eh12u13E6DSXIIksGAyuEdtsNhuePHmCTCYjwiASliwWi4jcI5HIRw9Zl8ulrK98T1fB5wj4zYW80WiIGz73V657nU4HxWLxTpITcFNfFAqFtcT39wVNWijEJgGYQhMAYorT6/UQCATEEIYivdPTUyiKgna7DaPRCLvdLgY2NBggYSEQCCCVSn1WIxP+bY1GI8QwOn2bzWZxfV4sFkI2Zs3EfYTvaiqVklrjc8JsNuPw8FCG8+12G4vFAoqiiGiH+9JgMLhD7NwE1ocUiprNZhEBmEymO9+R++4uZ1+DwYDj42MAkOSffD6PyWSCcrkse+NisUC320UkEhESaKPRQDgc3lswTvH958AmUjbFM0y62xdmsxnxeByxWExE3btSlLaBayqJjZeXl7BYLELkMhgMWC6XCAQC+MMf/gCv17vX0I9i/m63i0wmI+s17z3J7kajUQScTK7cJaa7PUjy+XzSRKdTsVarFfON4XAoZlT7ukkbDIY76+ByuUSj0UA8Hsf19bWIMXnfmEZvMpnw7NkzaLVaWYs1Go0kypOQ+zHvPsk0PLcwUX5bndZutzEajTCdTuF2u4XAq6qqJIgyjSSXy6FQKMBgMCAcDr/3u2EwGPDkyRPkcjkoiiJCCD6fTFAKh8NwOBz4b//tv8l94znNYDDIuc3lcsm1ms1mmEwmMBqNYgpBAUm1WpXzHtPGKVwKBAIIh8P3EgM+BTQaDQ4ODuB0OoVEOJlM5PuThB4MBh8Hcl8xlsslstms9FharZYkSRoMBrx580YS2GikAdwYb/R6PRQKBaTT6a1EyOVyifPzc9TrdeRyOTkncz2hU/Xl5aXUr6enp591EG4wGHB0dCTJMkwoYPrxZDKBwWAQw52Dg4NvggDBmnm5XKJUKgnpWFEUMeOgoYXf70cqlfqkNQLTRWhExvWR5xoShbmnUEzDsyAF6R6PB4FA4L3NcT4nMpkM2u02crmcmDKtPsOBQEB6qRqNBpeXl3j27NnG36WqqpiC1ut1EbvQhIWmDS6XCy9fvtxK0n/Ex4F1V7/fl+SSbXsrTTs/Ffk4Go3C5XJJr2aVMGy1WoVU8b0mo3xK6PV6Sawi0XLX+zKZTOByucQI8KF6uTqdbs3wr1qtyv2m4d9wOJSUPRrLnp+fo9lsrj2LXGPZU3n37h2Am/P8YrFAPp9Hv9+H2WwWMfaHiMg+FtVqVYz3Dg4ONp6BKIS5uroSs9Hb7xRnVzThdbvdcDgcQpTVaDRiWsh+MxOG3hfsu43HY5kH7YLdbke5XMZkMsFoNHrvvjJ7t+wvbdqTzWYzfvjhByyXSxGeqqoKRVHQarXWROzj8VjMKovFIlKpFCKRiKTEbxLyEUzg5XULhULw+/0yrwMgRqfshRSLRQQCAQSDQREwJ5NJlMtlFAoFDAYDtFotVCoV6Qt4PB44nU7Y7XYcHR0hn89L3/r29+92uygWiyJWnM/nWCwWaDabWC6XkhSpqiqOj49htVrh8/nkOcnn84jFYms1bLfblV7XdDqFz+dDp9NBLpfDcDjEYDDA8fHxTnHT1y4G/OGHH1Cr1dBut6WX0mw2Ady8c6tCMKPRiPF4vDN9/hFfDuPxGGdnZzIbpiiVhofL5RKFQgF+v18MPNi3TiaTUpv6/X4xDW00GjtnKc1mU1Jdg8GgzMNJ8uW7fNvUfV+43W7pZ5XLZZhMJlitVhweHqLf76PT6UifuVKp4PT0FKenp3j+/Dl6vZ6k8DYaDemlVyoVIYdaLBZJUU+n0zvfZZ6PVoVwk8kE2WwWo9EI3W5XTKGYHlYoFBCLxTa+L+zX2Gw2dDod2O32NYL57es1GAzEsJNrympy+jZiOedyNOsdj8fI5XKSKm2xWMTIi2dz/v/9fh9/+tOfcHp6KveQ8zam0ofDYUQikQ+6vwwXYHo5cEMuLpVKUFUVvV5PemnT6RS9Xg/ZbFaSAPV6vRiR2Ww2qV9ots95kc1mw2AwwHA43PpZLi4u0Gw2cX19DbvdvtYD5L3P5/N48+YNbDYbFosFvF4vnE6npJ3S9IaCv3A4LGnSq+/Ix4LCVgBrM9vvEeyzzGYz5HI5ZDIZqQ34PPJZptCfPIePwXK5xNXVFer1OlqtFnq9npiFTqdTEWdMJhOkUqkvLgjyer3odrtoNpv3Cr9pqutwOER4d3R0hOFwiFqtJn1pzjK4V+wDn88ngupKpYJkMrn1TFqr1TCfz9fSynu9Hmq1GnQ6nZhYM/GO77hWqxVxrcfjQb/fRzweh8/nQzwelzTWz8HfoAEga1/CYDDg5OQEVqsVlUpFZg6LxULqaz5bXEfD4TAMBoMYDO5jWsfe2Wg0WuPRfOuYzWaoVCqo1+uyx3NGoiiKmFZ/bL9jOBzi8vISqqoin8+jVCqh1WrBarVK/9tut99ZU6LRKIbDITqdDrxeL1RVxWAwAHDzTNAs2+12I51OP2hfRq/XI5lMyhmPIRNWqxWKoqBSqaDZbMqskDVGo9GQec27d+++iNnrtwiGNuTzeRiNRkSj0bX+EfvUNpsNpVIJy+USxWIRx8fH6HQ6vyvx99cChtmEQiGZS9Js/lPB4XAId6TVaqHZbKJWqwkfa7FYCBfYZDIJr+ZLIxAICEeV/RONRgOr1XqvMJ8cYdby2/qi0+kUjUZDDKw/xpD1e0Gj0UC32xWh4i4weIEzoIeoN2m4uMpZ+NwCZL/fL71A8kW3odlsyvnuoQxeviasCsInk8m9s1nWTFqtVgwkGZS0i4NBQ559Q02+JdAskKak98HhcOD6+lrE/FwLJ5MJOp2OcLt4xjebzfIz7EM/4tOCpoM03LsPiqKg3+8DgJhq6nQ6uN1u2a/IjX9fblIymYTFYpEABoa5UUdjMpkQDAa/iYCCRzzi94pHtswjHvGIR3xB0DmdhJHbhz+j0ShObfl8HmazGeVyeS/BDkFnaJ1Oh1wuJ8m7t8lPFLGyaT8ajXBxcQGtVotWq4VIJHJnYMxh+9HRkQjVLy8vtwofqtUqdDod/uqv/grNZlPSRZrNpgzXbTYbQqEQotEojo+PodPp0Gw2MZvNdjYS6YJeKBQQCATQarXem+DF5PNff/1VBqR6vR6qqkqCM3BTkHs8HrhcLnFFH4/HIoykEGy1uGbDqVarSbIhxXiqqqJYLArxblXEV61WpaFWKpXgcDgQiUSQTqdxeHj4XYsL6KrV7/f3upc84HI48bkF6BRez+dz5HI5nJ+fw+FwoN1uo91uSwIJHRaNRqMM0E0mEzKZDPR6PTQazdYGDwVHNpsNv/76qwijfD4fxuOxOAkXi0XYbDYRKZHIeX19LQf109NTEbzP5/O9nKvpXEaRzia4XC7Y7XbEYjHkcjkhlMViMcznc0l+I9nT6/XuRSz8WmEwGHBwcACfz4d//Md/FJE+D8RMwKlWq+KE7vV6xRFwdV1gukQkEhG3ufF4jEajgWw2i8VigfPzczx9+lQIH6FQSAh/JIIYDAYUi8WdJJBVjEYjISAGAgFJ02g0GmIkwPQq/k23243Dw8MHFVYul0tcXFygWq3ip59+EsHi6mAPuEnHphD96Oho66CBhCKn04lisShCQoPBgE6nI47KFDbTtdpsNouj32g0klRyvV6PXq93x71So9EgGo1iNpsJOddsNouIOxwO4+joaOO6ZrFY8OzZMxGwk1ym1+ulibMP4YMCYIp9mUJCUtwqwXufhg3JoxR5U/QUCATQbrcRj8dl3XO73dKsoshJp9MhmUzC6/V+0NrM6x0IBIR0xHWV14NDJ34fphSsOifS/Z5JWdPpFB6PB6lUCsPhEIlEAj6fDwcHB+/9GT8F+P2YMEGRYyqVQiAQwPX1Nfr9vpBUScpgupZOp8PLly/hdDq/WCNuNBrJIH42m0nSTKfTgdvtlpoGwF5Ji6xhKaa+r+4ZjUYibN4Fs9mM09NTVCoVMRv6+eefMRgMhBxMMbRer0exWEQ+n18TpX7Ne9enImXTMOlDUCqVUC6XhbTJ9Zl1NZN3PB4PzGYzDAbD3iQSJrVOp1P5PfF4HF6vV4x/er0eLi4uoNfrhTx533vB9Zy1kU6ng1arlXTx2+DeAeznlrwNGo0Gh4eHIt5eTd/kf7fZbEin05LASSMWEnU/ZQIJB96lUkmIcpveKZKNzWYzJpMJwuGwiNZvD4W0Wi3C4TAuLi5kSPchbrVMIozFYmg0GjLo0ul0cjYDboYgJycnMJvNOD8/RzQahcFgEIH9bbTbbej1elitVnkmT09PJXmDz+xisZAEGordPzfZhUYTTAniMJtioEd83bi6ukKtVkMul5Oa0+fzYbFY4OzsDJVKBf1+X9K8YrGYDKwbjYYI3A4ODjbWVI1GQ4S5FovljjkQ17NqtYp8Pg+DwYByufzZxcZWqxWnp6fI5XIyvF8VGFitVlitVkmI+lbABEG9Xi+1Ns0kaXxhMpmQTCYfJE2JxNFVMqROp4PdbhfCcKfTQblchsfjEQIyzwqKoghh6msFBSnlcnlrzavVakWYUi6XxbTmtkCBAlMKKx0OB6LRqKTLGgwG1Ot1ZLNZeDweZLNZBINBSTRWFEXqxa8lffVbRjAYRKvVQiwWQz6fx2AwgMfjEVNViinH4zHi8bisnwRNQ3nO32RWtQ2raw5JlAaD4ZE0/J5g0i+FxLsMEUnsJ+GcCc8PBZvNhpOTE0nM6na70kdTFAWhUEh6JrFYDL/++iuCwSCKxSI0Gs2aCQEF6J1OR56dxWKB8XgMo9GIRCIhQvV4PP5g32kbZrOZGI04nc6dz7FOp4PX692aJF6r1TAYDFAulxGJRGCz2eS8DEDOSoqiiDkIkzU/5HM3m03U63VYLBYoiiJGhJuwmpL4IUlzFEsrirLzbOJ0OpFMJtHr9USMROE2DSu5ZrB3RTHVjz/+iL/9278Vs+VtIKHRaDSKMIRiu2g0KgaV3NeNRqNc+9v7TygUgtPplB7Y6rmMpFKac/Ha3f4do9EIxWIR9Xpd0iUpOOz1eggGg7BYLJLorNFo8OzZMxgMBqRSKfmb19fX0vumoI1CgkgkAqfTiVAoJCL01d/1rZq9mEwmPH36FGazGT///DOWyyWi0aiY3NlsNjidTknpisVi0qt+xNeDyWSCt2/fiqGRXq+H0+kU85tqtYp6vS7p9n6/H+FwGH6//07fkmYXoVAI+XxeDMJW37vlciniimg0upZoxxnyp4BWqxWjydlshqurK7jdbjidTpll0Xw4mUyKMaXZbJbvdXV1BVVVpV/E2eZisUC5XEalUpHEdgoGVzGfz1Gr1cT8gz1D9mPZ66LRokajEeMsl8uF2WyG2Wy20bzb6/Wi1+shl8thNBqtmcSvCsoXiwXa7TZsNpuI8AGIwH413f02vF4vms0mKpWKJHZTWHebYD4cDsWstdlsyrmwVCqhWq3KOmwwGOB2u+H1emXW976gWTl7DACEf0GS/NOnT8WcZjAYSO+u1WoBuNk/mEYbj8fle92eMaqqujPlldekUCjA5XLduVdcC41Go9QYR0dHODg4WLvu5FQwnIBcFJpPfiw4o2b4AOFwOBAIBD7LujyZTNBut9cS151O5wcJqZbL5c5/x2R5t9sthHNycyqVijyPRqMRPp8Pbrcb0Wj0k/QGstksarUastmsGKE7HI417gKNwrPZ7Np7+SVA4bfD4UChUEAymdzYFx8Oh6hUKvD5fDCbzWv9BkVRkEgkPupz6HQ6MR/NZDK4vr6+Yzo6m83ECJrG6IFAAKFQCLVaTYzOp9OpCOBoKq6qqsyQyXmKRqNIp9M4ODj47AEYNCaq1+t3ziRMa9doNMKrazabYszLOa/b7UYoFNrLmP/3gNFohLOzMzGB6nQ6st6xtuFaf3R09MFzBQYJcD0h50+j0WA8HqPb7cJoNCIQCGA2myGRSEgdRFPt4XAoxrqr75vD4ZCwhM8Fvj/j8VhMvDKZDDqdDiqVCgwGA7xeLxRFEYMyo9EovXvg85u9fouoVquYTqfodrt4+vTp1vOfXq+Hz+dDpVLBfD5Hs9mUmu0RXw4P2S/1+XwwGo0olUpwu91QVVVqpvF4LMEfi8UCr1+/htPpRCAQ+KA+1CawriY/a9+eP+c/78uD8nq9KJfLEn41mUzWROucDVarVSiKsncAze8BNJrhuXEXaAgwHA7FEPxTodlsolqtisEaZ/bcQ0Kh0GcLq3A4HGIYWS6XpXe4CoYiNJtNMY78lByTrxV2u10M5fYxMqGxnV6vRzweR7fbhcVikV7eNtC4kzXM9wSa9ZHXfh9Y02k0Ggl76ff7wsOdTCZr3GQGZAC/GR0/4mGw6/5xLtput7eaM65CVVUJVXjfXgZDLhk0tWre9qG9kUc84hGfD9/mBO8Rj3jEI74DMDmB6aO7nMdIcm82myLs3LfQ1mq18Pv9ePv2rbhRbRLMcNBAV9hyuYxerweTySTkGZvNJsO9VaIVBW3ZbBb1eh2vX79GPB6XQyqHx6PRSBzK/8N/+A/I5/N49+4dGo2GuBg5nU74/X5Eo1EEAgH8+uuv6Ha74sC8CzabDTqdTghB7ytAv7y8RKVSQSaTQb/fRzKZFNJOuVxGPp+X9AIAIlJoNBqSwDebzUR8vkqEdLlccDgckmxIEbvb7ZaUEabpMnWVxTQTNwOBAJLJJJ48eSKpy98zeJBhg+Y+UJDLdNjPDYqHKGjtdru4uLhAuVxGvV6XxGq32y1DlOl0ilqthkwmg8lkIgLI+551vif8PYlEAq9evZLErlQqtTaEpJt1vV5HoVCAVquVtLAPwS5in0ajwdHRkRwMq9Uqrq6uhIg3mUwkKcfr9Uoi6deA2wN/JkLsc6h1OBz4m7/5G5yfn0uq3C+//CLDpmg0Ksk3TObkPsC/GQwGZShHMPFar9cjn89DURR0Op21a8aGKUFBOtfdXYJNNkIpRnG5XDg+PkYul4PL5UK/38d4PMZsNhOnc4fDgUQi8eAkgHa7jeFwiFevXsFgMEiKLknoHF72ej1cX1+j0+nAaDTihx9+2PrZ+J3ors/7QSMAVVWF1NnpdETUQAEsRYlM86rX65JOufrekjjq9XqRTCYlHcnpdCISidw7BGF60ftiuVwil8sJsWhVgG61WiWJw+12C8loX2fD5XIp143g+07jg06ng3a7jXq9DmDdodzj8XywQzkb3xys2Gw2mM1mGTIBvzXC7HY7xuOxCEN1Op2QKnq9HjqdjnynFy9eQFEUOBwOzOdzmM3mByW97wO73S7fodvtyvP24sULFItFFItFSU7lEMNgMODw8BB+vx9OpxPHx8dfhKzbarVwdXUlpIV+vy/7hdVqFRdiXuPpdLqXUJyik+FwuHPfmkwmGAwGexMSDAYDYrEYIpEI/tf/+l/yjFgsFrhcLklJGAwGMJlMsNvtuL6+FrHrqtnCI9Yxm81QKpVQKpWwWCzw13/91xiNRmi322vPBZN+aCyxL2q1mgwl6HDe6XRQKBTEfILEuvl8LmvIfesqSbc6nU7IV9uwXC4lpfG+dMdd6PV6QkRl6pDP54Pf719L4GG6dTgcfnAxlNfrRaVSQSwWwz/8wz+g2+1CURTZW+fzOXq9niTDkjxoNpvF9X7TWk9RKfeKDxGgE0ztWUW9Xsc//MM/oF6vy3mANVa9XsfJycnGGnswGKBer4vxG89YRqMRJycnIg5YdS53uVx7m8I8FCh4esS3g0ajIWJWnU6Ho6MjWTuq1SosFovULgaDQVKIbDabiMIKhYIIhWw22519lARqGqVsq7sCgQCGw6H0Inb97EPBbDbj+PgYo9EIjUZjbc1jvf8tguIT9vtWzUJoxvTQa4fD4diaBud0OuF0OjEcDtdqTavV+smIWg8Jntkmk8m9wk6PxyNJGLVa7c41oWENSV6KouD6+lpIo6whBoMBfvrpJxEDxWIxOBwOpFIpEUWkUqmvpq/xrUKv1+P4+Fj+/1arJWseAEmjp2jx6OhIkvPq9TpqtdraWZVJs36/fy/jK+CG5P9Y3384fD6fmP3kcrmthMjFYiEGnSSnLJfLB0+1sFqteP78Obrd7sbabvVZOTg4kOeJ5rkOh0PExezLccYSi8VkD/d6vRtFiKxN+XtNJhOcTucn3xNo3jYYDPYSwDNJnOR5npmY8t5qtUQQta3fztTEZrMJl8t1by9yFbPZDIVCQZLyarUaZrMZnE4nFEWRFIzbZzmKC2n88r4wGo0ijMtkMiiVSggEAmvnBZ75RqMRnj9/jkwmI0INEjbZs1g9L3HfqVar+POf/3zv/sD5m6Iod1IfNRqN9ImIRqMBg8Egf/s2KHqKxWJybib5dRX83ygC5Xen8KfX6yEUCsm9ZMq7wWBAJBKB1WpFqVQSEX8kEoHb7cbBwQG0Wi08Hg9+/fVX1Go1OW95vV45xzDljvd51RDgWyZUJ5NJeQdoVmA2m6EoChaLhRhbxGIxMXh9xNeFbDYrdYjP57tjuO71etHv95HP58VgKhqNbl3P0+m09K8LhQJarZYIamezmdTkq+/QQ8Hr9crsq91uo9VqyfwKgCQKco9bFUNXKhXUajVcX19Do9EgnU6vrfUUM5fLZUn/fvHihawt4/EY5+fna0I4pk9Xq1U5D6bTaTGnJ/r9PkqlEmazGQCsifSJQCCAer0uKVuc6ZjNZhHLs27k/H2179fpdGA2m6V3tQlOpxNmsxlerxf5fB7T6RTtdht2u33t30wmEzECMZlM+Lf/9t/i1atXMJlMcLlc8Hq9su4qiiLJ6e+bxMfk+ul0KpwO7petVkvMFCORCIxGIywWC/r9PrrdrqxNvG5arRZmsxnxeBylUgm9Xk8I4qxdaA49Ho/h9XrFKHsVtVoN3W4Xi8ViK6l/NBqJ8TJTh3u93p390uVyYbFYoFarQVEUMZb8GCyXS+GydLtd2ecByP1hv5f72acGTRxWhVQ00GLdw1nxNvB9q9frGAwGMq+g0IF1A2uSTfUFjQ9oHqXX62E2m2G1WhEKhT7J+Zam3LlcDjqdDvF4fO3dtlgs8j7TJDGfz+PJkycf/bc/FHq9HtFoFPP5HPl8HpeXl2J6qtfrhUdAo26/3//RYvNtiMViUtuXSiVcXV3JuzCfzyUohNfV6/Uik8nAYDDg+PgY3W5X1ggASKVSIhJm75DCbs4bGFzwueHxeJDP5+F2u1EsFpFIJNb2GI/HA5PJhFwuh0ajgZOTE6mj+NzeDnsxGo0wmUwSMLILnD/6/f7vwohuOp3i7OwM9XodxWIRiqIgGo3KrL/f76PZbIqxs0ajwfHx8Xv3hheLBS4uLtBoNOQ8FQ6HkcvlJHQhEAjIfwduenGrc512u41QKIR4PI7T01NJwzQajV9kzm0wGOTdpik8jZjJtwJu6pp6vY5gMAi/37+2jlWr1W/6PPM50G630ev1ZO8hZrOZmL1xbVIUBTqdTsRJD91X53rAWs1sNj8aj35mcLahqirK5TLOz8+lH8feWrvdhqIocLvdaLVaCIVCHzVzpnF6o9GQ/iTNwFZrq08NnU6H4+Nj6dtUKhXhkGu1WqkDXC4XAoEAYrHY4wziX0BTxX33rk89/+S5gr1TzgL5t2w2G3q9HlqtFtLpNEwmk6wr72Oc+z7QaDTS014ul8JVdrlcUsfyHB6JROByuZBOpz/55/gaYTKZ4HA44Ha7UalU4HQ6t54tyRljwI7JZILb7cZwOEQ2m5WQwdU6heezarWKSCTyXZpNrgZ5kA+0C5PJRELOdDodfvnlF+l9TyYT6VMzWdvlcqFQKEhAzIfwZR+xG+ScMZhy0/yG+iSdTodGoyFno011ObmU6XT6vTU6BPtz39v78ohH/B7wKEB/xCMe8YgvhOFwKIe7fdNHs9ms/Jv3GUQGg0Ho9XpYLBbM53MUi0VYLBYYjUYsl0tJCLXb7RiNRrDZbCgUCqhWqzAajZLUOxwO0ev1JD1hMplIArPb7Ua9Xpf/tkoSZGMmHA6LMMpoNOLg4AAHBweYTCaSKMdDBMUMi8VC0kjuA5uQdEF/H3BQm8vlpFHFJpLdbke73YbP5xOHcJJtKFKZzWao1WrQ6/WSZrVK6qFIj4ewer2OfD4P4OZZ4JCI3zeVSsFisaBarUpSQiwWw+HhIU5PT9/ru32r4OFFp9MJ+YApqmzcGAwGOBwOWCwWuXYU73wJUEjAVLFyuQyr1SppOkxKWBW2RKNRlMtl/PTTT9BqtXs/u3zWmRrAa3NbfL4Kn88noner1SpptDqdThx6d2EwGIggYtfPGgwGnJ6eimCaJEY6+vOakFTzpaGqKorFIjqdjgz8KUAn6SYUCt3bQCGZtV6vw2azYTwew2KxoNvtIhAIiOsprx2d/Xq9HjweD9rttpCPboOO/51OB9VqdWdj2efzoVQqwWazoVwu3xnqE7PZDOVyWdwe2RCw2+2Swk2R63Q6lZ/5XE3tWq2GYrEopFKSL1absUzqSyQS+PHHH2G1WnF5eYmXL19u/J1OpxPtdhsWiwWdTgcmk0nuMQBJFQJ+Sw3i8PXq6goAxPWagkkSe1dJT6PRSNIyKCAajUayZ+xLgH8fMDG+0WigUChAVVUhcc7nc5RKJUkhWSWW7ZMUtbqf326GczDCNJLb9cmncChnirzT6USn00E6nUYgEECv15P9gemvs9kMmUxG9gWmFJLctwq73Q6XywWbzSbr6NfinL36XBL8vIFAQFxBAQj5ikkpX2IP7Ha7uLq6EvIUDZO4V1EcrCiKiIM7nc694rLlcgmr1SrGLreb+QRJvFwT3sdRliYWjUYDOp0O6XQaNptN/s5kMhEhksvlQiaTQTAYRK1WezBy0beOer0u+1s6nZb63mazYT6fS62j1+uhqioKhYIkntx3xqEQotlswuv1SnKdw+GQRGitVguHw4FwOCzilsVice8g0maziUkH9/Jt9Q4NtGgK9L7odrvI5XJyvuL7TMLUYrEQUhBJL5/L6Ver1crAOxaL4fz8HFdXV7Ifs95mTTOdTuXdXi6XW4UQ/H40tvlU6PV6+POf/4xcLidDXg6uJpMJ9Ho9Go0GGo0Gnjx5ItdyPB6j1WqJcZnb7d4o0jGbzR9FXHjEI4hqtYpWq4XlcrlWn3Mw3Ww2hfBdKBTEyI9DVpvNhlgshuvra1mnVsls7CuQLH0fgcHj8aBQKCAQCNwxuPqcMJvNnz2B/aGh1+sRCoU+e2LU+4A107eGwWAgApL7zlRarRZ2u13MOFexagpKMsjl5aX0BXne63a7srcxKdBsNkOn0+Hq6gp2ux0ejwfz+RzpdPq9RSOPWIfJZMLp6akQewOBgPRneObx+/0iEu31eri4uBATwFWXfLvdjl6vh0qlgoODg0cCw2eAwWAQ8nU+n0culxODWqYq8l5pNBr4fD7Y7XZ0u92N4tiHwL6EFkVR8OTJE1xcXMBmswnReTgcQqfTIRgMwul0wmKx4OjoSET0m/qGk8kEpVJJTKdY97Mf5fP5EAqFPlmtv1gshCy7T2+AP7P674CbvhYTye5LhQFuRGL1eh2j0QjdbncvAfp0OsW7d+/EFLJcLmM6nUovkEKq0WiEcDi8Vqu0Wi05r20zXbkPiUQCw+EQyWQShUIBZ2dncDgcMBqNmM/nIgoNhUKYzWb44x//iP/6X/8rXC4Xer0eDg8PRVzIZCxFUaDVavHrr7/KLO/ly5d3khxXodfrYbfbxViWa94mMOHe5XLBYrHsNKSimFCwXGIxGkFjMECj10uyOcn8brcbs9lMxIl2u13+/Xw+F8M/zszYY6URZTgcFkGb3W5HqVRCLpeDxWIR00SbzSZ9uFVQ8Mf7GgqFvtmUE51Oh5OTE1xdXYnwtdvtisCQKcO8LveKKZdLzPt9LCcT6JxOaL7RdPg1LJdYjMfQmkzAV3afuYZx/rJqMr4Km82GcDiMUqkEl8uFVqu1lWxpNBplT+GsqNvtSv/K5/OtPRP7GngQy9kMGp1u72sZDAZhsVhQLpfhcrkwmUxkbk1zV+5z8jeWSxGgL5dLJJPJO/sWU8h1Op3MnGisPpvNcHZ2hkajgXw+L6a1FotFkmzz+Tz8fj9msxmKxSJisZisAzabDclkEldXV5JAenvtN5vNSCQSWCwWqFQqKBaLIoSmWe9yuYTJZJI9hbPJ6XSKVquFYDAoc4hN0Gg0SCQSmEwmqFQqIkw7OjoSXgJF2zTSDQaDYlIwnU7h8/mE5M/zyvuaC7ZaLZRKJaiqisFgIEagTLA/Pj5Gp9MRo1X2NmlqYrFYoKqqmEkGg0ExFri4uBChHQDZC5bLpQieTSaTpDLfFizw+d5lstNut6Gqqsxz+D029SRYX1BI/bE1fSaTQbValaAAl8sFj8cDjUaDwWCAarWKer2OeDyO5XIp5sufCu12W86dzWYTw+FQ6mRyaVRVRbfb3ZpIPBgMcHFxIWavg8FA+DUki5Nnw/vRarXECIMCCofDIc8mBex8Pj4VarWaiDd3JSyvmujTMO9L9iuCwaCIH2mk2mw21z5vLBaD3W5HOp3+4Fr0PpBbZDKZYDQaZY+iYQCNfex2OxKJBF6/fg2bzYZ2uy2pcavvDE2GzGYzer0evF4vIpEIkskk9Ho9ZrMZms0mCoUC5vM5ksnkg3yvTdBqtUgmk5KsmMlkpG5cFUwtl0v88MMPsFgsSKVSsNvtMtO5DY/HI6nqqqrufLa5X9BM/VtHqVRCt9tFsViE3++/U6O43W64XC4Ui0XpgbVarff+7gwSKJfLa0YqNPRmmE0gEBBjN6PRCL/fD51OJwIkt9sts98vxfFaRTwel7X1H/7hH9BqtaDX6+H3+9Hr9cTgxWq1ylyes2Ouwd/yeeZzYTabyVyXzwN5GuxNGAwGGAwG9Ho9CVx431p5X9BAqF6vr80vdTqdGFx+rhTjR9xAr9ej3+/DYDCgVqtJX5jcJxrLtlotLBYLaDSaD5ozFQoFlMtlDAYDtFotMScyGo1Ss7ndbqTT6QcxI2AP8vr6GjabDYPBAMPhUFKUV2u2DxX4fY8gZ7XVau3186qqwm63f7L+cy6XkzPhfD6X+lqr1Qrn5fLyEgaDAW/fvkUsFpP160OMc/eFxWLByckJLi4uhOve7/dlZuL1etd62r+ndS0UCqHb7aLf74tpnd1ul/2aIUWVSgVer1fmU8BvPWQAyOfzOD8/h81mg8lkwmw2Q6/Xw3K5RCQSgcfjweHh4XdXB/CsyHPmfZwC8g5oplUul6Weoo4FgPTgS6USRqORGBx/D6ZQXxto+sXwqk0BlgxdtNvteP36NY6Ojta4kwRNhRke9SF8uUc84hHfNr78yf0Rj3jEI36noCPUYrHYq0nGn/kQcfV8Pkc8Hkc+n4fH44FWqxUCpkajgcVigd1uR6vVEoIeU88nkwnm87kMsoDfmsanp6ew2+3w+/3QarWIx+MwGAwisl11xWYzma7jq+B/vw0K3ejAu+931Wg07934YfLRYrGAz+dDsViU68xDOIUrzWYTxWJRGpx0yPb7/Xj58qUIFm9Dr9fjyZMnYgAwHo8xGo2wWCwQj8fRbDaliQpADqdM/AgEAt8dMXsXmIDhdDpRqVSElMG0K+Dm2Wk2m1AURdJCvobkLqfTidFohFQqhfF4jGQyCbvdvnW46vf7RUDfbDb3Sqhhk52GBhTV3jeU9Xg8uLy8FJd5pnwzpWYXmODMJL5d0Ov1SKVSiMViaDQa0iSlc92XvkfEfQP/1XS428kLm0CxAQdt5+fnuLy8FAOOVVDsTHHvfD6XofBtkHxA4sguR0GDwSBE7evra2QyGXi9Xml4LhYLSXzi5w2Hw3e+22oK95cQ5Q4GAxQKBQA366HP59vapAuHw5IKXS6X8fTp041DShIhKXRhk3A6ncq6oigK4vE4HA6HkAe4PpPkzgaL0WiEVqtFr9dbIxnQrMVoNEqqdr/fRyqV2vk9PgalUgmNRkMc7g8ODtYaxvP5HK1WC8ViEV6vF91uV5Ll76tDmD5Px/jb0Gg08Hg88Hg8QqimKcynapoHAgGoqioN4VUDi+V4DM0//RMm5TK6nQ4CsRj66TT0/yJ+5nBmtYa6PcjeR4j/paHT6RCJRBAOh9HtdtcE6Hy/vwRWHX7b7TYSicQamYMJKx6PB9fX1xiPx1gsFjKw2/b8McmeA73lcomrqyshZup0OiyXSxGHLxYLpFKp9x4Cv3nzRhJ/Xrx4cWcNNhqN8Pl8QsIwGAzIZrMiVv3ehgefAs1mU9zHb99fuuMSXE+5D99HciFpjSZeNpsNh4eHYia1avYRj8cRj8dRqVQwHo/R7/fvrT/C4TBUVRUXY6YX8P2aTCZoNBrodDqIxWKw2WzvbabTbDZxdXWFRqOBZrMpYjYAsjdz6Lgp7e9zgAIwCvYuLy8lbYDGZmazGcPhUAbyNJ/gWXETSALg9Zw1Guj9f/8fFv/0T9AMBliazdCcnsL6n/8zTHsYPLRaLfzjP/4jzs7OkMlkMBwO5TOQRE2CoMlkwuvXr9f2YavVKkm2BwcHj4nij3gwkJhGcfjqOkhBiqqqMnSlIM9ut2MymUi/hEZeFOesCtDZu5lOp3s9yxaLBcvlcq0OfsTvB/P5XPYhCnxZ8+xjYPClwD7qvoRUnU63RlwkmOTX6XQQiUTQ6/VQq9Xw5s0bmM1mLBYLETrQPMloNCKVSsk+5nQ6EQwGUS6XZX/j/viIDwcT5yKRyNqZh/UFn83BYIDz83NUq1VJRfT7/UKc73Q6cnZYLpc4Pj7+avpQ3zMODw9xcXGBdDqNYrGIWq0GjUYDnU4nvX0KCbgf8szwtZkEKIqC58+fiziaqdcUi/n9/rXPvGndVFUVZ2dn6Ha7aDab8jv48yRbt9ttnJycfJL+CYXQJDzetyZRjESDUmI2m2G5XGI+n+91vjYYDDLD2WeOw3TcZrOJ6+trWK1WPH36VOYuPINRlMDPuGoIwCSaDxXvc1ZzcXEhQuFOp4N+vy9CSs5lgJtUZJpphcPhnSnFiUQCV1dXYpzSbrdFaLEJHo8H/z97/x1naV6W+ePvJ5ycz6mqUzl0VaeJRBFYRAkGDIi6pnVdXWUXDARhFxREZfW3whoWERRJv8E1MwysruQhSBwGZpjU092V61TVyTmfJ3z/eOrz6cpV3V3d0zNT1+vVr+6uc+qJn3jf131dFy9eJBaLSVfH7eh0OqytraFpGvF4/FDCANg2zXvvpXXXXaj33IOy8SytkRHUH/gBvBvukdlsFp/PJ4UH2u223JsL0RZd16WgrUA0GmV9fZ3BwUFZhCGerd/vZ3x8nHa7zcTEBIFAYN/1RTQaJZPJ7DjW4xG6rnPy5EmazaZ0AzZNU+auRJxhP/QyGep33on9yU+iCkK1pmE+/el4XvYygs95zg1XvL0f7F6P2qc+Re9jH0O7eBFME3Qd86abcP/ojxL87u++IYrrRZFap9M5MEcXiURkLDSXy+1bDODxeDh79iyVSoVcLifJycIZTYi3HmoNbts077+f9l13oXz96yitFqgq5ugo+kteQvCHfxjtgDVPKBQiFApJ8UhBRhfE0e0Qws2VSuVA0RSRaxR71oGBAVkcnUqlSCQSkpgquAfCUR0gnU7LtfbmfuJ2u6XIbDQapdPp7JifRLxH5PSFK7iqqpRKJSYmJqSgvjAGaDQarK+vy1z0QYYBkUiEqakp0uk0hUKBZrMpRQ8BSbwNBAIkk0mZc/X7/eRyObxeL319fVdEpjarVVIf+hDNz38eq1BA6fVwuVy0zpxBjcepALUNsftWq0Wr1drVgbVWq8nrGB0dlS5uFy9eZGhoiEajIdfWok2apolt20xPT6O2Wij/8i80/vEfWfP58CYSaE97GsHv/36Z596v0LPdbssCY8uyME1zx35NQKy7Go3GDlGxy4UooF9eXkZVVWZmZrbsK0VfFAWZmqaRy+UON98eAo1Gg/n5eTKZjMzHDw0N4fF4sG2bWq1GsVhkcXFRzhknT57ccYwLFy5QKBTIZDIyfiSE/MU6QazJRD44EonIMarRaEg3RJHHOXHixDVxhSyVSlJ4/aA9vFgDNZtNisXiY86HGR4eJhgMSkESwesSJghCYOJaF8oL4YuhoSEZoxDv1u/309/fL68hkUhQr9dZWlqiWq1uGUObzabcO4t16eTkJENDQ3Lc63a7rK6usrKyIvve9SzGjsViMuYSiUQolUqkUik5X4qcvRBsOWgtJfhekUiEtbU1JiYmdt1vtdttMpmMdFl/vDvLmqZJsViUAst7rU8URWF4eJi5uTlpfnC57zuXy1GpVNA3xK0EEomEHOsFJyEWi5FOp2U7E3FJ4RJ6IxVVulwuTp06xQMPPEAymaRcLqOqqoxvCJMNIeIi2oy4R8EJOY5B7Q0xfzWbTfL5PJVKhW63S6PR2JKnELHWbDYrC0evRR+tVCrMz89L0UQhSiHGnnq9Ti6XY3Jy8oYwV3myYHV1lUqlsqeYRiAQoK+vj+XlZVKplIyVXM7cnEqlWFtbI5VK0Wq1CAaDck8hxjCXy8XY2BiWZR25OJGA1+vl1KlTtFotKe4ozH2i0ejh94pPIsTjcWmQsp/oI7Blj38UfbjVapHL5UilUlJEZ/MeWZjYZLNZFhYWcLvdpFIpuZcWosnZbFaKWx4lAoEAt9xyC8VikVwut2UfJfY8N3IO8FpBGKiIdyP2TmLMaDQaKIpCf3+/FI4TMQdN0zh9+jTz8/NbYsitVgtVVRkYGCAcDuPz+Zienn5cin8fBHF/QiRut6Jkgc0iQ+BwWUXcyeVybeE1qqoqDfuEeOcxrg3Eml1wbv1+/67rqoGBAZaXlwmHw1SrVVqtFo1GY4vgQqlUwu12S/7qsWDAMY7x5MNjn8k5xjGOcYwnKTa7iB6m6Glz8u1yN4GCdDQ4OCiTfpuDZm63WxZcptNpmUAtFAoyiatpmlTEFWQfj8fD2NgYqqpKYtHg4CDT09MEAoEtBejhcPiKNq/CyaFcLh+Y5BMFtYFA4LIVmoWzcDwep9Fo4Pf7qdfrkkgmNvzCyWJpaYlisSiL+EdHRzl79ix9fX2cPHlyT8KYKIYZGRmRxRqWZcniMRFQEsVZLpeLWCx2xQnpxzv6+/tZX19naWlJuo8I4i84700QIxqNBjMzM3i93mumNn05qNVqNBoNSazcD4IQsdlFYL/+Itp6MBjE5/PJwMZh7lsoZbdaLSmcUK/XKZVKpNPpPV3aRFJodHR0hwv1ftB1/UDSxmOFgxL+wgF8YWGB8fFxmfA/zL0riiITwslkkkwmg8fj2ULg2UzKLBaLMnGy13t0u91Uq1X5u/sFXoSjgqIo5PN5MpkM6XRakn5VVZUuHslk8oZ05xOJJtFO97vfUChELBaj0WiQyWQolUr09/djr6+jPfCAU9TmctEdGyMfDHLzzTfLBFIsFtvidC9ce0ZGRpyxvNnEde4cZxoNLI+HarlMLRik2WzS19eHqqoYhkE+n5eK6r1eTzpvt9ttVlZWpJPJgcp/tk3z29+m/ZGPwP33ozSb4HZjTU/j/pEfIfg937ODkGdZFtlsVpJOxNy8GUKhWdM0+T1N06Sq+V7PVwivRKNRqXS4H/YSlblaDAwMSLJ2KpVynMgMg4EvfYm+++9HrdVQej2GdR3N76ebSJB96lOZv/lmGczcTz1dJCYeDyqvgoBxo0AIxBSLxS1rze0QDjOzs7Ny7bm8vMzQ0BDBYHDL2Fqv11lfX9/iJiEKzcWYJsYz8Tz6+vro6+s7lIiLQLfbJZ/PUygUCIVC+z7XSCQiFYJzuZwUNLgeTn2PN4hixsMkVxRFwev10u12DyV20uv1JOFLENKFg08oFJIEG3DmBqGqaxjGoQosY7GYdLvO5XJybPV4PFiWRafTwefzMT4+TjQaZWZm5rISIY1Gg8XFRdLpNJVKRSakRGJSONqJRIyiKNx0002PyT7A5XLx9Kc/HVVV8Xg80mEqFovJQoz19XVqtRqrq6tyHt0raSnIlH19fXg0jeIf/iHqpz6F0migl0oohoGtadjf+Aatj3yE+nd+J5E3vhF9j2Rws9nk4Ycf5tFHH5WuKGfPnt3i0ifcnwTJ7qabbpLuRsJpMxaLMTg4+IRMBh7jxkGr1ZLj3Pa1frvdlqR70Q4DgQCVSgXTNLcUoIMzHxUKBXq9Hr0NwvkxjnE5yGazrK6u0u12KZfLdDodOR+Xy2U8Hg8TExM3JOlWrOmFkNhB6PV6O8RvxM9FEYXP5+Phhx9meXlZOurAJfe+drtNLBaj0+mQy+WYmpqiXq8Ti8WwbZvx8XEWFxcJh8PkcrnLWoseY28ctOcRYj75fF7uFzYjGo1Sr9clEczlcnHLLbc86YhV1xuKovC0pz0N0zRptVp4vV5ZXCRi6m63WxJCxX5QiF3eaBAihlL87oB47WZ0u10uXrxIPp8nnU4TDAZlkY9t2zQaDSkiODo6ysWLFzlz5sxVk6x8Pp8UCBZCovuhVCrJ97L5u6qqyvzVYcSQxZgq8lAHoVwuU6vVSKVSRKNR6TLe6XTo6+sjn88TCASIx+MUi0Xp4BcOh6VLrXDpvRqIInRB/NxeKCyEWS5evCgd2mu12g7H1+3w+Xz4/X5arRadTgfTNEmlUoyOju7pZipIioqisLKygs/nk0Xbwh3W7/czMjLCwMDAgUUaZq1G+bd+C+3b30YrldBKJZRezylUXV3FXlgg6fPR/YmfoDM2xtLSEoqiSFECl8slBRJM02RoaGiHM6qIoZumuaOdiJiJeJYH9R0hALPbsR6v8Pv9V+QeWrnzTux3vQulVkPL51EbDRTbxnK5IJfD+PrXKdx2G9H/+T/RboA82EFonz9P8zd/EzWTQS8UUKtVFNPE1nWUdBrz/vspjY0R+MM/xHMd3VZ3Q6vVotlsyrH0IIjCmcOsTYXAsFhjX86cImDW65Tf/Ga0b30LtVJBKxZRej1sRUFZXcWem6N6xx243vAGgi94wYHH83g8h5p/Ra5TuPAdhEgkIoXbhZBjoVDYkRsR+fFWqyWdwlOplHQf3H4usRfudrvUarVdr12Ih4hYr8/nkzE4AeHo3mg06PV6RCIRBgcHGRkZOdT9xeNx6TLe6XQYHh6WfAvBn0gkElviTFclfmvblP7iL+j94z+iFQrE0mm8zSZuw8BWFPja1zBVlVwkwkODg3wxm8W94dC2vYhXCOOJ6wuFQnKuy2azNJtN4vG4FF21LEvOBZppcts3v8nk7CxKrYav28Wlaag+H9ZnP0v1ve9Ff8pTUJ/+9H3FaATfReTVNvNldoPgoFytgLAolBTOzrvNx6qqMjIywvz8/BYRhaPA6uoqpVJJitBvFloRBW6hUEgWQQnXTbE+syyLubk5WXw+ODhIJBLZ8uySySSzs7PMzc1h2zYDAwM87WlP23KvQkhpdXWVpaUlmSs+6uJPISzQ6XQOdWwRpz1sjuB6QAhbi1y1KEDfr9jiWsHlcjE0NLSrqIRAMpmkWCySTCZZXV2l1WoRi8WkmUOlUiGdTuP3+0kmk1I0WsDtdjM2NsbCwgKlUumKCpKvFolEQgp2hEIhKUIojFOEW+hhciWapjEyMoJhGKRSKebn56U7qRCuK5fLUiShv7//CSE4Leamer1+YHxKrE0ET2A3cZf9UK/XpUD/5ucmhGIACoUC2WwWRVFYXV1lbW0Nj8cji9KF8GChULgs/tG1hsfjYWpqimKxSCaTYWBgQK4Nxbgp+pdAIBCQQizHYq/7w+PxyOKmer0uTRNEnl4UognRlGw2SyKRuKL180Go1WrMzc2RzWZlHGJwcBC32y3FSxcXF4nH4zLmcSNxNJ6o6PV6srg4HA7vuZYQvKi5uTkajQbZbPbQe3DhoL6ysoJlWZw4cWJHHk6IEwkBo2w2e025j8Io5RgHQzj5RqNR0un0nkIzhmGwvr4uneT7+vqu+tzZbJZ6vU673WZ6enrHvkLkJIQwebPZ5MSJEwwMDEixzfn5eYfPuCGce9RmBKqqSu6UZVlSHEXknIVY0Y0y714vCBF44UJfrVblmiWZTMoia2Eythmapkmxyd1iyEJs8on6TBVFIZlMymLkpaUlBgcHt+xrhSlSLpeT3GvLsvD5fFK8q1QqUSqVJJ+n3W4DSJMtr9crY+lXKjr7eMLm+Mj1gBD+HhoakqKNwjRoszCeqFURIrMrKysyJiP41dFolGQyuauT+jGOcYwnPo4L0I9xjGMc4zGCx+ORBM9arXYg4V0Ufm8vYDwMxOYpEAhIN+ahoaEtAbr5+XkajQbgBFI6nY5MnrfbbbkJtyyLSqVCMBgkEAjIBItQN/X7/QQsC+Wzn0XP5UDXUYeHsV/wApQrcAHq6+ujUqlIh+f9CLD5fB6fz4fP57usoIFIXAqnMFGcW61WtyjliWS1+LkIXoyOjjIxMcHtt9++bxHfZggXje3w+/03lMLrY41AIEChUMDlcrGwsCBdkETgTxSBXbhwQbqO3ygk6e3F8gchkUjIwlXhjLcX8vm8dNYSSv4i4H0YCCdZ27bp7++XCe+VlRWZkBRBAkH26Ha70hX6qBLujzUOSviHQiGCwaBUvBcF4IdNKqiqytjYmCyWW1xcJBqNysRJt9uVhf9TU1PE43EGBwf3fI+bi84PetfCNUK4JPb399NsNmWiNhAI4PV6GRwcPJIg67WAbdtomka73T5w3hNJ/3A4jGmaZD73OQYvXCCxtITa6aAYBoZt01FVPH19mN///VSe/WyaG+qk4rmvrKwQjUZpt9u4MxmG7r2XvoceglYLj65jKQqGrvNIIkH21CmSz3oWbrdbOkAvLy9Ll2rhuiYK9oaHhxkbG9s3eWuUSlR++7fRHngAtVJB3SjIQ1WxFhcx77uP0nveg//3fx/vmTPy94rFonQsGR0d3bd9RKNRCoUCmqZJYlQqlWJkZGRHAM+2bdbW1jANg3g+T+jcOYqf/SxKIID7KU8h8PSnX1e3nampKVlE3rpwgfi73oUvm8VfreJrtVABFAVF1+mVy7izWfR776Xwa79G3z7k4EajIVVvj+fgy0ehUJDiGAeR9ILBoCwK1jQNv9/P6uqqXEMqikKj0cAwDBmwHB0dleQZoYQvnCiF04PX62VgYICBgYHLCuqLpEK73T5UYiccDrO6uirX1FdLfHui4nJEtq718Tc7DB62bQhygUhE1mo1qbgr2ls0GmV8fPyyCz8F6bVcLjM5ObljfhPu5x6Ph9XVVUm4eqxcX0RBhhBLEk5d4OxPhViPmIc3O+pshyB5hnw+Qv/7f6M++CDaygp6o4HpdmO73aiGgZbNYvp80GhQXV4m/K53oe9Cdkun0ywvL0sCwokTJ7aQ/4TavdvtliQn4fozMzMjRX+uN2HwGE9OCMIk7FzH27Yt1+gCm/+93QlM0zQ5tm3+zO12S/XwRqNx4PpZiOm5XK4npdjdkxVra2usrq6STqcluU8UQxaLRUnoMwxD7lFvJAhSfi6XO3CfaBiGJNxuX6Nu7mPFYlE+E3D2S6Kf9Xo9SXqu1WqyqNnj8UjyUDQaJRQKUSqVCIVCjIyMHKv0X2NUKhXa7bZ0JNxLJC0YDDI4OEgmkyEWi1GpVG6YmOETGYlEgsnJSfx+PxcuXEBVVRlnFGvpcrnM8vIysVhMikE+HnA5e01B4BFin5vbnnC8DQaDZDIZUqkUHo9Hku6v9hr7+vqo1+ssLCzsm09pt9uUy2UpArB5PeD1elFVlWAwSKVSOVCMsFqtous6Xq/3UMLA2WyWSqUiyWvg7D2EE42u65TLZfL5vBSb7O/vZ3p6mrGxMbmmPyrhAvE+9oJwFhXu6Ae5c4v5o9Vq4XK5SCaTVCoV5ubmiMViUoSv3W5TKpXodDrcfPPNMl8oCHvNZhPbtmUBhHDZO2h/aDWblF/9arQHH8S1uOjERcNhrGAQTBOtUkErFFATCcb+5m+wf+ZnKJ09y+LiIqlUimKxKIusQqGQnOu2E8pEgeFuwgOb9+CmaR6479q8Vt58rPb583TPn8fudFAjEfzPehbaE5jwX/mnf8L+sz9DXVnBVSph+P2YsRi2pqG2WrjX1rDTaWi1KL/mNcT+/M9RL1OM+3Jg1mrUP/EJzKUl6PVQYjF8L3gB3lOnDvX77QsXaL361aipFO7VVWxVxYhEsF0u1G4XPZ1GSafp1mo0fv3XUd79btwbooCPBTYXuh0GYs14JfGvyyUlW+22068feOBSvw6FMCMRFNGvczm6fX0Yv/d71CyL0ItedNnXteu5N8QhdF0/1HWLoiFACt2LvMX241qWJePEwnFXFLNtF1wT/968v94NQhQ7mUxy6tQpisUiy8vLkt8gCM6RSIRoNIrb7ZbiHodFOBxmYmKCXq/H4OCgFPv1er27jnfNZhOPxyOFkQ4N26b41reifOpTaHNzxEol9A2RDs2yENkcG/A3myTzeb7z3Dk+PTLCl2+/neLwMAMDA7Jov9lsygJMIUodi8WkQGO5XKbX68k8lxR/9Xh4xic/SXJ9nXC5jKtex9A0FJcLV7WKsr6OEQ7Tv75Ob26O2Ze+dM/nKQTCRD7B5/Ptu6/rdDoy3nGlEG2wVCrJ/d5eUBSFeDxOPp+Xa6qrLQhpt9uSyJ1IJPZcRwhh84sXL1KtVsnlcjIfXSqVaLVasvh8t/WdpmmS5F+pVJiamtp1fNI0jdHRUVloL+IAjzVu1FzLtRLdPmp4PB5mZmYAZ7zM5/MUi0UpbrS+vi7FjILBIGNjYzvGI+GKVyqVZB7uck0/rhZC/HdkZIRWqyXXpD6f77JjLQMDA3IcE0K1hUJBfu73+xkeHpZj+hPB2bjb7dLtdrFt+1DvTuSfbNum2+0een8l5mHLsnad90SBlxCRXFhYkDwVkacR42uhUMCyLEqlEtPT0zdMvkbTNKLRKCMjI0xMTMh9isvl2lOAWcCyLDmngtM/Q6HQE7Yo7XKRSCQYHByUa7pOpyN5K4JX1ev1aDQa6Lou47qJRIJGo3HgfvywsG2bxcVFKZY0Nja2Zc73+XyEw2EajYZ02NZ1nVtvvfX4XV5jFItF2u02zWbzQAFAl8tFMBiUsfnx8fFDvR9RQNput5mZmdl17BFiJouLixSLRQKBwA1rvvNkgxCOEqIfCwsLxONxycUUQjOlUmmLkM/VFrSKPFq5XCYYDO5YSwljBGGGMz09TT6fl9c0MDBANBqV4v4ip3vLLbdc1XXthUajwfr6uhSoEQJgXq8Xr9dLf38/yWTySTWmJRIJ4vG45ApvnquFoN1+8Pv9TE5OXocrvfHQ399Pq9WSBocrKyu4XC4pKirm7eHhYeLxOENDQ1Lk4+zZs9TrdVmLIgr/4/G4rNUQwrNCJOGJsDbfDa0NnnKxWJSxdZfLRSKROLTY1pXC4/Fw8uRJwMn/FAoFUqmUXMeK/eDk5CSBQECaRgpRdyEMJoSBj0V5jnGMJy9ujF37MY5xjGM8CSEWzkJVWriw7QbLsqT7qFCkPgx6uRz1j34U64tfpC+Twd9uk3O56L7oRbi/53tQNjbCwglZJHVFQbzP58Pj8bC2tiaJMUKNTShadbtdFEWhUCgQ63YJfuAD2BcuQL2OsrFZMHw+Kn/+59gvfCGRl7/8slT54/G4dNTbTAbd/nyEwtzk5CTRaPSyEribCzoty5LFebFYjHw+7xRKhEJyER2Px6V6e39/P8985jOZnp6+YYs4H8/I5/PSZeT06dO0Wi2pjKuqKt1uF9M0GRkZkaq8+ymrX0+IJMRhr0ck2aPRKJlMBtu2dzhIGoZBNpul0WjIRJggBLpcLkmc2A+macqEva7rqKq6JSEpSOfiujVNI9ZoMHD+PP6vfY1Efz/VsTECP/iDuK6SFPlYot1uU6tWqX3zmww2mwRzOex4HPvMGZRNQUeR8J+dnaVarZLNZi9rAx2Px2Vys1qtUiqVWFxcBBxypqqqhMNh6bKw37Gr1SqBQACPx3PowOjAwAD9/f1SOEMQqEKh0KHcHB5LhMNhghtO4wc537RaLemM9NRCgWd9+tN4azV8tRruXg8bUABL14nU69gf/Sj9mQzrP/MztFot6vU6g4ODTE1Nsbq6ivm5z3HzF76Av90mUK8T6PVQN4iTdZeL20olTq+tcV+vh/riF5PNZmUgRvR70aeHhoakWu1+RGKzWqX6a7+Gdv48rqUllF4PIxTC8vtRDOMSIa9UovXqV8M73iGL0Ov1OvV6XRYA7gfhJCcKJwYGBlhZWWFubo5oNIrf60W//37cX/0qdi7HVLFIrF7H5Xbj9vlQTBM0jV4wSHFsDO0nfoLIS19K++JFjHweRdNwjY/jvgYqh4qiMD4+TlxRqPzWb6Hl8wSyWXo+H82REdRwGJemodRq6Pk8kVIJq9PB+vM/p/7bv01wF1JOp9NhbW2NaDQqg8rHuDyIAm6/338oAkggEMAwDOmiLARYREG3cAPyeDyMjo7K9VU8Hicej1Or1ajValsUZbe7fRwWIskiCJQHKamKuVaIXdwoRIgbDUIwq1wuH/hdkYwYHBw8FLlFzIG6rtNoNA4MwDcaDfn9ywnWx+Nx6cBQKpWkc5vX66Wvr++KAv/CaVbs6/YjV4bDYZkYDQaDDA8PP2bJv3A4zMmTJ9F1nWg0SqVSkYUQoVCI6elparUarVYLn8+363VWq1XS6TQDAwME/+Ef8D34IK7ZWSyXi/b0NMomwkiv3UZbX8c9N0fXtqm86U0k3v3uLYInvV6PfD7P2toawL576WAwSK1Wo9vtks/nuf322/F6vccJkWNcVwjCnXCV3EwAVFVVErvFWl3EWcRnm9HtdmV739zuRQxns3PNfuNGsViUjrM3+r7gGEeDSqUi3dSEwNxmwp5QN19fX5d7WFEseqOgr6+P9fV1AoEAmUxmX0JZOp2WYpzb92GiMMTtdrO0tCTnidHRUfx+P/V6HcMwsCyLUCgki93Fnlp8Jgi3kUjE2UduuD4fFRHyGLsjn89TrVbl3mE/RCIRWeiay+WOC9CvA0Sc0bIsWfgg9m/1ep1er4fH42FwcJBYLMaJEyeecH3GNE1JYA4Gg/u2u2QySb1el8U/V1uADg4ZLJfLkUwmWV9fp9vtblkvm6a5pU/sJgIgxJyq1SqpVIp2u71nvkXc72FzV0IgRIhCbB7HvV4vk5OTZLNZfD4f3W6XVqtFKpWSMYHR0VFGRkau6/wUiUTw+/1yDtivQEo4C/n9fgzDoL+/X5IbReFLPp8HnOcciUQYGRkhFApx8uRJNE0jn88TDodlnESQ0Pr6+vB4PBiFAt35eax2Gy0axXv2LMqmdWH5z/8c7eGHcc/NYQQCmOPjW+LdxuAgvWoVz8oK2DbjH/kIvj/4AzynT8vvBINBeb5YLEYikdgx59ZqNRkL2v4+/H4/Ho8HXdelm8p+qFarsrDI6/FQ+9d/pfeRj6BeuADdLlgWpstF1efDft7z8P/cz+Gdnj745T2O0Flexn73u1GXl9HrdWevvCnOawGd4WG0lRXc8/N0NY3yu99N/PWvP/JrMatVKu96F8rnPodSq6HW62Db4PHQ/tu/pXHmDL6Xvxz/M5+590Fsm+bv/A7q6iqeVIpOMgkDA3JvbwHWyAhKOo1naYmOolB761tJ/NVfHfn9HBZCzL1SqRzKYbHdbsu93rVG+c//HO2hh3DNz2P6/ZijozLHbwPG0BBGpYI7lcKwbYy3vx3jqU9FP4JYt9iXivHvoOciRBwB6bBm2/aO+V7kUEVMFpx9rYgnbi9AFzmi3UQv9kM8HpdCUvl8XrqSulwu4vE4iUTisgsRBHchGAzS6/X2zSkYhkG1WmV0dHRHzvcglP/mb1A+9Sn0Cxeg1cLCed8uUfiHk/dSAA0I9Hq4TJOXLC0xVa1yV69H55nPlHu7oaEhWYAqxu1KpUIymaRareJ2u5mYmJDzjxjfpz/8YYbW14ml0xguF9XhYZqqis/vx+fzoTca6OvrhFZWSBoGRbebcjK56xpIiL7Mzc1JEc691kqi+Ey4a10p2u22zI0fxp0rFAqRTqfpdruHFq7dD6J4XIgg7wfhrCtEhES+Qji467q+Z0yz3W7TarVkvkMI3u/GXxEiUaVSib6+vssrNLZtGt/8Jp0vfxm7WkVxu9FOnSL0/d+PumG4IYQGxD76oPcnnM+9Xu/jotj7RkUwGOT06dOkUikZyygWi4RCIckt6evrY2BgYM9cTCQSIZfL0el0HpMCdIHD5LsPg6GhIRlDikaj9Ho9WdTucrmkEPYTbU96rSHmbyHUtRsSiQThcJiHH34Yn8+HYRhEIhFOnjzJrbfeit/vp9FoyEI+MT6eOnXqhiiEE+Inuq7T6XQO3M80Gg1ZmHjhwgVcLpcsanO73bLQTzjgPpkRjUaxLItkMsnq6qqTNwwGaTQaMt6n6zo+nw/btuX6RcyHR9VfRfFxPp8nmUzuOeYIV/T19XU5d95ooq1PNAjuiRA6PAjBYJB8Pi9FDA5aSwh3eyFOtN9+TlVV4vG4jNkJ5+hjPPbo7++Xe0ThqpzL5eTnYr8njICEANfVQORxO53OruNArVaj0+lQLpdJJBIEg0HK5bJc6wqEQiEGBgZkbPZatKtSqcTCwgKVSoVisSj3CWKfF4lEaDabVKtVZmZmnlRzkxAdOx7LLx/CoEOYbpXLZQzDkIIdwWAQv9/P1NSU3Ifbtk08HieZTNJoNOTvAFJoRgiBF4tFDMPYlyMshItyuZwUrBL75IGBgcds/3IQbNtmZWWFXC4nn4NYRwsDy3Q6zcjIyJGMV3vB7/dz0003kclkCAQCdLtdyZsUovX9/f309fXJeN1mfuZBIoLHOMYxnhw4Zksf4xjHOMZjiIGBAam4nEqlGB0d3RHYME2TtbU1WYx6KBKSbVN617tQ7rwTpdHAVSjg6nTwGAY+Xae5soLyz/9M5Rd+AZ7+dDqdDr1ej263KwM5whW31WrR19cngzWRSIRAIEClUsEwDAqFgqMyubbG6TvvJFitoheL6Btu6ti2o2ofDmPlclTuvZfgH//xoQvTVFVlenpaJp/T6TT5fJ5IJCID2pVKBVVVGR8fl26ElwNFUfB4PPh8Pmq1Gn19fYyOjkrytwh6+3w+tF4PZXERe22N4bExpp7xDIaGhki43dTvvhuzWkX1+/HcdNNjqtT/RIAgrzWbTU6dOoWqqrRaLZrNJu12G9u28Xq9UlXQMAxZQD0yMvKYF4UFAgGCwaBM0u1XVCXctGZmZjAMQzrgFAoFQqGQTGzUajV0XZfF56Kti2IgIdaw371XKhU0TSMQCMhEicvl4vTp02SzWYLBIO1229nMX7xI5M47CS8u4u52cZmmQxAKBGjccQfmc55D+Nd/Hdc13PheC1jtNun3vY/gXXdxNpUi4HI596XrdGMxqs95Dr3v/V7UjeSFCL5tT/gfFv39/fj9fjKZDJFIRCY3hapurVbb8j52Q71ep91uMzIyctlkVFFw/HgrtBoeHpYF99lsds+AqwhCaZrGqWqV537rW4RLJbyNBs1QiHI4jO12owGueh1/pYK6vIzH5aLndqP81E+xvr7O8vIybpeL+Cc+wW2f+ASBRgOvYdB0u8mHwyiBAIpp4qvXGa7XaVoWL3jwQVK33cbks56FYRiy0LGvr0+Sz/r6+g6lUlh+29vQLlzAPT/vuJcMD28hkVmWBbkcntVVOkDzzW/G8/d/j6LrMtB92HFPuJAIZXdd150k2+c/T/TjH8dXreJtNAjVang7HVTbRt0g1xo+HygKlsuFsbaG/Y1vUPyd30GNRBwioqLQUxRqt92G+2UvI/SCFxy5S3r3//wfAoUCnlyO7sgI6sAAW8JbiQRGPI6ytEQ0k8HSdR742McoP+1pRF0u9GAQMxqlUq1SrValM97Y2NhjPnc8GSCIA5FIhFtvvVW6Dmwm/wly9m6JjlAodGgxpsNcSzgclkSCg5RUDcOg2WwSDoeJRqOXlYjpLC7S+NjHsFdWwDBQYjHc3/M9BJ/3vCPvI481EomEdKur1Wr7vq9yuSzfw2HEnAKBgCywFIXce5FRBNlDFHtfLmHpqOfParVKr9ej1WodKmkQjUZZW1tjaGiIZrP5mJKgQqGQ7K/5fF6qGwsSSLFYlEWL5XKZqNeLp1jEbLWo2jbNSIS+/n7imkby299GX1nBcrkwZ2Z2vD/F68WanMRYXsa1vAyBAM1vfxv/U54iv1OtVmk0GrRaLbmu3Q/hcJhMJrPF7erxti46xlYYhiHb4ubE4pUKklxriLlGODFsTn4Gg0FZbFOv16UoYDAYlH1MwLZtyuWydJLdvicRMaZiscj6+jpDQ0O7Pg/x7JLJJH19fU8qYsGTGel0WoopTk1N7SgkFPOxy+ViaWmJUChENpu97BjbtYTL5aKvr49ut8vS0hJLS0skk8kt/UQQBwW5OhKJbPkckOsCt9tNoVDY4Wq33cmy1WoRi8UwDAPTNGk2m1sIo7quy9/Zz43xGEeDTqcjC/0PGr+E07Qo+DjG9YHb7ebMmTOsrq7i9/vlO7NtG7fbLYuUR0ZGnpBE/0qlIt2yDyoyAsd5VLifC1Gnq4HL5WJmZkau14Wjhsfjka7bInaVSCSYmJjY9T0MDAyQz+cJhUKsrKzs6vLU7XZZW1uTxL3DuMcKApkoMtrt+oWLUbValYWLg4ODzMzMMP0YFB3H43EplJnL5WRcYDeUy2VZqCmEkm+++Wba7Ta5XI5YLLbF8dfj8UhSl1jb7UWMbX7jGxQ+/GG0e+4B0wTLwlBVWokEyvd/P6Gf/EkUlwvls59FXVvD3Nhb7VgPKgpKJEJXUfAsLmL19TH04INM/fzPY1mWJL2NjIzsuecWRW3Dw8O7CkKLNa4Qe45EInuO2eJYQ0NDhINBav/zf6J++tNo+Tx6oYAqiloUBSMYxMznaX35yxhvfjPB7/qug17f4waND38YtVbDVS7TnpnZItQmoGga1sQE1uws6vo69qc/jfUrv3KkLui9bJbaa16DevEiajqNq1bDVlVsRUE1TSy3GyWfp3vxIsbrX0/4B39w1+PUv/Ql1FQK19oa3f5+2M0pTlWxh4fpWhau1VX4ylfIvfzlzv24XKgzMwR/9EevW/4pGo0SCoXIZDI0Go1940hCTGNsbIyw10vjm9/ErtVQfD48p06hH6FTk9Vsonz606hra9hu9579mmiUjqLgXVrCSiSo3XUXsV/+5as+fygUIhAISJHlg+IplUqFcDi8pxu4gBDXFrn57Xvf7WIflUoFj8cj99aXA1VVSSQSRyY+63a7iUaj9PX1sbKyIsXFt8MwDFKplNx/XE5uzzYMrDvvRF9fh43if5dpotu20x9t28kbKQrYNipOQbpuWYRsm1OVCi+9/37OPf3peKemZNwkHo/LXJUoTo1EIrKYfmRkhHa7LTkSgUKBiVSKYC5Hz+OhOTLivBshiK4oEInQCwZxzc4SyOWYfvhh7llYgKmpHXGaYDBIpVKRRGYR+9hx/xuuakJs72pMADa3p8PEjMR3dmuHVwLBwRFE/YPg8/mkQ7wg8zebTRkz2usehGi/2NsLA4q9IPb8vV5PiucchNqnPkX3Qx9CW15GrVah13PG8lCIynveg/3iFxP9lV9B9fmkmNHKygrdbnffHGmxWJROjNdTJFq4FIv8lNivJBIJKTj+eIPP5+PkyZN0Oh1ZfC4KP2ZmZg7MrYn16BMpvhEOh6W4U6PRkDyPUCh0WaYmjwe43W7cbjeKohy4lgFoNptyn3hY93MBYXaQzWb3dE8tl8uoqkq73WZ4eJhbbrmF0dFROWeK+XR9fZ2VlRV0XSefzx+JMNvVYnMuUhSp7jeHpNNpWTwt1omiGBacPWW9XqdcLu/ptvxkgaIohEIhPB4P8XhctsFAICDHrGaziWmajI+PMzo6isvlkqLdExMTR5IDEgKXwIHrW9HWK5UKhULhUEWLtm3T7Xal6MWxwMrhIdZgh33Poqj2sBBr3VardajxJhQKsb6+LvnUxwXoNw6EQEU6nSYej2/Zx3i9Xulaf72clMvlMo1GA1VVD5yDo9EouVyOarUqhSiPCvV6nYWFBdLptJzDhoaG8Hg8kp9cKBRYXFyUY9RjEW89xo0PsacUeYZQKMTQ0BADAwMUCgVZTK4oity3i7YvxPbB2XeJXN1efUPsPfYTHazVaszPz9NutymXyzSbTSmEHIlEZAx6ampqV163bduSZyTafjgcvi45qsXFRbLZrBT7DQaDRCIRbNum0WiwsLAgC/EFn/daweVyMTo6yvDwMNVqVb5DMW5uhqZpx8LexzjGMXbgybubPcYxjnGMxxjveMc7eOc73ykDJ5uDIZuDKJsTYuLPZrz4xS/mYx/7GJt+geIf/AHqxz+OtrTEXxeLvL7VchKAm85hX7wIX/satq5jbwRjRABHnGNzgm1kZISXv/zl0rG3UCigKIqTjHS5WHvf+/ipRx+VxWcbB5DXRLkMy8vwzW/CP/0T7BFcSyaTLCwsbPlZMBjk1KlTaJrG6uoqv/RLv7Tr89rt+eyHZrMp/93f3y+J2sJJdst5LAssSxbgSbz73XufU1VB05hfXNyzyOQwaqmHxVe/+lVuv/32PT8fHh4+lBPlQbjrrrv4vu/7vj0/f+pTn8r58+ev+jx/8id/wtOf/nTq9Tqjo6OyMFoQpn//93+fRx55ZEd/EU5xh8Xv/d7v8d/+23/b8/Nf+IVf4B//8R+v6B4EmQzgJ3/yJ3nNa16z6/eKxSJ/+7d/y6c//ekt/W7z37B/Wxfnevazn80f/dEf7dou2+02d9xxBx/+8IcP7C+2aaIYhtN/dwnWfqfbzUcqFWoPPYT/T/5kh+PI3/zN3/Dyl798z+MfFjMzMzzwwAN7fv6Zz3yGH/mRH7m8g264ptiAsnFvQWA2FKITCuHJ5Wjdey/l17wGZSPgnE6neeUrX7l3+7Ish/gnnpWiyDFgO2ZnZ6WDazKZJJPJsLS0xNraGh6Ph5e+9KU7fmfz+HzYce7hhx9mampqz8+DweCRJI/vvvtuvvM7v3PPz6enp1lfX7+iY4vAim3b/OzP/izf/d3fvSUZ3Gq1KBaL2LbNe97zHtIrK844LeaiZhMymd0ODLkc3HMP9rveBYritHnT5P/ncvFCw0C3LHqahse2GajX6VgWrYEB/nOlwudbLZRmEwoF7N/8TdhI5l4O3vjGN/KWt7wFgF46jfblL6OlUhh+P9b4OK+fm+Pvdrt224ZKBR59FIJBh9yxaS2x/Tp+5Ed+hDe84Q1bftbr9dA0DVVV+bu/+zve/va3YxuGUxQrzrEXarVL83uhIH/8vZrG320EokyvFyWfx7z/fgp33038d3+X97z//fzGb/zG1mNtbn+Kcqgi3Kfefjv/Eg6jZjIYwSD2NnLzv+Tz/JfNc5BlQbUK585hbzu+rWlOP92lXw8PDzM7O7vndXzlK1/hRS960YHXexA0TaNWq+35+fz8PLfccstVnwdgeXl5T6KWcBK5Emxvfx/60If2TVD8zM/8DJ1O57LXbdvxL//yL7zgBS/Y8/Nbb72Vubm5A4+z2b1SYPN1/dIv/RLPec5zsG17C2FwdENo6EUvehFf+cpX9j6BbYNhOG1xe99661ulAMof/M//yWtf+9o9D/Mf/sN/4K677jrwfg7Cq171Kv7wD/9wz89f97rX8Rd/8RdXfZ4Xv/jFvOIVr2B9fR2Xy7WjsOCOO+7gr/7qr65ofoNL655bb72Vv/zLv9zxu7Zts76+zt13380HP/jBqyaLnTlzhm9961t7fv6JT3yCH/uxHzv0de91r+FwmI9//OOAQ76xbRvTNGWRxn333cdzn/vcK7iDnSiXy3sSHtbX1y870WjbNvbGekjZ1Kc+ffIkydFRms9/Pv1+P3q7jd5o0JmexgZGvvzlvQ9qWfClL8EznwnbyGCb+66iKLzpTW/ixIkTux5G13X+9E//lHq9jqZph29vpun8EVBV/ubP/oyXveIVe/7Ks571LB588MH9j7vH/Ofz+ShsmluPsROmabKyskKxWJSkQVGALkhLQ0NDNwRJbTOEi2itVmN5eVmSuwEpuBQOhymXy9I1c2hoaIfYSaFQkMVMu91jIpGgUCgwPj7O8vIyCwsLxONxWSzQbDYpFou0221GR0eJRCK7FpMd48aCSIgXCoUtzjnCSegwaLVa1Ot1isUi8Xh8X4Ktz+eTYgmiQPRyHQGvJcbGxuRacn19ncXFRUmwNQxDEucnJyeJxWJ77on7+/u5ePGiJIUODAzQarUkoV9RFElGB0cVXtd1+X9RCAEOkWKz+9MTFc1mcwsp/7EqHBZ7j8Ou71RVlYT7Y1w/CAHN0dFROfcIJ+dYLPaEdkno9Xr0ej1s2z5UIY8QrRTk9KstQAdnfXHmzBmWl5cJhUI0m01arRaAdODx+XxO4eQec4nP52N8fBzbtslkMvzwD/+wJI5tJ/dezp7ud3/3d3nhC18o++Z2/PZv/zZ33333lp8dat+4OSYL/Nqv/Rpvf/vb97yO17/+9bz73e8+8Ho3X4MQgPzMZz4jr+Xmm2/mx37sx6ToQKfTIRaN0v72t3mmx8OXvvY1Xv6Lv+jEn65iX/rsiQnu7OtDK5edwuxWyxF/1jSMSIS//fa3+Y1f/VXnHBvF6SjK7nHRzbAsuPde+Na34LWvZXp6mve///0sLi5SqVSkOK7A1772NV73utdtyVvuNx5vzo9sfnehUIhPfOITdLtdVlZW8Pl8DuHv7/8e9ZOf5Nyjj/LCUmlrvNC2nRjf6qrzs89+1skzHtDuRFHbbkin03vuIy8X586dY2JiYs/PDxwPOp1LuZj779/za/982208bWAAz/IyVq1G7ZOfJPKyl8nPT5w4QTqdvtzLv4SN3MmH/H6+LxymMzkJoZDznLtdlHwe1/o63z0/z0M/8iPOXn23NtDrXcqXlMswP7/r6f5wepqfV1Xc5TJas4mWyWDFYqCqWKEQP/rmN/O5SmVHTOBy8aY3vYk3velNe37+X//rf+Wv//qvtxTc7Zs/23hXqm1viYH8h2SSt589i/3c5+L79/8e3623bvm9t7zlLfzRH/3R5V28aV6KLSoKZLN8bzzO///s2R1fVSIRTK+X98/P89uveAW86lWXd65teMYznsEXvvAFPB4PkUiEfD5PMBiUa97PfvazMschsD1Pu18sTKzrwuEwb3vb22i327hcLoTzksC9997Lq171qiuKIW6G2+3eN09+8eLFffPse13/buPcX//1X0vh9LGxMfr7++X6uVqtHixOaVlOPzogh/c14KyqOjE5RcFSFGwg2utxqlDgh9/6VtoiX7mtr26/7t3ek7IRG/uIx8NtG0KSxoZYuaIoqBvff87997Mk5qZsFvuVr8TeOJ885qY5gY3r3P4+f+d3fofnPve55HI5er0eExMTxONxOY6/4AUv4Gtf+9r+z24XbG+Hv/7rv85P//RP7/rdVqvFu971Lu67776tc5xtb43xK8quOeDNeO1rX8srXvEKub/cjj/+4z/mIx/5yI6fX04/Avi+7/s+Xv3qV8vnq2nalvHsgx/8IO973/sOPM++ME2eFwzyj4kEerGIahhYuu6MgbaNEQrxoQsX+O9veIPkAe21DtjrOjavr77xjW/s+f1//dd/5Sd+4icOvuZN2B53LZVKLC8v0+l0qFQqdDodbNvG4/FQKpXweDyMjY1d14L4o4SIkYZCIbrdLg1hHnIAhFC0cLF/IsHj8Vx2kfXjDdFoFI/HQygUolQq7Vv8Jtwjxf5wt1xRs9kkm83KwhRVVfH7/fT19dHX10e5XCaTyexaNC6EVMvlMq1Wi+HhYVwu1479p6IoDA0NMTc3R7lcJpfL3TCx/f7+fur1OqVSiXQ6zeDg4K5j2crKCqlUikAgIEV1PB6PFH9rNpucP3+e8+fPc/bsWUzT5KabbjqSIurHK0SRWrFYJJFIUKlUpECEruuMjIwQiUTw+/0MDQ1h2zaFQgHLsuR3rhaimNjv9x9a4LLdbu8r7gLOOJrL5cjn81scXL1eL/39/SQSiSfc+HrUcLvdeDweGV87SCyk1WpJ8Y3DtI3N6/fDvIvN67EnijjLEwlCnLzValGr1WQBunBiPkroui7FJBuNxg6+d7fbleOKoigYhkGv18Pj8exox6qqEggEDhSNuhKkUinpCj8+Pr7lOQgDpnA4zOrqKsvLy7hcrgNNJY7x5EKxWCSbzdJoNDA2BPGEmEpfXx8DAwPyz17wer1omobX66VarR6YD6pWq7hcLilgvNvns7Ozco4V3BBN0+h0OqRSKVlYbRiGNLsDZ9zPZrNks1na7TbNZlPek9/vx+/3Mzg4eM3EKvL5PLlcjuXlZWnGuHlMSCQStNttUqkUqVRKispdrvji5UJV1ePi8mMc4xhXhOMC9GMc4xjHeIxgGIYk/VwNtru3VO+6C/UTn0CfnQXbph2P01pd3b2YzLad5OUhYNs2Y2NjNJtNSqWSJIudOnWKvn/4B3KNBi1xju1/bz+nacIe977XMwkGg9x6662k0+lr4liTSCRYW1sjFouxtrYmNyeHwl4ERssCw6D8wQ8y+MY3gqLQXV2lt7KCbZrofX0oExNH0g6cy9ifSNlqtY7kXAcF1Nrt9pGcp9frSYKoSAZ5vV6ZlNc0TSbBrgabg767odvtHsn9VCoVDMPYEvAUzs25XA6Px3MkbbvT6bC4uEgikSAUCsmgVqlUolgsSjf1qz6PouCen6drGDTf8AZcH/wg2qZglGmaR/LcDgq0CWXSq4WmqjT7+vBUKniWllC6XfjTP6X8pjehBAJYlnUk7Q2c5J8I8IXDYUngTqVSrK6uHtkYd5gx4SgC5NdrTCiXyywvL+Pz+WT/N00Tn8+Hy+XCbjbpbL6WPcQTtl28Q+7bBLXbxW3bWJqGDrgMA8s00QDv+joG0N48z9mb3B4uA71N82/9Ix+BRgO92aRz6pTjIm5ZtPZ7trtc+0HncS7ZplKpSOfMK1qP7PJce7ZNa3LSKbyoVnGl01j5PJgmRcAYHDySdtDM5x0CWq3mECC3wbTtvZ/b9uveZ/y/bmPPAUkt27aPbJ1wEK7HekQUrRzFOHe9xh7TNKnX61LB9cyZMwwPD0t1c+Hmd0UQz8o0qX396/t+9ajWI9vHhN0+P4rzuHWd5OwskXvuoVet0g4G4cwZ7Oc+l56iyOLRq0W73WZubk6OaaZpUiqVJFE+Go0e2Xn2w1GNCZuvVRRjbFb5fTyOCbF0mkSnQ1+9jprLgWliut0QCMB+Y7aAEHE4YM2+39gjinkMwzhw7b8vTJPWO99J4dvfJvTbv417F+Xhoxp79kO1WiWXy1Gr1aRji3DSeiI7uxuGwfnz5ymXyzLp6XK55P4mnU7LPt/pdKRQyOVgMzFuu2L31RIUk8kkxWKR/v5+UqkU8XicWCwmHZ3r9Trr6+usrq4yNTWFb8MtCpw5oFAoUKlUGB0dJRAI7JoEVRSF6elpqUQuEsSbx5NIJOK4S4bDnDx58knttvJ4QKVSYXl5mXa7LV1gwSHNFgoFvF4v4+PjBxaii4K4brd7qHEiGo2SSqXkvv5aJ7kvB4qiMDMzQyqVwu120+l0qFarcm84PDyM1+slHo8zNja253pbuNkmEgkymQz1ep1Go4Gu67hcLjqdDvV6XZK2/X4/brebVCol3c/Fcxcuj7quH0nh6LWCYRgUCgXpBOJyuYhGo/u+X9u2JTmi1WrJeVQQR/r7++nv77+uznDiHVUqlUN9X7ilPxHGO+GeJOb/xwN5XtO0G4Y8fr2wuZDqMI5N2wWIjwper5dTp07RarUoFApyryHyOochFfb390txDUGavFoIcpdwvN1OcjIM40j2cI2LF7ENA2Vb37cNg/rdd1P71KeOZN3eaDTIZDKYponX6+WmUompz3yGZL1OzOXiwWaT1hE8t+7aGlqlgtZqYUQidJJJp+it3XZcwqtVWtsFtA4TGxXf28jZGYZBX1+fjFXPzs5K0q5woz2K9+NyuUilUtRqNQKBACMjI/Q1m/g+8xm0hQVsoLXfPVxFTHY7rmd+7tDYZ59s2TZKOOwUPtZqmNvEF48qD9j1+zGnp7cW+Lvd2MPDdCIROt/+trOfP0x72Od+zEIBz4YQst5qoRoGHV1HMU3c6+t0221ah4gJHISDxq+jirtZhQL6o49i5vN0vvAFOr/8y0T/43/cch1XdZ6Ntt/d75lGo5iplDMmXOU9CeGnZDIpSbrLy8sMDQ3h9XqxLOvI4rzr6+t0u10GBgbw+XyyEE7slY8iP7eb8MlmHGXcrV6vMzY2RiQSYWBggLGxsS2fH9nYI/5WFCm4LcT0460WXdumA1vnhytA2+PB3iBIi1y3silm2basrTyRK+yza2trrKysEAwGGRkZIR6PbxH3uKpY/CbsFx8UIgVH0bZFwYnX65X76817xqNa92wWnVYUhVarRSQSke/HNM2jOU+jgW7bGP39WH19KJrmiA5Uq+jZLBQKRzL2XOtYfD6fZ2lpiWw2K7kTIm9fKpXI5XJEo1EMw8A0zX0LK250CDE9IXx50B6gXC7LMfi4COnxB1VVSSQSNBoNFhcXyefzuwqTW5bF2toaiqIQiUR27NlN02RhYYFKpUK9Xpc8J1EsVy6X8Xq9KIrCyMiIjCH29fXJ2Euj0aBWq7G0tCTjb/F4fNf4kaIoxONxisUifX19O8bKxwqiQFqIvTabTeLxuCwsbDQalEol1tfXCQaDNBoNZmZmZDFhrVaj0WjIuECn0+H++++nWq0SDAb3Fa96okMUVo2MjBCLxfD7/bK4XOREo9EosVhMCoNs/t2jwOUKXAoxmf32faVSicXFRcmrrdfr0mE1EolQq9VIp9PMzMwceWHsEwmxWEwKOeTzeRKJBKqq7hrbNU2TSqXC4ODglrXPftB1HVVV0TSNdrt9YFHk5njaEyG+/ETFZsHga4l4PE61WmVlZWWHQMJ2A7pqtSoFFXbLy6mbzGaOCs1mk0ajQaFQoK+vb8+xRgjAXLx4kVqtRi6XO177HQPbtpmfn2dhYYFUKkWxWJTCL6FQiMHBQcmdO2guE+L7sViMbDZLPB7fcwwVHC/Bp95+XNM0mZ+fl8XnQ0NDO/qUYRik02mWlpZQFIXV1VXGxsbkPeXzefL5PJVKRc7/pmnKPGmj0WBkZIShoaGrf5DbkM1mKZVKWJbFxMTErnlrr9fLxMQEc3Nz1Go1stnsDZWbP8YxjnGMzTheER/jGMc4xhMJto3xT/+ElsmgmCbd0VG0ixeP5NBut5upqSnK5TLdbpdoNMro6Chnx8dxXbiAu14/kvPsh2upuqTrOpOTk7KY6eIRPTcA5a67yBWLqI0G6gMPOElXTcPQNFoHKZw/ibGZdCc2fI9n9Ho9ZmdnCQWD+C9cIPzVr+JdWWG012PS5+PulZUjOU8gEMDn87G2tgZcCoS73W4GBgYO7ZB2EGyPh97AAO6VFbo+H7WPfITof/pPR3LsxwpGNIoRieDOZgmtr2PrOo3/9/8wfvInD60MfrlQVZVTp04xPz//pFDevhqEQiFJthaEDbfbTbPZdBS3r5JAI6EoWKqKIdzcLAu128XT6WAA2lWS3XaDdf48eqWC6fXCNQ6Kl0qlfZ0zrxS2oqC1WtiDg9ihEJ3BQfT5efS5OdB1Ok9/+tGcp1aT7h92IMCTVxv8xsVeZMBeryfnpscT8vk86XSadrvN2NgYQ0NDPP2I2vMW3HMPtS98gdDzn3/0x34s8PWvM5ZKYRQK0Oth2Dbte+6h8+EPs/7Up0pC5NXC5/MRjUYplUqyyLLZbBIOhzlx4gSLi4tHcp7HAtVqlUAggKqqNwTZ5kph9/XhrtdRz5935opOh94RrUcPC6FifBTQV1bQvvpV6q98JYF3vhPP+PiRHBdw5rZdimQEut0uc3NzNBoNqtXqjgL0crmM3+9nenr6steUIrHXbDalo3g4HD6yvcNRYG5uTroTBQIBpqamJDFEOESLAklwEpuXs9bJ5XKsr69LcpggIwcCAdLpNJFIhPHx8V3dYA4Dr9fL9PQ04BBVCoUChUJB7nPF2BUOh+n1enS7XVKplCT0C5fSWCzGzMzMnkQaXdc5deoUuVyOQCBAf3+/VCfXdR2Px0NfXx/JZPLYaeMGR7FYZGFhgXw+T7FY3OKkWyqVyGQyJBIJut0uU1NT+yqzb3bnOAxJStd16ZZ2UJHGYwFFUeS6rFAobBkPA4EAfX19h4ohjY2NsbKywsjICJ1Oh0wmw+rqKoFAANu2icfjmKaJx+MhGAxKh+JIJEIsFpNjZ6lUcgQ6+/puSPciwzAkWaTT6dBsNgHnPQcCAfx+v3QW2v57woGqXC7LvSQ4RcXRaJR6vU6xWGRmZua6xe1isRjlcpl8Pk+j0djXiV2QfgWh9fGKXq8nyTWbC+jcbjf9/f1bSN7HuHJ0u13y+fyW9ZDo75cjsuDxeHC73aiqSr1eP5RIiMfjQVGUK15n7Aefz3dFwjwCfX19RKPRIxvfFEWR4juLi4uHIvteCeyvf53iK19J5G1vQxeiPmtr1N/4RtT5eZRU6kjOI9pKIBBg+utf5ymPPkqs3SZuGLgB9xHNo0q7jeJ20zl9GmVzOwkEMBIJjIsXr7rYS2BqagpN09B1XRZVlMvlQ7uJHQaiCGNiYkK62nnvuANqNfRWC2N0FDYVGRxjGxTFcRa27cMVgF8B7L6+Pd3llUAAW9OuuqAVcFzPTRPFtrF0nW44jHnyJABGqwUPPnjV57ieMINBbF3Hs7REr1jE+qu/ouLxEPnJn7xu12BvFIQeJZLJpIytrK+vs7CwgNfr3VIMdDVQVZVer0exWMTtdhOPx1lfX6fZbNLr9Y5+nrBtrHYbq91GC4X2jMNcDSYmJhgdHWVgYOC6OUopsCXmqh1hO7A21vrdXg/bstB3cfE7CoRCIWZmZmTsYmRk5LrusSqVCrVa7UjXZPF4nFQqRTAYpFAoXNW6bD/4/X5cLheqqlKr1Zienj56wrpp0j15EsXjuZSbUxSUSAQzHMZ66CHY2G/eqBBCGmtra9TrdYaHh6WgPzhrlHq9TjqdluKUfr//cUv+V1WVvr4+Go0GCwsL1Go1KVrZarVkLMXn8+H1eimXywwPDxOJRK7J3uQY1x5DQ0NSxDSVSlGv12Uxp2jfYv4WsebN8RLLsrhw4QLFYpHV1VV6vR7BYFCK2hcKBbLZLMlkklAohMfjYXR0lEwmI/MTqqqSz+dZWVlBURQmJyeJRqO7FsML+P1+KewlYuGPNVRVZWZmBnBiaKVSaYurtaZphMNhWVg8ODhIs9mkUChQrVbx+XzS7dowDFmw6Ha7+eY3v0l/f/+TtghZcH3AmScHBgakYKaqqjK/JeJ6mUwGn88n3YevFt1ul0qlQrlcplKpyOLQ/fa7QqR1r/VPuVxmfn6edDpNqVSSuRld1+l2u5TLZQqFAoODg1iWxZkzZ65JLGQ3iBi/EKu50eFyueh2u9RqNc6fP8/AwIBc58RiMaLRKJqmyb2J6IuHFYwRQiuRSIRyuXzgWr1UKklx2ieyCPgxDof+/n5yuRw+n49UKsXY2JgcO4Rwbrvdlrl8MdbvlrcXolFHua8qlUo0Gg16vd6BbVvkeMrlMpFI5FBCqsd4YuP+++/n4YcflsI7Ho9Hjn+maTI3N0cwGGRychLbtjl79uy+e4aBgQGZXxS52O3fF/lLVVWJxWK7juVCWDufzzM8PLxrvkXXdUZGRlheXiabzeL1ehkeHiaVSpHP51lcXERVVQYHBwmHw9LQrVKpUCgUZN5UGAccFRqNBs1mk3K5TDwe33cdI4rhS6USkUhkh8jFMY5xjGPcKDhmJBzjGMc4xhMIzXvvRU2l0ItFepEInuVl1CNwVxDwer14vV5isRiTk5MO4fvee7EaDdQjcBp4rBGLxZiamkJRlAOVlS8HrocfJvTQQ1iahqvTQdkg/Zq6Tm+D/HSMnXC73ei6jtfrpVqtXrfg67VCNBplVFGI/cmf4M/l8NTreFstPO027m6X4BGR0fx+P2NjY/T399NqtbBtG5fLhdfrJRgMHukm2U4msSoVlHwe81/+BX7+5/ckJT1uoCh0BwZwtdu4SyXCX/0q6z/0Q9fUyVLTNE6ePEn9Ogh5PJ5x9uxZZmZmyGQyMgGlaRpnz551kndHdSJVxdqceFFVLK8Xu9tFv0aEPlotsCyHHHaNYJomxWJRBuR8Pp9TNFGrHcnxFdtGq1QwNoRVFE3DOHECz7lzUCzS+/KXj+Y85TLeWg2908F+5BGMaBRzYADlWLzhhsHq6irxeJxoNIrL5ZJJ9WKxiM/ne1wkNjdDuIYMDQ1x+vRpbrnllmuSXFTqdbof+hA8QQrQlXKZQKOBDZiqCoZB1LLoBAJEv/Utjmo1Igoy+/r6ME0Ty7Kk+8LU1BSDj1OxJ0F+EMVSj+eCIjuRwBgeRsnl8G042em1GkddzrjXOsowDPL5/JGdx/L58Cwu0rFtGm98I+4PfejoiMqmSfF//A8Sv/u7O9bU3W6X8+fPUywWWVtbw7ZtwuEwXq8XwzDIZDJks1mGh4cxDIPTp08fqgjdsixWV1elK6Zw5BBFND6fj6GhIenE/VhBFNynUikikciOvi3cWgKBAIuLi+RyOUlWPkyyfHV1lbW1NbLZLOVyWZKcLMsil8vh9XoZGBig2Wwe+tnuhnA4zOnTp+V9NJtNKdwyOjpKu92m3W5LJ/der4eiKAQCAbxeL9FolPHx8QOTjaqqkkwmSSaT0vkanMTlZlLtMXbCsqwjd6C9EjSbTRYXF0mn01QqFZLJJOFwWK6jLMuiUqmQyWQk6dHr9e5JztxMLuv1egeSiAXJWtO0G1qoQNd12davBIFAgOnpaVqtloybJBIJKpUK3W5XCqAVi0Xq9Tq9Xk+6AddqNZaXl6Uq/9raGqOjozccIaDb7XLhwgUqlQrZbJZGo4HL5UJRFHq9HrquE4vFaLVajI+PS+EO27aZm5uThGHhoiWKvVutFsVikVKpJN0cT58+fV3W+olEgtXVVUKhEOl0momJiV3XSoZhsL6+LgnRRxkPu56oVCrMz89LkooQXBCuE/V6nUwmc20KTJ4kMAxD9ud2u029XpfroVAohNfrJZlMHnpvEQ6HJXm5WCzuW4AuhCz6+/sJhULSiU6sAbxe777uINcLuq4f6dzY399PJpMhFAqRSqUYHR098ti/Wiyi3Xcf1de8hsi7343d6VB/1atQ5+ZwLy2hHlFxlJiTzy4u8vQLFxhuNPBYFq1YDEZHMTMZOAJhNMU06U1Obi0+34xwGNLpqz4PIAvDBwYGyOVyhMNhKUiTzWaP5ByqqjI+Pk4ikaC/vx+vx0Pp859HLRToBYN73+cxALB7PVTDAJcL5bEiwOs6HIEjtWoYKICtqtiahr15vPP5sP3+a1Zkfy2gGAa2z0cnFsO1sgIrK5h/9VcYL34x+nUSw1E6HbgGa/jJyUncbjcul4tOp0OlUjnS4/v9fkZHR1FVlWAwKIWfotHo0eXnDIPc61+PtrCAmss5P9M0zKc9Dc+P/igcocvx2bNniebztP/6rymUyyi6jjY1RfAlLzmyc2yGYtuyINjmkjP6kWGjIFAIdGmadk32PclkkhMnThxIzL5aVCqVLQ677XZbCq4MDg4e6b3puk48HqfZbLK0tEQulztScWgBVVXlelJVVdrt9pELS9oez965OEXBSiRgw0H+RkUmk6FarVKtVpmcnNyxBlUUhVAohNvtlg7QmUzmcb3XGhgYIJ/PE4/H+cY3viHzdJ1OR4oVgrPPF0VSN2pexbIsisUizWZzy744Eok85rG8GwW6rnNyQ8zH7XZTLBZJp9PyXYvClmg0Sjwe58SJE1ue3crKyhYR2O0xl2QySblcJpPJAMhiUOEALvbTwWCQkZEROeYNDQ0d6h0dpQvsUUCIvebzecm1Ensj4Z4s7sswDJrNJvV6ncHBwR1xWhGXX15eJhKJcOHCBZ7ylKdc71u6Ioi+V6lUtohz9PX1XdFePpFIsLa2hs/no1gsMjg4KIvxu90uuVyOcrksBQlE3FWM3/vNbyJPXK1WMQwDVVUJBAIkEgkMw2B1dZVKpUKpVKJSqUjxfNGOBwYGdsTOm80m7Xab0dHRXXNllmWxuLhINpulUqlIobXNiMfjlEolWTC9tLTE6dOnL/vZHRbineVyOVlUB86au7+/n3g8fkNyNhqNBnNzc3S7XVqtFs1mkwceeIBAIMDAwACtVotsNiv3CJZlMT4+TiwWO7R7s6Io9Pf3S0GOYrG4Zw60Xq9LUQ/BfTnGkxs+n49kMimLZufn54lGo4TDYXw+H5qmsb6+LoVJQqHQrvmrzUXiRymc2+v1ZI7qMPFkr9cr527DMI7b+JMYc3NzPPjgg5w7d45Wq0VfX59cy5RKJVkg3uv1WFhYwOVysbq6ytTU1J7HjEQiJJNJbNuW/UUIGymKQqPRoFar4fF4ZH4ykUjsOI5YF7jd7n3XAEL4dmVlhW63y9raGvl8nlQqhcvlYmxsbMvcp+s6iUSCcDgs53G3200ikTj03kKsEQQ/fzsPo9lsSs7HYfbn4XCYpaUlLMui3W4f98ljHOMYNyQevwzWYxzjGMd4nEPX9SNR7dxMfO58+9sojQaqYaA3m6idDm7D4FBnUVWpXr2bopmu61KpcWhoiFAoRH9/P41CAbpdNNvGJ37HtrcS5jf/27adP6oKLpfz9yYc9EyutQNgPB4nFAphmiZelwsMA8W2sRXFUXDefA97YeO7AFgW3mYTNxsJX1W9dCzbxpfNOu9HUZxncRXEmoM2Pj6fTxLPJWzbIYlsvqftxxHvU1FA1w8MQu5HegYc51rD2P05iuewUXgeDoeJxWJkMpkdZDu3272l/Yt2e7lk8YMCLm63+0jaXMLtZua970VbW8OfzTpCBIqCu9tFMwy8lrV7X9307A9DGolEItx6661UKpUdjj0+n49gMLj//Zim8342rm+vgnKPqoKiYMZi6LkcdiZD+8IFvBtBcqGWfbU4KGFx6DFBtPVN92WLnwOebWNWOxYjmM3SrlQofPazeKNRPB7PpfZvGJfa8T7PCbh0zgP6eDAYZGJi4sjGuMOMCZsTy1eKqx4TDonJyUme+tSnUqvVsCwL27bRNE06fvk9HmceEnPMQdg+Bm38rib+vf3rLhf25r4qXGbEvy9z/N4SJAoGnfG118PcaFMuVcW3131sHq+3jYPij4Bwz6zX62iaJosZpXNmJuOcZ3Ofv4J24bZtlG3u8IqmYUQi6MUivpWVS+9nvz6z+TrcbmdMMk35vry9HrphoJomer2O0uuhVyp0x8chHEZTlEvn2Y7dnufmdcmmcfbIxp4DcBCJSlGU66byfhTnsSxLqpQvLCzIn3s8Hun2IlT2rxZHPfZs7jub/51IJPiO7/gO+vv7GR4eZnh4eMvveTye3c8j5pzDjEm2jbvZRLt4kdbDD+O7+eYdX9mxHun1nOOLPgO7r7nBOb+mwYbq8n5wuVxX3hYMw+mvloXbMOhOTkIs5sy5loVVLuPJZvGurhINBPDpurMfuAoEg0FuuukmWq0W3W5XFnD4fD6pLH9DrUdAzmGwc672eDy0221SqRSBQIBQKLRF5feqxoSNdyPaReEtbyH80pcS+M7v3HVMPvR5Nr333cZ3Ray7BgboZrN4ajW0TodWtYoSCu0918GlY2rarm1FOPratk2lUiGXy21xZ2k0GtJV0ePxYJrm/mNHp7N1XtgFSn8/3XYb98oK3VCI+he+QOiFL5Sf7xh72u0994476NO2jfrxj9N4yUsIPOtZWz5aWFigVCqRSqWIx+P09fVtuZeBgQEKhQKpDSfH+fl5zp49u/e94hQ8Xbx4kVKpRDabpV6vo6oqmqbR6/Wk8rlwmdg+/l1P5HI5qtWqnGf2gq7r9Pf3k06n6e/vp1KpHKjwXigUWF9fZ2VlhV6vt8P5qNvtUigUWFlZwbZtZmdnuemmm66Y1Oj3+zl16hTtdptisSgL0D0eD/F4HLfbLd03BJlLuKVcSeF7MBh8XJNkrweazSa5XI5isSj3RyLB3N/f/5gkdwUhulwu77pHFIl+j8cjCaHZbJaJiYldjxcMBvF4PHg8HiqVyoFk93K5TDAYRNO0J7QTTzweZ3V1VRYmT01Nsby8TKfTkWr6pVJJEgQajQa6rhMMBrnvvvtot9vyOQ0MDLC6ukqpVOLEiRM3hBuKGLOKxSIrKysEg0FHTHRjXWOappw/hRCncHLJ5/PSlSAQCOwgCns8HqLRKOl0mpWVFVwuF9ls9rqQ1DVNY2hoSBYNLy4uEo/HiUQiaJomi0ELhYL87uDg4GNewLsfbNumVCpRrVblekU4SywsLJDNZsnn85Ikqus6vV6PSqVCsVhkYGAAy7I4derUvo7wx9iJXq/H+fPnJYG+1Wrh9XpRVZVut0smkyESidDpdOh0OnuOs5shiKxiLbq+vs7g4OCOtYNpmqyursoCLiEwIARqxBpArP9GRkYeU8LRc5/7XOr1OisrK6yurgJXFkfQdR2Px8PY2Bi2bbO2tsbCwgLBYJBQKIRt23g8ni17pi3x/u35jD3W7ZrHg3t+nq6qUvmrv8LOZNAWFnAvLtKLxdB8PnzbC7a350g8nj3jV2Ifkkwmeebtt/Oce+6hv9fDqyi0ZmbwRyIogK4oW/c8IuYmYokHrSk39iguXUfZ53mrcCm+J3BQzFpcz6a49ea9r8/nY3x8nPHxcUzTRFEUCoXCkeyxg8HglmILs1ZDabVQ223MDeL3vnvFzde+x37xsLiesfh9ccg4kqooqMWik88NhfD8u3+34zzyXLZ9ab8uD7ARI9otD7gpf6IecD9eTbuU29R1589mbM6f7BOTdZkmpseD3uth6TrWtr2LR8TIN79vUfwu+utueUZxry6XE2s/oI3slwfcEse0bRQRk9s8LonrbTZxr62BqmK43bgKBaxkkvrHPkb0F37hyuJulnUpDrjRr9375A30UgnV58PXbF5Vzhm2chAURWFkZIRkMinXioODg3LcOKgP7Ja7EPB6vXzXd32XFJczTRPbtqUbervdvrznJp7XtnfntiyC73mP44Du9TrcCF2HbBbjnnsobwj3XjUsi8brX49vZQWlVkPrdkFVsQMBqu9/P5VnPevg82zO1e4D8dTVXX6mwNa88/b8zC4xhu3vSdngSbhbLbBtPF6vjLdufuPe7X1VnOsy80GDg4P77lf3jMUfEuLeVFWVDmjOZVhS4D0SiThuwRs8kQPvYfN9bhsPxdgzMjJCrVaTjsTVapVYLCbXPZimwxsQz10cQJxT0xyRjs3vZtP1uFwu6vU6tVoNr9fLiRMn6PV6pNNpWcwv+vOea6y9sGk8P0jMTlfVrfk5kUfdzkFRFGxVRdljbD6KWPxuwhWGYVAqlaSj3H7nEXte8d1ut/u4cATvdrv0NgxKPB4Puq7jdrsZHx/n4sWLuFwuLl68KAsgRAFtp9ORwoeHES683hD7hlwuJ+MzggMjhDsPGj+eTPB4PJw9e5ZsNkswGJQCIuDsx0RsYXtxzWaHc5/Px8jIyI5jK4pCLBZDURTS6bTkDU5MTMhCV8uyaLVa0iHW5XKRyWSo1+tSJEMI5Yu4Y7PZRNd12WZvJKiqysDAAAMDAzQaDRnPd7vd1Go1Ll68yNLSEn6/n3K5TH9//55jVDweJ5/Py2Luer1+w8fuM5kM6+vrdDqdLUXdwrU+HA4zMTFxWe9NxB/7+vpIpVJ4PB5isRj1ep1UKkWr1aJSqVCr1SgUCtK4Znh4mAsXLjA4OMjo6OiO46bTaTKZDJ1Oh1qtJoVbg8Egs7OzVCoVvF6vdAiu1Wp0u13OnTvH6OgoiUSCZrPJ+Pi4nCO2C1zuVhgnBJ6LxSIjIyN7xtNF4V42myUUCtFsNveNvfd6PRknFIKih0Gz2WRubm6HmKWmaYRCIarVKmtra8zMzNxQsf9ms8mFCxcoFApS/GV0dJROp0OpVGJxcZHZ2VnC4TDRaJTTp08zPj5OPB7ftwByNwgX6+HhYdbW1mi328Tjcfnee72eLE4XxYlDQ0PX4raP8TjE6OionM+EEUehUNjCUxGFsmNjYzvWnGJcCYfDcs97VBDjxOXwscTvPFZiPqK/iXi42+1+3Bs0bEa1WiWfz9PpdGSRciwWu6GEQKrVKg899BBLS0vU63XOnj27hSthWZack4WIfjqdluP0fjG38fFxyT9pNBpybAVnzTo6OipFfsbHx3f8vijErtfrhyrgDgQCaJom8ximadJqtS5xZHeBy+UimUyytrZGX18f5XJ5X2EI27blmrnZbMr1huAw9/X1kUwm0XV9CzfsMCJ7m2MUR8HpPsYxjnGMawHFvtGk445xjKvAH//xH1Or1QiFQrzuda97rC/nGMfYFaZpSjXQZDJ5pOrNxXe8A/UDH8C9uOiQW1otLE3D2iPQp5gmeqsFikL97Flav/qrpJ/1LMrlMoVCYYuKUq/Xw+fz0dfXRzQa5eTJk04A9Y47UP7gD/AvLGD4fCgiQQZOIkzTMH0+jNFR8PuhVsMzP0/ntttw/a//5RQ83KAovPKVaHffjSufp3v2LMrGAl9dWsKdz6N1uxg+n0yW6c0mpttNLxbDPHECJZUisLyMthHMtxSF3gZpSbFtx6EAsFQVxbZp3Hwz9s/9HPHrOH4VXvc6tLvvxrOwQLe/H3twcGcCtdnEvbCAEQxiTU8T+Pu/x3WFKvCVu+7C/t//GyWdRs/nUSwLy+Nxij47HUy3G2NwEHtkBO/b3053Ixm2tLSEaZqMjIzsCMx0Oh1WV1ela8bJkycPLDK43rBNk9LP/izqI4/gWVlxnrXLhSeVQtsg+Voul+P2YJoYPh+9WAxrwyXBDIWwpqbwvuc9ssD7WqH0nveg/OVf4llYoHPrrQeS1OxGA+/srNOn3/52As9+9jW9vitF7TOfwXjLW/A+/DCdU6cc9wzbptlqYfR6dDodFFVFF85whkFscZFcMsn8j/4okZ/+aUZHRxkeHsZqt6n82I+hPfwwimFgTk9fOpFtQ63mEFwUBbxelHIZvVqld+YM3ve+95q/w2sBkawGHnN1PUH6tSwLl8uFur5O66MfhX/4Bzyzs7gaDVrDw9jJ5K4EGgH9wgXo9VBNEwVonz2LvrSEu1xG7fUwdkm2KN0uqmli+ny0z57FNT+PGY1iftd3EX/f+674nsr/5//Au96F59w5OlNTsI86r22aeB55hO7oKOZLXkLibW+79NmGaqQoIBAq1ELZWShJbi46KP/1X8M73oHn4kXat97qkIgeegh3ve4IsGzMfbaiOGQsy8K1kVjt+v3Yuo66QYY1QiF6N9209XqrVbyzs6BpWKqK0deHvV9yptnEc/EinZMn6Y6P485k0OfnUTsdZ1yMRHDPz+NqNFA3yEmm243p8dCdnga/H3V5GXcu57ikgzM3RyKYG4rvWy/QxnXuHEYigfm8513VezwG0n1PFM0ZhiHJ9ELd90YoxtkL9XqdXC5HqVTaEgSWjlyHVHHvplI0f/ZnnbYVjzvj0X7o9fA88gidmRnsV7yC2Mtfvu/XWw8+SOdXfxXXuXOYXi/W5OTuwhmmiWt2Fsvtxjx7lvCdd6IdUv37cmFWKlR//MfRzp9H7Xbpzcyg7LLPsS0LfX4exbYxTp3C96EP4TlEEcehr+Ma7rWOCouLi2QyGUmi2CwOVK/XabVaMpE9NjZ21YVc5X/4B+w77oByGbVYdJywFAUrEIBoFGt0FO8b3oD/Cp0Wii97Gfr996O027uPs5tgV6uEHnoIW1HoBoP0brllz7Wmsr6Oq1Cge/PNaL//+4Se//wd31lYWGBlZYVvfOMblMtler2eFKjpdDo0m02CwSCTk5PMzMzwrGc9a899QuUf/xH7T/8U97lzdCYnUfZJYNmWhfuRRzAGBzFe+EIS73znrt8r3XEHnle9Ck+t5syB2wrCEtXqliJ0H5Dp76f7i7+4ZX5vNpucO3eO2dlZgsHgvm0im81SrVaZnp7mzJkz+5KXLl68SDabZXl5WTrDiqI1UbyXz+dlgdDU1NSu5JqDIBLum9WfReHsYfGtb32LpaUl3Btk5P1g27YkIU1PTx9YKPbQQw+RSqWoVCpMTk7umdDO5/MUCgVmZmaYmZl5zF3hj3H1sG2bxcVF6W4txhFwCneEcM7o6Oi+wgdXi+17rV6vx4MPPsjS0hIul+tAYtPq6iq2bTM+Ps5tt92259x34cIFlpeXyWazTE1N7UkKFK5s4+PjTE5O7prwfyJhaWmJVCrF0tISkUiE/v5+Sc4Te6pMJkM+n6fdbsvCyUgkwsDAgHT76Xa7NJtN+vr6GBgY4OTJk4d2XblWKBaLzM7OMjs7SzQa3dMpvt1us7S0RH9/P2NjY5w5c4aHH36YlZUVqtXqDjes7VhYWJDFGrfccst1IyqtrKyQTqelE7thGNKpQ4hIJBIJBgYGDlU0vBcEyeNKyEDCHVTsy30+H+FweMszymazpNNp2u22JIQKIu/KyooTr1IUxsfHd53XBWl0cHCQwcFBbt5FzOoYu8O2bR599FFyuRwrKyuEw+Etgi+2bdNoNMhkMjLuPDExsWdf2gzLsjh//rwUsVFVlWg0it/vl8ctl8tomkYsFqPVahEIBCgUCnS7XVwul2zPgUCA/v5+IpEIp06duiJBmqPAtdhrZbNZUqkUzWZTCn6I9aJwDwqHw0xPT8s1WuF3fgftox/Fs7RE+/TpvV04bRt9Yz9snDiBYhjO/tjlwpqY2LoPKRTQ83m0dhul20Xv9eh5vXTHxjB+8Afx/cf/iP/227cd3pYiEM3/9/9IfOADhOfn6Y6Po+xHUGu18F64QOfkSaz/+B+Jv/rVe37XKJWov/SluB591IktHGI94nrkEZRuF3vDtb5z8uSebuLK6iquUonu2bNob3oToWvkynsYmPU6tZe8BP38ecxY7FD3qj/6KGY8jvHSl27ZP10z2DbNe++ldeedqPfdh9Jqga5jTk6i//APE/qBH0C9Avc/ger//b9Yb3sb7nPn6MVi2MPDu8d6Wi08c3P0+vsxnv1sEh/84I7vGYUClbe8Be3BB6FaRa1UnOJBXceKRiEUwrz9diJvfat05e4sLtL6+Z/H9fDD9IaG4KD9jm3jfughumNjWL/wC8R/4ze2fNyem6P9i7+Ifv48tsuFOTW15TplnrXdxt4oiDd9Pjpnz+4qKKCurKB0OhhTUxCPo8zP41ldpRcOY/b3o4hYfq2Gms+jNxrOtZ06ReT970c7onhk8fd+D/XOO3EvLmK73aidDmq3K/PAAtaG6LtqGLT6+jCf/3zif/u3V3ZS26b47/892v33o5dK9E6e3LOwXFlbc2IpN92E+pu/SfiHfujKznkN0Ov1yGQyskBHEHTdG+IX/f399Pf3XxUB26zXKf/qr6I98giuxUWUXg8jGATDwFMuo/V6qJaFqWkYHg+dZBLVttErFWxFoTc1hXnmDNG/+IsrjqFW7rwT+8/+DCWTQS8UUDfEFbAsVMvCCAYxh4awZmYIveMde+bb7W6X0o/9GPpDD+HK58E08bTb0t3c3vScFMtir1nZxmmPpsvlzIGahmqadM6exf7P/5nYK16x5fvCZVQUAJhf+hKjd9xBbGmJVjwOySRut3vXNb+6soLWaNCbmsK2LFypFJbHg7XPntJutfBevEhnehrrZ36G+Otff8ATvjr0ej0pHCZyjULswOv1Eo1GHdEf06TyspehnTuHYlmYJ07se1xtdhbb7ca45RZiH/nIrnNvp9NhdnZWxh/K5TK9bpfBj3+cyfvuI1Sp4KvXHQEQj8fh3JgmRjiMNTRE79ZbybziFVQ2yP6tVku60fd6PQzDIBwOE4lEZEFjoVCg0WhsIbELF9hIJMI4YP/rv8JDD0GrBV4v3HQT/h//cbwbwpaF178e/Z//Ga1cxjhEfltZXMSXTjscIbfbyU2Ew6CqKO02rloNy+2mNzmJ+ZSnEPuzP7uq+XMv+P3+LUXoPp+P9fV1zp8/z4ULF3Z1p90OIbR88uRJTp06daTulEcJIWiWzWZpNBpbPhOxi3Q6zdLSEvfddx8ejwe32y0Lkl0uF16vl0ajgcvl4syZMwwMDFxTZ97LgWVZUlwvm81Sq9VkEYuYS6LRKP39/SSTycdlHOta5rVs26Zer9Pr9VAUBY/Hs2fbz2QyzM/PMzc3x+Tk5IFCD7Ozs8RiMcbHxzlz5syO837rW9/im9/8piziaTQaW2Ie4s/w8DArKytEIhHGxsa4aRvX4EZGOp3m4sWLfO1rX8Pn80mxkb1iY+12WwrRPfWpT+Xmm29mcnLy+l70ZWB5eZlMJkM6naZarcq4qCjmEgL0sViM06dPX1a8otPp8Oijj0qBYECK8onCNHCc4xOJhCN+oyh4vV5GR0cZGxvbEjcXzqWiYL5er2MYBqZp0m63abfbUmjj5ptvpq+vj0ajwfr6OqlUimKxiKqqTExMkEgkGBsbo9FoUCwWcblcjI+P75mzPXfuHMvLy1QqFaY3c8d2gWEYzM7OMjo6ytTU1I4xay/ncl3X6evro7+/f99i/1arJeNR6XQaXdeJRqPouo5hGJLDMTg4SCKR4PTp03v29VarJV3GdV0nFApd07z7ww8/TDqdJp1OMzg4SDQapd1uUygUqNVqUoyzUCiQTCYZHh7m2c9+Nslk8ori0a1Wi4sXL1Iul8nn8zSbzS0caSEaHY/HL1tw1jAMaRDlcrluOGGNo8CNxCF8rFAul+XaROQmisUixWKRRqNBIBBgcHBQiv4IQeBCoSDHlfHx8UPFmg+LdDrN/Pw88/PzW3iBeyGVSsncw+23335di9AF37tcLsu9mViriML84eHhx23/qdfrLC0t0Wq1qFarcm/icrmIRCJ4PB5GRkauSEDpSgVK9sK5c+f4/Oc/z4ULFxgZGdmTi9FsNslkMvT19dHr9XjmM5/J9PT0oXLoQpyoWCzKfYiqqsTjccd0atP61DTNLcYS3/72t5mbmyORSByqJmBhYYFIJCLXAYZhMDY2tu/vCEHv/v5+pqamOLFHDMCyLObm5igWi5f29RvjoRBVjMVihMNhTp48SaPR4OLFi8zOzh6qT4p835kzZzh9+vQNL1T0eMDjgUN4jGNsxuOhFvaJIRFzjGMc4xhPAPR6cN99kE7DiRNw9uxWs2PbhgcegLk557vBIMzPw733Orn4n/xJeGYgALqO1u06qsGWhenxbFGgtiwVw1AxLQU3FiqOi3EvW2P9jq9wn+s/02rlGRoqMj5eptfr0GzqnD/fT7EYwLKCnDwZZn7eRSgEvk+ucmsqj2La6PUmqAq2qmDbCmrXcNy+eybWhQWKiWkCvQa6x+sozu+iSLkZtRr82785nIThYWg0IJWCyUl46lN3chAMA774Rfj616HZhB9+yiyTj/wT+n3fQKnX6Vhu1t0naH/3Szj58y+kb3DTAWyb3BfuZeWjX6edb9A/ohG/7z7UZhMjGKJad9PtqqiqTaRmofcAW8MwdVTVRlUt55mbJkq3C6aJd3X1UjE+ToK3p/mwLVBUUNwWareLbjiFqp75eTqf/SyzP/CrzK14KZUckexWCyIRuP122M6XNE34xjcgk4GxMbjttq1GA5WK8zxM03lmm/eH3VQK62vfRFnOUnH3oSZGcCsWjaZGpazTaquEgibRqIo9MYF3bo5Ovc7y++7i/FP+K90unD7ttNXNaLfhnnugWoWbbnLac7sN9/3dfXh/9z3Ey2kiRpFybAh9MIrH72wWe90u5moe91yKegV6/+1N9P7kgzz66ASzszE6nTxra2uMjdkEg05hRLPZlEEbsQG27Sif+Qzkck4bOXHCuff99tq2Dd/+NqysQDIJT3nKJf6GbcPsLJw7B/39MD4ODz7o9Me+Puf+w2GnbXo8zr/X1pw2GwrBo49C4OF/45aFdYKrq07x+dAQ+oULjgiBZdN2BQAV3eNC77TQOl2MUp2SfxJXv5fI6kUqaoPCe/6JE3/8ZqpV53q/9jXnHr/jO5xrLZWcZz46Cg8/7IwXug433wwzM1ufweqqcx+l0tZnMTnnY8bU0E2FfF7H7VUIBkx6PYV6Q8PoKei6TTBo4vFYTuNSFDpdjXvv9dIpwtOfvrWtbWkfc3M077qL7n0PUVnrUjWCVEbO0vmel3L2xSfYvn9vt+HLX3b6//S0cy+b8fDDzljodsNznuM8c9OEb37TeSfJJGw2kLQslXJZx2y50DQbo+dCURt4fSrmRrGkZdt0GjphQ8M0AyQSfSRHRqTrY/v8eZR6Hb1apSuUnw0DI5VDL5WdgmbbwjQVDEvDcuu4rTo0m9S/fi/3ZE6TSjnC7zff7LQh0W9N03mvmcyldvW5z8F3fRd8//fD8rLz7ms1OHUKnvtcpx3uB9N0uAMXLjjP46lP3be++UDUak77X17e+vPRUWcM2hx3Lxad77Za8IxnOH1ot+N985vO8YR4XzTqXOfQkNP3HnnEecc33QRnz3pYXfXwyNfLxD70P+hfuBetXcdfLeCrd1AsC+/6Or1ChbonRmdoHI8Pmi0NBQgETFzdKlq7TS8YxDBszLrJ/GKEhJ2kz27htQzMto3t0tBUG0W5pNYvXb4rFUeIJJFAe+ELyWScPnXffQ5HI5HY2ueiUef+y2Wo1+GZzwTRfEIvfSnVO+7ACIXQ19fpBQKYtkajoWEaCoHARn+zbbS1NWxNw4rEmD/z4/zLHZDPO3Pj7bcrTE87ia98Pr+D6K4o/Zw7F+SRR5znOzUFit/vkPwAqlW05XWod8ECBRtlI/BnKwqKZYO1Tb/MtlEMk57moWH6KWfdmJaC0VNQVBu/6WbYcBzMVVXF6utjr+HYshXqZhifFkFdyRJYWMTyOC7o3dOnweVCAcxEwulnrTYmGlqnh60oaKur1MfOoBQt1LaCZoOtqdi6jrmtYM6yFBoNjWpNI2Qn8BfbWEtrgDOGnzvnjP/Petal/lIuO/Nbq+XMycPDcP/9ztwxOenMHZrmjL2PPuqMW4GAM47NzDj947Bx34UF5xh+v3Pc7fmsbNYZ/9ptZ2xcX3f+ffvtl8Zfw3D61vy88/2REWd9NDTkjD3Dw07/u/9+596e8hRnLWHbcP68c/5dTCIkNM2ZY2+99ZLWQzAYxO0OsrTUo1hsMzTU4+RJWzoQHIRSyXnGmua0zxMnDvfMHn300jOHSwY3203AhcnT6KjzDO6/3/lsetpZYwmXWMua5FvfMgkEFJ76VJWhoUsX0e3CV77itJFUCp73PKc/bc4LmZUKAErPoGYGqKfd9AyFUNAkELDodBXaLRXLdo5rGh6ShofiusLcZ9p0B522tzlO3+064+naGsz860eJrDUINi1qQxMEFEX2q1pNp9t15upQWMEYGsKzuIhVq1H/538m8rM/e/ADxXn3Dz7otMW+PueZ1mrOHuTf/Tunbebz8K1vQaEA0S/+KycXOyRyLVLRkyg5Pz6vSTBkoms2NlCr6himgic+QXzlEYxGg8adHyH946/l4Ycds/TbbnP6zX4oleBLX3La3enTO9fH5bLCN77hJhRy2vUB/ECJxcVL/a6vD5aWnHWWYTjz2eqq0y5Pn4bd6qbX1pxnpmnOn/vuc37P63X623Oe48zdExMTaJqGruu0Wi1KpRL1eh0Q7tljzM3FyWT6MYwIS0uQXW6SZIXJkS6PpOKUXcNMTCpMTDhr/WAQzpxx5pnlZefcpRIkP/9+pr/6NwSLKQLNIqai0/X4wLRwrWWw1RzGahnl9a/H/v3fJ/Cd34ltO+81n3f6j647x3O7nXY4NeWMJz4frC22uXmhQqLZxNhI9tg21Os65bKGYahEowaRiIGq2ijhMIbbjW2A2uhgzq/C2CDNjpd2R8W2oNkATzFH0ijTGJ1AGxwl+V3ftes7m5yclErCwl2y2WzS6/UIBAKSEDA5OclTnvKUfRM05n33oZXLTqHGAQslRVUxIxHUSgXt4YcvuRxtP+b734/W6TiCC4ckUnoqFYwvfxmzVpNk51wuJ8kyfQcs/BKJhCymzeVye95zvV6nUqmQSqUIBAIMDw9vSU4Kd+VgMMji4iK5XE6SLQ6bxDQMg9XVVYrFonQT3kzqDYfD+7pBCAjnM8MwDlXMKZLTQghlP1SrVdrtNqVSiYGBgX3V1BOJBOVyWboFHxegP75h2zZzc3Pk83lSqRSdTodQKEQkEpHEzIWFBcLh8BaH0+sBQQptNBqHIiBGo1FWVlYwDIN6vb4nESqZTFKtVqnX6ywvLzM4OLhljBCFdJlMRvb/a1l4f6NgdHSURqMhxbwqlQqRSIRQKITf75culV6vl1arhW3bnDhxgmg0SjQa3eI2LVx6VFVF13VuvfXWx8w1Apz5o1KpSIf2veD1euX8EY/HpaBZuVze4Ya1G2KxGJlMhv7+fur1+nUrvB8bG5Mu4bZtk06n6XQ68vl3u11CodCujkgHod1uS+ERMZfoui6FqQ4i1AoCa6VSkQVWgiTr9Xqlc9bKygqZTIZsNku5XEZVVdxuN4ZhMDc3R7lcptlsMjExsed+KhwO0+v1yOVyRCIRmZQ+xsEQY+Lq6irRaHQHsUpRFILBIF6vl6WlJbkeGhgYOLBfqKrKyZMnUTZcdwUZKZfLAch2ID7TNI10Ok0ikdji8CKItktLS4yOjnLx4kVuuummG8Yd5WoxMDBAJBIhl8sRCoW2rN3C4bAsvJduK80m6pe/jFYo0AuH9y4+B1AUjIEB3MvL2EtLoGlozSbGqVM7imBdpRJqr+eIw1oWimXhajZRFhexP/lJut/8JsZv/AbhH/7hTYdX5FxQWFhAb7eduNUBJDjF58MIBFALBax/+zd49atpX7xI8667sBcWnE1XMIj+7Gfje+5znV/aJBB5EGxVdYq8FAXFNHFfvEhvcNDZcItjtFqo2awT256YwJqcJPLiFx/q+NcKWiCAHQxieb2o9TrmQWuQblcKRCvXYY1mFItU3vQmtIceQiuX0UolFNN04qWpFNajj1L5wAdw/87vEHjGM67oHKHv/35K//APdBsN3EtLGN0uVjLpBClwRAbVQgF3NosRCGAnk3j+03/atfi8+opXoM3N4VpZcYRWg0Fslwul18OzsOCIljebVF/5SsJ/8RfosRiuwUGaXi+m349aqWAdtN+p1Zy9cCCAtj0wY9vo8TjGM54B7TauuTmYm8NIJlGCQad9GoZ0VmZjz9yLxfZ0s7c9HtRGA21tDavZxLO6Smd4GLbHmsNhrHCYXjqNe2WFrtdL9e/+bkeR7ZXCTqdRGg3UDYdtvd12cr8ezyVnectC7fXQOx0sVcWbz9NcXLzykyoK2n/4D9iZDFajgWt29lK/Fu5mnQ5aJoNeLjv9enycyPd+75Hc81HB5XJJkWdRQAKO49VRrV3K/+t/oZ07h3tuDiMQkOLRnkcfBZwcR8/tRtsQJXbVavTOnKEzMoK2soJ7fp6OolB5xzuIv/nNl33++pe/jP1nf4a6uOiIUvf1OfkQl0sEzNCyWdyzs3RNk+p//+/E3/c+lF1iEYrbjfJDP4RVLGJVq7jqdawNgXuFTS7WsGvxub1xv7aqYrjdqIaBq9XCEIXNuRzm174G2/qGx+NhamqKsbExpwh9eBjlc5+j1+0SymTo+v1O8mszLAslncZVKtGZnMTweNCbTbRmk97o6J75INg0L+bz2J/9LPZrX7urqOlRweVyMTk5yejoqCyyF7GqWCwmi2fqd98t88CdiYl97wGcnJU7lcIql2neey+B5zxnx3eEI3GhUCCXy9HX14f6r/9K/yOPEMjl0EwTY3gYpa/PMUWwbexqFT2bxXXxIlSr9GezuF76UryRCO2pKbmXFY6mHo+HZDJJPB5nbW1NOh+LAiiPx+P0OSDy3veiPPAASq2GWi47yWRNw3rkEdqf+hSN224j8nu/57Rf4WR+EAwDdy6HahjYpklzfBxlYODSWAW0ez20lRVnblAUyu9+9w4Rk2sF0zSlwNhhCqXEmlwUQNyIEHvHSqVCpVKhVCrJ8VWIKgjhtlarxc0330wkEqFSqUgxV5fLRTgcJhAIkE6nWVtbw+/3H+jMe72wuLgo90RiLhHxLOHGmMvlZFGP6AfHcKAoyqHn2UajIYUIDio+B2QMYrvwAVx6N5ZlsbKyQj6fl06QpmmSy+XQNE2OV2Jv9XhzsRdFcoZhUKlUCAQC+8YLWq0WXq9XFgO3N8xKbkTkcjkymQzLy8uYprml74FT/FYoFEilUrJo66abbjp0LNTj8XDq1CkuXryIz+fjgQceYGVlRcYnhSid3++XQkWGYbC0tMTa2poUkHe73aTTabLZLLOzs6yvr+Pz+WSOSFEUGo0G+Xye2dlZZmZmqNVqjIyMyGJ6UTC2vr7OQw89xMDAANlslmQyKV1qhfDjbmi321IQ+yAIN3dRFL/9OLOzs1K0sFKpyAK8QCAgjQAmJyf3zE8tLCxQLBZZX19ncHBwh3iKcHZdX19HURTm5+d3iFkKwY9Go0Gv15MF6C6Xi3g8TjKZPLRo/2FRq9VkcaIwxgJnLhsZGZG5j6GhISqVihR7EMIEVwKfz8eZM2dIpVIEg0H5TmzbxuPx4PP5CIVCjIyMbMkB7AeRP6xscEUEgsEg/f39xGKxxzRfcIyjhZi7Op3OFrGGQqEgcwv5fJ50Ou1w5TZMdmKxGIlEgsHBwSNfsyQSCbmWKxaLkue6GzqdDrVaTQpvXM+22Wg0mJ2dlSZ5zWZzi2hWMBik0WhQrVY5efLkodYlNxKq1aoUUMrlcti2LdcIgsMRjUYxDINer7fvexKwLEvuJTcLbR1WoGQvCJ5EtVrF7Xbvu270+/34fD7Z5tvtNt0NA6OD4PP5pJiL2FtpmibbnW3bUtRBcKfE/eXzeWzbPtS5xPdEjrDb7R5qDBf8kl6vt0VgYzvm5+fJ5/Msb5Cm4/E4wWAQVVWlyO7i4iLDw8PYts3MzAxut9sxeiyXDzRaKJVKRCIRdF0/9NxzjGMc4xjXG8cF6Mc4xjGOcQNgdRXe9jan6EHg7Fl4y1suFS796Z/CV7+69zH+/M8hWXkOb/N9CBtQez2nIHoTMaXT1TGMS/9XTZOu4sGwdNoVhcbFAh/7qAuUYSwryS23NPmO76jz3veGsSwNl8uNqqo88ojz+0PlB/m5h78EHQPbBguVuh3Ctp1zKIqNy+7iaXVAVfE3l7AVWAsPEz57G9E9CIC2DXfeCX//9w7fZjecOgW/9VuXRPhXV+G1r3WelWZ2+L4Lf0Lv3V9kza4y4V+j17Botd24tTXaX3uU+97xIcK//zqe9YtPp/rxj1N419+R/UYGd7tO0OrRVFTixjxuq0aVKKs1F7bi3JerqeMxnURbp+skQXXdQmx1FUDJZNBME9W2N9TIFSxLo9PZnDTVwNbx2S00y0SvtVh+sML7fvErXEi+YMc9Kwr80A/Bf/7PDo9heRne/vatRaDT0067icXg7rvh3e++9AwVBX72Z+Gnfsr5/8N/9q/Y5yzGa12WA6foXdwZIGy3VHI5F8mkRtLnp7FQ5vw7v8RfPuO/yGThc58Lr/qJBaxchsVFhb/4yDjL7UtKo2fOOMU8P/bQnTytWMTfLjPrP0unFYAF6B/o0d/fZS0fotyMEVbzDK4tsVBJ8Kmf/jj3TP4c7XaIdjuBZZ3k7Nk03/u951DVS4lwr9fL0NAQS0vD/OmfOu1ge3v57/99ayGXQKUCf/RHTlGvwOAg/P7vO4IQH/iAU7y3Fz760b0/E/jxBz/FQKGJB81xQW000Fot6Ji08dE1NlQ00XCp4DGa9CyF9kqFNe8wQ2YfSrrMyl/fy7uadebTQVmoC06Bcri5ymD9Al8yO7T1EGvRW2i7LhHPn/98eNWrHP7OXXfBhz7k5JK3YyJ/mp9JJZjppGimGmTce7scDiS7JJtlGmqIC3NB3vv/JmltxDJe+Ur4gR+49F2zWqX81rei3XMPvUKTxnIL3TSJKDrqA+dof/pTfP5tz+Tzr/4NfvFVURTFKe77X//LKYASuO02eN3rnOLsd7wDPv/5S5+9973wmtfA3/yNMyYInDgBv/HSQerZAPGOj3q6S8Ud3XQnHgLBLn19FRTVw/paGE+jStf0kq0kufuef8drfn1Eftuq152iW8vC9nig1cJ6ZAW100W3OrjtHiomCjagYLUUVCw6D8zx0T/L8X+3xY2mpuCtb3UKJ9/+9t3b28c+5vzZjH/7N3j/++ElL4Ff+qXduVmNhjO/iCJLcIrqfuu3nILYy8XDDzvH2yWPCDgFcG94g1ME+7nPwV/+5aUCVk1zxqB//++3Hu/tb98phHAQvN0yP/vgf8PTOI+/tYTXatLUQrRtLz7LxNXtYXebuFsqRm2R+cApbMUZf91Wi+neKr14hKbhJdcIkeym6HYgzQBBcx3V7qF3OzTNIF1VxeMx0FTrEsHCtnFnMvQiEexQiAdiP8SfvmL3PrUXPvAB+Jmfcf5ooRD84A9iNhoOkeTcPCvWCZqKT461A5EaSWMFvValNX6Cr5efwl9+5JkI5ssXvuD8/bznwWtf6zglblZ9/vzn4V3vgg2eCXfcAT/4g/Dzz7+FjseDqWlo55awLdAt41L7RQEVFAsU29pEtFHo9nTUnoVqqbTxsm4MUc9tbYhau0O350I1beywzyGr7IJOR2VlxUOnoxLvxEhWUxiqiavXo3P2zJbf6/UP0km3cKHjM5tYKCjNHmavTabeYLTdwm05N1q3Q1juIPqmJFyno7K07KXXVTau0Y1tGiw94OKdv+0INoh36fc7HCy3G975zr3bPjgFmX19W+eTzbj9dvjN35S80V1hWc67ueuuSz/zep0+K8yJP/lJeN/7Lr3L7filX3I4YO997959S1WdIuxSyeGLCrzgBc664Utf2vsat2NsDN78ZqewfXER3vQmqNVcgPPOnvc82MfQS+KLX4Q/+RO2zG9jY/CiF8F3f7ezttmOVsuZC77ylcNf7174qZ+Cn/5p+D//B+68U0GES9xu+JVfcZ7NyoqzzioULv3egw86f//qr8L3fZ/zb8Xjod1R6ba8FDI6dbfzLKqVPUIwtkVfF2oNN/c9EuQr74UPfhB+7ufgZS9zBAb+8A8v7RV+5Z5znKzU6ZBgfTWEp2AxPtZhdc1Ns3FpranrNpOTDplKKRYxvv51ZyA+AMvLl9ZBu+EDH4CTJ515Wqwzf/KBbzGZq9GyfZS6ESg4z1DVIJnsUsi76HbFCOIGM04gV2Dub+/jf3z+0rEDAfi933PWbrvhk5+Ev/iLS+1EUeA//SfnOSmK047+6I+i9HoKHo+Kojhj3X/5Lzs42RKW5ayLPvKRAx8N//Zvzt+33+60da/30t7lQx/a/3c/+lF42tPgjW9UGBsbo7+/n3w+L4uGAL71rTgf/vCQ3Hd97W8v8NT1f+GWwhfoWV0u4vQs0z/Fh5Mv4ZGBF2Hqzhre63XWuisrzjVN577ET1z4JzyNJVSrzYJ3hoYeBRTQQPWbRLo5BlZWyRdVYr/9u2Tf9iH+9x0DLCwc/AwAXIbFb6x4cDfduMIqGArLy15arUt7zmbTzdqam4nJNrpu07LjuK02LS2AluugZ+fo6lFM1YNu9xgySoDCivcE2foEf1N+G7+zqDA1tfNahCK3qvZjmmNMTq6xvFxDUTqEQgoDAwOcODGNZUXw+ZznIkS+thuc2/U6mCaW7qLb07Bs0DQbTYNeV0FRwO2+NED1NC96t027YbFwoSOdepJJ5110FhdxpVKohoEl1C+24Y+9XmSJjWXh6fUcgZVikV46LQvQG40G9XqdQCCwb4E0OIm6YDBIvV7f4o6wHZuL2gcHB/dMKrtcLvr7+6WadaVSOZSydLfb5cKFC9JFvV6voygKiqJgmqYsdqzX6wc6B4iEs6qqhyZ7CgGeg4qzKpUKjUYDy7II7+N6D5dUq0WxmmmaxwrJj2Nks1lJXnW73czMzGzpX4lEglarxerqKqurq5KseT2It5uJzYchRIvi24MI0ZFIhJENBaxMJkMqlULXdenG22w2sSyLRCJBX1/foZTYnwjQNI1Tp04xNzeHx+OhWq3KsUFRFAKBAJFIBE3TaDab+xL8gsEgg4ODsohfODw9FhCkPFFQfxB5KBqNSqeXXC4nRTwOQ3AIBoOsr6/T6/WkmMP1gnCJdrlc6Lou27FlWXg8HiqVCg8//PBlkZRWV1elo6BwRgQnBlmr1chkMgwODsr+tB3FYpHFxUXK5TLFYpF2u42iKLIwJBKJ0Gw2mZ+fR1VVUqkUhmEwPDxMKBSS7+rChQusr69TLBYZHx9ndXV1Tyd38f5qtRr5fP64AP2QyOVyVKtVbNveV6RB13X6+/tZW1ujv7+fUql0KCEaXdc5ffr0liIj27blOw6Hw9L1fm1tjfHx8R3zjCDaCsEUr9dLoVB43JHy94PH42F0dFQSigVZc7d1VjeVQul20ZpNuocRlwiHHUHEet1xV/Z4HCWtDagrK7hKJad43LYdZ1hdR+n10DYKY93Lyxj1OtYf/zH1aJTg85638zzlMkq3i+X1HkpFz/L5UNttlGyWwq/9GtoDD6DWaiiioNflwr7nHhrvfz9KNovldqM2GlgHiFHZvZ5TlD0yQvfECdzZLNbaGu71dVhfx3K7USwLtdPB9PvpTk1hTU4SfPvb94zXXTcoCvZ3fzdWOo1nfh6j09lXYEDJ550i6lAI/w/+4DW9NLNapfrrv452/rzjqCzcaAMBMAz0YhEll6OXTNJ9wxvgbW+7oiJ0xe0m+Pa3U3/1q+lqGtr6Op6LF7HcbmxFkTnfXl8f9uAg/Jf/QvB7vmfHcSpveQva3BzuhQWMSMRxM9/0fs1eD3VtDffcHF2g8ru/S+Id70D1erGf/3ysXA7PwgLtVgtlr7nTttGyWYxQCDscJryRCGqfO0fzzjtRvvhFlHYb3bJQ19cx/X6UbhfvwoLsZ65yGc0wHLEIn895pvv16w0hZH1DcNUIBvdV5rWTSaxqFTWXw/jkJ7F/+Zd3LfK9bJgmaqPh7N17PUxdd9ri5r6vqlgeD7am4Wq1sDQNbW3tqk4bednLKC4vwz/9E/baGu7VVez1dWy3GywLrd3G9Plkvw687W27OjDfCFBV9bLcAw+LXi6H9sUvoqVSmH4/1oaYl5JOo5gmmmE4QgG6DhsCAbauO2N4LIY1Po45P4++vo7x+c9j/vqvo13mdXY+8AF0IfAxPQ1+/6V8iqJAKIQZDGKnUrgXF+n4fNQ+/WnZh7Yj+su/TPH8eWcue+ihDfFrpwBd2VQM7GRy2PJ/cArQTV3HdrkcB/RuF73TwdQ09HIZs1rd817E+geg9Yd/SPt1r6OnqrjX17FzOYxIxHmW3S56pYJi23QmJrAmJrCnplA+9Sln3jnEGtwKhdCLRaxGA7NaRb8Oe6jN97cbzGIRej3nOR8iDmD7/Y4pgWE4v7sHVFWVRXStSoX6l76Eu1RCA4wzZ1A3z8eKguLzYXm9uEsl3LOzeFIpPEtL9IfDNIeHqT//+agvfCG6y/X/sffeYZac5Zn3r/LJuXOerAwCRDTJgE1OxosDn8Paxnh3HVnjiLENaxswDuuEjW3AxnjJSAiZnC2CkBDSjCZ3TifnU7m+P6qrpns6zmgkjXDf19XXTJ+uU+GtekM9z3PfNwlZRvnGN3Bvu422rpNOJMg+/vEY11+PtRYrUFWVJND75V9GPH0aZW4O0TR9YZNAqGRmBldRoNOh8drX4hQKSLqO3GphLy/j9fdvLRLQ6yGfP4+6JsbpiiKRlRWcRgM7nw/FcARFwZ2aQjh7FnF5Ge9Tn8L9+Z9HfBjiLZIkhbFCy7J2jbkE4kiSJF2VMcDAGbxSqTA/Px8KJEWjUTzPo9PpsLq6Goqd9Xo9nvKUpyDL8rbvxwExMSDGbPcO+nAheC8P3oUudpYO5pR4PM7s7Cyrq6thPPt7RbTr4YTrujiOs2sOIoAkSaEgiuu6G9p8dnYWSZJoNBoMDQ2FTp3xeJxoNBqKI9x///1kMhlyuRzpdJr8xSInVzkCB9N4PM7y8vKO8RjXdWk2m2GsUJblsP2uNgRCj0GMcHJyctNzoSgKg4ODyLLM4uIisVhsz/GSANFolGuvvZa5uTkymQzVajUUUglEAXO5XBgrkWWZkZERpqen0XWdUqnE0NAQKysrLCwshOIrlUol/L4kSdTr9VDkuFgsMj09TaFQCAl7QawlnU4zNzeHKIqkUikOHjxIPB4Pz2mn9grifntBQERdf/+DnFq1WmVxcTF0Lg9I8s1mk+npabLZbBhbujjuHDjIF4vFkOy8FQLSY7FYJJVK0Ww2SaVSeJ4XCjDWarUNoiZBe7ZaLarVKgcPHtw1p3YpCOKarutued5Be4BP9jtz5gzNZpNyufyg3i1UVeXAgQOhoEIgThSQ7feaH/E8j5mZGcrlMs1mk3q9HgoMqKpKJpMJ4/SHDh26KtcVVzMcx6FarYZxVEmSiMfj5PP5Pc9ZDyUCkacAqVQqzCcWCoWQnB4Yx8RiMQYHBx8SoXNFUUJBl7m5OTRN23JutSwrFF8IhD4eLrTbbY4fP06tVqNcLpPJZDYIW/R6ParVKnNzc6GA/jXXXHNV3Ou9wLIszp8/T6lUolwu09/fTyaT2bBOarfbrKyshITqWCy2Y/3FVgIltm2HfWEvAiXbodVqhUTtveTAk8kk5bVCbmO7osUdIAjCpntpmiZnz54Nc5rNZjMUkYnH47RaLer1Oq1Wi76+vh3znc21OEewBjBNMxQh2w2u64b1LFuhXq9Tq9WYm5sjEokwPDy84b6mUilSqRTVapWlpSUURQnFBnK5HIuLi6G4zlZoNBp0u10GBgZ2vc597GMf+3gk8eiYkfexj33s43sYpZJP2llPZAGf0PHOd8Kv/ip84hM7k88DrKau4d6Fa7lF+y5yq4W7LmBh29IG8rnsWUiegy5G6EoJP3npuQi4eEiIosSJE0lOnEhuJzjPkxY+QNos4wEdKUHE7aF5BrrnF9p4goApaLiCSNTpoggWPTFK2evna8Kr+Kkt9tlq+eSjb39752s9fdonbP7xH/u1E29/+xr53DV55fHf5UjjLoa754i4XRq9NLqYQBIc8uYqfcYSJXOYyhvexMJ9NyJ/9asYD9SZMMrInoUlKEg4RO02eC5Jocaod4qFNQKjJai4SCiYodudba21rSriyjJypRK6nwN4iLhskeQQBAxRI+74hFalXCad25rt43lw220+OfVFL/KJcBdzCs6d80k7/+t/+aIE6w3fPM8n5h486LfZXR8r8WSzgylGsKSdg2arqyqyVEBuNkknykiOgSdKXLP6WQ781e0svOcB8lmL7rkoP2KJzCSu456hF3K675mcPCmQ6i5yqHE3OXOVqtKHIV8oYC0VFRoNGdPwX5qaaoGMWSJjFrm59Cnumng1kUgEVVUxTYMzZ0Y5cCDC4x/for9foFDwi6OrVY13vGNrQt7p035fesMbfKfWAJ2OTyS7mGSzsgI/8zM7NsklYbRzioRVpyj0kbZltE4HxwTR9bCkCx3MA0xXQUFC9ixiTpsq0FTyjPTOobgGnePncbM3ht+ZLH+dJyx/lAPN7yJ5FqLn4AgyhhTj/tz3cdfIK6kmpvjSl3xy5I03+qS27TCXfzw1rZ+6nidnrtJSsiBsnaCrLENe77CoHOBk/xPpqRcCwX/7tz6x+tgxv0ir8brXIZ06hTw/j1t3kYUklqiiOV1y5iq6GENzuiy8dZnPT7yNZ7wow1/8xUbyOfjur69/ve+mvZ58Dj4J7q1v3Xye58/D/3zHdfx0b5Jjyio5s0hD6dtQFNRpq8SiGTodAccWKbgVmmqBTqSPu50n85a3+OONIIAYj/tjnCgidLuwWEE2TCJOd4206+GtlXsIeEg+hZe41eJF330rM+4U3x19aXjs6WmfXNrrbb7eveCTn4SzZ31i68Vxw/e/fyP5HPxj/Nmf+WPIpeReFxfhD/5A2jCuXIx222+nP/kTn+y8XkTEceBf/sUnLT7mMf45//7vbxaM2AtefOqPGe2cYrxzmpacZjF2EFvUUJ0eE92TRJwOMbdLyqkjeyajnTNUtCFSVpWUXaUhp1npHuD+1JO4Qf9PBH2ehFVFWetDimci4ZJ2atiOgu5EEKKgWJbv4KLr6IUC7vg43R/6Sf7snclLIp8HeP/7/TH5llsg8z//J9X5eXo9AeNkkTH7NKaoYYoakmsTbXawkyrugQOcsq/jPSNv2rKo9Stf8Ylv/+t/XfhsedmfEy4Wdbn9djh69Cg3HDuG8O37idi+WkBPiKJ6JjI2Ii6OK+IhIuIi46w95aBYJoLgYggalqjSVjIbD+B5ZK0yPTGGhIPejeE0ZdKpzQ/R4pJPPgdftEVzdTxXoCHkia4L1ruewNxSHFM9xqh9BhcRxTWIeV1iVpuD9gN4goAtKIg4NKU0S+4hBmsOuayFbQvMzESw7QttF3W6WKJKS0xvIo93u/66ZC9YXNwofnEx7r3XJ6hebLij6/5xcjn42tc2ks+Dv7/5zf7Y3Wj4wg47PW//+I+7n6vrbhSwCfD5z+/+3YsxPw9/+Zf+OPZ7v7eR0A7+M3nwILziFdvvY2EB/vRPN5tozM/71/2e98DjHueT0Z/wBL+2rdHwx5CzZy/9nLfC//t/PgH5YjFT0/THyxtugD/6o81r9gB//df+mPrc54I0NMrZpRxDYoq0XaW9g5gLQMKq4yHSkxKsxn11ENuGd7/bd7eem9sofhB12sieTU/y11OGLnLmzOaCIdsWmJuPckzV/B2uU4rdDp7n34vtyOfgP68X95WY3UT2THQxumFsch1YXtpc8Np245hFg7puwDphlmBt9u53+2PZepw65T//6/MDnudve911Po/g//5fEcvaODb6Y50vZLAVvva1vZHP1yPozz/3c3DXXbuTzwPcfbf/3uKT1/2CqdHRUTzPY3pa4LbbfGd3PI9bZt/Hsxb+lZjVIGuViDktBM/DEhVy+jIjnTPcsvwxPnjtH9CMDqPrG/v1kxc/QNZYJeJ2mYlfgy1uLN53BYmaNoglaox0zjH9rSxf/Jk7mB79iT23gyVFsUQNnQjGskOlF0Hvbb24mJ2NILoOh2yLpegUHSmJI2nErTpJq0rU6eAKEkVtjJaSoxgd56PX/C616DhvfrMvqnHxuuX8eX+MnpuLAiPAyFrhBRw4IPD0p/tj4vz85vM5cMAXZzpwYO2DaIxWV0OudTlnbk0cUVSPSMRF74mkmxJRN8rsuQRve70WVvpKki9Q8Lqnl4iwVhC8TXLoJy8qBlfWFlmCbW8YEAMi3V4LEgIXkZ2IqEFyMpVK7brfVCrF6upqmNzbjYDuui5nzpwJ3S8SicQGQpVpmtRqNZaWlsLE5LFjx7YtfAwcY+PxOM1mc1cXeF3XsSwrLGbbCY7jYNs2iqLsqShRVdXQuWCfgP7oRqlUCgvRRkdHt7z/0WiUsbExpqenH9bCW1EUw2fLtu1diyqCgui9iC4MDQ0hy3KoiN9oNDBNE0EQKBQKYdHkxMTEjoVbtm1TrVYxDCMsyMpms49awnpAEt3KoURVVbrdLp7nhYUCOyFw8g0EOB5JAjr4xS57uS/BcxEUBD8a0Gq1OHv2LKVSiVKpRDQaDd20LMuiXq9TLpcZHBzEdV2OHTu2a1vMzc2xsrLC4uIinU6HRCIRFlN2u11mZmaIx+Ohc97Y2Nimc5qZmWF5eTkUIBgZGUFV1bDQuFqthm5zlmWRTCa3LOQNil1d16VYLIbP4lbFKUGhUbfbvards642NJtNms0m6XR61/EzmUwiiiKtVotms7nnQq5gfC0UCuEaRRCE0I3kvvvuo1qtks1mdyzwCsbswEX9e4mAHiBwi98JXvDC7nm+8/TuO/WJTq7rx3LX9zNdR6lWkXQdD3Ci695l13JbrqZhTE2hzc5iqSrm3/wNPO1pm9f3suyT//ZYzCa4LrguyqlT2K0W8uJi6OaNJCE2m4grK1hrBHpsG7nbxbCsHYniYrnsE8aSSZJ/8AdYp05h/8u/4JVKIUkeUcRLJiEWw3niE0m9/vUou7mNP0yIvfKV6J/4BHYshjI9jR0ksi5GvY5aLmOMjuIcO0bk6NGH9Lwab3874unTqOfPYyeTOKOjG4h31vAwVKuoi4tYnofxpjcR/eAHES/DnUkdGSH5t39L88//HP7zP3E6HYQ1MVxbVX1379FRlJ/6KZJbuFv3jh9Huu8+lPl57HQad2xs8/OqKLjj49hzc8hzcxCLoZ85Q+TwYWI/9EPon/oUdiKBOjODOTm5mTzqeYjz80i6jnnwIN6zn42gqlT/6I8Q77jDf34rFaS1dz0nIEibpi/muqbkZwJeq4VsGFjptE8W3q6w0vOQGg1cRUFwXeR2G2N8fOMY4HmwdmxxLYYv2DZKu41TLNL9zncu251+A/J5RF1HdBw8UdxMPl9/SrIcOlZLnQ5WqYSyw/jt9nq0PvlJnDvugDXStJfJIHzf95F4xSvI/dIvUR8cxH7f+3CrVajVwn5tJ5MQj+PcfDOpN7wBZRdHpe9FtD/+cYROxx8vDx0K3eiVahXRsnxxwLV5wFUURMvyBUfKZZw1N3m7v5/I9DROu03rttvI/PiP7/n4vfvvRzpzBrlcxioUtictC4IvttBsIlarWLfeulHNe/2mskzurW+l9id/gq4o2KdOEalUQgd32Ew+D+CKIp4o4qjqhW0VBddxkBwHT9fx1sYyq1RCv+ce3FYLIRYjcsMNqOsEIaI33ID31reiv/GNuNksYrmM1GqB4+DJMvbAAF4uhzs2RvxP/oT2O98ZztV7gbfOXdvbKQH5cEKWL/TtPczv4RpAFPcs/mB96Uso7TZKs4k5NrZ5zut0UGZmEE2/HkWybYRuF/HUKexkEqlYJLGwgH32LOLgIMLnPofQaiE1m76AuizD7bej9PcTefWrSb/qVSAIVH71V5FOn0Y9dw47mcQ8cGDDOTuWhbi4SPSBB3DOnsXRNOReD8m2ic3NYZfL2Ok0zuCg/z3PQ1hdRS0WUVotxHVC3nKvh2hZSN0uTrmMPTXlK/6u9Tdtbg631aJ1xx2kX/nKPbXbg0Eg7hmPx2k0GrsSLOr1OtFo9Kp1nwvI5XNzcyQSCYaGhjaQFBKJBIVCgTvvvJOVlRVyuRy9Xm9Hgqwsy6G4aXsP+aQrCcdxqFQqVKvVMKYRkM0Mw2B8fHxbEoYsywwMDLCwsEBfXx/VanXXGPI+NkOSpDCmsl48bTuYphk6V65/pzYMg1qtRrFY5NChQziOQ7vdptVqUalUQhJQIMoRvCMPDg4+6oQDRFGkUCiQzWaZn59ndXV1y3iB67qUSiVEUSSRSGBZFqqqXrVkukajga7roVPnTueZz+fDfFOpVLpk4lsQkxwYGKDT6TA5OYmiKNu2TyQSCV1EK5UKmqZhGAbT09OoqhoSkjOZTOh+HhA+c7kcCwsLlMtlTp48yVOe8hREUSSZTJJMJtF1nWg0ysrKCkNDQ4yOju7qVAqE57vekXY7eJ6HYRikUqkNMZC5uTlqtRqLi4thHGk9MpkM3W6XhYWFsM9dnN8LSNyO4+wak87lclQqlZDEnUqlwmc4cL3PZrOkUilEUcQwjHDOCWJSR48evWKCvb1ej263Szwe3zXvFvSjbre7pzbfCwJBhcvF9PQ0xWKR+fl5LMsik8lQKBQQBIFer0elUqFSqTA2NobneRw5cuRRN949HNB1nUajgeM4CIJANBql3W5TLpdDt27HccJ+u7S0RD6f3zbH90gi6MetVot2ux3m+ROJBIlE4iE99ujoKO12m+HhYZaWlmg0GmSzWTRNw3VdWq1WuBYeGRlheHj4Ic/tOY5DuVymVCoxOzvLysoKy8vLIdF2/XgfjUYZGRkJSbyaplEsFvfkEr7VcTudTihcH4vFLssl/FJQKpXodDqUSiXGxsa2vN+JRIKJiQmmp6epVqusrKxsm3s0DINTp05tIDVnMhlUVd2zQMlOCIje4Le9rus7vvtIkoSu66Eo9INtT8uyQgGWhYUFRFEMr891XRqNBo1GI5wnpqenORAW+Gy+llKptEEgqNfr0Wg0dhQgDr6r6zqFQmHb/lAsFmk0Griuu4l8vh65XI5ut0ulUiEej3Po0CHq9TqdTiecRzOZzIZagGq1SqVSYXBwkEQi8T2Z/9nHPvbxvYOr8y12H/vYxz7+i6DV8ok62xFZ/vM/fZLSxa6320IQ+IT2am5OfhK13UaybVzLwlMUrDXyueCtuZK7BoaoYYsqjiBjCwpdORU6xO6GZG+Fw41vkzOLFNURom4XD5Go2yHu2liCiiNI4IHkOT4B0/OwBYXjuady2+LTqb3D58FMTvqkpIUFnzy6VwLm+fM+IeipT/WJ1wBPnnkvhxvfZrxzCl2KcDZ2I4544UWn6HmkrAqD+iwxu4nyf+9Cl6Jk3R5ldYimWsAVJPA8BrvnGTbnUT2TtF3D7Z1nMXaYppInbVXRvB6KZ2EJKjI2OGCiYSUzxNbUtALagIuAxdbFQa4g4Qoismejujqit3Oh5/Hj/s9O7fIrv7L93z/0IZidhe93fYfmLYnxW6DdU8muXVHcrPDCM29nsnU/abNMqlxEme0xZUoYYpSMUWSq9V1OFT/Pbcd+i6naXahOj4jbZSm6+SUwIJ8HqKt99BsLpM0yufYM5eQhRFEkEokSiUS5884kd94J2azAy18OL3uZTz7aSVit2YTf/V2foFQo+C6iO7k7XkloTg/Rc7FFhXpdZsB1cRwRBUKi8nq4CAh4CPjJF0tQ/d89B9VdKyT1PJ5+/u958spHSZtlMmaJiHtBkaAtp0kbJa6vfoWPH/rfnOt7Gp/7nE/G3wmeIHH3wAvI68ukOg8w3DvPSnTK7xfrILsGo92ztIUEbSXLt4desmlf3/i6x6H+GrXf+A3kBx5AnZmhp6Y4GzuwgQQluSZ9+gLjnVOAx9nffhtfTr2F7UwhSiW4446dr2MTBIG7B1/IYG+arFWiX5+lGJnYUBxUKingeRTMRSJOj5XIJPf0PQ9XVHjgAd919Qd/ECJHjmBEo34h1uIiZk8i4nTx8AsfJVzAw0XAQwxJ6DIOKbvOq87+HwQ87h19WXjsrYhRl4LTp/1+/5u/Cddc43/mOPCZz2y9/fy8T0y/+ea97d91fVLfWj3Tjuh0fMGHi8nOAf7t33yH1ssln/c1T3GgeS/DvWmaSobVyGR4H00pylz0MGO9s9iCQtxpE3N7aOYScadFS86wGD1IR8nwmbGf5t6hF3Pgrh/DEDUOdY5jCBoKayq+a+OdjEXCtfA6IOLhSBJ6Notz8CDej/0YX8v86GWRzwN8/es+AV2QZSK/9yd86Gv/wNHEfxC1WyStGrLnF0etKuNIySjDT72evz7/O/TE7QOGn/mMT5ZdXYWTJ3cWdfniF+GGxz0O+58+ioSLi4AjyuhIxBx/TPGfaf+5diCcNTQMdCKYYoTlyORGsQrPY0CfRcClLadxBYmMVeHsnEQzIzE8ZCJJfht3uhK97oXvxp2mX0jsedTcHJonIAoeHrAwr/kOz6LEbPwaMmaJrFVCshxsQaGi9NNWMmTNIgmnRV3rxxMklpf8MazRkDaQz2XXIO40mdWO8UD2KQAInoNmtfAECUNO7Mm9aq/41Kd81/t02l8H/tmfwT33+P21vx+Kxa2/Z5q+s/P58zuTzx8pHD/uk7O3c1x/97t9gv1Tn7p17fCXv7yZfL4ergvf+pb/k0rBM57hk36Xl6/I6Ye4mHwewHF8kuvCws7f/5u/8e9jNBrhm8nn8IxmiYnuSTS7iyFvnYAVPJe8uUJDydFU+5jue/KGvz/wwObv2MKa+9Uua0bw11iu6EJMXGM274wTJy5vfWSLKp4g7umcACQcPAQscXNCotv154jRUX/OV1V4ylP8sW275/+OO3whl+3u4Z13biSgnz3rE9NXV+H++/d0ypvwiU/4z8TFQi+74ZvfhJe+FJ7+dDh0yJ+Lx8YEvvCFC9s8Yf7fefb8exnunSNhN2nIWZYjk7iIaG6XjFUmb66g2R1+5P7f4L03/jk97UJRR1/zFCOdM2StElWlfxP5fD3aSpaumSBjFrlh6VN8beTHcIU9hgsFgRPZp5AxVhnpnWO542y/UPAgZZZxBImmmud91/wfbEnjscufYKr5XSJOB1PUWIgf5Z7BFzKbvyUcf0slX4ToMY+5sLtGwxe+uFiQK0iQnT/v/2yH8+f97//pn/oiTffZNxHtfIuD7goRp40uby7Es0wBy/TfFdN2jYo6wHz86IZ5wnF8oYG/ul/j14Jkqef5A9luiyjP84tuAXldUikowNnJ0Xw9TNPcsXgpILRblrUnZwBRFNE0DcuywuK/nVCpVGi32ywuLpLL5TYl9FRVZWBggHg8HrrXLC8vb5ssBOjr6wsL1Fqt1o6FkuVymXg8jqZpuxYaiqIYurLvBUHRQ/DdfTw60Ww2NyScd7qXmqaRTCZDJ+PR0dGHXHggKIiORCI0m81dCz8ajQbRaBRJkvZU8NXX10c+n6darVKtVkMXk8AJISjO2wqWZbG4uBiSz3Vdx/M8VFUlGo2STCYZGRm5Kguz94J0Ok06nQ4d5QVBQJIkTpw4EY4twfMSOISDX6AWPBeBIn+v13tEicDBeYqieEljXDDmG4aBJEn0er0N7h1bodvtIopiWGz5cMAX75mmXC5TLpe3LOTJ5/PUajWWl5eRJImZmRmOHTu27T7r9Tqrq6vMz8/jeR4HDx7ccD35fB7DMFhcXAzd5VKp1Ia5dGFhgUqlQr1eZ2JiYoMQSlA4k0qlOHnyJAsLC2GhylZzduCelM1m6XQ6dLtdqtXqtv086LdXq3vW1YbAacq27T0V/QUF8YF4zeUgEolsGNOLxSKGYdDr9fZUQJfNZkMihWEYu/bN70VIa8VznqIgdrt4u6xlPcPwCZyRCJ4gIPV6OGsCUWK5jOA4iK6LFYttWNOHhHVRREgmsQYGkEslvIUFOt/8JvEnPnHDcYSREbxEAqlaxd6FJI7nIbVaCIaBpyho09NY2SzWOvKX43l4rRbq0hKebSPZNh4gz85iHziAsNXapdVCKZexhoZwDh8met11RK+7Du8lL6H9xS9ifuUr/kuUpiFOTBB/2ctQL6Nw86FE5OBBuq98JXzgA76D6qlTWNksXjaLJ0kIuo5UqSC325gDA3jDw8R+6Zce0nOyikXEr3wFaXEROxbD3Yr4JAiQz2OCf8/yeVqf/ORlE+mUvj7yb3kLVrFI+9Zbcefm/GBHOo32zGf6z982a7XuRz6C1GwiWpbvfL5dTFMQcIaGUE6exG616H74w0R+4zeIHD1K7zWvwXnPe2BmhsiZM1jJJG4qBZKE0O2i1Go+gXxqCvfIEdKvex3VP/xDpM98Bnl6GqnXw0omsdeIJ0K7jdLt4kQieKqKNTCAcOONPnn7c5/DrlY3ODlviVrNF2lYI7Cr5fJGcm+zibK4iLjW54V1ax/JtvGOH6fzkY9cEQK6eP31iJ6H4Hk4orhz3Nj1hYnxPJx4nO4XvkD6h394y01bn/0s9jvegVCvI5XLiGvv/56q4pw9S+cDH8B9+cvJ/eIv4r3ylbQ+8xmsr37VT3yqKsL4OPGXvQztEXaqfSThnjiB3Gj4ogdrz4fnOIi2jeg4OBeNza4kITqO7x6/9pmQSPji/q0WblB4sEfoX/wiQrvtk90LhZ1FUgQBK5tFqlbxjh/HaTS2dVsXFIXc7/wO5k/+JO0PfQjz3/+d2P33I+t6+HxJ6wjS7tr85QkCViSyccwSBFxFQe52QVVxWy2qv/mbiF//uj9n2jaeJNFVVVqPeQyRV7+a+JP9WHXsMY8h8oEP0LrjDuzbbsMLVGEFAef661Ff+lLSz342gizTyefxIhFfeGK3eREQOx1cVcVTlIfF/XwvUA8exNA0X/iiXoeBgR23F+p1X5BCllGmpvZ0DOtrX0Ou1fxY4EX33zMMtOlpRF1HNk1cScKWZd/VPhLBzmaRKxWoVpHPnMFTFDxRRG618CQJT5J8MQ5BwF5exi0WqU5PE3vFK5Duugt5fh47mcSdmNhyXhN1HRwH1TDwOh3cNUEfybb9cdaykFotrMlJhHYbdWUFqddDcl08/OfQ1jT/+bQsRNP011fnzmEdOoSgKAiplD+GNps4J09ewt3ZGwK3yQABkTybzdJsNllcXNxRSC8g2g0NDYUEiKsJgUhepVJBVdVN5PMAkiTR39/PwsJCSAreKa4KF8RN9+oUuBfour7JtXT9u+3q6mooVhqIJQIsLy/TarWQZXlbAk+ARCKBLMshqWqfgH7pSKVSJBIJVlZWQlG+7RAQggYGBja5IJfLZXRdp9frcfDgQSRJCt2Us9lsGKcK8h6Ba3a1Wt1V+PFqxODgICMjIzSbTY4fP87MzAz5fB5N0/A8j16vR7PZRBCEkMwtSRKpVOohcb+9Emg2m6EIxW4u14IgkE6nw+8ELr+XAtM0MQwjjM3uhsDtdb0ApWma2LZNMpncQLxzXTd85tLpdOicns/naTabG565SCTCyMgIvV6PdDq957E/k8lQr9eZnZ3FsqxN4nqu64YOrO12G8dxSCaT4bGDsS8QP91u/IrFYgwPD7OwsBASyNfn37rdLt1uN3R93QkXk7h7vR6lUikk/01MTGzYRyDGkk6nWVhYQFEU5ufnOXqFBOGCON1enx1JknAc56qIg9ZqNSqVCnNzc8iyvKntAqfu5eVl5ubmkCSJYrH4oAjv32toNpusrKzQarXQdT0kxJbLZXq9Xuj0HBBebdtmdXWVRCIRzjeHDx++KvO2gcDFwwlVVTly5AhnzpwhGo2GTuNBuwbE80QiwfDwMENDQw/p+XQ6Hc6ePRuKMdx///2USiUEQQiFBCqVSnhOAXK5HM1mk1qttqXY0k4wDIPV1VUqlQq2bYf5PlmWSafT9Pf37zq/XQ48z6NcLlOv14nFYjuupRRFIZfLUavVyOfz24oPz83NUa/XWVpaCvO76xEIlAQ5q60ESnZCIEAUi8WIRCKhGcF233ddl263y+DgINFo9EGLcM/Pz1Ov15mfnyeXy21y/s5msxQKBb7xjW8gyzKnT59GURQGBgbCedrzPDqdDqurq8iyTH9/P4ODgwiCQKvVolwu0263d7wf1WoVRVFCEa+LYVnWBrOG3cabXC7H3Nxc6C4/tRYnUFWVarVKqVQK8zuGYaCqKiMjI2SzWQ4ePLirUO8+9rGPfTyS2Ceg72Mf+9jHI4Rbb41w++3ijrlp04RXv/rS9num/5l8dPVHeYn4DySdBmLPxNFdNPzCKBm/WMUQIxhihGV1jBFjlrnYYU7knrLn40zU7kJ1dSJuh6XoJGVBZrR71nci9cw1krsf6HEEEV3QUDybtpzm24MvAUEI3YvvussnRV8OvvIV/wdAtns8rngHBX0BS1BYjB7a7NwsCD7J3BM40v0uDjKqZ3A6+ZiNxAxBoBibIOXUSdl14m4b25KJ2G26cgpL0jA9Dc3zHVojno4lKvRsjfPFYY4Yq6S9tQKBNSqxJe5UeOT/4woSbfWhDTKfOOH/21HSWKIakt4vJhhfjIjTwRJVbEHmJaf+mAOtexntnsVDoKL0sSAnQfaI203y5jI5axXZsxFOvoWl+BFkzy9qtnYgwAQwxAiSZwMeEau5xRZ+x6nV4FWVOe8AAQAASURBVJ/+yf/ZC1wX7rtvb9teSehSHEeQUDyT1ZKC68Tpd33lCWGdWzYA3hrJFSG8J6pn4CHiCRLmGoHtibP/ylOWP8xo9yya26OuFFiWp7BFGc3VSZtlxnpniTtNXnHmj3m//Ics8FhWV3c/32+P/hBT9bsRPYex3hkOtr9LXc7TkxIIuCTsBim7TkdKsBQ7xJ2DL2cpe1P4/ahR4cblT9L3m5/m7l+e50DnuE/kESMsRPuxlY2BekdUWYlOQW+God4Mxkqcf3jzeUhsT0K5HNw3+HyuL30e2TUZ654h1ulQVfvpyH4SI2q3yJlFNFdnMXqA5fgBvj36Q+H33/UuuPVWgBiPr7+AJ3brHGoVURHXyOfeGqEOHCSC51TGwUZCwEX2LMa6Z/mB2b9nPvsYqvHJK3Z99Tq84Q3+/0dHdydKvulNPpHTtn2XWUXZmhgKMDd3aSSL7cjn4BOiL3aAvhQ8duV2YlYD1TWYjR3dVORlyAnOx68jaxbJmauk7RpdKU5ZG2I5doDj2adx9/BLKKX8ZMhi7CCPrX4R1TPRPAMLGUOMYOGiuL4LOGtSAg4SLTHDnZlXcnf+NSx/98m7tvNu+Oxn/bn+W9+Cd75Thv7X8dnsj3PtyqeZatxN1G5jihFWEoe4d/AFNGdG2ItuyF5du+++Gz6y0OO5coqE08BGJu6018RpJFTPAYRQECOg5ntr7SF5DroYwRV8lwoBl7jVIGeuonoG87HDrETGyZklknaDtFmi1hii3ZZRZH+fgfM5gOLoJJwWlqDiCeB5Ag+ciKFpLq4rbHA29tbce2vaIAdb99KREtybewa3H/41fuTE7zLZvp8BfYEZKYErKptcmAXPZag3Q0+Mo8sJlpNH+MGTb+Xa2tdQXP8h7kkJvlt4FvcOv4habHxvjboDHAde8xqfjF2tbvzbduTzAO9854M+/EOK3Ui873iH/zM4GJrNAH4N7V7mpgDNJtx22+Wd44PBTuI/ARwH/vAP/TEwM/xinlC8nYaSZ7R3hqXoAXoXEVpF12JIn0bybCqRYe7qf/6eiL/LsQMMdqfJm8uUvC3crNZBdg2spsUS/XzjOzdwzy/svO/LHdOK0Um6UpKsWUT07B2vQ/BcknadkjZCMbp1v7pYcOnrX9/5+J///M5/v/NO+Imf8NeC60xNHzQulXy+Hl/+sv/zT/8E2ewFAYdsd45nzf8Lw73zaI7OucR12OKFQrguaWrqIHljidHeOVxR4pnT/8Adx94QbjNevxfN6aC4BnV1d3XculJgwFggadXIdOaoXsIa7J6hF3FT5QuYRoQhfZql6CG8i9/BANXu0mcuUVaHKEXGWMg+FgSBO9LX7ek4b3yjv74B/5F/sOI94JPXv/AFXxjkXxZfxGvk/0dbTlMwlliQjmzbtzJmEdFzaKoF7hl8wZbbnGkO01L7iUkziK7rFyDvQBISLAsRsBQF89prSa9L1qVSKVKpFJVKZVeykWVZdDod+vr6tk2cBkk7QRC2LCQ0TZNutxsWCsVisfD/e0nel0ol6vV6mODbDolEgkwmQ6VSIZVKbVmYEyCfz4dK9ouLi4yMjGxK2AcOsZ1Oh4mJCXK53K4OIrFYjHg8Hhb+7EZAazabJBKJq9qdZB+7I3AIdhxnTwUGQVGV67oYhnHFXD22g6ZppFIpstksq6urZLPZbfuGaZqXVRAduPNcSjGuYRicPn06LIbrdDpIkoQoiliWFRLYO50OBw4c2FOx4NWKoBAlQEAEFgSBRqNBrVbbUMgeEJGz2SzRaDQkfT+SBXABKT4Wi9FqtXYt4G2323ieRzQaJZfLYZommUxmT8W/1Wo1dBR4uIqparVaWDAaOAJshWw2GxakJZNJOp3OtgIJgXuBaZocOHBgy3Fe0zTGx8c5f/48zWaTYrEYPuvrSeJ9fX0byOfrERR4tlotFEUJRSAuRlD4YxhGWFjb6XS2bRNd10mn0/vz0x6xXlBmryINgVPKlSpmtG17kyP6TohEIuHzEoy7/9WgDg/TOnQIYXUVuVLBGhzc8X1YLJdxIhGcXA4vEkE+d85/8crlkBoNBNv2iYbr97HmMGprGnbCFyV083mE1VVoNNA//elNBPTIS16C+bnP+WSvchlvp8LNet13XRcEBNPEHBzEGx7eSFQUBIRUCjMeRzl1yneQFUVE0/Rd0/v68LJZn4je6yGWyyj1OlY+jzc4SOR1r7uwK1km+ZznwHOec4mt/cgg94u/SLXXw779doRKBalSQVpTrPYEATuVwjh4EK9QQP393yd63d7eJy8X7Vtv3eyovO3J5/CKRYRyGecTn4AH6eSq9PeT/ZmfuaTvCMePIzYa/rMry74Y2jbnLKgqdiyG2GjgBklDIPszP0MNcP71X3GaTf/5WllB8DxcRcHq78fN5XCPHiX9trfR+cpXkD73Ob9/CQL6sWMbnHS9/n50w0CZmUFqtfAGBpBe9jKSz3kOld/7PfjkJ1HPncObm9vkLo/nQa3miwD39+PF4yBJUC5fIK3X62hzc4im6ZMtIRR2ExzHJxj3eii33UbtwIFLbtOLoR46hKsoPkHTtv136q3mBc9DMgyf5Oy6uPE43jZq9M3bb8d961sRFxZQqlU8WcZJJHzxyV6PyOnT/j39wAeo1mrkfu/3SD3/+du6Zv+XRa8HgevzGnYUN1hzSBfWx0UEwRe8cF28nZJcW8Cr1xFME1eWdyVcA3jRKMJaMNCuVrcloAdQR0fJ/fIvwy//Mrgu5b/4C7j1VtTTp9HKZZS18/UEAVtR8BRl6+L8tXWP4HkoJ0/C0hJSueyT0tfay4lGoVzGuuceaq95Ddmf/VkAxEiE9MtfDi9/OZ5t4+o6Ujy+aZyJPu95GB/7mO80v8u86FkW8poDuPukJyFcJWvZ6PXX052YQFhZQalWMfv6thaAwRc6UKpV7L4+3CNHiBw+vLeDNJtgmn688KI2lJaWEE0T2TSx18j5omn65G/AGxzE7O8n8p3vIBsGeB5GOo0xOYmQTPrPsusi1Gooq6uI3S72bbfRvOce38TCMLCmpjY/I56HfP6871xu277YhqJg5PNIuo7U7aIYBlqng22aiCdPIrgusmn6Yx1+ZtxZewYBbEVBtCzktfWPu7yMOz7ur7HW+tuODgOXibm5uS0/7+/vp1qtkkqlmJubY2hoiEQisaEt2u02y8vLJBIJ0uk0A7sIEDwSaDabIWExIFdsB0VRSCaTNBoNOp3OjjFYuCBueiUEGJvNJqurq+H5BsJ3kUiEeDxOf38/3W6X5eXlkGweiBx6nkelUqFYLFIoFFhYWNgyJrwelyKiuo/NCEQZ0uk0xWIxFLzcCgFxLJVKbXJoDNzOo9Fo+K5bKBTCuOF6Qcd4PM7i4iKdTmfHmMfVDFmWueWWW0Jh3kajsSEmqCgK6XQ6dL01TZNUKoWmaQ+atPVQwXEcHMdB2W49cREURQnva9DPHy44jkO1WsV1XWzb3nLMXi/amEgkQrJ8QCK7eH/Bd/Z6HX19fWGfWVpaYmxsDFEUMU2TWq1GvV4Pz295eZlUKhXmmoCQZKvrOiMjIzseK8hP1Wo1SqXShhyc53mhy/JuCFyBg7ij4zi02210XQ+FI7Y7fj6fD53me73etjHQS0FALA6ED3aDruvEYrGrIg5aKpVC1+7Jyckt204QBIaGhpiZmaFarRKPxxkYGNgzmfZ7GaVSibm5uVCoxDCMkHzeaDTodrskk0mmpqY4cOBA2C8D0YSZmRk8z0NRlB2Fzv+rIRKJcO2111Iul0kkEvT394c5rsAhu7+//yEXk+52u5w+fZpKpcLq6iq2bROLxdA0jVwuh2EYLCwshO7d4+PjG3Kw2WyWlZUVBgYGdhXHCdBsNjl37hztdptqtRrmvsBvl2w2G+ZVdxtzLxW6rmNZVrhG3w2ZTIZSqRSKNV2cf9Z1PcxDZTKZTeTzAIGowHYCJTshHo8TiUTI5XJh3jeYX7aaB1dXV3FdN3yGHgxROhCSCfJ3251zIpHg5ptv5lvf+ha5XC4klAf52ECEJnh3GxgYoL+/H8dxQsOExcVFxsbGtszx1+t1KpUKo6OjJBKJLbcJBLIMw9hT2wb7MAwD0zQZGhoKxecD0elgvItEIsRiMZLJJGNjY1dkXt3HPvaxj4cSj/wKfB/72Mc+/ovCMB66IMJHr3sz19a+ztHWPUQ8HRt5LW8loAtRbEHBEDVWtAkK5jK6GKUnp7hn6MV7PkbE7iC5AaE4AoLAXPwoKatKxiwRdTsX7L8F0MUoCbvJSmQCxb3ySSSAo8XPE7ebpOw6C9GDm8nn6+CTIgQ0z6BHdEtXQFeQKWnDSJ5DyqmTcFqMdM8xk7iGFXWMcecMqmeSdJvYyFiCQlkZQHItZMfcUDRkiJEtiRj+yXjI2DjIGGKUc31Pe3ANsUecyn8fjy/egatLJK0KDXX7lyPRs0lbNRajU3SlBBPtE4x1z9CS0xvchwF0OUlVG2S4d56x7mkcQcL18MmJgIiLy84BT8lz1kjZArb06C+mm0leT78+x6A+S9Ebo0mGgrCAK0iorokhXSAVSTihW3pX8hNoaatEU8nSkxIUE4dJ6EWevvTvDPemkT2L6fi1OOscRHuiQk9O0rDzjHXPYgkazz3/t/zzze/ck5OvKyp85No/4CUn34LV0EiaVTJWmZRdxwN0KcZ87DBdOcXXhl7JV6f+e/jdI8Uv8KJzf07MapIxi/SZS8ScLhI2lqcw2juPbq4wHzu84ZwRBEqREQ617yNqN3ns8if43OFffPCNvw6OpPHha3+fV554I46gkDVX6TcWkfRZ/7oFiYacYzk2xXLsAB+49i0YyoUkpmleIOZ1ki/n8eb7sAWZiNfFQUbEWyOf++NL4HzuAS4ijqAieC5xp0XSqvKYpdv4/OH/dUWvMcBeCYTNNX2HPRpqXhWYaN5H0q7RktO42wh7uKJCJTJCRRtmqn0fhhjlTOIm3n3z32EqFwKSEbPOWPvk2j3yxxxB8BA91x+7xAiua6Jg+c++GOXO7PN459G/8Q3BHyT5PMDFdW+GkuSesVdyz9iDK1TcK6LVRXQpTlPOAP4YrHgmoudioiIIri+z4IEuRhBxET0HU4wwFz1I2q4x0T1NMPEHfWlJO8Bc4lo+eO0f8urjv0nKLDNgLKBLcXqkMJyN86LoWgz3ztOWkkTdLl0xRsxpUWdgA0n9YoiuhezZNNQ+zqcfRyl1jC+P/Aix2Xcy2j3NVOcBytogLSXvC3t4Hgm7RsFYRsRlPnYYC4lXnH0bcatOxiqjuno43mWNFW5Z/QRfHv5vfH3y/7sijugXk8+vFkiOweHSlzlc/TpRu4ktqpRiE3x38AU0YqNX5BgrK1dkN1ctgtrEZnSIT038HM+f+VsGenOMd0/RE+O05TSuIBF1OiTtGqYYYT5+lDPpx3PXxN6Up74z+HwONL9Dn7FI2irtuIbLG8voYoyWm+IrsRdRv0Lj1sW4d/D5PLb8WWxDJWesUI5s/7ykrDICHk0lz70DD1+xbEDwvhqx/txuWvoEUbtFwq5zPr6RfB5CEKhERlBdnYK+xHXVr/JF8+foqX6xiuZ0kFwbD3HbuXI9bFFbE38Czdpbcj/AauoajueehuwajHdOMdF5gIo6SFvJ4gkikmuSMYvkzCItJUtVG+LzE//9ssbSByv6shU+8AEYGoJSL8V3Cs8hZreY6DzASO8sK5GJDetVwXPJGKv0m0ssRyaoaoOc7fu+LffbjRQ4lXky2eQDRBoN33VNFMPiyw3wPGTD8N2BIhG0dYQR8IuzlpeXiUajrK6uMjY2tmVRgud5rK6uEolEiEajmwq/wutYl8hqtVohAbXT6VCpVMIisMCRxnXdkFy4mxNzoDRfr9f3VDyVyWQ4f/487XablZWVMPl2MSRJYmpqKiTMLy4uoqpqqOAdFFyKosj4+DiZTIaxsbFdj5/L5VhYWCCZTIbuxtsVfLTb7dChdLu23cejA67r4rrunovIRFEMixOupPvTThgYGKDRaNBsNpmdnWVkZGRTwrnb7bK0tEQ8HieVSu25mOBy4DgOZ86coVqtsrCwQCKRYHJyMjwny7JCp+kgCX/06NGHnKz/cEFRFERR5OzZs2SzWdrtduiyE4ypnU6HRqNBLpcLC+AeSYV6QRAoFAq0221mZmbQdX3bMTwo6k4mk2HhT1B0WKvVWFlZ2bZYJiiOGRoaolAoPGxFc0EBWuBYsROy2WxYBByM9RcjKPAJ3Lh2Kl6UZXkDOT8QhwmKmYJCl90QiUTo9Xq0Wq2w+Gs9UqkUrVaLZrO5wYFuq20DEkEmk7lqi5evNgRuL4lEgkajsevc3uv1sCyLeDx+xQrzgoK/9SIXOyGYu4Cr0tHnwaLbhdOnoVKBm26CQsF/T7r/fiiXfb6cKMLBG17G6IlTCMsV9LMrCOPDaNoWBMNWC6VSwRwdxX76M+idXMTQmmhzJapGlr6eh2SDiYptKKiqDZ6HqFu4iLiyjLu2ThYkCVfTEHQdymU8Dx54wD/fb30LJPExvFw6ykCyQXJ5Dl3QqAqDNJsyquaSSjooiovXaJEpL+GKEoLr+G6vQ0PYjkC7JSFKEIs5yJIXHtceHESdn8cSFDpuAiyX+Owq0twyQRzTS0Qwxsbw+vtxf+UNnPEeT/NzcMMN0N/v8wmPH/fbUZKg04F22+cnx2Lw+Mf7QonBdmfPbuSBRaNw+DBMTPj349vf9r93ww3+vdqqLv38eZid9cVPH/vYC/zcWg3uvdePi6n1RabadzGUatAw4kyrN5C64Qg3/eobMJ70JIwPfQjvu9/Fsm30LpiuSldMsnL02Zy/4b8x1hzmiY1NprEhajVfNK5a9Y1rb7rJ56cGz5QoXviZmIDrrgNV9dthYQG++U3Q3j3HTQs9JFnb4LitGyLdjoSi+PfKNH3RzJSUJ1JtI5w7H5K/PQ9mZuDMGV+U8ZprNorA9nq+aPTKClx/PYyP++23sADxONx8M+i6L8xnGP42F7/qrKzAPffAoZN1hio1ZNtEaHSwbQlTiuDkM2gjaUTZv1muJ9Dtiih2FEV3sVo6d97p7//IEYHhn/1Zzh18OtX3fZyB418gqXQQBOjpIuWB67Gf92JufO33I6ky1kc+glwqIdg25uHDUG9gV7qYPQFJATWrIvTnMCYPIp04Q/lUB/2vbuXw9z2HzOtfT+PsWUzPQ5mZQT5xAjuVwo1EEGwHai0E06ZTGEAaHmL5+19D7AP/SkFQoNHEjqVQpxfBNBEdE0dV/b7rSniA5Bl4nonguEjFIs5730tt8jAPxJ5Bq+WLYsZifj85etQXS+90/BjBem2DuTm/v/d6YNwX5THKJBM0cAQRqWtgS4pPOpb8nBCmjexYIIKtRRBMm6YRw6hrXDxLG7OzuH/6p4hzc8iNBuboKGQy6IZEryei5jwUu4M8u4B48jye+xka111H+lWv2rCf+Xk4dQr6+vxzl2Vf7PT++/3x9NgxcE7fT+nfbqN1ahVcm9R4mpXD30/nmqdz7DqZ++/3Bd+HhvzviKLfJ44ehYMHt+bZPxgUi/CZz/hjSq8H3/d9/vM+MOALQf/nf8K118Itt8ATnuDPD3ff7d+LQ4f8vgT+WNPfDwOxOJ4k4XQdalUZRfOIaRKOKyJ4Ep4NriwgCmtzRtBH112Y57oIpkXLiLKwnKP4ACwu+nm0dhsSCRgehsc9bmM/tm0o1iIoVY1UW2ZhWiMSBb0nomkumuaiah7xmIMo+se3dA8siU5XQpP2JiTmOH5fbzZFRl/4K9w/8SssvPMjvODeNzNeug9XEpEd24+5XbSu8DzAdJBNG08QEW0bqd1GrtexMhn0wUEETcOzLJyVOurcCk65ifDe99KIx0n9yI9y//1+fxgYgBtukNEuKv53HF/o8T/+43pe6VzLsXwZbXUZM5GANcKobojoPRFZ8VAkG+n8IiYaHSXHmbFXkbp7+7nl4uv59rdhaQlKJX+euuEGX+TzYtRq/jgrSf42jQZ84xu+YH0m448DN9/s///8eWi1QNcFBsdeybUPTJOo1eD0DNX8QTxRJhJ1iUZcerqI0XbIVWbwRAk3n0d+2cv2dC8BepaCpCtEDXBdIXw2ME2UVgvRNLFFBUuIILoeggueJ2JaMqWygqR36XNlVKeLK0m40SgkU9SbMrWqQjptk8kW6EQyKGfPoZ9awfvuEm00NCeL2YmRli88kwBUKkjdLpKu4ypKGAsRLQvr0CGYnsZTFJ+MbtuIa2RDATCjUd+tHTYOGILgO8m7LpJp4jUaGI4TCnnYsux3rocJ8Xic8fFxPM+jWCyyuLgYOv4JghDGRTOZDAMDAyEJ4WpDQGRwXXdXIbhYLBY6r1qWhWma28YrAnHTQqHwoAXmisVi6GgYiMgFUBSFbDbL4uIivV4PwzCwbXsT6ajZbIbii5U1IZedHAED4us+oe/yIIoiAwMDGIbB7Owss7OzDAwMbHgHNk2TSqVCo9EIXekvFvgMyMsXx1YEQdiyPymKEgoUPFqRSCS45ZZbSCQSnDhxAtM0SSaTxONxFEVBVdWQIKVpGgMDAwwPD1+17/aB+N5e78l60vnlXJOqqkQiEcrlcpiX2gndbhdN08KYrWVZ9Ho9UqnUpucuEBOWJCkchyKRCKZpbhiXArTb7fA7u+XDAkQiEQqFArZtMzMzw8zMDKqq0mq1MAwjdGlvNpuhyIZhGJw4cYKDBw+i6zrdbpdIJLIngcJUKhWSxddDlmUURdlRzMGyrDCuGrhxS5LEwsJCGB/ebQwNYq2dTodqtXpFCJyZTIZ0Oh2S8Xci3wV5yECU95GEYRgbYso7PbsB6Xd1dZW+vj5ardZD4sD8aEK9Xmdubo7FxUXa7Ta5XI7xcd+8wLZtFhYWKBaLNJtNLMuiWCwyOTlJNpslHo8zNjbG7Owsx48fDwXKAxJnPp+/ImI6j2ZIkhSScdc7y0cikYetbQLRhdXV1TDHNDs7G46bkiTR6XRCYRtZljl48GA4DkWjUVzXxXGcbYV816PT6XDu3DmKxSKlUolkMsno6CiapuE4Do1Gg9XV1VAsRhTFK+oAH8ybruvuKT8oyzKCIIR1GRejUqmEa+Xd6h4CgZJ6vb5JoGQnxONxYrEYw8PDnDx5MhRVWVxcJJVKEYvFEEURwzCoVCosLy9z+PBhRkdHw/56uQjG/F6vtythP5vNMjU1FQoRjIyM0Ol08DyPZDJJMpkkEokwODgYitFIksTk5GSYW5udnQ1z6rIsh+8ZAUE8k8kwMTHxoK5pJySTSa699tqwJme9OHE+n98nnu9jH/t41GCfgL6PfexjH48QUqmHzgXHE2U+cPSNvPLMHzPZPk7SaWAIGroYw5CiNOUcIg4DxjwiLnOxo9ybf9YlOd3ZknaBUOzZuIICgkhTLdBUC8iugeKaePiO15rTI+KewRPlh4xQPNw+Tcxq4AoiXXnnIE3CqeMiIuAi4G3rAF5TB5E8B7WnI2OTcmocan8XAMmz8RDoCTEEXCQc+s0lMP0iIsNVUT0TBxHRc3xe3hZxuojbQ8SlI0W5N/cMdOXhcYlayD6WcmSErL5Cn7FIT0piSlu8yHgug71pLFGlI2eIOB2y5iqWoG4in4dfESSWogc50L6ftFlmqHsOQ4phCwoJq0pzFyfGlFWlI6WwRYVq7KF7sXu4cM/Qi7imfif9+jwFfZFSdJyOlERxTTRPx/IUXEFC8EBzdWxBxhFkGmqBmNUgYTeZjR/jO4Xvx5E0bpy7nYjdIWHXmY5dt5HIvQ49OUVRGyZvrtCvzzFc/+4Gp3IA2e4RsVvYouo/e2v305ajfOS6P2S8+i0eu3I7R+vfWvM99vv0fflncM/QiyknD4X7Olz8Ii878zb6e7PkrCJdKYEjyDiCCJ6E6pmonoGIw0TnFDPxYxtIUY6o0paTJK0a481drHQvEz01y/uvfxs3Ld/G41dvJ2usILu+YIQtKDTVAnf3PY+7R1+5gah8MWqxce7NPYOx3hk0R0fCQcDDRUQK6cweHiIOIqao4SAT9brYgkLWWOHGyhf40oGfw/keEFl4OKE5XSTPxhL2UJQjCPSkBJ4gYcvRTff0+pVPEXfaeAhUlD5U10T2LBTPXHP+BltU6AkxNNegLSWJux0iVuNhG6sfDsiu5c9DcoqV6CRpo0TariKHQjMqdTlHT04i4JE2SyiuxUL8MPf0PY/rq18m4nRQHANPEDDFiN+X+n+Qu0ZfhS1H+czUz9P3wAKKZzDePUNDzlFXC5hSFNGzSZlVslYJW1BZih303Xj1eca7p5FdY0uhmABZs4gpavTkFA8MPBuA74y+nJRZ4inLHyFnLFMwlhnQ53EEBdGzAYGGkqOiDSN6FlGnw0jvPJrboynnqKgDCHgk7AYT3ZO0pTTPXHwfsmfx1QMPzinnqoTn8ZiFj/J9S/+PhFUladWQXRMXkZ6c5CnLH+VM+nF86tAv0Ynsk+62Q6q3xE3Lt3ND+YskLV9lQHJMTFFjLnqEhNMgYTcQ8LBEjYXoIbpKmuO5p3LHkf+9rWt4f/Mkj1m+nZH2KVRXxxCjxK0GFaWPQX0eD4GmUtiwJhM8l7y+SNqqMhc7zJn046jHdidjXi5WU9ewGD9M3Kox2juHLSjU1f5NTnJJq8qgPs9KZJyaNsB04UkP2Tk9GiF6NjeVP0/GLNKW01hbrc3XoaoOMtl9gIjT4ZrVz3H32A8BYEkRf225Jhiy1XvOekieFW5jy3srqAghCNxx5PVodgdPEMnpywwac4j6NC4iIi6GGKUYGaOu9vPJqV9g5iq773/xF/6/Xzrwcwx0zwMew93zHGp/l7acwhCiSNgk7TogsBSZpBQd48PH3rjtOhzgNufl3DDyRdTWvaEbkOs4OLIMkr9iFG0b2TAQXBcrFqN3/fUUnve8DfsJkk6maTI7O8vc3BwDAwMbil90XadYLKLrOhMTE2Sz2R1dMQuFAq1WKyTkGYZBsVgMiW4BgRQIFaRXV1c5duzYjm0ZuFxYlrVrksw0TRqNBvPz82GBTbFYJB6P09fXRzab3VCslE6nOXjwIKIokslkqNfrtNttXNdFlmUGBwdJpVIkk8kdHRrWI3CB1nWdmZkZFhcXGRwc3FSk1Gq1Qgf2ICm+j0cvZFlGlmU8zwtdnXaCaZrhM/FwFWWkUimGh4fxPI/l5WVmZmaIRqPEYjE8zwtd3NPpNENDQ4yOjj6kztPBWLGwsLClc7qiKKFbwvz8PJFIhJWVlU2OE67rXrWFlTshcAqrVCpUq1UikQipVApVVcP7USwW0TSNbrdLr9fjlltuecSJwEGhSCqVYn5+ntHR0U1js+u6rKysYBhGSCIXRZG+vj6q1SpjY2PhOJ3JZMJC4cDpW9d1RkdHH3ZXuF6vR7fb3eRUtxUCMkHQb7ZCt9vFtm0Mw9hT0WQqlaJSqWDbdlj0alkWlmWFRao7IXB8q9freJ63ZWF2JpOhXC4Ti8VCV6GAsLwe692TVFUll8vtev4PFwKX+lqttqHArVAokMvlHvEiwEKhQLPZDAtvdxIOCNxiNE3b1mnkUhGNRolEIoiiuKei02azGRJjHgn383/7t3+juqaml06nURSFH/uxH7si+z5zBv74j30CWYB83ieCXtxtJfcH+fGFO5gSYLR8jm59kXquQP8B35nNM03EUsl3Is3lcPL9vF96HauN87zA/GtGzdPEZ8/hOQ6uK+CJAo4Npi2guSYCLm0xQc9Oo6gpVDbm8WxP5Ld+yydqX4DAPL/Bazr/m35kBqfniYstLLWA1dFoVSzSVoW402JZGaTgrKLEZNxshkpNZWVFDcWcJcljeMQklbTxHAdB1xHbvrOpIMkgCFiuiCVGaCo56kofiqoy8tybWH36T/DnH7mWNcNwAJ77XJ+YevLkzvfgpS/1iecbr2tnfPjDPhH6DW/wSdPgkzDf+lafLB1gbAx+93d90uTf/R0MVr/LkxY/yOHGt2m7Ouc9GxcJTYpwNn6UT93wMn7k/z6TA3/1TJqnZ/nQ22d44F6bnpxkKX0DdjsKdwJ3wl/9Ffzsz8KLL9LW/vSn4V3v2vz87IRUCn7/9+FLX4KPfcz/7BVLDq2GhOfE0EsqfQWThQWNZnPrcd4xYqStHstnokTuNjhyfYR3vAPuvPPCNlNTfpsND/vE2be85dLFGp/6VHjd6/xzfv/74YPvM3ju6T/jxtnjiJaJ4NmIAiiAKJjYizr2chnhwABks8zNaei6yGjHoyHFue+uAu/5o4uPchT4daT8LzGklUlGTKZrOXQhDZ+BidPwiy89Rd+ZM8jVKq4koZw8jdkWUT0LDQ+vB3ZLxlus0RCz2EI/8WaF1a+c4WdfOM9b/nmMob/6Kxq/9Vu+0kG9jlSrIdaatLsqDW+QhtZHq5fjY/YbmP/mzfz36tc4ZpVInq/TlqDP9NAcm46UBE/E6Yl+KtbziLsuuhCjJaaJNE3s+Spf/oXb+Jdjz9i1jW++GX791+HP/swnqgaImmMMmv2kxBxRp4srSii2iWL1wm08QUQXIliuRteME3F7LFYzfOjjBzhW8MVog+m888EPIjYaKLUaxtQUbiLF/JxGu71+jowgCmkmWydo3d9Cfc/HSP/QD4Eg4Djw9rfD1752YevJSZ+I/q1v+b/n2ud52fyfc4QTGMUuEbuDgEdd1LCUu+hq/8zbx3+K02tx9Xvu2dwex475/fhKvHJ4Hvzrv8IHP7jx83//d/9nPZaX4XOf27yPrT57zOLTeHH5HiY7VWrzBj05hUeEMStLxnPRbJ1eV0ZVXRTZJ846moazjlTXnmmidiPMSv28//TTmXvD1tcwOgq/9Vv+v5UKvOlNkLrzGC/Ws6Scebxml2rXn9e73QvvP7LiMTpiUK/LxJd0HLLMzOX5198Z4DU/Cc961vZ6jSdO+PGr5eWNnxfMG3mcNUBKXEJ1dCTRJWJ2wbJwZdkXQ7RBsBxEz8MQVCKegekqiLqJcfQwxOO+a7UjMLOSQu/1IcsGE7VTWPetov7dP/OX976Ibz1wYa0yPAxvfKP/L/ik7te85sJ5vUd8HT9WXuBwxCY9PY2ZybJojVLrRRBwSVo18uYKrudyLn4t3208i9tuvwk+6c8pv/7rF+aWi1GpwO/8ji8OcDF+6qfg5S+/8PtnP+vPFbtpyd166+bPBO/FvKR2D9fbKqPdMyTqp2jIOUpyEgGI2U3SVpWOqFHtO0z/k5/MwAtfuPOB8PvAP/8zVO+8jme17mGiW2b2tMDwpEg04kCthme7YENXjOKaEngQdz0sQaVlxymuqgz0lnBMAdcTcD0RudXm+PFIGOPtdlWWl/14R9ybZKQ+jS3IaIJNT4qxuqSxtARTB3RiUT8fKlcqIanc1TREw0DwPATHAU3DOnoUqlXkahW1UkFa29ZWVYyhIexmE6XTQbQs3PWxFkHAURQUXUdwHMRKxRd/EARIp1Gf9PDGivv6+pAkCUVRyOfzNBoNDMPA8zyy2SzpdBpN0xgbG7tia/8rjUDECtgTUXB5eRlZlimXyzu6gwbiprFY7LIEOIP324DYGDhbZ7NZRkZGUFUV27ap1+shGQn8+PfNN9+86b04eH90XZd6vU48HqdarW4ZfwgIqP39/fvkjQeBwcFBej1/XbOyssL8/DyyLIfErV6vRyQSCUVg15PEAgSE1u1iMBfDNM3wO49mBOIW+XyexcXFDcQi8OOnuVyOfD7P0NDQQyom+mARiO8FApW7CXE0Go2Q+HU59zGXy1EsFpFlmXq9vuPYaxgGnU6HqampcLtIJIJhGFuKx2qahqqqxGKx0F07EolsGbtzHIdarUY+nyeRSOyZgA7+/Q/yYzMzM9x///3UajV6vR6qqpJIJDhw4ADRaDR0MFfWxF4kSdqTMGGA9dut/146nSaVSlEul7cUI9V1nfn5+VCkc25ujr6+Pmq1Gs1mk2azyfDwMDMzM0xMTGxLnpRlGVVVMU1zQy7xwSDoN8lkkpWVFcbHx7d8loJYduDY+0jn6oLnLoit74ZkMhmK+Brrlf/+i2JhYYFyuUyn02FiYiJ8ZmdnZ5mZmaFer4dCsI1GIxTAbTabRCIRFEWh3W4zPz8fPsNDQ0OkUikWFxfp7+9neHh4X5gGLmk8u1JotVp0u11KpRKFQiHM7Qf3I1jLxuPxUAw/mUzSbrfDfOf6cX0v93Fubo5qtUq5XA6FcgIoikIkEiGfzzM7O8vS0hKiKJLP5/ck/rEXrBdjCeaEneA4TjgPbJU77fV6dDqd8HnfDclkkm63e8njS39/P+12m+HhYVZWVigUChiGQbPZDPMBhmGEdShDQ0M84QlPeNA5ioB8HgjR7IZsNouiKAwPDzM2Nkav18PzvFCo+uLaEvDzbFNTUwiCQCaToVarUSqVwpxcKpUim80Si8U4dOjQtucRPCOaptHpdHYVKe6uOYEF65D1uJIix/vYxz728Uhgn4C+j33sYx+PEAJl/IcKpweezX+YNZ439y4yRpGsVURzdRTHImXXcQWJupKnqg1xIvNkPnPkly9p/yuJw5hSDEtQSVlV6trGIL8tahvIYkljkY6UwhFkKompK3GJm6A6PUTPwRaUXR31ZM8GPN9lWxAQcGErV25BoBwZRXYM+qwVTEFjJn4NgueFLqqeAJLnYkoRJM/GFSQ0p0d+jfDmCQKyZxFzO1iCii0ovvo+DqqjE0HHEDTaUopbj/7GQ9E0W0MQ+ML4T1PQl9DcHhPdk1SUAepqn08K9jzido3CGkF3OTpBT4ySN1fImCVWIuM7trMniNSUAhmrRM0aoCOnaCh58uYqLSWHtw0RRnF1UnaVxehBjmeftiMJ+NGC+ezNLMYPE7VbjHXPIuBSVwrE7RayZxNzO5iC5pNABY+umKAu50iZFfqNRcrqEE2lwD0jLwPghvIXyJhFulISU946gRax26StMqqjk7TrTLQf4Omz/8y/Z/4M0bM5VPoKN6/czkT7RPidhlrgnr7n8d2hF9DT8iAIzOVvYS5/C7LdI2ZWcQWZnpbdRLaJmHVefO7P6dfnyNgV5mJH6MkpDrTvw3V7uIKELkTQXIOY0wUEBvQ5lmMHN+zHEjREz0FzezxUcOQId4+9irtHf4jh+ndJ636FV1vLs5h9zLYEwIuxmLqWYnmEeLcNeMhcUCP01mQpPAQMUfPvL2uEHEEh5nbQnB5pfZlqfPJKX+L3NEwpiitIyN7u6pYAimfRE5XNAhuex83FO0ibRXQpzmz8GqJOi4xZJGU3CCo/bUGlphQwpCijvXNE7RbXrHz2YXMnfzjQUdKYgkbKqeEgU40MU2V42+3z5jKWpFHX+vn00V/jS9bPcaD8n8StGq4gU4sMM1u4ZUNfWsw+ho8cegMvPfd2ulKKnLnKRPdU+HdTjFBRB2mofXxr4AXcM/hifva7/4OuFGeke475+JEt+2bMapA3V1iJTDAfP0opecT/gyDw5YOvpRSb5ClLH6CgLxKzm0iuP0935SRNtUBZG+Jg814muiexBZWziRs3CGM01H4UR2e0d5aR7lmeuvwhprOPZzH7mAff8FcLPI9nnvsbnrRyK/36PGmrgiNIGGIE0XPpNxfRxRhJGvTfN8u/Xf8nNKPbPx+PNHI53+UnqF8SBHAdj8piD0dS9zzGXxI8j6fMvJunLX2QiNMmY5bQHH8eM6QoLSVLxO7QlLOUImPh5w9kn8I9Qy+imNqa0BnTy7z4lC8o5QvS1BFxcJAxxCgFq4glyAz25igYyzSVHLagoK6tpUBgPnaIldgB7jj8K1f+utdDELj9yOvJ3vd6RDyGejPkzFUaSh5T1FBck7RdRXENVrVRypERPnbkNx6a+/EoRsSoE3E6xNwOZXVnlV8AQ45jCwoRu01Wv1B1uZi8Fn3tb0mrQkPduZAlbVVoyWl6UuKyxJ8cSeOj1/8Bj5v/II9b/SQZo0jE6SB4Lo6oYEgxppM38J9jP8JC9rGXvP+HC46k8cHr/g8/eOYdWNWvErHbpKwKmmfgIFLUxmgqecrRET569Ld3FXCbLjyZz+kv4flHdOJnTiM4DoLjoKxLOAqehycIWLEY+vAw6Q98YMt3rPHxcUzTRBAEVlZWmJ6eDh0eLMvCMAzi8Xio/D45ObnjuRUKBZaWlsjlcpw8eRJBEGi1Wui6TiKRCAsxy+UyrVYL13XpdDoUi8Vd3dWDf3dyiq7X66ysrNDr9SiXy1iWhW3btFot0uk0zWaTRCLBoUOHNiTl0uk0N9xwA5VKJXTdDY6XTqfp6+u7ZMeA4eFhOp0OruuysLDA2bNnQxdgz/NCZf18Pk9fXx9TU1O7Egv3cXUjnU4TiURC8uduxX61Wo1UKoWmaQ9rAevw8HBYfGkYBrVaLSwaiMfjjIyMoGkao6OjD3nBYqlUol6vo6rqjkVd8XicTCZDpVIhlUqFBYGlUiks8BMEgWQySV9fX0iovdoRFIR1u10UReHAgQMbxoF4PB4WdJ8+fZqhoSFc133EC+CCgnnXdUMhg1gsRiqVQhAEdF2n0WggimJYODy8xiARRZFDhw6FxRtBYfjyGuNFFMVQACGdTnP48OGHtVA4KLbfq6CBKIq4rrvt3OR5Xvi3vVxHsM36711cOLUTIpEImqZh23Y4v18MWZbJ5XKh2wv4RIlAyCEo6q9Wq6EQSzBuPNLwPI/Z2VkqlQrdbpd6vR4WXcdiMZrNJouLi0xOTj6iTkX5fJ6lpSX6+vpYXl7G87xN45LjOKyurtLtdpmcnCSfz1+xdUAwH6XT6bDAbrsx0bIsms0mo6OjuzoqPVR47WtfGxIRwCfQXwkCumXBm9/su5Oux5rB4SY4osqHrv0DXn3cf5/MG0vkiucRGh6q5iKsEQrN4WGcwgDv7f9dvjB7PQxfR9pY4alLH6ZgLJKyayhYKK6FIpiInoctyHTFBF0pwbx2iMiSyNRUD9e0EXUde3CQb0xPcHyL21RJHuT917yZV538fbpKmrRZpGCsrOWLRFpyhpnIGD0pTs9KMGQu4DgxVpY3xtgdR2BxQSM62iG2cB63bSG6DlF6yI5DT/Tjoqag4iFQVQd43+G3cyg1wszHfLfv9fjMZ/Z2Hz7+8b1tdzHm5+Ev/xLe9jb/Nea9791IPg+2+bmf8/9/w9IneP7035Ayy2TNIlH3gjObKUbIGKuM3XmSL/z4cca/9At84q4JPrYyATtonLzrXTAy4hOGPc8nsL7//Zd+Lc0m/MpFoRNdSuCIMqplML8q02jImMb2axfV1bEFhY6t8Udv1shk2SAIADA97ZNI//iP4S9/r0jy5AnyThdTirGYup5uZPf1w9e+5osFPPnJ8KlP2rzi+Js4Wv8GMaeD7NmIuJiCT1hUPAvNNTA9FWt6kWJZRfeiyK5B3GlR1kaYTV6/7bEcSWPBHoE2PqN9DbOz8J43zfLzdZv+ZhNHVnB7HnHPxhOENQFwD83VcVyZjOisxYcsJM8m357hf/yPMT7+8Sz5v/kbOt/8JvpHPoJ3990snBVoODKrsUm+0/dcPEHkMSuf5MkL/07UbCB6DqrTY9wqIXhr87fnYFkinuC/Z0fcri+QLqhUtCFUVye5VGc4cZxcZ2bXnMzdd8OrX735856a5VTmCaTMMiO9875grdBG9AJBYvxcmBijqvSTscs0lBxtJcf5wlM4c5vv1PzMZ4Kr6wif+xxiuYy95hC9sqReRD734QoyFW2QgrHM9DcqeJ/4FlMvvoV3vWsj+RxgZsb/ARhonODVD/wuBX2BrLGI7Jr0pDgeAlmrxIAxT83oJ332bXzKrPOdsVds2R4nT8L/+T/wh38I3a5PRL/cZfwXv7iZfH4lcGLguTxr7j205Qx9+iLz8YSfp1b7SdgtNE9Hcw0MU0NZcwn3ZBmnUMB2BFZmoVAsU1P6qGjDzGUfv+2xFhZ8cYK3vc3vy7OzoPY/i+fM/iNtOUPeWGFB2txItiUwMxNBtXsM2A1mY77weKUu8+d/Dp/8pD9eHjly4Tue54tivOc9W5Ooy4mDrMSmyOnLPqEbAUeSiYo9RMcB28Z25LA+Iep1MAQ/7j8fOUj/uqLnhQUNveevr21RYyk6yXjnDOfuG8NufQbW5cSWlnzxg9//fYjHN5LPAZayN/GxQ6/npWf/lPFEglitRH/3JMGbqyPINNbqVE5mbuGTR389bK+5OX9t8Hd/t9FIG/w2eNvbtiafA7z73XDokO90fvKkP0ddLjxB4hPHfgvn9NuwqypJq0bGLJHS/UWLLsZYjk7SljNMi9cjtp7Bi++4A3FggPjNN2/bST79af+eZoeez5NXPoYuxki1i8zNjnPkSBfBsLB1wbdRWKulUDwL0fN8seg1kf+kXUdxTWwUBNej21WQ4hbuFuvEjpzBEWQ/r+pZ2Oti8tPnI1xzbRfR6CHpuk8eX1vvBnFLL9inIEA+j53PY2UyJM+cwQOMQgFvZAQ7EkGan0cyTUTT3EhCF0U8QfDjo90ucreLlc3iDgyQ+L7vu/wbdZnI5XIhAaFSqYQEukDUK5fLXdUCeoqioCi+AFOv19vgGn4xVFUN4zmmabK6uoqiKBsII4ZhUCqVQgJYJpPZM1EpIIcHsR/P8zh79mwYb7jmmms2xNNkWaZQKKBpGjMzM5TLZaamtq4fy2QytNttarVaGKcNyPEX359KpYKmaZdNnt/HBUxNTYVOzAH5yLbt0CE9Go2STqe3JagGLpTlchnDMHYkJ9m2TbvdfsjFNR8OCILA5ORk6CQ6NjZGt9sN41exWIxYLMbg4OAjLlq5G3K5HAsLC+F9jMfj28YrAkHjTCZz2X0vkUgQi8XI5/OhEMZWxCzbtllcXNxEPo7H40SjUarVKqOjo5u+l0ql0HWdarVKp9OhUCiQTqc3xFaC+JskSWQymUuOtwuCEBLM5+fnSSQS6LrO2NhYeL6aplEoFEilUrRaLRYXF5EkCcdxUBQF0zT3RETXdT10dV+/baFQYHl5mXg8HpK4g7HStm3m5+ep1+uhU/To6CiTk5NMTk5y/Pjx0Bk4FosxNzfH5OTktvGnvcQ/LwVBbNO27ZB8HLSVIAjhHFBee8EOciJ7IWQ+lLgUQZiLt7nSbfhoQ7PZDPtlf39/uO5ot9ucO3eO1dVVWq0WkUiEgYEByuUynuexuLhIs9kE/DbUNI1Wq4VlWUiShKZpFItFEokEpmnS6/W2FEvZx0OPoMbAcZxw3tM0LRy/ut1umNNPJBJUq9Vw3ResCdrtdij4u9vatNPp0O12KZfLFAqFbcVTZFlmZGSE6enpUEB3L6LEe0EgxBKIWu9Ws9BoNEJi/MXn2+v1aDQatFqtPc8PwXgZ/Oz1uc/n86GbuCzLrKys4DgO8Xg8FGFWVZXBwUGmpqa4+eabr4hQV5Bf22uuQ5IkXNdFVVWGhob2/L1cLkcikaBUKpFIJDaIA0QiEfr6+sjn8zvuT1EUkskkmUwmFFXY6X2xWq2GrvSPZA5sH/vYxz4eCuxXy+1jH/vYxyMEVX3oAwn3jL2SWnSEpyz8O2OdU2hOB9m1cAURXUpQ1/q5a+BF3D32yksmf6ykrmUlOklOXyJvrtJU89vuQ3F00tYaoTj3tIfMNVaX4ziijOKZ4LkgbL/I9xBCN2cAdyvy+TqYUpSGl2MlMsGtU7+M6ukYUpyFzI1U45NEzDojjfuJ2C0sKcJK4jAvOPNnXFO7kyPt7+AKMh4QcbvrzgAEPExBpS2lef/B36QR30bO+yHC+b6ncof5On5w5u/Q9RX6zEWG9RlcRBDA8mQsKYIniOTNIpqjM2jMEXfaGKZf7NSRM9smUdtyhj5zCRGHs+nHkbRrJO06o92zLEWnNpGYNbvDSO8cHTlNR8lw9/BLHoZW2B2SBI6z+3bbQhD46LE38uP3/RoeAoP6HFmrgrvWbqLnEffauIiYqAg4pO0qniNS1EapRIb5yOHfoBUdBM8jbZbR3B4daXOgImK3GdDniLhdv9jIc5A9i6xV4qnFW8l+s4jq6sScNimzQtqqIHkWniDSllIUegs8dflD3HrwVznbf8EJwpajNOXtAy7Xr3yKmN0kZ64yHz1MT/bPzVkjYYueC6JPxhZdF83VSdl1Vl1rA9lT8UwsQcUUHwb1RUFgKXsTS9y0+7ZboKX1UVcH0HszSLiInuELYKw5n1uCgiWoeGv3WXJtHEFc6/0+ZOcSbFj2AcB84hij7ZOM9KZZ9ewd5y/ZNYg5LUraCPOJazb+zdHJGqvE7RZNJeu7pcspenKKZc/3rvcQ8FhjsXoetqASs5v0d84/1Jf5sOJ07ilcW/tPBowF4nadjrJ90lN2DRJ2k9nYMU5mnwKAoSR5YOgHdj3Ouf7v492xEZ648EGuq34V1dWR1tYltqgxl7iGu4Zewpm+Z4Ag8I2BFyGu2Ix1TjPVPk5N6aOl5HAECc3tkTHLpOwqJXWYSmSYz0393KZjPjD0Azww+DzGanczUb8HzelgCRrLqWOcKzyVn7rndWTNIgKwEDu0pTiKJUWYix3hQPt+klaNxy3f+j1FQL9h6RM8aeVWxjqnkD2bxejUhrlddg0OJ2bItU4wmOlhnvg93v3Yv9kwdl8t+JEf8YtDBQHwPFpf/jLmRz+KdO+91CsCi4saq9Fx7hl4AQ8MPAdTvjKqos8897c8eeVjDHXPk3AadKQUbdlf78acFmPdM7TlNHWpn68PvISvTv00jrSzGmxcL/Hj9/0aQ93zDOkzyK5JR05hCQqa2yNnFbEEBdlzMCWNppwj7rQQPQdHkClqY7SUHMvxg3z4mjf5wjIPMWqxcd533Z/wqgd+j46cIm2WSVlVJBwcJJpylrraT1PN8+Gjv8Nq+tqH/JwebZA8P9kheP5aYi/w1oq6ZfeCMMtS5kZKkTHy+hJ5Y4WWktt2vozYLRLril53eza3gyvIfGv8R/j22KuYKH+T/s45FNegq6SZyT3hUSO4Y8tRPnHNb/O1ziw3LX+Cw/W7iDgdLFFjOXaA7wy+kNncE/ZWaS0IfHDkDTxptAeRT6PMzaHW6wiuuyZkJuCoKnYshn70KMkPfABlcGvhgYAIuLCwEBZ+BUlbVVXp6+sLk/ATExO7FkhKksTU1BS2bXP+/HlmZmZwHIfJyclQubnVauF5HocOHQrdNldXV5FlmXw+v+UxNE1DEASi0ei2zhj1ep2lpSXK5TKlUgnP8ygUCoyNjaEoCrVajXK5zMjICI7jcOzYsQ1FJJIk0d/fT39//yUnJrdr28OHD4dOLu12O3RXD4jtmUwGTdOYmpra0R11H48OaJoWKpqvrKyQSqW2LVwISN8jIyOPSPHqwMAA+XyecrlMKpXCsvyxXlVV8vn8FSVCbgdd19F1nXq9vidCdSaTYXp6mkajwde//nWi0WjYp4L+mkqlQueqgwcPPiJOvntFUEgtiiL9/f0IghC6TSeTyZDY3O12cRwH0zSJxWKIonhViFUEz60gCMiyzOLiIjNrjKRoNMro6GjoCnLgwIENY7uiKBw7diws2NJ1PXwGg2L1QqHAwMDAw06GlWUZRVH27Kal6zrZbHbbexKIPQiCgGEYu947wzAQBAFJksJtI5EI0Wg0dP/ZyTUjnU5j2zaJRIJWq7Vt+/X391Or1chkMqTTaSzL4vTp0yEBXZZlstksuVyOoaGhh9WFfjt4nse5c+col8ssLi6G4jaBk0mj0aBcLpPL5XAchwMHDjxihdeiKHLgwAGctaDvyspKON6KoohpmjSbTWRZZmJigmw2u2Uh8+VCEAT6+vrodrvMzMywuLjI8PDwpjWWaZrMz88Ti8VIJBJXtUva5eBrX9tMPt8NnUgf77vxHTx15t3cWP4iMbtJUmwxPNwDVYV4HOPY9fyT8fPcWbvR/5Ig8JUDP0c1MsLTFv8fHgJD+ixRt4slKNiiiiPI1JU8pcgoniDR7cLx43HyxiIpK8Hi6gAfz74AtuFFrKav5V03/z3XLX+KxxU/SdZYvXDOcorv9D2X+weex2u/81pMY4VWSYStliC2hXhmFtHrIesWIh66oFFWhyhpoyTsGlmrjGpXyZmrPHP6XdwafePls0EfJE6f9h2TH/tY3z18OxwqfokXTP81g73zpKw6NaXASmQSS9KQPIuUWaFgLpOyawgnXd730jgfln9y1+N7HvzTP8FNN8Hf/z3ccceVu7bzmcdxU+XzDOrzfszU2H68Ejw/p7MSGedc6rHYjrCJfB6gduf9/OeL38/Lvv4dZNdE8mwcQcYWVU5lnsi3Rl7Bcnp7UjhAve5f65Nm/40jjW8x3jlFR0rgIhBzuwh46GIUAw/Zs4l4PbAh35ijHk/SZyygizF0OcG9wy+6rPZxdAv9fA3BdhAsDxGJrhTDQSZIgYienwuKOx1Ez0X1TATPQXH9OfxjH4OXv1wg/sQnEn/iE1lZgTf+jImDzOMWPsQzFv6NuF0naVaRPQvJtRgwF4i4PWQc37UZgYjbRUMIcy+uINET4zSVLC01j+waFMxlVKfHWP3eBxWjuGfoRRyrf4OeuYqIx0zsGiJuF9FzcBHR5TiGGKNfnyXi6qxEJ7mn77lhPvSDH/QJ6Ob0NEKng9xqYQ4P43oCjcb2a5CWkmdQn0M1O3z2r07zzGO38MlPbn+eEbPOq07+PgPdGQaNOcrqEDW1P4zpCp5LwqoyYCygujo/MPcP1GKjzOZv2XJ/x4/DK9b46YIAP/3T8JKXXPrQ8+lPX9r2e4Upx7lr4AVEnTYTnQcY65yiGBmjLaXpSTFEHGJOG9UxEBwPJ67hRCJ0hBSN420K3WUMMUolMsxXx3501ws7c8YX7zh16sLx7y08m4RVY6J7kn59jlJkDO+iWgXV7jLWO0tTztKTk3x3+IJj9unT8PrXw7OfDT/xE/6U9hd/sVnYYwMEgW8PvIDhzhmyVomeFKMsDTMeXUAxdSxLwkTCEPwJR3EMvLVcdMntJ6kbRCMujiNsEj/oSUkMMULaLHFj+XObRJnPnoWdtGjO9D+T90YGefL8v3O0903UhI7smniCgC7GqUUGuWvgRXxn9GWb4pYrK/Cyl8FrXwsveMGF23H33b4b/HbwPHjf+3xhgM9/fod22yMcSeMTx36be2t389jlT3C08U0/3w24CLTkDBG3x1T7OOqHv4N9bxdBkakODCC+6EWkfviHES8iBn/4w/6/tdg4Z1OPJWo3GeuewW4rnDg+xIARoeCtyyN7NhG3hyFq2KJCU8kBfhxZwMMVRCR2KWIQBAwxguICCMSdFrJrhGYSjYZEVvLf9QTPw11zYhUdBzsSwd2K3LxGChHAb3iAXA63WMR2HGTDAM/zSeiBUjAg2DZquYyVTuMNDyP/6O797aFC4Hh4tbqc74R0Oh066tZqtR0J6EBI0ojH40iSxPnz54lGo8iyjG3b9Ho9otEoExMT5HK5XcVNA/R6Pc6dO0e326XRaNBsNmk0GhSLRWq1GhMTEywsLDA2NrYp9maaJqlUitnZWVRV3TLulEqlQtLG8vIyhmHQ19eHaZrh/jzPo1KpUK/XQxLzI+Hy+b2GwKG7Wq1uIKDHYrFQQGA7FAoFVlZWiEajFItFRkdHtyVAlctlZFkORXG/F9DX10dfXx/NZpNOp4PjOGE89NHidimKIoVCAV3XmZmZYWFhgaGhoQ3xMs/zaDQaoVNq4Cp7uRgfHw9jj/Pz8ySTSbLZLKqq4jgOzWaTWq2GqqqMjIwwODgYPoeBY+u9997L4uIiQ0NDG2IrAelMFEUikQjFYjEU4QzuU6PRQFVVxsfHKRQKlxWrEgQhFIytVqvccMMNxONxZFkOBQgCBAKtpVKJgYEBRFEMSdY75aACIedAoHA9FEWhr68Py7KYmZlhZmaGvr4+EolE6DI9Pz+PKIqhEGkgOJJIJBgaGgoJ6sG4vNU9tSwL0zS3dHV9MBgeHg4FYUqlEisrKywvL4dz1Xoh6kKhcMUIow8GiqKEceWtXOcvRq/XC+P0V0Pu4JFErVYL89ABSddxHBYXF6nX67RaLbLZbBgHDeK3yWQSwzBYXfVjbn19fWE8dWBggEOHDtHtdikWi2EeJBDq3cfDC13X6Xa7JBKJMP8RjUbRNC0kaCcSCURRRBAE4vE4uq6H44DjONRqNfL5PIlEYtf+ValUaLfb2La9K+E3EokQi8Wo1+tUKpUrNp4E82e73WZmZoZut7vtOt22barVKplMJoz7e55HtVqlWCzS7XaZm5tjdXU1zFFms9kdxWl1XQ/HpUsVXRgfHw/zXf39/WFe17KsMCcdmCDkcrlLbputEOT5DMMIhZd3QnB9QW7uUhCsH4aHh8PjybJ8SfNYf38/jUaDSqXC0tLSlrkcIBRTCN6trgbR6EcrDMMIxa0DAYt97GMfjzz2e+I+9rGPfTxCeLhqK2cKT2Km8CT6WqeZrH3bJ0gLGqvJI8zkb9nWhXpXCALfGnopQ91zJO06Y53TLMYOYF9EGo3YLUZ607TXCMXfHn7pFbiqrTGTvpnO6u0AJKw6bXX7lx1DiCCtJQgNQduUAN4AzyNp1+nIKZYSh7h37OWbNtHVDOf6nrbhs49d+0Yi970BR5QZ75wi5TTC4wiei4eALmq00v18+brX8pXsz+94faK4tbr5g8XxwR9guPkAz1l6L+LaeUk4eJ5AwutgOQYdKYnq6oxaFSJuD3GNIB1zOjSULCvRqS3b0Fv3Mns2/yRUV0f0XEa6ZznUvo+mnKYnJRFwSdgNYk6bppxjJTrJ58f+v20dQfeKV73KL346e/by9/GsZ8GP/zjcdptfDHO56ET6eO+Nf8YLT78dvfkdonaLhF3HsJoknSaeICJ6DroUoyHn0OU4LSVHRRvmkwd/iaXs7iTpmNVgtHcO1dXRPAPB83AECQEP1TPJm6s8sfIfa+ILfrK5ruQxxSiSZ5OyauQ691NVB3jFmT/hw4ic69+b6vhji//hO0mLMbryBWJ8T4oTd5rE3C6i5+EKPgk97rQRPYeMWaIaWXPYci0SdpP52GHmk1c/Ke103zPozf4DPSlO0mnhINER41v2BcHzUDwTQ4hgihqu6C/De0rmYT7rKwdNgwMH4IEHrsz+fvzH4WlPg9/5nc3OMOtxz+CLuLHyJWx9npy+TDm6TaDY8yjoi2Eh3fpCHiAschNxcS4m5QkC7sWvSoKAI0iInofiGZd6eVc1Tvc/k/bcP1JX8gzo88xK8U0CIeDPXUO9GXpifMs23QuqiQPccewNfMF8LWP17xCxWliiRjF5mGpio8L9lw7+PKqj4yGSNktkrRJ95lJwNrTkNHOxIzTUfj5y5De3J7MKAvO5xzGfe9yGj4dr91LQF8hYZcra0I5rIkdUaSh5MmaRo/VvEDUqDwuh96GG4Dm+Q7yxiOxZzMSv2UQsj6RkxIlRrCWRxOosk7Ech0pf4fTAsx+Wc3zGM3x3qLm57bcRBPj5n4fnP9//3SoWaf7GbyCdOYNUrSLV6xQsC1WPktcXGe6e4xnz/8rHjvw6c7knPKjzmyr9J09cuZXR7hlEz+F8/Dos6YKbQxVQnR4jvXOMds/wpNWPM5t5LDOFJ22/U8/jZSffzFD3HGPds9SVPOXY8IZ7I7sG/fq8L6Ih5+hJCSqREQQ8HEHmfOom7h58EbP5J1z+ev8yUE1M8a6b/4GjxS9w8+rtjHQuLMKKkTHuHnzhFSX/f6+hp6TxEDBFlYjTocXO44zoWr54j6jRXi8eIgh8Y+hl3Jw/jXumznjnFEuRKUx5XYLL80jYNYb0WWpKPx0lw3eGX/ygr8EVZKb7nsJ031Me9L4eSdTiE3zx0P/gi7tsd801O6+FHFFl9offQt8rn4Xx0Y9i3XsvNBqIlsWrl5bQNQ1xeBgpHifyC7/Arbfeuu2+Aofa4eHhMLEVFI1IkkQ0GqW2xt4Jikp2QiaTIZvNhoVPsViMXq9Ht9tFkiTS6XToPDE2NoZhGCwsLFAoFKhUKlsWhMmyHO53aWmJQqGwIRFk2zYrKyuhI2osFgsJtoODgwiCQD6fp1QqhW4TCwsL27rgXE6CcSuIosjBgwdDp+Z0Oo3jOCGZvlAo7CcIv8cwNDREq9Wi2+0yOzsbunEH99iyLCqVCrVaLSzQfaQKkgP3j8FtBCoealiWhed52La9J6J4JBLBsizOnz9PMpnEsixkWSaVSiHLMqZpUiwWWV1dZWRkBNu2OXbs2BUtVruSqNfr6LqOJEkcOHCAXq8XFlRXq9WwODAogAvI55qmhQWmjzSCcwgcwIPfBUHAtm1s295URBpAFEUGBwcZGBgInRbAL9bYya35oUYmk6FerzMzM7Mr2TsoOkqn05sKMzudTjgnLS8v0+l0WFpa4vDhwzsev16vk0gkwiJpuOCmHTgu7dRnHccJ53XHcVhZWaG/v39TsUqz2cTzPI4dO4Ysy4yNjaHreliMExSvBnPp1YDV1VUqlQqzs7MoisLBgwc3CMn09/fTbrdZXFzEdd2wuOyRGgOSySRHjhwJ1z6NRoNOpxMWII2MjJBIJEin0xw4cOCK9+mBgQGazSaO47CwsMCZM2dIp9NEo1E8z6PdbtNut4nFYqFgxKOlYH0rzM/DrbfCpz7l/x6PQ6ez83e2g66k+dzhX+IrUz/D0eIXyHYX6EsamLEMJ7LP4NzK5JbfOz78Qk4M/SDXLH+Kn37g18kbK8iexao6TD0yuIkAl7CqFIwVlqKTnFOuoZQ8suV+15/Xt8d/mG+PvYpUbwnNbmPLURrR4XDfq9Fx8voiabtK1RvaRLoqGEuIponoWFiCDJ6HKUZpy2lMOUpVjlLTBhjpnmOsewZHULivfOcj+g72pjf5RMHtxAREz+Y5s+8iry+SsJvMxK7BlC/ETmxBoxoZpq72MdE9xVBvGuebHyL6uBfvKf42Nwcv35y2e9A40/8M2nPvoqHk6DcWmZWTW4u7eR59+gIuIi0lxz1D28dMH7PwUZ43+w8krQoZs0jMaYd/60oJUmaZY7Wv8+nJn+M7Iy/b8fxE1+Lxq7dT0BdxBJm5+DFGumcRbY+Y0wERTMEnLPZcgZjbwUFmqnMCQYC52FFOZm6hFrs8YWxb1EhbFQTXl9zuSvENOUEAVxDpiTEiXo+o2wUEFMegtyZU/s//7P8cOAC2vRb/ExS+/+xf8oTV2+nX50lbVRzBp5tn7CqqZyKukc8BfMlfBw/W/idhiBEaSo7l6AH/XAV/nhE9G9W+zIFnDXPZx3N33w8geQ6jndOMd0/RUPJ0ZD+mE7cajFrnEHCZjx1iPnEN3xj/0fD78/M+cftaweKVZ2Mc6oo0ezFcQd4xB+wKEh5+HrG6ZPFrv7bzed649AkyRpFBY56lyBQtdWNf8gSRllpAlxJMdh8gbRR50sIHtiWgb/iuB//4j/5PJgNB7fGa3l74E1xP8LnrQrG46+4vG1+b+iny3XnAY7A3w0T3JIYYxUZCdQ1EAqd6AU930AUR5fhp8gjUlT7KkWG+OPJjnBx4zp6O98//vPH3r079NOOt+xE9h9HeWVLtGnUljy7FET2XpF0lYTdpylmWowf4zPh/37L/ff7zl0aePjH0g1xb/jKyazHWPUPE6bLgDeHGYliehyZ0ydhVJM+mLaXQXIOmlAFB5Py56PY7FgQ6UhLN7ZEyK3s/oXUopo7x8eveREIvMlX5BlG7iS2qlOOTvsv8Lu8T73yn/zM66j9D2zmfr8eJE74je6Oxx5P0PBJ6kahVx5E0Gtogjryu3mVdfkm1O8TNCqrZ5AfO/zWj3TNkjSJpq4zsWbh3++7uLa1K8ysVSm/9Ol98xpsxYgUiEWg2fXJ9gE8d+iUG7ptF9FyGe9NkrTKWICPiInkOcbuFiIcpahhihKXIVBjr99ZGP8lz1pzSBZwdRINFz6EjpVE9A9eT6NMXWY5OgSBQqylk1/F+Bc9DNM3Q/dzdSoxubV3sCQLC2nsigoB14IBfmCEISKaJ1O3iiiJ4HpJtI0gSeiKBc/Ag7stfTiZQttjHJUGSJHK5HO12m9nZ2fCdZSsUi0Ucx+H6669HURREUUTX9VDcVNM0BgcHkdaEBwKCuizL4Xv0Vu/+uq5z+vRpKpVKSBBMp9N0u11c16XX61GpVBAEAdd1mZiY2EAcWk/66Ha7NJvNTQR0URQZGRnBdV10XefcuXMsLi6SSCTCmFOj0cC2bUZGRkJSzMW4FAfIfVyAJEkhmfpSoKoquVwOwzBC8nJ/f/+GuKJlWZRKJZrNJhMTEySTyUf1u+5WSKVSuzqfXs0YHh4O3VAXFhY4e/ZsSAB0HIdWq4Vt2yEZ+ODBgw8qZpJIJDhw4ACCIBCLxajVaszOzoZ/1zQtjOEPDg5uIAvedNNNzM7OMjY2xuLiIoZhkE6nkWUZ13Vpt/13rpGREer1OoODg8iyjK7rrK6uomlaGP8pFApMTExc9nWUy+WQRB5cz3YIXFQ9z6Pb7ZLNZikWi0Sj0S3jZJ7nsby8HAqQbNU3R0dHQwHRUqnE4triaWlpidXVVRzHCUUWM5lMKGYZjN/dbhdBEGg2m0Sj0S3jjbVaDU3TQtf6K4nJyUlisRjRaBRd1zeIOMTjcTRNY2Bg4KoQ4QSIxWJomkYmkwlFRHdCrVYLHXH/q4tdrxcyCGLizWYTy7Jot9thHw6gqiq9Xi/MBRiGEcbHBUFAEIRwP7FYjPHxcebm5lheXkZRlG3zH/t46OCtiVRdPA7mcjl6vR7tdjt0kg4I04Fzt23bLC4uIopiKDyxG0zTDIUg9nKv4/F4SLC+kmvFvr4+isUi2WyW+fl5hoaGNuXSdF1naWkJWZbJ5XIMDAzgui7nzp2jVqtRq9Wo1+vU6/VQ0F8QBIaHh2k0GoyPj2/IuYBfg9Fut7cUKNkrRkZG6O/vp1QqUavVwvkkEKNeXyvhOA6VSiVcDwTjdKFQ2HRu2yGTyZBIJMJ5Z6cx1HEcGo1GKJ5yuRAE4bLFqoIamLGxMebn5zl//nzori6KIt1ul1qtRq/XY3h4mFQqFQre7GPvcF2XSqVCqVSi1+uFnwuCQCaTob+/f9c6qH3sYx8PLfZXVPvYxz728Qjh4XBAX49S8siuxTGXihODP8BY4z4EPEa65zjYvp+OlKIrJRDwiNtNom6HhpJnJTrJZ8Z/es+E4muvhSNH4PaPW7iW4zvx7fKiN933ZJozfdTlPP3mIl0lta3Tn4CLB9iCsjP5HIjZTSJul0X1IPf0/+Cmv0eNCgmzgiPItCMDIaFGV9L8241/yjPO/z3tiu/EkTNWUFwDV5Boy2mcg5NMvfHH+OGXvhzn/fCBD2w+fi4Hv/3bIMvw53/uk7ACPO5xUKnAmmjfJUOzWrzixBuZat3nJyoF36laxEVxDQR8R2rNNVhRR6lqBYaNOeJuG83VUQQLwfJJ6ytrScoNbeO0cQQZV5BpaX184prfQj8Txy5/jpjdJG2WSFt+0ronxVmNTKDLCT479lOblNQvBYIAL32pT2j9b/8N/u7v4LOf3XrbaBTS6Y0JX4AbbvAV/A8e9H//qZ/yRQA+8pHLPi16Wp4P3fBH5Doz3Lh8O8PtM6iuji7GMMQIsmcRcXt4gkhdG+C7A8/bnIAXBBpqAaMXJe60qK59LLsGI73zaG4PzTUwRA1LVPGApNOiJ0bwgLjTRsKlK0RZiF+HISeQJO//Z++94y2r63P/92q793Z6mTOVmaFIUVBAQBFEUMH+0xjxaoqxm5iiJpZEjJqbxJQbY4nJNXpjULCAoogd1ChFYJhy5tR92u69rPr7Y5295pyZ02aYhp7n9dpwZu+19/qutb7983meh1DYYKrQhU8r09ey3Z1fm/8od111Pv/zRMgRL18JkqkSa8/j16tUl7jmApRcKaJaFlMQcVktWngxBQlTkJAtHfciCRjLItGeQRXdNOQQD6+QpDUyAppmJ+icDWgrQR6PXUG8NUOk9igAiqmhHuUcKljYzxUBS5ZwiTrzYhcL3kGqnrNjY/xE8LrXwTOfCR/+8BGXhxOBINguCS97mf33P/yD3c6+8Q1Yso/gYD68h7R/h+MKIGCR9/Qu6+9FUyPZThPUy0z5d3AgfImTyPM7v2OLSeTmA1gI6IKCawNO9IJlolgquijTlFexOlrl+tZqP2cDDMnNj/r/P/zj/wd3o8Vw/Qly7h6qStxOXLFMAlqJhDqHiMmUbycPJZ57XMmJf/zHdkJWJ8Gs5YpwKHXVmt+xBIlv73gnE9ELuWjuawzW9iObi+Iaokxb8vFY/Nn8T99LKPqPPxCYrI/jNprIlkZVWV+hs6rEiDYOIFoG8cYU6bOYgP7Od8Jll9ntajW43dCb/hnRdoaIlmfe3X8M+dztMenptZOHrJ4erGKRfs8cV1S/floI6MmkfS1g5yzdey/88IfLE9QDAXjLW+zrBdCLRapveQvSoUMok5MIloUeDmMGg7j9FuG5AvHqPDlXD6/Y/wH+364PHCNOcDy4ZO5OwmoWl9lizL9nRWd4VfIy5dvBlvrjhNUcF899dU0Cem/pV1zZ9TBydoyikiDrtduaP2BQX3Si0UU3s96t9DVHCRgV8p4+/vnCf6PpitgCEqcwmeaKK2xns9lZew71nOfAXXfZ7jJg9yn7eq5nX8/1yHoTl15Hl72bpPMNQJe9HApfRFjN0tWeJmf1rSkgEFGzqKKbphzkYHK5aNAz3nMDgV/9gtad96IfmGVLYx9NMUBD8iNiEtAryJZGwdVF1tPP17a+g4qvj2c9y15f7N9/qq/2qY9zzoG//Et44xuhUFj9uGZLIHjDtXDttajpNNr0NJau84PnPY9mrWbf8EcfxetdI+l3CURRpFarOUkXhmFQKBScpI9KpUI+nyeVSjEwMLBmwNQ0TRKJhJPc0gkkgq1C3iGom6aJpmkUi0X27dtHrVbjaU97GpFI5BiyXMcZxePxMDU15Tibg51c0Wq1KJfLeDwehxAYiUSccgqCQCqVQtM0MpmMQ7jaaLByKTrB1qUJKmv9jt/vdxLdNhMTf70RCAQYHBx0XJLy+TyZTAaPx4NpmrTbbTweDwMDA0QiEbZt2/Ybm5zScR/oJAyvhw6Z1uv10mw22bVr17I2DjbhMpfLkU6nAZiYmGDHjpO7Z3my0HGAB9iyZYvTf4XDYdrttkMk9ng8uFwuPB6Pk5ypquqG+/ZThWw2y+TkJNlslnK5TCgUIh6PY1kW7XbbSTo/dOgQW7duXTXxTRCEsyppNplMsrCwgNfrZW5ujoGBgRWdBnRdZ2FhgVAohNvtdpwZ6vU6U1NTNBoNarWaMzY1m03m5+dpNpuMjIysmMRQq9WoVqsMDQ0Rj8edhJtO4mAikSCdTjsJ10dDVVXS6TTDw8Pouk4ikWBhYYFyuUw4HHbEC8rlMpqmkUgkHKf5bDZLqVTCMAwURSESidDf33/WPBvLsshkMuRyOURRXPW5BAIBBgYGmJycdJJtz6RrUSAQ4Nxzz6VYLJLNZmk0Gg7xopPgdqoSWkRRZNu2bY5wRblcdlx+OslQg4OD+Hw+enp6ntIJS7/4hb2HqOtH3jtR8vlSqLKfR492b15HN9ISJPb13sAXjDrPm/w0vY3DJLQMMjpVJYaBgstqElGz+I0qC+4B8u4+vrP1TRsvmCBQ8a1crx/segG9jcPE1XmCWmEZKVS0DMJaHpfZRhckRCx0QUEX5GXHWYLErG8rI7VHCWp5Lpz/xhkXAVtLvHdL9gEi7QViWoZ598Ay8vlSmKLCrGeE4cY+3HqN8+bu5mfDv3VqCrwBGJKbB3puIaCVGKgfYKi+n4y7n7ocdvZdFKNJoj3rCOum/TvtmM4KOGfuHq6b/CQ9jTGCepmSEifjHkATXSimSljL0ducoKoVuW7ik7QkP/u7r121fNtyPyaoFQlpRea8w1iCyKxvBKVuL+g9ZhO/WUMXJCxBRMAiYFZRLJUnghcxFdjFN3f80Qnfn4CaXxQ7tsnMR5PPHQjQxoOHFpqgENDLzITPXXbI2NiRvy9I38ElC3cxWD+AiMmMd4S26GFLYz+GoGBaOiYCCjoCtguxiYyAHdu0/yuSWeI+LVn2WtcUZFRpbZfYdSEIfHf7W2yye1YioBWJqlkizTwsEkULrhRlV4LpwDl8efcH0ORjzzlfDaC2BVRToZyxqK4jxiKaGgKmsy++5rGWzoXZe4ioGZqi7xjy+VJokoeCkiKqZRiuPU6sNn6MUOxaKJXs19kAU5D52p4/p3D401yc/SZerUJPc4KQUcJcjAVJloEuSLTx0FRdlNwpKkqcoqeLH/a9mv09zzvh86uyn//a+xFesu8vaEteQlqBiJojrBVskQY5wKR/F00pyLcH38DDAyeHeGsKMnfu/gtevO8D6KJCWM0R1bIoJXtA1AUXRSVB2Z0k3M6imO31HbNPMmqeFI/2nbjw5eLSccPYCPlc0lvsztzLhfN30d2ccN5XRQ+/SlzFQz03UQiMLPuOKvsxBJlXHfw4w7XHF4VgZHLuHupyCBAW80EyBFsFzJbORV9/H/953v9GX2Hsq3m7+M+9f81Ln/gLmnKAkGqLkyiWHZMRsKhLAXRBYda7hYZyZL3WFr24jQZuWtSEIBU5vEhEX+FaTRWvWSfjGaAmh/EbVQYbB+hujpP19NNsuLAWiaGmICC17RwRzetF9/thBdc+oV63ndIlCbnVQjdNBFEEtxtt+3bM6WmsWg3BNBF0HUHXMSUJzeejvXcv8u/9HrFXvnL9B/VkYFlnzF39dKC7u9shUs7OzlKv14nFYg6holarUSgUaDQaDAwMEA6HOeecc9B1nWw26+yZdlyMwd4rnpiYwLIsZFkmGAzidrsdN+ylGB8fp1gsMjs7SyqVIhaLIQgCtVoNv9/P0NAQXq+XXC6HIAjMzMywtZN4g70WkiQJRVEc1+OV4Pf76e/vp9Fo0N3djdfrpVar0W63HdJORxBw+/btuFwuTNOkUCg4pAXLspYRmILBjcf5f1OhqqojziZJkiPCt1F03KzBJr6OjY3h9XpRFAVd12k0GrjdboaGhohEIoyMjKzzi5s43RBFke3btzMxMYHL5XJcwuv1uuPCGolEcLvdjIyMnJR2FYlE2LVrl7OXp2kauq4jiiKKohAMBkmlUscQz1wuF5deeqmzT1ur1SiVSng8HgRBIBgMIssyjUbDEVVwuVz09/cD9r5nNBo9Kfs/rVaLZrPpEOrWQidm1Wq1iEajjgvsxMQE8XicSCTiiIPUajXy+TyapjkE8pVcdQVBYMuWLXi9XjweD6lUytmrVxSFbdu2EQ6HicVixONxp4zRaNSJMXYESjok0aX725VKhUKhQG9vL8Fg8IRJfGshlUqRTCYpl8vOWCWKIsFgkHA4fNbF7ZLJpDPmZrPZVYmyHTfhwcHBZXvKv6noxJysJcl0xWLRET+QZZl8Pu/EdzvHNRoNx0VbEATa7TbVapXh4eFl9bHjiD4xMUG73Safz581wgW/KZBlGVmWqR+1ARyJRKhWq5imyfz8POl0mkAgQLlcxu12UywWMU0TRVEYHBx0iM9PFbjdbrZs2eLEF2ZnbZOdjiBKvV5HVVXC4TDbt28nmUwSi8U4fPgw2WyW6cWk6EgkQl9fHz6fD5fLxcLCAqqqOvGBoaEhZ262EYGSjUJRFHp7e1eNQ1iWxezsLJlMBlVVHXHdzng7NzdHNBp1HNXXQigUwuPxODGi1fI4LMtifn7eWZ88met7shgZGXGebalUolAosLCwAOAIlXd1dREKhdi+ffsJ5bf8JkNVVUZHR6lWq44IQ8cBvZO7VCgU6OrqYmBgFcOyTWxiE6ccv5lZU5vYxCY2cRagu9vg5S+38HoFgkHYs8dOvjlwwE5elyRoNI6Q1u64Y2O/e9NNNlF5NdTrtrPs4CB861vQWoFzt22bTb7tIBSCiy+GbNZWjR4bg54ecLkEmvV3UrjPjfnDb+LRa4TVHH6jAgg05QCV6CBSyMuhvb9DNnkL12y3iSpguyEUiza5qLMX6fHA9kSGkdGvwDe+zU2ZArWaxFw5yOzIM+l+3Qu44KV7GB+3idiqCoZh3ztNk3FvvxHffWnck0V26I8z7xmkpEec4JJoasTbcySMDFoshKUZBPU6Q8okVncKQRJoNiSMxZxauVUlnhunHo7jivrp+61ryY1BIqqzt3wvw/u+TmD6AJYl0GyKiLJIcfczmL/oxbS2XAD4gXcwWb+V6CPfIjs1jtloE+73kbrxGex95eUIi4ut17zGdu782tfgwQchkbAJPk9/uk2uApuA/qtf2QrZXV02Sf/xx22HifZxGvKKls6L932AbeUHnQDpvGcQU5Dob47iNlvLAtA9apqW6MFjNhGwkDAQLYOAqSNbJoI/iOqLgAWaJiAKFuGZLGUlTtHdxULoHBAEvr3zD5nY80JeGf4y5qM/oVFrYVrQ8sfJ7rmW2b0v5KGfpFYs8yteYT/zBx+06/HAgF1XZdl+SZJdl845x1ZDB3C54K1vhRtusOv83Bxs326TmS+4wK7fYBPQH33UbiPnnQdbthzD++Z1r7Mdmh9/3CbA7dxpv18o2KToG26wz/+xj618z5/3PNvZBYZR1T/gC3etfNzv/i50BSCRt6/XsmyioNttP+eBcy4jfu8TyE/MUxBrqLKfeHUeF208VhPN40E3XVimgMtsYwkChmAn4JiCiGSZmKJEXF1gVg7Q3a0SiejE4xqNhge92UN/Zhw1HuDNu++k9fbXsn+/TdbfvRsUBR54wO4PHngAUgGVRELDq2tYARMppJHN2AtoVfJSl0JIlnHEdQNPJy0JAZOhvirewixyu0KuextsPYfLf+tI8rnPZ9/r88+378XPfw6HD9v3RhTt/iyVgkOH7KQCl8vuE/bvt7/bs2gkIwh2u7n0Uvs5f/KT8Mgja7eTc86BSy6BanXlz6XiazD/46fUD00RbBbwWXWbHCspmKaAvOhMKgCmz4Wi2B4ckaEAgauv5aVPE3C57DKHQvb5qlW7Llartpr/I4/Y719/vV139+2zSbw7dtj9xJ/92coJPktdX3TdJgoGg/Z3JOnIWGH3nyuTpB9+eLnoRQeXX27Xd0Gwk0cfesh+Jpp25Lcl6UibrNXs19Gx40TCbm9LRYT9ftud4Oab7fqVyRz53le+AggCX931Hl7zqG3x0duaoM+Yo+EOoeJC0jW8agVddpGLjZD3bGP/te/irS+BZzzDLs9118EPf6hw+PELCKsZUu0Z8lafk5AmKxbxuEYwaIAFtbqEXMihtE083X56brqIm885Uub77js2oUUQbNL1yAj88pdHErEeesh2+giFYHLSHsOOF69+9ZH7nMvZY2exCN/5zsrHd3XZ97nT3wUCdtsxTfv8pgmm8SKqd89S/slXULIZEu05erUpVNOFZOlYCJSVGHlPH+M9l5L407fycmBhAX7wg7XLe/vtdh1/+tPhJz+xnWxWu+5y2e7nkslOHywAV9PiaibmDuGdH8UttujZHqT72U+nOR0ilLX7Jbfb/u0f/MCuU7feCrt2wc9+Zgu1dM4pSba4SOJHZTyfaiG1LHwB8Hg1XC6LUEjHAmo1CbUtYi62Dblt4tIMErE2119dp7FouP6FLxx7HYoC114LfX32+BIMriyoAPZnu3fbZT10CP77v+1+bqXjLrrI7vNaLfs+iaI9X9y3z/77ec+zz9uJrbz+9fDZzy7/HUGA226z+8Yn3n4/A3NZvLpKYMCPW9ewAFGwyefBoIEoWM4X9UgET73IFd0PEn9bk8l5L4ZhX2+nTnq99nU3m3b77euz69z+/TbHciX86Ed2XT66nK95zZGxePt2+/W//pfdjiYmbIedSy89MrcEKH/sY0iHD+MaH0ePRDD6+uzEJ0AC3H3QnKnQm50m6lX5UOh9FG+7nUcP+SgWj8wjTNMuc63GMZAkuz136eM8Y+IXRGszNEIRokGAIx2daQo06iLttoghuigpCSJqlq2Vhwk1ZlZNRn/r9tvx/k8Bl6eFPhzDbbVRFAuf16DZkhg77HFuUsbdz0j9cQJagT/Mv4WG5kVsNvGEZSrd21l42o00RtZ3k1kJtZo9/x8YsPuPUMgei1eKNbziFbYoyS9+YY+9uRz813/ZhOqlyXXJJLzgBXZ77LzGxuxxb3jYJrNv326fo1i0573z80fmep39+R/+cHly8kp43vMswmEBUbTbhyTh/L2YC8fWrfbf+/bZ49jYmH3u66+329jMzJE8tbEx+36cf77dXnt64OBBew4oSfb8fP9+u/2FQvacs9Wyr2vnTvt+dMxgOggG4SUvWT6G+Hc/H/n2+5HmYZt0CGN4kLYq0W6JCCK43fZCxSg3iLdmaPZ0s+s5vbz//TvYt89ue5ddBl1dArz4zylEIpi3f5VGtomQLeJutLAQKLqSlF0JakqUu0bexpV/9Cx+7zx7LWhZ9nw7nbbdrJZC0Rucs3AvI/VHkNsN5KAb/4XbKV10A0I4ypYt9n0dHbWf3e7ddn34+78/tu+Px+HKKyEatcep//N/Vha1ecYz7OdSKsH3vrf2cz9dcLvh7W+36+Stt8Lf/M3qxy7t/139/bg6C5UTaJeWZXH48GFyuRxTU1MYhuEki4BNqJuZmXHIa6Zpruj60oGqqpimSV9fH729vei67gTNRFHENE0ymQzFYhFd1ymVSuTzeUzTdBIQu7q6lrm8BgIBuru7MU2TdDrN4cOHCYVCBAIBZmZmSKfTlEolkskk3d3dBIPBFV0S4vE44+PjtFot8vn8cbk/NxoNFhYWnHJ3ElQ6ZL2NKCOfbUksmzj56KiwezwexzVKVVUniOn1egmFQgwODm7I+fvXFZ1Enk6C73ptZ35+3hF/2LNnz4pK+4IgkEwmMQyDhYUFAoHAMclsZwuWujR01NUjkYjjCNNJgPJ4PAQCAcetGNgQYf9UolqtMjU1xczMjJOoHQqFnP7NNE3K5bLjfiMIArt3735K1PeO44Ku60xMTDAxMUEikXCcJEzTpFKpkMvlkGWZ7u5uenp6EEWRarXK6OgohUKBXC6HruuOgEC9XqfRaLB//37q9To7duxwxqjOOJjL5ZyE1KOT1gYGBhxnnrm5OcrlspOM2ylTpVLB7/fT19dHT08PtVqNYDDofNZoNJzE+I7Qi2mazM3NUalUaDabGIaBqqrkcjlUVcXv97N169Yz5iLeQblcpt1uUy6X6e7uXpF83oHP58Pv9zvOP729vWd07BVFkXg87jzvky1E05lziaJ4zHOSJInt27dTqVTIZDLLEqk7zobJZPKs7CM3ClW15/pLyednAx7sfymy0ebqmf8koBWIqFkGGwcB21W0rMSY8AxQcqW4fed7T5rA8xNdz+XZ6f8k6+6jpzWBgElFSYAg4NeKSJaBbGkYSIiCRUP0U+qIRC6BKUiU5DhRNcu2ykN42/kNuYWfCezOfZ/gIgGzto4IZFv20RT9RLQce/M/OKMEdIBfDLyCWNN2jUu1puhrjmEIMqroRrJ03GaTphRgyr+DGf92vnLOX6y41pP0Fs+b/BTJ5jQ+o8a4/xw06Ui7bosKGdlPyZVisHGAZGuaayc/xaHklbZA9wrorh7Eq1cBi5ocAWwi6qR/Fz2NcQyj7NQnAQtdkJEsk6bk40DkEu7Y/QFU5cQJFv3VfaiCGwPJPo+poa/ivitZdgdgAZroWpGICXbc8plzt5NspRExmPSfgynIJJtTKGYbt9miKfrQBZmwUVqMYQq0RA+GICNaBh6zhYBFrL1AZlHUMazm0EQ3quRlNrz7hK+5A1OQuXfH29mffDYXzn2dnaWfIVpH5n5p/w5+2fUCDnZdvaJYJUDJP0hViVGRo0S0HFXXCg7DSxBRs2iim7bkZypy/trH1qcJqznCeoGMu3/d6ym5kiTUOdxGg+HiL4+LgH62wRRkfrjt93io74W88cHfQcDCREJAQ0emJXgQBRNNcDkO0ncNv4nH+m46KUTVpivKF87/32zJPsCF899gpPqrI59JAR5OPIdf9b6Aku/kJsqqsp/bz/0wu+bv5aL5b9DbOIxgGXZsUpBY8A7yUNcNbCn+gkuy3yTVnkGwzLVF+i0Lv2ELj5fXqZ9PNYSas7xs3/voak4RbmcJ63kUU8UURBpSgLCa4aLMPXx34HX8cvDly7577uxdDNb2MdA4RFWOsOAZWlZ3Kq4kFSVBsjXNQPMQuqjwtJk7+J+h/2/FspR9/Xzuaf+H7ZkfcOH8XQzUD9BujBLTcrjNJhU5wpxv5BjThZISI6YtJrpjUXKtnFsBEG/P0RJ9tOQAn9/7UZ5/+O8B6GmOs632K+pSCNJ2wFA0TQTLQvP5MDwe9KEhjmkZloWUy9EOhxEXJ3jS9DTm4KB9L1wujK1bMVotxHweoVrFVS7TCgRoXXQR8bvuQjgFifhmo0H1G99Av/tuHt23D0vTsHw+zN278T33uTzzFa846ec8U+gIo3k8HizLIhaL0Wq1GB8fX7aOCQaDDA8PE4lE2L59u7Me6QiAqarKwYMHnTVns9l0nEhVVWV+fp5IJIKqqqiq6pA1O+SZhYUFEonEsr3dpefvCJvm83nHZbKzr9RxspUkiWazueb6KxAIEI1Gcbvd+P1+BgcHnfWm3+93CKmdNf/Y2BitVotSqeSIBMqyTCgUolAoEAqFGBkZ+Y0Vm1wL1WrVEcnTdd1ZxyqKQjQapaura0XS69EQRZEdO3YwNTWFy+Wi1Wo5BCWPx0MymcTn8xEOhxkeHt58FmcpRFFkZGSEZrPpiCJ2COFer5dEIkE0Gl1z/+d44fV6GR4epr+/n1qt5pzP5/OtSXbu7+/n0ksvJZVKMTo6SiaTQdd1ZFl2BCu2bNlCd3c3sViMkZERZ+9WUZSTeg2w8fhSh4DbcVUFyOfzjkhiR4QabJfyvr4+YrHYmnE/QRDo6emhu7ubYrGILMuoqoqu64yMjKz4zDrCnZ09x9nZWUKhEMlk0rmHxWKRWq3m9Lmnkvi1dB/+bEcikSCXy9Hf3086nXYEBTrjXbPZpFAoUKlU6OnpIRQKHVe880yh426cy+VoLyYkd8aCZDL5pPdN3W43Xq+XTCaDpmlOPWs2mySTSRqNBrIsO27orVYLl8uFpmk0Gg0CgQC6rjvCCYFA4Jj60iGqVyoVyuXyJgH9NKPThnO5HI1Gw5k/CIJAf3+/I0ZUr9cpFArMz8+TSqVIJBJOLCsejzv9XSfWv1rd6whE53I5J263FjpiOIqinPS4RDQadYTNLctibGzMmT+DPX/tiFh3BIcLhQLT09N4PB4nbtQRYegQryuVCq1Wi61btzpmBLVazbnmwcHBVQVKTgY615LL5Zifn6dWqyHLMi6XC8MwyGaz+Hw+ms0mzWaTnTt3rvkcOq7uHbHsTpwvFAo536vX604/NDg4SDgcPqNiUh1B4Y6wfTwed/JrOvcimUySSqU257fHCV3XOXjwIIVCgXQ6jSzLRKNRvF6vI+AwOzvrmAl0+pJNbGITpx+bvdsmNrGJTZwhdHWZnHeexdFz7NX2iG691Sbn3HOPTYTo7bUT2et1m+hzwQU2Ie14YjWvf71NirvrLpvE09dnEyAuuGDl44eGbCL6ckjw5rfSOPxCfvXRrzH7nfvxNMu4vRaxcyIkb34uwZtvZlc8zus2UKbSf/0X3PZJqNUQs1lczSZeyyLucrG7OA6f/QbtQ1ew533v49xzV9hctF5J/i9HEb+lI6bTjJT3o+KmZvqxdAufWUPySVgDvWjJJJYgIGUyxKamsNIZ9GiUgMcDhoFcLCI1GmipGIGBFKE/fRNPe34EvVCg8u53Iz7xBEI+j1QsIGoaCAKGx4MxlYbqfZje5xN997sXCeYRYH0V53jcfta33rry54Jgk16WYs8e+OhHbdfJpYS1c8+FF77QJmw8+qhNepmftx3Vb7oJzst/g8Kf/Q+BzEHqriDl2BAJMUO0OIrLrCJiImAnhMgYgJ1AAnYg03YWsF2BfVqFVGMcfcuexedgIczOovgqCP19zA1cRzwhMDRkn/uii3YAfwqWhdlqIUgSgsvFBYtlf1nZvp5HHrHJqBddBK98JU57We3+rIVt2+DNb1798+5u+7WR39m2zXZYXw1Pf7pNuLz/fpvUtGuXTV73H2X8+fLrC/z4I/eRfriILFvEd8W59gPXEOw9NkF7KdRLX0TjZ/+FEPOxrb0fbWAL7tF5JK0BoojoEvGgo7cs3EYLVfQgySYuSUNuGaiCgstSiVgF5J4UwYi9uex2mbhdJoQDmBU3QjaLcffddL/2tcfcm6uvtl9/8AeA5af0HBOpJqAoVYLJKKmkSqGoUCzK5ORhvLUmBjpKu43LaNtpHYJFUsogTWUxFQVt5yDRrQMM/ct7eNYquXKCYJMNL13BOHbPnjVv2zJEo/ChDy1/b2zMbidjY3a/ev319mvtWEM3rRs/TOMNb0B/6CHkZhO/WceybO8PSxQxZRlTkkAE0TRpDw3h3tbDc//qBsRV9nx27Vr9jEcJjHPbbTZh9ZFH7PZ9xRV2/TxZ+2Pz8/C5z1mMjgooisU118DNLzKw2hqCx4Oi2HX+6U8/OefrIBCwibRL8ZrX2G463/teim+nPs7NBz+IKMpojSLuchmP0cDySpiDA0ihEIlzz2Xrhz7EdbHlLm6KYpMcy//6HDJv/iniZJpufZpmspeubg1ZWs7GdwstlPkZ9O4wyfNivOy2y1ia83nrrfDTn8Ldd9tkvd5eeNWrjtTJF7xg5WtUVZtwvG8fPPHEEReHZz4TbrnFJjrefrv9WTJpk4uf+9zVn+3LXgb/8R826S8chquuOiIUsD4EePWbqHytH/2LX0RMp6FaxaUalCpuikaUqjuO+rznc9PHXk0gfGQpeeut8H//ry0Y0GjY7/X12eINr3mNTT7v3PerrtpIWVbD9sXXEWzZe+xRb3/78n9feaX9OhqVhSBmUsCd0xhKFY9xcYiEj8qMzpeRPdDVYzBwSwDfBfbbr3gFfOMb8N3v2oTV4WH7nhzdVjeCnTvhve+1Say3324/y4EBu8+79toj93KjePGLbZL23XfbxM2eHnjta21yKMDO/iKyt4nokYjFTUBd/ccAy+NBqFQQRYtnnFvl8udsPAF+LVPLW2+1BS+++lW7D96yxW43l1xy7LEulz23ecYzjv1MW1hAuv9+5JkZ9EAAs7//mGCBKIJ/IITl7yGcTtMuRog/9nVevELy0VJBJgBL16l+5zvoDz8MzSbG5CSKOotLbWBtGcDrXf3+WUAxHcQ3M49XUXnuyAFe9Nd9jI7afXguZ1/3i14E3e/7FWK5jB4M4g0KeDlSF70eg61bm+TyCvW6hMsr4Wm1OEd7BG1qFDMYtLP6cyKm+ihU7sOc2oL/gx/EvUYw+mRgYMB+dfC0p8EXv2j3bf39ttDSM4/DkK2nx36thFtusevMl79s96OdvmdgwGJ4uMVLX9pgaKjrmLXWati+feX3LVWl+u1vo919N9fMzdkM6oUIQvMKAkMvpu/qjavrXnWVfT8eftieE15+uS22cky7ti4jX92F9PMKnrExrOkDeFMpSNkOa1a7jZjLoZQK6KkInuEkyhvfSPyo+w+AIBB729vQXvlKpK98BeuHP0TPV5iZ9zKl9jG+/ToSN1/D3/y2Z9m9EgR7/n3RRXDjjXZ/9KPv6+x99N+4NPt1+kM5FKEIsg6qhPVEGGv6s5hXXUX0pe9C9PmOESrYuhU+9zl7XuF22+vOZz5z+Rj10Y/awmsPPGALPJ17rr0GWDoP/a3fgj/5E1vgYiMIBGwxhd5eW4xnI/D5bPGWVssWElgJb3jDkfr57GevTUDv1M+TgWw2S6FQYGpqCrfbTW9v77LAXSchJJ1OMz09jSiKayZpHN1HLg2EmabJ9PS046jebDYpl8tIkkQ+n+fQoUOOA3Cz2WR4eNj5vU6wR5IkarWa44YzPz/vkNJ6enoIBAJOkPloeDweFEWh2Ww6bikbQYec33FiaCx5AF6vl1gsRrFYZGBg4Bjnnk385iEcDhMOh6lWqxSLRUdFu0NufSoQcU81ZFl2EuBmZ2dJJBKrBs0ty2JychJRFJFleV1H40Qi4SQDZ7NZBgcHT8UlPCkoioKiKJimSbvdduqEz+dbMYmj1Wo5ivZPVtm+3W6j67pTJ4/XFWVhYcFxVtmyZcsx9bnjVuRyuZiamsLv95PJZJ4yqvGDg4OOQ1oul3OcJGRZdpJTI5GIk7DU1dWFqqocPnzYcelOJBJEIhGnTnd1dXHgwAGmpqaYnp6mVCo57imdZNVOourWrVuPuadut5sdO3YwOjqKz+ejWCw6JHdBEBzntkAgQF9fn5NsWC6XyWazVCoVR/SgI4LReX92dhbLsgiHw3g8HjRNY35+nkwmQ09PD7qus2vXrjPqqNAh8FuWtSFX9s716bqOqqpnVZ97MpLOdF0nn88zOTlJNptFXVSj8vl8bN269Zgk+1Ao5BA0OuPRqUiAOxN48EF7j+usgyDw8+HXMBW5gIvnvso5xQcQLR3RMjAFmYYc4qHktTzc+0JqnpM3b9RkH1/e8We8cv+fYyHQ1UqTbM9RkSP49Qo+s46MjolIQ/RTl0PkPCsnUzXkEDHNXhyFWgtnLQE9pOZs4rDkX5vsuIiGHMRtNAlqhdNQunUgCHx7xzspenq4bO4O/HqJoFZENlVMQaIuh2nJfp6IXsa9295CSwmv+DO7svfh08tEtRwz3i3LyOdLoUpe5j1D9DbHyWtFdmS+zxM91614rGK2bcdvQV62uDYFmRn/dlxGk4iaxWvUkDCQTA2P2WLMv5fvbHvLkyKfA3j1Gg0pQEgvYgoSXrOBarlRRZfznAXLxGWquKw2qqBgIVKVV75HAFuzPya06Bg95x3CFGQEyyCi53GZbXRBdkjubcGD12ogYiKbKqrsxhAkZEvHZbYJ63myVh+CZRLVshRcXcz4t580MQmAdPRppKNPw6OVCTbnES2DhitG1bt+sNEUZB5MPo94a4Yt9ccJaEVqysqxQdlsE9My5F3dzPi3kwmtEUAC3JqtqCxZOqq4/vhqiDYZWzI1PPoqasxPIYiWzvMP/S1+o4piqbQkL3PeIepyGAsBr1EjqmbwGg0Kgsiz5m7ncOoKmq61Y7MbhSnIHE5dweHUFbj0Oh61hCkqNNyxY0jEJxOmILOv53r29VxPrD5BsLmAgEXVnSQfGAFBoOaKs6v0c7raaQJaYU3hA69RxW22SLuS/Cp+DaKln9Lyny5423le9dif0Fsfpa85hi7KlJQEbdGHiEFQKzLYOERVDnPt1GcwBYmHBl5if9myuChzF5F2BkOQjyGfOxAEsp4BfHW7rl2Y+Ra/GHwF1ioO5YboYn/3tezvvhZFb5CsHuLl+99PsjVNT2uKnuYYJSVJS/IjWiYBrbg4/gs0F8stmvqxjt+WRaw9R0TLkfZt53DoAhbCu/niuR/n+Qc+Rrvsw6NXCWpFNLWJFAhiGgKK1sYyLbThYZAVmi0JQwdBBNOAQHESqdmkMbyNpuHGZbUJzhzGGB2lFeui7Y7g9VpIooglCMiaRrunB3N4GO8HP3hKyOe1H/4Q7SMfQSiVkHM5bjh0CEcT9P778X72s0z//OdEP/QhxFNEyDgd6JBzGo2GI04mCAKapqFpGrFYDJ/PhyiKuN1u3G438Xicnp6eY/ZyOkKnHbJLKBSip6fHWZdZlkW1WiWTyTh7s263m2QySS6Xc4ihRztSKoqC2+2mVqthmiahUMhxTS6VSg4hz+PxOPu/7XZ7TfJnvV53yDZDQ0P09PQ4ztxLv1etVjl06BC5XI5sNuuQziVJcgiVsizT39+PruvrkmI2CtM0HZHCDlH2qehs23EhLZfLlEolmkuUdf1+P5VKhWKxyNDQ0IqCskdDFEWGh4fp7e0ll8s5BPSO63MymTwl7smbOPnwer0MDg6e1r3bzp708WBoaAiv10s8Hqfdbi+rc4FAAJfLRSKRoK+v76QTzpeWuxPXWg+WZdFqtYjFYiiKQnd3N263G5/PRzwep9FoLCPgd/rgnp6eDe0VCYLgkMpbrRatVotwOLzqtXd1dWFZFpqmUavVaDabTE5O4vP5HGfb4eFhAoEA27Zte0oLJZ5MSJLEtm3bHPJhPp8n3Un8wn7OgUDAIU5u27btjIuIrod8Ps/U1BTtdnvZeOByuSiXy8zPzxOLxZ4U8S+RSLCwsIDb7aZQKDiOxpZlOeTCcDhMPp9H13Xa7TbRaJR2u0273Xbiz+12m23btjkk9KPhcrkcofJNnF7E43FmZmYcV+yljt2iKNLf3+8Q1KvVKiMjI/T09LBt2zYSiYQTwx8fH6dUKjlxk05fGI/Hl81vY7EYmUwGWZYplUprzlXa7Tb1ep3h4eFT5q4eDoeJRqNkMhkEQcAwDEKhEH6/3xFSLpfLiKJIs9nEsiwsy8Lr9XL48GFUVaXRaKBpmiNuK8syCwsLjrBFX1+f47rdcVJfS6DkyWJmZoZcLsfExASyLDM4OLgsTtpqtcjlckxOTgIwNjbG9tUSsRYRj8eduElH9GJhYQFZljFNE9M0CQQCjqjWyMjIKbu+jUIQBBKJBIlEgkajQavVwrIsFEVxnu1TER1RhM7cyeVyndZrmZ2dpVwuMz09TSwWOyaPJxAIkEgknPitJElEo1H8RxMyNrGJTZxyPPV3hzexiU1s4jcIV1xhv04mOsSCJwvf1mEu/eRbgbcesdE9zglo6T//E/7lXxCnplBKJXSfDyMUAlFEqNdxT05iuFzQblOs1Yj9zd8gHJ30KgjE3/teit3dGLffjlmpIBSLBFUVS5Swgv1YwSDG7t0E/uzPwLKov/vdtP1+xHweqVhEKBRsMnkggN7XhxUOI73jHYSe/3zMRoPyO9+J9NhjKIu2wHo0iub3I1gWYqWCK53GzOXQdJ0iEPvTP33yN3gdbNliE9ZWw7nnHkuiyv+vrxMz5pH9Gso5XYSrWTwTk0jNBuKi0qchCAiW5Shbi1h0aJkWIHb+b5p4ymVq8/Pg8SDlckjNJurQEN5zhnj9p27ijSvlsggC4gqbkuEwvOlNx3cPzia43bYgxKtfvfLnajpN7ZOfRPzJT7iiXrfZLQC/8qD/1j9TeNazCPze7+FayWIUcPX2Un/lKzE+/3mkw4fx7N+PqKpIuo7udoOuI+k6LsPAcCvIbhHdpSDU2liSgOCWUZpNBJeMYBWBozZdBAEjFEKq1bBmZ7F0/di2dvTxF18MmQyumRnahoEgScSiGrHoohOsPowxOYlQExDbbQRNQ5Qt9ICXdnc3hEIYe/YQ/su/RN5AwOpUYGQETqS5enbsQL7jDorvehe+r38dudFA1DRbwEEQbEEG07T7lJ4ezMFBfH/91yctwN3Xdyzh9mSiuxve9S4TvVCg/rWvIf73d6j9wzwAlteLeemleG+5Bd/R6hinALIML32p/YIusP6R+oMP0vrKVzAPHLDbkscD552H95Zb8O5dgZ28BKFrrsJ45ucRfUW6JybQ1Spmu8shIlumiVAsoiwsYCoKVm8v0qtfvSgsshyrCSOsBZfLJqmvJp4QCtku6htFdze8+93HV4ZjzvnCF8JNN1H/2c9o/+xnUK8T8XhI7N5N8LnPXbEviMXgbW+zX08leC+/nPo//AO6z4eYy9nuDGtAKhTQw2HMaHRZ3RIEW1zlpptOXtl27Vp7XD8eXHXV6sR/we3GEkUEwzg2OWkl6Lrdr8GK4/eTwQUXrC6CtFHU7rgD6nWkRgN9+/a1rycaxVhYQMjlMO66y1YSWA2WRenf/g3zzjsR8nnESgXBMJDrdZRCAbnVQs3nsfr6Vj2nAERTFp68xshQja03VAhHbZL90UT7YquFYBiYqyReeDwm/X1tME3k0VFcZhvRMBAMg7bbjeVyga7jSqexZmfRymXqb34z1t//PZ6O+sBpwO7dx4q9nEysVGcMw2Jh4eSwfWs//CHaxz6GUCgg5/MIjYbdTlwuzP37qX/hC1gveQnRP/iDDa17kkl461s3cGJBIPKRj1B6+9ttYaGFBVzT0whTU057NTwe1N5eSKUQ3/Uu/OsMQEpXF9Hf/334/d8HIAU8bQNFAVsA6uUv0Xnuz/8YyXwAsZlGWahgyjKWotjzzvl5dL8fo1ajOD5O5BOfQDoqyDswAO9737qXzi232K/VkEjARz5iO6o/8siRMr7mNfb3NrIEtSz49KdtcbeOK/3QkE0kv/LK5QIimQz87d/C44/b//Z67XNdd1T+/+AgTE2tfL4N5JlsGNlslmKxiCAIqybJdJLoxsbGqFQqZLPZVRN1lqqBH+302XFunZubQ1EUUqkUkiQ56vzxeJxsNsvExASWZS1zzwGbhB6NRslms4RCIVRVddzVU6kU3d3d6waERFF0Aq4bQbFYZHJyktnZWSqVihNw7ajil8tl57NOWTaSLLeJX38Eg8Ezqph+JmFZFsVikVKphKZpiKKIx+MhkUg4SWSpVIpCoYDX62VqaoqBgYFjSK6maTI7O0uj0cDlctHV1bVusq0sy/h8Poe0ejai417t9XopFotrupNYlkWpVHIC3ieSSGaaJoVCgUwmsyxRsZMwmEwmNxRM77hQF4tFxyVsNXQSXorFIsFg8JQmYZ5MCILA1q1b8fv9eL1exxml4wjh9/vxeDx0d3eTSNjEmkwmQ61WI5vNMjAwcExSWigUYu/evYRCIcbGxggEAk5iUCeJze/3MzAwsOpz8Hq97N69m1wuRyAQWJYk0SH9p1KpZYk5HTEMWO7k8cQTT1CpVEin044799Jn09XVRS6XI51OIwgC4+Pj7FhL+esUo5MQJEnShhJTOklEne/+OqFWq/HYY48xPj5OJpOhUCg4pIpQKMTMzAyPPPIIl1566TFiHR2yyK8THnroTJdgZch6k6CaRVUC3LvtzXzXehPx2jiK2aYpB8mGdmCIpyYpeDZ6Pl885y+5+eBHqLgShNQcfqOCy2xjIWAgU5cCVJUoc94tq5K2rSWepAIbmzOvh1277D2+j3wEFvMFnzSsRUljYYPzenuPavn1nW5IeouQmkEwdZquKP8z9P/xYP9L2Jn5HiOlX+DVq2iCm3n/Vh7ref66IgV7st8jrObQBYX6olv5aqjJEdvhW81xbva7qxLQW3IQXZCRLW1ROGH53EuVvI4DOEC0PUdYy6NJHpryk5/7aqIbVfRQl0KImFiIuCxbgLhTFtEysBAwEVEsDQ2FkfoT3PrL3+Ghruezr/t5qPKRMXVL8ZcEtBKWIFCTbTKwT68imQaypdOUjoyfqujCbbQQMXGjopoauiCjiS68RgPJ0Im0M0T0PKropujq4me9a2w4PAm0lPCq4gNr4ZHeG3nGwtfJ6P30NcfImL2UXakjz9Ky8OslulvTtEQfRXcXD/S9fO0fBXTJHkdMbEL+erCdsk1MQUKTnvpEsB3z97G18jADjUM0pACz3pFlc5OaGKMmR4m25+lrjqOLbp4x9QW+v+0PTnpZVNm/rI6fLhT8wxT8w8e8P564lNJEiny7i+72NKrooS0fG8CXzRY9jTHagoue5hjXT/8r101/mpbk42DkEh7uuZG50J6Tp4R9GnH12L/S3Rinv3mYvKuLvLt32XXUlBg5o8lA4xDdzXGeO/1ZDscvpeLro6vyBInWLBEtR87ds/b1CwIFV4rudppIO0Nf6Veko+vvyGqyj9no+Xxhz228/Im/oC35iKoZ+prjCNhzZk10U3B1Me8ZtMnjgov+1hgt1UdFiaELMi6zTUTLI1oGM96tLHiH+db2dwDQVoLcufeDxOoTnDd3F0OVxwiIDTTZQ1BLE9KLdLencO+boiAnKRPFQsRj1oipGTRLY9y7m+nsbv57119w84EPs83lJVKbx5ebw8UcuiAiSypSUELr6cHq6kJ8xzvW3cc+EVR/8AOM978fMZ1GyWbtPevF+LwD00T60Y8ovfWtRP7xHxGfgqTX2dlZZmdnyefzzh6OJEmO+1swGESSJNxuN0NDQ3g8HscVfCWUy2VqtRozMzOOs/VSCIJAKBTC5/MxMTFBNpt19ouazSb1ep1AIHDM3k84HKZcLlMoFKhWq4TDYbxe74oiox2SvKIoVCoV6vX6srW2ZVkO0S0ej+Pz+Zw96qPPa5omY2NjZLNZcrkcPT09zjq7g1QqRSaTcUgx09PTT4qk0263yWQyjjtlZz0vSZJDmniqEDSz2SzpdJp0Ok2j0SASidDd3Y0sy2iaRqlUYmZmhlqt5uyrb5Qc7HK56O3tpXeV3KdNbOJkIpVKkUwmKZfLVCoVh8Dt9/uJxWKnXBwiGo06fWCr1VomsmBZlkOC7bj+6rpOKBRy3M+j0SjRaJRarbas/D6fj2g0ekJEMI/Hg8vlcgi7a7Xd7u5uh5gZj8cZGhpCURQkSUJRFOLxOF1dXWc9gfp0w+12s2vXLubm5vD7/Q5J2rIs3G43LpeLWCy2TOjlbEU2m2VyctIRt3W5XAQCAQRBoNFoMD4+jt/vp9VqYRgGQ0NDJ3Qej8dDMBgklUoxPT3tEB07404ymXQEdiYmJnC5XDQaDURRpFqtOo7JfX19hMPhVcWQDcPA5XI9JeIdv26QJImenh40TWN6enqZu7Uoipim6YgDDAwM0NfXR09Pj0MwHhsbo1wuU61WKZfLTvzS6/VSrVaZnZ1leHjY6T8DgYAj4LGwsIDH41kxhqPrOjMzM8viSqcCxWKRTCZDpVIhkUhw3nnnHTMvrNVqTE5Oks/nUVWVZDKJqqoUi0Wq1SqiKOJyuZz63xGcqlaruN1uIpEIqVQKt9vt5F6cKsKwrutks1nm5+cd8vnR7crj8dDf38/s7CzpdBqPx3PM/H4ldPrGjohMZ3zsiDp3RLXOxtjlamLlTyVomkYulyOXyzliAIAj3JNIJE658LVhGI4Agd/vX9VEQpZlBgYGOHz4sBPr3SSgb2ITpx+bBPRNbGITm9jEyccJLGRaBw/Cv/4r4sQEUqNBa+tWhCVJgBbQ1jSkyUmUsTGQJMr/8R9EXv/6Fc8ffeMbMV/9aipf/zr6/fdDtQouF8LQEL5bbsGzc6dzuPLv/071m9/E+NrXsA4fPnLOcBiuuw7/Lbc4RODyZz6D9MQTuMbG0EMhzIEBEAQnDcaMRFA1DXlsDGVsDP3uu6lfffUpCaY9GbQnJpAOHUIuFNAXF+LK7CyiqiIaBqYgIC4G5ZY+zaPTgzoEdREQDQP/4cNofj96NEq7rw9rZITAxz52DAHlNxnNxx+n+cd/jDgzg5TJINfrDplPsCybvFMsUnv4YXwf+9iyuroU0d/9XYqtFsaXv4w1NYV3dhbRNJEXF4KmJKF7PJiKgjowgFQoHEnw6rRRy0IwjJVT0yQJFpM/LVVdm4AOeG6+Ge2BB7Dm55HSaZvIubQvkGWMrVvRSyW8o6OoXi96KIR6+eUIe/duiCx8NkOOx0l+7nNU77mH2sc+hjQ7i9BJFvd4sPx+8PsxLr6Y4Lvetaq4wNmK6je+Af/wD4i1GlI+j9RogGliuVwY8/Oo3/sezcsuI/r+9590UuqaEAT8F12E/wSVVARJIvjXf031LW9BlSSk2Vnco6MYigKShLjYnrRoFKu3F/OlLyV2880n8wrOTggC/ksvPevGrpMNJZXCePrTIZ/HPTVFOxKxWf8rIZdDbjZp9/cjXHfdun3iUwXSrl1YoRDCzIw9V1rLpc6ykEsljGAQs7v7rBzbzdFR5GoV0+0+xtH+GAgCRjiMWKthTU6uSsC3DIPC+96H9IMfIM/OIpdKIAiYkoTUatmCLqaJe2EBVdMwh4dXJ6G3WraDtSwjrxHctTweLElC0LQ108fF6Wmkeh1J1zFFETUaxVhCMG8bBuLCgl2/gcZ734v7859fUURjE8tRvfdejL/8S8TZWTtpTRQxgkFbPKleR8nn0QMBjC9+kXyxSPy97z2pCZdSMEj0n/6J8qc/jf6tb2EWi1Cv2/VUUcDnwzjvPDyvex3+iy9e9Xdao6M0vvxlm6ldr9siLTt34rn5ZnwXXLDhMhf/7u+Q7r8fZXQUSxRpDw/D4v0A0Op15Lk5XIcOoVoWpfe/n/jHP34S7sTKSCTggx+EfB5yORgeti9toxAEeOMbbXf1bBaiUfu1ElIp+PCHbff0et0mqq+UG7BWl3OyCOgdx4FSqUQikVgzwNZxhygWi0QikWWuvUsRj8cpFossLCw4hG3AcYfIZDJO0LJD6gsEAkSjUYLBID6fj6mpKebn53G5XHR3dy9L4PH7/fj9fgYHB53f7CT+dAJVgiA4v7k0QGQYBqqqoijKqm7LS2FZFtPT0w65sONYu/SeeL1eotEoU1NTjnp2NBo964KVm9jE6UIul2NmZoZ2u021WnUcd71eL5lMhkAgwNDQEIFAgO7ubkzTJJ1Oc/jwYYLB4LLEo3K5jCRJdHV1Ua/XN+wa0Ek0OVvJpy6Xi3A4TCwWY3Z2lkAgsKKTBtiO44ZhOOIXxwtVVTl06BC1Wo1SqUSpVELXbcKQ3++nWq2Sz+fp6elZN2m3Wq2iqiqtVmtDCb7hcJiZmRl0XafRaKx6jWcbBEGgp6eH7u5ux+29Q4DuuEl3YJomuVyOYrG45nP0er3s3LkTt9tNLpcjEok4ZPVOwvt66LSFrq4uWq2Wkyzj8XjWTXTtfF6r1ajX68zNzTniA0dDFEVSqRSmaTI/P4/f76fRaJyxhBdJkpBl2Um8X+9aVVV1jnkqusOthnq9zkMPPcSjjz7K9PS0k3Ds9XoxTZO5uTmmp6fp7++n3W5zxRVXMDAwcKaLfUoxN3emS2Bjxw541avgk+98nKfNfZ09hZ8gLSFmjoaexoM9NzEev/S0EOrmIufyLxd/jm3ZH3HBwjfpbkyQaE3T25zAZ9aZ9QxTda89nviMKqpoz3lr6xy7HlwuW2zrhS8EUbTFdT/84Sf1kw6Knm6bQKdlESxjVQdYACyLgFGhKkcoubtWP+4EIQjwnOfAvfeu/HmycoAL577OnsKPUMwjSXfjwb083H0D+7ufy76e64/7vBE1i9tsUpcD69cvQaAhBXGbTUJqbtXDxiMX0pwNoQsKITW39v2yLCJanrIcY8E7RNP15B2e5gLbaRTDpNQZ0p4RYloGn1FDsgwkywAsFCxEy57nCVhI6HiMOnuLP6Svfohnpz/Pndv/mMn40wHwGHUkS0cTXM59kix9kXRpYXKk7piCRFtw47WamIh4zQamIGIgIWLgs2r0tSfIKynmvcM80P1iDqWuetLXfTLRdMf58s738PL978cQJJLtWRLtORpyAAvRJtJbOiUlTsYzyA/6/j8OJy9f93eLvkFbHEAOEdTyqzqrdxDUChiCTEv2Mx84c2I2JwsXLdxFWM1hAXPeLas6VBc9PfjMKtH2POfn7uPHw69Hl88MUfBoruypginI3LPlTbzswIeQLY3hxgFKSoyykkAT3cimRkjLk1DncFkqquCmJij01w8hWiaaqBBvzXBe/gccCl/IN3b92Rkh2J8ovO08e4o/IdGepSJHyHtWJqpokpe0dxtbGvso6FXOn7+LH438DqF2BsnSkC2NhrxGLGURDTmE2NJRLJVwa570ut84gkxoF/92/j9yUfrLXJD7Lj69jGzpWAjooosZ/3Z+2fUCGkqEF49+lLKWJKJmCS+Szg1BIe/qouKKk/EOcvs5HzhGLKXgHz5GeEEy2txw4KPsKfyIkJon2soSMRcA0AWFkpKg5Eox69/Kf+/+S+qeJF849+NcNvEfPM38Dn6lgkevIWCiiy58YQ+pK7bief2t+J/xjDWvuWNMeTxLA71YRP/whxGnp1HyedT+fnuj9/77lzcqQUA5fBhNECj94z8S+8M/3PhJzgJks1lmZmaYnp5GVVXi8TjhcNghoFcqFfL5PM1mk0gksqIz+Uq/WalUAFZN8Ad7TzWZTDI/P++QOk3TxLKsFfdUA4GAQ4gpFAoO6VHX9WUio61Wi1qtxtDQkOOIPT09jaIo+Hw+TNOkXq9jWRbJZJJEIsHIyMiqpMd8Pu+4Lq5EPocjRCjDMMhkMni9Xvr7+ze093w0yuUyY2Nj1Go1hyTUEXjtuIXn83kGBwcdUbyzFYZhkE6nmZ+fp91us2XLFuc+q6qKqqrOfkY2m0VRFBRFIRwOP2UdHn9dYFkW1WqVRqPhOKOGw+GznuB6qiEIApFI5Lgd1E8GOv1lIBBgbm6OgYEBDMOgWCw6/SfY7a5ardLT04PH43EIlB2stXd5vOj0y+FwmGKxuGbbNU0TVVXZvXs33d3dDAwMOHUrEAhsxtLWQIcU19fXR7FYdES5XS4XkUjkhMaa041ms8nU1BSzs7PU63X6+/uPqYftdpvZ2Vmmp6cRRdGJHZ0IBgcHaTab6LrO/Py843Kdz+dxu91IkkSz2aS3t9chp9brdURRxDAM4vE4yWSSnTt3LosHd6BpGo1Gg2Qy+ZQniD5V0d3djaqqCIJALpcjk8kwPz+PJEkYhuH014lEgmQyyfDwMJZlcfDgQfL5POl0Gl3XCYfDjlhwtVqlUCgQi8UwTZORkRGnDx0cHKTVajmk91AoRCQSweVyYRgGlUqFYrHoiOp3d3efsjEznU4zNTVFLpdjy5Ytq86be3p6mJycpFgsAnbMR9d1p952+utkMunkbnQc0TuE/UgkcsrnZPl83oklDw8PrzkepFIpRkdHj4sgHIvFHHJ9uVw+RoDl1yl+dDahWq1y+PBhGo2Gs6bpxPaCwSDVapWFhQVGRkaWxVtPNjrjZq1WY3Ad8yhFUQiFQo6o+kpiCJvYxCZOLc7+We0mNrGJTWziNwKN//5vxEoFpVqlvW0bwkoLf0VBHxlBOXgQcX4e/RvfwPrt316VRCP6fERe8Yq1HS0B0eMhfPPNcPPN6IUCerGI6PWidHUt+21LVbHuuQdpYQHD7XbI56uV07V/P5RKtO6446wj8enz82BZiKqKGQwi1GpI7TaipmGKou2AblkOCX0pTGznc1MUsQTBDqR03GoUBS0Ww9y9G+s5zyH0+tcjr8bm+A2ElsnQ/NM/RRwfxz0zgxYO0966Ffx+sCysRgMpm8U9NkZbVWm8+93In/3syo7ggkD07W+nduGFtP7+71HuvRehXkd3u7EkCUuSbKfeZBK8XqxyGUsUEXXdIZYjCHY22QoQmk0sRcFyuTZEKPZfein5vXuh2cQ1NoZx+DBGKuWQkyzTRMzlcGWzaNEoxsgIwrveRfzFL34Sd/TsQ/C66+B5z6P+85/Tvu8+rFIJZBmxvx//C1+IaxXVzbMZ5TvugL/5G6R0GqVYxPR4MEIhEEWEZhPX5KTttttuU3znO4l94hMIp1j57mRCSaUI/cu/UP74x+H++zHqdYRqFUwTXVEgEsFMJpFe/WpitvX6Jn6N4Hvta2n94hdo9TquyUm0RAIzkXDqsNVuI2UyKMUi7d5erO5uguvMK55KCNxwA5XPfAYtGEReWEBfQig9BuUyUruNPjyMeMMNpyQZW52dRZ+ZwTJN5O5u3MermKxpYJqYG9x8tiTJnsNYFma7vaLrRekTn0D6/vdt4i2g9vXZCUyCgKZp8OijuJpN5FYLq1xGS6ftOeIKEBdJy/j9eNcSzti9G3NsDFc6TdswVp7rahpKuYy06Bihe702QXoJBEnC6u1FFQRc6TRqKET9hz8kcPXVG7o/v6lQZ2bQP/IRpOlp5ELBdhqPxZw6bwJ6vY48PY08Ogr33EN5927CL3nJSS2H6PEQffObsX7nd6h+97sYY2OgaQjhMN6rrsK9Zcuq3zWqVUof+ADSz3+OWKshFoug6yBJmE88gfr979Pcvp3gBz+47txELxYRvvlNpOlpLElC27YN4aj5o+D3Y2zdijU+jjwxAR4PrQMHVhVSOhkQBJuI/mRyt4JB+7WRc603hVtrunyyTH2bzSaqqqLr+oacmoPBIPl8HtM0VyWgR6NRPB4P8XjcIZF3XH5brRaqqtLV1YWqquRyOUKhEC6Xyzl/h2A3NTVFu90mn8+vmLAoCALz8/PU63VarRbpdJpKpeIod4dCIYcA39vbiyAIlMtlhMX1Zkf12Ov1rpok0ClzoVCgt7d3xWQDsJ0Ienp6SKfTJBIJCoXCWZ8EuIlNnAp0nLM6ThYdl6wOSVdRFIc8u2PHDvr7+xEEAVVVKRQKlMtlcrkcHo8Ht9tNV1cX4XCYhYUFotGoQ5xeD50k1rM5Aau3t9dx1Uin00QiEWKxmJOMW6vVKBQKNJtNBgYGCIVCGybgd6DrupNUMzMzgyRJTh9tmibVapV0Oo3P53OcctYilncIwMCGHGkURcGyLEzTdL73VIIgCI5D0GpoNBrouk6lUlnVEWUpuru7aTabdHV1kUqliK+0N7cBrDYerYdsNkutVnOS6dZCIpFwCPjZbPaEnWeeLEKhEF6vF1mWKZVK65a7VCoRDocdJ6ZfFxw4cIDHH3+cyclJZFlm69atjhsh2O0zm80yNjbmJPHGYrFfa6eGkyXI9GTx17cZlD72Ud45911a83WiagaPaU/WVcFNSM2yvfxLDoUv4mvnvBdNPvXJqaaocLDrGg52XQOAW6vyB794DcO1x20SNjapXJYtwhGdSkVGUztitqbtwOrqYTy4d10H7rUwMAB/9mfL1z2XXmqH1r70pSdPinws9VzOy3+fVGua8DpEaa9exW22SLuSPJJ47gmfc8cOW7CsUDjynssFb3oTXHONLSL2ta8t+YJlcfn4Z3jW3JfxaWWiWgavUUfAQhXchNsZRiqPMDX7Vb6y+/20XJEVz+ty2cvvJ6Ot09urYo1ZWNaxe29ut13++++HWes8FryDxFqzpNppBMsAQcQQJBpyCF08sgaMqAsopkrZneSXXS84Kft6v+q+gctn/5um6CekF5n27cRltAjpOVxGi6iWxUBCwETCQMBCxELCoL81yWBznKISI/T4H/Efez7GZPzpaKILSxBtcYhFsUhTEDkilW0t+dt2+NZQQICGGEC2NGRLRwAMQSbt2ULOO8APe1/J/wy+6klf86lAOvo0/nP3bdww+ndUWgkCWhGvYYsQNlxBKq44NSXK9wdey6O9N27oN3XZy68SVxNWMww2DpHXG6ir9GmiZZBQ5ykpCfLuXqajF57MyzvtCDfS9NcPEtFyFJUklrB2EmpR6aK/OYpPrzCSf8Dpj08mPB67T/3lL20N2A7cbvjd34XnPhfGxuBTn4LHH1/5N7xeu0kcZR58QhhPXMbX9Hdw09jfU1fDRNUMQ40Dzuc6MiImJgKypSOjU5GimIKE22jarvHtNC6jie+xP+X/7f3rY4j7550H558PX/7yydsXOxnYs/Ad3Hodv1Fh3Ld7zWNV2UddChFVM1yQ/S4/2vJGBCxHrX8jQ6O1rO86fijdSX7o+T1+suVW+kuP4FOLGKJCwTdILrjNOe6T4c+ye/7bXLRwN7H2vPP+tH8nD/bcyKHklRjixubbhuTm6+e8lyeyP+TC+W+wpfqYLWiPhSWIZD39/LLrBTzWfT2GbK91NNnHD7f9HvcP/zbbsz8k0ZxEMjUacpjRxLOwWlt4yQLctIIGsGXBd78Ln/+8PWaHQnDjjfDyl6+awrAMtTvvRCiVcOVytAcHYTWyoSBgulyIs7NY3/kO5pvehPgUISKZpsns7CyZTAZN0xgeHl7mQCeKIpFIhFAoxNTUFDMzM8iyTFdX16qEGsuyHKHQjZCIQ6EQ8/PzjiOvLMvIskytVlvx+I7bZUcsreO4q+s6tVrNcbMMhUL09PQQCAScve9SqYSqqo7gWigUwuPxMDw8vOaeeC6Xo1QqOcTotZBIJBgfH6fdbpPL5Y6bONchaiwsLJDP5wkGg/T19aEoCrquUy6XSafThEIhTNNEFMXj3ic6negQisrlMgMDA7hcLmevq+NA2RH7azab7Nu3D6/XS7lcPiME303YbTiTyZDJZGi1WrRaLUzTRFEU3G73sra1idOLzp66rutMTU3x4IMPYlmW43jebDap1+u0222i0SjhcNghYZ4qCIJAMpmkXq9TLpeZm5ujp6fnmHMahsHMzAyCIDiO0ptt/PghiuIJ7yOfaXQEvjviCJIk0Wq1ljmIu91uBgYGGB0dpVAo4PP5TpiA7vF42L59O4Ig4PP5mJ2dZf/+/ZRKJZrNJj09PexdNDJyu93Isky9XmdmZoaxsTFisRgjIyOrxnhzuRxutxufz3fKXK43sT4GBwfx+XyO23i9XncIrj6fD4/H48RgwCZuF4tFpqam8Pv9x4jfJxIJarUaMzMzjiBSIBBAURQCgQAjIyNOnSoWi0xOTjrfdblcJJNJwuEwXV1dG4oTHS8ajQYTExP86le/YmJiwqnbsiw7Du1Lx+dOHT106BCWZRGPx1cUSOrEECRJIp/P4/f7VxQwOVXorB06uSRroSNa0jElOB4Eg8EN5cFs4smjVqtx6NAhstks2WzWaaOyLKPrOqVSibGxMZLJJKZpsmPHjlM2t2y327RaLaftrodAIMD8/DyWZTl5BpvYxCZOH87erJ5NbGITm9jEbwzMRgPhBz9AyuXQgsE17eMEUURPJnHNzWFmMtR/9CMCV1110soix2LIq2z+V7/7XcRiEblcRl2NfN6BoqBHIsi5HPzsZ+iFwqq/e0awpOyCZSGWSgi6jmBZGIqCqGlHPmd5SocgCPa/LQtLEDAUBUHXscB23E4kCP2//4d0ClWvnqqofv7ziLOzuGdmaPf2LmfMCAKC34/p96NmMrjTadpeL5X/+3+Jve1tq9a3wJVX4t61i9qLXoR54ACmLGN0d2MFg8sIa2Y4jFUsgmUhqartui4IWCu0N8swkCsV1P5+rEsu2VgykiAQ/qu/ovzWt6KKIuLcHO6JCSxJskUNdB1LFNFiMazubqxXv5rIrxn53IEg4H/GM9ZVcn8qoD01hfWJT9gkvGqV1vAw4pKgrQW0NQ15ehrl8GEQRUqf+hTRN73pzBX6BCBHo8T/6q/QMhlqX/uaTfJrtxFCIZTLLyd81VWbjsG/pvDu2YP+J3+CcdttaG43cjaLmM1iulx2MFDTMNxu2kNDWH19eG67bWVRkKcopEAA69prMUslpNFRxPFxjIGB5SISlgXFIq6ZGdRkEisSIfiiF528QlgW1e98B/XOO5Eee8x5WwNq27Yh33QToZtu2pjrfCCAJcuIqoqxiqP5Uojtti224navSD7X83mEr38daWoKSxBs4u2SvkBQFLR4HCGXQ2k2kZtNKBZpd3fbLtVLL7MjtLRlC9aVVyKtsWnuueUW1J/8BGtuDnFuDqu//5hjhHwedB3JMGzCvSTZ4gkr/J7Z1WVbRRcKtO+8c5OAvg5qt9+OWCqh5HK0t2yxs96Oht+PtnUrrkOHEBYWMG6/HW655ZQIMwguF6HnP3/Dxxu1GqW3vAXp8ceRp6Zs4QifD8vtBl3HNT2NNTsLlQq1N72JwD/9E64V6lgHtTvvRKjVkGs1Wlu3HkM+P1JQAbO/H2X/fvRqlcbtt+N5z3uO93KfslgrznayCDeWZTkuMRtR8e0cs5azsCAIDA4Oomkauq4zOTnpENeLxSKiKFIsFqnX6wQCAWKxGN3d3cvO7/P5UBSFcrlMuVw+hoBuWRbj4+NkMhnK5TLNZtNJVojH444zTccx1bIswuEwBw4cQBRFh6QuCAIul8tJFIjH48sSZAqFApVKxVFkXguBQMAhx+Xz+acEAV3XdSeRsuOk6/V6SSQSp1RxehO/nigUCo5Lhaqq9Pb2EgwGnTal6zr5fJ7p6WkMw+DQoUOkUinK5bLz+VJ3Y4/HQ29vL729vXR1dTExMcHk5CSapi1Lij4azWaTdrtNKBRaNzn4TMLn8zEyMgLYySCFQoHDhw8jiqLTLweDQYaGhohEImzbtu24kwZnZmacBOWOc8dSBINBksmkk0zeIaivlughSZLTV+u6vuZzABzSeaff/XVEJ2Hasqx17wccIe4vJfOfTtTrdWq1GoFAYF13B1mW8fv91Go1GmeQ4RMIBPD5fEQiEfL5PKFQaNV73XE0iEQiv1YJgJVKhbm5OeYWLb+HhoaOSZiRJMlJnBsdHSUejzM6Osr5559/Jop8WrDefDgeh23b4Gc/s0NDl15qa4BJkk1aCwahp8f+2+uFwUGYmYEnnrB16DpQVfje91Y+x1/9pUX5r29Duvtu+itjgEpWTjIvDgICAatCrz6Nps7hEVtsqfwpzT/5GJde4SIchoMH7fJ1cuaiUajX4etfP3KOq6+2ycfNJvzkJ0dcPN1u21U8ELDd4I8y4ySVOnJNs7NBWuKlaL9aIJkbRXZ7ELoTBAI2oba7S6Vak2nUwL8wiSRbiKkw4vXXcd0eaLchnbbv01KdkmzWvs/bt9v3+pFH4K677Pv6qlfBnj0rL2df/Wq44gr7+ELBvt/d3fa9LhahVoPRUViSy7kMF1wAw8MQDl1E72diGI/F6CtM4fbImKEQhiEgSRY+r4EgQqvQJrJwmGYoQqjXzbW3PZenNW3C5dQUZDJw6BA87Wnw9KfbZTo6jzEcts+7d69N9PzFL+zvpFJwySVHSPZveANcdpl9beUyDHzvX9je+hIR4zBuvWE7hSuDiAoEhSq9rTR96jRJoUZ4/E/49Nb/jSb7eNazbK5bMGhf67nn2s/6059eXj9KriRt0UtUzSKKJqa58njb06sSjajIngJlbxfhkQjXXWc/n+FhuOgi6OqCW2+FH/1IoP7xrQR/8C38Zo1Qcz9twY2OjClK1KQwZSWG16gT07LMereQd/XQUEJcOfPvXP/sAiUtxFjo6ci793Le+QJ79sBPf2rfs3AYhobsujk5adddlwvOOcd+Bo8+GCXzihQD9SdIqbME9SI5Vw8Fdw896iQS4NbaCIKFaJqYiAhgk9EFQIC4niVYr/Cu0ddz78t+Qlf3Fjz3efHNtugKFpECXiKtAt7pOpJh4DeqaJILQ1YAAbfZRpcVGq4gtVA3UqFEUC+BCSU5zj0D/4ufDv8WLeXIXLO/H17wArv+BoP2NT78sN1+9EXee0dLqavLrkvpNPz4x/Z9cLttTelzzrGfSbNpb7vt22fXU02zv9Opa5Jk9yGHD9ttZyUshHfzbxd+kr7SI5y3cA9JbQa3bCDFg7QuuZLWRddwZY+HS1WbpLlSvxqJ2H3N/DzIMkz95Cbq37yHshJjsHmQWc8W27F5sbEHgwZ6XSVZmcBCoODuZt/Wm/iDNws873nw2GN2P1ss2gTqSAQuvBD+9V/tdrMSbrzR/vml2teieOS9ozWxf/ADu7xg9y3nn2+f7+c/t7dWt261+61QyB4DJifhwQft4y+/HLZssdu5Zdl98r33QrCVASxcZovmokO112Pg9VmobYF6ffm8qiGHELDoClX5szdOYL7YJoqPjdlCEh6PfT9lGfbvt8tVrcKBA6yIc8+171M4bPf58ThcfLHdB9Xr8P3v2/UpmYQrrzwSJh4ZgQ9/2CagP/igfW7TtMeS7m646iq7vo6P2/dnbAweeODIOffuXS64sRKCQXj0Ubvs+7uvJe8f4uKZO9hT+DGK2Ua0dETRZGtzH5ruRrJ0iokttH0REASqNYmiJpCxBuhqTjLYOIAlCFw+8Vku+8wfsGuXPT4uxfOeZ1/z7OyR6zl0yL6H558PpZLdxw8O2tdeLtvk9YkJu67s2mW3OVG021LnJYr2++22/Rv5vH0vV5u2y7J9z2OfHyM8XcGQFJSIGzQL0wBZsfB6TEwLalXZ+Z2qEiGmZvDpFTxaGe9AgkjBQtIEenwF6v4UhfzKcQtRhKRSQGgJ6IKLinvtOe+VV8Lzn2+3vWLRrhOXXGL//fa3u5mMP/2Y71x3XeevMPAy9tdfys+/V0M2WrTlAFdc62V3FM5X7Dr4/e/bdWBdCAKjqWczmno2oeYs4eYcomVQc8XJB0ZW3QPXZS9P9Fx37AdFe1xUVTha5/sHP4BPfOLIvysV+MIX7GNf+9p1ymlZmHfdhZzLYbjddsNbA3pXF+6JCcxKheo3vkH45S9f5wQnjo7bbavVwrIsZFl2ROaOF53fKZVKDsl5JYiiSG9vL4cPH3b2W1cj1XTWuLqub8j1sbMvq+u640IZDofJ5XK0Wq1jrsvn89G/GHOQJGmZcKggCAQCAfr7+x3HukQi4QiQFotFNE1DEATcbjeJRGJdkrxlWTQaDWq12ob2STsiaCe6hp6cnCSfz1MoFBgcHDxGTCwQCNBqtZiammJhYQFZlolEImftfks+n6dUKuFyufD7/Y5DaUdwQF0ygXG5XFSrVR5//HG8Xi+XXHLJGSz5byZM02RsbIx8Pk8+n6dcLjsEZsuyHDJapVIhmUwSCoUQBMEREd3EqUdfXx+6rjsutZOTkzSbTbxeLy6XC1mWCYVCWJblOPVOTEywZQ3h8SeLVCpFqVRicHCQ6elpDh8+TCQSwefzYVkW9XqdUqmEKIoMDg4Sj8dPG6lxE2cHDMMgk8kwMTFBpVJx4h5gzzHC4bAjytshwhYKBeLxOPV6/YSFNX0+H3v27CGXyzkxqu7ubuYXF4rxeJyenh7cbjeWZVEqlahUKmzduhVBEFYUNjZNk2w2S7lcZnBw0BGz2cSZQyKRIJFIUKlUljksBwIBZ5wCnPlgJpPB7XY7ovVHozOXnJqaIhqNksvl6OnpASASibBr1y4WFhYIBoNomoZhGIii6JDUu7q6TonARqFQYGJigqmpKSYnJ0mn03R1dVGr1RyB/3q9Tjwep6vLFueUZRlFUWi1WhSLRQYGBtYU7FYUBVmWabVaTsz2dKATJ9voXMLtdtNsNp+Sgte/CbAsi4mJCQqFArlczlmbLUUkEqFarTIzM4MoioyPj7N3795TIprTyYPa6G8vjc8vHa82sYlNnB5sEtA3sYlNbGITZxzq5CRCq4VUq9nE7nVgRaMIMzNQr6Pt329HXU8DjKkphEYDwbLWDWABGKEQcrEIpok6NXVWEdDlnh40QcB0u22n30XyuSmKWIriZG6tNKW3BMF+3zTtZBFdx1AUdI8HQRAQfD60hYUnTUC3NI36j36EPjMDhoGUSuG/6qqnjBr10TBbLYR770XMZtF9vtXtGi0L0+0GTcP72GPoH/oQxTvvxNyxA+XGGwlefz3CUa5ASiqFeeWVaLUarnQa3es9liwbDmO6XJiqiqyqaG43hs93rOCDZdlEO1mGaBTPLbeseV1aJkPtK1/BWrw2yTCgUsFyu2nH47ZTp2XZTtKhEGZvL/Jv/RahGzfm1LCJM4v67bcjViq4SiWaW7cirLBpLCgKxpYtCAcPIs7Nod99N9Yb3/iUckHvQEmliL7hDWe6GJs4zQheey31aJTWP/8z1ugoVKsIrRYIgt1f+/0YF1yA/61vxbN165ku7klH5M1vprR/P5ploYyPozzxBFowiOXxgGkiVyoIuo6WTGL19aG85z3IJyngaLZaFN/7XqSf/hS5UEDK55HabfszRUFYWMA6cIDC3XcT/uhH1z2vctllmN//PsLcnJ15tsbxlmHYokL9/ZgXruzsU73jDoR6HblWo71164pCFGYqhVmp2MQeVbXH+4UFhzRuaRpiPo8rk0FNJLASCXyvWttxyXfxxTS3b4dqFdfEBLplYXR3L+tXxUoFueN+7vGghULHzA86EEQRPRRCqlSw9u9f89y/6bA0DeHb37bna37/yuTzRQiKgp5IIGezWOk09Z/9DP+ll57G0q6M0m23Ie3bh+vwYXS/H23LlmV1o61piAsLuCcmaAPV97yH+Oc+t2LioNlqod9zjy3+JUkI66gZ43KhBwKIhQLmQw+d3As7y3E6COidgKIgCLRarXXVf1utFqIoIknSmkS7SCTC8PAwgiAQDAYpFovMzs46pHCPx0MqlXJUxldK4nO73U4C4tHoBK6mpqYIBoPs2rWLcrnM/Pw81WqVcDhMNBolk8kwMzNDNpulUqng8/lwu91UKhXGxsace9AJdpXLZbZs2eIk7qmqSrvdxufzbSg4FQgEaLfbyxLozjZ0SOf79u1jbm4OVVVpNpt4PB4ikQiRSIRCoYDX62XLli0bUoTexFMLjUbDUR5vt9uOy0MoFCKZTJ6wCvz8/LzjbnS0cxbgOGW5XC5mZmbI5/MOmbJYLKLruhPcdbvdBINBVFVFURSSySSZTAaPx8PCwgJ9fX0rtknTNFlYWCAQCOB2u896V5BwOMzOnTuZnZ0lFAo5yYFLEzjj8Ti9vb3rkoWPhq7rFAoFstksgUBgVTKuoigMDAwwNjZGvV4nm80yODi44rF+vx+3243L5aJcLq8rtFEulwkEAoiiuK57wVMVS8n1G0l66RwjCMIZSRLvuNGvlWy0FJIkOS72ZxI9PT00Gg3H6aOrq2tFgYtCoUBPT4/j+vHrgoWFBWZmZqjVanR3d685NsfjcWZnZ1lYWGBiYoLdu3dvSBzhqYi15sMjI/B3f3f8vzkyYhOjj8bb3maTqzqOq6kUvO51MJD9Efo99yAfPmzv9+zZSVRROLJrkMAyY/gmJ9nZeAzNULEW/i/h8P8CbDftHTuOPd8b37hy+f7wD4//mjpQX/xa6r93P4xVSaQnscamHAanJQhELYuYaYIkoO7YStfTz+FVn7ga4TiGn23b4CUv2dixg4P2ay3MzNhu4o8+ahMg9+6F3/5t21ndhkBj6B2of/zHCFMt+nIH0YQAZixmMwg1DTFfJFouo8eCSMNdBN/2es65/snFmIJBWxhgNR2+PXvsV+Phh1G/+EWkxiiCS0fbuZWw282RKKAbzBhiOk1fZT/JboNLr/0k0Xe8Y9Vzv/GNNgn1zjtt4nF2z1X4J39GeGaCXd1zNFxxKlWZek1E00TcbpN4QiMc0qFURjR1fIMhLnjHFTz75mN/3+O2uOiRv0Gsfw0p3EIpaIiGgWKqWAiYpkhQr9DTnqYmB5nxb8MSRQJSnTdkPkiXv4B8r0G/LLM38O+Y08PIsVch7H0+l11mk/M7uOCCo05uWZQ+9zl2fPnL7BzIIpVLCLpFWC8RMsqY2iEkS0c0DMSlSeuChabIIEm2yLVpImgWHqNJV2OSF/74fxH/9KepvPRTiE0PPZWDCFUBQdeRTA0BEwUTyTJAb2EKgu3S7HHj6g0TTQUxNR/Wow3S8hC/SlxF8zVvZmvNFi9ot23hgt/+7WNDvHv2rPooAZsE+9znrn3Memg04KtftfuoVssmT996Kzz0EHzrW1AqCQTPuYDzXnQBz3zm6hqHN91kE4i/8AW7bnk8cO21Nqlz2TT097ZQ+tLrqPzVP9OagoHaITTFgxny4w8aSI0GslbHiHnRh4fZ8eyLuOy2FzuB4XPPtV9H40MfsgnSX/yiTQgfHLRJs895zvHfk1e/+vi/sxYuvxy++3cW0kELUbRIJNoEe+1L6sxFTFMgl1colWRMAzxuHY+mI8c1ECGWWErqXR2Vit2+77vvCFH69a9fPfQLtnDBC16w+ueCYPefiyZ/K2LLFvv1ZPDEE/Af/wGPP76Db+76Y76n/i7PjD3KlReVGJj9PsJ9ZQLj47QHB4lHfMCRfZNGQ2Iho7AgjjDY3M+wNM72vq8S2vvaFQVXw2E4mVq2TxaFh5pI+5tIOZ3hodXs5NuYpkAmo2BmBAQsQiGDv/1Ym769eym8LIpoBInV5gl1hUilRObnXdRrEqYFgYBBIqHhcRnIh2Yxu/3sOM/Hta+9gPt+YJPlWy277ZqmXS+uv94WkHC5ju2PolH4p3+Cz3zGbvPBoJ0a89KXruQQLsC7g8DKewXXXGMLz7RWu/QVUPH2UvH2rn/gBvCVr9hCFR3+j6rCpz618rG33w7PfrYtErIajEoFMZNBrFbRo9F1xWGFYBBLkqBSQV9NReJJouMem8/nnX28jgia1+slGAzS29t7XM5xpVKJarXqOAmuhQ6BuFQqUSwWVyWgd9a4oiiuuJ+72rV11tWJRILZ2VnHeW5wcPCYdXPHgfKxxx6jr6+Prq4uRkZGcLvdjiN4KpVy9iAkSSKVSh0jbroRLCUfbHRPRpKkNYVbV0OlUnEI/l1dXasS7jpijel0mkQiQaFQOGtFUJfuqxcKBRYWFshms7RaLUcQUZIkx8F+YWGB2dlZJicn2bZt2yZJ9TRjcnKSXC7H5OQkoiiSSqUIhUKIooiqquTzefbv3+8I/fb19TnihsFgkFQqtelqfRoQiUTweDxYluXc8068zu124/V6CYfDlMtlJicnEQSBUChEPB53CJiFQgFVVR1Bjg4p/ET2KUVRdJymO4LSxWKRbDYL4MQBQ6EQsViMLVu2nFJX9k2cfeiI7o6Pj+P1eqlWq5im6YhhNxoNSqUS3d3dRCIRQqEQmUyGdrv9pAjoYI/JXV1ddHV1MTw8zP79+xkeHqZcLtNoNJienkaSJCeeu3XrVhqNBrIso2kao6OjBINB59+VSgVRFBkYGHBi4Js4OxAKhdYUCyoWi7TbbWq1GgMDA2v2Q36/H6/XS7FYJBAIOAR0AK/Xy/DwMP39/Q7hXRRFx3H9VKBcLjM+Ps78/DwzMzPIskwikWB4eBjTNKnX647of2fOmkgkcLvdCIKAJEnUarV143PVapV4PE673UZRlHWFwE8WOuuAE1k7bOLsQ6VSodlsks1mnRjeSggGg3R1dZHJZIhEIlQqlVMiKK8oCoqiYBgGqqo6AuGrodFoOMf8usbUNrGJsxmbBPRNbGITm9jEGYe5GOUSLGulSNkxEETRdm82TaxFktJpgWEcsafYwEabIIoOgdva4OLrdME9OEht1y6ETAYlm0X3eo+4nAsChizbyR+L17vsagXBuQ+CaWKJIuai47tSLAK2q/2JwqjVqPz7v2Pdcw9CPo/QboNlYbjdlP/+77Guuorg61+PsqgE91RB/Wc/Q6hUbGfx1YQWVBV5YgKp2UTUdWRNs53DH30UY34e8/HHKf7bv+H94AfxHpX54XnZy9AeeACjUMB1+DDq0NBysrAgoEUiuMplLEFA0nXasoxlmraTpWVBtYqUySC1Wqhbt2Kcdx6+iy9e9ZrKX/gC1qc+hVCvI2WziK2WQ6A3IxGEdhu1rw/xGc9ACIdxX345/ssuOyXuoJs4+bB03RZNyOXQ/P4VyecOBAG9qwvX9DRmsUj13nuPy611E5s40/BffDH+z36W5mOP0bznHqxCwR7Hu7vx3Xgj7rWyXJ7iED0eIp/4BMX3vc/O/CmXEYtFxFrNbtvRKFYigRWJoLznPQSe9ayTcl7LMCi+5z1IP/4xyqKljB6JoC9adAm1GnI+D7kctNuU3/Uuov/0T4hrbLgHn/c8iv/yL/acZH4eNRBYWQzDspDSaTvZKBLBc/MKmbyA+cADyPm87Z6xWh/o9aIODOCamkI0DCRNw5tOoy7OhcR2G0uS0Hp7MXt6UN7znvWFDASB0F/9FdU3vQkVUNJplCeeQF90eBdUFXehgAAYkoTh89nO9Wv8pKUoCO22La6wiVXRHh9HqFaRq1XUJUGq1WDG44hzc1Cvoz766BknoKuzs0g/+QnS9DSGz4e5SCpeCkFRsPr7UQUBZXoagkHqP/2pPUdbRHtigvqXvoRw3314HnoIpVy21wX799t9QjJpWwetANPtRmy3bZGrE4ReLNI+eBCr3UYMhfDu2XPWC9usxfs9WUakoVAIl8tFIBCgWCyuSzYuFouEw2HHNXwtxONxfD4fmUyGUCjkBG06Qf9O0slqQVnTNFcNImYyGUqlEpZlMTAwgGVZTE9PI4oipVKJTCbj1NPp6Wk0TSMSieByuejt7WV4eBiPx4NpmlSrVYrFIqVSyUl86LgS/7qpG3dI+x0n6fn5eZrNJj6fj2g0SqlUQlEURyVdVVW2b99+XAmsTzXouk57cQ9IUZR1g49PVViWRaFQYGpqioMHDzoOTu1220nQ7e/vp1AoEAgE2Lp163EFVzvuTsVikXg8vuZ3o9Eo+/btc5TtBwYGiMfjDpm0o8jfabsAu3btIhqNOu4uU1NTJJPJZf1QrVYjm81iGAb9/f2kUqkNk2zPJHw+H9u2baPdblMqlZYR0E80+Q+WJ9Wslwy1dBwIBoOrJuF4vV4CgYDj/hCJRFa9x61Wi0qlQn9/P7FY7CnxLE4EPp/PSTKqVqvrJsWVy2UURcHtdp8RgQ9JkpBlecNCKaqq4vP5jlsA4WQjGo06JIdMJsPc3Bzz8/POWN5qtVAUhf7+fqLRKNu3bz/jZT6Z6CSRAesm4oiiSDweJ5vN0mg0qFarxM4iAduTibUI6JdffnLPJYo2ueqaa5a/n/+XLyMVCoiaRnvnzhXXF4IoYg4PIx44gJjJoN91F9brXreiGN2phKuvD+PDH6b9ilcgmCayqiItsXq3BAFDUdACAYyREaIf+chpL+PR6OuD3//9xfJZK2//+y65BPPP/xztr/6KdiiEmMvhmpqy15qCgO73ow4O2ky7N7yB8NEWqacQrS9/GbFUQmo27fqx0jxTFDEHBjBHRxHn5zG+8x3M3/99xDUSVy+6yH4BmM1rKN/yj+iNKK7ZGYQRN/7uFfa4Gg2UmRn0WAwiEYKr7LEXP/lJhDvuQB4bQ9B1mokEcquFYBiImrZI2tYxJQmfR2UgnkWIRtlSfAS5WEQo2LE9wTQxZRl9fh5zfJzi+DjRN71p9ZtlWRQ++EHEb38beWYGuVwGF4iWhaCBaBrIizbiAjhxR1MQ0Hy+ZQxpCzBcLty1Gkqzie8HP6D58MNYV12FdeAArmr1yF7XkvWeZJpYgAiYomi/4nEEVcU1MQF+SG0P8Lw/vZaXrkH2PRVoHTpE87vfxSqVQJaRhoYIXH89UjCIz2cTP4/WpRwaghe/+PjOs3MnfOAD6x8XefnLEBQZ85//GSoRlHwOsVFBqFmYLhftrVvB78e8/npi7373hmN3F15ov1Zr72cKF14Iez6UoDnRRHlCQBQKWCzf3xNFi1RSJZW051hWuYxYA8vtRu7u3vC5QiHbnXldh+azEOecA7fdduTflhVBEK6w2/fL/h2pUlnVTdrnM9gyvCjmVInYApe1KrW77iL8yleepit4EohEsFwuJFW113OrOklbdHeriFoewWfiHVIJ77QJzuKNN2Kl03YMJZ2G/n76eo/KV7EshNlZxHYbfWgI6fnX8eKXyLx4g+IvR2NgAN7//hP77lJIEuzebYtInAnUavDe99plKJVs9/O1tvO+9CX4oz9a/fNlOUYbWccKApYkIZjm8bHwNwhN0zh06BDFYpFcLketVnMEzTpO47FYzFn7b3Tur2kaqqriWTSDWA8ej8cRzVsNoiji8Xjw+/1UKpV1idGNRgNd1/H7/fh8PmRZJpVKOa69k5OTJJNJ/H6/U8ZarUYulyMUCrFnzx5nrdgRvjuZ60BRFB3izkbW0JZloaoqsixjmiazs7PUajVnjzsYDJJIJFbcM8vlco4z7HprvkAggKIolEolcrncWUtA76DjftsRUOjr61u2R9QhzjabTQqFAvl8nunpaSKRyCZR9TShXq9TKBSYnp7G5XLR39+/bD+y42QtSRKHDx9GlmVmZ2fp6+vD7XYTjUYdocrhFeKHmzh56MS9UqkU3d3daJqGZVmIoojf73dibaFQiPHxcYrFIplMxhEy6ew/d/o0j8dDsVjE4/EwODh4QsIPkiSxY8cORwj16HGoI3q7KVDwm4dWq8WBAwccEZLe3l4ndmSaJrVajbm5OYLBoONQ29lbPxExl7UQCAR42tOeRjqddsajer2OaZqOGI/H4yEcDjsEynK5TK1Wo9VqIcuyI6bu9/vZtm3bJjnxKYRGo0G9Xnf6yvUQDocpFApomoau68fEtmRZPm1COdPT0+RyOcrlMkNDQ46wuGVZuFwuxwF9fn6ebDaLJElO3M7r9eJyubAsyxGLXmmMLpfLNJtNR9BXFEUMwzgtddzv9xMIBMjlcusShE3TpFKpkEqlNsXzz1J01jSwfhwrHA47BhLZbPaUENA7wj0dUYmuNXghpmk689mO+MgmNrGJ04vNVreJTWxiE5s44xAXlc1MWUZotbDWcc62VNUODCkK4imY0K4GMRbDcrvtSFijsTbLAKDRwFxc4MlnoZOL8qIXYT7+OFYuh1SrYYkiommimyamy4W1uAHqkOixk5vAJp53oHs8tpO2y2W7ZgPSCT4XLZOh8s53Ih06hJTJIJdKR84lCGjBIGYuR/WBB/B97GN4du480cs/7TALBdsNvCNnfjQ0DWV0FLleR263HfK/aBgo1Spys4k1O4va30/rXe+C//2/8S6RwfdffDHF3/otjH//d6ypKTyHD2N4PBiBgB0Qr9eR63VUnw/RsjAUBbleR3j8cbvtmSaiYaAFg6jbt2Pu3k3ktttWzR4pfe5z8JnPIE5NoZTL9rmCQRBFhHod9+QkhteLEolgGAaxP/qjsysTZRPrQp2ZQajVkGo11I0IPoTDMD0NtRrGgQO27cUmNvEUg3fv3mV9628KRJ+P+N/8Dc1HH6X5la9g/fznCLUalqJgDg4iv+AFhG64YU3y9/Gi+s1vIv70pyhjY7aQzciILYiyCCsYROvutseZ8XGQZcr/+Z9E3/CGVX9TcLkQX/EKzE9+EnF0FNfoKFpXl5283BmD6nWkhQWkRgN1ZARjzx4iz3jGyr9XqSBoGuZ6KriRCKosIxw8iKhp9pi4OJ/VvV4IhzGHh3G/7W34O5nH60Dp7ib4z/9M5c//3LY1KZcRKxUwDCyPBzUSQanVMAFtFXf2ZdeiabbD1K+po+XJgrlIFumsNdaDIEm2kIFhYJ0slvGTQP3OOxHqdeRGg/aOHWvOvczuboRCAUolWnfc4RDQK1//Oubf/i1iqWQTAkolh+xg1Wq2qEI+jzo8DCsQXQVdxxIEe910nGg88gjNL30J6YEHQNdtGyBRpBUKwbXXEnzlK89aEarT4YAuSRKxWIxqteq4ia+mEl4oFGi1WvT09JBMJjeUSOT1ehkaGqK/vx+wA4m5XG5dcquu6zQaDeLx+DGq3M1m0yG6LiVnDg4Oks1mcbvdaJpGq9XCsiwnMVHTNM455xy2HGXr1XF4SKfTTE1NIcuy4wLtcrlwu93kcjknAWIt1Ot1h6B/tiGTyTA1NcWBAwcYHR2lWq3idrsZWhTEaTQaCIJANBpF13Xm5+edBMm9e/f+WhH5wFbhzmQylMvlZe/7/X5SqRTRaPTXJlnONE0OHz7M/Pw8jz76KHNzc+TzeQRBwOfzEQqFHOfwjkCDruvs3LlzwwkGlUqFRqOBYRjrBok7ydKTk5MMDAwwODi4rM0EAgECgQClUon5+XkURWF2dpbh4WEn0blTn2VZRpZldF1H13VHNT2RSKzqyHW2wu12rxn8Pl60Wi2azSaSJG3IfTwYDJLJZDBNE03TVu3HUqkU1WqVSqXC5OQkfX19xwiJLE0eCwQCJ+Q09lSBLMtEIhGi0Sizs7PEYrFV752u6xSLRWKxmJNgf7rRcQOZnJxc18Wi3W7TbDad5LqTDcMwnOTyTuKry+UiFosRj8ePGXd6enocoZBEIkG1WnUEGzqkhEAgwNDQ0ClzHDlT0DQNw7BJWRvplzuJnEu/9+uItebDp2OJqs3PIz38MHI+jxaJrC1uJQjoySSuuTnMTIb6/fcTWMlq/RTCMgwa//7vSD6fTRA2DExJWhabESwL0+NBKhZRp6bwrmfffBqx1rQscNVVtIeHqf/Xf2Hddx9mo4FlGPaehqJgXHYZ3pe/HN/555+28hrVKsL99yPl8+ih0Mrk8w469WN6GqNUonbvvYRuvHFD5xG9XqTf/33Mj38cdB3X6Ch6OIwRiyG4XFiqilQoIJfL6MEgZl8f0pvetCLBXctkEP7rv5AmJ8Ew0HbsAEVBtSwoFhHLZQTDgMXYkikIuIpFqFSwALWnBysatcW+NQ2hUEDJ5TDrdYwvfpFyIkH45S9f8TqKn/mMTT4fHQXLssWWw2H7wVerSHNzeHI5pCV9miFJ6D7fipVDEAR0lwtZVZEbDepf/jLiRRchqioIAoJh2KILkmSLni0KhXd+SbAsRE1DmppCqVYxPB70kRHMa64hcsMNG3o2JwP1X/yC1mc+g/T44wj1OsJi+S2/n8qnPoV19dWE/+APkE7BOL0ewjffjHHNNVTvvBPjnnuw5ufBNDHDYbjiCvwveQnuE7TUPhuXYe6hIeo7dqAvCrC3u7qW7Tkvg2UhZ7PowSAEg/ivvvr0FvYsQec5GrXa6m7SpmnnRhgGiCKW17vMTdo4ePDMFP444XnOc1C/+lVMWUYsFLDWWNdZhoFcLqP29mJcfLEzPoRe/nJK991nJ91PTGA2GuiJhL2HLwhQqSDn84jtNurwMObICOFXv/p0XeK62Lv3zBHQAQ4etF8bwY9/bIt2LG5VHgNp0aXNkiSEdpv1pCkt00TUNHRFQVjF4e1EYVkWo6OjDiHX6/UyMDDgEGc6a8z5+Xlai+T3jsDcehAEAUEQNkzuWksodCmSySTVapVCoUC1Wl3V9Q5sgkIgEHAceAH6+vocochMJkM6nUYURRRFQdd1DMMgGAw6Ynfbtm07pft34XCYUCjEwsICqVRqzeuvVqvouk61WiWdTuN2u6nVahiG4bhEz83NEY/HGRgYWPZb7XabVquF3+/fkBCh3++n1Wo59+psRGdfPZ1OI8sy9Xqdrq6uFckclmVhGAapVIp6vU6r1aJUKm26oJ8mZLNZarUaqqoyODi4rA7qus709LQjeNBx304kEnR1dWEYBtlslmKx6Ij7broCnxqoqkqlUqFYLBKJRNbcg5ckiWg0SrFYxLIscrkcpVKJSqXiiEtbluW4lcdiMXRdZ3h4mHg8jmVZVCoVh+SoKArhcHjVGJEgCMTjccc9t7OH3yFHbuI3E5OTk1SrVfL5PNFolGQyuWyO0hnLFhYWnHlJJ6YjSdJJj0lKksTQ0BB9fX3k83kajYZDQA+Hw4TDYQRBwLIsSqUS2WzWIal3xvFkMnlK9sk3cWphmqYj1rERiKLoCNSfTCGE40WHGF4oFOjq6sLn81EqlXC5XMtEZzt5DbOzs47QSEegKBaLUa/X0TSN2dlZQqGQE5dqtVpUq1VarRbJZNIRWgZOm8N4IpFwhIZzuRy9i8YyK6EzpnWETTZx9qETl97ImqYjCNFsNk/ZmsbtdhMOh4nFYszOzjoxxKNhWRbz8/OAvf7brF+b2MSZwSYBfROb2MQmNnHG4RkZodHVhT4zg1QsoieTa0atxVzOVt72+fCcJBfOjcB39dXUP/lJdJ8PMZfDHBxc/WDLQikU0KNRjKEh3Gfhxm3w+uspfOtboGm4Dh1CXlwgSKqK6XZjyDLSEoVgAezEDsNwHAt0lwvD70fbsgVlbAw9HMZMpU7IKdZstaj+4R8i7dtnB2xdLtS+PqxO8kqthpzL4T58mHa7TeOP/gjpU586a0koR0OQJCfwaZnmMS6l0uQkrnIZSddtsj+2gwOWZRPGBQFB1/GMj9MCmn/+53i+9CWEJYGf6BvfSMnjwfzsZ2nX64i5HOIikcryeGj39oLfj7ZjB1Klgjg7C4vkOksUIRjECoWwrrmGyFvegrhKgm3zscfgs59FGh9HbLVobd26zB3bAlrtNvLUFMrhwyCKlL70JSKveMXJuZmbOC2wTkA5HlEE03S+u4lNbOKpBe+55+I991z7H6fYwkb76ldth3PTPIZ87kAQMAcHEQ8etN3H7r4b69Zb1yRbR17zGgrpNPpddyHOzOCamYHZWUyXy3GB0v1+1K1bMXfsILyG2AqKYvdrur7+BQUCaLEYgqahJxKYi3ZrQiqF54Yb8F1wwXHfT6W7m/i//qstDHDHHZj790OrheX3Y6oq2oEDeCYmoN1es5+2TBO5UkHt7cU8i5LSz0aIi5vYpiTBGg4hHViGYSd1S9KyudCZgvnoo8ilEobXuy6TQ5Ak9FAIsVTCeuIJACrf+hbmxz9uCz8Ui+iBAO1oFKVcRlZVBMNAWUwec42Po27dukyUyzJN2z2+uxtr0ZV6oyh99rPwuc8hlUpI+TxyvQ6WhSWK9hpjfp7at76F+4MfxHfJJcd/c04xTgcBHaC7u5tisUgqlWJmZoZGo0E0GsW9SPhvtVoUCgUqlQq9vb1OkP14IEkSW7ZsQdd16vX6um6c+XweRVHw+/3HnKvZbKJpGpqmLUtiFEWRrq4uksmkkxyjqiqNRoNSqcTQ0JCTzHg0RFGkr6/PIWZns1mCwSDxeJxiscjCwgLVatVJLOgkwXUScGRZRhAE2u02kUjkrHMabTQaTE9Pk07//+z9eZhld1muj99r3vO8a566qro7IQmzCoqADEKCiYRBEQecUFEOoqIIHEU4yBhRVBT1yzl6HfE4EcAwivIDNSggQwgJPVXXXLXneV7T749Va6Wru6Yk1TV07/u6cgVSu2p/9t5rf9Za7/s+z7PC6uqq53ruJgSBI27L5/PkcjmGhoa89PpwOEyhUNhXYexhYts2CwsLFItFqtUqlUrFG9JVFIVYLEatViMajTIzM3PsHa5t2+bixYvk83m+9KUvUa1W6fV6nDp1ikgkQrfb9QYMUqkUtVqN+fl5TyA+Ozu7p+dx08z3MhhULpfp9XoYhkE6nd52gCMWi9FqtSgWi4RCIcbHxzl58iQrKyv4fD4v3ds0TS+lQtM0BgYGGB4evmYMBFzcgZe9vi7bth/WUI07XHXpc21FPB5naGgI27ZZW1tjfn4ev9+P3+/3EpF0XScWizE4OMjExMQ1n0YwODhIuVymWq2ytLTE6OjoFaL/brfL2tqal45xqSi/3W7Tbrc3DbNdrb0nnU6Ty+Xw+Xxks1lGR0e3PKZs2yabzXqf7X4nqrkCCncgyt2HfT4f1WqV1dVVxsbGrrgGSKVSJJNJKpUK5XIZwzAQBAGfz0cqldqT2cJxRFVVFEVB1/U9Db25yTyKohzYsNhBo+uOVm07DuJQ6C0sgGUhtdsYw8O7Pt6OxRBWVqDTwVhYgAMWoFf+4i+QvvhFlMVFLL+fxswMgmE4Zl+ArSiIrRZqPo+1ukr7f/5PtL/5m301DbyaaFNTaK9/Pdb/+B+0778fq15H8Pvx3Xgj8iFcm+srKwi67hgVjo/v/gvRKMLSEkK9jn7mDLzgBXuu90TuuINqs4n1gQ/QK5WQCgV8c3Pez41gkN7YGCQSCK961bbi9sZHPuKYzzUaToK1a6ogCJBIYF3yPpqZDMH5eWxBQA+F0G+8EUGSHhJwKwoMDtKLx1EvXMBeWXFMjl/4wivE+Fa7DR/+sJP4a9voJ09urtGFw5jhMB1JwpfJIG30wUxZ3vE9skURWxAQdR3xa1/DPn8ey+/HlGUMv9/pSbrH/0b/wRIEz4xB7vUw2226k5OOCeTznkf89a8/MHV07VOfwnr3u5GyWeRCAanbdcy6bRvBshxj7XKZ6je/Sfh970M5BMMfKRol9opXwCte8VDc8BG8Bu/Oz9P88IcddWyzCaoKs7NoL3whwe/8zj2vWb7jDqxvfxvyeeTFRYytEj5tGyGTQWq36c3MYD/72cdmH71aWBsFrE09wW4XMZ93DOM3DCAQBGxBwIhEHANAy8I+AGGl3etR/8xn0D/xCcSlJdB17HAYvuM7CLzkJfhOntz1bwQe9zjaJ04g5HIo2Sy9UGhr03jbRlpacvaceBztzju9H4mBAOG77qL+utfRUxTEfB51fd25dsARQxvRKMbkJNb0NKHf+z1PKH0UeOxjD3sFDzFY+zaPzXyaVHsZyTZoylHOJb+bM+nvw5R92DbcfTe85jVb/74YCGDecANCLoecz6MPD++4TwjlsvPzSAR1G1PiR0q5XPbEzNFolKGhoU0/d8UmoVCIxcVFfD4fa2trnDp1ate/7fP5CAQClEqlLZMdL6fRaBCLxbx68XYkk0nW1tZIpVKsrq4yMjJyhVDLNE1PNO/Wa91akiAITE9PEwgEvNpPvV73BPBu7SedTjMyMnLVaz/pdNoTaLo1061wzTzdxHPTNGm32/j9fmRZptVqkc/nCQaDntj85MmTV6RM75XjUPNKJpNUq1W++c1vIggCiqJse8/eaDQA5/02DINareYJFq8X3GPGra8GAoEDu58vl8tUKhUikcgVe0Eul6PRaJDP54nH44TDYZaWljBNk16vx9DQEIlEguXlZZaXl5EkiWQyuaP5RJ9Hhnt8dDqdbfeiS4lEIiwsLHjp9T6fb5OJCeAZOK+vr2OappeS22g0vP6aK0B3TSOHh4d3FJVrmrbruaLPtUOn06HX6yEIgmc8Ak5PstFoUKvVSCQS6LpOo9G4Qvjn8/kYGBggm80SjUZZXl72jqGrtY/Isrxjv9M16r6ezkHXOpcaWbvn2Z3o9Xre+fAwzdldQydXdO0KdiORCKVSiVAo5O3Hmqbh8/mo1+tUKhX8fj+maXLy5ElWV1c9o5FSqUShUACcYz0YDDI8PEwoFMKyLCKRCIqiHEj6ubvuWCzG0NAQi4uLiKJIOp3e9L7btk2pVCKXyzEyMoLf778qadl9Hj0Pty/tmj1cTaOHkZER6vU6vV6PlZUVYrGYl4zuXge5JtkTExNEIhFisdhVW0+fPn2253hPZfXp06dPn2sDQUB8wQuwl5YQi0WEfB57uwZ8q4VSLKIPD2PedNOemon7hTI4iPmd3wmlEtriIt1o1HH2vxzbRlhddZz5k0nk228/ko18QZKIv+tdlF//erqyjP3gg2iNBkqvh20YWIKAuZGKLgCmKHpiaEsQsCSJzvAw1ugoQrmM1O1iTE0h3nbbI3q99Y98BPH8edSFBUdkMjbmJBy4D4hEMCMRrFwObWWFrs9H/X//bxJveMN+vi1XDeXECbqahqUojtDnkiKZ3WqhFQpIGwI3ATwRurUxfCN3u1iShCWKaAsLdBIJ6v/yL0Se//xNzxP78R9Hf/7zaXz0oxif/SxiPg+27QwdPeMZhF78YtSxMbBtGl/8Ir3//E+o1UDTkE6eJHzbbUi7uG23//7vEatV5EaDzsmTCFs0oARNw5ieRj13DiGbxfzoR+GHfuhIfhf6bI20sb/ZkoTQbjtmEDtg67ozgKIoCH0n0T59jj9Xcb/uzM0hnT2LXCxiJBLbp9BsrMNIpfaePiYIJH7zN6mMj2P8/d9jFosIlQqCYTjpLJEIBIOY3/u9xF73uh2Hv+yJCaz770ddWaHrJnJt91jLQm406A0PYz3veSTvumsvb8We2GQMsIFRLlN/6UsxCgXktTXM2dltPzNxfd1JSrtsWK6Pg9XpUP/0pzG+8AUolVA2EsTkbBZjGwGsi1AoYCkKhEJoT3ziAa14h/V0Ok463h6FULaiILbb0OlgtVqYv//7SCsryNUqnelphFAIWi3kdhvbNB0xuCQhd7vO9enyMsbp0w89fz7v/I9EAu2FL9zzuisf+hD8n/+DND+P1GxiRCJ0p6acYddOB6lUQrtwgV6jQfdNb0L4/d8/Ugl/sEmHfwWt1v55emia5olMVVWlWCxy8eJFZFn20keCwSATExNEo1FmZ2cfkTAuGo3i8/kYHBwkk8kgiuIVDRzbtikWi5TLZcbGxgiFQlcMprnNK9jagfvSv+sKVwGvebodkiQRjUYpl8tEo1FM0/SE+IlEwnM+LpVKnljQTVl3G1SRSIRms7mt0P2wcFNM8vk8yWSSTCZDIpHY9P4pisLg4CBra2vUajX8fj+aplEulwmFQteMAH1hYcFLz3ZFsqlUCkEQaLfblMtlSqUS4+Pj2LbNqVOnjrV4zxVpnj17Ftu2UVWVwcFB7zuiqirhcNhLsRkeHvYGgf1+P51OZ09Jwm4Kljuott0grGEY3uBRKBRCFMUdBzgSiQTz8/N0u11KpRIDAwNMTEx4KRXuYK8r2r38uD7u9Ho98vk8xWLRS45RFIVEIkE6nd5xmM8dDHQNO3YbGGm3296wym57/NjYmGc20O12PVMBQRCIRqPEYjFUVWViYuLI7YdXg2AwyOjoKJZlsb6+zsLCAn6/3zv/tFotms2m97iBgQESiQTlctkb5HWFxZIkoaqqJ/Tf7yRvn89HPB6n2+2yuLjIysoK6XR60/O0221yuRy9Xs9LPtrPtCJ3D85ms1QqFc9wBqBWq1EoFLzzsGmaVwzVXo9DgMPDw95QVbFY3DEJpNPpUK1WvWSHa9UAYjczpoPQ+tm93kOCyz0MI3o1CtvekzHZfmJ1OnDPPYirq9iiiD47663HlbkIgB2N0pMk1LU1etEo9U9+kuiLX3yga320iIEAwX0Wfz0SrEtMmG1RvMI4eBMbpr9ys4lw8SLdP/1Typ/7HPaznkXwJS/ZkxF19Ed+hNYNN9D+27+FL38Zs9dzjA9lGVQV87u+C//LXuYYGW6D/fnPIxWLmD7f1sLJSxAMA2FDIGpp2ra1LUFV0UdGUJeXsSoV6p/+NJE77tj0mManPgXVqmNyODGx7d+yZRlLkpAtCxsccepOa7QsLElyepHVKkK9jlyr0TlxAvx+5z2v1xFME0MUHdNAUUTsdlFaLUxFwdY0rBe9CP+LXnRFDe1q0vzv/8Z697sRFxaQKxX0ZBI9lXLE+7btpMLn86gXLtAzTWq/8Rsk/uIvHOH/YXEEe3RWq0X5bW9D+o//QGw2EUsl53shilhnzqD/x39QOnGC4FvfuqfvWfi22yh95jPY3S7q/Dzi2bOYyaST+i4IzjHmJlRPTGBNTRH5mZ+5+i/0iCNt9PVsWUbodLBrNdTFRQTDQNR1r4cNjnmn2Osh6TpmIODUEK8ira98hc7/+l+IxaLz2TUajnGlLGPNz9P51KdoPu1pxH/7txF3ui4XBLRXvhJjYQGj00G9eBE9mcS69HtbrSLncgi6Tm96GutxjyP23d+96c8oAwPEPvABqn/zNxif/CRWPg/u+URRsNJphOc9j+iP/uiREp8DnDwJs7Nw4cLhrSHRXOC28+9ltHmeoF7BbzYQsNEFlRsq/8WzF/83/zb6I3xj7E6++lVhx7qqcscdWA88gJDNIqyvY+9w/a1ksxjxONbAAMGnP31fX1M+n6darSIIwo51Ob/fTzKZpFQqEYvF9lTLSaVSFAoFZFnelJK4FbVaDcMwvBraTkiSxPT0tFeDXVtbI5/Pe6KZbrdLrVZDlmUmJiaIx+OMXRZHLwgCw8PDntmbaz7oCtAPsvYTiUQIhUKMjo6ytLSEYRikUinv/XVrwrlcjkKhgN/vp9VqEYvFmJ6e3lS36XQ65HI5FhcXAVheXmZyI4BDURQ0TfNERrsJzNvtNoFAYMtaj2VZtFoter0ejUZj3+sKeyWZTHpGqEtLS9sKZt2an9s70DRtU4LytY5bgymVSpim6dWGXCH3wMDAVRXzus/Z6/WuqLO4QrWVlRUvPbbVaiHLMrquY2ycwyVJYmxs7Apz3z77i/sZwJWCSNu2vdqiK7QURZFqteqZP5w+ffqKPUMQBMLhMIqiMDc3R6lUIhwOo6oq1WrV28sFQSAUCtFoNKhUKszOzm4Ssve5vrhUDNpqtTYZ54ZCIdLpNI1Gg0ajgWEYTExMkMvlWFtb82rkl+L3+1EUxeuBPOEJTyAcDh/a+avPtUc0GiUYDCJJErVabce+gm3bVCoVkskkoVDoUAXovV6PbreL3+/3rn2TySTNZpNWq0UmkyGZTBIIBDyTXrfPbRgG4+Pj1Ot1hoaGKJVKJBIJzxAC8HpR7jXY2toa8XicdDp9oGZHk5OTtNttbNtmdXWVSqVCNBpFURQMw/DMqEZGRkgkEszMzBwLM6brEVmWUVWVWq22p8d3Oh1CodBVNTwIBALMzMwAzkxUsVj0jPjd+65oNMrIyAjxeLx/fPXpc4j0Beh9+vTp0+dIEH7Ri6h+7GP0Gg201VX0bhdzYABho0BrmyZisYiSy2GEw9jpNL6f+IkDX2fwZ36G9te/Tq/dRltcRI/HMVMpR4Br29j1OnI+76QlTE1hTU8Tu2xI4yghBgIkfv/3qX30o3Q+9CGEL3wBqdNxks9FEV2WEU0TwbKQNqJKTEnC1DQ6o6PYySRiJoNaKDipmuk0kUcibLJtzHvuQSoUsGTZE59v+dCBAYxGAzGbxf7857F++Ze3Teo+SvhvvpnW5CTC+jpyqUQvnfaGx5SFBed9Biwcwa9omtgbQn/T78e0beROx3nchsmBfs89cJkAHUBJp4m/8pXwyldun2YgCIS+53vge77nYb0Os1pFuPdepEIBPRrdUnzuPYUkYSSTyPk89soKrf/+7yOZWNlna5TBQcyJCeeYrVTQBwd3HE4Si8WHRHiXDWP06XPUMGs16h/7GOYXv4hQrYIsY4+P47vjjoeVptLnkWEsLzvXdr2eIwjfBTsScRJEul3M1dXdn0AQiP3Yj2G/7GXUP/tZ9P/6L2g0EFQV8dQpQnfcgbwHgY3vB38Q/d57sdfWEHM57B2S0jYJbw/g2k+Ox7Gf8xzMRgPlwgWYm8McGdmsgtV1xEwGuVymNzWFOT1N7GGe9691Kn/3d9h/9VcI1SpyqYTQ7SI2m0i9npMk9s1vos/Obq0u1nXUQsEZUpyaIvCEJxz8C7gM2+/HliQkXWcv3rOCrjtJY34/9Y9/3BvA7U1MPDQ4GghgBIMIponc6WAKAqYsI/V62J0ORqMBwSBCPo+azdIdHcUaGSG4x5RAPZeDD34QcXERsdOhe/IkwqWN6kAAM5HALJUcEypZpvXe9+L/4Acf/ht0FdlJOGPbzgzqfs0ehUIhTp8+zerqKuFw2HOtB7zBr3g8zujo6CMWoQmCwMzMDIZhYNs2mUzGE5m5Q0vuUMvo6CjxeJwTJ05c8XcURfESx93G1Hbouu4lPLiJMzsRCASo1WrYto1hGGiaxsTEhJfc/p//+Z+IouglP8iyTLvdplKpoKoqwWAQ0zRZW1u7YnjysDBNk1KpRLlcRpIk7/3fakDIFeFXKhVisRjRaNRLk9+LgPWo44qsl5aWEEWR2dnZTcMDwWCQZDLJ+vo6S0tLSJJENptleA+ppkcVd1i5Wq0SCATo9XpXJE8B3oBytVollUrRarVot9sUCoU9HcuBQAC/348gCNTr9S2fA/AGE6vVKkNDQ95A3Hb4fD7vO3fp4KkkSQwMDGxKkb7WWF9fZ21tjU6nQ7lcpruR/qdpGrVajUwmQzweZ3h42BsUubQpHo/HPSOJSqVyRZL0pViWRbVaZWBggEgksqehmuHhYVKpFMVikWg0uinpJJVKkUgkDnU456AZGhryDBU6nQ6VSoVms4lt22iaxokTJzwTlrGxsU0CbFfA7xIKhajVauRyOSKRCJZloes6oiji8/lIp9NEo9FHPAQxOTnpfV7r6+vMz8+jaZqXsN3tdgmFQkxNTZFIJBjfS3LvHmk2mywvL7O6ukq73fbMZi5/zPr6OisbaY/BYPC6H1weGhryhr9WVlZIJpNbDqBblkU+n8eyLBKJBGNjY9dsKvxREKBLySSGKGJLkuMOtUsvwW61nLqQqiIcsIFC45//GaFScQS+4+M7GvbZ6TR2sYhQKGDccw8cMwH6UUFOJunhCC7FZnPrOpVlIS4uotRq0Oshmib0eij5PIZhYGWztO+5h9aP/Ajxn//5XeuKgSc8gcATnoCey9G57z6sZhMxFML32MfuKR1b2KhfmHsQEsj1+kO/t1taZzSKvbqKUCqhf/7zcFl9S/+v/0Iul53v0g71PCsSgXzeMawFRNPcvkZh24iGgakojll5ve4kDgP2hmmlFQzS2+Z3jeVlxFbLqXm9/vU7C0+vAp0PftBJPq9U6J44gRAKPWRisJGya4bD2CsrqAsL9Px+R9x/++0Hus6jjNXpUHnNa5C++U3kxUXH7DwQwNY0MAzUlRXstTX0apXmq1+N/b734dsYSN0OQZaJvfOdlH7t15zvdqGAUiggrq8DlyRUT0xgTUwQvOuuPdWKr3VEnw/zppsQ8nmU9XUoFpG6XafOKEkYmuYYddg2gmEgd7sIto1Sq9Ftta7aupr/9V/ob3wj0uoqSi6HLUnO90qSEDsdtPl5TE2DDcOtxPve54jJtyH0tKdRfe1rsd73PvRcDqlQQM3nnb3NshxD3GgUa3IS+5ZbiL3jHVvu62IgQPxnfxb7p36K1le+grFxfMmDgwS+8zsRHoEh5EEgCPDqV8Nb3wql0sE/f7p+jpc/8EZSnWUGuqvIVo+WFMISJMJmmYHuKlUlQWDxz4j08vyb8PM0m7BdSTH8vOdR/tCHnBmjxUV0w8DcSgBuWdiKgjU8jPSyl+1oNvxwcQ38qtUqsVhs1/u/WCxGoVDwRCe71bKCwSCBQIBUKsW5c+fI5XJe4rLP5/OM5dz7s2Qyic/nI5FI7Lr2SCTC7OysZxJ66f2xLMuMjo4SCoWIRqNMT09vKyYXRZFkMnnoxnYzMzOeAD6TyXj30KIoous6pmni8/mIRqM0Gg3GxsYYHR294u+46cPLy8usr69jGIYnNLYsy0uq3KmuBnjGqK4Ix6XX65HL5SgWixiGga7rlMtlb53Dw8MHalDmipJTqRTr6+tkMhlkWSYUCnm9gHq9TrPZJBwOe+mXlUrlwNZ42BSLRRYXF7200kaj4QlhQqGQJ+aenp6+akmM7t7iinBcut0uFy5cYGFhgZWVFeLxOJlMxhOJDg4ObjrOZVkmFouRyWRotVpUq1Xv7wYCAdLp9J72sj7bI8sykiQhCALdbtczHnUT7M2N2Utw9htBEGi1WiiKwtjY2I69HU3TPNNY27aZmZlhaGjIM5Btt9uUSiUWFxe988sNN9zQTzq/DjEMg7m5OSqViveP27dxTVXdfpSu617Ceb1eJ5FIkM/n0XWdcDjs9Q7c9NuVlRVGRka880GfPvtFOBzG7/cTj8fJ5/M7Cl7d2no0Gj30/qMgCFecN4PBIAMDA54RRD6fRxRFNE2jWq1Sr9cxTZPp6WlSqRTT09Pe91FVVe8a07ZtFEUhEAjQbDZZW1sjkUjg9/t3NZx6JNTrdc/UX5ZlotGotwfIsszp06eZm5vD7/dTr9e92QRJkkilUkSjUfx+PzMzM9dsv+NaIB6PU6lUyOfztFqtHe893KAHdyboahKJRLjhhhtYW1sjEol4cy+CIKBpGpqmkU6nvT5vnz59DoejWfHs06dPnz7XHVIkQuAd76D9a79GV5aRMxmUs2cxVdVzsrdFET2VcsQ/v/iLBJ/61ANfp+/UKYw3vxn9LW+h6/Mh5/Mo5845TUnbRrBt9HCY3uws1vQ0ofe858AHHh4ugiwTfclL4MUvpnrPPXTe/nbEchmx0UBqtxHbbUcMvZGADmAqCnKthpTLYSkK3fFx7JERfL/7u8iP4Eajc/Ys4soKcrmMkUrtOqBjpFJoCwuY9TrNz3+e8G23PaLXfqAIAvLtt2PNzUGlgjw/jzE1hSCKKI0G4kYKhC1JCBvupLYoOoLejd83fD6UVgtLFFGKRayLF/f0vPtJ5/x5BF1HbjbpTE7unAgCWMmkM9TRbtP79rf7AvTjhCAg3X479twcYj6PkM1ib1M8trtdR4SXTmNOT++Y0NKnz6Fi25T/7M/gwx92hJalEkKvB4KAFQqh/9u/UZqYIPA//ye+G2887NVeu1jW5mHM3XCHgWwb+5Kkld0QZJnIrbfCrbc+7CUCBJ/yFErj4+ilEtrqKl1FgWRy85ptG0oltGyW7sgI1vDwzgnt+0j8V3+V0tIS2DbS0hLa+fOYfr+TaKXryK0WlqLQO3ECa2qK8Nvf3jdXuITyn/wJwv/7f07id6XiDC/6fNiyjKTrYNto9TrSgw/SPnUKwR0ksm0nAWhtDUtVsQcHkX/4h4/Eeys+9rHYX/kKUi6HsYuwwjZNR9QwNoZ9440Yn/40cqGArSiwkdroYk5MIG7E4kjdrjMgblmInQ7y/DyCKCJYFt3RUeyxMQJvfeueh/gaH/kIQqOBUqvRmZnZLD6/lESCXqeDks3C+fO0v/Ut/DffvLc35gDYrX/Wbu+fAN15Pj+zs7P0ej1KpZI3NOAmgO8l9bzX61EoFDx3bddxO5VKEY/HCQQCnDp1irm5OS912U0wFkWRVCpFJBIhGAwyOzu7pdjdbQxHIhEvnXsn6vW656C/lyG7brfrpXq4Q4DuQJzP52NxcRFFUbxE9UgkwujoKKIoIgiCJwx104cOm06ng2VZNJtNQqEQpVIJSZK2baAFg0GKxSK9Xs8RvmzcR5qmeewF6Pl83kttmpmZ2VIc66YsLS4uUiwWCQQCDA0NHcvhODfpqFwuo6oq7XabYDC47WcfjUbJZrPYtu0lODWbzT09VywW84Zti8WiN5y2Fe12m16vRzAY3NPgob2bqOkaZHV1ldXVVdbW1mg0GgSDQW+vq9VqzM/Po+s6yWSSVCrF0NAQqqqSTqdJpVLIsozP5/NS4TOZjDdcfjmuIYggCEQikR2F6pejKApDQ0P9gbANBgYGiMfjFAoFbx+1bRtVVUkkEqRSKTRNY2VlhfX1dZaXlzEMg3g87qXB9Xo9yuUy9913H4A3lO8arwQCASqVCj6fjxMnTux6DtwKSZI4deoUS0tLaJpGp9PxUiQCgQDhcBhN00ilUoyNje3rwEU2m6VWq9FoNDhx4sSWg6rBYJCJiQnm5+epVqtks9nrXoAuyzI33ngjlUqFWq3GN7/5Taanp4nH44ii6J3n8/k8lUqFgYEBJicnd0xKP+50Ojv//CDm0PyPeQztgQH0eBy5WMS4/L7+MqRCASMYxA4ECD3jGVd/gZeg33cfUrWKLQhX3JddwYZATqrVsC9exGq3EfuDfQ8bdWyM+vQ0woaBnz40dEXdR5qfR67XkTsdBMPAFgRMRaE3MIDUbqPNzWGEQph//deUDYP4q1+9p+dWBgZQnvvch79od7/fw7WXYBhg21iiuHvdQhCw/H7HQW0rQVG9DrqOpao7/61YDEuSnPTzjX+2Q9yoyyIIjoB04zVZsryjAYO33mAQqdl0fr9WO9B+bOf8eaQHHkAuFNATie0ToAUBa2wMu15HKBbR/+mfoC9A96jcdRfi/fejzM1h+f10pqY8c3qArmkiZrNoS0t0bZvWm96E9td/vau4VwqHif7BH1C/+26MT3wCVldho96HomAlEgjPfz7Rl78cabf99jpCueMOzG9+E/niRa/+Z2ga9iXvtw1Yooi4UacXDAP5i1/EKJcf0YzATpi1Gr23vAVxZQUll6M3MrKpPm8CRq+HvLyMMjcHgkDlT/6E+Gtfu+Pfjd55J83hYTp/9VfYDzzg1FHd/SgUworHEZ73PGKvfOWu+4ogSQSf8pR9esUHw/Q0vO998F//BefOwcWLMDfn/OxJT4JEwmnJTE3Bd34n/Md/wJkzztsjiqCq0GiALEOzCevrUCjAJXq6LVGNJi/99lsYaC8w3FmioA5R1gaxhI3jy7YJGlUGO8uMtc7x1PWPkA9MYZrP2/ZvCopC8J3vpPna19IVReS1NZSzZx0zgU0PFDBmZrBe+EISL33pI3/ztsCty/Z6vT0lgLqGnZemEu+GpmkUCgVM0+SBBx5AFEXC4bAnCrMsy0syTafTTE1N7fn+MBKJcMstt1AqlTxhvG3bnvllOp1+RPezh4GiKJw+fZqlpSX8fj/tdnuToD4cDlMqlTxx6E5p9a5x3/3330+5XKZYLHoGfktLS7TbbRYXFzl16tSW98uGYbC2trbpvh2c+vfc3BzNZpNSqeTVP2u1mpeUXqlUGB8fP1AxVTqdZmxsjGKxSC6Xo9vtbkpE9Pv9DAwMEAwGGR4eJhgMsra2xsDAwCM2wT0ulMtlFhYWWF9fp1qtEg6HGRsbQ5ZlDMOgUqmwuLjomQzMzs7uaEzwSBFFEUVR8Pl8nkC02Wx6tatMJkOlUvF6Q6ZperXKTCbD8PAwsixjmiaVSoWFhQXS6TTtdtvrQQSDQSqVCn6/n+np6X5y9i50u10KhQLNZtPrUUUiEeLxOIqiEI1GKZfLmKZJJpPx6npuErUkSYRCIYrFIuVymWg0uuO+BE69WRRFVlZWmJ6eZnBwcNMeHQgEvJrk+vo6iqKwvr7O1NTUVX43rm9cQ4qjgmVZXLhwgWKxyPLyMoIgEIvFvO90s9mkWCxSKpWwbZtms0k8HkeWZcbGxrBtG1EUPeG6a+biHsOusHZsbOy6rwX32X9GRkY8Ex/3XOX2ZMDppReLRer1umeae7XMX7rdLpVKxZuj8Pv9W/ZKVVX1EptN0/R62slkEkmSPPOXZrOJruvouk46nebEiROMjIwwOTlJKBRibm4O27ZZWVkhk8kQCoXw+Xz0ej0ymQyGYZBMJr3f3a9ZANu2yeVy5PN5Op2OZ7LtpmQnEgmGhoY8c+QbbrjBM9+p1WqeAVQwGOwb2ewjtm3T6XQwDMMzH9svM/FkMundq2QyGSYmJracLzIMg/X1de+e5iDmaVwDA13XPfMU17yhf2z16XM06AvQ+/Tp06fPkcF3ww0If/InNO66C+67D6PZRGg2HRd7VXWGFkZGUH7u5wg/+9mHts7Q93wPnT/6I5p//ufw3//9UFMSIBDAjkSwv+/7iPz8z+97o/WqIghE77gD43u/l/qHP4zxyU9iFQrotg3FIko+j+nzYSmKcyEvihiDgxCNYp48SfA3fgPf6dOP6KmNXM5JkdB17L00sEIhx2G918N0E0ePAZEXvYjSvfeimybKxYto3/42pqZ5TXpw0iAALElyhnQuvbnbSJwUN5zlr6aj/HbYG6IjbNvpLO+C4A42mabzu32OFZHbb6fyD/9At9FAW15G13WswUFvAMi2LIRSCTWbxQwEsAcHUX/8xw951X36bINtU3zb25A+/WnEpSWUeh1TVbF8PgTLeihNpVSi/drXYr3rXX0zhauElE5jSBKWLO8pfYx6HVsQsFUV6SAFgoJA4Ld+i/ZrX0vPttHW1jDzecxYDFtVEXQdqVJB6vXoDQ9jj43h/63fOjAhsqCqxN/7XsrveQ/8679iNpsI5bIzDO3z0R0YcJKWbrqJyJvfjNIXHHnUPvUpR3w+N4fY69EbH3eG+zc+u0a5jH9uDrnbRe52CT3wAO10GmTZMWjSdfRoFGt8HPvFLybyghcc8ityCN15J82/+zuMQABpbQ1zZmbb41FcX3dMtGIx/C95Cd3f/m3EVgsjHL7yd1SV3swMysKCk3RkmgidDpJpYvd6jhlVMok1OEjgd38X3w037HnN9mc/i1QoYPp8CLsMs1jpNGKhALUa7XvuOVIC9N22sVYLrkbvU1XVhy0mdBuXuVzOS351hx0vF8uFw2Fuvvlmz5m7dcn9Rzgc3rWJ6ArVm80mCwsLXlr3VrTbbS+Fxk1S3g7DMDhz5gyNRoNut+uJcEVRxDRNer0e2WyWmZkZotGoJ5qXJMkTeaqqysLCAoVC4cgI0C3L8l6LLMve63HXfzmXpg64zbdL//txpdfrUavVKJfLxGKxHV+PIAheqsvAwAC1Wo3oMRQPuOLXbreLz+ej3W7v+B3w+Xze5+4ODlqXD1dvgyAI3nDhwsICy8vLjIyMXDGo0Ol0yOVyxONxbNvedYDDHVDWNO2aHzx1qdVqXvqzYRhecjY4A8W9Xg9FUVhdXWV5eZl4PM7o6CjDw8M0Gg1vnwqFQoyOjnp72tLSEvF4nHg8jqqqnsmAK5SemJggGo0ey2P9KKEoCsPDw9umzXU6HbLZLKurqwCcOHFi0wCGJEmUSiVUVeXMmTP4/X6Gh4eZmZnBtm3y+Ty5XI50Oo2u65w8efIRDeSJosjU1BSjo6PeUKs7xBQKhTwjg/3EHexw9+GdUpJUVfWS8mKxGL1e77rZA7bj1KlTVKtVbNtmfn6es2fPeuY6lmXR6/WQZZnBwUFOnz7NLbfcctWTI/aDkydP0mg0ADzzjL2wWwL6gWhFBQHxttuwl5cRSyWETMYxOd6KSgWlWqU7NYX93d+NvIfkyH2l2UQwTccYbA+1BVtVETb6CWaj0RegP0KUO+7APHcOoVBAWF3FHh313n8hn3fE5+7BLIoYmoYej8PICCZgttsoCwswP4/5939P86lPJfikJ1219VqDg1gXLiBXq07/bpdjRdh4jLWXwVT3mnKrc8uGYbiwm8JQEOgOD6NeuOAJWAXD2NzrsiykjTR5w+8H00RPJp3agCAg1mpOD2q378GGIQBw4Md/+wtfQGi1kLpd9Onpnc2aBQEjFkMqlbDPnkXP51EehpnQUcdsNGh8/OMYX/+6o0b1+ZAe8xhCL3zhjvuoUSohfu5zSCsrWJqGeeLEFfUFQZKwR0boCQLq6iq9aJTGF76wpzkBQVGI/PAPww/9EPq3v425uoptmkjpNIEnP/nIJlQfJuHnPpfSH/4hliyjtloYkuQY1Lu46ee6ji2KTpCAZSE0GjQ++lFiP/VT+7qe+sc+hlAuo+ZydMfGHGX0ZQiqijk9jXD+POLaGuanPoX1cz+HuEuxLviUpxB8ylPonD9P+wtfwK5WETQNeXqa6HOes2OK+rVANArPe57zz2688IW7P6bXgze/GR54YPvHPCbzz8S7GYY6S6z7Jqipl+2DgkBTibEgBTnR/DbJ7hrfvfYPGPr3s5OdiTY5ifinf0rtHe+Ar38do9mEr399s1GLIMAv/AKJH/3Rfe/fuPUbQRD2XJuxLGvLpMStcOuXoihSr9cJhUJ0u11yuRxLS0v4fD6GhoYIh8NeeuLDrRdIkuSJ1+HoCekeDrIsMz09ja7rFAoFWq0WlmV5CY6rq6ssLi566d5bYds2q6urniFrtVqlWCwyOjrq1eN6vR7VapX777/fq9OoqophGFSrVa9eMDIywsjICLIs02w2uXDhgifwCYVCjIyMIEkS+XyeRqPBysoK9Xrdq3VfjWTL7bjhhhswDINut0skEkFVVa8u7Qr4k8kkPp+PSqWCbdtEIpEjUVe/WliWxdLSErlcjlqtxtTU1BX3wqFQiFarxdLSEpIkoSgKN99881X5DqVSKer1umcEmMlkKJfLXqpqPB73DO7c/o9bn1heXmZ8fJyVlRXK5bJnbhqLxQiFQpimydraGmfPniWZTLK+vs7NN9/M8PDwsd0PrhaGYbC4uEilUtlkFukK0DVNwzRNYrEY3/rWt8hkMl7Pw032FUURXdfJ5/MsLi4SDodptVq71vjK5TKdDae/xA7X2bFYjFarRalUIhQKeaYJ1wqtVotiseiJJBVFIZFIHJgY2jRNisWiZ9wCzrk0FouRTqcP3bwhn89TrVZZWloiFApd8T32+/0kEgnW1tZYWlpCFEUKhQITExMEg0EmJydZX1/39je3fxUIBDyh7cTExKGnTve5NnH7aIDXY8lms97e2uv1CAQCTE5OEovFmJ2d3ffzlJs0XqvVPAEwON+drRKYk8kkhUIBSZKoVqub9udYLEY0GqVWq1Gr1byE8ZmZGR772Mdy6tQp77EzMzPIsoyiKLRaLSqVCs1m0zORcHs1J06c2Lf+oGmazM3NeYZL1WoVc6PuJwgCoVCIer1OuVxmdnbWMz1xzbD67D+GYVAoFMjn897+6947JhIJBgYG9hTksBOyLDM0NISu6ywvL7OwsEAymSQSiXgGZ+40LyiAAAEAAElEQVQ9kCRJDA8PMzQ0dKDXEoqiPCwj9j59+hwc185dRZ8+ffpcZ9imSevrX/fEt/LICIHHPvZIJO89GrTJSbQ/+iO68/M0/+mfsNfWwDAgFsP3nOc4DtJH4DX6Tp/G93u/R29tjdZnP4tdKoEsI46NEXne83Ztah5l5Hic+M/+LPZP/RTd8+cxKxUETUOdmKD15S9jfv7zUC6DLCOMjuK/4w78t9zy6J70kua1bdu7pmpvahjuY7rP1UaQZeLvfCfl//k/QVGgVEJdWXEGcNhwjd8Q91uS5CQ+XIYtSYi93pVN/wNCikQwBMFJau90YJfCqSdYVxTEfuHh2CEGAgQudY7PZFDPnnWOTUFwEkoAPZFwBjd/6qcO1SCkzz5g21jtNoIsX3MDNtW/+zukz3wG+cIFBNume+IEhELedYVhmoi5HNrqKj3Lovtbv4X2N3+D1N+79h3/zTfTHhjAWF3dU/qYXCw6othw+MDTx3w33oh911203/QmutEoQqGAVK0iWJYzZBeNYqRSWMkkvv/1v7a8JjLKZcdsRxRRhob29ZgSfT6Sv/Vb6K98JY2778b86lcRWi1sTUM4eZLAi16E78Yb9+35rglsG+P//T+kbBax26V38uQV+50Qj9O+4Qa0uTnUZhNLURB7PSxVxYjHsZNJ7EgE4eUvJ/4TP3FIL+RKlMFBzKc/HVotlAsXYH4eY2Rkc6q4riNmMsiVCr2pKcyTJwk8+cl0LWvH4W5B0zBOncKo1ZAKBYRiEQAjEsF4+tNR7riD6Pd//8M7d9g2Yj6P0G5j7uF7ISiKM4zeamFnMnt/ngNgLwnoR4X5+Xny+Txra2s0m00CgYAnZnUTTlKpFIZheGkdqVSKVCqFbdve0Nlem7mDg4MUi0WGhoZYX1+n3W4Tj8e9Qa1Op0O5XKbZbDI8PEyr1drxbxuGwdzcHGtrawiCgKIo3mCXYRicO3eOUqmEpmnE43EGBgY8Mbebru7iNoRTqRS1Wm3fk0lcUfBOKeaXIkmSI7bYGOrzbYgvWq3WlklDbsKSKIo0Gg1GRkY89/HjjGsq0Ol0dk38AKfJvba2Rq/X8waOjhuXJoe7x4v7+e6GK0J/OE3foaEhLwFqZWWFubk5z0nfsizq9Tq6rjM8PEyv19vTMewOs7nfveuBXC5HtVr1hkXcz6DRaLC6uuoNa8RiMS8ZRJIk2u02pVKJwcFBLMvi9OnTBINBpqenAWeIpVQqUSqVNu2H0WiUkZER4vE4MzMz/eHPq4xrvNJqtTZ9vi7FYtETaYfDYW/QMRqN4vf7GRoaolKpeAO9oihy8803P+IBDVcwfxA0m00Mw6DVau1pkDAajVIqlTAMg0ajseMA7PXCd3zHd3ipU/V6nUKhQKfTQRAEhoaGSCaTDA8P85jHPMYbqDvqfO1rXyObzQLO9d1eDW928089KL1o+KUvpfrJT9Jrt1GXlzE6HcyBAYRAAAQBu91GKhRQymV6AwPYg4MEXvGKg1ncpQSDjunXRmr1rsLiDQEggHRMkimPIqHbbqN8993O8bGwgNntYqbTEA4jF4uI3S6CZTkGwYqCpSiYl16n+v30pqfRzp3DLJfpfPjDV1WALj33udhnzyLm806q9k73MZaFLQgIto21yzWaretIrRZmOo0wNnbFz8WTJ7HvvRcpm0VvtxF2+gIPDmIuLIBhINg2cqeDLYreWgTbxpIkdL/fEZArClY6jf7Yx6I+8ADC2pqTuL7Ta7Nt5EoFMxzGGho68O+AXSohdLtOWvseaiF2IIBYKgFglkrXhADdNgzK738/wic+gVCvI1cqTk9fkrD//d+pf+hDWM98JvFf+7Ut++aNj34Umk3kZpPuDiaK4Bgv2MUiQqlE76MfhYfThxIEAo97HDzucQ/7NV5vCLKM9ZjHYH3xi1iS5NSz220sV2C78f01ZRlLVeklk0itFmKhgPHP/wz7KUC3bayPf9wxrlRV2GkPEwT0wUG0pSWsep36pz5F9MUv3tPT+E6exHfy5D4t+vpFVeEtb4G//Vv4xjcgm3W2cZdoxOY7S58g2svTE33UlO1FtZaoUNCGSHXXSXVWaH3lyyRv/a4dn18ZGiL5vvfRXVig9ZGPwDe/6exHLppG7Md+7FG+ym2eW1EQRdG79t9NDOLWY/x+/66mTtls1hOaA3zXd30XhmFQLpe99MRqtUqr1SIYDHoGYY+Wa6HesN099PLyMqZp7lg/zWazXnpwt9slFosxOTnJ7OwsvV6PSqXC/Pw8oihiWRa5XI7iRr8EHKO2VCpFPB5naGjIM5BdXFz0kubHx8e9eq9pmgSDQe+f1dVVMpmMJ2Y0DIN6vb7JjM5/FW6kEokE8XicG264gXK5zMDAAJIkIUkSfr/fq2e0222y2awnRr+WDRJdsW+pVGJkZGTb72wgEGBoaIhsNksikfDqgftNKpUik8kQDof51re+5ZkjDg0NUavVPKGSrusYhsHExASCIFAqlQgEAszNzXnml36/n/HxcSYnJ+l0OhQKBQRBQNd1vvWtbxGPxykUCtx0000MDQ0xMDBwTewNjxbDMDh79iyVSoVMJkO73fa+H81m0zOXSKfTFAoFbNtmbm4OURSZmZnxhMmuiFJRFM90MhAIeMnmW6HrOu12m0ql4tWZd/pM4vE4CwsL3r51kIYWV4t2u83S0hKNRoNms+mJvzVNI5/Pe8f1fvf6LqVSqbCwsOAZfDebTcAR87livVgsxokTJ/bUF7wa5PN5SqUSsixvayIhiqLXt200GtTrdc/g2e/3Mz09TavVolqtegJIt8Z94403MrZFzaBPn/1ieHgYTdPQNI1UKkWj0fCM2N25hlgsxvj4+L73xMvlMvPz81QqFUqlEp1OB1EUvT5uNBql3W5Tq9U4efKkZ37vmt3ncjk0TdtkRCEIgvfd6na7Xk/g8v1eEASmpqYYGhoin89TLpc3pa+nUikSicS+7i0XL16kUCh4RjoDAwOEQiFEUfRmOJaWlrx+/Q033LBnY9o+D59Wq8WFCxdoNpuUy2XPEEAQBILBIPV63TPGerghFZczPDyMrusIguCZqmSzWe8ex70XSSaTDAwMeCZHffr06dMXoPfp06fPMcNsNKj93d9hf+pTiNms1zjpKQqd0VGk228n8pKXHHvRmHbiBNov//JhL2NX1JER1MMYRDoABEm6Ir0wcuutcOut+/5cytgYhihiaRpirYa9i6hZqFadwSqfD/mYFbVEv5/kXXfR+MIX6H7kI+if/CRSr4eyMShvSRKWqm5Og7gEG6fBb8sy5iE0xH033kg7FsOIRpFKJcxdHI3FQgHT7we/H99Tn3pAq+yzn/hmZzH/6I9ovfe98I1vYLZaCI2GI+BTVYjFsAYGkH/6p4n8wA8c9nL7PBJsm9Y3vkHn7rsR/v3fETodbEnCHhxE+P7vJ/SiF6Ecd/da28b8x39EWl9HsCx6s7MIlxWiBUnCHh6mq6poq6t0o1HqH/vYVRtKua4RBMQf+AHslRUnfWxtDXtkZOshw0IBudGgOzOD/X3fdygmP/5bbkH9m7+h8fGPY95zD/bysvcza+P6O3r77ZuF5bZN/XOfo/fRjyLdd5/3nzuShPWUp+B70YsIfMd37NsalaEh4r/4i/v2965lWvfdh7iwgFwqoadS2943CaEQvcc+FuuBBxAsCysQQH/qUyEWQ3rmM4nefvuBH4+9lRW6Z89Ct4sQiRB44hOvWEP8N3+T8sqKM4i9vIzv7FnMQABLVZ2Eog1BfW9qCuvECSJvf7sjuIhEsFUVqdXC2Ob5EQSIRjEjEYxOBz2dxrztNpLvfe8jej32huj94YysuMlm7DFN5qA4LgL0bDZLoVBgcXERSZKYnp7elGo6ODjoJWe4bso333yz17wVBOFhp2srisLJjfsWWZYpFovMz897f8c0TQKBAFNTUzQaDRRFIZ/Pb5uWvry8zNzcHNVqlenpaR7/+Md7DtuGYVAqlahUKnS7XVqtFrVabduBnWg0SjabpdlsUq/X92UoxXWlLhQKm8TQ4XCYgYEBotHotkNBrkDebV5rmkYoFKJSqRAIBK5oKNfrdVRVRdd1b8D0WnCCdgdZgD010d3389LfO26oqoogCKiqSqfTIRgMksvlvMHSy3FFjKZpemlDD+f4FQRhRyf9aDRKLBbzhpnz+TwrKyuMj49v+ZmUSiXq9ToTExPE4/F9d0A3DMMT5Kuq+rD3oauBm3Tl7lXua7Ysi7W1NcrlMrVajcHBQQKBgJea5Q59dTod1tfXEUWRhYUFHvOYxxCLxTh9+jTr6+tEIhG63a7X/Pf5fFsmK/S5erjnk1AodEWit23blMtlKpUKoigyPT3N6uqqN5Tn9/sRBIF4PI4kSayurhKJRCgWi3sy1jhsLMvykvv28n12H+Mar/RxuOmmmzh16hQXL15kZWXFG1aLRqNeStD1kBa/23XwQQnQpXCY4HveQ/NXf5WeoiBlMvjm5jxBrGgYmH4/3YkJ7KEh1Le8Bd/s7MEs7hLkW27B+tSnHPFttQo7iRY2xLdGPI554kQ//fxRIPp8hN/9bhq/8iv0JAkxm0VbWADTRG63kUwTc8Mw2FIUehMTVxjzCprm9EyKRfjP/8QolXZMfn40hO+4g9pf/RV6PI62vEzvxAnYoj5h12rOsa2qiKa56320mM87faloFN8dd1zx8+ALX0jrH/4B0+9HyuWwJia2FwyXSpg+H5ZpIpgm0kZSuSWKIIpe/8sSBESgOzGBNTpK9M1vpvUzP4MeDiNns44h5XbPUa0idbsYJ04gvuAFB25eLigKCIJjTrCXtPYNMwBg64T5Y4ZtGJR+4zeQ/vM/EVdWUOp1LFnGVhQwTaRMBiMYxKzXKc/PE/vDP7zCJMC8/37HREBVdzW7FjaMQMVqFfHs2av50q57xFYLPR53zFAUBdM0nT3EtrFE0ekdKQp6Oo2dTmOurSG22wgboQn7hdloIK6vI9XrmPH47qYskYgzO1CtYjz4IOxRgN5n/1BV+ImfcP65HD2bo/nSc8jNdQriEBgPfZ6ieOUpqq4kGeosEzDqGF/5MuwiQHfRpqbQfuVX4E1v2ixAv4qIokgymaRer7O8vEy3291Ub72cYrHoGfjtZN5l2zaZTIZcLodlWUxNTXn1kGQyia7rzvdTFD1xgitIvxrC12sFURQRRdFLsrycbrfrpUO7yfCapnn3vaqqMjAwQCQS4cyZM17NZmJiwvs83JruwMCAZ9LYaDRotVqeGepWZqPgiChHRkY8gfCXv/xlNE2j2+16xrDu3x8cHNzXz1qSJMbHxzEMA8MwyOfzxGIxIpEIlmXRbDapVCrU63VisRipVMoTOLsYhkGlUvHqeJqmEYvFjm0dyzWXdBPgdyIajXpJ6a4Adb9RVZXR0VG63S4XLlzg4sWLhMNhUqkU1WoVwzBYXl7G7/cTi8Xw+/0MDw+TzWY9E01BEDaZD9dqNbLZrJfMqus6lmUxNzfn1YrdlO++ISZeSqybWj4yMrKptuMaNKysrKAoCp1OB1VV8fl85HI5r9dmGAayLJNMJgkEAoTDYfL5POvr64yNjW1ZL3L3rWq1SiqVQhTFHUWAbn2y1+vR2wgWOc40Gg3Onz9PpVIhn8+j67r3+t3k4UQiQafT4cSJE1fFILNcLnPx4kVyuRylUglVVQmHw55Q0/3cx8bGMAyDkydPHvj+56YrVyqVbY0jWq0W5XKZer2OYRgsLS2haRpf+cpXeNzjHkcqlUIQBM9cE5x68dLSEqlUimAweE30IvscbVxjnFqtRqVSwTAMb99LpVIPS3huWZZ3bSLL8rY9xkajwfz8POvr61QqFRKJBKOjo6iqim3bNBoNb85hYmICURQ5efIkgiAwMTFBt9v1vlPhcJhYLIaqqpim6fUTg8EgQ0NDjI2NXdF7abfb3jW9KIqMjY1d1euoSqVCpVLxrh1GR0c37RnuHhAOh1ldXUVVVVZXV5mZmfHmIcrlMr1eD0EQ0DSNZDK57yL564Vut8v58+cpFApkMhl8Ph8DAwNomoZpmtRqNVZWVgiFQt59wV4MpHdiYmLC+5xTqRStVsv724FAAL/fz+Dg4DVhYtOnT5/94/h3Nvr06dPnOqK3tkbjda9DnJtDKhSQy2UE0wTAUhSM9XXshQVKn/88sfe8B+kadhrtc22hTU3RuOEGhFwOOZ+nNziIsN2NqG0jFwoYsRhWIkHwe7/3YBe7HwgCoWc+k9Azn0np9Glaf/zHhM6d89JK7O2GqW0budvFFgRsWUY7hOa5IMsIz38+1vo62vnzmMUibCdCb7Wc1JiREczHPx5tcvJgF9tn31DHxlDf+156S0t0P/5xR3yp6xCLoX3f9xH83u9FOAIigMPANgwan/scvU98AiGTAdPETiSQnvEMwnfcceTTs612m9LrX4/ymc/gy2aR221nYA4wNY3u/ffT+Nu/RXz1q4m+5CWHvNpHTv3f/g0xk3H2pOHhK8Tnm0gkMPN5hEIB8+Mfhx/90QMfYLweCL/kJVQ/8xl6nQ7q4iJmu+2kS7niqUYDsVBAqdfpjo5ijY4S/smfPLT1SqEQ0Ze9DF72MsxaDbNeRwqFtrzeNhsNKm94A9LXv45cKiGVSogbjV3T54NCgd6999J+7nNJvOENCNfA0OlxovulLyE0GoiGgZ1I7Cx+FgSMoSGUXA5raorQ29+OduLEQS3Vo/6FL9D7x390zAzcoXFJohoMYj/rWYR+9EdRN0yZRL+f+B/9EeXf/V34j//AaDYRymUEw8BWVbob3zPzppuI/M7voLgiqKc9DWt+HuX8eYx2e2clSKXiDFgnkyjf932P+HUJkoQVi2GpKmKrhbnL423LQup0MFMpOGLJmrt5EWwlvHnTm960adBtvwWbl2PbNrlcjkKhAMD4+PgVDVZBEDwh5fLyMpFIhEKh8KgTVwOBADfccAOrq6uEw2E6nY4nztY0zXMqT6VSnjg9k8lQq9W8tHTTNCmVSpw7d45yuczk5CQzMzObBr/chpib/lAsFgkEAjSbzU1O4y5u0rg7VPdocdNvLk11tywLWZaJxWJUq1VCoRCzs7NbNsYFQfCc3Mvlsjco5gpVU6mUN8Da7XapVCr4fD7q9TqPfexjCQQCJHcxCDsOuMlRsizTbrd3dVJvt9sIgoCiKFf9e3S10DSNQCBALBYjk8l4qSnFYnHL5nGtVsPv93sGC25T/+Gwk5O+O8CRTCZZWFjA7/eztLTE3Nwc8Xjcc79vt9teEtDIyIiX0L1fVKtV8vk81Wp10393zRauZpLJbrRaLQzDoN1ub3KZr9frm1Jl3AGtQCDgvcedTodoNOqJ+6PRKPV6nXA4TDAYZHZ2lm63u2nQxN0nr/chz4PCsiwMw6Db7W55nNXrdbrdLo1Gwxt2cg1BLj+fuMLzcrnsDYgfdSRJ8gaEdF3fdZjLfc1uMlqfh1AUhdOnT3P69OnDXsqh0ens/POD1ExrJ04g/tmfUXv/+7HvvRezXncu1G0bfD4IBDCf8ASCr3oVvkP6zMLPfz7VP/9zjEgEOZtFj0S27ZMI+bxzn5dKId9++wGv9NpDHRkh8oEPUPvTP8X+/Oex6nXElRXEjQRvIxDACIUwhocRthHKmrEYvoUFTNOke/HiVROgS+Ew4qtehfXe92IsLqJduIAejToJ54oC3S5SqYRcr9MdHESuVjH9ftTVVXqyDJfXq20bSiXUQoHu2Bjm9LSTFn0Z6tgY9e/4Dmg0UC9exFhZwRwd3XyMun9rddXpC6VSoKpI8/Oo+TySrmPZNoJpYksSVjCIPjiINT5O4N3vRhsfp/mc52BVq0gXLiDOz2OOj2+u5do2lMvOc6TT2NEo4S0E81cbcWoKOxQC2949iR4Qq1Usvx9b01CvgdSg8h/8AdJ//ifKhQvYkkR3aso5tjauV/VmE3l9HfXCBXq2TeV3fofkXXdt/iOdjtNP2eN9nC3LiJ0OQqezN9F/n0eGbYMsY0SjGOPjiMUiYqfjmCjIMlYk4qSRu+//xr+FfTYisjbSJIWN590VQQBJctax2wVInwPHrNUAEE2T2LBAMNim2xNQVRvDEJi/uLn2YwkShqAg2QZ2s3UYS35YpNNp8vk8wWCQ5eVlxsfHrxCh27ZNNpul3W4zNTVFMpncUZhRLpe92sDo6OgV91qKonj3aul0mmq16iUg9wXo2xMMBgmFQqytrW1p/OjWubrdLsPDw6yvr5NIJK6oT/p8PiYnJ8nn84yPjzM0NOQZiG6VUF4sFmk0GhiG4YnSd1pju93mW9/6FqFQyDMpdFFVlXg8Tr1eZ3h4mNHR0Uf5rjxEMpn0UhZrtZontHUJhUKMjY0RDoeZmpry0s+73S7r6+uUSqVNYlc3vTSZTDI8PHzsarfdbtczC92tJueKNS/tfVwNBgcHKRQKJJNJyuUygUDAO77AMQd0j5vBwUFUVSUSibC2toYsy5RKJc8EQVEUstks2WzWq1eGQiFkWfaSUDOZjJf8KkkSJw6hR3pUcFOiV1dXicViWyaP+v1+JiYmWFpaYnV1Fdu2GRwcJJlM0u12vSRpV5TuCrv8fj/VapVCoUA+n9/2e93pdOh0Op6wcbc6nGvYe9xryrquMzc3R6FQIJfLkUwmNxnhuuJOV5juCiG36gs+mjUsLCyQzWapVCqMjY1dYSZiGAaZTIalpSVEUWR9fX1f9+i94B5n7l5wKe61SKlUotVqUa/XvePaNUX48pe/zMjICDfeeCM+nw/LsjxjDYDJyUlSqdR1l4Bs2zbVapVWq+Xth7FY7Lp7Hw4a1zA7+gg1EK1Wi1wuR7lc3mScu12PcXV11TMenpyc3HQ9JwgC4XDYu45cWVlBVVVqtRrRaJRwOMz09LSXVF0ul1lZWfH2YdcUJhKJMDo6uqn3W6/XWVtbo9Fo0G63N/Vq3X7t8PDwvou68/k8tVoNy7IYGRnZ9lwRiUQ8I6VwOMzy8jL5fN6bVXCv+3w+H5VKhZWVFSYnJ3e95u2zmaWlJSqVCuvr6wwPD19xTxcKhUgmkywvL7O+vu7tQ4/W5DiVSpFKpajX69TrdW/eJhwO72rA1KdPn+uT43VX36dPnz7XMWa9TuPXfg3xzBnUxUXHZX9oCDscBttGrNWQikWEUgl0ncqv/zqJ979/Z4FVnz5HCOUHfxDrgQegWESen8c4ceKK4SrbNJEvXECpVDC7XcyLFyn91E8hPuMZhO+8E/kYDtuH7riD1t/9HfryMlKvh+gKaxQFJOmhBr5leT83VJXu0BCx7//+w1nzS19K4xOfoNdooK2u0ut0sAcGnOEmHHGQWCig5HIYkQik0/h+/McPZa199hd1YoLgL//yYS/jyNC49156d92FmM8jl0oIGwMxtqZh338/tb/6K4SXv5zYK15xJIehbF2n9KM/SuBf/xWl1UI0TS+FxQbkdhtlfh5jbY32O99JxTSJ/fAPH+6iHyH6V7+KWK87A4Hx+O6C03gcpVTCXltDz+ePfwL8EUQKhQi95z00NtLHxFwOdWnJGRDbOA6NUIju9DT26Cj+d7wDZYtG7mEgRSJI2wyV2r0elV//daT//m+U+XkEy0KPxTA2jiGxVkNdWcHK5cAwKBkGyd/5nSO5R1yr2I2GM6Qvinu6V7JV9SHTr40Bjv1flE3r61+n/eEPI3zrWwjtNrbPh33yJFa3i3LffUjFInKxiLQxQGkLAkYkglUo0Pzc59Df8haCT3kKAGIgQPJ3f5fu0hKtu+/G/MY3EJpNbJ8PTp0i8KIX4b/ppk1LCL34xbTuvhsjGEReWkKfnd3SXMbudlHX1hwzqHic6POe9+he+9OfjrWygnbxIka3i7BDOoxYKDiGTbEY2iFdB2/HbsKZ1hZzkm9605uuzmK2oVarea73g4ODOw6muENyrlhuaGjoUQ+oaJrG9PQ0uq57g2jgDMwlEglvYNJ1Cvf5fJTLZW9Ix30NrghzamrqCmG823h1h73cYYByubzloIlt296g4aNt2lYqFebm5sjlchSLRfx+P9FoFEmS6Ha75HI5crkco6OjmKbJDTfcsOXAnTuwOjQ05CXRp1IpKpUKa2trKBvpY6VSyUukv+WWW0in016i9XHH7/d7n3O5XN61Qe0ep24K/HFlYGCAdrtNqVTyUkzcAcBYLOYNLrsu+ZqmEY/HGRsbI51OP+LP3ufzMT4+zvj4+JY/P3HiBLZtI4oilUqFcrlMfiPZzk3yHR4eJhwOMzs7uy9pvpZlMT8/T6lU8tINLjWtiMfjlMtlEokEU1NTh+Kkb1mWtzddup+6SeeSJG3ad9zHXPp78XicQqFAvV73BjdcNE171M71fR45u53zWq0WnY1rMtdkwP1ctyIajXrigb0Iug+bYDCILMsEg0EqlYr3GrfDfYyiKNumuPW5ftkpAV2WDz4EWBkYIPmWt2CUyzQ//WnM1VWwLMRUisD3f79n7HVYiD4f3H47VqWCdP488twc5ujoJscr27IQs1nUfJ7uyAhWOk3sttsOcdXXDnI8TuKNb8T8H/+Dxqc/Tecf/gHuvRe1VKJz+jSC379zTVGSHOGmbWNfRdEJQPTOO6l2Olgf+ADdSgWpUHBS223bqxd0Z2awk0naz3se8j33YCwtOffewSBmPI6tKIidDlK5jNTt0hsexh4dJfibv7ltnSr6hjdQW1igZ1koS0vI1SpGNIqtaWAYyNUqgq47ovLRUbR3vQspFKL5p3+K/Y1vQLPpCEMFwbmRDgYxn/pUwq9+tSfKjr3mNVTOnUO3bZT5eZRvfxs9HHZqGpaFXKshGIaTvjw6ivKmNyEfwlBr+HnPo/qBD2CEw8i5HOYOae12t+uYbE5NYT396YgH6b5xFTBKJcRPfhJpaQlbkpwa0mXX5EIwiDkzgz0/j7y4CF/+Mp1z5/CdOvXQgwIBkGUEXd+ToFzQdSd92+fr11KvJskktqYhlUoYioI9MrKjaaTYamFrGtY+34+79XdbkhC6Xba/2t7Aspyarywj9K9Jrxp6Lkfri1/ErtVA01BPn3ZMS3b5Trr7ni2KYBgoiuWOFWAazv2yZBtEegU0q41gmQTNOorZwVa2rxcfFdzEQMuyWF1dZX5+nlAotCkVtVKpYNs2ExMTxGKxXQ38arUa9XrdEzTvhCiKRCIRqtUq9Xp9P1/aNUc6naZUKnnmpZfXXtz3PRAI0Gg0PJHoVvVJN/G62+0iCMKOhnOukNnv9+8qGK1UKrRaLTKZDOFwmNHRUcbGxlAUBcMwPJFqvV73RHBbCWEfKQMDAwSDQbLZLNFoFMMwvNqgLMvE43FPMAtOjeT8+fNUq1VKpRKNRgNBELBt2xNrNZtNqtUqp06d2pfa4UFh27b3OvbCQYl8E4kEIyMjXg3Ztm1GRkZYX1+n3W6j6zqyLGOaJqZpesLzXq+HqqqMj48TiUS8VPtOp8Pw8PCmzyYcDtNut0kmkyiK4iU7Dw4O7lonulbJ5/OekcRO6c9uIumDDz7oifanpqY8s1HbtlEUhUgk4olXBwcHiUajdLtd5ufniUQihEIh75iybZtms0kmk/F6ILv1TdzvomsEcZzJ5XI0Gg2y2Syjo6NXiEbd9PNAIMDCwgKBQIBMJsPMzMy+raFQKNBqtSiVSkxMTGzZc5RlmdHRURYXF8nn8/h8vqsiGn2kZLNZCoUC2WyWXq9HKBQilUqh6zp+v59Op0O73WZtbY1iscjY2Jh3nolGoySTSVKpFBMTE/ti6n0csG2bTCZDPp/33h9wPmufz0c4HPb6Y32OFisrK2SzWRqNBpVKxfvsFEXxeoyxWIzp6WnPcNtNOE+lUleYCbkIgsDw8DDnz5/3zJ9cgXwsFuPGG28kl8sRDocxDAPLsrx+fiwWY2BgYNPxUiwWWVxcpFwuUywW6fV6SJLkCeZd8Xe9XufkyZP7ZgBsGIbXf91LynosFmN+fp6lpSXv+1Cv1z0zFXCuYQuFAolEAtM0PdOtPrvT6XS84ykej28776BpGmNjY8zPz9NutykUCvtmDN8XnPfp02evHP+JtD59+vS5Tqj91V8hzs2hLiygx+PYo6MgCN6wg+3zYaTTiIuLKPPzoKrUPvIRoj/0Q4e67j599kr4+c+n9NnPOk3HixfRvv1t9HgcKxpFAMRcDl82i2BZWJKE2Os5gzFf/CLW2bM0PvQh7B/6IeK/8AvHavBAHRuj/sQn0isW8Z8/j6mqCIaB4iadb7wWcaOwYIuiM2zzhjcc2utU0mm0t72N7m/+Jl1ZRslmEYtFTJ8PWxCQOh1nnakU9vAw4mteQ/DJTz6Utfbpc7Wo/+u/Yr7tbUirqyiFArYgYAaDIAhIlQpSLuckz/x//x/lSoX4a1972Eu+guJv/RbBf/1XlEYDwbYxJQlbUbAFAcGynHRgy0LudAg88ADtP/gDOk9+Mr59bNQcFHazCYbhDCvtpcGjaU7CMI4J0FEVoPfW1mh+9KNYZ886U93hMPKTnkTo9tuRjkFhUB0bI/Jnf0btgx/E/pd/wazVHkqw8fmwQyHs7/1eQj/zM6gH7A79SKn+zd8gfeMbKBcvYgYCmJOTm445Kxajq+so8/PIc3MgijSe+UxCjyJFus/DQ/D7vTQc27J23RMEXXcG4wDhKrhY6/k8tTe9CenMGaRKBbFScZLARBG+8hWUZhNB17FUFT2VwhgbcwYpOx2kYhFtbo5eo0Hvt38b8fd/f5OwXJuYQNvj+UcdG6Nx++3Yd9+NcuEC6rlzGMkkVjKJIEnYuo5YKKAWi5h+P9bYGNJP/iTCoxwWCtx5J51/+icMvx9lcRF9ZmZL4TvNJkouh55KYY2MEPru735Uz7vfaJpzab6d9mwn4c1B0Wg0PGf2vaQGx2IxCoUCuq6j6/q+DYa5Q0rbEQgEvOasmxTrpq6srKx4CR1TU1NX/K4syyiK4iWNuOnnnW2SrxqNBrZtEwgEHlUSgmmaLCwseC7q4+PjVwyGDgwMkMvlWF5eRhRFlpaWmJ6evuJvKYrC7OysN9SRzWbJ5/NYloUkSZRKJSzLIhKJMDAwwOzsLMPDw5w4cWJf0xwOm3Q6TaPRoFQqkcvlthXiuulOk5OTJBKJY528G4/HqVarjI+PY5qm9x1ot9ssLy8DTtp7q9UiEokQDod5/OMfTzKZZOwqiuUEQWBmZoZ4PO6li1yadqyqKqlUioGBgX0xQLBtm4sXL5LP51laWsI0TWKxmDdM6Q7aFQoFbwB2PwfJ9oosy0iShCAIdLtdT1Dc6/W8/efSoVNd14HNydKuSL3dbl/VZKTjhmVZNJtNL+XB7/cf+JCkIAgoioLP56PZbF4xJGPbtpcCIAgCpml6wvKtvgeyLHsDQ5cmbRxVZFkmkUhQr9dZWVnZMu3NxRVSjIyMEIvFjry4vs/Bs9N18GHqL+V4nOiP/MjhLWAHYq98JaULF8C2kRYX0c6fd+7BfD4wTeRGA1sU6Y6NOYZ9b3874nUqALhaSOEw0Ze+lKptYy0uOkZ2OxiNuAjtNvaGqbB0AMZI0R/5Edq33EL77/8e+957MXs9p54pSdh+P/aznkXwZS9Dm5ykdtNNmHfdhZVMIubzKJmMUxeRJCfleGoKK53G97a34bvhhm2fU04mCb///dRe/3oIBqFcdmoZrRaIIkY8jp1KYSUS+N78ZgIbfSHf+95Hd3GR1mc/i53PgyAgDg8TvO02lMvEG6LPR+wP/5Dyb/0W+HxQrSKWy4iNhmccaqdS2LEYypveROh7vueqvs/bIQYC2M96Fla5jHrhAvbyMtb4+BV9O7vTQZ2fxwgGIRIh+NKXHsp695P6Rz6C0GwiN5t0Zma2r60JAtbYGMqZMxj1Oq1//Ed8b3yj92PpsY/FvvdepEwGvdncUTRsb5gP9IaHsR7zmP1+SX0uQXvOczC+8AVYX3eCD1Kp7R/cbiM3m3RHRuAZz9jXdYiBAObJkwjZLHKxiD48vHNfvFx2/h2NojzpSfu6lj7QfuABWv/3/yJ96UuOkYiugyDQ8/tpT06i3HknkTvv3PYzUoaHseJxzHAYqVzGvOS4EkydofY6Eb2EZJtIGEiWgWrrDHRXaX/2HupPfwzhI2ZIejlDQ0OeSLfZbFIul8nlcp7QMJVKEYlEiEQizMzM7FrDcoWjqqruSdSqaZpX+90q2buPQzgcJhgMMjw8zMrKCoIgeGn07nvnmse5AqNgMLhlXUIURTRNo9freXWf3djts7Rtm1wuR7Vapdvtcvr06U1CV/dYisViLCwssL6+jizLpFKpfTUmDQaDnplsq9XyaiCuYZ1Lr9fj/PnzFAoF1tfXiUQiTE1N4ff7vTpeqVRiYWGBsbExzp8/z4033nhkxJi7oaoqmqbtydjBtm3a7TbxePyq10bcelkymWR2dtYTuE1OTpLL5TzBfzabxbZtTyTmpqLHYjEikQirq6tUq1USicQV/R9Jkrw9bWhoiPn5eRqNBvl8nsnJyav6+o4qtVqNarVKJBLZdY/1+XwoikKz2aTb7aKq6o6i9VAoRDqdptVq4fP5WF1dRZZl77vk7u+jo6N0u10kSdr1O18qlQiHw2iadqyNe23bplAoUC6XCQQCO/Y3XWPjUqlEJBLxTBf2g3w+T6VSwe/379iLEwSBVCrFysoK3W6Xcrl8oAJMRVFQFAVBEGi3256Y0N2P3X1hfHwcSZI805WJiQmSySSLi4uUSiWi0ahnkuv3+/H7/QwMDJBOpw/MbOOwsSyLCxcuUCqVKBaLVCoVwPmMLctC0zSvhu4mw/c5GrgJ0cvLy3Q6HcLhMAMDAwiC4Jn85PN5xsfHsSyLkydPUi6XabVa9Hq9XfdMURSJxWKeeNu9TgJnH5qYmGB0dJRms4lpmoii6Bn4XkqtVvOu56rVKslkkng8jizL3nVUoVBgYWHBe45Tl5r6PQrca9dut7unY9fn83kifff1u9d8l1Kv11lfX/dmO4LB4La9pT4PUSwWabfbdDqdXXv+Pp+PQCDgCf73S4Dep0+fPnulL0Dv06dPn2OArevY//zPSPm8I/DcEJ9fgSBgTU4inTmDUChgfvzj8NKXHisxbp/rF0GSiL/jHZTf+EbH8b5YRC6VEPN5BMNA6vWwwTFdEAS6iQSCLCM2myjFIkYohPnXf02pWiXx+tcfq+M+8NM/Teeb36TTaqFlMk5iwEbaprCRmGEBgmXRSaXoPu1pJA45hTjwhCcg/uEf0vy934MzZ6Be99KfzVQKYjGskRHUX/iFvqiuzzVHb2UF4x3vQFpaQi6V6A0PQzLp7TuWbWPU6yirqwhzc5gf/jC106eJ3HrrIa/8IaxWC99f/iVyq4Vg2+g+H/YlxU4bsFTVMfvY+Ec+f94ZEnv96w9v4Y8QT3Bqms4g5m6C017Pe4x0BNMyzFqNyjvfifTFLzrDftWq87pkGfuLX6T2l3+J/YIXEP+lX0I44kmocjxO4nWvw/qlX6L5//v/YaytgW0jplKEn/3sbZPGjyS2jfXxjyPlctiShDU1tWUDTlAU9BMn0M6ehVKJ7t1398+VB4hyyy0YoZCTgL7bECU4A3GhEHY4jLaF4PXRYBSL1H/pl5AuXEBZWkIwTYxQCMvnQ+h08BcKiKaJJYqYmoadSoGmOdfDmoYZjWJWKmhLS3QVhdb73of/z/985ye1bXrr61j1OoKmoY6Oeknw8V/5FcrVKoYgOMOd+Tzi+rrzXlmWI4IfGMBOp7F/7MeIveQlj/o98M3M0HrBC+BjH0O+cAH17FmMZBI7kXDiEDsdxEIBuVJxUtWGh1Ff9aojd63vhrdtlXQOR0OAblmWJ2Ley4CXm5ABeP8+KBRFYXR0lJGRERqNBrquI4qilziz03pisRjNZpPV1VVUVd0k+rsUd2jFHaLYLS1iJ4rFIp1Oh1KpxNjY2JapRG4KjmEY5HI5AoHAtim4wWCQ06dPs7S0RCQSYXh4mEajQa1Wo9vt4vP50DSNqakppqeniUQi19zARyqVIp/PMzY25g0IJBIJ77113+9qtcrQ0BCRSGRfk34OA1fobds2siwTi8W4cOGCN0DY7XYJBAIMDw+TSqWYmZlhfHyc0dHRA/n8E4kEiUTCE0u767w0iWU/KBaLFItFlpaW0DSNkZGRTQN94XCYdDrNysoKS0tLiKJIPB4nkUjs2xr2QigU8tKeK5WKd2xul4LtDnLJsrwpJchNhDoOouSrja7rZLNZL+XBfU9kWfYGhA5yWDKZTFKv15mfn6fb7W4aNhdFEUmSME0Ty7Ko1+tIkkQgENhyje55DDg2IoCBgQFv2HBxcdFLYro0ealWq5HNZj0RxU4GM32uX46qAP0oI8gyiXe9i/J73gOf+Qxms4lYKjlCL0miNzoK8TjW2BiB3/kdfKdPH/aSr1n8T3sarT/5E8xAALFYxNpF6C+XShixGFYqtaOIe1/XePPN+G++GaNcpnvmDHa7jRgO47vppk3GBJFbb6V3yy00//EfMT/7WaxLRDTWwADibbcRufPOPSWJK4ODJP/P/6H+b/9G7yMfgfvuA/fe8cQJ5B/4AWIveMEVxgja5CTaz/7snl6XGAiQ/L3fo33//bQ//GHsr3wFodHAVhSsiQnkF7yAyG23HXqSePQXf5HK/ffTM02UhQXkBx90TOQDATBNxFoNpV53UudPnMB++csP7Ni4mlj//d/IpRKmpiHsZoSmqhihEGK5jPW1r236UfiFL6T2f/8vRiiEvL6OOTu7bb1HzGSc/5FIoN555368jD7bEHz60ykPDKBns6iZDL1AwEmrvwxb11GWljBCIQgGCe1DnfBy5Ntvxz5zBiGXQ8hmsbe577d1HTWXw4hGsdJpos961r6v5Xqm/s//jPGudyHl80iFAvLGPACApSgImQzWxYsUv/pVEm95y5Y9KUGSEJ7/fKy1NbRz5zCqVYRoFHSdwMIcWs9AtTrItokNyDj/9ltNrPVlzLe9jcr6OrFXvGJvaz6kFPDh4WFisRj5fJ5wOLzpXt+tZ8RisT3VUURRRBTFPSeMGobh3XceF3HvYeEKqwHW1tY8kaaiKORyOdbX1wkEAszMzBAKhfYk6tjtM3UFga656HafUa1Wo9frUSqVGBgYwLZtyhsGG7IsEwwGvSTykZERFhcX6Xa7FIvFq3JPriiKl+q5FZlMhlqtRiaT8Y5/F0EQCIVChEIhcrkcq6uraJpGoVDY1mz0qBGPx6lUKhSLRVqt1o7J366ZYjQavep1SjfR+nKBqWu0WywW8fv9GIaBYRiUy2VM00RRFNLpNOPj42SzWVobDa2txLRuLctN0HZroOVy+boVoLvGIHsR0rkGFc1mk1wux9LSkrefq6pKLBYjGAx6e4dlWfj9fhKJBJqmkUwmqVar9Ho9BEFgYGCASCSCruv0ej3y+TwrKyuMjY1tWWvM5XK0Wi0vffY4nxdarRaGYVCtVve0H7spvb1ej1qt9rBFwd1u1zunqqqKJEmeUXej0diTmNz9bFutFq1W60AF6OFwGFVViUajlMtlb3+4VFzris/BOe+EQiFUVWVgYIDBwUHOnj2LaZpEIhFmZ2cJhUJ7Mja/1pifn6dQKLC0tOSdd8PhMIIgoOs6lUqFTCbj7aW7nTP7HAxuz2J5edkzsL60Fx+JREin055AXZIkcrmct79uZzJ8OX6/n1qtBjjnzMvNiiRJ2vV7s7q6SqlUolarMTU1ten84l5HBYNB1tbWWFlZQdM0arXavn8ft+trXk61WgWc643Jyckt3yf3mnphYYFIJEIul2NiYmJf13st0u12abfbaJq2JyOjUChErVZD1/Ud7y0Om3a7TT6fp1qteqYEgUCAdDq9qd/Yp0+f48XRnkTv06dPnz4ANO+9F7FUQq5UnOGSnS68BAE9kUApFrEXFuicPXtNNLP7XB+Ifj+Ju+6i/ulPo3/sY9hnzkCrhXr+vCPGtiyaQ0NYExNOEiNgAUazibK8jDA3h/Hxj1O76SYit99+2C9nz/hvuQXjjW/EePvb6fj9yLkccrPpiM8tCwHQg0F6k5PYs7NE/uAPDn2wBsB3+jS+P/9zOmfO0Pqnf8JeX3fWm0igPuc5TjJl/0axzzVI42//FrFcRikU6J44AZcX9wQBIhF0nw/1/HmsXA79H/4BjpAAvXTXXQRbLSTDQFfVTeLzS7FU1Ulc0nWUeh39nnuwfvmXEY+ZQ6Xy2MdiRiLOZ1OpwC6NX6lcxohEsAYGjlz6uVEuU3v1q5HOnkVaXUVutRyDIklC1HXETAY9HMZqNCgtL5N45zuPvAgdnGuA8G23HfYyHhWNL34RMZtFKZfpDQ3teA4UFAU9HkcuFuG+++itrh6blPfjTuhpT6M0OIixtoaSz9OLx7dO3Aao150Un5Mn4bnP9YTa+0X1rW9FmptzkrCiUczR0YfWsrKCoWmoG0Yhkq5jLyxgXC4uiMXotVoo2SycOUPnzJkt7/+sVov6P/0T5j33IG4k6QK0QiHs5zyH4EtegjYxQeKtb6Vy882Yd9+NvbYGzaYzSK4osJE8pP7IjxB+znP27X2Iv+51lJtNdEFAzOeRi8WHBosBMxBw7oNTKYRf/mVCz3zmvj33frKTAH27/36QuMJzVyy3WyOq1+t5AwiHJZYTBMEbjgCnoSlJ0raJ5vBQcns0GmVhYYFUKnXFa9V13XPfTqfTj9qxP5/PUy6XUVV1S/H5pSSTSebn5+l0OhQKBYaHh7d8nN/v5/Tp07RaLYrFoif4VRSFRCJxzQ97iKLIyZMnAef4KxaLrKyseD+3bZtgMMjExATRaJTZ2dkDT0i+GkiSxMmTJ8nn8wSDQQYHB2m325TLZc+wIJFIcOLECYaGhg7lu+mmXVwtcrmclyQxOjq65V4lSRLj4+NcvHiRSqVCLpc7cAG6KIpeusXi4iKNRoNQKOSlz1yaaN5ut2k2m96g4KVDGZ1O50CSkY46zWaTCxcu0Gg0vIEbd+jFNQmpVqsMDg4yPj5+IEMJqVSKTCZDKBRiZWVl00CNm9ogCAKFQsEbJPT7/VsOoVYqFaLRKH6/f18Tya4m7hCRe+7JZrOegQo4x7VpmiQSCQYGBpicnNwx/afPwWDbNtVqlXw+76VjSZJENBolnU5f1f17O/oC9EeGIMsk3vAG9J/+aRp3343+X/+FUK+DqmJPTuL7wR8k+NSn9mvvVxl1ZIT6k58MxSLa8jLdWAwuuT+5FCGXQ+p0MCYmEG67bfs6w1VCjseRn/rUHR+jjo2hvva12L/0S3QXFx2xeiSCNjHx8I8lQSD8jGc4ice2jdVuI2javr9u/y234L/lFuf/2PaRO+alcJjo+95H9dd/HdvnQyyVkEolxFIJWxAwAwGnfxAOY7/85cR//ucPe8n7gtBsIhiG0zvYA67RrdBobPrvUjSK/dznYjYaKBcuwNwc5ujophOEretImQxKuUx3chJzcpLY05++r6+nz2YESUJ51asw3/Y2jF4PdW7OMVZIJsHnA11HKJWcWRRFwZyawnrRi65KbTv8/OdT+dCH6NVqaMvL9AwDe3DQqVMC2DZ2rYayYaBpjY4ivvjFx6IfclxofulLmO94B9LiIkqphB6L0R0exvb7EQwDymXnWKhUwLIoBwIk3vSmLf9W6MUvpvGRj9BLpdCWluiNjDgGqN02mtnDEGTaoh/V7qEj0BaD1KUIiWoJ5tuYH/wg9dHRPdWlD9P4y+/3MzExwdjYGLquewZ+D/deMBgMEgqFyGQytNvtXa/lq9UqsViMQCDQH+TfBU3TOH36NHNzcwSDQWq1GvV6nXa7jaIonsAxHo8zNja27WdnGAadTod0Or1rXTKRSFAoFMhms9RqtW0N9mq1mme66dZCLjXoUxSFWCxGMpkkEAjg8/moVCqUSqUDN4UzTZNisUipVCIUCu1oGjgwMECj0aBSqZDP54+NAD2VSrG2tkY4HGZtbY3Jyckta3i9Xo/19XVisdijNrvdC+7zRKNRisXiph6KoigMDQ2RTqe99G1d1xkZGfHSS12xvK7rqKp6Rf3VsiwajQaJRMKrA/n9fur1OqZpYtv2dbnPuIaUezUG0XWder1OtVrF5/N5tU5VValWq2iaxvDwsLcPCYLA2NgYs7OzWJZFqVTyBOiqqpJKpby+l8/nY3l5mbm5OWKxmCeKvbSXMDY2RjQa3bYHdVwwDMM77vaSZu7ux65hwF5w3+9cLkf7kkKWa37riordOttuCIKAJEmeMfhBIooiqVSKRqPBwsICxWKRaDTqmVyHw2HvNbjHimv86n6vR0ZGWF1dJRaL4ff7r/l+5FY0Gg3K5TLLy8v4/f4rjKBdQ49wOMzi4qJn/NEXoB8++XzeM3SfmZnZ8jpOkiRGR0eZn5+nWCwSDAa9fXSvYmzLsrxj4pGcE5vNJq1Wi1KpRDKZ3NbcRBAEhoeHOX/+PNVqlVwuty/fSVVVEQQBn89Hs9ncdC2xFeVymXa7jSAIjI+P73hv4/P5CIVCngnG6OjosTFmPixs235Y11dH/TrMsiwWFhYol8s0m01qtRqGYXjGCpVKBb/fz/T09I4GT3369Dma9Kuuffr06XMMMDMZ6HYRLAs7GmXXy8dw2BnYN02M9XXoC9D7HCMESSLyghfAC15Ad3GR6jveAdUqWiZDZ3YWIRy+8jsQDNKbmUE9dw4hl8P4h3+AH/iBIzeMshPhZz+bVjJJ+y/+Au6/H6PVgk7HeQ3BIHYohP30pxP5pV9CPkB3zL3gu+GGvtFFn+sGq9NB+Nd/RSwUnGSHnQp7qoqeTCKXStgXLtC+//6HBuYOm3/+Z8QNUYS1S1PcUlUkXUcyTaTVVXorK/hmZw9ilftG6FnPovz+92NEo8i5HHoshrCd8K5SQep2MaamEG699WidS2yb6hvfiHT2LOrFixjBIN1Tp7yhPNO2HcHs+jrS+fMAVP74j4m/9rWHuOjrB2NhAdptBNPc9Zrd1nXw+RBzOWzDoLew0BegHxSCgHjnnVjr60j1OsrcHPr4OMKlA1y2DZUK6uoqeiKBHY0S3OcUn86FC0hf/zry8rJjeHGpmMq2kSsVRMPAVBRsUUTq9bA7HYx6/YqBdzudRiwUoF6n9fGPX3Fd1jl/ntbrX4+4vu6k1FQqznEqCJihEFYuR/uee+i85jVE77yT2A//MPzQD9H80pfofetbznEdDKJ913fhv+mmfX0fwLn+T7zlLVSf8ASMu+/GXlx0lCqW5aSg+3yYT3oS/h/7MQJPfOK+P/9+sdMM4FFIQA+HwwSDQSRJ8gbYdqJarRKJRNA0bU9DHQeBm7BaKBTodDpbNmNlWWZsbAzDMJBlmXa7TaFQ8IYqOp0OjUYDTdOYnJwkkUg8qgEc27bpdDq0Wq09NXx9Ph+qqtJsNjcNsmxHIBC4bhtvqqpy+vRp1tfXCQaDdLtdT4jvHpfxeJyRkZFrQnzuIooig4ODDA4O0mg0NpkPhMPhI+tivh+4AxeVSoVEIrHjaxVFkVgsRrlcJplM7mkYe78ZHBykWCx6ieypVMobEK/VarTbbXq9HuVymUgkQjAY3CSUbzQaXjLS1R5MPcp0Oh3Onz9PoVDwBN+jo6Nomuali+fzeSqVijfUMz4+ftXX5Q6Bmqa5aaAzGo2iKAqmadLpdFhfX+fkyZNEIhGGtkhkrFQq6LpOLBYjnU5f9XXvJ8lkEkmSPPOLWq3mmcCkUikikQg+n4/x8fHr+hg+KrRaLebm5uh0OtRqNRqNhid2iUQi5PN54vE4U1NTj+hc8i//8i+USiXAGXZXFIXn7EH80xegPzqUwUHir3oVvOpVh72U65bAK15B92tfQ282URcW0FMprFTKM6iz222kfB6lUqE7Ooo1PEzkxS8+5FXvjKAo+1vbFYQr0s6vCkepRnsJcjJJ4s//nPpnP+sYaz/4oPcz2+fDfsYzCLzkJfguNxQ8zqgqiCLCHgUdbg3K3uK+Lf4rv0JpcdExwV1aQjt3DtPvx9I0BMNAbjaxFIXu1BTW5CSht7/9yB4L1xLh5z6XaqWC9cd/TK9QQC4WkYpF7+eWqmKkUtjpNNatt5J4zWuuyjpEn4/AO99J+7WvpSuKKOvriKUSpt/vGPJ2uwi67hh7jo1hff/347/xRqof/ahzTCWTBJ/yFIQd6lpmtUr9Yx/D/Pd/RyiXQZaxR0fRXvACgs94xoEbihwpbJvu+9+PlMkgl8ueoQbg9D5UFQYH6aVSKHNzyPPzGJ/5DJ1t9jxlYADlDW/AeMtb0CUJ3+IiUreLYJroqAjY+K0WpiDTFkNUlThr/hmU4Qqx9XNYmQy9v/xLePazj8U+4KbfPlISiQSrq6uEQiHy+fyOZmyVSgXDMI7lfedhoWkaj3nMY6jVauTzeZrNJqZpeomx5XKZgYGBHcU1pVIJRVEIBoO7Juy698/JZJJcLofP59uytt1qtXjwwQcxTZN6vQ7g/VsQBILBIO12m3q9zvj4OH6/3xMYHzRuOnOj0dhTnSYej1MoFEin07umiR8VJEliZGQEwzBYXl5mfn6eRCJBNBpFlmUvAbdcLhMIBBgcHGRsbOyq128FQSCVStFqtZifnyeTyTA4OLhpj5AkiUAgQKlUIpVKMTExga7rlMtl8vk8kiQhCMKW4ljXHDQYDHr1nr0K8q4GhmHQ6/UAp2dwWOaOoVCIUChELpdjYGBgx895bW3NM9hsNBosLS15fSjXgDMWi2EYBgMDAxQKBeLxOD6fzxMCb5eaPTU1BTifca1Wo1wuU9y4TpNlmVgs5onST548eWzMMLdDFEXvvd6LoNx9jCAIe/ou9no9zp8/75lkuOdUN6W1Vqt5Zh+SJHnH4m5rcHuUh/H+Dw4OUiqVGB0d9RKWW60WnU6HcDhMq9Xy6rwDAwObvuvgHOtu73MnQ/BrmVwu5/VvhoeHt70G8/l8pFIpyuUyiUTCMyruczhcel52z9XbIQgCiUTCM55wzbcNw9hTv7HRaOD3+73+ycOlUql4hjA7mfiAsw9Go1Gq1apnTvRorzUkSSIWixGPx1lbWyOVSu34fq2vryPLMqZp7um6LxaLsbKygmVZtNvt/vdiFxRFQdM0ut0upmnuKth3jbMuPUceFSzL4vz58xSLRVZXV+n1eoRCIa/nm8vlyGazDA4Oous6p06d6ptb9+lzzDjedxd9+vTpc73waAp5h1gE7NPn0aKk0yjz84idDno0irCD25qgKOiplOOyvbBA6777CDz+8Qe32H0g8PjHE3j/++nMzdH+l3/BLpVAUZDGxgjfeitS3ymxT59Dp3v+PEKziVyrOWmsu2Alk4jZLLRadL/2tSMjQJeLRUTLwhbF3Qc1BAFbFBEsC7HTwT6GTQZBlhFf+ELMfB7p/HmUCxcwxsbg0ua2ZUGxiLq+Tm9gADseJ3znnYe36C1off3rSA88gLK4iBEOY12eUCQIEImgh0IoFy4gLS9jfOITmD/900jXoTPxgWMY3rX3loNptg3lMlKxiNxqgWWhtNvw4IM0/vf/Rk6lrq1h1CNM7OUvp/jAA84g4sICvnPnMIJBLL8fwbKQGg0Ew0BPJrFHR5Fe9zrUsbF9XUPr7rsRGw2kXg99enpT49K2bUTTRLAsLFnGlmUkXUcwDMRCAevya2JFwdQ0hHYbO5/f9KPeygrtX/1VxIUF1JUVLEVBTyScxCLDQKpU0M6fR4/HsX7/96nJMpHbbwdBIPiUpxB8ylP29XVviyAQvfNOeOELaX3jG+jf/jZ2p4MQDuN/6lP3/f2/Guw0L3VUBOh+v59YLOalYmzXSKxUKnS7XcbGxo7U0GIqlWJ9fZ1AIEA2m2ViYmLLpr/bxEqlUgwODhKPx+n1eliWhaIoTExMEAgESCaT2/6NveIOXj2cZq8kSZ6TdZ+dkWWZ8fFxRkdHvePSTbqIxWLHfnhqN9yhtuuFTqfjpe/s5rYPzr6Wz+e9xKmDFqBrmsbMzAzgHKuuOUapVPJE04ODg95g6ujoqDdcbBgGmUyGaDSKpmlbJrh3Oh0KhQLVahXTNBFFkWAwSDqdvqaOi5WVFarVKplMhpGRkStSQnw+H/F4nOXlZVZWVpBlmVQqdSCf98jIiLe/V6tVyuWyJ8DtdDrEYjFUVaXb7V6RUmGapjfUOzw8jN/v3/JzPuq4ovtKpUKxWNw08JtMJjel4/Q5PJrNJufOnaNUKpHNZhEEgUgkgiiK9Ho9lpeXUVXVS2E8efLkwx5MuvPOOzeZ5/j9flqt1q6/1xeg9znu+G+5Bf3Xfx3r3e9G1zTkQgExn3eSn20bsdfD9PsdcezYGIF3vQu5b8px3SHIMpFbb4Vbb8UolTCKRQRVRRkcRNwmxepYMzuL9dWvohWLmL2eI0LdBtuykOt1esPD2NPTV/xcUFXi730vlXe/Gz73OcxmE6FUQjAMbFWlm0pBNIp5ww2E3/zmvoHnARJ96UtpnjhB50Mfgq99DaPTcerfogh+P9bMDPKLXkTsKhvC+2ZmEN7/fhpvfzs8+CDU6wj1OoJtY4RC2PE4djiMPjGB/OCD9D77Wdi4ZjUUhXIsBs99LpEf+7HNBu+2TfmDH4S//VuERgO5WETo9UAQsAIBjC99ifLQEL7Xv57Ad3zHVXt9R5nWN76BuLCAnM+jp9NXmKK6CJKEPjWF78wZjHqd1j/+I77tUtC/7/toSBK9d78bo1pF7PUQAEsQsQWRrhjCFCTKSoqcb9L5PDQ/vXTamQFZWqL5ta8RfNKTruIrPxrIsuwZ7i0uLrK6usrQ0NCmWpht29799MDAAD6f71jedx4mruGpi2EYfPOb30RVVVZXV5mYmNjSSKBarVIsFhkdHSUUCu1ao3DN9Hq9Hr1ej4WFBeLxOLFYzEtUrtVq3HfffbTbbSzLQlVVBgcHvb/d7Xap1Wqsr697NXufz4cgCIdyX67rOoZhYNv2nmo0rljereWpqurVeePxOOl0+sgY4V7K4OCgJ0Z1zQnyl/ThNE1jcHCQaDTK2NjYrmYE+8XQ0BC1Wo2JiQmWl5dpNBpeWrEreq5Wq6iqyvj4OIODg2iaxtLSEouLi+RyOSzLotfreUJZwzCoVCo0Gg0GBgbw+/1eDdIV2mmadmDHm5v0WqvVNv33aDRKOp0+8KRhV2SazWY9Yf9WNBoNzp07h67rKIrC4x73uE0mF655QC6Xo1wus7i4yMmTJ0mlUlekLG+FIAicOHGCeDxOLpcjFot5/SZRFPH5fKTTaVKp1JETpT0S/H6/Vxev1Wq7itRqtZon5NvN6MIwDM6dO0exWPTqzq4RgGuMurKyQiAQ8JLYK5UKyWRyx8+pVqshSRLBYPBQ0sNlWebkyZOAI2pcWVkhm82ytrZGr9dD0zSCwSDDw8OEQiEmJiY2iR3d88r12se0bdszI4hGo7sKQWOxmGdmUy6Xr6nezXGj0+lg2zbtdpuBgYFdHx8Oh1lfX6fX63nnuFAoRLFYZGyH2ZRut0u9XmdycnLX/WA7dF33zhN76XX7fD4ajQaAdx31aEmn05RKJTRNY2VlhfHx8S2P90KhQK1WIxKJoOv6nky2ZFnGtm0sy9rS7KbPZlwzBDdIYqd7OsMwqNfrjI2NHcl7P9f4ZGlpCb/fz8TExKZjfHBwkHK5TCaTwbZt5ubmuPnmm6+Ja5Y+fa4Xru0JrT59+vS5RpAGB9FV1RF/1euwi+uV0GhgKQpIEtLg4MEssk+fq0Dn/HmEdhu50aC3B/c0O5FAzGSg1aJ3DAXoLr6ZGXwbQ8x9+vQ5WljNJtg2gmVhq+qOCcfgmGMgCGCa2M3mgaxxr9gAG02DXQuibmPBtpF2uQ45qsR+8icpnjmDDkgLC2jnz2MEAtg+H7iCU9tGHxrCGh1FffObkY9Ysa794Q8jVSoIhoE5MrLt5yaIIsbwMNr8PGa9Tv1jHyP24z9+1dfXuu8+Oh/7GMzPQ6+HHYkgfdd3EfrBH7wuhm/FeNwZQBYEaDY3D2TpOvLFi0jttiMiNgxHYGzbyO026r330vm5n6P94z9O/Gd+5lgkeBxrBIHEW99K+fd+Dz75ScxmE7FYROx0QBAw4nHsVAo7FkP+tV8j/Oxn7/sS7LNnEatVTL9/xxQed72mJCGYJlK7zY4tmssawfV3vQtpeRlteZnu4CAMDGw6vsxkEqPZRJ2fh+VlzD/8Q4ynPe3wvrOCQOAJT4AnPOFAns42DIR9ErDuNGO1B23OgTA0NESr1aLZbLK4uMjg4OCmRrgrlisUCt5w20ENTO0FRVFIJpPekN7S0hKDg4ObBH/uEJtlWTz1qU9F0zQikQidTgfLsrwBknQ6vWXKzMNFFEUEQUBRFLrd7q6Pt22bbrdLPB7fdWihz0OIongkm6h99hd3GAHY08CG24w+zEGoSCTCqVOnWF5eJhaL0Wg0kGUZVVWpVCoIgoDP52NsbIxQKIRpmt6QsjtIPDIysqmxblkWCwsLlMtl2u021WoVwzCQJMkbfgkGg0xPTz+qNLWjQLfbpVqtesnM2w2OyrLM2NgYc3NzNJtN8vk8ExMTB7LGsbExIpGIN9BpmqY30JnP5zFNk16vR7FY9BJxLMui2WwiyzIjIyMkEglmZ2eP7b4vCALxeLyfcn5EsSyLubk5isUi2WyWgYEB4vH4pn3UNb1YXFwEnCGgvSSV7AfXowC9c/48+tqac78/NOQYzfXv8Y81kVtvpZVK0f7TP8W+cAFqNYRu1zHtDAQgGMR84hMJvfa1aJOTh73cPoeMnEjsqabcXVxEX1kB00QeHMR36tSx2SsCd95J55OfxFJVxEzGMUndBjGXc/5HPI76whdu/Rifj8Rv/za9n/1Zmh/5COZXv4rQbIKmwews/jvvPDLGvtcbwSc/meCTn+wYXH7pS9j1OoLPh3rTTQf6mWgTE2gf+ACdM2dofeQj2IuLjsg8HEZ84hMx//3fUR98ECGXQy6XEQ0DAFsUMaJRrGyW2uc/T+Dd78Z38iTYNqV3vxvxnnsQl5ZQajVMVcXy+8E0UTMZyGTQCwV6r3891lvfSuhpTzuw13tU6HzmM4jVKoJhYCWTO/ZDBVVFD4cRi0Xsf/s3eOMbt93TQk9/OvqNN9J85jORazVMWaGphzAFhZoco6INYIgP3evaCNiplGP2XavR+/d/vy4E6ODcj7ZaLWzbZnV1lQsXLhAKhfD5fBiG4SVzDgwMkEqlmJmZ6Q/uP0pcM0zLslhdXWV+fp5wOOwJ0LrdLpVKhU6nw9DQEPF4fM/1iVgsxuTGtWIgEKBSqVAqlTBNk1qtRrfbxefzoSgKiqJw+vTpTXWEQCBAIBDw6iiuGeLJkycPRbh9uanybpTLZZaXlzFNk2aziSiK2LaNpmmekcLQ0BCjR9BsZnR0lGAwSDabJZFI0O12sSwLSZJQVZVwOMzQ0NCBCk0lSeLkyZMIguDVISuVCoVCAXBEu8PDw4TDYdLptGfE66YZC4LAmTNnKJVK9Ho9wuEw3W4XVVUZGhoiHA4zPj6OIAi0Wi3a7TYjIyMH0i+yLIuLFy9SLpep1Wredw7wEsLL5TLJZJKpqakD2/ei0SiBQIDh4WFWV1cBRzB26fMbhsGDDz5IoVDA7/dz0003eUnllUrFMy8QRRFd18nn80xPTxMIBBgfH39YPRA36bzX63nGvbIs7yq6Pm64Pb1arcbq6irJZHLbPa9arXLmzBn8fj+ZTIZEIkEymSQSiWzZb1hZWaFSqbCyskIymbzCkDscDpNKpVhaWvLeY9u2KRaL234XdF33Eu39fv+ejHavBj6fjxtuuIFMJoOiKHQ6HUzTJBwOe+9hNBolmUxeIX51Rbyqqj6iZOfjjlv/d/fG3XDPBa6guM/hcWmvcC/nhkt7jG4fut1us7CwQD6f39Kk3zV7DYVCBAKBR2zk7+5Je+1tXvq4/TrvudcIpmmytLTE3NwcsViMcDjsXTOUy2W63S6jo6PU63WvH7bbtaeu6wiCgCiKx7YvdpC4x1MikSCfzxMIBLacY3HvUVyzhKMUJAHO/lkoFMjlcqiquqWxjiAI3vVTJpMhHA5713V9+vQ5HvQF6H369OlzDAg+7WmU43GMaBQ5n8eIRrdvQts2UrGIEY9jjY3hf8xjDnaxffrsI3ar9ZDQU1F2F3rK8kNCz6MQM9inT59rDjEYfCgRfCNFYSdsXXfEgJKEsIsb70Fijo5iX7yIbZqIuo69U3HQthEtC1OWMaJRQkew8bsnBIHE7/4u5fe+Fz75SYxmE6lYBF13BKeDg9iJBNbAANob30jwyU8+7BVfgfjN/z977x1mWV7W+35W2GvnHCqnTtMzMIooHOMQBBEEZISLekzg8aioiOegKIrnGo4oDqJyfa4eEQ9eUQwgUbIoYIArVxglzEx35b1r79o5hxXvH7vWj67prtDTVV3V3evzPCh07fDbe6/4vu/3+/135EYDMxodmxvsRySC5fMhNRqYn/kMHKMAfbS2RvdXfxXl0iXkVgu50xmbG/h8OP/2b3Te+lac5z2P5E/8xLWTwW8TQt/0TXTe+EbMSAS5VhMp1Y5l4VteRu33UXbEiZaqItk2lqLgKAr+YhGz28V6y1toAMkf+qET/CR3BpKqkvrZn0X/nu+h+453YP/jPyK126Ao2FNTqM95DrHnPAf5mFQR0nAIOwnnV/1NknAUBUeSkC0La8fMRNq5Ln40jmGgjEaYgQDSFYX50eoqyoMPohaLGMkk7GFOJoXDGPPz+NfWsNttuu9+N4mXvOTIPuupwnHofOIT6O98J/IXv4g0HOJoGvb58/ie/3yiz3zmYxak77epXOvW5L/+1/8qUkRhnCT6pje96TG992FJp9MiqbJUKglH/0AggOM4Qiw3NTVFMpk8lWK5ubk5IfQulUqsrq7i9/vx+Xy7UpAXFhZIpVJcuHDh2D9DIpGg1WqxtbVFLpfb9/3ctI5oNEriFjX18fA4Llynf3ew4aCkiMFggCRJqKp6ooNQ4XCYixcv0uv1qNfrTE9Pk8/nxTDGcDgkn88jyzKWZeHz+Ugmk6TTaaampnYlMdi2LVKMt7a2GA6HhMNhNE3DNE0KhQKqqjI5OYmu61y8ePGWFqHXajWGwyHD4fDAAWOfz0ckEqHRaOwagr0ZuIlouq7T7/eFoclXf/VXs729TalUEmJ61yzATUELh8NkMhl6vR7D4VB8Di813OOoqNfrDAYDtre3mZycvOb1haqqzMzMsLGxQblcJhAIMD09fVOu8+4UAbpjmrTf+16s97wHeXkZbBvHcTAVhf7cHMrznkf0BS+4PZOQ7xBCT3oSoSc9icF//AeDD30Ip9FAkmWkqSnCz38+2j7JSB4eAseh/cEPYrzznSgPPQQ7NR5TlunPzo6PFffff+qPFYG77qJ3773Q6aCtrmLs1NKkK4egHQepXEYrlxnNzmLPzJC47759X1ebnkb78R8/5tV7PBa02dlTcZwLXLxI4NWvFv/bsSzqP/mTqA8+iG91FUeSMLNZ7Hh8vD32eijVKtqlS+jDIf1XvQrlf/0v+v/0T8jvfS/q8jJYFqOlJYhExPzNyLaRq1V8pRKGbWP86q9i/Omf4jtEit7thFOtIo1G2H7/wf0owAmHUctlnJUVar/2a8iJBL7HP57wN33TVX0hu9+HdBqrVGI0Mc2l7b23L8cZp6zbPh8YBs6jknD3wk2GM00TWZYJhUKHSmk+TciyzPnz50X91k00dsW7qVSKeDxOIBDgzJkzXuLmEZHNZoV5e7fbpdFokM/nhTgpHo8zNTVFOBzm3Llz17VdZTIZAoEApVJJCEd1XafZbFIqlVAUBcdxCIVC9Pv9awrf4vE4w+GQUqmEJEnEYrETMZENBAJomoYsy3S73X3TqOv1OisrK3S7XQqFAsFgkEQiIcTNq6urhMNhLMvCsqybZjp4Pbhi38FgQLfbFQJ01xTiJFBVlbvuuot6vU6lUtkl3JEkiUQiQTab3bUdLS4uClGp4zhsbGxQKpUIBoNMT0+LZOTp6WlUVWU4HFIoFEgkEvj9/mPf1twkzGq1ysbGBrZti9RwgF6vx/b2NrVaTYhEz5w5c1PqbJIkcfbsWSEwLZVK1Ot1otEoqqqi6zrVapVms0kwGGRycpKFhQVkWaZareL3+0kmk0LInMvlaLVaBAIBYrHYYxaRaZp2IiYUN5NcLke9XiccDrOxscHs7Oyu/a7b7bK5uUmhUKDf75NMJun1eqyurlKr1QgEAszPz+86TpmmSb1ep1qt7ivi0zSNubk5Ll++LMxmq9UqpmmSSqXEd+84Du12m3K5jN/vF7X/k8Tn8zE3N8fMzAy2bQvDj7m5OaLR6J4i1mazSSgUwu/335F9TPd4IknSoZObbdsWYluPk0NVVSF4Hg6HB56fXXMTt8eYyWTodrvMzs5SKBRotVokk0n8fr/YxzudDuFwmJmZGaanpx/zNUAgECAUCmEYxjXXOhwORS9KlmXq9TqhUAhVVY+0pj8/Py+ucdvtNo1Gg1qtBozNFeLxODMzM2iaxmg0Et/LQeesZrNJLBZDUZRb7h7spJidnWUwGIggBtcUWtM0LMui0+lQq9VwHIeFhQXS6fSpM52p1WqMRiO63a4wP9qLeDxOtVoV5lqeAN3D49bBE6B7eHh43AJImgbPfCb29jbq5ctIxSLO1NTVInTHQd7cHIt1MxmU5z3vlnFL9/C4FvJOs9eR5XGixAHizdMq9DxOrHab7vveh7m8jGQYEIvh/8ZvJPyf/pO3/3t4HAP+8+cZhsOYsRhKo4F1gAuxXKuNE5FDIfxPfOJNWuXBSM94BsZnP4vSbKIaBoaqwh7FcHk0QnIcLL8f41u/9ZY+tkiqSupVr8L4/u+n+453YP7zPyO122Oh9MwM2vOeR/zpTz+yNNyjRur1kCwL+zBFREnC8fuRTHOcGHNMDC9dYvBTP4WSz+Pb2kIyDKxwGEeWUXo9lHIZMxLB7PdplEqkXvvaW3ob2g81mcT+hm/AqtXwr64yajQgmUTZ2kIZDFCHQyxVHacDGQYSYAcC6JOT2PE42toarK1h/emfMnzKU8ZpLB7HjjY7S+oVr4BXvOKmvq8TDuMoyrUTzSUJMx5H1nWU4RDTtpEcB2dHmP5o5Gp1PIAXixF8znPEv/ff/W6kXg9lNMJYXNzfzCkaxfL7katVzA98AG5DAbqez9P9+Z9HXl1FqddRmk0ky8JRFOxCAevf/53Gm99M6Nd+bZxSeJ3sd2i+lvDmz/7szxhc8YdgMHjsAnQYC7jdFBVXLOe6UCcSCeGwvLi4eOqaVjAevDx37hz5fF40O9vtNpZliVSPYDAo0mduhrAqm81Sr9dRVZXt7W2mp6ev+TjTNKlUKiQSCQKBwL5DeR4edyLRaFSkBjUajQMHpxuNBvF4HE3TTsWQdTgcJrxTizp//jyVSoXt7e1dgyLusEUoFGJycvKqVJvNzU0ajQbr6+uEQiFmZ2d3iesty6JWq4nB5+XlZe6+++5bVsys67oQZR9mUDISiVCtVrFtG9M0b7rxwLUGOl0TgVqtRrPZxDRNJElCkiSazSYPPfQQvV4Py7LEuTaXy7GwsHCgaYmHx2GoVCo0m01UVd13KFSSJLLZLBsbG+i6Tq1W22WAcVzszPFdk9tl9szqdGj+7M+iPPggSrWKWq8j7xhN2T4fZrGIs7JC48MfJv7AA6iPYZjKKJfpf/SjWJUKkqKgTE8TfuYzUU4oSetOJnjvvV4Ss8djwh4OafzCL6B8+tOotRpqrYa8Y64mjhWrqzQ/8AGir3/9qRe6xn7xF+m87GXojoO2sQGNxjhpWtOQDANfqwW2zWhmBmdmhtCv/MptWxP2ODk6H/wgyuc+h291FSsYxF5cHJt4ug9IJLASCaztbbTNTXRNo/OHfwhf+ALK9jaSYaCfP3+VuFqSZZxcDl3T8K+vM4rH6b797SR/7Mdu9kc8Wa7nXqVex7e1hTocoug6ztveBj4fZiRCI5dDfsELSHzf94njgDDakGUky9r3pR1n5/9Y1vj5B9wHuuZM9XodwzCwLAtZlvH5fITDYSYmJkgmk4f/bCeMoiicO3eOfr9PuVym1WqJzxQMBslmsySTyVu2LnBayeVyRCIRKpUKsVgMe8dgSpIkfD4f2WyWTCbzmOoSkUiEc+fOMRqN6HQ66Lou6jy9Xo9QKIRpmtRqNVRVvaZox+/3s7Kywrlz5zAM40S26Wg0Kmrc9Xp9z4Th4XBIsVhkfX1dmCVcuHBh12P6/T7Ly8si8do19juNBIPBUyWkkiSJdDpNOp1G13XR69E07apU43a7Ta1WQ9d1/H4/s7Oz2LZNOp2mWq1iGIZIP+/3+0JoF4vFhJj60a951FSrVer1OhsbG/j9fmZmZnYJOiORCJlMhkKhwPr6OrIsk0gkbppoyU2VXl5eJhKJ0G636Xa7GIaBoiik02kcx6Fer3PPPfeIul82myUcDrO5uUm32xUGlpFIBMdxCAQCDAYDUVv22I0r9rRtm2KxyOrqKsFgkEgkQrfbZX19ne3tbXRdF8njfr+fcrlMuVwmlUqh6zpLS0uiHl+v14VIbnFxcd/39/v9xONxTNPEsixmZ2epVCosLy8TCASQJAld13EcR9R+p6enryvR/jhx+6qSJLG8vMxwONzVn3Qch36/j2VZ9Ho9KpUKZ86cIZFI3JEJ6Iqi4PP5CAaDdDqdfX9Hd3+vVqsEg0FxPLgTv7fTgLvvx+NxGo3GgQYKzWZTGEDH43FUVeXChQtcunSJQCBAs9mk0WiInk84HGZ2dpZIJML09PQNmUyk02kKhQLhcJhqtcrsjuFbu90WhrOj0QjbtrEsi0qlwtmzZ4WJ91EhSRKLi4skEgkxw+Be9yqKgqZpZLNZstksy8vL4p7E7c1ei263K46tmUzGM2Y4JLFYjMXFRSRJIhQK0Wg0qNfr4/CSnd8jkUiQSqXIZDIsLCyc9JKvotfr0e12UVX1wDkjSZKIx+N0Oh16vZ641/Lw8Dj9nM6peg8PDw+Pq4h9//fT/sd/RNd1tI0NrF4PK52GaHTccGm3x81qw0BfWsK6+26S999/0sv28LghAnfdxTAaHQs96/XrEnoGnvSkm7TKk8HqdGi98Y1If//3SN0uSqczFib5fBjvfjf12VnU7/9+Ys9+9kkv1cPjtkIOBHC++Zuxy2X8ly9jtdsQi137wYaBr1bDyGSwz549VcOJoRe+kMHb3obW6SBbFupggKVpOKoqBkAc20bRdXyGgenzYYVCJH7hF0545UeDb3KS5I//ONxiiSqOpomBHOcQj5dMEzsYxDkm53VH1+m/+tXIGxv4NzfR02nsXE4MbNmOg9Hr4SsU0C5fxpAkGm9+822d7h156Uvpf+pT6P3++DtptVBbLRRdx1YUbEVBGY2QLQvT78f2+3GyWSRFQV9cJHDpEla7Tf/tb9+V5uJx+yF/5Vdif/azaJUK5mBwlfrCzmRw6nVsWcY3HCLZNmYwiPXo4YN2G1+lgjE9jXXmzK5zjb25idrtYvn9SAelo0oSViyG0u0il0o70TK3T4HfKJXovvzlyKur48FoEIPRGAa+chlKJfRmk8ErXgFvfCOBRw1BHcR+M0c7oeOnhsnJSSGabjabGIaBLMsEAgEymcypEHLuhyzLzM/PMz09TbVaFSnDiqKIpNmbmfgQjUaJxWLMzs6yvr7OxsYGmUxGNNZs26bdblOpVNA0jVwux9TUlNdEuwk4jiOGcr0G9+lHlmWRFL22tkaz2dxzQKTRaDAYDJiYmCCTyZzK/ckdynAHEV0BejQaveZx1jAMarUapVKJQCDAzMzMVZ9LURRyuRyyLFMsFgmHw7Tbbc/Q4oRxfxdXzFsoFPjXf/1XyuUyxWJRCNNVVRWDsltbW5w/f54LFy7c0in2HieLOyB6UNqci5uS0u126R2jUd2V7JeAfsoDfg+FY5o0X/UqlM98Bt/qKgBmKoUTjY4FcN0uSq2GvDPk33rlK0n+/u8jH1KwMFxepvemN6F8+tPQ7yPvKPrtUIj2H/wBzlOeQuxHf/Qxido9PDxuIo4zFp//4z/iW1lBsm2MZHKc0szYdFQcK0YjOv/tvxH/gz841SYTvslJIr/3e3Rf/Wr0SAQajbHZ7WCAI8sYuRx2KoUzMUHota99TEZ/Hh4HYbz73ai1GjgO1sLC3veFExOYvR5yuYzz3veCLKM2GpiZzP7J3okEZqUyNuz80IdwfviHT6158XEgTUzghEIopRKmrsMetTapUECrVlH7fSTHQRqN8JVKyJaFI8uYW1vY29vUvvAFUr/2a0iqiprJYMdiWOEwSrsJ7DM87wCdDrJtQzSKcvbsng9tNpusrq6yvLxMs9mkf0VRdnJykmQySafTIZfLHZjIdtpwDUM9bh6hUIiFhQVmZ2cZDoe7jAWPYtvx+/34/X6RaOgmAJ45c4ZyuYxlWZRKJUKhEJFIBJ/Ph2maQtTjpia7daKbjWty5qYMF4vFa9a8K5UK6+vr6LrOPffcs8u8Vdd10aOwbZvNzU06nQ7NZpPHP/7x5HK5UyPgvBXYKwm73W6zsbHBcDik3W4z2jFBcuuP5XKZubk5dF2n3W7TbDaRJIlIJMLCwgLhcJi5ubmbIvJ2TfaAq8TnLoqiMDs7y8rKCs1m86anZgYCAe655x7R8+n1euL4MBqNhADSNSqwLIutrS263S79fp/hcCh6Frqu0+l0eOSRR5iamvIE6IxrXc1mUxh6KooiTBCuTDZ2U3oLhQLdbhe/38/CwgJzc3PiWOT25ra3t4WA1E0dHg6HDAaDPY0+Ho1rjLqwsEAsFiMSiTAcDoVoTlVVYrEYfr+f6elpMpnMTfi2Dk8qlaJcLjM7O8vm5iaj0YhYLIZpmjQaDXq9Hs1mk263K0wpzp49e8cKAjOZDJ1Oh9XV1WumU9u2Tb1ep9FoUCgU6PV6wjD9C1/4AvF4nPn5eWZmZvY0aPE4eiRJEr+de4zY6/zQ7XZptVrMzs6STCaFwYqmadx9993U63UqlcqufVmWZVKpFNls9oZN/FVVJZVK0e/3WV9fp1wu4zgOtVqNdrtNu93GNE0Mw6BerxMMBmk2m7RaLba2tvY0w3+sJBIJEomEMI12j2uhUEhsv5OTk7TbbXq9Huvr60xOThKJRMTfbdum1Wqxvb1NJpMhHA4fmJTusZt0Oo3f76dUKhGPxxmNRpimKeZ4AoEAuVyObDZ7Ko8rlmVh2/ahDYtUVcW2x3Et7rWUh4fH6efOqYx6eHh43OKoySTh3/otej/90+h+P3KlglYoIO1cgDmKgpFMYmazWBcvEn/ggS87B3t43KJImgbPeAb29jb+S5cwWy2kPYbpnNEIrVbDyOWwLl4kcPHiTV7tzcOs1Wj/5E8iPfIIarGI2u2OkxsVBdkwxs3cUgk7n6extUXyv/yXk16yh8dtRfjFL6b/wQ+iZzJo6+vo09OQSn1ZqOc40OngKxSwNQ0nl0N98YtPdtGPInD+PL3nPpdBt0toYwPJcVB1HUfXQZZxHAd5J3HXkmXMSIThT/4kkVOevHK7Y587h72+jlouYx8gDnWGQ5TBAHN2FumYkrQ7H/wgcrGIls+jT0zgTEzsTliWJKRIBOPcOXyXLiEXCpjvehfO93//+Bx/G+JfXMT6n/+T0WtewygQwLe+jtbrIVsWlizjsyxsRRkLif1+jKUlkSQiBYMYkQhytYrzD/+A88pX3rbfkweE77+f/l//NVYohFIoYJ09u3ufDgYxUimo1fD1ekg7KS9WNAqGgTQcItfr+Fot9EwGe2qKwMtfvvtNTBMcB+eQw0eOLI/PYY6DY5r7D2DeYrR/5VdQ1tbQ1tYw0mnsqSmkK74XfWYGymX8hQIjSaL/P/4Hgbe97bpE+PvNJ+wnvDkpFEUR4shbFVVVmZycZHJy8qSXwpkzZ0S6baVSYWNjA0VRUBQFXddFEkc2m2VqaurUDZ/cTjiOQ6vVolKp0G63xb+7qVCpVMprYJ5iJiYmqNVqTE5OUiwW6ff7pFIpMeAzGAyo1+t0Oh2mpqaIRqM3JcH3RojFYsT2Mi27glqtJobWlpaW9h0eSKVSYhClUqlcU3jqDvEahgGMB2dSqdSpSt/QNA2/349hGIdKBun3+wQCAWRZPvbEpcdKoVDg05/+NJcuXWJzc5NAIMDExATBYJDRaESz2WRzc5Nms8lgMMBxHO6+++5T9bt43DpcOZxzPUM97iDQzWC/6+AbnNM7FbTf+U7kBx/8cvLqwgJccZ/hhEKY2Szy5iba6iq6z0frz/7sUMZ8vf/v/0P/hV9AKZVQymXUXk/c20mOgxmNYtXrtD/7WcJveAP++flj+5weHh43RvuDH0T59KfxrayM087PnEGSZVFHdcLh8bEin0dbW0NXVdp/8ickf+InTnTdB6HNzJD6kz+h84lPoL/znTif/zySroOqYt11F77nP5/oM55xW9WXPE4Pej6P8tBDqPU6ZjK5q853LexMBv/aGk6lAo6DbJrY6TQHVf6sZBLf9jZ2o8FofZ3APuLn243Q857H8F3vwtI0pEoF5xppxFK5jFatovT7KJaF4fcznJrCmZvDsW2kRgO1UkG5dGlsxvH615P6uZ9DUhR45jOxSyX8ly4RoMtQvbYhpmM7KLUyRjSKE40Sfc5zrvm4brfLysoK29vbfM/3fA+6rou/+f1+3v3ud1Or1Wi1WmK43U0a9PDYD9f09GYRDAZF+ncoFBK1H1cEGAwGmZiYQJZlpqamTiT93CWXy9FsNpmfn2dzc5Pl5WXi8TiKomAYBr1ejy996Uu0223m5+fJZDKiRtbr9cjn8/T7fVqtFoPBQCTv9vt9NE2j0+nQbrdZ2M9kxGNf6vU6q6urIlnccRwhtm00GqJvYds28Xh8V53VTfTOZrNXiS+Pg16vR7/fp9lsCsOQVqslkqVdIXIikUBVVRKJBI1Gg3Q6zWAwuKnJ9G5q56NrsuVyWZx/XMHY5uYmrVaLcrmMaZqEQiFCoRC2bVOtVoWAfn19/dB15NsRx3HY3t6mXC4zHA7p9/vifB0OhwkEAkxOTnLvvfdSrVaJRqOsr6+LxOkLFy4wPT29y+jT3b4DgQBra2uEw2G2t7dZWlrCcZzrElfLOzNUPp+PszvXg9VqVRiUqKpKMpkkHo+fyuOVLMucP39e7EvFYpF//dd/ZTAY0O/36fV6wjy11+shyzJbW1sMh0POnDlzxxk8ZzIZSqUSsViMfD7P3Nyc2LYsyxKGKe65zzUyKBaLwoxgbW2NixcvkslkWFpa8gwmbhLZbJZKpcLMzAyFQoHhcEg6nRbnMV3XaTQaNBoNcV3y6CRz1yw7k8mg67owsNA07Uj7y7Ozs3S7XSzL4otf/CL1ep3hjvloJBLB7/cjSRLz8/MEAgEkSWJ7exufz4eqqsfSG93LzAYQZvwA29vbFAoFVFUV322v10OSJHK5HOl0mjNnztx25suGYdBut4WRjHtOP0oikQjnzp0Tphbu9UQwGDz11wiqqor7gMOcY3VdR1EUJEnyZjc8PG4hTueEhoeHh4fHNfHPz6O+6U203/pWzA99CLteh50hQjQNe2IC+TnPIfnd333oBAUPj9NO+MUvpveBD6DncvjX1zEmJ7EzmS83kh0HWi20rS2sYBAnm8X/3d99sos+ThyH1s/9HMpDD6HtDLaNlpYgEgFJwrEs5FptLE4cDLDe8hba09NeErqHxxHin59H/9mfxfq1X8NQFLStLSiVMHf2Q7nfR9F1jFgMe34e+/77SZzCfTD5i784drP9+78nsL6OquvjYZsd4TmShKlp6Mkkw+/5HrI///MnveQ7Hu35z8f63OeQtreRymWciYlrP9BxUEolrGAQwmEiL3zhsazHfM97UGo1HEXB2ae4LSkK5uQk2uYmdqNB50MfIva85x3Lmk4DoSc9Cfn3fo/e//pfSH/zNyi6jjoaYfn9OKqKoyiYsRj29DQ8agDTjsfHA22DAXqp5A2QHyFWt8vgwQex223kcBj/3XfjO0HhrTY9TfepT8UcDPBdvgzLy1jT07tUGFY6jVqr4UgSls+HBAR20ruRJMxIhNHiIk4mg+8XfoHwV3/1rveQ4nEcTUMZjTBt+8BBTLnfx/b5cEKh22o4ePjwwyif/zy+QgEzmcSZmbl6uFSSYGIC3bbRtrbQk0m6n/wkkfvuO/T77NdbOo0CdI+jRVVVLly4QKFQECLDK5MPIpEIgUCAqampm5rGcaeh6zqXL1+m1+vRbrdptVqYpimGlDqdDltbW5w5c4boKU4TvJPx+Xyc3zFP8vl81Go1VldXxYCTbduEw2Hm5+eJx+OcP3/+1AqRr5dut0un08Hv9x841CnLMvF4XKReXclwOCSfz9NqtRgOh2LoMRAIUCgUSCQSzM7O7jlEcjNJpVIUi0X8fj+NRmPfgRnTNGm32yKR4jQO8nW7XR5++GEuXbpEsVhkdnaWTCZDr9cTx6JMJiOGqi9fvkw8HieZTDI3N3fSy/e4BXGPja7hzUE4joNhGMIk52ZwRejkVdzyLSzHwXrve1GqVZCkq8TnAknCnpvDfuQR5HIZ8/3vx3npS8fCqz0Yra+jv+Y1yOvraMUiRiLByL1f3DG+VKpVtOVldMOg9zM/g/rmN6NEri3c8rjNcRyGDz2EsbmJY9uoExOEnvCE6zJU8zhejPe8B7VWQ7JtIT6/CknCnp3FGg6Ry2WsD30I50d+5PTXZySJ6FOeAk95CsDY0FBRvO3P49gxi0UAZF3HOcz9/c5jpNFo3E+X5X3PxQJNQ7IsAOxH3Xvd7gTOn6f3uMdBo4F/c5NRMDg243axbXzlMvJwiGJZmD4fdiCAncshwfhYl05jJBL4lpdRVlfhAx9A/97vRZudJfKiF9F/17swEglmi5fJB88yVHf/lpJjESyvoxhD9HPncJ7+9D2vdzY3N6nX69Tr9WveL2YyGRKJBJubm0Iskclkboqo0sPjIDRNQ5Zl/H4/vV6PTCbDmTNnqNfrQqjqJgG7Ag1N08hkMjdU3+n3+1cJWxKJxHUJMs+dOyfWtL6+zuc//3lhuAdjM0nTNMlms0JYPxwO2dzcFAKwcDjMxMQE09PTrK+vi+TH0WiEbdvIssy81y+9bvr9Pmtra5RKJVqtFrlcjng8vqvO2mw2KZfLJJNJgsEgZ86cQVVVsT3eTNHpcDjEMAxarRaGYWBZFt1ul9FoJIS/nU6HarVKKpUiHo9TqVQwTZPhcHhTBeh74faBfD4fzWZT1DOLxSLBYJCpqald9RjXkNNNu11ZWeHxj3/8bVPvPiyO47CyskK1WqVardJsNoFx3809PiUSCQaDAZOTk8zNzZHL5YSp6eTkpDi+XItAIEA8HqfRaAgBpc/nQ9M0DMPANM0Dv/PBYCAMRH0+H7Is7/uepxGfz8ddd93FxsYGW1tbxGIxer0e0WiUubk5otGo2Ib7/T6rq6tYO9fB586dO+HV31w0TWN2dhbbtikUCqyurhKJRIhGo2xtbVEul1lbW6PT6ZBIJJBlmWg0KrbZcrlMu92m0WjwFV/xFRiGwblz57y+5E1AUZRdZgvVapXV1VUURUGWZQzDIBAIMD09TSKR4OzZs/veD+wnyL5R3NmChx9+mHA4TLlcptPpkEqlME1zl7lxLpcjFouxvr4uROiZTOamm0NMTk7i8/nw+Xxks1lhFOP+LRaLEQgEWFhYuK22936/T6lUotlsivOGJEn4/X7C4TC5XI7UlffKN4gr3o7FYkLUfSvgnkdKpRK9Xo/IPr0KN1Agl8vdVtuKh8edwJ11p+Lh4eFxG6DE4yR//MdxfuRH6P3Lv2CVyyDLqNPThJ/8ZK+h63Hboc3MoL/mNZi/9EsYioJaqSCVy5ihEEgSymCAZFmYiQT27CzO934vkac97aSXfWx0P/5xlC99Cd/a2li8Nje3a7+XdkSAejyOdvkydqmE+Za3wLd+q3d88PA4QqLPfCbdQAD99a/HyeWQajWkfn88fBqPY6bTONEo0n/+z6Re8pKTXu41kQMBUr/7uzT+7/+bwQc+AIUC6vY2smXhyDJWLIZ59izqS15C9ru/2zuGnAIiT386jT/8Q/RGA3+hgC5JONnsrt/GsSyUrS3UTgf9zBmsJz0J7RhSHBxdR37kEZRWCzOROHj7iMdx8nloNjE/+1m4jQXoMB7OCrz+9VRUFemtb4VGA31yEkfTsJNJpL2amIqCtJNE57hGUx43xHB5mf5f/AXSxz8+Pk67KfQ+H9aTn0zwO7+T0BOfeCJrS/zcz9HY2gJA2dzEf/kylt+P7fMhmybKYIAVCtGbmADbhokJsCywbRyfD0Ih7G/4BsLf+70EdsR6V6I99amYH/sYbG0hNRqwn+jVMPB1OowWFnC+7uuO6yOfCP2/+RvkTgdZ1zHOnNn3sfbEBNRqUK8zeve7r0uAvt9sy37CG4/bB0VRmJ+fZ2ZmhlqtxmAwEAkN10rD8DhaDMPg4Ycfpl6vUygURAqJpmlYlkWr1aJWq5HJZLAsi/Pnz3sNzWNkMBhc5QJ/WGf2UCjExYsX2draIhKJMBqNxACDm5idSqWYnp4+FSLqo8JNJD5sEraqqoxGIzEEBmMB9OXLl2m1WlSrVQaDAaqq4jgOlmURCoXIZDJ0Oh0uXLhw4oOZgUCAaDRKJpNha2uLUCh0zYEEy7LI5/P4/X4ikQjZEzQR2o/t7W3y+TzNZpNoNIokSRSLRYbDIaZpAuPfTdM0cY5YXV1ldnaW6enpYx8iGQwGDIdDMbQbiUROpZDf4/BIkkQ0GiUWi1GpVMjlcnv+pu5Aj2EYYmDyuDHN8e3LXpyC2fAbYvjQQ8hra6j1OkY6fW3xuYskYWYyaMUidrlM/9OfJvz1X7/nw3tveQtyqYRWLDKand0t9pIkiMWwolHsYhFtYwM9GKTz9reTOKU1SI/jwTFN2n/zN1jvex/y2tp4pwN0VWWYyyF/27cR+87vRD7iJB6P62O0sYHyhS+g1GoYB6U0SxJWJoOWz2PV63Q//nGiz3jGzVvsEbBnzdPD44hxHut1rCzjSBKSbeNY1sEidMPA2XmMfAemFoZ/6qcYrKygmyb+fB6zXsdKpcYXcjvJ5z7DwFIUrEAAPZtFelTKnaQoGHNzBB55BKvXo/uOd5B6xSvQZmYYvvzl2L/923QqVRb6D9NXInTUJLak4Lf6JMwaWsBGv3AG++JF4j/xE9dcp5ua69Z99kJVVWZmZlheXmYwGFCpVDwzMI9Tgd/vJxQKIUkSjUaDVCqFpmlMTk6Sy+VEwq4rCt7c3BTJmI+l5uwKYrvdrhCIu+nqgUCATCbD1NTUoWoGqqoyNzdHpVIRdS1XOO7WQ2RZxrIsyuUyExMTbG9v0+l0aDabTExMiORI18w1Ho8zPT1Nv98nn88Lw4ijTpi83bnye15YWLiqDijLMqlUikAgwMbGBuFwWDz2JHAch06nQ7lcJhqN0ul0RF1ZkiSGwyGtVotQKCRE5+7zXMODo6Db7dLr9URvxxXTHYZQKEQ4HCaZTFKtVjFNk3q9jqZpZLPZXfvUaDSi3+8zNTVFNBqlVquRTCap1WpM7BVCcJuysbFBtVplbW0NWZaZnp4WtVXbtmm321SrVfo7zVZN0wgEApimSa/XO5RBRSKRYG1tDcMw6Ha7JJNJQqEQmqbRbDb3vX5wzRomJiZ2mTjciqiqKhK6L126JMwcAoEAs7OzIqXbFV5vbGygqirNZpNEInGyi7/JuGa5kiTR6/VoNBqsrKxQLBapVqviHNztdkmlUkxNTYm0Z1es6vP5ePjhh/H5fEiSdEcaTJwEgUCAixcvUigUCIfDjEYj0RdxDaBjsRgzMzMnfm3hnh8mJiYolUrMzMyIc45rRJ1IJMS/TU5Oks/nyWaz1Gq1E+mVpdNpkskkjUaDer2OYRhCjJ1Op2+7eYh6vc7a2hqtVotGoyHORTA29kgmk3Q6HTqdDvPz8zfUcxuNRlQqFarV6q7ebyQSIZfLXZdR1EmQTCbF/lUulwkGg3v2PavVKo7jEIvFTm3P18PD49p4VzIeHh4etyiSqhL5pm866WV4eNwUIvfdR/91r2P4m7+JUypBvY68czNnZjLYqRQkEsgveQmJF7/4hFd7vIze+U6URgPJcbBmZ/e8qZT8fsxsFrVSwdnaovepTxG+zcREHh4nTeSbvgnn676Ozkc+gvHBDyIVi2OBYCKB/NSnEvv2b0c55aIWyecj9YpXYP/wD9P54AcxvvAFnMEAKRLB96QnkXrqU72hsVOEpKoEf/mXGf73/84I0La2oFLBjMVwVBVJ11HbbRxFQV9awj53jvgxJddbnc54TaaJcxjhjyTh+HxIloXT6x3Lmk4j6vw89sQETreLnc2OE1P2ebzU62HviI7UI3RIvVPp/N3fYf76ryPXasjVKr5OZ5wWB5jhMFSr6P/yL+g/+IMnMqgvBwIk3/hGGg88AB/7GFavh9RoIFkWViCAOTsL4TDW134tiV/4BYytLUYPPYQzGqHEYoS+9mtRk8k9Xz/8lKfQyOUwSiV8pRJ6JHLVACAAto2ysYGlaRCPE3zhC4/xU998nMuXkdvtsYHVAccrSZYxo1HkTgf78uXrep/9BDSWBYYBpz24zONoUBRl3xRfj+NhfX2dZrNJPp8nlUpdNdCVyWRot9tsbW2JVJ577733lh4WOo1cOcB65ZCpO8Cay+UOtX/4/X6WlpaYnZ2l0WgIAbrf7yeZTN6WQzpuIvFhUoyBq1KMR6MRly9fplKpsL29TTKZ3DX0NBwOqdfrbG5uYpomly5d4p577jnx73J2dpZer4eu62xubhKNRkkmk/j9fjFg2Gw2RdpVJpMRg3CnCcMwqFarlEolhsMhPp8P0zTpdDpYliWMBdzfTZZlarUasizTaDRoNpuk9zMLeow4jkO9XqdSqYgkdneg201Ly+VyJ74deDx2crkcrVaLcrlMtVq9alin3+9Tr9dptVoUi0UxpDszMyOSto6LwWD/v9/qYZNGPg+miWwYcBiTlVgMKZ8HXccsFPZ8mNVqIX3ykyiVCkY0elXSKJ3O+OZCknDicexOB6lSwXr/++EHfsAzkLxDsDodmq96FcqDD6JUq6j1+nhbBBxFwUwmsfN5mh/9KJEHHkCbnj7hFd+5mIUCOA7KaIQRi+1bFwRw4nGkjQ2k0Qhrx7TQw8PjanxTUxiApWlInc6BKejOTj/DTiTGBqW9HnKthnPA/alar2PFYtiJBP7FxaNZ/C1E4K67sH/91xm95jWMwmHkahWtUECybZThEMmysGUZMxhEz+VwJiev+TpSMIgZDiPXatj/9E/wilcAEPuO76AlSRR/5I9p6BMk9DIJo4qEjSH5KfnnicyFiX/NXcR//df3TD+v1+t0u11M0zxQHKVpGpFIhEajIRI/PTxOA+l0WoiFt7a2mJ6eRpZlIcB12d7eZjQaMTU1RSaTuW4RSqVSYWNjQ4iGdF1HURTsHXPseDxOv9+n2+1y7ty5A2unuq5z+fJlDMPAtm0mJiZEmuhoNOLSpUs88sgjFAoFVFXFMAz6/T6tVotYLLbrsxk717OyLKNpGqlUisuXL9PtdqlUKicmjL4VMU1T/MbxeHxfE8pQKEQ0GqXZbFKv15mdnT2RpE3HcURq8HA4ZGZm5irzxNFoJOpvvV4PWZZRVfVIahvVapVyuUy/3xemn6qq4vf7iUajQih+EBMTE3S7XfL5vDCmvFKQ5jgO/X6farVKLBYjGAwyNzdHoVAQ56Y7SYDu/qb5fB6fz8fc3Nyu446bfh6JRFhbWxOiXjedGjiUSa27jViWhWVZBINBIpEI6XSaUqlEOBy+5n7iOA7FYlGYEdzqIrl6vc7/+//+v6yvr2PbtjBKdRxHGFFMTk6iaRozMzNcvnyZdrtNpVK54wTogEjmrVQqRKNRLMtC13VxntJ1XYj3rzxuhkIh4vE4o9FIpKa7RhN30v59kmiaxtLSEnNzc9TrdUajkTDmTaVSom92Gmg2mwAsLS0xPz8vjIF8Pt9V12GRSESYQjQajRM7JsmyTDqdPpae1mmi3W6ztrZGsVgURhwTExO7zO/r9TrtdlsY18w+xoCgSqXC5uYm/X5fCN2vNGdqtVpEo1HOnTt3rD2dG0GWZWGetb6+zsbGxi6zKRhfo1arVZrNJrOzs+J46eHhcevgTRR4eHh4eHh43BKEnvQkQn/1V3Q/+UlGf/u3WKXSWMmRTKI+/elEn/1s5Ft9au0AHF1H+dznUOp1zHh8/6QEwE6nkba3odVi9PGPewJ0D49jQFJVYs9+Njz72Se9lBtCDgaJ338/3H//SS/F4wCCj3scvOENDH7xFxklEsj1OnK7jTQcgqKgz85CIoF18SKx17722ETMbtqHoyhIhsGBvuKOg2yamLJ860+ZXwfBb/5mhn/xF9iahlSt4uw38Gvb+JpNjFwO63GP21dY7HEwvU99Cvt//k/kjQ20Wg0jGmW4sAB+P5JhIDca40SpRgPrzW+mFQgQ/67vuunrlAMB0r/4ixg/8iN0/+ZvsB58cKzUCASQ7r6byAtfiLbTpFBiMQIXLx76tSVFQf3BH8R+3euwh0P8ly5hZDLja0SfbyzGbzRQKxUky8I4exbr676O4OMff1wf92TQ9XFq/CEHZhxFQdZ1pEMKAF0OSnAcDDwBusedxXA43JUCH41Gj21wbTQa0Wq12N7eJh6P7ylwjsVi2LZNqVQiHo/TaDRu++b4zcRNf75ygFWSJBzHQVEUEomEGGBdWlo61ICsz+e7YwwdotEo0WiUcrnMYDAQA2/uwKibXAxjsXqtVuP8+fNi8NFNNdre3mZmZuaqxPlAIMD09DTBYJBisUggEKBcLjN9woK0UCjEuXPnRNJWo9Fgc3NTJBf5fD4SiQTJZJJsNnuoRJuTYDgciuSiTqdDJBIRA2bhcFgMCtm2Ta/Xo9VqMRwOqVQq1Gq1QxsPXA9uwnqtVhMDSe42JEkSiUSCTqdDrVbj3Llz+w4je5xe4vE4oVCImZkZ8vk8lmUJEcLW1hadTod6vU4+n8c0TTEAtLGxQblcFsYOx8FBAvST2uTsfp/uxz+OXS6DLKNMTxO5777xPdJ14FyRQuLAwaJS8V8cYYx2LXof/zhSr4fa7TJaWhr/o2Eglcv4dgzLxPMlCRvQej1GxSKDL35xXDfyuK1xTJPmq16F/JnP4FtdBUnCSKchFhtvi70eaq2G2migj0Z0X/lK4n/4h6feKPV2xblinz3M9a94jOOMe6AeHh7XRJuZoXPPPUiVCr5ymdHk5L59c7VaxYhGcaamsONxJMNArVbRk8m9rwFaLZR+H3N2FulZz7pjzZpDT3wivv/9v+n8xV9gf+Qj2O02WBbq5cv42m1sVWV05syBhjx2MIg8HCK1Wrv+PX7//fzxW76J86vv56vKHyZm1MaPl2Quxb6aqf/8LJ7981+/r8mOruuMRiMCgcChzLVCoRDtdhvTNEWq9EnT6XSoVCq0220syxKi42w2SyKROBVr9DheEomEEIJvbm6ysrIiRJcAg8FA3NfPzs4Sj8eZ3MP0YS+azSYbGxsUCgW63S7pdJpEIoGqqjiOQ7fbpVarsba2Jup5Z8+e3fc18/m8EM3ncjlSV/SlXWNB0zTZ3t6mUqnQarVQVRVd168S4XU6Hfx+Pz6fj1AohKqqu4TRN5oqebvhOA6NRkOYHyqKQiQSEUmchmHQ6/VYPISBSiKREKaV3W73RIRAo9EI0zQxTZNYLHZNsbff72dycpKtrS2KxSJTU1PCWOSx4jgO6+vrlMtlYRLp1s/c90ylUnQ6Hebm5g6sVSeTSbHGQqFAvV4nEokQCARwHIfBYIBlWcTj8V0GorFYjE6nw3A4FL/nnUClUmEwGDAYDDh79uye5ztVVUXyr9sPch9rmuaBYjzTNAGEMSjAzMwMvV5PCOVSqRSJRAJN03YdE3VdZ35+nng8fkuL5KrVKg8++CCFQoHV1VWSySS9Xk8YmGqaRjKZFKYJgUCARCJBo9EgkUgcu5HlaSUYDDI/P8/k5CT9fp/BYEAymcQwDDY3N0mlUtfcX6PRKO12WxjVNhoNKpWKJ0C/yaiqeup7jIZhMBqNCAaDqKp64P2M3++nXq+LfTQQCBCNRr1rpGNgc3NTiKUXFhZ29dBkWSaTyYhrKNdsKZvNXrfBQaVSYX19na2tLdrtNpFIhEwmgyzLjEYjarUa1WpVmK/cddddN2Qqbds2jUZDzHGoqkoikdglFH+sTE1NMdhpDJVKJTY2NvD5fPj9fizLYjAYCMOZRCIh+sMeHh63DndmddTDw8PD48QZPvQQ/Xe9C2dzc5zWkEjgu+8+ot/yLUiHSdH0uDORJCL33UfkvvtOeiUngtlqCQGfcYgJPUlRsH2+sTCw3b4JK/Tw8PC49Rl8/vMM/vZvcUolcBykTIbAs55F6Gu+5tSkSAUf9zgCf/VXdD7yEYz3vAd5eRlpNMIJhbC/4isIfMd3kHjyk491vXIggL24iFUqoTYaGJOT+76fszOURCKBeruJW/chcNdd9C5eRKpW8RWLjOJxpGslNjoOcj6PAzjpNL7nP/+mr/W2wnEwfvd3UYtFfPU6o8XFcdqc+/dAADsaZTQYoK2u4mxuYv3RH2E+61knJvz35XIkf/RHj/x1Y899Ls1yGestb8He3h6nopXLYzH2jjO8mUhgT05iPfGJJH/5l498DSdOJIKjqij9PvYhHi7rOo6q4lxnuupB/Zh+/3DBiC6GYVCr1XaJON2k0r2a2R4ep4Fms0m5XKbT6YhBXkVRhKv8xMQEgSM2o6lWqwwGA5GMsh/xeJxqtUqr1aJSqdyyAvR+vy8G4TRNO3HhqJusXSgU6PV6uwZY3WHTarVKp9MRju2nVUh8UqTTaQqFApFIRIhC3aGC4XBIt9tlNBoBiFSSeDzO4uIilmWJc0Y0Gr1KfH4lyWSSVqtFo9EgEokwNTV14s39aDTK3XffTalUIhqNous6lmWJc18oFCKXy53q/dW2bTGY6CafT05OXnW8k2WZaDSKz+fj4YcfBqBWqx3LmtbW1sTgCoyHiaPRKLIsi8H1tbU1pqamcByHixcvHioxyONkcBwHa0eIqCiK2G8lSeLcuXNinymVStTrdZrNJv1+n2KxiK7rJJNJMSwbCoVYWVkhHo+Lbfc40lJOmwDdKJfp/O//jfSxj0GnIwynTL+fZioF3/ItxF7ykkOLdNWJCXRVHRvz9XoHfiCp18ORZfD7UfYZfLTr9bGJluNAOAz9Pr7VVWRdRzYMlJ2haQBHkrBlGcWyMJeXMbe2wBOg3/a03/te5AcfRFtdxQoGsRcXRU1OAggGMdNppEIBbW0N3eej/Sd/QvInfuIkl33HouZymLI87tV1uwfXGjqd8e/p9yMfk0GIxyFwHLr/9E/on/oUdLvjY/fFi7eNGbrV6TD8/Oexu13kcJjA4x+Pcj1Fq1OC79u/Hes//gOnUkFdW8NcXLxahO44SKUSSq+HfvYsztOehu+uu7CLRZxOB215GX12dlyzd++LbBvqdbRicZzqnUwSedGLbv4HfAw4loV0DDVD38QEqVe8AudlL6P/7/+O3Wox/O3fxvqP/0A2jEMVPSXbBknCuYZwSY9k+NTi9/Ophe9DM3sots7IF8WWffzARQ52+tnB2cfk56r1XHE9fZKYpsny8jKdTodOpyPEurIsE4lEaLVaBAIBzp49S/g669UetxaSJHH27FkURdmVbFmpVACEuWMymSQWi3Hu3Lnr7hEUCgWq1aoQJV8pjpEkiWg0SiQSoVAosLm5ic/no9vt7inuNQyDZrNJpVIhHo/vEp/DuAbiJqq76e6hUAjbtvH5fLuEM6PRiE6nQyaTIRKJiPpEOBymVqth27ZIpPYYC3q2t7cZjUZ0u11hphEOh/H7/aiqKgS3hxGMuoJbNx36ZuM4jqiPpdNpHMeh0+lcU4TuCogbjQaLi4sEAoEbOpYXCgW2t7fZ2NjAtm1SqRSxWEwYJbjGt91uV6TnJg/oJc/OzrK1tcXW1pYwFHH7fLFYTGzjV6aqq6oqvnu3p3In0Gw2abVahMNhJEmiVqthGIZI/3V/C/hy8q8rzlMUhWAwSKvVOrA/0m63hVGNez6NRCIs7Rj/BYNB6vU6tVoNWZbFNUU8HmdmZoZEInGgIcdpptPpsL6+Tj6fp1arEY1GOX/+/C4Rv7utu0YoZ86cIRwOU6/XgbHhz50oQHexLEscb3w+n+h17XV9pmkasixjGIYwmBiNRui67tXgPXYhSZIw894PXdep1Wqsrq5i2zamaaKqKpqmEQgEyGaz5HI5z7jqiOh0OgwGA+r1Orlcbs/zjKqqzMzMsLy8zGAwoFKpXFcK+mg0YnNzk62tLfr9PktLS1f1FTOZjLhWkWWZQqHAwsLCdX8my7IoFotUq1VGo5EQoLvmT+FwmOnp6X17zIdhaWkJv9+PpmnCNMY0TTHrFA6HiUajLC4uesdDD49bEO+O3MPDw8PjpjJaXaX767+O8tBDyJ0OUrc7buL5/dj/9E80/uAPkL/v+0h853ee9FI9PE4d8k4DyJEkJNs+OG2WcTPXkSQvbtHDw8PjAAaf/zz93/5tlEuXxoni/T4ATiCA/uEPM5ybw//ylxP+2q894ZWOkVSV2LOfDc9+9vgfHOemC+SV5z4X5/JlpHIZajXYayjStvFtb2PGYjjRKLFv+7abus6TJvhf/yv6pUuYgwH+lRWMdBo7kxmbLjkOTqeDWi6jDIfoS0tYFy4Qf+YzT3rZtzT6v/4rwa0t1EoFY2JizwE4KRjEmJ3Fv7aG3enQfc97SPzAD9zk1R4/iR/8QTrz8+hvfSvOysp4cNYwQJZxIhGcdBrp2c8m9UM/dN2pf7cC8ld/Nc6//RtKpYLZ641FHHvg6PqXkwaf+MTrep/DJKAflmKxSLFYZDQa0Wg0xGBKIBCg3W6Tz+eZn58/1UI8jzuTzc1Ntre3xYCkK5aVJIlIJCJSWM+cOXOkSRGDwYBer0cwGDywUekOU/Z6PeHAfatg2za1Wk2kglwpRITxUKc7LKgoCrFYjEwmc91O69eL4zgUCgUqlQr9fv+qAVZZloXwdXNzk3w+j6qqTExMHPvabiVUVSWTyaDrOmtrazz44IMAQkQaCATw+/1CPB4Oh8WA3he/+EV0Xafb7TI3N3fgeyWTSUqlEhMTE/R6vRtKCToq/H4/CwsLzM7OCgMLNxX9KFz3jxtVVfH5fMIMIxqN7mu2IcuyGEBzxQVHSbvdpl6vs7GxgaZpzM7O7noPd4CzXq9TLBbx+XxsbW0dKhXL4+YyHA6pVCrUajVx3JdlmWQySTabFcPlFy9eZG1tjXA4zPr6unhOPB5nYWFBJHZls1kkSWIwGJDP58Uw9EHb7GPhNAnQh5cu0f/pn0YuFJDLZdR2G2lnqM+RZcx4HHt7m9a//AvR3/5tfIdIIwo94QkMcznMZBK1VsNMp/etyyjV6rguEo8T/vqv3/uFFUW8jjMcoq2uogyHqKMRtqJg+v1jQzFANs2xMN2yUDsdun/8x0Sf8YxTY6DocTxY73kPSrUKgL2wcO3fW5JwZmawBgPkchnrQx/C+ZEfuS3v+U87gfPn6c/NYRaLKPU6Zi637z4quynNkQjhpz715i3UQ9B65zux3vY25GIRud0e19AUBTsapfWHf4jzrd9K8mUvuyUN9YcPP0z/L/8S6ZOfRBoOwbKwFAXd78f5pm8i9F3fReCuu056mYcm+i3fQv2DHxynMa6soD38MEYqhROLgSwj9Xootdq47j43hz0/T/SHfghfJkP9S1/CfP/7kTc2CKysYPn92MEgOA5Kt4vkOBi5HM70NL7XvOZQ1wYngWNZ9D75SUbvehfyF7+INBziaBr2hQv4nv98os94xpEmt0uaRvhrvgYA/ROfwNnYQNncxDxI+O44KJ0OZjqNMzV11Z/FUyUJ3bf7/vQK75090TQNv98v0nMPotfriZTlkxSgW5bFww8/TKPRoFAoYJom8XicQCCAZVnU63XK5TITExNYlsWFCxc8EfptjizLnDt3jnq9LowzLcsSZo5+v59sNks2m73uOoIromk0GvsmM0qSxOTkJJcvX6bb7VKpVPasG9VqNZFcvFcaeyqVotlsks1mWVlZodPpEAqFhLDFtm16vR71ep1wOEwkEtnV97geY4nTypXCG1eM/FiTSh3HYXV1lUqlIhLlZVkWgnPbtonFYvj9ftrtNo7jCIHafrj1BkmSTkS45h6/FUUhl8thmqbYvuLxuNheh8Mh7Xab0WhELBZDluUbMmYdjUZsb2+ztbWFJEksLS3tEn5rmkY2myUWi7G+vk61WsXv95NIJA78/SYnJzl37hyO45DJZIRxoM/nIx6Pi/W76Loufqc7RXwOY+Fzr9ej2+3S6/V2ncs1TUPTNKLRKLlcDp/PRyAQQNd10XNIJpMUi0VSqdSevSHDMGg0GkxMTIj9wyWZTKJpGltbW8TjcYbDoahLu/X4bDbL5OTkLS3qLBaLNJtNut0uExMTNBqNXZ9HVVXS6TQ+n49qtUogEKDZbB55rfB2wbIsYRSx33bhOA6SJO0yBbHtw9jle9xJ+P1+AoEAtVpNbDOPZjAYsLm5SbfbJZ/Pi+Ode96Ox+P0ej2azSbnz5+/o84jx0W9Xqfb7WJZ1oEzDZqmEYlEaDab1Ov16xKguyb77Xb7muJz+PL1uWmaVCoVQqEQs7Oz1/U7m6bJpUuXhHlUp9MR248rDk+lUvR6PRYWFsjcgCmmJEnMzMwwOTlJrVa76jo4k8mcuLG+h4fHY8cToHt4eHh43DSGDz/M4L/9N5R8Hl+xiGwYmMEgSBJKswnF4jh97/d+j0a5TPLlLz/pJXt4nCqUaBQ7ncYOh5FbLax9klIAnF4P2TQhGkXZce30uLWwBwOGDz/85RSCixeRvRtwD48jp/epT6H/4i+ibG3hK5eRDQNrp6gnVyo4W1uY5TLGq19N+1WvGgu/TxsnMCQTfe5zab71rRhTU/i3ttBteyysvqLJ4oxGqJubSKaJvbQEz3428i0gIDlKQk96EuZ//+/Yb3gDxvY2arU63q5UdWzE5DiY0Sj62bPYFy4Qf+CBIx0KuyP5+79HajaRHGe8Te732GgUy+9HqlYxP/IRuA0F6MBYhPCMZ9D/3OcY/fM/43Q6EAigXrgwHkS8jYfQo/ffT/fP/xwzHEYtFDDOnbs6DQnGg5D5PJamQTRK6DqTjY5KgJ7P5ykWi2xtbdHpdAgGg6IB02q1qFarJBIJ0dw+jrRKD4/HwtbWFsVikc3NTZG0Oj09jaqqjEYjms0mGxsbZDIZHMfhwoULRyZ6ddNbDzsApCgKtm3fUoMepmly+fJlWq2WEPibpolpmjQaDXw+nxh2mZycFINspVKJVCrFwsLCsQ1ItVothsMhjUaDycnJPQdYFUVhenr6MbvA3wnMzs4yHA7p9Xqsr69TLBaRZZmJiQlkWabT6eDz+Xjc4x4nhuU3NzeJRqNiPzjMUFgwGBTJUYZhHOtnsixLDGm4wwX7GUW4iV63Gq5IPhaLsbW1JX6Tvfa7fr8vEmOCweCRDwSVy2Ux1LG4uLjnOlKpFIPBgFqtRiQSYXZ21ksTO0UUCgVKpZIYHhsOhwBikLxWq5FOp1lYWEDTNC5cuEC/36darRIKhZiZmWFubo5oNEo8Ht+1nQWDQebm5lhdXaXX61GpVA5lYHE9nBYBulEu03/Vq5BXVtA2N7EiEUYLC7CTMia1WijVKtrly+iGQfuVryT5pjcdXIOVJOTnPhc7n0dtNJAKBZyZmWvWZ6RSCWUwQJ+ZgWc+c1/RorqwgBkI4CgKvvV1lNEIdTTC1LSrEkNtnw9sG8lxUIdD1MuX6X7840Q80epty/DSJeTlZdRGAzOVgv2u7yQJK51GKxSw6nV6n/wkkac//eYt1mOMJKE873k4KyvItRpyPo89O3vtWm6lgq/bZXT2LM5TnoJyjcRHj2PEcai/8Y3Ib387SrE4PrbbNo6qIlnWuIYbi2E3GtS/9CWSb3jDide5h5cvM/rXf8XudpECAbR77yX0lV95ze2r/Z73YP/2byM3GijVKmq3K0xtzUgEq1pl+A//wOgnf5L4/fefwKe5fiRFIfnrv07jVa/CUVXkahVfrYZcKgHgKApmPC7E5+EHHsC3U0dL/fzP00ilsN7xDuxOB6lWQ9L18fcxMYGTSmFns/h/9mdPjSnxo9HzeTqvfjXK2hpKvY7SbI63WVnGLhSw//3fafzRHxF67WsJXLhw5O8feP7zMf7+73G2tpDL5WsKywWNBpJlYafTqM95zlV/3u8W5DClm3Q6TblcFqnR+zEajej1eiwtLZ24uefGxgbNZpP19XXi8Ti5XG7XNXsul6PValHa2aaXl5e59957Tzy13eN4kSSJdDpNOp1mOBwyGo1E6vKNGBA0Gg1Rnzko1VBVVWKxmKhD7sVoNGI4HApR6LXw+XzMzMzgOA5TU1M8/PDD9Ho9IXpxhc/xeJxEIsHU1NQuI0LXMEJRlJsmptpL/HW9dLtdisUi7XZbiFplWSYQCBAIBMjlcuRyuet6L9cEdH19XYh7IpGISE517+9brRaWZdHv90Xy8340m02CwSCqqp6IEeSVhnvz8/PU63VUVaXVaglxOIx/m1AoxPT0NNVqlenp6Rv6rarVqqjFnjlzZs9tzO/3k8vl2N7eJp1O02q1DqxfxmIx4vE48XicdDq9r3jNcRxarRaZTIZoNHpLC52vl8FgQKFQYDQaIcuy+P8w7vsEg0EGgwH9fp/5+XlRb5VlmVwuR6PRoN1us7GxwfT09FXbr/v6wWCQeDzOxDVMhcLhMOfPnxd9rCuNUQ9jNnDaGQ6Hwhw6k8mIXs5wOLzq2OAmdbfbbfGdubX8Ozn9HMYCU3e7cM0/3F79tfbZK+u4V5p73En7t8fhSKfTNBoNyuUy7Xb7qvOFruvivmFjYwMYJ0yfP3+ecDhMt9ulXq+zurrK/Pw8sixz/vz5W/7YddIYhsFoNCIQCBzqGjQYDNLtdjEM49DXko7jUK1WhRHTQddr6XSatbU1RqMRtVqN3AHagSvfZ3l5WZhW+/1+ZmdnxZyGaZo0m03K5bIw79c07YaT0F1jocOu08PD49bAmyLw8PDw8Lhu7MGA7j/8A3apBI6DPDFB5GlP27fJanW79H/u55DX1/EXCuipFM7EhEhlthwHWi18W1tIy8tYf/VXtJeWiD33uTfrY3l4nH4kCelZz8IuFPA//DBWpyMG5a7CcVC2tzHDYZxIhOjzn39z1+pxQ4zW1uj95V8ifexjSIPBuLsuy4yCQZynP53wi1+M3zMV8PA4EvRCAf3//D+RNzbQtrfRMxnsbFaIMR3LQqpW0ba3MXUd+4EHGMzPE3zc40545SePHAoR+JVfYfSqV6FLEr5SCcplzGgUR1GQh0PUfh8rEMA4exbriU8k9eM/ftLLPhFiz3se/clJBm9+M84Xvwi9HuwMtBEO48RiOE9/OvEf+zFvuPQIUCoVpMEAKxi8ttD4SiQJKxJBHg6RK5Wbs8ATJPSEJxB6whNOehk3FTWdxn7Ws2AwwHf5Mr7LlzEnJ8fX0e7QSq+HWiyOE5HOncN68pOvezDzIH1Kv3/wazSbTUqlEhsbG1iWdZXL8cTEhBhactMSwuHwLZEM63F7MxqNKBaLFAoFbNvmzJkzuwSMqqoSDoeJx+Pk83khmr377ruP5P1VVUVVVXRdP/R6fT7foUWWblKMmzZ0swdEbNvm0qVL1Go1Njc3URRFpFGsr69jWRbr6+sYhsHs7CyO45DNZqnX6wSDQXRdR9d1zp8/fyxrd4e8JEk6sBl9Iy7wdwJu0lWxWCQWi9Hv94nH48iyjCRJ5HI5QqEQwWCQmZkZJEkS6VGukPwwyVCu+cJxphoNBgPK5TL1eh3DMLBtW6R9JBIJcrkc0dvouleSJBKJBBMTEzz88MNiYCOZTF41aDIajahWq2iaJo6NR5noZZqmGBJPJBIHDsekUikxtOImoXmcPJubmxSLRfL5PIPBYFcKXL/fJ5/P4/f7RcLZmTNngPH2lUwmqVarXLx4cd/9LBAIEI1GaTQaRKNRZmdnj3Qw7bQI0Dv/z/+DvLmJtrmJMTmJk8vtNilLJrESCexCAW19HT0QoP32t5P4vu878LVjL34xzY98BH00QltbwxoMsNJpnHh8XMtvt1Gq1bH4fG4Oe26O2Pd//76vGf6Gb6CRy2FubhIoFgGwFOUq8Tkw7s9ZFqbfj2RZSL0eo3e+0xOg38YYW1s4loWs6+OE3wNw4nGkzU2k0QhzZ3vyuPlEX/ACGh/4gDhW2IMBZiaDk0iMj0fd7lg42+0ympnBnpkh8tKXnvSy7zhaf/mXyH/91yjLy8i6jrEjQpZkeSzUbrdRt7dRLl0C26bxy79M+nWvO5G1dv/xHxn92Z+hfOELMBggmSYoCnooxGBpCd8LX0jsec8Tda/O3/0d9m/9FvLGBr5GAyMWY7i4OJ7TMAyUeh3/2hpGs4n9O79DJxwm+i3fciKf7XqRQyFSv/M7tN/5Tqz3vhdnbW2cWu844PNhp1JI3/qtxL7ru1CTyS8/UZJIvuxlmN/1XXTf/W7MT3wCqdkEVcWZmkJ77nOJP+1pp9YoVt/aovvyl6OsrqLtiBDMWAxb05AMA1+5DNvb6M0mg1e8At74RgLnzx/pGsJPfCK1pSWo1/Hn84w0DVKpqw0Q2m20QgEjk4Fkkug1DJ73u2U5TAJ6KBQiHA6TzWYp7nO+M02TfD5PJBIhEAjcUKrbjaLrOo1Gg1KpRCQSYeoaAn73PhOgVCoRi8VoNBqkUqmbvFqPk8IVKh8FhmFgGIYQcx9EMBik0WgIA8NrPeewtYxIJMLCwgKj0Yi5uTlGoxG2baPrOtlslnA4jN/vZ2JiYtc9rGEYdLtd5ubmSKVSxyqk6nQ6Qrjt1rDchMZUKnXd9bNarcb6+jqNRkMIPV2RuKIoxONx+v0+nU6HM2fOHOr1TdOkXC5TLBZRFEWIzFwkSSISiRAKhURNQZZlKpUKyWRyT/Gorus0m02mpqZIJBL7GkceF+72pSgKqqqytLREuVwmFAqh67qoe2qaJswYXLH8jRgT1Ot1ms2m2Ab3Ix6PC3FgvV4/UIAej8cJBAIkEgkqlYpY87WoVqsivf401eUcx6HT6TAajYCxAPnRye03wmg0olwuMxqNyOfznD17lkwmI7bB0WgkjFiy2Syrq6tYlkUqlRI1Lbe/UCwWWV9fJxAIEAqFkCSJfr/PYDAgFosxNTXF9PT0vv0L9zh0u+EaCI9GI5aWllhbWxMi6mudY6LRqKgrl8tlpqeniUajJ3JsOE0oikIymRSmoD6fD0mS6PV616y/tlotYezhmr/4/f47/nv0uJpkMinuTUqlEj6fb9fsR6VSodvtsrW1haIozM7OEo/HiUajSJJEPB4Xxsibm5v4fD7Rm/I4GQ57zeoazff7/UPdm7rHlH6/L0wuDkO73abdbrO5uUkkEmF6enrX31VVJZPJEIlEWF9fp16vUygUbliA7uHhcXtyOiulHh4eHh6nErNep/3Hf4z0d38HrRbSaASOgx0I0HrjG3Ge/nSiP/iDwjn6SrrvfS9yqYRWKKDvDPnsQpIgkUAPhfBfuoRdKmH++Z/Dt33biSR6enicViLf8R103/52jFQK3/o6xhVpLQLbRi4UUHu9cZrqN34jqteIvGXofPjDmL/5m8j1OnK1itrpCMd6MxLBrlTof/jDjH76p09nCrOHxy1G921vQ94RmI/m58Ed/NtBUhSYmGAUChFYXWUUidD/0z8l+Bu/cWJrPk2EnvAEpN/6LQa//Ms4qRTU68jd7ngQ1u9nNDUF4TD2U59K8ud//rZOWT6I0JOeROhJT2L4yCMMPvYxnGYTfD6UxUVid2Ay/LHyWAU8Ryj88ThdJF/5Suo7w4dyPo9/bQ3b58NRVbAsFF3HDIfH4vPHP57EL/3Sdb/HQbvwYRLQt7e3aTQa6Lp+lYDXJRKJMDc3x9raGt1ul3K5zOLi4nWv18PjKKlUKruSQvYapIpEImQyGarVKolEgl6vd0PJPS5uo317e5tut7tvsrppmnQ6HdGg34/hcCgEtG4KC0AikSCbzd5Q49UdUun1ejiOg6ZpTExMEI/Hr2pMu0nGm5ubxGIxJiYmkCSJ1dVVkTS0tLQkhvHcZI7p6Wm2t7dZX18HxmLGhYWFx7zmvXAHWAOBwKGa6sFgkF6vh/moKXI36cUVLMN46Mttet8pmKZJJBIhmUyKoVE3PTwQCIhhFJdkMkllx0THcRza7faBw+idTkcMHB/VIPOVNBoNVldX6XQ6ImHLJRgMkkwmaTQazMzMXHPI/lZlYmKCTCZDJpPBMAxqtRqj0YhYLEYgEMC2bZrNJtVqFVVVyeVyBINBcrnckRoBuPvPcDg89NCKJEmMRqNDG3l4HC/tdpvt7W02NzeFuPzK4UTXxKFQKIjUs1gsRiaTEcO1wKGOne7507IskehxVJwGAbo9GCB99KPI5TJWOHx1X8pFknBmZsYJspUK1vveB9/7vQf2puRQiMgDD9B95SvRfT7kchmtUEDa3AQY13NjMfTpaez5eUIPPHBgnV5SFKTnPAfn3/8d2baRbBv9Wl+W46AMh+O1qyp6PI5ar8PnPodZq6GecKKnx/EgOQ7X1TG9chv2ah4nhhwIEHvgAdp7HCuQJIxolNHZs9gzM4Re9zq0Rw2DehwvjmFgvfWtqIUCsmFgnD8Pmvbl/U2SIB7HjMVQVldR19bgX/6FwRe+cNONapt/8ifw5jejVqtjk5MdQRCA7fMhlUrYy8vUP/95Uq9+NY5pYv7O74z7xq0Ww7NnkcLhL3+2YBA7FmPU66GtrmIUChhvfCPOU5+KdIuIIyRVJf5//B/wohcx+MIXMDY3x/W+TIbQ13zNviJyNZkk8ZKXwEtectPWexR0fvVXUdbW0NbWMNJp7KkpYcTqALrjQLk8FoZLEv3/8T8I/PmfH+3cjSQRfvWrGbziFeiWhb9QwKpWsZJJbL8fyTBQms3xvEImgzMzg+9nfgb5Gteb+2kHryjH7Mv8/DyDwUAkzj2a7e1tms0mgUCA6elppqenDxQbHie1Wo3BYMBgMBBmUnsRj8epVqu0Wi0qlYonQPd4TEiShCRJwpTwIFwRtvvca6FpGpqmYRgGpmnua/bp1j8CgQDdbhe/30+5XCYejzMzM0M4HN71PsPhkNXVVSRJQtO0YxPlmqbJ8vIynU6HTqcjksNd899Wq0WhUODs2bOHrk+2223W1tYoFou0Wi1SqRQzMzNomoZt20LA3Ol0hCB96RDhE9VqldFoRLfbZWlpac9akizLTE5OUq1WURQFXddZX19ndnb2qnt+Nx06HA4Ti8VOLKHSFWVeKXydmZkRtXy31uXz+YhGo7TbbUajEcFg8IYMLt1008OYPEuSRCAQELXwwzx+cnKS4XDIYDBgbW2NiYkJkVgPY/F/rVaj1WqJJNKDhO03A9u22d7eFgnx7rnV5/OJev3ExMShDX73olQqoSgKo9FICPavrH+5yfPu+a/X6xEKhUSfCWBychJZllEUhWw2S6PREIL5YDAozvczMzO3pbj8MFiWJVLdA4EA8Xic4XAojlGP7pNpmoZpmlQqFTKZzKkzRjhJstkstVqNRCLBxsYGwWCQZrN5lRlGo9FgMBgwNTWFLMsYhiH6ih4ej0aSJBYWFsT11Pr6OtFoVPSLS6USKysrGIbBwsICyWSSycnJXddNkiQxPT3N5cuXabfbwnzG47GjaRqBQIBqtXrgdS5At9u96jx2EFfetx5WtO4aGl2PqXWlUqHdbuM4DpOTk3s+zjVCqNfrpFKpI5vj8PDwuL3wBOgeHh4eHoditL5O75WvRF5fRy6XUdttpJ3CtCNJmNEodq1G59OfJvTAAwTOnv3ykx0H673vRalWsTUNZ5+baUnTMLJZ1GoVZ3OT/mc+Q+hJTzruj+fhccvgy+VQfvInsV//egD8KyuYoRB2PI6jKEiDAWqrheQ46EtL2GfPEv+pnzrZRXscmu4nPoH12teibG7iq9UwIxH0uTmRQiDX6/hXVjBSKezf+A26waCXaOPhcQPYwyHS3/0dcrWKGQ7DPg09KRrFSCRQqlX49KcxymV8J9SEPW0E772XwF/+Jb2Pf5zRu9+NvbGBNBrhRKPw5CcTftGL8B+D2OlWJXDhwnUnK3tcH3YqheP3ozQamLZ9YAq60uthh0JjEwWP2xJJVUm9/vU0f+/3sN7/fuxOZ5xqtJMQZcbjEA5jPe1pJF/5ysdkCHGQVucwAvRutytSbPZrYgUCAZF44zr83+igh4fHjVCv12k0GodKCkkkElSrVbrdLrVa7Ugal8lkEr/fTzweZ3t7m0AgcM19wnEc4R4fiUT2HfYoFotsbW3R7/fFsAiMh9xarRaNRoN4PM6ZM2euK2Wl0+mwvr7OcDik3W7T6/WAcSO73W4TCASYm5sTg2aO41CpVKjX6/h8PtEY7na7DAYDKpUKsViMVCqF4zjk83k6nY5IYZmdnWVzc5NSqSQGrY76ePFYB1ivbKbX63Xy+Tyj0Yh2uy1EsMFgkGq1SigUYmFh4Y5odA8GAzEUtrS0RPAAhWYsFqNarSLLMqqqit9+ryFUy7JEynU0Gj3ygft2u83q6iqlUkmcp+bn59E0DcuyaDablEolMeygKMqJDbceNdFolKWlJba2tiiXywyHQ3G8uDIxLJVKkc1mhYg4FAodatD1sBxlmrrHyeAaj4xGI86ePYuqqsJwxHEcZFkmFAoxOzvL6uoqjUaDcrlMJpPBtm1s20aW5UMNLSmKIraZo9529gvhUNXxf46b3ic/idTpoHY6jObn9xfuShJWKoVaqeAUi2NR4eMff+B7aNPTxP/wD2m/5S2YH/oQVqMxNm8G8PtxYjGcb/5mYi996aFF4fHv/V4ab34z5tYWvsEAVdexdgxKJUCyLGTLGvfmAgGMZBI7Hkfd3ATHwSiVPAH6bYo6OYmpKGORabeLc8D5Q2q3cWQZ/H6U2+R8e6viy+VI/P7vj48VH/4wVq0mjN7x+3EiEZynPIXIS1/qic9PgM6HP4zcaKA2m+izs7DXwLAkYc3N4f/SlzDbbQbveMdNFaC33/Me+KM/QlldRen3MVIpjHR6LBQ3TajX8VWrOO025t/+LY1YDGVpCalex1eroc/MIO11TxUOo09PoxUKOI0GnQ9+kNjzn3/TPtuRIEkEH//4Q52/b2WGDz2E8oUv4MvnMZNJnJmZq69xJAkmJtBtG9/WFkYiQfef/onIN37jka4lcPEi9utex/A1r2EUi42N1atVJMvCkWWsaJTR9DROIoH6sz+7Z0/7RhPQYZyCfu7cuavqDTC+zh0MBsL4b2pq6sTN0Hq9Hr1ej0AgcOB9uSRJRKNRut0u/X7/Jq3Q43YjEAgQDAbRdZ3RaHTgdtfpdAgGg2iatmeNKZlMEgqF0DRN1Jr2wrIsWq0W09PTTExMoOs6lUpFmPLCWGzabrcpFApsbW2hqiqTk5MiTTybzR5pErplWTz88MM0Gg3y+bxIoHbNIF0TwWw2i2VZXLhw4VAi9Hw+T61Wo91us7i4uEv0LcsyiUSCWCxGPp8XSaW5XO7Aumer1RL144PM41yRsCv2DwQCrK6uEgwGRQ3q0enQMzMzNyTmPiy9Xk+kudq2jaIoxGIxYci5srJCu90mFouhqupVAjrTNKnVamL7uxHjUveccdh6iPu4w26D2WxWJJSWy2UKhQKKoggzguFwSCAQYH5+nkQiIc5jJ4lhGFy+fJlWqyXE8W7d3zVA7Ha71Ot1zp8//5iNDC3Lol6vY5omyWQSWZap1WooinJVndTt+zSbTe6++25CodCu/SWXy5FKpajVasRiMdFb0DSNVCpFJpO5o3uosiwjyzKO42Dbtjg2uGbRg8GAaDRKIBDAcRyazSaVSgVVVZmbmyOZTJ4KY4TTQCQSYWJiAsuyaLfb5PN5YYCUyWRwHIdOp4NpmuLcYRgGU1NThEIh0l6tzmMP3H6ze5xtNBoUCgVarRblcpnRaMT58+eFcc+1zn2SJJFIJGg0GiSTSQzDwHcLBcQ4jnPi58AryWQy4ljYbDb3NXt2zWampqYOZQrt4p6bVFVlOBweaJzvJqb7fL5Dn9ds26bVatFsNonH4weaYcfjcWH60mw274i+vIeHx/Vx515Ve3h4eHgcGqvdpvczP4N86RLaxgZWMIg+Pw87SU9Ou41aq6EtL6PrOv2f+RnUP/ojkeRglMvI+Txqs4mRSh3orGyn08ilEnS7jD79aU+A7uHxKGLPfz5twPrd38XOZMbN3FoNHAfH58OcmMBJpbDPnyf6uteheo52twSOaaL/1m+hbG2h1uuMFhfFcdbFTiQYdTpjR3tFQX/DG3C+4Rvu6ERhD48bYfC5zyF1u6jt9tjs4QDsdBr/8jLWcMjgU5/Cd6sNYB0jkqIQefrTiTz96Se9FA8P7Pvuw3nwQSiVkBoN2K+Z1+uhDIeY8/NIT3vazVukxy7s4ZDhQw9h93rI4TCBu+5CPuJIRElVSf7UT2H9l/9C933vw/zsZ6HXg0AA+Z57iL7gBTck0pDlsQh9L6HNYQTobvLoQc0lGDd/NjY2sG1bDAd4eJwEjuOIpJBIJCISmN0BMjdVIZFIoKoqqqoSCAQYDodHlrQryzJTU1Pous7GxgZra2vkcjmi0ahoVvf7fZHYMT8/Tzwe37NpurW1RaFQoFAo0Ov1RKKGJEkMBgNKpRKVSoW5uTls2+b8+fOHSi9uNpusrKxQq9WoVqsi+RwQA13pdBpd11lcXCSdTtPtdhkOhzSbzV0JHc1mU4jX3eEfdzCh3W6TSCTEAFsulxNp6dVqdV9388eCO8R4PS7wVwqfy+UyGxsbVCoVGo0GkiQRDAZxHId6vY6qqmQyGUajEefOnbvtj3e2bYthxsOYG7iPicfjKIoikpmmp6ever5pmuTzeXw+H/F4/FiE3xsbG1SrVRqNBvPz87sGBl0ThVQqxcbGBsViUQiyb4chQE3TmJiY4N577+WLX/wiPp+PRqNBv98X4nN3QHJhYYHRaCSSjw4yGrjedcB4uNw9hu3HYDAQx6PrSWfwOB50XRcDQYlEgsFgQL1ep9/vY5qmGM5WVZVoNEooFKLZbJJOp+n1eqiqis/nE0YWB+1bw+FQPOZ6DFUOw37Xvzcj/RzAKpdhNBqbKR9iKN2JRJCLRbAsrErl0O+jRKMkX/5ynB/9UXqf/CRmsThOKM/lCN933zWTPvdDDgTg278d85FHUHQdy+dDNk3knZQ1W5axNA1HVTESCez5eWi3r+s9PG5NAnfdRX9uDrNYRK3VMLLZfXuuSrWKGYvhxOOE77vvJq7U41ookQjJn/gJnB/5kfGxolAAy0LOZgk/5SkoNyCe8bgxjE9+ErXRGBd3DhJX+HwY0ShyvY7zqU/dlPXBTkr7m96EsrWFMhgwOnsWKRj8svDY5xsLjlMpfMvLKOvrWO94B/qZM/jq9bEZxUH94mQSp1RCqtUwPvQh8Pofp5L+O96B3OkgGwbGAff39sQEUq0GjQajd73ryAXoAKEnPAHtT/+U7rvfjfm+92GXy+JvTjwOz3oW4e/4jn3NNfa7ZD1sAjqMTcHuvvvuq4QLkiSxuLgo7oNjj+q/nwSucdRhr8EVRRHPOQpcEdphjas8bn0ymQzFYpFgMEi9Xt/XhGEwGBxKRBMMBolEIqTTaUql0lWiUBfbttna2hLp1XNzc2K7c9ezublJtVql3W5j7jhPuCLwQCDAxsaGSOo9d+7cdYlu3ARtWZYJBAKiVrWxsUGz2WR9fV0cH67cJ3O5nDBShPF+eO+99+5bB3aNIur1Otlsdk9xrizLIqm02+1SqVQOFPiYpolhGIc2k/T7/SiKQjQaFbXiZrMpBNFXpkPPzs4eu0GkruusrKzQ6/XodDp0u10syxL1Db/fL+rjW1tbGIYhhMku3W5XGMym0+kbrnP7/X6CwSCdTufAz2+aJoPBgHQ6fV2GnnNzcyLhPZvNiu1RkiSmpqZEIvXCwsKxCgXdY/5Bj7l8+TK1Wo2NjQ38fj+Tk5OEw2EkSRJmvWtra8zMzABw9913P6a6bqvVQtd1er0eFy5coFarAbC9vY3f7ycSiaAoCqZpCkGvpmkEg8Fr/laqqjIxMXHHppzvRzAYJBAIoCiK6B3N7cxG+Xw+kZbs9iRarRbJZJLZ2Vmmp6eFKNZjzOzsrDDWDQaDbGxsUCqVKBaLohcaDAZptVpMTU0xPT1NKpUSJqMeHnvhGquUy2Wi0SiWZVEul1FVFU3TWFxcJJlM7tvDCQaD4niq6/qpFqA7jkOr1aJSqdDpdIQA3TWQTyQSJ3rsca9rM5mMMHm/Vn9a13Xy+bww8rgeAbosy8RiMRKJBOVymWw2u++5utVqoaoq4XD40MYg7nW1aZqHMo1x+0uGYWDs9CI8PDw8rsS7mvHw8PDwOJD2296GvL6OtrGBkU7jTE3tGmiQ4nGseBx7exttc5OR30/7T/+U1CteAYwF7DBOZeAQRThJUbAVBQwDp9M5ng/l4XGLE3v+8zH+03+i+453YH7oQ9g7rsAA1ld8Bdq3fzvxpz0NySte3TJ0PvpR5FoNX7WKPj19lfhcEI2OUwi2tnBqNTof/SixZz/75i7Ww+M2wW63wbKQbBvH798/CQtwgsFxKo1hjJ/r4eFxKtG+/uux3/Y2jHQarVhEDwbhWqlguo66sYERjUI4TOT++2/+Yu9wRuvr9P76r5E++lGkfh9sG2SZUTCI8/SnE/7O78S/uHik76lEo8S/+7vhu7/7SF8XxkKavQTohwmneSyCQzdR1cPjNNBoNKjX62Nzwh3BpSsmrlarxGIxMQB01E3jiYkJRqMRkiRRLpcpFotiGM0V4UUiEdGgX1pauubr9Ho9isUi+XwewzA4c+bMrma+O4y4tbXFxsYGsiyzvb2959Bmp9OhUqlQq9W4fPmy+H7OnTvH5OSkGBx2HIder8f29ja6rgvh/mg0wjAMbNveJSLt9/v0ej3C4fCuZrQ7tOkmK7iJOG6CULfbPYqvexdXusAflHbU7XbRdZ1EIkEmk6HT6bC5uUmhUKDf7zM5OUksFhPbh5vWXSqVME0TSZK45557bmuRrKIoYgBc13XxWd30jGazyWg0EoPq7iCAm+QSj8fZ3Nzk8uXLxONxMVTb6/VotVr4/X7m5ubEAMdR0m63GQ6H1Go1JiYm9kz11jSNmZkZ1tbWjs0Y4aRwh5OXlpYYDAYsLCwwHA4xDEOkNkUikV0DaUc94OtuB4cdWmk0GoTDYfx+/1WpUh43n+FwKAyGJEmiVqvR6XTodDq7jFuCwSC9Xg+/3y9MXQaDAfF4XKTUHZTMAQihu5tatxdu8hggzu0Hsd/1780SoEuKwnXlurtp8DAW6l3v+/l8R2bMpy4uYpw9i/r5z2OGw2MhungjCSMSwc5kIBoFSUJut7EDAZAkfCec6OlxjEgSyvOeh7OyglSrIW9uYs/NXS1CdxykUgllMECfmcH55m++biMEj+PjKI8VHkdEqwWGgeX3H2ikD+AEAijdLna3Oz533ISh6O5HPoJUr6PW6+Mk8z1OppLPhzk/j//SJaxeD/nBB5H6faxI5OB1ShJWOIw0GCBtbx/Dp/A4CpzlZeR2GzMUGhsP7IMky5jR6Pg6YWXl2NakJpMkXvIS+IEfQC8UsFot5FAIbW7uULMK+5VCr7fs6ff7r6o5ybLMvffee6pqCa5J42HNGd17yhsxjbqWyAPG4go3WfowBosetyaappFIJMhmsyJ1+1r3i8PhkHw+TyKROJSIZnZ2ll6vh67rbG5ukkgkxHMty6LT6VCv17Ftm/n5eZLJpBDv3HPPPZTLZSKRCOvr64xGIwaDAYqikM1mmZ6eJhaLMRgMaDQa1Go15ubmcByHCxcu7Ls/6LrO9vY2tVoNXdcxTRNZlvH5fIRCIZLJJI1Gg+3tbSKRyJ61Xbd2ViqViMfj1Ov1fb+Ter1Or9fDsqwDjYZd4XWz2SQWi7F4QC/MrRceth/kCo5zuRx33XUX1WpV1KZhvE2k0+mbkg6t6zoPPfTQrjpvJBJBVVUMw2Bzc1Nso+12m2w2K0xcXfHzYDDANE1isRiTk5PC5PJGyGQytNttarWaqLXvRbPZRFVVYVZ7PeRyObLZLI1Gg2azKbbHYDBIJpO5LkH7YXEch0ajIVJMXWFfNBoVZiyPPl/WajXa7Tabm5vE4/Gr6rXRaJRoNEq1WqVQKODz+SiVSszOzl73+twUV8dxSKfTBINBJEkiHA7TbrdF8rqiKMLoolKpMDs7e+QGirc7sVhMGB3U63Xxv5eWlqjX64RCIQzDEL8JwPz8PI973OO46667PPH5o5Akifn5eRKJBMlkUoj1dV2n2+0iy7LYf9zz4dLSkpci7HEo3P7dzMwMg8GASCQiavHXa7Bxmvfd0WjE8vIyvV6PdrtNu90WpvbutVEoFOLs2bNHat58vczPz4vrj3w+TyQSIZFIoGkalmXRbrdFSvj09DQzMzPXLfrPZrM0m00qlcq+8wbD4ZBqtUo2m93T9OlaXLkdHNbMzP0tvHtDDw+Pa+Epkjw8PDw89sUxTZwPfhClXMb2+68Sn+967MQEdruNUqlgfuQjOC97GZKmieQ6R5Zhp1Cx/5s640QKRUHyBiI8PPbENzFB8sd+DF72MsxmE3swQE0mjzwt0uPmYHzgA6j1+vgYe1CjJJUap7rW6xjvfz94AnQPj8eEFAiMU03gcNco7mNk2btG8fA4xUiKgvqyl+G89rWYuo62vIyZSGCn0zh+P5gmcq2GWq9j+/3YCws43/Vd+I7Z3d9jN50PfxjzN38TuV5HrlZRO52xIcjOYKRdqdD/8IcZ/fRP3zJmO8EgXOELtYvDJKC7TZzDpFi4QkxZlo902MIwDDGI5KYw+P1+MpmMN4TocU0kScLn84kBVr/fL4YsXPF3vV4XqefuQGImkznywd/5+Xkhts5msyJFRZZlIa5Mp9PMzs7uuS2Xy2WRVHP27NlrNoplWRbi2VqtRigUYnJyclcTV9d10TzvdDosLy+zvb1NtVplcXGRZrOJrutMTk6iqiqSJIlhlLW1NSKRCNvb23umcrmpV/sdK9xBYhgnWRyXYYXb6M7lchQKBTRNu+aA5XA4ZGtri0QiIX6LlZUVkea+uLh41edRFEUM4bnN/UqlIhJWbkfC4bAYZmw2m0QiEfr9PltbW4xGI7rdLoOdk4qqqgyHQxRFoVKp8MxnPlMM/7mDD50dc0+/38/MzIxID5ifnz/ytbtDio7jHDhk6yavNJtNarXabSNAj8ViTE1N4TgOhUKBTqdDOBwWwu5+v0+lUiEUCjEzM0MulzsW0bc71OoOhM7MzFzzuNdsNmm328zNzZFMJr0UllOAe3x3U8nca7JYLEY2m0VRFAzDoNvtikH9brdLt9vFtm1xTE4mk1QqFSKRyJ7JFtVqFdM0hQhhPwqFAts7QrSJiYlDXX+edAK6Y5oo09OYgQCOoiC1WgfWXKV2G1vTkBQF3wmfa8LPehadN70JI5nEURTMs2fBMECScHw+pCuuURzLQm210KemsL7qq1BvcAjf43QTfcELaHz4w2OjmtVV7EcewcxkIBbDAaReD7VSQR4O0efmsOfmiL3kJSe9bA+P042mgSSN5xMOg23j7ByPbxb63/3duId4mCTzUAgrGESuVpFN8/oE8pIkDFk8TifSaDTeBg9ZD3QUBVnXx887biQJbXYWrlOItt9tyGFad4fhNInPYXzvGI1G2d7eptvt7jIdfDSWZdFqtYQY97Gg6zqXL18WIo9WqyVqZpFIhHa7TaFQ4OzZs/uuxeP46fV6VKtVhsMhjuMIM7tHJ0E/Fubm5uj1eti2TaFQoNVqiTqdKxbvdDpC4Ds/P3+giCYcDnP27FkhjqrX66yuroq/K4pCIpEglUqRSqV2mYJqmsbs7KyogdXrdWZmZlhaWtplahgOh0mn02xvb7OxsYGiKGxvbzM9PX3NNXW7XZEsXqvV6Ha7ok7q8/lIJBKsrq7S6/UYDoecOXNm38+YSCSoVqs0m02q1eq+4mPTNNF1XaSPH0QoFKLRaIi67X7PCQaDhMNhCoXCgY91k98nJiYIBoP4fD6mpqb2FDUdN8vLyzQaDfL5PMlkkkwms2v9pmmyvb1NpVJhYmIC0zQ5f/487XZb1EGTySTxeBy/38/U1NSRJF2n02kKhQKJRIKtrS3m5uauWUNpt9vCQPN6RF9XIkmS2A+Om36/z/LyMoPBgFarRafTEWLuWCwmxHLnzp3bdX4sl8s0Gg0URdn3+81kMvT7fer1OpFIhOnp6es+Pj1aEBeJRDh79qwQHV6Zuur3+wkEAiIB3etTXh+SJJHL5ej3+3Q6HfL5PNPT06iqSi6XE79np9OhWCxy4cIFzp07xz333HOqBawnTSwWIxaLMT8/T6/XEyaibr/PNZh4rNduHnc2bl87k8mIHtpBRikwvgZyz2On7f7HRdd1Hn74YRqNBoVCAUmSiMVi+Hw+TNOkVqtRLpeZmprCNE3uuuuuExOhh0Ihzp07J5LZ6/U6hUJhl5HX9PQ00WiUmZmZx3RtEo/HicVizM7OsrGxgWEYpNNp8Vubpim2gUgkQiqV2vMa+Fq4xmfhcJhOp3NgP9IV3Ls9XA8PD49H400SeHh4eHjsy+Cznx2LAXaGVw5qUJqZDFo+j91s0vvUp4jcdx++qSnsRAIrGkVtNMaDEPuxM7HvRCIoFy8e1Ufx8Lh9kSTUZPLggQOPU41UKiH1+5jh8KFSCMxIBKnfRyqVbs4CPTxuQ/x33YWpqlihEEqzib3juL4Xcr2OrWkQCKDdffdNWqWHh8djIfKMZ4xFGP/X/4Veq6FUq/gvXRJ/t/1+zIkJnEwG+/77Sf3wD5/gau88up/8JNZrX4u8uYlWq2FGIuhzc+PUHsNAqdfxr6xgpFLYv/EbdINBIk996kkv+0D2CHoFDidAd4cnms3mgQ2qZrNJNBoVDaOjYGtri1KpxHA4FOJYN4W51WqRz+dZWFjw0lE9rsIdkMvn8+RyOXK5HKFQSAynjEYjms0mpVKJQCCALMs87nGPI51OH/la3DQRV1TpDtOGQiHS6fS+g3lu2rabArHfgKUkSaTTaYrFIrlcTgxswu5El2KxiGmaIvnbbfD2ej2y2SzD4ZDFxUUh+nQTkev1umg6+3w+kfLiJgTJsowsyyKRwmU0Gom/Xbl+wzCOdTjLTXm2LItisUij0RAu8KZp0m636XQ6xONxJiYmWFhYwDAM2u029XqdZDK5r5g+Go0SDodpNBpiqO12HX5SFIV0Ok2322V9fZ1KpUKtVqPVatFoNEQSjCRJYmBscnISXdfZ2Njg3LlzIpU+kUiIQQhJkkgmk2Sz2WMbJtd1ndFodOhtLRwOMxqNDp34dqvgir1lWWY4HNJoNIQRgKZpLC4uisSK4zJTiMfjpFIpTNNkY2ODlZUVUqkU0WgUSZLEunq9HpOTk8Tj8RMxdrBtm3q9TrVaZTAYiOF6VxB9kukWJ4WqqkKA7jgOPp+PmZmZXeYAiqIQCASIRCJsbm4yHA6p1WriMdPT0/R6PQaDAevr62SzWeLxuDgHjkYjYRgxOztLOBw+lgHo4XDvvx3XTzv4whcYvOMdSP/yL0i9Hg6grq1hKwpKuYyVTO5tsGzb+BoNzHQa6/x5AufOHc8iD4maTOJ8wzdgNRr4V1bQQyGcHdO0Kz+BY9uoq6tjEVoqhXb//SezYI+bhhwIEH/gAVqvfCW6z4dcLqMVi0j5PAAOYCsKtqoiN5uYPh/tP/ojQt/xHQTOnz/ZxXt4nFKkhYXxXEK5jDEaIe1nCug4KO02ViKBMzNzU9LPAWg0kEYjrGDwUO9phcPIo9FYKB8IoDYa2AeltTsOcq+HlUzieGYmpxYnHMZRVZR+n8NYJojtoFSi9spXgq4jRSIoT34y0Wc/G/kUDJUfZQL6rUIqlSKfzxONRimXywSDwT1rVuVyWSQl5w4w0TVNc1fCsc/nIxqNsry8TL1eZ2trCxjfM7o1G1dUm8vlsG2bCxcueCmdJ8BgMOBLX/qSMB/s9/s4joOmadTrdfx+P9PT0weah+2HpmlcuHCBy5cviz5Eo9EQaczhcJiFhQXC4TBzc3OHTnmOx+NcvHiRYrFILBZD13UMw0CWZSEczWaz5HK5a9bzqtUqlmXh9/s5e/bsNc3xJElicnJS3P+GQiGmpqauer3hcMjly5dFimQ0GmV2dpZAICBE2W7yuVuDOIy4JhaL0ev16Pf7h/pOnEOaubgJk+5n3I9MJkO1WkVVVer1+r7bgltDjMfj153WfdS4tfBisUgqlbrmcUxVVWZmZsjn8zSbTRYXF1lYWKDdbjMajXaZMaTT6SOrcSuKwvz8PJZlYVkWa2trRKNRUUPRdZ1ms0m/3yeXy5FKpVhYWDiS9z4uBoMBjzzyCLVajVKphKqqxGIxYWhYLpcpl8tMT09jWRZ33XUXmqbR7/dFbzCdTh+4PbrnsdFoRLvdFr2Rw+IaCSuKQrfbJZFIoKoqmUyGTCaDaZrCaEFVVcrlMuFwWBxXPK6PXC5Hp9PBcRw2Nze5fPky0WiUYDCIbdvC9HZycpKpqSnm5ubuyLrsY8E1NrnefcDD4zC4fVZX/LzfNbppmrRaLSYnJ8Ux9TSyuroqTGnS6fRV5/VcLkej0WBrawtJklhZWeFxj3vcia03Go1yzz33CDNgwzDE7IFrtJTL5R5z31WSJM6ePStes1KpsLm5iSRJKIqCaZqoqko2mxXXIQcZgD/69TOZDN1ul9XVVYbD4b7Xvq7Q3e/33xTTHA8Pj1uP03l28fDw8PA4NViVCpgmkmXhRCIc2M6MRMbu4Lo+fi7jBELpW74Fu1jE98gjmO027OHu5tg2vkoFM5HASaeJPvOZR/uBPDw8PE4rtj1OFTjssIqbQuAlEXh4PGZ8ExNYX/M1UK2iFQqMcrk9h8scw8BXq2FmMljnzhE8wQKnh4fH4Yi/8IX0z5xh8Gd/Bp/5DNZoNE6MUxTw+7HuuQftRS8i8YxnnPRS7ygc00R/wxtQtrbw1euMFhevuj+0EgmsTgdtbQ1DUdDf8Aacb/iGXSl/p5H9evGPnkv6oR/6oV0O/j6fj3g8TiKRoFQqieSRa+EmKi8sLBzZsM3GxgalUomtrS263S7BYJBQKIRt27RaLarVKslkEsuyWFxcPBbhsMetiW3bGIZBr9cjEAgIUdyVA0p+v5+JiQk2NzdZW1vj7rvvxnGcYxtmdYW212uW4A6z9ft9Zg+R1hWNRikUCgyHQwaDgRgwWVlZEc3zVColBhy73S6zs7Nomkaz2WR7e5tsNkuhUNg1uOam8BiGgSRJIlG8Xq8LAbqbtFKr1UilUuI40G63iUajwtACxoOXo9FIpGsdB8FgkPPnzwtxdKPRoFwu70qgdwdY5+fnSaVSVCoVRqMRo9HoUN93PB6nWCwyNTVFv9+/rYehJyYmqNVqJJNJ/u3f/g1JkjAMQ4jHHccRA2Pnzp3DcRx6vR71ep1isShS0SzLEiYFPp/vVKbDHHYg91ZjamqKdDpNpVIhGo2Kc76qqqTTaTKZzLGnBywtLWHbNrIsiyQxN8HaTZVYXFwkGo1elXi0H24KRa1WE6IGTdPEsNBhB5tarRZra2tiuHUwGGDbNj6fj2azSaVSEQM1p3HbPS7C4TDdbhdN0ygUCnzlV37lnt9pIBBAVVUxlOvipu/AeJDfTQ3x+/3Yto2u6wSDQebn50kkEpw/f/5YvuObmYBuD4c0fumXUP75n5HbbeRaDdkwcCQJyTRRBwOU0YjhI49gXbhwVf3VsW3UtbXxf89kUJ/3vKNd4GMk8qM/Svdzn2Ok6/jzecx2GyuVwolEwLKQWy189TpIEsaZM1hf93Uk7rvvpJftcRNQ02mSv//7tP7szzDf/37sSgVnMEAplfB1Osi2ja0o4yTaz3wG+9Ilhn/7t/S+6quIveY1+G5AuOThcTsS+vZvZ/iud2H5/SjlMvbc3N4PbrVQDAMzk0F5znNu3iIlaXxeO+T1s5vmbmUySMEgUrmM024j7TOo7LTb48+WTqN88zcfybI9jh75q78a53OfQ6lUMPv9fR0xnX4frVrF3qmpOu9737ivrKo4n/gErTe9CelFLyLx0pfePDOFa7CfAP2oEtBPG7IsMzExga7rrK+vs76+fpVwYTgcUq1W6Xa7LCwsEI1G9xQ26LpOoVCg0WiIWguM79XK5TKj0YjBYMDExATZbHZX3c41ciyVSkiSxPLyMvfee+9ta/x3Gun3+6ysrCDLMrVaDdM0CYVCyLJMp9Nhe3ubRCKBYRgYhnFdqYePJhAIcM8991Cr1ahUKrvEya4pYjabve6aRTAY5MyZMxiGQavVEqL2QCCwb/qr+/hms3kosZSbWP1oQ1CXfD5Pq9Vie3ubmZmZq947nU6TTCapVqtsbW0Ri8Xo9/u7EtevhaIoOI4j/rPX/hEIBAgGg2xvb6Pr+oG1FrcXc5i6XSQSIRwOMzExQT6fR1EUksnkVWtxayqTk5MEAoHrEikdB5VKhU6ng2VZB/aVstksKysrDIdDdF0/MJ3+KEin08IIoNvt0mw2yefzOI6DLMvCUNVNDD9oWzlJHMdheXlZiM8nJiZIJBK7tpFcLketViOfzyNJEmtra1y4cEEI6tzjz0EEg0Ecx8EwjMdkLhqNRoUZb61WIx6P71qnm9gKY+PgZrNJLpcjFot5AvTHgCRJnDlzBkVR8Pl8YltvtVrCiHxqaopAIMDc3NwNmZ14eHgcHe61Wb/fZ21tjUqlcs390zWJ9/l8xGKxU7sP9/t9ut2uuLa91jolSSKVSmHbNqVSiWg0Srvd3vd68rjRNI3Z2Vmmp6fp9/viOte9hrtRVFXlrrvuYmtri1AoxGg0EmZUPp9PzAS4vd/rJZPJsL29TSwWI5/PC3OmK3Ech3K5TLfbZXFxkVQqdWpNDDw8PE4W78jg4eHh4bEvzpWFSvtgD2UxNChJcEVxNPKiF9F917sw0mm0jQ302VmIx3c1shxdR93YQLIs7MlJpG/7NiTvItbDw+MOwUkmcfx+lMOmEPT7WPE4eC6aHh43ROBFL8L49KexGg20lRWMhYWrBnac0Qjf2hqOquJks/he8IKTWayHh8d1E/qqryL0VV+FvrXF8DOfwel2IRjE//jHe8lfJ0T3Yx9DrlTwVavoU1N7mpMRjaJPT6NtbWHXanQ++lFiz372zV3sdbKfkObRApzf/v/Z+/Mo2da8rBP/7ClixzxHTpHTyTNV3bIKBRSLYZV0gQpVBRRYoiXdP1tsp0IcoCxURARaHFiKukBcKtDStiUikwoi9kKFpltESoqi7j03z8mMzIiMOXbEjjliD78/8uxdJ+/J6eTJ+byftc66VRk7It6I2MO7v+/3eZ6/8/f4L/8Fdnb2b42/6ItAVXt0u10GgwHFYpG5uTni8bjfdOGlYdbrdTKZDNFo9MTUm9PQarWo1Wrs7u7iOA7r6+vPLTr1+3329vb8RpxIJHLhwjnBzcAwDH8BdH19nX6/7yc4RSIRVFVlOp1imqYvePNE6tcN52ndy2suOwlJkpAkCcdx/HrYYDBgMBhQrVZJpVLk83lM0/SbE1VV9RsGYF8YqOs64/HYbyL2Fqsty8JxHLLZLIPBgK2tLZrNJtlslmQyiWmafjJ2KpXyG1Tj8bgvvvcWjCORCMFg8ELNIyKRCK+99hrNZpNoNMrc3Jz/2GENrM8KJk8jfA0Gg7iui+M4B4SWp2UymTAYDHAcB0VRiEaj59IYcBEEg0Hu3LlDq9UiFovx6NEj4vE4/X6ffr/vJ92nUimSySQLCwu+6F/Xdebn5333/aMS1C6CQCBAMBjEMAxf+Hwcw+GQUCh0auHzTSMQCLC0tMTS0tKBJPrLQpZl7t6964uPM5kMlmX55yJN08hms8zNzZ36WKjX65TLZSaTiS9sgP191jRN9vb2WFpaOnF+0u12efz4MY1Gw0/visViKIrCeDxmd3eXYDDIbDbDsizu3r37yogfJElClmX/GjOZTI48RrwmpHA4jKIomKbpp1Lkcjk0TUPXdTKZDIPBgOl0iiRJhEIhdF0nlUqxvLx8YefC4wTo5zkNcGczjG/6JpRf+RXUrS2U6ZRZLLZfN7VtFNNEnk5xZRm91WL267/ObH4eNxrFBWTT3Bdxy/K+iPtzPof0+953fgN8CQKLi4T/1t9i+NGPMgmFkBsNAuWyLyp0AgGsdBo3l8P+Hb+D1F/7a1cqHhNcLnIoROrrvx73D/9hBr/0S4y/67uQu12c8Rh5Nttfo3Uc5OEQrdXCDgSg36f3J/4Ese/7PrRzuJcUCG4L+sYGg3e9C0yT4PY2U1XFnZ9/3rCk2yW4u8s0m8VNJIh/4AOXN8j5edxwGKXdxrJtpOPm+a6L3O9jJ5M473gHcr2OlUoRKJWY6vqhJrzuZEKgXGaWTOLG4ySuiRmL4HmiH/wgg3/xL7AiEZRyGWtjA+mQey93OiX0m7+J7DhI0yl0uzjTKY6ioEynUKlgJRLY/+Sf0C6VSH/rt17ZPOK4lqAXvv2/QSZnCwsLjMdjAGq1GqVSyb9Xs22byWRCKBRibW2NZDLJxsbGoa8zGo148803fRPRwWDg31OMRiNarRbD4ZB0On3kvVUymcR1XV+Y0Ol0XtjYUXA2LMvykyAnk4kvGPUEH67r+kIdT+QZCoVe6veRZZlcLkcul2M6nfoimkAg8NLmZF6t4bR4dYXRaHQqsZRnzDiZTPzjx2M6nR4w0z1KoCPLMouLi5TLZfr9Pu12+0TB7XQ6RVEU/9g6ikwmQ6VSQdd12u028/PzR247Ho8ZDofMzc2d+jtbX19nMpngOA6VSoV2u00ikUDTNCzLotPpYFkWc3NzpNPpSxFwn0S/36fX6/kp3McRDAYJhUL0ej36/f4ljRDf9LNer9Nut/16vyRJ/j7t1VmuM6ZpMhqNfOPbw84T3vFv2za1Wo1IJMLouALOKThrzS6fzzMYDDBNk3K5zMLCwnP7iGVZN0JUeROQZZn19XXm5uZoNBr+vg77dWwvff667+cCwavG4uIipmlSKBQolUr0ej1SqZRvBNLr9eh0OqiqysrKCtls9krF2sfhmWvNZrMT5z7pdJpWq4VpmjQajWvxmWRZPnPS+UkoisLy8jKLi4sYhsFoNPLX8pLJ5EsZ4ASDQQqFAo7jsLe3x9bWFtFo1F+Tm0wmdDodbNtmeXmZRCJxKuN4gUDwaiJUfQKBQCA4Fm15mYmm4WgakmmeGE0hdbs4qgqBAOoz7uDa/DzqRz+K/V3fxUyWCezs4GoaViwGsow8HqP2+9jBILO7d7F/+28n/Uf+yEV/PIFAILg2qF/8xbif/CRKvc6s10M6rnDS66FMp1jZLPLv+l2XN0iB4BYS+bzPw/j9vx/7X/wL2N4m+Oab2OEwdiyGK0kog8H+HCUUYra+jvslX0JSNF8JBDeOwOIigctsChUcyfTf/lvUdnvf7OwkIWQ6DdUqcrvN7N/+W7jmAvTj1n2e7d8wTfiu74JPf/ozf/tn/wy+8ztjrKys4LoujUaDarVKvV73FxCHwyGSJJHP58lkMty5c+dcnP5rtRrtdhvLslhbWzvUzTgajbKyssLW1hbxeJx6ve4nawpebUzTpNfrkclkiMVitNtt+v0+pmnS7XYBfLGb5wz+bBrwdcLb9zVNYzwen7iY6yWma5rmP9dLdLEsy188f7Yx0bZtv4EnmUz6qb+dTsdftPbE1bIs+4lYhmH4zQWDwYB0Oo2maWQyGXZ2dvzGxoWFBSKRCKlUiul0Sq1WYzwes7a2RiaTufDmIVVVmZ+fZ25ujslkciDt6K0NrF7TJuw3kp3kpG5Zli/MfBFRdbfbpV6vY5oms9nMF6BrmkYymWR+fv5Ckmssy/L382f3Edu26Xa7fsJ9MBg8YDbikUgkSCQSvog5nU77jcbxeJxwOEwwGGR+fp5IJIKiKJRKJcbjMYZhXKjZwFF4DSG1Wg3TNJ9LoXqW0WjEaDRicXHxSsZ62VyVeFqSJL95cDAY+E3KmqYRi8VeqLG8UqlQKpWoVqt0u110XfePncFg4KcV2baNbdssLCwc+jqz2YwnT57484/DUhtmsxmVSoVisYgkSVQqlZdKeLtpeI0/c3NzNJtNptMp8XjcP4fbtu1fZ1OpFJPJhHA4/FziVDKZJJlM0uv1MAzDP496ovSLNn+4rAT0zj/+xyi/+qtojx/jBINMNjbgmeudtbDArN0msrmJoyjIsxlqvY5crQJPRdy5HG4mg/05n0Pqr//1a2WMrD94gPpP/ym9f/7Psf79v8fpdPajQCUJNA37zh3Ur/gK0h/4wPFiRMGtRVIUpr/0S6jdLqph4Goak7U1/wbVAWbTKUqlQmBri6nrYn7Lt5D5x/9YGBYIBM8Q+9jH6BeLTByHwO4urmFgPTWOliwLpdNBGY+ZZTK4S0toH/sY8iUmYOrvex+z//yfcff2kJtN3GcMv57DSzLPZlG//MtBlnH+/t9H2toi+OabzFIpnHQagkGYTJDbbQKGgR0K4SwvI//hP3ypn03wYmi5HM6XfimMx2hvvom2uYk1Pw+xmH9ed/t9Qq+/jvp0fjjTdaYPHvjXBtt1odtFrVaRNzexAGNpidQV9eccN4U5jQDdtSx6/+E/MPupn0J54w14Kmi9CXhGoIFAgMlk4teUZFkmEon4QuOVlZVDayGWZbG5uUmz2aRSqRCPxw+Yi+7s7Ph15lQqxe7uLuvr64fWh5LJJM1m0xd5CAH65dBqtRiNRjQaDR48eOCbinlIkuQnBW9vb9Nqtfza33kQCASu1BjQM+2E09VOvG2efZ5Hu91mPB779abjiMfjZDIZSqUS9XqdxcXFI2sk3j34wsLCiWniwWCQRCJBLpejVCoRDAYP/a0mkwmlUol4PE4oFDq1AF3XdR48eMDm5iaRSIRut+ufNxRF8esAoVCIjY2Na5HWbds2juOcei1LVVW/tnSZhEIhVldXKRQKjMdjv4YcCoVujClio9HANE0kSTrxHJHNZtnc3GQwGNBsNsnlcgfSsU8yEPbMTl7mHJLL5eh2uziOw87ODpubmyQSCSKRCK7rMhgM6Ha7BINBP5X7uHqz4HSEw2FWV1dZXV31Tcdvyj4uuFx6vd4BU+dYLHYtriuvGqqqcu/ePX890TAMGo2Gf53UdZ18Pk88HiebzbK6unrFIz4az8Q9HA6fuCYsyzKxWIx+v89wOLykEV49iqK8kJnTafFMo705grcfeX0O6XSaRCJBNBrl7t27Iv1cIBAciTg7CAQCgeBYQq+9xnB1FalaRWu3meVyB5LND+C6aK0WVjKJs7hI5LM/+8DDsS/9Uvqqyuxv/k2m+TxSs4k8GIDj4AYCTO7cgWgU5z3vIf2X//K1avIRCASCiyb6vvdh/uAPMksk9lMI7t5FOqxQP52ilcvMEgncWOxyExYEgltK6k/9KQxdx/6RH8Hu95FbLRTTBNhvXn46R3E/8AFSf/bPiuZMgUAgeAmkWg1pOMSORE4+n0oSVjSKNBwi1WqXM8CX4Dghzeuv7wfwSBL82I8dFJ8DTKfwHd8BP/zDOVRVJRAIkMlk6PV6fgpIPB4nHo+j6zqrq6vEYrGXHvNgMGA4HPqpNsctJum6TiwWwzAMYrEYhULhpRNRBDcfy7KwLItAIMD8/DzRaJR2u00sFvOby2RZRlVVv0HPE0B6KSLXBV3X0XWdRCKBYRjPNXy+FcMwCIVCBINBv/FpMBjQ7/eJRqN+Y7Cu637q+7PNW97fBoPBgZQRr6kqEAj4KfL37t0D9hsNDMOgXC4znU5pt9soisJ0OsVxHMbjMYFAgN3dXT9dem1tzU+5vSw8geNxeKnsmqYdSO09im63SzQa9QXtp2Fvb4+9vT263S6GYRxIRgqHw6TTaTqdDqurq+cmgu52uzQaDd+AwSMUCuE4DrPZjOl06gvQA4EAuq77SdTeedW2bWRZJp1Os7CwgCzLBwTt8XicSCTiv34kEkGWZb+B4ypE3YlEAl3XSafT1Go1P7XorcxmM8rlMrFYjGAweCHNFILniUQiB/aZF6HX61Eul/3089XV1eeazYbDIXt7e5RKJQA/QeGtNJtNP4VveXn50MQITdNYXl5me3ubRqPhG2xcp2vGReKZeRiGgaZpGIaBaZqoqookSb5pRyaTIR6P47rusY3csVjsXOaNL8plCNDd2Qz33/5blEoFV1Gw19efX7+SJKRMhqGmoT96hK3r+4JCzyRBlrEfPED7wAdIfNmXXUsRt5rJkPqGb8D943+cwX/9rzitFmgagbU19Le97aqHJ7hirHYb+T/8B5Td3X3j77t3n7vXlgIBnJUVrFIJbXcXolGG/+2/Ef7cz72iUQsE14/A0hKR7/1eBh/9KJNodH+NoNNBsqz9a0UshrWygptMov6Fv0D0Pe+51PFFPu/zaBcKWM0mWqXCVNfhMBHccIi2u8ssncaNx4m9730o0SiGaWL/H/8HTrOJ0moRaDb9p9i6zmx+Hjebxf26ryP5tV97iZ9McBZS3/RNtJ+a6SilEsHtbRwvVMKyUIZDZMvCBSxdZ/rOdx5UeUsSJJPMYjG0N99ELpexf+zHcD78YeRT3m+fJy+TgD5+4w2Gf+kvIVUqqO02crfLJ9JpHEXBjsUgmcQtFJiWSgSuaYrbwsICc3NztNttTNM8IEA/KYm0Xq/T6/WoVCrk8/nnaiuu6xIOh5mbm6Pb7RKJRGi1WoemMntC51dN5HHVeDWkQCBwrLjZE4YYhuGbzJ31Hv86oWmanzI9Go1OFLZ5NdRAIPDcsTGdTplMJmiadqIg1ltrUVWVVqvFdDo9st7YaDR8EdRp0peXl5cZDAbYtk2lUqHT6ZBMJgkGgziO44vGo9Eoi4uLFAqFFxLwhkIhXnvtNV84NBgM/MfC4TC5XI50On1t1m48E9LTGtJaluXX068CRVFu7LE1HA7p9/unMp1UVZVQKOSvEXpGk55pbiaTObYO1263SSQSvrnqWZAkiY2NDSRJQlVVTNOk0+lgPu3T0XWdpaUlotEouVxOmGJfANflPCG4XjSbTWq1GqPRiPF4jG3bqKqKrutEIhHm5+eFGcQlEwwGefjwIc1mk2g0Sj6fP2AgEY/Hb4RJh2cgdNprvCzLWJaF4zgXPLIXxxuXqqo35lzqGRU0Gg2/j8PDu9amUqlXZh1OIBCcDaHsEwgEAsHxSBLq+9+P+/gxtNvIxSL26irSWyfNrotcLIJt4+bzyF/+5YeKCaJf/MU4n/d59P7Nv8H6d/8OeW8PaTbDicfh3e8m/MEPoj9tqhWcD+NPf5rRz/88brsNqoq8tET0/e9HfQXShQSCm4QSjSJ93dfh/MN/iPTkCYFHj7AyGZxMBknTYDZDarVQ222cQABneRnpwx9GuYIGUoHg1iFJpL7+65l+2Zcx+Nf/Gus//kfkVgtgf47ynvcQ/qqvQt/YuOKBCgSCq8Du95lubeGOx8jxOMGNDWGW9TI4zmeU2KdBkva3v+S0g7NwnJDGtuGP/BH42MfgZ37m8G16PXjzTXjb21J+k0e73fYbc4LBoC80Oi/6/T6TyYTZbHZiegfsiwvL5TKO4zAajW5sI47g/PDSvb1Fymg0SjQaZTqd+kkhXjOToijU63V/Qfw6LmDmcjn6/T6tVotms3mkMNYzblhcXPRTqgE/neVZMwdVVYlGo/6ibjwe97f3Ftm978+yLAzDIJfL+elG8Jnmglqt5jcXzGYz1tfXfad7TdP8xstgMEg+nycUCpFOp1ldXb12C+CeGDaZTNJut/2mz8MYj8f0ej0KhQLpdPpUzQm1Wo1yuczu7i7j8ZhUKsXi4iKKojCbzeh2u5TLZfr9Pq7rHjBJOAuO4/DkyRNfKNrpdHwDEdd1/eZtx3EOnDtlWSaRSPiJLp6zvLdPeNuf1DgiSRKKouC67qUnBD3L8vIyk8kEy7IoFoskEgmSySSapmFZFqZpYhgGuq6zuLjI4uKicNK/AdRqNbrdLsPhkPX19UMbo8PhMCsrK2xtbdHtdqnVakcK0D0Dj8PE5x5eenu5XCafz5/KGOS2EAgESCaTSJJEKBQiHA4zmUz8hHNN09B13U9JK5VK6Lp+6jSxy+IyBOi9n/95ZMNA7XaZLi8fbZ4MEI8zSyZxNQ27UED7tm9DCgTQlpYIrq+fz4AuGEnTiH7+51/1MATXjN6P/zjSYIA6GDB+2rx/KJKEvbiI+pu/CZ0Oo3/1r4QAXSB4C8H1dbR/9s/o/czPYP30T+NubvqPOZkM0pd9GdGv/Eq0U4jPzh1JIvhN38T0ox9lZlkEt7exIhFsL8ncspDbbbRej1kigVMooHzDN6A8nW+lvv7r6T98yORf/AvcX/91rMlkv1ilKBAMYr/znQQ/9CGiX/RFl//ZBC+MpGmkv+d7MP7e38P92Z/F7vWQDGP/N5VlXEVBMk0kx2H8jnccabAjKQrW/DyB3V2cbpfez/wMia/6qkv+NMcnoFvW0Y+N33yT0Td+I3KpRKBcBllmlkiQj8WQp1PUVgueCkv7f+pPEf3+7ydwQiryVSHLMtls9oUM2lzX9e+vPDO4w7ZxHMe/N+t0OoRCIfL5/KF1omdNDs8Lr458E9OEL5rJZMJkMqHf758q0TyZTFKv1xkOh5imeStq8969bTKZ9O/7j9s/DMMgEon4982H8dZk9MOQZZlUKuXfexeLRRYXF4lGo/77j8djms0m/X7fNwQ+jblbMBjk/v37vPnmm4TDYQzDoNlsYlkWkiQRjUYpFAr+f71EyhdBlmUymQyZTAbHcbBt2xd6Xwaz2Yxms8l4PPaTM5PJ5KHfTywWIx6PUy6X/XEehZdgv7CwcCVGejcdz/T3tMI+RVFwHMc/5+fzeb+uXS6XWVpaOvR4rFarjEYjQqEQ3W6X3/iN3/CNVjOZDKlU6tT7oizL3L171zd1ffa49lJac7ncsTVEgUBwPriuS7FYpF6v02636XQ6/rXLdV0CgQCpVIp+v0+hUDjU0EhwcSiKwtzcHHNzcwyHQ9/kWtf1FzKyuUoURUFRlAOm7McxnU4JBALXZg3Rm/80Go0DxjqxWIx8Pk8ikTj3+5zhcEij0aDX6/nzKE8sfpZ7AV3XWV5eplAoMJvN/D6O6/IdCwSC6484WwgEAoHgROIf/CDtX/gFmM3QtrZQPv1prHQa52mxT+71UA0DXJfZ+jr2O95B6kMfOvL15HCYxIc+BMdsI3h5Bv/tvzH+gR9AeeMNpH4f6WnTK9Eo/R/+Yex3v5v4N34j2hmK2QKB4GJIfvjDGJ0O9sc/jtNooLZayPW6L7xyAgGsXA43l8P5mq8h/T//z1c9ZIHgVhFYXCTwkY/ARz6C+7Sr5Swi0/EbbzD8sR/bVxFOJrjRKPJv+21EP/hBcd0VCG4Q4zfeYPAv/yXyf/7Pn5lLA8NMBun3/B5iX/M1wtTpDLjJJG4wiGIYOCcJ0V0XeTjETiRwT9GAddWcJKRpNuGbvun4bba24G1v+0xjxWkaz14GrznJSxg4CVVVfXfq6+g2Lbh8wuEwkUiEer3OeDz2BdOBQODQBW/TNEkmkyem2VwV2WyWer1OoVCgVCoxmUzIZDL+5/IE4q1Wi1QqRSKRYMFLUGV/8VxV1ecSXTKZDL1ej36/T61WI5vNEgqFsCyLYDCIqqqMRiP29vb8RPW3Nh+qqsrS0hILCwuYpum/RyAQQFEUms0mg8HAb+j1HO+vmyDxWfL5PP1+n16vx+7uLgsLC8+lDfX7ffb29ojH4774/iQsy6JcLlOpVJjNZty5c+dAKpJnipBMJikWi34y0jve8Y4zNQe4rsvjx49pNpvs7OzgOA6JRIJsNst0OuX1119nb2+Pfr/PysoKiUSCu3fv4jgO/X6fdrvN1tYWKysrSJLE/fv3/UZBRVGwjuu4f2YMXkraVSUEwb5Ryfr6OpIkEYlEMAyD7e1t/3Fd15mbmyORSLC4uCialG4A0+mUbrfrJ3Yd18zkNYB723oNQh6z2Wxf/NHvkznFPNpr/B4OhwwGg1dGgJ7JZDBNk3a7TTab9dMKvfO+qqrE43F0XafVavlNR6f5Ts/KJz/5SZpPk1JrtRqqqvJbfstvOfY5lyFAt5+ufQCHp8C+dftkEq1Ww3FdAhsbBFdXz2cgAsEV4vz6r6MaBnYggHRC46GkKFjxOHKng/OpT13SCAWCm4Ws6/sC3K/6KmzTxOp0kEMhtGz29GaKF0Tksz8b99u/ndl3fAeTRAK50SBQLiM5DkgSVjTKZG0Nkkmkj3yE+Pved+D50S/4AqJf8AVMtreZ/Pf/jjMYIIXDBD/rs4T57g1EUlXSf+7PYf/RP0rvp34K69d+DYZDCIVQfuZnQJJwAwGkk8QIiQSUy0iGgfUrvwLXTIB+pL+a6zL8K38FuVQiWCoxmZuDfN4/Th1gsrSEXK0SLBaZSBK97/gOMt///ec+/qui3+8znU7pdDoHalPP4tWphsMh2WyWWq3GbDZjMBgcKu6cTCbnJkAYj8fU63VarZYvivSSrrPZLLlc7th091eBZ80HT/NdeKIdzwDztpDL5ej1erRaLer1OnNzc4duZ5om3W6X5eVl0un0c/tpIBAgGAxiWRaTyeTEmmgwGCSXy6HrOtFolHK5jKIoaJqG4zhMJhPC4TBra2skk0k2XuBa6aWUP5tU6iHLMul0mlwudy41clmWL1V4vru7S6fTYTQaMRqNfFFiJBIhFAqxtLR0QEScy+VotVqoqkqj0TiyFui6Lo1Gg3A4jK7rL2TIIdjHO+d7BoYnMZ1OD6TNp9NpUqkUtm2zu7vL48ePSaVSRCIRvz7nrY0EAgG63S6SJNHpdID9Y6rT6RAMBllZWXmh9c1EIkEikcC2bb/+pWnalda5BYJXjXK5TK1WY3d3F9u2yWazxONxf42q0+n465CwX6MW5+qr4bqusZ9EIpEgHo/7JjZvXQ9+Fu+exdsPr5p2u02xWGQ0GmEYBoPB4IC5ebfbJRqNcvfu3XO5x7Esi62tLUzT9NfRPQF6LBaj1WoRjUafW/8+LZ5xjEAgELwoQoAuEAgEghORVJXk3/ybdD72MQgE9hN4PVEk7AsiMxncbBb7tddI/q2/hXzMzYHg4jF/5mdw/ubfRK3VUBsN5NkMR9PAcZBtGyschm6X3m/+JpG/+3dFo5VAcF2QJFIf+Qi9hw+ZfvzjuG+8sd+taVmgqhAK4dy/j/ahD5H80i+96tEKBLeaswjPZ9Uq5l/7ayi/8RvIvR5ytwuui6souJ/4BIOPfxznve8l9c3ffHLTj0AguFK6/9f/hfsP/yFKt4vSbKIMBkiOg6soWIkETrmM+dM/jf5d30X4Xe+66uHeKNQv/mLcT34SpV5n1ushHbdg1OuhTKdY2SzK7/pdlzfIM3Iea31PdT2Xhte05rous9nsxAWq6XTqJ16Lxg8B7Au2K5UKoVCIZrNJoVA4clvPLT+ZTJK7isS4UyDLMvfu3fOTQhqNBltbW6iqiiRJzGYzAoEA8/PzpFIp1tfXDziMx+Nx4vE4xWLxwDHlpUu5rkur1aJarSJJEu12m7m5OdrtNrPZjFgsxuLiIgsLC0em+MiyfOhjNzEFJJPJ+Ank5XKZra0twuEw4XAYx3EYDAZMp1OSySRzc3OsrKwQOoVqsdlsMplMME2TtbW1I89tnhi6Xq+TTqcxTfNMKejNZpN2u83Ozo6f7O01fpbLZXRd9//Ztu2PzWusi8fjVCoVdnd30TTNT3wKh8PEYjE6nQ6ZTOZYcbxpmsD+fnDVzRjpdBpd16nVasTjcWazmd8Y4Qln5+bmrnycgtMxGAywLIvRaHQqw4BEIkG73cayLAaDwYEmFs+85rRpTN58w7btV8r4xru2eglhq6urh143B4MBjUaDubk5wuHwhV4H3v3udx9IJgmFQgyHw2OfcxkCdHc83hfdyfLpRIGqivQ0Dc8dj89nEALBVdPvI9k27ilrja6mIQ+HyCccwwKBAJR4HOWazVmjX/AFTP/pP2Xwoz+K/XM/h9Pvw9NroavruF/0RYR/3+9Df/DgyNcIrq0RXFu7vEELLhQlFiP54Q/Dhz8MgDub0f2VX0Fqt7FPY0gnSdiBwP5adK93waM9nLMkoPd/8ReRSyW0SoVpLgeHCFYlWcZdXGRm22jlMnzqU4w//Wn0t73tnEZ+tViWhW3buK57ZK0kGo0SjUYxDMPf1rbtQ43uLMvyEy3PUht5llarRbFYpN/vYxiGX/uRZZlEIkGv16Ner7OxsfFKpxw/Kxo+jaDcSyq+TMHxZZDJZGg0GiwvL7O7u8t4PCaTyfii1/F4TLvdptvt+qaGh9Un0uk05XKZUCiEYRjH1jAsy8I0Te7fv084HCYYDDKZTOj1egeSynVdJ5VKsbKy8sLrIc8mlY7HY984MhgM3si1lclkwqNHj+h0OtTrdUajkS8QnkwmKIpCKpViPB6zsrLi1zGi0ahf897Z2cF1XXK53AEDgclk4r/m2toa6XT6lTeoOAtenXl3dxfLso41ExmNRkwmE78+Dft1OM9oQVEUOp0OhmFQf9ofrKoqjuOg6zrD4RBFUej1er4Q0jAMGo0G6XQay7JYW1t7YbNEb81SIBBcLtPplFqtxt7eHgDr6+sHjkVPbO6tf9ZqNTRNO3HdSiB4lkwmw97eHpFIhGq1ysrKyqFzWtd1qVar6LpOOBy+8p4CwzDY2tqiWq1iGAahUIhUKoUsy0ynUxqNBo1Gg0KhgOM4PHjw4KUMvSzL4o033sAwDMrlMpZlEY1GUVUVy7IolUqoqsrCwgLT6ZSHDx+KeZNAILg0hABdIBAIBKdCiUZJf+/30vuZn2H2kz+5L4p82qiDJGHfvYv6/veTfv/7zyTYEpwfw098Audv/S3kYhHVMJhls7jZLHg3GYPBviP61hZT22bwzd+M9kM/hHxDndEEgttI7L3vhfe+l/HrrzP+1V/FHY2QdB39sz/71iyKCwS3jeneHv2PfARlawutVEKezbDCYVxVRRmNUBoNrEgERiPalQrpv/N3kEQBUCC4lnR/9Edxv+/7kItFNNNkFosxXV7e74SbTFDbbbRHj5j2+0w++lHkv//30e/fv+ph3xii73sf5j/9p8wSCQKlEtO7dw835ZhO0cplZokEbjRK/Cu+4vIH+4Kch5DmiHCPCyMWi6Hrup9McNICXrfbJR6P++nBAoGmaaRSKUajEdvb21SrVfL5/HMLxt1ul2q16qfKvEj6xWUTDAZ5+PAhpVKJcDjMZDI5kObiifyWlpaeE/vlcjnq9TrBYJBarXZAkJ/JZPwG0UQiwdbWlv89zc3NMT8/74uXX6VU6NXVVVRVRVEUxuOxn6AjSRKxWIxUKkUgEGBlZeXUDWutVstvAjjpXBWPx6nX65imSavVOlOTdb1exzAMJEk6ID63LIter+efO71GWK8B+9nmvoWFBTY3N+l2u36jXj6fxzRNX+B+1Oe3bZtGo0EikSAYDL50o/h5EA6HWV9fZ3l52Xfml2WZSCRyYgLVTcF1XSaTiS+mvq1pAV6DO3Cqphlvm2ef5+E1rSmKcqo0Jsdx/IbZ80jguylomsbS0hK2bVMqlXj8+DHJZJJEIoGqqkwmEzqdDr1ej2w2SzqdZmVl5aqHfQDLOlosBOcnQJdisX1RuW3jzmYn1lmkyQTX2w+PMHoRCG4coRCuoiA/TYk7CcmyfKGqQCC4mQSWlgj8mT+D88f/OOM33sDp9ZBCIfQHD1BuoDGZ4HyRVHX/PC9JSJaFe4rnyLaNI0lwRfdqx031PU2u6x6cXw5//CcJtNtIjoszN8dxshdnfh7t058G02T4r/81+l/6S+cy7qvmWbHPUYZd8XicWq1GOBz2k8glSXpOKOSJPDRNIxqNvpTIo91u+wINLwlwaWnJT+XtdDpsbW35Ro33798/YO74KhEMBlFVlWg0Su8UBhCmaSLLMqFQ6FZ9Z281BG21WpRKJVzXRZIkXNclEomwvLxMIpHg7t27hyZmBgIBkskk2WyW3d1ddF0/1MTTu9cOBoPEYjEePnzoi9ZN0/SF4uFwmGw2ey6CnreO13EcOp2OX1fSNI1kMnltxUOO47C5uUm73aZUKhGPx/1aNux/p15NczKZAPv7t2c+eefOHT/VulKpsLm5STgc9s8Lo9GIUCjE2tqaL/gXvDi5XI5Go0EgEKBarbK0tHSoMNRxHGq1GrFYjGAweKDmrCgK9+7do9Vq0Wg0yGazvoHJeDz2jXVTqRSLi4sH1kdc16XX61GtVrFtG0mSCIVCNzapVyB4lfASqQeDwXPi82fxDLrL5TK5XM43UxYIToOmaeRyOWazGdvb2+zs7JDL5Q7Ma0ejEfV6nel0yurqKqlU6tik9IvGsiy//8E0TVZWVp6bh+dyOWq1Gjs7O8iyTKlUYu0lTP+2t7cxDINisUgikXjOuMeyLJrNJru7u0iSxJMnT3hwjAmhQCAQnCevzmq9QCAQCF4aSVGIv+998L73MSkWsZ46nin5PPpTB0TB1TP6wR9EqddR222m6+vwVsfgSAQnEmFWqRDY2WEaDtP7yZ8k8Qf+wNUMWCAQHIn+8CH6w4dXPQyBQHASrkvvW74F5ckTAtvbWKkUs/l53/zFAazhELVcRtvcBEnC+Dt/h/RHP3q14xYIBM9htVo4P/ADKLu7qIMBk7t3D8Zax2JY2SxWq0WwXGaiKAz+9t9G/0f/6OoGfcNQolGkr/s6nB/4AaQnTwg8eoSVyeBmMvvnzdkMqdVCbbdxAgGc5WWkD38Y5QYkobyIkOb1178V1/2MQECSNB4+/A4uO9wzFAoRjUZJpVLU63USicSRArp+v+8vPGezWeFoLvBZXl5mOByyurpKqVSi2+36+5LXfGZZFrlcjmw2y8bGxrVP6dE0zRfPtlqtA82AqVTqSFGz19i4uLhIsVikVCqRy+V8wW0qlULTNJ48eUIsFvNTQFZXV5mfnyeTyVzbRsOLZGlpiVwu5wutZ7MZkiT5DXCZTOaFkk9msxnT6fRUzW1e4+50OvUbIV+EwWDAaDSi0+mQzWYP7Nu9Xs9volxcXERRFOLxOL1ej0QiwWQy8fcNSZJIJpN0Oh3S6TTT6ZRUKkU4HGZhYYG9vT1s2yadTh9oNBgMBtRqNRRFIZfLMT8/f63Oz6qqXmvDibMwHo9pNBq0Wq0DCWWhUIhcLuebTdwWnk0emk6nJwrBPWH5YYlFqqoSDoeJx+P+MXMc3W4XRVGIRCKvXCre3NycLw4xTRPDMDAMA8BPYltZWSEWi3Hnzp0LTT8/C8eln8P5CdCDv/N3Mv34x3FUFandPt7NyXVR2m2sZBJneRktnz+fQQgEV4z89rfj/n//H0qthjUaHXuAuY6DappMFxZw7t27xFEKBIKLQNZ1wu9611UPQ3DdkCSc9XWkvb39sILFRTjuHnE4RJ5McBMJ5Lt3L2+cz3Dc7f6jR/A93wOf+AR0u5/5+x/7lRoPOxaQp/EoRjxhMT8/RZZcTFOl3tCYTWUCQYdMRiUTDiObJvbjxxf9cS4NXddRVZVAIEC/3z9UmKEoCtlsFsuy2NzcxDRN5ufnD9R/h8OhLzpaXV0lmUyeWSxo2zY7OzvU63V6vR5ra2sHxhUKhfwE9HK5jKIoFItF3v72t5/5/bxkbE/sGIlEyGazN8L8TpIkstksyWSSnZ0dBoOBL9h9K7Zt+8aJoVDoyO1uKpqm8fDhQyqVim8I6tVjA4EAwWCQVCrFwsLCsSKkQqFAv9/HsixfKOQJl2zb9k0hFUVhZWWFbDbri4i8GuRFYts2lUqFZrN56Gf06spXKbQ6jHa7Tb/fp1Qqkc1mn6vnKIpCOp0mEomwvb1Nu91mb2/P3081TePBgwdsb28TDocZDAb0ej1c1yUcDvti9nQ6zerq6q2qq10mXiLr0tISxWKR3d1dcrncgbWMfr9Po9HAtm0KhcJzojb4zLkpm80yGo2YTqe4rsvOzg6WZVGr1VhbW3tuLVGSJOLxOIFAgO3tbWKxGPV6/aVEeALBcTiOQ7vd9g2kVVUlkUg8Jw51XRfDMGg0GoxGIxzH8bfN5/OXbrjuJSabpukb93opz1dlMGMYBp1Oh3A4fOI1KBqNoigKpmnSbreFAF3wQhQKBd+splqtsru76xsQW5bFbDYjGo2yurpKOp2+8muId59kGMah4nPYv/7Nz89jWRb1ep1QKEShUDiTqfJ4PKbb7VKpVIjH4ywsLDy3jaqqvpH93t6eP7e6TQZVR9Hv9w+c873gDIFAcHmII04gEAgEZyK4ukpwdfWqhyF4C5NiEeUTn0BtNpml08+Lz5/BnZ/H6XaRmk3sn/5p+NqvPX7xUSAQCAQCwaH0f/EXUZ48QdvdZZbJ4C4tPb9ROIx19y7K5ibK7i7uz/0c9h/7YyjXIJ1QIBB8ht6P/zhSr4fW6TBZXz8oPn+WTIbpeIxWrcLrrzN+/XVhGvMCJP/QH8LodrE//nGcRgO11UKu1/fvR1wXJxDAymZx83mcr/ka0v/L/3LVQz4VL9Kj+fjx9+A4n1HmyHLoSgToAPPz8/R6PQaDAcVikbm5OWKxmC9gtG2bTqdDo9HwF79fJgVHcPtQVZX79+/z5MkTdF3HNE1M0/RTrJPJJMlkEl3X2djYuHYiueNQVZW548Rsh7C2tsZ0OkWSJCqVCk+ePCEUCqGqKrPZjPF4TCKR4OHDh8RiMe7du4csy6+k8PxZAoEAi4uLLC4unttruu5pst5ejvF4zGw2w7Ks5/Ztr1nCE9MDfqKSbdtMp9MDDdFeIhnsi+gDgQB3797FcRxkWaZWq9FutwmHw8iyzGQyYTabEYvFmJ+fZ25ujrwQVF4ojUaD3d1dBoMBhmEwGAxwHAdN0/wm+lqtdmQa100kGo2iqiqRSIRut3uiEKHT6RCJRPwUt7eSy+UwTZNms0mr1TqyuduyLFqtFslk8lY215+GhYUFotGobxJk2zaO46AoCqqqkslkyOfz13JfO0mAfl5DDv/W38p4bQ2rWkVrNJgmk0hHCU3qdWTLwspmkT/wAbEWIrg1RL7yKxn+y3+JFQ4jVyo46+tH7t9yrbb/P1Ipgl/1VZc4SoFAIBBcJsqXfznuG28gNRrQbsNRgkrXRa7VsMJhiESIXtG14aR+8f/0n57/m+ZMkXGYSQFsG4y2yngkk0rN2Nv7zHxwPJIpl4KE5SB60EV6Kna4Dei67huLtlotUqnUoeZ92WyW8XjsC9a73S6lUolAIIBlWX49w0sefhmRR7vdZjKZYBgGhULhyHuVWCxGNpul2WySSCTo9/svVC90XZdKpUKtVmM6vhDf+gABAABJREFUnfoCdFmWiUQiVKtVEokEq6ur177e5olDk8kk5XIZ4Ln738lkQqVSAfZ/z3w+f63MB88LRVEoFAosLi6eOR08GAxy9+lCja7rtNttyuWyXyMMBAJkMhk/Kf0yk7ZnsxmPHj2i2+3SarUwTdN/zBNiZzIZTNNkY2PjSoz4er2eb8YK+99XOp2mXq/T6XRQVfVYM0HPTNQTJA6HQ7+OpGka9+7dYzQa0Wg0/JqaoijEYjFyudyR5siC0/Ps2kStVmN7extN03xhn2VZxGIx5ubmyGazLB3W1/IMoVCIUCjk16ENwyCVSh37W+m6TjwexzAM4vH4mUV4AsFROI5DuVz2z1fD4dAXI0YiEcLhMIuLi/4c48mTJ76o89l6fjwep9lskkwmWV9fv3DzC8dx2NnZodVq+ePx5i/RaJRWq0U0GmV9ff3Sz4ez2YzZbHYqMb4kSb6ps2VZlzA6wW1CkiQ2NjYIhUIEAgGm0ym9Xs8/FmKxmD+fKBQKV25K02g0MAyDUCh0osA7m82ytbXFZDKh1Wq9cH+B937D4ZDpdMry8vKJ79fpdHxzmdsqQHddl2azSb1eZzQa+fNUVVXRNI10Ou2bGQkEgotHzOoFAoFAILhFjP7zf4bhEGU0Yra6yrFLDpKEnUqhtlq4pRKT7W2C6+uXNVSBQCAQCG4Nk5/4CZR2G8l1cRYWjr7+ShLW/Dz61hZ2v0/vJ36C5A0RVQoErwruf/gPKM0mtq4fa+YE4ObzyK0W9HoMf/InhQD9RZAkUh/5CL0HD5h9/OO4jx7tq1Qsaz/yJhzGvnePwO///SS/9EuverSn5gxrSM9xFQL0RCLhL2DV63X29vZQFIVQKITrugwGAxRFYX5+nnQ6zZ07d0Qj0A3HdV263S7NZvO5ZICzNnp5KSa9Xo96vY5pmjhPd2jPuT+dTl/5QvFloCgK9+/fZ2dnB13XGY1G9Pt9bNtG13U/xSaTyTA/P/9KfCeXjZcaNBgMTtzWcRyGw6GfzvKiuK7rN7Gepvn32d/7rQL5Z/+/91qhUIiHDx+ytbVFLBaj3+8zHA5xHMdPDgsEAszPzx/qhC84P5rNJsVikUqlQrfb9Zs1FUVhMpnQ6XRoNpssLS3hOA4PHz68FddLL8W+1+tRKpV8QfhhjEYjTNOkUCiQSqUObS5Np9NUKhUWFxfZ29vDsiwymYy/reu69Pt9arWa39jspTm8isRiMWKxGNPplNFohG3bfjPlYeKS68JJAvQzBio+jySh/aE/hL21hTMcEtzcZDo/D6kUPD3fupMJSr2OZhhMlpdxFhZIvv/95zQAgeDqCSwu0vv8z8cdDAg+foxVLOIsLcEzAh3XcZCrVQLN5v5xsLxM5Au/8ApHLRAIBIKLJPblX07nh36IWT5PoFxmCpBOHzAocW0bpVxG7feZbmxgv/vdaOdR3DwDZ5nWjtQolqQRcMef+dtIZjQKYrnPF1itno2bDOLesqb8fD6PaZp0Oh12d3cPFfnZto3run7Ss5d2+6zg6bxEHs1mk06ng6ZpJwrKU6kUzWaTfr9Ps9l8IQH69vY29XqdWq1Gr9dDVVWCwSC2bdNoNNB1nfn5ecbjMQ8ePLjWIvRAIEChUMBxHGzbZm9vj3q9TjQaRZZlRqORL+JdXV31jQQMwyAYDJ45rf46I8vyS6WpRiIR3v72t1Or1YhGo0ynU2zbRpIk3ywvn8+TSqXOcdTH4zgOm5ubtNttdnd30XWdQqFAJBJBkiQ/WXN3d9cX1Tx8+PDShDT9fp9ischoNKLX6zEe759bA4EA1WqVnZ0dhsPhqYxDk8mkL546zMgwFApdqvD/VUOWZe7fv0+pVELXdSaTCb1ez0989s75+XyehYWFU5tZ9Pt931z3NPtBIpHwE9O92vt54l3DbqMZh+B4LMvizTffxDAMms0mvV4PSZJQFAXLslBV1TfASKfTGIZBq9WiXq/7x4BXz/cMeZaWlrAsyzeLvggcx+HNN9+k3W6zt7fHaDQiHA77hkClUslPNZ5MJjx48OCAefFFI0kSkiT567on4TiO/xzBzcJxHAzD8K8N3v3AZa7hS5LE0tIS8/PztNttX4CuKAqRSOTAetFV4jgO0+n01NcxXdfRNI3hcMjopAWSI+j3+/R6PcLh8In3MN45zQuauI04jsOTJ09otVoYhkGn0zlgfOF9fsMw2NjYeCWNrAWCy+bqz84CgUAgEAjODbfTQZpOcWX56KSPZ7cPhZCfTshtwwAhQBcIBAKB4IWRf+M3ULpdrHgc6YSCrBSNYmsaUqeD9YlPgBCgCwTXB9dFrlaRhkPs0yQLaBq2riMNBrh7exc/vltI7Eu+BL7kSxh96lNMfu3XcAcDpHCY4G/7bYRee+2qh3cmfvfvhn//78/+fNs+v7G8CHNzc75LcDabxTRNptMpsN8oEovFCIVCrK2t3Vr35FeFwWDA1taWLxIcDAa+AD2ZTFKtVsnlciwvL5+pccATygH+wvWriKIorK+vs7S05DfTet+Hl/KkaRqz2eyqh3or8dKCWq3WgaSdw+h2u0iSRDwePzKJ+ThUVUVVVb9Z9NmGaU3T/Cah0Wjkp8XIsowsy8+JR/v9vt9Y+qxwORgM8vDhQ4bDIY1G4znjiEwmc62EqLZt0263aTabflqVJ2LO5XKX2jh1XsxmM3Z2dqhWqwwGA9bX1w80AUejUTKZDM1mk3K5jCzL7O7usrGxcYWjPj/m5uYwDINkMsnOzg5zc3PE43H/HO84DqZpUqvVSCaTvjj/MGRZ5t69e/45sVarYRgGuq6jKArj8Rjbtkkmk+Tzeebn5890bF5XptOp3xz5bFPVScdGIBC4UYYGJ/VXnSJM59TEvvRLMXZ2sH/4h3H29ghUKlCpYAeDSI6DMplgB4NM1tZwCgXCf+NvIN9CkYbg1Sb5sY/R3d1l6rpoxSLapz/NLBrFDQSQbBvVNHFlmcnKCu7yMuHv/E6kazR3EAgEAsH5IodC6N/2bUw+9jFmQGBvD7dex4rHcVUVeTJB7XZxFYXpnTs49+6R/OhHr2y8Z7kkPU78Ngr9N1gdvoHiTLHlz8yVf7fx80z4jHgmiExJTTOJLKJ97ueex5CvDalUimw26ydqbm5ukkwm/frtYDCg2+2iKArvfOc7yWazpNPpAymDnsjjPOoKk8mE0Wh0KjG5oiiEw+EDKXanoVKpUK/XKRaLyLLM8vLygXq1d89VLBZ90e/b3va2M32eyyKZTCLLMsPh0E8yHI/HuK5LMBgkm80yGo0wDAPTNHn99dexLMuvM25sbHDv3r1rVRu6ajRN89PU+/0+lmX5ia1XkY7YbrcxTZPd3V0SicRzRnu6rrOwsEA0GqVUKhEMBqlUKqxfQg9ft9vl8ePHfi3Ptm2/ltrpdPxk0m63e6r6jFcr9dK2BZePLMusrKywuLhIq9V6Lm3+LCJD27axny5gnsbUwxMOOo5zakHrSZimSaPRoNvt+kauuq6Ty+WuXX1ccHE8efKEdrvNzs4OwWDwwDzAsiw6nQ6NRoPBYMCjR4/QdZ3hcMj8/DyJROLAuqdlWVSrVYrFIpIkUS6XT0wcPiulUgnDMCgWiwSDQe7cuXOgFmxZFq1Wi1KpBMDjx495+9vffiFjOYxgMIiu63Q6nRO3tW2b4XBIKpW6kWs9ryqu61KtVqnX64zH4wPr1t78I5fLsbi4eGnGAoqikMvlyOVyl/J+L4p3/fKuoadBUZSXuvZ5zz2tAN+bc9lX1WR0wWxvb9NoNNjZ2UGSJLLZLLFYDFmWmUwmvoGTtw89ePDgVhp0CQTXCSFAFwgEAoHgNqFpIMtIrguue8DF+lAcB/fpNqcRrAsEAoFAIHgLros0GoFt456mg1qScDVtP+X3jI6XAoHgYnAdZ/+YhpPn0R6StD/vvqUF/csi9NprN1Zw/lZeVh91FQnoHplMxneDb7fbzGYzJEnym9xipzFmEFxr+v2+77Bfq9WQJOmA2//W1hbhcBjLspjNZty5c+elFplfVfH5swQCgVMlggjOl0wmw97eHolEgr29PVZWVg4Vbg6HQ2q1GrlcDl3Xz+SM7iWnx2IxOp3OgSbrWCyGpmmEQiFM0/T/G41G0TTtwCK4ZVl0u13m5+dJJpOHNhd4aVfXGa+JyksE98TygUDAFyjn83kKhcKNSsdoNpuMx2M6nY6fOHYY2WyW2WxGo9EgEokwnU5vlGj4KMLhMGtra76ZQK1Wo16v+0noXppDJpMhm82ytrZ2bJOHrus8fPiQYrFINBplMBgwGo1wHIdoNEo8HicYDLKwsEA+n7+Uz3jReIKQdrvtJ4p5go9YLOaL99fW1m5Fw+x4fPzj5ylAB0h9/dfTzWaxf/iHcRsNMAykpyYvVjQK0Sj2O99J9KMfJXiBSWvD//E/mPyX/4Lb7UIwiLKxQez3/B7k8/7AAsFbUGIxEv/gH9D51m+FcBhME9kwkJ8aVk+XliCVwllZIfKd30lQmFELBALBrSf8uZ8L3/3djL/923EzGaRmE7nXQ3JdXFVlWihAMon9treR+Ot/HSWRuLKxniXc7n8svI/fUfs3jOUwc+Mie6G7R9b0JVwGShw1ECX9wQ++5GivH2tra37yp5f+ZpomsF+Xmp+fJx6Pk0qlWF9fR5blCzP5cl0X13VPfb/vbecJCU/CcRz/flSSJFZWVp67f/JqcYqiUC6XCYfDmKZ57dPw4vE4GxsbdLtd39DPcRxc18UwDGazGVtbWxiGwWAwYDaboSiKbyj6xhtv8Hmf93lHmsFdR7wETk8cq6oq8Xj8XH8rWZavxW/faDTodDooinLsbxSLxUgmk7TbbWKxGIVC4VRi37MymUx48uQJ9XqdVqtFPp8nkUj4x5XjODSbTUqlEp1Oh729PdLptF8POgzXdf16h1gfuFpUVT23c4KiKP5+MZvNThSdeuuL57EfWJbF48eP6fV6mKZJt9v1TTjC4TC9Xo+9vT3W19dJXOF8RnDxdLtd38wjGo0+t/amqqq/nv3rv/7rTCYTJpMJn/M5n3PovqGqKktLS+zs7FCv19F13Z9DnCeWZdFsNqlWqwQCgUMNwL3jVZZl9vb2iEQilzp/yWaz/hyk1+sd2xNgGAaKohCNRslms5cyvtvKdDql0WhgGIZvluP1ZZxnIrnrujx58oRms0m9Xsc0TWRZRtM034ghFosxmUwYDofcvXv3Rq3fXRSKoiBJEpqmMT5pwYP9edNkMkHTtDMnuHvXzdO8H+xfbzVNuxVrSm+l1+vRbrfZ3d0lEAhQKBQOHBOhUIhQKEQ8HqdUKhEIBCiXy9y7d+8KRy0Q3H6EAF0gEAgEgluEeucOVji8Lyo3DEinj91e7naxw2FcTSNwgY1XAoFAcBuwWi16P/7jOL/4i0jdLigK7sIC2pd/ObH3vhfpjMUjwQ1HkvaF57KMZFmcpkVCsqz9SAdh/iIQXCskRcGJx3ECAeThkJMk5a7jIE8mWJkM0gnzbsGrw8tOB65SgA77TX/pdJq02KdvHbZt8/jxY39xOZ/P++k6HpPJhEqlwvb2NrAvOFxYWLiiEQsEZ0dVVQqFArZtUyqVePLkCclkkkQigaqqTKdTOp0OvV7PTwtbOWNdzGvc7vf7FItFOp0OyWQS2G9OSCQSjEYjKpUKxWIR13XJZrMkk0m/gcMbp6ZpxOPxa+v2fxLtdputrS0/0VrTNKLRKLIsMxqNDphcWJZ1KSlO50Wz2cQwDEKh0Inu+el0midPnjAej2k2m7fGhCKdTqMoCoFAgHQ6jWmafkpdLBYjHo+j6zorKyunavYMBoPcv3+f0WjkC/y9hvNUKvXcNeom4zgOjx49wjAM9vb2GI1GhEIhP52i1Wr5aWeTyYT79++fuUHpunCZCegeia/8Stz3vY/+L/wCs//0n3A7HVBVpOVlwl/xFegbG+f/pk/p/8IvMPmhH0J58gSp19sXv0sSbixG9wd+APe97yX5J/+kSF4XXChKPE7me7+X8ac/zfDHfgznE59AGg5xg0Hce/fQv/IrifzO33l6wz2BQCAQ3HjCn/u56D/6o/T+3b/D/jf/Bndraz+gQNdxPuuz0D/4QZK/43dc+bWh33/x5xiRVT6V/nw0e8zq8HUKw0c0gktMlMih21dC60R+x+9m6YKE11eJJEmsra2RyWRoNBokEokDgu5EIkE+n78UAZOmaQQCAd+k7Dhc12U8Hvvmfaeh3W4zmUwwTZNCoXCs0CKXy9HpdOh2u9Tr9WshQj4JWZbJZrO+mMuyLF5//XX6/T6//uu/7qeiR6NRdF1nPB5Tr9epVCqsra1hWRaf//mf/1y69nXDdV329vZoNBq+0MmrB0QiEXRdZ35+/taI2rzP2Ol0SKVSJwq6UqkUW1tbTKdTut3uhX4PtVqNfr9Ps9lkeXn5gLEm7O+TXhpqq9Wi2WzSarUoFApHvuZgMAA+IwoSfEZ82mq1mE6nwP750jNyvEiTgfPCM1T1EpJPErZ3u12i0ah/XJ8V27Z59OgR7XabUqmE4zgkEglisRi2bWOaJu12m0wmg23b3L17V4jQbzGNRoNut4vjOMde64LBIKqqUiwWicfjxx5jXqLu7u4u0+mUdrt97uszrVaLyWRCv99nfX392OtAJpOh3W5jmualzl/S6TSlUol0Ou2bOh92DjdNk2azyfz8POFw+LnrhuB0uK5LqVTy08g7nY5v3KHrOt1ul3K5zNra2rmc08rlMs1mk+3tbRRFoVAoEIlE/H3Rmwtsb2/7htLX3Qz7MpAkiUQiQSKRoFqtks/nj12z8oT90WjUXyd+Uby1tna7faLBtWVZDAYDlpeXb2WQRKPRoNfrMZvNWFtbO/K7j0ajpFIpWq0W8XicyWRyolGOQCA4Ozd79VogEAgEAsEBol/8xRj/4B9gJZOozSZWKnXkgqE7maB2u0xXV3E///NRbuFNiEAgEJwLrovxfd+H9GM/hjQYoDabSNMpyDJuOIzza7+G8f3fT/BjH9tv4hO8cjj37+Ps7qJWqzhLS3Bck/xggDKdYiUSyL/lt1zeIAUCwen4gi/AKZcJbm1hTadIxxT0pXZ7/3+kUmj/0/90SQMUXHde1lz4qgXogttLq9XymxIXFhYOXbAOBoOsrKxQLBZpNBp+s59wORfcRHK5HLa9bydjmiaGYWAYBrDfNBCNRikUCsRiMdbX11+qkWdubo52u838/DyVSoXhcEg6nUbXdbLZLK1WC1mWKZVK/rhCoZCfgtzpdFBVlZWVFbLZ7I1oin4rk8mE7e1tqtWq3wj+1uaj6XTK3t4exWLR/w1ugtjecRym0ymj0ehUv00wGPQb7j2B9m0hkUjwjne8A9M0DzTNBgIBMpkM8Xj8ha8ZoVCI5eXlixjutWFnZwfDMNje3iYUCnHnzp0DDTBe0kmxWARga2vrxqc0nKQ30fWLeV9JVYm9973w3vdezBscQudHfgT+0T9CrdVQ223k2QxHVZEcB1wXKxbDMQw6n/oUie/9XpQbeI4X3Cz0t70N/S//5asehkAgEAiuCXIoROKrvxq++qvBdXEtC+maCd3O6r30s/f+PPFJEySJ/GiHteHrTGUd6S1W0S4Sr6c+j/sf+rPnMNrrSywW8wV5s9kM4NLT8NLpNN1ul62trRMb/3u9Ho7jEIvFTm2G2uv16Pf7furmcciyTCKRoNfr0ev1XuhzXBeq1Sq1Wo1PfvKTOI5DOBwmm80SDof9e++1tTWKxSI7Ozu4rksqlSISiVxbEYrjOGxubtJut2k0Gr5QR5ZlP9E4mUwyGo0Yj8fHCp1vCt7xOJvNTiXI1nUdSZKYTqf+cy8Cx3Fot9t+2vpRx5QkSczNzdFoNCiXy7RaLebn5w81znNdl2azSSKRIBgMnll8dZuo1+uUSiUmkwmGYfi1wkAggGmaVCoVFhYWrr0RcDAYJJFIkEqlqNVqpFKpI0VxXs27UCj4hpZnZXd3F8Mw2NnZIR6P+wnRHtlsln6/T7lcxnVdFEXhHe94x403dhQ8j+M4dLtdut0uiUTiWBGo4zgEAgF/nabX6x1rKhuJRFBVlX6/z2AwOPc1C28uEgwG0U8oij47f+mfxaXpjMiyzOrqKrZt4zgO29vbvhD2WVPn0WhEPp8nnU4LgfJLsL29Tb1ep1wuMxwOCYfDhEIhHMeh0+nQbDbJZDJYlsWdO3dIpVJnfi/LsnzDIm8d8q3HTzQaJRKJsLOzQ7lcRtM0FhYWjhU/vyrkcjkMw6Ber1Or1Y68XntrPKlUCl3Xz7zOm8vlqNVqhEIh6vX6sXPher2OpmlEIpEbsdb6InjHgmf6ftJcIp1O0263GY/HtNvtaz+vEghuMmKWLRAIBALBLUJSVaTf+3tx6nWUN99E3t7GXllBessE3B2NCGxvY0cikEigf/VXX9GIBQKB4JrjurS+8ztRfvZnUYpF1H4fOxjECYfBcdDqdahWmeVyzP7iX6T/bd9G9D3vuepRCy6ZwFd8Bfav/RpStYpUq+EeVchyXZRqFSschkiE6Fd8xeUOVCAQnEj4q7+a8b/7d9i6jlosYm1sIB22gDkaEahWmWUyOHNzJL/oiy5/sIJryVMd1pkRAnTBRdFoNDAMg0AgcKxbupe0Uy6XyefzGIZx6iZQgeC6MT8/TzQapVarkUwmsSwLx3FQFAVVVUmn08zNzZ3Y9HMSgUDAF4tqmkar1WJrawtZlnFdF8dxiEajvPOd78RxHILBILu7u8B+U6mXRJbNZm9s0069XmcwGGAYBmtra4c21AYCAZaXl9na2qLVahEOh29EU4SXHOe67qkTuZ/97W8bz6Y+CE5mOp3SarXY29sjFApRKBSeE+kHAgGWlpbY29ujXC6j67qfkn5TOU6ArihwzTRPZ6b38z8P/+gfIW9toQ4GzHI5nHR6X9TlurimiVavo775JlPbpvMt30LmH/yDK08ZFQgEAoFA8IoiSddOfA6wuHi251lqiI+/47t5z9YP8FnN/4hu9YnOOjAtH9jOkRR+4rW/yp+xX402WUVRLlV0/izZbJZKpUIkEvHTMw8by3Q69Ws1uq6fWqRqWRaWZZ06LVjTNEajEY7j4LrujTLZdByHZrPJkydPfFHlYUIgSZJYXV3FdV3a7TY7OzusrKxcWwH69vY2rVaLYrGIpmkHDAwdx/GTVYfDIbB/v5zP569yyC+Nt99JknSqOpHruv7+epH77GAwwLIs+v3+iUL/VCrlH9/lcpn5+fnnErBt26ZSqTCbzVhaWiKbzZ66jnZbqVarlEolKpUK3W6XUCjkp4EPBgOePHlCLBbDsixs2772hgtzc3N0u136/T7FYpGFhYUDxgWu62KaJtVqlWQySSwWe6nj17Is2u029XqdSCRypJgsGo2ytLTE7u6un4B6UkK74ObhmQ3PZrMT69KO4/jmJrC/L52Eoig4jnMh9XzbtrFt+9TGCKqqMh6P/c98WaRSKT+hPR6PYxgGlUrFX1OLx+MsLCwQDoe5e/eufz677ViWRafTYTqdIkmSb7By1mtcvV73zWgVRXnOrBbwDUq8fTkSiZxZDN5sNplMJvT7fdbX148ctyRJzM/P8+TJE8bjMY1Gg6WlpTO9520iHo8Ti8VYXl6mWCwym818QyjYP75N06TRaBAKhchmsyye9QaXfcOXVCrlm36XSiXy+fyB3382m1Gv1+n1eqytrZFIJG70WtJhWJaF67pMp9NTrUVqmoamaUwmE984WyAQXAyvRmVNIBAIBIJXiMQf/sMYv/ZrYNtoW1uon/40ViKxL5Z0XWTTROv3sSIR7PV13K/9WsLvetdVD1sgEAiuJd0f+zGUf//vUTc3wXUZb2wgPVNEnboutFoEKhVmjsPsf//fmT18iDY/f4WjFlw20fe8B2NpiWm3S3B3l6nr4szPHxCtutMpSrmMMhoxvXsX5z3vQbsBYguB4FVDv3ePwZd8CcxmaJubaG+8gZXL4abTSLKMO50iN5torRZWLIa7sID2v/1vQkAg8KnVXu75t1CnJrgG2LbNeDym1+udapEuGo0iSRKDwYB+vy8E6IIbTTQaJRqNMpvNGAwGfrNMNBo914bscDjMw4cP2dvbIxqNHljkDgaDaJrmNwx7jUxeI2k8HieXy93YVCDHcWi1WhiGQSwWO7bRQVEUstks9XqdXC6HaZrXPvFdURRkWfZTzU9Km3Ach8lk4jc8CF5tms0m4/GY0WjEnTt3jm0ez+fzbG5uMhgMqNfrN9aQAo4XoB8TOHSzcF1m/+SfoFSrqIMB040NCIXwf2FJQkoksOJx5GIRbXsbQiEGv/zLRN797iscuEAgEAgEAsH14rXXTrddNgt/+2/DN30TNJv7f7NVnf947xv5pbX/H2+v/hwF8zdxtj4J7meERrak4kgqT0NnBRdIIBBgbm4Oy7LY3d1la2uLdDpNIpFAURRmsxmdTod2u00kEiGfzx9q0nUU3v35aQVZtm0jy/KFC3kvgm63y2AwoFarIUkS0Wj0SOGRJEmkUimGw6EvalpeXmY0Gvn3pK7roqqqLyS+inTg0WiEYRiUSiV0XX/ut/fSz6PRqJ8MGggEbryQ2ROV6bpOv98/Mmnco9/v+wK3twrSzhNP9Oy67on1q0AgQKFQoFQqEQwG2draYjQaEYvFUBSF0WhEt9v1k1VTqdSRYmEv0XI0GvnvnUwmL/SzXgX9fp9SqeSnn6+urh5IYM7lcozHY8rlMru7u8iyTDQavdb14VgsRqFQwHVdarUapVIJVVUJh8O4rstwOMS2bTKZDLlcjtXV1ZcSxLVaLSaTCYPBgPX19WO3jUaj6Lru16eFAP328ayZh2cYexTPrvtYlnXiNcR1XWaz2YWZ+HivexohPOyPWVXVKzEUSqfTRCIR6vU6sVjswJxL0zRyuRzZbPaVWPeYTCbs7e1hGAbT6fTAel8wGCSbzTI/P//Cc6p6vU7z6c3M8vLyob+zlzxfLBZJJBIvJQbvdrt0u110XT/RjDsYDBIOh+l0OnS7XSFAf8rGxga2bSNJEo1Gg52dHf++xDt3pNNpMpkMhULhpfsqVldXmTy9ea1UKjx+/JhQKISmaViWxXA4RNd11tbWSCaTrK2tncOnvL6cdM5/63Y37b5PILhpCAG6QCAQCAS3DFnXSX7P99D5i38RdB3abZR2G63XA0nCDoWY3LkDsRjuH/gDpP7YH7vqIQsEAsH1xHWxf/RHUapVJNtmeu/e88kAkgTZLBNNQ9/eZpJK0f9X/4rURz5yNWMWXAmSqhL+ru9i9Kf/NBNA29tD8sSpqoo8maAOBtiBANONDex3vIPUN3/zVQ9bIBAcQfpbvoV2vw+yjFStotVqyOUyriQhuS52MMhsYQE3l0P6E3+C2Jd+6VUPWXCNyGRe7vlCgC64CLwGAcdxTrUQLkkSqqpemNu/QHAVeM2MF0kwGGR9fZ1CoYBhGMxmM2C/STOVSvnH33A4xLIsv5n0rMkF14XJZIJt2/T7/SObS58lHo9TqVQYjUYMBoNrL0AHSCaTdLtdyuUy+Xz+2HNpt9v1TQ5OEqsLbj+9Xg/TNAmFQic2U6uqSjQaxTRNer3eJY3wYjhOgH5bwjgG//W/IpdKqK0Ws3z+6A8mSdjLy6i/+ZtgGIz/9b8WAnSBQCAQCASCZ1hehi/+Yvi//++jtwmH4S/8BUin4Ru/Eb71Ww8+PtYS/Pfl38d/B6ztv3FAgO5vMz7fcQsOp1Ao+MLvdrtNq9XyRdSu6xIMBsnn86RSqRcWaITDYaLRKLVajfF4fKyIxkviTaVSB0SfN4XpdEqv1/PFLieJlj1Ryng8pt1u8yu/8isEg0FM02Q4HPpC306nw97eHnNzc5cuKmo0GgwGA6bTKSsrK0eKQ1RVZW5ujnK5TC6Xo9Pp3GiDVFVVSSaTpFIpqtXqiQYArVaLRCLhJ6xeFJ5wCjiVqUMsFiOXyxEKhZibm8NxHLrdLq7rEggEWFxcJBaLkUwmD01Y9RLSvSRWzxhB0zR0XScej7O4uHhrEnW9ZNLBYMCdO3cOrf/qus7q6ipPnjyh0+lQr9evtQAd9lPQFUVBVVWy2SzdbtdPBc5ms8TjcXRdZ2Vl5YU/i2eK4L3+cDhkOBz6+8hJxGIxer0e4/HYTw0W3B5UVUXTNMLhsH99PwpJkpBlmWAwyGw2O1Es3ev1cByHaDR6IWsV0WiUWCxGvV4/cf7inVvn5uZOvPZfFMFgkOXlZZaWlvzjSVEUdF1/ZYSdg8GAN998E9M0abVaDAaDA589FosxGAzodrvcu3fvVGt8juOws7PD1tYWW1tb5PN5RqPRkb9zOBwmEolgGAbRaJTFxcUzff+WZWFZ1qmNXoLBoP8cwT6qqvLgwQPK5TKhUIjJZOLPr1VVJRKJEAqFWFxcPJc1QUVRuH//PsVikWAwyHg8xjRNbNsmFAqRz+cJhUK++PwqzCouGk3TkGUZXdcZDAYnzinG4zGWZaHr+o1fcxcIrjtCgC4QCAQCwS1EicXI/L2/R/8Xf5HJT/wE/Lf/hv3U4ckNhXC/+IsJf/CD6PfuXe1ABQKB4Boz+H//X+S9PVTDwMrlnhefP4OUSGCHw8jNJtbP/RzuH//jSFfgHC64OvS7d+Hv/32Gf+WvMEsmwTCQez3k6RRX05isr0Mshv3bfzvJv/pXkW9gs4NA8KogqSqZ7/5uOv/8n2P/5E/iViowHIJtg6ZBKIT92mvoX/d1QjggeI53vQs+/vGzP19ofQUXgbfw6CUNnYTrun4qwG1ctBQILhpN08jn80c+fhMbn4/DM6rwEt5Pwksfu0kmF7lcjlarhaZpVKtVlpaWDm32mUwmNBoN0uk0oVCIWCx2BaMVXCcsy8JxnFOnwqiq6jfc3mSOE6Cfomf4RjD5hV9ANk0kx8HJZDiu/U9SFKxEAqXdhl/9VVzLEnVDgUAgEAgEgqdIEnzDN8D8PPzzf37wsXAY3vY2+F//132hOuzXX7/92+Hbvu3F3kckoF8eq6urRCIRIpEImUzmOfFSLBZjfn7+hUVeqVSKYrGIrut+yvdRdLtdLMsimUySy+Ve9iNdCY7j4LqubxZ6ErIsM5lMKBaLzM3NMZlMfPG6oigMh0NarRaxWAzLspjNZpeamOilaUaj0RM/jzfmXq934wXosF9XMgyDdrtNqVSiUCg89x14qdKTyYTFxUWy2eyFCv1CoRCKovhizpPqlaZpkslkWF1dJZfL0e/3/bUGSZL8Y+2wWphlWbz55pt0Oh2azSamafrPcxwHXddJp9P0ej3W19dvvKHjbDaj0+lgGAbJZPJYIZSqqqRSKQzDIJVKnShOvQ5ks1nS6TTtdpt2u33AhDWbzZJMJk+971qWRavVotFo+GmvsF8/HwwGWJZ16jUqRVH8WrMQoN9OstksvV6Pra0tRqMRoWNcLl3XJZ1OM5vNjhXS2rZNo9EgmUyi6/qFmEBks1n29vb8ZPHl5eUjj5FWq4UkSSQSiSufv8iyfKPXssbjsW964gmFU6kU8Xj82HPUZDJhc3OTZrNJtVolkUiwvr7un5v7/T7tdpvt7W3/t3z48OGR5xzXddnb26PRaFAul3ny5AnVatU3SHj23PlWEokEtVoNy7KYTCZnuj5463GnXW/wjKTEOfQgiqKwsrLC0tIS7XbbF6ArikIymTz3tUBFUbhz5w6TyYRms+kbU3nm17lc7tSmAjcRSZJIp9OYpkmpVDrRTMQwDMLhMLquk3nZxBCBQHAsYmVTIBAIBILbiiQR/cIvJPqFX4gzHGK120iahprJiOYmgUAgOAXTT3wCqd9Htu0TG0kBrHQarVrF6XSYFIvoGxuXMk7B9UHf2ED/kR9h8Mu/zPgnfxLnzTdhOoVIBD7rswh/zdcI8xeB4KYgSSQ//GH4g3+QwS//MtNPfxomE6RYDP13/s590wmB4BAePoRoFPr9sz3/hujwBDcMr5EsFovR6XTIZrPHbu81gEWj0Stz2BcIBDeHFzW5sCzLb/i5KSYXXpp5oVBgZ2eHYrFIJpMhGo0iSRKWZdHpdGi1WkQiEbLZ7KUniQmuJ4qi+PvIabgtBjCvQgI6nQ7SdIqjaUin+L2ccBhtMMC2bexuF1U0QgkEAoFAIBD4KAp87dfu/wNw3f06qSzvC9Tfym/9rfBTP3VwW4A/+kePfg+RgH65ZLNZstks/X6fwWDgC9BjsdixgrG34jgOhmH46dmNRgPTNP3/bmxsHBDjuK5Lp9OhVqsxNzeHrus3UswaCAQIBoO+qNyyrGNF26OnN2GGYZBOp2k2m2xsbJBIJA4IiMbjMZVKhZ2dHQAikcilCdxmsxmz2exUCdeSJPnCrNuQwBmPx8nlcti2ze7uLo8fPyaRSPh1pdFoRKfTwXEcVlZWSCQSzM/PX+iYNE3zk9n39vZIp9NHCqUty6LdbvsCuTt37gD45pJe7eMwXNfl8ePHtFotdnZ20HWdQqHgrztMp1M6nQ6VSoXx0xN1IBC40Unow+EQy7IYDofHmpR6JBIJms0ms9mMwWBw7QXosC9o9M7zZ6XX6/H48WNGoxGGYdDv9/39KR6P++JRT1h+khhyMpmgqiqSJN34uprgcLLZrC8ILpVKLC8vP3e8uK5LvV4HYGFhAU3T6HQ6AGQymQMCxn6/T61WQ5Zlcrkcc3NzF2L8oaoq2WyWyWTC9vY2pVKJubm5A+dc7zzbbrcpFAqEw2ESicS5j+VVYDKZsLOzg2maDIdDRqMRruuiaRqxWMy/Dh01P6xWq5imSbVaZXFx8bnfwVs7r9frlEolgsEgzWbz0PO94zhsbm7SbrdpNBpsb28fmNNKkkQsFmMymTCZTJibmzvw/Lcaa5yFUChEJBKhXC77AuajcByHfr/P/Pz8C83XXyUURblUc4hgMPjKrjXmcjmazSa6rvvn/MPuhwzDoNvtsry8TDKZPLUZtEAgOBtCfSYQCAQCwSuAHA4TuMGOdAKBQHAVuMMhkm3jyvKpGkldTUPykt+Gw4senuC6IklE3v1ukYosENwWxDEteEFUFf7yX95P4Hk2Vecd74BEAn7pl45/vhCgC47Dtm1M02Q2myHLMrqun1ognsvlME2TZrPpp2kc9R7NZpNEIkEwGLwQt3/BzaHf7/vNGaqqEo/HT5W4JHi1CAaDBAIB3+TipPNGp9MhEAig6/oLp51dJevr6376Q6vVolwuA/sNl7ZtEwgEyOVypNNpVlZWxPlTAOw388diMT8R6rjmF9u26ff7LC0t3ehGazhe3HNretc0DSQJyXVPtbnkOLhPm1ilY9LPBAKBQCAQCAT7ovPTasee3fa4lhiRgH41vIzB5Xg8ZnNzk+FwSKfT8eui3W4XVVXZ3d1ld3eXO3fukEqlsG2bXq+H4zjMz8+TTqfZ2Ni4kQmOiUSCRCJBLBZjOBzS7/ePFKW6rkuv12M4HBIMBhkOh7zzne88tP6r6zorKytsbW3RarUIh8OXJqJ50QROx3GQJOlCU8Avk5WVFWBfuGSaJoZh+KJITdNIpVL+73737t1LEdDm83kMw8A0TXZ2dlhcXHwu7XY8HlMulwkEAqRSqQPiuNMkpHrH7u7uLvF4nIWFhQOPBwIB8vk80WiUR48e0ev1MAyDu3fv+imWN60e7TgO7tNawWl+R28b27bPLDC8afR6Pd58800ajQaNRsM3C/EMTjudDt1uF8uy6Ha7zM3NHSvmdxzH3+6kdGPB6ZhMJr5Q9lljgJcxHXhZAoEAKysrOI5DpVJha2uLaDRKLBbzDVs8M4/l5WV//uEJzQ3DIBQKoSiKb+4Sj8eZm5vz/10UhULBN4vZ29vj8ePHhMNhgsEglmXR7/dRVdUXRm+IwJszMRqNePToEZ1Oh3q97qeGS5LEZDKhVquRSqWYTCasrKw8d16xbds3AojH48eaAOTzef+aFY1GDz1HbW9v02q1KBaLaJrG0tISuq5TrVZZWlpiPB7T6XR8AxZN00in0/7zZ7PZAQPos5DL5Wi1WqiqSrvdPnbeZxgGkiT5xjkCwVUSDofJ5/NYlkWpVGJzcxNVVZFlGdd1mc1mvvlMoVAgkUi8smJ9geAyuVl3ZgKBQCAQCAQCgUBwSUiRCKjqfoOobZ8oQpemU9ynC2ySMP0QCAQCgeCV5e1vh+//fvjlX4ZWCzY24N3v3k/sKRbhT//po597yt4rwSvGZDKhWq3SbreZTqdYloUkSQQCAUKhEPl8nmw2e2xTTTqdZm9vj/n5eSqVCrZt+w09HsPhkFqthiRJF+r2D/uNkbZt+8Jm0RB0fXBd12/8Go1GTJ52aKuq6jc/zM/P34gkFsHl4J0zer0eT548odvtHtmYM51O/cSkSCTyXFPrdUaWZe7du0elUiEUCpHL5Q4YNHhJEouLizdKWC+4WHK5HPV6nWAwSKPRYHFx8chtm80mqqoSjUZvfIPXq5CALq2u4kajyOUyDIfHq50AudvFiURwUimUMwpwBAKBQCAQCATHEwwe/ZgQoN8sptMpjx49ot1uUy6XUVWVRCJBIBAgmUz6wnRPxOM4DpFIhGw2SzweJxQKsbGxcaPqDs8iyzJzc3MUCgVarRaVSoVYLEbwLTu5V8ebTqcMBgPS6bT/7ygURSGbzVKv18nlcsfWcc6TUChEOBym0+mcKPKbzWaMRiPy+fytSeCUJInV1VXS6TT1ep1kMumLlOEzafTpdPrSauXRaJRCoYDrulQqFYrFIrquE4lEkCSJwWDAaDQiFouxsLDA/Pz8CxsuNhoNut0ukiQdmere6/VoNBr0+31KpRL5fJ7pdEoymaRcLpNOpykUCjdGiK4oim+4MJ1Oj0yW95jNZsB+/f1VSO52XZetrS2azSbNZpNCoUAsFjuwTTabpdVq8au/+qtIksSnP/1p0un0kftAvV5HlmUhmjwHLMuiWCzS6XQYjUb0+31s2/YNivf29kgkEiwvL1/Jul42m8V1XWRZJpVKYRgGzWYTx3HQNI1MJkMikSAajXL37l0AisUikUiE4XDIcDj05wzxeJxgMMj8/PyR56fzwltbKBaLBAIBRqMR3W7X/24LhQLRaJRIJML6+vpz13vBydi2zebmJq1Wi729PVKpFMvLy74hrOM4mKbpC9MlSSIYDB6YA3U6HabTKb1ej9XV1WPfz0tVf+ONNxgMBliWRSKRIJPJEIvFGI1GGIZBqVTyU9fH4zHD4RBVVRkOhyQSCUKhEJVKBcMw0DSNZDLpm7t0u13i8TiBQODEa8lReOtwc3NzlEolZFl+bq7huq4v2l9cXCQUCok1LsG1wJundjodWq0W7XabbrfrG6Nks1nS6TSWZXH37l1x7hQILoGbcUcmEAgEAoFAIBAIBJdM8LM/m2k0iqOqyK0W7jGOugCKYWDH4zipFMETCpECgUAgEAhuN9ksvP/9z/99bW3/31G8IuEGgheg1+vx+PFjPxWl3+/7jWmqqpJMJun3+3S7Xe7cuXNk4ogsy9y9exfHcZBlmXq9TrPZJBqN+s7vk8nEbybL5/MX4vbvpSa0Wi0sywL2m+8SiQT5fP65RiPB5eI4Do8fP6bdbvspPN7vBPtNib1ej06nw8bGhvi9BD7ZbJZareabXMxmM5LJpN8U6KVx1Wo1QqEQqVTqucSjm4AkSSwuLrKwsECn0/Eb1rxG+Jva2C64OHRdJ5FIsLCwQLFY9A0bnm2YtW2bZrOJYRh+s+FZEwKvC8Ph0Y/dEu0A0fe/n8GP/AhWJIJcq+Gsre3Hbx6C2++jDoeMCwWk3/27j9xOIBAIBAKBQPByHOeV9zRYUHBD2N7exjAMyuUy2WyWTCZzQCiTz+ep1+u88cYbBINBFEVhZWXFN/RKJpM33vByYWGB1dVVDMPgjTfe4JOf/CQrKyu+uHw4HNLtdhk+vQGLxWKEQiE/afs44vE41WqVwWBAv9+/FAF6Npul2+3SbDYxTfNYYVOr1SIYDBIOh680bfciiMVixGIxLMtiOp36ArazCstelrm5OWRZ9kU8zyax6rrOwsKC/98XreVZluXXkhOJxKHHpGEYVKtVPym9Wq0iSRKSJNFqtYhEIr4I9sGDB76Q8DoTiUR8g8FOp3NijafT6RAOh9E07ZWotxuGwXg8ptFoMD8/f+RnzmQy3Llzh8ePH2PbNm+88QaFQuFAwvl4PKbZbNLv91lZWSEWiwnR5Eswm81444036HQ6VKtVxuMxoVAIVVUZjUY0Gg3fLGMymfgC78sml8sRj8dpNBr++dTDmwekUil/P7l3756/rzxbz08mk6TT6SPXVc8bWZZZX19ncXGRRqOBaZrYto0sy/73GolELmUst5FWq8VgMKBSqTA3N/ecGY8syySTSUKhENvb27TbbSqVyoE5kGVZvinIcQY43lqXd93ySKfTNJtNQqEQsiwzGAyYTqesrKwgSRKhUAhd14nFYnQ6Hf96kU6naTQaJJNJTNMkmUzS7XYZj8csLi6Sy+Veal67vr7uzzn29vYwDMMXts9mM7rdLpZlsbCwQDqd5s6dO2d+L4HgPJEkyTcYgf1j1DtuXNdlMpngui6hUIhSqcS9e/cu7ZwuELyqCAG6QCAQCAQCwRUwfvyY4Y//OPzSLyF3OriqiruwgPJ7fg/R978f5RUoKgsE153w53wO40IBq1JBbTSYpVJw1IJWp4M6GjEpFJB+7+9FuiHuywKBQCAQCC6f49Y8hABd8Cyj0YjNzU0ajQb1ep14PM7KygrBYNAXc7ZaLd/pWZblYxeFw+EwDx48YHt7m1gsRr/fZzAY4Lou8XicRCJBMBhkbm7u2ITWs1Kr1SiVSozHY9rttp8c7CUXGYZBMplkfX39lUj6uI54ySM7OztIkkQ2myUWiyHLMpPJBMMw2N3dZTwe47ouDx8+vDUpRIKXQ1VV7t6966ePNBqNA4024/EY27ZJpVLk83kKhcKlNDdfFJIkkUqlSKVSVz0UwRkZj8eMRiO/4TAajV7YtWdtbY3J07jFcrlMt9slGo2iaZrfjK0oCoVCgVQqdSsavF6FBHQtn8d+97vBNAk8ecJsbw93cfF5cflgQLBYZJZMIsViRD74wasZsEAgEAgEAsErgEhAvx2MRiN6vZ6fEn2UADmfzxMMBqlUKqyvr7O6ukomk7nk0V4cqqryrne9i/F4jKqq7O7usrm5iSzLBAIBFEVBVVV0XffTNofD4anEl57g2HEcnEtalEin076hwN7eHrIsPyfMdV2XVqtFp9Pxhab6cc4SNxhVVa9Norcn1my1WrRaLb+G4aUJZ7PZMwm/PVHobDY7tIbc7/epVqvUajXG47GfyJrL5fzaYavVYnt7m+XlZTY3N3n48OG1N5dQFIVMJkO/36dYLDIYDI4UlY7HYzqdDktLSySTyRshsH9Zms0m3W4XRVFOrA+vra1Rr9cJBAI4jkO1WqVarRIIBLBtm9lsRiQSYW1tjWQyycbGxqXuH9PplOFw6KdYe6K4m8qTJ0/odDoUi0Xi8ThLS0sHzDHG4zG1Wo1isQhAqVS6sjpmMBikUCiwtLTEbDbDdV3/ungYXgL1dcAbu+B8aTQadDodAoHAc+LzZwkGg2QyGQzDIJ1OMxwODzUWdl330PNJp9OhUqn4ieHdbpdMJsNkMmFvb883dGm1WriuSzQaPbBfZjIZRqMR4/GYSqVCOp0+IFj3xODNZpO5ublzMePRdZ379+/z+PFjIpEI3W4X0zT9dYlUKuWnsd+9e1es+wquHNd16Xa7fOpTn2J3d5dyuYyqqjx48IB0Oo2qqv69TLPZpFQqoSgKmqbdivU1geA6c3NnugKBQCAQCAQ3ENeyML77u5F/7ueQ+33kVgtpOgVZxtnexn3zTcwf/mGUP/NniP/e33vVwxUIXm0kCeVDH8Ld2dlPKtrcxFpagljMbyZ1HQe52SRQrTLN53HTaaKikVQgEAgEAsExHCdAfxpsLRAAnxGpNRoNCoXCc0kQ3oJwqVRid3cXTdPI5XLHpmSEQiHe9ra30e/3aTabB8R3yWSSTCZzIQ0y1WqV3d1d9vb26PV6RKNRv6HMW2Sv1+ssLy9j27ZwqL4Cer2eLzAPBoMsLS0d+A1CoRChUIh4PE6pVCIQCPhu4gIB7Kf7eCYXiUSCfr/vG0146U66rrO0tHSrGsIFNwuvMazX62FZFo7j+I0pmUyGubk5gsepVs6A1xjz5MkTQqEQ/X4f0zSZzWZIksTS0pKfen7nzp0rSz07jlqtRr1eB/ZFHieJ9Y9Ll7xN/WuJP/fnMN94g6ltE9jZwe12sVIp3FAILAul00EdDJglkzgrK0h/8k+izc9f9bAFAoFAIBAIbi3HTeVFAvrNodlsHkiMPA4vAdVL1r5t9YZgMMi73/1u3yRydXWVbrfLbDZDURQikYifit7r9Wg2m35y53E4juOnrp63Gdt0OvVTzp9Nds1ms6yvr2PbNq7rsru769caFUVhNpvR6XSwLIulpSVSqRSrq6vnOjbB0aiqytzcHHNzc+f2mp5wz0uvfCvesTsej1lYWCAQCNDpdPznhEIhCoWCb2yr6zqdTudGGELOzc3RarXIZrPs7u6Sz+dJJBL+8eY4DqZpUqvVSCaTxOPxc/3urzOj0YjhcEgsFjtRLK6qKoVCgel0SigUYmFhgX6/z2w2888twWCQdDrN8vLyieczy7IwTRPLspAkyU8iflE8kxTvnOWZsnpJxnNzczdOwNnv9+n1epTLZZLJJPOH1K90XWdlZYUnT574RgArKytXKrqXJOla1lIFl8tkMmE8HtPtdo8Vn3skk0kajQaj0QjTNH0Buq7rBINBZFmm3+8/Z+ozHA6pVCo0Gg2GwyGhUIiVlRXm5+e5c+cOsizT6XSo1WoYhsF4PObBgwcHXiMej5PL5QBot9s0Gg0URcE0TUajEYPBgJWVFRYWFkilUty9e/dcjrFQKMRrr72GYRg0Gg36/b7/WCQS8c1oxNr85TMej2k2m36at6ZppFKpU5lK3UZ6vR7b29sMh0M++clPUi6XmU6nLC0tMRwOmU6n5PN5/1gPh8OUy2XK5TLBYJDRaHTjrsECwU1CCNAFAoFAIBAILgnXtml/7GMov/RLqNvbKKMRViSCE42C46B0uyiNBrN0Guev/3XM2Yz4Bz5w1cMWCF5pEl/xFbQfPcL6qZ9C3tkhuL2NHQjghELgOKiDAQDThQWcxUUC3/ZtaPn8FY9aIBAIBALBdUYkoAtOw2Qyodvt0mq1SCaTRzbByLLM0tISm5ub9Ho9Go3GqRpmPKHbZTAajSiXy774fG5ujkAggKZpvsgvl8tRqVTY2dlBlmWq1eqFpLBfBcPhkH6/j23bKIpCLBa7lgufzwoy19bWjmwyiEajfiJOPB5nMpmcu1hTcHPxTC4Gg8GBhgmv+S6ZTF77lCLB7WV3d5dqtUq328UwDD/RS5IkotEovV6PVqvFxsbGuTf3qKrK/fv3GQwGNBoNer2ef12IRqPkcrlLuy6fhXA47F+7wuHwiQ29r0ICOoCayRD93u+l/9GPMo1EkJpNlE4Hqd0GScKORJjcuwexGNKf+BMkvuZrrnrIAoFAIBAIBLea44KSRQL6zcETJ+q6fqLgRpIkP/l7dNyNyA1GVVXe/va38/DhQ3Z2dqjX61iWhaZpJBIJMpkMqVSKT33qU35N+SSBrmmavnjzvO5FHcdhd3eXVqvlC7ps2/bvub269fLyMpIkkUgkMAwDwzD85FrPcDUSiXD37l0hKrzhaJqGLMu+Gd+zadde+qtpmiSTSQKBgG9k6QkAPfL5PKZpYpom9Xr9RgjQg8Egd+7c8euizWaTer3uixxHoxGSJPkJ86urq0empN82XNf1BdunQZZlotEo6+vrLC4u+unA3r6VzWZPvFZMJhMqlQqGYTCdTrEsC9j/nXRdJ5/Pk8vlTlW3rlarlEolTNPEMIwD1x6vvthut1lbWzuVEPa64AlSLcvyxbGHIUkS+XyeYrHIZDKh1Wq9MuYJguuLbdvAvsnEaeYOXnqyZVn++QD2xeHBYJBEIkG73X7OKKPdbtPv9xkMBszNzVGv10mn00QiETRNA/YTzoPBIFtbW0iSRLvdfm69O5fLoaqqbw4/GAzo9XoEg0EWFhbY2NggEomwurp6aDr7WZEkiXQ6TTqdxnVdf31CrNldDePxmJ2dHXq9nn8v47ougUDA7yUoFAoH5k+3nW63y+PHj2k2mzx58sQ3e8hmswyHQ0zTJBKJYNs2lmWRz+eRJIn5+Xk2Nzf99beTTMwEAsHZEQJ0gUAgEAgEgkui+3/+n8j/z/+DtrmJo6qMHzxAeqZobrkulmkS2N3Fcl2cv/t3mbzrXQSFq69AcHVIEulv/maMTAb74x/H6fWQWi2k6RRUlenCAqTTOPPzBD/2MSKf/dlXPWKBQCAQCATXHFmG+fn34zjTZ/62vxgqBOg3F8dxGI/HfrOcflzH6ynwnNFHo9GJQmxFUfyGvUQigeM418qhvFarsbe3x6NHj8hkMlSrVQA/YcJrLFxaWqJYLNJqtQiHwywsLNzoRW/DMKjVagwGA8bjsb+Qr+s60WiUubm5a9NU5jiOL8h8NonlKFKpFO12m/F4TLvdZmFh4ZJGKrgpRCKRa7N/CwQA5XKZSqXC7u4u0+mUVCrF4uIiqqoymUwwDIOdnR2/wfL+/fsXsg+/KsfGcbqPl5wiXTsCS0ukfvAH6f3sz2L91E/hPnrkP+ZGo/AlX0L4gx8UNX6BQCAQCASCS+A4fzwhQL85eOLE0yZzy7KM4ziHJizfJmRZZm1tjbW1tUMfz+Vy9Pt92u02pmkeaaxmWRbNZpNkMomu6+diwOa6Lpubm7Tbbfb29vxU0EAggGVZlEolNE1jfn6eyWTCgwcPME2TaDTKdPqZdZLbnMBpmqYvovH270QiQS6Xu7XmnrIsk06nMU2TUqnEdDr1hYG9Xo/RaIRt274JgmmahEIhVFU9YLIrSRLJZNJPP/fq7NedRCLB/fv3KRaLJJNJer2eb4aYSCR8cdny8vIrJS5TVRVN0xiPx6fafjwek0ql0DSNZDJJMpl8offr9Xo8fvwY0zR98aiHoii++LPb7bKxsXHsuader1MqlSiVSgwGAz8pXFEUZrMZnU6HUqlEMpn0zQduSoJtr9fDNE1isdiJx1coFCIYDPrGEEKALrhqvONWlmVfjH4cruviOA6SJB045iVJIpfLMRwO2draolqtMj8/jyRJzGYz/ziJRCJ0Oh1UVSUSiTxnjOIZeTebTarVKm9/+9ufO7d4a+PeuWkwGLC4uMidO3d4+PDhhZvVSpJ0LsnqgrMxHA559OgR3W6Xer3OZDJB13UkSWI8HvvGTOPxmNXVVbLZ7FUP+cKZTqc8efKEWq1Gu90mHA771/9CoQDsG/g0m01qtZrfZxKPx1FVlWg0SrfbpdvtXvEnEQhuN+LKIRAIBAKBQHAJuLaN8xM/gVqt4koS1sYG0luLlpIEiQQTRUF/8oRJOs3gX/0rgn/+z1/NoAUCwT6SROqP/BHsD32I/k//NNYv/AJStwuqiru0RPB97yP6hV+4fwwLBAKBQCAQnICiwGd/9scPfeyW98jdSkajEfV6nXa7faDJUdd1crkcmUzmTM1Q0+mUyWSCqqqncmsPh8P0ej1c12U2m12bprXZbMb/+B//g0ePHjEcDrFtm9lsBuA3kg0GAzqdDoVCgXQ6zd7eHrlczm8ou4mUSiWq1SrtdhvDMPzPDBAIBEilUvR6PfL5/LUQb89mM1zXZTqdnqqBy0uwn0wmBz6b4GqxLItWq3UgeVzTNFKpFLlc7qWNMQSCm8p4PKZarVIul3Echzt37hxorvIaxfr9PqVSCVVV2d3d5eHDh1c46puLbcNxl4bblIDuIQUCxD/wAfjAB7DabSzDQNZ1tLk5JNHIJxAIBAKBQHBpXEUC+mQyodlsYhgGlmUhSZKfyJpKpW60ueJV4YkTTdM81faTyQRN0155EU02m6VarZLP59nb28OyLJLJ5AHB03A4pFqtoigK2Wz23ESDpVKJdrvN9va2n/z8bH3aE73v7u76osy3ve1tzM/PY1kWjuOgKMqNEBW/KKPRiCdPnjAajeh2uwyHQxzHQVVVEokEtVqNTCbDysrKrRPdw356ebPZJBwOs7u7y/LyMoFAwE+uVBQFWZZpt9uMRiMWFhZIJpPP7QvBYJBOpwPgP+8mEI1Gee211+j1erRaLd9wQdM0MpnMjREnnyeemcD29vYBU4LD8BK5E4nEmdaKRqMRm5ubNBoN6vU68Xic1dVVdF3HcRz6/T6tVgvTNH1j542NjUNfy7Is3+ByMplw586dA2PXNM0Xy+3s7Phre+94xztuxFzAtm1s2yZ0ysKdpmn+cwSCqyYQCKAoCpFIhF6vd6Kph2maSJJEOBx+LmF8bm6OTqfDysoKOzs7vtnEdDql1+tRr9eJRCKEw2Hm5+dJJpMHTFM8CoUCzWaT6XRKvV5nfn7+uW1kWSaZTDIej1lbW+POnTu89tprYi3vlmNZFpubm7RaLSqVCul0muXlZTRNAz5jGl+v1/15QzAYPHQ/u03U63WGwyGtVovl5WX/Htv7XmDfAGVhYYFyueybN3hzqWAwyGg0wrKsq/oIAsErwatddREIBAKBQCC4JAb/5b8g1+uonQ7ThYXnxefPIEWjWJEIcrOJ+x//I+43fAPSKQQHAoHgYlFiMRJ/8A/CH/yDVz0UgUAgEAgEN5jjeoiEAP1mUavVKJVK9Pt9Op0Og8HAb6BLJBL0ej1qtRr37t0702LxizSluK77wq9/0biuy+uvv06r1WJnZ4dkMkkmk/EbWEajke8W7zU7Li8v+4+NRqMbKUCvVqtUKhV2dnaYzWakUimSySSqqmJZFp1Oh2az6TdPqarqJ+5eFd6+dtP3uVeZdrtNsVj0k5xHoxGu6xIIBPxGhWw2y8rKyo1oeBMIzpNms8loNGIwGLCxsXGkKCEajZLNZv0kuOFw+FwDmuBkhsPjH7+NAvRnUdNp1HT6qochEAgEAoFA8EpynB/jKYNWT43ruuzs7NBsNhmPx3Q6HWazmS9m6XQ66LrO+vr6rRcLnDdeMnCtVqPf7x+bAGlZFv1+n+Xl5RdOxb1tqKrK3bt3/XTter1Oo9EgEokgyzLj8ZjJZEIsFmNhYYF8Pn8uNUnLsmg0GlSrVQKBAMvLy8/VnlRVZX5+HlmWqVQqRCIRP6X9NhsHeOmW7XabWq2G67rEYjFUVWU6nbK9vU0oFGI6nTKdTrl3796tq9t5QiHHcSiVSjx58oRYLMZoNGIymfgplY7jMDc3RywWOzTl00uqBW6kUD8Wi4lrwVNyuRy1Wo1QKESlUmF5efnQ39SyLGq1GvF4nGAwSPoMtZZyuUy326XRaFAoFA78Bp7wMx6PUy6X2dnZ8c1PDjMGaLVa/j67urp6pHA+FAoxNzdHvV4nnU5jmuaNSLiXZRlZlk8t3LMsC13Xb+TxKLh9yLJMJpOh1+tRLBYZj8dHrss7jkOr1SKRSBAMBp87PmVZ5t69ezx+/BhN0+h0Ov66brVaZTqdsr6+TjweJ5FIsLi4eOj7ZDIZP5W5XC4TjUafm9O6rkur1fJN2mOxmBCfvwI0m00GgwHVatU33nkWWZZJpVKEQiG2t7cJh8NUq9VbPY/wjst2u+0fK61WC1mWDwRAAH4fjnd99Y5327b9a5lAILg4bu/du0AgEAgEAsE1Yvabv4k0GCA5Dm4qxUlLBnYySaBSwen3mRSL6PfuXco4BQKBQCAQCAQCwcVy3JqHMIq/OdTrdXZ3dymXy/T7fWKxGIuLi8iyzHQ6xTAMWq0Wi4uLuK7Lw4cPn2tIGY/HtNttJk8jmLzUi1AoRDAYJBgMYlnWsQvlHoPBAF3XkSTpgBP0VdJqtWi1WtRqNVRVpVAoEIlE/Me9hX1v0V5VVTqdjt9IdhMFzpZlUalUqFQq2LbN+vr6cym72WyWRCJBsVikVquhKAqZTOYKR70/LlmW0XWdfr9/YkPUeDz2G4yCx3V3Cy6FZrPJ9vY2tVoNwzDQdZ1oNIokSYzHY4rFIqFQCMuysCyLO3fu3LpmVoHgOFqtFoZhEI1Gj01Vgv20lGaz6SdpPXjw4Fixg+B5ThL23HYBukAgEAgEAoHg6ggG4fM//xeBZ5vU94ux5ylAd12XJ0+e0Gw2KZVKjEYjIpGIn6jaarWo1+vkcjksy+Lu3buvZMrtWUmn05RKJRKJBNVqlbW1tUNFyq7rUqlUCAQCRCKRKzd4vA5Eo1Hu37/P1taWL8wYjUY4juMLpYLBIPPz8ywsLJzLe3qizH6/z9ra2rE1p2w2i2EYmKZJo9G41ceF4zg8fvzYT7fM5XKk0+kDopjpdEq1WqVYLCJJEuVymUKhcIWjvhg8cZ4sy/T7fQzDoNPp0O12GQwG5HI5stks4XCYlZWVQ4930zSJRCKoqnqrTQteBYLBINlsltlsRrFYpFgsks1m/Xq24ziYpkmz2URVVebm5nwDixfBE4u3Wq0jE4phf79cXFxkc3Pz2HOTV1/Udf1Ew8p4PE6tVsM0TV/oet2JRCJEo1FqtRpzc3PHft+TyYTxeMz8/LyomwquDfl8nkajQSKRYGdnh6WlpQNr0rC/hru3t4fjOGSzWfL5/KHzFlVVuX//Pu12m0ajQSaTIZPJoOs65XKZfD5PPp8/dv93HIdcLkcoFCIcDrO7u0soFCIej6MoCrPZjE6ng2VZLC0tkUqlWFtbO++vRXDNcF2XRqPhX0+OM9DSdZ10Oo1hGCSTyVP1i9xUvL6DXq/n36MEg0FCoRDdbtc39PeIRqMYhsFkMmEwGBAMBun1emSzWT8IQSAQXAziTkwgEAgEAoHgEnDHYyTbxpXlY9PP/e01DZ4227vnbcctEAgEAoFAIBAIrgyRgH7zmU6nlEol9vb2GI/HrK+vHxDhRiIRUqkUrVaLvb09FEVhd3eXjY0NYH8RbXd3128AHD+95wsEAtRqNaLRKHNzc34jS7vdPtJBHfYXzP//7L13nGZZXef/vuHJOdZTuTr3RLIjackLDGLO/FhFXUVXREVlDeuaBRUXV1nBdWEJorCSBIkqCMwAAwzDDBO6q7tyfHION/7+uP3c7urK3VXVVT3n/XrVa6af5z73nHvvueeee8738/3U63VGRkbWBbHdSAqFAo1GA8ANuL0aSZJIp9MsLS1Rq9VQVdV1kT+KgWT9gMt6vc74+Pimx+DxeMjlcszMzJBOpymXy3sW9Hkt9LOp1+t1FhYW0HV9y0QG5XKZYDDoLn5fiWmalMtl6vW6m208HA6TSqUOLDmCZVlUq9U1yR36LvQ3G91ul7m5OZaXl2k2m4yNja0LqtF1naWlJWZmZgDHcSebzd6A2gqOMo1Gw+3XDcNAURQ3wD8Wix3apAaWZWEYBr1eb8vA9mq1SrlcptvtUiqV6HQ6GIaBbdsEg0EGBgbWJAuxbZt6vY6u64ATDNMPlH280+ls/b0wlRcIBAKBQCAQ7Bc+H8TjT97wu0tTBHvC6uqqmwxOVVVOnDixJtnVwMAAtVqNlZUVbNtGlmVuv/32m3JeYj+QZZnBwUF0XWdubs6dP4tGo+68Z7PZpFgsous64+PjrkvgjabX67muhpZloSgK0WiUdDrtJjrbb0KhELfffrsr4Ox2u1iW5c6P9R0594pGo0Gj0cDr9W57DWRZJhqNur+5malUKrTbbVZWVsjlciQSiXXb9B3j+4lKfT4fg4ODB9ZWDpKhoSHi8bgr7u0LfvvJAkdGRjadX+p2u7RaLY4dO0Y6nRZzLzcBY2Nj6LqOJEkUCgUWFxeRJAlFUTAMA1mWicViZDIZV4C+WyqVCt1ul06ns+X6GoCiKMRiMSqVCrFYDNM0192HmqbR6/W2FZ+D09cFg0F6vR6apu267jeCTCZDpVIhn89TLpdJp9ObblssFvH7/fj9/i23EwgOEp/Px9jYGJZlsbq6ytzcHH6/n0gkgiRJdDodms0mXq+X8fFxUqkUAwMDm+5PkiRXeK5pGvV6HZ/Ph9/vJxqNbpt8oV6vu8lngsEghmFQqVSoVCpYloWqqiQSCWKxGKFQiJMnT26bPFdw9Ol2u2iaRq1W29E6bT/epNvtUq/Xb1oBumma2LbtvrOAc+yVSgWPx0OtVluzPqeqqpu0xrZtqtWqm3BLJCUTCPYXMaslEAgEAoFAcABIkQh4PI4IXdeRtgm6lrtd7EuTmfJNnPVXIBAIBAKBQCB4vCEE6EefYrFIp9OhXq+vE59fSSqVQtd1CoUCoVAITdPQdZ3JyUmq1aobAOj1epEkCU3TXBf0VquFoiik02k3K/pGQWqGYbCwsOA6Hh+WRbVOp0O73abdbpNOp+n1ejQajQ2dHiRJIhqNUi6XsW2bRCKB3+8/Eq4QV1Mul6lWq/h8vm0DkcLhsLtoeqMF6OC4A5RKJfx+PwsLC4yOjm4YjNoXl4+OjhKPx92FYNu2WVpaIp/Pu8EYVwrQl5aWSCaTjI6O7lsQZd+Bvp8IoC9A93q9eL1ekskkg4ODN5Vre6FQoNVqUavVOHbs2IbBBx6Ph9HRUaanpymVSq5o+GYP1rQsyw0WPCyJOY4iuq5z8eJFms0mjUZjzb0diUSoVqsEAgFOnDhxKIL9r4WVlRVKpRL1et0V2vfF5JIkEY/HabVatNttBgcHXbFJ/7kOzn3m9/vJZDJks9mbrs3l83mKxSKAGxC8WYDUdgL0mzRGSiAQCAQCgUBwCNhqrGmazt/1TknYtu2Oj2VZZmxsbN34v/8eoSgKCwsLRKNRisXiNQnoHq8MDAy4or1CoUA+n2dlZQWPx4Npmq7AYHh4mGQyecMdIw3DYHZ2lmq1SrfbpdlsYpomqqoSjUZZWloik8kwPDx8YPMx0Wj0QBzGTdPENM0di6Y8Hg+apmGa5j7X7MZSKBTchKtbuVtKkkQmk2F+fh5N0yiXy4dmjn+vCQaDjI+PMzo6iqZpxONxlpeXWVxcpN1uE41G190fnU6HhYUFYrEYgUBAiF1vEiRJ4sSJE4RCIfx+P9ls1k3coaqqm/w2l8td8/2g6zqapqGq6o76p2Aw6CbG6CfffDwRjUYJBoMMDg6ysLAAsC7htWEY5PN5ms0m4+PjxONxIZgVHCr6SUoURSGZTFKtVmk2m4Az/hgZGSEcDpNMJhkfH9/xmMzr9ZJOp8lkMnQ6HUql0pZJr3u9HrVajeHhYcbGxhgdHaVYLBIOh9ckpeiv1SUSiZtuPUGwMf3x707Hzh6PB0mSMAxjw0T/NwuKorjrcbquEwgE8Pl8hEIhkskkq6uryLJMPB5HkiRXsC7LMo1Gg16vx8DAAIFAYMtxt0AguH6EAF0gEAgEAoHgAPB/+7fTe/e7sbxe5FIJe6vFRdtGqVQwYzGsbBbf2NjBVVQgEAgEAoFAIBDsK0KAfvQpFotUq1U3CGYrEokEU1NTdLtdlpeXXeH5ysoKyWSSkZERV8Dbz36+srKCruskk0nX9WR5eZlareaKs/vOq9Vq1RWXZjKZdc7HN4per4dhGOi6ztDQkHvOqtXqhgt/wWCQpaUldF1nbGyMSCRyJLN494OaduKCARAIBNYIGG8kwWCQbDbrJjW4ePEi8XjcDfzrdrtuEO3g4CDxeJyRkRHACb6emppy23az2URVVTcoOJ/PEwwGXceR06dP73kAl6ZpnD9/nlqtRqlUotFoYNs2kiRh2zbhcJhms0mtVuPkyZOH5l65HizLolQqUalUtr1nZFkmlUqRz+fpdDo0Go0DCUK+HkzTpNfruYGHO+kTLMuiXC5TKBRot9vu5/32LQJ5dodhGJw7d45KpcLi4iKmaRKNRvH7/W7A4erqKrlcDsMwOHPmzKETocuyjKqq+Hw+2u32GpcEcJ7ppVKJlZUVTNN0k7nkcjlyuRyBQIByuUyz2UTTNC5evIjX66VcLlOr1bBt2y0nFovRarWoVCqcPHnSfb7fDJw4cYLOFcryQCCw5h67ku0E6IesiQgEAoFAIBAIbiK2yzf3f/8vPOlJ8IQnOEL0ZrPFY4/pGIbN6dMqiUR4WyFKf26kWq0yNDS05TtmJBIhEAhQqVQIh8NCgL5LRkdHXYfVTCZDq9XCNE0kSSIUCuHz+chkMgwNDd3QJHu6rnP+/HkqlQqrq6t0Oh0CgQCqqtJsNsnn80QiETRNo9vtcuLEiZsqKWA/8d9O5zf7ws6bWdxp2zatVsudf9vueodCIbe9NJvNm1aA3keWZfx+PydOnHATNczPzzM5OUksFluz/tFut4nFYgwODjI2NnZTJRZ9vCNJEoODg+RyOSqViitAVxSFSCSyo3tnJ/Tn7q53O6/Xi8/no9Vqbbsvy7LodDpEIpEjI9DuJwXoCxz7iX4jkQiqqqJpmrvu0xefj4+P3+BaCwTrSaVSxGIxSqWSmwgenPFKIpEgk8nseA33arLZLPV6nXq9ztzcHENDQ+vWrVqtFktLS4TDYSKRCNlsFlVV3fUGwzDcvu5mHgtdK7quUywWabfb7nmKRqPrEmIcVfrH0Hfv3o6+w7csyzfF8W+G3+93r3WtVnPXrwcHB+n1eti2TaFQoF6vEw6HXSOEfD4PwNjYGKlUiuPHj99U71kCwWFECNAFAoFAIBAIDoDAHXfQOXECY3UVT6FAL5FA2mxivFRC6fUwJiaQX/YyEC9FAoFAIBAIBALBTYMQoO8dlmVRr9fd4Dafz0ckEtnXhSXLstB1nXa7vaMMyj6fD6/XS6fTYWlpCcuyWF1dZWhoaJ3Dt6qqZDIZAoEACwsLBAIBN1uz3+93xen9Bcm+M0Q0GiWbzTI6Orofh3xNXBmsE4/HaTabZLNZ1x37SmcE0zSpVquUSiWOHTtGNBplaGjoRlX9uui3vd0ENcmyfGgWQ0dGRrBtG0VRqNVqVKtVyuUycFlcOTg4SCQS4eTJk+41XFxcpFgsMjMzg6qqjI6OrhF4d7tdCoUCMzMz2LbN9PQ0J0+e3LN6W5bF5OQkpVKJhYUFgsEgIyMjhEIhJEmi0+lQKBR47LHHSCaT1Ot17rzzznX34FGj1+thmibNZpPBwcFtt49GoywvL9Ptdmm1WodWgN4PGiiXy2vupX7AeSqV2jAwp91uc+HCBTqdDtVqlUaj4QaxhsNh6vU6wWCQkydPHjqR9GFlZmaGarXK3Nwc8XicTCaz5twPDAxQLpdZWloCYGpqiltvvfXQ9Gl9UqkUjUaDmZkZNE1b8/wpFosUi0Usy2JoaIhGo4HH4yEYDJJKpfD7/USjUaamprj//vvXOC8MDw+7banZbFKpVJiammLsUjLRM2fO3NRBQZshBOgCgUAgEAgEghvFdnnLPvIR+MhHbHo9je/6rhU+8YkQ1aozZvf5dF71qgs873khVyiyEa1Wi3a7jSRJRCKRbesUjUYpl8tuAsKbKVHVQZDJZEin067YxzRNZFkmFAodmiRzU1NTVCoVZmdniUQiDA0NrREcdjodVldXmZmZAZz5jX5Sx5uBvsCqUCjQ7Xa3TCDYn8/PZrOEw+EDrOXB0p+/tyxrx/e8qqqYprkjMdLNgqIonDp1yk32V6/XqdVqNJtNN9FEf31kbGxMuJ/fpEiSRDKZJJlM7ul+fT4ffr8f0zS37ZvAeb77/X4kSdrwvk2lUm7y23a7vaWAtV6vY9s20Wh0XTLMw4zf7+fMmTNMTU258+l9Z1lFUVz36EAgwLFjx4R49gZi2zaVSoVGo4FlWciyTDgcPjRjoxuNqqoMDAwwMDAA4Carvl4SiQS5XA7btlleXmZ6eppAIEAoFHKTz3S7XeLxOLlczl2nvLpugvWYpsn8/Dzlcplut0uz2XTXqyORCAsLCwwMDOxoLfQw4/P53Pu1VqttOx6u1+soikIgELjmxAlHAVmWSafTNJtNZmZmaLVahEIhPB4P4+PjzM/P4/f7aTab1Ot1VlZWiEQipNNpbr/9dvx+/02TfF4gOOyIp5hAIBAIBALBAeF5xSswL1zAbLXwXbiANjgIiYQrMLd1HblQwFssog0NYWezRL77u29spQUCgUAgEAgEAsGeIsswPf1X2LbhfiZJKseO/bwQoO8QXddZXV2lWCy6wZv9oJC+QDGbze7LIvuVYsidLlbLsuy68tq2TTAY3FL4Gg6HiUajrqvxrbfe6jolDQwMuE4//UzQmUxmR2L4g8Tj8bjZ27vdLmNjY8zNzSHLsuukq6oqkiS5TunRaJRcLsfx48ePbACiz+dz3XK3C2iwLIt2u00mkzk0zi2SJDE2NkYsFqNQKBCPx93s6oqi4PV6SafTZDIZN0jCMAwKhQIrKyuoqsrY2Ni6e8/v9zM6Osri4iKLi4v4/f5tg7R2Q7FYpNFosLi4SDKZJJvNut81m00WFhZcx45vfOMbpFIpzp07xxOe8AQmJiZIpVJHMiinH5Davz7b0c+Qb1nWoQ1mXV5eZmlpyRXztlotbNvG4/EQi8VoNBrk83lOnjy5Jmiw3W5z7tw5yuWy2xbj8TiqqmIYBtVqlWKxSC6XwzRNzp49uyNH9cczvV6PWq3GysqK2z9fjSRJpFIpbNtmdXWVSCRCvV4/dMkdMpkMq6urhEIhFhYWGBsbQ1VVarWam1Qml8vR6/WoVqukUik3+QvgOqgXi0VkWebs2bPrHH7i8TjxeJyVlRXm5+fxeDwUi8U1/dHjBSFAFwgEArANg+ZnP4v2T/+EfPEiaBp2KARPeAL+7/1egk984o2uokAgENyUbDe90heG6LrOu94VQNd77lxfuw1/9VcRVHWJ224rc+rUqQ3na0zTxDRNFEXZ0dygqqpuGYf1XfywI0kSsVjs0L1rgjPv1J+T6ouNrqYvnp2bm2NlZQWv18vg4OBNI9xLpVIsLS0RCoUoFAqMjIxsem/0E13257RvVvrzjIqioGnajn6j67rrJv94QlVVzpw5Q61Wo1AoUKvV3O88Hg/pdJp0On1gLtK1Wo1yuUyv1wMc1+tkMkksFjt0CRcFW5NMJllYWCAUClEul7dMemwYBvV6nZGRkU1ddlOpFIuLi0SjUZaWlhgfH99QqN5POnJlYsujhN/v59Zbb6Ver1MoFNY502ezWZFM5wazsrLC6uoqvV6PZrPpCtBDoRA+n49sNsvg4KDos65gL8/F6OgoiqKgqirdbpdKpUKn03ETp4yMjODz+RgdHRWJU3aIYRicP3+eSqXC6uoq7XYbn8+HoihuTEgkEqHX69HtdpmYmDiy7VtRFDdR+sLCAr1eb9MYAcuyKJVKxOPxI/k82S2ZTIZCoUAymWR+fp5cLkcsFsPr9XL8+HEajQZLS0vUajWGhoYYHBzk1KlTnDhxYtNnt0Ag2HuEAF0gEAgEAoHggIi88IVUZmYw3/lO7MVFvIuL2MvLWD4f2DZqp4Pl8dAbHcUeGsL/R3+Emkjc6GoLBAKBQCAQCASCPUSW4ZFHfg3L6lzxWUAI0HdIp9NhcnKSRqNBuVx2nQTAWbSLxWK0Wi2q1SonT57c80zi/SAwj8dDp9Mhsc07m2marksx4C6KbUcikWBmZsYVxg0ODpLL5Wi1WhiGgSRJ+P3+QyNcvpp+oEMsFqNSqRCPx5mYmGB1dZVAIECv13ODuFRVpVqtuuLziYmJG1v56yCdTlOtVsnn89uKMOv1OpZlEY1GD10QQj+oV9M0ut0utm2jqirBYHDdon6pVKLX69FoNJiYmNhygXdgYIALFy7QbDYpFArrBJzXSj6fp1qt4vF4XLGnZVksLCwwNTXlumk3m02q1SoLCwusrq7SarVYWFjg9OnTnD59+sgFTvUDlfvBrNtldjcMA8uyUFX1ULosrKyssLCwwOLiouvQPjIygizLdLtdqtUqpVKJ4eFhLMvi7NmzbvDp9PS0Kz7P5XLrknKk02nK5TLLy8vIsuw6dQs2p1Ao0Ol06Ha72zqzJZNJ95lcKBQOnSjA5/O5yQfm5+eZmpoikUhQLpep1WoYhkGtVqPdbpNIJIhGo647Cjgilf5zy+PxbHn/DAwM0Gq13MQxQoC+FkWBQ9j9CAQCwZ7Sfewx2r/1W8grK6jlMnK9DraNrShYMzNon/scnTvvJPb7vy/WAAUCgWCP2XqazBGfa5p2SSTiCAtVVQEkTNNE1zU+/WmdXK4BwNmzZ9eN//uCk/479naB7pqmrXl/F9xc5PN5Go0GhmFsKaiWZZlsNsvc3ByaplEqlW6a90WPx0MqlaLX6zEzM8Pi4iIDAwNr5tlM06RcLrvzOsFg8KYW0UiSRCQSIRqNusnpthJKNZtNTNN03eQfj/Tnoy3LchPwHuT8Zb1eZ25ujm63S71ep9vtAs6cUqlUwu/3u4lbBUcDVVVJJBK0Wi3m5ubw+/0buqwbhsHCwgJ+v59wOLxpX66qKiMjI5im6a47xONxYrEYqqqi6zrVapVarUY8HiedTjM2NnZkRZLRaHTTflrX9QOujQCcOerp6WmKxSL5fJ5arYYsy2vGpdFolF6vR7vd5sSJE0e2/R12hoaGyGQyFItFotGoe0/4fD5SqRSpVOpQrsEdVqampqhUKszOzhIKhTh27NiaBNLNZpN8Ps/s7CzgnOedxFocVrLZLMVikWAwyNTUFOPj4+sSthuGweLiIrZtr0u8frPi8/k4duyYm3R9dXWVQqFAKBRCkiQ6lxaebr31VnK5HENDQ4yOjt7gWgsEjz/E000gEAgEAoHgAEn81E9RS6Uw3vEOzFIJqVJB0jSQJHoDAxCNYp0+TfD1r8d/6tSNrq5AIBAIBAKBQCDYY7aKSRQC9K3RdZ3JyUmKxSLLy8tuJvFAIIBt265jbq1WcwM7Tp8+vecL7PF4nFqtxuLiItlsdstF5Fqthqqq+P1+JEmiUqnsSDTeF1UahoFhGIATuHZUnMElSSKTybjXpFAokMlkGB4edt2IdV3Htm3q9brrKHuUs5aDkzigH2CwsrKCx+PZ0OW71WqxsrJCOp3G5/Md2oBLr9e7rbtMvV6nVqu57u9boaoq4XCYer1OvV7fkzq2223Xtbgv5Ldtm4WFBc6fP8/MzIwrTu+7txcKBXq9HufOnaNWq7lORGfPnj1SweD9JBTRaJRarbZtQoxqtYrX6z2UmfJ7vR6Li4ssLS3R6/U4fvz4mrYXDAZJJpMUi0UWFxdRFIWFhQWOHz9OvV6n0+mQz+fJZDLrxOd9kskklmW5bgnNZvPI9Kk3gna7TbPZJBAIbJucQZZl95y2Wq0DquHu6CcukCSJWq3mBjQVCgW3/rlcjmAw6I4t+vRFKoZhEI1G3efyRkiS5LbVTCZDu93e8Dlw2DBNk0qlQrvddhNVxGKxawp830qAHgjAEX7MCwQCwbZ0H3uMzmtfi7ywgHd5GRswo1FsVUXq9fDOzmJ7PNBuU3vNa4j/9V+jPE5FRgKBQLAfXKFTWIem6ei6TqfTQVGUNaIGwE3W9vDDx/joRx/mO76jzsrKyrqEXNFolFAohCzL1Ov1Td9B+9RqNWKxGIFAQAhRbkIajQb1ep1oNLrtnFIwGMTj8VCr1ajX6zeVkGR0dJROp4Nt2ywtLXHhwgXC4TAejwfTNGk0GiiKwsjICPF4nJMnTx7pOeCdkMlk3ESlpVJp0wSklmVRKBSIRqP4fL4NBbKPJ2RZPnAHy0ql4oo6S6UStm27czm1Ws11s9Y0jYmJCVKp1IHWT3DtDA8P02g0GBoaYmlpyX1u+/1+bNum0Wi4awejo6NkMpktk7xms1k3QUKtVqNarVIul93vQ6EQw8PDRKNRJiYmRMICwZ6ytLREoVBgdnYWSZIYHh4mHA4jSRK27SRaKhQKzMzMAE6CmL1KAi1Yj8fjYXBwkMHBwRtdlSNNq9Wi0WiwsLBAJBLZUFgeDocJBALMzc2xurqKz+djYGDgSK3n9tE0zU3m3Gw2KRaLzMzMkEwmGRwcJBQK0el0aDab+Hw+xsfH3bXtxwP994R+Epn++m8/wUYsFsPn87nGDQKB4OARs1oCgUAgEAgEB0zse74H++Uvp/GZz6B/7nNQqYDHgzQ8TOA7v5PA7bff6CoKBAKBQCAQCASCfUII0K+d5eVl6vU6y8vLZLPZdcFYfbeB5eVl5ufnUVWVcrm85wFBmUyGcrmMx+NheXmZkZGRDQPmut0uhUKBdDpNMBh0A6esHVzo/jaSJB14wNVekclkKJVKjIyMsLCwQKfTIZlMEgwGSaVSrkMswIkTJ9Y5zh5FJEliYmICwzAwTZPZ2VkikcgaF4xarUaz2XSz4B910b1pmpimua1QvY/X66Xb7W4p4NwNuq5jGAatVotEIkGn03EF/rOzszSbTTKZDKlUClmWsW0bXddJJpOus/bk5CTxeJx4PM7w8PCe1OugyGQyNBoNKpUK1Wp108D3Xq9HqVQik8m4wRqHib7bdqPRWCc+v5J0Oo2maW7We13XKRQK1Ot1bNveVoQfiUSYm5tjeXkZr9fL7bfffmT72P3GNE1XiLwTFEVB1/UdPeNuFKOjo0QiEfL5PIlEAsMwUFUVSZLI5XLE43GSyeS69tfvZyzLwufzbdtnBwIBN4GMpmmHWoBuWRaLi4uUSiV6vR6tVgvbtlFVlVAoRCAQ2LWbyCWTsA05ZF2PQCAQ7Cm2YTjO5wsL+BYX6Q0MYGcySJfGGjbQ03WUpSW8U1NokkT1DW8g9Yd/eGMrLhAIBDcRPh985jOjmOblrEiKEuBFL5pH03oYho4ksU58fiVer5d///ezPPpol9/8zTJDQ0Nr3huj0Sh+v594PE6xWCQcDm/63lSpVNB1nXg8/rgRDTze6M8BbtWmrsTr9bpzaTcTsixz+vRpZmZm8Pl8tNtt6vU6hmEgyzJDQ0NEIhFCoRDHjx/fUYLWo048HicUCjE0NMTi4iKWZZFMJtf0F91ul5WVFUzTJJvNks1mxTzVAdPpdJienmZ5eZlqtcrAwACxWGzNWk69Xmd1dRVd15EkiUAgcKjnegSX8Xq9nD59msnJSfx+P+VymdXVVXf+0u/3k81miUajZLPZHTmp9gWC+XyeeDzuzhnKsozH4yGRSDAwMCDaiGBPMQyD1dVVVlZWkGWZ8fHxNc+LfgLzYDDI/Pw8CwsLqKrK4ODgjtfuBIIbQT6fp9FoYBjGljECiqKQzWaZn5+n1+tRLpeP3PtVPp934yX6SY97vR6KolCr1Wg0GgSDQUZHRxkdHSUUCpFKpRgfH990Tco0TUqlEuVy2V139/v9pNNpYrHYkYw/iEaj3HHHHVSrVYrFIr1eD9u28Xg8JJNJUqnUkUw+IBDcLAgBukAgEAgEAsENQFJVoi99Kbz0pTe6KgKBQCAQCAQCgeAAEQL0a6O/gFYqlQiHw5s6gUiSxODgIBcvXqRWq5HP5/dcgB4Oh0kkEoyOjjI3N+dmpo5Go0iShK7rVCoVyuUysViMVCrF2NgYc3NzBAIB6vX6tuLPer2Ox+PZkav0YUVRFE6dOuUK2crlMgsLC9i2DTjXKhKJMDExQTQa5dSpUzeFE1QsFuP48eNIkkQ0GqVSqbC4uIht2+5nExMThEIhVwx5lOknSdipoNw0zT1zstE0zXVVWlpawrZt/H4/i4uLLC4u0ul0iEQirvgccNufLMtks1nq9TqapjE9PU0ul2NwcPBIBXqmUilWV1fJ5XIsLy+jadqaYNZ+kGQ+nycUCpFMJg9dVnjbtimVSlQqFcLh8LZByMlkkunpabrdLqVSyQ3WCIfDm167fsKLvqtNvV53nZ5TqRTZbPZxEfy8G1RVRVGUHTuaa5rmOgYeZvrJJrqXVNI+n49KpeK6KmyGbdv0ej18Pt+Og/YkSTrUAT6maTI5OUmlUiGfz9NsNt3+2TAMFEVxE3v0+86dsJ0DukAgENysND7zGeSVFbzLy/QGBmBggKufApLHgzU2hjE9jbqwAPfcg766iueIJ+ISCASCw4LfD5pWwrIuD0pNM4BlmRiGia7rqKpn2/14vR6Wl00+9KEot9xSW5fsLJfL0el0aLfbzM7OMjAwQDgcdr83DINKpUKxWGRwcJBAIPC4dzW+WVEUZVfzYoZhEAgEjtTc006RZZnjx4+7SVnr9bo7DxgKhdykiI8XJEnixIkTGIaBJEmsrKxQLpcJhULIsoymaXS7XcLhMCMjI6TTaeGiegPoC9+q1SpjY2PrRMOyLLuO2TMzM4TDYVZXVzl27NgNqrFgt/j9fm655RYKhQLhcBhN01wXc1mWXfH5btzKo9Eo0WiUXq9Hu93GNE1UVd0yKY1AcD30RZiNRoOJiYlNxxGyLJPL5ZiamnKfx0ct6bLg8UW9XqdWqxGJRLYVFYdCIVRVpV6vU6/Xj5QAPZ/PMzc3x9LSEvV6nVAoxNjYGCMjI6yurrK0tIRlWYRCIQDGxsYYGBjYMkZkZWWF5eVler0etVoNTdPcRDmVSgW/38/ExMSRjEOQJIlEIrFt0nGBQHDwiJGuQCAQCAQCgUAgEAgEAoFAIBAcEFvFlt1kxid7Sn/hrNlsbutCIEkS8XicSqVCKpWi2+3u2IVmp0xMTLgBdMVikZWVFZaWlpAkCdu28fl85HI5EokE4+PjpFIparUa8Xic1dVVUqnUpoEopmlSqVSIx+MEg0F3sfEo4vV6OXv2LCsrK4TDYXq9HpqmAbgC+1QqRS6Xw+PZPgD4qJBIJAgEAhQKBSKRCKZpugJ0VVVJpVJkMpmbItg0FAoRCoUoFovour7ldbQsi0ajQTabve52Xa/XuXjxItVqlXw+7wa2+v1+arUa8/Pz2LbtBnX26Xa77nXo9wulUolisUiz2aRWWx9YfphRVZWTJ09iWRaKopDP5ymXy/j9fiRJcjPDJ5NJ0uk0IyMjuwqmOwhM08QwDDqdzo4C8v1+Px6Ph06n4x6fbdubBqcUCgUKhQKtVot6vc7KyoqbLCQQCNBoNCgWi5w4cYJoNLrXh3dk6QdS9t3ptwp0MQyDZrPJyMjIkTmHfr+fEydOYFkW3W6XZrNJPB7fcFuPx0Ov1wMuBx5vRavVwuPxoCjKoXaYmZqaolQqMTc3h8/nc101wLkv6/W6e/33SoC+x8MxgUAgOFToH/0oaqkEkgTZ7OYbShJWLodnchKj1aL5wQ+S+NmfPbiKCgQCwU3MZnnFHKdTG8uydyQM679ffuUrEYpFnVAIHn4YbBtuvRXS6TTtdhtwAv/7LpOaFmF5OcjKioTXG+DUqdPE415Onjy5paCi2YTpaeh2nTFzqQQjI3DsGCgK5PMwOQmZDJw65TxqwJlL/tKX4L774JZb4DnPAa8Xzp+HlRWIRJzPt9P81mrwwAMwOwvj485xGoaTLNWynPLicWi3IRaDs2dhJ8aupunU++tfh+VlmJhw6iLLznGNjMDJk87/g1PuxYswM+N8dvw4hELw1a9CtQqtlnOunv9851F7zz3OdTlxAp71LKfupRI89hik05f33e3Co486CbFOnoSNmkCn45y3btc5Z9GoU5/JSeezS7ll1uH1hpicHOWrX/XwxCeGGB7WOHGiy8qKF12XmJjo4vU671O9Xo9ut+u6515Nve4cf73u/DsYdI7jW9/SWV62eOITbb7t27woijPPVa06dU4mnbYiy87vazUYHHTaQr/d9NtSn0YDzp2DSsW5rr2ec9xnz8KVU3u2DXNzcOECaJpThiQ5/7UsncHBFrmcgaoqhMNhPB4P7bafQmEUj8cp07KcujzyiHM84+PwrW85+5mYcM71RlQq8M1vOtdGUZy2cOutcPW0nW0790j/XDQazme33ALz8855OnEChoY2LqePaTp1LBSca33qlNO+dN2p51bDu41ot+HcOT8LC7eQz6/Qbg/R63XQ9Ra5XJvBQR/Dw8P4fD6y2SzDw8MUChKPPOIc7513OvebZcHUlHMdTNO5h2691fluu+N5+OHL99JG0zqlknPe0mlnm92YSZqmc181Gs5vr74/TNNpNzMzmydejkbh9tudNvf1r8ODD8ILXgBnzlzu52zb2cfs7Np+8WrabaddaZpzfh58EO6/H8bGnP7Q54Ph4cvtd3wcPB6Tr361w733Rmi1nsDzny9z5kyHQsHD8rKX4eEeqZSBJDnzSLFYjHK5TDQaZWRkBFX1sLTknENNu3xt4nGnLTUaTpmjo859+dBDzjkPBOCuu7a/hjuh0XDKyuWce/3iRVhaco5xcBBOn3b+PTvrtOnBQeccVCpOHyDLzn3/6KNOn/pt3+b0kbWaczyjo7C66vz72DHnWkvS5b6hfzz958PEhHPfFQrOuahWneebYTjXb7t2Vqk4x1CtOtdsaMjZZ73unOdWyyl7fh7KZafut9zi1O3qfWias62qwh13qNRqg/R6OYaG2oRCTmIIn8/PxYteJiedfVydy9q2neOfmXHqfmU/oGnw0EM+ej0ft9/uHL9tO+c6n3fqvZk2stl0jqdadc5dp+P0+ZblPHNSKbjtNqc8SXK2uXDhct/W6zn9e6/n/OVyzrVaXHTO1e23bz4HNzXlHFMqdXlM0Wo5z85azTnvJ044+9wM23b2MzMjsdGUYb3u9J233QZ33LHxc/dK+sdTrTr9Qrm8dn7R63XO/9V5H/J551p3Os69vln/cCXVqtP2Bwedc7ZR7lBdd+4rWXb2ea25U2wbFhacc6XrzvW1bee/Hs/ae2orTBMefLDNQw/JxGIx/P71HbqmSUxNORf92DGJTifJF78oc+6czote5Nx/V5fTaDjtMBZzrvlO8qh2u86z2TCca3v1M7xQcO4Bw3CuRa3mnEdZds736KjTd8zNrd93Mun0Wd2u8/zKZJzzs5vp9X6fmM06bWJpybkGicTaZ0uv57T5ZtMpMx532tL58869+JSnXB5v9NtMMunUJ593+qCBAaeMXs8ZRy8sOG09EHCONxRy9p1OO/vpdC4/GxTF+e7KZ2d/HLy46HyezcK99zrn8IlPdPrUK6+RpjnPOtt27uUrlyz63/V6Tn1s23ke5XKXx7ylktO2b7nFeWe4klrN2abRcPrhYNDpB0dHnefntSxtaxp84xuX237//JqmiWmaQIyHHgoRiRiMjvZQlH5f4yef92JZkErp+Hw+DMNYl/xpdta5VpmMc5zT006ddd25BmfOrD/O7eh0nL7p0UedfvMlL3HO8/Ky0/Zl2bnuqdTa9lWpOGOSqSnnuo2OdllcnGdxcZFut8uxY8fodkMsLvpQFJsTJzJY1lPI5wuYZolcLoeieFleDjA/7+zbNJ3xTCLhtMP5+XlWVlaYm1vhwgWJcjnOysoYkUiPU6fmSKdXyWTS6LrOiRMndrQuPDfnnDddd47zttvWjx+d/qhFpaJx5oxBKOQhGo1ekQTeOWfFolPPq58l/edNMOi8l20UUmAYTh/Q6TjlN5vOs+nMGecee+QR57koSU7/NTzs3J8CgeBgEAJ0gUAgEAgEAoFAIBAIBAKBQCA4IIQD+rWhaRqapmHb9joXio0IhUIUCgVs20bX9T0XoMuyzMmTJ1ldXSUYDNLtdl2BVl/YGo1GyeVybmbpbDZLtVqlWq0yNzfHyMjIOmGaYRgsLCy4mZ1vVPbuvmuyruuA4xAbiUSuyclVVVVGRkYYGhpyEwmAI06PxWI3hQh7I/x+P6OjowwPD9PpdLAsC1mW17gc9c/vTuiLdGVZRlXVQ+Oqm06nWV1dxe/3u85em1GpVLBtm2g0el1tu9lscuHCBfL5PMViEa/Xy9jYGM1m07336/W6K1Lui2dt26bVahEMBvF6vfj9fmRZplKpuGLLvsj0KBEMBjl79iyzs7NEIhFarRadSxFa8XicSCSC3+9neHj4UDqu9YWt/SQNO6G/XV94rqqqe8xXUiwWyefz5PN5er0e4XCYRCJBKBQinU5j2zazs7Nuezx9+vSRTvqxGbZtXwriYccuPKlUisXFRdfZaWxsbNP+enV1FY/HQzgcPlKuE07Anp90Os3KyorrVHQ1tm1Tr9fJ5XKuO9lm2La9JonMVsL9G0mj0aBWq7GwsEAoFGJoaGjN/dd3Pw+Hw8zMzGy5r0cecYKwbBu+/duFA7pAIHj8Il+4gFyvY0Sj20duB4OYfj9SrYb52GMHU0GBQCB4HODz7Uw8s1MsS+ItbwlSLjuCBnCEGj/zM/Cc54zh9XrdhFWf+pSXT35yEMuSUBQFRVG57z6Ff/7nIG94g8Lp0xuX8YUvwP/6X04w/tWMjjrzxYuLlz87dQp+/ded7V/zmsuff+5z8Nd/vX4fgQD8yI/Ad33X+nNj2/DhD8M73rGLk4IjfHjNa+CpT918m8VF+M3fdERkfT7/+fXbDQzAb/2WI5T7nd9Ze6yb8W//tvbfX/savO99G+/7xS+G977XETKAI1L4b/+NNdfj/vvhzW++fI3BEYdWKo74Bpw5/e/8TvixH7ssbnvkEXjd6yZotZq0Wi3m5714PGvnej0em1e8YpUnPKFFoVAgEAgQCARI99VIl/jc53x84AMy/alCZ05bo9fT3Pf5v/97kKQev/u7Laan03zoQ5fLisUcscilvAjrOHUKfvVXHRHI5z4Hb33r5tv+6Z86Ao92G/78z50EB1diGAa9Xg9d17Ftm+FhjZ/6qSXicZl77x3m059OIcs7m3vw+5376QUvuPyZbcPf/A388z9v/Jvv/m541auc9myazraf+MT2ZT3vefDa1268PjM5CX/wB8413whZhv/v/4Pv+76d9TFf/7pzHp1z7AXGME3DdV62bZunP73Lz/yMxfBwmoUFlTe+0RGa9ZEkRxS2uOgIwa7me7/XaY8b1eehh+CP/9gR7IBznn/lVxxxMTjn7R3vgH/6p8u/OXkSXvc6R8SzHfPz8D/+hyOI7Z+f5z0PfvqnnT6nUIA3vvHy/bMbPvUp579vf7vTFv77f3fEXX2Gh50+48p6/tu/Odv3kzfsFF23aLUGaDZbBAIBJifXK1dvu63FK1+5is9nE4/HmZmZQdd1VlebvOUtCR5+ePfHCPBXf7W2Le8W24Z3vQs++EE2FABvxtOf7ghRP/IRRwx4NZ/73Na/P30afu7nnP7oK1/ZVZUBp5392q9tLG7+1Kfgb//WEUzulH59b7sNfumX4LOfhfe/HzZfdpFQlBA/8RPw5Cc7bak/7aUozrOyf191Ok4feOVxfvu3O/fSY48590Cp5Hzu8cDLX+4IPvv3BTjPjR//8bUC7M9/3nnub9YHX8nTngbPfrbzfN9qvu1qVBV+9mfhRS+6/Fm3C3/4h454uM9Tn+qcu/e8Z32C9Je+FF796vXts9eDN7xB5qtfdb7YbnkvkXCeuydPXv7Mtm263S6maXLunMpf/ZWPUmn7G+HZz3aus6I4fdhHPrK2/WezzlhiZGT9b23baV8f/eja7X/oh5zENv1n+ze/6fSf/esTDDpjnmc+c9vqraHRcNrP17++9XZ33eX0vZstYS8uwu/9Hpw/n6bdDgLjPOUpJq985aqb4OZb3wryvvdlaTYv92G93jC2bREMhvjwh517/xd+wRFF2zZ86EPwzndePn+5nFPOVokHvvY1p903Gs6/vV7nnvnRH3XawVvf6tzHexnrMD4Or3/9xtf0Sspl5znwxS9uXv74uHOMjzziXJvtlmef/3xn3P/Rj+6un72a7/5uZ0z/N3+zvn974hPhN37Dub//6I8coftGvO99zr34t3/rCKzvv995jhSLzvd+P/yn/wTf8R1Ocqq3vW3tGByc3w8MOO8Q/WvY//xHfsS5FyQJPv5x51xu9Izo1/lXf3V3Yu6vfQ3+7M8u31d+vzNmeOELQZJkPv/5cb785WG8XudGGBrS+K7vKvLxjyeZnV17c3i9fn74h5c4edLpfFoteMMb1vZtGxEMOvfAM56xszrffz/8yZ+s7av/5V82335iAn73d51768Mfvvz5Bz4AnY7FS17iIZdrMj4+wZe/PMDHPpbCNK/u94bR9SbPfnaeQkGlVLKB9X3j2bMd7r47zyOPFPnwh2+h1UqsSXb24IMThEJtvu/7voBlWUiSxG233bZpsuRGw3ku3XPP+u9+8Rede8G2be67r8wf/IFCrQaWZePxSPzwDy9x110zpFIpFGWA3/9975r3ue/8TmesoyjOvfTudzvPI3DGcq99rZNMps/kpNMH99v2TvB44BWvgO/5nr2dCxAIBBsj2btJWS4QHHLe9KY30Wg0iEQivO51r7vR1REINsQ0TVZXVwEYGBjYMsutQCAQCASHgStFETeTK6FAIBAIbi7Eu5bgqPCWt8Av/EIQy7ocLSDLAV72sjYnTzqLntthWRaVSoVSqbRGzJtKpUgkEgcm6O07dbdaLdd9NxqNEovF9lwcu7q6yuTkJDMzM5w9e3bb/bfbbWZnZzl79ixnzpxxReD7QV+Y1m63sSwLVVVdUdvVzM/Ps7y8zOLiIq1Wi0gkQigUQpIkWq0WjUbDdSHNZDIcP3583+q9EU7w1CrFYhFN09x3gb5YN51OMzAwcNOKxg+a7d61+kLGfD5P64pIZI/HQzqdJp1OHwp33QsXLrC8vMzc3JybOOHKNtI/jtXVVYaGhshms9x2223X3E9861vfYnl5mXw+z8jICJZlMT8/z9LSEp1Oh3a7zfnz52k0GgwMDJBIJMhms9TrdbrdLtlslnQ6TTweR9M0FhcXsSyLpz3taTzpSU/aUkR/2Ol0OhSLRdcZXFVVEonEvvTLe4Vt23zjG99gdnYWr9dLbqsoJ5xA5wsXLjA6Osrx48eRZZnp6Wmmp6c5fvw4vkuWd/3tVlZW0DSNwcFBTNNkaWnJTYqRTqdpNpssLCyQy+UYGhrilltuOYjDPhCazSaFQsFN/gCOAD2VSpHJZNxztRn9+2pmZgZVVclms2uSwHS7XYrFIq1Wi/HxcbLZLKdOndrXY9prGo0Gk5OTbkKLYDBILBbD4/Gg6zq1Wo12u40kSei6jmma5HK5DdupbdssLS3Rbrc5duwYJ06cWCcqOCxMTU0xOzvL0tISp06d2vLdqVqtcvfdd7tjToBAIECr1ebtb3eCPXfK05/uiGUEAoHgpsO2qT73uSjnz2OFw9g7GE8qFy5gBYMYL3whqbe+9QAq+fhCrGsJBI9fvN4gur523vWlL21Qrzdot1uoqmfbuRTTNOh0uoTDIUKh0DpRsaI44srTp5152i9+scbv/q7Hfe9SFAWv14uiOIqzQMARnVxtPLew4IiarhZ9bcfExGXB3E65805HQHDlK8o//IMjzr4WZNkRgT3nOeu/q9Xg539+Y9HsRgQCuxPWXS8+nyPgicUcF8vXvGbn5f/szzqivFoNXvlKAJtGo3HJ3byHz+e99Ny5cg7G5kd/9BtEoyuMj48zNDTEsUs2rqZp8sUvlvjDP4zi8/mQJAnLMmm1WhiGia7rGIaOZdlIktO2PB4viqIQCAS2fa+/ktOn4ad+ynkn26rNeb2OSOif//myGLhPr9ej0+lgGPqld2RH5ZXL1bnrrgKf+tRtqKpKMBjA69153X7mZ+BlL3MEXh/4gCOs3Yqf+AlH0PW+98Hf/d2Oi+GHfsgRqFxJu+3sbydi0N/6rcsi7s0oFuE//+ed3ddPe5ojFP3Sl7bfdiNe+Ur4gR9Y+9nKitOmrxa5SZJT/6c9zRFGvf3t6/cXjzsirq3c4m3bEUteKbLtMz7ulPGXf+kIca+XXM45nqvJZh1xnSRdFmFfC7qu0Ww2abXahEJBJGnjNY+nPrXBK16RxzAMJicnOX78OB/4wBOZnNw+UfJ2/NiPOW15t0vaH/uY81w5KvQTMBiGwdhYj1/5lTyxWIRMJkMgEODCBaddHQY1y8/8jONy+9/+m+NgezVPfaqTXGGnCQ9+7Mec5BXgOMv+8i/v/rl/rfze7zliUXCSYnzhC7v7/UZ9zFveAp/4xGWF707WCnM5+J//ExRFp1AoUCwW0XWdZlPmjW8co91W8Xq9eL2+bff38pc7fdTb3rbx94kE/J//s951/R//cfNnSy4HP/iDjpv1r//6emGyJDnHvZ0I+kp2c75f+lJnfHE1puk8T4pFaDYbtFptbNvC7w/wtKc1+NEfzZPPe3jTm0bRtLVrP72eo/AMBoNEIo5N+alTTpv42tfgTW9aX14y6Vynq13NwXF/fs1rLgtHr+TMGWdsevWYYa8YG3OeKxstb1mWM155z3t2PpaTpMPR1/S5806nPg89tP22uZzzDvLzP79WRA7OcX3bt11bchBw9pnLOc/x7XjmM53EADthcdERGV8taJckp739y78s8Ld/60PTtG2TQ1uWRbvdJhwO8OpXw4/8SIo3vGHn4yhVdYTW2ezWyTOKRSfZykbtfbf0Y0g6nTYvetEst90W5a1v3XygZRjGpSTIG7+HgrOmNTq6wuKijKYlN137PX26wnOfew/Hjh1jYmKC4Q2yDLVaTtKwqanNj+F1rzMJh6d5/esTNJumm4QKQJJsXvWqC9x2m5e3vvUMzWbcfQfu8+M/7vQRv/M76/edSjlJVvx+5x7+qZ9a37Z3ym//9voEaSKGUHDUOApaWOGALhAIBAKBQCAQCAQCgUAgEAgEB8T1OqCXy2Xm5ubo9XrUajXXrdfn81GpVFzX5f10ubUsi6WlJVfk2Gw2XRfacDiM3+8nl8vtqQuqE3zgdYXaW7mPArRaLTdob78FupIkEYvFiF0dRboBo6OjyLKMoii0220qlQrlS2nIfT4fIyMjhMNh0uk0Y2Nj+1rvq+l2u0xOTlKv1ymXy9TrdXcBUZZlYrEYrVaLarXKqVOnduyiK7g2NE1jcnLSPef1eh3DMJAkiXA4TKPRYGVlhYmJiRvuaj0+Pk6323XFl9VqlWg0isfjwTAMGo0GlmUxODhIMpnkxIkT1yyG7ovIi8Ui2WzW7QtisRiWZTE9PU21WkXXdTweD5VKBVVV6fV6+P1+UqkUwWCQ6KUomr5jk6qqBAKBIy9OCQQCjI6O3uhq7ApJkojH49RqNZaWlshkMtuKYVVVJRQKkUwmUVWVlZUVgsEgy8vLrlN3pVKh2+3SbrcZGhpClmXy+TzBYBCv10s8Hgdw+9xisUg8HqfVah15F3TLspiZmaFcLtNsNqlWq654OBAI0Gg0WF1dZXh4eEvB/8jIiDvOWFlZYW5uDo/Hg8fjwTRNut0uwWCQ8fFxEonEgSdN2QsikQgnT55EkiQikQjVapV8Po9lWciyTDQaZWBgAJ/Ph2maaJrG/Pw87XabZDLpOpw3m01X6D86OkoikSCVSt3go9ucarVKrVYjFottG3QT3SjqEMfBZzficxAO6AKB4CZGkrADAVAUJE1j21hm20bWdSxVdSzIBAKBQLBnqOp64ZAsK6iq4iaa2m6uUNN0PB4VWZZR1fXzBKbpiDf/+I+dObPPfCaxZXfe6TjCiJe8ZO3n73//tYnQdis+B0cM+prXOGKKZz/bcWC9VvE5OPPYb3qT8zc05AjU+i7Wf/M3Oxefw8GKz8ER5n7xi47g2XEl3Plv3/tex1X9y1/ufyIRDIawLPvSvntomoaqei6JyS0Mw+BrX4vyqlf5SCQSa+Z8DQP+8A8vv3NZlkWz2ULXNTqdLrIs4fF4kGUZ2wbD0Ol0OqiqApdGHDsVoZ8/7zgPb4emOQKOKx3hnc8d8Xm328EwTDweDz6fH0mSqNeDfPzjKXS9684ZS5K0oWhmI972ts3FhBvx8Y87rquf+czOfwOOO/KP/uhaEdtnP7sz8Tk4LumS5Ahlw2HHqbTTcQRbL3yhI97/1Kd2fl9/9au7q//V/P3fw7OeBYODjnjtvvsch+ONsG1485udvw99aONtqlVHePbGNzoOqxvx9a9vLD4HmJ11xJJ7xUbic3ASR3z+845j/LUK7Rwkd47atu1NXTMfeCDMd393EUUxAGg2vTzyiMpeTCO/853OdTx+3HGrfs5znHa0Hds5lR8WbNui1WpjGAamaWAYJo8+avGZz1R46lNLFAoFms0kf/Zn45smADho3v1uRzC5kfgcHPHubnjnO+F7v9fpO/7hHw5OfA5O//bEJzqOsrsVn8PlcxGNwi23OALsT3969/tZWYGPfrTNqVPn16x3feYzx1ld7aCqKoZh0utpBIPBLddoPvaxrcXDlYrTL1zpWG7bWzsXr6w4QtjNsG0n0cUv/MIWB3nV/nZzvj/xCec59OpXr50euO++yy7A/bFsp6MDNg88EOZ7v7fAAw+E14nPwcYwDHw+H7J8ed53chL++3/f3Pm77yL+i7+4/rsvfWlzMe65c5s7d+8Fc3OOG/VTnrL288lJR0x88eLu9neYxOewu4QtKyvOc3wjga5tX98z8d/+beeC6y99ybnXEontt73vvo3d1G0b/t//g/Pn03g8HXetdqv7X9M0FEUGFN773ij33APz8zurMzjj7p/+aSfpy+23O87vt966frsPf3hvxOfgPAdt28Y0TT772ZN84Qtbx1b0x9GmaWKa1rqxhmkamKbJI48E8Xi8eDybr7WfP5/g6U9PUalUCIfDDA0NrVmb73QcUfhW4nOw+ZM/afPkJ9tUq85J8Xq9qKqKJHGpLgkmJ+vMz9t4vU4Mz5V9z6c+xaZjplIJPvlJJxnP5z537eJzcJ6xVwvQBQLB3iMc0AU3FUch64NAIDLqCAQCgeCoIZwiBAKBQHAUEO9agqPC294GP/dzGzugT0xsvcidz+eZm5tjdXXVFeD1nUjbbSeIo+/0Oz4+vqcC8D6WZTE5Oem6GTebTRRFQVEUd9wYj8fJZDLkcrk9E0JalsVDDz3E3Nwcuq4zPj6+5bYXL150RdynT5/ekzrsJd1ul0KhQKlUwrwUcSLLMslkkkwms8Zhdit0XXfFtpIk4ff7icfjuxb36rrOY489RrFYZGlpiWAw6ArrbNum1WpRqVTo9XqMjY2RSqU4c+aMcEK/TjZ71+pfj3K5zOLiIoqiEI/H8Xq9mKZJvV6n3W6TSCTI5XIcO3bshovQdV3n4sWLNJtN6vU6jUYDwzCQZZlIJEI0GiUQCHDy5ElXsHktTE1NMTMzw8rKCqdOnXLbYF/8Xi6XOXfuHI888ogrVs9kMgwMDDAyMkIwGCSbzbrjhKWlJZrNJqlUimc/+9nceeedIrnCDaDRaHDu3DkuXryI3+9neHh4w36s0+kwOzvrXs+zZ88CMDc3x/LyMjMzMyiKQiaTYXl5mdXVVXq9HrFYjGq1im3bDA4Oum2iT98tfWRkhGPHjh14ApC9xLZtLly4QLFYZH5+Hl3XicVibn/ebDZpNptEIhGGhoYYGRlhcAuXVtu2WVxcpFAo0Ov1qNfrmKbp3tt+v98NoD/K429dX+vA08fj8ZBOp90xVT9RS6VSoVqtus9wj8dDPB4nHo+TSCQ4ceLEoT0fpmnywAMPMDU1RSKRILGDSLFnPOMZ6xzQf+M32tx33+7KvvtuJ5hSIBAIbkZKr389ysc/jndhgd6ttyJt9RxoNPBNT9O75Rb4+Z8nfrUVp+C6EetaAsHjl2AwSOcKRbHXG+BTn2rzla+0ePDBOo89ZqEoCn6/f8Pfa5qGpjkCqEAgsOUcxtCQI8p67LGd1e3MGUe4bZqO+Gc3gomjwp137o3z8X4zNOQIUPpOfJJt4tfrYFv0vDEsafO5IZ9vvbu0aZq02y1M08QwDAzDwLb7ImwVRVE5e9Yil4u7c1ntNjz6qL0mwWyn076UTK/jJmS9Gsuy6HY7yLKM3x8gGo2sEXnsB333xm63i2EYBAKBDeeFLcui02mjqh4CAf+lhGbXloRyO5JJRyy3W06fdsTjfe6/f2/qk0rBf/kvjrvsQXPqlCPE20v+63+FZhMeeMARQHa7TnLlrUVKRwvLsmg0nHl2WVa2TeYwOlpA13VWVob27b5TFDhxwkkY0ee225z/mqbzDDGMo3EdbNui2Wyi6zrdbhfLslFVBUmSsW2LgYEylUoYCOPxeAiHw4dGhL4fjI87SRoOmrNndz5O2SnWFdnUd7JGaBgGrVaTXK5Ms9lBVT1UKnFMU8G2LXTdEVR6vR58Ph+hUGjDBEC74clPvvz/zebae+p69un3O33u3Xc7otp77oFvfMNJjNFXY11LoqA+oZAjiDXNtc8nwzBoNpu02y1U1bNlMiVd11w36VAovOv34VOnnIROinL5b6+eldfDnXc6/Z+u7/0zT3BtXHmfbcb2bcemXm+gaT263R4+n2+DNmvT62nouk4wGMDn8+9pEunTp50xjiw79/Gjj+7ZrjFNg0bDWZMLBPzr3ME3otVq4vf7CQZD695Fe70urVaLdrtDOBxiu3G2YegMDi6TTqe58840d93l5clPdsTeb37z9vXXdZ1Wq3lprOS8e2xWTrfbu/T+7NR9Nzz5yXvTz+RyThJkRXH6MUmy6PVavOY1TSYmsod2zU4g6HMUtLAiikYgEAgEAoFAIBAIBAKBQCAQCA6Irdbit0oV2mw2mZ+fZ3FxkU6nw/DwMJFI5IrfOqKy5eVl1yU5GAzuuYvr9PQ0pVLJdUEdHR11y3ACdhoUCgU30NPr9a4R+V0rsiyTSqVoNpvMzMyQz+fJZrPrtrMsi8XFRSRJIhqN7osIHxwBeV/0vfFi6Nb0nepHR0cxTdN1kN+pcLzT6bC8vEy1WnWzgvfr4vP53AQA/eCPvmurbduue+2VrKysUK/XXffhq11b+w7vKysrzM/P4/F4KJVK+3Z+H+/Mzc1RrVZZWFgglUqRTqfXtI1EIkG73WZhYQHAdem9kcJpj8fD2bNnqdfrFAoFGo2GK1INhUJkMplrSo5wNb2e47YUCoXWBDdJksTw8DCxWAxd19E0jenpaRqNhusa33dM79ehVqu5yTzGxsZcN23BwROJRIjH44yOjjI3N8fMzAzJZJJoNIokSWiaRqVSoVKpEI/HSSaTDA8Pu78fHR11xbH5fJ6FhQVmZmaoVCqoqoqmaYTDYRKJBPF4fN3zQ1VV/H4/3W53jcj2KJLP5ymXy8zNzeHz+RgfH1/TrhOJBN1ul4WFBRYXF93+Y7PxgiRJrki9VCq5AnRFUQiFQqTT6ZtC1OXxeBgaGmJwcJBer+ceo8/nW9NvnT17lkKhQDgcJpPJuAJ0VVUJBAJks1lSqdR193X7Sb/vlGXZrf+1sFvxOQgHdIFAcHPj/57vQb/3XuzlZZSlJayRETayUbQtC8/yMnokgh2JEP3O77wBtRUIBILHD4oCz30uPOc5QSYnF7n33jZ///dBSiUTj8dLMChx++1NHnwwQLPpOM0FAn48HhW/f2sx4tKS87dT9tMd8rBwFMTn4Fy33/kdSLRmeeLSR7mz9Fn8pmODbcgeHkk8g2/kvoOV2G3rnudXi88BFEUhEomg6waa1rs03+u8U6uqis/nZWVF3dTRGbgkwtPp9TRUVd1UXNYXf7TbbSzLmfPdTAyyV+i6hmU59QsE/JsKDmVZxudz5ld8Pu8lJ8uduaDvlmsRn8PeiBA3olS6MeJz2B8h3hvesPf7PGzIsoyqOmsl3W4Pr9ezqQDati0eeyyAzxcnEFD3LemDaa5vow8/vC9F7TutVhtd12m3O3g8KoHA2jm2anUIXe+h6x2CQYl2u7Pn65mHiRshPoe9F5/vHptOp42m6UxOegkEoq4A09EBKqiqB9M0L60rS0iSTDSqcj0JTPZDMN3f5733Os72+0GrBV/96vrPVVVFVZVLyWq6lxLcrJ+TNwxnHOH3+1AUBY9n9+tdh1XcfVTGmI8n9uY+kwiFgvT9dLvdHrquoSgqsixhWRaGYQASgUAAj8dDMLi34979GhsC7riifyzb6Z8dx3Tnd7K8wZyibXPZe3j7PlKSZKankxQKQebnZT71qd3Vv9frXUqsZW+awA24NJ5y3ktU1UkuspukMnvVZ1/9rmXbEr2eZ8sYLIFAsDtEJI1AIBAIBAKBQCAQCAQCgUAgEBwQWy0sXZE0fh2rq6vU63WazSbHjh1b58YgSRKRSASPx8PMzAyNRoN8Ps+xY8f2qOaOy3pfFBsIBNY51MqyTCwWIxQKMTMzw+rqKl6vl0wmsydO2blcjlqtxsjICIuLi7RaLRKJxBpX12q1CuCKSePx+HWX28eyLEqlEvl8nm63i23b7vH3BY1XJgXYKbvNtlyv17l48SL1ep1SqUS73UaSJLc+sViMdrtNrVZjcHDQFW3aV6yu9YVziUQC27YplUqUSiVCodA68fmVDAwM0Gq1qFarFAoFIUDfBzRNo1qtks/nt0yiEAwGGRoaYn5+nmQySalU2pNkD9dLNBq95G60P9jbrBKHw2HuuOMONzHE5OQkhmGgaRqyLGPbjrNUoVCgUCi4ySDGxsa2dIEW7D/Hjx+/FIChUCwWWVlZYWlpye3f/H4/g4ODxONxJiYm1vS3kiRx4sQJgsGgKyRvNBqua10ul8Pr9ZJMJrcVB2/Xxg4ztm2Tz+cplUrIsszw8PCGz1+/38/Y2BhTU1M0m00KhcK2AZ6KopDNZjdM/nIzIUnSloE0siwzMDDgPg81TUOSJLxeL8Fg8ABreu30E9YEAgGazSbpdHrL7Z0xz9rPrjCV3RVCgC4QCG5mQnfdRen0aWi18E5PY1gWZi6H1H93t23sZhPP8jKSaWIdPw4vfSnKNbxDCgQCgWD39N8b4SK33FJhZWWO1dU6fn8bWQbDOM7XvnYCv9+PqqqEQqGb2on1cY9t84yZ/8uzl95PwGgQ1wr4rTYSNj3JT6yX587Sv/Ot5LP55OnXYSpbJyNwkDZM/LlTNE3DNC1M09z2/VKWZRRFcZMw7rcAXdN0dF1HlqVtnRtV1REMOWJ8bd8E6ALBXuDz+dB1HVU16HQ6+Hz+des1lmXS7XaRZUcou51TusBxfDUMg263i8ej4vOtn2tz5qf8QPfS+ZWxLHPfxP2CG0Pf3VzTHGfjzZ4hiqLg9/vcZBD7mcDkqBIMBjFNC7/fdkW6quq5JGy1MQwdy7IvrY3156oPb5JUgaCPoqiEwyFaLSdxk647zxDLst1nRT8JQzB4tN7R+mN2Vd1Zv6brBrIsu0lyrkaSJHd988o4lc1whOCXf7sb+uJ/TdMvvd9s/XuPx3spQZaTtMrrPTzjJUU5uuu+AsFhQwjQBQKBQCAQCAQCgUAgEAgEAoHggNhKh72ZAF3XdWq1GuVymXg8vmWAi9/vJxaLUS6XiUajjIyM7JkzaaFQoNlsous64+Pjmy5UqapKLpdjYWGBbDZLpVLZUtS8U1RV5dSpU4ATGFSpVMjn8657p8fjcd1tk8kkx48f3zMHUk3TmJycdEXu1WrVdR33+/0kEgkqlQrZbJbR0dF9cz7tdDpcvHiRfD5PsVgkHo8zMDCA3+93RfilUonp6Wn8fj8PP/wwqVSKarVKp9PBtm28Xi/xeJxarUY0GiWVSqFpGo1Gg5GRkS3LlySJRCJBuVwmnU7T6XQICDXZnlIqleh2u3Q6nW0F0eFwGL/f7yYEOAwC9P3G4/Hg9XppNpubLq57PB73/m82mzSbTVqtFg899BCWZbli5kwmw9DQELfeeitnz54VwYM3GFmWOXXqFKurq66TdLfbxbIsPB4nuDMWizE4OLihWFqSJIaGhsjlcm7iDY/HcU8ZHR113dQ3wjSdQNJUKrWpw9hRoNFo0O12qdVqZLPZLZO/eL1eotEolUqFSCTC6OjorhOiPN4JhUJH1pkpk8nQaDQol8s0m03C4fCG29m2zcpKGdNc25a2Spq0FWLIIBAIbmokiegf/zGN//Jf0CQJdWEBz7lzmH4/tqoiaRqKpmGEwxjHjmE+/ekkX/OaG11rgUAgeFyhKAqnTp2iWCwSDucZHo5fcqq2yWb9PPKIH4/Hh8/n27e5PcHh4NnT/5tnLv0jw+2LhMw6DTVGyTsASATMBsOdabTeEqrVw2t2+Mht/x1L2t9Qa8uyLokvpR0lc3UEOhqWZe/aZfDa6mbteN5AlhUsy8S61pdHgeCAUFWVQMARR/d6zpx8XywGzpyhZVmoqorf77uUpGRv1ttuZpyEGo54MBDYes7d6/XSarUxTZNeTxPrTTcZmqZdci9mWzduVfUgSZpIYLIJsqwQDodptVooioph6BiGgWHYgHTJgdiDoiiEQsFtE8YIjh6SbWJLN+cajqKoRKMRdF2n13OeIY4TuCNK9/l8qOrRbNM+n9dNlK5p2qZrkJZlXUos5RzrRuNuJ9GTgixLGMbOBO395FCKsrt3Bdu23Hopyvb9cf/9xXlvOFyC7yPadASCQ4m4nQQCgUAgEAgEAoFAIBAIBAKB4IDYKnbsko56HX3Rd6fTIZfLbVtGPB5nZmYGwzBotVp75gJerVap1WqEw+FtF/lCoRCKolCv16lWq3siQAcnGOXs2bOXAkXDZLNZDMNAkqRLi+ohMpkMyWRyzwJFDcPg/PnzlEolFhcXUVWVZDJJIBDAsiwajQYrKytUKhVM00SSJEZHR/ek7KtZWlqiVqtRLBYZGRlZIxiTJIlIJEI4HOahhx7i4sWLeDweyuUyQ0NDJBIJJEmi2+1SLpcpFouMjo5SLBZdUe5O3FuDwSCrq6vuQqgICNpbOp0O7XYbr9e7I0F0JBKh2WzS6/V2lO38qJNMJimXy+TzeZrN5hoX7CsJBAIMDQ1x7NgxN2jQ6/Veyrruxe/3k06nOX36NOPj40dadHwzIUkSuVyOgYEBarWaK0BXVZVYLLaje0KWZVKpFHfccQeRSMRNyLHVvVGr1VAUJ4ArmUzu5SEdKO12m16vh2maRKPRbbePRqMsLCxg2zbdbvfIiqkFuyeVSrG0tEQikWBpaYnh4WFCoRCmaVKr1ahWq3S7XSqVCo88ElvngH6tiCGDQCC42fFks0Tf+lZqf/iH8LWvYbRaSPW643geDGIkEtiRCPZLX0ryta9FEhGQAoFAsO90Op01813/+I//yN13300mk6HVatHr9QAnmd1znhPmzW/+NhqNh6+73DvueAtjY6/a9PuvfOU7KBb/7brLOX36tzl16r9u+v0DD/wki4t/f93ljI//DLff/j82/f7RR3+Dqak3X3c5udx38ZSnbF7fqam/4NFHf/26ypCxWZSD/JASQ7V1psK3o8uX3XnrpHmv7OGPWw+DtoBd/XfMmT/BugbBTzB4jOc9b/P2VCx+jvvue9maz3Yzv9ffVlGCvOQlxU23azQe5fOff8rOKr15aQDcddd5VHXj+Thdr3Dffbdd2vb65iif+9wHCYVObvr9Jz4Rw7L06yoD4Nu//dOkUs/a9Pt/+7fTdDoL113Ok570boaGvm/T7++55z9Qq33Ndam8Vu3+bbf9GRMTP7fp91/72g+wuvrP17bzKzh58lc5c+Z3N/3+oYd+nrm5t6/5zOMBfZeXbHT0P3HnnW/d9Pvz5/+Ayck/2t1ONyCdfiFnz74br9dE13U3eYKqKng8flZX383Fi9fX9wDEYk/gWc/60qbfr6z8E1//+g9fdzl+f44XvGBq0+8rlfu4997nXnc5AC97WXvdZ44o1sQ0l7n33ufsYC/b9xkvfOE0Pt/GCXhNs8snP7k3c7rPfvaXiUbv3PT7T396EF2vXXc5T3vah8lm/+Om3//7vz+RZvP8dZdz551vY3T0lZt+/+Uvv4RS6fPXVYaiwG//9u/x1a/+yro5RMuyME2L2dlfplL52A721t/Bxu3h2LH/wq23/ummv37kkV9jevqvdlbxLRga+n6e9KR3bfr9xYtv4rHH/tuu9ytJrDlHyeSzePrTP73p9gsL7+Gb3/zpXZdzNeHwKZ7znG9u+n2h8Bnuu++7rrscjyfKf/yPK5t+X68/xBe+cNd1lwPwkpeUUJSNJ5t7vTz/8i8Te1LO8573CMHg5vv6+MdD2Hswef6MZ3yWROKqc2PbjJe/ypOWP8Zvzb6JstUFJGxJwpIUbGR2W/JTnvL35HKbX+svfvEZ1GoP7Lb667j99jczPr552/3qV7+XfP6T113OqVO/zunTm9+LDz74s8zPv/O6yxkb+wnuuGPzvuXcud/hwoU/2XY/ztgdNurjbNsmHn8Bt9/+Hvx+/7rvAebn/zcPP/y6K/a1nQP6xtvEYk/mWc/64qa/W17+IPff///tuJzNygsEhnn+8yc33b5cvocvfelFO9r3VsiyyktfWt/i++suQiAQXEKsZggEAoFAIBAIBAKBQCAQCAQCwQFxLQ7opmm6Lt87ye7c38ZZ2N9E1X4NGIaBruubumReiSRJ+Hw+DMPY0zqA41Y0MDDAwMAArVbLdSL3+XybLshdD33R98LCAslkkmw2u+b7UChEOp1mfn6ehYUFFEUhkUjs6DztBk3TqFarlEol4vH4pvvvdDooikK5XCYcDnP8+PE1TtrhcJhUKsXKygpzc3PEYjH03UZ+XeJmFzvfCGzbxrbtXTgKyW5A3ONBgJ5IJPD7/cTjcVZWVvD5fK54vNVq0Wg0MAwDy7LI5/OMj48zNjbG8PAw1WrVzS6fyWRIpVLC8fmQIknSdSdPSafTrKysEAqFWFxcZGxsbMNnaLvdJp/Pk81mCQQCmyY1OArYto1lWUjSztzSZFl2+xzhSvb4QlVVJiYm3Os+NzeHaZq0247jVH+8Yds2y8ubB8PuFpHjQCAQPB5QUylSf/7n9GZnaX/wg5jnz0OvB6EQylOfSvS7vxslFrvR1RQIBILHFZ1Ox/3/K999QqHQmkRc3/d98Od/3sWyOlwvtr31fKhl9faoHGObcrQ9KmfruUPb1vekHMvStinHuO5yLMBCxU+LqfDtmPL6pIQmMl1X0mODrbFrhQ9gWd1ta7PR8exGx9R3h9xmqz25Pjthr8rZTsxlmp1t2+XO2Ho+xDT3pk+AzfsEVYVotEe5vBf30MH0CduJ/y1rfZ9wKd/HLsvZrk/Ym77HtnWi0QiapqNpmvuskGUZr9eDz6fuSTmmuXWfYNvmHpWz3T427nv2Ctu+LLK7GfuevSnrxvc9sDfjEcsCVTV43vPg39bl1rEBew/HCbvve66tnP0fjzjlbN0x7l2fsF3fszd9gml6ttni6I1HthsAOv3t9QvQ+27TfcLdPN/76O8y2LpIVC9hWj16/X7DBrZ5B9i8nK37hL3qe7Z/R9mbd6Ht+4S9Gvds3Sfspu/Z6jEjSSbBYGDTeKCr+56dvDNstM1u+57dvpv0Oai+x7Y3j59SVZubPHRCIDhQhABdIBAIBAKBQCAQCAQCgUAgEAgOiGsRoCuK4ookDcPYVoRuGMalsuQ9FVfKsowsyzsWlPdFcPspiN1vt1bTNCmVSmsc1zdCVVVGR0e5cOECzWaTQqGw5wL0SqVCr9ej0+msEZRfTblcptPp4PF4CAQCaJq2rt1IksTg4CC9Xo9ms+m2mWazua1rbrPZxOPxXAq+Eq7Re42qqng8Hnq9HpZlbSsi7fV67vXYieD0qCPLMsPDw+i6jq7rzMzM4PV6XdfnRqNBvV6n0WgQDAbx+/2Ypkkmk2F4ePhGV19wgPh8PnK5HKZpMj8/z/T0NIlEgmg0iqIoblKPWq1GPB4nlUoxNjZ2o6t9Xaiqiqqq2LZNr9fb1jG+1+uhqiqSJO0owY3g5iKRSHDs2DF3vHju3DmWl5dptVpuoo5wOMzUVGLPyjxxYs92JRAIBIce3/g4vl/6pRtdDYFAIBDsgjvugHgcGo0bXZO949QpWFy80bU4fCi2Ts2T2lB8fjPw1KfC1752xQd74MzZ5z/8hxXuvffoJu87TEgS/PiPw3/8j/D858PMzI2u0bXh9Tp/zeaNrsn1IUkyPp9vw/k0Sbr55933Ekly1qD2whVYcHR41avg3Lm14w5JkpEkWbQFwb7yvOfBE54Af/3X15bs5LAR7BZ5xUOvY6g9xVBnCgmbjRyzBTcfqqrg9W69rifYHSIPvUCwt4hoAoFAIBAIBAKBQCAQCAQCgUAgOCCuRYAeDoddMXG1WiWXy21ZRq1WIxgMoqrqngq0A4EAwWCQarXKwMDAltvquk6n02FgYIBAILBndThoKpUKmqbRbDa3FSaqqkosFqNSqRCNRhkbG9vTBAB9wa0sy5uKCg3DoNlsUq/XSSaTrrB8s8QFqVSKhYUFADweD+VyeUsBumVZVKtVEokEoVBoXxznH+/03e0lSaJer2/pAm2aJrVajeHh4et2iz5KpNNpt23PzMwwOTlJuVym1Wph2zY+n49gMIhhGHS7XXRd57HHHuPMmTPbCnIFNxfDw8OYpokkSVSrVSqVCoVCwf0+FAoxPDxMNBrl2LFj1+1+bhgGxWKRWq2GYRjIskwgECCTyex7whSAWCyGz+dzxwvbPaur1eqa3wgefySTSXw+H4VCAUVRiEQiHD9+nGAwSCAQIJFI8NWvHuPJT773Uh9rEg5HCASCuy7r+c+HTfL4CAQCgUAgEAgEh4ZYDObn925/z362E/SuKM68sCzDxYvwpS/tXRkbEQrBL/0S/OVf7v2+z56FN74Rzp+He+6BQgFqNee4rpfBQfiBH4CnPQ2SSfi7v4PPfvb693s1sm3S8DjJtmQZ/H4Lywaf10L12AwYGkztXXkvfCEYBuhXmTReuGDz5S9f//4VBb7/++EpT4FbbnGOybad6zP7gS9if/xP+dz1FwPAmTPf4vbbPVy8GKNY9KAocP/9TiJWa7MFjmvgOc9x2toTnuC0rwcfhHL58vcf/zjsME/ultx+Oxw/7vy/1wuVitP26nXweODee6G7nZH9DsjUzxNNLFEPDAHwMz8DL3qRU+ZB8e3fDhcuwOrq3uwvGoWXvhS+7/ucc/XZzzriU1mGfB7m5q6/jEzGaQvLy06/NjDg3EuG4Vx/WXb6or0im3XqDs61ecpTnDIUBR56aO/KiUSc8/a0p8HICHzrW/DAA/DWt+7N/j0e+K7vcvqcr3wFSiVIJJzr9eIXw8c+Bl/84t6U1U/cMjTkJD2xLGi3DZaXm+Tzizz44N6U8x3f4SRr6Hbh3e92rnsuBz/5k3DnnRDc/VTVjnjyk512J0lOef/6r077u15e9CL45V92koU89pgz9jAM5zl47Bjcfz+0Wtdfzq23wjOecXkMEo87/Vur5Qh1z51z2sdeEIvB//yf8OlPw7//u3NcZ8/KKEqXlZUOlcr1lzE0tPX3uRxMT19/OadOwctffvm58+CDB5+g6I47wO93+ob9YnwcbrvNSRrwla9c//4UBUZHnfb8rGfBf/gPcNddznjkoYec9vbv/3795QC84AVOe37Ocy4nPT1zxilraspph/uFqjrjrYEBpz/d6/wKd0++icH2NCPtScqeLCX/MGb136/Z9fxK7rrLeRacOwef+9z113Uzjh+Hpz997Wff+Aa023tbzugoPPOZUK06fVwy6TwDw2Gn/3vjG/fu3W5sDH7lV5znzac+5fQL+bzT7ppNZ4x1vaRSMs94hvOu2G9XiYRzLI88sjf929VMTMArX+k8v7/+defcFYvO/18vPp9zffoEg85xpVLO+P7RR52x9vUiSc77lsfjtAPTdJ6pmmbT6WztXi8QCHaHEKALBAKBQCAQCAQCgUAgEAgEAsEBcS0CdI/HQywWI5FIsLy8TCKR2FRE2e12qVarriDV4/HsQa0d0uk0tVrNFdfFYrFNty2VSvj9fgKBAOl0es/qcND0ej26lyLMgjuIYAmHwywtLWHbNpqm7amgr+8kv5VTgK7rrvNtOBymtU2ESt+lPRgM4vF4aDQarK6ubihatCyLpaUlwBFJZzKZaz0UwRZEo1H8fj+JRIJCoUAwGNzQad62bZaXl1FVlXA4vOPrYVmWK4xVFMVtV0eNXC6HruvMzs7i9XoJBoPkcjkikQiBQAC/3084HMYwDObn57Esi8nJSW699dbHhVO84DJjY2NEIhHy+TzJZBJN07AsC1VV8Xg8JBIJBgYGdtTHb4Zt2ywuLpLP5+n1eq4AXZIkQqEQpVKJYDDIsWPH9jVxh9frXTNeiEajmz6HyuUyvV6PkZGRI9mfW5ZFs9lcI/QXCSaujVqtRigUIh6Pc9tttxEIBNxnBEC9rhIMTqCqGp1OF1n2oihBbNve8TNkdNQJrhcIBAKBQCAQCPab7ebitpsT6M9nboZtOwHtluX8vyw7IpSrh8a/+IsKr3rV5uU8/LCPz3424M4HS9IG++lHzvcLvpJLG/7Q7Q/wM39h87a/kXj0Uadez3kO/NAPOUJKr9d7XfOTtu1UQVE8RCKOgPU//2en+DNnnD+AWs3DV796/fOgZ854eeUrL//7l37J+evzF3+h8uu/vnU5/Tr3T5ltO/W1bZCxUG0dvwSZQQNfvIPXu35S3pe3CfTbimU5O/B4trXQ07S1lyoU8vOhD23+s3/9V4n3vMeZJ+i/X1mW5c7/bvTOdeV3/b9gMMB/+k9rt5MkuKPwIW6//82c782y7dVxLIud/1fVDSttWRaBQADTfJinPjVOLBZDVVXuvtvkox+N8KUv2ciy/1J7lrZcC9mOV7xC4uTJy/++66613//RHwXQ9avC3y3rssrfttdejP7x9a/lpWN95StlnvWszevxjnf4abe3b9tX9gsAKiaybbjVeNbS/+M/Nr+I8uSznPrF7yDyghesueF3kxzQNJ2/fh/UT3AB8HM/p/L0p8P73w8PP+yIqF/4QnjFKxzR8ze+4WVmZvNyDIM1/VJ/31d/ftddHt7+9rUC+he+0PkDePhhDw8+uHE5V5+rK7m6L3za07y87nWbn4tez8OnP73FeduiTdhIgIQkw9NG8/zTP22+m//9v1U+9amNy7Gsy9dDki5fk4249Vb/huXccYfzF4sp/OAPBtz9weUkJldzZTvoX6v+dplMgJ/8Sef/X/3q9b89c0bes7Wrd71r/WedToBHHpnmy19e4p/+yQvImKZ0xTHZKJYOloV06UMbCRvJ8fiVbPfz/kn9/u9oM3jM+egJT1hbXre7/fN/J5gm+P0SHg+cPu04ep8+vXabaDSAbWvuPdG/D7e67hvx7GfLJBKOEP1FL1r//R/8gZ9y+fqP6SUvUdY8V6/ms5/1MT9//eX0E2B7PPCylzl/AKbp48EHSzz8sM7ysnfLucQrn2+bjdee/nQPf/qnjjjz//0/J/nN4KDT93zf98HrX+/hgQeu/3jGxrz85/+89rO5Ofjf/9tJGKEoKqoaWNP/XgtPeYqPn/s5p88ulRyB/d13O3+KAu95j8LHPnb94zhVvbwmkU47wuAXvMBJdiBJ8JnP7E2fEI0GeMtb1n/+rGc5fw89JPE3f7M3fc/P/ixcXeXhYfjBH3REwb/3e9dXTv/ceTwSgYDTpr//+9cnu/j1Xw9suW6/Hf1niKLIhELwvBMX+I7yvXhb5+mFI7SVIeyWjE+S8SOvfVfoP9dk+bLydhte9CLFvUe/8zud++j++53d3H47TE/7eeyxgNtfy7LTRvpJfzZ8Z9mAu+9W+emf3vz77/ouH5/6lPMudGV8jKru7p567nM9vP71m3+fy11+F+o/N2HtGGcnvPzlXv7qry7/++p+VdM8fPnLO29zV4+r+uf01lu9/Nf/uvnv/vqvVV73usCad9LLx2NfOp/ShmPEK8crXq+PF7/YuV+uXB7sj7c/+EHFPW+2bbt/TllrL/5G7yZ9Mhn/ltfnnntk/s//2Ztn0C/8wvrPTdNmdXUPsrkIBAIXyb6ep55AcMh405veRKPRIBKJ8LqtZh4EghuIaZqsXkolOTAwsKdOWAKBQCAQ7Af6Fam491LAJBAIBALBXiLetQRHhX/8R3j1q5+LZfXcz2TZxzOe8TmiUXjPezb+XbPZ5Ny5cywsLNDpdFyhZR/btmk2mywvL7uOrmfPnt1Tx1XLsnjooYdYWVmhWCwyPDy8zi3Wtm1KpRLFYpHR0VEGBwc5derUntXhoFlYWODChQssLy9z+upIkw1oNpssLi5y5swZbr311j0VoJdKJS5cuMCFCxcYHR3d8Nq2221mZ2eZmppC13UMwyCZTDIxMeGKy64W6D322GMMDg66YrP5+XlXlNmvf7PZpFqtAjA6Okomk+HkyZNHVrx8WNjsXatcLjM1NeXe7+l0mlgs5j7bms0mxWIRTdMYHx8nk8lse5/VajUKhQK1Ws39TFVV0uk06XT6SAo3H3vsMebn51lcXCSTyaCqqitEvfLeKxaLFItFTp48yYkTJ25IUgxd16lWq+i6jiRJ+P1+YrGYEMMfMJ1Oh3a77QrQw+Hwdc9z2LbN9PQ0hUKB5eVlms0mfr8fn8/niqRVVWVgYIBEIsHp06f31W281Wpx7tw5lpeXqVarpNNp4vG4GwTY6/Uol8vUajWGhoZIp9PceuutR2bsrGkaq6urlEoldF13He5VVSUSiZDNZonH4ze6mkcG27Z56KGHmJ+fp9frMT4+ftX38Ku/ehzTBE1zxhWWZRIOhwmFQng8O7Nq+7//13EfEQgEAoHgoBDrWgKBYL/pB/tf72t9XzygXqWjrfyv/4X03vciz83hqdcx/X7MS3OBSquF0u2iRyJY4+PYP/zDJH7+59397cd0na7vTGxyUNimSevzn0d78EHHSjEYxPvkJxN+1rPcSl5dZ9uG9je/if4Lr8H7rW+hDQ469ntb0Wrhu3CB3i23IP/arxH93u/dvm62I4b0+7c/Xxuta5VKJWZnZ6lUKm7yOFmWXXFHLBYjnU6TSCQ4efLkpu/z7fvvR/vlX0aemUGt19GzWWzbxreygtzroVxS6Uu2jakoIEmYfj96Mok1Oor9Yz9G4qd+as0+8/k88/Pz1Ot1KpUK7StsLEOhEIlEgnA4ysTE2IHPvzX+9V8xf+/3kBcW8JRKzj0Tj2OrKlK3i1qtIpkm2tgY1vHjhN/yFrzbWeleA/UHH8P4rdcjLy8hF4tOuZaFDVh+P2YqhZ1MYr3oRSR+8zeRrr75d0E/N8Jm7cyy1vZRRrmMdvEiVqeDHIsRuO22Dcu/LLpb//lG/dW1ouuXy+gLefdyeqr72GN0Xvta5Lk5vMvLGIEAZjKJ7feDbqBUK3jqdacvnZhAes1riP3gD15zeVcez17gJP/Yuh/Z7FodBiYnJ8nn88zMzBCNRslkMiiKU1H1058k/Q//QGRmho7PhzY8jNfnI3Bl4k7bRl5YQK3V0E6dwv6JnyBxyLIb2jZ0Ovvnvn5Y2e271urqKnNzc8zNzaHrOslk0k1gYpomtVqNcrmMqqqMjo66a8s7q8vBt39d333CgZ1gmpfFvnuNYTiu9x7P46+97gX7Nb6+Ek1zrk/ljW9Aft/78E1P0z17FmmDBOVuvSwL3yOPoA0OYr74xaT+/M+vqeyNnje27Tyb++3ctp22v0V1DgVWp0Pjk5/EPH8eW9OQolF8z3wmoac8BSRpjWb/RrMX97yTvGDtZ9tdq74If6d9mGVZzMzMuAYVlUoFTXNcxWVZdpNiRyIRTp06ta9JuHeLiCEUHDWOghZWOKALBAKBQCAQCAQCgUAgEAgEAsEBIcvwjGd8bsPvNnNAB8epenR0FNu2WV1dZXFxEVVVXcfWdruNYRgkEgmy2SxjY2N7Kj536i5z/PhxzEsrU4uLi/h8PqLRKIqioGkatVoNy7IYGRkhHo+vEzIdNTweD16vF9M06fV624p02+2261at7lUk1iX64vFwOEy5XN7w+qqqSrVaZWVlhWazSSKRwOv1srq6it/vd383ODiIx+Oh2+1i2zaBQIDx8XGq1Sper5dKpUKxWMS45LLUF6TH43ESiQQnTpzYVHzeT4bQF9r6fL7rchZ+PNJ3aQYn8UCpVCKfz7sBObZtE4lEGBoaIpFIcPz48U33ZRgGU1NT1Go16vU6tVrNDRAKhUI0m01WV1cZGRkhm83u+7H1ej0KhcIaEXA0GiWVSu1q4bfdbtNqtahUKgwODm4Z1JpOp6nValSrVfL5/IEGwHa7XZaWlqhWq/R6Pffc9wXK6XSaXC4nFr0PiKuTE+wFKysrFAoFZmdnURRlncu5YRhUKhUWFhawbZuLFy9y22237VsCj1AoxMTEBOC4Z/WTwni9XizLQtd1AoEAo6OjxOPxLYPVDxvNZpMLFy7QbDYpl8s0Gg03AN/n85FIJKjVau44SCRJ2R5d19F1nWazuWHf2Go5DlXgnGdVVdA0C9M0MQxzRwGeT32qEJ8LBAKBQCAQCG4+9kq00He8u5LWvfci/f3fo0xPI2sa3ePHkcJh93sD0FstvHNzSFNTmO97H80nPIHws5+9b+KYQyNutG2q73kP1oc+hJzPIzcariLW+Md/pDwwgPKDP0jsB34Aj2ftyZAkCN5+G5VUCiMWQy2VMLYRoMulEkYoBIEAgU1ssjsPP0znE5/ALhRAkpAHBgjcfTdS3x5+l6RSKbxeL8vLy8TjcbrdrjtHGwgE8Pl8ZDIZcrnclokNO+9+N0qx6Ig3jx/HBvwXLiB3u8iWheHzYSsKkmmi9nroPh96JILSbCJNT2O+6120bruN0NOf7u4zm80SCoVYXV0lFotdSlJmIcsyqqqSSCQYGBjYl7ng3swM3a99DbvVQgoG8T3xifgvJeQ0Gw2MP/kT5MVF1EqF7sQEUjTq/tYG9MFBpOVlvLOzaLJM40/+hNSb37ynddQWFjB+41eQZ2bwLSxgBAJow8PYgQCSZSFVq6grK9ilEoZlUfF4SP7Gb1xzedv1Q/3v29/4Bt33vx/5y1++bJcpy3QTCaQXv5jID/0Q6hX3Qt8k/mo26q+uhyvL2E2fahsG3UcfxaxWkf1+vCdOoG4w8dD+sz9DXlzEu7xMb2TEnZzo9wxWPEav08EzNQVzc5hvexvGC16w5lxc6/HsBTs515tdq8PAsWPH0DSNiYkJlpaWuHDhgpPMUFUZ/cQnkPJ5TFlGGx7G4/GsF6xJEtbIiJMwIZ/H+OQnsX/yJ68racNeI0lCzLsTBgYGMAwDSZIolUpUKhXy+TySJGHbtvv8SCaTDAwM7Fh8Dk7715aW6H3rW1jtNko0iv9JT0JNJPbtePbrntvPKXpVhVhs//Z/s3MQywyuWPjCBaR6HTMQ2FJ8DiDJMkYkglyvY128eM1lb9St9h3Cr/z3YRafW90utbe+FT71KajVnHcEywKPB/0DH6A8Oor6ylcSfclLDk1Crb245zfax3bXarfl9uOD4vE4hUKBZDLpvpsoiuKudfcTxQsEgpsbcZcLBAKBQCAQCAQCgUAgEAgEAsEBsVUgzVYCdHACvBRFcV2La7UavZ7jpJ5IJIjFYvj9fkZHR0nuk9InEolw8uRJN6NxpVJxReeqqpJKpYjFYoRCIU6ePOmKsY8qiUQCn89HIBCgUqmQy+U23dayLGq1Gul0mkgksucua4qikEqlaLVazMzMsLq6ysDAwJptSqUS1WqVYrFIo9EgHA7j8/lot9tUKhW8Xi/JZNJ1Oa1WqwSDwTWBi8VikXA4TCaTcZMNKIpCKBQik8mQTCY3FPUZhkE+n3edufsBJf1ECZlMhlQqJQSBOySXy7ni/f51NwwDWZbda5ZOpxkaGto02NSyLCYnJymXy8zPz2NZFrFYjHg8jmVZNBoNpqeniUajWJc6oP0Soeu6zuzsLLVajU6nQ6vVwrIsFEUhFouxuLjoBhftpI3096Np2o7cjuPxuOsGrWnagfRNjUaDixcvUqvVKJVKtNttN6hKkiSi0Sjtdptarcbp06fFwvwRxLIs8vk8hUIBSZIYGxtbdz+qqkomk0GWZZaWlgiFQlQqlX17ToOTxMLj8bCwsEAikViTFMTv9+P3+4nH44yOjh6Z53Sn0+HChQsUCgVWV1cJh8OMjIzg8/kwTZNGo0GhUKBarbrB76Ojoze62jvCsixarRamabp9/EH1B/2+v98fX029fnU9JCSJS8J/e0dlXDVUEQgEAoFAIBAIBNvQff/7UcpllE6H3qlTSBskxJRCIbQTJ/CdP49ZKtH7x38k/Oxn34DaHhy2aVL+7d9G+dznUJeXUatVsG1sWUY2TSxFwVhawl5ZoTw56Qh7r5pjklQV6cUvxlpcxHP+PEahAJnMxgXW63iqVXpjY5h33YXnqjmz9gMP0PnLv0SZnERuNJA6HaeegQDdD32I1tmzBF/7WgK33bbrY41EIkQiEbrdLtVq1Z0TDAQCxOPxbefOtIUFlPvvd0T28TiEQijT00iGgWKa6IGAu1BhqyqWriMbBkq3i3b8OL4LFzBLJbr/8A9rBOjgJL47fvw4uq6vmd8LhUL78i7bvOceen//9ygPPgi9HpgmtizT9ftp3Xorvh/6IYylJaR6HW+xSG9sbI343EWSsIeGMAwDdXkZvvENerOz+PYwgW7jL/8SZXER38ICWi6HfanN9K+WHQqhZbN4pqdRp6YwPvlJ2nffTfCJT9yzOqzBtqm87W1I730vcqWCUiqhttvOfaMoGLEY1tIS9Y9/HP8f/iHBJzxhf+qxRxilEo33vx/7k59ErlRcIb2mKFjf/u34f+AHCD75yYCTGEI+fx7P6ipaOr15ZrxAAH1sDN/0NFazSeNDHyLxUz91gEd186KqKmfOnGFmZga/30+73abRaCBdvEikWiXS7dJJJPD6fPj9fjbs1SQJI5XCu7yMVSjQ+spXCD/zmQd9KII9YHh4mGAwSCgUIp1O0+l0ME0TRVHw+/0Eg0EGBgZI7SIBROsrX6H7vvehfP3r2LqOZJoYqkrD78d+5jMJ/uiP4j97dh+PSiDYBzQNybKwdjimslUVuddDuhQv8njEbDap/eIvIj/0EPLyMmqj4Yz7ZBnZMLBUFWNlBWtujsrCgnjOXyPJZJJkMkm323UNDjweD6FQSMR+CASPI0Q0i0AgEAgEAoFAIBAIBAKBQCAQHBDXI0AHxwElkUhQqVQolUquS7LX63W/28r5ZC+IRqPcfvvtlEolwuGwK4IHXOFyIpG4KRabvF4v8XicVCrF4uIi4XCY8BWOQ31s22ZlZQWAWCxGZrPAxetkcHCQer3O6OgoCwsLNBoNEokEgUCAUqnE9PS068I7ODjIwMAAY2NjgCMQr9VqrK6uYts2586dQ1VV1wW3L5jPZrNks1na7Taaprku5uscKK6g2+0yOTlJo9GgUqlQrVZdV1qPx0M8Hqder1OpVDhx4sS+t9GbhUQi4YpHa7XaGgH6Tu71vvP23Nwc4XB4nTNSIpGg2+2ysLDA4uKiK4re6lpfC7quc+7cOarVKisrK3S7XQKBAIqiuI7o4XAYTdPodrscO3Zs2/7Dcd813KQc2+Hz+dyM7IZh7LvottvtcvHiRfL5PPl8nng8zsDAAH6/H9u2aTab7j07NjbGhQsXOHPmzE3Rbz6eqFQqdLtdarUaIyMjW96TyWTSTdrSdwnYTyKRCLfccgutVotKpeIK0H0+n+umdpRYWFigWq2yurrK8PAw0SuCyfsuSYlEgvn5eRYWFtxkPXvteL+X9Ho98vk8pVIJwzBcAbqiKCQSCddZbj/p95+qqtLr9YhEImu+r9fXitJt28aybCRJ3nF/tU9DIoFAIBAIBAKB4KZEW1xE+cY3UEsl9Hh8Q/F5H8nrRY/HUUsleOABenNz+C7NA96MVP7iL5A/+1nUCxfAttEGB7ETCSRZxrYspHIZTz6P3WphfPzjVFIpEq9+9br9RH7wB6l/4hP0Wi18i4vonQ5mJoPUf3/UdaRCAU+xiJZKYWezhH7sx9bso/n5z6P/3u+hLC/jyeeRdR3z0nu2omlYXi+Uy3RnZrB+//cJ3XXXNR2z3+/fMiHqZnS+9CXodlHabYyREWxdx9NoIBsGpqquW6SwPB7UXg/LMJAsCz2R2LZd9ed895PqO94B73gHarGIUiqhdLuX6+zzIRUKGA8/jNntolYqznnfxuLVHBjA89hjGK0WrQ9/GN9rX7snddVXV1Huuw9lZQU9FnPF51cjeTwYx47hffRRqNXofvCD+yZAr77jHUjveQ/K9LTTFmIxetmsY3Xa6aCUy/gmJ9FaLXqvfz3yX/0V/pMn96Uu10vnoYfo/sZvIK2uohQKqLUakmk6AuVgEIpFtHvuofcjP0Li1a+m89GPItfryLqOlclsLG7uE4lg+v3IhQLGpz8NQpi2Z6iqysmTJ+l2uxQKBSdJ5twcflVFtW28qRTydmsR0SjSwgJoGualNTjB0aS/3tVqtWg0Gq4AfbN1162o/O3fIr3rXU5ijXIZpd12vpAkjFAIs1ik+4UvoP3KrxB92cv24WgEgv3BDoWwPR6UdpsdhI0g93rYioK9y3vopsG2qf7mb6J885t4pqawvN7LyYgkCds0kUsl1Hweq93GfOc7qQ8MEH35y290zY8s/STXAoHg8YkQoAsEAoFAIBAIBAKBQCAQCAQCwQFxvQJ0Zx8yqVRqV5ng9xpVVRkYGGBgYADTNF2nk5tRWDw0NES9XqfX67GwsEAsFiORSLhC0kajQblcRtM0xsbGXIfp/UBVVU6dOuWKCKvVKpVKhdXVVRYWFmg2mwwMDDAxMYGqqmiaxsrKCrFYjEAgQCqVcl2xw+Ewt99+O9FolMHBwXVlBYNBgsHgtnXSdZ3JyUlKpRKLi4sEAgGGhoYIBAKu0LZcLruutJIkcfKQBrIdVq4lAMeyLIrFIoVCAZ/Pt6mzuN/vZ2RkhOnpadrtNoVCYc9dgy9evOi6sEejUYaHh9eIX1utFvl8ntnZWbdOQ0NDW+5TkiQkScKyLNdRfCtM03S3OYh+amlpiVqtRj6fZ2RkZI2oU5Ik18lqZWWF+fl5vF4v5XL5hvbrgt3TbDZpNpvIsrytUFiSJGKxGPV6nWazuaN2uxeEQqF9FzHvN91ul3q9TrFYJJlMrhGfX4mqqoyMjHDx4kVarRaFQsFNwnLYqNVqTE1N0Ww2qVQqNBoNN3FLMBikXq9TLpcZGRlhYB8txFVVJRgMEo1GqVarpNPpq+q5NpTAMAwkSbqU/MOzozKu2qVAIBAIBAKBQCDYgt7DD4Ouo3Q6GMPD225vJ5MopRKGptF7+OGbVoCu5/PIH/0oyuwsSBL6qVNIiuKKSiVZhnQaLRbDc/Ei8twc5gc+gPmjP4py1TukmkwSfMMbaP/qr9LzeFBXV/GcP4/l9WIDiq5jeTxoQ0PYg4Oov/7r+G+5xf1998IF9D/4A+TZWbyFAlo6jZ3JwKXknoamIRUKjgu2rqP99m/j+T//B+/IyAGdLbAbDbiUiBG/H6leB8tCNk30DQQj9pVzZYaxtl1985s3pF3VP/hBePvbXfG0nkigj40h+XzYuo5cKuFZXcWsVvG121iShJFOr3O9vxrJ58P0+5EaDey5uT2rb+sTn4BWC7XdprfdvevxYMRiKMUi9j33YHW724twd4m2sADvehfK7CyyptE7fXptQotgEDOVwiyV8C0u0lNVWn/+5/j/1//a03rsBd2LF+n+2q8599zSEkYggDY4CH4/mCZytYp3YQGzXMb8u7+j4vFgLy8jtdvOtfZsP39hhsMorRZyPg+2vW07EuwOv9/vrjnUZ2awAgFkSXL67m2w1/zD3mwzwRHieuera+99L9I73+k8H1otjHgcY2gI2+NB6vWQy2V8s7Po9TrWn/wJjXCYyHOeg1mv077vPqxGA9nnw3vLLfiOHdvDIxMIrh/5qU/Fvv9+lHwevdVC2upe0TTUZpPesWPwpCcdXCUPEa2vfx3lG9/AMzODGQxiTUysWXeTFAU7m0WLxfBduIC1vIzxzndi3303kqJssWeBQCAQbIQQoAsEAoFAIBAIBAKBQCAQCAQCwQGxVTyFaR5cPfYSRVFQbuJFukAgwIkTJwAnUKbvWixJErZtI8sysViM4eFh4vE4J06c2FdRodfr5ezZs5TLZfL5POl0mnq9jm3bzM/PMzExweDgIMVikUaj4bqegyOA7DtHh8NhZFnmxIkT1+UQu7y8TL1eZ3FxkUwms05Am0wmicfjLC0tMTc3h6qqVCoVEonEdZ0HwdZUKhV6vR6NRoORkZEt26Tf7ycSiVCpVIhEIttuvxv6YtulpSXi8fiGrk2hUIjx8XFmZ2dZXV3F5/Otc2u/mmAw6LbbZrO5zrX3ahqNBqFQCEVR9t35Wdd1qtUqpVKJRCKxZd0GBgZoNptUq1UKhYIQoB8x+glYPB7Pju4Zj8eDeelhb5qm6z4t2JpyuUyn06Hb7TKyTcC+x+MhHA5TqVSIRqOMjo4eiNB/NzSbTS5evEg+n6dYLLr9br991Go1FhcXCYfD7jgjs4824plMxhX4F4vFNSL0ev1yG7VtC13XCASClwToO2u/QoAuEAgEAoFAIBDsHKvVglYLyTCg3XZEkFsliOwLKy0LOp2DqeQNoPnBDyK1Wo7I5pL4fCMkjwdjeBjf9DRWo0HjIx8h/spXrtvOf8stSG95C82/+Av4+tcxWi1ot5EAw+dzHJHPnCHwsz9L8MlPXvPb9t/9HXKhgLdQoDs+jnS147bXiz08TC8Uwjc3Ry8cpvne95L8tV/bq9OxLZLfj33pXdg2DMcp+hK2JK1zg5auFHXKMna/XRkGVt9V9gCxul3Mv/1blIUF5E6H3smTSH7/5YQDPh/20BC9dBrv5CRyr4di2+g7LUCWnXtG0/auzsvLyO02tqpufc/2tw+H8ayuIuk6ej6/5yL/5gc+gNxsotbrzj1zpfj8SlIptE4HdXUVvvUtuhcuHDoX9Nb/+B8oi4t4l5boDQ2tm2iwolF6vR7eqSmYmcF897uxjh3bnZC8v51lYVuWEKXtI+rgIJrHg6WqSM0mbLM2JTUaTpIMrxclm927itg2Vq+H7POJhANHCKNSwXr721Hm5pC73TXJNSQArxcrEqHbbOKbnkb3eND+6I8o/du/Id9zD1KnA4aBKct0PB6at92G9/u/n8jzny/ageBQEPme76Hx3vdihEJ4FhfRT57cOFmHbSMvLmJ5vRCJEPz+7z/4yh4Cuh/6EEq1imQYmGNjm64FST4f2sAA3pUV7JUVWl/4AuHnPvdgKysQCAQ3ASKyQSAQCAQCgUAgEAgEAoFAIBAIDoi9cEAXHDzRaJRbbrmF5eVlIpEImqahaRqyLOP1evH7/WQyGQYGBg7EXVmWZdLpNOl0ml6vx9TUFJ1OB0mSOHPmDJIkEQ6HKRQKBAIBdF2n1+sBjttpPB5HURSy2ey2wt2tME2TUqlEsVgkHA5vKp6VZZmhoSEuXLhArVajUCgIAfo+0+l03DaxE/f0fnsxTRNN0/BtFpS4SwqFAvV6HcuyyG4RINYXWC4sLNDr9SiXy+uceK8kHo/j8/mIxWKUSiXC4fCmgQXdbpdGo8H4+DipVGrf79G++L/dbm/rXCxJEvF4nGq1SiqVQtO0fRfIC/YOWZaRZRmj7yq2DbquuwlbbubELXuNpmlOILHXi2cHrlWhUIhyuYxpmodS6D83N0e5XKZUKjE6Orqujw4GgySTSebn51leXkZRFBKJxL4dRzKZZGVlheHhYRYXFzEMg2QyidfrpV532uk994xgWZfFLIoS4O67Wzva/z5q5wUCgUAgEAgEgpsG2zRp/PM/o7/tbYSmp1F7PXxzc6CqWF4vRjLpuGxfPafR7YIkYSsK0g7mf44q9r33opRKmIHA9uLecBjT60UqlzG+8AXYQIAO4Bsfx/fnf462sEDrYx/DWl52hNipFMEXvxj/2bPrfmPWakhf+AJKoYAeja4Xn19JPI5RLiMXCtj/+q9YP//zyDsQJu8FnrNn0YJBLI8HuVLBvmKeT7Ksde1IMgwsRcGWJOxAAKkvzFZV5Otwqb1WGp/4BFSrqNUq2ugo0ibu4JLXiz46ivfhh7FlGbVSwRga2nrnto3U62EnkxCN7l2lL4n4d+zPLEmX3Zz3w9X5X/8VuVjECIW2vWesbBbvY49hNJt0PvYx/L/4i3tfn2uke/EiyoMPoq6uoiWTm2a5k3w+9PFxfJOTmK0WLC9j+3zIpZIjKN9mPlhut7G8XuxYTIjP95ngE55AN5fDWFxELZUw0unNhb+2jVIqYcTjWMkkoWc847rKtk2T1uc/T+/DH0b+1reQdB0UBfPUKTwvfzmRF78YSczNH2qaH/kIUqOBp1ajd+zYpsk1pHAYbXAQ38ICnnwe7eJFlG4Xtdl0+1zT50MqFDAfeojy177mJIoRInTBDUZNJrFf/GLMdhvPhQt4LlzAyOUgErncPlstlJUVlE4H7cQJzKc9Df+ZMze24jcC2+b/Z+/O4yS763r/v75nrX3vvXv2TBLCJglgDHABDRIgIQuLIrjfi/wUEQFjQPkJol4EFe8PRX/yu1xcUAQSEyOLgBjZEWQNJJnp6e7pvWvfq872/f1R3ZWZzEx3T6a3mXyfj8c8kpk6VfU9p85Sdc55fz7aV7+KXirhJRIbHr9lJgNLS1Ct4tx3H6gAuqIoynnbW1fcFUVRFEVRFEVRFEVRFEVRLmHr3eci5fk1ZVB2VigU4uDBg4yPj1OtVnFdFyEEoVCIZDK5ax1WbdvGtm1M0yQUCvXHoes6w8PDDA4OUqvVcF0XKSWGYeD7PrVajVAodEHhvEqlguM4NBoNJiYm1p1W0zRSqVS/+7kK2m4vKWW/c+5m6LqOXLtRcgtveqzVatRqNZLJ5IZjicViGIZBvV6nVqutG0AXQjAwMEC73WZ6epr5+XmGh4fPWJfb7TZzc3MkEgmi0ei2dhFe43kejuP09w8bCYfDFAoFoBdQVtvFxSMWixGLxVhaWqLRaGxY7KFarZJKpYhEInuuK/dedr77pL28bOv1Oq1Wi0KhwMDAwDnXGdu2GR0dZWZmhlwuR7FY3LCgxaOlaRpHjhwhCAI0TWNlZYXJyUlCoRAnT9o0mzpSBpx6G/35fCaZzDYMWlEURVEURVEuIUG7TfmOO9D/8z8xVlbQOx1EEGC12wS6TuA4aN0uQbmMe+gQnHLeQCuV8KJRRChE+Oqrd3Eutlm1Co5DsEGnXACEIFgNUYtabcPJrfFxrF/6pU0No/XVr0Kr1evEfuDAhtP72SzWyZMEzSatb3yD2DOfuan3uVCRH/oh2vv2IZaWMAoFuocOITWNQNfRfJ/g1PNnUqJ7Hp5t98I7hoG2uIgfiYBlEXpEB/id4H32sxilElLXYb2QP0A8jmdZaJ6HVa/3ztmvV7yuXO51yU2nMZ/xjC0bsxgYgFAIzfOQ1Sp6rYZeryM8r7dO2navkEQmg9A0tEaj17VU0zC3+Hxl0OmgVSpo7TZeKrXx2C2rV7Sh1cJfXNzSsVyo1r33ojWb6N0u7oEDrHvGJxLBi0bRCgV0y4JUChYWEKXSOYPrALLbxWg2e9vJDm2jj2lCoL3oRcjZWbRyGW1ujmB8/MwLo1IiFhbQO51eIYof/3HEo7mOJSXdkyfpPvggznvfi14ooFcq6NUqwveRmoZYXCS4/37KH/gA4d/9XcJXXbU186psOf+zn8UoFvFtGzY4Fy9NE6PdRgqBMTtLZ3Cwd+wOh8HzEOUy5vIyQaWCd889lCMR0q997c7MyA5wFhZo3nkn8r77ENVq71g0PIz+vOcRv+km9AsoTq5sr/Sv/zql1eOxNjeHPT1NYJoEponwPHTHwYtGcY4cIbjqKlJve9suj3h3BO127/u+6xJsohCX0DQCy0I4DrJa3YERKoqiXHpUAF1RFEVRFEVRFEVRFEVRFGWH+L7LzMw/4vsuAEJoGIbFxMRPACqAfjEwTXPdYOxu0HUdTdPwff+Mx9aC36cqFAr9MPCFdINe60oLvY6zG1nrSgsqaLvdDMPAMAw8z8PzvA2LDHS73f40W9VlV0pJEAT4vk9kkx2eTNPsdy3eyMjICI1GgyAImJ2d5fjx4yQSCcLhMEEQUK/XabfbpFIphoeHmZiY2FQgfCucTwD21CDnXg7OKmfKZDLMzc0Rj8cpFApEo9FzfoblchnP80ilUjtSCOFSYlkWlmXhOA6u627YBb3ZbGLbNpqm7blO88VikXq9ju/7ZxybHykSiRAKhahUKtsaQIdekZ0rrriCkydPEo1GabfbtFotgiCMbdsIIR5VQ7Z4HPbYR6AoiqIoiqIoe4uUlN/yFvQvfxnzxAmQEicWw+h0MLpdAl1H+D6G6+IHAUxO4h49CrqObDR6HUD378e/9lqMbHa352b76HrvpHkQbG563wfTRG7w+/F8BZVKL1AsZa9T+AbTy1Co13Hc8wh2MugiBMYttxBMTSErFayTJ/GjUTTX7a1XptmrlBsEGJ1Or/u5YRAMDEC73es8PjaGf801WBt1FN+O4ReLiE6nF4Lf6FyZELjJJKFiEYRAn5sjOHDg7M9zHMylpX435eT112/ZmCPPex6tv/5r8H0iDz6INAyE5/U+fyGQnQ56q4VcWqI7OopRreKMj+M/7WlomzxvummnbCdCiM11ZV/ryL6Jc7I7SS4uIprNXkD+HJ2OT+XH4xiVCpph4GezuNks1tISTiRy1k7w0vcxZ2bww2GIxYjcdtt2zIbyCImXvpTyZz+L0+1iTU8TtFq9TuhrHX7rdYxCAa3bxdm/n+DgQZKvetV5vYffaFD/p38iuPdetOlpzOPHiXU66N0uvmHgZLP4ySTC8zAKBcTyMk65TOfXfx3xnvcQuvLKbZp75UJohQJap4MfjW54fDCXlgDQPY9OKkVw8OApD5rIcBhnYABjchJ9ehr/ox+le9NN2Pv3b+csbD8pKf/pnyLuugvRaqEXCgjHASA4cQL50EPUPvhB9F/5FRI33bTLg1XORhgGmXe9i8p734v/8Y8T1OuIcrl3jNa0XnGZaJTg2c8m9cY3bv33iItEv+CQEJv+/rL2vYwt/o2gbC2vWKR+110E3/gGotlE2jbi8suJ3HILocOHd3t4ivKYpgLoiqIoiqIoiqIoiqIoiqIo28xxHGZnZ5mZ8fne9/47QdDuP6ZpYVKpFxIKhQgCc90u6YpyNpFIpN+Ft9lsbhgGr9VqxONxQqHQBQXQlb0rmUwSiUSwLItKpbJu0QQpJZVKhXQ6TTQaPS2A3m636XQ6BEGAYRjE4/FNrzNCiP4fz/M29RzP89A0bVPvIYTgyJEj6LqOZVk0Gg0qlQrlchkhBOFwmJGREUKhEPv27duxwhGhUAh79YbIzXTFbjQahMNhhBCqKMNFRtM0BgYG6Ha7TE9Pc/LkSYaHh/ufP0AQBJTLZfL5PMPDw4TDYTKqJfR5yWazLC0tYds25XKZwcHBc07reR71ep3x8XEymcyeK+qwVrglEolsKhwfjUbpdDr9Yi/bybIsjhw5QrfbpVAo0Ol0SKdTrKw8+sIdT3rSFg5QURRFURRFUS5BtU9+Ev1rX8M8cYLAsvAOHkR0OujHj+MHAZrn9YKXq0FhKQRiYQEZCmEvLeGm05DNEnnFK3Z7VraVnJhAPvAAxvIyQRCw3gl06fsYzSbO2BiMjW3pOEQohFz7nbmJMLxYDcNITdtUeHYrJW+9ldK3voUnJfrUFEat1uvOLQRmq9Vbl6TENwyCUAhncLAX0F5YwIvFIJsl9JM/uaNj7tO0hwPRm2GaeJaFNAz0Vgtx/Dje0BBiNcwqgwBRKvU63do2wdgY+itf+ei6KZ+DNTZGs17vbauOg3RdAsPoFZEANM9Dd1180yR67BhuKASpFKFbbtnwtdvf+x7db34T2WwiolHspzxl3Q7NWjiMDId7XT6bzQ0D6NL30RwHLxRC7LVCFmvrwCbP74hT1hvj534O+Ud/hOc4WJOTuJkMQS6HsCxkEKAVi5irhQvcI0cIbrhBhZp2iBYOk3jXu6i/8Y04pomWz2MtLiLm5gCQuo6XTOLt20dw8CCxP/oj9ESi95jnUf/MZ/C+9S1otSAcxnzSk4hdf30/jNg5dozW7bejLS6i5/OE5ubQXRctCPB1HTQNs9HAicWQY2O4o6OIfB57YYGuELTe+lbsf/gHhKqquPds9rjQbKJ3Ogjfx9d15Lmuu5gm7v79hB56CL/ZpHXnndivf/3WjXenSUnpd38X7VOfQp+exmg08CORXkEXKdGrVfR8HjedJnj3u6k6DsmXvGS3R62chTAM0r/2a/i/8As07r0X7xvf6O/ztMc9jvjNN1/axac2QZgmwdAQwfw8er2ONzKy7vSy3UbrdpGxGNrFXmjiEiVdl/If/zHapz6FaDYxymWE6yI1DfmNb9C55x6aP/RDJH7rtzBVoW9F2RUqgK4oiqIoiqIoiqIoiqIoirKNOp0ODz30EJVKheVleVq3W+iFPzudNr7vs7LiMTq6t7prK3vfWhfeWCxGoVAgEomcM3RXq9VwHId0On3BXXgty+oHLVut1oZdrlutVj9gu1EHW+XCRCIRotEomUyGlZUVYrHYObt/5/P5fjfetWBnqVQin8/TaDTwPK8fQDdNk2w2y9DQ0KbC0tFolHg8TqlUYnBwcN0waKfTwXEcYrHYhkUU1miaxuHDh2k0GuTzeRKJRH8fa1kWuVyOXC63o+tbKpXCtu3+fK8XQPc8j0qlwujoKKlUaku6zzuOQ6VSwXXdfhA/mUyqYhPbZHR0lFarhZSS+fl5Tpw4QSQSwbZtfN+n0WgghGB4eJhsNsuRI0fUZ3GeQqEQ8XicXC7HwsJCv+jKI/m+z9zcHKFQiFgstic7zUt55vfA9exGgN62bcZWQxrp9FkbhPW94hXwnOfAF74AH/zg6Y+l0/DTP72NA1UURVEURVGUS4B7zz0YxSIiCHrhc02DSITu+Dj23Bw4Drrr9gJXUmK2Whhzc7jxOO7AAHJ0FO0Nb1g3jHopsF74QvxvfAOxtASlEqxTZFArFJCaBuk0oS3u7Gldfjkd2yawLLRSCblB0EWUy/ihEMIwsI4e3dKxbEgI0r/zO5RjMfiXf8FvNDBmZ7GqVYQQaFISrBaO9A2jF/Dwfbx0utc5+ilPgQcfpDo3R/iaa7DGx3ds6HJoCBkOb6rgAFKiN5s4Q0O9ruPJJNriIvb0dC+souu94L2m4aXTyJER5MteRuplL9vSMVfe/34EvU67gaYh1n776zqBphEYBprjYHS7vfA/4D3pSaR++IfP+Zq1T3wC9yMfQT9+HFqtXkEDXacbidC67DKsl7+c+POed+YThSB4xjMQy8vY09N0HQfWOY8rCgWkrkMyiX2219tFIp1G2nYvMOb7GwaCRbNJYNvIUIjUjTdS7XQI/uzPcPJ5jGIRq1DoFzcIDAMvk0EODuL/t/9G5k1v2qG5UgDMwUFSf/EXVD/0IbyPf5wgnwfH6R3vLIsgm0X8+I+TfOUre+FzKal88IMEd96JViqhVasI30fqOv6991J53/vgxS8mcv31tN7wBrSpKay5OaQQvX2BEHimCUJgdLsEnoe1uIgjBHJgADk4SFfK3r+l0zTvu4/Yc5+724tJeYQglyOwbfRGA0/Kcxan0EolhOuiAZ5pEqxzfUiEw3jRKFqhgPe5z8FFHECv/tM/of3rv2IcPw5A9+hRCIf7j3tS4tZqWHNzMDND8N730nniEwnt9HcUZdP0eJzkT/4k7FZRoD1Ou+EGgpMnMYtFvFoNVouVnI2+stIrxhCNEn3xi3dwlMpmSM+j9MY3on/ta+gnT2I0m/irv7t0z0MvFvFtGxoN6q95DfE/+zPMoaHdHraiPOaoALqiKIqiKIqiKIqiKIqiKMo2CYKA48ePUyqVmJ+fJxQ6iBDitCLtQggcxyEIAqanF4jHe8FFRdksXdfJ5XL9Lrzz8/MMDw+fFmaVUlKtVllaWiKXyxEKhcheYHX0VCqFZVnEYjFKpdK6AfQgCPqduOPxuOr0vANGR0dpNpu0221mZmbI5XIkk8n+etFutykWizQaDSYmJojFYqRSKaanp8nn8/1u4o7jAL19VTwep9FoUCwWOXLkyIbdvQcGBqhWq6ysrPS7rJ9LPp8nGo1i2/Z5dyuPxWLEYjEOHjyI7/u9G2l3KeSraRq5XI5Wq8X09DRLS0sMDQ2dEST1PK8flo3H4xcclm21WiwsLFCtVul2u/0A+lpH9lwux8jIiAo/bzEhBEeOHMGyLEzTpNPpUKlU8DwPTdMYHh4mkUgQDoc5dOgQ4VNu+LoUNBoNSqVSfz9hWRbZbHbTRSQ2a3x8nGazieM4zM7OEo/HSafT2LZNEATUajXK5TK6rrNv3z4GBgY2LIqyGyzLwrIsyuUyQRBsuD02m01CodCuFW1ZLyv/rGfBT/xE7/9vuw0mJuCzn4WVFXj84+EFL4Dh4Z0Zp6IoiqIoiqJcjJy5OfT770cvFnHT6V74fE0mQ0fXsRYW0BwH4Xn9DsqeaeKMjhJccQXWr/4qsWc9a/dmYofEnvtcyu97H+7gYC8caBiQSp0+kZRQKmEtL9MdHSUYHyd67bVbOo7Q5ZfTvOwyxPIyZqFAd3Dw3IFY18Usl3GHh/Gf+ETsXei0KHSdzO2303nJS2jddRfufffhzcxgzs8DEJhmryuspiFDIaRhoNXriFYL60tfgs9/HmkYtGyb+tVXE3rFK4hec822j9t6/vPxv/a1TRUcYC2EOjiI+NVfJZifR/7rvxLU61Cr9TrVmyYkEgQjI+g/+ZOkNtF1/HxI10Xeey9GpYITiyGCAM3z0FwXo9N5eLrVAKzu+/ihENrll589PCklpT/5E7S77sJYWcEoldAc5+HgtGUhVlbwH3qI8kMPkf7lXz7jdSK33kr305/Gt22MmRncw4dP38esvVWzib2ygjs0hL9/P6mnPGVLl82FCj3veTgf/3ivuEChAOsFjVwXs16ne+AA8hnPACFIvuxlNPfvp/O3fwvf/jZepwOuC7oO4TDB+DjazTeTfdnLNt1lXdk6WiRC+hd/EflzP0fra1/DX1xESok+OEj02msRq9cxpO9Teutb0f/93zEWFjCqVQACXUfzfZibw0ul8ItFGn/xF+iuiz07S3doCK3bRXccBODaNkLT8IMAo9NB63axFhfpplJgmsiBAcjnoVSie/fdKoC+B+nPfS7y2DH0YhGv0YBzXEsXrtsrPgJIwyBY57oUQBCPo1eraJUK0vP6695FRUq8j30MY3kZ4fs4l12GeOQ5ZSEQySSuaWIfP063WqX1j/9I6Ld+a3fGrCgXKHbzzdQ/9CHcdBrr5Emc/fvP3C9IiVhcxKhWcQ4exH/qU7FWC/Aqe0f5Pe9B/9rXMI8dIzBNuocPw+p1xgBwHQd9cRFragpHSmp33EH2//v/1Pc3RdlhF+E3JEVRFEVRFEVRFEVRFEVRlItDsVik2WwyPz9PLpej08mcdbpwOEKr1aJcrrK4uKgC6Mp5Gxsbo9VqEQQB8/PzHD9+vN/12vO8fifrgYEBcrkchw4dQt+gY8hGdF0nk8nQbDaZmZmhVCqRyZy5jkspWVhYQAhBIpHYk11pL0WJRIL9qze3lkqlfldzwzCQUuJ5HrFYjAMHDpBMJjly5Aizs7MsLy9z8uRJfN8nnU4Tj8fRdb0frJ2enmZo9Wa/yy+/fN1AbTqdJhwOMzw8zNLSElJK0un0aWFsz/NYXl6m1Wpx4MABMpnMBXUCv9D1eiuMjIxQrVaZmJhgdnaWRqNBKpUiEokQBAGNRoNKpUIoFGJ8fJzBwcEL2u9XKhVOnDhBtVqlVCrRbrdPW8bJZJJWq0WtVuOyyy7bkk7rysOEEOzfv5+RkRHy+TzVarUfQA+HwwwMDJBYp/PCxajRaHDy5ElarRbNZpPO6g3VoVCIfD5PJBJh3759WxZEj0QiHDlyBCEEkUiEUqnE7Oxsv5u4aZqk02ksy+oX18jn8+i63u+Gnkwmt2QsFyKTyVAoFFhZWaFWq5F6ZGDiFJ1Oh3a7zfDw8FmPrbvtkff1PO1pvT+KoiiKoiiKomyOOzfX697c7eKd5ZyASCZxEwlkrYZeLiMcB7tUwk0kcF/0Igb+4A8eMzfcC8Mg9OY30/3N38QNAuyTJ/FXVvBTKaRlIRwHvVxG73ZxhoeR4+OE3/zmbVk+5i23EDz4IEGlgjk5iXvgAOIRhTZlt4s5NdXrxJzNYt9225aP43yEDh8m9MY3whvfiPQ8kJLqPffg/dVfYR47huh20YpFhO8TRCIY+Tz63Fz/+X4kAsUi7je+QfWXfqnXiXMb9QsO5HK9ggO2ffaQYbOJNTeHl8lAKkXi5pvRQiGC17yG+ic+gf/gg8huFxGPYz396cRWQ8lbrf6Zz6CVy71g08QEhMNoy8sYq+H4fnU3TSOwbTzHQUYi8JnPwK/8yhljqvyf/4N2550Yk5NojoObzSKz2V6Q3nURhQJGPk9Qq+H/wz9QSSZJvepVp71G+KqraD/3uXiui3n8ONaDD+JlswTpdC9Y2W6jFYsY5XKvM/zwMKHXvGbP7VMiP/RDdA4cwFtZ6RWXiEYRZymIKoMAc3q61xkzkSB86639x6JPfzrRpz+d7tQUna98haBaRYRCWFddReSaa/bcPD8WCV1ft2BI+b3vRfvc5zCPH0cCzugopNP9ogxUqxhLS+gPPojmukgpcbNZGBpCHDvW27dp2sNFGDQNLxTCbLcJfB9RKCBHRhCahpdIoNXrBFNTOzPzynmJ3XJLL2yaTGLOzeEcOoSw7TMndBw030dqGoFlwUbnhderwnmRaH3zm72uweUyXi53Zvj8VJEIbjyOXigg/+M/8BsN9A2KTSvKXmRkMuivfz3BO9+JB9gnTuBFo/ipFGgaotvFKJd7RRn27yc4cIDEm96028NWHsGv1RCf/CT67CzSMPDOUjhJWBbBvn14c3OYJ09CLEbzq18l+sM/vEujVpTHJnWHiaIoiqIoiqIoiqIoiqIoyjZZ6/prmia5XI6FhbNPp2kalmVRKpWp1xO02+1Lrkvqdmu1WuTz+X4I2zAMEokEuVxu1zqH7iRN0/rBUtu2aTabVKtVms0mmqaRyWRIJpOEQiEOHTq0YefqzRodHaVarTI6OsrCwgL1ep1MJkM4HO4HbcvlMlJKJiYmSKfT6wbulK2Vy+WwLIv5+fl+sYC1ztjhcBjbtslkMoyPj+M4DoVCgbm5OTRNY//+/aeFudfC057n8dBDD9HtdgmHw1x++eXnfH8hBIcPH8bzPACWl5cpFov9UHu326XRaGAYBvv37yedTjMxMbHty2W76brO0aNHOX78OJZlUalUqFQq5PP5flfykZEREokEg4ODFzTPzWaTEydOsLy8TKlUIp1OMzo6imVZSCn7Heunp6fZt28fk5OTHD169IyO7MqFsyyLsbExxi7x7glrBQ9KpRLFYhHP8wiFQgAUCgUMwyCXy9Futzly5MiWhe/j8ThXXnkly8vLxGIxXNftB/2llBSLRXRdRwjByZMn8TwPwzCIxWJUKhUikQiHDx/GPttNiTskkUgQCoXIZDIsLy8TCoX6y+5UnuexsLBAPB7vFzFQFEVRFEVRFOXSJYXgjF/p9TpaoYDe7YKUSE1D6jr+4CDGkSOPudBk5KlPJXj723Hf/na66TRaPo+xGpqWuo6XSOAdOIDM5bDe/nbCT3jCtowj8cIXUvzP/4QgwJiaIvTAA7jxOHK1AJvWaGA0GniRCP7BgwQvfCGxZz97W8byaAhdp/K3f4v8m7/BqNdB09DabcxGo9dJuFbDs206ExOQzfbD6db8PF6tRvC+91FPJom/4AXbN0bDwHr96/He+lY8z8OemsKNxwkyGbAspOuil0qYtRpuMkkwNob+2teirf6+1iIRkjsY+vfuvx+tXu/9JZkEIQj27cPx/V6Hds/rd5knFkOsrGAUiwTlMs7iItboaP+1/FoN+Xd/h37yJMLzcC67DE4tcGCayJERnGwW6/hx5Ows/t/+Lf4tt5wRHky/5S2U2u1eAGt5GSOfR1tcfPi9IpFeYD6XQ3vjG4n+yI9s63J6VITAfs1rcGdn8brdXrAsnSbI5SAUQnoeWrmMWSwC4B4+jP+c5xB+/OPPeCn74EHsgwd3eg6UC+SVSmh3341+8iRSCNwjRxCnFqAVAlIp3EQC+zvfQW+30aSkm04jABEEvXDxI4+ZmkagaWiui1Eu446MACB1Hc11Ed3uzs2ksmlGOo34mZ8h+Mu/RExNYR87hptKITMZpGlCt4teKmHVagS6jggC3KGhDb8zaY0GQShEkEpdnN3PAed734NWC811e8VGNpg+WO0Y7Xc6dI8dI/JDP7Qj41SUrZZ44Qup+T7Be95DN5tFKxQwV1Z6v58Mo1eAJ5tF7ttH9J3vxFwtsK7sHfW770as/obqHjx4Rvi8Twj80VGMSgUqFTp33aUC6Iqywy7Ob0mKoiiKoiiKoiiKoiiKoih7XLfb7XcMXutcqWnnrqJumiadjkun06FaraoA+iY5jsPU1BSNRqP/R0rZD6AvLi4yMDDA+Pj4JR+41DSNw4cP98P4Z+vCm0ql0M514e5RME2To0ePcuzYMSzLolwuMz8/3+9KaxhGP3SeyWQ4dOjQJf857DWJRIJEIkGz2aRcLuN5Xj8Enc1m+92w5+bmaDQadDodjhw50g+fe55HsVikUqn0A5+tVovvfve71Ot1TNM8I6x+qkgkwuWXX87k5CSJRKJfGMFxHHRdZ3x8nFgsRiKR4NChQ3uig/lWME2Tyy+/vN95PpfL9R8TQpBMJi+48znAwsIClUqFUqnExMTEaR2nhRDE43FisRiLi4vMzs5iWRbValUVglAelU6nw9TUVL/gwdpx5dT9RaVSYWlpqb+vedzjHrdloW/bttm3bx9jY2PU63U8z8N1XWZnZ4nH4yysVvpZC3p7nsfy8jIrKyuMjo7ieR5XXHEF1iO60+0UIQQTExM4joPrukxPT5NKpUilUpimSblcZmFhgXK5jGVZ7N+/n2QyuWuFdNZr/qMO5YqiKIqiKIpyYfSBAVwhCCwLrdFAroVH222MmRn0bhfheb0OygBBgB4E2NPTNL7wBeTP/uxFG5J6tGLXXYf3oQ9Rv+suvI9/nKBQ6D8WDA6ivehFxF/8Yox0evsGIQSZt76VcjiM9/GP460VCiiVeuMIhegeOgSxGPLWW8m87nV76gdU+b3vRXz4w+izs5jVKr6uYzabaL6PFgQEQqAFAaHFRbqmCek0QSSCk8thTk4i5+fx3vc+5PXXr99d9QLFnvUsarffjv/udxMkk2iFAvbMTD9I6sVidPfvh0wG7bWvJXHDDds2lg11OrBaCOG0z1rXIZPhkT+tpWVBEAAQtNunPbYWwDFrNboHD54ePj+FsCyc8XFC09MEtRqNe+8l+RM/cfo0pkn2f/5Pqh/+MP7ddyPn5qDd7r23aUIohH/NNYRf9SoiT37yhS6FbRO99lrqb3oT/rvehROLYRQKmA891H88MIxeKH1wkOBHfoTMb/3WLo5W2Wr1u+5CNJsYjQadw4dPD5+fQmgaXiSC1WwiNQ2tXkfG4w9vl2cJokvDQHgemuv2Htc0tG4XaRj9oiLKhZOOg99sosfjW/K9JfXKV1JuNPA/9CH8UgmjWERfLUKBEHiRCO2JCex8HhkECMc5Yz98mnYbo9mkOzqKeO5zL3h8u0W2Wr3vjEJsajlL00RI2QvpPuJYpCgXm8RNN+Fccw3NO+/E/9SnCKrV/mP+kSMYN95I/IYb+sWKlL3F/6//wiiX8U0TNmigIFYLj+nlMvI739mhESqKsuaxdQZKURRFURRFURRFURRFURRlh/irNwd6ntcPOq13r1kvlKvheV6/W7Cyvm63y4MPPki5XO6H3aLRKJqm0Wg0KBQKRKNRXNel2+1y+PDhSy78vBYOrtfrBEGApmnE43HGxsbYv3//jowhFAqd1pV2aGioHzw0DINIJMLAwAC5XO6SW/4Xk2g0elo4+VRSSsrlMpVKhXg83g+ld7tdZmdnaTQaVKtVWq0WUko8z2NlZQVN04hGo7TbbS677LJzhjojkQiPf/zjqVQqFAoFms0mQRD0C0UMDAycc2wXkyAIcF23P2+maZLL5cjlcnS73X73edu2+8v4QnQ6HWq1GqVSiUwmc85lKIRgeHiY48ePU61WWVlZUQH0beJ5HoVCgWKxSHe1U45pmmQyGQYGBnYt+LxVlpaW+uvcIwseAP3u56FQiNnZWcLhMMvLy+zbt29Lx6Hren8dfuCBB2i328zNzZHNZslms6cVWhkcHKRYLDI3NwfA1NQUl19++ZaO53ykUikOHDiAEIJoNEqpVOI73/kO5XK5X+QjHA5jGAbdbpdKpcKDDz7I/v37z9otfTutF0BXFEVRFEVRFOXChA4fpnnoEGJpCaNU6nXobLexTpxA63bRHQchJYGuI4VA932ElGiOg/H971N6y1vI/P7vnzOMd6kyslnSv/iL8Au/gLOwQNBsosViWCMjOxb0FrpO5jd/k86tt9L66EeR993XDxLLaBT57GcTeclLCB0+vCPj2az6Zz6D+Md/RD9xAs116R44AK0Wuuuiex5OOIwAjG4XpMSam8MJhyEUgnAYd3QUa34ep1Si8elPb2sXdIDEDTfQOXqU1j/8w8PLeDUkKm0b+cxnEnn5ywldccWWvad0HOqf/CTuJz6BmJ9HeB4ymURcdx3RW289rVt5XzwOponmeb2w4wbFX0Wn0wunA3oicdpj/r//O0axiG9ZGwdw4nF800QUi3j/9m/wiAB6byLRC6a//OW0vv51nAcfRHY6aMkk4euuO/v87EHxF7yA1tgY7b/+a/j61/E6HXBd0DQIhwnGxtBvvpnUS1/6mNsnXurkl7+MVizi2zZig+sHwjSRQiCCAL1exwP8eJyg0cB0Xfwg6G97ABLob61SguP0u69y9dXbNUuPCUGrRf3ee/HvvRdterr3j0LgP+5xmDfdRPz66x99GF0I0q95DY0nPpHuP/4jfOtbeI7TKwRiGAjbxr/2Wpr334957BjWwgKdSASRTJ75Wq6LOTODF4tBNErk1lsf9TzvNhGPIw2jFyh3HMQGBWFFt4vUNBAC7QKLJCt7W3dmhtY//zPB3BwEASKdxr7+eqJXX72niiRdKGt0FOtXfgX5mtfg5fME7TZ6KrW9hamUrdFs9vbhprmpdVJaFqLRQLRaOzA4RVFOpQLoiqIoiqIoiqIoiqIoiqIo22AtaCuEIFjtaLFR4+nVyba0Q/WlSkrJ8ePHKZfLzM3NkU6nT+vmDL1w5tLSEtOrNziEw2HGxsZ2acRbKwgC5ubmKBQKdLvdMwLo8/Pz5HI5xsfHd2R9MgyDsbExRkdHqdfrpwVtL4Vg8aXO8zyCIKDT6fQ7dXuex+zsLJVKhXw+TygUYnBwsB+AlFL2uyEnV2/eueKKK87ZwVwIQTqdJn0JXuzvdDrk83mKxWK/+Aj0gveDg4Ok02ls296yLtBrKpUKnU6HTqfD+Pj4utNqmkYqlaJSqZBOp/E8b0tC8MrDVlZWmJubo9vtUi6X6XQ6QK9rd61WY2lpieHh4Yv2OOR5HuVymXK5TDweX3ffHovFiMfj/WnHxsbOuW+4EI1Gg0ajwdLSUj/k/0iapjEwMEAQBCwtLRGLxWg2m7t6bMpms4RCIZaWlnAch1qthmmauK5LKBQikUgQj8fpdrtMTk4yODhIu93m6NGjRCKRXRv3qS6he9MURVEURVEUZdeYN92E/9BDUCyizcygNxpo3S5Gt4tvmgSrN+EL1wUhcEMhpBBYU1M4hkHl/e8n/epX7/Zs7A4hsHb593Xo6FFCb34z3HEHfqMBQqBHo3v2B5Pz4Q9j5PNonQ7O0aMIy8JYXERzXQJNg9ViB64QmJ0O0nXR8nmCiYneC6TTsLiIKJVw/vVfYZsD6AB4HsRi+E95CkE+D/E49nOfS/zZzz4jvH2hGp//PO4f/iFitaOuaDYRQYCUEr74Rdp/9EdUH/947Ne+lvhzn9sPT9pPfSruRz9KoOuIYhHOcm6iT0r0chkvlSKYmMBcPQ+7RpTLiG6XIBLZeD0SgiASQXS7iEplw2kjT30qkac+dRNLYm+KPOlJRP7oj3AWFmh/4QsEtRrCtjGPHiX6tKft2e1OuUC1GsJ1CTZRlFEaRq/7uechVot8y0wGubJCoOsYjoMXCvXXFREESCF6IVxNQ5uZ6RV/iMeJvOQl2zpbl7LWN79J961vRRSL6IUCeq2GCILePnJlBf8736H8f/4P0Xe+E/vAgUf9PrHrriN23XV0T57E+d73CNpthK7jl0qIep2g20UeP46TThOamcGNxwmy2V5RFc9DlMuY5TKBbeMfOIB86Uuxt7iI6k4KPe1pdMJhfNtGKxaRGxQY0UslvGSSIJHY0kIuyt7RnZmh8cd/jP7NbyKaTYxWq1egwLZxP/EJShMTWK9+NbFnPWu3h7qlhK5jDg/v9jCU8xEOg6YhTrm+vh7heb3fLaqjvaLsOHVniaIoiqIoiqIoiqIoiqIoyjawbbvfHbhWq5FIJBDi3K0ke13PNcLh8J4JF+1la92Y14LWuUfcrAW9ztz79u3j5MmTLC0tYds2w8PD2xKC20lBEHDs2DFKpRLLy8s0Gg0sy+qH11ZWVojFYjiO0+9MvVNFDYQQJLb45r9LndyjLWZLpRLNZpN8Pk8qlTqjY3Y4HCaVShEOh5mbm8OyLPL5PMOPsQv7KysrzM7O0mq1KJfLtFqtfgf0ZDJJrVYjFout2yH+0XJdF8dx+t3WNxIOh6ms3pCqAuhba2lpibm5ORYXF6lWq4TDYaLRKEIIms0mU1NTxGIxfN/H9/0t7wi+ExqNBq7r0mg02L9//4bTp1Ip5ubm8DyPRqPRL1RxqiAIKJVK/WIqUkoMwyCdTm+qY3yhUKDRaOB5Htlsdt1ps9ks5XKZRqNBPp/f9eIo0WiUbDbLysoKy8vLJBIJLrvsMnK5HOFwGOhtp6VSibm5OXzf5/jx41x11VU79j1mvcOTuq9bURRFURRFUS5c/MYbKf3Lv+A6DvYDD2B0u4ggwLOsXgc430f3PDTfx7NtglCI7uHDmNPT6AsLeP/8z8if+znEFp9vUM6TEOh7vHto54EH0B98EKNYxMtm++uM8LxeCHO1CyoAuk6g62iui1Gt4oyN9SrrCoEXiSA6HUSptK3jbX3727T/7M/QH3gArdFA73SQAJEI8pvfpPK5zxF73evOGV50V1Zof+MbyEYDEYkQetKTsNYp3tj43Ofw3v52tPl5zHweqWm9wGqrhe44aL6PFAJ7ZQXnW9+ifPXVaC9+Mamf/Vmi115LaXQUb3ERc2UFN5WCc5ynE/k8mufh5XJoN9105o/rtc9hs+eL1zo6X+TXO86HNTqK9bKX7fYwlJ1imr3twnU3nDRIpZALC73O5o4D9Lqiu+k0wvcx2m30Tgd/9TV1z8OzLHzbRp+cRO90cI4cwX/a0whdfvk2z9ilqfWtb9H9jd9Am5vDXFxEmiZeMok0TYTjYK6swPIyTr1O81d/FfHnf77uvnkz7H370KNRan/xF4j77kOr16HV6nW3DwLMahXPshCehz093d+/+qEQ7vAwMpslePGLyfzyL1/w/O+m0GWX0bzqKkQ+j7m8TDedRqyeYz5DpYLRatEdHYXrr0ds4tqScnHpPPAA7Te8AX1uDmNpCb3bJbAspBBojoPUdbzlZby5Oaqvfz3Jm2/e7SErj2HalVciv/Ql9OVlvFYLznaflO8jymXodrFWVnDSafxNXKdUFGVrqTtLFEVRFEVRFEVRFEVRFEVRtoGmaWSzWer1OjMcQUoAAQAASURBVLOzs3Q6HTTtHBd7AcdxiMUShEKhs4a0lNPl83lqtRpCiHVDZ2udT2dnZ+l2u5RKpbN2SL1Qa6E8z/PQdZ1IJNLvFL3VTp48SalUYmZmBsuyOHDgQD+sBtBut1lZWWF6ehopJSdPnuTABXQSULZWEARUKhUWFhZotVpIKbEsi0QiwcDAwK4E+A3DQNM0QqEQzWaTZDJJpVKhVqth2/YZ4XPXdfE8D8uyGBkZoV6vUy6XicViDA0NIR4jycR8Ps/JkyeZn5+nXq8Tj8cZHBxE13U6nQ6VSoVCocD4+DhBEHDFFVdsKii+WUIIhBAEQbCp6YMg6H82j5XPaCc0m03m5+eZn5+n0+mwf//+0wrJ5HI5Op0OCwsLzM7OIoQgHo+TTqd3cdTnz/O81WI5bKqYgm3bSCn7oftHqlQqzMzM9LeVTqeDlBLTNKlWqywtLTE4OMj4+Pg519dms0mj0SAWi20YyjYMg1gsRqPRoNVqbWKOt9/i4iL1eh3TNDl69OgZ+wfDMBgcHMQ0TZaWlohGoxSLRQYHB3dkfHu0PoqiKIqiKIqiXDKEaZJ897upvuEN+DMzvQC6lBiu2w/b+bqOGw4jLQvn4EFEKIQ3PIw1O0tQLlP/1KdI3HjjLs+Jstd1/+u/kO02muMQZLOs/cru/9qWshfYlBKEQAqB7nm9ToStFsRi/ekA5DaeV6rfdx/e7/4u+uIiZj6P5jj4pomgFyr1w2GoVGg9+CDBu95F+Kqr+s9tfetbtP/+79G/9jXodsHzkLpOy7KoP/nJhF7+cqI/8iP96dvf/S7ND34Q+2//FrteR3dduqkU0rIwazU030esLhMtCNBbLYxul84Xv4i7skLxgQfI/N7vof/ET/SCr80m5vHjuCMjkEw+HDB3XcTyMlapRHd8nGBkhNRZtls5PIyMRDBWVghW3/ecVsfjDQ0hh4a2avErG5C+T/O+++jecw9ibq63r04mET/yI8RuvRVzh87ZPFbI/fsJvvMdrLk5ur6PWOf8n4xGEVISmCaa6/b2XZEIcmwMdzWQrnW7mKvFXgB014VmEzeR6IXPH/94Ur/zOzsxa5cc6Xl03v529Pl5rMXFXrg5m+3vxyTgjI4ilpawT56kq2nUf+/3yL7vfY/izSStb36Tzuc+h3/yJManP43eavVC7q1W/xglggDftnsd7h2H7r59/U67mCb+E5+IfdttxJ7znC1cErvHfsUr8O6/H7/RwDpxoncsSqcf/gx8Hy2f74U3BweR2Szxl798l0etbDW/WqV1++1o09PY8/O46TTugQMI2wZW14NCAXNlBa/bJfjTP6U5Pk70mmt2eeTKY1Xs1ltpfvjDeJEI+tIS/sGDp32H1hYWMGo1hO+jdbvovo+sVvG//nVKb3878f/xP1TXe0XZISqAriiKoiiKoiiKoiiKoiiKsk0GBwfJ5/PEYjHm5uZw3bN3Nu902kgpSaezDAwMqGDgJtTr9VM6y6+/vKLRKIZhUK/XqdfrWxpAb7VaLC8vUy6XcV0X3/fRNA3TNInH4wwNDW1pQYFut0uxWGRhYYFQKHTWYF44HGbfvn3Mzc2xsLCAZVmMjo5uefdl5fw1m00mJyfpdDr9zr1SSmzbJpFI9EPchw8f3tKg8kaEEKTTaWq1GnNzc1Sr1X5RhbNtL/V6Hcuy+uM2DIOVlRUGBwdpNpvE1m5KvYQ5jsPs7CwLCwt0Oh0OHjx4WtGJWCxGNpulUCgwNzeHruvMzc1x8ODBLRuDbduEQiGklLRardNCz2fTaDQIh8P9fZSyNZaXl6nVajQaDQ4dOnTWfW0oFGLfvn1MTU1RLpdZXl6+6ALomqahaRoAvu9jGOtfZl4LnQsh+s9bUy6XOXHiBPl8nmKx2A+Ha5pGu91mamqKSCTSD72fbbvxPI9isUihUEDXdSqVCvF4fN0guq7rBEGw6aIN26nZbNJqtSiXy2Sz2XW3yXQ6TblcplKpkM/ndyyAriiKoiiKoijK9jPSaZLvehfNz3+eoFYjMAyCtd9bQiANAy+ZJBgagrXfm8kkzM1BtYr37W+DCqArGwharV6YHE7rdCqDAG2tC7oQpwWetSBAuC6sPk8GAUarhTM8DNtQ4BWg89BDeO94B9rMDFY+j5PLIXO5/rov2230QgF7Zoau69K+4w7MD3wAI5ul+pGPIP/sz9CLRfRCAeOU4nN+OAyFAu43v0n5Va8idtNN1H77t9EfeABrchK7XEZ3XXzDwKpW0X0fpERqGr5pIle3SaPVItA0rFoNcewYrpSU3/1uMr/5m5Snp/E+9jG0+Xms2VnkwgKBbUMQYLTbBKZJd/9+5NgYkT/4A7SznMczb7iB4JvfRCwtQanUC2+eS6mEkBKZyWC94AVb+0EoZ9X65jfp/N7voS0toZdKaM1mrwu9ZRE88ADND3+Y4IUvJP36168blFY2L3TTTbif/zxyYQFtZQU5MnLOabV8Hj8UAiHwQiGsyUncbJYglyM4eBBnYQFrZQXR6aAJgW+aeJEI7mWXIWMx5HOeQ/oNb0A7V9doZV31z3wGbWUFc2mpd5zI5c6cSAjkyAiu52EuLMD999N58MHz6jjf+I//wHn/+9Gmp9HKZUIzM+iOg+55+LqOk07jHTiAsKxeJ/RCAbNS6QWuQyGCn/95zH37sK+8EvsSK9wde+YzqfziL+L/1V8hFxaw5udhcbF3DAwC9HYbqes4o6MEo6NYb3ubCm1egmof/Sja0hL2/DzO8DBycJBT7yIQuo4cGsKJxbBOnMBZWaHzN3+jAujKrjEHBwme+UxoNLAmJ5HT0wTj40jPwzpxAt1x0LpdNN9HCwICTcPodDCWl/Hvvpv6N75B5N3vJnTZZbs9K4pyyVMBdEVRFEVRFEVRFEVRFEVRlG0SCoWYmJhASsni4iJTU3P06rw/TEpJEAREImHi8RTD6mLvhoIg6HdV3Wyo2jCMc3ZhfbRKpRLT09PUajVKpdJpHVVt2+4HeoeHhxkfH9+S9ywUCnQ6HdrtNocPHz5n+F4IweDgICdOnKDT6ZDP5xkbG9uSMSiPTqvV4qGHHqJUKrG0tARAIpFA13WklCwtLbGyssLY2Bie53HFFVdsGPDcSoODgxSLRUKhECdPnsT3faSUp4WqoRearFarDA4OEo1G+9vg2vblrnboutTl83na7Ta1Wu2M8PkaIQQDAwM4jkM+nycSiTA+Pr5l4e9MJsPc3ByxWIxSqbRuAN1xHOr1OuPj42QymTMCwcqj43kelUqFcrlMKpVa95hkGAbpdJpKpUI2m6XdbhO+iG5ojEQimKaJbdvUarUNi7nUajVCoRCGYZw2n47jMDU1xdLSEtVqldHRURKJxGnP7Xa7LC4ucvLkSYQQxGKx/vs5jsPCwgLlcpm5uTmWl5dxXZcgCDBNk2QySS6XO+v+s9vtEg6HN+yWvhNqtRqtVgvXdTdVqCaVSlEqlfr7lJ0oKrNeB3RVK0lRFEVRFEVRto70feTICF65jBeLweo5BmlZyEQC8cjf8EIQGAbC95GnnI9UlNNISfO//ovupz6F/4UvEFpYQOt2kbUaIpFAzM+jd7sIKdHoXad45A9Bw3HQV1bwk0lEqdT7x0wGe5sCz80PfhB9ZQUrn6dz4ADiEecLRDhMMDFBNxzGXligG41S//CH0Q8dQv4//w/azAxGrYaXTNIZHQXLQrguolTCWlzEr1Tw/uqvqP+//y+652HOzmJVqyAlgRAgJeYp5zY9TeuFz1d/BPu2jd7t4kUi4PvoMzPwiU/gvOpVpH/t16gMDuL/3d8RVKu95eU4oGl0h4YgkcC/4gpid9yBfY4ClfHnPY/yX/4lbi6HtbCAY1kQj585Ya2GtbiIOzBAMDBA8rnP3boPQTmr1n/+J9077kCfn8dcXgbAi8fBMNCaTcxCobf/7nYpF4tkfv/31cmTLRB9+tMp7duHWy5jz83RNc3TumoDvf1WqYS1vIwzPo6XTKKZJkGhgFEooOXzSF3vFdrQdbqpFNKy8DMZ/Ouvx3jiE4m/+MUYF1mx0L3GvfdejGIRKQRyg/PGwdAQ5gMPQL1O6847Cd1xx6beo/qRjyDf+170pSWMYhG91UJ33V43XHrBWqPZRJucxDl8GBGJEOzbRzcW660/0ShicZHEq1+9BXO8N6V++qeppVJ4738/frGIVi4jul0k4GezkE4TTEwQetObiDz5ybs9XGWLSd9HfuIT6IUCvm0j1ytkG43iptMYhQJ861t0Z2aw9+/fucEqyimSb3oT1elpHCkxZ2Ywv/99hOOg+T665wEgNQ0nEiGwbZyhIbRGA3tykq7j0PqN38B4//sx1ivepCjKBVMBdEVRFEVRFEVRFEVRFEVRlG00ODjY7/5ZqwGcftOJEIJIJIppmuzbd0B1P98ETdP6y3SzYVfP89A0bctCZ7VarR+gq1QqJJNJBgcHMU0T3/ep1Wrk83lqtRpSSnRdZ2S1O4OUkkqlQrFYxHEcAEzTJJvNkk6n110HarUa1WqVSCSyYfjMtm0ikQjVapVaraYC6LtISsnk5GQ/fD40NEQsFut/1oZhMDg4yMrKSj9wOTMzw+HDh3dsjJFIhFwuh+d53H///SwsLNBoNHAcB9M0+wHmVqtFJpMhHo/3u/DK9VKKj4LjOARBgKZpOxKyfDQKhQLlcploNHrW8PmpstksU1NTdDodSqUSQ0NDWzIGXdfJZDK0Wi2mp6dZWVlhYGDgjH2I67rMzs4SDodPC/IqF67VauH7Pq1Wa1PLNZlMks/n6Xa7NJvNiyqAHgqFiMfjpNNp8vk86XT6nEUyPM+jXC4zNDREIpHAtu3+Y/l8vt/5e//+/WctnGDbNhMTE0xNTVEsFolEIgwMDNBqtTh27Bi1Wo1isUg+n6fZbJLP5/E8j3g8TrvdptFosG/fvtP2H91ul3a7zfDw8BmB992wVrRD1/VNFRuxLAtv9UYbz/N2fd+ovq4qiqIoiqIoytbRYzEApK6DaZ4WFjnrV28p0TwPT9fhIvpdqeyc5le/Sue970WfmUGrVtFrNaxaDc3ziDz0EJ5l9boIrp5bXzuzF2gaCIG21i09CDCaTeSJExjNZi/wPDhI9L/9ty0fs1cson/5y+iFAm4yeUb4/DS5HF65jFYo4H3843iAvrCAUa/jHD4MkcjD245pIiMRugMDWMePE/r+93tdeDUNL5HAcxyMdhvftntB9CBADwIkvS7wdLu9rsr0tlGxuly8eByrUsFvNmneeSfWa19L6qd+iuC226h/8pN4X/gCVKtgGIh9+4jcfDOhK69cdxkI08T69V/H+53fwfN97KkpvFgMP5NBmiai20UvlzEaDdxcDjk2hvWGNyB2sIjpY5FXLtP9v/9vtNnZXsh5eJggl+sXBwkAr9XCmJvDPH4cVwgqH/wgqZ/92V0d9yVBCCK//du0X/c6nCDAXljAz+fxV0PkwvMwymU0x+l9LmNjRP74jwkaDTof/CDy+9+HVgtcFzQNolGCbBbxgheQ+vmfR2xRoVgFxPQ0otHAj8c3PnFoWXiRCKJWQ05Nber1G1/8IvK970Wbnu7t63M5pJQIKZFC4FsWuutittt4UvY6Ox892ttOMxncWg09n0d+4Qt45fIlXXAgcdNNyBe8gPqnP4372c9CuQy6DiMjhF/0IiLXXKNO7l6iupOTaCsr6JUK7iauQwYDA1jFIl6rRfvzn1cBdGXX6PE4yfe+l8pv/zZEImiTk4QKBXTPI9B1fMtCGga+bePt3w/hMMHAAM7yMtbsLN1QiPqHPkT6ta/d7VlRlEua+tWpKIqiKIqiKIqiKIqiKIqyzQYGBkgmkzQa5bM+HovFdrTT8aUgFosRj8cpFov9kP+5tFqtfjAttnpD54U6efIkhUKBarXK/v37TwsRGobBwMAA6XSamZkZFhYW0HWdXC5Hs9nk5MmTdDodarUa3W4X6AXLyuVyP3SXyWTO+r6e551X8MyyrC3v/K6cv0qlQrvd7ofP0+l0P0i4RtM0hoeHCYKA5eVlwuEw3W73tODmdtu3bx9SSjqdDu12m2KxyIkTJ4hEIqvFMiKMjo4SDoeZmJjoB68bjQamaWIYxqMer+d5/UDp2nYBvSBqLpc7Z0fj3bC2Ha6F8TcSCoUwTZN2u02n09nSsYyNjVGv15mYmGBubo5arUYqlSIcDhMEAfV6nVqtRjgcZnx8nOHh4XU7pSvnJwgCgiAA2FSBk7Vp5OoNzRebwcFBqtUq1WqVkydPMjY2dsY23+l0mJ+fx7btfnGWNVLKfvGGWCy27rq4dtxcK6xQLBaZn5+nUCiwtLREPB7nCU94AgsLC8RiMVzXRUrJ4uIig4ODnDx5koMHD6LrOlLK/n41FArtiSIMa8V0giDo3Sy5wU1/nuf11x/tkd0Pt8kW1xZRFEVRFEVRFOUctEiE4MAB/KUljHIZd3h4/WDQasdmkknMJz1p5waqXBRqn/wkwTvfibG0hJHPI3yfwLb7QT2j20V3HISUBJqGY9uYrosIArQgQK6ue8Hqf81mE6PVoj02hhwdxfq1X0NsUZHXU7W++EVotzGaTTqHD5+9+MIp/EwGa2GBYGYGqeuYxSLO6Cic41yDsG3cbJbYiRNIoJNOEwwNQa2GWP0BrHleb7msPUdKNN/Hl7K3TQrRWz5SInQdPxJBKxbxvvhFWA28aKEQyZtvhptvflTLIfbsZ1O7/Xb8d7+bIJVCKxSwZmd73ZtXQ/PdI0eQ6TTGb/4msWc841G9j7J5jbvvRhSLWMvLdMfHIZM5c/2MRHAPH8Y8dgxtYQHvrruQr3ylKg6wBUJXXIF897tpv+UtdJNJRKGAXq0+vE0kk8hcjiCbJfT2txN+whMAiF57LZ2HHqL9+c9DrQa2jXHZZSSf8xz1uWwDsXocCTZ57VJqWm/fu1qkeyPOBz6AvrKCUav1Co0A+vIymufhmyboOr6mIV0XvdPpvX65DKvdcINsFntqqlc05L77evvpS5gwDBI33AA33LDbQ7moBa0WXqWCFgr1ihbs8eB+UK0Cve8z0rY3/C4lbLs3T56H7HVRUJRdoycSZP/0T2l961s4L3sZQbmMBPxYDD8cxstmewWqTtkO5dAQwWqBEe9Tn0L+0i+p4jKKso3UN2hFURRFURRFURRFURRFUZQdYFkWIyNnrza9Fqq8CLNou2ZgYIBKpcLKygqlUons6k0EjySlJJ/PE4vFsG37nNOdj1qt1u9kPDg4eM4OtoZhMDY21u98/NBDD9HpdMjn85RKJTRNIxwOI4SgWCyysrJCOp3GdV183z9rQG6ti/sjw8vn4vs+hmHsWFhNObt8Pk+tVkPXdVKp1LrT5nI5Jicn6XQ6FAqFHe1cL4TgwIEDJJNJarUavu9Tr9cZGRnBMAwMwyCZTJLNZjFXL+BKKSmXy/3Q86MJNzcaDSYnJ/tdkev1er8zcDwep16vs7S0xOHDh4nH41s92+dtreP7ZkKja9am2+rQsWEYHD16lOPHj2PbNpVKhUqlQqFQAHqd7cfHx4nFYgwPD+/o+vRYoOs6uq4jhMBxnA0LMDirN9StPe9ik0qlGB4eRkrJ/Pw8J06cIBqN9rf7ZrNJq9UiFosxOjrK8PAwyWSy//xut4vneTQaDYY20YEjkUiwuLhIq9XqH0uXlpYYHh7u70sbjQau67KwsABAOBxmZWUF0zQpl8tEo1FWVlZwHIf9+/eTzWZ3vXs49LbNteIetVrttOV0NrVarV+waKcKk6wXQN/j99opiqIoiqIoykVHf9GLkMePI1ZWoFiEXO6s08kgwFxexkskkKkUsec9b4dHquxlnR/8AP9d70KfmcEol3EGB5G5HELX8SoVrOlprEaj3+FbSIk0DDwhMBwHggBNyl7379WAupASz7YJQiG0228n9qxnbcvYg0ql16VYSjjH+fbThMOIIEAvFgkMo9fdeINCkVq73e9sLoTodYSFfqi8bzVorq0WjdNctxeqXA3xIwRS1/HDYTTHQWxxaCtxww10r7yS5j/+I/KznyVot3vjEwIZjSJ/9EeJvuxl2Pv2ben7KmchJcG992IUCvi2Det0TRa6jjc42CuMUCzS+NzniF9//Q4OtsdvNGh8/OP4P/gBst1GxOMYV19N/Md+7KINXoef8ATsf/gHav/8z3j//M/Ikyf7jwVjY2gvehHJm25Cf8S1g9DRo4SOHt3p4T4myWgUaZpo3S4blsOWEs1x8CMR2ETR8Pb996M99BBGoYCby/UKjawGbYWUyLXz7EIQmCaa56F5HkaxiLd2TTgW6+1HHQdZKj36GVUuedLzet3j77kH/f77+/8epNOI5z+f2G23YZ5SdHdPWb3uITUNsYlrkTII+t8v2APXTBQFIKjXYXCQIJ/HmZiA1WuB57ok5WWzWPPzBJUKza99jdh11+3YWBXlsebi/CWhKIqiKIqiKIqiKIqiKIpyEdooA6wC6JuXSqWIRCKMjIywsLCAlJJMJnNa0Np1XZaWlnAchwMHDpDL5bYk8FcsFqnVakgpNwyMhUIhwuEwy8vLzM3NEYlEqNVqDA8Pk0gk+qFUKWU/ZOt5HkIIwuHwGR3bo9Eo0WiU+fn5fkD3XNZCfuPj46rj8S5rtVo0Gg3i8fiGgWXLsgiHwzQaDVqt1g6N8HTpdJqnPe1pJBIJHnroIWzbZmxsjGg0eto2thZChd42OfgobrpoNpscO3aMfD7PysoKoVCIbDaLYRh4nkelUukXe5BScvToUaLR6JbN66NhGAZCCEzT3FRHc9/3cV0XwzD6wf2tZFkWV1xxBaVSiXw+T+6Um9SFEGQyGQYGBnZ9uV2KotEohmEQj8epVqsbFkioVCqEw2Esy9oTxRQejYmJiX6AvtPpUC6XaTQaQO+YNzQ0RCgUYnR0lJGRkdOe6/u92w+DIOgX31mPpmlomobv+5RKJTqdDrFY7LRCHiMjI7iuC/SOz61Wi0qlQqfToVgsMjQ0RDwe58CBA6TTafbtkZuzk8kkoVCIZDJJsVgkHo+fs1hMq9Wi2Wxy8OBBcrncpgtfbJVrr/0sjtPb15mmhabpKoCuKIqiKIqiKFss/qIXUfnbv8UdHcVeWMDxPILBQcSpvxNaLYz5+V6H0bExuPFGtFBo9wat7DnNv/kb9Hwes1Sie/AgxOMPhzVSKdzBQYxOB+G6aIAIAsx2m0DXe4UW6XU+l5qGZ1lIw0DvdPBCIbzLLye3jZ1UhWX1u64L39/wYopcPccgPA9NSrxIZMNqaXq73XuuEL33sG0CyyJwnF7389MG1AuhCyl7066+l1wNn8tYDK1c7v19G0K99oED2L/xGwSvfS2dBx9EtlqIaJTQ5Zer7X4Hufk82soKeq3WC7JudEIklYL5eajVcL/9bdjBAHrQ6VB573sRn/40olZDq9V6XcINg+ATn6D8vvehveQlpF75youysqAWiZB6+cvh5S/Hr1bxGw20SAQjlboo5+eS89SnEkxNYU9N4TkOYr0wa6OB7jh42Sz6D//whi/d+Y//QDSbaK6LXLv2ceox4hEFRHzDQPc89HYbz3XBNHtB29XHUd1xlXNwl5ao/8ZvoJ04gVEsolcqCNcFTcOPRgkWF2l87GPor389iRe+cLeHewb70CG6loUfiaBVKgQb3MMgSqVeAYdIBHMninVIid9oIHQdLRxW+27lrPxCAVy3V0RhE/e3yFisN63rEhSLOzBCRXnsUgF0RVEURVEURVEURVEURVGUHaIC6FtHCMHhw4f7Ye2lpSWKxSKxWAxd13Ech2azSSgUYv/+/aTT6S3r/Os4Do7jEA6HN9VZPBqNMjMzg67ruK7LgQMHCD3iJjEhBIlEAsuymJ6eplqtsry8fEYAfWBggGKxiGEYFIvFdQO/a9PFYrGzdlNXdk4QBJsOXEKvO/Lac3bLwMAA5XIZwzCYnZ1leXmZdDpNNBpFSkmz2aRSqQC9QGo6nSa71k3iPExNTVEsFsnn84yNjZFIJE57PJ1OU6vVWFhYQNM0pqamePzjH78Vs/ioCSFIJpOkUimWlpYYHBxcd19QqVT622J6nS45F0LTNHK5HLlcrt9lWgiBbdsXZafti4WmaWSzWRqNBtPT0zQajTP222s6nQ6VSoXR0VGSyeSe6ML9aI2OjpLNZikUCsTj8X4A3DRNstksAwMDZ52/tXVx7Xi4Ec/zCIKgX+ihXq8zPj5+2jSaprFv3z5WVlawLAvHcbAsi3K5TCgUYmxsjFQqRS6XY3x8fFPH7Z0ghGBgYIBWq8X09DSzs7OMjo6eUaSi0WgwPz9POp0mHA7vyvE8nf5hut0uALZt73gAXlEURVEURVEeC7RIhPDv/R6dN76RrhCYS0uIQgEvGgVdR+t0ekGqaBT3yBH8a68l89//+24PW9lD3Hwe/ctfRi8UcFMpOEvhOzk6ireygu55/cC5FgQEuo5v23imSaBpuNkshMNIXUcrl3udxk8N+G0D48gR3FCIwDAQ5TJyg0KXeqXS6567GrDdDBEECFY7nq/y0mm0bhfTdXvh+1M6n59GSnTXJTAMAtuGWAz95Em8gQHkFl13OBstHCby5Cdv2+sr6/Pr9d7/BAFyM6FVTeuF+XwfuVrwYCcErRblX/s19G9/G21hAbNeR+o6ga6jeR7Mz+MlkwTFIqWZGTJvectFHfzTk0n0DYKVys6KvOQldD7xCXzbxpibwz948KzrmPR9zIUF3HgcGYuRePGLN3xtWa0iHKe3ba1th7YNQKBpaL5PcOo1EE2DtXPPngemiahUes8PhdD3SIFSZW/xikXqr30t2vHjWDMzIARuKoUMhRC+j1atYj/4IG4mQ/DOd1IXgvgLXrDbwz6NHo8TXHcdfj6PPTNDp91GhMNnnVYGAWahgJtOEwwPE3vmM7dtXO3vfpfWRz+K9qUvIVavM8hkEvljP0bsttuwHnHNR3lsE4bR/66+9ntlXWvf2TUNsQ1FoRRFeZjawhRFURRFURRFURRFURRFUXaIpkE4vI8g6Jzybw8HkVUA/fyEw2GuuOIKJicnicfj1Go1ms0mnudhWRYDAwOEw2EymQz79+/fstDZ+QavXNel0WgghOh3hj2XUChEKpWiXC6TTCb7Qbo1ax3Qh4eHmZubQwhBNps9bd6CIKBYLFIulxkfH+8/R9k9uq5jGEY/wLeRbrdLNBrd1eCwpmkcOXIEIQSGYVCpVCiXy6ysrAC9oGk6nSaVSpFKpTh8+PB5bxvVapV2u00+n2dwcPCM8PmaRCKB53nk83mSySTVapXkLt9gthbQz+fzLC4uMjo6etb573Q6FAoFcrkckUhkR7ZF27axV2/AUrbf4OAghUKBgYEB5ubmyOVypNPp/vYbBAHVapWVlRWSySTJZJLh4eFdHvWFs22bsbExxsbG+jdlb7QPsG0by7JIJBJUKpXTOpmfTbVaxTTNfihbSkn4LDdMaZrG8PAwuVyOSqVCNBpF0zRGRkYYHx/n0KFDmy4AspOGhoZoNpsAzM3Ncfz4cWKxGOFwmCAIqNfruK5LOp1maGiIAwcO7GjhgvWyBRfxPcqKoiiKoiiKsmeFn/AExHveQ+vtb8fNZKBcRqvXIQgIolG8iQmIRgl+/MfJvOlNCFVwTjlF+8tfhk4Ho9WiMzJyzrBGEA7jOQ666yKkJNB1vEiEIBLBz2R6HZzXOpEDolLpdQPf5t/V0auvpjQ+jre0hFEs4uRyiHOdz3ddjGoVZ2QEPxTCaLcxlpcJpFz3B6vUtP6PXeE4mD/4AcJ1MVaDwprvI9YeP+VHsZASo91GCkFgWbgDA1Aq9bpLZ7OYL3rRFi0FZa/R185Xaxo4zobTyyBA+D4YBmITXTu3Svltb0P/1rcwJycJLIvugQO9IhRC9Nb5chlzeRnt2DFcKSkPD5P+xV/csfEplzbpujjHj+MZBpppYpbLiHYbb3wcEomH18NqFWtpCQkE+/bBi1+MfpZiKWewrN5rBEHvdYQAy8KLxRCeh+44BKvnj4Xrorsuuu8jmk2CyUm8TAa9WsVLpQgGB0k+4xnbu0CUi1L1j/8YfWoKa3oaN53uFZdZC8EC/uAgfq2GdfIkHuD98R/jPf3pGI+iMPd2irz0pXQ/9zm8WAxragr3wIEzukhL18WYmUFIiRwaQrvxxm054S8dh/Lv/i7affehV6vopVIvgC4EQThMsLhI6667aP30T5P6uZ9TFx0UAIx9+3Asi8A00arVDYtSiUqldwwwTQxVYERRttXeu9KuKIqiKIqiKIqiKIqiKIpyidI0eO5zHzjn4yqAfv5CoRBXXXUVtVqNQqFAs9lESomu6ySTSQYGBrY8iGlZFrZtUywW8X1/w5BwuVxG0zS63e6mQrOpVIqpqSk8z6PRaJDJZE57/ODBgzirNzstLCxQKpVIJpMYhoHneVSrVYQQjI+Pk06nOXTo0KOfWWVLrIVO5+bm8Dxv3SDkWhGFRCKx6yFrwzA4evRov0N5Lpfrd2XXNI1oNMrAwACZTOZRdcQtFArUajWklBsGUVOpFPl8vr+t7/ayWft8xsfHOXnyJDMzM+RyOaLRKEIIPM+jUqlQLBaJx+Nks1lGR0d3dczK9rBtm8OHDwO9baZQKFAoFAiHwwghaLfb/WIhuVyO/fv3n3chAs/zcF0XIQSmae65rvab3f7Xun43Gg1KpdK6xSRc16VYLJLNZomccoPUGR3ITmEYBrlcjlgshuM4TExMMDQ0tCfD59BbHmvheMMwaLVaVCoVms0mQgiSySSpVArbttm/fz/pdHpHx7fNze0URVEURVEURTmL0JVXEvrQh2h86Ut077mHYHoaXBcZj6M9/enEbrsNc2hot4ep7EFBvd7r+ColnKPbJUBgWUhdR7ouaBpeOIw7MdELnj+SlOj1eq/L98jI9g0eQAj0F7+YYGYGKhWMEyfw9u9HPKLrtOx2Maemel3IMxnkLbcg77wTsbQEpRKsEwQLNA0hJZqUBK4LqyH83guvBs9Xp9VW/y7pdYn3TJPAtnFzOdB1rNlZ3IEBgmyW5POet9VLQ9kjzFyOYGQEb2EBo1zGGxxcNyAnyuXe4/E41lOesiNj7DzwAPqXv4wxPU0QDp/ZeVoIyGRw4nHMyUn0uTn8j3yE4BWvQNvBkLxy6ZGeR+UDH0D+8z8jikWMahW91UJIidVoYH//+3ihEH44jPB9RBDgJZMEY2P4z3422V/+5U29j37oEEEs1nvPWg2xej7Zz+UwGo1eUZJWq7f/lhJt9RqWCAKMVguz0UBISSsWQ9xwgyrgo5zBXVlB/9KX0BcW8GKx08Lnp0kk6O7bR2h6mqBep37XXXuumEf48Y+n8zM/g/+BDyBnZrCPHcOLRglCIUSrhd5uY3S7BIZB99Ah/Kc+lcxP/dSWj0P6PqU77kD/4hcxpqbQu13cRIIgk+ltp/V6b2yJBP7//t+UXZf0q1+95eNQLj6RJz6RzsQE3uLihkWpZBBglUp4mQzBgQOEr7pqh0erKI8te/Nqu6IoiqIoiqIoiqIoiqIoyiVoowbcKoD+6CUSiXN2T95q2WyWQqGArutUq9UzAuKnarfbtFotYrEYpmluqnPpWlDO9/1+2PdUtm1z+eWXMzk5SSQSoVarUa/X6XQ66LrO0NAQiUSCaDTK4cOHd7RbqnJ2AwMDFItFDMNgZWXlnEFk3/dZWVkhHo9jWda669ZOEUKQy+XI5XJ0Op1+8QPLsgiFQhf02p1Oh3a7TSwWQ9tgB6lpGrFYjHa7TafTuaD33SqHDh3C9300TaNQKDA3Nwf0xur7PpZlMTg4SDqdZv/+/bsemle2TyKR4OjRo8zMzJBMJmk0GnS7XaSUJJNJEokEoVCIiYmJTa8HUkqq1Wq/8MIaIQTpdJqBgQFiqzfeXUxyuRzLy8sMDQ2xuLjY7/C9FqqXUtJoNFheXiYUCpHJZBgcHGRlZQVd12k0GhsWrGg0GliWha7rW16EZqsJIdi/fz9DQ0Pk83nK5TKe5yGEIBQKkcvlyGQye7DowG6PQFEURVEURVEuYUIQu+46Ytddt9sjUS4iwrZ7Hb4BPK/XMfYsgmwWuXaeYS1kLcTZO6af2uX7hS/c+kE/QuKlL6X0n/+JGwQYJ05gP/AAXiJBsFrIT6vVMBsNvEgE/+BBghtvJPurv0r585/HzWaxFhfphkKIsxT+k60WVrXa/7vhOPiGQaDrCE1D8zw03+8tCymRPBxG9wyDwDBwIxG0ZhOzWMQZGECOjWHfcccZIXnlEiIE2otehJyZQS8U8NYpciB9H2tlpd9lOfqsZ+3IEFsf/SharYbuOHQPHz7nSRthmrgjI9gnT/aCkx//OMmXvGRHxqicn6DTwSsWQQjMXA6xB6/xSdel9Ja3oH/1q+iLixiVSu/fVwt9iCDo7V99Hykl3tAQpNPIVApuuYXsL/zCpk8wxn/8x6n+5V/iJRIYKyv4a13Vk0ncVArNcTBcF8HD++2AXgERw3EQUuKZJnqrhVcobP3CUC56jXvuQTSbGM0m3csuW7/QSCKBHwqh5fN4n/wk7LEAOkD6F36BimkS/O//TadUwp6exi4UEEHQ6z6u6whAK5fxqlVa3/420auv3tIxVP/+79G+/GXMyUkC06R75ZVwyvclf2gIv9XCnJmBqSn8v/s7Wk9/OpEnP3lLx6FchNaKUk1NQbmMMT2Nd+DAGSF0GQQY09MgJUEuh37jjerClaJsMxVAVxRFURRFURRFURRFURRF2SEbZYdUAP3iEI/HCYfDZLNZVlZWsG37rN1sHcdhfn6eZDKJ67pYloXrupgb3JDmeR4Auq6fM5Rr2zZXXnkltVqNfD5Po9Hod2OPxWIMDAyosOseEo1GSSQSjI+PMzMzw9zcHKlU6rQAd6PRYGVlBSklQ0NDDA0N7bnAYSgUuuDQ+amklEgpNwyfr9E0rf+cvUDXdY4ePcri4iKRSIROp0On0yEIAkzTJBwOE4vFGBkZ2bECGcruiUajPO5xj6PRaFAsFul2uwCYpkk2mz2vdcD3fSYnJ6lWq1SrVSqVSr/4QzgcplarUSwWGRwcZGJiYtMdyPcCwzA4fPgwQRD0izcUCgUikQiaptHpdPA8j1QqxdDQEKOjo4yOjlKr1UilUpRKJZLJ5DnnOQgCyuUy2WyWWCy2pfus7bRWoGBiYmK3h9K33q72IlrlFEVRFEVRFEVRHhOsK66gGwoRWBZauYwcGjr7hIlErwu6pqH5fq8r7Vk6pstaDXthYUe7fAtdJ/MHf0DpbW/rBegrFfRiETOfB8APhegePAjxOPLWW8m87nUgBNZrX4v3trfheR6hyUncZJIgk0HaNrgueqmEubLycDBR05D0gpLSNJFCEJgm+D5mpwNSIgBP0wgMA9+2wTDQXRc/kcDbtw+ZTmO+5S1Er71225eLsrviN99M9cMfxhkZwZ6fx3FdgoGBh7soS4lsNjHn5pCahj8ygnbbbTvXZfk//xOtXMaNxU4L952NSCSQuo4olfC+9CVQAfQ9pfmNb9C58070L3+5V0gEevuo664jdOuteyqYWf3DP8T8ylcwjx9HCoEzNgbpNAiBEwSIfB57YaF3gtGycI4eJfTzP0/8+c8/70C9Fg4jr7+eoFTCOnYMOTODv28fQtMI0mlYWuqdrFwtHgK9IHqg63im2QvF+z4A+sc/Tv2aa4hff/3WLhDlohZMTWHU6wSWBZHIhtP7ySR6pYK2skLQ6aDtwWsQqZ/+aWoTE/i/8iv9fxNAIARS15FCoHc6GF/7Gu6b3kT9zW8m/mM/tjVvLiXB3XejLy8jAe/QobN3sI5EcA4dwn7oIfxKhfZHPrLr+znpebS/9z2CchksC/vwYczh4V0d02NR4tZbKX3+87iehzk1hfWDH/QK/MTjAGj1OmalApqGe/gwwdVXk7rllt0dtKI8BqgAuqIoiqIoiqIoiqIoiqIoyg5RHdAvHfv27aPb7eJ5HidPniQej5NKpbAsC9/3+4HBaDTK0NAQzWaTWq1GtVolssHF62q1SjgcxjCMswbb1wghSCaTKmh+kTh06BCu6wKwsrLC9PQ0lmVhGAZBEOB5HolEgsHBQQYHBxkZGdnlEW8/wzAwTZNGo7Gp6TudDrFYDMPYO5e3NE1jbGyMkZERKpUK7XabIAgwDINUKkX4LDfwKpe2WCx2QZ3JgyDg+PHjFAoFZmdnkVKSSqUYGBjodwefn58nFArhr944t2/fvq0a/o6IxWJcfvnlp3WMb7fbSCn7BTts22Z0dJSBgQEABgYGaDabVKtVFhYWGB0dPSOE7vs+8/Pz/ePj2nOVR2eP1PpQFEVRFEVRFEVRNiH8+MfTOnQIsbSEUSzi5HLnDMA6w8NYlQq+aaJ5Xq+zYCaDtCyE66KXyxjNJk4uhxwdxXrTm3asy7ewLLLveAfN//ovOnfeifzKVxCr51RlOIx89rOJ3HYboaNH+8+JPfe51BoNgj/5E7qJBHqhgD011f9h64XDvRBnEOCEwwShEHq32/vTahGsBrHE2gUaIXBCIToDA/jXXIO+GoCHXgd58fznE7v1VsxTzztI2QuP3nUX4sQJ6HYhFoOrryb2kpdgjY1t/8JTtoUejxN6xzvo/sZv0NU0zKUlRD6PF42CrqO12+iOgxeP4+/bh7z+etKveMX2D2x1ndMeegi9WiUIhZCdDmK9IKQQBLbd26bq9e0fo7Ip0nUpveMd6J/7HPpq4Q1ttbhpEArhFwo4//7vtK+/nswddyB2+fqIMzOD8dnPYkxPIw0D9/Dh08KlQtNgaIhuJtMLqJsmZqdD7PrrH3U399Qv/RKV734Xx/cxp6cxvv99vGQSbXU9FmvhcyGQmoZnWUjTxItE8HM5ZDSKdewYolDA+djH4BINoHcmJ2nddRdychLhushYDOOHf5jYi16EfgHXLC55rtsrYLDJYtlS0xCr3zFktwt7MIDulUp4f/InaPTuJ+iMjuJns4i1+w5cF1EoYC0u4nW7eP/zf9IeGSF81VUX/N7NL38ZbWkJo1LBHRo6e/h8lbBtvEQCvViEL38Zr1zGSKcveAznyysWqX/4w8hPfQqtXAbfB03D0zT8pzwF+yUvIXbddTs+Lr9apf3tbxPU62jRKPbjHoc5OLjj49hpwjBIv/OdlO+4AywLUSr1ilIViwAEto03NITMZPCvuYb07//+rh8bFeWxQG1liqIoiqIoiqIoiqIoiqIoO2Sj65arOTLlIhCPxzl06BBCCKLRKOVymbm5uX5n5nA43O96PDY2RqfTYWZmhvn5edLp9Dk7sjqOQ6VSYWhoiGQyiW3bOzlbyjbSdZ3LL7+c2dlZwuEwjUaDRqOBlBLbtonH49i2zdDQ0GMifA6QSqVIJpMUCgXa7fa6Ye12u02n02FsbIz0Ltx8sBFN08hkMrs9DOUSsLy8TLlcZnZ2lkgkwsjICNopXyDi8TgDAwPMzs4yNzeHruukUqnz6rC+F0QiEa688koajQaFQoFut4uUEsMwSKfTpNPp0+Z7YGCASqXCxMQEs7OzHD9+nHQ63d9vNBoNqtUquq6zf/9+stms2iYVRVEURVEURVGUxxTz1lsJjh9HVquYk5O4+/cjHnl+udXCXFqim0yi+T7OyAhau425vIwIgl4H53ic7uhor8v3HXcQe+Yzd3ZGhCB69dVEr74a6bp45TJC19GTyXOGSxI33UTnssto/f3fI7/4RbxOp9fxVteR3S5yeRkAd3gYmU4TzMwgm81eB/i1TsOahmfbaI7TC6UfOkTqAx8gaLXw63W0SARrdLTXafcU3elpGr/zO+gnTvQ6otZqvfC7YRB8//u07ryTxnOfS+r228+rS2r7e9/D+d73kJ0OWiJB6OlPV0H2XRJ58pMRf/IntN/xDtxMBioVtEYDggA/mewVcIjH4ZZbyPxf/9cZ68iWkpLqRz+Kf9ddaLOzWPPzGO02st3GaLfxolH8wUE417nC1e18o27pjzXt++/HnZpCui56Ok3k6U9H24nislJS+u3fRr/vPowTJ9AcBy+RwE2lANAaDayZGQLbBtel7Dhk3v727V3HNtD62Mcwmk30dpvO5ZefO1xqmjijo9gnTxLUatQ//nGSt932qN5Ti0RI/umfUrn9dgiHoVTqFRupVtF8H1/X8cJh0DScbBaZyfTW8dX1XABeOo1RLCK//306x44RuuyyR7kE9h53eZnaO96B/u1vozUaiHq9d0w3TeRXv0rtAx+AW24h/epX7+q6s1eJZBJpmmiOgwyCdQPTAFq7TWCavSIkezTYX//Hf0RbWsKen6c7Nga5HKd98qaJHBnBiUaxp6cJolFaf/3XhN/5zgt+b/ehh6DdRvM8ZDrNRmucn0oRmpnB9zy6J05gXH31BY/hfHR+8ANat9+OtrSEns9jVKu975CAH4lAoYD39a9TuvFGMm96045sQ50HH6T193+P+MIXEJ0O+D7BanEN/2lPI/SylxHd4eW007RIhOx73kP905/Guftu5He/e9rj/hOfiHXTTaSuv17t1xRlh6gAuqIoiqIoiqIoiqIoiqIoyg5RHdAvLalUiiuvvJKVlRXi8Tie5xEEAUKIfiBwcHCQeDxOs9mkVCoRj8eZnZ1lZGTkjA65jUaDpaUlIpEIyWSSoaGhXZozZbvous6BAwcYGxtjaWmJZrNJEASEQiESiQSZTOa0wOWlLpfLsbCwQDweZ3Fxkf3796OfpTOT7/ssLi72Q/rZbHYXRqso209KST6fp1gsYhjGWbt8AxiGwcTEBJOTk9RqNfL5/EUXQF+z2Y7xmqZx5MgRhBCYpkm1WqVSqVAoFACwbZvBwUESiQTZbJYDBw5s88gfO/7t347g++3Vvwl0PcQLXjC5q2NSFEVRFEVRFEW5JEiJXyzSbTYxE4leR8MLCFAkbryR4je+AUGAPjVF6MEH8WIx/GgUAWj1Okaz2evUfOAA3gtegJ7N4n3ykwSrHQUBZDIJz3se0dtu6wWud5EwzU13egxdeSWht78dr1ym861vETQaiEgE54EHEB/4AKJWI0inEaaJf+QIfquFViigtVq97rmaRhCNElgW9tISwvfpHj9O5ClPwRwePut7diYnab3udegnT2IuLCB8v9cZ2zAQrRZ2oYAXiUCnQ3l5mcx73rN+B2Apqd17L+7HPoZ+4gS02xAEBIZBKxSifs01hH/qp4g85SmPZnEqFyB81VWEP/Qhml/5Ct177sGfne11GE4m0a+7jvhNN21711bp+5R/53cQ//Zv6CsrGOUyRqeDHgQIz8OTEuF5GM0m3dFRGBg4/fmOg9Fu0x0ZQahzZ0jfp3bPPXh3393b3hwHpMQzTarxOPJHf5T4K195zu1/K1Tvvhv985/HnJwk0HW6V1yBOKU4QJDN4rguxtQUxokTeJ/7HLVPfpLEDTds25g2Ir76VbRiES8SObPIySOnTSSQuo4olfD+4z/gUQbQAfREguyf/zmNz3+e7j/9E/Lf/g2v3cZstXCjUdxcDpnLwTnGFGSzaPk8dLt0v/e9SyaA7szN0Xjta9GnpzEWFtC73V4YX9fRazW0pSXcRIKg0aC0sEDmbW9TYc1HMJ/xDPxPfhLm5xGlEuRy55xW+j5GrYYzOop/9dWIs1zT3G3S85Cf+AR6Pt9bF9aZH1YLXuj5PHzta7grKxfcYVt2u70AtxAbhvkBMIxe4R4podu9oPc+X92ZGdpvfCPa9DT2/DxeOIwzMtIrdOH7aJUK1uIiQamEd/fdlC2L9K/92raOqf7xj+O/+91oq4U2jEajt2yg952yUMD9yleo/PzPk/qZn9nWsew6IYg/73nwvOfhLi/jLi4CYI6OPiY6wSvKXqMC6IqiKIqiKIqiKIqiKIqiKDtEBdAvPaFQiH379jE2Nkaz2cTzPHRdJxKJYJ5yk0g0GmXfvn1IKVleXmZubg7DMPqhu2azieu6JJNJhoeHGR8fJx6P79ZsKdvMNE2GT7lpyXyMdhsxDIPh4WE8z2NmZoapqSlyuRyJRAJN0wiCgFqtRqFQQNM0RkZGGB4exjhHpyNFudjVajW63S7VapXh4eGzhs/XGIZBMpmkXC6TTCZxXfeS35fous5ll11GuVwmn8+f0eE8kUgwMDBAarVDkHJhVu9potNZJAja/X/XtLC6R1FRFEVRFEVRFOUC+PU61bvuQn7sY9jLy3QtCwcIcjnEC15A/NZbMR7xm3dThCDz1rdSjkTgX/4Fv9FAKxQwKhUQgiAU6oVSo1HkK15B7n/8DxAC+Yu/SHd6GtlqIcJh7AMHztlp/GJgpNPEnvOc/t/Lx4/DWhDq1PmKRAj27eOMyzLNZj8IFbTbj3y0T3oerTe/GW1mBnt2FieXIxgaOi2M5rXbGPPzmJOTIATl//W/yLzxjed4QUnxHe9A/9SnMFbDxZrj9B/2IxEoFnG+8Q3c172O5C23nM9iUbaCEESvvZbotdfuytuX3/1utM9+FuP4cUQQ4OVyuLkc9vw8RqsFgN7tEnge9sICXdOEU86T6cvL+JYFsRiRx/j6E3Q6lH/rt9C//GWMfP607U1qGl4iQZDPU//3fyf8B39A+AlP2JZx+Hff3Qt+BgHeZZedPcxqmriHD2M/8ACiUMC96y7YzQB6rYbwPILNdIgXAj8UQjgO1Otb8OaC2LOeRexZz6J69910f/d34aGH6B49ithoPGv7f9+HTufCx7IHSM+jcfvtaCdOYM3M4KVSuAcO9AsD+FJCvY6xsIB27BgeUN6/n/Qv/MLuDnyPiT/rWZSGhnAXF7GWl+nG42cvriAl+twcUtMgncbeo/vR9v33o5XLGNVqL0y9gSCXwz52DL/dpv2FL2DeeusFvb+WTCItq/ddqtPphbnXIdptpK6DEGjJ5AW99/lq/K//hT4/3+sUPzJyRuGWIB7HGR7GmJxEP3EC/8476dxwA6HLL9+e8fzHf+D/4R+izcxglsu4iQTdAweQtg2ui14qYc3N4Vcq+O9/P9VIhORLX7otY9lrzKEhTNW8QVF21cV7hkBRFEVRFEVRFEVRFEVRFOUio2lQLP4HUj58S5MQGtnss4CHgz7KxUfX9Q27zw4MDKBpGoZhkMvlqFQqdFcreSeTSVKpFJZlMT4+zsAjLnAqyqVqdHQUZ/XGrkKhwMrKCktLS+i6ju/7aJpGMpkkl8sxODjI6C53Xdppnufh+z66rqvg/WNAp9Oh2+0SBMGmipDE43HK5TIA3W73kg+gAwghyGQyZDIZHMfBdV0ALMt6TMz/TlLfSxVFURRFURRFUbZe+7vfpf2WtyBWVggvLWHU6+hSgq7jx+ME8/PUP/pRrLe+leiP/Mh5v77QdTK3307n1ltp3Xkn8r77CBoNAIJUCn70R4nddhvW+PhpzwkdPrxVs7jnaIkEcrW7pnScDTv20u32wmVCoK9zzr/+mc+gzc9jzc/jDAwgR0Z4ZL02EQ7jHz4Mx4/3Qmuf+hT+q1+NfpbzPuX3vAf9k5/shYs9Dy+XQ6bTvdBkp4NWLGLNz+PV6wTveQ+NVOq0oL1yaetOT6P9y7+gT02BpuGshZWDgCCfJ/B9dMfBM0101wXHwVxcxF0N84mlJcxyme6BA/hPeMIl0/35UZGSytvehv6FL/SKQ0iJm80iU6let95mE71YxD5+HKfVonP77Wh//ufYW9w1vvODH6BNTmKUy3iZzLqdlIWu42YyGKUS8oEH6Bw7tnufoWH0CnoEAZs5fSiCgECIhwPgW0TP5XrdnTWt171+o5DrWujcNBGrxcEvdo3PfQ5tZgZrdra3Do+NnX4cEqLX4ToaxTx2DG1+Hu+uu5CvehXCsnZr2HuPEBg/8zMEy8v47Tb28eO4AwME2Wxvu1wN8usrK+idDs6hQ/iPfzypR/E9bSf4lQoEQa8L+WYKRaxN47oE1eoFv3/4Gc+g9b734YfDaIUCwcTEutMbpRJeKkWQyxG64ooLfv/Ncubn0b/+dYzlZdxU6ozweZ9p4h48SOjBB/HrdVof/Siht7xly8cjfR/nPe9BX1jAqFToHDrU31cJANsmiMXotttYJ04g5+bw/+qv8J///LN+r1QURdlq6k4VRVEURVEURVEURVEURVGUHSIEfOUrzz+ji+QLX9jrTKA6oF/6stks6XSaUqlEsVjEcRyklFiWRTabJZPJoK9zk4miXIoOHDhANBolEonQbrdptVr90HUkEiEcDjM0NPSYKczgeR7FYpF8Pt8vUgEQDocZGBggm82iadoujvCxp9Vq0Ww2++tlPB4nFApt+ftIKZGrqd/NfMZr05z6vMcSy7Kw1I1y22a9VUp1QFcURVEURVEURTl/nQceoPPGN6LPzmIuLOAZBk4qhR6JoLkueqWC+eCDOIODuL/1W7Te+U4iT33qo3qv0GWXEbr9drj9doJOB6Fpj9mwmf20p9EJh/FDIbRiEblBgUu9VMJLJgkSiXWDUO4992AUi70u8sPD535BIfCHh7GnpvAbDer33EPqp37qtEmcuTnEP/0T+vR07+9HjyJOLbQXiRBEIjiZDNbUFO7iIs6f/Rk8+9nqR/pjRPOjH0VrNjHabTqXX/5wWFnTcPftQ0xNAWCsXnPSHQfZbOJPTqJ3u4ggoLt/P8H+/cTf/OZdnJPd1/zKV9C+8AXMEycITBPv0KHePnJtglQKP5XCLxZ7XXEti8b73of9zndu6TicY8fAcdAchyCdPqOAxSPJVAptZQU8D2dyctcC6MH4OMGJE5ilEoGU6+6DpOtitFp0BwZggxDq+Qo/6UnUwmHceBy9VCLYoHOyVirhRyJg24Sf9rQtHctuce6+u1eUQAjkOp2uha7jDQ9jzc4SlMvUP/UpEjfeuIMj3fsSN95IeWkJ/6//mmBpCSOfRywvExgG2mqY24vHcY4cIbjiCpK///t79vir2Tb+2tg8b8Pp5do0QsAWfFe0xsepP+UpsFo4p5vJIKLRs09cKKB3Onj79iFuuGHdQhxbrXnvvYhmE73Vwh0fX3cfLGwbNx5HKxSQ992HvP12xBYX1Wjedx/aygpmoYAzMnLOQhkiHMaZmCA0PU1Qr5/1e6WiKMp2UAF0RVEURVEURVEURVEURVGUHbLRNTMVQH9s0DSNXC5HLpfb7aEoF0BKidijNxdcjAYGBhgYGKBardJoNPpB31gsRiwWo16vUygUEEIQDoeJRCJb9t6+71Mul+l2u/2CEOl0ele6KdfrdSYnJ+l0OpTLZer1OkEQoOs6iUSCer3O4uIiR44c2dJloJxduVxmeXmZZrNJp9Ppfxa2bROPxxkeHiaxTies82UYRn+963Q6G4bcO50OhmEghMDY4ptdFGU96vCnKIqiKIqiKIpynqSk+Xu/hz4/j7WwQGd0FDce73XZtiwk4A0O4lUqWCdP4moand//fcIf+cgFB1y0bSiidzEJHT5M8/GPRxQKmEtLOOn0uTuCViq9sOToKOLHf/z0EPippET/wQ/QqtVeh+mNfijHYgSmiahU8L7zHXhEUKjxsY/1wsWNBp1Hhs9PFY3iDA1hrqwgl5dpfP7zxJ71rA2WgHJJ+OIX0YpFvGgUYdunPxaP4xw8iDUz0wtLOg7G6h9Zq+GMjyOzWYKJCaLvfCfW2NjuzMMe0bnrLvRyGeG6eJdd1ut6fjbZLG6zib68DF/7Gu7yMubQ0JaNQzpO/8LwOcdw6vRrF5mDAFx3y8ZxvvQbbiC4/35EPg+lEmSz55xWy+cJTBOSSSI337y144jFkM9+NkE+jz01RbdWg3Odq282McplnIkJ/Kc/fUs/x90ifR/9u99Fq1TwksleJ/j1JJPI+XmoVHC//nVQAfQzpP/7f6c2Oor313+NXFiAWq23n1jtJC/jceRznkPyta9FP0c4eC+wDh/G1XW8SAStWiXY4BqWVi4TGAaEw5hHjmzJGMI/8zM43/oWXrOJfeIEzuAgMpfrB8xlt4u+soJZLtMdGyMYHiZx223950vfp/GFL+B973vIdhsRjWI99alEr756yy5OyKUlRKvV+362iU7xQTyOmc8TdDp4pRLm4OCWjGNN95OfRC+VemPLZNYPxMfjBJaFKBTwPv3pM75XKoqibAd1J4KiKIqiKIqiKIqiKIqiKMoO2ejarwqgK8re5fs+pVKJfD5Pp9NBSolhGKRSKQYGBlQYeIskk0mSq50qut0uS0tLnDhxAtd18TwPIQSWZRGJRBgcHCS7zs1NG3Fdl4WFBUqlEo7j0Ol0ADBNk1AoRCqVYnR0dFs6XZ9No9Hg2LFj5PN58vk8oVCIdDqNYRg4jkO1WqVQKDA8PEwQBFxxxRU7NrbHorm5OZaWliiVSpTLZdxTbuqzbZtMJkOtVmN8fJzh9bpcnYdUKoVlWUSjUUqlEqMbdOQql8ukUilCoRDhTdwgoyiKoiiKoiiKoijK7mh9/evo09O9APTAAORy4DhnTphK4Xge1tISMpej/pnPkHj+83d+wJeY0CtfiXv//Xj1OuaJE7jDw5BO9y/aSN9HW1nByudxhoaQ2Syxl76095jn4RUKBN0uRiaDHo8jXRd8H+H7yM0UsRSiF+7yfWi1znz8vvvQCoVeuHiDczwym0UsL0O5jPPpT4MKoF/6pEQrlxHdLsG5Qo/xOM6VVyKKRYxSCVGpIHUdL5HAe9azsG6+meT115+7uMFFQvo+fq0GgP7/s3ffcZLUdf7HX98Knbune9LOzszubAYlioAgIkYwoAiGO33onaeeOdwZUX+K6KnnnZ566qmYFRUDGUFEQQQlCJIkbJydHLp7uqdzV/r+/qiZ2Z3dibszs7vs9/l4zAO2u6bq29VV1T1V9f58EolFd8t1i0X0e+9Fz2Zxksl5f99rbia4fTtuuUz5pptIvvGNBzr0/WipFK5pghDISgUxTwdvUakgNQ0MAy2ZXLJxLFb03HMpfe972C0tBAYHsQwD9h27lIh0mkAmQ33NGtzNmwmfcMLSj+W1r6Vy221YhQKBnh7sVavwmpr2hFw9DzE2RmB4GCeZhOZmwq997ZKP41DwKhV/PTvOwrpWC4E0TX/6Umn5B3iESrz0pfCSl1C++27qf/4zFAoQCKBv2kT8pS9Fj8cP9RDnZba04J56KmQyfgfyVasQs2wj0vMwslmcVAqvvZ3o6acvyRgiJ5+M84EP4H3xi9jBIGYm43eUDwYRUqJZFm4wSL2rC9nRQfjzn8dobAQpyf/0p3jXXIM2PIwolRCeB7qO/fOfM7ZmDcZrX0vi/PMPeoxy8sachQba955OyoNe/n5GR9FqNZxIZP6iJELgRqNo1Soik1n6sSiKosxABdAVRVEURVEURVEURVEURVFWyHzXilx3ZcahKMri5PN5du/eTb1eJ5fLUalUkFJimib5fJ5MJkMqlWLdunVoC+hUocxvshN4sVhkbGyMUqmEnLigb5omyWSSYrHI+Pg469evX3Q3+nq9zrZt2ygUCmQyGUoTN9wIIfA8j0gkQlNTE4VCgU2bNhFb5m4GUkq6u7vJZrNkMhk6Ojr266zd3NxMLpdjaGgITdPo7u7mKU95yrKO62g1PDzM0NAQvb292LZNKpUimUxOKwYwMjIytd2YpnlQxRAmGYZBKpWiWCzS19dHPB4nPssNTel0GsuypopgKMpSW457qBRFURRFURRFUY5WteuuQ8vnEa6LN1/HxKYmGBlBZLPYN9wAKoB+0KLPfCb5f/1XvMsuQw4OEhgaQg4N4YXDfjfzahWp69Tb25EdHQQuvRSvXmfsf/4H8fvfI/YK67nHHYf5spchNQ2p6wjbZt4/oaVEcxw8XYd9C5lKiTY2Nne4eC9C03BDIUS9jlSho6PH5PnvuU7Y6DqytRW7tRW5bRtuJIJ97rk0f+tbKzPGZVR99FEqV12FdscdiIlCsjIcRp5zDpFXvpLQMccsaD726Ch4HpplYTc3z/8LkYhfPKJaxRsZOZiXsJ/omWcyHothx+Po2SzePAF0PZvFSSSQiQSR005b0rEshhYKoX/4w8hLL8X2PII9PbihEG4yiQwEoF7HyOXQLIt6Rweys5PoxRcvy1iC69djf+xjOJ/5DLZpYqTTiJER3IlCHvrEtmI3NyNXr0a8971ETj55Wcay0rTJYiW6Do4z/y9MhtV1HVRh47kJQfTMM4meeeahHskBC7361dj33IObzfqFd9atQ+zzvkvbxujpQQBeayv6BRcsWXdxgMT551NKpah/61vInh4YH0fU634xhEgEolHck04i9v73E1y/Hum6jH3yk+h//CP68DBmLofwPKSmITwPzzBwhofxenvJ7dhB6n3vO6jxas3NEAqh2bZfFGqeQg5aqYQXCIBhYKRSB7zcpSKFQABCXchRFGWFqAC6oiiKoiiKoiiKoiiKoijKClEd0BXlyJPP59m5cyejo6OMjY1hmiaJRAIhBPV6nf7+fgKBAJ2dnTiOw+bNmxcdhlamq1ar7Nixg3Q6zejoKPF4nDVr1hAMBpFSUigUyOVyjI+PI6VE0zTWrVu34Pm7rsv27dsZGxujv7+fWCzG2rVrCYfDFItFRkZGGBoaYuvWrbS2tpLNZjnjjDOIRqPL9prz+Ty1Wo10Ok1bW9t+4fNJqVQKx3EYHR0lkUhQLpeXdVxHI8dxGBwcZHBwENd1Wb9+PYax55JqIBCgpaWFhoYGenp6GBkZwTRNUqnUkhSgWL16NePj49RqNQYGBkgmk6RSKYLBIACVSmWqKENnZyfxeJzmhdwsqSiLNNd9S+pjTlEURVEURVEUZXFkXx9auYwbjSJ0fe7AshA48TiiXEYMDKzUEJ/0kq9/PcXGRuxvfxuZzcJE6BshcJubIZnE7eoi/MEPYj/+OPZHP4pWLKKl0+jlMkjpd49Np3EffhgyGbxYDDObxeromPuP5VIJzbaRyST6iSfu/7ym+b+/0BDR5HSL7P6sHKGEwFu1Cq23F71YZL6oqbRt9FoNZ9UqtDVrVmSIy0U6DrkvfAHt5pvRCwX0bBatVvP322AQb3SU2m9/S+XlL6fxAx9YnpNWyxTu0yIR5HOeg5dOE9y1i/rYGDQ2zjxxNotRLlPftAn5/OejHeIAcfSss6hfcgn25z5HvbERLZ3GyGb9C92ahpNM4jQ347W2Ev7c5wht3rxsY4k997lUYjFqX/oScmAA8nlEtQpS4qZSkErhtbYSeM97iD33ucs2jpUmDAN3/XrE8DBGLoe9evX8n0OOg0wk0I89duUGqhwS0Wc8g9wrX4nzq19h7N5NaNs2nFgMLxZDahpauYxRKOCZJvbGjXhnnEHyH/5hyccRO+ssYs98JpUHHqD2u98hczmEpiHa24m87GUE97q2m/vyl9Fvuw1zxw6klFhtbcjGRoSmIV0XLZvFTKdxi0XcX/+afFMTyTe84YDHFj7vPGq/+hVeIIDIZJDt7bNOK20bo1DA6uzEPfPMWTvKH5SmJmQohDE+jifl3PuzlOjlMm48jlyC4tiKoigLoQLoiqIoiqIoiqIoiqIoiqIoK0QF0BXlyGLbNt3d3YyMjJDP5+no6NivG7HjOAwNDdHT04MQgsHBQTo6Og7RiJ8c+vv7yefzpNPpqYDt3pqamkgmk/T19dHX14dhGLS0tCw4iD3Z8by/v5/m5maam5splUrs2LGDer1OuVzGsixc1+WJJ57AsixqtRqnnHIKq1evXo6XTCaTYXx8HE3TaJin00kqlSKbzVIqlUin00d9AN22bTKZDJVKBc/z0HWdRCJBY2PjAQXCM5kM9XqdYrHIunXrpoXP9xYIBGhra2NgYICWlhZyudySdEEPhUJs3LgRKSXBYJBsNsuuXbvQdR1v4otCLBZj3bp1NDQ0sGnTpiUJvivKYqgAuqIoiqIoiqIoyuII2/YDzAv9g0oIhOeBbS/vwI4y8Ze8BHnuuZRuvRXrD3+AXA50HdHWRvD880mecgrjv/gF8hvfQOvpwSwUcKJR7NZW0DREtYo5OgojI9ipFEahAK6LGB5GznbeUEr04WGcWAwZi5F4+cunPy8EXksLWk8Peqm04HCx29oKq1YtyXpRDn/auefidXdjZrM4xSLsc8582rTpNJ5pQkMD4X23tyOJlOQuvRTtD3/A2LULvVbDjsexm5sRUiLKZYLd3bjhMPZVVzFWr9P4sY/NeeLKXLWKmq7jBYNoxSLefF1sKxU014VwGG0Z9rfYG95A+U9/wqpUCPb3Y5fLuK2tiIlipLJaRc9kMHM5rNWrkatWEXvta5d8HAci9pznYB93HKWrrsL57W/xstmp57zOTvSXvYyGl70MPRZb9rFETjuNyM9/Tvmee6jfeCNyotM9zc0Ezz2X6NlnI56EBTuMl74UuX07YnQUxsZgtusTUqKPjOz5HLrggpUdqHJIpN73PnKmifuLX+CWSmiZDMbYGEiJFwxirV0LDQ24Z59N6hOfWL59RAgip5xC5JRTZp3EGhxEu/569N27kZqGvXEjQteZPJoLXUe2tmIlk5g7dyL7+3Evvxzvla9Ei0QOaFihLVsoP/WpkM0SHBigFoshZigOLj0Po6cHaZqQShG68MIDWt58zBe8AO+eexDDw3PvzwClEnq9jrNuHdrznrcs41EURdmXCqAriqIoiqIoiqIoiqIoiqKskPnuLVMBdEU5vGQyGarVKmNjY6xdu3bGoK9hGHR2dtLT00M6nSYcDrN69WoVCD1AtVqNQqFANpsllUrtFz6fpOs6nZ2d7NixY9FB7HQ6TS6XIxgMToXP+/v7p7qqCyEIh8PE43GKxSLbtm2jVqvR2NiI4zisWYauMdVqlXK5TCwWQ8zzYWEYBtFolHK5TK1WW/KxHClc16Wvr4+xsTFqtRqlUgkpJbquE4/H6e/vp7W1ldWrV8+7TveWy+XI5/OEQiHC4fCc08ZiMQzDYHx8fMkC6ADxeJxjjz2WwcFB4vE49Xody7IQQhAMBgkGgzQ2NtLR0TFrQF5RDpbqgK4oiqIoiqIoCydtm9Itt2DdeqvfVVnTYPVqgi99KdFnPEN9iVaQiQTSNNHL5fm7GgKiVkOGwzBDEEY5OMIwiJ97Lpx77n7PWf39yG9/G6231+82vHEj7HXOUQJWeztafz9mNovUNNxgkEA6jSUl3qpV0wJcslrFGBhAs23sri7keeehz3C+U7zwhXi7d2Nms9jFImKucHEmg9R1P1x8/vkHtzKUI0bsFa+g+LOfYSeTmL292Bs2wEznLsfGCGQy1Ds7cTdtInzCCSs/2CVSuP56xK23YuzYAZpG/dhjYaLrrJz4qdXrmLt3Y+7ahXPTTRTPOIP4858/6zz1WAz39NMhkyHQ30/dthGmOev0WjqNE4lANEr0JS9Z4lcIgc5O7M98BuvjH6ceCGCMjmI+8YRfQADQbBs3EqHe1YVsbyf42c8SmKNL70ozW1pIve1t8Na3Yg0N4VWr6IkEZnPzyn/3EYLoGWcQPeOMlV3uIRQ//3zyP/4xdlsbwYEB6lL6odW9171to/X3o9dqWJs3I5/3PHT13eLoIASpd72L2oteROXKK5G33opXqQAgTRP5zGcSuvBCIk972iH/W6V81VWIctn/7rVly+xh+EAAu72dQG8vXrFI8cYbaXjVqw54ueF3vIP6jh1YlkVo927shgbc5mZEOOx3Xc/lMLJZBGBv2IB31llEn/70A17eXOIvfCG5b34Tu7GRwNAQVjgMM4TrZb1OoL8fu6EBGY8TVwUlFEVZIeqOBEVRFEVRFEVRFEVRFEVRlBUixNzX71QAXVEOL5NB5XA4PGe4WQhBc3Mz/f391Go18vk8jY2NKzjSJ4+xsTGq1Sq1Wo3Ozs45pzUMg0QiQS6XIx6P09XVNW/QuFKpUK/XyefztLa2Yts2/f39ZLNZCoUCTU1N00Lg0WiUnp4eKpUKW7duxTAMIpHIkgWNJ8mJpOdCCxcIIZBSTnXEPto4jsO2bdvI5XKMjIxQqVQIBoPouo5t24yMjEwFt2u1GuvXr19wCN2yLCzLmjd8Dv77EAqFpn5nKYXDYTZu3IhlWeRyOWzbngqgp1Ip9Cdhxxbl8DJXAF1RFEVRFEVRlD0K112H+53vIMbGMHI5RK3mdzSORLD/+EfGOjsJffjDfrhDOWrpz3oW8sEH0UdH/e7Fc4W/qlWMSoV6Zyfimc9cuUEqlK68Eq1YxMznqW/aNC18PkUIvM5OXNsGx0FzHOodHZgjI4hsFicW87uq1+sYlQpOJIK9cSPuKafQ+L73zbjc+IUXUvz5z7EbGgj09WGtX4+Y6dxUPk8gnabe3o63bh2Rk09e2hWwDOzRUUrXXYfX1weOg2hoIPi85/kBLlWcY8GMxka0t74V72tfg927CW7fjp1I4KZSYBiIWg1jbAy9WsVqa0O2txP5wAcO9bAPin3NNRiZDMJ1sTZvnjGQKIJB7A0bCG7dihgbw7rqKpgjgA4QuvBC7L/8BW9kBHP3br/T7kzn5TMZzPFx6uvW4Z5+OmZr61K9tGmip56K/vWvU/761+HBB3HKZahW/SfDYYhGcU85hei7301o48ZlGcNBE+KwCsYfLbRIhOCnPoV18cVYQhAYGkKOjOAmEsiJzyGzWMQLBLA2bsQ97jhS//7vh3rYygoLbdxI6MMfRn7gA7iFAngeekMD4jAqbizvvBM9m8WNRGYurrK3ye17bAzn9tvhIALokZNOwv3EJ3D+4z+oRyLomQyhnTunLo54hoGTSuG1tuI94xmkLrlk2b67CMPAeOc78T73ORzbJrBzJ04yidfYiAwEEI6DGBsjkMvhhsN4a9Yg3vAGjFRqWcajKIqyr8PnU0NRFEVRFEVRFEVRFEVRFOUo92TJEVYqFQqFAq7rous60Wh01i7GinK4sm0b27YplUoLChtHo1GEEFOdrFUA/cDYtk29Xsc0Tcw5Oo9MikQiZDIZpJQ4jjPv79i2jeu6uK5LKBQil8tRq9UYHx9n1apVRPapJh8MBgkEAjQ3N1Ov1xkbGyMajS55AN0wDAzDWHBH83q9TigUOmq7X3d3d5PL5ejp6SEajbJ+/XpCodDU86VSiXQ6TU9PDwChUIj2Bd4At5hu6Uvxe/MJBAKsWrVqWeatKIqiKIqiKIqiHJz8j34E3/0u+uAgxtgYaBpuOAxSEhgbQw4O4mSz1D/0IeSnP01UhYmPWvGXv5zCj36EnUhgDA5i7XUeY2/S8zD7+nCiUb/j7kUXrfBIj17SdeGWW9Ayman1PyshcFpbCe3aRW3dOtx43O86m8+jTYS7ZCRCvaMDolG85z2Pxo9+dNZOy0ZTE9qb34z3zW8iursJ7tiB09CAl0yCYYBloWezGOUyVmsrsr2d0Pvff1gHuO3RUYpf/jLa3Xf7XU1LJZASGQhg33AD2a4ugm9+M7HnPOdQD/WI0fCa15C3LLzvfId6Po+ezRLq6fHXq67jNDTgdHYim5sJfOYzhI877lAP+YDVHn8cfccOjLExnMbG2bvhAsI0sVMp9GwW+cgj1Ht6CHZ1zTp99BnPIPvsZ4NtY+7cSeCJJ/yAYTLpF4+oVNAzGb8QSHs7sqOD2DvfuQyvco/Q5s2EvvpV6j09VG66CS+dBkBrayP64hcTmKdYr3L0ip56KuK//ovapZciGxsR2SxaqYSo15GmSX3dOkgkcE87jeSll6ItoPCu8uQkdH3usLKUVB56iNpVV8EjjyAqFWQwiNyyheArXkHsrLOWL3w9Po6wbf/vqHknFrjhMKJeR4yPH/Sy4+ecQ62tjfKPfwx33YVbrYJlgaZBKITX0YH+8peTfM1rlj20n3jRixgvFvG+8Q2sbBYjk8HcsWPqeTcUwm5rQzY1IV/7WlL/9E/LOh5FUZS9HZ13pSiKoiiKoiiKoiiKoiiKohyGjvQAej6fZ3h4mHK5TLVaxXVdNE0jHA4TDodZtWoVzc3Nh3qYirIgk52lpZQL6jQshEDXdVzXPWq7Uh8JhBBTQWHP88jn8xSLRcLh8H7hc9jTmdwwDOLxOPl8nqamJkqlErFYbMnGlUwmSSaTdHd3U6/XCQaDs05bLpexbZtkMknqKKxsXy6XKRQK9Pf3E4/HZwyWx2IxIpEIPT09jIyMTIW4F7IvB4NBwuEwuVxu3mk9z6NcLrNq1ao53zNFebI5jO9vVxRFURRFUZQVU7rjDvje99C7u/2Osx0dkEpNfWF2XBeRyRAcHMRyHKxLL8X8wQ9Uh9CjlB6PwytfifvjH6Pt3Elgxw68xkbchgZ/AikhlyMwOgpS4qxfj/eiF6ntZQU52SxaoYBeLmMv4DqGiEb9DpyOg3beeZgvehG1q6/G27UL6nVkPI445RRir3oVgY6OeefX8LrXka/XcX/wA9x8Hj2TIbh7tx8u1jScRIL6pk3IpiYCl1xyWHc/r/f2Uv63f0PbvRt9aAijUsELBEAIhGUhDQMxMoLT28v4u95Fw2tec6iHfMRIvv71VJ/2NKq/+hXyzjtxLcu/uKhpyIYGOO88Yq95zbJ1614p9a1boV5Hsyy8xkbmOxUlUyn0TAbbcbC2bZszgI4QNF5yCWOO4wfOMxmMsTG00VF/Xnvvb6tXE/r85+ee3xIKdnURfPvbV2RZypNH5JRTCP/615RuuQXr+uvRurvBtpHRKPK004hcdNERXZBCWX7O2BjjH/84+qOPoo2Po+XzCMdBahrerl0499zD2Nq1RD/7WYLr1i358qWu+98RpEQuYHrheUhNQy5RIDx0zDGEPvtZnGyW8h13IMfHEcEg5saNRE49dUUviDS8+tVUNm+m+rOfwb334tTrMPF5RSCAe+KJBF/zGmLPfvaKjUlRFAVUAF1RFEVRFEVRFEVRFEVRFOWwcSRnVoeGhhgYGGBsbIxcLodt2wghkFJiGAbJZJJyuUypVKKrq2vZOsUqylKZDKrquo5lWfNO77oujuNMdbJWDkwgECAUCmHbNpZlEQgE5py+XC4TCoXQNG1B631y2lAoRDabxXVdyuXyrB3Ny+UymqZhmibNzc0MDg5Sq9WWPIDe0tLC8PAw4XCYoaEh1q5di6Zp+03nOA4jIyPE43ECgQCNjY1LNoYjxejoKKVSCcdx5uwMrmkara2t9PX1YVkWY2NjtLS0zDv/pqYm8vk86XSaYrFIPB6fddrx8XGEECQSiVm3IUU5Usk57vRSX+MURVEURVEUBeo/+xlGOo1eqVDfuBGxT8c+oeuwahX1YJBgTw/1RILSr35F4/ved4hGPAcpqff14Y2PI0IhAmvWoM3SoVs5cKm3vpWxTAbnppsQQ0OERkcRIyNgmgjX9btqNzTgrl6Nd/bZNH7wg4d6yEcVOXkOWEq/6+V8hEBqmt/t3LaJnnoq0VNPPagxJP/lX6hMhIu56y5c2/YvHOk6MhZDvuAFxP7hHxYUaD9UvFqN8oc/7Bda6O3Ficepb94ME8U/5URxjkA6jVOr4X3jG5Q6OvzOqsqChI87jvBxx+EWi9S3b8erVtETCULHHoswzUM9vKUxGayHBe2PcuJ6jpASWa/PO70IBGj6z/+kcP312Ndcg9yxAyb3N9NEJhLwwhcSe93rMOc4B60ohwthmsRf8hJ4yUsO9VCUI4yTy1F417vQt23D7O1F2DZOPI4XCiEch8DgIHJoCHt8nPK73w1f//qSh9Blezve9u0Y2SyelHNegJCui16pYDU2Ijo7l3QcRlMTDa94xZLO80BETj6ZyMknYw8PU73/frxSCS0cJnjiictSAEBRFGUh1B1QiqIoiqIoiqIoiqIoiqIoh4kjNYCeTqcZGBigr6+PWq1GY2MjyWQSwzBwXZdCoUA2m6VYLE4F0juX+ILg0UJKyfj4OOl0mmq1iud5GIZBQ0MDLS0thA7xjaFSSgqFwrQiBMFgkObmZsL73IR7uDMMg0gkQiKRIJ/P0zxP15tCoYCu60SjURKJxAqN8smnsbGRwcFBwuEwY2NjtLW1zTqtbdsUi0U6OztpbGxcUGGLQCBAIpEglUrR29uLrutT+9G+JrfnWCxGMBgkkUgwNDS0LF3uA4EALS0tOI7D7t272b17N83NzcTjcYQQeJ5HoVAgk8mg6zptbW20tbUtqKP3k02hUCCfzxOPx+d9/dFoFMMwKBQKFAqFBQXQGxsb6e/vp7GxkaGhIUzTnPHYWi6XGRkZoaWlhXA4rPZ75UlnrgC6oiiKoiiKohztatu3oz/6KHo2i93YuF/4fJpkEieTQctk8G65BfmOdyDmKbi3UtxSieK11+LdcAPawMDU49VwGPmc5xB59asJbdp0CEf4JCMEjR/7GOMbN+L88pfU+vvRSiVMIfyuhokEXlMT2gUX0PQv/6Kqf60wo7HRD5WbJqJanbcDp7RtNNuGQAAx2cl+CUyGjpyxMepbtyJrNUQ8Tvj444+IwhDFG25A6+0l0NuL3dSEbG+f9vxkcQ4rHiewcyf26Cj1H/1IBdAPgB6PEznllEM9jGWhJZO4gYDfEbdahXmC9aJa9QtC6DpaMrmwhQhB4uUvh5e/nNrWrVi7doFtoyWTRE4//YjY3xRFUQ7W+Gc/i759O4Fdu3ASCdz29qliJhKoex7a8DDB3l7qQPnjHyd4+eVL+j3VeMlLkI8+ihgdhVwO5ii+rY2O+kVHUimCL3/5ko3hcGS2tWG+9KWHehiKoiiACqAriqIoiqIoiqIoiqIoiqIcNo7EALrneQwODjIyMoJlWaxfvx5zrxtBdF0nlUqRSCTo7e1lcHAQ0zRZtWrVtOmU+ZXLZXbt2kWtVmN8fJxyuTwV6G9oaGB0dJTGxka6urpm7Jy83HK5HP39/dRqNYrFIrZtAxAOhxkdHSUej7N27dpDHpJfjJaWlqnQbyaTmTWEbts2mUyGVCpFOByes2OyMrdgMEhDQwPNzc309fURDodpmOEGTsdxGBgYIBQKEYvFFhQsntTa2ko+nycQCDA4OIiUEsdxpk3jeR6ZTAbXdUkmkySTSVzXRUqJruvLso+tWbNmar/JZDIMDg4ihEDXdRzHQQhBMpmkpaWF1tZWVq9eveRjOBK4rovrukSj0QVNHwwGcRxnv/d4Npqm0dXVNbWc3bt3E4vFpgqr2LZNPp+nVCrR1NREc3MzXV1dCyqAoChHEtUBXVEURVEURXkykrZN5f77cXM5hGlirl9PaOPGRc+n/te/QrWKXq9jNzUx31dkt7GRwMAAXqFAbetWwieccGAvYAnVtm+n8pGPoA0NoafTGOPjCMfxu3BHo3ijo9Ruvpn6299Owz/+46Ee7pOHEDT84z/ivupV9F57LeKRR4hpGlokgnniiTQ873mIGQolKstPi0RwTzwRkU5jDA9jtbcj5jgHKMbG8AwDEglCz33unPO2R0YoXXcdXne339m5oYHg2WcTPftsP5A9A6OxEePMMw/qNa04KXGvuw49k0EaBnKu85eRCHZzM0Ymg3ziCWpPPEHo2GNXbqzKYS1yxhkUIhGcWAwtm8Wbp/inns3iJBLIeJzI05++6OWFjjmG0DHHHOhwFUVRjkj1nh70v/4Vo7/f73q+du1+17qEpiHb27EAc2AAu6GB0p//TOxZz1qyccRf9CLy3/0udnMzgYEBrInvV/vJZgmk09Q7OnDXriV5AMd7RVEU5cCosxSKoiiKoiiKoiiKoiiKoiiHiSMxgJ7L5ajVauTzeTo6OmYNleu6zurVq+nu7qZarZJOp2nfp/OFMrtSqcT27dvJZrOMjo4ihCCRSKBpGvV6nZ6eHkKhEJZlYds2mzdvXtEw5OjoKL29vWQyGXK5HFJKQqEQUkoymQymadLa2kqlUmHLli1EIpEVG9vBaGxsZHh4mI6ODgYGBnAch6ampqntXEpJsVhkdHSUYDBIU1PTURsKXkqdnZ2USiUcx2FoaIjx8XFSqRTBYBDP8ygWi+RyOUzTZM2aNbS2ti5qm2poaKClpQXXdRkaGqJQKFCr1aZCxJNFFDRNY9WqVcRiMZqamhgbG8M0zWUrMiCEYMOGDUQiEcLhMC0tLVQqFVzXRdd1otEooVCItra2RQXul4LjOGSzWfL5PI7joGka4XCY5uZmYrHYio5F0zQ0TVtwoNx13anfWahUKsW6deumjrWTBTaklAghiMfjrFu3jmg0yoYNG1TRCeWoowLoiqIoiqIoypHGTqcpXXEF8uab0cbHwXGQQuAaBuVjj8W88EISL3rRgr/sesXiVFh7Id3MZTCI8Dw/nFkoHOzLOWhWfz/V978fbfduAv39SNPEaWrCC4UQjoOWzxPs7sZOJPC+8Q3GNY2G17zmUA/7yUUIgmeeCWeeSWrVKvRZQsjKygq84hW4DzwAIyPo/f14a9bMfFyoVgmk09gtLXjr1hE5+eQZ5+dksxS++EW0u+9GlEoYpRJ4HjIQwLn5ZnKtrej/9E80vOIVy/q6Voo9MoK2ezdGPo+TSs17TJXNzWijo1AuU739dhVAV6boiQTes5+Nm8kQ7O6mPj4OMxSqBWBsDKNcpr5xI/L5z0cLh5d2MFJS/fvfqT30EM7WrYhQCKOjA/P444k+/enqRJmiKEesylVXIUol9GoVe55Cy15bG2JsDHI56tdcs6QBdC0UIvDhD+N88pM4nkewuxsnGsVNpZCGgVavo+fz6LUa1urVyI4OIh/96GF3/K1t30719tuR+TwiGETfsIHY85+PdgQV51cURZmNCqAriqIoiqIoiqIoiqIoiqIcJlz3UI9g8XK5HIVCAcMw5g0hhkIhwuEw+XyeXC6nAugL5HkeO3fuJJPJMDo6yqpVq0gmk9MuAtu2zfDwMD09PQghGBgYoLOzc0XGVygU6OvrY2BggEqlQmtr61Q4Hvzg6tjYGP39/biuy44dO3jqU5+KcQR08tE0jU2bNuF5HkII0uk0O3bsIBQKTYX/pZQkk0laW1tpb2+nsbHxUA/7iBcKhaaKKIRCIXK5HAMDA8iJdrzBYJCWlhYaGhpobW09oG19Mmx+3HHHEQgE2LlzJ/39/QSDQUzTpLGxkWg0Sjwep6OjA9d1GRsbI5VKEYlEFtx9e7GEEKxevZq2trapLtue56HrOvF4nEQisaLFJaSUDA4OMjIyQr1ep1AoYNs2Qgii0SjZbJZIJML69esJrdANFJFIhFgsRiaTYdWqVXOuD8uyqFartLW1LbrwRVNTE5FIhNHRUeLxOJ7n4Xkemqah6zrNzc20tLQQDAYP9iUpiqIoiqIoiqIoy6j2+OP7d/qeqATqBYOI0VG8xx4je+edNF566YK6T4tIBHQdpEQ6DmKWopxT09u2H5AQYumDcQeg+N//jd7XR7Cvj/qqVdDa6ofpJ573GhupVyoEurtxenvxvvUt7Oc8B7O19ZCOW1k4q7+f+s6dUK+jNTYSOemkebdTBWLPeQ65Y4/FrlYJdHfj7tyJ29oK8TgIgbRttGwWM5PBicWQbW0E3vSmGQNQ1uAgpfe+F627G31wEKNSwQ0EQNPQbBsGBrCHhpBf+hK54WFSb3/7IXjFS8vN5QD/mCcXcqwzTb+LvGUhi8VlHp1ypIm9/vVU7rwTq1wm2NOD1dyMbG6GicIvsl5HS6cJjI1htbUhV60i9rrXLdny3VKJ4q9/jfujHxF84glC5TJ4HgJwwmHstjbGTjwR/aKLaLjoosMuCKkoijIf7/HHMcbHcSIRxDzXuoSm4cTjaOPjeNu2LflYYs9+NsVPfALn85/HS6XQMhnM4WGE5yF1HbehAaerC9ncTPAznyF83HFLPoYDVfnrX6l+//vojz6KqFQQlgVC4MVijP/f/yHPPZfk296mguiKohzRDv+7uxRFURRFURRFURRFURRFUY4SR2IHdNu2sSyLUCi0oFBkOBye6tKtLEw2m6VWqzE6OkpbWxvJZHK/aUzTpLOzk56eHkZGRgiFQrS3ty+q4++BGhoaIpfLUS6XWbdu3X5hTMMwaG1tJRAIMDw8TCQSIZvNsmrVqmUf21IIhUIce+yx9PT0EIvFqFarlMtlwO+kHY/Hp9Z3c3PzIR7tk0c0GuWpT30qo6OjxGIx2tracCeqdBiGQTwep7W1dcb9YSGEEHR1dRGPx6c6eufzeRobG4nFYsTjcVKpFLFYjEqlwtDQEKZp0tTUtCLbrhCCVCpFKpVa9mXNZffu3YyOjjI8PEyxWCQYDBIMBrEsi5GREUzTpL29Hcuy2LJlC+EVuIm+paWFfD7PyMgI+Xx+znWUTqcJh8OEQqED6hofDofp6uqis7OTarU6VQwgHA6vyPFVUQ6liZofnHLKz7CsGuB/39A0dYuBoiiKoiiKcuSo9/ZS+dCH0Lq7CQ4M4EQiWB0dyGgU4bqIfB4zncYbGwPXJWeaNH7qU/PON3D88ViRCJ5p+p0A5zlXoOVyflg1FCK4ZcsSvboDU+/pQX/wQYyhIexkcvaxRyLU164ltGsX9VKJ0tVXk3rb21Z0rMoiSUnp9tupX3kl+kMPIV0XISWurpNLpRDnnkv8ta/FaGo61CM9bAldJ/GFLzD+vvdhaRra8DDB3buRmobUdTTbxjNN7JYWZFsb4h3vIPac5+w3H+k4lC6+GG3nTgK7d+PEYtQ3b4aJAonS8xBjYwRGRnBrNdyf/pRCZyeJ889f4Ve8xCaDwZq2sIrLUvoFQTQNVIEEZR/B9euxP/lJ7EsuoR4IYI6OomUyfiEHKdFtGzcYpL5mDbK9ncCnP01giQpf13t7KX/oQ+j33UdsYADdttEmLuJKwKhWkfk81sAAVnc32YcfpvGTn0To+pIsX1GUo4yUVO67D7unBxwHrbGR6LOehbbIwsqLVq/7Ae8FFo2XhoFWqyHq9WUZTvz5z8d6ylMoX3UV7s03442PTz3ntbYiXvxi4hddhHGIr53ubfyaa5Bf+Qr66ChGJoNeryN13b/xRwicRAIvkyH38MMkv/IV9Hj8UA9ZURTlgKirw4qiKIqiKIqiKIqiKIqiKIeJIzGALoRACDHVmXg+Usqp31EWJpPJkM/nMU1zzrCtEIKWlhb6+vqo1+uMjY0teyC6Wq1SKpUYGxujqalpzk7AyWSSXC5HPp8nnU4fMQF0gEAgwObNm6nVamQyGWq1GlJKDMMgmUzu15FeWRqmadLR0cHq1aspl8s4joMQgnA4vGRdpxsbGznnnHPYunUrIyMjZDIZhBBomkaxWGR0dBTbtkkkErS1tdHW1nbUdLkfHh4mnU7T09ODpml0dXVNdYKv1Wrouk4+n6enp4d169ZRrVY5/fTTlz2YnUwmCYfDtLW1MTw8PPXY3vug67qk02mKxSJdXV0kk0kCEzfAHghd14nFYgc9dkU5kkx+tWtru4D6xA1lwWBw4nvcIRyYoiiKoiiKoixC6RvfQB8YIDgwQH31apgoTjb5lVZGItRXrcLYtQtj1y6cW2+lfP75RE89dc75Rk4+mdq6dTjDw5jZLFZj4+zdpctlzGKR+vr1yGc/G/0Q/31Zue46RLmMXqthr1vHXF/vRTyOGw6jpdM4N98MKoB+2JKuS+4//xPtppswxsbQs1n0ml9MTGoaTjKJOzRE4Xe/I/z5zx9WXSMPN0ZTE8lvfpPxr38dedtteMUilEr+BZxAAOJx3A0bCPzLvxA/55wZ51G85Ra0XbsI9PRgp1LIjo5p3ZGFpkFzM/V4nOCOHXjDwzg/+Qm89KVHdBflQEcH1XAYNxJBy+fx5juPOhksi8UwNm5c/gEqR5zYWWdR/fKXqXzta/DEE1AqIapVP9QXDkM0invCCUTf+15CxxyzJMt0slnK738/+v33E+3rmzpJZgcCeIaB5nlotg2eR6BQQHvoIWpALh6n8YMfXJIxHA2s/n5KV16JfOIJqFYhEkE76SRiF12EeQDFZBXlSCRtm/ErrsC74Qa0wUGo15FSIgMBxmMx5HOfS+yf/mnJimvsJxJB6jp6ucxCblMRloU0DOQyBuMD7e0E3v1u5NvfjtXXh1epoMViBNeuPey+I5Xvugv5la+g7d6NMT6O3dSE3dyMmCiUwvg4xugo+o4d4HnkL76Ypq9//bB7HYqiKAuhAuiKoiiKoiiKoiiKoiiKoigrSNeDSOlN+/ekIzGAHgqFCIfD5HI5XNdFn6e6f6lUIpVKEQqFVmiERzbP86hUKpRKJRoaGuadPhqNous6pVKJUqm07AH0QqFArVbDsqwFdaJOJpNkMhlaW1up1WpH3HYQCoXo7OyccxrXdclkMmSzWSzLAvwgdWNjI83NzZiqk8uiaZpGfBkrwicSCZ7ylKcQDodZtWrV1HYtpaShoWEqvNze3n5EFU44GFJKRkZGGB0dRQhBR0fHVBf0UqlEsVjEtm2EENi2zWOPPYbjOASDQU4++eRlLcgghGDjxo04jgP4QflsNks8HscwDCzLolAooGkaa9euJZlM0tXVtWzjUQ4fUkrGx8cpFAo4joOmaUQiEZqamub9fqIsjro/SlEURVEURTkS2KOj6Pfeiz48jN3QMBU+35fQdZx16wg+8QTk89SuumreADpCoF90EXLXLmShgLlzJ87atVPdjQE/dFAoYPb1+ctvaCDymtcs4Ss8MF5/P0aphBsMIhZQ4M9NJNALBbRMBmlZfqBCOezkvvhFxG9+g7FrF5plYadS2J2doGmIahUjm8Xcvp16pULtwx9G+7//I3gEnS/xKhWqDz6IOz6OFg4TfOpTMVtbl215ejxO40c/ivvud1O88Ua87m6kbSMSCULnnEPk5JPn/H37uuswslkQYr/w+d5EMIjd0oKRTiMHBynffTfRM89chle0MrRQCPmc5+CNjhLs7qZeqUw/Lu5NSozRUZxEAplMEnvBC1Z2sMoRI3zCCYQvu4zatm1UbroJOToKgNbWRvglLyG0xMULCpddhujuJtLfj5zYd51wGCYKr3qAZ5rotRpCSgzbxty2Def667H+8R8JzHMN6WjnFovkP/tZ9LvuQiuXEePjCNf1Q6333Uf5iivwnv98kh/8INoRdg1RURbDLZXIf+hD6A8+6HfPzufRJq57SSFw4nG8TIbSnXf6xYNOOGHJx6Cdcgry/vvR02mcuT6z8cPyRrGI1dUFyzCWfQnDILh+/bIv52DUL7sMfWQEY3wca8MGiEb3FPcSApJJnIYGtN27Mbu7IRSidMcdxJ797EM5bEVRlAOiAuiKoiiKoiiKoiiKoiiKoigr6J//OcdEw9b9HIkB9KamJjKZDLquk8vl5gw8TwbCGhoalj0Y/WThTWwUrutiGAs7pW8YBp7nTf3ucnIcZ6or9ULGFwgEcF0XYOq/TyaZTGaqA30+n6c20WUoEAgwPj7O0NAQq1evZvXq1Yd4pE9Ok925q9UqnudhGAYNDQ0LCq/HYjGOO+44CoUCmUyG+kSXAcMwaGxspLGx8agKsOZyOer1OuPj47S3tzM4OEg+n2d0dBTP84jH4zQ2NiKlpFwus2vXLnbs2EE4HCaZTLJ+mW8KiUQiHHPMMezcuZN4PM74+DilUolqtYphGKxevZp4PE4sFmPjxo0LPn4qR65MJsPQ0BC1Wo1SqYTrugghiEajDAwM0NTURGdnJ9rEjaLK/CY7oM9EBdAVRVEURVGUI0H5ppugXMaoVKh3dMw5rTBNnIYG9GwW7rrL77Q3T2e/hgsuYOzvf8e56Sb0nh6C27fjRCJ4kQhIiV4qoVsWdmMjXmcn4j3vIbRly1K+xAPjOP4X/oX+faRpU38gSMdRAfTDUPXvf0f7zW8wurtBSurHHIMwzT0BmHAYp7ERJ5MhODBA3TQpfe1rBL/4xUM57AWp9/RQ/tnPEH/8I6JSAcfB03Ucw8A99VTC//APRE47bekXLCWV++6jft99yHIZEY8TOOEEYmefPe8fxV6lgv7oo+i5HHYyOf/0TU2IkREoFKj/+c9HdAAdIPLqV1P77W+x43HM7m7sdesgGp02jXRd9N5ehG3jdXUhzz1XBU2VeYW2bFn2z1G3VELcdhtmby9S0xCuixsI7P+ZKQRuKIRRqeAaBoFiEadcpnTllTS+733LOsYjmTs+Tv5d70LfuhWjvx+9UsENh/EMA71cRh8dxYlEoFIh199P6itfUccG5clJSvIf/Sj6ffdhdncjNQ27tRWSSaSmQamEkc0S3LmTerVK7eKL0b75Tb8L+BKKveIVlH/2M5xoFH1wEHfjxpm/t0iJPjiINE1oaCB00UVLOo4jUeWhh9B27sTIZLCbm/f7rjNFCNyuLozHHoOxMerXXKMC6IqiHJHU3RaKoiiKoiiKoiiKoiiKoigraK77+lYigC4ldHfDo49CczOceOLs18MWIhaLMzqaZMeOp+A4w5x5Zonm5th+01UqFYaGhmhqaiIUCpFKpQ7iVRw9JgOvuq5PddP2PHjssQi5nElDg8Pxx5entispJbZto+v6ioRlNU1D0zSklHieN2+wbzIQCDA2pnHXXeC6cPzxMM99wCuiUoH774dCAbZsgU2bFh6yGxkZoa+vj+HhYfL5PMFgkFgsNjHfCmNjY8TjcVzXxXXdg+qkXan4+/DAADz1qdDZCX//OwwOwjHHwLHHHl3hQNd1GRgYmOo6Xy6Xp8Lj0WiUcDhMe3v7vMcdIQQNDQ00NDSs0MgPX6VSid7eOg8/3MHAQJRIZIRCYYRQKERzc/O0fT0SiVAqRXnwQZ3RUY9EYoxUKkUymVzWMUYiEY4//nhyuRzpdHrqfdd1nXg8Tmtr64KKDywlKWH7dnj8cVi1yj+2xfb/SFSWWH9/P0NDQ6TTafL5PACmaeJ5HsPDw8RiMarVKuVymS1btqxoMYlsFu65x982nv50aGtbsUXPaXwc7r4bbBtOOgnWrNl/mrkC6IqiKIqiKIpyJPCGh9GqVTzDmLOb39T0sRjm0BCu62KPjhJct27uXxCCxo9/nFxDA+6VV+KUy+iZDFqt5gcNGhpwmpuRiQT6e95D4vzzl+aFHaxkEhkIoNXrSM9DzHM+T1QqSNNEBoNo4fAKDVJZjOqVV6KNj6NXq9SOPRZhmjNP2NyMXalgjIzAX/+KNThIoL19ZQe7CKU//Qn7P/4DLZNBS6cxikXExB+rTiQCmQzWvfdSf93rSL3tbUt2QrRw/fU4V1yB1teHKBYRrgu6jhOLMbZqFfpFF9Hw2tfOujxn4tyEcBzkAoKTQtfxDANh2/6J6SNcaNMmam95C95ll0FPD8EdO3CiUbxEAqlpaJUKxvg4UtexNmzAO/54//1TlMNA6ZZbEMUigUIBKQRCCORsxU2FwDMMtIliy9roKN4dd4AKoM8q/8lPoj/xBIFdu3CiUewtWxAT3y08wKlU0AcHMXfs8Kf/whdovOSSQzhi5UB4lQpuqYSeSKgCArMo3nIL+gMPYHZ344ZCeOvXT32vEAANDbgNDbiZDMHBQerBIKVvf5vgZz+7pOMwW1rwXvACqFYxt2+HnTtx29un/e0k63X0oSG/+/mGDbjHHUfkpJOWdBxHovpttyGKRTTbxmtuZq5voULT/GJnY2Nw//14tZraNxRFOeKoALqiKIqiKIqiKIqiKIqiKMoKmut+vuUO+rgufO1rcOutex5LJODTn4YNGw5sfv/7v/D736+jVCpjWav53e/K/NM/PcHmzQFM08RxHAqFAqVSiVQqRUtLC11dXaoD6QIJIYjFYiQSCbLZLOFwG//3fx0MD+/pNNTWZvGOdwySSLiUSiU8zyMWi61I8DISiRAKhdB1nUKhMG/gtFAoEIvFePDBBNdfH5pWdOEtb4GXv3x5xzuXbdvgE5+AanXPYy9+Mbz1rTBfVrFcLtPf38/AwADVapW1a9cS3aeyQ61WY2BggN7eXoQQBIPBAwro9vXBpZfC6Ojs05xwAnzykxAMLnr2RxzHcdi+fftUCLlUKqHrOpqmTRVjSKVSVKtVOjs7aTvE6c9Syb+PNB73fw5WLucHSFtbD35ee7v55iDf//6x2LYNSCwrxVlnOTzzmXKqiMSk++7r5NZb12HbfpGMxx5L8Pa3F3n965NLO6gZCCGmOtSDX4Rj3/GtFMeBL38Z7rhjz2OpFHzqU7DMDeGPaul0mqGhIXp7e3Ech1WrVpFIJCaOAYL+fod6fZCenh6klOzatYvNmzevyNhuvhm+9S3/+xL494+9/vXw6levyOJn9ec/w1e+AvX6nsde8Qp405sO1YgURVEURVEUZZlMnGw9oL8SF3qiVghS73kP9qtfTemqq3Buvx2Rz4Om4a1ahfGiFxE//3z0w6g6WfC5z8X53e+Qg4NomQxyjpMK0rYxCgWsri7kmWceXRUPjxDSthF33IGezWInEvN2qPdaWjC3bcOpVCjfeCOBt7xlhUY6nXQcynfcgdPdjbRttFSKyHOegzmxPZbvvx/70kvRensJpNPYsZi/HQaDYNvo+TyBgQHcXA738svJRyIk/+mfDnJQktxXvoK46ir04WGMXA7NcfwuyJ6H1DScwUG8oSGyjz5K46c/jZjhxPFUoEgIhOsy79FESjTPwxEC5nn/jhTJN7yBcV3H+853qBeLaJkMxtgYSIlnmlgdHZBK4Z18MsnPfU6FsJTDhjc4iKhW/e8BmuZ3Ip7js0/qOrrj4JgmWq3mfwdQZlR99FE/cNvTgxOL4XV17X8uPxLxOzDv3Ine2wu33Yb99rdjHkRBZ2VluMUipRtuwL3hBrS+vj2Pb96M8bKXEX/Ri9Sxfi/WtddOfS6669bNfl2ruRm7XEYfHYW77sLJZjGampZ0LKkPfYix/n4A9L4+gjt24AaDeIEAwnEwKhXccBhr40a8TZto+Oxn1d8EgJfLodfreKY5e/GnvaePRDCLRVwpccbGZiwCZQ0M4KTTCMPA7OjAUA0dFEU5jKgAuqIoiqIoiqIoiqIoiqIoygqaK3edzS7vsq+5Znr4HPwg5Cc/CV//uh+aK5Vg7Vp45jP3v3YoJWzd6gd1i0XYuRPuuw903ZgK27quwQ9+cArPf/6jbNgwRCTiEo/HWbduHZFIhPXr188auh0bg7/9DYaH/a7OJ54IszUWWIh6He69F3p7YfVq/zUd7LVtKeGRR+Chh/zw6Bln7OlmumuXvz6CQT8AfCCh/knlsr8uuruhpWU10WiRXbssvvKVDgL73IQ2PBzg5z9v5S1v6Wd0dJRkMkkwGFyRLvOJRIJQKERDQwPZbHYq9JfJGDzwQBxdl2zYUGXdujq1Wo1isUgotJlf/KKTUGj6Bvbd7/ohzRNOWL7x7trldw6vVOC00/zlCQG1GlxyyfTwOcBNN/n7wznnwAMPwBNPQDrtd41dswb++Z+huRl++9sS2WyQWEzyjGesJThD8jsUCtHV1cWuXbt47LEad99dIZVKcdppks2bp+9vUvrb2GOP+dvsxo3+2KWEH/5w/tf5yCPwne/Au9898/PbtsHDD4Np+h15OzsXvg4Xo1j0t+OeHr+j/NOetvSh+F27djE2NsaOHX2MjLSRzx9LY6PB8ceXaWysUSgUGBjIcuedCUKhCieeWOAlL0nMW1RgLo8+6r8/4+P+v1ev9l9bV9fsv+N58IMf+NuUZfnr/txz/cIL842lp8dfj54HJ54oWb26zMCAy1e+EqKvz0DTdFatgg98AI49dmGvIZeDP/7R3/aPOcYf/+Q2+NBD8MtfxhGijG1beJ5HtWpx++2n09V1D+3t3lQRkZ6eFH/60yaktBFCoOs6tZrLZZfFeeYz6/T3BxkYgIYGeO5zYSGN0rZt89exlP5+OlNn5tksV/jcsvx95vHH/fE87Wn+a9rbr341PXwO/nr+9Kfhe9+b+fPftuGvf/WP9atX+6/3YAsTDA/7n32WBU95iv95upjVks3C7bf7n6FPeYrfHbuvD+66y3/+9NMPn0C9lJKhoSFGR0dxHId169ZhGAZSwnXXNfGXvySo1zWE2MJJJ3Uj5XaCwSDFYon+/hgPPOAXCTj5ZH/9L6Vdu+Cb32RaoRUp4Sc/gc2b/WXu/fhf/+pvX5WK/1hbm7+uOzoWvkzX9fffxx+HVav84/u+XwWGh/1CCZY1/fFrrvHHcOGFcOqpMFHTYVbq/jJFURRFURTlSCBaWiAU8rsQV6tTHTZno5XLeIEAaBpGS8uilmW2tZF65zvhne88mCGviNhZZzHW3o4zMoI5OooVj8/8B7vnYfT2IgMBaGggdNFFKz9YZV52Oo2wLLRaDXshAb1wGM80oVJBDg8v/wD34dVqjP/oR8gbb0TLZqFSQUiJDAQof/ObuKefTvRf/oX6//wP+tAQZjpNrasLMXEiRgCEQnjxOPXWVgI7dyJ7e/F+8APsF78Yc5H77t7yl1+OuPJK9J070SwLp6UFr6kJoetIz0OMjWGm02jFoh9W//KXafzgB/ebj5FK4TU14cZiaOPjuPNVjpzssp5IoG/adMDjP9w0vO51WOecQ/mqq3B/9zu8yZOpuo572mmELryQ5BlnqJMMyuFl8mTe5HY5X0GavTsWw9wXgY9ylauuQi8U0Gwbe9Om2fd9IXDb2wlu345bLlO68kr/O5Zy2Crffz/1Sy5By2bRMxn0QsEv3KLriJER5BNPkL/8ciKf/zyhFSoOezizR0fRH3kEPZvFSSYR8xw3vOZmgjt34pbLlG+5hYZ//MclHY8IBEh9+cvkv/Ql+P3vcctlRC6HcBy8YJB6RwdEo7innUbDxz+uQtGTDMM/ju19EWguUu75zNgrsC5tm8INN+Bcfz36jh1Tj9d1Hff00wldeCHRM85Y0qEriqIcCBVAVxRFURRFURRFURRFURRFWUFzXUO84w4/+DPXPUbptB/UTqUW3/H2z3+e+fFCAfZtDrJlC/zHf/jh15074U9/8seXycw8D8MwiMfjWJaFZVnceecpPPKIwzvfOUh7u6SpqYmWlpZpwdzhYcjn/dBmTw985jN+YHXSSSfB//t/BxZYHRuDj3wERkb2PHbllfCxjy0uzLW3mbrbXn65H/IdGYGf/nTP40L4nUQvuGDxyxkd9TtxDw1NPhKnWNyCZa3DsmpIKQkEAtOClo89FmTbtn6CQUFLSwurVq1akS7AQghaW1upVCoUi0X6+vro7n4q1147vcv0mWeOcvLJ22hoaOCRR1r3C9FPuv325QugX3edHwKdvF/opz+F177W/3n4YT/0P5Of/9wP5u29LYEfyPyP/wApPQqFIJVKE5rWDJR49rPHZ5yXYRhs3XoMv/nNKkKhCPE4/PznOq97Hbzudf40rguf+5wfBDwYd98N73rX/sH2n/wEfv3rPY99//vw3vfC859/cMvb18CA36l97/tYu7r81zZfwLZU8o8JXV0wV4OwQqFAoVBg+/Yhrr32NLLZPVX/b7yxkX/4h1E6O0P87GcnkclYmKbJ73/vh8C/8IXFB32lhG9/G268cf/nhIC3vx1e/OKZf/fyy+Haa/f827bhN7/xw+dzNZq64Qa47DJ/O6vX/ePrs5+d5f77YxSLHiAwDAPbDvCJTwT40pf8oglzue8++M//nB5APeUU/7ij63uCxpZlU6lU8DwP27YRAu6/P4TrbiMej5NMJtm+3b+p13EcDMPANA08TwKSd79bm1ZE5Kc/9d//2YL6UsIVV/j73KQf/hDe8Y7Z1+tc8nl/O9ywYWHB99lUKv5n0V73fdDc7Bet2Pu13H77zL+fzfpFIU46afrj4+Pw2c/6hS0mNTT462gxofu9/fnP8F//Nf2+yJe+FP71X2f+/jE05H/ubtjg3ytz113w3//tf97N5qc/9Tt4v+ENBzbG+YyPQ38/tLfvH57eVz6fp1arkc/naW9vx5jY4G68sZHbbktOTSclPPjgOiqVOqtXl/jiF+s88MCeg0sg4BdwOOMMv2iN6+4pUHKg7r579vuO7rprTwDdtv3Pkgce2H+6ye8Yz33u/Mur1/3j2n337XksEvELIGzZsuex++/fP3w+aWDAL0gUDsOnPjX3/bXq3nBFURRFURTlSBB90Yuo/PjHuJEIeiaDN8cfW9J1MfJ5rI4O3FNPPaw6li85ITDf8hbcz3wGp1YjsHMndmMjsqXFr5gnJeRyGOk0wnWxN27EPfNMIvv+Yasc8eR8wcol5haL5D/wAfSHH0YfGcEYH/eD10KAlDiRCGSzVG+7DVGrYYyOYq9aNRU+35cIBrG6ugjt3Em9XKZ0zTWk/vVfD2hsXqWCvPxy9L4+NMfB3rIFTHMqVCo0DZqbsVIpzB070Ht64IYbsF//esy26efCEQJx3nl4AwMEt27FLRQgkZh5wVKij47ixOPIeJz4S196QOM/XAU6Ogi85z3w7nfjFot49TpGMrmgTqGKcihoLS3IYBA5cfJL8zzcvUKD+xKuixQC4bp40SjeYdap2x4epvyHPyBzOdA09DVriL3whYekE7V46CG0fB47FvO/b8wlEsENhxG5HO6DDy5qObVt26hcfTVy506EbSNjMfRnPIP4BRegH2z1V2U/lQcfxPrIR9D7+wkMD+MGArgNDUjTRFgWRjaLGB3FKhSo/tu/Ib7xDYLr1h3qYR9SzsQFZ82ysGMx5j3VHo36x6RaDS+dXpYxaaEQjR//OPZb30rpmmtw//Y3v2p7IID21KcSufBCgnNV4T4K6WvWQCyG5rr+TQYTzRpmnX58HDcSQcZiGBMVgO10muKHP4zYuhUjm8UYG0OzbaQQuKEQZDLYf/kLYy9+MamPfARxMNXVFUVRDpIKoCuKoiiKoiiKoiiKoiiKoqyg+Yrff/rTfuBu3+kcB77xDb+D+eS9Dmef7QdHZ8ny7mfv8Nx8tm3zl1erwT33LOx3NE0jFAoRCgXxPA/Pk1x3XZJvfEOb6pYL+4fuJu4v289DD/nhqze/eeHjnvSlL80cGP7ud/3Q4IG4+eb9u9taFvzP/+w/rZR+2PlAOqF/5St7h88BBNFodGod1et1ymUbXdcnCmv763poqIlzzgnT0tJC2743vS2j1tZWSqUSAH/9a46f/SyKrlfRNP8iqOM43HJLhI6OTjZvDnPZZe1Tz+3rd7+bvWv3wejp8d/7ff38534wb9/3dW+Fgv8zG9d1kVLiui6BQJirr25m7doa69bV95t2aCjAH/7Qgev6oV7XddC0AFdc4Y/j1FPh6qsPPnw+Oe5cbnoX23vumR4+B39b/d//9TvyzhdcXowvf3l6+Bz89+HrX4ePfnTm33Fd+NrX/M7ck8HNF7zAD9LPdE09nU5TKBT4wx/Wk81Ob9frOIJf/rIVIcCyBMGgv+8EAgF277b5xjdMLr54ca/pL3+ZOXwO/nr85jf9btP73oPhun6QfCa//S388z/PfL/Vtm1++NzzXEqlMo7jYNs2v/mNiZR1oI6mCUwzgOPY2LbNrbdGeOMbZ79dpVCAL35x/wDq3/4Gv/ylX5Dh2mtt6nUL27ZxHJd6vYaU/jH+r389hjVr7sKyLKrVKg899Fxc10FKORFAD2Db9tQ62VuxCJ//vL+eZrpf76GHpofPJ33zm/72udAGUPW6/xrvvXfP5+WFF/rr+UBCs9/61v6fn5mMH/T++teZ2MZgcHD2edx22/4B9Msvnx4+B//z8T/+w1/mYsc6MuK/7n3X+29+43e5f85z9jyWTvvL6e72/x0I+EUwfv7zucPnk371KzjuOL9wwVJxHP9YdPvte17Deef53wFmux8yn89TLBbRNI34xM2Drgt33TXTTd2Cxx9fy403DnDvvca0DvaW5W+bpukHwgGSSbj4Yn+fPhBXXDH7czfd5BdWAL84zkzhc/DH8o1v+Ot6vqJDV1wxPXwOfvGESy+FH/94zzH0O9+Zf+zVqv99dPJ3/vznZ+E4VQA0TaBpIV772lmqGimKoiiKoijKYSTQ0UHx1FMhnyfY00M9Gp1+omSC9DyM7m6kYUAqRfDCCw/BaFdW/IUvZDyTwfvWt7BHRzGyWbR02u9U6XlITcNpaMBra8M95RRSn/qUqkR1mDIaG0HX8YJBtHIZr6lpzullvY5m2xAKoc0z7ZKSkvxHP4r2wAOYu3YhDQOrvR2ZTPqFVKtVtEyGYG8vnq6j1esgBF5z85zBLBGN4oZCaJkMzi23+FX4DkDxhhsQxSLG+Dj1rq5ZA9JC13HWrCG4fTtOuUzpqqtm7Mwbu+giSr/+NXZjI4HeXupr1yL2CaFL10Xv70ev1bA2bUI+97lP3nCkEOiJBCo2pRzuoi98IaVvfxs7HscsFPxwuW0jZ7kQqzkOnqYhAK+tDf2881Z2wLOobd1K+XvfQ7/3XqhUEBPHVDcSYfz//g/5/OfT8La3regxR1QqfkflBV7U9kwT4bqI2apH78MaHKT42c+iP/IIWrGIKBb97zSGgbzvPgo/+hFccAGpfSs3KwdMOg61T38afWCAwPCw3ym7sXFq/UrAXr0aRkcJ9vVR1zRKn/kMwe9979AO/FBb7Pa390WfZd52zZaWAy7mc7SJvexllH78Y5xoFG10FG/9+tknrlQwymXqGzcin/c8hK7jFosU/v3f0R97jMDu3f7fX6kUMhoFKdHGxwn09+Ol0ziuSw5o/NjHVuz1KYqi7GueWx0VRVEURVEURVEURVEURVGUpTRfAD2fh61b93/8+9+HP/xhzzVGKf2u5N/61sKW67qLGibgh8AWGj6fTqBpOrpuMDBgMDy850VL6QfA9w7dzdVo5a675n5+JpUK/P3vMz93//37hrsX7g9/WPzv3Hrr4qbP5eDRR/d/XNN0YrEowWCISCRKOBxG1zWE0DAMk2g0SnPzajo7O1k/1wXOZSCEYMOGDbS1tVEsbiEUiiCEwHUdXNfFMHSi0Qh///sm1q9fjxAH0NL+IP3+97M/d++9sGvXgc9bSjm1jU52nX/44Zm7ZN1zT3yvzvRyWpeh++/3//uXvxz4WPY1GaScNNu8pdw/uHgwhof98PRM/va3/cc16bLL/H1m767Bv/+9//i+pJSMj48zNlagp2c1zHArqm0LLMt/3DAMpATX9UPc9903+zhms5Dj4Z/+tP9jjz/uF/OYiWXNHj71Oyh7lEplLMua6EbuEgwGiUQiRCIRDMOceq5et7j88sqc43v0Uf8YOZN774VyuUylUqZer+N5HsFgENMMoGn+sSYQMHFdl3Q6TbFYpFz2xxYImGiaX2zE38QFmrb/ezI4OHtQe7YO4rC4ogz/8z/+e7X35+VVV8EvfrHweUyScs++ua++Pr9bNvjB8bnsuz9Y1uyvd2jI70S9WA88MPtn/d7Lsiy/o/tk+HzysR/+0A/vL9RSFMrY27e/7Ref2Psz/+ab/aD+bBzHwXEcAoHA1LF1dDRAuTzz7cyeZ/DHP3ZMHHv3/3Kx9zEhn/e/rxzIe7EYd9899/OWBQtp8vPnWfLgxaJ/DJo0W1f2fZXLe4qvjI8/QLHo/4yP/43x8QfUPZqKoiiKoijKESP6jncg29qwVq8m2N+PtmsXslBA2jbSshCjowS2bkWzbewNG3Cf+Uxiz3zmoR72imh47Wsx/uM/cM4+G/vYY6lv3ozV2Ul93Tqspz4V58QTkW98I41f/SpaOHyoh6vMQguF8M44A7exEWN8HDnPCS8tnfY7KkajRF784hUaJZRuuw39wQcJdHfjRiI4W7ZAY6PfWVwIiETw1q6l1tWFWSqhWxbC8xbU6dGNxRC1Gloms/iLCROcP/0Jkc36HVtn61Y+KRLBiUTQs1nkLJVNzdZW9A98AG/tWpxkktDu3RhbtyKGhxGjo2i9vQQfewy9XMbasAH3qU8l+b73HdDYFUVZOkZTE+4zn4nT3o6QEk/TMGwbMUPVTq1W80OCrks9lULG48QvuOAQjHq68l/+QvU970H/7W/Rt24luHUrgd5egrt3E3z8cfTHHkP75S8Zf/vbsUdHV2xcMhTyC90spAIqfrgfTUMuoFt7vbeX0jvegf6Xv2A+8QTB7m70chksCz2X81/3E0/Az35G9v/9vwP+rFCmK916K9roKObICFZbGzQ17R+QFgJWrcJqasIcHETfvp3qI48cmgEfJszVq/0iO6EQ+lwV0CeVSggpIRJBW7Vq+QeoLIjR1IR31lm4bW0YpRJicHDGY4usVjF378aJxyEWI/qqVwFQ+O530bdtI7B7N05DA/ZTnoJcvRoSCWhowFu7FuvYY0FKjF270G66ifJ8F5QURVGWkQqgK4qiKIqiKIqiKIqiKIqirCDH2Uqx+MTUT6m0f9p8MtA2yXXhlltmnt9tt+3fyXYms4UfV8Jjj+35/507Fxf2HR1dfEh0YGDuewdmW5dzcZzpYb2Fuu66xU3/t7/NPnY/hB4jHo8TiYSJRKJEoxGiUT+IevzxG2lvb1/8IJeAEIKuri50vZNIJEI4HCYcjhCJhAmH/bEODa0iHp+/q85y3Pdx7bWzP3fTTX6n2wMlhDYVfJTST/bddtvMM7z99iSeJyd+TyDEnss0k5215+qivFj7Hhv++MfZp/3hD5duuXMVa6jX/eDuvlzXP57N5I9/3P+1THaez+UEjjNzR6LpBJqm4Xl+8N+yFr9Pj4zMP82+x2/wu2XPJZ2e+fFf/xrq9RqOY1Or1QgGA4TDfuh8MuwdCASIRiMIoVGtVrEsm8IcN6z89Kezj2PnThgYGMC2/W7qiUSCeDxGKBRC0zRqtSqO42Cafuh9+/btWJaFrusYhkk4HMK2bQzDRNc19FluEL7zzpmXP9d2M1Nn9JkUCrMHen/3u4WHbydlMn6AdzaTx/j5Auj7Lnfnzrk/lx9+eGHj29tVV83+3N4h+scfP/BCLHv7zW8Ofh6TarXZ9/8bb5x9/foFDwTeXiu4XJ798vf0z5f5E9T1+uzB7qUyPDz/NLMdIyY5ztzzuemmxY1pIVQAXVEURVEURTlShDZtIvC5z+GtW0d9wwYQgtDu3YQee4zQ449jZDI4ySTW5s24z3wmqUsvPaq+8MbOPpumyy4jdNllyLe/He+f/xn5r/+K/slPkrrySlJvexvCMA71MJV5hC66CFIppGli9PQgZzsBks8TGBvDaW3FPfFEgitYyLR+7bUwNobwPNyurln3M9HQgBMMonkemm0jF1ItTwj/j/6DObE8Po6wLD+cv4BjgBcOIywLMcdJocSLX4x28cW4GzdSP+YY3FgMfXwcfWwMYdtYnZ3YT3kK7umnk/zqV9EikQMfv6IoSyb21rciOzqodnSguy4SMGo19GoVYVlo9TpGuewH0wEnFMLZtAntne9c0Y7iM6lt24b1qU+h7d5NsLcXGQxS37IF64QTqJ9wAvW1axG2TWDHDrTHH6fw4Q8jF3KReQnIzZvxEgmMYhE5T9V0aVno1SpeQwPimGPmntZxKF98sf+au7txo1Hqxx6Ls3kz3oYNOMceS339ejTLIrBjB/of/0juu99dwld29LKuvx4xNoYUAtncDJUKYmAAracHracHMTQ0tX15ra1otg3j41SuuebQDvwQM5qacJ/2NJzJ4kHz7A9aJoMTiyFjMWLnnbdCo1QWIv7e9+J1dWGtXYuZzWI+8QRieBg5Pg5jY2jd3YS2b8cLhfzvv298I8F16/BqNbjlFrSREbxgEK+zc+bvn6aJs2EDWr0O+Ty1q6+e9rSTzZL/0Y/IfuYzZC+5hNyXv0zlwQdVkQ1FUZaFOjOkKIqiKIqiKIqiKIqiKIqygr797adhWdWpf2tamJe+dHo72n2v9T/66OxdSV3X70J61llzL3cxXU2XWj6/5//37sK5UNUqBAILn36+bu97d19fqIGBA+siv1i53PzT6LpOOLz/zWixmZtur6g77wwQDkM4HMbvLjv9YulC1n2pBCt9j04qdeC/q+s6QggMQ8e2bXR97ksv/jST3ev3BHQn75GsVuf45UU6VPv9fE0zZsolb906eyC3WvU7tO/dgEzT/JCp45jTOsnPTU51556c72Is5Bgw0z483z2rsw1fSg/LsrEsC8PQMc3ZDoSCUChEpVLGcWzS6QKJWTo1zRWm9jyXYrGIZcUIBMypY41t20gpqddrSClxXZdYLIaUEsMwJppURXAcB8/zCIdDBAIBZgv4Luf9bA89NPv6zGT84gddXQuf33z32E8WF5gvgL7vtjNfMYO53qfZzBdkltLfFucqRHGo7N49+3bhOH5H+3PP3f+5cDhMNBpldHSUer1OMBjEsmYPoDvOZIGE+bunTbriCnjNaxY8+aItpOHPfIe4cnnu5/f+HqYoiqIoiqIoR6Po05+O+Z3vUPrJT5C33YY30RETTYNgEG/NGvQLLiD5ylcuqNvyk1Ho2GMJHXvsoR6GcoAip55K7ayzsOt1zF27CGzditPUhEwmkYaBKJfRslnMQgGrtRW5ejWRd7xjxcbnFgroDz6IkcthJ5N+1/M5OPE4gXIZzfMQ2SzMU3RVL5XwQiG8VOrAC0joOgjhdxhdAOF5fuBunpNHiRe/GOeMMyhdfTXuTTchJ08K6TruaacRuugiks94xlFV+EJRDnfBri7cz32O+sc+RikQINzTg27b6I6DMVExWwqBa5rYsRj2U56C9t730vCKVxzagQPl734XfWSEwOgo9bVrp1dfFgLR0ICbSCD7+wns3k09FKLwm9/QcOGFyz620IUXYv/lL8jBQbSREeRsx3Yp0YeGcINBiMWIvPKVc863dOutaL29BPr6sJqb95+vEBCPY2/ahLl9O1p/P+7VV+O94Q1oC+iursxO9PQgikXcQABjxw6/SIPr+hV5hUBqGqTT2LEYbkcHTiSCVirh7t59qId+yAUvugjn/vuR6TRGdzfOhg37fz+SEjEyglEsYm3ciPfsZ6PPcu1POTTMlhZi//u/lD70IaxoFJHJYORyiHQahPALYqxfD4kE8g1vIPXGNwJQvvVWxPg4RqGAtWbN3N8DTROnoQEjk4F77sEZGwMpKXz1q2h33gnlMnqphJASGQhgXX011Q0bCL75zcTOPntlVoSiKEcFFUBXFEVRFEVRFEVRFEVRFEVZQQu5h3HfENZ8wbaxsfnneSg7oAeD/n8dZ+FdbPdWqUBDw8Km9cORDp6no2mCmQKQO3cufgwHcy18MvS3EAcTcl/J+2Ol9AOUnjf9/r9jj907ZL7/i37wwfnnnc/PH0B3Xb/TdFMT7Hut3fM8nIlEn2ma1Ovzr/yDCbwLIQgETBwnQLVaxXXdGcON9brfpde2bUKhIKZpTOuADv5+upRFySfuhVqwgQF/P+3sPLjtab7w7Ez3mM4XkOzv33cefvdvTYtOdOWeu0qF53l4nkTXdXTdH8Bc68dxHMbHx7FtG03TCIVCuG6c+bomz/Q6DjSAbts2nufiOC6RSHjOefhFEExs2yafH8d1XTRNZ2TEX9+trfOPxbL8Tuue52Gae268isfjaJqGrmvUanUMwyAej5NIJCbWp0G9Xsd1XcLhEIZhEAgEZ13OTPd0LWS7l9LfRkMhaG6eeZqenrnnMTIyfwBdSn97k3L+wh6TzeYX2wF9Mrg+m73vC1yoaHTuIHK97q+7++5b3HyllNi2XwhhstO4pglMM4Dj6Bhz3Gjtef57ommwdu3s2998xVfuvnvmAHpzczNDQ0OEw2EymQwdHR3U6zPfxO5/N5CYpkkwuPCqNgsJiB8oKRd2nC6V5n6+Upn7+eDsu6OiKIqiKIqiHDUCnZ00fvSjuO95D5W//AU3l0MEApjr1xM56aRZ/2BxcjlK116L+8c/InI50HXkqlWYL34xsXPPVcEl5fAgBMlLLiFnWUhdR0unMTIZtKGhqeedWIz6+vXI1lYCl1xC+LjjVmx4djoNUqLV6zgtLfNO77W0IEdHEZ6HXijgzhVAr1TQKxWcjg7E85534IPs6MCLRgnkcjiuO3cxCinRSyWc5mZkR8e8szZSKZJvehO86U1Iy8Kr19FjMRU6V5TDWOTkkzEuu4ziD35A+fe/R+/txczl/HCtEHiBAHZrK+5rXkP0da8jtGXLoR4y9vAw+r33YoyOYieTs59kFgKvsxNZLKJlszjXXw8rEECPPuMZjK1fj10oEOzpwQK8trZpoVvpuuiDg34oc8MG3Kc9jdDGjXPO17r2WozJLtxtbbNOJ3Qdp62NQF8f3vg4xZtuWpHg/ZOZsG20ahWzXEYKgW7bfgh24vNNSImnaQjHwa1WcU3Tv2iwnFWKjxCxs88me9ZZYNuYO3cSePxxnMZGZEMDUghEpYKRzaLV61hr1uCtXUvibW871MNWZhDo7CT5gx9QvOEG3OuuQ+51U4kMhZDnnEPkVa8idMwxU487vb2ISsUvfLSAm2DchgaM3l7wPKoPPID9zW+i7d6NPjSEUS7j6TpS09BtG88wEKOjON3dFN73PhIXXbQcL1tRlKOQCqAriqIoiqIoiqIoiqIoiqIcZva97jpPQ5AFBfcOZQC9UvE7iP7617NfU/Y7/Vq4rouUfrjNMExM06BSmf9GrGq1SjqdJpvNsmuXSaHQiRBiKmi2d1fqatUPMC8mYDtfqHEui+novZxBt6Xy6KPwxS9CNuv/e80a+PCH/VBnS8vcXc7vuWf++efz/jxn8/vfw2WX7dmmTzoJPvIRkLJIOp0mn89PdcTWNI1SqQXXbZuzM/m+4dDF8rvu2gQCJtVqlWAwiOd5U126Pc9j506LarWCaRoT2+X+Nyi/+93zL8vzXOp1f1/xO3r7+0ogEEDsc9PiYu/hmGx81NwM//qvcOaZi/v9SZOh3NnMVGhhvn1kpgBmS0sLwaDEdb1Zg/+TLKuOrutomjYVVp8p+Fmr1RgaGiKXy2FZ1lQAPRAIkE6vp16PEQwGmS2InsvtX3TiQO8l9UPzk4Hf+Q9YhqFTq9lUq4LHHrP56lf1qW70nZ3+fjLX54nf5dxCCDGtOIIQGpFIZKLYQpDW1tapoLpl1aeOtZFIBMMwiEaj+22Le5spDDtfwBbgrW/d0zm8sxM+9jH/v3ubbzuab9vcuhW+/vU9x/zw3Ln/qeD5fPPdNyCcTs89/Te+AaYJi7lvuqFh7gB6Pg9tbf46mikwP3ls8QsfeBOP7dm3XNfBdT1Aomk6pmnzyCM9tLe3s2rVqv3m95e/+Oty8r1NJOC974XTT99/2fN18J7tMzgQCJBKpahWq/T09DAyMkK1uv9G4LoO1WptomiFNm/BipXieQv7DnfjjTA6Ch/8IEQi+z8/3/ozzQMb31zUPfKKoiiKoijKkUqPxYjPVOFqX1KS+9734IorEMUiRjaLqNf9bo6RCN7DD5O/7DIC738/sYMJvSrKEtFCIRr/678oXHkl7jXXIPv6/Gp0ngeBADISQZ59NpHXv37eMN9SmwxzSyEWdiI2HkcKgWcYmMUiTrmMiEb3n862MXp7cWIxiEaJHkTAJvTyl2P/6U9+Z950es4QI2NjCNdFNjVhvuQli1qOCATQA4fHeQlFUeYW6Oyk6ROfwP23f6P8pz9hpdM4IyOIhgZCp59OfMsWv5jEYaL8hz9AtTpVlGNOQuCkUn5we8cOrP5+AvuebF9qQhD9zGeovPvd1AGzvx8xNoYTjyNNE2FZGMUiUtex1q/H27CBhk9+cu55Son+6KNo+TxOQ8P8F9QbGvwqt7kc7gMPrEjw/snMc13MchnNthGAY5p4prnn+pDnodk2xkT1baNUohqNzl/592ggBKlPfYrcJz/pFw/KZv3A+cRFPanrOA0NOGvX4nV2Evnv/8acrDStHHa0UIiGV70KXvlKrKGhqWJngc5OtJkuNLouTBZrWMDFFiGEH1b3POpf/CLG0BCBnh6/wNTGjX6FavzC5lo2izkyglOt4n31q5RXryZ6oBf8FUVR9qIC6IqiKIqiKIqiKIqiKIqiKIeZfcPi84XHF3LPVr1+4OOZe9nuRHB8766oJoaxJ+3005/O/vtSSqrVKrZtTXSu3jtUa6BpOv39Dhs3Ns76+wMDA4yMjFAul8nlcuzeHaBSSaFpGo5jYlkWwWCAcDjMZGi0txfWr99/frbtB6iTSb+z92Se9WA6oGcyCw+gLySEOZul7JxdqfgdgF0Xjjlmzz0bw8Pw//7f9ABxXx98/ON+0HC+bbGvb/5lz9UJ989/hv/93+mPPfig5N/+rcDb3radUqlIPp/Hmkheh0Ih+vqgWPRDw+FwiH2Dw8Hg4juF70vTdCKRCP62q2FZFtu3byccDiOEoFqtsn17M6YZIBDwt8WZwtJzhVI9z6NarWDbDq7rThRrkBPhX4NarUYwGCAU2vMa997vF7N9ZDLw3/8Nl1ziv/dNTf79INu3+89t3OgXCZitq+6uXXPPf6YA+nwFIWbqqu44TWzfbmGaJrValVBo5vVar9cnuohHME1zqjDAvu97sVhkx44dFAoFxsbGKO+V6tQ0jfHxFioVHdt2Zg1Z27YfBl3M/TPzvTeLDXr29AS56ipj2uvr7/f30/mOMVIKYP8BTW7j9XqdVas6cN062ew4pulvz+FwmFAoSDAYmlq/s5lpu5kMls9l72n6++Gd74Sf/GRPc4DeXti5c+55zBUUz2TgM5+ZPk21urD55fNzT1cu+wUhJu8vniwMMJevfMXvWP7MZ84/LfgB78HB2Z+fDKAnEvs+I6lWa9TrdTzPnQqgO46DbTtIKXFdl2AwOHV8cRwHy7Lo6+vDcRwcx6Fjr5saH3oIvvCF6dt2oQCf+5z/89SnTh/BfB3k02n//Wlu3v+5NWvWUKlU8DyPgYEBdu0aoV5vQNc1PE/iOA6e5xEIBAgGA1PFFA4Hi/nsue8+uPRSf73u61B0QD9MVqGiKIqiKIqiLJuxL30J7Zpr0Hp6MItF3GAQLxLxwzOZDGJkBLupCefSSylUqyRe+tJDPWRFQeg6Da95Dbz61VQeeAB7506kZaElk0Sf9Sz0BXRYXA7mqlVUAgG8cBitUMBrnPlc/yRZLuMFAkhNwwkGCe3ciZ1M4jU1IUMhcBy0sTE/OBkI4K5bh3z1qwnM1Sl9HtHTT2ds7VrssTGCAwPUAwGYYZyyUCA4OIjd0oKXStFw3nkHvExFUY4Mejx+RHzOy1wOUa8jNW3mKpb7Th+Nok1clHIymeUPoAPBri74+tcpffzj/onqXA5tfBytWvWD52vWQDKJe+yxJD77WYympjnn55ZK4HkIx9lz8n0uQvgBaddFHswFWQUAOXEtVndd6uEwmOb0K7CahhcMIjXND6Hjd0XXF3rR40lOC4Vo+sIXKPzmN9jXXovcunUqmIyu4zU3I847j8Q//ANGKnWoh6sshBD+99F5vpNqjY3IYBDhef4FnnmO2aJSwTNNyOXQq1UCvb3YjY3I9vZpF2uEaSLb2rASCQI7d2KPjlL7wQ9UAF1RlCWhAuiKoiiKoiiKoiiKoiiKoiiHmauv9jsPH3usH1j76lfnnv5QBND9MGx1Kqg2GYbVNA3DMNB1jVAoNGeXUSk9SqUStu1Qr9dxXRdNEwgh8DyPer2OYZh0d2fZuLFC5ww3P/T19TE0NER/fz+1Wo1YLEYymSIQCOC6LtVqDU3TkFIipSQS8StAb9++fwD997+H7353T5Cro8MPbHZ2+sHGA5XJzBx2n8l8Iby5LFUA/Y474Fvf2hP4bW72Oycfcwxce+3M4eFCwe92uxRjmCuA/pvf7PuIpFKpsHWry913j9LYmKOhoYGGhgaklJTLZbZvL1KtVia6okvC4ekXcRsbl6bzvGmaRKMxNK2CaZq0ta2mXvdvqIjH43R3ryMUChEOhzFNc6rD8EJ43uS+Yk+ERD10XUMIget6WJaFYehTHbP9MLyYFm6cL0i7L8eBj33MxXGciaA76LqBYfiXlnTdD6iffPKe33Fdvzv9fOtzppc+03a1t70D6K4LX/sa3HabiWW1EQz6O221WkXXNQzDxG+o5GHbDv77HsY0jYlCFL69O8RXq1V27NjB6Ogo6XSaZDLJunX+e+Z5HsViEdt2qFQqE4UFIBqdOWWey00PoM8X1JxtvxFCmzh++a9lvmC3fwzVuPPOBixL22+583Xp1nWNYDCClMzYUV4IjVAoTEfHFjwvR61mEImECYdDNDQ0zDu+SaHQ9H9feSX86EcL+tX9vOENfrfw73wHHnxw/umz2dmfu/PO+dfRvia3oYX8Xi4Hk43Ch4cXNv8bblhcAH0uk58v+05XrVap1erUajVc18Uw9IkCLi71eh3btgmH/cICQggCEzfy+Z+xVfr6+hBCEI1GSSaTgH+snmm79jz43e/2D6AvZP098gg897n7P26aJlu2bGHHjh2Ew2EeeSSM57m4rgMIDEPHNENTRRTM5WgHvpdaDb74xYVNu9jiJ48/7hfEWbdu+uPzdUBfjsZqKoCuKIqiKIqiPJkVbroJ7dpr0XfuRDgOtfXrEbHY1BdhT0rI5QgMDuK4Lt6XvkTt2GNXvKu0osxKCCKnnAKnnHKoRwKAFg7jnXUWbjpNsKeHmmUh5vhjVU+nsZNJnKYmZDyOOz6OnskQ3LFjahovEMBpaUE2N+Odfz6N73rXwQ1SCMIf/Si1978fy3UJ9vfjZjK4ySReMIhwHIyxMfRKBau5GdneTuDDH57zdSiKoqwoTYPJLrlSLujChJyYRhgrFykKdnUR/MlPKP35z9SvucYP3dbryHAYefzxhC66iOQppyzoBORUZ2FdX9iFPin9sLqu73+hQlkUa3AQvVpFuC6epqG7Lu4s596lYSBqNX+6cpnQ+eev8GgPY0KQOP98OP98ajt24PT2Ih0HrbGRyMknr+i+qaycyPOeR/nb38aJRNAyGby1a2efWEqMXA4nlYJ6Hc1xkJq2X/h8+gIi2M3NGNks8oknqG3dSuiYY5bnxSiKctRQn0iKoiiKoiiKoiiKoiiKoiiHoU9+0g/Vbds2/7QLybE+9tjBj2nP8lxKpTK2bVGvW1PBSCHAtj3qdTDNAK7rEQ5LgrO03iyXK9i2PREa9YNhewcYHcfGsix6etIMDZUJBoO0tLTQ3e0HDYvFMrFYHin7GR83KRSeSn9/ACkFhuF3YQ8EJLValWq1ghBMPBZg+3Y499w9Y3ngAT/QundgbmAAPvQhv1vrQrrVziaTWfi0iw0/HqixMf81p9Nw3HH+j6ZBTw986UvTt6lMxg8a/9//zRQA3+PRR5c3gO668Pe/T3/Msiwsy6JarbJ7dzOnn940FZAGSCaT2HYrUvohy8ltYO8AYqEwf/fYhTIMg3g8gePYtLevp1q1eOSRENlsiDvuSJFIGOzbgX1+fpDeti2q1RqmaRIOhxBiz77iui6WVada9V+IpumEQiGeeALOOsufZjEB9Mmg+2TnYCk9hBBomj4RUg4SCAT53Of8YgWNjf528s53+uHL+cwUNp/vOLb3vvHDH8Ktt/r/b5oBolH/Grtpmti2jW37qeDJsKxp+sH5SCQ6bb3tPc+HHhrm5ptNxsbCPOMZm1m9es92pOs6yWSSWCzB2JhLrVZF0wS2bc8YZv3Zz/yiExs2wNOfPu/qQEp//d1xh3+P1PHHw1OewlS3dl3XsW2LYHCuG6Iktm0TDAbZti1MNLqwMPjeTDOApgUwDH95uh6ecTrbDtDQEEOIMNFojEgkvODwOfhhWCnh7rvhpz89uAIfAIu5v7inx1/PfX1+UY0TTtgTzr3yygMfw3wd0AF+8Qt473v9bX0hHd/BP+a5rn8/3HzuvXfu5yfHGI/vecz/HK9TnThARKMRhNCo1+tT2144HMK2/Y7nfiEKfepn3bp17Nq1i1wux8jIyFQAfa5iAI88sv9jCym+cvfd/rrL5fxO7qec4n8233sv6HqAM854Cu3t40QidcLhPd87dF0jEAgSCJjT9v/l8tWvzv9eTFpcAN3fx7///QpvfvM4uq6TSCRIJBLzfoZt3epvf+GZd2lFURRFURRFUfZh/+pXGKOj6JaFtWULYt+//YWAxkbqpkmou5t6KkXlV78idPHFc89YSuyREdzxcUQoRKCjQ4VKlKNG5FWvon7bbTjhMGZ3N86GDbDvviUlYngYo1TC2rAB7/zzibzqVVQuvxzuvRe3VvP/mNZ1CAZxjz0W86KLSJ533pJUSgsffzze5z+P9YlPUG9oQEunMTIZv1OupuEmEjidnchkEuPii4k9+9kHvUxFUQ4/0rIo3nwz9u23+xcRdB2xZg3hCy4gfNxxh3p4s9LXrsUNh/1QeaEADQ1zTq+Nj+OGQqDrmB0dKzTKCUIQe9aziD3rWQc3G8PA3bDB/+zI5bBXr57786BU8sObiQT6U55yUMs+2lXuuAMBSE3DMU10y0KvVvFME7nX91vhOOi2jTfxvrjxOM7IiOroPYPQpk2wadOhHoayAsxVq3Cf8QzI5Qj29FBPJGDi+to0UqL19/uFHhoaEOUyeibjh9Hn+e4rm5vR0mkol6nedtuSB9CdbJbi1Vfj/eUviInPStnZSfBlLyN2zjmqirGiPAmps1eKoiiKoiiKoiiKoiiKoiiHoVoN3vzmhU07V3BTSvjlL+GKK5ZmXFJKSqUylmVRq80UhpU4jjPRpdlPmWqatl9I03FsHMehWq1hGMaMwUrDMNF1g2oVRkdHCQaD/OEPzfzsZ/4Fq3JZUq2upqEhSLXaguftHywTQhAOh6lUKliWjWHUpwLoe7vttpnD0+UyvO99i19Pexsd9UOPmuZ3U5/tepvrwsMPH/hyFtpQ+9FH4TOfmR66Puss+MAH/ODsTPOpVOCWW+ae7x13wDOesfDxzma2IOdMnYvrdQt7IsHX3b0Ow+jDtgXZrIHjCAxD8ve/xwmHJZVKGcdxsKz6tO2xXF54WHChDMPknnvWcdNNe9bzgTbdtW0H13Wo1WoEAiaBwP4FHfyQaJhKpToVHA0Gg1x7rUDX4Y1vXPi2NRlGnQxye96eHUMIgWmauK6L47hAmCuvFGzc6BdwmK+L+aSZtrH5tt9Syf/v8DBcd93050wzQDxuTBUk2Lu7vH98CUy859N3vh/+EE49FR580OZLX0pRqQTRtBYeeCDIOefkecUrpm90nicIhUKUy5PbkjVjAP3Pf/Z/wA/JPvHE3K/tT3/yx7LvOpg8dgYC/rFS120MY+YNqVarIYTAMIwZt5GFEEKQTDYRCASpVCrU6/UZC4hcf32Sc855AsOIo+v6opdXKMDHPuYfi1baww9P3xc2b4ZPfxqi0blD0J7n4jjOVOMYP4C95zLrQoqH/P73fgA9m11YM5ZJmcyezukHY/L17R1CnuxwLqUkEokghGAy6Ow4DrquT3wW61Srfof0vYsTGIZBY2MjuVyOxsZGqtUqrhuesxBFOr1/A56FrL+77vJ/ZvPjHwve9KYk8TgkkxIpJSAmXtPB2Xe8rusHuu+6y39vTjvN785erc49xn3nubAAuqRWq08c21z+8heLc87ZgWEYRKNRQqEQw8NrgcSsc+jp8X8URVEURVEURZlf9e9/R9+xA2NsDLupac4TOiIex47F0DIZ5G234b33vWiRyH7TuaUSpeuvx73+erT+/qnHy4kEvPCFxF75SgKdnYsapzUwQOV3v8NLp0HT0NraiL34xRhNTYuaj6KslPAJJ1B9+cvh6qsxdu0i8MQTOMkkbkODvw1XKuhjY2i2jbV2LV5XF4l3vQuztZXwF76APTpK9d57kcUihEIEjzuO0JYtSz7O6NOfTvDyyyledRXOjTfi7VVdVjY0wHnnEb3oIgLt7Uu+bEVRDr38L36B95OfoOVyGGNjYFkgBDIWo/7b31LdsoXIxRf7QdHDTOwFL2D8G9/ASSQwRkdxEonZL0jaNsb4OFZnJ+7ppx/RYWDjZS9DbtuGSKf9k+/NzTNPKCX6yAhOLIaMx0lccMHKDvRJRubzCMvCCwZxIhFkuYxWr2PU68iJ/QYpEVLiGgZeIOCH/1evxp2tErmiHEWib34z1QcewKpWCfb2YhcKeC0t/kU8KZGFAkY6jV6rYa1bh9vRgZHJoA0P4yyk2rBpInUdLAu5kCrQCyUlY//7v2jXXosolzGyWcTExS4vFsO5917G2toIffzjRE4+eemWqyjKIacC6IqiKIqiKIqiKIqiKIqiKEe4v/8dXvOamZ97+GG/w+xSsW0L13Wp1WoToc7APlP43cc1TadarUx0Uq7tF9Ks1y0cxwZm75AOfiCyWFzN+Ph9ZDLruOIKB9M08Tx3oiOrTS6XJBCYq6upmOqM7DgBXNdh1y6D4WG/iyrA448f0OpYkCuv3NNZt7kZXvlKeMlL/GvvW7fCPff4TVt++9uDW87FF8NLXwqtrX7Ab/VqeM5zpne7taz9w+cwPTA7mxtvnH8MS9EBfbYA+r4dg13XmQhD2hNdriVPPBHmF79oJZ+ffvljMjjtFyHwtx9NW0BL4YNwMN2U92ZZFs5EWjUQ2Hd/25sgGAxQrdbwPA/btggEglx1FVx//cKCjpPd5Gu1Kq7rYpp+cHtPKNXBtq29griC669ffEvdA+mAXiz6//3xj2fezjRNIxQKEQqFkNKbGt984dMPfQjGx11c18N1PUIhvxjG7bcnuf32JM3NNqmUg2lKMhn/OGYY/vHEMPzg7lzL+Nvf5n5dADt2zP5cKBSaWN+SarWGEDV0XUfT9Knl+sdSCIXCU+/Zgbrssg5Ms0Q4HJ7YDpyJALI2sSyHu+7SuPfeswmH/fWtL6Q9915+8Qs/VH042L7dP3Y+97kzPz9ZUMUPaXtToWZN0zAMnWAwyO9+F2DbtoUtT0q/iMJiXHYZvOEN/udUf79/bD/1VOjuhsce8+9FOfXU+edz+eV+QYTJ/cd1HRzHP4bu2c/Bdb2J/7pTn89C+K/Xth103Zja7q+8splKpYmRkTGOOSbG+HiNtWvnPyZMNuDZvt3/DLzvvsWtk5lICd///uTrW5rg+aQPfxgSCb9QweAg097vnh6/iMmdd8Lzn7/wYjD/93/wxz/ON5WkXK5MFdewbRshIJPJ4DgOmqaRTCbp6QlTrZqEV7DF+SJ3e0VRFEVRFEU5YtQfeAAqFTTLQi4gzO01NhLo68OrVqlt27bfTfa1bduoXHwx2tAQeiaDkc/73ZSFwI3F8IaHqVx7LbX3v5/E+efPu7za9u2Uv/Ut9PvuQ1YqaJMVwCIRSt//Pu6ZZxJ/17uOnHCslJTvv5/6ddche3oQloVsaEA/80zir3gF+jzdW5UjS+MHPsCY6+LccAPkcujZLKFcDqTEMwycVAqnuRlv7Vqi//3fmK2tU79rtrZiLmAfWQpGYyOpt7wF3vxm6r29eMUiIhwm2NWFMNRt94ryZJX7+tcRv/gFRn8/Rj6P1DS8UAjheeiZDJ5pYudyVN/zHuQXv3jYdUPXQiHkC16Al81i7NiB1t+PN1NVbNvG2LULLxiEZJLQRRcdmgEvkfhLXkL+Rz/CbmsjODiI5XnIlpbpr9uy0Pr70ep17E2bkM97Hnpi9oKeyvxEMOhXXgecdevwRkcxczk8x0G47lRVV6nreKaJE4kQKBb992XOa52KcnQIbdmCe+mlWJdcQj0cxhgdxdy2DalpCCmRQuAkElidnXibNhF+//ux3/9+pBALqwgvpX/BStP8/XUpSEn2U59C//3v0Xt6MEol3GAQLxwG1yUwNARDQ9jZLPUPfhA+/3kip522NMtWFOWQU38JK086p59+OmeeeeZU1xtFORw1NjYC4HnetG48iqIoinK4U9+xFEVRlMOZ+ltLOZId7Db7t7/BW98qefWrJamU5JhjIBTyg7xf+5q+4DDUQtRqNSzLQgg/hClnSRz7nXhN6vU6hmFg2/a0kKJtW1iW/5g/i9mTyw8/3ExzcyPZrIZlWei6juM4eJ43EZALzDqOSbquU6vVJzo3Owih8Za3wJe/7NLWBkNDK5OkGh2Fb34THn1UsmqV5Fe/mis4v3jXXz/931deCR/9qMuGDf6///pXQal0YMucqQP53oTwCx4c7PaWzUpse/+ZpNNiWpd7vzO4i+d56LpOPu/x3e+24Th+WHpfuq5Tr/vdsR3HwTCWLpx4sOY6BuzZV4x595XJUL1t21Nhe4B6fWHjqFQq1Go1HMclHA6jTdw8Mrl/GYYxsS9VqVarAAQCJkIsbpuq1939AvHl8vT3d1/j45BOu/zpTwvfV6WU8x4bymVwHBd36mK9mPY76bRBOj15Oc1/XNM0bNtCSonrulPraXkIgsEQ1Wp1qrDA5N/mQmgTwfsg0WgU0zQIh0MH9ZmyYwdEIhGkLCNEBNu2sW2Let1/7bquEwoF0XWDYDBIIBBY9PJGRw94eMuiu9v/2ddkQQZ/HdjTXudkUQvbtvnCF4ILDv5+/vOSk06Sc27r+7rnHv9nLj/72cLm9e//7t8T5nl7jqF+4QV9arv3g/beXv+e3O51bLuOlB6u6wfR77gjAUhKJZ3e3jB//nOIYHD+7WFkxOXmmwU/+Ym2JIVLlttCitQ88AA8+ujCPwNvumn+aarVKvV6nUqlghCCYDCIYeisW7cOz/MoFApkMhl6e7PUan6XormK+iylaNTBto+AN09RFEU56qnrWoqiLJZTLKJPFAKUuj5vtUVpGAjPQ3oe1vg45l7HHau3l9q//RtaTw+BgQE/uJZKIUMhcByMfJ7g9u1YqRTuf/0XOSB23nmzLqty3304l1zih9nTaYxqFU/XEQCuixONInM5Cg8/TOi//ovgYdiddW+1bduoff7z6Lt3I8bH0Usl8DykaeI98ADjP/oR3gUX0PCOd8zewfUIdzRe14q///1UzjoL6+qrkffdh+N5U4Xu3LY2xEtfSvSCC9BiscPic1xrb2fyLI4j5cKqfCpHNymp/O1veKOjIAT66tWETzzxSXsce7Io3ngjXHEFxs6daJZFvaMDmUxOFbm0bRuRThPo66PuupQ/+lHED3+IvncF6MNA5F/+hdJ99yFtm0BPD3qxiJ1K4UUiCCnRCgXM8XHcYBB7/Xrcc88lesopB3S8PeTH6ImAM4aB8clP4nzkI9QBc3gYJjrAS11Hq9UwSiW8YJD6hg24xx1H4t3vPvTjP9J1dSHDYTxdh/FxvPZ2am1tiLExtErF/36saXjxODKZRO/rwwmH8TQNrbNTrX9FAQKnnor35S9T+8EPkH/9K3al4l/UFwIZiSATCbznPY/4W96CCASoB4O4kQja+DjuxN8Rsxof94s8RSKwZs2S7HPjP/852i23YExUVq9u2ICIRqeedzwPkcn4xUAch+onPoH48Y8xUqmDXvaBOBr/1lKOXG9605v46le/eqiHMScVQFeedILBILFY7FAPQ1HmtNiOMIqiKIqiKIqiKMr81N9aytFucFDw1a/6N0IEArBunWT3boFlLd0y/NCZ9//Zu/MwO8oy///vp7az955OOunsCyAgi4oCioggyqaADi7jqMOoo/7UUdxwAxUdwBF1dNxw+erojNuAgOICKDOKiiKKC5CQrTud9N6nu89e2/P7o053OkmvSa/J/bquXEn6VJ96Tp2qOnWq6nPfo91SpxJ1nHaroe+xAXSN1tHzGcZ0qpwrfvzjk1DKJpUKq2M58PHpPMfYeY+46SaTZz1r/i84/d//KaY37iPT1we33mrwsY+FKAU/+tHczVPrQzurH45sdvwxHroua8aGsYvFqT4HluINXiPbyuRdtscyDDWt4PXBRgK2nucRj8cnDFVHIcg4xWKRMAyoVNzRruHTNd413rvumvz1heHU08yv+RlLGAaUSiUMIwoIh2GIYRjV91djGFGn5zDU1RD0kY9LKYN09SbfSsUlCJwxj0X7dseJHdXHXp7nUSqVqgUZfBzHxrLi1e2L0VB6EPhoHW13sdjU28Gvf6147LGFW4+1HtsYYf8+9MD9y/7PJ61DqN5mPTJNtE/aP3UY6tHfm+5+6uGHFd/61tIIn8/EbB5zhWGI67qUyyUMwyCRiDPyvvzgB8u49NJe6urqSKfT3HOPQblcqe6fHeZj/9TQcJS9eUIIIYQQQoxIp9EjHY49b8oOjcp1oy50SmGMufkeoPjxj2N1dBDr6KCyYgUc1I3Tb2zEz+Vw2tvxOzrwP/UpgrPOGjfM5ra3419/PWZbG053N15dHeXWVqgWRNOFAlZvL7Fdu6h4HuVrr8X80pcW7Gb/qZQefRTvne/E2rcPp7MTVQ3QYxiYw8MYvb34mQz+d77DYG8vdR/84FEZ3jyaz61MJvn0p5N8+tPx+/tx29rA8zDq60lv3nxUvs/i2BAWi+S++1348Y8xenpQvo8GfMticNUq1MUXk7n8cowZnksX80Brwm9/G6u7Owqfb9qEcpwDz7DZNnrlSlzbJrZvH+VMhsJdd1Hz8pcv1KjHZWYyJD/xCQrvfjc6Hsfo78fKZjF6ewEIEonRcH3w/OdT+453LPCIZ0BrCg88gHfHHRiPPopRKhHGYoRbtmBdeinWjTfifvSjhA0NGP39GPk8yvPQlkVl/XrIZAjOOIOaD35QtsNZkDzzTIabm/H37cPu68Otr0cZBjQ1ccglQNfFHhqisno1wRlnYE0VnBXiGBLfsoX4v/4rXlcXxV/8AgYGwDQxVq0iff75GGMKYAfPfjaq+p3PL5VQExTH1lpj9/ZGhThqakhfcMERj1MHAdx2G2ZXF2iNu2kTyjowjqoMA5qbqVgWsY4OynV1FO64g9pXv/qI5384jtXvWmJpWgoZWAmgi6NOpVIhn88viQ1QHLtGuunIgY0QQgghhBBCzB75riXEfq4L27bN/o1aY7uhmubU3VuVisJoh4Zh1ejj0w3JBgHY9v5QXBS6jB4bCWROJgyD6jyNAwJyXV3MehfyxebxxxW7dsGGDdDevvhv4BsaisLGB7+l1eZTo6L3MppopAv6ZEYqW0f3BC/+5RBR1fHCZJ3Px4rC6jN/jZ7n4fs+Siksa/JlaRgGpmnieR6W5R1GAP3QLvXTCeXedtvcbKuGYWKaRjXgHYx2kp+I7/sYhoFS0b5ormgdks8X8DyXcrlCLBZ1Oo8ei8bq+8HofrRQKJBOZ2Zp/VbYtoNtO2gdEoa6+noNlmYxh5kpl0u4boUgCEgmEwesE0qB4zjYtj0aUjcMheM4o/ukyWSzczny6Rv5jIaRz9HoNY58vpumie8HmGZ0OTkKox+6D/V9D8NQGIaBZU3v0vN996mjLnw+21y3QhhGHeqTySRjt7tf/aqeVCrgggsGsCwL267D933CUOO6Hs4UAZnZ0Ng457MQQgghhBBiQcROPx0/kSBwHFR/P7qlZdLpzYEB/JoawmSS5HHHjf68sn071t/+ht3ZiVtXh64GwQ/5Rp3J4La2EmtrI1y2bMIwW/Eb38Dq7sbp6qKyejUcFCxXqRRBKkXY0xMF3hMJCt/7HrWve91hLYe5FBaLuO9/P2ZHR9Qdr6mJcNmy0QBDoDUUCth79+Js344LDG/cSM3f//3CDnwOHOvXtazGRiz5gimOAl5XF8V3vQtj507M3l6swUGM6vYdWhZeZydhWxvDv/gF6ZtumtviICMh3Z/+FLq7o0qSjY1Yz30uqec855CwmIDiH/6AsWcP9sAAblMTapJza7qpiXBgALOvD+9HP4KXvWzRFc6wly2j9gtfIP/jH+PddRd6587Rx7RpEjztaTiXX07maU9bwFHOjLtvH8X3vx9z1y6sgQHMoSFUEKBNk3DvXsJHHsFduZLYDTfgt7fj3XUXRlsbuC46nUaffjrxK64gPeZYTRwZZZpw0UUE+/ZhDQ9j7dqFv2YN6uAC9qUSTlsbQSKBrq3FueKKhRmwEIucvWIFtS972aTTJK68Eu9nP8PPZKLCY+vWoZLJA6bRYYjV3o5RqVBZvZrwoBD74Sr+6lcY1WMcd8WKyY8n6usJ+/ow+/oIf/xjeNWrFuSz8lj/riWWlnw+v9BDmJJ8ixBHnd/97nc89thjXHPNNQs9FCHGFQQBvdVqcsuXL5eDGiGEEIue53mj/55Ol0shhBBiIch3LbHUTRWeXjyiLswj451JR2bDMA54nZZlYln2aOB1MmEYojXYtoVtW9Xgp41pmjhO9BxTHav6foBtW9XfcZZQAHl2/PznBscdRzVAutCjmZpSBge/pQeP3XEcyuUytm3h+/6Uwceoi7GDaVrV9WVh14FwTBvwyfYBlmVj2w6e505jWwmq24qNbdsz3rdoHWKaxrRCtJZljV64nel8DOPQ97e2FnK5GT3NrInFHCqVaF3yPJ94fOJ1SWtNEAQkEnEcJzZlWP1IlMsVwjAc7TI/0X7O81wqlcpoUYCZFgSYmsH8HFrt76S9kDzPQ2uN7/vE47HRAPbBlFIkEgmKxSJhqKvrztLpnGLbDqZZxrKioHksNvI6ozC91uHoOmiaJkEQjH4GR0UqFFqH1X1rDNu2px1A7+5eGp9FCykIogITlmWN+93mt79t4HnPG8Y0QakkpqkJAp8gCObluHLFCvuQ/bgQQgixWMh1LSHEkbBPOon+448n6O3F6emh0tiIisXGn3h4GLtQiDqlXnABsTGdy4e+/W2c3buJDQ5iViowOAhK4WUyhI2NkMnsvwm/tpYwFsPo70f/7GfYr3rVAbMJhoexHngAq68Pv6YGNVnXyOXLCYeGMPv60Pfei/X61y+6sOHgT36C1deHs28f3ooV0NzMAd9ilIJMBm/zZuxt27A6O/HvvBPrla9cdK/lSMh1LSGODkEuR/k978F6/HGctjZC28ZbsQKdyYDWGMPDWAMDqMFBPN+n9N73Uv+5z00acj5chd/+lsott2B0dmJns6hSCQWEsRg8+CD5W2/FfsMbyDzvebM+76UsfPhhjEIhKhrQ1DT5dRilCOrrsQYG0F1d6L4+nJUr52+w02XbNLz4xfDiF+N2dOD39aEsC3vVqiMqgLAQ37W8ri4q//Iv2Lt2Ye/Zg9J6tPiP8n3s7m5Udzfu8DD+u95F4jOfIX7RRfMytmNd46tfzcAjj+Bpjb1zJ9bWrdF7k0qB1pi5HFY+j59KEaxfj7rqKmqf8YyFHrYQS5Z9wgkMXn014Ze/DLt3k9i+Pdq+amvBMDCKRayhIbRh4G3YACeeSOOb3oQxC/tr/de/YuTzKK2hoWF6n5X9/ei+PhgYwF6x4ojHMBPyXUssNV/96lcXeghTOnrOxgghhBBCCCGEEEIIIYSYU1HwW1XDaj6WNVXoO2pZbZrmIRd1HCeG7we4rksQ+BMG/CDqAmqaJoZhjHb1VCoKyPl+QLFYxPe9CccTBD6+75NIJHAce1bD56ecAo88MmtPN2f+9rfo7zGZ50VtvM64Y+5rAUbWARvfdyiVSpOuR77vEQQBsVisug4tnQIEsZiD73tUKhU8z5v0pp5KxcWyRraV6V1MDsOASsUln89TKhUJgpAgCKtdde0JL8iOdFo/HNXc+pjngkLh8J5rNkQFDxyCIKBYLOG67rgdhLXWlEolTNPANC1isbnrMqy1xnVdPM/FNM1J33fbdvB9H8/zcF2XeDzGUlnHwzDEdV1c1x0tyhAVKLCJxZw5DfhPxPPc0c+vqT7nDMMYDf57nrukAuhRkDwqcDFSzGNkeTuOXd3fWKPFDXw/KrwQLRNFEPiUy5XR9TNa78RsCUM9WhRkPMPDJl1dDqtWuZRKBoahR39nrikFs9AwQwghhBBCiEUr9rKX4T/+OEEuh7NjB96qVVBTMxoY12GIGhjA6ezEbWpCNzSQ+ru/G/39odtvx/nqV4n19qLCELNcRgFaKZTnoYeH8VMp/PXro+6RShHU1GAUCuh9+6ITJWNOuhR/9StUsYiVz1NZv37K8fuNjTidnYT9/ZT+/GeSp58+68voSAR33YXV10do2+hlyyacTpkmfnNz9Fp6e8n/4hdkLrhgHkcqhBBTG/7mNzG2b8dpa8Orq0OvWgVKjZ6d1fE4/rJlGO3t2Lt24dk2w//zP1N2OZ2p3L33Enz0o5idnVi9vSitCRIJUAp7cBDV1YXX0EBwww0MDQ5SO+Zz61inczmU76NNM/pcnmr6eBw10uF+oarqzoDT2orT2rrQwzhswx/6EObu3Ti7d+M1NBCuXImqFuDUQLhqFaq7m9iePVSA4gc/SPyb31x0nemPRsqyqP/4x8ledx3EYpDNYg4MYFdDn0EiQWXDBshk0FddRf0b3rDAIxZi6at71asYNAzCr3yFSj6P0duL3dcHWqNtG7elBRoaCE46ibobb8Q4qEP64dKFAioIos/KaRRB1o4z+lkZ5HLzHkAXQsw+CaALIYQQQgghhBBCCCGEmKYo9B0EPsViiTAMJ+2y6bputQvzoeHJke7VjuNQKpVJJOLjhIc1lYpb7TacxLadAzozO04M13WJxWKUyxVsO6wGzKs3HuiwGsj0cBwHy7KITdSx6DB94APwta/B3XePH5peLNrb4TOfWbgO0zM13rI8OLQMEIvFcF2PWCxaj2zbPmAdCMMQz3OrnYljWJY1brB4MbOsaBuKx6P1HA7tLKG1plIpE4YByWQSx3Gm1cW8UilTKkW/57oVXDcKMEfzsKqFHaxqqPbAm3WCICpCMVE4cjJBcOC91JXK/BRHCMOQIPDRWgOqGtaPbiiLx+PVQhWaUqmM7/ujAfyRbti+72EYBvF4glhs4s7Ys8H3/er665NITB1qtm2nug7ERrsmL3aVSplyuUwQRPvqMIw2csMw8H2bSqVCLBYjMc9J1zDUo12/p2OkO3gYLuIPgQnE47FqgQ6bUqlU7WRuoZRBIhEfXfeLxQKWZVW3i4BisUAY6mqhgNiYYLqYLSP7x8mOLXp6ogB6uWygdVDtKj/3N1bKvZtCCCGEEOJolz7vPLJbtxL813+hOzpw2tvRlhUF+bTGqlbR85qb0StXYl97LbE1awAYvu029Cc/iT00hOW6UTCqGjJXYRh1Q3ddCEPUjh14mzZFN/CPhBXHOQEWDAxEv6M1OpWa+qg/mYzCAb4f/e44tO+Pht/nUzA0hNnejjk0RFBXN/X86+th3z4YGsJ7+GGQALpYIsJiEbe9nbBcxqyvj/YR8oX6qKN9H/2Tn2D29BA6zmj4/BBKEa5Zg/H446i+PoIf/hBe+tJZWyfKO3bg33hjFHIfGMBbvpywqWk0IBaEIWSzOJ2d+J5H+B//QWHjRlJPecqszH/JSyTAMKKLFAcVgRmX56Gry1YtoYKkS1H58ccx//Y37I4O/Pp6dGvrocdBhoFuacEF7H378OrqKPzmN6TOOmsBRnzsMeJxGm+8kcIf/kD5ttvgt78lqBb41ckk+rzzSF55JfGNGxd4pEIcPepe+Uoq55xD8bbbCO65hzCfjx5QiuC003Auv5y6c86Z1WNPlUqBaUbfM8NwyhC6cl109TqrmcnM2jiEEAtn8d99IoQQQgghhBBCCCGEEGLRiMUcXLeCbVuUSkXi8cQhIb2RMCxoHMcZt+O0UopkMklUnx5KpXK1G6uFUoowDPF9D1AkEgls2zokhGgYBqlUCq3BMBSVikuh4GEY0bzCUFfDojFs2yGVSs5qN92LLwbHgde/Hi65BBZ74fZ77lnoERyZ6v0KBzAMk1QqBURdrF13/zqgdbQummYUpLRtu7oOzDwwvZCUUqRSyWpoGiqVCq5bwbJslKK6rQQYhqoWarCJx6cO7JbLZcrlEuVyGd8PRjt6ax1Wu6KHGIYiFouhNQdsf0Hgj4ZPDyfQf/PNYFmwZQu8+MUwjeZdR8T3/dEO8lrr0e7tShmjhSls2yadTlEoRPuWqHhFZTT8aZoGsVgMy4o6c08nFH4kRsYJTGu/ZZpGdZ0P56UD8pE6dP0zRwP9YRh1oh8J/4MmkZidCv3Tp2ZQVGTpBc9HmKZFMpkc/RyNutFXRveTYRiilEE6nUGpqAiNYZgYhqoWx4j2r44j3c9nm2FE24TnucD4y/cb31hOEECxqAiCAMc5vKIgQgghhBBCiEPVv+ENDNXXE3zta4S5HKq/H1WpgFK4K1ZAQwNhczOxd76T1JlnAuB2dBB+5jMY7e2YnofvOBhBgK6eO9FA6DiYlQpWuRwFBPbti8JU5XLUETyTOTQoYNvokZ8FAUxxbktXq/wpw0CNnLfRmsLDD1O+7TaMhx5ClUpgmgRr12JdfDGZiy+ete54kwmq1TFVGKKnUyjUMAgtKwo6FItzPDohjlz58ccpfO97GP/3f9E+o6qwejXmJZeQeeEL52VbE/Oj8JvfYPT3Yw0N4a5cOXnQSymCxkasvj70nj2U/vY3EiedNCvjKH7nOxj9/Tj9/VTWrYOamgOvyBkGNDbiJhI4O3bg9vdT/q//kgB6lf2kJxGMfP5ms9DQMOn0VjZLkE6ja2txVq2ap1Eem4q33YaRy2F4Ht4U3XPD5ctRfX2QzVK+4w4JoM8npUg99amknvpUtOfhZ7Moy8KsrY0KHgkhZl1s7Vpib3sb+i1vIRgcJKxUsOrrMeaoqLd96qn41c9Klc1CY+PEE2uNmc0S1NQQNjdjL18+J2MSQswvCaALIYQQQgghhBBCCCGEmDbDMEkkEtVgXoVSqYRpGphmFBwPggDf96th2EQ1DDv+zYS2bZNMpgCFbVt4nl/tThyFbkfCnpZlkUolUePcvGOaFplMmlKpjGlahGF4QBddwzCxLGuCDutHpq5u/79XrYK1a6GtbVZnccwaL/g5XgAdwLIs0uk05XIJyzp4HTAxTQPLskkk4rNagGA+maZFOp2iWFSjXYijDtmMbmumaVa3qfG3lbGCwKdcjrqfax2STEbB/GJRAVEI1XFsgiCkXK4Qjys8z8W2HcIwpFyOus0bhoFtH15Hed+HRx+FG26A173usJ5iWly3QrFYwvc9PM8jCPaHs6Px2/i+TzweJx6Pk05nRkO4Uag26pYOVLs9OwvQ6Xn/GCacYnSbWfydlHx//PWvtbVCR0f0eaF1SKlUplQqoVS0nR/uujZThmFgmlFBi+ks+5GO80utuMUI23ZIpxWlUrQ9R/uWaDtJp53qZ6euFpuJloVhKGzbwbbtKfc34vDEYg6+71GpVPB9b8L9zre+tRzXdTEMhWlah1UUZConnvhJPK8MRNviCSdIt3shhBBCCHFsqH3pSwkvu4zcj36E/4tfRDfaGwZ6xQpiF19M6tnPPiBUU/j+91G5HPbgIKVVq4j19mIUCijfR1vV85JKEcRimOUyRqWCPThIpakJO5ejsn49epywlL1uHV48HgWxh4agqWnScRuDgwTxOBgG9rp1BMPDDL73vZh//jPm4CBmNovyPLRSqL170du2MfS1r+G8//1zHtYyqoUkMQzwvKl/QWuMIMA3TekyKxY3rcl+9auob3wDc2gIs68Ps1iEMETbNn5nJ3rnTgZvu43UzTcTm+uKnGJeBJ2dUKlERTIymSnPzOpMBqOzE3wff98+mIUAepDPo+6/H6OvDy+TgZqaiSdOJvHr6rD6++Ghh3D37cNZufKIx7DUpZ/zHLKf/Sx+bS1WTw9eXd3EnV2HhzGLRfxVq+DCC1GWRHHm1BNPYAwN4SeTYE9+TlIZBn4mg5HLET7xxDwNUBxM2TZ2c/NCD0OIY4YyTazJwuCzJPWsZ5Ftbsbv7MTu6cGtq5u4wEQ2i1mp4K9bh3HxxbPaiV0IsXDkqFcIIYQQQgghhBBCCCHEjEShTFXthmrjut5ocDwKw8arISi72rV24otKtm1TU1MzGvgcCYdGgcPphT1HumCHYYDreqPBOcMwcBx7zkLHYwPoMOW9D8c8w4Bwmo2ZZxJABzBNk1QqPboOjHSAHglIL9Vg6FhRsYUMnudXt5VgtFiDbVs4TqwaEJ1apVIhCHyCIKgWd4iWTzweJwxDtNa4btRJPggCKpUKWutqgYkA2466hieTiSMOn4YhfOELR/QUE/I8l2JxpMu2Xw2QxzEMRRhqfD9alr7vA3q08MXIn2i/FgWQo4IW87cemaaBUgZKqeoyn3zeQRAV/lBKLfpCCyOB2oPXv1NOyY8G0JUySCQSFItFPC8K4c5XAN1xHFzXRSmF53mTzjcIAsIwrG6D8zO+uWBZNpmMXd0m3AP2oY7jzHoBFzG1qMiHieM4lMsVEgk17vvgeR6u65JIxLEsc07eq7VrX0el2rUtFotxySVys5AQQgghhDh2GMkktS95CbzkJZNOp30f7rkHo7cXP5WClhbCbJbQtjFdF9809994rxSBbWNXKoS+j719O0EsBpkMySuvPOS5U2ecwcDKlfidnVh9ffgNDRN2QddBgJ3N4q9YQXDyydhNTWTf+EbMv/4Vu60N5fv4NTWEtbXg+1jZLKqnB6+5Ge997yP/kY+QfuYzj3i5TcSqqyNsacHftw8rm8Vvbp48kDA0FJ0orKnBevKT52xcQhypwa9/HfW1r2Hu3o2Vz+NlMritrdG2WiphZbMYfX24hQKFt78d4/Ofx56im68Q01H+859RpRJ2Lkd57dopQ/BhYyP2E0/gVyqUH3oI57LL5mWci5myLNSllxL29mI+8QT2jh14a9agYmOKa2sNQ0M4HR14jY3oujpSV1yxcIM+VlQqoPX+Qj5T0JaF4bqo6rlMIYQQs0OZJsaVVxJ2d2Pk89jbt+O1tqJGCowBOgwx+vpwurpwly9HNzaSedGLFm7QQohZJXcLCCGEEEIIIYQQQgghhJgxx3GwbQvXdbEsd0xwXI12CZ5uCGps6BP0aKh2pgzDJB6fv+DlwQH0JZw9nBerVkF3N7ju4f3+ZAH0EfO9DsyGWGx6DZ8i0fZlH0G1A611NbDoYdvWaPgXorBpIpFAqSjUHwWzfYrFImEYYJomiUQCyzJJJJLzFgg+PJpSqVwN6/ujXbZHGIYa3Y+VSiXK5QpKRUUrRpbJQoZuTdPCNKMO7VH3+cne8+g9tSy7GkA98m1g2bLonrq+viN+qgOEYYjnedVg9/5l7TiaVOrAChUjnycjIfAgCGbltU3FsixM0yQWi4K/UUf0Q9eFMAwpl8sHBIWXOsuysBZZ154wDKtFalzCMESp/cVFHOfoKDAyPkUymRwtBlAqlTFNA8uKus6HYYjve4ShJh6PYVk2yWRyXkY2D800hBBCCCGEWHK87m5ULodZKOBVQ9XeihUYHR2oMMQqlQhtm9CyqJ54Aa2xikVULEZ582aCZz2L+PHHH/rkSmFccgl6zx7U4CDm7t34a9ce0nFOex72rl1o20Y3NOC86EUMfvKTmI8+irNzJ346TbB69QG/561cCdkszt69eFrj3XADwfe+h5nJzM2CUgrj4ovRbW2YfX342Sw0NIw7qQ5D7O5u/Npawvp6ai+4YG7GJMQRqrS1wde+htHWhlEuU968GZVI7J+gpga/uRnV1YXT3o5rWQx/6lM03njjwg1azApj+XKCWAxtGKhcbsL92QiVy0WfA5aF1dIyK2MIhochCKKTudM4P6nj8egfvk+Yy83KGI4Gda9+NQOPPhp9Nu/aRXzrVvxUijCRgDDEyuVQnofX1IRetQrzHe+Q7vHzQKfTaNPELJWYTn1ro1JBOw56TCBSCCHE7Kh7+cvp37YNALOtjfiOHQSxWPRZGQRYhQIA7sqVhCtXEvvQh7Dq6xdyyEKIWbS47iAQQgghhBBCCCGEEEKIo9y+ffsA+M1v4N//fTq/oaudjqMuvKZpHnHH4dmilEEsFicWi3MkwfGDnnXSpjeLiQTQZ8a24fTT4be/nXramXZAX8ocZyYB9CMXhlHn9CAIcJz4IY+bpkkymTogJGwYJeLxGLGYQzKZJBZzFn2Xbc/zCMMA1/WIx2MThlRH9mOlUml0+tjYziYLKBaL4fsBvu9RLpeIx+NwSA8bTblcBsBxbBxndsZ+xRXw3e/OylMdIAgCos+18IDlXFPjY5qHbvgjhU60DuctgA6QTCYIw5AwjAoZWJaJZdkYhoHWIb7v43k+lmUSi8VIJOKL5rP5aFIulymXy6OFC8IwAFQ1gO5TqZSJx0eOQ44+lmWRSqWIio9YuK6H57mjx1uWFRUkMU2TVCo1b/tlCaALIYQQQghxqLD63RytYaSwV2MjrufhdHdjeB6G52G6LloplNYYWuObJm5DA8EZZ1D/gQ9M+Py1L3sZAw8+CL6PvWsXsccei4LZmQyEIUY+jzU0ROg4+Bs2EDz3uWSe9jSGb7oJc98+gniccO3aQ7+7KgUNDbhKEduzh0pTE7k776TuFa+YoyUFmRe9iOFvfxt3xQpiHR24vk/Y1IQae+6mVMKqhvfDlStRl1yCkpOvYpEq3nYbqlDAHh6mcnD4fIRS6JYWfNfF7OyEBx/E6+nBbm6e/wGLWZM+6ywG6+vxa2sx+/sJ6uuZ8AKX1pgDA/j19YQrV5I46aRZGYORTBIaBiiFms5FlJGLEaaJMd66eoxSlkXDTTeR/djH8O+7Dz+fxxgYwCgWQSn8+np0UxO6pgbrne8k89znLvSQjwnG6aej//QnzN5e/GIRJinAqV0Xq1Cg0twMp502j6MUQohjhFI0Xn89Aw0NcMcdBIUCqr8f5XlgmrgtLVBfT7hiBfH3v5/kqacu9IiFELNIAuhCCCGEEEIIIYQQQggxj+qqqeXGxiiQO5GDO35CFEBXCmzbmVGH8SN1441Rl+a3vW2yqZZOcHy2SAB95l73OtizB/bunXy6YymAPk952lFjl+1EgVmlou7gjuMQhiFahyQSCdLpNPH40rgpLQpq+qMhzcmYpolhGNXQvbtoAuiO4+B5HlonKJdLFAqFatjUYqQ4ied5KKVIJBLVjtCTv9bpWrMGXHdWnuogenQdHLv+1db6GMahG/7INNHvjLNjmCOmaZFKRTezWZaF57mjQf/oZyaJRBzTtEgkErMW/Bf7lUolKpUogO77UfGBkeOeIPApFj0sy0TraJ2Kx4/WELpNTU0G13UxzZFjwohpmsRiDrbtzGsBBAmgCyGEEEIIcaiRzm7aslClErp64lCvWEElFsPq6cEsl1EjXWq1xnJd/ESC8llnseyTn0RZE5/rVZZF/c03k33f+6ITtQMDWAMDWENDaKUIYzHc1auhro7w/PNpeO97Gfrv/0blclj5POUNGyb/3lBXR9DdjertJfjhD2EOA+hmTQ2xD32IyrXX4iqF3d2N7ukhyGTAMFDlMlaxiJ9M4m3aRHDmmTS89rVzNh4hjojWcO+9GL29+KnUpOFIgHDZMmJPPEGQz5O/807q/+mf5mmgYi4o24YLLyTs7sZ54gnCzk50S8uhIXStMfbswfB9/GXLMC6+eOKg+gzFTzyRvG3jJ5MY2WxUmGQSRn8/YSwGsRjOySfPyhiOFsq2abjuOsp///cUb7+d8IEHUMPDYFnolSsxX/ACai65BGOK7VzMnvQVV1D4znfwUymsvXvxN26E8YoNa425dy+B40AmQ/LFL57/wQohxLFAKRre+lb8v/97cj/4AcHYz8rWVpxLL6XunHNm7ThHCLF4SABdCCGEEEIIIYQQQgghFsBk+Ubf9ykWC/h+FC70fW80sGdZJr7v47ouiURizoOSlgVPehJUG7eLMQ4OoE9WUEBE9+I1NcHNN8O//Rv88Y8z+/0gmJtxLbRJ7i2eEyM3GyulCMNwWh1zDSMKfk7URXwx0jokDENMc3pjNk2z2vE6nHrieaNIJlMAGIbC83w8z8N1verPDGKxGJZlYdtRd/pDO6Qfntra/c1wZpNSxug6OPb9qakJxi3GMPJ+KKXmvcO4ZdlkMhkqlUq107lmfzEYhW3b81oM5ljieR6VSplSqYTWkEwmD9r/RMUxyuUSpVIZUFiWhTXfO9R5opRBLBZ1eh8pShRtEwuzT5YAuhBCCCGEEIeyGhoINm9GdXdj9ffjrVix/6b7+nr8+nr8fB41PIwKAlShQFgu4z75yaSvvXbS8PkII5mk8ZZbyP/v/1L5wQ+ik2sjJ41Nk+DMM4lfcQWppz4VgHDbNsxcjtCyUKnU5E+uFEFtLWYuh967l7BcxpjDQl/Jpz0NPv5xyh/5CLqxETUwgMrnwffRiQSVVasglSK88EIa3vUu1HxXcBRimvzBQVQuh1ks4k/nC3MySWjbUCgQ7tkz9wMUc67mla9k6Je/xHVdnLY2wnwev6kJMploH53LYfX1YXge7vr1BMcdR/1LXjJr87caGwmf8Qzo68Pp6KBSKqEm6mzuutgDA3jLlxM86UnEN2+etXEcTeIbNxJ/xzvgHe9Y6KEc8+zmZsLzz4dSCXv7dswdO/BXrECl0/uPs4pFzM5OzFIJd+NGgjPOkHVbCCHGERaL5H74Q/wHH4RCAWwbtWkT6SuvxGltndFzWY2N1F99NVx99RyNVgix2BydV+GFEEIIIYQQQgghhBBikZuoW3YQBBQKBVzXpVKpVLtbxjAME61DfN+nVCpjWdFNd0oxp51XR+5NnLvcqR4NVGodAgrDMHAcZ1EHyeLxQ4sILJKmyYvWyP2wmQw8//mTB9CPpQ7o872aR12EDWzbwnW9KbuDe56HaRoYhjHltGL2KaVIpVLVQK6LfVClC9u2cJzYIT8/UrW1c9MB3TTNald6q7puWdX5+ZjmoRt+NI2JYagFCXobhkEikSAejxOGAWEYBX9HXsdCam6GbHZuCgUstEqlguf5hGFIMpkad1mPvDfFYhHf96qFAhbvccP4Dj4Gil6XbTvYtsV4BSUWuhCIYRxagEcIIYQQQggRsS69FP3446ieHlRnJ3rlygMnSKfR6TRhqURsaAivpYVgwwaSp5wy/ZkoRfrcc0mfey7B0BBeTw8YBvby5Zjp9AGTateFMESb5rQ60GnbRlULwYWl0pwG0AGSp51G4rvfJX///bh33YXaswfleehMBnXmmaSuvBLn4GUoxCKjx5yY0dP8zq4NI/rGf7Se8D7GmLW1pD/xCfLXXIPrOBi9vTj79qGqFXW1aUZFSJYtIzjuOGo/8YlZ37/Gr7oK99e/xh8cxNm5E2/16uhCzJh9vy4UsNvbCWMxdFMTjnSIFktE/TvfyUBnJyiFuWcP8V27CGw7Om7xfUzXxU8mcTdtIjjpJOquv36hhyyEEIuL1mS/+EW4/XZULoc1MBAdhxoG+je/oXjbbeSe9jRqr70WSyoQCyEmsNSuwgshhBBCCCGEEEIIIcRRYaIAeqlUqgYNK8TjsYMCnwamaWHbUdfPSqWMUlGX1rkKJI3ctzgXT+95LqVSmTAM8H2fIAhRKgoouq6LaZokk4lF2V12vPDV0dQBPZ2GfH52n3O8UPlMHI0hSwDLOsIFcxgcJ4bv+7iuh+d5E4aXgyDA8zzi8SjgvNDBx5kwDAPTNKhUPEa6Vk8mCPw53ZceGVUNpEZdn0eCqkoZc77vn21KKRzHIQh8isUSQeBjmha1tT6GceC2EK1/PolEfMHXvyh0bo3bpX2h1NRASws88shCj2R2jRwTRPsmZ9Kgv1JRYQzP87AsjzAMF+k2fKixx0AjYXsA0zSwLBfDiI6BFlvhj/p6jWEsbPEFIYQQQgghFquaiy4i+53v4ObzOO3t+K5L2NwMySQAOgwx+vpwenoIkkn08uU4//AP0wqHj8esrcWsrZ14gnQabVkYnhedmJtiPqpcJqyG1c1M5rDGNFPKssicfz6cf/68zE+I2WbV14NpEto2RrFIOMX02vOisKTjoOrr52WMR0K7Lvn778dvb4cgwGhsJPWc50g46SBOayu1t97K8De/if/TnxL29++/oOE4hMuWoS6+mPqXvQyj+pkwm5Knnop79dWEt94Ke/YQ272bwHEIql2izUIBs1zGy2QI165FX3FFtO8VYglQjkPDJz9J9pOfRP/sZwT5PGpwMApPmiZ+bS2kUoTnnkv9O985J9uYEEIsWVozcN11GPfdh7FnD9bwMNqyCB0HFYaY3d0EiQTk8wzv3k3mc5/Dbm5e6FELIRahxXfXnhBCCCGEEEIIIYQQQhwDxgugB4FfDYVWqh3Axw8dGYZBPB6nWCxh2yGe5xKLzU1HmpEO6LPd6LVSqVAqlXDdCp7nAQrTNAhDcF0Pw4hCimEYkkqlFl1X0/EC6BMVFViKTjwRHnxw7p5/qvXp4LB6qQTl8tyNZyEtRFbScZxqkYs45XIUgLRtZzS4qfVIV96o47Zt28Tjsfkf6BGwbQfLcqlUKvi+P2mIMwh8wlBj2zbOIt+Qo/do7leauQxax2IxXNfFcRxKpTKO45BKuezfzesDOr5blkUstrTWv/lg23DyyUdfAD0IArQOCcNwWp/9tm1RLEaFJoIgWBIBdNetUCxGx0Cu61ULHETjrlSiIkS27aB1SDKZxLYXdr/00ENX4vslALZvV1xxRZzbbrttQcckhBBCCCHEYqQch/THP07+LW/BNU3Mzk5iTzxB6Dhow8BwXbRh4DU2olta4OqryTz3uXM2HufpTyf46U9h3z7IZqGhYcJpdRhiDw7ir1hBcPLJqEV2LlaIxUrZNsEZZ0BvL87evVSCADXJiTWjv5/QtqGmhvgiDgCHxSJDX/kK+qc/xchmUaUSaI2Oxch/7nMEZ55J6h//kfjGjQs91EXDrKmh/o1vRL/udRR++1uC3l4ArJYWkk972qTrxWyo+4d/YCiRIPjSlwhzOVRfH0b1okqQSuGvWQOpFPoVr6D+6qvndCxCzDZl2zS8610Er389uTvuwH/kESgWIRbDOOkkMpdfLoUxhBBiHNmvfAXjvvuwnngCgMq6dahMZvRmDa9cxuzuxtm5EzcMyb373TR89auzf3OQEGLJk7NEQgghhBBCCCGEEEIIsQDGy9K5rkcQBKNByMkYholpGqMh0bkKoI8Uip9Onisen15I2Pc9SqUS5XKJIAiJxQ7u9K5xXY9yuUIYRknkTCazqEJl4zUnWeS51Rk57rjZD6CPDZVPN4De3w+f/zw89BCEU7WPWaIWorO7UopUtbqEUqoahiyOdrUNw6jDbSwWw3Fskskkprm0LinZtoVhmNi2Q7lcIZEwMMe5yS8MQ8rlymiH7YUOeh4LDMMYXf8MQ+G6LkNDO8nl4pRKdQRBiFJLe/2bD44DZ58N3/zmxNOsXw+7ds3fmGaD1nr0M2Cy7ucjRqbRWgN68okXAd/3KRZLlMtlgsAnHo8fFLTX+L5PuVypvibIZEwMY25vUp5MT89PCMMogN7XB11diQUbixBCCCGEEIud09pK5gtfYPiWW+DBB6Munfk8aI3vOFBbS7hyJdZrXkPN858/p2NJn3ce2f/4D/zaWuzubtxMBjXeOWetMfbtQwG6oQHn8svndFxCHG3iL3oR3q9/je7uxmxvJ1y3bvwT4IUCdm8v3vLlBBs2kDzllHkf63T4/f0Mv+1tGFu3YnZ3Yw0NocJw9DX5qRQMDFB66CHCG24g+bSnLfCIFxdlWaSf+cwFmXftS15CcOGF5O64g+Ceewh7eqLCAQ0NqPPOI3PFFRLSFUuaWVtL3T/8w0IPQwghloSwXIbbbsPo6EAB7ubNhxTEUfE44Zo1+B0d2O3teOk0+V/+kvQ55yzMoIUQi5bcrSGEEEIIIYQQQgghhBDzaGhoCIg6Oo8ET227FoiCkEEQYJrGtEJXpmkRBD5BMHfJ3JEO6NPJfq9ZA9u2TT1duVzB96OwfSKRHCdYHnU/N02DUqmMZVm4boV4fPEEno72DuitrbP/nDMJoP/851GRhi98YfbHsdj09y9MBXHTNEmn05RKJUzTJAxDwjAAQKkorG1ZJvF4YlpdiBcfRTKZQOsQrTWlUgnLsqrBdIMw1Pi+h+f5WJZJLBYjkUhMa98rjpxlWaTTKUolA8uy2by5kb17o881xzEwTQvTNEkklur6Nz9WroRTT4U//enQxzZsgHXrll4AXSk1JlQeotTkweuwWp1k7O8tZpVKdAzk+z7J5PjHQJZlk0gYo/utSqVCIpFckPEKIYQQQgghZs5ubqbxxhvxurvJ33knwe7d4Puo2lpi555L6swz56WjnLIszKuuIuzvx9i+HWf7dryWFqitHZ2/LpUwe3qwhodx160j3LCB2mc/e87HJsTRJPWMZ9B/+ulQqWDv2IHxxBP4TU1RFVul0K6L0duLPTCAX1uLXrGC2Gtfuyg7S2rPY+id78T8299wdu0iiMVwV6+O9huALhYx+/qI7d6NWy5Tef/7MT73OemEvoiYNTXUvfKV8MpXLvRQhBBCCLGAcj/+MQwORt/11qw5JHw+SimCVauwhoZgYIDKD34gAXQhxCHkjg0hhBBCCCGEEEIIIYSYRy0tLZRKpdH/G0aCiy8uVv93JJ07NTD7Nyyl09Hf07kXqqZm6mnCMMD3fVzXw7adSbuajwQQPc/FdU3i8Thz8RoPx9EeQF+1avafU89g9f7a12Z//ouV606vwMNcGOlEHYYhrutWg5wapQwcx17yXactK+qeHf3bwvNcSqXy6ONRwDmOaVokEgmco2kjPgIT3X8wlfPPh3vvncl8LNLpDEHgc/LJrQSBIpVKYRgK23YkeD6Fzs7os/m1r4UPfQh6evY/VlsLb30r3H33wo3vcFmWhWGo6ue/Ryw2+QrpeR6WZVYLZyzudSYMQzzPw/M8bNue4hgoKgISHQNZxON63IC9bUfv/113wW9+M5ejF0IIIYQQQsyUvXw59a997YKOofZlL2OgrQ3/Rz/C2LsXZ88e2LuX0HEgDDHLZYJkEnf9esING0jfeCNKvo8LMTNKUffRjzL49reDYWB0d+Ps3Yvq6EAbBioICOJx3JYWaG5G/cu/kD777IUe9biGf/hDzK1bsXfvxq+pIVy9+oCLQyqVIkylqAwMEOvooBKPU/jKV4h/7GMLOGohhBBCCHEw//e/xxocRBvGlDfyKMPAr63FHBxE//nP0Y0di7BYkhBi4ciZIiGEEEIIIYQQQgghhFgklDIwDAPPi0KgU4WtwzDAMAwMQ0057eEa6YA+netLiWk0KPc8v9ppOcS2pz5FbdtR588w1NXu8IvjtHZynEakhxvaXGxME1papp6uthaGhqb/vGPXIbleubgYhlEt8HD0sW2HTMakUqngula1I3q0DiqlsG2bWCy2aPYti8Fk9yCMFCvwfb/anToKCjuOw+WXWzMKoI8wTYvNm1fT1bX/M0dMbSRwvno1/Pu/R+H/jo7o/2efDQ0NsBQzA0oZ2LaN4/iUSmUsK8Cc4AM2CHx8P6gWkLAXfQf0ke0mCEJisan3ubZtUyqV0To6BhqvKMMzngEnnQQnnACvfvXMPpeFEEIIIYQQxwClaHjPexhsbcX/zncIs1lUNgueB4aBn8lAKkXwtKdR8+53Yy9bttAjXlhak/u//8O9807Url1R5chUCp72NFJXXkls/fqFHqFYpMx0mobPfpbBL38Z/yc/IejvRxUKUXjHtiGZJDj5ZBKvfjXJpz1toYc7If+uu7D6+kCpQ8LnB2howMvlMHt64De/wevtlf2HEEIIIcRiUiiA70fFx6Zx/Uw7DkaxiHJdwkoF4yi9d0AIcXiW4G0HQgghhBBCCCGEEEIIcXRyHBvXtahUoqC2bdsTTqt1OBq6su2569o7ErSeqkNz9R6qKWmt0dVW2EpN3fbZMIzRztkz6aA918Lw0J8dLaEvx5leaPGss+DHP57+82Yy+/893xlBpRbX+iPml2GYJBJJ4nFNEPhorUeD09PZDx1rxm6r+2lKpRKu6xIE4WiQFhSWZVKpuBSL3YTh5km7Ok+mtvZIRh2F40eKtxzuGJaSE0/c/+9kEi677NBpJjmMWNRisdhol/BSqUQsFqsWrRn58NB4nkel4uI4DpZlEYvFFnLI0xIdA0X/jooHTW5k/zT22GmsjRvhn/4p+rdpwgc/CO97H5TLszZkIYQQQgghxNFAKepe+Ur0VVeR++lP8X/9a3QuB46DsWEDqcsvx1m1aqFHueDKjz9O8brrMPbtwxwYwMjlUGGItm3C7dsp3Xkn+Wc9i/oPfEDCGGJcynGof+Mb0a99Lbn77iPYuRNcF1VbS/ycc4hv3LjQQ5yU29GBuX071sAAfn39lCfxw2XLiG3fTlAsUrzvPmpf+tJ5GqkQQgghhJhSLAaGgQqC6U3v+9FNQYaBsQSuuQkh5pcE0IUQQgghhBBCCCGEEGKRsCwb0zSwbQfXrWCa5gQhOk25XMY0DUzTJBabuwD6yL2HU2X5mppg3bqpn08pNdqhdCQEOpmxgavF1Nh006ZDf7Z27fyPY6ZOPx3+/Ofo+uFELrww+vupT4WHHhp/mlWrZv56xxYomE6H9dl01lmwZQt87WvzO9/FxjRhuteY58OKFdDVNbPfWb48Wvd+97uZz08phWUt0UTuPDo0gK4pFIq4rku5XCYIAkzTqBYICSmVPJ797Da6unzy+RbS6RSGMX7H6vGcckr09wknRAUwXHf6Y9Va47ourlshCPZXBjEMg1jMwXGcRVdkwLIm3wdP13SaZS3V++FN0yJZ/dAwDAPXdalUKtVO6JogCDEMRTwew7ZtksnkjNY5mPm6NhuiY6Do3zM5BlLq0GOgV7wCXvjCA9/jzZvhK1+BD3wAdu6cnTGH4SL60BBCCCGEEEIcEeU41Fx6KVx66UIPZdEp/e1vlN/+doyODpzOTgD8TIbQcTDKZWI7dhDE41CpkB0YoOHTn0Y5c3dOXixtyrapef7zF3oYM+b39gJgeB46lZr6F5JJtFJQqRD29c3x6IQQQgghxEwYW7aga2owu7rwCwWY7PhOa6zhYfz6eoINGxbXjTlCiEVhcd1xIYQQQgghhBBCCCGEEMcYyzow3B2PJ0a7eRaLRSqVSrWraxRE8jyPYrGI1tG0sZgz49DVdCUS+wNuU11jWrMGzj03CrhOxrKiUL1hKPwxCbyamvEDTp7nYVkmSqk5e50zlUzC8ccf+vMtW+ZmfqlUtGyP1IYNcN118KUvTT7dU54S/T22u+7BTj99egUHxmps3P/vlSuj8PFsOvlkuPVW+Pu/PzDsft558Na3wjOesTTDmK95zeSPr1kzfkGE8Zx5Jvz7vx/5mGZDPA433TTzDs3Pex48/elzM6albO1aqK+fnedavvzA/5dKZVzXpVQqAppkMkkikSQWixOPJ0ilUpxxRpGOjg4cp0ShEE03XSMBdNue2b4uCHxyuWGKxQKlUplisUChkK/+v0SxWGR4OIfve9N/0jn2oQ9F++D3vx/+4R+O7Lme+tSpp5nuvmExuuQSh2uucYjHY6RSKRKJOKZpYpoWiUScZDJFLBYnlUpjz3BHksnAhz985O/BTEXHMyPFG6ZOv/u+h2lGx0Bm9QArnY72nVddNf5nWiYDt9wy+Wf4ySfDm94UhfAnEgQ+hUKe4eHcIY9prdm7d+/o8akQQgghhBBCLGVhuUzp/e9HdXQQ27sXb9kyKk96EuHatejWVoJNmygfdxzaNLG3b8f84x/JfupTCz1sIWZf9UKVVuqAwsAT0hqldXTxaKoLQ0IIIYQQYl5lLr8cUin8VAqzuxsmO77r70f5PmFTE5YULBNCjEMC6EIIIYQQQgghhBBCCLGATBP+67/gpS+N/h918kwQj8eJx2OEYUCxWCSfz1MoFHBdF8uKponFHBKJxOhzOc7+7tWz4bzz9oeTEokDA8QHu/LKKCj9rGdN/pymaWGaJpZl43kuWmte9aourr9+N+9/fxsf/GAbn/jEDk48sUAYhvh+gG07OI7NSScp3v1u+Lu/m/lrOeUUqK2d+e+N56KLxg/NtrREoa6J5n+4XvlKeO5zj6zQ9PHHw/veFz1HU1MUfhzv+dat2/8anvvc8cNtpgmXXRZ1K55JiPySS/b/W6loPNNppDIdL34x3HD1YbuMAAEAAElEQVRDFJz9u7+D//xP+PSn4TvfgX/5l+h1tLREAfyx68FiaFT0zGdOfLH3yivh8svh5psn/v2LLoI3vGHqcL1ScP750Xt8xhmHN9axLAsuuGD8xyYLPo64/vooMP2yl01/npkMXHxxVBijrm76vzeRs88+8ucYW+xgIV1ySVSMYDZcdln094tetL/DeKVSRilFIpHAMA68vHjCCSW2bGmgsbGR009/lCDw8bzptfjOZA4Mnb/uddMbYxAE5PMFKhWXQqGI7/vYtkM8Hse2HcIwoFCIirgUCoUDCp4spJNPjvbBZ5wRbd8jBT9m6g1vmN77feqpUfGR2WYYUdGDuTJyPPP85yc599wa0uk0iUSSVCr6k0wmSafTZDIZLMua1nOeeCLceCP827/BN74BT3pStO6l03P3Og5mGCaWZbFiRchllz2M1hMHuMMwxPN8HMfGth2Uira7b30r+vydfD7wtrdF29fBTDMqbHLhhdHx50c+cug+2/Nc8vn8aCGHg286D8OQ9vZ2tm3bRhBIh3QhhBBCCCHE0pb7yU8wenpw9u3DXbECvXw56qBzHyoWI9iwgdBxMPfuRd17L0Hu0IJdQixlTmsrKEUYj2MOD085vR4ejjqgJxKYq1bNwwiFEEIIIcR0WY2NBOecQ7ByJWaxiNHejvYOKtqtNfT1Edu3D2/5cmhsJPOCFyzMgIUQi5oE0IUQQgghhBBCCCGEEGKBJZPw8pdHAdpVq8BxYtTWpnn60w1uvrmHq68e4rzzSiSTyWoX0ATJZJJkMoWqpohXr466eb7pTfDtb08chJ6uM8+Ef/qn/f9XauKuw6tWwXHHRf9+85ujQNdIuNlx4OBrVLFYDMexAUWpVGLLln6UgsZGn/p6H8OAq67azoUX/p5zzunlBS/I8dnPGvzrv0aB0b//e7jmmul3Tl67Ngq7vu990XKaqZEAVzoddRx95SvHn24kVP2Up+x//ZYF//iP0XvzhjfMfN4QLc9TToF3vWvykHFt7YGB6o0b4R3vgM9/PgrdLVu2/7Ezzoier7l5/zjPOAM+9rHRRifU1UW/N7Ybcn39/qC3UtH7UFMz9WtYvfrQsOTatfCFL0TL5VnPitbZ1asPDcyZZrSOTUQpeM5zDgzU2zasXx8VThjrxBPh61+PAt233BJtK//5n1FH95EmLevXR49/6lMHBjcna+LyrGfBq18dBf1noq4OTj114gD6c54T/X388dFyHxv4jMej8PYLXgCbN8NHPzpxIDSZjN6r00+P/n/VVdMf43jbzKpVcO210fb+j/8Isdj+x04/PSpwMFnRhZNPjsKfAM9/fhTIncpxx8HHPx69lrq6aHmMHVttrea44zx83ycI/Cm79Pzrv0Zh9pnasCHan1x/PXzuc9E69O53z14xhamsWgWXXrp/fo2N8M//HHLWWTlOPTWH53kTdiU+/vio+/Ytt0z+/OvXR/8+9dQoiDpSDMRxYsD+DU0pOPHEAq9+dVd1LI0cf3wvSrm4buWQ537Vqw7cRjZvjt6Hse+/48DKlVMvh2KxiOe5nHLKEzz1qSVSqSS2bWOaFrZtU1cXJ5GI47ourutSLE6vK/sznzn1vA/XC14Q7WtHKAXvfGdU7GVk/7JsWbRdj7fPe+5zo8c+85lDP1cn4jjR59JEn98HG/lMgGgfc/XVcOut+9cJiD4LP/CB6LhlLigVfWZu2hSN//3vhze/2eKCC5JcdlmaD384zU03JacdPB/xnOdE+50tW/Yv76amaH+/aVM032idjraT1tbxnycWm35BGNuG97wn+jweccYZFm98Yz+bNpW54orfHBLgTqcDwtCnVCphWRamaRGLRR/ul102/Xk3N0f7zLH7yfr66L3btCn6v+NE++obbogKlJgm+L5PsVikXK5QLleqXdsPnenu3bvp7+9nx44d0+uKJoQQQgghhBCLlH/XXai+PrRposeeQD2YUgQrVmDlcqhcjvxdd83fIIWYB1ZjI8FTn4rf2Ig1NHRoQGksrbF6e/FratC1taQnqlQqhBBCiFmhXZfhO++k/w1vYOCKKxi4/HL6//EfGfz61wmGhhZ6eGKRqnvHOwhPOAF3wwbMYpHY449j7N6N6uxEdXRgP/YYTmcnbksLYWsrsQ99CGOqqvdCiGOS0nJFWBxFPvGJT5DL5chkMlxzzTULPRwhxhUEAd3d3QAsX74cc7I7V4UQQohFwBtzUcmebsJHCCGEmGfyXUssJclkklKpNPr/RCJRDcbtl81G4ULHgUqlwvDwMEEQYBgG5XIS309TXx8FiSoVKJUO7QasNXzyk3D//ROP5alP3R/IHeu668bvyuq6Uejyr3/d/7ORbtoHd1gtlWDPnqjbsuNAdze89rVjH486015yySOsXNlBMpkkkUigtSafz+N5HnV1dSxfvpx169bRNE5CdefO6DW2tU38Gh0Hvve9/YGtMIS//AX++Ef4wx+m/t2PfSwKSfb1Ra91usGv/n4YHo66bo+9RtfVNf0Ov44DH/rQgZ1JtYa77oIvf/nAaU8/PQoZGgbs3g0NDdH6MR19fVHoe2yIeCytoaMj+ne1CcoB+vvh97+HffuikPndd8P27fsfX7Yseh0ThfnGEwQwNAS+HwVsTRMGB6OQ3LZtB0579dXwwhdO/7kn4rrReju2Q/rIaw/DqNv7gw/CJz4R/XzE2rVRgHEk7B4E8N3vwv/8T/ScEwnDkEsuCfnHf9R89rP2Idvqy18OL33pgT8bGY/nReHC8Tr3Dg1BPh+NZ+/eKLC9fv3+wgIjrr8eHn544vFt2BDtC+rrYWAgWi7lcvSa6uoOXA98P9oely/fv/y+//2oy/B4PvKRAwPqTzwR7UfG7Bo588yoe73rRuvmRNe6d+/26O/vx7J68H2PbNbka19roaMjTm2tRRjGMYwDjwte+tL94dkvfhF+9KNDn/c//iMqrpBKRe9/b2/090RFLMIw2vZqaqJlsG9ftM7G49F74PvRMs9m9//O+vXQ2Rkt1+nIZKL1b8WKaH7d3S6e183AQD+e5xEEIXfe2cwDD9RjWTaxmENdnc3VV8MznnFgSL5SiYLzO3fu/1lTU7Stjn2NN9zQy49+FAXRk8noCU47Lc+znz1IU5NHKnVg2L2rq4vt22P89KenY1kNoz9/5SvhJS+J/l0uR38m6mL/uc/BT34y8XLwfZ98Ps/ZZ/+Jc8+t0NTUxOCgST5vUlfnk06HuK6iUjEolwe4916D3//+FJLJFLa9v0qHaUbbK0Tr8+teFxUlqFSi/cztt0efE7NxJfXZz46KQEykWIy263Q6GlexCL/9bbROrVsXBciPtMhBV1e0r/7DH6LPwbH3EJtmVFDivPMgl4um3bDhwMIb3d3Rslm9OlpeWkdh+HvvPXA+p59+6L5lxQq46abo90fW9x//OPoz1v/3/8Hznje91/O730WfCdNh2/DNbx5alGSsYjFaH0aKoOzcGYW1xza0W748Csf/4Q/w1a9G2zVE23lLS3RMMVL/IR6P9mtPfnK0rIaHo/1wJgN79+5l3759dHZ2Vo/xMhSLdcTjAclkH/39Jj/96elks/UkkwkcJ8YznhFtszP9mlNtXkEuF+1zJjuOKRTgV7/ayV/+Msh3vtOE46QwTZMHHlhJGO7fQcdiMb773e9SLBbZuHEjW7ZsoXbsh6cQQgixwOS6lhBCiOnSQcDQeedhbttGmEyip+rirDX2Y4/hL1uGf/nlNH7sY4c9b7muJRaj/AMP4L/nPVjbtoFSeOvXow4+ntIatXcvdjaLu2kT4VVX0fCOdyzMgIWYRDA8TO6OOwh//vPoQpZSsGwZ5vOeR+aSSzCSyYUe4pIj37WEWBj5n/8c95ZbMLJZVH8/qnpviY7F0I2N6HQa9fKXU/ea10z/ZgZxzAiGhhh83/sw//xnGBrCGBhA+T4YBmEmg25sJGxqIn799SRPO22hhzsr5LuWWGqWQhZWAujiqLIUNjoh5IBGCCHEUiMnj4UQQiwF8l1LLCXTCaDPFq1hx44oCLZsWRSU3bs3CkaeeCKccEIUZMpm4ZFHouDbk54UhSUnEgTw6KPRn5UroxDpdDpgQxQu/c534M47o3DpBRd0EgT7KJfLDA4O4rouSikSiQR1dXU4jsPatWtpaGiY8Dm1jsb+k5/Ar3996ONThdn6+6Mw+o4d0XLo7Y1C1I2NcNFFUZB7tpXLcM89cMcd0NMTLfMrr4wKAvz1r1F4O52OAocTLduhIXjooShUduKJ0XMcHDBeKL4frR+PPRaFzp/85EO7mh8uraOiClu3RtfPzzsvKhAwnzo64Gc/i9bnjRujrsTjfVXK56P3CKJ1fmwoH6CuLuTf/i2gvh4Mw+YPf4jCoZlM1Hn6yU+e29fR0wNve9uB4coRI6HJIylwPjQUdUkfKV4w4slPjgKcB6+v5TL88pfRcm1thdNOm/oeiXw+z/bt2ykWi2Sz2dFiHUopUqk09fX1pNNpeno2sHdvHWEYBbFPPvnA5/7DH6L9SKkUPX766XNzf0a5HAWbe3uj5bBsWbR8fvvb6O9kMlrmy5fvX/533x0F2Zcvj7rFjxQdyOVy7Nixg3w+z8DAALlcbrQLcbHYQC63nLq6FOedl2H9+vFvnh7Zn//tb1GoeLxtddu2J7jvvl7++McE9fWNnHRSgRNOKE64fPr7+8nlcjQ3byCZPJWhIcWpp85sX9rVFQVtx4b1R1x4IQwP97F+/RMEwU42b9486XGf1prt27dTLq+mu3sj8fgKTj01+gwqFKJ9SbEYvefjBeK3bo1C1u3thz52xhnRdv7oo4c+ds450brkedFn7YoV037586Jchj//OSqAoDU861nR595MaR19hv7mN9E2fdZZ0XoUBPCrX0WfscuWwdlnjx+c7umJPn99P/oMnCwgPp7BwWg+WkeBecuKQuMHF3V4xSvgqqtm/vp6eqJji927o8+as8/ev54MDkbHV5YV7a8ymWjdfeKJ6LWedtrkr6e7u5u9e/dS2buXXHs7bhAQNjURa2ykrq4Ow4jjumspFus4/viJi1/MplKpxKOPPsqOHTsolZbT0bGawUGLT3/6ODxv/0KNxWL88pe/ZPv27TQ3N7N27Vo2jbRVF0IIIRYBua4lhBBiuoJ8ntxFF2Ft3UpQX49ubp7yd6xt2whqavAvvpjGT37y8Oct17XEYqQ1Ax/+MMZPfoK1cyeG5+HX1hLW1qKVQhWL2AMDqCDAXbuW8Pjjqf3iFzGne5FIiHky+M1vor/+dVQuF4U1qycMdSKBbmpCZzKYb3oTNZddtsAjXVrku5YQ8y93990EN92E2rcPu68PbZoEqRQohVEsYrouXk0N4Zo16CuvpP5tb1voIYvFSGuKf/oTpe9/H+OPf0QVCuhYjHDdOuxLLyVz4YUox5n6eZYI+a4llpqlkIWVALo4qiyFjU4IOaARQgix1MjJYyGEEEuBfNcSS8l8BtCXAtd16evrY2BgAM/zUEoRi8VoamqioaFhRtvzn/8chbq3b486xj7veVFYTIiFVi5H6+bPfx4FLU8+Ga64wqOlJXp8ob5r7dkD3/52FLK1bTj/fLjssvED9YdjYCAqOvGHP0TB6dNOi4KglnXkz10sFtm6dSt9fX10d3eTTCapra3FcRyCIGBoaIh8Pk9NTQ0tLS1s2LBh0mIWS8nIa+/p6aG3t5dMJjNatCMIAoaHhxkcHCQej7N69WpaW1tpGVnZZmjHjh3s3LmTgYGBaQVMu7u7qVQqrF27ltNPP/2w5glRIP9//icKNzc2Rvvyyy+PCgM89thj7Ny5E8/zWD2NVG5nZydhGLJ+/XpOPvnkGY/F96NO3ffeGxVsWLMmWo9Hil90dMB3vxt9Bi1bFoWwX/QiaTKxULZtgx/8ICqssGoVPOc5cMEFCz2qA2nPY/hHP8K94w7044/jex6h1mCaFE85heSLX0zL+efP+3eaffv2sWPHDtra2ti8eTNWdWd99tlnU6lURqeLxWI88MADdHd3UyqVWLduHaeeeqp8BxNCCLFoyHUtIYQQ06Y1g895DubWrYSJBLq1dcrp7UcfxV++POqA/tGPHvas5bqWWKy075O94QaM++6DgQGs/n7Mchm0JrRt/IYGdGMjwaZN1Hz849iLrfKiOOZlP/951Le+hbFnD/bQEIHjEKZSAJj5PMrz8OvrCVevRr3xjdS+9KULPOKlQ75rCTG/Krt2Ubr6aoxdu7CGhnBXroT6+v0XoLSGfB67o4PQsgg2bcJ473upecELFnbgi5T2fZRpygU8iNado3g5yHctsdQshSzsLNziI4QQQgghhBBCCCGEEEIcHsdxWLlyJStXrjzi53ryk+e+a7QQhyMej7rvju3AO+Y+nQWzejW8851z9/wNDfCGN8zNc7e1tTEwMEBPTw+rVq2i5qAuO5lMhnK5THt7O93d3ZimSW1t7VFxgbm9vZ1sNktfXx+tra1kDmpbnkgkaGhooL29nX379mEYBo2NjTiHUbk+nU6TyWRGg6aJSVo6a60ZHh6mqamJ9Eir9sPU2gpvfevE8wEwDGNaz2UYBkEQEIbhYY3FsuDSS6M/42lthbe//bCeWsyBLVvgXe9a6FFMzO/vZ+id78TcuhWnrw8rm0VVPxCCRIJ0qQSPP87Qww9T/653RTdDzdfYfB/P87BtezR8PplEIsHw8PDo7x4N+1chhBBCCCHEMUYpghNOQHV3Y/f04K5aNWkQQw8Po8IQXVeHddpp8zhQIeaPsiwarruO/HnnUbn9dnj4Yfwxve6CNWuwLruM+ssuw4jHF3CkQhwq/3//h/qv/8LctQujUqGybh1kMqP79lBrGB7G7uiA3bsJP/95SieeSOIwCpcKIcRcK3znOxiDg9jZLOX161EHXQ9EKchk8DZtwtm2jbCnB+973wMJoI8qb9tG8fvfRz3wACqXA6UIly9HPe95ZC6/HKuxcaGHuDCO4vC5EGJuSABdCCGEEEIIIYQQQgghxKIRFotUdu1CVyoYtbXEN2yQC2BCiAMUCgWKxSJ9fX00NTUdEj4fEY/HWblyJR0dHTQ1NdHf309zc/M8j3Z2FYtFCoUCvb29NDU1HRI+H2HbNqtWrWLXrl2Uy2V6e3tZtWrVjOfX2NjIvn37SKfT9PT0sGbNGtQE++SBgQHCMKS2tpZly5bNeF7TZVkWlmVRKBSmNX2pVCIej0tHljniZ7P4fX1gGDgtLRjJ5EIPadEKcjmG3vpWzMcew9m1C20Y+PX16FQKtMYYGsLp6CDs7cW76y6yQMO1187b+JRSKKWmXawhDMPR/cFE+wUhhBBCCCGEWOzsyy4j/MtfoLsb1dWFbmkZdzodhthdXfg1NehMhpqLL57nkS5u5a1bKd19N2G106CxfDmJiy4iftxxCzwycViUIn3OOaTPOQevpwd3zx7wfcymJrlmIxa1yne/i9Xfj1kqUdm8GRWLHTiBUlBbi2fbONu342azFL/9bQmgC3GU0a6LPziIsm2surol+bkVFouoX/wCo7cXL50+NHw+lm3jNTRgDQygt2+n9Ne/kjjppPkb7CKkPY/sxz6Gcd99GLkcRn8/RrUYcLhnD2FbG7lvfxvjn/+Z2he/eIFHK4QQi58E0IUQQgghhBBCCCGEEEIsuPLjj1P43vcw/u//UJXK6M+LK1ZgXHQRmSuvxJzswqoQ4pjR19dHPp/H933q6uomnTadTuM4DtlslpqamiUfQO/v75/2a4/H46RSKQYHB+nv7z+sALplWTQ2NlIul2lra6Ojo4MVK1YcEOYOw5CBgYHRkHsikZhybEeivr5+tAN8oVAglUpNOG25XKZUKrFixYrRMfn9/RTuuYegekO02dJC6oILsOrr52zMRx2tyf/iF1Ruvx3zkUdGf1yybfTZZxO/4gqSp566cONbpIa//GXMbdtwdu3Cr6khXL36gBvfwtpaXM/D2rkTe+dO/LvvpnDeeaSe/vR5GV8sFiORSBAEAaVSiUQiMen0+XyeeDyOaZpS4EEIIYQQQgixZGUuuIDs//t/uMPDxNrbcYMg6oo49ntOoYC9dy8qDAlXrYJLL8WY4jvTsaL0179S/Pd/x3z8cVQuh1UuA6Djccq3307h+ONJvvWtJE48cYFHKg6X3dyMvcTPqYpjQ2XXLsw//xmzvx+vvv7Q8PlYySR+bS1mfz/85jf4AwNYDQ3zNtawXCb/i18Q7NsHWmM2N5N6znPkOqgQR0JrCr/9LeXbb8f8/e8hCAAIa2pQ559P8ooriK1Zs8CDnL7y1q2oUgkrn8ddvXrK6XVTE0ZPDxSLVP70p2M6gK6DgIFrr8V84AGsXbswy2W8dJogk4n2ubkc9tateHV1hJ/+NEOeR+3LXrbQwxZCiEVNAuhCCCGEEEIIIYQQQgghFtTgN74BX/kK5tAQZl8fZqGA0prQsvD37kW3tTF0xx0kb7xROqaIw1Zpb6d4223oX/0KNTQEhkHY0oL1/OeTvuQSzHR6oYcopqlSqYyGIy1r6ktd6XSacrlMZUxxi6XKdV0qlQrxeHxarz2ZTJLP5/E8D631YXUpbm1tpVQqAbB37162b99OKpXCcRyCICCfz2MYBqtWraK+vp6NGzfOaTfkhoYGOjo6yGQydHV1sXbt2nGXRRiGdHV1kUqlSCQS1FYq9L/vfZi/+Q0UChgjN0QnEuS+9CXCs88m8/rX46xcOWdjPxoEuRyD116L+ac/YQ0MYA4MYLguWinCeJygrw/3/vspX3wx9e94B8o0F3rIi0JYLsM992B0dxPGYoeEz0fZNv6GDTiPPw6Dg5Rvu23eAugNDQ3s3buXdDpNf38/ra2tE05bqVTI5/OsWbOGxsZG6YAuhBBCCCGEWLKUZZG68UaKb3kLFaWw9+3DGBjATybRloVRqWBWKvipFP769QRnnknDP//zQg97USj8+te4112H2dmJ3dOD4boEjgOA6bqEjgMDA5R37SL88IdJnXnmAo9YCHE0K//pT1CpREHDNWuY6mxV2NBAbNcuAtel/Ne/kj7nnDkfo5/NMvzVr6LuvRc1PIyqnncP43GGP/tZ9LOfTeaf/gl7+fI5H4sQs8Hr7SX/gx8Q/v73qHwe7TioTZtIXH75vBafCXI5Bt//fsyHH8bMZqPrFtVu10EySdjVRekHP6D0j/9I3T/8w7yN60iEhQKEISoMYToFYC0ruuYQBOjqvuVYNfRf/4Xx619jb9+OtizKxx+Pqh6jAvgtLfi5HE57O35bG+EXvkD59NPlPhQhhJiEBNCFEEIIIYQQQgghhBBiAZWmuADY0NBAuRqSOxI/+clPOGeSmydOOOEE2trajng+X//613nJS14y4ePnnnsuv/vd7/b/IAjA90Hr6M/BAab+fti5M/r5nXeC44BS3HTTTbz5zW+ecD5XXXUVd91115G+HN7+9rdzww03TPj4W97yFr785S8f8Xxe8YpXcOutt074+Mc+9rFJxzFd559/PnfeeeeEj3/lK1+ZdLlO18knn8yDDz444eM/+tGPJl1PpmvZsmWTrrcPPfRQtN77frSuaX3oRP/5n9Hftg2GMeFzjYRsx7Nnzx6Om6WL0jt27KClpWXcxzzPo7a2dlbm88ADD3DaaadN+HhraysDAwNHPJ/bbruN5z//+RM+/pSnPIXHHntsRs+ptR79MxJ6vPbaa7nkkkvGnV4pxUc+8hEeffTRIw5JXnfddbz73e+e8PHXvOY1fOc73zmieQD88z//M7fccsuEj3/zm9/kxz/+8bSea2Q5jbf+Xn755XzrW9+a8Hc/9alP8d73vnf0efSYbWjk3yPLVCk14fI966yzuPfeeyecz3//939z9dVXT/1ixsx77HhG5tva2sq3v/1thoeH6evrG/1ZfTZL5YMf5IHHH+elO3ceui9QCn7+c/joR6N9wRTrSSqVore3d8LH//a3v/G0pz1t2q9nMj09PaQnKJDR19fHmlnq2PGXv/yFjRs3Tvh4TU0Nvu+D60bLLwyjBw5eVvv2RX8/8AB84APRTU9j3HvvvZx11lkTzmfz5s3s3bv3sF7DWN/61re4/PLLJ3z8mc98Jg8//PARz+eWW27hnycJHlx55ZXRthqG4HnR30pBT8/kT6w1DA7Co4/CZz7Du971Lq6//voJJ3/Tm97E1772tcN7EWNcccUVvOENb2D37t309PTQPEGHsz179pBKpUgmkyxbtuyI5yuEEEIIIYQQCym2fj38x3+Q/8hHYOtWGBrCyOVAa4KaGvyGBkilCC+6iIZ/+RfUNIoCLgR33z4Kt98+GgLDcdDr1xO//HJST3nKlOc7ZqLS1ob7oQ9htLXh9PTgNjWhm5qi8/eA77qovj5iHR24rot7/fVYX/oSsbVrZ20MQghxgFJptOMxY4KGE9GOE52DC4Io6DnH3I4O8m9/O2rXLsyeHqzh4ShYCqAUXiZD2N9P7ne/I/nxjxPfsmXOxyTE4dK+T/YTn8D4yU9QhQJWNovyfVCK8E9/ovKzn1E8/ngy112Hs2rVnI4lLJcZvOYazD/+EXv3bpTv49fW4tXXo4IAY3iY2BNP4NXUEH7pSwyGIXWvfvWcjmk2GOk0GAbaMKLrMqnUpNNrz4v2aZaFSibnaZSLkNaEd9yB1d2NBryNG1HjXd/PZHDXrye2fTuV4WGK3/8+8fe9b96HK4QQS8XiPAsihBBCCCGEEEIIIYQQAogC6rMRQA9HbmKYQLlcnjIMPx3ByM0dExjpXDyh8QLCIz/XGqrLwvf9Sefjuu6svB6vWh19ssdnYz6u6076uO/7szKfqTpAB0EwK/OZap2drflM9RzhdOYzss4dQXdsrfWsvJ6R55rMfM5nNua1GPY9xWIR3/dnZV+60Pse27aJxWJUKpVZ6eh+tO17CoUCW7duRSlFbW0ty5Ytox6o/dSnMHbtwuzqojTeuj/ysyDYf5PiJCYqRrH/6WZvnzCV+dwnjLv+T/R7Ybg/dH3Ajxd+nwDTOB6ZpsPaJ4wc00xHGEKpNOXxyGzte2zbpra2ltbWVvbu3cvw8DAveclLRp/bMAzCMMRxHFatWsXKlSuJx+NHPF8hhBBCCCGEWGixNWuI3Xorpb/8heIPfkC4cydUKuh0GuOMM8hcfjlWY+NCD3Nc2nUZuOkmzPvuQxUK2AMDUUFOwyD861/xfvlL+jdsIH3ddVHYfhYUvvlNjJ4enJ4eKmvWQF3dgRM4DnrlSirJJLH2dirpNIX/+i9i1147K/MXQoiDqXQaXS0QoioVmCKAqSqVqDCHaUZBzzkU5HLk3/EOjG3bcNraCBIJ3DVroKYmmiCXw+jrI7ZjB5VKheI734l5663YExSHFGIhad9n4B3vwHzwQcz2dqxCgSAWI4zFUL5PbGCA0LZheJj8G99I6jOfITZLhXTHM/SVr2D++c/YO3cSJJMEa9agTDMaKxA0NxMUi1E4ffdugq99jfKZZy76btfx44+nkkziZzKY2SxBff2k0xv9/YSOA4kE8ac+dZ5Gufjkf/UrjO5urGwWd8WK8cPnI5JJvEwGs68P/b//S/i2t2Ecy+F9IYSYhATQhRBCCCGEEEIIIYQQYgFNt4PtkQpyuXmZjxCLSf6eexZ6CDOmpwgzipmpVCoUi0Vs217oocyKhoYGent7pwxAH6tM06S1tZVkMkk8Hqe5uRn7P/8To7OT2N69eI2NMA/dbIRYqgzDYMuWLTzxxBPE43EGBwe56KKL6O/vRynFihUraGpqIp1Os3LlSlpaWhZ6yEIIIYQQQggxqxInn0zi5JMXehjTpn2fgWuuwfz97zHb2rCKRfxkEh2Lgefh7NmD3rcPBgcpvulN6E9/mvjmzUc0zyCfR/3v/2L09eFlMoeGz8eqq8MbGMDo7UXffz/Bm9+MOcdBTyHEsSl++ukUYzGCRAI1MICeIkRoDAzgp9PoeJzEKafM6diGv/tdjF27cNra8Bob0S0tUfh9RE0NYU0Nbk8PsY4OKvE4uW98g4Z3vGNOxyXE4Rj87GcxH3wQe/t2QsuismnTAQUfAs/D6Ooitns3Fa0pvOc9OP/5n6Oh8NmkPQ/94x9jdnWhbZtw3TrU2G1rRDKJt349sSeeIFgi3a6NeBx9/vmEvb3Etm8nGB7eX7TiINp1cfr78ZqbCY4/ftGH6+eSt3UrqlBABQG6oYFx1oYDhPX1OHv2EJTLVHbuJHHSSfMyTiGEWGrk7hQhhBBCCCGEEEIIIYRYQOvWrZuX+RSvv56Bm29GT9FBU4ijhtYE99+/0KOYseGXvISBq65i8Ktfxc9mF3o4S1oQBHR2dpJMJo+awHY6nSaRSEjH4QnYts2mTZvYvHkzJ510EstqalD33YfR24ufSk14c46Yhul27BZLnuM4nHDCCWzcuJE1a9awceNGNm3axMaNG1m7di3r1q3jhBNOkPC5EEIIIYQQQiwCg5/5DOZDD2Fv344CKlu2EGzeTLhmDeGGDVSe9CT8ujpi7e2oPXsovve9R1wAs/T730OhgJ3LEUyjK3zQ2Iidy6EKheh3hRBiDjitrQSnn47f2IidzUKpNOG0Op/HHh4maGpCn3MO5hyeN9ZBgP7xjzH6+ggd59Dw+dhpm5vxk0mM3l7UffcRlstzNi4hDkeQz8Pdd2N0dKBNE3/jxgPC5wDYNuHq1bhNTTh79mC0t5Ofo+u1uXvvxchmsYaG8JubJ9y2AEgkRrtdq//9X8JJ9hGLRerv/g5dXx8ty7Y26OtDh+H+CbRGDw3hbN9OkEigly3DueqqhRvwYlAuQxiiDWPy7ucjTBNVvf4l+1whhJiYdEAXQgghhBBCCCGEEEKIeZRIJA74/7hVuA+afqppAHDdKBw29qKjUqOBMbu9HeP22xnYs4eGW25BHdQNOB6PHzK2w2FOUb08Fovtn0+lEo1XqckvCI8YmdY0sazJT287jjMrr2eqrsm2bc/KfBzHmfRxy7JmZT6xWGzSx03TnJX5TBWOna35TPYchd/8BquvjwRMbx0bCVcqBVMsp4MppQ7v9XhetF6PzFtr7J07sfr6CHfvJvff/435L/9CzcUXj/7KbCy3kTFPJpFIzMq8pgp+H8m+JwxDdHXZFQoFPM/Dtm2CIGB4eJj+/n4Mw2DNmjWjwe0jtRj2PSOvJxaLjb7+g9/PsT8f+TPReCez1PY96XSaVatWjf6/8OCDMDyMNTyMu3o1BpCYbJ3UOvpjGGDb0d/jmGqsh71POAzztk+wbfyR/dV4y2Vk2Y3/5NEfw0BPcVPXghyPHIEZ7RNmctwzMp1hgONMeTwyW/uekX2CYRg0NTXR1NREqVRi3759AKxevXrK/YYQQgghhBBCiPkR5PPw4x9HITDLikJgB31nV6aJXrkS1zBwOjqo1NSQu+ceal7wgsOf7+AgyvdBa9Q0vouqRCI6Z+B5BIODhz1fIYSYSuLlL8d9+GH84WHsnTvxWlqgvn7/ubgwhGyWWGcnXn09NDSQnOOwZukvf8Ho6cHKZnFXrJjyvGBQDe2Gw8MUfvUrMuefP6fjE2Im8j/8ISqXw87lqKxbN2nAN1yxAj0wAAMDuD/4ATz3ubM+nmDHjqjbtdZQWzvl9GFtLc6+fQTlMm5HB/HNm2d9TLMptmYN7nveQ/DRj+JZFk5XF7qriyCVAsPALBZRnodfV0e4ejX6ZS8jMwfLeSlRmQxYFioM0a6Lmup6RqVCWL2eZE5jHRJCiGOV0lrK1Yujxyc+8QlyuRyZTIZrrrlmoYcjxLiCIKC7uxuA5cuXT3kTlBBCCLHQvDEdMqe62VUIIYRYKPJdSxzr+t/6VswHHsDZuRM/mSRsaYGRG7+0RhcKWJ2dGJ6Ht3kz4QtfSMN73rOgYw7yeXIXXYS1dStBfT26uXnK37G2biWoq8O/9FIa/+3f5mGUYinLfvnLqM99jtiOHZROOAFVKqGCILoJM5mEgy84Dwzg7NuHe9JJxL/4ReInnDBnYwvLZfr/v/8P85FHcHbvxvB9/JoadCIBQYA5PIxZqeDV1xOuXo3xjndQc9llczaepaq7u5uOjg6y2SzZbJZKpYJSCq01lmVRV1dHQ0MDzc3NrF27dnrFPJaIwcFBdu3axfDwMAMDAxQKhdHHHMehvr6eurq60dd+rBr6/vfRN99MbOtWyk960iHFVw6mKxXijz9O5YQTMD7wgQOKPxzrxu5TK09+8v4bJbXGaG/HzmYxy2WU1oSmiVYKy3XxHYfQttGOg7d+PcGpp1L/mc9gTFGo5GjU/+53Y/7kJ8Ta2qisWQN1dYdOpDVGRwfW0BDu8cdH+/8rrpj3sY4l37WEEEIsNXJdSwghxFIwG9+1hr79bfSnP03ssceorFsHk3Xw1Rrn0Ufxmpvxzz2Xxs9//jBHDrm77yb4yEeIPfoolS1b9l+LmEipRGzbNipPehLGBz94ROF3IYSYyuC3vgVf/CJq717sgQG0YRBUOzSbxSJKa7ymJnRLC8a73jXn54BzP/sZwfXXE/vb3yhv2TJl4Q4dBMT/+lcqW7agrrmG2pe+dE7HNxPyXUv0v/vdWHfeidXbi3fCCVMWVDD27EG5Lv7xx1N/773TK0g/AwO33ILx//4fTkcH7kknTTm9Hhoi1t6Oe/LJxP7jP0icfPKsjmeu5B94APcTn8Do6YGBAYxSKboHxHHQTU3oTAbj1a+m9mUvW+ihLrjyE09QvvpqrMcfJ6ipQa9cOen01rZthMkk/qmnUv+976Hk+sdRQa5riaVmKWRhpQO6EEIIIYQQQgghhBBCLGGlv/4V849/xG5rw0+nCdeuPfDirVKodJpg40bYsQOzvR1+9jP8174Wq7FxwcZtplLoWAxt26hikakqpeogwHBd/FgM1dAwL2MUS5suFFCFAobrEt+6NQqfjzAMvHSacNkyyGSin9k2KgwBCIrFOR3b4Be/iPmXvxDbuZMgkcDbtOmAYKy/YgVeLkesvR0fCD/1KdynPAVnTGdnEV0wTiQSdHV1UV9fT7lcJggCDMMY7aK8fPlympqaFnqos66uro7jjz+erq4uMpkMnufh+z6GYeA4DqlUiuXLl1NfX7/QQ11Ypnlgd5spjOwDUCr6XTHKqKtDO060bIpFSKWiB4aGsLNZrHIZDfiJRHTs4Xlow0A7DpWNGzH7+rB37gTTZOib36T+n/5pQV/PQkhdfTWlP/4Rt1Qi1t6Ol8sRNjVFN+prjR4exurtxSyVcNetI9y0iZqLLlroYQshhBBCCCGEWKSCRx7BHBwktKz95zgnohRebS3G0BDmY49FHckPMwRmb9lCYNuEsRgqm42Kak4262yWIBYD28ZZ5J1GhRBLX90rXsFwTQ3+F7+IzmZR/f2oSgUAf/lydEMDYWMjztveRvrcc+d+QKY5eg1UTaNv5AHnqCfpLi3EgigWIQgIbXtaxxGh42CVStH1As+buhv1DKmaGnAcVBBMq9u1KpfRVhShM5fQ9bP02Wejn/508vffj3v33YTd3RAE6Pp6rOc8h5pLLsGoFto41sU3b6Zw4omovj7szk7c+vqJiyX19WGWy/hr16IuukjC50IIMQkJoAshhBBCCCGEEEIIIcQSVrrtNozhYQzPw920aeIOv4ZBsHIlsR07CAoFcrffvrDhL6XQZ55J0NOD09ZGxfMm7Uxr9PejDQNqa4k997nzOFCxVAXt7ST27cN0XVQQYIwJn4aGEd3skMvhLl+OXrECKhXC6oVlc7JuQUcoLJdRP/0pZmcnoW3jr1uHcfBNREqhampw164ltnMnlcZG8t//Pg1vfeucjWupqqmpoaamhlKpRC6XGw2gp1Ip0un0Qg9vTiUSCdavX8/q1avJ5XKjAfRkMkliqs5Txwh7/XrcWIzQtjGGhtDNzZNOr4aGCB0nuiF6/fp5GuXSkHz2syn8x3/gJ5MYfX2E1QC62deH8n3QmqAaPgcwfZ/QsggdB5VOE6ZSGNu2YfT04N99N/o1rznmbuaJb9mCf911eB/6EJV4HLO3l9i2bWjDQGmNVgo/k8HdtIlw82Yy//Zvx2SneCGEEEIIIYQQ06NnGALDcVCFAoQhYbmMcZjnj+KbNlE4/nhUTw9Wby/usmUTntvXnoedzeI3NxM86UnEN206rHkKIcRM1Fx6KfrCC8n99Kd4994L/f3RfnLZMpznP5/a885DWfMTo7FXryYYOU86PIyeKiQ6NIQ2TYjFsFavnpcxCjFtySQYBobvE0yjmM1IoVpMc9L7AA5X4tnPpvyNbxDEYhh9fZN3u9Yae2AAv76eYO3aJVf0W1kWmfPPh/PPX+ihLHrxV74S79FH8fN5nB07cFesQDc0oEaux7suqrcXp78fd+VKdHMzmSuvXNhBCyHEIicBdCGEEEIIIYQQQgghhFjK/vQnjGwWL5OZ+sJtKkVQ7UoS/OEPsMDdR+NXXIF7//1R9fP2dvz16/df+BurWMTu6cFraiJsbSV1xhnzP1ixpBR+/WusX/4Sw/MwwhCtFF4sNhryM3wfs1JB+z5OdzeuYWAODhLU1BA2NhKbw+Bp/mc/g8FBrOFhKmvWTFw0AiCTwU+lorDnPfeg3/SmebspaqlJJBLHbOjasizpdD6B5CmnUG5txe/sxOrvx21qGv9zBtBhGN181NhIsGkT8eOOm+fRLm72smUEZ54JAwPE2tqoDA2h43GsQgGjGjYfudnMcF1UGBLG4/iNjdETKIXf1ITT2UnY00PhgQdIn3POAr6ihZE++2zKn/kMhVtvhYceIigWoVKJll0yia6pQZ9/PrWve92cFkMRQgghhBBCiKNFMDRE7gc/IPi//0Nls2BZ6JYWnIsvJj2PAcOFoJJJME0Mz5tWCAzXjUJghnHEBc/sK64gfPRRdDaLvXMn3rp1qFjsgGl0pYK9ezfastCNjThXXHFE8xRCiJlQjkPNpZfCpZcu6DjiW7ZQ2LQJ1d0dnaNubp7wHDVaY/X349XXEy5fTlKuh4pFxnzSk9D334/R1YUuFFCTFYLWGmtoKCpCc9xx0yuWM0OHdLuurYVq8dyDqa4ulO+jGxuxLr10TsYjFofUWWcx9IY3EH7uc3idnThdXdDVFd0nozVmuUzoOFRWr0avWkX8Yx/Dkuuss0tr8r/+Nf6OHWjXxaivJ/msZ2FPUShcCLF4Hb1nVoQQQgghhBBCCCGEEOIYoAoFlO8TTvOGsdBxUJ4HxeIcj2xqyVNPpXTWWVCpYO/Ygb1tG/6yZej6epRhRBejenujQGBNTXTj4OtfLxeExeS0pvLJT2IODaHCEM9xou7npjka9g5Nk8C2sctljEoFp6MDbRj4a9agLrxwTm9M9bdtw8jnQSl0TQ1Trc1BXR12Tw/h8DBed/eSq8gvxIJSCvOyy9C7d0M2i7V7N/66dYfc4KeDAGvXrmi7bGqKbj4Sh0i95jWUHnoIt1Qi1taGl0xCGGJU97UEAabnYQQBfixGGIsRNjaO7ud0XR2qowPKZfz29gV9LQspftxxxP/t3/C6uijcey9hfz/KtjFbW0lfcMFhd6ATQgghhBBCiGOK1mS/8hX49rdR+TxWfz/KdaOHUimCP/yB7Oc/T+yd7yR11lkLPNi5YT75yVEIrLMTcjmYrJCZ1thDQ3jNzQTHH3/E59hrLryQ/t//Hh2GWLt2Ed+6FS+dRleDaCqfx87n8RMJ/PXrCS+8kLoLLjiieQohxFJlXXopeutWGBiY8Bw1WmO0t0cB2WXLMC66CGWaCzNgISaQvuwycv/5n/jpNFZXF8HGjRMeU6ienqhQbUMDzmWXzdmY4q9+Nd5jj+EXCjg7d+I1NxM2NOwv3F8sYvT0YA0P465dS7hmDXVyDeioV/vSl5JraMD70pfQ3d2QzY5+V/BbWiCTIXjSk0hdcw3xzZsXeLRHD+37DH3zm4Q//CFGdzeUSqgwRDsOhc9+luDpTyf1mtdIEXAhliAJoAshhBBCCCGEEEIIIcQSpke6Ovs+ehrTK98ndBw4qBvJ4XA7Oij9+teEw8OoWAzn+ONJPvWpM7p5rf7668kWClHXle5unM5O1N69aKVQYUgQi+G1tKCXLUO9+c2kzz33iMctjm75X/0Ko6sLq6+P0ooVOENDqHIZq1QicBx0NVyuDAPfcXDKZbTvU1y2DN3UROYlL5nbAVYqEIbRdjudbcWyQEdbd1gqze3YhDgK1bz4xQw88AAEAfbOncQefRSvro4wk0EBKpfDGRxEmybehg0Ez3gGDXN4M9RSFt+8Gf+DH8T78IepxGJYHR3YpRJGEGCVSiilCE0TP5GIPr83bDjgJsnRfZ7W4PsL9CoOT6WtjeJtt6H/+EfI5yGRgM2biV9+OclTTjms57RXrKDu7/9+lkcqhBBCCCGEEMcArRm4+WaMu+7CaGvDzuUIHIcwmYy6bXZ3o7q68JYtw3vf+8hfd91ReV45fcklDH/ta3iZDFZXF346DRN11e3pgWoIzH7hC4985krR8N73ko3FCH74Q4JcDqO/H3NgAIAwHqeyfj1kMujLLqPhbW+TwrJCiGNWzaWXMnD//eD70Tnqxx7Dq69HZzJopTByOaxsFrTGW7+e4MQTqX/5yxd62EIcwqqvR593HkEuh719O+zahb9qFWrMfQc6DKNr/r29VFpb0S0tpOewCE3q6U9n6K1vJfzUp/C6u7H6+jC6uwkcBxWGGJ6Hn0rhbthAuHYtqZtvliKwx4jM854HF1xA/pe/pHLffTAwAJaFWrWK5KWXSgh6loXFItl3vQvzD3/A7OnBzmZRQTD6uJ9KwcAApT/8Af997zsqv58JcTSTALoQQgghhBBCCCGEEEIsYXrzZsIdO3A6O6mE4aEV88dOW6lglUpUWlpQW7Yc9jyLf/wjpW98A/Phh6FcjjqqGwZuIkG5tRXzhS+k9qqrpnVDmRGP0/DJTzL49a/j//CHhL29UChAGIJtQzJJcNJJxP/hH0g94xmHPWZx7Kj85CeY2SxKa8I1a3B7enC6uzEqFaxKBcpldHXdVGEIRB3RDcD8yEewGhtnfUza9/G6uwnLZbRpgm2jggDt+1N2W1elUvQ7RDd2CCFmRlkW9TfdRPZ974s+VwYGsAYGsLNZILoh2m1pgYYGgrPOov5DH5LOMpNIP/OZlD/9aQpf/CL6gQcwH38cu1QitCxCx0GbJn5NDeHKldHyHqtQiI4NYjGMhoaFeQEzFBaLZD/6Ucxf/QpVKEQ30gcBGAbhX/+K+/OfUzr+eDLXX4+zcuVCD1cIIYQQQgghjgnDd92FcdddWDt2QBBEQed0evR8dKg19PfjdHbiBQHeRz+Ku3kzzqpVCzzy2WVmMugLLyTM5TCfeAJz584oBDYmVKWDIAqB9fVRWb0a3dJC5vzzZ2X+yjRpeNe7KL/oRRT/53/Q998/WkBTJxLoc88leeWVxI/gWoQQQhwNlGVR/6//Svbaa9GWhTEwgNnfj9nbC0DoOPiNjYSNjYQnnkjtzTdjxOMLPGohxlf39reT3bUrKvrT1kZ861b8ZBIdi0EYYuVyaKWi8HlrK8mPfnR/N/I5Unv55eSXLaNy663oXbtgeBjlulHx/nQaUinCZzyDzL/8C/by5XM6liNR+utfKd19N7qnBwDV3EziootInHTSAo9sCVOK9DnnkD7nnIUeydFNa7If+ADm73+PvWNHdO/QihXo+vro/qViEaO/n1hbG26xiHfDDRTr6w+7wLMQYv5JAF0IIYQQQgghhBBCCCGWsNgLX4j/u99BZydGdze6pWX8CbXG7OoiiMUgnSZ5+eWHNb/hO+8k/OQnMXt7sfr7MYvF0cdCy8Lv7ES3t9P/xz/S8NGPThmuheqNF1dfjX7Vqyj88pd4W7eiKxWM2lriz3wm8U2bDmus4hjV04NRKhEkEijDQK9YQUUp4nv2AKC0RlU7imul0FqD1ritrTQ85SmzOpRKezvF//kfuOceVD4PgJnPY3Z2QhBg9PWhV6yY+Am0xhocxK+vJ9iwYU7C8UIcC4xkksZbbiH/i19Quf12eOSR/Q8qRXDaaTiXX07dOedIN65piJ9wAvFPfYpKWxuFK64g3L0bw3VxV64kbGiY8LPf6OvDT6fRySSpZz97nkc9c2GpxOBb3oL55z9jtbdjlkoEySRhLIbyPGJtbYS2DcPD5F7/esxXvjK6kcY0sVavJvWUp8j6JIQQQgghhBCzTWu8734Xq6cH5bp4W7YcWgBNKWhqouI4xHftolJXR/7736fhrW9dmDHPofq3vIWBagjMbGsjvm3b/hBYEGDl82jTjMLnq1eT/Nd/nfUQWHzLFuLXXgvveQ/ByDnQMQUBhBBCROeoG265hdzPfoZ3xx3oRx/Fr16rQimCTZuwLr2UuksumfOwrhBHwojHqf/0p8l++MPw61/j5/MY2SzK89CGMVrwN2xtJfnhDxPfvHlexpV+5jNJn302xUceoXzvvehsFmXbqNZW0pddht3cfHhPrDWFhx+mctdd6H37ogK1dXXY555L5sILUY5zxGMv/ulPlP793zG3b8cYHkaVy9Gs43EqP/whxc2bSbz5zSRPPfWI5yXEXCj85jeYv/sd9s6dhPE4wbp1YBiMfhtIpQhTKcp1dcR376YSi1H6whdIfv7zCzhqIcRMSABdCCGEEEIIIYQQQgghlrD0s57FQGsr7uAgsT17cIFw+fIDOqHrIMDctw9reBh3wwaC004jvnHjjOeV/9WvCG+5BaOtLQrF1tXht7ZCPI72fYxsFqu/H4aGIAzJfuxjNHzwg9N+fmVZpJ/zHHjOc2Y8NiFGjITLR5XL2P39aMNAK4U6aNqRbuixtjbKW7cSP+64WRnH0He/i/7851G5HEZvL2ahAFpH3S1KJQzPI9HeTj6VwqypGf+1dHWhfJ+wqQn7sstmZVxCHK1Kf/kLpdtvR+/YEXW2SKcxnvpU0ldcgb1sWdTl4LzzSJ93Hn5/P15XFwD2ihVS3OEwxdaupfTmN+N9/vM427aB70/cPX5gADuXo7J+PfqcczBra+d3sIdh8OabMf7yF+wdOwgTCcrHH4+KxUYf9z0PY98+Elu3Eu7cSfDgg/jr1oFSeI7DwMqVGJdcQu1LXyo3jQohhBBCCCHELCk8/DBmezvWwAB+U9Oh4fMxVE0NfiqF0ddHeM896De+8aj7fqZsm4ZbbiF7441w330EhQJGNgueB6aJ29oKdXWE69eTvO66w7ouMP3BKMxMZu6eX4hjmdYU//xngr4+VLX44Zxuz2JOKNOk5gUvgBe8ALejA2/v3qgQ8fLlxNavX+jhCTFtRjJJ4403Ut6xg+L3v0/48MNQKIDjoDdsIPbCF5J+5jPnvxiNUiRPPXXWgtqlv/2N0k03YezejTE4iCoUUIB2HMLf/pbsF7+I+epXU/viFx/2PPK//CXehz6E2dmJ3dOD4XlRQwHArFSiIrj9/VTa2givu470s541K69NiNlUvu02zGwW5fv4W7YccK/SWKqmBrexEau3F/72N8rbt0szCiGWCAmgCyGEEEIIIYQQQgghxFKmFMmPfITSW95CBXD27oX+fvyaGrRto1wXa3gYbZq469cTrl9P7QxC4aO0xv385zG7urAGB6ls2IBKpfYPw7bRzc24TU1YO3Zg7dqFf++9lP/u74gff/zsvV4hpqCbmgjjcZxsFr9Swdm5E7NUwqpUCE0TLxZDjwQkgwC7VMIMAvxKheK73oV5662HXwW/auj730d/5jMY7e3Yg4P4qRReczOYJqpUwiiVMH0frRSZRx+lsH49LFs2ejFWl8uYPT3Yg4NU1qxBt7aSvuiiI100QhyVKu3tFD78YYxt2zCGhjByOQhDtGWhH3mEwre/Tfj851N/zTWjnbmtxkYJnc+SzJVXMnT33bjlMk57O0G5TNDUBJlMdHNZsYjR24s9NITb0oJesYLUq1610MOektfTg3H//Zh79hDGYgTr16MOullO+T5WoQBhiJ3PYxeL+MUi2jQJLQu/sxPd3s7AAw9Q9/GPy034QgghhBBCCDEL3D/+EVUoYHgeYVMTU8Wagvp6nM5OwuFhKjt3zlrxycVEOQ4NH/wg7tVXU7jtNvzf/x7y+SgEtnEjiRe9iORTnyodyYVYgoJ8ntz3vkf4ox9h9PSgfR+lFL5pUtyyBeuFLyRz0UUTF4UUi5bT2orT2rrQwxDiiMQ3biT+7ncv9DDmROGhh3Df+16MvXuxu7tRQUCQSIBhYA4Oorq68OvqCD/9abL9/dS//vUznkdl1y68j3wEo60Np7cXd9my6Pi2WjDJ8zyMvj5ie/fiui7eRz5C5YtflIIVYlEJSyXMhx7CHBjAr6ub8pgkXLYMp78fv1Cg9LOfSQBdiCVCAuhCCCGEEEIIIYQQQgixxMU3bYJPf5ri+9+PW1sLAwOYw8OoSgVtWaNdToItW6j51389rNBd8aGHMNrbsfr68JqbDwifj6UMg2DtWmKPP46fy1H8n/8h/r73HelLFGLaYs97Hv6vfgWdnZjt7RiVClalQmDbhI5z6C8ohW/bGK6LsXcvuW9+k4a3v/2w5+91daE/97loe8nnqWzaBGO2F11fj9vSQvDYYyQGBwkMg0R7O2FvbzS+MMSsVAgSCSrr1xOuWUPq5psx4vHDHpMQR6vK7t0U3/zmqNjDvn0YnoefSqFNEzOXw+zrw0+loFxmoKuLho9/fDSELmaHmcmQ+vjHKVxzDa5tY3Z3E9u9Gw2gFCoM8VMpKmvXoltacG64gdjatQs97Cnl77gDVShgFYvRfvzgm/RdF3vnTsxyGTMIUIBnWbg1NQRr1qCGhrD6+1HZLPz/7N15mFxlmf//93OW2qv37iSdnYQAIovCiDgIiIgLsoTVmUHHcXQcF1xBxtFxGVdUnBkdXMbxOzo/R0dEdhVcUMAFRBGGLWTrdJLeu7p6qaqu5Zzz/P6oEIlZOiFJV5bP67q4gNRddT4nqXR3nfPczx0EjL/vfbR98Yt6/4mIiIiIiOwlWyxiggDrOLvXcBmLYaIIqDdyHspi8+cTu/LKRscQkX2kunkzhauuwunpwRsdxcvnMWEIQBSLEQwPEz35JGO/+AWtn/iE7iGIiOwjQS5H9UMfwtm4kdjgYL0xvKtr68+eobUwMYHf14dZv57wW99iatkysmefvUfHKf7P/+CMjBAbGaG8eDGmuXmbzZWM72PnzaOcSpHo7aWSyVD89reJa+2FHECCXA6iCKdapdbaOmO9icWIPA/KZaJcbhYSisi+oDvcIiIiIiIiIiIiIoeAxJFHEv/2tynecw+VW2/FPvVUvQE9lSI67jiSF1+8V1NOyj/+Mc7EBCYMZ54sE4tRy2ZxRkex994Lugkqsyh9xhnku7qoDQ6S2riRyBgix9mu+dxGEf6WqehRIkGQSOCMjGB/+lOit771WS/WKtx0E2ZqCn98nPKyZTverMEYgqOPprxqFX6hgI3HqXR14UQROA5BJgPpNOHzn0/26quJdXc/qywihzIbBBSvuQant5f4xo1U29qI5szZOhkiAoJSCbevD3/tWgDy119P2zvf2cDUh6b44sW4X/0qk1/5CvaeewinpqBUqj+YSEAqRfiCF5D+u78jsWxZY8PupuiRR/DGxwnjcUiltnvcGRzEqVbxKhWCLc0MjrV45XL9+01nJ0FHB86GDfg9PRCLMfWjH9F03nkNOBsREREREZFDh0mnwfMwUYSt1bZeB9ipahXrOAC4mcwsJBQR2XtBPk/hPe/BWb2aWG8vUSxGde7c+ma31tY3PxwZgVwOgoD8Bz5A++c+96zvAYqIyB9N3XgjZnSU2OAglQULoK1t27URxkBLC9VkktiaNUTDw1S/8x3Ygwb0cGoKc++9uCMj1LJZTHPzTmtNczO1bBZ3dLR+D+Yd78DNZp/9CYrsS1s+a+0Ra+ubWD+b54pIQ6gBXUREREREREREROQQYVyXzFlnkTnrrH3+2nZsDFOpEMXjuzVZxqbTuPk80fQ0UbmsyQsya4zrErvySmrveQ+R4+BXKgS+D2FYvwFqLU4Q4AQB1hjCeJxqVxc2kSC2aRPR5CSl++8nc+aZe35wa7F33YU7OkqYSmF2tajVGKqLF+OsXUt13jyCFStwli6FWAxn0SJS559PfNGiZ/37IHKom/rpT3H6+oht3ky1sxM7b972m6OkUgTLluGvXYvT14f94Q+J3vQmnB00FMve8drbafvABwjf8Q4KP/4x0cAANgxxOjpIv+xl+F1djY64Z6anIQyxO2hksGGINzGBW60SuS7W9zHVan3BzJapegAYQ7R4Me6TT2JyOWq33w5qQBcREREREdkr8ec/n2o6TeT7mLExmDNnl/VuPk/Q1ETU1ET8iCNmKaWIyN6Z/PrXcXp6iPX2Umtvx86bt01zuU2lqM6Zg7d+Pf769eD7TP3kJ2TPOaeBqUVEDn42DLF33ok7MkKQTEJb205rTTxO0N6ONzaGXbOG6cceI/nc5+7WcaYffBBTLOIVClSWLJmxPmxvJ7FhA2GxyPTvfkfmJS/Z3VMS2a/8zk5sIkGUTOJMThK1t+/6CaUSThBAKoWjTfhFDhpqQBcRERERERERERGRmRlT/8fa3avfsmsxsFsN6yL7UuYlL2Hk3HOJ1q0jqtUA8MvlrY9HjkPo+1jfp9rRgZ0zBxsEmCiCapVwdPRZHTecmsLJ53GKRcJdLEh4mkml6s2NxuCceCLtH/3oszquyOGodttteLkc1hjs3Lk7rTOOQ23uXOIbNhBNTjJ1xx00X3bZLCY9vLjZLM0XX9zoGHsvmQTXxWz5HvJMZnwcE4Y4YUgtHq//4tM/H/3ptAbHIWhrqy9AW7WK6ubNxBYs2M/hRUREREREDl2p5z2P6cWLMYODxEZHqba1wU6moNuJCbxikUp3N7zsZTNPSxcROQBE09OYn/4UZ2iIMJncrvn8acZxCI44gtiTT2LGxqjeeiuoAf2wUtmwgeJNN2FXr8ZUKvXNwU86icyFF+K1tjY6nshBqbJ+PU4uhzs5SW2GjY4Aoo4OnOFhKBYpP/DAbjeghxMTEAT1ewvJ5Iz1JpGo1wYB4fj4bh1DZDYY3yd68YsJh4eJb9hAuVLBPH3vbAeckRGCdBrSadLnnjuLSUVkbzgzl4iIiIiIiIiIiIjI4c50d2NTKdxKBVutzljvTk4SJZNEra1a2CcNET/tNMpLlxJ5HmEiQS2VopZMUkulCJNJai0tVJYswc6fv+3mCsbAs9w0IapU6i9hLfZPmxB3wjoOxlrYQZOjiOyEtbhPPokzMUHQ3LzDBZjPZLJZrOdhJiYIHn54djLKQc057jhsSwtuuQyl0jaPmWq1vmEJ1L9fWIsTBPXvN6nUdq9lMxmcLT87BQMD+z27iIiIiIjIIc0Y/Msuw3Z1EcVieGvXwuTkNhun2iiC0VHiGzdS7ezEtrWRufTSBoYWEdl9hbvvhslJvKkpgs7OXV77NI5D0NKCm8vhPvoo1f7+WUwqjRLkcuTe8x6mX/c6nP/5H7yf/hTvnnvwfvxj+MpXmLrkEsY+9zlsEDQ6qshBJ5ycBMAJQ4jFZqw3vl+/JxwE2EJht49jEok/bmi7O39Xw7D+b8epP1fkAJK+5BJoaiJIJvF7ena67sEMDeFPTBDOmUP4ghfgd3XNclIRebY0AV1EREREREREREREZpQ67zzKt9xCGIvhDA9jdzW9s1TCLZUIFizAvOxlsxdS5BliS5ZQbm6m1tREkM1iU6n6zXnHwaZSmD/ZTd4ZHyfyPIjF8BYtelbH9FpawHGIfB9neppohnobBDjVKkEshmluflbHFDkcRdPTEIaY3VwAhDFEvl9fxFMs7v+ActDLrFxJ8X//lyCdxu3vJ1y2bKeLfZ1aDYzBeh5hR8f2Bc9sgphhswQRERERERGZWdN55zH21FMEt9yCs3Ej8Q0bCGMxomSyvmldoYABal1d2O5uvA98gFh3d6Nji4jslnBgAFMuY6II09Q0Y73NZnFzOQKg1t+vr3eHuNrwMFNvexvu+vV4mzfjVioEySTWdXELBdzhYYJMhrBUYmzTJtquuw7jqWVIZHe56TQ12NpUPhMbRfUNa10Xs4MNancmtmIFZd8njMcx+Tx2hinoJp8njMfB94mtWLHbxxGZDYljjmF65UrCG2/EW7+e2KpVBC0tRM3N9Y2cp6fxcjmcapXKwoVECxeSfcc7Gh1bRPaAfpoUERERERERERERkRkljjyS4rHHwvg48Y0bqSST0N6+XZ2tVIj19lJraoJ0mvRFFzUgrQgkjjqK4hFHYAYH8cbGqHZ3Y7bsJL9d+18U1RdotbYSzZ9P+vnPf1bHNL5PeNJJMDJCbGCAyvz5W4+5w/qxMazrQlMT8TPPfFbHFDkcOclkveHXcXa6i/42rMWp1Yj2cAGQHL78ri6iM86AUgl/7Vro6SGYPx8Tj2Njsfp7D3DLZZwoIkgkCBMJ2MGiYFMoEG7ZKMGfN29Wz0NERERERORQ1fbe95JvbSX83/8lmprC5HKYahUch2DuXGxbG1FXF/H3vY/0C1/Y6LgiIrvP2vo/u7uR4TPqzDM2QpRDkLVM/eM/4qxbR6ynh6CpidqSJZh4HIDIWmqlEn5fX/2aJpD/13+l7aqrGpla5KASW7KE6VSKIJvFHR8nbGvbZb3J5eobnKfTxI49drePk1i2jOKxx2JGRvCGhqh2dmJ8f4e1tlbDHxsjmDOH8NhjSSxbtkfnJDIbWt/1LsbCkODWWyGfx8vl8MfGALCuS9DaStDRQTR/PqlrryU2f36DE4vInlADuoiIiIiIiIiIiIjslvR73sP0+vVUg4B4Xx/B2Fj9pmsigQ1D3Hweb3KSMJ0mWrwYXv96TVqQxjEG//zzidasqd/k7OkhWLp0u4ZwG0W4vb0Ya7FdXTjnnbf7C7t2IH7hhQS//S12aAh30yaiRYt2/HrT08RHRqi2txMtXEj6BS/Y4evZMKT0wAME/f31JtquLjIvetFOFyGIHKhsGFJ68EGCgQGMtThz5pA+5ZRnN33FGMKjjsIMDeGNjlLr7t7139tCAScIsC0tOHuwAEgOby3vex/5TZvAWrzeXhJPPUWQShH5Pk61iomievN5KkUYjxMsWbLda9goIpbPU+vqIjz2WC2oERERERER2VeMofVv/5bwssuYuvVWgnvuwYyPg+dh584l9upX03zGGZr6KiIHHaejA5tI1P+nUIBsdpf1plAg2nK/wO3q2t/xpIGKv/0tzlNP4W/cWJ8su2AB5pnXxY3BpNPUli/HX7sWd9Mm7J13Ev7d3+HuYONMEdmek0hgzz6baGSE+Lp1BFNTmJ18HbZhSGx0tL7BeXc36VNP3aNjxS6+mPDRR7H5PP769dtsKLH1GJUK/oYN9Y1x29uJafN/OVAZQ9tVV1E87TTKN94IDz5IYC1EETgOUUcH5txzab7kEtzm5kanFZE9pCsrIiIiIiIiIiIiIrJbEkceSfSZz1D+wAeopNM4o6PEBgYwYQjGEKTTVBcuhJYW7GtfS+tf/3WjI8thLnvBBYzdcw8EAf769cSeeKK+CCCbxVBfmBXL57HGUDviCMITTqDtssv26piZ004j99znwvQ0sfXridaupTZnTn1xgjHYWg1nbAx/ZIQglcLOnYv/hjds1zwblUpMfPvb2B/+EGd4GKpVAMJYjPHWVjjnHLJXXIHX2rpXeUX2t23eyyMjUK1igcj3ybe3Y17xCpquuAJ3hoWUf8o/7zyixx/HDA1hRkawO1tcaS3u4CBBJoPNZGi64IK9Pyk5LDipFK1f/CLjH/sYwa9+RVAs4uTzmCAgSCaxjoNXrRI5DrXly+FPF4ZFEd6GDVjHwba3459/foPORERERERE5NDlZrO0XHEFXHFFo6OIiOwTmZe+lMnrryfIZHBGR4l2dd3UWrx8nqC9nXD5cuKLF89eUJl15Ztuwh0fx4QhYXf3ts3nz2Ach2DePOI9PYSFAlO33krLa187y2lFDl7pSy9l+oc/pNbaSry3l+qCBdDcvO293Olp/E2bsMZg58zBXblyjzc4z770pYw9+CDBHXfg9vTUN8HNZIjSaQCcYhGvUKhvgrt0KdG559Jy9tn78lRF9rn0C19I+oUvpDY8TLWnB1ut4ra1kTj6aIzrNjqeiDxLakAXERERERERERERkd2WOuEE/P/6Lwrf+x7BXXcR5fNbdy3G8whPPZXkpZeSOvHERkcVwbgurZ/+NPkPfAB8HzM2hpvLEcvlAAjjcWpdXdj2dsITT6TlU5/a+8nixtDy6U8z/s53UnUcnMFB4hs2gONgHQcnCIh8n1pHB3bOHKI3vpHsnywWqI2MMPXe9+I89RTuyAjelgVFANZxCJqbiYaGmLznHtLXXadFZXLAqg0PM3XVVTt+L7tu/b08OMjEvfeSue46Yt3du/3a2Ze/nPx//RfV8XHifX1Uw5BozhyM42ytsZUK3ubNONUqtSOPxJ599h43ukvjVPv6KNx4I9x3Hyafr38d7erCednLyFxwwaxswOGkUrR96lNUNmyg+P3vEz78MKZYpGYt7qpVEIbERkbwensJ2tuxmQwmimBycuv3mtoRRxCedBItr3jFfs8rIiIiIiIiIiIHN7epCXv66YRjY8TXr6cyMgKdndsXWouzcSNYi+3owDvvvNkPK7PKefRRnPFxgqammZv4MhlC38fk8wQPPQRqQBfZbfHFi6m8+91En/scwebNxDZuxPo+YSaDdRzcUgm3VCJIpwmPOILwzDNpv/zyPT+QMbT9wz+QTyQIb76ZsFjEGR3FHR8HIEokqCxbBuk0duVK2t71rj1uchdpFL+rC39nm4eLyEFHDegiIiIiIiIiIiIiskf8zk5a3/pW7N/9HeUnnyQcH8ckk8SXLdM0ZjngOMkk7dddx9TPfkb1lluw//d/BM94PDz2WPzzz6f5nHP22a7bbnMzrV/6EuNf+hL2Jz8hmpqCqan6Zg2xGGSz1BYvxn/d62g688xtnhuVy0xefTXu448T6+kh8n2q8+ZBUxOW+tR2b3QUb80aqpUKxauuwv2P/9DfPdnnymvWMP2LX2DzeYzv4yxZQvblL8dJpXbr+dH0dL35/LHHiG3Y8Mf3cjZbXyAzNVV/L69eTbVSofDe99L8H/+x2w3iJhYj9clPMv2ud1ExBn9gAJPLEWSz4DiYSgWvVCJMJKgtX0544om0vfvde/NbIrPFWvL/9m+Ym2/GKRZxcjlMpQLGEK1fj127lqlvfQvnzW+m+dJLZyVSfMkS4u997za/VtmwgeJVV1HduBFnZITYwMDWDRYi3ydoayPq6CA66SRaP/UpLQwTEREREREREZHdknnjGyk++CCVSoV4Xx/B1BRBeztkMhBFmMlJvNFRTBBQW7qU8NhjaVMD+iHPFIuYICDKZHaj2GBjMQgCKBb3f7g9ZS3F3/+e8s03Y9auhXIZ0mk48URSF19MYtmyRieUw1zTeecxFY8TfP7z9c2PR0dxSiWMtUSJBMH8+ZBOE513Xv3e07O9/m8Mre9+N+Xzz6d0883Yu+8mKhQAsJkM9qyzSF98MfGlS/fh2YmIiOwZNaCLiIiIiIiIiIiIyLNiPI/kccc1OobIzIypTxk/+2xqw8PU+vsB8OfOxZ87d78c0kmlaLvqKqK3vpWpH/2IcN06qFYhmyVxxhmkn/OcHS5GmLzxRtynniLW00PQ1ES0cOHWOgPQ2krQ0oIZGCC2cSPVWIzJ//5v2t75zv1yHnL4KT34INP/7//hPv54fUFbtVqfPJ1OM/GVr2Bf9jKa//7vcWdY5Db53e/irF5NbMOG+qTzBQu2fc+3tRG0tmL6+4n19lKNx5n81rdofctbdjtr4uij4QtfoPThD1NrbYV8HmdqCoIAG49TmTcP0mnCF7+Y1n/6J0ws9mx/W2S2WEvu4x/HvfNO3A0b8AoFgnSaKJUCa3ELBdxcjlpLC9EXvsB4pULLFVc0JGp8yRK8r3+dye98h+Cuu4hGR+sLOo0BzyNasgT3ggtoueACjKfb8iIiIiIiIiIisnti3d1En/kMpWuuoZJM4gwPE+/txVgLgHVdgpYWoq4uwmOPpfnaa3X96TBgEwlwXUwQYHej3gQBuC7E4/s9256obt5M4Z/+CWfdOtzxcZyJifoGzp5HtGoV5TvuoHjqqbT80z/NeB9CZH/KnnMO0WmnMXXHHQR33okzMABhiG1uhtNOI3PJJcTmz98nx0osW0biqqvgqquIymUAnERin7y2iIjI3tInDREREREREREREREROWz4XV34XV2zdjwnlaL54ou3+/VarbZ9sbVEd9yBOzJC5HnbNJ9vwxjsvHlExSLOyAjRT36Cfctb1Fwre23y9tuJPv953KEhvNFR3EqFyPMwUQTW1jdFyOeZ+L//o+lf/xWvrW3HL2Qt0Q9+8Mf38p82nz/NGGx3d/29PDxMcOed2De9aY8WSyZWrCDx7W9T+NWvqNxyC9GGDZhKBZvJwEknkb7kEuJLljy73xCZdZO334571114a9cCUFmxApLJrY8H1hJMTRHbtIlgwwair32N6ec9j+SxxzYkr9vcTOvf/z32jW9k+g9/IBwZwToOscWL6xskaOq5iIiIiIiIiIg8C4ljjsH92teY+sY3/jgRt1LBGoOJx4m6ujCvehWtV1yhJsXDRHTkkUSbNuENDRFFETjOzounp3HLZWqLFuEcc8zshZxBpbeX4pVX4vT24vf1YcKQIJMBz8NUKsTXryeMx6FcZmJ4mJbrr8dJpRodWw5jTipF82WXwWWXzd4x9TVdREQOMGpAFxEREREREREREREROQBMP/YYTn8/Xj5P0Nm568ZFYwg6O4lt2kQ0Pk7xV78i85KXzF5YOeQUH3iA6POfx9mwAW98nFp7O7WOjvrGBtZiJyfxh4fx1q6lGkVMXHMNbV/+8g6bxUsPP4wzOFh/nTlzZn4vd3QQ6+8nyuUo3n8/mdNO27PwxpA57bQ9f54cWKwluPFG3OFhTBhSPfJIjO9vW2MMNDVRPeII4mvWUBkfp3TDDSQ/+tHGZH46lueR+rM/a2gGERERERERERE5tPhz5tB2zTVEV15J4d57iXI5HNfFXbCA9KmnYly30RFlFsXOP5/w4Ycxg4MwMgJz5uy40FqcgQGCVAqTTpO+6KLZDboz1lL4wAdwe3uJ9/ZSbW8nmjt3m/dxUKng9vURW7uWKpC/9lraG3ztV0RERORwpwZ0ERERERERERERERGRA0AwNIQNApwgwGazM9bbdBoTRdhKhXBoaBYSyqGs/PWv4w0N4Y2PUzniCEw6zda2cWMwzc0ETU04mzbh9/RAIkHh7rvJnnPOdq8VDA7C0+/lTIYZZ0Bns5gwhGpV7+XDWOn//q++AcLYGLX29u2bz58pmaTW3Iw7Oor95S8JJyZwm5tnL6yIiIiIiMhhyIYhxXvuoXLHHZj+fggCaGnBOf10shdcoM9lIiL7iZNK0fSKVzQ6hjRY5qyzyH/ta1THx4lv3kzVWqKuLswzJqHbWg23vx+vVKK6bBnhn/85/ty5DUz9R4X77sPt7cXfvJlqRwe2u3u7ewcmHidaupRw/XrczZvh3nupDQ/jd3U1JLOIiIiIqAFdRERERERERERERETkwPDMKdHW7v7TALurCdMiMyivWoW7ahXeyEi98Ted3nGhMYQLFuA++SRmbIzqrbfCDhrQt7EH72U5vNUefxymp3FqNWhrm7E+bGkh0dtLWKtRWb1aE8hFRERERET2o9KDD1L+1Kdwhodxx8ZwSiWwFhuLYR97jMn//m+4/HJa3/jGba9xiUhD1YaGKP7wh0RDQ2AtpquL9CtfSay7u9HRRGQPGc8j9YlPMP2Od1ABYv39MDpK0NSE9TxMpYI/NUXk+1SPOIJoxQpa/uEfGh17q8ott+COjWGsrU8+31mhMYTz5hFfs4awWKRw0020/v3fz2ZUEREREXkGNaCLiIiIiIiIiIiIiIjsgSCXY+rmm7H33gsTE+C62O5u/HPPJXv22bue2rsL/vz5hJ5H5PswNQWp1C7rzdQU1nUhHsebN+9ZHVMEYPpnP8MUCji1Grazc5e1xnEIWlpwx8awjz5KODmJ29S0TY3X3U3V94k8D2dqCptM7jrA5GT9vRyL6b18GLPlMoRh/X+8mW9jG9+vb3AQRYSl0n5OJyIiIiIicvgq/PKXBB/6EG5/P/7wMABBJgPG4E5M4I6MUMtmCb/xDcZyOdquuUZN6CINVuntpXD99bi//S2USjjT0/VNI5JJSv/930ydfDLpt76VxLJljY56QCk98gjTN92E+cMfMMUixGJERxyBf9559Wv/u3HNSmR/SqxYAV/4AqV/+ieqLS0wNoY7NYWp1bCeR2XxYmhuJjz+eFo+8QncbLbRkbdyVq3CnZggaGraZmr7DqVShIkEZnyc8LHHZiegiIiIiOyQPgWJiIiIiIiIiIiIiIjsDmvJX3895vvfxxSLOLkcploFY7DpNOEf/kD+K18h9r73kfnzP9/jl08cdRTFxYsxg4P4Y2PUurp2vljXWrzRUYLmZqL2dtIvetFenpwczuz4OKZaJfI82I0NFGwyiRkfB+obMvxpA3rq+OMpd3cT9Pfjjo0RdHbu+r2cyxG0tBB1dZE65ZS9PR05SJlMBrvl/WfLZcxMGxeUy1jHAcfZ7j0oIiIiIiIi+0ZteJjaxz6Gs2kT/ugotTlziDo6tjaORdYSFAp4fX0469YR3HEHE0cdRfPKlQ1OLnL4mn7sMcrvex9ufz/e0BBuqYTd0jhtgoAwlYLxcaYff5zoU58i9bznNThx44UTE4x/8IO4jzyCOzGBk89jggAch2jDBqJHHiH/n/9J8qMfJXnssY2Oe+iwltLDDxP09UEU4XZ1kTr5ZDX6zyCxYgXxb3+b4r33UrnlFqJ16zDVKjadxh5/PKlLLiF53HGNjrkdMz0NUbT169FMrO9jwhBTLu/nZCIiIiKyK/rpXEREREREREREREREZCbWMvaxj+HcdRduby9eoUCYSBAlkxBF9elPg4PUOjsJPvhBCh/+MJkzz9yzYxiDd9552HXrIJfD6e0lWrx4+8Zda3E2b8apVgkWL8a84hVakCV7x/PAGEwU1SdKzzSl7Bk1Jhbb/nFjcF79amxvLyafx9m4kWjRopnfy698JcZ199FJycEmccopTCcShIkETi6HXbBgl/Xu2BhBUxM2kyFxzDGzlFJEREREROTwUrjxRhgbIzY8XJ+q2tLCNp/ujYFsltqyZcTWrMEMDRF+73tw4YWagi7SALXhYcrvfz9mwwbi/f3UWloIFiyALRv92elp3NFR4r29VKtVKh/4AN7XvkZs/vwGJ2+ccGqK8be/HffJJ/F7ezG1GkE2S5RKYcIQb3gYMzREdWyM8rvfDZ//PMnnPrfRsQ9qtlpl4nvfI7r9dpy+PqjVwFqiWIx8Rwfm5S+n6a/+6oCa3n2gMa5L5iUvIfOSlzQ6ym6zqRTWdevT2nej3lSrRKkUdqaNSkVERERkv9JqJBERERERERERERERkRlM3Hgjzo9/jLd2LVhLZdkySKe3Pl61tr4Yt7+fWhRR++Qnqa5YQay7e4+O07RyJWM//SlUq/g9PbirVhG0tWGbmrCAKRbxRkdxgoDq4sVEy5fT/NrX7uOzlcONu3gxNp2uN6AXCjDDwj5nYoIwmcQmk/hdXTusabrkEvJ33w21Gn5PD85TTxG2tRE1NdWb3aem8HI5nFqN6pIlhEceSetf/dX+OD05SMQXL6Zw4okwOkqsv59qWxukUjsuHh/HK5WoLF8O55yDk0jMalYREREREZHDgQ0C7J134o2O1icmt7TstNb4PkFHB97oKHbTJkq/+x2pP/uz2QsrIgAUvv1tzMAA8f5+Kt3d0NGxzeMmmSRauJBKKkW8r49KKkXhW9+i7ZprGpS48SY++1mcVauIrV9PkMkQLl+O8X0ALBBZixkZqf+eAtMf+hCJ7353a43smXBigvGrr8Z99FHckRG8fB4nCACwrkvQ3EzU38/Ez39O5rrriM2wSaUcPKJjjsH09hLr66MSRRjH2XlxsYhbrRK0tuKccMLshRQRERGR7ezipzYRERERERERERERERHBWsIbb8QMDmLCkNqfNJ8D9YlO7e1UFi0iNjKCGRujeNNNe3wo4/u0fO5zhM9/PrWjjiJoacHL5YivXk1i9Wr8oSHCbJbqihWExx5L9vOfx81k9tGJyuEq86pXYTMZgkwGd2SkPuF8J+z0NH6hQNTRgX3JS3a60NJJJGj+3OcIjz+e2ooVhE1NeCMjJJ56isSqVfhDQ0SZzNb3ctPnP4+zs2ZjOWwkr7gCOjoIslli69dDLoeNoq2P2zCsL6LeuJFqVxe2vZ30ZZc1MLGIiIiIiMihq7JhA874OO7kJEFb24z1UXt7vYmwWKTy0EOzkFBEnikql+EnP8EZGSFIpbZrPt9GeztBJoMzMoK5+26iUmn2gh4gbLVKed06zL334vX1EaZSRIsXb3+90xhsVxfVuXOJDQzgDA0x9eMfNyb0Qc7WavXm84cewl+9uv79pbOT8tFHUz7mGKrz5uFMTxNbvRpn1SoK73kPQT7f6NiyjyQuughaW7GOg9Pfv/NCa3EHBgjSaUinya5cOXshRURERGQ7moAuIiIiIiIiIiIiIiKyC8UHHsDp78fL56l1de1ysolpbiZIp3FGRwnvugv793+P8fbsdozb1ETb9dczcdNNhLfdht20CbZMAMHziObMwTn3XFovu0wNu7JPuE1NRGecQTg+TmzdOqK+Puz8+fWNFZ7BTk8T27CBIJOBpiZSF1+8y9f12tpo/dKXmPze9wjvuAM7MAC12pYHn/FevvxyvZcFgNSf/RnVt7yF6HOfIwgC4hs3YjdvJkylMIBTLmNdl0p3N7a7m9hHPkKsu7vRsUVERERERA5JUaEAgAlD2I1Jv8Z1sY5Tv451GDazijRa6fe/x0xM4E1OUl24cMb6oL2deG8vUaFA6f77yZx11iykbKxwcpKpW28l+uEPcfr6MIODJAYHcSsVSosX7/K5trMTOzKCyeWo3XornHvuLKU+dEzeeivOY4/h9/QQptNEixeDMWy9Ct3eTtDWhhkYILZxI9VYjKlvfpPWd72rgallX0mfeiq5Zctgaqp+n8Faorlzt/kZw05P4/X14VQq1JYvJ3zJS/Da2xuYWkRERETUgC4iIiIiIiIiIiIiIrIL1YcfxhQKOGGIbW/HzFAftrbiDw4STUxQ2bCBxPLle3xM4/u0XH45XHYZ048/TtDfjw1DvLlzSZ144naNwSJ7q/ntb2fysceohiGx3l7s5CRBays2lYIwxJmYwJ+aIshkCJcswb72tSSOPHLG13USCVpe+1q44gpKjzxCMDAAgNvVRfr5z9d7WbayQcDUbbcR3nknbqWCWy6DMTi1GmZqiiCVotLdjWlvJ1y8mOR731v/eigiIiIiIiL7hZPJAGBd948byu2CDUNMFIHrQjK5v+OJyJ+I8nkIAkwUYZPJGa9jk0phrIVajXBsbDYiNlTh3nupfeITmIkJ3NFR3EIBb3ISt1rFAPHBQcLJSYKlS3e86YYxBM3NOFNTOOvXz3r+g561hLfdhjs6Co6ztfl8O8Zg580jKhZxhocJf/IT7FvfionFZj+z7FvGkP3kJym87W1UjcHfvBn/yScJ0mms52GqVbxSiTCVqjefn3ACrVdf3ejUIiIiIoc9NaCLiIiIiIiIiIiIiIjsgi2VMGGIdRyM48xc7/v1xbZAVCzu3cGNIfnc58Jzn7t3ryMyA6+1lewXvsDU1VdTTaUwuRzu2BhOLoc1hiiVorJ0KWSz2Ne9jta//ds9O4Ax9WZhNQzLDoSFAuP/8A84Dz2ENzqKl8/jhCEmDDHWEjlOfUGq42De/37aX/EKbV4gIiIiIiKyn8WXLKHU0kLY1IQ3NkYwwwRSJ5cjcl3IZIg973mzlFL2VG14mMJttxGtWQPlMqapCe+FLyR7zjmY3Zh0Lwcwz9t6veTp69O79HSNMYd8c2/h3nsJPvxhnM2biY2MEMZihM3NmEoFt1bDhCHe9HT9923tWmrLl+/474PnYaanMdVqfdMN1539kzlIldeswdmwAW9sjFp7+66v7RlD0NlJbNMmovFxCvfeS/bss2cvrOw3se5usl/+MpMf+QhkszA5iTMxAVGETaWozJ8P6TTRGWfQ+v734yQSjY4sIiIicthTA7qIiIiIiIiIiIiIiMgumHS6vrAsinZrUZmpVrFbGtWfnhQlcjDw58yh9T//k6k776R2663YNWu2PmYTCewZZ5C65BISRx3VwJRyqLFhyPj734/729/ib5keFbS3Y7NZrDGYYhEvl8OtVgldl/Ab3yB80Ytwm5sbnFxEREREROTQZjwP8/KXEw0M4K9eTTAxATv5LGZrNfzRUYK2NqIFC0i/4AWznFZmEk5MMP6Zz+D++tf1z9qTk/WGP98nuvtu8l/+Ms7ll9NyxRXa9O0gFVu2jLLvE8VimPFxbDK5y3ozPk7k+xCP4y9dOkspZ184NUXtk5/E2bwZf3SUyqJF9a9lxhCVy4SVClhL5Lp45XJ9M86+PqIlS7Z7LVOtYl0Xm0io+XwP1QYGIIpwarV64/EMbDZb3xCgUiEcHJyFhDJb/Llzaf/KV5h+/HFKN920dUMU0mk48UQyF19MbMGCRscUERERkS3UgC4iIiIiIiIiIiIiIrILsZNOopbJEHlefRp0V9cu6918nrCpiai1lfjixZRXr6Z0223YzZshDDGtrTinn07qtNO0SE0OOCYWo+n88+H886kNDxPm8+B5xObP17QR2S+mfvAD3Icewl+/njCRIFyyBLNlEw8DkEwStLdj+vuJ9fZS9X0mvvEN2t75zobmFhERERERORxkLrmEwq23Uu3qIrZxI9V586CtDbZ8bsNabKGA39eH9TzsnDl4l16qBuYDTJDLMfn2t+OuWYPb14dXKhHG41jXxalWMf39BC0tRF/9KmObN9P2D/+gP8ODUOLIIymtWEEwPIw/MkKlq2un159tFBHL5Qja2giXLiX53OfOctrZU7j9dszEBLGRkXrzeUvL1seiTAY7MbG1sTyyFrdSgclJKrUaPGMKuo0ivIkJqvPnEx1/fAPO5DCz5WuQDQJq995L7s47cUZG6r/W3o456yyyK1fitbc3MqXsheSxx5I89thGxxARERGRGagBXUREREREREREREREZBfSJ53E2MKFBAMDeCMj1Fpbt1l4to3xcbzpaSoLFxKdfDJjV16J+/jjOFNTmGIRABuLYe++m8muLtw3vIHWV796Fs9GZPf5XV34M2y4ILK3gttuw83lALZpPt+GMdjubqJSCWd4GPvjH2Pf8hZMLDbLaUVERERERA4v/pw5+B/8IMGHP0zNcYgNDmKHhgjTaXAcnOlp3EqFWjZLuGgR9txzaVm5stGx5ZmsZfKaa3BWrya2fj1BJkPlyCMhlQIgtBbGx/EHB3HWriX4wQ8YX7CAlte+tsHB5dnwLryQ8MknsWNjeOvXEyxZgvmTa9m2VsPr7a1fb+nsxL/ggkN6w4HwBz/AHR0ljMXqk8+fwba3YwcHCV0Xp1YjjMXwy2XCMMSMjmLnzdtSaHH7+rCOA62tJC66qAFncnDz580jdBwi34epqa1fg3bGTE5iajXiGzZgymVMrYZTLgNgEwmidesofPvb8LrX0fLXf31Iv4dFRERERBpJDegiIiIiIiIiIiIiIiK7Ygze5ZcT9fbWJzqtXUtt/nzIZv84hSOKcEZHiQ0OUu3qIvJ9nJ//HGd0tL54s1olTCSwxuBWKtj+fmqDg9hrr2V8YoKWv/qrBp+kiMjsq/T04KxejZfLEbS27rj5/GnGEHR2Etu0iWh8nMIvfkH2nHNmL6yIiIiIiMhhKnP66RQ/9Skqn/kMdngYMzZW32jRWsKmJoK2Nmw2C5dcQtub36wmwANM4Ve/wnnqKfwNGwiam4kWLtz2z8gYaG2lmskQW7sWp6+P8IYbsK95zXaNy3Lgazr3XHK/+Q21KMJbv574qlX1P/dMBgCnUMCbmMD6PrVlywhPPZXmCy5ocOr9xwYBzqZNuIUCYVPT9l+fHIdaezsmDPGmp3GrVSxgwhBTLmMBW6ngDg7iTU5SXbKE8MgjaTn11EaczkEtceSRlJYsIRgcxB8bo9bVtfPvF9bi9/ZiwhC3UiE+MECYSNQ3PzEGt1jEz+WoNTUR/ed/ki8UaH3722f3hEREREREDhNqQBcREREREREREREREZlB0/nnM7Z6NcEtt+Bs2kR8wwZC3ydKJsFavGIRawzVefOI5szBhiHu0BDxvj6qbW3Yri7YMqk3iCLI5YgPDRGUy0T/8R8UFi0i8+IXN/gsRURmV62/H6IIp1olampiphYF29SEiSKoVAgHBmYlo4iIiIiIiED6hS8kdcMNFH7xC6p33IEZGIBaDdvSgnv66WQvuACvtbXRMWUHKjffjJvPY6wlXLAAs5OGT+P71ObMIdbXRzQ2xtSPf0zTuefOclrZa8bQ9pGPMOZ58POfw/g4bi6Hv+U6ShSPU12wAFpaCF/8Yto+8hGM6zY49P5jq9X6f0QReDtum7Dd3dQqFQDcSgUnijDVKjafJ6zVcMtlwnic6hFHEC1dStOnP62NNp4NY3DPOw+7di3kcjgbNxItWrT976W1mJ4e4sUikeMQOg6VI46ATGZrbQDUCgVimzZh1q8n/O53KRx/PJnTT5/98xIREREROcSpAV1ERERERERERERERGQ3tL33vYy3txN+5ztEU1OYXA5TrcKWxnPa2ojmzSM85hj8O+8k3tdHpbsbOjq2eR3jONiODirpNPF166gND1P5//4/NaCLyOHH2q3/uUfLdq3d5rkiIiIiIiKy/xnPI3v22XD22Y2OIrvLWtw//AE3nydoasI4zq7rW1uxAwMwPk7wwAOgBvSDkvF92j/yEYrnnUf5ppvgN78hDMP6g45DeMopJFaupOWUUw75RmonmcT6Ptbztk4035Fo6VKqfX34uRxOGBK5LpHvE2azBPPnQyZDeNJJNH/gA3jt7bN6DoeSpgsvZOwnP4FqFb+nB+eppwjb2oiam8EYTKGANzqKn88Teh4miigfdRQmk9nutUwmQ3XZMuKrVxPmclS+9z01oIuIHCLKTz7J9C9/iZ2YwMTjuMuXkz37bIzvNzqaiMhhSQ3oIiIiIiIiIiIiIiIHqGBsjOI99xCNjWE8D2/xYtKnnYbZyaQO2c+MoeVv/obw0ksp3HEHwT33YMbHsZ4H3d3EX/1qMn/+54xddhlmdJQwHt+u+XwbySTV9nb8XA67ahXlp54icdRRs3c+IiIN5s2ZQ+A4RL4PhQKk07t+wtQU1hhsPI47Z87shBQRERERERE5SEXT0xAEmCAgymZnfoIxWN/H1GrYycn9H1D2H2NIn3wy6ZNPJiqVqA0PA+B3deGkUg0ON4uMIXre8wiHhoj19VEJw51OfLfz51ONx3F6e6k1N1N57nMxy5fjrFhBauVK4osXz3L4Q4/xfZqvvZbJq6+m5vuYkRG8kRGcgQEArOsSpNNEsRhOrUZ57twdNp9vfb1YjFprK14uB488QqW3V39OIiIHscJ991H95jdxVq/GFIv1jeAdhyiTYfxLX4JXvpKWN74RE4s1OqqIyGFFK9RERERERERERERERA4w5XXrKH3zm5hf/QpTKmEqFTCGIJkk39mJecUraP7rv8ZJJBod9bDkZjI0v+Y18JrXbPdYed06nOFhvPFxal1dM76WbW/HGRmBUonyr3+tBnQROawkli+ntGQJweAg/thY/evmLiZveaOjBE1NkM2SOeOMWUwqIiIiIiIicvAx8Xj9PxwHgmD3nhSG9fqnnysHPSeVIr5kSaNjNEzioouoPfAAdnAQt6+PaOHCHV9/qtXwR0aodnURHHcc7TfdpM2A9wOvtZWWf/93Jr/3PcI77sD290OtBtaC7xOFIdb3iY2OEnZ2svMrhXW2tRV3dJSgVqPy2GNqQBcROUhN/O//Yr/8ZdzhYbzRUZxqlch1MVFUXyfR3Ew0MsLYo4/Set11h9eGOiIiDaZPRSIiIiIiIiIiIiIiB5DSgw9S+eAHcYaGcEdG8IrF+sIbtkx/aG4mGhhg/MEHaf7853GbmhqcWJ4p2jIZyAnD3VukGYthHQdqta3PFRE5bBiDe9552LVrIZfD2biRaNGiHS8CHhrCLRapLl+OPessLS4SERERERERmYFxXcLFizEDA7gTEwTz5u1y4zdKJdxajaCpCefII2cvqMh+lD71VHLHHAPT08TWryfs6SGYMweTTtcLogjyefyhIazvE82fj/sXf6Hm8/3ISSRoee1r4YorKD70EGFfHzaK8ObMIRgcxF57LeRy4PszvpZ9uiYMsdPT+zm5iIjsD1M//Sn2S1/C2bABr1Cg1t6O7eiofx+wFsbH8YaHcdesgSgi/0//RPvnPrfrn2tFRGSf0ScjEREREREREREREZEDRHnNmnrz+YYNxIaGqDU3U162DJNKYa3F5PN4o6O4a9ZQCwLGr7mGti9+UQuhDiBOMglQbyoPwxnrbRRhrAXH+eNEIhGRw0jThRcyduedUK0SW7+eaPVqgvZ2aG7GAqZYxB0dxZ2eprpwIdHChWTf8IZGxxYRERERERE5KHjnnotduxZ3ZIQgn4e2th0XWoszOEiQSkE6TeaCC2Y3qMj+YgzNn/40k1deSdUY3P5+EuvWEfk+1nFwajUAgpYWou5uogsvpO01r2lw6MOEMaRPOglOOmnrLxV+8QtqrgvGYMpliMV2/RqVSr0B0fP+uKmAiIgcPKyl9rWv4Q4O4hUKVJctgy3324H61/jWVoKWFtyeHvyeHkgkKD70UP17iIiI7HdOowOIiIiIiIiIiIiIiEhd8WtfwwwOEhsaorJoEdHixfUFM8ZgHAfa2wlWrCBMp/E3bMB99FGmfvKTRseWZ4gtXoxNJgnSaZzx8RnrTT5fb1ZPp/GOOWb/BxQROcAY36f5s58lPP54qitWEGUyxAYHiT/xBIknniC2aRPW96kuW0Z05JGkPvtZ/M7ORscWEREREREROShkXv1qbGsr1a4uYn19MDZWnyT5DDaKcDZtwisWCbu7iU47TZ+95ZDitbXR/JWvEJ17LsHRR1M56iiCjg7C1laq3d1Un/McghNOwLz97bS9972aqNpAyec9D5JJgkwGJ5+fsd4dG6tvnBGPkzz55FlIKCIi+1Lx/vtx+vvxcjlqXV3bNp8/kzEECxdiggDGxynfdNPsBhUROYxpJIqIiIiIiIiIiIiIyAGgNjiI+9vf4g4PU2tpgZaWHRcaQ7hoEd6TT2JyOWq33QavfOVsRpVdcJJJ7JlnEg0PE+/poVIqQSq1w1obRcRGRuqTVbq6aD7ttFlOKyJyYPDa2mj90peY+J//IfjBD4hGRurTi6zFxmLQ3AwvexlNr3sdXnt7o+OKiIiIiIiIHDTcTIbYBz9I7QMfoAbEN28mHBoibG6uTwwul/EmJrCOQ/WII4iOPJLmq65qdGyRfc7NZmn7yEcIxsYo3HYbwZo1UKlgmprwX/hCms86C+OptaLR3OZmoj//c8KREeK9vVSmpiCb3XFxsYg/Pk5l0SLCU0/VxhkiIgehyt1340xMYKKIqL2dXW0BY3yfoKkJd2wM+8AD9U2VtGmMiMh+p09JIiIiIiIiIiIiIiIHgOJPfgKlEl6pRGXBgl3WGseh1tJSv7n6+OPURka0sOYAkrrkEsp33UWtuZlYTw/VhQuhqWmbGlut4m/cCNYSzZ2Lc/75GNdtUGIRkd0X5POUHngAOzWFiceJHXMMiSOP3OvXdRIJWv/2b7Gvfz2l3/6WoK8PG4b4nZ2kXvQinERiH6QXEREREREROfykX/Qiip/8JNWPfYxKeztmdBS3UIAownoe1fnzobWV8JhjaP7Up3CbmxsdWWS/8draaHn96xsdQ3Yh/Zd/Sfm++6hNThLbsIHq3LnY9naM4wD1zX3N2BixwcH6hs7t7aT+4i8aG1pERJ4VOzGBqVaJYrHdulduk0mcsTFMtUpYKODubJMSERHZZ9SALiIiIiIiIiIiIiJyALCjo5hyGeu6kEzOWB9lMvhjYwAEw8NqQD+AJI48kvJb3kL07/9OsGkT8Q0bCBMJwmwWHAdTLBKbmiJMJKguW4Z94Qtpe+1rGx1bRGSXymvWUPzWt3B+9SsolTBBgHUcyrEYxaOPxr/oIppe/vK9njZhXJf0qafuo9QiIiIiIiIiApA+9VSSN97I5O23E/7gB9jeXrAWG4sRnXgiiYsuouXUUzVFUkQaLnHUUVSvvpro2mup+T6xoSEYGiLccu/MnZ4GoNbRgZ03D+fd7yZ53HGNjCwiIs+Scd36z5/W7la9fcbUc+P7+zOaiIhsoQZ0EREREREREREREZEDgHUczNM3V59x43RnzDNvrnq63H+gabn8ciZ9n/BLXyKamsKMjuIUChhriWIxKkuXQjZLeMYZdHz4w5p+LiIHtMLdd1P75CdxR0dxR0bwisWti4GieBwzPEz0xBOM/f73tP3jP2qxuoiIiIiIiMgByEmlaLn8crj8cmwYYqtVnERCn+NF5IDT9MpXUshkqP7bv2GHhmBsDKdcBqDa0gKtrURdXcTe8Q4yZ57Z0KwiIvLsmYULsek0bn8/telpzAwb9buTk0SpFFF7e/3nWBER2e+0Ik1ERERERERERERE5ADgzp+PTaXqjeWFAmSzu6x3JicJEwlwXfx582YppeyJposuIjjzTAq33EJ4113YwcH6VKFMhuCFLyRx0UVkjjpKu7OLyAGt+LvfEXz84zi9vcRyOWrNzVSWLsUmEhAEOOPjeCMj2PFxgh/+kHwqReu7393o2CIiIiIiIiKyC8Z1Z2zwERFppMyLXwynnUbhvvuo/uhHhMPD9Qc6Ooi/4hWkTz9dm/uKiBzk0uefT+m73yVMpXBHRogWLdpprS0U8EolKgsXYl7+8llMKSJyeFMDuoiIiIiIiIiIiIjIASDz8pcz+dWvEmQyuCMjhJnMTifP2FoNb2KC6oIFhC94AW5T0yynld3ltbXR8oY3wBveUG8+D0OM51Gr1RodTURkZtZS+cIXcAcG8MfGqCxZAlu+5xgA38cmk1Q7OvDXr8ft6SG8+WYqK1cSX7KkgcFFRERERERERETkoGcMmdNPh9NPb3QSERHZD2ILFjB18skwOUm8p4eq72Pnzt1+nUSpRLy3l1pLC2QyZC66qCF5RUQOR06jA4iIiIiIiIiIiIiICLjZLPaMMwjnzsUtFjEDA2DtdnW2VsPv6SGKxaClhcTKlQ1IK8+KMRhPewOLyMGj9PDDOBs24I2MUOvs3Np8/qeM71NbvBivVIJikeKNN85yUhERERERERERERERETnYZN/7XqJFi6gsWoQ/Ooq3ahUMDWEnJmBsDHf9euJr1xKk00SLFmH+/u/xu7oaHVtE5LChBnQRERERERERERERkQNE01veQrRkCdVFi/BzObynnsIMD0OxCFNTmM2biT/1FFhLsGwZ4TnnkD7llEbHFhGRQ1T5zjthYgIThkTt7busNYkEQTqNMzoKv/jF7AQUERERERERERERERGRg1asu5vU5z9PtGIF1aOPJmxpwR8bI75xI/7AANZxqBxxBNERR2De9jaaL7us0ZFFRA4rGrMhIiIiIiIiIiIiInKA8NrbSX/+8xSvvppqMokZHcUbGcEZGAAgTCapzpsHbW1EL30pbe9/PxjT4NQiInKosiMjmOlpongc4/sz1oeZDN7EBM7kJLZW263niIiIiIiIiIiIiIiIyOErsWwZsf/+b6Zuu43w9tuxmzZtfcwmk9izziJ1ySUkli1rYEoRkcOTGtBFRERERERERERERA4g8UWL8L7+daZuvpnwjjuwAwMQRfVGc8chPPFEYhddRMvpp6v5XERE9i9j9uh7jbF22+eKiIiIiIiIiIiIiIiIzMBJpWh+zWvg8sup9vcTjo9j4nFiCxbgJBKNjicicthSA7qIiIiIiIiIiIiIyAHGzWRoee1r4YorKK9aRTA6ivF9/EWLiHV3NzqeiIgcJkxnJzaZxB0aorYbE82dQoEokSBqacF4uhUtIiIiIiIiIiIiIiIie8AYYvPnw/z5jU4iIiKoAV1ERERERERERERE5MBlDIljjml0ChEROUwlzz2Xyg9/SOT7OKOj2Hnzdl48PY1XLFLp7oaXvnT2QoqIiIiIiIiIiIiIiIiIiMg+5zQ6gIiIiIiIiIiIiIiIiIiIHHiSxx1HuHw5tc5OYiMjMDGx48JqFa+3lyCTgXSa9MqVsxtURERERERERERERERERERE9ilNQBcRERERERERERERERERkR1KvvOdVDZupBoExDdsoJbNEra3QyKBCQJMPo8/Pk4YjxMsWQKXX0580aJGxxYREREREREREREREREREZG9oAZ0ERERERERERERERERERHZodQJJxB95CPU/vmfqWQyOCMjJHp7wVoAwkSC6ty50N6OvfBC2t7ylgYnFhERERERERERERERERERkb2lBnQRERERERERERERERE5tFlL8YEHqD72GJTLmEyGxKmnkjjqqEYnE9m/rKW8ejVhPo+JxYgtXYrX2rrHL5M57TQqX/saxW9/G/uLXxAVixAEWMfB+D7hiScSv/hiMqefvh9OQkREREREREREREREZPbZWo2pn/6U2m234axfj6nViDIZ7POeR+qSS0ged1yjI4qIiOxXakAXERERERERERERERGRQ5O1THz3u4Q334zT348pFiGKwPMo/7//R/Hoo4n91V+RPeOM2Y0VBEz95CfUbr+9vlilWsWm00TPf74Wq8g+EU5OMnXjjUQ//CHO8HB9WrkxVF2X8EUvInnppaROPHGPXjO+eDHx97+f8MormX7oIaKJCUwiQfyYY4gtWLB/TkRERERERERERERERKQBSo88QvkjH8EMD+ONjeFMTUEU4XgeUW8vlZ//nNLznkfLxz+Om802Oq6IiMh+oQZ0EREREREREREREREROeTYIGDswx/G/cUvcIeG8PJ5nKcnNkcRUTyOGRkhXLWK8Te8gZbXv35Wck0/+ijTH/4wZmhom8Uq1vMwGzfWF6uccAItn/gEblPTrGSSQ0t59WpK11yD09+POzqKNz6OEwRgDEEqBbkc1fvuo/Ka19D6lreAMXv0+m4mo0nnIiIiIiIiIiIiIiJyyCo9/DCVq6/G3bwZf3AQHIegqQnruphKhXhvL5Hvw/Q041deSeuXvoSTSjU6tsw2aynefz+V++6DqSlsLIa3bBmZV79a93lF5JChBnQRERERERERERERERE55OSvuw735z/HW7sWYy21ri5sWxvGdaFaxeRy+ENDhFNThF//OpNtbTSdf/5+zVR65JE/LlYZGNj5YpVSiYm3v53mL30JN5PZr5nk0FLdvJnSe96Ds2EDsc2bieJxanPmYJNJCALc8XFimzYR5nKE3/42ed+n9U1vanRsERERERERERERERGRA0JULlP+0IdwN28mNjBAde5cbGfn1g19LVCu1eqPr1tH1RjGP/c52j70ocYGl1k1+aMfEXzzmzh9fTiTk5haDWsMNptl8r/+C3vmmTS/4x242Wyjo4qI7BU1oIuIiIiIiIiIiIiIiMghpdLbi/PDH+L29IDjUF22DOO6bJ3zHIth582j0tpKfN06bH8/wde/jn3VqzDe/rl9ts1ilf7+nS9W6esjtn59fbHKZz9L+0c/ul/yyKFp6l/+BXfzZuKbN9ffY11dAFvf+1FzM5VKhdj69bBhA+G3vkXlZS8jvmRJwzKLiIiIiIiIiIiIiIgcKKZ+9CPM6Cj+4CCV7m7o6Niuxvg+0ZIlhOvX4/b1Ye+5h2BsDK+trQGJZbbl//M/Md/8Ju7gIN7YGCaKiHwfJwwxfX0EmQzh+Djjq1bR/IUv4LW2NjqyiMizpgZ0EREREREREREREREROaQUv/99nEIBb3qa8lFH1aee74BJJKjOnUtsYIAol6Nw991kzzlnv2Qq/PjHOKOj+AMDVObNg87O7fP4PtHixYQ9Pbj9/XDffQS5HF57+37JtKdqw8MUbroJ+8ADMDUFvo9dupTE+eeTPuWUrc300hjVvj7c3/0Ob2iIWkvL1ubzP2XicWpLlhBfs4awUKB4443Er7pqltOKiIiIiIiIHMKspfS73zF9xx0wMADWQlsbsZe+lMxZZ+kaioiIiMgBLLjjDrxcDuu6sKt7dMYQzJ1LYt06wkKBwq230vI3fzN7QaUhJu+4o9583tODMz1N0NlJ1N5evx9tLRQKuENDxNato2otE9dcQ/tXv6rPACJy0FIDuoiIiIiIiIiIiIiIiBxafvlLnFyOIJXCxOO7rm1rww4OwtgY1Z/9DPZTA3rt9tvxRkfri1V2MClhK2MI58whvm4dYbHI1M030/rGN+6XTLvLBgH5667DufNOTLGIk8thggCMIXrsMWr33Udu8WLSH/oQiRUrGpr1cFa8/XZMsYhbKlFbsIBdLmNJJgnSaZzRUezdd8N736uFLyIiIiIiIiL7QOnhhyl/9rM4mzbh5vOYUgkDRPE44a9+Rf7663He9CZ4/vMbHVVERERE/pS1uGvX4k5OEjQ3z3jvxKTThL6PmZggWLVqlkJKw1hL8M1v4g4M4E5PU1m+HBOP//GenDGQzRJmMtiNG/F7eiCZpHDffWROP72RyUVEnjU1oIuIiIiIiIiIiIgcpqJSiakf/IDgF7/A5PNYx4G5c4m/+tWkX/zinU4MFhE50JmJCUy1SpRK7UaxIYrHMZUK5PP7J9CWxSrO1NRuLVYhnSaMxTATE4QNXqxiw5Cxa67B/fWvcTduxCsUCJNJokQCwpDY5s3Y/n4YH2f6yivhX/+VxDHHNDTz4cr29+OUSkSxGCaZnLE+ymbxxsaICgWC8XG81tZZSCkiIiIiIiJy6Cr++tdUP/Qh3P5+vOFhTBgSJpNgDP74OGZwkFprK9G111J9zWuIrVzZ6MgiIiIi8gxRuVyfYh1F4O1ey531vHp9ubyf00mjFX75S5zBQbx8nuqcOTvfCN0YwoUL8Z54AsbGqNxyixrQReSgpQZ0ERERERERERERkcPQxHe+Q/SNb2CmpvByuXrjpTFEqRTB/feTnzuX+NVXkz7llEZHFZGDTFQuE4yNYVwXr70ds5uLM/Yp1603eVu7W+XG2nr9ftp4wwYBhCEmiuqLUHaH60IYQqWyXzLtrvH/+A/c3/wGf+1arOtSOfJIeEZjfyUMcYaHiW/aRCWKKP3jPxL7zndwEokGpj482TAEa7G7OcncOs4f/45E0X5MJiIiIiIiInLoq/b3U/3oR3E2biQ2PEx1zhyijo6tG72G1sLYGP7AAGG1SvJb32J60SJ41asanFxEREREnuYkEuB5WM/DVKvMeKfRWpxqlcjzIJOZjYjSQNX77sOZmKjf821rY1d35IzjEDQ34+bz8NBD2CBozH1zEZG9pK9cIiIiIiIiIiIiIoeZ/Je+hPnOd3A3bcKbmMB6Xn0Ki7XEBgZgYIDa6Ci197+fqQ9/mOwZZzQ6sogc6Kyl+MADlG++Gfe3v603TgM2kcCecQapSy4hcdRRsxdn7lyi9etxJycJnm4u31ltrYYzPU3Q1QXd3fslj/F9bCyGdV2cSoUZW32txdRq9ckK6fR+ybQ7onIZbr8dp68P6zjUli/HOM42NcZ1sfPmUXVdYv39VJqbmfrRj2jWBK9ZZ9rasIkEbrVKrVbD+P4u651ikSgWA8/DbW6epZQiIiIiIiIih6biDTdgcjliQ0NUFi+GlpZtG1KMgfZ2aqkUsbVrcXM5nJtuUgO6iIiIyIHEGMLjjoOhIWIDA1Tmz9/u3tg2JicxUUTU0oJ38smzl1Mawk5MYKpVolhs1++LLaJkEr9QIIwiwslJvLa2WUgpIrJvzfzVTkREREREREREREQOGVM//Wm9+Xz9etxCgcqiRdSOPppoyRKipUupPOc51Lq68IeGMBs3EnziE1T7+hodW0QOYNH0NLmrr6Z29dW4P/wh7lNP4T/xBP6TT+I++STOjTdSftObyP/rv+72RPK95ZxzDrazE7daxU5O7rp2dBTr+9DcTPK88/Zbpuj44wlbWvAmJ7EzTJu2k5OYMMS2tjZ0scrUj34E4+N4k5MEc+bsciGF7eysN9iPjhLedtssppSnJc85B7JZIt/HGR3dZa2t1fAmJwnb2wlPPVUTF0RERERERET2gq1W4Sc/wRkZIUinoaVl58XJJNXWVmL5PIknn6S6YcNsxRQRERGR3RBfuRJaW7HG4PT377TOhiHewABBUxM0N5N95StnMaU0gonHwXEwM9zr3SoMsVvurzqJxH5MJiKy/6gBXUREREREREREROQwUv3OdzAjIzjT01SXL8c0N28zGdg4Drari8qiRcRGRjBjYxS/970GJhaRfcGGIYW77yb3zneSf9WryL/0peTPO4/cBz5A8fe/f9aN4TYIyL/vfbj33Ye/ahWxzZuxsRi1OXOodXYCEO/pwV27FvO97zF23XX78rR2KnvBBdhsllprK7FNm6BY3HHh2BixkRFqc+YQLVtG6sQT91um+IUXQlvbHxer7OT33EYR/uAgQVMTNpsle+65+y3TTML/+z/MxER9YcRME7KNIWhpwZ2cxFm3rj49XWZV8rjjCJcvJ+jowB8dxU5N7bDORhHexo31jRdaWkhoWr2IiIiIiIjIXpl+4gnM1FR9s7fdmGoYtbfjVipQLjN9//2zkFBEREREdlf6xS8mXLaM2qJF+Pk8Tm8vtlL5Y4G1MDmJv3YtBogWLIALLlCD8WHAWbIEm8ng1Go7v//8DN7EBFE6TdTRgZNMzkJCEZF9T1vZi4iIiIiIiIiIiBwmph9/HHfNGrxcjqC9HROL7bTWNDcTpNM4IyOEP/4x9q1v3WW9iBy4ymvWUPrgB3H6+nDHxnAnJyGKsK6L6e2ldt995J7zHJo/8Qm89vY9eu2Jb38b9/e/x1+3jigeJ1ixAuO6Wx+POjooVyr4PT2469fDrbdS/PM/J33qqfv6NLfhZrO4V15J9NnPQhQRX7eOWiZD1NYGrguVCm4+jzc9TXXuXGx3N6n3vW+/Zsq8+MXkli2DQoHYhg0EYUg4d259p3yoL1aZmsIfGMBYSzh/fn2xSgMXI9hSqT6J3fO22axkp/WxGCYMAYiKRS20aYDEm99MraeHoFwm3tND0NpK2NGBSSSw1mLyefyREUwUUVu2jPDUU2k56aRGxxYRERERERE5qEVbrreZKIKnr/XsgtlyzcSEIbZQ2N/xRERERGQPGNcl+6lPUXj726kag9fXh//UU4SJBNbzcCoVnFqNWjZLuHAh0Zln0v7mNzc6tsyCzAUXUPj//j+CVApneJhoyZKd30OdnMQtlwkWLcK86lW7da9VRORApAZ0ERERERERERERkcNE5aGHoFTCqVaxHR0z1odtbcT6+ogKBcpPPUXyuONmIaWI7EvlNWuYfuc7cTZtItbXB1CfrO15mGqV2KZN2P5+KBaZfNvbaPryl/FaW3fvxa0luu02vKEhrOMQLF2KcZztykw8TnDEEcSeeoown6f8/e/v9wZ0gKbzzmOyViP64hepjI/jjI4S27QJYy2R6xI2NVFZuBDb0UHsYx8jccwx+zeQMTRdey1Tb3vb7i1WOf102t/ylv2baabIqVR9o4IgqDfIz7AwwlSr2C0bEDjp9GxElD+RPuUUJt/3PqLPfpZqOo2Xy+GvXl3/s7MW67oEra1EXV2EJ59M60c/qgUvIiIiIiIiInvJpFLgOGAMNgiY8ZN2rQaAdRyMJiGKiIiIHHBi3d1kv/IVJj/5SXjoIYJiETM5iYkigkwG29qKzWbhggtof+tbda/lMOG1txOddhpMTRFbt45g82ai+fPrnwWeaWqK2MaN1FpbsU1NZFeubExgEZF9QA3oIiIiIiIiIiIiIocJWyjUJ9MaA74/c308Xp/YYi1RsTgLCUVkX7JhWJ98vmkT8U2bqHZ2Es2Zs7VJ3AKVMMTt6yO2fj1VY5j4xCdo/9znduv1i7/5Dc7QEO74OLVnvO4OxWIELS14uRw8+CC14WH8rq59cJa71nTRRZSPP57S976H/cUviKantz4WtbdjXv5yspdeuseT358tf84csl/+cn2xyu9/v+1ilXQa29ZWX6xy/vm0veUtDV+s4h5/PNFdd2H6+2FyEpqbd15sLd7ERH3i9hFHaPp5AzW98pWUuruZ/uY36++z6en6wnbHgWSSqKsL5/zzabviCoyn28UiIiIiIiIieytx1FFUYzGCTAY3nyfa0TWUKIJyuT4pPZ8n8jxIJok997mzH1hEREREZuR3ddH+r/9KZcMGSjffTLh2LaZSwWYyeKecQva883AzmUbHlFnW9J73MLl6NdUwJNbbi52cJGhpwSYSmDDEmZjAK5WotbURLVyI++53z9q9aBGR/UErCkREREREREREREQOEyaVAtetT0Ct1TAzNKGbWg27ZWqLJtmKHHyK992H09dHrK+Pamcndt687aYvGdclWriQIIrwNm+GBx+ksnEj8UWLZnz92rp1MD2NEwTY1tYZJztFzc34Y2ME1lLt6ZmVBnSAxPLlJN7/fqJ3vYvKunVE5TJuNkt82bKGNN/6nZ20/8u/UOntrS9WWb26Pjk8nf7jYpVsdtZz7Uj2la9k4mtfI2hqwhscpJbN7nSjATMyggkCbEcH/vnnz3JS+VOpE04g9fnPU+3rY/q++4jGxzHxON6yZaT//M8xWybVi4iIiIiIiMjec7NZ7GmnEY6MEO/tpVIqQSpVf3B6Gmd0FG98fOuGr970NLVYjLBWw1+woLHhRURERGSX4kuWEH/3uxsdQw4QXmsr2S9+kcn3vY9qOo0ZG8PN5zETE2AMYSZDZcECbFMT7rvfTdO55zY6sojIXlEDuoiIiIiIiIiIiMhhInbCCVRTKSLfx4yNwZw5u6x3x8YIsllsMkl8+fJZSiki+0rl5ptxx8YA6pPPd1ZoDOG8efirVhEUixRvvpn4O9854+vbarW+aBZ2Pf38aU83e0cRtlLZjTPYt5xkkuQBNFEqvngx8Xe9q9ExdslJJODVryYaH8ddswZv3TrC+fP/uIAasGGIMzxMbGSESnc30Zw5NL3ylQ1MLc8Umz+f2Gte0+gYIiIiIiIiIoe81OWXU/75z6nl88R6eqgsXIhTqRAbGMAEAU6tVv+3tQA4QYA/Nkbhb/6G8BOfIHXiiY09ARERERER2S1+Vxdt//mfFO6+m+qtt2IffXTrY1FrK+YVryBz0UX4M6zJERE5GKgBXUREREREREREROQwkTrhBKaXLsUMDuKPjlJra4OdTUGfmsIrFKgccQT2rLNwksnZDSsie82sW4c7NUXQ1DRjg7iJxwmTSczEBHbVqt16fbe1lSgWq/9PqQTp9K6fUCphXRccB7e1dbeOIY3X8nd/x9jatWAtbm8v8TVrCFIpbDwOYYhXKGAdp76T//z5JD/+cX3PEBEREREREZHDTuKYY6i85S1E119PsHkzyTVr6k3nUVTfxNHUt4cMXZfI9wmNITk6Sm39eirXXIPzhS+QOOqoBp+FiIiIiIjsDuN5ZM85B845h2h6miCfx0km8Vpatv7sLyJyKNiNcRQiIiIiIiIiIiIickgwBv+SS7BdXdhYDG/dOmyhsG2NtTA2Rqy3l1prK7S0kL788sbkFZG9Ysrl+rRxb/f2I7aehwlD2M3p5MnTToNkkiCdxsnlZqz3xsYIWlqIOjpIPOc5u3UMaTzjebR95jNE559PuGIFlRUrsKkURBG4LtUFC6g95zlExx9P8gtfIHnssY2OLCIiIiIiIiLSEM2veQ3mPe8hWLyYyPMw1uJEEZHrEvo+tVSKMJ2mPG8exec+l1J3d31Cen8/pc9+ttHxRURERETkWXCSSWLd3XitrWo+F5FDjiagi4iIiIiIiIiIiBxGml79asaeeILg9ttxentJrF9PGI8TpVL16baFAiYMqXV0YLu7ca+6iviSJY2OLSLPgk2lsK6LqVaxMxZbTLVa/1qQSu3W6/tdXYSnnAJjY8R7eym3tGCamnZYa4aHccplgsWLMa96FcZ19+xkpKGM59H2/vdT+5u/oXDTTdQeeAAzNQW+j12yhMSFF5J+4Qu1oEJEREREREREDnvNK1cyNjhIbdMmvE2bqKbTWM/Deh5BczNRRwfEYlCtYltbqQUB/uAgtdWrmX78cW3uJyIiIiIiIiIHDDWgi4iIiIiIiIiIiBxOjKHtfe8j39JCeMMNRIUCZnQUU62CMQQdHdj2dqLWVmLveQ+Zs85qdGIRebZOOIGot5fYxo1UajWM7++01JZKuNUqQWsr7vOfv9uHSP/N3zD9u99RLRZJbNhAtb2dqLMTE4vVC0olnJER/IkJKgsWEHV303TxxXt7ZtIg/ty5tL71rfDWtzY6ioiIiIiIiMiBw1qKDzxA5Wc/w+bz4LqY7m7SF1ygzT0PV/feC0C1tZVgxYptNu0zsM1mkVFnJ04uB5OTTN92mxrQRUREREREROSAoQZ0ERERERERERERkcONMbS++c0El15K4ZZbCO65B7NlYaSdOxf/la+k6ZxzcBKJRicVkb2QuOgiqvfcg/U83P5+okWLdjyhOorw+vsJMhlsJkN25crdP8ZRRxF88IPUPv5xKvE43sgIsVyOyPfBWpxajTCVorJ0KXbBAlKf/jRea+s+PEsRERERERERkcYp/PznVL/6VZy+PpyJCUylAsZg02mmv/99CiecQOaqq4gvXtzoqDJbrMUZHMSZnibMZnd8Pe6ZYjGCZBJTKmH7+2cno4iIiIiIiIjIblADuoiIiIiIiIiIiMhhymtro+UNb4A3vKHRUURkP0ideCLTxx4LpRKxdesINmwgnDsXk0zWC6zFFot4AwM4tRq1JUuw55yzxw3imTPPpNTSwvRXvwqPP05QKEClsiVECtJpwlNOIXvllcQWLNjHZykiIiIiIiIi0hgTN9yAvf563IEBvFwOE0VE8ThEEe7gIGE8DhMTFHt6sNddR+KooxodWWZLFIG1MzefP82Yen0Y7t9cIiIiIiIiIiJ7QA3oIiIiIiIiIiIiIiIihyJjaP7EJ5h829uoAt7mzfirVxMmk1jPw6lWcapVgkyG2pIlhCefTNt73vOsDpU68URSX/4y5TVrmL7rLmwuB66LmTuXzHnn4c+Zs2/PTURERERERESkgYr334+9/nqcDRvwpqaodXURtbdjXBeAWqWCOzxMvLeXShhS+od/wP/mN3GbmhqcXPY7Y4iam3FisfoU9BnKbRjilsuEra2whxtDioiIiIiIiIjsT2pAFxEREREREREREREROUR5bW00f+UrjH/yk3D//QTFImZiAhNFBKkUtrUVm81iX/Yy2t77Xoy3d7eOEkceSeLII/dRehERERERERGRA1P5m9/EGxnBm5ykumwZpFI8c9a1iceJFi6kGo8T27yZaibD1M030/LXf92wzDKLTjuNaPNm4uvXE1QqmHh8p6VOPl//j9ZW4i996SwFFBERERERERGZmRrQRUREREREREREREREDmFuczPt115LdfNmCt//Pvapp6BSgVQK9/nPJ3vhhXiarnTYsrVafeMBY2YuFjkElFetotrTA7UaTlsb6Re8ABOLNTqWiIiIiIgcRMpr1uA+/jju6Ci19nZIpXZaazs7ifJ5zMgI4R13wOtep8/gh4HUxRdTvuMOwmQSb+NGgiOOwLju9oXlMv7gILW2NqKuLtJnnDH7YUVEREREREREdkIN6CIiIiIiIiIiIiIiIoeB2IIFtL3znY2OIY1mLYVf/YrKzTfjPPIIploFxyE84gj8V7+azKtehZNINDqlyD5lw5DJ224juPVW3PXroVYDa4k8j3xLC7zsZWT/8i/xOzsbHVVERERERA4C5V/9Ckol3EqFWkcHu2wnN4awtRVvdBQ7NER5/XoSy5bNVlRpkMSyZZRe/nKCWg1/3Tpiq1dT6+zEtrVhHAcqFdyhIeLj44SZDLa7G+9Nb9pxk7qIiIiIiIiISIOoAV1ERERERERERERERETkMFAbHmby/e/HXb0aN5/HzecxYYg1BtPfT7hqFRP/9V/EPvIR0ied1Oi4IvtEVC6T/8AHcO+/H29kBC+fx6lWAbCOQ9DcTDQ0xNTdd5O69loSRx/d4MQiIiIiInKgiyYmcGo1rONgYrEZ620igRMEW58rh4fWa65hbHISHAczOIg/NITT11d/30QRNd+n3NmJO28e5k1voumVr2x0ZBERERERERGRbagBXUREREREREREREREROQQF4yNMXXllbhr1uD39mKsJWhqIsxkMEGANzaGGRmhNmcO1WuuwXzmM6Se//xGxxbZO9aS//CHcX/5S/z168FagvZ2oqYmcF1MqYQ7Okp8zRoqpRLTV1+N85WvEJs/v9HJRURERETkAGbi8XpTsbUQReA4u35CFGFNfU66k0zOQkI5EBjPo/3Tn2biO98hvPlm7OAgFIv194PnUfM8ykceScsb30jTGWc0Oq6IiIiIiIiIyHbUgC4iIiIiIiIiIiIiIiJyiJu49lrcdeuI9fQQtLQQzp+P2bJA3gK17m7I5Yj391MFyh/5CIkbbsBJJBqaW2RvFO67D/fXv8Zfv57I9wmOOALjOJinC2IxwpYWouFh4n19VGIxCl/5Cm0f+1gjY4uIiIiIyAHOP+oognQa6ziQz0N7+y7rnXyeMJPBJhLEFi+epZRyQDCG5r/8S/iLv6D4m99QfeIJKJex6TTTy5fjL1tGes6cRqcUEREREREREdkhNaCLiIiIiIiIiIiIiIjIQa28ejWl738fHnwQUyxifR+7ZAn+q19N9uyzMd7hfUus2teHe//9eH19BJkM0YIFGGO2LTIGOjqoALGBAWx7O4Uf/5im889vSGaRfaFyyy24Y2OYMCRYsWLrpgt/ynZ1USuVcIeH4Ve/Ihgbw2trm+W0IiIiIiJysEi/+MXkOzsJmpvxRkaotbbu9PMG09P4U1NUli7Fnn46Tio1u2HlwGAM6Re9iPSLXgRAGIaUh4YaHEpEREREREREZNd2csVLRERERERERERERERE5MAWTU+Tu+Yaym98I84NN+D9/vd4jz2G/8gjeD/7GdHHP07+sssoPfxwo6M2VPGWW6BYxC2VCOfOrTeb70x7O9Z1MaOj1G6/fdYyiuxrQT6P+/vf442NETQ3Y1x3l/VRZydesQjFIsW77pqllCIiIiIicjAynoc591yiuXMxUYS3fj22VtuuzhaL+D091LJZaGoidemlDUgrIiIiIiIiIiLy7Bze4x5ERERERERERERERETkoBSVy+Tf+U7chx/G6+3FrVTq070TCUwY4g8Pw9AQtVyOytVXw2c+Q+p5z2t07IaIVq/Gm5wkTCQwyeSui40haGrCKRRwenpmJ6DIfhAMD4O1OJUKQWfnzE9Ip7HGQLlMNDi4/wOKiIiIiMhBreX1r2fskUcgivB6ekg8+SS1piZsKlX/LDI5iVcqUWtpIVq0CN74RhJHHdXo2CIiIrKPBPk8hVtvJfrxjzFDQ5goImppgdNOI33JJcQXL250RBERERGRvaYGdBEREREREREREREREdmnKr29VJ54Alup4DY3kzz5ZNxsdp8eY/wLX8B95BH8deuIkkkqS5dCLAaABarWYkZGiA0MUAUqH/oQie9+FyeV2qc5DgrVKkQRdoYJ0Fu5LiaKMNUqWLvriekiBypr9/w5eq+LiIiIiMhuMp5H62c/y/hHP0rwq18RTEzg5nI4+TwAUSpFZcECyGQwb34zza95TYMTi4iIyL4yedNNhF/6EmZqCnd0FGd6ur4BTTxOtGkT07feSvHcc2m96irM7l6XFxERERE5AKkBXURERERERERERERERPaJws9/TuXGG3EffRRqNYgiap5HLZ0mevGLSf/lX5JYtmyvjxNOTWF++lPc/n5sLEa4dOn2jaPGYLu6qLgusb4+qq2tTP3gBzRfeuleH/+gk82C7+NUq4S70VBuqlWs59WntqkhVw5S3pw5YAxRPI4pFLAtLbt+QrGIiSJIJDBdXbOSUUREREREDm5OIkHbpz5F6ZFHmL7pJuxvfoMplwGI2tsx55xD5qKL8OfMaXBSERER2Vcm/vd/sddfj7t5M34+TxiPE2azYAxOsUh87VqCZBJuvZV8oUDbP/+zrrOLiIiIyEFLDegiIiIiIiIiIiIiIiKyd6xl7LrrcG69FS+Xw83lcLcsuMYYgkyGcGSE6fvuI/jgB8mcfvpeHa5wxx2YqSm8qSkqS5bsevFWWxt2eBgzOkpwxx1wGDagey94AfaXv8QMDmInJzHNzTuttWGINzlJdeFCouc/fxZTiuxbXmsr4YknwugosYEBKvPm7XLakDM6SpBOQzpN5hWvmL2gIiIiIiJy0EudcAKpE04Aa4mmpzGeh4nFGh1LRERE9rHyU09hv/xlnN5evGKxfn9iS/M5QAgE5TL+xo14a9cSGMPE8ccfnhvjioiIiMghwWl0ABERERERERERERGR2WBrNaYfe4zir39N6eGHCQuFRkcSOWTkr78e5+ab8dauxR8cJMxkqBx5JJVjjqGyZAnWGOI9PTg9PdT++Z8p/eEPe3W88IknMBMTRJ5XX9y1K8YQNDfjTk7i9vRga7W9OvbBKHvuudhslqCpidjAADYMd1xoLe7mzVjXhZYWEhddNLtBRfax+MqV0NqKdV28DRuwUbTjwtFR/IkJwq4uwlNPxWtvn92gIiIiIiJyaDAGJ5VS87mIiMghqnjDDTAxgT85SXXpUmhq2m6DXJNIUFu2DBNFmOFhwptuAmsblFhEREREZO9oArqIiIiIiIiIiIiIHNJqQ0MUbrgBe9ddOJOT9UUexlBJJLCnn07qsstIrFixzXOCsTFKDzyALRQw8TixY48lsWxZg85A5MBW6e3FfO97uBs2YMKQ6lFHge//sSAWI2pqojwxQaK3l4rvM/1v/0bqG9941se009MQRfVG6V1NP3+63vcxWxpPo1IJdxcTwA9FTjIJF11E9N//jbNmDbE1a6jNm7ft4rhSCXdwELdUonrEEYTHHkvLSSc1NrjIXsqccQa5F74QajX89euJPfUUtbY2bEsLxnGgWMQdHcUrlajMn080fz6ZN7+50bFFRERERERERETkABMWCjj33os7Okotm4VUaqe1xnUJOjrwRkexmzdT+t3vSP3Zn81iWhERERGRfUMN6CIiIiIiIiIiIiJyyCo+8ADVD38Yk8vhjozgTU5iwhBrDGEmQzg8TPmnP6Xy1rfSfNlllFetovQ//4P59a9hehoTBFjHoRyLUTz2WGIXX0z27LMbfVoiB5TSTTdhikW8QoHyihWYZzafP4Npbqba2Yk/MgLr11N65BFSJ5zw7A6aTmNdFxMEWzeV2BVTrRJtaVZ30ulnd8yDXOvf/R1jfX0EgLN5M7GNG7GOQxSLYcIQt1olSKWoLltGtGIFzZ/85G4194sc0Iyh9eMfJ3/NNeB5mJER/FwOZ3AQAOs4BE1NVJYvJ+ruJvmpTxFftKjBoUVERERERERERORAU1mzBlOp4BYKVHfjGmLU3o4zMADT01Qff1wN6CIiIiJyUFIDuoiIiIiIiIiIiIgckkqPPEL1gx/E2biR2NBQvbFy3jxsIoGp1XDyeeIbNlBrbib6939n5Ikn8O67D2d0tD4NtVisN7YCYTyOGRkhfOwxxn7/e9re9z41ZopsYX/+c9zRUYJ0GpNM7rI26ujAjIzAxATlH/7wWTeg+yedRPTTn2L6+2FiAlpadhHQ4o+PU+vqInzOczDeYXp7zBjaPvpR8osWEd50E+H4OM74OAQBbGnCJZ0mevGLab76atxsttGJRfYJJ5Gg7brrmLzlFsLbbsNu2ADVav17vO9jm5qwL30p2SuuwJ8zp9FxRURERERERERE5ABky2WwFmMt1veZ6S6hcZz6vcQwrD9XREREROQgdJiusBERERERERERERGRQ5q1TH/uc7h9fcSGhqgsWrS1QfXpBSFRSwuVUgm/pwfWriX92GPUmpqIjY9Ta26mvHQpJBIQBDj5PN7QEHZ8nOC228hnMrS+7W0NOz2RA4UNApzxcZxymbC5ecZ64/tEiQRmeho7PPysj5t9xSsY/+pXCZqb8QcHqWazGNfd8TEHB8FaorY2/PPPf9bHPCQYQ+sb30h0xRVM/ehHBA8+CFNTEI/jHH00mQsuwO/sbHRKkX3OeB7Nl1wCF1/M9GOPUevpqX9/b2sj9cIX4iQSjY4oIiIiIiIiIiIiBzAnmwVjsK6LKZchnd5lvX16E0zPw2jDVxERERE5SKkBXUREREREREREREQOOcXf/x63txd/eJhqZ+fOpyOnUlQXLCD95JNY18UplykfdRRks3+cXOD72GSSWmcn/rp1uBs2EN5wA9ULLiC2YMEsnZHsT9OPPkrpxhsxjz+OKZWwiQT2mGNIrFxJ+qSTNO3+AGRiMVi5kmh8HGfNGvy1awnmzYMtC8AAqFZxhobw83kqixZhFy4k+7KXNTb4PmCrVSobN2Knp3GamogvWrTH71EnkaB55UpYuXI/pRQ5QBlD8rjjSB53XKOTiIiIiIiIiIiIyEEkcfTRTLe0EDQ3442NEbS377LeGR0lTCQgmSRxyimzlFJEREREZN9SA7qIiIiIiIiIiIiIHHIqd9yBMz6OCUOiri521ZpptjRuutUq1XS63sC6I75PbfFi4qtXExaLFL7/fdre+c59H15mTZDLMfHBD+I+/jjuxMTW94x1XaK1a6nddx+5I48k+7GPEevubnTcA5LxPKKWFqJ4HKd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" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(123)\n", + "\n", + "###\n", + "# Actual Expectiles\n", + "###\n", + "y_loc = np.array([10])\n", + "y_scale = np.array([1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7)])\n", + "tau_lower = np.array([lgblss.dist.tau[0]])\n", + "tau_upper = np.array([lgblss.dist.tau[1]])\n", + "\n", + "# Calculates exact expectiles assuming a Normal distribution\n", + "expectile_lb = expectile_norm(tau=tau_lower,\n", + " m=y_loc,\n", + " sd=y_scale).reshape(-1,)\n", + "expectile_ub = expectile_norm(tau=tau_upper,\n", + " m=y_loc,\n", + " sd=y_scale).reshape(-1,)\n", + "\n", + "test[\"expect\"] = np.where(test[\"y\"].values < expectile_lb, 0, np.where(test[\"y\"].values < expectile_ub, 1, 2))\n", + "test[\"alpha\"] = np.where(test[\"y\"].values <= expectile_lb, 1, np.where(test[\"y\"].values >= expectile_ub, 1, 0))\n", + "df_expectiles = test[test[\"alpha\"] == 1]\n", + "\n", + "# Lower Bound\n", + "yl = list(set(expectile_lb))\n", + "yl.sort()\n", + "yl = [yl[2],yl[0],yl[2],yl[1],yl[1]]\n", + "sfunl = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1],\n", + " \"y\":yl})\n", + "\n", + "# Upper Bound\n", + "yu = list(set(expectile_ub))\n", + "yu.sort()\n", + "yu = [yu[0],yu[2],yu[0],yu[1],yu[1]]\n", + "sfunu = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1],\n", + " \"y\":yu})\n", + "\n", + "\n", + "\n", + "###\n", + "# Forecasted Expectiles\n", + "###\n", + "test[\"lb\"] = pred_expectile.iloc[:,0]\n", + "test[\"ub\"] = pred_expectile.iloc[:,1]\n", + "\n", + "\n", + "\n", + "###\n", + "# Plot\n", + "###\n", + "(ggplot(test,\n", + " aes(\"x_true\",\n", + " \"y\")) +\n", + " geom_point(alpha = 0.2, color = \"black\", size = 2) +\n", + " theme_bw(base_size=15) +\n", + " theme(legend_position=\"bottom\",\n", + " plot_title = element_text(hjust = 0.5)) +\n", + " labs(title = \"LightGBMLSS Expectile Regression - Simulated Data Example\") +\n", + " geom_line(aes(\"x_true\",\n", + " \"ub\"),\n", + " size = 1.5,\n", + " color = \"blue\",\n", + " alpha = 0.7) +\n", + " geom_line(aes(\"x_true\",\n", + " \"lb\"),\n", + " size = 1.5,\n", + " color = \"blue\",\n", + " alpha = 0.7) +\n", + " geom_point(df_expectiles,\n", + " aes(\"x_true\",\n", + " \"y\"),\n", + " color = \"red\",\n", + " alpha = 0.7,\n", + " size = 2) +\n", + " geom_step(sfunl,\n", + " aes(\"x_true\",\n", + " \"y\"),\n", + " size = 1,\n", + " linetype = \"dashed\") +\n", + " geom_step(sfunu,\n", + " aes(\"x_true\",\n", + " \"y\"),\n", + " size = 1,\n", + " linetype = \"dashed\")\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/Expectile_Regression/index.html b/examples/Expectile_Regression/index.html new file mode 100644 index 0000000..2a5db4e --- /dev/null +++ b/examples/Expectile_Regression/index.html @@ -0,0 +1,2281 @@ + + + + + + + + + + + Expectile Regression - LightGBMLSS + + + + + + + + + + +
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+ + + + + + + + + + + + + diff --git a/examples/Gamma_Regression_BostonHousing/Gamma_Regression_BostonHousing.ipynb b/examples/Gamma_Regression_BostonHousing/Gamma_Regression_BostonHousing.ipynb new file mode 100644 index 0000000..5702f7e --- /dev/null +++ b/examples/Gamma_Regression_BostonHousing/Gamma_Regression_BostonHousing.ipynb @@ -0,0 +1,864 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gamma Regression (Boston Housing Data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/StatMixedML/LightGBMLSS/blob/master/docs/examples/Gamma_Regression_BostonHousing.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:39:40.836849400Z", + "start_time": "2023-05-18T06:39:40.819009700Z" + } + }, + "outputs": [], + "source": [ + "from lightgbmlss.model import *\n", + "from lightgbmlss.distributions.Gamma import *\n", + "\n", + "from sklearn import datasets\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:29:58.792846200Z", + "start_time": "2023-05-18T06:29:57.927953500Z" + } + }, + "outputs": [], + "source": [ + "housing_data = datasets.fetch_california_housing()\n", + "X, y = housing_data[\"data\"], housing_data[\"target\"]\n", + "feature_names = housing_data[\"feature_names\"]\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n", + "\n", + "dtrain = lgb.Dataset(X_train, label=y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Distribution Selection" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:29:58.807805300Z", + "start_time": "2023-05-18T06:29:58.794840400Z" + } + }, + "outputs": [], + "source": [ + "# Specifies Gamma distribution with exp response function and option to stabilize Gradient/Hessian. Type ?Gamma for an overview.\n", + "lgblss = LightGBMLSS(\n", + " Gamma(stabilization=\"L2\", # Options are \"None\", \"MAD\", \"L2\".\n", + " response_fn=\"softplus\", # Function to transform the concentration and rate parameters, e.g., \"exp\" or \"softplus\".\n", + " loss_fn=\"nll\" # Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\"(continuous ranked probability score).\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyper-Parameter Optimization\n", + "\n", + "Any LightGBM hyperparameter can be tuned, where the structure of the parameter dictionary needs to be as follows:\n", + "\n", + " - Float/Int sample_type\n", + " - {\"param_name\": [\"sample_type\", low, high, log]}\n", + " - sample_type: str, Type of sampling, e.g., \"float\" or \"int\"\n", + " - low: int, Lower endpoint of the range of suggested values\n", + " - high: int, Upper endpoint of the range of suggested values\n", + " - log: bool, Flag to sample the value from the log domain or not\n", + " - Example: {\"eta\": \"float\", low=1e-5, high=1, log=True]}\n", + "\n", + " - Categorical sample_type\n", + " - {\"param_name\": [\"sample_type\", [\"choice1\", \"choice2\", \"choice3\", \"...\"]]}\n", + " - sample_type: str, Type of sampling, either \"categorical\"\n", + " - choice1, choice2, choice3, ...: str, Possible choices for the parameter\n", + " - Example: {\"boosting\": [\"categorical\", [\"gbdt\", \"dart\"]]}\n", + "\n", + " - For parameters without tunable choice (this is needed if tree_method = \"gpu_hist\" and gpu_id needs to be specified)\n", + " - {\"param_name\": [\"none\", [value]]},\n", + " - param_name: str, Name of the parameter\n", + " - value: int, Value of the parameter\n", + " - Example: {\"gpu_id\": [\"none\", [0]]}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:35:09.370575100Z", + "start_time": "2023-05-18T06:29:58.929480400Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2023-08-11 12:29:16,191] A new study created in memory with name: LightGBMLSS Hyper-Parameter Optimization\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0751f545d27942b8b4cc7663d8a843c4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/30 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " concentration rate\n", + "0 12.671469 6.625142\n", + "1 9.840599 10.347679\n", + "2 12.215254 7.179342\n", + "3 11.811074 6.747012\n", + "4 13.350488 3.346810" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred_params.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SHAP Interpretability" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:35:15.172419700Z", + "start_time": "2023-05-18T06:35:13.620191500Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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", 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", 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Documentation built with MkDocs.

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+ + + + + + + + + + + + + diff --git a/examples/Gaussian_Regression/Gaussian_Regression.ipynb b/examples/Gaussian_Regression/Gaussian_Regression.ipynb new file mode 100644 index 0000000..0b530d5 --- /dev/null +++ b/examples/Gaussian_Regression/Gaussian_Regression.ipynb @@ -0,0 +1,1188 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basic Walkthrough - Gaussian Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/StatMixedML/LightGBMLSS/blob/master/docs/examples/Gaussian_Regression.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we model and predict all parameters of a univariate Normal distribution. Recall that distributional regression models and predicts all parameters $\\theta_{ik}, k=1, \\ldots, K$ parameters of a distribution $\\mathcal{D}$ as a function of covariates:\n", + "\n", + "\\begin{equation}\n", + "y_{i} \\stackrel{ind}{\\sim} \\mathcal{D}\n", + " \\begin{pmatrix}\n", + " h_{1}\\bigl(\\theta_{i1}(x_{i})\\bigr) = \\eta_{i1} \\\\\n", + " h_{2}\\bigl(\\theta_{i2}(x_{i})\\bigr) = \\eta_{i2} \\\\ \n", + "\t\\vdots \\\\ \n", + "\th_{K}\\bigl(\\theta_{iK}(x_{i})\\bigr) = \\eta_{iK} \n", + "\\end{pmatrix}\n", + "\\quad ,i=1, \\ldots, N. \n", + "\\end{equation}\n", + "\n", + "where $h_{k}(\\cdot)$ transforms each distributional parameter to the corresponding parameter scale. For the univariate Normal case, we can specify the above as $y_{i} \\stackrel{ind}{\\sim} \\mathcal{N}\\bigl(\\mu_{i}(x_{i}), \\sigma_{i}(x_{i})\\bigr)$. Since $\\mu_{i}(\\cdot) \\in \\mathbb{R}$ and since the standard-deviation cannot be negative, $h_{k}(\\cdot)$ is applied to $\\sigma_{i}(\\cdot)$ only. Typical choices are the exponential or the softplus function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imports\n", + "\n", + "First, we import the necessary functions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:24:10.418630300Z", + "start_time": "2023-05-18T06:24:10.403008900Z" + } + }, + "outputs": [], + "source": [ + "from lightgbmlss.model import *\n", + "from lightgbmlss.distributions.Gaussian import *\n", + "from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data\n", + "from scipy.stats import norm\n", + "\n", + "import plotnine\n", + "from plotnine import *\n", + "plotnine.options.figure_size = (12, 8)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data is simulated as a Gaussian, where $x_{true}$ is the only true feature and all others are noise variables:\n", + "- $\\mu(x_{true}) = 10$\n", + "- $\\sigma(x_{true}) = 1 + 4 * \\bigr((0.3 < x_{true}) \\& (x_{true} < 0.5)\\bigl) + 2 * (x_{true} > 0.7)$\n", + "\n", + "We first load the simulated dataset, filter for the target and covariates and then create the `lgb.Dataset`. LightGBMLSS is designed to closely resemble the usage of LightGBM, ensuring ease of adoption and full compatibility. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:12:13.097935100Z", + "start_time": "2023-05-18T06:12:03.538184Z" + } + }, + "outputs": [], + "source": [ + "train, test = load_simulated_gaussian_data()\n", + "\n", + "X_train, y_train = train.filter(regex=\"x\"), train[\"y\"].values\n", + "X_test, y_test = test.filter(regex=\"x\"), test[\"y\"].values\n", + "\n", + "dtrain = lgb.Dataset(X_train, label=y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Distribution Selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we specify a Gaussian distribution. By modifying the specification in the following, the user can specify alternative distributional assumptions. This includes the option to choose from a wide range of parametric univariate distributions, as well as to model the data using Normalizing Flows. The user also has different function arguments for each distribution:\n", + "\n", + "- `stabilization`: specifies the stabilization method for the Gradient and Hessian. Options are `None`, `MAD` and `L2`.\n", + "- `response_fn`: specifies $h_{k}(\\cdot)$ and transforms the distributional parameter to the correct support. Here, we specify an exponential for $\\sigma_{i}(\\cdot)$ only.\n", + "- `loss_fn`: specifies the loss function used for training. Options are `nll` (negative log-likelihood) or `crps` (continuous ranked probability score).\n", + "\n", + "For additional details, see `?Gaussian`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:12:13.097935100Z", + "start_time": "2023-05-18T06:12:04.423429800Z" + } + }, + "outputs": [], + "source": [ + "lgblss = LightGBMLSS(\n", + " Gaussian(stabilization=\"None\", \n", + " response_fn=\"exp\", \n", + " loss_fn=\"nll\" \n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyper-Parameter Optimization\n", + "\n", + "Any LightGBM hyperparameter can be tuned, where the structure of the parameter dictionary needs to be as follows:\n", + "\n", + " - Float/Int sample_type\n", + " - {\"param_name\": [\"sample_type\", low, high, log]}\n", + " - sample_type: str, Type of sampling, e.g., \"float\" or \"int\"\n", + " - low: int, Lower endpoint of the range of suggested values\n", + " - high: int, Upper endpoint of the range of suggested values\n", + " - log: bool, Flag to sample the value from the log domain or not\n", + " - Example: {\"eta\": \"float\", low=1e-5, high=1, log=True]}\n", + "\n", + " - Categorical sample_type\n", + " - {\"param_name\": [\"sample_type\", [\"choice1\", \"choice2\", \"choice3\", \"...\"]]}\n", + " - sample_type: str, Type of sampling, either \"categorical\"\n", + " - choice1, choice2, choice3, ...: str, Possible choices for the parameter\n", + " - Example: {\"boosting\": [\"categorical\", [\"gbdt\", \"dart\"]]}\n", + "\n", + " - For parameters without tunable choice (this is needed if tree_method = \"gpu_hist\" and gpu_id needs to be specified)\n", + " - {\"param_name\": [\"none\", [value]]},\n", + " - param_name: str, Name of the parameter\n", + " - value: int, Value of the parameter\n", + " - Example: {\"gpu_id\": [\"none\", [0]]}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:22:05.890475500Z", + "start_time": "2023-05-18T06:12:04.439051100Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "93dd30a563fa43c88e31de7efb451d2e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/30 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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locscale
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" + ], + "text/plain": [ + " loc scale\n", + "0 9.984035 2.921586\n", + "1 9.979074 2.909918\n", + "2 9.979074 1.065636\n", + "3 9.979074 4.529788\n", + "4 9.979074 3.121158" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred_params.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SHAP Interpretability\n", + "\n", + "To get a deeper understanding of the data generating process, LightGBMLSS also provides attribute importance and partial dependence plots using the Shapley-Value approach." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:22:07.616856700Z", + "start_time": "2023-05-18T06:22:07.020722700Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Partial Dependence Plot of how x_true acts on variance\n", + "lgblss.plot(X_test,\n", + " parameter=\"scale\",\n", + " feature=\"x_true\",\n", + " plot_type=\"Partial_Dependence\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:22:07.960311200Z", + "start_time": "2023-05-18T06:22:07.616856700Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Feature Importance of scale parameter\n", + "lgblss.plot(X_test,\n", + " parameter=\"scale\",\n", + " plot_type=\"Feature_Importance\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot of Actual vs. Predicted Quantiles\n", + "\n", + "In the following, we plot the predicted quantiles (blue) and compare them to the actual quantiles (dashed-black)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:22:08.982134200Z", + "start_time": "2023-05-18T06:22:07.960311200Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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k1lb5WluV7GGmJisQUDo/X051tczLL8tLJGSHQv1TiOt+2En3QZH/FNPWznLd9g+8+iq+fLms3/1OzoYNspNJJadPlyKR9us9SUnXlVNeLv+6dZLjqP6++1R02WWKzJ6tyH33Kb50qeJPPy1TWdlWz4gRCp1yiiIHHZRVTUB/yFRVSZLsdFrpaFQ9PaqscFjGcWQlk/I+nDULAAAAAABILa+/LvfGG2Vv3KhATY3S+flKTJwohUKyUinZdXUKbN4st65O7r33qj4cVuEnP5nrsoEhgQAWAAAAAGDY8LZskd3SIuM4UjTac/tYTP7t22WlUspUV/ffUmVFRTKBgJyaGrmeJ9l2t83teFxeICAvFut9aOsj4n/+s+yGBvmam5XYZx9ZnYTJLMeRN3my7FWrZFdUKPP00zKf/7wsX9vHB+GZMxWeOTOr/oGBYu2YRc6yZPUmoGhM28myJB8fjQEAAAAAIEkyRslbb5Wzdav8tbVKTpokxWL/+aFTMCgvP1/JeFyBdetkNm2Se889cj/+cTkFBTksHBgauv+EFwAAAACAvYnnSZJMX2ae+jDQYXbMWtUPAgsWSEVFbRfq6rpta5JJ+Zqb5RUXyzvyyKz6c5ubZb3yipyaGqVjsU7DV+0sS5kRI+RraJBdU6Pmv/89qz6BweIfM0bG75cbDstuaOixvWlqagtqRSLyjR8/CBUCAAAAADD0Nb/yiuxt2+Srrla6rEyKxTptZ4XDSo8fL399vazmZjU9/vggVwoMTQSwAAAAAADDhlVSIoVCstNpmWSyx/Z2S4tMICBZlnwlJf1WR96CBfJGjFC6qEj+igqZRKLTdsbz5Nu4UW44LOXlKXzuuVn1lyovl5VKyWlpkVdY2PMGRUWyPE9qaVFmzZqs+gQGi5OXJ3PkkXJLSuRrbJRJpbpubIx8lZVK5+fLxGLK+/jHB69QAAAAAACGsOQzz0h1dbKMkVda2n3j/Hy5gYCs6mq5zz8/OAUCQxwBLAAAAADAsBE+8UQpEpEbCsmuru62rXFd+RoalCkuljtnjpy8vH6rw/L55Pv852XGjJEXDiu4Zo2sbdukdLqtb8+TqqvlX7VKdiajzKRJyhx/vIJTpmTV346wmWVMj8sdtjW02mYJ87z2moChLHTuuVJhodxQSP5166TOQljGyNqyRU48Lm/UKJkFC2RHIoNfLAAAAAAAQ5C1fbvseFxuOCyrp8+PLEtuXp7sREJ2ZeXgFAgMcb5cFwAAAAAAwGAJTZ2qllmzpNpaBTdvViIvT1ZBwa4NjZFTXi7jOFJxsYJnndXvtcQWLlR9dbXc++6TV1kpX02N7MpKGduWZYyMbStdVCSvrEze0Uer4Nvfzrov58PlDj2fT1YiIdPFFPI7mGSybQasQEBWD22BoSAye7YSZ5whPfqonHXrFFi5UpmCAnmxmGTbslpa5Kuvl+W6Sk2aJG/aNBV8+cu5LhsAAAAAgCHDGCMjybKs3m1gWZIxA1oTsCchgAUAAAAAGFYil12mxAcfKJVMKlRernRhobySEikSkfE8WfX18lVVyfI8padMkXvIISo8+ugBqaXws59V86RJSv7v/8qsXCm1trbNOGXbUjQqb9Qo2WeeqeKLLlLG87LuJzhpklrGjFFm61b5amuVHjGi7UOyLtjV1XJDISkcVviYY7LuFxhMxVdfrVpJeuIJuXV1cmpqFNi0qW3pBL9fmaIimdJSedOmKf/nP5fTWfgSAAAAAIDhqrRUJhiUXVcn1/N6nEXdaWmRF43KFBcPUoHA0EYACwAAAAAwrIRnzpT7gx8ofcMNSkYicqqqFFyzpv164zjKFBbKKyuTO3u2in7yk27DSrsrb/585c2fr8SKFUq8/rq8piZZoZD8++2nvI997D9970YAS5Yle+FCmfJyWTU1siorZcrKOm/b0iJ/ba1SY8bInTVLwcmTs+8XGEyWpeJvfEMt8+cr8cgj0uuvy3Xd9qvdKVPkO/10FZ52muxQKIeFAgAAAAAw9AROPFHuq6/KqqiQ6uqkkpIu25qWFjmJhDITJsg6/vhBrBIYughgAQAAAAD2CF5rq1rfeUemsVEKBhXcd18Fxo7Nal95xxyjxK9+pdYHHpD+8Q+58biUSrWFncJheSNHyl64UMWf+pQsv79/D6QLof32k11QoJYnn5TZvFmpDRtU+/rrCh5/vKKHHrrb+88/6yw1PPaYUs3NCm7apHQiIW/kSCkcliSZdFp2TY38VVXKFBZKI0cq9NnP7na/wGCLzpmj6Jw5chsalNq8WSadlq+0VIFx43JdGgAAAAAAQ1be/PmqGzFC6ZISBbZtUyocliKRXRumUgps3Kh0fr4UjSr/7LMHv1hgCCKABQAAAAAY0pLl5Wr5059kLV4sq6VFcl3JttXq86lp9mwFP/EJ5WWxRGBoxgyFfvITpSsr1fryy/Lq62X5/fJNnqzoUUfJcpwBOJrOpSsq1HjrrXLeeENqaZHT0iIZIxMIKP3UU6qZMEHO5z6n6Mc+1ut9mlRKqYoKmXRa/tJSOQUFitx0k1q//nUlfT45FRUKgEjSpgABAABJREFUrlolLxBomyErlZLn9ytdViYzapTsr31N0TlzBvCogYHlFBQozDKDAAAAAAD0iuXzyf/f/y33hhuUSaUUWLu2bZb44mKZUEhKpWTX1clfWys3GJQ3caLMxRfL181MWcBwQgALAAAAADBkNb/6qtLXXy+7qkp2VZV8TU2yjJEkueGwVFWlzDvvqO7cc1V05ZVZLRXoHzlSBeed19+l91pywwa1fO1rcjZskFNRIV9rq7xAQObDUJTx+WRt3y5v40Y1Xn65Yuee2+3+EqtXq/Xhh2X9/e+y4vG2/5PkHnigAmedpejtt6v5V7+S3nxTbktLW6jNGJlgUIrF5E7+/9m77zDJyjLv49/npIqdw/TkPIAwBB1RREkmUEAQXXfXgIquusYVw6u4a9h11dVlVVxM7BoxkhSRFRCJioAgeZjUM8NMT+fq6sonvn+cjtNV1WE6zXB/rmuu7qrz1DnPqTpV03XqV/e9lsg//APJU0+dh70XQgghhBBCCCGEEItFzcteRjqdxr/iCuy+PvTeXiI7dows9yKR8Mt7zc0EF11EwyWXLOBshVhcJIAlhBBCCCGEEGJRyj/yCM6nP422dy9WVxdOTQ326tUE0SjKdVGpFFZnJ14qhXfNNaSiURre/e6Fnva0+Pk8uY9+FG3nTqy9e3Hr6iitWDHaFtDzUL29WD09OMUiXHkl+eXLqStXCSsISF11FerHP0YbHETr7UUvFMJwlWmienrwHn2U3NFHU/elL+GXSuRuuglv3z5wXVRDA9GXvYz4CSfMKMgmhBBCCCGEEEIIIQ5/dRddRH79ego//Sn8+c94pRK4LmgaRCJ4xx5L5G/+huSZZy70VIVYVCSAJYQQQgghhBBiUSp87WvoBw5gdXdTWrMGhlqJKYBIhCCRwG5txdi5E333bryf/hT73HOxVqxYyGlPS+bXv0bbtw/rmWdwWloIli4dt1zpOixZgl1Tg7VzJ3ZPD/YPfwhlAlip73wH9aMfoe/ejZHN4tTU4CxZArqOyuUwuruhuxunUCD9oQ9R/81v0vDOd87XrgohhBBCCCGEEEKIw0T8xBOJn3giTmcnhQceIMjlIBIhcvzxRNevX+jpCbEoSQBLCCGEEEIIIcSiU3jsMfQdOzC7u7FbWkbCVxNYFu7q1US2b8fL5cheey2NH/zg/E52poIA78Yb0Xt78Q2DoK2t8th4HLuxEaO3l2DrVorbthHdtGlkcXHrVtTVV6O3t6PZNsWNG1FDVbQAgvp6nKVLw7BXezu2rpP+1rdo/MhH5nIPhRBCCCGEEEIIIcRhzGxrwzzvvIWehhCHBW2hJyCEEEIIIYQQQhyscNNNMDiI5jj4zc3VB8fjuIkEWm8v3H77/ExwFtj796M98wzGwABuY+Okbf+C5mZ024ZcjsJdd41blr/mGhgcxMjlsNeuHRe+GqFp+CtX4kWjaJ2dqNtuwy8UZnOXZlWpvZ3UlVfS9+lP0/fpT5O68kpKu3cv9LSEEEIIIYQQQgghhBBiAqmAJYQQQgghhBBi0Qm6u1H5PF40ijLNScf7yST64CBaKkXguihj8b/d9VIpAJTrEkSjVI9fAZaFr+vgOATp9MjVfqGAuusu9N5enJoaVDRaeR1K4ba0ENm7F39wkMz//R91F1546Dszi4pPP03uiivQH30UlcuhD4fE4nEKP/sZ2RNOIPH+94+rACaEEEIIIYQQQgghhBALafGfkRZCCCGEEEII8ewTBNMbP0n1qMVIRSJDvyiU5006PggClO+H44dvC9gdHahiET2fx162bPLt1taGv+Ry+Hv2zGjucyX/wAOUPvUp9I4OjO5u9FIJfyiApzkOXiQCAwMUtm3D+/znSWzZssAzFkIIIYQQQgghhBBCCAlgCSGEEEIIIYRYhFRzM0EshtbTQ+B5KF2vOl7LZvEjEfza2sOi+hWAtXIlhWg0bJ+YTuM3NlYdrwYGQCmCZBJj/frRBa47+vsk91O4IkWgaeD7BLY9s8nPAXvfPkr//M9oe/ZgdXZiNzbirFkzEjYLSiW0nh4ie/di2zb2P/8z5ne/i7VixQLPXMwmL50mc8MNePffD7kcWBZq/Xrir30t0Y0bF3p6QgghhBBCCCGEEEKUdXiclRZCCCGEEEII8awSOftsnN/9jkDX0Xp7CZYsqTg2KBYxcjlK69fDGWfM3yQPkRaLEZx+On5PD5H2dkqFAsRi5QcHAUZPD05tLUFDAzUve9nIIr2hAQDfMFD5PEFdXdXtBraN5roQiaAmCX3Np+yPf4zW3Y3V2UlpxQpobBzXllFFIgQrVlCKx4ns20cpkSB79dU0fvzjCzZnMXsC12XgG9+A3/wGlclgpFIoxyHQNIIHHqB4003kjj+e2k99CrOtbaGnK4QQQgghhBBCCCHEONpCT0AIIYQQQgghhDhY4rnPxVu9GrelBbO7GwYHy44LHAdzzx7ceBwSCRKve908z/TQxF//eqitxampwWxvDyv+HCTwPLT2djTXxWtrw3/lK1FDbfkAzNZWvI0b8RoaMFKpSds3aj09YSu/eJzYmWfO+j7NhJ/Po/7wB7SeHpxkEqoFwxobcZNJtJ4e1O234+fz8zdRMScCz6P/sstQv/gFxtatRJ5+GmNgAFwXPZ8n0t6OuXUr+r33knnPe7D37VvoKQshhBBCCCGEEEIIMY5UwBJCCCGEEEIIsfgoRewDH8D+2MdwbZvI7t04dXX4jY0E0Sh4HloqhdXfj2+aeGvXElx4IZE1axZ65tMS3bSJ4iWX4H/3u7BnD5EdO3ATCfzaWgJNQ8vnMdJpAsOgtG4d3ubN1F1yyYT1mK95Df7WraieHtT+/QTLl4NSE8YF2SxWfz/20qV4xx1HdGwrwwWUv/9+VC6HkclQWr2aiTMfz2tqwtqzBz+XI3///SQPo8pn0+WmUmRvvBHvySehUIBkEvN5zyP5qlehRaMLPb1ZMfCtb6HffTfm9u0Euk5x7VpUMglK4QOubaN1dWG1t2MHAZn/9/9o/MEPJm1NKoQQQgghhBBCCCHEfJEAlhBCCCGEEEKIRSn+/OfjffKTuF/6En4yid7bS6S9faTCk29ZOC0tBM3N+OecQ+OHPrSwE56h+re8hQFdx7/qKkqZDFpvL3p/PyoI8C0Le8UKqK/HO+kkav/1X1GWNWEdNWefTeqaa7Dzeazdu/FsG6+lBYZCLNg2qrcXq68Pt74eliwh+va3z//OVuClUuA4qCBAxeOT3yAWQwUBOE542yOQXygwcPnlqD/8IWzJNzgIngeGgfeHP5D+7nfhNa+h4V3vKhu2O1z4+Tz8+tdo+/YRaBrOhg0o7aCC7ZaFv3IljqZh7d2LnUySu+sukoukgpsQQgghhBBCCCGEEBLAEkIIIYQQQgixaNW8/OUUV60if/XVBPfei1cogOOApkEkgnf00ZgXXkj92Wcf1iGU+je+EfuMM8hddx3eLbfgp9PhAl3HO/lkoq99LYmTTqq4j8qyqPnyl8l84APYuo7e2UmkvZ1A0wiUQvO8MLC2dCnBkiVoH/kIiS1b5nEPq1OWRTAUuglcd1yLxbI8L/ypaagjpArUWF42y8AHP4j++OPo+/djZLP4pkmg6yjHQR04gFtbi5dO07d7N42f//xhWw0qc9NNIwGz0urVE8NXY/hLl0IqBf39lG64QQJYQgghhBBCCCGEEGLRkACWEEIIIYQQQohFLXrUUUQ/9znc/n7yf/4z/uAgKhIhcswxRI86aqGnN2us5cux3v9+eN/78LJZAttGr6tDGeFbd8dxqt7eXLKE2m99i8ErryS48068TAay2bBiWCQCiQTec55D7JJLiD//+fOxS1NmbdxI0TTxLQttYIAgFqs6Xg0M4FsWmCbWhg3zNMv5M/CZz6A/9hjWjh140SjFdetQiUQYwAsCgsFBzM5O9B07sIHUN75B4wc/uNDTnhH34YcxBgYIDANVW1t1rNI03Lo69HQaHnssPLYP4+ClEEIIIYQQQgghhDhySABLCCGEEEIIIcRhwWhspPaccxZ6GnNPKfSamhnd1GhooPGyy/A+8AEyv/0t/p494Lqo+npiL3sZ0U2bZnmysyO6aRP5TZtwu7sxe3ootbZWrOgUeB5Wfz9OSwve0UcT3bhxnmc7t4pbt6Lffz/G7t248Tj+2rWosSEjpVB1dTg1NZjbt6M/8wzeb36D9/a3z/i4WVC5HLguvmlOKUwVWBZaPh/eplRCOwIroAkhhBBCCCGEEEKIw48EsIQQQgghhBBCiCOMXlND/RvesNDTmBbjwgvxt27F7+/H3LkTd80asKzxg2wbc/dufMMgaG7GuuCChZjqnMpfey1aJoNu27jr11duO6lpuMuWEWlvx89mydxwA/VvfvM8z3YWRKOgaWiuizeVilauG7ar1DS0SGR+5iiEEEIIIYQQQgghxCS0hZ6AEEIIIYQQQgghRO2rXoV/1llh6AiIbN2Ktns39PZCby/a7t1Etm4FwF23Dv+ss6hZwIpogedR3LaN3IMPUnj8cfx8fnZW/OCDaP39OMkkmGb1sckknmmi+vrw7r9/drY/z/RjjiGoq0NzHILJ7sMgwEin8evq8DZulPaDQgghhBBCCCGEEGLRkApYQgghhBBCCCGEWHhK0fAv/0LKsnBvuQU3nUbv68Ps6QHAj0QorV4NdXX4r3wlDR//+IIEcNz+fjLXXktw881ovb0j1xejUYLTTyf+N39zSG0RVS6Hcl38qbTWUwo/EkG5LiqbnfE2F1LyNa8h8+Mf4yYSGAcO4FWr+tXTg/I8gqYmzPPOm+eZCiGEEEIIIYQQQghRmQSwhBBCCCGEEEI8K9gdHeRuvpmguxsAbckS4mefjbVs2QLPTAxThkHjpz5F4TWvoXDttQT33IOybQACyyJ48YuJXXQRsc2bF2R++UceoXjZZWjd3ei9vejpNNpQSzwvmcTr6aF4yy0U//EfZ9wCMrAsUCoMGk1hvOZ5eJpGMFm1rEXKaGggOPNMvEwGa/t2gvZ2/BUrxrWfDHwf1dOD1dWFvWwZfmsrdWefvYCzFkIIIYQQQgghhBBiPAlgCSGEEEIIIYQ4ohV37iR35ZXoDz4I+TxaoRAuiMXI//CHZLZsIfne9xJZu3ZhJypGxDZvJrZ5M4Hj4KZSQBjUUQsYMipu20bpYx9Df+YZrI4OvGgUZ8kSiETA89BSKSLt7Th1dfjf+AaDpknta1877e0Ea9bg79yJ0d2NHwRVq3wFto1eKOC2taGGWjcejuo//GFSO3di+z7Gnj2YW7fiJpNhdS/PQx8cRAGlZcsIVqwg9q//ihoT0BJCCCGEEEIIIYQQYqFJAEsIIYQQQgghxBEr/9e/UvrEJ9A7OjC6u9HzeQIjfCusXBc3HoeBAfJPPon3hS8QP+GEBZ7x4uBlMmR+9Su8++5DDQ4SmCZq7VpiF1xA7Ljj5m0eyjQxW1vnbXvV5L78ZfSODqyODkrLl0NT07jlfn09pXweq70dd98+vCuvxD3zTIyGhmltxzr/fLy//hXV1YXq6SGosv96ZyeeZUFNDfEZhL0WCy0Wo/7rX2fgM5+B++/HzWTQ+vvRikUCTcNtayNobMRvayP22c8SO/bYhZ6yEEIIIYQQQgghhBDjSABLCCGEEEIIIcQRye7ooHTZZWh79mB1dODU1+OuWAGxWDigUEDr6SGyeze2bVO87DLMq67CbGtb2IkvpCAg9c1vwvXXo7JZjL4+lOOApuE/+CClW26hsGkTiX/+ZyKrVy/0bOdN4Ykn0J9+GrOzE7upaUL4akQ8jr1yJZHdu/EzGbK/+hX1b33rtLaVPOssUt/6Fk5/fxj20rRwe2MqYQW+j3bgAMbAAPbatXjHH09048ZD2MOFpyeTNH3lKxSffpr8Ndfg/+UvqFwOLAt/3Tqs88+n7owzULq+0FMVQgghhBBCCCGEEGICCWAJIYQQQgghhDgiZX/8Y7TOzooVi4jF8FetopRIENm/n1I8Tubqq2m89NKFmfBCCwL6P/1ptN//Hm3vXsxMBs+yRtrARfr68E0TJ50m9973EvzXfx32oZ+pKtx4I9rgIJrjVK1IBUBNDV4kgurtxfvd72CaASxlGMQ++1mKl16KDVgHDhB0d+PW1YFpokoljHQaAHv1avw1a6j91KdmtmOLUPSoo4hedtlCT0MIIYQQQgghhBBCiGnRFnoCQgghhBBCCCHEbPPzedTvf4/W04ObTFauWATQ1ISbSKD19KB+/3v8YnH+JjoNe/bAww9DNjs36x/4wQ/Qfv97jO3b0UolSmvX4h51FP7atXgbNlA85hjc2loie/ag7dlD/hOfWLT31WwL9u9H5fN40SiYZvXBSuHV1KDl82idnRAE095ebPNmIl/+Mt7GjZSOOQa3qQktn0dPpVClEnZbG/ZznoO3eTPJK65YsKpthUKBdDpNOp2mUCgsyByEEEIIIYQQQgghhFgMpAKWEEIIIYQQQogjTv7++1G5HEYmg71q1aTjvaYmrGeewR8cJH///SRPO20eZjl1v/oVXHWVThBAYyP867/CbHYADFwX/7rrMDo6AHA2bpzQ6k2ZJsHy5diGgbVvH3ZtLdlbbqH2/PNnbyKLle+HQaoxbQCrUgo1fLsZip9wAtGf/5zMzTfj/uY36Dt2hHPQdbzjjiNy4YXUnX76vLfkC4KA3t5eenp6KBQKeJ4HgK7rxGIxWlpaaG5uRk31vhJCCCGEEEIIIYQQ4gggASwhhBBCCCGEEEccL5UCx0H5PsTjk98gHg/HOg5ef//cT3AaXBd++tPRQkr9/WEg6wMfmL1tZG69FdXXhzEwgL18edVQT9DaStDXh+rtxfnVr+DZEMCqryewLLRSicD3UVr1guJaoYBvWfh1dVMPbZVbTzRK3YUXwoUXQhDgFwposdghrfNQuK7Ljh07SKfTDAwMMDAwgOM4AJimSX19PZlMhv7+ftavX49hyGknIYQQQgghhBBCCPHsIC0IhRBCCCGEEEIccZRpjoRUgqEKPdWMjNE0VCQyl1Obtm3bIJ8ff91tt83uNtyHHkKl0+F91tBQfbBSuPX16Ok0+vbt+M+C1nORl71s5H5RqVTVsUGphJHN4jc1wamnzt4klEKLxyeEr3wf9u2DSaZ1yHzfZ8eOHfT19bFr1y4GBgZobGxk7dq1rF27lsbGRgYGBmhvb6e3t5cdO3bgH0IFMCGEEEIIIYQQQgghDifyVUQhhBBCCCGEEEcca/16ipaFb1loAwMEbW1Vx2sDA/iWBaaJtW7dPM1yaualiFA2i/I8fMOYUnWlIBIJA1uAl8mEVZmOYInTTiPV2orT2Yl54ABOPA5l9jnwfYy9e/FiMUgmib/udXM6r3QaPv952LoVdB3e8Q549avnZlu9vb2k02n27t1LMplk6dKl49oMRqNRGhoaOHDgAHv37sUwDHp7e2ltbZ2V7fu+TyqVYnBwENd10TSNRCJBU1MTpmnOyjaEEEIIIYQQQgghhJgpqYAlhBBCCCGEEM8SqRRkMgs9i/kRPeYY/PXrcRsaMPr7q1bBCjwPo78ft7ERb+NGokcdNY8zndx8ZJuCeJxA01CeN9rrsApl2wRDbfj0ZHKup7fglK5jvPOdBMuW4SUSWDt3ojo6CGwbCINX9PZiPf00muvirlmD/4pXEF2/fk7ndcstYfgKwPPge9+DYnFuttXd3U1/fz+GYUwIXw1TSrF06VJ0XSeVStHT0zNhjOOEFd2mcJiN6Ozs5NFHH2XHjh3s2rVr3L/HHnuM3bt3402h0p0QQgghhBBCCCGEEHNFKmAJIYQQQgghxCxoamqiUKUV2xlnnMFvf/vbist/8IMf8J73vOeQ53H00Ufz0EMPjbvOceCKK+COO6Cn5/946KHXouuHtp2Ghgb2799fcfnDDz/MqbPUfm1gYADLssouO3DgAOsrhVw8D1w37NH2xz+CVuE7SENt0v68di2rX/OaskOCICCRSEx77uXcddddbNmypeLyNWvW0N3dPWbbMJTzGScer76dX/7yl7y6SjmkF7zgBTz22GPhhbH31b33Tl4Fy/fDMX19XPHTn3LJJZdUHHr++edz2yz0TLzsssu47LLLKi5/5zvfydVXX33I23nHO97B17/+9QnX155zDgP9/fzrJz/Jfw8MhInGnTvHD1Iq/Pfgg/DII/Dv/15xO+eddx4///nPKy7/xje+wcc+9rGqcy2VJl5XXz/+UD/55JO54447Kq7jl7/8JRdffHHV7UBYgSoIgrLBK4C2tjauvfZalFI0NjbS29tLc3MzuVyORCLBXXfdxStecTauGx7TmgZTKVwVBMHIPwDTNPnhD3+I67p0dXURj8cpFArk83k2bdrEzp07OemkkyZf8RR0dHRQX19fdlk6nWbp0qWzsp2HH36YoxZZ8FMIIYQQQgghhBBCTI8EsIQQQgghhBBiFhQKhaoBrFK5pMQYnudVvf1UFcuUv3nwwTB8BWGIwrYPfTvRaLTq8iAIZmV/pmLK2xkKWlVcfPLJ1J533qFvZ9JpVJ/HZMfS6LjqyyerCFQsFstvJwimVp4oCMDzcF236rBSqTQr991k27Fte1a24zhOxWX1b3wj3s03U6gU9Bq+73w/DLRVYZdL1Y3huu6M9ufgl5r5eu0Zuz+JRILOzk5c16VUKpFIJPA8n1JpdDtTuIsqWr16NRAew729vezZsweAXbt2Lc7XnkkE0ykHJoQQQgghhBBCCCEWJQlgCSGEEEIIIcQC6uiAwcGZBxGm4mtfm7t1H0nqL7108spP4lnPWrFioaew6Cml8H3o7LRoaFA0NsIzz8z+dqLRKCtWrKCjo4N9+/YRiUQqVuiabTfeOC+bEUIIIYQQQgghhBCHCQlgCSGEEEIIIcQCue46+MEPwoI5mczcbSefn7t1H0nUVPqhCSHKcl2XVCpFXV0dvb1FrrvuRPr7V1BTk+BNb5q8YtuhWLJkCdu3byebzU5aKW22VOkeOStaWlrGVdiKxWL09/fP7UaFEEIIIYQQQgghxIxJAEsIIYQQQggh5kgsFhv5PRKJjFuWy8FPfjLa6W1gQMeyYuh62Jrr4C5oug7GFN7BTdYaUCkNTYuVXXbQFKsau2/lt6MmHTNbpr2d4Tv9oEo5k1XOmc52giB8DIc3Nfbx0zRt0u2M3VYQQLludaYZAAG+r/A8NbQPYJrhT13XyefD29bVTSzuFY1GiURiBEG4bGRajhMehOXaoik1fiOAMcmBGYlEZuVYmGw7lmXNynbMSYJ4pmnOynYsy6q63DCMSbdTrrugaY55LJn42nMwXdentD9BEOD7fsV2eUop9u/fT39/Pw89tIQDB1qIxTR03eBnP4MXv7j8a0+l6QVBMPJv7HNz7P02+vzSqa2tJZ1OEwTBrL/2OA786Edw661QWwvvf39YtfDg/ZnOa+hY5V57isXiuJay81XZSwghhDhSOF1deP39YJpYy5ejzdN7EyGEEEII8eylgkpnzoSYgf/8z/8kk8lQU1PDpZdeutDTedbzPI+uri4g/EawrusLPCMhxFxzxnxaP9mHh0KIw5v8P7/4xOPxCdVK8lVKT91+O3z1qxOv//Wv4fLL4Y47yi+bifPPn9q4738fGhtntg0x3re/DTfdNP66H/0oDEJN14ED8K53ge/7uK5LPp/D9wPe/OZHqavr4sorn08+H0HXDSzL5IQTSnzxizXce2+S73wnDI+84AXwT/8E8fjoeq+9NqzANuxNb4K/+Zvw91J7O7lrr4X77kNlswSWRbBqFea551LzspehppIGfBaqkO2bM+We25/9LJx00uxv64EHHuCRRx7h0UcfJRaLsXz5cjSthsHBBDU1veRyg3ieh2mafP/7F9LQ0EgikRgJhS5ZAkP/bY1T6XVt27Zt7Nq1i0KhwKpVqyYsv//+Gq69tgXbVpx++gCnnPI0hUKWNWvW8LznPW82d53bboOvf330cjIJ2ezEcTfcMD78diim+3+KEOLwJ+/nhTh0gW0zeNNNuDfeiL5jx+j10SjBaacRf/3riR511ALOcJS8pxfi2Uf+rxfi2UP+n1+c5iPLImdMhRBCCCGEEGIB5HKVlx1c/WrYE0+EhYmOO25uAh5790oAa7YcHL4CuOsuOO+8ma/T88LwValk4zgOmYxLU9MyPK8W0wxwHIdczuEvf4nw6KM7+e//Ppbht/1//jPceSecc87o+m6+efz6f/zj0QBWZO1aIh/5yMwnexjwffjtb6GjA048EU4+eebrSqXgK1+BJ5+EtWvhgx+E1atnbaplVeq0Nxdfs8tkMvi+TyaTobm5mSAIeOqpZv70p1NQyqCmpsi5595HJrOVVCqFrhv4vj+u+lYqNb1tapqGUgrP88rMR+dnP2sd2dc776ynoSHJ2rX5SSvMzcTY8BWUD19B+JhMUthMCCGEEHPE7ugg87GPoe3ahdHXh5FKoRwHlMKLx/G6uynecguFt7yFhksumb/EvBBCiBkr7d2Ls3MnfrGI3thI/KSTUPKmSwixiEkASwghhBBCCCEWQJlMwYhy7eYAPvGJ8OfmzfC5z4Vt7WbTnj1hEEWU19sbBnYAXv5yWLp0erc/tGBMQD6fp1SysW2beDxGU9MKGhqK6LqBroffoHVdh2KxxG23QSaTp6amdmQN3/zm+ABWd/fErdj21AIkw53RJul4uah985vwu9+Fv//mN/CBD8DLXjazdf3gB/DYY+HvO3aE6/7iF2dnnpWMKY40ju/P/rZ6enrI5/M0NjbS1tZGPl/gxhtPolh0AIdCAe64YwmvfGU/yWQSCDDN8aecKr2uVRKPx0kmk/T09FAqlcaFue66q27C8+naa1fwmc/kiY8t8zbPqr2uT9dcPI5CCCHEkcpNpch+6EPo27dj7dlDoGm4DQ348TjK89DSaSK7d+Mmk3jf/z4poOEd71joaQshhCgnCMjcfjv2ddehP/ZY+EbL9/ENg1RdHbz85dT83d9htrYu9EyFEGICCWAJIYQQQgghxBy59lq49daw7dvFF8MJJ4wuq/ZBfaUKWMMeewweeABe+MLZmeewvXtnd31HkkIBPvaxMIQFYXDna1+D5uapr+PgwEhvL/zpT1BfDy9+cfUv4TuOg+/72LZNJBK2GyyVtAntzgzDJBIJOHCgiOeFLQuNMu0CK4XBBger71MQwNVXwy9/GV6+6CJ485sPvwICpdJo+GrYD34w/QBWEASk02l+8xuLIPABha5rPP64RX+/fsgV5TKZ8LkeicCWLeHPYZW60c12BSzP8xgYGCCVStHc3ExLSwtPPFEgCGqIxYKh/YYDB45hyZJ+LMvCMEyCIDxurRl+O7m5uZkDBw4Qi8Xo7e1l+fLlI8t6esa36wiDhzp1dXW0tLTMfGcPUaWqZNO1d+/E/wfmorKZEEIIcaQYvOIKtF27sHbvxmloIFi+HJRi+E9Uv7GRYqGAtXs37N6N96MfUTzjDKIbNizktIUQQhwk8Dz6//3f0X/3O4z+fvS+PvShb4AFmoZbV4ff2Unm1luJfeELxDZvXuAZCyHEeLNfl10IIYQQQgghBL4fBjo6OsKKOF/8IgwMjC6vFp6YLIAFcOWVszLNcSSAVdkNN4yGryAMxtx//8zXt2cPvPe98N3vwpe/HLavq8a2bRzHRSk1UlmoWNTKVskxTRMIw1e2XSq7vkrVdQYHq89j5074xS/C4zQI4JprYPv26rdZjPr7J16XTk93Hf08/vjjbN++nXw+Ry6XJ5/Pkc/nGRzMcOedeymVyt//U5FOw6WXwle/Cl/6Enz60+MDPvNVAatUKhEEAYVCgWQyia7r1NY2kUgkicdjxGJxYrE48XiMDRs2sHr1apRS+L6HfwiTsSyLhoYG2trayGQydHZ2jqzPMEbTSMNV3yKRCNFolIaGhkPe55marQDWd7878brZrK4lhBBCHEm8dBp1113onZ24icRI+OpgKhbDWbMGI5uFTIb8tdcuwGyFEEJUk/rKV9Buvhlj+3bMzk68ZJLShg0Ujz4ae/lylG0T2bEDbdcuih//OKX29oWeshBCjCMBLCGEEEIIIYSYAwfnDnI5uO660cvZbPnblUpTa9U1Nsw1W/bulSorlfz0pxOv+8tfpreOsfftb387PkBz993Q01P5tp7n43kehqHD0Hf5S6VKZacUkUgEzysfgNm3D554ovwtM5nx8+3tHR8W/J//mXib73yn8rwXq6mEHKvp6upi165d7Nu3j+3bt1MqlfA8F8dxyecL5PN5Hn88w9atWykO92scI5UK24i+853wjW+Erw8Hu+ce6Owcvfzkk+Mft/lsQXiwIAiPPV03ME0T0zTRdQM19GHn6Geek7+gnH9+2AKyXMBo1apV1NXVsWrVKjKZDNu3b6ezs5NSKYdtl8jncyPhK8uyWL9+/cgcFsJshaQeeWTidfLaLIQQQpSXvekmVDaLkc3itbZWL80ai+Ekk2i9vajbbyc41D8KhRBCzJrCE0+g3XQT5lCoqnTUUWGoNpFARSLQ2Ii3YQOl5cuJdHSg9u8ne8UVCzxrIYQYTwJYQgghhBBCCDEHyoUgbrhh9PexQZexcrlDD4fMVKEwWhno0Ufhm9+En/98aoGwI1ml/a+pmfk6b7554nUPPjj+cqFQ4JlnnqG9vZ1CIT8U8hlNeIQVsMp/wDR8fXBQauPqq+Ef/xE+9any8xqugJVOwyc/CW9/e9g+8447wuv37Zt4m3LXLWa//z28730zv/3AwADPPPMM+/fvJ51Os2TJEhKJxFAVqDiJRBxd19m2rUB/fz/bt28f97gBXH55+Hh3dcEtt4TPs4N9+9sTr/vJT0Z/n68WhGFFtbAiVWEo9VUt5FUsFgkCUEpDqamddvrOd+Df/33i9YZhcNRRR9HS0sKGDRtoa2vDcRxct4Dn+ZimSSKRwDRNYrEopmlOOObn02xVwBJCCCHE1Hl79kAuh6/rqERi0vF+XR16Po8qFHCqfQNCCCHEvCpcey0MDqIXCjhr16JMs/zApiachgaM7m70v/wF+3A7KSGEOKIZCz0BIYQQQgghhDgSVfri9eAg1NZWDmDl8wsXwIKw4tGuXfCv/zp63VNPwWc+szDzaW8PQyeeB69/PRxzzPzP4ckny1/f0jL+8iOPhO34ZjpHXQ9/FgoF9u7dSzabJZfLsW+fR7G4DsexcRyHIIBYLEqhoFcMwvzxj60YhpoQgPnFL6rPYTiAdfPNo9WWSiX41rfgRS8qf5vDqTJPPn/o7TsPHDhAf38/+XyeNWvWYFkWw1XJIAweRSIRcrkWOjqeJh6P09/fT8vQAWPbEysc3XBDGHabzNj7ulIFrGqPRy6Xo6+vb6Qql2EYNDY2UldXN1I5yvPC1yDLAk0LA1jJZJL6+nr6+vpobm6uuo2BgQF0fR26ro2Et6bigQeguxtaW8fv4/e/b7Jt29GsX1/gZS/roKFhkCVLGojFovh+QBAEKAX5fIHHHtuJaZq0tLTQ3Nw8re3PhrlsE7iAhb2EEEKIxc3zwnS4UlP7D1OpkT+YpAKWEEIsDoHjoO6+G723F6emBmVZVcd7zc2Y27bh5nLkbroJ613vmqeZCiFEdRLAEkIIIYQQQoh5lEpVD2DNdgWs6YZjenvDKkljPfTQxGDEfMhk4KMfHa1A9Ze/hFWB2trmdx4PPVT++rH37U03la9YVGl8OdFoGJDZvn07qVSKnp4eHMehUKhHqbANoW2XRoIyXV3lD5Sw9WAw0hpusjmEAZZwncMBrLGVliAMLv3lL+U/0zqcAli33Vb9+RVWbqq8PJfLkc/nSaVSNDU1YVkWmYxedmyxmMQ0TQYGBujp6RkJYFWqkuT7YeBpqqbTgrBQKLBnzx5yuRzZbHakkpVpmvT19RGJRFixYgW9vY38x3+Ez/eNG+FjH4MlS6ClpYV0Ok1fXx8HDhwgCNaV3XYmkyGVSmFZ4bGnTWeHgIcfhle+Mgz/Pf00XH99WI0NYOfOGIaxnne/G/761yJ3320TBN5QRSyHIAjYsWMHyWSSbDZLZ2cn69ato66ublpzOBRSAUsIIYSYf6q+HiIRNNcN/9CbJICt8nmCoQ/2jcbGeZihEEI8CwyfGJjhN0fc/n5UqYRWKuFM4eSTisXwTRMKBYKurhltUwgh5oIEsIQQQgghhBBiFnzmM5/BHfPp+29+U/7t1nCFlGy2/Hpyuam3/Lv22rBSUTIJ7343HH30xDHTDXP19JRvKbdjx+wFsIrFMOCzdWsY8njzm8Pw0cF++9vx90UQhLf78IdHL89HVZhHHy1//djg0a9/Pfl6hscPFR+aoFj02LFjB729vXR2dtLU1ERjYyOpVIyamlqUUigVhmmUggMHHLLZ8SmcIAgolYoYhoGmaUPVmSbMBMdxKJVsPM8duR913aCzUxEEccZWdBqWyy1sFR7HCSt4PfwwLFsG73zn9NtADrfYrGT4czvDCCtAHWxwcJB8Po/jOCPBngceKD+JXE6nrq6egYEUzc3N2LZd4fEIpdPQ0DDlXakYwPryl6GxEY49NryczWbHhfpc1yUWi6GUIpVK0dXVRUNDA7Zt87//G6W7Ow6E1dx+9jP44AehoaGBuro6Vq5cyd69ezlwoAPXXYJh6IDC98Mg1L59+2htbcUwTKLlntSTsKzwef+tb5Vffv/98Na3FkmlDmDbSYrFEpqmYZoWmqaIx5dQLPaxa9cumpqaCIKADRs2UFtbO+25zMRcBrCkApYQQghRXuylL6X4i1/gmyaqt5dg6dKKYwPfxxwYwG1rwzvhBPRD6SkuhBDPcqX2dnLXXgt33402MACaht/ainr5y6m58EKMpqapr2z4hMl0vuE1/CbpcPpWmBDiiCcBLCGEEEIIIYSYBR/72MfGXd65MwwzHWy4Os1stCD8wQ/Cn93d8OlPw49+NDE0UipNbV3D+vrKXz+bnby+970wOAZhCKtYhPe/f+K4e++deN0dd8Ab3gBf+Qrs3g0nngj/9E9hVbG5EARw4MDk46YyZtjAQPnru7vTJJM5urq6WLp0KfX19SPLlFJYlkUQBIAiny/Q06PR1zecKApwHBfHsVFKEY1GiEYjI5Wthvm+Ry6Xw/M8HMfFdd2RCliGYbB3b54nnsjg+8egaeMrOxkVziAcyrnOdDo8jp96Kmzd+A//MBrG6+0N2zJ2dYWhqzvvhI6OcNm2beH9+LnPTX1bxSJcd131Mf/yL2H1pXg8PCZPPXX8ctcN7zNd1zGG7pAbbyx/UjkIwPfjeF4vAA8+6HPrrZXb1PX3Ty+Alc9X348f/hAKBYf9+3fy9NNZ9u/Pcuyx9TQ01NPXF+Ppp+O0tNgsXdpJd3cXmYzH449vIJk0MYzwCf/734cBLKUU69evJwgCdF2nt9emVCqOCxPqus7KlSupq6sjkUhMOH6mIhIJX8cq6e2F3bt3k8m4FIsm0WhkZK4Att3MmjU1ZLNZ9u3bh6ZpQ/PazDe/qbFzJxx/fBhYnUE+bFJz2YJQCCGEEOVFjzmG3FFHoXp7MQ8coFRbi0okJg4MAvRnngGlCBobsS64YN7nKoQQR4LA80h95StoN92Elsuh9faihr49p+3Zg797N5mf/hTtne+k7m//dkrrNBobwTDwIxG0bBZ/kvBWUCqhOQ5Eo6ihatNCCLEYSABLCCGEEEIIIWbZjh3lw1cQfkDveZWr12SzM2tBWCiE1YFe97rxwYLpBrB6e8tfX6Vwz7QEwWj4atitt5YPYO3eXX4d73nP6O9/+Qt897tw6aUTt3PffWFgZ/Nm2LRpZvPN5So/VjP5giZUCmAF9PRkaGhIEY1Gx4WvhlmWhe+HASylYGDAZuvWXWQym4eqYylM08SyTCKRCJHI+ISJ73tks1kcx6FYLAJqpFKW7/vYtk13dx89PWmy2ZUkk8lxIRrHCVtoHqxUmnk1sm9/G+65J/x9//5wPe99L3zjG3D77dVv+9e/ho9Puc/Xyvne9yYf88QT4c98PpzDySePDx9qmjZyf41t3VjJ4GB4m717I/zP/5hVWwx++MNwxRWwalXlMUEwerxVOi4hfKz+7u/Cqmr5/Ery+TzR6NE88ojHK1/Zz//+71IcJ5z76afHOeecKE8+uX/o2CiRTE5MXBqGwaZNm+jq6mLv3gyJRGLofgBNU2iaxqpVq1i6dCmRiDGjMJLnVX4NCpe75HI5UqkSltU6LnwF0NVlsWZNiWQyydKlSzlw4AD19fX8+78X2bYtrOx1++3hY/re905/fpPZtw9++cswyPrCF8LrXz/950W5FpJCCCGEqC76jnfg7NiBWygQ2bULp6kJv7kZZVnhH0+Dgxjd3WilEva6dXjPeQ51Z5yx0NMWQojDTxCQ+uxn0W67Db29HaNQwE0k8JPJMOiazWI+/TROXR3+f/83A7ZN/VveMulqlWXhnXIK9PZiPfMMJcdBVfkmoNbTgxeJQCJB7JWvnM09FEKIQyIBLCGEEEIIIYSYRX198NGPVl7u+5XbD8L0KmAd7Be/CMNMl14KxxzjYNs2XV0K34+iVUt+jFEpOHbwea+ODvjjH8P2hy9+cfhzKgYHy19fKoXVb4ZVatNXzp13wrveFf7+5JNh+7V77hmtdqRp8PGPwymnTH2dwyrdH2NNNTAxHJwpdx94nk8+7zE4OEhrxV6Pimg0iq6HIaBcDgyjFcMwsCwLXdfRdY1IJIJlRSbcerh1XqFQxLKsCe3wIpEIsViS/fufxHE2k8/nSSZH27JccUXlfXv0UTjhhMnugfFSqdHw1bC//CUMVk0WvhpWKFQPYN13H3znO+HxVanqXCW5XFiFbexnc/F4nFgsBkAmk5m0tV1PT5HVqxP8/veNkz4HgwA+8pGwclWl6kzpNPzrv8KDD05lDwJs28ZxHHRdwzBMdu82+fa3l40bdeed9Zx9dj+1tY04joNpmnieh65PrGClaRpLly5l/fo2Egl3JIimaWH4b+3aeiCsljaTANZkIbkgKA2FCHXMMifju7tHj+na2lp6enro6xvkoYf8ca9Rv/vd3ASwvvrV0d937AiPzVe/enrryOVmdUpCCCHEs0LiBS9g8MMfxr/8cuyuLozeXqyeHnxdRw39Ee7W1uKuXIl3zDHUfelLqDJ/6wghhKgufd11qN//HmPHDtA0ikcdhRpzMscF3EwGc+9e1O7deP/zPxROOonY5s2Trjv62tfi3H03fmcn5u7dOOvXo8q9jx4YwOrvp7RyJd7mzUTXr5/FPRRCiEMjASwhhBBCCCGEmEU33VQ9eOB51YMgudyhtXTr7nb4yldyfOADO1EKbrutnsHBJkzTwLIiZUMLY1WqPjO2/dwzz4Qhs+EWaFdeCWefDS9/OVx9dRgwe/nL4RWvmFj9paur/Pr/4z/gn/959PKuXZPs6EF++cuwPWG5Ck2+D7/97fgAlueF93OltnrDqgWwhh+nqQZNqo0PggDbVnieRyQyMTw1lmlaWFYEz/OwrGXEYjGi0SiGYYy0xTtY2DrPo1QqYVnmhPDVsG3bWmls3EyxWELXDVzXrbjOsX784zCA1dcXHkMbNoTtA6u5//6J1w0MhJWnpqpadaHBQfjSlw6tLdzB1crq6+tHKpT19fVRU1NT9nYQVhzr63M48cQGnniigVhs8lJIxWIYojzvvPLLOzvDf1PheT6+7+O6zqTH1O7dURoaGvD94du4IwEs32dC5a7hamuVGMb0q+9B9epXAI7jk8/nMc3mstXHurpG56SUIplMks2W8P2ZHwSH8np8/fXTD2BVCqkKIYQQorra884jt3Qpxf/5H3jiCdx8PvyDRNMIEgmoqyN4+ctpePe70eLxhZ6uEEIcfoIA77rr0Lu7Ub6PvXFj+TBrTQ32unVEt2/HGxig8MtfTimAlXjuc+l7yUvAtjF37sTauhWnqYmgoQF0HZXLofX1YWYy2K2tBEuXEv/Hf5yDHRVCiJmTAJYQQgghhBBCzKJbb62+3POqV8CaafWTIAiGKhzZpNMujz++D9PMcuedzyWfz2NZ5lB1G4t4PF6xdVq5ABOMD2D87nej4ath//d/4b9h27ZBUxNs2RJefvrpsGLW44+XX/8DD4ThkyeeCMNSUw2ZDLv++urLH3lk9PdbboGrrgq3d8458M53Vg5idXdPvu3pBnzKjVcKHEdDqTCENbkAXddRqoVYLDZpwMa2bTzPxfeDiuGrYffdt4ogyOG6LrZdmlIA6+mn4YYb4H//N7y8dCl8/vPQ3Fz5Nn/+c/nr+/sn3dyIanfVb35zaOErANcdf1kpRUtLC4VCgd27d/PMM88QBOsnPJ88z6VYLHLHHZvp6VFEIlPv4XnffZUDWNMRDCWHwvaQ1atv+b4iCIyh8f7IbSH83HKo6NeY8dW3PYVDZkZ8f3i/yqf7enrG389hu0j3kEJUh9IScCqvHwd76qmZb08IIYR4tkts2UJiyxaKO3ZQuOMOgoEBME2MtWtJvuIVaJXKjAohhJhU7qGH0J55BiOVwm1urlpJUMViOHV16L29BPfei5dOo9fVVd+AUjR++tP0O04YuOrpwezrQxs+QaQUbjJJae1agtZWrM98htixx87iHgohxKGTAJYQQgghhBBCzKJJCkzh+9UrYB0cbJqagHw+h23bFAqFoZZgtSSTLaTTLRhGWPnItm1isQAISCQSwMQQVqWgwtjr9+yZ2qzuuScMYD32WFjdarIgw+23w7e+NbV1z1QqFVbsGp7LzTfD8cfDqaeWHz+VClgHh3R83xsJsGiaPiGcc/D44XGuq5NMJkmn0yQn6enoOC5KKQYH42VbxR3M8zxc18MwdMo97uMpDEPH89wphsFCP/jB6O8HDsCNN8Lb3lZ+bKEwPhQ3U9WOqZ07D3395R6rtrY2stksvu/z17/2kcvlMAwdXdcJgrDamO/7Q638IuzYEceypt7i5lDCQmONP+yqrzQIwLaDodupcbf9/vfh4othbKGIcI7BUAvC8DZjWyxO9jo4c2ErxVKpfJ/W3l4T1x0NgBWLRXQ9hqZNXn2skpm2hJ2J/fvh61+fv+0JIYQQR6rohg1EN2xY6GkIIcQRxXnyScjn0RwHv6Fh0jMLXn090T178ByH4vbtJIa/oVeFsiyavvQl0tddh3vDDQR79oTfngsCAtOERAL/tNNIvulNRNaunZ0dE0KIWSQBLCGEEEIIIYSYRZNVfplKC8Lpsm0b23YoFApomkY0GiWRaGHXrii6bqDrBpZlUSwWKRQKgMI0bSyretWkscaGQp54Ymq3uf12+NCH4Pe/n1oVmdtum/J0Zuz//m/iXL7//coBrKlUsBmuyhM+DiU8b3QDw63aIhFrpMJQuftCKYVhJKivr2ffvn0Ui0U6O+u4+eYmurvHp1mCIMBxbEzTpK9Pn1KFqjCAE1SsfDZxPhpBEEwrDHRwVuv66ysHsB5+eHaCLU8/Dd/7Xvj7G94A69aNLptCLm1S5QJYSinWr1/PI4/E+eUvjyYW83AcB3dosGHomGYUTdOJx2OY5tSrX8HsBbCGw3+GoeM4Drpe+TgJAkincyjVjKbp48befDM8+WRY0eymm8C2XXR9gMFBE98fnayu6/T0FGlqasIwqlfcminTNEkma3HdAM9zJ+yT74chrLY2B9u2yeVytLa2YRgTE2HvfCd85COwaVN4udJTYzaO0yAIK7Jdc014XF58MZx++sRxd901+vsLXnAzth32cTRNk1e9ahYOaCGEEEIIIYSYoaBUQg2d0FBTOA+hDCN8M+T7BIXC1DekFHUXXQSvfS35Rx7B3bWLwLbR6upInHoqem3tTHdBCCHmnASwhBBCCCGEEGIWvO1tb8O2bR54IKxipWkWJ574vQnjJquANZMAVqlk4wylBKLRKKAoFHR27x7bYkMRjUaH2hQ6lErTC2ANC8NG07vN7bdPbdyOHdOezrTYNvz0pxOv7+qqfJtqFbCuuy5sqfi3f2uTzRZx3eEgjjMSTDEMHdc1sW2b/v4AaKjYFq+9vYZkMkkymeTppzv5/veP4uC37UHgUywWAbAsk/7++JTavYVVjaba3hB830fTtCkHtqZrth7r//zP0d8feCBsgVhfH16eqwpYELa2u//+5cRiLroeBiCHq57puoZlRbAsc9LWf+UcSsu7sZRSWJaJ61oUCoWRx7Qc1w1IpdKYpomuaxNCfXv2wJveBI5jk8/n8TwNxynguh7DwT7DMNm1q53Ozk407Rjm4pSTaZqYZpRIRFEqlYjFJlaY6+62aG0t0dHRMdSeM1q2BWRXF3z0o2Fob9cu2LABPvYxaGsL28R2d4e/VzoGpuORRxyuvDIgCML76stf1kkkdI46CmpqRseNfX1qajqNUikMYEUiEY49dm6ei0IIIYQQQggxFaqmZqTccVAsog7uVX+wUolA00DT0GYSmlKK+IknwoknTv+2QgixQCSAJYQQQgghhBCz4Oc///lQdamQpsXKBrA8L/xwv5LptiAcbhPnus5QpZ3wQ/pcTsO2D/7AXmGaFo5j43lW2QoylXR0wMaN0N8/sdJRJWNbli0GX/1q5WUDA6PBHQhb6N1+O2zbVn2dW7f6/Nu/ZbFtn0KhiKaF93EYdAlwXZdCoYhh6PT15ejsLOG6bWXXlcsZdHSsY/nygFtvtchmi+i6NvIYeZ6L63roukYsFseyLAxjaiE6wzAxDBPbdqoGcSAMX3meRyQSwTTn5rTBUK5kVrku/PrX8Ja3hIGhauG56azzYHfcATfcEIZ2dN0gFjMIzzsHTN7ecXKzVQELwuCObduYpkGhkCcajU54zgeBT2dnF5alY1kmkUj5Y8pxbHK5PKVSaaiilk4kYqGUwvd9HMfhJz8pcdZZNoODKXy/EU2b3apNSimWLFlKMplHKUU+n8eyrKHjNLzvd+92SCZ34/s+a9asIZForjqPXbvCnzt2hNXUzj8f/v3fw6BsXR1ccsnM5+t5Lrt27eXHPzbJZpMjASylFB//uEFjY4RPftLkpJNmvg0hhBBCCCGEmA+xF76QQjSKF4uh9fURrFhRdbze349bV0dQV0fsOc+Zp1kKIcTCkgCWEEIIIYQQQsyjK6+EapXX9+yZ3vp83ycIfHw/wDBGQwZXX72k7HjD0CmVgqHb+FNu03b55WHbuJe9bHrzW0zuuafysre8JQxfNDVBZ2dYCSednnydxWKBQiGgUChiWRaWNb7STtj+0adQKJBKpdi/P02x2AiUb0t3++0NXHbZGtrbFYmEwrYdPG84BaSIxWLouk40GiE22bdNx7Asi1KpiK7rQ5WDKt+2VCqh6xqaps2oSlo52SxcfXVYdeiYY+DGG2dltRNccw28+c1hu7fZcHDYcOtW+K//qhSSmp0KRbMZwNI0nVgsNrTO0lBIUBt6rQgronmeh1KKdHrD0DE88TEPgoB8vkCpVMJ1nZHjcJiug2la3H//0Tz4YI6aGg/HKZBIJGdvZ4bcdFMryWSKWEwNtf20KZVKKKUIgoBdu2xOPjlOc3Mzzc3NNDRU/1BgrD/9KQxjDlcpTKfh29+e2TzDueXZu3cvf/zjBorF0fKG4XPLpKvL5b//2+Cqq8L7ybLCSn1CCCGEEEIIsdhEVq8me+KJ0NuL1dGB3dhY+Zt3/f0Y+TylZcvgla9EmRPbwgshxJFIAlhCCCGEEEIIMY+mEuqZjumHNUZDItO97R/+AFUKJx323va2sILS178+tcdpuOrPcIWhg8NXw5TSiEZjFApFCgVFKjUINJcdu3MnNDc3E436uK6NYdh43mhPOssysazIhBZxkwkDHxa+H4bBCoU8lhUZF6LxPA/bLhEEwVDrNqtqpaypSKWgoQG+8hV46KHwugcfPKRVTuq22+B3v5uddR1cAes735ndgFQ5M2lDWk0YqFIjLQkdxx1pRalpikgkRmvrUn7+86XE49Gy67BtG98PW2zGYtFxx81YpmlRKgX09nrE4y6+7816FaybbwZoIB63h9o9htX8hqtLwTJWrjRpbW1lyZIl9PdPLxj31FPjL8/k8XAch3w+TyQyyP79+2lsXMPAQA1KaQRBgOs6Q5XEXLZti7J/fzfLl7dSUwN9fdPfnhBCCCGEEELMh+ib34zz17/iZrNYu3Zht7URNDaihs4dBI6D1tOD1dsbLmtpIfn61y/wrIUQYv5IAEsIIYQQQgghDmOapoZCB+B5PoZRPTAzHLxQSqFp06/Y8/vfT33sXAdV5kJvLzz++NTGOo6D5w2366veb1HTNDQtzv335+jqKjJZuzrH0YhEokQiw4GYAN8/tDs0Fovh+z6gKJWKFAqFkeMnCIKRKmrRaBTTtIhGp15hq5KLL4ZTTx0NX82HK66YvXUNDo7+7jhhm7q5tm8f/Oxns7vOsE2fOVIxKjwOApTSME2TNWvWEouVDxBCGMByHBdN0yZtW2pZJrmcg+d5lEr2tCq1TcdwxTnXdXFdlyDwUUoRiWhs3tw28rro+5OsaNYFFAqFoWBmiTVr1rB8ucXgYBhEC4NwEUzTpFAIq4rt29fBkiWNJJOGBLCEEEIIIYQQi1ZiyxbS73sf/je+gdPRgdXZCZ2d+JEIAWAUCvimSWn5coLly4l87nOYS8pXaBdCiCORBLCEEEIIIYQQ4jBmGAaapmGaBo7jTFoZyXEcTNMYakMmbwkPtnv31Mf6vjdU4UdNqVLUgw+u4o9/tInHE9TWBoQhrDAEMxwWAThwoNyt1dD4Q6FIJBJoWtiGzvf9oeBKWDlo+FiyLItYLMpstdS7995ZWc2C+NOf4Kab4Cc/GW1LNx9+8pPZX2cYTooQiUxsMTjZS4Hve3ieO6XXDKXC9pWe5w0FveaWYRjj5uW6MObpNO9BUMdxh6qF2USjcaLRKHV17oRxSoUhy0KhgOM49PX1kUzKBxNCCCGEEEKIxa3u9a8n09iI881vEnR1QSqFKpVAKUqtrVBXh7dhA/FLLyV27LELPV0hhJhXcrZdCCGEEEIIIQ5raqgKjEc+nx8KWJllR4YVm8JqTWG7vNkJ2BxJ+vunPnYmwQ7f9ymVSgwOphl7/4ctDCOYpsENN8zl46KIxWJEo1Ecxx4KYIWBFV0P2yiODYMJ+Pa3F3oGc+/yy6stDUaO9akeGqPH0PyXwcvnx1+e7wpYth0+ryBsyQiV7zdd19E0jf7+FL29NTzxhASwhBBCCCGEEItfzUtfCmedRfbuu7Fvu42gvx+UQi1dSuy884ht3rzQUxRCiAUhASwhhBBCCCGEmAUL2W4vEolg2w7RaJRisYjnuZimha6HLa88zxsK23hEo1EMwyhbBWcuHG5tCPfunfrYsH2fhu8HI1WkqikWixSLJXzfJwj8oZ/helzXHKpgZrJ7dxyYvKLWoRhug2ZZ83MciMOZGjnWPc+jQr5zHN/3ME1jQcJ8xSJ4Hgy9/M17ACsIfDzPR9d1+vtNSiVVdQ66rmPbNj/8Yd246++44zm4bgEIn68PPxzlta99ai6nLoQQQgghhBBTpxTJ006D005b6JkIIcSiIQEsIYQQQgghhJgFC1k0SCmNZDJBNhuGeWzbplAojBtjGDrxeAzDMEkkEig1twEfCMNX8x1+OFS//vXUx5qmOVTBRuE4zlBVsfJKpRL5fAEIcBw1VGlKw/c9bNslny+gaYpksoYHHghIJJJSiUosGqZpYpoOhUJx0rCh6zpA2BpwuALUfCuVIB4Pf+/pWZApjPi3f1tNc7NTdcwvfnEU3d1JamtHr8vn9+D7o6/jPT2xuZqiEEIIIYQQQgghhJgFEsASQgghhBBCiCOApukkk0lKpSK6buD7YYUlCNC0sM2VZZlEIlE0be7DV8NKpUO5dcBibpOo6waGoWOaFqVSCcPQ0TR9wjjf98hmsyMhrXg8jud5+H6A57n4foBS4LouAwMpbDuOUhqJRGIB9krMls9+Fj796YWexXiu62LbNr7vjVRfC8NV1VtPWpZFqWSjaRqlUpFotHwYKAh8SiUb0zTRNB3TXJjTTsViGMDyffjnf57fbWuahq5rlEoOEJDN6mSzE18Xhnmex969DcTj8/e6LIQQQgghhAjZHR3krr8e//HHwxMYsRjaiSeSvOACzJaWhZ6eEEKIw4wEsIQQQgghhBDiCKFpGrFYnGg0wHFsfD8Yul5NGrCYK7Y99bG+72PbJRzHwfPC0lnDARHLioy0VFxMotHYUJjKJ58vDM3VHKkw5rou2WxmqL2gjmVZeJ6LbTu4rotSDIW2AoIAHCe8w5RSRCIWhjGFfm9iUaqpWegZjPI8j3w+j+d5eJ6L63pA+PwyDBNNKxKNRiu2JtV1A8syCYIohUKBfD6PZVkYRnhaKQgCXNfBtm10XceyIkSjURYqQHnjjXDxxfCnP83/tk3TwjBsSqUSrutWfQ4Pv3aY5sJVCxNCCCGEEGKmAsche+utOA89BLkcxGIYxx1H8pxz0GKLu4Krl8kw8MUvot9zDyqfxxgYQHkegWEQPPAAuZ/8BO+ss2j46EfRotGFnq4QQojDhASwhBBCCCGEEOIIo5TCssoHKebb/v3VlwdBGBbL5XLkcnmCwMd1XUANtTAzhipM2UPVo2IspqpYhmEQH+p1pusatu2Qy41vNxYEAYaho5Q2VEEorEAUiVhDoTI1Mk7TNPL5PIZhkE4P0tDQMK8Vy8SRx/NcstkcjmMPVb8Kj0cAxxmuWGUQBGHVvFiFD0pisThBEFals22bYrEIhK834bEbBj0tyyIWi1ZtyTnXrr02DGDdfff8b9s0jaHqX2FlPE3TylbGC6uFFUfGL+T9JYQQQgghxLQEAQPf+x7B9dej+vvR0ukwvKRp+P/3f6S/8x2Cc86h4b3vRRmL76Nob3CQ9Pveh/7UUxj79qHn83ixGL5hoOdy6N3duPE45POknnmGhq9/XUJYQgghpmTx/a8nhBBCCCGEEOKIUCzCJz5RebnruuTzeYrFIrlclmIxrBij69pQGCHAdV0cx0XXdYLAJwgCEok4iymEZZoWyaRGsVjEMEx83ycIfIbnqFQYWDEMA9d18X2PaDQ6UiVrmFKKaDSC6zqUSqWRwExUTvSKGQqCgFwuh22HFZksy8KyTMY+fzzPo1QqUigUAIaefxMDnEopEokEul5C13UiEQvPC1sZKqXQdR3D0IlEopjmwlduy+ehq2shtqyIx2Mjr1f5fGEoTGqiaWoodOqOVMWLRKLEYtEFqVAohBBCCCHEtAUBfZ/6FPodd6B3dGAMDIBS+IaB8jxURwdubS1+Ok3/9u00Xn45apF92WDgM59Bf+oprJ07ceNx3E2bYOiLKD7g5vPoHR2YO3YAkPrCF2j67GcXcMZCiMXE6ekhd8st+F1dKKXQliwh8cpXYjQ1LfTUxCIgASwhhBBCCCGEmAVBsNAzWBjNzdDbO/3beZ5LLpejWCySTqdxXQelFPF4HE1TuK6L67pYVmQomBAMBUTCyliVWqXNpVNOgYceglJp4jJdN0gkkvi+h+O4HHOMzZNPmkCArusjrcY8L2xJdnD4apQaCpuFIY0wgBVhMQXODrZpE2zbttCzEOXYtj0UsCoRjUbKtsPTdZ1YLE6hkMe2HXS9VKWCniIajRKNRoZahY4GsAzDGGlJOFt0HTxvZrd99FHo7p7V6UyZYZgjlfEMQ8dxnJGAG4Qht2g0imEYxGKxRVOxUAghhBBCiMmk/vu/0f7wB4yhcJK9YgXU14NSEAQEg4OYXV3o27dDEND/+c8vqvBS8emn0R98EHPPHtxEAn/NmnDuY8XjeOvXw86d6Hv3wp13Ynd0YC1btiBzFnPPS6fJ3n47fnc36Dr68uUkzzxTKp+JcUp795L91rfQ//QnyOfRht7nB7EY2auuwjvlFGre8x6sFSsWeKZiIUkASwghhBBCCCEWgSAIhiqmgKapKgGdxeWd74Qvfxlcdzq3Csjl8th22HpwuB1aNBoZadVlmiaOE1aCikQi6LpJEARDlaTC6jtTDSU997lhcOpQrVkDr30tfPSjlcdomk4korNhQ4T2dvA8n2KxNNRa0cP3AyKRym/Fh48BwzDHhLb8oVaFi9Nb3gL/+Z+QSi30TBaXxVDQyLZLOI6Dpmllw1fDlArbB9p22OrTdd1JwlTh+LkudHXssWGQaiYefhgymdmdz3SElfF0SqUShuGMVMRSSg3d3yaWFZn10JoQQgghhBBzxUun4YYbMJ55BpTC2bABNfa9qlKoujrcmhr0Xbsw9uyBO+6gtGcPkdWrF27iY+SvvRYtk0GzbZx16yq/cVMKb/lyItu24eVy5K6/Huu9753fyYo5Z+/bR/aqq1D33IPK5VDFYvjYR6Okv/51gpe9jLp3vAO9rm6hpyoWWOGJJyh8/OPo+/ejd3Vh5PMEWnjuVvk+biIBqRS5Rx/F/8pXMDdsWOAZi4VyeJzRF0IIIYQQQogjVNiGL8fgYJrBwQyZTIZ0epBcLofjOAs9vUkddxz80z9Ba+vUb+M4Dr4fVuXRNI0gCKvwDIevQmHAI6weY+N5/lALv7DqjjuNxNfGjVOfWzUtLXDUUbBly+RjI5GwYlYYfNFH2g8CVVuNua6LpikMQ0fXw7fswUHl1YY6IywaSsEnP7nQsxAHGw7vua47pZaApmkQBAGe5+K6i+O1Z9268N9MPPzw7M5lJnRdJx6PU1tbQyKRJJlMkkgkqamppbY2wcknS/hKCCGEEEIcPjK/+hUqm8XIZHCXLRsfvhpL03BXrkQvFCCbJXfNNfM70WoeeQQtlcJNJmGy1oixGF4shkql8GfjW11iUSk89hi5d70L7Ve/wti6lchTTxFpbyeycyeRp55Cf/JJtJ/9jPS7343d0bHQ0xULyOnqovj//h9aezuR9nYC06S0fj32ccdhH3ccpfXrCXSdSHs7qr2d/Mc+htPTs9DTFgtEAlhCCCGEEEIIMUcGB9NksxlKpdKEEA0E5PN5stks+XyBfL5ALpcll8tSKOQpFPLkcjlyuVyZ2y4eySS85CVw1VVhO8KpKJVsHMclCAIMQ8f3vZEqWAczTRPfD/B9b6hKGEOhkrAv2atfPfn2amvDVmaHqqUl/PmKV0w+tqkJ3vpWiEbBsqyhAEyA6zoVH8/h9oWGYaJp+kgVtIPzWsnkzPdhLngerF+/0LMQBxs+zoIgQNOmUo4rrMwUBBNDf9Pl+x6FQoHBwUHS6fTQa2EWx7GBqa87EoEPfzishDXdLxx3dk5v/FxSSsM0zaGqYSaapnH55fCZzyz0zIQQQgghhJg6709/QvX341vWpG9MVSSCG4+j9ffD/ffP0wynIJdDeR7BFMv5+paFcl1UPj/HExPzyd6/n8InPoEaDtQoRXHtWkqbN1M6/njsVatQjoO1Ywfa1q1kP/pRfDkGnrUyP/oR6sABIvv3U1q2DH/1akgkRgcMtTO129qI7NuH1tFB7qc/XbgJiwUlASwhhBBCCCGEmBNhwCoMV+UZHBzEtksjS/P5PKVSiXw+T7FYRNM0otEo0WgUTdOHluWw7fDndEIL82lsOOjEE6d2G8/z8Dx3KHQVBj4qtRNUSkPTtKF2fMP3QcDw/fGiF8HJJ1ffXk0NvPGNU5tbNcNVvqZSgWrVKmhrg9e9LmxFZlkW0WgUz/PJ5/P4vjcyNgh8HMemWCxhGAaGYQxVwTJQSh1UGQySycV1LLguaIdwdmHVqtmby2Lxylcu9AxGK60ppcY8d6oJhlrkVa/SVnUNQTBU0S9DPh8GScPXwTyFQoFcLlw21Qp2Z58dHh9f+AL86EczmtKiNRwKla4EQgghhBDisJHJoBwHLxKZUs91PxpFOQ5qIXuDHywaDduGTfE9iRp6wxtEInM8MTGfst/7HlpHB5GOjjBQs3YtqqaGoTfEUFeHt349TkMD1p49aLt2MXj99Qs9bbEA/GIR9fvfo/X0hG0Gq3z7NGhtDYOnPT1w220Etj2PMxWLhQSwhBBCCCGEEOIQdXZ2cvbZr2PJkktoaXkbLS1vp7X1YpTScF2HXC5HsRhWuSqVSjiOjW3bFAp5lFIkEgn+8R/7WbvWQ9cNIpEIiUQCTdMoFArYtoO9CN+0x+PjL7/gBVO/7Wtes51IJAyFKBWGkCoZDoMMj1FKG6kOZRhw2WXwpS+FYadyamrg3HPDqlSHYvj2B+93OcOhouOPD+dfW1tHJBIhHo/jui6FQnGo0lmBQqGI53lYljX0L6z6ZZomlmVNCMOM/ZLdYuC6Uzr3XtEUv3h82DjrLLjkkkO7T4YdSrUzTVNomoauG1NqKei6LkqFbfN0vXprvC98ody1AblcbiRYWigUCIIAXQ+rudm2TTab40Uv2kMul500hNXQcOjP2fm2bBl84hNTm/dwaLG2dm7nJIQQQgghxKyxLFAK5Vd+/z6W8jwCpaZcbWo+BJs24dfWYmSzBJ5XfbBtY+Tz+HV1qKOOmp8JijnnZTKou+5C7+7GqampHKhRimD5cnzDQPX04P/mN7CIK9SLuZG/7z5UJoMxOIg3hTf7XlMTRjqNlk7jPPLIPMxQLDbVz6gJIYQQQgghhKgqk8mwb98+Xv3qN/LMM8cRiUTRx/W7i+B5HsVikSAoAGEwI2zBB/F4FFA85zl5jjsuj+fBRz6yHlBEo1Hy+TyO41Aq2VjW4vrG5cHnHU44IazqMtk5zNWrbY4/Ps3y5Y/xxBMJcrle7rjjOAyj/ElZ3/fRNAPXDVsVapqGYYRvZ5PJ8P485phwu9dcM/H2NTVhK8B/+ze49lpwHLjzzunvr2WFPyergBWNjrYrHA5LaZpGQ0MDwFArRX+owpU2FJTR0XUNXddxHAfTNDAMnUjEGrfuD3zA5/77qyd72trCn/PVgu1QAljxeHh/HUk+9KHZW9cXvgBXXAHbts3k1grLsnBdl3zewfPcKsGqANu2R9pfmmbl00WWBStXTry+WCziODb5fAFd14jF4iNBSYBo1OK889qpr9/OH/6wBKVy1NTUVqy29da3TmNXF4EtW+Cf/zl8LmzZEj5mN98Md91VfvzwfxMSwBJCCCGOPIHrkrntNpy774bBQbAstLVrSVxwAdaKFQs9PSFmTK1Zg19Tg9Xfj+s4qGrBKt9Hz2Rw29oI1qyZtzlOJnbhhdh3301w4ABaZyfB8uXlBwYBWmdn2G6xpob4RRfN70TFnMndeScqm8XIZilNdmwqhdfYiNHTQ7B/P8WtW4kec8y8zFMsDn5/PzhOGDydwjcig0QiHOs4BP398zBDsdhIBSwhhBBCCCGEOATd3d0MDg6Sy+WJxWIHha9Cuq4Tj8dwXW+o8lUBx7ExTZPh1nvDGQRdh7e+dTg5ozBNC8dxRtr2LSaNjeMvR6Pw/OePXvZ9j0KhQDabJZvNkMtlKZVKeF6E2tpaTLOPM8/McPzxGXw/KFsRx/M8giBA0zTWru3ANC0Mw0DXdVauhLGfX1Q691tTE/5cvhw+8AG49NIwjDVTkwWwVq4cfTzHVjCKRKLU1tbR2NhINBolCMJxYfhKJwgCbNvBMEwikSixWHxc+8HnPS/glFOCSc/35PPwkY+E85hKta5DNcXODWW9972jrR3Hku4Oofp6ePObZ357y7LQdR3LsigUimUrYfm+T6FQ4Nhju4lELCKRCJVaggJ89KMTrwuCgFLJ5qUvfRpNU0SjsXHhq3jc5/LLd3HmmQFLly6hVLLxPL9qZb9yx9W73lV1dxfU0UePPu9NE449FjZvrjxeKmAJIYQQR6b0L39J6qKL8P/t39BvuAHjd7/D+M1vUN//Pvk3vpG+D38Yp6troacpxIxEX/MaqKsjME207u6qY1VvLwoIGhuxzjtvfiY4BfEtW8LWcsuXY/X1oTo6CA6q6BV4Htq+fRjpNM6KFXjHH09006YFmrGYbX5vL5RKYTWrKZSdDpJJNNcF38ft6ZmHGYpFxRj9gtrBrxVlDX8rVakjr+S7mBKpgCWEEEIIIYQQM2TbNgMDA6RSKWprG8YFDg6mlIZpmpRKpaFrAqLR2Jjlo2OPOy5HIuGRy+kYhk6pFFZM8jyfMvmuBXPmmROve+tb4S9/8RkYKIwJjoUhKqUUpmmQSrnk83kMw6BUKhGNRrEsC9u28X0f0zRRSg0FksKAhm3bXHRRNw88sII9e+pZsSKsNDT2fjMqvMMtdz7t+OPDL67lctPf78lCTWOrAx08Nh6Pj7SG830fx3FGWivquolpmmiaRjwewzTD6lf/+7/hucGWFh+lIJmsXvI+n4dNm+C//3v0uiCA+++Hz39+yrs5ZTMNYG3cCC95SRgy+sMfxi/78IcrtblbWLoOvj9/XQd0vfJxXU1zM/T2hpXXEok4EKAUFIsllLIxDB1Q+L439Lqi8w//UGDbtn4MI8LPfuYOhUnHB7HOPTcMWWaz47fnOOFzt61tP5a1ccJ86uvdMb/XomkKx3Gw7dJQ4GuicsfVaafBt789zTtjnpR7nIYr4ZUjASwhhBDiyJO64grUL36BsX8/xsAAAeBHIijfR+/sxIvFYHCQTHs70csvl28diMNO/IQTKGzcCAMDRPbupWQY4Tdqxr4xDwLo78fq7MRetgy/tZW6M85YsDlPoBTJz32O/HvfSwkw9+1D9ffj1tQQmCbKcTAGBwl0HXvNGvx166j99KcXetZiNun6yDEbBEGVrx+FRkI3SrGoTsqJeWGuXUspEsE3TbSBAYIlS6qO19LpsHKeaaKVKx8ujnhSAUsIIYQQQgghZiiXy+G6LoVCgdopfIpumia+7xEEPr7vV2zbpuvw7nd3sHp1keEARBj4qJz6qK+f9vQPSXMznH56uettPvKRpzj33F3k83kKhQKe5xIEPq7rkMvlSacd8vk8uq5TKBSIRn2WL88QiVgjVbNyuRyDg2lKpSIAdXUWxxyznMsus7jhBpNvfAM2bBi/7UpfLKv0hcbnPnfq+zv2sZqsZd6qVaO/W9bE5dFojNraWuLxOPF4jFgsTiwW/p5IxIeqg43esLk5rN41PIfJAmCvf335+c/VecLp3I9jDT8uz3kOvPjFo9c///nwwhce+rzmwnXXwd/8TeXl5547e9tqawtDgjMJYF166WgrRMMwSSSSRCJRkskElmWNtMHUNJ1oNIJSinR6gM2bUxx99BNkMlkGBzNDrVPDk82f+AT8wz+MBofGGq7QF43C6adPTDW+4AWDI78rFc7JdV08zyeokGYrF8CqqRlfZW82Hep50XLPr2oBrOHxdXWHtl0hhBBCLA7pG25A/eIX6Dt3og8OYi9bhnPssXgbN+IedRTFo47Cj0Sw2tvRdu0i/4lPEFSpBirEYpX81KcIVqygtHIlVnc35lNPoTo6oLcXdeAAxtNPE9m/H7utDX/ZMqKf/jRqJm9q5lBk1Sri3/gG3vHH4zznOdjLloHvoxUK4PvYK1bgPOc5eM99LjVXXolZ7Q97cdgxVq6EaJRA11Hp9KTjtcFBvEgElMKUQM2zTuy44/BWr8ZtbMTo769aBSvwfYz+ftzGRrz16zE3TvyCmjjyLa7/8YQQQgghhBDiMOL7YZAKGNcqrhKlFEopwlBVgOd5GIZGJDLxzfuKFTYf+tB+PvCB1UPrV1UrbL373fDFL85oN2bkP/6jfOBg586dOE4fra3PsHHj89m3r3ncvD3Po1Qq4TgOSimSySRBEPDSlz7Fr361iVQqjucN3zcmlmURi0VZsSLCunW1NDQ0VJxTuWAIUDHoVun6cv7hH8bfLhaDQqH82LEBrErb0DSNaDRKdLI0VxnVKuRbFrz0pdNe5YydeurEVpRTNRxO0/Wwrd0b3hCGbtatm95jM5+GH/tKXvva2dmOrsPf/d3Mg3OxGJx1Flx/PezZA4ZhUFtbg+M4lEr20OtW2PLS9z10XadUKrF161YAcrmlGIaJ57nYtk0iESeXGz2FdPDjEwa6wnDiCSdkueee0VRRIuFx8smjASzLCqivd+ntDUZuq8o84MNV+w+2dOn074+paGmBZ56Z/u2CIMD3PVzXpVRS4yp6SQUsIYQQ4lkiCPB+8hP0AwfQSiXsjRtRB30TQ0Wj+KtX43R0YO3dS5BMYt95J5GXv3yBJi3EzETWriX42tfIf+IT2LW1qN5e9HQa5XkEmoZXV4fb3Izf1ET0c58jfsIJCz3lsiKrVxP54Q/J3nsvpeuvx3/6aVSxCLEYwXHHEX3ta4lv2bJ435yKGUu8+MWkmppw6+owentxGxoqPs6B52GmUritrXjHHktk7Akf8eygFMb55+Pv3AmpFEZ7O+6aNaiDTtYEnofR3g5KETQ3oy+i1qtifkkASwghhBBCCCFmSNd1tKFP0R1n8j5svu8PjVdomo7jOBiGydKllb/5vHp1Dzt21KOUhlHhW6NnnAHPe95M9mBmzjsvrMp0sMHBQXK5HPv376ehoYHVq6Ps3z8+FaXrOvF4DMMwMAwD0zRpbW1lyRKHE0/spVAo8NnPPncorKYRjUZIJJKsWmVSJXs1I1M9j9rcDAd/LjLVANZcqBTAisXCYFxra/nl0/miZn09pNNw3HHw2GMTl7e2hoGpQwl7je24ohSsXj3zdVXykpfA3XfP7jorBbDOP7/882K6/uVfwupXK1aElytVdqtm+L696CL46lfDtolKKSzLGqmuVioV8X2fQsHB8xxM02TlypUYhkEkEsFxHHI5h2g0AgQkkzGgTEk3RsOlvm+zaVOBN76xi3vvrcOyAl71qj6i0WDMWHjBCzr57W/bADAMRbkvkFZqbTlJtf8ZSySmN97zXEole6iNaMCBAz08/vggsViMlpYWmpqaiEY1amogk5l4++FztRLAEkIIIQ5/2XvuQTtwACOVwlmyZEL4aqxg6VKCVArV24vxu99NfKMhxGEgunEj1o9/TOb//g/3xhsJtm8fWeatWoVx7rnUnXsuerVvDy0GSpF88YtJji3JLI54yjBQZ5+N39mJsX072jPP4K9cOeEk0UigRtMImpowX/OaBZqxWGi1F15I/7334nge5q5dRJ56CreuDr+mBgAtk8FIpwkMA2fdOrwXvpC6886j0Nu7wDMXC0ECWEIIIYQQQggxQ8lkEtM0SSQS9PWV+YT9IK7roOsGuq6PtN9zHIdTTy1f8jydTvO85z3D3r0vwbKsslViolF461vLt7qbK5VaZvX09DA4OEgQBLS0tHDWWSn++Mdy6QJFS0sL+/btY+nSpaxYsQKlFLlcDt/3qa9vwDTDcNZwC8aphCOWL4emJujrG73urLMqj59qAOsb35h4/1YK4USj1avezIZksnzLtg9+sHqIacmSMFD1+OOj173rXXDVVROrDX3/+2FoxzDCYNHB3vOeQw/9zdUxe/TR8LGPhQGetrZwf1Op2Vt/pRaQB3+xeyZflP7wh2HLlvHXzaRbx3AA64wzwuBdezusWQN//jP8/OcMtfosjrQYTCTiLB1TWuqNb0zx85+34jgOxWKJeNynoaEHCPt+HhyYCgOVYaIok8mwZYtiy5Zs2bkFQcBznrOD5cuLKLWKl760fqRd4liVKmBVCmC9/e1w660zq2IFk7f2HKtYDO87z3NxHAfX9di/fy87dvRSX19PJpOhu7ubDRs20NISmVEA68QTv4/jhC1YDcPkzDPlFJ4QQgixWNl/+hPa4CDK9wkaG6n6Z6BSuA0NGAMDRHbtws5NbN8sxOFAi0apu+ACuOAC/EIBL5NBTybRpvOHtRALpO7ii0n9+c/Yrou5ezf6U0/hNjQQJJMQBKhMBiuVCgM169fjnXoq9a985UJPWywQZRg0fPGLpD71qfBbcqkURn8/xlALSz8SwV66FBob8V70Iho++1mCmZQzF0cEOXsjhBBCCCGEEDNkGAYNDQ0MDg5yxRVXsHNn20hISimDtWs/MzLW930cxyUWC9vOeZ5PEATE4/0sWfI0xWLTSDu6YrFIKpViYGCAE05oo7ExxV/+Uk8kEn5oP9QlDIB3vGPmLeDGamuDzs6pja0UwBocHGRwcJC6ujo0TaOpqXJVsGQyiWEYZDIZcrkc69atG1lW7nzt0JfKqlIqDGF8/etQKoX7dNFFk99uMuXmU+mccpkvTc666VbqGeuTnwwDOPv3wymnhF+4f/xxuPfe0TFvfWvYHq1SS0eY2uMxmZlUdpqKc88dX4lqyZLZCWANP+aVHvupdpN8yUvgvvvC3xOJsDqS54XXn3LKxPEzOWc3trrY+vXhPwiDgz//OSNtCF3XIx6PT2hv+rznZenstPjzn2tpaPB4xSueJJttolQqEYlEKJXGb880TTRNp66ujp6eHhKJBHqFiff19eG6LieeaLBhQ7zi69d0K2BdcEFYue3yy8svn8xUn1dh+KpAsVjEdT1M0yAWi9LW1kxDg8fAwAC9vb2sWLEC3/dpaHgOMPG+GH5+VXo9Xbbs9ZSG7uhIJMIpp0jrEyGEEGLRymZRjoNvGKhqf0QPCSIR1MAAAP7gYPXBCykI8AYH8Usl9NpatBm0TxfPDloshlatV7sQi4wWj1P3n/9J+mMfI4hE0Pr6MPr70Xp6APAiEZy2NoLGRrxTT6Xhs5+VdpTPclo0StOXv0zmrruwr78eHn4YgqEvSCqFt2ULkde8hvqXvCS8XOlbZeKIJwEsIYQQQgghhDgES5YsIZVKcc89N+E4o6kETYuNBLBc16FUKmGaYdu9aDRKNArHH5/m5JN7KRY92tvbR9oZ+r5PPB5n5cqV1NXVccopS3nb28IP8IMA/vQn2LULjj8+/DcbPvSh8NzBz38++dhyFVuCIMD3fTwvDHQMe8UrUtxyy/jegXV1YbLCNE08z5vSSYmpBn5e8pKwytPAACxbNj6IcrBDOXdW6dzyTNsPKjV63mYyh9LFIZmESy4Zf90//VMYVtuzB577XDjnnMnXMxut9uYigFVXByefPP66JUvGhxZnavPm8OehBrAuuQQuvXQ0gJPNhgGs2tryx+ShVMA62PLl4U/HsXEce1wb1bFMM+CCC/q44II+bNtm585uisUEfX19LFu2jKam8H7I54dvoVi/3qClpYVCocCePXtobW0lOeZgdV2X3t5eBgYGWL58OfF4nIahvqKrV4fH31jHHFN+Hyq12Bxez0xVelyPPx4efTT83fe9kepXvh9WDhsOryWTEZqammhqaqK3t5d9+/ah6zqmmQImPmGGK8At9q4sQgghhJiCSIRA01CeF/5RP8kbDeV5BMNf3Kn2hmWBON3dZK+7juB3v0MbLi+sFN7mzVgXXkjyjDNQUtlDCHGYM5qaaPzWtxj8zW/wfv1rgp07xy33TjgB64ILqD/rLAlfiZBS1Jx+Opx+Om4qhdvdDYDR2orR0DDJjcWzhQSwhBBCCCGEEOIQxONxVq9eXaY9YECxWBgJF5mmhWVZxGLhp/xveUua44936ekxKBabaGtrG6l2YlkW0WiU+vp6li9fTmTMSXml4EUvCv/NloaGMOzg+zMPYCmlUEqhaRrumNI1p502wO231+O6o/fPa14TnsR3XRdN0yYEQDZtgm3bxq//2GOntz9TOe8xlfNnb397+eurVcAqN5/JKjBdfXUYyHjb2xjXruxNb5o49lAqYJVjWXDxxZWXX3ghXH/96OXNm8tXXYtEmFAZabLtzravfnViEKpaYGesj34Uvvc96O0tv/xtbwt/Vms/ORXJ5PjqYpMFcCoF1S6+OPx874c/nPptAM4/3+eHPwzwPH+ozWd1lhW+dhUKhZHXKF2HV70KrrkmHKNp8I53xFkyVJ6qs7OTffv2jQROPc+jUChgmiYrVqygoaGBDRs2jLxuXnIJ/Mu/jG6ztRVOOqn8fGKx8Hk2ttXgpk3hzzFdFKctkSgfBPvoR8PWjU8/DclkPz/7mU82O7FyWH396Otec3MzpVKJnp4eli1rIQgax41905tGX3+mUCQDmHpAUwghhBDzT9+0iaCmJmxBmMmgKvUYHh4/MICXTOI2NKAvsg9sB2+6Ce+//gs1OIje04Oey0EQhNW9enrw/vpX+jdtovaLX8Sc6h/aQgixSCnTpO7CC+HCCynt3o3X00OgFNaKFZhtbQs9PbGIGQ0NEroSZU3xNI8QQgghhBBCiEqamprKBLAUSmlEIhESiQTRaJRIxMK2S2QyWTo7n2Hv3r0jLb0cx6G+vp4NGzawYcMGNm/ezLp168aFr+ZCWxt8/ONhGODYY8M2XpOp1DIrkUiQTCbJZDIEQ2mBRMLnDW/oJhr1UQpe/OI0mzdnKRaL2LZNMpkkcVCi6MILx6+3qQlOPXUGO3cI1q2DN7wBzjuv/PLpVMB6//sn355hhIGk971vtNrX855XvhrVfFfM+bu/g1e8IjxWnvtc+MAHyo+bbqBqsuzPdFtrvv714bFysHKhuINt3BhWT/vOd8ovf+lLw4pqUPmxn+pTdbr3U6WAzkUXwdq15ZdVCxfq+nCSZ+qJnomvb/CWt8AnPhG2rPzyl8P2iatXr2blypWsWrWKDRs20NDQgGmaIxX91q9fz9KlSzn66KNHWq4CnHgiXHYZbNkCZ54JX/pS9X144xtHjx/LCh/7QxWPh8/5sU47LXy9e8Ur4H3vCzjppP24bgnDmFg5bOlSe9zlpqYmcrkcGzf2s2VLDgj36YILKs1XElZCCCHE4arm1a8mqK3FranB6Oqqnpzu68NIpzHSaawdO9D/9m8Z+Lu/I/Xd7+IMtb5aKJnf/hb/S19C37mTyLZtqGIRt7ERp7UVPxbD6OzEfOop9L/+lcwHP4iXTi/ofIUQYjZF1qwh/vznk9iyRcJXQogZkwpYQgghhBBCCDELyoUFhttTWZaF54WtqxzHwXFseno6qKkZwPd9dF2nvr6eaDSK4zgsX768bOBhLowNnCgVVnw64QS45Ra4777ynx1UCmC1tLSQTqfp7u5mYGBgpL3Yli1Znve87Mg2AA4c6CGRSBCJRGg+qJ/di14E73433HbbaMu8uehwUekuPv30sEVcNdMJYD33uWFY5cYbwbYhl5s4ZjjLccopYfDK98NAz3Ra0s1VhZxoNAyGTebCC8tXY6pksgDWJZeEwZ6pWLUKzj67/LIXvjA8jrLZyrd/61vDn4YRjr/vvtFliQS8972jlyvN++AKWNOpBlZNtWDXTB5zw9BGqtX5vgdUfyBc18W2bUzTnFAx65RTJo5funQpra2t9Pf3k06ncV0XpRSxWIyWlhZiFZ48L3hB+G8qXvSiMCS5d28YsDuUylfDEomwfWU8Dn/5S1iF69xzR5e7rovneaTTOpY1/kmYTHpEIuMfjGg0imEYlEoF3va2FO97Xw26DvX1o+vr7e0dasu4AgBNU5imiWVFyraGFEIIIcTipMXjBC9/Of7AAOaOHQS7d+OtWIEa+7eT76O1txPr7sbXNIxCATOdxshkUPv3E+zeTe4nP4GLL6b+4ovnvd2V09ODe/nl6M88g5FOU1qzZlzp4wCwly1D378fq70dW9MYuPxymj772XmdpxBCCCHEYiYBLCGEEEIIIYSYI8lkWMrItm2KxeJIS0LTtFi7dgVHHbUE3/cZHBykt7eXbDZLEAQYhsHKqZTtmUQQ+Liui+8HKAWapmNUSu+M8bznhf/+9Cf4whfK7Vf52zU0NBCLxWhra6OzsxPf92lsbBxqTxiOcV2Xzs5O8vk8a9asoampacKclArbm73qVdPd4+mp9JnGobQvbGmZeJ2mweteF/5Lp+HNb544ZmzAbKat+WYr8DNxvSUymQye56FpGolEgniZHozDLeny+amtd7JD8SUvCUNTf/hDWNnq+OPhm9+cOO61r4W///vK91s0Goa5vvWt8D5auzYc/+tfQ6EA558ftlUcdt558Ne/QrEYXn7968fPtdK8Dw5gVQt8TYdlhffF3XePXjfcmtJxpr8+XQ9DPq5rUigUsaygauBzYGAAwzBIJpMjocrJt6HT0tJCS7knxCxpawv/zZbhQ/q5zw3/HSyoknZbssQue71SiiAICIJgXHW23t7ekQqIqVSKQqFxaLyGabqUSjaWZQ6FsOb3w1chhBBCzEz9+95H6qmnwPcxd+/G2LoVt6YGPxZDeR5WVxdmoUAA6J5HKZHAG/oDxMznMbZtw6mpwb/qKlL5PA3/+I/zOv/sDTegBgcxU6kJ4athStPwV6zAdRz0Awfg7rtx+/sxplu6VgghhBDiCCUBLCGEEEIIIYSYUwHFYoFSqYTn+cTjYVWsaDSsbqJpGvX19dTU1PDMM8+wf/9+DMOgtbV12u0HLSussOT7HsViCcdx8H1/JDigaRq6rmFZESIRi8k+2Hfd8tdXymoopVi/fj2e5wHQ1dVFX18ftbW16LpOqVQim81imiarV6+moaFhVoJms20qnx/095e/fqZfVJ+NYjezFfgZXV+Wzs5O0uk0tm3jeR5KKaLRKPF4nCVLltA45s6Kx+HrX4drrw0vv+Y18NhjsG0b3HrrxPVPJWh2zjmjbRgfe6z8mDPPnHxdL31p2N4ulwtDcqZZudrS5s3hfjz9NCxZAkcfPX55pQpYB18/XOloNnzwg+G89+0L5zfcGtMun/upStMYaodqo2kaxWKRWCxadmyhUKC3t5clS5YQj8cntAs9kpTJFI4zHBRVSsPz/HFBvLa2iQ+E67o4joNlWeNCpt3d3ezdu5fOzk4GBgaIxWK89KU93HnnUjzPo1AooOsavh9FKSpWDBNCCCHE4qJFo9R/7WsMfOpTYbncdBq9vx8jnQbHQXNd/KE/+nOrVxMsXYo79MecsizcXA5r715UezveT39K7sQTSbzoRfMz+SAguPlm9N5evGi0bPhqhFL4S5YQ2bkTL5cj++tfUz9cTlaIRcovFnH7+1GahtHUNL46nRBCCDGLJIAlhBBCCCGEEHPojDPy3HCDj+M4RKNRlCqftNF1naVLl7Jr1y6KxSK9vb0sX758WtuKxSCfd8nlckMf/tu4rjeyXKnhyjcerusOVTGqnBjKZKa1eQDi8TibNm1i165d1NbWMjg4SDabxbZtDMNgxYoVJJNJ6urqWLt27YK22aoUlppKcKZcSGsq4avK4bXJbzuZ2Qxg9fT0sHfvXgYGBkilUhSHy0EBpmlSX19PNpslm82yakzfxdZWeM97RtezbBm88pXlA1jTPedd6VCZavu5urrK7TMPVq26UqV2mAc/huvXhwGurq7R6971rqlt/2CWNdomcayZVMDSNNB1A8syCYIohUKBfD7PwMAANTU16LpOsVhkYGCAgYEB6uvraWxsZMWKFTOb/GFismyZpmnU1dVhmgalUmlciLVcAGtgYADLsojH4yNBxUKhMBK0LRQKrF69mng8Tn19wJNPagwMWASBT7FY5JFHXk0QOEPBWZ1duyK8/vV3zPJeCyGEEGI26ckkTf/1X+T/+leK111HcP/9qEIBfedOPNdF8zwKxxyDKhOwVokE9rp1RLZvx+vvp/iLX8xbAMvLZtF6e9FyOdypvBlKJPANA5XJ4La3z/n8hJiRICD/4IMUr7sO7b77YOiLYkEkgn/aacRf+1pixx67wJMUQghxpJEAlhBCCCGEEELMoZe+tItrrqlBKTWuCoptT0yTRCIREokEAwMD9Pf3TzuAZZph+KpUKo0EnmKxGLquEQSMCWU5wHA7rcqpg8HBaW1+RDwe59hjjyWdTtPT00M+n8f3fQzDoLa2lpaWlrIt7OZbpdDTVCpgnXkm3HTT+Os++MGZb3M2zFYAK5VKsXfvXvbv308mk6GhoYFly5ZhWRae55FOp+nv7x9pmanr+rSPVZh+AKvS8TjTlo0zNdXtKQX/8i9wxRXQ2wunnQYvf/nszmUmbSeHg2zxeJywOJ7CcWy6u7s5cODAyLhYLMayZctGwpI1NTWzMufFqtpLkuu6DAwMEAQBGzYM8OijFsVikWg0/PD0mGPG993M5/P09vbS1tZGMpkcqWLV3d09Elxcs2YN0aG+lfX1Hh//+F4efzxBPO7T2Wnw8Y//lSAojlmnVMISQgghDgtKET/pJOInnQRAcccOim9/O+rpp3GSybLhq5GbRiI49fUYfX3w8MPY+/ZhzUMIPhiuxOX7lb9tcPBtNA2CADWTkqxCzDG/WGTgs59Fu/detFQKvb8frVQKK7hFIng9PZRuvZXCuefScOmlqCke90IIIcRkJIAlhBBCCCGEEHPkBS+AaNRh3boBtm1Ljlyv6wErV5ZPTsRiMQqFAm6l/n9V+H4W13WxbZtYLIquj77lUyqsXGSaBsVikUKhgFIamUymYrDiUMJCSinq6+upn2EftlwuNxLeGg751NXV0dzcjDlL7QIq7V9Dw+S33bgRzjgD7rgjvLx5M5x++qxMa8ZmoztcEATs27ePnp4ecrncuJAIhG3YmpqaqKurY+/evXR0dKDrOi0tLVjTTEJN92EcU2hrRDI58bq5pmlhVa/f/W70uje+sfzYlSvhP/5j7uYyg5eJMZXEFIlEHMMwsG2djRs3UiqVRsKSlmVRX19PW1vbYdN6MAgmH1NJuQBWsVjkwIEDpFIpbNvGcRzWrSvw6KObyGazlEo2L3yhTWOjAyhs2yaVSpFKpaivr6ehoWEknOh5Hv39/SOVxsY+rwCi0YAtW8IUZXd3HUqpQ9ofIYQQQiwO9vbt4DjoxSLuFNqvBw0N6H19uI5D6ckn5yWApdfWhsEU00QrFPAnm6PvozkOrmlWb1coxAIIPI/UJz+Jfu+9mO3tKMfBravDGTo3oQ0OEtm9Gy8Ww7nhBlKuS+MnPjG335YSQgjxrCEBLCGEEEIIIYSYI0qFQaRTT+1i+/bRAMOWLRlisfKntX3fRymFmubJv1KpxCmndPH44zWYpjkSvjrppCzLl5fIZnXuuKMeUESj0ZE2hd3dfRUDWGeeCVdfPf66iy+e1rSmrVQq0d7eTi6XG6kUEwTBSPWsAwcO0NzczMqVK6d9H03VVCpgKQX/9E9w4YVhG7iNG+f3fO0HPgBf//r4684++9DXm06nKRaL9Pf3s2TJkgkhkWGGYbBs2TLa29tHWmYuW7ZsWtuabgBr2TJYvRr27Bm97v3vn946Zsu73x22F9yzB445Bs45Z2Hm8ZKXwP/8z/jrJjsOnvc8+P73hy8pIpEIp54a4aijjhoJPA4/36YbqltosRisWQO7d49ed9ppsGkTXHVV9dse/KX3bDbLjh07Riq+5XI5ANat8znjjAN0dCwhmcyzZs1OnniiFcMwCIKAaDTK0qVLqa+vZ+3atSSHUoLD4bZsNjujinFCCCGEOEwVCiOtz6b0B/DwGM/Dz+erj50lyjTxTjwRenuxDhygtHw5qkqreNXfH1bAqqvDeslL5mWOQkxV+ppr0O67D3PXLnzTxF2/HjXmuec3NVG0bcz2dsxdu3Bvvpnsi15E8owzFm7SQgghjhgSwBJCCCGEEEKIOaJU2FbwOc/x+Pu/v59s9liam32e97zKveKy2Sx1dXVEIpFpbSuVSnHMMb0kkzqeFyaIYjGfc8/to7HRZffuyFAAC0BhGCaO45BOp/F9H63MCfbWVnjVq+C3vw0vr1wJL3vZtKY1LcVikaeffpqBgQE6OztxXZdkMommaeRyOXp7e0kkEjiOg23brF+/fk5CWFPtjqgUrF0765ufkhe/GP74R3jwwXAeb3wjNDUd+nr7+/sZHBxEKUXtJN9mj0ajxOPxkZaZ0w1gGdM8I6EU/Nu/wQ9+ELb0O+sseOELp7eO2aLr8LrXze46Hcchk8ngui6aphGPxydt1dnYGFZeu/PO8HIiAa9+dfXtrF4Nxx8Pjz4aXtZ1uOgiqKmpOSLaDL7znWHVsXQ6fM16wxugrg7uuQe2bp3aOorFIjt27KCnp4euri7q6upYs2YNsViMIAhYsWKQnTt30t/fT0NDw0gbTtM0iUaj1NbWsmzZsnGVw3w/DN0OV/QTQgghxLODSiYJDCP8Y7ZYnDyEVSyGYw0Dva5ufiYJRC68EPehhwg6O9H37cNfubLsN0yCUolIVxdOQwN+WxvJU0+dtzkKMakgwL/hBvSeHgDcdevKhgmVZeGsW0fk6aehr4/S9ddLAEsIIcSskACWEEIIIYQQQsyh5uZment7aWvrpL5+F83NzRXHZrNZHMehvr6+6rhybNvGdUu87W2PsG/fSbiu4pRTBmlsDHuUHfx5v67ruK5LEAQ4jlMx8PXud8NLXwrZLBx33PSrFk1VEATs2LGDVCrF/v37aWxspLGxEWNMSqdYLNLZ2cnuofI2sVjskCrJZCvk4OayktXwl99narjalOd5/O3fBpxzTpTW1lpWraoFDn3iw+G2aDRaNpR3sHg8TqFQwHGcaW9rJlmfurqw+teRJJ/P09XVRSqVwnEcXNdFqbAyVSKRoLW1lcYqZdk+/OEwiJZOw8knw1ReOj71KbjlljDIduqpcNRRs7hDC2zzZvjudyGTgfr60desL3wB9u6F226DG2+svo6Ojg7S6TRdXV0sX758XBhRKUVdXR3Pfe5z6ejooLOzk8bGRpqbm2lpaaG+vr7s6+lw6ErXdWzbnjRcJ4QQQogjQ2zLFjKxGG4igdbfjz/JH8Fafz9uMkkQixE98cT5mSSQPP10+o4+GvJ5rPZ2vF27cFtbUckkKBW2HeztxerpwYtGCZYuxXjLW6Rtm1hUcn/5C9q+fRipFG5zc/VKbqaJU1+P0dcHf/0r9v79WFKpVgghxCGSAJYQQgghhBBCzBGlIJFIEI/HaWlp4cCBA5imSV2ZbzIXCgU6OjpoaGggEolUDVxU3p4iEnE4++zUhGW6Hhx0TTDlc+UbN057KtM2MDBAPp+no6OD5ubmsgG0aDTKqlWr2Lt3L11dXUQiEZYuXTqloFD5bR7ipGfAL995clKlUokdO3aQzWbJ5/PYtg2AaZpADYODUZYvXz6j42as4faXQXDw8VLeVMddcAHccMPo5bY22LBh+vM70qRSKdrb2xkcHKS/v5/8mDYzpmnS0NBAJpMhk8mwatWqshXflApDVNMRjcL55x/q7BevaDT8N5auhxXrJisk4TgOAwMD9PX1UV9fX7US3NKlS8nn85imiWmaLFmypMqcoliWRW1tLQMDA9TX10+yF1N7bgkhhBBicTMaGwle9CK83l4ie/ZQzGRQlUJYmQzm4CClNWsIXvxijIaG+ZuoUtR98YsMfuAD2JqGfuAA0fZ2fF0HXUc5DoGm4TQ2ErS1EbzlLdSed978zU+IKXB37YJiEc1xCKbw/PEbGrD6+nA9D3vnTglgCSGEOGQSwBJCCCGEEEKIOTKclVizZg2lUgnf9zlw4ACpVIr6+npM08TzPNLpNNlsloaGBpYsWcLq1aun3aIqEokQiURwXZdisUj0oPSBpo3/MN/zPDRNQyk1FOJZWD09PQwODqJpGk1VeulpmkZzczP79u2jVCrR398/7WphwxYigDWTojeFQoFt27bR29tLd3c3tm2TTCZRSlEqlejs7KShoYFSqYRt27S1tc14fpZlEY1G6evrw/O8SY/DbDZLMpmctGXmhRfCjh3w+OPQ0ADvfa98WT6TydDe3k5nZyepVIq6ujpaW1uJRCIjrwvDLSGH24SuXLlyoad92CsWqy8fGBigVCqRz+erBqogDCzW19eTSqVobGzEtm0sy6o4tqWlhWw2y65du6qGsBzHYYrZRiGEEEIcBuJ///cU770XZ3CQyO7dOEuW4I95zxN4HlpfH2ZXVxhwamoi/vd/P+/zNJqaqPvmNxn42tcI7rwTL5OBTAYVBASmCXV1+MuWYbz5zdSee+68z0+IyQSlEsN/SAe6PmmN6OEKWYHvEwx9yemQ5+C6ZO+4A7e9HRwHraGB2OmnYy1bNivrF0IIsbhJAEsIIYQQQggh5shwwCQWi7Fx40aUUiQSCVKpFD09PXieh1KKmpoa1qxZQ/z/s3ffYY7d9dn/36eq1xlN0ZSdbe6EEAwxYAiEUAymGEgCKYTkIeWXYgIPAQwJJCEJhDxAaAkl4BCSQCimBNtg08GYZjBgXHZnd3pT7+XU3x9npd3Z6btTtnxf1+XLOzNH0lfSkWYk3ef+BIOMjY2ROIMjnZPJJHNzcwSDQQqFAunT3txT1ZOf5ntjBy0CAT/JZPKMG6S2U61Wo1KpEI1GV235OVU4HEZVVWq1GtVq9YwDWMWVRWFnNBZvK/x+eOxj4dvfPvm93/zNtbe3bZvx8XHy+Tyzs7MkEglGRkYIBAIAOI5DpVIhm812W7H8fv8m2nVW1xmZqSgKpVJp3TBcvV6n3W4zMjKy7nbgha7+/u+h3QZV9f672M3MzFAoFCgWi4yOji4bSdcJGiYSCaanp5mbm0NVVVKp1IpwpbA1lrX+zztjOCVJ2tRtHQgEyOVy3dOuFcAC7/GVyWQYGBhgYWEBwzCWjVrtPJ4zmRqShAhhCYIgCMIFwn/55ZivfCX2W96Cqeuo2Sza0hLGiYMY9Hbba5fq68NNp1H+4i/wX3LJnqxViUbp+au/wvrTP6X2+c/jHD+OaxhI0Sj6tdcSfuxjxZEUwjlLTiRwdB0kCale3/gFfrOJK8tIqop8hq+hO5xWi/LNN+PefjtyoYDUaIDj4Pp8NN77XqpXX4324hcTuOKKs7ocQRAE4dwm3vIUBEEQBEEQBEHYIae+Lx0KhbjyyivJ5XKEw2EajQaWZSFJEoFAgFQqRSqV2rBJaC2aphGPx+nt7WVmZga/379sHF0nY+W6Ls1mE0WRURQv0LHXHMfBdV0cx9l0G5eqqliWhW3bZ3y5p0x76zrL91w35c//3BvBNzEBj3gEPOc5a2+bz+dpNBosLCzQ19e3YsSgLMvE43GCwSCTk5MUCgUWFxfPOIAVDoe7IzMXFxfx+XyEw+EV27VaLebn54nH4/h8vk2F4CRp5Vi4i1VnlGQ+nyeVSi0LX51KURSGhoY4duwYjUaDbDYrWrDO0lOfunwcJsCLXrT8641CoKc6dQznRqdTVZVDhw7hOA6KopDJZCgUCt2gV7vdRpIkotFLYcPj9QVBEARBOJ9EnvEMapEIxjvfibu4CMUiTq0GgNHbC4kETjqN72UvI/TYx+7xakFNJIj/9m/v9TIEYUuCj3sc1WAQKxRCLhRwNghgqfk8VjyOE48T/PmfP+PLtatVyi9/OfJ996EsLaGWSkiOQ+eoCjsYhEIB60c/ov6a1xB60pPO+LIEQRCEc5sIYAmCIAiCIAiCIOwSWZZRVRVFUbBtu/vBveu63f/OxvDwMLVaDcuyWFhYoFKpEI/H8fv9GIZCu93GskwkScbvD+Dz+QiFAttx1c5Kp4FLlmWsjeppTrAsC0VRtjyq8VRjYzA5ufx7hw6d8dltmt8PL3nJ5rbNZrMUi0V0XV8RvjpV5+fFYpGenh7q9TqhUOiM1rdv374T+4rFzMwMoVCIeDyOrutYlkWlUqFSqRAOhxkYGGBkZOSs7oeLUaFQoF6vY1nWhmE5XdcJh8OUSiUKhYIIYJ2l4WF49KPhe9/zvk4k4Fd+5eTP/X5/NwjbGbG5nlqtht/vR5KkdduvOoLBIJdddhnT09NEIhHq9TqtVgvXdUkmk0QiEXK59RvlBEEQBEE4P4Uf/3i49lpqd91F67bbaMzMABAcHcX/jGeIdilBOEtqIoHzuMdh53L4Jidpl8sQi62+cS6H0mxiDg8jPe1pSGda0+y6lF7zGpQf/xjt+HFcTcMYGvJeaEgSNJvIuRy+mRmMRgP7zW+mmUyiXX31mV9RQRAE4ZwlAliCIAiCIAiCIAjb4IlPvIEf/9jofi3L+rL3ztvtNuPj49RqNUqlEuVyuduAFQqFqNfr3XabM22l0nW9O+rQ7/dTLBZZWlrCcRyaTRXbHsXn86GqKj6fj0Dg3KkjCofDRCIRCoUCqVRq3SaZTnAlEolsGI5YzwtfCG9+88mvJWn9cYC7zTAMWq0W5XJ53fBVRzweJ5vN0mg0qFQqZxzACoVCHDx4EEmSCIfDFAoF5ufnuwHBcDjM8PAw4XCYkZGRMx4BeTEzDIN2u43f799UeC0YDFKtVrEsC9d1t9TQJKz0utfB3XdDvQ7XXLN8Mkmn1a3zfLTec4xlWZTLZQYGBojFYt1Rghvx+/1ccsklNJtN8vk8rVYL8Bqykskk8/PRs7p+giAIgiCcwySJ8LXXEnjMYzCWlgBI9PeLAxoEYZuEXvximnffjdFo4JuawujpwentReq0jTebyNksWqlEe2gId3CQ6AtfeMaXV/va17zw1cQEdjCIMza2PEgZCOCMjNCOxdAnJ3F8Ptr/9m8gAliCIAgXJBHAEgRBEARBEARB2AZ///f/xV//9fLvdd5zM02TI0eOUCgUmJ2dRVXVbjOVbdtUq1WmpqaIRqM4jgNwxiGsQCDA5ZdfTjabJRKJYBgGtm0jSRIHD6rkcgF03YemaTzucWdxhbdZKpWiXC6TyWQoFotrBo5c1yWbzRIOhzdshdrIox8NT3safPGLoOvw278NfX1nfHbbrtMGZlnWppp1Tm1XO5vRjADRaJQrrriCTCZDOBzujnuUZRlZlkkmk/T19a05Ok9Y31YDVGfbjicsJ0mw1mQfWZa7LXKTk5MsLS3R39+/YjvLspidnUXXdaLRKH1n8OQRCAQYHh7e8ukEQRAEQRAEQVid/+BBrDe8AfOv/5q2rqNms+j5PI6mgesimyZ2MEh7bAx3aIjAm96E2nPmDbTtz3wGpVBAchzsffvWfq0XjWIkk2i5HPzsZ7SOHcN/8OAZX64gCIJwbhIBLEEQBEEQBEEQhG1wIje1TOd9t5mZGUqlErOzs/T29q5oDIrFYrRaLWZmZlhYWECWZWKx2KZCN6tRVZXBwUEGBgZoNBrdpq1XvCLAO9+pUa16QaNf/dUzOvsdkUgkCAQCDA4OMj8/j+M4JBKJZUeCG4bB4uIipmkyNDREKpU6qyPFVRX+5E/g//wf72v/uVMIBpwczdgJVW3EdV0cx0GSpO5pz4au6wwPD5NOp2k0Gt0AViAQ2HTTj7A6Xdfx+Xxks1ksy9rw9qzX6/j9fjRNE+1XuyCdTlOpVBgZGWF2dpZarUY8HicYDOI4TrfJUNf1bmth5NQaLUEQBEG4wLmWRe1rX8P8zndwq1Uknw/58GHC11+Pmkjs9fIEQbjIhR/3OJr//M803vMeuO8+rHodTrTOEgxCMIj9qEcR/rM/w7dv3xlfjl2totx7L2qxiBmPI23wOtzp7UXJ5zHrdZp33ikCWIIgCBcg8Y6pIAiCIAiCIAjCDpEkr/2qVCqRzWaJRqNrjmvz+/0MDw8zOTlJMpkkl8uRTqfP8vKlZWPofvEX4b3vhXIZenvPrcCRJEkcOnSo29a1tLREPp8nHA6jKArtdptGo0EgEGBsbIxkMsnQ0NC2XPa5dDucqjMuMhQKbWqkYLVaRZIkgsHgtjZTybJ8VqMehZU6t2er1WJ+fp6RkZE1g1WtVotGo0F/f78Y97hLFEXhkksu4ejRo/h8PkqlEsVikUwmA3jNVf39/cRiMVKpFKOjo3u8YkEQBEHYJa5L+ROfwP7oR5GzWeRyGck0QZZxIxGqH/4wzi/9EvGXvxxF/P0oCMIeClx5JYF/+Rdax47R/OIXcXM5XElCHhwk9IxnoKfTOK0Wta9+FTuXA1VFHRkh9MhHLh8huA4rlwPXRTIM3E20aEk+H46qQruNWyic7VUUBOEsOM0mtdtvx3rgAdxGAykcRnv0owk/8YlIYiywcBZEAEsQBEEQBEEQBGEbrDYhTJIgn8/TbDZpNBrs379/3fMIBAIEg0FKpdK2BLBWE4l4/52LAoEAl112GcePHycSiVCtVqnVati2jc/no7+/H7/fT09PD6Ojoxd8E5AkSfT09FCtVpmcnKTZbBIIBFbd1nEc8vk8sVgMn89HLBbb5dWen1qtFvV6HcdxUBSFSCSCpmk7dnmlUolMJkO1WmVpaYlWq8V9991HoVBgaGiIRCKxrA3LNE1mZ2eJRCL4/X4RwNpFmqZx2WWXkc/nyWaz9Pb24rpu93knFovR19dHNBrd45UKgiAIwi5xXQpvfSvyZz6DOj+PWioB4Ggakm0jz89jhcPY5TLlo0eJvetdKOJv0vNO86c/pfXNb+JWKkg+H8rhw4R/5VeQz9WjVoQd03rwQRqf+hTSd76DVKngahrO8DDqM55B5Prrkc+TcfT+gwfx//EfL/uemc1SeNvbkL70JegESSUJU9cppNPI119P7Nd/HWmjVvJTQxqbGRvvut5/krT8tIIg7BrXNCn+y78g3X47VCrIlQqS4+CqKvatt1Ls60P5tV8j9uu/vukwpiCcSgSwBEEQBEEQBEEQtsFa77W1Wi1arRaapuHfxJvWkUiEUqmEaZrYtn1WI/bOR36/nyuuuIJqtUo2m6XRaOA4DqqqEovF6O3txefz7fUyd00qlSKTyRCPx5mdnSWdTq9owrIsi/n5eWzbpre3l76+vgs+nHa2SqUSS0tL1Gq17mNNVVU0TSORSNDf37+tLWKu6zI9PU0mk6FSqVAsFqlWqxSLRer1Ovfffz+ZTIa+vj7279+PruuUy2VKpRLBYJB0Os3Q0NAZjyUVzowsy6RSKVKpFK1WC9M0kSQJn8+3o0E98fAVBEEQzkXlj33MC18dO4ZkmhiDg5BIgCyD6+LW66hLS+jHjmG4LqWbbqLnPe8Rv9jOE7WvfY32f/wHyvg4Uq3WbTZzwmHK//qv8PSnE/v93xdBrIuA02pR+pu/Qb7rLuRqFblQQDYMXFnGmZ7GPXKE8s03o7361YSf+MS9Xu6WtY4cofEXf4E8N4eczaKeCF8AOKqKNT+POz1N4a67iP/TP6GscwSb1tdHw+fDCQSQKxWcDVqw3EYDybYhFEIeHt7W6yUIwsacVoviy1+O8qMfIS8soFYquJKEq6rIJ4KY1vw8TiZD4fhxkjfdJP6OEbZMBLAEQRAEQRAEQRB2iCR5zUSu6yLL8qZOI8sy7ok0l7uZIygvUJFIhMi5WtW1i3w+H/v27cN1XWzbZmZmBp/PRzweR5IkWq0WtVqtu10ymaS/v3+vl71jXNelXC5Tr9e7oamt7iszMzMsLS11x8q12+3uz4LBYDcg1Rl1uR1mZmZYXFxkZmYG0zSJx+MMDg4yOjrK5OQkmUyGXC5HpVJhcXGRdDpNNBplcHCQaDRKOp1mYGBgW9YinBm/37+pEK0gCIIgXIhc08T+6EdR5+aQLAvz8GE4NYwsSUjhMHYohDs9jTY5CYEAjR/8gOCjHrVn6xY2p/Sf/wnvfz9qJoOazyObJq6iwIlQihWN4uTzFH/6UxL//M/nTfORsHWuaVJ8xStQ7rkHdXISpd3GjESww2FwHNRiESmTwUylsN7wBqqvfz2RJz95r5e9aebiohe+OnYMfXYWOxjEGBnBDYW8UYLlMmo+j1Qq4VoWpVe9iuS73oWkrv5xuuz34zzhCdjZLL7JSVrtNtI6B4ypmQxWOIwbChF5xjN26moKgrCG4t/+LcoPf4h27BiuqtIeHUWKRkGScB0HqVhEy2SQx8exbruN0sAA8d/7vb1etnCeEQEsQRAEQRAEQRCEbXDivellJMkbY6VpGoZhbKrRqtlsomkakiRddO1Xwup6enqQJAnbtunp6aFYLNJoNHBdF03TGBkZIRQKkUwm2bdv3wXbfrW0tEQmk6HVanWb0WRZJhQKEQgEGBgYoGeDI47n5+dZWFhgZmYGwzBIJBIMDw+jKAqGYVAqlZibm6NerwOgqupZj5ir1+tks1lmZ2eRJIkDBw50xwz6/X4ikQiFQoGZmRkymQw9PT0kEglGRkaIx+P09fWJMOJF6PLL34xptgBvP3z0o3eucUsQBEEQNlL90peQ8nnUUgljaGh5+OpUkoQ9PIx6//1QKtH81KdEAOscV73jDnj/+5EnJlDrdcyeHpzeXiRN82qey2XUTAbl6FFwHIqvfS09b3/7Od8IYlcqVD/7Wewvfxkpl/O+mUohP/nJRJ7znHVbjS5mpfe9D+VHP0I7dgzH56O9fz+c0sJrptNQKqHPzmI6DtY//iPmz/0cWiq1h6vevMoHPoAyO4tvdhZjYAC3rw+A7t6cSmH19iJPTqJPTGDqOtUvfIHo9deveZ7B5z2P9p13YgWD6BMTGAcOrBxd6LpICwuotRrtAwewH/tY1ERizfNs3ncfzU99Cr7/feRqFVfXcUZHUa+/nsh114kmOkE4A60jR1C+9S3UyUkcnw/7wIFl759Jsgw9PRjRKNr4OPLMDPb//A/OC18ogsfClogAliAIgiAIgiAIwjb46Effzvi41f1aklSe8pSXE4/HCYVCKIpCuVxet1HHtm3K5TLpdLrbcCQIAMlkkp/7uZ8jn88TDoe7zWqqqpJIJEilUgQCgb1e5o5wXZeJiQmy2Sy5XI5yuYzruiiK0g01xuNxGo0GjUaDkZGRVc/HMAwWFxe74xpPDUIBBAIBAoEA8Xic6elpNE1D13Wuuuqqs1p/NpulVqvRarU4dOjQsssEL6TZ399PKpViYmKCVqtFMpnk8ssv39YxiML5Zf/+P+u2s/l8Pp72NPH7QBAEQdg71re/jVwq4cqyN3ZwHZKiYEWjKMUi7j33eCEe8brm3OS6mB/8IMrCAmq9TvvgQaRA4GQYRZIgHseKxZAnJtAmJsDvp37PPYSuvnovV76u8sc+hvOhDyFVq6i5HFLLC7W7fj/ugw9S+Y//QH7pS4n92q/t8UrPTuvoURqf+hT88IfQaICu4x44gO+5zyX8uMdt+XHntFpw223I8/O4qop94MDK85AkSCRoyzL+qSnaPT3UbrmFxB/+4TZes51hV6vI3/gGytISZiTSDV+tIEnY+/ahPPAAUj6P+bnPwToBrMCVV9J8/vPh4x+H48fxPfQQViyGHYshyTJSs4lSKCCbJu3RUezRUUJ//MernpfTaFB8/etRvvc95HIZuVj0GulkGWdqCueBByh98IP4Xvc6Qo95zHbcLIJw0Wh86lPI1SpKu01r//4133OVNA0zncY3PY1TrVK97TZiL3jBLq9WOJ+JAJYgCIIgCIIgCMI2eN/7Xke73ex+LcsBJOnlRCIRgsEgiUSCbDZLMBhcdYyV67osLCx0R6qlNnkEqeM4yxqRFEUhFosRDoe37boJ5wZFUejr66Ovrw9N03Bd96II6c3OzpLNZpmamgJgYGCAaDSKJEk4jkO5XCafz3dbq3RdX3UMYy6Xo9lsUqvV2L9//4ogVEcgEKCvr49sNksymaRSqZxxC1bn8VksFolGo2teJnjjR4eGhpiYmEBVVSqVighgCYIgCIJwTnArFSTTxNH1TYU6XL8fuVRCardx2u2Lvq3FabWo3XYb5he+gJTNgm3jxOO0H/EItOuug20cIe7aNlYuh9NqoSYSKOv8HVu/+27k+XnUQgGzrw9prQM6TgRS1Pvvh2KR1i23nLMBrOK//RvShz+MMjODVirh6Dp2KASAUi6jZLOY8TjOu95FqV4n/ru/u8cr3jq7VqP0xjei3H03crXqPdY6IZ0jR7C++10KIyME//Zv8R86tOnzrd1xB5RKqJUKxsjIuo91KRbD9vuRs1msL3wB/uAPzvmgZe1LX0Kq1bwWqrGxdbeVZBkrkUAtFHAffBBjdhZ9eHjN7ZM33kjBtuEzn8EuFlHyeXxTU0iAo6pYiQRWby/28DCBN7951cYwp9WidOONKD/5CdrkJLJhnBz/aNuohQJSNouZSmG+9rXU/u7vvKCdIAib873vIRcKWOHwypa600jRKK6iIBUKWHfdBSKAJWyBCGAJgiAIgiAIgiDskM77j0NDQzSbTdrtNlNTU/T09BCPx1FVFdd1qdVq5PN5TNNkdHSUeDy+4cgxx3FYWFggm83SbrdpNps4joOiKIRCIYLBIIODgyQ2OEJdOH9dDOErwzDIZDLMzc2hKAqjo6PIstz9uSzLJBIJIpEIk5OTLC4uomkaqVRq2XYAhUKBUqm0ZgjyVLFYjEwmQ7VapVAonHEAy7IsHMeh3W5vKlTp9/tRVZVWq4VhGGd0mYIgCIIgCNtO00CWkWx7c9vbNsgySBKyz7ezazvHVW6/Hftd7/KCLfk8UqMBgKvrhO67D/eWWyi/5CUkf+/3zirAYszNUfvkJ+HOO5Erle737UsvRXv2s4k8/eneWMFTtL/6VeRyGclxvLGD65z/smaz7373nGw2q3/nO0j/8R8oExPIrZYXsolEuut0XBerWkWbmUGamMC++WYaV111Xo3JdBoNL6Rz332oU1Mo7TZWKITj8yHZNvr8PO7CAmapRPPGG+Ed78B/+PCmztt64AHkatW7vWKxDbe343HUfB4nn8fMZtHWapQ6RzhLS902NDYxgtKNRJBPjK80FxfXDWAhSSRf8QrqT3gCrVtugbvvxj7l+dIZGkJ51rOIPPOZa44yK73zncg//SnasWPYgQDmwYPLxr2arguFAvr8PCZgvvGN2B//+LohS0EQTpKqVS9MvpkD3SQJx+8H04RTfqcKwmaIAJYgCIIgCIIgCMIOi8VijI6OAlAsFikUCmSzWRRFwXGc7jZDQ0MkEgn279+/7vk5jsPRo0cpFotks1kqlQqSJKEoCpZloapqdyTb8PAwAwMDO34dBWEnZLNZWq0WjUaDAwcOrAhVdaiqysDAALOzs7TbbQqFAr29vcu2MQyDdru9qVYpWZbx+/202+1tCUK5rrvpbS+GYJ0gCIIgCOcX+eBB3EgEZWEBq9mE9UZfuy5KpYIdi2Fv0KJzoSt/+tO4b3878twcWqGAqyhem40koVSrhLJZjEgEPvQhCrUayZe97Mwv553v9NqQcjmUeh0cB1dVkTIZnPvvp/ixjxF+y1vQh4a6p3PLZSTDwNF1pDX+zj6VGwggFwpIpoldrZ5zwY/W//wPSj6PUq/TvuQSpNPDf5IE0SjGwYP4jh7FLhRofuxj51UAq/jWt6Lcdx/6+DhWMIi5f3+3ycUF2o6DvLSEb2aGNtB47WvxffSjSOs08Xa1295+oyiba7pTVTjxfobTbG6w9Tlgq89Fp76G2+RpQ1dfTejqq7GKRYzpaVzTREkk8J8Y52ia5qqns2s1pC99yRv/qOs4+/evPv6xpwdDlr37N5mk+tnPEv/t397a9RKEi5WqdsPkm3mHRrJt73G3QVuWIJxOBLAEQRAEQRAEQRB2yKnvl6VSKXw+HwsLCyQSCZrNJqZpIssygUAAn89Hb28vg4ODa4ZMOiYmJsjn80xPT6NpGsPDw4RCISRJwrZtyuVyd9waeCPZksnkTl5VQdgRpVKp21rl26A9IRwOo6oq5XKZYrG4IoB1Js42DKWqKpIk4fP5aDabxDY4ktwwDEzTxOfzoZ3WUCAIgiAIgrBXQs95Do3/+R+sYBB5aQln3761AwnlMophYPX2oj7zmbu70HNI66GHvFDU9PTJkW6xWPd2s10Xq1jEv7AAx49jf/KTVK+4gshTnrKlyyl/5jNeyGt6Gq1YxAqFMFMp70PmVgs1l0PKZDDqdWovexmRf/3Xk+PPVNVbzyYPFnBPab3aaHzTbjPm5lB++EPUfB4rHl8ZvjqF5PdjnWhv4gc/wJifR0+nd3G1Z8bK51G++lWUuTlsvx9n//4Vr1ckWcYdHMSQZfS5OYxYjNpXvkLkqU/d+AJCIVBVJMvCdZwNQ3lSu+2FteCcC+OtRhkcxDkRHnWrVaQN1izVatgn9iNtcHBLl6UmEqhbaCOv3norUrWKVq16zW3rvQ6Nx7GXlpByOezPfx5EAEsQNsUZHcWZmPCa+zZqcTQMlGYTa2AAad++3VukcEHYONIuCIIgCIIgCIIgnJHTX8tHo1EuvfRSLr/8cg4ePMihQ4e6/z3sYQ9jaGhow/BVvV6nVCoxOztLIBBg3759hMPh7huviqKQTCYZGxuj1WqRzWaZm5vbUgPPxaDRaDA3N8fU1BTT09NkMpllIwKEc4NlWViWteHIwA6/349pmliWteJnPp8Pv99PvV7f8Hxs26bZbOLz+TYMfq1HlmXi8TjxeJxSqbTquk5VLBbx+/34/X4RmhQEQRAE4Zyhp9PYv/iL2Ok0arWKNDeHe6L5pst1oVRCn5nBSKVwYzHC11+/Nws+B9Q//nEoFtFKJYz9+yEeX/4CUZJww2Ea+/Yht9tI2SzGJz6xpcsws1mcd78beWYGpVqldfAg9qFD0NsLySRuOo15+eWYiQT61BTyxATVt72te3p5ZAQ3FEJpt2ETDUZKpYITDOL09iJv8u/z3dL6yU/ANFGaTZyeng23d5JJlEYD1zRp33ffLqzw7FU/8xmo11HrdezBwXXDA25fH64kIeXzGJ/97KbOX7/mGohGcWUZqVBYf2PXRS2VsONx7AMHthQ22ivhX/kV3EgEMxJBPTFacC2u46AWi9jJJPaVV+54QM954AGoVLxA20bjESUJOx5HqVaRFxawa7UdXZsgXCjUZzwDt7fXa7ba4DlOXlrC0XWIRAjecMMurVC4UIgAliAIgiAIgiAIwg5Z6/3QYDDI4OAgIyMjDA0NkUwmNwxedWSzWWq1GqZpMjAwsGZDj67rpFIpSqUSrVaLSqVyplfjglKtVnnwwQd54IEHOHbsGOPj44yPj3P8+HF+8pOfMDU1tWFIRtg9nf17swFC13WRJGnVx0UymSQej9NsNrvtcGsplUooikI4HKZnEx/grCeVShGJRNA0jfn5+e7Y0dNVq9Vuc1csFjur4Jdw4REZWkEQBGGvxV71KpwDBzDGxtBKJfT770eanYVcDmlxEfXIEXzT05g9PbhDQ2ivfS3KRkGCC5RdrSJ/4xsouRxmJOI1C61B0nXMZBI1n0d58EFaDz206cup3XILUqWCVixijo4ihcOrXICEm05jRSIo8/PI3/kOZiYDeM1mhELYwSDyie+tqVpFbTRwUimkzbQp7bZmEzoH1GymSfbENpJt42ziAI1zgXP//UilEraur7tPAV5IJxpFrlSQN7lPhR/7WJx0GiuRQFtagnVGsUuZjNeU1duL+qxnbeVq7Bk5GMR94hNx+vtR6nWkhYVV/8h2HQd1chJkGbenB+05z9n5xbXbSI6Ds4Xxj9L5NP5REM4Bkac/HTeZxOzrQ5+fh1Jp5XOA6yItLnq/VwcHsa+8Ev+ll+7JeoXzlxhBKAiCIAiCIAiCsEPOcnrZqorFIqVSiWg0iqqu/5IuGo2ytLTUDXZsNP7sQpfP55mcnKRYLJLP57EsC1VVcV0Xy7IIh8M0Gg1qtRqXXHKJGAF3DvD7/QSDQQqFQjdctRbbtqnX68Tj8VUbs3p7e1lYWCAajTI3N8fo6Cj6KqNTarUa2WyW/v5+gsEg4dU+yNqCSCRCMpnEsiympqY4fvw4yWSSaDSKJEm0Wi1KpRKVSoW+vj5isRhDQ0NndZnC+e+ee16IZXkfpsiyQi6n86IX/c8er0oQBEG4mKk9PUTe9S4qr341RiiEVCggF4vI1SquLGOHw1j79+PGYqivfS3hJzxhr5e8Z9oPPojUbqPWarT27WOjl4VuMomczUKzSeveezf3Ya/r4n7xiyjZLLbfv35rjiTh9PXhO3oUu16n9rnPkXjpS9HTaaqPehRUKugTE5gLC7gDAytfyNbr6NPTmIkEbiRC+HnP23h9u0wKh732IEnCbbeRNnot125711NVkc+XoGCjAY6Du8H7AB2upiG3WkiGgWvbSCfGBa5JklBe+ELc+Xnceh1tfBwznV42OtM1DJSlJbRikfbICE46TfwZzzjba7Zron/wB1S+/30M00SfnsapVrF6enDDYSTH8QKN+TxIEuaBA9hXX038aU/b+YWFw7iqimxZ4DiwmfGPJ/aDizXoKghbJfv96K97HeZNN2G6Lr7paWyfDyse98avttuopRKSbXvPbyMjhG+6aa+XLZyHRABLEARBEARBEAThPOE4Do7jYJrmpsJUsizj8/nWHMl2MalWq0xNTbGwsEC5XKa3t5d4PN4NsTWbzW5Aa2RkhPHxcS677LJ1Az/Czuvt7aVUKpHJZCiXy8Tj8TW3LRaLqKpKOBymt7d3xc81TWNoaAjbtpmbm+P48ePE43FisRiKotBsNpmbm2NhYYFwOIyqqsTjcTKZDD09PSgbfWCxjrGxsW6ArFQqkc/nWVpaAryWr3A4zOjoKNFolIMHDxIIBM74soQLw9LS/+I4J49mLxbFPiEIgiDsPW1ggOQHP0jt61/H+PSnUX76U+wT7RHO4CDyM55B5NnPPi/Gke0kp1732phcd+MgEEDnoADH8UI2m7mMZhM5l0NuNLwPjzcSDOLoOlKthj0x0f129JWvpHrsGIbjoE9P45ZKWPE4biAAto1SKqHW65ixGM7ICMqf/AlaX9+m1rib/I94BHWfDysYRCkUcDY4iEIuFLBCIfD5CDziEbu0yrMUCoGiIJum97jb4LWqZBi4ioLr928cvjohdsMNFKemsD75SeS5OfSZGZibw/b5kBwHpdXC1nXa+/bhjIwQfPObkc+j1y5qTw+ht72N+itfieHzIWez6AsL3kgywNF1rGQSN5XCvvpqEv/wDztzZN1ptEc/Gvv222FuDopFWK+F2XXRSiXMvj7syy4758aBCsK5LHTNNdT+7u8w//ZvaScSSLkcarGI5Di4iuIFMnt7cQYHCb75zfhGR/d6ycJ5SASwBEEQBEEQBEEQdsh2v0/XCQNJkrTmGLPTOY6DLMsXfZBofn6eQqFAuVxmbGxsRUNSIBBgeHiYpaUlZmdn8fv9FItFksnkHq1YAEgkEvj9fnp7e1lcXOwGrE5XKpXIZrOk02lCodCarVX9/f04joMkSVQqFYrFIsVikWq1SqFQQNd1FEWh3W5j2za5XA7Lspibm2NoaIi+M/ywSZZlDhw4QDQaJZvN0tPTg2EYuK6Lqqrouk4ymWRgYECMHhR24zMeQRAEQThjkqoSefKT4clPxrUs7EoFORA4r0IYO00Kh0FRcCXJG+MWDK67vdsZ9aYoSBuNljvBabe9y3Jd2GS4xpVlL+R1ysE52sAAobe/nfqrXuU1m+VyXgNIPo8rSdjhMO2DByEaRfr//j+i52D7FYCWSuFccw3kcugzMxg9PWuP6avX0cpl2qOj2I95DOpZjhzfLcrP/zzut76FvLgItdq6rWeu46BWKhiDgzhXXLGly0m87GWU+/ux/uu/cIpFpGLR249lGSudhnAY+6qrCL/mNedlOME3Nob6wQ9S+ehHsb74RZxczntMnGhEsw8cQHv2s4k9+9lIm2wbO1vhJz2J4r/8C1Y8jpbJYESja4Y3O6MTnWQS7YYbdmV9gnAhCT/ucVgf+xi1z30O+9ZbcRcXuz+zDx1CfdaziF93nQg3CmdMBLAEQRAEQRAEQRB2yE4EsDoj2arV6qotP6cyDIN2u00wGLyoG3WazSa1Wo1CoUBvb++q4+k6+vr6qFarlMtlstnsngawDMMgl8tRKpWwLAvLsggEAvT29tLb23tRhOokSWJsbAzLsnBdl5mZGQKBQLe9zDAMSqUShmEwODhIMplkbGxs3fMcHBwkHA6TyWSIxWLdkFUwGKTRaJBIJEin02iaRrVaJZ/PE41GsW0b27YZHBw84+uSSqVIpVLU63WazWY3gBWNRs+qYUsQBEEQBGEvSKqKKg5YWCFwxRUYgQBWJIJcKOBs0FAl5/PeGEG/H98jH7mpy1AiEVAUHE1DbjTY6PAc13GQDQNL15FOa1P2jY2hffjDVG+9Fftzn8OdnDx5ulAI98lPJvSCF+Db4O/svRb4jd+gfffdWCdGKhqDg5BMnnxh7rpQKKAvLGDG49DTQ/BFL9rTNW9F5DnPofLhD2OFw6gLC16D1xqj6uSFBS8AmEzi22poTpKIvehFuM9/PtUvfhHzW9+Cctkb0TU2RvA5z8F/+PA2XKO9o8RiJP7oj3Bf+lKaP/oRVi6HpChoo6P4L7ts19cjqSrKr/4qTjaLPD6ONj6ONTi4fPxju42ytIRaKmHs24c7OkrkV35l19cqCBcCNZEg/ju/Ay9+MXalgl2vo0SjKBu0JwrCZogAliAIgiAIgiAIwg7ZiXxMb29vNxRSq9XWbPoByOfzBAKBboPQxapUKtFsNjEMY90RduCFZOLxOKVSiWQyiWVZ3TGFu8V1XWZnZ8lmszSbTcrlMqZp4jgOwWCQUqnE4uIiBw4cILTJI+TPZ52xfJIkEY1GKRaL5HI5bNtGURRisRjxeJxAIMChQ4c2FTaMRCJEIhFKpRK1Wg1N04jFYjz84Q9fMd6zXq+zsLDA7OwsAKFQiGg0elbXKRQKXRT3nSAIgiAIwsVIDgZxn/hEnGwW3/HjtCsVWOvvx3YbvVDAHBzEvuoq/AcPbuoyJFXFfuQjIZtFn5ujbdvrjpmTCgWvASsWQ/+lX1q55kCA2AteAM9/PmY2i10sIvn96On05sYongMCD3sY5o034rzjHZhzc+gLC7C4iHWigUxpNJBcF7OnBzedRrrxRgJXXbXHq948JRLBfdrTsGs1tKNHUY4dwx4aWt6wZprIi4uoxSLG2BjO/v2EH//4M7o8SdeJPutZ8KxnbdM1OIe4LvV77qF1++2QyXjjQvv6kGMxL6i3Bwc7xV70IgpTU1i33oo8O4s+M4M7P++NDu2Mf/T5MPbvxxkdJfTmN583j01BOGdJEkoshnLa+0CCcDZEAEsQBEEQBEEQBGGH7MR7dj09PczPz5NMJpmbm2NkZITgaSMtXNcln89TLpcZGRkhHo+j6/r2L+Y8YVkWpmmiquqmwlQ+nw/rxFiO3Q5gua7LxMQE2WyW+fl56vV6N0RnGAbZbJalpSXS6TSWZXH48OF1Q3gXilgsxlVXXUUulyMcDmOaZvdnPp+Pvr4+enp6ttwiVSgUcF0XgMsuu2zV8X+hUIjR0VEmJiYol8ssLS2ddQBrr7TbbXK5HNVqtRtgC4fDGzbDCYIgCIIgCFsTeuELaXz5y5jlMvrUFMbgIG4yidRpLHIcpHKZwOIiViiE29uL74Uv3NJl+J/3PMzvfhd3cRFlZgZn375VX4S67Tb60hJWMomTThN+7GPXPlNJQuvrQzvD0dt7Lfq851ENhzHf8x7cfB6pUEBqNgGw+vtxk0mcnh60P/uz87I9KPHnf05hagoAZXoa3/g4ts+H4/MhWRZqo4GjaV5IZ2yM8JveJOZbn6b+gx/QfvvbkWdmUEql7v7hBgKYd95JYWQE3403Errmmt1dmCSRfM1rKA0OYn/849ilEnKxCKYJknRy/OPDH07kppvQ0+ndXZ8gCIKwKSKAJQiCIAiCIAiCsEN24n1OVVUZGxvDcRxc12VqaopgMEgsFkOW5e5INsuyGB4eJh6PMzIysv0LOY9IkoQkSTjORoM5PI7jdMf77faYv6WlJbLZLFNTUyiKwv79+7vBGMuycBynGwJyHAdZlrnqqqsuivF1mqYxODjIwMAApml2A0RnGi40TZNSqUShUCAej68avurQdZ1EIkGxWCQej9Nut9fd/lxj2zbT09MUCgVarRaVSgXbtpFlmWg0ytLSEvF4nH379u1645uw0mafqwRBEARBOHf59u/HeNWrsN/0JkxFQV9cxF1cxD7Rgqo0m+imSTsWQxodhd/5HcJPeMKWLiP0mMeQ/7mfg2YT/fhxnPFxzP5+pEgEJAnXtpFyOXzZLHYwiDs4iPqSl1zwgZzIU5+K+8u/TPVLX8K84w6kXA4AN5VCf9rTiP3yLyOdp3/zSqpK8m1vo/j//h/ccQd2vY5ULCJZFq6m0e7vh0gE+8orifzVX4mQzmlqX/0q1hvfiLKwgJrNItm2N/4TUHI53Pl5rGwW86abqN50E5GnPnV3FyhJxF/yEpwXvpDq7bdj3XUX1Gqgad74x+c+d9MteYIgCMLeOD//whAEQRAEQRAEQbiIJRIJ9u/fv2xcXi6Xw3EcVFUlkUgQi8UIh8McOnToom6/AggEAgQCAVzX3XBsI0C1WiUYDJ5VuOdMuK5LJpMhl8shSRKjo6PInSPkT5BlmUQiga7rLCwsEI1Gyefz9J2nR6ifCUmStuV+aTQaWJZFo9EglUptuH3ntjZNk3q9ft4EsGzb5siRIxSLRRYWFmg2mwQCATRNwzAMCoUCfr+fwcFBWq0Wl1566YYhLMdxui1xmqbtelDxQmXbNgsLC0xOGit+5rouk5OTDA4Onjf7niAIgiBc7CJPfSq1YBDjbW/DzWSgUEA+0bZj9vRgRCLYkQj+P/5jEr/6q1u/AEki/qY3UbrxRgxFQV5YwDc5CbKMqyjIpomjqpi9vbgDA/C7v0v0uuu290qeoyRVJfr0p8PTn77XS9l2kqaRvOkmzJe+lNott2Dfcw9SvY7r8yEdOkTwec/Df9lle73Mc07r2DHMv/975OlptHweo68Pt7e3O7rTOhFY1JeWME0T6y1vobVvH/5LL931tcp+P7EbboAbbtj1yxYEQRDOjghgCYIgCIIgCIIg7JCdzCQkk0nC4TC5XI5cLrdsJFswGKSvr49EIrEiwHMxSiQS+Hw+IpEI+Xx+3QBWq9WiWq2yb98+enp6djVYUiqVaLValEol0un0uvddLBYjn89TLBbJZDIXVQBru9i23R0/uJnWJ03TAC98ZNv2jq5tO01NTVEsFrtteQcPHlwWYDNNk0wmw+TkJGNjY0xMTHD48OFVz6tSqZDNZimVSt3vKYpCT08PqVRKjDE8C6ZpcuTIEcrlMrmc0t03O1zXZXp6mnK5zKFDhwidaM8QBEEQBOHcFr72WtxrrqH29a9j3HYbdiaD5Dg48Tj1X/gFtCc9iei+fWd8/ko0SuJf/oXSu9+N++Uv41QqXmOO44CmQTSKPTqK/uIX736bj7CjtFSKxB/+4V4v47xR/+//Rsnn0XM52mNjEI1y6qt9SVGgvx8jGMQ3MUE7HKbxX/+F/2//dq+WLAiCIJyHRABLEARBEARBEARhh+x0dkfXddLpNIODg5im2W3AEiPElpNlmZ6eHhqNBpOTkywsLNDf378i4NRqtZidnSUSiRAMBjfVirSdarUajUYDSZKIRCIbbh+LxSiVSrTbbSzLuijvd9d1abVa3XF6fr9/06FDRVG625qmuWGrVifkqCjKeTPysd1ud5uvgsEgw8PDK7bRNI2hoSHm5+eZm5vD7/d3W7I6bNvm+PHjlMvl7n+d2yMQCFCtVslkMt3no45arUaxWMQ0TSRJwufz0dPTIxqcTuM4DkePHiWfzzMzM4Ntew2Hp2WwmJ6eZmBgAIDLLrtM3I6CIAiCcJ6QVJXIk58MT35y93u2bWMsLW3L+cvBIMlXvQrnT/+U6m23YY+Pg2EgRaPo115L/JGPvODHDgrbzzVNanfeifmDH+DW60g+H8oVVxB+5jNRNvF69VxiV6vI3/gGSjaLGYlANLr2xpEIZizmjSS86y6sYhE1kdi9xQoXJde2qX/zmxhf/zpuuQyahjwyQui5z0Vf5XW8IAjnrovv3VlBEARBEARBEIRdslvvcW/XSLYLWTqdplar4bouMzMzVKtVYrEYfr8f13WpVqvUajUikQjpdJrh4eFdb/PpNCspirKp5i1N03AcB/A+wLmYAlimaZLL5chms8va3zptTH19fRuGU0KhEKqqEg6HKZfLGzYKlUql7ui+jcZYnityuRytVotms8nBgwfX3bavr4/x8XHq9TrZbJbR0VHA2y/Hx8fJ5XLMzs5i2zaxWIx4PN4d6zk9PU0oFMJxHFzXJRwOMzs7S6PRoF6vYxgGkiQRCARYWFggFosxMjIiAkQn5PN5qtUqs7OzJBIJ+vpWhj8lSSKdTjM/P4+u6ywuLrLvLNoyBEEQBEG48MjBILEXvGCvlyGc71yX0oc/jPupTyEVCsjlMpJl4coybixG5UMfwv2VXyF+443I50kDbvOee5CaTdRazWu/WoVrmmBZoCg4iQS+yUnsVovm978v2uOEHVW59Vasm29GXlz0Hm/ttjdKNhym8YlPUH3kI4m88pXo6fReL1UQhE24eN6dFQRBEARBEARB2EGXX/5Ypqfb3a9l2ScOMj6HKIrC4cOHOXbsGLquU6lUKJVKVCoVJEkiGAwyNjZGIBBgeHiY/v7+dc/PdV0qlQrttnefa5pGLBY7q5GPnWYly7JwXXfDEJZhGN0mpvOlkWk71Go1xsfHaTQaFItFKpVKtwErHA5TrVbJZrOMjY2RTCbXPB9VVUkkElQqlW7wRVVVSqUS9Xody7KQZZlAIIDf7++OhozFYudN4LFarVKpVAgGgxuuWVVVQqEQ1WqVarXa/f7S0hLFYpGZmRkCgcCK8ZjxeBzDMJiZmWF2dpZKpYLP56PRaFAoFLBtG5/Ph+M4GIZBIBCgt7eXer3OJZdcsqxp62LVGeuoqir9/f0cPbr6drFYjFqtRqFQIBKJMDw8fFE99gVBEARBEIQd5rrk//qvUb70JZT5edRSCSQJR9OQLAt5fh4zEsEplymOj5P4539GDgb3etUbcioVME1wXVy/vzt60HUcpEIBNZ9HabW627uyjNRu49br3mkFYYeUPvQhuPlmlIUFtEIBAEfXwXFQFhawAwGoVKgdO0boHe/At0aAUBCEc4cIYAmCIAiCIAiCIGyDN73pS/zLv+z1KoT1qKrKJZdcQrlcJpvNEo/Huz9TFIXe3l5SqdS6rTy2bbO0tEQul6PdbmMYRve8O+PVBgYG0DRty+sLh8OEQiEkSaJSqRCLxdbdvlwuE4vFCAQCF037VaPR4OjRo2SzWTKZDIFAgL6+PnRdx7IsKpUKU1NTJBKJbogtsc64iP7+fgqFAtFolHvuuQdd19F1nXq9jm3bJ8bAuTQaDRKJBAcPHuyOgDvX1et1FhYWWFhYQJIkyuUykUhk3ZCgpmlYloVt24AXNMxms+Tz+e6owtWCgbquMzo6ys9+9jNyuVw3uJZKpYjFYt2QkGEYFAqFbpPW+Pg4V1xxxUUdIuo0lJVKpXUDgx2JRIKpqSkMw6BUKtHT07MLqxQEQRAEQRAuBsX3vQ/lS19CHR8H18UYHoZ43Kv3dl2oVlGXllBO/Lz4N39Dzz/+414ve0OS3w8nXnNIlgW6jttqoU9MILfbyKaJbNt0ZoC7soxsWfgmJqj/5CcgmuWEHVC98074939HmZhAaTYxBgZwk0mkE6/ZzVYLJZPBNzFB27apv+pVaP/xH+dN89z5ypifp37bbbiLi7iui5xKEbzuOnyigVrYpIvjHVpBEARBEARBEIQdduJ9umVEA9a5R5Ik4vE48Xgc27a7LUeqqm7YOGWaJkePHqVcLlMoFCiVSrgn7nhJkohEItTrdUqlEocPH97yCMPOSMRYLEY2myUcDq8ZTCmVSpimSTweJ5VaOa5sOxiG0W3mObUNqre3l1gstqkxidttcnKSQqFANptlaGiIaDS67OfRaJRms8n09DSyLKMoyrrNZIFAgH379jE5OYnrujz00EMYhkEkEkFV1W44JhaLkUqlaLVa53zYrVQqsbCwQKPRYGZmhqWlJUzTRJZldF0nHo/T29u76m1iWRaKonR/Vi6XabValMtlBgcH173PO+GtxcVFVFXl8Y9//IqxjrquMzAwgK7rLCwsEAwGWVpaQtd12u02ruuiaRqJROK8aRk7W6Zp4roupmluqg2ss41hGMvGbwqCIAiCIAjC2bBrNbjlFuSZGXBdzMOHkU59PSpJEI1iRSIoExOok5Nw9920HnoI/6WX7tm6N8N3xRU0NA3b70cqFHBUFd+xY8itFmq7jStJ2KoKsgyui9puIzkOSruN9oUvUHnMY4hed91eXw3hQuK6mB/5CMriIkqjQfvwYSSfj1NfcUt+P87oKIamoc/MYITDVG+9ldjzn79ny76QtaemqL3rXSjf/z40GsiNhveDQIDmf/83tYc/nNCf/uk5/3wn7L1z+11DQRAEQRAEQRCE88RqAayzmEYn7ILOyL/NcByHo0ePks/nmZmZwefzkU6nu41VnXF4k5OTpNNpAC677LItNWFJkkR/fz/NZpNGo8HU1BQDAwMETxnpYNs2pVKJQqHA4OAggUBgU605W+G6LjMzM2SzWVqtVjeAJUkSoVCIYrGI3+/nwIEDKwI2O6lWq9FoNMhms/T29q4IX3V0xuTNzc2RTCYpFAr09vaueb6NRoNoNIphGKTTafx+fzdYp2kafr+fZrPZbdQ6duwYl19++Z4E0DaytLTEzMwMhUKBYrFIqVSi3W6zuLiIYRjdgFqj0WB0dHRZCMuyLGq1GsPDw4TDYQCazSatVgvHcYhEIutedrvdRpIk8vk8hw4dWvexlUwmyefzPPTQQ8zMzDAyMtINYOm63g0iDg0NbTnIuBbHcSgUCpTL5RWBwu26jLPlrvaLZI1tJEk6J/dBQRAEQRAE4fxU+/znkapVtEqF9tjY8vDVqSQJe3gY34MPYlWrND75Sfyve93uLnaL9OFhqj//85DNoi8uYjcayO02aruNpWm4mnbyCDrXxTVN7BOv5eVcDvvtb8d+7GNRNmip3mmubVP/5jdp33knFAqgKEgDA/ivv57gwx8ujgI8jzR+9CPkyUnUQgEjlUJap4ndHRjALZWQcjmsz30ORABr2zXvu4/Wq17ljV5dWkJpNHA6zwGmie33Q7FI86GHsN/4RkK/+It7vGLhXCYCWIIgCIIgCIIgCNtgE5+bC+exXC5HtVplZmaGWCy2YgxdOBwmHA5TKBSYn59H13WWlpYYHh7e0uX09fXROHGU3cLCAlNTU/h8Pvx+P4ZhUK/XkWWZdDpNMpncMOjS4bourVarGzzx+/2rns51XY4fP04ul2Nubo5Go0EwGMTv9+M4DplMhqWlJfr6+rAsi8OHD3fDOjstl8tRq9WwbXvdsYJAt8GqVCqRy+XWDGBZltU934MHD5JMJqlUKt1WIk3TiMViRCIRcrkc8/PzBIPBTY2I3G2FQoGZmZnu/dbT00MsFuP+++8nn89TLBa7IxpHRkYwDINLLrmke/pcLoeqqoTD4W6rmuu63eDZRmGfer2OaZqYpkk0GsVxnDW3NQyDarXK/Pw84XCYWq2GrutIkoTjON1gVLVa5dChQ2e9jy0uLrK4uEi73aZarXYbwToNXNFolH379u1J65bP50OSJPx+P7VabVngcjW1Wg1JktB1fd1xqYIgCIIgCIKwFdbdd6MWCl7oYIODL9B1zHAYuVDA+cEPdmeBZ8n3ghdg/fCH2Lkc/nweAFtVcXUdybaRLAvJcbxRhJKEq6q0+/vR83mcSoXqZz9L/MUv3rP1V7/0Jcz3vhd5cRGlVEJqtbx1BoMYd9xB88ABgv/3/xJ42MP2bI3C5rW//W2kahXZsnB7e1n31bYkYcXjqKUS7sQEVj6PKkbRbxszk6F1001Ik5P45ucx43Gs4WE40T7tttvI2Sy+mRkMw8B4/etR3/c+fGNje7tw4ZwlAliCIAiCIAiCIAg7RBx8eOHIZrMUi0U0TVsRvjpVMpmkXq+Tz+cJhUKk0+k1x9+dyjCMbjhqdHQUXdfRNI12u025XMY0TVRVZXBwkEgkQjQaZf/+/RuOLLMsi2w2Sy6XwzCMbphGlmWSySSpVGpZ4GNxcZFcLsfk5CSqqnLgwIFlIY+BgQFKpRJLS0s4joMsy1x11VWbbhI7G+12m2azSTAY3NTlhcNhWq0W7XZ7zW3y+TztdptarcbY2BiBQGDVZi3DMPD5fNTrdbLZLIlE4pwLYM3Pz5PL5Wg0Guzbt498Pk+pVKJer3fDc52mp2PHjmEYRnd/64zUHBkZIRwOd5vNVFVF0zRc18UwjHUDSrZtd0c0dv5ba7vp6WmazSaLi4scOnSIdDrdDcm1222KxSIzMzPd++7yyy8/47DR9PQ0i4uLZDIZyuUyqqri8/mwbZtsNovf76evr49ms8mll16666EmXdeJRqMkEgkymQw9G7yRXigUiMfj3ZYwQRAEQRAEQdgW1SqSZeH4fJt6M8P1+5GbTaRqdRcWd/bCj388hRe8AOkd78AtlVBNE1wXxTSRXBfplKPqHFlGsiyUWg1HUZCyWezbb4c9CmCVP/EJ3He/G2V+Hq0THvP7vfVns7iqCvk8rVe8AueNbyR0zTV7sk5h89xKBck0cVR17ba5U7cPBJBO3PdWoSACWNuo+tGPIi8u4pufp51Ow2kH8Ek+H+7wMO1wGN/0NO1gkNpHPoLvr/5qj1YsnOtEAEsQBEEQBEEQBGEbrNaAJQJYF4ZGo9EdxbfeKLuORCLB7OwshmFQLpfXbGtyHId8Pk82m6XZbHa/rygKPT09HD58mHq93h0BaNs2fr+fVCq1YQNUZ91Hjx6lXq93249s2+6OEqxUKuRyOYaHh+nv78d1XTKZDNlsFkVRVoyoA2/sWSKRQJZlFhYWiEaj5PN5+vr6NlzP2eq0MW0m0AYgy3L3NGup1WpUKhV0XV8RZnNdl0qlQrFYpNls4jgO1WqVBx54gGazSTwep6en55wYA1cul2m1WhSLRfr6+sjlchSLRRYXF3Fdl3A4TCAQoFQq0Ww2mZub6450nJubI51OMzIyQiKR4MCBA93zjcVi+Hw+AoEAxWKR/v7+NdcgSRK1Wo1AIICmaWuGtTpr6DRP9ff3L2u48vl8DAwMEAqFmJ2dxe/3s7CwwNgZHF3aaWybnp7GcZxl4xUBTNPsjhV1XZfx8XGuuOKKXb9P+/r6uqNFZ2ZmsO3Vg1ULCwvdUZm9vb3nxL4nCIIgCIIgXCB0HVeSkGx7U5tLJ5qi2IMW2TOVvPFGcp//PL7FRe+AIsfxwleAe+K/zhs5sm2j1mremz2ShDs/3/33bqr/4Ae47363N66uWsUYGMBNJpFOvC62TBM5l8M3N4dhWRhveAPazTejp9O7uk5hi3QdZNl7HG1mv7Is3BP3ubzBgXjC5rmGAXfeiZzNYgWDK8JXy8TjmMWiF3r8xjewq1WUjdoChYuSCGAJgiAIgiAIgiBsAzGC8MJlmia2bWNZ1oaNUwCBQADXdbvj2FbTarUYHx/vBqw6LVeyLBMKhahWq2QyGUZHR7n00ku769isVqvFkSNHyOfzLC4udlt+Os0/5XKZ2dlZwuFwN9SkKEq3cWtoaGjdoFMsFus2LGWz2V0JYHVCPZVKpdvktZ5ms4nP50PTtDW3sW0bx3FWbOM4DnNzc5TLZWq1WndsXbVaxbIsfD4fx44dI5fLcejQoTXbnnZLqVSiWq12w2alUonFxUVkWWbfvn2A1+IWi8VotVr4fD7K5TKLi4vs27ePVCpFf38/Bw4cWHZb+Hw+YrEYiUSChYUFIpHImiPy6vU6kiQRDofXvc07YUDLsujr60OSpFW37zS9FQoFIpEIw8PDW7qdXdftNl/Zts3Y2NiK03ca7RRFYW5urhtS20zAcTvFYjF6e3txHIfp6WkWFhYYGPgDHMd7zEuSxNBQnWq1ysjICPF4fN0mPkEQBEEQBEHYKmn/ftxIBDWXwzaM9YNVrotSqWClUjgnXm+cFyQJ6YorMB54AGVx0fvadbFlGVeWcVTVG8EIyKaJ2m7jyDJytYrZbuNaFtI6r3V2QusjH0HJ5VArFYyDByEYXDauTtI03MFB2oGA184TiVD7xCdIvuxlu7pOYWvUSy7B6YR3ymWIx9fdXimVsEMh3EgETbwW3DaNH/4QuVxGLZcxhoY23N7p6cE3OYlVr1O/6y6iT3/6LqxSON+IAJYgCIIgCIIgCMI2+NrX/puZmZNHikqSgiz/xh6uSNgrpzYurRYSMgyDI0eOUCgUmJubQ9M04vF4NxxVrVaZmpoiGo12g0abad461fT0NKVSiYWFBQYHB4mf9mZeOBym3W4zMzPDwsICsiwTj8eXhWg2EovFus1LlmXteAgpkUiQz+dZWlqiVqsRWedIw1arRaPRoL+/f90wjSzLyLK8LNzmui6zs7MUi0WWlpZwXZdoNEogECCfz1Ov16nVahw/fpyRkRFc1+XSSy/ddDPXTrAsC9M08fl83TCWbdsMDg521zUwMEC73aZ6YkRIOBxGkiQCgQCxWKwb9Dvd4OAglUqFVqvF9PQ0PT09JBKJ7v3darXI5/M0Gg1GRkZoNpuYptkdUXmqVquFaZoUCgVkWSYSiRCLxdYcKZlIJJiamuq2yW00nu9UpVKpGygcGBhYd//s7e2lVCpRqVS6IyZ329jYGJIkoSgKR46oDA7+Be22Abjouo8nPznPgQNNYrEYhw4d2tP9TRAEQRAE4ULyve/Bv/0btNtwww3wnOdcnG3WwRtuoHXrrdi6jry0hDMysvbG+TyS4+D29KA/61m7t8htIMViyK1Wd+RgOxSCVV6POLqOK8tozSa2qkKptOvhq/b0NMq996Lm85iJBKxxMAwA8ThWLoecy+HceSfu//f/IZ1H7WQXm/BTn0r5ve/FikZRMxmsWGzNJx63VkOt12kfPoz71Kci7fEBYBcSu1gEy/Kez0IhNnrqd0MhcF0k08QtlXZjicJ5SDxCBUEQBEEQBEEQtsG73/1SDOPkGDlZDgAigHUh8Pl8KIqCpmnUajX8fv+629frdWRZRtf1VcewzczMUCqVmJubo7e3d0W4qtNSdGo4KhZbfSTZaprNJtVqlWw2S09Pz4rw1anXa2hoiMnJSXp6esjlcjiOg6qqmxptpqoqjuMAdP+/kxKJBD6fj3g8zuLiIj6fb9Xb17ZtFhYWCIVCBAKBdcNrkUiESCRCJpOh2WwSCAQol8tUKhWWlpbQdZ1UKoUsyziOg2maDAwM0N/fj67rzMzMoKoqmUxmTxuJJElCkiRarRau61KtVolGoytCOj6fD5/Ph6qqxGIxQqFQd3+u1+uEQqEV5x0Khdi/f3/39Pl8nlwuh6ZpuK6LZVmEQiHGxsYwTRNVVZmfn2dubo7BwcFlwSfLsmg2m93Rl8FgcN2wU6dxzjRNLMva0m1SrVap1WoARKPRdbeVJKkbKOw0ie32eD9JkhgbGyOZTPKjH9UIBgOoqvcBj65r9PRIHDrkI5lMitGDgiAIgiAI26RSgX/4B+i8nPnQh+DgQXjYw/Z2XXvBf8kl1K+6CioVfJOTGKqKOzCwPBTiulAo4JufxxgYwOntJfaUp+zdos+A/LCHoZim13ylKKuGrzrcU39m2zitFvIG7wdsp9Z3vgOtFkqziTkysmE4xE4m0efmcCoVmj/7GcFHPGJX1ilsnez3w3XX4RQKKEePIk9MYI+MrAz5VSro09NeAC8SIfz85+/Ngi9Q0olRkMDmxq92tpFlEXAU1iQCWIIgCIIgCIIgCDtEfEZ+YfD7/YRCIRKJBMVikWQyuW77TLFYJBaL4fP5VgQ/DMPoju3rjB1b6zKHh4eXhaM224LVaSMyDINkMrnutoFAgEAgQLFY7AZzLMvaVAClMzIRWLPBaDt5Y9iGuqMdJycnSSaTxONxVFXFtm0qlQr5fB5Zlkmn0/T19a0a0uro6elhbm6OcDhMNptlZGSEUqnUDe50wlcAlUoF8AJJPT09hEIhWq0WhUKBUChEf3//ngVj/H4/wWCQWq3WbfRaa1QgQKPR6IbZOu1ZrVZr1QAWeOE3TdOYm5sjHo93969Og5bP5yORSDA0NMTU1BSapjEzM8P4+Hi3PcxxHLLZLAsLC2iaRk9PD319fesGGjvBvk7AbCssy8K2bTRN29RpO+MtwQvx7dVYyWg0ytBQlGjUptlsAS5+v590OsEWCsAEQRAEQRCETfjsZ0+Grzre/35417v2Zj17LfK611H74z+m7broMzNQKGDG47g+H5JpopTLKIaB0d+PMzyM7/WvXxEYaT3wAI3PfAZ3ehoMA+Jx1GuvJXLddbsaXlqLJMvdYJXkuriOg7Ta63vXRemMIHQc3EiE1k9/SvBRj9q1tTrVKtKJtmZpM7ed34/kOOA4OCeaj4VzV/wP/5DCT38KjoM2MYH64INY0ShuIAC2jVKpoLTbmIkEzsgI0stehj48vNfLvqDohw7RVBRsnw+pVMJdr2UOkItFb0ypz4d64MAurVI434gAliAIgiAIgiAIwg4RAawLRyqVolKpUCwWmZ+fJ51Orwhhua7L0tIS7XabdDpNKpVaEfzI5/O0Wi2azSaDg4PrXuap4ahIJLLpAFa73abZbHYDVRsJh8PUajVCoVA3tFOpVDZs3SqVSsTjcQKBwK4EsMAbFddpQioWixSLRbLZLJIk4bouqqoSj8dJJpP09fUxvMGbk6qq0tvbS7vdZmpqiuPHj1Ov16lUKkQikW7zVWc8XSqV6gbyJEkikUiwtLREKpXqtk7thd7eXhYWFvD7/eRyOWD18ZfgNaS1221SqRSJRIJCoYDrustGZ64mHA5z6aWX0mw2KRaLmKaJJEn4fD56enq6+9rBgweXNcaVSiWKxSKSJBEKhRgaGiIQCBAOhzccKVitVrttchs1z51OlmUkScLezFGseIGtzn58Loz3kyT5lPXszuNLEARBEAThYjM+vvJ7U1O7v45zhT40ROid76T+6lfTDoeRCwXkUgm5UsFVFOxYDKu3FzeZxPfXf03wkY/snrY9MUHtTW9CefBB5EoFqVbzAk66jnP33ZTf/36k3/gN4r/1Wxu+WWJXq9S/9S3cYhFUFW3/foJXX70tb7K4jQZmIoFeqXgjBlstbE3DVVXv/F0XybaRT7RkWT4fimWB349dLp/15W+F5Pd323mwLNhgBKJrWd51kGXkE23CwrlL0nUS//zPFP/yL8Hvh1IJpVBALhZBkrDDYayxMdxIBOXGG4k++9l7veQLjm/fPmpXXomUzaIuLWGkUmuOGnUdB61QwEoksPftI/jwh+/yaoXzhQhgCYIgCIIgCIIg7BARwLpwJJNJCoUClmUxMzPDsWPHSCQShMNhwGsUKhaL2LbN6Ogo8Xic/v7+FefTCV/puo7P59vwciORCJVKBcMwcBxnU8GQTphms41Bne3C4TCaphGLxcjlcoTD4TWDVcViEcuyiMfj9PX1bepytsvAwACBQICFhQWSySStVqsbnvH7/QQCAfr7+1cNrHXaniRJQtM0FEVheHiYZtMbH3r06FGmp6cpFAooioJhGDQaDRRFoa+vj2AwSDgc7oacOm1TpmnSbrd39XY4la7rxONxBgYGmJ6eplarYRgG2mlvHLbbbTKZDNFotBtqkiQJVVU33fjUCQauRZZlDhw4QL1eJ5vNEo1Gu01WmqZ1w2qdfWity3Vdl2KxSDwex+/3E4lENnlreEKhEOFwmMXFxTXHK56qE7rz+/3nRABLEARBEARB2HniNftKvn370P/zP6neeSfm5z6Hcv/92CcO1rBHR1Gvv57I9dejnHgtDNB66CGaL385ytwc2vw8smliBwK4ioJSLiMtLmLFYjjvex+FpSWS//f/rnrjt6enqX/kI0hf/zrUakiGAZKE4ffTSqeRr7+e2ItehHQWbbWSz4fb04M1P4+racim6Y0kNAzcE2uSXBdbVbF1HVvXwTCQZHlzLVTbSL/ySoxg0GvcKRRglfcYTqUUi1ihEK6moR86tEurFM6GHAzS89a30rj3Xlq33IL7ne8gnXhvwenvR77uOiLPfS7qBu3mwpnTb7gB+6c/xS0U0CYmsPbvXxF2dE0T9UQ61+3rQ332s8UvEGFNIoAlCIIgCIIgCIIgCBuQJImDBw92wyrlcrnbvgRek1IikSAejxONRjl06NCqIY5OOGqzAQ9ZlrvNRBs1FHVomobP5yOXy20qtNVsNtE0DU3TGBwc7DZoTU1NMTAwsGyUnW3bFAoFcrkcg4ODBAKBDccc7oRYLEYsFqPRaFCtVrFtG1mWCYVCqwZ1KpUKmUyG8ilHLEuSRCwWo6+vj8OHDzM1NdW97tVqtXs/pVIpNE2j2WxiWRaZTAbDMADvtp6fnycSibB///5du/6rGRkZoVarkU6neeCBBzhy5Aj79+9H0zQcx6Fer9NsNolEIiSTSQYHB6lWq4TDYXRd3/b2rlAoRCgUYmxsDNu2kSQJWZYpl8scOXKEWq3G7Owsw8PDK0JYjuOwsLCAZVn09PSs2ia3kWQyyezsbDcwFwwG1zyPTsgxkUiQSqXO+DoLgiAIwoXEyuepffaz2A895IUPwmHURz+ayNOehrTOiGfh/Fatwr//O0xOwi/8ArzoRXDvvfDJT3qfNf/Wb8Hll+/xIrfRhf75+dIS3HEHBAJw/fVeyc5mSKpK9Lrr4LrrcE0Tu1pFDgZXHSHoNBo0broJeXoa3+wsRjKJ29/fDTDYrguVihfMOnYM6zOfoXLgANHnPW/Z+WS+8UOsN7yeQGkeNZdFrVbhxGtgR1WxFhZwZ2YofPe7JN7yFuQNRoWtRb/iCtp+P3Y4jK3rXgCr3Uay7e7luYqCq6qYiQRYFq6mgabhu/TSM7rMMxV8+MNpjY1hLS6i5fMYvb1Ia7VPN5to5TLtfftwH/c41ERiV9cqnAVJIviIRxB8xCPAdXFaLSRNO6ugobB5kac8hcLdd2M5Durx46g/e4iGPw7RILrPRa7XUctlHE3DPHgQ+zGPIXna85cgnEo8cgVBEARBEARBEHaIKFG5sMiyzMGDB0kmk2QyGZLJZLfZR5ZlAoEAqVSKnp6eNUNPqqqiaRrtdhvbtjcc3dcJR0mStOkxf4lEgmw2i6IolMtlEuu88WqaJrVajZGRERKJBH19fTQaDQAWFhaYnp5G0zT8fn83xKOqKkNDQyQSiTWDZrslGAwuC4idznEcJiYmKBaLlMtlSqVSt6nK7/cTj8cpFov09PQwNjbWDXVVKhXi8Xh3DGO9XkfXdXK5HM1msxvkabVaNBoN8vk8ExMTJJPJTTWb7QRd17nsssvI5XLYts34+DiZTAafz9fdP9PpNH6/n4GBAcAb8Tc2NrbuPrsdTt13O6E3x3GYnZ1lfHycWCxGOBxGkiQajQalUglZlhkZGTnjljVFUejt7aXVajE1NcXc3BwDAwPLwl6u61Iul1lcXKS3t7c7TlEQBEEQLmZ2tUr5rW9F/sY3oF5HrVTAtnFVFecrX6H4vvchP//5xF/ykgs/vXIR+n//D370I+/fR4/C9DR85zvdXAp/+Zdw882wR5O3t92FvAsvLsKNN0Kr5X399a/DO9+59essadq67TvVz38eeXERfW4Oo7/fC18tOwMJYjHMYBDt6FGkhQWsj30Mbrihu5hb3z+B/y//gf7GAmlzBqfXT3tsDDcUQnIcKJVQ83mkUgksi+JrX0vP299+Rndg4KqraB48iLW4iJrPYxw+jNVuI1UqSLaNK8u4gQBuIgGmie/IEYzhYexHPxptFw/WaDbh3/9dwqm/iKcwS1opo42PY46OIp3eSFytok1PY0UiEI8T+NVf3bV1CttMksT4yN0mSSRe9zqKmkbuY1+iNNUiXs3gKxUhYCKFNYzhYUgksB//eJJveMPaQUhBQASwBEEQBEEQBEEQBGHTJEkimUx2R98ZhoHruui6vu5Yto7O2EJFUahUKuuGoyzLolKpdMNOmxWNRgkEAiQSCTKZDIFAAP9qRyo7DvPz8/j9fkKhULf5Z2xsDF3Xu0GxcrncHfE3NDREOBwmFAqxf//+Vc/3XOG6LsePHyebzTI9PY1t28RisW7Apl6vs7i4SD6fx7IsXNflwIEDjI6OIklSN5g2MTFBs9kkn88TDodJp9PdkNXk5CSu65LNZmm324yPj3PZZZdtOiy33Xw+H9dee2230Wxubo5gMEgsFiMQCBCPxwkGg9RqNYrFIgMDA0QikVXHZe6k0dFRwAtJ1Wo1CoUC8/PzgBck6+vrIxqNEovFOHjw4BmHw4aGhmg0Griu2w17hcNh/H4/tm1TrVaxLItUKkVvby8HDx7cs/vudPfddyOW5YUFFUXGMHR+8zffucerEgRBEC50drlM+c/+DPmBB1Dm51HrdRxNw1UUJNP0ghOxGHaxSGFmhuRf/VU3AGHbYBjg84kDUc43tu2Fc7LZk+GrjrvvXv61acL//A/8/u/v3vp20oW8r37mMyfDVwBTU3D//XDlldt7OfbnP4+Sy+GqKu56B05oGmZfH9rSEu7CArVvfYvw4x9PvQ4L//QRHtmaZ7A9RUWJEak2CDROjPvSNKx4HPPAAdTZWbSJCdB1al//OuEnPvGM1qzecAPO0aO45TLaxATm2BgMDXFq57RUr6NNT2MHg5BI4N9iqMkwvOCirp9Z0O/d74ZvfhNwnw7Sz3hKxGGwcQT/kSNYoRBOKASui1KtorTbmLEYzugo/N7vEXjYw7Z+gUJXtQoPPQR9fXDipatwgZNUleRrX8s///S59LVv46rCN9EcA5/P4dAlBs411+B/3vMIXn31hZ3cFbaFCGAJgiAIgiAIgiDskAv9NXmtVqNWq+E4DoqiEIlE1m0jutD4/f4tB5AikcimwlGu67K4uIiqqoTDYVKpFJZlUSwWMU0TTdPQdZ1EIrFqYGR4eLgbEJucnCSZTBKPx9F1vRs8yefzAOzbt4/e3t5lAbJ0Ok1/fz/5fJ5SqYRlWUiSRCAQoLe3l3A4vMVba/fl83ny+TzT09P4fD7S6fSy2yoSidDb28vc3BzT09PIskw8HieVSnUDSg8++CCWZZHP50mlUsuud2ec4YEDB9B1nfn5eSRJwrIsYrEYkiSh6zo9PT3ouzimx+fz8ahHPYpYLMbY2BiZTIZms4nrupRKJfL5PIFAgJGREWKxGIcOHdrV9YEXZNy3bx89PT1kMhmi0eiyEZvRaJRUKtW9Hc+ULMscPnwYVVXx+XzUajXK5TL1eh1ZlrtjQ30+HwcOHFh1fOVekCSYnv4gjtPsfm9hIQCIAJYgCIKwg1yX0uteh3L//ejHj2MHg7QPHoRQqPtzymXUhQXk8XEsoJhOk3jpS5mZgX/6J29s3eAg3HQTjI3t4XU5D9Vq8J73wIMPwmWXwZ/8CezGn9yuC3/91/DjH2/+NN/4xoUTwLqQX7PfdtvK791xx/YGsKxCAXlyEqVUworHN7xB3WQSaWEBKhWM730PHv94vvmZJS4tfpfh5ji63SLqumimgap6Y99dWUZutXCzWYzeXpRGA4pF2p/+9BkHsKLPehaFH/0I03FQJyfxP/TQslCTXKuhNhqY0SjOvn24v/EbhK6+etPn/81veo/nRgMe9ziviWwrpUa2fSJ8BSBJ3HHJy2keC/PSoY9i1+vIuRxKrYYrSdjhMNbYGG44jPQHf0D81399azeGsMzx4/CGN0C57O3OL30pPOtZe70qYbfcW78CLr2CLzs3EjBK4Lr85//GVh2/KghrEQEsQRAEQRAEQRCEHXKhvpmby+XIZDI0Gg1arRa2baOqardJaWBggHg8vtfLPGcNDQ3RarVot9tMTU3R09NDPB5HVVVc16VarVIoFDBNk9HRUUKhUPc2NwyDdrvdvb11XSeZTJJOp9E0rXsZsViMffv20Wq1aLVaTE9PMz4+jq7rBINBVFUlHo+TTCZJpVLdRqJTKYpCX1/fGY1/OxdkMhlKpRLg3eartSipqsrw8DDHjx+nXC6TzWY5fPgw+XyedDrNt7/9bcrlcjdc6LourVaLSqVCs9nshrL6+/v58Y9/TD6fJxaLMTw8DHghvfn5eeLxOMPDw7s2njAQCHDZZZcxNzdHLBbr7m/gBbT8fj+xWIyhoaE9bTELh8OEw2Fs28YwvA9YNE1bNibwbHVGhzYaDbLZbLfRrTOWsbe3l0QisaejNAVBEAThXFD/4Q9RfvITtKkprEjEa1I59QWNJEE8jhmJoB09ijw7i/2pT+H81m/xn//pZ3LS22xhAf7t3+Dv/m5Prsa2yWbhv//baxB65jPhqqt29vLe/W749re9f991l/f/V796Zy8T4MiRrYWvwAsmnG/m5rxxiqOjMDR08vsX6mv2tWz3n/52pQLgje7bxGsdSZZxVRUsC+p1AOS7v0y6foyoVcJGQbdr2JKCe+Lvc8m20UwTW1XxZTLYmoZaKMC992IVi6hbaIs+uRCJxF/+JcVQCPtznzsZaqpWAXD8ftrDw14K8sUvxvfzP0/1jjtAUdCGh/FfeumaZ91qwbvedbJ97K67vOePZz5z88szzeVfu5LCNw79EX/2ruto3HIL1je+gVwsgizj9PUhPeUpRJ77XFQxTn1N2awXdB0bW/9x/6lPnXyOc1342MfguuvgbF+i1uve5Z4Pxyxalvd7IRCAyy+/eJ4nTzkmDFvWqfm998JcbY0TCMIaRABLEARBEARBEARhF7RaLbLZLLVaDdu2u41Rvb295/QYt9NNTU2RyWQoFAoUi8VuM5LrumiaRjKZpFarMTQ0xODg4F4v95wUj8e7gadisUihUCCbzaIoCo7jdLcZGhoiEAh0952lpSUajQaSJHWbnMLhMPV6nUqlwuHDh7v7UrVaJZPJkMvlmJyc7LZY6bpOPB7n0KFDDA0NMTAwQCqVOquWoXNRvV6n0WhQKpVIJpPrhmsURSGRSFAsFkkmkxiGwaFDhyiVSqRSKQqFQjcsB157UzAYZHBwkHA4TCKRYG5uDsMwmJ2dpbe3F1mW0XUd0zS7IZ9arcbhw4d3rSWu0+pkmiaFQqEbcOo0p+1269V6FEXZ1AjPsxEMBtm3b9+OXoYgCIIgnM9at9yCUiohWRb28PCafx9KioI1OIg+PY1dLlO77Tbuvvt5y7b5yU92Y8U7x7Lg//5fOJHl59vfhre+FQ4d2rnL7ISvOjohrJ12yy27czlb4bre+K922wuunO2E6Lvugre9zQu1aBq84hVeKxFcPMGCjm08zgEAufPaRpaRLGvZCL9VuS6u4dBo6biO99o1cuR79JiLKDg4kkxDDmFLCiHd6J7Mtm3Udhup1fIuxzCwXBcrkzmzABbec1nyla+k/YIXUP/kJ3G+8Q3kUgkkCaenB57wBFxJQrr1Voybb4YT189WVeoHDqA9+9lEnv1spNNu1C9/efnoR4D3vW9rAay1VEP7kH/n5SRf/vKTaZFTduJczgtzzs7Cwx8O/+f/bD7wYxhe65aiwDXXbH9YbzdMTHjBob4+eMxjTt40//Ef8MlPev++8kr427/1ngtW020eO6Fa9W7PM211tG0vlHzrrd56XvhCeNGLNj5doeA9Z/X17e7zVL0Or3mNN7IU4Jd+yft9eCGZm/N+xxw+DCMjJ79v26tv326fH8E54dwhAliCIAiCIAiCIAg7RJa9Nwo7AZhGo0G1WsVxHGRZJhKJsLS0RDweZ2xsbNVRcueSubk5lpaWmJ6exrZtent7iUajKIqCZVmUSiVyuRy1Wg3wmmx6e3v3eNXby3EcisUizWazO3oxFotteSRfKpXC5/OxsLBAIpGg2WwuG/PXaSjqBLQWFxcJBoOMjY3h9/tRVZVGo0GhUGBqaoqhE4dxX3755ZTLZe69916OHj3aHSHYacKSZRnXdZmfnycSiXDFFVdccOEr8AKPpmlimuamxsqFw2Gy2SyWZdFqtYjH4+zfv59MJkMmk2FoaAj7xLtxqqqiaVr3fp+dnaVYLFKtVtF1nXQ6zejoKOFwmHa7TbFYZHZ2tnv/XnHFFdva8LQRTdPo7+/ftcsTBEEQBOH8JN9zD0qxiBWNIm30uiQaxVUU5GKR1l3fBZ63YhPXPXfDLa2WF0RZ60+yr33tZPgKvOty64cXeXH/J3G/+U2kUgkUBWdwEPXpTydy/fXIOxwm3wk/+xncffder2I51/XCbt/4hvf1JZd4YYmz+fD7U5862ShkmvCJT5wMYJ1PJahOo0H1jjtwZmdxbRs5mWR25Mkcr6Q5cGBzLW133LG94yO1VAqntxc7EvHGEG7QnlyZrhNoqEwVU9z6lV/gJb8FiamfoLoWEi5NOYQjrXKnKApWIIDWaODitWIB23IH+sbG8L3ylfDKV+JaFpKiYCwsUHvlK1GOH0fOZlFKJWTLAsDRdaTFRewjRyh85SvE3/xmcF1vHKPfz9xML3B27+2cOC5rhd/7Pe959fnPhxe/eOUT7NveBvfd5/37zjshEoGXvGTjyzMMeNnLvGAKwIED8I//CGdT4FytemEmVYUnPWntwNN2ufde77nixN3Es57l7etLSyfDV+A9733+83DDDSvPw10jQXg2u9l993nhq875f/Sj3u+gZz0L1nq77CMf8Z6nwHuuevnLYbeOn/rKV06GrwC+/nV43vNg//7tu4xczjtfTYOnPe3s9rOtuvtub992HO+x9IpXeCEzOLnvnO70AFa9Dvk89PScnNIsCKcSASxBEARBEARBEIQd4jg2Dz30EMVikYWFBVqtFqFQCFVVabVaFAoF/H4/g4ODtNttLr300nM2hGWaJktLS8zPzwMwNja2LESiqiq9vb3EYjGmpqZYWlrqNmJdCKO9HMdhYWGBbDZLu93uBrBUVSUYDBIMBkmn01savRiNRolGozSbzRVj0eLxOLOzs9RqNRYXFxkYGFgR8upcbj6fZ25uDl3XOX78ODMzM/zkJz9hbm4OSZJIJBLdEFK5XCaTyVCv17v33y//8i9v2+10rnBdF/fEu6ebCZiduo92TqcoSnc8YzqdxnGcbtNbOBxGlmXm5uao1+uUSiX6+vooFovLRkH6fD4GBgbw+/0sLi4SCATIZrOiHU4QBEEQhHOL6yI1GkiWhbOJ8DqShKPrYFlYxeqqmzjO2TcX7YT/+i/vw3jbhl/7NfjN31wZFDs1lCS5Ntce/yC/8O3PIu3PoORySCfSPM74OO4DD1D+0IdQX/EKIk95yrLzefBBr30pFoNnP3vrH6B/5CPw1KfCTmTpGw14wxu2/3w3I5eDZtMbBXj6S8XJyZPhK/BGJN59Nzz5yRuf709/6o0ZvPRSr63MtuHoURgfX77d8eNnfRV2lV2pUH7ve5G+/GWkatV7rLou5VaIUu6jZOOP5pZ9L+bX/+IA1123/nmdmEq+fSQJ6brrcGZn0Y4cwSqXvR1+FY7tIC/lKWtJqlqCn0WfwG0fnOG5tSyKa+EgwXodWpKErarIpomrqri2jTYwsK1XZ2Ja5V1vrvLE297ApfZxRppHcH0aZn8/biiE5LpI1SpqPg/5PGYuR+VrX0NKJLpPJFfneinL1/HjwWdSCaTPaB1rNfGAF+L55CfhCU9Y3srUaJwMX3XccsvmAlhf/vLJ8BV4j5F774Vf/MUtLPoU5bIXbMlmva9vu80Lh23mraFVir025fbblwdo/vd/4Td+wwtcne7mm1cPYJ04lnCFtYI5m/He96783qc/DV/8ojeq9/Rmxfn5k+Er8H6HXHvtydDoTvvAB1Z+74474A//cPPnUa16rY6m6a371JK6bNYLlJ2YXsqdd8I73uHd75WKt+/l83DFFTA8fHbXZTX/8z8nA46u633dCWCdPvqzwzhZxsf998Ob3+yFtONxry3siiu2f53C+U0EsARBEARBEARBEHbI0tISqVSRqakpwuEww8PDy8IZpmmSyWS6o80mJyc5ePDgXi13XblcjlarRa1WY//+/Ws2+HTadubm5kilUhSLRXp6enZ5tdvLtm3Gx8cpFotks1kqlUp3DKBlWaiqSjwep9FoMDIysuW2oUAgsGL8muM45PN5CoUCoVCIeDyOtca7fj09PVSrVYrFIjMzM2SzWebm5giHw90xhh2JRIKhoSEefPBBfvazn6FpGsePH+fAgQNbv2HOYaqqoqoqkiTRarU2bChrtVrd+7Szb/v9fnRd74Yik8nkstNYlkW1WqVSqRAOh3EcB13Xu+MHTxWPxymVShSLRcLhMAMDAxdk85iwvkajQaPR6IY3I5HIst8JgiAIgrBnJAlX03Alaf1P/k89iW2DJNGWVp9TtV4A6/vf90Ixl1wCj3zk5pe5uOj9f2DA+6C80YBk8mST1T33eMEEVYXf/m3v/E81Pu592Nrx8Y/Dox7lBXZO1Q2puC5Pfeht/ELuDtKN4/iO5LCDQexgEMlxUItFpGwWM5nE/ru/o2IYRE/MGfvJT+D1rz/5Qe+PfwxvfOPmryt4H8Lffju8851rN6Wcqc99bvmHylu12YYz1zSpf+97OPk8KApffXA/H/r65bhIPOxhcNNNcOqf6v/1XyvP4x3v2DiA9alPwYc/7P1bUbzA2113QSaz+vaO44VB1gqE3H+/d5v39XktPl/4gtd08ru/C7t5LIW5tET1z/8ceXwceWkJtVLxYkqSRKShsE+KEDYLHKj8mK/+62t5xCMexQc+sDxMs1lzc/D+93vhmSc+EZ7znM3dx5HnPY/qJz6B0duLPj2NMTSEFUnSaKn4fA4+3cFtt5Em5lAcl1xwiB/0PQNb1pn5yhEMPYLsupiShs9p01TWrjtzFAWt3cZQFKzhYZTNBEY3wXXhW9+Cf/oneNzxjzNYGydZn6QQ6yNyScoLmnW2DQYx4nEC992HPjGBo6pYfn93h0qZCZ5EjmsWP8sXxv6In6av3/J61mrAOtUHP7j8OeVsQkL/+q8rv3f33WcewLrrmzbhB77HNZkvETOycC88kA8x+jtPJvzEJ64Y2wheMPOf/skLrh486IV0Th0Pt5HV2vweeMALe66m0VjZrJfPr77tsWNeK9iZWOux2Gh4TVx//ufLv39qW1fHP/6j9/y21Wmbrutdxsc/7u2eL37x5sKsp+uEpTaj1YLXvvZki9ZnPuO1GnZymXfdtfz8pqa8MNpXv+rd9x2q6p3P1Vdvfb3rOT2AOzt78t/rNWB1fOQjJxsySyXvd9bf//12rlC4EIgAliAIgiAIgiAIwg5pNhvMz893gzCn0zSNoaEh5ubmmJ+fx+/302q18PtX/xBjLxUKBUqlUnc83nrC4TCqqlIuly+IANbExAT5fJ7p6Wk0TWN4eJhQKIQkSdi2Tblc7gbUgG7z19moVCoYhkG1WmV4E4f9JRIJJicnKZfLZLNZVFVlZGQE3ypd7pqmcdlll/HDH/6QxcVFHnrooQsugBWNRtF1nUgkQqlU2jCAVSwWicfj6Lre3TYSiSwbBRmLxZaFpmq1GpZl0Ww26e/vJ5vNkkgkCAaDKwJY4N1HnTazRqNBSHTVXzTy+TyZTIZGo0G73cZ13e4oy3g8zsDAAMGzmasjCIIgCNvAGRvDmZ1Fzecx0+l1kxdus4nSamGNjNAcOATZVc7P8T7MvPlmr/0kFII//mMvEPOhD53c7vd/3xvFtB7XhXe9C770Je/rYND7XrPpfSj+2td6H5D+zd+cPM1993kfWEejJ7/3vvetPO/PfAZe/erl3+t82PqwhVt5RO5ORupHvO9fcgmcOLjBBRzXhXIZfXYWy3Fw3vpW2lddhW/fPm65ZXmI4sc/hpmZrQUKwAuafec7cP2JDEerBQsL3ofZZ/OS46c/PfPTgnfbS5IXelAUr6Hl1MCdVShQ/e//hi9+EalYBNPEsmV6J2L8TmCMewau52fu0/n611VOZNaAtRtI1mPb8LGPLf/6059e/zSm6Y29Wms3f81rvADAtdd6Iyk7HnzQ26d3o93NabWo/sVfID/4IPrkJHYgQHvfPqRoFCSJ8Z/IJIwMw83j+OwWT7r7H/jAX/4T388c2vjMT+O6XmCw01h0/Li3jz3pSRufVu3pQXvta7H+5m8wZRl5cgHTLNJS4jQkhd5AmZBVpq0EmQ5dxkOxR/O9sd8EQLebFIo6A5KGJWsE7Aa608aQV5lJ5roohoELuIqC9KhHbek6lsve/X3qc0LHxz7mjYZTHINHZO8gaSzSUgLMOGNcKTWWL8M00SYmAJBtG8lxMGWdbHw/qmQhNauMtR+grPXwjIl3A2w5hLWZANb99y//eq19+UzHwZ5pkXn161+HP/ggv95aImIW8DleAsr4rJ+l73+H6fj76X3dH9H/rOVN3Dff7AWmwAvLvv/9Ww+tnq5e7z5lr/CTn8A11yz/3loBrHe9y2sc8/m8/Why0gtorvI235Z85SsrA1jl8urbvuQl8MIXwotetPnzf+ih5Y1W73wnXHmlF2IGL/z0r//qXeZTngLPfe7q57OVEYHf//7yEYaZjBdu7DzPn/r7v2O1AKBleWG07Q5grWejBizXXdmodra/S4ULkwhgCYIgCIIgCIIg7BDDaGEYBiMbvMve19fH+Pg4jUaDbDa74fZ7wTRNTNNc0dS0GkmS8Pv9GIaBeSbvoJ9DarUapVKJmZkZAoEAQ0NDy0I4iqKQTCYJh8NMTk6Sy+Xw+/1nHcAyTRPLsnBdd1OBvEAgQLVaJZPJUC6X2b9//6rhq45OU9nS0hL5fJ5yuUxsjVER5yNZlunp6aFWqzE1NUWpVFpzPGSxWOyGqHp7e7v3ryRJpFIp6vU6ExMTzM/Pk06nuz+3bRvLsnAch1KphKqqhMPhNe97n8+H4zjd0wkXPtd1mZqaIpvNdkOspz4nBoNBkskkpVKJ/fv3k9jqIc2CIAiCsI3U66/HeeABpEwGCgVY6yAK10VZWsIOBiEUovzo58AqH0A6Dnz3u94YKPAaL972tpVj+D772Y0DWD/72cnwFXjNIR3Hj3vncXrTkW17p3ne805+76GHVp73iRzFMq0W4Lo8euHTJNuLKK7NZPhyLg+0cYFWU0FVXTTNgXictqLgn5ignUxS/8Qn8L3ylfzwhyvP90c/8gJYMzNeeGJqys/3v6+jaeunI774RS+ANTfnjayam/Nuxz/5k80FZFZzNu1X4LWGvP3tJ9td/vd/vfYaSYLW+DiNV70KeWYGOZtFLZeRHAfZVDhoBUi2FhhsHOOS/Lf54Hv+imc+8+RrzDMJfczPb320nmF4oYLOuLPVWNby8BV4jSff+tbJkVU75dgxqH76dkaPjqNPTWHF4zjDw93XIo4rYSoBMoF91NUYw81jtJUAg1/7b7ji9Vu+vHvvPRm+6njPeza/fwUe/3jmX/5y6m98N/mlYWJOnpBTQ8al2vKjHO6hLsW4p/Z07rzkz3Ek7+PplhrGkTXasg9HUkAOEHCaqK6FZHpBKwDJslAsC1wXV1Ew43H8m6zPs20v4HHHHd7++au/Cr/1Wyd/7rpe+ApgX/77RMwiMbPAfGD/iuSS64IztYjcaKK22ziKgutAy9DJ1mOYih984FPqDDfHGWhO8vTJ9zKReBSwflN2tertx729mysi3OxbLaa5/vjTQmH175/J+LfKrbfivOUt7K+USRpLADQV78CjmJlHOu6S1wpM/+FbkJtlUr92cg7gN7+5/Lx+/GPvufhsjk+s19e+7vfeu/kAFnj7z+WXe0HfctkLYf7RH8HTnuY9n9TrXrjv9HCmomy6WBJYe72d/fTJT/aa+Tbj9ttXnseHP+yFjh0H/uqvTrY5/fu/r/1rfyvPy53H0qne9z54+MO9fUqS1n/ePdXpIcOdtlED1lqPuTMNOQoXLhHAEgRBEARBEARB2CGNRoNgMLjhiClN0wiFQlQqFarV6i6tbms6b/S6m3ynxHVdJEk678esZbPZbtPR4ODgmtdH13X6+vrIZDL09PScdaBpq7eb4zi0Wi0sy8K2baKrHdaLF+wyDAPHcfD5fBiGQavVolKpXFABLID+/n4KhQIDAwMsLCzQbDZJJBLdQFur1SKfz1OtVhkcHCQSidB32juZfX19lMtlbNtmZmaG8fFx4vE4gUCAZrNJoVBgaWmJ4eFhhoaGiMfjRNYYg+GcOIxZluXz/nEhbM7s7CxLS0vMzMxg2zaJRIJoNIqiKBiGQalUYm5ujnq9DtAdSygIgiAIeyFy3XWUP/QhzL4+9Lk5DPAqlk75u8W1HdrHl/CVmhTSBwld/ShqwdUrQBzHa9s4lWGsDP5kMt731gsIrNaYcapOyOt03/nO8gDWalb7E7jdhuHSvfS25oibOXK+QRxJxbIMJif9tNvep9EDgwY9SRMpEsEKhZBzOdwvfxnnT/8UWJka+Ld/867vrbeCZcm0214Dps8nrfvhrSybZLMl/vM/dSYm/KiqimEo3Hyz18rS+cC/1fLCaMePe0GB9UbIbTWwdLovfGH5aK0jR7yA2cOGMzT+4i+Qx8fRZ2dx/H6MoSHccJjMooqVa5A0MsTNHIpj4T74d7j23yJ1gjZn8GfyeqGJtXT2w60EIzruu29nA1gf+IC3T//2j75MsFonrSo4neTCCc4p665rcYpWL0ljiUtL3yXYytHwb21m5fT0yu+d/litVr0RnlNT8HM/5z22ZNllfn6eTCaD0d/Pxx79FprlI1xT+gqD5jwKNk01yiOe/XMs/Pyv8YWbDy47z9nYz2HKPmpqHAeJgNTEkRQ010Azmt64RVfCkWRsXUOybWyfD/vwYUK/8AurXpds1nucjYx4gZh77/XCM+AFJT7+ce9xMzp64rY8pW0q2lpCc1pIONTV6ImfS8iyi2nKTB5TGCs2cG0HS1FxfSpKo43kgua0vQAW0FZDzAYOMtZ4kKLVz8MXPg/8nzVv/09+0htt5rrwuMctD4ht1lpv1TQa6z+/rhZMPROtBx/Eeetbkaen6TUaLPpGqGo9uJL3fCm5DhGzQH97Bq1k0Hjzv9C8/BCBhz1szfNcXISxsY0ve60WokZj7ba6e+9d+b31nku++U2vAa/TUGXb3vi5dNoLo+Zy3j51003Lm7E2CmBVq3DqS8CNJtT/13/B4cPe/X3tteuPJfzBD1Z+7667vPV86lMnw1cdb33r2mvcrLWu69//Pbz73RCPQ7G4+fOzrJNjhs/GN7/JqsFo8O6/mZm1WyU7vy9PlN6vsFHIUbj4iACWIAiCIAiCIAjCDnFdZ9VRZKvRNA3HcbohjXON3+/H7/dTOv0dmlXYtk29XicWi63bwnQ+KJVKlEqlbmhiPdFolKWlpW5r1tkEmvx+P7quo6oq1Wp1w2acWq2GqqqoqookSSvW2mg0qFQqNJvN7n7WaDQoFArkcjnaZ/sJzDlI13UOHfLGb6iqSj6fZ2JiAvnE4ZuO4xAKhRgdHSUWi3H48GHU097Zk2WZQ4cOIUkSmqZRLpcplUrk83larRaO45BMJonFYiQSCdLp9JrrqVQqBAIBJEnaVJOccH5rtVpkMhlmZ2eRJIn9+/cve1z6/X4GBgaIRqNMT0+j6zp+v58rrrhiD1ctCIIgXMxkvx/9DW/AePWrMQF9bg43k8GKxUBVkdptnGwNyVCZCFzKfPMQM6nX0r/GB7OOs/mWpVJp/UaPU4M+W7GZCb/Hj3vjpfr74YYbvIzL4iI8qvIAfquO6ppUtR4kGXI5rRu+Alha0knELWTZxU4k0Ofncep1jIkJ4PJVL+9zn9v8+k3ToN1uY9sVjhyZ5H//9zKgjizLqKqKZfm4/36NTn7hgx/02rLAGw3YbK49rups//w/tZGs4wMfgL8f/gDy9DT6zAxGf793wwISYGs+Sr4IZT3FYPM4I82jWCWN6h13EL3uOuDMGrByua2fptNkcibFtLkc/PM/e2PSLrkE/uAPTrb02LbXXhUKndl4smzWC19Fm/OkG8cINwvkYgO0lny4SCQTJj6fg+0sT6qV9D7213+Gz25wSf5b3Dv03K1f+Abe8x749re9f//4x2BZLvH4LF/+so1lOTz84RPM565kIv547oldi207yLKM3+/jrx+fIxjct+I8m74kD/z/7L15mGR3Xe//OufUqX3fel9merZMEhISCLvEBAFBMECQIIosrhd3vSpyEbxu142f6FWvG9ddAfUqiggCchHwBoGEBMgyW+9d3bXvVWf9/XHqnO7qrupteiYzyXk9zzzTVXXqbHW27/f7/rzfiecQUYpMtC+wFJgjoLdIqAUkv0i348EwBExBwEAm6qmiHDuG+cIXIg1ob3/qU5bIQ1Esocc732kJH7fzx39sRS5C/zEgsFXFJPQ+F/B6TfIFmWCriGRqeEyNlhnEj4ZpDlYNdj1hmlKEuLLB7aWPgfmWgdPl8/Cnf7r5+rOfhbGxgZPuYKtD1G4CrCFm0MDRCbBaf/mXiMUicrnM+dDNtMVw3+emIFLzpumKAWZaj7KxEML8vd/D94539IpQwjvmuba2U4DVaFgCnpERS/TykY8MjrADy5Vq2H1gddUS6m299wxzA7OX++ij/e9VKvCOd2y+Xly0fsu3v33zvb2uaW94AzzrWfBf/6u1Pf1diCaqqjnu6IIAH/2oh09+UkYQBP7xHy3RlC3guv9+SyQJ8K3farmpDRJPvepVO9/bjUHzqFSsCN9q1RKC3Xabdf8cJsBaWbHcAxOJgwmwKhVrOy6Hv//73YXc//Zvu3/ffpZptwd/fu+91jr+6I/CTTcdahVdnmS4AiwXFxcXFxcXFxcXF5cjIJudo1zeWg7lRRTFfUfwaZqGLMuOOORaI5VKUalUKBQK1Ov1XV1aKpUKkiQRiURIX25PyROILVTSNI1QKLTn9KIo4vP5nPjAyyEcDhMIBIjH45RKpaHxefZ6lstlstms42bVaDQcpyc7YrDdbjtuXrAZvVetVpmfnyeTyTzpXLBCoRBnzpxhZWWFSCRCt9t1xGY+nw+fz0cikWBiYmKoWFKSJE6ePEmlUmFjY6MvYvDChQs0Gg0EQdjVIU3TNKrVKqOjo8Tj8T1d8Vyuf/L5PK1Wi3a7zdzc3FABZzAYJJPJUCqVSCQSNBoNBg0+uLi4uLi4XA1Cz3gGwv/4H3Tf/W6UVAqhUECq18EwMD0eVjwzVLxpVkMn+Nsb3o3y+STfOCSy6CB1JeWyNQg+P28JPBIJePGLN51LDlujsh8BVrcL//qv1t8f+MDmMr16G9HUMRAxBAlJNCkW+5/hTANaLZFwWAdZRuitaLfW4nJpt9t0u11UVeGLX9R55JEMrZY1+iuKIl6vjKZpzM8b3HxzknZ7U3xl86lPDRdgXW4E4cB1LjYQLvxfpI0NtHDYEV9txxRE1gLHOd54mKhSRP3Qh2APAVajAeEhj0iX44B1mGbbF7+4+ffioiU6+IEfsEQK7343nDtnffYt33JwJ6PPf976P6hYCgXZ6JLrRmn3jr1S0YNHNkmn+vsZFCmAiYisd53vDsOOy/r3f7dEe4qyt8NMubwpvrL53/+7S6cTptVqAWPkclkkyYvf7+stx0BRFFqtNisrDbzeZWB2x7zvn7iXG8r/QUWrMtm5xLpvkouhG0kHa7Sx3LX9eouUukEtO46cThMccGDruhWvZv+2lQr83d8NFnCurW3+vfUYaPhSqKIPE5GAVqclx3oCLCiXPGSNNpKpYwoCOhJoCqYgYgqgiTvbk3U5QVLZIGJU0Uoly5Krh2nCX//14Mi2v/mbne8N4j3vgZe+1BJxnT49eJrWHpejYQKsQYKuhQVLtHrihOUwZqOVywif/SxSoYAai9FuDD5ZDdOkJfgpSwkirQ30z36W81/4Ar6RMer10/j9PmR5cz/+0i9ZAqdnPct6/Z//Cb/2a5YQZmwM/tt/gw9+cPi2NZvDXY3Acu17yUs2X+92LWk0hn+2lf/4j/5Iuj3q+ABLOPXhD1vCKLurQFG6dDpdDENH1/WeG71VbCdJIl6vj1zOxxveIHDiBNx6a/9x8wu/ADM7NY+HYrsAS9fhp37KErEBfOIT8Mxnwvd8z+73689+1jp+DsL8vCVoPWwN24MP7u2iuRMmYbUAAQAASURBVBf2NWWYAxZYwtz3vMe6prqG5y6uAMvlyLnjjjt4znOes++BJpcriz04cC27Kbi4uFwZ3Ouwi8uTH/c+f23xsz/7AH/3d5u9xe12G5/vHNVqlUwms8NZZyu2OGNqasoR8FxrRCIRJEkiGo2ytLTE5OQkwQEjGvV6nbW1NUZHR/F4PPj9/mtye/aDaZpOpN9+RVWqqqLrOpqmXfZ2x+Nxx21pYWGB8fFxR6Bnr4uu66ysrGAYBtPT047IanV1lUQiQblcplQqUSqV0HWdQCBANBpF13VKpRLBYBBFUahWqzz22GOcPHmS8LCRjesUURSZmppidHSUcrmM0us983q9JJNJ59zc6/cKh8OEw2EURUFVVQRBIJPJsLS0xPz8PPPz84yPj+841xVFYWVlBUmSCAaDxOPx6/accNk/6+vrFAoFAoEAoijuev0Ih8Pkcjmq1Srr6+to2s7IInCf711cnkq457vLE4l8661If/mXNP75nzH++Z/xzM8DUKr5uN/3bL40+grmE8+wRhgVg0cfNTGMnaON3a6OYexj5BnY2DDodEze9S7JEUN84QsmP/VTVjtXVaVDibC8XhNVtb5omuy5PlsHVztSEF2QEDEQDRUBD4MNZkxrYLzTxRBFajWJX/q5OAZ7r7DXK/fWzeyLeu92u3Q67Z57rYks++h2Q4RCAoZhomkqnU4HSVK5dKlCLqewuprasX0rK5YAq9mEl73M4M1vNh2BU7t9uH26G9mLn6GlNEnU63RmZhB2KDhMZx+agkhFThFX84hf+QrNhQW84+PkcuLA4+m+++DsWZPv/V7DEX4sLVmCiQsXBn9nN5aXDT7wAYEvfOHyR8o/+lH43u/V+ehHBR57bLNP4P3vhxe/WN81Imw7//7v1rZoptjbVwKCafQde6oqsJbbJvYxTQRMTAQ0pF37iC5e1HngAYE//uO9i79U1bKzefBBAcPYOr3ZO0Y7mCYEg35WVgTnM3rr7vX6ME14+OEqFy5MoqoKktTfXloPn+RDx36QV178DdSOl2x3hWxnmU47hA8I6E1MQWDDN06xO8H4d/wXZo4d23GvPH8e8vn+c+Df/31wBFy3u7ltX/3q5rXhQuIOWlKEmidOQlmnKcdQ1d45CoimweZxLCCqGl3BR0cM0hV3KkQMRATTQBJNuo0G3i0CrIceUvmLv9jfNXIYn/vcTmHcdqpVnWGPFboOjz02+Fqgqgaqam1puQzf//2SI+YSRfjRHzV43vOsz5f/9Qt4CgrJepPu8VnMAWIlQ9cxTBPTNCiKCU4qeYo1P7lPforWrc+g1ZpC01R8Pr9TTAbw8z8Pv/ALOmfOwO/9nkQvvZ2VFfiJn9g9Hq9WM+l2zW3H7iZf/KLJXXdtbnyhMPxasps71nZyOd1xbRKE/V1r/+iP4Ju+SUeSBJrNLorSpdtVnONcFAVM0zoWLQdEq58qFAry+OMCjz/ePz/DsM6Jo6BS2TxfwDpnlpf7j93777fETru5K95//8EF1e9+9+bfr3mNwTd/s8nqqqXvHVan+Pjj8LGPiXziE0ejhGo0rHOhVtv9OWJjAx56SGerqbXbd3/t8Za3vIX3vve9V3QZrgDL5cjx+XxPug7765m9YlJcXFxcXFxcrl/c+/y1xfa+ZZ/PSyDgR5Zl8vk8Y7t4yOfzeWRZJhQKkclkBk5jC2YajQa6riOKIuFwmFQqdVWOBVEUmZmZQdd1DMNgcXGRUChELBbD4/GgqirVapVWq0UmkyGRSDC73Sv+OkMQBLxeL4FAgHq9Tio1pLy/h+2uFAwG+zoMD0smk6FSqTA1NcXS0hIXLlwgFosRCoUwTZNWq+W4jU1PT5NOp5EkCUVRePjhh1lYWMA0TcrlMqZpkk6nnd+qWCzi8XgYHx/n9OnTNBoNyuUyS0tL3HDD4LiU6x1Zlsnulm2zT7xer+OWFQgEaDabmKbp/EbRaJRgMIhpmjSbTer1OoFAgMnJSbLZLNEtne4uT05M00TXdRRF2dUt0EaSJHw+H91uF0VR3IpZFxcXF5cjZXR0lPaw3JwBCIJgRXDfey/cey+mrvMvH1L5/T8JgiDQal3i//7L5v3tIx8ZPJ+Zmb1dlu666yI+X5aLF+HcObHPieZzn2sTjWYxTYaKB/biYx+Dn/5p+MxnPsP09M1Dp/v4x8dR1Vrfe/+CyS8ZihVJVvkkJoNv0ELZkpsIpsnvByZIr42xMjHNoMk//emn02ye2+famz0XlZ0zOnHiN0inX0O73aZQqLG83OLixc22yv33v4RS6bN93/nwh+GHf3jTYUpRhkeW2Zw+/XMcP/4jQz9/8ME3srb2d87rj5k6v2VqCBjwla9YW2HuvhwRg9c8IHHzz1a497+Nc/Hizu392tf+KwsLv8tHPmI5jHg81jwP6l41NnYvt976xwD84i/uFGRcuPBrPP74uw820x6BwOZ2plIv4I47PoJpwl/8hcjddxucPr257//8z/+ct73tbTvmoeub2yQAf2l0reOvnh94/E1LId4Xex4AIa2KiUDXE6QcsLIP8/mP8YUvvHrH94ads4OIxazCoJ/5mcW+9xVF7YkBNQzjPJ/73Mt3nc8XvmAOdQsG+AjwGxi8P3MvI8oIEbWEz7AUkRU5RUNOcN4T5zuL/4DwnZ/E872b37Xdhgxj8LVCEHYeg694xVeBSQD+/M83jwVN8vNg+m5eXvgHRAzM7iUoC878BczNmELdVpoomEoDU1nesew/880QF720uxJ1M87WHoU//VORT37yBN1ubuh+2S+33fZXjIy8YuBn998v8ra3PY+HHnpox2dbj7ntfOxj8KY3WfvPEqHBjTf+BtPT34lhwK/9msjKisHsLPzT/2dyz6LM9zTa/NtDD2OaD++5ziIGxsUq+m/+T0SPgKpax4cV3/4TnDljZURaTmEiP/IjBrnernr44e9lefnP91zGRz9qnXfDtvGxx97Mj/3YbzjnZrEobPv8XVy8+Ot7Lmc7k5OW8E+SrH2XybyM22//wNDp5+d/h0ce+QliMWtddR1HlDvovBn2WTz+DJ7znE8NXIZVvPdBvvrV7zzw9ggC/O3fbr5Opaa45ZZHdkxni69Kpc9w//0vPfBytiOKXl7ykk3l29/+reish98Pr33tY7z5zbc7n9vPC3vd2wZx992LeL2D7dLe+94Gr3nN+L6eRz7+8Z1Ojj/1Uz/F27fmUro8oVwNDYsrwHI5crrdLo1GwxVhXSPoPWm/O0Dr4uLi4uLy5MO9z1/biKJEIBBgfHychYUFgB1OWJqmkc/nqVarzMzMEIlECGzz1TZNk9XVVfL5PIqiUK/X0XXdifhbWVkhnU4zOTm5a4fmURCPx5mdnUUQBGKxGKVSibW1NUzTRBRFotEo2WyWYDDI3NzcvmL7riS2G5UkSU7km2EYmKa57/MmnU474qRms7nrNpVKJfx+q1rzKKIXBUFgbm4OQRCQZZlareY4WgH4/X6y2SzhcNhxxgqFQhiGwdjYGI9tyRIYHx93RHLVahVVVUmn00xNTXH8+HE6nQ6FQoF4PL7ndrpsIggCx48fR5IkPB4PjUaDSqVCPp8HrN9oamqKUCjEyMgIExMTT/Aa7w/DMKjVamiahiAIBAKBgY53LkfPlb6Ou7i4uLg89Wi32wcWYPW9liT+5VPylkwdE8PYe367RfVs54MfHOxO0unsf70HYQsyLl0y+ZmfGf78r+vtgdvUb5ozZER3y9tlIcFi9iUYwuBlGUZnX/uub/YDFmuauhN//rGPjSBJKuvrChDoLUcZuJzdnEkGL3t3hdP25RiAuvnhvpfT1UzOnRP5pV8afByYptq3nO1uRvvFNHdXBJqmduDfZxC6vrmcT3xC4BOfkJiZMfmFXzAIhaxn7f2ck3sdf4rZ28emSUrJUfUkaHjiXEg/t/e2cdnb025bpgvr6/3XBU3TnLaCKEr7Ws5eoggD+MNb/xdz1Ye4Mf9vRJUCABVvlq9kX8TjwXG0T3wAhgitDoLt7ATsEP3dP/U6uo9+v73WPRXhXnM0GTRRSKtSDRzjQuQWfv+dEf7X/9Lp1fJw8aKAYQy+9hwU0xx+UvzzPwusrnYOdB+Awafw9mvCX/2Vdc6eMP0YSHQx6ZgHcfkxwFBgy6lpiVza9KStADz0kNB33g+7xu1nG7aiKCrnz8OpU9YxVa32f7792nMQtgoC93vtGfQT7XbebP/MMHZe5FVVpdvtous6nU7r0Nuzdd263d1v8Edx7bEYflx3OlbE6EGP68Oy3+eRQfdZ5Upk/rocmsZ+80QvA1eA5XLkfP7zn+eRRx7hx37sx57oVXnKo+u60/E/MjLiDs66uDwF2Gq9bA80u7i4PDlx7/PXHpK0s8ppenqSdLqOx+NhZWWF+fl5wuFwzy5co9Fo4PF4mJubIx6Pc+rUqT6BlmmaXLhwgWKxSC6Xc6a3v18sFgmHwxiGgaZpnDhxwhHiXClGRkaIx+Pk83kKhYIjBATr3pNOp8lkMk/Yfch2Csvn805HjKqqNBoNRFEkFArh8XgQRZFkMkkmk9lVWDI2NkahUCCdTrO+vj4wetE0TYrFIs1m03GiOioBkyzLnD17lkqlwurqKvGex7rH48E0TdrtNrquO8eYrutMTU3RaDTw+/2sr6+j67rjmiYIAsFgkGw2y8zMDKdPn3ZctcrlMp1Oh1qt5izHZX+cOnWKRqNBPp93HMfAco5LpVJkMpkd4sprkU6nw8LCgnNuS5LkCBjt4yaZTD4pRUL1ep1yuUylUqHZbOLz+Ugmk8TjcWKx2IG3ORgMEg6H6XQ6u0bQwuYAVjgcdq5Rg3Cf711cnty47XmXa4ntx+Dy8mZbRxCOrr0hiuLQ9suwyKjD8Jd/KV/xdhJAS47x0PgrrviyBEFwijRaLS//8i9hgkEIhY52uZa4Zvg8j+qZUACagREa69KONvVRLgeu0vYI7FjO0hJ85CMS3/RNsLR0dP03gmmS7SziN9rkQrM8mP0GDE8AkZ3rcDl0Ov2/jb2rJElC14+ubSBIHhYyz2Uh89wdn4mdy3eKstF10bnObd9NHf/lF1PZmAjU5RQPjL2CalXkS18yePazrbaiz3d0v4+qqihKF1mWd0Q8AtTrR3VsD75mr0dvQBdlDDxskWEeGsvhTsPr9W1btv330V0T3v52mb//eytmcfumXW/XHug/7zudDp1OB0VR0DT1yCKuDWP368tRXnt2m9f2yNHLXc6wZV3u9rTbbURRdPvurxHe9773XfFluAIsFxcXFxcXFxcXFxeXI2BQVZrHI3HmzBkuXrxIIBCg0WhQr9fRNA2Px8Pk5CThcJhoNMqxY8d2DLovLS1RLBaZn5/H4/E4Tjo2rVaLjY0N5ufnHUHX6OioE50Xj8evSAPf5/MxOTnJ+Pg4nU4HwzCQJAm/3/+ECjNarRbnz5+n3W5TqVSoVquUSiUnpk8QBEzTJJvNEo/HqdVqFAoFUqkUMzMzA9fd4/E40YsACwsLBINBYrEYoiiiKAqVSgVN05icnCQejzM9PX3k2xaPxwmFQqiq1WkmSRKLi4tomsb6+nqfOE9VVbLZLLlcjmg0itfrJZPJIAgCHo+HTCZDKpViamrKicPzeDz4/X6nc87l4ITDYcLhMLOzs2i9jAOPx3NdiJU6nQ7z8/N8+ctfdly87E7CSCRCMpkknU5Tr9epVCocP378utiu/VAul1ldXWV5eZnl5WWnk9rr9eL3+xkdHXWudyMjI/uebyqVolarcenSJTqdzq6xpOVy2Ymh3Svq1MXFxcXF5WpzACOja5ZiEa5GCvS/T34rkm9whNGVQhTFnsuv9UOZptFXJHM9UPWm8fovPyr8Wuev/sqKRPvyl49mfpKpMdd4GAGDpcBJlkJnuH/6DUcz8200m9vfEXrRfofI+roGsCPprvSpkvdPUA/fwHz6WQB8/OOCI8A6Sq11t9uh2Wz1nJklfD5/n5D2Sl/Hm/4Mj8XvQK1+DvTLdyQyDANVVfsEWAeNHD0Iv/u7cOedV27+TwSK0qXTsZzPDEPH6/Xi8/n2/uI+OIjDpYvLUxFXgOXi4uLi4uLi4uLi4nIEDOt39Hg8nDp1ilarRT6fp9FoOM4y4XB4qAOTqqoUCgVWV1fxeDxMT0/vqLoKBoNMTEzw5S9/maWlJbLZrDOQ7/P58Hq9JJNJxsfHr4iTgiiK10wsWafT4fHHH3fcwmRZduIGu90uxWIRURTJZrO0Wi18Ph+NRgOfz+dEFQ4TlSST1iCKKIrE43HK5TKFQgHDMPB4PCQSCWKxGOFwmBMnTlxR1wpZlpFlmZWVFWq1GvPz88iyvEOcV6/XKRaLAMRiMUZHR0mn0/h8PhKJBKFQ6EkjoLnWsN0IrheKxSKPP/44jzzyCEtLS+RyOfx+P+FwmEQi4cRfKopCtVrFMAxEUeTYsWNP9KpfNrlcjqWlJb72ta+xtLREtVql2+06A0m2s9zKygpPe9rTaLVaTgzrXqTTaXK5HJFIhOXlZWZmZgYeF7VajWKxyNjYGOFw+Jq5prq4uLi4PLmxB2FtJ6WtbH+9faDVckW6Os6el7uczVqU3e/dkhTAMIY7g4imjmTqWH42Wxt+1itTENAFmVLkJJldl+Pf5zZtRm8NYjcXsmazhSDICIK/51K0/fe1otJMU9h1Gda0uw8hCoJ3x/ZIpoaEjmAavb01eBn2fjQRKIZPMLbrcuQjOeZE0bvr54LgOaLlDBc5WGny0r6XIwCiqSGaem9Pbh6DAQyK3hFq3hRL4Rv427M/i+LZ6sQsXvb2+HwQCAR2CLAsp1wP3a6CaZp7Lsc0zSNpfx7dtcdal499bO/lWGeKsdnpYynPkEzN+S0ETEyEvuPaRGA9OMeXzr4LsxdLGg5vLsPrta49h98m01klTTN7BTRWW1TTdILBgCNgkiQ/khTYMwZyL3a7Jvzn+KsQl34Xv755pA6cBwb09o8myJg7rmcmguDZIe7bKsAadO0Zvs7D++tE0WqfffSj1r+d332irj273wP62Zx289pj0ul06Xa7GIZBMBjsOYgNusbZO+cg5+fO4qL77oO//mv71eVfe2Dv/QYCHk/gqgjF97s9Xq91zKnqplNgseijWoXk1dVnuzyBCOb1Kk92uSb59V//der1OpFIxI0gvAbQdZ319XXAjSZycXmq4EYWuLg8dXDv89cef/RH8A//0P/eD/0Q3H334ea3trbGpUuXuHjxIseOHRvontLpdFhaWqJSqfDYY48RiUTIZDKOYCgcDpNKpYjFYpw8eXJXB5brnUcffZRcLsfy8jKjo6P4/X7m5+dZX19HURSy2Sy6rrOxsUE8HiedTjM7O8vq6iqqqjI7O8uxY8fIZodXXiuK4kQvalt6/0KhEJlMhkQicUXjRuz7fLfb5bHHHmN+fh5RFJmamhrYmf3YY4/xla98BcMwuPnmmzlx4sTQaDNN0zh//jyTk5McO3bsirh4uVx7lMtlLl68yBe/+EUWFhZotVqOM5qmadTrdTqdDslkkmg06sQRHjt2jDNnzhDeOoJwnVEqlbh48SIPPvgguVyOTqdDMBgkmUwSCoXodrtOJKHP5yMWi/HsZz+byclJJiYm9rUMW+C1vLxMq9UiHo87+1FRFMrlMs1mk2w2SyaT4fTp0wSDQf7lX+C7v/s70TRrxFuSJObmvHzqU39wJXeJi4vLE4zbnne5EqiqSjQapbNFReX1evmjP/ojPB4P8XjcKSYY9qxYKMBb3nI11/roeMlL4Gtfs+LfLhdJ73Iy/2luKHyakFbBQKLkH+ehkZewGn/a5kjrPrELRcASxNnP841Gg2azQavVxuuV+4QDtrjCukYIgOlEJweDQWTZS7PZpNlsOi44g5dt0Gq1kWWZQMBPJHJ09mDBToHveOiHeXb6y2iPbdCWQpS9GdpSGAGTsFohqW4gmCaLwVM8kngOf3fTzzkCFZfBjFUe5va1D3FD+T8QzU3bpqXQab40+nIez96JIR79veNDH7L+v+8+aLU23zcMg3q9RqvVRhDA7x8uTlBVlW63SzgcIhQKIct7iSquDh/60M7tOgizhf/HC5f+lNHWJQJaDdlQMASRthSh7Ynw5fRdfObYW/pEcYZhkExCJGIeIorSpNFooijdnthKwuv1Ov2BpmmiaSqKoiBJlsO1tb+v3jPFMxf/irsW/4TRzjwxtUhXDFATw5iGSdioEzC7VKQEa74pPjbyLXx67HU75tHptBEEkVAoSDgccd5/z3vgR3/0qm3KE0Kj0aDVaqLrBoHA3oIfq3jIElhFozHnfVVVaDabNBpN/H7/0H4gsO4HzWaLQCBAMBjc13IH8a53wX//78PFbk8V3v1uOHUKvvVb++/zsZiXD3xAPOijgssV4GpoWVwHLBcXFxcXFxcXFxcXlyPga1/7JPm8VXZlGDq6blIsPoNcTiMUChGJRPaYQz/VapVqtUogEBgonNI0jeXlZarVKoVCAZ/PRzgcZnR0lOPHj9NqtSiVSiwsLDA+Pg7ADTfcsGvHy5VG13U6nQ6maTqRd0eBPciwvr5OOp0mkUiwsrJCs9mk3W4zMTHhdDqmUimKxSKRSMQRm1y6dIlSqUQoFNpVgOX1epmYmGB8fBxVVR0HrKu9T/P5vGMlPzc3N7SSOJFIMDk5ycMPP+zEyqXT6YHTVioVPB6PI9pzefJjGAYLCwssLy+Tz+cJBAJks9m+3z8cDtNsNsnn84iiSCKRQFEUGo0GGxsb17QAyzAMx61u63UnkUiQTqdZXV0ll8uRz+eRZRmv19snaPb7/YyNjeHxeGg2m3Q6HS5evIgsy4yMjOzrvB8dHcUwDARBoF6vUyqVKJfLgOUeYsfPhkIhTpw44bhfCQLccssf9A3Kvva1bk+ti4uLi8vBMAyDc+fO7XAREQSBubk5ms2mc2+yxfenTp3aUVBwWHHCtUChcDTiKwBd8vHo6Dfw6Og3HM0Mh2KiKJZgRdMsYaZp0osTl9B1HVVV8fsDTtSgx2M9y3S7Su875q5RU4Ig4vV6UZQuXq8XTVPxeI5GpNHyp3n/jT/PXPe/Ug2lSCjrjLUXELHWVRO8lOU0VV+Wc7Hb+NAN/80VX+2DtfjN/FP8Zj6mNoh2coimRsubpHGFoxsXF2FkBNrbUuVEUUSWvfh8Bu12i263g9fr29E2tcVXfr8PUZSuOYHx5Vzf5tPPZj71LEZqj3Cy+DmCWg1NkCkEZ3h05O5tbmSblEpQKgkctHZLVTU0TaXd7uDxeHYILC0nZi+S5KHdbqGqCt3u1d3n/zn9ehQpyN2L76Og1YkrBXxaE9M0qYsRlr3HaXkifHTk9dyf/aYBczDRNJ1AQEYU+68LVzKC8FpBksResY66L9c4TdPwer079pWqqmia1rtv7OVkKOLxSGiaiqqqhxZgRSLWv1rtUF9/0qDrcPHizvePHTuwTtvlOsYVYLm4uLi4uLi4uLi4uBwBv/Vb34SqbvZKimKA1dX/4Ny5GoFAwBE3ZDK7hVJsous6uq7j9Q6uDi2VSjQaDfL5PIlEgkgkgqZpTid8MBgkGAxSLBZZXV3F5/OxsbHhiLGuJs1mk42NDcrlct8AkN/vJ5PJkEqlLsvFrVAo0Gg00DSNRCLhOPfU63XC4XBfh2M4HKZSqTiCpFgsRiqVIp/Pk8lknCqo3RAEYejvcjWoVqtUKhWCweCu65FIJByx2cLCgiM82U6z2aRQKDA6OkowGOyLMnR58lIsFul2uywvLxOPxymVSkSjO90PQqEQqqpSLpcJh8P4/X7K5TLRaNSJI7zWqFarzM/P0+l0qFartNttTNNElmWq1Srnz5+nXq+zsLBAMBikVqsxPj4+8DqUSqVotayK4NXVVWZnZ53zZT+Mj48TjUbZ2NggFouh6zqGYThuYul0mkwm84ReU1xcXFxcnpxsbGxQrVZ3CLDAKizwer3E43FyuRyLi4t4PB4KhcKOgoQnSoBlmiamaUVVieLecXmDOCrx1dVCUbpomuZEpOu6RjAYRBQlTNPofab1RFPW4LrfH0CSREc0rqpaT0y1+/6SZQ/dbhdd17Z852gohWb561t/n0j777hx+WOE1QqSqQICmuhlPTDNl0ZezlfGX4axR8yhSz+KHKYgn7hqy/v+77citQa52gQCtggwSKfT7jmveRBFsefGpPG0p61SKo1RqXgJhYIc5jy+phEE1mNnWY+dveKLsq8PewksRdESWKqqiix70XUNSbp659mXJ76ZR7N3cWPuo9xc+CSRzjq6rlMkyoOJF/JQ9hvoyINd9xRFRRAEJMmzo32kDk+JPTAez7Up6LKFtKIooCjKrr+zpqm9Nq4Hn69/XxmGiWGY+26ri6Il7rXuuYcjFHIFWGAdV+fP73x/bu7qr4vLE4f7ZOPi4uLi4uLi4uLi4nIFME2TfH6DhYV1J97DdmqamZnZs5JNEAQEQXAEVdvnXalUqNfr+P1+YrEYGxsbiKK4Y76pVIpGo+GIJ8bGxvZc9lGyurrK2toa9XqdcrlMq9VynGhisRj1ep2NjQ1OnDhxaEesTqdDq9UiGAzi8XicZXQ6nR0DSIIgEAwG6XQ6jrtMJBJhbW2NbrdLq9U6sFvZ1UbXdTRN27UzDnDEHdlslmKxyMLCApFIhGg0isdjDbhUKhVqtZrjHDY1NXWVtsLliaZYLFKpVBAEAVEUHUHQICKRCJVKhVarhSTZnbMmqqrueRxebSqVChcvXnTiQm1nN1EU6XQ6XLp0iWazia7rLC0tMTIygs/nG7odoigSCoVQFAXDMKhUKlQqlX0LsMASfobDYVRVpd1uO+551oDqtSdgc3FxcXG5/rHaInlKpdKu0wmCwOjoKK1Wy2kvPNECLFW1IrS2xnJaBRAyXq+vL25L17WewExAkqQd99WNjau55peHYRi022263S4ej9QrsFFRVQ1JstpPtqii2WwgSVKvIEMmEAj2BGtWnNT+xBbWM6BhmJc16D6ML55PkL3trfzfsTcyWXmQULeEIXqoBMZZj5xxrUAGYBgGiqI4wgpLiGLHzD1xw7mKMvh9QRAIh8O0Wk1EUXTEfLquIwjg83l54xsl0ukyv/ALo7Ra186QtCAMdqm5VrHaXpY4M5mEbncvgaVMt6ug6xqKohIIXN1935UjfGnqXr40da/lflWv0el0URSFgBhgUPmdHZ/o91vX+e3OTT/3c0e3ft/3ffBbv3V08zsqJMnTczfz0W53erGzOwt1NE2j0+ni83n7nOXsNrqidFFVq/0KfvYWPtoKy8Nfl0MhuIYNsq8af/7ng8Xfx48/xbMZn2JcO3c7FxcXFxcXFxcXFxeX65RisTgw2mN6eppTp2LUajWKxSL1et0RH01OTu46z2AwSDgcZnl5GU3T+jqfrMpmlUajQSaTQdd1Wq0WmUxmoIgpkUiwurpKNpul0WhcNYHR2toaKysrLC8v02q1iMViTE5OIooi3W6XcrlMqVRiYmICwzA4c+bMoVxgDMPoc+IxDKM3AGEOdLSxBWj2b2ZPY3/vWkcUxV4H905x3nYymQzxeByPx0M0GqXZbFIsFoHNDvPp6WkikQjHjx+/piPlXI6WTqdDp9NxBIm7IUkSfr+fbreLz+dzrjNXU8y5H1RV5dKlS6yvr1MqlRgfHycSifStp6IoPPDAA6yurtJut6nVasTj8V3n6/P56HQ6zj7QDlkuLcvyNRe74uLi4uLy5KTRaDhOkPsp/EgmkxQKBdLpNM1ms88RdXv02JXCNE1araYTnWRFfpsIgvX8q2mWM4jXKyMIQi+iyejF89kRfTI+n+8JjV0/LPZguapqhEJhTNOg0xGRJKv4wi4eAWuQ3moTSPj9ATwez5Z2jHCANo3Z00Ed/TOdacL6OiDKLCafeeTzf3Jh0m63URQFXTccARZYsZPdrnKk4v2JCVhZufy1Buu8C4XC6LpGt6sgy5rjXHfrrSq33nqMeDxOJiOwsHA0yzwKTBN+/uef6LXYP/Y5bZomiYRBLrfXNyznQLtf5Ch59rPhS18Cv3+425HXuyncs4vgrPWwjnWr+MeDIAgYhommqei6gc/nQ5a9BIM7Y/D2aLIeiGi0fx2vJYLBAIahY5pWm11VrYhY+/dUVQ3DMBwny2AwiGGYdLttFEV1xLzNZpNOp0u73XbujV6vF1n2sP2ab/U5ykjS4a8vwaDlgPVUZ5jzpuuA9dTi+nsKdXFxcXFxcXFxcXFxuYYwTZPl5eWBnwmCJVxIJBJEo1EWFxdZXV3F4/GQzWZ3FRtlMhmKxSIej4dSqdRXiW5HDZqmZT1fr9cRRZFgMEgikdgxr0Ag0KsQ1/sqya8knU6H1dVVVlZWUFWVubm5PuGBva7FYpGVlRUkSWJ5eZnjx48feFm2qKHRaADg8XgcV59Btu2KoiDLsiO8sgczPB7PZUUhXi0CgQChUIhcLrdnBJymaQQCAcbGxgiFQk5U5dYItGQyycjIyKEdyFyuX+xYPjvyVFGUodclQbA6fNvtNvF4vDfIeW11K+XzeVqtFsVikenp6YFxml6vl4mJCdbW1mg0Gvh8vj2Fh4ZhOI6EkiRdc8IzFxcXFxeX7VguOtYz32//9m8jCALdbpd6vY6iKDz++ON97YdgMOi0MRRF6buHXg0HLNM0aTabKIpCp9PuFa3IyLIEWFFmbUcJZiJJHgQBNG2zIEGSRGRZRlVV/H7/FXm2fe1r4YMfHPxZMHh5+0pRFBRFRZLEXptEIhiUUFUFVd10/bKfyazt9DnuVVtdTTVNG+iashWriMUqWHmi20DPfS48+ijsYdj2JGXrsd/tPW+KvRg/g3ZbRRQFfD4fhmEQDocQxcP/Xk9/OjzjGfAHf3CEm4AlCgwGN9sGL3gB/MAPWCIdgGSSa0qABVAoPNFrcDjC4f2phmzd1X6aLjfeCC96Ebz3vbtP5/fD29++Od+f/El45JGd0/3hH1oOhMmkNU0+LxMMhrBEhTKqqtDtKpimiShakYN+v7/n7Be64o5vkgRjY0d3TB4/vumoZguIres1vX4X777bkKIoEQ6HaTSaveu55YRouVRZokxZ9iOKEqFQCEEQaDQaqKqKqiq0WpaYs9vtOK5jPp+XbrfT27cSfr/PiZ3VdQ3DsPoFDlMQaeP1PvECrJERy4XrwoUndj22Ewwa7FGD6/Ik49rqKXNxcXFxcXFxcXFxcbnOKJfLdLvdgVWFW/tXJElifHycixcv0ul0KBQKjI+PD51vKBQiFAoxOjrK8vIyoiiSSqWcaEK7492OIsxms/j9/oGCA8t2fDPW8GpgCyEajQbHjx8f6vqSSqXodrvk83lCoRCqqh7YISYejxOLxcjn8zQaDUKhELIsEw6HqdfrfY5fdgRYIpFw3i+XywQCAbxeL7FY7PAbfZXIZDI0Gg3H5SedTg+dtlQq4fF4GB0d5cyZM05Eox2BFg6Hn/ABF5cnBlu46PFYnd0+n8+Jo9yOaZp0u128Xi+dTod4PE4ikbim4vNM06RQKFCpVAgGgwOvhTZ+v59MJsPFixfRNI1KpUImkxm6Pc1m03HBCoVCBAI7K7JdXFxcXFyuJba6npw5c4ZcLue4jjSbTVZXV52Y3Wq16giyB3E1HLC6XSsuyV5Hv9/HVocOj0dG01QqlQqapiOKIuFwiEDA3xOqWK4gnU4Xj8caqBYE4cijknd5vODbvx1+7/f2N583vxm+/GWTz33OfsdE1w0MQ+8THoiiiM/nx+ezPrcH4CVJpNVq94psNuMDvV4vmqbRaqmOcHwYitJ1Yhu93ifOodPrhbe9Dd7xjqemAKvd7qAo1rEvCOIOlyvTNHufW/Y/zaZAJBLmMK5lP/3TcNtt8G//dlRrP5y3vnVTfAUwoE7M5QCIotATKkl4vXVgeB8A0HNQMnvn+N7t/WwW7r7bEkP+9V/DJz8J1erO6ebm+vu5hplyx+PWP4D3vAc+8QlotWTC4SD/+Z9tvvY1k2Jx8wCxrnWWo5MgXPk2ZjwO+6lN/M7vtMRkgzAMy53wVa9qc8cdGj/xEyFHyLwZj2s7Fnbwer34/f599cmJohVDa8cJynK/2NjrtdysTNOk0WigKN0+AafH4+G22z7B5z53tyN2s5zGagSDQcDE7wdBEOl0Oni9MqIo7inc3Ytw2IrBVVXVuTdZ85WvSt/BzTdbIqxrTYD1ild0kKSjfR5xubZxBVguLi4uLi4uLi4uLi6XQblcpjbEd317v4rP5yMYDFKpVJx4rN04duyYI+5aXV2lXC4TjUbRNI1qtUqhUKDVajE7O0skEmFiYmLgfBqNhiO0OOpBiEGYpkmxWKRcLhOJRPZcZjKZ5NKlS3Q6HYrFIqOjowdaXjKZZHl5mVgsRi6XY3Z2lng8TrPZpF6vOyIswzAoFAr4fD58Ph+JRIJ2u02lUmFsbIx4PH5ZFX9Xi2g0SiAQYGRkxBk8SyQSfR159m9QKpWYnJx0BH3AFT0G7AECW+Dlxq1duyQSCSqVCuVyGa/XSzweZ319HZ/PtyOmtNVqoWkazWaTkZERIpFInyvftYCiKKiqSr1eJ5PJ7DqtLSCLxWJ0Oh1kWaZerw8UYLbbbUd8Fo/HiUaju4oeXVxcXFxcrgWsmCEZwzB49NFHMU2TfD6PrusEAlZknaZpbGxsOFHDqqoOjAS/8g5YpuP+JIrCUOcqRVGQJIlWq+24ANliJUEAn0/C65Vpt9t0Ol0EQejFFR7doO+wR1u/H77hG/YnwLrxRnjVq+AVrzD5i79o8hd/EXKcauw4xZ0IO8RUgrDpcGPj9XrpdrvIskyn08bvDwwUYVmRyjrBYLDnzPLEiep/8zct15TdxG1XgslJK35sY+PKzN8SBSpbRAhCT4Qg9U1jOdVYx+sgkb8tJBQEgU6ngyRJqKrWa9/DQZKxn/1seutyWZu2L7YvI5m88st8ciP0BJY6ExNrfOUrM7sKWhRFweOxxFe2wFKWrei9YnHn9HbTLhCwBKIvexl813ftnO4wMWqxGLz61fYrmXvukbl0yeQHfsBwXP0ux9XtoMzOWo5V+xFgzc7ufM+OhLVcrgzOnPkqS0tlXvhCgQ9+8GkAPeGbHQHZxeORHGGW5Vq190lo3cO8jtDKdtTa+t1Op4OmWeJjW4zc7XYAgbNnJ/nyl4NompdOp4umqXi9PjqdNrqu9yJD7WhCq6/ysAWb3/ItVrHj0lKdWk3sOSzahaBiTxQm90TTV+63ftrT2Ec859Xlx3+8xk03acC1X+zpcnS4AiwXFxcXFxcXFxcXF5fLwKpIUwZ2VAjCTlesQCDgdNbshc/n4/Tp01y4cMGpTq/X6051+ujoKKZpEo/HmZqaGiisMQyDcrlMPB4nGAz2qt2uLHbVX7vd3pdQwe/3I8uyI3Q4KKIoMjY2hqqqLC4uMj8/Tzwex+/3k0qlyOfzVCoVJ65vfHycSCRCvV4nn88Tj8eJx+MHFn49kRw/ftyxtc/lcpRKJaLRKLIsoygKtVoNwzCYmJggHo9z7NixI1luo9Egn8/TbrcdkVUsFiMajVKpVCgUCmhbRgECgQCZTIZUKnVNuSU9Uei6TqVSQVGs2Ahb1HPULmTtdrvP6SwSieyIC0yn0+RyOUKhEJ1Oh2AwSDqdplgs0mw2HfFkt9vl0qVLmKZJIBDghhtuIJ1O7+ow9URgd/Da27wb9nGbzWZZWVnBMAxKpRKiKPaJz9rtNhsbG4iiSLPZ5OzZswQCAaLR6BXdFoBz534RVbXjUSU+9CGZN77xp6/4cl1cXFxcnhzY9/FOp8P6+jqCIBCJRHY8d9jPJhcuXGB6epp8vrDjHn8lBFiJBJTL1t+qqvWcRFR8vsHiK13XMQwDTdN6sXtm75nTcoSyEQQRv99Pq2U9q1rRS0cTRfiiF1luTYO47bbhn23lTW+yxFfWusJNN6m9v4WeCEFA142hQq9NTAzDQJZFRHFz+61YyYDjvGI5ilkD34IAum6gadYyA4EAsuwhENjf/rGFEkeNva1X8tFychKWlzdf33MPfMd3wP/8n5Yzz1FimgadTgdFUXsiBKvtLghizwnGjlrz9ApXdDRN39Nh1eu1Yts0TUNRFJ75TJnv+i7LPezg63iYLRvMICEg7BRguQ5Yl4/Xa7XNbrlF51//tYphxAa2sbcKLLeKUF/zGnj44d0FWDbDmu4nTvS//vZvh3e+s/+9H/iBvbclHr+6oqvnPc9yvYrFLHGZKO4tXpSknftFUbq02+1e3J/l8vToo19jfX2dxUUPrdZcz13ai99vndN2tG+73QKCQItwOHyg9R/kZm8LOFXVEttZ90gV0wS/30c06nX6CINBiXa7jaZpSJKErncIBgO965GvJ8Y9fPHcK1/Z4ZFHHsc0vbTbMTRNd8TEpmm7dlkOjaFQaM/2+mE5fRq+8pUrMutD8cxnmj3xlctTDVeA5eLi4uLi4uLi4uLichlsxgHub3q7M3y/ndd+v5+zZ89SrVbZ2Nig0WhgmiaJRIJisUi9Xsfv9w90bjIMg5WVFcByfNnLFeZaYFCU434YGRlxRC2FQoFSqUSn06FWq6EoCsViEY/HQyqVol6vI0kSnU6HbDZLKpVidnb2mhOU7EYgEOD06dOcP3+eSCTiRFEahoEkSaTTaaLRKMFgkBMnThzY9arRaFCtVtE0zTnGK5UK3W6Xer3eJ+4RBIFisdgbxJFpNpt94qx6vc76+jonTpwY6mjwZEdVVdbW1igWiz2Hh00Bls/nI5lMMjY2dtmOYeVymfX1dZrN5g4nsmQyycjIiDO44/V6GRkZQdM0FhcXUVUVQRAYGRmh2WySy+VoNptOrOfs7CynT59mZGSE6enpy94nR409mCxJkrN/dyOdTuP3+zlz5gyFQoF2u82lS5cIBAKOULbVajnxTDfeeCOZTObIxIx7ce7cL2AYm5lP8/MBwBVgubi4uLjsD0EQnFjujY0N0uk0qVRqx3SSJGGaJj6fjy9/eZoPfeh2/uiPdL71WyXuvdcSV1yJCMIXvQg++EHrb8sVxMA0GTooq2lWpJ5pWiIE+15vGGafAAnoOWOJKIqKx3N0AqxXvxoef3zwZ896lvX/S14CH/3o8HnceWe/w1Ukstn28XplNE2m3e7sKXayCx4GOc7KspdQyFqOLMs9oYC1v2w3JY/Hg8fjIRjczYnFinVUFMUZ1LeWKeH1+pBl+UgEWXYzdi89wm23wZe+dPD5f93XWcKT3/kdS3jyrGfBt32bJa7YLX3+534Ofu3XBsewDcMwDJrNRm+/dXeIEOy4R1sco2laL1Jzp8PZToReFKeGpml4PDA1tf9128pBXLOGMT0NN9xgiVl+6Id2fn4QB6wTJ+D8+ctfJxtbmKJpmuMaJEkevF7vNVMUJEkHF8KJosh73mNgmmNMTio8+mgLSZKQZQ8gYBg6qmoJU22B5db29733wmOPDZ73dqHRsMNxuwPWTTfByZNw7pz1emzMijHci93qWfa6jh6Gl73MiqfbimEMntYmFusXhqqqSqvVptvtOC50Pp+feDzeK2iqI0kedN36HQShi8/nQ5KkXvuyQ7vdRhRFNE3lhhtkZ78dBuv4Npx1MQzr2mC5Ggp4vRqiKOH3B9A0S+isKAperxdJsgShdrHW5YjhfvAHdRYWzlEsFonF6sAdBIMBZ56GYUUSttsdfD7roI9Ewvj9Evtoth+IVOracsC6+eYjVLu6XFe4AiwXFxcXFxcXFxcXF5fLwOfz9QQN+2tYNxoNYrHYgYQogiA4Lk1gdWAIgsD58+fJ5/MsLS1x4cIFx+UKoNlsUqlUEEWR6elpksnkwEGXK4EkSY5deqfT2XN6uxNZluXLEqBMTU054olMJkOz2WR8fJxCodAbsLE6IhOJBD6fj1AoRDAYZHJy8qo42hw1gUCAG2+8kUqlQj6fp9FoOJ9FIhEymQzxePxAAyOVSoXV1VVarRbNZtOpsM7n871KbsVxvJIkifX1dZaWluh0Oni9XrLZLCdPnkSWZbrdruOKNT4+jmEYA2Ntnux0Oh0ef/xxarUaxWLREVGCdW6Hw2GazSbVapWTJ08eSqRmmiaLi4tsbGxQLpf7XLYAwuEw9XqdcrnM8ePHnai9iYkJR2RXKpXI5/OUSiVaPauLWCzG1NSU46J24403krhGy9dtMVs0GqVarZLcI+ek2WwyOTmJz+fjxhtvZGFhgVKpRK1Wo1ar4fV6CYfDJBIJTp06xcjICCdOnLgqLoIuLi4uLi5HgS2it58HV1dXiUajzrNGu92mVquhaRqp1C18+MO3kUh4aDS6/NmfBbn5Zjhz5mgcsN77XnjwQVhbg6c/3RJv2AIsi93bUoZhOm62WwVXw4o37EFww9j5+WGEPM95juWi9NWvDv78Gc+w/n/ta3cXDmwXNYRCJtmsTrVqxwcqiKJIp9PpOVPtfI43DKMXM+h13K22I8teIhGPI/w3tigNZNnTE1B5Bs7fXkar1XTaaaqqOaIwj8eKwJMkiVAo6MRAHhZbD7PXI9ZLX2oJ4LY0efbkmc+Et77Vcl/61V/d+fmwJuDLXmZFWX3v98Iv//L+lmWaZq8IQqXdbuPxWKILW1hlmgaKotLpdJ3j0nKPMvcdASkIAoZhRZGl0/3ubwfhco2f77wTfvRHrb+HCdS265yGCeze8Q5rX7/udZe3Tl4vvOMdJp/5TIm/+RtPz1lM610jBDweT6/NKvPDP+zn/vslvvzly1vmQdnaLH/Zyww+/OH9i8Huuw9e/nKIxZLk8zrPfW6VxcUgqmqd45bAT+jFz+4UWH7TN1n7aFh3y34dsCYm+l9LknWOfOYzlrDv+c+3Iln3QpKs6QZ1F91zD3z2swc71/diUA3NXgKseLxfgNXptB1hXzAYRBRFdN3qL2k2mxw/nnUKeexY0a0OZD6fD01roqoq3/d9S7z85bO88Y0itdrOZb/ylfDhD0PP/H4gpmn03eMMw+i5TVnXHK9X46abFvnSl0bRdW3Ldca+5ljnxWGLIG1GRwvU6w1WV1e5+eYMH/pQ/wEgiqIjROt0OoiiSLfb5Wd+Jsijj8Lf/M1lLd5Bkqxj/OxZeOih/s+OH4eLF49mOQfhppuu/jJdrg1cAZaLi4uLi4uLi4uLi8tlkE6nKRQKAz/brnup1+uoqko8Hr8sMZRdtTk3N4cgCMiyTLVapVKpUOz5yft8PrLZLNFolEQiwfHjx69IZMSw9YvFYsTjcdbW1shkMrtW9FYqFWRZJhQKXba4I51Ok06nqdVqTlyjXdlni4nAqgZPJBIHtn6/1hBFkWQySTKZxDRNxwHrMGxsbLC0tESxWKRcLjv29IuLi5TLZTqdDtPT08iy7MQ1VqtV/H4/xWIRWZadSm5736ZSKYrFIqurq4iiyMLCAidPnjzKXXBNo2ka586dc/ZBOBxmamrKcVtrNpuUSiXm5+eZmJhAEATOnDlzYEv+lZUVcrkcS0tLzv63RXK2EG55edk5Tk6dOuUc+zMzM4RCIUKhEKlUik6n0xtoU5EkiUgkwvT0NCMjI0e+f46aTCZDo9GgVCpRqVQc0ep2NE2jWCySyWSYnJxkYmKCmZkZlpeXKZfLKIqCx+MhkUgwMjLC+Pg4qVTqyKMiXVxcXFxcriTtdpt4PE42myUcDjticFuMI4oi4XCYWCzGRz96a+85zopDA0sg9c537l+A9ZznwH/8x873f/d3rUH7rQPgq6ubf1tCMat9Y5rGrmIUO0bPbtdsd7/ayc6B5Re96OACLLvJMCxd3U4w3i7osQbEN/f3dvcdQYDXv77F+94XRFU9eL0ypunvxUm38XplR2BlmqbjZmW7+dgRV4MQRSuO0e/3Y5rWwPygGKvtGIZBo9FAVRWnmMbjkRFFsee2otLtKvh8Pu6+u8TKygSrq4crohkd3dy3uz1mvfrVlnPVq14Ff/Zn+5//9mi07Qx5VOR7v9f6/3nPs0SIjz66+VksNlh0ZMcDdjrtnjNOvwOxIIiO+5jtgmMJIQTnGBnGbbc1+NKXwo4IThAEXv3qg7XttwqcnvY061ge0o3h8Fu/NThObmsTelhy4nYBz/j4zmlSKet3PQoXnJe+1CQSucCtt5b4sz+bcpzFRFHEMCxHLEkS+cZvrHLsmMlLXnKK17xmeJvL44Hv+i4ruvIf//Hy1+87vsPa1gceMMhk4LbbTD784f1//1u/dfPvTCbDm98c5UMfMlAUqU+E4/F48Pm8vcK2zWPE/p2GCbC2X9tiMev32RpXeMcdg4VZHo8lyjsojYYV42cYlkuZKEqEw17Gx0Xe8hb4zd88+DyHMajbZzdxE1jXc3t7LRdGA1W1rn32tVzTdMcdPJHw91wGvbTbnZ7jlIrP53Ou35YIs8MjjzzC2FiJUOg4xWJkhyD2rrus4+Ud79jvFpqOo5p9jVfVGtnsFzDNO1EU0WnjW8uxpmk2W/j9AQKBw8pFTExzg1KphN/vJ51O8ZrX5Pnbv93pvm85L0qoqsKrXlXm9tuP8fjjR9e+th0N77wT/vqv+z/7L/8FPvAB+Pzn6fULdvuExZIkDTxvLodIxHru2dg4ktm5XGe4AiwXFxcXFxcXFxcXF5fLIBwOEwwGB3Zkb32r3W6zurpKMpnE5/MdiRuVKIrMzc05DkjbxUvRaNRxQbraZLNZyuUyGxsbrK2tOeKS7bRaLQqFguMCVCqVqFarRKPRy4oEjEaj16Wr1eVgdxwdhkqlwtLSEsvLy7RaLVKpFPF4nHq97sQOxmIxxxlreXmZcDiMqlodiDfccAOVSoVSqYTX6yWVSjmdkqlUClVVyefzhMNhut3ugSMRr1fW19ep1Wqsrq6SyWR2nPe28KlQKLCysoLX6yWfzzM2NrbvZXQ6HdbX15240WPHjvUJuILBIMFgkFgsxtLSEl6vl6WlJW644QZnGlu42Gg0nAhJW3wVGDaqcg2STqdZX19ndHSUtbU1VFUlkUg4+8N2Jsjlcs5xOjY25jgMnj59uhfPYDjn00HFcC4uLi4uLtcKtmPUzMyM49qbSCTQe6POHo8l4onH4xSLY0iSiiVYskZxH3nEms9+BFiCYIkV7rsPfvInN11NnvWsnY4p25FlS9xjxwYOek60RVqGofUilq3vDBNr6boVvbRd8HTrrdY6xeNQqey9XTa2gevZs9bfWwUjthOQtS1gRfdZIiVtW9bbxYtFjh3L9LVTnv50lT/4A4P1dYnx8QCvfjWAgKIodLtdOp1uzykJTp4ss7g42it8CAx0D7abhOXy5nuCIO4oDhqGFTfepN1u94Reci+uSkIUZbxeH5pmOTkFAiU2NqJAgt0GrX/zN+FnfmbnPn/zmzfbzLs9cr7pTdb/r3oVfOpTsLS0v23Zi/08cr/pTfCud0G3a73+1m+1RIXbURQFVVV55Ssf4xOfuH3o/KzIOBlVtQT/oij1HN70oTFgt91Wp9US+eIXDXw+Hy9/eZvRUUttcMMNm+fqMPx++Pqv33zt8cDb3ga/8Ru7RywOq1Xa2u0wTNCzXaiTyVjnz9e+tvmeLQq7XIPkt78dJidXWF0tsro6z+RklPX1/iIwwzB461s/j89XolKZYX7+EjC4MCiRsCIop6ct1769BFjPeY417XOfOziO8Yd/2Nr/ggAjI0cTSRYK+bjlFnjsMX9PgGX2zvPB5+HZs9b/qjp4ftt/R0Gwjv33vtdytkokrOjOo6BcLrOyskKjMenE6IHV9hodrXDxYpOv+7pp/umf5H27FkWj1vp+7GP9gkmwXA8HsZfxky3o+aEfgl/6JbUv+nVzHga6riPL1nUSrOutJEm9gjYNSRJ7zncGmqaiKApra6tcvNjGNGM0m4LjWGbfs4JBy7XpV34FPv1p6xx52tNgZAS+7/usZdv3uE1hsr1OJpKkUCzmEYQqz3rWn/B//++bHad6WbZOONsh0XLs2xln+7rXwfvfv/s+eulLrftUtVrdV8FWJCJxxx0Pc/vtYWq1FJJ0dO7adrfn+Lj1m/3Jn1jH7rd8C5w6BXffrfHJT1rFZrpuidG2OuRpmoooHo2zI1juV1epBtblGsTtRXJxcXFxcXFxcXFxcblMZmdnB77f7XacOKt6vU4ikSCbzTIzM7NjMOCwCIJAIpEgkUj0Kuqs0QDLev7wcX6XSyQSIR6PMzU1xeLiIvPz8ySTSSKRiGM5Xi6XWVhYcJybfD4f58+fR5IkRzQyOjp6zUaePZlYXV2lUCjQarWcQTqwhFmNRsOJaiwWi6yvryPLMvV63YlqC4VCCILAxsYGiqJQq9X6hH+pVIrz58/TbrcpFApM7DUS9yTANE0KhQLlcplAILCr6NIWP5XLZcLhMKOjo/t2rMvn87RaLVqtFnNzc0MFQ6FQyHHsi8fjNJvNHSLHcDh8XbvCSZLEiRMnnAHnjY0NisWiExFhu3vZbiBjY2N9UYWiKO6IyDQMg06n47jpBQKBI7t+u7i4uLi4XEkkSUKSJN761rc6rlemaSLLMn/2Z3/muJcKgkCnI2EYSm8w13oGsR8J9hJgjY1Z7i7ptPXvN38TPvc5azB0mCvK1scNURSRZRlNs+LLPR7PjqICWfagaRLttjUA7vcPFh+Bde/WdQOfz+8MNAMkk/DGN1pCg/e8xxpY3i0ucCvPfrb1fyBguSr9r/8FtRp84zfCC1+4dUqdW28t8qlPyaiq1nN3sfa9JIksLi6hqmVSqRRTU1POt2Ixa/1A5JZbgnz1q9ZvZxhedN0ATJJJnR/+YZV3vCOA3+8bGD1obT/8xE9Yv0OxaEVo7SaysVFVlVar1eck7PFItNtiL3rQg9/vc+ILrebCKq3WDIKgDf09vvEbYXbWcrF63/s233/1qy3his1LXrLTtQT6p/F4LCeTt7997+3ZD2fOWMfv2trme/fe2z/N2bPW7/3IIzAzA1NTOwVYhqFz++1lbr31/5FMxvnP/9Sp1YYXxsiyTKul4vVKPUGhRLerDC18CAYNXv3qh7n55hanTs3yrGdt2sm99a3w4z++8zv33WcJr9ptuPvunbGDt98Of/iH1vn9wAOWGGsrkYh1Dr/61fB3f7f5viha7jw2w5osgx6X3/EOSxSRy1m/64tfvPnZ859vxdjtF0myRE8/9mMwPq7x8MN5crkcHo+Ht7ylw6/8Sv/+TyYNbropQy6nsry8jN/vR9e1HUKLU6fg135t8/VeTaPv//7+7XjBC+Df/936OxCAX/olS0hzJXjTm+DtbxccJ0CfD7792y33wq3nfCZjRb9CvzBzL174QrjxRlhfh7m5/UUL7sX6+jpLS0sUCgUmJ1UeeyzTc9cz6XZN5uYusLoq0mq1+I7vOM273rU/dV4sZrkbvuhF8NM/DV/5yuZn9903+Dt7RRDaOtk774S/+ZsODzxg9ESSmwe97UhnbJuZFU+o90Q+VvyoXegjSR5isWjvfC/RbAbw+wOYpkk4HEEQBCf68MwZ699W/uEf4CMfsaJ20+kV3va2NIqiOhG9mqYiCNa1vNFoMD6ewuORey7PsnOtPnFijfn5AJqm0W63+1y4Uikr9nU3Adbx4/CKV7RZXdUxDMO5ftXrg/sjfv3XL6CqOhcvrqFpx1BVdWjUJcAtt3CgmNCtdad33239s9F1nVjsce69V+XTnxZ4/PEksmwJYC13MsUpFLR+h9Bli7Buvvmyvu5yneMKsFxcXFxcXFxcXFxcXC6TQQ5YpmmSy60RjVqCCjvma3Z29ooJimRZfkJFV9s5fvy446RTKBTI5XKsrq4iCAKtVouNjQ10XXcEDvaAkF1BaItExsfHGR+UWeByJDQaDVqtFuVymXQ67YivTNOk3W7TbDYJh8MIgkAymaTZbNJsNmm1Wui67oh4bGFKq9VyIm9sZFnG7/fTarWcKJMnO/V6HUVRqFar+xKcJRIJcrkcIyMjNBoNInaWzR7YcXuRSGSHeGg78XicQqHgxPRdjsvctUowGOT06dPMz88TjUad49s0TYLBINFoFL/fz9jYGNlsduh8FEVxBFxWZfZmPEEqlSKbze7Lya1Wq1EqlRxxrO2AuJvQza2UdXFxcXE5CiKRCNFo1InXtfH5fDsccm0XD5/P6ww6hsOWe8TCwu7L+b3f6389OkrPxWk40ajlRvXgg9Zrv9/H131dnk99SqZabTvFJHYbSxCE3kD2Zhy7NVi8yS/+4iXe+c4ZWq02Ho+EJFnCakGA//k/LRGCLSBIpy0HoP0IsKanLecRm1tuGeyAZJom58+f50UvKlCvw6c/PdvniqJpOtXqGp2OFY2uadrAZ7Ef+zGBX/5lP/PzPlRV6wmhTN785ja33noDP/qjAX7/94evr6paA79/8AfW68cfHyzQ2YqidGm12jQadSqViiOEUxTFiYW0X4dCOppmPdsnEm2i0TrFom9gO9TrhXvusf6+5x6YnITz5+HECXjGM/qnHVar8IpX9L++8UYrCu3zn999m/aDIMAP/qDl8rO+bomSXvWqndOlUpZAyObmm+Hhhzdfm6bJa15T4MIFlVDIzx131Pj4x4e3+TfF/CaybMVyWe2kNj6fb5uzm0mlUqTTyXPTTRNks15itjUPlmBoELq+93no81n/7rrLcqp75zs33ete9zpL5PSSl1gOPHZc4StesfO32h7TmM0OjpSMRCzB0iCe+cz9C7A+9KH+1+vrRce1eXZ2lkBA521vW+GP/miMTkdEkkxe+tISomg5dZ8/f55Go0G3qxAM9l9HbKezreu8/ffeynOf2//6x3/cEmzWatbxtF34dhSYpkmtVsPnK/Dyl0v827+F8fkE3vAGnTvvjPOKV3j51KfgX//VWv+3vGVTEHcQ5z/YFNYeBbbr9srKCvV6nac/PcLCgt9xM0wmO9x8c53FxSqGYZBOn+PpTz/LAw/s3Tja2qx797utY6RUso7tEycGf2cvAZZ9mkkS/MiPlPjIRwr8+Z9P901z++0V/H4/mqbR3X7wYEVfGoYHXdfw+32OUCoWizI+PsqJEzG+/GWZTqfdE0O3CQSCBIPD10sQ4GUvs/7e2Mj0HPUswa8keeh2u3i9bRqNBvF4vPe52tsWyXHMetazynS7o6yv11FVldOny7z4xQaiGOPuu6NEox7m5uDChf7l//iPW9F6k5NQqwmsrVm/j9mzFFtbG9wnIYogSWZvGwTHvWsQH/iAdb9uNPrjN3djy2VxB4uLi5TLZeLxBb7t26Ikk13+7d9SfPSjdjHWprMjQLMpEIlE9l0UNwhXgPXUxhVgubi4uLi4uLi4uLi4XAEEQeDYsVlOn84eeMD+yYIoipw8eZL19XWCwSCdTod8Pk+hUHCclWq1GuFwmGQyiSAIzt/NZpNisUirV3IvyzKZTOYJ3qInJ5VKhWaz2asK3Oy1suNprKpsq/tAFEUikQi1Wg1FUXqDWpsDY1alvuF8dyuCIDhuZ08FVFV1hDv7ifELBoO9WALN6SDdC3v6bre7r8hNj8eDz+ej2+32DcQ+2QgEAtxwww00m00KhYIj8PR4PI5joCAIVKtVp6NclmVisRiiKFKtVrl48SKtVotSqUStVnMEWJFIhHq9Tj6fZ3Z2ts9BayvVapWlpSU6nY5zvgiCgM/nI5/PEwwGHWGui4uLi4vLlSCTybCxsbGvabvdbs89Q8LnswZPw+GdrjhHyU//tOXUsroKz362h1tuifP85y/w+c+baFqJ2dlVGo0of/iHt/cKNbxIkuQ4QnW7CrLs4eabW7zhDUs0GjVqNctNxe/34/NZA/sTE5Zr0UF42tMsZ6RQCF772v2Jozc2NqhUKqyuLvH1Xx/k+PE0H/zgZiTTi17U4NSpOZaXl1lcXHREZdvFcBMTlnuVqgq0WjLz8zIjIzA6aj1PvvzlljDuC1+Ahx7auR7bH/H2an5qmkqr1e49s9R7UXii40QmCJZ4TNc1FEVB1zWi0SjJZIdMxs/znneR//N/doqNjh2DN7yhP+bvGc/YKbzai0E1CT/4g5YTVq12sHkN4sYbN0WE+x1r/8ZvtBx27Aizb/5mHVm2XpimybOetbsAaxOhJ97QCQataPFms4UkST1XIANN0/F615mcnCSRSDA3N7dDFLDdpQr6RYP74fRp+P3ft4RG4+OW4xFYv99732tFB0ajVuThdl71KisqTdetffia1xy8oGDbaXAgarUa1WoVr9frtLtOnOjwkz+5yOqqj1RKZWTEal95PB4ikQjVanVHRChYjmHb+bEfg+/+7p3nFuyMTxQEywXrStFqtbh48SLtdptarcb4eIvXv95AlmVCoRhf+UqATCbDnXdO8vVfv/NHGOSGt49m5JGwtrbG4uIii4uLxGIxRkcv8bzndXj00REiEZNv+qYq09OjFAoex6Xs9a8v89WvJgfu+61srXH0enc62Q1ivwIsgEDAx623djl//iIPPnhjr+9K5667OnS71nFXqVSIxdpUq4FeoaEVDalplvjKWqbRi/6N9Jah91yXDBSliyRZ9y9buLsXmUwGWW4SCAQwDJ1Op9NzMyxRr9cJBAJOW1aWZUzT7Il6PWjaKq97XY3Pfa5LKCRy880GodAMsViM8+d9jI6O8upXj/Orv7q5vIkJ6/i2z29rXSV8Ph/1eh2/38/Zs00efri/jTs7ayk76/W6E39stYsHb9dh3NaGCbBUVaVcLrO+vk4oFGKsd1N66UvLfOYzMZpNSy3q8cj4/dDpdHuum2qfiybgxAHvh+npvY8xlycvrgDLxcXFxcXFxcXFxcXlCjEzM8MNN1idEk/VyCpBEBgdHWVkZIQLFy5QKpUwTZNyuUy9Xsfn8xGNRqlWq06EYrvdZmZmhmg0ysLCghN5l0qlnrL78UpiV+BbFfqbpcr236Io9nVOy7JMs9nsDdKJfZWedkzb9t/JMAy63S6JROKKuLTZ1ZaXU6F4JbDXx9xHL50tTLNjDFwun1AotEPgpOs6uVyOQqHQJ0SzxWl253GhUGChZ/kRDAaRZRmPx6ooXlhYIJFIYJomoijuGDgtFovMz89TKBSca56/14tcKBTweDyk02m63S5zc3P7Es+5uLi4uLgcFL/fTywW2/W5wnZ81PURAoEgXq/ci1iyBhrtKK1hTE/v/vnu62fFZW0S51nP8jA1tUq9rtNue6lUrMFpSbKfLwXHrevWWxeYnFxjaqrC/Lzl7GXF48n4fD7n3rtLCvRQ7rzTirI6CPl8nmKxiCRJjI+PMzHRIBJR+cpXTILBIidPrnDunPWMXS6XCYVCKIqy4znCRpatAeVbbul/XxAsN6l77oHv/V5LwLaV7ZqSyUlL3DLM+abT6aKqas/1SqLbNZEksSfEs44dSfKgaSKapjtuxnfccYFms8HkpMbznlfl4Ydjzrq96U1H5+g5aCA+GoX//t8tgeDqKjzrWXsfq7tx0HV9/vOtyMivfc0SK91xh4eHHhIIBoPU63XGxsKMjirkcoOdYHTd+pFuuKHD+noEn89Puy0gCCKGoaOqWs95TODrvq7J2bPHCIVCHD9+fGBB12teY7kd1evW69lZuOmmg20TWMfJIPFQJGLt42E85znWb3HunCV2PH364Ms+e9Zyi8rlrNderyV02E9diq7rPQe//n0Tj+vE4zszVL1eL+12G693Z9HQILPmZBJ+/dfhB35gX5uyb26/Hb74xb2n+57vsf5vNps8/vjjlEolNjY2elFpYTweD+12m1KpRDAYdPpVjh8/vuP6/8Y3WtGTW3nNa45og3ah0Whw4cIFHn30USRJIp/PoygKkcglnvMcSxSnKBHm50NMTk5SrVapVqvEYhv81E8l+e3ftuITn/Mc+Oxnd87/MAI+UbREg8PY2kRLpVJsbGzw/Oef55ZbDCQpy8xMh1hMZ3U1SqfTYW1tjdtu+wKf+MRztxSdCU7fSLfbxePxIIoi09OWxVUgYE3n9XpptdoYhhVXCPsr3LTiCkO97YkgSRKKoqCqGoVCAU3TnLamrmt4vZbLpa5rBIOQz6+QTpd7QtBRGo0GhUKBSCSCpmlMTam87W0zfPrT1v1o+7Xd5/MRiURIJBLk83mSySRnz7YQxX7x0Wtfm8c0TUqlEvF4nEAgQCQSYS+NeDhsRR1evLj53jd8g3W9286wY8AuyGq1Whw7dqzvs0RCcwRYYImwRFFBVdWeyHvzGv7c51qxhh/8YL/j3yDuucd1tX6q4wqwXFxcXFxcXFxcXFxcrhCh0O7W4U8l7KrUTqdDo9Egk8ng8/mYnp5GlmU6nQ7VapW1tTVM02RpaYnZ2VlGRkZYWVkhk8lQqVSGus24HB67U3a7M5UgCAQCAUKhELVajXg87rhY2R19fr/f6diz7PUNgsEgwW0Hfq1Xnh6JRIYOMh0U21GtVCo5AjFbqJfJZPaM47vS+P1+PB4PHo+HRqOxZ/Roo9FwRD77dcqz3Qn8fj/NZpPUHiOMtluW5Qrh2/FZsVikXC6jqiqCYFXkp1Ip57e/3lFVlXPnzlGtVimVSlSr1T7hWzgcplarkcvlaLVaZLNZDMOgWq06bhq2oNYeYPV4PESjUadjvdlssrCwwNraGrVajZGRkb7PdV2nXC6Ty+XQNA1BEDh79uwTfry6uLi4uDw5OXbs2MCo9JWVFVRVpd1uEwgEHPFVILD5DFcq7e30sD0e7nIJh8OcOnWKdrvdeyaxYvpEUcTrlREEEV3XuP32CvfcI6CqGTQtgSRJyLJMIBDA5+uPwzuMAOuFLzzY9PV63WnPZLNZBEGgWCwiy3nOnrWi0XI5S0kiyzKapvHQQw8xOzvL5OTkwVewxwteAO9/f/97L35x/2tJsgbNf/u3LTFLJoPjOqLrWs991XLqNAwTQaAXQ9l/3Hg8HnTdiiOcm3uQdHqDfF7g2LFj3HdflZ/8Set42S0G6jAMc0I5ftxyCjNNa6A7FoN/+qfNz1/ykqNdj+2cPWv9s5BIJBLUajVWVlZIp9O85jV5fud3JgaeQ4piubdFIgaveIWHP/5jgWAwxO23a7z5zes89libRx7xksno3HmnTDZ7etd48kgEfud34BOfsPbXXXcNjgC8kszMWP8Oi88Hv/iL8M//bLlQ3X03PPLIZpSmzSAhmF0ANMjRahB20dALX1jn/vv722jDrmnDuiEupz7sTW8aLsCanrauwS98oSU40XWd8+fPUywWWV9fJ5vNEo/H+wqfOp0OuVyO+fl5wCoiGdtqQYclYPrTP9108wqHLUe3K83XvvY1lpeXyeVypNNpYrGY40rc6XSo1+tUq1VGRkYwTdNxKWs2m9x6q8b73udBVS1h3itfuXP+h+li+IEfgPe8Z/jnW69lwWCQUChEJpNB05YYH9cdYVM2m6XVajEyMoKmPUatdhMej9zrN7H6WWxXca9XJhLxEotZ19dQyFKAiaKEKAo956xdVGEDsNqxIVRVxeOx7i+SFOPMmTNOn0QgEECWZURR7DltSShKlXTaEkMJgkAmk+HYsWO0223HrUwQBG67LchLXjLcjT6bzVKtVimXyywtLTE5Ocn3fd8KH/5wCk0TuOuuCqOjHVZWVjFNk0Qi4bjb33knPPBA//y2i5//y3+B//E/rBjU06eteNRBAqxh955Wq0Wj0egTZtu8+MVl3ve+/pzQl7xknZMn1/jjP35e3/uve53l7PjMZ8KXvwy/9VtQLFrXPVsgJknWPfjbvm3o7nJ5iuAKsFxcXFxcXFxcXFxcXK4QTwK9wpGxurpKsVik2WwSjUYplUqEQiFncMQWNtgdirIsU6/XiUSsKr5arUa5XHYFWFcAW2S1trbmDMLZxONxGo2G41gWjUZpNi2L+3g8jiRJVKtV8vk8uq47v+lWRx9FUcjn8yQSCceJ4XIwTZPl5WU2NjZotVpUKhW63a4T72YNcOUYHx/f0eF8NQmFQs5+sis9h4mYDMOgXC4Tj8cHCth2I5VK0Wg0uHTpEt1ud1fxVrlc7sVThPrOpVwux9raGt1ul0ql4sTlBQIByuUyPp+P2dnZ69qpyTAMZ9BiaWkJn8/H2NgYoVDIcXJYWVnhgQceoFqtOueB1+vF6/ViGAaVSgVZlkkkEoiiSC6XI5lMUi6XHfFbLpdzKrZnZ2d3dPJKkkQ6ncbv97O0tEQoFGJjY+OyBl9dXFxcXFyGYUXI9T9/2LHRsiwzOjqKLPvx+30EAsG+aXtGkAO5+2649daDC5X2iyUKC/T+7v9Mkjwkk2luvjlGo9FA0zREUSQUCnHPPX4+/vH+6Z///IMt+xnPsNynDkKn06Hb7aLrOuFwmHw+z8bGBvl8nlarhc/nc7an3W5Tr9edeKjFxUWmD2kl9spXWtFztkuQIMA3f/PO6e66y4qkK5UsYcdDD8HP/zyoqoZh6L19aBVaeL1eNE3vRRFuVfEIveOpw003fY1SSXSKDaLR6BWLMdsryds+ZN/6VkuI9LWvwZkz1mD51SSbzVIsFgkEAiwtLTEzM8V3f/cqn/98lFBI5zOfsdpA3W4HwzDw+/0oisw3f7PIC19oOS9lsx5EcYIzZwb/jrsRi1lRhNcz6bTl0GQzPg5/8if90X9bP7cJhUJEIhEKhQKKouxa2GAYBrVajWw2y8tepvGlL22eP5JkXdsGEQ4Pfv9y+nyGCdZuuw3e/e7+9/L5Ep1Oh/X1dUZHRwcWNfn9fqanp1lYWCCfz+P3+xkdHe27rmcylujoH//RWvd77z1c3NtBqNVqbGxssLa2hiRJzMzM9Dlv230EpVKJ9fV1R1RmC+o0TeuJl4Yv4zDN/jvu2OmutJXt3RYzMzO02210XWdlZcVpv/t8PjKZDMViEVEUuemmS5w7dwZd19E0HZ/PitsTRQm/38fIiOkcN5OTXcJhnUZDwnJ4hOc8pw0ctJJTQJa9yLLVbg2HVU6fPk02mwXg4sUu589Hes7cJqHQMqOjaUKhEMvLy46TGlj335mZGS5dukShUCAQCDiCqUHE43EymQyGYbC0tMT58+eJxWK85S2W0rfVanHuXAVJkpiamiKZTDrzu/126xpvR3/KshVdu5VTpyzXtm7XEmqKIr2owO2/z+D1MwwD0zT7jjmbm25qMj6usLpqHVyhkM7TnlbD4+nwHd+R4/77YxiGwH33Wc6CNrfcYolDTXNThFmvW+t1BQzfXa5DXAGWi4uLi4uLi4uLi4vLEeDzhTGMrfFtAVeA1aPRaNBqtSiXy6TTafL5PKqqDhSKJJNJms0mzWaTcrlMNBrtdU5bNuAuR08ymWRlZYVwOEyxWOwTgtj7P5VKUSgUaLfbtFotkskkIyMjdLtd4vE4j/Y82E+ePOmIUzRNo1qtOgMRmUyGiYmJy3ZSsmMpl5eXabfbRCIRRxjUbDa5dOkSkUjE6WgbHx+/rOVdDtlslkajQaVSYWVlhfHx8YHxjKu97Bi78/IgZDIZ1tfXCYfDLC8vMzMz43SebqVer1MsFhkbGyMcDjsir6WlJXK5HKurq04saCAQwDRNCoUCGxsbpFIpNE1jbm7usgV0TxTFYpFarcby8jKxWIzR0f5K13A43HOAUFlZWWFmZgafz9d3PtjH9MbGhuPKV6lUKBaLpFIpFEWhUqk4grvt4qvtywuHw5TLZcLh8MBjw8XFxcXF5UogCJZrUT7vxTTDjI8nCAb3574JkM3CD/3QFVzBfdBs4oiit/J1X0efAGtqyhrgPQiDItj2YmvcdKvVcgRYqqoyPj7e1+5JJBLU63Uefvhh51mrVqvt6ZY6iEgEfvmX4c//3IrSuvdea5sHkU5b/8ASHrz4xfAP/2BiGPa6W1FZgUCHel3uRZSLHDu2wNTUMo8+egboMDPzSUIhD8VikZGREbxe74GfX3fbHjtGz2a/g9mSBK9//ZGsxqEIhUKMjo5iGAbLy8tcuHCBaDTKPfeEEUWRT37yBqc9GwgEe7HulqrniAyCn3QEg/Brv2Yd36o6/PhOp9PkcjkCgQD5fJ6JiYmh87TjwaPRKMePp/jVX4UPfMD67N57N8+R7QiC9VmhsPleKnV5DljDGGR2ls/nnWKQ3RylRVEknU6zsrJCNpsdWMQ2PQ1ve9sRr/Qu5PN5ms0muq47fQXbEQTBaU9Vq1U0TXNc37ZPf8898Pd/v/l6UFTrfggGLWeln/gJ6JmG9bG92RsIBDh58iSiKBIMBqlUKuRyOQzDigsdGxtjenqaiQmRv/3bALmc5e4lSRJ+fwBZ9iAIIqlU05mnxwOvfGWRD3wgjWEYJJMaL3/5HtaTe+D3+wgGw0QiEdJpS2R1330G//t/iywuKshygzvueIRQKES73Xb29dZiK7toyG731mq1XYuxZmZmHHF3vV6nXC477vY+n89xhU4kEn3RmNGoFe/5N39jxee+6lWDXStFsV+M+7rXWeJMm7k5698gJElCkqSB/YmCAD/0Q8t87nNRNE3g9tsbdDoNdN3Drbe2efObh/ddCUK/AHMXk0KXpyCuAMvFxcXFxcXFxcXFxeUIeNe7Nvj0p/vfcwVYFrZ1vKZpxONxCoVCL+LC2DGtHQNmi7Z0Xcc0TURRfFJEoF2LeDwekskkrVaL+fl5crkcIyMjCII1CDM5OYmu6yiKwoULF/B6vVSrVVKpFKqqOjFrsizTbredaDdd1/F4PKRSKZLJJJOTk5ftYFYqlcjn8ywuLiKKInNzc30VzqlUik6nw8rKCktLSwiCQCQS2TWyYy9UVaXb7QLWQN9+4wHBGgwol8vous7S0hIXLlwgHo8TCoUwTdNx8BIEwakG3StGcDu2k9P2ZcRisV7EgEK5XKbRaJDNZkkkEkz1Ri5KpRK5XI7FxUV0XWdmZqbPfcs0TarVal/H8o033nhdxuXZgxaSJDEyMrLjc9M0yefzKIriOPJZERCG0+lvH8+SJJHP551BHttprV6vO3FO2wVeg4jH46ysrKBpGq1Wi3CvtN691Lm4uLi4XEkEQeDBB8/y/vdb7g0Hva0PigC72jQag9+/9VZ45zvh3/7NGgy9777d76tTU7C01P/eiRMHXx87RloQBHK5HI1Gg263y8TExEBhPFhCfbu4YWNj41ACLHt9t7vl7IfnPhc+9CFr/1gxflbM+Dd/8+f52MeexsZGkHB4gTNn/pNkUmd6eoVWq0WxWKRUsgpkMpkM0Wj0QO6tu/Ha18L73rf5+t57r6/nosnJSUeEYLsI53I5TNMkFJqm1Qojyx48HplQKEipdB1t3BPE7Cz8t/+2+zQ+n494PM7o6KjTns1kMn2ON6ZpOm3J8fFxAoEA0WiUWAx+6qf2ty7f9m3wG7+x+frlL78yx+f25qCu67TbbWq12r6KYcLhMIIg0Gw2aTQaR+oibpqmE7lqmiYej4dYLDb0OqdpGpVKhXa77fQ5tNvtodeMWCzGxsYGiqIQjUbxeDx9kbJgOf995Stw/rwlovru7z74fczG77ciHrdHXQKEQjvfC4fDnD17lnw+T6FQcESXdn9VMpnkrruyvO51Pj71qUv8/M9HUVUFr1fGjnUNBvv7wZ75zDqZzBqLi3We+cwxzp49dbiN6SHLXkRRIhaLsba2xszMDOPj8CM/cpGHH16kULhEOh1G1z0UCgVCoZDzO24lGo2Sy+Wc42g3AZYgCMzMzJBKpdjY2CAWi/UJk2OxGJlMZuDxOzkJP/zDB9vGe+6xxLr/+Z+Wq9t3fdfwczEajRKJRFhfX6fRaDhtbhuv1+TOO6uAda6tr1cZHx+/bgvPXK4NXAGWi4uLi4uLi4uLi4vLEWBeXpHakxrbVcbj8SBJEj6fz4mKGyQ28fl8NHqjKt1u13Fc2s1NxuXymJycpNFoMD09zfLyMvV6nVgshs/nczoUO50Ox44dIxAIEIlEnKiX5zznOTSbTUzTJJFI0OlYsRqSJBEKhQgGg4yNjRGPxzFNE1VVHQv4YR21w9jY2KBUKmEYxo7oAhs7euHixYvUajXW1tYIBoMHFvHZ0YrVarXv/WAwSCaTIZVK7Tk/QRCYm5tDEAQ8Hg/VapVKpUKhVzrt8/lIp9PEYrEd1aAHYXx83Pmd7GWUSiVnHSKRCLOzs4TDYebm5pwO71wuR6lUQlVVjh07tuP3EASBeDyOx+NhaWmJaDS6Z1X5tUi73abdbjtRgYP2caPRQFEUWq2WU32taRrtdpvQtt73WCxGvV6n3W5TqVSczmVd19F1HWBfIjWv14tpmr14Cu0IttTFxcXFxWV/fOADm+2XrfFe++Gg0WhXgmECLIBnPtP6tx9e+1orjsvm5puHO0jtRjQaxeu1YqYWFxdRVbUv0mk7tpDC5/PR7XapVqt7RqcdNZpmOYOIooQg0BPbw8iIzmtf+x/U63Wq1Srtdpv1dStusFQqOUUWo6OjJJNJThxGsTaEe+6xxCePP24Jb+6668hmfdWYmJggkUiwsbFBNBp1io5mZvzMzwfw+bw9QckTJ77qdrsUi0W63S6maSLLMslkcscz7/XEzMwMnU6HmZkZVlZWqFQqznmpaRq1Ws1xR04mk04b7SDcdZcVdfe1r1lxZ894xuWv9333wV//9eZrQYBv/Mb+aez2hWEYO8RIg7DbnoZhDCx6OwyGYTixqp1OB0VRnGPH6/WSSCQYGxvbUaxkFzJJkkQ0GkUQBCqVCoFAYOD+DwQCdLtdut0ugUCAdDq9Y7p0Gn71V6FctgRUw+Ih98uwy+6ww0OWZcbHxxkbG3McpCz3wEBf/8QLXpDB6zV6BV2Ks29Esb/zUFEUFGWVs2eTJBL+yyoes9ZbIBQKMTIywtLSEpcuXXL606LRLoVCh2ZToFwu4/V6SaVSOwSL1nqKSJKEYRjOMbgXtsuzrutOv48tUD5KJAne9Cbr314kk0mWl5eJRqNsbGzs+J22ks/nkSSJSCRyZM6OLk9NXAGWi4uLi4uLi4uLi4vLFeJ6qta9kthOSnbnXzwed2LZms3mjo5e22kHrMEJj8dDOBwmPSwPwOWykSSJU6dOcf78efx+P9VqlWq1Sq1WA6yOtOc///kYhkEgECAQCGAYhlMpmU6nkWWZSqVCq9VyBFixWIxQKISiKKyurlIoFPqs30OhEJlMZmgUwVZarRbNZpNKpUI8Hh/aaQY4Qq8HHniA1dVVp3MxkUiQyWR2HVwwTZP5+XkKhQK1Wo1KpeJU+NqRD7VajUKhwIkTJ/bsTJQkiRMnTlCpVNjY2NhRgWx37sXj8X0NAqiqSrlcdvajvV2Tk5NOp2I8Hu9zj5NlmVQq5cTEwGY0aKVSIZVK7bod4XCYUCjUF5d3PTnSKYqCYRhomja02tp26bNFoqZpomnaQGGU7dS3sLBALBZzjsWtIj9d1/c8pu15W4Ofbvygi4uLi8vVQdfhsGPyH/iANeC9Fdsxs1KpoKoqoig6EdZHUUAhSdY6b6XZHDztQbnzTiva6P77IZk8fISdJEmkUinW1tYcd5hhwiR7P6XTaaddpOs6zWbzqgqwRkbA45ERRRGv10er1XTioyYmJvB6vXg8HjRNo9Pp0O12kSSJyclJRFFkdnaWkydP7hqHdhhe8ILDxUBeSwSDQWZnZ5mennaEKm94g5f3vre//fLqV1/d9ep0OiwtLVGr1Wi3204bx+v1srGxQTAYZHJy8rIFIE8EHo+H06dPc+HCBfx+P41Gw3HiliSJbDZLNBolEAgwNzdHYGue2QG44Qbr31HxohfBpz4FuZz1+qUvtc7NrdhtjWExas1m04n4EwQBv99yqBNF8UjaGLquc/78ecrlMsVikWq12ifsCofD1Ot1KpUKJ06c6HMYsgtVbHcoj8fD6uoqGxsbpNPpHW36TqdDqVRiamoKv98/VAQjScPjIg/Kfu+HdvvObu8JgrCr+180GmVursy5c37a7TamaSDLXubmOs78arWa46ycSqUYHx8/8PrffTd84hP97734xUEmJiYQBEtoVSqVaLfbLC8vOw5V4+PjRKNR0un0wMJIW3h1mOPIjv27FhBFkZGRERRFYWFhgYWFBbLZbN9x2u12KRQK1Ot1ZmZmLttF3cXFFWC5uLi4uLi4uLi4uLgcAYMcsK4jfcIVJRAIEAwGMQyDRqPhRHsFg0Hy+bxTLWjTbDYJBAK0Wi1M02RycpJgMLivilxbZCGK4pFX2T3ZkWWZM2fOUK1W2djYoF6vO595PB7S6TSZTGbXgaFEIrEjPqVUKjE/P+84ENluWbZAq1arEQqFOHHixK7xfq1Wi263i6qqu9rBFwoF8vk8jUaD1dVVFEVB13VHOFUsFkkmk8zMzAzsSJyfn2djY8NxL4jH42QyGQRBcARLdqewaZqcPn16zw5JQRCcfWNVuFpWE16vd98Dbd1u16nm7na7zjxsR7lEIsHExAQnTpxAURTa7bYjhAuHwzvWsdFo0Ol09tyfNrFYjFwu5wzCHXbQ4olgq1jMHGJXqOs6Pp+PUCjkuPbthmEYdDodwuGws/+CwSA+nw9ZlqlWq3uKRu1jXxTFI4vucXFxcXFx2YvDOvf+8R/vFF+VSiVWVlbodDpOFK89KJ3L5YhGo0xPTx8ownk74TBsMyTd1QHroHzd11n/LpfR0VEuXbpEIpHgkUceoVwuk0gknG23I8Ta7TbZbJZgMEgwGKTRaBypU81+mZmBY8cEHn3Ui2HodDodnvGML6AoCqVSiWQySTQapdlsOs6w4+PjhMNhstksp0+fJpvNXlei/KuNLUYES+z3/vdvCm0kCZ7//Ku3Lq1Wi3PnzlGpVBwXI6/XiyAIKIriRG23222OHTt26EjMJxJbhNVoNMjn89RqNUdAYhf+xGKxa+qYzWatWMOvfMW61p09u3MaSZIcF+pKpeK0MarVKoVCgW63S7vddrZV13Wq1SrxeHxH3NpBMU2TCxcuUCgUWFpactzv7Pal3cZfXFx0Yt7PnDnjHPe2Y5csywQCAUzTZHR0lPX1dZaWlgiFQo7rdqvVol6vEwgEGBkZYXZ29qqIUndLjrePpUql4lyjbRfrdDq9a5+TIAh8x3dE+dmfbQMCitLFNOsEgw9y4YJVWCVJEslkklQqxeTk5KHOu9e8pl+AJUmWi1o0Ou7ci5PJpCO49Pv9GIbhuBgOixas1WrOuXO9i5HGxsbodCzhWy6XY3l5GY/Hg9frRdd1p39jdnaWeDzO3NzcE7zGLtc7bm+0i4uLi4uLi4uLi4vLEeAKsIaTTCaZn593HInS6TSqqtJsNp1KSTuGTdM0CoUC4XAYSZKYm5sjkUgwOzs7dP6maTqVg1vj4vYrGnLZxI6csx2U7Cpaj8dzqI7qUqnEpUuXyOVylMtlgsEg6XQaURRRFIVyuUw+n2dychLDMDhz5szQWAXDMBzxzLBqykKhwPr6unMs6LruCGREUWR5eRmv14uqqui6viN6wu7EXlxcRJKkHTGHwWCQVCrF+vq6M00ulztQpepBRFc2zWaT8+fPU6lUKBaLtFotR1BlGAbhcJhms0mtVuPUqVP4/f49l7H1991Pdaosy06n834jCA6L/ftIkrSvmI298Pl8PXcHryPw3I4tVLOP90qlgiAIA5ev67rjEuD1ep3KbFsomkgkKJVKTnzjIBRFoVqtOjE1rmDUxcXFxeVqcRgBViJhOURtxR7EzOfzlMtlAMdFcmNjw3EvabVanD59+tBuWIMEWNdicq/X6+XEiRNcuHCBQCBAsVikWCzi9/udZ61AIMDY2BjhcJiJiQny+Txw5dxC2u12X8xcKBTqe/Z917vg937Px/nzLY4da+H3L1CvqywtLVEsFolEIui6TrfbJZlMIssy4XCYkydPEo1GyWazR77OT1YkCX7lV+CDH4Ru13I+utz0Rl3XKZVKjrDC4/EQj8d3CPs1TeP8+fMUCgXW1tacwg27vaBpGpVKhfX1dScyzufzXbcFAnYM2vVCMAh33LH7NNlslnq9TqFQoFwuo2maIwyq1+tO4Y2qquTzeRKJhCO2i8fjh3bCqlQqVKtVlpaWCAaDO5yQbSFpJBJhZWUFr9fL6uoqx48fBzaPo3g8TqVSYXZ21mnjtVotGo0G9XodQRAcsarX60WSJBqNBg888IBzXGcymSNxVdzO055m3eN6tzEA3vpWnfPnL1GpVBz39q0C43q9ztraGjMzMzscrrfyghd4+O7vDvF//k8LSdJ4+curjI1ZblMej4dQKEQgEGB8fPzQosfJSfjZn7WuLZIE3/7tYGuq7L4de1+Hw2GWl5cdUdYw8ZWu6xQKBeLxeC+6cPB01xPHjh1z+km63S71eh1N0xAEgbGxMfx+P4lEgunpabdt7nLZuEeQi4uLi4uLi4uLi4vLFeJ6F2ApiuJY2dudQ4cRQ9g28raDz8rKCpFIxBFZyLJMPp+nVCrRarUIBALEYjFuueUWYrEYJ06cGOq2o2kaFy5coFarOXFxdieKXVG+vr7O1NTUUPt6l8Fc7kCQpmksLCyQy+Wo1WrMzMzs6MRPpVIUi0WWl5cRRZHFxcWh1YYej8fpCOt2uzuOiXa7TT6fJ5/PoyiKI7yLx+Mkk0lCoRDZbJbV1VUWFhYQBGHHwNHGxga1Wg1N03aIr2wEQWB0dNQ5rkOhEGNjY1esklpRlL4Bk3g8zsjIiNP53G63KZVKLCwsMD4+zrlz57jhhhv2FY0oSZITtbfX9HaFrv3do0bTNIrFIvl83hn4AavTPpPJDIyo2C8+n49wOEwikXAc0LYPQvj9fqe61/5dy+UymUwGn8+HJEnouu7EmXQ6HaanpxkfH+9z9bAHR+r1OouLi0xMTOxw/Wi1WqyurhIOh4lEIu7gpYuLi4vLVeUwAqztpo6VSoXl5WWWl5fpdDqMjIwQjUad+6uqqs4znmEYnDt3jhtvvPFQIoA3vhF+6Zf63/ue7zn4NlxpKpUKlUrFiX9uNpvEYjFUVXUKQ8LhMPF4nHQ6jSAI1Go1gsGgIwQ/CkzTpFQqkc/nnYhlwIlZTqfTZLPZXmwi/PRPi+h6nIsXSxQKz+WRRx4hl8tRrVYplUp4vV5nHScmJpidnSUWi3Hy5El3kPqAxOPwXd91+fPRdZ2VlRWKxSKKojjO0bIsEwwGnchw27XGdgfO5XKMjY3tiI30eDxEo1EajQYPPfQQy8vLLC0tMTc3ty+nn2sNu9DE4/E8aWK+k8kkq6urjI6O8vjjj6NpGq1Wy3F6DofDjvgqnU47hSe2G7UtiDooW4vcdmvz2m55tnBTVVWn7yiTyVCr1SiVShQKBaampmi1WpTLZer1ulNkVa1WWV9fd6Lz7PuHLMtUKhU2NjZ2dbI+LJIEb387/NZvWSKsF7zA4PjxC2xsWK5fmqYRjUYJh8NOe7BUKpFIJNB1HdM0B0b42fN+/etFXv/6MJrmp1SCdjuOYRiOsOwo3KWe/nTr3zBsoVwymXTEmqurq2iatkOg1263WVtbQ5Ik0um042z2ZGBsbIyRkRFKpZLT72O7UdvnjYvLUXD93DFdXFxcXFxcXFxcXFyuYQYlRlyvAqx6vc76+jrVahVN0zAMwxlISCQSjIyM7Lsatt1uc+7cOcAalIjFYrTbbTRNIxAIIAgChUIBRVGQZZnp6WkmJyeZnZ3l1KlTpFKpoZ29uq7z+OOPUy6XWVpawjRNYrGYY6ler9e5dOkSsVjMce5xRVhXj2KxSKfToVwuMz09PfCYEQSBdDqNoiiOo5CiKAM7viKRiDOoUKlUdgiwSqUSzWaTdrvNxMSEI6LxeDzOtB6Ph8nJSS5duuR8botfFEWhVqtRLpeJx+N7in3sTvBMJuNEPFwJ1tfXqdfrQwdMAoEAExMTFItFVldX8fl85PN5xsbGdp1vJBIhEAg4cXnDOo1tKpUKkUgEj8dz5JXH9XqdCxcuOBEWdhW3KIrOYFAul2Nubu7Qg5P272RHJU1MTPR1NEejUUfkp+s6ExMTBAIBms2m44Zld1bbg1Szs7Oc3ZYTkkwmaTQamKbJysoKFy9edJyxTNN0jtFYLMbY2BgTExM74lUFATKZF6FpbcCKr5mYOHx0k4uLi4uLy+UyM9P/em1tjVKpRLvdHhgTJcsyo6OjyLLM6uoqwWCQYrF4qGfxO+6A6WlYXLRep9Pw7GcfdkuuDLajiB23bRiGI1ywheDW/XyC0V7elS3YHhsbI5lMHonAxTAMLl68SKlUolKpUC6XnVhlURSJxWI0Gg2KxSInT550ROKSJHHy5ElGRkZIpVJcunSJcrmMoigYhuG4z4TDYWdA/koLchqNRl+EnO2icy3Fxz0RqKrK448/TqVSoVAo0Gg0EEURURRRVRWv10symaTZbDruPLZLnd/v39GWsB3r7IKpTqfD+fPnnTj6er3O6urqNV/QNKyYw44eTCQS17UYSxRFTpw44RSwzc/P02w2mZqactx8FEUhGAw6bUbTNB0H6FardWBHM1VVHcfy/bhoJRIJLl26RLfbpVwuO+3sVCrluF4vLS3R7XZJpVKOm1az2XSun9FolGq16rQ7befsxcVFfD4fiqKgqionT5480mvBmTPw279t/b2yssbSUonFxUUCgQCjo6N917t0Ok2z2WRlZQXTNBFFkXA4vGfUrsfjecILbzweD3Nzc5imiSRJ5PN5CoUCoVAIURTpdDp0Oh0ikQjpdBrTNMnn86ytrSEIAoFAwHGvv16vxaIoOsJSF5crhSvAcnFxcXFxcXFxcXFxOQLW179KrbZZTm51Rtz4xK3QIVlbW2NlZYVqtUqlUqHdbjufhUIhR6AyMzOzp2ADYH5+3ql0fPrTn069XqfZbNJsNmk0GgiCwMjICI1Gw+nMv/POO5mamtpz3qurq1QqFRYWFohGo4yMjPR1CiYSCTqdDktLS6yurjpiDp/PR7fbJZ/P7+jUz2QyyLJMqVRCURQEQXA6sd1quIOxtaN/u8BkO6lUiosXL9LpdCgUCgMj/WwBYK1WY2VlhVgs5nQia5rmDGKFw2GnEnx8fNyJH7QRRZFUKsXGxgaZTIZ6vU4kEnHiWWwXh70Ih8OYpomiKH2d/EeJYRhOfI3tmDCMVCpFvV6nXC4TDocZHR3dtVM0FAo5A1mlUolYLDZ0IK3RaNBqtRgZGSGTyRxpZ6sdr5jP5524IjviRlVVRzSVzWYxDIPTp08fKg4lkUiQTCbRdZ3FxUUuXLhAIpFwonharRa1Ws0R6QWDQaamppzYHcMwEAQBVVVpNBqcPXuWmZmZgR2309PTjsNYp9OhUqnQbDYBnIgJn8+360DWM5/5D30RMK9+9fXZwe3i4uLicu1xGAes6enNv5vNpuNekkqldn1GTqVSjjNUPp8/lIBDkuA974GPfxx0He68E47AMOTIyOVyrK2tsbS0RKPRAKznrGQySalUwjAM2u02qqqiaRrVapVMJsP6+rrjqrL12bPT6ZDP5514JEmSHBHJXs/Uly5dIp/Ps9hTq9nOOKIo0mq1qFQqXLx4kYmJCUzT3BH/HY1GiUajnDx50hFf2IUssiz3uZxdKSqVCmtra7RaLceNWRRFAoEAPp+PbDa753PukxXTNDl//jylkiUOCYVCfYUug6IENU1znqkHtXFsMeXGxgadTge/30+73UYURUqlEvV63SloMgzjmnTDqdVqTluyXC7TaDQch6FIJOI4zZ04ceKKRNhdLYLBICMjI0QiESKRCCMjI46jcTAYJJvNOn0XmUwGXdc5f/48jUaDfD7PzHYl7R7Y4s1ut7uva7ff73cEU/Z3wTpup6am6HQ6TE1NOQ6KpmkiCIITd+f3++l2uzztaU/b4balqiq5XM5xsl5ZWWFycvJA27MfDMOgUCiQz+eRZdlx49pOKBRiamqK+fl5EokEhUKBiYmJI1+fK0EkEuHUqVNOsWKtVqPdbmOapuOeZ0dbRiIR5ufnnfhF2yXa7/dz/PjxPe9JLi5PVVwBlouLi4uLi4uLi4uLyxHwO7/zTFR1U6wkigEEofUErtHB2djYYGVlheXlZVqtFvF43Kn2U1WVSqXCysqK4+7i8XiIxWJD52cPztg2+KlUyqnGtTtGbbrdLu12m5MnTw6NG9yKrutOhWsgEBjq9uP3+5mamuLSpUu0Wi3W19fRdd2p2K9Wq06nvtfr5cEHH6TZbDriD7A6OqPRKKlUisnJSRKJxHUVwfBEYJqm85vux1Lf5/M5lbmdTmfodKOjo5TLZVqtFouLi2QyGeLxOIqiOO5CgUCARqNBMpkkEAiQTCZ3zCcSibC2tkan06HVau1Yx/0M6NjTmKbpxCYcNbVaDUVRaDQa++pgTiQS5HI5RkZGaDabe7pF2dM1Gg3m5+cZGxvr60Q1DMOJgkin0441/1FhmiaXLl1yzuWJiQmi0WjfNMlkkmq1ytraGqIocunSJW688eDiVkEQnOgNSZKoVquUy2Xy+TxgVQMfO3aMdDrtRPUYhoHX68Xj8TgxHz6fz4ljOX78+FCntImJCTKZjBOval9PfD4fqVTquotycXFxcXF5arN13L5ardJqtVBVdde2gE08HqdSqZBOp/tiqQDnOXyvZy+vF172skOv/hVD0zRWV1dZXV11RCuyLDuxVJFIhHa7Tbfb5fz58+RyOVZXVzlx4gRnz55FFEWmpqYIBALous7CwoLzrLu1UCQcDlMsFgmFQhw/fnyg6K1SqTjCHJ/Pt8Pt047mLhQKrKysIMsya2trTG9V1/Wwi1D2U3BzlORyOZaXlykWi060vB0DDRCLxeh0OjQaDebm5q5rR6PDYDvFLi8vO231rWyNupyfn8fv92OapiOe2i4+so+ZXC4HwOTkJLIsO5Fr4+PjBINBVldXWVlZQRAEIpHIoYohrhT1et0p5sjn8/j9fmKxGJIkoaqq87w/MTGBYRicOXPmui6s6na7pNNpWq0WyWTSaQdLkkQkEukrqvF4PITDYae47qACrMOIHO12sV3gsrGxQblcxjAMJ77PNE3GxsaQZdlpw9vRqbfffvtAsZcsy0xOTrKwsOAU7YyPjx/5NaBSqdDpdKjVakxOTu66DwKBgCNIsoVL14swNBQKceONN1Kr1SgUCnQ6HUe0WK1WicVibGxsUCgUCAQCjsv8xsYG6+vrZLNZNE3j5MmTRxaf6+LyZMLt6XFxcXFxcXHZF8rqKs2//3uMixeh24VoFPm5zyXy4hcjbOk8c3FxcXHZ5DrpewGswY+VlRXW1tbodrscO3asr2PSjnFLJBIsLCwgyzJer5ebbrppaCeTHYmgaRqJRAKwOuLC4TDhcNjpCLbnb1dnFgqFPUUe5XKZbrdLo9HY0y3L7/c7gxZ29Mna2hrtdptgMIjP56Ner/PII4+Qz+cxDANN04jH444Fu221PjMzw8mTJxkfHyebzboVf0OwO15tS/79YFfv7iZmCgQCHDt2DLAGkWzhjh33Zsf0ZbNZIpEIU1NTfYN8W5dlr9/Wqn5BEJBlmXa7vacQ0HaH83q9A5dxFKiqiqqqmKa5L2FiIBBwOre3Vh0PI5lM0mq1ME2TXC7H0tISsiwTCAT6OsOz2SypVGrogN9hsattNzY2GBkZ2SG+sonFYmiaxsbGhlOlO2za3RBFkbm5OSeKJZlMOr+/7axgO4lVq1WazSbVahVVVfH7/WSzWeLxOD6fj+PHj+8pLvR6vUxMTFw31dAuLi4uLi7D2KrR0XUdTdOcWN698Pl8jrhZ0zTa7bYTz2c/9/n9ficSaK8Y6K3ulFciGvkgFAoFut0utVoNwCk+yWQyjouIqqq0220qlQq5XI5ut4vP50MURWZnZ4lEIhiGwYULFyiVSqyurtLpdAiFQni9XjRNY2VlBY/Hw+joKIqicPr06R1xV7a7r2EYuwoT0um04zobDAaZmJjYc59fDYrFIsvLyywvL9Nut0mlUo6YxDRNp504Pz+PYRhIkuSI658q2OeN7SI9DPt8KhaLeL1e51iwhWw2tsOVfcxsFbsJguC4RNsFTY1Gg42NDWZnZ6/YNh4EO3LTjlGbnJzc8Xxu7wdbQDY/P8+pU6cOtTzbScx2A7LFXldTdGM76cVisX0V6Pj9fqdP5qB4vV4ndq7RaOwptGm1Ws4yC4UCa2trNBoNyuUynU4H0zTxer1Eo1Gnn2BmZsY5pwOBwK5OW4IgkE6nWV5eptvtUiqVjjxGrtVqOe38/fT1RKNRCoWC4zR3PYn7BEEgFov1CalzuZzjOG5fY7fea7YWaNm/4U033XRN3ENcXK4lXAGWi4uLi4uLy65oxSLVX/kVpP/3/xBaLTy1GpgmpseD8alPUf7d30W87z7ib3jD9aU0cHFxcbkKXO5lUdM0isUi5XK5r5MvnU7v2dGnKIozIGCaJrIsk0wmh3Yi2dNWq1VmZmaGdhz5/X5GRkZYX193nGmGxaLZ7kLBYHBgh4woin0DA+Fw2BE87UW73abVajk26HsRiUR49NFHCQaDTkzd3NwcXq8XVVUdQVY+n6fT6TjV6pFIhEajQS6XY35+nvn5edbW1njGM55BqVRiamqKbDa75/Kfati/rSzL+4rnMwyDbrdLMpnccyDPdiBbWloiHo87Ihk7zsX+Nzo6OnRAzl6nrQOHfr+fQCBAPB6nXC4PdM7ail3parsJXAkEQehz2toLW0y09Xt7YVe6ezweFEWhUqmgKAqiKJLNZolGo/j9fmeA8CgpFArUajWn83c37GgHu0r3MAIssPZNMpkkmUzS7Xada6TX63VEbiMjI871wHZfsAVa6XSaRCLxlHNccHFxcXG5fuk9HlwWWx+L7Oc82+Vpr2cO+z6qaRrnz593BEvVatX5zH5GX1tb49ixYwOfC7a7qdj4fD4ymcy+xFtHTalUolwuO89phUKBeDzuPBtGIhFarRaNRsMRcZumiaqqpFIpx0locXGRUqnEwsICwWDQeT6zsSO6lpeXAf5/9v49xpV1z++Cv3Wx6+KyXb7bbbvva+119j57Ajpck8lkIFEgCVEuQDQIKYwS9EqgkIAYhEQUASGTREigd/5AASkR/EEEKBOUZCbJKw2gQZOECRNmyJwzZ87eq+++22XX1a4quy7vH72f53Svvrl79VrdvXd9pC3t1e22y+Wqx8/ze76/7xdHR0f41re+RX+/Wq1oVLyqqneeh0KhgE6nQ+d+H9vp6l3iOEa/36drse3t7UvrUeK8pCgKFWkR99zn5Mb0IQmCAI7j0PX3Xfedqqq0uYicJ9u26drZdV14ngfbtpHL5eg1M5/PwXEc0uk0FV4IgoBcLgdd15HNZtFqtZ6FkysR9miahkajce1ahYh2SDMHqTncR7jpui4GgwFdJy2XS1qbIeNPrVb7KOsD8hrrOjCTGPWHiMQ4joOqqigUCuj3+3c6+M5mMyiKgtlshkKhgF6vB8/zkMvlUKvVqCuWpmlgWRatVgu2bUMQBLiuu5ajoqIo4DgOjuNgPp8/ugCLNAlyHLfWOeM4jn4fRY/xZfuExHFMXa8YhsHm5uaVa5plWboWHgwGyGazmE6nSU0sIeEdnv4bMiEhISEhIeHZshoOYf+JPwHu+Bh8vw9usUAoCIg5DuxyCWYwQKCqiP7b/xazfh/F/+g/SkRYCQkJCRd4nyFxMBhQNyrTNGmRT5Zl6Lp+oyDC9310Oh2YpgnP8+C6LhUXjMdj2uX8rnhhNpvBMAwIgnBnEZvYkVuWhel0eqP4hLgZrVuIZFl27Tg38rh1N1miKIJlWfA8D61W65KdfL/fx+npKS0gqaqK2WwGWZbh+z5qtRoqlQoMw4Dv+xgOh/je976H3d1d+v4eu/D3dUBVVZimiV6vh2q1eutnRUQ4iqKsJWbKZrP49NNPMZ/PMZ1OsVwuwfM8stkseJ6/sytb13VIkoR0On2p0FupVGDbNqbTKUaj0Y2d5aZpwrIsbG5uolgsfrDNPlEUaQSebdvUSe4mHMdBOp0Gx3H32lSo1Wool8s0jiYMQwiCgEwmg3K5jFwu90E6u4mQMpPJ3DlOsCyLTCZDN4seA0EQrrhHECRJwubm5rWxPAkJCQkJCS+Jf+/f+5/wi7/4Q+cbhrn/vOXiNECWZSpaJuKN27AsC5IkodfroVKpoNfrIYoi5PN5ZLNZhGEIy7Iwm81QLBYRhiH29vYuzQmHwyGNQtd1nbp0kkh04syzv7+/lmvoY0HEGL7vU4eZd8+HLMt0fcXzPHUq9X0fuq4jn8/TGDgSrU7WcSzLQhRF8DxPBQz9fh+ZTOaSI+hyuQRwvhZcZyOciHB836d/+5SQtauu66jVajc2AzEMg0ajQZ2TJ5PJvWPVXirk+iLOrHdBGk2IY1Icxzg7O4MkScjn83BdF8vlEkEQUGcj4m6TzWbpWoCQy+XQ6XQQxzE8z3sWsWOapsE0TfA8f+c4VCqVYBgGdam7y0WbYFkWDg8Pae2DNIGROkA+n8disYBpmnj16tUHF4GKoghZljGZTK5Eul6HbdvI5/MPHhcrlQpmsxkEQUC320W73b72PRL382q1Ct/36Ti/t7d36Riz2Sx1JCfX03w+p46G60BET+86uj0G5L656PR4G77vX4p8fMmQ+EXDMO6Md8zn8zQqdjKZvHgBFqkXEhdx4qL/UiIlE54fL3s0SEhISEhISPhgxGEI6z/+j8EdHiJ9coIgm0Xw+jXw1YItjGPAMJAaDsEeHiL4W38L5uYm8j/xE0985AkJCQnPh4eu1c/OzjAcDjEYDGDbNkRRhCiKtCNtNBqhUqlgtVphf3+fFhtd18WXX34JwzCgaRpc16W28USgUiwW4boutre3Lzn8kI2DddykiBDsroI9iSm0bXut7njXdSEIwlpxbqQwFgTBWoUxTdNo92+9XqfHEkURzs7OMJ1OEYYhtra2LnW2tlotKq7KZrPUHh847w4eDoe0M/SlF9wem0qlgul0Cp7nMRgM0Gw2r70GlsslJpMJCoUCJEm6l7NRJpOh16yiKDg+PsbR0RF0Xb9RrEQiYDY2NqCq6qUNnlKpRAvynU4HnuehVCohk8mAYRi4rovZbAbLslCv15HP51Gv1+95ZtZHURTqyjWbzW7tdg/DELquo1QqQVGUtQVYxFFhMplQZzDiasHzPI1m/BDcV0hJiu0vvbv4IazZ5J6QkJCQkHCF73znD+CLLx7v+VRVhSiKUFUV0+kU2Wz2xrmC67qYz+eQJAkcx6HT6VA3lIubu+VyGY7joNfrATifi3z++efgeR6j0QidTgf9fh+O4yCXy6HVaoFlWfi+D8MwMJvN0Gg0EEUR3rx5c6PA+rEhzjKe58H3fSiKcqeoXBAERFEEx3HAMAyNVDRNE/l8HgcHBwjDkDrXcBwHRVFQLBZRLpeh6zosy8JkMnmwI+hzwzAM2LZNna5s26Yb4alU6pJYn+d5KIoCwzCg6/o3RoBF7jGGYdaaC3ueR+PEVFWlDmm//Mu/jHa7jXQ6fSmKOwgCTCYTAOdiq3fdgMla9znNxUkzx21jECGVSkEUxUsRc+s8/+HhIXUFyufzqNVqEEWR3sOz2QzHx8fULejVq1cfVLRRLpcxGo0giiKm0+mta1FyH6mq+uCGMSKYCsMQnU4Hh4eHUFWVjnWu60LXdVo7WSwWEEURpmlif3//2hoJy7LY2NjAyckJFdSmUqm1xKDEQZDjuA8idsvn88hkMuB5HqZp3uoOGMcxDMOAqqqQZfnF14Mcx6ECw3Wcr/P5PBVtrVOTe46sVisMh0Pa1EeSB9LpNERRpO7qift1wn15eXdDQkJCQkJCwkfB+cVfPBdfnZ5iVSggbjYvKwkYBigUsFQUpN++BdvvI/hf/hfE/9q/BuYFTrgTEhISnguaptFNhiiKsL29falbMY5jmKaJ4XCIKIrAsiw+++wzsCyLg4MDTKdTDAYDqKqKjY0NKi4JggCmaWI8HtPCVjqd/qCdq4VCgToJOY5zaxHH930sFgvUarU7XX6Ay4UxwzBuLSgS4RrHcchkMpfes2VZtBhNimukQ/hdIRjpFiZFQvL/5XIZ0+n0RrekbyrEzardbuP09BSnp6col8v0/IdhCNM0oWkajXbb2Nh4cMG6XC5jPB6jXq9T97hisXjpHiDiRFVVrxVPkaI5EQVNp1N0u10qIIzjGIqiYHNzE/l8Hvv7+x98g69ardK4kV6vd203ahAE6PV6tAt73Q5U13VxcHCA+XwOXddpFBBxIyNxf61W60HX92q1wmQyufS8kiShUqkgl8tRgddisVj7eLPZ7FoizQ9JHMewLAuLxYJ2a+dyuY/quJGQkJCQkLAu76uV+MmfvPxvhmFQqVTgui5OTk7Q7XbRaDSubL46joN+v08jvdPpNGRZRqPRuPZ1FEVBs9lEp9Oh6wgSY9Xv9+F5HnZ3dy+J52VZRqFQwGw2w2AwoCKv/f3993vTa0Ji2sim9W0b0FEUUXE/cW+Jogjz+Ryj0YjGxZmmicViQV2BSfybZVk0Htq2bTiOQ5+bzI1ILN9dTTVk7rVu88uHZrVawXVdOI6Dw8ND6swEnAt/SOQ3iSuXJAm2ba8dg/l1gLjckvd+W1yb4zh4+/YtNE2jLnNBEGC1WsG2beoGHEURVqsV+v0+dVNqNBqXYjQJxJGbZdmPHvV5Exfj4taBNHOsG9/X6/Xo+rHVal2qI7Asi1wuh2w2i36/j06ng3Q6TSMiPxSiKCKXy6FSqaDb7YLn+WtrIWT8LZVKEAThiqDuPmxublLHL1I7mU6nAC7HFEqSBEEQ0O/372xQI7Hww+GQXtumaaJSqdx6P9u2DeD8++JDCFAzmQz9XiEC45sc+abTKYIggKqqL94BCjivz4RhCI7jMJ/PYds2XcOTMfjiuUilUlSMSZq3XhKu6+Lt27fUAZ00jgLn13U+n8d8PodhGNjf3382417Cy+Bl3Q0JCQkJCQkJH43l3/gb4GczxACiWzZDmVQKq1oN6cEA0WQC5//4P5D93b/74x5sQkJCwjPguhreQ5qkRqMRLeRsb29fKWIwDEOLWZ1OB9lsFpqmgWVZzOdzDAYDVKvVaztWS6USZFnG6ekpJEnCcDikmxNk84AUtG6DOElVq9Ubi1HAeXe8IAhQVZV2aV5X4A/DEIPBALIs0y6zuyCFsWKxiMlkcqvjz3g8RhAEkCTpynkhNuOr1Qq5XA6LxYK6AF13rLlcjtr9x3EMURSh6zrt5P9YrFYrGIZBO/TIeX5unXk7OzsIwxAMw0DTNHS7XdrJT7oki8UiSqUSWq3WexWGeZ6n4ime56FpGg4PD6+4wNVqNRSLRWxvb1+7OZVKpfDmzRsMh0NkMhn4vg/f92mUpyAIKBQKaDQaH8VdoVwuwzAM2nV8cHBABYgkssE0TaRSKWxubqJcLq8lYvR9H19++SUVbQqCgHK5DEEQaBRQt9uFoih0Y2vdwnIcx+h2u5hMJnBdlwqwWJalMaqSJFFR43Q6hed5t7p2ua4L3/fRbrc/6KbGbcRxTDdIL0a88jwPURSRzWZRr9cfZTPg7OyvYLU6H4t4nscv/RKPP/bH/vh7P29CQkJCwjeP9xFg5fPAj/3Y1Z/X63U4joMoitDtdnFwcECdO+M4hm3bmM/nVBAEnG9Wf/rpp7e+HpnT67oORVEQBAFc14Vt29jZ2blx7VEsFrFcLqFpGhRFwXK5vHWd8liUSiVYlkUbOG6bGxLBVCaTQRAE4DgOLMvCtm10u13M53P4vg9ZllGtVsHzPBVoaZpGYxeJk8/F+K10Oo1sNotCoQBN01AqlW5dF8xmMyiKgnQ6vda88UOzWq3Q7XYxHo+hKAp9rwBoY4DrurAsC1tbW3TTnziQfRNgGAalUgm2bePs7Ay+79PrjTiwxXGMIAgwHo9xdnaGxWKBSqWCzz77DLIsYzwe4+TkhEb3kfl1Op1Gu92GLMvI5/NoNBpXzqthGMjn8+B5nkZqPjWkmWOdePI4jqlL3TpCEeJKR1yIb2pcuxiLSZzpPvRaZWtri65DiEiMiGNIk5PruigWi6hUKtje3n4v8QjDMNje3kahUMB4PIaqqtTJmGVZCIKASqUCSZLw9u1buK57q3MUgYjXZFmm9azpdHpjc10YhphMJsjn89SF8UPQaDQwn8+pyJgIX8mY6vs+ZrMZTNOkwrznMI6+LxzHwXVdHB8fw/d9uK5LhbDEcS2TyaBeryOdTmO5XNLr6qWJk5bLJRWpDgYDZDIZtFotOoch7naWZdG40tevX39jvm8S3p9EgJWQkJCQkJBwhTgIwP36r4PVdQSqCuauzdxCARgMAMPA8h/+QyARYCUkJCQ8CNu2aTxauVy+tTBINjjI5gTDMNB1/c7uRkmSaJd4Pp+nhdtSqQTTNDGdTjGfz2/tmrYsC3EcI5fL3VpYY1kWzWYTq9UKy+USx8fHKJVKtHAbRREsy6Ldk1tbW3eKui6ysbFBIwSIu5KqqrT447ouptMpHMehj333ueM4ppsXHMfR7mCO4260ywd+2G17UaDzMfA8D4PBAI7j0AhIIsASRRGlUgmNRuPZFMA4jsOrV68wGAwgSRJ1OiMOU0RIV61WH6VoKUkS3rx5g06nA0VRLolkiOObLMtotVq3OrJxHIdms4lGowHTNOmGRiqV+uhxkwzDYG9vDwzDIJVKwbIsGIYBwzAAnBdDieinVCphe3t7reftdDowDAODweBa57lsNgvf92nkD8uyV7peryOOYxwdHUHTNPT7fRo7RDYKh8MhRqMRqtUqstksdRUbDAbY3Ny89tolIs1sNkvHq49NFEU4OjrCdDqlm1bEhTCKIirMs20bm5ubawlJb+N73/uTiKIfxqN88YWE//6/TwRYCQkJCQn3574CrH/inwB+628FfB/4Hb8DuG6Pm8xPOI5DOp2G4zg0Ro6IpogAnjjxuq6LbreLfD5/61qHRND5vo/JZAJd15HJZO6MVy4Wizg8PITnedA0DRsbG/d74w+gVCqh1+uhUqng9PQUYRiiWCxe2aQlMdaFQgFRFCGVSkFRFBqhTdxIv/3tb18RtxDHm+FwSBtv2u32lTlTpVKBYRiYTqfo9/s3xn/rug7HcbC5uYlisfjk64YgCDAYDLBcLtHv9+makHzeRHjV7/dRrVZxdnaGKIqQz+c/WtTkc6FSqVCR2tnZGXK5HBXuERHgeDxGt9uFZVn4zne+g729Pbq2J3P+2WyGL774AoPBALlcjroRNRqNa+8zy7Iwn8+xs7ODcrn8bEQIqqrCMAx0Op07I9Dm8zmCIEA2m11LuKPrOl1L3jWWkHUSEUJ96Di2dDqN169f4+DgAJIkwTAMWJZFG58URUGj0YAkSdjZ2Xk0oVI+n0c+n8dyuaTr44uCPOIgtG7EPMuyYBiGNhY1m030ej0EQXDlO4I4BbIsi0qlglqtdq/rcLFYUDcnQRDubCQkohtN0zAejzEajWij0nK5hCRJ2NzchKqq2N/ff3aNcA+BCMvId7ksy0ilUojjGLPZjAp2Pc/D5uYmjc2VJOnFuV8NBgOYpnljLYQ4pg8GA3Q6HaRSKei6/l4NgwnfLF7WHZGQkJCQkJDwUQhtG4hjsGGI8I4CFwCAZRGlUmCCAFjDOSUhISHh6waJ9HsX110AWL871HEcWhhax0FFVVVMJhPaTW2a5q1RfARSdCVdndVqlW4ekCLD5ubmtUUp13UxGo1QKpUgiuKt0QfAuXvParUCcN5pres6xuMxFS0Qy/pSqYRKpYJWq3Xn8RPy+Tw2NzcBnHfU67qOyWRCrdCDIICiKNje3qaiqpOTE7iuS6PCiCsXy7Jw3XOxQxzHWC6XKBaLVwrQrutScZYoiveKMHhfHMehnbUkmoTE4jEMQwvwlmXh1atXzyJOBPihEK/RaEDXdRrbRmIwfN+nEYUcx9Fr4a5NtpsQBAH7+/vwfR/T6fSSeKpYLN4ZyfLusX+Mblbi2GDbNhX1ZLNZlMtlpNNpsCyL/f19mKZ5pauaYRgaO3ibqOwi5N4nz3XTexQEAa1WC8fHx3Bdd63NTLI5eHJyAp7nsbOzc+mzjKIIuq5jOBzSAn2tVkO328XJyQlKpRLtMCYiTU3TwHEcGo0GarXakxR4T09PoWkaTk9PwbIsFb0xDIMgCOjYRlw+iFgvISEhISHhqbmvACubBf7lf/nux7Esi93dXSwWCyqUchwHnU7n0hzVsiz6HSlJEo2a29rauna+Sho1SGOE67prfaem02mk02nq6vMx4DgOm5ubVABxfHwM4LxRhDi+2raNxWJBxQtEIHFRBLBcLiGK4o2ihVQqhWq1im63iyiKrm0mILFxrVYLnU4HR0dHKBaL9HGe59Eo9UajQZ2OnhqyNjRNE6VSCZIkXRKhZTIZZDIZOtcKggBRFGF7e/u9Be8vDVEU0Wq1EAQBfu3Xfg1ffPEFdb2KooiKHIlAYrlcXrlO0uk06vU6yuUyvvvd79J18nK5hOd5dO0BXI5wr1aryGQyzypqrVKpYDKZIJ1OYzAYoNVqXSvKCYIAw+GQivbWWd+tVit4ngfHcaDrOoDzsYk0k70LaY4jf/uh1yuiKOLTTz+9sgYBzseLSqWCcrn8QWoCZKx9l1QqRaNYPc+7M57d8zzaKNVutzGbzcAwDEajEQzDgCRJ4DgOnudR8Vy9Xke1Wl3LfTwMQ2iahslkAt/3EUURdc3L5/OoVCo31rNqtRrS6TREUUS5XIZt29R9nDi3q6qKzc3NZ1N3eR8uNnkRkXCz2aTXMRFSj8djRFGE73//+xAEAVtbWy9uHA7DELPZjDpB3jQeEHe7w8NDWjtJBFgJ65IIsBISEhISEhKuwH61QIoZBsxqhTu3leMYTBgCLAvmG9Z9lpCQ8M0miiJ0Oh1omnatCOf4+AiCcC5AWKc7NwxDKkpap2OQ53mEYYgwDMGyLIIguPN1SHwgETSQ/wqFAlqtFsIwRLfbxdHRES3i8zyP5XJJNxZUVUW5XMbm5uZaXYekkzaTyaBUKlEBzsXiVbVafVAxt1KpIJ1O03hB0tnKMAwkSaJuOfV6Hd///veRzWYxGo2wtbVFC2+kqD+bzZDNZrFarWjX6sXiPxGO5HI5yLKMXC6HXq93Y7TiY+J5Hg4ODjCZTDCZTFAqlVCr1SCKIo2hm06nODk5QbvdBsMw+OSTT55VJybLsiiVSlTsp2kaFosFDMOgsREkBnM8HqNard5YRF8HQRA+ivPB+xCGIc7OzjCbzeB5HmzbRhiG4DgO2WwWg8EAxWKROkORjTsiXgPOC+D3dS6YTqdwXRee56HZbN76WFEUIcsy3Xy57ZySrvvJZAKWZbG5uQmWZRGGIVzXpeKyfD4PjuMwHA6xt7cHnuextbUFTdMwGo0wHA7B8zyNLSROGbVa7Uk+08Vigdlshk6ng3Q6jVardene4nkelUoF2WyWCrU+ZCxGQkJCQkLCfbivAOu+6WKyLGNrawuNRgPf//73Ua1WMRgMkM/nUSgUUC6XIcsyut0udbUkTkY7OztX5que54HneTAMQ3+37nyQCL8+VoMEcO6C9emnn2IymYDneZycnMBxHCiKAo7jIEkSGo0G3cjmOA61Wg3lchnj8Rg8z9MmBF3Xb3R2EQSBOh2FYXhl05u4kgHnc5PZbIbJZILhcEh/n8vlsL29jWw2i1evXn2UmMbbiOOYrgmI0yo5J+8KIwqFAgzDwMnJCba2tpBOp5/EFfWpqdVqODk5oet+EvUpCALiOEa1WsV0OqXr4V6vh52dnSvrVZ7nUa/XMZ1OwfM8jZcbjUZ0jUnuxUajgUKhgP39/WclNiHu3q1Wizb0lMtlGhd4sZmDRNE3Go0718jL5RLdbheHh4cYj8f0fkylUphMJlAUBZVK5VLthQhOgfXHq/fl4vqajAssyz7ZZySKIl0D6bp+p9DNMAzIsgxBEFCr1VCtVnFyckJjSEkUKYmtJ+LBdYSjvu/j7du3VEBnGAZ1PpckiR5jtVq9sa5VKBRQKBRgWRZ0Xb9UZyqVSk8+fj4mg8EAhmEglUrh9evXmE6nlxoveZ6n0bbHx8eQJAnf/va3aR3uJWEYBpbLJXWCvA2GYei1UiwWL0W/JiTcRiLASkhISEhISLgCK4qImk2E/T4400RQrwO3LB7j+RxsECDO5cC+evURjzQhISHh6YjjGAcHB5jNZuj1etf+/vT0FIWCguVyiU8++eTOhTrHcVSsQEQYt0G6VcnmBMMwiG7YYSHFbeJ8NRqNEEUR3QgQBAHVapWKMUiRiXRxA+fdx6Tbent7+073q4uQ4tV8PqfRJCSCbh23r9sgohQS3XGxMFYsFul7LJfL8H0fJycn6HQ6VMBEOmfPzs5orB9xSSKfGemeXC6XaDQa2NjYoKKSer3+wYtOg8EAlmVhMpmg2WxeKmaSLnpFUTAcDtHtdpFOpzGbzdZyRPvYdDodDAYDdLtdLBaLSzEQi8UCZ2dnEEWRdnJvbW097QF/IMIwxJdffgld1zEYDKgzG+kY1jSNxjZ6nofXr1/TMYHn+bW6qkn8J+ksJkViEqdxV/wCQVEUWJaF1WpFRVTXoes6dddqNptYLpfQdR2maSIMQ7oZyvM8FEVBFEUwTRPtdhvlchmSJKFSqWCxWNAxkIg06/X6k3XXEqfB5XJJRWXXQcSkmqahVCrBtu21Xcku8kxSXRISEhISviZ8tee8NvcVYBGGwyFs28ZgMECj0aDzuyAIaByxoigQRRHj8RjpdBqGYVyaR5O5Qa1Wg6qqcBwHqVQKruveuZlPoqHS6fRHFyDUajX8rt/1u/DLv/zLkGUZk8kEYRhCkiTqzhtFESRJwsbGBorFIlqtFrrdLoIgoM5Pg8EA4/EYhULh0hyNuI8Qp2Se56+dY5D478lkgkwmg0qlgtVqRaPC0uk0FbU/ByENiZo0TROvXr2CruuI4xiTyYTOo1KpFMIwxHw+p1HmuVwO5XL5yeMTnwLTNKnIZmNj45LLEGkCIA1IJDZsPB5f23RB7starUYdgcicHwAVM2UyGWxubl6Jx3wOELdrhmFo/CLLsuA4jjoWqaqKSqWCer1+p3OS53n48ssvoWkaXaeRRrcoiqgL+Hw+x+bmJj3/juNAkiQwDPMkwpx114cfmkqlAsdxaGz7TfUIElvbarWgqiodj16/fg3XdTGZTGgDD8/zUFV17cjU1WqFL7/8ErPZjNZGSKxpFEWwbRuj0Qi6rtP62W01h1wu9941q6eENBiSa/PdtSxxpZzNZtjd3cVyuUQqlYKmaRgMBrSOQFz2fN+nUbv7+/svbhy+GKG5zphGvtOB83OZCLAS1uHpR+OEhISEhISEZwn7e38v4uNjcJqGwDSBmzr44xj8aIRAURArCrK///d/1ONMSEhIeCq63S5ms9ml7tOLnItiMuh2uwCAg4MDfPrpp7d2QyqKAlmWwfM8LMtaq2Mwm80ik8kgjmNkMhlYlkU7PglxHKPb7cI0TUynUyomyOfzsG0bBwcHyOVytJBSKpVgmiaNbSPRbblcDsViEdVq9cHFV+I29SG467lbrRaNGez1ejg6OqJFZ47jEEURDMMAz/NIpVJYLpfUkYhEh5TLZVSrVTQaDfT7fSiKQkUjH4rVagVd1zGbzZDP528VdNRqNVrMnEwmz06AZZomhsMhOp0OoijC3t7epQJ1sVjEarVCt9tFp9MBy7LI5XIfJQbwY3N6egpd13F6egpZlrG7u3tpLPF9H5PJBCcnJ1TQubu7u9ZzE4t80zQv/ZxsxBEh1Lrd2SzL0r+5zVHCcRzM53MqBj05OaGiy4uxGKIoIpfLUYFYuVym0ZOWZdEoRo7joCgKjfp7KkjXdDabvXNjI5/PYzQawbZt6qqXkJCQkJDwlPzFv/jbMJ169N8sK+JHf/Tv3/j4h5gKhWGI6XRKv/suukCSuD1FUWAYBhqNBhaLBSzLuuKeQdxmcrkcqtUqUqkULMtCt9tFtVq99XuYzOMzmcyTzB2LxSJ+5+/8nXTj37ZtuK6LVCqFdDqNfD5PxTGbm5uX5lQXxUSTyYS67JI4RhKDXq1WwXHcrbHtDMNQd2HHcehGM8/zNOb5uUCEAVEUQVVVZLNZdDodiKII27bpWow4J29vb2MymaBarV5Z835TmEwmsCwLLMvit/yW30KdsOM4RjabRb/fh2EYdH1vmiZkWaYOz9eRzWbx7W9/G67rYjabUQEWcZP+UOv3x4BlWbx69Qr9fh+iKML3feqcRBq+1m3mCIIAb9++xXQ6hWEYEAQBm5ubSKfTqNVq8H0fjuNgNBpRQcvOzg6NWm21WigWi8/qHvvYlMtlTCYTKjD1PI8KTAHQBh1d11Eul5HL5a44WkmSdKcz0W30+336vVEul6/URGRZRqlUojUH4kD4dRpTiHv7ZDLBfD6nPyeuaZVKhX4mpmnC8zwsl0vqgJ9KpcDzPFarFRzHoeNHpVJBoVCA53mo1+svTnxFILWFi851N7HOYxIS3iURYCUkJCQkJCRcS/YP/AFY/+P/iGWlgnSng2UUAYXCpZb8OAzBdbvgXBfL/X1Ev+N3gP8abpAmJCQkvAtxQxqNRkilUmi329c+rlwuQRR99Ho9yLIMy7JudY3KZrPUDn06nd662U8K+o1GA/V6Hb7vw7ZtWmS6KGoZjUYwDAODwQAMw9ACZL1ex+7uLhaLBSaTCX7913+ddnRe7NbmOI4W7d9HfPXUkA69TqeDVCoFz/NgWRbS6TRevXoFRVEwGo1ogWmxWODLL7+kEYflchnNZhPVahXj8Zh2Hdfr9Q/aQU4s0heLBba3t+98j6qqwjAM6vj1nDr0xuMxLMvCcrnE7u7utdc3uaeOj49hGAZ1Afg64fs+dF1Hv9+HLMvXbqIJgkAL14PBAKIowvM8iKJ44/PGcYyTkxNomgbLsi7FO6ZSKaiqCsuyMJ/PwbIsfN9fy21vsVjQbtnbHkvc+5bLJfr9PqbTKWzbhizLqNfrNDZ1Pp9jMpnQjmbDMOhYQxztngtRFCEMQ6xWq7WOi2VZiKJINxQTEhISEhKemk7n17BaufTfLCvd8uiHOTGapkkjfd5dGzEMg3K5jOVyieVyicFgQBtOisUinatomkYjgbLZLHV6mk6nSKVSGAwGaDab14obPM+jwhxZlp9sI10QBHz++edwHAeTyYRGrxPxE4lPB87nTUSc5fs+Xr16RdcYROhB/pa8L9M00Wq11l6PEZfcl0AcxxAEAbu7uzBNE7quXxLyETGNIAhQVfUbuSFOnNAMw4CqqnReTtaioiginU4jCAIsl0vkcjn0+30sl0t6v13EcRyIogiWZek1el+nnziOaWz4U4kxGIZBs9lEo9GAruuYz+d0bUHe0zrXC3Fe6/f7qNfrKBQKGI1GGAwGNFJPEAQIgoDJZIJ0Oo3RaITVakUd/p7Ksfe5wLIs9vf36fnXNA0nJydgWZaKBUVRxMbGBlRVxd7e3iUXt/clDEPMZjNomgZFUW5sSON5Hq1WCwcHB7BtG+Px+NHGyvs2Oz02y+WSvi8yXhDBoCzLsG2bOqvX63WEYUjvYVKfIUIr0zSpEJZlWeowOZvNqGv5c6o3rQNxAWcYBvP5/M7PfT6f0/f40t5rwtORCLASEhISEhISroXL5ZD6038awX/6n2IFQOh2EY5GCPN5xBwH1vPAWxZijsNydxfRmzdQ/4P/4KkPOyEhIeGjoGkaFTxtbW3dWFhhmPNO6NlsRuPj7trAr9frWCwWmM/nOD09RaPRuFRgJ7Eco9EIlUoFsiyjXC5jtVphOp0ik8ng7OwMrVaLigBI5xuJKUyn01BVFYVCASzLQlEUzOdzHB0dwTRNSJKEnZ0dKIqCdDoNx3HgeR4VIhCx0kuEZVlsbW2h0WhQkUoQBNjd3aXOO7qu4+3btxiNRvA8D0EQUBceWZaRSqVopGK9XsfGxsYHPWZSQAdwq/iG8Fwt0n3fh2VZmM1mtLPyJnieR6FQgK7rKBaLNJ7v6wKJVPA871YHA+Dc1ezg4IBGMdwk+ASAk5MTjEYjdDodrFYrGrfBMAwWiwV1UiMudwzDwDTNWyM0gyCAbdtotVp3CuFIjOpoNIIsy3AcB41G49I1mEqlIIoistksvvzySzAMg9FodKuz1lNCxvfbIl7fJYoiMAzzjdwYTEhISEh4+Xz72/f/myAIqAvPdXM2VVXpfHY2m0HXdYzHYxoTlkqlkMlksL29TTflGYahblbtdhtnZ2c4OTmhzrwsy15yU1FVFaVS6c651cdgXeGTqqqYzWbUeXdjYwOVSoWKSMimtyRJdE1YLpc/ePz5x0IQBKRSKXAcB8dx6PqUrLXIdUUi93Rdp+7FN8W8BUFABTgsyyKTyTyLaLbHgDhT+b5/rbAkn89D0zQIggDLslAul8Gy7LWNAcTZpt1uo1Qq3XvealkWba4h8/hUKoVyuYxKpfIkEZfE3af0ABs/En1JrrFisUivJdKANZ/Pkc1mIQgCWJala69Xr15ha2sL1Wr1WbuFfSwEQcCbN2/Q7/eRyWTg+z48z0Mcx0in09QNudlsPnpzn67rVMB6l4sWz/PI5/PQdR35fH6txqSbWC6X0DQN0+mUftcRJ/lKpfLRajJBEFyKX+Q4Dqqq0vhFy7LQ6XSgKApd25J1axzHl9yeeJ6/9n6yLIs+5iW6vamqCkEQkM1mMZvNbv2uJi751WoVuVzuWUT3JrwMvh6zjoSEhISEhIQPgvJjPwbnz/5ZrP78n4dfLoPRNLCOAyaOEfM8lq0WUCgg/Pa3of6FvwDuhW7GJyQkJNwXx3Fg2/ZXxeIM/vpfLyGOrxYsGQY0Qo1EKNxFqVSilvnD4RBnZ2fUgSmKImofXq1WUSqVsLe3R2MCm80moihCv9/H8fExLXZNp1MMBgPqsFWv12mcIHBeQJlOp7SjdmNjg9qSA+ddhLquYzgc0kL2Z5999qIL2el0GhsbG1fEU6R7/tWrVzAMg26IKIpCi62CICCTyaBWq300Z6b7WKQT8cfFv3sOuK6LIAjg+/5aorVsNovJZILVavW1E2CRblQi6IvjGPP5nDodkI02UuTLZDK0+/QmNE1Dr9fD6ekpBEHA/v7+pQIyiVoYDofQNA3A+fg0mUwgy/K14j4ynqTTaSiKgmq1euv7ymaziOOYbiI2m80bi83pdBqCIMDzPLAsC9M0n2XHOBGtiqIIx3HujPVcLpfwfR+SJD0b8WNCQkJCQsK67O4Cdxiu3slNoupqtYp0Oo10Og1ZluH7PhRFoRvUoihSsdXFdcb29jYVIk2nU+pIQzaMibtvoVDA1tbWvR18npJsNktjCcfjMURRhCRJV+ZcQRDg7OyMxqk9xznTQ1AUBaIoQlVVKqK7uH7heZ5eC3EcUxciWZavCF0WiwVGoxF0XUcYhnROzXEcisXis3VyJlFhmqbBdV3qPE0aKdZpwCGkUikoikKdm24TqXU6HUiSRCPK1yUIAhwdHcGyLJimCdM0sVqtwLIsbcAYDofY2tp6kBDqqZjP51gulzBNE/V6HcD59be5uYlOp0NFbaSBi6x3Go0GyuUyNjY2noX487mQSqWwtbWFZrNJRVFxHNNmtg+1TvJ9H77vA8Ba97uiKOj3+3QN+5Caw3A4RL/fh+u6lxyo0+k0LMvCaDRCrVZDs9n84PWZXq8H0zTR6XTod+vF1yTRpJ1OB/1+HyzLotlsQpIkGqV5V9OoZVlQFAUcx73I9S4Ras7nc5ycnGAymVw7BkZRREVsxMEyIWFdXm61PCEhISEhIeGjoPz2347wr/01OD//8wj/zt9BfHoKxDFiUUT8ne9A/MN/GPI/9U89zKM+ISEh4YVCYrZSqRR+/udL+Ht/7/oCBRkaeZ6nDlLrCGg2NzepqMr3fRrpwbIs7bySJAnb29uXurXq9ToYhgHHcXBdF7qu4+TkhEZZkA7DbDaLjY0NehzT6RSmaSKOYzSbTRoRRgoMHMehXC4jlUqh3+9DURRomkYLk18nRFFEu91Gu92mHYHEAYhs/GQymY8qBhJFkRa21rFIJ1ESwPOySCfXP4C1OkvJY6IoWtt56KUwn89h2zYYhsFgMMB8Pofv+3Bdl24WkQiRYrGIVCpFx52LxHEM0zQxHo/xve99D6PRCLPZDJubmxgOhygWi5euVYZhaGQp2ZjKZrPUTaJQKNDoUeJWFkURtra2UCwW7yxiq6pKBVUkevAmLMuisTtkQ/W5FjXL5TJs28bR0dGdYkASx0BiSxMSEhISEp4jxSIwm139+X/+nz+svEPmLSzLwnGcGzdwVVVFPp/HyckJ2u02ms0mPvnkE+pedZ27BIm0Go/HkGUZlUqFrq1SqRQEQUAul0Oj0XiRLr0bGxuwLAthGOL09BSqqkJVVTr/sywLuq7T9Vy9Xr9RWPMSqVarmM/nME0Tg8EAjUbjyno5jmP0+31EUUSFSRfRNA1nZ2ewbRuz2Yw2LQHnAgvy8+cmCiLzS9/3YRgGFosFFamQKPZSqYTNzU3qAgacr/Ecx7n2eq9Wq1gsFgjDEP1+H4ZhIJvNIgxDLBYLKpwicee1Wm3ttW0Yhnj79i1msxk6nQ6iKKLx4VEU0fejqipd+72U+XAQBHR9dFH0lk6nsbOzA8MwIEkSCoUC4jhGFEXodDqoVCrY2dm51aX4mwzP8x91jfeui9NdvK8gqt/vo9frod/v0/WvoijUgfrk5ASyLNPra2tra63nXa1WmEwmMAwDq9UKDMNAEASUy2XqFPguQRBgNpthMpnc2jwlCALa7TaOjo6wWCywXC4hCALy+Tym0ymy2eyNzlae59EkgFKp9CIdsACg0WjQSN9ut0sdGCVJQhzHcBwHuq6DYRhsbm6iWCxeisRNSLiLRICVkJCQkJCQcCecoiD/Ez8B/MRPnIuvViswX6NiT0JCQsJ94TgOHMchCIIbxVcXCYKARnOtW+BpNBqoVquYTqfQdZ0WXURRRLlcRi6Xu/a5iCsTKboQNy2O49BoNGhRgUBi0GzbhqqqWK1WCMPwSkQBAGrPrus6FEVBrVZ7Vg5LcRwjDEMqQntfSDEpnU4/aaE+n8/TjaW7LNKJi1m9Xr8z5u9jw/M8OI4DwzDwff9O+3bf98EwzKXO95cM6dqfTCY4Pj5Gv9+nESGu64JhGEiSRAVQcRxDURQsl0u4rotWq3Xpug7DEIeHhzBNE9PpFG/fvkWv14MkSRiNRnBdF5ZloVAoXLpXGYZBoVDAcDhEpVKhLhS6rmM6nVI3CY7jkM/naezAOsVihmGo0146ncZoNEKpVLokBCTiLl3XUS6XqZiVRDU8R8rlMgaDAfL5PLrdLtrt9rVuBLPZDIZhoNVqIZfLPVgA+YyG1YSEhISErymffQb80i9d/fkdxhc3Qpyc8vk8ZrPZjWsV4HwusFqt8Pr1a2xubuL169d3Pj/DMKjVaqhWq7Asi4rWSYTUc2o6uC/ZbBbFYhEcxyGTyWA2m+H4+Jj+nqxFVFWlLir3xff9S5v4z2luXS6Xoes62u02Op0ODg8PUSgUqMOV4zgwDANxHKPdbkNV1UuiHl3XcXp6in6/D8uykMvlaEPTarWCaZrodrvI5/OI45hGHD41lmXh4OAAk8kEmqaB53nqKuN5Hk5PTyFJElarFVarFfb396kzlqqqGI1GqFQqV9a96XSaujaZpokwDGHbNqbTKRUREUfsWq12L9emfr9Pz3c2m0W9Xr8kwCDR8d1uF8PhkLqBvwTB4MX48Hdd/FiWRbFYRLFYhO/7CIKANgi1Wi1ks9mPfrwJVyEiwPF4jG63S9emqqreKDJcLBb0+rxvvJxt2+j3++h2u1gul9jZ2bm0RiyVSlgul+j1euh0OmBZlo73t9Hr9TAajeB5HhVgAYAkSVQ8ub29fUXoPJvNaPzi9h1WliSCT9d1ZLNZ1Go1uK6Lk5MTdLtdbGxsXPmeWCwW6PV6yOVyVAz9UuF5Hq9evaJu1yQWmTS8ke9dUg/Z2dl54iNOeGk8n1lWQkJCQkJCwsuAYRLxVcKNrEYjLI+PES+X4IpFiN/6FphHEEEkJDw3SBzdaDRCFIVg2euvc4Y5776zLAvVavXeHdkcx6Fard4Z+/Uu6XQazWaTWpyTItB1sW/EeScIAupsxfP8jZ1sqqpiMpnQmK37RCJ8KCzLwmQyoS5ewHnxrFwuo1wuv4iC722wLItyuQzTNHFycoLhcHitfX0QBOh2u0in08/SIl1RFKRSKWSzWRiGcef9QB5D/uYlE0URjo6OMJvNqGPV6ekput0uUqkUjZLheR6NRgMcx2GxWMAwDHQ6HYRheGkjKooivH37FtPpFJ1OB4vFAqIo0uJpGIYYj8dIp9O0iHjRsU5RFERRBFmWsbGxgSAIqHtVEATUgUsURToGrSu2rFQqyOfz1DGORBim02kEQQDHccBxHH2cIAhgWfbGuKLnQCqVwubmJo1kPDk5gaIoyOVy4DgOy+USuq5juVyi0WhAVVVsbm4+9WEnJCQkJCTcyLe/fb0A66EwDINKpYL5fI7j4+MbnYzCMKSRPvl8/t7rHIZhqOPOSyGKIniehzAMwXEcRFG8stYqFovIZDIYDofI5XJYrVZ0TnYxCvo+wqEoijCbzTAej+G6Lv05OYfVavVZzLFZlsXe3h6A801xwzDoZjgAGlmWz+dRKBSwt7dHr6soinB2dobxeExFB++6F2UyGRSLRZydnYHjOCpiespGIhLjR8RXjUbjimhxtVphMBjg9PSUuuZubGygUqnQxoler4dWq3XleiKxjpqmoVaroVQqoVar0YYw8vnfJ64zDENomobJZAJJkm6MlJckCa1WCycnJ7Qx7CGiwY+NKIo0Us227RvrHIIgQBAE6LoOQRBoVPnHxHGcS5F+tzkWvVSWyyVt0CFOh7cxHo/R7/cxn8+haRpM04Trumg0GpeEhxc/qyiKYBgGvRfuK0wdj8dUELy7u3utgCudTqPdbuP4+JiOa7cJsE5OTmjMruM4kCSJujKRZi7SxLS7u3vJlcnzPCwWC/A8v5arHan/hWGIUqkEx3EQxzE6nQ4ODg5oY1UURXAcB67rQlVV1Ot1bG5uPota4PsgCALevHlzyTHsYkOnoiioVCooFArPqvE04WWQCLASEhISEhISEhLejziG8/f/Pvy//tfB/eqvAlEExDECloVbrYL5Pb8HuX/9Xwd3j8JOQsJzp1Qqod/vQxSz8P0lJEnCP/fPvb3mkVNMp1PEcfxkghgiFhsOhzTa7CJRFFGhBMMwcF0XlUrlxgIXicIAcCUO7WNDHIAsy6JRCiSqUZZl2LaN4XCIzc3NFxN9cBP1eh2aptFuZs/zaCcnsUg3TZMW+CqVyr0K6h8DlmVRKpVg2zZOT09h2/aNmz6O48CyLGr3/hiOZk/J8fExJpMJzs7OEMcxNjY2MBqNMB6PEccxMpkM5vM5BEHAcDhEo9GgUZdffPEFFosFVqsV/UxHoxEMw8DZ2RkymQxqtRo6nQ4sy0Imk4EgCCgWixgOhxiNRrTblgi4yDgQxzFkWUatVqNxp2SzT5KkW90rboJlWVQqFZimSWOGxuMxhsMhwjBEOp0Gx3GYz+fY2NjAYrFAJpN59kLJcrlM4yzm8zl0XcdgMEAcx+B5Hrlcjork9vf3X7QTR0JCQkLC159GA2g2gV7vhz979er9nrNarcIwDDpfPTw8vDRfnc/nMAyDCpuJq9PXFd/3qcAmDEM6j+B5njqMXkSWZezu7mK1Wl2KP5dl+d7x56vVCm/fvoVt2zAM41KMlSiKKBQK0HUd1WoV7Xb7yTeXeZ7H69evMZ1OqcCAOAyRCPhMJoM4jnF8fEyFJ8C56GA2m6HZbN4oCBBFEfV6Hf1+H6VSCbqu3+lC8yHRNA2u62IymaDdbl/bmJJKpdBqtXB6eorJZAJRFKlQq1gsIggCnJ6e0sg/IsJxXRe6rsN1XXz66aeXIgxJk8V9nX4A0GYDx3HujNuTJAmZTIY6Z29sbDz5NXYXxBGIiMZuW4MScSMZ3z5G9Gkcx5hMJphMJnBdF6vVit4HxK2nVqs96LN9LsRxDMMwMJlMYNs2/RnDMMhkMlSE+u61RCIAx+MxHetkWaaNg4qiUPeyra0tiKKIOI4xGAyoS9t9a3Wr1YqKRfP5/K3nned5OuaWSqUbI+0nkwnG4zHOzs7AMMwVR604jmHbNgaDARUKffbZZ3Qd/ZD4RdIEFccx9vb2wPM8UqkUdR40TZM6dW9sbEAQhPeqr5GYRCKuS6fTKBQKT3bdchyHer2OWq1Gay7EKfKlC8wSnpZEgJWQkJCQkJCQkPBw4hizv/gXwf6dvwNuNgM/m4FbLM5/xXEICgVEnQ7Mv/23If+X/yXE/f0nPuCEhMeBFM1nsxWiKILruhBFCQxzUdwUYzb7EiyrodlsQpblJ+nULpfLtPN5NptdKZQQ4VUURdB1HRzHQZblG7uriTiD/O1TcdEBqNvtIgxD5PN55HI5aj1/fHyMfD5P49yemyPUfeA4Dq9evcLBwQFSqRQVgGiaBuCHmwqkePhc3XdIrGa1WkWv10OpVEKhUKDdpkEQwDAMaJqGSqVCHZ1eMrquYzab4ezsDIIgoNlswjRNyLJMBVFkY3I6nYJlWZimiXw+TzcpM5kMFf4QF7rpdIp0Oo2NjQ34vg+WZcFxHHzfhyAItJjY7Xbp9UJeb7FYgGEYpFIpWuwUBOHeLhQXWSwWmEwmGA6H0HWdvsZ0Or0k2lwul/R1e70edF3HP/vP/rNPGvO5LuSaHI/HyGazdGMQOD9/lUoF5XL5xQsGExISEhK+/rAs8G//28Bf+AvAcgmk08C/8W+873Oy2N/fp3MM0zSvzFdrtRpyuRzK5fKdEUkvGcMwcHx8TOdglmXRjfFcLkcbRTKZzJU1YiqVei9xUBiG+PLLL+k6ieM4GkNPnEzIfI3M0Z7D2oFhGOpg7Hke3aAPggDj8Zi6XHmeB+B84342m0HTNOoSdhvZbBYcx9H47qcUYE0mE+i6fqd4hzgh93o9VKtVKhzb3t6mcYpEBDKZTAD80OGsVqshm83i1atX9xbwXYfrulgsFlQQdxe5XA6apiEIAgRB8CKEQaSJRNd1dDodtFqtK65IYRjSJoxCofBRagyk8UzXdei6DsMwEAQBANAxxXEczGazR/u8PzbEMVrXdfoZ+L4P4PxeV1UVlmVBVVXs7e3R9dZsNsNgMECn08FqtUKr1cL29jZOT0/putnzPHS7XTQaDVoXsiwLQRCg3W4/yFVxsVjQWuA69YpcLkcFYYvF4trPaDQaYTabIYoi7OzsXFlTks+a4zicnZ0hn89fcpgjYjxyz93l6OV5Hr0viQv+zs4OarUaHaPId4QgCHR8fkiELYliJJ8r+WyJo5yqqmg2m0/WxEREfgkJj0UiwEpISEhISEhISHgws//qvwLzcz8H/vgYrO8jyOcR1Ovn1VzXBT+dgp/NsFwssPgP/0Owf+kvIX2DTXlCwkuj1Wqh211CliV4nof5fAGO476K0ooQBCEcx8ann7Zokegpuj5zuRwkSUK1WkW/3wfHcZciF0RRhCAIWCwWWCwW2N7ehiRJkGX52uczTROKooDn+SftCOv3+9B1nToANRqNS4KwYrEI13XR7XYvdTa+ZFeaVCqFN2/eYDabwTCMK4IVVVUfNUrE8zyYponVakW7pVVVfS/hnSAI2Nvbo65BmqZRIREAKs6p1+soFovY29v7aNdZFEWYz+cIggAcx0GSpEfZKCDxmFEUodlsgmVZ2LZNN1wURYFhGAjDEK7rYjAYYLFYIJfLIZVKodlsYj6f08dls1nqVkWifURRRDqdRjabhW3b1CmLWOdbloVsNksLsaQbPZ1OP4owdDQaodvt0sJ/v9/HdDrF9773PeTzeeq0tbW1BVmWEYYhTNNEr9dDuVzGeDx+EZsywPmYubm5iVarBd/3EUUReJ5/0WNLQkJCQsI3D5YFvvMd4C//ZeDwENjcBB5DR0CcjIgg5F2RSz6fpzHEX1csy8LR0RFGoxGm0yl1AEqlUlitVjRWnDQhbG9vP2rDQb/fpzHWhULhSpS0oigol8vodrtUoFUsFh/k4hNFERWCpFKpR1vvkihsx3FwenpKr6cgCOjawPd9dLtduK6LXC5H3Vdvgmyw+76PxWIBy7KoQ3Qmk/loAvrVakWdpNZxgFMUhTZizOdzFItFsCxL48cmkwlKpRKCIEAcx+A4Dul0GuVyGdVq9dHm2FEUXeuofRMXI8YvNi08ZwqFAnVfIzFsqqpeamIxDAMcx1Gn5g8twIrjGEdHR9A0DWdnZ1SUR64L4gB3cnKCRqMBAHjz5s2zdxe+SBzHODw8pO8xjmMadQecn3fS1NRutxHHMV6/fk2jOafTKZbLJXZ2dqg4iLi0pdNpWJaFyWSC7373u6jVarAsC1tbW9SF8SG1OnI/AFhr7CCPufh3F7FtG57nwTAMVCqVW5+TOAK+6zCnqipkWUYqlYJhGLe6VEVRBNM0Ua1Wkc/nL70eWbdvbW0hiiIwDPNeY7vrunj79i0Mw8B0OsV8PqfjSBRFkGUZ5XIZtm3j1atXN9YiExJeEokAKyEhISEhISEh4UG43/0u2L/1t8AdHwNRBP+TT8BcLOxkMgjKZWA0gtDpwE+lYP83/w1KP/3TT3fQCQmPCMuyaDR2IQgeGIZFFEXUBv7colvE1tYWqtU0dnd3H31znkQLBEFABRg32eSTKIs4jjEcDjGdTqlN+mq1wmg0QhiGEAQBmUwGGzcIJReLBebzOXZ2dlAul58sRiCKImiahslkAkEQbow0kCQJ7XYbx8fHNEqg1Wo9wRE/HgzDoFQqoV6vw/f9SxbpD+lEvA7HcTAYDGBZFjzPo68hSRLtfHxX8HYfstksPvnkE3Q6HeTzeczn80sdkKSg2G63P0oXou/7tNs0CAIa90LEitVq9cGxEsvlErZtQ9d1FAoFes5IJ2mz2UQcx0in0/A8DwzDwDRNZDIZKIqCSqUCWZZRr9cRhiGCIIDrulT4c/G4CoUCXNeFZVmYzWZ0w5PEccZxTDd7bNvG1tbWo8Q7jsdjdDod9Ho9OI6DbDaLb3/72/i1X/s16v5A3Okcx4EsywiCAFEUoV6v040i0sX7lM5694HENMZxDF3X0ev1aBSEJEkol8uJKCshISEh4dlCvm5V9VyI9ZgwDINisYhisYjlconVagXgfCP8OQuuSSSc67pUYK2q6r3mS3Ec4+TkBJPJBLPZDJubm5fmsyTieT6f4/T0FBzHged57O3tPcp7iKII0+kU0+mUxkxfB8/zaLVaODg4gG3bGI/H95rvWpaF8XgM0zTpz4iQq1KpPIoDz2q1wsHBATRNw3g8vuKcS5onxuMxwjDEcDhEOp2+dfOeNDEsFgssl0tEUQSO4+ixV6vVD+oeFEURxuMxjo+PcXh4iGKxCE3TaETada/NMAw4jrsi3CDrQhJp5nkebXLJZrOPvlYn0WRkvXTXPeH7Pv2sHmud+jHY3t6m55ys44iDnSAI1MWvUChgd3f3g9dEiMNZp9OBJEloNptXBJWKotB49HQ6jcFggK2trTufez6fUycnjuOQy+WeRLg1m81oc50oitjY2Li0JpRlGaVSCf1+H51OBzzPYzKZQJIkmKaJ4XCIUql06bzIsozt7W06FpbLZZydnVG393a7jUqlgmq1+qD1J8/z4DgODMNguVzeed6Iox/5u3dxHAeu6yIIAtpMdRv5fB6j0QhBEMD3fYiiSB22i8UiRqMRMpnMtWMKqQsSR63bRITvuzYncbiapmEwGCCXy12KVvQ8j372RED46aefPuu5QkLCOrycb72EhISEhISEhIRnxeJnfxacaYJ3XXhv3lwWX12kVsPKdcENh8A/+AcIplPwLyBmKCFhHf7X/5WFLMsQRQG+f+7ccy7AOi9Av369j/39xxWQOI5DhQ6u62K5XNIN/263i2KxiGazeanIKUkSXr9+jcPDQyiKAtM0Yds2LZxubW3RqMKbCiyWZWEwGND4iqeM8yO25bZto9Vq3Vr0FEWRFiSz2eyVguVLhti1X4QIpsjv71tA1XUdx8fHNN7A87xL3cu5XA6LxYJ2Jz5UvCPLMj755BO4rks7VoHzjblisfjRuh5N08TR0RF1bnIch75XUpzVdR3NZpN24N4H3/cRxzF837/096SLlGEYNBoNeJ4Hy7LoxlehUECpVML29jZkWcZwOKQd53Ec082Xi/erqqrQdR3VahWj0QjL5RL5fB4MwyCOY3ieh36/j9VqhUajgVwuR4ucD2W1WqHb7aLf78PzPOzs7EAQBLiuS52tPvnkE3ieh5OTE+RyOUynUzQaDeq0IEkSjSEwDONJ42Duy3g8xmAwoOMRcYBQFAXD4RCqqmJzczMpICckfGDc734Xi7/xN8AcHQHLJeJsFtw/889A+YN/EPwLGlMSEj4mH0vvnE6nn70Ti+/7ODk5geM41GUoiiKkUink83l0u13U6/W15k2GYcD3fUynU9Tr9RubCTKZDGq1Gt6+fQtVVWGa5qPEMb+7TroNIjDTdR35fH4tUU0Yhjg6OoJpmjBNE4Zh0GYNURSp00yj0bixqWddxuMx5vM5RqMRms3mFVECy7IoFovY2NjA8fExbNumYovrMAwDBwcH4Hn+UsQhwzDUMXY6nWJ7e/uDzEcty8Lx8TGNgJxOp1gsFlBVlb4+cbO5+DkQl7GbhBvA+Xr/Q8fO5fN5yLIMnuevdWO+SBzH1IFHUZQXFc/NMAy2t7dRKpUwmUyomy/hIS5+URRhNpvBNE3adCPLMhqNxp0NG5PJhArAbmo8A87Xj/P5HNPpFJlMBq1W68bzPp1OMR6PsVgsqHCPOKfl83nU6/UHNyA9hPF4DF3XAeCK+IrAMAy91zVNw3K5RBiGePv2LUajEW1kymazKBQKkGWZNutVq1U4jgNJkmAYBhqNBl6/fv1ezTKZTIaKHYkT1W0YhoFMJoNUKnWtY/lFh7l1RE88z9OaAGnuAs4bvBaLBXzfx+npKXX5IrXK+XxOz9/m5iZyudxagq+HMh6PYds2BoMBarUaCoXCpd+LoohGowFJkjAcDiGKIkaj0YtvnExISARYCQkJCQkJCQkJ9ybyPLB//++Dm06xyuXA3FFQjSoVCIeHCBcLOH/7b0P9o3/0Ix1pQsKHw7aB//P/PP9/luW+KnjGWK1WCIIQQAzbNmHb0aNFwl0UxxDRSiqVosUaRVGoOOb169eXNjtkWcZnn31G4xvm8zn9XSaTged5cF0Xw+EQR0dHkGUZkiQhiiI4joMgCFAqlVCpVLCzs/NeGymLxYLGshG3IVmWaSHzLoGU67pwXZcWy+8im81C0zQEQYDVavXsN4HuSxzHmE6nmEwmWCwWtEDMMAyy2SwqlcqVQtd1OI6D4+NjDAYDKoRptVpXinUnJye0OPjq1av3ErRJkvRkxTXHcXB4eIjxeAxN05DNZun7DcMQlmWh3+/TgjfHce8lPLx4ngRBoJtUYRjSmJU4jhGGIRqNBur1OmRZRhzHsG0bpVIJoigilUrRa9jzPNo9yrIsNjc3aTSFaZoYjUawbZve77lcDpubm8jn89jf33/ve4G4RFiWRcVXwHmBmed5FItFGpmYzWbhui5UVaXXZLFYRCaTweHhIQzDuDaq6LnS6XQwHA4xHo9pFEk6nUYURbQju1qtYrFY4JNPPvnajTsJCc8B//QU8//ivwD75ZfgTBOsbQNxjJjnEf/ar8H+q38V8e/9vSj8qT8F5gW5byQkPDYXtAOUF2I4+cHxPA9ffvkldF3HaDTCarWCJEngOI6KfxRFwWq1gu/72N7evvX5ptMpTNMEy7J3CjTIuse2bWiadqcAi4g43hU9VSoVKvTyfR+e5wHAWuskEnFNnFJvE/FEUUQdqbrdLsIwRD6fR6FQQBzHcBwHnU4HsixTYcBDRVhxHEPTNOi6DlmWbxQIqKoKx3HQ7/cvfVbvit8ty8Lh4SEmkwltQmi32+B5nsZCdrtdqKpK5/2PGZNpWRYODg4wmUyo+EFVVSwWC7AsSx3TqtUqwjDE5uYmFWFYlkVjEj+kUOIuFEWhDSokWvMmAct4PEYURcjlcqhWq2u/hu/71OFbEIQnFW5ls1lks1mEYUjdxFOp1L3dvIbDIYbDIRVGEgGWoiiYTqe3NmxcdFNWVfVOYU6xWMTZ2RmWy+W1Irk4jnF2dobxeHxpLCFIkoRCoQDLsrC5uXlrhN1j4boujXa86Bh9HaS+8d3vfheVSgVBEODs7IyKt0hsKRG0EgdAIjbleR5BEEBRlPeOxWRZFqVSCY7j4OTkBLZt31j3WywWME0TzWYThULh2s+aOPFdFFzexmq1ovfHxfskm83S7ylRFDGbzaBpGn1u4LwmsLGx8eD4xXW5OI6Ta+smVFWFbduYzWbUlf+lOGMnJFxHsvJMSEhISEhISEi4N6vxGMxyCdZ1sVrHESSTQcRxwGKBqN//8AeYkPAR+M3fvPiv+CsHlyWiKEQYhojjGMfHxwjDNCRJoo4vD+Vdccy7EQyLxQKz2QwnJydot9s4ODjAmzdvLhUtSJGoVCpRoQexTY/jGL1ej0ahke5thmFQKBSQz+chiiK2t7cfXIwOwxDHx8cwTZMWxogAK5PJwDAMiKKIvb29Wx2QiOBs3YLsxWLT+xbanhtBEODg4IB2oF8sogqCgEKhANM0USwWsbOzc2sRq9frQdd1GIaBra2tSxswpEicyWQwGAzQ6XSQTqdhWdajbk58TE5PTzGbzTCdTtFut69sUsmyTIvYg8EALMteuufWIZ1Og2EYpFIpzOdzek4LhQIV7ZDPBwAtyF8s7pP7hHRbK4pC41V0Xb/kxpBKpbC1tYXpdApJkrBarXBycoJyuYxms4n9/X0Ui8W1uq3XYTqdwjCMr5wARfpzsvmXy+VQr9cRBAFUVUW320WhUKDHQ1AUhUanvAQmkwmGwyHOzs4QBAGazSYURaHF6+Vyiel0irOzM7pZ+a1vfetexe1c7nMEgQvg/P4rl8U7/iIh4ZuFd3iIxZ/6U2A7HaT6fbCrFYJMBjHHgVsswE0mCBQFoetiNhqh+Bf+ApgX5MCRkPChSfY1zzeHDw4OMJvN0Ov1qAMpce8hc5N+v4+3b9/i9evXiOMYOzs7Nz6n53nwPA+ZTObO733iguN5Ho3jvonxeIx+vw/f92FZFnVBzmQy1PVpe3v7klPPfbnrb0ej0aUotHc3yFVVxXK5RKfTQbfbpSK0h0SKLxYLBEEA0zRvdR7L5XJIpVKo1Wo4OTlBo9HAfD6Hqqr0MVEU4fT0FIeHh9T9+fXr1/TYBUGgc9GzszMatff5558/ijCBuIYR8dXGxgbq9Tq63S5dUxWLRUynUwyHQ7Asi/F4TOfQmqZBVVWIovikAizgXFBHmqFOT09RqVSQy+XoutzzPGiaBsdx6Prq4mdxHURYSJqJCKQOQdY/TwURxjyE09NTjEYjjEYjWJYFnufBsiyiKKLRk7VaDa7rXmmgA0DX9cvlci2HaPIY3/epw9tFer0ehsMhOp0OwjCkdR6O46hoazAYYD6f0zjLuz6/94WI7lar1Z2fcxAEdP25WCwgyzKtRVQqFRojS5piGIa5JAAMgoB+lo8h7qnVaphOp6hUKuj1eiiXy1ToBZzf+6TJqFAoIJfL3RgLm81mIUkSUqnUWo6IhmEgm80inU5fWdcXi0Wk02n0+32oqkpd0oloVxRFlMtlNBqNDypychyHClzXEeMWCgV0u12sVivYtv1i60wJCUAiwEpISEhISEhISHgIFwUM9ylIxfHlv01IeKFEEfDn/hz5V4z5fIH5fPCVDXqAOAZYlsFwOMByuYKqqrRQeVF0cB8GgwEVx2xubl4pwMmyDFmWMR6P0e12IQgCdF2/sXDDMMwlIQnDMGi1WqhWq7RL7WKRplwu39mReBthGOLLL7+kGxykwCZJEsIwxGQywXg8Rq1WQxAE+OSTTyCKIhWCAediFlLQSqVSCIJgre5A3/fpY+7bsfqciaIIb9++xWw2o65HxG7/omPVdDpFEASI4/jGDkfXdeE4DqbTKcrl8o3d7wzDoF6v4+DgAJZlYTwev8jCmG3bcF0XmqahWq3eWOxNp9PY2NjA6ekpyuUyptPpjUXT6xAEAbIsQ1VVKpwk95QkSSgWixiPx7TL3rZtSJJE4wwsy8JwOES1WoUoilBVFQzDQFVVlEoldLtdZLPZS8fP8zxqtRoqlQpOTk5QKpWws7OD3d1dvHr16tHugSiKsFwu4XnelW7fOI6/imNl6DGpqor5fA6e569s8JFoxffZNPxYxHGM4XCIyWSCIAiwvb195Zym02k0Gg1wHIderwdZlmlX97r86I/+Mh37BEHAH/gDX4/o1ISExyBerbD4T/4TsGdnEM7OsCwWEdVqNBI9ArCaz5Hq9ZB6+xYxAOO/++9Q+Hf/3Sc97oSE50QiwDp3F14sFnTjnESyj8djeJ4Hx3HgeR7CMISu64iiiMZ1bW9v37guujgHugvyuNvmQN1uF4PBAKPRCKZpgud5iKKIKIqg6zp4nke1WoXv+5eipnzfv1Nw73keFX3cNkeM4xiTyQTT6RSpVOrGWPd0Oo3NzU0cHh7CcRxMJpMHCbDCMEQURYjj+Nb3wLIsXT/2+3067yKusavVCp1Oh7oCvXnzBu12+9rPTpIk1Go1jEYjFIvFe8/dbmI6ncLzPEwmE7RaLWSzWURRRN3LxuMxgiBAsViEpmmYzWZIp8+buCaTCXieR7lcflAc+mOjqira7TYAQNM0aJqG0WhEBS+r1QqZTIY2bd3llrxcLnFwcADbtmkzEYn0lmWZxkLW6/VbXZNXqxU0TYNt25cavCqVyqM0nTwEIrwiDRmtVguKotD3RwRPp6eniKIIh4eHePPmzXuL/m4aS3zfx2g0Qv+rpth31zCCIKBWqyGfz+P09BSapkEQhA8uwLp4vOS9E9GO7/tUCJbP52GaJubzOWazGfb397G/v3/pcUQANRqN6Bo7l8vRRiHTNKEoyiVH6fchnU5jb2+PHiO5J8jreZ5Hx2eyJr9JSJfJZGjdYDabUXHpdViWBdd10Wg0UC6Xr71mFEXB69evaXMlcZaTJGktN7XH4Dwd4LwOtU5EKnEEJ4K8l0AQBHBdlzaIyrKcOHclAEgEWAkJCQkJCQkJCQ+AL5cBhkGUToOdzxHd4eoT+z7YIABEEcwdXTwJCS+BP/gHf/j/rutiufTxf//fnyOOf+jewrISdnf/Hnj+3PKbRMOlUql72fAD54Uby7Kg6zqKxeKt3Y+VSgWWZcE0TUwmkzs7596FCE4eGhdxE51OB7qu4/T0FIqiYGtr61LBr1arQdd1DIdDhGEITdNQLpexXC6xXC5p8T2dTtOCOs/zMAzjVmt80sFeKBSQyWS+VgKs8XgM0zRxdnaGbDaLer1+qfgmyzJKpRJ6vR7Ozs7AcRyKxeK1TmxkA4rEYdwG6WY3DAOqqlIntZfEdDqlBfq7BGRkE8cwjHsLsACgWq3CsizMZjMMh0P6OTUaDXpta5qGfr+P1WqFZrNJ4yE8z0OlUkGpVMLu7i79fBuNBkzTRLlcpnEppNMV+KEj3nK5xOeff45yuYxPPvnkgxQDr9tk5HkePM9f6mIHcOOGAtn8ewn3p2matJBdq9VuPeZKpQLTNOl4/BibeAkJCYD9v/1vYLpdpDsdLCsVxI0G3h1dmEwGq/19pN6+BdfrIfy5n0P0x/4YWDFxk0tIAIAXNnV7FIjTjqZp8DwPR0dH0DQNq9UKOzs7mE6n6HQ6OD09ha7rVAQjSRIymQwVUhSLRURRdG1jA8/z1CV2HTzPo4KA65hOpxgMBtR1k4g4CEEQwDAM9Ho9RFGEMAyRSqUgSRJ0Xb9VtBPHMW3YIQ6rN0FEDsSR6jaRSCqVQi6Xw2w2QzabRbvdvvdagWVZOm8lgpWbUFUVvu+jWCwil8vB930cHx/T389mMwiCgGq1iu3t7VvXOvl8HpPJBKZpYjqdPsrcbTKZQNd1iKJImxZYlkWr1aIuPbPZDLZtAzj/zEnEd7vdRq1WQ71e/yhxcOtQrVbpmrxUKmE+n9PGLVmWIQgCjbK/bZ4cBAFt0Op2u+A4DoVCgYowbNtGr9eDJEn0PL3bzBZFEbrdLr2nbdumLkeKomA0GkFV1Su1hw/NxYaNKIqwvb195R4gdRfSsCFJ0hV3aXJPCoKA+Xx+p5hxPp/T+MZ372dyjubzOXZ3d288H6IoolarYTweo1QqwTTND9pwlUqlwHEcWJalzXeO43zlMH/ejELETaPRiDYslctl5PN5OoaapglZlmkzUrfbxWKxoI7RjuNgsVigVqvdKFp6CNlsFp988gnOzs5oJOpFQRgRVrXb7RsjCgn1eh3z+Rzz+Rynp6eo1+uXxvuLjlrVahWyLN85Loii+GTiTYZh6Hlex4mePIZhmGcvYlosFjTKkzj0sywLnudRKpVQrVafTPyZ8Dx4/pWthISEhISEhISEZwenKAi/8x1A05AeDOBvbNwa6cFOJghFEZBlyP/Sv/QRjzQh4fE5Pf3h/0dRCN9ffhUbdm7ydhFSRMxmszg5OcF4PEY6nUa5XL5XQcEwDBpncVvnJwDqjkPcdpbL5aN0970PQRBgNpthNBpBluVrXcAYhkGxWMRqtcKv/uqvolQqYTweA/hhVySJwSuVStA0jUZuZLPZG4sbk8kEYRhCVdV7C9+eM+92ob8rviKwLItms4nDw0MqArlOgLVarbBcLi9F390GKRADl638XwqkoCvL8lrHnslk1oqHuY5isYh8Po/NzU0qqioUClAUBc1mk4qPLMuCJEnwPI92bbdaLRrLebH4KkkS9vb2AJwX5KfTKY1VIU5SiqLQ7vP9/f1HL2KyLAuO45BKpeC67qXfEVcuIuyTZZk6ZuXz+UvX2HK5xHw+R7VafRECJcuy4DgOANwZA8MwDO3Wtm2bFmYfQhwDvn++Yf4CdGoJCR+U1d/8m+BnM4BlEd+yqcSwLIJ6HemzM4SmCfvv/l3k/9Af+ohHmpDwfHnme5uPjmEYODk5ge/7MAwD8/kcR0dHmE6nKJVK+N73voezszM4jgPXdakI5GI8dDqdhu/7ePv2LfL5PLLZ7BVhPnFOIg04tzXOEOFKPp+/cQ40HA6pm+11rpvEHYnneQyHQ2QyGbAsS0X6mUzmxk3/4XCIOI5pzPVtkDjGKIruFBEA53OkbrdLnbjWiU+7iCiKNGLRtu07o8lSqRTa7TZ9LzzPIwxD8DyPk5MT2pBzl1iBiIhuinC7L8Q1bT6fXzlvoihie3sb/X4fsixjsVjA933Yto10Ok1dnxqNxr0bQD40qqpCVVXqXHXRXadUKq21nuz3+zBNE51OB8Vi8cpaXVEUlMtlGmlJaivkWiIx37PZjMbmkUYtEoUoiiIajQY8z8Mnn3zy0URYuq7TsabZbN663iQNG5ZlYTKZXBI7ETFjoVDAeDy+s46k6zoVU74rNJzNZtB1HZlM5k5hSC6Xo7GJs9nsgwqwyPGIoojvf//7yGazVIiVSqXAMAxWqxVdi/f7fezt7SGdTiOTyaBQKNCo2NFohHK5jFQqBVmW6fUpSRKGwyFKpRJkWb5zvLsvsizjzZs3WCwWmE6nlxzci8Xi2hGahUIBGxsbiOMYo9EI3W6XOh4Sh/PzaPoySqUS9vf3bxTvPgdEUaTu9bZt33ndOY5D61HPWbw0Ho/R6XTgOA5msxnm8zmA8++PbDYLx3GgaRr29vYe5ACZ8PUgKdkkJCQkJCQkJCQ8COEP/SEEv/IriMdjcKeniHZ2ro0jjE0TwmwGv91G+CM/AmF7++MfbELCI/ILv/DD//f9JaIoRBhGwBXvBYBlz4VDPM+jXq+j2+2iXC7fGg14HcSCm2XZtQosoihC0zT6t08twNI0Db7vYz6fY2dn58bHhWEI27axWCxgmiaazSY+//xzZDIZMAxDXX1OT0+Ry+XgOA4EQaDxcPl8nhY3Pc+jLkekU/y5iTviOEYYhmAY5t4CJtu24XkeTNO8UXxFINGExEHN8zxqi08g3YnrdCYCoF3I5G9fIveJu3uf98gwDPb39wGcizKJGHEwGAA4Hx82NjZocb7dbkMURQiCQN2vrtssyOVyePPmDXq9HrLZLDzPo93ngiDQ7vNms/nBNhsKhQJM00S3270UB0oiExRFwXQ6hSAIcBwHLMtClmW6ARVFEfr9PiRJolGnz50wDKm7xDpiqosuGCQS5SH83M+d/wcA9TrwMz8DrJHkcC/i5RLOL/4igrMzIAzBlkrI/Iv/Ivg7nE4TEj4mcRiC+83fBGcYWKnq3XHouRzAsmAMA+Gv/RqQCLASEgB8swRYuq7j6OiIulXwPE+delarFSaTCb788stLbinA+RwuCAJomgae52n8eRAEmE6nyGQyqFarl+aJxH02m82i3+9fK5oCztdow+EQ2WwW6XT62rUhicwmjr+3zedIbBV5bC6Xu+SUetFZiMSOL5dLtNttKvC4jTiO6fx/nXkxiZcG1nM+eRee51EoFGBZFnq9HorF4o0b8mEYYjabodFooN1uY39/H47jUJfeVCpFBTzr8ljR2OS9R1F07eeXTqexvb1N3VV930cYhigUCmi32/iRH/mRK3NHEtFFIq+e0kE2m82uJch7lzAMMZ1OoWkaFEW5sVGKCOsuRlpubW0BOI/mnM1mODk5QTqdxs7OzqU17nK5xGQywcnJCba2tnB0dITXr18/7I3eE8uyYNs2WJa9U3zDsixyuRwsy7rWOa9SqVBRZ6/XQ7PZvHY9oWkaHMfB9vY2SqXSleudNFytIwhhWRaiKGK5XH7wKDiGYVAoFKh4aTQaYWNjA5VKhda+wjBEp9OhTWXAec2LZVkUi0XaDERES5Ik0XF6uVwiDEPU63VUKhXs7Ox8MNGSLMv3Fpu+y8bGBhUtEXEecQVbLpfgeR6WZSGTyaDT6aBSqaBYLD5LxyjiHnmxFnXTcRKHSlVVIcvysxUuaZqGs7Mz9Ho9OI6DXC6Hzc1NcBxHozNPTk5QqVQQxzFtmkv45pEIsBISEhISEhISEh6E8qM/iul3vgP4PlJHR2C/+AJBuYy4UADDsogXC3CahpRpYlmtIq7XIf87/85TH3ZCwntzsQ67Wq2wWq3A8+s56PA8T7vw7iPAYlkWDMPQQvBdRe+L4pjHKMSEYYg4jsFx3IOEKPP5HI7j0KiCmyBuOa7rQpIk6hJEIAUtXdcxGo2Qz+epK9Z0OqUOY1EUYbVaIZPJYGtrC6qqYn9//9kIhUh3q2matLBPCmykY/MuSFd2FEVrdVQqikLdwHzfv/I5iKIIWZYRhiE9/7fhOA4kSaKbGi+NdDqNdDoNXdfXciW62FH9EHiex9bWFnVRIOc6l8shn8/TjbdKpUIjV9Z1Itvf38dyuYSu67RATsRXH9qZrFKpQNM0CIKAXq+HdrtNj79cLtPNxZOTE3ieh0ajAUEQIMsyjXYhsRyVSuVZd7oSyHgchuFaj7/oELfueHzXUDUcAn/trwF/9I+u9XR3EjoOzL/yV4Bf+AWwug7GdYE4RiwIsP/SX0L8W38rMn/8jyci+oRnQTSfn0/GwhBYZ0xmGESpFJgwRPxVh3pCwjeNf+Ff+P9iOPxhjBvD8N8YAdZyucTJyQmGwyFt8Mhms4iiCKZp0sYPXdcxGAyos89Fx5cwDGFZFkajETKZDIIggGmacF0Xtm1fcsTkOA4bGxsIggBnZ2c4Pj5GoVCAqqpUxGUYBnRdRyqVQj6fR61Wu3bORhpTyJzxLogIq1KpYHNzEwzDQBRFTKdTHB8f0/Ukcejc2NhYe51EBAHE0equOZvneXTe/FCBULVaxWw2g6Io6HQ6aDabV9Yoy+US/X6fNpzUajXqOkMYj8cQBIEKN24jjmO4rotCofAoTUzkc+U47lYnXRITRhyzGo0GqtXqpbnjcrmEpmk0NpOQyWRQqVRQKBQetPZ3XfeSYE1RlDvXgu+LrutYLpdwHAebm5u3PjaVSiGbzULXdeRyObTbbYRhCE3TMBgMkE6n6fV+kXQ6jWaziX6/j7dv31IH22w2i1wu90EFHqRhI51Or1WDEAQBi8WCNmhdHA9I1DwZU46OjqCqKrLZLBiGgeu60HUdnueh2Wwil8tREel1rCssJGPFxeP3fR+e5yGOY+oy9Rg1FrK+IuvIMAy/qrOdjx3L5RK+74PjOKTTaXqeANBoSp7n6X3mOA48z6Nj5+7uLnK5HHWHfu5Uq1WUy2XMZjP0ej0cHx9fimWNogjD4RDz+RyWZWEwGGB/f/+D37cPoVqtwrIsGIaBTqeDVqt15fsuDEP0+30AeNbO9UQIOBqN4LoudnZ2Ln0XkphZx3Go6Je4VCZ880gEWAkJCQkJCQkJCQ+DYVD4838e+k/9FMBxYMZjpIdDML0e/X2QycDf2kJcqyH1Z/4MpM8+e9pjTkh4BC7qVs47geMbRQ7LJQvgh4UhjuNogf+uSIrLrylSwcx8Pr9TcGPbNo2feGjh+LoCL+lOrFQqa9uoA+cFopu6fglxHMMwDFiWBVEUoSjKjYKzQqEAx3EQRRHd0CiVSjTGg8QfiKKIQqGAVqv1LERCYRji8PCQdreapkmPV5ZlOI6DwWCAra2tOwV6Fwunj1H0LBaL6PV6VMx2W9QlicYgx/lchG33oVgsQtM0jMdjWJZ1JaLhIp7nwXVdNBqNewknCb7v4+zsDJZlwXVdcByHKIown8+xWCxgWRZev35NozQfcj7T6fSTRJPIsoxSqYTVaoXT01McHx+jVCohl8uhVCrBdV3MZjNomoY4jmFZFrLZLL744gu6+Vcul1Eul9Futz/68T8EWZahKAotet+1eUPGY1EUH1UQ97M/+zgCrGA6hfnv//vgvvwS7GgE3jTBRBFVgQWZDMLZDItf+RUEP/3TyHznO+//ogkJ7wFL5k4cBwTB7Q8GgDgGEwTnj79FBJ6Q8HXm29/+/1y5/IlOg8TskQgxEhn0dWEymVBnq83NTfq9zbIsBEFAOp3GbDajQgMSO53L5eicjOM4yLKMdDqNxWIBURSxWCzgeR4Wi8UVcVStVqPnk8R+TSYTKoBKpVI0oprn+RvjsIiIg+O4teYQ6XSaihJIJFa/30c2m4Xv+1ecUiuVCur1+lqinXw+D0EQIEkSDMO4c96p6zry+fyldex9IXHccRxjMBjg5OQEkiTRdSgRDkmShM3NTVSr1Wvn6sViEaZpQtO0O+dujuMgCALk8/lrY9vvC4mkyufzmEwmVxzT3oU4ICmKcum6Ik5PxBHNcRzEcQye55HL5WCaJhRFwf7+/toNDYZhYDQawXEc6rzFcRwEQYCiKKjVareukd4Hsr4i6+C7yGazGA6HNNLcMAwa7bizs3PjOZ3NZrBtG51Oh4pAms0mbeyq1+sfxCWbiGWCdeYpOG/YIPfhdffjzs4ObYgjAs7JZEIfn8/n0Wg0kMvlboylIzF/8/n8zgi+IAjgeR7K5TIdIyeTCRXqkWtPEASUy2VUKpX3WucQoSUZF03TxHg8pjUPhmHA8zxarRYGgwF4nofneVgul1TkVq/XUSwWoes6bNsGx3FU2Pj69Wva6PRSIM7RpOFuPp9DkiT6vUHEvJqmoV6vIwxDvHnz5sHj7YeiWCyiXC4jiiJ0Oh0cHBwgn8/TcXg+n8M0TfA8j83NTZRKpQfVXD4GJF7SMAy02+0bx1pFUWijGhFk3ad+mvD14Oszk05ISEhISEhISPjosLKM4s/8DMy/+lcR/vzPIx4OAc8DoghIpwFZRvjP//PI/ORPQnz16qkPNyHhUXhXz8QwN3cRyvJ5AdxxHOi6juPjYwiCQEVFpPBdLpdvLVipqgpRFJHL5aBpGo3ku47lcklj9x5qRT4ej9HtdmmBl3Rjkiit6XSKQqGAnZ2dtZ6fbBrcZl9PCmgXnZVui7goFArodruo1+tot9vgeR66rl/aPCqVSs9CeAWci9C+/PJLzGYzdLtdhGGIfD6PXC6HKIpgWRbtZiVRFbcVnlKpFFKpFI1mvKugQ4Q/NzlWkZiPxWKBk5MTjMdjVCqVK+d/uVyi0+lAURTIsnxn8fa5ksvlIIoiisUiRqPRjZtDQRDQGJmHROS5rosvv/wShmHQDcDFYkEjZZbLJbLZLDRNw+7uLmq1Gur1+rPt+ryOra0tGqU5m80wHo8xGAzoJmM+n6fislwuB0VR6EamIAioVquo1WovRshXLBbR7XahKAo0Tbu185tEhbZarQ/S+WrbwANSXyjxcgnrp34K3Pe/j/TxMUJBwLLdBr7qDCdupsLxMZaui+Wf/tPg/9JfgnBLlGxCwoeG4XmE+/tghkPwuo5Vo3G7bZxtgw0CxPk8+M8//3gHmpDwjLguAS6OQ4xGGiaTCXzfv+R2Qhwo3p1frlYr6lwDnG/oFwqFZyvYiuMYmqZB13VkMpkrwptCoYBOpwPXdeG6LvL5PI3c8jyPuonEcQzbtlEul6krCxHA3BSvR0QemUwGpVKJurEQUUAmk0G5XKbn8jouusGs44J80XWTOMXs7u5itVrBMAy6ThIEAaqq3mvuJQgCFSURUddNwhnSwFMoFN57Tlur1eh5KJVKVHwE/DC+T5IkVKvVGxtISCxkPp+nzS7XrYd838dwOKRr78cS5lSrVSooISKs6yBxl+T1iVMPqSMMh0Poug5ZllEul8GyLHXBnUwmaLVaiKIIb968uXMN3O/30e/3oev6pXsaOD+vhUIBtm1jY2MDGxsbD3rfi8UCk8kE8/mcxiXmcjkazbXONU0g6woAtKnDtu0b13BxHKPX68EwDBiGAdM0YZomFZumUimoqor5fP5e7/EmyL0/HA7Xcpc2TZNGr113TliWxd7eHgqFAiaTCUqlEhV3kXu9UqmgUqncOB6XSiVYloXZbHZnM6BhGDSq1bZtjEYj2khIrhWO45DP52HbNjRNw6tXrx7kZuy6Lnzfh+u6+OyzzxAEASRJom7z5LUWiwXG4zFKpRL6/T7K5TIcx7kklCRNSZVKBVEUUTfAp2hUegyOj48xm80wGo1Qr9epIJJE3WUyGZimid/8zd+k5/9b3/rW0x70NRCRJIlPJPckcP7dUq/XaRTubYLKp0bTNBiGgXQ6fWcTlqqqVLQIIBFgfQN5njPjhISEhISEhISEFwOTSkH9yZ8E/q1/C84/+AcIjo6A1QpMoYDMj/0Y+GfauZKQ8BhwHHujsIhhYmQyIQaD80KpaZrodrs08mK1WkFVVTiOg8lkcmvBisR5EXHMYDC4tlvZ9310Oh1kMhnadXVfxuMxzs7O0O/3YVkWZFmGqqpgWRau66LX64HneWr9/+rVqzsLJLlcDtlslnbYXld8CIIAURQhCALEcYxisXhrkUKSJMRxTAtzqqo+66IGKQCfnZ1BUZQrn1+xWMRisUC32wVw/plns9kbHcxI56MoiphMJreK8oDzwj0p5t9UbG02mxgMBuA4Dt///vfBcRyNxRMEAbZtUzefZrOJjY2Nj9JhSTqdgXPh2WO4CDEMg83NTbrJdnJyQiNnUqkUjZqZzWZIp9NoNBpoNBr3EvSFYYi3b99iOp2i3+9DVVV6nc+/isGKogj9fh+TyQSapuGTTz6h0Q53xXE8FxiGwe7uLrLZLI1A8TyPut5dFF5FUYQwDMGyLBRFoTGiH4J1oiUfAsdxKJfL8DwPJycn6PV6qNfrVzY7LMuimwNEEHofRqO/jeXSA3B+3bMsj1rt9116zL/5bwLf+Q7wJ/4E8JDplv3zPw/miy+QPj5GkMsharcvCVmYTAZRJgN/NoPQ7cIXRTh/+S9D+Omfvv+LJSQ8Ivzv//2IfvADMJMJoGnATfOdOAY3GmGVzSLOZpH9V/6Vj3ugCQnPhHdTc6MoxNu3X4DnTbpOueicVCgUMJvN0Gg00Gw24Xke+v0+dZ0h8zJRFGnc28bGxqNEtj0my+USQRDAcZxr10WkEUIQBOqkslqtEAQBVqsVJElCGIYwTRNBEKBSqSAMQziOg9VqReOFbqJQKKBQKNA4djI3IeKlMAwxGo1u/PtMJkMFGZZl3RmdZRgGFEWhjRoEEnX+vjQaDeroenZ2hlKpdEmA53keptMpbQYi4rP3pVKpQFVVTKdTaJpGo/yIk9hdMdYcx6HVatH4qKOjIxQKBeTzeaRSKSpQMwwDmUwGtVoNrVbrQfNI3/ehaRpc16Vz4Xw+D1mW0Ww20e12EQQBSqUSPeY4juE4DkajEXVEq9frYBgGQRDQCE3LsrC1tXVlLVcqlTCdTtHtdsGyLM7OzrC3t3fjMY7HY/R6PXS7XXieR12HyLkgbmEXXbbuI6RbLpc4Pj6G4zj0P3IucrkcdbLieR5hGNJ77zZc16WPIWs1IqS6jtFoBMMwMBgMAADlchk8z6PZbGJ/f59GoS8WC/qcj9lYRERCiqJgMpncuq4j44uqqrceA8Mw1B2IiJaIo95dtQDg/Bz0+33k83n0ej1sbm5ee98QQVW9Xoeu6wjDEGdnZ9R9XFGUS9GHuq5T8eObN2/uLcgl3z1BEFBnsmq1Ctu2sVwuMZ1OLzXpkfi9bDZLYwcvjo1xHGM4HFK35ZfU2HQRMtaSxjhVVeH7PsbjMXWtI3VI3/fxq7/6q1gsFrTB8znBMAx2dnZQKpUwmUyufJeRa3+dqN2nZLlcwvO8teqOpOHsYhxvwjeLRICVkJCQkJCQkJDwODAMlN/224Df9tue+kgSEj4oF5uU02kBq1UA37/auczz54W/6XSK4XCI2WwGSZKwvb2Nna/cQ3Rdx2w2o9EKt3WrkoL35ubmJetuSZIQRREtbl4Ux6wbcUhwXRedTge9Xg+e52FnZ+eSwIZ0ww8GA5ydnYHjOAyHQzQajVuflzjGZLNZTCYTyLJ8paBNCoa2bVMhx22RBxft6J9rhxwhiiJMp1NMJhMIgnBth20cx9RF6Lvf/S7trvvkk09QqVQuFaVJ1+BwOKSFZU3T0G63USgUrhRSx+Mx7UK/qahrGAY6nQ7iOMZisaCd/bqu4/T0FOl0GltbW2i1WlAUBRsbG3d+7u8LESfqun7JZe6xCnT5fB7b29s0bmY2m+H4+Jj+Pp1Oo1wuo1AooF6v3/v9kpgTIpgkkYPD4RA8z6PRaECSJPi+j36/D0EQ0O126fUsCMKL6dZlGAbVahXVahWWZWGxWFzadHpIN/R9iaKIOgAQ1z6GYWin/cUoofel2WzSbv5ut4uDgwPq8kUc7YIgoJEcu7u7996M+H/+nz+CKHLpv1lWwu/7fYtrHgf8zM8Af/bP3v99BD/3c+A0DTHHXRFfXaJYxMq2wU0mwP/1fyGYThOBfcKTkv09vwfG//A/YGUYEPp9+FGEuFIBc2FuEfs++E4H7HKJ1dYW4t/9u8E9Y6F2QsKH5KJJ0/m6YQ5dn8K2z6gbpSAIVFzU7/fpGoPEvF0ULHAcR92fSDQSiVR+TvFHZGM/DMNrv4c5jkOlUsFgMKCNMcRhiEQ8eZ4HnudRLpeRyWSQSqUwm81ovN5doigA17pvrcPF1yBRRjeJghzHoZHZ17nYPgaZTAa7u7sAQNeBnU4HgiBQUUwmk8HW1hby+TxevXr1aGL4VCqFer2Oer0OAPdyTwLORVzEMYg4+UynU/p7SZJo5F6r1bq3cGy1WuHs7IyKFInrCc/z9HOL4xibm5sYj8c4OjqCKIrUxSoMQ7rWrtfrdM1GIq90XUe73b52fc8wDHVnG4/HkGX5RlFTGIbo9XoYjUZYLpfY2dm5ItYrl8vI5/M4PT2lorBSqbRWE8xyucQPfvAD6LqO4XCIIAiQyWTA8zwWiwWm0ykkSUK5XIamaUin09B1/dY1TxRFMAwDlUrlkuiG4zh4nnftMei6jvF4DJZlUa/XMZ1OqQCS4zgUi0Vks1n6HlOpFEql0qNdrzzPo1gswnVdnJ6eotfroVarXRqH4jima/pKpULdmddBkqQ7XbWuO6Z2u40oitDr9XB8fIxcLodcLgee52m043w+R7VaRSqVQjqdxtnZGVKp1BVRInHS0zQN3W4XPM9jMBjcO1ae3McMw1BHQSJUPTs7w2q1wmKxoGuvIAgQBAEODw8vRROWSiU4joPpdIrlconNzU3kcrm1xujnyGQygWmaiOMYhUKBCl8dx4FhGPB9n15Py+WS1hvT6TR+/Md//EHfOR8acr0R4SVwXnN5jAa7jwFx7luX516nTPiwJAKshISEhISEhISEhISEe/BVwy0A4sbDIp1OX7MQj6ld+Hw+hyzLqNfrKBQKVFh0sWDFcRz6/T62traufV2WZfHq1SscHh7SKEDDMGBZFo3ca7fbyGQyDxbHkIgAx3Gws7NzrWiC4zg0m02cnJxA0zRIkkQ7dG+CZVlUq1X4vo/T01Ocnp6iVqtdKiDH8fn58n0f9XodkiTd2lnmOA61279N3LFareD7PqIoQiqVuneh8jEg74t0g78LcRbzfZ9uYM3nc9i2jXQ6jeFwiHK5jGq1ipOTE5imicViQQuMvu/TeMNKpYJ2u00t+WezGVzXRbvdRi6Xu7YbUtM0nJ6eQtM06vhEYkksywLHcdS5rVwuY2dn597ivvsQxzHOzs6oZbuu67S4TiIxdF2/VwzmTZRKJYiiiOFwiHw+T10PWPb8vlYUhW7G3BciHhMEAZIkYTgcUhFetVq9JLTK5/NwXZe6R5GNgEql8kFcnD4kpLD6MVksFjg4OIDneTSa5mLUia7rUBQFe3t7j+LOQcZj4kJHCuGO49DoJuI4t7Ozc+/zcd/L7f/9f8/Fwfd5a/7pKdjDQ/C6jqBQuD3CDUBULkM4PEQ4n2P+v//vyP+RP3K/g0xIeERYUYT05/4cvJ/6KfgMg/RgAEwmCLJZxBwH1vPALxYIRRGr/X2E/+Q/ieKf/JNPfdgJCU/GRQGW57lYrVYYjQbY3q5e2fBXFAWlUglnZ2c4OTnB4eEh/S5VVRW1Wo2KrFzXxWw2w9nZGTY2NvD27Vt861vfejaRhOQ4iLjgOjKZDBXKZzIZWJZFnTyJCzGJGyqXy+j1elgsFtja2kIul/ugInMicCfig7OzMzQajUuvSeLYBoMByuUyFbd8KIjr13w+p6Ki+XxOBVIk/qvZbD7I9YOIl0hcIxG4v8tDNrYbjQYymQxGoxFUVcVqtaKvk0qlkM/nUavVkL1nvvNyucQXX3wBwzAwHo/huu4lcRWJNSwWiwiCANvb2/A8D/P5/Epcd6PRuOTYQyI0RVG803WlWCzi6OgInudB07Rrm35msxmWyyVM00Sr1brxMyKfZ7fbRaVSoevMuzg8PISu6+h2uygUCiiVSpfGA9/3MRqNqOsyaaBQFOVawchNbkaZTIbGuBOxGXESOzs7w3A4xHg8xs7ODm0wKpVKl2oBqVQKtVoNvV4PlUoFhmGsLYBah3a7TZtCLjZskPdtWRaA84jKUqmEvb29D77uI9F8DMPQWkGn0wEAeo53dnboZzGdThGGIba3t288tnK5DNd1oWkabQa8z/sQRZE2RFmWRa/zfr8P0zRp81K1WoUkSdS5kIxD3/3ud6nrMBFcbWxsoFAoYG9v78WKYFzXxWKxgKIo1L3PMAxMp1Na1yHrWlLD6HQ6yGazODg4wLe+9a1n50pJ4DjuSepy70s6nUY6nabOebcRxzFc16XC7YRvHs9jJpyQkJCQkJCQkJCQkPBCuFi7ZxgGkiTTDtyLGixSLOx0OlQ8k81mrwijSJTVdDpFJpNBq9W6sQOM53m8fv0apmliMplcEoWQTs5KpfKgYsZFl6bVagVd1+lrkkiCi++7VCphMBigWq3CNM07BSqNRoOKaIbDIe2kFAQBQRDAdV2wLIvt7W1EUYRms3njcxGxFhE4XFckt20b4/EYhmHQTjWWZSEIArVl/1iddqR4BpwXGIMgAMdx1Lr/9PSUxt0B59EvjuPA8zxaqF2tVviN3/gNZDIZ6pKUz+fx+eefo9PpYDqdYjweYzKZoN/vo1gs0jiJer2OfD6P/f39K8VQx3FwenqKfr8P27ZRq9WQy+Xo40gBn5zv1Wr1wTfVSCdyp9OB7/vI5/NUEDafzzEcDqnjVxzH711YzWQy2Nvbw3K5hOM49PORZfnBhUESC2FZFiqVCnRdx2KxwGq1ulawmM1mafxPPp+HaZr07x4juuXrzGKxwBdffIHZbEYL9Bc7ucfjMcbjMTY2NhAEwa1Og/eB4zjs7+9jPp/TKAXiYidJEiqVCorF4oM2Uj799P7Hs1jcT4AVTCYAAHa1QrxOhzQRXS6XiDTt/geYkPDISJ9/DvzX/zXc/+w/w7JQAHQdrG2DXS4RCwL8RgPIZBD+9t+Owp/5M2Ce6SZUQsLHgAiwSAz6cukjn8/eKDZIpVJot9v4h//wH4LneYzHY3z++edXnEQkSUKz2cRsNqNunpPJ5IO7pK4LadTI5XI3iiskSaJup7PZjDosSZIEVVVplDrP8zRSvtVqPcgd9SFUq1UaXd3r9XB0dETnqMQF+V3XzQ+12dvtdmkUXhzHWC6XNAaNRCXyPI9arXbvYzBNE6PRiMaOEQFcOp1GLpeja5T3hTQK+L5/SeilKMqDxHRxHOPg4ACz2QzdbpeKP4jw4WK8IIkYJ2uBd2MKC4XCpTVCHMfwPA+LxWItUZggCBAEAYvF4lpnKOBcgGUYBlKp1J2CLhJnaRjGWgIs0kRE1qIXhWRxHMO2bdpY0+/3EYYhHUM6nQ5UVaVuzuS83eRmVC6XMRwOIUkSJpMJJEmiTU8HBwfQNA1xHEPTNHQ6HRo//27NQlEUcBwHy7Kg6/qjCrBYlsXr16+p29nFhg2WZVEoFGhU+e7u7lqxZo8BERmOx2PaZEUaV4jbn6qq+MEPfgDDMJDP5++smxQKBXS7XerWdp/1K6lj2baNbreLcrmMMAxpPedi8xL5Dtvd3UU6naYuWel0GqIoYn9/H6Io0hjPl9bIdBHyufA8j9lsRh3kKpXKlWuFxLGKoojJZALbtjEaje7tRpZwO8ViEaZpUqHrbY6fxL3sKZrTEp4HiQArISEhISEhISEhISHhHlx0wALOix2yfH235mg0Qrlcxvb2NgRBQLvdvrawWyqVcHp6Ct/37yxuXnRXCcMQQRCAYRikUqn3EqFYloWzszP84Ac/QC6Xo0XbVCqFyWQCRVFoPAlwLhjp9/tUXHSXAIthGOqqlU6nqTglCAIatbaxsUGLsqPR6NruyTiOacGWCM7epdPpYDQa0UKq657HeHEcR+NFJpMJLdB9aBaLBRUUXYxOlGUZhmFgsVhQR6dsNgvXdcFxHBqNBlqtFmazGf7xP/7HAM47rJvN5iWhXqFQoAKswWBAN0FarRZUVUW5XEa9Xr+2cDocDmGaJizLwvb29pXzIYoifZ5Op4NcLofxeHytk9djQCLkzs7OwLIs9vf3Lwm+stksKpUK+v0+fUw+n1+rI/su0un0oxXdSeRNEARIp9PUXU6SpGs/B3L/hmEIURSpY5phGIkA6xbiOMbh4SEVXxG3sotjYbVapU6DLMvi5OQEr169erRjuBgrdN84nJsQBCCVuvp9cxuuez/nLObidXiPKAUAwAuJiUj4+iN99hnE//l/xvyXfgn+3/ybiE5OwCyXiBUF+Kf/aWT+1X8VwlexzwkJ32SIAGu1WiKHbnbnAAEAAElEQVQMI4RhhHK5cOvfkJjBwWBwZ/RzsVik825FUe50x/2YVCoV6vRiGMaVNQtxViVzsb29PUiSBFEUsVwu4XkePM/DarWCZVnI5/Not9v4kR/5kY8imCBrKJ7nwfM8dfv0fR8Mw1B3ZUEQHuS6uS69Xg/9fh+dTgee50FVVbx69YrGwBmGAcMwIMsy3r59izdv3qztvjIYDNDr9WAYBnRdh39hAiSKIgqFAnVsIgK594WIld4XEtnW6/WuiI6A888vm81CkiScnJxgMplAFEV8+9vfpveI4zg0tpy4fsmyTNeNpJFoHViWpcKN6zgXYC7XdjMmcYar1erOx04mEyrOu+jCRtyeieuX67pYLpfUpYs4hJE18XljW0wbjjY2NqCq6qWmGxJ/V6/X8Y/+0T/CarUCy7LUicm2bQDA0dERbe4yTfOKyxZpnFj3Pd4XjuPw6tUr2rChqio8z6O1APLePrZQSJZlbG9vo9VqXRICEiElEX16nreWox4ZC33fv9Ft8Daq1SptSDw7OwPHcVgsFgiCAI1Gg66TJ5MJoiiiAlgibhkOh9SBb2tr69l8/7wPPM8jlUphsVhQ1zpZlm/83lkul8jlcuA4DrPZDIqi3NuNLOF2yuUyBoMBrYVubm5e25zoeR7G4zGKxSIVgSd880gEWAkJCQkJCQkJCQkJCffgug3x6zp8SUG80Wig2WzSYsh1SJIEhmHged6lgvNdcBz3KC5Oi8UCP/jBDzAajTAej7FYLGhxMgxDWuBcLBZot9u0MEeKozcVeN+FYRg0m03U63VMp1MqwGJZFrIs01iP8XiMs7MzHBwcQFVVZDIZMAyDxWJBHa3a7TZUVb0ivOl2uxgMBpfckyqVCt0c0HUds9kMzWYTcRw/mhvOTfT7fRwdHUHTNIzHY+quRF7TcRxYloW9vT3a0btcLunnKssyZFnG8fExZrMZcrncFTEViYioVCpoNBo4ODhAsViEqqr4/PPPbyy6kUIeEX/dJkZTFAWKotBi3sbGxgcp5k0mE5imiSAIsLe3d+31TWIwj46OaNTHYwiwHhNS9CWdukQseZOjVhRFdKOBYRgIgkCjBBJuxjRNKtisVqsoFK5uJpMI1DAMMRwOIcvynR2rD+Uxi/33vb2+0pmuTarVwpJhEIoiWNNEdFdh2DQRMwxiSQL3gQSYCQkPgeE4KD/+41B+/Mef+lASEp4tv/ALfxiu6yMMw69E4jxE8acB3CzAnc/nNPZ8e3sbYRje6oJaKBSoGHo+n380N5e7KJfLGI1GqNfrGAwGWK1WKBQK9L2k02kwDEPXJEREw/M8fN+H53kIggBxHCObzWJ7exvf+ta3PqqzCHHvqdfrNOKaNOEQt5cPKeJwXRfD4RDdbhdRFF1pkBBFEaqqUic0juPQ7Xaxu7t753OPx2N0u110u124rotisYhWqwWe57FaragzluM4AEAdep4LZO3Csuyt6xGe51GpVDAcDlGpVKjz7Wg0oo5V5B4TRZFGY7Isi1QqdaOj1UWiKILv+5eu75uI1xTfX1yf3IVt27Bt+5Kb8rtuzyRqLp1OQ9d1aJpG3dtYlsXOzs6la5u4V1/nZrS1tYXT01MqbguCANVqFYqiUNEkEVaSWMjxeIxarXblvK37Hh/KxYaNi0Kvp44m43n+Tne1+56Xda+ti8iyjGaziSiK0O/38b3vfQ+2bUOSJMznc/i+j/l8Dp7nUa/XkcvlUCwWwTAMtra2LsVQfh3EVwCgqiry+TxtIlosFlcEngTyXUXOwfe//30af7mzs4N8Pg/XdWHb9iXXv+tiPxNuhtTdSNzj8fExCoUC8vk8dd8mQmLSONhqtR4kSkx4+SQCrISEhISEhISEhISEhHtwnT7qj/wR4O/+3R92lwPnhap2u43Nzc21IgOeqlC0Wq3w9u1b6LqO0WgEQRCQzWZpl1Ycx1itVhiNRrQYuvOVmwTplLxvJB3HcahWq9cWkMhz8zxPO+lnsxniOKYORYqiIJfLYW9v71Ih9l2nqb29vWs3BzRNQ6/Xo5sDOx/IHaPX66HX60HXdRiGQQvoJPbi6OgItm1DFEW4rkvPuW3byOfz1C3Jtm1kMhkcHBygXq/DcZxrO7Y5jkOlUsFqtaIbbLcVQC3LwnK5hOu6a0WoqKqKXq+HIAiwWCwefWONuD4RQdht4kKWZVEqlaBpGkqlEhzHeTYbfcD5tcayLDKZDCzLAnB+zMQZ613m8zmNWSERFSzLJh2rd0A67Yng9TbK5TIODg6wWCygadoHc3F7Ku4jwHK/+10sfvZnwZ+dgbdtcNMpfMdBWC4jKhYvu2MBQByDn0wQ5HKAqkL5nb/zcQ8+ISEhIeGD0u3+/xCGP/yiYBjxTqEvEY/HcbyWU5AoipdE588FEhlMIu3G4zGm0ykkSQLLslT4QjZKiWgjnU4jiiIEQQDf95HNZrG/v49ms4lPPvnkSd5LOp1Gs9m8Nar9Q0CcXF3Xxe7u7o1rv2KxCN/3oWkaFEWh5/ImwjBEr9fDaDTCcrm8Ep9IosdUVaUR5TzPPzji+bEhsXqmaSKfz9+5nidOPY7j4Dd/8zdptJhhGFitVrS5KZVKoVAo0HjJfD6PXq9HhYE3QebE2Wz2xnmxIAiQJIlG9N12zHEcYz6fo1wur+VmRtafpMkhiiJ0u12YponZbIZisYhsNktfc7lcgmVZCIKAMAxRr9chSRJ10CNr99uOUVEU+rcXRSY8z4NlWWxubiKVSiGOY5imCUmSUCqV6Hkk61oS35ZwDrkP0+k0XNe9U6jjui6t16zrfPcu5HNnWRaapsFxHOo8mEqlUK1WIcsystksNjY2LjU8pdNpBEGw9ncPcWMPw5A2Az6nWgLwQ7clQRDQ6/VoY+RFoiiCbdv4jd/4DbiuC0VRUCgUYBgGOI6jQkfHcVAoFGg9guM4iKIIWZZRq9UeNXrzMZnNZuh0OnBdl673NzY2nuSziqKIutfbto0gCOB5Hm0gJdejJEloNBrI5/PY3NxEoVDAaDT66Meb8PQkAqyEhISEhISEhISEhIR7cJ0A60d/9Dwu6t16TyaTwWKxuFOA5fvnHempVOqjd0GOx2M4jgNN0yDLMhaLBTqdDi3CEOtzEqMmCAKm0yk4joMgCLQw+liwLIu9vT2USiVq0w+cF2h1XcdisaAOUr/+678OVVVpp+tkMqERDru7uzcW5svlMt0cyGQyaLfb9xaR3YXjOBgMBuh2u+B5Hnt7e7TALggCZFlGqVSikYvz+ZwWAeM4psUz4Pz6IGIk0sF7G7lcDt1uF3Ecw/d9GjGxWCyouxTLsrRwBGCtQikpXv/QPeFxIRtgvu9jY2Pjzsdns1kMh0MaD/OciqYcx6FQKMCyLNq1KooiLMui3bqEKIpgmiYURaERna7rolqt3uiYlXDOYrGA4zjI5XJ3bnqlUinaRb1YLD7SEX481hFgRYsF9D/zZ8D9yq+AM02w8znY1QpMGELQdQSeh3g0wrLdBr5y5EMcg+12wfo+gnYb8e/6XWD//+y9eZQk213f+Y01I/c9KzMrs9Ze3qIWYGQENovxyBgZDJJskMeeGXuO4RwvnGPZg1mHxQwwMBgYDwxmsfEwB2MYIYSR2GQBYtECCPTEW7u7lqys3JeIyMjIyNhj/si+V1Vda3dV9fJefM7R0euqrMyIyFju/d3v7/sNF6hCQkJCnno8z4EgnDz+ZRgGtm2D53kEQXCm6+7BmO0nzYEkFovhmWeeQaPRQCqVgq7rVDBAGjs4joPruuB5HrPZDNPpFJFIBPl8njrBJpNJrK+vP9B8jTRGEKH900YQBBiPxzRe8qx9yGaz2N3dhWmaGI/Hp0YGjsdj6shbr9dPPK6iKKJSqaDVaqFQKECW5SfCBYvMhzzPO9d3yzAMBEGgTU9E4JfL5ZBOp+k5qKoqxuMxZrMZ8vk8HMcBz/Po9XpYXl4+9vqybZvOnSVJOjHyqlAo0MYrEql5EpPJBL7vI5VKnet4u65L4+R1XadzT1mWUSwWj9REXNdFPB6njUCyLCOZTJ4pNCOMRiN4nkfFkaZpwjAMxGIxDIdD6h5dKBRg2zba7TYsy4KqqnR/FEUBz/NIJBJPxDn1pCCKIhKJBDKZDMbjMfL5/Kn3deKSLYrihWpDS0tLyGazGAwGUFUVoigim82CYRikUilks9lj58dETHiWMJPUaDRNozU4IgKMRqNYWlp6Ys4DURRRLBZRrVbRarXo90DEnqqqotfrodlsQlVVMAyD6XQKy7Koo73ruofcK/P5PI03FQQBmUyG1s8epavjWXS7XXz605/GaDTCZDKhkbvxeBy5XA7Ly8t405vedK5G18uACMEsy8JkMqGOVkEQ0GNbKBSQTqchiiLS6TTK5TIViIa8MQkFWCEhISEhISEhISEhIQ/Ace7RxzWFMwyDTCZD49FOKwYpioJoNApJks50cblMfN+n0XiyLIPneZimSUVZJGowFotRMdloNALHceB5HpVKBclk8kq6RTOZDDKZDGzbxv7+PjqdDqLRKDzPw2g0omIwEmcQj8exv7+PV199lRbSgMWCSzabPdRtCyyKT7u7u7AsC7Isn2jn/rAMBgNMp1PM53Nsbm5ScZFpmuh2u7QonkwmqbhM13Wk02ksLS0hFosdciELggAsy54r8pG8Dlh8x5qmodPp0M51x3Fox3+v14OmaXAc58yFA1I8uipnJrKfwPkc4chrDv7dSZBjT1zbJEm68sXBUqmE8XiMRCKBwWAAQRDgeR50XafFQtd1aQd6JpOhBWeySPekFICfVHzfh+/7545i5Xme/s3rjbMEWL5pQnnPe8C98AKERgOsbcNJJGAnEhAMA7xtIzKbweM4MNvbsCoVgGUhyDIYz4O9ugr/2jWkv/7rH80OhYSEhIQAZHxzBWMWTVMhSSePM0h8NxlDnzVuIoIl4qxx/Gcuxu0kgouMdR5FDJIkSXjmmWfofMY0Tbp4ms1m6SL/fD7HcDg8EtVULBZpU8NZeJ4HWZYxGAwORceR/S0UCo89eux+iOsxOSZkbOW6Lnzfh2ma5xqXSpJEI/Os4zqXDkCaUwRBOPMcSCQS4HmeCnqehDEymQ+xLPtAzjvE6TabzWJtbe2Q2IhELKbTaezt7dHzcHl5Ga1WC3t7eygUCrTxxPM8TCYTjEYjRKNRFIvFE0VaAKhAq1AooNfrQRCEY89r4iydz+fPbLiybRt7e3vodruYTqfo9XqYz+cYj8eYz+ewLOuIw45lWbBtm7oaxeNxNBoNWJaF8Xh8JCbwOFRVxWQygSRJh97f931sbW0hnU5D0zTE43FEo1FEIhHouo7pdIpCoQBVVTEajVCtVuk2hHwGEpU5Ho/R6XQOuU4dRFVVTKdTrKysIJfLXbixTRRFrK6ugmEYTCYTrK2tgeO4E89p0oxVLpdPrUuNRiPs7e3RiLiD92ae55HJZKj4dm1t7YkQEtfrdWiahlqthslkgv39fUwmE+i6Ts9f4pRHGvyIuNg0TUiSBNd10e12Ua1WIUkSrl27BgD0Xnow3vU8ruhXzfb2Nj7xiU+g0+mg0+nANE36vAyCAKlUCv1+H6qq4gu+4AsutRn0OAaDAZrNJgaDARVsktoteTbGYjGwLItCoYCVlZVzuYaGvP4JBVghISEhV0DgeZj94R/C/vSnAdMEE49DfMtbEH/rW6+kaBISEhISEhLy6DiujnySACuVSmE0GqHT6ZxYCJ1Op1BVFcvLy8hkMo+0M5oIcu7cuQNJkjCZTMCyLOr1OmazGS2gW5aF4XCIaDSKO3fuwDRN3Lx5E5lM5sqLNOPxGLIsQ5ZlTKdTJBIJ6iA0n8/RbrfhOA4VsiiKAkmSqGtUIpHAdDpFLBY71FktSRJdWDprceBBIZ3LiqIgk8lQZ7OlpSUafzCZTNDv9zGZTOjPqtUq0uk0UqkU6vU6PV84jgPHcfA8j8annMbBIpWiKFRgpygKbNsGx3HwfZ9a7+u6jldeeQW3bt06VchCCtgsy16JMxNZ7CHisLMKV6ZpgmGYU2MwDcOg+0+EWgzDIBKJoFAooFgsnlu886DEYjHUajUq9nnppZfoAkU+nwfHcTAMg54bANBoNDAYDLC0tIROp4NYLPbELtQ9CZDv/rzXsGVZNNrzaeXmTaBeBz784cM/P8vUa/KTPwnu05+GsL0NLxqFvbkJRhDgBwH8ZhO+qoI1TfCOA9EwIOzuwkql4BYKCAoF+NeuIflDPwTuEXUah4SEhLxRMbe3YbzvfcBHPwpWVQGOg18ug/2bfxPJd7wD3CmONeeFYRiMxyPE49FjRQdBEEBVVcRiMaTT6UNjqOPwPA+KolCXm/vHcJqm0Rih6XRK3SMkSaIuvCsrK49EAEE+6ySi0eipvz8LwzCwtbWF+XwORVEOxVyReUmv18PGxsaZ7q1k7E/Gx1cBEZyNx+NDAvVUKoVisXhozP8ggoSzmiOAhTuYZVnn/t5jsRhs26YCvscNcbglkeNnicKII1QkEoFt26hWqyfOYQRBQLlcRqvVwsbGBhiGwdraGobDIVqtFhiGoY5ZHMchl8shn8+jVqudGifGMAxWV1fp/Hlvbw/JZBLpdBqCIMBxHEwmE0ynU2SzWRQKBSqGOQ7LsnD79m0qhiLOQsQ1bTweI5lMYjwew/M85PN5BEFAG9CIYxJx3SZuPOfBdV04jnNEdMOyLKrVKr1+er0eotEo3WdFUei2VyoV5HI5rK2tnesz30hks1nE43GUSiW0222YpkkdAUktRlVV6LqOcrmMVCp1Lhfr81AoFKBpGsbj8ZlO17IsQ5IkRKPRE69BVVWxt7eHTqcDTdOQzWaxvLwMURTheR79rOl0Sh0fL/IcOA++71MhGBG+EoEkadCzbRvpdBqZTAZra2tUAESifjmOQzqdRjQapedwp9OB7/vo9/vU1fH555/HeDymdapCoUDdtMhx4XkexWLx0p3hH4ROp4NPfOITuHPnDkajESqVCsrlMo0pnUwm6PV6uHPnDnUG/MIv/MIrcw3XdR37+/tot9swDAPVavVQYyep4/X7fTiOg+l0+rps9Ap5OEIBVkhISMhlEgRQf/7n4b///WAHA7C6DngewPNwfumXINdq4L72a5F+xztCIVZISEhISMhTynkFWMCiw7Rer6PZbKLRaFAnJiIwURQFk8kEpVIJ6XT60gpW58VxHIxGIxiGAdM0kc1mUavV0Ov1EIvFMJ1OMZ1OwXEc5vM5BoMBgiDAysoKisUiVldXr9T22zAMdDodGhewvr5+qMCayWQQi8XwqU99ijo5+b6PGzduIJFIwHEcKoQqFovwff9QlzHDMOdyT3pQSBHNMIxDRcBcLgdBEBCJRJBOp2kU4Gw2QzweRzqdRjqdPtIJnUgk4LouBEGg3Y2noSgK0uk0HMfBYDBAu92Gruu0YEoi2IgDGokiTKfTWF9fP/Y9id16rVZDNpu9ksJcPB6nlu2qqp4aiUH2k0QdHBez0ev16L4rioLZbEaLm6lUCtPpFIPBANevX7+yot3S0hJdIInFYnjllVcwHA6xv7+PSCSCUqmESCSCdrtNz5loNApZlhGLxdBsNulC3fr6+pV3eD5tpFIpGrlJCswnYRgGHMeh19nThigCP/qjwNoa8N73Hv39aQ5Y/nwO/NZvge124Ysi/PX1zyykMQz8lRWYmQz40Qj+ZALBNOFEo3ALBbhvfSv4v/23kfmqrwqjB0NCQkKukMB1ofzgD4L97d8Gq+tgx2Owto2AYcA0mwh2dqD9/M+D/ef/fFFXvCD5fA77+/vUdZa4ORCXKt/38cwzz8A0Tbiui3a7jWq1ekQI5LoujdxOp9MoFouHfi/LMhqNBkajEX1fSZIQBAFGoxF1/DFNE5ubmydGpz0NmKaJO3fuYDweU3ch0mDjui4mkwl2dnZQKBQQBMGx427P8zAejzEcDg85tBAhRCaTuTQxVrfbpS65B4UAZLtVVUUymYTv+zQi+6wxlOM41Fn3PM0DZD52Hogj8JNEsVjEdDqlzUInzY2DIMBwOKTONKVS6UwXaeL6NZ1Osbq6ikQiQRuN5vM5FefF43HEYjFUKpVzuWmn02kq6kqlUlAUhc5FGIZBMpnE6uoq4vE41tfXT/zOgyDA1tYWFEWh8ZCiKNK5SzQapRFco9GI/s3ByPl0Ok3H70RYcl4BA4mcO859LJFI0Aa4g8JHYOGwVKvVUKvVEI1Gce3atTD2/QC+79N7ELk3+L6P4XCI4XCIeDwOnufpuUKESgzD4M6dO/Qeks1mkc/nH6pukM/n0el0kMvl0G63UavVjgg1STyqqqqo1+tIp9MnNnC1Wi1a91hbWzt07XEch2w2i1Qqhf39ffo8O881+jB4nodOp4PxeAzLsg7ddxmGgWEY1E2SRO8RoSdp3uR5HvF4nEYIkhrBeDyGKIqIRqM0tvTatWvgeZ66x5MGL2BxzVWr1TPd54gTIs/zV3YP9n0fL774ItrtNkajEZ577rlD9x6e5w+JxprNJuLxONbW1nD9+vUr2aZer0cdx9bX14+cXyT1IBKJYG9vjzqfr66uXsn2hDxdhAKskJCQkEsi8DzI3/Ed4H7/98F3u+BVFYzvI+A4MJ4Hn+PgdrsI9vch372L3L/+16EIKyQkJCQk5CnkQQRYGxsbtDA6Ho/R7/fR7Xbp7xOJBC0WbW5uPvLCH7F1n81myGQytFu2Wq1iPB4jEonQIjopABMr8/X19SuPfiBRiLPZDBsbG0fcwUhnH1kQEASBFvtJ8TubzVIXKJZl0ev1UKvVYNs2XNdFJBK5dGehgxFn9zvtJJNJJJNJzGYzWjjrdrswTRPVahVLS0tHipSk+FYqlTCfz0917xmNRnAcB9lsloqmdF2n9vWNRgO2bVMBVjKZxGQywdbWFkzTRDqdPtI1bRgG2u02EokEEonEpcc1EhiGQaFQgK7r2N3dhaIoJy4iaJpGFyOIm9RB+v0+7VaczWZIpVKo1Wr0WCqKAkVRUKlUEAQBnnnmmSuziieLZaPRCIlEAru7u7BtG7qu04VO3/cxn8+RTCZRKBRo1AJZqMvn8/B9H9euXXsqxUNXRbFYxGg0giAI6PV6JzoNknsFEezl8/nHsLUXg2GAzc3Ffx/3qDhNgKX/5m+C0TTw0ymslZWjx4hhwKTT8NJpeI4D/84deJEI3LU15H/u54AnbLEzJCQk5HVHEED+9m8H9wd/AL7RADefw00k4CUSQBCAm07BjUZwsln4P/IjmDgO0l/zNRf6yKWlJUSjUYzHY+zu7tKfE0eNfD6PbDaLIAggyzKazSbu3r1LGyCAhZuuqqoQRRErKysoFAqHxm6GYaDRaKDb7R5qOiELuMSNo9frURHFc88999TG9zQaDSiKgm63i3K5fGQcm81moes6dTACFuNEcjx0Xcf29jZt1NF1HZ7nged5JJNJunh+7dq1CwsDSJMLGSsnEgkUCgWwLEsbb4bDIer1OgzDQCqVQq/XO9M9VlVVRCIRxGKxU52YACASiUCSJOpUe5rD1sHmlifp/CBCkUKhgHa7jUqlcmSs7rouer0eLMtCOp2Gbdvndv0izk0khrBSqUBRFDqXI8LHs9zU7iebzSIajWI4HCKZTB5yWuY4Dvl8HsVi8dTzTFVVzGYztNtt6tiby+Wwt7cHAFTAI0kSNE2DYRiQJAkrKyuoVCpIpVJU7EEiMA/GX55EEATU/ScSiWAymRzrEp1KpRCNRqEoCmRZxmw2Qy6XQ7VaRb1eR7FYfGiB0OsV0zSxtbVF7+2TyQS2bWN+b6IRiUTg+z51XHNdl94/BoMBjb8TRRGqqqLT6aBcLj+wazrHcVhbW4PneQiCAM1mE9FoFOl0GhzHwbZtqKoK13Wpk/xJjlWapsE0TciyfKqoiuM4VKtVbG9vwzAMev+7TFzXxZ07d2h84HQ6BQDalKcoCliWhe/7tNEIWIi2SL2LNE9mMhk4jgPTNGGaJhVmEUFzIpGALMsAQJ3kJ5MJre+RGhiJ01NVFbIs02uSNGuORiPqWgks6lnFYhGZTOZSYxplWcZwOES/3z/2PkrgeR7Ly8s0HrPRaGB9ff3Sr2PbtjGZTGhN6rTnTjQapU5/0WgU1Wo1dC8PCQVYISEhIZeF8qM/Cu4jH4GwtYUgCGBXKgiyWTAsi8DzwI7H4EcjBLMZ3F/7NajFIjL/8/98qdsQuC703/s9uC+9hGA+B+JxRD7v8xD//M8PxV4hISEhISGXxP0CLIYBOG4RL3awe5e43ty4cQP9fh+xWAzFYhG2bdMON0EQkMvlUC6Xr6S77ixIJ5tt24cKBDzPY2lpCa7rQtd1WnBhGAayLKNarV55RIjneTQ2L5VKHRvNqGkaLYysr6+j3W5DFEWMx2PkcjmwLAuGYZDL5eD7PrWnJ5bukiRBkqQzFwceFNIZSCIOj/tu4/E4rl27BpZlIYoiXn31VVrYPIhhGOj3+9Q9jeM4tFotOI5D4xqARbGU2OYvLy8DWBRIO50O8vk8BoMBdTQgsXckilAQBNpt/7u/+7t4y1veQr9fXddpga9cLqNer1/pd18qlajdfLfbxXw+Ry6Xo8fwoHhqaWkJyWTySJembdtot9vodDqwLOuIeC8ajSKTydAFMo7jsL+/j2vXrl3ZfomiiGq1imq1ire85S3UBeL27dsYjUbUiWtjY+OQrT1ZqGu32/Se8uY3v/mJ6/5/XMRiMWQyGdRqNezt7WF/f/9IXI6u69S9r1wuo1QqPdURhMCDC7Dc27fBatpCaHWWs4ggwC0WwY/H8E0TrqKAfwoFayEhISFPE5P/8l/A/dEfQdjags/zMG/eBHNgsc8NArjTKYT9fWBvD/7//X/D/OzPhvSQrg8MAzzzzDPodrtIpVKwLAuu69KYZkmSUCqVUCqVEAQBdnZ2wPM8NE2DqqpQVZW+lkTykKiygxCHWlVVsbq6eqTZhCwWS5KE/f19xONxDAaDS1/0fhTMZjPMZjMMBoMjQrSDJBIJOs5Np9OYTCZ0vLe9vY3RaIThcEgdXnmeh+M4UBQFw+EQy8vL8DzvQs0DhmGg2+3SOcX9Y2USP9jr9dBsNlEsFmFZFgRBQKfTwfLy8rFjUV3XMR6PUalUkEwmz5zf5vN5qKqKwWCA6XR6qvsZEdkQceCTAsdxtOkKWLiKDYdDpFIpsCwLy7IwnU7B8zxWVlYwGo0eyH2ZvJbMDViWRT6fv5RjIEkS6vU6lpeXMZ/P4XkeOI5DNBo911yDzC/JNgGLOc/q6io6nQ5WVlbA8zxGoxHi8ThmsxkqlQoymQwKhQIqlQr9nNlsBs/zkEgkTjwPLMvCcDjEaDSC53nQdR3dbpeKHo4T4AiCQMf+rutiY2MDN2/evLKGoqcZ27Zx584dyLKMdrtNnfAikQh18BuPx5AkCYIgQJIkOI4DXdfRaDTAsizi8Tg4joOu6xiNRkgmkzT68UHv65lMBhsbG2BZls7dx+MxjQjMZrNIp9OIx+O4fv36sfUiANSdLgiCMxuZRFFEIpGgLuqX+SwijnFE0ByNRlGr1ZBIJGDbNu7evQvbttFqtbC0tIRCoYBarYZYLAbTNDEajfBnf/ZnYFmWugFqmob5fE6bKSVJog1Ho9EIqVQK8/mcNnqR+5TneWAYBqqqQtM09Ho9WhcsFotgGAbNZpNG6R50EifPLVLPOum4Pyj7+/tQVRWWZR1x0rwfSZKQzWYxHo9pDew87n8PwnQ6heM4mM/nKJfLJ77u4LVB3Nfn8zmWlpZQKpWuNDEg5MkmFGCFhISEXAJ2pwP2gx8Et7eHgGXhbG6C4TgQyRPDcQhKJTjZLIStLbD7+/B+8Rfhv/vdYM+IkDkPgedh8v/+v/B/9VfBjsdgplMwJPrwl38ZcrUK7u/9PaTf+c4Lf1ZISEhISMiDYLdaMD78YfijERiOA7u8jMTb3w7uKZ6E3i/AikQWixgbGxvHvp5hGJTLZSwtLWE6ncI0TSp6OWi5f1UQpyeySHKwmErEP9FoFJZl0aIrged5amfu+z4tjPI8f+UdXcSKfTaboVarHfsaRVFoQbtYLEKWZTAMA8/zMBwOUSqVaLE6nU6j1WrBsiwaaVitVpFKpS69izoajR6K0jspNo5lWdRqNdrlZ1kWer0e5vM5LdhbloVkMol6vU6LbSS6cjgcQhAE2gUcj8exsrKCTCaDaDSKdrsNz/NgGAY0TUO326VCoIP7XCwWaVwjy7KYTCZgGAYMwyAej2N5eZkW6K/a9YzneWohz/M8dWUg56XneYhGo9Q57tq1a0e+v+FwiPl8jul0ivX19ROLgsQljDhTWZb1SDrqBUGgiw+z2Yyeiyd1BpOFuk6ng2w2C1mWr/x7uAqIIxu5H5Eu5ot2zq6trcFxHDAMg36/j0ajAUEQ6IKl53lIpVIolUooFouPPOr1KnhQAVZgmsA9d+bzNOYEPA/cW/DzT3vjkJCQkJCLEwTw3v9+cPfisdyNDTD3C4UZBkil4KytIbK9DWsygfHLvwzpW7/1oT82Fothc3MTtm1D07RDz+eDYnCGYXDt2jU69jw4riXxO8VikS4yzmYzjMdj6LqO27dvo9frIZ/PnyrGSSQSSCaTkGWZRoc9bWLz0WgEXdfhuu6ZC8GpVArD4ZC6y2QyGezs7NDYr+PcPwqFAl3gZRgGjUYDN2/efKhtJS7DhmEc6zIMLOYplUoFe3t7NAqxVqthf38fu7u7yOfzSCaTdM5C5ifEOY00hJwGcRXJ5XJ0nnLceTKfz9Hv95HP5xGJRJ64SO5UKoVr166B4zga3XhQsEBEFsRpeH9/H4qinOn6Rebf5XL5SucoRDjzIPi+D13XMZlMjoznRVHE2toaYrEYWJal87dIJIJEIoGlpaVD54fneRgMBkin01RYcT/9fh+tVosKQgzDALCIXHMcBy+++CI0TcOzzz57pNGC3LtqtRqy2WwovjqBZrNJ4yiJO9hBMpkMSqUS9vf3MZvN8Morr1A37XK5fOQ8mM/n6HQ62N/fB8MwiMViDywczGazSCQSdL7uOA79XTweR7FYRDabPfV54TgObNs+Ugs7iWg0Cl3X4TjOmdfog0DETvv7+0in04dEPePxGLZt0+hfUjccDodYXV2FJEmo1WpQVRUvvPACUqkUEokEWJalcY9EvChJEn1OkOsvCALYtk33n9yvbdvGdDpFv98HsGjge+GFFzAYDMDzPGzbRjQapcfYtm3aPFar1ej2XrSmSdzAidD3NFGXaZp07j+bzWhM8mXjeR48zwOAE2ufpFGONHiS4xgEASzLgqZpiEQiV1JzDHnyCQVYISEhIZfA7P3vBzObgdd1WDduHC2UEAQB9vIypEYDvqZB+8AHkHn3uy/02YHrQv62bwP7R3+0iD6cTIAgQMCyYA9GH7bbkLe3kftf/pfQDSskJCQk5Moxb9/G7Kd+Ctyf/RkCwwB7T7UUxOPQfuZnEHzJlyD1T//pU+eq4XmL/x3kvPNohmEO2YhfJcTtaTgc0uIksCiu5nI5FItFxGIxMAyDbDaLWCwGjuPQ7/extLR0pGhJiqJEkHPZ3WXHQbp9SXffcViWhfl8Tou7pKuedAl2Oh2kUinE43FaFGq321BVFW9605uQyWTOtTjwoDAMg2KxCF3XaeTBSS5bnuchmUwinU4jk8kgnU5jNpsBWBTz0+k0IpEIlpaW6HuSmAfDMGDbNo0FIFb8kUgEvV4Pk8kEAGg3fiQSOSRKI0iShGeffRZ3794Fy7KQJAnLy8sQRRGiKKJQKDzSeIZoNIpnnnmGxh5alkUXfsjCDPnujlukGY1GUBQF8Xj8zM77XC5Ho17G4/EjFeeMRiMqyDyryzOVSmEwGEBVVQyHw6dKgDWbzWj0kGVZtGBKHOgKhQLK5fJDL7QSp8FWq4VIJALLsmi8I8/zSCQS9Boql8uXGpXwuHhQARaTTAKCAMZ1Efg+mDOONWOaC7EWAC6MvAwJCQm5UvSPfQxsrwdeVeEsLZ1cUwSAeBxOIgF2NELwkY/Af897wF4wwpyM9c4inU4jnU7DdV36LCeOqsDCVWlvbw+GYUDXdQyHQ7RaLfT7ffA8j+3tbepeehyZTAatVotGWj1trhGmacIwDMRisTPHzAzDIJFIYDab0eYHy7IwGAyokOG4vykUCvA8j8Yqz+fzB46w9zyPusme5DJ88DPz+Tza7TY2NzfB8zzW1tYgyzIGgwG63S59bSwWQ61WQyqVwsbGxrkEPSzLYmVlBa7rwvM8NBoNOi8SBIE2iBB3E+Ky9iSO5VKpFG7dukXn4CSKjYiyisUi4vE4xuMxNE3DcDg80/WLOM2lUqknbux/0EnnpHOoWCxiZWUFyWSS1hKi0eih74/MU4GFE/LS0tKROUG/30ez2USn06FRaoVCgd6DXnvtNViWhZ2dHSiKQt3hHMehIpNSqYRcLof19fUrOiJPN6RRhohsTxJKkXi1T3/604hEIpBlGW9605uOjcCMRqNYXV1Fo9GALMuIx+MP5dxGmpcqlQocx4Hv++A47ty1iYvcLy7zXjMcDqFpGhiGOeTg7XkeJpMJdZXK5/PU3SqZTB5qEiOu6LZto1wuw/M8WJaFIAgQBAGm0+mhhknTNOn1qes6YrEYJpMJPM/DaDSCYRgQRRGu6yKZTMIwDPzxH/8xbcz8rM/6LFQqFXieh+l0CoZhqAMWcT1rNpsnNqOeRBAEGAwG6PV6UFUVHMdhZ2cHvV6POvAfFD35vo/pdApN02iD1Xg8poLBfr+PXC53qS7XHMfR9yORpweZzWZotVr0eRoEAY3eTSQS1LWyXC5jOBxeqdt6yJNJKMAKCQkJuQSCj34U3HgMNxY7vhp+ACaZhC8IYGQZ7h/8AXBBAZbyQz8E7g//EMLWFsAwsKtVIJMBjos+/NVfXUQf/sN/eKHPDAkJCQkJOY3Zxz4G+7u/G1yvB344BGcYi4XcIAATBHATCXiKAu2FF5D4P/9PiCe4Gz2J3O9+BZxfgPWosG0bW1tbmE6nUFUVk8mELpLE43FomobRaETjQnK5HDKZDDiOw2w2o/EfpJhvmiZ0XYcgCEilUgiCgHYfXyWkoMbzPCzLQuw+11BSZCICLdKhWKlUwHEcWJaFqqqQZRmj0QjAQvBCug1LpRI2NzePvO9lUSgUaAd7t9uFZVnI5XKHBCKk83B5eRnVahW1Wg2z2YwW7InFOxE/OY6DRCKBmzdvwnVdjEYjNBoNmKZJux8dx0G73YamadROvlKpwLZtLC0tnVhEDIKAisRWVlZQr9cfq1OQKIpYX19HrVaDoiiw7l18oigim82eWOj3PA+u61Ir/vN8jiiKtOPyIui6ToU/HMchmUyeen6Zpon5fA5Jks5VQCZFUSJGexpQVRU7OzuYTCaQZRnz+RwMw9Bu4kwmA8MwMJ1OqXPAw0AW8KrVKsbjMWaz2aHvgUSSvl54UAGW+PmfD/e//lcELAtGloHTFvGCALyqws3n4V2//lQ7VoaEhIQ8DTivvgpmNgPrughyOZy13OtnsxBbLfjzOaydHUSff/7Mz3iAxLMz4Xn+yLhF13XcvXuXjrtd14VlWTAMA5PJBL1ej4qwiQvo/RBX14OuE08TB+cl54E42wZBgPF4DMMwwHHcmRFZ+XyeRkINh8NjY9dOg7isGIZxrrE+EVZYloV6vQ6e56nzjGVZ1NlZFEVkMhlUKpUHEoVls1msra2BYRgkk0koioLXXnuNOkiRz8rn89jY2Djz+DxOWJZFoVCgYqnj3HOy2SwkSUI+n0e32wXP83S+cPAcIhHaS0tLkCTpkTRxPQjkPGdZ9kT3GYZhUK/X4fs+WJbFSy+9RPeZOMS4rotEIoFyuYxisXgk6ms+n6PVaqHT6cA0Tayvrx9qsEmn0ygUCnjhhRdoTGWj0UAmkwHLskilUtQdenNz84Gdvt4ojEYjzOdzmKZ5ZoNaPB6HbduQZZk6L50Ez/PI5XKQZRn5fB66rp/6+rN4GBd2EqtLnk1nzbt1XaeObZeF67q0PpfJZI6IEEn9ggizkskkjeObTqd0W0jz0ssvv4x+v0/jHRuNBvL5PI2GJK5XRJxFrtF4PA7f99Hv9+H7PqrVKhzHgeM4qNfriMfj6Pf7ePnll7G6ukqduebzOX2uA4saCnGi43ke9Xr9XN+N67ro9/t48cUXqROjrusIgoCOH8h4Ym1tDZlMhoqO5/M5dF2nz4ZWqwWWZemzEACuX79+ad9bPB6nx3MymRxyzvN9H+12G4qiQFVVlEolKsBOp9Oo1WrgOI66VsbjcbRaracyXjnk4QkFWCEhISGXgaaBsW3455lgMgw8SQJj28A9V4KHxW61wP7Wb4FvNODzPNzNzUPdzMdGH/7CL8D/mq+5lOjDkJCQkJCQ+zHv3oX93d8NttGAOBjAyWbh1GpgotFF5V/XwQ6HELe3YTsO9G/8RmR+9mefmufSky7Acl0Xd+7coRN90nErSRLtGmu1WrTwUq/XIUkSrl+/jkajgUqlQgsbsiwDWBRXisUiotEoNE2joqWrLl5KkoRIJIJ0Og1FUY64bhFLdY7jYNs2PM+jxbWNjQ3oug5JkuhiD+nSLRaLqNVqeOaZZx64Y/xB4Hke165do0Kq4XCIF198kUbOWZYFnueRzWYxmUxQr9eRTCbPdEIiSJKE+XwOnuepE1YikaAFt/l8jtFohG63i9lshkKhcGqx0TAMpFIpWhzUNO2JiGoTBOGhYyLO27FKBEHBA6xOOo5DRT6kM99xHJimSbtOJUlCPB5HuVw+dqHR9/0HWqjjOO6Bt/Nxous67WQl13C1WoUoirRDV5ZlNBoNeJ4HlmVp/OTDwvP8oY7i1ysPKsBK/NW/CrlSgdvtQuj34aRSwAkiRqbXA+N5CAoF8H/7b1/SFoeEhISEnAiJiWXZMx0KAQA8D+YBYmKvetjgOA62trYwHA6pS2cmk4Gu62AYBo7jUMcMEh8lCMKRuQQRXbEs+1SKponjyHQ6PdfrTdOkkcmmaVLXr7PGrzzPIxqN0qaNB4XMic47BiWx5ISlpSUsLS1hOp1iPp9Tx1HiWvUw5PN5xGIxvPbaa+j1epAkCa7rwvd9ur2+72M8HiMej5/q2vUkcdx3ybIs1tbWqOvX9vY2nRMQocRsNgPHcdjY2EAul8Pa2tqj3/gzIK7JiUQCmqad6NAlCAJ1THIcB7FYDJqmwfd9ZDIZrKysIJVKYWlp6di553A4xGw2g67rWF9fP1ZcEY/H8da3vhUvv/wy5vM50uk0Njc36TYSAd9luuO83iAOfpIknXl9WZZFY+7uF8wdRzqdRr/fp419FxFgPQz5fB6DwQCCIEBRlFPrLYZhYD6fo1qtXqrrHBFAOY5zxKHbdV24rosgCOj5zfM8OI6jvyOQmE5d19FsNpFKpTCfz+E4DrrdLmKxGARBQCwWg6Io1F1LEARcu3YN8/kcgiDAdV0sLy/Dtm2Mx2NkMhlEIhEqgK5UKjAMAzs7O4jH4+A47tDzhtS4xuMxotEoisUiNjc3Tz0Gpmni7t27uH37Nvb29tBsNmHbNiRJQjQapU5co9EInuchFovB932YponpdIrxeExFyq7rQpIklMtlJBIJdDod+jy7jEhEAFT4ms1m0e/3kc1m6TNO0zTqPk/qbLPZDKVSCclkkn5+oVCAbdvodDqIx+MwTTMUgb6BCAVYISEhIZcBxyFgGDC+j/PUNZggQMAwwAUHA/r73gd2NgNnGDBv3Di5UCMIsKtVRJpN+NMppr/xG0j/3b97oc8OCQkJCQk5jtl/+k/g+n2IgwGslRUgk/lMFzfDAMkk/EQCTrcLsdmEHYtBe//7kfkH/+Bxbva5se2jP3uSBFjtdhuTyQStVotGDR4s/KZSKdi2jWaziXa7Tbt08/k8Lcyk02nahQaAOmdZloV8Pk/f91FAIvdkWcZ4PD5iWZ9MJmkkH8uyWFpaonFjkUgEuVyOCp50XYfneXjzm9985eIrAonS29/fx3Q6Bcdx8DyPxkJyHAfDMFAoFKCqKl599dVzd3Z3Oh3Isoy9vT1IkoSVlZVDhSbiZrW1tYVerwdBEKiV/P2YpgnTNKlt+9PqPAB8ZtGOuFqdFZfpui5s24YoiudaNNJ1Hf1+H5PJBK7r0muOOF+R2MMgCKhbl67rqNVqRwrUgiBAEASoqnpsh/z9HFyoexogXaGKomBlZeWQGxiJU0kmk+h0Omi1WhBFEZPJ5Il2NnhSOE6zfOoaLMOA++//ewTdLvzZDMLWFpxyGchmaTx9YFng+n0IqgprZQV+rYbMl3/5ubbHHY8xff/74X/604BhANEo2OeeQ+Jd74JwjoWZkJCQkDcyTCKxEFX5/mKycZa4xLLgP0BM7D39ypUxGAxojFitVqPRgZIkQRAERCIRsCyLcrmMbrcLSZKokOYgmqbRWPRHMU6/bDKZDI2MPktkYNs2ZrMZdRyRZfmhRPn+Q3y5ZLxLhF9nLQYTx6z7nc+SyeSlxkQSsUAsFoOu64hEIohGo+B5HvP5HJ1OB67rwjAM3Lx581SXE9IgcdCthTg+Pwmk02lsbGzQfR6PxxgOh7Asi46Ry+UydaF+UhfsyVydxCoe59I1Go0wGAwwmUyowFCSJDpncxwHqVQKlUrlyN8S0Z2iKHSOfxI8z2NzcxOtVgurq6uo1WooFotPzbzpcUMEgOdpYPJ9HwzDULfhs/6O4zgwDPPYagyxWAyJRALFYhHtdhuiKB4737QsC51OB+l0GpFI5FIFWOT4kON2EJZl6e+JWxyAQ/W4g68lAs3hcIh0Oo319XWoqgpFUWCaJmzbpk5SHMdRh0JN0xCNRqEoCuLxOI0gJCKjcrmMVqsF0zSRSqXQarUwm80wGo2wvr6OpaUl+my2LAuapqHT6aDX6+G1115DvV4/Ubznui7u3r2LnZ0dvPLKK5hMJsjlctQpirhlxuNxlEoldLtd3L59mzZVqqqKaDSKdDoN0zTRarWQSCRQKpXw1re+Fa7rotVqged59Pv9M13cjoM0IZK4QbItk8kEmqZhb28PlUoF8XgcqqrS48swDHq9HmKxGGKx2JGaZS6Xw97eHubzOYbD4RN7Pw+5fMKnT0hISMglECwvI7h9G/xwCD8IaBH72Nd6HrjZDHY2CzzEYOAQH/0o2PEYbjy+cBY5BSaVQsDzi+jD3/99IBRghYSEhIRcMs5wCO7jHwc3HMJJpxeRuMfBMAgqFfiaBmY4hPfBDwJ//++f+vx8UniSHbBc14UsyxgOh7QYcRyiKKJer2NnZ4cKgdLpNFZWVtBsNuH7PgqFAi1wWpZF7fxXVlaozf+joFAoYDAYoFqtotPpwLZtGuMHLAr/e3t7kGUZmUwGiUTi0LYxDENdoRRFoYscZ4lyLhNRFBGJRJDJZNDtdhGJRHDt2jVkMhkaCdnv97G3t4d8Po/RaITP+7zPO1WE4vs+hsMh7eSs1WpHip6CIKBSqaBer+PTn/404vE4dF0/8r62bWMwGNDjFIlEqLvY0wjDMMhms9A0Da1WC6VS6dTC+2QyAc/zSCQSVLR2Ev1+H61WC5qmQZZlKjAajUaIRCKoVCoQRfFQYW40GmE2mwFYLAwcPD8zmQySySR6vR50XT91Ect1Xei6jnq9fq5oxUcBcT44GJdJjjVx0iPCyZOiGBmGQaVSwd27d6FpGi0khywWtci5w7LsocXoB3XAAoD0O98JudkE3vteBO02xHYbQbeLQBQB3wdnWfAiEVjr6/BXVhD/P/4PsPd1aN+Pb5pQf/iHwf7O74CZzcArysI9i+MQ/PEfY/b//X/wv+iLkP7mbwb3iLvdQ0JCQp4WpC/4Apg/+7PwIxEw4zGCY4QIB+FlGV46Db9QQGRj48z3v8r17iAIMBqN6ILuwbEMcbVIJpNUfJFKpaBpGpLJJBXAA6BOEtVqFZlM5qGdlB4nuVwOrVYL6XQavV4Pa2trx45Bfd9Hp9NBNBqlbjMkVsk0TRQKhTPH4aZpUnHSgyKKImKxGDKZDBRFObJYfD+KokCSJEiSdGVjUFmW0el0sL+/D9u2sb6+jlQqRec3JO5sf38fvu/j7t27eO65544cJ9d10e12MR6PqeMwsIggE0URuVyOjtcfN7ZtIxaLUZe4UqmEaDRK/01ELcSFZmNj49zuvo8KEqNYKBTQ6XTg+z7S6TTdztFohE6ngzt37kDTNNTrddTrdSwtLdEIclVVaXzY/ft4MC7zPE5LRNhA3K9D8dX5IQ5+JFb0tHsQy7LUzZuIsU6DuDtxHPfYRJArKyuYz+fwPA+dToe6M4uiCM/zoGkaNE1DIpGgNZTLPH8EQaDzuel0emi+SxrBiMtUMpmkc2xSnyFomgZg8RxZXl6G67poNptwHAeGYcBxHLqfHMehWq1ieXkZ6+vrsCwLrVYL/X4flUoFyWQS1WoVkiRheXn5UBSqYRhgWRbD4RDr6+vIZrOHtiMSiaBYLFLhl67r6PV6J0bidrtdaJqG27dvg+M4FAoFLC8v0/OMNGYFQUAdyIbDIR1bJBIJCIKARqNBxxBra2u4ceMGfS7NZjMq1qxUKueuZ43HY/R6Pcznc8xmM+rMHYvFEIlEwPM8lpeX0e/3sb+/D1EU0ev10O/3qZNhMplELpdDuVw+4nBGYgwNw4BpmufappDXB+ETKCQkJOQiBAHsdht47jn4n/gEGM8DZBk4ZfLKDgYIOA7IZBB9xzsu9PHMZALGcc4ffRiJgLEs4Jx22CEhISEhIQ+C8ZGPAPM5eMOAdYb9NBgGXi4HfjRC0G7D3NqCdMHoqUfBkyzAkmWZ2rqfFVMQiUSQTCahKAqSySSef/55AIvCGymiEKtzQRCQyWSQyWSQTqdx7dq1RybO4TgO169fpwW74XCInZ0dCIJAi9Oe5+HatWu0WHKwo9D3fWiahtFoBJ7nUSqVUKlUHmnx2jRN9Pt99Pt9ZDIZ3Lp1CwzDoNvtotfrwbIseJ6HIAhw584dzGYzaJqGz/u8zztWWAUsFkIsy8J0Oj3xNQBQLpdRqVTQ6/XQ6/Vw+/Zt3LhxA6Iowvd9zGYzGIaBeDyOQqGAQqGA0WiEYrF4omDmaaBYLGI8HkMURXQ6HdRqtWPPWdKFWCqVzozVHI1G2N/fR7vdhq7riMfjiMfjmM/n9O/n8zlkWQbDMKjX6ygUCkin02g0Guj3+xAEAfl8nn5fpJCZyWTQ7/dPXEgLggCdTgeRSATxePyROdAdRxAEGAwGGA6HME0TlmUhCALq5pXNZlGpVKAoCubzOWzbPlPwyLIs0uk0VFVFJpM50antjcbOzg76/T6ARdzPwWPyMAIsAMj9i38BtVyG9/M/D0+WwSgKGMcBGAZuMgnE4/A+53OQ/OZvhnhGBKlvmlD+5b8E9+d/Dq7VAj+bwRNFBKII1nHA9fvwYjG4hgG12UTmx38c3CU6ZYSEhIS8XpBu3sTsmWfADIcQ+n1YmczJTZbjMTjThLuyAuZv/S0w53heXqUDlmEYcF0Xk8nkWAcb4kY7m83Q6/Vo9DZxgCKOpe12mwq4HjZ6+nHDsiwqlQocx0Gz2USj0UChUEAqlQLLsjR+eTQawXVdpNNp6nxFnEtI9BJxKD5uXDibzejfP6wgqlgsQtM0jMdjDAaDE4+5YRhQFAWVSgWpVOpUB6KLcFA0tba2dkSAJ4oiyuUyeJ5Hu92mLi4HxWOWZeHu3buYTCYYj8eYTqeHnHkSiQR0XcdkMsH169cfq8uaYRhotVrUDeWLv/iLwbIsFb6QKLB2u429vT0AeOzXBokqM02Tjv2z2Sw2NjbgeR51gRmNRkgkEnAcB9vb22i1WgCAW7duoVgsYmVlBSzLIpvNUjfhZrNJnb8OznOIA9BZgiACaSK67Mj2IAho3YHjuCdCwHfZZLNZ2hikadqp9xaO42BZForF4rmOM3FaJ4LckyDiGyKAiUajl1Z3ikajuH79OhiGofF8vV6PCsji8ThqtRoSiQTq9fqlz7dZlkU+n8d0OqVCU3IeEaEPESsnEglMJhNaGzh4zIijXKVSoTGFhmFQ8ZXjOOB5HrlcDjzPI5VK4a1vfSvW19fRbDYBLL6/9fV1sCyLTCZDhWjA4hriOI7ejzmOQyKRONZt0fd9iKIInudpg9ry8vKReTxxsiOu9SzL0ufiQUgNxTRNWlMhDW2e58F1XUSjUWxsbCCVSuHatWu4du0a/XtSd4nH4+h2u6hWq2fW/drtNrrdLkajEVRVhed54Hme1kLT6TQ9F5aXl6nobH9/nwqvarUaBEFAuVw+sZGM3JcexrUy5OklFGCFhISEPAS+YUD7wAfgfeAD4JpNcEEAYW8PrONA2tmB6brA0tLhPwoCQJYhDoewlpfhr6wg9pa3XGg7gnsT0nNHH/o+ApYFE3aAhISEhIRcAb6qLoS+wPH5SPe/PhYD6zhAEMAbj4ErEGAFjoPphz4E5zd+A0yns2gBz2TAfPEXI/nOd4I/o+P3fk4TYJFONQLpbntUmKaJ+XwOnufPVVBOJBK0mBEEAW7cuEHjDwqFAi06kPcrFosoFAoXEi+ROEHHccAwDCKRCPL5/JEusYNIkkRj/OLxOI3LIxFv169fpw5Euq5jf3+fdlc6jgNg4TRULBZRqVQeeeF6OBxiPp/DMAxs3hMm7u3t0UUPy7JoZx3P87hz5w4cx0Eul4Npmrh27dqRY04WsliWPTXehOd5PP/88xgMBjBNky6QEaLRKCqVCqLRKJaWlmDbNnWDepwin4sSj8eRy+XgOA729vbQaDSQy+Vooc+2bRqNl8lkkM/nUavVTnw/z/Owv7+Pfr+P+XyO9fV12n1q2zaWl5chiiIsy0Kv16NW/5ubm7QY1263USwWoSgKddpiGAbVahW2bcOyLOzu7tKFOhLnQFykXNfF6uoqcrncY1sw8n0f29vbkGUZsizTIiUhHo/TTnZBEGDb9rkjE2OxGL1fEtv/kJMRBIBlDy+qn0eABQCZd78bwbvehemHPwz3D/4AgaYBggB2bQ2xd7wDkTMEvAT1B38Q3J//OYStLfiRyEJ4fUDE6BoGuF4P4tYW7CCA+t3fjfwP//AD7GVISEjIG4fI//A/wH3tNbi6DnFn5zMxsSSKyHHADocQRyPYlQr8Ugmpd73rXO99let9pPkhCIIj4hzf95FMJpHP5xEEAYbDIfr9PsbjMXV/mkwmmM/nNIJseXmZjm2n0yltCiGxZfl8/okWPywtLdH5BxE39Xo9uqBL3HmJq08kEkGz2aRNNLIsw7ZtOI6D6XSKer1+6Lh6nod+v49kMkkdnR6GXC5HY5parRZs20Y+n6djTNd1oSgKxuMxstksMpkMqmcIsx8W4qiqKAoKhcKp7mf5fB6qqmIymWA4HFIBlud52Nrawng8RqvVomKKeDxOXaRkWUaz2aROSs8+++xjc1ojMZW6rmN9ff1YYRvP86jX69jd3YUsyzQO67LxfR+TyeSQsOqgC51t29jf34eqqrR5hUSxRyIRxGIxVCoVJBIJZDIZqKoKwzDQ7XZhmiYSiQQ2NzeRzWZRrVYPiS7IPu7s7EBRFAwGg0NzUDKHEASBvtdpEJHU/XGZp0EiIMk+EYEOOReHwyFGoxG9roHPuP8UCoXXzZwllUpBkiRks1kMh0PEYrFj77VBEKDX61GBD4BTI1eJS3s2mz2x4cm2bXqciVsWEdPl83mUSqVT60XnJR6P47nnnqOO8eVy+dBnZbNZ2lh1FRSLRSq6bDabWFlZocc4m81iPp9jOp3izp07EEWROkKSc8w0Tezt7WE2m0GSJKRSKdpQmcvl6HuRZzLP8/T8fv7552kjIAB6f7xfBJVIJCCKIqbTKSzLoq58xwnhiFMzuS+RWML7m69IHCx59s/n8xMb3xiGQalUgiiKyGaz2N7eRiKRwNraGpLJJHV5J/8mx4XUJjqdDoIggOu6GI1GKBQKKBaLx57Lg8EAnU6HOoiR5jkiltJ1HaPRCI1GA6urq0in00gmk5BlGbquUwFcrVY7VlBGCIIAlmUhHo+HrnxvMMJvOyQkJOQBsfb2MPumbwLbbIIfjcCrKhjXBeN5YO4tVCa2t2H1+3CqVUAQwFgWOEUBZ1mwKxUEy8uIftu3XThqKajV4G9tgR+Nzo4+dBxw8znsQgE4ZYEpJCQkJCTkYWE4brFIcK/j8CyhDuP7i2cXwwBXMBHV//APYf/QD4Edj8GPx2AMY7Ftoojgtdeg/+f/jODv/l1k/9k/O/cz+TgBFpnLe553SIzgXWXexzEE5zzuBFJYIH/LsiyKxSKKxSIMw4Bt2wA+E1NxEUgcnGEYmM1msCwLDMMgGo2i1+shlUqhXq+fWFgTRRGbm5u08/ag604ul4MkSWi32xiNRrBtG7quU9csEq1XqVQeWXTiQWRZplbooiii2WxC0zT0ej1Eo1EUCgVaECoUCtje3sZsNsMrr7yCaDSKSCRyxMqdFNXOU/AlncmWZcG2bcTjcerCRJyH0uk0dF2Hoii08/NpdsACgNXVVdqNTRbAut0uGIZBEARUfJbJZA4V8I5jPB7TaJxarYZIJAJN06DrOo2OARbF+FwuB1mWkUqlaKxgIpEAx3G0M/TgYlmxWIRt22AYBqPRCKPRCP1+nxZMgyBAOp1GrVZDLpfD6urqlR+7k9jd3cVoNKJd6qVSie6baZpQFAX7+/soFov0/nfe7vOD8RXk/03TxHA4hKZpcF0XLMtSB7DTvq83AgyzcMG6V/eG6M6Q7W5j+vsquFQS0rPPnhofyAgCUm9/O/D2tz/U59udDtjf+z1wzSb8SATe5ubR52gsBm99HUGjAX5vD/jTP4V55w6kGzce6jNDQkJCXs8kvviLoX7d18H/D/8BQacDsdsFul14kgQEAfj5HL4gwKrXEVSrkL73e8GfU3xzlVMS4mbBMAxc16WOScR9CAB1ySwWi7Asiy7GWpaFdDqNSqVCI5CWlpagqira7TaNMyaNG9FoFN1uF5lMBvV6/YmNKazVajR+sVAoUJdejuPoPkiSRH9eLBZpk0mv18P29jb+4i/+Auvr6wCAtbU1Oo4cjUZgWRblchnlcvmhxR8sy1KXYZ7nMRwO0Wg0wHEcWJaF4ziIRCIol8vIZrNYX18/1Sn2ImiaRo9FKpU69bVEHNNut8FxHIrFIjKZDB0vtlot5PP5I40ksVjskOuNJEno9Xqo1+tXsk+n4XkebQQ5y1WMCFCIMGk6nV7aGNjzPOo8dtDVVhRF6tCby+XQbDahqiparRb29vaoS5Gu69S1ajgcYnNzE4lEAtVqFaZpYjabYT6fo1Kp4Pr16yfO88k+Ejciy7IQi8XAcRwVGhKn3INOwsehqioikQii0ei5Is07nQ76/T6d55H5RiwWo8K3bDaL2WwGXdfpdUzmeb1eD9euXbuya+N+iFiOzB0jkcihqM6LwDAMlpeXac2g0Wggn8/TiPmDjUGO4+C5556DqqoQRRGtVgtLS0tUvEKYzWbodrvUBXrpfrMCLASYpP6hKAo0TaPzwkQiQV0DV1ZWLqWWIwgCqtUqKpUK5vM5/c6j0eiVi+mi0Siq1Sp830e73cbOzg6SySQVFpF64mQyQSaTgWVZkCSJ3iOHwyE8z4NlWVSkm06nqdBUkiSk02lEo1Eqet7b26MO99euXUMmk0EqlYJlWcfeS7LZLHq9HgDQ+EGWZY88b4lIl2w/cagiTZwHIaJi3/fp/e60Y00cDkulEhVFfu7nfi6KxSIkSTr0t7Iso9/vwzAMTCYTdDodOI5DG9x0Xaf3p4P76/s+ut0uBoMBXNfF+vr6IXEUwzBIJpOIx+NotVpotVoQBAHLy8tUpNdoNLC/v49EInGqU9tsNoPv+1SkGvLGIRRghYSEhDwAzmCA2XveA3ZnB2KzCZ/n4eRyCKJRMK4LfjBARFHgsyx40wTbbC4WeTkOXioFd20NfrEI6Xu+B9F7UT8XQfhbfwv+iy+C6ffPjj4cDmn0ofTVX33hzw4JCQkJCbkffm0NbjSKgOPAqCpwxqIAO5nAi0YBhoF4yaKC6Yc/DO97vxdcpwNhOETAsvCSSYBhwM1m4MZjuIkEvF/4BYxlGfn/9X89lwjrnibpEE9KBKEgCBBFkRY+zuqums/ntJBy/2tJkfoyGI/HaDQaGI/HtIs9EokgCAL0+31IkkRFX9evXz/0ub7vH3LiEkXx2HgTAKjX66hWq5BlGYZhwPd9WiBNp9OPNHaQQLbfsiykUilajB4MBojFYkcWBziOo9F2JKaQdD8e/I4OOnydJ5KhUCigXC4jmUwiEonA933a5er7PhXU1Go1usjytMOyLC20kahAy7Lg+z54nkckEqGLfmcVzcfjMS0wk+5ecp3dv2iSSCQgyzJmsxkmkwmSySRdNCTFx/tZXl4+slBHXKBIJAFxcHsc5zGwWBxTFAXNZhORSORI9GU0GkU0GkUqlUKr1UI0GqXHaD6fn+naRcRsZOFve3ubdtCrqgpVVaHrOl28TCQSeO6551Cv1596seDDEo0Ckf4O3tL5Vbxp/AcQfAv2t0zBCSyseBzBf/ffIfH3/h7EK2i+mb3//cBstogcvnHj5Ocnw8BfXobw2mtwp1MY73sfpG/91kvfnpCQkJDXA5n/6X+Cls/D/Q//AcFwCCjKwl2YYWAtLQGpFLzr1xH/pm+CdPPmud/3OAesyxpOkIgoSZJw+/ZtRKNRGIYBXdfpwikRCZDxKonIXl9fRzqdRj6fP+Q202w2MRqNoCgKPM+jY1fbthGNRlEqlTCbzXDjxo0ri8S7KCTaW9M0TKdTGqlFXIZ2dnZo3JqiKABAx1bxeBx7e3u4ffs2VFWFLMtUTELikJaWlk6cE50XInRRFAU8z9MYKhKxRsZbB13JroKDc9fTFuVJIwOJ4AJAx8+j0Qjz+ZyOmU8im81SEUk8Hsfy8vKlRZydF9M0aRT8aQ68hGQySd2kDMO4FAGW4zi4e/cuVFXFeDyGpmm0kSsIAsRiMeRyOfz5n/85YrEY+v0+ptMpEokENE1DNBpFNpulrjbb29tQFAWf8zmfQx2vyHutrKyc6l7keR40TcPu7i6m0yk0TaNuwKlUCvP5nDo5k+j44zBNE+PxmM55z5p7NBoN2qCj6zoEQaD3mlarhfF4TMWIGxsbKJVK4HmeirVkWaYOSjdv3rxSh2LHcdDr9WgjGhGlErFcoVA4Elf+MORyOdqER5rIhsMhOI6jzTWkMSibzeLWrVvY2dkBy7IYDofUOYtlWczncziOg1QqRQWj9zv2zWYzbG1t0Xj7eDyOcrkMURTheR6Nekun07Rh8GFd/+6HiDkfNeVyGYqiQBAEcByHwWCAfr+PaDSKeDyOer2OlZUV+L6PWCx26F5XKpXQbrcxm80ONeNNJhOIooh4PA7DMOhzNZ/PY2dnB/P5HKqq0nt6LpfDYDBAKpU64gxFalGJRIJee4Ig0PskuXcpikJdqgqFAgaDARiGObZWQX7GsizdZtu2T3x+H2wS5XkesVgMgiAcqdmoqoper4fBYID5fA6GYVAoFFCv11Eul+E4DnZ3d1Eqleh1Sr5zRVGoIHx5efnE2imJFt7a2oJhGBgOhzSicjgcQhRFdLvdI7URguu66Pf7SCQSNLY15I1DKMAKCQkJeQCmP/qjYPf2IO7twSkWEZTLAMOAPF7dXA7OdArp7l2wngfwPKzr18FEIvBLJbBf+ZVIffVXg7+kh23iy74M6k//NJxiEWKnA4vnwdzf4REEwHgMcTSCVa/D29hA7LM/+1I+PyQkJCQk5CDxL/5iKPk83HQa/GgEN5s9sbofOA74yQR2tQrvLW+BcIlxZ3a7DfcHfgDc/j54WYZdrS7EYPe2xQfgzmbg9/fBb20Bv/3bmDz3HNJ/5++c/sZBAOfFT+NGXwaDAFqkhG76TYhEHo8g4n6IrbsgCFBV9dQOQdI9WSqVkE6nr6zbT9d17O3todvtQtM0FIvFQ5/nOA7G4zH29/fpIs1zzz0Hy7IwHA4hyzItwDAMg1wuR7vEj4N0Qj8sQRBQ0QcR6qTT6UsrzimKAsMw4HneqcVDUjwibmGj0YjGZQCLiABiGa9p2qmddEEQwDRNPPPMMwiCAOVymS4GGYYBjuOwtLRE33Nzc/ORRbsEQUCLuVdhx84wDJaWlrC0tARN0zCfz+n3elbH+UFs24ZpmofOA1Jgu9/hiXSf3i+2OngeHwcpkpKFh4OOT9ls9rEJrwjEWcDzPFSr1RO3J5FIIJvNYjqdwnVdiKKI8Xh86gITca2r1+vIZDLY2tqCLMtot9tQVZV2jZJ4Q2CxsCnLMp599lmUSiWsr6+/biJAzstz7Q/irX/x75GwFWSdIeLuBNGXbYBj4KZS8AcDzD70IVjf/u1IfsmXXOpnB5/6FDhFWYioz1pwEkW4iQRYRYH/wguXuh0hISEhrzdSX/EVCP7m34T+kY/A/t3fBRQF4Dgwy8uIftVXPVQzp+8D169/C3z/M+OSTOZy3KM4jkM6nYZhGNSViIxfEokEgiDAbDaDpmlIJBI0Xvv555/Hc889d+i9JpMJms0mdc0tFotUiAGAij2azSZ838fW1haeffZZsCxLXYUOuvhks9nH7pKVSqUOuTq9+uqrUFWVulj5B9RxDMNgZWWFLuoS8T4Zz5Kxa7lcvlAUnaIo6HQ6hxzGBEGA67pwHAfRaBQrKysoFouPJPaauKgR59f7x5gk8kyWZUynU/R6PSpaI+I/4g72pje96czPy2azNHbxrHnUVUCcjM/rZsxxHBiGge/7h86XhyUIAhrXuL+/D0mSDsU1Elfbl19+Ga7rYjabIZ1OIxKJYDAY0Fh38j0RUUgQBHjppZcQj8fhui7d1tP20XVd6g49HA5pfHk0GqWOP+Q6AEAjOu+Py1RVFaPRCOl0Gtls9kxxYr/fx2AwwN7eHgBgZWWFzvOIwMRxHHS7Xdy4cQMsyx4SkeZyOaiqSt2VG40Gnn322Qt8Kyczn89x9+5daJqG8XhMnb6BxT0jk8lgNptBVVVcv379wve8crkMSZLQ7XaRz+dhGMYhl6j7G4Nu3LiBRqOBTCYDTdOoyDSdTiOTydBmsuPcr/b29qioslarHREXkmdGs9mk4p2rrF1dNb1eD/1+H5ZlUVGb53m0CSmTyeCzPuuzaC2h1WrRObXneRiNRrR+MplM8OyzzyIWi1HnOCLsIudHNBpFLBajdQnXdVEulzGdTmmdrlwuI5FI0OuZNOpZlkVjZ+fzOf0OSM0ulUohk8mgWCzS5kAiCLwfURQhCALdFkmSMJ1OT6zFENGXZVlU+GgYxqHng+u6h0SJ1WqVCmsTiQQ9P6fTKR2b7O3t0etUVVVomgae588UGAuCgEQigclkAlVVUa/XqQi1Vqthb28Pe3t7KBQK9L1836fPJVKjXFpaeuSC35DHSyjACgkJeSDM27dhfvSj8KdTMKII4eZNxL/4i8G8AfJrncEA7Cc+Aa7Xg5tKIThhMM8kk7CeeQaRu3dh1etw//pfR/qf/3OIy8uX12J2D1aSIH7Lt8D9ju+A4/uQGg24sRi8bBbg+c9EH9o27GoVQbWK+Ld+66VvR0jIk0Zg25h94hPwRyMEDAO+Xkf8cz83PPdDQq4YhufBvP3t8Pt9cHfvgt3bg7eyAub+SabjQNjZgS+KQDaLyLvedanbof/yL4NVVQijEaz1deC4KIN4HM7mJsS7d8H0+/B++ZeBd73r2PuEP59D+6Vfgv/BD6L+moyvad+b9LMClEgZzNLb4P+Dy92Hh4F0tBEL/3g8fmzRnBSxia12/hQHzYvS6/WgqiomkwnW1taOdL8KgkCLfCSS71Of+hRYlqViC+te7mMkEsF0OsV4PEahUMDKysqliVLIMRkMBrAs65BQJxaLIR6Po1KpnCvK4CAsy1K3JcMwYFkWZrMZjVW4H8/z6CLIQYGVpmlHBFjEYn40GiGRSJwoYBqPx3BdF6VSCZubm+A4DsPhELquU/esZDJJF7kehdCHFNQVRTlUnCeRIad1ST8s9y+AXRRSYJxOp2deQ57n0cWLs/aN2Pg/SRDBpqqq5yp6kxhGSZLA8zx0XcdoNDpWFOo4Dvb392m3rWmakGUZe3t7tLPbtm3aZbuysoLZbIbxeEzdsT7/8z8fruvSBZI3AtoHPoC//vKPIW00kXZkaHwWregmltdciLDAjceIbG3B1jR4/+bfwPjBH0TsL//ly9uA2QyM6y6e4+cgEAQwjvOZzMSQkJCQkBNheB7Jt70NeNvbLuX9fB+4ceM7D/3sMs2Hfd+nY50gCHDjxo1DzRKZTAaTyQR37txBLBbDfD4/1gWi2+1SF9G1tbUji7ORSATVapVGXkmSROPDZFmmsYbAYo4hSRIymQyq1eqVjC0fFOJgpKrqieJ6EvFMFnJ3d3extLSEUqmEWq2GTCZzobF6v9/H/v4+dSYmDmNkDhKLxZBKpY7MPa4S4rxMxHr3L4b3+32Mx2P0ej0q0lpbW0O5XEa9Xke73Uan06HjQxJPfxIknstxnGOdaa8anufBMAw4joNlWWeK3Gzbpk4wl9GwQhyvWq0W0un0ke9ZkiRUKhUMh0O88sor4DgOkUgEpmkim80emQ9HIhFks1kYhgGe59FsNrG2tkbj7k3TPPH76HQ60DQN+/v74DgOtVoN6+vrVBRnmib6/T6NlZckCUEQoNFo0ONI3NOWlpZoXPtpcyniwj0cDum5ZNs2Op0OdF3HZDLBYDCALMsolUqYTqe0ueTgvmcyGerKlU6nMZvNLj2KkDiVjUYjdLtdJBIJ1Gq1Q9eLLMvY3d1FvV4HwzC4efPmhedDmUyGCrsmkwl18IvFYkfuQcSVuN1uU9dnQRCQSqWQy+WQy+WOnTuSZrDhcHhqvLwkSahWqzReVJblCzXcPQ6CIMDu7i51CZtMJmBZlrprO45D/5vUgSaTCWKxGEzTBADq8t7pdAAsjkssFoMoivB9n7qiDQYDjEYj5PN5xONxWv9IJpOwbZs6mAGLe2u73QbP8/Qanc1mYBgGb37zm+nztV6v0/sQcaMWRZHWj3Z3d5HJZCBJ0rHfIxFwZrNZtNttGluaSCSO3BuIcInEXm5sbABYnC+dToc2gpEozul0ikqlAl3XYVkW8vn8oedrMplEoVDAaDSi5zQRiZKo3fM8UyVJgmEYcBwHlmVB0zTq6l8qlaDrOlqtFhUKEhf/TCaDfD4PlmWfuvM25OK8/hUTISEhl4L+0Y/C+rmfA/faa4BhgHUcgGXhxuNQikUwX/mVyPzDf/i6FmLpH/gAmNkM/GwG6/r1018ci8GNx8FqGvi/+IsrEV8REl/4hdC/67vgfP/3w8pmwY5GEAYDML6PgOPgptNwCwX4pRKi3/d9D2RTHhLytOGOx9B+/ueB//bfwKrqIiuMYeCIIuRqFexXfiXS7343mEfk7BES8kYk/Y/+EZQ//VPAdSE0GuBefRVuJoPgXjGKnUzAaxr8SATuxgb8L/9yJP7KX7m0zw8cB8yHPgR2OIQbjx8vvroHIwhwCwXwwyGCVgvGJz95ZIHaGQ6hfeM3grt9G/xwiOxAQ3JRA4HH8JiYLXC/vw31n3zo0vbhIiwvL2M2m8E0Tezt7SGXy9Hub1KkIwXFRCJBiwQsy9JIi8sSqViWhclkAkVRkM1mTy2EZzIZqKqK1157DZFIhIouSDQHsCgGtVotRCIR2lV7GVF5pIOeWOxPJhMAoJ3YgiAgl8thNpuhXq8/cMc5cTVqNBp0u0/qTJ1OpzR2jhR1ScfjQRiGQaVSgWmamM/naDQaKJfLtHMZ+Iy7GLFVj8ViyGazYFmWFrWP6zI/L7quYz6fIwgCWmQ9S5hDCuayLEPX9UMCO9IJORgMUKlUaMflk8BBsRUhk8nQfThYcCeOY7lcjn7PqqqC47grFzxeFY7jIAgC2LZ9LpcAQRDoOQF8xmlgMpkgm83SYiURF8ZiMSwvL6NQKNAFNlEUYVkWFWLm83nanVssFpHJZKhD3muvvUYL//V6/YqPxuPH03V4P/7jyM32EXUn2I09C5tfdO17kglIUXiZDNzJBJG9PTg8D/Pf/lvEfvEXL29OKkkAy4I5Lpf3OFwXAcct/i4k5BjsVgv6+94H/MmfANMpwPMI1tYQ+aqvQvyLvgjMU+p2EPL4CRwH0w9/GM5v/zYwGi3ug4UChC/7MiT/xt94XdcxCceZ5lyWXtn3fRoRXCwWIYoiBoMBRFFELBajjjrz+RylUgmu61LxwEEMw6Bignw+f6pLaaFQgKqqGA6H6PV6dHFV13UAoG5BsVgM+Xwemqbh2rVrVxqjdxa2bWNvbw+NRoPOCTzPO3HexbIsqtUqdRAulUoXji4icV7tdps6jKXTaSrWIBFue3t78DyPOow9yFzB933IskydikgEZT6fP1E8RsaGqVQKo9Ho0HyGuDH1+33qhkychIvFIo1IJFFUmqah1+thbW3txG086B50//Z4nkcdeYjwged55HI5FAqFS4m8lCQJoigilUpBVdUzx9aKokCSJHqMLspwOKQubMe5EgGgTlQMw8AwDIxGIxo1fhzJZJLui6qq8DwPpmkinU6fuI+madLahO/7NBb+oIhJkiSsrKxQ17tIJIJSqQTP8w7NQ8nfLS8vn9nIoqoqTNOkbuAk0s0wDOqwNplM4LouMpkMPScTicQR8Vk2m8VoNMJ0OqUReufB933qKnWaYxVxEe92u1haWjpyDyANRr1eD61WizoPX5bQ4/7v435c16WCH8uy6D4RYRyJDTyO8XhM41nPugaIMIlEZj5tQpZ2u43hcEgbjEisK4npJM1KrVYLiqLQ+xKpIXAch9FohE6nQwWH0+kU/X6f1lB0XcdsNoPruvQ8LhaL2NjYoPd58r7lchk8z0MQBBqVS1zmyuUydcC6fv06ut0u5vM5FT6xLItEIoFEIgHf99Futw/dk4+DZVnk83nMZjOMRiMapdrr9ZDNZpFIJMBxHIIgoOd8NBrFrVu3aDzgZDLB/v4+dnZ2kM1m6X+3Wi1sbW3B8zyUSiXEYjFcu3bt0OeT6/hg/Cy5/99fZzsJ3/dhmib29/cRBAEsy6LCUBK3SaKUiVAtHo9DkiQUi8VLcS8Mefp4/c8wQkJCLszkF38Rwb//9+AHA/DjMVjLQsCyYIIAAcvCTafh9/uQ/+IvkP3BHwT7Oi2q+s0mOF2HLwjAOWJo/FQK/HgMfzqFq6qXFjt4HIm/9tfgPPcc9F/5Fbi/+Zvw71kxA4BfqSxEJ+94B7gnrJs+JOQyMbe2YHzTN4Hd3wc7HIKfTMDcG+D6PA+320XQbEL+wz9E5od/OLweLhlvOoXT6SBwXfDFIoQLWOKHPN2wkoTMj/wI1G/5FkCSwIzH4GQZ7L1nkxeNwq7XgUwG/tvfjuw3fdOFFoTtTgfG7/wOgvEYAcMg4Diwqgp+OoV9hvU8APj5PNhuF5jNYL3wwiEBlj+fL8RXL70EcXcXPs9DTVfQ5RcuLjFXQ84eIN7tAy/NFqLPx0wikaAFZ0mSoCgKxuMxWJZFEASYz+eYz+eIRqMwDAOiKNIO0lQqBUVRkEgksLm5eWH7ek3TqJvUecQ0LMui0+lAEASsrKzg2rVrh7p80+k0SqUSWq0WtUBPp9OnRvmdh0ajQRcbeJ4/VBAjkQbD4ZDanguC8EALIMViEf1+H/F4HNvb24di9w5COuJJpIIkSXBdF5FI5NjCZaFQwHw+B7DoXmy1WuB5nnZBzudzRCIR1Go1ZLNZXL9+/cj7PKj4KggC2rk5n8+peIp8bi6XQ7lcPnZxIggC7OzsYDgcYn9/nwrs8vk8FQfu7e0hHo/T7vbl5eUH2r6rIp/PYzKZYDweU7FVKpWiIiNiLS9JEmb3HH7Ia0hBvlwuIxaLPXHuVueBnCcPcr6QTn3SMStJElRVhSzLtCM0FouhVqvRmADSJW8YxiGB23Gxo6lUinYGW5aFwWBAO6Sf1liK+/nkJz+J0WgEAFTQ95a3vAX6Bz8IRtOQNofYk65T8RVweJGdSafhlMvgBwMEnQ70j30Mib/6Vy9n427cgP/CC4jIMjzbBk5pbgg8D7yuw67VENxXEA8JCRwH8g/8ALgPfxjsbAZWloF7DX/+7dtw//RPoVQqiH7ndyJ6jmipkJCDTD/0ITg//uNgxmPwsgzmnqAikCT4f/qnUH7yJyF8wzcg+Tf+xmPe0qvlKgVYJPaPZVk888wz0HUdmUwG0+mUClgEQUC1WkUkEsF8PockSRiNRjQiCFjEDxJ3ifOIvVOpFF555ZVDwo+DMWKmaVLBEVlYfvbZZy9FQPMg+L6PZrNJRT1kIZ7jOJimScUnxzV43C9SuyidToe6qKytrR2JHJckCcvLyxgMBmi324hGo1AU5dxzreFwiHa7DcuyoKoqFWBJkkSdUVdXV4+IWIgziGmaaDQaaLfbVCCgqirm8zls20Yul8N4PKZRjOQ8IePxTCaDfr+PXC53qusSEQzeH5clyzKazSbdfiLwEUWRCruIE9lFXMhI1Px0OsXu7u6pIiwiQCuXy9RF5iJYlkXnnKe5qZG5KqknBEGAWCx2opiG53kq8BBFkTqZERe34/ZRURTqWuY4DjiOozF7xE0H+Eyk/O7uLpaXl6kLnmmatLEpm80eioo/jel0SuPNSazlYDCA53nUpZoIpPr9PliWxd7eHlKpFFZWVg4dAyKyMAyD3u9OIggCTCYTDIdDyLJMfx6NRlEoFFAoFA5dk9PpFC+99BJ2d3fh+z6i0Sh830c6nT7ihLa0tERjCImj1FVjWRaNRlQUBZPJhIpMeJ6nczlFUXDt2rUj8zPLsmCaJuLx+LnmbiSOkNQfnhaISK3b7YLjuCPnEHGlj8fjeOWVV2idq1wuo1qtIplMQlEUFItF6qi9s7MDhmFolKxpmtQBi9T8SO2K53ncunULsiwfqu8VCgXkcjkoigJZlunfRyIRFAoFpFIp7OzsIBKJoNls0mc7mZMTISfDMKjX68hkMqc2KlYqFUwmE2xubuL27duYTCaIRCKQZRnD4RCe59G4xXK5jLW1Nayvr+P69euQJAlbW1vgeR7j8Rgvv/wyPvWpT1HxF2ng9DwPoiji4x//ONbW1rC6ugpBEKjD18HzhziIEbf4s9wFybPNsiw4jnPoeud5Hul0mh6/SqUCQRCQTCaRTqfh+z76/f5DnT8hTzehACskJORUpv/tvyH4iZ8A22iAn07h5PMICoVFgdX3AUUBPxyCu3MH8Dwo3/M9yH//9z/uzb4aXBcMgOCcVYrgwOAxeASWxkKphOw/+ScIvv7r4XS78OdzcMkkhKWlMHbtQQmChYNK6JL01OAMBjD+9b8Gu7UFsdX6jMAjHl903KgqOFkGoyiA60L9pm9C7sd+7A3R7XrVGC+8gPl73wvu4x8H7i2oWgC8mzchfPVXI/nlXx4e5zcgXDqN3I//OPTf+R3Yv/qrCF566TO/5Hl4X/AFiP6dv4PYX/pLD/0Z5p07mP30T4P75CcBwwBjWYvndBAgsr8Pbj5fCMbPeB+G4xbPds9DYBiHfqf90i+Bu30b4u4u3FQKfr0OcxiBO18ULjSxCE0oYFPYRqrRAC65SP6w5HI5iKKIbreLTCZDCzLz+RzD4RCu62I+n1Ord47jYNs2jeBbXl6G53m4efPmhWIOiHsTKcSeBek6TaVStOh+P0TQsbOzQwuMFxFgzWYzKIqC/f19SJJ0pKDO8zwKhQKSyST29vYwGo0gSdIDCbAikQjK5TI8z0On00G324Vt27STk1inG4aBbDaLVCqFpaUl2LaN+XyOpaWlEztP6/U6LTgVi0VMp1M4jgOWZWkHYCqVwurq6rm+g9PwfR/b29uQZRmKokBVVSqkYRgGiUQC0+mUFlnvdxkYjUYYjUbY29uDIAhHBHbZbBa2bWN/f586s6VSqSdCsJTP56ldfrfbxcrKCkRRRLVahe/7tFuT53nMZjNkMhlYlgVZlqnjA4nEeBohiyqSJEHX9TOjOE3ThOu6kCSJutiNx2MMh8NDMYTEyYAIrLa2tjCdTukCLYkfOO78J923JMaIxA6Mx+MHdql7UvnSL/1SKrIEQIWz7oc+BH48hsOJmPGHvwvfO/zU8wsFMIMBoCiwfv3XL02AFXvXu2D+5m/CF0WwvR78lZUTX8v2+4vnbCYD6Z3vvJTPD3l9ELgu5G/8RnB//Mfg9vbAGwa8WAy+JAG+D7HbBbpd2OMxzH/1rxD80A8h9lmfdTmfbduYfuhDcF9+GYFhgEkmIb7lLaHb1uuIyfveh+Df/TtwnQ6E8Xjh4n9vgV4YDIBeD04+D+97vxcTXUf6dXx/ukoBlmEYmM/nYBgGtVoNruvS2GIS70YaPTKZDHXTIfFvZHxK5g3njVkzTZO6sb75zW8+ItqXJImK39vtNiKRCHq93iMdi/m+jzt37kBRFBq1ZpomGIahAh/iGmrbNvL5/CEhAomCul8o9DCQY6UoCvL5/KnzgmKxCE3TqFjkPHOtbreLdrtN3YNIjLvv+xiNRhgMBsjn83AcBxsbG0fmUiRCKggC7O/vY2trC8lkkr4nGQ+RWLNqtUrPk0gkgmg0ikqlgtu3b8MwDGiadqwAKwgCyLJMY8HJPGM8HqPRaKDf70NRFAiCgEQiAZZlMZ/Psbu7i1gsRs/Ti7owF4tF2qDR7XZhWdYh91zP8+jxT6fTyGazlxIJSa5JEjd5EmQ+TFxridPLSQRBQJ2VBUGA7/vIZDIIgoDuo2mayOfzdB/39/fx4osvUrdsXdeRSCTQ6XSgKApisRgqlQpEUYQkSZAkCZqmwTAMGqH2MHieR4+vJEno9XqIxWI0Ko80GXEcB57nqfvaYDCA67pHrh0iejlNMOk4Dra3tzGdTqFpGkajEa2TpNNp6li0urqKRCJBhYjENa9QKKDT6SAej2M4HCKbzaJUKh1qkslms5BlmTZpnRVteRFc18WdO3cgyzKNg61UKtTdUNd1yLIMVVVpNOL169cPnUMHz5nzcJ7jfF5836eNikSgK4oija+7zEh74g6m6zrW19dPfG8SXTccDhGLxXDjxg3qOGfbNizLgiAIqNVqsCwLo9EIiqLA933Ytg1RFCGKIhUbWZZFaxOdTgc3b96kPzv4mfl8/kSH7vX1dTAMQx2oVFWFcq+xVhRFFAoFpNNppNNpbG5u0u/S8zyMx+NDToikWc/3fdy6dQutVguNRgPAZ+5LJEa0WCzi2WefxdraGj2Pb9y4gX6/j36/D8uyMJvN6HVKngGk4Y+I/2zbxrVr1yCKIm1IJecPuQcLgoDxeHyiGyCwEF8RtzGe51Eul7G8vAxRFOG6LjRNgyzLtEFUEIQ3hCt3yNmEq2EhISEnEwRwfuZnwPV6i47Va9eAg4M3lgXyeTjZLPidHfC7u8Af/RHmL76I6K1bj2+7rwgmm0UgiuBse6EKP8MVgp3NFm5ZHAf+vi4P3zCgf/jD8DodwPfBFgqIv+1t4C/o4AAsFpLFC0xE3qi4oxG0974X/kc+Aq7bXSziRyLw/9JfgvTOdyL+1reGQrYnmOl//I9g9/ch7u/DXloCDgycGQBBqQS3WATbaEDY3QVEEdPf+A2kvuqrHt9GP+0EAeQf/mGwv/Zr4FQV3HgMbj4HggC+IIAZDOC/8grkX/kVpH/oh8A/hZFLIReD4Tgkv+zLgC/7MrjjMdzhEOA4COXyhR3oZp/4BOzv/M7FGGU4BGcYC+FzEIBxXXC2DdbzEGk2YcfjpztzOM7CLY/nwRxc5A8C+B/8IPjhEL4gwK/XAYY5uoDBMDBLNSQG2hMjwAIWHYLXr1+n3d+2beP27duIx+M00uJ+EUWpVMJgMKBdd/v7+xcqLpNoQ1K0Pa2zkUQQGIaBer1+qvsWx3HIZrO0I/siBcbhcAhd1+E4DlZXV08sAJK4g8FgQG3aHyQCYnl5Gb7v4/nnnwfDMLhz5w7u3r2LZDIJhmHookE0GkWtVkM0GkWn04EkSYhGo6d2sZZKJRQKhUOiKJZlaTftadGPD8Lu7i5GoxGazSbt3E4mk9S1SFEUNJtNWrx65plnDn32YDCgBbt6vX5s8VEURaysrGB7exuapmEwGCAej9Mok4Odmfl8nnYaXjUcx6FWq8HzPLRaLezu7iKTyVBXNtLBS7qk5/M57dwm0ZCbm5vnjqV40mBZFrlcDpqmodVqwXGcU69RWZYRi8UgSRJyuRx1NigWizSegnyPB+8LnufB931wHEcXdE+LCyLi0UgkQgvMxn1C2tcjbL8P1jBgSUkguC86575nFMOy8KJRsPM5vF7v0rZBunkTs1u3AF2HuLsLp9WCX6kcEq4Evg+214M4GsFaXYW/vo74533epW1DyNOP+pM/Ce5P/gTC1hYCQYB54waYA89zy/fBDIeIdDqwgwDmd3wHpF/8RbDndLk4jsB1of7MzyD49V8HqyhgNQ3wPIDn4f7qr0IplcB97dci/e53P/L5v91uQ3vvexF8/ONgp1MwkQiCWg3CV3wFkm97W9jU8gAYn/40gh/7MbB7ewtX3GoVyOXod2oHASDLELvdRTz0//V/YX79+uvWZe2qIwjJsxtYiK1OW9A1TZMugB5cSCfzBuKCetL4jiy87uzsYDQa0fHFSRA3UlmWkUwmUavVHplT5t7eHmRZxt7eHqLRKG7evIlUKoV4PE7jz1RVxWAwALBwqDooECNCoUgkcqb4/SwmkwmNpzrLYYyIQiaTCXRdP9MdZDKZoN1uo9VqwbIs6m5KIM4//X6fjgGJ2y+BZVlcv34du7u7EEWRRkWRaK18Po/l5WXqpnZ/gwYR8PA8j0ajgUQigWQyechJOAgCdDodeJ6HfD5PzxvTNLG3t4dut4vpdHpk+4HFedftduk8KJFIXMhliOd5XLt2jV47xBVJkiQa28lxHAqFAvL5PBXmXJSDgp3TIrFEUaRjdcuyEI1GqUjiOAzDoK/3fR88z1MnZuLCMxwOsbW1RV1vXnzxRdrAwjAMUqkUdYohsY+WZWF1dRWRSASCINDozovAsix838dkMqGOfAe/SxL55jgO0uk04vE4FY3qun5EkEii50+6RjzPOyRWAha1GnKsDMOgc8v5fE4bnO7evYtOpwPHceg8hwjTXNeF4zhYXl6m32ksFkO/34fv+3Ac50oFWCTus9VqUfeug2SzWWQyGXQ6Hezv70MQBBovSxAEgbrLnUeIZRgGJEm6sEu7LMvY39+nMZTEESkSidC4z3q9fmGXdYKqqlTsd1pdhjhWke/7OKcvhmHofYHEfBJBKIkMJcfRtm14nkdrE5PJBIPB4IHqewzDYH19HblcDsPh8MizI5lMolgsHnLT6/f76HQ61EnQsizqhEjEScRJa319HdPpFNPplLq8VSoVXL9+/cj5S6L+WJaFruuIRCL0WbCyskLvaaSG1G63wbIsIpEI1tbWMJ/P6ecDi8amRCKBpaUleo4e951Pp1O88sor1B3/+eefPyReJfeqdDpNI355nkepVHrkjpshTx7hzC0kJORE9I9+FGy3u+iuLZUOi68OwLAsvNVVRF59Fa6mYf4rv/K6FGBJb3sb7F/7Nfg8D1aWEZyijA48D/xkAnt5Gd7nfi51UnIVBdrP/AyY3/kdMNMpmHtihUCSMP2pn0LwV/4K4l/3dYg8pd3xTx1BAP33fx/zn/gJxH/ndxB1HLD3FuI9UYQbj8Pr9eB87GMYP/ss0j/wA5cikgu5XDxdB/ORj4Dt9+ElEofEV4dgGPirq+BefRXMeAz3134NCAVYD438wz8M9v3vB7ezA8404aZSi2gZhgFrGOCHQ2A4BEwTk/e8B5mf+Ikw9vENDJ/PX5oIz7x7F/Z3fRfYRgPiYAAnm4Vbqy3GKUGAQNMQu30bjO9DMAz4Oztwb9w4cZWBkeWFYDoeR+RzP5f+fPZnfwa23wenqnAOuEn6wdHCEMMxcPN54F7x/EmCCEBGoxFSqRQGg8Gx4isA1Oqc2O0TMdDDFrlIYZ3jOEyn01OL/ZZlHXK7OcutKZlMUjcv0zQfusBIREvJZPLMbvtUKoV+v09dnh5EgAUsREepVArz+Rwcx6HX6yGZTCKTydBCFOm8JUVNEtNxVvHmrO7Fi0KiBZrNJiKRCJaXlw8JqGKxGI3Xa7fbEEUR7XYbm5ubAD7Tda+qKgqFwqldpeRYkLgLWZZpkfy4KJO1tbVH4pJVLBZpwZ90gJL4CJZlkc1maXxkuVym32uhUECxWDzXdUS6cXVdp6LFZDKJbDb7SIRmp1EsFqkDXKvVQr1eP/aakWUZmqZhZWWFHoODRCKRE89nsuBBCs4HF3SPg4gNSTGaLAK/7rm3jwzLAN79vzp6ngQMsxAIX/KxSX3Xd2H6T/4J7CCA0GyCURS4qRQCQQDjuuA1DQBgrazAr9cR/9/+t7ChJYTiz+fAr/862E4HAcfB2dwEc39MLssCS0uwOA5ip4Mgm8X0gx9E+mu/9oE+a/7yyzB+5VfA/MmfgH/hBUR0feFwHgQIBAEBz4NxXYBh4HS7CAYDyNvbyH3bt136OWvt7WH2y78MvPQSYBgIolHg5k34oxGET34SrK4vnJvvxTAGr70G/1OfgvJTP4XIt3874m95y6Vuz+uV+S/8ArjxeFGXu3YNuF+0xzBAPg9bkhDZ3oY1HsP4L/8F0e/7vsezwVfMvfX8Q1yWno84VhFhxFniJtu26WsOvjYajdLxPIkvOwgR5BDH3O3tbRqd3mw2USgUTpxrZLNZ7O3twbZtTCaTS1tYPw3ihNrtdhGLxahQggiFiMNUPp+n+1YoFGBZFiKRCAaDAebzOcrlMgqFwoXHgUSwwfP8uQRokUgEqqoCWAhITpsn9ft9qKoKwzCwsbFxZOzHMAwymQxEUcTe3h6SySQGgwFW7nPPZFkWm5ubMAyDNmEQUUKhUKBz2OO2n0S3k3kscYiJx+N0LkXei8RlEdHNcDik8W0HHVfuPx71eh27u7uQZRnxePzCMW+xWAzPPPMMms0mkskkjbELggD5fB6JRILOxy8qwDu4HwCoq+1Joi7i5EKisKPR6CGXOs/zYBgGHXePx2MatcVxHFKpFFKpFGKxGG7evIn9/X3qXHtQiJdIJJDP5/Hcc89RUQNxlBkMBtQRbWNjA67rHhLUPSzxeByu61Ix58bGxqHfx2IxRKNRzGYzKjojAjRN0w7dP4hAMZVKneiQ3Wq1oKoqms0mMpkMlpaWDonIisUiDMPA/v4+dYqORCJIJpOo1+vo9/soFotgWZa6/Xa7XQCL75GIn4ig9arnjMTVbjweIxaLHRFfERiGQbVaxfb2NnUuP1irIJGipL5yWm3FNE3M53NUKpUL3b+HwyH29vZoYxhx6gMWdSHiLuY4DjzPu5QoR3LvPaspbjabUeGlJEmHzhHynPU8jwr0RFGEZVn0/pZIJOi9UVEUmKaJdDpN92E8HmM0Gp04hz8N4nLlui5s26bRrPff6zudDnUt1DQNoigeOb65XA6lUokKKw822J3mymfbNsbjMe7cuQMAWFlZAcdxNEJQEASwLItMJgOWZTEcDjEYDGgcIIl6PHj+rK2twTRNeJ6HbrcLRVHo8SRunuPxmN4Pnn322RO3keM4er6T5IGLOPWFvD4IBVghISEnYv3e74FTVTC+v4guOO3FggAnmQQ7HiP42Mce1SY+UmKf9VmYr6+DGQwg9PuwEwnguC72IADXbCLgeSCbhfSudwFYdDPq//Jfgt3dBdvvg9e0Q8fUjcfhyTKMP/szeP/7/35p1vohx+NpGtRv/mawH/0oktvb4C0LrOchYBj4LAvWtiE6DoLpFF40ChgGtG/4BqR/8ifBPeCCa8jVMvvd3wUznUKYTmGtrZ3+YpaFm8uBH48R3LkDa28vFDw+BLNPfnLhfLWzA8bzYN28SYWmDIAgnYZTLi9cyXZ3YXMcJj/zM8j9q3/1eDc85HWB8bM/u3DUGAxgrawABwvtDAMmnYZZq0Ha34dg2+B1Hd54jOC44oltQxyN4OTz8NfWEPvsz6a/8no9wHHAui6CZJI+s4PjOsgZIHjCnw3D4RCqqoLn+TMLuIVCAbu7uzBNE6PRCJVK5aE+M5VKQZIkZDIZjMdjpFKpEwumtm1D13XEYjFkMpkzFwbI7y8iuCB/S7pbz4J00JHIlIchnU7jS7/0S/Hiiy+i3++je895IZvN0u7n6XQKnuexurqKTCaDtbOebY+AwWCAyWQCz/NQrVZP/B5TqRSNlkkkEtQOn0Q5uK57LrFUKpXC7u4uZrMZta7neZ5ayw8GA/T7fRQKBTiOg83NzUtbmDgN4mbV7/eRyWQOLTYKgoBUKoV0Oo1oNEqLp+cpgpMIw+FwSN3gfN+nMXvEga1cLj82IVYsFsPS0hJc10Wr1cL29jYymcwhFzRVVWGaJl0ge9CiYzKZRDKZRL/fpwu0J11rvu9jNpshn88jFotRYeOjcrV4nATZLIJIZCEiuW93/ftNAYIA/HwOJ5kEHiA69TwIpRKSP/ET0L7t24BEAlBVsKoK1jAAloVdqQC5HPyVFcS/7/vCMfcF8OdzTH/rt+Dt7gKOAyadRvRLvxTSzZuPe9Memulv/RYwmYDXNNgrK0fEV4fI5xEMh2BGI3gf/CBwTgGWN51C/a7vAvfJT4LTNAiNBgTDAOt5AMPAjUTgsyyclRUE0SgYRYEwGCCYzeD8xm9AWVpC9uu+7lL215tOoX7v94L7+MfBzmZgVBWM6yLgOLC/8RvgTXMRh81x8OJx+JK0aCYYDIB+H06pBPubvxn4vu9D/PM//1K26fWKMxiA+5M/ATcew81kjoqvDhKPw0mnwY3HwMc/Dnc8fl05NzebwM4O0Okc/d1lmaOk02nEYjEaUXTa4ngQBFBVFdlsFvF4/NAicCaTQSQSQSqVosIZMuaRZZku0muaRhftPc+DZVkYDAYwTROGYaBSqRwZKxFBzUXG8A/KaDSiooGD0UxkrOh5Hvr9PtrtNnX20HUdjUYDgiDAcRzU63Wk0+lLiZ476DB2HnzfP+SWdBKmadLmlIMResdBmjUURUEymcTy8vKx47ZYLIa1tTWsra3RZgQAJ55b0+kU7XabxrczDAPLsrC9vQ2O4xCPxxGPx3Hjxg3UajVkMhlsbm5SJ6TxeEy36bSmHo7jkM/nMRqNUCgUMJ1OL9wEIkkSbty4QefdpAlBEARks9lLbzLheR6ZTAbZbBa9Xg+FQuFEMQaJh69UKjBNk86BRFHEbDaD53mwbRuaptGIQs/zsL6+jkgkQoU5kiTh+vXrVBRH5vzFYhGbm5vUhergNpKmpOFwCEmSMBqNMJ/PsbS0dGFH4Vwud8gZ6P7Gr1QqhclkAlEU0ev1qBMwz/MwTZO+znVd9Ho9Ktg47vx0XReyLGM4HCKRSJx4LZMayN27dxEEAW7cuIFqtYq9vT16f4jH44hEIjTSUZZl6tpDXIEOOpddFcQ1irjFnQYRX5LoU9M06XdN3KKz2SyNdzuuCc91XXQ6HSQSiUOCswdlNpuh2Wyi0+lgNptheXkZiUSC3t9IdCKpz5CfW5Z1xIX7rGbBg5B7r3ucEvoAxAkawJFrMpVKIRKJQBRFTCYTGvPK8zyKxSJ4nqcRrmSOTIRO5XIZs9kMuq5DURQYhvHAjYSE0yKCJ5MJdTxzHAcrKyuHxEoHjy+5Xzz77LPnvseNRiOMRiNMp1NwHIelpSUoikKdyw42+SUSCRpB2Gq1oOs6bt26BUmSDu17JBLBjRs3sLW1hXg8Tt3KyP2BiGBlWQbHcWBZFqqqnljXFEWRvo8sy6EAKyQUYIWEhJyCLIOxLPiRyOnFsHsE8Tg4RYE/n8M3TbCXFHfyxMAwiHz918NtNOCaJsSdncWCbaGwEB4EwaKAOBiAcRw4GxvwPvuzkfmCL1hEDn7jN4K9cwfi3h68aBTW6iqYew/9wDDAjUaI7O7CtixY3/Zt4H/qp8IowSvCn8+hvuc9YF94AYnXXgPjeWCCAI4oImBZsL4PzvPgcxz8e9FJ4u4ubIbB5N/+W+S+53se9y6EHMAbDMCY5uIaPMfAPUgkwA6HAAC31wsXgx4C81d+ZRE7aJqwD4ivDsEw8Ot1+FtbYPt9BB/+MPx/9s9ef8+GkEeKMxiA/cQnwA2HcDKZw+KrAwTFIrzxGJznQTBN+O02rHz+M+OZe05ZYqcDXxQRLC2Bvz9q5kAkhiwLMNwI4nEfqnp0CsWwT0704EnM53PMZrNziVQkSUIkErmUOK9SqYTZbAZN07C/v4/l5eUjRRvTNNHtdpFIJGCa5qmdbwf/hmGYU4tAZ3HeCIaDkMWIi3TeSpKEW7duUUGLpmnUIYrjOFoMTCaT2NjYeOj9uyw8z6OOTyd1nR8km81iZ2cHpmlClmWUy2X4vk+7cs8jkCHCq9lshlKphI2NjUMd2sRevtfr0QLec88990hs3olIyLZtuvjAcRwSicRDucU5joNPfepT6Ha7GI1GMAwDiUQCiUQCruui2+0inU7TRbz19fXHJsIiMSIcx1FXtIMuYJlMBtVqlUagPkhxGgDy+Tw6nQ6SySRarRZisRhdqL1/nxVFAcMwiMfjEASBHreHLSg/TTBf8iXwt7Yg9rYQCQxY/GfumUccsBQF8H0EuRyEt73t0rdFKJeR/4//EbM//mOY738/gpdeAjOfI5Ak+DdvIvLOdyLxRV8UOl89JL5hQP2JnwDz4Q+D0TSwur4Yn4gizF/4BcyeeQbSP/7HiL/1rY97Ux8Y78UXwU4mCFgWOOu6ZRi4mQy4yQRBowHfMM6MIfQNA+q/+BfgXnwRwt4eGNNcNF3de94HAHjLWsRX7+zA3thYOCKlUhC2t8G1WvDe+174f//vXyjyELgnvvqGbwD7yivgm01wlgUvGoUviuBlGRFdXzQ+chzsZBLO6iqYe936dhAAoxHEbheO78P+N/8GkV/4BfCXLKh8PTH/8z9HYFngZzOYlcrpzaQA/FwOkd1deJaF+ac+heQV3CsviivLmH/yk/CnUzCShMjzzyNyn0DfcRa3WjJs/LmfA973vs/8/i/+4p/C923679FIxHd+578//vNcF+PxmArCeZ6nDi/3P48TiQQVDxAB/knPf+JUcdCVg8CyLAqFAubzORqNBtrtNiqVCnRdR6/XoyIrEkW2srJCBVe6rtM5C3H0PQgZ5x+MZ7pqptMpJpMJotHooePBMAyWl5fpeGoymaDb7ULXdWxtbaFSqeDWrVtUfEMcSIGFKOc8Y/HjiEajiMVidBH8rDg7TdOoSO608a2u67Asi0YPnkUmk0Gr1YLv+2dGTQOLseF0OqWL+vdvi67raLfbGI/H2N/fRz6fR7VaRS6Xg2ma9Nwgjk+macL3fWxvb1N3Fs/zMJ1OUa1WT90W0zTBsiyNzJrNZpcmkJIk6ZEt1heLRTqGb7VaqNVqR+abRCyZSCRo/JiiKLh79y51miER7ETk1ul0UK1W4bou8vn8kfckrk6FQgGqquJzPudzoGkams0mNE074mCXTqehaRo91qVSCdFo9MKuRGTOkslkaDTmQSEaz/NIJpN0rplIJOB5HkRRpKKr4XCI8XgMURSxsbGBfD5/bH1AlmVYlgVd189sqiJzK8MwkMlkkEqlEAQB3RYiPCPOy4PBgLpypVIpqKpKndOuck5s2zYcx0EQBOcSw8XjcQyHQxqvRwRY5F5I3m93dxe5XI46KBMHIkVRqLvQ0tLSQ7uz9/t9ej6tra0dcaQiDkmiKOLVV1+FoihotVrUEQtYXKedTgeZTAb1ev1cc11y7yXxpyfdv8n5M5/PEYlEDm0fEfak02kMBgNomkad19LpNL2Pep5Ho1gLhQLW19dRKBSwvb1NRcpX5Rbd6/Vo5CCJRDwIOb6CIKDRaFAnxNVzrsnouk7jesmzkIjWRqMRhsMh4vE4YrEYGIaBIAhQVRU7Ozv4y3/5LyOXy6Ferx8r0H7++ecxHo8xHA6pwI8I3SzLQhAEsCwLnU6HxmASF8X7r/tYLEbjfkNCQgFWSEjIyXDcYvYenHNR8V4XIQAwj3mh6KpIfOEXYvKe98D/d/9u0VU3GkEcDhFwHBjfR8AwcNNp+Gtr8G7dQub7vx9gGGjvex/Y3V2Ie3twcjkE1eqhBz4Tj8OPx2GNx4i027AkCdP/9J+Q/47veIx7+/pF/emfBvfyy4jcuYPgXgHGiUQQ3BscegA83wdvmuBse1HwjUbBdTrAH/7h664rMiTkQfA0DdzHPga+0wF8H+KdO4v7H8vCkyR4hQKQTi+eBwwDt1iEuL8PT1Whf/jDSH3lVz7uXQh5ijE+8hHAMMAbBqzTCqSCAHt1Fezdu+BNE8J8Drz4ItxkEmBZcPM5WMeBk07Dr9cRvOtdSH3FVxx6C65SgS8ImLsRGD0LmpjEvTSlI7AswEynl7ejl0wQBPR/5xUOkY7gixZoisUiptMpgiBAq9XC1tYW7S4mCwCGYSCbzdKi5sHO0pMgXcqCIDx04Zt0MUajUei6fmY3pW3bsCwLsVjsTAv5s5AkCc8//zwmkwmNvSCRDWSx5awFiUcFKTjatn2uiMNIJAKO42gxFTjcLWlZ1pnHr9/vU0Ha2trakWNBCueCINDYjkdt8y6K4gMLjA7iui7a7TZefvlldLtdNBoNGiXJ8zwYhqHucyQugXzu4+ymJG4Mg8EA6XT60L1FFEUUi8VTu+lPgxSLSbzGaDSC4zjUVQFYFJZVVcV0OsXS0hJEUYSu60in09Rx7zyQOAiGYSCK4oXjTB4liXe+E7Nf/EVYsRQq2i6asRvw2cU85uAtO5jPEel24eZy8LNZpL/sy65mgxgG8c///NCV55JxFQWT97wH3Kuvgu31wJNxBsuCcd1Fo9xoBOfOHUze8x6k3/GOx7q9D0pgGIs5BKk7nfV6QQBz7wT3ZrMzRVHqj/wIuJdegri9DTceXzhcyTLY+Rx2LAaGZcE4DnjbhgtAaDTgPPssGEGAU61CajTgaxq0D3wAmXe/+0L7qn7v94J95RVE7m2Ls7YGJhJZuKe/8go8jgMXBGCCAKxtQ2g24d6L8AXDAMUibI5DpNWClc1C/6//FZl/9I8utE2vZ4LZDMw9l59jG4Xu5953AdeFr+tXvHUPhvnaazD+838G87GPAfM5dU2bCwL0W7cg/t2vwR+Zfw3//oCO6uu+DviSLzksvgKA/f2fg+/P6b97vSiAwwIsz/PQarUwHo9pPBeJn0smk9R9ZWlp6dDfVSoVGIaB+XyOvb09FIvFQw4RJI5vMpmgVqshkUgcG9dFxFS+72N/fx93796Foih00TyZTGI+nyObzdLIJeIS2u/3MRgMwHEccrncoTEaccwQRfHCY/jz4roufN8/dqzIsizq9ToVaJF4bUmSsLKygmq1iiAIYNs2Go0GbHshnCPRZLlcjsYqnZdcLodWq4VkMonxeHzIYex+iBtXrVZDLBajsYjHvf6ga8t5tofneQRBAM/zzuXGVSwWqQvS/RHYQRDQRf9+v09jAovFIlZXV2lcVr/fx+3bt8GyLG7fvg1d18GyLCRJAsuydN5x3NiVjDsVRYHjOPB9H71eD0EQQBAERKPRR+LCe5mkUikarb6/v4/t7W0q4iBuS6qqwvd93Lp1C0EQYHNzEx/96EcxHo/R6XTAsiySySTy+Tw4jkMQBPTvybl7HERMRVyVbdtGoVCgwor7nWVisRja7Tbi8Tiee+456pR3UarVKnZ2dqBpGr3fxGIxCIKAIAjo3Hx1dZWKL7a2ttDtdpHL5WDbNqLRKHieh23bVAy1urp6SGBDmuCIk/NxmKaJXq+Hu3fvUjfhbrdLnbeJcFAUReRyOTAMg2g0CpZlYRgGdF3HdDoFwzBIp9MolUoXPj6Pinw+T92mFEWBoihUcAqACl2Im9Py8vJDfY7jOPQ6Js5bJ0EEc4qiQJIkVCoVWrtyHIeKAHVdx40bN051zQMW7vLj8Rgcx1GnpuOIRqOwbZsKhe+f05ZKJRiGAcuysLu7C03T6D2oVCpB13Xouk7jUXO53KHPCoIADMNciVs0eWbIsnymE6IkSUgmk4dcos6zTcS5HgCtleRyOdrkN5vNaOMn+bfneUilUvTYtttttNttCIKAfD6PbDZLHcqKxSKKxSJc16WCX8dx0Gg0oCgKjV8NggCxWIw6Ltbr9UPbH5x3HT3kDcHrUyEREhJyKTD1OoJEAly/D8eyFgWiU+CmU/jR6MIR6nUqwAKA9DvfiVm1CvP/+X8QvPwyXMMAbHux+hqPw89mwbz97cj+43+8cHoJAvgf/CC40Qi+ICCoVk8uMObzcDQN3GCA4A/+AN50Cu6S7Ybf6PimCeZDHwLb6y0iB1kWge8vIiMPwrJwJQnCfL4oJnAceMOAN5th+v73X1oUQcjF4SoV+JKEgGEQaBqYM4ofzHQK/14RTHjIydsbmdmnPw3+tdcgqSo8ngcTBIuCNcOANU3wug4/EoGztrbINUinwTSbYAwDXrP5uDc/5CnHl2UwpJh3Vm5GKoV5rYbEzg7cWAxOPk/PVzebRZDPI0ilwPyDf4Ds//g/Hvnz2Od8DuTiEsZQkLUH0IT8ic9vFj740ejBhOuPEOLYREQxZ0GKSxdxlzr42RsbGzSiTdd1au0NLApNS0tLdPFhNpuh0WhgMpmcWMyeTCa0i/Sg1fjDQOIjiNvSaV2csiwjGo1CkqRzCZHOQzqdfiKK9pZl0c7m+y32D4qfHqSgdHCRJpVK0S5zRVFOjbU0TRP9fh9BECCXy50qsIvH40gkElAUBYlE4tR4xCcJx3Fw584dDIdDvPrqq2g2m7Twp2kaZrMZUqkUTNNEpfL/s/feYZZldb33Z8eT86k6lau6qzrMDAwDDIKggIioIEgQ0/uqCF71Xq+IKAoo3ovI5QXEa0DhekVREQQZhgxDkJwZZphhZnq6K+eTc9rx/WPXXl3VXamnI9Df5+kHpurUOfvsvfbaa/1+3zDM1NQUy8vLbGxsoGnaRal/LwXi8TjxeBzTNIWTgF8AvlhnibGxMbrdLjfeeCNf//rXaTabzM3NUSgUhCuYqqoMDg6iqqpQpB8motF1XUF6bGxj1PoF3IGBgYuONbkS0AYHcZ//fLrldyM1ljjSvp+KNkhdz2Lb4Pb7yKUSerWKHQ7jjIyg/vqvI13FMXMdFwbXtqn/4R+i3Hcf+vw8TiBAf2LCc9CWJDAMpFIJbWMDq9PB+cu/pJ3LEfnBH7zah354RCJe/J5lib3EfpAMwyNrAcoBBGWrWkX67GdR1tawg0GcqSm0U6eQTRNHloUjqqtpWIBqGF49oFKBbBYpFsPRNKRKBeuLX4SLIGD1l5ZQvvpV1JUVrEgEZ2rq7DxVqyGbJoptYwUCSI7juXK125idDtL2+SiVwi4WkUslrA9/GH7lV647y+0BKRwWYwXThAPmPneroecqCspFup1dSjQ+9jGcN74RuVxGKZVQ222xz7CDQaRSic1PP8B31NNw5L+I8fAP/wBbXIZ9ce7wsSyL06dPCzJLu91G0zRBMNjc3CSZTArXm+2uFalUSpDDi8WiiIoOBAI4jkO/3ycUCjExMUEymWRmZmbX9Zosy8zMzAiy1MbGBuvr62xuborYQp8IZlkWnU6HSqVCMpkkm82ytrZGv9+nVqsJEoLrulQqFUHUvtSRbntBURRP7LlH5KHfYPe/jy8umJycFHGOPmnNj3rz3XCGh4ep1WocO3bswOa/j3MdxtbX1xkaGjqv8V2v13nggQdEBFMoFBKNfp9kv30N6jevwRtDB61PfbKFvzc9CD65wG+Kz8/PC3cwnyg0NzdHIBAQ5D6fIKiqKs1mU7g0PfjggwwNDREMBolEIpTLZWzbpt1uizGz3YnZMAyWl5fpdruCBGiaJvl8XqwdA4GAIHxdLYfah4KJiQmAHa62tVoNOBt/mEgkiMfjzMzMcObMGWZmZqjX6wwNDSHLMoZhEAgESCQSQkDi7wvq9bpwZtoOy7LEWl6WZUZHR4VTTKlUol6vE4lERAy578Q3MzNDIpE4tFvOQUgmk5w4cYJarSai41utlrhfU6kU4+PjdLtdNE3jnnvuEd/Ftm2SyaSI1PTrF71ej16vx4kTJ8TY9l2g99qfdrtd1tfXqdVqFAoF6vU6kiTR6XQoFAqoqipiNYvFIt1ul1gsRjAYxHEcqtUqhmEwNDTExMSEcJC6nPBFSH7t5iDRmP8s8UUv58KvBfnzfK/XE05RwWBQ1Isupv7SbrcxTZNOp7MvQa3X67G+vi5IdceOHSOXy4lITJ9MvLKygmVZzM7OcsMNN+xbM4vFYoTDYQYHB1lfX0dRlF0JyJZl0Wq1BKH5XFf47S55iUQCy7KQZVmQu3yHqWg0SiQS2fEMbDQapFIpNE07lNv8haLdbtPv9zEM41Bu1IlEgtXVVWzbptPpHOq57NcTt5N3JUkim80SCARoNBrCpRy8eaxerwsH+3a7TbVaBbwx7McXjo+P7xhb9XqdcrnMxsYG/X5fjM9SqcTY2JhYY2x/ho6Pj4u/90lwV8KZ/TqufXzvMiSu4zqu46IR/qmfonfbbdiBAEqhgLPtYXIu3GYTpdfDmphA+vEfv4JHeXUQeexjiTz2sfTOnKH7+c/j1utIuo4yPU3iqU/dUdzu3n8/8sYGarWKNTBwYJHMyWQILC5it9u0P/tZ4s985uX+OoeC3Wxi1+vI4bBnd/9dtLHcjtanP41Uq6FuxR3Ipomtqrt/H1nGURSvKNrtemSTRgP7zJnLdnyubdP+8pexZme95kkySeiHfxj9OlFoT0R/5Eeo//VfY8XjqKUStt+Y2AWu46BXq5gDA9gnT+4a82k3GpibmwBouRzKNdAYv1ZglcuYr341kVYLeWvDY6sqriwjuS6KaYJheHFQc3MY09MQCuFukVLcPYqP13Edh4Y/Xx+SBCKFQljhML2pKaxbbkHZUre7iQTKk59M4pnP3NNBQVIU2k98OtXPVEiaJQZ6KxSD4+fPL66LXlhF9nM/rkECFpwl+qyvrzM4OLhvwbvVamHbNrFY7JKQgyRJYmpqisHBQYrFIpVKZYdSOpvNCnXeAw88wNDQEOvr63S73R0K9n6/L5SRQ0NDxGKxi1Z4ZrNZNjY2SKVSrK2tMTExsasislwuU6vVhPvP90pBxXcmKxQKoonhKy+DwaBouNTrdYLBoCAG7Qe/aBoIBMR52q5eXVtbIx6P70l02djYoN1uo+s6ExMTBzYz/HFt2zbdbnfX9/WL/X7h92o2SFzXZXZ2lnK5zH333ScKlUeOHEHXdREd4F8L8BpIQ0NDLCwsCHeoc+N1rgY0TbvkRDBJkpiZmSEQCPC4xz2O06dPk8/nhSNWPB5HVVXa7TbpdJp4PM7g4CC5XG7f+cC2bebm5kRDyI9I8FXkzWaTcrlMLpe7qg5jh0Xqv/5X1k61aax+iqRRIGUWGTTWULsOgZKFHQxiDg3hZrPwohcRf9azrvYhX8cFoPXZz6Lcfz/awgJWLIZz7lyo67gjIxiJBPr8PGY+T+8f/uG7ioCl3XILzsc/DmtrUK/vGSsNgOui1mpYmQz2zAzyAYSD1gc/iNRsehF0R496bgK2jeS6Z4k5/ltrGq5pIlsWarmMJcvI7TZSv49aLGKeOXMogtheaN92G3KrhdLredGC295H3ubU5GoajuuidDpgWSilEu7255kkYadSqOUyTqGAWSqhXWQM0/cqgjffTEfTsMNh5HIZ54Bmo1KpYIXDSLpO8Oabr9BR7o/Wl76E8/rXIy8vo1UqmIkE/SNHcINBJMtCqlTQNjcJ9Ts8IfBe+nKYr0/9v+Lv77jjwj9zfn6earXK0tIS4XCYI0eOiDWx71rrrxcB4YblY2hoCF3XCQQCZLNZ4drix93560L/Gb8XFEVhZmaGZrNJq9UiFouRy+WYmJhA13URHdbr9VhaWqLRaJDP50U8WavVotFoMDg4iOM4bGxsYNs2mUyGgYGBK7IGtCxLxPKtr68TDodJpVJ77r98V41cLidIIKurq4KI5hNdOp0Oa2trzM3NMT09Tb/f55Zbbjn0WmxkZEQ4jPnOxD4xzbZt8vk8S0tL6LqOpmm0223m5uaEQ2+r1SKfzzM9PU0sFsN1XRHHpOs69Xr9QEdhP9ZOUZRDk8fGxsZwHAdZlkUkWalUIp/PUyqVkGWZo0ePEolEGBsbE+O20+mI+ErbtkmlUqTTaaLRKBMTE9i2TaVS4e677xYExMHBQRF/try8TL1ep1AoCFKST4aJx+O0222WlpZEg/9SkYOuBPzjTafTwtV2O7a76PhOL4qi8OQnP5lkMinW0X6UZjgcFu8xOztLo9GgWCyet2/0iXe2beO6LoFAgMnJSdbW1kR0lx/fJcuyuF7JZJKTJ09etDjMRyqVIhgMcvToUeGu50cNyrJMIBAQBMl77rlHRFWOjo4yOTkpCKFwVsjkuxUvLCwwMzMDnN0rmaZ5XvycTypsNpuUSiWCwaCIGM1ms6RSKUGMsyxLEFB9cmY+nxfk1yNHjpBOpzm6tea5nEgkEui6Tjwep1Kp7EvA8qMs/fO119zvi3t6vR6NRkNcB59MdLE4rFNfqVQSe0L/2BcWFiiVSmIOGBgYIBAIsLGxIYg5mUyGUqm0wzUyEokgyzK1Wo12uy0ieM+cOSPqIqqqYlmW2OsGAgG63S6GYXD33Xdz8uTJHYSpaDRKJpMRz2ifdJRIJAiFQmiaJs6lT/qrVCp0u11OnDhxnlvTpcJDdUK8EMd93yV+YWFBxCn65yYWixGLxQRB3I+VVVWVRCIh3Lb8eMJqtUqxWCSZTGJZFrZtMzg4iOu6rK+vUygU6Pf7TE1NYVmWcMFqNpskk0lCoZAgmuu6TrfbJRQK0W636XQ6RCIRarUa9957r/i+PnHzOr6/cJ2AdR3XcR17Ijg9Tfvmm6FeJ7C0hBEI4O5GIOp0CCwvYyYSEI0Sfc5zrs4BXwUEjx0jeOzYvq+xy2VwHGTLwj1MlEw06hX4+n2ccvkSHelDg9Pt0vzoR7E+9CGU+fmzP8/lkJ/xDKI//dMeGeu7CPbCAlKr5ZFBVNVTRO6zOXFUFaXf9+IOVNXL9ej3L/lxuZZF/R3vwPnwh5E3Nz17ecfB1XU6b3kLzUc/mtCv/ArhRzzikn/21YJrmjQ/9SnshQVPxZ1MEvzhHyboxy0cEnI4jPuUp+CUy+hzczibm7hDQ+dfV8dBWVz0nLIyGfTtMR2uS+tLX6J/++0o3/ymIFD0JAn71lsJPPvZRH/oh75riYeXCvU//VOUfB6118ORZWxVxdnaQLuA47pelEe/76nJl5YwJya8+ycQQN6KD7qO63ioUCcmsEMhXFmGWg0OeAbJ9Tp2OIyUTJL6i7+44PjY1hN/ntXIN1Adg/HuLOF2i4o+SEf1Colhq0HaKBBwmhhHJmFrg30tYmBggEqlgqqqFAqFPR2ILMuiUCgIe//d1HkPFeFwmMnJSSYnJ0Wh5Vw16MzMjIh0KJVKzM3NiSKOb/nuk6BmZmYuKgIOvGKIX4B3XZeFhQWi0SixWAxVVYXCzTRNRkdHSSaTQjH8vYDFxUUKhQJra2t0Oh1R5PQbbb6rgF8Er9frDAwM7Fv89mNN/IgUH8PDw9TrdbrdLisrK2SzWZLJpHgvwzCoVCpC/drpdA5s4gAiKsKPM/Hhuq4orrW2IoXa7bZQJfoF/Uwmcyil5qWCX7xbXV1FVVXC4bBosIHXePSLqBsbGyKeZmpqimAwSL1ep1arXTIClmVZVCoV0UTRNE0o+q8WZFlmcnKSkZERhoeHOXXqlCCkOY5DIpHY4YIxNDS07/lwHIczZ85QLpdZWVnBdV2SySSZTEaM9dXVVdGAdF13h5L1moQkof7X3+d9X3s0P7BxO6PtWXS7SzRkMDxuQTCI/fCHE/iFX/DWsNfxXQXj/e9HrVTAdbHHx/dupkUimNksaqmEe+YM3e98h9DDHnZlD/YhIvbjP071//wfrEQCdXMTMx4XzlTnQioWkWwbJ5NBPYRAzX7gAdRGw3Ox2mrcuf453IUo76gqimEgVyoo3S6SbaP0+7iKgnvXXVSe/3yUn/1ZEs9//gXvB93vfAe5VsMOhc53lffJV1vvKUmSJwCzbZROB+vc9/LrEYDT6VzQcVwq9Obm6H3hCzj1OlIggHrsGNEnPemacsHXR0dpPupRUCqhr6/THxzc09Hf7XZRazWM0VHsW29FuwbIzbguxl//NcqWkLN/5AhsuUNIAJqGOzqKMTCActcqw84CT1p/F/cN/TjtoEfKO0zJavtQ9klLfkTdyDlR776zRigUYmlpiUKhIKL//LW87/KSTCaFq2q32yUQCGDbNpqm4TgODzzwAOl0mpGRkX3X8f5xdLtdstksk5OTO/YNfoyhH0O3srLiCcEkiXA4TKFQoFaribi/ZDJ52aO5DMNgfX2dSqUiSBWlUol+v8/Q0BCJRGLXdXSlUkFRFCzLIhAIsLq6iqZpGIYhCAntdltESNm2zX333Uen0yEQCHDzIYmDsixz7NgxFEURjiH1ep12uy2EGKFQiG63y9DQEKOjo4I8UqvVmJ+fZ2BgQDgV++vu5eVlms2mcKTdK97Lj6nyHUcOSwTwyUJ+BHYqlRLCCt9Na2Rk5LzoyXK5TLPZpNPpMDIyQr1exzAMIXBQFIWBgQGOHz/O3XffjWEYLC0tMTMzI/YP/r44mUyKWOxsNsvQ0BBjY2Osrq6ysrIioi+vlMPapYJPXPBJg/5eYPs+oFqtCnen0dFRdF0/L4Z0OxKJBNVqVYyR7ddZkiSxv15bW2NwcJBAIMDRo0eFAKXT6eA4DoqiUKlUmJqaOlTU24VAlmVyuZy4x3q9HkNDQzvIRIZhsLKywsbGhiCY/eAP/uB5azLffW19fZ21tTWCwaAgh/h7Ft9pbHttxZ8jy+Uyg4OD6LpOsVik0+kIN7JOpyP+VlEUSqUSQ0NDIhJvcnKSY8eOMTMzQyqVuiIEU99Nz3f+KpVKu+7ZfTKLv+8aOARp3I9ivdRQFEU8P3yn93Ph39+zs7OCdOgLB6PRqJgbS6USo6OjBAIByuUyjUaDbDZLv9+n2Wziui6dTkfM2T6BFRBRkv6113WdUqkk5mSfvGWaJoVCgc3NTQYHB0X9yXfhu+WWWxgfH+f06dPi/toeCQoIJ7rZ2VlyuRyJRIJj23qYvoOaHzkMiDrAhczP/vn1X2+a5oE1ugt1QgRPODkwMEA0GqVUKlGtVs9z3/aJ3uvr67RaLfr9PuFwmJmZmfNcClutFhsbG+JYQqEQtm3T6/VErcUnUIZCIVKplCDiRSIRstksKysrdDodqtUqkiRx6tQput2uIJeVt/q6vshuY2ODYDB4qBrXdXxv4NrZHV3HdVzHNYnoy15Ge3GRvm2jr67iVKvYqRRuMAi2jVyroTWbmPG4p8r8zd9Eu8JZ0725Ofr33AO9HlIkQvDWW9HPKRZcVWzPAXYcDloKi2gXSfIIP1cJvdOn6bz85cibm6ilEkq97qkzZRl7bQ1neZnmu96F9id/QvQJT7hqx3mhcPt9L4JKloWSVXIc9vQrkSTvmrkusmFgaRrSJd5QO70e1Ze9DOWb30QtFFCrVe9cbzmp2OEwVCoYd9+N9bKXEf/Jn7ykn3+l4fR61N/2NtyPfQy5WkXyrfQ1jd4//iPtm28m+IIXELn11kO/Z/zXfo3GN76BYZroy8s4zSZWJuORHh0HqdFA22pimEePYj/qUSS23PqcXo/q//yfKF/6Ekq1ilIuo2xtPuxAAEolrK9/nfLjH0/q1a/2okW/D9Gbm0O56y7UQgEzGES2bWTLYodWRZJwdR0LULaiPJy1NW8Mh0KEn/KUq3T01/G9guiP/AjVv/5rr1lXLGIlk3s73pnm2YbKYx5zweQrgHI7zHtu+jN+9r4/xpJ1UkaBXH8Vpee1xBxJpaZlCM0MI52YhgcfhC1V+LUGv7g6OjrK8vIytm2TzWbPU7bn83kURTlUnNfFYC8b/kgkwsmTJ1ldXSUajdLr9URBKBAICGXodnXzxcInYfhNI5+04ziOUM0lk0kikcglIX1dKygWixSLRRF/Nz09veO7DQwM0G63WV9fR5IkDMNAlmVWV1cZGxvbtXDpN5wmJiZIpVI7XhMKhThy5Aiu64pio1/E8mMvQ6EQY2Nj1Ov1Qzcwtjdgthf+ZmdnaTab1Ot1Njc32djYELESfnHOj/cLBoNMTExcESJWsVikXq8jyzKJRIJ2u70r2UnXdRKJBI1Gg0QiQa/XIxAIYJqmiOq4GPjuCtVqVahFfSX6+vo68Xic0dHRi44p8BXw7XZbNFPi8TgDAwMHvremaUxMTDAxMUG/36dUKommjKqqJJNJUqnUgbGT+XyeWq3G8vIykUiE4eHhHX/jH4/fRPPngmu9iRYKS5zOPYXTuacw2DjFSPMUE4Ntnv8rCoHHPOaCRQ3XcW3AbrVQvv1tlGoVK5nck5Tkw81mkYtFaDbpfvrT3zUELEnXkZ79bOxKBWV2Fm12FnN0dEfsnmvbyPk8eqlEf2wMd2iI2GH2wt0u2LbndrW1jnF1HUdRUEwT5xxHK9k0kW0bV5LQOh2vYeY4OK6L2mhg33UX7uYmlfl50n/4hxdEwpLabbBtnN3WDlvPrO2R7r5zsLSLI4BkmuJvDophvNTofOMbdN/+dpR774VOx3N+lWXsSITqwADSM55B8gUvuGaIWMGf/3nMO+/ErtfR5+Ywxsa8Os42Ip7bbKKvrGBHIpBOE/z5n7+6B72F1pe/jLy+jloqYeZygnx1HnSdtfBRploPELTaPHzjo3z1yK88pM/0ySaWZe1LrFBVlVwux+rqKgMDA1SrVTKZjHCC6PV6wkWr2+3S6/WwLEusYzY3N4lEIrTbbRqNxqEi9Pwm7W7P+uHhYfH7TqfDysqKIBSZpinW8L5w43LGVHc6Hc6cOUOj0aBcLtNqtWg2m9i2zezsrHDSbbfbjI+Pi2Z7Pp+nXq8zMjIi1mKWZYnoxFqtRjgcFsQt3yl1ZWWFhYUFgsEgo6Ojh47nUhSFY8eO0Ww2KRaL1Go1HMdhdnaWQCCAoijccsst5zl1JJNJyuUyd911l3A0S6VSomHd6XTodDp86Utf4uabb95BtvAjsPwYy2g0+pDIcNvdz7rdroiyCwaD5xHxDcMQxEI/Cl2SpF3dVkZHR1lcXKTVajE3N0ckEhHnPhQKkUgkhDAlGAwSj8fJZrOoqsrY2JjYcxSLxWty7ejfl9tj5n33HR+qqu4psDEMg36/j6qqh9oHh8Nhms0m4O3TfCJVoVCg1WqxublJs9nkO9/5DhMTE0IQE41GdxCg/DlpeHj4shAWhoeH6fV6AGxubgpxjK7rOI5Dt9sVjty1Wo2bb75539rI4OAgs7OztFotisUik5OTgriaSqUoFotEIhF0XRfuSs1mk2AwSDQapVwui3j3fD5PtVoVMXz+OVRVVbhTHzlyhOPHj3PrrbdeFlej/TA0NEStVhMkxFarRSqVIhwOi7pStVrFcRxBgE1fRUFuJBIR8Xu+uOlc1Go1VlZWqFardLZI7gMDA+RyOSRJotVqUa/XSSaTuK5LOBzm1KlTpNNpKpWKcAD3nQqr1Sq2bZNOpxkcHGR4eFjUmiRJwnVdms2muF/8PaqqqqyuropjaDabzM3NMTIyIp5pvlP4yMgI9957L47j0Gw26Xa7BINBXNcVrs/JZJKjR4/y6Ec/WtTParUaS0tLggzp3weBQIB6vc7a2hqjo6OHnqdjsRi6rhMMBndEAO8F3wnRF6MdBqqqMjg4yMzMDJ1Oh3w+j+u6DA0NibnLF5b5ruHBYJCZmRnGdxGz+E6ICwsLInrZj631XQ59DA0NCWetQqEg4ox9casfs9nv98V6pN1ui+9WqVQEKXV+fh5Jki4qUvM6vntwbeyKruM6ruOaRWByEvdNb6Lz8pdjRKNIxSJqqeS5OckyViTiqbHicaTf/E0SP/uzV+zYWp/9LP13vxvlvvs8eZfj4KoqnUCA5q23EvrFXyT8qEddsePZC/rkJJYsYweDyI3GgS5YUq3mFQqDQZSrpLzuLy7S/d3fRV5eRl9dxVVVrGQSV9fBslBrNbRyGWNwEOtVr6L9utcReexjr8qxXiikZBJ0HclxsEIhJNNEMYzzirDi9VuFWMm2PcVpMol6Kb+r61L9H/8D5etfR5ubw5VljOFhXF+50ushl0roKytY7TbOG95AJ5sl/JjHXLpjuIKwm01qL30pyr33omxuotbrXhSELCPZNnYwCOUy5n330XjpSw8dwalmMkTe9Cbav/d7GIEAcrGIvrEhIh0cTcNKp3GyWZxHPYrk616HpCi4tk31j/8Y5YtfRJufR7IsrEQCY6tYJDeb6BsbuIUCGAbVV76S9BvfiHSFN7fXAjq33+7FZ/T79IeHCeTzXrOi3xcuWD5cTQPLQu73CfR6dI8fx7711utRmtdx0ZA0DeknfgKnWEQ9cwZ5eRlnYuL8+ds00ebnvbGZShF87nMf0ucVi9ANZPi3m/+CR66/n0flP0ayX0B1PdWsJenUAgOkXvQk0r/083D77Rf7FS8rpqenhTK8UCiwsLCArusoiiKcZXx1eC6X27cBcznhF0oMwxCFSPAIKecqmy8VstkssViMYrEo4gd8hEIhBgYGyGQyO1T+/X5/R0zBXk2dc9WFvrI4lUqJ4v3VQKFQEKrOycnJXQu3kUiE8fFxFhcXBWnJNE3m5+cFQUWWZaGa7/V6ojg4usucn0wmOX78OKurqySTSdrt9o7Iw2AwiK7rtFotlpeXqdVqBxbcfbKWX6AulUrcddddlMtlUZgDxHF2Oh1s2xYF2FKpxJEjRzAMg6mpqctaHLYsa0fx1idibR9v2xGNRoXqvNlsCuLRxTYQe70ep0+fFt+/3W6jKAqSJGFZFsFgkEwmQ6vVYnp6+iER03q9HvPz83Q6HRqNhoi88QlYfpzg0aNHD9U0CAQCu46pg+C6LsVikXK5jK7rjIyM7No80TSN8fHxa76Jth3beweF+EkK8ZPUj8CvXRscguvYBU6nQ/NjH8N+4AGvhhGJoD/2sUSe+ESxv7AqFQAkw8A9THFe03BUFQzDi/L7LkLqRS+iPDuLCSjLywTn5rADAZxg0HOhardxFYX++Dju+Djh173ucGKYaBRU1SMJbe3zrUwGpd1GMgwky/L2K4Dc7yNva8hbgYC3h7RtrFAII5tFrddx222sD3+YWi5H8ld/9dDf0Q2FQJaRtppb22EHg54785aLsKtp3t5Ykjy31x1v5HqkvEQCZ2wM9Qo2Muu33477V3+FUiiglssovZ6IoHdl2TumzU0qd99N6o1vPDAi8kog8tjHUv2VX8F++9txV1YILi5i6zrOFsFPbrdRDAMzlcIZG4MXvvCCBGCXE8YddyDXakiOg5PN7ivi7MsROkqUhFHk5vJnHjIBq16vU6/XicfjBz6TfTcXn+TuCzh8Yr3vqloqlQiHw+RyOUGsSKfTVKtVlpeXhRvvDTfcsGdkUSAQIBQKieimc9fMkiQxPDxMPB5nc3OTM2fOEAwGiUQiYk0jSRK9Xo+FhQVBfLrU8In/pVKJzc1NYrEYU1NTHDt2jMXFRYrFIouLi9x1112MjY1RqVQYGhoSDfbh4WERebayskK/38cwDOr1unC72Y5wOIymaZw6dYpCocD999/PE57whAtaH/riHH+dZFkWc3NzDA0N7RqTZJqmIIA88MADgkyQyWSwLItutyuIBXfeeSfj4+Ok02lc1xWxXH4MpC9+eKjw9wwDAwNiXX0uOp0OpmliGIYgg/X7fSKRyHnjSFVVTp48yV133UU6nWZxcZFKpUKlUmFwcJDl5WUAMYb9Me3/rU+q2M3x6WrCF9I0Gg0havKdXlZXV0mn08LlbD/4QhchGD8A575ueXlZEIqq1ao4Hp8AODk5SbvdZmhoSDhENRoN1tfXyWQyhEKhy+YY40et6rpOv9+n0WgI5+ehoSE2Nzep1+tMT0/vG7UH3liIRqM0m01BQAOP4OcTUhcXF8lms2L/12q1SCaT5PN5sS/3yZt+1LpP7PFFOPfddx/Dw8Nomsbo6OhVGW+qqgo3JU3TqFarbG5uCoKjqqqkUikhlpmenr7kwj7fEfAw0DRNHMv6+jrJZPK8OWhjY0MQLaPRKGNjY8KxGzxnt06nQ6FQALxx3W636fV6zMzMcOTIEcBzGA+FQoKM49eKfPKr77zkE35kWebGG2/c4To1MjJCo9FgdnaWSqUiiHwTExNks1kymQymaXL69GlUVeXUqVMivtYnPm93lL755ptFLGa5XBYu6P6c7j8zG42G2KNbloVlWec5Yu6GQCBAPB4nlUqRz+cFSWw3dDodms2miEC9kOfW2NiYuJfm5+dZW1tjfX1dxNr68Y2maRKNRpment631uATiWu1mogW9B28to8tX6wnyzLBYJBWqyXcz7rdLrlcTrigRaNRTpw4sSM6c2BggOXlZYrFIhsbG4J4dild/a7j2sR1AtZ1XMf3OcxCge5Xv4rTaCAFAug33kjoppt2vCZ44gTav/wLzdtvx/7wh3E3N8Xv3FAI90lPIvz85x8YxXfJ4LpU3/xmpPe8x3Nm2uZWA3hOK6USxje+gflbv3VFSWG7QR8bo3nTTR55rVDAyOX2Jm+4Lmqp5BWwBgZIXCVnqdZrX4uyukpgZYV+LgeDgzua29bgIFa9jr6ygilJ9F/7WsLvfS/Sd4EjROjJT6b3z//sFTsVxSNVGQayYXiq1HMW77Jl4SgKkm1jDQzgpFLCOelSoH3nnShf+YogClhHjiDJ8tkiWyiEMz5OP5kksLiIGQjQfctbvjsJWK5L7ZWvRL77brT5eVxNwxgfh0TCU/v2+x7ZbH0dq9PBedObaA8MEHnc4w719oHJSdS3vY3Gu96FdccdOKUS+A4RmoYzNYXyUz9F8rnPFcrcxgc/6J3/uTmPpHXs2I7700km6Y+MoC4soM3Pg6rSeP/7STzveZf89FzrcE+fRqrXsUMhyOWwGw0kx0Ht9ZBcF0fTPPKo+AMX1TAwQyHcXI7wC1949Q7+Or6nkHjhC6l+85sYto22sIBy//1YqRROJILkusiNBmq97s2pR4/i/MRPEPnBH3xIn+UnAVtqiG9M/ALfHP9ZRmv3EOt5RZdmcJC15M08/TcVJAk+9alP7VDVXk6V9UOBqqqcOHGClZUVQqEQvV5PuNKoqrojzutqka+2Q9f1PaMSLwf8+IDR0VFBSPNVpj4sy6JUKlEsFkV8BXhqct8WfXtjp16vs7i4uENd6DtA1Wo11tfXGR4evqLfExAKSZ/gtF/h1lflgjeGhoaGhKrVt1X33ZyGh4eJxWIcO3Zsz6JbNBrl5MmTdDodKpWKUGPruk4mkyEQCHDq1CmazaaIQNyLpLa9gCdJEvfffz8LCwssLi6ysbEhrOS73S62bYv4Et9xwFc4b25uClepYDB40a5Pe8H/DNM0CQaDQplZLpdxHOe8OcNXo1uWJZT8o6Ojuxbrms0m5XIZwzDEGPML99sLiLZtc+bMGSqVCmtra8RiMdF8AE/lXq1WWV9fF2P85MmTF1Qg7PV6PPjgg1SrVTY2NnBdV8wvhmGIaJ3R0VFM0+TEiROXrXlQr9fp9/vU63XhlLEXzm2i7dbsvaowDCrPeQ5uIoH8Qz+E9mPPAXYSdK5RE8ZrE9udpy/3RxkGtb/9W/j4x5GaTeStdbSrKFgf/jDVwUHkn/s5kj/7s8j+3ClJIm5u/zc/657EAU3Uaw6SRPq1r6X6V3+F+5GPYLdaSJWKICMZ4+OQTGIfOULkVa8iuNU8OgjqrbfifOYzsLEBjYa330ylcDY2sHUd1TCwttymFMtCdl1cwFYUj+hkWViBAHY4jDs2hmGaaLOzyCsr2P/+7zg/93PIh3xOSDfeiHPPPQTm57EMw6uZNJvIpRJas4na7yO7LorfMHMczEDA23NtR7mMYhhY2SzKM595RcYtQOvzn8f9y79EXlpCrdcx02nMqSkv0s9xoFr16nJnzoDjUH3Vq8i88Y1X7Pj2Q+pFL6IWjWK/7W3YtRryxgZKq4Ury9ixGNbgIG4shvxf/osXL3mNwC0WkbpdL7Zyn72EP4V1lBghu0XcKF/Q55w1A/MinC3LOpBg4P2dJOJ01tbWCAQCwl03EAgwMDDA5uYmD3vYw1BVlVqtxsbGBuA9Z0dHR8X6IBAIUCgU9iRYZ7NZSqWSeJ+9SBi+w5IfnRaPx0kmk8iyLIjm/r9jx45d8nWe7+izubl5HoFpbGyMXq/HkSNHhFOYqqqEQiEmJycJh8P0+32SyaQgrcuyTK1WI51O77n+isfjJBIJKpUK7XZbuJFdKCRJol6v02w2RQz2bvCJPO12WxCRfFIDeNdqcXGRBx54gHg8TqfTIZlMCqePRCJBIBBgcnJyV4LXQ0EqlaJarVIqlWi1WjvGr23bYl+uaRq9Xk805XcTFiQSCRFBpyiKIMdEo1EROaXrOgMDA+cdfyQSoV6v47ouhmFcE031er3O3NwctVqNSqVCr9dDVVVxv0ejUbGPOn78+L7ERN+F2nc7O4g81263CQaDyLJMsVgUEaGmaZJKpZicnGR9fZ1yuczCwgL333+/IEH4ZDnLssTe+ujRoweSxC4Gw8PD5HI5QajwySvhcJhIJMLa2tqeYplz4Ud2bn99MpkU0eblcplyuUytVhPnxrZtgsGgqMv45A+fBOg7CzWbTRFnKkkSmqZdVbFIIBDghhtuoFAoEI1GyeVyYq/rz3GDg4Nks9lLRr7ynZUbjQaO4yBJEpFIhIGBgR0RjL4DlB8Ht32P12w2BQk4Go0iSRKmaQricL/fZ2JiQkRIbofvRri6uoplWRSLRW688UZGR0cJBoOsra2JZ83k5CSqqrK5uSmeY77TWzqd5syZM3S7XY4dO7arY1Q8HueRj3wkS0tLogZz/PhxsWcOBAJij57L5SgWi+TzeRGx6Asrp6enhUNgt9tlaWmJjY0NGo0GIyMjxGKxHden3W6zsbHB6uqqOL97PRe2I5fLCVL20tKSmE/PvSabm5uk02kikcgFOyH6Mbq6rovv7UfNuq4rxJq2bWOaJg9/+MN3EKF2g/8c9UWfezklhsNhjh49SrVapVarkclkRJSkoijCGfLkyZPnzVd+jLPvXueTLqempi7o+1/Hdx+uoUrSdVzHdVxJdO+9l8473oHy9a9DrwemiSvL9INBOjMzaM99LvFnPEPsyJVYjOQv/zL80i/RX1ryihehEPrExBVXt9Xe/nakd78bZX4epdfzCkCplOdq1O8jl8voa2tYzSbOm99MIx4n/hM/cUWP8Vzoz3kO9r33QrmMNj+PefTo+SQs10VeXkayLJxcDuknfuKq2Ld3v/MdlAcfRFtfx0inYbcGrCRBMolh2+jr67jZLM077ji0W9HVRHB6mvbDHgZbRB9HVbECAbQtEomt6148IZ4aVnIcZMCMRHDGxpCf/3ykS7jx691+O0q16jkvzczsWWCTYjHMbBa1VMKdnaV7772EHv7wS3YcVwKtL34R5e670RYWsMNhnKmpHYVZKRDAHR31yGbz85jBIL2///tDE7AAlESC1G/+Ju6v/RqdO+/ELhZBUdAmJgjdeON5hWD7gx9EKRbBdbGOHt31/EuKgnX0KPqpU0jFIvYHPgDPfe41UVS+oti6HxxVBVn2ztfcnNe4MAzULVW3K0kiRsNRFJxgEPVVrzqP3Hsd1/FQIYfDJP73/6b+B38AgQBSuYxSqaCVyyBJ2MEgxtgYJJM4P/mTpC4wKmY7SqWd/+1KCqupR573Ov/tH//4xz+kz7mSUBSFqakpRkdHKZfLO+K8fNXa5Yod/G6BH81wLtrtNrOzs3Q6HaHe9R3FYrEYrVaLQqEgXJSq1Srz8/PCfcdXxfrvVS6XicViQl04fgWdTzudjlBHHsbdKJFIsLa2xsmTJxkeHqbdbpNMJnEcR7gaBQIBUSg/DGklHA7v2QDL5XJC/bu0tMTw8PCO126PMkmlUrTbbWRZplKp8J3vfIdisYimabTbbRFbNzExQbfbJZFIoOs6uVyOtbU14eRVq9WIRCLk83mhYL3U8AlWvuo5mUyKqBo/IuBc+MXlRqMhxtD2WBe/yNntdkVh3o8SLJVKhEIhxsfHReHUjxlaW1sTzm7b4Z+bSCTC6uoqwWCQzc3NQ58T13WZm5ujWq2KiKJz1a2WZZHP51laWgI8BfHlOue9Xo9er4fjOIdqUkSjUWq1mnC5u6oErHOLv46DeueduJqG+8ADuP/2Tp5Q+iW+NPUC8SC6TsDaH+bmJq33vQ/3U59C3iKQOskk0o/8COHnPpfAxMQl/0yn06H60pei3H23FyvWaHhCIEXxHJrW1rA2NnAKBSqLi6Re+lLccBg7HEZpNLAPcH1w221k24ZIBPkyHP/lhqQopF/6UqwXvIDW+9+P9dWvIjWbHplsaorAT/80yUc96oLWcrGf/Enqf//3WPE46sYGZjSKpCiY4+PIi4ueUKTf3xH15+9hVMPACgRwAgHM8XEkPAdWc2SEwNISTrNJ86MfJfEzP3OoYwk/73n0Pvxh7EAAZW0NV1XRKxUky0I2TSTHQXJdJED1G5emSTcWQ2YrhrFQQC8W6Y+MeEK9n/qpCzvJDxWuS//v/x51cxO10cCYnoZw+KxYTJYhk8FKpVAWFz3BUiBA5667rgk3eoDQYx9L68EHkd/7XhTTRLJt75r3evTGx4m88pWEb7nlah/mQ4Lrbr8nHvra3Xe32avpuBv89V9ly7HPdV0mJydZWFig1WqJyHDwnGQ3NzcpFouoqko2myWVStFsNqlWq0Sj0fOigX1EIhHR8N7Y2EDX9fPWrf1+n/n5eWZnZ7Esi9HRUR71qEeJtbxhGFQqFVZWVoQj1n6uWxcKx3EEqSISiYi1XK/Xo1Kp0Gg0AG/tF4lEaDQaNBoNcrmccDPxIxk7nQ6tVks02A8ixIVCIbEmLpVKDznSyI/0294sP/f3vnAikUgQiUSEy8h2TE1NEY1GWVhYIJPJ7CBeZTKZS77PTKfTrK6ukkgk2NzcZHJyUlzX7ePJMAyKxaJwDtuNTGBZFoFAgOnpaRH16I8nf598LlHBx3Z3qMO6RF1OdDod5ufnKRQKlMtl0uk0Y2Nj4tx0u13K5TJLS0siQvGGG27YUzzmn+dIJEK5XN7XkdayLBqNBmNjY0QiEQqFAuvr6ziOw5EjR8S6enx8XJB0Go0G8/Pz5PN5LMvi5MmTpFIpgsEgU1NThyJ/XCxkWWZgYGDHHgs8gYssy/S3Cf/3gz/HnHsuc7kcgUCAcDhMJpNhY2MD0zRptVpks1nh2uw7DvljyjAMIZzqdrsMDQ2J+yqRSFAqla7I+dkLiqIwPDwsBFr+nBAIBA4kvlwITNNkbm5OxIo2m01BlItGoyJW8MiRI8LJ2N8D+u7jgUAAy7JIpVKoqsra2ppwIvKFSbVajVQqRSgUIp1O70o29Oe/QqEgnP10XceyLJrNJo1GQxA2ATKZjBA21et1MpmMcJ/udDqoqophGLuK1yRJEi58vjt8JpOh3+/jOA6apjEzM0O/36dYLDIzMyNqe77j1/axWCgUhHvlXk6EkUhEPM/9aL7DjLF4PC7qWfl8Xpxf/znV2Yr49utF2wVgFwJZljly5AhjY2OsrKywtLREvV7HcRwikQjRaJR2u00+nz9UncufF23bJhKJiHlrt2uiqioDAwPC7cpxHHK5nKiH+cTkveB/32q1Ks7XteKYeB2XB9cJWNdxHd+HaHzkIzh//ucopRJKqYS6ZXkM4Og6Uj6Pc/o05bvuIvPHf7yz0CVJBK4iO9eqVHDf8Q6U5WVk06R//DiSrp8tNYTDOOEwRiaDPj+Pub6O9Za34D71qVeFzOQj9mM/Rvk//9OL75ufJ/DAA5jJJE487hX02m3UahVcF/PIEewbbyT1y798VY61+4EPIDcayKaJe5D7RTqNu7mJVC5jfuQj8F1AwAIIvvCFmA88gNXtolYquJKEFQwiGwZat4sLyFuFUAAHwHGwHYfAxASubV+SCDrXslC+8hWUSgUrHj+Q2OVks8jFIjSbdD/xie86Alb//e/3yGaOg73lVLEbpEhkJ9nsO98h9LCHXdBnSap6YCxm79Qp5Lk51GoVK53eV10qyTJmJoNWLuMuLtK9774LPqYLhuvSuftu+l/7Gm6rhRQMot14I5EnPenqRCCGw7iahtLpePeEpmEeO4a8vo5bryNZllfM3oqiULaaF91HPpLMk5505Y/3Or6noaZSpN/yFlqf+ATG+9+Pe+rU2V8qCvZjH0voec+7aLfAYvHg11wqzkC/36dSqYjinu9eczHxDPtB0zShhLuOg9Htdjlz5gzlcpnNzU2CwSDZbFYU2xqNBouLi6RSKVEsXV9fF7EFu6kLu90uGxsbrKysCHXh5Yy/2w6/cSZJ0qGKPr5i2ncyGhkZod/v7yj+hcPhS9ZUSaVSDA0N4bouGxsbLC0tiaK1H2ViWRaZTEYUufz4G98y31eC+iTDZrOJruvk83lGRkbQdZ1wOEyj0SAUChGPx0UxbHuT4lJC0zQURSEUCtFqtRgbGxNNqVKphKIoO0hC3W4Xx3HEeT558iThcFg05Hx1e6VSoVQqiWhF8Ap7+XyedDpNv98XxMBSqUS1WhWfuxei0ahQhMZiMdEoOQh+A3FjY0MUKM+F74CxurrK5uYmoVCI0dHRyxIv6o9bv7l8EGRZFn9zNZtorS9+Ec5pbAIgyyitFkq5jBWN8qTuO4kaZe44/nsgSdcJWHvBdam+7W1I//ZvSK0WSqmEvCUecAIBnLU1urffTvu5zyX94hdfUqFF9dWvRrnrLrTZWRxdpz85iRSPew7AjoNUraLl88idDtaHPkR9aAj3KU/BKRQIzM9jd7s78ybP+V5KoYAVjeLGYsSvFDHnMkBNp0m+8IVwCVxz5WAQfvqncRoNlDNn0GZnsYaHIRajNzVFYHkZR1HQ22227wAdTfP+6brn8rTtvEvxOK6qIlUqWF/4AhySgBWcnqb96EdDp0PwvvsE4Up2HGxVxdQ0FNNEMU1xLLJtE15YwKhUUHs9L4ZxbAx3dJTQa1+LcoUcL9p33omyvIxaLmMMDMBerkGyjDU+TuDUKajV6N522zVBwGp+8pNYr389SqWCXKsJgR2ShCNJaKdPY/zu72K++MUknvOcq324ZzE4iBsKefP8PrUn/xEVsRp0lQg1fWDX1+2F7dOcv7aoVqsMDAzs+7zs9/v0ej0Rv+03cvv9viAUbCcxyLIsGtC+I6xPwlpbWxOk+70apZOTk/R6PeG45btU+s3006dPi+b0yZMnmZ6e3iGk0HWdoaEhQSgPBoMUi8VDxSodBo1GQ5AkxsbGAI8cv7q6KohV3W0P52g0Ktxfu90ukUiEdruNruuiUd5oNAiHw/u6KXe7XeHSY9v2gQQRy7JEFKBPBvPXutvXSruhVqvR7XaxLEusDf2/Oxe+K0gqlWJgYIDjx4/ve1wXA0VRGB0dxbIsVldXWVhYIJ1OiygpVVXpdrvMzc2J6PeBgYFd9z6NRkO4X+VyORFh6I+d/dDpdMSYu5xOTYfFxsYG9XqdcrnM+Pj4eUSYUCjE2NgYpVJJiC3K5fJ55CMffpxcu91meXlZkEDOhWVZrKysCPdkP/691WrtIF+BNy+Mjo6SyWSo1WpomkahUCCbzZJOpzl69OgFx5NdDviEjnK5jGma+15f27ZpNpuMjIzsSj5KJpMkk0lardaOuMp+v08oFMIwDJLJJBMTE0II0ul0hNBLURQURRGuUj4Z6VqAL0i7HLAsSzgrr62t4TgO8XhcuLKVy2UKhQIDAwPMzs6KseoLanwyWygUEiTQWCxGJpMRTsn9fp9YLMbY2Bi6rqPr+r7fx3VdQUb0CV2tVks4b2+vswUCAXRdF85YmUyGTqeDJEmC7NnpdPbcB4fDYVRVpVAo0Ov1RHSsj1AoxMDAACMjI/veL7ZtU6lUqNVqxGKxfec1TdOEw2AqldrX+c4wDEF08521fbK1/3wEGBwcFNdtcnLyUOSo/aBpGkePHuXo0aM7ft5oNHjwwQcpFApYlnVgfcEnDfqO9r6LYblc3tOl3o849l0jLcuiWq3uS071EY/HWV9fx7Zt2u32RZ+H67i2cZ2AdR3X8X2G1he/iPPGNyIvLaFVq5hb8VpuKASWhVypeKSHWg3XcaiEw6R/7/eu9mELNG+/HanVQms06B09unfkXTiMMTyMvrGBWy7T+s//JPa0p13Zg90OSSL9p39K5dWvBlX1bNrLZfStTauj61jZLG4mg33jjSTe8IYr7izmw11aQmo2scLhgyMMJAk7HkdutZBXVq7MAV4CRG69lcZLX4rzpjdhBoMohQJKu+0R+VwXZZvqz5UkHEXxCCW1GtarXkXlxAkSb3gD6kNUl/mw63XvvjMMzEOoCSRNw9Z1pF4Pd2vsfLfA6fVQ7rzTI5slEvuSneAcstmnP31ZyE7G/DyUy6jNJo4sQ7uNq+s46TTssll2k0nkzU0wDMzFxctKwGp+4hOY73gH8uIiUquFZFleYTsa9SJKnvUsEr/0S0iKgmvb9O6/H6tSQdY0tKkp9IdaTHRdzFJJuBxqQ0OCvCo/+tG43/qW1+Rpt71zpCg44+MYo6NQqSBtK/rpxSLG0aNIT3zipTglDxn9pSXsQgFXktDHxtCuJcKJ69L59rcxFxbANJHTaSKPf/yhI02+3yGpKrGnPx2e/nTMfB6rWERSVbSREZRLtIk91wFrN/zWb13cZ/R6PVZXV6nX60KlB16zYGNjQxSCLlck2nUcDktLSyIuZXh4+DynokQiQbfbFWSqYrFIKBSiWq2KeJFzEQqFmJiYYGFhgUqlIuzorwT8Aq7rugcWk8FrtimKIgqF4BUT94uruFiMjY2JCD6/YecX8NLptHCy6nQ61Go1TNMkl8uxsbEhjnW7er3b7VKpVNA0jU6nIxy4arWacEdaXV0VzcNUKnXJv5Mfw9JqtVhcXKTX64nGkV/I9YuiiqKwsbEhbO0f85jHkEqlhFOU7/jgq9v9SBS/qeQ4Do1GQ0QR+M4WfhzfYSz/U6kUCwsLOxS7B8GPhQAOHM8DAwPMz8/T7XYplUqXrBm6HX6MqOu6h4pN6fV6aJq2Y6xfaZjFIuZrXnO2u+5DknAmJnAAq91GXV1lov0gjiuxET3OPaPPwjDAtuG6mHYnqn/7t0j//u8oS0uojQZWOIy9RWSU220CZ85gRaPwnvdQ6XRIv/zll4SE1XvgAZSvfAV1YQEnFMI+cmRHg1vachAy4nH02Vnk1VXsd7+b0J//Of2PfAQrGkVbWMA4cmQHGQjw3JvW1lDbbYzpadwf/mGUq+iCcC56Z85gLizg2jZKOk340Y++oqK41G/8BuXlZVzXRVlZIbC4iK1p3n5PktAsy7untsQjZiSCHQphZTK4e4hzXEDN53E+/WnKL3858swM0Wc9C+2A+TTxJ39C/Rd/EVdR0Lb2S/bWuVC2YgddWcYGcd/Lto3S64kYRmdqivAf/RHBEycu5WnaF/1Pfxq5Xkeybdxsdl+PJUnTsOJxlEoF96tfvWSitYeK1pe+hP2//hfK8jJauYwZjXrnUte9fVelQmBxEbNWw/nLv6QRCl1153wf8vHjyI0GSqdD4N57ccJh7FgMN5Pxjn8LjgsBq03IabMROsK92R95yJ8Zj8cxTZONjQ0MwxDrrN2i1Hx3TcMwxFowFApRq9WwbVu4cG6H/zrfHSSbzQpXDMuyznNS2o5wOMyxY8cE2ch35PDFBK1Wi1AoRDabZXp6es94u2QySb1ep1arUSqVDowlPix8J1vXdQkGg3S7XVZXV6lUKtTrdSKRCCMjI0LMUKvVmJ2dZXV1lZGREcLhMMFgkGAwSCQSIRaLkc/n942x812votGocHDZizTe6XTI5/NUq1UsyxKR175DSi6XQ9M0sabeDdtJd/663F+rbkev1xMR5evr61QqFSRJYmBggEQicVlclgcHB4XrULVapVKpUCwWkSSJzc1N8Trf3Wi3tWyv1xOuTT5Ry48yr9Vq+4qWHMcR0Xl+xNTVhGEY1Go1yuUyyWRyXxeibDZLvV6nWq0KAsteGB0dFeSi9fV1ms0myWSSYDAohC61Wo1AIMDY2BiDg4PifguFQnuuv4PBIENDQ+RyOc6cOSPIRXvFjV5pDAwMUCgUCAQCBxI3K5WKENPsdy6j0ShTU1M4jkMgEODOO++kWq2i67qYOyVJEvOC70yYzWbFns53f3YcZ1/y5PcClpaWqNVqLC8vi1i97USjwcFBarUad999N5qmcebMGY4ePbpDAGcYhqjlpFIpBgcHRWyhYRjIsoyiKITDYeLxuHBtzGaz581zvquioig7XA/9awGct4f0iVb+M9K/Zn7cqf932127fKKsqqo0Gg0KhQKpVIpIJCLcpPwY2GazSaFQYGZmZs/6TKfTEdGqPll4P/gOa/1+Xzhbbocfo9loNHBdd4crei6XQ1EU6vX6Dlc0373tco7XcDgsrk2tVjuwjlqv1wkGg2iaRjqdptPpCNcxTdPIZDLnHW+73WZtbU2Q7/y632Hm/+2OW4d1Hb2O715cJ2Bdx3V8P8F1Mf72b1E2NlBrNY/AtFV0lAB0HXdoCCOTQZufR11cxP7AB+j/zM8QmJy8qofuw/nMZ1BLJexAQBz7nvDdmSoVjDvugKtJwAIkXSfzZ39G63Ofo3/77XD33VjbNsjO8eOoz3oWiac//bIWJo31dcz1dQC0oSH0cxZdkmkibRUhDwNXlj2nqH0KJtci4s98Ju2BAXr/9//injmD1WwiFwoEi0XPAct1sRSFfiaDk0wi9/vom5u4+Tx0OjT++38n8da3XlyR2z/HkoTkOBxGYy+5rvf677KuilWpgON4ZLNDNDSlLfUx/f5lIZu1PvtZzP/1vwgvL6OYJnq97qnQZRm3UvGK72NjO1S+ogjvuriXcbxX3/pWpH/7N5R8HrVSQbYs7z7bKsxb6+vYm5uUv/lNlIc/HPeOO5BLJa/ILcuYskzzllvQn/tcYk984qGaR06vR/PDH8b60IdQFhbEz91YDPdpTyP6vOcRe85zaL3znViRCMr6Ovb09NkxLMuQzXpj2HWRl5ZwAgGIxwk973mX50TtA9c0afjfZ3ZW/NwE7JtvRn/2s4n96I9etRhJ17Ko33Ybzgc/6JFXtyJQbE2jHo3iPuUpRH/plx46ke77EFouh3aQa+MFwrKgVjv4dceOPfTPaLfbnDlzhnq9LmzS/ca/39TwLcqnp6evq6OuEjqdDu12W6hy92rshEIhRkZGWN5q+EqSRDwe37foo6qqiCv0I0gupV3/XvDVh4FAQDQN9kOtViMej6Np2mVzZdsNQ0NDZLNZyuUy1WoVwzBERGQ2m0WSJGZnZ0VzxC9G+i5GPhRFIR6Pi/iGZrMpnAXOLZT6jbTLBb+Q7xO+xsbGmJycZGVlhXA4TLPZFLE8hmEwOTnJ9PQ0Q0NDzMzMiIZcPp+n1WpRKpUYHx8/L6ZGlmXRGFlcXCQSiaAoCo7jYNv2oa6jX8A1TXPf5uh2tNtt4WRxkGo9EAgQDAZptVq02+1Dvf+FIplMCve0g5po4DmH+c4JV3Ksb0frttuQttyR90Qkgjkzg3znIpn+Bo/duJ17Rp4pXLAO2ip/P6H91a8ivfvdKPPznov2sWM71vg2YHe7qEtLqHNzWB/5CI1HPvKSkDE6t93mESkMg/709J5rT0nTMIeH0VdWsGs1jAcfhBe8APttb4PFRYJnzmDGYjiJBMgyUreLuuUsbExN4Rw9SvzFL77o471ouC6Nj3wE6/3vRz592ltMuS6OqlJNpZCe9jRiv/ALFy1kOhQkifRrXkPtLW/B/uAHcTY3UdfWzorgVBXHsnCBfiKBdfy4cKQ+7yqVSmiFAprvNLGygvWBD+DGYrT/7d+wH/94Er//+6h77HPVVAr35puxv/EN3F4PMxBAtm2v7rLlxu0qircHDYcJ5PNIrosTDGL9+I8T+rmfI/LoR1/xvYu7JbBxdP1Q9QcnHEYrl5FME7vR2PN8XG64to3xpjehrK2hVir0p6bgnDW0k0jQa7cJLCxgrq5i//Vf4zz5yZ572lWCsbZG89WvRjl1CqXRQHIcAq0WVr+P0m5Dseg56Y+Pe7WLXp/R3ioNNUVfjXDP8NOZmYGXvATe9ja46y7vfcfHYS+TlH6/z8rKCtVqlWKxSL/f57777iOXyzEwMEAwGGRwcJBIJILjOCK+aHJyklarhWVZ4lm53WlyNzKQ71hl2zaAaDwexqEyGo1y4403UiwWKZVKwiXVb/zWajVuvPHGPdfoPpLJJBsbG+RyOdrt9oERfw8F+XyeRqNBvV4nl8udR6SKx+PChWljY4NEIkEmkyEUCjE4OCgaupubm7u6NbmuS6VSwTRNBgcHMU1TOLacCz9mrtlsUqlUdqy1wuGwcDjRdZ1EIkG5XN6VrO4312VZFk38WCwm9qa2bbOxsUGz2aTT6VAsFul2u0J44JNwjh49ellERT6RbXNzk3Q6LRzT0uk0xWKRarWK67q77qXb7Tbr6+tEo1Gi0SiDg4OC1JDNZsX6fLe/9Z16ZVkmkUgcuJ/aDtM0RUy6T1yIx+MX7fpUr9cxDINut7une8t2pFIp8vk8mqYRjUaFG96541bXdY4fP86ZM2cIBoOCtOXfz6FQiFwuRyKRYHBwUDhsGYZxqGsuSZIgdh52z3El4MepDg8Ps7S0JKIKt9+X22NI/ejFw7hBDQ4OMjc3h6Ioglxz7txp2zbValW4NmmaRrPZxDRNERn6vUy+8kVYm5ubxOPxXfdxkiShaRqhUIhTp04xNTVFJpPZcc/quk4ulyMWi7G8vEwgECAWi3Fii9QeiUQIBoPk83kx/+XzeVZWVohGowQCAVzXpdvtCveqRCJBPB4XtRtZlsW1sCxrBxnHtm10Xd8R+eu6rnBB8wmMvV4P0zRFjKAfT7i2tiYIy8lkkrGxMWRZFqTXUqnE2NgYruty4sSJXYlAPmnZdd1DiYz89/BrBz5c12VpaUmQr6rVqhCRKooiXN6i0SjHjh27rIK93eDX15rNJqurq/umCviEVX/uCgQCTExMiHl5dXWVWq1GMplE13Vs26Zer9PtdkmlUuRyOZLJJMWt6ATTNA/8vj6J0hdEXsf3Nq4TsK7jOr6P0P7a15BXV1FLJcxcbk8Ck6RpmJOTBE+fxm63ad92G4GXvvQKH+3ukCsV5H4f+zCNKUnCDoWQej2ka8UtSJKIPvnJRJ/8ZE/ZvLYGjoM6OHgeEeqSwnVp3HEH5gc+gHLffeLHJtA8cQLtWc8i9pM/iaSquPE4rqZ5rkBbZJ/9IPd6uJqGewGFC9uG++/3/vcRj7hqHAgij3sckcc9ju5999H+4AfR/uEfMKJR1H6f1tQU0rbCsAP0R0dRVlfRFxYwZJnaX/wFmVe/+iF/vppM4kaj2KEQcrOJfZATQbeLbBi44TDSdxkxQ5AKJWn/ZtJ2+OPvEBsDu9nELBQ895vBwX0d5Oq33477l3+JtrSE2u0iuy6ubeNKEopp4hoGkmUhz81hTE2Bv3Futz1ioqYhXyaHkvptt3nkq/l55F4PM5v1FNiadjaipFhEeeABOHUKOxwGXUet1ZC2NkR2KATFIva3vkXlJ36C1Mtfvm/BvL+wQPsP/1A8H9RaDWmL9GVHIjj5PJ0PfADpxS/G+fEfh24X7cwZmJvDGhryniX+TdzpIG9uonY6GEePYj/ykYRuuumynKu9YFWrNF72MuQHHvC+T6WCvFXAsQMBpFIJ++67KX/iE6T+9E+veJHd6XapvvzlyN/4BkqxiFqtiuNzJQkrHscplWh//vNYr30t4VtuuaLHdx1nUS4f/Jpw+KE/w0zTZHZ2lnK5LJR42yO4TNMUKj2/CHnDDTdcNULA9zNKpZJoMh3kiuQ3cvwYtsOqC31L+VardUUIWMFgkFgsRjqdJp/PE4/H9ywW+UWm4eHhA2NpLgdUVSWXy5HbhWTpN5XAa2j1+32hMN2uKPSLmOFwWJCwAPF6ONssVBTlssZdBINBxsfHcV2X9fV1FhYWiEQiwhmr1+vhOI5wJcjlcvzAD/wA2WxWHJcft+DHA+7XQAwGgySTSarVqnCD8s/JQfBfI8vyoc+Jr+49bLFVVdXzCruH/ZxyubyjcZVIJM5T1fpNtEwmw+rqqmiw7YZisYhpmiSTyQtqol1KuLaN+7GPoRwiA1dSFGrhHKnWBun+BhOVb7Cc+YHrBKxz0PuP/0CpVFC6XfonTuzuoh0KYU5PEzh9GqlUwnzf++BSuOF8/evI1SpmLHawu3Qigbu+jlytYn3pS2Te+EZqrov9z/+M3Wggl0poGxseMUdVPaembBb7yBHif/7nV43s4sM1DCqvehXyF7+IUi6jlsvIhiHiya1EAmdjg8anP0349a8neBljqXxIikLyv/5XyuUy2rveJaLnZMdBchwsVUVxXYKNBr2lJewjR86zj5PW1tCLRWTDQDVNHEXx3KlKJZT1daxIBJpNGrOzxP7mb3Z1w3K6XZQ778QNBukODOAGAh6hxnFAlrHDYZxsFiIRz60vkfDiJycn0R79aCK33nrZz9WuUFVvoXtIhb5PKAMEme1yw7LgPe+B2VmYnoaf+znofu6zyIUCWqmEMTx8HvlKHG8kgjE6ir66Sr9Wo/mxj121KML+8jLt3/5tlKUltLU1IcJyXBfVNGFr7GFZ2J0OdiRCuFqnLKXYDB3hM2P/L91ABlWFiQl49athYwOqVZiZgfl5eNe7zv/cU6dOCRcL38VJVVVOnz7N4uIiuVxuBzFAkiQmJiZIJpPE43FWVlbE89t3R1JVlU6nQ+IcsaJP4PGf0c1mE03TDk3u1zSNkZERhoeH6fV6WJZFqVRC0zSWlpYOJF+Btybym96XimwfDAbRdR1VVQXxyG/Y7uZi5UdNDQ8PiwgiQLgvTU5OUqvV+Pa3v83s7OwOEpfv1OQ4DkNDQ6iqKgj35+5RarUai4uLIo4ukUgwOTmJpmlYlkW9Xmd9fZ1Go0Eul6PX6xGNRllfX2diYmJHg15RFBGxZRiGiC+Mx+M4jsPy8rIQFFmWJSLVdF2nUCiQz+fJ5XJYlsXx48cvCwnLj3frdDoiCkxRFKrVqviuCwsLwgHXdV1arRb9fl800sfHx8WxjYyMiPistbU1ms0mqVSKUCgkHJ98V7Hx8XESicShxqBt2ywvL1OtVul2u7Tbbc+pccs5aXV1lVwudyjy1G7wHeV8wcp+aLfbFItFFhYWME0T27YF2SQajQpygY9gMCiIkP7v/UhLn5Dix4wBO4gmh8HVjP7eD1NTU8KVaH19XbgV+/dSs9lEkiRxD58bibYXfPdr39XIj2tUVRVd10W0m6ZpZLNZYrEYkUiEYrEoxDTf6y7ppVKJbrcrXKP3gk9S850IffHYuQiHw2QyGeEQ1+12CYVCJJNJYrEYm5ubJBIJMef580mtVhP3VC6Xo1KpEI/HSSQS4tkWDAaFi5bvzAiIOdEn2vrH4TuRVyoVEV3YbDZ3PJv8OWxtbY2jR49y4sSJHTWRcDgsiKarq6uoqsra2hpTU1PnfXe/xuHH5x2E7dF824lCS0tL5PN5lpeXBSFsaGgISZIEIaxSqYg62MmTJ6+4q/Tg4KC4RsvLywwPDwvCoo9Wq8XGxgaRSIREIiHOq6ZpHD9+nNnZWeGC2Gg0hNNiNBpleHiYUCjE1NSUEHkFg8FDuYxvj7v9Xr9/r+M6Aes6ruP7Cv1PfAK5VkNyHJxMZn/78GAQKxxGLpVwPvtZuEYIWBcKCc8m3r1aDJ99oA0MoF2Bwr7T6VB95StRvvlN1HLZKz5v2d47uo5UKODcfz+Vj36U5BvegPqEJ+B+/etI+Txus4m0j9uG2++jttv0jx6Fxz3uUMdjWfCa15xV4508Ca973dWNyQjddBP9r38dN5tFKZfpzswg7eJsJckyzvi4l/O8sQFf+AJWpYJ6DhnHNQyv4HjQuJMk3Kc8BSefJzA3h93twj7EIblY9Ag3kQiRZz7zIX3XqwU1ncaNRLDDYZRGA/sgO+lOB9myIBJB2at57bq0vvAF+rffjvKtbwliV0fTcJ/wBILPfe555JX2nXfi/tVfIS8uorZaWMEgjmkiAXYwiO26SKaJ2u9juy760hLG8eOg6yilElY8jhuLEXnsYy/+pJz7dUwT5+1vR1lbQ+71MGZmkAIBMVeLiJJwmPC99yJbFlKjQS+ZxBgagnAY17aR63X0zU2cSgXLtqkqihejsguM9XXaL3kJ8sIC+soKrqpiJZMillap1dAenKUVzmK95i+J/vHvYP3ADwCgrKwQXFjA1nVcTUOyLJReDzscpj89jXPyJMnXvOaSn6f94HS71H/v91DuuQdtYQEkCSuV8giirovUbKJubkKxCKZJ9Y//mMwb33jlWKCuS/VVr0L56lfR5ucBsLJZnGTSO4ZOB7VcJjA3h9Ht0n/lK1H+7u8I7LJ5vlww1tYwzpzxGkWpFKFbbrlokpprWbQ//3nM+XkwDORUitCTnnTNO3wdJn7wEY946O9fKBRotVpsbm7uGmmnaRqDg4OEw2FWVlYIhULk83kmt7mSuq5Lv9/HsixR9LmcxJHvV/R6PVGcO0zxyFdgG4ZxKBtyVVVFXN6VtCEfGhqi2WzSarVYWloSBXN/DFmWJWJEcrkckUjkmomD8OG7VQUCAeE4FgqFUBRFxAlomka32xXNsX6/LwqezWaTdDpNJBKh3W4TCATQNO2yk+D8wpwsyyIasbIlGPEjdPxIg2PHjp037trt9gVFCCSTSRYWFoRzQSgUotFoHFj0azabKIqCruuHJn/6RdrDqtdN0xTX7DDw1ajlcpl+v0+73RYEu2g0KqJMthOohoeHqdfrZLNZVldXSaVSpFIpQXjtdDpUKhVxPqPR6KHiFi8HjNVV5GoVpdk8lHChG06Rba4StpqMNe4TBKzr8GCsr3v74EoFK5ncnXy1BUnTMNNp1EoF99QpeqdPXzRJSG42kQwD5zCMOEnC0XUwDM+uRpJIvvCF9H7oh+jcdhvuZz+Ls+3i2seOoT7zmaSf/vR9v9cVgetSefWrUT73ObS5ObBtrGgUWVVROh1k0yS4sYG0sUF3bY3Ob/828j/902VZC7qu4DUhSVB9wxtQ7rgDpV5HtizMgQGcWAxkGaVQQC2VUGwbbct1zp6ZObs3KJXQSyWUXg/ZtrEVBSscpn/8OITDWJ0Ocj6PPjeHYds0//APSf3DP5wnfjHzeSTDQO52MYeHIZ1m36d9JIKrKNBuY6+uXvJzdFjIo6O4W+7HZrd7fgzmua9vNHBCIZxkEuUKkMkB/vEf4cMf9v7/N78JrRb8zOmPomw9U910et/aJ8kk7sYGcrmMdccdcJEELLNYxK5UQFHQR0YOFS3v2jbtV7wCeXGRwNISRjqNMzOD1G6jr6wgmyaSYaBaFoF2G6fXw+h2aQ9NsVoZ5QsjP8c3x38O2MnzHB72/oFXd9N1djwfXNelVCrtEIL0+30efPBB4YqzsLAgmtQnT54kl8sRjUaZnp5mdXWVcDjM+vq6IAP4BP9Go0EsFhNrSsdx6HQ6pNNpAoEAjuMIx0mfEHNY+E45cDYi2/+Mg/ZBD4VYfhB8t6BEIsHi4qKIlNrNAcePh04kEiI+0I/RHhsbQ5IkHvnIR7K5ucnRo0cpFovUajWq1Srg7RlisRixWExEk8ViMeEM68N1XZaXl4VDydTU1I51nO8Wk0qlWF5e9kiqmsbQ0BCrq6ssLi4KBxlFUQiFQsLlKpfLkclkRDTXxsYGjUaDzc1NgsEgAwMD4nUjIyOCrODHAc7NzfGwhz3ssgk6zh1Pg4ODrK2toSgKvV6PWq0mhBjRaJTx8XECgQDj4+M7zmEwGGRmZgbwyIWVSoWlpSXxe5/45hNopqenD/xOlmXx4IMPihjNTqdDIBAQ62ZfFGMYBv1+f1cSxUHYTnraL5quUqmQz+fJ5/PCvcWPxNN1nVQqRbPZZGhoaMdeQ1EUERnY7XYxTVPUIc7d9/r7B18ssx9s26bT6Qjn2gvB5Y7gU1WVEydOMD8/TygUotVq0Wg06PV6yLLM0NCQcL2enp6+oOP33QZHRkYwTZPV1VVKpRLJZBJVVclkMgQCAeGO1ul06HQ6wpXtaglGrhTa7TbtdlsQOneDH0XbarXIZrOidrMXUqmUcJ+rVquCgOWLlkqlElNTU8KlvNlsCnKgJEn0+31uuOEG8SyrVCpks1lUVSUejwvndF/gtj1e0q/5RSIRQQSuVqvC9cp31JJlGcMwWFpaYn5+foeYbDcMDAzQ7/cpFouEw2HGxsbOqx/4++1oNEqtVjvQAdInFem6Ll7bbDYplUqsrKwgyzLT09M79vDbz6FPCPNJvVcS4XCYiYkJXNelUCiwtraGqqqixtPpdIToKpfLMTY2tuOZHQgEuOGGG4Q76Hb3SE3TGBgYIJvNijkvHo+LdZPvlrUbfBL15OQk6XT6ugPW9wGuE7Cu4zq+j+CWy0j9PnYwiCTLuI0GSr3uVackCScU8ooT/uY1EkFptZBrNS/a6iE8FGwb/v3f4XOfg2QSfuu34KGmGbbb0AgPEQ+cQW/WMQ9wZ3IdB6XTwUgk4EosSLdcdC5nfOCFwrUsqq94xdlmv+NgpVJYw8NejF2rhVosel1mw6D2e79H8g1voPG2t2ElEmjr65ih0O7qRcdBXVnBDgYhGiV8yJixe+89S74COHUKvvAFePKT9/87x4EvfckbB8EgfOxj0OnAs54FP/Zjhz8nu8J1cT760bPxlvtFPEkSTi7nEababVof+ADJF7yA9p130rv9duRvfAOp1/NeNzqK/PSnE/vpn0bZw3448rzn0f3Qh7CiUbTFRYyjR5F22axJhQJarUZ/agr71lvR91F+XIuQVBX3R38Up1A4HNmsUMDaIptFn/Ws834viIV33umRCisVT2ENOMEgdqmE8dnP0nv600m97GVi/ur967+ilEqojQbG9DRyterFSXS7OKaJreoYbgQFk2C/g6QoSMUibiCA2unQn5nB/dEfvSyuSa1PfhKpUkGtVjFGR3cdBwBKPo+rKCj9Pqau48TjsFUokvBiA/u5nBclu7CA9dGP0vmJn9jVSan5xjeiLC8TWF7GGBjAHRraMa9WA0OUOgaj1TnazSKd//n3TH3yn2n+y7/gfvzj2K2WF5FjWaAoWBMTEIngPOEJJF/+8j3H/eVC/V/+BeW++9Dn57FiMZyJiR3fx43HMYaGUBcWvDlR02h8/OPEf/Inr8jxtb7wBZSvfQ1tfh5H17GOHEGS5bNNgUQCO5HALpcJrK3R13Vab3kLgde//rIfW/Xf/x37zW8mMDeHZFlIsowVi1GbmICnP53oL/zCBc87rmFQ++d/xv3wh5HLZaROx3tW6jqdt76V5mMeQ/hXf/WKu6QdFruZjzziEd7z6IEHvEbGb/zGQ3tvx3EolUpUq1XC4fC+atloNEo8HqdarRKLxYS1eKlUolgsYmzNfeAVRDOZjIgMuZbg26r7yrFQKEQmk7niariHgoMK2OfC/05+lORu6vft8B0HLrfz0rmIx+PCialUKpHP5ykUCqKh0+v1UFWV4eFh0Vg4DKHsSsJvoPkqzmAwSCgUIh6PU6vVRKGtVquJ+Bz/OvpKVj8molgsMjAwIJpalxuDg4MkEglKpRLRaHSHEjUejwtC3G7jzneZcl13zwLfdmy/bn4BdXNzUxSLd4MfcZNIJAiFQoeK0gBEJM36+jq5XG7fwmK326Xf7xOPxw8VsWrbNqdPnxaxJ+12G1VVBeEOPLJZr9ej1+sxPj4OeAXn6elpwGsG+YQ3RVHE/R2NRpmamiKRSHDs2LGrRmZ1Wi0AJNtm5aabMLcK7Zqu70oikBUFW1KQXQvd9poN3a6355qbg4c9zHM/uRywbS+F/hp73OyA8eCDnqig08EcGUECTEtidTVAr6sQjtiMjPTRVK+x4qTTyIUC9Pv077vvoglYrqZ5TKBDOrxJPnNo230dPH6c4CtegfO7v0ttdgNMg9hY+kAxl2V5b3UlhnLry19G+fznUefncRQFV9fR63XPVXgrhhC8eSVcKGB++cvUf+d3GPiP/7iwD3Jd2l/7Gv3PfAa3VgNVRZ6YIPrTP402NMS998Kf/7nn+nPrrfAbj/sSgY9+FHV+HlwXw2ehbMHaanTrjQZ6rwf1OvZmHic3jCw5aIUCcr+P7DsNBwKeG7vfAAuHcaamsFZX0ZaXMSMR2p/7HNGnPGXHYTum5Z0CF1xJ3p8QBN7+xZ/7L2Mk7kGIPOtZdN79buxQCKVY9PZWW7C7JuZGFd3soikmOA5ao0H35EmkH/uxXWt1tg1ra97pu1R8bp985eMjH4GfieeRu12sSMQTMe0HScKORpG6XaR8/iEdg2tZtD7xCYz3vx/l1Cnx866u4zzhCYR/5mcIPfzhe/59+/OfR15aQl9ZwchmcbfmKpJJjEAAuVhErddxLMsTU265hNcf8yTeufwCVtJnHdIuZJnmui6bm5sMDQ0Ri8UolUrUajWxZvCjmuv1Op1OB4Djx4+L57UfEe07dGSzWVKpFO12m2azSaFQYHBwEFmWaW092/w119raGuA1wi+GQOA35VVVpdFokN7HqdyyLPL5PK7riqj3S4WBgQFarRanT5+mXq+jadp56x8/wtF30EilUiK6zCc4gbePeNzjHkc4HBYOZfF4nGg0Kr6roihYloWu68IVbPt6zo+yKpfLDA8P77muDQQCDA8Ps7q6yszMDLquMzk5SblcplQqsbm5KdbZnU6HgYEBNE0jlUqRyWSEk1alUhFxlYVCQRC8ksmkiCG0LEuIAXwB0pXC6OgoAwMDlEolEd/of/9sNksmk9l1vRqNRrnhhhvY2NggFouJeDxJkgS5aGBgQIzzgzA3N0e1WmV5eZlIJMKRI0d2XJtWq0WhUBBEL//6XAh8IqQsyzSbzV3X1/V6nc3NTQqFgoh2Gxsb44YbbhC/L5VKIuJTVdXzot8kSTqQOJnNZmk0GpTLZdrt9r4Cl1qtJlzADiOCaDQaIhLV399FIhEGBgZIpVKXnJClaRonTpwQ5Jpmsylc1vzPPcxe5lwEAgFGR0dpNpvCmTmfz6OqKqlUSpA1/Qi8YrFIp9NhfHxckD6+l+HHn+63n/T30H4krr+32wu+I/T2uEtJkhgeHqbf79Pr9VhcXCSbzTI8PMzIyIggCFarVWRZZmJignq9TjAYZH19HUmSSKfTpNNp4XDkk35s22ZkZESQbovFIpubmwQCAVELcByHmZmZHXt2SZJotVpomiZiFsvl8p578kwmw+LiIv1+n0qlcp4Tk18rbDabLC8vCyLfbjAMg2q1Si6XIx6Pi3mqUCgIZ8BzyVfb4RPhSqUS4XCY0dHRy0o2cl2Xer1Oq9US4yUajXL06FERSViv1+lvGVL4joWBQICxsbFdRYayLJPJZMhkMoIs54v7zsXQ0BCNRkMIG33Hre3H56+LxsfHicViBzplXcf3Bq79avd1XMd1XHLIvR7SqVMoWzFbuF49zFUV2NzESiaxR0eFe9SO5rVt0/7c5+h/+MNIa2sUNyVmS4OsjD+ex77yx3nkE3cu/D71KXj3u73/v7np2WC/7W0XbjZy6pTnkjQ5/0yeWV/khn75QHcmyc8OSqUIXKbmut1s0vrQh7A++lGUlRWvsRwK4f7AD5x135EkTNOz+/7GN2BoCH7zN+FSiKrtep3WRz6CvbwMto2UTBJ66lMJbmVYNz70IeQvf5nAAw/gyjJmIuFdbEXxVJWxGGYuh7y05JERVJXmhz4Ez38+9j/9E/LcHPrsLObgIG4q5RWvXBfqddRCAcmy6B2ZofOYHyE6OX2oY/7CF87/2V/8xcEErNe9Dr72tfN//jd/A4ODF+dC4nS7yPk8cquFnUgcPEAjERxNQ2o2MR94gPKLX4zy7W+j1GoohQJKu41imh4J68Mfpvm7f8DXb34hxee9lJ/+5fSOSJDAkSN0fvEXsf/1X2FhgcDp01jxOHYyCYqC1Ot58Q39Pv2xMdzxcWK/+7sP/cteRZxHNjtyBOncQpDrIm1uojabXoTd4x/PZjfDR/6P1zx4znMgkzCp/v7vo3zzm2gLC0i2jZVIYGaz4LrIzSb68jJOMIj5wQ9StSzSf/RHGGtrKHfdhVIqUdcHqDeTJGIB4rUaluN4rld9CVeSMSQdxTFReiaBjQ1cTaM/Ooo7PEzsl37pspwf43OfQ61WvS+6xybaNU20RgPZsnC2ZN1Ko3GeglrSNKwjR9BPnYJ6nd773nceAau/vIzyrW+hbmxgJpO4uxR2SiWNrhJgPXiE8e4ZFtcGaX3ow6R///ex/8t/ofmBD2B961seGzIUQj55kuhzn4u2S0zU5YZrWbgf/zhKoYCjaTvIV44rgQuy7CIpCq3hGaJz90OpjHn77XCFCFj9229HqVS8MTs1RbevUauqKIpLNmuiKFtFgkwGs91Gyefh61/HLBR2jTO5FLBKJWrPfS6Rb38bud9HsSyv+YdH3LZXV+mfOkXrk58k+Gd/RvgxjznU+9qtFrWXvhTlnntQ8nnUet2Lydxy87DDYahU6N19N5XfeiWVmacwPu6RtX10OrC66s3xh3Dzv+TYzQHrppvg53/+bELquZiZmaHX64n/DgaDzM7Onve6druNaZrU63VGDuH+4KuTDcNgY2NDxG5Vq1Vhhy3LMpFIhGazSbFYZGJi4ppwK6pWq6yvr4uIBb9oHQ6HWVtbI51O76rQu5agaRq6rlOv1w9FxPLVw6FQSER+7IdarSaK+IcluVwqDA4OomkagUCATCZDo9EQ1yibzRKNRolEIkxOTh5IJLsaCIVCwnGs1+sRDAbJZrNCkV0ul7EsSxQsbdtG0zRqtZpQLPu/03WdZDJ5RYthftF9ZGRkR/TPQU2c7c4NfgNuP/hNPVmWRZOoWq2ysrLC2NjYeYVE3+XAcRxB6jxsI8O3/PcLzOc2bHy4rks+nxdNhcM0W+bn56lUKiwvLxMMBpmYmBCFY9d1aTQaIv4HvPPrX894PM6JEydYX18nHo/T6/VE0T0YDBIIBEin04yOjl7W+chXJ/uNG1mWiUajDAwMEAqFkLc2Cq6ioJgm9tZ5lyVpV9KGgoXm9En38zx59R08ovRpqs/WuMd6OHcNPYN/TD6Cl/6edOBe60LguvDP/wwf/KCXUPbzPw/Pfe75rzMLBVrvex/O17+O1GyCruNOTRF69rMJ33rrJXUgNQxvr6jr8JjHnCUdOb3e2fi0reu6sR6g0/aaAK2mwuamzvhYX7zGRcK1HaStIr1lwX/8B3z1q56u69d+zdvTHwbO+DjO4iJqtXqwgMwwUHo9rJERpKmpHWsN14W3vzPIBz5wBNeFZz8bfvVXPQLcbrf/xz8O//qv3u+f/nT4lV/Z/3Tn8/DOd3rkvac9zRM9GQb88A8fjmDXv/12lGoV2TCwg0G0ZhO110NyHGxV9dycXBfZtpFsG63bJfLJT1L9p38i9au/uv+buy6de+6h/b73IX/yk56TFR5JEVnGjUSo/cu/czrzBP6m9xI6QW/t881vwlO++mEeVq16e+kTJynUIjQbCrruMjxsoCgS5sQE8uKiJ07r9VHn11naiBDTOwx226iG4cWEB4M4gQDWFrFTQJKwR0dR63WoVOjffrsgYNk2/N//C5/+QI6X3B9hoh3DWLaIRmQC+t4eWG6v5xHXAgGkfdZyxtoa7fe9D+euu5DabdxgEOnYMfSnPhVtZAQlFts3mrJU8ghLigLPfCacu1zRx8ZoPvrR0GgQWFigHwjgZjJIS2tQ7KA7DqprosgWumtgKwqBpSW6/f55i+VqFV77Wjh92vu8F70Ifuqndn6ebcP993t/+vCHHzxF7NlfvVA3Ud9t8JB/Z1nenygKWOUy9T/4A5RTp1DLZSjWwTCRFSAaxC4W6X/mM3Sf9zzSL37xrl+q//73C8cuZ2hIzPX1horRTxAdjOLGDYKlNQJyFb3TwdZ1HNOiGDu2470u9PEVDAaJRqMsLi6KmKVutysIJtujj31Sj08yiMVihMNhBgcH2djYQFVVEomEIETk83nhktFut0mlUsL9V9M0JiYmSKfTF+U4GYlECIfDpFIpKpUKyWTyvDWU73JZq9WEC2axWOTMmTNks1kGBgYuujE8MDBArVZjbGyMYrFIuVwmEomItbMfLaUoinA+9gU2e73fTTfdJGLKS6XSjj2mT2pKpVIMDw+fR9Ipl8s0Gg0URTmQFLLdiWtqaopMJkM4HBbrad9ZbHBwkHq9Trvdptvtiia8YRh0u13S6TSbm5uYpsnw8LBw5vIdvLrdLvl8XpADHvawhzE4OChIWpcbPlntMHvv7QgEAkxNTTE2Nka9XhdiIn/fdthjbzabNJtN1tbWiMfjuxKrfDc1P+JL13VyudwFiQISiYQgdvhR5duP0V9/VyoVQcAYGRkhk8mI/UA2myWZTLK0tMT6+jqKouwr2tgLmUyGtbU1kskk6+vrjI+P70oGbLVaYs8QDof33Q8bhsHc3Bztdpt6vX7eerperwvhxeWI9/KJc51OR4wHWZaFMOZCx3IqlRKxd/59PTo6KsiifjSd77q1ubnJkSNHGBsb4/jx49/z7ue+s/J20eG58M+5JEmYpomu6wfO6f612n69fBcp8GLp/fhUTdOE+CkajTI5OUkqleIRj3iEqPVtbm5SLpeJx+NiX95qteh0OmSzWer1OrZtc+rUKSzLEmPcd4/UdZ21tbUdTt6+47UfUSlJkohj3H4f+cfmE399gdNuGBwcpFQqkUqlWFlZIZfLibHnw4/mC4fDO6L5fMJtrVYjkUgcOB/4n+GTuQ5Tm/QJ0j65Udf1A+8pX0TY6/XodDo7iJG+M7aqqpTLZTGONE0T64/D3EPnxjCei+1C2Xw+L9y//KhJX4zqxzVOTU1dk7W167j0uHar3NdxHddxySENDSF1OgSaTWxV9VR8LtiOgoSL3LdwVQnNtpE7HS+6LxzGTGZ5/wcVql+6m0f85+uRCyVCnQoxGqg9hWlJJ7v+HZo/8y7WX/F0Rl7y66Ko8Ld/u/MYSiW4554LI8vU6/A//6fXiG0PPY0fWfkXyvIgAysr9I8e3d0GvdlE39zEHBzEGRpCHR2l8vrXY3ztbtrFHlpEJ/rIowSe/WwiP/ADD6n42/rP/8R8/euRGg3UUgm51UJyXVxNw97YwPjc5+g+8pEk/+zP+MAdMd77Xu/vlpagVoM3vvGCP1LAqlRovPnNSJ//PFKrhdRq0+vKSEEN6d3vpn3yJNpznoP9J39CeG4OxTCwdZ1AuewVKUsl7FAIa2gI4nGcqSnkBx9EKhSwP/Qh0v/+71TyeayPfQx5YwN9YwPW13HEuHGx4nHqAzN8ofZD/MeDf8TUy+AVr/AUd1/8onecT3zi+UW8hxKJcebM7uQrH296E/zLv1z4+/pwthamkuviHnJD6coyrmmifOxjSMEg2sICSruNtFUFlC0L2XGg14eWwa2fewsL3/wsf3fbH/KyTz1zx5BL/fqvU7Us7He/G7tWQymXCS4tec5cW5Fw1tQUzsgI4f/v/0M/RNTMpUK36xVCI5GL75EEjhyh80u/hP32t8PiIsHTpzHjcS9+TZah20WtVJAtC2NiAmdyEuP/+R1e8hKvAQDw+c/Dm37439Dvugttft6LDZyc3OHQ56TTGIYhXI6sO+6g9cQnYufz0Oth1SxWgiOYJY2ypHEkN0MiP4vjykg9m4jT3rKSclAtG1tS6YyP4x45QvB1r7t85KJqFfp97EBgz5MtV6s4loNiOZiKhuK6XnF+N+g6ZjyBvV6lc8dXuW2wy133hxgdhf/234APfhCp3UbpdjEnJs5r6lWrKt2OtxHpqHF6cphYt0jvg3fAi16IkkiQ/OVfhl/+5Ut3DvaAYXjPgFhs77jS9pe/LNTBxlYxy7VtmlXYKISxXJV43MZ1odlUyJgjpFfKqA88SH9x8bLH/Nn1ukd4q1SwEglqzQDra2dVt82mwszM2QnSyWYJnDmD3W7T/vjHvXN9iWEWizSf9jSiZ86gdbtek0xRcFTVa5I5DoppEi4U6H/ta/Rf/nLkv/1bgidP7v/Grkvtj/4IZes+dVWVbm6EtU6Ofl8lHmiRk9YJrKzQqPaZ/fW/5B03DpFP3MjrXueRnJaWPNJ2qeQ1Fn/7t+FJT7r472wYXm/lMM1En8e9HX7NYK/50Cca+dhrU21ZlijSHcZpx1c8ttttTp8+LRTrvurWLwo1Gg2WlpZIJBKiWL6fEvxyw2+8lMtlqtUqtm2j6zqO42CaJpFIRBCzjh8/fs25K/nwbeo3Nzf3VBL78ItlvvNOu92m1WrtafFumqawrfebSFcaqVSKZDJJvV4XFviSJAlV+NU4psMik8mwvr5OJBKhVCqJ+Di/qaHrOqurqzQaDWq1GuVymXQ6LQhlrVYLVVVJJpOMjIwwNDS0ryPd5YKvaj0swuEwiqIQDocPFSXYaDREpMjQ0JCIx1ldXWVubo5YLEY0GkWSJNFQUBSF8fFxUqnUBZHSotEosViMkZERlpeXcRxHzFM+er0e+Xwey7KEK8FBRdxWq0W9Xmd1dZVIJMLoOa6MkiSRSCSIRCIsLi5SKBTQdX0HeSwcDjMzM0O/3z9vrF/uGADbtllaWhJNyEajIRpGvgNbLBZjamICJ5vFjsdRarX9GcimyUjhAcJOG9n1iANpo4DdVHistsBNlS+wHp7hvW/5Y570pKELWss3m55rcSbjPZe34xOfgPe9z/v/luWRsW65xSMltVpw1zctrL9/MycXPko6UEOtVJBME2QZ5557ML7wBXpTU4T/5E8IzszQ6Xj7jWj0wvYbrusRh8BbM2wZqvDwh8Of/Zn3XnI87jkySRL0ehAI0GzuvM6NuoozauA6kJ9zGexozC8mUDaTPBrPefld7/Jeu7DgrU3+9/8+3LFqz3gG9gMPIBWL3lo/ncZxJcpllX5fJhq1SSa8tbySz2PrOkSjfC3xXP75F7z3+PmfhxMn4P3vP/u+73+/90+S4MYb4Q//8OxQWVyEt771LJfkfe/ziGPPeMbux2hZ8Ad/4B0eeEQzH//2b9653c/F3K7XUb7xDZRyGUdRUDsd1G4XV5Y9R+NtJ8oGHNNE73ZRu13MN76R/pOfTODIkfPf2HWpv+992B/4AMrXv05kdR3ZNFBsj/1i6TpOIIBkO7StJEHV4pfDS7zzpv+PRniUUL8M3/w2ql7GSiYpNcKUit481O9Ds+mt0XQ9xOSwRHR5FqXTx5UkBvvr6N0eMhauJGGGwziBAPXcNPnNBLgSAwMG0ahHbpVkGSuRhtACZQABAABJREFUQKlW4Z57cE0TSdP4whfgox8FtBhz8VuIG0UGu2sU88OMje9N9lHKZaxgEMJhwk996nm/d7pdaq97HfLnPofUbqNWq0iGgdTrIZsm8hvfiBGJYIxP0Rw+RuDZP0XuZ70BYNVqyMEgHSXFf//vElvGRnzyk/B//o93D99/vzdmbrgBYi99KfUHzlAtSiQXlwguLOO4MgHbRnG3yL2OAxIo2JiShHL77VTabdJ/8ifi+n/ykx75Crz7/e1vh6c+9eya3DDgT//UqxeCdx+/5jV7O7iVy15tYDd86dQYN7ejZO1N+gYUywFwIZMxCQTOOe+ui9zpYMdiWLEUhrE7qdHHHXfAO97hTSc//sQ2z/naS1AeuB9tcRHTVNh0hujJESTHYahfIra0jB0OwXveQwVI/87vnPee8v33o9TrWIkEPUOl1VIolzVsy5PFusubJI0SuBauZCM5DoFqlfSnP8xvKevcnf1RPnvkN7DV4AU7YMXjcVZWVmg0GuTzeUKhEENDQ2J/YlkW5XKZ2dlZ4vE49957Lz/4gz8o1h1HjhzBMAyxN/Eb0JqmEY1GKRaLwvG31+sxNDQk4rrS6TRHjhy5aPLN4OCgWCOsrKzsIFL75KVGo8HCwoJo7PprIl+4MjMzc1HNUEmSmJ6eFnHxc3NzgjAB3nrUj7xOJBKMjIwgSRLtdlu4FZ27BvHJKPF4nFwuR7/fF+uGYDBIJBIhl8vt6oDjR9iFw+FDnd9IJEK/38cwDLLZLNlsVsSU+3tK3yWtUChQLBZZX18XZLNGoyEcjkdGRoS7x9LSEq1WS5CP/DinVqtFPB6nXq+TTqc5evToNU8k8ePgHip88r1lWfuuqxVFYXBwkNXVVUFWuxBRlSRJDAwM0Ol0WFxcZH19neHhYXF+feJcrVbDcRyi0aggMW6HqqqMjIwIR51SqbSnqGK/7zIxMSEIIouLi8L1VlVVTNOkVqvRarWEc9V+UWWmafLggw9SrVZZW1vDdV0SiQThcFjMVYVCgaGhIWzb5sSJE5ec5FCv19nY2KDdbtPpdAQBKxQKEQgEGBgYYHh4+IKcs9PpNO12m+XlZeHGFgwG6XQ6tFotUV8yTZPR0VHGxsZ4zGMes6+j2PcK4vE4iURCOH/ttuf1xUuKolAoFMjlcvuem263i2VZhEKh82pxY2NjwkXOdza0bVs4rPmiobGxMWRZ5sSJE2Iv3Wg0aDabyLLMwMAA0WgUTdOoVCrC7dAwDDH393o9UcfTdZ14PI5lWULM55Oc/Mi6Wq1Gp9OhVqsxODgo6hvb649+3Ope91EwGOTI1prbd1srFArifPnnZns0n1/7Mk0T13XpdruHmgv99+z1evsS6Pz39h3+fYEU7B7358N1XRYXFykWixSLRSGWVFVV3Jd+hPLg4CDHL9LV+CD4zt+qqgrSnX+9fVJrIBBgZmbmqtZor+PK4joB6zqu4/sIbiiE1mwibTVULT1AywyD4qm9FNcmYPXROl0k20Y2TTrj43xGeQaff9M3eP6DryHYXWXA2MAFWkocV5KIWTWy/XVaaoL8a6s4+Q3GXvc/9qxIbm4enoB1113wP/7H2f821AifmPw1fmrhb0iF2wRmZ7ESCZxUClfXoddDqVTQmk3MdBpncBArEKD3ohdhVTq0FzvItoUpyTTuO0Pyi1+kfPQo0T/9UwL7VBV7PY9Y5O+Hm5/6FPZrXgMr68j5Cn0pSEPNIOsSkX6b6MoK7sYGdLvUXvIS/m3z70A522h/8EGvcPtQjCmM9XVav/M7yAsLKJubyM0WnV4QyVVQXQMrqKGtr6O8970EOh1Uf5Hj26q6Lq4kIZkmSq9Hf2wM0mmsTAatUMDd2KBz552kX/lK6tPTWO95D06hAI0GkmVhKYpH2kokeH/lZ/j01C/jSCpzc14h6v77oVDwPvKjH/WIZn7PsduFlZXdv5dp7m2X7he890Kt5v17qP2yYjtGqxIgaQXROj2aTRWja5M08+jdBtheTKcbDuFks7i6jmyaKM0mbreL2m6DbeOqKlLPQHYcHFXB1jQcA3THQLXrHG99G/Nbf8WnX9rhqf/7584egCSR+q3fovP4x9N973vhK1/B9qMaJAlncNCLMnzO83AjiT2dVy4W577vxz8Of/d3Z//7DW/wIrdaLY9ceddd3j0xMQFPeYpHjtB1r3j6D/8Ad94JuZxH9vE5Y6kXvYgqYP/Lv9ArN3E2agTKG+iqhat7ZLNeaoBGaorkn72Or5waZPs6vV5zKf/rHYwWCriyjDM1tXusgK5jHj1K4MEHPSXybbehPOIRGB0b2ZYw5a1Nlgtr1RThmRmMhQKS2UZxbWQcZNdGcnpYoRDmox9N/HWvO2+e+MpXvEJ1o+FFYb7whbCfk/32c9ztwmc+46lUn/QkvJPpu8ztAaNp4XZlFFfGsjUkyWsKbEe3q7C+odPrykSNHLn+KsuzCp97f4VGeJTVVY/M9FJzGbXV8mI3z9l0Fksahfy2yrMk0VSTRK06rBdwLeu8uNXemTM0/v29bHziQdrFPoGUzsSPHSXyM8/l7tbDqVY9R4ILNRa5915vHqnV4OhRePnLd3cdsFZWoNfznB0NA/XBB5H7Bk5HYxroyWGqxiAtLQ0StNQE2c4GrbqEvrx82QlYZj7vFfj7ffrpLNUVg7jVQML1SH5ugnZHIRLeiqgJhz23gF7Pm4MvNVyX+otfTGR2Fq3TwZUkjEhkB8PNdhwUw0C2LAKtFs53vkP7L/+S4Fvfuu9bt77wBZRvfQttYQE7FKaamWF1LbhlqwklM46bCZGbKiHft046uMGTlv6J99z8Rl7xCs9R473vPetAZRjeffbYxz70mCPXhX/6J69ZqaqeU8f0tBclePSo997n1uZ2iyC8VInGsiyLopxzCLW9/5pCoUAoFKLZbAr19Hb4ri7Ly8vk83kURSGRSFxWUsFe8N111tbWRGRFPB4Xx9LtdimXyywuLjIxMcHs7CwnT568IgroC0UymSQYDJJKpcjn80KleC4cx2F9fZ1QKCQKr77a3lcXbm8u+C4A/ntfaFH7UkKSJJLJ5FUhH10MfPVit9tlcXGRzc1NoabXNI1isUg8Hmd5eRnTNIWqP5fLEY1GGRoaIpPJiGt2JeNQLgaappFMJkmlUqyvr5NOp/d0wbIsSzRvotEowWCQ8fFxJElCVVXheFHYetZomiaao8lkkqNHj17wfXn06FFBbtrc3GR2dlaQxkzTpNfrCWc1v4h9EPyGnWVZ+94rqqoyODjI+vo6AwMDVKvV84qcgUDgit5vjuOI6MSNjQ263S7hcBhd1zFNk5WVFTRNY3h4GMMwGHra03BWV9EffBC5VvPECufCNNEefBCt3wLXRaePKWmYSgDVMRnuLuL0VglbDaKf+30e/OLrqaqjJJMeqWI7DMNbgvpLu7U1j9Cz1TfmWc/yXJ/A2xe/7W07/951PcJTqQSya/HM+/+Mm6pfRO0sIMs1lLiGEwiAaaKvruJubGBWqzT+64t53ejfcFfFc1K+9Vb4/d8/m+62H0olz0X5O985/3f33uuROB72MAjd8kgakQhWLOaRWmK7nEvggfvDRKM2kUaBthKnT5A3f/yxvObpHnFkO+bn4c1v9vY6qgoveAH80A/tfpzRn/xJ6m97G+bAAPraGoYss94apF73Nr71mkqpqJJob5LstKkOHkEafSRv+dBR8R7/+I977y9cF+67z3O7+u3f9n72qU+db+TzznfCj/7o7uuor3zlLPnqXFQq8Ed/5F3fvZbK5uamV0/q9cC2kbcco/YSldiqjiWZSKaNU21Se9dtaL/5+/hpjZGI5/pb+eM/RvniF1GXlghsFFAcBwkHBxlH9VwLJNelHB3HqJhMdE6juBbPf+BPePsj/pZUZ9UTGra7lIJjO/c222AYEivNQeK5CLmle+jJIUqBEYJ2m6yRR9cMivGjbBg5rM2z66mlpSDHjnXRt5ysnFAIrdXCdl3sRgM1k+Ed7zj7Od8a+ilm6t9ioL9BaHMVxgZ3PT9uo4FWqdAfHcV5xCPO26M4vR4bv/E7dL70APHiEhG3iaTLqJ2OV8ezLCRAbbWR83XKgTrt//wOyitfgT6SFDfYujHEwzvP5J7hp9MNZKhWPTLlZz6DIGU99anw3/7bGH8z9Nc89q4/4RGU0K0eAddz4rFRsCQNU9axUGioaQalGursLBZQGhmj8+MvIhxmx7nwzrv3Wb4R8be/fZZ8Bd59/M53emtvWfYOOxr1yIjFIrz0pR6Rbjd8WXkKA9ZdxNp5Nk8ZNBSvCVirq4wM99F1l7C/52q1kHt9FrWTvPe+ZzD7Qvit34If/EHvGD/3Oe/zn/AEj/T11reeTRStvfUdtKw5BooLWPE4p3vHMc2zY2TWSaNKfaabpwgsLMBtt3GH8qN8s/4whofhF38RAqqF1O/jmg4NJ8zS3PYNictoe5a4VSPgdFHds3t/yXII1SpM6vcTslrk2gu852GvQ9MujGzQ7/fpdDoUCgVBitoOVVXJ5XJUq1Xxb319nZmtbNtQKCRie6PRqHDnVVVVuPkcOXJExPz4jhoDAwMHOsQeFplMhnq9juM4rKysCLJYt9ulUCgwPz8v9lD++i8YDIrvMzIyguu6nDx58lCxzntBURRuvfVWEdfnH5sPP3bQF2UYhkGr1WJ8fHxPIr1PVun1euc5/VxKgcRu67xYLHaeE5F/TX1ygu/O45P2/di0TCbD6uoqtVpNxBjG43ECgYAgUfhxgL5L6/T09DW5D7xUaDQaNBqNQ+3NfVeyZrNJo9G4YFfrXC4niP6rq6vMzs6KSPG1tTWWlpaEY/bg4KDYH50L/1rXajUqlcpDWjtnMhkREdhqtUSNALxxF4/HmZqaIhKJMD09vadoCWBxcZFarcbKyoqIL92+tx4cHKRarbK5uQl4kY833XTTJRtXhUKBlZUVEWVumqaImAPPfcx3q5qZmTk0qXBkZIRGo8HIyAjr6+sEg0HS6TSBQEDc+z5Z8+TJk9xyyy1XtW5wMXAch2azKYgpuq7v6yTnu6hFo1FBcD33vPoinKWlJeF8vV80o+9Q6AtwzoUfS9poNIRzlSzLhMPh8wQ7oVCIm266iXq9LvaqfgReLBajXq+zsLDA+vq6IF/6DoGnTp1idnaWQCBAJBIRoiXfbckn9RUKBeFwZZom3W6XhYUFMdZ8kpjjOFSrVeLxOHNzc6TT6V3PQyqVQpZlQf5rNBr0ej1c1xWxl7tF8+0X63gQ9vvbRqPB/Py8cPjfHu8Zi8VotVrk83mOHj26Qwi5tra2I7LVr19IkoTjODQaDUqlEu12G9d10XX9std6stks6XSaarVKuVwWZDJ/nkgkEpds7XMd3x24TsC6juv4PoFr20if/zyu4+BKEi7QM/WzBR9J2moAK0SlFlq3i61p9HsSH6/8EP/P6Vcw2Fkia2yQD47T0LK4koxudXHdPEG7Q85YZ7CyQfcv7mft23eT+OOXgfv4i2KK7OZqdN/IM9DsPjdE/wqnVkEuVlDOLCHLDrIMVjRK/8gR3EQCU9NxvzOLurEITQvVTdKXg6iuSSS/htq2oV5n85d/mw894c184jsTDA15xdpf/VVPDerbpA8NwUteArlgGen1b8Ca24BCk6XgMdpq0vueW4WYTLrLiDGHcmaBUiXAEwL/xOdnfnPH92g0didgOY5XSFpY8JrBU1NnT6HT69F62cuQz5xBmV+mH4xRiZ+kqHifrzgGKaPAkaUHkVwHxbZxATMUwt1iN7mu+/+z995htt1l2f9n1d37zJ5ezpnTc1JJAgRSRKQrICKoINhABcTyqkhTrPgqiPwEAV8rFixIDRA6oYQQUkg5OWV623t272XV3x9r1jqzp5/kBIKe+7p2Tmb2mtXXd32f57mf+0bUdWRNw7QsfMvLdFQVYjHE1VXsrsbnP5jhq/8hcOjQS3np374Y6d6voN11F2atiRj0I192guzks/jcW3szuF/+cu+xrKzAJz/pJO5KJSdxW6lsf60XF51i+HZwOyV3w1e/6sjm74RczuleBocs5DoHLC/D7/yOzNNaT+JavcLAyhKdXJa4XkSwDWxbx3liBASlha9YxJQksCykbhdDt9ENEQUToaVj2hINMYyAgE8xaBsKmmAQNBsotsZk8yGm/+kf6bzqCvybqh/Bq68mePXVGOUy2vw8dreLlEjgP3KEuXmBN73ZIbAdOAC/+ZsOEaqv7/x9NDcHc3MCBw/abBIF2AJddxKHPp+jLvYf/+Gs+5pr4PWvd+7PjeQrcDqjg8HzCVkXDz3kfP7zP52u1S99Ce7870Umit8mYNT4+1tV0k86QOTG6/mxH5fI3/Tz/MV/3sRl5ic5KX0Fxa+hqDaHD7UoRib559KLuV96OsJbAmxofABgrHwPtTMlJswy2uDgzi2xODZ8ejyOVChS/tKDZKwTDLZUojjFIUtwnglNE3hoOoksJ7AjBhGthGJrqGabpF7APH6c4Otfv4V8tbrqWGO6+PSnHdWet751a+HIsuBjHztPJnz+8+ELXzhPMPnEJ+CtI2PYoRByPo+x3jVtWQL1uoQs24RCJvW6gpPCE5BtAx0FlPMVGRtYXvahaefHdmGd9SK5AxROQlsbN5Ata8s57HbFbQsUluCoJeqGwOkHdRq6zIkT4DeqVN72Nqxv3k1+WifYqBC2TaxFieriw6z9y9eYNa/iE0ffwD/FU7z97c49vB/YNvz1X58fN2ZnnXPounC6MZwgrJ/kZhO53YZs1lEG0y1CVgcLAdXqEjCbaN1VlgOHsXGOe2VZxc5b7JzmuUhYt7awuzrm2RzjdhYRC7CxEbEECXMxgn0ghbC5yrbhPZrNOkW2QsEpDFx77dbHIJt17ilRdMbF7Uhvha/fg/zNO53zBXR9ISwkJNtGENZPrChi+v2wro7lK5cx77+fzJ1nKYWPMDXljCWnTjmn/7rrnEKka7VoavCgdRy7vTXBWCwqJFNxyqpJUlvjQP1Bks15SqFJfu/34J57epdvNBxFhp0slFot5zTt1OD44IPnlSMMwxnzNkJR4AMf6LUH3s6C8GLYB4PTeeZ2NtZqtT1VsGq1GpZlebLf/f39OxJl/H4/w8PDLC8vk0qlKBaL3zVLtXa77SWezpw5Qz6fp9vtcvnll29J4AcCAUZHR8lmsywvL+P3+6lWq49LApAgCIyMjHg2G/Pz8ySTSeLxOLIsewme4rps2sTEhEeQm56eRhRFT8Z+o3z8xu7C4eHhx+Wxfz9gdHSUZrPJ+Pg4Kysrnu2jz+cjHA57djk33ngjqqp6nf1u0jeZTG5RaPp+gFtkqNVqLC4uMjw8vKV40ul0WFlZQVVVEomEZyEAznlLJpPk83mvM9dFPB73SJOPBIqicOzYMebn5wkEAl4Ht2VZ3hjl2kW6ZLC9UK1WqVarPUTOnRCJRJAkiXq97qkrfC/hKl8tLCwQDAaZmprqKfIahkGhUGBxcREA6aqrGEyl0AYGCGQydHQde3DwvOKrbSOfO4daqyHaFm0hyELgEBX/+aRyzjZJdlYZbU0DAt/5qf/Lv1z5LhAEXvhCJ9btdBwS0513Oo0sv/iLztziYx87T77qr5+l/ee3svaJb6C0KhQzFj9ZnOShvpt4YPA5tAIOM9l9Z14//y9cVv46483TmMicVS7j8IaG465pIq6toSwss7gQ4PrQ27j/mr/BlHx8+9uOXVswCNGoM4fZPKe2bYc8/ad/ujNpCOAtb3HIRroe5Jejz+Davjz+2Rm0ShXYvmhtVFrE9SLLgSkeSjyFtprg//yf7de/kZT1Z3/mNKlsjO0ty1HnqdcDXPOaNxH4v2+mUZfxPZwhLlYRlSSGIKNaXeL1IqJtshI4SNY8yD8V3gib5jM7EU027s/P/Zwzn/n4x7d+X6875Jbrr9/63eYYfjNqtfMkrI1z+FLJiX8q35H4gYyP0ZaNiI1iGjuSr2xboNNRkBExEdALOg++65v81Tc0TNF5Ji67DH6+9SekvnU7yswMcrWGYcsgGLSECAgCiqWhtjQs2cbfzLEWPIouqgy352jLYY7lvkDNP+DYF7YVCiX/rhnwTltEEgJooo+lwCE+P/ZK+lsLPDF/K0ktx1J7aNvjKZVkBgedbiHBMLDXJ+ViINDTmAYw1/ckZjNXolgdxltnkc9VMfr6zlvPdzqIhQJquYyeSlEJjPCh1i/TeBO8+MWOyhxA4U/+jPxnzzJYmqEjBlhTDnFQn3H2wbTRUcHGUacSbCbbZ7ERMZoySnEJPRgERUFpFvghJcdTMv/FJw++nrMDT+PWW3uP7/Ofdz5wgOqBX+JA40ESepGOEKAj+p0mNUSqSpKKmqYrBVkzDSZqp2ndW2Ju+jbe+9GXYUo+JLNLunYGv9HAlBTyoYN0Oucn1l/60tbrsnm+Ds5zHYvt/kycSf8AjcW/oyT10d9aohUKYog+bAtW1hWIw2GT8cEa6vIyNSVJoRvn1OAz6NQcguWJE07jj6us99GPOrkSl3wlGR2uyn8eu1VAD/npDkygT299Nxmijxn5GJc17qO22GLm3Z/izmMnAYd09id/IrNWDuGvBemK9Dz76fYiUaNC0GpgIdKSQpi2SJg6Gj5qcgIBi4nmw2DbPOvcO+g86807npef+ZmfQdM0zwa90WhQKpU8xY6dCtauGoc7V3dVHdz3WCgU4rLLLqNUKnk2QC58Pp9X0JUkCUVRLrrNryAIHDx40Fu/W6w9e/YsMzMzGIbhqWEKguBZIgaDQXRdZ3FxEUmSWFpaYmqnpOQ+EQgEGBkZQRAE5ufnPZVZWZZ7jtswDE/RMxAI7Kkm4iqy7BeufXqpVNqXLVqz2SQWi+1rLjwwMODNQbvdrtdwMjIy4s3RCoUC9XqdbDZLKBTyjm9lZcVTTIvFYlSrVe/8J5PJ7/l87bGEaZoYhrHv6+gq4xg7Kd7vAlEUPQKQqqqeUk6tViOXy3k2ka714G65gkAg4I0bjxT9/f2EQiGP6OmSRVy1GHcfdiNAdjodz4Jvoy3aRgiCQDKZ9GzAXJW1ixFjl8tlFhcXWVlZodlskkqlvGfbtm0ajYbXXGbbNpIk7Xs8UVWVI0eOcO7cOXw+H4VCgXPnznmKaaZpkkwmvWauRqNBoVC4YGLe9xKGYXgqid1u12vUURTFI3Sm0+kt5CpXEU7TNBYWFlhYWPDUpVx0Oh2azSaWZTE0NOSRgLdDoVCg0WgwMTFBMpnccTmX1LUfssxOjWzdbpdKpeI1B42NjeFbz/ValkUsFmNwcNDLJQ0ODhIOh3tUnl2L11Kp5FldulaF+XzeUydUFIVisYggCJ5a3ezsLAcPHtz23R6Lxbj88supVqsUCgVvXFYUxbO33Xwt3PeDz+ej3W7vSpYEPFKXqqo7vlsajQbT09OeilUgEPCUsQ3DoFKpMDs7S39/P5ZlceTIEcLhMLquk8vlyGQyHgF4Y37AVb8Kh8MsLi6SyWRQFIV0Ov2YN6eKokgqlep5r5umyZor23wJ/6twiYB1CZfwvwTNr3wFMZcDUUT3BzC6AgGzjSHo6KKChVNYV2wNwXTkwy1ZZlkY41DxDqJakX5tleXAFE0lAbZNX2eZlJZFsg1US0OwLRQMJN1Evv12jDcUeeHKLXzi2Bsx5PNZhE99Cp70JPjQhxwFmGuvdbrJNseDtg0zM9sfz31jP8oXnniY61b/jeX/vB9R1RCx6BswSY0qZI7+AKVzRQLf/jaj9UUKUpSsf9xL6gGs2SYT6iLRuSUq80FGp/8A4Zr3kc1KZLOOjd6GvAHZrJOAuW7hNp69rDNZK7MUOExL2ToZK1YCiP2H8efnEFdzXB35HN+YfEXPedgcV3z6044M+2aLvvFxh/j1pS9B7K7PcdPpBdSzK6wxTMEYBVPA9Q4zRRVDUNElH4Fuw6FdCAI6Kt3W+cmOLCv4/F2UTgdb15GzWYx1ckkhL3PnN0TODDtKXbmcxOWXP43PLjyN5WXn758Wgiv2WY/51391PnthZmYrAatcdooCbgFgN/zN3zikhN/+bScxtvF+areda+cWBj79aSeJOTzsJKirVacj9WjlTiZbpwl26gi2jYyJJYhYiAjYSF0TWwOfaGJKMh05jNG1sAQZ0dIRbGiJjs2CbYHWdV6zhiBjCDIWIjGjTEiv8Plf/zjKrx7n8GFHfWUj5EQCedME+W//9rx62NzcuoXcOn7+5x0i1XvfC6YpoqrwW79l8aQnbT1PnQ68+93w9a9vL7R0xx1OMnMnlZfN5KuNyGbhL37uXk6e+hCvqt2ParaRbQ0Lie5SkNInBvnQfz+L/9ZfjOU7wsqRX+eLxi8R7WRQrC5tJUZNGoL0+sXbJr5PtpaQ9A56V8CMJtg4bdYNgUpFQVUtIhETUbAxo3HM5TqrhsJnv5biGZUYAUElqpeo+HoDdsMQQFC83w+05ygrfUQDQXzrUrXlskMMkSTn3tyMU6ccEtqf/IlzD547B+9611blt3//996fFxZg/ikv4HD0M1iKgpjLoaXHmJ31O/sFKIpNxFQxBRHRMrER0MUQVizpTegaDfk8+QoIGA26oh8bgabam9T78OdG+CnFh6Jp2JaFIIo0WxLzc9snhPxmC11UmV+O8Rdv8YMAo4k6b26+FmX6NMaZLANGl4YcQRd8yLZGODdPSFQ54W+TuP/X+Kcr/oJPfjLldejX606X+27WEu644+JLX3JIgv/8z3DrrU4S/hWvgCsrFQKrGQTTQmm1sSQRXVQwbBnBdt5vPqtLxzYYb5+hJKcwBRlLkHj7+4f4zasdUl0i4RTRPv5xh7QzN+ck+QcHHbfFR9owU9LjyA8vobYNInYVQQDBdovdAoYgo5V05G4Z8+A4iCKCZUEggLieXDIMp/i1Xp/l1CmRTkflppucwk8u54wDG8lLt97qjIvHjjld429/u1NoffbDn+UlS1UEEzqCn7Z+nvSlyBaqej7RZ/p8KK0WgmWhz2T40Ku+xJcmtso3j43BO/6ohX3XvXSWGtSMAWxl5+BW64qU1TQpLYvfbHAk9xW+eWByC/nKxTvf6dhQPuEJzs+djjNevf3tDgnUxcc+dv4dcN99znebn7nN0HWnGP2CFzj/CsL2BKyLlePy+XxEo1ESiQS5XG7X5I9pmpTLZURR9BKVeyWnw+EwqqpSLpcpFAoXjYBlWRadTsfbD7/fjyAIGIbB3NwctVqNZrPJ2toai4uL5HI5z0LBtYXbfJxuh26tViOfzz9uSUipVMpLPJdKJc9CY+N1icVipFIp+vv7PVLJkSNHvAJMrVbzJOI3dhe6FmyX8MggyzJHjhxhZmbGIzXWajVarZaXiJ+amvIk9/+ndB26CVrbtslkMiwsLHiWNK61QbvdJhKJMDQ0tK29YjAYZGJigrGxMQzD8GT7L0ZiUpZlDh06RKfT8bpPN3YEb2clsBNs2/aKUJvJnNvBJdcZhvGIClcXE5qmUSqVyGQyBIPBbdW+ZFn2OtlXV1cJHjqE+du/jfgHf0DbtvEXCoilEkY4DKKI1Gjgq1bBttFkHxl5jIqvtxPeEiQKAafjZLA9T0uOMlR7iEzsJB//uKME+Td/c95urlx2YoQDBxwVXL9e5bmn/5RDtXuJdXMIM0v47QbDlkBa+DY35j9G69zv8pXBF/O1sZexmrgSyexy3don6GstYtoCc/4pbETq9RKKIiMrCrIkYQ0Ps1oMM9RYoCnHOJz7CqeHngE4DQou7rij1+pvZQV+6Zf2f+7dWP4f6y9l3P4ig+k6gaUFkoZNxZfGEpz3kWibRLUC6e4KJaWfmtrHXaMv2vd2bNuZZ7zmNefP5StesXGJGxjX384LzT8lHB4kruWJ6Q7pyhQUiuoANTVFLjDOh4//Ho3AI7M7f+Ure/MXm7HdvAZgD1cQwJmzv/71Tg6n1XKO8fd/34mj/Xo/V5YU0qZMwNKwEbElCd2Q0DRnLJElC9VnYpgigmWCDbqootgafrNJtJOlHHTsUvJ3nKZ05uvEWovopoRMED8tuoIfU3SumSkEMGyFgN5ClbqkO0ssBY8S00vEtDxPWPsUt079mmNVKCj4rRbdPdotjJrD6DEkH5XgCE01xWWVbzDQXcZnNunKW/++WFRotyUCfoORahUrEsYaGOC+hwP83tt6l7UFiY8efys/9tCbsQSJYW2F4HQOVV5Fli2wbbq+MMXEARr+Ad6rvJWF/HHIO80zr341/OAT8lQ//FUSlSW6op/lwCGmmg9imQaCbdIUQhiiDIKA32zjt9tYCIiY+C0Dw5KxLagoaXxmk4PaQ5SUNC+Y/nM+IoicS9+y4/k5mf8CuqjSlMIsBI8i4iirdwUfluBcZ8EGS5DJ+UYYac8R1stcufxRQmaVq/KfJ2jUEW0DGxFTlDHedw13FJ+PctW11Ot734cA7bZNtaqtW+FYgIAkiaiqz5tjmpKPL469gufN/X8orWkONE9RUtJU1T4MQUWyddRCAamygqb6mbUOc/v4T9JZz+3V6476t0u+AideXxdZAOBw4auEjAqhbpVZ8SCt6Z3ViAzRx1onjZStcFnka3zReA2aHGJuzrEYfXHjCq5W8vR3V8jboyCISLZOXC/gs9pYiLTFELaAQ100wZAUakqSsjrAcHua4fYsesnPmcoc0Nvt1O12aTabvO1tb/Pm7w8//DDT09OeffpuCiTNZtMrKJqm6dn4biRASJJEf38//f39HmHAJVd8NyAIApOTk/T395PP56lUKj02v+7++f1+DMMgl8t5hf1Wq0U2myUcDvcQyx4pxsbGaLVaXqOJpmkkEgnPht0lw7gxwNDQ0EUvCKdSKcrlMrlcbk/79Eajga7rxOPxfVvsufPr48ePo6oq09PTKIqCJEnYtk2lUqFarXrkGldJZmlpyVMSdhsE8vk8kiRtq8D2Pwku+c9VS9oLru3ZI7VmlCSJw4cPe8o8bgwgyzKKonjqdBdC7Hs0CAaDTE5Oes+HGxMEg8F9HWOhUKDVatHtdhlzO6p3QCKR8KwxC4XCRckvrKysUCwWabVaTE5OekQacK6ta+e+srLC0tISqqrSbDb3bRPo9/s5fvw4mUyGtbU1r4nINE2CwSCBQABZlmm1Wp49ZafT2ZeK8Pca7Xab06dPezF6rVbzFJFcokyz2aRcLnP48OEt743h4WG666zrtbU1lpeXvfvYfScFg0FOnjxJp9NBkiRmZmY8BUFBEHrs50dHR4nFYlvs7C828vk87Xaber3uKUG6cC0T3feCIAiUy2UCgQCGYXjnIJlMUq/XKRaL5PN5LMtCURTq9TqxWIx4PO4191mWxaFDh4hEIrTbbdbW1rx34Hbv4gtVQFcUhUgkQjwep1AokEqldn12Xfthl4i9GbZtMzc3R7FYpFAoMDo6uiXOj8fjNBoNlpeXEUWRubk5Tp48SaFQoNPp0Gg0OHDgwI7vUDfGXlxcpNvtflebUy/hEuASAesSLuF/DfSvfQ0jW8fUfCwqU4yIcxgoKLZGwGzj+gKZgkxHCiLbjh+cXuxyjXkbcS1HSwo75Cugr7NMn5YlYLWQbANDUOiKfgxbx2dr+NtNzAce4DJklFO/y3+d/CMs0Umwz83By19+ft+++EVHIelXfsX5eXraUTPaqxH6g3dezge5nMA1ZdK1s6hWG0MNEr3iBGcf1Hjt3T9NX3OJthRkJTC1ZYWWIFEOj1GrqwzqTvJ3ongX830Oa2Xb5KVtc03uM0Q7edpiaFvylYt83kdQHWK0PU3IqHIs9wUeHH6e9/2//ZuTQA6HIZNxVF62w+KiI68O8PJ7b6NUbpIwfBTCo9uepLiewzbBWpfkxwa7Y7GRqWIYIqKgIiqO1aTcbKIVypi2RKkepDF0Pui+6y7nsxFf/KLzuZh44AF4xjPO/6xpjv3k/Pz+19HpwNve5iS/n/nM87/fqDQETtL4H/+x928XUtcj2BamIBO2autJriC6oDoEN9tGtTUCVgvBtLEsEds0QZARbQvF1ugKvZ2+pnX+/w1BQbF1LCSS2hrVb3+Dv/rLJpoc4tnPhle9yjkH7TZcfXWvPYRh9Mrxb8b/+3+9P2safOQjwhYClm07ifLtrDo2Yifyw164LPMpnjv7V8S0PAkth99qYSMi4JzXvu4KxS+v8vz4KT5+4i0OYVAOUAof3Hvl65BsHQEL3RApFH0MDhlYtkC1KrG6skE1SIDjx1rUGiph2yG6lPyjZAOTJDsZUtoaNTXlFV42QzE7xPQSK4Ep5GueQiKRYGnJ6aYvlXbfx1OnHKLpgQOOGtZ+ihoAf/CvJ/iHI0fQy2V8S0uUW2EMfcgjWOq6QE1NMdhdQsTCEBQMUcaKpZCBQkFhbYNylWibxI0iWd84Z2PXosm9gf/Z1FM4feazXGGvYGSz6PE4y0tRL9EjiKK7aUTbIGqUWQkc5HTiSd59fs3X/4K2OYM/u0CdBAvhIz1EV9HS6euuMtY6B8ALTv8R/6a+gxe9SOB973PIMQCHDsGb3wwb823ZrJP43g4veMH5/2+34f97l8Gflb+E3ALVFtBtGd0XcEhqtjP4abaKamv4rTY2MGousOqfpOAfZsV3hF/91Z2vTaHgkES//nXHvuXJT95eQcA7t2edz4kTzn3w2dtsFl/7Pp7REInZJhImLSGELirYCEi2iWprBM0m3YYP4dwSQkSho0aYz6X4r28+l5v6HbKaS75ycfvtfm66ScO2HXWCzU01rpKjJDnqTW4n/sHaffjW1cHMTc+BbojohooggCQ6pGxJ7CLaFlRahKLbePPhEA1//sUVfunhAFMdnbZvj46oruARhxWzS0Tf4+HCUZn4m79xOuI/9rHtl3nd65zO9U98wln2QvDRjzpk4Ouu20r+DYcfuQXidkin01SrVU9Gf3R0dAsZwTAMVlZWEAQBv9+PaZpbOqh3QiQSodVq9XShP1J0Oh3y+TzFYrEnaezaoBWLRZrNJqurq54suq7rnrWGYRj09fWh6zoTExM9CRI38VOpVIjH41iW9YgTzY81BgcH8fv9ZLNZUqkU7XbbGzMDgQCBQIB0Ot2T1AmHw5w8edJLAHe7Xa+7MJFIkEwmH7fH+/0EV3HJLTJsTOy6XbWpVGrXApdLdnQTvK5q1HerePhIMDg4iCiKiKJIX18flUrFe+b9fj9DQ0Pev7tJ7rsd8o8F/H7/oy4OuEVh11JgP3i0hauLBTdB3G63Obi562IT3GtYr9epT0zQ/3//L9Xf+z2sXA610UBstWBd0drw+ZC7XQrRCXL2xI7Bc8k/RLKeJ6RXuCpzK5nYSUwTPvxhR6FpI5pNJybx6XV+4v7fZKx5monGw0TNGpYgItoWEiZ+2yGShqwGz1n+Ow5X7+az4z9P0Q4T6JSIGmUW1HEMG7At8nmZeiMCtjO/lEQRhCARIU9MK/CEtVs9AtZGzM468691pyv2cEDeEbXgCO9P/C6/E3sTXctHfKlAn5alIwawBQG/2cISJHK+EUr+YT526DfIR7YSzXfDbbc5segv/MJm8pWDxdT1vD/6d5zIfpZr124l0T0/YVsKHeWewedyLn1zzzz6QrHX635x0Zmf3nWXEx+3Wk5D0vT0/rfx0pdus10lxnTsGiYap9bvFYFWx9fT7GOYIkbLeRb9dhtTkDAFCWN9Diqb53f+qswnUZo17LZOSeknbWYQbQtd3DRHEmQ00YdqdQmaDVSrQ0VJEdOLDLVmaalxSr5B+jrLxLUCVaV/1yRTXM/TlKJoUpDZ1JOxBZGnLf8jDSlGn7bKinR4279vtUSEagtTN+BAH6UnPo/fe9v229GUMP9++Z9y3eKHuCZ/G1EK+MwWwwNtdNHPYj7Ow+L1fKP/J8iHD7Fx9Hr/+2G69BmevKxz0KwxFzxB2KygWF18Vpe2GMDYcI4EbARsZGx0FDqCimLqdJoyWRJ0Q2MEjBoj7RnoCPzwzLt4b/wqOmp8230/VL2HmF6kpiTpyo5Vjm1ZTr4J04sbBUGgLsUwBYlUZ4WfOPcH1NUkCS1HVC8j4IzhLSlM5Y4C9W/fxWeHf4LvTP70nknATqdDt9vFskx03fAIWKIorBddHds7SZJ4aPi5BI0aT1v6Jy8/0adlzl8L0c+SME7FSvON4Rfx7bEf79nWAw/suisk28v4jSZg05L3JnY3lDhDnXlkSyfcWevJgdwz8DymqveS7i6T6K5R9g8R6xaQbBPZNmhKDvkK28ZvdTAEGVOQqKp9IAis+Sc41Lgfv9Fg+L6PAo5cdK1WY21tjVqt1kOKcou3rlqUO0/fDpZlUa1WiUQihEIhDMNA1/Vdyc37jVUeC4RCIYLBIPfcc49H+J6cnCSRSPTMAd05XyaToa+vj6WlJUZGRh6xzdpGuLaMbnxQLpdZWVnx5qWuFaKrfjI0NHTRlTESiYRnr5XNZlFVdVuijaZpZLNZ4vG4pwp7IUgmkywvLxOJRMjn8wSDQY8k02g0SCQS5PN56vU6q6urni10t9v1yAG2bXPmzBmPoLNxPzVNo9ls9thRPZ7n5bshFAoRCoUol8uk0+ldVcm63S7dbtf7m0cDV8XHbWQIBoPE43Fyudy+5v6NRoN4PN5DHnk0cK/jhaLZbNJsNgkEAns2cLikE1flb2lpybNkFEWRUCi0RUVpN7gWbaVSiXQ6veO5EASBwcFBpqenaTQa5HI5DuxX/h9n7Gy3256F+sjICH19fZ5950ZbNbepy+fzPW4buZrNJplMhnvuucdrXnObUYaGhlBV1bOlrFarXgPb0aNHe54PQRA8sqDP56Pb7Xo2dYIgeLayrj1gLpfz1ru83tEry7JHNgqHwxw6dOgxVb+2bZtisUi5XPb2bzNisRitVovV1VWPPOyqZrnv5FAo5P29pmmespurUqVpmhM3rhNtI5EIExMTmKbJ0tISyWSSYrG4rWLcI0F/f7+nmpXJZBgeHt52LHPznGNjY8Tj8W3Ptftc5fN5BgYGdhwXwuEwAwMD5HI54vE41WqVcrlMpVLxbFJduGp0lUrFy+m4xMVisUgikbhEwLqE7yq+P2csl3AJl3DByJ5tIWZFIlaQlhJnTjpBQssR14tIorEuhSNgCwJ1OY4hSCSsGmKrRUzUiRgV8j6HGR4w6qS0NYeIgk1DimALTmrIsBUU06QlBBF0iXH9LJYocc3yh/n2+DbZunV8/vPwwhc6xc7f/d39ExYA2mqChb4nnv/FObgu+6/4jCah9cTUTomcSllG8A2R0PNE9BLXZD/pEbC2g9+oEdfyhIwqFWXvCW5LjmIJEkG9xkBzho28l69+1fm85z1w9917H2e4k2O4NUNML1BRUtsek2J18FkdZEvHUH0o3S42Aj67g2b7PCIHgKZLCLIPn9Gi05Iw5stUlDS1QILFxDV779BFxle+4nxe/WqHiPUrv+Ko0TwSvOc9DtlA1x21ta98Ze+/UdfVfQTbQhcULET8VgdVcM6huJ4o7IgBsG0kTCTbQrB1bEF0/k7cefJu41jB6YJC2KyhWBqJ5gJrsRN8+tPn7RHBUdj58z93lF5gZ9vG3fDQQ1vvj7//+73JV48Uk4Vv8tzZv2KoPUtEL1NW06yoUxiiD8G2iOglUlqWiFFBtE06Z9/Jp4+94YK305Jj6KIT7DZzOgVFpVBQ2dJEZjsdyZ21FgFBxhYk2r443x76EYZaM0SMMmPNs6wEnX3cCL/RYLg9S0OK0ZDjzA/8OIOmo76zF/nKxfvff8GHBoD26t/Gt/w6uqZJ6FyGCbFCRe1DF3xItk5ML6FaXTTBh2SbVOQU5eUQAwNaD/lKsC0G23OYgkxdSXLv0PO2bGsu8QRyYh9FMUEsl6NiK7TaMcBCFARYV8WSBIHB9rzT8azEvXVFWyscL9+Br7FCRYyTCRzYSnQVFXKBCWwEhtpztOUIA7WH+cVfPNGz3PS007X/h3/oKAyJovMuyGTYFyZzd5A72yBtWbTFAKqlYXct9I1FLEFAE3wItk3QamKvdxLfk37Orsl+yzKxLBtBcBI5X/iCyBe+4JCwfvRH4ejR3uV/8Rd7x65AAFKr9/FT5a8TMFuO6pYtOhaSgupZALeRkS2dgNVCb5kIrTbTkau5M34TDywmeOA92+/f9LTMu98dxraFLeSrjTDNXhsUv+nK2Tnlme1g207BDEA3FVSh69wX28nnuedrne3rjHm7F8nXsu71sUEQvO753dBqOed4ty75xUWHePWJT+y5um3x13/t2q304mIrvMdiMdLptCcjPjMzQzgc9hKBrVaLWq2Gz+djYmLC605u7SZFuAGCIGDbtpfsf6QolUrMz897XYn1et2T1Y9Go5w6dcojIg0ODtLf3+9ZG4BDECmXy2SzWQAymcwWMobP56O2zngzDOMxI4JcDLiJO/f6bEzk7tZd7nZgXsJjC7fIYNu21026FwFH13VWV1cplUpewQMcApbbsTk8PHzRCg8XG+l0mmQySaFQoFgsoq0HUq6FwIUoTT2eEQwGCYVCng3rbthYuArs5E37XYKbFA8EAnveQ7LskAfq9TqNRgMhlSL/lreg3XsvobvvRsrlGFFVxPvuwy6X0SMRWrFRKOwyjxFk6nKUsFFhtHHa+71ry7sdnnnuLxhrnuFg40FUW0MXFRRLwxAUWkIQGwHV7uKzu4TNOsdqd6MtSKzJaYJ6GRORhhh15pNAtRZ2iBqCgASYgCSKVJQUEb3MaPMsoqV7DVsb4TYiXXZZr9rlhWI6cj0fnnonU6X3EIwsENIrqGYbAZuyOkBDiZP3j/G5A7/IUvIJj2gbbjy7EzpKjHvGXsw9oz+Gz2igmG06cqRHJfuxxKc+5Xw24kLIV7vhnsHn8aS1j2Frjk26YFqeHd9GKJaGYum0pSAdIeAt01bigNP0cbL0VeJ6gZqcRMCJa2xB9HJOG6ELKqrVRbJNonqRthRG0Z0JccCocffAc0i3F0hqORLdDCX/8Laz3pBeIa4XWQoc8uwnAR5I3UJIrzLROs1AZ56cf3zdln0dtk3IqDDcmWdNGUar9fPWzzyPpt30COuOxY6Mqvoc9RPJxzcPvIJvTf4UBwrfJNlaRLJ02mqc6WtuoK7urECTXHmIsF6mIwbR5CDp5hKKrWMLArpw/vkRbRvZdggyNmALoIkqqqkhYxDXC6zJYdpylFX/Qcba56ioaU5mb+Pb4y/Zsl3RNlCtDrJl0JTDmKaBbYNtWb3zTNfiTRAwEBnortARA6T0NZpSlJXAJJoYQLINonqJwfY8Ma3ALSv/iimp3DX+Ezsee7vd8hQ/DMP0CLmGoaPrBoIAiuIojsTjMQKBAHeN/wS50BRPXPkvDtQfQDE7SLaBJUhoUoDF8DG+NfSjzKRv3HG7O8FpDLOd+3IfNroWohdDyVZvwnOu/8kUFkcJGVVG2nOIWISMKoqtYwqOVb1gW/itDiImLTFMRenzmslMUaUhR4nqRVi5F3Dm264tc7lc7mnICAaD+P1+z364WCySSqW2vC9N0ySfz3t2cS6ZxrXye7yiWq3SarUoFAr09/dvSy6TJIm+vj5s26ZarXrEhu5u3pYXgFAoxIkTJ8jlcoTDYQYHB72YQRRFLw507dguNgRBYGxsDF3X0XWd+fl5L4ZxrZ3ce8Mtbm+2cNoPRFEknU7T7XZZWFhgcXERVVXRdR3LsjzVmaWlJdrtNqOjo55SsGVZ1Ot18vm8p9a1tLTE4cOHPau8arWKYRheg47bwDIwMLDFdvvxDpe4kM/n97TFc624AoHARbOZkyQJSZIYHh6m2Wx6+7GT9SjgETTj8fj33O7Osiwsy7ogAt7q6iqNRoNOp+PZ+bnkrGKxSDgc5uDBg3vmHjbmQPaK52VZJhqNUqlULjj2r9fr1Go1VlZW6O/vZ3h4uOf7jbZq8/Pz5PN5fD4ffX19j6sx2bZtFhYWKBaLzM7OMjs7y8rKCtFolGAwSK1WQ9M0+vr66O/vJxaLkc1mWVpaQlEUb+zeCEEQGB4eZnBwkFKptCUPk0qlvHg3mUySz+eJRCI9cxTX3m6zcpNpmlQqFS9/paoq8Xj8UTXxuITHdru9I7E1kUhQKpUIBoNUKhVPqWuj3acgCESjUWzbZmhoiGq1SrFY9Kx0bdv2SHiuOrb7Lvf7/ZTLZSKRyEUjYMXjcWKxGGNjYywtLXlK75FIBFEUabfblMtlarWaRzTefB+7KBQK1Go1z+5xN8RiMa/JrlAoeGTwjeQrl/Dnqn66FoiKotDtdmm1WoRCIY5uTuJfwiU8hrhEwLqES/hfgDNnZM7dFeUpiEg4gZ0h+sj7xyj4RvCbTScJgUhXDmIKCsnOKp2ughgw0XEK/cZ6QieurSHbOiImTTHckwizBQFbAEGwydkDhIQ6yW6WJ6x9irvHXoy9S3HVley/GBirnyJsVOiKATR596DMFkRqcsIhZDQe3nVZ2XSCcXFdKWnHdbLOdVovrItYKOb2gfx2x22aBt2u5iW2BEEg2sliA4ql05G274CRLCfJJtg2LSOIhAHYiFiodheNXhJW11BQEFDMLpJgsKQe476+p2NK37sC0/vf/8jJKxvx7nc7/+6kLLYZJ7KfxWd10QWVqpwkYDWRbBPJNhBwFMVMFDTRR1EdYLx9DgELn63TZe+kuYiF5fah2jZgo1jb3xOZDHzuc3DDDY4tyG7qV7uh1XKsCe+6y+lo363Q8qhg2/zgwv8j2c0QMSosBI/1PHe2IFJT+6grSUZa5xhpzWAUv8LdtReQix67oE3Np66nOx+kKUWJa3myazt3E+VyChNajqqSoqL2k48cJhc5ylj1AQRsRlozTDUeoCFHaYvh9QR6lYDVoiInyPjG+VT/j3PXbQfIRpvMzDy6zrP94FV/cJhnn/wLfkx8A7MZx6Ik1c0i2waWINKQY8yETjDQXaYlhkjqOaSqSbHdB5KKiElYL5PUcki2yWLwCGfj17GQvK5nO5Zl0mi2+FTfjxFdynBMrpJYWaONTFlOYq6TaGJGhX49h4rOYvAoZ2LXkY065KkrM7fiNxv4rRYrwa0qgxtR9A+TqBcI6RWuznySz8RObLvcm9/8yM7bVWufJqqXHCUDBCxBxG92kG0DTfR5xyNjINoGEiZN0bFRfGDoOVvWZ9s2uq6tj8PnE6JOYl9BVX3ccYfMHXc4qkgveIGj/nT77VutPdttuDrzScJ6GbDJqiPEjRIBq0XIaqAJ6nr3v+ANz367Q0OKUFH7+erEz+x5/Pfco+LzCfvJ/XvoSCEsQcS2nUKZzu5JJ8F2OtwNSd21MNTyJdFEPx0pSNioUld3T9T5jCayrdORgpT92wfmm7Efi5JHSr4CZ+z8zne2/v6xyDmOj48jiiKyLHsEp2Kx6CVSRkdHCYfDnq3d3Nycp6C0V5Kt3W6jquqjIl6Uy2Xm5ubIZrNe8mZ4eBhZltE0jVwux/T0NO12m4GBAU8tSBRFrzAmy7JHyioUCl7X4kYigkuUAb7najX7RTAY/L5L/P9vgiAIaJrmWQ0IgoDP5yOVSvUk7judDmfPnqVWq1EsFmk0Gt53tm0TCoVoNBrUajUOHz78uL3mrrz+o1VteDyjr6/PS7rWarVdixrFYtHriP1eF4xM0/TUC/cDVVXpdruYpsmJEye8DndwCgetVovSS16CeOedYNvE4gaFwu7jvCGoKLa+Y+yxEdHWCsfK32SwNY9km1iIqHaXlhTqiX87BLEtEcXSUO0u462zpJlHExR0UUHcqHSIABKYloVpOVRtSxDQRWfuCk4zTEfcOfn+aMhXLj41czlM/jUDyYc5mfucY61o29R9fTzU/zRW4lfui0jxqCEIdJUIXeXCFSAer5hPPZH7EzeT7q4SNSuErBpdghiC7Ki9YqJYGrJt0BH9dAUfggAVOcVaYJyGzym0+fS6c09ZHYrqAKrZcUg9ls16YqRnu5YgrKuzmci2hrh+zwK0DbgjfCNXyB9FVhuMdedQjSYlNU1XdgpFstUl3s2R0nPk1WEqvgG+OXa+cfArB1/FSOMM2CajrRmm9O9QkeJ0xQCSbRIzK/jtLkV1kJxvhA/6Xk9REzGMFqZprs/XHNJAp9PF51PXC2MCliAz0/9UZjafzF1U/nxmCxnDa/zyWQ6hyMCJJdaF7ZFtzVPAcog/TkxuChKSbeAzz48rLSVGpxskpuW4JvfpbQlYFhKWIGEiIlg6lmU7FvaCgChK3mWx1on/pm0TNapIWITNOtPhkzSU8wVIHejIEUq+QcZbZxlqz3LL8j/zcPoHafi3KhNoWne9eObst0Pw0ADbuy0Mw6DT6eL3+zBN07ODWkhdz0LqehLNBUYr9+MzW2hSgJXoZRQjUzue673QVBLoos+596zulqauzfCZTTRBxbQsCoafdruNoijYtoVpWvzrgd/mZ86+ERsY7CwRXW9cAwiu52xNQaIlhmlKUfL+XgsuTfCj2Bq+bp1cLsfKygrLy8u0Wi3i8bhnA24YBpVKxbM3B2cO4cYYgUAAQXAUxVqtFoqiMDQ0RCwW82IKv9//PSc374Z8Pu+pJu1lreQonS9hWRaVSsVrXnHVNh+NNaCiKJ7FoLs/rirWd6PZJJlMetus1WqUSiXm5ua87wOBgGdPPTo6+ojnS0NDQ95cJZvNsry8TKFQIJ/Pe/GfJEkcOnTIs6QHPCKaLMteE0E2myUYDJLJZDwFlY3zoFAoRK1Wo1wuMzExsW/LxMcD4vE4fr+fgYEBrzEpFov1xPQu6bHRaHjKbRe7iSIWi+H3+0mlUqytraGq6rYqW51Oh9XV1R41te8lXIv0jbHaTjBN02sgc5vXQqEQiqJ4CuNu7KRpGkePHt21ScIle/h8vn3lKvx+v0fsdNXb9gPXMtQ0zV0JM25+JZvNesS+i2GzeDFg2zazs7Pk83kWFxc5d+6cp+rV19dHu90mk8kQCAQwTRPLshgYGGBwcJBms0mlUiGfz+/YbOOqPu82XrnKcWNjY17uTJblLddY0zQymQylUglN07wmJrcJK5VKee9O27a9pkNRFPH7/bteV5f4tVveTlVV+vr6ME2TXC7H6uqqd3zdbtfLZTSbTa644goymQzRaJR6vU46nfZybaqqEolE6Ovr6yEkuSpwLhHpYpD0BEFgamrKa8gslUqsra2RWe+edhXJxsfHiUajHDp0aEebU1chOhQK7flcuUS7drtNt9v1muzcd7VrU1ipVKisqxi49qYbbYaz2SyLi4uMj48/6nNxCZewH1wiYF3CJfwPxswMfOADYe6/X+WmwCQtOcJAdxHVbKNJTrBsCyJteVPiz7aJGBWacpSib5iwUcUUJId0ZelEjSqqrWGgbOlCFGzbSe4gYgoyRXWAgXUZ7fHS3SykdvFsuohQzTaSbeyqSLQRhqDgszqo1u66/V3ZUaUwBWXLspZtb+kAFLCQLQ1DkOnIe0vbWpZFq9XEMExM08AwTG+S1DRMTMPAAi9RvRkbr4dgm1gIgIghSPisLpJgoovqeoIOJNtAtk1ELFbVcXKBcb49+mN77uf/RExU7yOsl9EkH0uho4iYxLQCPrONgIkpKDTlGE05BoJAVS+S1POItokorHe3Ym9VkrHBxkaxNLqij66gAgKGaVIzlR0nwf/wD47F1j6FTrbFdhYRjwVGK/fR11kmqa2R843sSHq0BZHVwEEON+4nqFe5KnMrn40ew7IsdF3DNC3AXu8SVpDlrc9vR43zUOKpRLUCY61z1PW4Z426GYluFp/VYVWd4p6BZ3sE0NuO/gYdOcQT1z6J32wQ0/IEjSrYNk0xyJIySlMM8bHES7gz8kzUdpt//VcZRXEm8I91Z9Gnzx7nC/a/cHLyMzxh7VaS3az3XUuOcl//08kFJnn2wl+T6GZJdteYaJ3xljEFmYrSR9k3wLnoE/jEsTduKlbYNJtNdF3nrsB1RNI/gVgUSQk5+owCQ0YWQ5DXE742VSnBiv8gc7Fr+OSx3/HWNVY/RViv0JLCeyacXfWFiFFmvHERKmibkG4vEjRq1JQkFbWfsdY5LKR1S5Kmd9zgdId38WEKCnPhk1tUByzLotlsbrBXcIoMwLplg4GmOcmXQMBPoyHwz/+8877JRptjlW+uW5+kyPnHoS1g6yKKrTmJcu99ImAIMoINuuDjgeRNNAIXp1NpM5bDx6hW7iCtZZBt3VMX2A6ibaGgYwoSNSXJqfTTdlyvJSrc33cLcS3HWOsc+T0KEknN6YjvSGEeHvjBR31cjyUeSZ7ctm1OnTrlEUDcZGcikfCSDKOjo6RSKa9Lz00iiKJIIpGgv7+fUChEpVKhWCyytra2Z5LNtf6bmJjYtat1N1iWxcLCgre9ycnJnmJLMBik3W4TDofJZDKMjIywurrKwYMH8fl8+P1+LMui0+l4kvBLS0t0Oh3K5XIPUaRWq3lJ0e9XW4lLePyg2WyysrJCvV7vSdIFAgFWVlZIJBKMjo4iCALnzp2jVCqxsrLiJQtdkpVrNbG0tOR1oh4/fvxxrdD2eINt297Y5apKKIpCMpm8YPvNVCrFysoKfX19rK6uIgjCFqsC27bJ5XLUajVv/PteXy9XcWC/drC6riOK4u5J/VAIJAmh1cLv29uSUbE66IJC03bspmzbQpLkdSUOAUVRvfntldlP4TcbhKwapiARtJp0Bf+2zUea4ENFQ0MmZRRYk9MotoFiG9uSZSRRxLRMLNtGsCwUS/OUM3Xpu1TMFwTWYidY26EZ4BIeIQSBD13xZxyr3slk6wwBq41qdfC5jKD1OW5LCqGJPupSnJhRourr556B53r3yuZYuisFzzdS2IbXFNiL9W3YAhG9SEMIUTEkZms2XavNu5Ov5XXan6PZImljjQPts5jI2IKAgkFHDLISOEDZN8SHj76RUvh8g09HDPA3k2/hxTNvp2mrRPQycbNMwHTUdGtylAWln7YS599GXsPDwavoNp3YQ5ZlBEFcz/G0kCQR27awLJtQKAjbanHtjq4YwEQiaDnvJMF2nn8nij6fhxJtc4MSrdMkufH8CvR2jNTkBFGjTKK7hmR2tzbkCQKZ4EESrSUSep6smEaUpC1xsSgIIAiEtRI+21ESLyjpHvLVRhiij2X/FAdap/AbDa7I3Mo3DmxuPrHpdLp0uw7hyufz0W53PGUfZwxzGmR0XafTaWNZNpIkrhcCQ0iSTDk0QTk0sa/zvB9Mp26gu/j3dMQg8W6OQmBsx2VN0ySu5SnKfcwrE6yZIYxK2WtAkCSZppziPeO/y08u/SU1McrR5gPEjDIiFoag0BXDTiwmJ8kGJrbEbS5hVvArrKyskMlk6HQ6jIyMeM0RrjWgS7pZXFwkFothGIaXx3TJDbIsk06nCQaDRKNRBgcHWVpaIhaL4fP5HjfF/u3gFnXD4XCPksh2kGXZU1xptVpkMhnK5bIXj7lqov39/Y94PuEq7nwv0N/fTzAYZG1tjVgshq7rHiFElmVisdiu1k/7gSAIHDx4EL/fj6qqBINBNE3z5mqhUIjh4WGi0Sj9/f1b5jemaZJIJGi32xSLRTqdjkecicfjDA4OIssyuq5TqVRYWVlZn8vY3jF8P0AURaamprwGv2w2S6FQIBKJIEkSuq5TrVaRZZmJiQni8ThjYzuPK48GExMTaJrTbLi4uEgkEvFIlq46mmtplk6nvaax7yWi0aingtNqtXZtillZWSGfz2NZFhMTE0xNTfUQ2UzTpFgsevZ0MzMznDix87zQVf3brw36I20uq1arVKtVotHonqStaDTK2toa9XqdUqmE3+/3nonvpfKxS6ZcXFyk0+kwODhIJpPpeZ/ouk4ulyOXyyGKIuFwmFAoRCKRoFwue0StR0v0lSRpx/uk3W5z7ty5niYs9z3oxpjNZpNCoUAikaBWq6HrulfDEUXRezdst5/OHFBAVdVdY8D+/n7H0tm2KZVKGIbB6uqql7+IRqMcOHCAUCjE4OAg2WyWtbU1BgYGPBJUJBLZ9pq7Fq+wOxHsQiFJEocPHyaXy3l2npqmeWpTrp3t4ODgrsRGl/C832fEPR7LsggEAgSDQU95f3l5mVKpRLVaJZVKEQ6HveN15+HuOV5bW/NU0y7hEh5rXMpuX8Il/A9FqwVvepNAtaoyO/unfHHuT/lDq+skWqq372g3BE4aSMDC0kSs9lkveXOjWeFPwtc49mu2SXdTQfWDVoc3WeudCKaG1aoC68o/5RzmHV/el71QOHyS66//MrKsoCjrnXwbkM1+grvv3tqVtxG32brXLWZ35rddJin6+M/4zQAotoYpSHTF8xOzSuUuvv71m7f83SfddWtgt3YnEjiJLZvbhMs5F7lq22VarUW+9KVeFaDzkz5nLessHj693slIPbftNfyv2FOZQMQQZFSrCwg0pRA2IrptctCqgLk9o8fqzmPkVrE//+FdjwngqU+9g1jsyh2//+xnh9H1yp7r2QvXXfcR0uln7vj9V75yNY3GmR2/3y+uuOJ9+A2nq1AXfI4VFjJln1Mc/vX6t/mO3us953aUAmCty7GYu/vT/aYt8YtARY5REaIsCgNIjYbH9r/33leyuvofj/p4JiZ+iRMn/mz9JxtdN7AsE9sGURQ4d+4tzM//lXMcwnlSga7v2nC7BUNDL+Kaaz7I0cLthHSnQ7OunJ/Afqg9x9+0z235O+ceLmDXv4U+/ZY9t5NMPoUnP/lz3s93jr2EY5Vv4jNbjLZn+YeOypv1Xq869/rYiNiV2zFq38R+4Oe87z+5voxom4i2k7IeFoO8PfQkvh3/Ae7rfzpNJY5sml7nSbf7JU6detn+T9AOkOUIz3zmzl5xtdqDfPWr1/ORDcfSc2QFR9rnL7z930rItPU8Vvssz3zihxE3EYzcpFu9vsJDDz2Z7wDvsq11tTfb26Y7xthmGUufx27cib18XlLuNltbP3fwT/5xhqWdEyA/VPoc9noRwG5Po9/6nzsuuxtuuOGLJBJbbWJlW0PE5pbODLn2We8YNhcXnB3Q1o+ri5X9R4xb/3XLIocPv49Q6GnrZEB1PSC0MQxzXS3lhbTb+yOSCdh8ytLWiyAidvvM+u/hrXKCX0B1yMvg2Yf4zTZrvlH+bvUfeHD6Tfvazm44fPgNHDny1p7f3Tv4HK4sfJE+PcvrbYMP7jF+AWCbmK2HMb66vU3t+PjPcPnl7+HeoR/m2txn0HxBxlrnWAwewRRV/q49zb+0Z73lN3bmW9oS5udG9nU86fSzue66nd9V8/Pv46GHfn1f69oNsdjVPPWpX/d+3py//8hHPsJP/dRP9fxuY4cuOO/z+fl5L/kfCASoVCr4fD4mJye9hHEgEGB8fJyxsTFPMcFN2pzfH6dbNZFIkMvldrSzsiyL1dVVLymxl1XXTnDt2Mrlco+U+Ua0Wi1EUaS/v99TpGm1WkSjURRF8Y7XTZz7/X6azWbPeWq1WrRaLQYGBr7nSjWX8P2PSqXC7Ows5XLZu4cdhQkbwzAIhUK0Wi0ajQaRSIR6ve7ZPGxOwvn9foaHhwkGg2SzWfx+P2traxelGOIWXx6NssLjHdVqlYWFBbrdLtVq1Us+q6pKuVxmeXn5ghQXRFHk4MGDXvFjZWUFVVWJRqPIsuxtB2BsbIx4PP646G4Nh8NEIhEKhcIW9b/NcOZndUZGRjw72o1ot9tOIcEwnI9tQy7nWIFtM+X5t9hNpC2bsFFj1neQB4UDrKysrCe8dWZmnOaoje8aN47umUPZGru5Cn/OFrhSkJBtExkTyTYJmXWasqNS9mP1r1K3txbB3W1Y5RWMT8d33sA6rrvuv0mnn+kpRL/sZfSQ4G+//QnU67srWu8HV1zx14yNvWLH7++88zkUCl9+1Ns5evR3OXTot3f8/r77foaVlX/f9jtB2P6ab4eJiVdx8uS7dvz+4YffwOzsu/e3sl0wNPRC/vbkX/ILD7yOsfYMPruNJvjQ1xv33mt3+WOzAiYIetbR6S5/GbP6Dez7nHUIwMfdebO2go2A6N58lnMfXicofFhy5k+ibTnq5KKEZVn0yznebaZ4W+02zPtPevv2VZzmM29dG4hhUMLWVzGbMvYdn/X+JhSa4pprvo5u+3jP0G9xqPUQN1S/zGWt+5Bsg3v0Im/sPIDVnXZUtyq3effm9rC3/V6WQzzzmfkdz2u9/hC33+6oGX/GNhHXj8MqLffGO17TyWaYYJtgFFjAh0+QMDfl5SxRomwb/GDpsxifSWwXQfFp20KydUQsbCO7a07xdjGGhYCIzap/K+npGaXPYW7YipOn/Cp29evop7aXxneLhxsbHg8e/Gei0Sd6PzvvVOf7VqvFQw/diKZlN6/qgnHNNf/C0NALe35XC44wHbuGqF7kF+r38J32zmOPmxe0jTWM7hmswge3XW509K2sjv0+R7Sz/Nzi27m8eS9Rq0ZdilGX47zayHFHZxo6W71DRSxsRPj2LPaTP95DANiIvr4+/uqv/orx8XEGBgbodDqe9a2maaTTaRRF8Qq/rvpNNptF0zSGh4cfd3ZXm+EWZ6PRKI1GY0/Cd7vd9qzwXOs7VwklGAxSr9dZW1tjdHSUdHqrQtvjHaFQiIMHD6LrOo1GwyNghUIhj1RmmmaPhfWFNsQIgsDIyAiDg4PkcjlarRb5fJ5isUg6nWZycnJHAlu9Xqe/v592u83q6iqSJBGPxzl48GAPqUCWZQKBAIlEgoWFBa/Qf/Lkye/K/ehaIbqExkeCUCjEkSNHmJmZ8Wzqms2mt97h4WEikQiRSISpqakdr0Oj0fCUsizL8kgYLuFuL0QiEQ4ePOip1ZTLZW9+CM7cdXR0lEgkwvj4+Pdc/QrwGjAikQi5XG5HUpimaczNzdFoNBgeHub48eNbyCmSJJFOpxEEgdXVVU9ZbSd1Xb/f7xEZNU3bk4zZaDQ8pbv93psuCceNV/eC+z50m47K5bL3XSgUIp1Ok0gkvutjdS6Xo1QqYds2AwMDLC8vY9t2jwqSoigMDAywsrLiqcC7qlW5XA7LstA07aIrLbpKh41Gw1PArtfrXswYDAaxbadhuVQqcfr0aW/f3feJ+25135uFQoHx8fEt8axrq+faK25HPnXhvosHBwdJpVKMjY15ClsuCTidTrO6ukqxWESW5X01MnU6HRRF8axvLyZEUWRwcJCBgQHq9XqP3V8sFttXjsNV76rVavvaZrvd9shmfX19lMtl1tbWmJ+fp9PpUKlUtrWnrdVq+Hw+zwqxVCpdImBdwncNlwhYl3AJ/0MRDMIP/7CTkLRtHdNqb9JM2k+2zvIK1QCGbRDU3Zeijb1pEmfaFr1lR6v3/21tX5s1jBbNZgtZlhBFCZ/P15Ootm0Ty2rvsgbQen7afqPdddKCYJtEjTJZ/zjzkfNJOqc7cet29rPuzViT05zyHSdk6Nuo+tg7Hs/mpGqvacTWbVuCRE1JoFhdwnaHruBHF33Mh44T6mRodyq77KUFeyiA7bbtnjVZ7T2v0b62Yu/OBrKszkXajoku+bHWld42Q7cturtVHfYJAwFZsCgr/XwjegvNThf/ukVCOBzGtrWLdDw6TqdoB03TsCxzXV3KIWB1u82e7bQf4Sbt9fEhrJUJim26YqCHZGli73HeTNjH8RpG7zLl0AT/deTNvPjMH6CLfuTW9C7bcRP2229n47i4qvTzV5e/D5f0KQGSJHsdK93uxbnfLGuv6dfOY8JmbK+F567GxBIUNodZ3a6GrhuA0LOdbWhcPeti07Ohbb/ktuhibrhC+7vu22GnMaElR9AFBe2CnlUbbMP5bIKmdYjHnU7qjXDvB9vuXtC9cP7c9u5bXk0zp6RRrQ4CNoYg05YjTNXvxxJl9Is0xlnW1nFtMXkdi5ETpLtLaN0FLuhodtgny3LuilL4IKlffh6BL9ao351hqvEgFSWJYLZ3uD4W2NaWe2wn2La2x/fGRTpvvW/dG2/s/d40zS2Eq637Ynvde7ZtUy6XyefzntT4wYMHicViVKvVHqs0VykrGAx6yThJkhgZGfFIofPz86RSKeLxOLIsY1mW18EHTmdrf3//jpLf54/T6Qhz5dQDgYBnBVGpVFAUZceuaMuyMAyDeDzudSpXKhWGh4eJx+N0u10ymQyFQoFkMunJt7vkk1arxfLyMolEgkAg8IjJYpdwCeDcT7Ozs6ytrVEqlUilUoyPj3vFi06nQ7FYZGFhgZGREc6ePYuqqt7zthPi8Tj1ep1isUgoFGJkZOQRJTHb7Tb5fJ5SqdRDwHK7Z/d6Vr+fUC6XmZ2d9bqgRVH0VETdcTAej2MYBoZh7Ns60bUycG1rKpUK9XrdK1yl02mi0SjhcHjXwtV3E65FSCAQIJ/PMzo6uuOyhUIBWZYJh8M7jodb3ju7dE9YhkFaz9BGpWr5uE26nkajvj4PNLFtJ/bbGHPu/obdGea6RZlgG1SlOGk9w7wUxhZENPaYn+0wH9uMZrNBpeKQ7Px+m6uuqvOP/zjiJflN8+LMm1qtBq1WE1X1bXsPWdaFzQN3gmXtfsyWdXFiw06nRbPZWC/mbHc8+kU6Ho25/hv4jyNv5pnzH2CwPUtCzyNi0xVkdNug0xM1bB+n7ZW36m64Yd1GOmyblLhGXZURlRZGvveestk8C9+0ftvacg8aRhtd12m3O6iqSib8JD6cfhIftm1Uq02+9k20B1/kxEkbV7VHYLT5e9Pc/X3idPs756j3GC48P+GoKanU1xWkIxGTel3CZ7ZoCIrznO40zwd6z+jOB+qz2hiCjI5MWwhsKT50sXoIWOexe5y4+dyJ4tbCsiTJmKax/u/Fyxdth6+Nv4wD9ftpNRW6+9pOb451MyQJJFnmtH2Y/2/ibfzawls43H6YhhwjHxhHq+f3iHUtMC0wtx9bYrEYr33taz2FqGg06hW44/E4oijSWG8QdBVY6vU6S0tLCILA+Pi4Z2f4eIYkSSSTSRqNBrque3ZW283dWq0W8/PzBAIBqtUqhULBI57Ztk29Xmdubo5oNOo1yexmDfZ4hqIoW5SRXSJPuVzuITe6ZJ54PH5BBA5JkhgaGuKaa67xrLOCweCOhBVX8cUlvrTbbQzD4MSJEzuq+Lg2fmtraySTycfUfk3XdYrFIvl83iOogZO/TafTF3x+wCHHnDx5knK5TKFQoNlserG+qxK2U+ytaRozMzM0m00ajYY3B3YJWO79e+DAgT3nwPF4nBMnTpDL5YhEIh7BzFVhTSaTpNPpx43dqEuacvMgCwsL9Pf39zQsdDodzpw5Q71eJxgMekSWnZBKpSiVStRqNfL5/LYELPfaFItFKpUKDz74IGNjY8RiMVRVxTRNKpWKl0/pdruUSiWuvPLKC8ptuPfRfpW2XIs/RVG8hraNBJharUYkEuHQoUPfNUWsRqNBu92mUqnsSW5x451arUY4HMYwet9bF5M4Zpqml5PqdrssLS2xtrZGPp9nYGCAeDzuxRIuuUqWZc/K1+/3Mz4+7hFJ3Wu+tLRENBr1SFmbjzmdTntxr6sYv91xue/kEydOeIRVN7bdaM8Xj8eJRCJks1lqtdqu455hGNRqNUZGRh5TxUqXrL0TeXE3JBIJz3JyL1W7VqtFt9tlbGyMRCLhNaf29fVx9913A+eJkhtRr9e9+zEYDJJIJCgWi6RSKRqNxrYNT48E3W7Xe5e6OV2fz0dfX98lotf/cnzvs1GXcAmX8JjhRS+y+exnTWZmLs76dMFHWltBsQ1sQXAkrgUJbBBtA3WPguh+Yds23W6HbhdkWcGyTCzLJBDYu4vjkSDVWcVCpK4kuXfouY/JNm6LPY+upkPTUYVwJ1y6rtNq7dSp+MhQkfsY6CwDApJt0JCiGKKPfGAcOqcv6rb+J2ElfJRm6Wuku8uoRhtNvvhBpohNRh2loqa5f/iHkS2FTqeNKIro+sV5fly4Fmqa5liobUxWbg5sHg3+7M+g7+/ayB/r0ph79CS17eBaFCrK+aTNcvIa/unkn3Pzwt9TyLWhWXzU23GCmu2CPOF/UFHU9oqdinLxVDdkqwu7KGA91jgbfyIjzXMI7UeviAfOvbCZfOXCkR+/eN1DmhTwbIEBVKONbOt0xADavkmxjwCCwH8fewvh+4t0inmwHv27yLXRfcpT6vDSp1GwM1Tn7kRp1IlreaJ6ee+VPI7xpK3ia3tCEAQmJye9n23bplarkc1mMU3T67R0/98N1hVF4dSpU3S7Xfr6+vD5fIiiSDweJ5VKeSSGUqlEPp/3OiDdjuFkMkl/f/+uSj2appHP5ykUChiG4RGwXOvDSqVCp9PZtQPTtTMxDAO/3++Rw8AhHbiJwLW1NY+gFQqFaDQazM/P0263SSQSDAwMMDEx8T2Vy7+Exx90XfeIgK4tm5toi8ViWxKYmUyGarVKqVRibGxsy73r9/sZGRmhUCgwMzPj2cIdOXJkz31JJBIsLy+jadqeCc/NsG2b5eVlcrkczWaTSqXi2fH5fD7q9Tq5XI7BwUFGRrYqAbpER8BTpXg8o9PpMDc3RzabpVqtMjAwsOV6NRoNstmspw7oWlLsB9FolMsvv9wriG20dXALV9vdH24CdjPR9bG2KFRVlUQiQafTYWFhgeXlZQYGBnrGO8MwvHt9bGyMcDh8URLCg90FInaHafkA96snmbfTiKKwTu69eLGHjYCFiC0IFOQ0dTnOgfZZxjszZHwXzz5H07q02y1sG8bGamSzOTqdCKLou6gFQl3XaLXatNvtdfvSIJIkIstOF/mJE3D77Rdtc9viJ3/SUTVfWXn06zIMwytqB4PBnpjqscB9oy+koSb5gcV/INnNENYrKFaXlp4Drfqo1y9h4jfbgI3P7mIg4aNDLRSmkUywMjAA+Z0VpfYL27bRtC6KIvfGBYKAJgUd1avvMxjICIJEXUmRSukIAjRrEDPKrKrDoC1clO3U5Dhdq0tLcJ5Lxwj+uwNRFDAM+zEj4L785XDkCLzlLZCLHuO/D70B/du3P+IGo40wTROfzyF+LrZHORc4RshsMNGdwRLE7dWdLwDj4+N8+tOfRlq3j4xEItx88834/X4GBwcZHh72VCTy+Ty2baOqqkdujkajTE1Nfc+tyPZCJBIhnU6zvLxMX1+fV/yORqNeMdudzy0tLXnEqtHRUQ4dOtRz7ySTSTqdDsvLy6yuriKKYg9x7fsVtm2ztLRELpej0WhQLpc9greqqsTjcarVKrFYjKmpqQtWbB0cHCQcDpNMJqlUKqytrRGNRj2btHa7Ta1Wo9vtMjAwQCgUolqt0mg0iMfje6o4xWIxz3K6VCo9JgSDSqXC3Nwc7XbbI5O5KtXRaJRqtUo4HObQoUMXPJd07cvcwvx+7ME0TeP06dOUy2UymQymaXpqMK4Fl6IojIyMoOs6R48e3fO6+Xw+xsbGGBkZ6WnICgaDj0uVXneMAlhbW2N5edmz3HPy35qnkiOKIpOTkxiG4anzuM1m7hjmPs/1et2zX90Id+zodDqemu/y8jK1Ws0jg7kNZs1mk06n4zWeLS4uMjAwsKvy0UY4882Ap8a1m0JwPp9neXmZbDZLLBajr6+PwcFBRFGk0+lQLpcpFouMjo5iWRbHjh37rlzPdrtNt9v1SJXtdttTdW+321tic/dYbdtpIN+o2PRI4zOXVFqr1bwcVz6f98b9er3OwsICxWIR27YJhUJermp0dNTbx0wmgyRJtFotYrGYR0x2n9NwOEyn02FxcZG1tTUvD7fxPLvWnmNjYywuLrKwsEAymSQSiSAIArquezm9WCxGKpXiwIEDO8bEGxXx8/k8oVBo2/yZbdtks1lkWfbeh49HpFIpT9Uum832NM5thGEYZLNZIpEIPp/Py4ceOHDAUzU/deqUp8ovSRKGYVAulz2Cn6uK7vP5vLioXC7T7XZ7cgMbr/F+sDHP45IPNU3znudarcbKysquDViX8D8blwhYl3AJ/4Ph98NrX9vgrrseXZDuQpMCZJURDrTPINkmAbuFZBtIWIi2uasE+YVAFEVCoTCGoa9Pns313ztqWJa1q+bLviFJNpPCDH6zzGLgCJngFEuJJwAQiUB/P3ztaxdlUwSfdy3a17T1wEhDVX3e5HI3L+gLRbKzwqhZwxAV6qgItkVMLyK1TIrShbPR90Iw6CSGH2s87WnwxS8+ttt4YPDZ3LT677SkCKPCAuLUOO2uhN9vETpjwqPPFVORE2T8Y3xw8rdoKnF8gGka6LpOt3vxiiC67jw7nU7XY907k1i3k+biPKu2bXP4sEVleBg7FCIoFJCtLoa4PXHl0Wyn0+luKRYUI1P898k/pDDTD6d+9aJucyuEbTvGv99ge53Z9kUlEaW6WZDjrm/q9hu9iHjhC+HJT4alJUf44YN/9lyenP3oRXsP7ZWwfyxq35GoSSxmIC2s0JbDWAGFSMoP+3M6fESoBUf4pyveRf6OB6H16Am6hqHTarU4cOAUs3MdfD/wA3xz/qlEb/8GVzTvpigAndk917MXvhc5/9/8TWde9WjhSpHLsswDDzyA3+9HVVWvEOLz+ajVap5UuyzLyLLM0NAQ4XDYS/RKksTw8LBX1DcMw0so+v1+0un0romWer3udc+Wy2Wq1arX9RwMBqnVaqyurtLtdndVpgkGgwQCAXK5HD6fr6dzWpIkxsfHvWRwtVoll8uhqiq6ruP3+xkaGsLv9zM5Ofm4sDa4hMcHXDuFXC7nFQJdAlYwGKRcLhMIBDhw4ICXJNU0jUql4hVhdiMOuhYWLslwP0U0t2DkkpgvBIuLi2SzWZaXlz2rH7dQ1Gq1WFhYIBgMYpomlmV5xEnXDqdSqXjPliiKHsFyPxYjFxMugbRarfaMOalUque9ubGYNzExse1+hsNhxsfHmZubo1qtks1mL6hz1u2CT6fTWJbldf9vlzCtVqssLy/TbrdpNps9SdFMJuMlxh9LItbExIRHultdXWV6etqz/jEMg0ajgSzLXlfv1NTURdmuYhtMK1Ockyf5f9EfQZVUfD7fOmn34hViDEHGBrqCnzOhKyipjjrISGeeQ+2Ht7XLfiSwLBtJkhEEeOITV1layvDUpwp88Ysne94/jxaGYVKplNffwwrNZpNQKORZgYjiY9N04uLNb4brr4dPfOLirE8QBDqdrqeIHAqJ/N3fySwuwn/8x8UheW3GdPpmpvtvYrz8bY4WvkZQrzBfvROKq4963TYCqt1FxMJEQhNUcgN9SAMxzjzlKayt7Wz1fqEwTWvHRpwrrmjw4IMXbVPb4ld+BWo1+MpXLs76OmKAljqIGhTo6+tQKAj0t3KYtkjtIuaLMuoIYctkWFtef/6/e3G0ZTl0r8eKrPziFzv/fuAD8KpXwVz/DRQDY1B/9AkjVyVWkiRUVeXfBn6BtJ5DEmG4M0/QqD+q9T/wwAM9P8uyzPHjxz1bN8MwCIfDnqKHi93IzY9H9Pf3UygUGBoaolqtMjw87M0RC4WCt5yqql5R1lVt2i4P4Pf7GRsb82zNcrkcExNbrTW/n7C0tEQmk/HmprFYzLMra7VaFAoFCoUCY2NjWJbFkSNHLoh4pygKR44coVgsemrOa2tr3rvaVZlJpVKEw2GGh4c5d+4coijuqP60EYLgkMk3Nv9cTFQqFWZmZjxVXfcZkSSJbrfrNQGMjIxgmibHjh17VI1E+3muZmZmPAvvZDJJKpXqIXsYhkEul2NhwSHSLiwscPDgwX1tXxTFi6YE81hCEAQOHjxIIBDA5/PR7Xap1+secSwcDrO6ukq9XneUKzMZTyXMJWBtbFhz7TZ1Xd+iOrWyssLq6iq5XM6zOHebgaanp3nwwQeJRCLYtu2pnbs5jna77anvGYaxLzIcODGqq2her9e3fRZckpfbTHTgwAGOHTvmjV3uc7W2tsbS0hKiKLK6urprY97FgBsnlstl6vW6R7hSVdXLY22Oz90xxY3lKpUKiUSCUCh0wU3QhmEwNzfn2QpWq1U0TfPsc1utlmetOTIygm3bnhrz2tqa1xTkqk+5OYjh4WFM00TX9S15A7/fz/DwMMvLy6RSKc9ydSMOHjzoKcsVCgVWV515sKt05vP5GBwcJJFIMDExsWs8LAgCw8PDdLtdut2up4i/0fKv0WhQKBTQdZ3x8XGPPPZYwFV7c+uarsrifrcnyzKDg4Pous7S0pJ3PK4Cp9uk6touDg0NMTg42HOvHzhwgLm5OU+tr1qteuOCKIooikKn0/EUApvNJktLSxiGQbPZJB6P9yhW+Xw++vv7GRgY2Nc7b35+nlwu5+UaXCtNy7I8BfV4PI6maYRCoUs5z/+F+P6vJF7CJVzCrhgfN7n5ZouZmYBXAxcAAQsJExHLEQ+3nG8swbEO2C59uRa5jHl1DAGL0c4cQcshYOmCiiYGMG0dv230FMBFLCxELEHEliQkyVUPEbAsm/X5DYLgFFRNU0AUnUmWLCtIkky77chMugx4wzARxcDeHSK2jWQbiFgI2Oe7xQQBbJugaRAXyugnRpmYSHPs/3sTPzZ2fn133SXyb/+2fUHGtsHQbUTLQHCtDN3v3IUEASQRJImXvaxOu13j3LkRNO28DUqn015/0W83sXTXtI2sum0i4W7X9pYPmC1yvhHqShJdUFBsnYSvQbBRINldw++ty/krWxCxZQU3x2DbsL/Y1VnPq18N73rXdlLwAUSxu/XP1uHzgWmev+4uLAvvngAQBBFFgec+FxQFbrutdz0DA35M8/w1MgxnvT17ur6NjXmUzcsJgsRv/kkfvvdei/KtPLGVcxjZOdSREQRFwSeKBETROU57/V5yV6yqGAZYpqMEJ3nWBef7PN1nIu8b5wMH/4B88HyyRlHUDR7yMqK43iW64aQKgo0kWeu/F7GsjZPA7e4TiXa7gyzLW4IWZwLq854hRbERxd4EZXfnS9eDcNjk3nvvxX/8OGmfDzWskGitOWprgISAb5vOYBELWxCwRAlLcIooggCGsWHsEG1E0f2dgmmaGIaBLMtcdRW89rXwznfCqVPQ8fV55207SNLW+2Ij3LFEEBzi2M03V/jKV+JblnOIED4kybkO7jpl+fx9bJrOZ7ca0OZ9/a3fgo98BM6dc38j7Ho8jwZPeQo87WkzzM4uYNsaL3vZ5u3YO+67IIAkWdi24N2Dkm0g2iZBq0mqPUPeP9ZDwJMsjb7uKgHAEgQQJVB7E1NunmNzbGOazrPauw8it9ziqAL4fHDsmPP7pz99hC+84CbkT30Jv5c46e0Rdp9G578CtiiDLCGK7mvB9j6RyO6Blij6kCQ/kuRITbuB2Xb7LElgmTaypXl75I4JEq7MOIyPdSCfx2fnaRwcIXT1APKZCqqqbnnXmaawzVjXu4wkOfemZYHPt3MysBYcYbr/FuSleYT1d4s3xgkCui3v2uHvxPk2puls64YbThGP19A0p+idfIqfj+ZfxSdqFrnltyOv/v26aYmwfi7Xz5HFhrHg/DMky+42zuO661Se+lT46le33ydBkHueIVG0kSTn3nbGFGHT8s47ZvP7T9xwL99ww3bHLm0hbrjJPfd67JQ4cjt/M5kM4XCYa6+91us0bjabBAIBL1k0PDzsJYxWVla8In0wGOTYsWM0Go2ebtW9SAytVovp6Wny+Ty5XI5QKMTw8LAnp+52abkJc2BHMperlCVJEvl8nsnJyZ7ChSzLTE5O0mg0OH36NMPDw4yMjHDw4EEikQh9fX309fVdUr66hB64iazV1VXvefD7/ViWRS6XY21tjXQ6ja7rHDp0iEgk4iXb2u32vqxx4vE4q6urXoJ1v+QbN47ZL8rlMrlczrPvmZqa6rnfk8kkmqaxvLzsLRMOhymVSl4Cu1wue6Qhv99PrVajUCgwMDDwXeumLBaLrK6u0ul0aDQaHgErFAqxsrJCKpXy9qVYLFIul4lEIruSxFxlKJc01+l0HpHiqKvctx1ce45CoUC5XPbURSzLQtM0AoEA6XSaZrPJ0aNHe2zvLyYkSeLIkSPMz8/j9/tptVrUajVM00SWZUZHRz3Vq4MHD3r3iFvQgV2Kc7p+fjK1YRJnA63RJIV0jE8kx3jS4MeJRK7gc597mreMG4NKkollrc9NRBvJNBAulNAkCJSkJPfEb+abg8/n2Uv/j+srX8RvtvA17sTvzYEABBDBlhSMPezXRNHCNIX1xLhDWr7xxipPeUoMXQ+iKCs88ECGQmEIUfT1vP/dOfpO8a0sW9j2Zgs4p0nBVRFw7OcUul0ncT4+3mBtTd+TuKlpO8cDiuLs1/Hj8pb53J//uaOuUy6XabVa6+RmZ+5n2+7cyUZRzt8PlmVjmvb6NTz/e+eWsVEUP4FAgHa7jSSJ/MzPrDI4OM7goEP0esMbFB5+ePvj2Xwc283L3NhaFDeNo4LgWF4nrwNgdvZdSJXb2TzcunNId07oxqJunCF4d43zrw/QRB+aoKIJKsvDE7Secw3aLbeQbzQQPvOZbcf0jc+S+zydjwPFLfeJIKg8/emLLC9P0WhIVCoyfr9FMGjxohflaTYbfPjDPizL3kCSddal6zu/J5xtOkckSeD3B7jySvjOd3qXO3LE5ulPh4ceErbcb7ZlYRsGwvqzv9/ntRUOIw4kGQgV0Yp14mtVMGzm/VPM+A4hit/a8jeC4Fx3N0/jKK4CpoVoWwgbLPEsQcRC4l/Gf5NfXf4AA3qWuF6kpgx7y0SiJr6S2GNBeD5v6AQwmzkw7vza3b57r99yyyzRaAJBsJmYKHHrrZexsCAjyxKi6OQWnfyigCCAqrL+PG3ND0iS89luvBAE56a/8srzvxschFtugS9/GUw5higGELARbYuNMaclnFfZ3osoatsihmF4ZM+yluT9B3+Pt4p/iCTKcPeD+DcppwuiE0DZskOE3Ugi2PjecOPcjfshyzLdbhefz8fq6qoXEwwMDJBKpXqaQb6f4MZDY2NjmKaJaZr09fWRSCTQdd0juhUKBVRVJRaLcfjwYRRF8WKqze9cn89HJBLxiFyjo6OPS4Wg/aBWq3kFY9u2t6h+hcNh+vr6yGazLC4uIkkSa2trF2w9OTo6yuWXX+6RFvr7+70mBlmWPaWtVCpFuVxGkpym60diZXUxYZomZ86c4cyZM2QyGa/RoNFokEgk6O/vp7+/31MhEkWRpaWlfZOdHgnq9TrNZpNMJuNZA26GLMseGSSbzXrq1BdrbmsYBq1Wy2t62GiN9t2ES0IZHBz07APd5zYUChGPx7n33ntZXFwkHo97Smvu3waDQU+pZnR01Gvy2fg8F4tFMpkMS0tL6LrO0NAQkUiEqakplpaWCAaDTE9Pc/bsWcLhMAMDA0xNTfUoFOm67pHh5ubmOHTo0J7H5ioCpVIpTzVnMzGuWCyyvLxMtVrlyiuvJJlMbhmjBUFgcHAQTdMoFAre2H6xxyzLsjzrNVfpLpPJkMlkPItB0zQJh8Osra151m8uNE3z4rhcLgc4MfqFWDeC88yePXvWIyi6Clxu85QbfyWTSYrFoqd6GIlEvJxeLpdDURQKhQKiKHqKcAMDA556/XZNWOFwGFVVqVQqFAqFLc+mJEkcPnzYs6NPp9Oe2qBbs4lEIgwPD++LBJlKpTzSabFYpFgsksvlkGW557hGRkZIJBIcOHDggs7lfuDaOLqqcK7CnKIohMNhQqEQIyMj+yLTDg0NeQSoYrFIoVAgm816BCxZlr33xMDAwJb3UDweZ2pqimq1iizLiKJINpvF5/PRbjuOM7FYDE3TPKLd0tKS5yTgbkcURXw+H6Ojo16cfujQoV2fmXw+Tz6fZ2FhAUmSmJqa6ok/0uk0jUaD1dVVzzHgu91Adwnfewj2xWwTu4T/9XjHO97hMbR/4zd+43u9O//rYZqm1/3Xbg/wa792/qWRTsPb3w7JYAu9XOHzXw3yzx+NYSNw883wmc9snzAUbJND2S/z86d+nUFrmaBeR5clysEY3b5hZL9IOafgr1ZJm2t0khHq/SOsvv43sCaj5PN5Dh8+jM/no1Qqsby8zNTUoXVJUmg0RGZnA/z9359XWzBNg3bbscAJBPx0Ol1arZaXlHAnGACSJHoqNbZt062XeUn1n3lJ36cJYSCWSghOdhArGoVoFPOyy4j+7u+i7KLwsB2+/OUvc//991N6+GGOLi4iLIkIuoic7JKfSlM7fpxUOk0kEmFiYoJSqUS73ea2257I6dOOVKlpGgQCAZrN1rqChQ4I60UWZzI7NJTlmmuWyWav5OzZlOfLPi7kebp2J9c178Bvnpehmo5ezT2Dz2O+74nE2qu86Qn/Req+z1JZ0tANkUJJ5Uzseu4deh6Ng9fwjncKbFS2NU1YWIChISfRtroKf/mXMD299Rx8/OOQzcLsLFQq8L73nf9OUWBgAJaXe//mZ38WXvCCnc/r7KxDRCkWne3fcAOMjjqdn+9+N3xrPSf4mtfAM57Rq0RTKMAb3gC5nJM8e81r4OlP37qNBx6AN73p/M+jo/Ce94DVqFP55V9GevhhlIUFRF1Hj0SwVRXBNJFrNQC04WHskREC7343/qNHe9bX7XQYzHyV44Wv0Cc0sQSJkjrAPakfJBvcGpA7ll0tQqHgujJKl6UljW7XUTRRVZUbbpjj+PEHaLVa+P1+BGESyxrlyisDSFKBTGYNRTlJKHQAUVzjb/5GYWFBIhwOsZva1Q03zHHttTMcOHCAkydPeoH5j/wIbCZ2hcMGL3/5NO99b5JCAaLRBj/yI/eiqiV8Ph8n/v3fSZ86TXC5RtY/TlXdmhBQzDZjrbNYIZnWSJTZN76RvqNHve/X1hTOnAmSTOpcdlkLy4I/+qMxlpY0AgE/sViQv/3bIO582zSdbuC//Vuo79IM+oEPOJ9vf7v397qu0Wg0aDbd8y8iivDGNy7wh3+4taMxFDJ46Utv4/DhAxw5cmRHSWjbdu77btf5/OVfOs/JwYPO/b0Z//zPznKvepVzTC7pNBBoUyz2JlFf/vJZPvzhk8iyRDAYQpZl/viPYXwcvvENiMed5+53fue8Ot2TnwzPe56TcD5yBM6cOc3i4uK6MsUUv/VbTiJA05z7rtPp8Ixn3EUkkkAQOtx3X4ChoTA//MMGyaS6nqgLYZoHueZYP8E/+i1yX5pjqDWH32rRlCLoog/Z1ggbdTTRR/CyfqypKSLvfS/KPoPpO+6AP/mTrb//+Me3X97WNEq/+ZtI3/oW4uoqUqWGZsq0dT+y7VQMqnKSgn+Umakf5Mdu/y38gfPPx/LyMtPT02QyGSzrSv7939NUKtsnm6+9ts6znjVPobDE0aNHOXHihFcY+frXYWbGIYddf72zfLMJ3/7Z/8vQ3bcyWD2D3rDJi2kachJLFEmHK/QZWaR2m+7wMJVUigdf+Uq09S4ocN5puVxuXSZZ433vezKNhkSr1SYQcKSSb75Z4nnPi3PjjUrP2NjpwBvfuP04fvw4PPyw8/+Dg844u7H+/Yd/eH7c3QzTNHniE5d4whMe8mymotFoTyebo6SUo90e5PjxBJp2jL/+az+aBhMT8Pu/D4lE73rn5+H++2F4GJ7whO0Vx3Qdfv7nobyDq6FpmoyO5rn55nkkacHr6C4UQvz1X5/AsixkWcLn8/MLv9Dh5S8f5Cd+QthR1XGn+24jMpkM09PTLCwscPTo0V0TkvPz88zMzLC4uMg111zjLT87O0sul6PVajEwMICu6xSLRUZGRjzFq0wmQ7vdZnJy0rMMuBCcPn2a1dVVMpnMjokRTdO47777PFvEG2+8cceE0OLiIktLS5w+fZqpqSkuv/zynsSaruueIs7ExAR9fX17JjMuYWfoG5jq/xOJa2traywsLDA/P48kSQwODvaQcizLolqtsra2Rn9/P+l0mpMnT5LL5ZienmZpaYljLjt3F5TLZe644w4UReHYsWN7dgWXy2UKhQKHDh3i2LFj++4SP3PmDIuLixSLRQ4cOLBjEdMwDGZnZ+nv78e2bcLhsNedGYvFPCJOs9mkXq97agyubeF2xcKLhdXVVVZXV3vUuFzCpq7rhMNh+vv7SSQSDA0NMTMzw5kzZ7YtGmyGpmnMzMwwNTXF4cOHd7XbuFDUajXOnTvHysoKjUbDU+9wxx63a7darTIyMkJfXx8nTpx4zItJnU6HfD6/pWO/r69vS8fwxnh+YGBgx3FTz2Zp/Pd/Y91/P618nkq3SzaRoHjdddy7ssLc3BwHDx4kGAzx8Y8/l0bj/HWZnLyXI0e+TiwW82KPZ587x6HTp4k++KBD8LDBCASc5iIA20bUNETDoGmHkTBZkweYUw/wp8fehxZw3gGRbp4b2x9mePlhgmadriWx6oshPdfAd921JFOX8d73XoWiyASDvYW8cLjGs5/9MDDD5z9/E3Nzzj6fONHiFa/Ioqq2d45mZ+dYXZ3kE5844b3TFAX+7u9Alptks3k+/3mBf/u3xDqRC266aYVDh+5HFMO85z1PRNcFLx6PRjNMTS2RSCh8+cvHaLclfD4VWVa4+upFXvSiCgMDAxw9enRLIt1VAKjVanzjGzIf/WiEcllBUVT6+gSe9jSnkUCSnNj1Na+B9ToMV10Fb3sbZDLOM1csFnsUAA3DRhQlUinHpqS/v98rJGSzOT70oadTrZ5/L7z2tSsYhsA//MMgnY5jvfKUp0zzkz/p47LLLts34fGOO5y57XOfu3XOBk7svt/030/9FLzkJbsvs7DgrE/XbF4w/g2ea/8HygP3YXRNR31dlbCiUSpXXM/a9dcw06gwPjFBp9NhaWmJ1dVVJiYmtjzLpmmyuLjIyMgI4+PjBAIB5ubmOHr0KEePHuWd74xyzz3Oss1mg1iswMteds+uSjeWZXmFetu2OXbsGIcPH+bjHw/yhS848bAgiJ4FzxVXNDh27G6OHYswOTnFoUPuPB/+z/9xzqVlWQSD8I53mIyNbW8tc8899zA/P0+sViN9zz3Ub1/CP7NKSsthCjJd0Y8mqETMKjGzgk9qgQhIEqaigGkimCZtn59Z7SjTvqO8b+S30dQIqqqiKOq63Y7MBz/oPE/z8xpf+MIsuj7L2JjNwMAgDz8cpFSUmBht8s53ny/y2ZbFL576Va6zv0W8UWYpeJiO7DybR460WcspVCsyom0w2jyHLQgshY7xgavex/951wSXX957vA888ABLS0ucPXvWI0duN/+t15t85CNT5PNH8Pv96LrTwBUMBnjtayP86I+eX3ZpyWkmnJ524qFf/3UnT6rr8DM/4+SfNuM974GN04VOB376p51/XThNZE4M1Go1UVVn7LBti2azRbvd4sUvvo9Tp44gijazsyksS6TTaa8XYwPes5lMrvLCF1a55aZBUgsLlP/lY2Q+O43eMJCDEgM3j5P8qR8m8kM/xJn1GGRxcRFVVVlaWiKdThMMBmk2m2SzWc9yKRaLUSwW8fv9SJLEFVdc4Vn3uAXIC5nrPB5hGAZnzpyhUql4FmKAR8B2C74uEevkyZNeERnOk8Q3zhvq9TorKyscO3aM48ePX7RC6n7f9RcLMzMzLCwskM1mmZqa2nFuats2CwsL3pzz8ssvv2BrpgceeIBMJkMulyMej3s5R7dQL4oi1WqVTCbjWUElk8k9yUyWZTE9PU06nebAgQMXjfxkWRZ33303Dz30EDMzMwSDQa+gHgwGCYVC3jPi9/tZXV1F0zQmJye5/PLLHzM11dnZWRYWFlhdXeXw4cO73iPu3PrAgQNMTk5ecK5gM5rNppeHctV6JUlClmVSqRTpdPoxa2B4JLj//vu56667eOCBB4hGo57Kmmv51mg0aLVaJJNJotEohmEwOTnJxMSEdx89+OCDLC0tUavVtjSY6brOvffey+zsLKVSCVVVPQtXl/Ti3gcrKyt0Oh2mpqa2jBk7xfTVapWZmRlyuRyFQsEj6MiyTLvd5v7776dYLNLX18fU1BQTExM7PsOu2s+hQ4eYmpq6aHFWrVYjn89TKBRYWlqi0WhQr9fRdZ1qteqRYdxYsFqtEgwGKZVKnoWnz+dbz706KlDj4+OMj4/T39/PoUOHLmismZ+fZ2VlhYWFBSKRiDeOzs3Nkc1mqdfrxGIxSqWSp4gEjtqV+yy5qlHDw8MoisLa2hqdTodkMsnq6irj4+NMTk4SDoedemO36ylblctldF1ncnKSq6++esf9tG2bSqVCu932CM4uae9CUS6XWV1d9RSe3eaoYDCIz+ejr6+P4eHhix7Xttttzp496xHO2u221zTsWnKmUimSySQHDhwgsV3gsA2KxSLZbJZ2u+0RPd3jCQQCDAwMbHv/bpwjNptNL74vl8veO9x9PtrtNt/5zneYnp72GkUnJiYIBAIe0b/T6eDz+bjqqqsYHh7m8OHDO96LG8eJAwcO7DgudzodpqeniUajjI6Ocs0111zKhT5O8N3gsnx/tTBcwiVcwiPG5KRTPKxWnaJ8X5+TRIEgvmCQ574UnvvS88s3m3D77VvXYwsS54Z+kNtu+Rg/ffb1FM+cQcnliHXr2GszIAj0mSZGQKEdj2P29dH62ZcTu3oCy7JYW1vz5DA1TVv3K9/YaWNxxRVNnvzkGnfc4XS9ODYDAoZh0N9f5oYbCiwtLfDFL15Nq6VvUJs43wEdi4lcd51Gtdok9oTryE08mYnTp7Huuw+h2cT2+xEOHyb4oz+K//jxCz6fmUyGQqHA2toa3VCI+vOf702kdUDVNKR1X3ZwCvvuJO2HfmiGU6dOous6gYAfQRB52tNmEIR7+NjHbkHXpXVymYwg6Fx11cdIpwcYGlJZWPgBJEmiXq+x6h/kY4Ov4Jup1xPslpBsnbaaQJPPJ+7F0TGm/ujXkIRfIV6pYOs6ST3Owlf8PFWCm26CzfMXSXKIIi4mJuAVr4C3vKV3uVe/2vl3cND52LaTRL71Vqd4/7M/6xBBfuu3ev9uN/IVONveLnaORh07Bjep7XRZOIGKK0/a1wfvf79Dcunvd8go2+Hyy+F3fxc+/3lnvS996bq6TyRC/D3vofKWtzj+itUqYqWC2OlgiyLa4CAkk1gjIwR///fxr5OHTp6Eyy6Dhx4CBIGZ2BN4QDm+JwHKgZPg0TSdw4ebPOtZd/Lgg2G+/vUpikWJkycrXHHFApbldIMVCgUCgSYHDrQIhabw+WLk83lSqRyjoyrVapVnPSvLxz9+BfW6gKraaNpWtZef+IkcV17ZYWam43nEuz7itdp5yXlRFJFliec8Zwmfb5WXvORu6nUJRWmi6xq1WotKpcLixATPnZujHuxjoLlIXCtQVvuJDwpUSyKBVoWYUaahxBDGIkzfdAO+TaTHgQGdgYHz0v2SBL/xGyt86EMaPl+MF784xNBQsOf7pz3N+Zw65SikfelLvWd3ctK5P9/0JmdMq1ScxGp/P8RiXV76UufcmKaFLIu8/OVZUimDm26qcvvtsZ5z9rKXOewpRVF2LXoLQm9i9gMfcLq6NQ1+/Me3Lh8MOvfh7/wOvP/9dWq1FidPnubIkVVqtVG+/vVJQOSmmzL09ZW47rr7ueuuE7Ra8LKXBbjsMhVBgGc96/w63/1uuPNOZ7033dRLYHGTq/l8nkajwpvfvMCttyaZnm4yMDDNkSOnGBkZApwA6JZbRAYHBxkYcBI9rvz+6GieYF+I0X98N83X/DHZL/gQmi0iehnJNtAEP4vBQdpylGM3TBF729uQN5Ay9sLAwL4Xdc67qpJ8xzuovO99mJ/6FGalglCrETJNLFGGeAxRTCPd9KP8+Ft/CtXX+1y419U0TQ4erPCmNznVsG9/O8J//Vc/pilw9GiLn/u5LIpik883vcTKxvvhKU9xPhsRCsFN//LrlP6gifUFEyFfIrVaYqCTQZQsFGyMeBx9dJRuKsXaK19J1TQZXT9frh2XG+S2Wi2e/ezP8J3vXM7KSpe+vhluuqnJDTc8mXg8ga4f60n++f3wx3+8tUDwpjfBtdc6BCtdhyc9iS2qBM973s4ELCfp4IwBbuC6EaIoeonWhYUFLEvk4MEM//IvBygWHaLvdrHs5KTz2Q2KAq97HfzFX2wlYDqd8XWe//zTlErLJJN9JJNJJElidBSe/WyNL30pSrfbob9/lf7+HKdOlXjTm4Z5wxuiW4LhfbiTAeel03dTYwHWFTXaPbLstm1TLpc9dZnh4WF8Ph9+v596vU6tViMYDJJKpRgZGWFhYWH9fRBgaGho34mVVqtFs9kkn8/T19e3Y1eaqqocO3aMr33ta8iyzNmzZ7nyyiu3TRT09fUxPT3N5OQkpml6BDJXzr/ZbOLz+ZiYmCCRSFwiX12CB9M0e9SUAoEAa2tr5PN5AMbGxrbcK6IoehYgKysrRKNRisWiR0ByFR72StoqiuJ1IVYqFY/EtB1s2/ZUmgKBwL4Lku1227PiSyQSuypIuN2dCwsLyLKMJEmEw+GexDDgKUWdPu2Qqfv6+hgfHycSiXgWQdFo9KKRsVyrhMXFRTRNI51O9xBt2+02hUKBhYUFbNv2EqZuUnkvuO9PV6HiYiKTyVAulz3y5+bkts/nY2hoCEVRWFlZIRAIUCqVLioJbDu4hcyLCWVwkMQv/zIA9fl5cjMzlItF6tUq7XYbn8+3nhyHJz3pW3zjG0+i1QoyOLjG8eMPo+sWqqqiqir1ep3FZz+bMVGk1W4TnJlBMgzUVhNLFLEFAdGysEQRS5II2i26iQT+4QgLQz9IKetD6XZRVYXxa1qkrhniI586wczMEH5/g2c+c5qpKWW9KzhPLJahXB7AtvEKZLZt8cpXztNsrqIoUV73ujVmZqoIAhw82OmZO0iSRCqVBOZ43euC3H//EQIBHy98oUYmM0uz2aTRaDAyUufXf91C02SKxSJra3N86UsZhoeHGR5uc++9V2AYJrJc4dprb+eKK9LrxI1ZPv7xCWxbZXS0w/Ofb1IsFlFVlZWVFQ4fPuzti9u17RZD+vpMfuEXBILBEH7/1mKIG7vefrszZ7/hBpO1tTyrq6ssLS3R6XQ8axFZlmm1WpRKJbLZLKVSCcMwvEJOMhnnV391hU9+MkWrJXHDDVWmppxJ3xvfuEixKBONtllbm6XdnqRcLu9bzeTJT3Y+O+HQIYc8dt99jtpoOi3wjnc4+YFXvtLCMJy4UlFEnvnMvecrExPwX/8FThz9FOApaMvLaNPT2JqGGIsRvPpqpHabwpkzcLbqNee5RCenmWXrvBTceaLlzVVci5LXv95p/jh9GgYHLZ71rBU0Tdv1neK+u2RZ9ubpgiBw+LCOqgY5dWqUYNDmxhuX6O93LHVyuQbR6BDJ5Pk5mCA4277tNqjVLJ72NHtLnmY7aAMDFF74QvTn6aw1FRYNkbHle8l8KUc9D5EBgdHhKuJ99xCu1VCaTSTLwrAs7EgEIhHakzdwa/dXSBgBikWbTqeLbdv4fBa/93shFMUJDiYnVW6+WWZlJcTS0hJ+v58TJ1g/pzA4qJHNqoBNp9vhgSc9hycunaU6KzLeOktFSWEkEsiSyEhfh1irgb9aRENhOXiEuwefw7N+vpd85ZwbgYGBATqdDisrK17Bb6Nqom3b1Ot1SqUSz3++QaFgUSpNMjurccUVfp76VIlnP7t3vWNjjuKce/5dKIrTGOV+5+JXf7U3xgcnxnrLW5xGxEbDicdf9CL4j/+wueuuBpK0yH33HcAwzseKl19+hqGhIkeOOP6VmiZy991jLC21CAYDHDwY5OabFfr6DM6ePcvg4CCiLBO+5RbCt9zCGGDpBsJ6E6sLN74YHBykVqt5djuDg4NUq1VPzSOdTntzplKpxMTEhGe5E4/HmZ2dpV6vk8vlvq8JWLIsc/ToUY9A1G63PSsyURSJRqOUy2XOnDkDOPbJzWYT0zQ9m+JWq+XZzLlkIReb7cq+X6DrOpVKhXK53EMu2w6CIJBIJMhms6TTaarVqmehvR8IgsDk5CSGYWDbNoVCgWAwSCwW84hXrh34wMAAgiAgyzLLy8ue9e9OqNVq2LbtEWwuBizL4ty5c5w9e5Zz585Rq9WIx+MoioJlWZRKJUqlEslkEsMwmJiYIJVKMTs769nCDQ8P772hRwCX4OLmvneDqqoEAgHq9TqNRuNRbXdtbY3l5WXPWq613q3mKvbW63UKhQIHDx4kFovtsbbHHpqm0W63qVadeUEwGCSZTHrvcFV1yMVuPqRareLz+bj88ss95aJarebZz6XT6S3xTLVaxe93GiAPHTpEJpMhHo+TSCQY2JTEHBgYYHp6mkajQT6f35d1aSwWY2pqysuluYrIbnyVSCTWm/WHdiVfAYRCIQRB8N6ZjxaWZTE7O0ulUqFarfLQQw9RKpU8Qm8sFkPXdSRJotVqecqK/f39tFotBgcHaTablEolGo0GlUqFoaEhRkdHOXDggEe+vJA41jAMSqUSuVyOYDDoPYOaptHpdLznxo2PV1dXkSTJsyV082GRSMQjVG5UTGu32yiKgiRJno2g2xzholKpoCjKluu/Ge6Yul9S0m5w1+PmCjcSsBKJxGPSUGQYBufOnfMU4uLxuKek737vKqG559B95vZCKpUilUp545ZrxxwOh7fNWbr501bLaWB3lc4CgQC1Wo3BwcEtcxhXwdKyLAzDoN1uk8vlSKfTHqlOVVVarRZ33nknT33qU0mn09u+9+r1Op1Oh2q1Sn9//67jskuidG04XRLrJfzvwCUC1iVcwv8yxGLOZy+89KXbE7Auu8whx7zkJZMo1b8i89a3onzrW/gMA3XdM8j2+bACARoHDtB40YsQ1kkqrpyjruvey3G7gL7T6XDLLQu020MsLkYYHTW4/vr7SKcdAkq5XKZWO8OP/EiFxUWZ229/gve3jjy6zLFjizz5ydV1tYsadvIQkVe/+qJ0o7hEsnK57E2INyf3VFVlYGBgPbm7tm6daKwniMr87M+e4lOfipNIJPnBH1zD769w3301BgfvYnHxSeukNIHDhxdQFCfZ2teX5cYbZ/na18YRBJGDB2d55jOjhMPXMjU1wFOf6hA8/vIvHSWc/n54/etd6z3JIz4MAC972YUd8xVXOMo5Z886P/f3w1Of2ruMIDjF/Z/+aScB586X3/lO+OIXHTLU859/YdvdCPe85/N5ut2uJ7kqyzI+n49kMsng4CCqqrIhD74jnvAE5wPOxHx11fGCtiwL6XWvw7+ygv+LX0S8/36HtKeq2FNT+F74QkI33oiwYbIkCA6h67bbIJczSaeXuPPOAqdOHSUSkdF1kdXV8/fei16UR5Zt7rwzyuKizROekGV0tMTx4w0Uxc+113YZHPwclUoFn0+lVHICALeo6MqX2rbNZZddRjgcptVqecF1MFjj135tBlVNEQpZ/Ou/prnrLmfC6vdbvPzla5w40QJUZFlG0zSWlpa87pwjR2Lce6/jSy2K0npg8RVOndLX7WUaNJsGtVoNTdOcrkrb5t8vv5wfMb+DXh0k1u0wbi2g5nWSCDSDMRrhCUIjQZaf8QOOQtw+klahkMX/z95/xciaZtlh6ArvvfeR9vhqU909GtPjNDMQJA7ZkBoCKBAUMENQ4mAexAdePQmXgiDyQrgAH8Q36oESnyniXooCoSvgYiBWd8/0dJc9Nk14772P0EOcteuPzEh78tS0yQ0UeqbqnMww///9e6+9zHe/e/o2Zudiy+bHj9f//PZvr939RqM16eW//C/X/12jAX7ndzb/zmplxd/5O8f45//ciMFgjN/93TmePRsAAP76X69Dp1vi3/07B0ymJf7T/7QGs7kArdYOvV5/LStdZanV68i8bcV5+dmzEf7kT968JaosEYvtQqvV4lvfqr79kyoAMfzhH3bwjW/8H4hEoojHI1Cpzl/wfj/wh3+4/fe5XC6YTCYEg0GUSiX4fAv89b9el/gjl2ttDd9oNDCfzxEKhWTZDZy334/FYnj8v/xjTFIpvPwn/x989q+OYFwMMFGbkLc9xMehP8Q//6c3VyQmk2uCzlseK4C18v6yUmm1cP3pn2L5d/4Oev/232Lx/DlWwyFUViu03/gGdn/v97B3AUDhcrlgNK4jWlqtFoJvCYL/3r/Xw4cf9jEYqOF0rpfDVC6RwHKdJbNKq4XnH/5D9H7jN7D4V/8Kui++AN46OE41GixdLqj+4A8w+c3fxKjZhKpYlGckh3yCBSTn7O7mUSqV0Gw2YTSGkE6nAQDHx8d49OjRBnBhNK7PqX/2z9ZE7D/8w7VDl0p1+VLta19bE27/9b9eOyKyVqslZrMpAoE87Hb7pVE8JpNJFGc2mw2xWAzh8PnPjAP7fD6XqK3LiATf+tbaAa/dBv7H/3Htbrj+OTN8//t5tFoF+P3+c6Ds977XwHe+00Cl0kK9/lO8eKFBrebD/n4Hvd7jt85YBlk4XdcBXaNZE6g50F+mRJxMJhiPx/Idq1QqdDodIVopVaQ2m01UddPpFAaDQWzp/X4/2u023G73tV5jvV5Hv9/HdDqF1WqVJcTZGo/HUKlUsNlsAlylUil4PB4Bfvl9NZtNAcocDgd0Op2AhHq9Hl6vF2azGU6n8xyZ5L5+OWs8HqNSqaDZbGI2m4kL0Gg0kvi63d3dS68VEq9arRasVitisRiMRiPUajW63e6VS4BerycgqU6nQz6fRyQSOfc7l8slisWiAN43iUQYjUZy314nTsVut+OTTz6B0WiE0+ncSgrr9/soFApYrVYol8uyvIpGo7JMZETGuzqkrVYrlEol1Go1UfWe/ZkmkwmxWAylUgn5fF4IHRTPXFUEr89Gf7xrkfzWbDbh8XguVRZ7vV75HKvV6nsnYL3v4mKdLtFnySOBQA1/42/8bwDWMeeDwUhiwDUazfrv6PXQ/eN/jMX/9D9h+C//JQyZDDTTKdSLBdSLxZqEtVphrtFgFI0CgQA0f/iH+PD3fh/f/7ctvHoFGI0lhMN/iefPq0gkgO98JwC32y19gNlsht/vxx/9UQb//X9vh1q9Js0YjUYEgwu4XFNUqwPE43GoVMD+/sWLI7vdjkqlglisiz/5k/WC+NWrV2i1WiiXy4I7zOdz5POneP78OXK5HAC8Xdi8wc7O/4Hh0ASrNQ+T6QHy+TXh8OFDwGp9g8XChP19F0KhPWg06wgTh8MhJJB8Pi8irXa7jeVyKcT+5XIpz9PBYICDgwO5t02mCT78sI56vY5PP51KRLBKpcLTp0+l5147oE6EqFgoFCTOxGq1wuPxwGZb4G/9req5z8dmW8BmWwBQo9FYk6OVi6N3qdlshnq9jv/kP6kjkdBhOlXj618fIZtdX1N/9Ecq/J//53rR9Hu/10KtZgTgu9ESHwD00Sj0ZyJXNdMpNBqNqO6tVit0Oh3MZjO63e653pT4AZdozWZT5qL1P8D/8D+snYkHgzmOj3U4OloThS+b/Ww2m7hq1Go1WepoNCo8e/bm7ZLIjPncJip5g8FwbgGn16/789ns8qAKxp93u12cnp5KJC/JC0ehIJx/8hD7Hs96yTif4/joV2FLp2F/8waqXg82jweq3V0sfuM34LRY8P/Amvi8dpjWIpfr4Hd/1wCrNQLgy889Go0KcbpcLqPb7coM9R/+hwX8i3/hR7u9gt8/xm/8ySFUjb8H2z/7XzDMGeHu12DuF4G3/bpep8M86obW58Pe7/wavv3//HtQ69aE3EajcW6haLPZ8OTJE/zwhz9ErVbDy5cvJfqWpACXywWn04nHj3VoND7D7/6uDrFYDB98kLzgs9z+Gf/mb64FVP/u363ngP/sPwMuugSePVs7Yynrb/0tDT74oIlcroVf+ZUqvvji6yiV9DCZ0ojFjjAaQa4pvX6Jb37zDYLBIqLRKKLRKNxuN0ajERaLBUwm0zl3GbXu/IzB816n0yEYDMo8kslkhIhqsVgwm80wHK4FNDabDVarFcFgECqVSiLhSM6ZzWY37iUmkwnq9br0eADEjYNRbl9VabVa7O3tSURWv9+XxSdJWUajEaenpzLHmM1mLJdLDAYDdDodOBwOrFYriSrj6/95i2VkTSYT9Pv9tYOew4FWqyWEM5fLBZvNttEzkJQwmUxkoX6Tstvt2Nvbk7my3W6jVqtJtBSjssxmM5LJpBAAS6US4vG4OK4S/6TAp1KpSH93V5GFxWIRzWYTmUwGs9nsnGOQx+NBt9sVJ7l1osceDAaD4LHj8VhIvXfpTEuh13WdphhH9i5EwVqtJjhxv9+XaDiNRiNEvkwmA6/Xi9VqhcPDw79y0ma9XpfnxuHhoexk3G73xj6IceSVSgUffPCBED2ANcGKZ8W2a2swGGAwGAixxOPxCMHpLLbCGD46o163HA4Hnj59ikajIf0jsJ6h1y6VunPOXBeVWq3eiKC9ba1WKxwfH4vrVavVwnw+x2w2g91ux2QyQT6f33AYnE7XqRONRgNPnjyR5xmjAZ8+fQq3240nT54gHA7D4/Hc+L6p1+uYTCYYDAYbcXucQ+fzudw3NpsNJpNJdifdblfEHwaDQeZXRu92Oh1Mp1M4HA6oVCoUCgWMRiN0u90Nwi7vj1wuh0qlciUR6y7LZrPdeD9x26rX6xKFGggEzvWxWq1WsL9sNguz2SxOi9etq96PEp+YTCYYjUbyjOf9HggEzpEd+/0+Xr9+jVarBYPBAJPJBL1eD7vdDr/fv4FvdrtdjEYjvHr1Ch6PZ+vMMhgMMBqNJO7yqrLb7eL4Nh6PfyGd7O9re/18dov3dV/39d4rGgX+4/8Y+F//1y//3d/7e9hQjS2tEYz/9E/x6uOPEXj5Eo7xGKrFAgu7HbNf/VWo4vFz3j/L5RIqlUoWlAQQF4uFAIuz2drV6tvfTuM731HBYrGg1+tBpQpgOByiWq2i2WzC4Vjg6VM7NJoC/vzPD7Bard7+vBG83p+g1fKg3+/f+WDcbDbFajQSiYgbyTZGt9PpRK/XQ6/Xw2QywTe/+U1MJhP4fG387b/dQji8HmA7nfWQGwr9/2E0TrFaHcLlauPJkzfodvWynPrwwyz8/o+xWMygUk3wwQffxYMHDSEKGI3Af/1fr8GiuyS7q1RrUsuf/dnaJeW7312TWy7786z9fSAcHr5lwc83hvvrLlrIsueit9PpyCBJ5Q3VE/v7+9e2Ap/P5xLFRtcRAjJ2ux2G730P/r/7dxEOh68cAozGNcFstTLh889V0GrH+PDDP0cymcRyCXz0kQOVig77+yN8/etrks93vrO2FiaoMJ1OEQhE3xL81ln2VI4QbKBbBMEwm80m7ilffv4qLJdLWCzrz+hv/s0qvvnNHrpdLZ49G8Bk+nIIZ6wZgdTBYIDf/E0HWi0LcjkL5vMJfuM3/r84Pk4hFAoh/zZTkoBdIBCQe6zqduNf+3x4lM/jw2oV+okdZvM6WlGn18P+3e/C9P3vQ7NYwPrWpvUq5QdJcRaL5Vrf6ze+sXacKhTWBJ7L/opKpcLv/I4FOztppFKpt033msSg0QB/7a818df+WlPeb7k8RDKZhMfjuZWaRKVaOyN99NGX/+673/3y/67VatLE7+7uXnh2cdnTatXhdNo21L/XKbVajb29PRkWuQTvdrvodDrQ6/WYTCbiDMGBRFl2ux3FYhHL5VIU5oadHTz+f/9X+H9Vv4w/BNbPjtuUWr2OZfkn/2QdSfroEfD971/z7xqNcHzve1db7ilKr9fD4XAIscVqtQoIo9OtNshXdDd0OBw3WsZDpYLt938f+P3fxySTwez0FMvJBFqPB6avfx0qnQ69bBbLel3OR6ot6fASDAY3rj8uYdxuN1wuFwqFAsxm81aF6pMn66iNm5RKtSa+/Uf/EfDxx2sSFwDM5wvE4yPYbFU4nddT8hHMJEDBGo/H4lSiBGwMBoMosgOBwNb7zmhcu9z9N/8N8L//7+sI2nC4AKOxiFZLu5WYNBwO0evloFKNsFjMkM2mxYr+4UMdPv888TaGaQaz2Yzf/u3rgUAEdHQ6HTqdzoVqXDq9UAVIgHaxWGA0Gm0FMvjM4//y2qRF93UIWMvlEuVyGW/evBGwHYAsAW02m7gHTKdTOSPa7Tbsdrs8L8rlsjgNkWDlcrkQjUbhcDg2FhxqtRo2mw0+n+9W1ur39YtXnU5Hlit0J2IPwwXhcDhEKBS6kkTFxZE8hwwGIXteRt5kPMKa2LE+a7LZLI6Pj+FwOOT+4oJSrVYjFovB7Xbf6MznsgTAtfqG8XgsxIhkMrnh1kJ3qXw+j1arhXa7DY1GI+pQtVqNarWKarWKaDSKxWKBBw8evNMcRAV4q9VCIBC4FCgMBAJCzB+NRhI3cdUihspzg8Fwp0ubzlvnp+l0ei2iBxfOXq8X0+n0vUXIvEuR/E2XMUZ4nO2PGZHD64+OQEqwV63+cm7gAlqr1WIwGIi4xeR0wvbf/reY/hf/Bbr/9J9C/W/+DfTlMrBaYWkwYOHxQGW3Y/HBBzB8//sI/fZvwzUew2bPo9Pp4M2bBnI5LSwWC5LJpBCFlFE1KpUKiYQff/qnH+Pf/JvHqNe1CIUM+OM/ngBYX/ez2Qyj0QhqtVoiLs4WeyY6G52cnKDVaiGfz8PlcsHj8aDT6eDVq1eo19eig1qthtVqJcptjaYErXaE5VKPk5MTIXYEAgEYjcByOQDgBLAmeTQaDYxGI7RaLWg0GpRKJWSzWczn83NOcXQAT6fT8p3s7e2hXC6LAKbVaqFarQqJK5FIIJvNYjAYwGw2i/sFF0q8Hui+FDzjLHxREY+5C2V8tVpFPp/HaDRCu92G0znGYrHAp582ZR5drVb4lV+xv43+MSCbdaHdbsPlcmF3d/edXofFYpH4FvYqDocDo9EIpVIJ3W53YynS7/dhMBig1WrRarUwGo0QDofPnesGA6DXu95Gx2nx6tUriZs6K7xYrVbodDri1lKpVCR2hwSwyWSCarWK58+fyxx3myUjazgcyvVXLBbR7XblzCI2QhJqPB5Hs9mERqtFb2cHo4MDmM1mWMNhrHDeq3vtMD1HLtd86/Rg2HBtMxqNEsdiNBrRbDaFFKzVAn/8x6cwGr0Ih80IBn1IJP42xh9+CO2/+BfQ/MVfYDIaAfP5esAwmbDY2YH2e9+D63vfA946U1QqFUynU3Q6HVmkWywWmEwmLBYLPHz4EA6HA6enpygUCnA4HELuJD5SrVahVqsRCoU28Iqb1G//9vqf25bX633b66TwB3+Qe7uI7KBYtKFSqWyQm9rttkSd8ZptNBqwWq0iNryqtFot9Ho9RqMRfD6f3Ov8WfV6XeYPg8EAi8UCt9uNcDi88fy12+3IZDIyl1x3SUjHZsY2tdttIezQkaJYLCIWi33lRGdGxikrlUpJdBTdUZSzm9PpFJccAIJjMy7q53GumU6nePXqlTgpTyYTcQHkc0YZrwecdw68TZFMwsgyJQGYsz7Jeexh8/k8Xrx4AeBLvGM+n0t8M68j9vLvWovFArVaDZVKBTqdDm63+9y1T5KeWq1Go9GAxWKR85//23trzb1OaPDC5/PdSTwfhV4kEl9VFIDfVtywJqznUS6XMR6PhTjMYt/e7/eRz+eh0WiQyWTw5MmTW/2+uypGN0ejUSGV1mo1FAoF6XUXiwXG4zE0Gg2cTqf0EQBk1srn82i320K2V84F3GXxuaLT6YTQQ1GRsoiv3tTpV6vVIhAIIBAISE+/Wq3w2WefyXu4ingzm80kXvymhI/FYiH3m0qlktmdDphOp1OIkQaDQa7N0WgkZwWfIyR+HhwcSOz73t4eHA4HHj9+/E4udiQ/6vX6jXOZ5wJxK5bNZsNotI78nc/nqNfr8kzin2MPf3R0BIvFgkBgvY+kUJkRtTqdDpPJBI1GQ5zmc7kc1Gr1zXDin4NarVao1WpotVowGo2X7nIoviQmc1ezNaNnW62WONjRsaxWq6FaraLT6UCtVuPFixfi+DmZTDYc/Ox2OxKJBFqtlhCiOFN4vV5otVpks1lUq1UUCgXs7++f6yN5T153ptqG6d7XL0fdE7Du677uS9SxVA+y/vP/HNjZAY6P16qu73xn8+8RBDWGQqi4XDAp8oK2jT/j8RjT6VRYxgTq3G43stkshsMhut2uuCYAkAaqXq9LpA+bmr29PVitVnzzmxUUi17kci6YzcBv/3YOVutSBqdoNCoKlLsovkYqoZgvTDBPWRqNBhaLBfl8XhY6BOb4HlerFabT6VuS1ggm0/8Pdvufv1VuWsW+dT6fo1qtApggGg2h1+sJKH223oPTKLRa4N//92/2d3q9HgqFAgaDAYbDIWazmdj008EjHA5fOhRSZdFoNJDL5SRfnSoFLscymYyo7h8+fHhlczefz/H69eu3xJp1VITJZJKhtlarwWq1im3tdW1wVSoVvF4vBoOB5I0HAgF897udjT+3XC6Rz+fFGY7XgdVqxfHxMQaDgSh1HA4HTCaTDA4GgwHlchnVahWNRgOLxQLxeBxGoxHD4RBGoxGDwUCGGJUKePhwdO61Urmh0+kwGAxgs9mwu7sLg8GAR4+aGI3aaDbzyGSWODqaolqtypIvHo+fW/h4vV6MRiNUEgn8WwC/lkggYrPBF41Cn0xC83Y49BaL4qYyHA4vJFbRptxqtcqAc526rtMfAPj9fjQaDYTDYRSLRSEy8DWNRiM0m030ej0Eg0Ehnd22/viP1+5cx8drcuIf/dH63zPeiE4eV12/brcbzWZTXt9Nrc7NZjMePHiA09NT2O12nJycCLGR9yYX2VTEKosqKmBzeNDrgX/wD9YRiJ0O8Hu/d/Nzg7VarRCPd/AP/kENvd4IOt0ChYIGvd46Yuk6NsY3rUgkIoRZnttUdSujLabTKeLxOOx2+40V/CxDIgHDFgtyrVYLnU6H+XyO+XwuhMvhcCgDpLL4jADW8ZKDwUAiJm/72i6qb3wD+O/+O+D/+r8AjWaMR4+yyOdxrWGaf0ZJvgbWzwlef81mE4PBQP7bOlLYIf3BZdF1RuOaOL5cLvHxx02k0z1RqilrPB6LYq/ZbMJkMsHj8cDr9cLv9+M/+A+6+OKLEYbDOVYrQK1W4Vd+5XqkXhJFlWDDtt6DTj/T6RQWiwVOp1PuJ/ZjylIuUJT/SzL7arU6RyLmwoY1Go1wfHwsQEK73RaAkER3nj+z2QzdbldANxLi6GhFMEmr1cJoNIpynsBlPB6/1ud1X7981e/3cXJygmq1inq9DrvdjlgsBoPBgPl8LtcoY+0Igl1UBNGB9b3DeIN0Oo1CoYBQKHTufqLrJ8kgDx48QK1Wg1arlXiNdrsNYL1cIJGCZIGbLHmonmUc1lULYILcvC+VEQeLxQKFQkGA9Gg0KqQPm80Gl8uFcDiMarWKbDYLtVqNXC63oQS+abXbbVkoXKXs5HlNANxqtQox4yKHxNlshlarBZ/PB5vNdqfLTD5DNRrNtRbvvAb5d3+WCFgEvKvVqpCRSOIxmUywWCwbhEW32418Pi9RByQq9no9WCyWjT6CpD8uEejOQ1IuAOjDYXj/0T8C/tE/wqxaxegnP8Gq14PGaITh2TMYFNeY0WjE/v4+ut0u6vW6kPeCwSDsdvuFn2syacXf+Bs/QiJxiMePD6DRrPCjH9UlhojkAEZ1no305JJfo9GIy1SpVJKePpVK4fT0FLVaDScnJ5hMJhvkZ6PRCIvFgkKhAI1GI7MW5x6dTge9Xi+fHWOrp9MpptOpuKfN5/OtjgTEAAqFAvL5PAwGA05OTtBsNlF8Oxfxu2GUXqfTQS6Xg8FgwHg8htfrlcU/v7t2u41CoQCXy4Xj4+MrI0co6jEaje+8EK5UKsjlckIA4rVYLpfRbrdxenoqgjWVSoWdnR0hctfrdekV6Ixym+Lc3e/3kUql0O12hfTi9Xolqo7nV71eBwA8f/4cVqsVgUAA5XJZ+mWKbJbLpQgDRqMRarUaSqWSEM5tNhu8Xi8WiwXq9ToqlQqSySSy2SwePXqEyWSCdDot74tni9PpFAzttsSz6XSKo6MjLBYLNJtNuW7C4TB0Op3M91TZP3/+HFqtFi6XC+PxGJPJ5FrzgcvlQj6fF+GE8hlgsVjw+PFjVCoVwUu4oKWjjdfrhVqtXosvDAao/+7fhe5v/k2Y37yBdjyGymiE7sEDmD/4QNR7qVRKiP4kPxuNRqhUKtTrdRgMBgSDQYTDYbhcLtjtdnF2pSBJrVYLVsfePhKJ3Oqzftfy+XxyfeXzecRiMSH7MnLZ7/ej1+thNBrJOa7VakXMkEwmr4wwZrlcLnQ6HaTTaUynU7jdbhiNRiwWC3Q6HcxmMwQCAcG7NBoNdnd3z+EIShHQTZaE2WwW5XIZxWJRCIkWiwWr1UocUJxOp7jynBV4vUsRF+UcdBX2y/6j3W5L7BGdZpRnI/GGWq0mroaJROLncrE+nU7F/SOXy6HX68FsNsNut8t5whjmxWKBZDIpzx/gy+febUun0yEcDiMUCsmZwectAInu1ev1cLvdKJfLmE7XGCRFWgDk7J5Op1gul3dG3icOUavVoNfr5VzZJgZROlPy7+n1ehEhqFRrIXm/30e1WkUsFnvna4ZR4/l8/lKnbeDLONJQKHQrZ5zVaoVUKoV8Po/T01MkEokLyUMk1jcaDYlD+6rceM4WMXUSk3gdkSRDca9OpxOMvd1uIxAIYDAY4NWrV0KgKZVKQnKv1+tCwqFLErERAPL8A7C1n6G7+7sQzrkPA9afucPhQKPRgNfrvbSHotsPCVPXqclkgkqlgkajITOoSqVCJpNBp9OBTqdDJBJBOp0Wl1m6QTGGsVqtwmKxCFGM5Gqdbh1BnkgkEA6HxcH6XYrPqrMzP/t8RspxHuWcTRErxQR8NtI9slwuy56v3+/LbpIpCPzdxOM9Hg/0ej1KpZKk5fwiuBzR1bJer+P4+BiZTAbxePzKc8jlcuH09FQI9Xfx3Eyn02g0GiLspVHBcrmE2WyG0WgUInG/35f5i9fiOuVAJ/gTsL4/z55vPB+63a5gMdv2vVqtVtzcrurTptOp3MP3iQC/XHVPwLqv+/olKTZEyoG02WxuqE+YRezz+d6SW4Df+q31PxcVAa7T09NLiRQAZNFpMpkQDoeRyWRQqVTw/PlzLBYLVKtV6HQ6uFwuGfTq9br89/l8DpvNhlqtBovFguFwCKvVCpNpju9//1OMRjpoNEsYDAssFn6kUin0ej1EIpGNzO93LTpXMIaMkYrMP1YC21TFjkYj7O3tiRq/0+kISFsoFMSJYjKZQK1Wo1KpQK1Wi3rR4XAIgElHGjZyd2lrfJfVaDSQTqdlKJ1OpxLDsFqtJDav1+vh8PDwwmal0WgIAG2328+pe+lS02q1UCqVoNfrUS6Xr1wAUxmdyWRgs9kQCoU2wBa6rTHSy2g0Xhu8CwaD4qyQy+XQ7/fhcrlgNpuxWq0wGAxELc3GVanm4nKNcRXK+0qlUsmCX6PRIJ/PQ6vV4sGDB1CpVOKgVKlUhFhw0aDHiDlaK8fj8Y3vQa+fYTweSb48h32Px7P1Xie5jtdk02KBJRJB7OlT+Xd0tqDi7rPPPsPh4eE5VSedWiaTCRKJBLxe73tpUrVaLQ4ODsSun828EjDnkqter8NsNuPo6EiAupuqab1e4B/+w/P/nufKZDK5lsKViyBlHOdNy2Qy4cmTJ0K04M9hxrnT6bxwUTYejy+03//wQ+B//p83o0hvWqPRCCcnJ7JEUBJLCDaQMHiXLocmk0nskY1GIxqNBlKplJAfVSqV5Ny7XK53WhpdVE6nE2azGVqtVpTDw+EQGo3m3H1HQJsOGFRNkyj8PuprX1v/02rNJZb2OgMnl09K8IikIC6WHQ6HuGQsl0shvKVSKQF+qLq/qJRA2DbAo1qtys/1+/0yjAPrQTgadeLv//0O/uW/1GC1auF731PB7U4AuB7hLxgMii19JpMRwjCLyvNutytLCCUYRuBH+V33+32YTCZZ2vHznM/nGI/HSKfTqFarAhixz3M4HOJa8/r1a3EpYK/B3qLb7eLo6AharRbj8RiBQAAejwcmk0l6FZJI2Mfs7u5Cp9NdGT95X/elLJIfG40GYrHYxr2h1WqFQPXixQshfTPWclspCah0COx2u1gul8jlcuJoZTKZ5Lzs9/swm83SU9Pm3uv1yjmkLLt9Tfq9DaHVZrNJ9MLnn38uikqr1SqKa2V1Oh0B+whokxTc6/VEXR8IBNDv9+W1kojJz2A+n0sfGI1Gbw3+MlbiumQBRrD4/X6JMcpms/I5K89uEmS4ELiI3D6bzdBoNNDv92VZZ7PZruzBlMuRsxF820qpWL8LZ6C7qtVqhXQ6LZF2FODwvdGlYTAYIBqNiluk1+vFeDwW90yKNFqtlpB0KIayWq3QaDSoVquy+Dy4IM9d5/dDp7TEvqBUKhU8Hg9qtZo81y+rNfawgkYzRatVQ6/XQ7VaxWg0QrVaFbcgq9WKwWCAZrOJaDQqy3EuyfR6PcbjsZwDWq1W4nO4UFOr1eI0xyUlFzPKODuj0SjOsP1+H7u7uxvPZhKnO50OJpMJ2u02IpHIhdclnTO++OILWT6TpLmzswOj0Yh0Oo1OpyPP7kajgUKhICRn5flkMBiQSCRkqche6jInrGazeSNHnYuKDhF07+J3TEfdUqkEg8GA2WyGYrEo81QwGJQ5mURRp9P5Tm44Pp8PtVoNgUAAxWIRHo9HRFlarRaNRgPHx8eoVCpCtvN4PJjP52g2mzCbzSIYq1Qq2NnZQTabRbvdFlHDcrnEcDjEy5cv5WxjpBoXccPhEN/61rcwHA5FbMfZni4bXOQZDAbU63WEQqEbzxHlchm9Xg/1eh2PHj0SzKJUKq2draxWuFwuVCoVfPHFF3C5XHj69CksFgusVisKhcK1yHd02D4rnGBR6BgOh8VJm2fNYDCQa0NJ5LdYLDC+dbxgtBiwPn+z2SxyuZwswNc4nwmTyUTcyfnnotEofD4fDg4O5JygMyCdfEwmEwKBwF8ZUWaxWKDVagFYP0uHwyE+//xz+Hw+6HQ6Wfz/5Cc/gclkEhxGrVbj+PhYiNYOh+Occ9NF5fP55NlfLBZFMBeLxWTB6PF4BAv2+/2wWq3nCNb8vui4c51qNBqoVqsS7UpRn/K1DYdDFAoFFAoFOc+v61x/UU2nU9TrdYn+ZJnNZvh8Prjd7q3P9EajgfF4jOFwiP39fVSrVUwmEyHM8bkIQMSK3W4Xjx49ksix91nsHXl+XEegt63o1kJspdVqodVqwePxiFMpI5wYu1gulxGLxaBSqXB4eIhWqwWLxSJn37sWnWeBLwWXtVoNw+FQzho6utLJhCQNRt/x+TudrmN7lbG+t6lut4vnz5/j5cuXaDab8twvFotQqVRwu93nnu0mkwnZbBYqlUocrPV6vZz7xWIRwBobqNVqQqCie6DX673Urfds+Xw+NBoNwSUvetaT6Ewywk2fr/zsnz9/jkwmI2kfdCwmaY7/t91uF9fBXq8nON1fdZHwRJdLPtPZ75tMJjidTnH4Oj09hdVqRaPRkP1Ft9tFKpUSoctoNEIikYDJZBLHee7a6IK+zSWHYt67EpD6/X50Oh0hgF90LQyHQzSbTRFBXIfo1O12cXJycs6pmhh+r9dDNBpFsViUJBuKRznnApDPh+dGv99HLpeTvkCr1WIymWA2m70zAYtEGJ4NvKc4c9vtdiEA09xAp9OJqJcOZqenp3ImOZ1OHB4eCq5Kl1q6bfJ77Xa7UKvV0m/Y7Xak02l4PB7p835e62xiDIlq5XIZZrNZCP2BQGDrOaYkOCmfz7ctzrG5XE7wTD6bKYghKZeYa6/X2+hHXC6XYKt8jqjV6nOvnz1ru92WOOizxXtKr9ej3W5fedZ2Oh1xav15dNC8r9vXPQHrvu7rF7zYJB0dHYllPeM9aKnLJkWv10ueucvlws7OzpVDjNfrRblcFoVcLBY7B7Cygel2u0gkEnA6nfD7/ahUKsKiZ2a0SqUStxnGXoxGa+ee169fC1gWCASQSqWEsKVSAWbzphPUcDiEWq3GYrG4U+CFD+fFYgG32y0ENhKNms3mubx1Ehp2d3eleSuVSnjx4oUQTQaDAUKhkMRAMFKPqhvagzscDthsNhQKBZhMpksf3Be5m73v6na7SKfTKJVK6HQ68Hq9G4umwWAgBK14PI6TkxMcHh5ufY21Wk0UkJc5D7lcLvm5FosFkUjkQsCIEQ60rN/WFJvNZsTjcVHy0XnrOiCUWq3G/v4+gDVgQ3XU2sHsS5ttfq9msxmnp6dYLBZi2V6r1aBWq5HP5+H3+2Gz2eR3k31vt9tRq9Vgt9vx/PlzuYdbrRY6nQ5++tOfIhqNwu/3n1PG1Ot1NJtNGI1GUVVrNBr5+7RmzefzAnzo9Xo0Gg34/f4LCRe85+x2OzqdDoLBoIAp+XxeHOy49BgMBvjRj34Ep9OJnZ0dmEwmjMdjdDodIai5XK73qlw1Go149OgRisUiLBYLJpOJONt1u12530mObLfb6HQ6KBaLiEQid6reBG5GqmQs0buU3W7H1772tQ0nhave0zpmxCludtvqXchXJIsw6sxutwsxhaTDaDSK+XyOBw8e3JicR1ITySrK+8tut+Phw4fiKkJggGAhgayzUYB3VYwOcrvdqNVqGxFTZ6vVamG1WomLEgBxmOTS5H0tkmnRbDKZ0Ol0LnwW0Xa8VCrJc5HgU7FYRKfTQa1WQzQa3QDrSBBi3CUdELdFK7KU75fgirKm0ykGg4HE1PB18P7mZ7y7O8Pf//trclg4HEWtZr42YOZyuRAKhbBarVCpVOR6pYvaYDCARqPZeN6TlEY3qV6vJ++R12ooFBKgfDabodlsolQqyUKFhFUWbcE7nY7YpReLRSSTSbjdblmQOJ1OiRlMpVLw+Xxiw88FdTgcFmCv0WhgZ2cHOp3undwAqbQ+u1xwu933irBf0KIbKpWj2xTrVCh6PB40m0243W60Wq0Ln0mdTkfAL5KM9vf3pWehapEuTkajEbFYTByDlK4PjJ+Zz+dCSH4Xtf9oNEIqlRLQMvc2eplLcIK7BC17vR6GwyG0Wi2m0ylKpRKm0ykMBgPMZjN6vZ70Tb1eDzqdTgDss0tSr9eLVColYpbbgr88m64bmUGirNFoRDwel3O1WCxCq9XKOUpXXAorON8oiyQ6LkmVsaZ8Lni9XokYOVtcwAPrvv+qZRAdiDQazc+U+1UmkxHyrNPplCWKSqXCfD5Hu92WxSXFBJwZB4MBHjx4IKKP6XSKYrGIer0OnU4nYh+qvLVaLZ4+fYpkMnmja4ZReHSxnk6nQjhQOjheVvwzvV4Pg8EA8/kc3W4XRqMRs9lMSEPECFwuF1arFRJvnUTb7bY41Y5GI7k36LZUKpXkGqQbndlsFrEUSdt0KOLvZKQFHdroxjsejzGfz2Vu6fV68hzbVsRY+GfT6bRE9O7s7Eg/wM+C/QLJj3ToO3ufcOFVqVQwGo3w8uVLuN3uc9ewMnKefcC7PGv5s/r9vsQSrVYrvHr1CtlsVsikdI/l2QWs73+fzyektVqtdq0FcbvdFmcdjUYDo9EoUeD7+/uCeVQqFREH0uGGv9tkMsHtdsv5T2yp3W6jXq8jHA6LOzCFXR988IH0Xb1eTyJP6CJmtVqh0+lkAcjnxmg0ktid2WyGcrksi8BSqYTf+73fQyQSuRGRY7FYoNFoSDRdNBpFs9kUV3ASF0nINRqNEqNzcHCAfD4vP2eb2yrj6GezGabTKSqVypW9Hs9kVqlUQqFQkJgauk/wGjSZTELGCYfD6Ha7aDab+OKLL/D8+XNxo6bLhkqlgslkkme9VqtFsVgUIvXTp0/R6XTO9ZNXRRi/r1oulygUCiI2IzmNhHFeVyqVCjabDU6nU+JvHA6HzAAkAR0cHFxbbESMaT6fI5PJIJVKiXCO8Zm9Xk+e0263G36//9wZ3W63JWrzuvNPtVpFs9kU56Rtr5n4WiqVEmegpCJF4TrFyM/RaIRGo4FSqQSNRiMYkzKel2T2/f39c3PqcDjEcDgU0jHdvekE2Wq1oNPpxFlLo9GImPgyR+Z3LeKUvG6IuXE2CwQC1yK3LBYL5HI5NJtNwbUogHa5XEKYrVar8vOVLqB0LWGvEYvFru3Edt1aLBZCCmu32+IyQuI1Bdc7OzviyM7vQa1WCz7AZ/11Y3jPVqFQQKlUEkdH4lycH/g8CQQCG7uOXq8nhCO/3y/nE0m7Go0Go9EIH330keDTsVgMer1ePn+j0YidnZ1ruXhZLBbYbDZEIhFkMhksl0shdLIYd0sR67YYxYuKhH/GRJ6engoxP5PJyOxB/IdufUxUIL6rxCPuspbLpRAGAUjPprwX6cJIxyM+X0m2ObsXWiwWIkYlCYezRTqdhtFolFmMJO5sNotIJCLEi0KhIL3YNoyKz2gKeu6inE6nuEhns1lMJhMhVQJfOvw1m024XC5xSb6qhsOhOFWz96VTUK1Wkx1Kv9+HWq1Gu92WufksSZEkdxKpKSzXarVoNpv47LPPoNPp8MUXX0icKF1Gb4pf2u12WK1W2asp7yeXyyW9TaVSgc/nQ7/fh8fjgd1uRzQaRafTwdHRkfSHoVBIdjAqlQrHx8d4/fq1mEYQa1USEJXPU5PJhFarJUL/n8falhjDf09cw+12y/dP0q6yiB/cVew5nUHb7bZck3S5Za+q0+mETExxEgAkk0lMJhOJh+TrZu+unJtIOCyXy2i1WigUCjg9PUUoFNowOKCbOl3ULnN6JibrdDrv1CDkvn4+6p6AdV/39QtclUoFn3zyCXK5nETcEZwAvsyT/vrXvy6W5IVCQdRsi8XiSqcJnU6HeDwuRJ90Og2LxSJADy35F4uFKLhisRjUajUSiQSeP3+O2WyGXC4nIAmXJXTv8Pl88Hq9MvwyEsdgMODNmzeIRCLi1EJ1Iv+72WzesOq/i6KysFqtilOEwWCAwWAQ9RAXECaTCeVyGdFoFI8ePRIlPxeu2WwWy+USDocDu7u7aDabaDQaEoNBFwtaqkciEdjtduRyuQtj2ZhhTdCHxYZ/WyTTXVexWESr1UK320UymTz3+XPBxkHXYDBsXapTlVar1aBSqZBOpzcUK4wGY7lcLrneO53OhcpeNm7z+fxSoolarYbf70c2m8V0OpXh+jql0+nw4MEDATZ4DQMQwM3n88HlcmEymcgg8/r1a1HmaLVaWcgRoKN6hDa40+lUYhjT6bQMAlqtVmyDI5EIgsEgYrGYxFVMp1N4PB50u11RMZ2enor1NwGsRqOBwWAg1uNUoV4UpUOnBDrY0N2OjhfNZhPz+Vzuz1arJdGir169QiAQkDgrLkP39vbeuxsB7eSj0SharRay2SwGg4HEUCgVmiT6ORwOIZTcFvBhMeLEYDBgMBhcqS4kkGswGO5kUUiA3uVyoVwuw2azXehWQOdEp9N556ri1Wq1Ecni9/vPDeGMY81kMgDWy8nd3d1r/Xy6D3a7XVHj85x1u90IBAIwGo0wmUzY3d3FbDYTYIFg9VdxhnJxOh6PcXx8jMlkskEmIklxMBjA6/XKYgKAKNDvatC9qPR6PRwOB5xOp4C5ymuRYG+n08F4PEY+n5clYTqdFmCTarSLwGSVSoVQKITj42P5/nh/rFYrGYqbzSYACBAIrAlqyqVet9sV1zie5VT8Kpf1/Dl2ux2tVgt2ux2JROLa33skEpEFnM/nk2UW3S/olECSFoE8i8Ui13e73RZHKqfTKctdxhycnJwAWD/PIpEIdnd3EY1GRf1HkKlWq4kijODWycmJRFJxob1arWC326XX6vf7GwtGArhcVjP+4aa1Wq1QLBZRq9U2FlMkQubzefh8Poksuq+7qdlsJnF27FHpDnBX0R1XFV2M5vP5hZHCFosFBoMBLpdLHEguIgWzlzn7LNJoNDg4OJAoVuUSVq1Ww+PxwOfzXfiM4yLqXYpEYjoN9Pt9iRSkew+JL+PxWIDacDiM58+fQ61Wo16v48GDB0LebLVa8qzmEh6AOOUo+2EuBeiKetuiwpvq8KvmKZKYjEajLIW0Wq0s08fjMVarlTw79Hr9ViL7YrHA0dERms0mKpUKBoOBOMfMZjNUq1Uhx4xGo63zKt1m6JrJ6PJtRVJMIpG41Dn2qy7lglmn0yGZTG4s0hjRabPZkMlkJKKLfRMX97/+678Op9OJly9folQqYTgcQqVSiauH3W5HOBxGPB5HLBbDr//6r1/7/GUk+mAwkPmPs1qtVpNemg5PFxWfifV6XRZeT548QaVSkRmCcxlJUSQPK/sjg8GAfD4vDhTAlwQ8KqSVi22bzSbxKFqtVj4bvhYupcPh8EY0FaPfiAE0m02JpTlbJIF1Oh1Z1haLRaxWK0QiESEtjEYj6HQ6WSpotVqMRiN4vV4Mh0PY7XaJ9CQRkwtCztKTyQQ/+MEPsLu7+1akppLZb7FYIBaLweFwvJOwhc4+7I/4maTTadTrdRQKBXFrJsmj2WxCp9PJWQJAHID4/i5ywmk2m/jJT34ipC9l3xaPx+Hz+eD3+3FwcIDXr1+LeGMymYhzLTEkq9WK3d1dPHv2bKNnpVPBm7fWri9fvkQoFILL5ZK4aKrmQ6GQ9KOcVReLBTKZDMxmsxCPSNaoVCoytxUKBRSLRYm1NJvN2NnZEcLqVdXpdOQajsViACBiLgqwlMIc3lfhcFgcbEgwUfbH7Bn5szn/0MXl5OQEe3t7Fz67WezJ6U6gJIwCEDwlm82iUCjgxYsX8Pl8SKVS+Pjjj+WzmU6ncLlc4vo2Go1QLBZlyTmZTNDtdsW18qw73PsuxlKejQ32eDwolUqC/xCzoQsHvx+SOSnmpMsxPyeTyQS/33+he9NlRbcr4EtXquVyiVqtJk4eyWQSfr9fRKLKIgEomUxe+3lIgn273b6SpGMwGGS+4nL/OmQmzkwkl1SrVRQKBXGLdjqdSCaTIp4iqTISiWC5XOLhw4fnFqzKyCqKRfn6RqPRhgBrPB5juVwimUy+N5I2o04pilT2b3Tr6XQ64nZ5Uc1mM7x580YIrpzp6DSl0WhQq9VEbDidTgVr5HdXKBQwGo3QbDbxa7/2axeKVm9bxHxqtRpyuZykcVgsFuTzeYlNJsbebreRSCQ2rkdGoDUaDZjN5gtdWC6rUqkkRC6SRbirYLJCpVIRgSZjGWez2YbQirM9SYtqtXrDxYuOgHa7HR6PB9VqVcggs9kM+/v7V0Z9A2tnOYpESqUSjo+PhdRC8prJZBLy1dlUCAqg6vU6ptPpBnGf5xoxNpfLJUJu7jj4fPX5fEJ45axFsuB1cbnr1mKxQKlUQqPRkO8BWGNRJFCGQiG5Lz0ej/RH0+n00vuVJDv+mZ2dHSGdOp1OTCYTFItFLJdLuFwuNBoNEQROJhO0Wi2cnJzIWe1yuaSfYTQdsRolXnebojkBI9d2d3dFmFKv14VgTeGM0WhEKBQSofN1yLS8D+r1+jmnarqrc+ZuNptotVqYTCZYrVYbc/VoNBKSPt2Z9Xo9fD6fxLVNJhOcnp7CZDKhVCrh8PAQ7XYbRqNRjBuuW9wLORwOcYHk2c7+jcYHL168kGvYYDCg0Wig3W7D4XDg8PAQdrsdBwcH4qoLQJwBo9EovF6vfO56vV76XOXrtVqtsqu5jgvzRbVarUTAwc+ZO8DbENVuUqenpxuJMeFwWDDm1WolexA6/W1zYiN+SXz9XYtkXWKbjFlV3uNms1nMBTqdDkqlElwuF+LxOKxWqwglSLxkioQyCrder0tspkqlQr/fRz6fx2g0QqFQQCQSkfsgGAyi3+9L+oHf79/oe+fzucyxFHC/bwfN+/rZq3sC1n3d1y9o1et1fPLJJ/jss8/QaDRgMpmkKWcjZLPZYLVacXp6im9/+9sIhULw+Xwol8vIZrPQaDQol8tXDlp8eLDhbLVaqNfr0hi6XC4430bzHRwcCHBMl5xutwu32y0PUi6dOcjQ5YrLVafTiVgsJo0AySp0m2ITTlvR2wxil5XX6xWL93q9jmg0CqfTCYfDIQ9eAjEkqxweHsqQrFKpEI/H8fz5cyyXS2HRM7aDDeDZgf/k5ETAg+FwKKCIErCgg8V4PEar1cJgMBBCDpfdVqsV+/v7W5UwjDjhMtRisdzYFpzqs0ajAY/Hcw5sJ+C9Wq3gdrtFyVyr1aRpZewYlREnJycIhUJCmCABq91ui/qRSmbmvl8Wy0YFBF11LisOtJ1OB91u90ZuR4wAYQwI3dLOkmb0ej1arZaoFRnXqNfrBQgdDAbodDo4PT2VyAMy7rmYikajAv5XKhWUSiWUSiVZRNRqNYmwIKBst9uRSqWQz+cxGAxQr9fFEl55ZtRqNRiNRonC2VbT6RTj8Rhut1uGDqojSAClMoif+8HBAdLptERMMk+eC9J3GVK3Fd/LWZUsl9B0Plgul5hMJrIcPDswDIdDFItFGXSpSLtt0dq82+1K1CNJFLxuqHwkyE7g/l0iRJQVCoVEpZ7JZM451zHKhrbT78N+n4rWcrkMv98Ph8MhgCeHN61Wi3A4LPbHXNRdBYbyz3c6HXEkASDXO8+c3d1dAfFJoPmqi4QfYH2tvXnzRsAfOiyazWaEQiEsFguEw2EZhvlMvSuL88sqEAgIYTqbzSIcDsNsNsuQSgIt3U9IPs1ms0L45OB8WTGihsA+74+PP/5YgFOeoXwW6HQ6DIdDOBwOObep6OfgvVwu0Wq15M+fPW/4nF8ul9JTXLfoPEhwis90OqhZrVakUikYjUakUil89NFHmM1mEidYr9eh0Wjgcrmg0+lkSUJCPZ1GSHyjsymJnLx//vIv/1JA06dPn8r3RoeSTCYji1yScmkvz6WmklzncrlQrVZFPXgT8g7B9kajgXK5vLGYogMLLbybzSYeP358p0rrX8ZarVbI5XICJDEqgctCuoZeRY64i2KPwAiRbcVnIZ1AS6USHA7HBnjJ5X+9Xhc1+rZnERfjvH8BiJvG+6zlconj42MhzgQCAUSjUZRKJek/h8Oh9Gd0zTs4OMB4PEYkEhESCGcbLtSValv2i7zXz74vjUaD5XIpP+M25fV6xR2UM89FxZ6P4DTwpUKbi1Dlssfj8VzowkPXp0wmA5PJdO76ZNQ2Y8Kz2aw8M1kE10ejEdLpNAqFAkKh0LnfNxqNkM/nJcr3ryqualvR2Wo6nSKZTF4ItPPMZ1QfnZLUajX29vYQCoXg9Xrh8/lQLBZFXGE2myUSx+FwYH9/H0+ePLkWoL9arZBKpVCr1VAoFPD69Wu5x0lwGo/HUKvV8rwKhUIX9qwUZCyXS4xGI0QiETgcDnHO0Wg0Iu6hOOkv/uIvxFXCZDKJcxwXniTBcOEzGo02nJj8fv9G7BLvMy57SX5ibAx7lUajgW63Ky69JLVuu9e4fKtUKpjNZvK9cPlEwj0X7VarFUajURbfjLHvdrtCauJMyPuK3x8XV3RooIMQsRQSwPf399/p2cq+XKksn0wmqNfrQgwlEX06ncrnCaxnGZLn/H7/xiLuLOYwHA7x+vVr/OQnPxEiCZ8hfF+VSgUOhwMffvihYEU8r0gspMtNoVAQQms2mz3n0uP1evH555/j5ORECPPExOiA6/F4JEaRM0q/34dGo5FrptVqyWzPKMLJZILlcimL28FgIJH2RqMRk8nkWhFadCZdrVYbnxcXrX6/fyOOmn1Xp9NBJpOR3qtSqUhsOZ3Z6fBFYQojWur1urh4J5NJOd8peJvNZuK2xfOFRIWzfbNer0coFEKpVMLr16+xXC7FlYlkJsaojkYjmM1mETxOp1NZ/qtUKolT/SprsVgglUrJd6jEzaxWKz799FNxxyNBkN8TXe0Y7e3xeIT0ulgs5HsjCei6RUcoii+JwZJwx+cgY5O4FKZrg/K9kbDDWKbr4l7Eb3nGXVV2ux35fF7O5atmieVyKQKter2OVqslca2MZeaylctXr9crhFCVSoVsNisO9cCXkVXKBbnVaoXVapWoeAqwGG3L5ff7cL+q1+soFovikOZ2uxEMBsVZqNPpyP3L73hb76sUs2WzWVitViSTSSGCmkwmzOdzFAoFdLtdaLVaxONxefYCX7oItVotIbAyAvCuqtFoSJSUyWQS4Q1nfADY29sDsCZ6U4SkJI6StEUiU6/XuxF2SFdQxuQ+fPgQ5XIZuVwO8/kc8XhcXA7z+bxgqsFgEK1WS6LC6c7FBT9dbZXOKuwTer0ePvjgA3Fz4jNCrVbjyZMnV2INWq0WDx8+RDqdhslkwnA4FIdYJjbQ6fEssZfXBJ/JymuaBB4Kv0jwHQ6H4kI+n88RiUREWMJkktlshuPjY4zHY8Ff7qqm0ymOjo7Q6XSk91KKmok5t9ttHBwcyP1fqVQkXjgWi23teTiLUVy8u7u7gZ/x7CT5k3uOo6MjHB8fw2AwCBbZ7XaFQGy324WArVKpEIlE4Ha75Xq+aTFJg585e2I6gBmNRomD43dKYQ77oOvggryWuMc5ey4Ts6GpQK1WEyMGEt5Z3F8Qq6Jj/mq1Qr/fF8IuxRFWq1WEV9zl7ezsXBvnZlrKZDJBJpMRIgzfQzAYlLhaisU7nY7ED7JHttvtW3dldJ4jjkd3XwqVtr0e5ex+G+yh1+shk8kIfkPsWq/Xi+gjGo2+FzIP+5ttiTHsI0gspPkDRUG81yjm5zX6rnsdkr5o1EE35LM7ALvdLvcqe8l+vy9Rum63G+VyGYVCQUwq+NoY595ut8Uggf0+k4xINp3P5wiFQhITvVqtUKvVhLTLeWM0GkGr1cqfpXPgff1y1f03fl/39QtYi8VC8supEE4kErKYVKlU2NnZkQWpSqXCp59+iu9+97vQarWiJKnX6zCZTDJQXPb7SGSgcw7VhiQmcYGn/DnD4RCVSkUUIg6HA8PhUABFMoc5IKhUKrHit9vtiMfjAgyFQiEBYxin0O124fF4LlSj3rbYoI1GI2QyGZRKJfj9fnFvsNlsAoR0Oh2EQqGNyAIA0shz8dVoNKRxIXhCZSQJRRy68/k8/uAP/gBOp3PDRrbdbotdbKPREAUABwoqcOhu9vDhQ3nw93o9VCoVUVUqI0SoJrpu89tutyXuSKkC4GDEhok1HA4xmUzgcDgEpGZT/vLlS6RSKZyenqJWq4mCgw4hGo1GVEPKxQSvhYuK1+x1QQSDwSB/57ZlNBov/H20zu73+zCbzUImiUaj4iLHuAOq+WnxymVLJBIRNwVgvWBntM3R0RGANVkykUiI+5ZGo8Hx8TFqtZoMIbxn+flZLBasViuMx2Ok02khDe7s7Gy8BzbYVGFzIKGSjKq1s0Qmk8mER48eIRQKoVgsIhwOIxaL3blV72QywaeffopyuSy27lqtFk6nEx6PB41GAzabDbu7u3IvjEYj7O7ubgVCCKqenp6KAvddCFjAesiv1+sAILGMdOsB1mdPq9XCcrnEbDbD7u4urFbrnahJAAhAB0DU9ycnJxiPx0LWsFgsAi5dZ1Fw06I6l4qWWq2G5XIpURokk7rdbvh8PpyenkrE0mW22gTX6JDndDoRCoWg1WrlGZPL5eDxeLBarXB4ePiVucJcVF6vV4iYJEzQeYyKLIIGBILpBKFUp73PIhixWq1QLpdFMUnnK6ps6cLndrs3LMx5rl5n0UDiIbAm6v35n/85jo6OxBqbiiwAEhvKaJlvfvOb55YIVOFz2XMblflVpSThbqudnR0BmVUqlTi+0HWA17bJZJK4rUgkgnA4LORYn88nC0/2d3y2U8Wdy+Xg9/uFkGa1WoW0RyUsF5cEK3gOu1yuDZW1xWLBYrHAbDbDZDK50X1CIlAmk4FWq5UoOMb/8D4l6TqXy+Hp06fw+/13ds79MpWS8FYoFDAcDiU2lssXjUYji83Dw8Ofic+Z7hbtdluI5ASzufSnmtbtdl8ZBUOF6ldVjHEul8sIBoPS/+v1elQqFYlIm0wmAtDZbDYEAgFZJno8Huh0ug2HFS7E6MpAgJ3zUb/fl+WrWq1Go9HA3t7eO4F8dK8iiYnk6LNnZb/fR6FQkLlLCQar1Wq43e5rzxEUkdBFZ5sbHtX92WwWxWIRer0e4XD4XL9GYjnjDI+OjsSJeLlcyrxBcDkej793IuJNikpfRv5eVna7HZVKZQMgZpnNZuzt7WFvb0/cowjm8ztmT3TdYsTY8fGxfAec/WKxGGazGfL5vMTR/+Vf/iW+/e1vy8L07Pvs9/syx9OBEvjyuy6Xy7BYLBiNRtIjqlQqzGYzdLtd7O7ubqjySYCkuyudoNhTGwwGmbOB9ZzGSF/Ge1LQQyICP1sSp+gkVa1WYbFYUKvVMJvN5Dqk6wzdfyORCDQajYivFouFAPHlclmIshRiMVpYo9GIcxZdkdtv4+09Hg8CgQCCwaAQN9grGo1GIcGYzeZzrrKMB2QfQlI8F3gU0rEfZbEv5xKQ3yE/X2IhJF1S4AWszwOz2YwXL16gXq/DbDbDbrfD6/XK/EmXlkwmgx//+MfI5XISRUtXhNlsJt/RYDDAD3/4Q/j9fnmdz549g8ViQafTwaeffoperydxlp9++qkQNpWEjFwuJ0sXu90uUYT8LOiKwxgjl8uF09NTOJ1O1Go1uS8493IGJlmdQh+la3W9Xsfz58/x5MkTIYVdVTwPty31GM+lvE9JdBkOh+L2xz6aJDjOvYyhrtVq8Pv98pztdDqCD1HsoXQhoONGqVRCq9XC3t7e1vmZBOVCoYBsNgutViskzWazKdeHzWYTUhEdO4lfkHTXbrffe9y6shaLhThbFotFORt0Oh3m8zlSqRTK5bLMmXt7exu9B8kCZrNZHAvNZjPC4fC57+261e/3RXxJxyT2BnSZtNvt4gIVj8dFpNlsNnF0dCTPPC4J2bfs7e1dmwimJHtf5zmi1Wrlu7sOSZzYNJfRJNGOx2P4/X7o9XqJeOXPt1qt8Hq9mM/nqNVq4prJ90T35XK5fC7e1Ww2byzVl8sljo6OZBF717VcLpHP58UdmEQSFkVoTqcT2WwWlUpFzuez1z6dFvP5PJxvY4v5O/iz6KIOQM5Nq9UqRECScvg8CwQC8tneVXHJTYIKzzL2sZPJBG63G0ajUQgIFFwozz2bzSaR3XSYuslr4Jm4s7MjxAar1YpWqyVkW35W3D2YTCYhg1NsTlyOpUwe2dnZEZIr43fpVqrVapHP52Gz2a4dGa7VarG/vy84AQlY3Ed4vd5zc0+9Xkc6nUalUkG73ZbvnPG1x8fHgstTHOFwOJDP54X05/F4hLROkT8dtxh5xzjHb3zjG9f+Hi4qpaCFRD2KQPm6SSrjzurhw4cwGo0IBAKYz+fI5XJIpVJCsFGmtBBbByDntrKIh1Cgy8+JCR5OpxMHBwcbkdVMfODz89GjR7BYLNjb27vxfLFarZDJZOReabVa0sNS1Mj+gs79Z/u3m5B56bI+m822kpsZt0zSuNlsFnI9RY0ul0uI4nSV0uv1QoKhKIoCUrosE5cPBoMolUpCcGJ87nUqGAzKOaV0pyMBkmRuRtcnEgl5XlksFvh8vguj4fhzmF5zFQl3OBwKRnub/qTT6QiGQ3MLkugY38temETIuyxlYsxZTJk9odFoRLlchtPplH6/0+kI/kqnX0Yzvmvxe+HszpSSs8UUJLqH06HZ5/Nhd3dXZkcSCinAIZbBOGX2ZwcHB3j27Bm8Xi/6/b6QdJW/i27ker0eXq8X3W5XZiuSGS0WC6LRqCQ13NcvV90TsO7rvn4Bi0qfUqkEk8mEcDgsC7jBYCBAv81mE8tIh8OBarUqmcW0z/b5fOI4sa1oIc4mdjweQ6/Xy7KD6sFtoDsbyH6/jw8++AAWi0WyzK1Wq4D3FotFmkE6S5FcNZlMEAgEpKEmKDQcDjEcDhEOh98LIzwejwvAUSgUcHR0JHm/ZGWzEfJ4PBsRau12W5zJ2ER0u11RCQAQBw+qJlQqFbrdrgyCx8fH+LVf+zVpGBeLhbhmtFqtc3axwJrcUa1Wkc1moVarkcvlsLOzg1qtJmoDpSsMAHEEYfTezs7OlaocpcMIwZVSqYR2u43BYCDkCr5PEsTm87kMfRzeGo0Gjo+PMRqNNr5fv98vKr1yuYzVaoV8Pi8DOe2ILyq1Wi3q4+sU1WLvA9xTxgJRVcGFEocR3keMcvH7/YjFYphOp8jn81uJTcCXUY9utxtv3rxBIBCA3+8XW2gy93k90M1NWRy+k8kkCoUCOp2ORGUAEFY/iUGM6BgMBuJiQfX2ZcAi3W16vR7q9fqVxM+bVLlcxkcffSTKrul0CrPZLMB8Op1GMBgUUidtYuk8c1Hp9Xo4nU5ZdF3HhemyMpvNQvar1+s4PT0VIqVarRZ1KcEAEhjushgz9vz5c1QqFXHyoysbAfb5fI5KpbI16/1dajAYyAKECxulGpOqVJ5zRqNx4+zcVnREKBaLWCwW2N3d3QCHqRJ2Op3I5XLQ6XTIZrN4/PjxnbwnkmgJhtxkwelwOPDs2TMByTKZjAChPO+azaYAYc1mU5SPX5UqnI4iWq0WPp8Pn332mSyNGS2l1+slSgNYuyh98sknsli5zr2jjFT54Q9/iBcvXuCnP/2pxHawqJylkxtBZqr0OTiPx2NxtHA4HFtJUuyZuBC+62Lcn9lsFiKuy+WS+5oxjoVCQZxBuMxzOp1CTuBilgsIJQjDPmI0GqHT6cjiQOkswueyx+ORCBQSe88+E257v1N1WCwWJb6aryuXy2E0GskiuFqtolwuCxmCNvh3Ad78MhXjGtLpNLRa7TkXITpJFQoFIWs9efLkvTlE0aa/Xq+LcOOiCoVCqFQqCIVC8Hg8cDgcApaFQiGJyd3Z2bmxU+v7LmWEqPIcNplMSCaTskiazWZwu90SFe10OoVctb+/L8sdLgm4VOt0OggGgwiFQhL9xvODzjQkPdB96l0W1NFoFIPBAPF4HPl8Ht1uF3a7XZx2ut0uxuMxPB6PzArvcl4qXZ8u6zFUKhUCgYAsn7ctrlQqFQ4ODmR5QjEIF/0mk0lc1GKx2M+E+5VyLnj16hUKhcK5a2lb0f2QzzkSBIAv3aEpkohGo5e6mV3nNVarVSFb2O12EYQol58Oh0OIbpVKBZ9//jlGo5H0NeznGOtGIjCfWywSiXjvNJtNDIdDGI1GWK1WfP3rX8fh4aH8+UePHmE4HKJQKKBcLovDDHtaq9W6EeHOZzyXzsrXQAeUTqcjTrQUvRwcHAjYTjIlIyYACADf7/eFuEjSCmNCp9OpOBszaplYBh2Ya7XaBjlsPp9Dr9eLOwBJNFarFfF4XBzGvV6vqPmVPRaFTIwo7na7KJVK0t+TtMHzh2IzLsk5mxIHYYx9qVQCACHl87unsw9jPOr1ukSqUKRGwk2/34fBYEClUsGPf/xjIabThYwENRIh6IpWr9dF2Perv/qrMBgMSKVSIvhj78GfcXp6Ki5AwWAQtVpNiIlK0l0wGMRisZCzQxn/Q/IDsQy1Wo1ms4lAICARRHQlo0hnuVwK8Z9EGUYcGQwGhMPhS/thumjzsyRBjOp+Om2R6JXL5VCpVFCv15FIJGThyQjFTCYj7lXsJ+k8RQEBsbZ0Og2VSoVSqQSr1YpGoyFESC66hsOhOMcxvoq1XC6RzWaRSqUk3oyxfIxIM5vNgqMx7on4kc1mk6iYTqcjhJKvKjI2m81KHA/vNeWzjsK4bDYLAPjoo4+E2MoIJM4UgUBAnGiIs920ut2ukBPoKkdhK/EUm82GUCgEk8mEeDyOaDSKTqcjoiM6m9N9lAQxiq2uW8r5VklyuqjG47GQJ0g6cjqdW3GXwWCAVqslghun0ynRtyQB8Bqku4ler5fX73a7cXJyIrFCjF91OBxCoqjVapcSnYmJ0C3jrqvZbMpZHAqFLvz8DAYDQqEQ8vm8uCyfnV8pZgOwMQ/yrGo0GkLCIrGKczDnUt7PNpsNGo1GPjuKd961RqPRRmSl8meS3At8OXPa7XY0m01MJhMMBoONa1NJKL6p4ytduBidDayvF2JH5XIZHo9HFujHx8ew2WwyjwQCAQwGg3Nx7iSKkzBNYhQdsdvttnw3nGf4ed8EAzUajdfq5ZTC8X6/j2g0eu7+HgwGKJVKmM/nklBBfJM9CZM5BoMBJpOJCIyVJJpKpSIizkgkIpG8ynQPr9cLj8dz5azAXiGfz28lcZAoyZ0Ae4dYLIZIJCJ9HyOTSZQGIO6pJLCwHzlbGo0GsVhMHPrNZrP83Hg8jt3dXXEBY2wm3eZIMH/w4MGt7hs+v+nIZrPZYDQaxTW1Xq/j6OgIu7u7WK1WePjw4Tvtvjg76vX6rd8Nia3cHfK75+6H8cUApB9RkvaIx1mtVgQCAZnfST5cLBbQ6/WIx+NIp9Ow2+2o1Wo3wrsZEarX6zfSXVQqlRgjOJ1OISWzn7vqmeV2u0VYSueli2o+n8t9dhs8ltGMNFYIBAKyDwAgPR8d+AC8c7zl2aJjt81mO3ctKJ2Fa7Wa3KfK3UG324Ver0cikZBkk7sozk69Xg82m+3C/s/j8UjsOGdRzo9OpxMffvghTk9PRZxCbICELb/fj+VyiWAwiHA4LHgozU1SqZS4IBJbdbvdcLlc8nuU6Qcej0dErPcErF/Ouidg3dd9/QIWWfLT6VRsi2lVyuGWFQgExG0hm81KU2uz2VAsFkUJua1xoDKRKgq6cVCNUCgUBGxaLBbnhgMC9srIL9rQM+qMsTlc9ms0Gmg0GgyHQ4lII+DFRRLtX8nI5mv3+/13tkBlrCAJEgSNmHFNJYzRaMTe3p6w1bvdLk5PT1GpVFAul6HT6XB4eIhyuSxWvQRPGBtJcFalUsFut6PX60GtVuP58+f4rd/6LahUKlF8NZvNrQMVAHEL4GdpMplgsViQy+VQKBTQ6/XgcrkE+ONC55NPPhGHhJOTE+zs7FyqDiC5iQMw1T6lUgmz2Qw2mw1OpxMqlQqTyUQsOieTCfr9vsSeud1ucbbigEFAkrnJXq9XlLJURxFMuUydZrFYYLVaJb7msoGIVr6hUOi9RHpR7TwYDLC7uyvfEQcZgkokv/R6PSQSCVEuKpVXl71fqlGU8YFcNjBmQKkA4qDEppEgAwfwH//4x9jf3xeQhEQvDmEGg0GIEdVq9VpqLqfTiWq1Kr//LpaqJF+dnJwgk8nAbDYLcWU0Gm2QqKbTKQKBgCwLrvuaU6mUEAjfhYBFohMHQYL+/IcqGlo9A7cnQ1xWJFuZTCY0Gg3Y7XZRPNGCn9GL8/lcCH13UYwYoZsHB3SNRiOAULFYhNvtls/pKvUs1Y39fv/SxTCVT7RKHgwG73TPM0KLLkssRl6eBRwvq8PDQ4lTKhQKcr1qtVpZTjgcDgSDQSH9vu+YLWWRXJXNZiWK+ODgAD6fb6vVMsmDXPgVi8UrVf9UndZqNeRyOXz88ceycCShkvGMdE9rNBpoNBpCsuKSnuRUDtTbwE5ebwRP3sfnmclkcHR0hI8++gjA+tnGODGbzSbOViQ6cOEWiUQwnU7lOUfSKyOKfD6fABJ8joxGo3OkYzqLUKlot9thMBjE6W7bc4XuQ+xPrlv1el0A952dHekBGJPUaDRgMBgEvCgUCpjNZnjx4gUSiYS4+lzkJnZfm0W3Sqrkz8ZQAOtelp8nl1NUOPPcv8tyu92oVqvQarVot9uXgsRc4O3v78PhcEhkCt01vV7vO7tOvo9aLpcS4XBRX6Z0RF2tVjg6OsJ8PhdAbrlcwm63y+dlsVgwmUwklp33nslkEvdhuk8xirtarUKlUgmZ6fj4GAcHB7c6x7RaLQ4PD3FyciIxG3TqYpQlBQQ7OzuX9qPXqW63i263K84il5XRaBQnAi4uz5Zarcb+/r7EJZCwAKyFJozmu0nE7LsWXYaWy+WGC8HZuNBisSiOvzwz4/H4hWfv2eg89vwGgwGlUgk2mw2xWOydne74Gunww7i3s+A/r50vvvgC8XhcZnQ6DZBYn0gkYLfbYbFYUC6XL/y9vHc4I9hsNoTD4XN9ncViEfHIxx9/LGIzzlKcXRhNRUIRlcxcAnE5bDQa8c1vfhMGgwEGgwE+nw9er1fIJYzW8Pv9QoZkFCSdIxjJXq/XJTLHbDajWCyKCxOXaiaTCU+fPhWCGWPgZ7OZ4AXBYBBWqxV+vx8WiwXFYhFWq1Wi0zkzUrjBGo/HODo6EmFXqVQS57ThcAiLxQKv14vRaASn0yluIOwT2EdTId7v9+FwOISMxyXkcrmU+5Nnlk6nE6HJcDgU8ilj6F+/fi1nGqPBtFotEokE3G63RIp4PB6Uy2WZD5RRIZFIBG/evAEAIZiPx2NZTplMJvR6PZRKJeh0OhwfHwtxij0XXfJ4XlKMZLFY5H2SjEcXKrvdLjMJF8Hz+RzL5VJmieVyKec8XcL6/T6MRqM4UF3l6KskjRAHKBQKQkygmK1SqaDb7SKbzQoOwBnd5XLB5XKhWCzi888/lzhSr9cLo9GIcDgsS3IuzUjefP36NQwGg7iskCzCZ9nr168lrj6fzwvZHoC4vDHmiktVg8EgmN5wOES9Xpf7y2KxCOGCfYHZbEalUtkat05HLmUE013UdDpFs9kUJz6SeJTVbrflGspms+Lkzxml3W6LAy2jz0muvikBazabiUM88SzlghZYP2dKpRJyuZycb3Saczgc6Ha7QvwBIO6Vt+n9bDabkOyV5JKztVgs0Gg08PLlSzmDuVClIxCX5CxG2dMlqFAoCBmHmCV7Bo1GI5FJnOP5uugAx+JCfjKZiMsno4h4zXJuJAmdkb13XY1GQ4j7V2EPxMC55FXORTxj6Dyj7Pk4LzPimm6UxGFJlgAgPSnPvcFgIAKnu3CUpfh2Npude790xaNTEe/5er0u4lFl8TtlLO11i2Qz9tcsZe9Ur9eFWMQoyG63i3A4jGfPnuHzzz/f+h7oBknhFIuEW95zLJIAeX7dxLXoOsX4um63i2QyufVcJKmY53ez2UQoFBIMcLVaoVgsCp7PXQA/f/YiTOz4wQ9+gN3dXSyXSyGec4ZjLxiPxy+dBSlo0el0l56RTqdTovOsVqv0QEx+oEBAGfNKDF2tViOfzwtedxFOyJjr2WwmJGoSigaDAZxOJ/x+P6LR6EZE5Nnv+ro1HA5Rq9UEc+UzREkq4nn/2WefibDka1/72q1+33WLLkAOh0N6aJKshsMhNBqN9Lrc7TmdThgMBjQaDajVavh8vo1Zkb0TI+YsFosQFum2fxHRht+70rmPzrB05Cdhjs/A25xhJEq6XC5Uq1WYTKat99FyuRRhAjHfm1atVsNgMBAB4tlnMt3PKDQgAfGqZxOJhtue+3S9Y3H/cNG86PP5xLWY+zJlWgt3km63G4lE4s5wVPYwJLheVhqNBtFoVGbtSCSC/f19EcM8efIEqVRKRKx0KyOJOxQKIRwOIxKJbJwLOp0ObrcbrVZLXML5OSmToO7rvpR1T8C6r/v6BSw2zVxYVyoVWXBSqUaVh9lslrg+5mlzIONDcttiezgcikvPcDjcSvoZj8coFAoy9BP8ASCgCBsyJYu83+9LPNh4PBayQfttHvRwOEQsFpPmQa1W4/T0VKI96vW6NNRkhlNddnBwcGdADEFlWoB2u92N5o6NpZKpTrvxXq8nSl/asJJIZrVahSxAVxEOgQ6HA5PJBC9fvoTb7RZgmMoSpdrrovJ6vUilUhiNRnj+/LkAdsphjNb/o9FIgAwCnWSP22y2c4pa4MtlCAHIbDaL09NTzGYzUdpx0etwODAajUQtqNVqxZ2Gn0kikUCj0RB7fA7kXOi7XC7Jp+byx+v1XqqG9Hq9aLfb4hh2WSwK4/mY9/0uRetdXidWqxW9Xk9iEfgPXSqy2awoZ6ja5ffCBRjjXmiZrRxulU04ozXOghYEDNxuN3K5nKjjCBoTnBiNRohEIgKQEeCx2+1wOp0yOE0mE7GHZuY2gGsBCQTlOXy9a7VaLRwdHSGdTiOfzyMSiWy4KcxmM1GW+3w+DIdDsSomueeq4qByG9Xd2apUKhgMBtBoNPj617+O+Xy+ETXE84AuDqvVCpVK5UriCkmsAGT5dFFNJhNRyE0mE3zta187d6ZMp1MUi0VkMhlZnt2FM81sNtuIIEwkEhvni8FgkFhLgvfz+Rzf+MY3LnVS2aZuvKgIgA0GA4mIvU3R9pruDsPhUK4pp9O5cYZeByxUq9U4ODiQ5RXjeql093g8SCaTYnH+VxEjRjDL7/djOp1if3//0nuIzgy0lb/sOp7P5+h0OohEIiiVSjg5OcFgMEAwGBSHLRZJ3Yy2Y+SowWBAIpGAwWAQpRaXqGdrsVggn8/L8vN9OC8Vi0X8+Mc/FlK2MmpzsVig0+mg2+1iuVwiFothPp9LxHOn04FGo9k44202m8QfkaQOQGJKGU92tkwmkwBSJD+S2Mylo91ul3ODjn8Elq5bfD9KwKpUKolK/yyhht8Z+75yuQy1Wi3Pm/u6vAg6lkolRCIReTYri1HYzWZT+th6vY7d3V0h6Ssj9N61uPzxeDziFrPtjGU8IiObHj9+/JVGCL5LsXdh7PtVRfKHsn9g30+HDYLts9lMVKfsYakkZmQonSL8fr8QMXO5HDQaDUql0qWL/ctKp9PhwYMH6Ha74hLDIiHlOmr26xR71esu0jhPXuVqy+spmUxKH/5VOaeweK9xmans7Sji4X9n1LBWq5U4Dl4rfJYpq91u4/T0VNwZ+v2+3DdKl6XhcIj9/f13IliSNDCfz6HRaETAAHxJQuQMr9FoJGaHDiI2m02i8bgsocMzsQqSjs9+R+PxWOYg/v1t193Ozo7MriTij0YjqNVqcd6YzWZCIlTG69KFmvMmHeqSyeSF4pBEIiEk1nw+L851dGopFovo9XpC9jIajZhOp0JWslqtWK1WGAwGiEajspTk85gEH/YCJKDRKXw6nQr2QDebVColTpp+vx+z2QxHR0cbbpTAun+Yz+dwu93yGbCnC4fDMs/TvULpYp7P5zGZTMR5tN/vYzQaod1ui6iq3W6L48psNpP3S1eQSCQCk8kkkZUU7U2nU4ke5/1LkgtxAcZkMYYJgAiDSMjy+/1oNpsyH5tMJiwWCwwGA5TLZekbJ5MJHA6HLGLPzhYk1jH6hWRBzu90RmJUJJe5fN8kwzmdTiGo8R4CIJjAZcXvYDwe482bN/j444/lWjIYDFCpVGi1Wsjn88hkMuIkNRqNhJDtdDpht9sRi8XQ6/Xw6aefIhAICPHX4XDA5XKde+7SyZTuk3R64+uy2+0S+1qpVKDT6YQ8xZmbZAaKDdmXkuBFESYd5+g2wmg0zkB0NmAP3263ZYZUipT4zGRkIX8fscLrEh1IOh2NRluFRxQncYYdj8cS02Q0GoV87vP5MJvN5Ay/zrProtfDBf22BS0Acb1Kp9NoNBrS67O2kWZvW1y6ulwucYQ4O3PP53Nks1lUq1UhU+l0OonIpPtZp9PB3t6ezATlchlv3ryRM4fnFK9FYqckMDFiXknQI/H2LFbDODhgjfmVy2WUy2VJNSC5JpFISCzj+ygmCFx3fjebzVuJPMoe9Oy1zZgog8GAWq0mz2SNRiPOfMCXswGJax6PR4iF74p1bauzi3mj0QidTgebzSbOd8pSOmID67mUJKG7iodkpDXPb+K3nEsTiQQikQj6/f6GW7vyNdJ5lsJkEpy3nTkUkAG4EwxUWYvFQgT3dFDaViqVChqNRnZDDocDw+FQHLdJ0lsulxIBzP6VBF/OAcViUdz7+exmj0PCjMvlwmKxwHK53EpSIZmYvcRV5XQ6pR/pdDoyuzICnQkp7KV5/lCcodVq0e12t+4F2NcdHx+L8Jbulq1WS6IYO52ORCS63W6Jbe90OjcmZDACjs8SugWORiMRAyyXSxFyp9NpcUS7rQsWcf7pdHrhHKt0XWu32xIHZ7VahRjL62E0Gsm8QXE3BQvK+5oCK16DAITgRWHlWbyg3+8jm82K6InnuF6vl2dDPB6/0zM7Go0KuTKdTotoUKfTyW6E/Smdn24qKF8ul6jX63KuXTar0d2YyR28VrZVpVIRsj4JkQBEAEDCPK+d6yTGEMulw5zBYEAkEpHnpc/nu3MDA6/XK71fKpVCIBDYKshjskogEMB0OkUymUQkEsHTp0+h0WiwWq2QTqdlZ6x049/d3ZVnAB2Pz5bD4dgQef9VYO/39fNV9+j1fd3XL2BNp1MBx2kxr1arBZiiU4bb7RalJZVrBIpIvrgoLokqil6vd6GKwmg0ij0jFwVshPmzCW72ej0YDAZ0u11RBfN3M5dXCbzwPdH2kSoyi8WCZDIJt9stMUzNZhOpVArhcBir1QqPHj26U5XzVY0Rq9PpiIpqd3dX1FR06Wg2m7JwMBgM6Pf7Mvhx6CuXy9JsuFwuJBIJ/NZv/ZbYeF8HRKHKjOr44XCIQCCwQb6idTAVxnt7ewKk0a2CbPKHDx9uXCNutxv5fB4ajQY/+MEPMJvNRI1Sr9cl8mM4HMp37/V6xRFsOBzKApkKTw57i8UCpVIJGo1GFAjj8VjIb0+fPhUXmMvK6XRK7EipVJJsaiUAsFgs5DpPJpNbgcjrFgFHDlEcyI1GI0qlksRcsUKhkKh+eZ3YbDax9Wc8AtWhi8VCQHYuE0hAcLvdcDqdAvCeXWTo9XoEAgFRafP3AdjIx/Z6vTCbzfj6178u/87j8QhR0GKxwGw2C0ju9/tFWczP86qsdC5blARQqpdpZ8yl/3VUFKVSSc4ft9st0RksOh9otVrUajWEQiG0221RCPN9XlYcXghg3baWyyWazSaazSZsNpsMtuPxWJRlXBJpNBoUi8WNP7vtdXJR2m635d9xKXPRQMTFX7vdRjKZ3DpM6PV6xGKxDUD3LggqjUZDlotUzLMYOcqIyPF4jJcvX0ocXywWEwKxspTqxuuc0xqNRtz0bqtYq1arG86CNptNome4CKvVanKGPnjw4FpLa7Vajd3dXXF36na7EtlqMplwcHDw3pyarlu0Lqea/bLSaDTw+/0CnDAG+WzN53MhQ1E1SXKQ2+2+8FzWarWIRCL47LPPZGny9OlT7O/v49WrV6jVaqhUKuLySSCr1+uh1WpBo9EgHo/D5/NdCJ5QZUwVotlshtvtvvK9l0olpNNpIShzGejxeASw7Pf7AtgGg0EYDAYh6xIY5fMTWD/b2ccpwRK6eVwURcbrhct7nmV0euj1etDr9QgGg3IehcNhUb5dt/i6CDzzOyGh6yx4QkKuVqtFOBxGJpOBx+ORiNr72l4E7j755BMcHx+j2WwKQEx3AS7o8/k8Op0O2u02ut0uqtWqLIjpDjkYDBCJRK7lCHmdisfjGI/HmM1m4hBBZSwV93TVDYfDCIVCPzfkK+BLUjZnqauK6mD2xsvlUggLBEEJtAPrZWE2m5VlAN10ptOpxB5ZrVa4XC54vV74/X5kMhmJiggGg7cmHTEOm64v74vExJ953SUUz4nrvg6S3r7KItDLnoxOQXw9FJ2030b3UojBZRnnYs6/uVxuw+lyOp3ixYsX0i8mk0kEAgGZd2ezGZrNJorFolyX70JsJGmA7gd0dTg5OZF4Tc60JF9otVq43W54PB6Ew2ER8TgcDrlvSCx5+fIl2u229BKMwOCcUiqVUCgUxCVt2/xrt9uxu7uLbreLVColhAA6DpEQwudvsVgUIRMdAEnSpgvdtsUpydN6vR4PHjzA8fExTCYTOp0OUqkUcrkcer2ezEgOh0N6wpOTE8xmMxGr0QVcp9NtXP+Hh4cYj8fyZ+gA3W63hZxC9wliLUajUaLxuJgqlUriPMH4ELrH0OmZDnp0Hnc6nULioBsIIzZsNhsePXoknyNJXiTBEPvwer3SezBymk7DnOnptMX/1mq1JJJmNBoJfsGo1lwuJ+Q7Yko8C+r1OtLptFzfGo0GBoMBo9EIdrtdCFck87A3YuwkST/bvm+z2QyNRiOYWiQSkWdrMpnEmzdvRDDHZSQFGFarVUhGJFsQ4yDh47Jzr9vtIpfLifN6rVZDoVDAdDoVkiDJaJ1OR5zglK6RdM5lrKLP5xP3XI/Hg4ODg62/m2QP/p1t8yGJaPyO6NZttVoljmg8Hov4glGcPJMZA8T4GovFIg4HdK1jzI3b7RYsKJVKibCi1WptOOJYLBa8fv0ay+USfr9fzj+SS0iwvGp5RtzTZDKduy74PB4Oh9KfK3vzQCAgzk8k7VEgq9frb/wMZewP57/L5lviNRQ6cC59HxUMBtHpdDAcDpHJZITUR/IToydPT0/h8XgQi8Xw9OlT6HQ6jMdjtFot5HI5Oa8ePnwInU4nGDbFWQaDAc1mU8i/AEQoybjWVqsl3zVTIOiCeLai0ajclz6fb8NdhoQZt9uNWCz2TljPXZcSL1P+O/7vWbIUyZc+n09EqCSDAhCcgvcq476VosS76p3o6s54w7P3FEmjnAl0Op2QM5TfIQl7sVjsWvO3snju6PV6DIfDDdKKSqWC3++Hx+OR+G+S5KLRKB49eoTDw0OJSWOMKJ85JI/TpZ2iBa1WK88gZSmJuHd9jZH4wki0i4rPRDpmKwl+dD0kZqJSqbBYLOSs83g8EiHK30dx9bNnzzZwbp/Ph8FggGKxKNgvxWLKotPUfD6/FrmBDsB0Vtv237fhORQHuVwuiSc7eybTKbRcLqPRaCCRSCCZTCIYDGI6nYqrJh36k8kk7HY7SqWSJMrchIC1Wq2ENMdndalUglqtFodKFgntr169QqVSwU9/+lP8zu/8zq3uVQqkdDodWq3Whe5NdHIsFosIhULiQOZyudButzEcDiXRg+JfCsGY8qC8zukIS/wB+DKpYxtxttPp4OTkBM1mU/pEXiNKjHAymWBnZ+fOhGQOhwOJRALA+npqtVpoNBoSgcue2e12w+v1yp+9SVGAwr6cRCL2huyxeE85HA7U63URa23byxaLRRQKBZkBaMTBPr1er8PpdJ6bJ6xWKyqVCgKBwIV9il6vh8vlQjAYRDweRyKR2HA+BSDPZCUB3u1232onq9Fo8LWvfQ21Wg3NZhMnJycSUa5Wq4UculgsxN2ahPtAICD3hUqlwu7urhDZuYNQqVSIx+PSv1x0HvO1K8nL93Vfl9U9Aeu+7usXrJbLpTBx6QbF4ZPNNNWXw+Fww55ZSc7goobAhLLm87nk49rt9ksdRaiIOGvPyCgFDspGo1GsSklSiUajEhNGAohGo0Gn0xH1BEHfxWIBm82GQCCA/f19+Hw+eV0ciqj0LBaLt2qGrlNUahCIZSQjsCYXdLtdsWklEHB0dCTWzwDEUpMsesa4EcAgoMXF7eHhIQCcW6peVsyuZowQv+PJZIJarSYWyIwj5H+jitlut4uivlAobHyeJDD0+32xCKcKgix62hqzqXa73eKupVTXsEFi86+MmmRMHdWzer0edrsdwWAQ6XR6I9rD6XQK0QZYN1x7e3tyb1QqFYlao/MA75NEIgGn03nra4Y2zMyVVpI6qLoha55DscFgQDweRz6fl6iEwWAg5ACz2YxQKCQDPbOqed3weyAAyr8fDofPDbBarVaUb7xuzWaz2LCy6RyPxxI/GAgE8PjxY3ESoxJNrVYLuYcqRi4Aer3elYMn47MIiBQKBdTrdSFDAV+CAz6fTxYY24pqnGq1Ks5JFzXQTqdT1GsE87jYuGrgp/sEQd7bVr/fF4WdEhxRRhWdfc2ZTEaGDOVguVwucXp6Kkt15aKPymICRTxn+ffoFkWb5bPFpUi/30e73UYqlZLoT+XPuk3V63VZ8JKQRhv4crksChM6XzHuLpfLSTzS7u7uV+5ooazJZCLkq/F4LEtUls1mE/X8RWfoVUVFF7BePFcqFQA4FzXw81D8LKiaI3hjNBrlvKOdNaMA6drICK7LissmkhGB9T2wv78vyng6qvEcoxsHQepYLCZLp+VyKWeEUvXERbTFYkGhUIDb7UY0Gt165ozHYxSLReRyOcznc4m6YOQmfw6X7CQf+v1+AZ/ZF0ynU+nhePafBeQHg4FEOwGQiA3+LgIYjPzjM5CLMqqlGRH06NEjiVq6SZ1V0jEmi5/B2SIYzWWoyWSSs+megLW95vM5Xr9+jW63i3w+j1wuh8FgIKAgwXP2lu12WwBdr9crceG7u7uiUObyk4Tldy2r1SrEEbPZjHa7jVwuJ/efwWCQGM5AIHBrx6a/qiIRk04tV31m7O0IdLJ3qtfraDQa51TM7Et5fsRiMUQiEbhcLjkX9Ho9vF6v9AVer1dU4SSkv2u9TxITPwsuZy5bRjH+2ePxvJeY8LsqkuAymQyWyyVcLhdsNpvMdR9//LE4ggCQPsbhcKBSqci9S8UtezmenalUCtVqVVTXZ5dsBLuHwyFevHghBPBnz57dCgDnc4aYwOnpqRAoKeLgM5oLcTojWK1WPHnyZKOHV7pEM1b5+fPnMvuTUEFyDpXcOzs74mKrdGpk+Xw+HBwciFOS3W6H2WxGJpOR+waAOHHb7XaJXVutVnA4HCKKYQSZz+cTJ6B6vS79NZ2NEomEvMYXL15IRPDOzg4CgYA4PwHAs2fPcHp6imw2KyTY/f19NBoN6Tf29/cl4rxYLCKbzYpD7nQ6lUUYHfOU9wuXQsCX0V8kbXo8HhwfH6Pb7Ur0MP+Oz+dDPp8XRz273Q6Px4NMJgONRiMzxN7eHrLZ7AaO0263ZfFMYQRnyNFoJFFyyiUV3T6JjeRyOcxmM1SrVej1ellwkIzH70ylUiEWi8Hr9aJUKonbLInn7Mm5rOKcRyIOiSvElsxms9yfXICdFYTwfqLDApfPJDhxKUl3K97HAGTpxfdKoR3n8MvEPM1mU0hwzWYTo9FI7kNG+5TLZSEJuFwuLJdLRCIRcZLXatfx33SNdzgcmM/nMJvN0ste5HrBiMr5fH4u6o5lt9uFxEihiM1mk8hJOrdRHDMej+F0OuV8s1qt4sQ2Go1k9mOUIB306bLw7NkzZLNZVCoVeSY6nU4EAgGZ41++fClCz+PjY+nNudSnO/v+/v6lxCQuJi/6bDibeDweuXfp6g18KTqpVCpoNpvycyhwO1uc3zlfkGBEpzbO/4yDUjpdE0tRfi/lclkEL++LgMU4WWA905PISeFIJpNBo9FAOBxGMplELBaT54/RaJS4s3w+L7G50WhUIr6U7kEk5zFNgJgm41hJ9OdnuVqthPB/togBMeqa8Vd0CL4rd8/LShmTeNbh6WwRj9smQiKhyGKxbHXzCQQCMlsfHx8jnU7LNUa8zmQyIRwOi6Mdhb8Wi+XOPge6kjNK9ez34nQ6xc2VgliKYUmIbrVaMh9fR4C7rdxut1ybJAcri0QOt9stLmD7+/sSQbq3t4darYbj42MUCgXp7UgYpoiMOOFsNhNSt7LoTsXoq7ssukxd5e5PEk2r1RJHL2IOOp1Onisej0dSOAKBgDiwAxAxPc+/i7Bai8UiYk4K0oltsbYRDC8r5X1zUzzM7/cLsTmXy21EjtHRs1AooNvtCj7EyDa6avJ9UCxBEsptUhJohkDhG7+PbSIatVotPQhdsxqNxoUxsJcVCb3sqdj/sIfjnAmsd1t7e3sIBoMi0srlciLkIiGM7l/RaFSI10oCGY0EQqHQRm8xnU6hVqs3XLH45+kez57c6XTKn6GLfLVa3cC/78qhiFHNpVIJTqdT3H6JKxqNRnGhvu51qEz/4P1Klyq1Wr2BNSpd1xgBzb+3jQjEOHv21YlE4lzPQYIf34fFYoHP50Or1ZK48cvwDCbGWCwWhEKhDXEQReN0BgXWz7tCoQCn064PPeQAAQAASURBVIlwOHzjhKJgMIgHDx6IGIOJIey9bDabzNir1Uqw5W3vwWw2I5FIyPeq0WgQiUSu7JPYc/1VuGnf189n3ROw7uu+fsEqk8mIFTtBdo1GIznIdKIaj8dwuVzodrvo9XqiZlar1RiPx9K0bVvCK1UUZxvlbUWweDKZbNgz2u12+Hw+AZCo2COAzCxtp9MJvV4viwguBE0mk6jml8slHA6HuCWdHS6YS81IqYsWoywOtmx0lOrzbX9WGdnHhzHVlj6fT+ytx+OxEJEImNhsNjSbTfkeaHettFGlYxOBeH7+tVoNpVIJs9lMBqOriiowAEJcY5NE1eBwOJS4ORZt1Jl5zrgws9m88Xny5yu/B71eL9cXo0KoLrPb7ZjNZvD5fEKsUOZJA+vGhjnrtFynoojAJa/hVCqFTqcjMRC0oy4WiwgEAjIwm0wmPHjwACcnJ7Db7XIvkGhGu1EqqG+jRqrX68jn86KK9Hg8or6jdTEVm1yEcoAxGo2i3Ob3wvdMgKrRaCCTyWA8HgtTn4okKjUJnmu1Wjx48OCcksVutwuJwGw2I5lMinIdgCxLSIgqFAoCNu/u7kqEJgBRp7EIQDidTiG4XdSgTqdTdLtdsW3mUoAxSkoAk8TRVquFg4ODrWACFZhUk18FGHFBQuKpTqeTgfGigY1KVxJD3gWUUoIj1wFe+GfOut2sViucnJygXq8jm82KOo12/ZVKRZ4TkUgE7XYbT58+hUqlkvu73++fcztZrVYolUoSe0fCWLlcls+s0+lgd3f3xlbLLN7vHo8HKpUKpVJJiJaMFmAk3Gg0EpLMcrkU9wCSK1kqlUqA+8FgcOUyfLFYiFr/NgAYnwO9Xu8c+Ur5ms6eoWez5b+q4oKKoNx1ozguKhIkJ5PJtSK4JpMJkskkVquVXI9K4rjJZEIwGJR+wefzbZz91xl4udhSgl9WqxWPHj0S8hdVliQV0MVPq9WKo8d8PhcCsJLUSLB0uVyiVCrBarXKounw8PDcZ1Cr1cQGn7b0Z5/d0+lUlpWdTkfOB7pSms1miWap1+sCjvF5yO+R0X0HBwdYLpdwu92o1+soFAriREEXHaULACMLuSSs1WrweDxC3Do4OLjxM5HADO3k2edsi4+i44WSTG+1Wi+MUfwqi9eAskd71/P/XWsymaBSqeAnP/kJms0myuUyer2exGdFo1E5l7iQ6fV6ct9zPlACYx6PB3a7Hel0WpbgPJvftRwOBx49eiTgKRfGvM94dtAF7WfJceA65fP5xKmPsQDbiqIZiln458rlsizxZ7PZxpKN5GTeH36/H4lEQkjmdOVVfk8Ug/BcugsClrL4nOb1pBSf3KZ8Pp+4WxBcv6gYO0+w+Gex6NqUzWah0WiQTCY3rmku7fnsGwwGqFar4m4aDAbRarUAQJ6RvJ8Zh0G3H0YbK4tnOGecXq+HV69eyRnv8XjOzXxXFRfVb968QavVkqj58XgsTgmcD+hu1+/3xYW1VqttkK9OT09Rr9dleUPXCc7HnPtsNhsMBoPEdXM5xRi/eDy+0RcwOicYDAqBg2SV6XQqBGj2+l6vF6FQCLPZDJVKRQh+xDGA9fKHvR6V/sD63OTcwiXl06dPhcAUCATOidoMBgP29/cxnU7F+c/pdMLv94t4iRFQdEHm6yBZ1ev1IhwOC+aj/N7n8zn0ej10Op0sc/r9PmKxmMTiUZCnLLVaDZvNJiS/2WwmLpokCT158gQajQaJREJIFiS9NBoNRCIRITmZTCbBKujoHAwG5bpjz1Ov12GxWITAxqg5OgIwBomOEg6HQ/4dZ8VerwedTgeTySRLc0b0EJPiMpPkF7rusBdlpEy/30cqlYJKpYJerxdXmVqtBp1OJwR94lfKmDqDwYBYLIZmswmPxyMuELxubDYbJpMJgsGgzLiMlDxbw+EQ6XRa5jAS8vn5cAYrFouyoCMuQnyFTut01VZGStEpn06n2xyVGAXOBeO22ZgkRLp/VSoVBINB6Z9JNmYPRTKVz+dDuVyWPo+uTfwseW0FAgGJInr48KFETlHQkkgkNpawfK90dz44OIDb7ZaodIoPJpMJVCoVHj58eOEikFFbZ6PGAIgwiXhJpVIRwZxyEcm/y/OEbtTK2bTVaomIh647JKxz4evxeCSCbzgcbjhusnciwZH369mYufdVFDwyGqnX60kkIAni+/v7Fy5dbTab4EY2m00cEs+6XPN80mq1QgbhzM9rmrMGUxjOCosZuzQajcRhC4DEiVGk+lX09x6PR8hk/X5fyPd0CmaPRXIrz79tUWN0Ojs9PcVwONzAZVQqFfb39wUrzWQyQkwmQZjOlIFAAI1GQ0iXd91j+Xw+IfzQZYVFF+psNivCQRJLAIjjTSQSgcPhwN7e3o2X+HwN1WoVZrMZhUIBsVhs6/fNPiAQCMg8C0BSLSgs7vV6aL+N3KUrzWq1EsfveDwuZHAWCYChUEjcLe+qKOiuVqtoNBowGAwIBAJbnzMk0NIBm1g+0w3oaMU5LRAIwOl0ys9ieoRer0ev15Oe5CJCITE9iqui0ehG/2YwGMQRic6llxVjvIlF3aRIBF+tVsjlcjg+PpbncbFYRCaTQblchtvtFtLy2WcgiYHtdhsWi0UENhclylynBoOBCHTpkn5RMfKP7pi3IWABQCwWw3A4RKPRwI9+9CNotVrB0lUqlQi+fT4f9vb2EA6HRfSxs7MjMwzd3srlspDB6QrW7/cFz+eOgD0bq9PpiDhe+VlXq1U5E+Lx+LlrmaRJo9GIbDYrLk7JZPJWn8e2IhbGJBalE/5NYn1J2uMMA6x79zdv3kjKD3EQ7tMoonO73WKYAFycxlGpVNB+G7+8s7OzVXRDAUcqlUKv10O1WpXI9WAwiEKhAADSU7Dm87nMl2cTYxiTXavVxAGazzCNRoNAIIB4PI5ut4uDg4Mbi6g++OADib4+OTmRiHvOPHydTqcToVBog/C9rZQ4Ch1+LyvObDzH7uu+rqp7AtZ93dcvUBHs4APa6XTKw5NNi8FggNfrRaPRQKPRELUQSUZUntrtdrhcrq2RI8pF5nWWImw4zy5Ayaoej8diUQ9AnBxIBCI4aDKZUKlUZFlKi9tgMChAVKVSgVqt3uoo4na7kc1mxcFl2yJ+sVhsOEBxIUsg0u/3byh0FosFjo+P0W63JVJCScByOBwyLCqjJoD10BGJRETZzCg6qloJqBF8pQUmB28qSAnmOBwOlEol+Hy+S7+XbrcLAJLDzqGIFqRs6vidKIl5ymHG4/EgnU5jMpmIEhaAALxarVaiBSwWi4CfBDETiYQsoZXA0Wq1gslkErUmrWubzaaoXXnNUhFBAJyZ9KvVClarVRQD9XodNptNlnw7OzsA1o3WkydPZFDhoKTRaGThf9uGarlcolAooFKpiBOOcrlBVzeTyYQf/OAHYqHq8/nkniGQSUtYxkFRqcrc8/F4LEoyOlc5HA7Y7Xa8efMGR0dHSCQSG5ETHA7YiJL4xs+N1zedGBaLBfL5vCjN6/U6QqGQgGkXld/vl3iLs4oi1ng8Rj6fl/dAUIgOYOFwWMAjAjW0G1epVDg8PDw3kJKYRHXxNitqZZEUp1arEQqFZNjMZrMbFvr8bjudDiqViih3bzvkspQqr+VyKZ8ZvxcSCQk+EEA9q7qgmw0V6aFQCGq1WoY7LrZLpZLYz/d6PRweHspzggonFsEIqoloIc1rrdFo4PT0VMi/h4eH7+REEQ6H0e/3AQBv3ryRWEzn2yhN5tt7vV643W48efIEWq0WuVwOOp0O3W53Y/ilupluXZcBOCQr0h3gJsXlUavVgtVqvRIoUp6hzWbzxo5CJMCl02lRStEK/irgieBIq9XaAMZMJhN8Pt+VIM9l74mgy1XuLyTyJZNJxONxGI1GOV+UQDuVhna7XUAvpVr/MlCLPQRjWJSl1+sRjUYRDoc34ia4XCqXy0K+oltTr9eTZwVJjNFoVACj0WiEer2OTCYjZMgHDx7I71ytVuIy53K5MBwOBRji3+eClUtWPmNJYF4sFohEIvI9U5XZ7/dFaUrFLd3xfD6fuGgwYoPPEEbn7OzsCFjKZQafy1z0sDe8DTnR5/NJzCiBamC7SpXkIUY2na1tcaPvu2az2YUqPn6GZ2MBvooqlUooFosoFos4OTmR56bZbBbBAh1SbDYbGo0GUqmUWK0/fPgQKpVKnNIYwQRAIl5LpZI4Xt6VjT9jysPhMF6+fClLV/alXFAWCgX4fL5zEcI/y+V2u1EsFhEMBkVReha0HI/HKJfLohBXqsQPDg7kOVyr1XByciKLCCpt6Xh4eHh4pRiGvTIX4HdV7AkYA87XTAcuWv/ftEgOCAaDyOfzQtpRfn4kQbTbbcRisQtdO38Wior06XSK/f39czPaYDCQZwFd4cxms8y67OcJXhOcp7KcpNnxeHwuVnA4HEpkGQF4RsPzmZdIJNDr9XBwcHDuM+QsRuIwZ6R2uy1x9aVSCa1WSxZ3BoNBrgVGzY/HY+j1epRKJflz2WwW4XBYouYymQy0Wi2+853voFaroV6v49WrV0IaAiARnR6PB263G1arFU6nE+VyGWq1GtVq9ZwLhtFoRCwWk2ffT3/6U0wmE+TzeXHLsVgsEq3LmA66OZOwtVwu8dlnn+HNmzfyOfAZRcduOl3EYjG8efMGKpVKrmc+/872xzqdDqFQCMfHx3A4HLBYLHjw4IF890rh2pMnT2AymVAsFmU+2t/fP9ffABCFPEkHJycn4hZEtwA6M2zro0wmE9pvI8yVQq/5fI5IJLIRGxmPxzGZTOQzAyAOQ4FAQFT8s9lMXL04O5GM12w2MRgMJGqR5AslQX44HEq/NhgM5LsuFArimmmxWCSa0mw2i5ME4y2Jd/GeIrGMZHqfz4dQKIROpyNiE2WsHWdKkgRVKpU47ijdUDOZjMxKXMhSGEQiId266NzEHvxslctlEWslEgkhXebz+Q3iPZ0D4/G4RDKuVisYjUZ5fkwmExFIpVIpIXOQiK8k7yivVfbsdHi8qPf2er0iMDg+PkYul4PVahWhG3EousFT9ORyuUQARnIWSb2xWExwK7fbjUePHiEYDKLf78usrCRf8fqnCIFEoFKpJKJNg8GAYDAIq9WKXC4nff9FS1o6KtD9VjnjsXen4NNms+Hk5ET6qXK5LI51xHPK5bKQhfizqtUqstksGo2GkHHoCFypVOT6qFQqOD09FZEagI0YOQpBut2uuLBwiftVENqdb6NWSUgdDAby/qPR6KUxaAAkVpLzMZ/vSgzAYDBI7DvdCCnQ4j0xn8+RSqXkcwuHwxvJBOl0Wj5r4q6MYCUxcDgc4vDw8NbisusWZyuSRN1ut8yeJN/S4YRkjGq1im9+85vnfpbX65Wo2Hw+j0gksnEvq1QqHBwcSF+g1+sRCAQQjUblmbZYLOTciUajEm19l8W5MBaLiYuJ8twhkU6r1eLx48fSF9KRiSLPbQ7/1y1GhM7nc+RyOXH7Iu7OvQFdMt1u90bP7XA4EIvFsFgs8Pnnn0sShDLKvVarCameUa8k8xJPZHrAXTgNA18KJ0k4Job8+vVreQ56vd6N3oHxdrVaDQ6HQ/ZFJKprtVrZpfDs5OudTCbiCEp8kASGy84ch8OBQqEgux/lfcZnmsvlQqPRODcHnH2/zWZTsNLbuPzt7OzI9cZ7o1aroVgsYjgcYm9vTyIOL3r+2Ww2EavSyW9bosxFRYIN3dlJnlGS/rYVSXF0ZGKvf5sZjK5XJOCVy2Xp64D1DikYDGKxWIixgbIsFgt2dnaws7ODbDaLcrks11+73cZ8PhfiJ5MPiKFwfmHE887OzgYpkU6uJO1d9pmQDMVnCEUtd1kXJVVcVXT9JbGU8xmw7ifYr5tMJnzzm9/cuC+UDl+8Xnjdn/08SOQm7njZ9WAwGOBwOEQ0EovFNhJjGL9JUhyjN5WJMTwbKdB58eIFjo+PRTRLZ3sSVPP5vAjBHz16dCN80WAw4MmTJzKD0IGXCTiMZKfb3rbemuIX9u5Mb+KO7aLnPt2f6Wb4vvuD+/rFqHsC1n3d1y9QUXW3Wq02AE3GCZpMpo0FZrFY3MhHrtVqMBgMSCaT8Hg8SCQSW5tXAqp0rbqq6aCa6OzQb7fbMRqNoFarkU6nxd0kFosJEYgN32w2EyW/xWKBXq8XG/NqtYpAICADAIfms+AIH4zj8XirU9RkMsHR0ZE0KXSdACDLtU6nA6/XK+AMnWZevXqFyWQiynM2rlzcRqNR9Pt9aDQa9Pt9WR7abDbs7u7ixYsXqFarG3a/zCvmIEDgj02Qz+eTeEePxyORBpVK5cLIFrLUnW+zzQ0Gg0R8LJdLTKdTAaHoNEFbb6oIrVarqI40Gs2GGhdYLxq4EDg4OADwpQsNi+CQyWTCq1evsFqtBCDTarVwu92oVCqYzWY4OjoCAFHu05WIdtCnp6dIpVIC0tKZ6eyiq1QqIZvNCjBKsgOVz1fF4920CNby+7+oobRYLEgmk+LodHR0hMPDw417Zblcolwu45NPPoFGoxHXq3K5LNdRNpsV8oLX65X33O12heTYaDTw8uVLUa4mEgmMRiMBPknMcblcQjagdSxtfDUaDd68eSNLfo/HI5bB28rn84nTRj6fx/HxsSyZGCU6HA4ltoHxqPl8Hl6v9xwQQWJLrVZDNpuFTqdDo9E411STmMR7qFKpwGw2b7jZEXSmsoiRiVSUk0hYrVZRrVYF3BkOhxLR4fV6sbOz886LP7PZLMuQzz77TMBTKl/1ej2azaY4AvX7fVF4KJVnuVwO5XJZVNyr1UqiTRhVScJtsViUZQqjOoH1+aMkrDFGolQqQafTIRqNChGElu8Oh0N+78nJCZ4+fXpjAg+V0NPpFIlEAvl8XmIsgPWSUq1Wy7mu0WjEejkQCIhrGhcqLKWymuTcbQP4YDBAvV4/p268blHhPRwOrwWg8TlBEuVVRVUd70k+PxlT1+/3ZRHpcrnOLSNYhUJBHHK4oAAgKkcqnw4ODm5MtDGbzaLmLpfLF6qC5vM5CoUCLBaLLOjo5kAnSV6nymcHFzEE3Emm3kbM4LnJOJSLlirsLZRVq9U23AudTieCwSAymYzExlIJX6lUROVnMpkQi8VQKpWQz+cldoPXI6+R0WgkLmt+vx+FQgFarVbAOsbxcKifTqcbykuSaQhEkzxM4ibvnXg8DqvVip2dHdjtdnHhYH/I8zcWi208N71erxDC+bnzmcne6KZLHKvVCovFgkAggHw+Lz+DSkrahPOs8vv9G2ASF/gkYH+VNR6PcXR0JM9RJRC5Wq1gsVhkEXhwcPCVATGFQgHVahXFYhEvX76UPi0YDGI4HKJSqYg9O52uSPwkYZO96mKx2LpgsdlsG+fFXS5gFosFTk5OJPaBLpR8BtGhjoQ3Rhf+rJdarcb+/j6Wy6WIQxqNhvTpdPWg66jX690gjRiNRjx8+BDZbFYEDIyl8Xg8MJlMyGQy0ktdVSSkv4sKW1kkZVcqlQ2XVuDLnprk/f39/VvdD3t7e9KHkPhI0Hc2m0mMOh1IKaz4WavlcimuiRc5aShdHe12uziycOYkUV6n00m8H+dEOppwxlar1UL6U7o48Tnk9XqlNybpj9FdAPD48WN5jcPhECcnJxJbSses2WyGTqeDzz//XBbrLpdL3LXpaDQajYRcptfrUS6XBdzvdDr45JNPxAWY+EUsFhMhxHg8lmXW3t6eOKLRycRut6PX64nbVqPRgE6n2xAi8XUHg0EhUiljPJTOQIvFAl988YWcQZPJBNFoVMRU4/EYr1+/hk6ng9VqxePHjzcWsS6XC36/X+ZNkkl2dnbEPZwYBeOR2bvRBU2lUsHv9yMWi229nkioJmn7IuCf92UkEsFwOMSrV69QLBblmUGCEl2mtolUzrpW0NFsuVxuuK2z/+Bs/+zZM5nTGJNGMReX1Vz4r1YrTKdTcQDn90mHcxIAgfU5Rvczzm4GgwH9fh/FYhEAEAqF4HK5UK/X0el0hBBIl3GTySS9ZzQalVkvkUgIYYtLKi6pOYexl2XMJnGs4+NjIQOqVCo8ePAAP/zhD0UIqVKp0Ol0RCBB8j0j7DOZDBwOh0R+nSVx83NmTBcXb4yTJOZFt2DO1hTr8VnDHotzCoU5Op0O1WoVy+USx8fHInDs9Xoy5/b7fVSrVVnuXnamq9VqWYbF43EhMvCMoVNxs9mEXq/HaDRCrVaDxWKR+3YwGIhAYHd3V0gSxAVcLhcikQi++OILtFqtjfghXrvVahWnp6eCG5GEwPMyEAjA7/fDarXKuXTZkpaRTMQeKQKja7/SpZlzKAVlvJ7ohs95jaTWk5MTeDweibAngU0p/qKLVCaTwWw2Qz6fR6fTkX6D1w0dOej8zvhwuoh8VU4NXKzz/JxMJhgOh9fqIel4xvnA4/FAr9dvkGxJlmSf1ev1UCgU5LzpdrviOESXRwqr+v0+MpkMSqWSEJAYlcrXPhqNkEqlEI/HcXx8vPFsvEmRZEMnJOItZ38W7xsK3X70ox9tpAGQrEehLHGjbbihTqdDIpEQUh4X+TabTXpQnj/f+ta3ZN6kox6dmU0mE+LxOJxOJ/b39+88ZkmtVgtWTHEOSZIAxN3kW9/61jmnOGKXdxHXF4lEsFgsoFarpV8j9gRAnhd2ux07Ozvn7iGSFOgS1Wg05LXPZjNxAQTWZ9t0OkWhUJB+zu/3w+PxYG9v71aEmbPFs5zxYYwFJKluOp1KOsdkMtkQANI9koQKigJMJpOQc5XR3xSaTyYTmM1mEd7SvWhbhK+ylA5Z2wQidAonIUlJ/mZxjzCbzeS5fptZUaVSIZlMwufzyRlOJ1EA8jyjWyuf68qi8JdY0dOnT4XwfFU1m01JqiFZiecaCcAXxaF2u10RNxCTvq3gJpfLYTgcCua6u7srz5fZbIbBYCC7oOl0KvGw2yoej8tnQrJxJpMRUmk0GoXT6ZTkEQpKq9WqkBKVGD/Fl/1+/0oiL7AmA/OZSVHiX3UxsaVWq4n7KmO+V6sVnj9/LteBXq9Hp9PZ6Lm4v6XbpkajEaLR2d6M5w3nmavKbrdLgsZwOITdbpfEGBLpuZvVaDRCzuVOk9dmtVrFq1ev8Pr16w0hAIV2xNH0ej0+/fRTcZO/TrqSskwmEx4/fizEMCWezmhOn893bk8zHA5RLBbl2cw+nwkcy+US2WwWoVBIiILKz7RUKsl++DbRt/f1y1n3BKz7uq9foOp2u+h0OqIqz2QyiEQiGAwGqFQq0Ol0ooCmjTvj45idHAwGEY1Gxc53WzH6hqDodewZqQBU/tnhcCigkd1ul1gXtVoNl8slKjsCt2azGXt7e9I4UWWhdDji3xsOh2i1WlsdvLbVbDbDmzdv0Gg0UCgUZBFMJRTj/mgPSiDu9PQUn376qShaCaKazWYZsE0mE3K5HNxut4CxjB4EvozCo309iVqMSdBqtaL2ZINAklI4HBabe5PJhGg0ikwmg1wuB4/HI00Y7YcZ1UGCSb1eFyUzs6w5MJAModFoMBwOUa/Xxc47k8kgkUgImKds8KnUojLUZDKh1+uJowpBXzYybrdbFm8EJGgLv1wukU6nZWjk4pVOSblcDo1GQ1670WjE6empZGITKDAajYjH42J/SiXcXS3yqMjjAsVkMolj1za189na2dkRIlylUpGhXa/XYz6fo1qt4sWLFwLU85rlQlW5UKAt73Q6FScCLkAIwNMB6+joSBQJZPDX63W8fPlSAEK6t7EpJTmCi7xHjx5JnMU2FyiVSiURjoxzoGMcs9JJeAmFQpIzzigQ5fWrXPJx2GZDf5aAxcVKr9eTxXi/35fFDK9ZLiZJYHU4HOj1evjwww8lDtDtdqPb7Qpxk0QGXlc3sRq+qGgrTFcaxhbw3/P7JnBPtwSS6gjycpjzeDyiyOagQBIFzwXGQFSrVZycnGB3d1eGrXa7LfF+rVYLjUZDAGveN8qYj0gkIqRAxqreVMXHz5mOToytmU6nYsEMQBYIXAIyDo0AOhc5BGj0ej3C4bAQ1E5PT+F0OgWQJrGp2+2Kq9ZNB0C+Lv7vdc8WgvcX2bMD60GvWCwKmJxKpYQwyAWU2+2W5x6dMMbjMR48eLABVHEBx8gFxpMxgpLEnGg0itVqJS4MN6lYLCbnRC6Xk2czv08SOeggxcUo6yIyIz+jx48f4/nz5ygWiyiVSgBwbmlF5592uw273Y69vb0LF5pni25/5XJZ7Lppf8/rcWdnBwaDQZw/2BPxew8EAtKTVavVrWcEvyM6DKbTaSF9ut1u+Xl0hKCDI2NVCDaQCMlFcTgclmW8x+ORXoZFNxnax1MhrwTWlctGZZE4RdLsTWt3d1eihk5OTkS9z+cml4Z0PGNkMIGzRCJx5/FpV9ViscDR0ZH0hyS2KUn9dB1kz3lTFd9tiiKFYrEo7mck6dE5hBGazWZTniculwsmkwnValXIVcq4x7PXKokKymjiu6p0Oo1msylOIWdjW4fDIarVKtLpNABIpNPPQ5nNZjx48ACnp6cSc83lFlX87HO4yFUWY3rogMfFOvsAgqRXOQ0CkIWdxWK5k34ln88LyXQ6ncr8yGVhq9VCOp2WaK2HDx/e2BnOYDAI6GuxWMQ9hVGrjKa1WCx3trR6H0VX3slkcuFcTcHAYrEQ5wCq2Xmfs0cNh8MSw+X3+xGNRlEul3F0dCQzKt0LSdzqdrsy13PGAtZ9MnsjzlDVahXhcBjD4RCvX7+WSFO6GWi1WqTTaWQyGVSr1Q3iPH8nHW3oiECSBdXoPOtfvnyJ4XAozjoPHjzYeA5xVgkGg/B4PLIk1mg08pyjSIJEHH5mjLVJp9MiGmP0+3g8hslkEpePYDAormDsLX0+H549eyazPUVDy+USmUwGNptNZn7ljKfVahGJRJBOp+X78Pv98Pl84rDJHoh4Ape77POVLi9niyIUuhu1223BFJTxduxn2+22OIBzxh8Oh4J1sI/M5/Pnrk8ufwCIuyxnuXa7jePjYyFVKN/LYrGA3W7fcBzn/dnpdOByuSQurFqtCtbC64bXqM1mw2q1gsvlwsnJiZDXGflH51u6rblcLokkUalUKBQKgl1wdqQzTygUQjAYxN7eHlKplMwRZrMZlUpFyMu9Xm/jeT8YDJDP58WNmfdEOByW98gz9s/+7M+EhM94c0ZA0T260+nA7XbD5/Oh3W6LG4pynuZ3NBwOBbOpVqsYDofi7up0OuX65+dMEQNd3OgiRlyBv48EYJKYOPvT7Ynk+UgkIm5Tw+Hw0l6MhO5EIiHiRJ/Ph1QqhUqlAovFgsePHyOfz8vcR+cCkne5PLPb7Ugmk9jf35d5PxgMCnlhPB6fwx/a7bZc8xSMUkxI8Rod1WKxGFwuF9LptMyV2xyQdTqduHal02kRgbFP4T1N3IUEQpVKhXq9Lm49VqtVsFGn0ykCjHQ6LWSuZDJ57pmp1+sRCoXQ7XZxcnIikZrsHbj45OfdaDQEU2o0GhJVfJveneciCUQ3JSIp+5vL5l3ln+FnZzabMRgMEIvFUK/XxXGOgh8KMOfzuTjBq9VqPHr0CI8fP5ZEB+XivVwuo9VqIZ/PC9ZGnIfv0WazYTqd4osvvsC3vvUtiei8yWdWKpUkho4/m057brdbRGQsl8uFaDQqrvCZTEaevXT4o1M+rxk+E86S0Hl/Elcn8ZnuIIFAQNxW+RnQqZPPKl6jV0U3vUvpdDo8ePBAzgXGg/K/GQwGeDweBIPB9xYFqVKpRHherVbh8XiE/KfVaoXsxb5923ugaIIYzGAwwGq1ws7OjvQ4FGHwPCLJ0mQyIZlM3hk5ks5ujAyMRqPY3d0V/KtUKqFUKokAjzuLbreLSqUCv9+PQCAgxPr5fI6DgwM4HA4cHx9jOBxKT8OYabpyE0+ie99VMzvxamC7UxbjnEmIODk5kXMUWM+JfHbE43GJznyXoosm3SYpJPd6vdL7kaTFeGb2SiSS93o97O/vS/zZVVWpVJDP5yVRhefQarXCcDgUdy/GFisxBorvfD6fRKJd9HleVSRnUxD47W9/GwAE1yMpazqdIp/PCzFO6fZ+tkKhkLjdUURPYiDJ+dwNUCgcCATgdruxt7e38V65x+Se7KpSfi/vO373ukXyPF1/9/f35Wzr9/sSk0zRw+npqVx/yqK7Ku+Fbde90vn6pslF/LyMRiMeP34sIudtiTHKOWi1WuHly5dCUmS0qvIZwnQKCmBevXolROmbXrcajUacxEmYpNHENtJwu90Wcn6z2cRoNBI8ni5Yy+VSXNgpWlGr1RgOhxJfHwwGEQqF7tzE4b5+ceuegHVf9/ULVGwsaLucTCah1+uFtMPYCrJ1ucijemQ8Hott/mXDOQGRXq8nbhAXDQzj8RitVkscUpSNEpndjPBiZA9fMxVzFotFnGlInGEzQTCVTlMkcpCgoCyqxw0Gw7nFGBnQhUJhq+sOM6lzuRxyuRw0Gg0+//xzpFIpiZviUoDWqHStoM08vwM6xfB1kBBFJTIXfWazWVypgPWAwjhEt9uNxWIBm82GVqsFADJ4E9DMZDLi/kMCDaOcQqGQqGLpDkNFAzO5/X6/uFOMRiNRJRPMz+fzYm2rbKiWy6V8/pPJRLKt+/2+5KtT3coBn/FKzI5XLh6Ugwyvhzdv3oiClao6i8Ui6mfGJMRiMWkkNRqNuLJQ+fWuDdPZGC+Cm0ajURo6qviUnw8XSfzzBoMB0WhULMeDwaAobEk0AtaAUTgclkGFds0krvFaYGTnfD4Xi2he/3zdxWJRFgqME/D5fOKgQcCXZwVJWgTsGYX5/PnzDZeUbaAlFUV+v18i8jgMcKBgZAHVfVy8T6dTZLNZIVPRlYVD//HxMYB1I09lx3w+l2iU+Xwuy6NOpyOgPR2cSNRizjqdQ1KpFPb39/H06VN0u92NGFGCZ8CaCEqnn20Ki5tcS3z/4/EYGo1GwFK+JyVIbTAYcHh4iFarhdevX4tbAompjFSkw5HH49kgTwFrkLbX6yEYDGI0GonLF4lMyuiR4XC48fcJUpNkSgtmkl5qtdqNCVhU9ZvNZlEGERClcp0LplKpJOcuYwcsFotEMVC5yQqFQqJu7HQ6QioDviSbxONxUfDcBgTjWUOl81V24zznLgOzW60WUqmUqN9zuRy63S6q1ao4CHBQjMViovrOZDIAINcxFWWnp6coFAoAcG5pbbFY4Ha7BXjRarUoFAoXOkddVFyIA+tnZ6vVkiUZAIl1oHsa7/VtNZvNUKvV0Gg0Ntw03W43kskk0uk06vU6er2eqCxJTmJPwzhRqu2uukep5GTMqdIOfTQaCVhEJV+j0RDiJhdvdHrodrsCMCljwOr1uijpF4uFLKtbrZYsOvjcpjMmHShJcGTvkUgkhIhAEoRSHX1RKWOhrwNqEwh8FxCLpIrj42NYLBZ8+umnGI/HotR3OByyTIhGozAYDBLny2fVNgvx91kkxjMK7+wzjiQGs9ksvV2lUkEsFpNFMYllGo1GIhvedZlRrVYlKjoYDKJYLGK5XG5c316vVxZTdPEiQbLX6wkJmn0/yZhni8v1u3SfIlGgUCjAbDZvjRg0m82Ix+OySGVU1/tawtx1Meaa0aV0wdFqtXA4HNeK6DMYDFtdbafTqQgrGo3GhZG5JJjzWX2b5auyuGzO5/NYrVYbile+XpK4SfzO5/PyTLhJnQV9+/2+kKttNpsQNX/ei4KoRqMh5z2flyRKTCYTBAIBOJ1OFItFWWJwWUliwXA4FCJjtVoV4Q3P2VAohOFwKKIsusKyL2K0CImb5XJZfi+JDFxOsVekSx0FA+zTZ7OZzCnhcFjchnnWe71ecdycz+c4PDyUz4TgN/tXzmsmk0miuzwej5CNSXamUMXhcIhbrdKNi30tCaf8M/v7+xLnyiU330ulUpH3qNPpJG6PzsVnRTbESRjN3Ww2EQgEsLu7K0smpeMUBQRcXKrVamSzWQSDwQ1iNwBRngcCASG0cQ4BNvtZEtCLxaI8exnltVqtYLVaMRgM5L0Ui0U5a0iEoQMGSSQej0fmaC7rjUaj9DadTkeW+RRx0ZF1PB4LIYSOZePxGKFQSIRfDocDg8FASHAejweDwQAHBwfiGEI8hUr6cDiM6XQKv9+P3d1drFYrcciiGw6df+isZjabEQgEsFwuEQwGsbOzg1AohHK5jHK5jFQqhcFgIHMYYzDH4/FGvCFnyUajIaQgChzi8bjgV/x7XGKORiNoNBpEIhHodDqUSiW5hums/PjxY3Edp0Mbz3y6HHNhSXzLZrNJH89ebblciqKf1zzJkSTuMYae2FO73UY+nxfCWDKZlOt8MBiIoG1bPzabzZDL5QSze/TokbhekyhOZ+ZIJCLYDQV/dE7iXLe7uyvxOs+ePZP7QSleUd4jjDdiZCPPHjoG8ixLpVLiQsXl8VVE81gsJoK7n/zkJxJJTSyIBEOfzyeiidFoBLvdLr9/Pp/Dbrfj0aNHch4TyyAh9KLFMucPzjdGoxFms1m+b6vVKgtHCiGPj4/FYeemTg0XxdVz6aokgF1WJGbQYf+qGVvp1s2YQbrF8zpX3gsajQZ7e3sS1Xh4eIj9/X187WtfO/e7ON+l02lxAKJ7DM86Pi+NRiPm8zlev34tDsLXeb/T6XTDNVc5B/LspfPr/v7+xjOEhKN+v4+dnR04nU6JwFsul0LMMBqN6PV6+PM//3N0u12EQqFzpF0KMnivKxMg2IPS3YP3C8lBRqNRnMfed5FUTYKhkoCldIF738V0BYqJlSSDq+YOrVYrIqN6vS5/n/0+MftWq7Xhwun1es85rLxLUYxHAo1yriNOyvSAfD4vJAi/3y+EZ5/Ph3g8Lv+OhKpkMolUKiXRspwpGF2m0+mElBQMBrFcLq8l1CeB6SKXKO44NBqNCHKJ4ZEcR7e9u3Rq43fE10axOPE2OqYVCgURlzP6dW9vD5FI5MJEmbOfQT6fR6FQwGw2g9/vlx2H1+tFrVbD0dER/uIv/kKusXg8Lj0BCdE86ygSvs2szFlrPB5vRKafPUf1ej08Ho+Qqq7C1ygaDwQCePToEer1OgqFgjg28p6nuMZoNG4lJVJQAUDws8uKeBWFLj8LpXymcr/GosOrwWDAs2fP8OrVK5kJ+BnzGUV8nFGd264z7oUBiGvVZUXskVgiS+loeVX1ej1UKhURZQcCgXOfPc0G1Gq1EA/piPwuLmXKpJNtNRgMcHp6ikqlIq6yJEKvVis5DymI4l6Ee3S73S6fOQVR93Vf162fD/Tyvu7rvq5VbCwIHFApRfIP2cpUEI7HY2EHk4zV6XTw+vVr2Gw27O3tXdi4+f1+1Ot1eL1e5PN5+P1+IdQAX+ZXVyoVIdsoh366XTWbTbjdbolEICFFrVYLqKfX61GpVGTopvsC1Yh6vV5eC+04aZGvLILEtM9VvpZmsynxIBeRBmizeXx8jDdv3oiTCBXsFotFGkKqMQh2k1Tm9/uFpKXMmNdqtTg4OJClt0ql2vgsOAgTtCT4QFCYA8KDBw+Qz+dhMpkEUKVinq5R4XBYFjWJRAKTyQS9Xg+fffaZkJoIfE8mEyERcSAbjUayJHC73WLXz2LsDd1xut0ugC9zo91utwBSLpcL1WoVwNr+mfbTuVxOFCwEt6i67Xa7AuoSHADWy2mlCoYNIp26gPVAQsUP7d9vW6VSCYVCAZ1OB5lMRmI+1Gq1qHbn8/mGWwMVD3RuUzL0aVEfiUQQjUaFoHVyciLOSHt7e9jb25PM7/F4LHaws9kM4XBYIgK4zKB7SqvVEoVXMBjEdDoVhVSj0dhQwNJFyGKxoN1uw2w2SzwgIyyNRqMAhbVaTe5Vt9t9IYhgNpuRSCSQSCQ2rm9Wp9MRhaXZbMZsNkMmk0Gn00GtVpOlG0FMKlxtNhtOTk5gNpvhdDpxdHSEer2OYrGIdrstREKXyyWROSSucXFANXQwGJRIVgB4+PDhxsBBe/RsNotCoYBut7sxoIdCITx+/FjOtOsW41aHwyHC4TAGg4HEUnCg5UBDde6rV68QDAaFMKJ09RqPx6hWq3Lub7Pj5usjuY5KeS53GNXQ7/eFLMmFVLPZFLcU5ZlJFTaBp5t8BnQenM1m+Oijj5DNZkUd7/f75eyfz+eIRqPiEtHv91EqlTbuZz6DlIrTSCQi6kbeIwTXdDod3G43AoHAtWzCt5VarYbdbpfYEaoDL6p2uy1n87Zhs9/vI5VKSUSC1WqF0+nEbDYTC/x2u41yuQy/3498Po+dnR1Eo1EUCgUUCgVxKRwMBigUCigWi6hUKkgmk2IPz3OTQz9V4gDEJfOmIA6XC3TZmc1mQoCjWxT7houqUqmgUCgIUYMOBoycrNVq4rDAZwpJVnzfBoMB8XgcKpUK+Xxeou0uIpoA66iGly9fyvej1WplUVmv18Wxivcb/39g7c7Fn0uwjqQlEuXosnFyciKqMVqP93o9cQalKp7L4Hg8LspnktS5tOJ7TqVSQoSlk8pFxbgqAOKkdlkRlDkbJ33TMhqNePLkCdrtNtRqtThO8BkYCoXEoYuADJ9dXq/31vfnbWq1WqFer8t5dxHJBfjy7KOD43Q6FWfCs8SRUqkEj8dzDni7bpF8Q6dCZYSDslQqlSycee5R3ccFTiQSgdvtRiQS2XoNkLxFwsJdFVWUBJIvuh/VajWCwSBSqZQsje/K7p3xr+yTuYy+62J0xF2W2+0WUn6xWJQIboLRBBJJ1j0bc3jbUoLzl82JzrdRVSRIk6Bz0yLoS2EA+2YSv3/Wi0sZg8GA4XC4Fby22+1CgCeJiKRc3utcblGYwrhQimJIemHsmM1m25jhGo0GVquVELDphMd73uVyCdmKBBL+/8r+REmu4tnCHpjPBxJf6GDNJSPnb6X4xOv1ynO+2+2KWwKfr1yus3h/LpdLmM1mmYW5XOZ87HK5pA9k38R5OhKJYLlc4vXr1xLdVqlUpB/qdDrI5/Oo1+sAIK5BdPIKh8OyNLJareIKrCwSnehYNJlM5P0q3TXUarU4bHJZypmlWCxCq9UK3sH+1+l0bkTd0hWK5xg/c51Oh1wut0Fw5ZlXKpVgMpmwv7+PTz75BCqVCplMRj5TPncZ4UelPv/78fGx9CXK+3o2m4nzGmeIWq0mn89qtcLp6ak4tzLCnnHcOp1OCOjFYlEEYVSy12q1jdkiFoshHo+L4zOJwiQhlkolcSEPBAIiPiOpOhqN/t/s/fePZPl5NYaf6so5x+7qHCZSFHctiYZeGF/AsP9eAwYMw4YN2NJr8RXF5e7OTupUOeec6/vD8Jy91dPd0zM7S67IeQCBFHdnuuvWvZ/7POc5AXt7e4q0s9vtePPmzQbZiKIjRtVxHo3FYlgsFnL79vl8yOfzEkyYzWa59jBKlNE9h4eHcnYgQavT6SCbzcrVstfrYW9vD36/H1tbWyKpcU4kwYeCLZKcjM8IxYvFYlFkdorE6E4ej8cRjUaxs7MDl8sloVwoFEKhUBAB8vHjx1itVnj9+rVcergsczgcIu3xeuzs7CCRSOg+pRiLkWqlUknu3sPhUO6vjJbjTE5BG/sYLrx4tvKacFau1+uKiLqJew4GA6zXaxGHGF3P/z2VSt1LhGDcH/EyCt8YH0nCHXsVI2GP88RgMBARKRgMIhAI4N///d/1uz569OjOn8+eiZ878KeoTpPJJHIHxUoksM7ncxFx7uthjbVer1EoFFCtVuX2RlcfknA5t3z11Ve3RlLSSYX9b7VaxWw2Q7/fRyQSuXeupMMvXcMo1tzd3d1wkTP2vXQo/uqrr7C/v4+Tk5NbiV4UxpCYSrHiTfIABUDj8RjFYlEk0g+JeBj/1mw2df7u7OwIJx6Px++55hpdQulqHw6HcXh4KKc1urcT6+d8wdjW3/3ud/jnf/7n9+YzzlUUovGdevM+p7v7X7LY7/2li+5fn1J0Xb+tSED5OYvP3G2zFcnZ5XIZXq8Xbrdb5Fz2HxRAsRdjfwdAUbZbW1uIRqNotVrCH9ljejweHB8f60xnX3ZbUUREh5z75sCjoyOEw2Hh0MCPjvNutxvRaPReDPpTi67hFJ+TXL6/v49qtaqdCyNGSarZ2dnBb37zmweJUcvlst5zR0dHGI1GuLq6kqCWz0Wv18PV1ZXOegp/k8mkCPlGEvDH1nq9RqvVEub4IewvGAyi2WxK+PiQiDsW93b8LHyvsU++6yxiEo/T6VTyz33FvRFJ83+OoviEeBlFzSTfF4tFCTNuYg7GWPlQKIR0Oo1qtQq/3w+Xy6U9r9PplABnvV4jHo8LazSWx+NRWkWn0/kgDmD87j+U4HJXcU84GAywv79/L65Con2r1UKj0fjsTus3q1gsagedTqc3PiNJYdw1jUYjBAIBzTic9TweD05OTj7a2ftLfakvBKwv9aX+isrtdsPj8aBQKMBqtYo8YSQ/0ella2tLyxgCAgRdLi4usLOzg8VigbOzMykM2+32xhIpGAzqvzcaDdTrdSmH6LZjVFEYh2AuPEajEXZ2dmCz2RD4U/4znWiMakGqfBaLhdSakUhEEYUktkQiEVk0G1/2BAN3d3cRDAY3mpN2uy1r4A/FTlksFvh8Pnz77bdy5KCyz1gul0tAeq1WQzKZxHw+h8vlgt/vlzqcCg42u1SSmM1mAQ78uQR5bTablhn5fB7/+I//qKUaXXN2dnbQbDbfU9/cVNjQqYRqapJeCBJymKGFMDPMr66uZL9LFyUAipebTqcol8s4OTnBr371KzXnJLpVq1UtDUkcS6VSIrcQxGs2m3K0CQQCGuIIIMdiMXg8HtTrddjt9o0Yg3w+D5vNhna7LcCJi0cqST+1KpUKCoWC7FVpd071MsE8i8WCcrksRRe/a8ZM2mw2LZk5uLTbbfzDP/yDwDm6OLndbuzv72u5wb8vHA7DZDIJiCFATyId3Qp8Ph9SqZSADUb5NBoNOSe9ffsWfr8f6/VajlRUFXPpTyAvGo3KEYZnDSMUHqJceMgCv1KpyDmCynlj8V4g6Y/Lk6urK3z33Xew2Wy6DrPZDOVyWZ/BarWKZJJOp5FOp3F4eIjT01P0ej1le1cqFezt7QH4Mb7t9evXyOVyIrfxPCO5p16v4/Hjxzg9PX3QwrFer+OPf/wjLi8vtTzlgooRmw6HQ9eWymmCI6lUCqFQCM1mU5Eg8/lcSzeSqLxe78ZZRaU91X0kTnGIo9NfqVTS2UPgnIrUeDy+cbaTYArgowlYAPQMhMNhZDIZRTnk83m52wUCAbjdboTDYS14OQRZrVY0Gg28efNGS0L+71xCHxwciMzF85G28D+1+AzU63VUq9U7bccZRUICzm2gAIfETqeD3d1duehMp1Nsb29LmcNrEIlEpCyOx+P45ptvtByxWq148eKFYj2oyOUCkecAVZk2mw1v3rzRIH5yciILdKrUSLi8C9BgROf29vYG4fQhi3PGW5VKJblb8T7jUE/AnDGa2WxWSxabzYadnR2cnZ3h8PAQbrcb+XxeaubVaqXnmjUej3F9fY3z83OpEAFooclFzXg81nMAQN8Lwa/t7W0Ryo0kR+CdIxndOtgnxGIxLXhJiG+1WrDZbJjNZopPfPToke79arWqqE7gXR/DWBX2S9VqVYri28rj8Wj51+12EYvF7v1Out2unpOfCmKZTCYEg0F8/fXX2NvbQyaTQa/XQ6vV0hnKJUEgEEA4HEYkEvmkaNCfUuw3u93ug8grgUBAKkUSsqhY5IKZvTLdDk9OTj76nGQs82QyEZmAccLj8XiD7GMymbC7u4vxeKx/x+fzKY42Fovd63LXarU0U3ysq+F9ZTx/PgTy8h3IP8PvYrVaCbQj+E01dCQSufX9u16v0Ww2Ua/XtZwkcYGOnLFY7M+i+v8pxUUw37NU1BqXr8vlEoFAALFYDMlk8sHL17vKCM4ToLyvgsEgrq6uFNP5KQSw6XSKWq0mF1K+R/jejsViP0kt+3MX4396vZ5c9G7e7zzn5vM5Xr58ueE4x3ma9/9isRBR3OfzIZPJoNvtwm63Y29vD/1+H4VCAYeHhwKLI5GI3Ij53ibBgkUCBRfljIa92Xfz3+HMSScPOq/s7++LPMS4Iy6P+N+n06n6ADonsAdh9B77Mbo4s+jIy3uAKvnxeKzecXd3V+95Rm+T1EKxGF1/zGYzKpUKut2uyO4mk0mkIbq88Hyh4yVjRenwdZtKn39ua2sL5XIZ6XRai28jcD+fzzUXx+NxkVvoYMOfTZcHzm98nkmIZLQUiUC1Wg3z+Rx7e3ua/dknMj5nOBwimUwim83Cbrfj1atXivJKJpMSsZBswX6LTjB04jG6Es1mM1SrVYl5SIyi+M7j8WC9Xm/cG4wHHw6H6vXNZjPevn2rZ5095cnJiaK3/H4/tre30e12NdMBkFsm3xc8F4lX0UHt4OAAwWBQDhsWiwV7e3toNBqIx+Mb7wG+I8xmMy4uLvTMtNtthEIhXF9fq0+2WCy6b0laJMmXQrtSqST3a/4+wWAQhUIBl5eXwoUGgwEAiLhhs9m0UCQmRMGFyWSSgIsRoZVKRe4+PAdIZiGBzbiIMs7bLpdL2Aqfm5OTE/WaxC8o9nE6nSJt3uZwy7ixq6sr4R8XFxdymuc8RAIX7+dMJqOoUYpb6P5E4VgoFEKj0cCLFy/krMFIcYo16T7VbDZFDuDyOBKJCLu6r0h44f3o9XrlMsbIGjrZNptNRZdyPmVkIt0p6d5PwuV9/RCdk41OtxRieb1e9azr9VquxtVqFZFI5KOIH/l8XnMYBbu8nt1uF7lcDiaTCel0GrVaDc+fP1fywmKxwPX1tbAw3o/Exzp/ikj96quv3psF1+s1qtWqnF3D4bDmxr29Pc0FrVZL5FU69LB/4313F5FnsVigXq9jOp1KpHDbIpUxfSQ5kRj1oWo2m3oPB4PB96670+nE9va2yC82m20DayLp02Kx4NWrV3J0Z9RpMBgU9mWxWNDtdvHNN98oau4+t9HPgXN8qc9XFIF3u131VS6X6yeJjZgMctds5XK5cHR0hOFwCJ/Ph/V6jUAggGAwiJOTE+0U7ipiStwdEHvudrsSnJHEMB6Pkc1mFbXIWZcO/RTUkED1oeK7aTabbQgsf24iRDQaVYxsoVDQu4nR0ezTiKOdnp7i9PT0QZ+J4ke+wwaDASqVikjSFosFu7u7GyI+Y8oKdzHcBXA++JR5j87o0+n0g9GRwI/42Gw22+jTH1p0i/2Y39Vmsyl2uVqtIhQK3TmL8j6LRqN6Z/9cxfc58QgA+g5Jvl2tVoq94/10c5amOJ1uh9FoFLPZTKQ0utFzJ7ler7Uruw1L4o41GAxKrHzXdRiNRtqX0mH4U4r7kZuzzl3ldrtRKpUkTGEMNq8j59afShBmmkqr1UI4HL6TYMa+4I9//CMGg4F6c1a/38cPP/yAUCj0SSLlL/W3W1/ulC/1pf6Kis0h7dA9Ho+ILFTPkZFcLpc3FoV0Gzo4OFAmMdWQXPaRpESw3+Vyod1uSwnI/Gk20eFwWCCh1+sV89vYVBiVrWw6acNK1jEVfHTxojWr3W6XMo7WkAQE9vb2YLPZ0O/3FcFFleVNVQpVCwAetFQkqWU8HsPv99/ZnJhMJkQiEeTzeS2FgXfuYQ6HQ2Aro/K+/fZbqYap3GUuPPCusaNdtMViEflmtVq91+BTmfmQ8vl8SCaTqFarODk5kZqy1WoBeNcUxeNxqcSoymTDa7yepVJJERW0HjaCuIxpazQaIpHEYjGk02mcnZ0JIOeist1uC+Df29tTfAQHLiomGH1AkJcNGpURbOzpSmW0Y/3YIrj2+vVrKUlHo5GU3Vwu0ILf4/Hgf//f/3ecnJygWq1itVoJbGNxqUjw+Pz8HE+fPtVCbTKZiDhIBy8qOY2Wv1wGtFot/PrXv1aTD7wj6dx0YKAddjAYRLvdRi6XkwKOBEm73S6Qst1uS3nCZVCpVFKEHVUsn7oMY7QflZxUXdL552aNx2O4XC5ZtU8mE/zX//pfUSqV0G63RR7lfUjlKQARCSKRCKLRKP7xH/9R90kgEBBh0OPxiBRUKpXwzTffoFaryaGP3+V4PEaj0VBcHIkRjx49urcxr1QqePHiBQqFAvL5vCJhzWazhhfeU3Q7Ozk5QalUQr1eRzAYRDqdhslkQjQaVVwnSUmMGeTQYTzj+v3+hjKGbok2mw27u7vIZrNSa1erVSl5qCYmmYD3JwmcxsXZxxbBJ5/Ph52dHSmeGb1KsjDjKAiGkIjLe5zqUH4PXPIwMubk5OSzL235d/N9mcvlUCqVsL+/r3uLsbmNRgN+vx+hUOhWpSRJRjznXS6XHM3otjYej5VbzzObsR2j0Ujv5EajIeUg1dmlUgmNRkODLrPtCe7TUYrPECNLCe4DUNSa2+1+T0VkLMbrPLToTFAoFDCbzeRkYKzZbCbLai5gGKNJAlo8Hkc4HN6wPedzRhIK74HRaIS3b99KldxqtQSY9Pt9ud1xkUDiIZfRJLomEgmYTCbs7Oyg1+vB5XLJuYEOHwTIdnZ2UKlUUCqVdE0JklQqFdTrdZFCHz9+rN+VYAvPvK2tLQT+FEdFtzMuJ9lr3XaPMSqMzoBGlddt92O/30c6nUYoFPqsID6dlQgMccnDBRvP2T939CAAObfxHfKhIvGezgjJZBKHh4cbABTPNMaE5nK5j4755PcKQER2LmK5HGSxD6ZTA59TEnIZPXFb/0uRwM7OzgbZ/nMU45EeGsXIWF0uwDqdjiJk6JBHAhbj7+h4wmXCarXC9fU1ms2myK0EjU0mkxaYzWZzw2Hml1oOhwOPHj1CNpvVuU/CDhcgtMn/HM8PSS2TyeRBZDySBCaTyQZp9aHV7/dxeXkpwJQuJcC7fpHEplgsdq+L2l+6otEoms0mbDabXHdunqF0lma8RLvdFuFiOBwqkomRs+yP2NvRbRl4d5+XSiUtdjg7d7tdOTPdBLONUV4kkUynU1xeXm44VrEnJdnD6/Wi2WxquTAajUQO4H04mUw0e83nczidTrjdbrm3hEIhOfKORiO5afHfo6LfZDKJ7GWxWN4jMYXDYZycnMgxjw657Hfm8/kGUcpqteL4+FjEAL57WKlUCtvb2wgGg1itVshkMhgMBqhWqxuO37e5D7IXjkQicDqd6Pf7yGQyCIfDWkQul0u5JTFSmi7VwWBQ5BbjEoL/zNhfezye9whg3W5XREnjDGKxWOTMbLVaRSygazkdWemEur29jYODgw23GfaudMTudDpwu91YLpcolUq6ZwEofmS9Xsv1kjgHyUVv3rxBLpfTWex0OmGz2eTWwpmdEYJ0VicxkcQ9Ln1sNpsWSR6PB6FQSDM6XeH29vawv7+P3d1d9W0AJEIIhUKKNCPuZTabkc1mUSwW0Ww2RSBrtVo4Pz/X+4I9Awk/FAWazWaRsS8uLoR7ff311xv9s81mU4zTv/zLvyCRSGhRXygUsLe3J0dBCi9NJpOwlW63K8esbrcrkhoxJhI2ucR8/Pjxe+9ho+Nwo9HYWMba7XY53BqXbHSyury8RCgU0hxNx1t+RpKwuECmGyivTzAY3LgexIqKxSJisdiGsI4ku1arhRcvXmA6nSr6htgkl+PElwaDAba3txEIBDTXDAYD2Gy2DcdtRj+SAMQZtF6vo9frYWtrC8+ePdOzuLe3hxcvXqDX6yGTyUg8Mp/PhV1x6UfssdlsKtKV88Rtjno3i/ceAJ2lxMhIwOIZSfHRQ4vOmYwZpssJ8T+eAa1WC99//z12dnZE0Do8PEQ2mxVmwjOXn4fvgk6ng3/5l3/B48eP5UZGt+PVaoV0Oo3An6KBWCRj0eXX+Fl5JtDt+j4MlK7yjBO7b0FsNpvhdrtF2BqNRh90MyV2x9nzrjK65hJrAt713dlsVk6EdMXkM3B5eSkSK9/Nw+EQL168gNfrVbzvl/plV7lcFl5Ecr3RFdrn82F/f/+j4+opPvxQzKfx3CEp8KG4mMfjUcrC999/j/l8LqyYomSKhJbLJSqVCqrVqsi8jBDmzzw+Pv4oEgPx4j9XkdC9WCxEFg8Gg3JNpiCY74RIJIJUKqV59TbHOVa329W71mq1olqtolarKXacczfjwc/Pz1EqldS3McqbaQ68nj8Fp/mYWeq2/vfnrng8LiJvNpuVc5ux6CRPAtLP6TxHZzLuQiigZ0IJe9W9vT2MRiMldNBFlXtLYJP0TldYOrDeNLQAgEwmA6/XC5vNdudZwes1GAyQy+UQj8c3TBlWqxV6vR4qlYqiLB+6R2Q6wHg8luiFhDAmvHwIPyMZmukAdAU2Jh+QsLa7u/vJRCzO1dPpdCOh5rZqtVrac1SrVezu7kp0QRdQYvVnZ2cffU5/qb/N+kLA+lJf6q+oaAtPJSOVniRN0L2EsTahUAj9fl8xQAQWEokEFosFyuXyhm06AR2+WKlEIyOfoLHZbJa69eDgALVaDdfX11pkMTaKQwf/XRJ9ptOpXBDYrNN1otVq4fT0VJb0VDtRMUc79kajIecUDjBerxcnJyfvNexG0PkhRYLDYDCAyWTC+fm5yAA+n29jkCeYRHCaakWSXEiiWS6XysLe398XgYIuGT6fD/F4XItPXkMuiH9qtAnJDVwqZzIZLXJI2CO4TiCHxC3+bC5oh8Ohlr3FYhHn5+cCU42K54uLCzlwffXVVxpA6AKWy+WQSCQQiUTQ7XZFwJlMJgL8Cb7E43ENPlwMMK6EjkFOp1NxcS6Xa6OBJegFQMrUu4r51NlsFjabTSq9m4tJv98vJxw24IFAAKlUamMomk6naLfbSKfTIg2SfEaAiM5yjHbsdDqYzWbw+XyYTqdaVjBGjEA3la/xeFzgtbEICpNIWKlUBKi63W6p61qtlmzXXS6XljtUMvKavnnzRpELn9KI8vkJBALI5/MiHt22OKQSncShra0tvHnzBoVCAZlMRgRIEi0IvhIQjUajAq9jsZium9vtFgCcy+VEPCsWi8hkMnIQ4nlFwICOUMwUPz8/RzQahc/nu9O1pdFoiBDC2KrDw0O5SBE4Bt4RRAh8jkYjxYPY7XYRaklUC4fDiiclAWQwGMi6mGf4bDZDNBoVcZCkBy6+Hj16tEHkZTwaVdC9Xk9RHlTbcTFLQs9Da71ey9WFn+3p06cCO7kYohsbi2dqNpuVCxHfVVTVAT8qx6+vrwVyfogc99CazWZS7pOgw9jGZrMpUlAsFtMZSoX4/v7+rYMkicOMzgR+XOpMp1ORkxghO51OZVfu8XhQq9UwGAwwGAxwcHAAt9uNy8tLfe9cupXLZZGjdnd3RbCczWZ49eoVrq+vBbrze+U7jorjcDgs2/TPEV/Ac240GuHo6OjWs4TgGz/7ZDKRExzPWH7nFosF2WwW+/v7SKfTuLq60pKFTp6Mi2A0nNPpxGg0QiQSQb1eh9frVSwpF8IEL+i4wzOakZZ0JyIQ3mg0FB/y/PlzlMtlbG1tiZDXaDQwGAwUB0i3i0gksgGKcinbbDaRTCZxcnKycY1CoZDuES7meD7crHg8LjJXNptFMpnc+PdI9iJBi+S2z12MaGOsE4Fot9v92aPbPqZINgAeBjJOJhP1TXQjvVkOhwPpdFoOb4yr+Bgw2Ugin06nAhgJ7LVaLQSDQfUrJFLxLF+tVnI3HY1G+O677xShw+effQYB3ZuOcT+1eG0f6kZKkv3W1pbO8lqtpmecDjx0snO73SJskeCWyWRQr9dFfjMC6IxqzmQycmoyxsL8Ustms+Hk5EROiOPxWMtXEjp/qcSk+2o8HuPi4gL1el1RtgTZl8vlhksuCRIfAlT/UsV4++VyiWw2i+vrawQCARFxjDHhdJchiYhkS76XGElht9sVj2QUXVGY4Pf7FdvJOCdGENFhwVjs7+i8mslkFJdBciPnXJIuuJymM6/FYkEmk4HFYtFZPh6P5fYbi8UUXUmBAAA5O7KnJNnDKMzqdrvCF0hCMpJmarWaxFgXFxe4uLhAqVRCKpUS6YSzJB2YgXdn6f7+Pr799lu9R+muylmRi0r2WdVqVcp/EmtuVrvd1rLA7XZrrq/VanIiJwE1GAwiHA5vECU5lz/EheC24tl323uf8TkU3tCZibM6HYiMkcdPnz7VEjXwJ2f1QqEAl8sl99xSqaTzh8IQRtvT1Yqu0cvlUq7WRmGV3W7H6ekpwuEwFouFZn3OJey7SOyiGOGbb75Bv9/Ht99+q3gz9iskAI1GI+Tzeezv7+P4+Bj/9E//9N5iiPMNi/98Op0im82iVquhVCqhXC6j3+/LTXk+n8Pr9WI0GiEWi6Hf74vIZRQdxmIxRYFmMhmk02nkcjn13sSz6DxGVw3eQ6PRCBcXFzCZTPB4PBJhjEYjxUPRHR6AhBokZjMukPOu1+tFPp/H3t6efh4dWRaLheZ/4obxeFxufcTa+B1WKhV9VmJ/oVAIx8fHImJxxuU96nK58OzZszudWlkUTTFylQSsUCiEer0Oj8eDi4sLVKtVjEYjiYtI/u31esJ3GHXF6Kxerwez2Yzd3V2MRiP8x3/8h9wy2u02FouFIsnS6bSc3Oimx+I9x+eJswJjgRhR7/V64fP5JDqkyyifi2azCavVqveaMQ6NUYbT6VRzNh3obiNitttt2O12ua4/pGq1Gnq9nvC1crksbIguZYxyzefzmM1meP36NQKBAAqFAjweD/L5/IZolBWPx/Hq1SsUCgU4HA5ks1mJR+hST0JWMpl872zlXMSeluRUzqIPEYjwmtFN8SFFAcCH+lW+LzudzoNcXUKhkLAmEgV5/UkiJA5Qq9XUvzOJwufzieRB0tzvfvc7/A//w//wQQLOl/rLFR396PRPp0/iOh6PBwcHB5hMJjg7O/uo+dDooPOQMjqufkwtFgtks1kRrYi18/9n30gxVSAQ0L0aiUR0Xu3u7n5Wcc/PVel0Wp+n2+3KwQv4UeRJwpzD4cCbN290XtAJl673xiLplu83Cidu4gI8S/f392VMQKL08fGxRFX7+/ufLB7ijslut2M4HH6QkMd7liKwP1d5vV6kUikJRK+vr7VLJCGV+HEymUQikfjZok37/b5m1Xq9LrfVra0tCQFoTrFarfR7v3r1CsViESaTCfl8HoeHh+ptuZ+giILz1s33FYX4HxJZ8TkDoHOHsfcA1C9Go1ElRXyINDWZTJDJZERg5x7FYrEIf10sFuj1ejICua3ohGq1WuUe2W63sVwu9fu1Wi25nc1mM+zv73/SbMT7lfPIXdXr9fQe7na72N7exuHhoa4J8ZZMJoPd3V1cXFzg0aNH/ymxli/1560vBKwv9aX+yurg4EBqoOVyKUvyarWKarUqtXcymcR4PMbu7q7Y9avVShE2drsdmUwGTqdTbOjt7e0NNxUqAanIJXGGsU6VSkWsbEbPsRFlXjaBpK+//hoAFJNWLpeRSqU0WJM9zeVROp3WS3A8Hkutabfbsb29DZ/Ph3Q6LYA0HA4jHo/fucgl0MHog7tqOp3i4uJC2erz+RyFQgHT6VQgJEFLAgA2mw21Wg2pVEqOEyySkWhbT3t7AulcwHM4W61WSCaTUqo4nU4tigiGGWOkCLJ/aIFpJHV5PB48efIE9Xpd98tNNR1zk43NT6fTEav89PQUL168EEhFMg2XhlSvMb4qFovJ2pv3D7PoaSNPYgPz5ReLhdStjG+iioxxCQQqqMih64zD4YDf71e8HcFfXotwOCxXjpvVaDSQy+UU8cLvFYDcRwhcRaNRZDIZKZz/6Z/+SURHuomNRiP4fD7ZmPI5peKN9wkJj4ykOz4+RqPRkNMNs81JSqOrCglzkUhE9yQVvfyd7XY7jo+P5RhmJEEy6o4xEnSt4FKEjkkkQoRCIbx69Qqnp6cfbfVLYHQ4HOLt27cYDoe3Kjr6/T6y2Sz6/b6iSFerFV69eqXIxu3tbXQ6Hf29jONxOp0Cj6mo4PlHApjD4ZAL3Xw+l/Ixn8/D6XSKZHiTvEMXKi6nrq+vEQwGsb29/R4gSMv4UqmEWq0mhzM277yH3W433r59qzOC0ZAE57kAIOjn8/kQiUS0sOKCh3n0JHgwYpSfl2SdWCy2AYRsb2/j4uJCJKj1eg2XyyVS4Gw2k1qYSt+Dg4MHK2f4542575eXl6hUKojFYtjb2xOxitEh/J54RjJKyufzoVwu4/DwUGQhXk8SPEh6s1qtOpd/ShF4ZgQcz9B0Oi0XIyPYyzg8j8dzqysdaz6fKyqT9xldMQioUzHHc5cOZz/88AOAd/cYVcK0Le90OqhWq1gulyiXy4oXdrvdG4pCq9WKUCgkgGmxWOAf/uEf8PTpUz3XVDHzcwPAkydPfhJhgS4drVZLS7vbiucsl7yMYuWyD4AcCOjMSKt7Ov4Fg0G9W8bjMSqVCpLJpJZjjM+gqwKdHOm4wwUPn4loNKrr1Ww2cXZ2BofDoUU3nzES4nd3d3We89pT9cpeymjZz2o0GiIkHh4e3nqN7HY7dnd35R5DEuDNYq8EvCO+FQoFucxRfbdarRQBSODv5yo6Xv1SilFYVON96HcrFAoSGtyM3jGWyWRCLBZTPFuj0fios4ikB8ZZc7FKNyACbCSq9Ho9LddjsZjio9izkDzLhSRd6wJ/iqY+OTn57N8LSQ10Br1vEcZzjov1TCaj2LDt7e33ztHJZIJyuSxXXMZttlotXFxc6NlqtVqK7aObJN2zLBYLisUiDg8PP+vn/rnK4XDcSvj7nMUeharW25xJjcX3GJ2wPqby+bxEDDs7OxvfMWcRLnx5btFt6JdY7GUolqCDNPCj+9rBwQG8Xq+IAMC7XrBYLCIUCiEcDm+4Z7Ef5ww3Ho/1jqNDEcFwLqZJYDIu5zirUXDVbDbx5s0bTKdTeL1ekb8ZRc3fi07XdrsdtVoNNpsNFosFlUpFRFTgx3Pd5XLh+fPncuRhcXFEQQH7dJKCgsEgrq+vsV6vsbe3J9dLAHKk4nJiPB6jWq0il8vpbKQT6HA4FJEgkUhs9OWcI51Op/oZLhFms5nu/Xg8jsvLS5TLZX2nJL4Z3WsZuen3+3FycqJlRzQalVKcEWIkX4xGIwlZ6Az0EJEAl3b8O0lSum8JS3zE4XCoj4lEInj06BFOT09F/CG56Pr6WgQE/rmjoyMtSyqVCvx+v1xLU6mURAl0UO31eprNPR4PfD6ffga/Z2PvY7fbkUwmEYvFUKlURBTnz04mk9jd3UW5XJbIqVgsAnhHoiMBNRQKSSzi9Xqxt7eHRCKx8U6jizEFN3Ss29/fx3q9lqNaLpdDr9cTIYP33Wg00pxJoUUymUS/31csG3EQuluxl8xms8KNSMgpl8uKpbZarTg8PNRzUSqV8OLFC8WBA5BLOWc6EjvogOZyuSSkIk6yXC6RyWRwdHQkwiAdhUmeZDyizWbTZ+f1bDabut4A5JREUQTjN7/77jvFws3nczx69Ej3JgmhHyqKHYwumMC7xfbR0RFev34Nl8uls5B4ZqPRUI8+n88RDAYlXOP5MRgMsLOzg+l0Cp/PhxcvXsh1Hng35y2XS3i9XmSzWUW5npycYD6fS+BA93NiNO12Wy7cJF/R6QuAziZ+R3TsevHihfA1vm/5nRJD3NraUnTYXUs/RgYHAgEtpz9UnOsYH0osgTOQcclOgiFdvF6+fAmPx4NyuazY45tltVrxq1/9Ch6PB6VSSRF9/B6M+NTl5aVi2o3L5U+JrTKW2+0WZjYYDCTWu61ms5lEiQ6HA61WS3gKyWJGXJf/jOTCD5XT6RTWRDEOnT5NJpN6fbfbrYQCJjDwuaQT/Hg8RrFYlODq6dOnX6KRfoFFh6NsNitR5GKxEA7NlItisYjT01MAwLNnzx683KdYttvtfhCLm8/nGI/H72F/D6nr62u5tRGPuOn2TLG9MTrZZDIpneKX7jJsLO67/H6/cFujw5Xx/WRMsCDO3uv10Gw2EQqFsL+/r++TZB2+Z3q9nmbWm8V9F89Iuh+mUql7McWPKbrlZ7NZ7U/uqlarBbvdDqfT+ZMj7j+2KLZkz03hKvCuH+J+M5lMfpDg/anF3pAiSs6qFxcXmM/ncLlcOD4+lrM2f1+Hw4FEIiH3fN4fJB+TaFQqlZDJZCQ+5jNKcWSlUtHc+yHXQxL4SFbmrop4lM/nk0vXh8S04/EYb968EaZMUwg659JxnXHkxJ5u7kJWq5X2cJyFGo2GxOHGvRXFoHTK5Uz5MUWi6YfIqfV6XX1PLBaD1+vd+N2JtxA3ZRz3507W+FJ/ffWlI/tSX+qvrKxWKx49eoTLy0uBNtlsVlbkBBfj8Tj29/exWCwUYZPP5+WAYjabcX19DZPJhMPDQ+zv76s5pLp+sVjg8ePHIrnQSpZuEm/fvhXAw0aSblkE1fnn/vCHP8gaHvgxCrBer8udhypUs9mMi4sLlMtlBINB9Pt9rNdrxONx/NM//RPsdjv29vZgsVikQrtvsUM1ICMVb2vSlssl6vU6Xr16hTdv3kiJG4vFNgZii8UiR41kMomtrS3F5Bktoam6slgsKBQKqNfrqFQqmM/n2N7eRjqdVqPe6XREwCKJgEpOOhvl83lFgZGARRUks8KNzO2bRTVeoVDQMMjIHxJrGBfJRpwNCV3MGA/C759OEIvFAg6HQ5bIvAcJ+KZSKczncwGYk8lEzjwEFtxuNyqVysbvzO93vV5rQCDwYCRerNdr/X4kyESjUVxdXaHdbm84SgFQVEStVkMymdxYii6XS8znc9RqNYErHDaXyyVqtZqcw/jZGWHl9/ul1qfLhM/nQyKRgMvlkpsYiRSj0UjOJXStIehLwI2kA6vVqlgFfg6CgBaLRfnwVGJwQDEC0slkEvv7+xgOh8jn84pcYh73dDrFcDhUTBmvM23fSV5sNBpygHry5MlHWyDH43G0223E43F89913AjfpelAsFlGtVjEej6W6NZvNArEZC8D4MSPIStInADli8fdmtAnPDLpP0ZGu86eII6PC9bYiEYkqRjpo8dmnlW21WsXl5SXy+by+YwL+xsUUF+IkOJK0GI1GBQiTyAe8G+STySQqlYrU0YyaI2EqGAzqvLXZbMjn82g2m5hMJvB4PDg8PBT4N5vNdA/QWYFxHiw6LRQKBTx58gSj0ehBQ0i328XV1ZUc3xihmc/nMRqNdC7w2jEupV6vC6iilTlV97z22WxWC9pQKKR3QCQS2Yi2JOGm1Wqh86eYXCrtSe67ry4vL9Fut1EoFBAMBjeIjtvb24rw4WBtfEcUi0Ut2kgsYN3mukNCLlVAJJNS6cVlF4nR/X5fbkgkz0WjUZRKJQDQQob/R7IBlwBcjHD5CGCDhMKFt8PhEDmRNs2fWnTzICB4VxFo4WKJ96jL5VLUH+8JLhDoiOnz+UQq44KJ6qtAIKDITToDkMBJkIPxfo1GA5PJBOFwWKD7cDhEtVpV5OP+/r6uHRcLRkJdJBIRQZyLFhJ72bMZ7wH+/cPhEI8ePboX8OLn4SLlrlgT9hH8Pvm+4bNH4JTA499SORwOeDweLRaDweCd77T1ei23UL/f/8FrRdCSEcQfQ8Did0NHDy62SaYjsbJUKunZCIVCIhlRDblarXB5eSmC/cHBgc4eRkaHw+HPGjnJImGRLlb3gYd02nC73SKNtdtt7O/v37o0oPPM9fW14kStVitevXqFXC6HSCQi0JC9Mh1nGU3ESFsSYb/UuzOLRL9isfieu8XN4lKfzk0PLRJjGX1111KBc+fl5SWGwyHq9fq97x++D4xRCYFA4M9C+uQ87ff7Ua/X9d7gs0dQnK4+dEXy+/1wOp2YzWbv9SNGMJlE7Gg0ing8jkgkArvdjnw+j1KppO+BTqrsiRaLhUjp/H3evn0rURIJwXS54kzqcDgwHo8F5Pt8PvXZdDrgPMooPhII9vb2Nq75bDbD9va2nPxITiOphIRnACJcl8tlTKdTLBYLzQgkffT7fRG4GUdlNpuRyWRQKBSwu7urGQ+A+oPxeAy3241isSjyF+cmuldT7Z/NZuV6ROdYvquXy6WcK/kzSCJg3Dx7cvYqJJWTROX1elEsFhGJRBTxdbM4T7AH5j1N0c56vRYp92aRhE+MgrgQiUlGoRKd00ejkUh8vP/YUxnne84wjHnlvM+oW95DJPo3m00AUG9McgZJI3RkuLy8RCAQwJMnTxAIBBQJcnBwgJ2dHTk5NptNXQ86QHJe2tnZQSgU0jkxm80UF8demuKmt2/filSTz+cVX202m7FardBut+XsZrVaFRO4XC4VV8SYM0avcZ4kyYL/SXyBZxkdnonVMbqa8yCdh8fjsbAh9iuMRWSfwPk5lUrB7Xaj2WzKxW0+n6tvsVgs+s4YRblYLJBIJNQ393o9lEolCWDC4TC63a4WglzkrddrOWLR/YCzSTabldvDx7pg3nQoA97NRqlUCqVSCWdnZ4ogNM4rk8lEYksKC0iKIvmSgqtSqSQXCi5xB4MBLi8v8eLFC5GqFosFdnZ20Ol0FPXHv4fLYX5fPPuMvRQdshhbxPu8WCzKnZi9OYmJLpcLw+EQ8Xhc8/ltxX50uVzKeeUhxcgf3leczSnCu1kulwv1eh1ut3sjhujZs2f3/pzd3V290yj+IxGl0+mg0+no/czZ1tif/pQieZj9XbVavbWXIY7DBX+325WDH9/bHo8HDocDyWRSmJwx1ulDxXmXy3AKnNPpNPL5vNzviPPwnUYxNN2SSGhh38R42J8zduunFnFuY3w4CbMf46D+n6l4LtK9lIQFngPGVI7FYiExcyKRePAzHIlERDq/GUl/s5rNJux2+62uqPcVSbrslW6bXzmHcUYNhUI4ODi4dZ7i+5T9CPEQRjb/kpxl6KJK3J1nAcU7jP/2er0IhUJyqCwUCrDZbMKL+A6k4+hkMlHffR+GSnyMBJXd3V3tZD5HRaNR1Go1uFwu9cu3zXmM/iXp6LYIQO6FuMf73LMW02EajYb6c+Cd+UEoFEIkEvlZnbm4G6jX60gkEsLkOYdQ+OLz+dQfUvDq8/kwHo8lyCdWB0CE2n6/j0qlormBu1Pig5FIBNFodMNZ9L4i/k9jAF6vj3nWVqsVLi4u0Gq1UCqVNOsYfz7f2XT2a7fbelcRszGKPKxWq2aunZ2d9wSjdJDj7EXx3sHBwUd9X8Td+N69DT/i3pM7NwrOb7u+sVhMLllMVXhILZdLCRS+1N9WfSFgfakv9VdYJGExloGqhm+++Qa1Wg3RaFRKLzYIJFyQMNXv99FqtbBarTbAbLL5e70eksmknBvottPpdNTsEEClU1OtVtMwajabEQgEcH5+LjAnHo8ry57ZyVRO7uzsqGk32ql3Oh04HA783d/9HQ4ODpBIJHBwcPBRih8uKcPhMPL5vKIbWHRdyWQyuLi4kJMEmwOPx6PmkkQtAjNWqxX1el1gMy2ljUXXKg4qBMe5wJvP52rAqDpNp9PIZDJwu92yyGTDz0i2xWKBUqkEs9ksoObs7OzWZiMajaLZbMJiscglBIDi6Vjr9VpMb0Y90kXl8vISb968weXlpdSRJDHQeQHABsDAgYtRQyaTCd1uV8Mgr8N4PJaqjmo0Rm3xvqUFK228u90u+v2+lmzRaFSRhhz6c7kcFouFokAASGHLvxvAxmDJwckYjbdarRSb1Wq1AEBRXRyqSYJg3F0kEsF8PpfynXEqjMoIBAICdN68eSOC2e7urppjKjwIxDL/nXEfdrtdAF65XNazQ3IYQdZut4tsNqtlRigU0qDIf5eky+l0ikQioWeMikpa8/v9fhSLRcWj8F56aJnNZpycnOi5z+VyaDabIskQcKKzzO7uLsxmswAnkihvLk0dDofc+AhM93o9Lav29/fl5EUyFEksVKyT6MZrxgEI+DF6wOPxwG63K16Dvy8AxVc0Gg28efNGLgFGVWU2m4XVatXCpNVqSVXNZ2i5XMLtdiuDnE4e/Fxut1vLNwLQXMCl02lMp1NFf9Cth+B+uVzG//f//X8wm80ClhkJ9vTpU53JdCZjdKDD4dAZ/hDb+36/j8vLS10Pt9uNRCIhB4GrqyvFfAKQs1G73UY4HJbFOSNPCJDyf9va2hIBbTAYbKiOQ6EQSqUSYrEYLi4uMBwONWyR9OV0OhVrYVwW0gHAbDaL9FQulxEKhW5dctntdjx58gTZbFbRGHz2uLTwer2ymz88PJRdO50YSO7lkpULNrfbrTPKSKzjO4tkycVioQheAvuMvCDgT4tsRnT2+30AUISn1WpFq9VSFKGxPB6PgHqv13ur49tDy6h+vuvvIFjBpQBJioyv5DKS13o2m2E4HMJut+PFixey/16tVlJ/8u/iz00kEnINKxaLmM1meqYYz0oV9nK51HMCbLqXUbVFxwHGTRmL74CjoyO4XC6Uy2U5+TFWjd9xNptFq9USoftDzxq/F4J6d/37XI7T/YQKM4Iy96ns/9orGo0qbqBQKGw40LAYy0vS4kOVoEZXyY+tWCyGRqOBWCymHpxRgbPZbMMly+/3K/KU8af8DKlUCpVKRZGJ7Bl+7jgBn88nF1fa8BOsZhnVkSSOkXxFsO6u4pKNzqfn5+fI5/MSbTidTsX90sWRpBjGa1FV+1BHx7+F4rzAnjSdTt8KItLJharsjwHeW62W+uYPxQrSNZj3BBX2xqIrM91MuTRhVAhFCJ877pSAOxf+dMx49OiR+nEub24TC/F3i0aj+OGHH0RYMf5zutbm83mEQiGRmIB3YPtwOITJZEK5XJYrNfvcdrut+fPo6Ai9Xg9XV1fqC0hGN4LKgUBAqt9IJILr62sA75Yxe3t7ighlT2gymXQPhMPhDYEM8O69ZnRVZiwq43oByH0SwIYjF/tfm80mstdwOMTJyQnK5TImk4kc/nw+H7a3tyV6Ip5A4jhFLxSYOJ1OiTPYW5A0Xy6X5V7z5s0bCdb4PcZiMWxvb2Nvb2/jnWsk565WK7x9+1YuouxRSIoh1sA54vj4WPfHer1WnCqFWsRPGOVH4R3dbm8Sgnm9SC6jI/dNVzsKBOgW3O/3FTtC4iCXh8PhUD+HUWokbB0cHGiuZ1/COYvf4Ww204KGjlnsX4gbWa1WCYcGg4GcJ6xWK7766ivFJFJkQ9IOZ7NoNIpUKiVivVHFP5vNRPpifDjJ/pz3EokEwuHwRqQvZzzOBXxu6NjN+4k9BMVm/NxcwJK8UqvVFP/De2IymYisYLPZRBgkDhAKheTAwVmTf/bw8FDzIX8WHR6DwSCKxSJWq5V+B85ivM8ZHWgymZDJZJDJZABAs1GtVpOzB79/usXl83n1JjabDZVKRRgSBXadTufWdywJhHR4rlQqCAaD7xFRGV0aDAZxcHCA//P//D+FQfDdYxQzkJDG+2Z3d1f4wWg0ksNyOBzG9va25n2Px6Olo8vlQiaT0VlJFyvjQtPlcomoahSysLicvLq60vdC17XRaKTY18ViochKOriRWNfv93F1dbVxXdgjLZdLRcvP53O8fPlSuBMxlJsRxYzKBKD7lDPkbWU8k+ju9hAih8ViwdbWliJQ6VYXCASEYRIX6/V6EoF+jihsumqlUikJ1BinarfbhWvQMY4x18PhEJVKRRFTxAtIsJxMJohGo7q+dDy+rRivWiwWN6I7iQvY7XaEQiGJW91ut2Y4OpA2Gg2Rvxg/SpECRWa/RALWcDhEJpPZEEsBEM5QKBSwvb39YMLRf6YiEaHRaAgrMGJgjIcdjUbodrvI5XIol8v4/e9/j//5f/6fH7SwD4VCKBaLCIfDKJVKSjC5WewddnZ2RGJ/aNXrdTnq3/dM0l3n+vpabs83he7NZlNpIhSAAe/OaRKR9/b2Pnt//lOL7q8AtG+gkP3o6Ghjhg4EAojFYigWiyIi+3w+Of3YbDY5LPIsva04v6RSKaUdfCqGcFfRnWm5XCKfz+Py8lJxp0z/aLfbIgLT2ZFFpyL2Ruzf+BlJ6P5cZbVaf1aXq/uKTkl0DQd+jAVkzDOLYmvux+hels/nUS6X4Xa7hS8z0n2xWODRo0eaw+hYZRTh0jn5oUUh1ae6NXGmLZfL6j1vltvtxrNnzzCdTtXzchdJ8vB4PMZ8Ppe4nQKY+4TPTqdTEcQ+nw87OzsfJZDjfsvj8aDZbGJnZwfr9VpOviQu12o1kZ9TqdSdGChnOu4c73Nz586zXq+LvAhApgORSOSL2O9voL4QsL7Ul/orLi6OgXcMcbfbLTcsl8uFUqmEer2O2WymRQYH4uvrazGXZ7MZyuWyANBerydQAfixAR0MBlre0nHIGE8Vi8U2YgNJ6Gg2m8pNZs4xI9gIqNA+fHt7G4vFQtEiZrMZqVQKOzs7+C//5b98spVtKpXS8r1UKqHf7yMYDMoO+uLiAhcXF+j1eor44KBAMNlms2mJ3+12ZatNMNPv96NUKm24rNAlqtFo4OTkBJPJRMpGNlME3F0uFyqVihTNFotFRJRMJiP3AuMAxVgJgmsXFxd4+vTpe82BMRIrl8vJKcU4AMxmM4GCVqtVpAxa5ZdKJRSLRVn0ezweLeIJCicSCQErL168gNPpFPGLbjN0wjK6MDAuYzAYyLbZ6XRq+bZYLHB6eor5fK6FOBnyfr8fT58+FUhpNptxfn6O7777DsC7xTMJYUZVC2NNOCRxgQ5AYCj/f0ZYtVotKdcJ2hOgIRBKq/bJZKKoT4L2wWBQsQRUHBP4ItAyGAz0nbFR4+/DWA+n04lut6t/dn5+juVyqUgiu92O2WymgajT6SCXy2G5XMoGnw5IjAxgNBg/b7/fx87Ojhb1iUQCTqdTJDgj+POxi3ubzYavv/5a2fYkISyXSy1H9vf3ZdXLSMRgMChXHJLY+PxxUWA2m6WuIvHBarVq2eT3++F2u/Hdd99pAbC/vw8AugdJghkOhxsELAK4VG4ZgclSqaRYlOVyCb/fLxII4y6vr6/R7Xa1zDEuX3w+34a7DgmIBOmDwaAct6rVqiLI/H4/fv/738PlcsFisUjRYXQIstlsODg4kGKO6l+6IhqjVWKxmK43B36q2gkgAu8GirscL7g8IvmLi3XW7u6unp1isaiffXx8LNeX4XC4YY9NF8OjoyMp9amsicfj2NraEpmY8WrFYlGRct1uV8QmLo7oBDgYDBAOh6VqYjH6kCqku8pIFtva2sLr169F3CWZ1OfzCZj553/+5/eGRC4EACCdTqPb7YpASWJkNBqFz+cToY6/M9XPXArR7ZDgMgEkxvMYzxZjBApjd2+rYDCIfD4vwvCHoqnuKtpz033ttjhDLlK4EOE9QPc7uncR4CCYTiUzF2/878C7Z9s4+NIJiyBTqVRCo9EA8A5c4dlCB53xeIxOpyMHBbpbpNNp7O7u6swl2fI2W3dajgPv7q1cLodoNAqXyyXwkt9zv9/H3t7eB89WvqN41txXPzXW46+1qKJcrVbI5XK4uLiA3+/XmcVI2K2tLcUuPSTqBYAivT5FBWez2XB8fIxMJiOyP8+KarWKwWAAv98v4Joq2Zv3jM/nE1g6Go0+mjT9U4rvMqrCW62WFpl0/eBzFAwGkUgkFAP+ELDV5/OhUqng8vJSgoFkMont7e2N/pZky6urKxF9ecbc5TDxt1oejwfhcFiL/qurK7l78GylawaXCx8bjUiB0G0R1Hf9TiREsI9lLZdLnJ+fa55jH8uekovOwWCAvb29z3L+caHbbDYxm83kcnObY8aHam9vD4PBAPF4HLVaTe63dOHgO4qzolEgwX+XAgXG6VIE4PP5NJszPrXRaKj3WSwWWlDTwZNCj3a7LWfN3d1d+Hy+jZ7j8ePHaLVa6jE4U3M+YG9VqVS0iNjZ2dGyhuR0EjV5rehOxOtJwiidsWOxGPx+vyLp6WzHJXkkEpEggZgB7x0AisGjsKVer2O5XIoMQpJOt9vFkydP8PjxY71f6WBAcp/RvfJmZTIZtFotRerdnN/Zs5Dsks/nhekUCgXUajXkcjnNEIxEJsGfxFWz2Yy3b9/i0aNHG0saCpVIQuPfwb7E6BREYoXJZNLSnI4WdP4hPsBemM4AdDUx4jMko/Ns5b1bLpfVgxCrItmOv+tsNsN6vRZxg/dJKpVCOBxGIpGQk3K/39dCy+12bzirrNdrXFxciBRFRy0+O36/H6FQCP/2b/+GXC6HyWSiiCV+z3SJIp7DeMt2uy3Xd2I1JOpTPGCMfwSg92+/39csyvOPGIndbkc2m5VbMx2VSG4EIMIG50bGWNrtdrmUxWIxLBYLrFarjQhBngf8LFarFZFIZGMxRALncrnE/v6+zh/+eWPR9YKRSru7u8hmszqD6LjKdzOfGSM+xhm4Wq0CeBffxd+H95pRGEM3PxJ2nE6nSHqM7ePvy1i9QCCASqUi0iMdzCh6MEaS8zu/Se7kGbJarTQrUewTjUblcgP8SLynWIZucz6fD0+fPpVzjfF+8Pl86PV6umd++9vfbhBpeU4B2CA0cdGZzWbR6XQ0w9GJzuFwYH9/X9+d1WoVmZSzFOeym0WCMf8exs6yf/uQGJZuaw6HA7/+9a83zj+6Rft8PuTzecUMf0pU2s2iG/6jR4/w7//+78Ie+/0+BoPBhvsOcRVi3UdHR+rv6bBHNyPgHSbu8/mEydzmIDuZTJDL5TAajXB1dSWhkNlsllCa93A8Hsfr16/R7/c1L/A7nU6niMVisNlsCIfDcuKm0yXd+35JS91+vy/3FAqWeT0pvg4EAsIdP8YZ+D9D9Xo9PbdOp1PnoPHdzOhJir2y2axcmB9CSjObzTg4ONC7kw7l7JVms5l2EalUSsKdT/kc9yUCsCgU4J8xzm7VahX5fB7ValWudzwHarWanqHZbIbDw8NPxpZ+7qIYfjKZCIe8WRaLBel0WgkcfNZ5HnEG7XQ6iMfj7/0d7AlJKiZ5+a7z+afU9va2yCR8x9CplO+jg4MD4f4UpjebTe26SDZkcQ7gLu3PiTX8XMU+hftLYNPt3ojtMHqQ7wrjPpE7J/aNxAv//u//foN8xZ0SHcpvEqiJXzcaDaXXsAePRqO3YqofW5wtLBbLvc7W0WgUBwcHwkMZMcx3U7fbldED56fRaITLy0vs7Ozc+Z4PBALIZDKYz+eaNx9a/J15rhILp2BjOBwqTrLdbmN7exvNZhMnJyd3/p2MjwR+xPRu1nQ6xfn5ufpAPk8kco9GI1QqFRwcHPxiz7gv9XnqCwHrS32pv6GiCr5QKGzE23EIp7p0a2tLAzFVdgRPjJbjxiKpZrlcivlOBQdjFIzDBaOkCKpRTUTHoPl8LvckKvnpbAS8s+ZMp9N6OR4eHv6kHHGHw4Hj42N9llarhT/84Q+oVqtievv9foTDYRwcHCjGgqzuQCCgQZlDt8vlwmg0ksKM15dxPuv1Gj/88APy+bycdehiwpeycYlHe+5yuQyz2Yzf/OY3AoNpQXtzicfYh62tLUWS0UHmZh0eHkoZVyqVcHFxIdLGbDaTyw0dA4xxgVx0k5k/Ho+xt7eH9XotS3cCXBym2QTSfpygdyQSwevXrxX3w+hFulxxmUHw0ufzIR6PY2dnBzs7O1gsFjg/P0cwGJQC+eDgAJFIBOv1Gv/L//K/4LvvvhN5rlqtaqHUbDYVp+ByuXB1dSUnqWfPnskVhvct1W0E5miRymaYADQVuIzKoiOL0aKVURW0iycQTQISARSSjOr1OgDouzCZTDg5OVFs4rfffovhcIg//vGPqNVqAN6BfiQa2e12xGIxLbpIzvR4PEin03C5XFL08GcwgpARkbTXjUajUrUAPyrk4/E4RqPRg5fRxgoEAtje3sZ6vdZ54vV6sV6vcXR0tAGi858D75TyJBjV63UpdoB3QyNJF3SeMgLEVDiTlEPwdjabCWAlCMZYAGO8wWAwEAE1Go0qatNkMqFSqcgtbn9/H4VCQSA2YwNNJhOur6/R6/Vwfn4Ov9+v626M2qQj0Xw+R7PZlFsSibC9Xk/qK7peUWVdKpU2ljZcqrXbbcWjkFhpNpvlYkFbeLorEJil0pMxPm/evJGzw11FkKLRaCAej7+nzCOwQKDi6upKLk0+nw9utxuFQkEkAzoDkJzI+9jtdqNer2uRQMIOsEmadLvd71ke8xqcn59LxcTPzQUhFYC8R+5SD9KJoVarYblcIh6Pa2HHd1+1WkWtVpMi6P/3//v/bQyJjNuhmtVqtWrJzWUPiSDr9Ro+n0/LM6OzBl3OCObTIYxLIo/HI2U1z7n1en1r7Iex+E77KUo8goUE8BkBZiwqiLiApNMXHeSazSa2t7f13mN5vV4N+dfX1zr7qFjjEGwsRggSFAiFQlr4ud3ujXcyf3cuTF6/fi1gNZVK4e/+7u/kWEmHg9tICYlEQt/tzs6OiNxc2HO5xyXRh4B1Rkwz8vhLfVox+oQkVi6OAOhd6vP55K5EsvN9oOhisVA//SnvSOAd8eTs7ExRtnTzczgcCAaD8Pv9SCaTiMfjd/bIPMPvI1j+XOVwOHB2dobLy0t4vV6pCElwp1LW7Xbj6OhIC3tjlOd9RWdavsfsdvt7ca8860ejEabTKS4uLhCNRjcWuBRBfKl3tb+/L3C+3W6j1WppQQ5Ay3afz/eT57PbirMmhTOMD6NYw1hXV1dyvGXEM2O6GKlXqVQ2XLF+ijp7uVzi7du3WorTpYeLr5uOGcbI6dvKbrfj9PRUZKatrS00m00JeXw+H05OTtButwFgo4/hgp8xz+z3eVbR5YPz3/X1tZwTgsGgnLArlQpKpZJmI/ZAjMv1eDz4zW9+AwCKReeyj8sYun4S6Gb/GwgE4PF4FNdcq9XUyzmdTni9XhwfH4uoett7M5fLieRGIiDJBxQ/RKNRhEIhYRP8OSTUdDod+Hw+kUIsFgsGg4EiKBg9x0XhwcEBdnd3sb+/L2IHiS6NRgODwQC5XA6BQOC9s4rvcTrpbG9vv0eMdTqdiu8pFotS+a/Xa9RqNRQKBRFC6KzNOJtgMIjDw8MN8l+hUBA5wGKxoFKpaO4jSTeZTMqRjA5ZdGW5vLzU9x4KhdDv90Uk4kw7Go3kWEHnz8lk8t6ShiQ1XjfgR4yDLlCMXBqPx8jlcnIH4mfnDM2ZiCTjvb09xGIxhEKhjZ9LV2IAmhFHo5Hu95uLbV7rZDKJYrEooQPviUQiIQEQoypJwiGRgESi4XAoN4/RaIRWq6XYRj6vjDibTqcbTnd0RqOIKxgMKpqVLvbEEdiH8/uhowJnA7pcGyNPp9Mprq6uMJ1ORajjwo/ELqPAhH1Qv98XGY44xW2CAKfTqRmW7sJcdCUSCbx48QLD4RBXV1cSg1KkQLdYXnviY5VKRe7AdGA0Oqgz9pH3K3FFzoZcgnFG5vM5mUyEC3CGJfmLRE46WdEF/uLiAv/4j/+IZDKJQqGg+5cRmyRoMXbI6FbHd5bNZoPH48Hp6anctNiP8Pf2+/1IJBIYDociCp6dncmpORqNavZi30KhX6lU0qxKhx26PEQiETm20FGDMz2dWkgq4/fN75zRS5PJRDNYp9NBsVjE06dP73XU4XkxHA6RSqXu/HfNZvNHxQw/tKLRKOr1Op49eyZyFx0P+XMpiCSR+fnz5xu9Ot/lHo8HmUwGtVoNNpsN6XRaEU83XXNnsxlyuRza7bbiv4+OjhAMBtXHEAfY3t4WaZizPfDjEtliscDtdiMajeqd6fF49PwDeNCc+OcqRqzS+ZwEAuOsTlI2hZI3HTj/sxffQ3y23W73ve41dGBmEsZDXcH8fr+wUopKmCZgFBy73W71V3f9vsD7juTEeh56bzGil6JV4B0WWCgUUCwWMR6PN3pz4EesnTi0yWTCkydPPguJ5HMWzz1GyN03m1LoRnyIuCRNDhgven5+jlQqpXc6HWF9Pp+EFoyk5Xvmc1c6nYbP50OtVpMokfcPhVOMZwagJAY6GoXDYTkUUcRt7O9JLP/PXJw3jb0P+5HbsFDj7oJGGXSZDIVCcgo1xoDv7Ow8yJ2Ocy4TiYwiaz5HsVgMOzs7n+xoz37yoULb09NTYdQ+n0/EY4ro+LuMRiPkcjntc0jwv+1z86zqdru4uLiQ2ywFOR+KneQzFovF8Pvf/167TJvNBqfTKUyB3wt3qEbHO2ORYAbgVpx8NpvhzZs3aLVaMlcgLk+R+9XVlfCL4+Pj9xyLv9RfT31B4r/Ul/obqlgspjirRqOBQqGgKK3RaCQigNVqRalUEqO71Wrh7du3Ut4anX9Y/LN0raD1Ne1GbwLibA7oxsJG5OzsDJVKRQpXOvtwyerxeJBIJDSwECxk3NBNkJ+kBYLqN39vLmypBKHjBYcdquQY9xUKhUSSWS6XSKfTKJfLqFQqirhiHBhVhiQ7UVlofDnH43EMh0MN6owwI8Bqt9vlvMQl0Wq1wuPHjxWXks/nsbOzc+9ynEA61bz8nag0BH6Mrry+vhZgRra6y+VCIpFQ/ORkMhHIm8vlRKJjPB7Z48lkUuQsqjRqtRrW67XY/HQrCgaDePr0KUqlkoAhgsskfblcLni9XpyenmI8HsuGlaArbdddLhfOzs4QiURwdnamBf3/9r/9b3j16hVevnwpJbrdbke73UYul4PX65X7C6M7Xr16hUqlgrdv34rMw8zuP/zhD3j06BGm06miF3g9p9MpyuUygHcNGdXeBO34fHAgJSjHHPf9/X1Fh6xWKySTSanzuIiYzWZYrVayNk4mk3jy5ImIHiT1UUlJNTmXCYyonEwmaoB3d3cRCoWklKdqgQ1qMBjUojYQCEi1nU6n1SiTvHFz2P6YovvWdDrFaDTC69evBeQTtKfL0Xg8FqGCDkYEpgmuMxZwPp9reGScBp8rPvd0gJvNZsjn8wgEAnoGO52OGmRjI87seUaJ2Gw2xONxORfx99nf34fFYhEZrtfraVHPCL9MJiPyFZ9BOrgxio6AnM/nQ7vd1jnXbDZFhppOp6jX6zg+PkYgEFB0azAY1Hllt9vR6XTg8XjQ6XREOqElPiM7GLvCZdtdajlGi97nttNoNNDr9aTyvq3odnh4eIgffvgBl5eXAttpXz8ajXB0dCTXNapCOXiZTCZEIhHk83kMh0M51GWzWbx69Ur3ws7ODlqtluIoSTz1eDyKhXS5XEin00in0wLfr66uUC6XkUgkkM1msb+//95wZrwPuVjp9/tIJBIilgLvQP6rqyu8fv0aW1tbePnyJZ48eSKnsX/9139Fq9VSXC6HxeFwKLDd5/MhlUppccO4EqOKyGazIZVKacExn881GK9WKy2yGEvCqB4uhO4ilBBouI2oxfcq1VhcIvFaUdXXarXkEkCwcbFYIJ1Oi+Sdz+cVEdpoNORwxmUuSaFGwPZmJEg2m9VyjQM3yR83wSD+3ev1GovFQkQV/metVtM7gWcVY6PMZrPA9Ewmo9hZALKevqlIYz8TDodxcnICh8Oh9zu/4/Pzc1xfX6PT6XxQQdjpdOSk8FNV43+txXOy3W5vRC9GIhG5R5hMJhwcHCAcDqNer793bjFKwGKxiHxHguld1Wg0YLVatUD51GK/Q+t03kN0iHjIsoq94F8iZtLpdOLp06dybSHJlfGndPYDNpcBjDC6r+iixfc3HU+5ROM7m+9GurvQkcDr9aJer+PNmzc4Ojr6rLEJ/5nLZDLh8PAQfr9/Q6HO891msyEUCok8+rFls9lE7Ljp4NBqtVCr1QSSkjBitVpRLBbxww8/YG9vD6FQSI7EnHcZPcayWq0SEGSzWdjtdhSLxZ/0PV9dXaHVaiGXyylanvfpXY4ZH1pCOJ1OnJ2d6b1h7KtNJpMimur1Oi4uLkTAZiRKv9+H3W7H3t6e+jM6N3PJM51OkcvlUCqVFLXO3pXfKx07Q6EQdnZ2REbZ3t7G6enprb87XYAYt9ZqtTYiMLrdLiqVilxOuHw2m81wuVxIpVJaYv+X//Jfbr2f6JZhdBMMhUK6xnRZYMQUCf2cwyKRiGLvSN6g8ykA9eK8Dpyr6FC1t7f3Xgx5NpvVou3me5qOZJPJ5N5lCGPcrq6uJFpYrVZotVp48+YNYrGYzkwSXDwej9zIt7e35X5Bgkav15PanL0eZ4LJZKLvgLMVfy6jt87OzhSxyFnTarWqH+t0Ojg7O5MLFUmDxuJs63K5FPVOHOnm/EBXqWq1il6vt+H+MJ1OUa1WcX19Da/Xq3+30Wjg4OBA9xdjYozXlXFzW1tbtz5/dCGOx+Pw+/1yqmVkuDH+kgKeZDKpBRK/K7r4kChF0QzfTQ6HQ79bo9GQM7XP59P8wJlktVphsVhI6EVXXpKkHA6HsLVgMKh4XYq9SOAgyQqA+nu/349UKqUZlVHKNxe7jEcjZgZARKe77mFjGWfEfD4v3HA0GqFUKslZmvGKjCl+9OiRXMz43QHv3hV0cW+323K7YhQzzx9G2fEzUAxJVwqKGSeTifr9wWAgAi1juilUpEtmtVrF7373OwlG6/W6Fp7RaBS7u7vweDwIBAKaT/gMAJDDOt16z8/PRZykAzHPQroeklBOtzdiicZZZzgc4vr6Wtf4psMeiZWc9U0mE549e6Z3IgmkrVZLxEGbzabkAsa3M+KUsxsxrkKhIPLHbdVut9Fut4Vt3FckGBAr/RwELKvViqOjow1BFa83Y7YYpbu1tYUnT57cSXKwWCyIRqMiyBB7YITXxcWFiFrlchmFQgHZbBZ+v199FB3wGcFKgiuFb8TpSaAlIZjvOc6NfL/x/voUd92fq3gW1et17Ozs3Eo8Ir50fX0tgdxfEwHLiBcA+OAMz/0I49xIEn1IBQIBPHv2DI1GQ3gWy+VyiZR+8x7h3qXZbG5gOxTculwuiVkfKrhjr2V8T1QqFTmHHxwcvEeaoDjZZrMJo6/Vah+MJf+5ajAYCFczOgtRmEcs+kPl9XpRqVQUJ0jS2aNHj9Q3kEjDXt/pdCIej4uIb7fbJWpnNPPPUYzMJq6wWq3Ui1HYQbEncU6z2Yyjo6ON38tms0n0S6INiaS/pDPqY4sOpUanL4qiuacw4hQUwPA5sNlsIppyR8F9F6OGH1KNRgOZTEYCEZK6mSqTyWTgcrmwWCywWCxwcHDwSZ+Xz/tDyZec9dbrNbLZrGYLOt2Gw2H4fD40Gg3NJJwRC4WCzDGMNRgMUCgUMJ/P5f5MUZ7f70e5XEY0GkU6nb51trJarTg9PcX19bWSaIgHU1xP52n2db1eD6VS6dbr1uv1tDu8jXxJ99FisShc0RhVyHd4sViUUcDz588/u6vdl/pl1BcC1pf6Un9DRRWW0+lUnJ7P59NSksrDWq2GarWqBQcbCIJAJNIcHBwISDEOfgQN6Jh020KWUVcEWIx2uz6fD6VSSY5btGyncpmW7FSxn5+fy32GTc54PNbwYATyvF6vnApKpZLAOjaSJMx0u13k83l9LjaUBBYikYiANgKt3W53Q7VJMg6j8jhkETyhuxCbB6rr2DRRJci/17hkp81+sVh8zynrtiLRodfroVKpiGzDBoDqOb/fj5OTE4zHYyl4+bMZg8fF1Pb2NsrlsiziU6kUxuMxKpUKEonEhkoxlUpha2sLhUJBWe6xWExqcA4YJEzx/qBKnMBYJBLBzs4O2u22hgAqTy8uLpTBzTx7OuO02238P//P/4OLiwv88MMPUnwzVotNb6vVwnq9loMTXbD8fr/Y72xoLRYLqtUq/vjHPyq+MxaLyUq01Wpha2tLP4sRVXTFIkHG7XarQSYAnUqlsFwucXh4iEwmg0QigfF4jOPjY5GMjORA3tepVAr9fl/kEoLctP3lMoUxIqvVSq5WdMhzu906GxKJBK6vr7FYLOTkBUCKNMZW3lR4E+T9qQtdRnJNJhMRHTnk8rkmsMm4OZPJpPOH4D5dfHgeUMVKZzFGIthsNilASeK6uLjQQpG2t7QF5+Bk/Nwkh9Fhja5JBNoJegcCAZ2lVJIYIx+pEKcrIIduEmVIGiMIQAIL74l+v49oNIpIJCKQ7+3btwgEAohEIiKDkpxXrVY3nAoZ3QS8G7SotiaJiaQsY3GwI+B9VxFEIhHwtjKbzdjb21M0FiMA6DZFkIMELC5geO8a1SiMzFgsFuj1evjDH/6AfD4v9xk+U16vF9FoFDs7O0gmkyiVSnr/8doYFfW7u7tytgoGgyiVSojFYmi32wJoGAdCos50OsXe3t57oDRdGF+/fo1isYh8Pq+l5vn5OXZ3d7X0A35UjjHyh6RNvkepLuZiy1h+v19EtlqtpqgVgsZ8ZqhmoxqL9x3fgcbq9XpS6BujYUmo5XPM+5XKLrvdjnq9jn6/r3f9fD5HrVbDYrHA999/j3q9jtPTUy3TqeY2m80IBoOIxWJwu904Pz8XyXe5XCpmbDQaSdFOt7nFYqHzn25+JGMbvxsujsPhsJw8XC4XJpMJms2mCLJc3DidTqTTad2f19fXqFarIg5zccEF1tXVlZzyuNBwOp1aHp6dnb33nNGNk0SBu0gofNckk0l9r1/qx6IriDE2iAssLkBKpRLS6bTudzpksg8CflxgsigUYITxTTc2LkU7nQ52d3fl+vBTy+l0blja22w2nRf3Fd+X7OH/EmUkZACb4gBjMX7H5/O996zeViRMm0wmxSSztzSZTHJRJFGAZwmXp1yc00X00aNHPwuRkc5+xsgfr9er3/OXWLy3I5GIiKYkqBgB5k+pcDisnrvdbus8paNls9nU7ETwm06eRkdCumQtl0s51txWjDpvtVoSIHyKKx2jZe4ifN3mmMGoo4fEyZIkzl6LrjfAu3cN79d6va6Iu62tLTx//lw9o81mU0wh3cuSyaR6asZCAO8iMgKBAGazmRZPXP5xgXcbwcZYLpcLx8fHG6p3uhPT7eLly5fI5XIbgin22S9evJCb1XQ6xf/0P/1Pty7qSN5hsZ+m+xjJLCTF2O12JBIJRVJXKhVFMfZ6Pc0CvG50LCNZhksN9lrhcFiLD8ZktdvtDXdgFnsZY9zzXcW+l7F/vV4PL1++xHw+R6VSkYsvZ2n2bdFoVIQCRoG6XK6NRRHnkoODA5GvSIxwuVzqZyk6slqtctVZrVZIp9NysUkmk4pPJ0bjcDhQr9c3YlxJQvR4PCIScT6+OT9QCFetViWio/iITuOPHj3CbDZDuVzGcDjEd999h0ePHqkvY99PcgivKUlZ4XBY9wXnZd7jvH6RSEQYDPEX1nw+R7vd1jNAnMBqtWo2pjsQiYUkU/G+JMbl9Xpht9sxGo1gtVrRbDY1lzNOczabSaBD0g7FF5xjeH+RVF6tVuUgReyChEESmaPRKPb29rC7uwuHw3FnL8B+hwJH4nx3uWcyhhKAyIsul0tEJTo47e3tYX9/H5eXl8K2GLXDn3FwcKAlG5eNVqsVT58+1QKuWCzi8PAQ/X5fxCT2bOzTGTtLRzijizCJ14zTNJvNaDab6rPpiMeZj6LWVqulxADieMA7kcXjx4/hcrn0+3Am+vrrryWiGQwGKBaLEoGSGEanLJIPiTPcjGbj7Mei8+NqtRJeayyTyaT7kw6q9XodqVQKkUgE9XodwWAQf/zjH9HtdrFcLuH3+9HpdDAajdDr9WCxWIR57uzsSBRExzy+n28jLhFfMMZn3lc8A+nC8jn6Igp/SYai2JAEPcY57e7uflAkYYzy7nQ62N/fB/Duvc3zp16vI5/PK4KX5Dz27XThGo/HcmIk5nN0dISLiwvhhhQqNBoNRZj5fD7s7OygUqnA6/WKyP5LKJJSOUPf5/pEwRcTGz6mH6MQiucBRcy/lHK73SK8A/jgzEV8kDj5fD7/KFEDscNkMilske+g26pUKgnbJ4YAQERj4j/8Dtlv33eNiYtzvgLevRcogCXJ8K4izk2h+vb29oNJO3Tn5Od+SGTizaITPYXpRmchnuUkBD/k9+K1ohM+a2trC19//bVcGKvVqtwT7XY7AoGAxBW5XE54xIcIrJ+jeJawP87n85hMJtq5kBxbLBYRDAYxGAwkTDVeb84fhUIBkUgErVbrP7ULFp3krq+v30up4M7U6XSqz10ul/B4PIo5Pz8/R6FQ0J8haXwymWiGOzw8vPeeZaRtuVzGYDB4L9EBePccMAGD4oJPEf7x3jXu7u4rCqbpmtrpdHQ/MwqVcYTcB9M0wuPxbCRX8LO+evVKO1saMRDjbbVaIprNZjMcHR3d2itQcNdsNrWHY2/FdzCfL7rXMtnIiLmORiOMRiPE4/Fb72PumOkid9e9zn0anx/OcF/qr6++ELC+1Jf6G6vDw0P8v//v/wuz2ax8YAJpZN0yIimfz2O1WmE+n8sVhLFrJAaQ8EECFVWjVDRSHXhzYUHC03K5lLsU8CNxiipK2t/zJfnDDz/IeWq5XGJvb08KDC6Zy+WyVHQEDtkok6hBshNdlggCdTodlEoluQc4nU6cnJwgm82iWCwqDpF21LRDd7vdsvdOJBJoNBobizqCCxzA2axTkUdFaj6fx/7+vpSoZrMZsVhM7j2VSgWHh4d49uyZwGZGnH2oyD4nwGQk/HCZ5XK5cHR0pCWysZhP3Ol0tCRjnjtBZCpkRqMREomEFkrValWgaSwWg8fjQSwWQ6/Xe4+gt7e3h+VyiVevXqHdbqv5YYQfwZp8Po9Hjx7B5XIpftFqtQpsJsFkPB7j5cuXyGazyGazCIVCig8AIACjWq2KjEVgnj+TueYksxwfHyui8/z8fCMXnSQEWgYHAgFEo1Fks1lMp1MthtbrtVS8o9FIKhiz2YzRaCTSUygUQqfTweHhoRb2fr9fAyjVYWxmv//+e5E+HA4HUqkU0uk0PB6PGkW6mBnViWwojfGZjFXksM4YALfbLdXLbYz/fr+v7+xT3A+MlUgksFqtUC6X5fhE4hO/A5IQCPzxu3C5XCKd8rrzrEgkEiKlkqBG9TytrguFAsrlMhaLhQhToVAIq9UKpVJJ4JjNZpMLH+9DDgR0YLoJmrhcLtn8N5tNLZwAyAUvHA4rPuno6AjxeBzRaBS1Wk2LNpI3SRKj48Jvf/tbxVDRTYpnKS2hgXcq1H6/r4Wv8fcjSY/E2lgsJneEbrf7XqxIp9PR0HnTIYaKYj5zHHTuK7PZjFQqpeV0NBrF4eGhQHdGtwA/RnPSXYIqVOAdwMWFKM8Cgrg8W0ks4n3d+VO8GC3B1+v1RnwHrxEjIXh/8r1FkiDdvkjyoNr1tiKxrd1uo1qtIpPJKArLZDLpWaR7Gd+7wDtSB8F6nu2hUAitVksODRwyjTELNpsNq9VKUSX9fl/kTRKS+/0+vF6v3NUYD8AFwGKx0PdDd4LZbIa3b9+i2+2KzEtCJiNb6AbGpYxx+bWzsyM1XT6fRz6fl2Mco1N4ppGIlUql5ILGhQGdGbi08Xg8IkIS5AiFQiiVSojH44qOM8Yi8DshIZwLfy7AgR9jNQl88FlnjCZjn+PxuAZ5Kv9IkuN7ic/t3t7eraBmMpmUc2kul0MkEtkgwdIRpt1uI5lMfjLQ8tde2WwW1WoVxWJRy0wuNKl0NdruG6/hfSRTRj8B7/pRLuFJsuv1enqO6SL5uSscDuueuwlg3Sze04wf/RxFhbeRTPExC7P7HGHozsDn6i63IpLqvV4v5vM5otEo5vM5LBYLarUaHA6HiHd0oQCguSGVSmFvb0/R7Q6HA5VK5bN+X+v1GuVyWTE+xkUBY07pcPFLrpvL359aJBJEIhGUSiUB2CRgzedzxbUyVoGLYzowAz+6LTMO774KBAJy8Wm325/0eUgmXiwW9xK+bjpmdLvdB8U5sG6LQjg6OhKpejweIxAICMSdTqfY2tpS3BPPLrputNtt9fvAu3c6e1vO8H6/H+PxWK4ldAchgcnYG91WFEv4fD7s7u4iEAjgu+++w4sXLxQjwbmZcxDw7ixrt9t48eIFzGYzvv32W3z99dcb19bpdMLlcqFSqWyQxEwmE5LJpOYoj8eDxWKBYrGI7e1tPH78GNvb23J+7nQ6MJlM+mwkvPT7ffXKnBFJAmLxbKFbktVq3XC6MBYJdHctHW8SDOiWPJ/PkclkNlwjbka/8bPStXw2m2l2PTs70ywUj8eRzWZxfX2N6XSKSqUiogAxHy4x6Jy7v7+v6xOPx7G9vY3BYCACGsUns9nsvaUIRUD1el3OzYlEQgvmXC4nkpPxLKYLLAk/i8VCkT18BpxOp+ZTv9+vmZ5zEX8esRgS+YvFohb97McoIuB8w2eAszoFRizG13Y6HfXXJOGRAMbvnH0B3z18jrlU5azNa0xREKOiKCaiS9ZwOBQ5kY6BdJulSyx/dz6ndKunKI6xRvx36Dp+VxHX4vWIRCLweDyo1+vvueQyii8UCsHtdsvRkCKGRqMBAHjy5AlMJpOiiBmrCvyIJxBTAKBIvnw+D6/XqzOJWMF6vYbT6dTMb7fbcXBwgEqlovmRkZZ831Mgw+vP+5AzHd3/+J3PZjPFhc/nc0XKcC7gPVutVvEf//EfihmlE/L+/r5cyvL5PGq1mohYkUjkvZ6JmCFJ2hTh3VacRzqdju7Du8rj8WhRTOd6CjPpuEQsjz3dzWjW+XyOcrksl7DDw0O8fv0aV1dXsFgs+i4BSGDDe4ekow/V5yJd3SyXy4XHjx+LGMfPSOIuiQ8fKjpKzudzxQMfHBwgFAqhXq8L6+F9QUzH6JzPxXyz2ZQr2ng8Rjwex9bWFtLptGY/9q3RaFTu2zs7O3KqSaVSv6i5j89Pr9d7kEtQIBCQUyhx5vuK7zz2X+yjicd9qhvr5y4KXVwul9wS77qv2W9QkA7cPRs9pD5E9i4UCnovDodDkaSBd+cP56DFYiE8vlqtfpAsUK/XhbtwxqSj13Q6vTVW+mb5/X4Ui0VFoH0oco8id0YpG4lnvB8eIqYhSYaR4vzsW1tb6PV6qFarcDgcwh8p6Lyv2BPehiOQVE7yerPZlNCCvRTxvVgsJmzvz1FGV10mQfD3p3vn9va27u2tra1bHWBJgqPQ8z8zASsSicgdtlQqaR9F4QrNHoz7Qu7LXr9+jcvLSwDvvvdkMgmbzaYElVqthp2dHSwWi1vFmCzuMnu93r2xfel0esNZ8FMJWJzPKWC4qyaTCd6+fat+hDPe4eEhACiZgntMYtCpVEqCXM5twLt+8vr6GqVSSTvgp0+fbuwSeO0ymYyu602y+mQywX/7b/8N5XIZvV5PSQScY9kzmkwmJX8sFgu9d7kvGw6HIkzRZfhmNZvNDbfj+4o4IXuxX9L7+0t9vvpCwPpSX+pvrKgMabfbMJlMmM/nArdIsAB+tBinsxSVfLRNJEnD6ExE1R9Z3AR8bTabIuTYPDCKzag4JLjEppkMaTbOdOCZz+d48eIFzs7OpB6gKpLWzsbhwUjEqdVqOD8/h8lkEhuZ4C6JSScnJ+h2u7i+vlaDyL8jk8nIipuKR35ONvZsvmnJTSAon89rGODylpbrXBBzGWu0umesIYGAdDothykCHx8CJubzOa6vr6XuYv67y+USGFKtVtVkPHr06D1wn8AnLbJJ6JjNZlLpE6in8ojAGZfl1WoVfr8fyWRSjlput3tjCAqHw4qNqNVqAjUJlF1fX4uYFo/H8eTJkw2L25tgNIcFqvlcLhcajcZGrAnv/16vh3g8LrUO7ecZ7Wd0hPnv/rv/TgqzH374QQAsIypXqxWi0ahsn/n9MpbB6P5Fa2ESIqnYbbfb2N7eRr1eh9vtxmq1wq9+9StUKhUNxmy0h8Mh3r59K/viaDQqIiEJRD6fT4qV6+trRR4uFgs0Gg2RKXmv8blhHCHBdDrXud1u5PN5uYYBUEwfneY+h/qOxBtGIlI1zSGAYCcJoSQP8Tt2OByYz+cCx6kYDoVC8Pv9SCQScrhjfj2VWVy60g1uZ2dHxMF2u41KpSISo8vlgsvlwsnJCabTqVwFSDy5GcvH6DyeqXa7HV6vV+Sd1Wql6EijA5bP50O320Wn09HzP51OUSqVcHp6isPDQ0XwcDg2qpP5fKzXa0wmk1vBDCMgSJeu8XgsRyE+oywC/tFoVEA+8O6sZ7wXFxdUUPEafKhmsxkSiQT29/dxenoKl8uF5XIpZR4AkWvj8bieeZI3GUFyfX2t757nGIEBs9ksZW8ul5O61+l0apEJYOM7DAaD6PV6cDgcuL6+VtxMIBAQwYfDIIdMkp3uKrrTvX37Fg6HA+12W+9lxlx2u13s7u6KiHN9fa2seg6sBDtJWqKrJYm7dD3gIo9OdyThWSwWhMNhkQoTiQTS6TQWi4WUyyQ/FotFLYpisRhWqxUuLi7QarWQz+fhdruRTqc3Yr+azSa+++47vQf39/fhdDrlNkG3D8YrkZwQCASQTqf171LxzkXOwcEBstmslgRUpPHZoosDI6L29vbklshntVarybKfivn5fA6/3y9yeqlUkosev59QKCQ1HItulnQaOTo6UoQd45QJslKdyX7qrnK5XCKBUIHJRZvRlYAEHyNI9qXeVaPRQK1WQy6Xw9bWFo6OjjbeVdFoFMPhEKVSCaVSSWTHD0XeAe++k7OzM1xdXclRkucnSaWMMTo6OvpZogPokBYIBAQK3gbKUdGcTCa1fP3UouMfl2s8k0gmSiQSH1wGPqQikYhIN3wOg8HghqqYzjpcsrL47qhUKri4uNhwPGOvQ8IOiYs820jmYgztT631eo3Ly0tFbrOvsFgseq/TBZZn5C/VDevnKLo0zudz9QzsP+PxuNwnzWYzEokEgsGgSMCMWxiNRjq7P1QWi0UkyU+NzqbA5yGEr5uOGR9DwLqr3G439vb28ObNGwyHQ4RCIc2fJMjTkZF9NOcBkrdJEmafZ3x+ON+x7w2Hw4ofJwn7rqI7E2e/169f4/vvv8fV1ZV6JjrO8PpQoMSovWw2i0gkgpOTk43rRYdKl8uFZrMpYj6LMVeMgl+v1zg8PMT29rbmP84KjF4jeZ5u3iRmkNSTTCblCGWxWGC323VOsO/gfXdzRidmYMRc6MRHkg/fOcFgUGQt4hSdTgeRSORWFxHOSKvVCs1mU7MNfz6JBbFYTFjL69ev1asG/hS5Tmcv/l7b29sihNL5ZzqdbvTw4XAYf/jDH+DxeHBxcSFSUL/fx/n5uXoxOieTmEN3NBJid3d3hbMUCgWR9t1uNyaTCRKJxHs9Ep2fiIuUSiWYzWbNDTcFH8Rb+MyGw2EJA+iARPyK0ZSMniMhglgBhQR0LE+n09jf35eLJl11OMN2u109SyQo86y3Wq2w2WzodDqw2+2IxWKaUQHIrZ7uXIlEAtPpFN9++y2sVquWWEZSJYk0jKOm8zGXfQ6HQ8IB3lt31U2nbsYVms1mtNtt/Vk63lAoxrhaCiecTqcIzfxsJpMJXq8Xx8fHiMVicn+icLHVaim6kXPzeDzGq1evEAqF5PhJ8iHFPo1GQ4TCeDyu93epVBKuRvJ9NptVXDGXoP1+X0tjOuFToOr1ekUaI1nO6/ViuVzq3o/FYhiPxyIN0tEsnU6jUqlowc8419v6fyYV/PGPf1QUFwkJFJxROEJSJGPlP1R+vx/1eh3z+VyO/81mEzabDTs7OwgGg+j3+1qm8r4kqbdarYromkgk0Ov1MJ/Pkc/ncX5+rhg1Pi8OhwP7+/vCAT5E3gUgUeFN5/HPVbeRyDudzntJCvcVUwWMvx8jvObzOa6uroRLnZ2dvffn6aJIh7hIJCJSICMQX758qeVsLBbD4eGhBIXEz+lM+UsiNnxsdBXft0aB9V2Vz+dRqVTk+keCC51eiOfdJ4D7c5XZbEY8HkcsFkOxWEQul5OojmUUAfH9Tzzk53I0Y0IGxfc3Z2L+HoVCQdjJYrFAMplEoVAA8O7+vc3tudfrSczCOZQ4OfBhYhj/nfV6/aD7oVwuo1gsCic14pXEXFqtFvb3998TkhprsVjg8vJSeEEkErlz1qQTXaFQ+OA9RoEHMbSbFQ6HRR4Mh8PaGwHv5l9im+l0+s8az0miz3g8xtHR0caZTYK5w+FQVG+n09E+7OY8xB3iXc6Z/1mKov/FYoFMJoNMJiNnaEZSZrNZzaTcs/37v/+7XPSPjo5wdna2gcOzP89ms7qv9vb23vv57PPb7faG+PO2MpvNmm0ikYjEtR9bxtQAzru3FXey8/kcwWBQrpLsbzgzUdRLh2aKJ+g6zKrX68jlchgMBjg9PcXR0dF7fb3dbsfu7i6y2az2KjRUAN49sxcXF4qlv03sOp1OUa/XlYRDYjZxWofDIUwvGAwiHo9jf3//1nOMuBJniw8VZ4+bO5Yv9ddTX9D4L/Wl/oZqOp3id7/7naK6SJ4iqYbOEdPpFE6nE16vV0484/FYS3DGAPzhD39AOp3Gr3/9a/h8PrmE0G7Z4XAI3OOSg802m1USNagCnEwmYs3zZ3NhvFwuNVxtbW3p5weDQTHOSb6aTqc4PDzcGB7onPR//B//B96+fSvSz3K5lGOW1WqV6igQCKBYLGI+n0tdGwqF1FwThCWoxsx0Kj+51Go0Gvjmm2+kcqfdOEFHXv9WqyWQmNeJKn2q4p8+fQqXywWPx4PpdAqPx4NKpSKy2V1VKBRQrVYxmUxwdHT0HhhJwJ1D1NXVFR4/frzxdxhVnzfBD6PTjDEaAIDuMZvNprxnu90uQMzhcLwHEtDKnH/m6uoK5XJZSykuxS0Wi7KeqS40Fp1ZuCxg7KHVasVoNJKim4x3o9sFCUmMHyCYQeB5Mpng7/7u77RwyOfzcsQJhUJSEhOgov2ykUxIpyxeW7fbLUIf1aQEIcxmM3Z3d+V61Gq1tDThs8UYTqrr2fDTEcBsNut78Xq9qNfrIu/0ej0RGkgCMYIYBMgIiFBdWSgUpGC0WCwoFAqKCHqIyu0hRUA8mUyiXq8L+DMWrzljE8PhsAgRBLkPDw9lwUuCoMViEZhKMiqVpaFQSCQpOuQxYoQOAQTs6F50eHgoBToBdiq/arXaRu433aqotu33+2g0GqhUKvD7/YhEIloYGeMcOZg7nU45OA2HQzx9+hT7+/t4/vz5e40+B2WC9yT0AT9GJxqLUTC0v+fvz0GCf3a5XMp1yu12IxQKiXiWzWblwEKbYf7dw+FQZwCt+m+r0WiE6XS6ATTQJYnxt3THoGsfv1sSj16/fi1VfiqVEqmN5DASkxhxQpttRqiQmANAoDuXCozfGQ6H6Pf7ODs7Qzqd3nBGogMal269Xu9O1ZAxEjefz8u9wOPxoFwuYzqdIpPJwG63y1Xr/PxcZxzjTLgU4P/W6XSklCPBcrlcolqtKvqHNtChUEiKHpfLJVIoYyD7/T6y2SxarZbItdvb24jH43IR6ff7KBQKUukZi0TdQCCAf/mXf0E0GkU+n9f3yIUR4yMZH0pnxu3tbfh8PpydnemdRTU9F/IkZPHzMraP8StUk19dXcHr9cLtduPs7AzX19eK66WTGJVpJEYwumkwGCCdTuu/BwIBLbWNRbX/fD4XQMVn4SEExNsqGAzCarWiUCjI7p1AltPpVMRjOp3+i8XK/ZKrVqvJ1ewupzESBzOZjM6K2wCw28put+Px48eKr2OMqtVqFVHxQwren1KMcCXInclk4PF4RAzhEp9kFoJQn1qr1QpXV1eKICCJFYDcFFKpFHZ2dvCb3/zmJ92TVqsVx8fH6gXr9breJSSNs5+Lx+Ny07m+vkY8HhdJkU6DnFUAbKi0jQuIYDCoiIEPqT4fWrlcDs1mE5lMBmazGTs7O3K9WK/XipIjIYMx7H8r5XQ6cXx8rHcCwWjeO3yWSCaiS5jD4ZCrL51eb7rW3FXG+OyPLZLm6Q7zobrpmPE5arVa4dWrV6hUKiiVSiJkjEYjRVkb537O3xQFkUDQ6XRQLpdFnGBRBEXHZ6fTKUIp5z72FiQT0oXpj3/8oyKk2u02MpkMzs/P1XeQxGG8Pn6/X8RyAtPVahXVanWDgGW1WuWCSmdDEjiMNRwOkcvlALyb2fjzisWinrVgMKj7gL2y2+1Gq9USWToWi8mhFsDG+Z5MJlEulzVf8BoYiwQMui+Wy2WRzI0ueJzfarUavvrqK2ExFMjcVcZIztVqdSsBl3PeZDLBxcUFarUaut0uIpGIYuYmkwkqlYoibem27HA4MBgMFNXKCofDWv7TvZiupRRBeTwe9Yz8XVerlXpJm80mEkypVMJoNMLu7q56UPalN4sOxnyeSKC6GXU+Ho/laE61Oe/zWCwmJ+XBYIBEIoHBYCChwe7urtzKvV6vYi0pauz3+3Ko3t7exuXl5UacIQkudKzl88R/RmKcMULLKPBjBG8mk0E4HMbBwQGcTqfecXRwLRQKmEwmIifzOTJiKMT4bDabFr3EZkjIullG9zhG0vG58Pl8GxHbfA8b404XiwV2dnbk1sBe1fj3k1jGZ4bnQqPRkMCFLkAUVQaDQZ1BvE5bW1sS1ZAIGAgE9C7lcnhnZ0fLM5PJhFKpJGyEMbaMyKTLP8WskUhEC0KStfmZuehkz7C/v49IJIJoNCqyHwCRJUejkRy57yr2+OVyGV6vF69evZKLPPGASCSid9FNh/m7iiJZAHL6JfH77//+72GxWPDy5UudByTK8YximgHnrEAgoLk4FArJEcrhcCieldgmZ8n78CLi07yuf64i6bPX632QzERCqsvlutVZh45hLpfrzmhwnpskjtDxjH9uPp9vOB5vb28jEAjoWViv14jH4wiFQu8RJP7S9bHRVcRief7dVcViEeVyWXgJ3bApgOt0OsjlcnpPnZ6e/tkcg+6qJ0+e4PLyEnt7exK0USyyWq3UA4RCIQnHwuHwZxO13lZ8/0+n0zvvHYvFIheddrstASOdfFutFnw+n4iq7IH29vYQCAQ2sD7ibgAkjLuveIaQQH7f5+AuaDQayWmTpIpSqYQ3b97IYe2rr7669dmm0IA9ZyqVeo9ownOQUdWz2QzValXP5XK51I6BAmAmCJDYetfnDgQCinxlzCEjJIPBoPqlP1cxiYDOe7f93jeJp5VKBYvFQi6nxvq5HA3/EpVOp/VubjQaEu2R5M8+g0INkpDcbje2t7dxcnLy3ndJsR7dMZ1OJ7a3t997LieTifYXD8EHfD6fnIKJhX9shcNh7d7K5bIcZo3fZ7/fx/X1NbrdLvb29iRyu9mPsOctFAraXzFlhL0cnak5L8ZiMezv79/5u29tbSEajaJQKGA6nSrKFoBELoz3vE0sRZGIMS6dcYgU6BEXdjqdErzfVh/rXEi8gv/31/KMfKkf65fTlX2pL/WlftYyMn65aGAcESNvKpWKmMpc0pP5S9V6sVjUcoCNRSaTQSqVQqPR0JBMAJyWtdVqVQtok8mEXC4nW+9eryfAEngHMBCUopMWnYFMJpNexCaTCc1mE8+ePdMimkzjm+Qr1vX1tchOBLIITtdqNREngHfOFVtbW/p3qbwi8SQSieDw8BButxv1el2uKtPpVIDay5cvxZIm6YwOPLlcDtPpVE5IHo8H4/EYV1dXsvH2eDzI5XJYr9c4PT2F0+mU84XFYpGzTKVSwfb2thYRRgCHzl9U/9zGGDeZTIhEIliv13JXuhlbQ+CeQJ/P54PJZBJIy+iB4XCIxWIhFxI6nDWbTYGTJNHRgv22Rj4WiyEUCuE//uM/pHij9S+jAvL5PDwejyy3uaxhUT0/GAwEGlPN2m631RCS9MSlDcHi5XKpQQr4MfaD4GEkEsHe3h7++Z//Gf/6r/8qNeTe3p5UARcXF4rSIGGETTifTbrAuFwumEwmudXwnxEw9Xg82NnZQalUEvhrJDeazWZUKhXs7e2h1+upmaWK22jjyiGAkX28T3mNuPSgKpyLBIvFgsFgIOD+7du3UiXzOUmn04jFYp9VaRaLxdDtdtFsNlEqld4j3DEyggQpRqaEQiEpUelAEo1GUa/X1dzP53M5zzFKg7bf6/UawWBQlre0zPX5fIrcIOAcCoXktNPr9QQyMeeejm7GRp2xn51ORwpYWs0fHBzIMa3b7eLi4kKuPgB0ztDVzOVybRC8jEXLcy7XqLAioHETEKPKlaQXDpQvX75UhAMjP8xmM0KhECKRCNLpNHw+n1RjBMW4KAXeuUTk83kMh0O8fv0aFovlVmvexWIhoNlut2uA4hnn8XjQbDb1Z0k66Ha7qFarAj44oBLEpz023QTsdvsGkMx3UK/XkzMjIxaM5XQ6RZiie1w+nxcBkYAFI+UIKDLO5TbSW7FYFEmTMRwEC3meXF5e4v/6v/4vXSOeVVQPRaNR+Hw+xTFZrVapyI1A7ng81r1LEo/f78fJyQni8bjc1viOpDMcSYtUXKdSKcTjcX0PJCtysL6t6EpmNpsFHJNYBUAgJNWsXIZEIhEkk0n9Pbu7uyIv8rsznrMABJwx6oXnX6vVwnA4xMXFBZ48eQKXy4WnT5+KUMj7xmKxIBAIIBKJiODBuJzt7W0RIo3Rjiwq8I3xi5+rPB4PHj16pOtEAIjPyhfi1e1FdwCCMveBuXRZ7HQ6aLVa2NnZebCDE+9ZqnbNZrPua5K7fw73KxbjchnX0263USwW1U/4fD4kk0m43W4cHR09yN3rrrq6ulLUDvsDLlq4vKxUKorG/B//x//xJy0SPB4Pzs7OkMlk4Pf7pcpdr9dwu91aZG5vb8Pv9+P777+X++nOzo4cLHu9nmKpTSYTBoMBwuEwjo+PN2IT+CzN5/PPotiliwYJ60bSLvCuJyfBO5fLoVKpwGazbbh3/C2U1+vF48eP8erVK0QiETnS8p3Nvu+mitzv9yObzeo+HAwGH1SKkyR7kxTw0OLZf5vb6V1FotJDluQPKZL6SqWSYubYz3JupysB8KPTJXs5CoDYb5dKJQBQPAPJMjabDWazGYeHh3j+/Ll683/7t3+DzWaTays/Wz6fF4mHsz9nec4+nK1vLmrY5/HdTcLLTXCajmmr1UqOaXT8IZBOJbPL5YLNZpMTcKPRkLsnPx8dg/mOGI1GcqXi7MA5i31mv99HNBqFy+XC1dWV4lhuVjQalbPKd999p56Kn9dsNisam44ojUZDThl0OOa1A350Y+j1epjNZlgul+qtuIggrjKfz3F+fi5BHbEaihZ4bxjjwWazmUQ+TqdT7nLG74HOskdHR4hGo3JjGQ6HCIfDmE6nqNVqmE6nKBaLIuzZbDYkEgktYrmwJDmIS3iKVe5aSlAQB/wYo3lztqlWq+j1enKqInGK5wMdjMrlMq6urrBcLtHpdOTw+PjxY+FfxFb4fuescnR0pBhsLlLYm04mE33HdO0mGYjkf7qt0oGrUCgo6r5arQoL4VzFiPvDw0ORhprNppwt2cfwvbK1taU5kk4cfFbomOf1ehEIBIQVUFRms9mwt7eHYDCI4XAoMdfFxQXm87mepWAwKBc7q9WqOc/v96Pf72tWKhaLil67zS2a/3+lUpEDs8vl0hxP9z3G/vD8iEajEpbRwYrxP8PhUEu6cDgsPBSAMLpyubxxny+XSzmKbW1t6bnn70eCIH8GSW90tObC9easSyIkn0uKim7WZDJBJpMRsaxer2Nra2tjtqSDNnsUusV8qGc1OlDR/YoiMj7/Z2dnG6kF7In4LITD4Q2R2N7enkiQ0WhUrnF0FnU4HHj79q3OHrph3KzZbCZH67tifn6uokvIQ6K8KRK6TVTKogMg37e3zWjBYFCCH5IHSWZ+9OiRzmGSCefzOUwmk1zn6W795yRnPKSIIxIf+xA+aHQhv4swxehc4iaHh4cb/bHFYhExkGLRfD6PR48efdbP9rFlNpvx1VdfSZTF55OCATrwcj4kdvFz3fskibL/um/GMJvNCAaDcjs8ODiQeJ1YGMnE29vbOttvkro8Ho/e/SR+31fdbld7mbuiA+nQRdL44eGhRPYknPDMvrq60mz629/+Ful0WnMZUzIuLy9RrVb13N5VjJR98eIFvF4vzs/PtbNiz02S7XQ6lfP6zWi+m0U3062tLbk9UjjZaDRgtVoRiUQQi8V+ViwBgByKSIS9WXTu7PV6WK/XwsPYYxprvV6LwPzXgFOZTCbtA/mOooCTkb5MJPH5fLi6ulL08/Pnz++d/yKRCDKZDKbT6a1EZaOQ+iHYFPvohzjJ3fd3HB0dCV+q1Wqo1+sS/U4mE4kAAoGA9jXlcln7MGORnMzegrMXz0caZEQiEWH+H3p22GMzyYb7g3q9LjGS0+lUH3yztra2RGaPRCLaVadSKRwcHKivCYfD9z577H0pav8QoYouuLcJd77UX0f97SB4X+pL/Y1Xu93GcDhEpVIRaE3iDw94ggR0Z6LNN4FAvryoaqcylEMUm6/FYoFEIoHZbKYIEGZjE3i1Wq2w2+1YrVZq6vr9vtSfBOBoz8kFFhuayWQi5WWj0RAJiS/7216G7XZbcTIEiNn8BQIBkWdarZZUbj6fTy4fyWQS+XxekU60eacrVyAQQDKZxN7enuKGuMjl9aLFPMkgXBb5/X6USiUNmdlsFqvVSo4BJEO4XC50u1188803ako4gLIpabfb8Hq9SKVSWK/XuLq6EsDFAf2uCoVCIkrV6/WNAZ6RkFzkEaQcDAbI5XKwWq1qvBnLxPiZ9XqNTCajRtHhcOD58+eKk7qrmDNOm2Mq51gkzmUyGakajS4OjK8keEFXBKqIG43GBijNQYnErUAgIOY7y263C4xjpdNppFKpjSV7IBCQ4pSAONU4JGswFoBgDACphtrtNgCoUaQi1W634+DgADs7O4qgA6C/czgcSp1MYI+Wy1z48nrwHqJa0ul0qkHk/co/y+eWCm/GWTLabXd3F7/+9a8RCAQQjUb1vY5GI1lQ0yqf2eMk8T2kqMxPp9PI5XK4urqSAoi/LwHus7MzPeMEFkmmcjqdePnypVzEeF+3220tRKhwICmHCy0OLoyiCgQCcsjidzabzfDDDz+IWEhgm05GtVpNjl4si8WCSCSiiJHnz58jmUziN7/5DcxmM/L5PPL5vD63UWUcDAbl1MRh5e3btzg5OXkPRCEgzcijeDyu2KlWq6VBfzweYzAYKELT7Xbj4OAAl5eXCIfDUlhSBcWYNZIV6/U6ms0mstksbDYbdnd3N34XgvrfffcdWq0WXrx4ofOO5L9ut6vYvlQqJUCS14vgfzablZ0yAKls6S5BRyQ6XJFMRYCAMU90jiLRkwRDkgYILhcKBREruDDkUo/EUBJRfT4fjo6ONhTwXPSQjGosKolo126MGAXeLaMPDw/RaDQUX8vnfLFYyD2g3+/LtY/P72g0gt1u13fC5R2JbLPZTE5QyWQSkUhk43ujY5nNZtO9yhjUp0+f6vfkMq/T6dy78KaSiOeN3+/fcCmh+5XL5cLbt28xm800kBuLsa5Pnz7F1tYWXr16teEG5XK5dDYwsmq1WolwRcIxieYARDy7q4zkNr6XSeS9qcgiiYtODPzfwuHwZ1Oy3had8aXuLoKgXNB+qAje85l/yLVeLBa4uLiQEx/PHS7u+v2+yNL3RSAsl0vFgbE/JzHnIe9OuufVajWRKVlWqxXRaFSOJ59aJKf9/ve/31h+870AvLvnS6USXrx4AQD43e9+h3/+53/+SQCTy+XCkydPMBgM1Mut12u54Rjd6EKhkNwDC4UC3G43UqkUBoOBYs94JjPOxdhrsnf9XGQZEjzH4zEODw/v/DsZr0cAttlsyunpb6WoRu31ephOpzg4OJCI4S7wnuCl1+uFxWJBs9mUSOCuajabcLvditD42GLv8TGOGbPZbMMxY71eo91uo9FoaCbn0pJuqvf9fc1mUwujRCKhmYQLfs7yJEJS2MGeh2TASqUi90mel7z3uSBMp9NyH67X65hOp+qRGF1Mt0fO/PF4HKenp3KcisfjqFQqWK/Xio8jkYXFRRZnJM4RdBFmWSwWnJ2d4eLiAk6nU9FyvV5v4xpQuECSBM9BzgV00mT/S0dVLhUZs0NHAvY5s9kMs9lMUYuMM+WfNZ6x7Kup9J7P53JEMLqtUthE51XO5STC8xrabDbU63UMBgOR2obDIcrlsiLLucA7ODhAPp9Hs9mUczN7GjozrddrEc+JVTA+EsCd92Gn09Gcyh6cDj9G0QNdhpfLJabTqVzFTSYTOp0OarWaZme+U0hcNpvNEvn4fD5hDMS56BrFHn80GqHRaIig32630el0kEwm1SszioTfE7EVuqCPx2M9zxSg0N2VjtherxfxeFzL1lKpJOdu/u6r1UoxMYx5pHiGjmIkEB0dHam/pMs6cQkS/3mvrddrPHv2DNPpFG/evNHiNhaLIRAIyNWLrjBcchFfoqv6kydPRO5qtVoi7hkd0BKJBJLJJPx+P8rlMjqdDiwWC46Pj4WdELNjpDafM16LUqkkt3uegZx/SWwMhULC//jd2mw2HB4ewul0Soy0XC5xcHCA7e1tvSeJKx4eHuLg4ABv376VeIpkx/F4rNmf8wUj9HhdKUIDIMEoe3lGKLGXZESVx+NRX0lSAh2Meb8Y31vL5RLxeBwej0czCVMIiL9y/uISj2fk/v6+SBnsgzKZjGZ9xmJ+6H3W6XTg9XrlPM/PZSSPcjnK3oo9KEWDfL6Mxbi14+PjW0VWjBql2KrdbktgxevN65dMJrGzs/OzEw2MxWjbUCgk57ebxKb1ei2Ma3d3F4FA4M7Zjn8PY+nvWiLTVSefzyMejwtTs9lsEueSnGt0F4tEInJNJEmZf+bnck56aJlMJoTDYQm3jeThm0X8n9HEdzmd1Ov1DeH1XcQhnkN00LvvZz+kOJcR1+RcRxe8h1QqlcLTp08RCATw6tUrTKdT9Z92u12YNYmrdET6OYq9Jh2RP1Q+n09EauIp3HcMh8MNQnIsFrt1ZiY+FQwG9b3c9f2R3L6zs4NQKHQn0YRYHF13lsslstmsSJSr1Uqkb6fTifPzc7nK0XGHjjl0r6/X60ilUri+vpab1m1zKxM+2N/U63VF5gLv7mmHwyEXfpJZP0SQzeVycidrt9vqh4ib8dw/Pj7+WXEg4nO3JSYAELGaDkPG3+WmAzF7Wjom/txFYj4xFKPD5ucqk8mERCKBeDyOTqfz3nPAZ9foiETC0n3F6Dr29TeL9w6dzT90rrE/o8DiU8vj8eD09BRXV1cSDvAz2+32Dcz+4OBAu0WmYdyc3SmkjcViSKVSaLfbcqWNRqPC8J1O54MxELppEfcyilTi8Tim0ylKpZIi428W3XPplvub3/wG6XT6PcHcfcVdaaVSkVPsXUXjA573X+qvs74QsL7Ul/obKTJ+jc43tIk1Nkx8KRIoot17NBrFkydPAEBRBkYA1GKxIJlMotVqSfXldrtxenqKb7/9VksRDpCtVkvOO1R0t9ttLYpJBLBarQJYSMoisEK77VgshnQ6jT/+8Y+oVCpypDKyxBlhxCg2YwwJAR7GFFFdRnBhMBiIqBEIBFCpVBCLxZSHPh6PEY1GEY1GcXBwIOeJFy9eoNvtwul0bgzJdOaxWq2oVCoCCHkdCKySeEB133g8xh/+8ActikjQIUu71Wrh+++/h9vtRjweR7FYhN/vR61WEyt/f3//3qaBYPxtjZ7JZEI0GsVgMEC73VacQKfTEcPcZrMhlUqh3+9rGc37jcQzj8cjEJaWrLcVlwmlUgl2u11KVWO5XC7s7e0hl8uJWJdMJtVIkTBD4hGjYwhYWa1WubEBEKGCIDuJQsbhyOhmwrJYLIjH47K/BiC3CV4vLgCYGc2YTT5nJD9QQRqPxwUYBoNBEWVYBICbzaYIL/l8XhGJHPpIkCHhjN8Jn99utyvlJN05GI3JBpARIsPhENvb2wiHw4r8ikQisFgsGjwODg7gdruxXC5lP8uGl4sTowPZ0dHRnWqmm/dfJBJRJjcVtLx/OMz85je/gcVikYKCgx/JenT7YAQI/y66kPG77na7IvIwxoDESavVKkUVAIFb0+kU2WxW9zqBFIK2vV4PwWAQhUJh4xygJfV4PMb29jai0ShOT0/laPb999/j9evXuL6+ltLP6J7CYcfv94uMeHl5iVgsptgHLkNsNhvS6fSGIwEJWHQFo100QSteX6vViqdPn8Lr9cqJj8tuI0DGeC+TySS3kZvFeJJvv/1WqkQ6GxqX6ATEbwKV29vbAmSo3A8Gg7DZbLi8vEShUEA+n4fb7UYsFsPbt28BvDtXEomEIhYmkwmazaZiU/gu5DlVq9Wwu7uLVquFvb09eL1eLJdLFItFkcxIrGCcDwdsLh2fP38Oi8Ui23ZaybNI+qPznclkkkLuJjBDNRnVfCRNElDiO+nNmzdSKDK6qtFoaNlKN71QKIRQKIRyuSw3iH6/D7fbLfcsI+hD0jbjOePxuGIaSWLjgvO+ZTEjIQHo7LnNNcRsNiMSiShutdls4uTk5L1/j6Rxq9WKWq2muEladhNMWy6X2N3dxXQ6xdOnT5HL5dBut0VQfkjt7u7Kua1cLgvQZbQz3z+8liRt0rWQ79k/Z5TGl/qxjPfaQwAoY1TwQxSDy+USb9++1QJzvV7D7/fD7/eLYHl1daXFNID3FmR0cSGgbIylImiXSCQedM8y6nZnZ2cDhLzNse1TipEP/X5friU3Y4JJWH716hUuLi7gcrlwenr6WchEN0nyt9Xu7q7OsFKphGKxiFqtJgdBOuIyGvLmspCLf5vN9knuSDeLccZcBtxXnHWoUP9bI2AB0LVnH/yha0ZXCLo70UXjrkjWer2Ofr8v4vOnLnmj0eiDHTMajYZi/OjudXV1JYER5wMS9imKuDmTGD8DicD7+/toNBpoNpuKfRuPxyKi+Hw+LfTprkaHY5IC2S9SGGWMkOr3+4jH40gmk3JSWi6XcDqdODw8BPCOkET3GRJ/KI4A3p2rdPgxEr0pGrnpCDeZTOByueTGfNvZZbVa8ejRIznEMOppvV4jm80qMnp/f/89gdFvf/tb+Hw+/Lf/9t9gs9nktEs1cjAYlGqdBBu6MblcLkynU+TzeRQKBTnSDAYDXF5eYjgcIhgMbvxcuhaQ1EbchYR6kpSOj4/VY7J/4GxGpx6K0zqdjuYs4jJ+vx/BYBCZTAaJRAK1Wk0x24FAQH8fsR6eff1+XwIF3q+MeBsOh1qYGkV8vV4POzs7mM1maDabyOVywlxOTk6ExdTrdbmC0z2c93S1WkW320Wj0RBRlc5NJPaRnEHRA1X4FGzZbDaJvozOqHQTHgwGODs7k1iQpDa6MvV6Pc3wdLcIBoNypaGDG12X6GTMeLrz83OJDnmN6EjN2ZxkbpKlKIqiezTJnHTCpqPds2fPFA//8uVLEQ7Za/B3Yqwt52EKUYjT0BU8GAyiUqkooi8Wi6Farcpdx0geXCwWInd4PB7hF7yP6XzH+ZEYIQldfEboUEJX6mazKccXunwMh0O5sXOu2N7eFl5A4iI/D2N+3r59C5/Ph36/rzmJbt0k/PHMpZiq2+3qXEqlUiJu0m2RJA+j4IVJAWazWQQmkmOMwlQAG5GMk8lk4x3Eni4ej2upTjID8SbGk3HGCfwp7vw2UidnU57XdA+8q9elQ3oqlUI0GpWQj8RYktacTqec1m5GF90W8TsYDCR6uwvjcTgcODk50fNBMS3JCS6XCzs7O4osfeiM9rnKbDbj4OBAfX8+n98gx9E5aLVaaWm6u7t7599H0i9Jnw6H49ZFK11uwuEwjo6OcHZ29h4JKZlMbrhBs8dhD0FBgt1uFx5sfH7+EhWNRpU2kc/nkUgk3hNiUqxBHPa+fpe4Pkm/LLrZ8zx0Op0IBAJoNBoYDAZoNpufRMBar9cij0+n0w0ClsfjQaVSQTAYfE90eFuZzWbd+0xfoKiWzxMJBPV6HTabDd988w38fr8ErJ+rKCoE8CBiAd/Vxj/3IeHabRWPx4UPZ7NZpFKp9+7Pfr+Pcrkst8ebZ8B4PFav9/btW51bdP5kL0FHRn4+ztzD4RD/9b/+V/U9VqtV+LDX60W1WpXDOF2DbiOTUvAM/OiGG4/HhQNyn8SZHnjX6yQSiQ1Sq7EKhQLK5bKI0CRAsxdklHcikcB6vcajR48+y3x6W/FdQ6L0zdmG8ZicLXhv3CT6MP6Nu7Kfk2gym81Qq9XQbDY3nFA/l/jstmI/d5cAlc8Lr81Diu7pt2GkJPOv12u8fPlS56XNZlMfZjxfO52Oer+feoa43W48e/ZMiQYUaxBzdbvdmh15/tLNl85gN2u1WqHRaCCVSmFvbw/Pnz+Xi7HRIOFDxf7VGGFrvPYUIrFHuotYyeSMeDwOp9N55+99VwUCAb3na7Wa5oDbft9KpaKd3Z/T6fNL/XnrCwHrS32pv4Far9fodDrIZrMa6umoRAcmEnzYtHL4BaAXOPCuMXe73QJD+GKh6ouL5FAohGg0qnhCxqixuYzH4xv538y8Z9NmJI0Yrb2Bdy98s9mMVqulBsbn82F7e1sZ5Iwe40uOoNh4PIbP51Pzy+GMCzba5xtBPi7JqNih4qNarapB9/v9ePz4Mfb29gRQsVnmgqfT6cDn8yEYDG5Ykg8GA1nPk/RDFWc6ncZ4PEar1UKxWJQ1Za/Xg9frRTQahd/vV/zearVCsVhEtVqVS006nVbz8JDGnKqb2xo9xiaQ9MDrQXY3yVYAZP3LeCQuH4EfWd4XFxdwu93Y399/73czOgQcHR3J5t2oAGQ0YTgcxsXFBYLBIGq1moYjNjrBYBD5fB79fh+z2QzhcFhkDVo+d7tdKbZ9Ph/i8Tji8fhGo0c1OIkzLP4dJycn+pkEM4F3zf4PP/wAq9WK1WqFaDQq4hNJQSRg0WWGg0M4HEYoFHoPhBiNRoo3ZHwdyWQkUo7HY0wmEw2sdJljzASj92KxGBKJhJ4LksXoZjOZTLR4ZLwV/x0+A8fHx0in0yJfcQFNBTEBEsZicPBcLBY4PDyUQpfLA6rJ3G43arWaYkZ5b1Kxv7W1JRKJzWYTYGOz2RQ9yAhWWpWHw2FYLBZMJhPFJREs5CDDYT8UCiGXy2k5wtgSu92Ora0t1Ot1NBoNdLtdTCYT5HI5LTNOT08xn8/laEf1397enpZ0RoUMwYejoyMpHP/t3/4Nr1+/xtu3bzGZTBCNRhEKhXQvcqlQrVYVqfm//q//KwAoWolxFiSplEoluFwujEYjObHQMjgQCAhI3NnZgcPhwHA4VCzJ9vY2dnd371RuMgqKMa33DSpUglWrVZ1VvK8cDgdcLpfOsZtltVpxenoqq29eg3w+j2w2i06nIwcDOvPRHY4qQ8ZxTSYTtFotnSf8rgeDgRafv/71rxU5WSgUdO8xqoSxM3/3d38ndSTBcmbFz2YzfPvtt7i8vJQbA4l/BKpLpRL29vYU72esxWKBfD6Pi4sLKfmM7lV2u13L0slkov/kkoqLB4fDgX/8x3/UNTaZTIrV5HNRqVRQKpVEBB0MBno2qT4nSXO5XIrcNJ1Opfyji8xt9wCdB/nPSN6iy54RTA0Gg1LyNpvNW+8lEjoZAQJArhrsaehwQPe41WqFQCCAVquFaDR6pwrqZkWjUTleXl5eCnhzOByK9ySpkj0GiWDtdlvOc7+0iIi/lSLRAMCDvnO6v3Cp/aEqFAp6H1PBZ3wGQqEQhsMhisUiAGwsLoF3y5fz83O0223UarUNN8v5fC6hAV0bjo6OHvS5qdL+nEX3x3w+L2L6zc9r/Pl7e3v44Ycf0Gq1cHl5+ZPIROwPCfgxnuK22trawunpqRwASUSbz+fodDpa8tEZy/g9cwnp9/vvVeN/TC2XSywWiwfHL9AR4mac1t9KcTnPM/ZD9w2deKiWLRaLKJVKuL6+lkMJoxIokGE81m3LlYfWQx0zarWaekE6I5+fn8udlD0h+3Ajsf/i4gKPHj16Dyym45Pb7UYoFMJ4PJabViAQQLfbVa/EHpguzqlUSi7TdFWKRqNIpVKoVCpaMpMM5nQ65XjDiJd0Oo1QKKT5brVaoVwuw+12Ky6+XC5jb29PxAUjkYEkMUaa86wyzn0Uu3DpcFsZFyCcFUgmOj8/v/U7Ad7Nvr/61a8AAP/3//1/A3h39nN2YQQh8Qcq03mdx+OxYuGAd8QGkqPYM3z//fd49uyZYuLp4huPx+VKykgi4gFOpxOJRALZbFbXg4IoRty0Wi2Uy2UtOhiD1uv18PjxYxweHioChGQkkm0uLy9FnnM4HFome71e1Go1OUPHYjGUy2VhJowbp0tXtVoVJsQ4R+Cd+t34XcXjccRiMZjNZnS7XRSLRbkJZzIZOYtw9uYZP5/PJSrhe5F/djgcYrFYyEk1m83qOSL2Q+ejTqcDk8mk5yIUCqHVamkZRwHJer2W297BwQF++9vfYjAYIJ/P677mAqvf7wv34uxL8hUd8ehSzF693+8rkoYOYFzCptNpEab5uRkvFgwGsb29jVevXokQQuc0zjDD4VBkxVgstoFz0E2aMzPdPZ1Op+519uDELJbLpcST19fXAN7NI8QmOEfRzZnfM6/P+fk5ut0ucrkcnE4nnjx5ItEYna74DHHOIMGtWq0qKp5YBvFCzrdc2rvdbkWu0gmc7vedTgdbW1tIpVJ4/Pix8Ih8Po9Op4NYLCbRDd3TSNDifVWr1XRO0iWKbrac+40EAeJhxNWIsRqLM1goFMJyuZTzL/EZijvo7MWfQyIYRVlGF6RMJiNhi9Vq1ZLeeO6RsEnBKnubly9f6npxXqFwbjweYzwea/bjHMVz0oiLNZtN3ev3uSG73W48efJEAiGKs4xiH2JB9xVFhq1WSwRs4lUfihq/r/x+P46OjkRsJImT+HMkEoHP54PH48Hx8fEHF/o7Ozuan+mKQbdriiJzuRy2traws7ODcDj8wZ5zPB7j7du3ei5Go5F62MViAafTiXA4jMFggOPj47/Y7EkHf2Jt5XJ5I22BPb3H40EqlUIsFrvTSZTEVOIfACRa7Xa7Iq2SQM3ngc/RxxYTJRqNBiqVivAKYo31eh1Op1Of4ezs7IPzotVqxdnZmUjJkUgE6/Ua/X4fpVJJxNb5fI6rqyudd0y6uBnr96nF+ZJOox+aS4gV/1QXHRKK1us1SqUSMpkMHA6HznM+J8FgEPF4HHt7e/pnRpEvHSaz2Szq9Tp8Ph9++OEH4bK3pX9sbW2J/E182e/347//7/97fY/s0SiyK5fLACDnLmOtVivUajXFKj5//ly9K+8P9nTX19doNpuIRCI4Pj5GPB6XgyLTIxg3TGL1ze+aLpadTgflchkWy7uo4uPj40/+Pu4rPkMk0tzEd7e2tpBMJvWeu7i4EHGIuy46wjK5YW9v72eLWSM5bTQayc2S7xWfz4fBYIBqtYqjo6PPMtM/tPgeotvah4r30F3PGoUKw+FQYmSKlCj+TiaTcjlljCFdFn9qcea4SaQrFApynOWcx51ouVyWeJX9D6Oe6Qy/u7u74SjI/bPX65W42HjvEOPv9/tYLpfane7u7m7ssPmfJLvP53OUy2VUKhX1VqzxeCy8jckQH+skaTKZkEwm5SR7fX2tnTmLfep8PpcI7HNjdV/ql1NfCFhf6kv9DRQXHQQCOZgQcOIQQwtwqi+o9GQ8gnEgJrs5nU4jHA6L9OFwODAej5FMJqXSYrNmtKMkeYYOVIPBQKAeB0e73a5BwGQyaWkK/BgDMp/PUa1WBUIyp3c2m4kAALwbymg37na7BUIS5O73+1L68gVM1SWdURqNBvL5vNQ2ZCg7HA4t12u1muLQCAKPx+ONeAmqAGmtTyKHcdnPGMR/+7d/09K2VqsJpHv06JGcewh6UnlA4J8kFoJgt1mX3la85reBFVTsEAjnoEvlKQBdZ7Le5/M5XC7XhvPYaDSSajoej2MymeD09HSj4aB61OVyiRlO0IPXkvcPAXGSvZbLpSxL6d5CdxlGVpJM4XA4BI41Gg2Ew2E8e/ZMBBMSlaxWqxp4j8ez0Wy2Wi24XC54vV7leXc6HTWQv//97wWMzGYzWR2TPEhVJJXvoVBIVvwcUI0kFKo5m82m1OtsRm02G/L5vBYW0+lU6h8SG9lUEhAOBALw+Xwi8LBZ5jNKdS6JgnRl44LD6ELVbDbxww8/KO4zGAzi+Ph447uNx+NSF9dqNbx9+1aORnRr8ng8AssdDoesa6k2ozU/71Wv14uzs7ONgZBxq/l8Ht9//z0ymYyufy6XU9TecrkUccjv98vNitbyo9EINptNiwjGqnQ6HVnkc1mQSCQE+l9dXSnCpNVqoVqtylp3e3t7Y4nIPHE6igHAN998g2w2i6urKzll8DwCoIiOarWKFy9eoFAowGaz4fz8XMSPcDgMv9+P5XKpxRxJbYE/xbVub2+j2+0iEomIcLdYLJDJZLSo5rAcjUbvzV6nA8NisXjQUJtKpTCfz7G/v49kMinLXy7D7yu73Y7Hjx/LiYrvNhajSDj48DPyXRWNRgWauVwuWbDz3KCjFF0MGc0ym80UP8d3Fc9EEoUSiQTK5TIajQYsFgueP3+uPzcYDFCv1zeIZiRiRSIRpFIpRQgaiy5ZXOzQCYekxMFgoMiS0WiEwWAgctd0OoXFYsF8Psc//MM/aOlBq39GgpCwVSgU9G4nGZFAIxca3W5X/x4Vp/w9F4uFVLi7u7vvgdJOp1MLuF6vp/6CDg98n9E1iosQOigY3aNINJ1Op1rGuVwuuZ2wZ7HZbLBarbomfL8YLeLvG65ns5kIl1T322w2vHjxQoqywWAghwm6YwQCAWxtbWE4HCKVSols+HMUF5W3KQ+/1Lvy+Xz6vrh4u6/4DND+/L5ib12v1+Fyue48K91uN7a3t5HL5RTBQDU7wdlcLif3Kr4/Cc6Xy2Vks1ms12st8P8SRRIYleAfIt3SdZIR3cPh8KNjE4wLeIo7SGiNRCKIxWK3PsdbW1uKKqrX65oHqJje2dl57521XC4VDUv31M8BGJOstlgsNlTv7AlvPrd0Lfsc8Yf/GYvx58FgUDPPXUsivvs4n6ZSKV07zgh02TDOqQ9doN5XZrMZ+/v79zpmdP4UvZtOpxH4U5zY69evBaQnEgn4/f6N+ywSieDNmzd639KdyVh0+iFRjy45BMDpKsq5odfrAYAI94zNIBHD6XSiXC5vRJ4sl0tcXl5qjvB4PMhmsxKKAFB0HQA5JtFVBoBcpekqQ6dIirR4pvDMo2MRo6nYhz6k6PjDXp2g+11lMpnwq1/9SqQMRsDTGYyuVHxn0K364uJCcZderxez2UwCAC6ar66uEIvF5OjDCLWzszMEg0G5r7IXous2gXoS09gvzedzpFIpYQwkwxAfGY/H2NvbkyMPnYboHMT4L5Lh6fDV6/VEwKEgjJ8tEAggk8kgFAqhXq/D7/dr3giHw3A4HGi1WqhUKigUCtjZ2UGxWJQLG+9pLklIjrNarbi8vJQALxqN6p6ls6jT6ZRDOuP6SEBh3HcqlYLJZEI2m5W7WzqdlnKdGFez2USr1QLw7p3NJRzPjvl8rnc3ScUWi0Vin3Q6jevraz2DFosFlUoFoVBIhCW6JfP/J/mGMy77ZuI3JB2s12vNQ3yWuTQMh8OKEp1OpxvzMEk4FH0sFguRhYyOclwos5+nCxl7Zc6SjAhMpVJaVF1eXmIymaBer0t0QkIX32WVSkWf8cmTJ5pTGf27vb0tJ1Cv16veJ5VKacHNviaTySCbzUqYQVc9ku0oSjI+vzw3KKaiAy2/V7vdrv/kmddsNjW3d7tdnXtcfpKMFYlEhK9R1MElImdt47k9HA51D3Dm4budxMVarYZyuSzybTqd1u/CZ5nPCXtLm80mYSV7HxKwiMMSW/T7/ajX67i4uNC5zmUlnVR5T19cXKDdbqPVaok4wN6DxDQSzyqVyoZwz+12S2hJh7NEIvHBnhB4R0RJJpNIJBLCoCiIfMgMUywWJSTrdDrCBhlnWSwWPxj3fV8FAgE8f/5cs5+RwEMxbCAQeFBvaLfbRZLgeUmSBQA5Evp8PsRiMezv79/79y0WC5G36RRkFNzymeFCHIAIiH+JIibIhXyn08F4PAYAndMOhwPxePy9Hue+YvxmpVJBs9nUZyUWn0wmMRwOP/lzF4tF/QyLxbIxlwEQ1prJZLC3t4fLy0ucnZ3d+fcxpo64Jn9X/ifPLPaPJPFkMhmR87rdLr766qtPJheySNj2+/0Si/O9dZvwqN1u68/8VBcdujTyzOPzC0C9Ofs+kp6Wy6Vc3hkhRnH+er0WSY7v3PueId4T7XYbh4eH6jV4fjICmEKOXq8n0rOx6Ki8XC5xenqqs2a1WolQyV3J9va2CKqj0QjFYnHDHfPo6EikGoqs7joHKeDg2fQQAt2nFsXjvL7JZHLjzPN6vUgmk+pJmfxQKBSE65MQc3Bw8LNFana7XVxdXaFarSrBhikzFF2xH1+v1zg9Pf1Z4xuNRcc7JtRwV3VXcU4zRhuzyuUyisWihDPcNXC/aUwlMgpcuFf6OSsUCimuu91uIxqNIhaL6Vxmkg7vHwqXkskk9vf3EQ6H38NJef/V63W02209Y51OB41GQ2IL7oZ9Ph/y+bycgCmIIInr8PBQZO1Go6E9IXF1im6IG93nbnlfUeDLn1Ov11EulzX/er1eCSvolPyl/nrrCyr/pb7UX3l1Oh3k83lZx29tbQnYMplMYutTWQxATjQEfKjwMDYIvV5PC83d3V01laPRCHa7Xc0fQQ5aW7M57fV6Yu5zaVOv1wUgE9gNBAJy62KOPV0tAAggKZVKcDgcCIfDyu72er0iYBFEcLvdWK1WGwtpDjZ0nOFSvd1uw2w2I5FIwOVyodls6ufRHYZKf4I4jH8igYWAZTweR6lUEuGBiyMjkYyLtVarpWaaYCSJWnQhYoQdAUmC3G63G4eHh8orj0ajUknQ9va+xnw2mykO8a6hymaz4dGjR3IA4+fm0puOPr1eT85AjOdyu92KSiBYxbiGi4sLPH36VIMkrX4nkwmur68Vb8DryxhMglN0JwLeAYz5fF5RAb1eTyphq9WKbDaLt2/famCjMoODERWvJCmR/NZsNjUM8vpwkZhOp6U4Bt4Bsn6/H8lkUoDl7373O0SjUYFYVGCSeDgcDhEKhZSBHQwGb21C6fBTLpdlXwxAMRgApAoym81yw2FT2Ww2BXLHYjEcHx/D4XAgk8kAgKLpjKC62+3GdDp9j4jG773T6eDy8hLj8VgELIJ/2WwWHo9HJEy6VtGRgIQuqguWyyUKhYIUq4PBAIlEAqlUSs4FvAeobCGIenMxUyqVcHFxIRvnUCik2AI6+wyHww01HUmUlUpF95vJZMLJyYnssglYr1YrOWmxyR6NRoqC5H1gNpu1YFmv10ilUjg4OBDhiMplFpeEuVxOKjiSmxgDYLPZtFDLZDKKzyAwTxeEer0Ot9utWBUuobi0oZKfMUzD4VCKKkY7cil2U+Vys3i+AngQKMT3CkmEHzuQk3xH23CqsJ8+fYpYLKYFw3w+V4Rku93W5yBJj6TbwWCg/+SZulgscHFxgevra92f/X4fkUhExCS+O6juIuhXrVZFRvrVr36lv5egPJfAiUQC4XAYzWZTit+bRdcMDtNUWnLZEwqFdL7TmYFnJO9j/r7r9Rq5XA6ZTAaNRkOxFXzfEnA/PT197/whiez8/BxbW1tYr9eIxWIIh8Na/NBtk4DY/v7+BoBEYIYOVXz+zGazFn9cNi4WC6nMDw8PBUyFQiGRORhpY7PZcHBwgNFohL29Pd2LvD+4KKPzB0E24714WxHQGY1GikumFft0OlXcCR0r+LxwCRsOhxUreXBw8NGKO5LIeb/cjKc0ksOMJETG6LJn+FI/KkcZvUNniduq1+upJ7qpuLutSLAfDAYbIIqRGMfznvcK1cxcENAVxOVyYXt7e+NnEryz2WzI5XKyNP8QiPdzltGp9iGqPRJbeS4+FPikswmXFu12W0A931OMeDg+Pr7z72UkK0nQ5XIZ+XwexWIRXq9XRAEKVsxmM9LpNILB4GeLwOF7ls4UBCU5b1HgwR6R8c9/yfiYv3TF43GRcXK5nBw9+HzcdOIxxspypqnX63JCYPH5oVPkT61AIHCvYwadi91uN46Pj9H5UxxyvV4XUHyzuKBm/A0Vufx92ddTfMEenc6uJFGRhDCZTNDv9+FwOBTVzf6Lzk0kyTDygYIFm82Go6OjDfL1bYttvtc5X19fX0uRzb6exG273Y5msymHZZIIer0eKpWKCGxPnjwREeVjir3SzWjD28pkMuH58+f44Ycf1AOSRM25IRKJIBwOw+l04uXLl/rOKFDKZrNaQh0cHGwQAbLZrMg/JN2zN4tGoxtuFCySbiluoFsqiQ5msxmHh4fw+/1yJSQxNxQKqVfgUpcEEhL2VqsVUqmUIiqbzaYWaEbyfqvVEvGcAoJgMAin0yk8iU4WjPcej8fo9/uw2+0ixbOHJA5ijAEnaYWOx48ePVKcoNPpFIbEaDT2/3RVKxQKiqMjXuT3+yWkCQQCmM1mcgMmaTIajep9n0wm1b+T4HTTxZJiFUZJzudzPX9cZrMno6s5cZxAIID1eo1gMCjBy87ODqxWq3pWr9e7gcOQGDqfzxW1VK/X5XpnsVjUJwLvetZwOKyzhwJKABI7kLTCWen169d49eoVarUa5vM50um08Cm+b81mM549eyaXL4ol6AbkcDgU/86ldLfb1Xf88uVL9f6j0UizNl0BBoMB/H6/yI+hUEjkScYS0nWdn+vm82uMogQgsRn7PAqX6ETNWYMLMZKiOQvR3Z9/r9/vl9iFDr3GWQKA3Ix4v/Hed7vdmM/nuLi4EJGQ2N9gMMC//uu/wuv1yhWZcYq5XG5DuENcl7G8FKtsbW0Jh/j7v/973ZvE5RhBm0wm5Wa4v7+PbDaLdruNQqGA3d1dObJPJhOdd3Sapsjy+voaw+FQUZ50pdva2sLu7i6CwSBSqdSDzmh+dx/r/JDNZlGtVlEsFoVHUVDF7zfwJ6dmisk+pSwWCxKJBBKJxAYx7VN6Brp+VatV3Q88C00mk4RQdC68ryhGKJfLiMfj7xFDKAhzuVwoFotwOBxyGflLld/vx/Pnz9Fut/X8Ee+mO86HiPAkBjFikURGEr2Nscr9fh+NRgMA5Gb0MUXyU6VSEfnq5vdut9uVxMHrfFsM9Wq1wvX1NTqdjrBnzuzEkrrdrlz07Xa7HLfm87n2ExSo9no9fP311x/E5u4rk8mESCSCfr+PH374Ab1ebwNfMAoVKCDf2dnZSBr5KUWiJonRnIfoYHezN+dZRWyZkY/GXQpFvnSXunkOrVYr7S+4H2I/SiJtIBDAeDwWyYsENd5DPKuWyyWy2awiI40uuozhrlarcigkbkYX5KOjI1xfX8tdn9eCgv8PPQuhUEjk6FartRFN+jmLxNB0Oo1cLifxBeew6XQqDPb4+Bg2mw3pdFo9P51dY7HYzxaVyMQaPh98l938HL1eT/1fJpPB06dPf5bf57aKxWLodDpK97iLDMWo68CfnGWNsyFxIsZT/uY3v0Gz2dS9v1gsRJrn8/vs2TOkUim51/6cRZf/cDiMSqUiYQ/nO+4v6EQWjUZxcHCAR48eIZ1Ov0fuAyBnqGQyiVKppH6s1WoJl2Ufwj0s9xnj8Rinp6fq84kjpdNpuawywYhihul0KlH0119//ZPEd9vb2yLI0yykXq+LTMsIeIpIvtRfb30hYH2pL/VXXIvFAoVCAc1mE7PZDHt7e+h2u4oYMpvN8Pl8aLVaImcRyKMSm8OQUa1NYsfu7i4CgYBchdhQ0t6XxBVGBjmdTkynU1SrVUVU9ft9NQGMfiPZgYABwUm6k5CEQNCNy142gRaLRXm+LIKZq9UKDodDMUpUs7LJphsIlzvJZFKqoaurKwDvovXYxPAlyZjBcrmsmJjJ/5+9N/ltNM3W/B5SEklxnklRFDXHlFFZWePtawMNu9GGN17cjQE3vLGBBnrnP6MXBnrZGwPe2A0bXtlGA1644Ybtvu4ablVWZWRWRGiWSIozKVIiRWqg6EXk7+RHhRQRWRmZWVU3DlDo25EREvkN7/ue5zzDaGTf6fT01IApgGT+DoQZwFrAAwC6ZrNpw/q5uTlTuFL83W63aw5aqDfOzs5Mkeb3+1Wr1VQoFO7c3G9ubrS/v6/ul3GVbrfbFBXkzfPvZmdntbi4qIWFBbMNRdE9Go10dHSkdrttZBOIc61Wy0Dwer2uzc1Nzc3NmcVnu922gQkAPZEH/X7fwK5msynpFbmGaAR+j9vtthgDcqMXFxctPvH58+cWJ1EsFk09CDGBgfre3p6BU0dHRzYo8/l8isfj6vV65q5FprXb7bYYwPF4bOBhMBjU4uKiHj9+rLOzM0myZ/fq6kr9fl+BQMCa0I8++kjD4VCrq6uvETFQ6BJP6ARbUI3hfoRlPs0koNzCwoI2NjZsyEjzykD05OTEFKcQISEn4RiVTCYt1gA1LsB7q9UygkqlUjHy5MXFhZaXlzU3N2f3kOZiaWlpamA9HA7Vbre1s7NjDlxOdens7KwSiYTC4bABcHNzc0okEvZ+9Pt9VSoVa1BwAYNI9ujRI52cnGh/f9+ytyFZYl2Ngh1yiSS7hwCsqNUhq9EADYdDFQoFzczMGKFub2/P4lhmZmb08OHDO0mRh4eH5vQRCoWM5EkTy/0BqICo4vF4plTJqIZRYKXTacXjcRsKAjTTWDNwwQEwnU4bMDgajbS7u6uFhYV7Y3rYNySZ09SbimfIqQz+ugUY02w2bUidTCbtfhUKBXNa6nQ6RppE3c+gEnIT4NxwOLSceGIpu92uxRolk0l79mkCnSAYimdnhN/Tp0/NqYDnSPoqZx4S313FgMCpBp+dnbWBhROkwUWK+8jwNh6P6+zsTF988YX29va0t7dninIAvlqtZiB9JpMxwiH7IOtrr9fTxcWF8vm8DSkhtLndbtVqNT179kyzs7Py+/0GgF1cXBgZEfIJeyGRlxBs+bmBQEBPnz61eA6narher6vVasnv9yufzxsBG+Lo7eJ3dLtdtdttTSYTHRwc2PDL6dQZDAbNVZBhms/nM4fQtbU1bW9v22fnu4bDYYt6yOVyU1Gy7wo84fbVaDQs8oTPx5qazWbV7/dt4HtycmLPNGtmr9czd5fvS/X8p1bZbNbep2KxaBEF7B24OeAyAJHtbYVLCm5ZADpOkF36CtAOBAIGGgHy8x6+yZ5/dnZW6XTa9jZcPb/rgswJ0PW2YRTrEa4yt+N43lSlUsliZomlYB/lGnBOm0wmevTo0VvfNWfsLcOQZrM55WQYDocVjUbfaRj2LoUL0mAwMJECQxpcWSORiAaDgfL5vLkCQ9D4+1qhUEhLS0t2rkU1jqMH/QZOZRBfqPn5eRUKBeXzeSMNspbeVURAcf6hN3uXcjpmMBh3fo90Om3nWdS8Ho/nre5MsVhMp6enFiWRTCZ1eXlpDhhnZ2c6Pj42V2mXyzVFQl5bWzPCMNF2GxsbNiwBrC6Xy9ra2tLNzY2JDfx+v5F5wBAODw/tfH7X5+X31+t1i2hJJpN6+PCh/H6/ksmkyuWySqWSDSzL5bKdp8ASfD6ffvSjH+nhw4cWC/d1CjLYuw7LPR6PiSQuLy/NMSoYDOrs7My+F4Tn6JfO2R6PR/v7++r1elpaWppS+adSKXW73Sny6M3NjSmiGRYjTsLx7ezsTI1GQ5VKxX4XJBuiwobDoRYXF01sA75DxC5EFSIGiWJ2XkcGAHz/8/NzlUolI8AxUIFQF4/HzemL9RgnVc7LqN8R2IzHY62srFgviZjG6/XaZ6evgjDFwKLRaNg+CbGPIUutVtPMzIxevHhhbnmRSMTwANxPOc/ihjcYDLS+vm4xOZeXl7q8vFQ6nbZ+GQeo2xWNRs3ly+l8DBbAtSXSHBwKUq301YANMQFCkPv6IaJ9wO08Ho8ymYz1wpCHtra2zOETnKbT6difMaweDAYm/vF4PPrd736ner2uer2uRCJhrt3Eo/R6vSmScrPZtGEmvQ8O8QyPm82m/vCHPxj+h/Obk4QdCoXsO/HflpeXTUwIQWVxcVFer9ewnoODA1WrVYsDA9ejt6vX65JkrioQ61gDie3kejjdqugHeK8RMoJFIVDle4KRnp2dmZMLexNERd6Bra0tczmVXg3QHz58qK2tLTUaDX3++ee2X0ky7Ix1jN4RNxaIbJA1wF3b7bZ+/vOfGwmMZ9DtdmtpaUmZTEahUMjIcNVq1Zy46SfZa7nvRP3hBk48I/sCZGdc6b5NYQD9Edfx9p4vfYUDOZ0FvymR3Rlj/sfW3Nyc8vm8crnclPs74qR3KfYgHLvfFPUIFt3pdGwQ/k2/w+2C2M21flPMFQ7t36RvAZ/5P//P/9PODJFIZMpFlnQByDUQYnHHe5dCwNzv9+8kXzkrnU5rZ2dH3W5X5XJZa2trdsa8ubmxsxqzhVAoZITLo6MjIxI+fPhQw+FQ1WrVEiEQxyJaffHihUajkQm7vkl/EIvF9Otf/1rX19d68eKFibxZV09PT7W7u6u5uTkVCgXDh95Xzc6+ipd+28+k12Nu4HRJi0Qi5rDKWg5pDjyeZx7yFXgOglqnOANn0nA4rHq9rlgspvF4POXSenFxYS7JzHCcLoc4qc7MzEw5KHPe5V1ZWlrS/v6+CSAQ974LYZTzjXOm9G2Uy+Wy8xLE+ePjY3uX2C8594L3c/4BE/026+TkxM5huVzuXiEWmCZOrmdnZ99ZFCGRtfl8XsViUdfX1zYjlGTCAVytUqnUa4Qk0hCYFTHHqtVqCgaDhhkGg0GLu5yfn9fa2tof7QT5dQv84vr6WqVSyYSPsVhMoVBIlUpFZ2dn5na4urpqKTK3i/M4ronZbFaHh4eW7kSMutv9KgoTQYfH49Hh4aEKhYL29va0ublpvS+ilvPzcxO6M3/mnIdz2vn5+Td+PtjvTk9Pp86Ny8vL7+QU+qH+MuoDAetDfai/4EJV0u12tbm5qbOzMw0GAyMicHjEBQgigfRqkwAEAajDRQQAEEb23t6ems2mWddfXFyYrSQMfwAlDq0oyZLJpLLZrFZWVhSPx82WX5Id8Ihlg5AFkQp3HYB3bMDj8bjZo+ZyOfn9fnN2AdTDTn52dlbHx8fmenV5eWnxDDiA8Jmvr6+VSqUM9HMehGgcKpWKqXZRqWYyGSMyoQblEAqQ4fF4lE6njSAG2QqCBa43DAJuFwQNFPvz8/OmxICocnl5qaOjIx0dHSmdTk8NEnq9np49e6bBYGBqO5QBWHUGAgGtr69PDXDd7lexjzQ8Ho/HbDUZQjoV3dfX1zo6OjK3kFKpZM0pgy8IWB6PR/1+3xwqcrmcvF6vOp2ONSa5XM4AMGxEcT2BBPb48WOtrKzYYTSfz+vw8NBUlFdXV0qn0/YsTiYTc0QIBAJGjMrlcnYfGo2GXRvcZ9xut/72b//WCEzEOJCZjjpyZWVFg8FAlUrFVF9Yt+dyOT19+lSxWEz7+/vW/Dqr1WrZgPauQ7STMJlIJIywiOsPZIDV1VW53W5Vq1WL9wKoh5ACmIjyFKCS+Mbz83MlEgkjcqEYAnBcXFw00BZQvlQqaXl52ZRvKDedTa/0CrTGtQn3ECdhkwJUPz4+NkCcmIZ6vW5r0sLCgl68eGFubc4sb1TCHo/HhgG4P+EMgCsW5Crc3pxkDeIvGY7jbMEzEo1GlU6n5fV6Va/XlU6nVSqV7rQpbzab2tvbm3JtYmDC+uf1eo3M6na7bQCJOxJOZ5BZWJsBEHiHUag41Xp+v1/Ly8tKJBLK5/P2jgP8zszMmMpqOByaUpYoVumV8utt6teuI97rj8k8Z61pNBqmKnJauksy8Pv4+NjW8lqtZqRjnjH+h3tGLBbT2tqaZmdn1Ww2LY6UdxarfOL4GOAnk0kDGQClnVFETpIGLh2BQEDNZlMHBwfa3d01lZ2zUN2i7iU6lX3RGQ+BcwVDPo/HY89qqVSS2+3WZ599NkXYJY4LAuL19bWeP39uTjz1el3n5+dGUp6bm1M6nbYBALFeDHRmZmbUbDb1f//f/7f6/b7++q//WoFAQHt7ezo8PFSv17PhLteYvQ+nMRTR+Xxey8vLevDggSn+GYzy9y8vL7W2tmZkPAYgt/dMQM2zszMjHw4GAyNhXl1d2R59c3Nj6qi5uTltbm7aXsyQ45NPPtHp6alKpZKRtp4+fap0Oq0f/vCHNrj4OkNjVLKo+CBKSjIr97OzMx0cHMjlcllEKzEpTlJKs9lUPp/Xzc2NHj169I0itv5SKhQK2XCr2Wyq0WioXq9b3DYqTgZ27xpNhgMgpPFyuWyOehDjZmZmLCbUGa3Ne8eQ823PC64X3Pvvi4AF2ReV4ZtAKoBoyCTv+iyyvhBlsLa29ppCOxgMqtfrWcRzqVTS5ubmW382Su5ms2nnSyoajZoA4X3Uzc2Ndnd3DSAGBE4kEkbUQRHK2cnr9Wptbc36tb/PlU6np+Ime72enWUgUfp8PnMbvKsgotxX5+fnqtVqtubS77FHQmJ/W72LYwYuv/1+/52eMZwhWU9isZgN9Hi36M8jkYj1o0TB497Q7XZtT93Y2LCBwNXVlf72b/9WpVJJuVzOzvMM4yD+zs/P65e//KWRgu8r+tlOp2NOV8lkUuFwWIFAQNVqVYVCQZFIRFtbW7a3Q6yJRCLK5/P65JNPVCgU/ujYEs4I7zrYHo1G5jCGUyKAPGdrnJbm5uasN9nf37eBwsLCwtQ9JRYJBzOGwIiVRqORPB6ParWauSAznBkMBup0OlpfX9fDhw+VTqdVq9V0cHCgly9fqtPpqFKpqN1uG/HI6/WaEI/75vP5TABGRAdEZCeRH3U9RCJcc3kHOPv7fD4dHx+b+3M8Htfy8rINPSGen56eWl9QLpdNUIfgweVy2b3hHMs1dblcNqiEqIibOv0j/ShDFCLpcZXrdDpG3MWlkuEU5yknWRGHeM7GdxEbiByHkIZTOkQ2Pgvu1pxjIcENh0MTNELmkV7tqU43O86UuVzOCA7lctmer3q9biReCHzr6+va3t6W2+02crbX6zVCENfg5ORE2WzWyFC4Inu9XqXTaXOgQ8yBe7n0qkeEuOwkYEmv9s3j42OVSiVVq1Vz6wLXoW8BIwEjjEQidqbAdeCnP/2pDg4OtLOzYz0XJAUiZSEh4PJUKBRsnaQXjEaj5irW6XSs96UPYO+H/Ab+2O/3DaPgWYAU2Ov1VKvVrDdGsITLNr3nX//1XxuBend3V9vb23r58qX1md1u1zApzu8QyHhfcL6KRqPW/0WjUfV6PXM7hlCHwI33h16NMyjCTPr9ZrNpbsTsbcRpSq/6bFzLwIVyuZwymYxF3nHui0Qir4k2v60iYgpi511nZhxyDg8PzZXuXaOEnDGfRHS+7wLLpnD4e5eix+/1eu8UKRWLxWxdOT09fSNh6+sUGB/kQydGFQqF7Dl535VKpfT//r//r4nD1tbWlM1mXxMp4zDndOVzRli9rXj234ZTQYCFIIRbt8fjUSqVsjW3WCxqfn5ey8vLU88s/xZXvN/97ncWI836wT4RjUYtrn5ra8vcw+kpeFc5u9124nLW9fW19vf35ff77XsSwYU4kH1qZWVFPp9Pa2tr34sjL1jjcDjU+vr61H8jVQMRNWcnBMbEKRP9ibu/JHvPnc+Ox+PRwsKCEYza7bY5WSG2xQkrl8tZZCTFzK3f7xtWflchYopEIjo5OfnWovneR83Ozurhw4eqVqvy+/1Kp9NTDnZer1fRaFQLCwt/FKb7TQt8EtHSmyoajarZbJpD3ndFwHK5XNrY2LBnrl6vW8ICs0GwaFyRnEQ8zoEnJydTOIrP59PKyoqdLfk5kDyTyeQ3csr7usXckM+Gax01NzenBw8eKBaLKZVK3Sk6xD0OTJp3kXOP2+3Wzc2NUqmUiW55BjOZjFwul0WiQ3rk2o/HY3322WeWruN2u+0ZhqhN5PvOzo4ePHjwxnVU0jsRe+l/uW9O44AP9ZdfHwhY32ONx2MVi0Xt7Oxod3dXu7u7Ojw8NIX006dP9c//+T//o372Z599pn/7b/+tKWxwBfnxj3+s/+Q/+U/uda74UH9ZRUNNBODBwYGSyaQODg5scIFScG5uTjs7OzbgSyaTduBnQyOiqt/vm+3saDTS/v6+Tk5OJL1SDJfLZVMmSLLYHIhBkoxsFQ6H7YBG9BoNCsATA+ThcGgEEchTAGClUkmRSEQrKys6ODhQoVCQ1+tVsVi0jbBWqxkQAZEIchOKPFTOKOzW1tbss6O6DAaDBmg6bV5povf39+2Qz3CXgxMMbkgZZ2dnZs3Pph8MBpVMJm34Mjs7q+XlZZXLZQN6bzeNuJ0wkOZeQK4rFArWZNTrdRWLRVOE4AQF8eby8lIrKysGlEFaSaVSKhaL+ulPfzp1EMRNp9FoqFQq2SCb++880ALwMWhClRmNRtXpdMz9BbtaPh+kFSzFz87OzEFCkgFwDEna7bb8fr9OTk4MgMzlchZxkUgktLm5KbfbbTEca2tr2tjYsOeyXq+bJf7FxYUNIXA4wUmG6AXyp4mO5Pq1Wi3Nz88bkaBeryufzyscDtv7AADMZ0bNBtjmLNyJcFu5q6LRqFwulw4PD83ta35+3tj3S0tLRmBwxjDhihUMBrW3t2egOGAtTSGuZDiEoepZWVnR8fGxOp2OrTEMyiBjzM3N6cWLFwbOer1elctls2HlcHt6eqp+v28OeP1+35RIt2s8HtvArNlsamlpSdfX1zo+PjZQFFVet9s1pydJ5pbU7/ctagM1lCQbsECimZmZMWAC9wTiXQA8INSh2u73+7bWMvzDNY1oDGej6oxaIpqBwzkgOjGlFxcXRv7BpYxYJxTOZ2dn9r0Avvl+qLuJ+by6utJ/9B/9R/Zu8j4tLi4qGo3q6OhoipyJ24Iz+oKBTqlU0sXFhcVjOUlAzvcWtcofA25CSGSNYtABWCTJXNpQRzNIdLvdFsnAPkVcG1EwV1dXCgaDZtn82Wef2dCBQUo6nbbIsIuLCyNbQXCUXjWiRON5vV5T5QO4JJNJs68nFms8Hk9Fx0FUIl4mHo8baQh1tCQbGgOoR6NRI40SnQeIFYlEFAqFjKDH0BZFUb1e14sXL2xYxe/BmYq4vVAopEAgYOSuubk5G9xdXFzo6OjIVF64y4XDYVsrQ6GQOTPwHLIe0gz7/X4DEnK5nBGzARcBVPk5lUpFjUZDqVTK1iMiMhic5PN5I7zs7e1N7c8MNHi/AQf4fMT78Ayk02lT4BLH1uv1lM/nv/azzfsPWBGPx22gwRlpf3/fBkyRSEQbGxtT4F8oFDIXA9ZYSM8fSvaOezweG8QRQcveDxH1XYFEzlU845DnGMTNzMzYfsY9YQ/hv7HmvK1QyzOM/r5qZWVFL168kN/vV7FY1JMnT+583p0gF05w7wp44gw2Go20sbFxr4qW9cvpPPQurm/cZ84OAOLvW63rdCl6+PChiSYg0OC45/V69eLFC8ViMT169EjRaFSrq6vv9bP8uRbOrzgY3o4rcTqlft06OTmxqDxIQxSO0PQg97kJ31X3OWYw4HaS+d/lZ00mE3OUOj09NUIGghli6cPhsEKhkJ2TENhIr9ycM5nM1DC6UqkY+YJYKYapOCDxnXF6YJ+8ixwIgYt9NJ/PT4kWFhYW1O127R0NBALy+XwqFAra2Niwvh6R1u1ryADjbfcBJ4NWq/Xaeft2DYdD+6wrKyvmLNvr9dRoNAwLWFpa0vn5ucWwQWyYTCZaXFxUOp22z4XjJudNIpU5wxN1gVocwcX5+blF5ayurprbkdvtNlESYp5er2dn0Gg0au56oVDIogldLpcRTQ4PD5XP500Ytr29bbFhuL0RhQX5vdlsmrAMBTpOZRsbG0qn03bGPTs7s6Ej1wWnUs5XRIfQ6zMwYlgqyRwkiduGFMW7zvMUi8VsP2RYGolE7PqmUilFo1HFYjF7t4mlgmjB4Pro6Mh6W+7V7eL3DIdD7e/vG4bixJZ8Pp9WV1ctwo99GqI2ZAUcsQ8ODkxEdXR0ZK70iOzAF1wul0Wycl7HvQhBH85pkswJn/4UQQHrQSaTMfwZnMbr9SqVSsnv92t/f9+EUAyNwFs4sziL9+Xy8tJ6c5/PZ4ImcA36U1wGnCSi9fV15XI560Nwy5NkQhAI3xA/Tk9PFQwGTTAKvsNaCKmKXpgBndNFAZcT7hGEDcQqOJfyexnin5+fa2dnx/C4mZkZLS4umsDrZz/7mY6Pj/Xb3/5Wh4eHRjxEjAN5DKwUEYaTVIg73PLysjloIspyvv9cT5/PZ8/y3NyclpeXDasjLSCZTNp9oA+jcJLGNYm1DzL4xsaG/vqv/9pcTiCufRc1GAxs/X1bFJrP57MeMRQKaXFx8c79eDwe2/7Z6XTkcrmMrE0iA9HQ71p3xWS+r+Lsj3j3bcU7COHyfRSRe5CHnA48EBZPT08tNeHr1NuG2TjvskcjfAF/YQ9FsIurfbFYtP79XYph/X1uqdKrcwOYl/Na8O5yTpNenSVvx8s719FCoWCYzcnJiZaXl18j5SAud7lc2tvbs/Ujn88bqRRhMrghYmN+L88CjvutVkuPHj0yQjN7J9cA90iPx/O9iUFwqwfHc5bTAQqXWMhBCKhxRVxZWTEiaqlUsrPFbVIZuC3YLVgyYl16WXBEHFxnZ2ftdyKcvP1ZWdvp/Vmj3G63OZ6B8b3penOvEUB+2+V2u7W4uKhcLqfulxHq9MzRaPQ7+Qz3FeQ8Z69yX83MzCgQCNiZ/7ssUjjAi3GA5TqGQiF5vV7Dx53Feefi4kJLS0uv/ezbz3E8Htfx8bFubm50fn7+TvjS+6pwOKzHjx+b4IOzF/jVm0RN7C2kdTBjkGQpL90vYwQ585Da4jwPICZACLK5uWkkQnpDznSI+SBxzc/P2/x4ZmbmNYcup1uZ0/0R13zmcR/qQ1EfCFjfU/3yl7/Uv/gX/+K920Sen5/rX/7Lf6l/9+/+3dSfQzA5OjrSv/7X/1r/5J/8E/3n//l//l5/94f60yoOzyj4AMJubm60srKi7e1tAzWcCkdIVpeXl6amwUUKIHBtbc0OnuVy2WKaANPi8bhZ/rpcLnNb2d7eNoVXIBCwwyxDckBDmkIsIIl4oolIJBKamZkxMGQymdgAIxaLaWFhwdS/DLawhu31eup0OkZqkl4pTnB8AkT42c9+ppWVFaXTaVWrVVWrVS0tLWk8HuvFixcaj8fa39+3THCcSCCNYLNJczgcDhWJRAz0gOCGYwXXAxXdZDKxQRWxTslk0iIJl5aW3tjQD4dDi0jCkpxmFDtTnhOckU5OTrSysqKHDx8aoQXSG64CKDzIaCYiAXLJs2fP7L46naG4n91u1+LRIJNhkQ8wyf8LSIa6otvtWpMyHA5NnQdI63wWcF1LpVLmpBQKhQwwffDggT13o9HISBKA4IlEQr/85S91enpq1x7wV3p1cIawQySgk2B0dXVl7mAAROfn56YUvrm50Y9+9CNz3uFzoHyaTCb6+OOP7Rl2ltNh477CwphGBGcqHJkAeYPBoEVFHB0d2XdfWVkxkshwODS1DzbHzsOz9KoZYLCCwv82IMtAYGdnxwhMvV7PGtBOp6NisWgqYZzmAJWcBB9JpmTe3t5Wt9tVtVo1l7fNzU15PB6dnZ2p3W5rdnbWAIhisThFaqP5DYfDBpYQe4LqC3CU74pydGFhYSourtlsmqU4YD6uHOFw2IgbHMaJKoGUF4/HlUwmtbu7q2KxaLEDWOLiLgVpBtckgEgGUcFg0OIo+P64YwFQzM/PKxKJGFlrfn5e5XLZyLEQ34itAFDI5XLmXvTLX/5Ss7OzajQaurq6MjDo8vJSh4eHOjs7U7FY1OLiopaWlqw58vv9FsOUzWYt5vSPqdFoZARDmv/BYKDj42PbIxjsQEbn+0Ic4r9DhiU+AfULpDXWu5OTE9XrdbXbbYXDYbMJhzDJUAZSFw5I/X5fmUzGnvnRaKTDw8OpqDJiPc7OzlQqlYzoy/PJekek53g8VqlUMnc2Sab2u7y8NEAe0JzBxPn5uX1eGnnWs/F4bGpf9oKrqyutrKxYZORoNLK/w3OFSrterxsBESc6PpvL5VK325X0Ki4CtzbiaAD2iSNxuVw6OTmxqJ3Dw0NzcES9GwgENJlMzIEskUjY805kGY5WpVJJe3t7mp2d1YMHD8wJr9/vKxaLmdscKlDeHZ6fSqVi+zv3kQI8Y70huqfX630t1Vm/3zcHhLm5udf2e4ajrOVXV1dmt327XC6XUqmUOTdCzPzggvWqYrGYReh0Op0pwJq14utUJBIxJwxJRtS7Db7iPPKHP/xBKysr5kDL/96VUHVzc2OOl99XMaDv9/v64osvtL29bX2DJBuMQhy/urrS+vr6O1uu49jR/TJm+21ElXg8rr29PY1GI3U6na819GG48G0Vznw4ETG0IYoShwq3222KztnZWa2vr79zdOn7LoQPvBv3Rbt+l+V2uxWPx99rlAKOgtVq1Zw5M5mMuTHSP6KInZmZ+VqCNnpEiDAIbiQZ2fttxcCMczaubQyCIP9CdoHUR8/y8uVLxeNx/dVf/ZVisdhrpD7ne3bbHYOhLb97YWFBR0dHRv7Y2Nh47fM6exzO7861EMcuiCE/+tGPdHV1pb/6q7/SJ598MhUvQ/V6vSnHFukrIQX4wO1KJBIWo1WtVl9znnB+x0qlYsMPxBndLyNkUZPjtnNxcaFAIGBxEpyLbn9u3LchS0P05loHg0EjctMnM1CtVqvKZrOKx+NTUdMQBYjqq9VqJhhxuVx2VsNVcXd318RtbveriGhEevSxkuws12w25Xa7tbGxYVHH19fXdv4ham53d9ciyKRXvR7XzEnMC4fD5vjE2QlX1YuLCyPkc27GsRcXZ95DyIesRwgD6H+Gw6EODg4sHhGSI8SkcDisQqGg09NTc1JqNBr2nTn/LiwsmDDwrup+GWuHAJB3mzUCsUc+n7d7CvEL7AsX3qurK718+VIrKyvWs9FPX11dWfzu+fm5Tk9P9eDBA+vr6f8QP7KuMFiEfISIDfEaTuROMRz4k/MsGQgEFA6HLWrI6dpJ3T5/XF9fGwELZ49KpWIEOP5+KBTSzMyM9YJ+v99IfsPh0D4TA2ZEfjc3N1pcXFQmkzHXuUqlIknmZkWfs7q6qmw2a66mzueJ9QcSozNuFtIO7wnPGHFxYCMfffSRyuWyvF6vDcCdPSY44W9/+1uLvC6XyyoUCtYXXF5eWrz76empDf1ZM+j3IO+DY9LnQJYFMyUuGfd1CLmIEff29kywRiyiM37dWfQbfGcclcFAvg8XHOkrd5mrq6t3co/EIR2HW+cZZjQaqV6v6/j4WPv7+7YeIsqLRCK2BjYaDXMHv6vAVcFmIBGFw+H37gzmPPu/S5y30+HvfXyGZrNpYsrRaKRYLKZ8Pm84KHu1E5t4G9ZzdnamZrOpXq9n/Q1kWARPFG700iuBVDAYNDdHyufzKZFIGD41HA6NkPquRV92H2ltNBrp6OjIzoiIsCHw9no9HR0dGc70k5/85N7rz7zi6urKxLi3n29EWK1Wy4R5n332mXZ2diwBgD+XZNhYv99Xr9dTKpUyp6Crqyvt7u6qXq8r+mXUusfjMXEn51L2JHCZdrv9XiMI37Ugw91eq0ajkcrlsv33XC5ngnn2D8wCcrmcrq+vtbKyYgRbSRY1e7tCoZA2Nze1s7OjfD6vdDqt9fV1i/0khvnFixeWypFKpcxVX9LUc8ve5EwIkb6KLNzd3ZX01buA0DAajb72DkgyoSIOlncV5ynOsvSkahoXAAEAAElEQVRyyWRySuj5dQon6z+lcq5x71JgmO+yfr7vmp2d1erqqgkhmLPyvt4nJuIZ52e8rYhTvrm5+V7EerhS5/P515z57hPDNBoNlctlHR8fW8pRNps1MfnOzo6RKWOxmM0S7ioMMnq9nvWN9FvtdtvImPQxOJM6cdJarWaCafb+y8tL7e7umkPo2dmZiYLAfufn57WxsfG94Tcf6k+vPhCwvqeiaXifdX19rX/+z/+5nj17Zn+2vLystbU1XV1d6fnz5+p0Orq+vtb/+D/+jxqPx/ov/ov/4r1+hg/1p1HEyf0//8//o729PXk8HjtYAyp+8skn1uhD1kH5xDAFRRhqNIAr1DawqRl+TiYThcNhU3o6QS+ifFCdzM7O6unTpwYy0qguLy8rEAjo5uZGW1tbymQympmZUSwWM1KN9OrAVCgUbHCNMtXr9RpY+PjxY3NYAViAUFEul7W7u2ssZ4/HYyDg5uamNjY2zLUC63+Y8o8ePdKzZ8/k8Xh0fHxskU04/aRSKWUyGYvH+/zzz41d/9FHH9lwF2ASNd3l5aWps25uboywwVrBgDkQCJjii4LkBuubKJ1Wq2UA3snJiQHgKC9w26rVavr4449NIURMIM0Zg2gcQubm5nRxcWG5xUtLS2q32/J4PNrf37f7dX19bY5NqGIjkYg1EM1mU/F43DLuW62WRQxAyIP80Gq1bECFgrnb7ZqiknxmFNs8jxCaYM87bdd5PqWvlGTSK7XoZDJRrVbT+fm5wuGwuX6hNIbUdnp6qoWFBf3+9783wh/gJ4D0/Py8KpWK5Xl7vV4dHR1NxQ44gTUsZO+yFCeaku9++3B+cXGhYrFoymTUrLjIAaZyb4PBoEVL8PcBIonJgPzBswT4BjmIaMdIJKJAIGBEBAgwXDcAVZ4F4kn8fr8NIjOZjK0xPPcoPGkgiEUAlG40GgZURKNRUwR3u101Gg1lMhkjCPl8PiN+OZ2QAN8hgLAuQNqhOTs9PZ1SRAJIn56emuKUPye6gPWR4cNkMjFQhkEM3+vTTz9VpVIx8I93jWcLFTVqawAlCJc4tPFcALB7PB4jsRElCfFFkr03uIShUj4+PjZFHxnyuJhByiNmBMUXzmrEkbbbbe3v79taNzc3p2w2q4cPHyoajWpjY+NrxbM5i2sLeADRLxqNql6vG+EEIuXZ2ZmBSjRstVpNtVrNSLOQ0xha9no9xWIxW1OIFfviiy+scU4kEspms1pbWzNVOspjcuSJRXK+s5eXlyoWi/rbv/1bxeNxG8QxLLi5uVG1WjWwC3LhZDKxSFziqljfIP+5XC5rMuPxuBqNhubm5lQul81Bi/UZ9x8AbZTM19fX6n4ZN8iahWMkDo2QaQEN4/G47RnBYNCGJajlcK58/PixkYohyrE/QvIm3gT1KAMuJwCOOxZ7mc/nUz6ftygRwPRKpaJyuayZmRk9fvxYq6urNlgfj8dTvws3MIhr/D2fz6d6va5QKGTkRNwnIaY6CcIoo74OAQuS2P7+vnK5nLa2tmxo5lR2AWLgooB75F0Vj8fN5a7Van1tJfJfcuHQ8T5s/4l/8Pl82tnZ0cOHD+8FmBgcQZTkHWKg73RQuaucboffh9U/5XK59POf/9z2psPDQ+3u7lqsMeIOhjI//OEPFY/H3xm8v76+tqGZ0331vmLfhsT/p1KcgZzn9/n5ea2vr+v09PQ19wAcSJeWlnRxcfGdxSNQqMWd8fOSLIYllUpNqen/nIqewAkGl0olOzcWCoWpdwpBRTQaValU0vHxsYlp7gNWnY6UtVrNhn7szTidoUjmjPWm68l5jR6GIRm9AkB3uVzW/Py8+v2+gfper9fEVYFAQA8fPpwiUnB2gDAgyfb/k5OTqeEjQhrOmrVa7c4IqJubGyUSCYvZJBoDNTFCq5mZGW1sbCgQCFjvd3svu7q60t7enp3JnPcOAmOlUtHa2tprQzTctq6vr60/gtCEq+3p6amRXxYWFpTNZm3AjFMT4hrO3jh8SbKeHVclCnI7Th24+RJnjdM3xI6LiwuLUYU0gwCL+9zv91Wr1VSpVKwnd0YM0n8cHx/beb/ZbOr8/NzI8ZlMxkQn0WhUc3NzKpVKajQatm7iFEPvEovFTBDH84BAZDAYaDweW18bDocNg8AtFjFfKpWa6n/C4bCy2aza7fZU7BcuRjxLKPs5q0KCwZUW96l2uy23263BYGDvE/eBHhVhVywWM0evYDCoZrNpsfX3DRchedFrQqhicBOJRNRut3V9fa1oNKpcLmfuvNxnsIlYLKbBYKBEIqGTkxN9/PHHKhQKJqTgPazVaharRHQg4i7IOcRr81xdXV2pUCgoFAqZgx2R541Gw87LxB7yc3Bp49olEgmVy2VzhFpaWjKyHOd9Z4GPDAYD5XI56zMQezrL7/ebizJrYbPZNKEozxVucTh2QxYFF0KENzs7az0YzmL08J1Ox/Ad1irccsAy6Bd5lhgUcg3575Ckg8Gg/rP/7D9TMpm0uFdJ5uzb6XTMyRbCZC6Xm3KuwKml0+mYW6Hf77fzvdfrNcIn9x+3OCJbnZGSkPDAaBGkdbtdc2Vgv+G+sA7dV85IJ0i/79sh1FlEjkIyQwTF3ufEid4FP2BdBEeiTk9Ptbe3p1arpT/84Q+q1+vW80JEvrq6UrVaNSyA9f822eH8/Fx7e3t2ziM6FZEfThsbGxvvhejP2ofj4NuGvOBUxGN+kxqPx4aBX15eanV1dWodIHYP5/RaraaZmZk7nSylr2LwGGSzv7tcLgUCASPMrq2t2XMItg65DZweQiECS3BOUjsg8L5rsWewN9wmoB4fH5uIJx6P27MBuQVM/NmzZ+ZWd9v1HQxrZmbG4oiJdnUSjhDX9/t9wyxDoZCJyHm+wKNwPme9RNgFDtxoNKYw1L29PTvb30U+pldpNpvfCwELN9nbjkXMd7rdrjY2NjSZTLS9vW0mFOFw2Fzo2XcvLy9f2zvuIyIxS/rBD36gQqHwWvwhuHgikVCtVjNRKPeN52Y8HqvRaBgezRkDIlyn05Eki0nEHXl5edmElghaJdncAAfc2+81KTHNZtPOPPQgGBN4vV6trq6+E5H1TXV1dWX3gXUPJ/Zvc6+gwMEQfd11NrldEEe/T1EimPi7ltNR+fr6+q2fnXWU9fD7Kua5byui06vVqi4uLrS6umoCDPYHhCKtVkutVksej+dOERDFHsnPJwVCkn7wgx+8cT+ORCImaGo2m0okErq8vNTLly9NIIyZAXPpVqulRqNhfeejR48+kLA+lKQPBKzvvaLRqDY3N+1/n376qf71v/7Xf9TP+l/+l//FyFcej0f/zX/z3+gf/sN/aP/96upK/+pf/Sv9r//r/ypJ+p//5/9ZT58+1dOnT7/5F/lQfxI1mUz08uVL/e53v1O73dbe3p6KxaKkVwfmUChkDOJ0Oq2PP/5Y3W53KiqlUCgon89rYWFBrVbLXBgAJp1knrOzMzsQ4g4EEImjEQAbAAYg5vz8vA1dUY5ls1k7qBHRiTU5zGWAPjaxYDBoA9N6va4HDx4okUhodXX1TqUNlvmQEQBpYClHo1GLIQkEAubiBRAIgO3xeOw7B4NBG/TiUlMoFLS5uanBYGAWmpLMRSYYDOr6+tqIKJlMxq4T0UUo2prN5tQAhMEBmz2RVHwG2N0onF6+fGkNMq4oAEd+v9/im7CgLxaL6vV6arVaBorOzMwom82qWCyaM5IzFisejxtYCZDbarUsmoAIBKL7iIZBFTQej+X3+1WtVhWJRFSv1+1AQ8wDzwqqU4BeCDSQFZzsfqeqAbAG4IwCiOE+j0YjU19dXFwYccDZcEciEVWrVe3v72t+fl67u7tmXe71eo30AhkGAmIymbQhDK4sNELEWwLcQkq67TQQi8XUarVUq9V0dnb2WsNUKpW0u7urUqlk5AbeEyfpJBKJWJQZRLx4PG4AIGsCjWk+n1cgEDDAANeY4+Nji7u8ublRPp83AiDXQ3oFPNI84izSaDTs2ctkMlNuZMQA4mDGPTs9PVWlUlGxWFSxWLTnnftar9en4s3cbreOj48tlhRFCeuGy+UyEhfRHEQrejweU+JmMhn1+30jqzFMhXwJ0AuxhcEAQzziWQFC+TNA3evra8sih+CzsrJi4HI8HjfQDKCc5xXVstvtNmU+imUiTVDD0YCh9IIkwnvCUJ97DKnKORhmKNLtdvVXf/VXRi65urqy9ePy8lLLy8sWR8paMxgM5PP57PM8evToa9n5O4vhYLVaNbcwQBAA0oODA/s8DIR433kOuEYLCwsWn8f9j8fjarVaRnLif+PxWNvb22o0GorFYgqHwzo/P9f5+blFA/KenZ+fa21t7U51jsvl0s3NjQ3UEomEnj59qsePH0852TGQ/OSTTxSLxVSpVMzZyuPxWGQfiuvJZGKDcvYarPilV6ANZLzul1bnxNtBImL4B7DPswpZCzIVrmbD4VDRaNQcUyaTia3VEA1+9atf2fCAaFaIVvV6Xf1+3xrhdDptP5tBablcnlIq8ewtLS2Zw1ypVFI4HDYFGYMbnr2lpSU9ffrU1iCGdBCBIZJyHT0ej/x+vxHAQqHQlOsAroAAqxBOcIzEtextNZlMdHh4qE8//dTcB1F4MahvtVrKZrNG9AHgA3C6j6DCkO37sFr/+1aQD71er7loQqyUZITW0WiktbU1Eyy0Wi0bTHMGeRNxr91um+r1XYhJ32Z5vV7943/8j/WrX/3KBhUMmnEuzWQyWltbUyKR0MbGxt87FzYIF84eRvoKlCT6iL3c7XZbBPjXGRa9jxoOh9rZ2bHBjfOMMDs7a1F83W5X6+vr31ns0PuoZrNpwK6T9NJsNs2N9L4zyezsrBYXF23I2mq1plyw6B+cYoZarWZkCoY40leq3NnZWduDiYS6q3A0XVxctFheyInOPsHn802R+hDeXF9fq9lsanFxUevr6/e62NCPcNbmOhE/zHrGn0G4OTg4UDKZtDPoZDLR2dmZRYen02kDqJ1DdXpRv9+vhYUF7e/vv/bdr6+v9fz5c7t3kIacsVxcu5ubG21sbLzWG+EALMkGW7iAO1XniUTCnEoPDw/N3cMZece5udVqqdvtyu12q1wum+OS02USUYIzBg78QZIRc3i+ZmZmdH5+biIeHMwQZyFIYp9IpVJTLliDwcCc0HC54PzudrstUhvhh1PcFggErAdj3WYP83q9GgwGCgaDOj4+VqPRUPfL+EWEJRcXF/L7/crlciaaqVarRv65uroyQRZuWsSv8P39fr9Fa3a7XRs+c6Zl7YQYcXp6akILzpxcR7AWxFL0V4VCwQi9RN3x/B0dHWlra0u//vWv7SwYCoW0sLBgQ/EXL16oXC5bn8+fE00/HA6NeNZsNhWLxZRIJMwFC0wmFArZuazT6ehnP/vZnRHRrDvFYtEEckSzMAwHs8CBnucUUmIwGNRPf/pTG14T30rkpNvt1uHhoWZnZ424ybqCWFF6JfLM5XIWzxoIBKbI68QOejweE+js7+8b6e2uCFAwQVx5IQYg+Jqbm7P7x7n/9PTU/tvCwoK5Vrndbh0cHGhvb8/6i8FgYIQH3h0cqxCUSK/wIAgUrE/co3g8rrW1NZ2dnVkf6fP5tLi4aMPTu9xH0um0xYWzfjv76U6no06no3q9bj0H2BaiK9x0IQ/hygsuCX6IgzPrFO8VfVA8Hrd3uNls2sAaVz9nfNZ9xfO1uLj4jQf2dxXPLH0bOMvs7KxhCQsLC/aOS3onV1+wT8i/0leEqUajoWfPnuny8lIzMzN68uSJuSnhapHNZi1Rgp9BX8rP2t7eVrvdtj0fURzu5Y1Gw2I1Hz169I3PwF6v10hirDP33bubmxt1Oh17379pDFWn0zEcF9eruwpyAftjp9N5DZsHTzk5ObFnkLMCosBWq2VubZubm/Y+0GtNJhMtLCwYDs9eyZogydZHMLt3rWQyqWq1auuU88yHqQJpEpzvAoHAFNmAaKxisWhudU5RBQTlUChkff94PLbUBEn2HLH3e71eu/YQ1tgXLy4uVCqVtLa2ZhjVr3/9a3NbTqfTymQyRtCcmZkx3L1Sqdg5COyecka+fpvxmvdVKBSyWG1ITRDbiVzm8z58+NDWfpylM5mMms2mSqWSTk5OtLq6as8ZTnhO4g44I8LOSCRyr4CNecX19bVKpZIRdj0ejwn2IEEijISUVSqVVKlUNBgMbD6cTqfV6XR0eHioo6Mj1et1rays2HqEECGVSpn7nLMmk4n29/fVarUssYaYbwiu4NKHh4daXl5WPB7/2u7fzOwwPQD/c2LR6XRauVzuWxHs4PDO3JLUlsvLSyNB3vV9cHUl3eDPpZzmEb1e7634T7fbNQzqtmMl53beZZINvs/i3T49PdXKyorFtbdaLXvXG42G9e87Ozsm8rnvPiLqkF6ttcPhUP1+37DRN5XL5TLcl3768PBQ3W7XkkNuO7pnMhl1Oh2b/+7v7+vJkyfv4/J8qD/z+kDA+p7qxz/+sf77//6/f+0AurW19Uf9vG63q//9f//f7f//T//pP50iX0mvDsD/9X/9X6vVaunf/bt/p8lkov/hf/gf9N/+t//tH/U7P9SfXm1vb+s3v/mNtre31Wq15PP5zNEoFotZBFq/39dkMtHGxoZ+9KMfSZIBQhsbG3r8+LHZhT979swcfRhgMuheXV2dUlbNzc2p0+moUCjYAJbsXJS+kizeC0WJ3++3JhIQ4+DgwA5LCwsL9t8Hg4GBgk5FFwfG+fl5bW5u2gC+0+mYuwXNDQQzpxVrrVaT9Kph+uijj6bIRThFAa4xuD47O7NYAFSQsLDL5bJ++MMfKhwOK5FIGKiP09ZoNFKr1TJ7ccBqXFfW1taUzWbVarVMAQf4jUtNr9czIgeqqmazqc3NTWs+S6WSMcWJ5gNwKZfLNgB4+PChgbrYMyeTyanmcGZmRn6/X7Ozs0okEjo9PZXf7zeiQTqd1srKirpfRkDNzMwYwSiRSBgZgTgzIqW4Htls1gYEgJkXFxdaWVlRPp+3gQnRdPwPwgTglfQViI2KjT+jAXM6Rw0GAwNQcYHiPqdSKSN4Oevm5kapVMoUsKiMIVMw0AfIbLVaajabkl4d/LBBpilwDj6IwCmVSioWi68drKPRqKlmIDrxHfv9vl68eKGtrS1TygOcoyBjyIsyqtvtKhqNanV1VXt7ewZAQd5wEiUYFKAYB0TsdrsWhRUOhxWPx+0e8/4D5IfDYSM3nJ+fK51O23rAQKnT6SgYDBqJq9lsKpvNam5uzpwEiMfg/YEAwdCbwQB20MlkUpeXlwoEAkYkhHwJiE9Tys8lohGnhMlkoocPH9oawGcEjIUECWGTwRBDON5lCImSlEqllE6nlU6nLSqYgRWkG6cVucfjUSwWM0IdkYiQVJLJpD3Po9HIrIp9Pt+UU9T19bUBGQDADGMYSDEEZpBCoYaPRCJTBAGi5gACI5GIAW6owLEwR/3lJE0CjqJgR+GC+49zPSIaFgV0vV43tTlDF5/Pp3Q6reFwOKWkXFxcVCgUUiaTkcvl0vPnz+3ZAFAej8fmMHVzc2P28jwv5MMzAGy1WkboglSIS9zi4uK9YKfT7YwokM8++8xAQ64b5KZQKGT3uFqtmhqbQYrb7dZkMpHP51M8HrfBSrlcNmIVACTDEBzhbm5ubLhFhA3PjMvlUr/fN9cHnj3U41je43ID+RUSJ88nLhGo1HF0wq0QcCCZTMrr9erg4MDW76WlJYsZxXkLQmUwGNT6+rpcLpf8fr9OTk6MhCq9GkA8efJE7XZbjx8/1tramkVNQEwmGofhBQQ1rkc4HDbSi5NEy/kIhT3qfOmVut+pdH9THRwcqFKpmKoYp4LJZGJkL4awt2MBIAa+qXg2vg+r9b9PRXQNwHi321Wn07F3zEmyD4VCdoYcjUZaXl6Wz+dTKpWyPeP2kAsXil6vp0KhYKTg77v8fr/+w//wP9Tq6qrK5bIGg4ENgqPRqMU13Ca0v63YU30+n1nRv6l4F1iP/lTqXeJinOrU9x0X865FZDxEf/omzhBONT1E5zcpT/+UqlQqqVar2fuDE1WtVrPY4mQyqaOjI3OhuV2QvCEl5/N5TSYTlctlczjpfhm/dnx8bE5skUjEyB44FDGw4HzSbDZ1fX1tezzV7/etZ0ulUkqlUjZUqVQq1iMjgLnt6se6g4vJXc8TxGGfz2drT7vdtpg64sIgX7daLes16W8hq9+OQV5aWlIul1OhULAzMX0YrkH0KPRAHo9HNzc3KpfL+vTTT1Uul9VutxWNRq0vAj/A3RSw2+126wc/+IF9T84kEG0CgYA5cnHWxV0pm80qFovp888/t77ytruHkwDG+f7m5kbtdtuGoTh03nbeuU0Uor8Ho4AYwrk7+mXMHPs+bqfEJF9eXlrEImeB0WhkDueQMJzn+mazqQcPHpjzNtefXgzXbwhtPMOIq3AG5VnHZY1BcSgUsmGbs/dstVpGpFhdXTUCH47GXq/XsCaEJjj/4OTBwARiA9eRdzkSiWg8Hht5BWdyhHwzMzPa29vT3NycxQMvLi5a34HDHfGEEH4gLSBeYoi+ublppB1+BlgLPSiEK8hmOJY5yVuIHSAa3C6wFz4f/R1rCP8e3Am3qLOzM4sFrlarU/GYEI2kr6IGT05O5Ha77V6xVzPUdrlcKhaLGo/HFuHtdKQ5Pj42nAfxVDweV/fLGEZ6NnoHvgMu5rwbuJDF43FNJhNzbA6FQoaH9ft9LSwsGCkVYUQ2m9XZ2ZnFF/IscC+vrq6MJM7vo7fhnrFuSq/6kcXFRRUKBRvI8+6/bXALCR2sDPc43mcc1bnGLpfL3HTPz89NwEdPwX1GhAVpjWsE0Q/HXgRFk8nEcDPn3oKbMu5/ODw4UwzADSaTiTlLs+6+zxqPx9rZ2THcx0l24nPjGJdKpWytxNnrTdXr9YwQRZ9ZLpfNwSISiajT6SidTts9ZY2v1+uq1+vK5XJKp9N2jdrtttLptBEdIF9lMhlFo9GpfTadTtue7XK5dHh4qM3NzW98zdLptHq9ng2D8/n8a+cW3Kr4Tm+7Vu9SnAHByN9UkIjphW7/fu7D0dGRYTPOszDCFMjB7O+zs7NGYgFT9Pv99577u92u7Ulfx70J3I2EiGq1qlQqZeR5sPzJZGLxc86IaT4zokdmF7ddbePxuOEs3W536ryFu9Dp6am5PRI3CEaQyWS0tLQkl8tlqSDMSIhrfPHihRFsILRB1pVkzzpORrFYTD6fzyJimVdIb469hODD7IEzFtjOH1tOMlyj0VA+n7f9eDQaTZFgifljrYJs7vf77e/ybDLH4buDe0OyzmQyisfjWl1dvddx2u/3mzMW+DXO6p1OR9ls1lwTIbhJMqJGvV7XxsaGnj59angZAnPI45999pmtJexJoVBI6+vrr30uvgvE6vX1dSObICK+vLzUwcGBfD6fjo+P9fDhQ3NWW1paeuu7PR6PtbW1pZOTE4tJRmALYTAYDNr9IR73fVW/3zdn3JOTExNWcM5YXV01caLT0Q1nYvCT9xlnf7smk4nNKt7Hdwd3RJDvjCa/XcwrC4XClBMZ8zb6OArzCs5230ext/h8Ps3Pz1sKBDGNXEfOSAjenz9/rp/97Gd3fm7E/Dh30l+9qyMY/wYC/NnZmWq1mqLR6J1OgS6Xy5xXSU1gBvah/n7XBwLW91Tvm2X7b//tvzWAZXFxUf/pf/qf3vt3/6v/6r/S//f//X+6ubnRy5cvtbe395qN5of686tOp6Pnz59rf39fvV5PT58+NZAJUhCD8G63a2q0WCxm1uPJZFLBYFDhcNgY87jyQHphyIjqhDgiLNidtsYccIm/AhA+OzszoDgejysej78WNxCJRAxA4XPShANyEUWUTqcVCoWUTqcth3t3d3eKoAC4HQwGVa1WzemqUChIejX49Hq9ZltPAcicn5+rWq3axgvogQLNGZFydXWlg4MD/ft//+/1wx/+ULlcziIiIDT4fD6dnZ1ZVAvEnaWlpSm3DNxEDg4O7D4SpUjc49XVlQHyjx8/1tzcnDlLoc7lZ3L9aLaKxaLdmydPntiB1QmoOYsDCOoh3LVarZY1W4B/RHs1Gg1TDzKApCnodDryeDw2dEyn01OWyJeXl9ra2tKjR4+M8Y9lbrPZNJIepCdJ5q7FkOijjz7S/Py82S1LMmUnKmaINgCWEKQAa+462NLE47ADoYVmCVLGYDCwxgBQBELM5eWlNTcMKXFkajabKhaL+vGPf/za783lcgZQHhwcmDL75cuXOjw81PHxsb2LKHgB5zwejxqNhiaTiZaXl00p9fvf/97+DkrB+fl5U08TA3B9fW3q8ng8rvn5ebtu8/Pz2trasvUHsB6XLEiLHKDT6bSR2ZzPfKVSUTweNzJYp9OxSFBs+j0ej6rVqjXNsVhM8Xhc2WxW4/HYCH4+n0/D4VCffvqpgcqQjXq9ng2msEaHUFMul41wCBgyPz+veDyuxcVFe694diAb4hwmyQBPGnlA6UQioWKxaKQZlJZEbPj9fmv6C4WCqXZRqbBeSDK720AgoFwup0wmY2AczwvRCvw7QH+AyHA4rHq9btF1Pp/P/q7TZUd6NZxlbXWeZVCldLvdKUV0KBQygijkoUwmo+3tbR0eHk4NGyD1QfgBZMdWfX5+XouLizo7OzMCFu9eOBzW6empJE0R2HAkg3AE6EQ0GANlhjg4mUmvADP2IQAFXAkymYzy+fyUgg1beBp9lG73EQYgQNXrdYvRhVg4Pz9v6mun68LTp0+1uLho7/HZ2ZnF6gG4Ly4uGmkB0g1772AwsLUD4iLqRwaTKAl53533HqcFBgSAjsPhcCq2CMIWcRsQLYkJkGTDVEkWz8MAwem+xXCQvTObzapardpQh+cwEono8ePH1uxCgnQ6G87OziqXy9m+wVkCEhrXA5Bkbm7O/j2DYVTRuI5B2iK+CaV/tVq1qNG3FUAIrqXpdHqKBB2NRjUajdRoNOza88xCvHoTsMO1xJ3pQ317xdAcJWEkEpkigUKajkQiSqfTNryHNOh0XMUhljUAxfnV1ZXF/i4vL39n3wtV+X3l8XiMNI/THX/+JnX+m8rlchnpuFwuK51Ov/HnoDSen583EsSfQjF0wEXhbQpjho/ENX9XValULOY5k8m8hlnQs+FEyhDqbcS477uazaZqtZqKxaKur6/NSZDe8uLiwtxQIS2vrKzc+bOcUWI4FzYaDVOZ4/YjvXJ/ZeDLHp1MJo0gRQ+FYw8xKpwB6KMZIt3c3Fifgup7MnkVxd5qtcytx/meEgeGOOU+YD2RSKjb7ep3v/vdVGThbdV0LBaz3md1dVWJRMKIUfQKiHCIyYO0xPPkFMJQnU5HkUhEbrdbnU5H/9f/9X/p5OREL1++tOFrJpMxJfLJyYkNxvje9P+4OdAHRKNROxfmcjmLrYOUFAwGpyK2cCpcXFy8d7gxMzOjzc1NtdttxWIxI9q02217TnB0xonc6/Wa4y6EDAgSCAGIUcY1x+VyWWQdxFZEBgzRpK9IOjMzM+bSTd8BmYazDjjL4uKiLi8vFY/HtbKyosXFRf32t781UYHL5VI2mzVHEklG8MI9CPGW85nj73c6HRPzTCavYhQnk4m8Xq/+0T/6R+r1euZ6hVuEM+oRkYbTTRGxEsI8yPOxWMx6Dsgt7AH0VTs7O4rFYvr4449tSPib3/xGL1680LNnz9RsNq3H8fl85jzH0J5efmZmRuvr64YjUcRh4owGLgDGQIQiAhX6WHrSer1u0Z18Z+5DIpFQMpm0aCUcNRCO0Wsy8OOs7na7Va1WTTQE1oD7FntROBw20rTX61W1WrXY+Lm5ORNTQdYJBoOGISJMQmTlJAkFAgHD+fx+v7nGcJ4grliS7XnZbNY+JyIdsEyiyhgS4xZLD4Gwgnh0v99v7iW81z6fz8iouGFD6CF+sful4zqOfUTvsWcgbntTITodjUZGVJW+cs4Br+JZjkQiRm7m3IiTCFgrewIiSsgeYJEQrei7pa/6a94XekScvnGXev78uRGdIPNCoOd6rKysvEYSfh8FiQlsMp/PG0ZGbGe73TYXislkong8rnq9rnA4fC+xg953dXXVYn4ZTJPuwDW7K742k8lYRDEpCJCQIUDxDDoJXM7CdQgMlJSFb3q2g1AFWXl3d9feQQbUvV7P8CTWkG9a9M7vGn/OHnBbKMR60mg0zD3xrkJsValUFI1GTdiXzWYNT221WspkMnc+B6PRSMfHx5bW8HV7AxI/JpOJuQ+GQiGLAj47OzOCfDgcNsySQnDu9/vvdTrl72SzWf3hD38w4RXk0nq9bvgl+wOi3pubG1tjA4GABoOBTk9PTfhSrVZ1cHCgfr+vubk57e7umnMQBDhSP/guRMBGIhFdXl6aex+46H29YL1ef81llhkMpIVCofBHCWRwVby4uNDR0ZEJoPkdOGJ1v4wAXVxcVCwWUzKZNDw7HA5rZ2fHMBSwMtZUzh0kD3Bf8vm8raeITXHI4wzJLKbRaFiqy97ens1XIHNx/weDgba2tnR8fKxsNquFhQXD0lwul62BnI2Pjo4sTWR5eVmxWMzeAWch1ELIXygU7LsNh0MT6He/TFTBde3ly5dKJpMmUFhbW3tjb7e/v2/kSYj1zs9CZObh4aEkmWvb+6jhcKjd3V1zLw4EAioUCopGo6rX6zo4ONDLly+n3pVoNGr7CAk0i4uL793JDUyn2WxOzfV4Rphr/rGVyWSMZH50dKSFhYWpswjYdrVatTkDRKF+v6/d3V2dn59bohHPcCgUskSC1dXV76WvB6MOBAJGvIKknslkND8/b8RR0guazaYikYiKxaKJcSnOprlczt475hpvil12FuseM9h+v6+rq6u37qXxeNyETMxuP9Tf7/qAwP+F1C9/+Uv7v//RP/pHb1zQU6mUfvCDH+izzz6zf/uBgPXnX6hgu92uVldXbRgI6IgDUigUsjgiFAQ4/6RSKeVyORWLRTWbTR0dHRnw9uDBA7Ns/P3vf6+trS0NBgOLmBuPxwa8OMvr9crv91szyvAXa954PP4acxgwNB6PTykAaK45PKJsTCQSRnyiKcIBR5IpHMbjscUeZjIZDYdDPXz40AaSDDIBjqloNKqTkxOLI8AKnwNcrVYzVSskIQZp0qt3DgcXJ+gNkcjv9+v4+Ngch3BPYnCHQ8rc3JwuLi5ULBbtAAnxRvrqUMK1ODo60nA4NOvRYDBoxIqrqyuLY0Id8ezZM1PN3XeggIxRr9cthgD3Fq7T+fm5Xrx4oZ/97GcWm1Cv11Uul42kUqvV9PLlS4XDYfvcKARh319eXur3v/+9fYeHDx9aNAgW5U4F3uLion1/nM+w2ceuGhU3qi0IE4CAe3t7qlarKpVKmp2dVT6fvxNIAZSC7AZ5BrUI0X8oNpvNprm+YCldq9U0Ho9VKBSUyWRM4ejxeDSZTFQsFvXgwQMD5pyVTCYNxGi32zo5OdHBwYH+7u/+TgcHB7q8vNTCwoIpbTjosw7QbMXjcc3MzKharRqQS1QYzRk59rjJpNNpiz5ETZDNZlWpVAyUvbi4sAYZohCA+GQyMTU5ZK92u21KLlwrIGmNx2Pl83kDeBk84M4AIQ33J4BiVL7dblfD4VDlclmBQEDn5+dToABkM6zkUZsDYqCMLpfL5pQlyRzv+v2+Nc00vAyPIPJAUkO9Xa1WdXZ2plQqZYNOQNXZ2Vklk0lTX+PCwPuFYtfv9xupol6vW1wjJK7T01O7VqzRNCtENUAmGo/HFsUA4QkQhr8PCNr9MhKEISLljL9wAmhEQ+IkJcnIkexBZKRDPoLssri4aApAYpqIAuz3+xqPx1pbW1Mul1OlUjG3AlR/EAakV0PQSCRiz9np6akp+lGjNxoNW3cBllGxM8wHBHn48KH+wT/4BwoEAgZcZTIZI93gXrS9vX0vSI2z2osXL8zRCpCWtazb7apWqymbzZpyNB6P68mTJ0qlUmo2mwZeQsoh5gZlHy5okUjE1mgGGgBgDCggV8/MzNi9YxgDiQk7e9yueM5QQzKU6/V6Wl9fVzQatThN7hv7AINbovqIDMBNAhK4EywNh8MWUcPgk/L5fFpeXjZVJKDC9fW19vf3Va/X7Z5Uq1VblyCn4kTBu+v3++3ZRUFKbOHl5aUymYwSicQUMZr7//nnnxuh7W0RGfV63QjguVxO9Xpdw+Fwau33+Xz2jDHQJgoW9737itgBYhA+1LdXTiLnysqK+v2+PffOmBjOkpD7WT9R5iGsub6+trWd6AKA/G87yu8uEBEgPZVKvabyd16D96Gyp3BogJS2tLR0J/hP5BnxOH8KzmAUpAeIGm8aYDLsjX7peOqMePo2CwfSdrtt0TZ3FesUZJdms/knva7wnjUaDXsvb197BjuQmyHb3jVgdyr/cbgtFotyu91aX1/XzMyMdnZ2JL0a3EUikSmCNYRySRZvTUREPp83JxfOc0T+ERFcLBZ1dnZmhGgEK5AHLi4utLy8bN8RZwYG6feRBlKplP7whz9obm5OR0dHevLkyWvkK+nV+Rd8AOLHgwcPjJjDWsdAZn9/3xwecUa6vW7U63UjXDebTT1//lzHx8fa2tqy8ywCCwhMnLulV2T/drttPS0R33xW1syHDx+q2+0qHA5rfX39znfw5OTECPRvcwDwer1aXl5Wt9vVysqK9RsvX740IorP5zMyHfcWzAFsYGZmxpy2c7mc3G63lpeX9dlnn5mTBGcTJ04ByWw4HCqRSGhhYUGlUskIOMlk0pzFIAZyXuFZ4NyPm9mnn35qf+fx48evvds42Ho8Hv3iF7+Qz+dTqVQyV3VJRhTHgYqIm7W1NQUCAVUqFV1dXWllZcVIFMViUZ9//rlhMTgiSTLxF+6wHo/HSOWcHyHEu1wuI7EHg0HNzMyo0WhIkokHq9WqIpGIqtWqfv3rX5ugjMEPhBQIi5eXlzbAPz8/NxHH7ZqbmzPHltFopFAoZNHx3IN8Pm/vYL1eN8fgyWSi7e1tlUolJZNJG2SGQiEb2iFQOTo6Mnf7RqOhQCBgRDX6W3peCHkQj8DFwuGwnek5Q3q9XsViMSOKnZ2daW9vT9JXTlAQGPn+g8HAejkcQLvdrq2BuMuFQiFz82DwBbkQt6ZGo6HV1VW7b5JsOOqM5zw6OlIkEjGsDZKN00nZGfs7Go20tLSkbDarZDKpTqdj0Y2xWEzD4dDIgVdXVyZWzOfzRo4DLyCW8F3IJ07XWchsrFUQHCAN4PrHYHw8Hk9FxuEcCCEX5xbWOVy5er2eAoGAnUXBdd1ut2ZmZtRuty2OELdsoqOIet/Z2TEMF+wwn8/r8ePHisfj7+zs+651dnamXq9n4rfFxcWpPQJyaSgU0uHhodrttrlw4x6eyWSMVC3J3NVwfg8EAoY5Q1qEFEmk5V37Ac86nzEej5t7vyRzJkIs8KaC5Hx+fq5ms/leruPy8rL16gg+We88Ho8WFhbMbXdlZeW9OtC8q6MyhOvbvxunw8FgcC/hnWK96na7Fr8bCoW0sbGhFy9eGJ4J5o0T59nZmSqVignUPv744699DWZmZvTw4UPt7++biIL9ejwe2zsBweQ2IYb3CDHwXY7VLpdLS0tL5l51cXFhRCYwOVxU2F+Jmoa4zZpEckc0GtWLFy+MrCTJfiakRvCHjY0NLS4uGk4MzgueCRl6eXn53vN+sViccplF8MkMBhyDPfSPcdkhQpjvghs+BFnOBQsLC7bfcpbDgAJMq1Qq2bNCMoLThRWBAfv11dWVOS5xPziP4YyLExaC0Xw+r8PDQ9tPiSmHUM/8CJfY271tNBpVNBo1snSr1VI2m9Xa2tq9vSGY7MnJiYLBoIrFos2HiOe7vr5WJpNRNpu1Z47fBZ7qcrn06NGje/sAhFHBYHDKXZPy+/0qFAr2XLAevavz0JuKGMl6vT4ViUsMus/nU6fTMbEI/QcEtkQioVwu916xCunVddnd3bUzl1Pogds/+M0fS8BFSDKZTMxBFqwaRzwE36lUSisrK/L5fBoMBtrZ2VGz2TRxEG5+rAcI+0lL+q5JQ06HPYxDcDB2ngVJHwArJjnj9PTU3ot+v6/j42PF4/GpuQUCEcTt9BUIFJz3hT0kn88bEZ5+420kdM78EN8/1If6QMD6CyiiAqgf/OAHb/03TgLWs2fP9F/+l//lt/b5PtS3X8Ph0OwZAfgp1LKoIU5OTuxAMBqNNDMzox//+MfGAPf7/eagMzc3p6WlpSlWOPaxo9FI5XLZ1JeS7owG4YDsjBDkAOAcjlM41/h8Pj148ED1et0O6qg+JBmoCBCBkgzlO4MaGP+oIi8vL41cNplMtLe3p0ePHikajRoZgeaaQgkG2Njv93VwcGCDakAOnGtSqZQpbYiGcrvdOjs7mxoiA14DbA2HQzWbzSmHm52dHYuuwUXKGeFIvF0ikdCLFy90enpqyt9arabRaGQRZ87DLuSSy8tL1et1HR0d6d//+3+vtbU1tVotA/EYEkpfuZ9wQMHiNRwO23fnoHR5eanf/va3+tGPfmSDfAgox8fHqlQqBswDPLtcLiNjQbCIxWJGHqDJnJ2dtSb2/PxctVrNAHu/32/KF1Q2u7u7isViBp7hTFUsFs1hhibl/PzcIqCWl5ftz24rtTnkSrLGGKcUSRbp1v0y4hJwDYCNZx1QcDweK51Om2IXMJL3+i43MoBOHH1+85vfmJInl8tZFjYKa0kGcDabTQOTw+GwHQ6JU7m6ulK9XlepVLLvAHFyf3/fVDeoK7HzR12EsxjPrDPXHkLX4uKiyuWyLi4uVC6XVSwWDYTudrvqdrvKZrP64Q9/qPPzc8XjcT1//twIOKxlmUzGYtIg6qBM5UBdrVbNiQwgcWZmxpT6c3NzGgwGqlarmpmZ0aNHjxQOh02lDBjdbDatqWe4hHMPpNL5+Xlbr3q9ntmBQ5oEAMARD5INBBYGVxzWIbgtLS0pnU7b7yOqgvjXk5MTUyKj3qLZlmRRdTwTqFxQ9Dldt1i3cJJCAYYDD2CvE3xGDemMrJBkZAIU60TUnJ2dqdFoaGVlxZwJz8/PDQxZX1+X1+vV0tKSPU+AFuxFH330ka29qN+IZr2+vtbW1paB2UTMoQDv9/sG1qDqll419JFIZOr7ttttdTodc2AqFAra3Ny0pt15HRgwYCMPgeyuGo1GevHihQHmDNxw5mB/azQaKpVKCofD+vTTT01Fvbi4qLW1NX3xxRe6ubmxz9xut831gJgUni9iblDysn4BBEHYYnAxPz9vRGic85LJpBHPWGtnZmZ0fHwst9ut09NTBQIBLS0taXFxUcvLy6pWq0bKQpV7eXmp58+fKxqNTqkPcfZiUHibqM0w9eLiwq7tcDi0SFTIzJxHxuOx9vf39emnn5rqKxQKmXqMSEzuJ/E1uPiFw2EbTDAAgTQKaQv3B8Cr8/NzUyWzdtyn7CUmuNvtWmQmQ7XbcSEMRM7OzkyV6vP51O/37wVJLi4u1Gw2zbHwgwLr261YLGbqeIh0PDfEN0A6393dtWiaZrNpz7zzfSXik4EULi4owr+tOj8/197envUMkMD4bADU6+vrSiaTSiaT7wVUvas451xfX6tYLGp/f9+IoM599OzszOJf3pfK9n0WLq+dTufeuBgA1cvLS1Nsfpv32Vm4lvX7/bcOw1ATQ+x5G8n0+yx63m63a1HIzqLH6na75uKEOvgushK9rMfjUbPZNAeC5eVl6yOvr6/tjMo5DmJ9p9OxYQVD25OTE21sbGhtbc3OZpAuGaCUSiU9f/5cXq9Xi4uL5qBzfHxsziwoheljeJ6ur68Vi8XeOGzgncZBDpEV5DCcbIfDoeLxuDkJj0YjtVotG/Q6C/LX9fW1SqWS9vb2pnoTzstXV1dKp9PWd+3s7FiUH0STYDCo8/Nzi9XCpQkRzfn5uY6Pj811iV6AzzgzM6Nnz55pcXFRGxsburm50cOHD19Tv3OepV94W/G7IPzQU3U6HR0dHdmQjihxHE3pJXHu4fzEwAZnG/odHCr4zjh/tNttO6fTHxAR53a7TVwBMd85LMTliTMU+w891ZtIJjwruMDizkC8jSTr/T0ej54+fapsNiuXy6V2u20uUSsrK5qZmVGxWLS+jagiRAUMObluOICdn5/bvgmJjJ6JYSaCA+mVw99oNDI3iePjYxsOtlotEyPhREccSyaTsWeYGLz7rk04HFY6ndb29ra5GSFoAPeQvnLBRbjlcr2KjpqZmbFoyNnZWesHET7w7ONuQzQiz3kgEDC8yuv1WvzR2dmZRbjhKjk3N2dYG3hVKBTS0tKSEaXi8bgymYwWFhbMkcPtdhsJBWwjm82aQ+Xnn38+JWhi8JjL5YxYh3sueNrp6am9R04iC7hTIBCweFSuC8Klm5sbw+rAM6RXa/Xu7q65g62uriqXy2l7e9uiCYmc54zWbDYlfZVYEQwGVSqVzIUM5ww+w5vIx/QNfEbENAhhcapjYE4kKdcUwSRrkiQbxhNzRe/NwJK/RwQUBExwO9zdBoOBPvvsMyOC8TvX19etJ4TciJO01+vV5ubmez+T4CZzfX2thYWFe3++x+NROp1Wo9EwAQ+ix1qtpnq9PjWEnp2dVTabVTwe1/r6up1RWFfAIKU3uwgjmuW5dRLrzs/PLR7zbdeF9xeH6PdRLpfL3K2azeYUcRX8CbLS+yoSCXDgfNP35lm6K9aKYTbX5W0ViUSmsKtcLmeOmEdHR0YoPDs7kyTbC30+n1ZWVvTgwYM/2jV4dnZWDx480GAwMJwCpz0EtU7M9Xbx/f1+v2q1mhYWFqyHkWR7eb/f1/LysnZ3d21tAdN0OtkMh0MjHcTj8SnyIbhzq9XS1dWVCT/ZVyFY4AIXCoXU6XQMJwDzQixQr9cNZ4AQfLsajYYRqSFz48TG9wN/v7m50e7urh4/fvy11xK3260HDx7o6OjIMJGtrS3ba5klkAzgPO8j1mTfXFtbM0yE5xTsxefz2ZxHenUufPnypcWWjsdjhcNhw9tI70BUynwpGo0qk8mYUYITmyWS2OVyvZWQBsmZs8ebyI/EOJdKJeVyOVtvOp2OEQcRUIRCIQWDQVvfIpGI/H6/isWi7bmrq6t33m/wxTdFerrdbmUyGR0eHhp2/XUiQO8q3nFmYLdxrYWFBc3Pz5vT5eHhocLhsLmG4zT3vlOpzs/PtbW1pU6nY6YCiHqJqG40GsrlchqPx9rY2LCzIc/BuxbPttMdHUwXEwsEIqwjh4eHRj53ktYoCM1E3s/Ozk7FuX8XRR/KOYxkidt9GsR5hLLdblelUkk3NzfK5XKGrXJ+XV1dtXPY6empisWiBoOByuWypTNwpoIEjukEfXg6nbYZ8teJL4TA+qE+1AcC1l9AHR8f2wvtcrm0trb21n/jdLwql8vf2mf7UN9NAYCMRiOzA6dw03Da0ONY5Pf7lU6nlcvltLy8rHg8bpvRaDTS+vr6nZacAKHtdtuG/2S709RLssE3ikSajIWFBTukoq6QXh0We72eZmZm9OTJE3O44oCLIpJBPk5Yg8FAlUrFlCeAmDc3N9ra2lIoFDIVZyKR0OLiomq1mo6Pj031FA6Hzamr1WqZs4+zEomEjo+PzfEKZjkHHD5Xv9+3ASks7JubG21vb5tqkEgZ3DIgXp2cnBhJAdCSZmJjY0N+v1+7u7vqdDqmVqjX69rd3VU6nTYl0dbWlqRXDdja2tqdhwTAzNnZWbXbbR0eHppqEJDz9PRUsVhMk8nElAY0yalUSplMRqlUSolEwlj2Pp9P+/v76na7+tWvfqUnT54omUzasB7L7aWlJWOk93o9LSwsWJOE0huwAFJLuVy2BgniBOQE1IAoFLFlnpub0+eff27ECgb7kAAHg4EBwhw2Y7GYHZZwKFpYWJi6ftxzbPxnZmbU7Xbl8XiswYGYw8EbICAcDiuVSunq6krdbtcUpxAH5ubmTIXZ7/fvffdjsZhisZj6/b4+//xzzc/PK51OK5FImNr0ds3MzJgqlmgLnmeG/6VSycBxnLog5gyHQwNAHz9+rFgsZmpMwGnpVWMQDAYNhC8UCha9BMCABTtEFUh2o9FIKysrWl5e1sbGhhYWFtRqtezfEVXB9SdKBBDS7/crHA4bOZNml0YYUBnFMYMGLOnPzs70gx/8QIFAQMfHx9bEA+6n02ktLCyo2WwaaQriI0QeVIasiQxFaIqI3ITkAzEOxwVUWPv7+7q5uTEVNQOF3d1dU7ARgwfhDcUn7w5NHcpv1MgQZZxOZqFQyEAUlLeBQEDlctlIkQCY/P+laZWvs3D0g1RGBCTRqdls1tahm5sbPXjwQOPx2Mi0nU7HBnBcj8FgoLW1tSlQJZVKTcVuAmSjSkXxvbm5aXvO5eWlul9GSbJPOIEcGjHAmlgspnw+b2qiuwrlN6RM1I532d2jcmbok8lkpvYel8tlUTTlctlIppDQJJmaGoUP6xv3BKcm54Dq+vpakUjEgFMIqBCmIauFQiH5/X4bGAFIud1ura2tWXwi7j6ouhjCbGxsKJPJGOmViEqnDXgqlTISFE5RqMRx0mPoy6CPwvnhxYsXpvZijUPpFIvFtLW1pc8//1zFYlHValWdTscUpCcnJ3b2GI/HCgaD9jk4T7HGQniB6Oz1eo2UyXsEsfn8/FyLi4umsG632/cSsDjD4fjg8/lsL6nX6/aOOJ8x4houLi60vr5uqkbIcVjy49Tp9/tN7fehvt1KJpPa2dlRu922/ePy8tKGsKFQSL1ez/YyzowPHjywZ2wymZh7Js6TiURial/5Nms4HGp7e9tcXiG6QizudDpGwqhUKvroo49UqVSsp/g2wLpCoWAChW63a85c0leuXMvLy6aIf5dhznddnP3G47FKpZINWHDZYSA8mUwsSiGbzX5nn89Jyn2X6+fcj+4iYBH3xHmNs+63RdS7ryBmQy64XRDJnA4rONfdLkgTDHkQDOAoK2nKfYWeeG5uzpysIMpwhsFZESKC0xUDd+dPP/1U1WrVyFcIWYjzrlar5qBbqVTsDAJBYmlpyZxi76vJZGL7fqFQkMfj0enpqcX/8Z6hIs/n80ae5/xwu1wul+FOxFU6HTogLkNCCwaD5grKdajX6/J4PEaOB3/gfDUcDi1uDJepyWRirsl+v98cJCCeHB0dmUDjtnLfSXJ6l+Lv4di5urqqSqViP4ueA6dN3JvYE4jCgCQGAYM+mM++vb1tjskQv3HARGyGUzD9CgMy7g8ELP6HWABiLRjIcDhUMpk0p1KuId8Xt0HEcvl8Xvv7+3b25tyJ++rS0pJarZYJJxhE9Xo960fAeLgfkCjokxm84xQJcR9xDaSlWCymzc1Nra2tWbQjJAGn+wzXttVqTREE+A64bRM77Yy0g0wXCoV0enpqf98Z60ZUFv1DLpezs7TTTQfndobpuI8wWCIWEYfvQCBgggsG8zhdcW6lp9vZ2bFemL54NBrZ+ZhIU3o81gcEYuFw2GKIHj9+bO4SiGIgsfCeXl5emvBvOBzq8PBQo9HInKCJxEEI0m637Ux7cXGhdDpt53yiPOmleJYRWxHpFA6H7XM7zyyQTs/OzrS1tTXVh4O3LC4umjMyLuSQtxHOIf4bDofqdDpaWVmx53t7e1s//elPDS8FDwADkmTuFPV6XcvLyyYimZ+fV6lUsjXr9PTUzjh8L/YIegR6l0wmY2RpJ3EikUjYs4LQ7Pr62hxLcbaEBNlut414l81m9dFHH1lvDAkYcSBueUSFvs/qdrvq9XrmRv6mCofDqtVq6vf7SqfTWltbm3L7g4AG0dfv92t5eXmKTH3bXQux1H1kIie+MRwO5fF47LyD0OzrDGQRyr3PogctFArWIzjFoe+zEomE4SNOxxFqOBzaZ8AlyvmeUlyHd42z5Nrxb8PhsBGqwuGwDg8PjXCIa2IsFjNM8Yc//OE37k8QEEtSNpvV/v6+9vb2TEx8XyF+xt2wXq+rXq/bOQ0hF33gP/7H/1jD4dAwOX4GwtdGo2Hid3o0in0P53FJhrnzc5xuxqwvxWJRiURi6kwjfRWZms1mp6JNnb8P56vxeHyvyyykkUqlYqkbf4x7LmTmxcVF2zvAt4iZvO0uhAijWCwaRuUkoHA95ufnpxybMpmMZmZmdHh4aCQPSI3Odz6dTpu4RpL29vb05MkTSbJ0icvLS3Nel2Tut6z576twBez3+yqVSkYu4TogiOR9YlYlvXqv/H6/IpGIOp2OEbJvfz72ORzE3lQk7nDW+ToELMQszsjidrttQs77SFRg/k4cOhKJ6NGjR2+NDv5jajKZaH9/38hXxCg7K5VKqdVqaXt722Z64Mm4PKZSqXd27wYbQihMr81+6Iw6RDyDe+B9YsxgMKhsNmsGBycnJ1PGFN92cYbZ3t62z36X2IFoZpz9SScCkw2FQlpcXJTP57M4RYifkODZp+r1up3l5+bmlEgk7Kx/dXWl5eVlm1nTOzHfeluBjbzvyOYP9edZH56Cv4DCTlR6pQp4l4bIyVoHAPiu4g0+1Psvp5PUXQd/hp3JZNIUOriX5HI5PXnyxDZ6SDJEFzmL3xGNRo29vrOzo3Q6bYPvSqVi2btYRqLYq9frKhQKNmziQFutVjWZTAwsCofDisViymQyBgqh2MDlZHZ2VsViUa1WS8fHxyoUCgbEYl/NtSiXy+Yo8ejRI+VyOaXTaTsENptNhcNh5XI5A/YODw+VTCYVjUbNscBpc4kbAREP8/PzprIl/pCDAHbouG8lk0n5/X61Wi0Fg0GzxK5UKgYcJpNJU/d4PB49ePDArNkhjwEQotAAUISEdnp6qmw2a05EzrWBgfjFxYURUG5ubnR+fm4ORM64GcDZq6sr3dzcWJY6ZC2anOvra/udh4eH5gQxPz9vkWJEEq6vr2s8Huv4+Hgq8gtFDm41EL78fr+Bb+l0WvF43IB/iChExpEPfXR0pF6vp+FwqNPTU6XTaWWzWRtIE5dGs4slKcP1+fl5IxuhCKFQNUIQILqFoQ1gFQ5YDGMuLi7MWSUUCk250kFKQN2HCvZtRcPOYAbLaCcw6iwOlACRkqwBhGiHdbTX67WhsSSLIfP5fGo2m5ZlT9wWYB+AttOhiBhGAP7FxUVls1lz1Gu1WgbGcM+KxaIR6fgZPLO4peHYg4oOgJb7cXl5aUA57yrvNaQLQAoUiWdnZ4pGo/L7/VPxHVhpLy4uqlAoqNfrmQMEjc2TJ0+MCIj6kHg5ACicpYgdPTo6ss+A+wDvFZbn6XRa/X5fs7Oz2traMpLH0tLSVKTBcDhUvV63OA3AGQZWkCohKPH9sDDnXhODhVIsn89PRUgcHh6aChebcQBrCFU4LzQaDZ2cnBixD2IXa2a/35fb7TZVHpbixGW4XC6Nx2PNz8+rWq1a9IITcIpEIopEIqZGZfBRKBS0srJiblJ+v1+hUMhAfa5BNpu197TT6ahUKhnoBHjBv+U+3bXfAlilUilTKDIYcla9XjfQDWAMhbEkIwUCXkL2ef78uf7hP/yHmpmZMTW2JHOg41oRTwS4QkQR94aIFAhUzggGgML19XWl02kD89j7ALjn5uaUTqdtDcYt4OOPPzZSMCROCI+Q6S4vL3V4eGgOIgCAONMRb3Z9fa16va7BYGCunOxf2MCzHznXw36/r1//+tdmQY7qPx6P2zWpVCra2toyAhYuXJAa/X6/ut2uZmZmVCgUVKlU7H6wh7TbbRsIETGHEwhqxTcB+6x5kElxqhuPx3YGCoVCRqxiDZ5MJuZ6y1C5XC7bz+E9hCB9X6Tuh3q/hTCA4T7unpAucCcFLNvY2NAPfvCDKaDO5XLZPQcIJtriu3AZAkSsVqsGuBeLRRvK4kTaaDRMifrgwQMj8Kyurr53EhbuFcS5cfZi8AUBGcehP9UqFAr2jkJi6H4ZmU4MHnvZfUKYb6ve1z3DTZY9BlAY1yjIoN8VIOl0oL3rO3LmQoRDP0d/zT0gjpqzJM4AV1dXrw1y+D3OdR81OZGjzr7nrsK95fDwUJVKRcViUblcTsVi0fZyhveIGxDvnJ+f23sJ8X9jY+ONz5OzzycKGNdWhh8QpuLxuA2u7xsmc93dbrc2NjZMPISbnfQVYRpix7Nnz0x0g2CC/Yx3nchoziWQjjjHgDPQg/v9frunnU7HBtT0grjaUvR/nU5nKu7vrsJdAjCfzwMZhqGHJDsfVatVIz7hNAHhgvMGbuCPHj3S0dGRrf2QQHh+cAHmXeOaX11d2XCB8xkkL/4skUiYszV9FW6ZNzc35sZGfBnrKmQuXAhxGOd5xEUBIhBnWa/Xq8PDQ62trRlZpF6va2ZmRq1WS71ez0gnuJpIsqgRCHw4Wo3HY4vJ4Xkinsd55uJz4xhFXzQ/P69PP/3Uvg+kRQbcqOshoPCuHR8f2/NTqVQs2pFnBVzG6Ux0enqqVCqlYrFo8ZCJRMLwIXqjxcVF631wNQNL2dnZMcFUNBq1a0I/ybNL3JUkw05wWHVihpB7XS6X6vW69U70dBDnIDXzDu3t7VlMJw5Mksw9rN1uK5lMmvAE53Pc1ff29uwcAfmYMy/YVqlUsnuKy8NgMFAikdDOzo69JzMzM+Y2S5QWkYjRaNT6/5ubG8PhFhYWzPUfjLFSqdg1BCeH4ACWIcl6NGcP/dvf/lY/+clP1P0yGg3RCGsDPWuj0bB3FmIm1x+nlaurK8Nlstmskf8gCLFO4lh2cXGh3d1de87n5+cVj8dVLpfNZTwejxt2CbE7n8+r3W7r008/1WQysWhG6SuXKWeNRiMdHBxoOByq1Wq9VzHHeDw2YtfbIl8hk0ISG41GKhQKmp2d1d7enjlbcUYlQux2OdcmHN07nY5hj7cLki/4FJGgkqZItO9STuz22yjnWv1tFQQXSLpcTwjWEFLpV1OplJFAneW8dm9z0pJkax5naGnaWYoIPVwfA4GAIpGI0um0OZq8z0KcHQwGdXh4+Np5KRQK2YytVqtpcXHRojNxfecs4/V6zZ0IpzlElplMRvv7+6pWq0ZUz+fzJlYEl+K+45rnjAJuNBoaDAZKp9MmWL64uNDKyopqtZrFNSNMAz/ie4AL3V4bpK9cZm8Lqu+qVCqlbrf7XuLLIcy63W4dHR3p8PBQku58/lutlo6Ojozkfde6IMnW12KxaHsz5IxaraZIJHKnMAbnTshonP+cZDUIXdTp6anOzs7kcrk0GAzeOo9lLwKnu6sQzRNHurCwoEgkYkRo1nfOMC6Xy9Ykvr/06tx1cHCgdDqt09PT10g47OfvKnbCKdXpSPqmQqSGsxm9GLHTOKu+DRPBkR+y37clzjo9PTXCJITn2zUej01IUCwWDYMmIh1B6tLS0jtHIyLCueu9dBapGYi6mfWQHsBZEod1IppbrdZ3SsDiPOb3+81V9659AYHX+vq6zYOYKULCxCSC9ahcLqvT6ejw8FDxeNwI+t0vHdSlV/eRn5PJZPSTn/xEsVjMTG7C4bDC4bBarZaZStxXGClA4OI8x7oSCAS+sRvch/rzqg8ErL+AonmT9M4HmNsDGKJMPtSfZ0FwwDECN5rbxfCfv59OpxWJROzgAqjjbIBRC/R6PQNsOPTFYjElEgkbloZCIRWLRX3xxRemyMPKkt+NehU1JWAQhaIgHA4bGer6+lrVatUcqojbqlar2t3d1cLCgrLZrCqVylRWMASG6+trHR8fm/U+TOZIJKLT01P1ej373ahtGZg3m03Nzs7q5OREpVLJhtvpdFo//OEPX2swILsRjYeLEox9NnkAvlgsZk4CbrfbXHcSiYSOjo6MHOLxeFSpVNRqtbSzs2MAKwASxBEcw9xut66vr005ys+RviIOMQBk3YDkAADhcrlsKA2L3O12G8Hj5OTEmPyrq6umnoTpjXJakp4+fap4PG5OKzRzAFVOsINrdnFxoWAwaPEXuVxOW1tbZv0eDAYNbD87O1M8HjdXLQiAOF4BwGcyGVOglstli9CA/MDPJirR6/XaIANLUooYjFQqpVwup52dHZ2dnWlvb29KCTsYDIxwRXOLEx3vHIe9q6sri/zCLetdAB0cqiAlQYhst9t3qoAgi8XjcXOLAniHuLK8vGyAMC5VvNMMDIlp6XQ6psaNRCI2yGAdkF4d/FlLsCOGhIOTEsoESXr58qWppGhqy+WyTk9PtbOzYwoFAHOAS2IjPB6PAcREdczOzhoRFVLN+fm5Ad0M3Or1unK5nKn8iTNJJBJmx31wcGCfPxwOq9PpaDKZaHl52Rrb09NTc7Qhvo54BekrFXm5XNbx8bEB37hzJRIJxeNxG9qXy2VFIhEjuS0sLOiTTz7RgwcPLPKUWL1kMqlIJKLd3V1TAD558sRiTa6ursyCGiXd2tqa1tbWjFDD98VRjig4t9utP/zhD4pGo2ZvTTPi8Xh0dnamQCCg7e1tU5AwaHZG0M3Oztoa5LTvd7lcdk1RbgNqBQIBey+4XreLgQkEnuXlZRtqECMA4CXJAGvn80jUpHPAkEqldHl5abFcw+HQhkw85yhjXS6XUqmUms2mstmsqtWqqbGdimZAMpzb+J68dwyuANrZi371q1+Z62MwGDR1eKvVsmEDEcEMCqRXAxJc2XhmncpdVDrRaFSFQsGI0pwTiRojgmU4HOrBgwdGWj0/PzfXzc8//3yK0ITTQTqdNgJnv99XrVYzd7ubmxtzw/D5fDbgGA6HisViOj4+ls/n097enoLBoCqViq397FHsT9i9Hx4eWqyGx+MxEAIHOAhlkEYzmYwR7BhaOF0BZmdnrakHTAZYYm2LRqNaWFiw88ibiiESA4i5uTkbqgLKOc8qEOtYo548eaJer6dms2nxJSiKA4GADZi/Ccj5od6tiLvExerRo0fmdoEbH5FcrF/JZPJeoA1XgoODg6mYr69bnDkYEOMyd1f1ej1zt0un04pGozo4OLB1MxgMWvwOKlScV46OjiR9FZXwbVQ8Hlc8Hrf1B9cgnF3+1Iu4GKKzIaZSKNKdytXvqhiiSXqN4CzJnNmI9G632+a0x/N0enqqvb09db+MWkTVz15DjNzp6akePHhgMUvdL6OYGSSzf7+P4gzzJuAfMQDiDUk2wHeKaxAv4fTGvXM+exCZXS6XhsOhqa05G992DMXV10kWGY1G2tnZMbU07k2rq6u6urpSsVi0/o7fh8My5PlwOKxCoWDEn7eR+XBwwDU1n8/bwJtzAlFekqxXg4zFn9E/I+SRZOfvjY0NIwMhMgoEAqpUKkaqxoXISSbhTAQ5hOhdzrK9Xk8ej8dcBxHwXF9fq91u27mGfiMSiZjgBQczKplMqtVqWf/PUA2BzfX1tbnr9vt9bW9vKxgMqlarWU8YCATUarW0u7urarVqZ1JclnFJk2RDUkRNnP0YHHPGwzU7FouZyAJCyPHxsV1zopHAG4iAg7TAmRxHJN77s7OzKfIUgipcBPi89AYM8j799FOL/UJxDvZB3zgajSymemZmRsvLy+a2+otf/MLIIkQMEgOIQAuBCr2x8+yKCzXvVLlcNmzIee7hd4DB4FwlybAGp0MIzz09B8MqhDpEc4dCIRPW8LxBauMZm52dVaFQsDM2zzaCON5/1qDbpEb+G0RuRAoIQ/x+v61zzuL9wRkvm83a36FP4ZxOZBIOw16v1wRvEMn29/fVbDaNSEaviuMu2Ml4PDa3aEhH5XJZtVrN1kOeu9XVVXuPrq+v1Ww27drzOem9cXbCCTsYDMrr9VrsK47HyWRS6XRazWZT6XTa+mZEcbFYzFy5stms9fjsQ6zJw+HQ9qLxeKzl5WWtrq7qwYMH+vzzz83F+t/8m39j2CVrBG7oNzc35lr/i1/8wnp7iMn0lzyPfr/fCOW4OtG/zs3NaWVlRclkUsvLy9ra2rL3IJvNWl/EvUulUrav+P1+ew55D+fm5lSpVGytv4844fP5TJDyvglY7Cdvch1EzMS1RQxzc3Ojo6MjG8BC2pNerauHh4c6Ozuz70wh4iTG1ev1WgwbjvoU4j+cgsAOObeTaABp8E3kEyJT+Td/rgW2wjXf29szoiD7MY6U7F/j8Vjb29va2Niw68tcgIi8N10ThGOZTOa1v8f+CAbIWuDz+ZRIJL613gBC+vb2tj2TECHBJvf39yW9euby+bxh959//rn1/85UBgjB5XJZ2WxWGxsbFs3MOQFXd/Zw9kic/RBWsg6DZbPmIiCTZJhDtVo1DBIcA6dXiFn3RbtDfEc89KZC8IwQ/l2Id2+rbDZrgkunuzDnkn6/rxcvXmg4HCqfzyudTr8xXpk0il6vZ/s+jvKFQuGNnyUej6vT6ejs7MycNu+rUChkqSgnJydvnMeCHbB33ff5OfeBmyEGBuOWZIRFnOQlWUQbwuKLiwuVy2U7izx8+HDqXXqXvspZ7L3vIioiWh3smDMX+DBChK/jZMV3fxsm98cWTqXS67N26dX3xxiAPZ1EjGQyqbOzM4tKZB98VxLWuxTuzTc3Nzo4ODCiuJOARSQp6wvR5N9lzc7OanFxUeVyWdVqVeVyeWpfdZ6NOcM5e0ZMJxBW8e8wB4AwvLS0ZE6giB04u/M76BWdUfWIaBBU4Wp6u5gnMYvG9ZczQK/Xk8/nMzOIN7lTf6i/nPpAwPoLKKfl+7vaAd/+e/fZxn+oP48KhUKW/c1g8U3KFw5auGIB/Dk3D9j7uPM4Y41oUjlw0UiUSiX7u+SU+3w+c7WCWMOwFEtzFOwAaNT8/Lw2NzctCoOBKu4op6enBkCggCTKyalmJRN9PB4baaJWq8nv99vwBrIMbl4M5Nkko9Gout2uOc5ls9k3NtioSmiofv7znxvYMhgMjChWr9cN6MTC9cGDB6a6xK0F1cre3p41X+l02ggLqVTK7hdEAiKhUPcBWkmyphiHMhQVfr9fl5eXajQaGg6HNlBmCJNMJg2oc6rQ/u7v/k7hcNgUogBKxCcQffXRRx/pxYsXNlS/a3DhPCDzzDlJhjD0acS5xzTkfr/f3FIuLy8N+ERZhCqTBiUSiSgUChmzneckHo/bMw0hLZvNWrNSrVbNFatQKBhRotlsmmoXxydUYADFs7OzFtHA/wKBgNnyM5CAXPG2guCIpb/P57OBaKfTmXIpAzwGKF9dXbX4NYgfkCWxvAbYzOVyFlGCww7EIciSHo9HCwsLWl9fN3XRZDLRr3/9a3NBSiQS1jgxwMBJIxwOW3zG0dGRKcUA3uv1uil1Li8vrWlBhYNlcrVaNQU1z43TJhlbdrfbraurqylXLFzVcJtieA047fF4zLUMa28GXYCUp6en6nQ6isVi8nq9Zt9MnIXP59Px8bEajYaq1apKpZINxSG84mqGE1ez2VSn09FoNFI0GtWDBw+MCJpKpczKmngO8soZkD948MAAbOdAsdFoKJ/PKxKJ2HAblR7XCAAGF7xcLmeuPeVy2YYVknRwcGBkVdZX4nlQ5LEGEJ2DoptieIViPxgMmkvAzMyMDWHuK6dqGCttQBG/3z9F9AS8xgFqc3NTDx8+VKfT0cnJiY6Ojsxxye12a3t7W+VyWYeHh/rBD35gAzDcC3Aamp2d1cbGhg2iUGfPz89bZClrPdFIuC4AYI7HYxsier1eDQYD7e/v69e//rX29/dN+c3aTVMXiUSM3Anpzhlth3W/k9gGERiFI1F6kmxgx7o0mUzUbDYtqjKfzxvpq1KpaHd319xy+Cw8W+yzEOxw8kBhyf7R6/XMlcHtdmtvb8/cF3EXYqBFvOXi4qK9M8+ePTOnuHw+b+RcAE++M9EizqhVFOOsaUR1QGaem5vTxx9/bAQpXLFY3yGuQch7UzRBOByWz+ezCJ10Oi2Xy2UuWljA87wzwFpZWdEnn3xi54eFhQU7s0HAikQi34rV+l9KMSBlcA1BibXz6w5IUMS1Wi198sknBrowgOZ59nq9Ojg4sP3iTS4rgEgAwV+H2ASR6uTkxNY81tFoNGrDfekrcOr58+fmhMnwj7NNJBKZAhaj0eiUopLzI2T2b9O9CQeiP9cKhUIKhUJTZBTIEt9XcV4JBAJqt9tTwxbIsvQ2w+FQ1WpVqVRKJycnev78uansG42GWq3WVOyWJIucOjw81NLSkv7whz9MESGcUYUQyW9HsP4xBbka18m7FKtEo9NLSq/OvgcHB5K+IhXimJzNZtVqteyzORXkCGeCwaARfSXZUJDht/QVYSmVStkeKMkIVrVazXo4oqLYA3EMDoVC1gfhhEmU+crKytfaA3DiJVKUvoVzjHMfg1RKBMT5+bl+85vfWGwzpGrcaDgDrK2tveY+QDQaYhJn9ASDTIjerKH0ecQpQ57HXRcshH643W7L6/WqXq/bHo/Yy1m4U6dSKXO3oqc9OztTvV63f7ezs2PEtEePHsnj8ajZbKpSqVh/f3NzY/gEohpwEf475HH6OHCdZDJpDuIbGxsWV7u/v29iIb43/SrXBaIR+MNk8ipikIhDrns2m1Wz2VQikTDxTSAQ0OzsrDY3N6ci1SDoMTind7u+vtbq6qrFTd4uhFTEZV9dXSmRSOi3v/2tDRaJsiTqDEdv3FOIqGbgTG/BIJrv2O12tbu7q3g8rp/85CeSZAQwBjTz8/NqtVrWA7IGUHxP3CkgRl1eXtp+zZkb5yh6b9xd2+22NjY2lM/nNT8/b8Ijzruzs7Pa2dkxUaPX67WYdkggXBOfz6f19XXDLlgv6enuOquwJkWjUTu7E//JeQT858c//rGCwaAKhYKRWfr9vvWnCGcGg4E5x+Vyuam1mYjdo6MjffHFF0okElpaWrLe9vHjx3r48KG9T5BQPB6PSqWSms2mxTfxOSSZI3apVLKIevpwziacb4LBoJHawS456zjXXwjCx8fHJvJhjUXc12g0bFBNzwDmEw6H9fOf/9xcdrmuRLxLMpfnwWCglZUV1ev1KWEf/44z4NLSki4vL+3M1e127Wd0Oh1zXvnxj3+sxcVF3dzcmAsbbtj0GwzXcUWHkDEzM2PYHv0Xg/lut/vGIWAoFFL3S7fhtzkDfp0Co51MJmo0GlOu+JLU6XQM12MtItYeB53hcKhEImH47MXFhQ4ODuxZvry8NExZkrkuIu7DaY44d7BBSYZRdLtd5XI55fN5JRIJWy8QW3k8HnM5us/tGBED55s/10Lsgyt8pVJRrVaz1IBwOGzkIc4QuOdLsoE2bpvRaNQw7/vw9UajYQSM+4gJxDB+VzUcDg2rgKjHngHWhTNXNps17H5vb8/2aQRSs7Ozevnypfb3981Nj2SJWCymjz/+WNVqVZVKxRwkJdk7TDQncyDedQShYE0I1OnPwJfYG+fm5rS0tKR0Om1iOI/HY73jXX0dpGDI+/cV5GJcTSXZ//+blNvt1ubmpvb29jQ3N2f4JkJv1nFE9O/iOkPyCoRcnFXf5qKGyJw0j/vq5ubG9otYLKbDw0M1Go07ya3MviBev4mYw3cGlySRw3lfILAEg0EVi0VzoWOOwzqLize4V7Va1fr6up2ROQMizr2vmJFwBnpT1et1EyYPBgP7vpzVMGHAwatQKLx1VgL5EPzv2yjO3+DVt6vZbFr/wPxobm5Oc3Nzin4ZlzgajVQqlSw1413Trd6lcGIlYYUIZ/B97rsTI8cN97suDBOePXtmUdZOcjUGImdnZ1pYWFCv11OxWLRZIQ5w3S9dZRcWFqaSE1ZWVqacqcHc+TNwicFgYLNGClHu1dWVDg8PdXR09Bqhk3PpaDQyN2YEMPQbGFIwIxwMBhan+6H+cusDAesvoJzuKO9qq3pb9fyulrkf6k+zsEpdXFzUycmJKenuOuD0ej3t7u5qfn7e1EQMIBOJhDkF4S5Rr9fNvhu1abfbNbUR8VrLy8t6+vSptra2LE4IxSVRQwCw2WxW2WzWGoc3HYQCgYCePHmiZrNpA0aGpNVq1f6MARMHY+fBB5UkEXt7e3uaTCZ2mJM09fc5BDlVNG63W7VazUBV5yDrdgFQ0VwQJYJS6OTkRKFQSMvLy+p2uwawZLNZa86dQyUOC4CtECCkV2Q6DpMQUPi8TiKH092EYTGHRGe0F88AnwMSxWg0MnCUKMG1tTWLGUEVB7EAYgNELqLSAL2j0aj29vbsc3E9+/2+BoOBERVubm7sUO9UjRLvgC3t3NycxT8BLKNehNAkyZ7fubk5BYNBU5ygyMc5Z2Zmxg5cp6enplhotVoG2CwtLZm6BUX1ysqKNb5YQZ+fn1szxoA+FosZiQSrdMAYAFLA0ncZAANexGIxAxCJ/gyFQlPKZcC/ZDKpQqGgf/AP/oFOTk60vb0tt9tt5CyU0Fjj8zxcX1+b3XepVJoayKKawjWDoeJ4PDZFOX+vUqno8vLSnh2c34gOnJmZ0dLSkvL5vK0pECMgefBsMvg5OTkx0Hg4HBqA7nQWg4ABqQPikHMtgGDI/87OzpRIJBSNRvX06VNz0SF6TJKpVVOplLk7OCsej5vKnuE27w/3h+EMSvxarSZJ5jwEGWNhYUHz8/P68Y9/bCQ7J8ESogbfFxcqSWYvjCoUQg7NNEQWiAkAlpVKxQgo1PX1tTkTnp6emouFx+MxZ4Z0Oq2nT59ahCtOSoVCwZ4zACJnk3eXYosYGZ7T26Rhp4X9ycmJ9vf3dXFxYSpX7IiTyaSOj48t3pMIV4bGKPtZg6LRqNxut46PjzU7O2tDCYbQNP8HBwdG4AIc8fv9evTokQ4PD80inncMsJiG9+rqyhxDWNtRKdJ40lzu7OyoVCqZEi4cDuvw8NDUQM4se4/Ho1AoZCom4lozmYwNJACLGDKh2kFVDfkCxwlIqPPz8+bMgTPh5eWlDg4OtL+/b45SuLDhlrazs2NDz3g8PmWXD1CEg2C327VnoVarKZvNKpfLWdRf90vraJ7NfD5vf58YGciVLpdLfr9fS0tLFtHEYPjzzz+3mELiaCXZOwQBGbIssU68c3wOiKmLi4vq9/vmzPim9Zto0FqtZoQs9n4n6MDa/vHHH2ttbW0KmCHC80O9vbAq/93vfmcgKe4q8XjcSNp+v18bGxvvDICxVzmjXCFDQ/LDdYChGkTJN8VDOoHgd61Wq6VisWgufk4gmHX75OREuVzO3IwuLi60s7OjarVqMVj8Oc4ozoLcjUtfKpUyl5FOp/NnPWD6roo4iT+F4oxFLDJOqRByul+6mDK4hyzb6XRUqVR0eHho/y2fz78GiuPU0mg0jHiAMwJ7Ous0ccKnp6fa3Nz8RtcokUhYr9Jqte5V8QcCATurxmIxExAxuMaRiHcb8sD8/Ly63e5UpBdD4Wq1atfx9PTUnNpwCSqXy/azOTcQ60kcHOdZiDyDwcDU8ul02tyaiPorFotqNpu6vLzU/v6+nj59+s4OA6lUSp1OR5L0hz/8QT6fb+rcx14eCARs+I1I6te//rXa7bb29/eNlEx/QxxQIpFQrVbTT37ykzsHTXwPokq4NpyLDg4OTHyEi+zl5aU5IeP44xSiEeuLg4qkKQLBXYr45eVljUYj1et1PXv2zJzQGFA1Gg0jPuGYmkgkVCqVbLBBj5FMJtXpdExpjer94uJCqVRKs7OzRpZnSIqrJxHnnGFx9Xj69KmePXtmgjev12uuC7f7THpPzpWQ17a2tjQ/P6+lpSU9evRI6+vrisViSqfTKpfLpoQHx7hdk8nE4jCvrq7MRfy+gmB4cnIyRTjFySiRSKjX69lAeTgcmiodoc3s7KxhFuFwWPl83oQIkJRPT09VqVR0fHyshw8fWu8PEYj3F5IafeFtcub19bWdmzmzg5ldX1/r4cOHRmoj7h78hx7W5/OZoyGuYhQkqMvLS62urlqMpaSpYTgEKp7tXq9nhHHIJWAYzp99cnJiuFAgENDCwoJWVlYUjUbt7B+LxRQMBuX3+7WysmIxkbh04ohJfB+Cx5///Oev4XcMiRuNho6OjuT1elUqlfTo0SNz+kgmk/rhD384tR5VKhVJr/CWQqFg2Ag4I4PzZrNpAh+ey9FoZCTG8XisWCxmLvSTycR6JIhqRIXhdtzv91Wv180pCvyHGOlQKGTCLIRbp6enmp+fVygU0ieffKJ/82/+jREbiHmlt7++vjZXaSJucZlBfOLxeMy5P5lMKhgM2lmS5wqyWiqV0uLiosLhsA4ODizu6KOPPtLS0pI5VkwmE8MNpFe9I2ezbrerVCpl7yDrBDjR7eL5Yv93upN9k8LtjO+KGzzrIs75tVrN8ESwCohVLpdLW1tbWlhYMCcZzh2Xl5eq1WoWS3Z8fDy19y8tLVnvWa/XTWCDO/fR0ZH1j/TrDx48MKIqBb69uLioo6MjlUolpVKpKSwXstjNzY058Pw5OLbeLvrb09NTjUYj1Wo1bW9vq1QqqVwuG5YcCAQMD97c3LR9H7JHo9FQNps1cSsE7MPDQ8MSWCMuLi5MYF4oFKZi/b7PwrUfJ9j19XVbhxBCsSdAKPb7/XY+Ozk50fLy8tS6DYGUWcvx8bHK5bKWlpZMxMh5olQq6fz83PZO3kvIVRBCmDlwxoDoLslig8E5Li8v9eDBA+Xzea2urtoz2mg0LEngrvnSm9yQEHyenJwY7sN8JRqNvjdHIkTsCOYhSPMZwBOd8cJvKs5NfC+nW/zbint1F3mFs333S4f08XhsMbW1Ws2wCOa1kLzH47EKhYK5Tt5XzHjy+bwJISGVMk/hHMOZg8g59h72hWQyqVgsZi536XTa3LCSyaROTk5sBvemz4QAYX5+/o3kMbCZarWqi4uL13AusBWfz6ff/va3hln86Ec/kiRz/Efs5vF4TET9NizumxbP1129Kk7RzMoSiYQajYZ9Zuf3W1paMiOAZrOpxcXF9/L5IK/X63WFw+E7XeBYa2u1mn2fSCSiUqlks6y3Odx9088IqZxkGchLzD4k2ZrqjJGORqNaWVkx8V+9XrdYwZmZGYutxKFQerUvk+CDEAwcYjweq1Qqye/3Kx6Pa3Nz097/paUl6w2c+xqCaRx1w+HwVLQrM1Dp1TsB0ZFzP+5fH+ovt/400L4P9Y3KuSm9KzB/u8F6n2pbmqJ3taP8UO+nIFO0Wi2Vy2VT/rEZADBsb28bEUF6pbCFdAXIQnQASpVUKmVKVgakknR4eGi24H/913+t9fV1/eAHP9DBwYGpAWlAANkZmGYymXd+Roj0wUIcBSKgBQdNgJZ2u23NBZvaycmJRelVq1VJr1xaPvnkk9fAZQr3LkkWLeYkLTQajalIB0g/3W7X3JZQ4gDqwDRnkFYoFCwDGjUoCiIi27BlPjk5MXIOSkSaW34/5ATnZ+r1emaPD+gKCSsUClmEk8vl0ubmpjUZABEcWFD0eL1eJRIJU0M0Gg0bLszMzKjVaunk5MRsTXGwwZqZQ4vH47EYsN3dXVMz4l7UbrctogHwB2IEBxXAR0AsVC84lwGMk7ENoCvJXF86nY76/b79PhQXl5eX1lRgP0zso9fr1ebmpikYDw8PFQwG9eDBA0mvhi8obSHBoBhFKc17gQUpwxgIIDc3N1peXr73+bxd0WhUq6urphDr9Xrm/oY1K2Q4j8ejhw8f6uHDh2b9j2IABywacXLCZ2dnLZLT4/EokUhYBOPl5aUdOHGLw4qa+8VQYHFxUZVKxQBswAQiKSGQMcTjvXj27JlmZ2cVj8eVy+XM4ajVak2pJyGk8uxfXl4a+YLnAXIP5DKeG8B3BjfdbteU4MvLy0okEnZoz2Qy9yqocAlwuVw6PT1VPB6X2+1WLpezKKpisWhqDMAABnihUMgcFCDRDAYDLSwsGJE0FAqZTTRW77gZ0gASaVcqlbS0tCS/3692u23KkH6/b+SulZUVG/SdnJxob29PuVzO9gHiFCAPORXwOM0BUP7+97+36Ba/32/KFchZuLWl02nV63U1m00jfNEMoqbjWvL/J7oQ90I+L25ROGyg2sa5iXUU1TogOg4N+/v7Rnzk+kD0GY1G+uKLLwyUZTAC4Ws4HFrk7PLysl6+fKlIJGLNKo3b+fm5uQ84B9hffPGFxYCw/jCsZN0BQCC6dmNjwwAx3q9sNqv9/X0D+pxDyrm5OXU6HduL+A7dbteIYdjX41iFivjo6Ehzc3MWSUwM5OzsrA0TIKdGo1Hlcjkb+s3PzxvRCfeJWq1mcYk847gZhMNhu0ej0cjOHtKr8yVgHTFFrN/NZlPPnz9XoVBQo9HQ4uKiAec01P1+f0oZy7pLTSYTlUolc0AbDodGQidW4ac//akpddvttrmfsQZDXgbYwnksHo+/cR3HRYYYW/Z1HAsBca+uroyU+sknn3w4a/8R1Ww2tbe3Z5ERjUbDHAcBS46Pj23QdXFxYc4mbyrWn9PTUyOVULfdmjgn8iwPBoM3kq0hhgPuO3/2XUIACKjVatUc8fL5vA27cUedn5/Xzs6OQqGQuVF2u107Q3H2HI/HSiQSU4RYiv0XEYAkAxrfRCr7UH+alclk1Ol0lM1mzaWTqG9iM+kPUOaHw2EdHx+r1Wrp+vpaKysrb8QCAPmvr681MzOjJ0+emIMo70Sn09He3p6WlpYM8P9jY0pwZEgmkzbUukuxjTp5Y2NDqVTKnHEZrjMQ4Xuxx0ciERMFobLGScrpCnZxcaGVlRV5PB7V63UjxCwtLdn6Mx6Pjdxzfn6uXC5n71a73TanEvYuIsHZw52xM/wMZ9z828rv99uQrlgsTvXbXIdqtarhcKjl5WXF43GNRiO9fPlSL168sBgxBiGQ8wG9/X6/9vf3dXh4qL/5m78xEpbTXYEzOrHFrJG4ySA6IAbV6Tw5GAysh7pdgUDAzk0Q2Phdt59V+jfOQoPBwHrafr+vVCqlTCajk5MTO7vg0EWk/Orqqjmv0k/gyiu9Atshu0tSOp02JxcU6gxXWX+lV+eCvb09I8MQs4HbAS7KOH1DooV8xDCS6ItYLKbHjx/bc8iZut1u6+joSIuLi68NHiHE46DE2e1tZxLwJkiOo9FIm5ubikQi1kOORiNTtUOkgYSIQyUkM1zyEE1BoGk2m9re3lYsFjP3EXoZ1Oa4Al1fX5tozbnPn5+fG1GJvQ0sifsXCATk8/lMkODsQxkYEf/IPu90xGM/Z43q9XpGikokEkZAAsPhfHFxcaGZmRkbVOIYTaRJv983pyci5Z88eaKHDx+aOMHtdhvOh+vY1taWOp2OyuWyPdv0H0dHR2o2mxYr+NFHH9nQdTAY6IsvvjBMqNVqmTABkQlOxPv7+0ZkYVCGgy3DegbUEGJKpZJhMoVCQfF4XB6PR5VKxcSJXA/IiisrK9Zr4q7jcrnUaDTsvac/Z6gLxofoC8wRIgOfbTgcmksdOEcul9PS0pKtxZyx6NkhVyEicmJZuBj6fD7rDcfjsbxerxYXF819HKIRcUYM2nn/6O0QU/I+sr6CAfK9IQo4n0Xn8++M/QOz83g8Fk/2x57xzs/PtbOzY/sTbmQnJydqt9taXV2d+t24p+BQjoPcaDTS2tqaYSjHx8daX1+3tSKXy6lYLBru5CQ+zc3NaW1tzVyiT05OVKvVDOfACZfrXigUDI++vb8sLS1pNBqpUCioVqtpb2/P9iWuLy6V8Xj8Xhzc+Wd/av0d8dKs35AMt7e3zU2QuE4iS29ubszNBad2cINkMimXy6VEImH7e71eV6lUMvKE00WK53xlZeVP4tpAvD0+Pp56F5yu2AhuOBtvb29rbm7OolGJa6XYh+r1usWDgmsTfcl8gbQSt9ttgnTwl0AgYMkhoVBI+Xxes7Ozth73ej3D9Or1ugKBgK6urkwkx7mM3q7dbiudTpsz5u3rD56CSyhY3mg0Urlctj2J+9xsNi2G/bPPPtP6+vpbiR2cfZ3ieMgsTsK9y+Uy90fOwGBBYMecF+8jDbHnxeNxE1Pzfd7l2bstisURB6wRQr8zDhiyCWcep3CaOV8kEtHq6qrNae4q53qDCUGr1bI99eLiQs1m02YqnLmYoSWTSc3Pz6tSqZib5MrKinq9nqrVqp1xnjx5YjHUx8fHGo/HNk+ixuOxzbMQq971/FD1et1mkRAA7/q7iURCyWRSpVJJlUpFmUzGegbm4aw/YN1er/etWNw3KXB/3n1ndb+MUO/3+2YmgdurpNfWAJ/PZ+sk3+2bFlhor9ezWdbtz0mixvPnzy2Fgr6U+RVu0O+bzHZ2dqb9/X0Nh0N9/vnnNg8lqYGZ88LCghli4BjX6XS0vLxsM0qEO/T7kgz/okemx6U/A1+HEA/GfXR0pOXlZU0mkykXzdXVVev3EQE5XfRZ5zmnLC8vT8Uosl+GQiGVy2VzmmW+9qG++0JU/m3WBwLWX0A5h0fv6mR1++/dZcV/u/7Vv/pX+p/+p//pjX/n7OxM/+Sf/BP9zd/8jUVXfajvrhKJhAqFgobDoRqNhorFojkLYZ3vtHUHvMDCulgsGjDKQWFtbc0IVZBSGMqiYCyVSvrbv/1bc25x2jKj8sdunebkmz4fOCy1221Jr9jHuMegJJBeHYCHw6Ex7wEdIVWkUikjAbytLi8vFYvFDMQplUqm1MRaFUAHhdn19bUp3nCZuX2IIkoJgIWIM5pZXH1gb9PYYi+M6tOp8mBgz3/vOmInIf5cXV2pWq2achUHrmazqYuLC4XDYVOYYfsPEMkBBqAEEgkKNZSXqGXC4bDa7baePXumVqtlh89+v6+9vT0DpFG40hxC6oGYsLa2NtV0YgmMFSgHIMiERFxeX19POTvxdxhatFotI3+cnJwY0x8VLoAtyk6uJ8AaQCNArBN4wMEmkUgYeeTy8tIGAOPx2AAxVJgomrh/7/J88p5ls1lTnpLhTuEskEwmDbRmoDM7O6vDw0MjbkEeJJLr+PjY3N+Gw6GtBc5oj/Pzc7148cKII06FOdbBNBIc+gEIIcOgCILkg6sGrjtXV1d6+PChFhYWbC+7uLhQMBg0UB5nrOvrayUSCbVaraloNohKKFp5pgG0Gf5BeEmlUuaI865rF8360dGRXXdAf64VAymnKoLDOUAxgD5kFsCtZrOp3d1ddTodi38KhULWWAwGA1UqFbvOPJ8QNRnIoYZ2ur4BxPzd3/2dxdgwOON3YO9/c3Nja8fGxobS6bTOzs40HA7l8/mMrAOJCrCXoRpA9NzcnDlM8exihy1Ju7u7pkbBZegXv/iFNfNcV1S0nU7H/jyfz5tiExc/SVP26rilDQYDc+YgtshJ3uS+SLIBMpGSZ2dn6nQ6Wl9fN2c2AA7Iadi8S9Ljx4/N8Qji5dzcnCmjISOiQsfeneZyZmZGg8FAqVRK2WxWv/3tbw2sIOOe9xOFIw2/03WMIROAFkA8Ln5EvuIa6Ha7LRYXBVK73Va329XCwoI++ugji98jtvHo6MiAM84TDFBx5wHUgTROPCtAGq6HfDYG2hBCiH6rVCp6+vSpjo6OLB6V9f1NwMvV1ZVZgnO+wc1Qkq0Z7ImQh1l7eFeq1apardaU0h+V05uK3w3ogB0295H/u1KpKJFI6Fe/+pXW19c/NOpfo1qtljlfMWhhKHZ1daVWq6W5uTnF43Eb9i0uLurs7EwbGxtv/Nk42EGSfxNghmMU7wSOl3fVeDw2gJ9zwptqMpnoxYsXqtVqOj09Nbe40Whk6mNcWH73u9/Zu7++vm6K1tFoZOcVIq5YR3K53JSzUafT0fz8vK27AM0QSXApwDkHYuifQzH8wUmWvYLzBUOiPxUHq/dVRDlHIhHt7e0ZSElED2sebjxcC4gn9znmUDhIlstlPXr0yNZWzk3SK7ec4XCoL774QisrK5L0jQh9rKPBYNDATpxicVK6uroy5yBIDhTD8ttF31QsFvXFF18YMZ1zNJErOLsWi0U7A0WjUbuOxKdLMgU4Tlis/dfX16pUKhZ3Ri9Mf847fnFxoc3NTUmvxFJXV1dGJntbEe+EOpfe0nmdbm5utLCwYM/I9fW1fvGLX5jTyHg8ntrXib7w+XwGSiNG+I//4/9YKysr1mfRs7pcLu3v79vZkX4PElSv1zPitiQTnESjUXU6nTuJqQxjiE2B7EC8ubMmk4kODg5sDSAejt40FotpPB4bcYjzK4IPRGCSjLyDIwvEn9nZWXMFIG6Os1a73dbKyoo58LhcLovQZAjeaDTs/IZYB5wDB11+RzAYNDIZvcfFxYXm5+eVy+UsBpM9AhFApVIxxTznXyLh6A1wV8Q57W3PV6VS0czMjMXc0kPj3jEYDFStVs1hmUEQzqhcU2L7cMX2eDz2XRFX4DCKIGV3d1cvX740Yhp9JQ7lzj4RgqRT6IDgK5VK6fr6WvV63fYz4obz+bwp8nHvgUzz8uVLFQoF5XI5Oxuen5+rVCrZfcTZy0naRuCHmCmTyahcLiufz6tSqZh4jTWUmEL6YIiDtVpNS0tLU4RvBDnEW+GsgtiF/R8SbrlcNuzjk08+kdvt1suXL1WtVlWr1TQej+2adTodE2xApEin09rd3dXS0pKd90ulksWTI8rAWRHxGMLFm5sbc3ACy2FoKclcrel7Wc/C4bCGw6GOj4/l8/nMNeP6+lrHx8fm0nV+fq5ms2mDtWAwaFGXgUBA5XLZSFvcO0Qh9GWVSkW9Xs/ck6SvcAEnSZLrT/Qg5B6eBVztEBNBkHO5XOp0OtZ3sMewRtMPOYlCDIZ5rrpfRty2Wi1zqOcd7n4Z38WzMRqNzAkZElaxWFQqlbrTyfBNNRqNtLu7a4JfnvmVlRUjM3766adG/IUYsr+/r5ubG3PK4/mMRqMaj8fmpAbeSnGO9Xg8NqB1FjFLEKgQXtB7JpPJKedLnEtuF66dOP0hmmEYjGAHF9C3Fa7xfwo1HA61t7c35UjPs8bA2ev1ajgc2mAfAhFrR6FQkMvl0tHRkQ3zuU+IlUhS4J1wxhRCun2Xfea7KEQ84MvOz+WMOkMEd3R0NNVDJBKJqX8zHo9VLpfNcRYiJbgr5BziRfv9vjnZENmKo7ckbW1t2XONm2AwGDSMn3WGmUU0GpXf71c2mzVcGgc6p7vVXT0ouOP19bV2dnZMzORcv4k3Z78dDodmBNBqtQzjv6s6nY5qtZrh/PQcOOOHw2EtLi6aU1O73TZcjXdvdnZWu7u7KpVKJiDIZDJ3ihMg2UK4Yi8lRpaZBf0hZGaw5mKxaCIInvfDw0NLT5Beze2cEdG4xiNg4FoQoUiCChFy9xXv2+npqRF2mU2A+Umv3mlE1ggaV1ZWdHFxYWsp/TrPKQ78nGdIy5mfnzdhBfMLRFxut9vmPm/D0be2tqyPvN0T3q5kMqmDgwONRiP9H//H/2HPPX0TzwkkJjBoUjy+btGbgDsi8Ae7gVRXrVbl8XimfgdiY/ozHOGcAmdnQXr0er1GHv4mhcC/2WzK6/WqWq1OJeRQk8lEzWZTx8fHNo/BdYznPBKJmEjx6+7999VgMND+/v4U+f/09NTcEdvttonBarWaEVUvLi5ULBbN2TIQCExFBvp8PiP0smaenZ1Zb9NsNo10iNCYHoiUlpubmykcwoltzM3NmRCH6+x2u21WKb3q5xcWFu7FgM/OzuTz+cxFC3Loh/ru62/+5m/03/13/923+jv+stDCv6flbKAhBbytWCSod7ESHAwG79QsvGmj/FDfboVCIX300UcKBoNGFMGBimEn1rIPHjyYGoBg4w1QS+YyQ29AE2KSZmdntba2Zi5XpVJJmUzGDtfODHYAEkASXJTi8fgUuPR1KhKJmHqDIbJTacT3gKCBmxX/rt1uy+VyqdVqWVRCKBQy94+7BkQooIgOg7CAKpPrBFiCm02lUlEgEDA79Xg8bhaVkuxzzs7OqlAoSJKRB46Pj9VsNg0Ic9rG09RzoASIYLCHuwKANgA2NTs7O6Um5RAFkENMHyQiQO3JZGKHQpzOGJ5w6CbiEccgCAUoaZwD9WAwaCoqDjrhcNgY9ycnJzo4ODDV4Pr6uv2sZrNphBvIJahbAJgBnVGgAEqi4EJ1yj2gwcFZp9vtGqgOY35ra8sU0FiKX15eWgQYBKV+v28APU5okIogKGHBncvlTMUcDAa1tramYDD4zu+I1+tVPp+3w/LR0ZE1O6hdIcusr69rbW3NmpVcLqetrS3FYjGVSiUD7FEqAUCinHUSsAC4ccfrdDo6PT2199EZE0LDBmlkfn7eFJi8S6PRyOyRiVOFlIJjlsvl0sbGhqk1nfEDgI0QYJw2s81m04hEDJ0ZpF5eXprDDv8uHo9reXlZy8vL+vjjj9+Yb3+7EomEEcoajYbFxABWJZNJ+Xw+GwIFg0EVCgXFYjGz+Ye0MxqNDMwHyLi8vDR1bSaTmWqiGB76/X69fPnS3AA++ugjbW9v25q1vr5+bxPv9/stvi8YDGp9fX3KqpgYtna7rVqtZspjlGhE40AeA0Q7PT1VtVo1chuqOafaJBwOG5gNSZHcegZGLpdL1WrVHAeCwaBltXO9iHUsl8vK5XIaDoeqVCqm9AIkZcDIO8oayjrBUAxVCs23k2zLMHQwGGh3d9fUXk4SV6lUUjabNaAmEolYBOHe3p45CQAU8a4BpiwsLNhaS1Qt71U6ndaDBw8MDIpGozaYdQ5iUA5LMkcy9s+lpSUjWV1eXhr4CxmViA7iXSUZoHxzc2OgLOs6+yHkaL/fb6BDr9eT2+025dH5+bntFSjbUM5hq89zgDoc0J17wXeanZ3V8+fPbX/kfXlToXAnMploUog0xE22Wi0joQWDQSPeQfqWZIOTaDSqRCLxzgBFPB63wdDs7KySyaTFKjNYpDlvt9vWsK+vr7/Tz//7Xrz/h4eHNuBbXl6eAl/H47EBt7yrkO6Hw+Eb92PWUoZ2byqcLnjH37S38K7giPG2AiDs9XpKp9P3imxubm40Pz+vly9fanFx0c49gFoM3SWZyx/xBQguIEkSOcLnxYn29PR0KqYXwJHf96dazuE8rl9O4JohEesD7kr3DQ/+3Mrn82lzc1P1el3VatVcOZPJpBESOKez9rKvQdrBhfZ2cU5nnX0TMTaVSplTTbvd/kYELPpWhmCQ/iFlM/yjx4Lsi4vtXQVBgPPnxcWFkYYQ07Afrq+vWy/K2ZdIvtsDIEhL/F76sUAgYBFcuI/edowaDAZGhF9cXNT5+bkBym+rm5sbi8gmPplzN/swQy7c0Gq1mjqdjvVCRK1KMocj5xCLd6RYLFqsG9eFXhNyz2g0smEVTkZut1vlctl+B4QA1iDOSBCtnMW6z3eFAHcX+RUh0Pb2tq2NxMVB1ICsGo/HbVjEZ5FkjtCdTsdciKWvHA0ZxjrFI5KUzWb1ox/9SIVCQYlEQgsLC/ZcHB0dqdfr6fj4eErUcH19bcQ/hjz8dwZBDM0ZvEGGwS3cec2IE8SJi+vB8wghpd/v21Dc+czeV7h64oSNEyuEB8gukDPAS4hUQiGeTCYViURei6eBKAfRIpfL6dGjRyYyQjSGk4OT1E9MCMQriO+ILEKhkMWULiwsKBQKTWFkvV5PuVzOegVcigeDwZSD1t7eno6OjpTNZqcILqurqxZFetdZA9Ef7n3cm83NTdVqNdXrdVsDEBAwkA8Gg3bmv7i4sPXI+e7jRETfgDM4zyeuSl6vV/V6XbOzs/riiy8UDAZ1fHxsg1jc46vVqp2p6McQvrG38mdOYQ59PHsv/b/TfUySuVOfnp7a8+/3+83NAFIjQ35IXKVSSQsLC9rf3zdnNNz+i8Wi4Xys47j7JhIJw0F3dnYstq7RaFhEI+SRcrms4XCofD4/tQ/yXOH25yQxg7fgrAi5/fz8XOVy2a4VPa706lyFm5Uk21cgcUajURs+gtMgPri6urKooVKpZM4nuF3h2McwcXFxUalUyvDAs7OzKce3dy1cfRqNhjKZjJ0zcQCDBPL73//enrNkMqlwOGx9l8fjUbvdnoqppJck6pfivAap5PY52uPxKJfLGfGE9/52lO3byu12a3Fx0QayYAecAbgXf47VaDTMEXdhYcHO+KPRyEjYyWTSnLJ55lOplOr1uq2PCIGdGD2VSqUsfs9JsoFs+z4TW75pgQefnZ290b2Y4h3inUFM6SzWybOzMyUSCVsTmJuQOIB7qtfr1U9+8hOl02mtrKyYwz+1vLys6+trvXz5Ujs7O0okEpZEgNgAV9BSqWRCIGYJuPQFAgFls1mlUql7vyu4oyTt7++bABLCA/HQnD9Yd2OxmIkSZmdnTZDhLPY2RAEIEiCVQJLkjIY4fGFhYWod8Hg8hsHyGXk3ORdCpGMvJ9mH2Mz5+XlLk2GuALEDhyFEE5IsYYI9Auw8GAyaCBFSfzgcNsechYUFI2Fz/kbk5HK5tLq6eu9ZC0Eu+y97GI6qgUBA3W7X+jDOrTi906eDOzv3XMQ5CE6Xl5e1vr6uo6MjBYNBI2mz7hEnyl79NiIR2Ou79NKsqeVyWZPJRPV6fWq+Ar7P7AvhUKfTMbfXdyn2TAwR6IXAP0OhkLLZrAm+fT6fms2mkewpzscQuebn5y0V4nY5E27ucvP9uuUU3y0sLGgwGJgAOBAI2NkOwQUJAqlUSvl8XolEQuPx2PYAnhki1r9J0dfw3oIvjMdjw0+JyCQpgPUil8vp/Pzczue3z80IWxEABQIBc9YEW7hr1gbeE41GbZ5A6srt54aZzu3rsL+/bz3n255nZpb0WR8IWH+59YGA9RdQzpxQ4pbedkBtNpv2f79rjnYgEFA6nX7j3zk7O9PLly/1v/1v/5v+2T/7Z2/9mR/q26mVlRVzwTo4ODBVFQNQQLW7yu12G8gdjUbl9XpVLBat2XW73YrFYqY4iEQiqtVqUxFRzk2j3W7r+PjYLGkhT6DeCAaDFrP2dYrNcDwea2dnx0DXZDKpFy9emJqCA6XX6zVA7erqSouLi7YZ4vhUKBTs3RmNRsrn81Ngv9/vNxcfyB07Oztm4Yv6mIGyJAP39/b2tL+/b6BINBo1EN55bXBiWFlZ0c3Njba2tnR6eqrf/OY3BqqPx2NTsXENINkQuwYjm4EhymkGwzR0fJ/l5WU7uADWj0YjU9wRLxYMBo3BzXeDVBQOh+26Y5cPUHl1dWVqilgsZvnfDLUgCgAWOQkAkMBQpwLGTCYTO3hBpGE4iENMLpeT3+83tTUDIQgXgOFcN9wMOKiiFI7H41pcXDSHLNQ11WrVwDiGr4BZPEuodVC98Q5g65pKpawpwFo1l8spGo0aaPyuhZ0ykWeQFCEpAXQtLCwYoE89fvxYOzs7yufzFifJcIc4BK5PvV5XuVzW+vq6lpaW1Gg0jHDldruVTqfN+hgXOukrtWU0Gp0icM3MzNiAm3tL0wvZgmEFzfLMzIyy2ay5DA0GA3P8uri40Gg0UjabNae758+fq9vt6ujoSKlUykAKiHK4FPl8Pq2srKhQKOjHP/6xcrmcNjc3v7bLDAB5OBxWqVQy5ctoNLJG2O/3W7TI5eWlstms5ubmLNMc0iaAKgRXFJ+NRkNLS0v3DiWJGaBJkWSkpzdZb0uv1sVEIqFqtaonT57ce1ZIJBJmPc2zEovFjEgEcMEavb+/L6/Xq2azaU4ZThcI4hkhkOL4I8mcnrxer0qlkvb3981+nOEVTi5+v1+DwUBHR0e2pkFeYa9isIfLGiRlnjNJNiRyuVwWJwJZBwLaeDw2Ah8uEV6v15w0sIGPRqPq9/vKZDIGEvCOB4NBffHFF0ZOw7EGFyTeS6J4IT0CDk8mE/3oRz9Sv9/X0tKSKd9arZZqtZr9XEhjTncx1hrIGvzu+fl5izwBOGDPhniHMhWnPsi5qDYBbxh2ZjKZKVU3cUKTyURut9ucOZ0gOIQ0Z7xwMBjUcDg0FTm/EyUfIO/JyYkN7njO7ioUhexlkuwMUCqVTJGHOh8COUQzp4MYir9Hjx7p5z//+VvXCuri4kKxWEyZTMb2T4jafCfuN04HgD9OcuRdP7fZbJqLGqpRBhp/XwpnSGK9ut2u8vn8awMR4sQmk4kikYi9zy6X662gCOAw8Ur3AaQ4DWSzWc3MzCgQCNxp5w7g9+DBA2UyGSPbOaPkb+9NxCtEo1FTnN9V1WrVnE7j8bgGg4EN6zlP+/1+2/+r1apFyeIYxtqZTCa1urpqjgmAy+xZkgzYTSQSOj09VSKR+JOMKEQRyfAAJT7DVc7NnBcgSRJP6+zL/9wLggH7H46Odw0lOZMTtw3J+HYxVCaSJhwOv1GQ43RPetsQjjXfCZA7103p1brO8BeyGORuHJog+xKjt7CwYH0LxTMCyF8oFJROp00Bj3NmLpdTOp3W48ePlclk7EzvdMO8XfST19fXRgAbj8fa2toykBZ3F5yWIIutrKzo6upK4XDYHE/ZV95W7XZbwWBQtVpNjx8/NpcESJn01BDeq9WquZfSZxKniwOOz+dTIpGwAYn0atDuJHz2ej2tra3pk08+sX317/7u72z/BzM4OTkxYdnjx49tHW80GrYP4mrIe8vZE4yM93dpaUmrq6t6+PDhneA4imtIEVdXVwqFQja45BwlvcIMIMSNx2MjJEFcghALkZx15Pz83KICEWWk02k9evRIP/vZz7S4uDjVA+LkeXx8bJgEPSfkSIjnRN34/X7rz3gncM1xuVyKxWJGPne73VPPCTGAnB+chTvwZDLR/v7+lBiMcwZxaNT19bUODw/N3ZWIIPZgBHvEmoIN4GKE41Y6ndbS0tKdPQz9diQS0cOHD7W+vq58Pj8Va4UQDlyAqBeIR5CqJpOJuZ1BwEmlUnYWz2QyNni9uLhQo9HQ2tqaYTLdbtfOnpDpIICzL/Nd2Rs3NzftXMb5j/97MplodXVVS0tLFlsCmcXj8SgUCqlYLJrjRyAQ0MrKihYWFpTNZo1gFY1GpxxpnO8+jsqnp6ean5/XysqKDdO5LvF43ERakL6IH8rlcjbwwgmLZxhsCvGZJLv/Tvc+ztTcJ8hdEM4QXyaTSevZeP+I6sSRFCIa95nhoVMoBsGD+KRer6d8Pm9EiGg0avcVB3ZEquATCwsLWltbk9frtf2EIaGz70CsyOekn+R6c66C4Io7XDgcVjab1fLysg0ncazB0ca5t9CDXVxcKJ1OG5kCgSmkS8iDHo/H1hKElDMzM0biBXOJx+PK5/O6vr5WsViccll7F7xqNBqZG+XKyspr+xJ4G/v4y5cvlUwmlcvltLCwYOdRxGSQTqVXvQ54w+3zNOSDd90Lv2n9sedAhEeSbE3/vuvq6krHx8eaTCZaWloyLEN6JeBEjA2+7RTss0cgVmBvTCQShi3fLtaG910IMcHQSedIpVJfew6BCKXT6ZhL/tuKKG9Jtt6APyPw6na7WlpaMsJguVxWv99XIBCYIlEMBgNtbm4qHo/rP/gP/oPX3j3iziA112o1dbtdJZNJwxry+bxCoZC954FAwLBb6SuXeK/Xa+/ffXV9fW3E53A4rIODAyM7I7yF1AFRwemiS38+HA61urpqPxdSGv3pgwcPpoQfl5eXarfb5iqIQ+va2tqdxBC/32+JL9vb2+bimk6np3CxJ0+eKBaL6dGjR3aGwano+PhY7XbbcGlnqsZoNDIcGUed4+Njm0VFo/8/e28WW3mapnk9x9uxz74v9rGPt7AdGRFZWVVdXdXdI8QAM0JCwAiNhmloECN6mgsQaBAg5gIkJCQ0LQEXDAimGqGWGKn7AvUFN2hQM4jeoGomO9dYHF7Pvu8+x8uxzYXz9+bfDjsiMjOyKisnXqlUVZkR9jn/5fu+93mfJaR79+4ZaQvSCfv71taWkfrD4bDN7C4vL1UsFnV0dKRkMmnnmNuKNd7Zo0u6dj1wPczn83a+5XdCcuWccxNDwNne6/XaWprNZs01EfE2pLx4PP5agqvLy0tFIhFbq18VcYdwl1hBUj0gl6XTaSPpQ0rf29vTu+++q8FgoFQq9UoM7OzsTNvb2yYc4OwGMR/32na7rdXVVd27d09+v98c+J1YHs6x4P+QGm/2lvzeWCxm++FXJe72+301m02biw2Hw2tkeJ4X+t5EIqHp6Wmtr6/bWiXJnF8PDw+vPV9f5fO1Wi3DmDY2NlSr1XR8fKxEImHiJJxlwf7BA4h8dmIJN4vPmclk7HtCYIV8fRNHg8RPrDTJDQg7Xues02w2TYR883PdTCeiL6V3f0vA+vnU//Q//U9f++94S8D6FhSLMiqhvb09bW1tvfTv7O7u2v/Gsu9V9Ru/8Rv6jd/4jZf+mf/6v/6vLXrom9Aw/JNcsHlnZ2fVbDbV6/V0cHCgRCLx0ntDti8HXYAu56GNSDmfz2fgCso6rFOxtywWixbZxiEbgHx6etrA6nv37r10gHizJicntbi4aIBuoVAwEAAV6s3oNQbWOIAQpZHL5bS4uGixMfV63Q5zODlJMmecpaUlffLJJ8bQn5iYMFUCACR21oC6gEVcm1AopFKpZA0bEX2Hh4fa2NgwAsD6+roRFBjyc2gHkMfpBPILACyHy2g0qkwmo4uLC8tBhhgCKQSygzPfmWg+6fO4Gg7hPCPNZtOuV6FQsO+PLXsoFLJrPzc3Z+40kUhET548uQaSbm9vW749yieINjhekYm9uLhoBDOUJhCvUP9igw0JAyv40WhkUT6dTkeNRkOhUEjT09MGTEIOGgwG5kIUDofNWpQIDSLraIwZwqMMoAmiaaPhDwaDRjyMRCKm2otGo9eGA19EeUfRQOEy47TKDYfDisViLxwcGSJEo1G5XC4VCgVJMnU28YaxWMzuBQom1G3hcFjj8VgLCwumeELdjKWt89pwPXu9nhFVaG4uLi5MUYedLLEcxBXw7gUCAQMLsd2H3AawAhlnamrKFAw0Cxy+IUc+ePBAS0tLWl9ft5iIL7ufbWxs6M///M8VDAa1v7+vw8NDVatVzczMaDgcKhKJKBKJGFAN8Qbbb9x+GGQQtQrYhQq31WopkUgoGAyauhYXvoWFhWtqZkBZ3q3bioEn5E4nUe62SiQSRvbFghpiajAYNDJWMBjUwsKCXK6rGNVOp6P9/X2LEsAme2Njw4b9p6enRhYjQoQIENRkAN4MdFAB4jiAKxIqYByZAL1SqZSR1Pb29myAhnKFZpuhMfE2DNJQJmYyGbPuHo1G2tnZseE8wzynGxLEdsidPAOdTsfIbLjzRaNRbW5uWgwfQ2zuiZNUAxgOmdOpamMoyR7JgBrHH8Ae1mpAQIADmndnzBqDu1QqZa4FgAfxeNxARdylyuWyDV5RDxLRMDc3p3q9busOAzhU8Ky5XHdszQFHGEaPx2Mlk0nl83lT7HU6nWvAnfS56r9Wq+ny8tKIxBBjIBpLVxGErCe4ItHwc/aBxO7z+V5wpntVtVotA8LW19dfSvpMpVLa2dmxiJfbwK2LiwvlcjkjrnU+i0UGOOt2u5qbm7PB0re5iOJk6AZ54bZrPDk5eW1IMDU1Ze/Lq+5nOp3WYDCwONTbgDVJdhZcWFgw0Hl/f1+hz2JoITqNx2NTH66vr1+LtHB+3pvfdTwey+/3vxQkQuHMeRXlJkNeIoYA8NkLzs7OTImYTCa1uLho68729raty+++++41Ehr7vXPIB9D7Talms6lCoaBKpaJ+vy+/36+FhQUVCgUTpJyfn2thYcEGksSK4cTgJDx/GwqH2Xg8/lLyE+pSp1PNXe8LzygEcSfp+Wb5fL5rkTe3/Tnihxj4OAlYEMHYuyQZUfzs7EyPHz9WLBYzhweG16enp6rVagp95jxMbAHODQcHB9Zvrq6u2s9mmA4hg7isSCRy53pws4hlQzwS+iwm2uPx2HUg3pwBPq6nvL8nJyfmNEpk/asKdTGEX+7hbYOsubk524MGg4HG47GtpziCQYAi+g4Xas4uKPErlYqazabW1ta0sbFhxDUI1DjgTExMmNI4HA7r3r17Go1G+uCDD9RsNo0gcHh4KOkKRC8Wi9f28PF4rGw2a9gZfauzLi4uLDaZHgXxztzcnGKxmDnKQJDg50kyFyKiynBQpYfhLIMAq9VqKRKJ6Ec/+pEePnyo9fX1W/d0otGJrCiXy+bew+dBGECfy/OKAAdCGy5/g8FA6XRa+/v7tq7RAzSbTesPidZkoMVacHl5aevi+++/bwM78AgIvn6/X7u7u3bOoSfzeDyGKeAa3Ww2FQwGbUAC0RxyRSAQMCdSyqnOhyi6vLxsLtiIphBydLtdPX782MQJEOydLl48HzjZplIpc5CHMMR5mv+PY/fl5VU8N6QkHHYQMDx9+tQES1NTU1pZWbF1g/MBhL3OZ1HF4XDYMB1cO4hO/+CDD4z4SLQ8kVQ4bafTaYu59vv96vf7RoJm3WSoeHJyooWFBROVIOZj3QmHwxoOh+bQi+sGuAX95vHxsdbX1zU7O2vxhGDWYC8ff/yxxb/TwyaTSYu8PDk5MfIUUY3dblcLCwvy+Xx2PnO6K/K+BwIBc5rm3fN6vRbZubOzYwRRsAcEVIeHh4ZVQqKiN+r1evL7/XYu5P0Ih8MaDAYqFouGLzlFZfSsfN6zszMVCgUj1ePiBT64trZmZ0FIE1zPnZ0d66Oq1aq56OPYRRRpqVRSqVTSxMSEERXYd7/73e+a23ypVNLh4aHhRul02nq5mZkZnZ6eajQa2X2i36H3ex3iTKfTsT4nHo/fui9BaNvY2DCBXCwWu+b2i6DKeSbgM7H23FzTp6enzZXyF6HodX/ehRP58fGxuafgzgQGDfEXIiju3JAHEf8jEsbx42XfDwI8+yuY5BctnEmdogrwWPD3cDis5eXl1yYSTE9PG+EYDONlxfoyOzuriYkJiykF7wNPx7lpenpaLpfLyFCIhDmP0b8iSnBel36/bzHF0uckV85ac3NzJrqG+M6a4ySWI0x9VWTb5eVVXDPkjUAgoNXVVQUCAZt1EDmIQCmdTiuXy5ljKmQQhNKQg2u1mtrttsbjsVZXV1/o2efm5pTJZOR2u7W9vW2f8y6Hf5/Pp5WVFTvPcYajByEtxePxaH19/QUcnjWGdQeSK/cRwcHk5KRCoZCGw6GWl5eVz+fVaDSUyWQsDQJy24cffmjvSSKRsHkaYgO+8+LionZ3d+3MeFc/4Yw1PDw8VKlUeoFkyB5I7C/GApwTA4GArZU33zkI/DfX2dtcgL5okYrwOngLDo0XFxd69OiRuaudnJwom83aGVKSuepFo1FVKhVNTU0pn8/r0aNHd64pPNfdblf5fF5zc3PKZrN2Fjg9PVW73ValUjEh/r179wwbrlarFrfNHIj4eIisJBs46+LiKio9kUgYCf2rFns+szIwdUjvGCWwzkciER0fH5sDdjAYtOcHgwPmft1u9yvdd/pOnHXBY2/2raxXnc+iEUOhkBYWFswIhM92s3hOmTni/OmcTVNghf1+35xowYWJRXQak7xMFOaMpH/Zs8zn47/5e2/rZ18YRnyd9ZaA9S2omZkZbWxs6OnTp5KkTz755JUErE8//dT+97vvvvu1fr639fMvyCgMaF9WEBpyuZwBWJCjnBELxAg57XcBE2nui8WiCoWCNWzOWD8OIsQIuFwuPXjw4M4NB8ARAgzDTQ6+4/FY77//vvL5vJrNpgHWbJo0JzQwgP80FagbseJHvSbJ1BzS1YEetR3uU7CpIUrMz88biQJCCXV5eWnEIIhAWMpD4nr+/Lnu379vg/8HDx7o4ODACAQ0rIPBQJOTkxYxSOPCNQkGg0omk1peXtbi4qJGo5FisZg5sXDg2N7etsOMJIuJwtVmfn7ePiORFmQnAx471SEAUqhCAOpoWHCHQvXMIAGCBuAMKsz19XXNzc2pUCiYspZIvwcPHqhcLtvBejQaWeb66empnj17Zp8REHVra0t+v1/Hx8fa3d21GK9Go2EDSVxaYKEDUKJugbzhdO3BKYPNO5lMmtqT9wXr85WVFT18+NAUSjDxaXQmJia0t7encrn8QkzA65TL5VIkEnltBj1ACo2w1+tVtVo1BSZD2VarpYuLCyUSiWuKAdSXNIqRSMTWA94jbH+lKxXYxMTENbKEJFMjcy2I1aRBoHFwZqdjH9toNGxwjionEokoFotZdFIikTAFMOQSIiXS6bTu3buncDistbU1ra+vf2lFB8TQer1uADrkSVwPAGRR4PR6Pe3t7VlTi1U+MWgoRBKJhBFTJFnUSr1el9/vN5tgQBpIaAwSXge8AjTDveBVBaBEjCXvY6lUMiCbaE6uNVGQzmdmc3NTHo/HGr5qtapEImHkOO7tzs6OuRCdn58baQrCF5FwkI+xBaexdLok8I6kUilTB+fzeYXDYQPMnNfs6OhIjUbDFNbsMSinAQl9Pp+Rwtxut+0duMUQD3FwcKDFxUUbJODKgYscEQbxeNzet3A4bMNZlHDOf8f3YAgAoIVqmkgRhrlEitZqNRtMo0DjmSEWg4EspFdc2iTZ92cYhasYANdgMLA9kNgXrMGdlvJYstO4QsZiUMpAVpLdT0g1w+HQCGLOWBH2kEajoUQiYW6ErVbLnkuUoNLnzpiSLEaGa8Kgm59LJIcki+5gGPVFajAYqNfr2X74skJVj937zbq8vNTOzo6pNfmOXCcs/VOplM7OzrS5ufmtJmE5z0ler1ftdvul5CQcN05PTw1svCsmzVm4rEHagQzpHFYPBgNVq1V5vV6trKwY0ZV1k/Ub0v/c3Jydg16nOMu9qhiEotKVZM5OHo9H1WrVIpnK5bK53DF4nZ+fN8V7JBLRBx98YMOVjY2NFxR/ExNXMbQzMzMWV1CtVpXNZl/r837dhaNDtVo10Hx2dtbW0OFwaMRISKb9fl/Ly8uKxWLK5XKmdA6Hw9+K94lhpiQbrt1Vfr/f+kxn33NbEdeLmOdl76Lzeb7t/AIRHUEFDgP0egxEer2eVldXba1mjQRQ9fv9WllZsTMPpP5cLqfd3V17H1kb2u22Go2GNjY2rl0Xr9dr4ptsNmtK6Hq9brGmryr2Z8BoBvYM5CAPsJ8DBkMwajQaJgjBuex1CryA3uZlBWke8gB7M4M0VPI4JUOA52wJWQiXl263a66oDJbG47EWFxfNEQxX1WQyqUePHl1zaUa8Qc9WqVTsDE7sGkIXrmG9XrezJE7QbrdbrVZLzWZTu7u7tmZzboHQH4vFlEqljISC0zCEHGJRzs7OTE3udBKl7+R3QtTY2Ni48345sZBer6fJyclrDgX0tpDOcP7BeZo1mSEDBH2ctYi/yOVyRjK5iYW0Wq1rjuEQDnF+KxQKRtiFOEWU9GAwsIF1Op22CETp86iq/f19u1b8Tp4DCG0zMzNqNBqGv0DA4ayIeHB+fl7xeFz1et36+7W1Nc3MzFh8JiTai4sLc7JwxkziWo17O05rjx8/vhYRh0MXmNjk5KSdtSFCUdFoVD/4wQ+0t7enZDKpVCplceb1el17e3t2HkZgxzDOOQDkWUfARnQJPRNrINcNR0sGSfV63fAN8JnRaGQ9OW5Rzufv8vLy2j+jt2X9gdTHuoWTpiTrvSCZX1xc2FCZiKnV1VUjAkoycsJwOLTzOg76XG+GrpeXlyaAkK6wBvAGxHisEThJEefp7B8QN0M46na75vKAMEqSRRzSNxE3hZs3A0zIU7geQHZwRlMh+CEadzAYGAkLd2L2Qp6xeDyuw8NDcz7jPZqbm9P8/Lx9J1zb+RyQk1kLeadGo5HK5bI5BTK8LpVKhvlUKhX7OxBoP/74Y3P0eFUhjHK73a/scyACN5tN68fZP+fm5uz3s65BIrj5cyEMEaX8tr5YOeMCEQGCjdCvejweDQYD+/+QLOlnLi8vzcUS4sld92I8Hqter1sPRk1NTZkL4V0CVZz3IW25XC49f/7ciBIIEBHxEUkN8WhjY+O1zmicf/x+v7rd7ivj4Vk76bk//PBDeb1eI77ihoRICKEKDlKsmeyfkO3ZA50JHru7u6rVaiYg+JVf+ZVrPx+hAiTmTCajhYWFLy2GoXfN5/MKhUK6f/++nj59anMOyECNRkOtVssEnogfKeK1p6amdHJyolQqpePjY7XbbTtD3VWxWExPnz41AvbLeoq5uTmtra1pYmJC5XJZ0WhUq6urkq7OUAhanWdgzn/SldBqZWVF4/FYu7u7Jtp+8OCBuXCdnJyoUqlYykW32zVnOIQekq45/SNkJm2E+8/3RpjZbrcNZ7/rPVhZWbGzC+cqCFXMryDK9ft9m2uA4bbbbUmy8yr4q/S54+sXSeh43UKUfHBw8MpUJ2J+IQjR93CWd5bf77dZF+YUCODvcuLmWUXIeXN/I1nE6/WaSKtSqejevXsqFou2rkD45Dx4dHRk5KHb+iywF/b4N1EI6AKBgDqdjhHUOFeBc4AX34yN3t/fNzMCSXb/mcd8FQIWZ3PmxBCRbsPcEG30+32Lc2UWe9cMeTgcGqFwaWnJUo6azabF4xJ1zrw8kUjI7/drcXFR5+fnKpfLNrNmjonAKxQKaX5+/gXMB4d08LOX7Sv0mbjLvq1vb70lYH1L6kc/+pERsP7wD/9Qf/Wv/tU7/2y9XteHH3547e++rW930fS8LugLS3o4HJqSuVarWSYwDQ0kIuIQONyjNAQoQWngLMAkmnkyeW/GXLZaLVUqFbPOZgPDpSmZTOrhw4eq1+uqVCp6/PixJievMqcByzkkAAyiVCDfGgIXzRoDr4ODA1OLQMCamppSMpk0p45wOKxarWZRMjhkYHsOKIhbGGoV7sv29rYWFxfl8/m0tramYrGo6elpVatVO5h5PB6z/gXE5ADVbrfVbrfNbQbXFwgAuBPwczwez7UhNo4yHDCJBiOai6H81NSUyuWyKRISiYSBhTikAFZ6vV5TTnc+y7imiQCQhZziHDZi1c7AXpKBw0QnorDn+k9OTmplZcUao1qtplwuZ+pjVMZ8/wcPHtjhF2tY4raePXum4+Nji6s4Pz83e2waXBpBriGW+xAyPB6Pstms8vm8EdMmJia0sbGhXq+nQqGgWCym9fV1RaNRDYdDe/7n5+e1sbEhST+z4TjA9dHRkba3tzUcDnV2dmbgOIMS3M6mpqYMmGRAs7+/b0MWwBQiNXAhQIWPPTWW/ygby+WyJJn6GCAElTVOZ5lMxizDt7e3jWxDo8A1ouHNZDIWwzA9Pa35+XmdnZ2ZZf/p6al8Pp/i8bgRMCCLftm6vLw0sJOhkhOcBuhkPWagV61WzTnQebg/Ozszsg+uagBQgGIMvIi7XFpaMtA/lUrZoIIGA4Loqwi5kL+ILHnZd65Wqxa/FggElMlk1G63TWkHKQqQanl5We12W51ORysrKwZoRCIRi8UKBAJaXl6+psBioMVQiHWIaDqAasAfSIW4M6DiPjs7s3uBpXI4HDYQolgsmtsdRFdn7Bs24wx4uZao4CEW1ut1LS4uqlQqGZh/cXGhhw8fKpPJqFarWeOORTLA9Pz8vLl6OCPHGAbxWQBB2MeIDOF5K5VKtgZy7yFWRSIRu29EBnPNAIMgUuPEiKsBRBYG73xG9mkaVefAxUnmkmTEXP4+w0Wi/Yj3ZQ8AfHQOVfl3ksyhr16vW3wKMT9+v1/tdlu1Ws3WcCJ/fT6fBoOBWq2WTk5OTGUvfT6sRPm9vr4ur9erw8NDG7YCbjB4YV35IuUkxLxOOeM0b1axWFSr1dLBwYERAJ1AFs5fxErs7Ozo4cOHX0pd/ItQDK4YPOHkdFdxzuXsAiH4VeVyubS+vm7nyWq1qt3dXSOpElEF4T+VSmlhYUGtVsuiMSiPx2ND8y9CBmbfZGgFaQOXTv7MzXUEEiRnoGQyaUQ1n8+nJ0+eGNGCd489/tmzZ+p2u0Z2wYnhtmKIzdqRyWS+EWo/hhLtdltLS0uanZ015yOGpgzRicQmCnVubk6Li4umsq/X6y+9Br8o5fV6zakIksZdBQAaiUTMQeemSw1/rtVqmcL/VTETCAF4Pp3F2tVsNk2hjDuM9LlCuVQq2XkGojd9a6FQMJeYm5+T9bBer6tUKikQCGhlZcWIegxbDg4OrpFSnD+DmGaU7a/6vtTi4qIJPg4PD+055Hucnp4qGAxaH8N+T38FoWJlZeXa57q8vIp5c7obEWfC0PR11xscSCQZoRKHGOcZiZ95eXlpiunl5WUj4jMwBWNg/8XxgsEB5//JyUnlcjm718Fg0KJzIEX7/X7DM3hXV1dXLVJjMBjoo48+MqfrQqGgTCajQCCgJ0+eKJ/P2xonXSmribqBKEOvwHfiOp+cnJjghO8MAYDYMAYHxM9LukZq57zEWU76nLiIWyrEf4qzNq5Io9FIwWDQCGyQ9nAhx4X16dOnOj09VbFYlNvttkGpJHvnu92uqtWqgsGgvQtTU1M6ODiwHpFYWc5l/X5fxWLRXPSITfzBD35gA0nOlC6Xy9wnEEfRxywsLCgWi9lgt16vy+PxmJMOBGtIfxAYcXOi14CEzrVGIDU/P69+v6/Hjx+bqw8kQZyTIPvg9jY5OalyuWzCRdy5jo+PVa1WNT09bfgPbhPOwimm3W4rEAhoa2tLpVLJfga/nz2ZvupmZM7MzIx8Pp+RO9fW1lSv1801yRkHhHvr8fGxDg4ODC/is4H9QWLt9Xr2c8D9cG2CdEg/hsrf5XKZM/np6alisZjtkayXPLP8dzQa1fPnz02051x/cM/G0Zs+LhaLmTvLYDAwR8KjoyNVKhUTctGDM6z2+/0mmHS5XEZ8hKCB6GdxcVH5fF7RaFTHx8cqFArm3lKr1aw3hNQTiURULpeVy+XMUQRndX4+ZFSizOfm5nR+fi6/32/OZUQD8T6zvsdiMXU6HRPn7e/vm0NYrVazIXCpVDICDAJEZy/0zjvvWI/J/WMvTCQSOj4+1uHhoa1BvPP7+/uKRqO2/tLfMlx+/vy5Hjx48MpzMmfx1+k1IGhXKpUXnAlYw3F75zvzrlAXFxeq1+vmnv0qoszberGcRHz6dfZzxLL06ziaszYzHOfch2t0LBa79RkYDAba3d3V0dGRCZ8gBkAgjUQito9TEHwQR1AHBwcm9iIVwtlvnJ2dqVqt6vDwUC6XS/l8/rVFIfF43HCDXq93Z5wZDsPBYFDhcFgHBwdyuVza3t5WNpu1GEeIB07Hv2AwqEwmo83NTblcLhN3IyiH+EuVSiXbW4kYlGQutlxPZjrpdPqaKPvLFBHF7OHSFRYDnpdMJlUuly3uG7J6vV63tYi9pV6v6+nTpzo/P9fDhw9t/3pVVJzL5ZLf7zesmnPFXTUxMWGpAdlsVu+9957tCXd9RxxFHz16JOmKKEMUeTweN9JGr9fT48ePdXl5qWQyabgDc7PBYGDJD5xz6a3ZK6XbxVQej8eEBbgpSjICOaQrt9utzc1N5fP5a0QgRI6sh8lkUi6XS++//752d3eNLEdvz5kbsTP74cLCwjVS+ZsqiPhEbS8tLd16H3F9hACDEYKkWwUMk5OTdh2i0aidKRqNxp0ELJ5r5op3FUI01jew3HQ6rVarZWf2VCplsy/OzLieS7J/NhqNjCD+quf+dYv55GAw0P7+vrmWOue9mBV4vV5dXFwoEonI7Xar3W7bGY8zkyRLk3BGZX+Zold1kqs9Ho85A998J4mbBsdE3HzbnIzZSOizCPGJiQndu3fP+mmEADgsk3JA3yFd7SG5XM6IozhUIZ6MRqPq9/taW1u7Nj+KxWJG8iJi/K5iZkBP/7a+vfWWgPUtqX/mn/ln9Hu/93umevsH/+Af6C//5b9865/93d/9XQPLtra2rtn5vq1vZ01OTtrgHfXEXYWlcygUUi6XMycsbPhpgFHXjcdjs3KXpHw+byoxNpuXDRNh8gNmOglYxICQ58zAnMMj1q6JRELvvPOOqtWqNWwovRic8r0hyzgPrKh4nBs8NpUcUo6Pj6+prbCwRclFBvTs7Kz8fr+pU+PxuA3Q+Xc4+qD8dblcBibRdEEOubi4UK1WUywWs3zpZrOpZrNppAiAOqyNpSswi8/E8FmSOcCQeY7b0fn5uVqtlhFDiElIpVK6vLy0uD7cEPL5vDqdjqlIUdQBxBHPGAqFDByhGbu8vLTGEtIScXMA5djj7+3tmcMMwz0ajlQqZfFZgPSVSsWcwFAqFwoFs5+HZY9yj0MzjmJOVytAHJ4LCF3EYUlX4BDgLCoKt9utTCaj3d1dcxohumx9fd0Oq4VCQdVqVefn57p//76R7HguncPx3d1dPXjw4I0Nx1GncvhsNpva2dnR3t6e/H6/3nvvPVNkjsdjA1k4fALqct1OTk6MJCTJHGCIHgG0RpU8Go0Uj8eVz+d1fHxsriyQrnw+ny4uLq69uxD9pCuSy8bGhg15qtWqKb9xxQJoDofDpszc39/X2tqastmskWic68mbILkdHh6q0Wjo8PBQExMT5nAFGWl7e9sIE81mU7lczoii1WrVlH00bZeXl5qfnzewgGFKp9MxN7JIJGLKe2zFGW4xsEmn09YYhsNh1et1a0ZuFv8MB6zp6emXktJwmjo/P9fq6qoePHhgFr0oDlGfAKb3+315PB7dv3/fBivO4dVoNLKhhrOcRAocEyTZwHVyctKGlBD8aH6JXGDwJMnWLBwRIekdHByY8xrPPOvG0dGRDUkg8eA8gZIa8tfR0ZH29vaMMJrL5bS/v6+DgwP5/X4tLS2ZaxJrGxb/lUpFkkyBRExLv983IN+pfAT4ZEh8cXGhjz/+2PYW3AsgwUJ4gjCLAmppaUkPHjywZ424GicIGwgELEKGwRv/H4IG6tWTkxOzk2dvmJ2d1XA41N7enu0FDJYhEgMWOYfHfr/fyIgoUInZGI1GNnjEpQonyK2tLRtOoyzke6CyRCXLvkJMJyTI0Whksb0oGp0xmKxP2WzWhh7OgtiG5fVNUAliNAPfVxVDwNsi6Or1ug0BIWE7a2LiKsoJBSgxX69LDvhFK2dEiXS1XpRKpTvPxDxjADNfBAibmZnR1taWDg8P7d1hLQ8Gg0ZWT6fTduYlqo8hOYOGL0NMikajRjTEac7pLMp1IOYA0qwkI3JIV89INBpVKBSyHsDlcikYDBqpADIbwFKj0dCDBw9eSeAIhULmEMb7elsx9GadZ2j9dRSxkRD9Jdk7PhqNrsUKoqZlEMJwmfcI5eQvOqFxamrK4niLxaLC4fCdg5rT01O5XC5Tgp6enmp3d9fiPHCgo3eAoPYyUhdRnPPz89eAaooBXaFQeKlC2efz2SCiVCppfX3d3I8kvSD+kWSYCmct3AtwGYUoUC6XlUgklMvltLq6+sKzz9nEGfn5OoUjl3R1Bvj//r//z/pJoh4BviGL06cgogkGgzo6OtL+/r4ikYhOT0+vuRux18zOzqrVapmAhf0XUgWRb871CCI0xO5+v2+9GhErkLPpD1mHGDg5B2+4bO/u7qrT6ZhzzL1794wUisjA6VDJABOCP6QphEknJydKp9OKRCK2TjndeRG8gCHg5oqbHdEbnB9xDuIcODU1ZUMAXGogWyCaoDfhvMn+Mj8/r8vLSyPhVioV/eQnP7FIQem66wcDO3r+m+8OUV0QOiqVihGRiAfiWZ6dndXx8bEJu46OjlStVk1URxQNzzO96Icffmh4h5OUlM1mLeIE8R6kctziJiYmtLW1ZdFmU1NT5krKWolwBDc0hD/0x16vVx999JGePXtm+yrRYpwtcX+anJzU4eGhiVWcgzmwGI/Ho3Q6rSdPniiZTJprEK7cYFg4luXzeUUiEfX7fYtsqtfrFlXKGgVxjnfktgoGgyoUCnZPcMolNhoHzkgk8tJhJ8S6qakpFQoFczkZjUZ2RkTFDykCUgPCOZzpiQwHK3ESAFlfeA7Pz8/NdYLzOoQhZ79OhBnXA0EeewluVpAoC4WC7fODwcAEgDhHQ+zlTOPz+ZTNZvXpp5+qUChoZmZGnc9iz509CyIJ3C4jkYiR/HCdpl/EDR1y6uXlpUUNSVfnLMhV9IVEsCPECIfDKpfL1lc4Cf3OvWA8HmtlZUVLS0vmANPr9eT1em0dheSxvb19LQ6Svofr63QL5Bmmr9ja2jKBzvT0tK33x8fH5t7PeRES4/n5uTk1QwijotGo9vb21Gg0ND8/r4ODA62vr9/5nLJGQeB/FUkCZ3li4pwunKxxOPsfHx8rlUpdSy04Pj42XBDHmEKhoNnZWYs/fluvrtnZWYtmBruBOEP/AMZXLBbN6ZC1A7Hu5OSkvvOd71jM580aDod6/vy5Go2GqtWqEbwhczUaDW1vbysUCqlarer73/++xb47SVvgUaPRyFzwiNC++bxNT0+bYzHETafD/8sqFospn89bcgikFOe5//T0VOVyWefn54bvgcVFo1HDlXkXwCcgZEWjUSMiSrIY7O3tbSMssY5zxiJS8WavBI4oXb27u7u75s76ZYmJTpGK87s7UwxYkxqNhomDEF+wnrD+4woI1kMc4+uIAthnwfRe1T/jxPc6saSdTsdmBKwbiHKGw6EWFxftn9NHQ6Lm77jdbg0GA0WjUSNcgWVJn+/jzLtuw3du9pSQ6cDbIEtDVI7H43r06JG5SHJOIKYQkSbn5VqtZo787LGc3XB6XlpaMlLwmy5IYePxWPl8Xvv7+wqHw+YqCv7tJNQnk0k70zNvua04fzDjwTnztuK9vPlc31XhcFh7e3sm4komk5qcnFQ8Hr8mxqzX68rlcpbG0Wg0jDCIs+fy8rKlcbxJDCEej6tWqykej6tYLCoej6vX69nZliQT0lrC4bBSqZTFKRIrzfPMtfyqn5HzCPeCFIZWq6Vut/vC2Zc9YTwe6yc/+YnhEgsLC9eIXCcnJyqXyxabzho3OTmp733ve4btM0tmHsvc6Pz8XPv7+zbTWVhY0MbGhrnlca+LxaKJmba2tuxcyzwmHo+rUqlYEsXNOjo6UrlctrPzXYTAt/XtqLenzm9JhUIh/cv/8r+s3//935ck/b2/9/fk8Xj0F/7CX7A/Mx6P9ff//t/X//P//D/2z/7Nf/Pf/Jl/1rf1sy+INrhAveyQjUqVJovYIIhPDFaxaccmHMDE6XYzGo1ey44a1ng8HjcCRrVaNSUZTQrg7/n5uXq9noFEDLfC4bAWFxd1eXlpTRef2efzGYAaCoVsqIabEc0l1wDr42KxaPbmADXkBkP8IScYJjQHOjZZoo44rKDgQwXEARI18s7OjtLptDW52J0DiBDfhULBGSUFaxvnhoODA11cXGh+fl5TU1MWydDr9VSpVCwH2uVyWWSX2+3Wo0ePlEgkVK1W1Wq1bDALa1+SMeOxy2SYx5DOqepg6A7QXi6X5Xa7TW1HvIYz877zWcwDZBpiIi4uLuR2uxWPx5XNZk158t5775lak5+1v7+vn/70p0bQCoVC5noDyQ53MgasTktfp+32cDi05gViCgprZ6OJu1okEjEXuJ2dHYsZmJiY0O7ursUlvvPOOy+AETQ6kMoY9r2JQxnRoBDOhsOhisWiDTtOTk5ULBY1Pz+vxcVFA8lcLpey2ax2d3eNUILLDS41qIempqYsFov3YWZmRul02mL0IBmdnp4a6IrzmCR7hgF6Gag0m039xb/4FxUMBlWr1fTkyRMD3wGEUZsxZAoGg0qn05qdnVW321Wv13tlVO+XqdFopGazqUKhYMQHJ/gLAQ0gnPeu0Wjo7OxMoVDI4uVYQwCbUfs+f/7cBnlOS2OIlKyhNACA0ACnqH5wO8LRzFkQYCCEOIcwt1Uul9PJyYn8fr/ZemcyGUkyBTLxlbj2LS0taWNjQ6FQSPfu3XvBoeEuFwb2J9zbULagfMfdjr2BdZh13Kk449lmTRiPxwa84QjFtUflCEiGmwFuBTRqzoaee3N8fGwDlZOTE4v3isVi+uSTTxQOh63pZngEkNhoNNRsNlWpVJTP5w2MYV+B9IUbEnFDDAlwbWy327auNZtNU7QRjYqabmpqShsbG/J4PFpaWlKn07HBAWCVJHM2g1Cby+WMiIQj3tnZmZ0DIC8QpRmLxSzexknSBBgBFGNNcg5xnbEw/B6GkaxJfB/WI7fbra2tLf3oRz/SYDCw4TlOc3/yJ3+idrttQ3YnyWBmZkbr6+tyu93a3t42ohrX2wlAcCZg/8lms6bKdQI9gI/EIUqyeMNWq2XD6buKgZfT7YViXR0MBkZ8u6vC4bCBHPV6/VtLwCKiKxgMGiGcc5dz+EvhYEnsMoOe163p6Wmtr68bqZ6BOWdQCFdnZ2d29oLMJ8nOKJFIxADo1y2I4H6/X++//76CwaARUiFgcR517vsoX28WZ/xMJqP5+Xklk0kdHh4asTebzWo8Hts59K5IBGfhdgnp4madn58bqMwawXUlWhSxx5sqCN3O94nz683vRS/kvGeSLA4ZQvrL4hN+UYroZuJ5Occ5CwDR4/FofX3dBme4BbXbbQM3l5aW7FkbDocWNXzzHUSowFn7NkdBFMoTExMvVSh7vV5b6wKBgJ0Nu93unecb+l9iJiFJ4w5DDB2qfrfbbREezsIZDJLFFyne5WKxqMePHysQCCgajSqVStk74iS14YgJiRs3mk6no3/0j/6RERMYgLH3MBSAhATJm96m3++bGIh1EHchoqCfPXtmKnOcLJ1gOO85YiH6NIQ8iCxQpQeDQTtP8GwAonOGgtQ/NTVlz938/Lw9czg8oNpHzMVAOBqN6t133zXwezgcqlQqaX9/X6enp0okEpqcnNTe3p59D5fLpXA4bM4Hkoyww9mVsyn/n3UXkgvOV7joTExcRTeWSiU7/7EWE7eBYMfn8xk55rbn1ufzaX5+XrlcTh6Px4bgkAZZk87Pzy0mx+VymXOP1+vVvXv3XiCQl8tlI8nv7e3Zc8D3RM3PM4HDF+/mxx9/bL0k58dEIqFKpaJyuWzOYNLV+0q0z9LSkhKJhBYXF+3nQrjgXAs5DkI9uAYOCbwL3Ae+PwK8g4MDw3wmJyetXyQShXi4Tqejjz76SLFYzAbGkrSwsGBYgBOP6vf75h5IDCrCJj4DzxCfjV5Z0ktdOp3FsLZSqcjn86lcLmtqasrOsghWnD0FYhRILl6v14ZNPDs3zwQ4a9A3TE1NGT4ISQBCE8Q0hqXcW/pRYo24DmCVmUxG4/FYvV7PHOYHg4H9fM5CRM4736tms2lijmQyaZ+N9xNCGcRqXJ8YjDudFfL5vJaXlxWPx5VKpSySCYwRJzDio6UrFwMiCVkHwLQ48+BsDTHKKWbMZDK27tZqNYs6h4hcLBZNtEqcM2skhEecuhgkghfggCFd4a6VSsWIl5CveP4gmrJW8JwfHR1dc2SdmJgwfK3b7RqZ62VuOvS7vE+vwqTdbrf1Mc5hPMKw7e1tIx/wHBKjiKtpIBAwUQzu38ViUZFIRAsLCy8QsdgzWa94F74JTq0/jwoEAoaZtlotE/iBTbJX0FfjhkSCxng8NlIBKQC3XcuDgwO1Wi3VajW7Z+D5EJoRyOGM/6u/+qtGZIBAnEgkNDMzY7HgiDTB3Vg3KN6N/f19IwqxBt9W4/HY+vrhcGgOcv/4H/9jEwLggjccDs2lB/Er58WFhQWdnJzY36/X6+ZeCj7NmuXEBEgRGI1GcrlcFrsK/g159GUFJs47+Kq4t5ddC0nmFkmxxrPn8D3Bk/x+v30felDOl4gqJBm2+TpF0gcOvC9zIWf9X15efi3cg/XeSeJ2fi7n84zLIY5CnG89Ho8JDp29OOdbcC3cpjjXOOvo6MhmJefn53ry5ImRVSDZ83N55jqdjtbW1m5953D3x2Fubm7OhBcIHwKBgAqFgj7++GMlk0lzD/26CKwLCwsmemUGRXyjJIvY5Vlij+Xa31ZggtwLJ1Z4W931XN9V7MNgg3cVJMxSqaRIJHItkQBiazQatQSYN1nT09NaW1sz0l+pVNLh4aEZHGDowNpAryVdxV3jfMs8iijFrxqZxzqEWIKZbSwWs/uOmId51ezsrJ1DET74/X4jmDmJ5KlUSouLi9euJ+d8COahUOiF9aLzWbTqs2fPFI1Glc1mr+0LmEHcjKBcXl62P7O8vGzO9yQTMIvs9/vWW4Bprays/MIL997Wy+sbQ8B69uyZNjc3f94f42da/8V/8V+YvTdFcydJOzs7+g/+g//ghb/3n//n//mtzcq/+q/+q3ry5Ik++ugjnZ6e6rd/+7f1+7//+1pbW9PZ2Zk+/fTTa7/vX/vX/jU9fPjwDX6jt/VNLez9I5GI6vX6rVboFOArQ0uISk61FU3u9PS0xWVhw10qlezfud1u5fN5hcNhszi9rRiySp87OpRKJXNOWVlZuXbIo7kAODw4ODAVPSpppwKNxpADGkxzZ4QIg7nLy0tTPNPM0QihuiVW7MmTJ+bygatWIpEwVSGRRLDsUWsBSIdCIS0uLprSZXd316z/ATRDoZA1sBBjNjc3NTExYYBAKpWyIZ9zSDEej3VwcKD9/X2zOj86OtLu7q4pft577z0jwJGnzAEG0A3lc7/fN2eF8XhsQKvX61U+n1cikbAhIgNkv9+vZDJpABIEum63awQY7JQl2eegOcGOE/cyHL1QOgA4SVeH63A4fG34AdmLvxOLxUwV4nwHJicn7e8eHBzYgbtUKhnzHiARpRLEGJ53wFqeT9xxGNJHIhHNzc1ZlNXMzIzu379vANBtB21IXAzHvywBC7Bge3tbh4eHqtVq5kKVSCQUi8XMpr/f7+vw8FCXl5daXFy8BsJDyqzVakqn08pkMhafd3p6qlKpJEkG3DkjyQALiZWYmLjKssehDVJhp9OxuCaAY+IOUAYkEgk9fPhQf/RHf2RWtU4yoHTVVHLPANohAL5uM/9FC1X8ycmJEZEonIlodlGieL1eW1sgLPZ6PaVSKaVSKQMTcBpCBTg9PW3qV2zJJZnaA2V9rVZTKpUysEy6eq5QAO/u7hogCmAGEWV5edncRyDI3SxAcgAsmrWpqSktLS2p1WoZGH92dmYuhDirOJ2vKIZyzjguZzEU4ZnAhQXgotvtGtkAchHvFwpnnA1Q583NzZkzGuvW+vr6NUv4YDConZ0dIwVzzVqtljVqKJhwTUEx7iQUskayb7jdbnOE2dzc1MbGhtLptL0v2LQTJekEDyF5ttttvffee7aeQJbFTW9mZsbuxebmppG6AP9xvJOu1otEImEAzsTEhNLptLkfQmaWZADTeDw2Fwx+J+5vyWRSHo9H+Xze7OdxzyG2dW9v71o8KXsFA0fIWc54klKpZIAba8Dc3JxdV5wdAbqcRCcngYB1Cxvr2wYIuNkVCgWzf6dhT6fT5pKBI1exWNTFxYVyuZzcbrepJwFaeO+bzaYWFhaUSqXMen12dtZUiHcVQ+fbog4hDUxNTb0yZhRwDQLct7Wc9uusb/F4XOVyWdVq1YgOkmxww7u2srKiWCz2pcBpQPbb6vj4WNvb2+ZWgeMMn5dBfLfb1b17914b5MKF6OnTpzo7O9OHH35oQwFiqBuNhrnGQYZEOOF0Jbi4uFDns4jbVCplEbidTkdLS0taWlrS/fv3bdAF+Rl3EUkmgnC6Jzmde26SCE5OTvT8+XMjiNZqNSNrck/m5+eVzWZ1//79NwoC39znIFDctvdBCnUCuE4g+IuSbb6p5fV6tbS0pMvLq6jhQqFga48kI5tAfshkMkqlUup85mzsPLdCPI1EIsrn82q1WhbZjSswPRH7eyaTsWgPZ52dndlw4XUVyq1Wy8j8EERuIwziDtntdk3FSu9I4Y4YCoVMfMLQ21lO9+kvA6h7PB6trq5qaWlJh4eH9j4NBgMjDuCANTMzY8NSiG2c0YmJRmEeDAavOds8efLE4hoZfjAgZMjKd4nFYqpUKjYMd7lcev78uQliGIBdXFzY2Z64M/ol4gGPj4+1urpqDl69Xk+zs7NKp9Oq1Wq3Rl8SUc3gQJJWVlaUzWaVSqX00UcfKRwOmzsd5w2GDefn55qfn9doNJLX67U+HZeKf/SP/pGkz91g6bGnp6ctmnl2dtYIH26324Q3RJzQ60mfkxrAHijIWTjeNptNtVotwzLoyXBMRUDmJJHc9vz6/X4j3nKGpSd3RkTX63V7psEa6GvBUIhRw6FrYmJCKysrFiXH2Xk0Gtl75XK5tLa2ZmszBJxKpWKD9HQ6fW3IRh+B60K/37eeDrc33Fa+973vaXp6WltbW+YWgZN4JBKx74nDKmf8fr9vYif2HlxNcedg7fL7/QqHw0Y62N7etl6L+Em/32/PZjwet2ei0+kolUpZb91oNMxdDSIUg8XRaKRPP/1Uv/Zrv6bvfOc71ld/kQLHgvA6Nzdnbl58nkqlYgIV51q1trZmTirlclm9Xs8cZxAKQtg+OjpSqVSyQS8DR9YcImtxHEfUwe88PT1VOBxWNBpVvV63QRWiNrfbreXlZQ0GA1ufTk9PFY1GzUFUkrnctFotc9blvcYlM5VKmXMH74hTiINLTr/ft8hz1lLczHDXYI3gucrn8zo8PNRwOFQ8HlcoFLII0vn5eetVnFFP/F2indh7iL9EuMI6jhMMZCj+HcQ63LmKxaJF8NH3SDInz+npaTtjIhxi/UDg6FxD+LxE57De5vN5u3apVMqii8bjsTKZjGFyjUbDBFh3PauQNCGZ3eY+Th/F3u/1es3B6vHjx2q320ZKIdYaJybIBAiTeJ6I5R6Px0Zk7ff72tzctHWuXC5b7+7s1wqFgqLR6K14HQRHXNqcvbPzTP0yd5ZvctE/DYdDHRwcqFQqGX53dHSkTz75xJI2hsOhYrGYEaj7/b4RuSFG3dZLDQYDDYdDNRoNW6sbjYY9o5Ch2bcODg40NTWlP/zDP1Q0GtVgMNDCwoK5S3U6HeXzeXs39vb2jCD46NGjF/oG1p9X9cK9Xk97e3saDodqt9uWAgDRisjoVCplLtrEASPMAgOEAJJKpQzzhuyOQJp100k4bbVaFvfN/19cXDTRMC7bryrwEklfWjDCGfImwTkQCFiUnNM5EKdQiPX0mM7z6/T0tNrttoliXC6XEWRfVswMJicnTex329qCcJG152VELYrzws0egO/PGVP6XGAEqQUyE2JzBMvS52JK5mv8JxaLmVCLQpweDofNwbDRaKhcLsvr9SqTyRhujmjUSSRdX19/oU+CRDg1NaXNzU2LwG02m2o0GvbOgUlw715GUPyq5XK5tLy8bCQ+XPzBcmdmZhQKhZTJZFSr1bS/v29rzF3vLqJl0k0Qot4lbnM+16/TyzuxgFf1ogj+udbsM+yLX+ce4ff7tbGxocPDQ3NwwmWKPRSXZSfWDFYNOVmSnW++qhMa8xBcezOZjDKZzLW0EHAoSOiXl5eKRCJ27p6YmNCHH35o32F5edn2g0wmc+tnBCvPZDIqFovq9Xo227y4uNDz5891eHhoIlzmeLdd02AwaO7nmUzG9hev12vvHYTUer1uPQ6JSjhev6nIybf1za1vDAHr/v37+qf+qX9Kf/Nv/k391b/6V19LPfuLXgyi7qrj42Pt7++/8M/vYtVOTU3pb//tv63//r//7/XHf/zHkq6ikA4PD1/4c3/9r/91/bW/9te+wqd/W79oNT8/b83x4eGhDb/ZIIbDocVI0TCi9iHSDxAa9wkO7U+fPlUoFNLq6qqi0agdgLBqpcG467DmtInl0Mww2GnpenFxYcDPcDi0pqdYLBrBhiznhYUFhcNhcxuBwAGwj1J5bm7ObLWnpqYssqVUKsnlcikajWppaUkLCwuKxWKq1+v6+OOPzYUGVTNErnq9bgo9GhmUhcR9ochjI8fyFZCY4dnKyoqBrR6PxwZ1P/nJT0xtSswH1rEoSNPptB2Q6vW6uc4wbN/c3NTi4qKBOJOTk1pdXb0GRofDYQNvUPhxfUulkkajkYEeyWTSDhKAGERHAQoBDp6fn9s9AuT95JNPdH5+bsSg09NT+5lHR0fXLGkZbpydnalSqVjjdNuBlQMPzy4K2dtqYWHBHMM4FNOIT0xM2CEV0JEhw3A4tO8Xi8WMXICC4PLyUisrK5qfn5fH49GzZ890cnKiUCikzc3Nlw7IXa6rTHun89gXrU6no/39fQ0GA3344YcqFAqmDsWJqd1ua2ZmRu+8845qtZoODg7M4YJIlZOTE+3t7Rkon8lktLGxocFgoJ2dHVtfuDfHx8e2xjiVJhDSxuOxSqWS2fCTyc4wg2YA5TcuR8Fg0IAGgG2Xy/WCugF75VqtpnK5bM/IbXnib6p4RpwqXa5H57MIJ55x3MecTTiDqkQioWQyqXg8rqmpKVPjQyoKhUK2PkM+JWKg2+0qn8+bsjkejysSiVxTZBBrgzsVDQH/jMaWJqxcLuvw8FCxWMyy4dkXIN3OzMy8ALri5EY8HU5FU1NTymazBiDfLAZnBwcHt6rynNEBOJzFYjGLoqvVakYkxn2QaK/xeGwOUTghdTodxWIxvf/++xY/98u//Msaj8emAO92uzb8RDkMEQGiGMQk4o4AglFMsx4xJJA+tzBm3+MZfvDggRYXF/X48WOlUikNh0OtrKzYHgKYxzX1+/0aj8eKx+Py+/2q1+s2QO71ekZOYv3EndIZ2YlTB2pL1tilpSVz9uJ+A7SxvkOwwNWwVqtpcnLSml4+ByCr0/mGZrnZbMrj8dhzxNqLYh5XIuILIYwxmPT7/QawLiwsaG5uToVCQZFIxK7/bQWJjcjFg4MD268Bzrxer1nHM4RMpVJ2PuDaMgB5/vy5PvroI83OzhqwSwQCca6FQsHeJ5fLpWQyqVAopHQ6rcPDQxs+ONcSBmXdblfZbPbaAJByxtG8TkG2l3TNseTbVqjpUqmUisWiEQ273a5KpZKmpqZ0cnKiVqtlsUfvvvuuIpGIFhcX3+hnGY/Hev78ua2hPp9Pi4uLNnjFAe/g4EALCwtyuVza2tp67XuKk9Pl5aWy2awmJibMfUO6AtMePHhgZFXI3oCXrHucO5LJpPx+v8V9QDzh/WY4Vq1W1W63NTk5aYNG1kSG2vPz80aqQBV823U5ODgwNzmIjQyAIM7V63X90//0P/1GntnbiL9TU1MmEnHGSxElRAQZBbmen/dtKdzGcH3qfBbzK30u0oBs6IzVhKyCspuYc0na2NjQ8+fPzZUJsh3DIwi8uN/crJsDnVcVv9upUL45UKH6/b7F5/KMszbidOTz+SzC1+fzqdfrWd/pxLEYLhGJ+2WKc7Gz5yNqD0cpnBg5j0EKSiaTuri4MMzpL/yFv3BtkMUwClIRQw/EDwySIWf1+32VSiVFo1ElEgktLS3ZGR1ClMfjsZ4AkQoONwwlGThBmgmFQuZszTkFYQekfecezqAgn89rfn7eyMgop4vFol0H3FmIDPV4PCYYQjCFYydRSpCicPTCMRNyqct1FfOCUwEuLZz5vF6vYRPOoT9nAGI0IFYcHx8rEAjo4cOH19zDQ6GQjo+PVSgU1PksDpP4s4ODg2tEJ65Zu902IhUkBJ4bRCv0sYFAwN67/f19+f1+/fSnPzUHDZyD6T+I6a5UKkaYJ+4M5x6v12vnat4zYu6IwOOa1et1E+zwPOOuxD/b29vT7u6uRRZeXl5alCEkNqdTmvS5sAoH7+npaSM6M5glCq5Wq5mIi+Go2+22czFkDhy9IBpCrAiFQrYGIlikX83lchqNRibAgjyE29p4PFalUlGj0dAHH3ygd999V++9955WV1fVaDSMaMKzDLnBWTgpTE1NqVwu6zvf+c418R7RpZyhEbO1Wi0VCgUtLy8b3oMAgedekt0fcLSJias4b8gpkIdOT08VDAaNGDsYDIwQwHvFmjs5Oal8Pq9UKqVut2uxyPl8/trAE8d4ztwnJyd2/X0+n6LRqH03or1x7eNdJZKPdR+S2OTkpL3zR0dH5kILJgE5AOevmZkZPXnyxJxyIOPgPoKzOec4hD7r6+vmIgLBZDgcWjQvhKdMJmNYFe5Y4Gwej8fEJR6Px3ohSIGnp6fmQo0zBZHXnc8c5RuNhp4/f27EAcS5znIKHcF+JV1bAxHmHh8fG76GUPN1sKrFxUWL2Dw8PFQoFDInDUgoiCDACs7OzvT06VP7XP1+39xJY7GY4vG49ZW4Q+KaRm/FOolbOdGKz58/VygUsveEyEwcVti3IGbdu3fP+ibOoQhDGcQjgIIsQS/LvvlVXUN+1pVMJo20WCgUrjn3jUYjPXv2TC6XS7FYzISrDJdDoZDhLp1OR61W64XnDsdRcEDipREQOiubzer8/Nyi0+r1ut59911zZwGjwaWKs32hULD78ODBgxf6Ke43IhWEJc6YW4iz9Xpdc3Nz5kiayWTM0Z7zFaI7XBjZh1l7EfNNTk4aJjsaja455nFWwMESYjfRYMwUOGN/EXzzpnjkyxTPNqQ59ibey/F4rGq1auRrzk2cS9jPiU6enp62WQSfC6IYjky31WAw0Gg00r179+weOteWmZkZE4C3223r8ZPJ5GvNnTk3tdttc+xlHcbtimcaolAoFLIzJmcBcENiOp0Okp1OR263W2traxZv6LxXpVLJxH8XFxfq9/sql8tKJpMvCD/C4bBCoZCKxaJhws6ztXT1fDabzWuOuBCHOW87n2GeT3rAr9sxHRIiRE/Os6SPMCv0er1qt9vyeDzqfJZ+4FwzcMh0ppacnJxocXHxTmH7zMyMPXsk+7ysWA85O71OIR79WZfX69U777xjBP2LiwtL7Gg2myYcuHkdPR6PrV2np6daWFiQ1+t9JTFSuno/nevpzMyMCRwQr3AeYN6QzWZVLBY1NzennZ0dPXv2zM4k8/PzFtkHrs78Gbes+fl5i9O8WYik4/G4jo6OtLKyck30B6kWUn08Hn+pq6DTwOFm2pTf79eDBw/MKANHLIi0y8vL5pr4tr799Y0hYEnSH/3RH+mP/uiP9O//+/++fuM3fkO/+Zu/qUePHv28P9YvVHm9Xv0n/8l/or/8l/+y/q//6//S06dP7ZARi8X03e9+V3/pL/2lNz5IeFvf/AoGg8pms5L0Qu4wh3ufz6dgMGhgcSKR0Pz8vHZ2dmww7CRYEOEH0QTrykqlYoeyw8NDc8SACHOznFarMzMzRhSanp62AzjOVO12W9Vq1YDMeDxuiiWyiTOZjCnxAFxR7xH1Va/Xtba2ppWVFWvO2Qxh/GN7iwsJ9pg0QBwCaCoBo9iwU6mUDWBwvoK8wyEQ0hYgI/bMxBJKV8Dl2tqa3G63/vRP/1S7u7vmQuV2uy0eZnp62mIFaYIZimFL7PV6NT8/r1QqpUePHl3LS5+amtKjR4/sfjAQnJ2dtQaXZmYwGNgwGrv/qakpU9Xh5AHhyu126/Ly0pjpCwsLSqfT8ng8Ojw81Nramj7++GMDRSYnJ62ZAmwajUYKh8MGsBPnFY1G71SVeTwexWIxPXv2zMBPFJq3/VkU2hCopKtDI8AkQBvqSUgKNAKovRnyALavr68rm83K4/GY01EgEHitgzfkEZ6fL9Ikd7td7e7uqlaraW9vz8DU5eVlAztpaohaWlhYsHdBulIe0LjyPJG1DRlvamrK4tGwaAZ0darAIZ1Fo1GL8wLs416jjGeI0PksKoQmtdFo6MmTJzboAky+7bpMTk4qkUgYcfP4+PgFV683WYArzsM+DnlEQEHGAejBihxyGG5Ui4uL9n7QqPO8Ex+Bwh13u9PTU1M/p9Npvffee/J6vVpbW7umqJ6YmNDq6qrC4bDFjkGQI64tHo/re9/7njkTNRoNHRwcGOEIlz7AWBrhUCj0wuAZ4pEkc+V6mRME64fH41GpVHqBXEdMn8/nM9UMxFViWgCLUWAzNKA55t/hVsd6Go/H9S/9S/+SYrGY7t27p16vp93dXV1eXlpUwdTUlFqtllnEQ/S5uLjQwsKCNbQ0WUTPMcicmprS0dGRkQRRxKGI2dvbM6AHEtAPf/hDe0+Hw6EpKHmXGP5WKhWzuYfc0W63NTc3p7W1NbsOkOMYLFSrVRtG9ft9bW9vm+sPhNmJiQkdHh7a9Xe63DGEyWazmpubU7lctliXYDBo4ATuYAy4ORfgYHd+fm4OZwwaIcsBZh8dHVmUzszMjBGbcJDANTEYDNp+NTU19QKYQAEKQzhhfwe0wzGNGFqU+JlMRt/5zndeONegGKxUKpqZmdGjR49eOHejGsvlciqVSjYsXF5etuelVCppZ2fH9h/IltPT08pms0Z8v1kMoF5mg37z+zO8/LaSryRZTBWAIlHX/H9iM4jMXFpa0vr6uhYXF984abdararX66lUKtl77CxIf8QizczMqFarWZTnywpQ9fz83IBc52CDNQVQaH9/354B4ls4s0ejUXOoXFxcNOJ0KBSyd1aSDfchH0KeYZ2tVqvWVxDfs7Cw8MJ+wbmRiBEcRXAlxYmoVqvpk08+sWv1y7/8y1/5Ht1G/GVtAmAHrGU9Q00rycD9VCpla/23qaLRqLlI4dQrXQHVDKxv29fZM28WDjb0FzcVqqFQ6FbnK+qLKpSlzx3OGCh4vV71er0XfrdTSAGZAoISf49oeFxbnbFiFMPiWCwmj8fzhZ1tnN/1+PjYHA4QOU1OTpq4A0IWwhHiSyBENBoNi/ZwFqTlVquldDqtUChkUT7EtHJ9iThbWVmxaDhJ9r28Xq+dC+nrOSuenJxYD8eZ9+LiQtls1kQ/PFdEXwyHQ6VSKRve8T4ifmk0GkokEhYTxufAIbvT6ZjrDmcKYm64fzik+P1+i8pxujNMTEwYEZrzEee4eDyu6elpxeNxra6uqt/vq1Kp2GAFl2yuCU7YOJfx75rNpg0LbgP5Z2dntbi4qP39faXTaZ2enmplZUXPnz838gD91+npqbxer9LptLlr8VxyNgC34RwHhiBd4TYIkBAVQKi5d+/etXfcGa/I/ec8Sv8tychOMzMz1xwKIIERa8c5C/I9Q2qciyHC0oPgan1XPxEMBm1QTlx5qVTS0tKSAoHANQdhnl0cy8GkTk9PLaKHQTEuxxBmOp2OyuWy7W8o3XGCk2Q4Wq1Ws/eXNWU0Gmlvb0/FYlGHh4f6v//v/9sGUCcnJ+aMQET13NyclpeXra/i2jLYhtiFo9xoNNLExIR8Pp/hFDzLxEniagXJi3WV/h3RIfs351yn+zdER2e0FIQ2n8+ndDqtlZUVra+vG4aCawGk8z/5kz8xjAzRBvsx5w3cyXBLccaKnp+fmzOc3+83EQ6OJOPx2JzheO4nJyftnUQMiavB5eWlkcIgfODcRlwkJC7i0hhk9no9pdNpLSwsyO122/3gGWZ/cWKIa2tr5ug4GAzM6Yb+JBgMGim93W6bkOv4+FhLS0uGH4KRJZNJra6uWtQOwkXctW+eU/L5vK0JPL+QjnHwhsgHATcej6tSqVi8FsP8WCx26zDa7/ebKMzj8ajVal0TneMghVsmeJAzdpcoQdb/QCBgJKyPP/5Y4/FYzWZTv/Irv/KC8JK4Rxw9T05O7HsdHx8rFospEAjYusJec3BwoMvLS+3t7Wl1dVU7OzvmFgM5EUIn/RRuux6Px0jbjUbjtaPPvik1MTFhTh68m0RAIUhDHAdxnv0gnU5rbm5Ou7u7d8bdQ9h2ut6D795WwWDQRI1g4QioIeiBW0L4LhQKOjw8NFEIWCjFXoMDr1NogLgYElomkzFhC+5mW1tbFseN8xX4E64oiFNw2atUKkokEkY263a7hh+DaeCYB96UTqeviUylz0n6kIlexzGK9Y57+mWfi2g0qn6/r3w+f000SUQ3bqblctni7lmvESbQb0oyrAaXm9nZWVWrVRWLRXPfI4IY58Jms6lUKmXxvzhc3VxbcDGNRCJKJBIvdRp3FqSjer1uwk9mTezLTidKekVmM5Bo+e+TkxNLKKE3J9ZvOBza+si6T3+9uLgon8+n4+Njm0PdRSICF3W+d84/yzn56dOntgdDcuKZCwaDCofD1r9sb29rNBrZueBnUXcRlXCo5JzBfa7X6+ZEydkVvCIcDhs5l7PObQUm1+v1VCwW7ax5s5iHEeENtvuLUB6PxxIKeN4ikYgKhYIuLy9VqVQM72Uuh3vT2traC+Lu2+ro6MjcQhHggv9CNl9ZWdHKyoqRSGu1mpFledfz+bwkGUEepyrmbaQ7JZNJHR0dKZPJGAHbWewZzCfpbTEXyGazmp6etns6OTmpe/fuvXKv5pxzVwTl9PS00um0iQhOT0+NUA+Z9m39k1HfKAKWdNXIt9tt/d2/+3f1d//u39UPfvAD/dZv/Zb++l//6z8XhujXWb/zO7/ztf3s9957T++9997X9vPf1i9mcXhw5g4zdAEobDabpmYIBAIGeKAyQrkHgEYzAICJomk4HJrqh4PlzMyMxc5RsNqJeEE9ejNzGWcZBprpdNo2K8BMDu6tVkvRaNSGPwDlKFBpiCCYZbNZG6iPx2NTsHGwBdQC6ERZCnhMZAHONgynzs7ODBCGEEAjmUgkTP3rVPjzeYl7pACMJFmjT4yD0wZ+cnJSBwcHGgwGWl1dtQE1h9Pvf//7WlhYMHKHsyBfRCIRU/eSUw2JBAIbTSmRZYC5ThvO0WikQCBgrlCoFWGTBwIBAyZ5DrkG3PPJyUkj18zPz5vlfzwe1/b2tpF87rIPjsViFsPBsALltvP7n5+fW7QYAx9s2yFRcI2I0igUCvZ5UFdWq1VzcgJ44LlmgAcRzQkqQJwh3hA1Ee4BEJC+yIDx/Pxc+/v75oCGQmByctKuVzAY1P7+vp48eWLfc2FhwYg9AEa8D9wrQE8cIXBlOj09NUUnKgkILtPT01pcXFQ8HreGF9BsYmLCIgpYW3i/AEoY4rRaLYtTIU6i1+tdU145i0FzsVhUIBCwocnXUajPONBXq1XlcjnlcjkdHR3ZUCqVSlmMBf8MUAVFDkokrvlgMFAgELBhByAXwAeDQRoR4kmXlpZubRqJAoK0A1F2bm5O8/Pz9nc2NjZULpcNaEK9DGHJ5XLZ/8/lcqakuUsBRJQcau7bijhXfub+/r6t6QyZ+K64b5ydnWlvb0+FQuEacYkBAlEqDAvYZwDnUefjhvP9739fjx8/VqPRuBZ7SuyqdDW4hEjK2ttsNs1BD+W1dLUWAb5BCub/47LBujExMWGRtMFg0OJ2aBaHw6GWlpauxZe2Wi3t7e2p2Wya4xiONpCJiEgAJJ2ZmVE0GtX5+bk8Ho+tcwwnlpaWbI8GGFpbWzN3LadbFs+nx+PRr/7qr+r9999XsVi09Qd1oxNgBSBxRuVxPRg8cI/ZF9kjm82m3Tfs7VEsS7LnHwfM09NT1et1LS0tvfC8MTRleM3QuFarGQAwPT1tg22Gxex5NwtiQrfbNXes2wrXuP39fY1GI9XrdS0sLGhzc1MHBwfXnL74brgkBQKBF+KZKRxMIOO9DIRlwBKLxV5LxXZXYVH/TSedeDwebW1tKZfLmYqOvYdhqMfj0fr6+p3X96sWYgCIkbfFylOxWMxiExlgvuocAOEea3Wfz2cDPwgFuJZIV8p24jtwgSBKyuv1KpFIyO122/NErOjS0pK5MO7t7ZkKm6gQzkCSbBi2v79vEdj8nNuuCw61ED5Yf5zX5fHjx9rZ2bE966uKi24j/vI84LaDa0ar1TIAF7cghi4MAr+NBXH3q0YO3PbziCmm93jVu8d+4PF4LD79ZcXZkrNSPB43p42bf985PCX2gz6Z/m92dlaJRMII5U5XLVwOcQOKRqOvRZ68q1wulzmxnJ6eKpPJaHp6WuVy+ZoA4fz8XIPBwPbTdDptpIzj42OL4XYWThOcRdnrcK2IxWLmZoerUzgcNsfJo6Mj62953yG6oSDHGQgyJ4M3hvfr6+u2HoEDAJB3Oh1dXl7q9PTUzibEnfJ76OkpCNWdz6JScShDMOMUQqH+x9UOERL/jiEl5+Vut6tyuWznJVygcR7FbYbvwUAO0irXCFyA/ndpaUnZbPbO9Z21pt/vm3gMJyKi3D0ej7kz7+7uXnPakj6PxnHiF5VKxRxbnO5u3C9ceyTZsBnRDH/e6Q7o8/lsiExEGCQXCPE8g0SLMLRAxPf06VNTsHPOhJCF+wr93g9+8IM71wpwIjAEiPwHBwe2bktXw+t6vW6ut7Ozs+YyUa1WTbAIEY+1ivuSTCZNOQ8JxO/369NPPzXXCo/HY+684Ce4B4RCIdVqNQ2HQ3388cc2JH769KlWVlbsuePMHovFdHp6as5KkoyskE6nVSwWzaUAl1VihnFfPz8/NxJiuVy2PgfcD7cR1kjO+ay5CBLC4bA2NzeNjLq3t2ffDxEEGBTuyrhr/fSnP7X4P6KaarWaDaZ4J3Hb2N/fl9frtbUJ8hMx97gWBgIBOytxHnK73Ra3zfCf9xGXLdZ5ekHpyi0YMU65XNbc3Jz8fr+y2az1MtPT0+ZscHFxYU7jPPc4ikDkg4DnXJP7/b729/ftWeUdgXjLekj/CWbJHgXhFVEWhDpELfF43J7RarVqRAZwG0lGkpiYmLD+BoFCr9cz9xrWNYQe9XpdhULB3jXOfwy6s9nsrU7WnCsDgYBOT0+N2MZ+Ab7TbDaVy+XUbrcVCAR0eXmpBw8eWCSS01kDXOmTTz5RNpvVcDi80/ke4gvXHWLYTcyEvapUKqlQKGhmZkYffvihjo+PbfDO81YsFuV2u1Uul3V5eWl4Sjwe12g0Ui6XUzgctnXY6XLzTS+G0ZA5pqamdO/ePRNP+nw+c3MnJszpWuR0JMRpj+I5dkaxv4ywzhkN/AlhcrPZ1NHRkRHTG42GRUkvLCyYQ9b8/LxqtZqlc+CYhQgULIC+uFar6fDw0OIXiSjlDILANhgMKhKJ6KOPPpLb7VY4HNb9+/evRblBCE4kEpqYmDBhLs8D5GjwkEgkYi5HuLQjQHc67rKe+f1+IwTdVePxWJ1Ox+KOv0qvG4/HVa/X5fF4lM/ntbi4aJ+L64vgaTQaKRQKGRkfLBPSKU6kfr/f8Grc/vL5vJ48eWJCOPYvHKbOzs6sz93c3FS1WjVnc649pKlEIvGFCJDhcNjcVVkjOSMxh+I8DvGVtf/g4ECffvqpiVi8Xq/NA3CSBt/EAZgIQOnzZAKIJ6FQyJItXkWA4Zlqt9vm8IkL687Ojp1vcHvlnWI9BHekP+Ls6CTe/zxrYWHB+sbz83Pt7e3ZswbhzZnogsFDOp2+Nku8rZh9EMG7uLhoewNrIA6j4MH9fl+ffPLJnXG136QCB0bkD1GeFAKc/xETdDodeb1eLS4uKplMan19/aWCdgTMiLaazaYR+BEgBINBVSoVVSoV+7mzs7PmdEo6DkkQ9+7d07179+z30vMipoGsB9HZmb5UKBRUqVQsqhsSIc86CSlg08w4XsfRjPfhVS6E/DxIXm/rn7z6xhCw3nvvPX3wwQeSPlc0Xl5e6ic/+Yl++tOf6m/9rb+lX//1X9dv/uZv6pd+6Zd+jp/0bb2tX+wKBoPmRsFmRTMQiUT08ccf68mTJwZ2AVJCRJFkYCoxHYAPkmwAE4lEDKBBgQrTGvY9Wb7hcPhaBvdNEoN0BTTgTkAUhiQjfLlcLnMCIcaIQ0S/3zcgh0xfVKtOogHXhChDwKNIJGLOW7gBLS0taXt7+5pSEzD44uJC5XLZQF7UW6jUyLVGZSzJwCDco9xut+r1ujVF+XzeVKlnZ2fmSAIAhMoaEo/f79fe3p7FykhXA6vFxUVTzjhrcnLS1EPSVaMEsIDrCpbA4XDYAKSjoyMVi0XNz89fi2kKBoPWfOMutrq6qsnJSbOrxlmt3+/bM8hzKMkUm5lMxiIgA4GARVHRoKG+uVk4xUBuQAGB6xCKUUg9WJ7Ozs6am8Lh4aGpqFEqY7UPgQLQazweq1arqd/vWywNQCbAXKlUUjqdtmYBEK7X6xnpwDkgxRng4cOHmp6eNkAQV7aXFYBUs9nUwsKCisWiRW3w7qCcYsiB9TDA9Pn5udLptFZXV43E1G635Xa7dXx8bO5tvL9EHvBOnJ+fmw1+u902O2Gfz2fKdgC26elpA0+x/o3H40ZIcX6eer1uVsiAiDjg3Xy2+XudTkeZTEaTk5NfiWjwsmJY2+/3NR6P9eGHH6pSqdizKl0pjjjYE7FKLCfkRM5BHPrn5uZUq9WUSCSuObKhXKMpz+Vy2tjY0NnZmTY3N291o6JYp1D8O22Bm82mwuGw0um0KUqwVW+1WuZ6h43+s2fPtLOzo/39fQNy7yIvOAfXL4vjyWQytj/RvFUqFdsbpqam9PDhQxUKBSMRsT4DhjCkwx0MJzeGA4BguBFBxP3www9t0FSpVIxgmEwmbcjx9OlTuVwue+6cLl+420gyRTnDMhRmrKGj0chInajTGYJCOka9DBkzlUopGAzaWk5TizLv9PRU2WxW7733ntn6dzodVatVxWIxA6nZRxkODAYDJZNJJRIJZTIZZbNZIzddXFzohz/8oZrNpgKBgIFkWK0T1YHTRyaTUSgUUqFQsIEU7zmk3NPTU6VSKRswQOzESS2RSJgrZLvd1uLioprNpj788EMbwgCuQQSOxWLmXOTxeJRKpYwwdluze3Jyonq9rqOjIzUaDW1tbdlafHx8bHs8SkvIg/F4/M6BOsNHnDpfBlThpgLBBsBmbW3N1liUvKxd8Xj8pes/CjYGwsQN3Va4tRFX+kUKNVzns1gkSUZYY93FYcDpfPLzrtnZWW1sbBjg7oyYYL/+Ogv1HwS9m8UgGjK21+tVq9UyotSrPh9uehAmpM8Ju7cVhMbV1VVzEeRsAsjI+ob6FgKoJFt/RqOR3nnnHXuXSqWSrceSbIjI+o99PQVBtFwu2xkYtePNcrvdWllZ0d7ensrlsgqFgtLp9FcaItxG/MXZie+3vb0t6eo8zdrU6/VsGLq0tKRQKHTntX5bd5eTnPI65VQos8+8LPaRCAqGYjiaEFeHMITPwgApl8tJuhok4a4oSUtLSzo8PJTL5bJ4btzqOLvw/GQymS8dPyhdrbVEgOA0ATkaUqAkG2zhqsB5nvPIzfcDkszR0ZF9d8hDvOc4P+FsUi6XFYlElMvldHl5qe3tbRWLRVvvuYe4jODEBUEoGAzaepbJZCyKnd7n5OREn376qZF1KAgj4Ann5+dGxsf1JBKJ2L58dnZmZPu7CqIf5zjELjgv4xp8dnamdrt9rU+B0IJLzmg0sgguMAiicBDtED/vdCubmZlRNpvV8vKyxYPfVTgUZjIZ/dqv/Zr29vaUy+Wsz8BtiCg4HHcgcEejURtcdrtdw3q4L0Qlgb9AnsHdYnp62oRY/AfHEFyeEeVwzxDrTExMKJlM2vuCOA+ncpyG8vm8hsOhotGokWnC4bC5Zc3MzOjjjz/Wzs6O3G63dnd3tbm5eev1wk0U8lQsFtPExIT1pPQ+kOydpLGjoyNzZeI9ikQiJgR0RoZLMndWRDCXl5fm2sF9GQwGJmjkGQkEAoZ/8PsZUuFCC+GFn1ur1ex9efDggRF9FhYWdHBwYO8OAsDLy0sjtOBqC8EAhynuO+d5Bpenp6fXRIJcG9ZCPjsu+kdHRyZEXF9ft3eMCCnIXJ3PIokHg4Hcbre2tra0v7+v5eVlIzIhhMC9CZdYzsGIbCAzulwunZyc2FoNAQ0HbByaLi4ujFSKs+bR0ZHhkx6PR+Vy2UhjTjKJ3+/XD37wA4vKAmvCNY6hHfsA9+zo6EgHBwcWIQ7xi6hQhJa7u7tGHPN4PJqenlatVjN8rvNZjFQgENDs7KwRcyFL3dZvMMCHKIjrbiAQMLxzMBiY48Xp6akePXqkbrerYrEo6XMHTPZZiJTgSLlczgbanAEhJ45GI21ubr6wv+PQjYM26wTvxmAw0OHhofL5vL2DuNVxdkSwVqlUbK8Cn6Mv5Vm5WZB3Ie/cJVijksmkdnZ2VCwW7flEGCFJu7u7hiNvbW3Zu0qPmslkjIQFOQMR4i9KIbiFoEfk49bW1itdlEh9gFg5OztrIrFCoaBqtarDw0PrEV52XVi3cbVrNptGyEun00a4ZA8El8RhG+dSSFD5fN7OF5zZnL0WBE8G951OxwS7pAYgrObP93o9JRIJ+9+ctSAKEdPNuYEzMO/2eDxWJpNRJBLRysqKpSew7nU6HcNUOdvFYjGLaq9UKrbnOms8HqtQKJgg8asKRhDBQD7d29szgTJ7ht/vvxZZv7q6amQJ7jNkC7fbrbOzMyNyPXjwQAcHB5KkJ0+eqFKpqFQqmVMYJEbIuvl8XolEQqurqzo7O7MzEGvLlzH1IDmAn/eTn/zEiK88w6yJrHnz8/NqNBqanp7W5uamXC6XkYFwDAZ/wxGaeRfRhZBeEY3gNgge9TqJDkRuS7KzRC6XM8EpEYipVMrECMzEnA5wxIgj6vgmlMvlMkECsY3FYlGdz9IaEK1yVoHIlkqlrkV931Zer1epVMqe693dXSMLY0oARpxKpQzXIXZ6MBhoY2PjG3OtbhYpGMwfOf/4fD6tr69bCgkOtCRBPHjwQA8fPnwpuez09NQSWJifsT4yT4S8TQzmeDxWOp3W5uamnb95n3EdfPTo0bX1bHJy0noXyFXsK2BhMzMzKpVKKpVKyufz1oelUinrl9rttg4ODpRIJFSpVLSysmJzln6//8o1A1GyE/N6W2/rtvrGrAbvv/++3n//ff29v/f39Hu/93sGFDFo6/f7+vGPf6wf//jHevfdd/Vbv/Vb+tf/9X/9LcD5tt7Wlyxn8+osmtGJiQljJgMioKKanZ01JxQsrgFEUQPAOp+fn1ehUFAulzOXi62tLQ0GAyNqJRIJLS8vW+OGOnlvb0/n5+emOrstfgViE8Qdj8ejo6MjFQoFO+iGQiEDKVDqxeNxLS8vK5fLmYLz2bNnpqxEkQ148vz5czUaDQOlUNN1Oh1rlLAh5TOhiIdokEwmjdR2eXmpQqFgyi2AFobgw+FQxWLRHMmGw6FyuZwRNYjc4prRtMFkZ5CeSqUM8PP7/VpbW7sVXECNWCqVND09bQ5WKDlghft8PlMbzs3NWYxDrVZTMplUIBDQ/Py8KYABY/x+vxYXFxUIBLS9va2PP/7YiFQo5QA3sUU/OjrScDi0xg3L3lqtZoPFYDBoudXS5w4KuJEBrna7XXMEka5AbNj1o9HI7KHPzs70wx/+UOvr6xqNRma9u7+/r36/r2g0amAozbwzFx0yF4A/YDduUDR/gIV//Md/rEQiYUAwrHhArcFgYIdeVMeSDLwluug2tj1OEsRecH2kK/IVrke9Xs9IlS6Xy1SW3W7XLGhXV1flcrms6aVh7vf7Bjrw3vp8Pp2dnanRaJjSCUctrjXAMIAgA6JqtWqfI51OG3iMotLtdmt5eVmBQEB//ud/bkpPGlDeJZRTOPMBoqLc+LqKYeze3p5arZYqlYq5okm69tywpq6vr9s95h10krAA5xkWEikDiEQRB5tMJu05cUbhOIuBScdhnY+Kj3er3++r2+1qbW3NwKHBYGARks5Kp9OmcH7+/LneeecdNZvNF6JUqtWqkeYYhNxVzqba6cJAzjvK2YWFBR0eHl6z+odIhl04AD3kKxweJdnesb6+rsFgoHw+b03h9PS0Kc7Pzs6MWLe4uKhIJKIPPvjAiFq4AUIyCofDBowzOJRkAz3y5VnvuF+tVktra2sWj+LxeEyRz3sPEIOrXb/fN7Ll4uKiMpmMrT0LCwumaiauAlIsAydIM5ubm2b5DvkMdRzNaCqVUjweN/W206WOoRANPW6azog73m9UQOPxWK1WS53PojPD4bA5eKEaTqfTWltb09OnT9Xv95VMJi0GiGFmNBpVKBQywijXDEDu6OjIhnFOElOtVjOgnmiPQCBgSvnBYGDnFFwXIY/cpVzmDEDM1avAdZ/Pdw3kcLp7fpn1isikk5MTHRwcKJfLKZVKXQMFuO6tVksLCwsGXL1OXVxcaH9/X+1228h9AMvsJxAKcVLtdruqVquKx+NfS6Tfl6nZ2VlT+v0si6E1Q1GKGDBnJAHrWqPRUDKZfCmZgHKKF17nOjv/DCB5vV5Xo9FQKpWyvQGyO8RbCpe72dlZLS8vW4w0BHs+s8/n0/379y2S7eZZlOFzr9czd9eXPZP0G4gBbttzvmjdJP4CCuL2Qd/Ed5NkA31IWa8iUbyp4izldE39sjEiv6jlVCjncrlbB6gQIUajkZaXl6+dPVZXV21gXSqVjPQ3MTFhBMTz83M9ePBAfr//msvSzMyMlpeXVSgULN4dxxf6Fmdc91cpXJpY2zmfMNznM3PuxY0SQhRDFPZ6ijPixcWFXTf+GWQk4p6pXq9nkYAMyxAutVot6wMQBxH1s7q6ak6qLpdLjx490sOHDw3Y5lxM3ARAOo4FrVbLyKv04DgMN5tNc8giygvBBo7RRIZxFuN7Q/aAOAPhEocKSO0Qz8EBIMVx/ZvNpvUkENCj0aj29/etf3f2ejinxmIxJRIJ/dIv/dIrzwrsB9LVevqd73xHsVjMzmPOa9BoNLS2tqZkMmlKfc74EKUGg4FmZmY0OTmplZUVO4txTaTPsdjRaKRyuWwEa4ZxuL5w3nQ6YeL6USgUjDQyMzNjzlAQcnFBAiPBhSgQCGg4HNo+wLOwtram/f19VSoVE6TdJqxBsc/7yDtTr9fl9/vN9ZK+m1hDvjOOd71eT4uLixYbFwgELA7RWQgW5+bmzBH3/PxcbrfbRGWcgZ09Kr0FbsZcO1zK5ufnTZBFzFK9XjdCA24vkAGCwaAN+MHUibKD7Hh8fGxOLoi/IMg4h7u4rhHBe3JyolQqZYNb8AnWFs7n7M8rKyu2pyLsQawIbhKLxaz3abVa187rkARw/4M4yPp2cnJiBGjuJ702vYcki13EmRh8D+clSLKnp6fmXJvP503kQx8e+iz6JhqNGomDQTnPkNfrNVyP3/XkyROLbMOpAVEQfRSE0YODAztvOZ1Eua44D4PZsjZQCNnAchD+QHyhH0M0d3JyokQioVAopE6nY/F4iEh6vZ6tael0WjMzMyoWixZxPzExoYWFBYuX5PM1m02Lb9zZ2dE777xz65l0ZmbmVlfNer1u95G+kJQCZ7ndboVCIXv3IKRxb4kFva1475zu1ncVw979/X1NTEzY8wyBzenWxr6GUyQDZ1zqcFJCnPiLWKxnX5SQAS5aKpVM5ATxBMcq1rPb7gn7HGs4BCcirZziRQj3iFI5ow2HQ52dnVmkFgTR09NTLS8vv0BsKZfLhktwH53ku5OTEyM34rQDhgpGDCEA0uZHH32k9fV1O0+QjuDz+bS7uyu/36/V1VXDzJxVrVZtH3a+D+l02varfD6vwWCgUChk6z7iGrfbrUwm89KY7y9SrOsQjXEaIwHl4cOHcrlcqlarOjk5MawD8in4gdNNMJlMmjhydXVVuVzO9kHINLOzsyaGwakPEhwRpW/q/ULEjXNhtVo1h0vnful0/XPGaUOolWSJI/F43PYQSSauh7AHbuZcv17lsnOznJiAJIsvrFardr4nnUCSERUhIuM4f35+bgLwb5pzH+S0paUlFYtFe87AIokJxQzidd3PwIiI4Mvlcjo8PLRoUN6hQCCgTCaj09NTNZtNHRwcKJvNvlQg8PMu5gvhcFi1Ws3WT0mWFoI7dT6f19ramjY2NszR72WF2ytn7W63axhxIpGwfZqffX5+rk8++cTmv++88445veJKyN+5rZxngKmpKXO34sxTLpdVKpV0fn6utbW1a/sV7s/9fl/FYtHOl+wVzHHu2uOY/UEE+0WJoHxbP5/6xhCwJOl73/ue/sf/8X/Uf/Pf/Df6/d//ff3O7/yO/uzP/kzSdVesDz/8UP/ev/fv6T/+j/9j/bW/9tf0m7/5m/rVX/3Vn+dHf1tv61tTs7OzikQiBmZKMuASdjRAC64dOAzR7IdCIWsKpqamtLi4aAPJXq9ng6TQZ3b82Wz2Gjg9NTVlFtkAmo1GQ71ez5jnuBQAKjD4QWFUKBTU6XQsxomDMdF7Dx48sMPj48ePbThIwwSQAzELEACmNOSedDpt4CEDmuPjYx0eHpoKB5tbbIE5CPA7AIM9Ho+py3BLAoRst9tGPMCmF6DHScKCeIKaAZcvYpicEU0USkSnejccDhsACWlBkpGZUqmUKZQgmjBsnp2dNaVws9nU0tKS5R77fD795Cc/MWePjY0NU4UD+HIPsEbGjpSYBazJ2+22kS1QZu/t7dkQAHtTSTZAw/2FJnF2dlbz8/M2qOcwLckOorjUAJoRP1OtVo0gSKQGzz1qVxp8yAJ8Tprycrmsx48fa3FxURsbG3YvIWBBNpOkjz76yJps3LXy+byCwaDu379/jZB8eXmp4+Pja6p2YiAAOyBfoQKABESUhJPYsbe3p3w+b4Bro9GwAT5DWizIGW5BmCJSr1qt6v3339fh4aHm5uaUzWbl8/k0Pz+v999/Xy6XS4lE4poKDNtjyGc4cQWDQQUCAYtY29zcNHIKzzC2+c5h2urq6tfmfiVdEe62t7c1Go309OlTAzWdCjTUj04XgcXFRVUqFQWDQR0eHmo0GikSiVhjzDOD4o4BytnZmY6Pj21gT1SUM97uZgGEYukPmYTfg0oYlQjPBAP224AMYgDX19f18ccfa3t729wHJiYmNBqN1G63TdEWDAZfqTyiaKoBrFgH/X6/NjY29OTJE+Xzee3v72t2dtZ+br/fN9Iff57/7fF47JpifS7J1otarWYgysOHDw2gJb738vJSy8vL+tGPfqSPPvrImkyISajFcQsgroT7JMlUjhAyGQxCHLt37549yzwLU1NTpp5zVrfbNTtzLN2lq7MzkYEAEbjFEP8F+QgSEFbk3W5XH330kQ1iGJy53W4DoWmeUcth2w9huNFoyOPx2CCVd++m2wCOMZBqsdiH0Hd4eGiks62tLYsIk67WadZ6hg07OztG8iEWGMAPxz5Ukwx6UJUy4GINikajNogcDofa3d2VJBsS3lbEKEAUfNWa43T8vAmUfdlKp9NGxCyVSvZ+uN1uIwROTU3ZUGVtbe21AD3n+pHP5825jGegWq2a08zCwoIR0A4ODiyuejwea3V19Y18z1/Ucva1kgz4Ic6LeyfJAO18Pq9UKvVK4JDoaZfLZaCvs3CjQ6nfarVsGEsU2Pz8vMX7OAk+NwkCDD8Gg4Gd46PRqILBoBFYWPNQ0OLwd3R0dOt+ArET98tXfdfJyUkbEH9VAhbEXwY/EH8vLi60trZmhCfWKYikrE+3qc3fdCFEwEmEcyUuF29qoPKLULgcQvJAoczA/vj42Bz6FhcXbYBMBQIBra+vG5m30+lYBA7kAQgpOKg6i/5jY2PDhCYMyCGcvInCMeni4kKHh4dGguKci/IWQgSDSQjbw+HQehPns8H3wZ3TuR6MRqMXHEucYLbT0eH8/NwIE5AM6AkqlYrt5+l02s7sgNsUTsCcZRAUgAkQbQeZnnu6tLRkhJTDw0MjnwSDQevdIAgxuGUggzIeURquG1wH8AnEUFxX4ofo/SD4AfoTOcial06nTXUdDoeVSqWMqIwT4Ovs/bh+SJ/3gevr6zo9PVWr1VK73dbjx4+NtJDNZs05udfrvXB2PDw8NBcxrtfU1JS63a4NfIfDoX1npzsucfWlUkm1Wk0HBwcmiKNX7Xa75iCFAzcEW4hsYEeSzJ3C5/NZpA/EMmKSfT6fjo+PjeTSaDTU6XReOGcNh0N1u11lMhmLbpKuzoxLS0vKZDLWu0D8QoARCoWsh4cs0Gg0dHBwYH3D9PS09UmRSMT2XRzccLmB1Fer1a5FvfBu4ZgORsNg+vz8XPl8XoFAQAsLC0qlUioWi+ay7fF47Pun02kjMdG742YLboLjuiSL+Ia0e3Jyck2YSa8F2QBMwOlmRl8AkYz1h95/dnbWcIiJiQmtrKwoFospm82aIzdkdMRi/AwIlLjbX1xcKBqN2vX74IMPLH4LvMPtdhtOiRsW0VOIxs7OzmxtmZ6eNszSGaOGQ3I4HLaz0s7OjrmXB4NBEw8idCiXy0aq5HtJMgI68Xm4WNGn0SsiDMJRpdVqXXPZxGWPfhrBpZNoxhnM+Wdwz8XZB8IbPTEOVWA6vEORSMTuNff78vLyGhENoR3O/vQSrE8IdqamplQsFjU3N2dC2NcpMA+ErpJecFl0lt/vN4IrhDb255tESWeBPd6FmdwsBvEul+taZCxCH0nX1qJAIKBisXiNnOX3+22dbTab33gC1sXFhREjnWRBsCZ6jVf1usSMHh8fa39/X6PRyJ49yC2SVCwW7RzH8+/8LI1Gw6JA6fvZe2+euZLJpBGsIc6xhxwcHBjWDI6fzWZfIF+Nx2P1ej3Dd1iLmIGAFySTSZtf4Krm9XoNf3BGsvOcfvLJJ/be0BuxHkoy8jcFWW00GmlpaekahiV9HhOJSJt3CCdjcLJAIKBEIvFGRanEGdbrdbVarWtufF6vVwsLC3r8+LH29/fVaDTU7/dtX8ZlkHUbF3Pcufb395XP59VsNo1QxvWEHI7QdjgcWqxjPB5/Iz3Z2dmZ4bvS1bxsaWnJsH1mBKHP4iERSEMu5R5yznvZZyKB5a4Ci5NkjoAvK5xy+bsIKEajke7du6dqtWqxbDefpWg0aq7Y/X5fa2trFlH9OkU6gdNtHPIZqSNvshBQ3r9/34TDTgf5V12r24ozZL1et1jLiYmriGycQgOBgBGiIa7l83m53W6bX34Ta2Fhwdayw8NDS9SYnp62BBwEQBCzX+UGxRrdbrfl9XptroCYwLnPQpBlHWVG1G63jVAFORW84y5cyOfzqd1uWw8p6Vp/NhgMtLq6eieRyu/3KxqNqtFoKBgMWo/TbrdVKBTM0fnmd4XYFY1G39h687a+vfWNImBRHo9Hf+Nv/A39jb/xN/T48WP9+Mc/1v/6v/6vZp3IoGI4HOp3f/d39bu/+7va2trSb/3Wb+nf+Df+jS+U5/u23tbbul6xWEyZTEY7OzvG6MXZiveOGo1GFquHkwVW0OPx2ByuXC7XtSGn3+83sOhmZESz2VS9XletVlOxWFSr1dLc3JwN8AEAABS63a6BhgCdKCCwm5+dnbUN8/j42GxKM5mMcrmcZmdn1Wq17OB+fHyslZUVswKnGQKwYlAOGBsMBu0z8R9U+YFAQL/8y79sziIcJE5PT6/Ft2Etv76+bhv3ycmJOZDQjE1PT5vqz+Vyqd1um+pDulo/Gfxy2N3a2tJwONTMzIxardYLLmIokIiD4iDBYCubzWo8HqtUKplymQGUE9RqNpsaDoc6PDzU9PS0ksmkstmslpaWlEwmNT8/r0qlorm5Obt25XLZ3KkYZtGQEWvodrsNAAIYB7Sj8X769KnZiB8eHkq6Argg76BMwkrd6/Uqk8lYEzE9Pa16va69vb1rBzuUNLVaze5pIpFQPB43wJgmGfe1WCxmrkxra2uKx+MGIOO2RswDUVWtVktPnz41q1uA43g8rnq9bvcPJQKKlPPzcyWTSRUKBW1ubiqZTBpQJ8kAO+kqfhSLeVToNLHSlRUtDg+AlicnJ9rf31etVlMkErkWq7iwsGAkCunKcYsGIxqNWlxENBq1yDGa0MFgoL29PSOnZbNZI+0x9ORzAQDifAXZMxKJKJ/Pm3PM2tqaKpWKRZY4XXaIbWP9+boK4JlrD+iH8rnf7xuREKKiz+czpQ7gM+B3tVo1ZwJs0iHCQEABTAoGg/J6veZYCEB2s3BYAdh3DiOlK1AhnU7L5/OpUCgY0XM4HL7wZ53FurK+vm5xqU+ePDGlPZb1DDxfB+h0FnbmzpqbmzMynt/vv+bCBOAAAQjSGveFNRc7/fF4bI5VDAsZCMzMzNj7Xa/XNTU1UJ+X6wABAABJREFUpWq1qmw2q2w2qw8++ECRSMSUNzy729vbmp6eVqFQMNAGgIZ7JsnsxSXZHoIiXrpSOnK9iHjFCRESHtGAEL/a7bba7bbF5EBYnp+f12AwsLUvkUiY+pN97vj4WMlk0lwCnS4amUzmmusRMR4MjLDXB/TApQL3OyJ8aOhxMazVagqHw7avdLtdGyZLsu+FW9PMzIz+8T/+x9YTMIDkuX/+/LmpX2u1ml1rnHJQVbHXQZyYmppSqVQyV8bJyclrznWQCl9WnJUYkrwKYGJYAnH1TRTkO66bk6g5PT1t+x8kg9eN/qpUKrbPQqS9uLiwSCEIEQx+IIYmk0mVSiUdHh7aNcch85+04kzqdCApFotm1Q5pj7Mh+xqgdqVSeenAJvRZZA6DPCdYBpGUvff4+NiU+J1ORzs7Oxbj6XK5Xgtok2RrAEWswm2fk7P8zagc1lren1cNpYhMhmx2l+PjlylccHHXuo3QjavH3Nycga9fd+GwyRDGCTSiZG2325qfn78zIvXbVsR4Mhhtt9sql8umvI9EInZOWFtbe2HNCQQCevTokSnr2aulq2dVuupN9/f3FQwGrR/G9djj8WhhYUGZTOZruebj8ViffPKJ/vAP/1CtVkuTk5Nqt9vmpsAgnM8WDoeNLFEqlWygRUQMQhJJth/jNO31em3A0Ov1lMvlFAqFTFjR6/UsFpdoFc5GS0tLRtxibWGdef78uQaDgd555x1lMhlzoXYSUbe3t7Wzs2NOFBAWIpGIRqORfXe/369Go2F9N2QGHB/29/dVLpdNkcyAdzgcqlKp6PT09No6x5k6GAyaswV/F8dnt9tt5LXj42N7rjhn0pNDWOfsznkeV6W5uTktLCzo3r17Ro47PT3V1NSURWW9rBhKcAai6MEHg4EajYa5gEH64ZnOZDJ2fsEZZnt7+5oLKD1Tr9fT/v6+xdBKnzvrEA3LZ5mbmzPX7pOTE3OPxQHCGW/4J3/yJ9rd3bX+B7ItzkQIomZmZozUxnCTMxX7y8HBgWq1mjl5sg8wcA6Hw/L7/bcKPiYmJrS0tGRRwOVy2YaRkgwz6Xa7dobHQSUYDJr7EXtoJBKx/tzpcjQ/P28Rd1x3eg+Iw7iGOQlYvJu7u7vmtgfZOBgMKhKJGIZwfHxsgo5oNKpyuSyXy2UiNYRk7XbbSEILCwt2PSGcMjDms+FCzufGoQ5HfIZnrVbLyD+8z1NTUzo5OVG1WrVIQKfbB0RN3NsymYzhYvwciN+QlRCReb1e++dECOJMwrnC6e7Nu8K5A0GIx+MxR+O5uTk7UxCbBZ4JcQi8k2E0axFxlJAeIOyNx2NFIhE9ffrUBJpgJ/weRHFONz3eC3pBSKPz8/MmbmIfgMyBUBLHrcvLS7teuDLQD7J+ZTIZdTodra2tqVwua39/3xyMeQfojSYmJq65k1UqFTUaDWWzWcNXbiPAR6NR60V5J1+nWD+JGQMDlHTNKdj5PjvdZbgOzr93W41GI+vRbgpU+QzcW+mKEONyuXR2dnbtfMx+fJvrMVgijspgmbwj39TCeR0RlzOmFLzp6OhIfr//VhLszWq1WobTBAIB1et1IxoSKT07O6s///M/Nxc46XPCCZFU4ItEmsbjcUteuHkOx5Wv1WopnU7L7/crFAqZe+fi4qISiYQ6nY59hpsFiX88HhvWBNYB+VOSEUaJGuX/83v5PolEQqVSyWYQYPy487z77rvq9XrmSri7u2viaukKD1teXr5TPEXkHWdap3sWuMuXEWogiHeSWogwdGLh2WzWxPfgPrxbYF+7u7vmIM+ZA/fAtbU1xWIxS0cZDof64IMPlMvldHR0pK2trWvXE1exRqNhZ99ms2n39as60EoyYT9x55zpEAPw+Ylqnpub0+rq6guuxG+iT3T2+JBc7irmJLg04c7G7CUajdrMrFgsqtfrGQ7HOW44HFo8MAT816lWq6XDw0Mjn0IAnpmZsdSLr9MV/a60ny/7s8bjsbLZrEajkVZXV82d7bZKJpPq9Xq2731TCVihUEhLS0uSZLgxxGrITjh8R6NRZbPZV/5MZhg8S6QJELt5szibMOOi52A/IZmAM+Nd6xZiJOZnuNuSjuJ0E72rENxwhmfeVygUtLOzY7MWSMfdbtd6iXA4/JVFgG/r21/fSAKWs9555x39t//tf6u/83f+jv63/+1/0//8P//P+of/8B9Kuq4efvLkif7D//A/1H/6n/6n+lf+lX9Fv/mbv6m/+Bf/4s/zo7+tt/ULWeFwWD6fT0tLS9rb29NoNLK8aK/Xa2QWXIbOz8/tQBcOhy3OCWtO7MEzmYyp5XChKJfLKpfLdnh3u906ODgwZRsgEVFmgIX7+/sqFot2OAdw6vf7BqjMz89bdBHEp4uLC4tGKZfL5gA0Go30wx/+UH6/Xzs7O3r+/LnZskpXShyAYCKRnIoXGnA2fkB/BjjZbNYOL6iHd3d3NTExoUajoUePHuni4sKAUlQFbrdb8XhcpVLJBuYXFxfmHEasD8pgiA4Mr0ajkVnzQ4LL5XLXooaOj48tHg91JapApwuaJMvPZvh/fn5uSnEAvomJCaVSKS0vL2thYUHLy8tKJpM2SGs0GsZgn56eVr/fV71et+YF8tX09LQN7YmlcLvdRgICxKEZlGTKwunp6WuxhFzLVCqlUCikw8NDa/gfPnxoxBgOUaPRyBomHGgAE7HYPz4+NnIRAJ10NaRBcQOQQEwDag+3261Go6H79++bPTqgYL/fNyIg6r/NzU27D9vb2xbVcHl5qXa7rWq1qlQqZW5qPp/PGnIiJKWrQzY53JVKxdTbkuweQvKBFEW0CEA4B8xms6nRaGQKWtaGdrttSrlMJqPZ2VlTNOG0RcQYz/Py8rKpcYrFonK5nKkrz8/PzakG5wKAMsCXTqej58+fG1gNSAIRq9vtajgc6rvf/a6BPK8qJwEGAPh11D5Y+8/MzCiVSplartvtmrL/7OxMU1NTNizjnSITHdIK2fOSjPBWqVRMUQoQw+8jFiGfz5sK7bY4onq9bgDWy1yoAKkYvNyMy7pZExMTymQympubs0aL9ZfnHFXxqwgsX6T4mcQ24syIAyPvFiCs0y0ChwVJpgDCdvvs7MwUQHxelIw0cqgfedZwMiA+h+eTgRIkTJ5pYhogMPNcSFIulzPCM8Mfhg2AkKiRGTjdv39f0hVJhuft6OjIBpesu5OTk0omk9ra2rKfVa1W7b1fXFxUKpVSuVy2GJX9/X2dnZ2pVCpZQ836DAiF4x+EXp/PZ46CDAhw4pqamtLR0ZE8Ho/FvfL+oLAbj8fmoNDpdMxx0eVy6fDw0OJgWB9wtHBeL6/Xa4MiriXuFv1+356RX/qlX1I0GjUA8OLiwiJrXS6XvF6vNjY2bFDkHPABwFxeXsWIoiJ0ulXcVShrUdi9SRDK5XJpcXFRyWRSu7u7thcAZhB98Lp1eXlp0XRTU1N2tqhWq0bwYo32er0qlUqmfINU++mnn5oykPvMfvkm14WXFWdYp5o7EAjcCQ696WJ4GAqFLLJ4b2/PFLy4kvJ8Hh0dWaxdq9VSqVSyc/dtRdTQ0dGRDg8P1W63LYqI82y329Xk5KSdd3BLODg4uOZS9qrnkesF+PyqYqDGOuwsCKTBYNDcPe8qzvWoInGiedN1M36NehVR4uuoo6Mj7e7uqlarma1/JpO5FjVUqVSMcPyqPf7bVMT8Mdhzkvuca8xdz/Pk5KSp1iXZvn9+fq69vT1T4vJeQu5fXFyU1+tVOp1+4+QrnF3+z//z/9Sf/umfKpfLmZsUpAPON0TXh0Ih60FxZUYh3+125ff79ZOf/ETr6+tKpVIWS3x2dmZRUbhD4LJFXPL8/LyRDQDYiYCEiEMUMsNSzo/FYtEEWqlUSisrKzaQGA6HevLkiT799FMjm0CmHI/H+vTTT62HRwBDH+DcvyDEbW9vWyyqx+NRIBDQ0dHRNXIPrjtOUQwD+9FopIWFBSNDdDodG6RAkiKaECyD/jgajarVakmSdnd37UzHmYt4JZy6fvSjH5nTdKVSeamzQL1ev+awQPX7fe3s7KjVaqlYLNqAlMEaJCaiHXGf5OzJdcYdKBqNWh9Lb8ZagiszEfKSzF0ct+RsNqv5+Xk9evTIBiAQgP70T//U3KFw8HG73UZkZI8iXhtSFu+Dz+cz5xUiR46OjpTP5w0DgURDzMzq6uqd1xSsgUF6q9WyvRPx0c7Ojp0/+d0Q809OTux5QdhB/CZ7KAQavgOOkpybnW7mnIFw75NkcTac24bDoRKJhJG1pM+J0Kurq9re3lYymTQyzdTUlHq9ntrttkUJZTIZG3ghTMTFe3Fx0dzSEDg1Gg1zsccJk57c7XZrOByaUJo+FxI+ostQKCS/369KpaLDw0NVq1Uj9OMAxfvN2QbSSrfbVTQaNUd0zvU4gU1MTOj/+D/+Dx0dHandblv/54xXRPzIc+LxeLS0tGTRsQh36M3p/3DWJsYdkVOxWDSBRa/X08zMjBYXF40sinsavU+n07nm4M2eDZmEeCRElKwvPM+ZTOaas5bH47G4aJz88vm8EfXoKYl7hui2vLxs0aTtdtucHZeXl+334S7kvA7glxAipaszGwQWSPs3C8KH06n5dfoc559BBETvjijxZuH+CImPfvOuoavTXQ+hDeTDwWBg2J7L5bK+HbKcpFt7FufnhsCJgzixm7iL4RZMPNs3qS4uLmxfqdVq5uTPsyXJzhs8N/Qat1WtVrM9B2FUJpO5toeHQiGtrKwY8T2fz1syAsQ15zt0cnKipaUle6ZwuoSc6YzcA/cF+zw7O9P8/LyWlpa0srJiZOPbenYwHr/ff008AnbqLNwCWb8RxjprcnLS3OD39vbkcrkUi8W0vr5uxCqwtF6vZ2R2zp24c990k3HWxMSEzSEQLhNn90XiIqWr8wHCyuFwaO8xotBKpaJIJKJsNnuNPHub+GxjY8PSD1qtls2SIMEz41heXrbrViqV1Gg0VK/X7Z9DumJ9l2Si4fX1dRO8E537VQrCMH2X8/zl7GXpCfb39xWJRNRoNL4WUQbOVMPhUAcHB2o0GrdGuF5cXKhYLNp94nNz3nPGtfKulctl5XI5wxtxyMSNMZvNvhaZCNyQdBnORsxZfhFd0ZknsA8hJgNDcr5XEJdwAn/dfe+rFPdV0heKhaWnKpfLNltzCsy+qMM3Z1POPwhA7iLDcd7jjM+7y9pKUgREX9b324r1IPSZGzgzIUQKryp6CzD7lZUVcxhH4FWpVOxMArExFAq9Fmb2tt7WN56ARc3MzOjXf/3X9eu//uva29vTj3/8Y/3u7/7uteEIL/nv/d7v6fd+7/e0urqqv/k3/6b+rX/r33ohK/xtva23dXthJU0zVSwWrWl2Rg0Qa4Lr1eLiooFh0ufqN5oGLDhRxtLAAE5ifVqtVlUoFK7FRPn9fhUKBYtsA+SFjLO+vq5ut6tSqWTNgdMRBjVioVBQo9EwwLhQKOjo6EjxeNxAF74fmyugHQ1Cu902twpAjZsNBqzpaDSq0GdxjNgoF4tFDQYDVatVFYtFA7zIoC+VSqbqHQ6HBnxjN0zEBY0owzkaIkkWlUDsH4Pdp0+fKp1Oq1KpWEPCvSXDHGcr3EcA1RhgkLuOMtA5jKAhjEQiSiQSevjwobHqJRlgtbe3Z65mKMn4b8gERM80m031+31FIhGtra3p+PjYoh3i8bgWFhZ0cnKip0+fmgPLj370ozsHuUTktdttRSIRe1Z8Pp81f4B3fGaeX6e7AjELc3Nzpijm2mHjjnqAyAbnIAyC3MLCghKJhHZ3d60ZSqfTqtfrNhSYn59XtVq1ZqXX6xk5b2FhwZ6jQqGgy8tLU6YQw1EsFpVIJAyIczrU9ft9i8E4OzszcHJlZcWUWE+fPrWD66/8yq+oUChYVJeTcBWNRm2IA9iaSqXsYN1ut5VKpbS0tKSNjY1rtu8ul0uffPKJgRwQzBiOEE/oBDEA9cfjsf7sz/7sGsiPcwhAYTwetyHazdgTZx0fH1uEFi5rqCp9Pp8SicRLG3kiVNxut+7du6cnT56Y2wDP+8XFhVZXV81yF/USrjQofaPRqDKZjDwej7lKpVIptVotUzpDsmN4TrTcbRF/uByRX49662UVDoft+eKzv6pQweJc53a7beDO4KlSqZjbIp8BkI2IGSLeXqYwkj4ny0G6IsKE5wcCMZE0kJ6kK2CMYSNxFl6v19b0265PIBAwQAxVL8/Y5uamkSZyuZw5yTEcZU8ZjUYKBAL2zvPvZmdnlUwmNRqNLKaFQQMkzrOzM1NuAlo1Gg1zq4EwyqAFhSKOLTgj4nA4HA5VLpd1enqq7373u4pEIuZMhGMG0RVPnjxROp02xWkqlbrm3LW9vW3vAG5Vw+FQi4uLFl/TarXMJWJ2dlbNZlPdblenp6d65513tLm5aYAswzGGRJCjO5/F1NKMO2PAvF6vzs7OtLu7a/sTym0GCuPxWMlkUjMzM+YUUS6XdXZ2ZiAW9vyQg3Ga4z4FAgGLj2AghTNAIpEwEhfDz9sGgIBdAFNfB6mjVqvZEF2SvUs4K8TjcSMvvqo6nY4Nkvg7l5eX6na76vV65ughyUBu4o9arZYNGnO5nDmtEUmEA+jrOnF9mXKCyMfHx6biBTwrFou2r37dIEoikdBgMNDBwYGeP3+uXq+niYkJGzxQEEH9fr8ePHhgaw+xQ3dVOp1Wt9tVOp22wSHnPSKMxuOxZmdnLWYrlUqpUqno4OBAkq6RgO8qlPCoE1+1r/A9+TvOYgiRTCZNoMFZ11mcmZ1DOYax3+bK5/MW+7y4uPgCMQwCyOHhoUqlkhHxbhtUfhvL4/FoeXnZlPecAe4iKr6seOaIc0HF7DyDcn0Znr+JIjqFKKU/+7M/Uz6ft1i5cDhsETlEUUJcRjyDEyWDn1wuZ4NLBC+7u7vyeDxGJC8Wi+bKt7CwYPGx9NaRSMT+mzWaKCH6QUkWw0MkMWcMzsE3ib/D4VDPnj3T4eGhDg4O1Gq1tLKyoomJCXMfxkkGVyBnZDD77ng8VrlcVqVSMRdtXEtwH4FM4XQOGg6H2t7eViaT0Wg0UiaT0enpqZEKIEiHPounhEzT7/ct3ofe3O/36+Liwpw9iFQCD+F6O/e+TqejH/7wh9rf31cgELCzOQR9rkGtVtPR0ZEWFxc1PT1tbv+cder1uur1umEOl5eXSiaTdr58+vSpzs/PVS6X7WzG9UFYB1GfAWcoFDKiB4NV9kqeGdyTEJCsrq4qmUzq0aNHeueddyx++PT0VH/+539u5zfpyhmHvQIXkHK5rM5nMaDr6+tKJBIWK4eYiZ7aKbRIp9P2TBBVCDHsVSRzyGOSzMlrZ2dHxWJRhULByH+4GT169MhcWiG+oPQnMpxeIpVKaXJy0mK0GQ5BWsDlCPyF55s4PKfrOWfU8/Nz6wMRhHBu8Hq9+s53vqODgwOLfR8Oh/YcBoNBnZ2dqVqtWjTcaDTS8vKyDewRD7BWcKbGpWA0GimRSGh5edkc2MGJiPvETYrYQc6Jz54902g0UqFQULPZNKxtNBqZA3i327V9H6cmCJBEJT58+NBiTCVpZ2dHW1tbarfbRpKDAMYeAHkPQRDvJO5ZjUbD3FggWkA+BGcDk3O5XDaMgxQZjUbNKahWq2lqasriaMC9IJSxZoMNErsOcYP9hvvh8/msF0fAgkjE6eaLoIX3mmEqfRZi0I2NDUUiEYtGBJchKk26IjWDNbDWgLHiAppMJrWysmJDyruKeHpJduZ+VTldyMABiY9n/sO14NpC8CGuFlzotjPA8fGxCoWCEomEOa/98R//sX1XiG9EneFW3mq1FI/H5fP5TBwryfZPhFO4brDmzc/Pm7sZvzsQCKjVaimXy72Ws8jPshBfkWqAa7L0eYw5Atjz83PNzs6qWq3aGsi6hQhsOBwqk8lcc868bW0GP+YZ4R1jT+Ke4zy1tramubk5bW9vWxwULl38t9/vN9fGdDptxIn19XVtbW2Zwxt49E1cH9c8XOoajYZFpd7sT05PTw2HwanuLvIFeN35+bkWFxf1ox/96BouJsl6bkiFs7OzX1iwhGD8y9R4PNazZ8/U6XQsEthJdgYDQZC4sbHx0s/mcrkM+0bMD9mD2Yrz+5ESAfnX4/Go2WzauQ4nMkm2fz5//lwej0cbGxtvxBmZWG6nGOquwnmfvejrciKmx2cOQH/AfoZTvSQtLS2ZY6kkE6Senp7q8PDQSL+cpSGGgzEzP1heXlY0GlW3233BGdrZfx8fHxvW2+/3r60dFDOvw8NDI7h+0+b1zDec8e/7+/uWQOLE1dgjMJSQZEIUzndflPj4utXr9VSr1Qxvo3AdfZmYjaKHAX9m/oGI5YtgYk7XSZ6n29wJKWYLrAG8z6QS0Kcy1ymXy2aAQJ2fn9t68uDBA/n9/hdS0b7Id6CPcrlc9v7gZOYk3X6Ra/y23pb0C0TActbq6qr+q//qv9J/+V/+l/rf//f/XT/+8Y/1D/7BP7CseF6K3d1d/e2//bf1n/1n/5n+xX/xX9S/8+/8O/pLf+kv/Zw//dt6W9/cYgMMBoPmoBOPx7W/v2+Hf4bYS0tLFveDAgzAsNvtmjUjAAMD6JOTEy0sLCgUCtnhr9FomJU1QHEymTTyTr/ft+xmnFsgkEASwSZyf39fJycnWl9ft5iuYrFooOODBw+M6b23t6dKpSKv16t6va5CoWDuP1ik00BFo1GVSiVT6mEhflNRjWMHrhw4KkDcgvhVqVTUbDYVi8VUrVbtsFutVpXP582Vg0E6+fYcYACn3G632cienp6asndyclKrq6u6f/++DdDu3bsnSTb0LhQKRogCPATcQ5kKmQzlr8/nM9cTwBhJFskAwYlGXJLdx2q1asr92dlZI9RAYADYm5mZMYAMwhX3aG5uzpyPUqmUHXiePn2qSqWiRCLxygMWrj6AAj6fzwZ/g8HADte4dgFk4SDD8BaQ1zmooYjlgmBxc7AO6ArZJpvNqlqtmroT1TURnhC2hsOh3nnnnWsEIoh08XhcExMTZn8MuWRqakqVSkXz8/MG5nGA5Po61b0A1ljK44Tn9/ttWJvL5WzwmcvlDIhGiQpAwudEQZ7NZhWJRF6wBW632wZeoYZmmAE5DkAzEAio2WyaixwES0iMFLF9yWRSZ2dnqlQq2tvb0+zsrL73ve+9EG/U6/W0u7urfr9vUWycJ2ZmZsxB5GVKT2d8BGsVZBPIB9Fo1NyJAHkGg4FFKy4vL+vg4MDWBAhfNG5TU1NGTES9hK0zcY9LS0t2fY+OjlSpVOwe5PN5czpgoHAXIQ31E8q5wWDwShAHZyDA5G63a6AVwyRA8OPjY62vr6tarVoMo9N5zOfz2dBvcXHxVqVfuVxWo9GwAdXk5KQBqs4o1bOzMxugEhk1OztralfICBBCIDjdBJJ4Z4ntgfSHYjEWi5lrwHA41PLyshE9iLMClMSGm+cepwaa+3fffVff//73bSiL8ofYC8i2Jycn5siAIprnm0gY3BpRhONiRwTC/fv3zdmB6wzJNJVK6eDgwJx67t+//8JaSxRPPB63OFKIdzhi4ADG77y4uLD7jiPeXYNsYts4LwAUcC1qtZopjJ1qTwYaEIdR6Hq9XnOzZA2E/IZ7IbbogJ64I+B0xj3l/QPAYA1vtVoql8s6PDxUOBw29S3gdavV0sXFhTkLvmkCCS4D9XrdgBmG9FxDQMX19fVXArqQyaTPnUcgGd4WGUeMKXsDcTYMorkf9XpdmUxG5+fn2tra+loII2dnZ9re3lan01GlUjFSHKo27M45R33dSjbOfkdHR7b+4ZZKZKRzuM/6yaAasvZdNTMzY2c+t9ut58+fK5/Pq1ar2bkUJTlOdaiyUdRyvn7VdYjH4zYw5Fx7W0EAJY7qNjUiZ/92u62PPvpI29vbRgZhUNvv93VxcaFkMqnj42Mjgt71e78NdXR0pH6/r0qlYk7FtxWDsv39fY1Go69Ngf1NLpxZ32QFAgFz1sL9YXp6WqPRSJ1Ox5yaERUBxr9uAe63Wi1zbNjd3dXz58/1+PFjWzfZVxGOsH47Y4WJknOS0Gu1mnq9nu2/LpfLhDaBQMBITxA08/m8ndMhYS0vLxshnOiho6OjF86PDGQYeu7v71u/R5wga9ru7q5arZaq1aq55UIUg2jEedzr9Rp5FNfPTCZjIqVGo6FSqWQDSlzLOH8Rce73+7W0tKRCoWD4AkIHVP+IIHCuAvOAdP/48WNzloBwTZzY8fGxwuGwnSGPjo40Go0sNhyHKZwMcaeEoFCtVlUqldTr9ewcyXMFeWNnZ8ecOI+OjsxNBNIcsWnS1fr/zjvv6MMPP9TU1JRqtZqRNXAFInZ5dXXVrs/U1JSRYjg74r6dTqetT5+YmLBhCAQ6xCpPnz5V57NoWwhcuKUlk0nbf7kPiD3AeZxnEvq/6elpc1aLRCK6d++estmsVldXzQkal0nO2S/rWyD1tNttuVwuI9p1PoudYoDO8GdnZ+eawzrnzEqlosXFRXNAgfAEOY0YbunKScHZY+CM7BxUIfCLRCL2TuHkViwW1e/3FQgEVCgUNDc3Z2SmdrttzxK9GEQjiBLEgD19+lThcFij0Ug/+MEPjOTAcwd+5vV6FQ6HjeSCMIJ330lIY4iKizpETbAmnmFclBC94DISDAY1MTFhwzNnHDyEQN5TCBOsbVtbW3auoveHMAl5ql6vm0CmUqnYeo5TF+JNXPIRzSByQfABAQBcEFIbWClxN7in03PzHiEaxAHL5XIZUaXValkk5He/+12dnZ0pmUyq2WxazNZ4PNbCwoLW19dVLBYNfwBD47rw/QKBgDKZjJGviEfDFXg0Gmlzc1Ozs7M6PDw0Z32KBADpCuvI5/PmlP06Tr9OZ5zXrVgspn6/r/39fVvHnTFJRGbiKIXD8/T0tILBoIkpiA7iHIlzDu8cuOinn356DX8Ih8NGoC2VSmq1WlpaWtLJyYnW1tbMeYa1yek0h0Cn3+8rnU4rmUwqnU4bdhuJRIwghnPnm4hKexNFXGmhUNDs7OwLpCAc8Hw+nw4ODmwtW1xcVLvdNid7SZZ+sby8bESgvb29l4o7cFMPBoP69NNPbS0HkwCzJQ5tYWHBMEOeA8harP+IonFAhiQGcZR9FvzCWexdxLIdHByYEJx+FQe9crks6WpdQ9Beq9WMrEqRHAGxANLuzX6L8wy4NdgB5M2vi9RB7e/vq91u6/Dw0Fx3nYRGyLqHh4eSrnCHV5GUJJlQ/FVxdk4nQJ/PZ+S3VqtlPTHrFGJBIqTL5bK2tra+wre/KrCP20T3t5Xf7zcCNGTfN13T09O6d++eXC6XuTFVKhU7YzCjYhblvCeQCT/99FMFAgFVq1UzYJiamjI32cFgcE0Ednl5qU8++cTSUyA4InxFfM2z2ul0tLKycus1m5mZ0dLSks1cPB7PN4aAhXMY+Cnx0x9//LG5doJtu1wuiwrFkTWTyVif8mX2vS/yORGugGNCCuPzYDhAj/eqet3Ej1f9DIR2rVbLTAruKoTL7OX8WQQVkAnBSHFlZH0HU69UKiaImJqa0qeffqpwOGzutMPh8AVS1s0i0tkZXS197oa+tLRkBDHmVm/rbX2R+oV+YiYnJ/VX/spf0V/5K39FxWJRv/3bv63/7r/7716wrD07O9Mf/MEf6A/+4A+0vr6uv/W3/pb+7X/73/5Sisi39ba+bXV8fKx6vW4KBOlqQwHUollBPYrV/sLCgjY2NswSttPpGBgHYcbv9yuZTKrzWa5657OIpNXV1WsbYDwe18HBgXZ3d81ZI5lM2kEhFArp/v37yuVy1uDyn0KhYHGD9XrdnCT6/b6ePn1qzgxLS0umWFpaWjKgJBwOq9PpKJ/PG4GTxg0SFPFIvV7PcuaxDWdz5s/hVIWbAC4w1WrV1LIA45IMHOh0OuY0hKLYGZWF65ckA1dp1pxRWM7hzC/90i9ds3T1eDx65513VK1W5fV6TYnnPGQAhDFMn5ycNPIVA2ws3z0ejx2OcE2KxWKKRCI2RAC4wNGg2Wzq4uLCVHkA+JD1IJTNzs7a51tYWFA2m7VGE1Y7B8SLiwv5/X7t7+9rYmJCvV7vpSAGCkeG9BTOaKhJRqORxcERbYYycWlpySKEnIUzljPGChDAWZCUsKh2DoIBniEjMKwfDofWTDkL0Pvy8lKxWMwUxAxXM5mMcrmc8vm8otGoDUtxhoG8GIvFrBkm2gywFRWsdNXUraysmEMPA3niMQABW62Wnjx5Yk5g6XTanOmOjo4sW77b7Wpvb8+Uo+l02mK+IP2Uy2UjjfE87u3tmWsRwAkxG4CdRKtsbGyoXq/rww8/VLVaVa/X0/e//30j9dyM9kFZD8jJgCMcDls0zeLi4gvPFkA6QDgOcigBGcZwfwF4iThjUEP0Q6vVMlVkMBjUvXv35Ha7FQ6Hr8VaMOiCNMIz0mq1dHBwoG63a8OjXC6nUqlkzzMRNXe54MzMzFiTAXnlribu8vJSz549k8vlUrPZ1ObmphEyqdFoZI5uOJkwFCIWiwaMtQry4cbGxrVmh9hWwNSlpSUjhuAWRvMFERY17NnZmb3/EPiJMTo9PX2taDaGXAw0GIJA4CKe7ujoyEi5nU5HU1NTarVapnKXZKQkQA+GJsvLy2o2m0bm476zTicSCdsziP9h/0KtS7QiETK8gwxhsU++CYQRHYFDIZ8dAhqgK+8kSnUAIEgeuE0CjhLNyqAZS28ihtfX1289oxMxANjAXs9gg+HUycmJPB6PDczcbrf9c4YKuOMxKEONnEgkdHp6ahEhvEvERTB42tzc1NHRkTmKcXZgjQZ0lq7W93a7bfGJgFbBYNBcI5eXl1/5vH2RKpfLqlaryuVytv76/X57xweDgRqNhr2HrOsvK1wUUApLsrMM/9xZl5eXFifNsAgrcfZPpyOpy+VSLpfT+vr6G70WkrS3t3cNRJ6fn78GrjjXJekKoFxYWHjjn4PC8ZP/nUqlNDc3Z4NT3BOcYgAiO2+eXe6qubk53b9/X5VKxZ5vhufsvxADnSBhNBpVpVK55jj6siJajLMTDrBOW3knOMbw+rb9ZnJyUt/97ndVq9U0Ho+1v7+vw8NDA/QYRKO4Zt1imPV1FwRPBvOvA8Z/1ep2u/rwww/19OlTuy7cl3A4/ML3ZiDR6XTUbDb/iSNgfZ1F38e5FQfhZrNpJApU/Kj8EUfcVZeXl9rb21Oj0bBh+OzsrL23OHnwZ4PBoJFDGOZLV+cYnBogjeB+QxQw8fE40LCONJtNO/PQf/H+MwiF/LKysmIRJUdHR3cSH1Evs48ykABch2BaqVSUTCZtsFgul02FzRCT2C8+I+489XrdnD/BHiqVivVTECQkmXOv00HW7XZrfX3dyOcMNUulknZ3dxUOh83FwufzmTApk8loMBhcE9ecnZ2ZGykqePZAJ8kcZxoiX50CGnouyFM45UDY4KwyGo3MSavT6RjBBjLbcDhUrVYzkpPH41EmkzGMAMHV2dmZRadwFsGJl3sIuajdbtt95LnBUfT4+NjIU9zPRCKhWq2mDz/80Mg4jUbDCCJO5zAIHRAnECLQfzMAlz53FZientbR0ZHu3bun5eVlG4BDqschEbIvQ32Gt7lcztw46RHq9bqePHlirhB+v99EQDhP4v5wfHxseyXiI4aUTiciepDT01OLi6MPcUYSQgShf3XiLRCNzs7ObLjU7XZtGL63t2dn4E6no8PDQ11cXCibzcrtdmt6eloul8vIQ6PRyARk7McrKytKJpN2RqN3xd16ZWVFjx8/NhIieIXL5bLeJRKJaGtrS4VCQaFQSKenpyoWi+ZO4+y/+I7D4VBLS0smXgJfI/qm3+8bcYOYdOfeC1ZzcXGhxcVFhcNhlUolHR0dmavG9PS0KpWK9vf3zbmGvh7xmXQ1wIeABpECfOXBgwf2DlYqFQUCATUaDYsvJUISAuzMzIxarZaazaYR2iYnJ9VoNOzacwZiPafXpXfy+XxKp9OGF0CEw200Ho8rk8no+9//vp48eWJO+8+fP7dnaW5uTpFIRKlUSqlUyoQlkowkDMEAklcsFlOhUFA8Hr+GYeFQxp8ZjUbXSFp3FX0m57jXLQRNc3Nz+ulPf3rNwQyxF4TZ8/NzI82urq7qBz/4gZFbarWayuWy7Y84tCPsoxf2+/323ONIy/4CIQ4yHD1woVBQJpOx/dHv95uYmKExAhswt0ajYVFm4ONgTN+EgkBxenp6p+BQkpF0C4WCstnsNZc5BJEQoGKxmLxer3Z3dy0m+WUVjUZNkBkKhZRMJq1XgeCeTCatP1laWjL8fXt722YZiD7BJRGXIIj69NNPbR8ldhmhNc4vnKlZS91ut7n8VSoVe+85U8zMzCiTyWh1ddUExPl8XnNzc4abEOMcj8eNpHczztDp2sye73RtLpVKtgZ8HYIhYmchi9/WS7jdbuv9SqWSzs/P5fP5LCb7qxIUOC+yXxKpd3l5aekOzpqbm9PTp0/VbrcN4/miNR6PDafhd32RCDn+jqQX7umbrNnZWW1tbZmALJlM2vfFOSgejxvhliIVot/v29zN6Ujq9XqVTCZ1cXFhDt2lUsnOws6Ye8QN4XDYsORms2kOYC/rkcEbarWazSZeJybu66zxeKznz58byReMkX4sn88bxhgKhRSLxWxOU61W7WyM+JZ1400TsC4vLy1mOpfLmUsg/RjnGxyELy8vzVzj6y7mq1wTDAxuSwMAQ0YcDdGSPhaxIvHbpB+Fw2E7u/Mck0Dj8/lMeNrr9a45HkPov6vAuX0+361kLWfk6Nt6W1+mfqEJWNQ//If/UD/+8Y/1B3/wB7cuKs4hwfPnz/Xv/rv/rn77t39b/8P/8D/on//n//mf9cd9W2/rG1PFYtGcOm6ypgF4ABgZ1t67d0+DwcCs62mEYC+jSo9EIqagnJyc1OPHj615utlgQj6g2Z2fn3/hwIpaCBcHwDkARBj+jx49MtJLoVDQ6emp0um0gQjRaNQUatVq1VSIZF+7XC6tra1d+34408zOzqrRaGgwGKhYLOrZs2fm7IBrAofJ1dVVxeNxix2jEc9mswYSQupB8YKyg5xtCC+oLgCZ2u22njx5omw2a4QXwCQON5ubm/J6vS9YYqKOn5+fV6/X0/b2trxer4bDocVGAPL4/X4b9DF4Rb1H8wzAhPMI1zkYDCqXyxlhT7o6ZJ2dnSmdTqtYLBoznoM7h/RQKKR8Pm/qyFAopJWVFVMN39bMoUKAiPaq4l4794ypqSlzjHC6DHGAJZ4PAA5gmwKskK6sgQF1bjvoAZa32207DE5PT1s0gfPd4J6gVryreGcgeUSjUa2trZmar1arKZ/P23vT7/fNLYls+Gw2e+360uwT7SV9bocNqefi4sIIIDgeEWeRSqUM3AVw393dvXbfGo2GhsOhut2uHj58qFqtpmq1qna7bQ5WqEtdLpcODw91dHSkdrtt6sHBYGCEMVRwo9HIos1wKDo+PjYAttvtKpvNanFx0QD5UqlkBAmnsw1gb6FQUKfT0Q9+8APFYrEXGgmPx6N6vW7EpmAwKL/fb8r3YDBoZM58Pm/DsWQyaercYrFohMSFhQUjfEmyGKp+v2/PpCRzCggGg/ZM93o9HRwcqFwumzsY77Lb7Vbns5gPr9dra95NRSKRkLigtFotFYtFzc/Pv9CAXFxcaH9/377T0tKSDf2cNTc3Zy5qjx8/NtUL3xdHOp41p7Jub29PGxsb9rxXq1Wdnp5aBMnx8bEB6bwvkUhE4XBYf/zHf2xxOLFYzIhFvH/sQSivcVp59uyZOVpAYEERdnJyYs0ibl3dble1Ws3Wi1QqZYOFVqtlzynuFZBK/X6/pqen1e/3jVhIRB5uf6iunIrtmZkZI2EdHBxoNBpZbCnqSlTCxJ+srq6aqvDk5MTWKvYzZ0EM5DmGUAzBicEbLhAojHu9nnw+n4EzOGtEIhGdnJyoWq2aU+Ds7KzcbrfK5bKRNb7zne+8QCyA0AhpMB6PKx6PG7GReDbAUNw+IF+xn0OURtXGPr2/v6/z83Mj9AKwDIdDlUolI9Ulk0m7Ni9zrUIdW6lUFAwGzaESQBcnyNdxbvwixcAUIujy8vIL76vP55PX6zUnTIg5LwPLIGJCTEXZL12BEjyXVOezqMjBYKDl5WUb7BE9yzkrFotpPB6rVqsZofxNRhHisFEsFm3wcrPm5ua0tLSkXC6nSqWimZkZiw/6OgpnUWKGcaJxFoPjubk5FQoFU6O+SsHnLJwgV1dXLaIAl627nt1AIKByuWzv2asIWBMTE7p3756dq2q1mrl0QSoAHEwmk0qlUi91q3K73fpn/9l/Vv/v//v/GhEF0h5qx9PTUyO5hMPha3HXX0eNRiNz+HKe0YiDvqlufxM1Ho/NNeTg4MDOxbh/4JRBb+Usos6I4vtZgK7/pBRn6VqtpidPnph7L/urx+NRMBhUOp3WgwcP1Gq17iQVS1eOR0T9MLjb3t42ojFnBtZXeiPiV3geiVSHFAPpZ3V1VVNTV7HIkFqI/yEiJJfLKR6Pm+Mp+7gk20cZqOHsMjs7ayT42/ocyEecnwOBwLVoCgDyyclJpdNpA8eJlCFOBdIZMeuIAjijQ3DCBaBarZq7JUQR+isI+xCNcHBkfQQjWF5eVrfbtZ9H/E84HNa9e/fUaDR0fHxs57JyuSyPx2P4g3ONJroK90uc1HDxDgQCSqVS5tKDe+vp6amWl5eVSCRMgIRLULlctn0Nwuvu7q6daXq9nvWwDKAZHkejUSM9IezBsYxIR4gZrD0M6SDDEL/C2gKph2iwUqmkjz76yIjenN2mpqauRRETdw/BhPsICW00GmlhYcGISsTBzM3NqdfraW1tzaJHhsOhGo2GOeHiPDw7O6toNGp4VrPZVL1e19OnTy3KiKjtcrmsfD5vZ0XOptFo1J6lSqWiWq1m/RRneCKYfT6fEfZ7vZ5SqZQNZGdmZvT48WM7r/IZcVFwkid5diCNQTKcnJxUsVjUvXv3tLS0pK2tLU1OTurjjz9WuVzW3t6euc0tLCyY0G08HhsxLxKJXHOI/s53vmOiD95dhJc4CMfjcb3zzjva29sz155ut2t4RjQatc8ZjUbtuQ+FQhYNEw6HjZy4s7OjRqNheATPAc9+v9+3s3kkErFeA2ciCnxKusJeEDrQc+A83Gw2dXl5aY5fCANw1Ac/+vjjj414wXkXnGRxcdH6oGKxqKOjI1vPwA6IYSYyHpEh6yDu45lMxtyY+N0QtYhoZnA+OTmpUCikUChkv3tubs7c+SCxsWa63W4baDJMBJu7eRbgeYOwuLq6auJJRLZOt20ieSC29vv9F9IAnMXwk3v/RYpz+Pb2tk5OTrS3t2dCmUgkosFgYPsnRLNYLGYxvZOTk6pWq7Z3si5ATg6Hwxbnef/+fX344Ye6f/++Li4uzHmeyFOwXScxY35+3si6oVBIfr9f3W5Xg8HAXCQXFhbMuRqSD0IEeklcSkgB+HkXGB8u+i8rr9drfXY6ndby8rKlZkiyZ5EoT7DX1yHGzM7OKpVKaX5+3oQWuJPdxN9cLpfW19cNswSvBxPAkRusDMFau922ZAPIy59++qk6nY4WFhZM1IhQfTAYKJVKaWZmxkgr7INzc3Oq1+u2525ubpoIDOItKRLEUXINwaCos7Mzi/6DFMb7PBgMtLe3Z+eNYDCo9fV1w7zeVC9Sr9et73xZRCbv17Nnz9TtdlWpVOTz+Wy9XVpaulPk+arieeH94RkiFvm2Pw+u6CQCvqogSENYoSCrQxwGz3tZgYnzWb7OAv9MpVJ2doYIeJeTUTAYtD2p3+9fI+U6q9/vW9/Sbre1s7OjxcVFS8jg/AIpl6Sb4+NjDQaDV7qbSZ/jDYgif94ELFx5SfqgT8B5lLWExB3WbrCaWq2m6elpI0WRPvKmC3I3feNNnM/n85mYBEIwhLGfRSWTScMnnj17ZkRj5nWSzI2QMzRYMXNQ6Wr/YE5TKpVMcMJZGXOH0WhkEbNra2smyj84ODBxCDMWnGpvFqLaVCplTv1v62296fqFJWDVajX9L//L/6Lf+Z3f0d7eniRdAxg5HP9z/9w/p6dPn9rwjkPSwcGB/oV/4V/Q3/k7f0f/0X/0H/3cvsfbels/r2LIRDwMbjnSFYh7eHhoQA/xRA8ePDDLfhpMYgFPT0/V6XS0tbVlOdGpVErPnj2zfGfiVW4brJ2enhrITAyCc+gDkAIQBJh6dnZmoBp2m7/yK7+i/f19c5Pa3Ny05k+SOavgeACRArUcIC5AJUzsZDKp7373u3r69KmpJQFtGEAzYFpaWjIXKYbszmxuFKmDwUCzs7NGdEGVg3U2dvIwrgEHiUzk52Ftj/PW6urqS5nugIE4LOAcdXx8bA0uLicQxFDmMdjmPna7XVNjctifmprS/Py83Yfd3V1jrtOEcu3D4fC1QyNAMOQkGqm74jxQxjrdmF5W3DOAVWehJiGOIZVKmYrEGefgVLDhrnJwcGDNViqVUqfTMdLZzQKQJE4okUgYkQ7ADyAc0HBmZuZW8ApwAeegiYkJNRoNA6w3NzdVLBZN1e50d8MhyxklQAF2o9p2uVymgiaDG/c73LqITUin02bVTSQnDj/YFjOAJ7rovffeUzQa1eXlpSnbiRPhZ/HuEbdXLBbt+UPRG/os3hTSBIA9JD+Xy6WnT59alNnOzo79TOlq2NRoNCTJog6cyvZarabt7W0lk8kXAAkafZSRi4uLajabRtokjpDvzEAccM6pcJuenlaxWNT3vve9F+45hJ2XVaFQUKlUMnUfwFG9XreBw9LSkrrdrj755BONx2OFQqFrBAxszon99Pl8yuVy2t3dtZgGSRbrgAvh6empkRlvK9yBfvKTn5ijyfe+970X7Ndx9CqVSioUCqaG9Pl86na7Oj4+1mg0sga5XC6b4pyGi3UtFArp7OzM1JmQXufm5gz0BuRClToej3VwcGAOEFNTU/Z3JyYmFAwGlUgk1O/3bcCBAhAw7fLy0og4z549kySLm0ilUraGQICiuU+lUjo+PrZIjrm5OSPrMHAYjUbK5XIWkYtCKJfLmXIREhRW2s73gHgB6fOIKwYww+FQ/X7fVJpYqQO24oSFmwSRaLlcTh6Px9Ytnhu3261UKmXRBgD+DMhwOsOBiDgc1mjieHG5YyhJeb1eJRIJeTweIypAdOOsQJwSymmGr1x/XBi4jqx7x8fHCgQC5hD4KkKKs3w+n9bX182VAUIsA/CvgxiBNT/A2l1gIc/gzs6OvcO3OftRPIe4eUJOJx6LSByqVqtJkjmeSZ9bjbvd7mvgRiQSsQFyo9F4o+5TtVrNBtUvGwBNTEwokUhYPE2z2fzaLPERPZyenmphYUG9Xu+aAvqmtXkgELB1JhaLfSG3J0i8DElfFY3GwO8mIfxl5Xa7tbW1ZdE1rKk4lKKETafTr3VNvV6vfu3Xfk2pVErNZtMIw5OTkzac9Hq9CoVCWl5e/lrs/SkINxC/iZFFhc4wen19/Y1F36E8brVaRpyHqEwUValUsvVN0jfGPeHbXLiIFgoFPX/+3AaxkUjEBDY41hKL/P3vf1+StLm5eatLICIP3GhyuZwJEXCv5H3E+YdBPm4L/FwEQZxr7t+/b4QWeuKZmRlzqGafhExLr8q/5xwxOTmpSqVi71o2m1Wv11MoFDLiNGcDiE4QhJLJpLlRcJ5HUZ7L5QyYZ+2r1WpGsGm320Ymox+G2BkIBOyMx5mIASpEHkRRTgcanGf4O3wWv9+v4XCo1dVVxWIx7e7umgiBYRoxyvRw0Wj0/2fvvXoly6/r8FU555zr1o2d2CSHEmlKgP82LNgPfvGDAcOAYcAQREKfwJ/DLzYg2fCjXw37wZAAyxYsEWCamQ7T3TdVzjnnqv/D5dpzqrpu6DTTJHsDAkcz3fdWOOd39l57BRE+FQoFAHjtecvFHYkpjOvlOcxIbD47GUV5fHyMw8PDjTOFhFqz2YyzszP5TOhCRWfVQCAAh8MhS2iS8zhjOBwOaDQafPbZZzLzc87h+2D0F0kwKpUKyWQSRqMRxWIRjUZDekw6b9Np5Pnz5+h2u6jX6yJe4JKFxIbZbIazszO4XK6NpR3Fa3R0YoTJfD4XcgIFFrFYTAhndGhQRt4wXvHVq1dCTLRarTg7O5NZi+SR8/NzWe4S32FfyAUbnchIBuS8QeIHn0sajUb6Dgpk9Ho9lsulEG6Ar8mUyngrkqOJiRF74aJKo9EgHA4jkUhgb28PJpNJxITsybvdrgha6BZDMm6v18N6fRWZfXBwIDM959HJZCKuFySR8d7UaDSIRCIyv1J4NZlMhHTk8/nQarUwHA7xgx/8QO6x6XQKn88nva/b7cavfvUr6cMnk4kQXzh70pGLRELO+EqCo5LMT9ElBX50nyFZaj6fIxwOi5s13dANBgOm0ykKhYJEGiuJYT6fT2ZeLjpJQmXfyPlXrVZLHCYdkPiM5hlLXCISiYg7F8mhxPHo/LctSOBZyogrEvNIGvR6vTJj0In0OkEDCWgk2QJXff7R0RFyuRycTqeQWPjf6La9t7eHbreLTCYjblmcpfhsIxmDWOObiAdYdGBfr9dIpVKCea7Xa5hMJjmT6KjH38F4YGKJxI35LO31ehgOh9jf30e1WkWxWBR3f5JrKUCmEIHXRiaTwf7+viy+OVdSuMX4JX5+er0epVIJWq1WPkOPxyMxtvV6HfP5/KMhYJGUeJcFNMkexAg0Gs21YlHeLxaLBdVqVf49z67tYrQoyY+3zS5arVYc+OgWrMQcSUTmuXtxcSHujxTMnZ+fo1QqSf9drVYRCoUk+o5Y+9HREcxm8wZWQNEyhWvr9RqxWEyezXzmkLRMYSjjq5UCOLo2M/0jEolAr9dLf6nRaFAqlZDJZBAIBNBut3F0dCSObG9KdtxV3L3cRMQbjUbI5/OCCz958kQI95z5zs7OEIvF8ODBgzeeqekgZLVaUSgUpBcgiVaJoSwWC5TLZREmUni4S9RFUSrd70gCarfbcs0oE1GIAVPgfF1R4Evh5DclfiGmeZdiL6pSqRAOh0UMYbVaBfvs9/vSH3Fvw2eT8jrl/WOz2ZDNZkUYy7P2tlL243fFGz5U0TEpn8/DarWK49vTp09xenqK0WgEu90uZE3lXozPCLov1et1fOc734HRaPwgpCfuX4CryNZdGAj7tcvLS3FZ/KYIWGazGbFYTPqATCYjPTHJ6ZPJRPoURkxT+KB8nTzL1Gq1iG5Go5HsbkgsSyQSODw83DirOAN0Oh0xDEmn0yI84Z67UqmgWCxKj2+1WpFOp0Vc/Kk+1fuq3zoC1v/6X/8Lf/EXf4H/+T//58bCVNnsh8Nh/Omf/in+9E//VNwX/vqv/xr/6T/9J/z3//7fZWBer9f49//+3+OP/uiP8A/+wT/41t7Tp/pU33R1Oh1Uq1VxfeIQyaKqqFgsol6vS9Ov1WoRj8fRbDZlcKD9b6FQEKISF93pdBqFQgGr1QrJZBIOh+PazHeCyHTfUioWCDx1Oh2MRqONKJ1UKoXj42MBDxg/QCDQ7/e/tpSo1+vissGmnstmOj/RWpxqd5IVwuEwotGoDNN6vV5ALpKvtFqtLBkJfnU6HXkdXNyPx2OUSiUZvKfTKfx+vzR4JO4who+vlUMcrfwZU0ibaw4dtzlIkGBHEINOK4wBpJKbC3ylSk+tViOfz0scQafTgd/vx5MnTxAOh8WtipFcSkVqsVgUlxc2U2Sak7BGEIKLg263C7/fv7OZ52DAyDYCayS2bBcBajZ926XT6ZBIJBCNRsXm/eLiAt1uF91uV77byWQiKmQqnOgKU6lUBDjN5/MAsLE05eJuOp2iXC5L5KHX692I7MrlckJqorpquzgc6/V6mEwmWRiwGJcXDofFlcpkMklEmzIGDfg6LkiZCb9cLpHJZMSendEkJApZLBZZAHCZ8P3vf19iWhqNBsbjMR48eIBwOCzksq+++grVahU6nU7sikluYEQjFawcCEk2o0MbSRyMU+ICiC5tVHnzNRLsZ3b65eWluElEo1EBj6jWZ1HRTBeo58+fv0bAYpweIz16vZ4sXOjwMplMBKji93Dv3j2xEB+Px6IWGQwGKJVKO52kbir+vV//+teyuKbbHM+L6XSKXC4n51KxWITb7cbBwYEsBcfjMZLJJHw+n7he8X0R2CQ4SzckxlBks1mJY9t2iqvX6+KS0u12kUql5CzkIKX8O4FAQBYmtNrmdQEAh4eHyOVyAK6Wd7xe6ArIyAzaZVMVTttrLhGo9ONShe6M1WpVbPOXy6UA1KFQCMvlEgcHBzg7OxPXNafTKQQkLnNoj8zr+uDgAMfHx3KNEthptVpwu93iBDkajWTpqLynga9FBSqVSlzmSOTiM5PkCy4+GdepjBExm80Si0E3AX52dKWguwfPIkZ70MlJGa9CYjQdAvR6PRKJBIxGo8TMLBYLiUSjCyIJflyGMu6I9wLBTSq86cKkLC4D6/W6nJ08rwkq87tgJAqJllwUcqnpdDqxWq1wcHAAg8GAcDgsiuU3LYPBIMDjYrFAs9mUaBOtVivq6fdRVC+zJ7qpdDqdEBo7nc6NBCyTySS23LVaTXo+AnQEa6lUpWMBwRNldBr7AxZdRnndvc9i1CvBlpuK1wKBuA9FwJrP5+LYSGIplz67ymAwiLLSarXeCABv12q1krNWpVJJ1NZ1nwVjwt7Ual2v1+Pw8FCimigM4PV9U3ztrjIajeL4yEgUgrp07XxfhKfrivb+xWJRyL/sYRhv1mq1JCbo5OTkvbi3ETDM5XKwWq3Y398X0jsJaHQSrNfrACBOvMvlEuVyWaLCPrlfvb8i8aRcLsPpdEqkr/J+dDgc4pCjUqlwdnYGnU6Her3+2vODjrFc2NCRitFHfEYqnTf5zyQNkNzCWAw60zocDunt+fwcj8dy37TbbQG9HQ4HzGYz3G43Op2ORIORNHVxcQGz2Yxut4tQKIRYLIZsNitRgXSH4wKefQ/JC3S/NplMQrTOZDIoFovSH/V6PZydnaFarUoMHR2zSM4olUoirDIajeLExLmR8VFer1cWoHSIYt/HOdHhcEisPPscj8cjTtN0i2Vfzt55NpsJuYgCFrpO0umKghSSMPhcpHiEynSlqzPdcPidaTSa13o/FjEHOk1z+ctlgXIRSQcgzpXsvY6Pj+F0OpFKpWCz2cTFi3FySsJYKBQSIQLncy6MSWjodrs4OjqS6/Orr76S5QjnNYvFIm5euVxOzi9GyU+nUznnSZ6z2+0bz0XG5dDBV6fTSRQT70M+80i+mM1m+PzzzyWWmq40kUhEhFD8blUqlQjkGP/JxSDFUHSwHQwGQp6jAMrj8cDlciGfz4uLHQn/vBZ7vZ7MoUr8ihgAHdf4uRB3MJvNCIfDcLvdCAQC0jNyzlOr1SIAY/8diUTEBZZO5c1mUxyrODNTaHF2doZGo4FSqSTRwrwGB4OBRNHzOUhcjw5PJLHR1bjX60kPqJyZ6TTXaDRE4MJ/3+l0BDMiVkMyG52M6ZZM4qfdbken0xEMj8JCusfT4Y6EGDo7LxYLIVFOJhMRL1DEc+/ePdhsNukFSYKkCIwxkfyOwuEwzGazLGjtdrvgF8S5+GygKxxJkiqVCnt7ezg+PkYoFJJzcZtMwfdWKBTEKdput6NarcpZy/vjpud/p9OR80opLLFarbh//75gjco4OZIRl8slfvWrX0GlUuHVq1d49uyZYIa8LlerlXxWqVTqjZ15VquVuKR5PB6ZIXhvkMgUDoflPKTTOYV5dDskEbbb7eLly5fo9XoS51apVFAulwWj4z2p0Wiwt7eHRqMhjuuMfea5yHmIn18oFILVasXFxYU4cfGZSFcafoa8b/hev20CAovP3W2XquvcVCkavUt/v1gs0G63RUDJeC6LxSIkS/7MVqslznx3JZdoNBrBcCnaUs7ZxD3y+fxrewniJIzgpNBvOp3i+PhYSHZ8Dzy/SejjvoNnAc9KOh3v+iyq1SoCgYAQfIGvXZtLpdKGa3On00G5XBZiLEnFFPyuVivpYRaLxZ0ciG4qzoLXka8mk4k8x58/fy4R6cRbaHphs9lw//59TKdTHB0dYW9v785zCc9XPuuV8Wbsl0jKIrmb+xqbzSbOj8pIaM6TAIRkR3c/uohRfMdIa+5pOIPtcgtfr9col8sbM+rHWHT+J4bH51O/35cZm8IpOr69evUKbrf72jOKPy+fzwsZepcAfbt4nWyLzr6NUgqUlfdcoVBArVaTfpa9WKPRkHOAuDSfzQcHBzCbzUgmk+/9fdEtjDjlTecuBUKtVgsej0fidL+J8vv9QrCjSKVSqciZ4HQ65Vmq0+kkVnlXr+Dz+WC322UHQCdQ7iQfPXq0EaPJ0uv1iMVigrkxyaDdbqNer2OxWKBerws5nuRB7kBoRJBKpTZIwiSWMcmBz5c3EUd+qt/P+q0gYBUKBfzn//yf8V/+y38RVRYbQC6fAOCf/tN/ip/+9Kf45//8n7920/7Jn/wJ/uRP/gSnp6f48z//c/zv//2/5e/+h//wHz4RsD7V71XRNnw2m0k0wXbRmpnqVKrRqND1er0C6MZiMbx48UJcoJhBvV6vMZ/PUavVEAgENixOyRYngWI+n4taot/vbxCwuOQnQUWtVksjEYlE8PDhQ5hMJhSLReTzeVnkKc8HFkFCOjvZbDZxRuBrI3Dk9XpRLpcFDCZAwUVsNBqVZR2HMII2VETys+x2u6+R3EjKury8FOcvpZqGoDBfJ0FVZp4rl/RUFBoMBmi1WoxGI2QyGaRSqWuvA8YXWiwWcY8hkYjxTFx0NxoNsfOmcpPAFd9XoVAQIgFt6akAVxJpSHzQarWigCJRjQpZgtIkZJEAoARh5/O5xLVRKa5SqZBOpyWjnOAhr3ESHGKxmFxv1xWzp71eLwKBgLiEEJikexmtqXu9ngzljPeiOxwt22OxmACZwWBQrvOXL18KcaHT6aDRaCCfz4vCnAAOSXIsOp0w+lGlUolCXqXajD9ULsIZccJ4jXQ6LWQ+WizT3cdqtaJcLuPs7Az5fB5erxdut1vIIYytolqWYAlBCTra7e3tybUMXA3VBBhJ9iqXy+IORGIH1aIc5HiNMiJpOp1Kw8t7mIpaOvYMh0N0Oh2oVCqJAaRC7NWrV/KzaOW8y+GGlvG9Xk+GLqWqilFb/HwBoNFoCLGF0UEkknJpZjQaJXKERDr+N5JaQqHQGwGXz58/x89//nN8/vnnAuoxdm+1WkkMJQkoBPsJYhOYjMViEqWjVquxv78Pr9eLWq0Gu90ubk+MFKE7EVXvVKDG43HodDqxUu/1ekLMI1knn89vKBaVFsEEA4rFogDqhUIB5XJZSE2JRELUK9VqVeIiZ7OZLEgrlYrY/JOga7PZEAqFkM/nUSgUkMvlBMyORCIol8uw2WziRqdUWhNc5uKtUqnA5XJJXKZGoxFQjIsgOleRxBiPx4UURDIiSbpK0J4xJsqizTlwBRwmEglxRmR0rc/nw3q9lqUqn+d0xOLrKRQK8Hq9EktM8jGBL8ayAZDnEVXtvKZJLKQ6/vz8XJyMuIAj2dHj8YgjH68hOgUAkBghqg273S5Go5GQoqhi3SasEKhlj8FYBIIVJJUpAQg+y9gz0CmCFvqMUOD38bZEqcVigUKhIJFJSpUlQcZwOPxGDlu7ip/nTc83ZXGRvx0huKtCoZAsi7LZLHw+nzjJeTwelMtlcb6iupdnMMn7drt9pwqP1/d23/auxV7nTT4POjLQmYA93vskygFX961er5coKyUhmm4QXB5WKhUcHR3BZDLdScU4nU5RKpXkOc9IZZ5DAGSBTsUf3TMIML5NHIDRaHxj0vBNdRfHxw9R8/kc+XxeemM6LbAoemi1WgK65/N5HBwcvNPv5dKRz9FwOCxzhFqtll6YTjeMKaEog+6+8XgcrVYLL1++fO21/74WXR3plO5wOK51nGBkgdI1g66UnLOo/laWSnUVq5rP52WpTxB3m4DFhREBXAK+LpdLYsro0sglEgCZjUgeZ9/A+EC6XQKQiF46FPKe53OCPQ0j8fiMpBuL2WyGWq2WRRZd3/gsZ/Q4SZ3s3SeTCcLhsMxJHo9H3IIbjYaQsBaLhRBMSUzm89dkMsnrZ3/EGL/Dw0P0+30Rw7BXoVsDFdYkggHYmGNJeiNphOT4crkMk8mEXC6H+XwOr9crZA+SvBi199VXX+EP//APxekCuJqFq9UqtFqtOCg5nU4RiXi9XiFLejwecRejArtcLkOr1QpZfTvunuXz+fDs2TMAV65ZJycn0teQCMJnCq9zOtUEg0FEo1FEIhH5O9FoFOFwGJ1OR5Tf8/kc/+Sf/BOJPefSnORaEsn6/b6QDRgn6Pf70Ww25T7S6XSieHe5XDJHUVQDfN0DUAVPMQSJTUrXoWQyKYIhxgwCX/dZJFDRtb3b7cq9RcIEZwx+H1zS0jmX/zwajTbOT0YWsqdmn723tydOSZ999hl++ctfitiEswujq9jDcs6luyXJynQ3oss1XUMTiYSI3/i5KePQB4MBTCaTRPixF1S+fuW8RldWXod0E1MS2liMuyJRNBQKiUMyABFpXVxcyHfV6/UAvN7XmUwmcUkYDAYbrm3EbhgxTiEVZwI6bQQCAQQCASFcptNpceyjYz8Xp6FQCA6HQyL1lsul4JUkvZHsTZdBRpbF4/GNe3CxWEjULMWCFChR4MQoReKHdIIlYYcuwgaDQdz4PB4P7t27hz/+4z+WyLNKpSLiB37X+XxeSBf1el1+Hx2v6LBVr9evFRGQKB8IBHZGugFX2Nwu4st0OsX5+TlWq5Vgb3wt7XZbnMpOTk7gcrmQSqXuRKDh/cbzZTgcolgs4sWLFxLdymcRSVgksNHR4ssvvxS3raOjo42FKJ3k2+02MpmMxDQTv+I8yeeizWaT30kiDvB1igCfRRTN8rnCnt7n8wmRiRjvdh/Gz+9jICCweG/yuySJmPcwl9j8TsbjMfx+/42CCDrrsa8hmcjpdMpnSHcUl8slPTXjUt+0uJNQ1nq9xuXlpYhPd4nCLRYLDg8PEQgE8OTJE7kHjo6OYLFYhNRFAhDPZ2JsJpMJ9+7dQ7FYFIISRdTbnwcxK+KtLGIoStdmzqMUyNFNkA4vPD+Hw6EIfynWeNuiY+h12ACxrydPnsi1z3QNh8Mh+9rhcIhnz55hMpmIuG6X2IvibOK9fFb6/X5xwWcEJVMDSCzm7woGgwgEAnjw4AEqlYrcp/P5HOfn5+j1eiI4YyQZ3RwTiYRc2yy/3y9iawpvstksPB7PRqIE48tmsxni8bg43X2MRVdKk8kk8dt0VGRRXEc8lX3KTfgMiegkE3c6nVvxLJKAiQ19W8X9YLfb3UjN4fUxGAxErEDiOmcXClnG4zECgQDcbjecTicikcgHcaZWCkrvgo0wDppuwt+koxN72nA4jCdPnki6w3g8lp6I/2c0GuUz3lWMhKSoYD6fYz6fIxKJ3IiJ8TlCUwVGCnNXarfbJW0oHA7DarXKjpF7odlsJnHfFI6w7yaOy7M8HA5/cHHgp/rtrY+jy9tRy+US/+N//A/8xV/8Bf7qr/5qI99d6Xbl9/vx7/7dv8Of/dmfIZlM3vpzj46O8Fd/9Vf4wQ9+gC+++AIA8Hd/93cf6m18qk/10RWJMFRlXDfskbnudDoFjFWSDVQq1cZS5N69e+j3+/D5fNjb25OBVa1Ww2azbSh4aQdOgIBkSjauRqMRzWZTXt9sNsNwOJSmh3b7Xq8XHo9HyCm0uSWhiq4cZPoDEGIMY6v6/T7m8zkymYwAahysuSCnmo/LQ4PBIKorEkoI0nPAprMN1YQcANjQ6fV6WVDZ7XZkMhkZIuhSsF6vpXGhvTlBhkgkglAohKOjo42hfb1eo1gsolAoyCLmuiaA34/FYsF0OoXb7ZaBm7FpVACS3EXCFIlDVO1wMcSYuslkIksAOtAQSOLC0WazbahIGYHG98PvlnEcSvXQfD5HNptFr9dDsVgUJxsuEp4/fw6r1Yp4PC4gIQdlgnA3OX1sl9VqxYMHD4RMQ1LBZDLBr3/9a1myzOdzAekIvo3HY/z85z+XJcve3p7Y1BMgJ/GG5IXpdAq73S7OJAR6Gadrs9lEQUvHs9lshnQ6jYuLC1neXKcw0mg0YlXN74kONyTBcbkXDAZlIUEgtFqtirqcy8LZbIZEIiHED0aPLpdLabjpCMUisE2nMNrK0olmuVzKNUzAjWC+MsYCuFIssk9gtBOXTATP+DlzAUUVbLFYlFjJcDgs17PSPYikNofDIUsFZVQWVUFU7dPRhNeB0k2Jgy+BPw61jJjiPU5VO0HS22o8HuPy8hJ/93d/hxcvXghhiCS2er0Oo9EozjVUHzI6z2AwIJ/PC8Bvs9lweHi4ATxzUHr16pUAaQQaCZrxjKWDDAGZcrkskSgcvNRqNaLRKFwuFzqdDkqlEoLBIFSqq9halUoljl7dbldcbGq1mtwrJpNJIiG5KKIDIZdn3W5XwIUf/vCHWK1WqFarsFqt4rREQildp7jw43fCP2O32yX6g3GqnU5H7KCp2qeCjmpuvV4vi9bBYIBsNiukIJIOgatzvF6vw+v1yrJYSXZlUf1ERS2f7XShpKKIij0SK6jCtdls6Ha7KBaLWK/XOD09RTKZlL9LZRXP73A4DJXqKkLzZz/7GQDIctZoNEo/wPuHTpi0oVcSQACIu8ZqtRJy7HK5hN1uFyCe6nfamgcCASGNMfZW+blwkUVi93g8FrIXVY5ceFClSAIio0K4CBuNRrLE4HDOaJhgMPhGLkTz+RyvXr1Ct9sVYjKX5jyrXC4XxuMxYrHYOzkvsafaVi9fV4xPuIt62W63Ix6PA4C4hHLBxd6Lynu+L7plMh6ExEdlkcyndEJ4X3UbiLxdvGcZbcRlEJ1GTSYTotHotW5VdymDwSDuKlz+sUfi9UnHUUbckSCgjBy7robDIc7OztDtdiXCjxERdLbgz+Gy22q1Cqk9lUq9ker8d7EajYbMATcRmNxut5ClrVbrzriLuxRJAIz26vf7MidwcbdN1GOfQVJtKBQSt8dut4t2u41yuYzJZCKRKb9vxecpHUg5a3FZTocBgtL9fh/ValUiY9frNdRqNer1upA5TCYTarXatZFKJP2QQE2lLF3UWCTIU7hCQlMwGESv14PFYhECg0ajkecXzwjg6/hgxgi63W40m03p/0n25bPVbDaLWEaJr7E4BzocDlk4k0yk0Wjw8uVL6f3puMmFNt2BPB6P9I6BQAAHBwfyWeZyOVlmkcxBsimdE+kWxWge9s8kX6/XazQaDVkm8DONRqOw2WzI5/PiMkPynFqtxnQ6FYdDAOLkTZccRo5REX1wcLDR/9IljC4rnF2SyaQ4exJHcLlcIsqheIt9Xq1WE+dDCl8YO0mSEEnvLpdr57yqjN/hAnH7GuSygYQiLgbj8TiOjo5e+5l83fl8XkhcsVgMnU5HMAPOEnRHoKiB5DZ+b9FoVBxrSB6iOxtJ8HTCJKmKhHySf3ktctFMUkyr1YJKpYLP58NoNJJ+abVaifiiUqmIWI0zCO/pQqEAu92OSqUi7oXr9VqcHzlDUswzHo+FpMT4udlsJrGe9XpdZi0un+nQ+rOf/Qz1el2i0BkzSPId7+d0Og2DwSBKfwoD6IpLQhrJHcr+g6+fZxrdkABsOJhwDq9UKlCpVOKwRle8QqGAZrMJi8VybX/LJTeJWp1OR+bger0u5Cz25SQoEVdTPkdJvGDPTrf8UCgElUoljjWFQkHOq8lkgvPzc3GwG4/HCIfDaDabCAQCQr6jUx2FRyqVSiJuSIRqtVriPBMIBAQvpCCCjqHbPSkjHLmUrlarsgzlecPvhPF9XMoqcVVipF6vV3DPVCol3wldu+gubDKZRKzT6XQwHA4RjUaRTCaF2NBsNoVQms/nxdmPWCAdc5rNJux2O1wu1xs55cxmM7x69UpcjPR6PY6OjoQkrnRHZ2zgbX0kXbf5jOaC+dWrV/jyyy9RKpXkLCP2S2ECyXt8NlC0uU2+UhYd3+jET6IHMS7+bEY2kazD5x5je5XXBe9rPj9IELlNjNDpdASH+DaEBruK1+PTp0/lM2J8HgARC9OVlwkA1y3BiYU3m00Ui0XYbDYhfrCnarVaIgI0Go3iYkTR9/so4qadTmenI5Wy7HY7Tk5OhKgdi8VgMBgQCoXE1ZCOQcQPlGJYumyXSiVcXl7KmQJc9Wx08QuHwxJXp3ydSrwLgMS6DgYDhEIhmTEY38gItEQigcvLS3S7XVSr1XcitXDHUSwW5dnI4px8cXEhPSgApFIp+P1+wVYo8mSP89VXX8FoNCIQCGzsZhhjSHE/ABGoUmQeCoXEXZYCceJsy+USfr8fyWRSHO/Y/6lUKpyenkqku0ajke/JZrOh1WpBr9eLIyEAwVyJ1XM+o8Ci0+lI6gHvC7vdLhG3Nwngv+3iWcW9nMfjEYdBzjv8rilQZo92Ez6jTOogXnETCWs2m6HZbG4I+b6t4nc4n8/lXuSOgZgWr1e1Wi39Au99Rg9SALK/v/9ByFfA1wQ6vpbbSilu/DZcFkliCoVCGI1GEnlJ4Sv7nbu6pPN8pWjpLk5zdKY2Go0SZ/v06VPpx+7du4dgMLhxfZNUTkLr2dmZOBM3m01JC+LOlkLUXq+HVCr1Thjhp/rdrY+OgHV5eYm//Mu/xH/9r/9VBsdtt6v1eo1/9I/+EX7605/iX/yLf/HGQL1arcaf/dmf4c///M8BYCOD+lN9qt/1ohpuOp3eOHzwXiPoRZt/Eqm2i/eo1WrdcLriEj6bzYpdZK1Wk/gqJRBONner1cLe3p4Qjhgvp2wcqI7jQhiANNSdTkcac5PJJLFrAEShBFypRxlxZ7FYBETjayHgxn8ejUYolUriEBQIBPBHf/RHAqpzOFES07rdrgCGdDNikSDEBXyz2RTg0el0Ym9vT4YUfjbD4VAGdSoGtr8HZtgz6ovL0u2iwokKPTp70KmEZCKDwSCLbAAb/0053HBhP5/PxdlmuVwiGo0iFAqh1WqJHTpVsFSpzedzUXrzs0kmk0K+oEMLYxiLxSKazaa8T0YU0PmF6s/nz58LkEBLd6vVioODgzd2A9DpdAiFQkImWi6XOD09hcViweXlpTTkq9VKhhiSXObzOX7+858L4SgWiwn5xmazIZFIoN1uy+fidruF3LFYLHB6eoqLiwtUKhUBDkKhkEQ/MfYwn8+LKp+KI6o3t5taugZFIhEB7JSkBDbC4/EYDocDGo1GHPPOz89l6c6IALvdLhEVXOISkDaZTBJ9wWJ0D13RlBGCjKdQEvu4OCAgT+UBl1Lr9VqITlQ7sbhsogW91WoVRTEXPXQDKJfLAtoqY5gYrbharYRwR2AVuAI1M5kMyuWyXC9U4QNXIEmlUpFlGM8yxikwsokxFCSWMArrNgLWaDTC6ekpMpkM8vk88vm8EFyXyyVKpZKQgJQuW3Q5oEuBy+VCPB6Hz+eD2+3eOQzxNTGGjPnzk8lE3JMIUpE4QJtmgkeNRkNUxDyrTSYTGo2GEJ9ICmQ8IZc34XBYvkvGBtBpiorUfr8vMTVc+tEmmoAUXW64LDAajTg5OUGtVhM3RbVajcPDQ1FBkkRHsJRgNm2d4/G4kE55HdDmm+5SXMLyuUmiMYlAVG/TNQ3YVI9TxUnXKrPZLIA1CcxcjhEgJJjHiCLaP0ciEbk+qtUqJpMJvF4vQqHQRsykz+cTIjQX1LyeEonEhvNCJBIRAhV/N1VC3W4XkUhEHFyCwaDcD7VaTZ6xPp9PiHSz2QydTkfIVy6XS6KK0uk0vF6vLGebzabYgAOAy+WSBRqXNwSRLBaLgJcABEyhIm82m4kLBr9r9hkkgN7F6We9XuP8/FzAP6vVKs93XofdblcIHwCEGPA2ZTKZZNm5K6Zx+7VxKXVX8Iu9E10nhsMhZrOZkCgJ4I3HY4nCZCwLAert4nWqjKF9X6UExxg7c131+32k02mJjeFie5sox2X220YMcCHPfosLTI1Gg3Q6LWQMXpOTyUTcDUjmOzw83Llgms1mOD8/R6PRQKVSgdPpxKNHj5DL5bBYLJDNZtFoNGQpTbFFqVTCbDaD1+sVsvrvc9Ghhc+mm8rtduPi4kJ6+duWO8oikN/r9bBer8UJh9HbXPAQ8CVRD4A8c3u9nhAJ6Nrq8Xig1WpRLBaxWq1wfn6OBw8evHEM0G9z8VnZaDTQarXENYRF8nKj0ZAzk89pEpFJAGJ0zWg0gtfrlV7wuiLBhpFTJOcoi/2u0rGRhBTl4p1uVxQkccblfDydTpFIJHB4eChkBi6m2ePwOqY7Kfub+Xwu0Wp8Tfzs6IRF1+ZOpyOkIiUBRunuyX82Go34//6//w+Hh4eysCoUCkKCYmw1Y5gZicZ+gWQIzhF0eOD3x97VbrfD7XajVCrh6OgIvV5PBEOcH6jIbzabACCfIVXrdOJot9sintomX7FUKhWOj4/RaDSk72b08cHBAex2u0RhqVQq+TkqlUqIgFzgcylJIgn/lxHadPHQ6XSvuXWsVishYTqdTjQaDQCbxCwA4lzG2LjbFrKLxUJ+P0k4JPovFgshjJAko9VexZKTiMpIEJ/PJ/fcbDYTdwQS7thr8rVyyakUodF5kpGLLpcLmUxGejKSvvV6vVyfjUYD3W5X4kwoEqK7qRKTMRgM6PV68ownIY6OXbxnF4uFzAAUAhI74sIlkUhInw0AsVgM6/Vaoi/pXkmXOGIh/X4f+XxeSFNut1sIVyQImUwmTKdTVCoVxGIxcadQniNKwRxJlqPRCMBVj8lIlclkglqtJp9ZMplErVbDy5cvxeX1tr6WZJtGoyHzFmdlRhnN53OJSSQxpdfrvUbAYqwdxZCMCNTpdDg/P8dsNoPb7UYikYDP50MgEBCnKr4/vV6P/f19ITwVCgXk83khH3ImoluX3++X+4TORyS+0YmX5+KuBTIJtly25nI5jEYjmM3mDfEHn+lWq1VIh5xNiI3QzWUymcjrXS6X0Ov1gt/pdDr0ej28fPkSrVYLpVIJ4XAYBwcHiMfjsNls4ho9HA6RyWRkab5cLpHJZOQ5xpmAM34ikbjxTOD8RUIrZ1OSonbF/pAAuVgskE6n8fDhQ/ndzWZTRGz8LAeDgXzWdKep1+s4OzsTQh1dyXg+Op1OxGIxeWarVCohC9Ah7rrifU4CGwmXxI+Jz/Bncmbc398XgjtdvraLbn2DwQCZTOZG1+TBYIBer4d4PA632y2iwW+7HA6HOHl/9dVX4lpMwRQdcyhY/t73vndjqgD72WKxKM56vEboPEfsqNPp4ODgAF6vF5FI5L266fJ75j15W1ksFtTrdRHEkPSkJMASB92uWCwmZyKdU+mST2yf0YTbsxZJgEqMgKIM4hbKIgZG4iOdeelC/bbut4yr1Wq1rwlB+f3xWbhcLgWPYl/M16LT6ZDL5bBeX0US3r9/H/V6HZFIBO12G+l0Ws4F5fsmBuT1egXnj8fjQsbmM5i7AaPRKNdXoVCQ85mENsZO8n00m00RF1LoR8ySjvw8+4jTUkS9v7+P0Wgk4mC6TLI3uguR5NsqPnftdru48wLYiRXRwYxmCrfNwqvVSvp0i8Uiolyl6cN6vUa73cbFxQUAiBtouVyG1+v9VmLclCk4xFu5v6FL5rYRBT9DXnPsu0jKehsh1l2Kzqt05b3t8+IsQtfNb7PMZvNGFPXbljKy9y6YBv+McpfM/dGuM5j/nXMXBeR2u10iiUOhEGw224bYms6HAHB8fPx7KXr7VDfXR0PA+m//7b/hL//yL/F//s//2WB1Kt2uPB4P/u2//bf4yU9+gsPDw3f6fXt7e/LPd1VEf6pP9btQygfWTUs5jUYj4BPBSUYCkK3scrlkqOTwzEGEf4fW65VKBV999ZXEABGc4zKs3+/j4uICk8kEbrcb2WwWjx8/FvIXm1sOQclkUggoyiIYSbCHMRA6nQ52u10GL54tJATRmlKn04m6QknwcDqdEo3Q7/cRi8WkWbwJvOBA6nA4BEhTNuVmsxknJyfQ6XQyGAaDQYTDYSSTSXl9+Xweg8FgI87gOnCBzi9Uol1XjAuy2Wzi7OT3+4WUoFarUa1W5XMnkDSZTGSg6ff7El1ks9nk++LCmw4qVEvt7+8jl8uJSo+xg4ynYLQfrUg9Ho+ozC0WizQ2lUoF5+fn8t2RcEOnC5VKJQOGyWSSaExa2r+Lwwav/+l0ilwuh3w+j9VqhVAoJLncVJsDV2QyDnS5XA5ut1sUl3Q54pBJJT6L6lcOl1Rcc3kyHo+h0WhwcXGBTqcjoJparRaAiy4ah4eHOwdCLjauKy6inE6nKDJpRb5arUTFazabMRgM0Gw2ZdHLHHWqmvR6Pfb29sRFi0C9y+USghRJeVQfdrtdHBwcyDBHgiSVo3SSIbDBJptLBKre6fhBJT3jQwm4UqlFYJrqVH5GVqtVVOSMmOB3PJvNcHp6KpF7g8EAe3t78jsrlQrq9TqAq8UPVVsEj7mM4gDAe4iD3219ynK5xPn5OZrNJk5PT+VzHA6HGI/HAojwWmCPRectjUaDaDQKj8eDRCKBe/fu3fj7arWagMTJZFI+J6qVm80mer0eXC4XptOpRCxS0UyVKs8wJTjkdrsxHA4xHA5RqVSwXC7RaDRQq9UQjUaxv7+PaDQqqhOVSrUR90IwTxm5wfg/ABvXusPhkOhQLiA5RNdqNSHa0v2E1xYJhYzjIXHR4XAIeUFZVO9ZrVa8ePFCVDsul0uU1LSTZ0RPrVbD+fk53G436vW6uBU0m028ePEC3W4XFotlAxwi6WoymUjEL8nBjCvhIoruinq9XqIDcrmc3E8kDjLyZDqdIhgMCnGSTiLhcBj9fh+9Xk/O2wcPHkh/8Pd///fQ6/XifkciAR3+qESmsx3vEf47tVqNUCiEvb09AcepRKYLSalUkkUdyUA6nQ4PHz6URdfz588FiOR1R9cLRuCu12u0Wi2YTCbU63VxMtjf34fdbhcQlIRVAPL53VSMbygUCnC5XK+dt7THtlgsyGazaDabQsR+m/L5fBKjQleA64pqdYJIdy0C0HRdo5KNjha9Xg/tdhvn5+firre/v7/zGbRYLCQSiETQ91lc/tdqNYmv3lWr1QpPnz7FbDZDu93GgwcPhDwJfE2Uo/MeAHFEfdPikobLMqPRKGRog8Eg7iWMBNNqtYhGo/B6vchkMgiFQjg/P8fx8fFrP5sxQpVKBeFwWKIgWHye8XrW6/VC9uQzQavVynv8fSy6HIzH4zt9v3q9Xogt2+f/dcVlBB2vSMKnsxydKTudDmKxGCwWixC7TCYTXr16hVqtJstvko/dbjc8Ho/0klqtFpVKBVarFa1W661Jg99UkeCjdDflEudNK5PJoF6viyMr3ffoctNoNPDFF1+I4+l8PhdlLhW5/EwZo8rvV0ny31XKWfM60ifJFXq9Hu12W8irwNWMFgqFJKKY7lB0Du73++j8Jlo7EolIHxEMBnFxcSEkbbpE0VmXr8dqtWI2m0mcMB2gSI7hDOH1eoWsMZ1OZRlDF0r+LIoa2BtQOU7npNlsJtcqSTmRSATdbheXl5dCYCAZm7McSfN8HpPM1mw2ZUkLQFw0SMh2Op0Su1Gr1eQ8I6mLoo2Li4uNWGOKpW4C9Q0GA+7du4cvvvgCNpsNhUIBjUZD4nzpcso4MvZqtVpNxBwUpfDspRMWI50YJfb8+XP5HJRLY5JZfD6fOLPSiYmgP3t+4iGcx7evz+3rlqV8XhsMBnEGJdGeS6h8Pi/3EK9hzle8Ntjz8znMz4UOV5wD/X6/OFzxvqejM10u+PcGg8FGZFG5XBaswmw2o9vtymdGIitneMaI9no9+Hw+qFQquWb5PQKQZS8d0fn3+LMMBgNSqZTMwxTK8bP0er3o9XryefH70ev1MttwjmcPwnhnnU4nQkI6clmtVrjd7o3lHq+NarUKp9OJfr8PlUqFTCYjC0WSSOnWze8HuOpRlaKXm8gro9FI3EpKpZJEaPPebLVaIpI6OzvD0dGRuFUpBU78fEg4VboXEe+y2Wy4d++eEByJGdrtdumXKcIJBAI4Pj5GvV4X4gExLbfbLT+D5DZ+bu12G8PhUJyIGIfucrnEWXybhEUsIhgMyj3t9XpF0EDHF8ai8hoi5kFSgcfjEbc7zrCdTgeZTAaRSAQmkwl7e3toNBri/DwajZBMJpFIJJBKpV4TS1EIlM1m4XA4xDmGQjVl5HkkErm2v+H3S0IscdFyuSzxj7xnt8vpdIqrlcPhkDmVGB6fJQDE+bjZbMLj8YgbPzFlXqvsjSgKpOiKzi3tdluEXmaz+Ub3K86dOp1O3NWGw6FcmzwPiAsxJYBnNkVr1312jF/1+/0oFovyPRN7pHCr0WjA6/XCZrPdyeX8m6pSqSSkQBLsSIimeKler6PRaCCRSGA4HMpzeLuIYxJnVpJ6ScyZzWZC3CAx1u/3v1fyFbDpHHOXukmsc5c/Ew6HxYWcTscAxAGRYqbtohCP2B8FZKPRaGdsIqPQeAYonfoZc/s2xedKKBRCPp8X0Q5Jyoz45evk2bp979HdiGJbivtcLhfS6TTK5TI6nQ78fr8Ifvm+lGROk8mE+/fvC+mdz0CdTgeHwyHXFHHqRCIhwrBGoyH4EQC55rh3IhZeKBRQq9XQaDTkeiRuymuYzqmhUEh2VCaTSXYeH3vR+Zr4A102dxV7eIrPbqrFYoHJZCL7PRoZMF6ceFyxWESv15PZhzgxe4TrhOMfsoh/0LmYMdA0RiCRn3MOdyAUiZbLZYknDwQCqFQqODw8vLbPfpdimgcdsW/DKLh//JhcFt+1ODPx2XTbGcd+g6TYer2O4XAogt+byuv1SlJEsVhEIpHYMAABrvYYwWAQVqtVUoiKxeI7c1Y+1e9efTQErH/9r//1xoCodLv6oz/6I/z0pz/Fv/yX/3Jnk/I29bFkbH+qT/VNl/KBRSB3V9ntdtRqNYmuoKsPlT9KFxqDwYByuSyLql/96ldiRcuhq9Pp4MWLF+j3++KSRQcmAhInJyeiUlA6KXBQXiwWcDgcOD4+FhXELltwPhDj8biA0z//+c/FAp2/g4DzaDRCIBCAyWRCpVJBNpsVtyc2i/V6XWziHQ4HVKqraLBarSaONbuKYCJVxIVCAeFweON1kyhEG+dkMon9/f2NZoLvX7lAvqkR5iLoJuIGHTb29/dRLpclv51Wy71eDxqNBs1mc0MtRCUona44kNP1hKAzgTzmPMfj8Y2IiW63K/FVBFi52GAGe6/Xw8nJiYBbBMPY9FKR6/f7xU6YID8X/d1uFz6fD36//706OrDZpo073T7W67XEFFHxZzKZUCwWJZLRaDQKcY8Lj3K5/FoTqNVqZSAlgE0no8vLS1naWK1WsbslqEbQI5PJAAByudydonq3SwlYOJ1OUf5rtVpxpVGr1Xj16pUMufV6XdT1BDe9Xi/cbjcymYw4la3XV1HCjJgjaEcCm9JtZjAYIJFISNThs2fPBKDlkDeZTFCtVsVNgIC9VqsVAg1dtAgA030A+NqFJRaLwePxwGAwCJmQ78PlcglRhyBBOp2WZYDf75dlE110dDqdfDZULtMxjgA774NoNCpEPgLHtw2hzWYT4/EYlUoFLpdLwHeewySn8HNnVA3fM5dsd1Ee0a2u0+lsACUARIHm9XpRq9Xk311eXspzJxgMigsBiaLpdFrUnwR8SO5kXIvL5UIwGJRzjwQGEu0YhaAcmHke0amL3x+LxDeS97iY4uJXrVbLskdZJCgtl0sBt5SuSn6/X85ePnP594xGI3K5nCx1GbnBMhqNODo6gsViwdOnTzEcDpHL5eD1esXxi+TG5XKJvb09jMdjIVtRZU2r+kwmg+VyiXA4LKQ1JRmv3W7Lz6AFu8/nk+WP0WgUgi3JsezRW62WWJnzGUWQMZFIQKvV4sGDBygWi/L5+Hw+hEIhUR7R/YEW9jqdTpwd+B2YTCbs7++j1Wrh4uICWq0W3/nOd+Q8IVmaxLvBYIBgMIijoyNEo1H88pe/FFInPwMq1KkQt1gs8j13Oh1x9WPECAABoGw2GzKZjDihcbF5XZFIyuvpujIYDPB4PGi327LIu2kRdl0xXpJgokaj2alS73a7qFQq0ru9DfFJ6fipLKfTiXg8LuBtLpeT61i5EO31evK84DP6fYNVdO8IBoMolUoAroDibZeQ8/NzWeAkEonX3ENJlDObzRIfWi6X3zrigS6B0WgUhUJBHOPUarU8qxlTwZjWaDQqbpe8X5VuS1xk0eWOZwuvfY1Gg1AoBKPRKGAnnff29/eFeLVer1GtVuV59vtcd70e6QZw1wUPibYkrBPUZ9x6Op1GNptFKBSSeCbOC4z1JGmFTjmMbFFek1TD01XiYyVgzedzlEolIWcoCTOcCcLh8J1VxcPhUFwHdTodYrHYxrVM4NxoNOL58+cYDAbQarWypOfSi66q4XAY8/kcn3/+uSzHxuPxtYA247S5HNulPuaC3uVyIZfLycwAXD0P4vG4KJc5A7I3Xq/XElcbDAbx3e9+FzqdThYZvIfZNy8WCyyXSyFYcrFCYh8jcEnKZWwE+wpGCDOGjrGHOp0Oh4eHogQejUY4Pz+HVqvF5eUljo6OxDGTPTmjqw0Gg5CGJpOJkDD4+tmTce5lH1KtVsXd0uPxiOMR5x4SXWw2Gzq/iYbm+yYhiEIKOolxIVer1fDgwYNbr69gMIhgMCjLYr72VColcVb8jtlD0DGYix7GwHe7XZmJ6JbK53Gv10M6nYbZbBZxxHK5RLvdFgES50y73S4kfF5DjNByu92YTqcisLvuXGO/uouESzcy5f+yF+XPZS9IR0v+HwnzFJfQSZdzHImjhUJhgwBDJyFGijudTpjNZsznczx//lzczfx+vxAH6R7F3wVAHHzY8xF3YNw9BQCMeqP4zOVyyfsEIJgVXUyOjo4QDofFhbbZbEqPQ9IKxRnbRVcZngNarRbZbBbr9VrEBBST0DH+/Px8Q0gMXPU4jKh68uSJOFBrNBqcnp6KUM5qtYpQzeVy4dGjR3C5XLBarXj16pWIOdgrbBcXUHQq4pnMnwFAIv9evXqFXC4nhKDhcCjiADqXk9BCJzT2rnQE5izucrlec96gO0Eul8N0OhXCC0UhFPExspIiEuUzQDkP0X2Nc2gqlUKhUEAwGBQs1O12C4HTZrNJ3GI8HhdyKslxdMijyIdnNt9HMBgUJy5ehxS7dbtdzGYzcS2kO9t8PkcwGMTx8TFSqRSCweDO+5jxiCaTCYFAQO4NYgG3OUT1ej1cXFxIhPV4PBaxYKPRkFk6k8nIrLdddOXrdrt48eIF9Hq9EPB4npNUlc/n5TlIJ1ZGMvKZ1el0YDAYZM7nOc/ZhS5LdHm8rk8ggZSOw8Qx+b2TmE3y9Wg0EvL1q1ev8J3vfEfcwonvbH8HFJzwmUNSPUWAJOkGg0G43W6kUqm3Jsm8z2Lk0rNnz1AsFoXoqHQA5dkQCAQQDAYFN2Z873aRTD8YDGSuIWYzGo3krOJ3oNFoRLSdTCbfq4sMsXb2Kbe5YPFaIT73tr8zEokIRg7cPlMQB6vVavD7/a+5tiiLxFylizVfK5/N71KpVGqD4Eyiaj6fF2dZulOShLSr6L7Os4xOvsQTE4nEa2c8iapGoxH5fF4ECScnJ2g0GvB4PIKjAlfXFc8on88nrk3T6fQ1Z0d+nkrRAwVIuVwOWq1WzhO6JdbrdVSrVUls0Ol02N/ff++YxTdRgUAA7XYb4XAYpVJJznQKMjg/rVYr7O3tiVPoTUVMkM98AMhms9KXjkYjFItFABAHXPZzdLWkEHU8HuPo6OgbdWz2er0YDAZIp9MiJFEmhPT7fYn65BnearUAQOLI+/2+zOZnZ2ew2WzY399/r85TKpVKXuttBLpOpyOk7V1YJZ+lxH3Ye3+bcZB3Kc5RJHjfhsNRwMtkH/axjCRXErSUZE4AEjPKqNKbcEoS/XkuKl3TPtWnAj4iApay1us1HA4H/s2/+Tf46U9/ivv377/33+FyufAP/+E/fO8/91N9qo+9GF9ntVqlid5VdE4Yj8eo1+vC7CUhoVAoyJKS6kWCxPl8Xpx3jo+P8dlnn4n9PR/wtImmUo3A6v7+vhCgXC4XIpEIarUaKpWK2GzTqWnXA40DINnNdHPSarVoNptim0yghJFM0+kUPp9PyDq0XiaJyWAwoN1uy1Kaqjo6VdyUT+/3+4XYUSgUcH5+LqpnEgT6/T78fj/m87m4dCmLKhBaVdO2XFkcJtRqtbjN3DYUkBR1dHSEr776SmzLSUqgai0ajYoSzOPxwGQyyTVSr9cRjUbleqhUKnKNkSQFXDW2dAdYr9eYTqcoFApCoLDZbAgGg+j1erBYLPLfUqkUTk5OEIlE0Ol0BFTl575rIUPgh25otVrt2ijGty06D3Dh1mw2pSneXvK4XC4Mh0M0Gg1RsD5+/BgA8Pnnn6PX68Fut782aCyXS5TLZSGj+Hw+uX7VarWAfUdHR6IgpksdXY3y+TzK5bI4br3pEEBAhOQ2ksK4kGGsSKfTQbVa3QAYaVVO8mOr1UKv18Pl5aV8bmxsR6MRKpUK7Ha7ENWAq8VLsVhEKBSCyWRCKpVCr9cTZSavFy6CZrMZGo3GhhJbuUAj4EWwSxk/R+INHaiUy1QSNunMQyIGiS50kyNAdX5+DovFIgAs4wVJ/CBgQaCFTgL8c+12W5YEt5Ew6vU62u22AFgcXqnU5rKHxThZgsT8bqiQuan4GVHltF0k/wGQKFIq50iSowKcZMVisYhWqwWXyyUgM797ui3t7++/5jbDMyUej2MymeDs7ExIVYz4XK1WcLvdEsewfbYqHSqUjmaMadhF/N8+ay0Wi7guUiG36/nEz5bnPi3Vt4ugeywWw2QyQTQaFZdEktMODw+RSqVkGQpAHBAqlQoikQjG4zFCoRBKpRLa7bYoJBlJwyUfCbIGgwEXFxd4+PAhUqmUkCiLxSIKhYKQ5PgcZVQKATLGCXEBA1yRTIrFIiKRCKrVKvr9vigLq9WquEIsl0uJBzGbzWg2m+J0wQUeXRUrlQoSiQQODg6QSqXQbrfR7XZRKpXQaDRQKpVweHiIWCyGaDSKVqsl/QBJoYxDAb52MeKCglGmPO92gYLBYBCFQkHO5OtclZbLJQaDAbrdrpCEbyqHwyELqk6n81YELABIJBLyrCXQ4HA4xNmU1uq0Tk+lUh8ERNzb2xMnwlqthkKhII4o/Pcul0ts+z8EOUSlUmF/f1/OpUqlIsRpxvYwVtNqtWK9Xt84e3KxxzOLJJg3La1Wi6OjIzm3Pv/8c3HU5KKJsckOh0OIVuyTSKhRErB6vZ4sO2KxmPx7EogJGPPP6HQ6dLtduN1uUa0OBgMUi0X4/f4br+3f5eI1qtPppBe5qThrMCbytuJiM5fLSRw0+z+Sp6fTqbiFqtVqiQCh8wKffwBElLK/v7/z91HJzZnmYxOiTSYTnJ6eotfrSe+udKogobTX6+Hg4OBO5yJj7GezGQ4ODl4DnrkMbrVaQnDQarVCiqYTHmOlgavPMRQKoVAoSETgLgIWSdF0JyLZaNfMHQqFRKSQzWZl0c8o2VgsJmRv9p10VU6lUvjDP/xD/PjHP5bITEYCn52dIZ/Pi+BoNpvJPM++lO6XDx8+lNe9XC7hcDiEpGo2m/H06VMsFgshMTDWktGX7FtJQo/FYiiVSlCpVHj27JlEFbKHbjQaEmuojAznPECSCIlnXAyQ+AFcLZCcTqfMYgCEQE7CHqNPSH5wu90bghLGdnW7XZkfGB99W3H5n0gkEAwG5e8DkJhvOrLSVYjOD+v1Wpw7O7+JxaT7SyaTgcVigdFohN/vFzdDLrW4PFCpVIhGo1iv16jX69KfcW7nfGW32+F0OrFarXBxcYFkMnntsgaAkEvYi9ANBvjayYJ9IF0wGdNOgg3nU7pQUHkfCARwcXEh3wHdyaPRKB49eoR2u41YLIbxeCyCET63PB6POIpQiES3KZLj5/M5JpOJ9IsUe9Ath3OP0uWVpD32RiaTSYhbfN10QKjX60KW9fl8QoI5PDyUe1cZM3oXHIYzIJ1SGOnJqHWe2YwirVQq+Ju/+Rs8ePAAkUhEXIVJZGBEYyAQEKIanXA5n9+7dw/hcFjIObyWvvzySwCQGWK7uIBUiir4uQIQdyP2dhReajQa6VN4H19cXGAwGEjc36NHj+B0OoVAulgssLe3dyMBnE5HjK0jWYHCCDqAZrNZFAoFcchTFgUl8/lc7iNG03Jm1Gq1qNfruLi4kLmIJAI6BPNsmc/n8Pv9sFqtEvdMIm8kEhGRDOfLSqUiGCuvR5PJhGg0Km5avE4SiQSWyyUeP358K3mE9wBFinet8XiMi4sLcYFxOp0IhUIwGAwieKEAlQ79iUTite+JxEq+f2KtiURC+mVGv7IXffHihTyDVCqVkKJJGObsZrPZJKZbSQBarVaCPV7nPDIajVAul2U2Y4wYSYIAJEaTAiC6opMYxuUs8U4K2JR4gc1mw/HxscSe0ZUauJo5Kfqhu+m3WcvlEsViURxcLi8vUSwWEQ6HJaaYJFkWkzBIUNiOqGNRIMXPcrFYSNQoZ386EjIClQ40z549w+PHj99bv6rswyheva4Yk0YywpsQEihuUzpDu1yuO4tZvF4v2u02qtWquHITy9wmVBHbUYqoeGbQuehdSq/X4/j4GJeXl5J00O/3pU9jagFdm697j7wfKVThXoNEhZs+Xz4jiXnzeiGxkXjbLmJjv98XQrry++Y1RSGG2WzGbDYTErfdbsfe3p5gdOyL/X6/EKV5xn1M7nXKYoIFnUqVJE8KJwCImKtQKGyQBB0OBx49eoRqtYpQKIRyufwansxqt9toNpuIRqPSJwLAycmJxDdeXl4Kbh4MBpFMJjd+1mQyEcdi4EqkdN1M+yGK+z+73S5xvzyPuAOgcJHiFZLHiFeyJ6OoLxqNYrlc4vj4+L3O3YFAAM1mU76X8Xgs9wZw9Vm2Wi3UarUNbHo0GsHhcMBut0uEqFL0otfrJV45EoncKar12yglCS2dTssebVdRbGg0GlEul6HRaPDll1/ixYsXMluw2GtwRvX7/ZK+xASk285xErAYPbvLtfBT/f7WR4W+rddr/OEf/iF+8pOf4F/9q3/1QZmX3//+9/E3f/M3H+znf6pP9bEWnRAGgwGy2SwGg8HOhytdNujoQrtLNsCMLDk/PxfVNl00qGA+OTkR5i8BC7/fL9nvHOicTidsNpuQEVarFTKZDPR6vSxkOVzs7+9f64RHF429vT2MRiOJiKNlLt1ScrkcqtWq5JqvViscHh6KY85qtYLf75eICgKLbOip8JzP5+h0OvB6vej3+9eqoFUqlSzKlcQmgrwmkwnf//730Ww2BRTMZDLyuS2XS1QqFbFdJzDD90xAWqmOabfbrzW2u4oLQDaWdrtdIpDoDuLz+WSAYeNN0JVkN8YA0LWHalA2p3xtVBWSIESlp9FolO9+PB7j/PwcDocDiUQCrVZLiD20n9fr9aIq2nU90A2gXC7L5/2uKqDtIlmOhBwCnmzMlOAOl3Oz2QzZbBanp6dIpVKIx+Mb6mVlLZdLZLNZibzivUTXLEZUcBhkFJzS9YyqYKo5Go3GG7uA0XGBpCCtVot+v49KpYLhcIinT5+Ksxnt7nmPLJdLlEoliTvqdrv4xS9+ISALY9GouAcggwSJiIzmYgxJPB6XKAiqVulwxKg9o9Eoqg9mspO4SNXldDoVAM3lcklGOtVxJHcBV/cJySaVSgXJZFKWrYVCAf1+H6VSSdyBCHan02m5BpRAOsECuiARpCcJCoDEq1ABdtN1OJlMJDaF0UUAhBRE8IFKPi7zGAHC905i2U11m7uHwWBAIpGQ2IbBYCCkAKpVlsulRDqQYEMyH50iZrOZ/PPx8TGcTicikchr6uVoNAqtVov9/X0B9Xhuc3hkfAFfD89Fkm0sFouQw7joIOi7TaSiuxoHXRLKuBRXqVRioa28p+kgqYwtBK5IalTpUXVG4tjjx48xGAyEuMM4gx/+8IcChCrPNToREhhj5Bu/X4IIPD/5vFGr1QJ68x7hOZHNZuXZSIIhAUUuo4PBoCytCoWCLDEDgQAMBgN8Pp/Y0pfLZQFXeG0wmoHPhel0itFoBKfTieFwiF/84hcYDAb4zne+IyBHOp2Gy+WSRbHNZhNyqs/nEycYRsQwQlBJbKD9v91uF6I041PoREIXzO3idcXosOtIKrwXlff9TcXrju/zbctgMODo6Ajn5+cSxaNU4tvtdonR2N/f/2Agi1qtxuHhIUqlkpAc6LDAZb3RaEQoFPqgRB+TyYTj42NcXFzAZrOJmns2m4mTAonUh4eHt4LWXBTSMeZtCFjAFfB5cnKCs7MzISQwepBus1Tib/9+PiO4rAYg142SCA1AXCRJ0KZDgM1mQ7FYFGcylUolC20Cj7+PBCwA4sJKMcBN4KnSOeO2ZyhwRQ4iCVJJvgK+JuDa7XZ0Oh2o1WqJa2DENJ8fdJkJBoM3LgE4SwAQd6OPpRaLBc7OztBsNlEqlTaIN+xXSqUScrmcLAzu3bt3qyt6u91Gp9OBzWZ77f0STGUsCQBUq1VYLBY5d/V6PQKBAKrVqrhkGAwGETawr2J0kvJn12o16dXpqKUksrAIettsNsTjcfT7faTTaTx//lzcTWazGex2Ox4/fizPdS7d7t+/j8ePHwvhh+SVVCqFp0+f4vnz5zg7O0OhUBACtEp1Fe3Ga8nn82G1WgkBJBqNIhgMChB9cXEBk8m04XhKoYTRaBSyP0VKwNfPR8ZOMUJNp9NJjDGfs8pYbDr+8BnF746uWbwv6MxJ0Rf7JwAyjw8GA3GwdLvdiEQiGwRwt9st5DGDwSAODSQ03bYUJ/mRivHr+nSC77FYDC9fvsRiscCLFy9QKBTgcDgk7pK9p1qtFrc19iAkKjx58gQul0v6bJ/PJ3MF46B2OR3zHuKMdBvRmYI0Oj7wPXC+orMYyTQk8vt8PvR6PdhsNvlewuEwotGoPHf53D89PYXBYMCjR4/kvbx69Up6QhL52U+Hw2HEYjF0u11x7I5Go3JP1Wq1Dfc4Lp0Yu0fildlsFmddPt84s7ndbiEskrzFpTHvm/V6jVAoBL/fj1QqJW7Xu4rYFYlbN527JCNyziWZjTON3W5HPp9HrVbDixcv0Gq18N3vfhehUEh6+FwuJ/HVJI5w9uF8GwgE4HA48Ad/8AcbrycUCuHly5eCU2wTsAaDAb766ivk83khETocDomZIw7C+Z3Epkqlgr/9279FLBbbONNcLhdSqRTsdrs4ePv9fuj1eollvgthgWeBcu7V6/VC1CTRhbFAXKRzGc37uFqtYn9/X6JfgSuCF0k0Vqt1Q0To8/mwv7+Per0Ok8mEUqkkYjQuPekIXa1WBR/l62HEMO9Hj8cj8TXxeFzOKJI4iMNQ4HhbkejAXuGuxRjPRqMhy3RlGQwGBAIBaLVacQEm/rDrNbTbbXktFPawGGFGF7JCoSDnscFgkKUq8QB+toyB4r06Ho9FLEqXc7osKmu1WonbS7/fh8Fg2Hi2Wa1WDAYDcb+02WzyfTFCjYIavi+KoqrVKpLJ5Eb/x+uHZFBlrLLH43nrueF91mKxwOnpqQg60um0RF12u90NsZjP55OYNd6bJBlTwLR9z1LIyDOyVCqJaJH98nb/e3l5iXK5jGQyiXQ6/d5inLaj329yjqlWq1gul0K2vUsNh0OUSqWNqFfiJXSjpAP4TcVrmAI24oQmk0nuFzqyTCYTBINBwV4BiMsc9zbvWpxTh8OhPJP4fklWugkbpHsrn5XEZZUpJLcVXe7C4TAikYjETPP5yLl2m9jISOLtZzTFdVarVc4KYpXj8RjJZHKDsESHdovFgkQiIUIus9ksJOaPoRiVRyIzyaw8qxitzuvy5ORECP7T6VTIe5x3vF4vTk5OkM1mJSWGkcMUsHV+EykeCoXgdDqRSCQ2XhOvZTqmxeNxxGKx1z4zo9GIWCyGQqGAYrEorrLflDugXq9HLBaTuYdxlBRIKGeCXq8nAgBekyToPH78WOKJc7kc1Go18vn8a86l71JarVbORRoiMHKaPRbjtOnGXCgUJBGE5GE+P3l2zOdzSfkYDodIpVJv5Y7/TZTf70e9XkcgEECpVMJ0Ot2I+l2tVmi323j+/DkACKbw+eef49mzZ6hWq+IqSgHMbDaTvoxnFB2Naf5wWzEZiD3xp/pUyvpo0Lef/OQn+MlPfoLvfve73/ZL+VSf6ne26DrEZa/P5xMVr/KB1e/3US6XUS6XcXBwIK5T3W4XxWIRjUZjI6qLjhd6vV7sSqkuMZvNotojEMFlKAEKNsp8jcDV8JDL5QSQYKQSFW7bxcHdZrOJpfHZ2ZlEnFFNazKZEI/HUSgU8Itf/AKVSkUWWHT0IeGK7Go6VVERajKZ0Gq1EA6HZVAfDAY35iqr1WqkUikZXpQLeA7if/AHf4B6vY56vS7g4mKxgF6vx4MHD1CpVCRWRqvVihUw7fkJPs3nc8znc1gsljs1e3q9Hg8fPkQwGMQvfvELWCwWXFxcIJfLwefzieORcoFLm2Fm0vPfEQim4omkE0YZmM1mWQyTcEXiTL/fl4WJSqUSG3bG99GNrVaryc+8aQFD9UapVILb7RZl2/sqEnUWiwXa7TZsNpsM1bPZTCxdqYRn9GKv10O5XMaXX34pi9JdqiYO8VRHkmxGJyGSRgiOVCqVDScMFm3ouUx9UwIWY5f6/T6++uorcYejVXmj0UCtVhNyXqlUkmUxYzXouMLvFIAQbxjlwugwxohQuR4IBKDT6WRoBK7AicePH4vSVafTyYARDoeFyDIajQTwpEKaRE1GCw4GA5hMJrGypjrV5/OJ8xdVZHQyYHNNBXYmk8FisUC9XsdqtcLBwYEMZpVKRe5JKmnpOMWhhkAtyYw8j0g+u2n4VC5V9Xo9TCaTOEfQZUrpWgVAwApa9XNZTxeGm4r3HO/t65ZUHO65UFISr4CvyWDJZFIIiuPxGK1WS9TVtLn3+XyIx+M7Bx8uq0jWIZg2Ho8lSo9REcvlErVaTciMVLhyAcnIXDqUbcevABBSntVqhdPpFHInozOofKlWq3JGrVYrUYCazWY5Bxn1ylg0vh/+bCqQ6I5GAvVN7gV8buXzeVEKk+jG64mfPUkeJAk2Gg2JZWHxe2EMAe8HfkZUS/HfM/6W1xpdpxKJBEKhkDhz0CWBSx5G1hDwZeTLYrHAF198Ict5OhlSRcclGQA5C5ULjfF4LERho9EoC0Dev1zIGQwGWRLz2l2v1zcCpUaj8VaiFMFokrvvUgSx3zX6zWg04sGDB2i32xugKQD5jKhc/ZClVqsRjUYRDofFiYkELLvd/tYRfsq6Tu2uLJPJhAcPHshimeRo3qd0iLuLGv19EeWAr6Mt4/E4xuOxRBqQKLmrzGYzarWauMeQRKL8DHgd0fmC8TPK2hWJwUUtAaf3XSSiKq+BjyF+Zbt8Pp/ExpRKJUSj0Z33JFW7dIe9aRYAIPFbJC7suv84l1BJz/6p3+/LsrvZbKLf7yOVSklk6nXFJS6Adz5X3nfV63WJaKdTCBekyoWlWq3Gl19+Kc+emyK16XhzHamcql8KJwqFAgC8JmIAruJKGVHAReTe3h6y2SzUajUqlYq4FbKvJOnb6XRKdKRydqUymRFcLMYOcs52Op3irkRCEUnkh4eHCAQCG2eEVquV6+/Ro0fiVjCfz6HRaOT1URBEcdXFxQWy2azEnGo0GlGsU/Vfr9eFlMXns9vtlp7aYDBIb8Wla7Vahdfrld6C4ivGEjkcDokCJPma8d2MQ2Hfw6V/r9eT85qqe0Y4MRqn1WqJ8KPVaomgjE4ndJBjXB8FVhTVlEolJBKJG58p/B16vf5OCwqz2SyOnexf+v2+RA1RjJRMJoWwzdmJ85bX68WDBw9gt9vh8XiQSCQ2lr2ZTAZOp1NckSh8IVkgHo9f6yCuLArjotGoRKTyfPN6veJ48vTpU+mxuaTm3D0ej6HRaDCfzzdcqoxG40bEl8fjEeei7VosFhuLPRI6GH/p9XqFCDibzUQYRJLKarVCPp+XvpnXJpfB7CtNJpPMphSNcSm8Xq/FHZjYEN8P+3KlA6LyfvR4PEJSabVa1zoRzedzmaWj0Si8Xq8s0EgMczgciMViOD8/FwLokydPBBNZr9eIx+OyXHW5XMjn8yLai8fj4jQUiUQwn883CFh0qSoUCuh2uxJ/BUBeW6lUEvdpg8EAlUoFi8WCJ0+eiJCPpBZGn5LoTsebcDiMH/3oRxIFE4/HpRfnufKm/TJnGgBCZGdUnsViERcK4IroRieP+XyOSqWCUCiEVCqFaDSKaDS6MWMZjUYcHh5KHJEyqof9Mx1DvV6vxBKSkEKHFopb2G+y92X0OD9rtVotWK1ykW2xWJBOp8W1+rZ+lxjcm7ieU1hKl6dt8gbJ0ePxeMMJvd1u7zwDKYZdrVaIRqMb9wZdyonVTCYTjEYjtNttxONxmYG63a7Mn+wDKHqgkJWRgIys5HOZmAiL8zvnRka4hsNh6RfsdjtCoRBarZbgWiReMKaWgivOrBQErVYrpFKp13oOpXD2Y6r1eo2Liwu0Wi3kcjmYTCaEQiHpN4LBoIhYlbG2yh6XQmbg65lDWZxj+CwiDk7Xv+2ig5xer0etVoPT6dwQmbxrBYNBtNttcY6hsyjxMkavTadTxGIxeUbdVu12G+l0GuVyGblcTnAb4gnhcFicR4+Ojm4k417n2jyfz9FsNuVsoXjIbrcLSVopgOP58r6K5CbgilDO90MHfkapKoskCDo7E291OBxyH97ljCL+vVwuhTg0Go02iI0kjiixQp6BvL+VZxAJXDzzhsOhnC3EeQHIToZuaGazGW63G7VaTUjnu9zsv+maz+c4Ozvb2KcoBSUul0scr2kMwHh2Yo38LA0Gg4iKAQgJ3fmbuONqtboxw/MeOjg42DnPU7A2Go2wt7d34/OLTqlMylG6eX/oohBAKaolIdntdiOZTIobI2d4q9Uq7leJREKc4ILBIBaLBarVqjwD3mcUoclkwsnJiew5mD7EXpgzGePhKX5Pp9OC74TDYdy/f1++M+60KpWKYOAnJycfBVl4u3Q6HQ4ODgSrrdfrEkEOXPVAdOokFkwXP56LFPuTJB4KhcQtulKpyO/i378rVsUe7WMhZn6qj6c+GgLWf/yP//Hbfgmf6lP9zhbJSYy0Aq4eSlT/sPHkw4hqVavVinv37sFut6NarW4MyRy+6YTU6/Xw5MkTsW4Nh8PCiKeykw9INvC0NSZZgE0KWeW0tt7b28NwOMR6vcYXX3yBSCQicYjMZCaI6XK5cHFxIQs2KgYJEtlsNrHpjcViaDabWK1W+OqrryQKgKo+LoW8Xi9GoxGGw6GA+b1eT8g1fP13Kb73RCKxM8+dyuNmsylkJy7Kmdedz+fx9OlTqFQq1Go1zGYzUXEQFOX3SmeeXcQcZalUKvh8Pvyzf/bPkMlkxHKf7jIcSuh0RacjgpK//vWvoVKpZCnZaDSEKU5FZLlc3rCSpWqfoIUykjIej+OHP/whXC6XqCsILBPAJyHmpiGWsV18Xe+zqDIFIAM88HWcAYEpLjkYM2kwGDAcDvHs2TOoVCqJRuv1eqKQn0wmYoFqMpkEmJvNZjJAEvwj8YckpV3NHt0E3nSZysF6MBggn88L4Uqv18Pr9cLv96NWq4lyhqrVXq8Hs9ksqlQqkKi4IIjG64eW4HQ4IgmFbnTAleJe+R0ajUb86Ec/QiAQwN/+7d9KdCYjgObzuaghGM0CXF13HMRJzKSLkcPhkJhGKtd4fzJqjoQofvej0QitVkviqMLhsCyMZrOZxEKQwMrhn9EDzWZTSLEE80wmk6iUU6nUjd8RXx8Xh1xS8L05HA4hwhkMBvnzXM4YjUZYLBZZdt0G1vB6JtHI4/G8tvTP5/NyLjscDvz4xz+WOErGIBHEpcsil8sE6kjAI2l21/DH5wbJP263W4B2gqd0dXG5XBKTWq1WJTeeZ5ler8discDLly9lkWe320VJyLiddrst9zGH7vV6LWAigUPlwo8AxWw2w3A4hN/vh8Vigd/vl2GQxB9aPzudTng8no34oNVqJdcdl0i8xpTfG0H+fr8vhNhUKgWfzyeEKC6PAIjqmgskfleMY1OCqnq9XpY9arVaBnvl9UhgHbg6Q+hC8fjxYwQCAazXa3EgIgmMz1KtVgufzyefrUajQalUwvPnz4X4HQwG5fPikpSvhbb/XAbqdDpotVp5dnBxYrPZJJaQDpIkotG1y+fzyfNrF/jLRdFNAzafPVRi3raoJTmJThXvWiqVStz7GOfFs+GbLqqPrwOz77JUYjFqdpeqnET/XUUAcft7IIlgFxn6utfKe+l9fJa8jqjgve1n7iJOARClNcm8fB7wz26/Nz43Schgsfd8X9cJXWVqtZrMELzf6agSCoVeA+9Ho5GcNySXfGjCIMtisQgpI5fLIZPJwOPxwGazyfzC+YmEjrss2ZSE6OsIz1arVRYqer0eL1++xMXFBWazmZxlvN61Wu1ONbGy6ErD3vFjKV4XJFWYzWaJB+JzEPh6Zu31eshmswJm36Wv30Xs4EzHKBPWrvNcp9MJ8YlRj5VKBQ8ePMBoNBLHVJKMLBYL9Ho94vE49vb2YLfbcXh4KM5l6XQazWYTnU5H5mTg6nzk4pfz1Gg0EgCZzym/3y+OjUdHRzvf83w+F4JYNBrFvXv3oFarxYmFswcBa5vNJoIjOq4Nh0M4HA4cHR2J4KRarYqYhv0xyct0MGavRSI2RTZer1fuGfYfdBJeLBawWq3ikkXSqFarRSAQgNvtFoIqSSTj8VhiWkOhEGKxGMrlsjjblkolId6zh7PZbPL7qtUqAMgi3WazSc81n883nJ+2i4KkeDwOt9t95/nS6XRKnA8jCumczHNbSZonaZqYh9/vRzAYRDwel0UoyYjEOVqtFtLp9Mb1S4KC3++/0xnFBTCfcfV6HZlMRogu7NXpSk0yfKPRwGQygdlsxsnJCRaLBXQ6HS4uLmTmYNzP4eGhOAdymUyCBx2r6MzACCf2s4PBABaLBd1uV3puRgjStYD9I+cbLtNJACgUCuJEVKvVhOih1+txcHAAAOLsTUcpuj7QlZtkBF7TVqt1o78hNuPz+cTRYdeilvHMytmR+FQoFBLBgEajEddyxt2RuEd3PAod6BrIBRNj8hgj2Gq1XnO5IlnSarXKe1epVOI8RFdxPp9UKhXa7bZgUeFwGCcnJ/Ie9/b2ZFYgqZBOvS6XC8PhUM579qbFYhHVahXdbnfDmdrpdL5Gjl8sFoK/sF8mYdZut4uLDl3Ca7WaCCf4uyORCJLJJO7du4dYLHatgyUFXruKsc7RaFTOVLo+X15eIpvNwmg0ymxJUYjD4YDL5drovXg/bOOKjHurVqsiHCMZjG7NfHYxdjKRSLyRe0Wn0xGX2l2uNIwCI4GZ7tVOp/M1csNisUCz2RSn6F3XPQlqdMKhSM5qtYqTGUUzJAxSNMXluNfrlVjwk5MTcR6hizz7CK1Wi2KxKLMu3f3phMh5mnMIhZskPnP+jkajgpcAEOEesWZiW99Un/ou1ev1xOWVxDMSyomtUHhM0e82AYuO4wB2vmeSbk0mE7LZrIhWtq9LxjzRUTQcDgtmUq/XRXz1rqWMXjMYDK9FrxE3Y3zprvjq7RoOhzg9PcWXX36JWq0mOCd7JJVKhXQ6jUKhgO9973tQq9U4Pj6+sWc3Go3i2swY+pcvX0q/FYlExLk4EonIzFcul8Ud6kNGX9GpkYTT6XSKcrksLo7r9Vr6bKXgPJlMwmq1wu12Cy7L5/9NxdlVOZeSUHFT0Z3TZDIJMU35HsxmszyfGQ0fjUYFQ6BYhiIXzmx8vpBU9G0TsJbLpTgJ5/N5IajzuTgYDNBsNtFqteSzpNgLuPpcb3JvpoCT/YFSOEL3U4/Hc+332Ov1xL3tNhIL5yieT99k8by32WyS5EPSOYk7xH9tNpskJ5jNZiFfKZ+DXq9XUkiazeZ7vyf1ej2SySSi0aikAJyensJms2G9XiMSiQhOAFz1tN1uF8+fPxcBCd8zAMFgLRaLkHK393cfU1mtVhwfHyOXy8HhcIioBoBg/41GA/F4HIeHhyL+NRqNODg4QLvdRq/Xe+1MCQaDYoRA0c82XnBdMVGCeNyn+lTK+mgIWJ/qU32qD1N0EyEpYjAYyNKD1p4ANogbo9EIwNdxhQSWCMoOBgNZ4Pb7fVnukx1PEpBGoxESkVLVzCZVr9fL76KCfDgcigW11+sVu+P5fI5CoSBEFr4+Ao4E4qkU/+KLL8S5i6AHmyiSPthIc+FPleF6vZbhxWq1CjDKWCgA4hYzm83ks3iTUhI7touA8zb4EQgE8PLlS7TbbTx58kSaYDoacdl+eHgIk8kkQBbBgLtalnu9XhwcHGA6nSIajYr6a71e48WLF8hmszKMMM6B6ufVagWTyYROp4NwOCzuC7T0XK1WQtap1+swGAzi1lWv1yVvPJVKyZBDwkev18Pl5SWAq4Hf4XDcqCgFIDEdVH68z/J4PEgmk/jVr36F9XqNTqcj1yeXRcxBbzQaAvDzum232/j1r38tVvgEmRKJhCwvaI2qfD/K6I3VagWXy4VisShOCbvccQh83nWZ2u125T4kEHp6eioWr3SyorsVFyZutxuDwQB6vV4WCYyupLKGEW5UtVGlBVyBhgTTGAVSrVZF7b7rGvb5fHj48KE45XHw4TV1enoKrVYrwB7VzFy6FItFUXuGQiFR8VCNqSSO6vV6zGazDdIRnerG4zGOjo427muC7QSfGePocrmg0+lkWKBiY7VaiZo8FArh4ODg1uuWIKXNZpNIDoKkdBkhmYM57/wcnE6nxLw4fxPxd5diPEmz2US5XEYoFJLPg2p8kgL550ki6nQ6MJlMCIfDaDQacr3EYjGx7OWykAs2Ov+xqFjjswCAPKd0Oh2CwSAsFguy2SwymQzcbrcoBOfzOXK5HJ4/fy7XAQmKnU5HXivvGbpU8Hv1+/2w2+2IRqPyd6xWq5xlBPc4rNP+m4Td73znO6Iq4s8kYYkECSWYTrIawQ6VSoV6vS4AKQmOarUas9lsI76M5CGC44wf3HUN0XaazlyFQkHIsbQZn06n8v1S2bUNfikjhKik4j3OgTefz4tjSL/fR61Wk36DhLnpdCpuBzxXCea1222JX1WpVNjb24PBYMDe3h4KhQLOzs7EMYakjUAggLOzMyGb8Pul1T+X3SRgc7FKYs42GYpxQW63+0YwiQuCwWCAdDp9q5K32WwKKPK+n1nvk1DzPmq5XMrSloQcuhPSlYznpM/nk8+Ny/XxePyaMpvxuMFg8M7nGf8uz3kSR28qLsYZF/eupYzqppPbbb9fr9cLaYulFBmQLE8FNns2XlcklfKZx7OBrqZ8pr5rrddrXF5eSvwAXQMZUWQwGGQxzOglRiiMRiM5u0hm9Hq9Em/6oSuRSAgZrdFoyLWnfO3BYBDO38Qu3AWEVxKCblrAsE+g04/D4YDT6YTX60UsFkM+n8dwOJRl7nXFyGrGsn5MxTmqWCxivV4LgWY7pkUpeCgUCvD5fDdG02g0GokYGQwGr/XFSjIq8PV3QoLudlFBv1wuodPp4PV6JZaIBAVepzyv6Ark9/vl3M1kMqjX68jlctK/c84cj8dot9vQaDSIRqOYTCYSQ9n5TaQ253b2EOfn5/D7/YjH4xvXEs/UZrOJWCwmvTMXzZyVg8EgyuWyxLYfHh4KYYYuQIxZI+mFkVsUQPH1dLtdcfppNBpCGqFz0nK5hMvlQrlcFqcgEp+51KDDFaOg+CykYxRj0DmH0NkauOo95/O59FB0sCNB/uDgQBxNSMICIIQyOnxTFc6oO7fbLX0JXVp7vZ44T7ypq3AwGEQikRA3If59uokql9nsK00mExaLBb7zne8gGo2+di57PB6JFbHb7bLkVp79fr//jeJkdTodTk5OUCgUYDabhZixXq8RCARETa+c31QqlURSs3dk9DNfD5/tXOaXSiWcnZ3B4XBIHMrZ2ZmIQ0g0orsaAHGV1Wg0MJvNQkBj1Wo1GAwGcQIjOdbr9crSNJFICImF78NgMIjQLhaLoVariYMal4vj8VgEjCRd1mo1BAIBcfVWFomDjPqiYy6f38Rq+LnabDZZ6jKGXllarRahUEiuW4pNbDYb7t27h8lkgmw2K30RP1POI9PpFOfn5+Ksrazlcilk3kgkIi5njUZDIuuJ6czncxiNRsECKKi4vLzEo0ePZJ6z2WwIh8PweDzI5XIioMjlchI9PxgMZGFPASKJIHzW03UrFArJ3KEU2hAzopPXYrFAPp/H5eUl3G73xoxfKBTQ7/cRDocRj8dxcnIipLu3LRISid3RnZ4x7xQIEUfc1YvT0XSXaJDRowaDAV9++aWcfyS4kaRGYgCdWt7kmU9CLM+N7WLkJJMA6GwNfE2QALAhuFOKM5VFpy+1Wo1arYZQKCQR94vFQs4Vzi2c2xwOh1wDi8VCXCATiQTMZrNEj6lUKhHlFAoFLJdL5HI5jEYjHB8fYzAYiMCUKQ3K98zrhxhXNBqV5/12v2U2mxGJRFAoFOB2u9FsNm/EJt93MWKcZy7ngNuqUqkgn8/Lc2g4HGI6nQpuT0cjujzS6Wc0GoljFEmwJMRtF/+u1+vF+fm54MS8HmazmQj2SFwkmRW4wj1vc2Z/0zKZTLh3795G9JrSKYjPjEAgcCd8P51O48mTJ0J8Ab52eScOQ2HeL37xC/zxH/8xOp3OrTM+XazpvmSz2eTcpLMcCcGc25xOJwKBgMT+fqgi9sIznOIaErWNRqMI4EhgODg4wMHBAZLJpDij6XQ6dLvdW79jRlAzpeSuRZzH7XaLUxCvLbVajVgsJmLOfD4vnzWFkRqNBk6nU84d5d/l+fumMa8foojV5/N5OByO14g+RqMRbrcbxWIRuVxO0i1uctTfLoPBgEgkgnA4vHH93WUWXy6XcpbepUio/xAu3DeVknhO4UwsFkOxWBRx/MXFhbi+0dHSYrGICFdZjAJlOs+HKs48xEwrlQoCgcBr9xXdhxldzffhdrs3ehLO3K1WSwTEN6XOfJtFwQf7RxLTM5mM/HcmAlE8w+cVcfdWq7VhIMDZIpvNCr5DnJ1Y/3VFHNdgMLx3HPdT/fbXJwLWp/pUv8PVarVECUhQmMMEF+HMNwcgjH+SmqbTKVqtlsRcEPhQLgQYJWI2m8XWn83rcDgUdxzGjbHJppUsh7VmsylOMqvVCl6vF8lkUuykOQidn5/LcETmPoGNfD6PdDqNr776SoCh0WgkwB0VuFQXk9yiUqkkdoCAmEqlunEBRjIZl2/vI2P9ttLr9Tg+PkYmk4HdbhdbUQIWZOPr9XqxOb24uJAF911fozKGh0tqALLoJEj28uVLUSy1Wi0EAgGEw2H0ej1ZTBwcHEClUuHzzz9Hu92W2AFapHLBTcvkf/AP/gHcbvfOhoWEuZcvX0pjNB6PUavVZFHF4s8bDocb1rjvs7xer9jGUyWnVMNotVpRjHe7XYk0I5mPiwxmtDudTjx79gyj0Ugi9ThsMh6NxAeqw+v1uhAA6FC3PUwxzz4YDN7pGmg0Gshmszg/P0culxPQnXasVBJRqUwlPEkXVLFzMcmFBZ3RCKDR5WM8HguBgpa3VNAvFgucnZ3hBz/4wU7VDG12mb9+cHAgyn0CmcfHx6J0ZOTHdDoVRysOISSgPHjwQAhySptvZt5TyapcBFxnNUtiGlWlBCOZGe/z+TAYDCQmhteCzWbD48ePN4hNNxV/TqvVwng8RjQaRblcRjabhcPhkGuDwCYJVwTajUbja7ELN5XL5ZI4jFwuh3Q6Lcu2XC63AZxzUenxeMTRjdeZSqVCq9USZwhGITJu8vz8XEhQBFm5JKtWq2i1WqIAn06nODg4QK1WQzwel/uv2WyKcpvucgTfSMYDrgC5H//4x2g0GjIwv3jxQtS+VqsVfr9fIli4wDQYDPj+97+P4XC44WrGaB3GTa3XaySTSTm3fvWrX6HZbArhggAOQToCn8PhEJFIRADK09NTIVLQYYvqJf4sRsSQIMCoiGKxKMRijUYjZzkXYSSAMsKE5Bi6vNC5K5VKCQGlXq9LNBBVP1ya5nI5Ab+U8RqJRELcTPj89fv9EhvT6XQkZsLhcAiRFYA4DHBZn0wmMZ1OEYlEsF6v0Wg0JPaFC0232416vQ6fz4eLiwvY7XZx5iIhnfb4jFWm5TlBo+3iIpjuBrfdn9VqFU6nE4VCQYB8Za1WK1QqFYzHYySTSXi93t8KBfXbFl0tJ5MJOp2OuCMpXcV4LrlcLnnW6XQ6lMtllEolcczhc200GiGdTsuScLlc3lk1rVKp4PF40O/3ZXl53QJjvV7LPc4l77sWl78klt/Uf9KtlernbfDT5/OJa2mxWEQ4HJaenMRVRhjyGmbPxb6CP/dNFvbXVS6Xk74C+JrEqnSRosvuer1GsViUHq7ZbIo7EqOuuVT87ne/i2g0+kHjC9VqNQ4ODlCtVoWYrXTkusm967q6a4wvADmrueSKRqNCtojH4ygWi8hkMsjlcgiHwxsAKZdjlUpFCIzve5H1rkWAvdPpyGtXEpaURTenSqUiEQnXEbAAbBBft+9nLlF5tjNm/jqSMkk9fC5pNBrEYjEcHR2JKILgukqlEuGIsn/rdrsS9cOFrvJ96vV6iWWhkwAXoS6XS4jk8XhcevJOpyNnpvKso+srY6/oBsRzla5Ry+USkUgE5XIZBwcH2N/fF9ImewLOToFAAMViUQQV4/FYyCh0k2T/QgA8HA4jn8/js88+w3Q6FZEFBSMWi0UEXIxpdv4mTppk/UajIS5Idrsd/X4fGo0Ger1enJJIDOfrIgGPmAbnEfYGyuf6bDaTOJVOp4NkMonJZIKDgwPpm5SkSavVKnjH4eHhGxNBKQjxeDwSk3Xd2TEcDiVykzPXdX2BxWIRJx6el3TQflsirUajQSKRQCQSkfmCZx8diegMS6IJncMZ99fr9TYcVXgOqdVqpNNpmEwmuYZyuRzK5bK4PXOGGo/HyOfz0Ol0sgDp9XqIxWJCMAOuolKMRiOCwSCq1aqIMigQ42xot9sRDodhNpvhcDgEK3M6nZhOp+LGTjIznYc0Gg0qlYqQAxuNBhqNBux2u9wPz549w97enpC5u90uRqMRTk9PZYHHnp9Ee4oZ2PcxYvG6RTMXvw6HQ2IqOQdls1mJzJnP5+K0BkB+t9FoRLFY3Lh+l8sler0eQqEQotGo4EwkTAUCAVQqFcznc3GmVP6vVqsV95fz83NxbKJwie+nUCggn8/DarWi2+1K1CUdaMfjMdLpNFarFTKZjIgi6V46n88lQpFuJcpoLODqrKLLVaVSweXlpYiR6E5zfHwMt9uNYDD4QeLhiA/SpZdk0JtEEBTTWCyW13pApUi22+0ik8nIjEOXXTqRkUxLQdtdS4lj7HKkJaF2sVigUqmgUqmICzT/LEW4dAQbDodCtN5eWiqj60wmE3q9ntzjJO3SjY1xhnR8I1Gt2+3i+PhYXJOAK7cW9rg2m01IgyqVCsViUTCCFy9eCNaldC6ZzWbibEIHZ56jfK5sF+dezjPfBAGr3W5L76zEq+hSRZLwrmo2m/j1r3+NbDaLxWKBcrkMAEKI4TP18PBQ5oR2uy3PfH6+wNX8ctMcHA6HxSnt5cuX4ujEqCemVlBc2+l0hEg8GAx2urG9a21Hr/GMNBgM15LJdtVkMsGrV69QKpUwmUwQjUbh3IoWpxscr6mzs7Nrse5dRTzp4OAA3W5XZlSmcazXa8HlDAaDkI0/dAUCASSTSWg0GrmWuD+ieA24Ip3fv38f8XgcDx48EKEK3SlbrZYI5oCvxUF8b6vVCvV6HclkEg6H441dfHn9MRGGjqDscb1er7gjJpNJcQ/jfU+HJyWGzt78QyRsvGlxbmeazXUuS2q1GpFIZCMp5jbcl0KObafxN92pkLDG++y2ep8O429SPPu50wC+fp5QQMbrmjPeer2WPeWu2k4G+JBVr9elj9jGb7h741lMzH46nQrmTDI/75HLy0sR2HxsAq7tUjriDQYDEYpz3uAzcjQaCSbC63E6nW44xgIQASB3fSTv5/N56SG2q16vYzAYIJlMvpE78qf6/alPV8Sn+lS/o0Xr5VqthsFggAcPHrxGvqD9a6VSQSwWg0ajQSqVEqeLdDoNnU6H4XAosVrT6fS15QGt6BnnRDDY7XbLEMfoAS4Zo9Go/Cw2sSQpABCwWakmd7vdODg4QL1eRzQaRSgUgtvthsFgQKFQQCaTweXlpahxSTggKMxYPYfDgUqlIu+HzQaBQeDrrGr+/7RYptsVBwuqsL4JAhZwpU4LhUICKigXBWRaK62vnU6nKOdvY2wrf47NZhPbTo/Hg1artREdQwVKoVBAqVSSyDiC61S9MkudAOdsNkOv15OlJclbdDybTqc3Lp79fj9evnyJQCCA4XCIWCyGRqOBQqEgzfJyuRRQnspyDlTvUiSbrVYrcR2KRCL44Q9/iPF4jMvLSyH5MEau2+3KvcCIHwKZBwcH8nlS1T6fz+UapqqCznAajUbAFK/Xi0KhIMsgEgh2KUWoViQoflP1+31kMhl8/vnnco8AECUQP1OqoAnC033O7XZjPp+LyojNLi29SYCiOwCHq2aziUAgALPZjOFwKMpnfq+MHuPnQsCZTmx0oFAupEgYq1QquH//Ph48eIB+vy+LAp5TtL2/uLiAWq1Gp9NBMBjcOcByqeFwOGSw4QBmsVjkfbAYo0glMuMHSFQxGAyiTK/X6zg/P5clIwk/dymv1ytL1mKxKA5NjOv0+/2Yz+dot9uiaqG7IK/teDx+52U740l4bmcyGVENVatVIQfweozH4/I6SqWSODINh0MZFKlgNRqNMugQ3NPr9chms+JiWC6XZVnHpYpWq5VnVigUklgJLut5LRIsUf4zXS/cbjd+9KMfCSjocrnEtYhDGkmVJpNJ7ke/3w+32y2xMp1ORxwTGTPBCJN4PI7/+3//L1QqFU5PT4WIR1LyaDQSchfP1ZOTE5hMJnnvVOpSkU93jEajIS5Rw+FQ7vdQKIReryfP43a7LZ8dCYHBYBCNRkOWbVxSuVwunJ2dyXfJOAcuTKrVKgqFgixBNRoNGo0GgCtQNhKJSJwmi0ptnh9Wq1XI4VSekXzF+8psNsvgTJLScrkUUijVWYlEQtwfcrmcvF46DU4mE7RaLXG55GdP5SgXGTzvSfJWFsnEXPzcBkQxjor9TyaTgdlsljOQi2OS0xhl+rta7XYbl5eXG+4SBEepXmMkVqFQEOUrSf/AVS+0t7f32nJpOp2iWCxKHAh7mbuUz+fbiK7eRZRbLpcol8uyBCSw865Fp7ThcIh0Oi3qxe0lwGq1QqFQkGiQXYCY3+9Ht9vFcrlEPp/H+fk57Ha7OG9cXl5Cq9WKopeuCVyqTadTJJPJG+ME7lrj8Vh6NI1Gg3g8vvF5MTqZyu7nz59Dq9VCp9PJNcH+iNcN+8QXL17g3r17ePjwoTwz7ro0eZOiq0sgEBDRCZ8fPFfepKiWZX++HeO7XcViUSKJeD1y4cyZqVAo4OLiQogqdCdaLpfw+Xzwer1IpVIfHRhIAi4dIj0ez433E5fxg8FAosium798Ph8qlQpsNhsKhQLi8bjMTFyO12o16RndbrcoYbmYASAOp7wfq9WqnClKBfRtzwEuGheLBZLJ5LXv0+PxIJPJoNFoYL1e47PPPoPb7Uar1ZLXzgiXQqGAQqEArVYLj8cDi8WyEfsOXN2DLBKS2beyP6lUKqLQZmwJZ3xWKBSCx+NBPB7HL3/5SyFA0MlksVhIX0aCU6fTAQDEYjG591UqlQgG2DdOJhP4/X6Ew2FZIvH7Zsyg1WqVSDLOP8QZqPhmX2Y0GoXQ/9VXXwmRiY4XjPBivJJKpZJ4tX6/j1QqheVyiWAwCL/fL/cZZz/GVr2tC9/+/j5OT0/hdDqRyWQQi8U24lU5P/d6PbjdbjmTSfq8qRjF/j5Lq9VeO49wObyr+Jy67r8fHByg3++jUCigWq2Ke4xyvqKrWrvdht1ul0U3yVHK16VSqTaEJYxHoiMbF0vhcFgIdMR+lO6oGo1GsAme95PJRIiNOp1OFjsUQTEuez6fi5perVajWCyiWCyi3W7L/Gm324WAQPGPco5Xit92FZ05iMXQga9YLAqZjfd5oVAAcNUz+Xw+ibofj8colUriDMBoFhI36B5UrVbh9/ths9mwWCzQarXE5ZokmeVyCYfDgVKpJBH0JBoy4vTy8lLc7DkD0l2Pcw/d8L7//e8jk8nILMDzqlAooNVqIZ/PS+yoy+VCMpmUz4bO2e12W+LseV+RUBMKheDz+RAMBm/tD+fz+Wtx17zHvF7vBq5GQYuSbMjeq1wuy1xGXGybpNFoNGRW3CbO0IGcRCK32y0E5tlsJrGbkUhEnO/fNLZN6QI1HA6vdR3nPJxOpzEYDJDP5+XZSvfUZDIpxMRCoSCuxywSqVarFRwOh0TskjDIz5rEK7qJkZxHTCwcDotgV/ldkqQeiUREjEs8mI5MjLkkWZGkXD5P6dxDLIfzxHX9mt1u34hA+pBVKBRQqVTkbFQSG4gH93o9RKPR1/CsRqOBs7Mz1Go1FItFEToBEPyAzoWnp6fiLqbVaoUQy3gmOmze5P5qMpmwv7+Pi4sLBINB6eV5z08mE3Fh52c8Go1QKpXg8XhQKBRu7PXepW6LXruteG3XajXs7+/Ls4sEeuCKgBEIBAQfK5fLMvu+aU/OpAgK45S9icfjgcvl+sZIKyqVSiIaHQ4H6vU6zs7OJPWBzoSMCXvw4MHG3iIQCIgYL5/PIxgMbuC03Oe0Wi0Rmd8kumAaBedfEtA9Hg9SqRQASITj5eWlEJkByHlFsQ33Yna7fScRmqIRCgO+zaJba7fbvRU74vVOzF7pLKqs8XiMXC4nxGieaXq9XswViFXfpeg81m63b73uefaQJP8xlNFoRDgcht/vlxh3mgEwyWVXkRjvcrk+uKhyvV7L/UQhjbKUbpVMKGi1WiLoZRwye0iv1yvC7V1C1I+5xuOxzKUUuJDwzWQTtVqNUCgks3a5XJZnHGdlOuDdv39fzlXiasSviePy3IpGo3A4HG/kwv+pfn/q40LhPtWn+lTvrdiUU812nYrO5XLJ4E5iAIcoKgUjkYioBwGIUobAEXCl2rLZbKK+arfb8Pl80tRRQRSLxZDNZvHq1SuxOKfCpVAowGQywe12IxKJSKzg9uttNBpCXGEU4JMnT2RRRzcYqhuogms2m7JwpmMQ3bH8fj+0Wq28HwJPfAgz5oM2oqVSCeFweOdg+yFrPB5LxNPh4aEsrAkQktlOIJfvg+DhXVUjjBhrtVp49eoVlsulWNDv7e1BrVaLA8VwOBQ1BAcdr9crChYqLUhiCwQCsggh2FooFGQpxSjC66ys3W63qCmp5iB7X0mkAK6uS6pt3tYlgUpzurOxSOy4f/++WLienZ1txD6QcMNBkOpq5msDV+Qmn88Hn8+H58+fyzKKanH+HqrvIpGIKEmp2plMJq8trhaLhcSncal/27KgUqlI1CCVkHSboV2xVqtFs9mUOBKdTidAMB3SSFQkgKZSqTbiOxl9QBed4XCIer0ui/j5fC4EJLfbjePjY1FLs0wmkxA7NBqNOPW1222MRiOUy2U0Gg0heI3HY8RiMRk4+T3pdDpRkzF2bdc9PZlMUC6XBWwnYENSGl3LuJyjtTbB6clkIktXg8GAxWIhimoC9hxQrVYr9vf373y/arVapFIpOd+oJCSJiUsyEmq4vOUw5na73zieiMpypVqWylAuErg8o1uMXq+Hz+cTO2MO7iRbMB6CMSB0YNHpdCgUCvj5z38uLjl0UFutVuJI5nQ6MRqN8POf/xw/+tGPZDnf6/Uk4kyr1Uoc3nVFEmoqlcL3v/99icPi9c33TAIPwZcHDx6g1WoJgZdlMpnkWs7lckIE4nBGMjIj13itL5dLPHr0aOO+bbfbiMfjElvDnHm6AFQqFVSrVYkVJgGJ75lOfIwAo9sXHWlI+goEAphOp8hkMuLeRHU7Fy90qxwMBuJaw2X//v6+LEO3h1A6UgFX559S+cioQQIbdNMgEEAiAnBFPCRhtdFoyFKNCyU+/548eYJsNouzszN5dgBfL0tcLhf29vbEQWaxWEg8EB2u+v2+OIeSCO10OjcWPzcVyXAEvxgrRUIvzzKbzYb9/f3fWfer+Xwu1xTJrtVqFcAVOHJ4eAij0Sg9rMfjkbhYknMA4LPPPtt5PhoMBsTjcaTTaQHF7wquG41GiSMol8uvEeXG47EQnuPxOFwu1xvHT91UgUAA3W4X8Xgc+Xxe+lhGfTCCS61Wy+/f9d5IkOXzjaRL4Oo5xvuKzyaer4PBADqdTvqF9+EKQQL5ZDLB/v7+tYsB9kpPnjwRwIvOKJlMRl4/I5SePXsGu92OVquFwWCAe/fuwev1Sn/6Ieo2MsGblM/nQ6/XQ7PZlMjM7Z53MBjg/PxcFmTdbleeH/V6XZyHSSSmmxyJox6PR3rfvb29DxpH8rZlMBikP+73+7dec6PRSGLJVKqrqNrrFgAUSTBq+vLyUs5Yo9EoZKtutyvLGUYeF4tF2Gw2WCwWcYumSynPHYvFgufPnwsRiA6Zu0opGth2R9j1ZxnLRDfjXaXVahGNRnF+fi6AOV13Li4uUC6XxdVZ6eRK8lEsFpOfTYU2n60kiLRaLVnQaDQaPHz4UM6lly9fipMPl9MUYRE/YIRyPp9HIBAQcYVarUYmk4HT6ZS4RLrSRqNRzOdznJ+fy1KRpNzlcolqtSoOVfz+SWRUXuMqlUqWnovFAk+fPkUwGIRarUYgENggPtJt4eDgQMjkXNKzZ+J8qexX3rbcbrdEVReLRekH+H1QMOXz+cTtixGCJLhxLvsQpNObigvL9/V76TzHnvMf/+N/jNlsJgQ0EkmJEzEal+6D2/c/n890cD84OJCelG5YXq8XPp9PvkeXy4V+vy/EHvbFdLYbjUYya1NM8vjxYxFhuN1uESKenp6K8IiuuJxBSG7iDBaLxbBYLJBOp8WtSRlHvquWyyVGo5E40k6nUxEq8ZlCNxQA4pJFt675fA632y1iBs7MjADmDNpoNPDy5Uvk83mo1WohTI5GI4TDYemf+bwhJjMYDIScxuU0nd4o0BqPxzg8PJTzkpgCfzbjqegUw/OScY/JZBIulws+nw8HBwcyz61WK1xcXEgMKkVwxPd4D6vVanFavqmq1apED7XbbSE78Iwol8tCDi2VSvIe+LvoONZsNrFYLMQph7gs3wPFS3Rj2e4HSKilwCoejwt5iOIikpnpTsSz/02KDjp2u11idHZVOBwWHPDo6Ag2m02EhnSC8nq9iEQi0ssWCgVJTOB1TIIDEwtIbgSu4nUpkqPQd71eiwMt8deHDx/i4ODgWpElsVDgitxMkaTX68XJyYkQqJvNprj302mQEYuj0UiidG8Scyo/710OYu+rKpUKyuUycrkc5vM5XC6XPJfoKksBNgC5roCrHiqXy6FarUo8s9/v3+hrPR4PLi4upN+mGJIir+l0ilgshkQiAafTKfPGTUVn99VqhZcvX2I+n0v/R7Gc3W4X9ymz2SxxWcQiHj58+EE+z3epSqUi5wyFxHTiVzreEPvkM4VYwk29BF1sOUfxnmG89bvGpr6Poktwv9+H2+1GIBDAer0W/Nfn8yESiezsjV0uF/x+P9brNfL5PP7+7/9exG+8zhaLheCp8/lc3M233YZqtRoqlYq4TLKPtlgsKBaL8Hg84pJPd0fufIghRqNRjEYjZDIZOZ93FcVifr9/w6H92yru4xi3fVtx10Jy0PZuYDAY4OzsTNz8SBIHID0AsZFkMoloNCqOQtcVBW46nQ6NRuPG3Vmr1dq4zr/JIqmOiR27IqAZN01SNnecu85AigZJ/v+QxfmNuM528fWR2Mh9rclkEpyL7sMklhGT+qbnjHct5dlLTEj5v0xKUqvVch9zl0YBAkUZ3FnybCNe0W630el0pK90OBxwuVwbkfef6lNt16er4lN9qt/RYmyQTqe71Xre4/FIXBubItrkKl2gqBSgclUJSNKSnAr7arUqjhQcpvb396WpOjs7E1cdtVqN0WgkKtdIJIKjo6Odaks6GhDkAIByuYxms4lWq4VkMilg83A4lIW20qWHSkOdTofBYCBNpd1uF7tJNiLdbhelUgkAxOqay5B4PC4W5t9UKS1Mp9PpxjBMdjobQafTucH6fpOMcjqQLZdL/OxnPxPSAxW4k8kEo9FIlp17e3ui/OIwz2W+0+nEZDJBKpUSwtTe3h6cTueGC41er0e325WIiesUJXRg8nq9EklDYgs/H7L1Dw8P4fF43npJyqijwWAgtrNK56derwer1YrPPvtMFJq5XE4WY1RLM76ATk271MlWqxU/+tGP8OTJExgMBvlOA4GAgFtKJUg0GpUl6rNnzyQ2k+QVLlm4TE0kEje+1+l0inq9jnw+j9lsBqfTiUAgIMArACGvmEwmnJ+fy+dBMg1VMttxfCRWEVQhSScUCknz7/V6xWGK159arcbJyYlEC5KASAKI2+0WF6ZCoYDxeCxLEwACRlJFxxi4bfJNMBhErVZDvV4XdS5trrk4Y2a43+/fcPFgbFk6nUYoFEKz2USxWIROpxN1CZ3g+BlyqWMwGITYQRCX186bLpGp5ua1aTKZ8Ktf/Uri7Agw0FKb57nVasX3v/99hEKhN8p3LxQKApr7fD6xIb+4uEC1WpXnTr1el8GUajTgCsClItXr9SKRSGwM5FSe+v1+BINBPH36VNTpJDAaDAZYLBaJviRQ2mq1cHFxAa/XK0vBt12aczij6onEPT5blKXRaIRMSXIDyYkARD0+Go2QSqXkecrri1bWarUa4XBYCBG8FkajkTg8kdS2fZ+t12tMJhO5fhnrQHISHbK49KabBvsFZZxLvV5Hv98XoicdQxgfwjNar9cjHA6LZXU8HpcF1y7Sp81mEyD71atXG/cjyad0sKDangAcvxPWYDDAV199hV6vJ0Rwr9eL+/fvo1qtikKYzhx09eBCmc9GtVqNYDAo51ClUhF1/mw2Q7lcfs3NTLn4uUt5vV7Y7faNRRiLMY3ftpLyQxeX7a1WC/F4XFRq7XZbForAlYsTVcJ0Njs7O0OlUkEkErmRwEDnBSo8lX3sbUWwj6QKLsbZ64bDYQHS3jfZR6PRCKCu0+nQ6XTQbrfFCdBgMMDn8wmJhETDm35Wt9uVaCUAODo6khix0WiEXq+Hly9fCjGQ0cpUNb9rEaAiSfmmorKeJMlqtYparSZnAuNpeH6QcJTJZDCdTvH48WOoVCoh03zMRRJqPB6XCAClM1G1WsWrV69kKTsajRAMBjEYDMTNhcue/f19cTCkulgZBfahnMHeR/F5Y7PZZNa6rhhnZrPZJEr4tkiHYDD4GmBaKBTEbTIQCAg5+9GjRxIlPBgM0G63kc1mxSV1uVwiHA6L40ixWJSeli474XB457zBZdRkMrnViZYRh3wO3hTZwZ680+mg1WrB7XYLAaFer0s8JnsnuvDQCYdOXFw0EDCme5jJZEKlUhEA3uFwIJlMolAo4PT0VIgiRqNRehW6jJDw1e/3RU1/eHgo5HOXyyVxOovFQnprOsqSfMo4u1qtJsIoumvxec4FwnYtl0sh8afTaVxeXoq7wWQykVmJ7s12ux2JRAKNRkNcF+iE8L7r8PBQ5jy6zSjnCi6n1+s1gsGgRHN9+eWXMu/z3FaKej5EjcdjESKRGESSxbYL0JsWSffVahWBQECewVxs0i2J9z9JmIwVovv6di/M+O5gMChkG4rBSLCnk5HH40G325Vez+FwCBFwOBxiMBiIm3ssFsPh4SEODg7EFYZFgcLZ2RmcTiecTqdgRCcnJ+LQQSyq3W6L8+//+3//T0gtFDawN1VWs9kUgQJdbmw2mzi8MU6Vxc8PuHJR6na7Iia8uLhAvV5HIpEQp/BEIoFf/vKXyOVy+PWvfy2uk4xsjcViModrtVpxseP5Smd5Yi/3799Hv99HtVrFixcv0Ov1xMGQeA0/X7vdLnOox+PBYDAQwRk/x2q1KsSue/fubXz+mUwGzWYT2WxWenpikCSmMNY4m83K0mxXlUolFItFERsSawKuRGN0PFY62bRaLfls+PdI2mIcZKvVQqlUgs1mg8fjkTmXbm6hUOi1ZwQJGJwded3umueIKfR6PcFh71pqtVqcWDOZzLVOrMSHDw8PRbRGFziLxbJBmvP7/UIKzOVyG8I5ABJFuVwuxVmZLja810OhkPTEdCn2eDyIxWLY39+/82KbYppmsykuy8TqfD6fiGIYp8nvkkKrWCx241KVLu0fkhirFAgtl0vs7e29dgZxTshms6hWqyJMJbGb4tlYLCaYhPI+MBgMiEQiIirVaDTyjPd4PNjf3xfB3HVRTLsqGo0KEb1UKonoZZfLEKPYrVaruGvyjPiYitGzRqNRrlti5Lz3ec0ajUY510ej0Y0z1mQywcXFhcxpJCLTyZERzvv7+290j3+ostlssNls4vxF4utt90EikZDzkk6J/HmhUEhSPeiWVywWAUDie4ErR5pKpYJarSZkOBKo6Xo6Ho8xHA5xdHSEWCwmBDgKEZ1OJ9brNZ4/f45gMChGA263W/obCsvpAPW+RVhvWySmAncjxiuFs9s1m81wfn6ORqOBWq0Gt9stwndi25PJRBzm+WxiasB17r7s0UOhEHK5nBAWlb31arUS4jwd5r9p4ZDH4xHcmmkO28X9T7VaRavVwv3793f2EYvFAvV6XYTX70O8dVMpCUa7HKu40xiNRtJbWSwW+Hw+MU8AIKLWTCYDrVYrkcC/TaWcZyk85A7EarWKWQVruVxKukMymYTJZEKj0ZBzm88dRszWajU4HI6Ne4gJHrc5eX+q3+/6RMD6VJ/qd7ToEHUXJjwt7bkI49KWUQblclksbbkIoXuU1+uF0+nEYrEQ4IQRQul0WmwYqWgIhUISv2CxWKDX6zEej4VtfXR0dGsUFgFe/kwOc4wC0mq1Qk5SLt5IEGFclcFgkGUNwXiTySTuIFyeM1qCD9NgMIhYLIaHDx/e2f3ifRUbCirjuZQiSMAmnO+jVquJG9GbMrH39vYkEoSAH6Ol2LT5/X6xkWf8X6/Xg9lsxv7+vgBek8kEbrcbg8FACFtUs3W7XblmqJ5k1MB2zWYzLJdL+c50Op00/wT/qSjy+Xxwu91IJBJv1cBXKhUUCgUUi0XJxg6Hw6K6V8ZQeTwefO973xNr8/PzcxkY+/0+1uu1gGuBQEBICUo1PYHbUCiEyWQigJrdbt+ZK67T6ZBMJtFut8V6nZEGGo0GsVhMiCckx9xU3W5XFHH8/HjNk0SitLq22+1Ip9MCNI3HY5jNZlEqGwwG6PV6TCYTcXriwoyvhepHt9stDasSjKSCkkv0breL9XotSwk6s9E9S+kaRWIJY/54DVerVcRisY33TsCQDfh6vZYlHXB1dkSjUdhsNnEeYdGmvt1uC/mxWq0KCXaxWEhMqVLJxc/K4/GI4jgWi4nS8zr1801ls9nEmpzA/+XlJarVqvxckk4YhxePx+H3+9/IKnc+n6Ner6NUKr0WK8WInel0Ku4DXBhz0crFoBJMUpILSVSidfl6vYbL5RKHAq/XK0RIuobx/GP0RKFQwP7+/obLw7vWmwygSuIVi8uJ4XCIvb09UfLyWcvFOVX6s9kMFosF+Xxe1HhcOLZaLSGp8XlMRwC6RzLCr1AowOPxyL2wbelN0IPEIi7DGScym83EbWIwGAjY0u/3BXzns5vL3tsqFoshGAyi2Wwil8sJWZvL1dlshvF4LEQYi8Uiz2g6alBZzn4AgKjtCRhRPT2ZTHD//v0NRTVdjbjMJVDX6XQEXCYwx+KArVwwvEmRrMZFPsH+j5Ug8b6rXq8L6dZisaBcLst1rFwgq1QqATkYAcNnzt7enjiUXFd2u12Um7xu7lpcXmwT5fiaGJn3IUqn0+H4+Fhin+kmSlIzFaF36e35ep1OJ+bzubyPo6MjvHz5Up73rNVqBYPBgOFwiFqt9s7AMperdLS8qSiaoJKXfZfSCYWxaBRs8J5n/5HNZsWp80MrTd+1GN3BfqjZbKJcLstcUy6XRZkciURw7949uT+Wy6XExxNkvX//PiKRyG+l5X0wGJSFbKFQ2NmvMlZ6vV5Lb0khzW3FJQ6V75yTDg8PJRpzOBwin8+LOxaXZHRH5IJyMpnA5XKJmxMXMvl8XqJrGVn5tsX4JfbDt5HMrFYrqtUqVqsVTk9PJZI4Go1KNBifL3RfrNVqUKlUsvTi4lxJqvZ6vZjP58hms8hms0LyoXsYzxUuqJRxHFRgBwIBiY4dDodQq9XybGdPQ7GI3+9HIpEQ8QJdi9RqNcrlMnQ6HaxWqxAx2+22RMVdR7okgZx9Uq/Xg9FoRK1Wk0U5o224DGZ8Uy6XE9fL9x3px+8ilUrBaDQim82iVCqJo6uSyMbfzdindrstbqx04mFsXyKReONeYpdYgLVer5HNZtFsNkUZr3SN6Pf7KJfLiMVib+1U0Gg0ZLmsFLTR4ZHCHt5b5XJZZgA6THGmYBTIcDiEyWRCMpmU75/OS3RV4XvgNcHlGCOK8vm8vBZlhEwkEsHDhw93YirsORivxiU71fTAldMlRVkke9psNkQiEXFr63Q6GI/HG33OdDoVPIUuWjwn6Eh0fn4u2JayfD4fxuMxNBqNRNHy+w4EAuIo5XK58Nd//dcoFot49uwZCoWCfO8UW/b7fYnwMxgMCAQCKBQKct+73W4hiWs0GhSLRXFbIFHLaDTi5cuXEiWpdLplkYhJJzvOTJFIRNxpJ5PJRnRau92WOLxoNHpt3HE2m0Wj0dj43pXV6/VQKpVQKBQwm81ei7tmrP35+TnOzs7kHInH43A6nRKnSfzHYDAgkUhgsVgglUqh1+uhUqmg0+kIqT8ajSIUCr0mPqUQpd1ui7sUa9c8p9frcXl5KXPimz6LgsHga06sTqdT3n+/30e324XRaEQ0GkU4HH4NU1GWyWQSsYBWq0W9XketVoPZbBb8tt/vi4sS3yfdEvndMdIxkUhIdDxnwbuSzN1uN0qlkgg8lsslXC6XiEAdDod8NwaDAffv3xeXcjpgXVcUGnJB+6GK7sD9fl9EsbtKr9cjGAyiWCzC6/Wi3W6LqyXJTbynGW9JkhvFkMDX+wTea5FIBN/5zncQDAbfmPjD+42EGGJgyvcwn8/RbrcxHo8RDAYF/+v1ejIHfkxFfJ0OhpxblG5p8/lcRGK9Xk/Es9f1r9PpFK9evUK73ZZEApLU5vO5OJ2Hw2EsFgscHx9/FCQsANK3vkkRHy+VSohGo3C73eLgzBmWRBDGczOJgM5XuVwOi8UCoVAINptNzoPxeIxGo4FsNgsAuLy8vFbkD0DmMpIw2+22PFPp9kjSO8mD33bpdDo5I6+LjlXWaDSSncn2NcjkjGq1ikgkArvdLvMRyeOMf6UI9/nz55JcQ/H7rvN4b29PiNfsZTm/Uwyh1+uFVPttiKm4S/R6vahUKuLOrSzuFoCvYy+Vr5VzIbFvn88n+6MPWRQEUVS73ZMznYLkMj63ldHD/P+DwSC++OILIcJ/G9c5o9jZr5PEdheMmTtpOiSbzWa5d0lqHQ6HG2k0FAoajUbMZrONPYTy2c+dDftWnhdvm7TzqX6/6hMB61N9qt/RIsB814c9/xwXGoFAQB5CjDagWpTLMyocSDSh9TTdcaxWK2azmdjz0jb8+PhYGhGLxYJ0Oo1GowGVSoVHjx7d+ppnsxl0Op04UlFB6nA4MB6PBdC12+0CtFksFiEA8O84HA7cv38fPp8Pfr8f3W4Xp6enQiKiCp9xEXTlSSaT2N/fx/7+/rt9SW9RdrtdhnwSyRwOB4LBoACnJKqUy2Ws12t4vV55729SVML5fD5Uq1VxtQIgS2wqQ1lU5XCBwGGFsW0EUPlaOVRy4Wc2m8UalUXLf5IVyEQ/Pj4WFQoBApKy6CTE4eFNazKZiPJxMpkgGo0K2MPrnCoJLnCsVit+/OMfy30BXDme1Ot1iYCLx+Py3klUYmwG/z3vLSpIqbL3eDwb4PhyuUSz2YTdbkcqlRJwkUpVEsbu+r1zqccIACUgYrFYMBwON/6PSxSSAkmmGwwGQizgtajRaER5wGGFr4tETLvdjl6vJ+QrRpoQiCZQR8WS2WxGNpsVZ6ztSCqj0ShqUC6SOp0OTCaTLKjoaqbX60X1zMgxv98v1yqXun6//7XP0+v1olQqiVsKFct0BlCr1TAYDLLU4xnJqAe6xJFgNBgMNu6RNy0OGC6XC0dHR/jss8/EBWg4HAoJjPcIo1jepBgDSyKRchhy/iYGsFAoSIRYqVQSlzReS5VKBcvlUhYTvE+5ZOISLBAI4OXLl6LafvTo0bWAI997p9MRIidBxY+hSGCmOwkAUdnzvqG7FhU6dF5aLpdC0iuXy+IAMxwOxSlDqUKmWxO/63w+j2g0+tqAuFgsRFFIUIkEAC4EubDhn+90OhIhyvhiu93+Rst/u92Ox48fiztGu93GYDCQaDo65JnNZlkgkzjd7XaFlMNlHEGcZrMJk8mEbreLV69eiYr5Bz/4gXym9XodGo1GbNS73S7S6bTYbQeDQezv78Pj8eDw8BDz+VwinN7ntbRr4fm7XCQB8blHcg6jebaLgAljVoxGo8SsTCaTG3+X8jn7Ju6frG2i3DcZ98Tey+v1St/Nc4HA9k2uOLuKAG2z2UQ+n5dnOF2XAEgMr81mExfRmxZrt9WbfFb8jOnupyRaazQa6QuohGefOJ1O0Wq1kEgkJIKJitOPvbRaLY6Pj1GtVmE2m0XQwIV1q9XCyckJIpHIxkJDo9FIvBOJP7Va7Z2+q2+zSN5IJBKy9FYCnpPJRAizgUBgoye/KxHSYrFgb29PnI8IQn/ve9/D5eWlREX1+33odDpEo1GJEOh2u3LvxGIx6Q1ZjDPJ5/MoFovS0ymftXxuGAwGjEajWxckSrHDXZ85JLLWajXs7+9LpFq325X7gT0S4xf1ej36/T78fv+GyhcA4vG4nD2VSgX5fB5arRblchnlclmWXSSJckZSntFWq1WW5ATTScIPBoNC2lCS2rgAj0Qi6HQ6sNlsEhcTjUZhMplQr9eRyWTkHBgOh0JCUn4e8/kcgUAALpcL6/UaxWIRfr9/YzaikwJJgOv1GqvVCpVKRQigjKS4K/n1rsVIea1WK64M/MxNJpPMRYyw5wzhdDplDi8UChLvRoHSbWcvSff1el3cxIGr+ZPiJbVajcvLSzQaDeTzeYl7ZGTgYDCQWE+6T1+31LypiCVQSLFd2yQTxntTzOR0OsUth6QsXs8kapXLZYlAU76H4XCIdDotzh0ejwdOp1PwK86bvV5P+rUHDx7snEFIFKPjbK/Xk5l320WBgjSShuho2Wq14Pf7Ua/XMRgM8PLlS/j9fnFDM5lMQih1OBwol8viuks36Ha7jVwuB4vFIo4Hw+EQAPDw4UMRNTDWNxQK4dGjR5jNZvjZz36Gly9f4te//rU8ZzlLcZ6lGKvT6eDRo0dCyqKjWDweFzyu0WjIYpWkVhLT6EBlMBiE7LH9udIVGPi6pzMYDHKPKl0e+JnNZrMbXaSNRiN8Pp+cF9tOCADESWU8Hgu+suvnGI1GtFotrNdrOQvpPlWr1SRSXOnq4ff74fV6EQ6H5Xlhs9lw7969jef8dDqVxTRxjruQHOnOvI2l3bU0Gg2Ojo5wfn4usZqdTkcEmCaTCZFIRKLq7xJVTfKiXq+H2+2WyFeeF1yQhsNhNJtNZDIZHB4eQqPRCAZYq9Xg8XgQjUbx+PFjDAYDcYuj2+1d3tv+/r5gkbVaDY1GQ6JMOaMEAgEEg0E5YyioczqdO6+F9XqNSqUi+OPbnIN3rVarhU6nIxjXTcXnb6/XQ6vVgk6nw3K5xHA4lFglRk3SgZQzGsVJrVZLUglisRjMZrNErr5N7e3t4enTp3Ju5/N5ES+yv2fcE53rSLIlnujz+d4Y0/5QRUEm5ye6WSvPMgpC+cwl6fO6M+ry8lKEhMrnMSsQCKDRaKBQKEClUiGTyeDo6OgDvssPW/V6HbPZDA6HA4eHh4Khbhef7RQncddRq9VE3L/9DGHcNmcLg8EgfdSuMpvNOD4+FocnRvVSCEoHZrq4fgzFRAUSb27qUUnopeBSeYasVqsN91eKIovForhZUzRMlzEKVVQqFbLZLFQqFYrF4s7nAgVmdNXMZDKC/ZG0bjabJVGHMaXfdMXjcUkCojCHLlacY6bTKZxOJyKRCNRqNYrFImq1GjQajRCGKNgLh8O3Oh+/r/L5fEL+4jOLxeceheG1Wk0IyMprhvGKJpNJHOJGoxFUKhVarZb0xQaDQVw832fR/IMRj/w89Xq9RIgHg8EbjSXohN/v95HL5WC32+V7bDab4kLO/SL3FQ6HQxxbKRK6jsSudNn6VJ/qrvXpivlUn+p3tKjm6ff7t/5ZPngYWdRqtcQelOQElUqFbreLfr8Pn8+HxWKBZrMp6txGoyGOLk6nU1TGVNkRZGEjtVwu8fDhQ1E9pNNpXFxciIvOdcXBkFbpXOBZLBaYTCbJ/aZjkEajkRgw2qfXajVh2MfjcfzxH/+xxC8GAgGUy2WxwyYJxO12w2azidrvXdTN71I6nU4W+LVaDQ8ePHhNvceGOpfLoVQqCZnubRaHy+VSiEypVEqaIIKK6XRaliNUqHDZyf8lEZB/xmQybYCsjAUgEEDyDYFEukHR7SQajWI6ncJkMiGVSsnCjopgnU4Hl8v1TnEMJE1dXFzA5XKJElalUsFqtcrPJ1mhXq/DbDYjGo3i0aNHsFqtuLi4kKGISxe+79VqJY064xNZBBsjkYg0v4xistvt0Gq1AnhqtVpxZDo+PhY1G+tNyAIEOndlnttsNnS7XQBXFv86nU5UXIw9ZMQgI0AZC8GFDB2wqDogmYruR81mEwBESUb3JK/Xi4uLC2g0Gllqrddr9Pt9iTIloKcEJ51OJ9rttizQ6OozGo1weXkpygVahc9mM7GXJ0jPZjwej4sia7v4HSyXS2SzWVQqFYxGIznb6vW6nKfRaBTr9Vrctmj5y+uCDg0ktb6Poi043fIIolsslre+R2izbjKZXnudHG7oXsN4EpJiA4EA5vO5RDPyXK7X62LlTvIgIzu5qKD70U3F+42xJspziMpOqv15L3/T7kPbv49n1k3Fc1Sn0yEej4sLH5+ZjHd1u93odrviyMZnVblcRjqdFiCQ0b90q0skEkK4JfF0MBhskK+AryM0WV6vV1T5SjXRXSqZTAr5Op1Oy/lmtVoxGAzgcrkEAGo0GvB4PGg0GmJ7TiA2mUyK6ng2m6FWq+HLL79EtVpFtVrFgwcPBFzgooVE2tFoJM+QVquFg4MD7O3twePxSKTxx6Is/dDFiB+eE8rn0/uw01bei1x0K///XaVWqwU04vJvNpvdSqpS9irvCpB8myS5bWXiu9RkMkE2m0W5XEa/30ckEnlt8UgCbD6fF5HEu8Rsc0k0GAxuBSAXi4X0FSTpcSZRRvhSfUrCc7/fx3w+h8lkQrvdFmeej4V4e1Op1WqEQiEEg0GJZiLgH41GbyS1kkhAsJxA8G9bqVQqPH78GKPRCKPRSFyMKe5gfA6fLYxu8nq9b/x+t4Fm4CoabDAYyAJf2R94PB5cXFwIgLvLjRbYjKIejUao1WqIx+Py3+kaS8X+Ta9dq9ViPB7L9XsbyYyOcIyk0Wq1iEajSKfT8Pl8qNfrWC6X4ngBXC1szs7OMJlMZDmw7XhHl7ZisQi9Xi+R3nQEoMsMhS7s/YGrGYpxcSRPE/T3eDxYLpfIZDIYjUZCHun3+3jx4gXa7TZcLhcuLy+FnGG1WiVqx+VySUx7NpsVogcjuBhnz5mArhv1el3uE7pwb5Oqp9OpxJk3Gg1RN08mE9jtdjSbTSFVvC/gPRAIwOv1SjSacrnBs58OQ6lUamMh5XK5hETP5Zfdbr+RqDEajXB+fi7u4+yJSQQhmYZxydlsFlqtVgiJLK/Xi0ajgfPzc5TLZVQqFTx8+FAcWO9ajIG+7e/weyD5OhwO49GjR+JGzSUpFy9er1feay6XA4DX4ppIci4Wi7LMPj4+lqgsfheNRkPiOK97rlBMsFqtRADIqPddxCJlDAoJjIxKDIVCqNVqyOVyQn7y+/0yc9FBgkRtfjfspzk3bbvIGgwGEWJkMhmYzWaEQiFoNBqcn5/j5cuXePr0qRB3GMHtdDqFtE0h3GKxwOnpKQ4PDwX/ows9MR8SwOgOEIvFMB6PMRqNYDQaUa1WhfhCUpXyXOQ8qPz8lPGbyt6MC+LtP7+rHA4HarWakMuUZyxJlzcRboBNcSDnWIpU6fhFEonb7d4QsCUSCQAQ/NNgMKDZbCIUColDIV2IFouFOOTxurnr/XWbe+J2LRYLNBoNefbyTCWWA3ztrOr3+9+IjGq32/Hw4UNx7GOsKCNo2+02yuWyxAuz79Tr9Wi1WoK90mWZ33O325WF6l3KarXi6OhIyKPEpIhfJ5NJIW/G43G0Wi1JgEin0/B6vRtkXeLH8/kciUQCTqfzg5KDKHC9i6svibwkk5LUsFqthNwdDAaxWCxQrVYFhyEup1KpRLjI+EY67d27d++tyBEajQaJREIW3Jz3SZ4lPjYYDOQ+ZbTqcrmExWJBo9GA8wPEwL9NEddmhPq2WJZFd1uNRgO1Wg2z2bxTKE+BdaVSkfN9u9RqNfx+vxDF6RDOn0lhOsWv3/ZndFuR5KmMJr2u6HLe7/clfp6iwJvOxUAgIJgxhYPXldFoxNHRkUSxKV1uSNb/mEqj0cDj8Uh0LOPIdxXdckkOUhZFur1eT2ZPko3oqKTE+Gw2G8rlMgAIqbtWq8FoNIor53bRabBarUpUntlsFqxDq9WKwPz09BT7+/vf+OfNaE/gipDXarU2UjFMJhPC4bCQHrmbI+bMOdNoNO6MFP6QRYENzTAikQgCgYC4D9P5jLiMMtmIgke6FadSKXQ6HahUKnz++ecwmUzSB5KYVSqV4HQ6JW2CeDDvF6fT+UYY6ng8xtnZmfQJfDYAm6YNnU4HR0dHN/7sYDCITqcDv9+PYrEI52+SQWKxmOy7crkc+v0+gsGgxBMDV+c6CeYf2/3+qX676xMB61N9qt/RcrlcYsV9m1K+1+thvV7DbrdjMpmI1Xyv1xPlLCMEZrMZQqGQKAG63a4wwp1OJzweD+bzORwOhyia6GTh8Xjg9/uFsHNxcYFUKiUOMoxRULoEKYsqRhKMuPjlIMMoq2aziUAgIFFCwBWoQHeeXq8nILrVahXiVTAYxGAwEHUU1ZQEhGw227eyrFcWc+YXiwWCwaAoGJXKUeUSy+l0vrUDBHA15HEBymWssqjQ5YBOBxcAsjDVarUC5u5a5tGmeblcol6vo1qtCkBHdahSbcDm78WLFzg8PITBYHivhLjVaoWnT5/i5cuXcg1sR+/1ej3Y7XaEw2F4PB6k02nU63X88pe/lFhEtVqNXC4nkW98T1xiMKaErkssxnEQJLq4uIDFYkG/30e/3xcCHhWINpvtWoXmmxTBWY1GIwpjltJBjS4zVB5TMcwIIA4iq9UK8/kcPp9PyF20LjabzQKYk6RJUlC328XJyYksf0ulElwu12uuAxyAnjx5IgAYY0QAiJLG7XajUqmg0Wig1+uJ289yuRQAkIBfJpMRYhHdgFKplBCEGBFIUJ4W2FxE8HskcW44HGI8HotDAglE1WpVHOJIoGO0KCMz3jdgYjAY3joiZLtIfN1FDNBqtYhEIgIs8XNXuqVNp1PY7XYkk0k5W+fzuShgDQaDELCAr88SKq9vA/1I2CDIWKlUUKvVZLDkmc5r3u/3IxgM3ni2U+H/LmQMnU4n8ZLK93GdWyUXq1xeMN6RTlDBYFCcsQBIVDD/Hs8JxgNx4aYEO2OxGCwWy0Z8DReB/HxuKqfTKYA94xTfpBjl4vP5kE6n5ZnLWGBG1JJoAFyRQEmIttlsci4DV+BOJBLBs2fPUCwWNz4fFsFlnqk820gSjcVisFqtyGaz6Pf78vyjO951wB2jE9hz0cVt1zVDsJY/m2D3t1X1eh2VSkXuESVRk2dHKBS69jUSHL/JvY/gKKMEeK0QhNsuZTQSo60YQXfb+dj5TSy1Tqf7YHGBH1PNZjNRPM7ncwHYuCyi2n8wGKDb7SKRSOxc4DDiJZ1OC5j6LgQsRvS2Wi1ZEOwqjUYjRAC6z9psNnHu2AbZSIzkMonRcOzVCDR+U8WzijGyZrNZXGruUiqVCg6HQ4gy1Wr1Ts6UTqcThUJBXE5+W6/1QCCA4+NjGI1GnJ6eClGdPYYyYp1uRNeRod6mGCmwXVTmMz79pqICvdVqwWazIRaLbXz/Pp9PnERKpRIikcjO64NOcG63W8D86yKpqcYOBoMYDofiOsVo6PV6DbVavRFZTCB+OBwKQdJgMGC1Wkn8u9KlmN9Dq9WSM+P58+dCtFeC0zyXGRVIgYrSxdBms6HRaEjk6Xg8RiaTEQIMexg+h5bLJZLJJGazmTh6BoNBIZDT6YuzFaMnAoGAfA8ktXEZrYyEY5HQ1ev1UCgUxD2X91SxWJT+lrE/74uERZe/becUurONRqONiHVl0T2Lrhm1Wu3aXn88HuP09BTNZhOVSkUWJBTvkMwQCARwenoqvy8Wi208c+k0yPOWr5M9kNvtRjQavVO/zAin61wt6brKZ1uhUJBZim4z17l/pNNpdH4TBb+3t7fz+zIajYjH47i8vBTnIjqgsugIQDxlV//Ba5UCNc61q9VK+hhlUZjFv8u+0efz4fDwEN1uF1988YVEZjPKjs8XurilUikhpT548AAvXrwQISRnIc60dK6gazgxiEajgWazKaTMxWIhvysYDAq5udPpyMw8GAzEZclsNsNut0tkfDqdFocqOmqzR1epVHLGc2lMwi3JkiRx0SVZecZ0u11YLBbpcfj5rVYrIYvdVnQVZmzVdDqVuE+Snvj5XFeMNud8tVwu5XMhjqMkLZHcSCd7znUUptH9ju5bJHKRpESMZbVaIRaLXUvA4Xyu1+vv3AOtVivk83mJt+v1enKd83szm83ye99lDrbb7RtOi6lUCq9evYLNZsMvfvELFAoFIefNZjMh3bjdbng8HrkveV2T7PYmZbFY8PDhQ3S7XcGi+Kx1Op3wer0y+3o8Hvn5zWYTzWZTImzp0koHaLfbLXGLH0txli4UCmg0GshkMnIdhUIhOJ1OcfWhMEmn0wk+ZrFYEIlEsLe3h2g0Ko6t5XIZyWTyrV6T0WhEIpFAt9uF3W6XOELipcR5GaWpFF79/Oc/h8ViwfHxMWazGY6Pj79VgpGSkGO1WjGdTiXRQafTYb1eS4QjCe88j5XupKx6vb4zkndXcQ8yGAxQqVRgMpnEdZBFcg6dFD+24tl9l6h6AOIgz15DKZi4qegK3+12RVB8WzFm9behuBMKhUIol8sYDodwuVxCylOmAsTjcdjt9tdmTEZjstcArojNJFNv43t8viwWC6xWK3i9Xkk/YHzbdnW7XRSLRREOHRwcvOa+1O12Ua1WBVN68ODBNy6EtNvtuH//PqrVqmDUfD1Mn/H7/fI8bzabG5iew+HYiH//0LVNouV3/eWXXwqmTjx8NBrJDK3T6SQZBICkr1gsFthsNumTOActFgs51yh883q9uLy8FCGO0hCBuyImGtxU8/kcZ2dnaDabshem6yJwRRBstVrIZDKIRqM4OzvDycnJtf2I0WjE/v6+4N00VqCRwWQygdfrFZyc8aU0FohEIt8oee5T/X7UJwLWp/pUv6Pl8/lQqVSg1+tRrVbh8/l2PqA47LtcLiFd5PN5AcIMBgOGwyHMZrMwn9vttlhO0o6S8SCMg5tMJhI55vxNjnM4HBbb9WKxKMDww4cPJXojl8shm83KkpONy2Aw2FAYERCmGpBABrPinzx5IiDYaDRCo9GQ164kxNB+/8WLF/B6vXC73aK2fl8P3clkglarJUAlFT53US9tF8EvOgENh0MBatgIE6QOBAKiHKYa5v9n78+j7Uzv8kD02fM8z+OZB6kkuaocX8xgoJNACCvQ6SS90qTTCZ3uptfK6ktmoPumISwydYcMQBpuFpCEuJPbDGEIBmM72MtgY3CV7SpVSSXpTHue53ne94+t56dvH+0zSDpylY1+a3mVJe1z9t7f973v+xue4UmbxVQW02g0aLVaS4XiYDCQa9zv93Hv3j1pRPB7Uk2o3W4jGo1KQnc6aEVhtVoFoFcqlQAsCtpwOCyJrMViEVs+YMFev0oJ0KOjI7EWNBgM0vhhUlkul5caL263G/l8Xq4PC4l+vw+NRiNe6mwwejweaVQyKWVQ+YCMdL1ej2vXroltFiX8CVq5SjsMKiU5nU4Ui8XH2CtarVYajU6nU2RoqVBjMBgQCoVEQpyMO663er0ujIhWqyU2R2Q5EIj00ksviXVgu92GxWI5c9DGtUrlBBafjHA4jNFohHK5jGw2i4ODAxnmu91uAfQRnEJFPwAyoEokEsIiu337NoLBoDS1jUajSPAqbVRod0JLjkQigVKphFarJfLXjUYDrVYLKpUKxWIRnU4H165dE2W193IQmHnWUNBqtSIajcpzmkgkhLWpVqvx0ksvSXOflptK9iNtUxlUbiBI7aImUrPZhMfjgdFoFOn6crksTB6qMcznczgcDlFBO23dQps7ZUOLFj5Ps/ZcLheMRiN0Oh2Ojo4EQABAAAdUd5hOp2g0GggEAvD5fPJZGo3GuWcTLWqj0ag07/x+P1wulyj78bvQWtPn88l5xKKadjMXBc9ervknDaPRKINMp9OJVquF+/fvYzKZ4OjoSM55MssIICGYgwqVyr2KoKterydWjae/C/cxp9MpuchgMBBrUwJCCMDiIIIKGErQ63A4xPHxsQz7BoOBNGACgYBY2RE8VCqVxLaEv5vKYj6f78xGE89x2ppeVdM5lUpJ44Y5hvKMU66Rra2tpTXCs4k/Byz2ZX4X5QCIIG6qQ3CNclh/ullFFh6B3lSLOGvfYRAME41GBdz61Rrz+RzpdFoApkrVDovFIkp48XhcgJVKe7dVQZYv79F5wKmLwuv1Ip/Pw+l0IpvNIhaLrQTukuBBpRqy4M8KrlU2REej0WOqalcRBJAw3wEgZz4VVtjs7vV6sj4JarkMuFcZHP4Cl1MwZSOUw//nGcPhUAa0fG+3230lag9arRY7OzsAIOqZfJZZu1KRym63Y3t7+0oAdtPpVOwuuD8p91XuewQ9XBQk9RBQodzLnQ+tpmOxGNLpNI6Pj0Vdme9Vr9fFHoj5khIkrIzZbIZcLgeNRiPqkZVKRfY7vV6P9fV1GczR0oqfk9YOBC7RMo0qU1S65XNNUAnPXa7VcDi89JmUDGheWzKs7969C2CRm4dCISEi0PpEq9VKvZdKpSRPajabyGQyAhh3uVwCUqEKBEHDBE1ZLBYEg0GxSSFxicoQpyOTyQgAR6VSYWNjA+FwWIgC0+kUpVJJlKaSyaSw5J9HEHhEVb/znne1Wi0KoV6v90yLHQJr8/k8gsHgY8qrHOienJwI8PDVV19dWhOVSkXWJ4EaVGRinkYAHYFRXF+rzmIOqtgnUeb3tPbsdruivsaeFpW+yXCPx+NL+2y/35e+z0V5gE6nE+Kgw+GQPgqDlu1U3FmlWEvbJOapBARrNBpRalYG60gAYnPI96A6r9/vlxpEeaatyrF41vJ9dnZ2Vp47XC/s6/l8Pty/fx+5XE5U5kkiIhhKo9Gg2WxCpVKJHeh0OkWxWFwictAulyQ0j8eDcDgMvV4vPTruR+xn8NkBFntgIBCARqORWoe5NJ+HVqsl9Y0S4MT/XnT2E6yQy+VkXU+nUxiNRqn12AM9D2g0nU4xmUykb0AgAfea0+tVr9ej1WrJzyr3aarY2Ww2Ic66XC7pw1ChLJFIwGKxYD6fY319fWUtXK/Xodfrpc9xUcxmMxwcHKBWq6FQKKDb7YqN4Xg8RrlchsVikVqF5/RVhV6vx/7+vgxVWX/qdDpRReF1Zk/6WRTuGVTyugxgLxQKCamPoEGeN+xD01byeQ/cDQaDACIvivF4LHX0aDSSnkir1RLCE595EikJogQWeTnX9f7+PvR6vRC12ed5mvqKSt3ValXqZABS99M2iq4Kw+FwqW5oNBr4whe+ICTwzc3NJ/4MVxW0nYxEIqIOSsA3leRoX0/ArBKweToILLdYLAKmPEvJWavViiIYVeJImOfzSaXFcrmM9fX1ZyLUPI9Q7t0EgSuBLBaL5bFcht9NuddeZt3RGYbnzrtJervq0Ol0sjdTUVGp2qRWq+FwOBAOh+F0OrG9vf1YD0lp/chzcTQaodfrrdxzlb+b6ozMd7rd7speZaFQEHeTjY2Nx85JtVot9Qft/8rl8rlq0M8rDAYD4vE4IpGIqIOxFlTmh1qt9koJQU8a/X4fb731FtLptJBH6JJgMBhgt9uRy+VkhuPz+RCJRODxeMQxgkINvI/sjb799tswGAwYjUbY2tqC86HbDQABm/7BH/yB1Kjs4zEXM5lMQsTb3Nw8V02qWCyKAq/f739sryJRijM65prnAeVtNhv29/clv1L2qnd3d2UvYI1FR4p3W3DjRXz1xldvR/pFvIg/5MEDLxQKIZvNIpFICFKb/r+NRkOsPbxeL/r9PkqlkoB5WNiTHcSGC5kpTPwJgGLxRtYrmelEFLNhxUIlnU6LegaBEmTpZ7NZqNVqkS8nC44MI8p3E6XscDjQ7/fhcrmQyWQAQBix/Hm+N4fngUAAb7/9tnw3NqJCoRAikQgcDofYGj1N9Pt9pNNpkaVnoaXX61EoFKR4vaiRQDbAcDhELpcTpZ9oNCqWZbRRAiC2BWazGVarVeQ0nwaAZbfbYTQa5T7ZbDZhI1F1qFAoCLu6Xq8jEAgI+p7grbW1NVFPOy+hYeN4NBpJc5GFJ6VDdTodKpUKMpkMtFot8vk8YrHYE32vs6LRaIgSyHw+f0x6lHZM5XIZ+Xwew+FQmhm0wyHzVKPRyHBEyaCjdPvpxg9tEJwPfcaVzBSCo553bG1toVKpoFwuI5lMCnOcln86nQ5OpxODwQA2m01Y9hsbGxgMBmIjVq/XxUJjMBiIbCwHLVtbW2IVQguEW7duIRwOCwM8k8mce2+VDY3hcIh2u41msylSu8CjJvXJyYmAHFjsUR6Y4AvaspGdSXY+5e9LpRJMJpPYerJYsVqtqNfrwj6nEhztGAgAIeCz3+9LMdPv93H37l2xgBsMBohEIu9JlpoyuL8UCoUz2eBkNtFOgXK+a2trohp2WRCq2+1GIBBYsj48axjKa0y2HwdmwGLoZ7PZpDCk7VAikQAAGSQAC6ZrMpmUoSjtTTiUpPrDxsbGpdnAbLSTAWY0GuVnadPIYRuHhHa7HWtra8jlcvB4PMI+PW8gSyUOJZBPp9MJEIjgwPeKLL3JZMJLL70k+6/ZbMbnPvc5hMNhFAoFAAsQGYFXtPowm82iLnA6yP4ej8eiMnBWKBtOuVxO1LGYdxgMBozHY1HACIfDGI/H2NvbQ7/fx6c//Wnk83mUy2UBmhIkxSHK3t4evF6vAGnr9bo0Zzno4vO4tbUl+z3tF5VqE8AjVistlp42CoUCCoUCUqkUptMpAoEA7Ha7WOcQ/J5IJOT5X1tbw3Q6xfHxsVhTU52CezKHXPF4fKkJ5/P5RD2jWq3C5XLJkJQgLODR8Jk5HkGJHDjSXlbZDKIaDPcbm832rtlFfzliPp/LPchmswI45LlWKpVQLBbh9/vFzqbT6VyK0cs8h4oOTwuyoToL11YikYDVahWridFoJICNra0tsXwgsJ/ntdJSsNfrodFoSOMQWNQpfr9fXnMVbNlsNiuqcGSTAovzr9Vq4c6dOzJEJ0CLgHMAAlxst9srm92rguBm4BFo4bzg3qf8uasOgiCazabkMsDiTCkWi7BYLIhGo89MBjAajdjf30cul4PFYlkahDMXc7vdMtB/lmC9W6vVllRtlGoBXEeXPSu5vqjW0+12H/ucm5ubMliidRrPOKqkrK2tQafTiS1fJpOB1WrFbDaTYTRV5ebzuZAX9Hq9rCWGTqdDLBaTdTYcDuVMofVHq9XC8fGxfAaz2Yx79+7J+UerLf67w+GQHCyfzyOfz0tOTDv4dru9RBgh0DYUCkkdtbe3h2azKefP9evXEYlEBOxhtVrhdrvhcrmEna1Wq2Ut2e123Lp1C1arVYDnBEYq8+jpdCq1FQEXmUxmSaGJaj6VSgUABITmcDjk3ms0GoRCIQGwGI3GSymyPm1wCNnr9S5FCLPZbCgUCmKFcno9UkmLAKazbK9Zw965c+cxmzcC1Aj4parSYDBAqVRCKBTCeDzGyckJarUajo+PlwCoVNP0eDxybpMwaDKZUCwWRTluPB4jlUqh2WyiXC5LPsd7TLJQvV6XfVipUkfF6dFotKS4c1YQgEV1UuXapUKM86HF+iqbO/ZemG9ubGwIYImqeMpho9JWnYQLDke5/tn7MRqNaLfbGI1GsFqtQt5QKj3q9Xp4vV6Mx2Mkk0kkk0l4vV55DqiSWS6XMZ/PJScFFnt8sViU17HGIACCACWdTie1EG1JCVJdW1sT9c1AIIBer4ft7W24XK4lxTnWxQDw4MEDGI1G2SsAiLoQeySDwUDuCVURqHzGUKvV0Ov1Qt46Sz2y0Wggn8+jUqkglUrB5/OJPSABoDzfOahbBXwkEIz7Pe1M2ZdYRVBS1sqnCSNU8aQyy2nrd+ZQVLSIxWJiu2U2myWf6na7qFarCAaDFwLtGclkErVaDclkEgaDARsbG0s1Bdc26+RkMnnlKk8ED7AfWy6XpVYYj8eiFpdKpQSIRQIdyYpXFST08Zzms8uhLElABIFQ7fTLVU8TJEulpLN6g7PZDHfv3pUeNK2/CBLLZDJiw9jv9zEajRCLxaT/RRA1++W8xi6XC4lEQnKKpyEr0/nCZrMhm81ibW1NwK1UomSvpVwui9oMAXqtVgvFYlEshKPR6HNRyKE1Jvu3fA6Ua3M+nyMSieDBgwcIhUJiv0xCCBXgSSY2mUyyv3PP42tms5nY4RIMy6BqFuc4ys+YzWZhNpvlTPH7/dJvarVaSCaTQgwlifAyQSu40+q+T9K/uyi4d1Phj7bAXH/sFfPMp+IdFduovnSZOA16/moLk8mEa9euiS0lSXsAZN/kel61X3FmQ1VGnqO8F6ej0+lIX/20WvGqe0JQIlVSzyMVWK1WIZFx7bxb94y92PdijEYjfOYzn8Hx8THS6bQAEvV6vfQqSaRhz3J9fR1Wq1XOcQLIlcqtAHD79m3pe7zyyiuP7Rt0dNDpdHjnnXcQiUQQDoeFDEEQWDablbp0b29vqZ/EZ3Q+n8s88SLwuM/nW7KPptXzWWEymbC9vS2fh/Nm9hS+nGrpL+JFvABgvYgX8VUca2trKBQKwhIoFArI5/PSGNDpdPB4PHC73cKoun//vrD3aGHncDikCczBx2g0kgJ/e3tbWEnFYhG1Wk0kLQmgAiADa7L7yOCx2WwIhULY2NhYYo13Op0lBofBYJDGqjKB9vv9YmFQKpXku0SjUbRaLVGZsVgssFgsCIVCMJlMeOONNyRJJHCCVmUHBwcIh8PodrvY3Nx8YkWaTqeDw8NDKZIpb06mPoEhVJRYldhNp1Pk83lpaFOtrFgsild6NBqVgb4yyXY4HDAYDMJY5LV6mvD7/aJi9eabb0qT5+TkRCTYq9Wq2BASeU8Ay/b2tkjWZzIZ+Hy+pQKSA97JZCL/NpvNEI1Gxc7IbDYvFQsEDNLyKhKJXEnzgxY9ZEqvGuzTKiKdTuPk5AQqlQrNZhM7OzvY3t5eagpGo1EBzU2nU3kGGGSH0x6TA+N4PP6uJIT7+/s4OTnB+vq62NpwGN9sNpHL5WA0GmG328UCcG1tDfF4XJLaUqmEaDSKwWAgCmpUFWAjuN1uY319HbFYDJ1OB9evX8fe3p4k0ZlMBv1+X9btquCwitZntIskU5JBGWY+e7RE4NDAZDJJk4mMb/rRU27ebDaj1+tBo9HA5XJJc7Tb7SKdTuONN94QJsja2hq63a4w4Gi9ZDQaZXDU7XYF9MnP0u12BYRx2obkvRY+n0+Y6FRJWRU8e2KxGLa3t7G3t/dUQEKfz4eNjQ2k02lRISBQTsnEaTQayGQyCIfDst/zDDxtbcvnx2KxIJVKCTsoGAyi2WwikUjI96MCD1WSCMRhU3xnZ+dS+0+xWMR4PBZALpsQHCBmMhkZslqtVnzgAx9YGgL2+30Mh0OkUikZTnB4Q3UAAl49Hs+ZoAmyk876NwKwLwIuARDFu/Oa4FSh5OCGCju0TOT78u8MBgP+yB/5I/jiF7+IaDQKs9mMfr8vwCsqN/CcoWoBG4JUrGMT6aLvweEtB1qVSgU+n0/Y9wza5KZSKQDAvXv3cHBwsKTUwcYk93We16VSSf6NtqQEuLKRfXJyAr/fL8MxlUqFg4MDUfejtTAZamS1ngY5XTaYYxBIub6+vtRYVq6RdDqNbDYLnU4Hv9+PZDIpDMvpdAqHwyFnNwf6TqdTmnD8fC6XSwAbmUxGgLw+nw/lclmA4s1mE3q9XkDgiUQCJpNJ7E3J3K5UKtL4o/KY3++Hx+ORnPK9EGxkVyoVaWhptVpRoHua4UGxWJR7oNVqsbm5uQQGCAaDaDQaApIhg/MyQHy+RjkkeNrwer3S+CfYjiq4tCYiqIZ7WalUEqtmtVotqhy0Ruf+xLO10+lI8/608tDTRCqVQqFQQDabFcUF5i+dTgdvvfWWDEdMJtMSM5TAxWq1KlZMGo3mUmo5FosFer1e1DIuWtdUTWHT/Kqi0+kIsYL2RyThKGsZ2hX0ej1sbGxcerBzVhDgGY1GlwZfZKVehZodwdUEjtKSV7mvVioVYWcT7HDW2mENxOeWFsBUiaICJQBRECmXy7BarfK8ci3QBisQCGA0GuHw8FCUWXK5nKgMcwDtcrnE9iidTsPhcEjurvyser1+idwwnU7l2YnFYlJvDQYD3L17F/l8HpPJBDabDTdu3BA7xEqlIoNPnrOJREKa6MPhUOpOqpdoNBrJ70OhkIArOCDsdDoIhUJL6rXKAQ7BLiQBKdWY3W432u021Go13nzzTWmskyzW7XbRaDSg0Whw7do1sSHkmU0AUbFYRKFQEMBqIBCQ+3M6fD4fjo6OhIz0NMz80Wgk+8JZtR7POeB8JR4GX0PCDC3Q+WxRsYpqveeFzWYTS7dWqyU9Cipf0UqJn52AbQ40Hjx4gJOTEzgcDrGspyJup9NBPp8XoDmf+fF4LPZYfr9fQOqlUkkAxcPhEC6XS1Ry1Gq1kJ+U1inA8tn1pNdv1ZlHUFGn00EqlUI4HF4CqhCsSCtYo9EoJMher4dCoSA54HA4xGAwkOFPKpWC1WqF3W6H3+9HLpdDqVRCt9tFNpsVVTYOkB0OB2KxGCqVCsxmM9bX12EymUQ5j8rKmUxG6lrmuFTrqVQqUu8Aj6zrqCrC70ZrU6o22e32JYUVntG5XA4qlQqbm5sClIjFYo8BFGk9SOBSsVgUgAKVt0icJNjtc5/7nKjUBwKBJVsaBlUWTk5OZJ0ro9/vS48vm83C4/Fga2sLOzs7shcxV6ciZ6VSWepBKPsNtVoN9XoduVxOQItU+DSbzSiXy0ugq16vJ+uAe/N4PBZyG7/39vb2Y+cMFYwIgHvjjTcQCoVkPaTT6SUlXZfLdSmgPcGB7O3wHDj93rFYDJlMBrlcTga7Vw085XkTDAYF9EEFyHK5LK8jKIo15cbGxqXsmi8KEu1oiclhNHuqwWBQ8sCrUp5/mqCSttvtRi6XQzweX5n7JZNJpFIpDIdDhMNh3Lx5U2pv1vn8zrTcjEQiArij2r/RaBTlSK1WK+9FlfunCa1WK4r57KW63W6p6Xq9nqhmUgXc5/Mtqayo1WrJ+e/evYuXX375qa/p6RiNRsjlcqjVaqLSCawmAlA1hXsv1b0SiQSm0ylMJhOsVqvUBrQrIxGlVqsJaD2fz+PevXtidxsMBkX93Wq1ijIWewcA5HyhQs1phRlaPKZSKQFU0+5caRV2+hnK5XJiA8oZA3uypVIJNpsN6+vrz1xrkWgwHA6RzWbRaDRgNBplVsU+BM/M2Wy2pDqXyWQkN7iMOr7T6bwS1d73apB0EYlElhSx2U85bxbEvY7gduaJGo1GZkwM1hDsvZGwQiLSqpyLM6PLWgU7nU7kcjmEQiH0+/2num9U92W+QgLIlwvMtQrESMGHq6jZX3/9dRwfH+Odd96B0WiE3++XPIoEOo/HI6r+tDInGZRuQ6dzDgLeAZxJ2FDWGXa7HRaLRZ43YLFfclZMV5lcLofNzU3UajVxIeLvSqVSqFar2N/fv/B7u1wuIYKcpdB2Ovh5XsSLeDfjBQDrRbyIr+JQqVRYX1+XwpVKEGzGERgRDAalAKe6SLvdFsllgphms5mwFNiMsdvtuHHjBlQqFVKpFCaTiRROymZKPp+H2WxGpVIRuWEq5VClSqvVIhgMioQuFSgI3KDNEJVj2HDz+/0ymPzsZz8r0upWq1UG1lQECwQCcDqdSCaTknyw4UX1gOFwCJVKJQCj+XwOo9F4aeDAaDQSG7tisQi3241YLLbklU0LABav165dW2rkjUYjHBwciERzq9WSpirtfvj5YrHYmQlFu90WlsjTAnr8fr/4Lp+cnCCXy6HRaIgU9XQ6hdfrFTACmZOdTgfhcBiRSAQ2m02UKai4QXDebDaD3W5HIBCQRisLgPOCoBoCL9xutwC9WLQpFWYuCqWKEhtbtVptJbONRWyz2US320UoFJLmAItpNm/4uShzyqKoVCrJ56QaGv2u3y3Paa1Wiw9+8IMAIKoOtVpNBi8Eo1AFxu12Ix6Pw2azSSIcDoelyHK73QIwMRqNoorF4ROHVFqtdkn6+LKqA263G61WS1Sq1tbWlhrnSgUXYNFAZwGuVqtlwDiZTAQsRpYxh160V9ve3pZrQYs7DkcmkwkODg5kKOlyuTAajZBIJKTw41CR+0y/35c9aXNzUwoZAly/3H73TxIE4Pn9fhQKBbFzOx0EoIbDYbGtedr3c7lcuHXrFt544w0ZyDebTWlqDYdDDIdDOBwOrK+vi0oiwX5n7QNqtRrBYFCa9plMBvV6HYVCAe12G2tra48V/X6/H/l8Xhpa+Xz+wiFcr9dDJpNBpVIRdR4+SxyScY8nAFStVou0vs1mE7YS95mTkxNpghB4RR0/UOAAAQAASURBVOAQv//ThMfjQbvdxsnJyYVNrUajIc3B003wwWCARCKBbreLTqcj57ZOp5OhnFL1Dlg0K2q1GhqNBjY3NwVEQhUODqnX19cxm80EGM69CVjsXT6fDxaLBZ1OR/KXs/YTgiTZ1PV6vSvlxDUaDYLBIObzOfL5PB48eIBWqyWA7dN2DF6vF/V6HZlMBm+//baAN2/durV0TQlqbbfbyGaz0Gg0ePDgAVQqlSikUBWRIBUCIxwOhwzlTl9/KrVxmEDLMDbg2VxmHnEWuEGtViMUCsnQ+e2338ZkMkEqlZIcUrm+aAVNFUMOXjl82t7elvtRKpVkyEtVrclkAofDIfaySjvHYDAIt9stIEQqlwCL55Z5J8+l90JQXYC5ChWECLIpFAoIBAJL5IKLggPpSqUiANPTzzdBjRqNBsfHx9BoNNKov0g1jbnpWY3UJw1atpbLZdn7GHq9XlRR1Gq12KTabDYcHx+LiolerxelJ6W1h8VikQE3gWalUumpgcwEFxNoeRrYRvIEVQCuX78uaiHAI+Aim47pdBp6vf5S1jmsy1wu1xJphU1GrhuSEk6rpjDInleqNJy+57S1pZ0O7zOfkWazKUSSTqcDr9eLYDCIaDQKg8EgA2sOaYBFg/Mqhhsajea55MH1eh2JRAL5fB7NZlPUhGlL3Gg0kEqlhH0biUSEgV+v1x8j47RaLeRyOTlnqAJL1UCXy4VWq4VAICDqPCQOkWjE/ZngBq5jWpCzpuU1p3UrczCv1ytnXqVSgU6nQ6FQWLmfULHlwYMHsNvtWF9fX9o75vM5zGYz5vM5ut0uYrGYkF2oRkJyDVnvBDYolSmZN4/HY2xvbyMSieCDH/wgzGYzTk5OUK1WUavVoNfrpVY8ff/5PNKenn2BRqMh+aZarZbPGAgEhDDGM4EDCbvdLurZqVRK6jeqkKVSKdRqNXg8HgF3xGKxlXsflXZos3LZWGVpzc/I/U/5fmSiq9XqxyyYVgWVIdLpNAaDgZBEePZXq1XZfy+qrWiJzTwYgKhTt1otsYhRfje1Wo3xeCxAnEKhIPt4IBAQsli1WoXP58NsNsPu7i6sVqsMr4EFsPj4+FiUigigY8+HgBWCliwWC05OTsRmm3us0vKQihnnBYFLZ1kler1euZ75fB4nJyeyDmezmajDx+Nx9Ho96HQ62Gw2OYeq1SqKxaIMnNkTI2gmEAggGo2iUCjIa09OTqTPwGfNbrfLGReJROD3+zEcDrGzswOLxYLt7W1Rws1ms0gmk+j1egJQn0wmQvjhwKvdbgt5UafTif0plXBoD8kaGYD0L2gXB0CA0fF4fMnWBljsLfV6Hb1eT2zlG40G1tfXUa/X4fV6YTab5R7wnM/n81hbW5Pz56weCZXUCEI9bXdcrVZFmdVkMiEajcJut0utzftgNBrxpS99SSyQvF4vLBaLWNnTmnwwGAjpdDabIRwOi6oPz2eCRalKFwqFBMDG2oBEPKoWn95zZrMZUqmUDFSpdEiwEIGuwWBQnoetra1LDSQrlYqoWp62F1eGSqVCIBDA0dERBoMByuXypQBelwmlLTtBmNyP7Xa7qGrQSpZqZZVKBevr66Kk+yxBG/Z6vY56vb4ELDKZTAKqIbnp3Qzm+wQpJpNJsYzSarUYj8eoVqtCqPZ4PLh165bsaXxW2KNkjjqfz/GFL3xB+vx0uRiPx7JuqeAPnE/kukwQgMVatFKp4OjoCIeHhwJQZh2ntGpmRCIRFItFFItF5HI53Lx580pqlX6/v9R/73Q6S71JEiZbrRZ2dnZgMplgMpng9XrRbDbh9XpFxYUkTxLpSDDl/sqeBsHfVATtdrsCiqI6GJ0WqDa6vr4uhBRgQbo/y96LZM9sNgu9Xo/XXntNemfstSrzkFQqJU4PnU5HSHnsz+h0OgQCAemN8nwiqNxut18a5JJMJoVQxf06EAjAaDTKemfdTIeWmzdvwul0IhQKoVqtwmQyXQiGZ6/X6XRKHj8ajVCv1+U5pGr5e0Ud/lmCVn5PGn6/X1R80+m01NjsnRMoXCqVBAzL96HFLsHkp4PuEKsAP6tCp9PJM7qKEH9eVKtVFAoFsaJmjUuwGIkWzxOIRRAjiSkEYDGfWAVinM/nQnJgXXi6vmewD0wC6P7+/tJ15eyUauatVktyBo/Hg0wmg2AwKHsTo9Pp4M033xR3HbfbvZKApFTypyrneDzGYDBYyr3oPkRCO+tePmckwVarVXS7XSH8nHd/lG4jTwsEfhEv4t2IFwCsF/EivsrjdKNXKavscDikUU7GF+VyybABIEojlC8l+2symQiwggCgfD6PwWAgwwg2YZm0m0wmYZPY7XYBMCibNCqVSoAbyiBohYM6Fo9kKJdKJfT7fQEH5fN5kWvf2NgQ6WDaWFFhwefzIRqNCpPQ6XSKOle5XMbdu3fh8XguPcyjilKxWEQkEnksCdVqtYJ8T6fTIrvPgflsNsPBwQGq1ar8O60Kp9Mp3nrrLQyHQxweHkoytLGx8VhiNBwO0el0EIvFZLD1tBGLxXDv3j3o9folS0Ilul2r1UrzmxLzlJtl45sDYA7imYhyaGM0GnHv3r1LDXDYLKQ/+Wg0QrlcXmpsswDx+/0X/k4+p8PhEJFIBKVSCY1GQ9RQTieBtAyYTCYiS87CmqplZAtptVpoNBrcunVLJKSVA361Wg232y3MhXczfD4fPvjBDwq7LpFI4Pj4WOxLLBaL2EewyU77qtFoJKAH+o57vd7HmgJksVutVlEeUD6fXNNk5J4FHqQykcPhEIWuQCAg17vb7YqlJAtIDgFoCUirExb4ZPKTzU8Wr8PhEFsC2rhks1mxwiJ4Yzwey3Co1+uhWq2KegSBW1S9icViIvmu1+sFBFStVpfUAN6LsbGxgdFoJLLB1WoVDodDbELIvAqFQqLQ8CyxtbUlBeA777wj0tdka8/nc7jdbmxtbWF3dxcajQapVEoGJOcFX9NsNoVlzMHAqp/VaDSIRCIC5DWZTAiFQufusTwXOp0OdnZ2pOloMpnkeePggUMk2mIyqA7FIQPPUp55kUhEwKDPEl6vV2xys9ks4vH4Y+fLfD5HsVjEgwcPpMD3+/3C7ur3+7h//74M6CeTicibUynE4XCI6hSb+QQFdTodeV8OW1QqFUwmk1iosEHM4RFBa8DifLDZbKKUkM1mRX1DGY1GA4lEQs42h8Nx4eDf5/Phzp07SCQSqFQqcLlcWF9ff+x1BBFQQZQ2a2eBK202mygTNJtNGdBR8UsZdrsdg8FABgfM6bRaLTqdDhKJhDRc2MgxGAyo1WowGAzSLCGI8aJhjV6vFzl4gvLIhF/VIDGZTIjFYkuDUd5jg8GA/f19pFIpaVIT9MKGy2w2kzyQii+hUEjOzWvXrondilIlhyCs90oQiEElNL1eL4zDfr+Pk5MTmM1m2QNWPUergudxo9FYCb5Sht1uh81mk3XTbDYvlNNvPLT4MxgMVwZkIzCOCpmsRQjOBhZ7z7Vr12RgaTKZZAjdaDREhYNgBRI01tbW4HQ6BXiUTqeFiPGkwUHcZDJZmVuz4cgaaTKZyMBFGSqVCqFQCIeHh2IvepmhaCAQENbu3bt3YTQaJTdlvgQsmt1+v39pyE+LMQ4RCcAiGCAQCMBms8mAk8x7Djq63a7UiXxWqKA2GAzkHlB1gUPzVCoFk8kkyjbvxeAgm+pq6+vrj4FarFarWB2xvmTOyzOROUG32xWFWwL3qdZJq18lgYZ5w+n3O+8ZpT01n6PJZIJAILCkyMZg/RqNRkX9QqkaMZ/PkclkcHR0hPl8ju3tbXi93qW9o1arodvtyvtSBdbj8cizQQtnm80mqiUEwhL4Q5WbcDgMl8uFV199VZQYptMp3njjDej1eiGnsEFPhj6JHrRINBqNsFgsQgBTBsFCW1tbApxSDoftdjt8Pp/k5Zubm3C73SiVSmLLA0AIIQQ0nrenKq3ZLhNUhSRYnJZrHFa2220UCgVsb2/L/TKbzdDpdGKNd9ZwlVEoFGTdKy3SqSKTSqVE4TmXy507rLRardBoNGItT6UOgtlPP3vtdlvqKBIElAohtP4maSaXy0GtVuPk5AQ3btwQZbhsNiu5Rb1eR7PZFDtSvV4vKmpUoyUYRqPR4M6dO3A4HHIeEuRjsVhEofu8oCL7efkQ+xharVbOXzL+vV4v1tfXhXBYr9dxeHgovTaukVqtJqD7eDyOeDwuORlrxkQiIbZhBK6TjEH7M553uVxOQPgvvfQStFqtWHmWy2WxeOLA0e12S/8in8/DZrPJe7H2VgK+2DskwYjqsnwtiW6hUAjtdhs+nw+ZTAZ6vV6s5Ehwm81mYqFmNpsFcMpe0vr6unwe1uuZTAZmsxkWiwW3bt06E2RB5Q/aeCntjlmvHx8fQ6VSiR39KqIF7Y6oikzbOyr5kmBqNBrRbDYRjUYF8NJoNOD3+2WQn8vl0Gq1pHdC0DQVDanm3O/30e/3odFocO/ePen5keBTqVTEJpYADKfTKapMPGOotnPRXsEggMNsNl9I+tLr9TCbzWI7fhVB8CsVnNlHI0BXaafkdDoF5Ass7hOBld1u96nzflpNp9NpjMdjAcsoLbJzuZyAL9nveTeDNadKpRIycyaTWcoPWUu+9NJLj10bp9MpQFOCAdLpNEajETweDwwGgxABdDodstms9CoaD+2AWR88S8RiMcllaClJRV+CvZSzitPhcDgEXEoQ57PEeDzGwcEBKpWK7I3KPhBV7VOplPTorl27Bp/PJwCCg4MDGAwGaLVa7O3tLYEb+v3+kgpkoVAQRa1utytqngSyHh8fQ61WY39/X87gYrEIlUqFUqmEbDYLYNFDvqh25PlMVUHuOwCW+vfvvPMONBoN8vk8VCqV3CPGZDKRdVsqlfDgwQNEo9ElABb3x0AgsHKPZfA8zOfzCIfDCIfD8mele4LZbMbx8TF0Oh2uXbsmZ6dOp5N9MJFIoFAoSB9KGcw5CLA1Go04PDwUy0sCOIxGo5DdwuHwlQD6vlxBIC33qNP9w8uG3W5HMBjEbDZDLpcTQGC73UY6nYbBYJB8gb0ph8OByWQidqEGg2HlHknyOpXgLgJhsS96ekZwUWSz2SUAP/sG/H20Xe10OucCj58lTk5ORHTgIhDj3t4edDodisWiuOYo1emoeEwHH8bh4aGcmy+//PLK66lWq+Hz+ZDL5UT1/pVXXpF+bLFYFPcgAGIDSAINZ2yHh4fSoyQZiO44VLLlbHZVXeRyuVCpVIT80+12hbzNmStJqPl8Xn7uLIEJ1m0v4kV8pcULANaLeBF/SEKlUoll0Vn/ziYLEe2ngw1+m82GZDKJUqmEXC6HdDoNnU6Hg4MDAUEFg0FpGjPovUsbhWazKQnaeUnYfD5HOp1GsVgU1hgbsLPZTJg6TKQJDKNkMAc0TMrv378v6htstk0mExgMBknC2ZSm9Hsul3uMTbcqqBRUq9XOZAAwWKA8ePAA+XwexWJRhqe0//B4PEssfso2q9VqHB4e4vDwENeuXZMhrfJap9NpseF61oF8rVYT5QwmbEp5ZNoUUa67VCpJgU9lJL/fL0MkSsn6fD6EQiG4XC5J5IDL2SXyNcPhEPfv35eGp9KmiY2parWKaDR6biHIgRPwiGFKhZ9CoQC73S7szNFoJOpQkUgEvV4PVqsVDx48QKVSERlesh1ol6XVavGt3/qtiMfjAvhisf1eKvSoVkGWssViketpNpuxtrYmtqNkahEQQwYerfx0Op0MdJikM1Fn04wNXYbT6ZRCuF6vn6tkEQwGcXx8jM3NzSXgJABhRtAaQq/XCwPDbrfLoIJMVVrNsFk1mUyWvN9ps0HlIjbnuPay2awwVzhcUjJh2SzgUIFrB1gU/jqdTgBe7/Uwm83Y3d3F0dERbDYbGo3GktIQVezovf6swEKCNmjzRnYTGYQulwuBQAB+vx9ra2u4e/eu7OuXCaPRKCqRBoNhadC6KjhwIbCGKnyrggV3vV4XMAmweHZ9Pp9Yg3FQEg6HBbhHiWiG2WzG5uamgNxok2EymbC5uXkl+wjBLtPpFOl0GsfHx3C5XHJelstlHB8fo16vw2AwoN1uw+l0SsPBZDKh1+uh2Wwin8+L1bHynCcTnSozVKlk40GlUj3WdHW5XGg2m9BoNGg0GmKzqNxzeW3b7Tbi8bjYY1E50u12w2w2y3XjwISWDj6f78KmEFUYyJa+efPmua+nkkCv1xM7l7OeLUqu8/xYX18/k8VoNBoRiUREsaVarcJoNOLo6Ejs7rgHqVQqtNttkZWnFSYtaS4TRqMR5XJZVA9W2ZWcfj2VS2w225Iii06nw9bWFobDIarVKoLBIPb39wWIRNAtG/GrWKnMb96rQYAcWcRUuVTGcDgUNT2qYV5miMB8nepiFwXZh3q9Hs1mU6y3VgXzG7/fL6CHZrMpFlMcTj1tsKG+Knw+H0qlktgTU0X17bffhsFgQKvVEsWR0WiEQCCAcDiMeDwueX4qlRLg0JMO5ciWrdfrZ1retdtt9Hq9JQVe2siefj1VjRqNhigOXRQ8y4rFoig9MBeh2uN8PhdLJg48arWaqG7W6/UldR4OhOv1ujTPC4WCnHeTyUQAxfP5HPF4XIAWtB1ttVoolUqYzWZIp9NixUzgT61WE2Dwe1HBk7Uj8/az9j2z2YxgMCgKxrTGHQwGSCaT8Hg8cDqdKBQKovLJfDUYDIpiA+vuVColyoZsXj9pKFVGT1vjKmN9fV0AIaVSCalUSggJVKQcj8dLapkMKuOwoW40GnFwcCBD92AwKNZT8/kcTqcTh4eHcsaYzWYBUZE1HwqFsLGxAbPZjFKpJIoSVF6hogTtdagGzHXT7XbFooz59+nGO2vmtbU1rK+vw+/3i5oR643TwX4IVaMILOEw9KKgiuBlWPztdhuHh4eiWMhch0QMKm1w6LW3tyfq4T6fD51OBycnJ0vKX6ej0+ngwYMHoijGvEH5nAyHQ7zzzjvIZrMyiFyl5kI7dQLnU6kU7Ha75HRUaOX6IfiQgDzunczTZrMZqtWqAEK5Z7BP0Gg0RJUzGo1K34gKV1SSphIIg/UTFWuZ07GPQyA7iTjAAryyapDHZ2h9ff1CwhpVW6kKzfVGS6T3v//9ODo6ErCZkqjFwTEBS7RL9ng8omaTy+XkGjHHV1q+kbRXq9WEcEGQbLVaRSAQkEHbYDCAy+XCyy+/vERkot069ycC9J1OpwwA6/W6ALWowEbVTn4+l8uFWCyGUCgkgAyCDAh64lqx2Wzy9z6fD/l8Xiwqg8Gg/A9Y5Hbcm3w+H6rVqgDxzqvLfD7fY3bHBEHVajWoVCrs7OzA4XAgGo2eeU5RBYQg7nfeeUeUtLkmh8OhANAIHLh//z4KhYJ8TyrMUkWJ679arcrzToX4+/fvI5/PIxgMIplMIhKJQK/X4/j4WGziTucZ8/kc4XAY+/v78v06nc65xDVlUEHisuc1c4XTdlRPE/V6HclkUtQoSUwcj8fodrtCwqMqhsvlErWhQCAgdaZarRZV9SeJyWSCfD6PL33pS2KJvbOzI3sR8Egtis4JHIa/2wAsAGK3Sjs4Wq5SWZhOF2f1z6jS2mw28dZbb4lKCi0+lUqJJJZS3ZDr/TI2YhcFiTZ0ePD5fEKAO0/Bh2AftVothMlnDYLz8/k8AoHAY+9vMpkQiUTExcJgMKBUKiEUCsl5xt4IgdzAI6tR2j7zvYbDoeTOVLyKxWKigFgqlUQF833ve59Y9ZbLZdTrdQAQFciL8kqekblcbqlfplarRV2KwIvBYACPx7Oyp8S6J5/P4/j4GFarVdR6NRoNxuMx8vk8nE6ngILOqntoQTYcDrG7uyugDJvNJmqnVL6MxWLodrsyO+D+RsXJeDyOTCYjsyWCXUgG9Xq9ovB1cHCARqOBWq22lFsCi3XBecTOzs5Tu4c871ACuqmCxjkGZzFerxd+v/+Jv0M0GhWwlMfjwe3bt9Hv9+W+EsDM54iiAxqNBn6/H8FgcGVviHMvnU4nhOTzgirFVK66TBwfH+P27ds4OTkRS9BAICDAUlrMJxIJAI9s708HAUgABAh/2SgUCiiXy9LXicfjSz0PghgzmQwA4P79+3JNaFdIUD3rK4/Hg2azia2tLdjtdunTMDc6T2SAhAQq2qpUKqytrYk6H5WxqBjGXgsJrIlEQtTF2+32kto/++zMu84CyzF3BBYEv62tLfj9/qXnJBgMot/vC5GMn3vVdyOoTavVXrkd8ot4Ec8zXgCwXsSLeBEAII3Efr8vSi6rgvLYlUoFoVBIgFTVahUABNgQDocfU3AxGAxiM8cmvt/vvxClTzQ01YWIAp/P5zg8PES1WhWmBrBonMTjcRwdHYmk7Xg8loEj1TXY8Dor2BikJDBZuOcFB+mtVutcdilVYxqNBpLJpBQqtHKjwthpWwbeg16vh62tLdy5cwf37t1bGui2223xiY9EImKh8SxBSyiDwSAKVqFQaGXxbTabodFoJDk6OTmRBhrlZJXqaLSlZAOITMOLFKt4rclO7Pf7sFgsCAQCIlXearWQyWSWlJbOakbodDpoNBqReY9Go1LIcMgCLIAXBBjR3tFoNIp1EgtQsvb6/b7YcxqNRoRCIUl838tBthubja+++qoMy30+n8hDN5tNZDIZsSpQqqQQdMShKZsPZrNZ7k80GhV2K4PNWrfbLaoDZz0PtBqiSp1er5cBJYtQFti0kCGzqtVqCVPNaDSKFRlZYTqdTgCcs9lMvO21Wq2A/U4rJOj1+sfY5WT0ks0LQIZ43PvI7OQgQMlkfK+GxWLBSy+9JF7uvG4AxOP+WdX3lEGJZQ5UotHokoqAcrhJS8vLKhRMp1OR9O/1epcCQfCZZcF41uCOrLRut/uYbYMS4Kf8npStJrjzdOh0Oni93ieW5b5shMNhAYg2m02xCOD/H4/Hws4iM5lgbL1ej3a7jfF4jHg8vrKJarVasb6+jkQiIaBINmPOeva5d3i9XhSLRQB4TLp7MpmgWCyi2+3i5s2bslenUimx8yNA12az4caNG0tglMsCS2gJRqbhecHhVrfblX3grCAgnPbHFw2DLRYLjEajsLQByKDb7/cv7TG0uCwWiwLwAnDp9ck9EIAw7S8KKi9RzfD0oIc2lF+NQdW7Vqu1Um0HgChfnJycCHD/MnsP1VeYs1wUVInhc5/JZMSyS/l8EBhpMBhkoN1qtZZUxvR6PTweD0Kh0JU3pznMpIoFgfC0XGF+CUBsSSKRiDSa1Wo1nE6nKMY8KRiINlLj8fjMIRvPCgIA8vk8ptMpBoPByjVBpQzlgOyiMBgMcl/JVGWOyrONAJbpdIqjoyM0Gg2pK2jFq9PpxLK0XC7jwYMHMBgMGA6HCIfD2NjYgNFoRCaTgcvlQr/fl0YwzywCrdxu95J9bKPRkGeVAFzmfe+WjfZ5wfPrMnu2w+EQxWW32y0DJOaJ6XQapVIJ+Xxe8lPnQ2thJTCVajTHx8dS71xkV/wsodFosLu7i2w2C6PRiOFwKPYbVqtVgBmnCVIA5DygjTbr0Gq1iu3t7aXaifamPp9PVF1cLhfi8TgikQh0Ot2STR3rfg4daV/IHI1WFLRGIniaFl4EAynXP9U4qFJMJbgnUebga8PhMMbjMTKZzIXMfCrlUlnwvJjP5zg5OUG5XEa1WkUsFnvs2fN4PKJGpFarkUgkcO3aNQCLer9cLiMYDAqgxu12y57Ga0A7vG63i5dffvlMi7bBYIA33ngDxWJR6nnl+UHQMG3pZrMZ9Ho9jo6OhA1P5WMOg2m7xPp3Op3CbreLUk46nZYBOesyo9Eo9RIB1gwCFalMS1DeWfsmgaXtdhu1Wg0HBwcC5OcQt9fr4Ytf/CJ8Pp+A9JTXj88QQbwXhVarXQIMnY69vT3Zc5V9EubtXq/3sXOpWCxiMBig2+0iGAyKcgKVTRlqtRoejwfZbFauKclK5XIZgUBArKdpm3bWOU2Vkmw2K0NupSI+Pw/VmVhLUw2Ja46q1lT1JQCPZIdQKASPxyPgfwKzqCDNen9VmM1msW4cj8cX9k74eZR2xwQ40Kr9NDjxdPD7hcNhIZW222243W4hazIHo5JMIBCATqcTpRb2LOx2uxBX9Ho9RqMRNjY2sLe3J+phJE80Gg0cHBzA4XCgXC5jNpsJ0SMUColyaKPRkL2T9umRSAQnJydiEXiZvJrgjssCqrg3PmtNf5YaZblclmu6vb2Ner0uSrlGo1FUkW02GzweD+r1+hK4fVUuzGtI5X2C83q9HjKZjNidRqNR5HI5UXxUPo+0rCqXy3IuXaQi++UIXpNIJCLfj4DV4+NjAQ2eFbxesVgM2WwWgUAAdrv9sb48FfTu3bu3pGx+lT0qm82GWCyGZDIpDhXnRbfbFeArbZOfJUigrtfrAi49KwhaqNfropi3s7ODVCoFm80mSqLJZFLWKR03lCpFVG8kAIjXns87rflI1KVKWKFQEIAayYCnQ6miMxgMxIKYwE8lIdloNMLlcuHo6AhHR0eYTCbnAv0JxKtUKpjNZggGg9je3gbwqI7kLAN4ROo7Hcz7SPRwOp3SQ6ZykfIz1ut1eDyepV4jlcaOjo5gNBrRarXQarUEnEkyKD8DbeEIGqNSJwAhvycSCVFS3d/ff8/0YufzuVjh9no9JJNJcW8gcMb50IrU4XCg0+lI/v6k1vDhcBgej0cIJkdHRwiFQmg0GhiNRjLr4PNCAC2BfauCQCElYeesXJsgONq+XnTmDIdDHBwc4Pbt20gmk6jVakJwpYCDzWZDKBSC1WpFOp0Wtb1gMCggolqtJs8u9xSS43w+3xJAd1XQHaBUKgGAAO6VwffUarXIZDICIK7X69Dr9QiFQtKvobpfJpORdb63tyf9agLcLwq66FBEgLlbs9lEpVLBwcGBOJiwf9BsNpHNZmUGQgIngKX1PJ/P0ev1pKZblW9y5gNA1FiVQRVOh8OBbrcr/W/ux6d/F2tOi8Xynp+nvYgXoYwXAKwX8SJeBACIjYRGo5EDclWw2GFhqlKpEIlExB+4Xq8jHo/LgOJ0YsVC4c6dOzAYDALCOiv6/T6KxaJIK3MQACzsDon2p2UHZenJIgEgAwkiyamuQTY50dqUCOfvJ/Kajb3LDLqZELEhtirm8zmy2ayAeiqVirACOOxOpVLi106mMUOj0QjTY3d3F8lkEq1WS5Ro+N0JwDqrQfgkQRQ8wSxKpZFVwUYSv89gMMDa2hrW1taEiU2p1Vgshul0ir29vSWfaI/Hc2aSO5/PkUwmcXx8LA0HAmtqtZpI4lqtVng8HqTTabE8cDqdKwt7i8UizWAq1cTjcRk2UcUKWDzHZNlQeYDKSFS8YiLKYvTNN99ENpvFyckJstnscx3CXEVQ5aFUKglL8+TkRAAO3DNov0iAA1ndZNOTvcJ/o8IL2d20SD0dkUhEWKapVEpUCKgsRfupwWCAeDwOr9eLvb09dDodaUQpWfRvvPGG7FsEfFCKmmpdWq1WVNq4/gGIhQdVeai2RtAf8IiNzT0kl8uJjdF8Pke/30c6nYbb7cb+/r5I+ZLB1Hhoheb3++VzkLH1bqpJcM/ksJ92GAyuPZ/PJ2xajUbzXBXdLqN8YzKZYDabRXb5vM8zm83Q6XTgcrlEbvkyn195jihtdFf9fp5Vl1FM4Gv4c+9W0B6zWCwKIJnFvkajwfr6OtbW1h6z4rp9+zaAxbq5cePGmb+fVmKVSkWs+nQ6nYAGVoEnIpGIDAsqlYpI8FerVZhMJgwGA2G0uVwu7O3todFoSIO5Wq3KoNBoNGJ9fR3Xrl1DOp0WdZDLBF/H+39R8Cy4DLByPB4LIJuNDO55VKAAIM0KpaWB3W5HpVJZOeilXYXBYEAikVj63BcNnQneMhqNotZ2mVBen3fzWf5yB1U/2KA/r0HGgUm1WoXX631M9e6snyFY7zKgHuZstDmmladSen4wGGAymcBisYjNLkkIpxuR3W4XjUYDOzs7V978YmNPrVaLJQ2BAFSJmc1mIuHPxjztYwgGYg7yJOencs+96AyYz+dL4LWz9gEO/YDLqbtOp1OxlQqHw6I+w3XPPMpisYh9Du2WOHBX3hOl7Tkb08FgUCxZyc5vNpuiRpBIJFAulwV0zuBeQwVA5unKM+Aq1DGeR4xGIwHJXXQfmOtRkZSgl1wuB6fTiUQigU6nA6fTia2tLRl8rBpa0c5HqQT4PEOtViMWi4mSZq/XW1I6oRX8eTGdTqUGoFLGWcEaAHgEOFMGbbVJvIpGo2I1p1ar0ev14PF4MBgMUCgUoFKpBEBMwLpKpcL9+/elz0CwHwcZtJ9+WiY0SUJarRalUulM8AKHK1TuvQhoSNU1qlSfdR0JYqclWbfblVpie3tbcsdyuYyjoyPo9XoZpGi1WlEVZj5AdQxaTHEQSavobDYLv9+PdrstdQ5B9LVaTaylqbLC/IKgotFoJINnAl4AiCU3ACEeEaxPRQUl0C+VSsHhcGBnZ2fpetBavN1uP2aTuSr0er0o1BF42+/3YTQa4XQ6hZhGMJzf7xdA2ulnCICoB5Kg86RBmxuHwyFAHZVKde7vajabaDabMJlMUodS9WjV9zUYDELYcTqdAroiUYqkuYsGr3a7XfIAk8mEa9euYTqdCjmCCl8ARHHAbDbD7XbD6/ViNBrJwKxUKgkwi+cQgdy8LwQKl8tlbG5uCoHqLEALz2OlrehFobQ75udSqhdddLbzGSXIcjgcisU7FV70ej3UajXcbrdYoR8eHortt1qtlp+x2WziGGCz2eB0OkUhvNFoiIocbbRpd0jrZb7e6/UuWQ9TSYMqGyaTCc1mE61W61IALCp8XQZ4OplMRMn1WZVnSdKjVSnzB5JluW/RSj6XywmALxAIyOc0mUyi9KtUzgUgFlq0jWTvMp/Pi9q1UomRdow2m01UEZX9U4KyCfR8LwCwGFRBZXCdsid93vNOAAeBriqVSoirJJkSzEqV+6uwH1wVPCMJ7jnrOWNtQjCkEuz9tNHpdESR8jI5mtvtFtAaz9FgMIhsNot4PC4KmwDkmtGel8rfPONHo5EAD4FHtmFqtRp37twRYjKV32jn63a7YbfbUa1WpbdA0A1rBmUtWigUsLu7e+a9U9prr3p2RqMRisUiHjx4gEKhIC4DPOO5R9tsNpjNZiSTSQFar1KfJBFMWa9Qcdfn88nnZ73L3ujpGkOn0y2Bngkg5nfy+XxS19CGcZUiNdVYC4WC2OjSjeTdDhJtCMg9ODgQUB2V1JjT0M74/v378Pv9GAwGeN/73vfEa9ZgMCAajSIajeLmzZtIJBJLVvXz+RwWi0X6SueB0hlUzOp0OkgmkwiHw0v3n72mfD4vAJuLQPGDwQD3799HIpHA0dERCoWCqByPx2MUi0UBv5MYGgqFcHR0hH6/LyQHEpn4P6UCFhX4XS7XuW4DPNu4j5y393o8HmQyGTQaDaRSKWxvbyMcDi/lOSTukzjHOpRq/TqdDrPZDMPh8Nz7q1arRUGU/ROqNJNE02q1sLW1Jfsf8/zBYIBQKCSKizqdDvV6XWp/2iyTCLMqj6BjBYCV95OknsFgIHsx1cWV+xDJeQDkmX8RL+IrKV4AsF7Ei3gRACCMtUAggHfeeUcs505Hv9+XYeloNMLu7u6SNR8L+GKxuJQ8MZlgcTOZTOB2ux8DF52Ocrkslj1bW1tLkpfNZhP1el0YfOVyGYlEAmq1Wopjt9stjAbaErJIoEw6E/rpdCrNCIZarZbG2WVt8fi6s4aNZHbk83kBj/n9fni9XmkEUvqzWq1CrVbDZDItJVZarRZra2vi2e52uxGPx+F2u4UdyYHLVQS/12UZRmySa7VakQYmo5nh9/tRLBaFIZTNZuHz+URRLZfLPZaIAosi5M6dOzg+PhZ1CVrF0DqDVgnRaFQKiJOTE3l+VzWmCA5st9tIJBJio0Cbjna7Lc9Pr9cTFSgmnmRX8h4ofy/vK2X9j46OlppI78Uol8vCgCZblMpzKpUK5XJZZIENBgNyuZyAIGkHRLAd7Ug6nQ4ODg6kQNzd3UUwGFxZ3FosFmxubgJ4pDpQq9WWpKLZ8HC5XNje3pbCX8lKr9frAsbSarUyQKYdE+9BtVqV17VaLVHUms1mMmy02+3CjAUeSaADkOaIwWCA3W4X6W02Q7VaLbxer8jZR6NRsUnq9XoCDuGghcC9crmMra2tS9lMXWWMx2NUKhUZwjLITvf5fI8Ve88bePUkQXvARqOBu3fvCnPZ4XA81qhjIelyuVCr1QRwq1RLWxVsUPHZOivIGCbQ9yK2krJp925fTw6RhsMh3njjDRko7e/vP3a+UCXFbrfj7t272N7eFqvA834/FauonELlAwIFlKHT6bC+vo5MJgODwSAWWrQC8fl8sFgscDgc2NjYEAl1j8eD3d1d9Pt9uWfKQYzdbofdbkcqlbrQvmM0GmE+n8tZwyH1WUGmIP//eYAQKgkqnymqVpERpmwKccBrMpkwHA7FQnXV0G44HIqdDBtgfL5pOXdWNJtNTKdTyddoq3PRGcYhOXC+1fRXWxAE3Ol0LtXM5zqg4sRFACylKmS73b7QEoWqSD6fD/F4HIlEQljcZBNTUSqXy8FsNotyZSQSkc9DRmIymRSg1LVr165cCSsUCglggPaitM7jMH8+n0ujVK/Xw263Sz4MPLLXeZJQrruzmKUGg2FJMZMkjrNAAlxnSlWr84KD9Far9Zh1wekIBoN48OCBKFNtbW2dmfcT2JdMJrGzsyMKVt1uV0gWXq9Xhju5XE4UfZWfwWazCUmC5xmv83nX4UmC9lFUKyTx51mesydVSDh9r6xWK3Z3dwWURXXKeDwOu91+7vc2m81iDfXlUjelHQiD+xFt2U4PkPnc6/V6GcpSDXjV3s3BNS31SqWSnEfKdUM1CNrW8hpQQYlgD5vNJqCDdrst9XgsFhNLH4/HI0NIo9EoQ/qL1slZQXUU2oF0u12cnJygUChga2traV8lIWwymSASiVzKrlipunbRHu10OlGpVCTvZ95pNpuxv7+PdDoNq9Uq9wWA2BYlk0lR70smk5IjcEDjdDpF1YW5/fHxsagNq1QqUTm9f/8+9Ho9LBaLKCzSyp1Dt263i0AgAIvFAp/PJ4NnADLAJwmLbHfuS/z5RCKBYrEoak/KXM9qtQrI7CJAJ8H6g8EAo9EI6XRaAIjMtWKxGBKJBHK5nIArptOpDGGVQBSqdvC7cMjztDXYZfdEgldpnUSg+1k1AF83nU6h0+nkOnEoPhwOL7UmVCqVAC3W19cRj8cFpPnaa68hl8vJnsV8kQQik8kkitBKMlw0GsX29jaSySRKpZLY53BYSLVyqmacN0TrdDpCaHvSATL7Zz6fD5VKRdQdLwJO8szjfSPhhM+q1WpFu91GKBSSvY4K4GazGX6/H5FIRMDStD6kSjcBJjqdDuVyGQ6HAxaLRRSLeO+V4AQqYfT7fbhcLrHHpRUaexpPkvcogae0rzwr+DoqgTxL0GaJalaMVeei3W4XYjBrt4tiOp3i4OBAVOFIoCsUCtKD9Xg8mM/nUjc6HA70ej3UajWpwQmIAyBq99w/3svB50mj0QiwflWwn1mpVLC1tQW32y0Ei1arJUrMJDOGQiFUq1W43e6lffKqgr3wtbU1nJycwGKxiKKP8vPWajXpN3k8HlFwepYgGIg5zUVBhSrut8Aj4BTPO/YKeP1ol0myi81mE/LG6byQvysSiWA2m8FkMiEYDEqveWNjA4FAAP1+X4CBBLu3220Bdc5mM7EeI/DwrPyTezNtz9vtNpxOpyintttt5HI5ZDIZ1Ot16R2k02lRk+J5xfwzk8ksuScog2q+q+Ylq5R0zpvHKEHPfC1/PwAB/9ZqNTidznNrbIKEms2mqN69mzGfz0XRLpVKyX2lZaPH45H+ut1ux/HxsShUf+lLX0IgEECtVsOrr74Kj8fzVH1Fo9GI/f19maMoic5Op/Ox2cdZ4Xa70e12MZ/Pkc/nkUgkpOcHLM77yWQic6z19fVzz/35fC7AtMPDQ2g0GoTDYVEwAx6RQ0nwKBaLCIfDsFqtAsgjcDGdTkvexxychO9arSZ5FmcOp4P7Jt0zzgvO1UqlkhDMzlqbVqtVlMM433Q6nUJYbTab5/bw2FtaW1uTn2MQtDidThEKheQzEBCVy+VQLpfh9Xphs9nQaDQEsG80GnHv3j0BZp911pTLZdnHzwLpuVwutNttBINBJBIJpFIpcU+hiwAFNTjzvIxl/It4Ee+l+MPTCX8RL+JFXBi3bt1CNpuVRiSHilqtVhq3g8FA/Hj1ej3cbrdIWBJUxUSJ3sRK67nRaCRy/zqdbiXAhjGfz5cUBJTJAgFKvV5PhkAs0jiIILPE7XZLM5BSv2xO+f1+UWvi65XD9k6nI8yVyzC+aBVCYM7p4Q2/EwdttGKjHQMTO6PRCI/Hg2q1KrKkpxMWgk1CoRDi8Tg2NjbE6/uqLL8YHG6zGCcbZ1WRyu+oUqkEyEIJdgDCDGDB2ul0cPfuXRQKBaytrSGfz2MymYj9DG0BVCoVer0e7ty5IxL5tKSkT3u/3xcWG60sqNBiNpvFauAsZqDf7xemcqFQwGAwEDUru90uAKp+v4/d3V3cu3cPhUJBvhsZS8oggIeDrn6/L2vjvGbXux29Xk8GdizWaFek0WhE3pjqSBaLRdjY0WgUXq8XzWZTFIWoKkUbKr/fj7W1NbF2WRVOpxN7e3vI5XJwOBzStAAWa4QM9FAodGZBScAVAW9U8zAajdJ4YrOJBQXZUuFwGLlcDgcHBwiHw9KIVzZl2bShVRKl2ev1uuwFHEx5PB5Eo1Gsra0JC7hUKqHZbKLX60mDNxKJiK2I3+/HfD7H3t7eUw2YniY6nQ4ODw9l2M7CjAUl5cXX19efa/HD5hZtSy67r7XbbWQyGRQKBRnIuFwueL1ekXqmVSmVCM1mszB0+PNk0BsMBrGMVEaj0RA2+HnDNZPJBJ1OB4fDIUCI86LRaMBkMgmo4L0QbHxPp1PEYrEzG5TT6RQ2m01Ys3zuzwq1Wr0EgiZIjhYrq8BEBCAfHx+Lil40GoXL5YLNZjsTfEz1ulXh8/mkCVIsFh+zimSwWeL3+0V5iOf0WbkMLb+MRqMMFs8KgjWNRqOwL4vFoigR8ffR/pmKEnq9Hl6vF/P5/DHAz2QyQT6fl/2ag8pisQiLxYLBYCDvu2o9t1otFAoF+P1+2O12sRhWWpCdda2UMuF/mABYVLy7rOodB92XVb1zOByiusM97awzkExOnl9WqxU3btxAq9USdQBgMWyitQ4BWqdzFAIeyuUy7t69C5fLhWKxiFAoJGfnVe1ZZrMZa2trokBBa5/ZbCYqG3q9XpQZCPQgQOZpAMFUnWO+uKpR6HA4UKlUYDAYUCgU4Ha7BYx+OmhpHA6HL23LV6vVxILlojOf7NRarXYhyKPT6QjLvtfryfolkBh4ZOnKfYpEEOV14D2gSg0AUUcwGAzPRPxotVrIZrPo9XoCDAMWZ2g2m4XL5UI0Gn0qIJZerxd73IuCqqWrQF+0I6KqznkkolXxbtmZUJ2X1/g0gIj1r81mQyqVEkVKi8WycpBAkIJOp8PGxoaAOZvNptS+VNJqt9uyRzHfIgObhJlCoSAgdZVKBb1eL8+hx+PB/v7+kmUt//5p8+J2uy3WkOxhcFCfz+dlbYfDYbHQo0qI2+0+M0dQBq2KL6O6xlySexjwSHWGirgkaCn3bKPRiFQqhWazKfmIUjXIYrGg3++j3W4LAC4QCGA+n6NQKAhw5vj4WCwiqd6hUqlkvZBIQ4Uy9oMINAEWg6p8Po96vS5WJqzdCKRkTUZVVdZCtJDlvWXu1263RRHsdNA2ajwey7PDQbAyeObF43HcvXtX1Eg2NjbgcDiQy+WQy+Xk8yjrTLfbLQB15VDvqkNpB8c+znm2tVRypcUVX/Ms/R+z2Yxr165hY2MDn/rUpwSE3Ov1YLFYhJhEVbLpdIpOpyOgAALZWENTJSabzYqaFG1vqSp43rlIpRvmnk+rBm02m2GxWIRkowR1MCaTieTH+Xwem5ubMhBkzGYzIVVRRZs/y/4T+0UEiPLn1Wo1bDYbXC6XnHHA4lmPxWKoVqsAFoPKer0uYAOqBNMOmmBTl8slhC+SD/lcX/YZ4N4eDAaRyWSEiKj8eaWCDoesz6qARcCk2WxeWk+0uJ5MJqKAzH2DSjvKIDiPCjkE1H7hC1+QPdxoNCIWi8HtdotlIwmv1WpV+lccuBuNRlEwJxCRn1GpBPteDrohuFwucVlYtXam06k8a+y3aTQasYVThslkkmt0FmjmWUOlUuH973+/qLFQdZEOEQSe8FkfDofY2Ng409ngSd/7IgK1MpTPgrInSFXZVYAwKkIqCQt8PfuJp/Nng8EgloAEOJHUQYs4p9OJe/fuQa/Xi2qh3W4XFanRaIRsNgudTic1ySqlZbVaLXaOw+FQgMrML6rVKlKpFLrdriisdToded/JZLJkuWa1WoXU2mw2HwNgUam+Xq/D7/efu664P1+WXH669iP5rN/vr7RDVAYBOARqPqmV/VUH85J0Oi1gadplWiwWAaiSQG6z2ZDP5+F0OqHRaFCv18V2rlQqYXt7+1K2datC6TDztBGLxYTsRGIg7a2pYm0wGAQsdF7QbjKfz4t9HYk7DK1WK6R/uppw/kcFLNY9Vqt1CYTE70xSUjqdFtvgVbnLdDpdUpg6L7g2m80mQqGQ1Fy8x6fD7Xbj6OhIehwmk0lsNXu93pnWuOzpsFdNcjs/b6PRQL1ef0wdlGA52g+m02mxu1YS0SwWC2azGZxO58q1WSqV0O124XA4pJ+/KtRqtdRVkUhE+vHJZFJUF71erwDQNjc33/Nn8Yt4EafjD08n/EW8iBdxYTidTrz00kvo9XpSiFMJhEk5Bw3z+RzBYBAmk0nUgdiM8fl8UkSTJaxkMRsMBgG2nIdqp/TsYDB4LMmhugwHlQCE5UnrnkajIWAhq9WKfD4vFkUsfugRzoaJEsE/nU5RKBQQCATgdDovxX5kM8TlcqFUKsHtdmM0GomcZqFQQDKZFFAZk75WqyUJm0ajWRoysdGjbE4yOp2OMPw4kHse4fV60Wg0UCqVxMaI/umnE6lOp4NutysS2JSG1uv1SCaTAsYZDofCKuCQXsms0mq1UmAwoeQAWaPRyHUej8fSDCJgoVqtolAoQKPRoFarwev1wmq1CnDvrIamVqvFzs4O5vM5dDodKpUKTk5OlpTXTCYTotGoNMoJWlzFgCfYDIAAF5WqDe9lABYbv6eblLTZa7VaMtit1WoIBAKYzWaiwEAGO5tiHNbFYjFoNBph212UPFssFuzs7EiRxkY7rSUuajSSad/tdmWdVyoVFIvFJUtKsiEJElOpVFJ4cF2SJQk8ku4ngIuNT7fbLZLBgUBArtd0OhUgIG0cWQzq9Xph1NM6jE3VbDYLjUaDZDKJa9euPdtNvUR0u10cHByIPZXJZILf75emaLPZlOE8r9eTDiAviuFwiHK5LA0d4BG77aIBf61WE+sMnhN6vR6ZTEZYeh6PB/l8HlarVRhyVJ3h4Ig/z72fjXkOosnooxXneQ1ppbreycnJmUUy8IjlHAwGn+ue/qTB4SpBr2cF1yMHZ+Px+NzmFa0jlCoBkUhEFAczmQwcDocAYQkGr9VqmE6neP/73w+fz4ednZ1nKsS5n4TDYSSTSWQymcdU3mhl0u/3RQEsk8mgVquJPcjp/Wg6naJYLMJgMIidKJWjyGBkfjSbzVAul8U2ZTAYiER6r9cTqxNljtJsNnHv3j2Ew2G0223JHxjj8Vhsimu1moDg1Wo1ut2uXHeXy4W7d++KQh8VFWin4vP5REUsk8nA7XZL/nLWM1qpVMQW6Q+bTDgHoxwKX9QwpirMZUFDKpUKgUBA2InJZBLBYHAJjMDhSrlcFoCUEmBH1TdlvP3226JUsSo/oQUXbXNKpZKwYbl/mkwmbGxsXBlgmAoIVLfd399fah7TGtfhcCCfzyOVSsn3etJBIesIDo7NZjOGw6HUQgSGABDVCTJRT6/9wWCATCYjtc5lAVgkiFwWyKTVajEYDESdEFg9hGcOZrFYBDjO13LvVFoIU/qfe5TSGpc/R5BAtVqFw+GAyWR6aqWYarWKRCIhaqcEIlABzWKxCDHgPBuVs8Ltdgvbudfrnft8koXNuvB0kJRCpYCLQJYcFL+bQxzgkeKJwWBAJpORfJxBwAFVFTjIPb0ncegRDAYFWEEbbiro2Ww22dcGg4GcGbR2Yp04GAyk1lCSXLhv+f1+vPzyywgEAtjZ2bkSVdB2u42DgwNReaUSy87ODtLpNMrlMmazGUajEcrlMmKxmCgqU+HnMvkGgbiXzU2o7Nfr9XByciIqcOzDEMgfCARgtVoxn89x9+5dUWqw2WyIRqNiU0TrqHw+D5/Ph/l8jvX1dbFzp7IThyO02ByNRmJnSMA3FQ9oI1kqlZDNZmEymTCfzyXHfvvttwFAej7379+Hw+EQmxrmF+PxGNFoVOxTWKsDj2yIuKfRIo8gHKpo0WqIBA2LxXKuBY7dbsf169eRz+cRCARgNptFfTiTyUg9w+/U6XSQy+WWVP7i8fil7uWThtI2ndedFp2nB+XT6RT9fl9y1GazKcNupSIs+w/nBZ831nnAoufz5/7cn0MsFsNv/dZvifIbCQ5Op1NU6Fnbsubn2mdwkGY0GhEMBnHz5k2EQiEkEgkkEglRP1uldp7JZEQ95llzSIJm2+02UqkUIpGI9COpGtPtdlEqlQRcTctjEjXtdjtms5mooDM6nY70tUgEpVpUvV5fUu1mP4H17f7+PtRqNbxer+w5er0e5XJZbCb7/b7kJmazWdS2AYj1MEGW169ff6Lca2NjQwCdBB7xc47HY7RaLahUKiG5KIe3zxqn7zlVTw0GgygiM5Q2z9wzOp2OgKtarZbU/g8ePEAymRSAKvsWBHCtr69LbUaLKoI2qWRPYEur1YLD4RAAiNfrfdfP8ctEOBxGv99Hv99HMpmU3glzQ1rj8jpznREgq7SPpc0tsAAIXpXS6aoIBAK4desWNBoNjo6O5J6TkKDMGfb39+FwOC5lt3lRGI1G6HQ66HQ6tNvtC1WPCJbgngtAzrmzlLR5TlE1kCA5EtQ7nY4oUgKQfjX3lPl8vgR+IIi13+/j3r17ODw8hNFoxObmJgwGA2azmYCfzGYzIpGI2BQWCgVsb28vfT6eQbPZTJSR8/m8AOQ58+Hn6ff7QrCmulA+n18CpxM8uUrJkrMFKvCcRyAhmd9isVy6llIGAZxqtfpStQPzewBCcnu3olwuC3nY4/Hgd37nd1AqlQQET5AYe0JUUWZ/kv3zN998E9evX5e1825+p1AoJMqU9Xpd+u4kvLpcrkvtMXx2gMUck3nqqiD4mdeDNT0BYFqt9jHwFUOtViMcDuP4+Fie2VXPIdf4ZVQoSW4sFoswGo0C7iLJ3ePxLO0her1+ifjt8/mwtbWFYrEIrVYrqtw2m036pb1eD6VSCY1GA9euXXuMDMZ1OR6PH6t1ee5zz2L9TSGBtbU1vPTSS9LrZb7JPYz97Ol0ing8Ln2U8+wSSbCdTqdy3m5vb4uDCXPQZ1U8fB5BYCv7KLTxfREvQhkvAFgv4kW8iKXY2dnBZDKBw+HA0dERNBqNNMtYcBGAEw6HEYlEpCiyWq2o1WrCtKdCjnJwQbn7ZDIpjYSnibNYgUS5k/VJZRmizCn3aTAY0O/3ZRjhcDjEfgVYJCTHx8eYz+cIh8PY3d299GejIlg2m8Xv/d7vweFwoN/vI5/Po1qtolQqiSUiUe79fh+JRAJ+v18GGPSartVq0pRRDp1YiDmdTrmuzyvIRqBfNZkLpVJpydd9MBigXC7DZDKJ3LDD4YBer0c6nUa73ZZEdzQaoV6vo9lsolQqodfric+22+1GsVgUf3HK1afTaczncwGksXmiDDIGCX4iS0hpoXieFYnJZML+/r6or5BpwOfGaDTC4XAgFAphMpngzTfflGYFB+20TZhMJjLYpy0dm+kX2Sq820Em1yqpdTapCcRIJpPI5XLY3NwU6yCu/fl8DqvVKuuLACSbzXYpJQKGwWB4KsAawX8Wi0WUpaxWK9bW1kSSl0pLZJYBi0HV9vY2qtWqAE8ajQYAiO1Cr9dDsViE2+3G9va2SKErr1MsFkM2mxXGUavVQjKZFJAgi1Cq2Skb7WQaVyoVOJ3OM1ngVxnJZBK1Wg3lchmRSOSxAT3VM9LptKih3bp168qaccViUdSnOIgjINLpdApLZ3Nz87FBHG1N8vm8ABzj8Tjy+bwMeEqlEo6Pj+FyueQMazabUKlUGA6HWF9fF6n9QqEgIAZ+Dg7BaBHj8Xgu1fzz+Xwol8sIBALI5XIYDodinQo8YlaXSiXY7XY4nc5zB0lf7qBCFQvxs4INTLvdjlwuJz97VnAPUILYTCYTtra2ACyaYNVqFScnJ7KHq9VqOBwORCIROJ1ObG1tXQkLan19Xfa7QqGA4+NjWetkuJtMJqytrYkyxuuvv47RaCQKUzyPgcVQhs/erVu3pFF2eHgowwSCzDudjoCsdnd34ff78eDBA2QyGWg0GsTj8ccaFvP5XCwCOp2ODCF4tsznc6TTaXmubDabKAHy+7DhQlD8fD4XQDibHdFoFEajEVtbW8IO7Ha76PV6S419rkflQIsWP+/FZsnzDKXqXbPZvBCk2mg0JL+4rIKU3+8XIgKBR3q9XgbmnU5H5Ph5Rp0HXiA4vtForBx2zudzZLNZaT7SxpLPX6fTQbFYhN/vF4vyqzivCKKlCt9ZZw0B/+l0WhTqnqbBHAwGpSH82c9+VoAOXBMcTCaTSYTDYQwGA1FDpC1Uq9USBZhAIIC1tbVLK8A9ieU3Aam02Tk4OAAAsWRRDtxYw53ejwkM0mg0S9Z0bNzTuodseg7jO52OAC/m8zncbvdTD8nJcqXigdfrhcPhkGvGPSWZTCIajeLw8BDXrl17oryDqoo8m9bW1lYqaZEsw7181d5F5QGtVnuhEiCB4KFQ6F23MqE6zXQ6RTqdxtHRkShHEgRMxQna3XFwxRyp3W5LY5y/r9FoiFWh3W4XtQW9Xi9AjcFgIOfHaDSCTqdDPp8XFVta6Gm1JmQyH8Lx8R6s1iH+1J/KYDweXxn4ajqd4ujoCOVyWRSPHQ6H5BC09Xnw4IGALqxWKzY2NmT4eNlgPkRFq4uCBKNUKoVWq4V6vY5utyv/zoEtrzdrllqtBo/HI4QzBgExBGhptVoBqG5vb4sKWqPRkCEQgbqnCTJmsxlOpxPZbBa1Wk3OOA7JG42GKFgTcEiCSr1el8EJ62DmuQBkSMx1RBXw/f19vPPOOwgEAmi32ygUCrIvajQa2Gw22O12vPPOO/L7LuqF2Gw2sWQ6PDzEbDZDOp2WvVr5na1Wq9iZMxfjernqIDhSo9HIgMpisSyRRQCIYiyJkQaDAZVKRZ5P9kCazaZYWp5nhcPBLvNThkqlQjAYxIc+9CG8/vrrcLlcohjUarWkn8XzigoF8Xj8sUG62+1GPp+H3+9Hq9VCIBBAo9FAPB4XRRX2BJm7NBoN6HQ6xONxeL3epyb60LKMZBCTyYROp4Pj42NR6CMZsN1ui9IPiUA8L6mQR1Clch9gv4cWktw/la+hlSfvIfcE1gsE1fO+Ml/nQJaqNrR+ZDDHYW38pBaBer0e+/v7ODw8FHId7cs0Go2AbM1mM7a2tq5kaM+zgT0V5d/bbDY4nU5RJGbvzGw2y5qlHSzPm+FwiMPDQ1FmrdfrABZrikQe9lKp8qHRaBAKhcS+tdFoyDNGFTEOnKl6ShX7yWQi/df3qgIH1UKBBRmmVCqhWCxCr9dLT5kqgKt6S0o7bgaHy2fZ3F9FqFQqXLt2TcAQiURClHpJViKIwG63n2u7/SRhNptl7dfr9XMBIFRa457FWcZpJW0CnpTfjXn54eGhAKEJ+Gu320v1Yrfblb3VarVKHysYDArIwmg0IpPJyJoxGAzI5XJLNQSBUzyDm80m7Hb7Y2eD3W5HOp2GXq9Ht9uV/bFSqQgZs9/vi3Ip15Pf70ev10O5XIZer18i3XFPXHUtSazzer3I5/MCWjgdSgU+kgKeNE6r2F0USuD8u7nGqQLE/YkkwFqthkgkApfLJWdTp9OR59hkMsmZMJvN4PP5YLFYhNSbyWSuFEz7NKHVahEMBp+p18neA+tWAtLZ01cGVZCVCsvsq9HC77x7TbJivV6H1+uVnqAyqCpZKBSW9lU6ZXAeyvw+l8tJfUv7Q7PZLGrCVJ1kKJX32Fd++eWX8cYbb8jeXi6XhdzK54LnpMFgwO3bt+H1eh/LE1atDapS0RFDWf9Fo1G89NJLQpzpdDqo1+vIZDIAIPaUnCEOBgNks9mVrj6nYzab4aWXXkIwGBSnFJIL3mvB+okiEZxPMw+kkMeLeBHACwDWi3gRL+JUmEwmkSAOhUKCTAceyc/TIz4SiQggwuv1StFBuwoO7043qsgoIKONAz9KpLLYoBqVwWBAr9dbOrxYGFIli7Ltk8lEDjuVSiXgG4PBIMMRqtTQcocDVzYQyQrq9/vY29tDMBjEzs7Opa8hbdrm8zlqtRoePHggjOtyuSyAMLfbDa/XK1ZA9J4nmI2KFWz+KBMjemcTse7z+Z5rgaBWq7G2tibgoYODAynAOKQmI5ONJY1Gg0AgIBKv/D5skrJQs1qtwvBRqVQYjUbY2NgQWVXaPlAmfTabwePxCLuIcs7KRJsgQSp+UHJZr9cLaOS8MBgM2NzcFHWn0WgkampkHAGLZ3htbU3k300m09JrzWazgBjZ9CMz9Xmxx64qaB1B1uB5li/9fl9UUnZ2dmA0GqXhziBogozC4XB4KYbIs4bb7UalUhF7uWg0KoBMMqfZAO31erh9+zZ8Ph/C4TDi8TheeeUVsbxsNptoNpuo1WoAFs/J7u6ugEiUexSbyMViEbFYTOwNzWYz3nnnHXS7XUQiEayvr4t6zqphtdPplCY6rVWfV7Tbbdmn2NxaFWazGeFwGNlsVuw5noaRdjqKxSJSqRRyuZxY1nAINBgMUCgURCFoOp1id3d3aR0ReNVoNLC2tiaF8dbWllhMsvlE6e7hcCiKIBySU4qaiivNZhPpdBrHx8ew2Wxi+RoMBhGLxS60FAQWjeWtrS1p3JdKJVSrVbFkoUWLx+OB1+vF2trac2twPk0QfE0gwnn7F/c72gSfdTbRPoaDbqfTKfkAFV0IYiVDlDkBlSiUAOBnDY1Gg93dXaRSKRiNRlGPoDUiVT/dbjfi8bgoe6hUKlE2415AGXK73Y7d3V3E43Fsbm4in8+LjS7zDQACWiO4nAMCWh/1+30BxQ4GA2mm05poMBiIxDgt6chUK5fLoiLGoL2yw+FAPB5HLpcTpr9S3YOgjfl8LuAOvV4PnU6HYDCIxkNbX2Vjfzwei32cw+HAzs7Oe/68u+p4EtU7NtX9fr801S8ba2trsFgs0Ol0AjonAC8SicBqtcqQ5TIqXARarnpttVpFo9FALpeDVqvFxsaGyP5TFaNer8uQ/OjoCDdu3Hjme0+VSKqnELShtK+higdzTK6hpwmqx3BgenBwIPZsBD1yOK3VanH9+nUZJlLNz2KxyNkej8efCHjDAW61Wj1XPWcwGCCdTgvIk8oeSiszg8GAWCwmgxqTySQEGOZ0HOKT+EHA2Xw+l/210WgglUpBpVLJgGgymaBarcLlcuHGjRvweDxPDcCibVmz2cT6+vpja8BoNCISiaBcLiOTyYj1wZPkHQQjTiYTpNNpnJycwOVyweFwQKfTiRUEc4NAICDs39NhMplkcESbo1Xn9Xg8RiaTkXX9XlAC5NmlVqvl+7IuZ63n9XqRSCREEYYALKpjhEIhWCwWxGIxqNVqGY65XC4BkDAXo9IiiSu0NCuVSmLnyMFks9nEG29EkM1+8OEZrMYnPhHAH/2jTRmEP2tQ3ZR29Kv2ZZPJhFu3biGVSgnQx+fzPfGewuHDycnJhTY2nU4HzWZT+hVUpVpfX5dat9lsIp/PC7mFKjDOh/aymUxG6lFl0Aaw0WigVqthb28PRqMRGxsbUi+/9dZbMiw9a7/SaDSIRCKittDtdkVhmOcMrfEIvB6PxwIayufzABa1s9/vl39n/0YZzNVbrRZGoxECgcASeYlnAi3EqA5x0XmjBNFWKhWxpD9rIKTVagX02W63RWnsqsNqtYqCVyaTkcEryTq08CSQyOfzieqpy+USC2AAouDI83IVgB+AAFlIHjq9vggKW1tbQ6VSQSQSkbUzHA4xHo+FiNlut7G9vS32e8qgRc1oNMJwOJS88PDwEFqtFs1mU4DdJGsGAgEBHHEA9yTR6XSQyWTQ7XYF3MkeEz9HvV6X/pzT6RTlToIvSMSj5VaxWBQgNtcKgwo2BEKSbMbvTysu9h7OOtt5Fut0OlGCsFgsS+oYyuCQlSBV5nxnxWw2Q61WQ7vdlrVqsViwu7sr9T+BNrRNvEpraWABYCZgM5/PS6+MIA+CO2i9SNIqwVS0BwwEAuh0Ovj85z+PwWAgZLvpdCq1NACpmRqNhjyrNpsNwWBQht7pdFpyae4R7Gvev38fx8fHsNvtKJVKMJvNoiRJAPF7scYJBALo9XpiNdvpdMRdIRwOi/I768d6vb5S8RN4VK9Tne+s111FaLVa7O3tSc+Q6nIk47EWoLLvVQVV8hqNBrLZrFj+KWM6nQq5zOVyLeV07GNEIpElJW3lfkiXB842SLrm3kGQKwAhQPAakBhAAqHyenEu43a7pb/KfgkVkbiXNBoNsSxT5q38rn6/HwDEDYXnLwBRxy6Xy1hfX5faiEAw7quBQADj8Rj9fl/I5adDrVZjY2MD0+lUCGMmk0lUtUh8nUwmCIfDcDqdWFtbe6p7yxoIwIWgZL6GteBVgz4ImleqPq06NwHIjGo4HMLtdqNQKKDT6cg8C4CAiUajETweD+r1uvTNePYBi+eVAB32PZ/Gzv29FLPZTFQ4qTip0WjQbDZX7lFUn2+32wIQoorzZXquFosFpVJJenOnn2u3241MJiMuN3RiITlyMpmg0WjIfKBUKglJn2cXgb9ut1tUa1k3MV9lnUDXFoPBgKOjIwHFT6dT1Go1zOdzUbMlEL7ZbAqhgTWu0WiUmmNVEGhKAPLm5ib29vZkryARzG63L80rzWazWFKfnJwICOs8EC97SCTaPk+BiWcN2pfX63WZYzMoKtFutxEOh69EqfFFfOXHCwDWi3gRL+KxsFgsuH79+pKcPRsORH/7fL6lg5MJczAYlMH1aTseKjaVy2WEw2GYTCbMZjPcuXNHpLPZIDEajSiXy2g0GjCZTKhUKvD7/cLGUco6drtdsfFjQ87tdst/Dw4OMBwOxdorEAhIA5MocRY20+l0aXi4traGb/zGb3wicFO9XherDw4kyQjl9WBzms0USo82Gg3s7u4K2IhSpy6XS5qDZGrN53PE43E4nU5JgJ5nsDnFBvy9u3dhvXsXlkwGs34fE70ezfV1DLe2EIlEZIhHmzYOGC0WCyqVirDCCbAgu63b7QrDJx6P4+TkROSQaQNAa5RarQa1Wr2yMc/ni0ofVON4kqbBWTY8DK/Xi3A4LMmi1+tdshKiqotGoxF1rPF4LIP85xbzOUb5PKbDIdRWK/ShEPCEAD2v1yuMaCbpq9YBgYZUc3M6ndIM4PVXqVQCsAMg9+0qG1az2eIrnv6ILGrdbrdY6hmNxseYfdPpFKVSSdgWL730khTDZJyXSiVRyOLP1Ot1GWQdHR3JdWCzg+szGAwiGo2K1/poNJIh7Xly2LRZpRLb84xqtSqN14sUa2w2mzDnacX3LNHv95HJZIQNtLGx8Vgzwu/3i9KLRqNBoVCQgqb15pvofvjD0CeT2DIYYNzYwORDH4Lq4UCGgzACY5LJpNgYAIuhJJ8JvV6P9fV1sUNicVoqlWRwoxwqXjasViv29vaQTCaFfUgADln1RqPx0qCuJ41Zv4/2b/wGJp/8JFS1GqBWY+73Q/dt3wbbH//jUJ2j7EBWHZlbVqsTv/d7DpTLOty61cXOTl9ey+G83+8XMDOVARlUjZrNZvB6vbLflMtledaV9p9qtVoAJbQbex6gY41Gg42NDRn2s2HNgeJp+wnafQSDQeRyOWHMc6hBW9GbN29iNpshm80KENjv98uwhJZ1AOR8p0Inm7NUEaE9FwABR1itVpRKJcznc9y+fRsOh0OYb2zgMmgtSMswgg1yuRzC4bCowJVKJdnzGo2GrBU2vwmO297eliYfLZGpHBONRr/iG3xPG2RD+/1+5HI5se/kPkOwYbFYhMViWcrzniRo00WAFM9csp6vAsjJc57nXSAQWGqysT7weDyyN9tsNlFCfZZgXUCFHSo0KsHTtBa32WywWq1yPq0KDgUI/LdYLEt7CQEnwGIvi8fjYscCQCzC2+22qHxxD2NoNBphmD6pVR4b6AR+rzoLhsMhUqkUstmsNJKpuMFcutFowOPxYDabYW1tDS6XC+VyWXJU5e91u93o9/vodrsoFosC9uRAilaDmUxGQJbA4lzQ6/XodDorbaQuEwS6UsXnPAAiQRnnWUCcFwRx0HZCCT4CIIN/p9OJaDS6BERh41hpC1upVKDX63F0dASPxwPnQ0trWoE0Gg0ZdobD4ffEXqhSqbC+vi5sepfLJQMwjUYDvV4Pr9eLa9eu4e7duzg+PpZ6mtYzzoc2nSqVSpSBqExK8ku5XBbQEmu2tbU1Gfz1+32Ew+EllYHpdIrj42+A0WiCxWJ+eJ/muHu3Cre7fCV5EXsLVFc57zp5vV6k02kByT7pXubz+VAoFMRaLh6Pr6x7JpMJ8vk8BoMBxuMxHA7HYzmwVqsVBaxUKoVUKiX5I2sLt9uNarWK2WwGm8229F5GoxEPHjyQYaiS7U9VqnOBq9MptPU6dIMBLN0uysOhKE5Fo1H0+30UCgUZ6o5GIyFtEVhGEgrXiN/vl57R6b2D1q6bm5tIp9MAIINeku44mPf7/ecOjx7/KoshF7Ag3igtk1YFB3VUlLiM7ejTxMbGhqiD37lzR/Z3KiRw2EjVbY1GA5/Ph2AwiI2NDclNNRoNYrGYDPxOTk6EVKXVajEej9F4aCPsdDrh9XoRj8cfuwckXa6vr8sgnoQhXn/mrltbW/B4PMv9qPkc82YTaLWgqdcxV9g3EmTBveI06I/A7qdZ8/V6XSw8aROrtLMlaLRWq8FoNMo5TsAH7y3XmkajkXr+nXfeERAawQgEDxMARNX1TqcDnU4Hj8cj1jvValUG30rVLACiQKhU0XK73Tg+PobzoQp2Pp+XvZiKlP1+H1tbWwiFQtjY2Fh5TUjcLJVKAtwkYNxqtSKbzcLr9WJjY+O5g4loWdZsNpHJZBAIBKDX62WdkUSrUqnw1ltvia2W2WwWBwbWTqVSSRRPtre3YTabBbDFIFi6Vquh0+mg1WoJyM3tdmM8HkOj0chgmOQsWnwDizVFIhfB6A6HQ86znZ2dc/cE9jyZd57en686+v2+5Cq0YWTOTkCsVqvF7u4uDg4OEAwGpSYnkJoxHA5F3TAajcLv9z/3Z4SWX6FQCM1mE4PBQAhsSgLBVQaBgbPZDKlUCkdHR3A4HEJ8ZP9dq9UiHo/D7XY/tm+tr68LsKZQKODk5ETAEjwTI5EIjEYjisUiarWagLwHg4EAPEheU6vVQvYIBoNwuVzY2NhYer6pKLq7uyuWwgRgmUwmRKNRpNNpUYIj4eC0gwQBvbQxTqfTyOVy8uyzZmVvggp57OcajUZR8wYgIEWCMFYF1TjZTyOQgbkIe3cWiwXb29tPXE8xqMLDuvS8uphgdypMXYXyKrC4vul0WhSYCLCjCrHVakU8Hl96tpVnMh0bBoMBbDabAIBoNco9lGE2m8VCluBFi8UiQBgKJnwlh1IhUqngWiqVRCGRMZvNpJeVSCSwtbWF4cM89vRaOCtOzzVXfR6v14vhcIi7d+8ikUjAZDKJEMB0OkWhUEC1WhWFLM4EaWnK3IBOE4VCAevr63KuKQGwJpMJ165dExW8fr8vatEWiwXdbhdWq1VqPvYuTk5O5M+c7RIwet7eSpIbc0MGAVXxeFzAskp7VmBhi0sCPj+vy+US1fZutyvg/ng8Drvd/tzU81k7AIueztPM5GjDnslkMBgM4Ha7hdDFPYQEel6PL8e89kW8t+MFAOtFvIgXsTJ0Op0UPlRsYiJ/VoG5vr4uBdIXvtDE22/P4XBo8KEPVWEwDNFqtTCdTsUOxeFw4Pj4WMA5RHXP53NUKhUZKLAYymazIrlvsVhEkpcS1rStIJubycvhYRtf/OI1OJ0m/Ik/MYXDsUheksmkJAa03VCySEKhEK5du/bENonZbBaVSgVGo1FUxHq9nkhuU+GCiltsqLlcLvR6PRiNRty6dUts+IxGowyrycoia9v50H7py8W8crvdMBmNyP7UTyH2G78BdbkMVbuN+WSCuUaDWamESTaL7Ac/iOrNm2KtRP95ovs7nQ663a4AqsjAZZHHYk2r1cLtdqNer0Ov1y816s1mszA2G42GMAIYfO1sNpMG+JNKs18UXq8XgUAAgUAABwcHAshSBpkVlKo1mUxwOBxXohh0Oib1Opq/9EvARz8KzUMm6WymQn4aRP7lb8er3/8dcK9fDoBG0NJoNEIikUAqlXqMxUQWOxnClF5lqNXqx5hDtC9gA+1ZI58H/sk/AQ4Pge1t4G/+TeB0Lz0WiwlbN5FIwOl0YTLxw27XwmweijqbWq1GPB6H3+9fGnyQna9ka5MVNZ1OkUwm0Ww2ZYBJOwcyBTkgBx4x5lh4XaaZwOL5stLZTxuDwQC9Xk8aKxcFLTova69yXpTLZXS7XXQ6HWxsbKy8LmzIJZNJkUR35HIY/Kt/hdnbb8NTrcLZ7S722S99CbOPfhStl15C/7u+C6qH657FutfrxZtvvgm9Xg/nCjtcDqRMJpMUxsr9S6vVot1u4969e9jZ2bn0OUGL016vJ6xENkbcbveVsoyV0fzFX8TsZ38WqlYL2moVqoeF59xsxuz111H/qZ+C7m/+Tdi+6ZtW/jyBjE6nE7VaDb/yK7u4fXvBSvrMZxz4H/6HPG7cWDTdxuMxrFYrHA4HDHo9qm++iU6vB7PVinkggL7ZLOzC9fV1YQhRTanZbAroez6fi5KlRqORIffzjnpdj3/5LyPIZICXXwa+53uAVSQsjUaDrYeg43K5jHq9LkMpAmq4Zx4fHwvb8f3vf/+SrRqHksx1OJS02+2iOlCr1TAYDITBR4sSp9Mp8uBU9nzttdeWGrpsNgwGA9TrdbE4YQOFwwCezWy4U42OYGpgwXQ7OTmBzWZDOByWz8HBFu2D3gtgg3czdDodtre3BdhANj+BjMyXeSbEYrGnXv8ajQZ+v/+JmzvD4VCUbrvdruTkBoMB6+vrkltSuaHT6cDn88mzQktW5b12Op0CBiMI4yqCzTiCPTqdjoCJaBPGBuaqOqXdbosKglJpTqfTwev1CsmjWCyiUqnAbDbj+vXrouDKBq1er5chPIeHgUBAgCzMe54WIKq05SgUCtDpdI+dL7lcTux0Nzc3ZQ+hdQqVRgkuymaziMViaDabCIfD6PV6AkZm87Xb7WI2m+HBgwcyfMhkMjCZTKIEu76+Dp/PB4/HIyQH7mlHR0fY399/4gEJlQBJlLkonE6n2MtfpCq0KsLhMCwWC4rFIlwul4B9CT6y2+2ivgIsmuwnJyd46623UC6XBaiuVDChfSP/Hlic9aFQSAbG77VBBwdbPBfYICYgMRgMwqDXI9hooPn5z0MzHsPo8UD/gQ9AZbGgWq2i2WyKRa7H45Fmuc/nk3Ufj8dxdHSEo6MjIVpxWKR8VkiUAYzQajVQqWi5pkMqNV0iPzxtTKdTqUsvs9cyF1YOFZ8k9Ho9XJ0O5rdvo5HNoq7TwXDjBiwf+ABUD0GNXKfD4VAsvsPh8JlARNYUDx48gFarRb/fx9raGmw2m6zHWq0m4D8SQpinU902GAxiMgF+9VeBz3/ejG53CxbLgYBCuIdq6nW4X3sNvi+9g2FBh+lUjTVtA7csPWRu3YTuYV3YbDYxHA6xvb2NXC4naim0eVEOKIDFPler1UQh4/QeR1APAAHeUlGN/07bwHK5LJZhFwV7WszneZ8vCvabqMb6PABYVPq7d++e2GEZjUZRSAcegSfYS5hOpzIoVgZBPyqVCu12G/V6HclkEgCESEdLp42NjTOBTuvr6/Jc1et1IQ+SWGUwGKSej0QiCxvBfh+az3wG1t/5HZgekhudwyE0Ph9G3/mdGP+lvwTdQ5CNx+OBx+ORGp219dPmj71eDycnJygUCmIVpARVDYdDsZ0DFuDF69evr7yfVEbL5XIymNzZ2RF7cIJbSMJoNpvy58lkImqR7AXQ+om9PYKXLRYL+v0+6vU65vO5KHVzzbInZrPZ4HK50O/3ZXio0Wiwvb2NYDCIV155ZeV5yDOsXC6jWCwKaFsJ4qLCVK/XuzK711WRzWYFzEMQQC6XE+AaleIJLDAajTIg3dzchNFolOtgMplw7949FItFAbeQHKXsRfFesn5lPUSbT+YwBCwQeMIajIDV/f19USWhKnkikZAzbnd397G8j2B6At4IwCIwj+Czq4zBYID79++jVquhUCgICY3ASSpfmkwmHBwciNKaWq1GsVgUtXUqLvf7fZjNZqyvr8Pj8SAUCl3p5z0vThOInvd7bW5uyt7WarUEqA88Aujb7Xa43W4hJSuDStrpdBoGg0HUaWezmZBDNjc3cXx8jGq1KsQzpb06AUeTyUScOHZ3d+FwOC7snSqVntif48zB6/VKn8Lr9crfsy/QbDYRDAbF5pT5EoHhAETFlKrco9FI8jiSU6iQ1Wg0pK49j1hht9tx48YNVCoVAW8zlAo6zzLroDBAr9dDIpFAoVB4zHIYWPSv0um0AGCvCjBBUHC1WhUSHYEn7Bn5fD70ej1cv35dchLuDUajEa1WS5TKjUYjGo2GqEuyX8lrxz2zWCzC6XTKXsZ6XXmGfCUHbahpcRwKhaSmq1arArYdjUbI5XJIJpPQ6/Vih02RByobbm1tnVu793o96SOflaNEIhGxk67X62I36na75RxotVpiT05XBOUs0mQyiZK41WpFJpNBp9PB+vo63G730vms1+vFtaVarcq5TuBWPB5fej0JV5lMRgDddGfJZDKIxWIr8yHaNMfjcbhcrpWvofjAqjCZTJLTU5mPfVEG9zin04nt7e0rJ9pSCIQqwlSDphrzZVR0gUUtmc1mJffY2NhYeh7YV3I4HEgmkygUCtBqtdLLfhF/eOMFAOtFvIgXcW6oVKpLW17pdDrs7e3h059O4T/9p5sYDhdNvDt37Pie73lTmn4mk0mUsjjcY0HDQ48SnZVKRRpIg8FALCM4iGk2mygUCjJcoMIVAJycnODtt0v4+Z//Ouh0DlQqZkynOnzf96Wh0UAYIblcDtFoFA6HQ+xcyLo77+Cfz4GPfxz4yEcAgwH47/97IBZrSSFDWfVeryfDoX6/L0kwpfTJ+h2PJ0ilXsIXvvBB/O7v6vHd3z2RZqXT6RSWDe8JC5Ivpzf5fDJB74d/GN7PfAaqfB7aeh3zyRxTlQZaTDBvtzEZDuFpt5Ftt/H6Sy+h9dDWjEUMB2aDwQB2ux3tdhsWiwV6vR4ej0fsGHu9Hmw2GxwOB0qlkgyLNBoNer0e9Ho9/H6/NO3y+bwk4jqdDr1eD71eD8ViEaFQCLFYDG63+4kBdco4OQHqdeDGDUCvXzRCb9y4gXw+j263i6OjI9TrfRwefiPqdTt8vhJ2d78IrXYmQ/D3ve994hF+ldF9/XWMfvAHoa5UoKlUoGm1oJrN0OkboVNXYb9Xxpu/9uu4/u/+NgJ//IOX+p1ra2tSzBWLRSQSCWHYEIhktVqlcKaK1HlsYhYllLV/1vjZn12Ar4DFf3/6p4Ef/uHl19DGY8GotOJnfzaIRMIKtXqGb/iGY3zN19Rgs7ng8znh9/vO/fynh81qtRrb29syyFYyHGn7dTqZJxjzMsOC2WyGwWAgygrPM5Te8pcJWpOdBwxrNIB/82+ARAK4fh34S38JOE00YaFcr9eF3XNWsGGfzWYRODlB7+d/HvpaDapiEcbhECOVClqNBppyGSOjEZp2G+ZEAtW/8TegUgx3tVo9Pv/5LeTzUYTDOgQCc7jdC1WhwWAgTMFmsylDVLLyKCmfTqdlDz+rgX9WUM75eUS5DNy9CwQCwP4+UP+Zn4Hq534OmkwG2kYDc60WU7MZmM2gKxaBQgFjrxfTH/ohtL7/+2H/k38SjQZQqwFra4BGs7jutIbIZEb4gz/QwmicyZD0N3/Tgxs3FrLZuVwOIY8H3rfegucP/gCmQkEaz3MAzc1NDP/4H4fh678eXp8Po9EIlUpFmObBYFAAQbTNyWaz6PcXKlt7e3vP1YoTAP7FvwDu3Fn8/09/GnC5gL/yV85+PZXLzgIQUHGgWq0Kw3tVEGRYLBZlUBiNRjEej4UZb7FYsLOzA5fLtbRWCfYiwOTg4EAACnq9XlSqzGazNH40Gg3m87k036fTKRqNhjQhJ5PJY0octDzMZrNIp9OIx+MIBoPPRbXtKz2UqndOp1NsIQkastvtMBgMT6zO+axBuwcqzvGecyD+4MEDjEYjRCIRsU+m3abZbMZ8Phe7Oj6TyqACGxmfz9JwIlCD7M/t7W20Wi0BhQGLM9XhcMButyOVSsFgMCydlxz60e6cewmH+J1OB5VKRQbBHETQTvCs/YZ2y+Vy+Vy11CcJlUqFtbU1ybGSyaTkwjqdDs1mE4lEAolEAqFQSJj4tCwvFAqinGez2VAoFDAcLoDmsVhMVH4bjQaazaY0fNVqtTSryVSdz+dIpVJwu914+eWXBZjPBjCVpPL5PPR6PQqFwhNbhFBd7LyGtjKMRqMAy6g0/KRBNRiCcfiMclCujIODA7z22msywKZiCBVOaCfD2m5/fx8qlUrY+7QWfa8GLX2XYj5H+9d+Dd5f/EXYHzzAvNvFbDTCCMD0Yx9DLRLB7Ju/Gf6v+zrY7XZ4PJ4zlVfIRmfNTfsb7gk829rtNux2BzQaLfT6ZTLNeDy/EstyDuUJfL5M8Iw8PdC/KDqf+QyG//7fw33nDsz1OkLDISazGYaf/CQ6bjcKX/M1qL///VA/BGvzTDYajReCwwg2pV0zgeprPh8KzSbsTidaKhUGw6EM+pgv0NJNpVLh//6/gV/+ZWAw0KHZ3IHf38KHPvQ2isUi/H4/nPfuYf8jH4Gu14MqN4N73IAGE8ygRr+uwXr79zHJZvHgz/95dB4CRS0Wi4AhqDY8mUxQqVRgsVgQCoVEQS6dTsPhcIiq0umgxQotc3w+n/QQWEdRtWdBuDs81+6XBD+r1Sq1LFVCLwrla56VDNPpLFSbV33MZrMJn8+HRqMBv98vioMEzZJMRjvcYDC4NLBWBlXHS6WS9J0IuiXB7Sx7KOX33tzcFHVJqjvo9XoB+QcCAamL5qkU3D/xE9BXKtA3GtB3OsB4jLlKhUmrBe0v/AI6H/sYtN/3fbB967c+vJ5Aq2WA12t4UrHux4IWO/V6HfF4/LE6i/sdlfZ5hp1FiqOaJmvPcDgsCtatVgsej0dUZwKBAJrNJmq1mlgAc38kwEmn08FoNAr4gQQu2hYGAgEhXhJkrVarsbu7K0TFfr+P8XgsKpA3btzAzZs3z6ydqc6bTCahVqsRjUaXelC0Kk8kEpjP56IOctVBUgdVBaPRqIAi6vU6isUiksmkAB88Hg92dnbQ7XZx48aNJeslAJKTEqRFdQ3auylrEt6HYrEo9w2AKBJRtcxqtaJQKAhxlgojsVhM8hOVSiXroVAoIJ1OQ6fTiWMBAMmdCPpX2hPx5wni2t7evtKe4KIXWUculxOl5UajIQAOglUPDw+xs7MDo9GIvb09IdbQjYBAT9aWXq8XsVjsy9p3ftqgWhPJfewf22w22RdXhVqtxtbWloCVToO/LqPMRyvnSCQiCijK9/d4PLhx4wbeeustpFIppNNpAVs6HI4ly2a73Y69vb3HrAyVQfBEt9sVlVjWOePxWAC0BMwTqKh8X6rXUJHKarVKH50EdwIVNjY2BIxLIL7VapWagedMNBq9tG0gFb4WwPCJ5GhXCVYIhUJotVqIx+NIp9Ni30zyf7vdRqvVkn5OMBi8kl4TQcH5fB6NRgM+nw9Op3MpBy6Xy8hms5hMJjg8PMT169eFUMQ50IMHD0SEgABa9iVJCCG5gf2cfr+PWCwmin/Ao1zmeZN6vxzh8/lQq9WEOBUIBLC2toZ0Oi2AWqq504rV4/FgbW1NnD3u3buHbreLRCIhIPiz9jiqoBqNxnP3EIIveQ4RlEgxCub8BMGRtMj72O12RWX09ddfx/b2Nt73vveJLd+q0Ol08Pv9AvbhtVj1XUwmE2KxmMxVqXyXyWRwdHQkwHFlH3YwGCAcDp/7GS4K1sj5fB52ux3D4XDJ/Yi2vsFg8ErX/mw2w8nJifT1SZYFHinc0kZxZ2fnwl4ESWnNZnMpNzgdOp0OoVAI6XQao9HoqZSUX8RXV7wAYL2IF/Einjg++1ngD/5gMdz9s38WUNYDer0e2ew2jMYx1OrRQ/s3E1qt67h5UyVJ59HRkRSD9DZWhlarFaufTCYjjDImm8ViCW++GcHR0TU4HH7s7T3AdDoRK8Fer4e33irjc5/bg07neGifZEappMLduxbcvNmFTqfD5uamJEnRaBT7+/vyWeZz4Jd+afF9fT7gv/vvAOVs9c03gf/r/3r05x/5EeCHf3gh6c7ig8pe87kTr732nahWg7Db69jf/zcol+0YjbIwm1sPm9Ffi0Ti2x4qrBjx4z+uxd//+xMEgz5sb29Lgn2aNfzljPqP/ig0v/M70B0eYq5SoWqLI9MJYq5SQ6eeYMOWhblehKbTgW80wpZajU/b7VJwAEC/r8abb+6hUAhhff0efL6F7K7L5YJer5fijc12NloMBgOm06lIFDsdDlgfNnPJXuNQTqVSoVwui1LBtWvXpOn8tPEf/gPw//w/i//v9wP/6B8tngu3242v//qvf3h/fPi3//brHyLqNUgm1zEaTfDKK29iPB7jlVdekWYxABwcLEBDm5vA3t7T35feG29g9AM/AHU6DV2hgJnRiLHHg8HMjGpBDeeoAveoiNI4ivrf+GFY/+3fh+X977/w96rVauzs7Mga5LCKDRyLxYZ799zo9VxYW+viwx8GPv95LTQaLTweLb75m5tQqQC1GkindXjnHRWsVh3+wl9wPgSh2fDJTwK/93uLa/kX/sJqlZmzYj4HPv/55b/70peAwWB5XwIWxY3BYMBnP9tGpWKCxTLDbDbH66/fwoMHc/T7euzsaPFDP2R6qgawwWC4dEHicrnQarVwcnIizcKzotls4Z13AvjCF7bx8ssehMML8N/zCA40yAy5qMlGxT6tVodPfAL41KcAjwf47u9e/BcAfvJfTlH8zc/jlcJvIPr/u4+Tf9ZCaEuD2c2bMP5X/xUsX/M1GI1GmM1m6PV6l1KJsFqt0KdS8P7iL0JTrULXaqHvdKJrNqPZ12A6tcM468MzzsGWy0E1ncL94z+O2t/7e1A9LJh//dcDuH17MWDvdo34uZ/T4q//9QxUKog0dLPZfGijF8av/Moe6vUZ9vaS+I7vaCAYDIo9p8lkQrlcXskKrVYBp3MBYnpe0WiocHioxauvAu32AoBIbN/3/JHP4hv+87+D5uQE6n4fo3h8MfV5eG9H8zlQq0Gfyy2UD/7Jj+Kj2X38zEc3MB4DkcjibPN6F8Ocxfq3AZij2+1Bq9U83OuAz38+D7u9CReA7X/9r+EqlaBvNKCp1aAajTAHMNbrYRsMgGIRyOdh/Dt/BwcnJ0in0zCZTI81PshAt9lsSKfTD3OMLHZ3d5/4OtXrwM/8zAKg9o3fCHz7ty/2ptPR6z0CXzF+9VfPB2Bd/N51UZi7CKhBJuTh4aFY7RLkUqvVsLGx8Vjjp9vtiuJKLBbD0dGRNHHa7bbYeVBNYTweo9Pp4LXXXhNAcDqdljOUg7Wtra0zlQHW1tZwdHSEZrOJUqn0XABYVG4g0JHKQF9J6lpmsxnXrl1bkldXqt49LzvNs2I+n4vSFVUcaO1E+7tMJoNSqSQqn2xMAQu7i3a7jclkIo3h05+fKh0Azre1ukR4PB5UKhUUi0Wx2aJ6z+kgWz8UCsmATTn06/f7AorRaDQi/X9ycoJgMIhCoSDX6DIKOQ6HQxQVaCt0FUE7N6rD1ut1ZLNZzOdzUU50uVxYX19HJBKBzWaTIbrJZBJry+l0KqAhr9eLzc1NeDyepcEKLa60Wi1u3rwJvV4vgHJaPVKpcJX1C62RCJCjUvFlg/ZjyiHCecH8kz/7LKFUC1gV1WoVX/rSl3B4eIharSa2hAR9EYh4eHgo19FgMGBjY0OAru/lGI+BbneREsilnM9R+5EfgfrjH4euWISh2sC4O8NspoJVPQUsKtiHQ0TrdXQNBgT/5//5gr1fjXZ7B8WiAdGoBu32W5JjkaluMBjg8/mgUrlgMhlP2XzM8LGPbSMQ6CEWW9RfF8Wv//qiXlOrgf/2vwW+7dsWf8/nknZAAFCrafHbv+3CbAZ80zc1EAwulB7yeT3+w3/w4/DQh729Gf72375827Tx4Q8DP/3T0D4kxDiGQ8wekhWmajXsnQ7crRbqtRomf/WvwmSxiGUlz7qLgmouqukU409+Et433kAgkcAagNl0iq7BgNzNm6i9//0wNpswJJMI9HoIOZ3QTadCIgMAlWqhZJhK7iB07TPQH96DPZPBteNjGIdD2HsDzGdaTKGBCoAOExgnQ7irXQy6XRh+6qeQ+y//S/g7HXg7HfiqVcTmcxy6XMD2Nux2+5Lig16vR6VSQbcbwPb2JrRax5nrkJa3PIeU56fdbhcSG8+rXC73mMUqAFH6GQwGWFtbg8lkEvAagW/nRb/fl/X8tOt6NluQhH7jNxZ/3t0FfvRHH/07bUvq9bqAUwj+W6WqmMlkxJ4tEoms3HctFgs2NjYQi8WWgAiXVToGHhEwaEnb7/fhcDiQzWbFmt1oNGKez8Pzz/4ZjPk8rMUiZlotuhYLRioV9BoNzL0eDA8eYOzxYPoP/gHaGg3eNv8xycu9XuAHfxB42lbNaDRCo9FArVZ7OOC2oN9XwWR6BJ6cz4EvftGF3/7tP4nhsAudLgmLpSEAnFXf3Ww24+REi2rVgj/zZ7bgdrtx9+5dVCoVNBoNqFQquN1uZLM5JBJ2dDo1RKN69Pvvw2jkwObmAlh3cGDFvXvvRzQ6xksvNdFqNUV5hkRLv98Pn8+HfD4vpC69Xi9kIIIS2u02Njc3EY1G4fP5zlRq4dooFAqiVnj63OQwNpvNIpvNwmAwiGr7ZYIqPyRyWK3Wx36WCiS5h7UmawvmDi6XC5PJRMDwZrMZvV5PFJdOW73xuzFnoLoIrZh5HnNdU3HcYrGg0WjIXmIymWAwGARwDywUXwmC393dPVPxiUqRBH6Wy2UBYGUyGQFnTSYTuFwu2Gw2aDQaGWgnEgn4/X7M53Ps7+8/lQ3S6aC9b6FQgMfjwWAwWLKdVFrT9ft9fPGLX8TXfM3XiBJcu91GuVx+jEzInvxXQgwGAxwdHUmOqwRAWSwWZLNZITKdBXwjSH8ymUiucNri7aIgqGhV2Gw2vPLKKwgEAtjc3BR1UCr8EogQCoUu3Kc9Hg9arZYoylBNlHaBDAIT5/M5QqGQWHaSWOp2u6W3BUBqhkwmg+3t7aXvTnCG1WpFu92W54YqRFSG397efuLn5iwV42cNjUaDnZ0dHB0dyZ5Ksr9KpRK7RpL5L7ImvmwQFEzQSa1WQ6VSEbUgl8sFv98Pg8EgZPJyuSyOGj6fD/V6XUDDBoNBAME8g8bjMer1upCG2OOhmhp/F/s/BHd9pQeBsOFwGOl0Wkiym5ubojLFvoter8fW1hauXbuG3d1d+f46nQ5vvfWWKD5Tdf90lMtlDIdDRCIReL3eM/P0arWK27dvC+FyMQtdkL5ms5mo0BP0SBtSAn65T1MVk1bEXq/3sXV4OggsarfbAjA7K6jyVq/XEYvFBIjFdVGr1QBA9iSq7+3s7DzTWUA1SxJTqXxKV5vn4eqTSCRQLpeRSqUwm83gdDplfkgAeK1WExLt3t7eufsuFXnpjnFeWCwWAWi/AGC9iK/8XfdFvIgX8WWNT30K+Of//NGf/+APgH/8jwElweyXf/m0SswcDx448d3fvZj5kl1Oqwxl06tc1iGf18NsnmJ9fSA+w3q9XtiPXq8XH/mIHb//+4tmSbE4wb17X4etrRE2NlIwm9v45V9ex+3bbozHY9hsVhiNRklCPv5xF27e7Mp7+v1+HB4eotfroVwuy+H7sY8B/+7fLV5zdASkUsCP//gj8MNHP7p8bXo94KMfNWBzc4pUygyHY4h+vw+DwYDPf/67MJkYodGoMRgE8IUv/J2HMv9jTKcDbG//Q9y79yHM52NMpwu242hkQ6EQwzd8QxRqtRlvv70AqWxsAKNMBr1PfAKzahVQq6GJRmH9tm+D5grsqyaThWLN7dvA1tZC2UujASa5DNQf/Sg0iQTmGg1GW9vIHtgwVwGq+QymQQOzwUJm2dCsQdds4lqhgMxf/It4fTZ7COKx4Fd/9VtQLi/85wuFP4I/+2f/M9xuoxQMADCbafHLvxzDyYkHPl8fH/hAApubGrjGY4w+/nFoP/MZWB4m006nE7O1/xc+VvxO5EduXL+eRDR6WxTN1tfXsbGxAb/fL8/AnTuL5vx8DvzX/zXwvvedf006HeAXf/HRn0ulRYP/G78R+O3fBhyOKG7f/tP49Kc7sNuHGI9HDxtDwL171+DzhfCX//JY5G2NRiN+5VeAf/tvF59BpQL+l/8F+JZveYobNp9j8A//IdTZHLSFEgaRGFSeRQOonjeiqdeiqffDOq4h3D9B8USN4T/6RzD//M9DdUFRPxgABsPCli8cDov1yoJNpcZP/mQQBwfmh1Zh3oe2YmMMBn0UixP8x/9ohkqlxnw+w2QyhVqtQrcbxq/92hzf8R0OfOpTC6UZRiKxALadjuPjhdKcRgP8mT8DsBd2lnryd33XYp3evAn81b8KUFjEarXiE5+wwm6fKiwdVAA0sNm0KBQWz/7f/buL1+fzwCc/ufhd73sfsLMjuJVnCp/P93CY7MMv/IIG29sWfPCD3aXfPRioUCjM8Lu/q8fv//4eTCYjDg4sqFaBv/7Xn/0zrApaghSLRXQ6nXNVGzjoXjQfA/jX//rRvx0fA3/trwEf/sdJvP9jfx/f3E/BNq7BOmlA255A05tAlU5j/Hu/h+rODnQ/+IMAlger54VKpUL4M5+BvtGAodHAaHMTc7MZzRxQrhigVqmhUplQ0zvgURfgTpbRbA8w+einoftz346DAxN+93ft0GoHD993jlRKj2xWj2CwJ42rhRS6B7/0Sy8DmGMw6OPu3V1cv57B/v5iAEZmq9VqRTAYxHyuwn/6T8AbbwBf/OLi8xoMC2vMr/3aR99hPl+ApT772YUi2Ic+dDZI67XXFueRwwH8xb+4UGMajRbP6kc+osZwuFjvZrMap0UaOh/+NUBbgabXw3BnB6rTYD+VCvB4MNLroT1MIFUP4UH+1zHe+V4AQDa7AJ9+7/c+YsIXi3WYTOqHKo5jTKcTACq0Wj5sh60I/9iPwl1Ow5zNYK7RYOz1Ym6xQDWbQd1uw1avY9ZuY/LpT6PcaqH1rd8K0xtvIOxwYHQvhap7D+7rXhiNj9h5tKfgYOVJhgOMH/qhxR4DAPfuLa77H/tjiz9/9rPA7/zOYgD07d+++ue5Vz9NjEYjaa5eNOgDIHL7FotFLOHa7bY0kE//blrD2Ww2AZJoNBpREI3H46KIo9VqkU6n8eDBA7F7MZlMokpCa6+1tbVzGz1arRYul2sBiH5oZXUV4PDpdIqTkxPcvXsXrVZLGsZsshH0cx7r7L0Y5ykpPUtMp1NkMhnkcjmxXyFAic+EMkqlEiqVCpLJpFhospnm9XpFGenw8BDpdFqYkL1eT+z4rFYrdnd3pTF4OiaTiTSvnrWZRnARlXRIyDgd4/EYmUwGNptNgHrD4RC5XE6YxVtbW0vPDBUqms0mcrkctFothsPhUs1wXtByA1jch6t8HmmxUy6XYbPZhNihtM9QDjdoJcXmLl/PQbvH48He3h5sNtuSOsBpBSifzwe9Xo87d+5gNpthZ2fnQstsl8uFVColA/BVzeuzwmg0yl5+Ud4BQGzKV1lcX3UcHh4Ks5qqCMqgPc7NmzfxxhtviOrEyy+//Fw/11XEW28B/+f/CTSbC/vu7//+BbGq/mM/BvXHPgbt4SFU0ymy8yiqFh9m0EAzGyGqy8LVKGE6HsP6678O1f4+Rt/2nbh9ewF4euWVR+fkdAr8r/8rcO+eDvP5JmIxH77zOxcqMMfHxwsrqVYL4ddfh7dQQLdkxV+r/SaS5l285vkTKFg2BbjzK78SxMc/DnzHdwD/0/909veiEi7jJ38SeOmlBYGKA0O73Y5qtQqbLYB/8k9iGAwWe9QXvmDD//a/JeFwTPELv+BDIqHGdKpGMmnDD/yAFd/yLYv8IBhcAOtXResjHwF++qehPjmBttvF0OVBO+SF1qKHFmPMy2VYqlWYBwOY7tzB9BOfQPjv/l3cvXsXjUbj0vdPp9PBOhjgfb/6q3A3GrCNRjC021BPp5irVDAbjfBkclD9wkfQNHoxM09h0E+h+/3fh+rXfx25D/5RoPJXAKMfOq0Wr9Y+ia8t/Cpu/tKXYJ604S2VoBuPoZ/NMIUaM0yhxRRTLO/n5kEPkVQKf/5f/SuUAgGYJhNgNsNwNsPLGg1yt2/jjWvX0Hlog9NqtdBsNnH//teiUPg6/P7v2/G5z1nwD//h4vlbFcnkAry0vm5dOTDXaDRwu7fQaKjg9S4GnuVyWZTVW60JBoMm9PoFAMVodGN9fQMnJ3dEDfA88sd8Phd1G/akLhvzOfBbvwX81E89/m8PHgA/8AOL+nIwAF59tYvxePzQljH4MOdcqOlVqzrkcgZEIkN4PIuhutPpRCaTwRe/aMDrr4/w8ssmfOADqz9Hv6/FnTsOWCzArVuLNdrrLa6r1br47zvvLP7+2rWzc13mkV6vVwbIVFLZ+bmfg6FQgLVYRMfpRM9mw3Q2g95gwFyrxTwWw7Ddhj6Vwkilxsn/+8fwo+tfi7F2cZ5XKou1+w/+wfJ7lkoLVV+fb7GWGYPBguTi8SxIV1TF7PX6eOutG/jUpxYP1Ic+1MSf/tMVqFRANqvHb/3W7kObeSN+67c82N19A9Ho8Mzc/OMfj+Hznw/CbDbj4x8H/tk/8+Hll19GMpnEYDB4CPLo47d/+1uRSCxyh7femopF8+c+18CtW1389m+/DJ1Oj1RKB59vij/2x9I4OTkRhdHhUIfPfMYIjaaF9fXF8JxqbYscrI/xWA2LRYO1tTWx5zrPupeA7U6nsxJ8xVCCibrdLsrlMuLx+Jm/F1gQJIrFIprNJkajkYB7DAaDqFgRkFSpVDAYDNBut7GxsSG1BfPixX3ryVprNpsCBDsLAEUVTuYCBCpOp1NYrVaUSiUEAgFRqPR4PDCbzTAYDGLzTovNSqWCZrO56C88VAClWsp5wRyAtSmVS0ulEjKZDFQq1dL3BRa9KAJXstksdDod0un0UxGLTke5XEaz2cR0OhUCDoEDVAqbzWZCqBgMBrh37x58vgXZl+v7KzVov1iv15HP54VMMZ/PUSwWUSqVoFar4ff7cXBwgL29PQH4rRq4Py8wEPDIkisej6Nery8BEWgbd5nwer3I5/MYjUZIpVJSu9B9Q61Wi9I4lX6VCjsqlQqBQAChUGiphvf5fKhUKtBqtahUKktnJPvZVBWmklw0GsXNmzcRi8VWknPe7aBbC61BlfWrSqUSMNRV1epUMrx9+zbm87k4fBA4arVaZa8Lh8OSj1QqFQGIOBwOeDwe7O/vo1KpoN1uQ6fTiRUrbazb7Tb8fr8o+pB4s7GxIXlTo9EQq/Evp+r284ytrS1ZO4VCAYeHhzCbzWIfPplMYDab8eqrryIUCj2mChUIBEQhPp1OYz6f433ve58ojfZ6PVSrVfT7fUSjUdjt9jNBNO12G1/4wheE9BUMBuF0OuF2uzEYDMR+st1uS6/CbDaLRanT6RSV8el0ivv378NkMgmR76I9od/vo9/vy7N1UdhsNnEwCYVCcLlcKJVKcDqdSxZ9tG30+XxXpkyl0+m+LGAkKvWlUinodDpEo9Gl60hlbwKmNRoNCoXCuaT6xZx2dGknC7PZjNFoJHPOF/GHN1TzrwbtwRfxnol/+k//qRwof+tv/a13++P8oY/pdIpisYgf+7Efw0/8xE888++7efNbEAj82mN//4EPLAALP/uzP4Pv/d7vPRMUoVYvBsxqNVYwnVWYTID5XAWL5SW8/PInEAiM8Kf+VBUuVxkHB3W8/PIaPvjBHfzmb/4m/pv/5i+f+Tm12jkmE9W5dloazRwulwe/+ZuL7/P662WkUl7cvOnBn//zG1CpgO/5ntfwsz/7TUs/p9M9YgmfbZ09B3dWlWoGlWoGn++zAFRLDMbZbPpwKJtDo/Fdip9ZXA/+V6fDw2sDqDCHFhOo8XDKvvxDeP1v/S3sfv/3Q7uiYXB0NMSNG64LB8jT6eL9lKFSAerZFBpMoJrP8LHr17Fh9COZMMI0aSPcP4Z+NoBxNoBmPsEGxmjwaqhUmEGFmUb78PcvJ49q9RxqNTCdquSaASrM52pcv/5hWCwfAuZD/OTX/TjWXv99jGo1qMp1TLtT/IlBDUezCeYPG8JTlWbB0FVB8T/VQwutR+85Gi1fun/xL/6/+Nqv/UsPwVTAN38z8Au/sGhGbm0BH/nIt+H27d957FqpVI9+z0Wh1QI/8iN/D9/3fd+HN98E/vf//fHXOBzfjf/4H3/hzN8xnS6apFxLABZ/MR4DsxnmUGEOFf6cOYZ//r44KmUDarVHjQPHqATfMIf/j0WPD3eK0OjOTuQf/koJ5foFFt/b4/mzePXVD8trWKiMx2NMp1Ok0z+BbPb/ePivyw+eXr94zk4DRvT6xxu/VuvX44Mf/IT8+b/4LxasdgD4H/9HIJP593jzzdUTGY1mce15n85wagAAWCzb+OZvvo1r1xZgGSWoaPGz/xmf+tT/n733DpPsKq+9fydVztVdndP0JGkkISGREcHIBCEQAgyYjG3ANhcbnODD4QLXNhhHjH3N5QLGxlwnkkkWCIMxmCCMENKMJvb0dO6unKtOnfT9cfrsTtVhND1CmFnP04803VUn7LP3Pnu/73rXeu4lqxlFo1E+8pH7ePe7NRqNNuBw0001XvMaVx78rrsu8Fu/9Yp1hI+NDeL3u/fjObJs7t/r4VWSwppt6je+4SY6br01xzXXjOE4a2Nelh3cOcxBkuTV467v5NJqW7oPTpIkTFPmKU85QSi0ZkGTaM7xivt/jUxrhv72DAeMFW/Wco+5fgDKMraqrm72JBxHWv311sH1/ve/n6PpNMnf+A3SS3kMfxT1wCilssLyksJLqt+g6GycnKXV67eRcTQN23b7nTsXO+uSHA6y7N77C17wAkZHX8qXvnQ94FWNdQgEgoRCQcrlW5iaOrXaro4IAnebP73b9HI2juOOL8eBY8f+lPHx13Pdda7S1ObneMstz+erX71zy3G2O8/m+1btDjI2b0om+Y1rrqFUUikUNBxHYmhYJxR0O9Gbz5zjH1ZW1V+QMWXfhqfuxeR2O++jY4/ivY7EZPsBlP4IjAxtuKk/mJnhvfPzGwa+N2+th4OMJSmuuqK2vnrdbeubbrqFn/iJz6EoLnH1UUdLLP7956ned4FYQOfjs8f57X/9VzFhOU73se/3r02hot3Wzevx+A088Yn/CbjvhM35mU996lO8zJuMtkEwGOQHP/gB586dY3FxkSN7kDus1WqcPXsWv9+PpmnMz88LO4j1CYF2uy3UHvv6+hgfH0eWZb71rW+J4L+n+BOJRIRFWb1eJ5vNCiK8oihkMhlkWRYBwquuuooDBw7sqATkWUMfOnSIw4cPi2TLg0W5XOaee+5hbm6O+fl5CoUClmUJkoaiKPT09HD11VeTSqU4fPjwQ64IapomxWJRWDdomralmOByw3vHzszMcPz4cWq12gZrL5/PJwKNyWRSEI8cx+H+++9nbm6OZrMp+gu4Y2thYYFCocDJkydFQNezu6rVahQKBQYGBpiYmGBoaIhHPOIRXZMT586dI5FIMDo6ylVXXXXJ9+uNh+XlZUqlErFYTNjymqYpkgDrrUD7+/tZWFjgwoULTE9PC8Xb7ZDNZoVFj6ZpXHPNNbsGOwuFAuVymcnJSa677rrLRgi0bVvYOZ4+fZqlpSWRKOwGr3rWsixRaX3o0CGuu+66PZ2vVqtx+vRpTp06xfj4+J7IridPnmRsbIxDhw7tqmRp27YIjINrmbOysoKu69taJoBbwDM9Pc3IyAjj4+O7JogvBc1mkzvvvJN77rkHy7K4+uqrd0wmzczMkMvleOQjH8mznvWsfQ0se6plsDeLaMty32F/9VdTvPrVA1vWrI4Dr3+9wYc+tKYSpCigKs7aBslxcCS56x5HlvEWUSBJPO+nvk29ucb8eNOb3HX6V74C730v3HXXMJ2OW03tDhEby3KQLct9+286ibcesJGxJFWsIR71qE+SyTyTJz8ZXv96lziyHjfeeCP3339SrI3XQ1Xd65aktRiEbcvY9sb2VBSHX/7lt/Htb/8PWq0miqISDAaJxaJ4a/H/+q9bKZX+vfu6W+/g2DYSW9vP25e6N2fz1miUX7rhBkIf+xiz7TYXLlwgn89z8OBBJEniHe94B1/yZKo2wXEc5NXggLRuPe2dbn2b/hwy75AiKAEHRQUrHqcR7ef+wgH++ej/ZPr4L/GN4ldXV2IPPjR8myTxgWAQw/YhmQ6GLNOIKJQCfv45+Vi+gMXp0678k2UpG/qyqnYvQlgfa1FkUCWTRyZ6+NtrbybaIyMfPMCXlRfw/+69AcO0uOaaFcLhD/Oud/0etr0WWwAJWXZWFbPE1gNZ3l55b2hohNe97k7On2/T17fEE56Q5IYbxslk3DXYV77yFW677bYd28Q06dof10NVwzzjGTk6HZ3x8SXm5prUagPIssPTnlbEcY7zjne8dN3nHSTJWW1HsG153d/W2tGbB8Adro4Dz3hGlltuiaAobgGZprkKcefOwb335vnyl0fFvhm2ft9ts7VrFzb0to1kmkiOgwN8dmKCiWAQVdNQFYVgMCjep3ahzNgDxzGQsCQVW1p78Ov3St75vf3S+vvzfv/Yx36Zgwcfz1vfCu12lgsXznPhwhKf/ORTAfjud2+k01kSbWZZkriX9efUtO36vUSnA4cO/RVDQy8kEonw7Ge7849HFsrn89x6609z/vwDOz/o9UfdsCd0xD58dPSd9Pe/huHhDr/6qyskEi5h6HvfM/jQh1LcffdrKJW+LOIxu3E0vOezuY+/8pWv5PWvf/2Wz8/Pz6MoLsHr2muv3fa4npqEV4TkrYfBVQpKJpMkk0n6+/sZHh4WdmveO34nWJbF8ePHhXLIDTfc0JUQ4TgOZ8+eZWVlBdM0abfbZDIZYrGYsPVqNBqEQiFCoRC1Wk2oQSUSCWG/7Kn+xGIxYUntWUbtRIryFEpWVlZYWVlhbGxMrHer1SqLi4tbSP+bsbKyQqPR4MCBAxw7dgxN01hZWQFcYsDFJrvvu+8+8R6RJImlpSX8fj+pVGrDddi2Tblc5vz58wwMDHD11Vdz2223PewIMxeLEydOkMvlmJ+fp7e3V9hz53I5oXTi9Yv+/n5GRka4+uqrhb35Q7mP20/MzMzwta99jRMnTtButxkfHxeWpZ5qcaVSodFo0Gw2GRoaIplMMjw8LFTOvEKXsbExMVefOXNGEBO8op71fdKzF/YUYoeGhvaFSPhQwTAMQd7x+Xz7ansGsLCwwFe/+lVOnjwpFNQ8cpAXa3EJ5ClBqCqVShw5coSjR4+Kec+zUDt16hTnzp1jamoKVVWFkpdnGxoMBpmYmBDW6IcPHxYWwq1Wi5mZGaHudfTo0X291x8mDMPgwoULVKtVGo2GKBafnZ0VhWlHjx6lr6+v6xyn6zoPPPCAKOKKxWJi3+spUafTaWKxGIcPH+5KwjdNk+PHj3Pfffdx8uRJZFnmyJEjG+II3/ve9zh58iS1Wo2rr75aKG/WajX6+vrEntYVaTA5efIk6XRa7Ct3i0nMzs5y7tw5stkshw4d2rXdarUaS0tLHD58mGPHjon5zzAM2u02tm0LZa4f1XfDmTNnmJ2dJZfLcfDgwR3bcGZmBp/Px8jICNddd92293z8+HFh2bxdHGY9FhcXBRn72LFjIkcPD+49fwWXBw8Fl+WKAtYVXMGPAVx7rdYlH2d6Wu9anfjd77q2X9/6lrnjeWx794Sx+zl3E7+y4uNDHxrAtvtoNpt85jNhXvYymVpNw7a3P8/mJGu34LFtQ6lk8NnPpjl0qMlHPnI9tg3f/GaIZtO1Gjp50t5ynu1JV92x/tyOY294kcuysio5quKsIwx43/H+u/mcXeN4qx9WvvhFqufPE/6TP8G/LjHxmc/ABz4A7faD7wcWIHLUfj+OLRE0a4w1ThG2a2iO+1cHiRYgzuS4hA7M7swXy9o+OGnbFkZH5/n5j9H88D0YzBFzyuT1BDUpQtMps6phs3ouGzC2PPPdCOef/KTFXXettblnDwCu1eTcnL5jn9sLOh04edJibm7N9mEzvvrVziWOVbetdQsWF/0Egxsboqr10Ksv0mnodEwd9jAePXQbv46z8ZlKkkQ4HMayLDodHU2TxXjejHb3X3f9fTC4cRB89auuytIb3uBdh7Xt89lMrtgJ3rWePOn+bEa5bNPpXPpcqqoq3/teBp+vheNIdDoG3/tehBtu+AGhkMG//EtMXEu3+WtzF9nL/RUKrprdejzwAPvybgBwNk52PO/U75JpzTDUmiLnG6RtLAsC1ob8jsck2y1DsQrbtqn827fRsmFS9RyzjGKeWd0kyzYGNjr2Nt+2wFg7z6ZL3kAGLJcjTE1d437LcsmymqahKDKKorKw4Aj/+L2iW1M7jjuo7rsPbr/dffccP+72v/FxOHFi69xzMY/Mu9tOMEi9rrC4uEZAmD4fIJk0GRjUyZcV2uLBWPAgz5lsz5GWfDSIoAcm6JXWOqfjQEOXaG1mXa7OWxthg2OC0/29e/q0TSoFfqNG+u//L53C1wiaNUJmjRoODS1Law/PZ7f7su21Y5TLrvLFenhEh93g9/vx+/3Ytr0nu5tGoyFsU73KrMXFRcLhMLIs4zjuuq7T6RCJROjp6SGTyRAMBllZWaHT6Ygq30gkQj6fF1Y79XqdQqEglEA8yfve3l6hMnb8+HGhCuSpzTQaDVG95clue+Qb27aFEtCDRblc5gc/+IEIbFiWRV9fH5FIRKhCgKuS81//9V/ceOONnDt3bk9VgfsBT2HJk3j3rsdTEItGowwPD++5Iu5Sr+X48ePcf//9om84jiOeld/vp1gsMjMzw4033ohpmhw5ckTYflUqlQ2WK+C2f7lcZmVlhd7eXiYmJpiensZxHJLJJI7jiEA7uFWDuq5vIWB51dBesH4/4Kk7gauYUCqVmJmZEX8PBAL09fURj8cZGBgQBKB8Pk+pVBL2MzshmUwyPz8viOTVarWrzeF6VCoVYrGYUBa4XPDU8MBtC0+NdDsoiiKqX4vForCE9JSuLgZ7qdVbX/CyU5C20+mwsrJCoVAQJEJJkigWizSbTVHZ3d/fv+U62+028/PzRCIRoYh8OVGv16lUKlSrVcbHx3cNPmcyGebn52k0GmSz2X29vlartSHBvVf84z86/Pu/w7Oe5SrCevn0UgkWF9mwtui6Vna2mdO9X68SteQT34eJNQLWn/2Za2v43ve6/7asljjX3pZNXp9bWwe4p3NP/LWvuT8f/ahbNOOh3W5jGN3fxzsVX6yHbcOnPhUjkWji2tv7VueOtedvGPre9tOb22/TUsfudKDRoP6JT5B+5SuFAm21WiUej2MYxsWtM7cZqx3AlDSmQ4cZH2qgFgqEZ6YYlB3+xz2v4T2dhR3WzXuH7sicCFyDrodRZJOkWaCvsoJV8XFLIcudgVFM0+tkG/fqe3k+tu3GIdqFCs637qUqywT7TjFcvodXhib4zJG3cvLkCMeOZbq22+Ylym7bjmzW5OMfD2HbfoLBfr7xDR/XXRfhNa9xi1g8YuylYq0wTeLEiRiNhkoo5BakfOlLaRqN3g1jdae22ks7/vu/r/2/YbiK3h5su3VR++btICshgo5NIJ+HuoWpS5iomKEw7ViaNg4mDjidLcv/nZp08/1NZv+dZvQYv/EbcXQ9TqNxmGZzmEjEVbi27Ta23dqxXZxt9hkb7kdGJF0//3mXgOUmTpM4TpJcTrroOFG3+/T5FMLhEJVKFNPMcOSIiuO4Cr6q6qCqjnhGlwJzm4CsZ0Fs7TA4vPXX4uIitVqNZDLJ4OCgIMR7tpjVahXHcQQJpN1u70mVQ1kl7BUKBcBdN3QjYHkFJs1mk9nZWaG86ikIy7IsFMry+bywSj98+DBjY2Pouk4mk0HTNJFk7+3tpV6vc+7cuW33NLZti3tvNptCVVbTNKFo41kN7bYu9AhCrVaLYrG4q0X9bvBUdur1urAaW+8A4EGWZVKpFIVCgXw+L9a8l8NK/mLg7SU9dbOLgWe/uLS0JBwzPPvybDZLu90mHA5z+PBhCoWCcL7wCg1s2+bo0aM/MlaL6yHLMtFolHA4zPDwMK1Wi8XFRcAdJ94+zYtDrKysEA6HMU1TkHu8Yqd7772X4eFhsbf1+/2MjIywtLREuVwWStqezZ2qqsKm3Nun/ahgo2PL/sGzDp6amhL2ZlddddUWZTPLsiiVSiwvL4vved9dP0fLsszk5KQYn5FIhMXFRWRZZmhoiEqlgq7r4lyefWQqlcLv97OysiLUylOp1I7qPj+K0DSNQ4cO0Wq1Vu2tG7RaLXRdJ5vNMjk5uaOaod/v59ixYzQaDWFB6e3/fT53D5BOp+nv799WEc+zDPba34vDrUc0GiWVSgkypN/v36I4qCiKsKr1bGtjsZhQmNwJqqqiadqqQ4G5q3rf+hjO+s9ernHxUMMjn3rFpru1n2slvUAmkxHW0N3g9/sJBAIitrrTu8pxHBqNxo+Uje8VXD5cIWBdwRX8GGBu7vIP9be/fc3aZ7/hBVwdR+LTn9ZYVU3eF3zlKwm+8pUEptlClmUkSeLTn15T19kvKIqCruurJKzVCsDVClxV3T/Ws3JuBgmYeuXb+OYLP8CxG0NEIvDBD+5dqWkvqNYUFisKBxuniVkVFKzV+NlmHZNLg67rPMq8h5vLdzGmT2FhcyJ4PUYkiG3bWMYFN3h3iSgWt1ZSXw58+9trpKHLjVpNRVE2BtgcSaauxFCNfRxEXeAGz0L4/ZevkmxmxrVw/FGEYcDdd0sEgyEURUVVdUzTx3e+8zjOnAnT6XRhfz1ItFquhepv/ubWv01N7dtpNmCk9D36WjP0tWfJ+wYoBgahed++HPsHP/Ax9bkUz2srgIQpr/UxWdo/Esb3v3+Mnh4Hy1qrANI0TfTptar6/cV61bX779+aMHqwkKtVrNo8CTtK1ZfGltx1Qamk4vPt00kA1daJ2Q0WghNYdZXejIFhSszNBWg1ZSrN/XvfBfUiP338LQw1ztKrzxMxK5iSD1uS8TXL+3YeD7ncVgLWXpFMJvH7/YTDYQqFwo7BL9M0KZfLDA4OMjY2RiAQYG5uDtM0BfkHXGWtTCYj1K889Skv+B+NRhkbGxOJFC9J0Gw2BUnHsyHwbAoyGTdx6RGxotEo586dExYGXkDQq7z0Aj1ewOjBwjAMUd25tLREKBTiwIEDG4IVnppPp9MhGAxy4sQJbrrpJkql0kVZnj0YtNttzpw5Q6VSEcE9VVWRJAnDMERwzqtkv5xJDNu2OXv2LPfff7+wzx4bG6O/vx9JktZZ4rjWct/97nd57GMfK1TEPPuDzcpmxWKRarUKIMhZBw4cIJ/Pk8lkSCQSwt4yl8sJ5QMvMeYFknO5HAMDA6Kaer/gqYytrKwIhQPLsmg2m9TrdXRdF9L+S0tLpFIpoYqwk4qbB03TiMfjlMtlEViLRCLbBjDL5TKdTodkMvmQyOh7SCQSxONxYemzHZmz3W6zsrLCqVOnGBgYEBY9XpIxHo9vGzT0+/3CCqVer+9KKqzX66KCfDuiW61WY2pqinq9TrFYpFarCeKWz+ej0XCt4efm5qjX68TjcQKBAI7jiOruaDTK4OAgw8PDF21Be7HwElO2be9JZU9RFEF0ezjZDFQqruX6P/yD+++f+Intrd4eLCZK3+MHm/Ju73zn/p6jG17xCvjLv3TtBfcThmGgKIogTl8ulUWnY+OcmKb2d59l5H+8kU4nzNmzR5mdzXLzze1927fLgCzZBFpNTs4PEgr1o8hFhlrTSNjEL7HAaO08DnZDwlHBklTyWh8VOcrBznn6nSLX6HXu34/zOBY2Mqplos0uMilliRglXn7/r/Gxa97DD36wP/ZBhgGyLOH3h9A0jVAozPnzklCw3q8cpmp3GC1+lwuJRyJJslAi/VFOgKmzJeL+BpJpYesOPtwIkWnUCVXz+3aexy99nCOl73AidTNfGXkVLVlDklhNPu5f+0mSvOF5rKzAxz/u2sPvJzTNRzDovm/Pn3cLpe66yytOu/wKFB4peru1geM4zM3NCRWhiYmJDfOjpmn09PSQSCQESctTe7kY+Hw+fD4fsiyL5Gk3JJNJisUimqYJJa5QKEQwGBRkfa/4ZGxsjL6+Po4cOcLk5CTJZBJJkgThx4NhGGJd1Wg0NpC/HMdhdnaWSqVCNpsVNm6edZRHBms0GgQCASqVyo77AU3T8Pl8Qk0rEAiI7z4YKIoiyAedTkfYmG0Hr5DCU1P+YRCwTNOkUCgIq0RgAyEuFovtiYyVy+UE6S+VSqHrOrlcjuXlZWq1Gj09PairauuZTIbFxUV6enrQNI2FhQUkSWJ+fp4DBw5c7lved3iFFocOHRJqb17Rhad87c1f4+PjVCoVwuEwgUCAeDwuCgfK5TLxeJxcLieKWoLBIK1Wi7GxMWEjahgGsiwzPDwsLDUPHDjwI/3OulR4ioi5XE7sWe6//35Onz4tLN43E0A8VW9vDDiOI9xSupFFenp6eMYznsGJEyfI5/OUy2Xq9TqxWIxsNkswGBQkLm9erNVqBAIB+vv7SSaTjI+P/0jbjO6EYDAoLHmbzaYoetzLGt7n85HJZOjr66O3t5dMJiN+H4/HdyXv5HI5SqUSkUgESZJYXFyk0+lsiGNFIhH8fr8gGHtF64FAYMMzb7fblEolQcAKhUJ7mgPj8TihUAhVVSmXy/T09Gz72c3W2pfLavWHCTf36hZM76bMDW6Bn+M4dDqdHYtfenp6hI3pTkQtcGOyXuxlp+dxBT8e+O83yq7gCq5gA0wTKpUfTclID24QRVm1iNp/iUbHsbEsa1V61p0Wf+qn9vccXqWIyzTXViXwrVXLqv1jRi05AyRPL1EL9jBb/SKf+dId+3bs9chlfUyay0SsKopbw4iD4lp8Aeu0si4JiqLwlMpdpKwiPkfnfPgYtuxu7mRZfghCUT/aaDS2jhdbktmqNvOjiQf2rvb/sIJpwnj+21y//AUGmudRnQ4tOciZpRto9d3K7D4mFn/mZ/aXfLkX3LD8BaJGCRmbov9BMla2wb/+a4TnOk0cSUHCtdnbX9qnC8uy0PU2Pp8fn09DlhWCwcCPrEywVG+TkLNEpSIZfYGyliYXGMGRZFZW9q8iR3EsJGwaagJaEidPhveNRLYeEvCCk29npH6KkeZZmmqEC6Gj6EoYJIlcaxpap/f1nL/5m/Av/7K93edOUFWVVCpFo9FgdnaWYrHYlZxiWRYLCwv4fD6hShUKhYR1mEeO8shPHhljfeCkWq3S6XQYHh4W1d2KomDbtrCR84IFnspWKBQik8mIIEwkEmF+fp6TJ08SjUZJJBI0Go0NSjfhcJhOpyNIEnupbN8OuVyOYrFINpvF7/d3rRTzbBKXlpZERVm5XCabzV5WApZlWZw7d45iscjCwoJQY1ov1V4qlVhaWqKzKrNw9OjRy0YQyefzLC8vMz8/T6fT4dChQxuCP8FgUPx4AdhTp04Ri8W2rS5vNpvouk6tVttQoasoCrIsEwwGhcKBpmm0222mpqaERL0sy9TrdRRFYXBwkFQqtavU+4NBMBhkfHyc4eFhcrkc58+fX1Wp8QvLmJWVFZG4K5fL2La950p6WZZJJpNomoZpmszMzNDf378hAdeNaHa5CYDrkU6nWVxcJBKJsLy8zOjo6JZ2zuVyZLNZZmdnqdfrok3y+by4z0AgwNjYGPF4fAshy+fzEYvFSCaTYnzt9O4rFArE43H8fn/XBF6z2eTcuXPiujy1OL/fj2VZVKtVMY68gLOu64LYFQqFmJiYIBgMMjQ0dMkqEXuBpmmCZLkXFSLDMFxbOFl+WCehvvKV/T9myKzt/0H3iDe8YY2EtV+8N5/PTyAQJBAIXFaLW9mxCeoNzHvv41MHfol/nHgLy/YQuj7Et75VIJvdmzLsbnCQ8DkGSTNPiQGaTRl8PXT0ReJmEd8+FDKBux6P2jVy1lqbWZKfZTVDxsoTY3+IUboSIhscB1wyVlJfZqg1BTi88OTb+Z+Zp+zLeSTJffe5xNLAlvfIvffuy2mQHYuXnPyf3Dn+83wz/hNomkano6/Gvi6/uuflQNisIRsdZMfBkVQcSUJxLHy27tqK7hOGWucZN6pEjBJjteN8YOy36WgROp3OviYUvaS4h499bKOS2OXAP//z5T3+ZnhkZ28v0A3lclkkh/v7+7edH1VVZWhoiOnpadrtNrquEwgEBGG+XC6L5Ki350gkEoLE3G636enpQZIkms3mtnsmTdPo7e3lwoULDA8Po+s6s7OzhMNhJEmi1WoRCAS46qqr6O3tZWRkZIt18ea1TTKZxOfzEYlEKBaLG9rCs+ZeXl7G7/cLIk8mk2FgYICenh6WlpY4f/48hmHQ6XS46qqrSKVSXdehnU6HQqEgyGP1ep1SqYQsyxiGIdaYe4Vnq+hZde9E5PKKFnp6esT+8KFGuVxmenpaqPc0Gg1s2xa2eN6acDcrR3DJcrVajVgshizL5HI5FhcXmZ6eJp1Oi/Wvqqqoqkqr1RIFHaZpsri4yPz8PK1Wi6GhoR2LFB5O8EgmjUaDVCpFT08PlUqF5eVlWq0WrVYL0zTJZrNkMhkCgcCqWqghxqIXN2i32yiKIogICwsLqKoqrAo9dS1v3+fFKbabL35coOu6KIjylHMXFxdZXFykWq0SDAY5e/Ysk5OTXedMT4HZU87z9t3doKoq1113nSAtekVVx44do1AoCMVAj2yoqqqwWB0YGLikWM2PErx53cvB7QbTNLEsS5CIL2af2el0hNrS0NAQhUIBv99PrVbbEB8IhULE43Hi8TiVSoWVlRX8fj+Tk5OEQiFxjHq9TjQaxTRNhoeHdy1y7HQ6FItFMY86jiPU7bd7R+fzeSzL2le18ocz9jKXe58R1trbIJFIEAgESKfTIr7SrVCt2WyysrJCOp0mEAjsqqp+Bf/9cYWAdQVX8N8Y7TbMzys4jg9ZvvREkCzvHISUJPWiz9NNtlFep2hi2zaGYRIMBoREtCQpez6Pe3zoVjXmnceTCHWrXdcSfm4V4oNvt/Uvbklybas8KytPCctNioIkbdwgr28TCVdVRNqDxlTJ10+71SDeyXHjyue5d+h5G7LF3e5HlsHb03Y625M1VMdAdizv5ujX51BWPewsFBzWgoRBoFu6wkHClDRsSd72POvvvZcqk51zbtBY6xXkKw8+Sca/7rzr28iU/XumGUnSzqQKWfbvyxiSdgk6StLFj9X1z8XrG+pqG5rG1r4StJookopPUjGlje2pqq7izl4JE5K0M3HjwcwJ3bD73LP3OWHn8+xccXipcwK4fTRsG7zk1P8kahQJm1VkbExJZbg5xeOKX+Su0CTfkYMXRZOTJPD5XEuN9YWmhuE+1+2wH+3mnn+trw00zhI2ytSUBM7q2PKjYG9zR964tSVlXZ90us4RPp+fUvAAnVIEu6MQMUrUfGubW58k43c2JizWzwvGLn0J3Lk7GHQriPx+H5rmE+8fD96c4L3DJAkUZW82uxuubZc54cHMPYpjIjsWEo64dz82fkdHstuYkorimAStJrPhwziSgopEYHX+cJAxZd9F0zQVx0JbpcV52DyXBB2Dbndj0z3JJGNjI2NJCraYpyViVoPh+mmGW+eoaCmygdGN7zpJEe8GGRsHCVuSMXeZs7acf1N/OXsWDh9e+7dnlbEe21nSDA4OUq1WGRwcFEG5ZDJJMOgqOnqBd0VRGB0dJRgMCgu1TqeDYRgEg0Gq1eq2AX0vCBOJRNA0TVQOHzx4EMdxKBQKpNNp+vr6kGWZUCgkAkKSJAnbrXK5zOzsLLZtEwgEGB8fZ2hoSCRA2u022WyWxcVFjhw5QqVSedCBas/abmFhQbTpdtWZkiSJ6txoNMrCwgI9PT3CauNyIJ/PU6/Xxbk2V7B5Fh+hUIj5+XmCwSBLS0uXrYo6l8sxOztLp9MhHo9vq+4Ui8VEoLxYLFKv14UdzGY7una7TafTwbKsDYF0r8oZ1hJqmqZhGAZzc3MEAgFhgzk4OEg0Gt1Alrlc8KzsPHsBj2TlBb49BQEvyTA6OkogENhRyclTMvCIQbIss7y8zNzcHJqmCaLhZqLZ5OTkQ0rM9fqbYRjMzMwwMzMjEiCSJJHL5ZiZmWFqaopms8no6CgDAwNYlkWlUiGXy9Fut0kmkywtLXHkyBHK5TKhUIjJyUmRsOvt7aVcLlMsFpmfnxdB4PVwHEc8h8HBQZEw3Yy5uTlRGT48PLxhfHttm0qlRL8OBAJC3Q/cvpdOp+nt7b2shJj18OZmb77xqqG3Qz6fx+/3P2RVrt3Gl2lutlXbeU7evLbQ7A7SqhXdXojtYm21yx5KUYJYlnsub33ivdt3g/v+dtcA25FR3vAGuPVWyGYD+7KmDQRCO6pubLcu67YfAzasxTZ8HlAdk4hV40DpHn5GfwsfPvpuVgIxCoU+ZmeTG+IDXjxDcYxVsruDs2k/LXexElSQURwTzdaRHBtntR0dHDTHEOs/75q7HWMv8CGhYrkX6jir3U+ipKQYslYIOQaapO2ZgOPGP7b2SW1dv7ElhUJgCFuSGWjN0FTj9NfP7Us/UNUg0ej2/cDbE3or57WCkLWnrToGkmOv+83GniDhEMJhoDnFMy+8n/JkgvsC12GapiCPgLTlfnaKb10KHmy7KY6J4pji7oJ2G1P205H9OOvX5o6N324TBEzcVuvssPeWcFb7wFr7rh9bWf8Yjt1krHEaxbF41YXf5y8O/B41E1qtNrLcfU7YGKOTWL/nXLvctfNo2sb3zmby1UMVJ5IlDZ+kIjm22Nt061MSjnsjkoStqjhs3Kd3I6d5idud3mGeeqUsy7sqi3qqq+VymWDQJbTef//99Pb2YhgG7XZ7AwErn88L+0BvvQkuscYrcEilUmJd4K1p8vk8hw4dwnEc+vv7N1jDJ5NJQQ4fGhraU6JZURTS6TTNZpMLFy4IS2HHcSiVSpRKJbEm8ZKvwWAQ0zQ5f/48jUYDRVG4cOGCWD/39fWRSqU2JOPr9ToXLlwQVu+GYYj2DYVc1b1yuczo6Oie1xTeGkmWZaEG1W3+sm2bQqFAKBQiEAgQDocv2Ub+YlEul5mamiKbzVIoFAgEAsLezlNw9daMlmVx9OjRHdfalmWJPU673ebkyZMsLCzQbreFnaUHn89HNptF0zSh4uT1MUmSqFarRKNRDh48+KDVyC43Wq0WuVyOQqHA2bNnWVxcpFAosLCw4LpEWBb1ep35+XlhURePx4UiWCAQEMpYHsGxr6+PlZUVYfU9MDBANptlZmaGsbExVFXlWs/L+iGCt4/zim0uZa/v2YZaloWqqkSj0Uveq3Y6HU6fPk2pVGJhYUHMjZ566nqyXyAQYGhoaMs+RlVVFEURCukeGXU7SJIk4hHr28crkqpUKrRaLTG/enPgjxO89o/FYkLpaSdUKpVVpdPQru+2RqNBLpejXq9j2zadTkcQHvv7++l0OiQSCbLZrCioBFcBK51Oi+fjqe165240GkLd3iXidxgaGtp2b63rOvPz84IY3W63aTabokDx29/+NocOHaKvr29DvKdQKFCr1cTct59q5ZcTjuNQqVSoVCqYpimIiul0uuu84MXuNU2j2WzuOtabzeaGcbQdJElibGwM0zRFsV40GiUej4sYWaVSERbNvb29jI2N/UgQeq/g8uIKAesKruC/Maan4YMfDHPgwFu46qrf2XHSD7XzhIwylqxR9fdhqRe/2Rgb+znGxn5uT5/tdDo0mw2aTXeDHAwGtgRWvQooVVVQFFWQo/r7n8Ozn93c9RyNhmtJ0my20DQVn8+/oQ1cSco2hmGKja4sr23sEolH7ek83WDbNpVKhXq9TrPZpN1uIcsSluXK2WtacDUuIiFJIxw6dO+q3LaGoniVClFkWeGaxc9x+9SfMFm/nwuhq9HVna1ASr5eevRletoLpBoXKEZcXwhF8W97Px/7GJTLO9vjXbX0RZ439UccrN3HitUiaLWQRTBs47ObQUHGwUShqUQI2C0Ux6SqJPjH4TfwT2Nv3qCkIctuAMl7PJIk4/NpXJf/Cr5sB7/dZt63VWHgr+NP2PBvv1lnvHmKs9Eb+Iej72C653E7ttVe8djH3rkvx9kNN9zwEW644SMX9Z1Hzv0zz7rwV6v94yhtdfvKFr/ZIGA3eW3kGkbHXsd3R396w981zU3e7FcMZnLyV5ic/JX9OdgOGB5+GcPD++wb2gW9vbc86DkBoKd2jpedeAu97Tn66vcBDjU1gYEfv9NmtHmGthzkVqvN0aHX8Y/XvPui5uKeHshfhLOD3997SfezHXy2joKNLq1t9u9K/eS2nx9onseSFO5P3cyHr/8r2u3Wqr2UjW27ySyPsKqqCks+H5Xs31DW06Q7y9S1pEgo/VPiyRuOrdo6B+onWAhOcHfmNu48/KuYSmAHGSOHVqtNs+lK+MuysoV8BXDddf9Kq9VGkiAYDO1oVXUpeNSjPrnj3z1pZXCDDY+Z/X88be5vGGmcRsZGtk2CdgO/o9OQwsiyg99uE7LrAAy2zrMQOsRv+sf4v1abudAhvjHwQu46/OaLvtZji5/neVN/AvX78FsN9C5z0a/LIf5AiROwW+iSD80xqMsRTvuP0pH9KJuUXMZaZ9HlIPeHr+f3x/5wVdknwCunfpdY/i4AcoGRLc/zeYFRnhdwq5zjnSw9+hJT0ev42NXvYj55w0Xfm4d3vMN9X3q44447aDY3jqFQKNSVhOXz+Th06JAIIpZKJZaXl0XQ2+fzkU6nSSQSq2ukppA09yrAdV2nXC5z9913MzAwwFVXXSXUfzyyRDgcFhYcXr/1FKsymYxQkQFEgFZVVbLZLLVaDV3XWVlZoVqtksvlxGcrlYqwdvCqCfv7+6nX6yKYPTAwcNFtahgGhmFQq9UIBoN0Op0dg+yeFLtt26LtL6ddj/cMPGWu7eBV8BcKBaLR6GUhhbnrSdcqxJOl30llKhaLsbKygqZprKysiCpLRVEEARA2JiPXH69erxOJRPD5fGJ+6+vrIxwO02636evrY3h4WNhU9PT0XHZrA0+RrFAosLy8TE9PD5ZlUSgUhLW3bdssLS1RrVbx+/3U63UR+N4u0Fgul0Ui8OjRo1SrVREALZfLwkJnaGiISCTykBDNtsPQ0JCY93O5HPPz88iyjG3bzMzMkMvl0DSN66+/nkQiIawYG42GsBes1Wr4/X7OnDlDMplkcHAQ0zQ5cuSIqNT02nZ2dpapqSkSiYSwV2g2m5RKJWzbZmRkhHg83lXiv9VqUa/XyeVypFKpbfuHV10/NTVFKpVicHCQwcFBoX78UAcuVVVlfHycXC7H8ePHt1XgAHecZLNZ+vv7u5I09xueleh61GrwsotYBsuytmX998rvv4Fril8nZpaYDh/bUe5RdiwO1n7AYnCCb0Sv2/FcP/mT8+L/X37vL/GIwlcIWk3mwkd2vc7B5jksSeVk4rH838wzt/3cF74AT3rS93Y93n7gMY/5QtffP/3Mn/DExX9mqHWec9HrsCWVgeYUCSNP2GpgSiq6HMReJSiF7QYtScWQfAy3zgHw4ql38+Fr34ve6XDs2P/Btt+Pq1osiUTCi6ffwxOynyVuFJiJXC3O7zObTDQfIGpWacohLNmds312B2lV5colsbD6/+7zfafk47XBSfKBYQAGGlMM69NomJir6tYAyioRyljd46tY2CC0r5tSkFlFW1VesJGQkCQZSYKOGeA1Wg9HRl/P56/u4oneBdcufJbnTL+Xydp9zAUP0dK2T4qVfP2kOllinQIvM1ool2Fvsxkj0eu57eq/4pG5LxI1SoLcZsoaJ5JPZC56Nbdd+Esm6sfRZT+LwUnoQiL0WS0izVOk2ovcsvBRzlz9Xtb2Ii1keYybbpoG3Pefa4uu4ve7BJf9Sqz6/T0Pek94eOUrvPzkb3JV7R7AoSP70bsQq2xJpi0FKDptDEljLjjJbz/xP2j5u6tIvuD+t3FD/ssMtGeYDR2irW59fyw6Dj36AkPNKXQlxCOr/8ndiafSarW56qqvoWlucttxwLJMTNNCVRUCgYBQpfcUDNeKaqRV23n/nvZ2T3ziNy66zR4Mfnrsl3iBXudA/TgNJcpycGLLPK2qDod75wnMz6OPjZF/9KNZfMUrmJubEwo569d5nhWR9w7ziMfd4Kn5r99f7ARvTR+JRFhcXETXde677z7S6TThcBhFUWg2m4IEXqvVaLfbXHPNNUJt9syZMyiKQj6fZ2pqSiic6rouLLzS6TRjY2NC6dNT19I0jWQyKYhde8Xg4CC1Wo2RkRHm5+epVqti3VwsFkXBhc/no7e3V6jBFgoFYeXdarVYWFgQRAhd1wVZvdFoMD8/z+zsLOVymUQiwaFDhwgGg8LCbG5uThQUjIyMCEXkndo9GAySSCTIZDKcP3+ebDZLIpHYMEd4doeWZdHX14dt26suEA9dIYFpmkxPT5PNZimVSoyMjGxR5slkMkLJVZIk5ubmGB8f3/aYnkpMq9WiWCwKm8hkMimUoz315Pn5eaE+I0kSR48eFSpYnipXX18fi4uLPPaxj92TjflDiYWFBZaXlwXRYmlpiXw+z/T0tFCs9shl3n7NU+TxyAue0o+n0uOtzb0x2Ww2xVrcay8vfnC5VV69sZTL5cT48fr9etXevSKfz7OyskKr1aLdbgsCViAQIBKJMDAw8KCf8czMDOVymfn5edLptCDLVCoVIpEIk5OTlMtlZmZmmJubQ5blLWQMT7nII3PuVvCxHoFAYAtJMJFIXFHbwSWkerb33jzbDYZhUCgUSKVShEKhbRXdDMPg/Pnz1Ot16vW6sPq0bVuQIQEmJycF0c8jO8ViMTRNQ9M0IpEIrVaLRCLBgQMHGBwcFKQ7r1964y8YDHYlDrdaLc6cOUO5XCafz9NqtcS4bLfbImZx/PhxlpeXSSQSq8IWBqFQiLGxMRKJBAcPHvyRIAbl83mWlpaEUqRXIBgOh1lcXCSVSgmlPg9e4V0ikaBUKm2rROnBs5D0+Xy7jp94PM7EhJtjjcVigoDpzVXRaJSxsTHC4TATExM/FHvfK3j44QoB6wqu4L8xjh6FQmH7zZRidzi88lVuXPk8Q42z4vcdOcDx9JO4Z+C5FKKTl+XafD4fpmniOKwmBZob5DVN09wSYPLsAfeKUCiEbduEQojEgyfn7jg2pmkhy9Iq+UolFNq/JIosyyQSiVULCwWfz0ez2URRTCzLXJVXVlBVTdhbuf927zcUCgoyWMiooNlu4FRXdr9GXQ6hOh3AIdQpUWRi1+/cfberfrUTzmSeQm32w+R9/Qy3p5GxAGdLBbOMs1rZ7CqWWCgYkobmGPgcnbRVJB6PbSBgbVfBqdkdZMcN+Np7qJi1VhWyJGw0u73r5/874IH+p/OU+b+jpiYYaM8wEzqCLW9tK9kx6W/PUFfjtJUIx/u3JjL2y77jYiA7JjbKg/P1uggEjApXL32Rm1Y+R0pfxkEiFxjmvsxPcrz/mbR9iV2P8exnw+c//+DOL9sGLzj1TjKtWQbaM2R9g5T9fYI4BO6c3NeeZax5CiSJp0z/H/7t0C/v+RwXQ766nNCVEKak4nP2NgZ9dlv0S1eVJyw2ibZtuYX0kiQIqgD39N1KWl8iVi8z0JpiOXhgnUKSC81qM9I8A0Bfe44bc1/kWOk/MWQfJ5OP496B21iKbU4ySgSDASGv3mq1VwNZ6ob3k2laq+pHgT0H6C83VLPF45Y+SaY9i4TDTPgoQbPGcHMKzTEJOQ3aBGlKIYJOi6DdxDYkUu1FUsYKZS1NXUvyvcHb935Sx2Go/APi7WV8VgvFNmgoMZKdHMtdCFjq6pxuOTIaBobkoy2HsZQAOA6WZSHJEhIySNCQw4SsOolOnmDArXyM+GQOVH9A3ChQVtMbxlA31LQ0fe15wkaFycK3L4mAdakODcFgkKuuuopsNkskEqGvr8+951WSQSQSWVXJdJidnaXdbhOPxxkeHmZ4eFgE8LzgfD6fZ2BgAFmW8fl89PT0kEwmBSGr2WwK1R9vjeWOK1tUSHuBwmg0yvz8vFCZikajIoExPT0tAvtekDGZTJJIJLAsS1QD9vf3dw1ueEGobokRL+jrVWp6VfHbq05Iq/bNlgg27bfVnYdWq4Wu61QqlT2RKhKJBNPT03Q6nT1/52LgElMtDMPYU8LE7/eLpKIXdLNtm0QiQT6fF9Xm69ffnU5HBOoNwyAajW6pHDVNk1QqxYEDBzh69OiO9hNeladHPNxsLXmxKBaLtFotlpeXhdpBPp8nm82i6zqxWIxMJoMkSZw/f57l5WXy+TwnT54U42xzIKzZbJLP5+nr6xPBV4+wWCgUBAFLkiQCgQC9vb0/VBsHSZI4cOCAUC/IZDJCpc3v95NIJJiYmKC3t1dY/nhWK4lEAkmSWFpaQlVVEomEsL8AOH/+PFdf7ZI6vGSXl3gslUoiwOxZoCYSCWKxGAcPHuzaHwuFAs1mE8Mwdq2y9Wx/yuWyCF7+MHHVVVcxNTXFwMAAp0+fZmxsbEMlsTfPzs7OEgqFGBwc5NChQz8Uq+IzZy79GPdlfpLh+mnSnWVCZpWmtn3AOK5nsSSVupbkvr5n7PkcfquJ4lgYe1SjNCQfmtPB9yOwp7u/7+nckLsLs62S0LNUfWliZonAqvJoSw6J7a7iWMiOjSH7WAqMoToGA+0LNLUYY9X7mU89UqhYeGpHiqK6xANfAlPW8DkbFa3WY70mjoyFjbyqBLrWN11al/d/a8gHBhjszOA40jZKZZL4jleIJeHGjwwt7BYteJ9YPYmCgyTLYp++F4xUjxMxyliSRqsL8WbjJUlU1BRRs8Ro/eSqAtfl21f2V07wU6feSUJfIdHJEjeLq+pnEg0lSlzPEZ37EBIOmt1hJny0K/kKoKMEyfmH6NGX6G3PcbB9mqmQOwdblo1pGjirsRBVVdE0Vdii+3wPD1WLc703Ezhex5IUt+CDbZ6z4xB0WphoyI5NU4ly3dIX+M74K7Z8NN6c52D1+6Q6KxS13q7kKwAkibx/iJhRIqbneFzp3/hB5hlIkoxlmcIKDlglrrsxtvWJa3esmWLt6Y21hxtuWP48sY77Dl4Jjnft46YpYUR7UBJ1lGyW1NmzNGwbZ3hYEExisZiIxVarVUzTpL+/X6zrdkvI7mTPsxm2bbO8vCyI1J5FnM/nEySser3O7OwsrVaLAwcO4DgOIyMjBINBjh49ytzcHJFIhHa7LQgZ3veDwaAoBAD2hSyjquqGghlPrWlhYUGsT7xkvVdssbi4iKIoTExMUCwWkSSJRqNBJBKh0WgI0rynULO0tMTS0hKHDx8WKleeCq2qqui6zokTJwiHw8zOznL48GGxz+vt7d12LX306FGy2awo2llcXBRW9J7NVjAYpK+vj2g0KlRcGo0Gi4uL+Hw+ksnkZV3HFAoFdF2nUCh0JV+Bu6/r7+8X9nnec94u3hKLxYjFYtx7r1vUXC6XCYfDJJNJQUDzbCk9m2tPLaxUKok1PrgKNydPnqTValGr1XjiE5/4kFhf7wVzc3MsLS0Ju8RIJMLo6CiWZTE/Py/UWCcmJrAsi0AgIJR9POJgvV7H7/cTCATEutuD685hbxjjPT09wk60UCh0LbbYLxiGwdmzZ6nVapTLZcrlsij68uIQpVJpT6oyjuMwMzMjxsP6vZw3h6RSKer1OsPDwxf9jFutFtVqlZWVlQ1Wbo7jiLbyCvL8fj8LCwsiBuOp4XlkN0mS6O3t/bGyCbzc6OnpYWVlhf7+fpaWljAMg2QyKeYQT9XaUydLp9Oib3skJu8dYNu2UDpbWlrCNE1RfGuaJsVikWq1upp7DIkCIi+e4hWJeTGcwcFBQfKORqMbxmGz2SQajTI4ONhVMc00Tc6ePUs+nxfkqoGBAfG5sbEx5ubmmJqaQpIkstkspmkKNS6PWDkyMnLZyZT7gYWFBZaWlshms5TLZQBRfLq8vEw4HKbVatFoNDh8+PCGe+rt7RXEaa8vdEO5XKZerzM6OkoqldrT+y+ZTBIKhchms0SjUUHG85yV0uk0mUzmx0597gq2xw8/W3QFV3AFlw2SBL/yKzXe856tAYtwO8cLH/gdBpvniHfyxI0Cmt3BkSSaSoREJ8sjc1/iq0Mv4+6xl12WIFYoFBRVZrZticQSgKoqaFrgkgJMkiQTiURoNpvIsmu54pK+HCRJJhh0k1fupiS4rbXBg4UkSUQiEfx+P61Wi3g8RqPRFOQyN5gmiSo8n8+PorhSmuvv15ZUIbe+Jm+/6VyOhd9qITvWuiC1tOdA57e+BbsV8VuKn88e/FVedPodxIwCMbsqhM83/9jI2JJMSw6CBJajIK1KpbuBYmlPbPu2GhH3oFntrooq6+GzWm6AGWVHJajdMDAAS0sP+usPKdpanG/134F/oclI4wzjjZMU/P1UtTRIMpJjEzWKpDtLOMgsBSf4dv/ttHw7S/FeNjgO/ZUT3LD8OQ6Xv0vAamJLMtngGN/PPJOTfbdg7KLydrG4cfYfed75P6G3vUDUqghSnyUpPKL4NbIzH+Bfx3+Re4ZfsONc95rXuASMr38d/H7X5nWvOJj7Oil9mb72HCv+Ycr+rZt8S/axGJxkuHmGTHuWR+S/wn+Ov5r2DkmwhyOm4jcw1DjLSPMssm1ssQ4F8PkdOrqEz2wSsJssageYjl8v/i7L8o4blu8Nv5Dx8r3IjsVI8yyTtR9Q0dK0lDASDhGzTMrIo9oGuuzHkFT62rMAmLKPhJ7lusLXOJV4DJ8/8hZMdf0EKK1WmdI1gO9JHiuKQiDgJxB46BVQuuGq7FcIm1ViRpH54EFsSaWhJcn7B+jVl/A7LUK2O94sZFTHIGpV8bd1FgPj5AIjfG7ijRQj21un+cwGmdoZAp0yk8W7majdJ9YP4JDsrBC2qvhtnZYapuLbXMXnKj8oWDhIWIpG2Z9BVhTw1GscB8ez8VlNokmK6gbbI2GCnaKwJdG13QkutqRgyD5UxyBkVi+hhV2Y5s62nrtBVVUGBwcZGBig0WgIckogEMA0TU6dOsXc3ByO43Dw4MENweZjx44xODjI2bNnKRQKxGIxQqEQAwMDopKzp6eHmZkZwA1ajIyMiL951dONRkMQrzRNw7IsSqWSUM/yZNFHRkZEkiGXy4lkgkfA8oLjy8vL9PX1Ua/XtyjcFItFZmZm6HQ6lEqlDckT7/z5fF5Uv4Mb1NzOLs6zz7Msi56eHhRFuWRSz3bw1oymae7JhsL7jGEYovpwP+GtmRVFQZZlEZTeDaZpCqJVJpMRgaoLFy7Q398vLCsDgYB4JuVymZ6eHgKBwBbCUrlcFlLr3Z5Tt+pl7/pjsRi9vb0Pujo3l8tRLpdFFamnhmUYBkNDQxvGy9DQEM1mk3Q6TbFY5IEHHqDVanH99dejKIrok5VKRagkjIyMiO977XUx1cgPFSRJYmBggL6+PnEPnupuf38/hw8fptPpCOLjZpsBz1ovmUwSCATo6elheXmZSCRCrVYjGo0iSRITExMkk0lyuZx4Zl6SOhQK0dvbu6O6RKfTod1ub1BR2wnhcJhyubzFJvOHgWg0yuMf//jVohmVubk55ubmRAK52WwKq4kDBw5wzTXXiIrYhxqVyqUf44G+n+Qpc39LSetlsD3NnHS4q+pyxCiR6SyyFBhlJTjKUmLvljS6EsKWFAL23pR2NKeDtaoc9XDHUuwYy6EJYp08A+0ZQmYNxbFQHJOGEhXkK9mxCDpNDFnDlFSqvjQgkTRyRI0SNyx/nvnUI5Flueu4Opt6HDdm78RhlqhRpOpz10GmsmrNJSkojoWJhuQ4aLZBWwlSVxMbjuOskrIUx8ZYZ0lvKCEqSpKkWUDDRMXERkbyiquQXELVOmvAjuTDkP00tISIUngOeT6zieboNJUIheDwntvTbzWRHdO9tj3EDEzZh2zV3PWh1d60rt8/pOrnefHJ36GveYF+fY6mEmbZP0pHDqA4JlGzyFDzHEG7hYRDRU3uWkS2VihQ5kj+P5g/dAOqqq7aT3eEPZ6iyGiaD5/P97BSLbBlzVUjbklYKIScJrrtX3t2joPqmPgcHRmHphJGXo1djdZO8J0ux5wsfAuf1SJgN1kI7VIQKkmUtB4SZoGB1jR9cp1ytAdd11cV2byPufYyfr9vg+q9O9YuzzpyvxDUC0zUjpMw8pS1nm2KTxxkxyKfVRns6cF/9ixWq8XQhQtoj340oVCIarVKtVpdVXmWRTFFMBgUKqnbwSNtlMtlkWjcCZ71tbNaYHP48GGheOsRLDx4tn+ePbqXjPb7/Rw8eFAQdrwijc1FHPsNn8/H0aNHKRaLIoEty25s2VNC9azrPJJTX1+fUFRLp9NCJcWzCltZWaFSqdBut7lw4QKjo6P09vZuIH54yk0+nw/btjl16hT9/f3CMspTFJ2cnOx675lMhoMHD4rnnEqlhB25d/2e2qtHJujr68Pv93P27FlByvGS9JcjQZ/P5ymVSmJvuhM88o/3/LcjyfT29gqL7Ww2SyDgFsit3yfpuk6n0yGXy9Hb2yvUpmdmZrBtm4GBAVKpFIlEgsXFRU6fPk2n0xEWlJeTeLQXeGSfubk5bNvmwIED+Hw+DMNgfn5eKLF4tmteUZNHqvJU4zwFnkqlsoXE5K27149tr0/U63UajcZluz/Lsjhz5gyFQoH5+XlUVRUqQLZtC7KMp74L7KiK5il3zc7OYts2PT09xGIxQUb0lP8ajYaYUy7Gji2fz9NoNOh0OoyOuqrrhmEwNzcn4h0eQcYrdNJ1nQsXLgBrNnkeGWtiYoLJycsjfrAXmKZJPp+nXq+LfuDFlR4OhaYXC0VROHjwoJj7PLvTYDCILMtCDS2RSNDX1ydiWl6swIMkSRSLRUzTpFAoCIXo9W2iqioPPPAA58+fJxqNChJkqVSiXC6TTqexLEso2QPi3eupWHuqdAcPHiSRSDA4ONh1zvHsD73Y2+YiOU3TSKfTVKtVpqamkGWZSqUiCILrx/LDXSmtUCiwtLTE7OwshmHQ39+/wSK+3W6Tz+fF+3RqaoojR46Iv6fTaXK5HCMjI6KwNZVKbVHzrtfr4tgXo+bv9/sZGRlhaGiIVqslxo3Xx67gCtbjR28WvYIruIKLwqFDJn/+52Xe9Ka1l3fAqPCSE29lpH6K4dYUFgplrQddCSFhEzVKjDTPUldi/MT8R0GSXBLWvkNalU73oesdVHWtws+tjPLj82kXRYxKJuGtb4W3vGX1DJJMOBzBsiw6HR3TtETiQFUVenp8NBoPbio8fHhvFcee3KhpmgSDIUzTEPZaqqoKNQfXgtC35WVdCI6gK0FsSSFslKn71jYGPqtFspMlZhSRsZAcV01GxqanPYeNzC/+Ipw8CV/96vbXePfdsJf110z60Xzi0Nv4meNvImXmCawG0iRMQMZeJT/ZkkRLDotKWwU3EWlKGjPRa3Y/0SrmEtdjyH7avggZaYU5dt6kJ4w8NTVBWw2zGNv7eTbjVa+CL38Z/uu/HvQhHlJ8a/xVRDsFHCTS+iIZfYH+9iyWpKE4BrakUNHSFPyD3Nd3C9+Y+NkfynX6zAbPOfl7HKx+n7BRWiVuGNiSTE9rnqHGGZ4697d8+tBvMJN+NACvfa2rPPW8521/3ETCtdDshief+0t+eurdxI0iftrgsEZidGBInyHTWSJxJofPbPDtiVdtf/0++LVfg1/8RZeAdeoU/H//397u/ZHLnyfWyWNLCuUtpJR1WK3kHWueImjWuHr5S9wz8lN7O8ke4DdqDFROEDBrGEqQlehh6oG9JZePHYM3vcl9Jjvh3v7buCl7J7rsp689Sz0zRq2+Ns9KMoyPtzhzKkCfPkdDiaGrYe4buHXP92HLGp+6+u3cdupdGLKfiFEi0ckTsaqi6t5GxpEkVNvEklVqahJbVgiaDYZa5zF0H5rVxv9Ag09c83tY64LvbnI5jM9n0ul0C+Brq6TZi08Ov/718H/+T/e/JZPwkz/p2oGut7rbCyZL3yXaKWJKGk11rQK44B/EklQy+gIdyUR1DBRnVRFBkmnKEWYjV/OFif/BVObmrsdO1ae5cfHTXFv4GgGzzkjzDCGnjmobOI6EKWnYkgyOg29VgXGycYIVo0zBP0BbiSDhoDgmmmOgYlFXomhJlUAqgpyVXTK2YyPZa1WX0U6DthyiFsgQibobbms1yShr7ltnVzhuQsJBwtyHBMviIqzG2S4JHkl7Paanp6nX67Tb7S3kKw/JZJJHP/rRnDp1CsuyiMViHDhwQCRQAGHDtrCwwIULF4hGo8TjcWzbRlEUzpw5I2T8q9WqWIv09PRQr9dF4HZ8fJyenh4cxyGRSAiCRjQaFUFCr5rTNE1BUvRQKBSYnp5mZWWFcrksKvxcdbkWFy5cIBgMigC5ruuEw2Eqlcq2FieNRkMkQgYGBoTc/+WAR3gCRLB3J3ifWf+9/YRXrR2LxWg2mxuCPd3gSeBLkkQqlSIWi5FOpwUpamVlRQS5vX63tLREJpNhYGCAaDTKyMjIhuOXy2Xa7TYDAwP09vZuuU/DMDh37hzVanVL9bLf7yeVSolg5MTExEW1k2VZouI4Ho9TqVQwDIN6vc7AwMCW8eJJz3tjrVqtUigUuPfee8XYC4VCDA0NiXH0w6w83ktCczNkWSadThMKhUQQcXBwEFmWKZfLtFotTNPcEmT1kqyeLU4qlaJYLFKr1cjlchsSe57SVafTEQp9HmHvvzvGxsZQVZXvfOc7DA0NsbKyQq1WQ5Ik0um0+Dl8+DDHjh37oQVcu7jeXjQ6apg7J36R28/9MUrLYrx5kqoap6qlMSUNzdZJGnlCVo2sf5hCYIh/nfzliyrSmo0eY3JV1Ua1dUx5e6K9bBtEzArzwYPMRo9d+g1eJJ77XPjMZ3b/3C23uHtGJIl/nfwl0ifeiuKYjDdPozjmqlaUg2ob7vrHMTBkH20pyHJgVJBzakqCiFFipH5yx/PNpB9FMdBPSl+iV1+gqUYxZT+2pFJX42i2gd9podt+Ak4LR3LXaCXfVhuTjqShODZsWkvNhQ4TqX0P2XG1sxSs1Z2TJbSNbORVhSx3z1/UeummaJ3qLNNQouhqhOP9e1dLc8l6Kppj7EnRSrU72JJLDTOVyzc3PX3qL+lpz9Ovz7EYGKfm22if19TiWOkkYwvfR3YswlYNxTGwpO2JDLakoMsBNLtDuFMGEOoJ3nv/cuJ3fgd+93dhD8ucbVHXkjTlsNvnkPA7OgG7jS1JboGeA4as0ZYC1NQYtiOhYOG3upMxw0YJzdaxJWXHecKDroTQOm71XNAoUwv2EwqpgLOOBP7wTojFmgtcv/Q5JsvfI2g1MGWNpdAk3+9/NrrivrP9dpucMrTuWw5Ro0SikyVkufby1ECqqGBZ0GwiVypcddVVwlr8bW97m1iXaZpGNBrlgx/84K7rsXQ6TaFQEGv6ncgKnk3Q+jjngQMHqNfrooBkvWKEtw/pdDqiQGP9usXv9zM4OPig2vXBQpZlYSscDAZRVbcYaGJiAr/fz8rKilhDDQ8Pb0j6VioVYbXofWZhYQGfzydI78PDw6JIBlzylUcwAZcEnslkiEajYq80PT0tCEGHDx/eokCrqiqZTIbrrruOqakpdF0nk8kIAkgoFBJkgeXlZfx+P7lcTqhGAUK1q1qtcujQoX1d69m2LZwp9kKe8xScGo3GFuvl9fDaIZVKMTMzIwrlvIICb02cy+WECpgsyxSLRUEy6e3tFYolBw8e5Pz581SrVU6dOkU4HBZ26z8sZLNZKpUKuq4zOTkp9jzeu0LTNMLhMI7jiL299/elpSUikQjxeBxZlkXhynqCmrdn7GbD5sbBnD3thR8slpaWqFQqzM3NkUwmt5DtwuEw6XSa+fl55ubmUBRF7G03Q9d1stksCwsLwvZv/V5WVVV6enqIRqPMzs6KMZBMJve8L/XUq7y5wTRNZmZmqFQq4jl5xaSZTAbTNKnVagQCAWRZFkQQb5wNDQ11tZu73LBtW6in6boulJy8eMPi4iLpdJqRkZEfOVJJKBTiyJEjzMzMEIvFqNfrIgYSiUSIxWL4/X56e3vFHnp97MKL53gqVtuRorz5vNlscu7cOWGP29vbKwi4nqV9NBqlr68PWZY3KGx5jgqdToeJiYmu71fHccjlcoLAupl8BQhibb1eR9d1sV9NpVLous709DTpdBrDMBgfHxcEwIcbHMdhcXGRXC4nrnVznCcQCDA8PMzy8jLz8/MEAgERIwJ37jt48CDgEvKKxSJLS0sb4nWRSISxsTGi0SiHDh16UEWdnh3iFVzBTrhCwLqCK/gxQDDo8KxnOdx5p7uYvHn6www1zjHcnKKk9ZAPDG8IatW1FHm7zUjjLIOtKZ4y/zHO9TyBYnj8QV/Dc54Dn/1s97+5KlfBS97Q/MmfuMpF3d59LhN5a0XXrbfCxAS85z0Xd66nPhXe8AZ44Qu7//3lL3eT5+sVuj1fZ1i7T89WoFvA8pd/2VXbKWQfTf0dPVTaKdKdZepaEkWVCLfy9LdnUB0Dn91BdUzARsWmI/kImzXeab+ZaOBtjD3zKTsSsGB7AstmnO99An9/+H/y+hO/xIh+AXCDsPaqtYEh+TAkH47XpxyHgN3GQsWQfdw3+Jxdz+HzuYpclUqKc/2P41BngejyBXJOZlsJ+pBRIWaWmY0c4b70UzHVILffDi97GfzSL8Hy8t7u77bb4HGPg0jk4ghYmrb/Fn5jY6Dr8FM/BdddB5/7nEv6Safhm99c90FJ4kuHf4XlyCFek/g7SrM5zFITuwMWCi01Ssnfx7cHns+vf/V2Xh+XhIrL//7fcOed+3O9j3wk3HNP97+pZosXHX8rE9X7GW6dQ7UNqmqSqhZBdiwSVoEhfZ4FfZgXnX4H/3z4t7nQ81iuuw5kGV75Svjbv914zN/8TTh0CFIpd6zNzMDCAnziE3DuHExmv8ZLz72LhJnH5xhYioSjaTiKQqcjI1sWPtu1xhxpTfH883/MYvxqZlOP2nL9r1jnjuAVh159NTziEfCDH+zSMI7DSOM0EbNCRU3umkRoK2E6sp+IUWa4dpJtmnRH3Hyz209aLfjiF10CzaMWPsGx4tddqzjHdC1CJY3TiUfxvcHnCWu2N7wB7rvPVfrycPAg/P7vu5f+nvfAb/zG9ucuRCc5HX8UPqvFWPM0seYZWr0DZOtxZNkh09tB1euMtmeFNcf3e26h5U8zNgbZbPdk4tvfDv/5n/Dtb7tzo6kG+fSxdzBcvpcblj7H0fJ3kB0LzW4z1jiFIWkYksp86PAG+5KKD7LOCP2tacZap/EHYW7m//GtiVdvOefavO1grxKDZHl729Tt8Au/4PbVyUm3DfN5t5+ux+HD8Ed/tPbv3l5473s3vkc2Y2TE4dw59/+DZg2NDsGUxMhAh7nZ1YSFJFH291H19RDr5IkZRTTHwG83kR2H5cAo//umv8VSuic4ji59iedM/7lLctNX6Oss4qOD7FjYskyLIDYyS/5RbFmlt71AyszTkXykjBxRsyLeB4pjYUkKkmOj+B3MAwdISDbZrLsJViQFRwYcB7/VIOw0WQlMcCJzy1q1kxqj76og/jk/6WKeCjsTCANWHdUxaKpRcsFLt7Sam9sfAtZmeCpUpVKJeDy+a7Xh8PAw8/PzxONx/H7/hjVUT08Ptm0jSZIIJnl2BaZpEg6HRZV2Mpkkn8/T29uLrutC6coLNKdSKdH2gUAAXdcF4coLyHpYH7Rst9vMzMywvLwsKrc3k1s6nQ6Li4tCkcs0Tfx+v6hQ7unp2RDs86wovGRHb2/vZQ1WBgIBJMktFqjVarsGV6rVqlDvuxzkFL/fL+wmjh8/LoLo2yk0VSoVOp0OmUyGVColLBGHh4fx+XxomraBhOclvgzDQJZlhoaGxH10Oh2KxSKlUklYJGxue8uyhDrb3NzchuplT+rfq0D1FG8PHNhecW8zvO+4FuWaIAx5Ca3NcIstVGGpubKyIvq0p5blqSh49oMPJWzbplQqkcvlaDabokDEUwlbX+W5l2OBu6/wxky73abZbBIIBLbMJ+tJgo7jiErnnSrcvUKRi4GrNuKOaW/O2AnNZhO/37/BFvOHjaGhIW6//XZh6VCvu0luv9/P2NjYthY6+4UHHniAXC4HsG0l+g55yYvCqb5bUCydWy/8bypGmmQny1DrvKtuLClU1STZwAgVXw+fOPJbrMSvvqjj3ztwG49b/jRtOURve46loLso6uvrUC6r6PrqfO849OgLGLKPphbn+wO3dT3eW94Cf/7n+0NAW49kEl78Yvi5n4O/+zv4t3+DVfdNAb8ffv7n4WlPgze+EWZn4dSpo/zD776DF57+XZKdLEkjj4pF2KoDEqak0lTCWJLGUmCUmm9NxdOUNQJWs6vdYjgM//f/wktfCo6k8B/DLyeprxCwmow3TpLzD1LT0pS0DBGjjN+GuFPGliQ6/hC6Gqa1SsyXZEi0lvA5Ok0liq0oHFBnKCZkZspuwlNXw1wIHWakNUXIbgrVUhsZXfIjOTYB9NWiK4WWEqIU2JSUchzS+qK7Nw8d5oHk42n5957omY8do174N/raswStmrj+rnAc4maRgm+A2chVl81+MFU/z1j9AXr1RQq+vi3kKw++mJ/WUshVXcYirucoBnYmj8irSuG2vPMcOTLirkP3C4riFtj8zM/AX/+1y9l5MOjIAWpaAt32IwM+q42KieQ4OBJYqJiyRknrpeAfZKxxkjYKutI9/mdJGo4ku6rVeyDgydhir7Gx2OLyEOL3E5/6xzbl3/s9qp/5TwoLJrFOAdUxcCSZ0fpJri79JzU1iWa1Vy0/3XW3ZrUZaZ3FZ7UFudNTy3eqEpqt48zO0lxZEfbi8Xicz33ucxsUPoLBIB/60Id2vc5YLEYwGKSnp0eoNHV793lkfm9NAYiEqKccpeu6ULPyFCMUReH06dM0Gg3q9frDSp2jt7dXWFJ7Fm61Wo16vU4wGNygFOUprY2OjgqFG8uy8Pv9NBoNVFVldHSUyclJsaayLIvl5WWhnDMxMeFazuq6sM0bHR2lXq+LwomZmRlhGb0ew8PDom0XFxdptVqifavVKmfOnBFrzlgsxjXXXCOeT7PZFMrFQ0Mu0e+qq67aNwUcb8/oFQTtBYqi7In8MzQ0xKlTp0in06iqytLSErVajXa7TTgcFkpmnmqrpmlir+y5VniEGUVRiMViOI5DqVSi0WiQzWZ/aNbYnrV9uVwmkUhseB66rhOPxwkEAsRiMVqtFuVymdOnTxMOh0UfCwaDxGIxenp6qFQq2LYt5gHbtimXy4RCITRN20Jq8pSxL9e63LZt8vm8UMTeTulMVVWGh4eZmpoShNLN12qaJgsLC0IV+MCBA9tet9/vJ5PJsLy8LNplr/OO1ye9Y3vxjmw2Szqd3lD4YxiGsK+XZVnEeTwrVU9x/KG2hLNtm7Nnz1IsFlleXqbRaAgyn2EY5HI5QSJttVocPnz4R46E5VnZNptN8vm8mBtVVRUqiqdPn6ZQKLCwsCBiF4FAANu2mZqaYnZ2VhR7raysbOmfXr/0VJ+z2Sz33HMPo6OjYt6dnp4W5+3v7+fIkSNif+zF6trtNoFAgIWFBWKx2JZ5t9lsYhgGlUqlKxGs2WyyvLwsFMjHxsYEWToejxOLxahWqywvLwuLxWAw+JDHP/aCarVKu92mXC6TyWR2fAdlMhmh+pjL5TYot2uaxpEjR4RdYSaT2VBM5vP5hP1ktxiHp7K1WX3zYmI0V3AFcIWAdQVX8GODW291uPNO8Bl1ri38Oz36Ik01soV8BRAM2YRDCnMrhzjQOEHIrHL94mf5yqE3dj22JMGf/ikMDcG//IsbqNyMV7zCte357nfdwNEv/AK87nU7X/PBgy6p5eTOhaCAG5hcJTcDOyuMrEcsBk98ovsDroXDq1+9e/DpFa9wr60b/u7v3OPm87sTWyRJWquc3YSbb3aJSKBStJ6E/v4LhC9kuTbyAEY4inphGcXW0WwDU1JpywH8to6xSoaK2BWk5fOY73gHI+8KMjHxGKand2mQdRgYcMkl3e7h5OAzOTX/aOL5EkG7iYSDKWnokh/bk3RflZv3O20ULOpylB8knkg9uL2/eiDgKphddZXbvqUSaHPPg1/7NyyjzJHiA8zo41R9PUJdS3ZMEnqWPmsRZSzB+ESIzLuew89f4x4P4Ld/G971Lpifd0kQb3mLSzB53/vWyA0vfzk8+cngrafX96f1CIW2Jjre/naXjPPRj25PNLxYPPGJW0kuP7sqXOU4cPvtm74gSZwYfS6j//hMGt/4Bsb3v8/3vwn3no4zH7uGmfSjmDykEFtdj3pr2Oc859IIWG94g5uE6Olx2+A1r3Gf22Y8afqDjNeOM9o8TUOJkg2PYa0GmTXNIdKfwjILDE4voHZsXjL3bvS3foSxsQQAz3++e47jx10SyzOfuXHqkiQYH3d/nvAE93ez1/4mMaeMz+nQ0XwQXCOYSJpC2/HTkWyCVh2f02GgPcOTZv6Gv+tCwLrjjq33JEnus//a16BahUYDPvlJl4inaRCNQrEIimMgr9qQeJaaPp9Dp7PNon21Wl12LDRrZ5/DZzzDJeWtOo4BcOQI/OqvusQ1gGurd+H8yfuI6XmSRpaIWQUcAgGTqh0jls1ztHw335p8Gc/7fy/n4EG46Sb3nk6ccNv7TW9aa++jR+FDH4IvfQlWVtzfTU1tTAh84ehbiN+fB+AgZ+lZOkkyEMBRVeQ5A7nTwYlEOWsc5Uz8Jr568BcBdxwuLMBHPrLxPh/5yLWfN77RTUR99rNuW80nb2A+eQNfsHQCRpXHz/wtypLBWOs0F0JXoasuaaKnxyAWs1hY8KF3VKqZcdKt40z4prlV/gzfsV/W1S5x9aGsEq824tAh9753sqN8wxvc57QeP/VTrrLCetLmz/3cxs/8xE+45MKPftQlnHUjkb7oRQ61Wo2zZ1WuClj0nWyj1ExiUZNoTKVWXQs42ZJC2d8n7C97W7P4bJ1CcGhb8tVk9us89/yf0d+aJmEUMFAxFRXN6WDKGg4QsHRwJAb0WeaCh5iOXEO9PcOAPkdHClBRUyBB1CxjI6MoFpIEvk4LK5tFGdiYlJJwx8xwe5qamkRXIxvU0W56lEQqfQvOR+ZIlM8wGC2yWNumCttxXOKyGqelRlk6egsH+tzndfCg+45QFFfpoq/PJbx+73sbiXCbMTu7NsfsJ3Rdx3Ecms0mw8O72/R4ZKB2u027Swf0qqaz2SzFYnHV+tihUCiIf5dKJRYWFiiXy8iyLNSU+vr6qFarwobMg2dfAm4wWFVVkUxQVXWDbahH5CqXy4yPj3cl2ft8PkZGRjAMg1KpRCaToVQq4ff7aTabzM7OCnu8ZrPJ0tISlmUxPj7O0aNHRXLjckFVVRKJBKlUisXFxR1l+B3HoVgskkgkRBD8csBLAoVCIZrNJo1Gg5WVFRKJhGgLL+myvLxMOp3m0KFDW4JXmUyGnp4eisWiqPQcHR0lm83iOA6NRoP5+Xk0TcNxHAzDIBgMMjIyQiKR6KrQtry8TLlcZm5ujng8LuxYPIRCIVG9PDs7K6qX9xrs9oLcnv2ip7q2HdnNs73xbHai0SimadLf38+hQ4dEguWhDngDokrWCy7W63URyPdsRCKRyIYE3U7w7sGzHAmHwyJZ1S1Y7inXrU+oqKoqfr9fSKVSrKysbLC13A6empmXiHg4QVEUxsfHhd2JR5a7GHj2HsVicYMqnFeZvF2CaGRkRPSBvr6+rp/bTwLSicFnk378UbS7Psd1+X9fJQQ5gERDjfH9zDO5d+A2GoGLJ79WQ0M8kHz8KkH/FAOt8zCSIdUjk04b5PI+mmWTHn2BoF3hQvAI09FrycWObDnWzTe77+Jrr4VvfcstnHnc49w93/33X1obfOQja2vel7/c/QF3jTA97f5tbGxtn+n9e2wMyuVH8DcffT+v/ebLua7ybXxOm6YcAcktWKpoKcpaH51NFnma3cGUVHS5e8HYep7Dqf6fJKZneer8R0m1l+jRl+hrz2FKGj6n4yoOyQ6yBIrmEDyQ4LBUxKq0CNVzqFqbct847WiKeEjHyK2Qyp7H6ZQoqz2rqqYqUkiFFuj4cGyJjuTHWiWRteUQCaOAotgkfFWGmmepqilsScFvtUgYeRTHYj44yXJogq8ceD2wdwLRyb5beNrsX1NTE/S2F5gLR7axXYNkZxnZsan60tzT/+zdD74DdoohXbv8RYJmFZ+tU/R1j2cEQzbxhA1BDauu4LM7JIz8jgQszWoTlJpEBzR+8jUpUofhW99ySKfdPfY3vuGuVV/1Knff/uEPw6c/vfu9ZDJuUctOeP7z3aK35z7XJRNWKu5e+y/+YutnX/5y+PjHu+95ZqPH6G9doL9d5Vz4WgJ2k7BZdYs1kGkrYRpaAkeS8ZmuteCSNsHcNup2+dAYbSWEg0TYLNPQtio9rEfEKNNUIhiyj9omMqBi6fisJr/7x0E+9NEADzyw9fvbxeM8qKoby9xv3HBMp/wrb0a55x56szNEGxYNJbpqaWnR354h054j5x8koy/SkTTCZoWOEmSseQq/3SJouUp3hqThICPh4DdbyI6F3OmgfP7ztF/xCgJXXXXJ1zs2Noau61iWxdzcnFDY9dT+vSRoNBqlp6eHSCRCLpfbss5aT3ZZD1VVhQXxwwmejXsikaBQKBCNRrEsC9M0t6zRPBvlwcFBhoeHCQQCdDodZFkml8sRCATo7e3d8D1P0bVcLhMMBllZDbB4do8egS6VSgm7vXg8Tr1e30KCk2WZgwcPCtJ8u92mUqlgWZawiveu7+qrr97wbEKhEKFQSJARfD4fuVzuomyZdoKn+qJpGrqu7/p527bFfnM3EpimaYyNjZHL5Uin0/h8PrEHbrVahMNhNE0Tx1pYWBB7m3A4vGXd6RG/fD4fpVJJqAL/MAgo7XZbEKY2kz88m0+vfTw1HUVRRB9QVVUUZHhjT1EUoaZcKpWwLEtYmq2/x3q9jmmaRKPRy7a3LZfL6LouCrZ2gldY4xWsearYxWJRFLScO3eO5eVlbNumt7cXRVG27T+xWIyVlRWq1arYx+8F3jHr9TqdTodarUaxWBR9yVP4kySJcrlMPp8XhKuzZ8+STqcZHx/nwIEDJJNJRkZGLrbZLhmzs7OCcOnZ5q3fU3tkIs82cWZm5odmtX6pCIVCwipyPWZnZ6lUKszPz5NKpbYUtYXDYZLJpCCkeQTFzXGtUCjE5OQkxWJR2L16c4hpmqTTafL5PEeOHNlQfOZZvnpK1IuLi0KZcPO867roWGKMb0axWBTEw8HBQVRVpVKpYJqmUET3Cjc9hfxsNrujlecPC5VKRdgY7zbveGptnvrc5j26LMsMDg4yMDBAtVoVMVdN04jH41331bquMzMzQ61WE2p3sKYa6qlvdVMhu4Ir6IYrBKwruIIfE4yMwFOeAsWPfYWgWSdiVpgJHd7AYEimTDTVId3jVk81GkFqepxEJ8t1ha/ytcnXY8k+AgGXZLGy4ibon/508NYQ2ylDBwIu6eoXfmHtd095Cvz7v6/9+2lPg/5+WFqC6693yTCOs7P1mIfHPW7jv2+9Fc6fh7vucv89NraRoOBh81ojHofHP36j8stmvOQlLhFkO3hr+9e/3r2fs2dd1ZZu+IM/cMlG9923MUCWTHrkKxeJ17ya0n99F1vroE1PE1xawsGt9jP9PpyO5KpMSQotOUTRl4FEmKHCOQxVpfPHf8Tvffif+MQnJWo19/j/+I/b3wO4BJenP90lWWzOxdiSyj2ZZ3EoeIGB2ftWw/I2YbuO5bjy/zI2smMj4WCpKjltgM8c+vUdz/niF7sECw+9vUDvMaq//uvY73kPjn+JocV5emsLtFel2INWE1NSMUb6kcb70H77txi6aXzDcUdG4C//0g2ce3uKW25xE/Dnz7uVn5sLbUIhtx/ee+/a717wAlel5g//cC0I97M/u3bNr32tmwTI53duWw/9/e71/NIvwW/91kZi13N2EAqTJDfpsLlfPfvZ7h/DN98MN9/Mk96gMfXXULsbrht2iSubMTLikkO++MWNvw8Gd07oXHONS5bbvB5+9avdtu503Pt7xCPgq59v8Ij8V+htz9OWg5hjIxzN6IAbeHH7D0ASR5IYnp2ldzAExU8DrwZcMtFTnuL+7AXNBx6gZ/4EPruFrcgbyFcAsmIDCrYk01IiqFYFv93mhsJX+UxrkWpwbTJ78YvXxvVmKIpLlPFw660uWaa/3x3Dc3Pwkb/WCJ2U8ePQ46sRziSJRi1sG06f7lJxsmrjpvVaRDLbkwue8QyX3ANuwPw//sN9bk960hr5qv6Nb3DNnb9PsZUloeeoqElmQ4cYnHAwFZ1wsUgkP4XOCofl/4P2beDgy0mn4X/9rzWC4uY8Y2+vqy63Hvfc4xLSwLWy+ftr/5CnnXsf470SeqeOVKkgWRZmOIyTTCJFIlRTt/Jp/Y1Y+HjKU+DGG9058Z//2SW0eXj0ozee64YbtpIdLcVPW4pzVenbJMwcdTUuyFeHDrXw+Wwcy+JgfAWps9r3/BpaucTY8ArvvPXf+E/pmWSzLglnN/h88OY3u9fxr/+69vtAwE2UHTni3s/Tnrb1u6GQa/fx4Q+7Y+WOO1xi22Zcf737A25ypBtuvNHgxhsNfPYIzkIMJZfD0HWGhyWWl3yUSm7nTacNCoXVIKvjEDPLFHx9LAcnSCTgMY9x35nefK9YOncs/Tm97TliZomZ8GHGnSn8HQNMCSsQAEnCNm18rQ6OLTHQnmEqei2V2CjhWoug2iTdzpGV+jBkjbDawlZ8yKvZm/DMDMbyMpnoUQp6AsU2iRkFkmaelhxmKTjBV4dfLlQTXvWq1XYoPJ/6Jz+BkUqRqZylaF29RR1xbKSJOb1MyKozFzlC5/FP4L0firFTkZequmuA3l5YFRzZAi9xuLDgttV+xcvWK0ntBesDC9t9NxgMMjY2JqqhPcJFq9ViaWmJ+fl5bNsWSliBQEDYUPT09NBoNISFG6wRObxg73rSkRfABjf4WygURMB6J4VTRVGE+pamafT19TE9PS2qs5eXl4VcfCqVYmRkhOHhYR7xiEfsSObYL/T29lIsFgkEAszOzjIyMrIlkWRZliCHpdPprtZ8+4VkMkkqleLGG2/kO9/5Ds1mUyin+f1+LMuiXC7T6XQYGRlhYmKCgwcPdg1or7dX8eBV1zcaDRqNBp1OB0mSCAQCBAIB4vE4IyMjWwJ/XvWy11bdKjPBfd6bq5cvJtjtVXBXKpVdP+/1oUAgIMiDgUBABM48KxRFUR7ShEqz2eT06dOi4lhVVVHp2ul0yGazZLNZBgcHMU2To0eP7koS8/l8IjlYKpVIp9PIsiwUFDajWq0KdSrPBqbT6aBp2r5WuHtWJul0muXlZQKBQFfFDK9iPRAIEA6Hfyg2HBeDix3fy8vLLC4uouu6GJ/gquxVKhUWFhYYHR190MSzB6OAJcuuevTwsLvej8VcgjHAyMgkf/zHv8xffPW1pBoX0Kw2uhqmEDmwA1HdXfvupDj8znfCn7/7zaT+07UKG2xPk1g+jl0P4WgaA4aB2mpi+3zUD46yUDzEZ49u9fpWVXc9AO51rye53367W7y1nizxtKe5sYrHPtZ9v2ezbqFCt2nk0Y/eXmzH7+++VluPF78YnvOcNN/79Rdw9u+aTDbuZykwhq4EMZSgKCBaD9kxiZplFoIHuBC7ZsvfvT3hepLI3WMvoxwc5Pbm31JaXCLYqaI6HXIM0qdkiUpVLFVFBgKrFViOomDG43R6RwgMD5P6wz/EXFjA+L3fQ08kCC8XCRXmkGwLxQ9OKk49Pkol5zDbHkJeVd4xZL+raKVGeGn04+j1GtZ0jYy+IIqySr4MFV8PK8FxPn7sf/GWd6U5dMgtTnnd63buJy9/OZw4EeK/Zp9F0Kwx1jjJcPMsy4FRjHWKSbJjkdSX6ekssRg8QCEwyLne7lbam/Hud8Pb3rYxxhEIuPGPVmur6jJAsr1E0lel3Q5uGQfDwzqq5hAKukUGTm8co9kgYLfw2y0UxUFWwNhUfBMKWwx2ZtGCPuKDfkKvfA4Tw3D77WsX9qu/qmwQgbrxxq0ErDvugE99au3fquoWSG7eqwH81V+58Y++PreQxEM47P4MDrrP5+MfX/tbJOLGQZ7+dHef/53vrP1NUWDiF59O7fe+SaY9R7qzRD4wQkuNkenrEA44FOf8ODbgOPR25tF9YdpqhPv61wosBgfd8fPxj4M+8ASUcoxqO0laX6GhJrYMTNlx1aIiZpmezgplX5qJR60q2Fg6h7Jf45ErX2CkcRqA3l9o8Otj4/zJ4os4mXka5ioJ8qUvdeN7b3yj265//dcb2+sRj3CLZP7pn9xY0nOf6yqTf/Sj7n51M1Ipt8ivW9uvRygEP915H8o996CdO4ft9zMVuXqD5aLsWCT0FQbas3QkHwG7ja+jE3Fq+O02QbuJqfpoOcE1cWbHQZUMJAkU0wRdp/n2txP4h3+4ZHU4jxguSRLhcFgUcnj7kEgkIhRvBwcHWVxcFAowu8FLVD+cFDDXY3BwUBBWZmZmhFqoR1r3lEnq9Tq9vb2iGEOWZbH20XVd2LCvTxJ79oOeMlGz2UTXdUqlEpqmoWmasGjr7e0VZKp8Pt91TSXLMuPj4wwODpLL5ahWq8JyziMDHD58eNt7jUQiLC0tceHCBWzbpqenZ1+KFTyVV4/0kslkdlx/e0U/kUhk172Cpwbd399POp2mp6eH6elpMpkMjUaDpSV33eEl6FutlrCw6+/v31LM4RV4xONxGo0Gtm2j6/pFuXZ4RRaenZlnG3ax/du27W2LKlRVpVwuU6lUkCRJEAs8ld9oNCrUlubn5ykWi+i6LshDp0+fpr+/n76+PuLx+Ib1t2VZZLNZYdV2uYojPJtOYE+KspFIRFhmVioVlpaWRNFXtVrl/Pnz5PN54vE4CwsL5PN5BgcHuxI5vOfS6XT2NE958BQFc7mcWN/rui7aT1EUBgYGyOfzYsx7+7ROp0M8HicSiTAwMPBDIfZ1Oh0KhQKLi4uCTLJ5bxMIBBgZGWF+fp7FxUV8Ph+Dg4OXtfjtoYRlWRQKBTGPdlMU90h0AwMDwgq1VCp1nQeCwSCTk5O0221isRgjIyOYpolhGDiOw9VXX72jil4qlaJarYr4WX9//xYi0Xr16vUwDINarSYI0D6fD3PdRmj9cbx3k3ee4eHhfVM53C+YpikIznsZG4FAQLyTLcvqej8eAW03tFotzpw5Q7lcFsr7niq+ruusrKyQTCbRdZ2RkZFtFfuu4ArW4+E1wq7gCq7gsuKXfxm+d/IsfbkSfscmPuiDio2qOvT3d/D7N7JsxsfblIwI4VIWv9XicUfz/MxbB3ckH910kxu4WZ84345I8trXusGhqSmX1PKa17AlMSpJbmXrdpWs117rqrOstwfzvvfGN7oB2UbD/dznP++qtngYGHCDJ5vxjGdsT8B62tPcwNBO8PamiuJWFUL3xHkgsBbEvf12V9HFw22bnBbkQIDEn/4p5be8Bep11GYTVddxJAnFNLGQaClBTDSKvgy5wAjDmQ5Guw81l8NZXobvf51XvepJ4pjDw/DBD3YPPoNLSgqHXeu3zWo0AN8deTGv7/saxa8bJGdOoTomhqO5AcJ1nzNlDWN0gPvGf5a50E07tt12hXmxZz2LeixG533voyhnacw38VktQKLo66ehJTj6+F78b34TofUMrnWQpDXylQdPMWk7vPnN8P73w+Ki24de8hI3+P6nf+oG9w8ccPvuerznPa6M/3r4/a4d3OLi2u9e+cqNFpa///vwN3/jVpU+97nbt4WHxz9+IwFLkrYSPboRH7vh9a93ySKzsy6x5dgxNyD9wQ+6BLz1uPlmlzC23b7rqU91x1Wp5PYxx4HS332JkFklYlaYCx1mOLGxqnHDVi8ex/L7kfJ5rC98wc2wPIhgYeNv/oaIaSLbNp0uGyRZWtu02JKMgYYPgx5nhUPmcb6HS8CKRPZGAvWQTLo/HkZG4Ld/R6KQO4Dyb9P4lpfRov1Isowsw8iozsK8H9sGRXEYGdVRGxWii006A6M87ZeOcORatx2vvtpVcbv3XpcE+vjHr50nHu8y1zoOnT/7M5TlRfqVHAuZw+i+GH1pg0DIAlSccBg7oxOamsJamsX667/GfPazUVdvYi9Nbywv0/yP/2C8VOGJ+Riz0gRzqRvpqGG+ftNb+dk/fjWNT38K64EH3A4eCqFcfz3R5z2Pn0wmuankJgJjMfd8sRj82q+5Y69ScUl3t9yy8Zw33uiS/T73uVWlPM3tby976iLtb9UJmzWy/jXCgYaOPLuEukoCk1Yrax1ZRm21sM+eZXD5m/zCO58JuLaSX/qSey0vehEkEhuD8pLkWs8MD7uJpFQKzpxx5/RnP3vru6wbrrrKJXPuFbfeCl/4wtq/JcklQq7mcQnfcQf6Zz6D5fejrKxgj44yOKgzOLiWfNd1mXpdIWYUUByTqi/N0//4ybx0dS560pPc96Suw4szd3FtaRFfZR59bJjJUBv/2SayaWJp2lrnUGWcgEpAb4OqcNXAInIqgTzjEFooYikKw/I8RjyGk8jQURTMUongKsNJ6XQYL9xPRgljOQpNIqz4R6j60nx98MX814j70n3mM93kDwD9/chvfCP2n/wJAAdXTlLRY9TUJJak0BOskpjN4agSxrXjTBwZ4vr3/zzKHp6LqsKv//r2NpszM27S2iOQ33yz218vlW/jBbY1TaPdbu8agFxfvbVbUFxRlA32eZ4SkZcMMAyDnp4eEUysVqvCGjCXywlCUbvdRlVV1y5SUVhcXMS2bZLJ5IaArVcd78m174ZYLCaUtwKBAIODg+TzeZaWlqjX6yiKIogcR48e5ciRIw+ZapEXALNtm/n5eaampohGo0QiESRJotlsUqlUhD2IZ2d3uSBJkqg+fcITnsD58+fFs6rVasiyTCaTEVXMj3zkIxkbG9szYSQSiQipfk+px7NTSafT2wZdK5UKuq5TqVSEbcl2UBRFEIW8QPRere16e3up1Wrk83k6nQ5+v19Y3KyHZ6cRjUbx+/2Ew2EuXLiAqqqYpimsNL0K5nQ6TSaTuexBZcdxmJqaEuSrvr6+DSRHcNXJ8vk88/PzyLLMhQsXOLQ+S74NPDvJQqFANpslEokQDocF4cdr43a7Ta1WE+oUnu1ko9FgdHR03yvcR0ZGhP3p3NwcsVhMKLbZti2CzaqqMjIyQiaTuWRb+ocTFhYWWFxcZHFxkVqtRjAYFLaclUqFfD5PIpEQFcoPhny2FwLW+sKNZNIluXgFVN7wW58XeNWr4OtfD+3ZZvDJT3b3Nm95C5RWdFS7g6EGsSU35PiRj7hrpXf/eYRPfew9xD/3bkbqYJg15FIJLAvH70fv64NoFPURjyD5jLcz8OUkmuaumR54wF0Pv+QlrrpPNzz60e47+vRpd412rIvATibj7uW+852NakeK4q75LhWhEDziN57L8U98ipqeIGaWmPf1dF8oOA497QVMWcWMxLh3k93i4x/vrkHBXQP927+tFUecyTyVyb96CqHpu/mvv7qb2mKDRJ/KyO0jdDQF60tfQjl1ypX2dhxQFOyeHqRnPpPYi17krvUnJzGOHKH+iU/AnXeirCY1HcBKJpGe8QykJ7yQP/ztDIqlIzuWS4KSJH7+5yF19a2ce9+nWProNwlaa3PxQvgQzrOewcLE0/i1ZwZEMQFsVH/thptvdp/DLy69hsxdF5BwGGhNc6BxgqYSQZcDKI5J1KxiywoLwUmygRH+5dBvoKrwtt9xFclXVlxF6Ze8xCX/TU25x3/iE93+9NrXbnz+T36y2wde+EK3uOpNb1r728AAPGuoSujfdCxroxRSIGgTj2/8nZRJoSwt4OgSYafO5ECethxj3tvzqQ6jw23CtUV81QL6xATWTTfh20b9Y33Xue46ty988pPu2H3xi10Clm27+6Jw2N3bR6PuePyDP1j77s/8jKtav8trmhe8wI0L3H23259/5Vfctkkk4P/7/9zCk29+0x1Lr341xKKP4RN/OknIqDLcOofkOBQCg8RjFj6fzaFDFvWCRbQ0RzBYo3PwIIOPuQl/tYdmyd3HvvnNbjziqU8F8FN875OY/f0CA6XT9LcusBIcQ/NLGB2H3tY8CSOPancI2w0cCVJSgfbCaf5MupXceQNdCRI3CkSNEqpkop42kJaXeVN8ivNTf8Nnrns7408/Jvb5kuS24623wt//PXz/+247vf717vX9+qY6whe9yN2/fv/77n9HRtzPPeMZbtvvhr98TxX5Z+5EWVjA8fmwJidJ5eUNRZm2pFAMDOJIEv3tudUiR4eEtQLYOD4NW/V5NW2uhbuto0gWZjCA5ffjz2bpLCxQ+4//IPrkJ+9+YbsgHo9z9dVXk81mhbKobduC9O2piIRCIYrFoiDNp9M7W5BWq1VB7Lqctr4PFolEQhQzeKSLQqFAs9mk3W5jGAZ+v5++vj6i0Sijo6MicWyaJs1mk76+Pmzbpl6vU6vVxHqrUqmwvLws7L69PZpHlgoGg2SzWUKhkCiAabVau6pI+Xw+hoaGGBoaEuv0VqvF4DZV0/V6XdzT+rW24zgMDw/T399/yeuzTCZDuVwml8uRzWa3LdjwVEO9AqHd1qZecYWnDDY2NkZfX5+wjuzt7WVxcZGZmRlkWabZbJLJZBgaGtpC7vLINN462VOa3muxVKPRYHFxUdhoecUswWBQKJ8ODg7umYilqiqKoggCwPq9SqvVol6vi2c7NjaGLMvC6iwej9NqtcS+oNlskk6nMU2ToaEhWq2WUJVaT8LxbOPBVV/t6+u7bCQhr0/vhFarJYrJ2u22ILacO3eOVqsl7OMSiQTpdFo8Q8/OzSM8RrtMzt7fLqa4Ip1Os7CwQCQSYXZ2VqilrY9PyLJMb2+vsCBMJpOCyCnLMiMjIz80W8tcLke73abVaglSbTdIkkRfXx9TU1O0Wi1yudyelNp/FFAqlYQFYDd1LHCfoaIoWJZFOBymVqsRDoe3VSQ2DINoNCrU2kulEu12m3Pnzu2pcDCZTLK8vIyu6zQajQ3vwkAggCzLBINBqtXqhrm42WxiWRa6rot3bbPZFEp4m+MbiUSCmZkZLMui0WjsiZj0UMJbT+xVDdNTwvO++2BhWRbnzp0T5ERPnc4b117cwiNmeQTOh1v7XcHDD1cIWFdwBT9GUBQ4ON5GjnWQm9DTY9LTs72Wtiw7pHsNNLPNsWMNbnqDjn+XNYOnjvWBD7hJ4ZGR7VU7olGXyLEbnvSk7gSsbhZtm7GeHPPsZ7vKRN/6lht4/oVf6B4Hvfba7sf6rd/aqsLSDXslj//O76yd/znPcauGT592g+HriRUelFiM1F/8BYVnPxtzeRnZNDFXs/zNpp+qmqTs6xUqIJGwhR3pQcpmoVhE//zniTxpjYD15Ce7iief+pT7s15O/vDhtYrI5z/fJaFslqJ/5WsDxJ/yx/CWt9A5EcBcXkbLZnGaq0EBOURRyxA62kvgp57F2E/+Cvyv7Tc1qrq97R9A5AlPgMc/num/uZtvvesbRDsFACr+DPItP8ET3nftpWfANyGZdIOMm+HZTHRDT89W5azXvc5t0/e9by0YvJksc+AAvOMde7+2Jz7RtQj41391g68vfzlMTOwe1O4GVd1KcAGXCPiNb6wldlKprcHHbkin3R8Pr3j8/ZjTReSOzMBhFZ9vB3sbScJKJFDKZZzlZaxGA+VBBOGcxUXk1QW7rSjs1jMMScPvdFAck5+5fYaDvW6+4rbb3ED2pcJ/xx2Y3/8+LC8jLy/jrAa+YlGT0GGLTkfG77eRsdDmFzFjEZxYjPjtz+WadaSRnfreZtS//nXklRXUfB5zIENfxgds9Y2Q/H6MkRH809PY9Tr1T3+axGtes+vxWydO0Pybv0G5+25otZA7HV5S9zFfSFAM9PNffbdx9NfuwDfQj28HFmA35d4bb3TfI9sNaUly5yaP5OqhfarNuYSJXLOFykDM38B37iyyrqN0OqJfOJKE5DhIto1WrWL+0z/RfP7zCV1/PY94hFvxvB533OHOUVNTbkLPmyMVZXdi7n7gjjvc5KlHmr3tNrftPBtI3/g4zRtugFoN34ULGAsLOAMDa3JoQCppIBfL9LdnyPqHCY3GST/3meLv114Lf/Zn7v8XfuHzyMUCqApyOuGe2HGQHAdnU8DB0VQko4MPHalUwIqGUctlJNtGAdoDAzjrAht2Ok19cBDf1BS+eh3T70NKhJFHR4loGsHrr2fip36KaOyJDN7rJkA2q9/Fn/c8KoD1vvdBTxXfbJlMcxlVtlEViU5PP6RSWEeOEH/Pe1C6BG4dy6LxH/+B/vnPIy0tgW3jJBIMPPWpfPofbuPMbGjLOmNubqN9zte/7ioCeP1lcHBwgyXgdvZom+HJ0q9XsNkpEOhV3wUCgT0rCG3G8PAwxWIRx3GQZVkEbJLJJNVqlb6+PlZWVpifnycSiVAsFolGo7RaLebn53EcR1jSrScNeFW6juPsKbjsVfSpqsr4+LiQaZ+cnBSS6Z5N1+VysSgrAAEAAElEQVRSltoJXkBaURTq9bpIHHgEuP7+fkEqOXDgwGW/RlmWmZycFFYkngJWq9XCtm3C4TCjo6McPnz4QVc0ejYke4Wu6yJJs9fq5ULBXcddDAErlUqxtLREX18fZ8+eFfYkzWZTXK+u6xQKBRzHIZFIkEwmmZmZYXFxUVRv12o1wA0sRyIRkWg6cODAZQ2iVSoV2u22qPzvJl3vkegsy2J5eZlQKES73d51LkmlUmSzWYaGhpifnxcqX8FgkEKhQE9PD/V6nUqlQjQaFT8eGQzc6t1uFcCXgnA4zOTkpDh+qVRibm5OJFs8cl8ikSCTyfy3CeyDa6uytLTE3Nwcpml2tWOt1+tCQc9LPl/M2IPuBKxXvALOnXOXAHfcsXFPvF5NZztkMu4+cb3SzXpEoy5RfmHBVV8dSlSpf+ZfeE/jC5z+dtk9DxJTseu5p/82kvHHAAqZDLz+zRF48++iT0/T+MQnsO+7z72JUAiOHiX0ghcQOHKEm4Gbb+t+/p2wW4ENuPu1Zz/bJVt8/etujOAxj9mdmLJXxMYyDL/iMdT/oUYmf4Yj8gOs+Mcot9fmR8Xu0KMvEDdKtMfH8B0+yJK8xhhTVZfU5mFoyN2HeSrWP/3TMDgkwdBjuOWJj9l6ES98Ie2pKczZWRzDQE6lCF1/PdKm94LW10fyF38R53WvQ5+ZwWk2kUIh/GNjSKpKEpcU89Wv+rFwyUjveY9LNoFx6i96M39x+n8QaWdR7Q4tLU7Ln+Iz21j5eYUD3fC0p7nHB3jn72t8YOjtKHe+D3n2Lmi0iRl5VMfAkVWMkQECEYdYXqfVLvGzJ3+V/n6d2B/08jvPehaR228XBSV/9Edu8ZSquiQfSXKffTTqKviOj2/cmx844KrUfvnLbpHHs54FrT9O4/j9BJwcgwNNKjU/ms+hr6/LDfl8WKEAsm2CLBOZOYc/EiGYTmI6Gj6riW+uiGwY6CMj2CMjRH/lV7ZvmHWQJJcg+fKXu0pvXl7tZ3/W/VmPJzzBJSSeOePG5baLc21GOLymzh0MbpwvZNkdO8/e4PYoEXvX21j+tbeAJNHfukC6kUOb94Gq4u90CDebWH4/nYMHsY8dY+IPfoMP+121rb6+rTG0xOteR+vb95H7d5Oe4nmOtO5FjoZQGjXkThufraM6JrYkYasqKBKB6WlCxhnihoZsW9SUOBVfD6EEWKEWSqlEOJvl6ECRw5U3E3jsHxMIbGwUv98llb361Tu3kd/vqlxtp3R17BicOLH195Lkks2Ur/2LW1hZr6NPTIAk0dPTwbQk6nUFn+aupe1yAxyQHRtLUlAx3P2VbWPgIFsmsgOKY+FzOsiOja4FkQM+jAMH8J89C6USnc98xg0A7gP8fj8jIyMMDQ2JxK+XGF6/7vRI84VCgUqlsu36yrIsoVjj9/sftsnM/v5+kcz2+/2cOnWK2VXpSE8ZJhKJ0N/fv4GIUSwWUVWVgYEBTNNE0zRWVlYIBAJomiZs29vtNsPDwxusqCORiFBSWV5eplgsCsXdi4GngOUpw27GysoKhUKBWq0mbJc8Ao6iKMK6fmJi4pJsl2KxmFBdmZ2dxTRNenp6xDV5Ce58Po+mafT29jI4OLjr3kqSJKHePD09zfLyMv39/fT09Gywem6328zPzwur9M2kCE/d2bPrCwaDQklmL3uqcrnM+fPnKZfLFItFobTrkYwikQiNRoNqtcrhw4f3VFDk2Sd61nseGc1xHPL5PLZtC/KPR8LwyBrxeFxYMuZWi99GR0cZGhoSdmbtdpt6vS7IaZ1OR9gO9vf309vbu282lN2gaRp+vx/Hcbbsd8rlsiCxeITDWq1Gu92m2WwyMjJCpVLZoOhcq9XQdZ1msyn2RpIkCXvO9bEJ0zRptVo7Fhl1g1c4ZhgGFy5cYGVlRRRXeH213W4Le8eDBw+STqcZHh4mn8+LArMfFqrVKpVKhVAotOse3FNZrlQqQpXuvwN0XafdbiNJ0rb7Lm9Pls1mSaVSlEqlDWqNm4/XarXo7+8XRD9PyanbvOsVP3kEZi+uZ9u2sLhdD5/PRzweJ5FIsLKyQjqdFnOS4ziCrKSqKrZti/2+3+/fcn/evPNwtPwFN14QCoUwDINWq7Ur8dcjxvn9/ksiYBUKBaGY2NfXt0X1T5Zlofx/4cIFisUiS0tLD9s1yxU8fHCFgHUFV/BjBmnV2042TTf6tctiS2q1cFZfzuoeN3lPf7qbPK/VXCnxPeZUtsVTn+qqjmwOLq9XD9oLVLV7YGozPGWT979/43f3GrDqtjfsZue22bboUY9yf3Y8tqKgaBpWNEozFsPJZECSWFiK0WitNXQ0aqEorkmhFQoht1pYXpZ+HQIBN3h7660uCeuBB1wi2EtfuvE+nvY09+8LC97x3fiNmkyS+t//m9pdd2H8y7/gPPAAtiNRKKh0LA3fk28i+tJbsBcXGf/7X+dd6Mwsh2kOHeIHfc/hB4trFU9Pecoe+ookce1LH8NffeMxojrQ74f3/RbsyrB5CPG2t8HHPuZaWD360W4fliQ3+LtfkCSXROipbV0O0YbxcXjve90K12h0o9XexSDqa6Ek2yi2jRLcgXy1CkdRkFYT+Har9aAIWKgqXh2VtE1Fld9noXfcjZO0+mlZhUhPcFfbgItF+MlPpjQ2RqdWwz8zg2FZ2P39oGmoioMatHAaDbSFBSTLwj5wAOfWW5EvMgm3HvoXv4hSKiE5jlvxvtOHIxEsvx85n8f88pddJu0OqN11F+a7342SzaLk86jNJkgSvY5DAo2ameYRxnlC3/oa9vP/EPlBVEs+GA6DHIvRlzFwlhXXftIXYrx9CkVvoeo6tqJgBAJu9kCSwLLwNZsohoFSLqO/7W2oH/jAthXo116793fBfqOvz63Uv+cel3h17JhLElyP2NveRm1ujo7j4JudxSmXMeNxd3K1LJLlMknJpNw7RHiwh973vxVpm4lXWllBarWwwuE9PQxHlpFsG9kwsPN5V2nMcTB8vq3yg4AUCmFccw3W1BRyp4OdTMKv/iqhxzxGtP91dFeq9BB/3vMwHvc46p/8JL4773RVNABLkrCuuw7/HXcQf/KTkboEuBrf/jb6e96DnM2iFIvIrRY4Do7fj/ODH1D50IcYeNWrGBl56QbCVTd84AOuJQvAuXPndm2r7dDb20ulUqFQKLCysrJtVbBHAhoaGiIej++ZvLIZqqqSSqWo1+vMzs4SiUSIx+NIksTI6iJFVVVqtRpzc3OCtBKNRkkkEuLn4MGDGwLjXkBKUZQ9yfl7gSlVVVFVVUjhP5wwNDREMpkkl8tRLBYFyQwQBLT9Vg7aDclkkkwmQ6fTEUFpTdP2TPr7/9l78yjL0rLM97fHM89DzFMOlVmVJTIUQ1/kUmBhAzKqIG070A7LRrx0C4oDDUg7ICBt29IO3d5ubVRaUWimQrCvgKCAQmsBNWVmZEwn4pw48zzs8f6xY38VkRlTZkZmVhbxrFVrVVVEnLPPPvvs833v+7y/56h1NTGa2xH6V/N3iqIIYx7AP/zDP9DpdGi1WoI85keE+HSp0WjEI488gmVZtFotZmZmSCQS4vpsNpusrKyQzWZxXZc77rjjhtEXKpWKiAk5qIGVzWa5ePEi/X6farV64GfCbyw4joOqqlSrVRGX4BO1/EaUYRgUi0UeeeQR2u02hmEwPj6O67pks1mmpqaOlDIXj8e588472dzcJBaLYRiGaNhqmkY0GiWXy92weJNbpc3NTRqNBqPRiBMnTuzavPObq8vLy4I4MH+Qe+gy7RYZ/rSnwatetfvvH3Z99aY3efuZS5c8us75896ghyR55oRYzCN/dj//eTo//itIzSZatcod1hBjKGFJGgmjzDPVL1H/4Rni73432jZCYGBhAfkHfoDuRz+Ks7wMoxGS42BtbnqOsZtguJUkb9jrRmj6l95Mo1VAeWCEsrrKQufrdNwYA0IE5RERu42phzBPzBC5c47sf/pFXvhhif/v//OGn370R71aynZ927d5/xxWwZMnPVz4ISSpqvf7u+jf/ltvWKfX8+hQ25cd0Sg4skY7fDj32nd/95V07fl57/VuX2Nns/ALb9fh7W/C2PgX9D70Iewv/wNmo4eiuqirS8iuSyptkm08jGSaUJZxehGc5WU6f/InKG98I/EXvhBF8aixO17v1nu/1/ufzXr0LF/uffdhfvrTuBsbpO1NUvN7m1Xd0QjFthlOTCBJEk4yiVytEi6ve2tNVcVKJrGyWZypKcLvehf6Vbr/FMX75yCdOrX/kNt+uppt6Av+5RSfsN5D6T+8E60bZjxUgV7TG24IBBhNTEAkgv3P/hnJt75V7HH3etlyKMT47/4Hgj/3c8hfB6leJ7C6ijYcIjkOriRhBIK4uo6Rz+OOjaE99BDqYEDEHjFQwvT0BPbEGNEJA1dKYo2PQ61GYGODkSQxeMc7CP7pn+66T7he/dAPXTks+oIXeJ+jO++E2s9+A7XVwtE070OEd01OjHvrOalcRq7UMPpeFKHmjghsxQuCiy1JSI6DbvdRHRlXkjElDVPWsbUA6okZpEAAKxZD7nSwl5bY3Ny8Ys3lOA7f+MY3yOVy5HK5q2peyrK873opk8lQLBbJ5/MUi0UsyyKVSu14juFwSLFYRJIkcrkcY2Njt2TY4rBKp9OkUilarRaGYRCJRKjVasRiMWZmZnbsyXwzT71eF9GMuVyOpaUlTNNkaWkJXdcxDIPhcIimaRiGQaVSQdd1sc/yYxlzuRybWzVd//cPq+2En8uvgWq1SqVSYXNzE9M0xVCJ67rEYjHa7TYPPPAAmUwG0zS56667diUJHVYnT57EsiwkSaJSqbC0tCQisH1alL+3mpycPLTZbGxsjGazyczMDGtra/R6PVKpFOFwmFAoRDQaJRQKiTWoTzDyzUaGYQjDzMTEBMlkkk6nIxruB+23e70ely5dolQq0Wg0SKfTTE9Pi/ep1+tRr9dZXl5mdnaWixcvcvbs2UOZy3K5HN1ul+XlZZrNpjg2f/jFj6YsFos0m02xnwdvn+1fV3feeSeTk5M8//nPR1VVVlZWaDabwoTlm8T82MF8Pr8nMe2olEwmxSBavV5ncnIS13UpFos0Gg3a7TadTkfUC3z63mg0otPpMDMzs8NI58dG+nH12WyWSqUiKN/b91/NZhNVVYlGo4ciFG3X5OQko9GI2dlZOp0OpVJJ7Gu2G2gikQiWZREIBMSe3a953Cr5xpu9runRaLQjutQfwns8mnWuVX4NYr/PXyqVotlsoiiKuJ78v71clUpFmKj8wcTd7rs+Zdy/prfvhxVFod/v70lk8wmCzWaTtbU1cX/xBwXhsfcOvP33bntrvz7nE74eb9pOmKtWq6IuuZu63S7D4ZCpqalrokhvV6VSodlsouv6vjUJn2RYr9dJp9M7BgGPdazddGzAOtaxvskU+vZvZ/g//yeOriNVq4LCsptcx0FrNrHGxrCf9KRd6RF76XICzvVI1z3jym/9lje5GIvBW9/6WGzCjdCLXgTFohdBlUh4hqzd/APf933wJ3/y2H+LeKTL9GM/Bv/pPz323898pj81eg1yHM9Qoqq4W1/ykzM2a2sOw4FMOGIzMfHYJKYrSR61xNnb+JJI7D/lpyheVMNf/qXn23vBCx6j1kiqSvxFL4IXvQizXMaqVJhRVdR8nvb/+B9Yv/qrSJ0OaqPBtGkyrSi4Tpx7K3/MP4X+bz658CZOfGviwClDX7ruxXZ9/OOeKe87vsMzjT2eFAwebPQ7Kt3oCPaxMW9y/roUjeKqKrJl4ToO0gGFPWk0wlVVkCSUayzuKHfeiasouJLkGUJ22Vioqo1pyTiOhO4aOEjIKoSf85xres79JCkK0Xe9i+7rX89IltEKBbSHH8YKh3EVBdkwUEYjrEgEa2EB+1nPIv3611/fk5bLnvkyFDrwnCNJ2NEo8nCIVK3u+6v9//N/sN/5TpSVFbRaDTOVYjQ1Bf7UV6NBolJBKtUw/8Gg8da3knnPe25KI02fmEA+OY1aXWeuuY4V7aH2hqijEZam4V5WZJBtG0dRsDUNtdfDLpXo/uEfkn7LW274sV6LwuH9G2/a2BjR3/otum9+M0Y0ilSvIzcayN0urixjJ5O42SyRdJrAL/4i4Xv2iYV1Xe8f/9rRdZAkjxxmWVecS3fb36mNBoph4EoSrqri7nWjkiSciQm0xUWP6Njp7Gl+2+81p173OvjX/xq73cYZjVDiceR9TCjdz34W6x3vQNnYQNuaBrW2jGZqvY60uYmVTOL8zu/wnPSQP+GH93ws8KJBj0LpdJp4PM709DRra2sCI+43N0ajEY1Gg2azKSIDr7coOjExQbPZZHx8XETZpVIpQqEQs7OzbGxs0Gq1iMfjnDx5kmQyycLCgmgi7EbqCgQC6LpOPB4XReL95Bc6DhMxcSsVDoeZm5tjdnZWTJH7sRC+ut0u3W4X27ZRFEVMr99I6bp+zSa8o5Kmaei6jiRJDAaDA4tQ/X5fFAyv1ugTiUQ4c+YMS0tLPOMZz+D8+fNsbGxQKpWIx+PkcjlB8LIsi4cffhjTNAmHwzztaU/bUVDzaQXdbpdCoSCaEefOncM0zR0xhUehfr9Pr9cjHo8f2HDRNI1QKCRMVIeRruucPXuW9fV1wuEw+Xyeixcvkk6nxcT4gw8+iOM4gkoB7KBsfeYzn+HUqVM89alP3RFder0KBoPMzc0xPT0tis7+xO8TKXLQlx8L4zfh9ruG/Htfo9EQZIirueZ2M2AdRR348v1MsehRtWZnH6Oxdr/wBay3vQ25UECvVLB1HT2fYDQMIvVNZowVtJqN+bUGnde/nvjv/R5qJoNZqdB+73tRvvQlpF4Ptdv1jCmahvWpT1EfG0N97WuJv+QaEFiPE8nBIKnf/E0av/Zr8NnPYvf7BBsNQpaBq6jYiQXkaBT1KU8h/ra3oWYy/MRPeHUH2AEwveWSpL0HEO66y6Mk1+uP/b/99vXPf75XP/FJWLoOv/zLXuz3XtInJ9F/8ifhJ8Hp92m87nVew2p5Gcm2sRIJnFgMbBul3fb2J5kMzjvfSdt1vVrFdSryrGdRn5zE2txEK5cxIpFdMcmubaOurmIHg7izs0hvexvOV76C+5nP4Gz7sDozMygvfSnJl73sugZuHi+SJHjJayfgh36T/gMPMPjwh7HOn/dyzUMheMpTiHz3dxPYilE+jJREgvT73kfvc59j+Id/SGB9HUeWQVGwwmFvrZ7Neo/f63mUY9fFlSV0xWRKKzLKJUHSHjvIbBbDcdBLJYx0mt7nPkf0WqfM9tHZsx7B7k//1Kul/cAPXEYN6/fBtj161/b1gOuiLC2httvIpknAtXFtkHGQ2RpQ22rO2qqKpQcYDVVcCQw5SEPL4WaSTAe26BmyjGuYlJY7fO2vS1c0jl3XZW1tjW63S71e59SpU0dmwPZN8z7ho1wuU61WiUajyLIsiCHRaJSJiQlhwHq8yzcIPec5z+H8+fNUq1XK5TJra2skEglUVcU0TVqtFpZlMTk5KfZPgUBAmI+azSYPP/ww5XKZXq8njBnj4+NEo1FSqdSOPVQ4HMYwDLE+vRoSlU/bUlWVbrcr/tY0TarVqiAp+Wb/lZUVGo0Gsiyj6zqu63L+/HlBdrr33nuvee2mqipnz55lbW2NUCgkaEX+AEE0GiUYDDIxMXFVphhZljl9+rTYW/j0pHK5LMxVuq6LqO9isSgiwxRFIRAIMDExwdjYGPF4HFmWsSxLEFoP0sbGhnjOmZkZVFWl1+uJYwuHw8zMzFAqlVhbWyMQCIjIyYOUy+WoVCqMj49TLBYZDocMh0N6vR6yLDM9Pc3DDz8sCD2VSkUQVgFBwqnVauL8gmeGMwxDDG3470E8HieTydyw2EFfo9FIxFH6A2j+++QTgx3HIR6PE4lEaDQaOI4jojzb7Tb5fJ5utyv224lEQpi0/JhLf6Cs0WiIa98nrU1MTBAOh69pz7OwsMBgMBDnLhQKoWka3a4Xyey6rqgZrK+vs7m5SbVa5dy5c7eUmuNHvF1OWfLPuU+GbjabWJZFu90mGAyK+7d/fd/O0jRNmOX2onEHg0EikQj5fJ5HH30USZKwLGvHazcMg3K5TL/fZ25ujnQ6LR4rGAyi67q4/gBKpZK4drfHyOq6LsxYtVpt14G6eDwuomzX1tZYXFwUn+d+v0+/3+fChQtkMhkmJiYEMetytVotIpEIqqoe6V7/MDrI9Abed0Qmk2EwGLCysiKIVJffj7rdLuvr62QyGYLB4FWTIbfLJ6K1Wq1DDYQlk0kqlQqDwYB2u31swDrWvrq975bHOtaxrlrBM2fonT2LVK2iFYuM4nGk3RpDrouyhXxw02n0l7/8Jh/pTk1Pw7vedfOeT5K8gvMP//D+noHv/V7PaHb+vGcIe+ELd/+9++7zjDJf+YoX6bDXRPJh5KRSuIEAcq+Hb6nSNYeTJwa4XAaCcl2UwQArm8W9DlQ0eEX4V7xi/9/R8nm0fB7Xtqn//M+jfOELyGtraJ0OjqbhaBqSbaNsbqKGw9wz1eWp2QvEX/M+1MjhF0uplFfEOtbtIe1Zz8L+5CdhfR2p0djfnem6aM0mZj6Pfddd+5oo9lPk5S/H+M//GXUwQDVNDE3bdUw4FDSxDQfdMjF1ncHp02QOm/F3ldKnp4n+zu/Q+aVf8jIJOh2kdhvJcbCDQax0GiIRnBe+kPQb33hFRMjVai/y1/Vq8Lu/i1IqodZqjObnd3RMJEmCTAYrlUJZXka7dAl0nd7f/z2RZ+4Sj3LUkiSUl74Ud3ERqVJBLxY9458sC5qjkE9r0nWseBxH05ArFdzPfQ77DW+4ZvPfrZS7VUQJve1tGN/4BtZnPoPy9a9jb10L9twcykteQuKlLz2w2eOm07iBAEqrheO6EAphB4OeWdCysDRtxxek7DjefV5RUPt9FMvCURTvvO/XVYtEvOZDp+MRMK5VkoSSSHDQDJdRKGD+yq943021Gsb4OG4mI0yKjuvittvo6+tIi4s8tfzHfDl9B4u5vZ1vR/VRkySJU6dOieg+n1rj/8wv7Pnkq5MnT163cSEQCHB6K09T13VqtRrLy8tIkiQKJL7hyo8Si0Qi+1KWtk/q+oaxvUxYhmFQq9XI5XJiMvnxLkmSrijS+UXjfr8vogB9c0k4HGZ8fPwJR/fZLn96ORqN0mg09i1Cua5Lo9EgmUwSDoevidgVDoc5d+4cMzMzxONxlpaW6PV6YnpbkiTRpEqn09TrdZ70pCft+R74hsLNzU0sy6LT6exoQIZCIUFoup5JUb9pcNjH8GMEnH2GKHb7G9/oVK/XmZqaYmNjg3q9zqOPPkqj0WB9fR3HccTnLhgMoigKhUKBarXKYMsk8PSnP/3IiWqKolxzbOrtJL+ZahjGoZosiUSC1dVVHMdhMBgIusT2999xHBGjsl27+fNuxK10YuKxeDjwjDDmr/4q8vo6WrXKaHbWm+qRJPzqgutksTY20JeXMWSZ1rvfTewNb6D7b/6NZzLY2EDp97F1HWQZ2TBgfR2zWMR517tolEqkfvRHj/7F3CTJoRCZd7wDY2OD3oc+hPNP/wT9Pm4wiHTmDOHv+i6Cfqa1/zdX2+90XYxiEafXQ45E0CcmbsrQgy9J8qIRf+M3oFaD5zxn75oIQDIJ730v/MVfeP/9qlftv0y8XM3f/E2Uhx9Gv3QJKxbDnp7eQTCyxscx220Ca2tYrov93vdi3nMP2nVOxiNJ6K97Hdbb3441GqFfuoSZSuFkMkjBIK5lITcaaLUaSBLmyZM43/EdJO+7D+67D+ff/luMtTWPjpVMesSrxzHp55olSYSf/GTCT37y0TycohB9/vMx/s//YfjAAwQWFxmdPIkUi+3ch1SrSJaFBJihENpwiO043tDpZfFZTjYL5TLU64w+8pEbYsACj9j/3d+9x9sciYCiIJumt0/b+iV5dRW13Ubd+h62dc0bTDMMb5DNcZBt29vbqir9VJ71RgZL0jAULy8yJtmAtyd0BwaVTpRvGJP8+q9nMM2dByNJkiDr+pSJM2fOHJnxIhqNCtO8T6Dp9/u4rks4HBbRfWNjYzectHPUikQinD59GlmWxdBJp9PBtm1UVRUGqnA4zKlTp0TE2djYGKFQSJhN/LWrb7ZPJpNks9krGuM+PScYDAoT2OXq9/sYW+5WTdPEY0QiEUKhEMlkknq9TjKZZDgcsri4yMrKCpubm4yNje0w7vjrdX89PBwOuXjxIqqq8tWvfpUnPelJ1zw4oygK8/PzTE9PU6vVxDXhrxEPM6iwm1RV5cyZM9RqNSqVCtlsVjTxfYOOoigkEokd8XbbjQq+QXA7ueygPZxPnanX6yJWcjAYYFnWjmGOeDxOKpXaEWl/GNOApmliv+5TbpeWlkTMYb/fZzQaEY/H6ff7V1w/PmlH13Wx1/FNJLqu3/TPnuu6rK6uij2Hb2KwLIsHHngAx3GwLItcLsfc3BzD4ZByuYxpmszMzDAYDDAMg9XVVXq9Hpubmzve34mJCbF+9o12hmEQCoUEQWgwGDA2NkYqlWLuOuq/Z8+exTAMFEWhUqmI69i2bWHAqlar2LZNr9cT9ZZbaWCKRCJEo1EKhYK4Fnq9Hmtra1SrVS5duiTO8WAwoFarkUqlUFWVv/mbv+H06dN8y7d8y3WR8G61kskkwWCQUChEo9HY0/zrk87C4TC2bVOpVIjH44JY6Efkzc3NXXEt+bWtZDLJ6uqquDf0+31isRjZbBZVVbFtm1arxcWLF8lms/T7fZrN5q7HNDMzI4hX/n2k2WxiGAbBYBDTNMnn86RSqV3jW32TkU93vxnXYa/XE3Qpn6IWCoUEmXu3usj09DS9Xg/XdSkUCrTbbeLxOMFgENu2BcE7nU6Tz+dZWFi4rtfiH5dlWYcabPSHMP2YyWMdaz8dG7COdaxvQoV+7McwLlzAGgwILC1hptM4uZwXQ7TVeNTKZeTRCOPECey77iLxvOfd6sO+JTpozydJHg3qBS84+LGe8xzvn+uVfO+9OBcueJOdvR7Sto3VFYfbaiHZNm4mg/bt3379T35INd73PpQvfAHtwgVcWfZMGtuKZOZggFIqoS8uYrgurZ//eTK/93tPzCLksYjeey+NfB6zWETb3MSMx2GPyUppY8O7D2UyaNdh/AyeOUPnmc9E/8QnkAYDtOEQS9cFWQvwXBOWRWg0xJElnFAQ6XWvu+bnPIz0yUkyv/M7DB95hP6HPoS7uOhNBsdiyE97GrFXvvLQca8Hyc1mcYJB9EYD6xDkMaXXw4lEcPcpMA0feQTlkUfQKhXMbHbvjoksY83NEXjoIWg2GX7oQzfHgAXEX/pSmh/8IEalQnR5+bEYvG3vu2RZKKaJqyg4moY1NgaSRGB5GbvbpfeFLxzJtPzNklUq0fzgB5H+6q+Qt5DTgOcM/omfIHLvvajpNNJVTDQrz3se7kMPoVQqWJ0OxONYmQzKYIBsWcijEU4gAFtELMl1cVQVOxZD30J0O6qKmUodeG93ZRnJdXEPEVd3ver+2Z8hNxrolQrDhQWkeHznd6ckISUSGKEQ+vnzxNQSz1z/830NWEcpTdM4c+YMxWKRcDgsprH8nwUCAZLJJJOTk0dmVopEItx5550Ui0Wi0SiGYYgIiEAgQCAQIJvNMj4+fmjjSDabFcV8v/C5vcjjui7tdptyuUwkEiGVSu0Zufh419raGqVSiXq9TqPREGQfx3HQNI1kMkmv16PX6+2LUL8dZNu2KPK5roumaeJ93R5nuZ/pzp9m9uNFrkfxeJynP/3pnDt3jnK5TL1ex7Zt0ezwzYTJZPJAqsL2KA6foOU/Tjwep9PpUCwWOXXq1DVPOfqRE9snXveTH6d4LaYvP64GYHZ2lq9//esicjAWi5HJZAS5TJZlVFUlEAjQaDS4cOECmqaRzWa58847D/2cfhPIP2+xWOy2n5C+VvmNF2DP9880TTHt7P+O4zg7Ij4SiYQwxIFXsL6ciLabAetmDOK2P/YxpEYDvVxmND3tOWsukyTLuFNTXoNnYwP3i1+kff486tIS+soKViyGdfq0OGDXcZDqdfTNTezhEPsP/5D21NRttSbbTYLgdISy6nU6H/oQ7v33I2+j13bzeeTv/E6ir3gF6nUOYB1W587B7//+4X9/bg7e+Marfx6700H6zGdQNjawg0Gc2dkrm/T+Om5rXe9ks3T/1/8i9WM/dvVPeJmi995L69/+W5zf/E2MSgW1VkPfMlyxtQ620mncfB77Oc8h/XM/J/5WDgavMNsd6yr01a+itFrYsRjSLvtPeThE2iIboyg4soxsmsjdLpeHJkmy7EXzdbs4q6s39LD32gKp3/qtuJ/7HHKxCFv7LPp9tGYTdTjElSTsYFBcW7JtY2satq6jmCZ6v49sGAS6Lfrqwo4n6nQUbEdCNofQ6lNT5vh64ByqqrFLxZCFhQU2NzdZW1tDVVUqlcqRkqh803y73aZSqTAcDneskf0m9O2oaDTK3XffTb1eF6YbXz45JZlMXmFoi8fjxONxer0ehmGgaZpojLuuKwwzflx2p9Oh0+kQCARIJBLk8/kd0VZ+jKBv+gFv7eHHYeVyOUEKqtVqfPnLXyYWi7G4uCj2ac1mU6zh+/0+J0+eZGFhQVCohsMhjz76KKurq4RCIbF/vB6jvqqqR049kySJbDYrjAwPPPAAiUQCwzA4d+4c6XSaVqvFhQsXqFQqYlioXC6j6zobGxtMT08zNzdHIpHg1KlTBxoS/Rg/PzZrOBzS6XRE1BcgqLY+TajVatHr9TBNc0/q3GAwEKY+WZaZmpoS5hn/GvHNGNFoFE3TeMpTnkIgEBDPrSgKoVCItbU1Go0GnU6HQqFw1XHXRyXXdVlcXKRarbK+vs5gMCASiTA/P0+hUKDZbLK+vi5MZb6xJBaLkcvliMVixONxNjc3SSQSgtR9OQXLdV1kWabf77O+vk6tVkOSJEKhEIlEgomJCTFcdj30GlmWmZiYEDGPfrycH6sYiUTEcfi/PxgMRA3kRpOyd1M2m6VaraKqqjBXFQoFNjc3uXDhghjEi8ViYk8cCoVot9s89NBD9Pt9FEXh3Llzt+T4j0L+vTSdTrOxsbHne+HfR5PJJOl0WtDfHccRQ3bBYJB0Os3c3NwV94pcLken0+FrX/sa3W4XTdOYmpq6Iq7WMAwmtmKrDcMQZKfdvhunp6fJZDKi9uE4DhMTE6yurtLv95FledfvnW63y8bGBrFYjFgsdiiy3/XIcRyWlpZoNpvCLLY9mrPdbrO+vs7CwsIVdSNFUbjjjjt48MEHiUajFItFNjY2sG2bQCAgzJnRaJT5+fnrHu7yz5Usy4eK2nRdd8/BqGMd63LdnivcYx3rWNel8NOfjvXTP43z67+OsbmJWq2iV6s4iiKIKVY8jjUzg3P2LIlf+7XrJrEc6+gUe+Ur6f7Jn2DGYmhra1gnT+5qZnFHIwLr61jJJG4ySfQ7vuOmHJ/d6SB94hPIa2u4sox5+vQVxg8pFMKZn8daW0NbWYFIhN5Xv0pkvzisY922khQF5TWvwS2Xcft9tIsXMcfGPJSZXzgaDFDKZdR2G2NuDmdujsR9913X84bf/GaGjzxC6MIFFMNAHY3AMEQcobxlGnElCTMcpvesZ5H5oR+67td7GAXPniX4C79wQ58j8MIXYn3hC1AsItXrsA/C3e31UIZDrLk5pH3Mmv1PfQq520U2TdwDGueSomAlEij1Ovz93+MMh9dMNLsayeEw4Xe9i973fz9WsYg2GKAaBq5t4+KRmlxJwlFVHF3HmJiARALXcTxzlmHgbMUy3Q4affrThH//95GHQ5RqFaXbRXIcHFVFKpVwz5+n8+d/TvjXfo3gyZOHftzYy19O+3/8D8xEAq1QwDh1CimTwWo0vJjB4dCLuFQUFMvCVhRwHNRWS8R+OoqCMza2S6n/Mbm2jWyaWLq+a0PlKOUMh0j/+38jVypY0ei+zyfpOlYmQ6ReZYZHyXYuUo2duqHH50tRFKanp5mcnBQFXd/s4pOGjlqBQEBMIjebTUzTFAasRCJx1YUFVVV30Lz8aIFgMIgsy+I1pVIpcrkcMzMztxTFf60qFosUi0XW1tawLItMJkMikRCTr61Wi1qtJqIINE27LY1mlmVRLBap1WqMRiNGoxGu64q4Sb8g6cdZ+nj9dDpNaCue1o+3GY1GzM7OEo/Hj4wKFg6HxfW73QAEnkHu8uaQ67qiSSVJEqZpUi6XGQwGIjpkZmZGNL6azSa1Wo3x8XEcx+Hs2bPX1HDyG27bJ473Ur/fxzRNEonEdX82DMMQEUOSJDE/Py+iS/xrE7z7QDAYFFE6a2trLCwsHPha/caV32zyz7+maaTTaUF7+GaSqqrivmmapiBf+PeFRqMhKBXgvUeXX7uH1eURhKq655zDkcr5xCdQqlVsTXssm343SRLO2Bja+fPeuqxQ8Gi36TTu1BQ4DlSryIOB9++KwiifRy+XcTY3sd7/fg+pdDyoI9T70pcwfvEXkRoNlEoFpdPxhq4UBXt9HWdtjc4HP4j+7//9E2p/3f34x5E6HdRul9HCwv7XRDzukVurVaxPfhJ+9EeP5BpKvPKV9GZmGL7//fBP/4Q1GIBpeqTlUAhnehr5Fa8g8+pXH1+zRyip2/Ui0PeKNd+qY/pQWleWvX3dHo00V1GQDQNp2334Zir6kpfQ/u//HTMWQy2VsKJR5EoFybaRHMeLZt+6fiTT9AbUVBVzchKpWMQejVANA1UdEFS6DNWdJJJHHgpyVrlEz4nT0yL8ffo+8T20XY7jxRmOj4/T7/dpNBpEo9EjMcVYlkW/3xdGonA4zMmr2IveLvIN77lcTpioFUU51N4pGo2Sz+cFKScajSJJkqAp+fLX2u12m6mpKWGwt22bxcVFYSLy93D+cSWTSWG6OnXqFLFYDMdxKJfLgopq2zahUIhSqSRovrOzs2Lox1cwGBQN+3K5TLvdplgssnAVsaI3U67rioi+VqvF9PS02Hek02nuvvtuYVrr9Xqsr68jSRLxeJyZmRkymQzT09OHiuQ0TZONjQ1arRaj0QjLskRsnizLwkTnR+OFw+Eda8TLn6PVaom91HA4FAasYDBIOBxmYmJCmPcfeughscb2SdW7yd/T93o9Qfe6FcbHUqlErVZjZWUFTdM4efKkqG1MbNEKR6MR9XqdSCSCoihMTk4SDodJJpNkMhmKxaKgitm2Tb/fp91u7zDQJJNJQTfqdrvE43EmJia444470DSNXC5HNps9ksjVsbExyuUyjuOwvLyMYRgEAgGxnvej3qanp8nlcoIup+s6Z86cue7nv1r5ZqPx8XEKhQKVSoXRaMTS0hLBYJDhcEgmk8G2bbrdLlNTU8TjcRKJBCsrK2xsbJBIJAiHw9x9993XRKt7PGhycpJOp4NhGBQKBZLJpCBjua5Lp9OhXq9jGAZPf/rTicViJJPJHd9rvjFwr/pcLpdjdXWVTCbD2tqaGOry99Z+ZL2maUxPTxMKhQTtsFar7fld7FO3ZmdnsSwL13U5deoUGxsbbG5uUigUBAXRvxf6kapjY2PMzs7e0PhBx3G4cOECtVqNQqEg4l5jsZgYxPTNf47jcOLEiR2xun5sp18vcV1XUM796200GnH69OkjIWvruo6iKMIYdlDtpd1uI0mSMCge61j76dhRcaxjfZMq/p3fSW9sjOF/+2/wjW9g9fsehUWWIRLBTSRw77uP5Oted2BM0bVoeOECxuIiWBZyKkX4aU+7KY35J4LUTAb31a/Gfv/7kS9dQj9/3itgZ7OgqriGgVyrodfr2OEwzvQ0yo/8yE07v35hVOt0GM3P703dkSTsqSnUhx6CRoPRX/zFE6pAfKydSrzqVdRXV7H+1/9CXl9HX1/HLRZxdR0cB2U4xA6FMBYWcObnib773ddt/Izccw/td72L/i/8AoHFRdTBANlxdhivLE3DikQYPPOZpP70T3dEWNxUuS7D8+extghgytgYobvuuq7CfeQ5z/HIY+UyeqnEKBTaQcwTT20Y6KurmLEYRCLEXvnKvQ+zXofhEEfTDtXdcyIRtHIZ27axm03km2Q8CJ48yfCNb8T8N/8GdTTC2toQS4AlSbhb5itzfPyxpqFfoJekm9O5PAJ1PvlJIr/7u6gbGwRbLaxIBDOfB1VFGgxQGw2kSgWj26X/Uz+F/Du/40WeHEJKLIb0Az+A83u/h3TpEoGt7xpzchLW173rdDRC22pcSICjadixGENZRhl4sbgHXcNyreY1SeJxAs997vWdkAM0unABqdv1jJ6HOA9uJoNWLhOQ+0w3v3bNBizHcURBdruR6qCipyzLNz22TlVVMel8vYpEIpw5c4aVlRUxmeobr3wjSiAQ2FEQv9kaDodUq1W63e6OQlo2m921UbVdlmVRKpUoFovYts38/PyO91RRFNLpNPF4nJWVFYrFIqqqksvlrivG7mZrNBpx4cIFWq2WOFe+XNclEonQ7XZptVoiwkLXder1Oqurq4IC5JOoJicnDz1RfrXy4z18+efZL9RZliUiR3yjEMD6+rq4Fubn5wWW31cmk6HRaFAsFpFlmaWlpasiQ/nK5XJUq1U0TaNUKjE1NbVr0dpxHBGnoev6oeJJ9lOtVqNcLovCeqPRQJIkEomEiLIxTZN+vy+mmpeXl5mdnaVWqzG1z/3SJ8D5pqLtdK9oNCqK1ydOnPimiB705b93wWBQxEgMh0PW1tYEGaHb7Yq40n6/TzQaZXl5mTvuuOPQz2Pb3hZ+u67X6ybi8r70JaROBzQNd26OwEtfSvS5z32MyLK6itLtYm/FDu6rUAg7EEArl0FRPCP8+LgXX9hoeCRN2/ZMFFvrNNdx0ItFRmtr9L/yFcJPf/r1vbAniHpf+QrGW96CvLaGXi57MeaZDK6uIxkGylYMnjE+jvHzP4/8H/4DoW/5llt92Eci6+JFlHYbV1HgENQFO5lErddxqlXsdhvliIzekXvuIXLPPYxWVhh+8Ys4nQ5SIIB+552E77nnphmvjEIBc3MTSZbRpqbQbjDNYE9ti9C7YU+had5+Ya9IXkUBSfIi0UHs9fc6Lnk4xA0EcG9RhJISi+G+8IU4nQ7KhQuoi4vIvZ4XSbj1WnBdZNNEMU2sQMAjYmUyWMMhkmUR6HbRh31yyjprkTPib8JWm+xoA8O1KQRP8A+RZ9GNzuzaALLtx85POp2mXC6LGPFrJZv0+/0rqKQ+aTOVSj2hTdmyLF/V2taPyS4Wi0QiEcLhsDBj+ftGWZZ30Ern5+dJp9OCJFSpVFhbW0OSJNLptGiqDwYDQef1Cby+yVtRFMbGxgQ9yx9ISKVSIpY6GAzuagAIhUJomka9XicWix3apHQz5LquiPbrdDosLy9TKpUYjUaMjY2JaxEQdKLhcCga7pubm+RyOaanp69qYMYwDBqNBo1Gg/Hxcaampnbs9fzzGYvFKJVKO/Ygl+8DSqUShUJBPJ5Ppd5eQ+j1eiSTSRYWFgQ1yn89u8k3X0xtEUlHoxHVavWmDwW5rku5XKZSqaAoCjMzMziOI2jfPlF5dnaWXC7HYDAgnU6TTCaZ3Ua83G5isywL0zR3jQHzKd71ep18Ps/MzIwYLDlK05DrumxubjIYDHBdl2g0Kki4240j/s/8z1Y0GhURdjdbCwsLjEYjHMfhS1/6kqCRWZYlaFfg3aPC4TD5fB5VVclkMvT7fZaXlzl58iStVuu23d9tNwUHAgHq9TpLS0vivfJrF1NTUySTSU6dOnXVpkV/v+3T/nxiuF9P8ff5vpEwEAhQLpdFzWw77XCvx/fvv5OTk+i6Lup5zWZT7Mt9c1kgEGB2dvaG196KxSKNRkMQE+fm5nacu2w2S7fbZX19XXzP+RQ/13W5dOkS1WqVQqHAcDhkbGyMubk5EeU5Go0YDAasra0hy/KuVPVOp0OlUqHdbgsTayQSIZfLkUwmd5xXWZbJZDJ0Oh1WVlYYDod7DqA5jkOtViORSAhj67GOtZ+ODVjHOtY3sfzC0fDiRQaf/SxuswmahjI3R/yf/3Pko14Eui6dT34S48MfRnn0UbAs2KJ0tBIJeMELiH7f9926wtFtpNSP/zj1dhvrox9FqlRQ63Xkcln83A4EMMfGPELNa19L/Lu+66Ydm/UP/4DaaHgGjQMKWpKiYMXjyM0m7gMP3KQjPNYtkSSRftObaE1OYn/gAzj1OjQa3kTnVgwAkQj2PfcQ/9mfPbL7QPxFL6I/Ps7g//1/UT7/eeTNTZQtw4gTCmHOzMBrXkP6x3/86O95h5BrWXQ++lHMj34U5dIlYQKyFIXB1BTKS19K7Lu+65oMlJKioL/hDVhvfzuWaRJcXMRMJrHTaQgEkEzTi45pNLwYj7k5+P7v3zcCUdpWED7cC9z2ezfZcBB6+tMZnDrFyDC8Rl8w6BXjVRUnmbwiPlFqtXBUFYJBtLm5m3qs1yKr0cD5zd9ELRbR222GJ04gbbvnuskk5vg4UqlEYHWVkaLQec97yPzH/3jo50h+//fTaLex/+f/xKlWvaiVSsWbKHccXEXB1DRcVcXI5XAnJ3FyOYyTJ1G/8AX08+dRCgWcmZldGyDuaIRWLmNlMjhTUzc8ptLpdh879kBgXzIXALruFU0iQwJ276qfz7ZtisUi1WpVIPRd10XXdQKBAKlUiomJiQONPrezQqEQZ8+epd/vi/Pguq4osCYSiVsyNWlZloib6/f7dDqdHXFzpVJJFID3Mkv5NKhOp3NFUWm7VFUVWHh/mvFGIN8ty6JWq4nzbFkWvV6PUChEMpkkEomQSCR2GJQOkm3bXLx4UUwvRiIRZmZmdjR3/OKaP7V88uRJUVQ2DENM4vtmlGw2y9jY2E0xofkRe36MxdraGr1eb4f5xZ+a7na7Ykp9t/cylUphWRblclnExlzt5Kg/vT09Pc3Kygpra2vkcrkdhfdut0u5XMZ1XcbHx8nn89d9rnz6wWg02hGlsL1JqOs6uq4TCoVYXV1lMBhQKBT2jUvc2NgQBDjDMEilUqLpZBgGzWaTQqEgzGynT58mdoua3TdLvV6ParUqGtD++6lpGpVKRVDxNE0jHo+jaRrdbpdSqQR4Tbzl5eUrzIR76XL6FVx7/KBrWTTe8x7kT30KqddDq9U8so8s4zz8MNaXv0x9cpLQ295G8MwZzwjhup4Z5jCSZY9IKkmMJiZQL11C7fWQRyMU2/bMEmwZJ7aItYplYS4uMvz8548NWHjU0NGv/irKxoYX/Tgzs4M+5gJWLodVrxNYX2ckywx+5VcIfeADTwwa03DoRV77+5ED5CqK2Is4g8GRGbB8BebmCNzkPYNrWXQ++Ulv7/joo+L/G5KE/eQnE3jFK4jee+8Nfb+dwYDOJz6Bdf/9yKurSKaJG43i3HMP4e/5nhti+HNPnMB59FHPlOk4Vwz52fE4Sq+HYprekKdtYwUCOLvs793RCLXfZzQ5Cc94xpEd4/DCBfof+Qju0pJ374zHUZ/9bGIvetGue/nUG95AfWUFAGV5Gb3fR3Icjy48HCJv3RetQAAnGMRcWPCij6amsEzTiyp0IW1V0LsjLElDdS0U16SjJikETvBQ6En8ef61BA9xnw6Hw9i2jWVZOwiNV6Nqtcrq6iqdTkeQZ3xFo1FBdpqbm7tuc/kTQalUSuwJV1dXhfHHMAwGg8EOomipVGJsbIxAIEAmk6FWq9FoNFhbWyMcDjM5ObljT+UTrPx4SdM0MU0TRVE4c+YMmqYhyzKBQIB+vy+oJDMzM0iSRKvVElF2mqZh2zaj0Yh4PE4mkxHxia1W68iGd3aT32wHb13vU8Iu12g04uLFi/T7fVqtFq1Wi4sXL1KtVkkmk6yvr1OtVpmZmdnRWA8GgzvoXoFAQBiCruYYXddlOBwSjUb3XLcHg0FSqRQrKyvk83mGw+EOco5vOCgUCjvMR6qqYlmW+Px0Oh0RWxaNRllaWkJRFGEa2S5/D55IJMS56/V6O+Iyb5b8QY1ms0kikRD7DV+SJLG5uYllWeRyOXq9nhgS2f66YrEYgUBADC5cbqbw5bouxWJR7D/n5+dviNlpeXmZQqFAuVxmampKXEt+RKRt25imKT4v/j7Jpw7eCgOWruucPXuWf/zHf2RyclIMTZmmSTKZFLGDgUBAmK/AO/e9Xk8MC2UymdvWgAUenfrs2bMUi0Xi8Tij0WgHCd6/dsbHx695cMyyLEFbC4fDO74XW60Ww+GQUCgkSHrNZlNQsiKRCHNzc4c+x9lsllQqRb1eF/QueMzolU6nb3hknuM4VCoVKpWKiFzc7fMZjUaZnp5mdXWVZDJJpVJhcnKSSqVCtVplZWUFRVE4efLkFSbfXq/HxsYGGxsbSJJENBoVnyPTNFlcXKTb7dLpdGi321iWhSzLxGIxms0moVDoivjRfD5PpVIR9yY/8nW7LMtiY2MDx3HIZrMHGuSOdSw4NmAd61jHAoKnThE8dWNjdVzLov72t6N87nOo1apnGNraRLmyjBWP42xu0vnf/5vQO9/5hJnSvGGSJNJvfjPtb/kWzA9+EPfiRa8Cb9sCf2+fO0fgX/wLov/3/31zj63bBcvyDFiHKYzqOnK3i9S7+ub27S7XMOh+8Ys41arXlJiZIfK0pz0xCuS7SZJIfN/34b7qVXT/6q8w/vZvod0GXUdeWCDyilegT08f+dOGn/IUwu97H6PlZQaf/Sz2ltlUXVggdd99SDcgzuswsjsdmm9+M/IDD3j3xUYD2TeHqSpWsYi7vEzj058m/p73oB0Q+bebovfeS/tNb8L5jd9gFIuhVKsEL10SzQg7GMQcH8fNZHC/93tJ/at/te/jSZOTEA4jm6Z3zzmgWCC329ihEG4ohLpfPM0NUGB+nu5dd3km1XIZe3Z2b8KZ66JVq1jJJM7Y2A03Ah2Fuh/5CFK7jd5o0JuZQdutqS1JuBMTnqFgYwP+8R8ZrawcvlkkSaRe/3q6d93F6E//FPehh2A4fOy7JhDAmpyEe+4hcO4ccj5P+ClPQZJlaj/+45iGgb60hL24iD025tEKJMmLHazV0MplnHAYd2IC9Yd+6Ibf+6StOA9Xlj3z5wFytyI/smM23/o0jQea3su/4nGlx4b//+7v/g7HcTBNk0KhQL/fZ3Jykna7LUhA8BgZptVqcerUqRuKAH88KBwOMzs7e6sPA/AKJ48++ijNZpNischwOCQcDqNpGv1+n1qtRigUYjQaMRwOueOOO3YtpPtxH4FA4ED0eDgcRtd1QQk6agNWtVoVZJ1arcb6+rpAo2uahqZpZDIZTpw4QSgUYmJi4lCNr2q1SqfToVAokMlkrpguDIVChEIhwuEwpVJJTI/Ozc3tGWd5M4tU6XSaVqvFhQsX6HQ6DIdDKpWKaF4FAgGKxSLtdls0mvym125KpVIiUrJarR76c9vpdMRUdCwWo9/vMzc3R7lcZnl5GU3TUFUV0zSxbZt4PE4+nyeXywmq2PXIcRy63a54/MvNV9vlNzTq9TrD4XDPJpRhGBSLRVGEPHHixA7jmqqqwnC2urqKpmkEg0Huuuuu6349j0f50R1+rGO326Xf79Ptdun1etx///3EYjEURSGfz4sYIP/3Tp48SSgUEkVrRVH4lkPsh/v9K//ftRiwXMui/uY3o3zxiygrK6i9HlY4jBsMwhaJimIRo15n+MY34r773bjBIK6qIg+H7MGk2fYErkec2SLYKK0WymjkGQgkCSsYfMzItRUbphgGkuOgN5uMvvAFeOMbr/6FPcHU/exnkctltM1NjO0U1+2SJMhkGFkW2uYm5vo63b/9W6Lf9m03/4CPWFI87l1zpom7iwnnit8fDnG37ktHbb66FbJbLZo/8zMoDz6IWqt5NbWtvaMdDEKlgvXVr1J79rNJveMd10VAt1stOn/5lx6x2raR83ki3/EdjC5cwPzVX0VqNlGrVa+O47pe9HmhwOgzn6F/zz2kfvmXj5SkH3j5y7G+/GXcUgl5cxN3y/Tty8lkcMtlHEVB27rXuKqKc/lax3FQ19awQyGIRIh893df97EZhQKdX/1VlG98A7nTQep2PYOqruP83d/R+q//Fek1ryH5Az+wY68jqSrpX/91Gu9+N+6nPoXZ7aINBh5VeMt45aoqdiiEOTeH5A9sSBL2wgJWswmOS9Uep6OlUFwLW1LoaUn6apy/T9/HJzPfgz04nJnqetdnjUZDxFO1223i8Tizs7NomoZpmrRaLQqFAolEQkSU386N+6OQJEnMzMyI9enq6qogWSWTSSzLotVqibXrxMQEk5OTqKpKpVKh2WwiSdIV5qvtGhsbYzAYCLKLruucPn0aVVUJBAKsrq6yvLws9jrRaBRZlimXyxiGQafTEdQWWZYJh8OMjY2xsrKyJ3loL9m2LQaS/P3JbvLJXJVKRcR9+QM8uq6Ty+V2DCgYhiEiFdfX1wXpJBaL0Wq16Pf7rK2tCdLM/Pz8rnQTP9LO3iO6dL/XNRqNSKVS9Pv9fYkowWBQxLrBY7ReP3Jrc3OT4XDI/Pz8DnOWP7wUj8dZXV1lY2ND0OTS6bSgwfgUGcdxhHktkUiIIYlqtYrjOILAdTPV6/Vot9uUSiURb+fvT/xrs9Pp0Ov16Pf7TE1NiXjz7dHtkiSRy+UYDoeCRpPL5QTN2o9cq1armKbJ3NwcqVTqhkSFjUYjSqUS5XJZfJ53GzZxHEcM5vkx5dsHlW6FdF3n5MmTtNttQRXzjSXBYJB4PH6FOUzfGlAMBAKCbn67KxQKceLECUzTpNlsCiJgMBg8stqFJEkihtKyLCzLEvRBn0jn75UlSaLT6bC6uko2m8WyLBYWFg5Nrdoei3sr5Bst2+0209PT+54/n/zYbDapVqtMTEwIgibA7OzsrjULfyhweXmZXq9HpVIRcYzbvwts2xbEb/+cb25uMj4+jm3b3HHHHeK+4NPBfHPh6uqqIBdKkiTiIgOBgDCRH0Vc87Ge+Do2YB3rWMc6Grkuva9+ldEnPoFbKoFtI2UyaPfdR/Tee2n88i+j/PVfe2ht2/Yi8xIJXEVBGgxQqlUCFy8y6vcZ/uzPIv/2bxOYn7/Vr+pxr/iLXgQvehGDb3yD0QMP4A4GyNEogac9jeDp07fmoAIBkGUvRuIQkiwLZBn3CUwAuVxWrUb7j/4I6dOfRmq1wDBAkjB1nfrkJPJLXkLie7/3lhmDbrQkTSP24hfDi198U583MD9P4LWvvanPuZdcy/LMV1/5CvrSEoAXH7K1WZe6XZR6HblWwxiN6LzpTSR/93evqZAdf9nLGJw4Qf8DH4C/+zvs0cgjECoKaBr2t34rgVe96lBmzehLXkLvj/4IKxxGLpc9atZer3E0Qut2GS0s4D7vedcdKXkt0l7+cpxvfANqNbRLlzBPnLjChOU6DsrKCjgObj6P/JKX3BYmSOcv/xKlWsUOBA6MYbHHxtAeeQSr26X3v/4XgX/zb67quaLPex7R5z2P4YULjP7xH3H7faRw2Puu2cJ2X67Er/0arTe8AUOWUYpFAktLuIqCI8te40xVsbJZ3PFx+OEf9r7PbrCCd9zBKBTCisVQ6nXsA4r+cr2Oo+tIkTDPf/2dPP80/O//Db//+zt/z3W923ggAPfdd98O84au63zgAx9genqaSCQiNu9+PJtPDbrzzjt3FFmPdeO0tLREs9lkeXl51+gMwzAol8usbFEJVldXWVhYuOJx/Enyw06thkKhHYVW35hh2zaqqhKLxQ58LJ/Y5DeuNE2jXC6zurpKqVQS0/C+saPdbmMYBqFQiGq1SqlU4syZMwyHQ0aj0YHmnkqlQqPRIBAI7FvESyaTgrIUDodJp9Oi0BgIBG7ZZGA2m6VUKmEYBqVSCdd1yWazVzRG/EZNKBSi0+lgGMauhCvfVHSYyXHXdalUKiJGYDv9TVVVHMchn88zPj4uCGyqqhKNRgkEAoyNjTE+Pn4k507XdQzDEOSCgyZfFUXBsixc193zdVYqFYbDId1u9wrz1XaFw2FyuRz1ep1UKnVdsUaPVw2HQ2HqLJVKWJYlrv1gMEipVKLb7QojYzQapd/vi6ZFKpUSERd+gyaZTNJoNA587qMiYDX/639F+dKX0C5exFFVRnfcscNkP3IcpEqFwMYGhusyeutbse++G6lYRC8UGNn2/jHezaagZbmShNrvo1gWrix7xpHt2oogtGQZvddDtizUhx7CNU2kx0nM0a2S8bGPodTruICTze5L8nRzOaRKBRoNRh/72BPCgKU/+9lYH/847vo6Ur0O+1FXXBet2cTM57HPnbslpOOjlDMc0nzjG1EeeADN3zum07hb31NSu41eKuFWq2BZNN72NjLvetdV72nMcpnOb/820he+gNTtIg0GSK4LwSDd//AfUGo1ZEVBaza9z2k87tV+hkMCKyveEN5wSPMNbyD5vvddlwlsu6Lf9m3Up6cxGw0ChQKGLOPm8+L1SaqKmUohDwaorovtk9L8G6Lr4rbbaJubSJaFeeoU9r33XjfBbLi4SP+nfgpldRV1YwPFMLBCIZBl5FYLqVTCSiRw/st/oV4qkf6Zn9lpwtI00m95C+aP/Ajd7/gOuHQJyba9AdVgEDubRYpErvisu6MRKAqDEwv8beN5tILj6PYAUwlSiN3FI2PfzgAdt9tFkoytteb+3/29Xg9FUYTJ5WrkOA6rq6uC/Hi5wUXXdSKRCOl0mpWVFVRVRdO0W0bCfTzJJ+42Gg1arZYwS/ny1+DJZJKJiQlhqOr3+zSbTVKp1IHnMJVKcfHiRTRNY3x8XKzZ/JiqRCLBxsbGFUYn34jvE2/T6bQgRoFnKjjM++fTQJvN5o6BpFgsdkUclG3bLC4uikGXRqMhTF6yLJNIJOj1etRqNU6fPk0gEGBpaYlGo0GhUCCbzYohEz8eLRgMoqoqtVoNgEKhwMmTJ684dsuyRETjYeW6LrZtEwwGyeVygjiVyWSueHzTNKlUKoKStZ10up0ONTU1tednUFVVJicnWVpaIp/Pk0wmBaXXN3f1ej1kWRbHpOu6oLlsbGygqup103V98rJPFFYUhVgsRjqd3vOxTdMUNGKfzBOLxQgGgziOw2AwENFi3W5X0HmBKwxjPt1tbGyMeDyOaZpcuHBBmOhc1yUejzM1NUU6nWb+BvWXfPK0b8DbS37EWbFYFHuybDZ7y+9/PhUonU6L62K/Pb//Pmw3Dj5RpGnaDTEtBQIBAoEAzWZT7Pd9AnelUsGyLMbHx8V9dTAYkM1micViIspXkiTC4fCesXiPJ/X7ffr9vqgFHaR4PE6tVsM0TTEA1mw2yefz+9YsgsEg0WiURqMhano+4d6nauVyuR33o7GxMRqNhiBPX7p0iXPnzonPof+ZVBSFdDpNs9kUxDJN0wQJPp1OMzc3d8s/v8e6PXRswDrWsY513eo/8ACDX/91lJUV5GYTqd/3ChzBIPbnP09d01AqFdRKBVdRGJ0+LQq0EkAggJ1MYlerBDY2GOk63fe9j8Cv//otfV23k0J3303o7rtv9WEAIJ89i/vFL6JsbmIdRMhxXdR2GzOfx9mjif9E0/DCBfo/+7PIa2vI5TJqu420tYkR1KPVVeqf/zzJ974X5Qke1fLNqvaHP4z8wAMeHSgUwpmf31GMdcNhrFwOeW0NfXkZQ9No/fEfk/qxH7um5wvdfTehX/kVrFqN/t//PW6vhxQKEbj77qsqPGvj49jPfCa0WgSWljA2N3F3mfpwDQNtaQkrEoFYjPARTBdfi+IvfCG1z38ebBt1cZHAww97RfB4HBeQu120ZtMzQC4sYD/5yaS/7/tuybFejVzbRt7YQO71MLaoUvtJCgSwg0Gkbhd3be2anzd4+vShzb1qOk3yd36H1n/6T7if/Sx2p+MREh0HNA3icZyZGbTXvpbYd3zHNR/T1UgOhXCf/3ycSoXApUveMe11jzVNtGoVM5PBPnGC8Ld+KwAvexncfz9sbOz89dHIM2BdLkmSmLvsMxYKhUQRtFgsous6xWLxit871tHLL3atr68Tj8d3NSDpus709DSFQoFisUggEGBycvKKqEi/4HLYwqPjOMiyTLPZ5MEHH2QwGIjJXT8WLhqNMjExcUX02GAwENOA22NhFEWhXq+LCWdN04jFYpRKJeLxODMzMyiKQqVSEVPBa2trwlTjk6D2Old+I+AwxK5oNMpDDz0kyG7+a9A0jWw2Sy6X23PS/UZJ13UxxdvtdrFtm0wmIyJYTNOk0WiIyMTJyUls2xaNpt2KhrIs47ruvu+74zhcunSJWq1GvV6n2WzumKYPh8OCwJVMJsnn81c0L44yHiASiZDJZETB178W9zr2drtNJBIhGo3uWTit1Wo0m01hNNpPfqyAb9J7IhmwXNfl4sWL1Ot11tfXRbwDeOfSj9us1WoiIqHX65FIJMhkMkQiEXRdZ2JigkgkIox7rVaLarV64PPvZsC6Wp+JMxzCxz+OvL7ukalPnryCLCTJMoyNMVIUb7+eTMIznwmpFG6x6MUNz87uHjdsmuilElY6jdzvIw2HKKMRkuti7ecWcxxcSUJyHCTXpfPpTxP/zu+8uhf3BJNUKCD3etix2MH0J1nGikSQez2cQuEmHeGNVfTZz6Y+MYFVLKJvbjKKx3cfWHJdpPV1ryaVyaC9/OU3/2CPWK0/+iOUBx9EW1rCjka9eO9t14Abj3vRnktLaIuLoKq0//Ivr2rAYbi4SP+nfxp5dRVlcxO12/WiQSUJybJQRiNv/+Q49GZnYXJSfOZdYGiaKIUC2uIipiTRfN/7SP/0Tx/NCZAkwu94B4M3vIGR66JvbOBWq1iJhBcZPhqhtFpIkoQRCgmSnv7ggziahmxZ4DhYsRjOwgL2055G+i1vua5DcoZD+j/3c8hLSwTW1jBTKW/YZ+uatF0XWi20YhF5cRHrox+lNT9P4tWvvuKxtPFx3B/5EUbvfz+BCxewZmYgHN7TZKmUy9iBAFIixqdyP0UzPHPF76i4yLKMqmoYhomqXrkG237LbjQaghRxtd/TjUaD4XBIvV5ncnJyzyZxMBhkfHycYrFIOp0Wpp5vduXzeU6cOCEimxzHwbZtZFlG13VisRhjY2NigMAwDBFrdpgGdyQSEcb67VJVlWw2K4ZPfIOST/XYvv/wI5MTiQSLi4tYliWM5nvJdV3W1tYol8silnI4HOK6rlgD+wawU6dOIcsyi4uLVCoV1tbWkCRJGNclSRKRbZcuXWJ6i5w/OztLt9tlc3OTVCq1Iw4xFAoRiUQEicV1Xer1OpFIhF6vt+M69/cJ0Wj0qq5/33DlOA6WZZFIJAR1y4/K84lM/X6fYDBIOp0mnU7vGLyp1+u0220URTnw+YPBIKFQiGaziSzLnDt3jkuXLpFMJnfsITVNI5lMihjDVqslaLz7Ubr2k+M4FAoFYTy63IBVKBTI5/O7Utnq9bqI6D5x4sQV8V3+3uNrX/sa/X6f5eVlZma8e9vl+xafLn7u3DlkWWZiYkIMNvmkNt+8ODExccOMEs1mk16vRzgcFtfoXnHrwWAQXdep1+s4jkMoFLrlhppQKISmaQQCAQaDwYHGvF6vh67rYthkrwGcYz0mPzK2XC7TarVERKA/KDc9PS3O+2AwwLIsYR4MBoMsLy8Tj8cpl8uPG6L8fvIJe4qiHOpz51PrwLsPDwYDQQQ/SIlEgvX1dRzHodVq0Ww22dzcJB6PMz4+fsXv+4RJQPyeb9T1lclkSCQS1Go1qtWqGJhSFIVUKkU+n78lsaHHun11fJc81rGOdV3qfelLGP/u36FsbKCVy0i27eG8AaXRgGLRm9LfahYNnvSkvadjs1nMfh9lcxO+8hXMUgltly/MYz2+FXnlK+n/2Z9hh8PIpdIVxpLtkioVcBycdBrtZS+7uQd6C2SWy/Tf/GbkixfRCwXsUAhjZga2Gi5ys4lSryM1GrBFSEr/1m/dEnLQsW6gXBf7Yx9DqVZBkvb+jEgSzswMzvnzyJUK1v334/7wD+9PGDhAaiZz3aSh2BveQPfhhxlZFnqhgNNsevSuUAhsG6XZRG21sEMhrPl5ePWrbx2RT5JIv+Md1CUJVBUaDZRaDb3ZBMDRdayxMdx0GvvJTyb5znfeHuS5rUa+5LpenM8h5Mqyh2q6iZhzJRYj/Za3YL/hDXQ//nHsS5dwDQMpFiP43OcSvuee66aNuZbF6OJFnE4HKRgkcOrUvpSDyPd+L4NPfQozlUJfWcGYnPQifPzjcF3cbhdtfR1H13HzebRXvWrHY+zmNdhKVb4q+dSger1ONBrdUXw51o2RbwLx4wj8aA+fSqWqKvF4HF3XyefzLC4uMhwOqVarIirCVzAYJBwOUy6XcV133wKTT6RyXVfQlfyJbkmScF0XXddFxMf09LRofpTLZdbW1uh2uzSbTZpb9y9FUbBtW8Q5nDt3jmazSblcFtQhX2NjY2xsuQaj0Sij0UjEmfi0qsvlx9X5U937qdvtsrGxQblcFrFrsVgMWZaJx+N0Oh1RbD9MMe0oFQqFSCQSIoqk2+3uIAvZtk0qlULXdcbGxgQyPxqNCtPadg2HQ1Kp1L4F5+Xl5R1RMn7knCzLjEYjMaWfyWRQFIUzZ87c0BjSRCJBNpslHo/Tbrcpl8u7TpX65p/BYEA8HieXy+0a1+G6LqZpMhwOD9Wk8g2Go9HolkZt3Ag1Gg36/T4bGxvC5LaysrLDKNlqtZicnBQ0gnA4zOTkJLquk0wmd5xD36TlGzQP0m4RhFdbE+5++tNenFi7jTEzs7+xJ5PBrlSQqlXkhx7Cvvtu6PfRL13CvnQJa2wMKRJ5LJu30UDf3PSiwKamGJ04QeQjH0G2bWxF2XsN4LoopunFmm1l/Jp/8zfwTW7Awra983pYg+ZW5OPNXPvdUEkS6vd/P06xiD0YoF+44EWpp1KPXbf9PvKWeciYn8c5cYLEfffd2uO+Trm2jXv//SjlsvdZ2sPsKCkK1sICgUceQarVMD/yETjkvs9ut70hscVF9NVVrFiM4YkTj32e19YIbWygGQamrqM3GhjZ7I5FsaRpOPPzyBcvIm9s4P7VX2H/63+NckSm2+Add8Bv/ib9n/95jGQSqVbzKFO9Hq6iYI2PY6bTOKMRbjyOMhxCq4VkWViKAokEbiyG++IXk37966+7xtL55CeR1tfRCwWMXA53YmKnYUqSIJnEiETQL1xALhax/uzP4FWv2vX9i37P99D/8Icx43G0lRWMEyceix3crmoVrdFgNDeH86Qn0Vy90ny1dQAEAjq2bdHvm4z22az4tNDp6elrIoDUajVarZYguu4nv5HcarWo1Wp7GrB8spAsy0dqSn+8yjf+JJNJ0YCWZZloNHrFOvxqqTOSJInzeHlkYDabFcYk8JrSgIh+i0QiTE5OYpqmiAZstVokEgmKxSJPetKTdjyev+exbZu1tTXa7baI1UskEmLfMRgMqFarVKtVZmZmcF2XTCZDs9mkUCiIgaXte5REIkEikaBarVIoFFBVlYcffpjhcIhpmlcMlfh77UajIWLWfMNMo9EQ6y/HcahWq8IwdRhTYKvVolgs0uv1KJVKNJtNDMPYQQjyB3/A2zfm83kCgQCdTucKypVpmoJafBjTgk9W9gdN/Ai5+fl5HMcRFBdflmVRqVRIJBLC/HaQHMcR1GX/v8+fP0+j0WBzc1NQqvwY9c3NTaLRqIhz304Zc12X0WiEYRhomrbn6wwGg5w+fZqHHnpIxIVNTk4yGo2wLAvDMMR76Me1nzlzBtu2abfbWJYlyMb7Ra4flWzbxrIsMpkMlmUJwtVeBDNN02g0GuRyuUNfazdSvklxcnKS8+fPi/3dbnt//xxHIhFM0ySXy+1L/TqWJz/KMZPJsLm5KQjT7XabRCKxI0q1UqkQi8VETco3NtbrdUF5Osprut/vi6hX3zTl1w2udXDOp31blrVrLeVyGYYhfkeSJHH/Oszr3G7e8uncg8HgQMp7KpWiWq0KCtnl90NVVRkbGxP1uINqfcc61n467uge61jHumYZGxsYb3878uoqermMkcvh5PPCHGA5DlK1SmRxEclxsDUNaTj0yBd7yMnlCJw/j93r0bv/fpI//MM36+Uc64ikT07SedazoNdDX1zEWlvDnpraaRpxXaRKBb1UYjQ5iTs2RuwFL7h1B32T1Pn93/eIRmtrGGNjsI0cJAFuPu9Rj5aXvWgBXadz//3EvwnMad9MGj76KPLyMmq9jpnJ7G9AkSSsbBa9WMSpVOh98Yu3PEJEn5oi/N730v/Zn8UIh5GqVbTNTW+yWJKwwmGM6WlIJnG/53tIv/71t/R4JU0j88u/TPfv/o7Rhz8M//AP3kTyluxv+Rb0l7+cxLd/+3WZ226mJF3HDYVwNA1lOMQ54Pddx0EZjbAzGaRrnHa8HimxGIl/8S+O9DHNcpnuBz+I+6lPIW8ZUgCGoRDuvfcSfvWrd41HDMzPM/qZn8H5tV/D3NhAX1/HLZWwt0hicr+PMhphxmI4c3M4r3gFycsavkdlwAIvdmJ1dRXDMGi1WgcW4QzDEKadQCDwTdGQOEp1u106nQ6BQEDEgvmmENd1xQRoNBoll8sRCoVot9t0Op0rHiubzYoC9OWTc5fLbzJFo1EsyxJNAr/oZpomrVaLcrlMr9fbUUh68MEHWV9fp9/vk0gkyOVypNNpTNPkwQcfpFgskslkWFxcRFEUHMe5ogHhx1z4DZNUKkWz2SSbzYri3+XaHiuyX6On1+tRKBTElGAoFCKfz5PNZsXrWlpaIpfL4TgOd9xxx00lII1GI5LJJJOTk6TTaRFv0W636ff7omng4/KDwaB4D3xSlq9utyum2/cqOPumyrW1NUFT2/45DQaDgnK2traGpmkUCgXOnDlzw86BruvMzMwwNTUlmmzD4ZBMJiMmtnu9njAo+vE0frTL5TouQD4mn1ZlWRaNRgPDMMS15ZPGut0u/S2nlCRJ5HI58fnfTbIsi+nhg7SbAetqIwitr38dudXy1qMHrREkCTuZRGm3cQsFwu9+N91f+AUMSUIpFgleuoSjqriKgrxl+rGSSZzJSZyXvpTEv/yXWB/9KK4kITsOtmV5Bvntcl1kn5AVDOKCF22/1cT8ZpYbj+NqGkqvd+DaD9dFHgywEwncW7D2u1GKv+xlNAoF7A98AGdjA71YhGIRR9e9QRDD8PYiJ07gLCwQe/e7b/thpt4Xv+iRs5tNjImJffeOkqJgplKotRruQw8xWl4mcIjopfaf/ZmoU5j5PO74+GNmItdF63ZBlrEVxYsPNQzkzU3PDLbjACSssTECy8s47TadT3yC5Pd+7zW/9ssVPHOGwJ/+KZ1PfxrzIx9BOX/eMxkqCvZdd3l7uuc/HxyHzqc+hflP/wTdLlIohHL33cRf/GLka8lp3UXWxz7mnWdF8SLV95CkaZhjY95+vlSi+7nPEb333it+T5+aov/a1+L8/u8jLS8TePRRrETCi0xXVaTBALVeRxkOGU1N4U5NEX3Tm2CfZHldD2AYJsFgiOFwsMt6zmVpaQnDMJiZmRHrzKuVH6nlx63vJz+WyDdjXK5Op0OlUtkRV+cTTa+nKXy7SNM0dF3flx7i/1xVVQaDwYGDEv1+XxCBhsMhlmXtGCTI5XLcdddduK6L4ziYponjOMTjcbFO9iOsS6USnU5HxFU/+uijnDlzBkmSKJfLVKtVTNOk3W5TKBQolUpEIhHuvPPOHXulaDRKJpOhVCqxurqKoigithu4wny1Xdlsln6/T71ex7IsdF0nHA5fMRwRj8cJBoNkMhmq1Squ6xIMBsVeALx9gh8f7RsQD9pfl0olCoUCjUZDkGx8WtjY2Jh4PzRNEyQoSZJIJBJisMV//b6ulqzsGwJ805gfv2VZljBl+b/nD8Koqko+n9+XCGVZljDG+edIkiRhZut2u6ysrBAMBpmfn99BgvH3EsvLy4BHWvYJVr5BczQaMTY2RqvVIhKJ7GrOSKVSxONxms0m6XQaXddZXV0Vx5JIJFhYWCASiXDq1CkxLHKQ+fNGyH9vY7EYg8GAcDjM5uYm4+Pju96rms0mw+GQdDp94EDPzVI+n2dubo6VlRVBapqamrrCwFcul5FlmeFwyMzMjPhsHWt/SZLE7OysqOOdP3+eTqdDt9slm80yGAzo9Xp0u11CoRDpdJrx8XFx/n3Kk0/pPoqhLcMwWF5eFsfhDwr6xq9isUg2m2VmZuaq9/3xeFx8tg+qb7quK6J0/e8oVVVFHeag73vDMMRnyCfyhUKhA2OUJUkiHo/v2KMf9PvHOta16tbf5Y91rGPdtup+8IPItRp6ucxodhaSyR0TX5Is4yYSWMEggW4X23U9VPZ+i+JQCFdVYTDA2Zq8Odbtp8TP/Rzt1VUM10VbWUF96CGseBxX15EsC7XdBsfxzFfT04R+6Zdu+8LoQbK7XaTPfhZ5c9Nr9O8S2wZ41KO5OZSHH0aq1bA++lEv8+pYTxiZhQKuZXmNqUOQQNx4HKlQAMPAvjz37BYpePo02h/8AZ0//3Ps++/HLZe9iXxJ8grgz3wmwe/5HiL33HOrD9WTJBF99rOJPvvZWI0GVqmE6zio+TzaNRSZHw9yn/lM7GIRfWUF07Jgn02m5NNekkn0XQr+t5v6//APjN76VqjVUKtV1FYLyba96KRoFKdcZvjpTzN6wxtIvPKVV/x9/EUvohsMYrz3vbj5PFKtJuKT7VgMa34eNxaD17yG9I/8yBV/f5QGrFAoJAoMe5FhHMehXq9TLpdFdBx4Tfp0On2Mwb4K2bYtog8CgYAogvoFLtu2CQQCJBIJer0eqqoKCtTl8mNastkspVIJVVV3NRZ1u13W19eRZZlWq8XU1BRzc3M7ipp+TF88HmdlZYVHH32URx99lNFoxPLysijQ+fFwfpNrcnJSmGn8hsr4+PiujYNoNCoiDDVNw3VdQdXazQjiG/wCgYAoZl0u13XZ2Nig0WhQLpfJZrNMT0+LaDV4bPq8UCggyzLLy8ucO3fuphayXNclEokwPz9PuVxmdXV1K5ZHJZFIiCbg4uIiiUQCy7KuQPxbliVQ9ftNLF9OWduriePHAtZqNUE7uFGfY0mSOHHiBE996lNFkbHRaLCysgIgGm6xWIwTJ04gyzJ33nknqVRqz9ep67owq22Pe9lNtm0zHA7JZrMHFkT3Ur/fp9fr4TiOIGxc62MdlfzPkB+tI0kSjUZDRIH6079+FKUsyyKScj8D1nA4RNf1QzWZd4sgvFpvgdNsojabYJpI5TKupuEmk3uSsFxdF/HpkqaR+O3fpvkbv4H7uc9hdzpInY4X9aWqkEziZDIor3kN6de8BoD+7CzqI48guS7aaIRrmoKGJTnOY4b+YBA7HPbMFZK07zrnm0Xyt30b7sMPo9RqWP3+FW+2OxggGYZH8zQMlOEQ68QJlFs8vHHUSv3ET9CamMB+//u9PUir5b3urXUg0SjOs55F/Kd/GvUJ0By019ZgNPLWuvH4nrF0vtxEAnmLNG6urh5owBKErUoFJxC4Il7edRxkw/DIdZqG7DjIponaamHYNlzeQI/FcBUF2m2cRx+9+hd8gCRN8+JIv/M7wXVxRiPkQOAKY1r8pS+Fl770yJ8fwO50UC5eRGk2sZLJfU1xruN49y/ThEqF0Re/uKsBCyD5Qz9EwzSx3/9+7GYTpVYjuLLifaYVBSuZxJqdxRkfJ/TOdxJYWNj3OH2zE4AsS+Ryr8a2R/gej1DIi+qanp4mHo+LGLibLcdxxJq31+vR6XRoNpuYpokkSYTDYTqdDsVikdnZ2QPXHbebbNumVqtRq9UoFApij+APhKRSqR3r5nA4TDAYJJlMUq/XSSaT+66r6/U609PTwvBWqVSYmJjY8Tt+HHYqleJLX/oShmEgyzILCwuMjY0hSRKdTod4PM7CwoKI1Ws0Gnzta19DVVV6vR71ep1Op0OpVKJardLv9zl9+jQbGxsMh8MdsVB+dNzKygqlUonhcCjMPgftE9LptDhXhmHsakKTZZnp6WlBE6vVajQaDVRVRdM0YWDzyVGZTOZAcor/HhUKBQaDAel0mqmpKZaXl4lGo7TbbUF9SSQShMNhJiYm0DSNfr9PoVBgfHyccDi8w0gRCARErOBhqDHdbpdkMkkgEODUqVPYti1McEtLS8KkZxgGruuSSCSE+Wovk2Wj0WB5eZnhcEij0WAwGOA4DpqmUalUKJfLNJtNpqamdjVmRCIRQqEQq6urFItFNE1jYmICVVUZDocYhiHMGZubmyKKdDtt17ZtWq2WMK9NTU1xxx13EAqFBKHHj87M5XK3fC/gv4/lcplMJoPjOFQqFdbX18XPJEkSsffVapUzZ86Qy+VEjOatVjqdZmxsjHPnzvH1r39d7O3z+TzhcHhH1KSmaaRSKU6dOsXY2NgT3hB7VIrH45w4ccKLSTYMHn74Yer1urhvhUIhcrkckUiEsbGxHWZVv27jR9NerwzD4JFHHqHZbFIsFkWcpKIo9Ho9qtUq4XBYGKtPnTp1VXWbWCxGKBQilUpRq9WIxWJ7Xie1Wk0Mt+XzeSKRCMFgUNTpDjKEN5tNQa9XVVXcIw4jRVEYjUZHck6Pdaz99MTudh/rWMe6YXINA+nTn/ZiscJh2A9fK8s4soxs26jdLvZotHv30n/soz/cY91kqakU8fe9j9a/+3cQiUCzidxooHS7uLKMmc/jpNO4Y2OEfumXCJ07d6sP+Yar99d/jdTtonU6jA6aQpVlrHTam6g8f57RygqBubmbcpzHuglyXVE4d+HAIvr2v7ta3PyNlBKPe5TCf/WvGD7yCHajAbpOYGHhcd3sUFMp1CcAKjv4Xd/F6DOfwVZVj+J08uSukTTuaESgVMJMpXDyeSLPfe4tONqj0+DrX2f0C7/g0Tc3N70o14kJryFs28j1OoGlJcxkEuc3foN2MLhr7Gb0ec/Dffazven4++9H2twE28ZNpVDuvZfYy1++53VylAYs/zO9nTa083FHXLx4cUf8nE/AikQitNttEY83fhzbfKAcx6FcLtNoNMQ53B4B4U9sVyoVLMtiOBzu2YySJIn5+XkxJb62tkY4HCaRSKBpmqA/+ZQlv5h0+UTpdvnF8gsXLmAYBsPhkNFoxNzcHIFAgMFgQLPZFFEThmEQiUTQNI319XVxXLtpe3yE/1ymaV4RReIrEAgQi8UEoj2dTl9x3J1OR0RBqKpKJpMhEAhcMZkZjUYZGxsTiPe9qFs3Qrquo+s6juOwublJrVZjOBzS6XSEUca2bfHzUqlEJpOhXC5zxx13CNNbtVpFVVXGx8f3NLm5rkuj0aDRaJBIJA6cavbPrT/Ff3nM5VEql8sxPz9Pp9NhdXVV0AP86fZQKEQwGCQSiRCNRpmfnyeZTO5ZMM1kMrTbbZaWlvaMqvDlN7u2T/uPRiNhCNwewXn549TrdTY3N8Wx+gYsTdNIJpOMj4/f0PjG/WTbNq7rUiwWRbRKNpu9wojpF9yXlpawbZtKpcL8HnsBP6I0n88fKh7meiIIjY0Nuv/9v6N9+MPo9TqqYSCtr4Ms425seN+jExNX0EElwxDxx3IkghKNknnrW7F+8ifpfuxjWI8+6plF4nHUZzyDxH337Ri0sZ/yFIbVKsFqFSsYRDZNZNv2aMCShB0I4KoqViSCOTFBaHERKxZDPgTF54mu6CteQe9P/gQrEkFdW8M8dcozym1FbKtbZnJcF3U4xFZVaLUIP//5t/rQj1yJV74S92Uvo/e5z2F84Qu4rRaSriPNzxN9xSvQ9hp2ug3lWhbCMXOYxtK2WG0O0VgafP3ryNXqnoQt33DpD9s4moY6HHpkulYLLjfqShKOoiA5Du4holSvS5KEfAD950bI3iLySbaNu0dt0x0OUSoVMSiiDofIa2sM//t/pxGLEX3Vq668TiWJ1I/9GP2nP53BBz8IX/yiRwr0z30mg/TiFxP/nu8R+xRF2f9t9mPsDGPEnXf+1lasmPee5nI2MzMlcrncrtHEh5VPbWq1Wof6/X6/L8znFy5coNlssrq6SrPZpFarEQqFhKHftm06nQ6XLl0imUyKdewTxYTV7XY5f/48GxsbghLkx8f59NpwOMypU6d2rJFyuZyI1/OJO7upWq0yGAx2RNOtrKzgui65XG7HOi+VSlEsFgkEAoyPj2NZFqFQiG63i6ZppNNpsU6cnp6m0WiwtLSEoiiEw2EGgwHRaJTx8XERQxcMBsV+yG+Ob2+qS5JEKpViaWlJEJIOsx6ORCLClCDL8q40NfDIs3Nzc2xsbBAOh7FtWww9hUIhxsfHCYVCh6K9uK7L+vq6IHXNz8+LPaRP+gXPSN/r9YTxKx6P0+/36XQ6gmR8+aBHNpulVquxubkpjPp7qdPpYFkWyWSSbDaLqqqcPXuWtbU1QqEQw+FQnG9/aCEQCOxrvqrX6ywtLVEqlWg0GoRCIaLRKIqiMBwOefTRR6nX6+K62SuSS5ZlxsfHWVpaYjQaUa1WGR8fx92qZSYSCbG+9QfMFEVB13Vc1xWDUf71/dSnPpW77rpLmCtUVRX03seDstnsjtcxNjaGLMvi/fbJaz7RbnJykomJCZ7xjGc8LuhXvubn55FlGdd1SaVSrK+vs7a2huu6whwYDAaZnp7m7NmzjI2NHWhWPNZOJZNJ7rzzTsDbG5umyeTkJJqmiajBdDp9xUCWb0SWZfnQ5qL9dPHiRRqNBoVCYcc9xNdwOGRzc1MMagWDQUGyO6wmJiYYDAb0+32Wl5fJ5/PE43HxuTUMQwwlTU1NEYlESKVSyLIs6k/lclkMvu0mf5BycnJS3C980+lh5NOzHk+fw2M9MXV8hR3rWMe6Jg0eegip00FttzH22xxpmijAKJYFjoPU6exdpBgMvKnXUAjpNqWSHMuTmsmQ+e3fpvfVrzL80Idw/+mfcHo93GAQ58QJtJe9jNgLXvCEJ1/5sstlL4LTdeEQaGQ3GvUmV7f+lmMD1hNGSj6Prao4qorU6x3YKZN6PVxZxtV1tHz+Jh3lVUiSCG5tJI918xR+8pPpPeMZmJ0OoZUVuHgRO5/34oMkCde2katVtGoVOxjEnZxE+cEfvG1iFneV69J/97tR1tfRNzcZzczAVvPBL8E5ySTDXo/A0hJWoYD9H/8j9nOeg7ILmUjS9Wuajj9KA1a320WWZXRdv6K4YJom58+fp16vUygUUFWVVCpFIBDAcRw6nQ6FQoFoNCoK6mNPoIbjjZBhGEiSxMbGBnfdddcVMXKappHJZAgGg2Ii0DCMPSPzYrEYJ0+eFNEDzWaTcrksmgHxeJyxsTE2NjbEpPVeRSTwohsGgwH1ep1+v08qlWJmZka8r8lkkn6/L6IhfHpaPB4XRqput7tr0d5vNiiKgqIooimwX7Mtl8vRarXENTg9Pb2j8NdsNgWlIJVKiaL6bkXxRCJBuVwWZqbtBizbtmm328JcGAqFjsxUk0qlCAaDaJrGhQsXcByHfr8vpnr934lEIiQSCb7+9a9TrVZZXV3la1/7GuFwGFmWRYHSN2DtJsuycF1XTHgfJN9IZBjGngS8o1I4HGZsbAzbtonFYjQaDWzbFo0+n3bmOA6Tk5Mkk8l9J7N98lssFmN9fZ3Z2dldzVrdblc0X8LhMIqicPHiRVqtliC3gWf4KxaLxGIxpqenCYfDrKysUC6XRYF2+znyJ2tbrRZzc3O3JAJDlmXRYGs2m0xPT+96r/CvrUwmI4rQw11MCY7jsLGxIZoch4mButYIwuEjj9B/85uR19dRmk3ULbqNNhh4MYOqimSa2N0u5okTSD5hwHVRm02sVAp7YWHHd6uaSpH8wR888LmVF78Y5/x5rF4PMxr1ntsvlkuSFwGcyUAshrq2hh0IQCRC+BWvOPiFPcGl5XK4L3851p/9GdrFi+iPPooLKIaBZFkopulRxBwH1zfBDAZ03/hGIu95z6Gi6G4nSYpC9PnPhyegwWy75GwWW9c9Sly3ezBBud/HUVVQFJRDmFSceh1s2zNU7Ua73PredyUJyXVx/HWD6yJZ1hUDlO4Wyc5SVaSbGDl8MyX750mWdz0H1OsECwXYol4rtu0ZTR0HrVLB/MM/pPuxj6G//e1E/q//64rHDz/5yYSf/GSsRoPRpUswGiEnEgTPnr1iL/ev/zX85/+8//FKkkRIV0nSw7UMekoMW9ZJJODuu7PXbWZIp9M0m02q1SrdbnffqGk//rvZbAo67KVLlyiVSlQqFeLxOKlUin6/TzweJ5PJkMlkGAwGFAoFisWiaNLut6a+HdTr9bhw4YKIbdM0jZmZGQKBAIZh7IgDcxyHM2fOiNfsG3ZmZmZYXV1lMBiQSqXEue/3+4JiND09TSKR4NSpUywuLoq90MWLF4lEIjviuFVV5c4776RQKDA3N7fjvfSb5NFoFEmSyGQyPPjgg8J4dM899xCLxej3+4La4hsMNjc3qVQqggy1nVwUi8VE9OHVrod9k5D/WnejyQaDQU6cOCH2ablcjomJCXFeDkto9Rv+zWaTiYmJHX/jk4olSRLGuLW1NTH0MT8/L87nyZMnr9jn+NSYXC5HqVRC1/VdI/UGg4EgR20n8iqKwvz8PFNTU9RqtR0GrP32Z+DtEZeXlymVSrTbbWZnZ684Pt9M1O12GQwGVCqVPesOwWCQYDBIu92m1WqJOD7fZJVMJsUeyDRNEfHux4L59B1/MMNfFz8eFY/HCYVC5PN5SqUS4+PjLCws0Gg0aLVawnjWbrfpdrucPXuWU6dOkUqlbvje72okSRJzc3PkcjkeeeQR0uk0w+GQwWAg3jN/fzg2Nkb+8ViXvg0UDAY5e/asMFX5lMNgMLinEajZbBKLxVAU5bpp2b4ZdmNjQ5D0dzvG2dlZVlZW2NzcJBAIMDk5eVXmr3Q6LYa8yuUypVKJzc3NHUbLUCjEzMwMyWSS06dPi5rU+Pg4nU6Hfr/PysqKMG/5P7csi3q9Tq1WY2JigkgkQjabFbW3SqUiYnf3kmVZdDodQf881rFupL45ut7HOtaxjlzOVoSc5Diwz0JYkmXMWAzJNFFN04sQ2GPSHkCpVr2ogXCYyC7EimPdZpIkIvfc8/iJITvWsR4HCj/pSQzHx7HW11GrVaxMZu/YAtdFqVax4nFIJok8+9k392CP9bhW8hd/kWK5DLJMsFZDX1uDtTVcRUG2LBxVxcpkcMfGcH/wB0ne5s3L3le/irKyglYuY+Rywnx1uaRIBGN6msDqKk67TecTnyD5vd97ZMexW63/Wgb8XdelXq+TSCQIBAJXbP4LhQKtVotCoUAmk7miGR+PxxmNRqytrbGxsSFMIrd7M+JGyS+A+tPcfrNgN0UiESzLEtF0e8WwgWcsOnfunDBFbS+m+tGCPqHqoKJZo9EQEXHhcBjLsq4oHgUCAcbGxlhfXxeFLcMwRLNmL/pAp9MRNChFUTBNk3A4vO8xpVIpQR9YW1vj4sWLJJNJEadQKpVYXl5GkiTGx8fF5OZukmWZSCQiqF7gUZBKpZIgIVmWhSzLaJpGOBwmn8+TyWQObAw6jkOr1RITj47jYFmWeC8GgwGmaVKpVHAch5MnT+74nEiSRDabFU0Hy7IE8Wt6elrg8MfHx28YceFmTHJPT0/jui6yLJNKpUSzChBFy3Q6TTQa5dSpU/s2o3RdZ2JiQlwbPpkikUgIpL9v0MtmsyLO8JFHHqHVagkig2/aMk2TQCBANpul1+sRCoVotVqsra2J9+Pyxy4Wi/T7fVzXFQ2mmylZloWRqtvt7too838vmUyKmEL/XrGwsICiKKIxU6vVcByH+fl5crncoe7l1xJBaG5ueuarS5cIFAqY0ShGJIIyHCI7DrauI5sm2mCA5LpIly5h3HEHkiwjVSqe2SCbRb3GaK/4S19K6w/+ADOfR6tWMWZnvdhf1/VoTpLk/XuphNZsMlpYwH7KUw6M2vpmUfL/+X9o1GpYgPbQQwS7XUEocvFMMqau4yoKriyjtVq458/Te+MbkX/v927b6O1vZkWe8xza0ShmLIZcreLs1yxyXdRaDTuZxMnnCd1998FPoKpiL+puIzX7kmQZKxxGMk0U0/TMXeKHV353iejzRALtGc84+PlvQ6lbZGFry8RqbTciNJsE1taQRyMUy8JRFGxVxXVdz0gHBC5cwMjnMd72NqR3v5vwU5+65/OoT3vavsfyvOfBxz4Gq6uP/b+nPAUeesgbEEl3l3hy8eO8UP8UtYKHynIkhYdTz2JZ/04knnxd5wI8Kub6+jrJZJJiscjc3NyuawjDMFhfX6fb7WLbthgeGI1GInouGo0Kg7e/Vk8mk6JZu7S0RCqVolKpPG4ivK5Fruty6dIlQT3ym8zbibO5XI5arcb6+jqSJLG8vMyZM2cAb21x6tQpwDP0+8TQYrEIeOvKRCLB+Pg48Xic06dPEwgEOHPmDMvLywSDQUHpcRyHYDDI2NiYoJb6TXF/uGA3UshoNEJVVVZWVnaYtfw9l3+cqqqSy+XE3qXRaOww7/hkF98w5K8D91Ov1xMmMJ/ou7m5yezs7J6U2k6nw9zcHAsLC5w7d+6qTT2NRoNOpyMMgJfLH+5oNBrCMOG/9tnZWTKZDPl8fs/13fz8vNgPFQoFwuEwyWQSXdexLItWq0Wn0yGVSpHP53eNOtc07aqp2JVKhcFgQKPRYG5ubk/jQjwex7ZtGo0GgUCAXC635yCPf8x+vJdvuvPPz8TEBKlUina7TafTERGKgUCAZDLJxsYGiUTicW2+gsdi3k3TxHVdSqWS+PzkcjlBr45Go9x5551kMhlOnjx5qw97T4XDYZ761KfylKc8RcSJSpIkSMbbKUY3SrZti+f24zh9ivJREKButUKhEIlEgqmpKarVKoFAYE/zVa/Xo9vtiojU640IrlQqwhi4X01DkiTy+Tyrq6uMRiNqtdpVm+58k6p/r+h0OsJ4NjExQTAYJJFIMDs7u2O9EI/HBSGwWq1SLpeFEQw8QpemaUxPT5NMJjl16pQgsa+vrxONRimXy3t+F4BnCvOv6cMMPB3rWNejYwPWsY51rGuSHAp5UUeShGua+0ZoOZkM7lYzSDGMvSMG63W0RoPR3Bz2056GfgNjOG6IXJfh4iJOu40UCBA4ceKxybhjfdNLGR/HCQZxJQm33UY6oEkkdTqiQKdOTNyMQzzWzZIkIX/nd+KuriI3GsiFAs709JUF7K3mkzIYMJqaghe84DECwbGOBcjhMMov/RL9P/oj1L/9W88c3el4ZmdNg1gMe3YW7V/+y11j+G43DT/2MZRmE8m2cfL5/eM7EwlsTUOqVrH/8i/hBhuw9iJgua4rCkeX//9SqYRhGAKbvb2YZZomjUaDSqVCLBbbszAQCASYnp5maWlJTKPezs2IG6l+v4+iKFiWxezsLJ1OB/CaOtvfH7+w7JOYgsEgo9FoXyKTrusiUmA0Gon3PBAIIEkS5XIZSZL2NX35k3jtdltM/u4VO+ubTfyYhk6nI6YiTdO8YgJ8NBrRbrfJZDIiujIcDhMMBg8kBy0sLIgolHa7TbPZpLHVXO33+0SjUYGKn56e3rcw6Ecb+E2QxcVF2u02jUaDbrcrXq8fL9ftdmm1WiLCbbdzViwWqdVqjEYjut0um5ubmKaJbduCkqQoCmtra6Lg2Gw2BdLeJ2K1222CwSBPecpTGA6HImLDj1g4LNFKURSCwSDdbvfACLnRaIRpmiKe8mZoZmaGRCJBpVIhkUiI+BY//iOXy10RRbCXJiYmxLR/q9Wi2WxSr9cBxFTv7OwssViMmZkZ1tfXqdVqFItFUqkUk5OTO+I/G40GGxsb9Ho92u22oLUtLCzsOB7fDJdMJllZWUHTNAKBwE03YAGC6qXrOr1eb08TVjweR1EUxsfHCQaDgnrh0+j8Rmk2mxUxOIfRbgasg7afnT/4A+SNDQKFAsb4OG4+j1kqIZVKyMMhsmli6zqubaMMh7iyjLS5iQTolQqjqSmcsTGSL37xoY7xcsnhMOrP/Rz229+O6boEVlawg0HsRAJXVZFGI9St7/rR7CzO7Cyxn/3Za3quJ6IkRSH97/89FcdBO3/e21sqCramefGRioKj61jZLG42i1Qqoa+uYmganf/yX0i/5S23+iUc6yqlRKO4996LU6sRWFxkVK3Cbo0z10UqFpENAyubRX7JS/Ye8tkmbX4eS5axg0HkVgt3l/WOncmg9npgGCijEa4kedfbZY1x1zTRNzextgxgiec975pf9+NakoT0ohfhrq2h1GpYzSYkk7iOQ6BQQN6iCprBoEciGwxwNA0nFMK86y5otwmsrmKoKsN3v5vwBz5wqPdqN+k6/OIvwkc+Ar2eZ8j6lm+Bz/+Ny/9503/jnkt/Ti7YYEzaINF1wHUx5CBxo8KTO5+n9rPfSuod77iu2qUsy0xPT2NZFpZlsbS0JEzZ22O5m80mvV5PDA3k83lh1PBJkuFwmOFwSLlcFo/tx20Hg0Gi0agwuExNTT1uosiuVj5NqVwuMzY2tuua3x8QsG2bzc1NotHojjW+pmmcOXOGcrlMJBIhl8uJ4QOfOJTNZhkbGxPrKFVVOXXqFMPhkEqlQrfbFetAf5DCHwLy12F7qdVq4TiOIL/6ZFU/bs2PrwsEAsJ84+9BthuwBoMBuq6Lv/Uj+PZ7b/1rIBKJkMlk2NjYYGVlhZWVFXK5nBgWAc/EUK1WMQyDubm5XSOvd5PrugwGA7FOGw6HGIZBMBjc89ii0SjRaFSs7/v9PlNTUzzpSU860DwRiUQ4deqUWEM3m01KpZJYa8diMebm5ohEIszNzV1Bcr5WVatVms0m4XB4T/OVT02ORqP0+31M06Tdbu/Y67iuS6/XwzRNarXajsfSNI1UKkWn02FtbW3H/vHy11Eul0XE4u1gjgiHw9xxxx0sLi4KIrZvbFQUhYmJCWEivTxK9PEq39x4MwnDruuysbFBuVzGMAxhwPINj+vr62SzWaampq7biHSr5VOeut0uKysrjI+P76ANuq5Lq9WiVCqJ+s1RUMc6nQ6dTodEInHgOQyHw6iqKv7mWp4/k8mQTqdptVpsbGxQq9VwXRdN0xgbG2NiYmLX75hcLifI8el0Wpi3hsMhsVhMUPZOnDgh7jOKopDP5zEMg+XlZUHP2l7DGw6HVKtVer0ec3NzxOPxI6OuH+tYe+nYgHWsYx3rmhQ4c4aRrmNFoyjNJs5+Be9YDEdVsRXFK9yWyxiShJtMeiaufh+lVkPrdjEmJnDHx4n8xE/crJdy3bIaDTof+hDuJz+JvFUkABiGQrjPfS7hV72K4OnTt/AIj/V4UPT5z6f1W7+FFY+jVqvY8fieRTbXcdAbDcx8Hvuuu9CPm+lPOMVf9Soaf/VXGKMR+soKzmDgNUq27qVSp4NSraIMBhgzM7hzc8R/8AexOx0699+Pc+kSmCYkEgSf+1zC3/qt11y0PdbtLUnX0X/4h4n91E8x+Ou/xnr0Uc8NFI0SePazST7taV6cUKdD92Mfw/6rv4JKBcl1cTMZ5Oc9j+grXoF6RAW8G6piEanXww6FDo5SlCTsWAx5MMAplY70MK42gnBxcZFEIiHiGvr9Ps1mE8dxBHb7coR/vV5nNBrR6/VYOID68URqRtxI+aYcH1EeiUSoVqt0Oh1CoRCqqmJZFoPBAFVVmZ2dpVarkc1mxYT+QZIkadeiaiAQEESfveIahsMhtm0zGAyEoUOSJEGLulyRSEQYvHRdp1wuCxNKuVwWE9a9Xo96vU44HCYajWLbNv1+n/n5eVKp1K6xcZe/poWFBdLpNJVKZUehPRgM0mg0UBSF2dnZA6+7wWBAIpHAsiwuXrxIpVKhXC4Tj8eZmZkhGAyKeM16vS4aO7IsX/E5GI1GnD9/XpCDKpUKpVJJTIoqikI6nRav2zAMer0emUxmR6QCIJpO8XiceDzOxMQEFy9eJJVKkUwmD42mlySJdDpNu91mbW0NwzD2pUjV63Vh8tuPsnbU8l+nX8j0iQPhcPiq7x1TU1PEYjHK5TLJZFKQEvypf3/av1ar0e12KZVKohGxXZqmCXLAAw88IChtz3jGM/Y0gwWDQfL5PJVKhUwmQ7vdvukxAvF4HE3TSKfTgmAVi8V2FLX9RlQ0GuXEiRPIsszc3JwgiMmyLAyR+XyefD5/6PdhtwjC/XroTr+P9Nd/jby5iRWN4m4V093xccwtN5cyGqENhzhbcWNqv4+ysoIZizGamcGdnCT0y7+MfJiswz0Ue+5z6b797ZjvfCejTAa5UkFtNMBxcBXFWxNnMjjT00Te/W70yclrfq4npCQJudfDzOeRRyOMbBYJvMjyaNSLo96SOzGBPRigbG7ifu5z2G94A8oeRsFjPX4Vfe1r6f7d3zEaDAhsbGB2u9jZLNJW48htt1GrVZR+H2N2Fmd+nsR3f/ehHjswO0v37ruRKhXUzU2MsbEr19mpFHa5jLQVVeooCo6igH8tuS60WujFIq6m4UxOorz61bd39PkBir3ylXT+7M8wcjn0tTWMrYQAPw7UCgRAllGGQ6Qt+pWZSnk10GSS0WiEtrmJu75O70tfIvLP/tk1H0s2Cz/yIzv/37mv/AbntL9ACyyh9vqYkShVfRwXiZDdZWKwjG1oKJ9r0njzm0n/xm8gHcJ8vfcxPLZebrVaNBoNYcoGj/qRzWYFjTaTyTA+Ps7m5qagSfrGIv/70I8mbjabwogRi8WoVCoiJvxyA7vjODQaDarVqjDU+8b+XC53qKi5m6FqtUqr1RKUTH+4YTdlMhkajQa9Xo9KpSLoIOAZY8bHxxkbG6PT6QgarKZp+9JqgsHgrmZv0zSRZflQex9/6MM3T/lrn2AwKCL0ut2uMMj7Awq2bYv1PXhmKj+uORQKsbKywsbGBpOTk7sev99A96kw09PTwnhWLpcpFAooioKmacIUGI1Gxd5nbm7uwNdVqVQE2dh1XSRJEsMch7mGNE0jEomIPcZhDSOxWIxz585RqVSoVCqMj4+L51cURVCxj8rE48c+7hVh7ysYDBIOhwU5d3uMuOM4Ii58NBoxHA4pFApMTU2xsrJCLBZjfHyciYkJEUHuE/NSqZT4DPt71l6vx8zMDLFY7JZEjF+LIpEId999N/V6XUSgbf9ZLpcjlUrd9sahGyXXdVlcXKRWq1Eqleh2u4K6Z1kW5XKZaDTKaDSi3+/viKy7HeUTsAA2NzcpFApiL+4Ph/mUqlwux8LCwpEMazmOI4bUDiNd17FtW5DsrkXtdptischgMBCUcsuyWF9fF3v4qampK4xYvjlze0zx9uFW13W5cOGCID1Go1EmJyfFfalUKrG2toaqqsLoPRqNCIfDwsB6UJ31WMc6Ch0bsI51rGNdk5R4HOfZz8auVAisrDAaDPas9rqWBZLEKBYDVcVOpdBqNeSthqgrSVixGKOTJ3HHxgj88i8T3EIpP941+MY3GPzCLyBvbnpmiWYT2ba91xSN4pTLDD/9aYave92Rxh8d6/aTHA7jPv/5OLUa+uIiTqmEOz5+pWnGcVCWl71p5nQa/WUvuzUHfKwbKjkcJv7e99J54xsxdB25XEZfX0daWwO8BooVj2NMTeHMzRH6xV+k/Xu/h/SZzyB1Osjdrkc50nWMv/gLBgsLBP7VvyJ677239oUd65ZJDgaJv/SlsEskUPtDH8L+3d8Vxj55q3DmBAK4jz5K54/+COkHfoDkD/3Q49vI52/8D1ts2Yox4joKBrtpt9rHVq17l0Pw8N0+NchvAqRSKRKJBIlEglOnTl1RQBoOh/T7fTHtfZBisRjValWg/g9Dr/lmkz+568fK1Wo1QqEQ/X6ffr+PZVlici4cDotpbU3TrtvQ5sfbVatVut3ujglHXz4dy6foJJNJVFWl3W7vSlHzX08mk6Hb7ZLNZpEkCdd16ff7fOMb3xAUL/+x+v2+MJclEolDU3YAcb36xSvwojYKhQKLi4sMh8N9i3n+RHQymRTXt09s204N8o1TyWSStbU1UTjL5XLivFmWxYULF6jX6xQKBdEISSaT2LYtyE6WZRGLxUQUxPr6ujCt5fN5cQ63T1gmk0kkSUKSJNEguhrlcjkqlQrhcJhCocDs7Oyun8dGo0Gr1RL4/FvRENQ07UAD3mHkG7oMw6Df7wtDl994cl2XarVKo9EQcSp7yX+PS6WSuG73k284bLfb1Ov1m27A0nWd2dlZLl68SDQaFQ2ocDiMLMuiORkIBJiYmKDT6XDq1Cny+fwOA5bfeLzae81uBqz9fFHdv/kb6HRQOx1Gc3M7SJLOwgLm+jpurYbkOMimiWtZSI6DrWkYc3M4584ReetbCd5xx1Ud526KPu95WE96Ep0Pfxjr/vtxqlXxM/v0abSXv5zEC194TH/dRVathvKP/4jaaGBms7gzM3tTxgE7myWwvIzd6dD7q78i/l3fddOO9VhHI31yktA738ng53+eUSCAUi4TvHRJ/NwFrFgM4+RJnIUFIu95z1UZ7fRXvAL7a1/DrdVQL13Cmp9H2v79IEmYs7OoDz6Iu7VmVYZD3PPncWUZeTRCchysRAJnehrnpS8l/QSvf6npNNpb3oL19rdjShL6+jrKYIBk2+C6yKaJPBp5+/pQCCsSwd1mJnWzWaRyGRoNhh//+HUZsC5X78tfRv7wh1GXLoFtMzxzBvQA7Yc8w16HLNXgNFPDS8QXF0FRaP3xH3t7wevQ+Pg4kUiEzc1NksmkGH7YboZRFEVQPYAdUXXbvwN9wkW3291hwPLXFdv/1le73WZpaYnhcEir1RJme9+AVSqVyOfzTE9P3/JhlX6/L9b8Bx2LqqqEQiF6vZ4wwFwuSZKOZA0UiUREDGSv1zuQDtLtdsUwwfb9bCqVYjAYCDPe5ZQj/73rdDq0Wi1mZmaYmZnBsiwcxxHx1n40tj/EVK/XGQ6HTE1NiaEJf1gkFAoRCAQEFXd7fJlPAzuIntPtdllcXBTXnT8M4j+/H9uYSqUOpDP5pJmrNUtpmibIyj6ByyfsHrXpxN/n+MMTeymZTIqhF3+t7++11tbWBLW42+3SaDQYjUbouk4mk2F1dZVGo8Hp06c5efIkrusSDAapVqtcunRJEJIBYZTbq0byeJYsy4Jk6ziOMBneTq/hVqlQKFCr1VheXkZVVWZmZnbcewaDAeVymeXlZVzXZWVl5bY3z0xMTAiTWTabpdVqYRgGqqqKSNpgMChITUchvxZ22OFC0zTFfv5aVC6XWVtbo1ar0Wg0sCwLVVXF58OnOna7Xe64445d9/3NZhPLspBlWdDDfYOjTztvtVrMzc2RzWY5ceKEMAEbhiFqaT5FMBgMkkqlmJ2dfUJEWh7r8a/jyvyxjnWsa1b41a9m9NnPYtbr6JcuYczMeBNw2zePgwHa6iquqmKePIn1nOegFIu4y8seLsJ1QdNwo1Hc5z2P6Pd//21D+xlevMjwZ34GeWWFwMYGdiCAOTbmMcAdB7nRILC0hBmP47zvfbQ17bjY+U2u+I/+KO0vfxnDNNFXV3E6HaxMxptUdhykdhutXgfXxTxxAvupTyXxz//5rT7sY90gaWNjJH73d2m///1Yf/mXOPX6Y/fFQAA3kcC97z7CL385vbe/HeXRR1FKJdROB0eWQVGQTRNHVZE2N7GWl2m97nUkXvOaW/3SjvU4UvOP/gh+7/dQCgW0RgNb13EiEVxJQul2UWo173vq93+fRrdL6id/8lYf8t5KJkHXUZpNbNc90Cym9Pte9OsBMWBXq6shYMmyzOzs7BWGA38SMp1O71p094uQ19IcuFrDyDeL/KJ1MBhkMBgwPz/PYDCg0WiIKUNJkohEIqJpYNs2uq4fekpwL6XTadbX10mlUmxsbDA7O3tFMV6WZVHUHx8fF7GUfrSkbxjytT1iJBQKkU6nicfjhMNhVlZWGI1GwijlT9f7zYpoNMqpU6euyajnFwrBI3v9/+y9e5Ak213f+c13VlVW1vvVXf2ex525V0ICDEKWQMYg0C4IJGEwAQYv1rJALHj9io3wwtrr8Hoty2tvONiFsGPtWIdDiwmwMXhheawhTMgGh0FIwNW9M/2s9zMzqyozK6vytX/0PUfd06/qme6enpn8RNwIabq661Tl65zf+f6+XxKT2W63zxQbkahAUjxkGAbD4RCZTObMyDaWZbG8vIzt7W2Ypol+v0/FOURw02g0aJGbCHCKxSJ9nWEY6PV6KBQKNP7NdV26mUO+YxKLcnS8YRjSKJTLEIvFUC6X4fs+6vU6tre3kU6nkUwmaRQLiZ0hTlDXHRtqmuaJjajTRIDPiiiKpwrJTNOk8UNLCzgZxeNxTCYT5PN5WJZ1btGZXNPz+Zy6PtwkxMFQ0zTqdkA2SD3Po4JbSZIgSdKxa7FcLj/z+5+2D3ueACsYDMDM52DC8EvONUd/vrx86H4zGICbTMDMZhBNE/NMBsEnP4ncD//wlQq1+VwOmU9+EvgLfwGeriOYTsGlUuCu4fx8mXBbrUOBh+PAvSiSGfjSsZ5O4bfb1z28iGsi9q53gfvpn4b5j/8xws9+Fr5lfWkCGoshTCYRfv3XQ/2v/2vwl3RVTH7jN0L77d+G5/vgd3chvfXWoZjqHdduxjQP1xGxGFxVBVgWfrkMxrKAIECQSiHMZhGmUmD+7J9F9s/9udvd1HFFKF/7tTD/9t+G+3f+Dmb5POTPfx6C6yLEOw1VooiQ4w7XWmtrx74ThuPgJxJgbfvKr0vn538enK6DdRzM798HI4onRJoBw6Od2EKW+YNDV8Rf+iXg+77vmY9bMplEMpmkIhgSw0WEWdvb2+A47lhsEInjfbLhgMRmE8cMnucxm83oXPPoaw3DwO7uLnUu4nmeCnccx8He3h7i8Tg8z4Prutjc3Dx1/GEYYjabHYtnvg6xFonkXlScwXEc3bS+TkgEoaIoGAwG57qjTqdTMAxD3baOQly9isUijRQjzRrEYavf79PXpFIpLC0tQRAEOgcnTmadToc634iiCFVVYds2jesDDtcWlUoFpVIJuq4fiy8jMYUXzelt28bjx48xGAzQ7XYRj8dRLpepC0y320W328VoNMKjR4+osO80yNw3nU4jf1pk7AIwDHNmJOBVcTSa8izXZeBQGEWaVRqNBnXuJXGVnU4HHMeBZVmoqopSqUSjQ/f391GpVAAADx48wGuvvYZWq4VkMgnHceialkSy53I5Kk55UXlewisijDt6rvu+T9eB5Jx6VjelIAiOiQPJdf00EMe5VqtFG8We/FuxWAyrq6toNBpoNpsQRRGVSuWFiHM8j0KhgFwuB03ToGkaXNcFwzD0Onia5pzzINGfJPr2vL89nU4xn89p1OtlGY1GqNfraDQasCwL+XweqVSKXte2bWMwGGB/f582M92/f//YmBqNBrrdLmq1Gnzfp02sHMdhPp/DMAy0221YlgUAtOmOrLM1TaMCLPIsyOfzV9KAFhGxKC/ukywiIuK5E3v9dcx+8AcR/PRPw6vXD7sqJQm+ohza4ts2eMuCpyjw19cRftM3ofATPwEAmP7xH8Pd3UU4n4PNZJD4mq95phiD54H19/8+uGYTUquF2dLSoe/3EYJ0GjPbhri3B69eh/9//B/wPvShSxfDIl4e+FwOiX/4D2H9lb+CuSSB7fchttuHXZIAAkGAl80iyOcRfPmXI/OpT73Utv0RAJdMIvMjP4LwB38Q5mc/i6DdPtxQKRSQ+MAHwAgC9P/mvwH3xS9C3NuDL8uYra9TsWvoumA0DWK/D286RfBTPwWzVILyp/7U8/5oEbcA+w/+APgn/wTs/j5424aztgbmSPypF4ZwTRNiowFmdxf+v/yXMN9449Y6qQlf//UIfud3gE4H0HXgvOfpdArOtuEtL4P52q+90nFcNoLw7t27cF2XFjUFQbiw6EWcaVzXpRsO5+E4DnVqepGLldcJKdhnMhn0ej1ks1nEYrFTxVVBENBohFgs9sxiFRLRR7q69/f3oSgKLULN53NaeCuXy2AYBpubmxgMBiiVSmi321TARIRkhmFgPp+j1+vh4cOHWF1dRaVSged52NraogWneDyORCJBO+MLhcKVFfN4nqfvWa/Xsb+/j2w2Swtjvu9jNBphOBxCEASUy2VIkoTJZALHcS4UHvE8j1QqBV3Xoaoq1tbWwDAMdVMi0X3b29sYj8cnjlU6nYZt23AcB7PZDNlslsYakA3CszotRVGELMtP1XG6vLxMN9XG4zF0XafRMizL0k2mRCKBu3fvXkmkwGlomoZOpwPbtqntP8uyiMViiMfjKJVKNxLtQZz5SMf7RYiiSLvqF4k8IOLJ5+Fmkc/n0W63kcvl4HkejaA8unmrKApUVUWn00EqlYIkSU+9Gfckl3XAYjjuS5vw4RmeSTyPsFyGVy4DlgV2exvB+jrE11+/PkEFw0Rr5EsQHt2AX+SYMAxC8rqzjnvEC4FYrSL7t/4WPF2H9Ru/gaDfP4y5W1qC8g3f8PQ1NYZB5m/8DWgsC/y7fwcYBrjBAGKjAQAIRBFeuYwwm4X/3vci9kM/BO/Xfg3+/v6hDayqgv+ar4H6zd8M9gXfEL0syp/8kwh+7udg/sqvwP2LfxFsvw8wDLx4HL6qIsjnz04LeMetl1nQjWIRvOEQ3H/6T+CGQ3jvNK8AAAPgn0234R25Bwgsg0+v5SF0uwi6XVj/8T8i8f73X8k4iPD4KEQccHRznThS8DyPyWRyTNBy1BmHCJYMw0Amk6HiDgCYz+fY29tDt9uFpmmoVCon4vdmsxna7TYODg7AMAwURUHxnRhe4GTsHP2OBAGFQuHKN21JQ8N5opejzGYzGpl+nXAch0KhgNlshv39fTQajRNiGHIcbNtGpVKBZVkn5mukCYmIQQaDAZrNJnK5HHVAicViWFlZQSqVwtbWFp0j3rt3jzZepFIp1Go1uK4LURRphBXDMDTCisTbkffN5XKXmt+6rovJZIK33noL3W4XhmFgfX39xBpAURR4nocvfvGLODg4gCRJeM973nPimEynU7RaLSrAv8mY8ctC5qmpVAqaplFH5dNYXl7GcDhELpfDdDrF7u4ufN9Hr9c75hpdLpehqirW19chCAJ0XUe73YYoirRhZ2triwoojopO0ul05EpzSVzXxWAwwGAwoM0oZL0HgDaFEAEWz/NQVZUKHy+D4zjo9XrQNO1YlCiJxywWi5de15K4WMuysLGxcaaQi5xb29vbtEHtMm7et5WjzmnXTaFQgGEY6Ha7GA6HZ75nGIbo9/tIJBJPvW5tNpsYDoc0LvbJ8yIej2N1dRWdTgeNRgOSJGE8HtNzcj6fo9vtotlsAgDW19eP3WslSUKpVEIqlcLBwQEGgwEkSaJ1LvIsu8ip8CpwHAf9fh+j0Yi6dcVisSutu0W8uETV+YiIiGci/T3fg5Esw/+pn0IwmYAZDMA6DhCGCCXpUJiUSCD89m9H9r/772iBMPbGG4i98cbzHfwz4Lz9Nrg334TQ6WCezZ4QX1HiccxXViDt7yOYTGD+wi8g/QM/cLODjbhVSGtr4P/P/xPj//v/hvcrv3LoekQKboKAYH0d3Ld+K9If+xiYaBP9lYHheSS/7utO/Pvk//v/wL71FoT9fXiqimBl5XjnrCAApRLmqgpxZwdup4P5P/tnwIc+9Ep0HUecz/RnfxacroM3Tczv3AHzZPGfYcC8E1ciPXqEYDDA7Gd/9tYKsJIf/jCMn/5peJkMxHYbs0QCzCkFntD3IdRq8BQFSCSQuGL3ydP2lS6qnV826otsLPA8D8Mwzi16EJFLuVxGKpV6JW3uwzCkQpejRdx8Pn9MYHU0DrLRaKBarZ4omvu+j1arhTAMF4qXWJRsNks3nVRVha7raDab9N9SqRRee+01GnXheR6WlpZo7IVhGNA0DZ7nYT6fYzAYoFKpYG1tDaurq9R2/UlIcZR0JV81lUqFfueapmE4HKLb7YJlWQRBQB2AstksjVzr9/sLXxMkcoUIcoiYyjAMLC0t0aKyZVmnHitVVTEcDqGqKliWhSAIGA6HKBQKsCzrROF5NpthOBxSJ62ndT+rVqs0Hi+dTh/bdBRFkW7mXddGWr1eR6fToZF4ruvSY8LzPDKZDCzLgm3b1168PhortIhzA8/zEEURs9nswoIlOSfy+fxzi3EsFotwXZcWf/P5PN34JfemVqtFNw5KpdLCx/03f/M3MXgnmi+bzZ64Zk4TYJ13ynLLy/BkGSHHgTEM4IINSmY8RiCKAMtCeEFcql8FhFIJcxyKYljTRHiBUDS0rEPXM0kCewMbPBHXD5/JIPVn/syV/k2G55H7m38T1kc+Audf/2vgd38X/pF7tv9lXwbx274N6T/9pwGGQfz116/0/V9k2FgM6sc/Du3nfg74nd8Ba9vwLopqDUNw0ym8fB7hE/Fsz8J8dxcIAvDTKWal0rGf/YvpLmb40jGVwOLvZd4HttkEplO4OzvAFQmwToOIjo5GE8qyjFgsBlVVaYQveZ7P53MqxiAiHs/zkEqljs35BoMBbNvGcDjEysrKqY0TkiRhZWUF+/v7GAwGdHOUYRiMRiPs7u7Ctm0YhkHdkziOQzKZhGma6Ha72NraOtO59bKQaO+Dg4Njgq+jkDmOZVmYTCZYWVk5N8b5qlhaWqJz70ajQWOWJUmC7/uYTCYIggCrq6sYj8cQRRGapiEejx/77gVBwPr6OhXGra6uIpvNYn19HYqiQJZl2pRwdL5NosJTqRQ+97nPIR6PwzRNKlQgEe2apiGZTNJGp/X19Ut9Ttu20e12qWPW9vY2Wq0W0uk0NE0DgBMirLW1NTiOg0ePHqHdboPjOGxsbEAQBLomN00TmUwGpVIJa2trt15QVCgUMBqN0O/3qevyaRBRARHNWZZFXZdzuRwSiQQSiQR9DZmzkjXHcDhEIpHA8vIydZc7672IoO+2f3fPm8FggFqtBsdxoOs6jV11XReWZYFhGHieR5uxANAIttFohFKptPA6UNd17O3twbIsaJoG0zRpE0wymcRkMkG/38fGxsaJyNHzGI/HGI/HNHb2PIjb8Gg0wng8Xvg9XhZIrOtRkU86nV5Y4JNKpRCPx1GpVGgdKpvNHrvOXNdFt9uF4zhYX1+notnLYJombNuGrusoFArnivKKxSI9B3q9Hq3N9Pt9+vzb3Nw8cwyyLKNUKtEGy6MirusmCAIcHBxA0zTYtk2PDRF5G4YBWZZpLGLEq0m0sxsREfHMpD7xCXhf//Uwf/EX4f/aryHo9YAgQJhKAV/3dVA+8YkXJlZwUexf+iWwkwnY+RzhGQsGiqrClyQwgwH8X/1VIBJgvfJwqRQyP/RDCD/5Sdi/93vwB4PDjY3VVcQePoyEMxGU+S/8Avh3Yin9avXshVUshnmhAGE4RLi/D/tzn0P8y7/8ZgcbcavwhkNw//E/ghsM4KVS5+7IMqIIN5sFp2kI/+iP4OzsQN7ausHRLgYjCGB/4AcQ/IN/AH8+h/T4MdxcDmEuB4giQt8HOxxCGAwQchy8tTWEH/0oxAUiry7DafUDx7nSt6CuRblcDt1ulxYznyQMQ1r8Je5GrxpETOU4DiaTCd3EiMVi6PV6UBQFq6uriMViSKVSKBaLCIKAbiioqkqLkqRwQuzvc7nclX6npDjd6/WQTCapKIfEdySTSfT7fWiahkajgUKhQJ2SSHRiv9/HbDbDxsYGVlZW8MYbb2Bzc/PMDSES7XKdrK6u0qI76Yomoi9ZlmmERzabxcHBwTO9F3GFC8MQ8XicvlcYhqcKumRZRhAEiMfjCIIAxWIRb775JhVZEYhQhlxvmUyGdtM/DUcdnMgGGsuyyGQyWFlZeSpnrUXpdDrodDrUMYBEGBx1JTtaOCdOZteFLMv0XCAF9vMgIkUSR3QehmHQmKGb6Bz2ff9YTAQA2vW/trZGo3La7TZ4nqdFWLJZXCwWsby8vPD7feVXfiW63S4AoFQqnbiWT4sgPE+AlXj/+6EXCvBSKfCDAbxs9sx1RxgEEDQNXqEA//79WzkveFURymX4r78Opt+H0OthVi6DOUdgyw0G8BIJhIoC5cMfvsGRRrxwMAwS73sfEu97H/zxGO477sx8oQD+BhwTX3SYD34QwfY2hLffhmfb51sSjkZgfB9BNgv+CptfAscBguDQ7W6B+R+9dwQBwgXdmJ6WdDpNG0xGoxF1Bsrn85hOp3AcB+12G5lMhsYRZ7NZ8DyPTqeD8XiMarUKRVHoBn8YhhgMBlS8dZ5rLXFpIfHU4/EYLMtiZ2cHvV6PRu4VCgX6DCfRhoVCAWEY4t69e08Vx/QkhUIBvV4Psiyj0+kci/oj8/1OpwPDMNBqtSCKIp3zkHXidcGyLO7evYv9/X2IogjLsjAejzGdTqkISlVVxGIxvPvd70aj0QDP86jX69RtmLhVjcdj2LaN1dVVpNNpZLNZGjWYTqfPFM+HYYjt7W3MZjOMx2OUy+UTjQuWZaHb7dK1RSwWQ+kJ0eFZEDHJZDKBpmmo1WrU2YfEx1mWhVwudywyWpZl3Lt3j76OOKcRdyFFUbC+vo54PI61tbVLCVGeF5lMBoqiYHl5GY1Gg64dyHydHMd+v0/jvZLJJN588030+32USiV6bZMmsifrlplMBvV6HfP5/Ni1fxTHcTAYDDAcDuG906TMcRyy2SwKhcJTN8W8rPT7fRwcHKDdbmM0GlEh62w2Q7PZRLvdhm3b9J65tbVFj2Wv14NhGHS9flFEPBGpdjod6LqOZDKJ5eVl6tg+Go1wcHBAjyupTS0CWS8v2kgjiiKNpX3RcF2XNq6JorhwjWYymaDdblMXcfI3YrEYJElCoVBAuVy+sNmOYRhsbW3R9Wmn08FwOEQymaSxfpZlQZZlev96mkYtwzBgWRYVTJ8HaUQ0DIM2rbEsS5vIiAvXeaiqim63S+/nNyHAIs+o4XCIZrMJx3GoUDkIAvR6PXS7XZRKJepQH/FqEgmwIiIirgQ+k0H6+78f+P7vf95DuRHCRgOMZcGXZWrpfR6+qh52p3Y6h4WQSGATgcMu08RXf/XzHsaN4ek6nM99Dv5oBDaRgPTw4UsnzrxKAtsG94UvgNN1eOn0uRsrABDm82C7XWA8hvNbvxUJsF5xnD/+Y8B1wdv2ie7n0wgymcMoy9kMsy984dZutKY+9jHomgb///q/EPR64DUNbK+HkGHAhOFhlGsmg6BYRPAN34DcX/7LVz6Gy0YQPi3Ly8uwbRuz2YwWtDKZDARBQBiGME0Tw+EQruvSTtTrFHXcRnq9Hmq1Go2kIxFnZDOGuNNMp1PcvXsXiUQCq6ur1H7fNE3ouo5erwfgsKBHYhMymQw2Njau3DJclmWsrq6iWq3CcRz4vg+e5yHLMhiGQTabxc7ODgRBwGAwQL/fp9EsruvSzYVCoYAv+7Ivu7BgelOQuA/TNGGaJnUOUBTl2GaYJEmQZZl2q19UUCNFQOJedTRqjojXCKe5K5HoGp7nUSgU6OaBaZo4ODjAdDoFwzBwHOeY61m1Wn3q62k6ndJ4hPF4TL8Pcgyn0ykthF9lnA1wKBAikZW+72NjY+PYZhXZyFBVlRbtSbzOdblxSZJ0Iv7zrPcijm2ZTAYsy0LXdVrMfJLJZILBYIByuXztm5HA4f2m2WzSyBQS80EcHGzbpiJPEsfDcRwVUBaLxYU3BhchDE86YMkycN50keF5MP/Ff4Gg1QL/+DHYev2EuypwKL7i9/YAlkWYy0H4tm87fyzzOSa/+qtwf/mXwXQ6YDwPQToN9gMfgPLxj0N4BcXB143w0Y/C/8IXEPb74A4OEKyvn15jGA4hjEaYbWwgeN/7oqjHiIXhVBXcKzavfFaUj38c1s/+LLxkEny9Dndz89Cx+gnC2Qxiq3UYEZhOI/mRj1zZGLhsFh7LIuS4Q5XuRc9GouQVBLDX7K6kKAri8TjS6TQGgwHdWFUUhT4fDcOgoicidCbuoUTEc+fOHbrRPJ/PaYTcIk0TJBLYtm26liKij9Pcs9LpNCaTCZrNJjiOw97eHt64gjQHEqM9n8+xu7sLXdepWGdvbw+NRoNu4Pq+D1VVEQQBfu/3fg+PHz/GV33VV+HOnTvPPI4nCcMQo9GIuoodna8TZ2HiHkacVyRJomIAXdePNVsIgkDXsIVCgcaJXwTZwG82m2fGcyUSCaytrWF/fx/9fh+SJKFYLF749yeTCfb29tBut2EYBp27MQyDRCIBVVUxGo2oCxjLsseaNmKxGB48eIBOpwNFUY4dB47jkM/nL3R9uS6IwGY+n9Njk0wmz/1OGIbBnTt36Ly12+1ib2+PCkSII20mk0E+n0e1WkUikaAuN2tra4hfEH8bj8epMxOZPx+l1Wqh3W5jOp1C13U473S3iaJIxV+FQgErKytRnBcOxWq1Wg2tVgu2bWN9fZ0K1Pb39yFJEuLxOL1v+L6P8XiMfD5P7z21Wo3e13K53JnnaxiG1OV3NBqdON6yLCOZTMK2bdRqNXAcB57n8a53vWuhY0WckhdxSQYO14pEJPkiEIYhrTdZlkX/ndR9CoXCuWvY4XCIg4MD6LoOTdMwm83AcRz9vlKpFKbTKSaTCe7cuXOhqEuWZdy/fx+7u7tQFAWTyQSmacLzPOrSHYvFkMlksL6+/lTfMxHICYKwkMhMlmVMJhP6u6Io0mfPIut7lmUhyzKdD9wEzWYTmqbh4OAAsizjzp07x+o6pVIJhmGg0+nQelShUHhhztuIqyMSYEVEREQ8De90Y4eLTvwZBgxwGM3o+1G0XMQrhfP227A+8xmwn/0smOkU8Dz4HAdbEDB5z3sgfed3QvmTf/J5D/PW4ek6EIZgXBfhApslDMfBFwQw8znC0egGRhhxmwks6/BZFYYIBQEXPq2ImNj3T7fUuEVk/sJfgLm1hdlnPoPwrbcOd6A977DLOx5HUK2C+/jHkfmO77gWwfNNCbCSySSNUpBlmca7sSxLF/GpVArLy8tIp9PY3Ny8+kHcYsbjMer1OprNJmzbRrFYpDFzwGHxhjhJka7k119//Zi7Vb/fRzKZpN8ncLjRUigUrl3MxrLsqcXqVCqFe/fuoVarIZVKwTRNzN45wbLZLGKxGGRZxsrKCnK30I3iScHVk2SzWTSbTbpJc7Sr/Ek8z8NkMkG1WkU2m6UbCSTCxjRNKIoCjuMgSRJs2z7hrmTbNo1fJK5KxImIbNSFYQhVVaGqKiRJou/3NEynU7z99tvHohiTySRisRhc10W73Ua328Xy8jI8z8Nrr712pcKn4XCI2WyGyWSC1dXVM/82cb0i0SHD4fBKxUFPQuI/DcNAvV5HtVo9IT7zPA/NZhM8z+Pu3bvgOA6WZWFvb48KTDmOg+u6MAwDpmmiUCggm81idXX12sYOAO12G81mE51OB6PRCIIg0M2k4XCIXq+HXC6HZDKJbDaLcrlMu8oVRUEqlbryDSPXpUtSyiLmAKnv/V7on/0s5r4PYW8P3FtvHQqXFQVMGIKZTCDoOsCycDc34X/1VyN1jjjA/Pf/Hu6nPw1G08APh2De2VzgRBHB22/D+pmfQfjxjyPz3/63URPSFZL88Ieh/dIvYT6fQ9zdBfv224dRZqnUoQrPssANBhBME/OlJYRLS1B+8Aef97AjIl5qhFIJwbd/O/CzPwt+exvi48fw8nkEuRwYjgNcF8xgAHE4hB+LIahWwf75Pw/2CqNp5NdewzSfh5dOg9c0eBe4Q7KDAbx4HIjHEf/gB69sHGdB4qMdx8HBwQHy+TxSqRSN2Q2CAI7jIBaLYWlpCZlMBvfu3aNRS0ejzYAvRZURoftFkI37MAzpZjYRc581f00mkygWi1TEPx6Pr2SdsLa2Btd1EQQBHj9+jEePHmE8HqPRaEDTNIRhiHQ6TUUTAPD5z38e5XKZCtbvXRR1eQls28bu7u6xBgLilJpKpahb75Ob8qlUigqSVFXFfD6nay9RFJFIJFAsFi81t+73+9Sh7Lz1DsdxKBaLaDabKBQK0HX9wvep1+sYDocwDAOrq6uIx+M0qps4cyWTSXQ6HfT7fXAcd8Kti+d5pNNprK2t4fXXX6eiEEmSnssGO4kNGwwGx0QIoihClmXk83mUSqUzxyaKIl577TXU63XqMExchrPZLI1lX1paQjabpWKJRTm61n5yPtxoNNBqtdBsNmFZFhRFoeI+y7JQr9chyzI8z0MQBJeOmnwZ6ff7sG0bk8kE6+vr9P7gOA6m0ykMw6AOyK1WC4ZhQJIk5HI52oi2vLxMr/d+v4/qGc3RRBA3GAxQqVTOFNsRx+tut4tsNovRaLRQZCppotF1HZ7nnXsfD4IApmnS5pvbjuu62N7exmQygWEYGI1GVBxJXB6HwyHK5fKpDsnj8Rj7+/vU5SyXy9H1/dGGzL29PXoPunv37oXjkmUZDx8+pLGRtm3TdStx1X+WyDzSMLeoqC4IAnpfePIedfTecR6kMe8mBJq+76Pf76Pb7UIQBFRPSSsholXiNJZIJGAYxlPXmCJeXCIFQERERMRTwGQyCEUR3GwGLwgudKZhbBuBICBMJiPxVcQrxfj//X/hf/rT4AYDcIMBeMs6bNkH4MdiQL8P7/d/H/p3fzcyP/zD0cbMERhS4GEYML6PC5cdYQg2COAxzKmdthGvFmwyiYDjDq+p+fzQFuMcGKIgEgQw54gnbgvKhz4E5UMfgvP223B+53cQWhYYWYb4rnch/pVfea33kvMEWE/GvT1r9Fs2m4Uoimi1Wkin03Ach9qtX9Zy/GWDWOBbloX19fUTXZs8z6NYLEIURRrtQTZYANDIvNXVVVqk5nn+2uP6FkFRFDx8+JAW1UinsCAIUFX1zLjBFwFRFOlmEimon1ac9TyP/lxRFCqWInGc6XQamqYhnU5DURQkk0lomgZVVWnhlsQKqqoKWZYhiiJs28brr79Ou9NJhzXP88hms88klAnDEDs7OxgOh2i32yiVSrTwRiAbeLVaDQzD4ODg4Eot6XVdx2g0giiKC3WjS5IEwzCg6/q1CrBSqRRKpRKN/9zZ2Tkm1rNtG+PxGJIkYW1tjXYDNxoNTCYT6LqOWq0G4LCgqaoqNjY2kEgksLW1da3uV6PRCM1mk0ankCgPAjnPOp0OLX4/fPjwmQrXi3CaVnqRvQg2FkPqH/wDjP7aX0MoimAHA/C6fuiiCsCXJHjFIsJcDv5XfzUyf/tvHwoHTmHy678O/3/+n8G2WhD6fYQcB19RAIYBa1kQhkN4igL/Z34Gmq4j+xM/Ec31rwiG45D+1Kdg/JW/ApfnwfZ6EDsdMM3mOy9g4CaTmG1tIahUEPtf/hdIa2vPd9AREa8A2R/7MQzHY+BXfxVMtwt+MADbbiNkWTBBgEAU4RaLCAsFhN/zPUh/53de6fszHHfodNhsgn/7bTC9HsIj7j1PIhgGZqur8L/qqyBc4zyAQERUADAYDKBpGnq9HhVfcRyH5eVlbG5uIh6P05jtfD5/qmsombcTl9GLIMIljuMwGo1oRPRFkUXpdJqKggaDwZUIsMhmuSRJ1OWHiB0KhQIqlQqSySRSqRQdn6ZpePz4MYBDh9FSqXQlcUu2bePtt9+GpmnodrtgGIbGUhE35qMOI3fv3j229pRlGevr66hWq5hMJvA8jzabXDY6LgxDTCYTjEajhebliqKAZVmYpkljK89iMpnAtm0aRU7myqS5Yz6f0/OjUCig2WxiNpudmCdPp1NIkkQFTs8Tx3Hw6NEjGr81Ho+paIGc25ZlwTAM3L1790yBiyAI2NzchOu6GAwGcBwHQRBQsdnR84y4nkmSBMuyLlxzWJZFBXlHhWxk/lyv1xEEAba2to79PJPJwHVdNJtNunZKJpO3sgnppgiCAMPhEJqmQVGUY+efYRiwbRu+71PnM+I4SBygSR1BkiQqChkOh2cKsIbDIUaj0UKxgqlUCr1ej94nFxFgFQoFDAYD8Dx/YUOQpml0HbiI4+F1QGI0yT0uFoud6jLn+z4eP36M4XCIRqMBlmWRTqepW/tkMkGj0UAsFqNC4idFWMRlaTweHxPaAaDXgqIoaDabaDQa9Fmy6PMpmUxeS10pFoshHo/D8zxMp9MLnwHj8RjxeByCIND7kyRJiMVitOHqPDzPg+M4yOfzC0dZPguapmE+n8M0zQtd+cj1R9yzIwHWq0ekAoiIiIh4CsSv/3p4/+7fAa0WGE0DzukqC+dzCKaJ2cYGwg984AZHGRHxfLH+w39A8KlPgTs4gKBpcFMpzNbXD4UgngdG0yB2OvBHI/if+QyMeBzpP//nn/ewbw18NotAVeEnEmDHY/jnFE4BHApQfB9QFLDRBssrT+zLvgzjWAyeooAbDhFcsAhnh0P48TggipC/4ituaJTPjnz/PuT792/0Pc8TYF22G3QRFEXBvXv34DgODMM4Vuwh3aGvGsRmnXQ5nxfxkE6nqcCERJwc/c5It/Jt5DQ3qZuyVb9OqtUqtbonjkKZTAayLNPoDMMwaEfh0Q0S4LBQa5omDMOg4sTJZILJZIJut4tCoQBBENDr9RCGIS1O1ut1sCxLLfWvung/Go0wnU7R7XbP7PTnOI66I3U6HcRisYWiGBeFRHssutkVi8VuzK6fFCh5nodlWdB1HYPBAACo85iiKMjlcjSiRlEU9Ho9GqFBNsV4nkcul6Miy+uk2+3CMAw4joONjY0TG8AMwyCVSkEURezv7yOZTKLX6127K9eT8YPAYg5YwOEcM/vTP43xL/4ivF/6JYS7u4cNEu/cG/3XX4fw0Y8i/c3ffKZgat5swvvUp8DV6+A1DfOlJSCbpa8PAHiWBb7RAL+9DY9hMHrwAKk/82ee5uNGnAKXTCL7kz+J0Wc+A+/f/lsE3S7gOIfup6IIJJMIP/QhKN/3fRBP6ayPiIi4BhgGuR//cYxffx3ez/88wnodIM7EPA/E4/Dv3oX43d+N5Dd8w7UMIfmJT2D8//w/mFsWpEYD7nSK4JRaAosA81wOYbGI+Pd937WM5TTIszsWiyGXy8GyLHieR11BJElCNptFtVq9sDFCkiQadWwYBo3xO4vRaASe5+mmt6ZpSCQSF66nWJZFIpGg7l1XBcMwqFar0DQNX/ziF8EwDEqlElZXV5FKpRCPx4+NjXwvb775Jm3MefDgAeLxOAqFwgnh/yIEQYDt7W1omoZOp4NisYh0On1MYOW6LjqdDg4ODsAwDJrNJhXSHYXn+QuPwUUcdTVbZI7HMAwEQYDv+/R3z0LTNBoNflRQpKoqdF3HcDiEZVlIJpP0HJ1MJkgkElQYQjbeq9UqfN/Hzs4Oje8j4qCbauhxXRePHj3CcDhEq9VCIpFAtVpFLBZDGIZ0vr23t0fn4ffu3Tu3cUsQBFQqlXPfVxRFJJNJZDIZDAYD5HK5c/+mpmlIpVKQJOnY997r9TAajTCbzbC1tXWqOEwQBKysrGBvbw+GYaDf77/SAiziMGfb9gknac/z4Lruseg34tg7m83gOM4xwU0ikaCCySAITj2G5PcWuU+SGM/pdEodvC8iHo9DURSUy2UqVMrn88feizgW9vt9LC8vIxaLXbtb+ZNYlkWdqMjamdSRSCzr0QhU4jxWr9dpI9LRz5RMJlEoFFCr1dBoNMBxHDKZDK15WJYF27ahadoxF8QnYRgGlUoF29vbVMh709/NkxDHc0VRzhX3AYd1Pdu2USqVjkXN5vN5jMdjDIdD2LZ9rsjTMAzwPA9FUU6Nq71qLMuCaZrgOG6hJrB0Oo3RaASGYRZ29Ip4eYgEWBERERFPQeKDH4ReLsPtdiF2OpgnEqdWvMMggHBwAF+WgWQS8U984jmMNiLiORCGmP1v/xu4Vgu8rmO2sQEc7awQRYTxOGaFAsTdXYS1GoJ//s/hfeu3gn+FF9NHYTgO+MZvRNDpQHr8GL5lAWdN7sMQfK8HT1EQJpNIfsu33OxgI24dXDKJ4Gu/Fv5gAGlvD7PJ5Pg1eBTbBm8YmC8vw//Kr4w26S7gtNrHdUQQnnxf+dy4tleJ8XgMx3Ewm81OLf4/STqdRq/XQ6lUorEmEc8PSZJw9+5dMAwDWZah6zparRYtSMmyTCMli8XiiaIdiRhZWVlBvV7HdDqlHb79fh9vvfUWgiCgDnGWZWE4HCIWi2F1dRWFQuFaCvfEGYFhmAu7G/P5PHZ2djCdTjEYDE6NHXgaLmv5f5N2/QzDYGVlBfl8nsZ/knGS74w4XxESiQQ2NjawsrJCo1A4jkM8Hr8R1z/HcajYM5PJnOq+QSAd0LquI5lMYnl5+Vo34J7WAYvACAJSn/gE8IlPwNnZgd/vAwwDYXkZ4jmFcoL5cz8H1jAgDAaH8/zTiv2JBNzNTYiPH4PpdOD//M8D1xQP/KrCiOJhA8v3fz+s3/1deLUa4HlgMxkkPvhBcC+Aq2lExEsHw0D9+MeBj30M1u/9HtwvfAHhdAomkYD0J/4EYq+/fq1vz2cyiP/dv4vpX/2rmAkCuE4H0qNHYJ7w1A7BIlhbh/DX//q1j+lJ0uk0dWQ52mAiy/KlBSyFQoG6/xiGcabriud5GA6HdJPb8zyEYbjwe5HowuvYQD26yUzmSk/i+z56vR5dA+3v7wM4FPeTyC9Zli/tDKrrOqbTKTqdDnVvfRLSFFGr1dDtdiFJEpaWlq5lnkPmdxzHUefniyAOpBfNDV3XxWw2gyzLx8aeSCSoOEjTNIiiCEmSIMsybNum5wpxcrUsC81mEwzDYDAYUPFKMpmkcYjLy8vXPr/udDqYTCZotVqnNn8Q9zTiMiUIAobD4ZW4BxUKBeqe1Gw2sby8fOr33+12MZ1OUS6Xkc/nwbIsZrMZOp0O3nrrLTQaDSiKcu7ahcRA6rqOXC53oSDjZYZ8T2cJpp6ErPPI+fskF93PyD1v0XUXOecvc5/c2Niggq1mswld16GqKgRBgOd5GI/H8H2fxtJepXs0wXXdY65vR8WvRMQ4Go2gaRqtPZDnB3GZG4/HdGy1Wg1//Md/DNM0EQQBDMOgDZSqqlLh6OrqKnZ2dmCaJnq9Ho3Y1HWdNqxd5HJIxkCef2R9/7wgzuK2bWN/f58+W54c03Q6RaPRQCqVQiwWO/bcy+VytNGu2WxidXX11Ia1yWRCXe5JnOV1Q5w0F4k9Bg6/D3LtXSQSjnj5iARYEREREU8Bw3HgP/lJBH/n78BzHIg7O3CzWYT5/KGwJAjAaBqEwQBMGMK9cwfB13895Hv3nvfQIyJuBPOznwXbboMfDOCWSmcKPxhJwnx1FfLODmamicm//tfIfPKTNzza24vyiU/A/oVfgJtOQ9zfx3x9/aQIKwzBNhrgLAvzrS2EH/wg+GfsOox4OUj82T+L6W/9FtzR6PD8qVQQZrNfis0NQ0DTILbb8JJJIJeD/N3f/XwH/QJwngNWxM3g+z7tlD9PEEEQRfFYN3XE8ycej+PBgwfo9Xq069X3fTAMQyMOCoXCqZtoDMPgzp079PgTh7PZbIYgCGjhTRAE2hWay+WQSCRQKBQWEu09DbZtwzRNWlQ9D9JVb5omLMu6sjHIsoxYLIZer3dhYT4MQ5imiWKxeKPxKUQIt7KyQjfWeJ4/9zvjef65RG+aponZbIbZbHZu9y4hnU6j0WjA931Mp9MTDnZXyWkOWE+7FyVvbQGX2MwIXRfMr/0a2MEAXiJxuvjqHRhBgJfPg+/3ETabsH73d5F43/uebqARZ8Mwh99r9N1GRNweGAaJr/xK4Cu/8sbfWr5/H+xP/RQm/+gfAf/pP8GzLOA//96x1wQMC+Hv/30oX/7eGx8f4TS318uSy+XoBm+n04HnechkMscENqZpotPpQBRF5HI5lMtltNttOI6Dfr+P6TuqZkEQaLz1k0ynU6RSqYU3XS/DZDKhzlqnbSD7vo9OpwPTNKHrOoIgoO6lxB2GNJv4vo979+4tvBHd7/epM9h5kWEMw1DHlvl8TmP8rhri9KwoCkaj0YWOJsRBTVGUp958J85jxEm23W5T91UA1C1tOByi1+tRl596vQ5RFGkEJlnXzOdzTKdTul65DkgU3XA4RCKROLf5o1QqUbFjv9+/EgFWJpNBLpeD7/uo1WrY2dlBJpOh1850OoWu63BdFysrK0ilUkin09je3sZoNMJgMMDBwQEajQZWVlZoPHmxWDxVaJFKpdDv9+l3+6oKsMj9h+d5zGazY/cqnuchCAJc16WubMTdiuf5E4JJ27YhSRJ4nj9zzSgIAo22WwTbtpFOpy/lUCyKIu7fv4/t7W3E43GMx2OMx2NMp1PqiKWqKuLxOLa2tq503TqZTNDr9WAYBhWpcRwHQRBQKBQgyzL29vbQbrepwKlSqUCSJOrcrWkaJpMJdWvWdR2PHz/GwcEB4vE4Wq0Wva9NJhPIsoxqtQpZliEIAnXgU1UVq6urYFkWnufB87xjsXznIcsyTNNEGIb0954nxPF8dXUVjUYDk8mEuuCFYYjxeAzLspBKpVCpVLC6unrsnOF5nroMNptN7O3tQVVVqKpK42INw4BlWSgUCshms1i7oSQQcr24rruQ2G0+n9Nr76bcESNuD5EAKyIiIuIpUb/5m2EMhwj+8T+G2+uBHw7B9vsIWRZMECDkOLiZDIJSCcHXfA2y/8P/8LyHHBFxY8x+5VfA6TqYMESQz+O86SiTSMCX5cNNnN/4DSASYFHEahX2D/0Qgp/8SXi1GqTtbXiJBPx0GmBZMNMpBMMAwhDzjQ0Ed+5A/bEfe97DjrglyHfvYv5X/yqCT30KLs9D7HSATgd+PI4QAD+dAkFwKCBeXgZ+5EcONwkizuU0AdYVJlFEHIGIU0ghTFVVyLJMO5xJkeyijkwi7AFwI645LyrT6ZR2mJKolesUvQiCgOXlZSwtLdEOT+J+cFEkH8dxuHv3Lo0jyOVy2NraosW4+Xx+rEhH3JWu0/3saETeIpBuyEXdqhYhl8tB0zR0u10a7XgWo9EIYRhCVdUbset/kkUFlM8T0uEKYKGx8jxP70tXIfbc3NykAj1SuD84OADwbBGEz8psbw/MZAJ+PMb8gpgaAAhyObCdDmBZmH/+85EAKyIiIuIGEKtV5P7e38O81YL1y7+M4Pd+BAi/FDnsMQLkdz8/8dVVwfM87ty5Q9cE/X4fg8GARmaRqD4SA1WpVBCGIRU0ETdV4no0Ho8hiiIqlQoV9FiWBdd1kU6nnzli7zSCIKAbuafNI4fDIUzTpI6upVIJhmHQ6OZMJgPDMNDtdgEAOzs7eNe73nXhxjCJqSOb4xe9Ph6Pg+d5TCaTK20geBLiajYYDM51NQvDEIPBAIqiQBTFCx1oRVGELMsYDAbwPO+YsCGZTFK36clkgvF4jFarBZ7n4fs+JEnCfD6Hoig0grBUKtG1BRHJNZtNHBwcYHV1FY7j4I033rgWERaJ7ptMJhc2CTAMg0wmA13Xkc/nMZ1Or2RNtLGxQV2ARqMRdF1Hv98HACroI44/5XIZjx8/hmEYGAwGGA6H0HWdRsibpol0Og3btrG6unpifGQNf9Vrp5tgMplgOBxiNpshDEMIgoBkMol0On3pc4M08aTTaRiGcczVOZVKQdd1cByHyWRCXQaJiOqoWItEaa6vr5/rDE3c9QaDASzLOlfkSNbzT3OflCQJDx8+xGg0Qr/fx2QyofdERVFoY9ZVXkuNRgOdTgfj8Zg6AQKH51oqlYJpmuj3+/T8Xl1dPSb8Y1mWnt/NZhP7+/uo1WrwfR+7u7vQdR2FQoGu+ck1ksvl4Hke1tfXIcsykskk2u02giDAfD6HLMuXdhI7KgR6nu5XBI7jcO/ePezs7ECWZYxGI/ofiarc2NigjVmn1SIKhQL9XER8XK/XARx+xlQqhY2NDSQSCWxtbd2YKDOZTCKZTNI5RDKZhG3bGI/HtLlMFEWkUimIokhrMrFY7FYcm4ibJRJgRURERDwD6e/5Hphra5j9i3+B8M03DyvhrguwLJBIICiXwX70o8h+z/ccxolFRLwq9PtgHAe+LH/Jbecc/EQCrOOAGQ5vYHAvFunv+i4YAIKf/mnMJhOw/T6Efh8IAoSCALdYRJjLwb9zB+rf+3uR+1XEMdSPfASmomD+D/8hwl4P0HWw0ykQhnCTSQTZLMJcDvwP/zDUj3zkeQ/3hSBywLp+BoMBjSsgVvAcx9ECVTweRywWA8dxGI/H53ZrA4cFakVRqLjnPEhHHimQkuLJyyzcGo1GtIBENqnI9002ea5ToMMwzFMJvRiGQS6XQy6Xg+M41AmAbKARAcxNdRpyHAeO4zCfzxd6/Ww2o+fxVZFKpSDLMgqFArrdLgRBONXFgThBkM7ei6INXlU4jqPXvuu6F3ZzE2c+lmWv5LgedeUAcGwz6iodsC5L8M6mKxMEwALCNIbjELIs4PsII8VyRERExI0iLi1B/OQn4f/QjwG+e+xnL5iO4UwSiQRee+017O/vQ1VVmKYJ27YRhiGy2SxUVaWxeWEYYm9vD5Zlod1u02i/QqFARU7pdBqe51GXkk6nA1VVqYPWVSNJEkRRxGQyOSHg9n2fbu5KkkQ3+4nbDcuyVGDDMAwdq67rFwqSjsaZXbaB4DpdhUn8VLFYRKfTQRiGJ8QXnueh0+lgPp9jfX0d+Xz+ws+Qy+XQ7/fB8zwMwzixvslkMhBFEcPhEKIowrIslMtlLC8vo1AooNlswjRNrK2toVqt0thv4qBDHFEGgwEVF7mue6bA4FmYz+f0/RYRHiQSCeqQ67rulQiwWJbF1tYWstks+v0+stksFRNyHAdJkmis/FtvvYV+v49ut0tj5Mi40+k0fN9Ht9ulc+mNjY1jzQ8koo7n+WtxobsOLMvC/v4+ptMpdbkLwxCSJCEej0MURSrkuwxHBYrD4ZDek2KxGGRZRjqdxvCd2vpR8Qepg3ieh0ajQWsq5zmi5XI5NJtNqKqKdruNtbW1U5tSXNdFp9OhLkdPc59kGIaK9gAs5C70tDSbTbRaLdTrdcxmM6RSKRSLRRqRqes6vvjFL2I+n2MymeDhw4cnrrP5fE6v89lshs9//vOQZRmz2QyZTAaVSuXYdZ/JZKgYDzgUgG1tbdH7CPClezKpw5BoxItqWJPJhApkb8v1IQgC7t+/j9FohF6vh8lkQn/G8zzy+TwKhcK56+tisQhFUdDr9ZBMJo81vC36N66aTCZDI2sPDg4gyzJ15iP1KEmSqOiS53mkUimo5zhGR7y83I6rMSIiIuIFRvnAB6B84ANwHj2C8x/+A8LJBJBlCPfvQ/ngB4GXVN0czucwf+M34L39NsLZDIyqQnr/+xH/si97aT9zxDVCzplL5MS/SqS/67swe9/7YP+rfwX/138dgWnSn/mvvw7hox9F6hu/EcwtWWhF3C6UD34Q4fvfD+u3fxuzX/kV+N3u4bWWy0H4xm9EMjp3LgXHHf53tOYcCbCuhjAMsb+/j36/D03TYBgGLcKSYmU2m0U6nYZlWbS4qKrqmQIpx3FgmiZWVlaQy+XOfB0p+g4GA8xmM8znc9qhSoqI5XL51jv2XJZOp4NGowFd16HrOubzOf2+SeeyZVl0s+G2du3JsnyiMHnTFu+qqiKVSqHRaJzoqn8S27bhui516boqyIaF67oIggD1eh3xeBypVIpGUoxGI9i2jXw+j3w+j/X19Vt7XK8asmF4VFh1HoqiQJIkSJKE0Wh0YWQLcaPgOO5a3dYA4Igui3JTDljsO6K+gOOABQSHoeuC8X2A48A8ZTxQRERERMTV87IIsIBD8cGDBw9gmiYGgwEVO5BYwWw2C9M08fjxYzSbTUynU7z22muwLAudTge+71NBuq7r8DwPo9GINn+Uy2VUKpVrmV+WSiWoqkpj7o46Gk0mE8znc8zncxQKBTiOA8dxaKTbUUeaVCqFwWBAXWQuEmBxHEddt8jG8XmEYQjXdWnTwXVBhD1BEIBhGPR6PQwGAySTSXAch9lsBtM0IYoi1tbWkMlksLy8fOHfTSQSiMfjyOfz6HQ6kGX5RKNCIpGg4qs7d+6gVCrh4cOHsG0bQRBgf38f5XIZDMPQ+L2jDjocx0EURbRaLepe4/s+HMdZKM56URiGudT8fVEnnacZRzabRTabheM4VCglCAIVrNTrdZimiW63i+XlZRrv2O/3kUgkMJvNUCqVIEkS+v0+JEmCpmnHhElkjk3coy6L7/tUGHYT657xeIzt7W1omobBYADf9xGPx8EwDEajEVzXRSaTgeu6YBjmUnGeuVwO3W4XS0tLaLVamM/nyGazkCQJ5XKZxj+++eabyGazKBaLyGQymM1m9Fwl4q9yuXyu8zTHcVhaWoLneajX69jb20M2mz2xttQ0DbIso1QqYXl5+Uqa167rODmOQ93qgiDA1tbWsXU7aU56/Pgx9vb2AHxJAAgcXkvdbhe6rlMXOvKdE+exeDwO13Xhui6tHzEMA1VVaWxsPB6n93ciICKvzWazaDabiMfjGA6H597fHMeBZVnY2Nh4Lq7W53FUVOf7PnWm53l+4eMbj8exvr6OarWK6XRKIzVjsdhzaZIkkbX9fh/7+/swDIPGVpJnFIlLJbUZ4tgV8eoR7bREREREXBHyvXuQ79173sO4dkLXhfFP/gnCX/5lsLoOxjQPu595HvOf+RlMNzch/rk/h+Sf/tPPe6gRz5NCAaEsg9M0eEFwoQsWZ5oI4nGE19BN+LIgra1B+kt/CeGP/ii84RCB44DPZsFdYzxUxMsDw3FQPvQhKB/60PMeykuBJB13H5nNDjVtr4iG4dpoNBro9Xqo1WoIggD5fB6qqtJNARKtYZomYrEYLVTVajUsLy+fEEdZloVWq4VkMolEInGmeMJ1XTx+/JgWDw3DoEVqYntvWRZ0Xcfdu3evXVhxUwyHQzQaDTSbTViWhVwuh3Q6TaM2xuMxhsMhjQDgef5KNw5eNgqFAobDIXieR6/Xw9LS0qmvI53yRNxz1YVSRVFw584deu4ahkE73lmWhaqqKJVKiMfj2NzcvNaYydtAEAQ0EuVoXE48HkehUEA2mz2zeEtc9zKZDPr9PtLp9JkiTNLdvrq6ilwudy0bk54H/OqvAh/+8OkCrJtywJI2NmBnMvBVFZyuw7/gHGY0DYEgAIoC6Su+4mYGGRERERFxIS+TAIugKMqp7p/AYeOBYRh0s1oURbTbbSqKIFHc8/kczWaTCqNWV1dRLpdpRN1VUygUkMvlYJomms0mMpkMFVbNZjPqohGGIYbDId3ULhQKx+YlxFWWxLifRxiG0HUdo9EIk8kEjUYDgiAglUqdOYexLAu+70NRlGt381AUBffu3cPu7i5UVcV4PIZlWZjP5+B5HisrK0gkEshkMlhfX194I55EAxJBiaIo1PnK932MRiMYhoFEIoFqtYqVlRWUy2W8+eabGI1GSCQS9DtvNpvUSZiIX0hDCDle/X6fCpEkSbpQzL8ooihCEAQwDAPLsi6cz1uWBVEUwbLshVHvT8tpDTFBEGA4HELTNCSTSXrekJg327bRarVgWRZdc4/HY+rMxLIsTNPEeDxGtVqlosLRaATf98Gy7LFjcpTZbIZ+v4/hcEijwViWRSaTQaFQODdO71mYz+fY3d2lcagkPo9cV0SA0+v1MJ/PIQgCYrHYwmsylmVp7CoRe+zu7tLjO5vNwDAMNjc3wTAMPM9Dq9UC8KVzkESyLiJcLJVKVDhDmrZI1CRweC7m83kqhLyqc/y66Pf7sG0btm1jc3PzzKYp4hCmaRp1NxIEAa1WC7quo9frwXEcxGIx+L4PQRAwmUzoPdh1XbTbbayurh77u6qqYjQawbIsaJoGz/OQzWapwBA4FGJlMhnYto2DgwP0+/1Tv9fZbIZGo4FkMolYLHbrBFhHuYxw1/M8aJpGncUFQUA2m70VdYswDDGdTvHWW2/BMAy0Wi1wHAdVVTGbzSCKIjiOQxiGYFkWYRgiDEP4vn9r3Mkibo7oiEdERERELEwwnUL/y38Z3B/8Abh2G/xodPgDlgXj+wgEAUy3C393F0athvR/9V893wFHPDekb/omeP/+3yNst8EOBgjP6eYJLQuc48BbXQXzDd9wg6N8MWF4HsIlLaojIiKulicFWGF4uDFer+8e6y4lha+Ii5nNZuj1emg2m2BZFmtra8cKNIIgoFAoQFVVHBwc0Ii51dVVNJtNbG9vI5lMQpZlhGFI4/QymQxKpRJWV1dPFU4FQYDt7W0Mh0McHBzA930q2JIkCQzDwDAM7O/vo1KpAAAePHhwI05Y8/n8WOFJFEUq2nlWwjBEs9lEr9fDdDqlm1AEjuOQyWSgqipqtRparRZ4nkexWLxRi/cXiUQiAVVVUa1WcXBwgEajgXw+f2wjwrIs9Pt9GmtTKBSuRaijqireeOMNWvg/6mogCAJ1v3rZj+V0OsX29jZs26YbjCRiM5lMYjweo9Vq4c6dO2fGt5RKJYzHY0wmExwcHKBcLh/b2CWxpZ1OB5lMBoqiXKqL/TL4PvC//++HafenRRDelDaU4Xkw3/RNCFotSI8fwzcM4Kwo2Pkc4mAAN5dDsL6O+HveczODjIiIiIi4kFfJgNxxHEwmE2iahmw2S+dAlUrlWHQXiT8nEVCxWAzr6+vXKioQRRF3795FGIZwHAd/+Id/iGq1imKxCM/zMJ/PqajKdV3k83lUq9VTN9s5jqMxcGdFeHU6HXS7XereMplMaNQfEVIUi8Vjv0saCEicYyaTubbvg5BIJPDGG29gMBig0+lgNpuBZVkqFCsUCpdujEkkEjT2K5FIQNM0NBoNuoaPxWKoVCpQVRVLS0tUdOd5HjzPo+eNbdswTRO9Xg+xWOzE3I+sI0ulEjzPQ6/Xo40XV+HsQ6LeUqkUhsPhucIE0oyQy+WubC25KMThZzKZnGjkyWazGI1GNMKQxLMTUQqJcBwMBsjlchAEAdPpFF/4whfgeR5tLiGuzaVSiYqq2u02Wq0WdSY6Gv9Hmoyy2ey1OAH3ej265ltZWTkhCCVuOLIs4+DgAIZhoNPpXEpcIssyXnvtNdTrdSQSCTiOg+l0Sq/hBw8ewLZtSJIEWZapgIq49BSLRRr1twhLS0uIx+PodrvU7Yx8/8RJrlwu33qXHyJi1XUdiqKcuxYmzUztdhuz2Qyj0Qgcx0HXdXQ6HTAMg2q1CkEQ0Ol06Bp7aWkJpmkiCAI0Gg2k0+ljglVy75lOp2i1Wsjn8/R+dpRqtQrTNFGtVtFoNDAajZBOpyHLMoIgwHg8hmmaUBQFS0tLqFarN3ptXwdE/EzcxYjzmCiKaDabSKfTWF5efm6fMwgC7O7u4vOf/zxM04Su61BVFQzDwLZtzGYzKIqCVCqF9fX1Y7Gfw+Hw0nGjES8+kQArIiIiImJh9L/xN8D9/u9D2NlByPOYr6wAqRTAMAhdF6ymQej34VsW/H/6TzEuFKB+y7c872FHPAeUD34QWrkMr9eD0O1iFouBOWUxGc7nEGs1uMkkkEhA+fZvv/nBRkRERFyS09b7sxnwxhtvYHrEliQWi8E+bZf8FYEUe0lkA+kMO61jut/vYzqdwrZtbG1tnSlKkSQJxWIR3W4Xd+7cgSzLWF9fh23bMAwDpmnSohYpQhFHmtMYDocYDAb4/d//fVoMJZ2doihCFEVkMhnE43G0221IkoROp4OVlZUr/a6OQjoZR6MRjRkhn53neaiqeqrj12UwDAOO40DXdSwtLZ1ZfCSxA7u7u3AcB4PB4Exnp5cB27ZhWRa1tk8mk5cSKW1ublKxU7fbxd7eHj1u8/kcnudBURS6abZI1/HTIggClpaWUKlUMJvNaPQe2RB62XEcB2+//TY0TUO73QbP8zQuw/M8GIaBwWCAcrkM3/dx//79U0VYqVSKHicS2Uk61cMwhG3b8H3/WKTjk93/V82//JfAV33VyX+/KQcsAFC+4ztg/pt/g3mxCKlWw2w2Q1gofMn1NgwRjscQWy34ooiwVAL/Xd8VWUVGREREPCdOu/1e1gHLdV2YpkmdZy7awL5NTCYTuqH7pBCERCRZloXpdEqf64ZhYH19/UyR9lWytrZGI5UajQZarRbq9Tps28Z4PIZt26hUKiiXy9jY2MDW1tap43JdFzzPnxq1djTuvdfrYTQagWEY+jtvvfUW8vk81tbW4DgOVlZWwLIsbNtGt9tFGIYoFosolUrXHv0UBAE0TaNuNQBodFUul3umRgJVVfHgwQPqSOt5HhWocByHdDqNYrF4TBDDsixYloXv+wBA17i+75+6ziRrCUEQkMvl0Gq1UCgUYBjGlYjXGIZBPp+HZVnY399Ht9s9dXM/CAK0Wi26zr1pdyLy3YZheGJ+LAgCqtUqdVo2DAO2bUPTNDAMg+l0inQ6TSMfXdel0W9PutpmMhnouo6VlRV4nodms4lWq4XJZAJFUZBOp+nfbDQaEEWRRrYTp6ir4Kjj13lufMDh2j6dTkPXdaTTacxms0sJS0RRxNbWFubzOQaDAWazGYIgoO5JyWQSvu/TBhSWZRGPx596nULuk9PplD4HOI6DoigvjEM4icFzHOfM+hCBNL8BoO570+kU4/EYQRBgaWmJ1qyCIIDjOBBFEbFYDEtLS/jDP/xDiKKIvb09VKtVqKpK6zfz+Rz9fh+pVArvfe97oarqifuCIAi4d+8etre3IcsyDMPAaDTCYDCgx5I4ARLB7ovMdDrF48ePYRgGhsMhLMsCy7I07jUejyOXy2EymeDOnTvX5mB3Hvv7+2i323j06BH6/T7u3r2LpaUl+pwmz2pBECAIAjY3N9Hr9agI9EU/RhGXJxJgRUREREQshP35z4P7nd+BsLeHQJbhb2wcq+AwgoCwVMIsnYa4vY2g3Yb3T/8pwo98BMw1dPZH3HIYBuKP/Ri8n/gJePM55L09uKkUgmwWkGXAdcFoGkTDgC/LCNbWEH7v90K45VbFEREREcDZAqyIQ8IwRKvVop2sRIDF8zwSiQRisRjK5fKxopeu6zTu4aJiuqqq6PV6mEwmKBQKqFQq6Pf70HUdruuCYRjaYZzL5c61+t7f38cXvvAF+t71ep12QJONJcdxqK37cDhEIpHA8vLytWw82LZNC0+DwQDT6RQ8z9OiMynim6aJBw8enFvUPQ9N0zCZTKgT0HlIkoR4PA7DMKBp2kspwCIxEJZlHQpJwhAcx0EQBFr4X6TIx3Ec7t+/j1qthlgsRt0WwjCkcS2iKKJYLGJpaelGhFAMw1y7IOg2sre3B13X0Wq1UCqVkMlkjn3fZGO10+nQ17/++uun/q1KpQKe58HzPN08m8/nYFmWRqXKsoy1tbUr6/wmzoqn0es93whCABCKRYj/0/+E+Y//OOY8D6HbBdPvw4vHAYYB5zhgXBdeKoVgZQXBd3wH0v/lf3lzA4yIiIiIuJBFBViWZaHb7cIwDLiuSzfeibi5VCrdimig8yAb7wDOXGskEgk63yOOMkEQ0N+7bt71rnchk8ng85//PJaXl+ncW9d1jMdjbG5uYnl5+cz5BonQW1paOrXhpd1u07h3AFheXoaiKNjY2MDBwQF6vR4ODg7wuc99DtVqFYPBAJlMBrPZDMlkEuVyGYVC4dqiGAmO42B7exuWZcEwDIzHYyogURQFk8kE3W4X6+vrTy1mkiQJKysrWF5ehmVZxwQqpzW5xONxKIqCVqtFRS2maSKRSJxoHPJ9H9PpFMVikcZZsyyLyWRyZQIsACiXyzAMgzrkWJaFTCZDxTCmacIwDACH0YvZbPZGnMuOwjAMnX8Hp9xw4vE41tbW0O12kUgkqFAymUyiWq2iXC7D8zy4rkuvg1QqhbW1NdpUMRqN0Gq1jsVUEuetzc3NY6Im4u7WarVwcHBAYzuvShgxnU7heR7G4/FCa+Z0Oo2DgwPMZjOMx+OnEsiJonjmexFB4VVCnAFfREidJ1zA/jGVSqHf70MURUwmE9i2Tc+to5GSwOG57bouvd5Jw5ymaRBFkQp0SCSdruuQZZnGahJXvieRJAkPHjw4IUYFDmtVuVzuqZwAbxue5+Hx48cYDAZot9tQVRUbGxu0hkEaB+v1Oo0TffDgwY0KwE3ThKZpePToERW0lstl6shInsmO41DHxl6vh3Q6jXa7DUEQaGNlxKtDJMCKiIiIiFiI6b/6V+BGIzDzObwzJoYAwEgS3FIJYqeDsNeD9du/DeVDH7rZwUbcCpQPfhDj//6/R/DpT2OmquAGA0h7e9Tn3pdlzMtlIJdD+F3fhcwP/MBzHnFERETEYpwmwIrW0oeQSD9SJBqPx7SbmBS2M5kMptMpptMpFEWBaZqo1Wpot9vI5XJnxmUQiNW967q00LWysnJpVypd1/H48WM0m00aPVgulyGKInW2GY/HaDabKJfLkCQJYRhSC/qrLmC7rovt7W1aeEqn06hUKrRoTDolm80mfe2DBw+eyoLddV3MZjPIsryQCIiIiY5G2b0skA0n4nx29DPGYjEaj7G2tnZhpyxwWOje2NigG1fEUYs4wGWz2WuJHXyRCMOQxlZchwiNxPR0u10UCgVks9kT72+aJr0nbW9vI5FIYDwen7phCQCFQgG5XI52tROxpyiKyOfztLP+qtjbO4wcPIvf/u2T/3aTAiwASLzvfWA+/Wk4f/fvIszlAF0H+44jgZfJIMzlEKoqmO/9XmS/93tvdnAREREREReyiABrMBigVqthNBpR1x9CLBZDOp2GYRhYXl6mcd23EY7jaPOE53nnNmiQ1zAMQ52Pboq1tTWsrq6i0+mg2WzCtm3s7e3BNE3E43E8fPjwzLH3+33aXPGkkMP3fXS7XRqdtbq6SuejoihifX2dOv/WajVMJhPE43FUKhWsrKxQF+LrbiCYz+d49OgRNE1Ds9mk8XIkfn40GmFvbw+ZTIa6Fz3Lmoxl2YXEg4VCAZqmgeM4DIdDhGEI3/dPbXIgUWXEmYnMF0mM4VXBcRzu3buHx48fQ5Ik6LqOwWBA34M4IaXTaWSz2St1elqUWCxG3WDG4/GpsZmkiWE2m2Fvbw/Ly8tYXV3F/fv3UalUsLOzA13XYZrmCadZ4kpLztvBYEAjRO/evXuqmI7neVSrVezt7VGnqqsSYPm+TyNAFxGHkNccFYhGXB+ksU2SJNi2fa44jZxb8XgcjuNgOBzS+9DRxiziDJlOpzGfz+n9uVAowHVdqKpK38fzPLoOXlpawt27d3H//v1zawOk4Sefz1NHbZZlIYrijT6brhPSXNlut2nj1FFkWaZxwZ1OB5IkodvtXqsr/ZP0+32YpgnHcZDJZGis7JOQOOPRaIRkMol0Ok3vC1d5/494MYgEWBERERERC8H+zu+A0zR4qgrmgsibMJsFOh3AMDD7zd+MBFivMOpHPgJnYwP2Zz6D8LOfhTedgvF9gGUBQYD/ZV8G6Tu/E8oHP/i8hxoRERGxMJED1tns7+9jOBzi4OCAxgokEgkwDIMgCDAej9Hv99FqtfDo0SPk83lIkoRGo4FerwfbtuG6LnK53LnFdCLSepYiMtlc0HUdDx8+PPF+yWQSiqKg1+uh2+3SKDfShXvVkMJTp9NBpVI5URAURRGlUgmxWAytVguKoqDb7WJ1dfXS70W+u0W6P4FDYR0RypFCtiiKyGazyGazly7+kagEx3Fo7EQmkzlT/HJdNJtNdLtd1Go1eJ6HTCaDVCoFjuMwn89hGAZarRYsy6LjXNThSBCEW70RedOEYYjRaESFmYRYLEZFUlclTCMFUt/3j13XJBZE13XM53Pq4tHpdKjo8t69e3TjhOM4pFIpujHHsiwKhcKNxLf82397/s/n85P/9jyan+PvfS/iP/MzMD/7Wcx+6ZcQtNuA7yNMpcB/6ENQv+VbwN60MiwiIiIiYiEuEmAZhoGDgwPqLkOi2Y46z3S7XUwmEwCHgpDbGrGTSCQgyzIEQYBhGKcKQY5iGAYURQHLsk/tLhIEAXW1JQ6rgiAgm82e69LLMAwqlQqdR969exf1eh37+/uo1WooFovHXHBnsxmd+6ytrSGZTJ4QFQ2HQ8xmM5imeUx8RRAEgUYPJpNJPH78GNlsljqR5PP5G2kgODg4gK7raDabVHRwFBIV2Wg0wLIsbXC47rElk0nE43GUy2U0Gg0YhkGFFIQgCDAajTAej1EsFhGLxahYKAgCKui7SgRBwGuvvYbBYABFUVAoFKiQh+M4JBIJOs9+HhHksVgMiUSCRgRmMpkzjxWJznzttdewurqKO3fuQNM0Kn6pVCpnuvpKkoRCoYDt7W0IgoCVlZVTxVcElmWRzWap4/RkMrkSFz8i2GQYZiGxBXnNTQs9X1UYhkEmk6FNdsVi8VwxriAIUFUVqqrCsiz0+33alDafz2FZFkzTRDKZhCiK1B1PURTqElkqlVCpVOD7PjzPQ6/Xw/LyMt797nfj4cOHl7p3iaL4wsT+LkoYhtTNnogpzyKdTmMymVDh5HW50p82RuLYr6oq5u8sxIkb6JMoigLDMDCdTqHrOoDoGn9ViQRYEREREREXEjgOmOkUzHwOfwHrWoZlEYgimPkceMfuOOLVRX7tNch/62/B0zRM//N/RmiaQCwG6eFDSGtrz3t4EREREZfmNAHWaZvhrxq2bUPXdTQaDWqpfrTQy7IsUqkUxuMxdnZ24LouarUaVlZWYJomJpMJhsMhgMONBMdxThWweJ6H6XSKXC73VO5PwGGn4mg0gmEYiMfjZ26sMAyDQqGAer0Oy7IwPS336woIwxCDwQC6riMej5/bjamq6onC02U3HSRJQiwWg6ZpZxaOCJPJBG+++SZEUaSxa8BhAZAc72q1euFGFnAovKrX6zAMA47jwLZtuhmVTCYhyzKWl5dvJB5jNptRdwEA2NjYOFaAlWUZ5XKZRkMIggBJkq4sYu6yY30yYvNFctKaz+c0SmY8HmM0GsH3/WNRMq1WC5ubm1ey+TGdTmFZFi1+A4f3jXq9jslkgvF4TKMhGYahm6PdbhfNZhPZbBayLB+LTV1aWrrR2JZFY6GO8tzSJxgGygc+AOUDH3hOA4iIiIiIuIi1tW/GZPKljhGWlY49a550oA3DELVajQp7nnSe4XkesixT55l2uw2O45DL5W7l/CQej1MhiKZpSKfTZ268O44D0zSxsrLy1J9nNBphf3+fOudOp1OEYQhJkmAYBprNJiqVykKCtUqlQqOLOp0OGo0GeJ6nG/6O4yAWi2F9fR3pdBpbW1unjmc0GkEQBMTPEUYTZ2EANPq8VCpd+vM/DY7j0GaddDp95tqCxME3Gg1ks1kMh8MbEf5tbm7CdV2EYYhWq0Xdc1mWheu6sN5xAS0WizSCDDicx89mMzqnvGpYlkWxWESxWKTNTMDheu82xJAXi0U6/6/X66hWqyeuPc/z0Gg0aGQeOZ6apmE8HlOh3XmIokgdysh3fx6qqtKosOl0eiVrkFgsBpZlkUgkMBqNjgklT4Nck7IsX/jaiKuBuNmJoohWq4VqtXqqMMayLEwmE7zxxhvwPA8cx+GLX/wiXS9yHAdZllEoFBCPx8FxHFzXRbvdRrvdPuZQRa7DwWAAURRpjOxRJ63njeu6cBwHQRCA53nE4/EbEW2apklrc4vEdmYyGTQaDczncyoMv26Ic9lsNqOunxzHwbKsU+9LPM+D53kqVCdO2U9bu4x4cYkEWBERERERF0Idr1gWTBBgIa+EMAQYBrjALSvi1YHPZpH88Ief9zAiIiIinpmX2QFrPp9jPp9TUcxlirZkg2Y+n2N1dfXUgk2v18N4PKaCktdeew2VSgWFQgGZTAaPHz+GruvHnJGedJsxDAM8z0NRlIUi4U5D13UEQYD5fI5UKgXHcc78rCSaot/vQxAEcBx35cWTyxae0uk02u02XNfFZDK5dOEpn89jMBiA53noun7mBodhGNjZ2UGv14OiKMc2bIhoJZ1Ow/M8+L5/7gbNbDbD22+/DcMw0O/3MZ1OaaF8Op3C8zxks1lYloWNjY1r30ghDlyWZWFzc/PMTThZllEsFtHv95HL5c6Nqbssvu9T4d18PqeFOeIA5zgOms0mxuMxHMehr5FlmW46LS8vXxij8zxxXRdvv/02NE1Dq9UCAKRSKbppOBqNMBgMaMf+vXv3nnkDIgxDhGFIi95hGKLRaGA0GqHT6UAQBFosJ+4UpKt1Op0ilUrRYjzHcTQ29SbjlZ5mDygymoqIiIiIOItv/dZ/hZ2do/8SwjDGGI0OHViJQw9x0SGbjZqmoVwunzlPFkURlUoF9Xods9nsxsQwT0OpVMJkMsFkMsHBwQGWlpZOCGJIDFIqlaLfxWXRdR27u7sYDAZ0vk0cgU3TxGAwQDKZhO/7mM1mCzmabGxsQJZliKKI2WyG8XgM3/epWxYRw62urp46LyTxd4uuYUjjxU1GJg2HQ0ynUziOg2q1eu5rFUWhzSDJZPJGzjlZlnH//n3s7OzgPe95Dx49eoSdnR3qPpXNZqnIqlqtUgem4XBI3bAWaVh5Fs4T1z0vstksvafU63Vsb29DVVU6Vtu2MR6PIUkSVldXaV0AOKxNzGazhcQgQRBAkiS4rnvMmewsiLAyCIKFXr8IHMchm81iPB6j0WicW2PwPA+aptE1yW08ds8CaYghzV7JZPJWCFDIteq6Lg4ODrC7u4tcLodkMgmO42idajQaIZfLoVgsUjc2EmFPxLwMwyCZTNLmHdLYNRwOsb+/D1mWqehqPp8jHo9jbW0NmUwGm5ubz/mbOGQ8HqPX62E0GlFXP47jIIoiCoUC8vn8tdYaiCN1GIYL1R5lWUYYhlce6Xoe5N7DMAxisRgVbBKR5WkCvjAM4bouZrMZ1tfXkUgkbnXNJuJ6iI54RERERMSFMByHoFyG32yCG4/hXbDxEM5m4GYzeIoC5gbzmCMiIiIiIm6C0+oCL7oAi4hixuMxjaVjGAbxeByFQgG5XO7CoqdhGLQIcVbhX9d19Pt9JBIJ2hU4mUxQqVTQ7/dRLpcxmUxoRB3P88ecfsbjMQaDASqVCu1kfxqIkCUWiyEWi2E0GkFV1TNtwWVZhmEY2NjYAMuyVx6VRwRMixaeJEl6psITEVPl83l0Oh2IonjiMzmOg/39fTx+/BgMw2BpaQnvfve7jx1b0zTRbrfheR49X07rHg6CAI8fP4amaWg2m0ilUiiVShiNRtRdwbZtGv/w4MEDfN3XfR3W1taurfNS0zQYhoFEInFhMTiVStGISE3TruT4d7tdtFotzGYzGIZBrezJuTafzxEEAXzfx3A4hOM44HmexuMlEgn6nd29e/dWFLRP4+DgAIZhUMFYoVA4dkzz+TxGoxHa7TYYhsHu7i7eeOONZ7LoJ5EPpmkCOLxvmKaJTqeDRCJx7H5Gzr0gCGj8ZC6Xg6qqqFQq1ImBuN8R97HrJhJgRUQ8QRjC+t3fhfMLvwBme/vQejSRAL7iKxD/2Mcgn+L4EhER8SWOPlaDIIBlWdje3oEoaphMJtSZkmwqDgYDKma+aN5D5vWGYdxqAVYmk0G5XAYAtFotukEej8cRhiHdXM9kMiiVSlhbW7u0Y9FsNsPe3h46nQ5GoxEqlQpUVT0295lOp2i1WqjVagiCAIqiLOSyWalUUCqVoGnaMTdRMqc/T8hF4o8WFZpcV2TeeRDBBomKvIhkMgnbtqk72E0gyzIePnyIcrmM6XQKhmGg6zpUVaXCISK2C8OQNh8R0cUiYrswDDEej6FpGl2ziqJIRSIvIuvr62BZls7PdV3HYDAAcDi3rlarUBQF+Xweq6ur9PdIXWIReJ6n5/lsgeIMOW+IW81VUSwWqciyVqthaWnpRHOJ4zhot9s0kvSmXOZuAsMw0O12YZomZrPZMUGPqqool8vP/TxeW1uj9zhyLnY6HfrzRCKBarUKVVWxublJYwhnsxkURUGn08HS0tIJUc3y8jIkScJsNkM2m0U6nUahUEA6nYaiKMeEss/bKZIIIon4ijQiAYfPi3Q6DdM00e/3cffu3Wtz0zv6jFnk+XS0VnlTsaocx1HHM9M0kc1m6fOq0+mgVCodO57z+Zy6EVarVSrSi3j1iARYERERERELwX7zNyM8OAA3HMIbj4FzCjBctwtfkgBFQeLbv/3mBhkREREREXEDvEwOWEEQYH9/H8PhEOPxmLpPAYexE+l0mgoQ7t69e25x0vM8uK57pnuNYRjU4n95eRnD4ZAKjziOQ7lcpoITXdehaRpisRi63S7i8TgtCpVKJWQyGaw9Q4wtKdaQ2DvyGQuFwomNhjAMMRwO4bouVFVdSIz2tOMBbq7wtL6+jtlsBt/30Ww2aRwLcSZ69OgRDg4OEIYhXnvttVNdohRFwdraGvb29mix9bSCqqZpsCwLzWYT+XweiqLgC1/4Anq9HnUeIgXaVqtFi30f/vCH8fDhw2vpFiQd1YuI+FiWhSzL1CHuWWk2m2i1Wmi325hMJhBFkW7+6bqOZrNJY0x4nsfq6iqWl5fpps10OqWdtSTq88GDBze6SbYIJHan2+0ilUqduSGbSqUQhiE6nQ5SqRR0XX9qdzvg0CEulUpRlzNd12GaJhiGOXb9Oo5DN5lLpRLu3r1LHbESiQTCMEQmk4GiKNjf30e/34ckSbTj+TogArtk8nJF+WLx9GdTRMTLgLOzA+tv/k1w+/vgDAPseHyY08nzCN5+G84v/iKs978fmf/xfwQbKREjIk6FPLaCIHjHeXWO3d0DZLNT6kbjeR4GgwF6vR7m8zkmkwnK5fJCz7x4PI7ZbHYl86TrZGVlBTzPU6eToyL4dDqNdDoNSZKoYOay9Ho9Gsu+trZ2qqtNLBaj82dN0+A4zsLvxbIs8vn8pZ2UYrEY4vE4NE2D53nnzq3DMMRkMkGxWLyWyLzz3hfAwvNZlmWp6+mTEZrXCZlP/qk/9aewvb2NbrdL17WO48DzPDoHBg7PuXQ6TaMdz8MwDNTrdRrHSOIEiZNOPB7H6urqCxdXxzAM1tbWkM/n0e/3kUwmjx3vTCaDQqFwYl0miiJEUYSmaRceY0mS4Hke0uk0FZKch2EYiMVikCTpSpurYrEYVldXEYYhut0uGo0GRFGkwjzbtmGaJhKJBJaWllAqlV4acUaz2US73abxnEefB0R8NB6PUa1Wn6vojGVZKqzq9XrI5/OYzWbUBV4QBCrYPXoPLJfLVKjb6/Vo3Cg5L4krYSwWw8OHDxGLxbC5uUmPfz6fX0hcehM0Gg202200Gg0arVcsFsGyLBzHoQ7RS0tLtB60iID0ssiyTCN1yTVJxMWSJJ3qUkmO0U1FrJJ7PnHPLBaL1IG+0+mgXq9Tl0Pg8DowTRNra2t47bXXsLS0dOvqNBE3QyTAioiIiIhYCOVjH8PkM5+Bm8lAqNUw39gA8+SGVRiC6fUgGAZm6+vw3/MeSM+wORoREREREXEbeZkEWPv7++j1eqjVavB9H+l0GqVSiRYHiRCKFIzv379/ZvGAiIHOEhCNx2NMJhMan0GKqOTvpdNp+m/JZBKPHj3C/v4+LMvC8vIyUqkUKpUKEokEtra2nsmmXxRFSJKERCJBxV+dTgeNRgOqqkKWZTAMA8dxqOtRpVJBMplcqHh+WWKxGHiehyRJ9Ds6j/F4DEEQIIriUxee4vE47t69S+NmdF1Hr9eD7/sIggC2bVMHtPX19TML06IoIp1OQ9d1pNNpzGazE25M/X7/WHTkH/zBH6BWq6Fer8PzPKRSKaRSKQRBgF6vh0ePHmE6nUJRFARBgHe/+91XXrQihdJFO6uPit6eBcMw0G63Ua/X4bouVldXj53LYRji7bffxs7ODhzHwf3796kwjhCLxbC8vIxer4dGowFZljEcDp8qJuc6GQwGNEpmeXn53NcSwRSJtnwWAVY+n0er1YKiKLSobJrmCQeK0WiEXq8HjuNQKBSgqiqCIECz2aTxPtlslkYWkgL9eDxGKpV66vE9ieu6NKaIbFTUapfbCHrve69sOBERtwpnZwfTH/sxcPU6hGYTTBDAUxSA58HMZpB2dw+bvxwHer+PzE/+JNgbFAxERLwokGkUcWWYTh2oahp37iwfm2MVi0VomobPfe5zCIIA3W731kQlXRUk/nwwGEDTNLiueywGOpvNPtW8MwgCDIdDaJqGZDJ57lqFuPwOBgO67rpOiPBFEAQMh8NzxQ+6riMMQ+rqdFMQJyIihLhozj2dTqmY7qbEV0dRVRVbW1tUQKTrOo3z5DiOijMURcHW1taFwovBYICDgwN6XoZhSMUHw+EQHMchn8/DcRzcuXPnyh2Zb4JEIoFEIoG1tTUqLhME4czjl8vl6Bp1Mpmc+5mJe1oqlYLneTAMg4oknsS2bRiGgaWlJRqNfpWQpi5yzI42ucmyjEKhAFmWUSqVjjl+vcgQd2cSR5vNZpFKpcDzPObzOW3KMU0TYRiC5/lnWu89KwzDoFAooFAowDRNTKdTBEEAnuehquqp1yuJqQ/DEP1+H51OB91ul8bikXvS8vIyMpkM7t69eyvFkrZt0zpCGIbY2to6JsqVZRnpdBqapqHVaoHneTSbTWxsbFz5WIgAy3Ec7Ozs0KZM4vxHhHvpdBpBENCmwXg8Tuco5HxSVfVaRGIAaC2A1BdWVlaoM9Z0OqWO26ZpYj6f4/79+6hWq9ja2kI+n0e3272WcUXcbiIBVkRERETEQvCZDLi/+BcRfPrTQBhC3t6Gl0jAz2QAjgMzm4HTNLCeh9nKCoLVVST/2l973sOOiIiIiIi4ck4TYN1g8sGVQWJCarUaeJ7H+vr6MevsWCyGbDaLbreLWq0GjuOo3fppxGIxJBIJjEajU51ufN+H53kQBIF2BudyuWNFkkwmg1gsBsMwqANQpVLBnTt3qAjiKjoHs9ksGo0GkskkgiBALBYDy7IwTROTyQS6rgM43AiIx+NQFAXVahXvete7rqWoI0kSkskkMpkM+v3+sdjFJyEOPblc7pliGIFDB6vXX38dg8EAiqLQQrhpmnQz4+HDh2cWrwmkQOe6LkzTPCbAcl0Xtm2j0+kgDEP85m/+JnZ2dtDpdKAoCjY2NqAoCi28Z7NZ7O7uYjKZ4K233kIqlUKhULhQwHNZJEmCLMuwLOvCzSXS0Z7P55856q/b7ULXdcxms1NdxXzfB3B4PZHubV3XUS6X4fs+daoj0RqNRoOKdm6bAMu2bViWhVgsduF1Q4SXtm0/8yYkz/MoFotwXRePHj2iTmPEXYIUyGu1GmzbRrlcphsEZCxPxnuqqko3DQzDuDIB1mAwQK1Wo05d0+n0ncia0++zZ/Hw4ZUMJyLiVhF6Huy//tfB1mqQ6nXM83kEpRKYI89HbzYD32xC2NkBGAbG//q/IvvjP/4cRx0RcTvhOMD3vXfm4A6NvmJZ99jriNNDsVjEzs4OgiCgEdHnYVkWUqnUrY1EfhKe51Eul2kk4VXgOA5838dkMjlzvXSUVCqFdruN2WxG1z3XBVnPlEolNBoNGrn8pPDFMAz0ej2Uy2UqZrkp0uk0nW9dJHb3PA+maaJarT6VU9lVkUql8MYbb2A4HEJRlGPRd0TAlkqlLhSIWZaFWq2GdruN8XiMUqkEVVWpEND3feo2TFx2Hj58+MJcb09CxBUXkclkqPtsp9M5s/loNpuh3W5jZWUFs9kMmUwG7XYbjuMgm83S9yLCrMFggEwmQxu8roNcLod0Oo3hcEjj64BDwVkymbxVbkjPiud51N3Zdd0Ta1xJklAsFpFKpVCr1dDr9ajL1G1wBlIUZWGhVKlUoo1wpDGHRIXmcjkoioJ4PI719fUbdRC8DL1eD6ZpwnGcE+Kro2SzWTiOg8FgQGMZr/qcJU5btm1jOByi0+kgk8mA4zjwPI9kMgnLsjAej+nvEId627apizpx0Uqn06hUKs/UrHkasixjaWkJQRCg0Whgd3eXNq+Ox2MEQYDRaEQdNNfW1vC+970PyWSS1nciXj0iAVZERERExMKo3/qtGHkegn/0jzDL58H2+xA6HTBhiJDj4GUy8HI5BMvLiH/qUxCr1ec95IiIiIiIiCvnZXHA6vV6GI1G8H3/hPiKwDAMyuUyHMeBpmlIJBKoVCqnFpFJAWowGJy6UUPcrsIwxGg0oi5UT4p7ZFlGuVym3c+rq6t48OABYrHYlXU3cxyHbDYLy7JwcHCAXC4HVVWh6zp1YgJAI+Gq1SoePny40GbK01IoFDAajaDrOur1OqrV6oliGClukmi0qxDcCIKASqVCj7Pv+/T4hGG4UDGSjJO4Zx3Ftm3s7e2hXq+DZVns7u6iXq/D933k83mMRiNMp1NkMhkIgkC7F1mWhWVZaLfbqNVqqFQqV1qgzeVyGI/H0DQN0+n03ALpUfeuZ+nUnU6nME0Tuq4jm82eWuycTCaYz+cIggCFQgHj8RjxeBzxeBydTodGAtm2jTAM4TgOdnd3wfM8ZFnGw4cPb00xPwgCBEGw8HHjOI7+zrOyvLyM+XwOx3EwHA5xcHBA3d6ORp+qqopUKnVq9MfR+w2JQXBd95gw61no9/s4ODhAp9OhkQvk3GcYZmF3NuAwgjAi4mXD/K3fAttsQmw2MS8UEFYqeHIWwEgS/I0NYGcHXKOB8Dd/E96P/Aj4lyTOJyLiqmCYw/hl3/cQBCFiMRFBcPa8em1tDY1GA7Zto16v4+E5St/DSEMX6XT6pYnSehp836dxeIvMxXieB8Mwx+bPruvCsiwawaQoypXN67LZLJrNJiRJwu7uLrrdLgqFAkRRpMIUz/NoJNrW1taNOkuR+Md0Oo1er4d4PH7qZw/DEO12G6IoQlGU596AwPM8SqUSSqUSncde1pWLzAVHoxHW19dPiIyIYE6SJNTrdSQSCfR6vWtxZr5NMAyDlZUVOv/e39+nUaE8z9Pz1jAMKIqC5eVlFItF6LoOURQxGAyws7MDURTBMAzm8zk9XtlsFhsbG1cu1DgKcUJ7sjmNND29LAyHQ+oevLa2dqagR5IklMtlNJtN5PN5GIbxQj4zstksMpkMRqPRCRfFfD7/TA1y100QBNB1HYZhIJlMXvh8yWaz2Nvbw2w2g6ZpVxodaZomdnd3MZ1OMZ1OUalUYFkWGIahDle9Xg/NZhOxWIyKw03TpK7VR9flpIF0NBphY2PjXHFuGIYYj8ewbZves0kc81lUKhXqzkiaNknjpqqqVBCsKAru3r17awV4ETdHJMCKiIiIiLgUqY99DLOv+ArYP//z8H/91xGYJv2Zv7oK/qMfRfpbvgXsNS5gIiIiIiIinifPKsAKwxCWZcHzPFqoedoYOYLrulRQsEiRnog4dF2nHWbnkc1m0W63qUjoNEckUtTP5XJotVpYXl4+Jt6RJAmSJKFWqyGRSKBcLiORSJzauev7PqbTKZaWlpDP5y9dGJ3P5xgMBrAs61hBJZfL0c+6tLSE8XiM5eVluhmRyWSQzWYxn8+phfjy8jJWV1dRLBYv7P5/FoiginTVbW9vQ1VVWsCzbRuapkEQBKytrSGXyyGXy1FHLMdx6IbPk5F1i8AwDC0SkS5C4PDcuuhvkcIXx3HHxDbz+Rzb29vQNA3dbhee52EymcD3faiqCsdx4DgOFEWB7/soFArgOI4KvxiGgaZp6PV6V16gJTF1qVQKzWYTq6urp35O0zQxGAxQLpef2XGMdJrO5/Mzu/qJUxzDMMhms+j1erBtm0YSHhWDkY2dg4MD1Ot1FItF8DyP+/fvn1n4vg6m0ykmkwk8zwPHcUgkElAUBTzPQxCEYx2r5zGbzWj8zLPCMAw2NjYgSRJ1l/B9H8lkEizLQpZlGhlw1AGCuI5JknTiXkocB65iM9JxHNTrdbTbbViWhbW1tWP3uXw++Y4Ia7G/d8vMzyIiroT5v/k34DUNIcMgPG/Dh2HgVyqQdnbgmybMX/gFpH/gB25uoBERLwAsS+YYPhWHnKd3VlWVOpyS6KHTNhOJ8wyZez7PSKnnDfleWZaF67oXbr56nocwDMFxHGazGXWAdV2XCtgFQUAqlUKpVHqqKCsSldXr9ajLpmVZ4Hkek8kEhmFAVVUkk0kqbInFYs8c9/40MAxDBfSz2Qz7+/vI5XJIpVJ0fWCaJobDIVzXxdraGjKZzI2P8zxYlr10w8h8PqcuMOl0+ty6gKIoSCaT0DSNCo5ug4PQeZBz7qhI5TLChGw2S+fgpHlmb2+P/jwWi6FSqdB4OCKGq9frUBQFjuPQ+b0gCFAUBYlEAsvLy9e6tn8WPM+Dpmk0tpBElj5rzei60DQNhmFAluULr0eyRiTipRdRgAUc3q/IPfNFgjxfptPpqa71T0LWzKSGcZXU63Xoug5N0/DlX/7lGI/HMAwDtm1TZzFBEGg8JBkDca4vFApIJpN0fIZhoNVqUTdt4qB1lEOn6R56vR69N5DnMHH0J/eT01haWoKqquj1ekilUgjDkD6vRVGkjv03WYuJuL1EZ0FERERExKWRVlch/aW/hPBHfxTzZhOh44BLpyEUi4dtdRERERERES8xTyvA8n0fvV4Pg8GAigEYhgHHcVAUBcVi8VIRCr7vYzgcot/v0+Lc4fgkFAoF5HK5Mxf+xF1qNpstZLuvKAqCIIDruseiFY7CcRw2NjZo93e9XkcsFqNFc9/3oes6JpMJ8vk8ksnkme+t6zo4jqP2/IvieR5qtRp0XYfjOLQ7jrgqNZtNFItFLC0tQRAE3Lt3D48fP4YkSdB1Hb1ej1qEy7KMarVK4yPW1tYWHsfTsra2BpZlwfM8dUrq9/sIwxCSJKFSqSCZTCKbzWJtbQ3NZpOeT+QcEAQBkiQhlUphaWnpqTYl4vE4FfmMRqMLRUek4EqK2oT9/X0qYMrlctA0jUZHkDgIIt4BDou3yWQSYRhClmXEYjFaiHOuOOeT53lUq1XaPbm7u4tUKgVVVcFxHFzXhWEYtLiXzWaxurr6TO951OHgPKEkcT9iWRa+76Pb7UKSJMxmMxQKhWPHQxAEzGYzxONxNJtNKIqCer2OjY2NZxrrIozHY7TbbSosI24N5NiRoieJ7jtv45CI86rV6pXF3ZCNvD/xJ/4EYrEY9vf3UalUIAgCYrEYZFmmAjdy7x2NRtRxQVVV+rdmsxnm8zni8fiVdLP2+31YlkU7dBffUAmBEx5AwCu83x3xEsO+/TbY0Qi+qh6qR84jkYAvimAMA94f/dHNDDAi4gXiD/7gH6PRMOkGqiDICMNvOPP1xAV3Pp9DFEUcHBxQ10jimDQajajzTKlUwsrKyoVNHS8zZO6jqioVNp2HYRiQJAmO46Db7dJ5wdEoZuLoYRgGVlZWFtowJ4RhiN3dXQwGAwyHQ+p8zPM8HMehTRZkzNVqlc55n5eoJ5fL0XN0OBxiOByi1+uB53m6xkylUlheXkY2m8X6+vpzGedVQkR30+l0oUjMdDqNRqMBz/NgWdatFRH5vo9+v49+v09rH8DhGiwej9N6xSKNDYVCAfF4HN1uF6lU6ljzGc/zSKfTx5qlJEnCnTt3MJvNqDsTABp7d1sdilzXRbPZhKZpdH0fhiFEUUS73UYymcTy8vKtGz+pES2yRmIYBrIsYz6fX7mgJ+JyLNpURGoTl3FnvgjLsmDbNgaDAfL5PI2BTKVSIrQ95gABAABJREFU0HUdiqLQ9/M8D+12mzZ2lctlVKvVY88p4qKvqipqtRokSUKj0cCDBw/oa057JgZB8I4gPaDRzCTe9qz7MRmr53lwHIfWG6/SsT/i5SASYEVEREREPDUMz0O6gc3IiIiIiIiI28Rp++QXCbAcx8H29jYmkwk0TaOLfQAQRRGZTAbj8Ri5XA4bGxsXLtwty8L29jam0ykMw8B4PKbiB1VVYZomOp0Otra2ThU9HC2gLFIkIK+5qOiSSqWwtbUFlmVp8WQ4HNKusnw+D1VVYVkWjZx7EsMwjjkOJZNJWJZF3azIJsGTbkWe5+Htt9+GYRjodDqYTqeQZRk8z2M8HqPX60FRFBpLtrm5CUmS8ODBA/T7fbp5RIRx5LssFAo31tnIMAxWV1eRy+XQ7/epGIn8LJFIoFAoQJZlPHr0CIZhYDgcUochUjwimzWTyQSbm5uXFrTwPE/PyUajcW7HLenaLpVKSKVS1NHMtm1MJhN0u10sLy/TwhnpfiYd4olEgna0i6JIoy5JEWsymVxZLN2TFItFWnQjjnC1Wg3A4XepqirW19ehKAq2traeudjNcRzdmHRd99TzX5IkiKIIlmUxHo/hOA4VX1Wr1RNjIPGJhUIBsVgM3W4XsVgM1Wr1WqMIu90uGo0G7Vqdz+e0OEvOn3Q6jeFwSEVY5Hp8kjAM0el0qIDvMpuLi1AsFuk5SKIdyUbedDqlm53EkY10vR4t6g6HQ8RiMRp/8CwEQYDhcAhd15FMJs+8th48+Gdw3Rl8P0AY+ojFlHfOm+P3vlQKeIX3uyNeVsIQjOOACQIEC97LQp4HfB94x+kiIiLiS/zqr/538LwvXRssG0MQnC3AAg7FCsvLyzRG6+g8CTgU7BM3iGq1eqmmiRedo46lgiBQh8x8Pg/TNLG3t3eu+Nx1XepsSxpHxuMxstksisUiBEGg8WqtVgvj8Ziupxadh9RqNQwGAxwcHAA4nA+RqGPP8+g6LRaLIZPJoFwuX6oZ6LqoVCrUSSeXy8G2bXieB5ZlEY/HabNRpVJ5KTa7fd+n4qRFHIwFQUAYhsd+77Yxn8/x+PFjjMdj6LpOxX8AqFsz+RmpHVxEIpHA5uYmXNeFaZpUgJVIJM783iRJwtLS0pV+tutiNpvh0aNHGI1GtFGDxJS6rgtJkqg4ZHNz84VzXjqNl+H6fdEga3FRFGHb9oVC4fl8Ds/zTnWHfhY0TYNpmvA879i5nEgkkEgk6DOWjNl1XfR6PTAMgy//8i8/854Rj8eRz+cxHA6RyWRgWRatn9Tr9XOfiaQOaVkWgMN71XnPW9KwGBFxFpEAKyIiIiIiIiIiIiIi4hKc5oB1pFH5BK7r4vHjxxgOh2g2m5BlGaVSCbFY7FiUgq7rVPxznnuNbdt49OgRhsMhut0uFXAJggDXdTEajTAcDqm45N69eydEG4Ig0FgL27YvdF+xbZtuMFxUeInH4yiXy+j1epjP57RDDAA2NzepnXi/38dkMkEqlaJjNwwD8/kcpVIJ2WwWiqLgzTffhG3bmM1mVIAliiIURaEuNgzDoFarwbZt1Go16q51NN7Qtm10u13s7+8DOOySI5tK5XIZpVKJFvhJZ+ZlY/yuClJ4Wl1dPVZ4Ip3fb7/9NkajEWq1GnXqIt/xbDaDrutotVq02/f+/fuXdsIqlUrQNA2pVAq1Wg3lchnJZPJYodQ0TbTbbSQSCaTTaZSOREQNBgOYpgnXdbG5uUmPS7PZhOu6x6IN4/E47YAk54+qqtQZ66oLfkchn6vX60FVVSr2ItdHLpdDsVi8knMhmUzSKE7DMFA4JTdOURR6frdaLYRhiOl0ilQqdeI69n0fk8kEhUIBiqIgn89jb28PjuNgMBgs5G73NGiahnq9jmazCcuyaDyMIAjwfZ9GSkwmE8TjcSq4Ozg4oFEB5Dwix91xHKytrSGVSl15lEwymUSxWITv+6jX69jZ2UEqlUIsFqMCvD/6oz9COp3GysrKMee9MAwxGAwwHo+xurr6VPGeTzKfz+H7PizLOnbNPEk2+2HM5y6AELPZHPF4DL4f4MlLoVp9puFERNxOGAahLCPkODDzOS7suQ9DsK6LgOeBWxRHFRFxWzhtn/u8vgoiclhbW0O5XIYgCEgmk1TMTBxbifPMq7AJGYYhDMNAr9eDaZr033meRy6XQ6FQQKFQQK/XQz6fR6PRoA0KRzeLSaOMJEmwbRtBEMCyLLzxxhvH5no8z1NRVK1WQ7fbpSL3iwQr0+kUg8EAjUYDPM9jZWXl2O/wPI9CoQBVVXFwcIDBYABZlpFOp2+FKCKTySCTycA0TSreIQKsdDp962P3LgPLsvQ7J5/zPMja8GniDm8Cz/NoraLRaECSJBSLRcTjcVr70HUdhmFQUdadO3cWPu+Ii9XLhO/7ePz4MTRNQ6PRQDKZPOaQ67oudF1Hp9OhTS9Ps76/LiRJgizLMAzjwtcGQQDbtq9sfR1xOTiOQzqdxmg0QqfTQaFQONe5Utd1yLJMHcyvCtd1MZ/PIUnSqQ1aRxvXwjCkdc5FhLfpdJrWonRdRyKRgOM46Pf7aDQa4DgOq6urJ56JpFn04OAAvV4Poigim83eimdixItJJMCKiIiIiIiIiIiIiIi4BKeZCQ2HZ29itFotjEYjNJtN5PP5E93hsiwjk8mg0WigXq/TIv5Z3Wh7e3vQNA3dbhflcvlE92M2m8V4PEar1QLLstjb28Prr79+rHBAnFzS6TR0Xb+wmEJswEVRPNNNybZttNttKqLyPA88z9POzXK5jEqlAtM0sbOzQ+M5NE1DEATgOA6qqiKdTlPxU6fToQVa13UBgFp9E5FUoVCAIAhotVr0O6lUKicK0vF4HGtrazg4OECn04EoiiiXy7SwQxymbhMsyx4rTB6NcazX60ilUies0SVJQrlchqIotOjdarVw586dS713LBbD2toa7bZvtVo0MoJhGFiWRTsWS6USlpeXj0VgWJYF0zSRSCSoqGplZQXdbhd7e3vodDool8tgWRau68K2bbAsSyP2YrEYarUa0uk0crnctXb5JhIJbGxsYGVlBdPplJ6P8Xj8Sjc2ZFlGMplEJpNBr9ejMT5HYRgGmUyGHmeWZZFMJk/Ei3ieRwWY8XicupSJogjLsjC9JgeYMAzRaDTQ6/UwnU6xsbFx7DNwHIdsNkuFe7PZDIIgoFKpUBcHIm4jna2KomB9fR2ZTObaohNXVlbouUw67g3DgOd51J2PbIBWq1VYloX5fE43/arVKhVoPSvEzY2cZxdz6KqhKB6+4itm+KM/Oi6Y/fCHn3lIERG3kuDhQ/jNJsR2G7MgAHPe/dg0wbouwnQa3Hvec2NjjIh4cQnh+6cvXsIwpM6V8XgcKysrVPBvWRYViSQSiWt127xNuK5L3YxHoxFGoxFc16WioMlkgl6vh5WVFWxubtI5R7/fR6/XQyKRAMMwmE6n8DyPCsEFQcBoNEKxWDyzIYbEkDcaDeTzeWiadqHbGHHQcRznXIchIo7p9XrI5XIYj8dXFgV9FRxt5HlZSSQS1AF3NBpdeGzH4zESiQRdq9w2Op0ORqMRGo0GFSYeRZIkZDIZtFotWvtYpB7xMjMcDmGaJr3GnzwHBEFAsVhEIpFAvV5HLBZDu93G1tbWcxrxcXK5HHUPmkwm58ZiEjc/VVVfKdfE20ShUICu6+j3+2i1WqhWq6eKjIiAiThdSqd1oj4ji8QakiZE4gR3kSCKNGySpj/g8Jlo2zam0ynu3Llz5jNRFEUUi0UqThuNRi+F21zE8yESYEVERERERERERERERFyC0xou//APgfn85L/7vg9N0zAcDqlDzWlwHIdqtYrt7W2Mx2P0+/1TBViTyQTT6RT9fv/caDxVVeF5Hvr9PlKp1KnF9EKhgMlkguFwSMUwp2EYBkzTxOrqKrLZ7KmCAcMwsLu7S51vjoo/OI5DKpWiIpvNzU288cYbGA6H6Pf71KUJABVUEbeqWq2GIAiQzWahqirm8zl2d3cxHo/R6XRo1CHLsjQ+rFAooFarYWVl5cRYWZZFsVhEvV7HfD6HpmmnuhDdZvr9PkajEViWPdc9R1EUpNNpaJpGv7vLdpnmcjnqcJDL5TAajeA4DsIwRDqdRjqdhiRJp8bOBEFAN5+Aw07E1157DZ1Oh3Yg6rqOeDwOjuOQTCZpjEShUEC9Xofv+8jn81hZWbmWgt+T8Dx/bsH4KiiVShiPxzBNkzqLHd1YIq4SpmliY2MDtVqNdv+T42dZFmzbpptmuVyObsBwHHdtkY3A4bXuOA4Mw0ClUjnznOI4DktLS9jd3cXGxgYSiQSSySQKhQKNGyDHW5Ik5HI5VKvVa+vkZxgGa2tryGQy9L4IHBZ9x+MxNE2DLMv0/5MoRSImUxQFd+7cuZKNZtLly/M8ZrPZseNvmuY7m7g2plMFnueD5zmEYYj3vneCH/3RELlcCj/7s0CnA3zN1wDvf/8zDyki4lYiffu3w/u93wPabbDtNsLl5dNfGIbgOh14ioJQUaB+27fd7EAjIl5AwhBotdool+VjDQhknUGcKTOZDBUG8Tx/q8Q5NwVx9CHuNAzD0DlwEASYTCbY39+nTqpra2u4e/cuRFGkUWuO4wA4dHYikd2z2YxG610kMlIUBTzP0/jxi4QLJPZNUZQL5y6qqqLb7dI47lfxGD9PYrEYdRPWNA3pdPpURxjg0Ol4NBpheXkZmUzmzNc9L4IgwGAwgKZpiMfjZ66zWZbF0tISdnZ2aOTeqyzA6vf7MAwDoiiee22T84REvD/N+v46yGazaDQayGQyaLfbEAThVEGpbdvodDrI5/OQZfnC+LuXGdM0oWka5u8UEcl6+CZElaqqIpPJYHV1FbVaDbu7u8jlclAUBRzHwXEc+gwhNcfls+bgTwlxTZvNZnBd99znFBEvi6K4sPPfk8Kup3kmkhpBJMCKeFpu1xM6IiIiIiIiIiIiIiLilnOZ9beu65jP51TAdB5EqEQK38RB6ij9fh/j8RhBEFxovU+st4mg68liei6XQ7/fx8rKCur1OmazGY39YxgGtm1D0zSYpkkj4U6LNDNNE7u7u9StKp1Oo1wuQ5ZlGpFG4siIuGRzcxOlUgmlUgmu68L3fXAcB0EQ4DgO6vU6Go0GWJbF2toaOI7DfD5Ho9GgcYSFQoFGs1mWBUEQMJ/P0W63aazY6urqiQ65RCIBnucxHo8xHo9fKAGW67qwLAuGYSxkh57NZrGzs0OLaOcJts4ik8lQp7ThcEgFc8SSPZvNnloEI7bx9pF8znw+jzfeeAOj0QiTyQSGYSAMQxSLRYRhCEVREI/H8fjxY4zHY+pKtba2dulx31ZSqRQtYHa7XRoNQyJJbdtGGIZ4/fXXMZvNoKoqOp0OLMsCwxw6IR2N8shms/S4hmGI+XwOnucXdFa6PORaJs5c5yFJEuLxOAzDQCqVwuuvvw7DMDAej6kAKx6PI5fLXdt4n0RVVaiqSmMPgiCAIAjUpWIwGNBOWeBww7NQKCCTyVxZ/IAoitSB0DAM5HI5Gkc0n89h2zb6fReOswzfDzCdHjplMQzecU0Dvvd7r2QoERG3msQHPwhtbQ3z8RjSwQHcIIBfLoM5unFi2+BaLbCuC3dtDeGHPwzuFd7Qi4hYlMM5BU8daARBgOd5ND6cOFOur68/76E+d5rNJgzDoM6spVLp2JwgnU7T9Uu73QbLsnjjjTfw+uuvYzweYzAYYDabIQxDCIJA589f/OIX0e12EYvFFppjJBIJzGYzumF/FmEYHjuWF8GyLGRZPuYWEnGzlEolTCYTTCYT1Go1LC0tnRCw2LaNVqsFRVFovPZtYzQaYT6fYzweo3pORjZpnGIYBvV6HaqqYjab3UjDzW2DfBej0Qi5XO7C12cyGezu7lKn3ttQyyA1G9/34fs+9vf3kUwmkUqlwPM8PM+DYRiYTCbIZrMoFApYW1t7JaPdiJO5bdvUpRA4bEQkbokrKyvX7sy+sbFBa3OapqHX66HdbtOfK4qClZUVqKqKra2tKxeG5XI5GsWr6/q59zPP82itchHR6Xw+x3w+RywWo/cU8kxc5HslTvuu69LI14iIpyESYEVERERERERERERERFwCUQQSCcCyjv/7N3xDjf5vjgP++T8/XPyTosoiRQtFUWAYBgBQIcVRHMfBdDpFIpG4sPOLdFPbtk3H8OTP7969+854OWiahmazSbvF/n/2/jxIsjU/78Kfs+U5J/Pkvi+1dVVvd+5IMyNhKRzWEsgYLIwdCALzM+EAG8I20h+AsTGWQxECBSCzycgBDtkyNkIgE2i1ZFljeVFIsiWNmH1G997u6lpyX8+SeTJPLmf5/ZH3fW9lV1ZVVndVd3Xf9xMxMbeqM7NOnvV9v+/zfZ5l5JWG7e1txGIx2sn9PPV6HYZhwDAMbG9vr3xPQRCQSCQQi8XQaDRQq9WohT5ZEJAkaaULrd/vw3EcOI6D/f19Kszo9XqwbRv9fh+ZTAbRaJRGiXmeB47jUKlUsFgs0Ol0IIoiRqPR2s7KUCgE13XfuIIKWRSZz+cXxpSchXQJvux35TiOLhZtChG6DAYDTKdTuK6LbrcLz/OQz+fhOA4V+J2cnGCxWND4glKphIcPH2Jrawvf+q3feueiIV+WYrEISZIgiiIymQxdrOA4DrlcDrFYDIqiYHt7G8fHx/jqV79KXd14ngfHcYjFYjR2kEBEjqTgvQ7P8+gioCiK117oIMVLRVE2KpqHw2E4joPFYkHjFa8Sj74Knr/vAECpVEKpVMJisYDv+7cqZCMOhP1+H0dHR5jNZjBNE5ZlAQBmszg9VouFC02LYDgcbRTTwGC8LXCCgOgP/zDs7/s+zDgOUqMB6b334EYiCEQR/GwGwXHgRiJYHBzA+6ZvQuo/+89e92YzGG8MhUIJu7sRjEYjGiuoaRpkWUYmk7kxZ0oiMCdjAUVR3hihheu60HUdvV4PmqZd6BisKAq2trZwfHyMVCqFXq+HcrmMeDx+K45Si8UC/X4fhmHQZhYyziKNDdfh7PzvRfA8D8PhkB5jWZYRjUY/lgKL60CcVx3HQRAEUFUVw+EQx8fHCIfDCIfDCIKACjVIo1OlUrmT8yMifAiCYO32EUcZMjcgjnskUv3g4ACqqr6GLX99kDk6iVe7ChLBdtfEIclkkoqqYrEYTNNErVYD8FFdaWdnB5qmYW9v72PpfkVc44lDvuu6dC7f7/dpbYDUwW5zH/E8j4ODA/R6PepAflYoTMTCpLHyplEUBdFoFJlMBo1G40JHtMVigXq9ToXPPM9fKdbUdR2qqkJRFCpqJI1sm7qEk4ZO9gxjvAxMgMVgMBgMBoPBYDAY1yQePy/AkuVVu/jf+i3gM59p3OjfJbFumy6G8DwPz/MuLMJLkoSHDx+i3W4jEokgl8vRomkoFKIuRxdFjZHOPSKKukhkxvM8isUiDg8PYds2XcRYh67rMAwDmqbRv+m6LkajEbUNJ847mqbBMAzqyuT7PjKZDOr1OsbjMQzDWFvIIYtMr8p156YgBSCO4zZeWHnZBZUXJZPJoN1uIxwO4/DwEIIgwLIsDIdDGp9IzuXRaARFWUbgfPrTn0axWESlUsGnPvWpW48EfF1kMhmkUil6vhMBFokfiMfj4DgO+/v7NJ5GVVUUi0WIonjuHkAEbiQS53mR09JVqQdd11cKjyT287ouVJuef6+qeBkEAXzfv5Fr+iZiBq8inU6j0+kgmUziC1/4AmazGQRBoOLU0Sj1oesVB0kS4XnL/Xh4eIh33nnnlWwjg3EXCFUq0P7G38DoB38QeP99YDgEPxwCvg9P0+BubQGRCLzv+i6k/tJfWnXHYjAYl7K/fx87O/o5AVY6nb6RaDPP89Dr9WjkOBEJCYIATdOQy+XuhCj7MnRdx2w2g23bV7qBkbEsmceUSqVLxz+hUAiyLFPxzVWMx2PqSvvVr34V0+kUrVYL/X4f4/EYsixTAR2ZM7que2Vcoeu6NMb9usK4+XyOdruNwWCA+XxOBSGhUIiOMXO53LWEfERcNhqNViKrX2T77jLdbhedTgfT6ZQ64JJYSiKwcRwHwNL9rFwuQ1EUVCqVO+F6dF3IeUKcvogbbb/fp66+i8UC9+7d+1hFfp2d328iDiHX9l0Uh2QyGUQiEXS7Xei6Ds/zqMuSKIpIp9PIZrO3Iui56ziOs+IaT6L9yNyVuIS12216D3j8+PGt3vNI81cul8NoNKLPIhI3fNsRp1tbW5hMJnBdlzpNkloGEfValgVFUfD48WPYtk3dJnd2dtbOh03ThGEY2NraonG/wFK4qKoqRqPRle6B5JmYyWTeqmcO49XDBFgMBoPBYDAYDAaDcU02qRn9P/8P8C/9S0sRUxAEmE6nVxabJpMJneSvKyiQbrThcLjRdk6nU4TD4UuLJ4IgoFwuo1gsUice0vl2tii0DsMwMB6P4brulYVSUsgxTROxWAy7u7vnioZBENBi7FmBFinSOo6DUqm0YtdOOurD4TDG4zFSqRSi0Sh1v3o+ynE2m2E6naJYLN64lfptEwqFwHEcwuEwRqPRleIk27YBLBeERFFEt9ulnfLEgeCs2Oemt5U4Wn3wwQcYjUbU/SwajdKuy+PjY1SrVYiiiO3tbezt7eG7vuu7XqjjMwgCeq6Q730Xu8MJPM8jk8lcujAWjUYRjUaxs7ODarWKbreLTCZD7yXkO3e7XYiiiFwuh2KxuHI82+02Go0GbNuGaZr0vCDXpG3b6Ha7ODg4uPIepSgKVFWlRfWrBE+2ba8UP28Sz/OoKwVZoOI4DvF4HNls9k53VpPz/+TkBJFIBL1eD7FYDMPh8MPjyWOx2KVOabKsYDrVYds+er0eSqXS6/4KDMYrI1QqIf03/yacr30Nk5/7OfhPnwKzGQJNA/9N3wTt3/q3IL1AxC6D8XGH4wRks9lbEXNMp1McHh7SKHLLsqi4IBQKIZlMYjgcIp1OY29v77ULCVzXpRHJZIwcjUapOIbERV9FNBqlziae5106B0un0xgMBvB9H7ZtXxo/Zts2XNel0d66rqNarcJxHPi+TxtVfN9HIpFAJBJBNBrFZDK50sVW13VIkoRIJHKlWOss4/EYh4eH1NXItm0qJON5HolEAuPxGJZl4eDg4Moxo+/7qNVqGAwGcBxnRRgYjUbR6XSQSCSws7PzQsIAInAi41fiJPSqCYIAx8fH6PV6GAwGNJKdNE6dFdYQ4VkoFEImk7kxceRtQWoVHMdhPB7T+Xy320W/30en04HruojFYohElu57oihCFEV0Oh36Offv339rm3Ceh5yHRBxyVWwoiYInc7K7hqqq2NnZQaVSwWQyoQKsTRzc32ba7TZGo9Fa13gA1P1KURTUajWEw2F0u11sbW29ku0jNYfLmM1mK/flcDj8UrUWVVVxcHBA61uGYaDZbNLniKqqKBQKiMViKBaLEAQBtVoN9Xodz549QzweRywWgyAImE6nME2T1vkSicTKvstkMhiNRhgMBrBt+9LrjDwTNU271jORwXieu/u0ZjAYDAaDwWAwGIw7yia1ruEQsO0kZFlGJBKBruuXLpr7vg/TNGkE2ToBViKRQDwep4KDy4puJK6QFCCuguf5a3ehz+dzLBYLKhK4ClVV0e/3EQTB2kWJi4rgJPbMtm10Oh0q0vJ9H7quIxQKwfM8yLKM4XAIWZZplNdisVj5O71ej1qSS5KETqcD3/chSRLi8fiddpYhkY6j0QjtdhvZbPbS7dV1nYrRarUaFovFStwdEdIoioLd3d1rFbpd10W/38dwOFwpwp3tat3a2sIHH3yAaDSKXq8HQRBod6XneZjP50gmkzg4OKCiv2KxCMdxriWecV0XnU4H/X6fdt9zHEcXy3K5HDKZzGtf3HtR9vf36YJMt9vF8fExQqEQBEGg10EikUAul0M+n1/p6ux0OqjVamg0GhiPx4hGoyiXy9S+3zAMDAYDlMtl+L6PR48erXW7I6TTafR6PYiiCMMwLi1K2raNxWKBRCJx48XL4XBIo/uIEJTEBsbjceo+cXBwcGcXqjiOQyaTwcnJCe7duwdN02j0QzyeQK32x+H7Uxq3UKsB/+a/+VfR7/fPiewYjI8D6rvvQn333de9GQzGW8OGSTzXZrFY4OnTpxgMBjRaKJ/PQ1VVBEEA27YxGAxWYsT39vZuZ2OuYDqdUkeSsw5OxC2DCJyu4z5MPvf09BRBENAxbiqVWhnfJpNJhEIhOrfL5/NrF7NnsxlarRaApehpPp/DsiyoqvphVPECuVwOu7u7cBwH7XYbtm2j1WqhUCjgC1/4Ar7lW75l7TjfNE0MBgNUKhVomnbp3HI+n8M0TSwWC8xmM1SrVcznc+psvLW1BVVVqaDMMAxYlkWP8f379y8cu/i+j6dPn0LXdbRaLUwmE6iqClEU4bouBoMBFEVBsVjEdDrFw4cPNx7fLRYL9Ho99Pt92qQBLMVCmUzmyvnUy0JcW4izjGma6PV6OD09BQDk83nEYjEaG29ZFp3fplKpN8rxirjXaJoG0zShaRrm8zkGgwF6vR6CIEC5XIYgCDRaMZPJoFAoIJfLoVqtIhQKoVar4Z133nndX+eVIIoiFaQ2Gg1kMpkLz8cgCKDrOm1uIfcT4p5E7l+KotxKg9V1EAThYyOiuwrXdWEYBnRdRywWu7QJkLi9G4axMm9/ndi2jXa7TetI5J6uKArC4TDy+fylIt/L0DQN77zzDrrdLo35JbUlIuQlDXzAR+7a5BlTrVYBLJ+98XgcxWKR1gDONoCl02k0m00kk0k0m036vHoe8kwsl8vQNO2Na9hk3C3uZhWKwWAwGAwGg8FgMO4wn/oU8PWvX/06WV4W1GzbRrVaRTgcXiuGCoIAzWYTPM8jFotdWGRNp9NoNBqIRqNotVrY2dlZ203seR7a7TY0TYOiKLfWuUWKeteJw7uqEEgWPGzbptsdBAG63S5M04TneTSmgeM4DAYDJBIJKqI6Pj5GoVA4F73neR663S7teCPCKyJ4kCSJduQXi8U7a42fy+VgGAbtjtza2jpXpA2CAJ1OB47jIBwOYzqdotFoYDQaIRQK0cUREn2RzWaxWCxwcHBwpfApCALU63UaJ0PcAkhsTbfbpQ5nvu8jlUpBFEXs7e0hGo1SoQwRbJGFp3w+j9PTUyqcyW/oZjKdTvH06VMMh0O60EOOvSiKSCQStEC3v7//xsVOAsvv8fDhQ9RqNbrYNh6PqYgxGo1CURS6eEGYzWao1+toNpuYzWbY29tbKURGIhGkUin0+300Gg0IgoB6vY579+5duC2aplGHhHa7jVAotPacmU6naDabdCHmRYuy6xgOhzg8PKSLaUQ8yfM8jcLpdruoVCrwPA+PHj268ePuui50Xaed3UT4FYvFNl7sGA6HVDy3vb2NyWRC41RNM4Pp9AP4vkNfP5spsCwLhUKBXtsMBoPBYLwoGw7fr02z2YRlWVRM8Pw8RFEUJJNJ1Ot11Go1Gk31qp0rR6MRnj17tuLgRCDPdeKM5fv+Rs6flmXRZhEiVAKWrl/9fp9GyCUSCXAch+3tbXS7XXieh9PTU6RSKdoQQkQVRMhC3H/n8zlkWUav14PneahUKlSMFA6HoaoqOp0ONE2DZVlIpVL4whe+gP39fcTjcSrgJzHupFlnZ2dn7XdyHIce0+l0isVigVarBdM00e/3sb+/j0KhQLeBLJrH43E0m03UajVIkgTDMC4cD1arVei6jtPTU8iyjL29vZW5GBF6nZycYHd3F8fHx7h///6Vx5gI9sfjMUzTpO4tRBxCXGTv3bt3o+dfEATo9/vodrtwHIc6TAdBgNPTU4xGIyQSCezv76+IK85eC6enp+h0OpAkCel0+rWLMDaB53mk02mMx2OcnJxQN7jZbAbHcVbEV6QpS9M0JBIJ2shD5vZXudS8TZDvraoqqtUqtra2zjXE+L6PZrMJ13VplN98Pker1YKu67QxjuM4GgFKxG1vwrnzNkMcusfj8ZVRtsBSyFiv17FYLGDb9mt1de73+zg9PaXPIuI6DYA65tu2jdFodOEz5CokSaKO/CSSkOd5qKp6rs5VKpUQi8Woe3QQBLS2RJwCs9nsOYGuIAjY2dmB53kIggAnJyfQNA2xWAyiKFKBMXkmJpPJF/4+DAaBCbAYDAaDwWAwGAwG45r8a/8a8Mu/DBjG5a+TJCCXK2E4HKJYLNKO5EQisdIFTuK8tre3acF6HaIoolAowHVdVKtVHB8fI5PJ0K7ZIAgwHA7R7/cBAJVKBblc7ta6emVZhqIodIHhqpgx27ahqioEQbhwASOdTtOFEOLyRRYITNMEx3FIpVJQFAWj0Wila3g8HkNRFBwdHdGu0GQyCd/3MRwOqTCCRCI8ffqUxqEAoNF+pmlif3//TkaYRSIR5HI5cByHWq1G7dc1TQPHcdR+3fM8Gi+3WCwQBME5u3vf9+lCEekmfPfddy/tun327BkGgwFarRZs26ZOYp7nYTAYQJZlFAoFTKdTJJNJGie5s7MDx3EwmUzowoeqqkgkErRAlkgk0O/3kcvlNorsXCwWePLkCQaDAZrNJsLhMMrl8rlr62z3PbG5f9MQBAG7u7uoVCro9/vnhD9kIe8sxClvNBqdE1+dJZPJYD6fo9vtIhKJUFe7i9jZ2cF0OoXneWg0GjAMA4lEAqFQiHbuk+jBQqGA7e3tGxNAeZ6Ho6MjKr4iRdiz5HI5dLtdnJ6eguM4VKvVG3PW8DwP9Xoduq5TIRw5DkTwWiqVLo0ROvtZnucBWF7XZ10vms31HeO+7yMIAtrhzmAwGAzGi/LhI+iGP3MZDzwYDC6N7xEEAZVKhUbYkcXUV4XjODg8PES320Wv10M8HsfOzg4URaEOTiQKj8wjTNO89Pk+Ho/x/vvvIxQKQdd16hBCGkba7TZSqRRmsxl2dnaQyWSQTCZRqVSoc6tlWTg5OaGfqaoqjV6azWYYDAb0v6fTKcrl8rmF5nA4DEVREAqFwPM8RFGksezNZpOOW2KxGMrlMiKRyIUx1KPRiB6jfr8Px3GoiGgwGCAcDmMymeDk5AQ7Ozsr40eO41AqlXB4eAjLstDr9dYKsBaLBXW+kmUZW1tb58a0oVAI5XIZzWYT9XodiqJgMplcKka3bZsK9nu9HhX4SJJEXYGPj4+RzWbh+z4ePHhwI4If3/fx7Nkz6LoOwzBWnImIeH88HiMcDmM2m611YZEkCfl8Ho1GA9lsFqZp3mgzw21SKBRgmiZKpRKazSYVocmyTN2QSQNPoVCApmm09pFIJNDr9TAej6Hr+sdGgKVpGorFInzfR6PRwNHRETRNg6Zp4Hkek8kElmVBEARsb28jmUwiEongvffew3A4xGAwwHg8pp/HcRyNADUMA7u7uyvxkIxXi+u69B5wmdM0IRQKUWGRdxsP6g0xTROnp6doNpsYDoe0UZG4z1uWBcMwMBqNqMNfuVx+4b9HGvquglwbW1tbmE6n9Jmmquql53cymcTu7i44jqOO2aQOtukzkcG4DkyAxWAwGAwGg8FgMBjXJB4H/vJfBv6L/+Ly1/H8UqREOnRlWYau66jX69Slh8TKpVIpJJNJ3Lt379LCQalUwnw+B7DsSOt0Omi32zSigRQUstksstnsSxVBroJYeRORVKFQuPC1s9kMtm1jd3f30ji4TCaDVquFeDyORqOBfD6PxWKB4XAIVVVXIkGImC2ZTCKfz+Pk5AS2bSMIAlocms1mEAQBpVKJdod6nofFYkEtynmex3Q6hWEYqNVq1IHs0aNHl0ZxvC4qlQokSQLP89TdqdlsUievZDKJeDxOrdyJAOf5BRoSOylJEmq1GjRNQ6/XuzAqs9FoYDAY4OTkhLpanS1MLRYL9Pt9VKtVGpEALIvA4XAY4XD40oUrWZZpcXITgQkpBjabTeRyuXMLE8lkEolEAo1GgzosmKZ57ajNuwQRYW4CifeJRCJXFhBTqRSOj4/hOA4Gg8GlfyMcDtMYmUgkAtM00W63qYiPFESJ8O4mF4yI8Oki8RWwPK+JULXT6UBVVXrNvAyu6+LJkycwTZO66UmSRJ0kyGLH2UXRyyDRCgA2ctUAltcSiSRkMBgMBuNlID0I0ylQrwPFIrAmAe9akCg/27aRy+Woq5QkSeeE4IIg0EVQ4jb1qmKD6/U6LMtCv99HpVJZicoiDk6xWIyKIUKhEKrV6oVjKtd18aUvfQm2bSMUCuHdd99dcaYiIq5Op0PncYqiQFVV2lgSBAF1SiGOHsTlk7glk4YCEkF40dhG0zQ6N1ssFkilUohGoytjslAohGw2i0wms3a/T6dTPHv2jAqYEokEjQr3PA/T6RThcBitVovGx+3t7a2MUTiOQzKZpAKi+Xx+ToBAGgYmkwn29vYunQfncjkcHh7Ctm30er0LHUqCIMDx8TEGgwH6/T4dl56FOLfU63XwPI/j42O8++67Ly1QOT4+pvMhEjcdjUbBcRx+7/d+D71eD6PRiDpk7+7urm2S0DQNgiBQl983RYAlSRKtfUiShF6vh06nA1mWMZvNwPM8otEobSAiAkRgeU9QFAXT6ZReJxcRBMFKVNmbTrlcBsdxEASBCqeISxgR5MXjccTjcZRKJTx9+pTu23g8jt3dXdqENBqNUK/X8fTpU0SjUbz//vvUOZ24Z93FGsfbynXnfER09TrnfEEQoFarod/vYzQaYXd3d+XZR9z64vE4arUaddPOZrMbicxuAtIAdR3S6TSteWmatiJwk2UZ2WwW6XT6lY1FGG837CxiMBgMBoPBYDAYjBfg0SPgv/wvgR/+4eXPrjte86rlKkY4HMbjx49pHMQ6i/hcLodsNrtR0Xd3d5dGTGSz2RVXIdL1nMvlNo5xe1FCoRASiQSy2SxqtRpkWV4rblksFqjX69A0DaqqXhqJKEkStra2aAfoV77yFVroz2az6Pf7GI/HkCQJsVgMkUgE6XQagiAgn89D13VkMhnk83ns7+8jHA7TIhfP82i1WojFYiiVSiv7WlEUFItFRKNR1Ot1yLKMRqOBg4ODm99xN0ClUkEymaSRf2djICORCFRVBc/zODo6urKIpGkawuEwjRIpFovnzkPP89Dr9ajYb3t7+1xBUJIkFItF8DxP4+cArFjDXwYR8AC48rXPOyxctChBuu9JxEyv13ujBVibQjptHcfZaMGGOJlNp1MacXMZmqbhE5/4BHWiIrGfZOEglUohl8vdeOdor9eDaZr0+r+MTCaD4+NjTKfTK0Vlm/Ds2TMYhoHT01OEw2G60AF8tLBK3CGAZRH3sntdOByGLMsQRZF2FV9FJBKhkQwMBoPBYLwMf/WvAt/5ncDnPw+MRoCmAX/lrwCf+MSLf+Z0OkW73Uaj0aACCbKITJxP4/E4He9pmgbTNAEsY+ZexaInidDu9/tIJpMr4quz8DyPUqmEfr8P13VpTGA6nV5xcB2Pxzg8PISu6+B5Hru7u9jZ2VkZSxNRl6IoODk5ocIlEvscDoeRz+fh+z6NmRYEAZFIBJIkoV6vUxdMYLmv1sXaEwRBgO/7CIVC1C313r179G+IoghZli+dd7ZaLViWRWOdyX4aDoeYz+dQFAXb29swDAPdbheyLK91alJVlbozLxaLc4vzo9EIo9EIqqrScSOJcARAnXuAjxbch8MhhsPhhdtO4hJ7vR7y+fyFi/Qk9rzb7SKRSMCyrEv361VYlgVd11GtViHLMsrl8sqchgjqIpEIBoMBRFFEr9dDpVI591nEQY3UDd4kFEXB48eP0W63UavVMJ1OIcsyvW5CoRCSySSSyeS1BCZBEMAwDPR6vZXIUFITeVOiGi+iVCohmUyi1+shGo2uOHXH43HkcjnEYjEcHx9TF+tyubwyJwqCAJZlAVjem05PT5HJZGAYBrLZLEajEXq9HgqFwq026l3E2fM5FArdmlP7XSIcDiMUCiEUCmE4HF46PwRAGw+JY/jrYDgcYjqdQtd15PP5C+f0oiiiVCrh6OiI3nNfx3l1HWRZRqVSQalUwmw2W3kmMhg3CRNgMRgMBoPBYDAYDMYLctYo6LOfzcL3Hfozz6sIggn9ORQKYWtrC+VyGcPhkAqwZFm+sPB/GUSwNRwOMRqNaKGe2Pi/Knv5SqUC27bhui4t1CcSCciyDM/zMBqNYFkWjYcrlUpXijJIFATHcdB1HZ1OBxzHwXEc8DyP2WxGnWdSqRQtxg6HQ/A8j2w2i+3tbfyBP/AH6Gc+ffqUdpGuExgRNE1DIpHAYDBANBpd26l9V4hEItjb28P29jbm8zl834ckSQiFQmi325jNZlgsFhdGWp6FOGYtFgtMp9Nzxb7BYEBdzNaJr85CojKI2xjP8xiNRldux3A4pAKTqwpg5PPJ9lwGWfQyDIPGIt7VY3pTkAU6IoraBI7jEATBipjvMiRJQqlUQrFYxHQ6heu6tHP9NhY/giCgznebnNMkfmc8HmMymVz5+ssg99larYZYLIZisbjy7+Qc0zQNJycndCEynU5feq8hi9GDwQCxWOzSjuggCOhi1U1FOjIYDAbj482v/dpH/23bwE/8xFKY9SK4rovDw0M0m00MBgN4nkcdTUm8j23bGA6HKJfLr+1ZZhgGFZxftVAsCAKKxSK63S5CoRDS6TRtWBBFkUZEjUYjaJoGURTx6NGjC5/9iqIgmUzS+ObZbLby75IkrRUAkfh2Imi7CjL+BrDy/5dF9p3FdV0YhgFd1xGPx1fmqsSNk5BMJjEej6lo4HkB1lXjShLJLAgCjbQiYnZCOBymLl4kQvCyaK7BYEDj568aM8bjcfR6PSrKexkBFvkcz/NQKpXOjYfJviNiM8uyEIlELnR/Iw0sb2JsnCiKqFQqeOedd6jbNHGkjVxgtee6LqbTKTKZzFqntC9/+cuwbRu2bWM+nyMcDkOSJOr43Gw2sb+//0ZHF6qqiu3tbWxtbWGxWFAHLHIuLRYLGIaBwWBAnfoIxLXINE10Oh0EQUBFmOR+W6vVoKoqPM9DEARrxX83TRAEME2Tur+dJRaLIZvNvtR1d9dRVRWapiGZTNLjdpHYeLFYwDRN6nj2umoWuq7TaMGrmp5kWabNfLqu33kBFuFlm5omkwl16guFQhs/XxkfH5gAi8FgMBgMBoPBYDBeEEVZFo3n89naf+92e8hkUisLDGSh/iYgReVNxAi3hSzLePDgAQ4PD2kUYbfbpUVxIrzSNI2KNTaBdCu3223ouo5cLkftwkncVxAEGAwGAEA7aWVZhqZpK243ruvS+IZEInGlOCSZTNIuPsMwbt1J7GVZ1x1JFjN4nt9ogUuSJFr0tm0bgiCsFPxId7ooilcWl3ieRywWoy5MZwVtF+37+XyO4XCISqWCVCp15TbP53O6aLVJsSscDtNz5eMgwBJFkTrsOY5z5T3HdV0sFosVl4FNIQtJt81ZUdmmAi/iAHG2g/xFIAtqvu9fej8QRRGZTAadTgfZbPZKJ4V8Po/xeEzFXeVyGZIkYd1aJYl3zOVyL/VdGAwGg8G4iPfeA548AR48uN77fN/H06dPMRqN0O12MZlMUCgU6MKt4zjUtYjMEba2tjCZTKjo/lU5oZAxJHHiuQriOlwsFpFMJpHJZDAej2n0O4mXGgwGePjw4ZUuXslkkkYqX+bidJZoNEqj2A3DgCRJmM1mFzbxjEYjRCIRcBxHnbCus39Jo8N4PMbe3t7Kv53db47jQFVVRKNRWJaFZDKJ6XS60mwzHo/pz+v2tyAICIIA9Xod0WgUtm3TKEbyHvJ7cj6dnd+cjasjztLT6RSO49DGjsvgeR6RSASO45wTxF0H13VhWRZM00QikVg7l5FlGbIsYzQaIZ/Po9FoYDabwbKsczHtxMk2nU7fuKPsqySbzcK2bRwfH0NRlEu/i2madP4ILB3FFosFarUa3n//fXQ6HXQ6HfA8j0wmg/l8jkgkgsVigX6/j1wuB9/38eDBgwtFXm8KZB73PKZpYjabYTKZnJuTDAYDjEYjtNtt6sRL4i5JjGEul0O9XqfRm6SB5LbwPA9HR0e0WY7E1AKg7unEOe/5CNO3iVwuR5+BZM73/PGdTqdoNBpQFIU6nr0uFosF5vM5dVW/inA4jMlk8sa59V0X3/fR7/fR6/VoAxqwfC4SJ75MJvNGimYZNw8TYDEYDAaDwWAwGAzGCzIe9zEaiR8uJJxfMT89rSIIWrh///5bHRmlqioeP36Mfr9PoxyI8w7HcUgmk8hms9cu7kUiEVQqFWoNns1mcXBwgOl0CtM0afczWfwIh8MYj8fIZDIrhSJSCJrP58hms1f+XVmWIQjCSvzFmwbplCed+ZcJmhzHQb1eR61Wo8UiRVEQiUSQzWaRSqWooGtT4RJZGCICwdFohEajgWKxeG5hajqd0ohKTdM2OkaETd2aNmE6ncKyLLqgRtyJ3sQCGsdxNEql2Wwil8tdeg6YpkljXe5qRCNxABBFkRbuLyMIAhpn9LKRRpZlwbIsxOPxK4vQsVgMnU4Htm1fKcAiCyNk4fHw8PDDRczS2tdXKhXWXctgMBiMa3Odocxf+kvAX/trwM7O5u/pdDor4hPyrCLiKlmWEYvF0Ov10Ol0IAgCDMOAaZo0VutVCbCed3C6CjLW1DQNDx8+pEIbMl70PA+KosD3/Y2e0WQsTYQmmyxuk7jwVCqFbrdLm15SqdS599u2TR1UhsMhZFmmzlubQoT5AM4JZsixUlWVul4pigJd12nsJMHzvBU3l3XHOBwOYzgcotvtwjAMCIKAWCwGRVEQBAFtiCFiHNM08alPfQrz+RzvvfceJpMJFdoTMZVhGBvFnxN4nr+WC+w6yP6azWbnxFSERCJBj89isQDP81gsFnQR/yyGYdCx+VWRZXeZTCaDdruNaDSKRqOB7e3tc+cBGQe///77iMVi9Bz/+te/DsMw0Gg0YFkWBoMBUqkUnfMTcaGiKPTa4DgOR0dHePfdd9/IOdxVEGEMiagkEJcp0zQhCAJyuRx1Wycxr67rUoetw8NDKpi9LQFWEAR49uwZ+v0+arUagiBYeT6Mx2N0u10a8xoEAfb399/K45ZMJmmNrNFo4NmzZ9QNGQB1bI5Go7Rp8SrnqVfBpvfEs7W/txXXdfH06VMqJCSCUeAj98rRaARd17G/v/9KIpUZdxt2BjAYDAaDwWAwGAzGC9Dv99HtnmI6La8tmgZBgJOTU4TDCQRBgEePHl0Zq/YmI4oiCoUC8vk8HMehwihFUV6q+KAoCjRNg2EYtJObFKLPdp2FQiHwPI/Dw0OEw+GVgiQpBBHXrE1404tImqZBURRIkgTLss7FgRC63S4GgwGOjo4wm82oCIsUkSzLWuk2X3eur4PE0WUyGUSjUfi+j1qthsPDQ8RiMaiqiiAIYNs2xuMxLTaWy+WNF69kWQbHcRiPx1d2OU8mE7rg9byIzLZttFotDIfDlXOKOA1ks1kUCoU3riM3m81C13WIoohWq4Vyubz2fHYch3aNh8PhV9IxTjrVyQIUuVdks9lLI1Tj8ThisRja7TZyudzaY7JYLKibwGQyobGsLwqJ6HBdd6O4WBKhedGC2vPs7u5CEASIoojJZALTNDGdTs/dqziOu/NufAwGg8F48/E84J//880FWEEQoN/vYzAYQFVV7O3todPpoN1uQ1EU+uwkMeGNRgOj0QhPnjxBJpOhEVSvCjKGJM6vV7kLEQcnnuchSRJkWV4ZK43HYwwGAyrGugryGkEQrjW2LBQKmEwmSKfTqFarcF0XvV6PCi3G4zEajQYVu7RaLYiiiGKxSJsgCoXCRvOys3Og54VMPM8jHo/DcRy0Wi0YhkH34dn3eZ6Her0OSZKuPMaLxQKj0QjhcPhchKOqqjQmkMzzqtUqeJ5fGUsCy0VwEvFo2/bGwhLHcRCLxV5qznp2vnkRRLwWj8fR7/cvdNwiMZflchmRSOSNdnMKhUIoFovwPA+1Wg1HR0c0Ok8QBDiOg/feew/9fp/GUIZCIXz5y1/GYDCAZVnUKe7g4IC6aZPGmVarRd1x8/k8ut0u4vE4TNO8s00lL8NF9YnxeIzZbAbbtpFKpda+hvxOFEUkEgkYhoF4PH5hBObL0uv1YBgGqtUqVFU9F8sZiUSQyWTQaDRQrVYhCAKSyeSFAsY3ne3tbdok5zgOTNOEbdsAljWvfD4PRVGu5Rp/W8iyDFVVaZzwVe7kpB7ztrqME5fPwWCAWq1G61zhcBhBEGAymUDXdRiGga2tLQDAgwcP3thaIuNmYAIsBoPBYDAYDAaDwbgmi8UC1WoV/X4Lnlf4UDCyOrnmOA6ZTBbN5ikEQUC1WsX9+/dfzwZfExLZR0RU4XB4Y9cV8vqbIpPJwDRNGgF2Vkjx/IJJt9ulncJni/ySJFGBh23bV4ooJpMJgiCAoihvrGiOdMonEgnour52UaHX66Hb7aJaraLVamF3dxf37t1DLpejHandbhc7OzvwfR+qqqLb7V65WBUEAYbDIXK5HKLRKPb392mkoW3bMAwDuq5Tl6m9vT2oqopyubyxwCSRSCAUCq2I8y7C930YhoFsNotoNLpSGNR1HScnJzAMA4ZhYDqd0i54AHSBaTQa4eDgYKM4x7tCNBpFMpnE1tYWqtUqTk5OkEqlEIvFwHEcFosFPRaJRALpdBqVSuVWtykIAjSbTXQ6HbpwMp/PqYOdaZpQFAV7e3trF82y2SyNOe10OivFaXJu2baN+XyOVquFSCSC09NTlEqlFy4Kk4UCnuc3WlgFQOM/N1lY5TgO29vbyOVy6PV60HUdtZr6oWD07OuuvekMBoPBYAAAtrc/jV5vSn/m+ctFR3/v7wF/4k9s9tmj0Yg+0wuFAlRVpY60xN2EuCQJggCO43B4eIhcLod33nkHiUTilcapp1IpNBoNhMNh6LqOUmm96yTw0ZyoUqkgmUyufa4TcVYkEsFwOLxS9DMcDiGKIhVyjcfjjbebzFEcx0Gj0cDp6SmOjo7geR50XacuMo1GA5qmoVgs0jHw6ekpDMPA/fv3rxSdkag8nudXov8ImUwGtm0jl8uh2+2i2WwiFAphPp/D8zwqvBAEAdvb28hkMhceY+ImlkwmwXEcBoMBksnkypg7CAIIgkDnp+ScIo6vZJ+TsSBx/KlWqygWi5fOEyaTCXXtfRnBDplvyrJMm0ueh+d5lEol+L5PHYjD4TDS6TRs26YOX7PZjEZe7lzHiu6OUiwW4fs+OI6jTnnEMa3T6dBmB1mW8fDhQ+TzeTx79ow6zZCaxHQ6pWIQEmfY7/fR6XQQCoWQTqfB8zwsy0K/338rBVjk2gSwIjKczWZYLBbwfX/lfD/bhHS2FqBpGkzTpC5zt+GCRe4DPM9f2AhE/u3o6IjWfN5WARYAlMtlZDIZOuc7Kx7NZDLIZDJ3QsREtlEURRiGcakL32QywXQ6pd/tbaTb7cKyLNRqNcRiMeTz+ZXzmbhMtlot1Go1iKKIfr//SsXljLsHE2AxGAwGg8FgMBgMxjUZDAYfOqyYtOi+jlgsjiBIUmv3TbqsXyfT6RTtdpsWg0i0hizLCIfDyOVyr7wglkwmoSgKMpkMWq0WBEFYWyA0DAODwQCVSgWRSGRFBEa6KYfDIRqNBrLZ7KVdnrquQ9M0hEKhN7pwm8/nMR6PYds2Tk9PUSwW6X6ZTqfo9Xo4OTlBv9/HgwcPkE6nEQ6H0W63sVgsEAQBDMNAq9XCw4cPqUim1+vRzr51kII66XbneR737t2joq5YLEajQkRRRCaTQTabvVaxURAEpNNpTCYT+h3WFfx830ez2aTd+meLYKPRCCcnJ2i1WjBNE6lUCpVKBZIkUXeuwWCA4+NjKqh5U0SUhL29PbpQ0e/30W630Ww2qRucoigoFApIJpPY3d3dyOHpZTg9PUWn00Gr1YJt23RB1vd9KqrKZrNYLBY4ODg4t+AXi8UQjUZRqVRQrVbheR4ymQyNJSFRGv1+HzzPI5PJUKeIwWCAg4ODay8wEPFmOBzGaDRaOc9838dwOMRsNkMQBBBFEZIkYT6fIxwOXyt6VlEUbG1tYWtrC93uzUVrMhgMBoPxAz/wz/HTP73566+jN5/NZpjP53ThXxRF5PN5Gis1HA6pcEgURTpvyufziMViuHfv3it1iSAur5lMBrVajcaXPY/ruvTfL4vIFgQBqVQKw+EQ9Xod6XT6wgYOz/MwGAyQSCSgqio0TdtYgAWAjlMFQUAkEsFXvvIVdLtd1Ot1KiSSJAmlUgmlUokK8SVJwsnJCcrlMgDg8ePHl86F4vE4ZFlGPB6HruuIRqMrx0gURWxvb1MnkA8++ACe58EwDNTrdeogG4/HkU6nsbu7u/bv2LZN58elUgkcx6Hb7dIxoiRJcF0Xk8mEjusGgwEcx4EgCNjb2zu3CJ5Op1Gv1/Hee+9BEAQ8efIEn/zkJ9d+X9d10Wq1EI1GIcvyS4kHBEFAIpFAMplEs9lEJpNZ+zej0SjK5TKdTymKgtlshnq9DlEUEYvFaOT0/v7+tcaSd5lyuUxjuhOJBI2n9DwP7XYbW1tbePz4MWRZhm3bNKKzUqnQeMr5fA7TNFfqEalUCuPxGOPxGJZlIRqN0ji3NxHSBHTRPTGRSECWZUSjUVqzIO8j7yX3WM/zYNs20uk0otHoyvkoCAJ9z8tEb16EbdvU5SmTyVx6j+d5njpypdNpOI7z1pz365BlGZVKBZVK5crj/bogznskQpScc88zm83QaDSQSCTo/fdtIwgCKpiTJAmFQmHt6ziOQ7FYxNHREW0YYwKsjzdMgMVgMBgMBoPBYDAY16Tf78MwDKiqik99aoqvfnV9V20QAOl0GoZhYDKZYDAY0ML3XcOyLBwdHdFOU2KHDny0UDEajTAcDrG7u/vKikSkU7jZbMJxHPzO7/wOIpEIcrkcUqkULd4uFgsUi0UkEom1Rf5cLgdd16EoCmq1Gra2ttYWxXu9Hmzbxu7uLlKp1BvlePQ8RKAUBAFarRZOT0+pnXyn08HJyQl0Xcf9+/cRDocxn8/R7XYxHA7hOA6Nc3AcBx988AHC4TAeP36MTqeDer1+LsbE933ouo5er4dSqUQjQwiRSAR7e3sIgoB2Mr/M/i0Wi2i324jH46hWq9B1Hfl8HuFwGL7v0y54YGn5n0wmVwR1jUYDuq7DNE3s7OysFHo5jkM0GoWmaWg2m3QxaTgcnhMF3WWIaKzdbiMUCkEURXQ6HSwWi5Uonb29vVsXGxJHtWq1Co7jsLe3tyJIDYIAlmWh3W7TuJt333333HW6v79PxaHdbhdf/OIX6X2LFOy3t7fpYkMoFMLx8TFyuRyNg71uUT+TyWA0GmEwGNBFwV6vB8uysFgsMJ0uXUVEUaTRKb7vv3AR+q4V4RkMBoPxZnPdFOUXXUPlOA6TyQTtdpsK2skzG1iOFRVFwXw+RxAE2NnZeS1j7UqlAtu24Xkems0mhsMhXUAmTq6madKF8lwud6mLUjabRb/fh6ZpqFarayO15/M5FcGnUinkcrkX2vZ8Po9EIkHHPoZhIJlMIhwOU+E5ccIi+1ZVVYTDYTQaDUiShE6nc+mclOM4ZLNZTCYTHB8fo9VqnYvjDoVCqFQqeO+991AoFJDNZlGpVKBpGnieRzQaRTabRTKZRBAE0HUd/X6fxixLkkSdoIIgoHMMWZYxmUwwHo9pTHU2m0U4HEaz2cRoNKINHBfFrFUqFeq2ats2dYGNx+MQBAGe58GyLBrVTeIZX/ZcJPHfoVAI9XodW1tbaz/TdV2kUilEo1EaPUYg4rV0On0rsXCvk2g0img0ivl8TueXk8kEsizj/v379HgS8Q4A2gThOA5s24Ysy0gkEnS/8jwPTdNg2zZs26bn222Iim4LEs1OmvyA5ZwimUwim82uzFuIEHE8HuPk5ASdTgf5fB6iKNJ9MpvNIIoidQePRCJIJBIrf3M6nUIURXAcdyvnGXHkcl13o+aXaDSKXq8H13Uxm83eagHWWS6b8wVBANM0YZomXNcFz/NQVfWVuWRtb29jOp3CdV3U63V6HoVCISquJq6PhULhwvram45t2x8231pXPrc5jkMymcRgMEAmk6H3N8bHk7fvamAwGAwGg8FgMBiMWyQIAsxmMziOA03T8N3fPUC3K134ekmSoCgKHMehi/R3jfF4jKOjI3Q6HQwGA8RiMezs7ECWZerwYhgGhsMhFeW8ijiExWKB09NTWJZFYwcEQaCiGZ7nkUqlsLOzg2QyCVVVcXBwsNZljIi2SAHp2bNniMfjiEajVGRkGAYWiwUqlQri8fidFctdB1IIkyQJs9kMpmliOp1iNBpBkiTcu3cP8XgcnufRLlXS4SgIAlzXheM4ODo6wic+8QnU63Xs7Oyg0+ng8PCQOoW5rovRaASO41Aul5FMJrG/v3/hwsjLFOd830e/36dFWsuyACwFPrVaDZqmQdM0WqBPJBJIpVK4d+8e/QyysEOKYxcVeUkn49OnTzEcDtHr9d4oARaw/A5kEZTneYiiSOMOZFkGx3E4Pj6G4ziXxvC8DEEQUGcq3/ext7d3bkGKxMgIgoB6vY5oNIp+v498Pk9jWTiOQygUwqNHj6jrgmmaGAwGCIVC1MWLxJAQR4vhcIhmswlJklCtVvHw4cNrbT9x4CNuWr7vU/Hn2Q570zQxn8+xv78PwzAwGo3WumowGAwGg/Equa6u9wLDorVIkkTj10zTpI0quq4jlUphe3sbnufBdV3qwEqcRYlT0KsmFApRwYcsyzAMA51Ohzq0ElFMLBZDLpe7MqI5HA5ja2trpemBOFwBoOIRVVWxs7ODbDaLbDa7cbTx87RaLYiiiEQigU9/+tOwLAuj0YiKiMi4tt1uo9frUbeo8XgMwzCgaRp1nLqIfD4Py7JQLpfx3nvv4cmTJwiHwzRKUhAE+L6PRCKBd999F/F4HMVike5Tsug8Go1wfHyM6XRKzw8yp/R9H5Zl0bHhzs4OnZNNJpNzcXOLxQLVavVKx1aO43Dv3j188MEHVOQ0GAzQ6XTA8zx836cCl1QqhXw+vxJr/aJEo1FkMhl4nodqtYqjoyMkEgnqIDadTmnkOWkWePjwId0XgiB8LBbrQ6EQFQjO53OkUqmVc5G4XxFREYmyJxGEk8lk5RyQJIkK+SaTCT1H3wR6vR5qtdrKuQEsv5NlWej1eshkMtje3qb7qFgswrIsbG1toV6v0yhC0izSbrchiiKdG52N6SSYpkmd7m5D7HRWBLdJY8nZ17xJ4rnbotfrodVq0boNETJHIhG0Wi0kEglsb29Dki6uQ74skUgEBwcH4DgOmqbBMAw0m016fDRNo6Lbra2ttzZ+cD6fY7FYwPO8S8WEQRDQY0SiVWez2cfins5YDxNgMRgMBoPBYDAYDMYLQCbYhcICf+Ev1PBzP7fuNctCEon7uqvFpHq9Dl3XaYTf2cICiXpLJBKo1Wo0HoF0Il9FEAQ0zu5sZ+ZVzOdzfPDBBzBNk3YwZ7NZjMdj+L6P2WyGUCgERVGom046nb7084nNuyAIK13jAOgiRjKZhKZpuH///p3q4FssFmi32zTqLBwOI5VKbVQwLRaLyGazGAwG1N2MfA6JBtR1HZZlUQeps8TjcRpPmc1m4bou9vf3MR6Pafwaz/PI5/OIx+NQVRX7+/u3Erfpui4ODw9hWRYMw6AdobPZDNPpFOFwmC767O7u0hjEdDq9Utg1DAPj8Riu657rCH4eEl9ICtXEnelNgbhOtdtt6s6UTCbBcRzG4zGOj4+haRpc14XneZfGS74oxFWNdI5edp1Go1GoqkpdErrdLubzOS08k4XFXC6HfD6PTqcDwzBQqVToYufzUTmxWIwu+sZisWtHWwiCgN3dXRpHVKvVqFsGcVOwbRuxWAzhcJi6O5ycnCAUCl079pDBYDAYjJvkusMW1938tbFYDLIsIxaL4dmzZ7RZ4iLXKCKg0HWdNgdcNRa7DVRVxePHj9Hr9agrD3FoPRtdfdbN9TJyuRx9byaTWXEUDoVC2NnZQTgcRjabfamxluu61HFWkiREIhHqshwOh+G6LnU6zeVy1EWYxG03Gg3k83kMh8NLvxvZB++99x6NV+52u1gsFnQsmU6nkc/nqZvVyckJFcuTednXvvY11Ot1NJtNWJaFSCRCo9FGoxGNF/z1X/91fOYzn7mwMcKyLIzHYwRBAE3TrpynaZqGRCKBbDZLxfmO49BxvKqqUFUVhULhRmOzdnZ2aNMDma/0+/2VfVosFhGNRnH//v1rLc67rkuj+Mh+TiaTb4zY6CykPkBiu89CriPf96FpGlRVxXA4pPOB54WLvu+D4zgaV0lc4u463W4XrVYLrVYLlmXReQzHcXAch0agku9MIjcFQVgRkB4eHuJ3f/d30W63MRgMMJ1Okc/naROgbdsrLs79fh+z2QyVSuXWItIkSaIOW9Pp9Mq5kOM44HkegiDcqqjoTaDRaKDZbKLb7dKmQ9IY2e126Tx4Mpng4cOHt+qGFY1G8c4776DX60HTNBSLRSqgJs2QuVxuo7rgm8pFNdwgCKiomTybSKMh+Znx8ebuVJMZDAaDwWAwGAwG4w2AFL1IpyUALOu/6yfYRCyUSqXuZDGJFOWIE9BFxTFBEFCpVHB4eAjbttHr9S51wXIcB71eD4PBgHaTA9h4MePZs2cwDAO1Wg3xeByVSmVl/xFhFgAkEgnaIX0ZHMfRKLput0u3gRRLFEVBNptFJpO5MwKb+XyOer0O0zQxGo1oV2w4HEa73YamaSiXy1cWNUVRRD6fRz6fh+M44DiOFq8XiwVM07xQVEfi+Hieh67rODg4wL1792gkJbHEj0QiyGazt+YQ5fs+Dg8P0e/3qftRJpOh22zbNlqtFubzOWKxGEKhEB4+fLj2WJLFKVEUNxLaqapKF9Fc130ltv83wWQyQa1WQ6PRgOM42NraWlkMzWazmE6naDabqNVqtMP1puMIbdvGZDKB7/sbnR/hcBhf//rXkU6nMRqNqPAS+Egsads22u02giBAPp9f2WZyTZ8llUpB13XqfHaVm8XzJBIJuigoiiJSqRSNURIEAalUCpFIBJFIBOl0Go1Gg3YKP3jw4Fp/i8FgMBiMm+S6w9oPjTI3/Gwe6XSaOlLOZjNkMpm14itd1+F5HnZ3dzGfz6m76OsSS5yNn5tMJlTsrSjKC431SORev99HNBqlcWKSJNE4sZdtUCDCtfF4jHQ6TV2WifsNafohC8Gu68I0TYTDYaTTadocs7jiILdaLdTrdQiCgMVigXA4TIVRjuNgsVjAtm3UajU6RiPfV1EUHB4e4r333qPzDCIKI66lZD74e7/3e9B1HYvFgjYEFAqFc9sTBAFGoxHC4TBCodCVQnoyDozH43jw4AEkSaKxk4IgUMH+TUMagxKJBHq9HpLJJDzPo01A5Ltns9mNm31msxmazSYMw8BsNsN8PgcAep6mUimUSqU7WWu4jIuckYjjGZlziaIIRVGg6/pKXYFAYr6IYxsROt5lHMdBvV5Hu92G4zjY3d09d04vFgs0Gg2cnp4CWIoKSQSaIAhIJpN49uwZarUaRqMRFbXJsozxeIxwOIzxeIx+v0+Fr57nYTQa0frBbbkWRaNRKszVdf3KWoVhGNSR67K417edfr+PZrOJarUK13VRKpVWGovm8zn6/T51ZD48PMTjx49vNb4+FAqhXC6jWCzCcZyVOMS71LB4W5B7EM/zmEwmiMViWCwWqNVqcBwH4/GY1ip4nofneZjP5zg5OcH+/v7r3nzGa+TtvzoYDAaDwWAwGAwG44ZJJpOwLAv1ep3a468jCEDj86LR6I2LGm6CwWCA8XiMxWJx5eKHKIqIRqMwTRO6rq9Y4Z+l2Wyi1WrRjjDiBiNJEhKJBAzDQCKRwL179xAEAS2Ek/1IOpwbjQaSySTy+fy5v5FIJBCLxXBycoJutwtZlpHP5zcSTkWjUUSjUSwWC+oERaIi7xLT6RRPnjyBaZro9XqwbZt2kvb7fUiShHQ6jfF4jHv37m28eEWK86FQCN1uF7PZDIIgXFjsdF0Xvu8jk8lgsVjQ+L7t7e2b+qob0ev1YJomarUaotEoCoXCyvknyzJSqRTa7Tbt8u/3+7RQfRaO46gz3Sacfd1tFjhvmm63C9u2Yds29vb21nbZK4qC7e1tHB8f0wiem75X+b5Po1WuukZd16VuXdPpFLIso1AoIBwO005TXdfR7/fR6XTQ6/VQLpepOJFEbiYSCaTTaXpfEUURsiyvLFxdl/l8jlwuh/F4jEQiAfeMRUgkEqEiLI7jkE6noes6EonEtR23GAwGg8G4Sa67RnodARYAFAoFHB4eIpFI4Otf/zp1FyJja8dxqINNPp9HNBpFOBym7leXzadeBSQ26CYQRRGFQoGKiNaJwl8G27bRaDRolOOTJ09QrVapkIKMhziOQzgcBsdxNA7S3dDazLIsNBoN1Ot1zOdzPH78GJFIhEbDAUuXlidPnmAwGKDVamF3dxfJZBJBENBtIm7DiqJQV5B4PA5N0+B5Ho1Q/PrXv45Op4Ovf/3riMViUBTl3LzGsiy4rkuFHVftU+KsRcaFZP73qkgmk0gmk5hOp5jNZvB9nzqWXed8mEwmePr0KSzLovP2s+/XNI26Et+/f//OzWcvgggcz4qFCPF4HL1ejzpfxeNxTCYTGnNKhIQcx2EymWA4HFJ36HK5TJsk7jKbzNEkSaJzNF3XEYlEkMvlMJ/P8fTpU/R6PRwfH2MwGFCHK+JWLQgCXNeF4zh4+vQpZFlGPB7H/v4+tre3EY/HcXBwcGvuaUSYa9s2Tk5OYBjGhfNL4npcLBaRyWTeqLn2TRIEAdrtNvr9PlzXxe7u7rnnYigUQqlUgiiKaDQaUFUVpmm+kjojabj7uEGeOfF4HLquQ1VVnJ6e0ohQANSV0XVd1Ot1Kvglx4jx8YQJsBgMBoPBYDAYDAbjmmSzWfT7fciyjGazeaGTiuPMYNs9WgS8LWegl2GxWGA+n9POrqsIh8O0+9TzvHPvaTQa9H+TyYR2VvI8D8dx0Ol00Ol0EI1G8f7776NQKFBBhqqqyGazMAwDlmUhCIJLu1dJtEa9XsdsNoOu69fq4pQk6c52Cnueh6dPn2IwGKDZbCIejyOTyawUk03TRLvdpmKSR48ebWT/LooiNE1DPB7H06dPIYripe8bDocIhUKIRCIQRRGTyYS6v50lCAKYponxeExFdbFY7MbO+16vB8Mw6MLWuuLsMha0AMdxYBjGSqfwWWRZhqqqcF0X0+n0yiL9aDSCqqorQsG7Domo0XWdxgNdhCiKSCaTMAwD6XQak8nkRqMEiPDK87wrIxx1XcdwOIRpmnjw4AHu3bu3cp2SSL/f/d3fxdOnT+E4DhzHodc+WUR1HAej0QhbW1vXine5CNd1MRqNMBqNsLu7i2w2uyIse/58TCQS6Pf7NPaHFV8ZDAaD8brY27ve66+rUxZFEZVKBUdHR8jlclBVFd1ulwrYSTMKcdvd3t7GdDqFZVkAQF1J30ZuSkwQBAGazSbef/99tNtt2phQrVZpHHM2m6WRgK7rYjweYzQaged5GiVOXJguGxt1Oh1YlgXHcVbGYcQ5lsSZi6KIdruN+/fvIx6Po1AooN1uIxqNwrZtdDodcByHRCIBjuNwcHAA3/dh2zZ14Eqn0ygUCvA8D4PBAO+99x4AUAGWbdswDAOTyQSVSgWO42zUQEEcdV53FDRxc3oRiNCm3++j1WohHo9jb2+Pfh6J8jw9PUW5XAbHcXj8+PEbEUkYCoVog1q/30c6nabzA1EUaXx4q9Wi94ZCoQBJkjAej+E4DjzPQ6/Xg6IoSKfT+OQnP4l0On2pS/ddwPM86LoO0zSpuOMiiJCp1+vROkmj0cBgMMCXvvQlWJYFURTxmc98BqFQCJPJBIPBAJZl0c8uFovodrtIJpOIRCKoVCrn3MVvg3w+D13XUSwWaXNeKpWic0zSrGfbNsrlMhWQfVwZDod03pjL5S59JmazWViWRV0k72Kj59sCx3HIZDKwbRvHx8f42te+hiAI0O12EYvFkEgk6L2L1COJA6au6zg6Oro1pznG3ebtHNUyGAwGg8FgMBgMxi1COiwXiwVOT09xcnKy9nXNZgP37kWQyWSuHXf1qtnUCQj4aCHh+QWF8XhM4ypc18W9e/dW4jtisRjC4TC+/OUv49mzZ4hEIlRcRMQ6o9GI7s94PH6lWw7pNrNtG6PR6K0pbvT7fdi2jWaziVwud65rXRRFGodWq9WgqipardbGNue5XI6K3AzDQKlUWvs64lhwNkomCIKV+IcgCNDpdNDtdjGdTmnUHHHVUhQFhULhpY6NbduYTqe0IHnZYhbHcbSYn8lkMB6Pz3Vrkoi4SCSCwWCAcrl84efNZjPYto3d3d03qivXcRz4vr+xWxnpNiexNjcpwNI0DZFIBDzPYzgcXujW5vs+FRbGYjHkcrlziwO+76NWq0FRFFiWBc/zEI/H6WLLdDrFcDhEq9WiRfzd3V0Ay2MZCoVeaMGBXH+LxYIufPE8f+E9SpIkiKK4UcwPg8FgMBi3yenpL2A286Dry585TkCh8McufP2LPLZUVcXOzg6NxlssFvTZKUkSQqEQEokEUqkUBEG41tzj404QBDg+Pkav10O73aYCKTIHIm7DHMfR+VA4HEY4HKaC/PF4jOPjY+qSdJEoicQZ6rqOZDK5dsyk6zqN89vZ2YHrunR8R+KXQ6EQ8vk8+v0+bWQg43FVVdHv9zEcDiGKInZ2dmhMomEYqFarmM/nUFWVivf29vaoa2+9Xqfj/HUQR51SqYRsNvvGjN2fp9PpYDQaod1uo1gsnhs/k2Pc7/fRaDQgyzK63S6KxeIL/b3FYoHRaATP88Dz/Ers5G2QzWZpdGmz2USpVKLj6lwuR8V2h4eHsG2binfI/M1xHBQKBVQqFezt7WFnZ4cK0e4y5Hol88uriEajaLVamE6nODk5wWQywenpKZ1rE1c5YClsi8Vi6PV6mEwmiEQiyOfziMViCIKA7q9X0YQmSRLu379Pt2swGKBara68RtM07O7uIhaL4f79+3dWiDscDjEYDGjjWygUQjqdvtEGy+FwSKPsrvpcjuMQj8dhWRZGo9GVDU6MlyOfz1OR8+c+9zkYhoFMJoN4PE6v5eFwiMVigXw+j0QiAU3T0Ol0EIlEqEMd4+PF3bybMRgMBoPBYDAYDMYdZ3d3F57ngeM4DAaDc/8eBAFSqQzK5WW816bxcK8aWZahKArm8zkWi8WVxbjxeAxZliEIwrnuWmKl7zgO9vf3z32W4zhoNBoQBAH9fp+67BCrfMuyVuz4Hz16tNF3IHbfm8ZqvAkQtycSq3cRkUgEiUQCg8EAsViMupldBYlwLJVK+OpXv4p6vY5QKERFN6QAT4pFxLWg0WggGo3S4mgQBDg6OkK/30e/34dpmgA+OiY8z9MINsdxsLW19UL7YzabwXVdeJ63kfV9JBJBu92G67qYzWbn3kMcn0gBm3QVP898PqeRh6qqvlECP+LOBGCjTnhyTM++76Y4GyfT7/epcPJ5SLygaZp4991313bzEpe1breLcrlMi52z2QyqqtLFKBILIIoiBoMBdS/TNO3Sa+oizgpPN90/vu+D47hrF8SDAHj33R/FYjEDsDw23/zNrITHYDAYjBfj3/v3/n8r7qU8r+Jf/9cnF77+rADL84Bf/EWg0QC+5VuAb/omYJ2+gbilJpNJVCoVeJ5Hx+bk384KI8bjMRRFAcdxG41dP860Wi30ej2cnp5CURTs7OzA8zw4joN4PI54PE5dkgqFAnRdRzabpSI3EssdiUQQiUQubSgYj8eYz+eYzWZrGxTm8zmNvCPj43a7Dc/z0Ol0MJvN4DgOEokEjZImEXFEJEAcjohLj+/7NHJ+OBwim80ik8kgk8lQt650Oo1cLoenT58CAOr1OiaTyYqjDnGEIm5Z0Wh0rRPum4Dv+xgMBtB1HZqmXVpLIO4s5LUXOQVfxGQyoS5qRDjJ8zx1DyOCgpuGCDI9z0O1WsXR0RGSySQ0TaPOaa1WC5FIBNlslrpfz+dzaJoGTdMQDoepY+6b4PwFLI8tuTY32WbitOu6LhUXkua1arV6TnDH8zyy2Szq9TqN4KxUKjg5OaFikVflmKSqKh49ekSPI7m3AMs6FKl1FIvFO+lMPhwOUa1WaYPP2W0fDAZQlGWt7ybENaSeFQqFNpo7hkIhDIdDAKCiyU1YLBb09ZIk3XnB4l1AEATcv38fzWYTyWQSpmliPp9TQSHP89A0Ddlslrp88jwPwzAwHA7heR4TYH0MYdUbBoPBYDAYDAaDwXgBOI7D/v4+otHoWqcYjuNQLJZw//7NdsbdNKlUCq1WC4qiQNd1Wvxex3w+x2g0ws7ODtLp9Mq/eZ4HwzBo3MPzBbQgCNBoNGBZFkzTxM7ODhzHQTgchiAISCQSyGQyMAwDv/3bvw3P82gn7FWQAtKbUnS9CsdxMJvNYFnWRosGiUQCx8fH9D2bWPeTGJB6vY7xeIyjoyO0Wi1a4PV9H7IsUwv1crkMz/No8YgU4avVKvr9Pk5OTsDzPEqlEqLRKP2M4XBIxXYAaDf8i/AiLm2XUalUYNs2KpUK6vU67dyXZRlBEGA4HGI0GiESiaBUKqFcLr9whMjrgMT+cRy34tp0EaSjd5248iYg0ZBE9FYoFFaEcb7vo9vtotPp0HMsHo+f+xwSUSiKIvb39/GVr3wFoiii1WphZ2eHbns8HqcObhzHQZIklEol2jl/XUgxXFVVjEajtdt2lvF4jCAIoKrqC50329v/4UqR/9u/nRXHGQwGg/FqMM2lGJjjgB/9UeCf/bPl7z/7WeDP/3ngO7/z/HuSySRkWYamaTAM41L3X9d1YZomSqUSEonEWzOGvw3I+Kjb7YLnedy/fx+1Wg0nJydU1E7cPdvtNur1OgqFAmRZxnQ6pSIo3/ep6OKy+QWJiwawVhBBot/m8/mKkOv5sfNisaCfM5lMaOw3mTerqgrLsqjjFsdxVOhTLBZRqVRQLpepcxoRFxBHHVEUoes66vX6StRlLBbD7u4u4vE47t+//8aeW8PhkM69N3HSTiQSVAA3Ho83jl00DAPHx8cYDodUvEYIhUJIJpMYDocoFAov3EhzERzH4d69ewiCADzPwzRNGIaBbrcLYClqODg4gCRJWCwWSKVSVKwZCoWQzWaRTqfvpHDnMkRRpA0apIHkMubzOYIgwHQ6BcdxVPyof2hpeLapxfd9zOdzeJ4HSZJgWRZ16QFA5/OvklAohJ2dHVQqFSpcIb+/y/d/cm30+30MBgM6ryP/1ul0kEqlMJ/Psbe399KiNjJv3/T4nBVdXSW+IoLOXq+3IsaWJGlF7Mq4mFAohFKphE6ng3K5jHQ6TUXmoijS+2UymaTndDQaxXg8ZiK3jylMgMVgMBgMBoPBYDAYLwgpFF8Uiba7u4c7rL0CACiKglgshkwmg3q9DkVR1goLXNdFvV5HJBKhrlVnIVb6pOP4eWzbxmw2g2EYSKVSiEajODk5oV2Q0WgUwHLxplAo4OjoCIPBYG183Fkcx6FdsC8iqriLuK6LIAjgeR5kWb7y9UTccV0XMFEU8a3f+q2Yz+eYTCYIggDxeBwcx0EQBLqIlslkIEkSTk5OqEApmUxiNpuh3+/T7lrS6Ucg7leapuH09BTtdhuSJNEO5utAFpZ4nsdkMtlI/EIcjy4qJkqShAcPHuDw8BCKotCiv+u64DgOkUgElUoFmqahXC6jUChca5tfN+FwGKIoIhqNwjRNeo1dhGmaUFUVoVDoyte+CJlMhp5n7XYbtVoNkiRBVVUalTgajZBOp+li0/MuWdPpFIvFArZtI5FIUEeNSCRCRZ6xWIx2zsuyjJOTE4xGIzx+/BjJZPKFF484jqPbVqvVMJvNLr0+iQsBKcYyGAwGg/GmMJ0CrRaQSgG/8Rur//Z3/+56AZYoikilUhiPxzg9Pb0wIs7zPNTrdciyjGg0ulHjwMcZXdcxm80wHA5RqVSou42iKJAkCY7jQFVVyLKMYrGIZrOJwWCAwWCASCSCdDpNX09ec9k4/KwQfz6fnxORkzkKsDzm8/kcHMdR4QBxVCaxh6QRQ9d1SJKEVCqFSCRCYyld18VoNIIsywiFQjRKvFgsrh17y7KMR48eod1uQ9M0zGYzGvUsSRJ1yyoUCnc2zmwTSIT1WdHHZZDx9FnnuasYDoc4Pj5Gq9WCaZqIx+PI5XIIhULwPI820gyHQwRBAEEQNmqOug5EZEXEIZlMhm4/mY9mMhlab3kbGq8URQHP8/B9H1/96lepWx2ZtyUSiZXrzjRN+h5RFBEEAaLRKAzDALC8pxIBJIkGDYIAi8WCujSRufNtNdpsgiAI55r47iqO4+D4+BjtdhuGYSCfzyMej9N7p+/7sCwLnU6Hnq+KorxUZGc4HIamaWi32+dqYMQNnDhGiqKI4XCISCRC3ekv+y6Hh4eYTCYwTROWZdHrSNM0jEYjdDod7O7usjnrFZDnk6ZpSCaTWCwW4DgOoiiec/kEluf8WTEy4+PFmzsCYTAYDAaDwWAwGIw7wkUdTdcw7HmtbG1tYTwew3VdNJtNWJaFZDKJUChEXYxM04Qsy1SIsq4YT/5/XVGfxIYBoA5JxCXpeWej7e1tNJtNjMdjVKtVPH78+MJtJ3EapNj+NkD2zaYdkKSg8yJRZ5qm4dGjR4jH43jvvfcQBAF1HiJRg8PhEI1GA6IoIpfL0YUb0kFJIicv+tuiKCKfz6Ner2M2m0HX9WtH+WmaBlmWEY/HqcvaZZDXyLJ8qTBPlmU8fvwYuq6j2+2ubBfP80ilUshms2td7u46PM/TSJKTkxPYtn1hN/x0OoVhGCgUCnS/LRYL9Pt9KkrjeR6KoiCTyVCh3nXZ3t6mYjri2LZYLCCKIgqFAorFIjqdzoUCMBKZ4HkeFEWB4zhIpVJQVRXRaJQuUJIFCbIQlU6nkU6nce/evY0dAdaRzWbR6/UQiURQq9Xo9zlLEATodDqYTCbULfBNXiRiMBgMxseTP/fnAE0Dntdy6PoylnDdo61UKmE4HKJcLqPRaGA0GiGZTEJRFARBgNFoBNM0qXA/m83eiuj7bWI0GsG2bQiCQMcwiqIgm82i2+3SaDLXdbFYLMDzPFzXRTqdxv7+Po1DchwHpVLpyjGJpmlUIG9Z1rk531nxCxFPqaoKQRCgKAomkwls28Z8PqcR847jQNd1RCIR2kyRTCYRBAF838doNEImk8FisaCxcpedF6IoolKpoFQqwbKsFUedsyKJNxkyFwQ2cwE+6wK26Ri9VqvRCPmdnZ0V8YggCMhkMkgkEqhWq2g2m/R3N+2Uw3EcdeEhTtBBEECSpHOihld1bMm5SVyJbhLDMNDr9TCZTGjtRVVVSJIE27bpPLZYLNK5c6FQoCI7YCm4E0URsiyj1+vBdV3MZjPYto3pdApgOW8iIsjFYoFsNkuFIozL6XQ6GI1GMAwD29vb52oBPM/TZ9vp6Sk0TaMiphcllUqhXq9D0zT0+30oioLRaERFuEEQ0HOR4ziMx2OUy+VLRcyz2QxPnjyBrutoNpuQJIk2MXmeB8uycHx8jGQyCd/3sb+/fytxo28LoihCFEU4jrNRTWE+n9OmQMbHD3bUGQwGg8FgMBgMBuOWeFMEWIqi4P79++A4DqqqwjAMNBoNWshVVZV2/eXzeZTL5XOfQYoKkiStxEsQZrMZHMehRVRipS+K4rmFgGQyiVgshvF4jGaziXw+j2QyuVL8dF0XnU4HjuNQkcPbUthQFIUei8tEM4ThcEg7hF+k63J3dxeu60IQBPR6PQyHQwwGA5imSRdySERksViksSWkgzISiVxZiNc0DaIowrIsGIZxbQEWKczbto2joyN0u90L41M6nQ7m8zktSF5VNCdCpUwmQxeLiHvSm76AQwRD2WwW9Xod2Wx2JeqBCCy73S7tuM7n86jVauj1ephOpyvnQTgchmEYUFUVe3t7L1TALxQKyGazGAwGK45jiqIgnU7j5OQE1WqVOkid5eyxJNsei8VQKBSQSqXoggXpNCVuboVCAQcHBy9dUFZVFaVSCb7vo9Fo4OjoCJqmIRqNQhAEOI4D0zQRBAG2traQSCTW3i8ZDAaDwXidbKonsO31v282gXWGkpIk0TmFLMs0Tow0FCiKglwuh1gshmw2i+3t7Rf8Bh8fiKPR2Zg1nucRjUap4E1VVSpSTyaTcBwH2WwW3/AN34B8Pg9ZlvH06VPIsgxRlPCLvwi89x6wswP82//2qpiONDwkEgm0223qfksgDquyLGMwGGA+nyOXyyEej2MwGMC2bfR6PepoReZwRChGxCa+72MymVBxv6ZpcF2XigPC4fCK4GAdRATxNqIoCkKhEARBoM6vl2HbNn39Jg7Ko9GICuNyudyFc0hRFFEqlXB8fIzpdIper3erY1tVVV/KRehl8DwPuq6fi2kj96sXbUA5y2AwwMnJCXWSVhSFCiw5jkO73UYoFMJ8PodhGBAEgUazZzIZtNttWkshDUoffPABjRxVFAWpVAqCINAYy36/j/F4jGg0Ct/3WdTcFbiuC8MwYBgGYrHYpY1YqqoiFotB13VEo9G1Ds6bQgSO0+kUh4eH+NznPgdVVeE4DkajEXXa8n0f0+kUhUIBg8EA3/zN33zhZ56ensI0TTSbTeRyOaRSqZV/TyQSGI/HqNfrVFz7yU9+8qWahyaTCRzHge/71NXtbanTxWIxxGIxWid5XqB8FiKArFQqb2QzH+PleTvOegaDwWAwGAwGg8F4zfzZP/u7+NrXPlJccRz3xgiwACASieCdd95Bt9uFpmkoFArwPI8KGEgkQeyCTEVFUai9PYkZPAtxuiJFy7NRE893OPM8j1KphFarhWg0il6vh8FgQEUOpLtTkiRsb2+/VKzYXUQURSQSCSSTSTSbTaTT6ZVFl7MEQQBd1xGPx6kL0HXheR73799Hs9mEoiiYzWaYTCbwfR+CICASiUBVVRQKhRXhFOm0v6zwdBZFUbBYLK4Vk3iWXC4HXddRqVRQr9cxmUyQSqVoQYvEm8xmM1QqFcTj8WvH2oRCobeqKC3LMvb392lsSL/fR6/XQzgcBsdxcBwHQRBQp6+trS30ej30ej3qQqeqKhRFge/76Ha76HQ6yOVyWCwWuH///gs5SgmCgFwut1ZEl06nqWvX8/FFRLApCAIajQYkSUI0GkUymUQ8Hkc8Hsd0OqWRqGfvEzfVzVssFgEsr5vxeAzDMNDpdGinfjqdpg5yBwcHzP2KwWAwGG8dJyfrBVjAcrz3+PFj9Ho9aJqGfD5P5xQ8z1MxA3PZuJrf+i3gf/lfcjDNMD71qafY2flIPBeLxZBKpeC6LnXKIY4mvu9jb28Pe3t7AJYxhsRB65/8kyx+/ueXn/Gbvwn0+8D3fd/q383n87AsC7Zto1qtolAo0Hhn4qAjCAKOjo6wu7uLSCSCeDyOer1OY6VzuRwSiQQmkwmNKhyNRohEIjQmjed5vPPOO6hUKhiPx5AkCYPBAJIk4Utf+hIVWGWz2Y+da4+maVAUBYlEAoPB4FLxj+d5MAwD6XSavu8qBoMBHdcTx2NJklaivAmKoiAcDsM0Tei6/lY2F9i2jWfPntHmE9u26Vw4Ho/DNE1EIhEcHBy88FxxNpvh9PQUrVYL4/EYn/nMZ2BZFnVams1m4DgOvV4PtVoNuVwO9+/fR6lUws7ODpLJJG1QIfWW9957D67rot/vY3t7m7pEB0FAnfBc16XXILlPvKr5SRAEsCwLvV5vZZ+S+NmLakuvk/F4TCPvNxEJJxIJnJ6e0ve8zLOtXC5jOByC4zh6jUYiESSTSaiqivF4TAV70+kUqqqiXq/Te/1ZptMpRqMRut0uEonEufocIRKJoFQqodFoIJVKQdf1F4oGJts7mUwwn8+pAIsIcde56G8CEUaOx2N6/hBR4k27011FJpNBs9mkUZHb29trG/aII3YoFIKqqm+NUz/jejABFoPBYDAYDAaDwWDcAPn8J1Ctvu6teDkkSUK5XEaxWKSRhDzPQ1XVjQqNuVwOtm1jMBisFU6Qwh/p4iOxIxeJi3Z3d8HzPAqFAizLwng8pjbelUoFmqYhHo9jb2/vjXcqeh4iNlJVFdVqFVtbW+e+o+d5aDab8H0f6XR6I7eni+A4jh57wzDOFbjWRf6djZHcBBLj8KLHShAE3L9/H8DyXDUMA61Wi/59nucRj8dRKpUQi8WY+OVDYrEYHjx4gNPTUyQSCYxGIxpNEY1GEYvFoCgKKpUKZrMZer0eTk9PIQgC9vb2Vgqlvu/Dsix0Oh16PN99990b3c+FQgGmaVKhnW3bVGgnCALm8znG4zFGoxG+8Ru/kd4HCEQMCgCWZSGVSkFRlBuNOCoWi0gkEuj1erSbnKBpGrLZ7DnXvuvwJol3GQwGg/Hm8bJrlqenwLd928X/Ttwn8/k8JpPJitvl2yR0v4xGA2i3ly5T1zR+BbCMevzhHwamUxmTiYt/9I/28Q3f0MbDh8uDl0wmYZomJEmCruvI5/PgOA62bSOZTNIGBeJalMlkEA6H8fM/v+ou9NnPAt/7vavnRDQaxdbWFoIgQLfbpVHkqqoiCAKYponpdIpcLof5fI5IJILZbEZdePL5PPr9Pp2zzWYz+L4P27ZpJOFkMsH29jZc18VwOEQoFKLv13Uduq4jFApRZ95kMknnhmdFHaRpRBRFxGKxS92c3jRyuRzG4zEsy0Kj0UCpVFo7HyTuNZs2n4zHY7z33nt49uwZLMui+1SSJJimCVmWkc/nV8bOkUiEClPuCq7rUkfd+XxOnffS6TSSyeTGc07btvH06VPahCJJEuLxOJ13EKFauVyG53l49OjRhfWLyyDnq2VZNPIxmUyi3W4jEonAcRzM53NEo1EMBgPEYjGEQiHcu3ePOr2l02mMx2Ocnp4iCALqMp3JZDAejzGbzeh1Y9s2YrEYjXtdLBZU5HiRk/RNMp1O8ezZM0wmEwyHQ4xGI3ieR12RDMNAJBLB/v7+nXouuK5L53abbBd5je/71O3xRSHuhqFQCKIoYmtrC6Io0mbGdDqN3d1dCIJAG/GIGPf5ek2/36diqKuaFUlNzjRN9Pv9awmwgiDAyckJer0eTNOEYRgr9wlN0zAcDmEYBu7du7e2rrQO3/fRbDbR7/dpAya51xP3t0Kh8ErOZYIoivRaIq7dzwuEHcehDllEOPkiwjPGmw8TYDEYDAaDwWAwGAzGDbBuIWNDXcqdgxR+rks6nUa326XCiel0inQ6TZ2ZFEWhggrSobsuim4ymWCxWGB7ext7e3sIhULUOp84+ZAC89tq561pGorFIo06e/bsGVRVpR3J8/kclmVRd59UKnUjxSee55FOpzfq0lNVFeFwGLquXxkR4roudax6mQJUKBTCo0eP0Ol0oGkadWIClqIsWZaRyWSQz+eZ+OoMmqbhE5/4BEajEQaDAWazGYDlPjtbsP3qV7+Kfr8PjuPWdnQSJwKe59FqtRCLxdDv95HP529sW0VRvFRo57ouMpkMVFXFdDpFqVQ6d+6RrtPZbIZyuYxMJnPjHbKqqmJ7exuVSoVGHkqS9NZELDAYDAaDcREnJ5u9juO4j51zEQD82q8BP/qjgOsC4TDwV/4K8MlPfvTvREDUbrepa1Sv16OOpIqi4Kd/einIDoVCmE6nEEUBP/MzUfzlvzyiYrZ4PA7XddFqtdBsNsHzPBaLBTRNQyQSQafTofHM6XQalUpl7fa6LvC8niSXy0EQBEiShEwmA8uyMJ/PAQBbW1sol8sYDAYQRRGTyQTHx8fUucXzPOp6q2kaHjx4AN/3cXp6Cp7nacRgLBZDqVRCLpeDZVmYTqe0yYY4tdbrdYRCIWxtbcH3fRSLRRwfH8NxHCoyIYvy8Xgc/X4fiUSCihTeZLLZLEzThOd5qNVqODw8RCKRoPNfImgTRRHb29vIZDIXutwQhsMhnj17hm63i2aziclkAlVVwXEcRqMRFXQsFgvacHAXabfbaLVamM1mME2TukepqkpFZDs7O1dufxAEODo6oi7BxWLxnEAkl8uh3++jXq+D4zicnp7i4ODgWtsbBAH6/T4VHRGRINnO6XQKy7KwWCwQBAGy2Swmk8m5JqxisQjTNFEqlfClL30Jw+EQiqIgFovB8zyYpolerwff96kIdmtrC/l8fiVW77ZFK9PpFB988AF0XUer1QLHcYjFYjQutd1uo9PpoFQqwXXdFxa13QYkDhJYChyv2i7i7v0yjWYE3/dhGAbC4TD29vYQjUYxGo3g+z44jqNuUsQ57NmzZxgOh+j1eufOWyJ0VRRlo30bjUYxHo9X4jc3oVarodvtolqtwvd9JBIJxONx8DxPr89arUZFXQ8ePLhyXOD7Pp4+fUqdponrF2noBECdrx3Hwc7OzrW2+WUgomJgeR+q1WoQRRGSJMF1XRoPuru7S4XDvV7vlW0f4+7AqkIMBoPBYDAYDAaDcUt83FxMeJ7HwcEBdU7q9/s4OTmBIAjwPA/dbpcKdZLJJMrl8jkxjud5aLfbtKstk8lAEATa9flxolwu0wjIyWSCXq+Hfr8PAAiHwyiVSjR+bW9v77VYsJumiW63C8uyLi1wG4ZBuxXXie6ugyAIKJVKKBaLGA6HVIBF4iwv2g9BEKx0biuK8tZ0yG9KNBq9UFxJoi8sy0KxWLy0gByPx2nHOYkXuklkWcbjx49XhHbEPUMQBHS7XQDLRaRqtYpEIkGjECeTCQzDgOd5NIrjprfvLDzPQ5blG//c3/3dPwbXdejfqNdl/Af/wd+/8b/DYDAYDMZ1YWuJFxMEwE/91FLUBACTCfCzP/uRAGs8HuPo6Ig6PRE327NxUel0Gl//+g4A7sNFdwm+L+PkJIJG433k83lIkkSbNYIgQKPRoOJ4IngIh8MoFApIJpPY2dm5MO7roqYh4iRkGAZ0XV8Zc2cyGfi+T5tuSCwzabIhMdeiKNJo6GKxiEqlAo7jqDBMURTqmLazs7MyN0wmk8jlcmg2mzg9PcVsNsPJyQk4jkOn06GiDp7nMZ/PUavVIEkSFcc/ePDgjXZJ5jgO+/v74DgOkiRRFxld1wEs5zL5fJ4K7HZ3dy/9vPl8jqOjI3S7XRiGQWPNiNiOxEfquk5FJUQsZ9v2nXGwq9fraLVaaLVaGA6HkGUZ4XCYOgKRa8h1Xezt7V0qSjMMA9PpFN1uF4VC4ULX52w2C9d1aSTcdDrdqKkoCJb3AtddRv+RRrTnOevgCywFKE+ePIHjOBiPx3S7JEnC/fv3wXEc8vk8TNOk9ZXFYgFRFFEsFqFpGmKxGB4+fIhUKkXdvGazGRWP3CZHR0cwDAONRgPZbBbpdHpljp7P59Hr9aio7fj4GA8ePLj17dqEcDhMHf8sy7ryOBMRnCiKL90caFkWnY+Xy2U6b7+o2Y3EYyYSCepwRgiCAEEQbHwPJE54wTWKmMTtibjw7ezsrAhfw+EwFdvW63WIooh6vY6HDx9e+rknJyfQdR2np6eQJAlbW1tUtBUEAX1WErEYccN6FZD7sqqqkGUZs9mMurvxPL9Sx6xUKtfan4y3CybAYjAYDAaDwWAwGIwb4A2u7d4osizj0aNHaDQaCIfDmM1mtOiuqips26ZFY1LgITF2o9EIvV4PgiCgWCyiUCi88Z3LL0upVEIqlUKv14OiKNQFSJIkJBIJGuP4OiB26tlsFu12mwqsnscwDPT7fZTLZUQikY2cEIIgWBHcrCs4chy3kYU9Ef8R+3oSDUCKpOuKwm8ytm2j3++vLHwlEgmk0+lLryfbtqnL3CbnVCKRoKIt13Vv3PnprNBuNBrRRT9ZlvGZz3yGFvZJ1AHpLBVFEclkEolEArFYDPfv338jF996vX8M3/+oA9myPl5iQQaDwWDcHi875PlwKM9Yw2QCtFqrv/v855f/T6LOBoMBut0ujXMWRZFGGhGHGMOIA0gA4KAoKjzPh6ouRTTPnj1DJBKBLMvUZUSSJHziE5+gsdJkETgej6NQKKwdoxMuS826yh2XRCH2ej3M53PwPI9SqQRFUeC6LizLWolqJnOYyWQCXdepyxZxPX4eURRRqVRwdHSEr33ta5BlmQqPno97dl0X7XYbp6en4DgOtVrtlTqj3AaCIODg4IBGLj7f8BKPx5HL5S4U152l2+1iPB6j1+vh4cOH1NVpNBohkUhQF2xZltFsNqGqKgaDAXieh+M4KBaLG7kk3yaGYaDdbqNarcLzPOzs7KyIXog4o9VqwfM8KkS8SETT6/Woi9hV88pMJoPDw0M4joN+v3+hoxzh+Bj47//7ZRzpzo6AP/JHROrWdhU8z9Maif+cQlJVVTx69GilGSaVSlGhXigUQiKRQCqVgmmG8Gu/pkEUA+zv9xEE8yv/9ssyHA4xmUzQbreRzWbXNl/xPI98Po8gCFYiGO9Cc1QoFEI8HkcikUCn00EqlbrQQWp5rzaQz+cRi8VeuimHCORIvCThojqFpmm0tkaeAwRJkqib9FVu5QA+dFoU6fcSRfHK93S7Xdi2jel0iv39/ZVaQxAAzWYIrstha2sp6B0MBkgkEpcea8dxYBgG6vU6VFWlTYln90UsFkM4HMbp6Sk6nQ5CoRByudwrm/NzHIdyuYxCoQBd188JsNLpNN2XLxtLyXhzYQIsBoPBYDAYDAaDwbgB3qYIwpdFkiTs7u6iUqlA13VaRBIEAZZlYTQaod/vo9lsUoENEdskEgkaIVcsFl/3V7lVPM+DrusYj8fwPA+CICAWi51bTFAUhUYHkG5kEhXxOuE4Dnt7e3BdF0EQoFarQVVVagff7/fR7XYxn8+RzWYxGo3wzjvvXFoAJCI80s0LLBde0uk0stnstYuas9kMT58+pZEaZxeByAIQ6Sbf399/I4U6BMdxcHJygvF4DNu2MZlMVgRYjUYDhULhwuvK9314nrdRsRVYHheyL8n7bgNSZH2eg4MD6LqOXq+HdDpNHSAEQYCqqlRY9yYfUwaDwWAw7iKWtRTtfMz7JK6F53l49uwZHR/n83lEo1EYhgEASKVS8H0frVYLJycncJzdD112VBrlyPMc7t27h8lkQp1SOI7D/fv3EYvF4Ps+crkcdWN5XhBwkRHHy6wPE2fj/f19iKIIy7JWhCm5XA7z+Zw6sYRCIXAchydPnmA6nQIAtre3L3VW4nkekiTR2PNv/MZvXOtqtBTRbOHw0ITvDyDLMsrl8hsTDz0aAV/6EhCPA9/wDR/9nsyRicvNWSeyTb+b7/sYDAbQdZ26y9q2jUQigcFgQJ2uyOfGYjHq7DMejxGNRqEoyks7Gb8snU6HurHt7e2d+/5k3iBJEk5OThCLxdDr9bC1tbX284jDFIm9vAxJkqCq6sYxbX/rby3FVwBwfMzjV34lhW/91mWE2lUNSa7rUrHWumNMXIEWiwWq1SqNjpckCeFwGBzHYTAQ8SM/UsF4vLxRx2IB/tyfe3rrUX/9fh/D4RC+718ZiUkctYkwcHt7+1a3bVOIu9hwOMTp6SnK5fI5wZDjOGg0GrT+sYnjsmku64YXaf3OuiVtMh8nr1nnXJVIJBCNRtFutzEajS4Vadq2jZOTE0QiEXq+8TxPXQgvcvbSdR2maULTtJXzyvOA//P/zOPLX16KyA4OHPypP+VhMBhgPB5jMBhcKGDs9XoYj8eYz+fY3t6+cD+Iooh8Po96vY7ZbAZd11/5/UkQBGSzWRqvyGCc5c0YeTAYDAaDwWAwGAwG441DFEXkcrmV35VKJXQ6HSqQmEwmVHwUiURoR/Pz73ub8H0fjUYD/X4f8/kctm3TfUC6jguFwrkiHulqJf99F4hGozg4OADP84jFYuh2u/jKV74Cx3HoopCqqphOpxAEAdVqFYPBAPv7+ysLQr7v4/j4GIZhYDQawTRNzOdzcBwHRVEwGo3Q6XRop+EmuK5LXQbq9ToURUGhUICqqgiCALZtUwcl3/dXYj7eNCaTCZ48eQLDMNDtduG67ocLdTzG4zH6/T6i0ShdtFlX3BYEgYohN+mSXSwWVNz0OpzqOI6jjgzT6ZQu7oVCoTvRPc1gMBgMxl1l3WOb56/XPGIYwGvWYtxJLhI56bq+EnWWSCTOOWMQUcXp6SkcZ4LZTIUsK+A47kMnnwg+8YlPoNvtYjgcwnVd8DxPHV1jsdil47eLhFY30TSUSqVgWRb6/T7G4zEVmPA8v+I+NB6PUa/X8cEHH4DneUynU8RiMWiadmmUOIlvC4fDFzZkPHmi4sd/vIjZzMdsNsb3fZ+Jcnlwq1HUN0WrBXz/9wODwfLnP/yHgf/4Pz7/OuJqswnzOfCP/zEwnQKf/rQD13UxHA5RLpcBAMViEfP5HJ7nodPpIBwOIxqNUhelarWKyWSCvb09lEollMvlWxPvzGYz2oRD5hhE8JVMJsHzPCaTCY26TCaTl4rPVFWlAsdoNIpyuby2KYM4TG0qZCNRjc+7Uj2P5wFf+9pHP/O8gC99KY4/9IdiME0TyWTy0vebpkkFXxcJZ1KpFG1GEQThnIPXF7+oUfGV7/toNlXU61v4pm+6XBQFAP/8nwO/+7vLe/z3fA9wnWQ90gwUjUavbIQhjtSkeeiuQM6ZIAioKFZVVSpEmkwmcBwH0WgUpVIJpVLpUoGT5wH/2/8G/OqvLgVY3/M9wL//759/Hbn2AGzkCOY4DiRJWqkTERKJBBRFQSKRQLfbhaqq515DHMjef/99jMdjuK4LWZZp5OhwOMRgMEAmkzknhvI8D77vYz6fnzufj48VKr4CgMNDFR98EEc8HsJ8Pqci0nWYpgnLsqBp2pXXJXnNcDiEaZo3JsAKgpd3CmUwmACLwWAwGAwGg8FgMG6AdbWliwrwH2c4jqPiItM0Yds2jd+IRqOIx+NvpABmU3zfx9OnT6HrOrVsJ52t8/kc7XYb8Xgcs9kMjuNgd3f3dW/ylcRiMbz77rtoNptoNpvQNA2O40CWZSSTSaTTaaiqCsuyMBwOUSqVsFgs8OjRI8iyjCAIcHh4iMFggGq1Ctd1EQqFsFgsaCye4zhIJpN4+PAhPM+jCweX0Wq1YFkW6vU6MpnMuYIc2b5ms4lqtQpBEJBOp68siL9KgiCgi3MXRTF6nkf3X6vVQjabRTKZXBFFjcdjGtdBRHHPd2pqmoZIJAKO4zAcDq+M4TBNE7FYjLosvE4URbkwWuRNhD07GAwGg3GbcBxw/z7w9OnyZ1EEfv/vB3791zf/DF2/fQFWrwf85E8uY/3+yB8BvvEbr36P6wL/+/8O/NN/CmjaUsTyTd90u9t5lot0GURcQhxYL4LjOGQyGcznC/i+j8VijlDoI8GRoigv7BLzoZHuxtt8HdLpNBqNBmKxGFqtFnZ2dlYW+13XRb1eh2VZOD4+plFbwNIxJwgCyLKMSqWydkw3n88xnU4vjQ3/5V9OYbHgwPMCgkDA3//7cXz7t49f/su9An71Vz8SXwHAr/wK8O/+u8CLTkuCYCnoevJk+fNP/ZSEP/2neQRBQI+LqqrUhUZRFFiWhW63S910iLCiUCigUqncipCNuCj3ej04jgPLsrBYLOh8xTRNKIqCvb09TKdTTKdTLBaLS68hQjweR6PRgOd5cBxnresUmYcTN7armM1mKy5BF3+v878LhZZzz263S0Vk65jP59TNZ7GI4m/+zTAMA/gDfwD4g3/wI2FIIpGg89lOp0PjOQm/8RuJD7clwHTqQBQFfPazRfyH/+Hlc7xf/3Xgf/wfP/r58BD4wR+89C0rEFfi64jagiC4UtT2qikWi+B5HoIgYDabwTAMep6oqopisQhFUVAul6+8Nr785eU1DizPjZ/5GeA7vgN4vtSTSCQgSRKNFrys5hEEAQzDQCKRWBuzyXEcSqUS5vM55vM5Tk5OkE6nEY/H6T7/2Z8d4Zd+6R4cZxvf8A1P8Yf+0AClUgnAR2JZWZapI9ve3t5G++6Xful8VOnP/EwGf+pPXT3JdV0Xrutu3FBFts+96AG3Ib6/HG/88i8DoRDwH/1HwLd/+0t9JONjDhNgMRgMBoPBYDAYDMYN8EM/lMN0+pEVvSCo+KEf6r7GLbrbkLiKuyR2eRUcHx9jMBjg9PQUsixje3ubdlISV6Zut4vT01MAy4LSmxDFKIoiHMdBNpuFbdv45Cc/iWw2uxInQqIv6vU6AODo6AiPHz9Gp9OBYRg4PT2FJEm0G550OXueB57n0Ww2Yds2er0evuu7vutSlzTytwaDASKRyIXdkDzPo1Qq4dmzZxgOh+j1enfinHQcB71eD7quUwEWiQHIZrMrCwi6rsNxHLTbbRSLxbULEpFIBDs7Ozg5OUGv16Md5WcXsOLxOBRFQTweR6/Xg6ZpFzpbke50sj0MBoPBYDDeLP6b/wb4hV8AbHu5EPz5z2/2PhJZfHzsIJ8PVsYLlgX8f//fUvj0+37fyzlIeB7wF/7C0mkLAH7nd4D/4X8AHj68/H3/9J8Cv/RLy/+eTID/+X8G/vbfBi7Sad+008U6DUEQBHAcB6PR6EqBO7AUxQM8PM8955J1EbXa8rtsbV38fS4yHbnsT/zqrwI/8RPL7/Un/gTw3d+9/vMFQUC5XKZCq+PjYySTSdpc8/TpU7TbbTreLxQKCIIAuq6j3+9jMpkgnU7D8zxkMvcwGMRQLs8QifjUZcX3fciyfKGrzunpRweZ4zg0myp8f3jxl7tD/PRPr/4cBEuHnL/39y53HwqC5bXx/vvA/j7wbd+2/P0Xv/iR+AoAxmMen/98HLu7WDmnNE3DvXv3oOs6wuEwdcJ1XRccx6FYLOLBgwe3Mh8NggBHR0fo9/uo1+tUJKUoCnzfh2EY6PV6yGQy8DwPmqZRkc4m4h5Jkqiw56LrKB6PIx6Po16vw3XdSz/Xtm24rotYLHbldbzuPkBiHguFAtrtNhaLBXK5HP2bQRBgOByi2+0iHA4jkUjix398B63W8v1f/CIgScB3fufyZ47jsLW1RR2FTk5OkEwmkUgkEAqFEAQ+5nMPi8WcOoqNx+ddkJ7neSHuF74AnJycFwtdhCAItLlsE+bz+aXzztdJPp9HOp1Gv99HLBaj3ykUClEn5k1c4f7W3zr/ux/7MeC/++9WfyeKIlKpFGzbRrVahaZpF55rnU4Hvu8jkUhcOB9finmX20wiSLvdLgRBQKMxx8/+7LfA8zyIoorT029FLGYgkVgKYxOJBL2fkyaueDxOYyWJe7YsyxiPxys1FOK8dpbxmMN8Pocsy1dGznIct/Gzz/M8cBx3pdvaVXzucx/dhycT4Ed/FHj3XeCKFE0G40KYAIvBYDAYDAaDwWAwboD53IbnfSTACgKPuZgwVnAcB6ZpotFoIBwOo1wurwhgOI5DNBpFOBzG6ekp2u02QqEQcrncnSxInmUymcC2bbRaLWQymbUxgTzPI5vNUqv7SCSC0WiEbreLfr9PC23kd57nIRKJQJZlWjg/PT2FIAj43Oc+h+/8zu/8cIHqPKZpYjabYTQa0c7ui+B5HolEgnYiz+fzS4uCt029Xken06ERG7PZDEEQrMQApNNp7OzsgOO4jR0VRFFEJpNBt9tFNps953LFcRzy+Twcx8FkMsHp6SmKxeJK96nneTAMA/1+n8Y5ptPnO1wZDAaDwWDcbRQF+ON//KOf/8W/uPz1rutiNpthsVggCAJ87WsdJJNDSJKEVCoF38/jB39QoYKpb/s24C/+xRffvt/6rY/EV4S/+BeBv//3L3/fL//y6s+j0VJE9G/8G6u/H4+B//V/BX7zN5cuQ3/+z2/msHUV601cAgQBrhV1xnEcgmApyriMIAD++l9fRs0BwLd8y3I/rRvKXmQQctE6d6Ox/GzCj/0Y8PgxcO/e+tfncjkq3DFNk44ZO50OjZUqFovUTcmyLKiqSqO8er0eqtUM/sk/KUOWo5CkAH/mzzRxcDDFaDSiDi8Xjf9X90sAnuduZQ41Go3gOA49nvF4/Nai+f6P/2N9FCHhF35h6fhGaLWAf+ffAf7v/3v1dYLA49d/PYlPfCICy7JWmjlCoRAKhQJyuRwmkwkVPymKgv39feTzeXQ6QLsN7OwAG5hPbUSn00G/38fJyQlEUcT+/v7KHIwIklqtFnzfh2macJwQfuZnvgGWVUalssAf/+NdFArrlYUkzp64GK0jm82i3+9DkiS02+1z83MCiWmMxWKQZZmKUK4Dx3E4ODjAbDaDIAg0SlRRlA/ndAH+xb/YgSiW8If/sIvJpIBWazVu8+d+7iMBFgAkk0k6JzRNE6ZpQtd1AIAsS3DdDEIhGZIkQpaXjsFXiU4/97nzv/v85zcXYMXjccRiMVSrVSwWi0uvDeJqdlWE3+tEFEUUCoW19Y11GAbw8z+/vN/+0T8K5PPLe+nzfP3rS7Hb8y5LxWIRlmWhUCjQBrRUKgVVVWnD3mAwwGw2w9bWFmKx2KXnY6lUgqIoCIfDSKfTmEwmcF0Xv/qrYUhSCDzvIhpdntc//dMhfP/3V1e++9bWFo6Pj2GaJrrd7srfSqfTGI1GOD09Xamh+P75E8x1l+Irsh0XEQ6HEYlEoOs6giC41B1/sVjAcRzkcrlLHbOePFlGsj56tHT8XMev/drqz/M58P/+v8Cf/bMXfiyDcSlMgMVgMBgMBoPBYDAYt8SrFmARccR0OqW278lk8q2K5nqT6fV6GI/HmM/n2N7evrCYJAgC8vk8arUaZrMZdF2/8y5DvV6PdgVf5DZFSKfT0HUdtm3j6OgI8/kclmUBAHVyCofDSKVSK8XyZDIJjuOogO3Jkyf41Kc+tbbbkVjtB0GwNm7ieSKRCPr9Pn3v6xJg1Wo1tFot2gWuaRotUE4mk3MxANvb23AcB8PhcKMojlgshna7jclkstaFgSy8AMsIx5OTExpn4fs+bNuGIAgoFotIpVLY39+/8+JABoPBYDAYV3NZAhdZJHfdBRaLBTzPx9OnAxQKJ9A0DcPhEL/xGz663QpdbP+N3wD+5J8ECoVl/NJnPwtEo5vHqm3qyPU8R0fnf/e1r50XYP3Df7gUXwHLBfP/6X8C/u7fXR8rfx3WC7A4+P5yjL+4yIbqDMsI6gAchyuj2Z88+Uh8BSzdkL74xaUQ63muK8D6yZ88/7u/83eAH/qhi7enVCohEomg0+kgmUzCtm3MZjNMp1OkUins7u4ilUpB0zRwHEfHpBzHIZVK4f/6v7YxnwOi6AIQ8Q/+QRp/5s8cwnVd5PP5jURsnufC9wMIgohoNHrpazclCAL0+310u104jkMbJERRRCgUQiKRQKFQoM7GN8U//IeXC7DOiq8A4B/9o6UAq1pd/T3H8fD9pXCoXq8jnU5DllfFPTzPQ9M0uK6LTqdDXXd/53fC+Ot/fXn+RKPAD/zAUsjwMgRBQJtweJ7H9vb2uTkdcdwRBAG1Wg3lchm/9Etp1GoxiKKHkxMFP/uzWXzv9zbX/g3LshCLxSAIwoXijKXTVAKVSgWnp6eo1WrIZDIrDtWj0Qi9Xg88zyOfzyOfz1/ptnNRml44HMajR49wenqKaDSK2WwGx3HgusBP//Q3YDZTwPMC/vbfVvAv/8vn56PHx+c/M5vNQlVVtNttJBIJzOdzeJ6HSiWGfn8ZlyjLIUjS8vM6neV9+TrUapf/u+MsYzM9D/i2b8siHO4iFAqh0+kgkdiCLAOStFocC4IAnU6Hup5dVUO4bb70paXr0WCwdIX83u+92DnxIqZT4D/5TwDTXP78q7+6FK5y3Pra4N/4G8C3fuuqYFaWZRwcHABYiiN1XcfJyQn997NNe8PhEOFwGF/72tcQCoWQSqWQTqfPzc9TqRR11hoOhxiPx7BtDqIoIBIJ05jbXu+8WE4QBNqsRgRc5PrIZrPodruIRCKo1+vY3t6GKIrnzv9lROAMmUwG0Wj0XH3y6GjptJbJAJ/8ZIaKvSzLurTGMRgMqKjronrZj/wI8M/+2fK/HzwA/qv/ClhXHlonQv/t3146EQLXPxcYDCbAYjAYDAaDwWAwGIxb4lUJsFzXRbPZxGAwwHw+h+M4CIIAoVAIiqIgFovRYjjj9UE6vzVNu3LhIBJZFksty4JlWXdegLUs4tl0uy9DEARomgbbtunPi8UCgiDQbut135fjOCoQ0nUdo9EIuq7faLH2KpeB22Q4HKLT6aBWq8H3fdy7d29FCJZIJJDL5dBoNHB6egqO4+jCied5G3Xe8zwPURThuhdH2uzu7iIUCkGSJMxmM1iWhcViAVEUUSqVaMF3b2/v0k5TBoPBYDAYbw4XJWotxTMOHGcK3/cgihJkWYGqlrG15cE0TdTrdfziLz6CoowRiXw0zv0H/2C5kP0DP/DR533+88sF6av02xcNyV4kMrDdPv+7n/iJ1Z9Nc+lI8slPfvS78Xgpyup2l5GKF8XvnWXd8IrjOKiqhlgsRseulwmrRqMRgBhEUbxyXP3jP37+dz/5kzcjwDobYUd4771LNwfAR7Fu0+kUT548QTabxXw+Rzb7jfjsZ3cxmfD4/b9/iE9/etk8lMlk0O/3EYlEMBjEIcseFosFeF7A06c8Tk9PUS6XMZ/P4bou+v3+JVHkAWazOURRBM/zSKWujny8irNReYZhwDRNuGd2JmkcMU0T9+7d26gp4jp43vrrxXHO/67bXV4j6wSV5XIImqZB0zRUq9W19YHpdIpmswlJkpBMJpFO5/BX/+pH585oBPzszwLf//3rtzUIlk4ygwHwzd98sWuSZVl0nlEoFKigaTAQ8Su/ksJ8zuPbv93E/v4UmqZBVVVMJhN8/vNJSBKP+XwOSRLx9KmK8ZhHJLKq+CBz093dXWQymUsFU7u7u1gsFuA4Dt1uF9VqFaIo0jnq2Zi3fD6/URzjRfcv3wcURcHDhw/hOA4sy8J0OsXnPqcgCGKIREKQJBEAh9/4jSv/DEXTNOquRZqiUikN0ahyTozz7NnNCrAWC+A//U9BoxJ/4RcU/Of/eQqFwgw/9mMqnjwpQFVD+KN/1MBnPmPjC1/QwHEOSqVDcNwCOzs7yGQyt+YitwmLxVKE+2FPGH7t15bn7vd8z/U+5zd/8yPxFbC8Dn/xF5fX77r773i8dHv8ju9Y/X0kEsGjR4/QarUQjUZpcxnHcZjNZtQ9fBkl2EAQBJBlmbqtl0ol5PN5+nlBsPxuiqKB5zU4zhDR6BAcB4ji1fs9Fouh1+thsVhgOp1SAZaiKCgUCvA8D7VaDc+ePUMikcBisYUgWMbHLhYLuK4HWQ5D14v4fb9va+Wzv/SlpSiKPIf+lX8lie/4DgW5XA7tdpsKMc8SBAEGgwFM06QuYOuaTg8PPxJfActn2m/9FvAH/+D575jLLe+fZxkMlqJx3wf+2B8D/vSfvtnIYsbbDRNgMRgMBoPBYDAYDMYt8Sq0HPP5HE+fPoVpmuj3+7Btm1rtu64LRVGQSqUwGo1upSB825AOPc/zIAgCotHoRrETm+D7PkajEY3KUFX1VgUlnufBdd2N/wZxOnIvWi25Q/i+jyAINnZDIiIgsu9t20Y4HIbjOCiVShe+j+d5qKpKxVq9Xm+tAEuSJIRCIdpZf5X4cDKZ0Ne/ruIv6fKczWbY399fu+AmiiIqlQqOj49hGAYVYJHr/SqWjgrLa+myhYhSqYRcLkcLm2evEdK5ymAwGAwG482gWCxiekaRsW6h8l/9V5exZWfnL77vYzp1MJ3OEAQ+wuEwOG45fhiPQ4hEIohEIphMJh8KZnjw/ATR6DJKaj5fiq3O0u0CH3wAvPPO5du8WACuu8B8vhRAAMsYtW7XRz6/nAt4HlCvA7HYR65aodDy755lnQBrHR+agFL+2/8W+OpXl//9xS8uF16/+7sv/4yLnG+SySwcx0K328VgMLiwgcDzPPR6PUjSPbiucOW4dF201Yemrue4aKh40Ta/7FxWUZaRZ6FQCJlMDn/n7zzEaLScK5ycLM/BT35y68O4QB6GYX7YJOB+OE/wwPPLBoxUKkVFCPV6Hb7vI51Or+wfz/Mwm80ALMUIsixf6VS0CScnJ+j1ejg9PQUAOhYWBAGz2YyKHoiL7IMHD25svgosRU/rpvDPiwUI684JAFCUZZwYiYOvVqtQFAWRSITOmZb/WwqNcrkUbDuFwWD1c377t5fX2GwGhMOr4rAf+ZGPorx+6qeA//q/Bj7xifPbMh6PMZlMEAQBjZ5bLDj8tb9WgW0vP/CrX43gT/7JDj79aRuJROLDqEDxw3mPB8dxIMsy+n0JkcgMwPKeNRwO0W63kU6nEYlErmxkEgQBDx48+NBpWMF8PqP1B1H8/7P35mGOXIW5/lubSvvSknqf7tltYxtjFoMxBgwJYQezQ27Y+SUQuJAQLgGSAAkh5EIIWxJDEi6QEINZDAkXsNlu2GywjbHB2J7NPT29qrXvUm2/P0pVvUlq9Uy3PbbrfZ5+ZiSVqk5VnTqqOuc73ycTDofx+/2dYzLCt75lizX37oUrr+wujut17ej6qoAjEAi4+/7Vr9rH8kxpNFS+9S0VQbDbs25lO3oULrus9zoUxW5/19JPgHXzzaviK7CFPseOTTE352NmJoBltahUGlx9dZAvfCHUEeb4mZq6kLe/PUcyOcSePXt6b+Be4I47VsVXDp/97PYFWF//+ub3vvxl2z2pV/tbLnd/X1VV9u7dy+TkpDshKpfLkclkCIfDFItFqtUwS0tjxOMtxseXyOVyRKNRd7LV+Pg41Sp86EO2w5SDpvmpVnWgPpCgyGlHTdN0f48dJiYmME0TURQplUoUCgUqlSr1uj2RTFFkgsEgsizxr/96kOuuk3nnO1fFmV/72noR8He+I/CCF+xH13Usy2JxcZFsNkssFkOWZdc93TRNJicnicfjTE9Pdy33Zz6z+b1///fuAqxev4PO+1//ui3EXivS9vDohyfA8vDw8PDw8PDw8PDw2CV2W4BlmibHjh0jl8tx6tQpQqEQU1NT7ow0TdMoFAosLi52OqPhnHPOuV84Ya2srLC8vEyz2aRer2OaphsfEAwGGR0dZWho6LTW3W63WV5edh3DDMNwhTdOJ60T+7aTOMK4Xs5DGzEMozOQdeYDB7uNM0u4vnHkqgdOzJ+qqkiSRLPZRNM0FEXZFIexllar5XaENxqNntuLx+P4fPYs70Kh0LfOW5blWuqHw+G+298tnI7EQqFAPB7v63YgSRJDQ0Pk83mSySSmaRKJRCiVSlteE5VKBcuyCAaDWw4MybLsxmx4eHh4eHh43H85fvz4pvcsy+pEX9ki61DIz6tepfCZz9gDjhddBD//eRvDMNF1nWAw4IqvAEql1XuVYDCIqqodVxoFXdeRZZlWyxZbbeTYse4CrO9/3x70brfbLC013W2vFWDdfPM8+/YphEJTfOQjYY4ft0UGv//78NSnwsjIZrFAN6egbnzrWzA1BWNjkM+viq8crrrq9AVYt9ySYO/eIKnUJDfdVGNioszhw+vvOev1uuv44fcHME0V6D863jGUXUckYg9of+c7thjr8sthenqzqMLBeTSp1Wz3lpER2MJ4a2CcAfu77x5xxVcO//Efw1x0UZAXvUgkl1vG77ejyJz6Ewj4O04nESYn7YiplZUVpqamWFpaolAoEAgEOnWtRaMxgixLqGoAVfXh95/5PX2tViOfz3Pq1CkURWHPnj3rns0cB5hoNMrs7CyqqnLXXQtEIoeZnh4stmqrPoNyubsAa2Wl+/LdnMvArivDw8OIoogkSaRSKQqFgivO9PlUvvvdh/PrX8fx+1WuuCLA4x/ffV2vf729/elpeMc7YHzcrmuO+Ars+nb99d0FWIZhYBgm+XyETMbHyIjG8eN+V3wF9nH53OdGKJUkhod1ZmfbSJKMz6cgCGLnnDe5/fZlZLmGZVmuqCudTpNKpdi/f/9Az3aNhsgXvjDFDTfsQZZbvOpVOc45p4ks2zGWiUQCQRD44hfh85+3v/PDH9oiuDe8wX69tGTv8+Rk73bAMDZfW6dOwU9+smURt6TZhDe9abOQaCPHjvX/fGxsc4Rls9nbie2LX9z83uc+J2FZ4wQCDSRJxDTNjqAGBEFGliUKhQiNRoiDB0e3jFrdbRa6pFha1nrHRcOw42x//Wtb3DY+Dk972vrY2l562X7dObWa/dtyyy122/vGN653KJNlmWQySaPRYGFhgWq1SrVapd0+xDXXHELT7AI++cmTPOlJcywsLDA3N9f5XQ/x3e/G1omvwO6Xco653c/Wv7+p1WohCEJPV0bHhSqTyRCPx4lEogiC1On/ElAUnyuIXVqCf/3X1SjbjWUDmJ8Pc845h5AkiVgsRi5X4Hvfi7Cy4ufgwTKXXKIQi0UJhUIcPHhwnWv4+vVsfi+ft//NZODnP7fF27fc0lu4vJZ/+zf43/976+U8PMATYHl4eHh4eHh4eHh4eNxvsWeXVZibmyORSGwSSiiKwvDwsBszoKoqi4uLHDx48D4q8WDMzMy4s8OLxaI7o86JWYvH4+4M3cnJyW2tu1arcezYMTe+zhGkAG7UQrlcplQqsW/fvh3tDAwGgwSDQXK5HCMjI33XrWkajUaD4eHh+0XMWzQaJRqNksvlaLVafTu6NU2jVquRTqcZGxujWCzi8/koFotbdpBXq1V3FrIz492pH2uRJIlkMkmtVuPkyZPk8/mu4iTLsljo9LjGYrFtRz2WSiWy2awb+ynLsrue7ThpNRqNjstEk9EBMiEikQjLy8u0223i8TiGYZDL5SiXy+5M6o2Ypkk2myUej+P3++93bngeHh4eHh4eZ47jsLSyskK73cY0TXcw9oILYnziE8P4fFHSaXjSk9qdmGgRUVw/8r646OOjH52gWpWIx3UURabdbqHrOu22HQHXy6HHcatay/IyfOQjoGlt6vV6x+1IAyx32+12m9/8ZhFVlbnxxiB33aWiKAqGAZ/+NDzpSdDrtrlWg1DIHkTvVa5f/MIWU6yNXNsuvYQX//qvAnAemlamXLadmh72sJNceuk8oihSLBYxDINwOMzY2Bh+fwDD2Hxf/PWv24KWfo9z0ag9SHzDDfbrL38ZPvCB3rGPtZo9EP3hD9vCgomJ9bGRZ4JpSnzta/v5xS+iXR1+brstzL59aZ7wBB+1mobPpyDLEqIoYRi2MOC//muc0dETKIqCLMs0m03GxsawLItarYZpmgQCAUKhIKIodcRXfrYSrw3CyspKR+zQ5uDBgz0nxgSDQVKpFP/v/4n86EfDhEI6kYjMH/+x7ZrSi5mZrQf0K5Xu7/eqx91Ej2ALsCzLdvCKx+OuW85dd8E996g0GirHjqWIRn2IosgNN/QW7Tnir5Mnbee8P/kT+NnPNi/3gx/AH/3R5vctS+bqq/dyyy0K4XCIRz+6wsGD3ZWSX/96Ck2LoWmTBIPg99uiO0EQ8Pl8XHfdhVxwwS0EAibDw8O02z4ajYL73O88mwlClHJZIJXaLIz74hft+DgQMAw/V101wQtfCL/3e/bnmgbXXrsqvnL49rdtMdqnPmVHroJ9vh/zmO7HbaMAK5uFP/7j7sv2YqMQqlKBL33JdhIahGPH+ke59monFhagm1FVL3GR7Z4cRFX9aFobXTewLMsV8vh8PhqN2K5FulWrtqvYIHPZel1jy8u2GErX4d3v3izKvesueOtb7f+bZm9xWz9B6zXXrP7eLC/bblUf+lC3sixTqVQol8vs3buXq6+ecMVXAD/6UZynPKXA9PQ0x4/fQ6FQZHl5mc9+dnMMqyTJCIKIosid3/j+UpFCoUA4HEZRlJ5O2E70bLvdZmrKYnZWQBBssefGfq9bbtF47Wsb1OtQr9sT+RxhJdjHMhyOcOrUBfz3fze5+WbTncR4/DgMD1d5wQtCxOPxvn1qG937AFIpW/T49rd3FzD34667tre8x4MbT4Dl4eHh4eHh4eHh4eGxS/TqgN8pMpkMxWIRSZIYHh7uuVwwGCSRSFAoFIjFYlsKZO5L5ufnWV5eZnZ2FtO0O1Gj0agrwCqVSuRyOarVKpZloSjKwA49zWaTo0ePks1mWVpaIhKJMDk5id/vd+MKCoUCxWLRjcLY63ij7wDpdJpisehGzfUTwORyOVRVJRgMblsUdF+QSqVYXFwkEAiwvLzMnj17unaGWZbF8vIyfr+fQCDA5OQklmUxNDTE4uIihrHaMbsRx3o/EomgKIrbmddrIMQRd42Pj7uzRROJBIFAAMuyqFar5PN5TNNkz549xONxEt1GBLtQr9c5ceIEzWaTUqnkzrZ2BFhLS0uk02kmJycHEvFZluW6OwzieOYsY1kWiUSCdrtNOp1mYWEB0zSJxWLrtttqtVhcXMQ0TVKpFMPDw/f5TGMPDw8PDw+PexfnXrharVIoFCiVSu6gZiAQIB6PE4sVO88VezBNq+OO0X0YyYmRy2Zt0bkoSusiin796+7lWOv4k8nYrkvXXAOGoVOv12k2W2iahqqqKIrMqpDGQhSTLC8f4RvfeCiqWicSCSOKEs2mHYXVy2h2ft4ejP3oRzdHDa7FMGwxSS9M044L+3//zxaS/cEf2IKltZ/3RkKSoqhqA0mS+c1vDvOIR5wiFGoRCsWRpBSjoxKjoyNEIhFqtY3ft7jqKg1d13nlK/NcemmbVmu8E6O9ev+o66viK6dMX/wivOhF3Uv1oQ/ZogvnuMzP2zFNW6Hr9vHy+XqLOW69NcWdd1qYZh3TNDYJ+QC+9rUUT3hCCRABAVGUkGW7LrXbbe68U+TUqSUEQcfn81OvTyKKAS680HZrdZySg8GQGym+EzguucVikWg02tehFiAcjvPd744gigbttka9LvM3fwNvfrP9+aMeZYsAHXQd/vqvbdFFP3pFlPUSYN1xR6/9sc9xKLTqdHvnnSN89rOr1+RGYdLNN/cvG9hOUH/yJ73LecMNtjhJUeAlL4FzzoEbbkhw990tBKGGpun87GdRZLm3FZimaciy0hGDivh8KoriQ9NsoeaNN17Mk588w4c/HGN+PkgoNMwVV9zA8HAZVVUplQ7y3e9ehChGmJpSede7bPcu57h0Ey996Uu2e9zevfDSl26ONnU4enRVfAW2mPHnP+++7Mb26TvfsaMct8Ovf207FDpl/1//q3fsZDfqdduta2ys++e92rDZ2e4CrK0uN1EUUVU/3bqfehgXAbbr1jXX2PX88Y+3r59rr7Xr2/AwvPa19r8b0XX44AftqExVtUW1T3xi/zL2uga/9S07cq7R2Cy+Alu09/u/D+Gw7erUi16iNqe8azlyxC7P2i4uwzAoFAoUCgWi0Sh+v5/bb1/v8N1uC8zOqtx0U5of/3g/stzkda+rdW13HfGiYRjU63V03b6+ulGpVKhUKkxPT5NMJpH67Qzg8/mYmFgfS+mgaW2aTdvd8s47dUzTQhBaHUd0EUXxEQj4+c53RBYW4NOfloAQkQidSZMWIPBf/xXnda/rW4yeDA3BN7+5ffEV9Bbz3XabfX2cd15/cbTHgwtPgOXh4eHh4eHh4eHh4XE/pN22Z4c7Qp6tOpoTiQT5fJ5ms0mxWDwrY8U0TWN5eZnFxUUEQWDfvn3rOnic6LVoNMrMzAzLy8soikI6nR5ItDI3N0epVGJpaYnx8XFisfWzAVOpFIlEgrm5OWZnZ91ohq2i2gYlHo8TCAQYGRlhaWnJfW8tlmWRzWYpFouulbt/kOyK+xifz0cymaTdbnPy5ElmZ2c3uXc1m00ymQzNZpPp6WkSiQSqqjI2Nsb4+DjLy8scO3aMYDDIyMiIe+6deD7HNcuJJonFYj3dnsB2NDt8+DBHjx7F5/NRKBSYn593Hc9kWXZFV/F4nAMHDgw0YFOr1Thy5Aj5fJ5MJoOmaRiGQbvdptFooGka8Xicc845h3a7zf79+7dcrxPhKAjCQAJJJ1JUlmVUVeXAgQOuE9jy8jIrKyuEw2FEUXRjPIPBIHv37iWVSp2V17+Hh4eHh4fH7tFutzly5Ai5XI6FhQX8fj/Dw8PuRIRKpcLy8rLrxCSKIk95is7XvrbzkzacAedvfhP++Z9XRQmtlu2gpWkawWCgi1hHIJsdpt0O0G63kSSJVqtFIGBbKxlGb+eqv/qrraO5BuH734fPfc7+/+ys7Yryz/+8PqaqH6IoEQ6HMQydVqvFv//7bwFWR2zmw+fzoSjCpv1otVq0Wi1M00DXda65xmRk5AT1epxms9kZuA4gCELXaLpbboHnPa97mQqFze/ZbkCbcUQoN9wAH/+4PYj9mMfYIqNuid9f+EIMUawgy865CtDNmUrTBK65xteJsZTRdQtZVggE/EiSzP79hxGENp/6VILbbw+hKAoHD8Kb3hSl2QxwwQV0FXicCYZhYJomrVbLjafXdbjllgg33hilUJC56KIaz3xmjhtuiHLnnUE0DRTFxLLMzjpsZzGwxSIf+ADMzdnOV/X61uIrgL//e7j00tXX2awtqLvuuu7Lb4yPW0ulsv48XXPN1hGIg3DDDb0FWH/zN6v/P3LErof/8R8BJMmOn3cc837yk81OPQC6rmGaJoqi4POtnmRbRKLi86n88pcip07FOHlSpdmsUSzqXHvtfq644hoEQeS7330sklQmEjG5554w//qvQf7yL+319IpyBPjMZ+AJT+gtvgL4yld6f7Z5X9a//vKXt1peXzdBSZZlrrpK4p/+yf781lu3J75yOHastwCrV32YnYXLLtv8/pnoHbt14WSztij3H/9x1VHqhz+0ncUcYduJE/Y19MlPbt7+NdesClCbTVt0e8kldHXgc+glZrz2WvuvF4ZhX8OhEPzXf/VeLpczaLVsR0m7bRCQJLHT3m8WjZZKduzoXXfBj34Estzm/PPtCWR7uqngOvz4xzFuuy2MopjUaiKf+1waRdHx+TaLplRVRdPaqKpKs9lCUUx8vlURlqZpFAoF8vk86XSaSCQycB9Cp7lcR7PZpNls0m630bQ2luVMKrMwTQtJklBVE8PQueGGMD/72frKYR+j1ePUz8VN13s7wiWT6wWT26Fbfb3mmlXBsiTZgsi17bXHgxdPgOXh4eHh4eHh4eHh4bFL7ERnZi/0Tu+dpmkDCXR8PjtKQNd197tnG9lslmazSbVa3SS+Wossy4yOjjI3N0c6nSaXy23pEuWIeLLZLIlEYpP4ykGSJCYmJjh27BjVatUVsuwEgiCwf/9+dF3HsiyWlpbcSDhJktYJjUKhkBsLceTIEeLxONFodMsZh/clU1NTtNttBEFgaWmJmZkZfD57IEnTNFqtFqFQiL1795JIJFx3sVAoxIUXXsjy8jL1ep2FhQUajQaqqmJZFrquo6oqIyO2G4AjTopGo32d3wD8fj/nnXcey8vLhMNhRkZG0DQNQRBQFIVAIEA6ne7EUmzde2wYBsePHyeXy7G4uOh2hjsz9B1HruXlZbLZLIcOHcLn8/XtKHWOgaIoxGIxd2ZrP4rFIuFwGJ/P587IP3z4MPfccw/xeNx15TIMwz12fr+fdDrd053sgYDjVODE0UiS5MZjenh4eHh4PJiZn5+nVCqxsLBAOp12BSUOwWCQZDLJqVOnmJubQ5ZlHvtYkR/9KEkuZwF9rEoAexDVQFFkRLH/fYZp2gPjn/vcqmDJNE00TaPd1jrr6H7Pa4s0Yshyo+N+I+P32/djH/xg723uhPgK4GMfW/86k7Edhy64wH7dT6ixFkmSCQQkdz9VVXXvzzY+qjUaDVqtpjuALwiwsCCTyRSp1eprBq4NwuEQ8bjYNVJrO0KRbliWia4bfPjDbW64QaHRkBFFkRtvtJ0/rrxy83cEwRYZmKZJo1Gn0Wjg86nrnmlM0+QLX7D40Y8SyDJomk44HF43IUHXBe64Y4gTJ4YJBAwajQb33KPyrncZhEK9oyfbbbu+nc58Fud82Pf6FjfcEOX66xMUi6vDqj/8YYwf/nDtc2WtIwrYfA1kMvDqV2+/HM2m/ef329fLn/5pb8HIVrznPXbUp99vC+/m5k5vPRv51KdsZ6utqFTgs5+1/6+qfgzDFlw0GnVUVV3ntmdZVqdNaOP3q52Yss3tULPZoFwuMT8v0WrVkWWZaDSCLCcIh5/ON75xDrreRtMamKaBIAj87Gcid93l59xz7ePZiyNH7GjSfmznGG4UaPr93WMe2+02rVbLFQE6z5yiKDI/L1IqWcRisa6xj4Nw9Kjt7tWNXn1YV18Nz362LTZaWrLjFwMB2x3qdCmVbMFVMmmLaX7wA/iHf+jejm50FVtasmMR1zoQtlrwhS+sX84w7PP4sIf1LsfpXk9rt9ENy7JoNBq0220Mw+iICe0DLEkSmqYjik2CwSCKsip++sAH7OPioGky3//+OM961m/c2N1u3Hab3W/ltF2tltgz/lAURUKhUEcIJdBqtanVNI51VG+OC+X4+Lg7WW1QF/2NQiXH+arZbGAYJqqqdhwF7XLafSkt6vV6R0hc6/TB9Y8XTKW6f/aVr2yOC3Xo9VsxCBv3q922BVgOhmGLYz0Blgd4AiwPDw8PDw8PDw8PD49dYzcjCNd2CFsDKL0sy3KXO1vFF07EQyAQ2FJUFg6HkWWZUqlEoVDYUoDluH81Gg3Gx8f7LuvEyDlCmOnp6YEctgYhFApx6NAhRFEkGo1SLBapVquYpomu69RqNRRFodFo4Pf7mZubQ1EU1+VsbGyM0dHRHSnLTiOKIgcPHmRubg6fz0er1aJSqWAYBoFAgEgk4goBRVHkyJEjCIJAIBAglUrxqEc9ClEUURSlM5PfFkipqorf73e/W6/XmZqaIhwODySskWWZiYkJxsbGqFQqrgBLVdVti+vy+TyNRmNdXKIjHHQ6Ek3THgDL5/OIooggCIyOjq7rVN2IIAikUimq1SozMzMUi8WeEZXVapVSqcSePXtIJBJuHEo4HOaCCy6gWCySzWZpNBpuLGI8HiedTncdtHgg4AgaM5kMrVbLFZ/JskwoFMLv9zM6OkqqVy+th4eHh4fHAxjHySKbzRKNRjeJrxxkWWZycpLjx49TrVYJh4O84x3z/PCHBW688RFUKr0HXzVN76xD2fJ+42Mfs51L1kZvGYYdR2SLx7ceIbXvl5tYli3g6BWfdG8wNwd33207xPRyATpdbOerpntf5/f73Xs/n+88gsEg7XaLRqMOBKjVBE6d6n5/u5WQpBeGYdBqtTquJRbXXguC0HYnNKiqyv/5PzJPexqcOgWxmO321Gza3w8E/K6IpFarUa8XEQTbDcwRlfzkJ+PIsoFhGPj96qaB/mZT5AtfsCde2EIchXbbjs0yTYNGo7tg7zWvsUUeD3+4HRV2ww22G1gqBc96lj1gvmdPd3ccSZKQZRm/38+3vx3jppv6P29alolpWoiitGPPjg6vfz383d/BL395ZmKRhQVbOPOMZ8Bb3rJTpbPFEL/85fa+40SggUWr1aLRaCKKdn2wLDpOfAJ+vx9FUQiFgpv6MWxhX4NarU67rXXqjt9d7vrrH4rPZ7dLzWYDTdOpVCooio+rrvLxuteJ64QuGxkkpuzUqcH3+atfteukQyDAJrFko1Gn1WrRbmvrBDtgC2UUReTo0XkmJyeB0+sXOH6892f9+rCuuca+bt74xsHFpv34zGfsv0c+0o6x/Mxntrfe17/edrd61atAlukZS9dPJJbL2WKu06XV6uW8aFGv1zou2U1M00SWJSRJ6kwys8WFPp8Py7LcCVnApjppT3Dzs7gYY2pKo9XqY+cFbp0RhP4OZZIkE4mEaTTsWFzTNEkmUwiCLQp2+nAmJycJ9rMQ28BGgZjjfGUYJsFgYF1kLtj9SH5/gFarRbPZQJJENE3v23+yvNxbgNVLfAVn1ke7sVk/cmRzfT1xwq5PZ2mXnce9iCfA8vDw8PDw8PDw8PDwuB/i89lW5cFgkEqlQiQS6bt8rVYDbEegszXSTtO0gR29AAKBAJqmDeTo5cwitaMLthahBINBVlZWXAemnRSuhMNhLrzwQvL5PCsrK9Trder1OrOzsyiKgq7r+P1+RFHENE3XuSsQCLixLPv379+x8uwkoigyNTXF+Pg4uVzOFWBJkkS9Xu9YzmvMzMyg67pbhx2nsYc97GGMjo5y4sQJCoVCp1OeNREzAaanp91ZmNstWy/ns0FZWVmhWCxSLpcJBoMsLi7i8/mYmJhYV0d0XWd+fp6TJ0+SSCS49dZbueSSS/que3h4mGw2y+joKIuLizSbTYaGhtz16rpOoVBwHd+i0egmMZ4gCCQSCRKJxBnt5/0Jy7JcV7KVlRVKpRKCICBJErquI4oi8XicRqNBo9HY0o3Mw8PDw8PjgUY+n6fValGr1di3b1/fZRVFIRKJuJMiQiGFhz5U4/jxecrlvZsGTgFM0+hEKPuQJHEgMdRa8RWsnywyiHjFWcayGGgyym7yr/+6eX92Bst1vrIsi0BgvQDlYx+bQBTpDFw3OwIW+/5H7mV7MshWO+dCEGxhXaNRd59BdH11ZN0WQum023Zk5FveorKwYMcw/f7v28IIB1EUMQy9838BXTfcZwRRVBAE3DjubvXnhz+Mr3ttR9dprnOaqnYXYDnOZ7/4hV2mtfzgB/a/kYjtTHXnnXbkWSwGL3gBXHghtNvDfPGLQ/zmNwLBoNm3brbbGpJkTzRZG+W1E+Ry8H/+D+TzZ76uT3/aXtdOXzb1+va/EwgEOhNWRHw+e0KSHasOPp+CJMnIskQwGOzqitdut2k2W2ia1nGCU7tONrPPiUqz2XRdte66S+P979/5iNV+fPOb8MxnwsiIffw3iumazWZHjGYLLhXFhyzL7qQ7u+w15jq2W5VKGNi+W/exY70j3PrVCyeObyfEV2u5+Wa7He0Wh7oea007ZLuC/fSnCnNzMv26Bnq1z6XSekHc6dBud19/s2mL6BqNBoIgdgSEa9sPFV3XabWarsNZJBLp2sY4bePs7CTnnDOLKA71LZOua+51tdVvgShKhEJhTNOg3daYmjqAz2f3Nw4NDa3rmzt1yhZaTk3BRRf1XudaAZauaxiG6cYddruHcFBVH7pu9++1262+AqyZGbj+evjVr2By0hYFDg9v3a6dqQDrjjvgP//TFvz16rb8//4/+Ou/tn9DPB68eAIsDw8PDw8PDw8PDw+PXWI3xwIkSWJoaIhKpeJG8fXroHBmvPv9/p7OOvc1TmfpoIMoZ7ujVz9EUSSVSpFKpdA0jV/96lek02lWVlYYGxsjkUis63yr1WrMz89z6tQpBEEgGo2e1W4+siwzMjLCyMgIACdPnqTZbLK0tESlUnFnkztOUYqiMDIyQjweZ2pqilgsRrPZdAVcgiC4TkaJRIKpqakzGlgCe1Z1u9N7rCjKluszDDvuJJ/PY1kWy8vLGIZBMBhE0+xOTmcdsiwzNTVFoVBgbm6OiYkJN1qyF4qicPDgQde1KpvNcvz4cRRFQRAE2m2709KpHwcOHDhrxZT3JidPniSbzXLy5Ek3QjQcDrvRkOVymWw2S70zKuTz+dx6ebZyH48je3h4eHg8wLjxxhuZnZ2lUCjQbrcRRZEL+4wMhsNhMpkMkiSRTqexLItw2KRer3fumRT3d1bXNdepwufzETjtfB/BFQPYLkL97+9XnwPs796X7I74yp6c4giM/H5/32ceVVXR9VpnQL91WvfJuq7RarXRda3jQKTTbDY7biqWe35F0Y7k03WNRqPpbmtmRui4Gtkxc+edB2B13Ils9xPH+UpR7HU4Yi9Jkjv7ayFJmwfof/Sj9ZMoBEFEEGwHpLUOQadDpQIf/ej6926/Hd7/fvj7vx9mbq6CLDdoNhudeKzN5bOvA41AwHZr6hWheSb893/DFqbPA3M23WuqqupG1muahmWZgIAk2fGVa2MJN+JEm1mWhc+n9BV32O5DIoZh0mw2CQYDfV39dovrrhN4+cstvvSlzW5erVazsz9sElwKgn19BYMCt98e5uqr4ywvG0Qi1rb7QxoNePvb4bd/2xaDNZtw4IAdBbiVQMURYe00N9/c71OrI2hqY5qG60RtT7qRuftukZMn/ShK90lzvdroH//4zF3rSyV45zs3l7fdbtFutwCBQMBPt98pW1znp9FodkSl7a7P9/a5V7j11inuucfHk57Uu9CrolS1b5zvRkRRwu+XGBnZS7c5a7feakeYOm3Hy14GL3lJ93WtFWBpmo5h6FgWA/wu2QLcVqvdiSe199MR6Pp8qruOT35y9VvZLHz84/BXf7XZUW4jZ3K+63X4i7/oHhu6kS98wRNgPdjxBFgeHh4eHh4eHh4eHh67xG53bKbTaXK5HIFAgNnZWfbs2bPJqcmJ5mq324yPj5NKpXY8EmGn8Pv9BINBcrmc26HWC8MwqFarjI+PDyRC8fl8qKqKpmmdGfr9O1trtZo7g/ZMhT5bkcvlaLVaZDIZxsfHu7o0hUIhpqammJmZIZfLnfUCrLVkMhkymQyzs7OYpsnk5OS66D9d18nlcszNzWGaJpIkcf7551MqlSiVSq6LUTAYJJVKnbEbWaVSIZPJUCqV1on9otEo6XS6p0DRceNaWVkhn8+zvLxMKpVieXkZURSRJIlAIEAsFnMHyRKJBNVqlUqlwsrKSl8BFtjn+dxzz2Vubo5wOEyzaXfCA50O9yDhcJg9e/ZsKwbggUqz2SSXyzE/P4+iKOzZs2dd++a4X4XDYWZmZlheXkZRFNLp9FnbDjocOvQuNM0eLZBliYc+9L6LVvLw8PDwuH/zkpe8xL2fAFvw8JOf/KTn8mvvwUdGRpAkiampBr/4hUq7rdFur44+2vc/dixeMBg87ShAWZYQBLHjjrS1+6ym6R2hju16+UDEcfoVhEEHruWOe8j2zoETDajruiuosyyTer3hRsTZwivJPdaSZJ97RbEj4FqtlhtJKAgCtZod9edET9kRiQKBQHDd+TJNk3bbjp1ynIe3M8Fgt+bhGIYtUAGpUx6LRqNJrVZ3J27YIkQDTbMdXvx+tROhvnsTJFZWdm3V9ymOuGi7z3mmaWGaphtlucVWEEUJwzAxTWNHnPNsV6q2KxQRRbEjwOtdllOnBG67zeLqq8V1kWaOSFHX7Qk+vfpC6nWF6657GI1Gk3DYFts4x812I2y7160giCiKjKL4Nq3vrrvsP4dw2BaX3FfivN7uV6siTsdNG3CFoE67Y1+DBn7/ZhFwLwHWpz515uX+u7/b/N6qeFbvKb5ykCS5E7mndQRYatflVVWl3W6Tzab4/Oc1/H5t0++tLZptdeJTFVR1+23RnXfCox+9vm01DPjHf1xfN669Fp7/fOg2B3StAGs1mlXsul/rsR3OGo0GqurriDFxhXZ27Gx3R7zbbrPFV7lc/y2cqeBuEPEV2M5cV18Nd94pkEgEeM5z+uRgejwg8QRYHh4eHh4eHh4eHh4eu8Rud16FQiHGxsYwDIP5+XlOnDhBJBIhHA4jiiKNRoNSJ3dhamqKeDzO2NjY7hbqDEilUhSLRVcc08+pq1gsIkkSkUhkICHS0NAQ8/PzBAIBCoXCpui2tei6TrlcZmJiYpMT1W6QzWYpFAqueKcXsiyTTCbJZrM0Go2BoifvaxynqEwmg2EY7N27d9MAkuOWJcsyCwsLBINBSqUSyWSSZDK5o2Vx3JLK5TLFYtEdjFRVlXg8TrFYJJFIsG/fvk2DeZIkUSwWOXXqFPl83u38dXAEUvV6naGhIaLRKGBfp7VajcpW0zE7+P1+Dh48SKvVIpfLrXPpGhoaOgNniQceKysrbrTgwYMHe16rsiwzOjrqugXm8/mzXsB46NA7aXVGC1RV5VnPuv85/Xl4eHh43D9xnK7A/g2dmJjgnHOqBAIgy8qaiDoBURRQFHuiw5kIoURRQpZtoYATPdRLgGBZjiuUuqXY4f6MZbFm4HprRFHqOAhZW05mWd2GRa1Wpd3WXBGUfY6dCPAmoiiiaTqiaN83r3VdFkURv1+l0Wh2XIza+Hz2RJd83qTVatNqNREEoXMPu75M9vfte9tqtYogiLTb7YGcaS2Ljihsd8//qiBMcKMYGw17MN0Wx9lCB1mWCYW6R+V57B5ONR9MULUzDtqOW5Udg2lgmo5QRHRFWH6/v2ub2GjAhz+8+X3HAcyJseyHJMmIooCm2fVRlmUajUZHDLjqECWKtqOQKDZRVbWvuLFatUU1Z5M7GkC93qDdbnWuOQG/37+mfbDQNL0jXrNFZ07c5Fp6CbAEYXf214lYBfo6uDnIsoKmtV1Hv24OkKIouROwBEGg1WrRarU6dUzo1EMLRVFQVZVAIHBaEwnf/36YnoY/+zOo1eC66+DoUVheXr9co2GL5oaHN69jrQBLEBx3y60OtEWj0egI7Vrub5EkSViWRbNp/474/SqmaXb6PNdfR7/7u1vv309/uvUyO8XVV4NlCbRaASoVkXe9697btsd9jyfA8vDw8PDw8PDw8PDw2CXujc6r8fFxwO4gr1arroDJsuzOl1QqRSwWIxqNcuDAgbN6gCKRSOD3+0mlUiwtLSHL8jqnJIdyuczKygqjo6MEAoGBREg+n494PE4qleLUqVM9xU6GYTA3N4eqqkQiEYa79SjtIO12m1arRaVSGUhsFIvFWF5epl6v3y8EWKVSiWazSalUYnR0tG8n4NDQEIVCgVKpxMrKyo6KrwBmZmZcJy5N04jFYu426vU6mUyGbDbLnj17ME2TQ4cOreucr1Qq5PN5ms0my8vLjIyMkEwmkWXZjSd09tfpJLQje8Koqup2wg6Kqqru9e3RnWKxSLFYJBwO941gBTtOSZZlV3x3tguwPDw8PDw8dortiA0Mw3Dv2+LxuCsgGBsLEwqxIf5JdJ2rdgI7Rk/rRNvV8fsDm55dTNOg2Wyucfi49yPE7i0EwT5324lnd071oOe81Wp2BEV1JEl23VeazQaCsOoibAup2jiOY2vPy6oYxHZHc8QP2WwN0zTQdaOr+GotquqnXrdFTXbUn79vvbJFWlJH7HJm7riD4Ig+2u0WmiZ3nvMtbFclsXOcfDt2LXgMhhMB325rrgCnVz2zHdbsZWzXodMTypmmQbVqO8a12y103VgjAqMTc+hD13VCodCm59877ugtLLUscxuRcSKmaWIYOtVq1XX6th2lRQRBQNNsEaQsyx1xj0Ew2NuR+YYbbHe7swXTNDoOek0EQSQQ8DPUmCdRW0YAykqSTGC6I0Cr02q13etxbT34t3+Dyy6DjY/Wu+WgB5YrgBsEUVzbzvdu7xXFRygkIAgiPp+Cruuu+5qi+DqRg7ao9Uxcw0+ehNe9bmuBWrm8tQDLcU603eqMnvXbjr/V3d+iYDC4wfHbot22Y28B2qUs+1hGNeq0pCArkUPo8tk7Ue3HP1bRtLPr+vLYXTwBloeHh4eHh4eHh4eHxy5xb80eHB8fJx6Pu9Fo5hpf7Ugk4saqnelMz91GEAT27t2LruuYpsmpU6cIBoPEYrFOpIZOsVik0WgwPDzM0NAQe/fuHXj9ExMTVCoVNE1jYWGBcrlMIpFAVe1ZdJVKhUKh0IlZmSKVSm0ZGXemOKIcwzC2FJAAbtSdM7P1bKdSqVCr1QBcR6heCIJAPB6nXC5Tq9UwDGPHImUKhQLZbJbZ2Vn3/K7tDA+Hw6RSKRYWFjh16hSSJLGysuIK8CzLciMUBUEgGo0iiqI76CdJkuuAlcvl3O05TlqRSOSsFj/eX3FcCAZ1BfP7/W6cz/2Ns7z59vDw8PB4AGBZFouLi0iS5EYzOziaf1GUds3hx3busB1ams0m9XodSVqNvTMMe7BZlm2h0HonlAcezsB1q2XHrG11L6nrdiSVLA92fizLcmO97EH7VXcce7Dccs+3qqo0m003cm+j8E2SHIHH6nNoJtNC00xEceuYSMfZxHGnbTQaBAKBroImW2RiEAgE8fk2R6vtFrIsI8sylmV2RA9WJxrLdqDxuPfx+XwEAgGazaYb39erTbD7GIxO++E/LYGKE9epabYoSJbtCHqnftuxc1rHEc6+FiKRzU49O4lTjkajiaLIm8SLhmHQatkOUoJgP7f2i6Y7m7oYnDhFy9C5pHULjzn1HaYbRxE60XSWILKk7uFnQ7/NzfHHU21rqKoPTdM2CTPf+Eb46Edhz57V93ZrX23XJzsisZ8ocLUc5pp2rP+ysqwQjcqu85fT5jpulP3cI7fLVv2Z110HBw5sfk5de1x9PoVmU0KWpU7E4ubndjvKU+tMYINgsJuAzI4pTbfmuOSe/8ujGz8nKGiImFiCREsK8KvkE/nFxHMpBKdOb4d3mcVF2Lfvvi6Fx73FA/fu1MPDw8PDw8PDw8PD4z7m3rRvDwaDTE9Ps2fPHjd6wukkPh0cS3PHSWv97LPdw3HqckQuxWKRlZUVV4wTjUYZHx8nEAhw8ODBbQmk/H6/62oUCATI5/PMzc25sw19Ph/JZJJYLEY6nWZqavCOm0aj4caFKYoycLmcgRRJkgYShdizXA1XiNVrmbXrvi+xZ2XaHd2DdAQ6blLAOlcpZ5+DwSCJRGLbnYorKyvueqanp7seO1EUmZiY4MSJE+TzeUKhkCvAcuIKNU1jcnISRVE4efIkkUiEWCzmHmvn3M/OzjI0NIQo2hES0Wi0q5vbgwHnuFWrVcrlsjszW5Zl91rrF4fRDzv6SBxYjGgYxr06UOfh4eHh4XE2YlkWCwsLrvusMxEhn89jGgZTjQbxT38afWmJQqOBFQgQmDyfsdKLWYyev6uqYFt0I2BHKplupBbQEQL5kSTb4WNQ9ytZhvuh9hqfz0erZbt9tdutrgPXDra4xH5uG1RYousahmGiaTqBQP97MefZQ9N0JMkWOWwWCqx/+C0URCzLGPiZxBaP6Yii7apWq9Xd51lBoCOwsZ9z/f4AiiKf9j3kmWA7v933z1ke9jXi8/lQFB/NZoNms0kg4N8U+2YYBs2m7bDm9/tRVd9A0XAbabcdl56mu+21OBN0HEGUJIk0m82+rlOr37XFjrquAVu1bVanf0QALJpNDZ9P2RS9B3Y/w+SkTLFYp9EQ3Yi++8PzkKZpCK0yr134KBc0bieu5xjScvgsW6ipCT5S7SUmmyd4ROEHfHLkzWia2lWApevwta/Bm960++W2o/PkjguZvuVEO0c8u9FdsDcCiqIMNIFvN7nuOgiF4BWvWH9bsEaH67p1GYbacSlrdn67V7+w6nzZJBi0Y1y77dsF+R/ygrl/ItTOkTRyxIyy7dKFQEWOE29luHjlO/znwT/myPAVu7jnp8f8vCfAejDhCbA8PDw8PDw8PDw8PDx2gDe+8RvccMNqT4MgiPeqAKvVarkiFVXd3KFmmqbrytNoNFwBRDweJ51Ouw4yhUKBTCZDtVrFNE0sy+rMklRJp9Ok0+ldF/bEYjEuuOACVlZWyGazaJrmfubz+Uin06RSqdMSl4VCIc477zwymQyhUMgd1BEEwY08TKfTDA0Nbbkuy7LI5/OsrKxQq9VcAZUkSfj9frec/WZ8Ox234XCYYrFIPB7vu81KpYIoioRCoXUir0aj4TqgOYNUiqKQTCZJp9NnZEF/JkiShCAI2xLIiKJIuVzm7rvvptVqUa/X0XXdFWA5dXFsbGygjmMn4rFQKKyL0umGIAgkk0kymQzpdNqNeczlcpTLZWRZ5pGPfCQ33HADlUqFlZUVGo0Gfr+/08Fquys5bli1Wo39+/e7TnQPFgzDIJvNusK3ubk591w6QspQKEQsFiOTyZBIJNi7d++225ZAIEAgEKBQKHRid3rXB3uwpEEymRzYMeu+4t787fDw8PDwePAhCAK6rjMzM+O+J0kSCcti+gtfILawQKDdRiqVEAwDS5KIHjnOyxdvZjZ8Ll87989oqDsbFb0Wx52m3W6jae11kwsUxb533s49w6c+BW9+M1Qqu1Xi3cHeXwVVNanX67Rara7CJ13XaTZtQYgkDR7JZxgGpmnfo28Uo4iisOYe3nZwsQVYbSyLTfddhqEjy8q6yK1SSUIQ9I4LzNZYltXZBxlFUTAMvROrZostBEF0Y7ZkWSYYDN0vRCQeu4c9sSpINGpgWSbNZotqtdoR89l1WtM0NE1DkiRCoSDhcPi0J5fZEZTampi77th12I5GdFzTtoqn9PkUNE2h3W5jGHpfgZim6YCwLuauX3ne8IZFPvnJJPfco+Pz2cLW+6p/YDtYhs7LZj/CRfVb2Ns6AUBeTlKTo1hA0KgypOVI6isoZpvXLXyIT+17D6bZ/VnvN7+5d8oty3KnLVY6cam9J6NpmuaKZ+32/f7FV78Koggvf/nqexsFz35/oBP/GaDRaKLrtY7gTMSyLKrVGo1GwxXVOn0razlc/DkvOvUJxlqzDOlZSmKMWf9+TMmPYrWJaVmm6neTV4Z57tEPco0YYCb1mN0/ANtgYeG+LoHHvYknwPLw8PDw8PDw8PDw8NgBzjvvSRw7tv693R5E13WdbDZLNpt13ZfA7vBJpVKu8KZSqXDixAlarRbFYpF6vY5pmiiK4jpMxWIxLMuiUChQKpUoFAq0223AHpCJx+NUq1Wy2SyHDh3a9Q47RVEYHx9nbGzMdcxxYt7OtKPd5/MxOTnJ+Pg41WoVXdc7sRf+gYUZpmly/PhxCoUCxWKRQqHgCsW2c7wEQSCVSrnnqFqt9nRKckQtsVgMv99PNBp1o/Gy2Sy1Ws09b87+VCoVlpaWmJiYYHR0tO8+2TODm+6xXnUgOH0coZhpmn33zaFUKtFut8lkMrTbbQqFguug5QjcYrEYzWaTarXKwYMHtxyAsyMp7OiF8fHxLcscDodZXFyk3W7TarWIRCLu/wOBALFYjEOHDmGaJgsLC+4xco55MBikUqm4orOxsTFCoRARJ7vnAU6z2eTo0aPUajWWlpa46667KJVKlMtlV5hYr9eJxWIUi0X8fn9ngFXj0KFD2xpQTaVSlEolVlZWKJfLxGKxnsvm83lXZJlKpXZiVz08PDw8PO6XCILA9PS0K0wQBAFfrUb6gx8ktLREcHERwTDQw2FMnw9B0/DPz7K/liWgV3j57X/E5x7697sqwnIi8XbC4SiVgr/7O/jAB+DEiR0o3L1IIBDAMAyCQWfgWusIneyBa123B+99Ph+q6tuWKMkWUtF1ecd5ypms4ri5rP2u89IWcm1236pUfMiyHZM1iABF1+1ow0Ag4N4fttua61bslEtVfR13FE985WE/20ci9jOmLFfdWE2nL0OWpc4kHh+hUIhoNHZakYCGYbiOcYP0hSiKz+13sQVP/V2tFMWHKDY7kW1NAoFA13Iaht4RY6od8Y7tuNTrenjrW0+RTOo8+tFNZmYCGIaOru++AEuSekf8fexjEAzCa1/bfx0X5n/I+fXbmG6doCkGmFP3Yq1pR1pikIKcZrQ9x1TrBG1L5HHZb/Dz6Cu6rm9+HspliEZPd68Gx+/3Yxhmx9W7jqqq60R1TgSspmn4/SqSJA0snj3b+PKX4cIL4eKL7df1+vrPBUEgHA5Rqwmuy5um6RiGLSR0JpECXaOFJVPjyvlPMtxeIG4UOK4epC6GkEQRWZZpE6SmxPHrFfY0jmE0ZZ5+4mNclXwkpnD2yGDm5rzfrAcTZ0/N8/Dw8PDw8PDw8PDwuB/TrZ97NwVYtVqNY8eOUa/XKRQKVCoV1yUoEolQrVbJZDIMDQ2Ry+VcNxpZlolEIkiSRLPZ5NSpU66oyRGMWJZFIpFwxTKNRoNCoUChUGBiYgKAc84557TjDbeDI2rZDZxouO1iWRYnTpwgm80yOzsLQDweJxwOIwjCto9XKpVieXmZdDrN3Nwco6OjxGKxdYMczrlyBFvDw8MIgsCJEydYWVlhbm6OZrNJOBwmHo9jWRa1Wo177rmHSCSCaZqYptlVgNRoNMhkMnbsTMf1TBBsW3tHyHe69vaJRAJVVYlGo2SzWUKh3oNC5XKZXC6HqqqdgRrLjYSUJKkzO7JKLpdjZmbGdXHbv39/3zJYluUO3AwyIOUsY1mW67iwkenpaQCGhoY4efIkjUYDVVVdRwbHOW50dJREIrFlGR8otNtt7r77bjfec3l5GbHTMeo4XDUaDXRdR5IkhoaGaLfbnDx5EoD5+Xn27Nkz8Pac+pVKpVhcXESSpK4iv3w+Ty6XY3Jy8oxmvQ+CE5vptMeBQIB4PO45NHh4eHh4nDU0m00e97jHrXvParcRLYuVaBQ9HscYH0fouIZaQMsw+M0tGZ5V/glm+Qas5avRBfv+UHCXsl8N8gj0W781g6oOd/1M1+tcd93OiKUvv/xnwIWMjsKHPwzNJrTbq24d118/iqaVz3g7l1zyn6TTv9Xz8//+74uoVo+e8Xb27/874vHndhx8beGFoiiuQ+5NNz2dfP7H21pnNxfR6el3kUi8wh7cbrdRVTsS0lnuyJHXk8v915rvQy8BSL/Px8dfy759f+lOZJFlGZ/PFioEAjInTryVmZl/2tb+dGN8/IVcfPFne35+7NgHufvud5/xdpLJx/OYx3y75+enTv0bt9/++2e8nXD4HJ7whFt7fp7JXM9NNz33jLejKHGe8pTeti2l0m38+MeXnvF2AJ761DyS1P25v9lc4nvf6/08JQCCZSJgup0wliBiItr+bWvq9xVX3EkwON1zXf/3//Z/TtjKddfhoQ/9LwThHEzTWicidPj+9w/Sai1tsR1Yf91Ym0ST+/b9I+n0M7quI5XS+PM//12OHDkCCNiXmTN5yHYuGtComgsu+BjT070VUz//+XNZWbl+y/X4fPCQh9j/17T1cXUAhw69i8OH3wXAY/LXM2TkESzLFV/9XeNOrtcWN61XwAI9gzlzI/rs23puf3jYcWx6NfCJnsvddddfcOLEhxCEzWXcDkNDv83+/Z+i0WgiCEJnspGFYZiIokCx+HlOnnzv6W+gQzz+SC677Ic9P19Y+Aq33vp7Z7ydQGAPT3rS3Zvev/VWW4D13e/+mI997CkD9YVKkoVhrPa9wGrdFgSFxz7W7iM4r3gDUa1AUsuw4NtDQwoxb9R5XfXnm9YpAIK2jFn/NcY3v4pJf+GvgMX79r+LSa2MYJmIyRg/U5/ETOrRmIKMppW5/vr+ExkHYd++tzM39+dnvB6P+w+eAMvDw8PDw8PDw8PDw2MHuDfH1huNBkePHiWbzbK8vIzf7yeZTKIoCrquUy6Xueeee4hGo9x5552oqkqz2ewq7NF1nWPHjrkRJCMjI1x00UXrXGj8fj+JRIJMJsPc3ByyLLO0tMTk5OS9t9NnEYVCgXw+z+zsLH6/n/Hx8XXHS1VV4vG4e7wURel7vBRFYf/+/e6gxvLyMisrK66gq9lsujF3Y2NjpNNphoeHXfezkydPIkkSBw8eXCfySiaTNJtN5ubmmJubQxAEotHoOoGKIyKrVCoUi0Wq1WonykAkFotRqVTIZDIcPHhwS/eqboiiSDqdptVqMTMzw6lTpxgbG1sn6LIsi1KpxOKi3ZFarVZJJpPs3bt33XKCIBCJRAiHw+4++Xz2zOJ+ghpFUZAkyRX/OLMre9FoNNxISmf7tquASqVSwbIsZFlmenoaRVGIxWLU63Xq9bobZ2maJqlUipGREc4999x7NeLBMAxyudy6qNFYLHZaYsPtMjs7S7FYZGFhgUAgQDqdds+5s/12u+3WcVEU2b9/P4uLi2QyGfd66hcTuRZRFNm3bx+GYWBZFqdOnSIQCBCNRt0Bw1KphKZpjI+Pk0gkXPHcTlMul1lcXKRarboiMzsWJeA6f42Ojg4sxLrttteh63bkjiRJVCo+fu/3/nlXyu7h4eHh8eCj0Whsek8A9FgMc8+eTVIZQZJoJkdoLBwDTLBMsPRN6xiUZ//mL/GpI5xIPJK70ldgyOuFF6a5uXynR2cU2LIwsiuIpRLBUAisMRAEDKOxI9uyrP4j9IbR3JHtKIpMMBhwB7ftqHgfiuLr3IO2Tms7GwfLLUtHVVUsy6LVsmg2W+i6jt+vIooCptlet52tBtt7fW6aTsxga40LzFqXGH1Hjptptrco305tp7XFEsYObae51RI7tJ3+z01g7eC1ulVZtrmdNXVuff3rX1kH2c4g4hLLskWSvcSHlrV129NrO+vfN7sKvMJhgxe9aIV3v7u1zi19LYOKr+xt9m/vB217mltUXcuyxZiJ2kmmWycY0nPkpCFMBARAw6RFv/bWgD7lcA7F976ncd553Ze5+GIoFjWOHj3zui0IJoGAH1X1oet6Z4KXgM8nIUkSpZLwgGh7nIjfY8dMDGOw7XQTtjlVWRBWK+fD8z8gpucxBYmynLB/bwVhi3pgwpbHxObSpa8zYrZQVQPaPqZbP6Y4M8z3pl/DbxIP36F7BI2FhfXukR4PbDwBloeHh4eHh4eHh4eHxw7Q7SH6TGbK9WNmZoZ8Ps/S0hJjY2PE4/F1n8fjcWq1GnfccQfNZpN6vc6ll17aNZ5LlmUCgQDtdptardZXnDA8PEyz2SSfzxMKhTYJjx4srKysUCqVsCyr7zEYHh6m0WiQy+W2PF7RaJRDhw6hKAqJRIJSqUS9XseyLILBIGNjY8iyzPDwsOtilclkKBQKWJbFnj17uopW/H4/09PTHD9+3BVTOUIqx0lqcXGRcrlMJBJhcnISSZJot9sUi0XuuecehoeHsSyLc84557Scg8bHx919mZub49ixY4TDYVRVxTRNN64vGAy6Vvyjo6M9XbcEQWBsbIxjx45RrVZZWVnpW2/tyAl1XeRdsVikVqu5ordAIEAikSAYDFIsFgmHw/h8Plc0lEwmyefzLC8vu1F3siwzNTVFs9mkUCi462t2epXPP/98LrzwwntNfGUYBvPz8+RyOVqtFrVazRWLhcNhV8CXTO5OXFCr1aJUKpHNZonH425MpCRJ6+IXfT4fw8PDLCws0Gw2KZVKpNNpZmdnabVa5HI5hoe7O2J0IxqNulGUsVjMFUg6LmSxWIx4PE4wGOTgwYO74qi3srLC7Oysu+12u40kSa6DWjwep9FoUKlUBorNBJif/491nb2ZTADwBFgeHh4eHruLMTbWM9gtMaRDbzOcbfGw3PeJiQEuyP+QJ5/8NDeMXclNUy/d8ZFJRW9Q/PznMb/xDcT5eff9Pzw+xc3J3+HbA/l1nT3Y97TxLV2ndgJZVrDnLQg0m41OlJqOKGqd6KgzR9dXxVeKYkco3p+jBXfJuNnjfogd3WlPGNl47x+LQSi0tRhpEERR3BQp+Ixn5Lj88hKqev9q39YSbSwhYeKzWlTlKKZlIrL96MhetPtoMt/7XvvzH/3ozLfjuK47kb+maXXcxyR8Ph/5/FYix/sHTl3+6U93ft1DWoaAWacqRVzJ1U7+Sow1ZvDLEXyGia+8QkhbJN8aJnb0b5H3/B69PQ0HJ5UyeO5z7T7iAeeaedzP8QRYHh4eHh4eHh4eHh4eO8C9NYupVqtRr9dZWVkhnU5vEl85hEIhVFXlxIkTDA0NdZ0VCbYDVr1edzsGDcOgXC73XO/Q0BBzc3O0Wi2KxSJDQ0M7tGf3D1qtFtVqlUKhQCKR2FJIkUwm3eNVKpVIJBI9l41EIlxwwQUUi0Wy2SzNZtMV0CQSCWKxmCtKqlarbvxkMpns6xikKArxeJxCoUAsFkPTNERRZHZ2luXlZarVKnv37l0nTAkEAsRiMcrlMgsLC0iSxMmTJzmv1zTRPgiCwIEDB1AUOyKlXq9TKpVotVoIguDGDGazWXw+H5VKZUu3JkdUVCqVKBaLfQVYTmxjqVTirrvuYmlpyXXOcqLwAoEA5XIZXbcHkw4cOLDuuMbjcfx+P0NDQywvL6Oqqnu8HGET4MbpDQ8Pk0gk7rXrwzAMjhw5QqFQYGVlxRU+OWI6URRdUVSr1eoaRXmm5HI5161tbGyMEydOUKvVusZOOsevWq1SLBZJp9PIsky1WqVWq21729FolAsuuIBsNks4HKa9pkc9EAgwPDzM0NDQrghGi8Uis7OzzM/Pu+5t8XgcWZaxLItKpUIul+Oee+7BNE0kSeLAgQM7Xg4PDw8PD4+dQOgTOx30b8MuZQv8Ro2UXmKkNUdZSRA6VWKoucB1h9+6Y9sAeN6d74VPFJBWVpBLJQTDwJIkHiYvkpid52+MBtqObvHe4d6KNrZdZAUEwY65tyfv+Hcsil4UJcLhEKIoEQoFB3ZBPV0OHIBLLoETJ+BnP9v59e/CLXZPpnM/5+LFbzBdvQPVaNCSApwKn8cvRp/JirVz16rH6aFpOsGggiiuurql0/DEJ5o85zkW55yzM9u5IvufXFj8JXXfEHcmL+N49OHEYsb9WnwFIGIiYNm+V6KEIIiYptGzX+vsRkBRbKfCByqNBnzqU3D8+M6vW7RMsMAELNNEFKWOH9rOcE/4fMJSgPSwRjCgkT9aZ6Q1h89s8qTZ/8P/3oFtXHZZmxe+0PLEVw8iPAGWx45zySWXcOmll7q53R73LU6Hv2ma7uxbDw+PBwdeO+zh8cDH+50/uzBNAdNcP7ivaSaatrMdRIuLixSLRVqtFuFw2BWMbMSecSnRarUwDMMVJmzEiU0D25mmWq1SqVR6xs2pquqKtmq12jpnmwcDtVoNTdNoNBoMDw/3PP4OPp9v3fEaJMYvHA5vuVy5XKZWq9FqtQgGg1uWIxAIkM1mabfbVCoVGo0GtVqNbDbLnj17kGW56zqCwSDxeJylpSXC4TDFYpFQKLTlPnRjYmKCZDLJysoKoVAIXdcRRZFAIEAqlSIUCjEzM4Msy26d7IeiKFQqFZrNJu12u+9AVCQSYWlpCUmSuOOOO1AUhXQ6TTAYRNM0V/CmKApTU1MoikIymVx3PzU6Okqj0aDZbHL8+HFisZgritN13RWDhUIhkskko6Oj91r7fOTIETeOMhAIMD4+7rqVmaZJuVxmZWWFSqXiRuOlUqkdLYPTdkiShCAIGIbh1qlu51NVVarVKq1WyxVMtVotms3mad/HJpNJ97w5YidngNAwjIHq1XaZmZlhaWmJUqnE1NSUK8xz9j0YDBIIBJifn+eee+5BFEWSyWTf60jXu9dl7/7ew+PBg3e9e+wkgUCg/wLtNpgmAmw5uB0QhPX5V537L9Na/9slbHCWErCwOsPpAAvBAzQEHyG9xHBrjj21u7GwyPonuHH8WYjiFmXuwtqiCVjIpsZw4yS+O+cxfT60RALL50PQdYKFEgcbvyJggY6IISqYnJlQu989nyj61+2TKMJGrVuPlLANCJu289KXmvzsZwKFgoAo+roeO1ledd3otx3n/Gua4UaTS5JEPB7rxB36EQQBRQmc1jnaiKKoBALBTmQ4GMbGYyhv2o6igK4PFgPn4PMpfPCDGvv3268//GER01xbZ6Uz3h9FgXjcx9iYwfx893s5yxI2bUdV7Si4LR7nXAQsUq0VXnLnnxPR8kS0AhIGJhLDjZMcKt5EQpC4SfS719sgiJgIluleuxYCCjKmYfScbWZH1w9+3Hw+e1Xd9td2Ju5+DZmmiSgGEAR7HQBWqwXmall7sXZ/dNGHaVpbXKur+7OxuXOw99vCsnq1GRaWZUeGKoqCaVpMTpp8/OOr2w0EAgQCgQGv+/X7I1k6Qif29ILarTxBOEpLUHh0+f+R841RvvW30S+2J04piuK6YwmC4P5txDSh262H7dYEIG5x3Na3PbJsHzvn8UuS7Pd6YVnO9mX7+VUZwkBGFxXCZo22HMASJRRE1B5ttYiJuaE973UOBUHuuj+PepSFppluP4Vl9XfL2gpBULboD+jd9oji4K76oqj23U63tmcj/a7P1e34u27n5pud7Wy+hkRxc9xlv98k2+HRR71eQxBE8kRoohA3i4iCYK9T6F0PnGveFCR0Yf0PrWQZiJaOiImFiAUYgoQFyLJJMGSyGB7ipBRkb+1uou0sPkHetJ7tIAgQi0W9vvuziFe/+tV89KMf3dVteAIsjx1HVdWBBhU87h12e9aIh4eHh4eHx32H9zt/9rMbz9ZOpGAgEOg789jpPFdVFU3T1jnC9EIUxS07BZzOOsuy7qezD+9ddmt2umma7vEfpC1wlnHObzabpVgsYpom7XabVquFLMuEQqFN64vH4+RyOarVKtls9rQFWGC7Re3Zs4c9e/Zs+iyfz7tlHAQnPrBXJ/Ja5ufniUaj1Go1JiYmEEWRdrtNtVoF7LrvuIuJooggCJuiAxOJBIZhIAgC5XKZfD7PyZMn1+3b6OgosViMiYmJvgInJ/LTEQmFw+HTdhKoVCpUKhXm5uaIRCKb3K0c96tQKMTJkyfJZDL4fD6SyeSO1k+nTRAEwXWaclz1urF225ZloWkasizvyG9br/jKncYRABYKBdLpdM94Q0EQGB0dXRcFum/fvnuljB4eHh4eHktLS30/L77iFfhuuAGx1aKfBmTa7yczNIQ/n0ewLAxZxugIvnVDJGskKSjD1OUoomVyuHIrPquFaJks+KfJ+PdsEnLUlDgnpTDT9bvYY53k0qVruXXiuTztadVt7+f73mfwZ38mgWXx8tvezDnlm5honKA9NYqVSrnbtgA9nYZiiRMnTarREe4UHspHL/wchrg79xCPf/ytgD0QvX+/xR//sUnHQNXlyitP7x7oRS+yeNGL7OeCV7ziOsrlzcu86lUmz362vczb3y5y5Mj686DrOs1msyNY1zHN1ecMSRKRZYVAwI8g2Pd4D3/4v59WWXtx7rkWr3udyVvfuv4YPOQhH+QhD/nguveuvdag3YYXv3jw4/Uf/2GwVoe4cfjqwIE/4cCBP9l2udfy0Y8aTE3ByZMm73+/RCazeZnJyd9jcvL31r137bUGV18tcM01WwsAI81lXv6rtzLamGG8ejuCZVFSEjSEEIrVZrg5z3BrnpB/mktTz+HfLvgQpUB/W649xV/y1BP/QKo5T1gvoBp1AFpigKqSIPeL13LdvtdzcuiRm74bjV606VoNBqFe37yd//k/Ta64oiOGsuBNbxJ7CtU24veP8rSnVXnf+wzOPx/KX/4yrb/9BModRzkVOEhdifX8rmRqHKrezqnAQX409nyuD9rOyT4fPPOZJrffLnDs2Go51u5PMgm53Pr1NRoNWq0Wj3jEXfzwh/ZEJlmWO8IVA02zW9FAwI+iKKiq/UxZra7f17vvvhuAUgle+crB6nKqepyX/eZdpJunSLfm8ZsNmhgYZhm/2cDU5sm2ltG/eZRi5PEUHvtY3va2t6EoChMTEwwNDXH48OGursDFIrzqVZvL4fPBa15j8k//tPk7r3udiWHApz8t8shHXrvusz/4A5OnPMVicdFueje2d90wTVhagj/8Q1gJHyTrnyDVmmNIz1HyjWBi8SfBh/AnPGT1Sx0xzp7GMQxB5p7IBXziEf+GLtnPZVdcYfGDHwz+zPvQh9p19D3veQ/vec97ME14/vN3r+917943sHfvG7p+9pznmHz96zvj4Dw29nzGxp7fdxmnDavX4Xd/9/T2eWjoceuuode+1mRy0uI971ld38gIfOxjhiumXPvb12g00LS2O5nLsixujT6Ww627GW1kCFs1mlKcPUKY7w49ZdP2g1qJPY1jHA8/lP/c9z859KanUyoJfPe7AqKp84ZbXsGBym2oZpPZ0HobOp/PFqLv3dcgm/XRzCc4z1rk11OX877xT5PzT7F/v8WLX2xx6hTs3Qvve9/W5+fwYYt3vtMTXp1N3BsaFk+A5bHjOJEcngjr7MDp7PYGaD08PDw8PB54eL/zZxfvfvfDKBRWp25Jkp/Xv/7WHd+OI3LYKkrL+dxxNuolanFEH5IkrYst60W905uqquomgcqDAUVREAQBWZZdIVw/nOPlxO/tFE5HL9jPYL2EHw6tzrRC5/wuLCxw9913IwiC67IhSRKKohCLxUgmk2557aiRgNvZvBs4UYs+n6/T6aZtebyc505VVfsu58RlFgoFDh06hM/no1wurxMmOuen3W670XHdnmtTqRSBQIBMJkM0GnWdlkRRRJZlYrEYw8PDPZ3hKpUKy8vLbtzhWpemeDzOyMiI61w1KI6zlWEYjIyM9FzOcf1aWloinU5TKpV6Ro2eDs7s6nbb7jANBoOEw2E3rnOj2MsR/QmCQKPRwDAMwuHw/cpVr1AoUK1WMU1zoNjMSCTiOqV5eHh4eHicNVx4Ieadd+I7cQKt1ULodW9VKuErlZAsCxMQTROhEx0siSKjcptEu0BOS7EY2IshiKhGE0sQKCnJni46piizok5w2LqL88cXeNPDr+fO0Wfy7W8PPmj+F39hCzP277eo//IuxuvHGW7OoaeHkNLprt8R4jF0Qye2sMCFh4d4x+Ovw//UpwHYQq4d5mMfM5icXH8Y1j7TX3SRxW23nZk4/hnPMLn66s3PiU94wurEmVe/2uTDH14vEHLivZ/whCa/+Y3I7KwE2KJ6n8+365GH732vySCPli96kf1Mu53H0Mc9zmLjI1s6bcEORlhNTlo480ump+Gqqwye97zB69CgXTrPPvK3jDZmmGwcI+cbIaeOYwmr5zvjNxlqLTLZOIGFwLOP/C3/9tCPbLr23vpWk8OHLZo/+C7zb/kHks0Fku0lRMukIdnPIkPtFazWKRKtDIkj7+Vr+/+Yu4ev2LKM551nccstm4/t2NhqHRSE1Xq4nfRxZ16HeeONKJU8uqBQl/vfgxuiQlmOEdVyHC7ezPWd9+Nx+L3fswCLt7xF5OTJzWV+/vNNPvWp9ddTIGA7cT3kITF+/nM/mqbRajWxLJAkkb/8y99w1VUPo1bzdSYu2estlbqXLxaDL3/Z4DWvkXouA6DqVV5013sYbdzDeHOGvDLMXOAguqSCZWHqbWJ6gZH2Au1iifA3v0kjkSD2mMcQDoeJRqPs27evZz9Sr24NUYTzz7c2OUk97nEWT3+6/canP939u4KwvVhOUVyzvCBwy8jTGG3cw1B7heHWKVvE61hr2QuBAInWEiGjwsnQufwy9WRXfAVwzjnbE2BtvBZFEV74QpMvfWnno+y3YnLSduS66aYza6uGhqAz160vzr4Hg3Yb9gd/cGa/g095il1HBAHe/naT739fIBi0j2evNjwQCOD3q7TbbTTNFmDdPf47NItfpaQnmWif4qQctOv9BhSjyVjzJCUlSV2OcOfIk3n+Qy0e+lCLYlFk9vvHCetFYlqeJf/6CYGKbBEM2A2MLFmMjrSwYmHUIwukgmXe+7wf0/6tF7N3r12vL7lk8OPQaFhuQoHH2YEzEXM38QRYHjvOz3/+c+68807e+tadzWv32D6GYbCysgLAyMiI18B7eDwIWBtTcG/N/Pfw8Lhv8H7nzz5WVk7Qbjfc16IYQJLkTdESZ0ogECAYDJLL5dyor16Ew2EURaHVaqGqaleHHaezXVEUms0moVCIWCzW042nXC4Tj8cJBoOk0+kHXd1TFIVoNEoqlaJYLDI8PNz3HFQqFdd9KJ1Obymc68fa3/lkMsnCwgKRSGSgCTDVapVUKoXP5yOTyZDJZCgWi2iaRq3T6y0IAoFAgHa7Tb1eZ8+ePa7AzHEmkmV5R+8x6vU6Kysr5PN5Go0Gy8vLZDIZTNPknHPO6Sksq1arGIZBOp1mbGysb5kymQyapqFpGgcOHHBjISuVintMZVkmGo0iCAJHjx6l2WxSLBZJJBKb1hePx4nH4+6xMwwDURQJhUJ9RYmLi4vMz8+7ApxGY7W9CIVCNBoNqtUqe/fudSNmB6FWq1Gr1RgaGtpSjJZIJMhmszQaDer1OukeA5Knw/DwMPl8nmw2S61WI51O02g0KJVKVCqVdcfSMAyazSbDw8PEYjEKhQJDQ0OEQiFGRkbO6Dq5NxEEAcuyCAaDWx57sKMwC4UCkiQhimLP9rOXGZp3f+/h8cDGe573uK+IvOhFNL/1LQy/H3lxEdMZYVyLZaGcOoXUySUSAF2WsTo/WoJhIGttJDRMQKhbyJaOhEGTAIql0c+P1z8awspAdPEYD/vme7nogmt5SGGIG6oP5/bRp9NQ+98bXXKJfe/wV38FP3rJN0grOUK+Jvr0VH/xUDKJlcngK+c4dOIbJB/2bIpFJ3JrZxkbE9cNOm98pn/zmyU+8AE4dmx761WU1cJeeSV88YvrP3/kIyGVWl3mggvgqqsgm4W3vAWazdVln/UsW3yz0fVnLU99Ktx5J6wxgj0jLr4YQiERS9N4auj7xH92HcONWWRLoy5HORK/hNvGn0n4nCme9SzRfb4e9BzZcY/r77kmJ8/sHF98MRQKMDNjuyT94R+Cz7d+G4OuX1HserHV8iPlO5mq3c1oa5aCkibnnwQ2yMgEkbx/AhGT0eYseijKeOVOluIXrFuXzyeSrB+l8Zm/w2gskWivkFEnKPlSWIK9H4JlENVyDDfnkK02zznxET4b2sNK5HDfcu7bB7d2mYcWConr+kYe/WhbuFMswg9/CFdf3X//AQTBXofYaCAYGjXJP5A4sC0GCBhVQkZlzUS11WvnrW+1r4W1vOQl8Fu/JfIv/7J5fcFgiMc/fh9f+pLt4rtWmLRv3z6mpqLMzalsFPkpitj1t15RYHQUKpXe+3Dh8nUkW4uMNU+y5J+i5Bu2j4l9YJAUlbI8QkOOcEi7A7WhcOjWW/H9j/9BOp3ecpKPLNt/G+ftyTLs3Sty5ZXwta/Z701Owh/8wep11a3uBoPiafeFPfvZ8I1vwK/HnsHDst/DZ7bYr99NoFYnrw5Tl2NYCAT0MgltmZBeYSGwj0xgmpv2vGTds+SFF4rbutZleXO5X/5y2+3onnts4d4vfwm/+EX377/0pYPV5UHYv1/k0CG45ZYzW8/f/R285jVbL+f3r+770NDpt5HBoN0mXn45gF1HLr/ceb36nsPm7YgEAvIaUWCMH06/kuDJTzJRP8a++p0UlDQlXwpd8CFbbWLtLAlthaYYZNk/zQ+mXomhhDh82L6+nvIU+Px36whYiBi0Rb97dYoijI61EcUNUcp+PwgCgmEQk2okDm+u0FsdI8uyOHy4Qj7f8PruzyI+3Us1uoN4AiwPDw8PDw8PDw8PD49dYjcS+hKJBPl8nuXlZarVal+3GCdOzu/394wBA1sUoOs6wWCQcrncM2KuWCxSrVaZmppiaGjoQdt5kE6nKZfLFAoFlpaWGB0d7drxm8/n1wlqdlJUoigK8XicoaEhN16vV6dqqVSi0bA7fGq1mit00nWdQCDA1JQ9MNVqtahUKiwuLpJMJgHYu3evK86LRqM7NiBsWRZzc3NkMhmq1SrFYpFms0k2m0XTNE6cOEGj0WB6enrT8a3VaszPz7uCI6esvahWq1QqFQKBgCuQkmW5q7gK7OuhXC5vOSvOOQeDkMlkmJubY35+nnq9TjweZ3R0FFmWabfblEol5ubmiMfjWJaFJEnEYr2jNBwMw3Dj+wZxoRZFEVVV0XUdXe8XMrR9wuEwwWCQRCLBysoKU1NT+P1+0um0K6qLx+MIgkA2m3VFfdVqFUVRGB4evl+JrwA3/nK7sZnOdz08PDw8PM4G/AcOUHvMY7AaDdTjx9HvuQdjbAxhrSXK/Dy+SgXnV1pXFMw1QnlLljEtC6nZJGxVEEwLGQ0BC1MQEOj9YCRaOsPlYyj1OlazidhsYhSLPEKWuSj6Y35zyxf4ZerJfP/AGzC6uF6sJRaDx04cRT6SQ5BUxK3uXQUBIxJBrFYRFhbcdewGWxjnMjwMH/4wtNt2DNdVV8Gvf93/O0996vrXwSD80z/BP/+zLYJ5xStsUdZGfD7baeb974d//3dotWzRw7nn2tv80Y+6b+/SS+ENb4BvftMu307w+tdD/aabaL7vfTz3ZIFyvkpAqyBiEozDo8Rf8+Lil1BilxMLvBOw693evbYAaiuGhze/18c0dh0bXX8cRkfh3e+GahX8/u6OXH/wB4Mfo0Fufy9a/CZBrYRstsmF+tsK5dUxhtorhPUCFy99g29tEGBpGtT/4z8Qs1mG2hlmg4dobHCSsgSJkm+Ylhhgun6EejvKI+e/yrfO/dO+2+4VNddtrkIoZP8N+ojpdmkoCoIkIpn9ZJ2riJaBhUhbXC3E2mO+fz+885329QD2NfOyl9n/v/zy7tfD/v1BgkHHodzEskAUBc49N87wMMzNDbZPm/atG5bFw5e/Rby9QksMUFK6T6IRBQFDCVFTR0jWsqiZDP5mE/8ADsuCYLdRGx3JnEeWV78arrjC/vzAgfXt2fOfD1/5yuprn89uK06XF78Yvvc9aDT8XPOQv+Klv3knkymd5t1lxhonEbEPloVIWY6zNHSYQmiKa/b91Sax7p49vWMxu9Ft/pcgwBOeYP8BPPe58NOfwgc+sHnZpz/dbnvvusuu8695jd1OfO5z2zgAG8r+F38BX/+6fcxvvHH760mn4SEPgd/8pv9ya6/RYBAuughuu231vQsugIWF/m5a555rCxoHbWMH5ZeTVxLUS1y+8EXirRXiWoZUe9H9vCUGWPGNU1LT/PfEy7ht4jkkk+B0lz7mMfCNERXuFLAQUNCZ2N9ElCwkyUKWNjf0lq7TsbazxVinyeMeN1g75fHAwhNgeXh4eHh4eHh4eHh47BK7JcBSVZV4PM7y8jJ+v7+rKMYwDKrVKrFYjEAggK7rLC0trYuWA1ucUigUiEQijIyMYBgGJ0+edN1oRFGkXq+74qvR0VGi0ShjvXpWHwQkk0my2Sx79uxhdnaWZrNJIpEgHA67cWr5fJ56vc74+DiRSGRXjtfo6CjFYpFarcbs7CzpdHqde5mmaeTzefL5PKOjo5imiSzLzM7OkkqlGBoaYnFxEcMw8Pl8BAIBAoEA1WqVlZUVZFkmk8kQiUSwLItoNLotZ6Z+zM7OsrS0xNzcHM1mk0gkQiqVIhaLceTIEer1OkeOHKFYLJLL5ZiensYwDEqlEvV6nUQiwfDwMNPT01sKAQ3DcPd9EBxRVD/R4nbQdZ25uTmWlpZotVrs379/3TUoyzLBYJB4PM7s7KwbxXjBBRdsKdJxxEqiKA5cXsMwXOHQTjM5OUmj0aDdbnPy5EkikQjBYJDh4WGy2SzLy8udWeIWiUSCUqlEKpVidHSUdDrN6OjoQNtxIiKbHcsGx5nu3hZvqarqusY5ToP9WCsEvD8JzTw8PDw8HvjE//zPKS4v0wbkuTmUI0cwAgFMRUHUNNRcDsGysAO7wOx2XyUItotWo0FAaiBZOgggSwa60F1hIVo6U7W78beKSLqOJQiYkoTRbiPVavgyGaZbWXxGnURzga+c/74tRVjOoKk14G+tJYoIloXQcaa5rzXSPh9MTcFzntNdgHX55bbw6JxzbFHERiYm4D3vGWxbBw9uXvYZz4Af/ABOndq8/LOeZf+7nVixrTB+eSOtD74LaX6eoeVl4rJEMxJDVEQUrY48dwzD70f/Xp1COc/QRz6CIMtceulgAqxHPGLze71uOd/+dvvfn/wEGg34nd9ZFeVsRBShXwL1U55iCx5++EP7nD70oXDzzd2XHcSUdrx2hLBepCZHMYX+zzWmIFORowTbJcZrRzd93s6XEH70I6RsloIS3yS+WktTjlBUhki0M5yf/wn/r12g4es+kQV6142dmMfjzB8R9u+HaJiAeQrZbKGLfdoEyyKqF8n5RlgO7HXf3tg8POYx8J//ufnrU1PdV6uqtjPS5z4nIHRcw377t+0Yt9MRca4xxtpEvDFHsrVITMuTVUfXNVKiuNm1ShgeQpybh3qdxo9/jP+ccwYqQzcBVnuNdmTfvu7fe9rTbGHQ/LxdtJe9bGvBaT9iMfjrv3ZEXUnOef+HUG74LMff91Oi7Sw+swVYaKJKXY7ysD++BP8VryL/vs1qS1GEw4dt16qtEEV42MMGK2MvPU4sBn/zN7aLoKqu1gVFgX/918HW7eDo5h75SPsPbFFtoTD4Opyukuc9b2sB1sZ9+p//E/7hH2wx4YUXwmtfa5/fO++E97538/evuML+znbnafYSum5c6Kf7XsVi+BwevfAVpqu/wWc0kDAIxSyy1Qgz4fP52cQLmEk9BlhfX0UR/tfHp5n/bRHdDDEVWEAITPfdpFgoYCoKBIMo558/8P489rFwww32Nn/3d00mJnamX8nj/oUnwPLw8PDw8PDw8PDw8NglNnYiOFFlZyJ8EEWR8fFxNE2j3W4zMzPD0NCQK7wxTZNyuUyukxvx8Ic/nGaziaqqbuyc3+9HkiRarRa6rhOLxXjUox5Fo9Fw4w2XlpZcVxdBEAiHw0xPTxONRjl48GDfqLUHOoIgcPDgQQAkSSKfz5PJZFhcXHQ/j0aj7N271z1euxElFAwG2dfpVVJVlVwux8rKCqqqYlmWKwiZnJx0nZpyuRymaXLuuedy4sQJAoEAKysrjI6OukKmcDjsRscpikK5XCaVSuH3+wdyZdqKYrFIJpPh1KlTCILAgQMH1h2faDTKzMwMs7OzVKtV5ufnabfbRKNRwuEwIyMjBAIB9u3b19PFai2SJCFJkivW2QpN09zv7AS5XI5Wq0WpVGJ6erpnXQgEAoyMjJDJZBgaGqJcLm95vAVBwO/3EwwGqVQqpFKpvss3m03a7TahUMiNl9xJnHrvxFkWCgV0XadcLmMYBpIkoaoq4XCYRCLBueeeSyAQYHh4mPHx8S3bRtM0yWQyrKys0Gw2XTGXoij4fD6SySSjo6P3WnRXKpVicXGRQCBAPp/vK7RsNBo0Gg3Gxsa2PE+7Id718PDw8PDohxSJEP/EJyi9//3oP/0peq2GUCwiGAamZaGrKnKrhS6KWIKAaBgY3X5vBQFDlpF03f6uKCILBmrCR7NLvNZ4/QQBo4rPsO1JNL+f9sQE1ugoJqDX60h3ZZmu3QXAk47/I985/Efr1rHJaSUWw/L5kCsVTMvaUlElNhpYqoq1Rk3j96+P5tsO4bDteHKm9Hrcetvbznzd/QgEbHent74VSqXV9/1+W8gAOyfAUrUKyof+EvHUKZRMhvb4OCSTyJ1zZgB6q4V86hTK8eMgihSvuorEG9/Ii19sn9pf/9p2irnxxs3RiY98pO3aspEehs8kErZTzGWXrb532WW2IGstj33s1vsmy/Anf2I7hgUCthCrlwDr0ku3rnM+s4WIid4RX4XCBrVq7+cVQ1Dw0Ubp4hKVWL4NGg3kapWyv4fCaA0lJcV0/W4Us8l48dccH76863IXX7w9ByyHQc1lnPkmwec+l+bXv05TDDLUWiSzRli1kaiWRbJ0yr4Uvxx9mvv+oF0y/R4Jn/9822HHqYPPf779/uk8MvebS6NqduMpWRptYfVgjY238ftN7jmx+p4kW4SHRKxFCTQNaxuNUbdz1E8Y5jA8DB/5CBw/bu/7xMTAm+zJwYOrgkiIwCPeyFXX/QHTuZuINZfAsqipQ8iPfjRPfnuA5nzvde3Z012A9bzn2dv5yldsgdTv/Z4dvTcI/eqzJG123utnVu3UxbXPgGvboDPB+Zm+5JKthU4b9ymd7i7mPffc7t+/5JLti68A/uiPbPfHQbgn/VjuST+WP33pEQo/v519o1VqVoh//s6jyIf2rlt27/qXxMcjmC9+NOJX51FnZmjV66sqtw1YhoGSzaInEph79hDqpuTtwe//PvzxH7seG9oAAQAASURBVNsCLEGA5eWBv+rxAMITYHl4eHh4eHh4eHh4eOwi5XKZlZUVSqUSVqe3w4nlSiaTpyXySKfTaJ2esFwuR6FQIJPJIEkSpmkiiiKxWIxUKkUqlWJiYoKTJ08SDoep1+vU63VM0yQUChGNRlFVlbGxMTcqLBQKuUINsB16VFVlaGiI0dHRB7X4ykGWZQ4fPuweL+ecrBWDpFIpRkZGdlUMkkgkkGWZubk5EokE1WrVPW+OMMdx4Dpy5AiFQoF4PO6eT8MwWFpaYmFhwRU4SZJEMBjk2LFj1Ot19u7dSzqdZnJyckdckzKZDKVSCU3T2L9//yZnKlVVOXToEKlUil/96lf4/X6GhobcOESnXg9aDyORCJFIhGw2u6VLka7rVKtVJicn+8Z7bodcLucKH3vFRDpEo1GWl5ddEeUggrdUKkWlUiGXy1GtVvtGEeZyOYLBIH6/f0sR0OmSTCZRVZXFxUXi8TjNZpN9+/ahaRqNRgPLsojH44yMjBCPx0kmkwO5k+m6zrFjxygWi+TzeUqlkuv6JQgCkUiEWq1GoVDg0KFDuyIw24jP5yMej5NOpzl16pRbPzfSbDaZm5sjFovt6rH38PDw8PA4E6RIhKG/+Rvac3NUv/IVrDvugHoda3ERjh7FKhTQhoZQ83mkRgNT17G6/IZbsozYatluVoqCJQiE2jlKrFdmqHqdkF4mYNZBsDBkGUtRsNb+TgaDCOdO0/r1KUYbMzws+z1+PP1yGqodQS0ItkvRWuTHPQ7rppsQlpawymWEPvdTVquFXKvRGh9HWKOqGR+HEydO4yBiD0J///vr3+sWg7cVD3nIZlHO4x9/emXaLsPD8Od/bkdsZbOrrjbO7fdWtzIjI4MNOj9k6XpEqYAvk6E1OdlV/SCoKsb+/QjHjiEuLGB885uYr30tot/PS16yutyll8KHPgTFov369a+3Ixp7Pb6ce64dE+YgirZQYyPPe57tZuK4DO3ZY7tZDYpz+99P+KAo8I53wMc/bh/vbsT3qBglmYBRY2y8zVBC4ze/CfVcr89sYko+Jg+v72+IRODQWNG2k7IsdEmFHmnasbhOqSjTFm2Bj2Tq+PUyYB+D3/99+OpX4Ve/sp2i3vhG6JXQ3u+R+Ior4FOf6v25gyNS8h84QO3ii8ncZrKnfgRTkMmp41jCGlsryyKi5RltzpJRJyn5UhxNr15Ag5rR9uuucdqfjW3QFVfA1762/j3HPa4X/dLhNck+/pYgIrG6oChYBAMG09NNcjkFUbIYGdHAMhFME0QRYRt9N2eQsoaq2m3WbnLxI2VuuWW94vajb1ndfi96PR5fcQVMT8PjHrf9smxhfDxwGcB25LvsMvjyl+3XweCqmG8jhw7Bz38++HbXXneTk92dDcHen0GviV7X8umavj/2sXbE4vHj2/jOSw/DS21F8JEjkP/p5mW6ObYFX/Qimt/7Hlo0iu+ee2jv2WM3imt+KKxGA2V2FkuSMIeHka68clvWmLK8+lu5Q6bqHvdDPAGWh4eHh4eHh4eHh4fHLjE7O8eRI0uUSiXK5TK6riOKoutWs7CwwL59+07LVWh8fJxAIEAoFCKVSlGv112HLSdKbnh4mOFOb//hw4dpNBosLy9Tq9UQRRGfz0csFmNoaMiNwxodHWVkZIRKpUKr1XIFRdFodMccgR4oiKLoHq9qtbrpeN1bEWORSITzzjuPWq1GPp+n3W4jCAKqqpJMJvH7/dTrtqtAq9Ui3cm4GB4edgUslUqFUqlEPp9HEAQsy6LdbhMOhxkeHmZqamogt6mtaDabVCoVCoWCKx7rhiiKJJNJLrjgAnK5HGNjYxw8eJBoNLptEVgqlWJpaYlAIEAmk+krJMtms8iyTDgcdo/TmeLE020lvgLc9qHVarlCuq1IpVIsLCyQTCaZn59nYmJikwjLsiwymQyVSoXp6Wni8fiuCgPD4TCHDh2i2Wy6QilBEAgGg6d1Di3L4vjx4+RyOWZnZ1EUheHh4XWxn4VCgZmZGdeF6txzz911saiu6wwPD1MqlZiYmGB+fp5SqeSKHA3DoFKpUKlU3OjWqampe82hy8PDw8PD43TwTU4y9OY3u6+Ln/885t//PUKphDk8jFGtgmkit1p21LOiuCO3gmEgtNsIloWmqhiBAPrQEMMrJ6maCmUl6Q5kxrUMsqWhWDqWAIaqosdi9ujlGoJBi8zwKEOLR/HrVZ6tfJ3AK19NuQyPfjScd9768kee8QxK//Iv6LEYvoUFtGCw64ixZZrIs7PowSCEQgTXjHj/9m/DJz+5fvmnPAXuuMOO2QK46CK47bbNx++3fgv++7/XD7y+6U1bHfXNqCpceSVcfbVzHOCZz9z+ek6Xw4ft+Km77oJkcn0U21aPOfv2bRZgCYLtkPSLX3TesCwuLf1fFCGDoaq2/VQvRBF9ZATf7CxGsUjl298m9tznrlvkoQ+1z1mtZkcDbnUb+PSnrxdgXXaZPQ6/kUOH4H//b/jOd2znrBe+8PRiKrdyOL34Yjui7BOfsLe1kYe87CHw2VsIHD9F058CAoyNtVhY2KwEkc0WIaNCI7WfkaddyJMDdqxkPA5veQtIGT9659lesrorf/YfaBLwG5SKMnJnGVOQ0KQAn/70qghvTVPhkkqtF5JFIv3FPb0cyTay9pqKvvOdLF3zJwiWyUTzHuJalqKcRJNUZFMjqhdQzBYr6gTZwCTXHn4HhrhaKQZ9VE8mB1tuLfv2wcMfvlrX/X540pP6f+fwYchkun9W8o/TkgJUpShRLU9VWS9UDIcNwuE1BydXsCNYQyGUjQ1kH7YrKrq3cWL0Gg379Wtesyqy6Xe9d3PskyRbfHW6bPdY9TsNsmzHWT7ykXYduOCC3iLXl7zk9AVYIyO9BVjbEd/1mjt1ugIsn88Wz/7gB/Cxj23/+2vnXalahYcsXc95uR+x7wML5D8hYE1M4HvGMwg/8Yn4zz2X1utfj/kP/4A+N4c6M4OhqhjhMAgCYr2OXKuhh0Loe/diPfnJxHqp4XrgdZt6gCfA8vDw8PDw8PDw8PDw2DXK5TLHjh3DNE1isRiRSATDMCiXy+TzeZLJJKZpcuDAgdMSYSUSCRKJBLVazY34EkWRcDhMdE2EhmEYbjzd2hg2QRC6CiGcCD2PwXDcd3bKMel0CYVChAbtvcYu9/j4OMFgkHw+TywWo91uY5omkiQhCAKjo6Ps27fPFfKdKc1mE13XabVajA+QXRKNRllZWUEURVfEs118Ph9DQ0M0m01OnjzJ3Nwcw8PD65ywNE1jZWWFcrnM9PQ0kUhkW8fyvkSSJPbt2+dGhp46dYpAIEAkEnGjRkud/Jg9e/YQj8eZmto6amQn8Pv9+M9kKnUHx/Hq1KlThMNhxsbG1tWFcDhMOBymUCiwuLiIz+djcXGR6TPpVe+BYRhu7KjTnrZaLYrFIkNDQ1iW5UYviqJIKBRienqaYDDI1NSU537l4eHh4XG/QxwawvD5QBAQNA1t3z6EEyfQBQFR01Cc0fAOpiBgShJGMEj9ne9E/uY30WWZydkZmq0FynICQ5BJtxYImjUsQA8GMQIBzMnJrmUY3isjNmT2hxbYP34jyee9und5g0GEl74U81/+BfH4cZSjR9HSaaxkEkEUbSVMoYCSydhCsYMHMZ/yFNQ190dPeQp86UuQz9uvBcF+7/WvtwUA8Ti0WnbM0EZSKXjXu2wxTb0Oz3nO9hyT1vLSl9rfXVmxB/BHRk5vPadLIGALg7bLU59qH59bb11977GPhac9De680xZQBM0y54ROIi1X0BOJrVVN0ahdB8tljDvugA0CLKe8g5qgPvGJtvDn5pttB5pnP8vCqFSx2m2kWAxhjcrg8OHV+MXTZZCIaUGARz1qswDroQ+F8POfT/2rX8UIBpHn5zEOHCCR0DEMgVJZpt0SbZcuy2KkOUtLDhIeUQm+9AW8eXq9UKo9fz6GomAEAkTrOWrKZnuegH9V0BPVcjTFIIaosBw5vOWpevGLbfGew+/8ztaCJ0na2i1mrUuUMjLC5y/4IM+/89005DBRLU9UyxM2yliIlOQhSmqaipLkzuf+KYuLF65b10UX9d+Ww2MfuzlW9DWv2fp7f/7n8N3vQqUCj3mM7T7Ujxe/GH784+6fGbKf25NXEG9lmKofwafXactBdH3zibBME2VlBT0exxweJnR597jIbuzAY9uucuGFdr06etS+Ztc6HPUTRF100eb69Yd/eGZl2e48n2jUvg6uu27zZ47z3kMesrWL2MGDdv275hq7TdkqYXKtduilL4Xbb4du87y2c+5F0RYmro18DQYHb3u7IUlw+eWnJ8Dy+wHL4lGzV3P5whcJ6BWi7RzRXxdBEDCDQYyf/5zC8DD+d7yD2EteQklVMf7xHzErFYSVFcRmEywLy+ejNT4OoRDWs55F4o/+aNuK2wHMvT0eBHjVwMPDw8PDw8PDw8PDY5fIZFa44IIIw8PD69yQnMiw+c70aVEUufDCC0/bYaqf8KZSqXDixAkajQbFYpFKpeIKbKLRKKVSiVAoxMGDB3dEKOFxduI4Afn9/k0xdfF4nHg8Tr1edyPiHBetgwcP7qhgxDRNN4pzkPruXDemaboCo9NhamrKFcssLi5y4sQJAoEAPp8PXdep1Wqoquq6Q+3fv/+0t7URn8+HqqrUarUtlzVNk3q9TiQS2ZZ7Uzwe58CBA4iiSDwep1AoUCqVME0TWZYZHh52IyYPHjw4UOTf2cTKygrFYhFBEDaJr9biCFJzuRyhUIjJyckdde6r1WocP37cddyqVCrrhK9giwwdYSPY9TyZTJJOp7021sPDw8PjfknocY+jHA6jRyKIuRzmvn20Dx1CXFxELpcRzE7cFWAJAlKrRSsSofnoR5N6y1uoP+EJtN/7XhgaQl4sEMtX0JoCIia6IIMsokci6Hv3IvT73fbLKIKG1nF27Uf8Fa8gn8mg/+d/IiwtoWQyiIuLmLKMYBggCOixGObYGMallzL09rev+76i2PF7X/iCLSR66lNXBTgXdnQcJ09237bPZzuZPPKRWxZzIM4/f2fWs9O88pXwmc9sfn942BY8jI/bg+l33w3nnAOvfa09aP/xj9vxjmmhRuJtGsKiOdiItSDY58807ZOyAzzqUfCw6QzVr3yF0guvR3QUBaKI8fCHoz73uYQvv/z0LK82MOijzKMeZScxOuI/sMU5vslJKk98IjSbKMeOIRw7hjY+TioZJJXSAKittBDmllGFBuY5+zAvfxxqlwkJvokJKhdfDNksqdoyK+YouriqYBkZWVVoSGabuLbCijrJiehFlIJbKImwhSbpNPz617Zz2hOesPV+/+Efbi2+2CjQevorR/ncF/6BfdkbefjSN9hXvh0B+1lzKbCXX4w8nTtHfosPvT3Ad9+y+j1B6B3xthFZtl3DPvpRW0x12WW2Q95WSNLmaMJ+TE/b18i//ZstrHn0o2FmZtWx6NbxZ/OIlW9TkhPsaRxlIbAfTd+geNE05JMnESwLc3QU8ZnP7N+mbuBsd8ACW+DarXtCVW1x0rFjq+9d2kkrjMfhj/4I/vmfbfHnU58KT37ymZXjdIyW3/AG2xnullvWv/+8521vPc95Djz72XY9+cQnbKFfNyIR25HR4dAheP/74b3vtevyWrqkv/blqU+Fz39+9fX/+B/b+343VNU+ZzfcsPpeL6fJtQwlLJ469wkeduq/GG3MENWLtmgcCUwT39ISLC2hZbO03/52zPe+l9iVV2I86UlUvv51jOuuw1xeRjAMzEQC4fGPJ/SCF+DbSjWJLRr70Y9WX5933unVDY8HHvevHjcPDw8PDw8PDw8PD4/7DRY+n5/R0c2zScGOjXMiq+LxOLlcbsdchhyq1SpHjx4lm82ysrKCz+cjGo0iyzK6rlMoFFhZWWF8fBzDMDj33HPXuQJ5PHCQZZl4PE4ikWBpaYlUKrVJhBMMBl3RyNzcHENDQ/h8vh2JHlxbDsddq9VqbRnF1mw2EQQBWZbPSDQkiiKHDx/m5MmTqKpKs9l0XeNUVSWVShEMBonFYuzdu3dHBUrJZJJSqUQul6Ner/eNIiyXy64DXXKbeRfxeJwLL7yQbDZLOBxeF2EYidhC0FgsdlouYvclrVaLWq1GsVgkHo9vWf6hoSFmZ2dpt9sUi8VtH8de1Ot1jhw5Qi6XY3l52Y1wVRQFTdMolUq0223XXW1qasoV0t1bcaQeHh4eHh67gRSJYD3hCRjZLOqJE7RyOUgmMffupa3rCIWCbanRie+xJAnt0CHE5z0PQZIIPfKRBL70JSrf+hbiN76BPDODVTHQjrYQtDpWOIBx8CBb3aGIum4Pqg7yvCIIDL3tbZQPHkT/whewFhftEWddt5URkQhmMonwrGcx9KpXdRUpjI7awote9LqNfbAMvj71qfC9762PtHrWs+BFL7IP8ego/PVf2++vvX0bHrb/jEqECmBJEkK7zZYGUaaJqGl2dN4A0d6DULr2WsxPfAKhXEbKZhFrNVu8oiiwsoJ+003kzj+f2N/8DfIZPhN1E+Rddtnm9yQJ/vZv4XOfs91tnvrUVdFf4h3voNDJdpTm5vAfP47h82HJMqKuM9Ruo0dDGJN7ES68gPif/VnP8vhf9CK0W26BQpHp/N3MqftpyWEk2SIe71hN1WpM1TO0xCAlX4qbxp4LDKZHe/jD7b9BefKTYWHBrlNDQ1AorBehgS1KWsvzngf5vMwvfvE4fvPExzF+hc4//X0TXVIxxdULdP9++F//y3YNkmV4xStgOwbkl1xinw+w3X9263Hq2c+2/7p/Ns1107/P02b+kZHGSabqd2Ms+hEsP5YoItbrKJUKhqqiHTiA8ahHMfTyl29r+/f3rqBXvQo++EEoFm23wJe8ZPWzxz/e/jPNweMn+3E6ifKCAO98p90uOvGUv/VbtjDqdNYlCHYs7c032/ssCLbLoKLY/3/ucze7Uh0+DB/+MPzlX65vu7eKyNzIi19su7odP267du2U4PiP/sgW2M3P2+3eueduLcCq/tfX+J3WV/HV7kHCYDZ0mJEDMmbIVr22TBMxm0VZXkYzDLT3vQ/tc59DGRkh/vKX2/mPp8n/+B8wNwf33GP/5r3udae9Ko8HGJ4Ay8PDw8PDw8PDw8PDYxewLDoxfr1naDuD88VikZWVlR0VYFmWxYkTJ9zowfHx8U2xgslkknw+z8LCAqIoMjMzwznnnLNjZfA4u0in0xQKBXK5HPPz8z3dgbLZLNVqlenpaYaGhnbUQSgcDqMoCtFolEKhsM6JqxvFYtH9zplGPIqiyL59+5iYmGBlZWWTe9FuORQlk0nm5+eJxWIsLCwwNTXV1d2q0WiwvLxMMpnE7/efVgyoLMuMjo4yOjqKYRiu2939WQCkaZrrytZPvOYQDAbd5TVN27FyzMzMkM/nWV5eZnR0lHg8vu5zR2i3uLiIKIosLy+TSqVOS/A2SESNh4eHh4fHvUn4Fa+g9pOf0G40UOfn0apVzHQagkGsVAqqVcRsFqlepz01hblnD7EXvtD9vuj3E7vySrjySgAswyD/znei/9//i29+Ht0w+jq1WK0WUqOBPjaGcN55gxVaEIg+73lw5ZVUf/pT2jffbCta/H6UCy8k9qQnrYuZ2y4PdgFWMGg7Ft12mx3RdujQZmFKv9sgKRLB2L8fYWkJuVBAGx3t/4VCwf48FkPZgdH+0rXXYv393yPNzqIUCnYMZiKBJUmIjQa+xUWs5WVoNCi/+c3Er7oK8QyEX/G4LXL4/vft16raNUURsMUjb3vb5vdFv5/ERz5C8cMfxvre9zCqVYRiEQwDU5Kw4nEIhTCuuILE296G2OfZJvTYx1J46Usx/+M/CIizHC7cQVsIIkUDiBkLqVpFajQoiCMsBvbyk7EXMJN6DLA7UXWCYOsgHC3EsWO2WKVjYszjH7/ZpScQgDe9afX1zIxMu0ucIsDjHmf/nS47+Eh8Wlx2GfzkJ8/GEGWeOnMVeX2UvcE5pGoVOqLB1t69EI1iXH45Q+9+97bbt/u7We+FF8JVV9la23i8u6Bspx6LQ6HNTnWDuBUqCrz73bZgR1FW4wdPl/37bVfBI0dsh8FBjLxHRmyh2re+ZYuwLr54MJe6jVx2WXcR6Zng968XMa11NOuKZWF86UuktDmEYI38+LnsiYko8qrloCCKWMPDtFUVdWaGZjxO9StfIfGGN5xxecfG4O//3jZl9Pnu+3bC4+zBE2B5eHh4eHh4eHh4eHjsEorio58AC2yRVrVapdlsYlnWjrnTFItFms0mmUyG0dHRrmIOQRBIJpPous7y8jLhcHhLhx6P+y+Oq5JhGMzOznL8+HESiQSRSARBENyYymazycTEBLFYjPHx8R0tgyiKpFIpqtUqMzMzlEolYj2mH1cqFSqVCtPT0ySTyR0Tgvl8PiYmJnZkXQ6NRoNareYKntZGCMqyzOTkJIZhMD8/zz333EM8HicWiyHLsuvUVC6XicfjpFIppqamzrgtkCRpR8Vz9yXbORbWLqiXKpUK9XqdlZUVUqnUJvGVQywWwzAMMpkM0WiUcrncs357eHh4eHjcn/BNTqK///203vEOWqqKlMmgHj2KJUlgWQiWhR4O0z5wAHNqiuDf/i1Sn99AQZLwP/e5aD/9KdbiIuLyMlav+07LQlpawlBVCIcJdkRcAyMIhHdhpFgorXDhws8ItguYokw+sId7ko/e0uH1gYQkbc/laCPKs5+NceQIwsoKZDK2MqALlqbhy2TQ43HMdJrYE594+hsF2vPzWB//OOLsLFKlQmv/fjuvq4MJtCYmkE6dwnfiBG1RpPixjzH0p396Rtt905tsN6XlZTtqcICEq02Ifj9D73wn+utfT/Xaa9Fvvx3qdQgEEC+8kMiVVyIP6ACbeP3rKQaDWJ/9LFathi+bRWjaselmMIg+NUWjlOB7/ldw09RLAbjggh0zIOvLwYO2wO/GG20xzSACke3GqN2feMUr7Gi2O8aezonkpTyq+C32T3wNfWUJACsYxLzsMoLPex6B08wtvb8LsMCum/dG/RQEeOEL4VOfsifPSFJv97Ju3x1EKDUosZjdnmyHYHDwGM77kq26M+o334x46hRyoYA+kiKeFKCXn2IshhEMImWz6N/+Ntb/9/+dkQjbQRQ3O415eHgCLA8PDw8PDw8PDw8Pj11AEAZzMZEkCdO0Z2c54o2dYGVlhXK5jCRJWwoAkskkhUKBarVKNptlampqR8rgcfaxd+9ewBZClUolCoUC2WwWwK0r4+PjRCIRDh06tCsDSMPDw+RyOUZHR1lYWKDRaLhxh2A7HuXzeQqFAsPDw0QiEUZ6DMbc1xQKBTKZDNVqlXa77V7DiqIQj8cZHR0lFAoxPDyMYRgIgkCpVKJYLJJfM102HA4zOTlJJBJh79697jVrWRaGYWBZFrIs3+/iA3cCp16oqkq1Wt1SIFqr1RAEAZ/P19Vp7HRwHNNM09wykjMej7vtbzab9QRYHh4eHh4PGIIPexjSP/4j1U9+En72M4x6fdWeJhDACoexLr+cyOtfjzKAs2/oMY8hv28fWrmMevIkbcAcHUVYY1FiGQbS/DxyuUx7/36Mhz8c/4EDu7SHg9E8dozapz+N8OMbefZxUMw2FgItKUDZl6L4T48j9spXnpFb0oOF8NOfTvHf/512uYw6N0db17GGh1ftxSwLq1JBWVjAEkXM8XHEF7zgjAfNa1/9KkK5jFIo0DxwAKGLK68giphTUxjHjyMuLmJ9//sYb3wj0hYOvv2QJHjsY8+k5KvIiQTxV7/6zFYiCMRf+Uq0ZzyD6te+hv7d7yLkcgBYQ0MIT3oS0898HomvDTP8S9i3D3bANGZgRkd7u4R1IxqFRzwCbrll9b0Xv3jHi3WfMDpqR8f953+CqiZ44QtfRir1MizDAMNA2IHnnvt7BOG9zTOeAQcO2BF0Bw9Cp6vFYwfZ6lG6dcstCLUaoq7bscJbrE8fGkJZWsIsFmnNzOA/eHDHyurhsRZPgOXh4eHh4eHh4eHh4bELWNZgTiytVssVVuykW43jyOO4G/VDlmUCgQD1ep1Go7FjZfA4+xAEgX379rkikUQigWmaWJaFJEn4fD7S6TTpdHrX3JN8Ph8HDhxwRUXZbJbjx4+7Yi9N0/D7/YyPjxOPxzlw4ADqWdgbfOrUKZaWligWixQKBdrttvtZIBAgkUhQKpVcB6+xsTHC4TDLy8vE43F0XXcFW7IsMzQ0xMjICIFAgFarxcrKCtlsFsMwAPvcxeNx0un0Gccx3p/w+XxEIhESiYTrQNUvUjGfzxOJRFBVtadT1XZpNpvU63VCodCW14UTaXkm7ammaUxMvBJNswe1JUnivPPOvmvAw8PDw+PBh7pvH+oHPoCWyVC7/nqsbBZLEJDGxoj8zu/0db3ahCAQ+qu/ovaHf0jLslDm5xHyefRIBEuWEdpt5GoVS5Jo79uHeeAAsb/4i93buQGo3Xgj7Xe/G2lpCTGT5UC1DdiOGyYSJWUI4bPHKd50E9EPfxh5C+H2gx3R7yf4t39L4y1voSVJKAsLiLkcejCIJYpIrRaipqHFYpiTkxi/8zskX/ayM9qmZRhw/fWI2Sx6KNRVfOUiCBjDw6gzM5iVCtVvfpPYi150Rts/G1HSaRKv+//Zu+8oue76/v/P26fXne276rIMNqaYYFowiUno2AZi2jcQviRAAgkk8ANMB38JBAgkoRdDAklo7mB6jw0YCDbYWJZ2tXW2TO/ttt8fox1ppW2Stkn6PM7x8c7MnXs/9869o5k7r/t+/+Xi3l/HedWrNnlAZ+C1r4Xrr4dkst2S7nnP2+oRrZ/du+E1r1l8n6Qo69b3bBt+5d72Dhxo/ydsjFgMLrwQ7r//2H2LKnfV62BZuLK8YhvjDk1DOnp+xalU1newgnAcEcASBEEQBEEQBEHYINXqyj++O45DsVhctkXgmXBdF9d1VwwqHG+hEtdCNS7h3BaNRolGozSbzU77S03T8Pl8m1JlKRAIcODAAaampggGgzQaDRpHKygYhoHX6yUUCjE4OIh3G9Zzn52dZXZ2lqmpKUzTJBqNEgqFFrUUnJ2dpVardcJtkUiEYDBIMBik1WotalkYCARQj17JPzc3RzKZpF6vk8/nqdfruK6LrusUi0VyuRyRSITdu3ev+fg+23V3d1MsFslmsySTSQYGBpZc93Q6Ta1WY+fOnasGtU7FQkjxVN5PFyqXnYp6vU4ymWRiwmVo6N00mwvHhIfLLy8yMjLC4OAgnnOhP4ggCIJwVtO6u4m86EVnPB9jxw746EepvPnN7VIX+TxysYjcaLSDV0NDEIlgHzhA+B//EXULe4w1Dh2i9ba3IU9MoM/PY4bDJCO7qdh+JFz8VoFuZxb98Bwt26b4hjcQ+9jH1qXF0bnMs28f0kc/SuU974GDB6FUQiqX220t/X7cWAw3FILnPpf4//2/7VLTZ8DKZpGKRZRqFbOra/UnBIM4sgzlMvbo6BktW9h4odDJISVhbcRXDGE7eu1r4SMfgdlZuOSSE6raeb2gqkiOg2vbq4ewTLPdOhmQz6CaoSCsRnzyEwRBEARBEARB2ACSJFEqlbAstROsOFEqlUKWZUKhEN1raNNxKlS1vdxms7mm6RuNBtFodNmxCucmwzC2pLqU67p4PB727dtHs9kkl8t19lVd14nFYpsaMnFdF8uyOpXoVgqhmabZCWA5jsOuXbsWHTcej4fe3l4ikQgTExNomoau64TD4c58l2uPNzs7y/T0NDMzM5TLZQKBALFYDEmSaDQazM7OkkqlGBoawnEc9u3bd160JYxEIsRiMSzLYnJyktHRUaLRKIFAAEmSOmG1ZrPJ4OAg4XCY3t7edVv+wvtpZY1XyTYaDXw+3ym9n1YqFUZGRigUCszNqdRqnk71M9t2SKczJJN1KpUK+/btw+/3n9a6CIIgCMJ2Y+zYgfGFL1C54w6aN9+Mc+gQUqOB6/XiXnQR3mc/G9/DHnbGwZszVf3Up1Dm59Hn52kOD0MkQrioUklKuK5EzYhhD3mxsmNoY2NgGFS+8x2CT33qlo77bGDs3InxqU9Rv/de6rfcgjsxAa0WBIOoj30soWc+c91aOjoLbTMdZ22VgySpPZ3rwhq/WwvC2UhUwNp89fvuo/Gzn+GWSkgeD9qBA/j/8A9FcPc4vb1w3XVLP2Y84hG0/H4cRUHOZtstbFeg5nLYoRBOJIIhekYKG0gcwYIgCIIgCIIgCBtElhXGx8dJJBKEQqFOUKLRaJDJZKhWqwwPDxMIBNa9AlY4HCYcDjMxMYFpmp32bkupVqtYlkU4HF63ll2CcKJKpUI6naZQKHQqrXm9Xrq6uuju7t6wlodrGVM+n+9UK1IUhXg8TiKRWDIElk6nqdfrlMvlk8JXx/N4PPT09JBKpYjFYpRKJcIrtOWp1WrMzMyQTCZpNpvs3r37pHBcIpFgdnaWyclJFEVhbm6Ovr6+M9gCZ49du3YB7ddnoRJYOp3u3BeJRBgcHCQYDLJv3751DZNGo1Hy+TyZTIZ6vb5iVbZGo0G9Xqevr2/N76etVouRkRHS6TTz8/MYxgX4/T4sqx3AUlUFTdOYmjqEaZpIksSFF164ZIhPEARBEM5KkkTgcY8j8LjHbfVIltSamUH51a9QUinMSASO/hsfCVt4PQ6tlozH46BpDo5vCOX++5GyWVo33wwigLVm3osuwnvRRRu6jIW2kK6mtYN+q0zv2jayabZbV51Ki01h3Zhzc1RuvBHn/vuR6nVcvx/loQ8lcOWVos3nOtq9++T7xObdGJUf/YjmF76AcvgwUrWKZJogy1iBAPlEAunpTyfy4heLINYqfJdeSmNwEGtuDjWToRWNIi137rNYRKnVsAYHkf70T8W2FTaU2LsEQRAEQRAEQRA2SDyeIBjMMTc3x9zcHIZhYFkWpmni9/vZsWMHkUiEvXv3rvuyE4kEqVQKr9fL7OwsQ0NDS1bKsW2b+fl5gsFgp/KQIKwn27YZGxujUChQqVQoFoudEInf76dcLjMzM8POnTs3LQDoOA7j4+PkcjkqlQqFQoFms4kkSXg8HsrlMqlUir6+Pvr7+xc9N5fLUSgU8Hq9q1bpCoVCzM/PUyqVyOVyKwawUqkUlUqFarXK7t27lwzXKIrCwMAA4+PjZDIZvF4vvb2950UVLFmW2b17N9FotBNqW2jzp6oqhmGQSCTo6upa9zBfPB4nmUwSDAaZm5tjcHCQer3eqZqmaRp+vx/XdZmbm8Pv9+PxeOhaS1sb2m0nK5UK8/PzDAwMkM1GjrY7tDvrHomE6e/vZ2ZmBq/XSyqVYnBwcF3XUxAEQRCEpdW+/32o1VBrNZon/PtrGA6GcVwbd1nGikZRcjncgwcx5+fReno2ecTCcpRgEPvAAaRUCjWdxuzrW7G6mpzJ4KgqhELoj3/8Jo5UsMtlCu99L8odd0C1ilosItk2rqLg/vznlL/4RdwrriD6D/+AJC5MOGN79rT/O77T5jp0mhVOUPj85+H661FTKdRsFrnVwlWUdhs9WcYKh3Hm58n99rfE3v9+sW+vRJJQ/uzPcKemcMtltNFRzIEBpEDg2Pu640Auhz47S6u7GzcaJfCc52ztuIVznghgCYIgCIIgCIIgrINXv/ozfP/7due2JCkkEgmGhmokEgnK5TKWZSHLMn6/H8MwiMViDA0NbUjlH4/HQzwexzRNJiYmmJiYoKurC7/fjyRJuK5LqVQinU6jKAq9vb309vZuSRUi4dzlOA6HDx8mm80yPT2NZVmEQiFCoVBnH8xms50wzZ49ezY8hOW6LkeOHCGTyTA1NUWr1SIUCtHV1YXrulSrVSYmJvD7/Z2Az8DAQOf5pmnSarXW1AJOlmW8Xi+tVotWq7XsdLZtk8/nyefzhEKhFSsbSZJELBZjbm6O7u5uisXieVO5bmHdY7EYjUaDZrOJ67qdANRGUVWVnp4e6vU6v/3tbzly5EjnfVySJFRVxbIsLMsiEomwc+dOuru7V6w8uMBxHHK5HNlslmAwuGI1xHA43Anz+f1++vv7jwa1BEEQBEHYSE4uh9xs4ioKrFAJszO934+WzQJgpdMigLXN6FdeiX3ffUipFFIyiTswsGQIy2000NNpzK4u7OFhIo94xBaM9vxkl0oUX/UqlPvvR5meRq3VsD0eXFVFMU3kVArL78euVslNTxP70IdEUGUdvO1t8LnPQS4Hj340XHHFVo/o3FL6+tfh+utRjhxBqdUw43Gcrq521SbHgXweNZ1GOXwYHIf8ddcRe9e7tnrYi7kutXvuoXX33bi1GlIggPGIR+B98IO3ZDjhK68kd/Ag5te/jjI5iWdsDNswcLxecByUahXJdTG7u3EHBtDe8hbxb7Kw4UQASxAEQRAEQRAEYR084Qkv4NChxfcFAnDRRRdRLBYpFoudANZC27X1bJG1lOHh4U6loVQqxfT0NLIsI8syrVYLSZKIRqMkEgl6enrOm1ZmwuZJJpPk83kmJyfx+Xz09fUtCvl1dXVRqVRIJpNAO7B08cUXb+ixkU6nyWazTExMoOs6w8PDi5YXjUZptVpMTU11jplwOEwgEFg0n4WWhWshSdKKVaqazSaO41CtVtdU1SgUCjEzM0O9Xqder5/VASzHcVbdPkvxeDyrViBbT9FolN/+9rcoikK5XGZiYgLXdTsBqIVqZJFIhGg0uii0t5JyuUyr1aJSqazptY9EIiSTSUzTpFKprHv7WkEQBEEQTiYtBJ5dt/3fKp9bpOOm2W5tjlozM9R/8QvcSgXJ68W4+GI8F1yw1cPaVIEnPYn8l75Eq1ZDHx/HMk2c7m44Guh3bRs5m0VLpbB9PtzeXvQXv3jV111YP4W3vx3l/vvRR0exfT6a+/aBzweA7bq4tRrqzAzayAgA+fe/n9ib37yVQz4nRKPw93+/1aM4N7m2jXX99SgzM8j1Os29e5E8HjrvKrIM8ThmNIp65Ajq2BjWj35E44EHtsd7tOtS+sY3ML/6VZSxMajVkGwbFIWmz0dt3z705z2P4JOetLnjkiRib3wj+WgU+2tfwymXkXI5pFYLJAmrpwc3FsPp7sZ4wxvwP+pRmzs+4by0vT75CYIgCIIgCIIgnKWWOxcrSRKRSGRLAhKyLLN3715mZmYwDIN8Ps/MzAyFQqETBKvX6xiGIcJXG8B13U7o7nysLGbbNtlsllQqhWEYDAwMLBmyCQQCDAwMMDU1RSQSIZPJ0Nvbu2HjSqVS5HI5JElicHBwyQpCC8Gs0dHRTjvChQCWYRh4PB6q1eqqy7Jtm1qtRiQSWbGqleMca1uzlopGlmVRLpdJp9P4/X50XScSiZw1+1mlUiGdTlMoFDrr7vF4SCQSxOPxbbcerVaLw4cPo+s6lmXR1dXF4OAgruti2zaNRgPTNLEsC5/Pd0rhPNM0O5XW1hIo83g8neValnUmqyUIgiAIwhrJg4O4Pl87WFUuwyoBaLlUwvZ4QFHQtsn3rNrdd1P/4hdRfvUraDTAsnBlmYbHQ23/ftRnP5vQk598XoSMJE0j+E//RPnVr6alKCizsxijoziqiivLyKbZbgUWj+P29cFLX7r5oYLzWOPgQZRf/xptYgLL78fZuXPxfilJSH4/9t69SCMjKJOT8L3vYf7lX6J1d5/Ssqx8nuoPfoCTToOqogwMEHjiE5E38UIPYfNZuRzlm27C/d73kNJpJMfBicWQLr8c/9VXo/f3r/syqz/+MXIqhZbL0errQ1pmH5NkGXt4GOPgQaxymdrXvoZnq8OFrkvu/e9HvvXWduvEXA7ZNNvHpevi6DpSKoV96BD5Q4eI/s3fbO74JInoK16Bdc01VG65BeunP0UqFkFRcPv60J/+dMKXX77tAtHCuUvsaYIgCIIgCIIgCOtgqfPUx2UqtowkSfT19VGr1chms6iqiizL2LaN4zgYhkGlUuHee+9lYGCAHlGK+4yVSiVSqRSlUqkTxNB1na6uLrq6utbUluxckM/naTabVCoVhoaGVqxwFAgE8Hq95PN5AoHAhgWwyuUyjUaDQqFAd3f3imEnTdOIRCLk83nC4TCWZaGqKvF4nGKxSDabpVar4Tt6JfZSCoUCiqIQDAaJx+PLTrdQgUtVVRqNxrLzbDQaZDIZ8vk8s7OzOI6DoihYloWu68Tjcfr7+ze8ut7psm2bsbExCoUClUqFYrHYqdLn8/kol8skk0l27dq1rap6zc7OUiqVmJub44ILLiAcDlOpVKhWq7iuiyRJeL3ezn3FYpFcLrfia75AluXOsXF8EG98/P9hWU0AFEXmu9+1ePKTX4ptt1vdnk7VMEEQBEEQTk/gSU+i9IlPYAaDKOk0djC4fFDJNFGLRVqDg9iPehRKOLw+g3BdmuPj2Pk8kq6j79iBEgyu6anFm27C/Zd/QclmUTMZlFqt85ij61ipFM7Bg+TuuYfYG95wXoSwtN5eQp/8JKUPfQjuuAO7UkGqVMB1sTQNwmGcgQHUl7ykHUwTNk3thhuQy2XkVgtzz56VrnbDGhjAGBnBrlap3Hgj0Ve8Yk3LaE5MUP3sZ5HuuAOpWkVqNkGScLxeiv/2b/CkJxF62cvWfIwJG6dx8CC1G26AX/wCuVzG1TScoSG0pz2NwFOegryGtrDHK37pSzif/jRSpYKSySDV6wDIhoEzPk7tq1+l+pznEH3Vq9b1vbD1wx8i5/MAuNEoK85Z1zGDQeRsFvfOO9dtDKcr/5nPtMNXo6NIrRZmVxduLNZunWiaSJkMaiqFUyph//d/U4xGCb/gBZs+TjUaJfKSl8BLXrLpyxaE423PM3KCIAiCIAiCIAhnme16jtp1XUZHR8lkMmSzWSzLYmBgAK/Xi+M4VCoVxsbGCAaDnR/2RQjr9FiW1amYtNB20jRNZFnuhEtmZ2fZsWPHmoIZZ7tqtUq1WkWWZfxH23msJBQKkc/nabVanbDTeqvX6zSbTWzbJriGk+nBYJB8Po/jODQaDQKBAPF4nJmZGSKRCDMzMwwNDWEYxknPXajy1NPTg9frXXF5C+30wuEwhUKBWCx20jTVapWpqSmq1Srj4+OUy2UMw8Dv91OtVgkEAtRqNUqlEvv371+x4tZWcByHw4cPk81mmZ6exrIsQqEQ4XAYx3EolUrkcjlisRiO47B7926i0ehWDxvbtsnlcuRyOQKBQCcYFgwGT3pNQ6EQo6OjFAoF0un0mo5zr9eLpmlomkapVKKrqwvXlUgmP4bj1DvTzc97gJdSqVTQdR1VVfGe4o8NgiAIgiCcHiUQwL38cpxsFn10FGdmBre//6Qvga5poo2N4RgGRCJ4n/3sM162XalQvuUWnK9/Hflo226Ahq7jPPax+J7zHLwXX7zs8ys//CHuhz+MPDGBWixiRSJYAwO4Hg+YJnIuh5ZO4xQKWLfdRj4QaAcPzgNqLEbs3e/GymbbVVMmJtptq0IhjMsvx3/ZZdv3i/657De/Qc7nsQIBWO3iJZ8P2zCQ8nns//3fNc2+dvfdNK+9Fnl2FiWVQl2obOy67cpnoRBOJkPxl78k+KEPoYnzI1vCqdfJv/OdKHfe2Q7kLbSVk2Wc8XGc+++n+NnPor3pTQQe97g1zbPw+c/DZz+LMjmJVixiG0Y7UAvItRrG4cNYgQD2l75Erlwm9qY3rdt7gJvPI7VaOIZxrK3tStN7vSjlMk65jGtZW1a9ycrn4UtfQp6YANvG3L8fNO1YgEzTcPv6aMXj6CMjuNPT2P/+7zhXXom8wsVqgnAuEwEsQRAEQRAEQRCEdbDUOZm1dKFyHId8Pk+9Xsd1XVRVJRKJrNsP6/Pz8+RyOSYnJzEMgx07diwKtsRiMRqNBtPT08zMzCDLMqFQSPywf4ps2+bw4cPkcjmmpqZwHIdwOLwoXHLkyBGi0Winys25HsJyXRfXddfcTk5RlE7FMGeDysc5jtOZ91pa/S2M3XXdzvNUVWVwcBDbtkkmk4yNjXVea0VRME2TQqFAuVymq6uLWCzGjh07Vl1WIpGgUqmQzWbJZDJ0dXV1Hms2m0xPT1MoFJifn8c0Tfbv38/Q0BBDQ0NUq1VyuRzj4+MMDQ0xMjLCgQMH1rSOm2VmZoZ8Ps/k5CQ+n4++vr5F+0Y0GqVYLHYqe8myTCAQ2PKKcYVCgVarRblcXvV1XGg5WygUiMfjNJvNJcN5x/N6vfj9fqLRKPl8fsXQmW3b5PN54vE4gUBgTS0LBUEQBEFYH6FXvILSb35Dy7LQJyfblT7icRyfD8lxkItF1GIRR9Ow9uzBefKT8T3ykWe0zMboKLU3vAE5mURJp1ELBWTLwpUkbJ8PO52m+cMf0njBC4i+/OUnfyl1XVof/SjK7CxqqURr9244emGEBGAYuH19NLu60EZHUSYmsL/2NVob1IJru1LjcSIvfelWD0M4SqrVkCwLZ40XlDi6jmRZcFxlt+U0Jyba4auxMfTZWcxwmMaePUgLQZFiETWdRh0ZoWWalF//eiKf+pRoSbjJ3FaL/N//Pcr//i/q2BhKq4UZCOAEAmDbqPk8UjqNmUhgvfWtlN/xDoJPeMKK86zdfTdcfz3K2Bhyo0Fz1y447oIaG7DrddSJCdTRUazbb6f80IcSfOpT12elFKXTsm9NHAcXQJaR1nhOZSNUbr4ZqVJBK5dp7N7drnq1BEnXaQ0MYExM4JTLlL/+dcJ/9mebPFpB2B5EAEsQBEEQBEEQBGEdnGoLQtu2mZmZIZvN0mw2OwEsTdPwer0EAgH6+/vXVKVnOa7rkk6nyWQyKIrC4ODgki2rPB4PQ0NDjI2NUalUSKVSawqMCMcshEsmJiYIBoP09vYuCr/EYjFqtRrT09O4rossywSDwVOqUmRZVqeSmeM4nf0lFouRSCRWDXpsNkVRUFUV0zQ7gZqVNJvNRa34NoKqqp1AT7PZXDXA0mg0kGUZWZYXjSmRSOA4DpIkUS6XyeVyTExMAO0QTjAYZMeOHQQCAXbv3k0gEFh1bPF4nFQqxcDAAMlkklarRSwWw+PxkMlkKBaLTExMoGkag4ODxGKxTkjL7/fj9/tJpVJMT09jGAbZbJZEInG6m2pdOY5DJpMhnU5jGAYDAwNIkoTrulQqFfL5PNWjV543m03uvvtuWq0WkUiEXbt2benYW60WzWaz0yZxNT6fj3Q6DYBpmms6Lru7uymVShSLRaamprDtpVsVTU1NoSgKkUiE7u7uU1sRQRAEQRDOiBqNEvjwh6m87nW0vN52y6VMBtk0QZKwvV5afX0Qi2H/yZ+0W/mdAXNujtrf/307KDI1hatpWF1d7cpVRwNfxsQElt+P/cUvkldVoi972aJ5lH/yE+S5ObRMhlZvbyd8dSJJ07CGhzEOH8auVqneeCP6eVIFS9h+XMPAlWUk22YtURXJtnF1Hdbwubty/fUos7Pos7M0BwYgHl/cCi4SwQqHkaen0ScmaHk8lG65hcg115zu6ginIf/xj6P85jdoIyM4Hg/N3bvhuHMnputCPo+eTGI6DtZ73oN18cWoS1SSXlD/yldQCgWUapXm3r1IS1106PVi7tmDcegQUjpN64YbYJ0CWNLQEK7fj5JKYbZaSCudC3JdlHIZOxDA6e3d0kp89o9/jJrNtivNrXJeQwqFcDUNKZvF+uEPQQSwhPOUCGAJgiAIgiAIgiCsg1M5H2JZFocOHaJQKJDJZCiXy0iShCzLWJaFruvE43Gq1So7d+5csh3ZWpRKJRqNBsVikd7e3iXDVwsMwyAYDFIoFMjlcgwODq65ctH5zrbtTrjE6/XSv8zV4j6fj6GhIcbHx4lGo2QymWWnPVEmk+HIkSM0m03y+TyWZeG6LoZhUCqVmJ+fp7u7e9mQ3VYIh8P4/X5kWaZUKnVaty3FdV2KxWKnss9GVW4Kh8Pouo7P56NQKNDb27vi9IVCofOcE6vC9fT0EAgESKVShEIhbNvGcZxO8Cwej9Pd3b3mYJyiKOzdu7czj3Q6zdjYGJIkMT09zezsbCe8FA6H6evrO2lM3d3dlMvlTgu87RLAyuVyNJtNyuVyZx+1LIvp6elOq8pyuYxlWZ1Q26FDh2i1Wvh8vi1vi7oQFnNdd9Xjay3TnGghROm6LlNTU8zOzsIJPzctVIcbHh4mkUhsi/aMgiAIgnC+0fv7iXz2s5RvuQX71ltxp6fb1UwkqR3CuvRSjKuuIvLYx57xD+alD30IZXISY3KSViKBe8KP8E4kQqPZRD9yBMbHsb/wBZpXXIGxc2dnmtZ3voOSzwPgxmKsOCKfD9vnQ85ksH70IxABLGGLuPv24Rw+jD4zQ9NxVmzX5rZaqLUazd5epP37V5yvlc8j33EHSjqNGQrBchWpJQlncBCnUkHKZLBuu60dJNkm37PPdU69jvStbyHPzuJqGvbu3Sdve0mCWIyWLGNMTtKMx6ncfPOyleysbBblZz9DyWSwwuGlw1cLs9Y0zFgMNZfDPXyY+n334X3wg894vXzPfCaNW2/F1nXkVAp3cHD5ictllGYTa+dO5Cc/+YyXfSakfB6p2cRZSzvBo2FkqdlEKhQ2fGyCsF2JAJYgCIIgCIIgCMI6eOMbr2Bystm5LcsGz372906aznVdRkZGOm0BvV4vg4ODnQo5lmWRz+eZm5uj0WgAoGnaaVXCqtVq1Ot1HMchFAqtOn0oFCKZTOI4Ds1mc03VXoR2uKTValGpVOjq6mJmZqZT0UyWZXw+H9FoFMMw8Hq9nfBPJpOhr69v1bBGKpVicnKSmZkZisUihmEQiUSQJIl6vc74+DherxfLsrAsa8srBi0IhUJ4PB6i0SjpdJpAILBsZatMJtNp27iRlX00TSMSiRCLxUgmkwSDQfzLVALI5/M0Gg36+vro6upa8nXy+/3s2rWLoaEharVaJzy1EDw7VR6PhwMHDjA9PY3f76fRaDA3N4fP5yMQCLBr1y4ikQiJRGLZcUejUXK5HF1dXTQajW3Rpq5Wq1GtVjttBR3HYWpqimKxyPz8PK7rEgwGMQyj8/4zPT2NqqqMjY0BbFkIyzAMdF1HkiSq1eqq1cyq1WondHcqVel27tyJJEmoqsrhw0tdDS2xc+dOurq6GB4ePpVVEARBEARhHcleL+HnPQ+uuYbG6Ch2JgOahj48jLZO4Xdzfh7lrrtQ5uYww2Hcvr4lp5MMA3PnznblqkqF6te+hvG61x2bIJdDajaxPZ4VQywLbL8fuVZDzuXWZT0E4XQYz3oW1s9+hjs7izw3h7vCRUvK3By2rkMggO/qq1ecb/VHP0KqVlErFRq7dq0cSJQkrFgMLZvFnZykMTKCZ9++01sh4ZSUv/WtdivIUonm8PDK50siEez5eaRMBvv22+Ev/mLJoFzj4EGwrHZYbw3nG5x4HDmVgkaD1joFsDz791N98IPhaPXClq7jJhInj7daRZuawoxGcYNBgs961hkv+4ycauvEhVDyBl1UJwhnAxHAEgRBEARBEARBWAf3338nzWa9c1uWvUuenygWi5TLZaampgiFQvSdcDJdVVUSiQSBQICJiQkMw2BmZoYLLrjglMd0fMWWtVRkOT4w4qzUP1FYpF6vUygUmJubw3XdTvDNtu1OGCefzxMMBunr6yMUCpHNZjFNE8uyOi3xllKr1ZiamiKZTFKpVBgeHsbn8y0KMrVaLWZmZpicnOy0v1toTbfV+vr6aDQa1Go1xsfHSSQShEKhzv7YbDbJZrOUSqVOEHGlSlnrobe3l0KhQL1eZ2pqiq6uLiKRSGebLlQZKxQK9PX1EQgEVq0kparqmkKOa6HrOrt378Y0TbLZLJqmYZomAAcOHFg1UOX1ejFNE9d1MU1zWwSwFlpmLlTVS6fTlMtl5ubm8Pv9xGKxRe8/CxUBW60W09PTaJpGKBQ6qeLXZohEIp0KgblcbsUAlm3bFAoFuru7CYVCKx7bJ5KkdsAqkUhw990VOOEnIUmSOHDgwLLBO0EQBEEQNpkk4dm7F/buXfdZV267DalSaYcFBgZWntjrxfL7kTMZ3O9/H/e1r0U6k0rGkoQrfjgXtlDgMY8hNzyMWSy2K8BJEk5Pz6IQoWvbKDMzqMUirV27sC+5ZNWAlJPNIjWb4LqrtlIDcAMB5Lk5cN120FIEsDaFff/9yKUSSBLSGr5j25EIaj6Pm0ph5fNLtiF0Gw1wnHY4aC3f0RbOtzhO+7nrxP+611F/9atpWhZGMomdz2NHozgeD5JloRQKqJUKZjiMMzSE/KpXoW5x5WO3pwfX50PNZHAWwlXLTes4qNUqrb6+ZYPDgnA+EAEsQRAEQRAEQRCEDbJUACuVSlEsFgFWbH/m9Xrp6uoil8sRjUap1+unHD5YaIW2EMRYLQzQarU6IZTlKhUJJ2s2m0xOTlIul2m1Wti2jdfrRVEUTNNkdnYWXddJJBKYprkoYOSuchVhOp2mUqlQqVQYGhpaMkyj6zpDQ0OMjY2Rz+dJpVLbJoAVj8c7ldxSqRRzc3PMz8+j63qn0pHX62V4eJhIJMLevXs3vIWiz+dj9+7dQHvbZbNZMpkMmqZ1jpWFynSRSIR9+/ZtyfGgaVrnPaLRaGCa5poqKjmO0wldbpd2lKqqoqpqJ3RYKBTI5/Pour7kvroQHOvq6upUz0qn01tS+UmW5U5L2PHx8WVbOzqOQzKZRFEUQqHQabd/9Pv9dHefHLKSJET4ShAEQRA2iGvbVH/8Y5q33II0NobUbOL6/fDwh+N79rPxXHjh5o4nmUSqVnE0DdZQldgOhdAyGZxqFbtQQD3aWk3q6cH1elGyWSzbXjWYpZTLOIEA7jb5LiGcpyQJ37veRf1v/5am66Ink5DNYoVCuJqG1Gqhlkq4ikJr506c3bsJve1tq89Wlo+FR1YJkgBIjtNpLyqJ8yObp9FAchychcpLq3BVtR2uot2+cClKKIStKO1wabMJq5xbkxZCV6qKdBrV6Jfj2bMHPvhBam98I81gECmdRs1kkGy7XXUtEKC5ezeEw0ivehXhK69ct2WfLu0pT8H57W+R5uchn4clAm4L5Eym/Uc0ivH0p2/SCNePa1mUv/c9rHvuwa3VkHw+1Ic8hOCTniTeA4RTIvYWQRAEQRAEQRCEDXJitsa2bcrlMsVisdNCbiWRSIR0Ok2tViOfz68YwDo+dLEgHA7j8XjweDzk8/lVW7vl8/lFzxHWZnp6mlqtxvz8PPv27SMej3cq/UC7rWQul2N2dhaASqXSaSmprPAjiG3b5HK5TvWslV4TRVGIxWJks1ni8TiVSmXVVmmbZWBgoNPGLZFIUCqVsCwLSZLw+/14PB4ikQjDw8OnVDHoTEQiEfbv308ymSQSiVCtVjtVphb2/0gkwuDg4Cm1kdsIHo8HwzCQJIlyubxqpa1yudzZV7Z67AtCoRB+vx9FUUgmk5imSa1WW7KtoOu6lMtlwuEw4XAYVVXJ5/OEQiEGBwdPq7XjalzXpVqt0mq1gPZ2Oz7s1NfXR7FYZHBwkOnpaSqVCtFotLOdK5UK+XweWZYZHh4mHo9veCU3QRAEQRDWR+OBB6i99a3IMzMouRxyuYzkOLiqinPkCI3vfIfqpZcSeec7Udbxh/gV2TYS4K41TH98ZSDL6vztedrTaH3/+7izs0iZDKzU0rlcRmk2sXbuRHrSk05z4IKwPjx79sC//iu1t7yFVjgMuRxKqYTUarWDV4OD7fZz+/cTes971tT+UxkcxPZ42iGcUglW+bwulUrYR79PqatVohPWj9+Pq6rIloXrOKu2T5WaTdyj51WUZb4rey66iKbPhxUMouRy2Ku89nI2i+3zgWHg/YM/OK3VWI7nwgvRvvAFyjffjP31r+POzXUec71e3D/+Y/zPfS7Grl3rutzTFXzyk8l/6lOYXV3oMzO0NA2W+LfQLRbR5+cxe3pwenvxP/7xWzDa0+S6FD73OZybb0bO5ZCLRSTbxlUU7FCI/Mc/jvSsZxF96UvXFAoUBBHAEgRBEARBEARB2CAnBrCsoyfD19oWTFXVTvsx67gT6QtKpRLpdJpisdippLRQOSsej+PxeAiFQkSjUebm5giFQssuN5vN0mq1iEQip1255XxULpdRFIVKpUI4HMYwjE6oqtlsYts2ANFoFNd1SafTOI5DT08PwWBwxQBWo9HAcRyq1SqDg4OrjiUUCjE/P0+z2aRarW6bABbQ2ScLhQLFYrETwFrYX3Vd3/QxBQIBLrjggk7AcSGAZRgG8Xh8S8a0lIVQZCQSIZfLEQwGlw1vWpZFsVikt7eXSCSyaYG21Sy0D4xEIoyOjnbGv1SotFAo4LougUCA6NF2C6lUCtu211wFbK1s2yadTpNOp2k2m539UlEUvF4viUSCRCKBqqrs37+fw4cPYxhGp9LcwvGt6zrxeJxwOEw8Hmfnzp3rNkZBEARBEDZO4+BB6n/3d8jJJPrMDABWKISj60jNJsb4OI6u49brFF79aqIf+xjyGipSnSkpGsU1DBTTxDRNpFU+08kL1bIUZVG7Kt+ll9IYHsbKZNBnZ2l6PEjh8EnPdxsN9KkpzHAYNxAgeNVV675OgnCqPHv2YHzxi1R/+lOaN92Ee+gQUqOB6/PhXHQRnquvJvIHf7DmQETg8svJ/+u/YoXDKOk0dji87HNd20bP52n19mJfcgl6f/96rpqwAv1Rj8K67bZ25bF8Ho5W9FuS66IWCljxOPbevcuGZGWvF/eKK3DSaYyREexiEZZ4LwSgVkMtFGgNDGBfein6OoXv7EqF6k9/2m6Fqaqou3fj/6//opVM4pRKSB4P+tAQ8ja7GFLSdfTXvQ7r7W/HsiyMsTGsQAA7Gu1UpFNyuXbrwUQCt68P43WvO7NWuJvItW3yb3kL8k9+gjozg1oogCThKAqybcP0NFYkgpPNkhsZIXrddWfNuglbRwSwBEEQBEEQBEEQNsiJAayF0IEkSThHS6SvZqnKVpZlMTo6SrlcplQqUSqVME0TWZbx+/2Uy2VmZmbYtWsXfX19lMtl6vU6ExMTdHV1EYlEFoWEcrkcxWKR/v5+/H4/8ZVOcAmLpNNpbNtG0zT6+/vJ5XKYpkm9XseyrM7rLMsyiqIwOztLV1cXruuuGHSzLIvZ2VkOHz7M2NgYjUYDwzAWVQU60cJ9tm2vef/aTJIkEY1GO6Ga7cLn8+HbhB/STpckSZ1WfGNjY8zOztLX13dSCMuyLKanpzEM44xa4G2Uvr4+6vU6U1NTTE5O4rourut21qPValEsFqlWq3R3d+Pz+QiFQlSr1c7+vJ77davV4vDhw5RKpc574ML8VVXF4/EwPT1NKBRi3759RKNRDhw4QCaTIRAI0N3djW3bSJKELMsEAgESiQSxFVoyCIIgCIKwfbimSe3aa5GnpzGSSZo9PbiJRKfaigs0TbP9+OgoLUmi8P73E3v72zd8bJ4rrqB50004moacyeD29S2/HraNWizSGhzEfuQjkY6/iECSMP7+72n9f/8fpmXhGR/HCgaxYzFcw0CyLOR8HrVYxPb7cYaHkV/2MlTxeUbYJiRFIXD55QQuv/zM56XrSH/6pzjpNPrhw7hTUzhDQyeFsFzbRjtyBEdVIRbDEIHETeV//OPJ9/Zizc6izc9jhkKwTAhVmptDsm2ceBz1Gc9Ycb6B5z2P6ne/S6tUwpiYoNXdjZNIdMI0ruMg5XLoc3NYoRB0deF94Qs7z3fqdcq33451111QrYJhIO/fT+Cqq9BWqDbfmpmh8vnPI/3oR1CptKu4SRKWYZDv7kZ66lMJv+hF2y54dbzA5ZdTeuMbsT/wAZx8HjmTQZ+ebleLlGWsUIjmvn24sRjatdfiv+yyrR7ymuU//GHkH/8YbWQEF2gNDEA02n5fcF3I51FTKZTDhzFdl/yHP0zsH/5hq4ctbHMigCUIgiAIgiAIgrBBTgxgaZrWqaxSqVRWbVFVq9VwHAev19upXGXbNocOHSKXyzE9PY3jOITDYfx+P47jUCqVOm3oHMdhz5497NixA9d1MQyDXC5HOp1G13Vc16XVauH1ehkaGiISibBv374VqzIJi1UqFcrlMsPDw51wSTKZRFGUk8IirutSKBQ6lamWe/3T6TRTU1MUi0Xm5+fJZrPYtk0gEKBarZLL5ejq6qKnp2dRCGehfZqqqksGtISzi+M4neO1Vqt1WvdNTk52Wop6vd5O+7xCoYDH42FwcJBEIrFqq8LNFovFaDQaZDIZCoUCY2NjjI2N4fF4Ou9FhmHQ29tLMBhkaGgISZJoNBqdSl7rtV/bts3hw4fJZDJMT0/j8Xjo6enB5/ORz+eZnZ1lbGyMVqtFT08PU1NT7N27l3g8Tm9vLz09PVSr1U5lQsMwRNtWQRAEQTjLVL77XaT5+XZLpd5e6O7mpFo4moazcyf2kSMoyST8+MdY2SzqBl+w4r34Ymp79yKl02hzczRDIaTj2iN3uC7KxASuqkIkgufqq0+axP+IR+C+/e2Y111HMxhs/3A+OYnkuiBJWD5fu51bNAoveQnh5z9/Q9dN2DqubVP98Y9p/e53UK8jBQLol16K/1GPOm/aaoX+4i8o/vKXtCwLfWwM9+BBrGgU1+8H10UqldALBRxVxdqzB/sJTyDyxCdu9bDPK5KioFxzDc7cHHKthjYygtnX165YdXQ/dVstlPl5tHye5tAQ7sAAwac8ZcX56oODtN7yFsx3vpOWpqGmUkjpNI7HgytJKI0GAGY8jtvfj/y3f4vv4Q8H1yX/qU/BTTdBqYSaz4NlgSzjhsNUv/Ql7Mc8hsgb3nBSC8T6vfdSf+MbkWdnkVMptHK5c6LQVRSsmRmcmRnyP/sZkX/+Z5TlqnJtA6GnPIXmgx5E9atfxf3e93Bqtfa6SBJuIABPehL+5z4XfQ3V27cLM51G/vrX2/+OyjLmnj2Lq1tJEsRimOEw2ugoysQEfP3rmH/+52tqeyqcv8QZWUEQBEEQBEEQhA2yVAWseDxOuVxmYmKCZrO5YjutbDZLIBDAMIxOVZXp6Wny+TyTk5OEQiF6enqQj16lDe1Wb+VymWQyCbQrL1188cXs27eP6elpYrEYlUql3cpCkvB4PJ32ZkNDQ9um7drZwrbtTjiqWCyiaRq1Wq3TZtLj8eA4DrVaDdu2kWW50+ZsqTZy8/PzTE1NMTs7S6FQwLIsotEolmVh2zZzc3OdoIfjOPQf1wphIYCzUAFJOHsVCgXGx8dpNpsUCoVOGLNWq6GqKuVymUqlQigUQpZlPB4P/f39BINBuru7GRoa2upVWFJ/fz+O41AsFjtV+3w+X+e9yDAMgsEgvb29qKraCS1Go1H8fv+6tVScm5ujWCx23hO7u7txHIdkMkmxWKRer+M4DoVCgVQqRXd3N41Gg127dpHP59m7d++2avEpCIIgCMKpa916K2ou1/4RfaUfUiUJu7cXY3QUu1qlfNNNRF/2sg0fn+ev/gpzbAyr0cA4cgQzGm1XazGMY1U50mkky8Lcswf7EY8g8qhHLTmvwB/+Ic1Pf5rql76E+4MftH84t22Q5XbI7LLL8Dz3ue2wgXDucV0K//EfOLfcgpxKIVcq7ddfVTG//GVyg4Mo11xD+Mort3qkG04JBAh96EOUXvc6WoaBlMmg5vPIqRQAtsfTDmTGYthPeAKxd7zjvAmnbSfh5zyH3MQE1s03I09Po09N4SaTOIaB5DgojQa2rtPcuRN3aAjfe9+7pgpSgcc/nto//ROND34Qd2oKCgWkeh1cFzsahWgUp7cX7ZWvJPjHfwyuS+5tb0P+wQ+Qp6dRSyVcRcHVNLBtlLk5LJ8PymWKY2OEPvKRTgXB1vR0O3x15AjGzAxmINAebyAAto1UKqFmMiiHD4NlUXj964l97GNI2/hiOmPHDozXvQ7nVa+iefgwTrWKHAjg2b9/cfXFs0Tl5puRqlXUapXm3r3LthaUFAVrcLDdvrJapXLzzUT/8i83ebTC2WT7HsWCIAiCIAiCIAhnuRMDWACJRIJUKkUwGGRqaoqhoaGTQliu6zI/P0+1WmXnzp3E43EURcGyLLLZLKlUCr/fT98yrSiCwSADAwMkk0kikQjZbJbu7m4e9KAHdSooHR/AisfjInh1mhRFQVEUUqkUqqoiSRKDg4N4PB6azWanRVlvby+yLDM9PY2qqtRqNUql0qKgVK1WY3p6mmQySaPRYOfOnSQSCZLJJDMzM/j9fmKxGOl0mrm5OaDdPi8SidBoNMjlcvT29hIKhVYM9gnbWz6f58iRI6TTabLZLKqqEgwG8Xq9qKpKKpXCtm3C4TCBQIDBwUFkWSYSidDd3b3tg0GDg4NccskleDwexsbGSCQSnfBVJBJZFLJaWNdIJLJuLRVd1yWTyZDL5fB6vXQfbRcxMzNDoVBgdnYWaFeo6+/vZ2ZmBsMwOsHZhekvuOACvF7vuoxJEARBEITNJx8+jFwqYR5XVWVZfj+2riMVCtj3378p4/Nfdhmlf/gHnA9+kFYqhZrJoGezuLKM5Lrttk/hME5PD/ZDH0r0Pe9ZcT2MHTsw3vAGnFe/mvpvf4tTLiN5PHge9KANr+glbB3Xssi9+c0o//M/qLOzqIUCOA4oSrt1m6Jgzc3hTk+TO3KE2Gtfe84HjtR4nOgnP0npttuwbr0Vd2zs2IOShP3Qh2JcdRWRJzzhnN8W25YkEfuHf6DQ04P93/+NUywi5XLQaoEsY/X1QTCIffHFBN/0plOquuR7+MPxffGLVH/9a5rf+Abu3Fz75F08jv4nf0LkD/+w87rnP/lJ5B/8APXwYQCaw8NIoVDncatWQ56bQz9yhBZQfPObiX/sYyBJlD/9aZRkEmNmhmZ/P3R1tVcN2uHXeBwrFkOenEQbGwPDoHz77YSe+cz13JIbQvZ48F588VYP44w5P/sZai6H7fGAz7fyxD4ftseDlMth33EHiACWsAIRwBIEQRAEQRAEQdhECy3CHMdhZmaGsbExAoEAwWAQWZZpNBoUCgUcx2F4eLgTAoB2Raxms0m1WmXXrl0rLicYDKLrOoVCgXQ63QkN+P1+/Eu1rxBOSyAQwO/3c++996LrOrFYjJ6eHur1eqfqlSRJ6LreCbm5rovjOKRSqUUBrFQqRaVSoVKpsGvXLgzDQNd18vk8sViMTCZDOBymq6uLTCZDuVwmk8l0nhsMBolEIvT29m7JthDOXKvVYnx8nPn5eQqFAgMDAwSDwUXT9PX1MTMzQ71eJxqNEo/HGR4ePqtahw4MDFCr1VAUhWKx2KnCt9BisF6vk81mqVQqDA0NEQgEOlUAz1SpVKLZbFIsFhkYGACgWq1SKpWYm5tD0zS6u7s7lQUjkQilUone3l66urpIpVLouk4ymWTv3r3rMiZBEARBEDaXa1lIponkOLDGCpuuqraDK0fbVG2G0NOfTq2/n/q//zvcfTdmrYZ0NICA348TjyM95SnEXvrSNVcfkX0+/JddtsEjF7aL/Ac+gPKTn6CNjOBKEq2+PtxoFEmWcW0bOZtFTadxKhW48UYKiQSR//N/tnrYG07SdcLPfjZcfTXNiQnsdBpXltEHB9F6elZ8rmvbSLIswlkbTZKI/J//g/Pc51L+5jex7rgDSiXQdaThYXxXXYVn377Tnrf/0kvxX3rpspM4tRrcdBPy9DQA5r59J1dI8vlwdu3CmpxEGx8Hn4/qr3+NsWcP8h13oKTTmKFQJ3y11DicoSGU++9HymYxb7sNzoIA1rlCKpWQTBNnDdXTAByPp/3ZoVTa4JEJZzsRwBIEQRAEQRAEQdggjrP0/T09PbiuiyzLVKtV8vk8qVQKx3HQdZ14PE44HCYYDLJ3795OKKFWq1GtVtE0rdOGbiWhUIhKpUKj0cB13SVb3glnJpFIMDY2hm3bzM/Ps+/oCUCv17uoOo7ruqRSKXw+H7qud9qwWZaFqqrYtk0ulyOfzy+qYKWqaiewB+3qSMViEdu2SafTnfZ0/f39dHV1MTw8fFJgRzh7ZDIZarUauVyO4eHhJcOSmqYxNDTE+Pg4lUqFcrm8qA3p2SAYDLJjxw6gHUrNZrNkMhlUVcVxnE5bz507dxIOh9m3b9+6vX+1Wi1M08R1XXxHr3LN5/PUajUsy6K/v3/R9vR4PORyOWzbxuv1Eo1GyWazBIPBVdvICoIgCIKwPUmqiuvx4CoKUrPJEoWLF3NdZNPEUVXY5ItZfA9/OL6HP5zmxAT1H/8Yt1gETUPdu5fw5Zdv63ZVwtZqTU8jf/ObKOPjuIqCuXcvkiyz8KlaUhTc7m7MaLQd0Jqexv7P/8R5znOQz5dKr5KEsXMn7Ny5/DSuS+3uu2nceCPSL3+JVKuBquLs2IHytKcRfNrTzp/ttQVkj4fwVVfBVVdt6nLL3/gGUrmMWizS3LFj2fZ0SBL24CDq738P+TzNG27AesQjkCoV1EqFxu7drPhNVpaxYjHUXA73gQdoTU+fUkUv4QyoajtIadtrm962QVFAdBAQViE+mQmCIAiCIAiCIGyB3t5eIpFIpwqSfdwX/kAgQCKRIBqNLgodOI6D4zhrrnSjKEonuHMqzxPWLhgMomkakUiEmZkZarUauq53QnMApmmSy+VoNBr09/fj9XqxLAuAZrOJqqrU63Vc16VarTJ4wsk2n8/Hzp07mZ6eJhAIUKvVaLVaNBoNgsEg/f39DAwMMDg4SCQS2czVF9aR67qdUJ3P51uxUp0sy8Tjcebm5uju7qZYLJ51r/1C69NkMkk0Gu3s15Ik4fP5MAyDWCzGwMDAouPpTLkn9IZ1HIdKpUKpVCIQCKwaZovFYoyOjtJoNMjn86LinCAIgiCcpZwHPxg7mUSfnaXpOO2KNsspl5FNEzcSQX34wzdvkMcxduzA+PM/35JlC2enyg03IFerqLUajf37l9/HNQ2zvx99chKnVKL8jW8Qfs5zNnew25RdLlO49lqUe+5BLhSQ83lky8KVJKTpadwHHqD4uc+hv+Ut+B/96K0errCOrF/+EjWfx1XVdtvBFUiKghUKoeTzuL/5Dc7QEFKz2X5sDaFd1+9HTqcBMOfnRQBrk7g7duDcey/6zMyqnwNcx0GtVmn19+MOD2/iKIWzkQhgCYIgCIIgCIIgbBB3lUupPR4Pw8PDDA4OdiqyqKq6bNhg4bFWq7WmilYL4R5Jks66Cjlnk+HhYUZHR+nq6qJer1OpVPB6vSiKgmmaNBoNDMOgr6+PUChEPB4nmUwumsdCuG5hHziRx+Nh586di1pUmqZJX18fu3btYv/+/Zu1usIGabVaWJZFuVwmkUisOn0oFGJ2drZTGe9sC2BBO8B44MABarUa+Xwey7KQJAmPx0MsFlvX4NUCXdfRNA1JkqjVahiGgeu6mKa5qCXogkajgaqqKIqCqqqd5zebTVqt1rqPTxAEQRCEzWFcdRXWr36FOzeHPDODu8wP3q7joM3NYQaDuMEgwac/fZNHKgin6c47kbNZLL8fabUKTaFQuyJcLof14x+DCGDh1GoUXv1qlPvuQxsfR7IsrGAQOxgE20bN55HSacxEAvPaa6lcdx2Bxz52q4ctrJdyGUwTR9fX1G7S1XXkSgWpVsM9g+rNK4aBhXVlPOMZWHfeiTs7i5ROwwrtR6V0uv26RqMYz3jGJo5SOBuJAJYgCIIgCIIgCMIGWS2AtUCW5TW1sVpoSzg3N0epVCIcDi877UKLu56eHkKhkGg/uIH8fj/79u0jk8kQi8WwbZt6vd5pKRmLxTAMg1AoRF9fH7lcDv1oyXJN04B2tTJZlpEkiWazuWyLSY/HQ29vL7Ztd6plybJMMpnEcRxUVSUYDBIIBDZt/YXVtVot8vk8pmkiSRKGYRCNRhdVpTu+Wt1agkeSJHWq3DnL9Ts9S/h8vk47wI220OIzHA6Ty+Xo6+vrPHZidSzXdSmVSgSDQQzD6ByXJ063EXp6noFl1QGQZYVdu0SbA0EQBEFYT4HHP57s7t1QqaCPj2M5DnZvL9JCayHXxa1W0WZmkGwbZ/du3Kc9DUV8zhbOElKxiGSaOMt8t1w8sYTt9SK1WlAqbfzgzgKFD38Y5fe/Rx8dxfL7sQcHkY5+fwcw+/shn0dPJjEB893vxv7qV1GCwa0btLB+DANkGekU2tO5koSr6ygDAzheLy7glkpIK5y7A5BKJeyj5wS1gYEzHLiwVoHHPpbcwABmLoeRTNJUVYjFFgfuXBdyOYz5eZr9/Tj9/QQe97itG7RwVhABLEEQBEEQBEEQhA2y3r/Rh8NhDMMgEomQTqfx+/3LBjVSqRSyLBMOh9dUTUc4fdFoFJ/Px969e7FtG4/H0wlgKYqCz+cjGo2i6zqu65LP54nH4wQCgU4Qy+fzoaoqkUiEQqGwYrgOIJ/P02w2mZqawnVdLMvCdd3O8nw+H729vUSj0c3YBMIy6vU6MzMzFAoFms0mpmkC7SCdruvE43H6+/s7FZagHcZbS2Ul27axbRtFUc679qKNRoNKpdJZ/0AgsGxo8USyLNPV1UW1WmVsbIx8Pt8JxdXr9U540XVdMpkMjuMQDAaJxWKdZVuWhcfjWVNw9nQ94hFfonm0bYVhGLzoRSJEKwiCIAjrSpII/uM/UnnVq2gBajKJdvAgtteLo6rIzSZKq4UVCGDt2oV92WXE/uZvtnrUgrBm7tFzBZLrspZTE5Jtt6v9bEAV2rONXSoh/eAHKDMz2B4Pzs6dJ1/UJkkQi9ECjOlpmvE45VtuIfKiF23JmIX1Je/fj/s//4MyP49Vr8NKVeRcF7VUworHcXbtInzFFRQ/+lGsUAg1k8EOhZatouXaNnqhQKu3F/uSS9C6uzdojYSTSBLet76VxmtfS8txMJJJ7HQaOxrF1TQk00TJ51FaLVp9fbiDg3jf+tY1VUQTzm/iX1FBEARBEARBEIQNst4BLEmS6Ovro9lsUq/XGR8fJ5FILKpw1Wg0yGQyVCoVhoeHCQQCq4Z5hDOjqirRaJRKpcLk5CSxWGxRVZ0FrusyOzsLcFIwTpIkEokElUqFsbExSqXSku3QoB3qeeCBBzqt0yYmJnBdF1mWsW0bTdOIRCJUq1UGBgaWHItw5qrVaidQZRgG3hNOyJZKJUZHRykWi2SzWer1OrIsd6onBQIBarUapVKJ/fv3o+s6Ho+HcDhMoVDoBH6WUywWUVUVn8+37L5yrimVSszNzVEul2m1WjiOgyzL6LpOKBTqVPxbTU9PD/l8nsHBQaanpymVSkiSRLlcJhgMYpompVIJy7Lo7e1d9D6az+fx+XydNokbYRMKbAmCIAiCAOj9/QQ/8QlK73oX/Pa3WJVKu2qQ42B7vVjRKG4ggPuUpxB7zWuQRDBFOIu4/f04hw+jZrM4rrtiaMC1bZR6HburC5Zpx3k+Kd96K1Klglqp0Ny1a+XARTSKnUohpdPY3/gGrFMAy5ybo/rNb+LMzwMg9/Tgf8pT0Hp712X+wsr8V15J7ctfxvb5kOfmcHbuXH4/yGaRbBu3qwvtGc9A9vlwn/hEnGwWfXQUZ3YWt6/vpOe7joM6NtYOS8ZiGFddtfErJiziffCDcf/pn2i+9a00IxGkTAYln0dyHFxZxg6Hsbq6cOJxPNddh/eii7Z6yMJZQHxaFARBEARBEARB2CAb8SN6IpHoVEVJpVLMzc0xPz+PruvYtk2r1cLn87Fz504ikQh79uxZ/0EIJ+nv76dUKtHX18fMzAzlcploNIrX68V1XcrlcqcF3fDwMOFw+KTqVIlEglQqRU9PDzMzMzSbTaLRaKfK2UJbyfvvvx/HcSiVSgwODtLb29sJ4VmW1Qn81Go1oN3msKura9O3ybnIcRwymQzpdLpTCWmhFaDP5yORSBCPx2k0GoyOjpJKpchkMkQiEfr6+joVk6rVKrlcjvHxcYaGhhgZGeHAgQOdEF42myWfzy9bwcw0TbLZbKf62vnQcnJ2dpZkMkmhUOhUgFvg8XiIRqMUi8XOMbESVVXZt28f0D4+ZmdnOXjwIJlMhkwmQ1dXF5FIhHA4TCAQYHBwEEmSyOfzFItFhoaGiEQinRaigiAIgiCcvbTubuIf+QiNw4ep3XQT7sgINJsQCKBceinBK69EFVVlhbOQ+tSn4t53H1IqBbkcxOPLTiunUriKApEI3mc9axNHuT05hw6hlEo4qgqrfdeSJOxwGKVUwk0mcRoN5DVW511Kc3ycysc+hnLXXVCrIdfbbcnxeqn++79j/8EfEPjrv8bYufO0lyGsTu/vp3zZZVCttttQTk1hDwwgHV992nUhm8WYmaHV14fT1UX4yU8GIPRXf0XpV7+iZZrok5M45TJWPA4+H7guUrGIns/jyjLmnj3Yj30skSc+cYvW9vzme+hDMf7rv6jcdhv2bbfhJpOdx5yBAZRnPIPwM54h2osKayYCWIIgCIIgCIIgCBtko6qYDA4OYhgGhmHQ3d1NuVzGsixkWe5UZolGowwNDS3bolBYX4ZhsHfvXgB0XSeXy3XaA0K77VkkEiEWixGJRNi7d+9JLQw0TWPv3r2dVoLpdJpsNovH40GSJKrVKq1WC1mWMU2T/v5+Dhw4gCzLnXmoqko8HicUCjExMdEJ58Xj8ZNbJginxDRNDh8+TKlU6gRxLMsC6FQdK5VK5HI5JEmiVCqRyWQYHBw8KSDl9/vx+/2kUimmp6cxDINsNks8Hmdubo7e3l5mZ2cxTZNYLNY5jhfCfKlUCsMwiMfj50WFs3Q6TTKZZHp6mlqtRiwWY3BwEE3TME2TQqHA/Pw85XIZaB8Hq4UODcPgwgsvJJVKddqBzs/Pk0qlcF0XTdOIRqPEYjFqtRr5fJ56vU5vby/hcJiBgYHNWHVBEARBEDaJZ98+PP/f/7fVwxCEdRN88pMpfOYzmIkE+swMTVVFOrE6tutCLoeeTtMcHMTetQvfwx62NQPeRtxmExynHUpbw/doV9OQHAcAp14/7QBW/Xe/o/GGN6DMzKDMz6PWau0QGCBbFrbPB4UCtXvvxXnf+/BefPFpLUdYm/Ab30hpfJyW46BNTKD+/vdY4TCOYSBZFmqxiGTb7fDVwACed7wDSdcBUGMxAh/+MJV/+AdaHg9yJoM+N4dk2wA4hoHZ3Y0bj2M/5jFE3/lO0dpuCynBIOEXvACe/3ysQgGnWkX2+1EjEfG6CKdMnIkXBEEQBEEQBEFYB6985f/j29+2OrclSd3QNlKJRIKuri4KhUInCLIQwIrH46Iyyxbw+/1ceOGFzM7OEggEaLVanYDOQnu5RCJBd3f3otDU8QKBABdccAGTk5OEw2EqlQqNRgOAYDDYqWrl8Xi44IILlp2Ppml0d3czMzNDIpGgUCgsW01JWJ1t2xw+fJhsNsvU1FSnqpjP58N1XarVKvl8nkKhQLPZJJvN4jhOp4LSchYClIVCgXQ6TSKRYO/evTiOg6IozM/Pk8vl8Hq9yLJMo9HAtm0ikQg9PT309/ef86+rbdskk0nm5uZoNpvs3r170fubrut0d3cTiUSYnJxkfn4eVVWJxWLLHh8LFEWhr6+P3t5eduzYwZEjRzrtIhuNBqVSiVKphCzLhMNhenp6CAaD7N27t1PNTBAEQRCEzWcXi1TvvBO3UABdR9u7F99DHiJ+JBWE48geD/ob3oD11rdi2jae8XFsnw8rGgVNQ2o2UfJ5lGaTVl8f7sAA/je9SRxHAIEArqoim2Y7pLbKNpEajXYbOUk67So55twc9Te9CWV8HH1mBjMSoTk4CAut7ms15EwGY3ycVqtF/U1vQv3MZ0RLwg2kRqMEP/IRym96E6bfD4UCSj6P2mjgyjJWPI670J7une88KbyoDw4S/uxnKX/ta1hf/zpOKgW23d6fZBn7wAG0q64i8uQni+Nuu5CkdtXLc/w8i7CxRABLEARBEARBEARhHbzgBa9ldHRzlylJEtFo9JwPYJxNdF1nx44dDA4OUiwWMU0TSZLweDwEg8E1VaHy+XwcOHCAWq1GJpOh1Wp1KmnNzc11/l4tZBcMBlEUpdP+UOwnp29+fp5iscjU1BThcPikFncej4dYLEYymeSBBx5AkiRM0+QhD3nIqvOORCLk83m6urpoNBqd139iYoJgMEilUqFer+O6LsFgkGAwiGEY9Pf3k0gkNmqVt41cLkez2ey0F1xuv9d1nd7eXqanp+nq6iKXy6259aYkSYTDYR72sIdRKBRIpVKUy2Vs2+5UpNN1na6uLrq7u0VlQUEQBEHYIo3RUWpf+ALS//wPUq0GrRbIMi2Ph8bOnSjPehbhq68WP2QLizTuv5/mwYPQbCKHQngf9SjUFdrxnUsCj388lbe9DfMf/5FGLIaSyaClUkhHqztZoRDWzp04iQSe667Dc+DAVg95W9Ae+Uicb38bZmYgn4dYbPmJHQe1WMTq6cG+6CKk0/yuUP7iF5Hn5trVygYGTm4Z6fPhDA/T9PsxkkmaPh/lL36R2Oted1rLE9ZGSySIffrTVH/+cxo33ID7298iNRqgKNi7d6M+/elEnvKUZaueKYEAkZe8BF78Ymq//S1ONguqijY0hLFr1+aujCAIm0KcMRIEQRAEQRAEQVgHS53jP1qBXjgPKYpCbKWTtEc5jkMulyOXy9FqtQA6reWi0SjDw8OdaU3TJJ/P02q1CJ/YOmIJkiRhGAatVgvTNE9/Zc5zruuSyWTI5XIYhnFS+GqBJEkMDAwwOztLtVrFcRw8a2g94fV6SaVSAJ2KaQsVzhZCeI1GA9d1UVW1E7o8X1pK5nI5CoUCmqatWE0M2hXkVFWlWCyeUgDreJFIhEgkQrPZpNFo4DgOmqbh9/vPm20uCIIgCNtR5X/+B/Nd70JOpVAyGdRKpdPz3dE0rLk53NFRsnfdRey665BEReDzm+tSuv12zBtuQBkZgaMt5WxNo+LxYD/60fj//M/xXHDBVo90Mdel+r//S+PGG5Huv79dWcnjgYsuwnP11fguueSUA4aBP/ojzAc/mMoNN2B961s4hULnMaenB/npTyd85ZUoa/iOeb4IXnEF+Y9/HCscRpufpxUMLvueIs3OIrkubiyGfuWVp7U8p15H+v73kdNpLL//5PDV8eJxrEIBOZ3G/f73cV71qtNueSiskSThf/Sj8T/60QC4loW0xvaUx8/Dd8klGzRAQRC2ExHAEgRBEARBEARBWAfn4u/yrgv33tv++6KLzs113EqZTIbp6WmazSaFQoFWq9UJTeXzeQzDYMeOHUQikUXPkyQJZ43pPsdxkGV51VZswvJKpVKnAlNfX9+K0y5UUpqfn0eWZer1Ot6FlhHLcBynE+w5MeDj8/kWhfDOR61Wi1arhc/nW9P0Pp+v85wzYRjGlrUZdF0YG/s3TLPdflRVVb71LY1rrnn1loxHEARBELZa/d57Md/xDuTJSfRUCjMUorlrF67PB7aNlM+jZbPtSjWOQ+6664i/851bPWxhi7i2Tf6665C/+13UdBo1l0Ne+GwoSe2ASzZL/Re/wHzjGwleccXWDvgoc26O0pvfjHL4MEqhgFwsItk2rqLgjI7S+tGPqD/oQYTe/W60U6yEq/X0EP3rv8Z9+ctpJZO49TpyIIDe379pX/TtUonybbfhPPAAbrMJgQDaIx9J8IorTrtq1EaRVBXlmmtwslnkkRH0kRHMvj4Ihzvby200UObnUUslWjt2YO/aRfiJTzyt5dV++Uukcrk9r6GhVae343H0qSmccpnaXXcR+MM/PK3lCqdnu+2vgiBsL+IdQhAEQRAEQRAEYR0sdc7y6AXZZyXbhne8A+65p337kkvatxVlK0d17pibm2N6epr5+XkKhQKqquL3+3Fdl2w2SyqVIh6PY1kWu3bt6lTT8ng8+Hw+yuUy3d3dKy7DsiwajQZdXV1bFiQ5FyyEeRzHwe/3rzr9QmDOdV0KhcKqAaxKpYLX6+2E74TFFkJp7hrfUF3XRZKks75a1f33vxHHqXduj4x4+dznRABLEARBOD/VPvpRlLk59FSK5o4d7RAEIEH7C0pPD62uLrQjR1CPHIEf/pDaVVfhe+hDt3LYwhbJf/CDSN/+NuroKLJlYcZiOLEYqCrU6yjZLMbEBGalgv2e91ALh/E98pFbOmZzbo7yX/81ytgY2tQUkm1jBQI4uo5kmugTE7iaBpUK5Ve+ktAnP3labRQlRcHY5As8nFqNwr/8C9IPfoBUqbSDZY6Dq6o43/42+Y9/HOWaawg///nb6qqv8POfT25iAusb30CenkafmsJNJnEMA8m2URoNbK+X1q5dODt3Enzf+047mGNns2CaSI4Da7nwxOdrT2ua7ecKgiAI24YIYAmCIAiCIAiCIKyDc60F4d13HwtfQfvve+6Bhz98y4Z0ziiVSkxPT3eqXw0ODi5qrea6LqVSibm5OSzLQpIkvF4vqqrS1dVFuVymUChQLpcJBoPLLieXy3Xatp1OKzahba3BnwXBYBBVVfF4POTzeXp7e5cNA1mWRaFQoL+/n0gkgiqupD2JYRh4PB5yuVwnXLUc13Wp1Wp0dXWh6/omjlIQBEEQhI3SOHwY5b77UFMpWvF4J3x1IklRsHbuRL//figWadx4owhgnYcaIyPIX/866tgYAM39+5E0jc4nyEAAJxCgWSqhT0xg6jqNf/s3fP/+71sa/im99a3Io6Po4+NY0Sj2wEC7xRngAk3TRJmdRR8boyVJFN/+duIf+ciWjXet7HKZwt/9Hcq996LMzKBWKjiqiqsoyKYJMzNY4TBONktuYoLYG9+4fUJYkkTsjW+k0N+P/eUv4xSLSPk8mGa7ktrQEPj92JdeSugNb0Bb5QKpFRel67gLF57YNqttAde223/IMrK4iEcQBGFbEWf2BEEQBEEQBEEQ1sF2OUe4Xj7xiZPv+9jH4DOf2fyxnGvm5+cpFovUajV27dp1UlBkoY2drutMTEwQDAZJpVL09/cTi8VIJpPEYjFmZmYYGho6qTVbrSZTqRTIZrOdcNda27cJJ9N1HU3TkCSJer2+KCy3lHq9TjQaxev1Yts2s7Oz9PX1nRQcsiyLqakpPB4PwWCQxCm2ETlfxONx8vk8qVSKfD6PLMtYlgW0w1mBQKCzbUulErZtEwqFROhQEARBEM4R9W99C6lSQWm1MBOJlYMJmoYVDqNks7h33olrmkiatllDFbaB2g03IFcqKPU6jQsuWP71D4UwEwnUdBp3fJzaPfdsSWDPnJ+n+K//ivemm9CLRRxFgWIRHAc7HkcKBECSkDQNZ2gIC9CmpsDvp/HAA3guuGDTx7xmrkvhLW9B/t3v0EdHsT0emrt2wdF1wnUhn0ebn0ceGcH6xjco9PcTefGLt3rkx0gSkRe/GOeaa6h85zuYd9wB5TLoOvKePfivugp9cPCMF6Pv3k1D13F0HblYxF2lirJcLOLoOmga2u7dZ7x8QRAEYf2IAJYgCIIgCIIgCMI6ONdaENZqa7tPODXNZpNSqUQ+nycaja5Ypcfr9RIMBikUCuRyOXp6elAUhZ07dyLLMo7jMDExgd/vJxQKIcsqN97Yzc9/HsJ1u/jTP03w4Adb7NixYxPXcG3Gx2F+Hvbsge2ekwmFQhiGQSgUIpfLrRjAcl2XfD7Pzp07sW2bWCzG1NQUIyMjRCIRvF4vrutSqVQoFot4PB4GBwfp7u5esZrZ+SwajQJgmiZ33XUXXV1dyLKMJElomoZhGEQiEXw+H3Nzc3R1deHxeDqtIAVBEARBOLs5mQxqo4GjaUhrqHDp+P1o6TRSq4WVz59RVZqzgWuaNMfHcapV5GAQY+fOTuWk85H04x8jZ7NYgQDSKpWBnHgcKZVqV0z75jc3PYBV+PKX4ROfQH/gAbRSCfloVSOpVkNutXCLRcxgEHvnTiRZBknC6e1FO3gQymVqN9yA59prN3XMp6J2zz0ov/kN2vg4lt+Ps3Pn4hMnkgSxGK1gEH1kBHl6ul1p6pprkD2eLRv3UmSPh9AznwnPfOaGzN9z4YXU9uzBmptDzeVodXe3X/MluI6DlsthxePYe/fiOXBgQ8YkCIIgnB4RwBIEQRAEQRAEQVgH1177Qn7zm1bntizrPPax/7mFIzozS53rO5tbKm4XlUqFVqtFo9Ggv79/1enD4TDT09PYtk2tViMYDHZa1UmSRCQSIZ/Pk06neeCBCD/7WQhV1dA0lTvuCPPCF2p4ttnJ69tvh09+sh1Q9PvhLW+BBz94q0e1PFmWicfjVCoVxsfHyWQyy1ZXmp+fx7Zt4vE4w8PDlEolNE2jVCpRKLSrki20lBwYGCAQCNDT08PgOlw1fa6qVCo0m01arRa1Wo27774br9eL1+tF09r790Ig64ILLqCrq4sdO3as2KpQEARBEISzy2lf13IOfx5oTU9T/epX4bvfRapUOvfXurqQnvIUgldfjRqPb+EIN5/TaCBVKsiNBtYawviSpuEYBlKjgZvJbPwAj1P87/+Gj30MeXISbzqN5Dg4ioKtaUiOg9ps4rZa4LrIo6NYe/e292ddx/L5kAsFnIMHN3XMp6px443IxSKSaWLv3bvs53NJ0zD7+tCnpnCKRcrf+hbhK6/c3MFuA8ozn4l76BDk86hHjmDt2HFSBTfXNNHGx0GScLu6UDcoECYIgiCcPhHAEgRBEARBEARBWAc/+MFNNBr1zm1Z9p7VFbCEjWHbNs7RJJu2SjsU13VpNptkMhl8Ph+yLNPX10c0GiUajeL3+0mn0wQCASzL4ktf2oHPp6IoMrpuoOs6t90m8aAHbcaarY3jwJe/fKw6XLUKt966vQNYAL29veTzeQYGBkgmk1SrVWKxGD6fD9d1qVar5PN5ms0mw8PDhEIhenp66O3tpVAokE6nO5WcoN1mMhqNkkgkKJcDTEzA8PDSwcfzWb1eZ2RkhFqthuM4dHd309fXR7PZpFarUa/XyefzqKrK7t27sSyLnTt3Eg6Ht3roZ0T82yEIgiAIx8i9veDztSsCNZurVjWSy2VsjwfX40E97vPXuaT0zW9if+ADSMUicjqNWi6D4+AqCnY4jDM9Tfmmm9Df9S78l1661cPdNMtVDFrbkzcvrNcYGcH9xCeQJyZQqlVafj9avY4rSbiahgs4uo7SbKLW2+cY7Lk53L4+AFxNa3+xqtdXWMrWk371K+RcDisUWr4VpOtCpYKcz6PUauj33kvjHe+gUC4TfNazUEKhzR30FgpfeSXZn/8cbBv1yBGMgwexwmGcoxWY5UoFtVjE0TTMPXuwH/c44s961haPWhAEQTiRCGAJgiAIgiAIgiBskLP5R/RzraXidqEoCvLRHwZM08RY5gekfD5PJpOhWCwyPz+Pqra/vtfrdXRdp6+vj/7+fgYGBujv76fVamGaKqGQ1Jk/wNzcxq/TqUgmIZ9ffN/PfnZq85iYgA98oP3/Cy+E170OEon1G+NSVFVl3759QDs4l8/nSSaTuEcPClmWCYfDDAwMEA6H2bt3b+d1WAjMmaaJaZpIkoSu6yiKwuc/Dzfe2F7GQx4C114LPt/GrsvZZGZmhlKpRCaT4cCBA51tXywWcRwHx3GQJIlcLofjOCQSiUX7vyAIgiAIZz/fU59K/UtfwvZ6UVIpnKGhZad1m020cpnmrl24T3gCknru/QRW+cEPcN77XpSpKbRsFsvvp9XXB5oGrRZqLod26BCtUonWtdci//M/473ooq0e9qaQdB0nEsHxelHKZeyenpWfYJoozSaWz4e0ia0qa1/9KnKphFYq0dy7F21yErfRQDr+C7ckYRsGSqOB3Gqh5XK0entBkpBbLRyfD9fv37QxnzLXbVcjsyzs5Vq412qok5MozSaSZaHYNnKthjs2hvnRj1L6j/+Aq68m+opXnNPV7Dokidi7303une9sH8+FAko2izY7C4BjGLQGByESwf7DPyT29refH9tFEAThLHPuffoUBEEQBEEQBEHYJs7mwJIIYG2MQCCArut4PB6KxSLdS5zon5ubI5vNks/nmZyc7FRYKpfLlEolAoEAtm1TrVbZt28fsixjGAaKcvLytttrZppnPo9PfrIdvgK4/374z/+E17zmzOe7Go/Hw4UXXsj8/DyBQIDu7u5OoGqhFV5XVxc9PT1LhoA0TVtU9SyVOha+Avjtb+GOO+BJT9r4dTkbtFotCoUCmUyGSCRCMBgEoK+vj+7ubhqNBo7jIMsyF1xwAWNjYzSbzZOqjQmCIAiCcHYzduyg8tCHQqGAMTFB0+eDJVrrLbTmsrxeCAbxPfvZmz/YDeY0Gpgf+AByMolaKNDcvRuOfkZaYHV1YeVyGNPTNBWF2nvfi/cLXzhvghrSH/0RzswMxuHD2PU6eL3LT5tO42gaBIN4n/a0TRmf02gg/ehHyJkMZiAAPh+2349Sq6E0Gtiue+y1kiQcVUVttXBMEwoFXJ8PpV7H6u9HuvjiTRnzaVmo5iVJ7WpdJ6pW0cfGkJtNlKOtFhe+vCqmiXzwIGYwiPPFL5JLp4m99a3nxT4s6Trx666j+otf0LjpJrjrLmzbbj+oKNh/8Ad4rrqKyKMedV5sD0EQhLORCGAJgiAIgiAIgiBskO0WfjkVIoC1MQzDIBQKEY1GmZ+fJxKJoOt65/FsNks2m2Vubo56vY5hGOzYsYOBgQESiQTVapVMJsP4+DiO46CqKnv27Fl2eUud695KlnVmz2+14N57F9/3gx9sTgAL2pWwBgYG6Ovro1wu02q1OhWtgsEg0imcBP/KV06+79/+TQSwFhQKBZrNJvV6nb6j7VYWKIqC/4Qr/iORCPl8vlNtbLUWn4IgCIIgnD38f/u31A8domWaGMkkVj6PHYvh+nxIto1UKKDn8zi6jrVrF+4zn4nnwIGtHva6q9x+OxQKaNksrYGBk8JXHbEYrWYTbX4eJiep/vrX500rQt/VV1O/6SasYBB1YgJr92447vtWR6GAnsnQHBzE2b8f7yb1RG9NTSHV66jVKq3+fgDsri7cXA5XlpGbTRyPpzO9q6rQaiE5DlKthpzPY3s8EAjgf85zNmXMp8vZsQNnago1n8fs7++cZHBtG2N8HLnRQG21sDQNV1HQGg0sr5dWIoEbDqMmk8gjI1iSRHHvXsIveMEWr9EmkST8l12G/7LLsCsVrHQaADWRQFmumpggnAnXpf7732OOj+OaJko8ju+Rj0Q+7r1IEIS1EwEsQRAEQRAEQRCEDXI2B5a2KoDlui7FYpF6vY7ruqiqSjgcXrZV39mop6eHYrFIuVxmcnKS3t5eAoEAjuOQyWTIZrNUq1UURaG7u5tAINCp6OP3+/H5fMzPz5NMJjEMg1qthm+ZvnXbbR880wDWmT5/vSy0HDwT5fI6DeYctdCycaHC22q8Xi/ZbLbzXBHAEgRBEIRzh2fPHpz3vpfGm99M0+dDzmTQk0mko1cb2B4PZm8vbiyG+9SnEn3ta7d4xBvD/OY3UTOZdihnlYqfTnc3UiYD+TzNW289bwJYxvAwtWuuwfrP/0Q9cgT90CHMSAQnGgVVRarXUXI51EqFVk8Pbn8/3k3cX9xm8+gfLq6iIAGS14sVDILjoNbrSI0GtmG0v5RLUqeKlJrJICkKrT17sC+7DGPHjk0b9+lQn/Y03IMHkdLpdh/6WAwAOZtFMs12+ErXcTWtHTyTZVAU3EQCDANzzx70kRHk2Vmsr30N95prkJYq+3wOUwIBEboSNoxr25Ruvhn7lluQx8fbV7y5LpamUQyH4UlPIvDCF6IlEls9VEE4q4gAliAIgiAIgiAIwgbZbuGXU7HZASzHcZifnyedTtNoNGg0Griui6ZpGIZBOBymr6/vpKo3Z6NQKMTg4CAA8/PzTE9Po6oqtm0zMzPD+Pg40WiUrq4uotEovb29eDwerKPpI0mS6O3tZWRkhEqlQjqdZscyJ9+3WwWshe4Jp6vVWvr+4zt1bJRUCu66C8JheNzjznx52+212a7cNb7xuK7bqUB2KpXIBEEQBEE4O/guuQTt+usp//d/43z3uzjFYvvDpSS1W3NdcgnGs59N4AlPOGdbc8kzM8i1GnYgsOo6SoqC7fMh1Wq4yeQmjXB7iL7yleRbLewbbsDO5VCzWfSjQX1XlrGCQZp79uB2daG/852bVv0KQIlE2uNQVaRGo/3lArB37EA6fLg9TbOJVqvhyDIuINs2kuPgaBqtvXuxH/QgIm9966aN+XQFn/pUitdfj5lIoCeTtAAiEZRcDtk02+unqsitFoplYXk8WIEAHL34QtI0zO5u9NlZnFSK6k9/SuDyy7dylQThnOE0GuTf9CaUu+5CSadRjx6XcPR9MhTCmZ+n/MMf4nvf+87JqpKCsFFEAEsQBEEQBEEQBGGDnM0BLFk++b4T16dUAr8fzvQiVNu2GRkZIZ/Pk81mKRQKnTCF67p4vV5isRilUondu3cTOXrS+mzW29uLoiioqkpXVxeFQoGJiQkajQbhcJi9e/ei6zp9fX0El2gtoqoqgUCAYrFIsVjcgjU4PUfP5637801z6c4i6+XIEbj2WqjV2rd//nN4/evPbJ5TU2c+ru3MdSGbBU3r/K50SgzDwHO05UG1Wl01fFmpVPB6vZ2WkIIgCIIgnHu07m5if/d3uK98JbXf/AY7n0f2eNB278YYHt7q4W28oxdkuEt9WVuCK0lIrnvmV0GcbSSJ6GteQ+XSS2l+7Wvwm9+0L2ZxHFBVXL8f94orCDzveehHL4zZLPrAAJXBQazZWdR8Hqu7uxMitPbtw5mchHIZ27aRLQvZNHElCdPno7l/P84VVxC59tqzoiqS7PWiv+UtmNdeiwkY09PYc3No1SqKaeJIEmq93q644/FgezzYQ0OL5uFGozAzA+UyrV/9CkQASxDOnOuSf9vbUO68E+3IEXBdzHgcNxxGkmWo1VAyGYyREZr1OvXXvx75E59AHxjY6pELwllBBLAEQRAEQRAEQRCEk6xUAcuy4P3vh5/9rB2+euUr4U/+5PSXdeTIETKZDFNTU2iaRn9/P8FgEEmSaLVa5PN5ZmZmqNfrAOzfv5/AWXDCeTWJRIJ4PE4ulyObzdJqtbBtG9d1GRoaIhQKrVjJx+PxUK1WO5WxlrLdqiwtN9TXva79/xe8AB7+8OWfv1wFrGZzYwNY3/vesfAVwE9/Ci9+MXR3n978vv/99u8I56p6HT7wAfjlL9vvES9+MVx55anNIxqNous6gUCAXC63YgDLsiyKxSIDAwNEo1GU86w1iSAIgiCcbyRdx/+oR231MDadG4ngahpKrcaqnbldF6XRaFcUOsPW2WerwOMeR+Bxj6M1M0Pr0CGceh0lEsH70Icie71bMyhJQnnGM3DHxpAzGaT5edze3vZjioKzaxfNZhM5k0EuFtFLJVo+H40HPYjgV76y6YGxM+V/9KOpXHcd5rvfTTMWQ5qdRS+VkBwHSZaxj7YgtA0Da/fu9tUbx5GOVsnCsnCPng8QBOHMlH/yE5Sf/QztyBEcTcPavRtJlumcfdJ17EgEJ5XCSCZp6jqVT3yC2LvfvZXDFoSzhghgCYIgCIIgCIIgbJDtFn45FSsFsL7xjXb4CtoXU3/sY/DoR8MShZpWtVDBaXp6Gp/PR39//6LQka7r9PT0EAwGmZycxDAMkskkF1xwwWms1fYjyzJdXV10dXUhyzK6rlOpVAiv8CNJuazwve8lmJ/385CHzLB7d/sKeEk6uUrZdqvCtlwFq0OH2v9/xzvgE5+A/v6lp1suwNVonN7+t1Zf//rJ9/3oR/Bnf3bq83Jd+NKXznhIp6xWay97M7p4/vSn7fAVtN8jPv95eOITT+23P0VRiMfj1Go1xsfHSaVSdC+ReLMsi6mpKTweD4FAgEQisT4rsYnuu68dyrvvvvZ+fA4U+RMEQRAEYQNIj388zugo2uHDWPU6rBQiKhaRbBsnFkM9z6sG6f396Mt9wdgCwWc9i8INN9CqVDCmpjBbLexEAuno6+mqKq6mITsOjd5enF27MP7f/zvrwlcLAo99LPZXv0r5llswb7gB/Qc/QKrVsDUNMxLBjsfbH4CXOQkhWRYoSmf7CIJwZlo33oiSyyHZNtb+/e2qV0twu7sxazWUVAr3zjuxcjnUWGyTRysIZ5+11SkVBEEQBEEQBEEQznsLYZ7Pfnbx/Y4Dv//96c0znU5TLBaxbZu+vr5lKz75fL5OtahKpdKphnUu8Xq9+Hw+6vU65jJJJdeFj3xkmDvvDPG73wX5ylcuIplcPtWy3UKAa2lB+OUvL//YShWwNtvpLrNSgfn59R3LSlwXvvhFeP7z2/995jMbH8z7yEcW33YcuOmm9t+maTI3N8fo6CiHDh1idHSUVCqFvURrnP7+fsLhMIODg+TzeUZHR8nlctTrdarVamc+kiQxODhId3f3WVcd7zOfgTe9qV1lbXa2HUa8666tHpUgCIIgCNuR/+qrwe/H8vtRp6Zwl2kt6LZaaDMzWJEIRCIEn/KUzR2osCLZ58P3vve1q13t2oXcbOI5dAj14EHUBx7A+P3vUdNpzK4unF27kF75SgKPe9xWD/uMKMEgkRe9iK4bb6T+R39EfWAAKxjE3rMHotGlw1cA+Xz7/6EQ6kUXbd6ABeEcZeVyKHffjZrLYUYiSKtUj7YTCdRqFalSofqd72zSKAXh7CYqYAmCIAiCIAiCIGyQ7VZ96FQsdQGc67bDI0spFE59GY7jUCwWKRQKhMNh5GWuulsQjUbJZDLU63Xy+Tzec+wK2K6uLubn5zEMg2w2S+9CK4rjjI15yWY1bNvEcVxUVeP66/t56lPbr9mJv8Gc7j7YaMCnP92u8hSLwWteAw9+8OnN63jLBaiO94tfLP/YcgGuRuP0xnMmTjeAtZ7d8aanYWIChodhaGjpacbG4CtfOXb71lvbFevW4/U8FZmMw/j4JLlcjmazSblcxnEcFEUhFAqRTCZJJBIMDAx0gpiqqrJ//36+/OVpbr99P5bV4FGPOkhv7ziSJOHxeOjt7SUUCtHd3c3Qchthm5qba78egiAIgiAIa6H391N5xjPgxhtRR0bQDx3CTCRwY7F2qzbTRMrl0DMZHMPAGRxE/vM/R/Z4tnrowgk8e/Ygf/zjlP/5n+FXv8KqVpGqVXBdLF2HcBhncBDtZS8j+Md/vNXDXT+ShPzMZ+LMzKA98ABWPt/+wrkE13HQUimsSAQnFiN8xRWbPFhBOPeYs7PgusiNBtYSFaZPJPn9uJIEjQb2Zl5JJghnMRHAEgRBEARBEARB2CDbrfrQqVjuAtTf/nbp+zXt1JexUOXJNE1iayhjrqoqmqbRbDaXrRB1qopFGB+HPXtgowvnzM21QzvDw8e2b7ncroAzNgYPfaiHxzwmRC63l1/9Ks/DH15kz57Qoqpghw75sCyLZrOJYegoikyrpfNP/3Ry+ApOP4B1883w3e+2/56fh/e9r91G7ic/aVdAM014znPg2c9efl9ZylpetpUCSss9fysqYK0lTLaU9Qpm/uxn8IEPtLeJorRDck94wsnTfe5zJ9/3mc/Ahz50est1XbjzznZbRp8PXvKS5cNfx57jcvvtVSqVFhddNEGjUcUwDFRVpdVqkUqlCAQCtFotGo0Ge/bs6ez3k5M6X/nKLkyzRbPZ4vbbL+Ptbx8jEGhvyEgkQiKRIBQKnTTOteybqRR8/OOQycBjHtNuK3mmIbnpafjNb6CrCy677NTfTwVBEARBEJYTfe1ryeXzIElIc3No8/PIMzO4stxuOajrWPE4bk8P7jXXEHne87Z6yMIy9IEB4h/8IK3paaq33oo9PQ2WhRSJYPzRH+F/1KNO7cvWWSJw5ZWUv/IVWt3d6NPTmJaF29W1+Cqweh1tagrJdbH7+pCe+UwkVfykLQiCIGx/4l8rQRAEQRAEQRAEYc3uvnvp+08nALMQsJAkCWcNabWDB7387ne9PPzhCvv2nfmJ6F/9Ct7zHrAsMAx4xzs2rirQ5z8PN97Y/vuSS+Daa8Hrhfe//9g2HR+HG2/cQ6VSwTRb/OQnVZ73vN+xf7+CrutHqwY1aTQMDMNA13V8Pj+SJHHHHUsv13XbAR1JgrWer/7f/4X/+q/F9xUK7fDVhz50LED0H/8BF1wAF1+89u2wlv1kpUJomxXAmptr/3+JImRLLnN0tB3mu+AC8PtXnvdqAaxPf7odzOnvh7/8y3aIZyk33HBse9g2fOlLSwewpqdPvm9qauUxrOT++9uBvAUHD8InPwnZbPvi9WDw5OfUajVaLZPbbvNRKg3x7Gc38RxXiaFWq5FKpRgfHwfAMIxONasbbmi/R+i6ga4bOI7NwYMX8sIX2hiGgXZC+nNuDv75n9vH0yWXwGtf2w6KLefd725XEYP2/4NBePrTT2fLtN13H7z97ccCek99KrziFUtPu1Q4bkEk8gfYdrvVqiTJ9Pcbpz8oQRAEQRDOGZKiEL/uOopf/jL2DTfgzs21yxQ7TvsDv9+PvWsX2vOeR0i0Hjwr6IOD6H/911s9jE2jRqPob387rTe/GVOW0VIp3HQaOxAAWUZqNFBrNSy/H2vXLpzHP57YX/zFVg9bEM4JWl8fTUnC8XiQymXccHjF6d1qFcl1weNB6enZpFEKwtlNBLAEQRAEQRAEQRA2yGa2ILTtdshmlS5+a7bcfNYzgKVpGoqi4PF4qFQqRCKRZaf9+tdjfPe7IWq1EL/4hZfu7nYlqTPx7//eDl9Be/xf+AK8971nNs+lzM8fC18B3HNPu4LQox998vaUZZlAwE+1CratMDKyh8HB31MqlXBdl0gkht/vQ1FU/H4/6iqpqnS6XaVK1+FlL4MnP7l9v+O095cTL6h23XaYZinXX3/yPv25z7XDLmu1lgpYC/uebbf/Pn6My1WdWsv+NzICuVw7JLXcOUbXhY99DL797fbtyy+Hv/u7lZf5X//VDj8BdHfDO98JAwPLj2O194Xbbmv/f2ICarV2QGgphw4tvp1MtrfvidXolso2nkmFp4V1XVAuwwte0P7b54PXvW7x47ZtY5omjUYDRVH41a/28sIXji6axufzMTw8zOTkJLOzs2iaRl9fH6qqnhQulGWFe+/1Llux7gMfOLZtfvGLdsW2V7966WknJo6FrxZ86lNnFsD65jcX76ff+Q686EUnV9hzXahWl5/PYx7zQ5pHdzLDMHjxi8+96geCIAiCIJwmSSL8vOfBNddQufNOzN/9Dup1CAQwHvlIfJdcck5WThLOHf7LLoP3vY/me96D29WFlMshVSpgWbheL83BQfD7cZ78ZGKvex3SevZxF4TzmBqLYT/0oZDJoM/O0uzrW/H4UtJpLL8fNxAg8Cd/snkDFYSzmAhgCYIgCIIgCIIgbJDNCmDdcw/827+1wzaXXgp///erV+E5XQuVgU60VAAml2u3OpuZgYc/HJ7//MXhEEmSiMVilEolpqamaLVa6Lq+xLwlfvrTCK1WA1VVcByFn/wkyOWXn9m6nBi8+P3vz2x+y7nhhpPv+5d/Wb5ylCwrBINBTNPkvvuGeMlLagDU6zYf//gF+Hw6huFZ1JpwNa1Wu83aox4Fd93VDqU0m3DllfDnf34skFMowOzs0vMoFE6+b2RkzUMA1haUyufhE5+A228Hj6ddPeiP/ujYeiyl0Vh5nl/+Mvznf7b/jkbhXe+CHTtOnu7w4WPhK4Af/Qh27156nq1Wu9jA8a9vKtV+/ktfuvxYTuV94Z572sfct74Ft97a3h5/9Vcsu+9XKu31O95SrSnPJKi5XAgT2oGxT31q8X2tVhPbtnEcB693+TcmWZbp7e1lbGyMZrNJJpOht7cXSTp5my23H9VqJwfTvvtd+Ju/aQejRkfhIQ9pVwqTpPZ75lqMjLT3jR074EEPWnnan/xk8W3Lalc0e8xjFt+/1mULgiAIgiAsS5IIPPax8NjHbvVIBOGU+S+9FN9XvkLlBz+gddttSMkkkmnihkJIj340/quvRu/v3+phCsI5x7jqKqxf/xo3lUIdG8PavRtpiZMEUiqFVirR3LUL9zGPQY3FtmC0gnD2EQEsQRAEQRAEQRCEDXKmAaxWq12l6de/brfFeu5zT67eY5rtME8m0779y1/CzTfDC1+4/HxnZtpVi0ZHYd++dsWa7u7F05xqQGOpYMwHPwi/+1377yNH2m3+rrlm8TSJRIJ0Oo3f72dqaorh4eGTWorNz+tUKiaWZePzedE0nV/9avEVeq7bboGmactXNzpx+qUcOdJus5dKtcNKr3jFyu3L1mK5Kjcf+tBKz5LQNB1N03nYwx6Gbdv89KcOwWB725xK+GqB67YDcf/zP8fW/6ab2kGwSy89Ns1GWksFLGiHr6AdrPrXf4VHPrJ9DCxULDvRSsGueh2+9rVjt/P5dhhnqbZwn/nMyfddf/3yy/zpT09ep5tvXr8AFsAtt8A3vtH+u1JpH+8Pe9jS05ZKJwewlqqAdSrHt+vC2Fj7tbjwwtWnPzGkaZoWlmWiqsqq+63H48EwDEqlEqVSid7eXgzj5IDdcoG75e7/9KePbcPvfa8dJrzyyvZ70mp+/OP2sbpQNe7lL2+3FTwVS4XglmoNKQiCIAiCIAjnE0lVCf7Jn4CorCMImybwhCeQvewyME20I0fQDx7EjMdxI5H2d/ZaDSWTQa3VaA4M4A4OEnj5y7d62IJw1lin5hSCIAiCIAiCIAjCic40zHLjje12ZDMz8MADcN117R//f/nLY9Pcddex8NWCb3wDfvYz+Nu/bbfeuvPOxY9ff327Soxtw8GD7TZyZ+rEAFY2eyx8teDE1mUAXq+Xvr4+BgYG0DSN0dFRZmZmqFQqVKtVcrkcExOTtFotvF4Pqqrh8XiAY9u30YB//Md26OXFL1664tSJlntt3v/+dmWser1d/eiWW1aeTz5/8vY/0XLV3O+7b9VhAu3KQJqmccMNZ34N1U9/unQbwQVLhXXW03IVrFbiOO3qTys9f6UA1i9/efLjCwGvE632Wh6v0Vi5hdxyTvV9YSE4tMC2l9/HK5XFy7n99sX3LVhrBw/XhY98BF7zGnjjG+H1r1/b8xbPw8V1XeTjUl/z8xof/OAgr3/9bj7+8X4KhWMD0nUdy7Kwj6aWjh7uiywXtFpu2564DRdCdSe2a1zKjTceOy5ctx2wO1VLBbCSyVOfjyAIgiAIgiAIgiCcEUkiet112I9+NOb+/VixGFo2i+fgQYz770efmsLVNJp79+Ls2YP3n/4JfXBwq0ctCGcNUQFLEARBEARBEARhHfzFX/wt3/72sVI4sryGX/ZX8V//dfJ9tVq7KtYjH9m+vVTbvEqlHUha8N73wl/+JTz96e3bd921ePo77jh5HqcaxDkx4LJUe62lQggA/UfbCsiyTKVSIZ/PM320PIyqqhjGEH6/H1VV8Pv9nSBHqdSudnXnnfDznx8b93/8R7tFWzy+/HiXC2qcGIr47/9ut05cyuc/364g5brw0IeCrsP4eLuq2MtfDpFIe7q1hl2W4zjtdo7T06de9WotpqaO/b3ca7RelqtgtZqF/Wm5ClortSCs19e+nFNZ/0pl9YDeUtajytgDDyx9f6l07O/vfKfdynEpa62ANTXVbuG34MT2fmuxUPXKPW7Fv/a1BNPTxtF5evnWt2I873ntF9m2bXRd7xzn6xHAWs5y73Ou2652Be3qX8ebm1v8+FqICliCIAiCIAiCIAjCdiF7PMQ++EFKN9+MfcstuOPj7SveXBc0DTcchic9ieALXoB2Ysl8QRBWJAJYgiAIgiAIgiAI6+BNb3rvST+or/Tj/ve+B/fcAwMD7daCqnqsYs0tt4DXu/yyJiehXG63ZBsfX9v4Pv1pGBmBv/7rtU1/qkGZEysTHR8EOZ5tLx1I6u/vJxwOk06nCQaDi8IaDzwQw+/3oWn6ohZm2Ww7gPXhDy+e10KVmv/7f9t///737QDTIx5xrJ3gmYZgjhxpV8ZZcPfdx/5OpdphrNe+tn37TANYb31ru5rYRlengtMPSK3V6a7DQmBouQDWf/5nO5S4e/fJj51KAOtUxjc7u/Zpj7ceAazl1un4alcf/ejyz8/l2sdNV1f7/WepVnzFIrzpTWc0TAAURUFVVZrNJobhAhIjI4vf4H7xixDPe14a0zSp1WokEgl8Rw/WpQJY62W5wN3vfw8PfvDyz3v5y9vb7MUvPta+cyVLVW4TASxBEARBEARBEARhq0iKQvjZz4arr6Z+332YExO4rRZKPI7vD/4AeSO/jAvCOUwEsARBEARBEARBENbBUtVQlgto3HorfPaz/z979x0fx0Hmf/wzdYtWvdhxj+30hEB6IIEAaZBOQujl4ChXgOMIBA5+B1e4UMJxhA539HIQCCGBUI4SagiEFkiI47jLTV3aPvX3x0iyZKusLFmy5e/79fLL0u7szDPbJM1+53n2fX/77XD88bBp09Sj1Mb63vfg+utn9iH+j38MT3tabcvOtBPS/nX39k683MDAvs5UDz0EDzwAS5fCpZdCXV0ddXV1rFy5kmq1ShzH2LbN4GAK1z1wXX19EwduIAmxXXllEpL6zneSyxoa9oVOZhtmuvvuqa//8Y+TsW2GMfsA1v6jHA+F+++H00+fu5DXZB2CDnb9I/fhVK+Pm25KRsuNdB4bMVV3rLHiOAkdHWpz0WWsVJr48nw++b+WsaI/+lHy/+bN8M//PP66ri64+eZ965sN13XxfZ9qtYrn+bgTvZiH9fT0kEqlyGaztLe3A5MHsPr7k25UxxyThFGjaObhtskei7e8BZ7zHHj+8ye+fs+e5P9bbkney0eec5Nt/6MfhW99C/7qr/YFthTAEhERERERObLEnkd1xw7wPMzGRtxjjplZe+TDkWGQOfVUMqeeutCViCwKCmCJiIiIiIjMgYmOtwwMJJ2RHv/48ZePDV9B0nVoolGCU/nc55Lb9PfP7Haf/ezEl+8fmJltAKuvb+LlRgJY9903fkzio4/Ca16TfG1Z1mj3G5i8289kIa8Rr3jF+O+HhpL9f8MbZt+F6JFHpl+mWk3CI7MNYM2Hd74z6ca2/302Uzt2wAc+kIRLzjkH/v7vGReeOzCAFeP7Pp7nE8fJlaZp4jgujrNvjGdvbxJ6+cpXJt92EMBPfwpXX518H4Yh+Xye7m4L309hmhbWmAfj9tuTZVOp5PnwH/8xu32vxcaNSVBstiZ7TeTzSZewb3yj9nU98EAy4nE47wTAvfdO//oaEUURvu8RDT+4hmHiug6mmdzXjmNjWSapVJpKpYJhgOPsH8KK2bVrL0NDg6xatYqGhgbSw8mryQJYf/M3BwbRhqeZ1uTLX568Ux8kz7Xzz596Hb6fBGpf/OLk+6k6yO3YkYyD/dSnko6H071379p1O76fpAdt2+G++2xuuOHZU99IRERERERE5lx12zaKt9+O8cMfYhSLo5cXVq7Euuoq6q++GnPMcSwROXopgCUiIiIiIjIHJjvh7Y47DgxgzZUHHpj5bR57bOLLwzAJBUAyLqvW8MXYWm68Ea67Dp773MnDBb/4RdK1av8g2P/9XxK02LsXzjgDLrssCS6VSvClL028rslCXlP5yU+S8WH2DP4anqibUy0Brnz+yAlgAezcmQRvZmLs/RLH8Hd/t+/7e+9N9v11r9t32dgAlud5VCrl4QBPMBrgsSwT2/awLItMJott2zzwQG3P99tvh1NOqXDvvUMYxgCWVeVnP2ulUPAxDBPbtkilUjiOy+c/D7/6Fdx6Kzz4YNIF7FD7yEfmZgThVB2wHnpo5uvbsmV8AOsLX5j+NlEUUamU8X2fMIyIoiS1aZomlUoydjCTSWNZNtlsHVEUA/FoJyzbtjEMgziOCIKA3t4Sa9eupKmpiTVr1oxuZ6LxiDDxfbBrV+37/OUvT7/MG984/TI//em+ANZ0HQw9D379a1i1avr1/uEPLyWK9iXtHnoow/vepwCWiIiIiIjIfBr8+teJP/QhzKEhzJ4erEIBI4qIbZtg927izZsZ+NrXyL7rXaTXr1/ocg9fcUzlkUfwtm2DOMZuayPzhCdgzOQAncgRQM9oERERERGROTBZAOsPf5jXMg7aSADL8+Af//Hg1lGpJKGGk0+ePBz1ta8l3XsmCkqMjPX79a+TdT3zmUkHpcnGoB1MAAuSENaKFbUvHwQwphkTUNsovaGhJNRypASwYOYBLNPc9/W///uB1//wh/DqV+8L0Yx0VqtWq5TLZarVKr7vYxgGlmUOX5eMq3NdlyiKyGbrxnXDmkpvr8+rXlWmXLbw/XqCIAtEQAnTNHAchyAIcN2AbDbDxo0GmzZN3hluLowErgqFZMzoXJisQ10+nwTpZsrzZrr9kGKxONy9zCMMQ0wzeROMohjTTLpghWFINpvFcRxyuRylUhHbtvH9gCAIgBjDMEilUixbtp729hTr1q2r+fE+1Hx/+mXGdtGqZYTs3r3jXzciIiIiIiJyeBr8xjeIP/ABzG3bcAYHCerq8JcuTQ6gVSrY/f2YPT14hQKlf/xHzI98BHcmB5yOAnEQMPTNbxLcdRfWli3JQbY4xrNtKm1tGJdfTsPznofV0LDQpYrMCQWwRERERERE5sBkAaz9TTWiaiGNBDp+9SvYvn126/ra1yYPTQHcc8/06/jMZ5LQU0/P5MvMtEvXiJFuW7WqVMYHsOK4ttGPI8GMIymANVMj+5bPw29+M/EyP/4xXH550rFo61YIgoByuUylUiYMI9LpNPa4Mx5jfD+gWq0SxzFgUF+fGx1pN5kwDCgWS1SrSSgo6cCUwTTN0XV6no/vB8NdzQwymQw/+xns3j3beyImimLiOAkUQZKw+e1vk5GMAwNw6qmz3cb0CoWZjR8cUUvQaEQcRxSLxeEOZhUcxyaVyg7fz8n1vh9QqVSHu15BLpfDsizq6+vxfZ9qNQltjdxftm2zbNk6Tjqp/oD9mY/OZLNRqSRdx3p6ahuBONUYTREREVm84iAgHBwE08RualrockREZBpBby/Rhz+MtWMHVqFAdd06qKvbt0BjI0FHB3R1kdqxg6plkX/ve2n9wAcWrujDTFQq0X/zzZi//S12T08SWBs+Ayy2LILGRqJduxj84Q+pe+97Sa1evcAVi8yeAlgiIiIiIiJzYKoAVqEAuVzydbk8+XILaSSA9cEPzn5df/wjNDZOfn2tI9g++cmprz/YDlibN89s+XI5CRpt2AArVyZhrFo63dxzD3R2QlfXwdV5JBjp5PPLX06+zBe+AA0N8F//lYRVqtUKQeAThiGZzL7gzj5JpyrTNCmXy1iWTbXqkclkpqylXK7g+x6e542Ovhu7Ttd1cV1nNPxlGMllcWxRqRzM3idj+Dwv2WY0pi3aQw/tpKWlnfe/v42hoWT//vzng9vGTAwNJe83M7V/B6zGRhgcnGxZjzAMqFQqw+Mcx3erMgwT13WxLJNyuYJlmVSrFbLZOpLH1sVx3APWO9HUgf/935nvy0J4y1sWugIRERE5LMUxxd/9jsodd2D+6lcYw6n3uK6O8ClPwX/yk3HGjF4WEZHDR/4b38DI53H6+6muWTM+fDXCMGDJEvxqFXv3bvj976lu26YgERCHIf1veQvW/ffjDB+IC1pbiRsaiE0To1DA7u3FfvRRvEqF4hvegPXxj2O3ti5w5SKzowCWiIiIiIjIHHjHO97An/+8r42MYTiccsr7gOTD+dtuS0bf/fznta8ziiJ83x8NdpimORpMmWsjnblqCRbVYuxYrkPlYDtgzdTDD8NHPpKEhywLrriittvdf//h371ntioVuOMO+NOfJl9maCh5/lcqEEXhaCeq6Z7LlmVh29bomLt0Oj3cXepAYZiMtPM8bzj8M9nhDoN0Ok2xWCIMQzyvyp13Zmewx/v4vk+plKzH933CMBjt6LR9+3Z+/vOYzs40dXXZKeqZW4VC0mlspvbvgNXcPFkAK6Za9fA8H8sypxwVaFk2jmPjeT62bRNF0ZSP90R133VXbfWLiIiIHG6iSoX+d7wD65e/xBoYwOrtHe36EWazsGcP6W9+k9I118BNNy1wtSIisr/4u9/F6ukhTKeTs8qmEC5ZgrNhA0GxSPHOO0m97nXzVOXhK//972M+8ADOli1E6TTBmjUYw8cEDIBUiqClBWPPHtzt2/Ech6H//m9abr55QesWmS0FsERERERERObAZz/7Ucpj2luZZmY0gLVtG7znPfCLX9S2rigKqVQqw6GOiDjeF8AaCWGl05k5DWKFIXzuc3O2upq7XM3G4OD8jHT8z//c93UYHr2hkDiOiaKkVZphGKMjAT/zmelvOxKuCYKAOI6IogjHmX4OpOM4lMsV4jgmDMP9RhXu43lJR60oiqcMBSW1m2OCXTbTNNaaUBD4lErF4TCSh2UlXZ8MwyCKYv7wB4eurl48bwlxHI+O4DvUphr9OZX9O2C1tCTjIveX3McRQeCTSqWmXa/juJRKpdHbuO7ktzmY4JiIiIjI4SgOAvpvvhnrV7/C2bIFw/cJGhrwGxsxogizUCC9eTNGNotxxx0MZDK06sN6EZHDRhwEmF1dmMUiYX39tMsbqRSh62IUi8Q7d85DhYc//5vfxO7rw4iiceGrcQyDeOlSwmIRs6uL+Ec/InrNazCzB3einMjhQAEsERERERGReVBr+CoMAwqFIkHgD4/6ikbHG8Zx0hXIdUOCIKCubu5CHffeC1/72pysal4NDCx0BYtfGAZUqx6+740L1lmWRSqVjJObrDPV/uI4CXJBEoSazr5l4tHbTbzeaLTDUi21WJaN7/vEcTzatap2MaVSeTR8tf+4Q8uC73znLLLZEpVK0lKuXC6Ry01/0Ha29g9S1Wr/DliTHV9OHoOYOGY0gDeVkZDoyP08lcN1PKuIiIjITA1+6UtYv/kNzubNRI5DsG4dxvBJAjEQdnQQlEpYW7aQ2r6d6Gtfo/ikJ1F31lkLW7iIiABJACv5IoZaT340TYiig//DfBHxOjuxHn4Yq7cXv7l54vDVCMMgaG9Pfh7m8xR+9CMarrxy/ooVmWMKYImIiIiIiBwmoiiiWCzieR7VahXHsUml0qMhhmQkoUe5XCaOU0CRXC43J52w5rL71XzaPzgiUxsZa5l0VTOwLBPbdiYNIFUqFSqVCmEY4Ps+QZB0wDJNA9t2CIIA265SV1dXUyAn2UyyrTiOpg1hjXR/A2OGIalDJxkLGg6PRUxNOl6wVMriuklIy7YdwjCYt1GEM7X/8eHJ72qDfY9fLW3uRpbZd7vJ7N4Nn/980mXuyiuhra2G1YuIiIgcbuKY6K67sPfuJTYMgrVrJ/7gOZulvHo1uU2b8AYGqHztawpgiYgcJsxUijidJnZdjHKZ6f76jaMI0/MIUimM5uZ5qfFwFuzeDYDleQS1dBBraMCIIqhUCPfsOdTliRxSh+eRPxERERERkaNQtVolCAKq1SrpdArbHj9KzTTN4UCWT6VSxTSt4RDI9KPcFqv5GEG4GIRhQKVSHQ4PReMCWKaZjM9LpdLjQk5J+KpMuVwhDEMcxyaTSQPGaBhwpANUHFNTGNCy7OFRmga+H+C67pTL+36AbVsYhjFlt7eR8ZzJvk3f0SoMg9FuWTMNdiXdwILRENpUXNcZ7mSXdBHLZg/PwzC+n4xK/djHYO9e6OmZeLnkPgPLMgmCYNoOfEEQYBjJ7aZbdmwHvu99Dz7ykZnuhYiIiMjCK953H+bevVgDA/hLlkzZ9cNwXbz6euzeXsL77yfo7cVubZ3HakVEZEKGQXT++YR79+Ju20bV90c7GU64eF9f8kVTE+5TnzpPRR6+4iiafiGRRWr2p0mLiIiIiIjIrMVxjOd5eJ6PbVtTBjscx8GyLHzfx/OqMO25eIuXAljT832fQqFIuVymXC5RKpUolyuUy+Uxl5cpFgujHaeiKBztfhXHEXV1WVKpZNSeZVk4jkM2W4frupTLFYLAp1KpTFuLZVnYto3juMMjDSd/7kZRRBAEOI6D607epQvAcVwsy8I0TXx/6nb/cRwRBOHweqcOgE1WV+3drJLgWBCERFE4423Nl/5++Od/hocemjx8BUmQyrYdHMchCPxpumAl72nJ+5WJbdcePisW4Z57aq//cLZs2UJXICIiIvPJ37gRymXMICCuoQuK39iIVSxCGFLdsmUeKhQRkVpknvUsaGwkdl3sHTuScYQTiMtlnL178VtaiJYsIXfhhfNc6eHHXroUgMhxMAqF6W+QzyetuFMprI6OQ1ydyKGlAJaIiIiIiMhhIAiC4WBIEjiZjuM4w92M4tGxcEejm29e6AoOb2EYUCoVqVaTMJVpWmSzWXK5HLlcjkwmTRRFlEolPM+nWCwBMdWqRxiGBEFIOp2ZdFSg67rYtj3cFcobMzJwcqlUCsdxME2Tcrk0YTApDAPK5RKOY2PbNq6bmnKdI8Eu13XxPJ8wnDiZF8cx5XIFy7KwLItUauYBrNpG7+1zmExOnNJPfpKEsGqRSiXd+ZLHrzzJYx6PBvIcx5328ZvIV74y45scli66aKErEBERkfkU+34yU9kwDux+VS5j7N6NuWMH5o4dWL29EMfJoOY4Jq5WF6JkERGZQPb00wnPPx//2GMxKxXsRx9N/nAePiYQ+z7G7t2kNm0iymaJly3DfvnLj4yDAIdYavVqwuOOI2hpwe7vh2k6Ypk9Pfj19cT19dQ97WnzVKXIoXF49r4XERERERE5ysRxRBQlB3FMc+pRXZCMAEtuF9cUelmsami6dFSrVCrDndJ8MpnMAWPgLMsmk7GpVpOOWMloQB/P8/D9pBvbdGMFXdelVCoRRRGe55NKTR22cRxneJkkpFMqlceNqAvDgCiKR5ebqO6JZDIZwjAkjiPK5Qq2bY0GveIYgsDH931M0ySTSZNOp2p6re3PNA0MIxl3WIsoinAca8ajDg9Xtm2PPn7lcoVisTTctS85xBSG0fD9bJDJZHBd56CCbotFXR2sXAk7dix0JSIiIjIfzIYGYtdNPqAvlyGbhUIBa/du7FIJI4pGP4h2DAMjijDiGMIQq6lpYYsXEZFxmt/xDvr/8R/BNDH37MHt7ITOTmLTxAxDIsfB7+gg7uiAl7+chmc8Y6FLPmw411xD9Mgj0NuLuW0b0Zo1E4bTjK4unEKB6rp1xE95ClYuN//FiswhBbBEREREREQOC8aY4xAxMHVYY3wTnsUR7JC5FUUhvh/geT6u60wZYkql0gRBEd8PhscOxkRRWNOIPtM0MQyDMKx9zF4mkxkOMhlEUTTczS35IGpkxJ1pmmSzGRyntvCOZVnU1dWNfu15PuXyvoSeaZqj3ZtSqRTpdKam9e7PcRwcJxgNnU0VUBsJk43s02KRyWSG36+Sx933PTwvGf1oGCbpdHp41KRDNpvlaH6PSqWghvygiIiILBKZCy6g9JGPEGazmL29RNUqqR07MHwfy/cxoojYMCCOsQAjiggdB3P7dsyGhoUuX0RExjAzGVpuu42B//5vwnvuIezrS0bqRRE4DuRyhOvXk3rRi8g99akLXe5hpf6Zz6TvO98Bz8PZvBlrwwb81tZkrKNhYBSLWD09WKUS1ZUriVasoP6v/3qhyxaZNQWwREREREREDgOWZWIYJqZpEAQhjjN116EgCEYDLCPdsETGGgk1JR2Y0tMu7zj2aNermQZmDqa5UyqVHh4Z6OF5/nAnNwPTNHBdF8dxZ9w1yrZt6uvrqVarWFYyEjEZGZi8VhwnGWc4mzCU66aoVKpYlkW1WiGTyTDR/RXHMdVqFcexMU2z5iDZkSKdTsJxnlfF8+zR0YyGkYTUXDc12hXraKYAloiIyNHFXbaM/FlnQW8vqa1bobsbMwiwgoDQcYjS6dFfno1KBSeKsMKQ2DAovvnNOP/zP5jp6X93FxGR+WE4Ds1/8zfEf/3X5H/wA4JHH4VqFaOhgfSTnkTmtNMWusTDkmHbNL373QzcdBPYdtLpqqsLc9cuAGLTJGhowFu/nmjlSrLveQ/OkiULXLXI7OlImIiIiIiIyGHAsmwsy8K2HXzfw3FsJg/BxPi+j+u62LZ9UGPUZuNZz4I9e+CXv5zXzc5aOr24RhamUrB0KWzbNvH1URQTRRGGkXQlmo5lWfi+P9yVKsYwTMIwYroMTdItK1l+ou1Mdb8bhkkqlSaVmrsPmZIRgxnS6TRhGIwGsCzLnJPXykgnrTiOKZdLlEql4dfiSKgrxvcDfN/DMIzhblvpRTOCcCzLsshksmQy8egIVcMwFuW+HqxUCqaZ4ikiInLEiMplqps3E1UqWA0NpNauxVDS+ADZl7yEyu9/T7xtG26pBHGMn8kw+ot1FGH6PmYY4g3/suDk88RbtlC45x4anvWshd0BERE5gOE4yYhBjRmsmdXQQMuHPsTg//4v4be+Rbx7N3he0tbfdYkbGuCSS6h/4Qtx2tsXulyROaEAloiIiIiIyGEilXIJgoAg8KlUKqTTafYPYcVxTKVSGe3mk0rNf1ed5z4XPvWped/srK1bBw89tNBVzJ22tiQM9/73T75MLTmYk08u8fDD2TFjLQ0cxyKKQqrV6vBzbPIV+b6PaRpYljVhZ6mvfhV+8Qt497unr2UuGYYxJhQ1tzKZ9HDHLvA8j2q1SqVSxTAM4jgefn06uK4zHDBLHZI6Dh9J57LnPQ++/OWFruXwog5YIiKyGFQ2bqT0ta9h3HsvRrkMgA+U2townvlM6q+/Hru5eWGLPIxkTjuN8otehPHb347+Qu5Uq0S+jxHHGHFMZJr4qRSR6+Ide2wyprCnB/+uu5Jf8kVERBYBw3VpevGL4UUvovTAA/idnRAEWO3tZM87T10fZdFRAEtEREREROQw4bouvh8AMeVymWKxiG072LZFHEMYhsNhl6TDz8iYtvn0qlclHY1yuXndLDD7DlZr1sDDDzMmaHRk830Iw8mvH+n4NPLcsSZJgThOEiQKggDLsjAMg3Q6QxhGeJ43HAbMTHjbMAzxPI90Ohk3N9k26upmtm+HP4NsNjvauS6KXMIwJI6Tz9iSy83RMYuLzbXXwp13Hnj52WcrgLW/mQawVq+evKudiIjIQhj86leJP/IRzMFBzJ4e7EIhCRBZFmFjI1FnJ0N33kn6ne8ke/rpC13uYcOMIqrHHIO1aRNhKoURxxAlv3fHlgWmiZfN4i1dipPLETQ1Yff3E2/ejLdrF+6yZQu8ByIiInPIMMiefXZy4EBkEVMAS0RERERE5LCRhDpGRsYFgY/v+1QqPgCmaZFOp7FtG9d1yWYnDsUcKjfeCM98ZvJ1Q8O8bhpIQl+zCWA1NSVdo7q756ykBeV5MNyAYEKO42KaFWzbwvc9LGvi54vrxsRxRBAEZDJpXNfBcRwcxyadzlAulymXSzhOMvISII4jPC95fibLOsMd2yZ2pASwrr4a7rqr1qWT8YKpVBKcHBl3ONJ5y55uduMR6vLL4YwzJg5gqdPTgWY6grCpKQlgZbOrCYLkBW4YBi0tOitYRETm3+A3vkH8wQ9ibt+OMziIX1+Pt3Jl8kO/WsXu68N59FG8QoHqm96E+cEPkj7++IUu+7AQDQxgOg5BLod37LEY/f0YQQCGQey6RM3N+GN+SYiyWYyeHgDCvj5QAEtERETkiLM4jwaKiIiIiIgcoQzDIJutw3V9qlUP2/bHXMfwWLOk29Cpp8Kf/zx/tb3whfu+XogO4Q0NMPyZxEHJ5SCbnbt6FprnjZ5EPyHDMHBdhzBMUSqVhscJHjgKz7ICyuUylmVhWfZox6Zstm60o5PnJWMxx9/OJJ1O4TgO2Wx2ysDRQnRMOxgveclMAlgjjNEQ2tEgnZ58tOXhHsAaCcjNp5kGsBobk/8vuuhhqtXq8DpSvOxl81u3iIhI0N9P9JGPYHV2YhUKVNevH//LdH09QVsbQW8vqZ07qVoWxVtvJf2JTyxc0YcRw3GSX5qiCLJZ4ro6DmjE63n7lh/5xZtkXJOIiIiIHHkUwBIRERERETkMJR10HOI46U4ESVesseGBd74TvvAFuP32+a9vnjMMANTXz+72uVwShlgsqtXpxymm0+nhzlYZKpUyYRhg287wqMCYk07aw8BAHsNYQyaTJp1OYVnJoQLDMKirq6NatbEsj1TKJQwjIMYwTEzTHO58te82+xvJZB0pHbAcJ+nu9Lvf7bssDAM8zxvedzDNsYGrxROKOesseOCB6Zc777xk/OVEDremX3Ec4/s+nlclCJJ5nYbBcBfBFI5jc6gfw5mOIBwJYImIiCy0wje/iTE0hNPfT3XNmsnPZGhtxatUcPbsgUceofKXv5A+6aR5rfVwZK1ZQ1hXl/zyMTiYtLmcgjkwQJjNErsu7ooV81OkiIiIiMypGZyDJyIiIiIiIrWaq4CSYRiYpoVpWgd0bjEMOP/8udnOoTS2+0sqBU984sGtZ7ZdlI6kAFYtnbrCcOoOWJCE9urqcsMjK+uwbYcg8IfHClZ42tM2097eSCaTIZ1Ok05n9ru9QTqdpqGhnrq6HHV1daP/J5fVTRq+Arj00uT/IyWABfDKVyb/h2FAoZAnny9QKpWG/xUplUoUi0WGhvJ4Y7oWHOmuuGLfiNHJrF0LU32eunTp3NY0G2EYkM/nKZWKw8/3MqVSiWuu+S3lcplisUA+XyCKwglvf8opc1OH686sA1Zz89xsV0REZLai730Po6eHMJWa9kyIuKMD0/dhaIjSN785TxUe3nKXXAJNTQQNDVjd3VOeORFXKjhDQ4StrcQXXoi5mNr2ioiIiBxFDrNzE0VERERERGQm1q1LPg/J56dezjCm75Z0qNxyC7S2QrkMK1Yk3WDe+1742c9mtp6GhtnVUV+fdDf6059mdrv3vhfe+MbZbXumnvxk+M1voLd36uVqeUxN0ySXy+H7Hp7nEQQut9yymXQ6xjBW0N/fSn19bsog1cGO2RsJnlhWMrpuvymGh6V0GoIgoFgsjt5nURRjWRaGAb4fUKlUcRyHKIqI43jC0Y5Hmic8IXl93HPPxNevXAn/8i9Th0tNE04/Hf74x0NTY62S8Fzy+FUqVUzTwLZtDMPkhBMqvPjFP+OHP1zHrl3LieOYXC6HuV9KKpw4lzVjqRQEQe3Lz7bTn4iIyJyIY8w9ezBLJcL6+unPLnEcwnQao1Qi3r17fmo8zJnpNFx6KVFvL+7GjcTbtxOuXImxfzK7UsHduZOgrg4aG8lcf/3CFCwiIiIis6YAloiIiIiIyBx4znOeM64bzm9+487Ldg0DXvtaeM97Jh8L9uY3w2mnwQteMLfbrUU6DWvWQGZ8YyXOOWfmAazZdsCqq4NLLoEvfxlqbVx02WWwevXstnsw2tvh/e9PwjD/+7+TL1drqM4wDFw3heumiKKQxz/+BAzDIJVK0dlpzWhE2kyM/Xzp5JPhd7+L8f2AMAyI46Qu27axD4PZdevWJf/bdkCpVMTzPKrVKq7r4jjOaAe6444r8Ze/pKhWq6PjQUfGMR6pXvWqfY/V2rWwefOBy7z61bWNx7vqqtoCWOvXw2OPzazO2sSUSqXR8FUqlRp9bOrqQpYuXcqSJTGZzAAf+EArYFAqlcjt9wbT3Ax/93fw4Q/PrppUavL35om4k/zoWKgArYiIHMVGWq3W+It/bJrJD6yZJI8XucZXvpL+P/wBLwxxtmzB+stfCJqaiLNZjDDEGRjALRQI6+uJjj2W+LnPJXPqqQtdtoiIiIgcJI0gFBERERERmQOf/vSn+eIXvzj67/LLP31It7dy5b6vzz03CRVN5PTTk5F/03VVeepT5662sV7+8gPDV5B0eJqp2Qaw6uuTLlpvelPttzGMJEAxVyMla1VXB01N8PznTz2y8WBCGVdfbVFXV0c2m8WyrEkDH3NhJNQTxzHXXNNFOt1HsVigWCyO/isUCuTzeXx/9uP8Hve46Ze54Qb4yEcOvPzVr07+HxrqJghCqtUq6XQa13XHjf9sbQ2wbZtsNkMYhvi+T7VanXXth8Jzn1vbcmNHD7a3z26bra21LffsZ0NHx+y2NeKDH4Qzz0xCXTfeWCIMk8cvlXLHBeNuuKEbSIJ/bW3NOI5DtVohCALC/VpePec5cOGFs6/NMGAmT4/J3mum63IoIiIypwyDqLGR2HUxy+VpF4+jCKtSIXbd5JdYAcDMZml8//sJzzgD/6STCJYswSoWcXbtwunqwgBKq1YRrltH/MIX0vw3f7PQJYssTnFMPJOzIkRERA6SAlgiIiIiIiKHwESho4k8+9lTX++68MpXHnj5/sfmJwvRLF069fpvuAFuvRUuv3zq5fZXayDpsstmd/uxZnu8NJ1O/j/nHLj66tpuYxjJv5Hbzpexj+dkz6UTT9zXmKBW6TRceeX4yw7l9LykEULMpk2b8LwtvPjFv+LEEzdQLleoVquUy2VKpSLlcplisURlFjMKX/1qeMc74NprmbKj14tfnIzCfP3rk88H6+uToODxxye19vV14/s+lmVO2Jlr5LlrGCaO4+L73oQBnrlUS+epiVx/fW0d3Ma+HmsJYE31+m1rm/72kHQc++AH4TWvqW35yTzucck+vv3t8J//Ceedt5tguPPG2PDVC1+4l9NPL45+b5rgui5RFBOGIZ63LyV1+eVw7LGQzc7s9bH/++3ppyf/z+S9a7L7dmio9nWIiIjMiSc9iai1FbtQIJ4mTWz09ydfNDeTuvjieSjuyGE3N9Py0Y9iveMdBE97GsHxx+OfcgreySdTWb+e4pVX4n784zS/+tXzf9aHyCIW5vMMfulL9L7whQxcdBGDT386/ZdeSu9b30rxt79Vi1kRETkkFr7Hv4iIiIiIyCJUawBruhPEr74arrgCdu2Cb30ruezyy+GUU2pb/9hMyLnnwv337/v+cY9LwigA3d1Tr+d5zxv/fS2fDfz939dWY61qOPl+SmNDObV+tjHSwWmqba9cCSedBN///sHXtr+x0+wmC3+94AXw8MMzW+9ttx0YEqk1YBKGAZ7nEUURcQymaeA47pSj90wTtm3bRk9PD9u2bcM0TVatWskjj4y0M0tGEnpelVWr+sjlGtm1qwXXnbqoT3wCvvEN+M53ku/Xr0+6Fdk2vOxlyb8Pfxi+973J1/HUpx7Y+a1cruD7PkHgk5rkjhn73HEcZ/Q+CYIAawazHA0jOeb/xCfCzp2wbduByzztaUm3uOOOm/kI0ZHw0Hvek3RzqtVkAaxaXzMNDbUtl8sl75PnnFPb8pPZf99KpRInnljhoYfagKToJzyhwJlnFsYtZ5oxhmFgWRZhGBCGSZrx//0/OPvsfcvNJDz1kpfAxz4Gg4PJCMORx6zWkaeGkXTyAujt/eloKMxxHP74Rws4iNaBIiIiByl7ww1Uvv1twkwGe/t2gnXrMMwDz+mPy2XcPXvwW1qIOjqoO5hWt4ucYVnUX3wxXHwx4dAQfnc3sW1TimNc1yW9ZMlClyiyqBTuvRf/llswhoawe3owCwWMOCa2bcI9e/B/9jN6Tz+dpv/4D6zp2oWLiIjMgAJYIiIiIiIih0CtAaypPtx/5zvhtNOSr1/5SrjxxqTjUUtL7XWMXf+LX5wEPTo7kxDOX/3VvuumW+dIJ5fpuG4SNnjSkw5uzOBUJgtBjWxzOgdzQnkttzHNJGx25ZXw2tfOfBsTGZtpmuy59LjHwUMPzWy9E3VEm24EYRgGlMtlgiAcF1QxTQPbTjpFpdOZCYNYYejR29vLzp07cRyHlStXsmvX2B0ycBwH27Z48pN/z7HHmnR1nchvfrOWvj6Dk06C886DO+9MnruOk4Rali5NusBdeilUKkn3qv03P9PuYEm9SWIxjpMOVxPx/X1PipHRhHEcEddwBvUHP5gEo9rbk5CO5yUdo/7t3yYOYJ15JpxxxtQnZ7/0pfDZzx64zHATKDKZpMtXrSPsau1gNRnDSAJxjz02+TL19fue1xM0GZuR/UceRlHExRdvBxz6+mxWrqxw7bU9B9xu5PPjkSAcJHfg2PGukATkfv7z2mp53OOSAFZfX1JXNptcXmuI65nP3Ddq9f77n0EU7XvTe+CBDP/936XaViQiIjIH0uvWUbzkEvB9nMcew9mwgaC9nbilJQlieR5GTw9uXx9BXR3xsmXYr3gFxgwC6Ucjq6EBq6GBMAwx9u5d6HJEFp3Cj39M8C//gtnZidvTQ+i6hA0NxJaFWang7thBvHs3lMsMvva1NH34w5gjv7iLiIjMkgJYIiIiIiIih0AtY7wALrgAPvOZAy+/7TZYs2b8ZdN1y5rI2A/+V65MAiDlctJVaWzwYfrPSSLy+SJhGA53jMkCB97oc59Lum7lcnM7QePKK6FYnPi6//f/4EtfSkIU11+fdEeaqKPXoQpgjdS1Zk0SVPvjH2e+nf3VEsDaFxyZnak6YAVBQLFYxPe94U5PMY5jEEUmvh9SrVZxHJcwjMhmMwd0rioW85RKyWjBdevWYZrmhM81wzBZsaKJgYHtnHJKL9dc00Jzc/Po/X/ppckINtuGurp9t1u3bvLaDyaAZY7p6jBZoKqra2zSKx6u35j2uXLxxePHAY4dKzhZ16iR1/xU6776avjKVw4MKI4NJWYytQewZtsBC5LRqrfcMvn1l122b32zCWC57oEBLNu2aWiAq6/+Cyv3T1ONYZrJYxdFMbZtMNIta//GHjfcMD6AZRjJuMN3vOPAddp2cl+PfY4CTDa16RvfgJ4e+P3vk1BhrUFXERGR+dLylrfQVygQGwbm3r04e/di7txJbJoYUUSUSuEvWULc3g6veAUNz3jGQpcsIkexoK8P/13vwtyxA6evj+qqVckfXsN/fERANQyxtm/H3bQJzzAYuO02Wt785oUtXEREFo2JT+cUERERERGRWbnootqW6+g4cATXm950YPjqYO3fGcqyknBUraGHKAopl8ts2LBh3L/NmzdTKpXwfZ9qtUK5XKJUKjEwsAvbLs9p+OqMM5IxYxdffOB1l16ahBbe/e5k1Nq5504+su9QBbDGBuNqHb82nbGPz1Td1CYLYD3rWbVva7IAVhRFFItFPK9KpVLFth3q6up48pNDMpkMdXV1pFIpfN8bfg6UCUbaLg0rlQoMDAyQy+VGO2RZ1sRFL1mSxbIs8vk8AwP94+57w0iOm+8fbJnKwQSw0uk0lmVh2zZBMHHboj179rUM8/0AwzAwTQvLmvxFdfzx8PznT77dyZ43J588db3LlyfPlYmCO2MvqzUQWuuy070uzj8/6cw1kX/4B3jRi/Z9P5sA1lOfeuDzt6GhgcbGxuHg4OStp0wzeX+LogjHsUefn/vXs3Yt3HRTEmBdvToJfJ5xxsTrnCzIOlEZzc3J8kuWJGNlH//4fffryGjYWtYtIiJyKBm2Teu73oXxt39LeMYZ+CefTHXdOrxVq6gedxz+iScSPOUp2O98J01jf8CLiCyAwp13YgwM4Pb24q1alfyxvt8fL4ZlEa1ZQ5hOY+3ejfGjHxHWeraKiIjINNQBS0RERERE5BBYuzb5wH7HjumX/ad/gh/9KBlJ9qQnTTwm7mDVOvpq4tv6lEolgiBgz55dlEp7RrsC7dy5gv7+DHEMjmNjDacDNm3aRDabpb6+nuXLl1M3k8QMSUejTZuSr103CVWtXZt8f8opSeBk587ke8eB6647cB2TBbAORi3339iA1HQBrFtuSfbhpS+dOiA0tgvPVPsz2TpmEmqZLIDleVXCMKRa9chk0qMBo/Xry9x/fwNgYNsOpmlRV9dFPt9KtVrFHrNxwwjxfX/c82Ck89D+XJfhQJd/QJDrYBxMdzDTNGltbcVxHMrlMlEUYprjky8rV1Z57LEMEOP7Ho7jYFnmuP0e6/OfH9/taiKTPW/Ghm4c58Dn40hnpeuug1/9avx1V1217+uOjtrHVTY317bcdE46aeLLn/a08d/v33FqOk96UhJaOuaYJIC5v/b2drq7u3Fdl71797J8+fLRUZHjRcPPVwvTtHDdkYDggUs++cm1jVSdLCQ10YjUqUZ/PvOZB4bcZno/iYiIzBnDoOmFL4QXvIDCL36B/8gjUKlg5HKkzz+f9AknLHSFIiIQx0T33IPV00OYSk39R5hhEHZ0kNq6lTCfp/Dtb9P43OfOX60iIrJoKYAlIiIiIiIyB97znveMC43Yts3jH/+mmgJYpjlxd6e5MJMAlmUl4wMhGT1XKhWpVpPRc3Ecs2LFChzHoVgsct99feTzeeI4xrIs0uk0tm2zbds2UqkUra2tFItF1q5dS+N06ZMx/uVf4L77YGAgCTwcc8y+60wT3vc++OY3oVRKxpgtX37gOuYygDXZ6LCxzj5739fTBbBOOSX5/6Uvhc9+dt/9vb+xl0/VAWuyANZMuj9NFMCK4xjP8/B9D8va190pnY54/OML3H13G0NDSdrENE2e/OQh7ryzAdv2iaJodJRfOh1jGAbRmIKiaPL2SVEUDY/zm30Ltcnu2+m0t7dj2xUcx6ZcLg93xdp3+OSJTxzi0UdTVKsVwMB1HVLDd+IZZ8DvfrdvXRdcMH34CmrrnOa6kwewTjoJrrgCvv3t5PunPAXOOmvfch0d069/xGRBn8HB2tcBcMIJ0NICfX37LhsbChsx04f65punvj6TydDc3Mzy5cvZtm0b27dvp729nWw2O7pMoVCgs7OPOF5COp3BdV0MI9nx2XTkmuy+m+j1OFUAK5tNrt9/rKSIiMiCMgxyF1yQ/IIjInKYCfN5zK4urHyeoJazSurriS0LhoYIH3300BcoIiJHBQWwRERERERE5sA73vEOymM+Lc9kMrzjHW9awIoSE3VemcxLXgKf+lTydblcwvN8PM8jk8mwbNlScrkqQRDQ29tLW5sxHKyJMc2kg0w2m2X9+vUMDAywe/duvOGNn3jiiWSmShGNkU4nwarJZLPwvOdNvY4aN1WTZcuS/w1j4o5KL3gBXHPNvu8nCtJEUYTnVfH9gAcf3IJpmjzucVluu62NdLqBl7/8wNuMDQ9NNIZuZGzlZF2eZhvASkazxfh+QHpMoi2bjbBteNnLdnPnnW2UShbnnDPEOeeEfP3rEXEcEQQ+rpustL7eJpvNMjg4SMdwCsjzJk7cBEFApVKhvb193DYP1nT3wUiHtyiKsCyLurq60TDhlVcG3H13GqhSLlcwTQPLsjnllAHq6x+mVDoby7LIZNK4bmp0fy+/HJ7+dPjtb5Pw4PXX11ZrrQGsYnH8ZWNf3696VbK9KDowcDWTEYSTmWk3PdNMavrAB5LA5Nq1418rB6PWhnpr1qzB930Mw6Crq4vt27dj2za2beP7PmEYkk43kslkSaVcMpl9z7fZjPqbLEzmuge+F+dyB78dEREREREZL6pUADDiuLazKgyD2LIwooi4ljOvREREaqAAloiIiIiIyCFy9tnw858vbA0zCU1ceincfjv09/uEYRIaSqVSWJY1GvQZGBigVCph27tobz+VfD5HtVoljmMuu2wrjuPQ3t5OJpOhs7OTTCbDnj17OPbYY2uqwXEOYif3U0t2Z7KgRGPjvk4/tg3nnrtvnft3o2lshOc8Z/xl44M0MeVymWrVI4oiVq/uZcuWJIBVV1dHQ0MfmUyGMDxxdITjiLFBk+bmZGzbD36QfG9ZyYgymDyANZMuPhN14omiGEhWbo5p65PNJsmw1aurvO51O0cv9313uJ54XE0dHQ00NTXR09NDPp+nvr6eSmXiNkF9fX04jkNdXR1tbW2178AkJgtgFQoFurq6GBgYIAzD0QCWbdu0tLSwZMkSbrwxx69+5dHTY+C6LsuX93PllX8gm/VIpVyy2SymaZJKpcaFd2wbzjsPLrxwZrXWGsDa3/6hnsnutpl0wAJ41rPgjjv2fW+aSXevmTr/fDjttCSA1dIyu+5SUHu3LNM0Oe644+js7CSdTlOtVikUCoRhiG3b5HI5DCNNJpMhnU4B+1ZcawDrxS+Gz31u3/dTTSz5+7+H//zP8Ze99KW1bUdERERERKZnDf9RFds2RqXCdBPp4yjC8P1keZ0dISIic0QBLBERERERkUPkzDPHj/VbCDMJYGWzcNtt8JzneARBckPHsYfXYxDHMQMDA+TzeVIplxtvfIQf/vB4urp8jj9+C+vX54EkaJXL5WhsbKSvr4/6+npWrlyJXUP6Yg4mz82qA9a//Rt84xvJ/faMZ8Dq1cnlEwWwJjpJdl+QJqZYLOJ5HpVKhTCMsKwKtm0ThiF79uxh7969LFmyhAsuqOPnP18xOuaurS0ZKTfW614HJ54Y0dMDT3hCzCmnJCGmyQJYz3gGfPWr4y970YsmXnaiDljJCEBjeBsRkGwvk5k41RRFI+M3jXGP4ZIlDVhWipaWFnbt2sXKlSupqzswbeT7Pr29vaxYsYJcLldzx7SpTBTAqlYrPPLII+Tzefr7+ymVSknVhkEulyOfz9Pb28vatWv5+Mcb+eUvLQwjz7p1VTxvOXEc4zgO2WwG102NC6fBzINOI2oJYE08KrK29c+0A9aVV8Ivfwl79iTfX3891NfPbB0jcrm56/Y0ZorgtEzTZNWqVSxfvpze3l4KhcJo2K6+vp6GhhbS6QPDgLWGxK6/PnmtbtyYdPd62tMmX/bcc+H442FkssnZZ8OJJ9a+LyIiIiIiMjUznSY85RSM7m6cri68ZcumPMBg9PUl1zc24p5//jxWKiIii5kCWCIiIiIiIodIQwP867/CZz6TfEi/v5ExcnPFcQ4MXM1kBCFAa2syMi8JDFmMhHB836BSqeD7PoVCgZaWFurryzz72X+kr69veKzXMePW1dzczJYtW/A8j8HBQVpbW2exdxOL45hCoUB1OA3lui6uW8/YjjYzsWYNvP71B17e3Az9/eMvmyhUMhJSqVQqeJ5PuVzGMEyy2SytrTFLlyYPSBRF9PX1sWvXLk491WTnzgzbtnXQ3GzwutcdeJzYMODpTz8wbTNZl6fWVrjiCvj2t5Pvly+Hpz514mUn6vhjWSaGAZZlEQTBaDhspAPW/gYHBzHNFZimOa6bV12dxdKla0Y7TW3bto22tn7iuJEoMofHHPpcdNGjHHPMMTQ3N7N6JPU2S/sHH6vVZJxgZ2cnxWKRpqYm2tvbsW0bz/MYGBgYrq+NOI45/vjjufrqHNA0/G+fybqsrVp1cLUebAesWs00gNXWBh/6EPz5z9DUlASMxpqLoOTBePzjZ34by7Lo6OgYHYE5YrLXTq37Zhhw0UXJv+lkMvCud8GDDyYBr9NOW7j7UERERERksXKuvprwwQdh716MXbuIly+feEHfx+nqImhuJlq6lLqZtjAWERGZhAJYIiIiIiIih9Bpp8H73pd0qnnd62Dr1uRyy4JnP3tutzVRAGsmHbCmEgQGYRiOBmncGtIgqeGWPUEQEATBAdefcgo89NC+788+u/Z6oiiiq6uL7u5uqtUqvu+Pdifq6VlCpdJBKpUa7uR0oKc/He68c/xlV145+fZe8Qp4y1vGX/bKVx64XDabhMKqVY9qtYphGKPdnC6+eF+CyzRN2traME2Trq6dvOhFWY491qa9vXVGwYypOiC96lVJOKRQSO7ryUJDE23PMEwcx8F1A8rlCo4TYZrmhB2wgiBgcLAf23awbWs0rAVJ8KSpqYm1a9diGAZNTU309/dzwQUP88MfriWKLE4+ucRVV9XR0pJm/fr1pGuZIVmDCy6ABx5Ivo7jiEqlzLp1u6hWq6xdu3bcc9h1XXK5HIVCgc7OTmzbZvv27Zx88sk1b+9gu19BbR2iJuqAVSvXTbZRKMzsNgczdvBQuvzyuVvXfAegbPvwuz9FRERERBaT+ksuoe+LX8QrFnG3bSMIAsKODoyRDstxDP39OHv2EDsO0bJlWM97Hkatc8hFRESmoQCWiIiIiIjIPDCMpAPK174GxWISADr++LndhuMceNnBBLAMw8SyTKrVfTdOpWIMwxgduRaPSf5UKhVSqdQBIwbD4RZEhmFMGIT6q79K7pOeHli6FF7wgtrqC4KAjRs3Mjg4SF9fH4ODg+O2NTRkUirl8H2Puro6TPPAg6mrViUjIn/72+T7XC4Z2zeZk0+GJz4xGcsGcPrpE4cpWlrA9z2iKCIIAjKZJEx06qlFli498MFobm4e3ocB+vq66eiYWZew6UbQnXDCjFY3TiqVwvN8bNuiXC6TTqfJZscHsKrVKrt27cKyLFzXwXXHp4RGjnM3NzdTV1dHd3c3uVyOG24IuPLKHjzPpL3doaNjNa2treO6Z83WhRfCpz8Ng4PgeR5xHHH66RtYvnz5pAHCXC5He3s7PT09NDU1kc/nycYxlYcfJi6VMOvrSZ9yCnDgLLy2toOv1TSTLlObN++7bP368cvMpgMWJJ2sZhLAmspCdG9at+7A+2Q21IFKRERERGRxMWyb+ne/m8JrXoNnmti7d+M8+ihhKkVsWZiehxFFBE1NRMuXEz/rWTRdf/1Cly0iIouIAlgiIiIiIiLzJJuFF7/40K2/2d/NiVt/QGN1L2YcUXBbePSYpwFrp73tWM9/fsxnP2tTrVYJAp/mZpN168pEUdJRKpVKUSwWSafTVKtVqtUqbW1tNDY2jltPoVDAtm1SqdRoF6ixjj8ePvrRJBRSX19bwCSKIjZu3Ehvby87duzAcRw6OjrIDbcQKpVK/PnPVUql0mj4KZfLYRjmuPUYRtLR6v77oa8PzjsPliyZfLuGATffnIySjKIk2DRRgMN14YlPLPDd70YYhoFl2axfX+ZlL9szyXoNGhsbyefzFItF4jietGvXRC6/fN+YwRFzdfzYsuzRx61arVAul4njbXR3lzEMg1KpRLFYpK6ujmXLlpFKpQ4INo1tZuW6LsuXL2fZsmV4XhJSS4Jbs0wWTcJx4P3vhy9/GbZtG+CEEzbQ0hJN+Fwcq6mpie7ubkoPPUTXF75Ay5//jFGtJg+8aVLNZnna9iv53fJrGciuHL3ddGG46Tz3uXDrrcnYUNc9sEPeRAHLmWhuhs7O2a1jIV122dyv84wz4He/2/f9eefN/TZERERERGT+uMuXU/+xjzH0nvfAb35DUCxi5PMQx4SOA83NRC0tWM9/Pk3Pfe5ClysiIouMAlgiIiIiIiJHuOq2bRQ+8AFe+NO/YJZKpMIiBuCZaZ60++v0vnIlmb/5G7JPeEJN63v2s+v4/e/38sADLqZZ5PrrBzBNME2b+vp6Ghoa6O3tJZfL0d3dTTabJZVK0dTUNLqOKIro7e2lsbGRTCZDfX39hNtKpWY2Wq2np4ehoSF27NhBQ0MDS5cuHXd9Q0MDy5c34Lou5XIFwzCpVquk0weGblw36ZJUK8OorWvZc587QBQNsWOHwdlnFznrrPyU3XZs2yaKks5SI6GkWq1alYxu/M1vku8bGw8uqPKc58BXvrLv+/POgyc8AT760RSGkex7c7PPWWeVKJXKQNIhq6Ojg3Q6zZIlS8hkDuwKNdE0wZEQ33xoa4PXvAZ+//vdbN3aRyZTN+1tLMui/fe/Z8X3v0+d52EXCtiFQpKwMgz8+nqePFTk8T0/4JvrbmJTR/Ikmm0A67zz4EMfgu3bYcUKWLZs/PWzvcvGvDyPOM3N8NSnzv16X/5y6O2FbduSDmQvecncb0NEREREROaX09FB66234nV2UrzzTsKtW6FahYYGnAsuoPGSSzBsfUQuIiJzTz9dREREREREjmCVv/yF8k03Ye3cyYrBflyvhG+kiA0DN6pSNVNYv9hOddMmore9jVwNKYaGBoN//dcUjz66hT17OikW8+zd20xjYyONjY309fVRLpd58MEHWbJkCe3t7bS1tY2OIAyCgF27dhHHMS0tLbS3t8/Z/nZ3d9Pf349lWSyZpGWV40S4rksYhvi+h+dZpNNpYH5mjrmuxYUX9tHd3c1xxx037fK+74+Odhz5v1aGAW9+MzzwQDJq7+yzoXVmUwyBpPuSacKf/wzLl8NLX5p0XNqxA37ykxR1dQ7Pe14fHR2rqFarADiOM/r4TtbFarZdm+ZKHMfjxmZOxfjZz1h7zz3kenrIVipEzc1U1qxJDtD7PmZfH6tKG+gLlvCsx97N/9oZdrScNesAFiSjOPfLFI46GgNYhpF0pvuHf5j9/k9k5Uq47bbR5mYaSygiIiIisoi4K1bg/v3fL3QZIiJyFFEAS0RERERE5AgV9PVRuvlmzK1bSe3cyYC9lE5nLb6VtB0yI59mr4vmndvxq1X8d76TyjHHkD7xxGnX3dLSzLp1AY4TMzAwwMDAAH19fQAMDQ2RzWaxLGs45ORj2zYDAwOUy2UGBwdxHIdVq1bR2to6ZwGsUqlEpVJhYGCAtra2SUf1uW6ShHEch0qlQioV4fsBMD9poFwuRy6XY8+ePRQKhdHxiBOJ45jBwUFaW1uHRyXOPAHiOHD++bOpGCwLnve8Ay9/5SuTf2ACbcP/ane4BFpc1yWVSlEqlaZcLi6Xaf3KV3B6ekhVKhRXrybV3Dwuuhc1NrJ9KGRF6TECw+GZm27jE82fJo5r71x2cPswu9s3N89NHfPlpS+Fq69OnpuH8nlkGMk2REREREREREREZkMBLBERERERkUOgXC6TzSYj2S666CLuueeeSZf97Gc/y9/8zd/MfCNBAGGYtG8xDNamQj6ePXb06sh0iJcvwcuapLZu5dtRxItPO23GbYn27x5UX1/P5z73OUqlEul0miAI6OnpAZKgS6lU4jWveQ2GYRxUoGisgYGB0e5KnucRBAFBEJDNZunp6eGaa66ZoF6DIDBGax+pIXvghLxRGzZsYOXKlRNeF8cxdXXTj67b/zYjYwXH3gef/OQnOfnkk0e/7+vrIwxDGhsbaW9vZ82aNXR1dc1oWxO5/fbbueKKKya9/txzz+VPf/rTrLfzwQ9+kJe//OUAXHUV3H33vutuuAGuvvpqfvCDH8x6O29961t561vfOun1r3jFK/jiF7846fUjz+Gxz4eJXHfGGbxrcJBcsUjxmGNwGxrGXf/OrVv58M6dyUuOGCPoIio+SHjPV+G7Jh/5SG37c9VVV/GVsTMf9/OhD32IN73pTeMuiyLw/fHLmebUz+tzzjmHe++9F4Azz4TPfW789bt23c7vf/+SKdcxkTgGz9v3fSaziqc97ZFJl//pT3/K5ZdfPun1w03Vxvne9w4MRqVSKfr7+yddz4YNG3hCjaNWp7Nr165xY1XHGhwc5JhjjpmT7fz+97/nhBNOmPC6pUuXsmfPnjnZjoiIiIiIiMjhJiqXKdx7L9HevQCYS5aQe+pTMdPpBa5M5OAogCUiIiIiInKIlMtlgNGRbZMJw3B02YMWx/hGhGXHhMPhI8OA1hYf3Ab8piYYGKAcBElwaxYymQwXXXQRkIwE7O3tHRc2sm172n2erZFAWC3bGcmOTXUXTzeebtaPz7CR+ykMQ/r6+ujp6WH58uVks1mam5spl8tzsq0wDKe8vlKpzMl2gjHPpZe/PBl/uGULHH88XHEFfPGL1TnfzkQ8z5uT7cQ7d2J5HpFtEzc2Yu+XAPLjmPLwY7hPBFEVotpfWt7Y9NIEgiCoaX+iabY59vVx7LHQ0QFj831xHBJF5SlfG7WIoso010czfnwmuouiA+778eI4nrPX6nTmajuTvfcsX76cT33qU1x66aVUKlPfvyIiIiIiIiJHkqC3l6H/+R+MH/0IhoYwho9fROk0g7fdRvz0p9Pwspdht7QscKUiM6MAloiIiIiIyCJhELNuXZnubpc4htZWH9dNAgtRaytWd/fcbMcwyGQyAKxatYqVK1eOBmRs+9D9mem6LrZt4zjOtKPkDlc9PT10dnZSKBSwbZsVK1bQ3NzM+vXrZ90tbKGZJjzrWQtdxexEpRIuENTVkVqEZ1t+6ENw4437vh9+Gc+pqFwm/+1vE/zkJzAwAJbF4CxDn0ej7373u6PvsyIiIiIiIiKLRXXLFopveAPm9u2YXV3Y+TzG8AlXsWkSNDQQ9fQw9KtfUfe+95FavXqBKxapnQJYIiIiIiIii4hjxyw7ZoKuUNnsvlZQc8wwDJwZjjU8GNlslkwmQ1NTE319fTQ2Nh7ybc61kQ5hK1asIJfLkc1mWbt2LalUaqFLE8A2TSzAdByM/effLQLpNHzjG/C730FfH2zfDr/4xRytPI4Z+OKXiD//eYyhIey+vuQMVsPAOkTvPYvZmjVr2Ds8gkFERERERERkMQj6+ym+8Y2Yjz2Gu2MHYSZDddUqjPp6YsDI57G6u3EfewzP8yi+8Y3Y//3fWA0NC126SE0UwBIREREREZkDU3UqmS5cY1nWzDudRBH4fvK/YYBhkDLNSRePgwDTMMgYBjgOzCJcMl2tYztkzbX29nby+Tz9/f3s3bt30vvW9w3imNGuUpYFUzXnmq771Gz3Z2TM2PLlyzn22GPJZrO0t7dTV1d3wHbm4r6zpnl80+n0nGxnuo5nqVRqXrbjuu6MthPH8YSj31K5HFYcYxeLBMkTaNz1jmGQGX6dxTEYcUSMAZaF4dYeQnRdd8rrbduek/ttoteHZcHZZydf3377Qbz3kOz72BGBppnmSVs+BR/9NFZnJ/bgILFtE6bTGHGMOzhIBkbfq3CcpGXasIkmie63yKT7M9ahfO/Z31xtZ7r3nnQ6Pa/7JSIiIiIiInKo5L/4Rczt23F37MBvayM+5hhG/io2ABobCRsaiHbvxt2+HS+dZuhLX6L51a9ewKpFamfEEx1xFDlI73vf+8jn89TX1/OGN7xhocs56oVhOHrG7JIlS6b9EEZEjny+749+PR+dSERk4ejnvPhdXRSf/WzsRx8lSqeJVq6ccnmjqwu7uxv/5JNxbr2VunPPnadK51Ycxzz66KP09PSwY8cOTNOkubmZuro6DMOgVCrR19fHhg31/OQnZxAEGU48sY63vc1k+fKFrn529HN+blUqFYrFIlEUYVkW9fX1FL/wBfjYx0ht2EDl2GMx6usnX0F/P25nJ97JJ2PefDMNV189f8UvsIcfhje/ed/3x3f9mOs2vpvHpR7E9H28ZcswGhpGA2xxGGL29ODu3Yu3ZAnR2rXUf+EL2K2tAHz/+8l4xBGuC1/4QtKx62iln/MiRx/9nBc5uuhnvcjRRz/r5WgX+z4Dz3oW1p//jFGpEK5ff8CJb/sWjrE3biTKZAge/3iab78dY5qT8w4n+jl/eJqPLMuR8ywVERERERGRUU5HB+FZZ0FvL+7OnXhLliSphQnEUYTd20vQ3Ey0bBl155wzz9XOHcMwWL9+PZB0CRoYGKC3t3f0oIZlWTQ1NXHxxU1cdlkvS5euZ8kSc7K7Ro5i6XSa9H4Jn9y115L/whfwGxpwOzvx162b8HUVl8ukdu0iaG4mbmqi/vLL56vsw8K446NxzPk7b6e5sgeLKt5xx2Hsd58ZlkW8ZAlVxyHV2Um1sZH8175G86teBcBTngKPPAI/+EEyLfU1rzm6w1ciIiIiIiIii03xV7/C6O/HHhjAW7588vAVgGEQtLbi7t5N1NND6be/PWJPJpWjiwJYIiIiIiIiR6j0DTfg//rXhL29OJs34x17LMZ+I7pi38fetg0DiDo6MK++euoDHEcAy7I4/vjj6enpobu7m7a2NoIgGL0unU7T3t5Oe3s75hRjGUX2Zzc3w/OfT/TpT2Ns2oTz6KMEra1ELS0YjkNcrWL29eH29xNms0TLl2P+9V8fEDg6miwdepilpS00+914K9omDYIC0NJC2NOD2d1N8N3vEv/1X2NYFqkUvPa18Hd/l4wdPMLfokRERERERERkP2FPD/g+RhRBLjft8nEuhxGG4PuE3d3zUKHI7CmAJSIiIiIicoSqO/98+q65Br7xDdiyhfSGDfi5HFFDAwBmsYg9NETsOPhr1xKdey4tz3nOAlc9NwzDGA1ZlUolqtUqAK7rUldXt8DVHbmiSoWoWMSsq8M8SlsQNb/sZfTn84Rf/zpRdzd2Xx9mV9fo9WE6jbd0KbS1wctfTuN11y1gtQtv5cAfSQcFnNgjbmlhuuxU0NKCs3cvUW8v1a1bSa9bN3qdOvKLiIiIiIiILFJjzraK43ja4wfE8b7b6YCBHCEUwBIRERERETmCtbzhDfQ5Dnz964T5PGZPD87wWWGR6+KtXAlNTURPehLNb387hr34/gzMZrNks9mFLuOIFZXL5O+5h+Duu7E2b953+bp12FddRe6Zzzy6wliGQfM//AP5U0/F++pXiR95BCoVCEOwbUilCE8/nfTznkfdE5+40NUuuHRYwIoDIsPCdpzpb+C6yRmsQJTPH+LqRERERERERORwYK9Yge+6RLaNOThI3NEx5fLm0BCR44Dj4KxYMU9ViszO4jvyLiIiIiIicjQxDFpe9zoqz3wmpa99jfjee4nK5eQ62yY8/3zS111H3Zlnaq6XHKD80EOU/+mfMLu7sXt6sIaGMMKQ2LII9+wh3LCBgc9/nux//Afpk05a6HLnVf3FF8PFF1PZsIHqH/9IXCph5HKkzzqL1Jo1C13eghr7VuKZGSLDwoxD4ijCmG7sp+8ncwYB8wjoVhcWChS+9S2C73wHc+dOjCBIugyefz7Z668nffzxC12iiIiIiIiIyGGv7swz6Vu2jLCzE3fnTsL+fowoIjZNonSaqLV132jCOMbq7SVobiZauZLMqacubPEiNVIAS0REREREZBFIH3cc6be8hfhNbyIcGCAOQ+zmZoxaOtLIUamyYQOVf/xHzB07cHfvJrZtgqYmYsfB8Dzs/n6M7m68wUHKr3893HbbURk2SZ9wAukTTljoMg5buxpOpmznCA0bu68vGc04Bau/n6C+nri+/rAPsuV/8AOC974XY2gIu6cHs1iEOMZ0XaIdO6h85zuUnvIUmt761qOrS5yIiIiIiIjIDMVhSAhkentxymXscpnIssAwiAsF4oEBwkwGf8UKrL17MeKYuL0d66qrdFKpHDEUwBIREREREVlEDMvCbm1d6DLkcBfHlN7xDsydO0nt2oW3dClxe/voAa0Y8I85BqO7m9TOnVQNg+K//ivpz39eB71knB3NZ9CTXk5bZScruvfgNTdjWNbECw8NYReLVI87jvjSSw/rgOjQd79LdMstmJ2duL29hK5LWF8PpolRLpPasoUwlSLwPPqHhmh53/sW5YhXERGRuRTm8/h79gDgLF2KVV+/wBWJiIjIfIiDgL43vQl761aMMExOborj0Q5Y+D4YBmalgtvXR1BXR3X9eqLTT6fl+usXunyRmunIkIiIiIiIiMhRpvDLX2J2duLs3o3X3k7c0XHgQoZB3NGBF4Y4u3dDczPFX/+aunPPnf+C5fBlGPx66bV0lHew3OzCeewxgpUrIZvdt0wcQ38/7s6d+G1txE1N5G64YeFqnoa3axfhrbdi7diBPTBAddUqaGwcF1CseB729u04jz0GhsHAf/83za9+9cIWLiIicjiKY4r33UfljjuwHngAogiAimkSnnUW6euvp+688xTyFxERWcQGPvYxrF/9CmfzZvy6OoJsFqtaxfR9zCDAAIwowgpDfNdNxhKecQZN73nPYX3ylsj+FMASEREREREROcpU77oLq78fI46Jlixhqo+7oo4OjJ4eGBig8s1vKoAlB/jTsitZln+EE9sK2Nu2kdq4kSCbJcpkkgOohQJGGCbhq2XLsN/0Jtzlyxe67EkV77gDY3AQp7eX6rHHQkPDAcsYrkuwdi3Oxo2Ye/YQ3HMP8ctfrgPDIiIiY0SVCv3veAfWL36B1d+P3duLWa0CEKZS0NODf//99F54IS3veAeG6y5wxSIiIjLXolIJ7r4bs7OT2DQJjjsOwzAI+vqwe3uxyuXkxC3DwPS85PhBRwfOFVeoW6YccRTAEhERERERmQdRqUT54YeJCwXMujpSJ52ElcstdFlylDK2bsXM5wnq6jBMc+plLYsgl8PM54m2bp2fAuXIYhh8/4R/5PlPswm/+U3CYhGztxfT84gNg6C1lbitjai5Gfemm8hddNFCVzypOAjge9/D7OkhzGYnDF+NMEyToL0dd+dOwt5e8j/4AQ3PeMY8VisiInL4isOQ/re9DevnP8fZvBnD9wmamgja2wEw83ncnTuJu7rA9+kLAlrf/W51whIREVlk8t/+NkY+j53PU121at9xqNZWgtZW/HI5GUEIYNu4W7ZgxDH+t74FV1+9cIWLHAQFsERERERERA6hyqZNlL76VYx778Uol4nDEMOyqKbTxE9+MtnnPIf0ccctdJlytPH95OxCy6ptedNMlve8Q1uXHBEm+lw0NiyaX/96/Oc/n8LXv07w859jDA4SWxbx0qU4V15Jw6WXYqbTh6SmOAgo/OhHeN/+NsauXRCGxE1NWBdeSO7aa7Gbm2taj79nD8bgIFY+jz/RaM79NTfDzp0YhQLhI4+AAlgiIiIADN11F9Z99+Fs2kTkOEm3izG/e0ZNTVSXLcPesgVn0yawLAbvvJPG665bwKpFRERkroUPPog1MEBsmhgTdZjOZCCTGf0+aGrCGhwk3rBh9DiqyJFCASwREREREZG5FMcUf/c7qj/5CcHvfkfqvvuwwhDD97GLRYzhltpBLkfY3U3lhz/Ee/3radAZXTKfcjlix8EslWpa3KxWCevqiKfoBiQC4CxZQvPf/i387d/O2zaLv/oV1Xe/G7O7G7uvD6NYhDgmdl3ihx4i/4UvwA030PzqV0/bVSMqlwEwogjsGg6bGQaxaSaBr0plLnZHRETkyBfHBHfeid3dnXy9du2EXVcNyyJYuxb3kUcwenoI7r4bFMASmV9xTDR8spiVy6kLnYjMubhYTP5mdpza3mMcB8IweX8qlTSGUI4oCmCJiIiIiIjMkcKPfkT105/G2rYNa9cusrt3Y/o+ZhQR2jZeUxPB0qUYcYzZ10dqyxb85mai972PfCZD/SWXLPQuyFHCOPdcor/8Bbenh7BcHnem4QFKJaxymWDVKsxzzpm/IuWwdTh9JlP46U8J3v52rF27cLq7iQ2DMJcD08TK57F6evDr64m+8AX6+vpo+ad/mnIHrOGQYWxZUK1Ou/04DDHDEBwHQweFRURkMYpj/O5uYs/Damys6UPQ8sMPY23Zgt3fj9/aOuXIa8M0CVpasPv6iB97jMojj5A+8cS53AMRmYC3cyeFr30NfvhDzIEBAOJ0muj888necAOZ005b2AJFZPHIZMCyMIIg6a4+3UEF30/+JjcMzGx2fmoUmSMKYImIiIiIiMyBgS98AT7xCew9e7D7+nAHBzGiCDOOiQwDI46xi0XMXbvw1q0jamykUiwmIaydO/H/67+oHnccsedhpNO4y5Zh1NJ9ReQg5K6/nuLttxPU1WF1dhKsWzfhB2NxFOF0dhLkclBXR+5Zz1qAauVwU+M0v0PO37sX/9//HbOzE6enB++YY6ClJRmZCURxjF8s4nZ2Ym7aRPCd7zB00kk0TPE8djo6iFasINi9G7u/n6CjY8qDw2ZvL5FtQ309qXPPnfN9FBERWSjerl0U77iD+PvfHw1nAIQnnoh77bXkLrkEw3EmvK2/ZQt4HqbnETc1MV12O2psxNy7F3wfb8sWBbBEDqU4ZuCzn4XPfAazUMDs6cEsl0c7yIZ791L98Y8pXXghLW9/O4brLnTFInKEs087jfhHP8LctQvyeZiqu3ocYw8OErS2Eq5fr/GDcsTR0XwREREREZFZyv/wh/CJT2Bu2YJdLBLU1RFUq9jlMl4qRWyaWL6PXS4TxDHOli0Exx2HUVdHdckSMtu2EQ4OUr7iCmhtBaDU2AiXXUbds56Fu2zZAu+hLDbO0qVEl10G1SrOY4/hbNxIsHRpchDMMJIzEgcHcfbuxYgigmOPJbr8cpyOjoUuXQ4D7e1w/PHw6KP7Lnve8+a/jsLXvobR34/b1UVlzRqMxsbxCxgGRi6Ht24d7saNGHv3Etx+ezLaaLJQlWFgXXUV8ZYtmD09GN3dxJM872PPw+3uxm9pIVq5kuxZZ83xHoqIiCyMobvuIvzABzCGhrB6erAKhSScYdsYXV2EDz1E35e+RP173jPh3yqx7ye/T8KU3a9GjXy4GkXENXSgFJGD1/8//4Pxmc9gbtuGMzSUHL9oasIwDIxSidS2bYSuC55HX7FIy6236uQwEZmV3JVXMvTpT+PX12Pv2UNQXz/p3+RGdzdGGBK3teFcc808Vyoye/qJKSIiIiIiMhtxjP+pT2Ht2YNdLFJdtw57926MMATDIB4+Kzw0TUzPw65WwTQJBgfBdXG7urA8DzMIsAsFwt27iU2TMJcj2rOH4p13UrnpJhouv3yBd1QWm+Y3vpG+7u6kpfvOnbjbtxObZvLBWhAkwauGBqLlywmf+ERa3vCGhS5ZDiNveQt88pOwYwecfTbceOP8bj8OAuLvfQ+rp4cwmz0wfDWG4TgEbW3Y3d3EnZ0U77+fuvPOm3T5+muuYeCrX8XL50l1duJ5XhLCGjn7P46hvx93zx7CVIp46VLs5z//8JrNKCIicpCG7r6b6NZbsXbswOnrI8xmCVpbk98RKxXs7m6Mri68YpHCa19Lw8c/jj18EskIs6WF0HGITROKRWhqmnKbRqmULOs4WC0th3DvRI5u5T/9CeNzn8PauhWzUqF63HEwPN4rHv5X8X3sbdtwNm0C02TwS1+i6cUvXtC6ReTIZuVyxJddRjQ0hLVxI9amTQQrVmCk06PLxFGEuXcvbnc31eXLiZYsoeHSSxewapGDowCWiIiIiIjILBR/+1vMHTuw+/rw29sxMhlMz8OIouRDhBGGQeS6mOUyRhBg7d2L5fuY1SpmFBHHMb7r4q1YAUGANTBA6tFH8VtbiW65hbzjUP/0py/cjsqiY9g2Le95DwOf+ATht75FODCAMTiYnGloWdDURNzYCFddRcsrX6m27zJOayu8+c0Lt/3qtm2YAwNYQ0P4S5dOu3zU2oq5Zw8UCnh/+MOUASwzmyX7rndR/sd/pGpZOLt3Y/b1EWQySYesahWiiKCpiWjFCuLnPpeGK66Yy90TERFZEEFvL+EHPoC1Ywf24CDVtWuhvn70+hjwjzkGY9cu3G3b8CyLwfe9j9b/+I9x66k75xwG6+sJGhqwensJpwlgWT09BA0NxA0NZM855xDsmYgAlG+/HXNwEKtQoHr88ePCDyMMxyFYuxbn0Ucx9+4luOsueNGLdLKBiMxK82teQ9/mzRBF2Nu3k96wgTCbJUqnIQyxCwViw6C6YgXx8uVk3/lOzAneo0QOdwpgiYiIiIiIzEL1Jz/BHBrCCAKitjYMGB23Ee+/sGEQ2TZmEJDq7yeybWzPI7IsIsNIDjoMfzgRtrURDg3hbt+Ob5oE730v0fnnYw6fnSoyFwzbpvlv/5boZS8j/53vED74IHGphFFXh/W4x9Fw+eU64CWHpahQAEi6DQ53GpyKYVlJKDYMiUulaZdPn3ACxoc+ROHd74aHH4Z8HmN4/FLU1ETc3Ezc0oL5ohfRNN/tv0RERA6R/De+gTE0hNPfT3XNmnHhq1GGQbxsGYHnYe3aBffdh9/VNW5UtZnJED/96UTd3aQee4ywpwfa2ibeaE8PdqlEdf164osv1u+eIodIODSE8YtfJIHHxsYJw1cjDNMkaGvD3bOHaO9eCr/4BbkLLpjHakVksTEch+Zbb6X/llvg3nsJikXMvj7wfTBNvGXLoLmZaMUKsv/yL6SPP36hSxY5KApgiYiIiIiIzMbgIIbnETnOaIegyHHANDF9n2i/xWPDwIwiDMDwPELHwQxDsCxie78/0Roa8FaswN2+Ha+9naG776bpOc+Zl92So4uZTtN43XVw3XULXYpITcxcDiDp1ub70y4fhyFGFIFlYdQYZE0deyypj32MyoYNlO68k3jHDggCaGzEfcpTyF1yCUYN4S8REZEjQhwT33NPMt43nYaGhsmXNQyiJUtIbdxIWCxSuOsumv/6r8ctUv/CF5K/916qpRKpXbvwi0Wi9vbRcWeUSpjd3TiDg1SXLSM+5hjqX/CCQ7iDIke36ubNGL6PXSxSXb162uXjlhaMnTuhVCJ49FFQAEtEZslMp2n9l3+hun07pTvuIPjNb6BQANclXruW1NVXk3viE9VxT45oCmCJiIiIiIjMhuuOhqqI4+TDiKYmokIBy/MwguCAYJUx3CHLACLTxPJ9onR64tEcjY1JuKu3l+A73wEFsERESK1eTampibChAbuvj6C1dcrlzd5eItuGXI7UmWfOaFvpE04gffPNsylXRETksBdVKpg9PZjFIsE0IwMByGaJXBcjnyfcsuWAq52lS8nccguVm2+m6rpYe/eS2rgxCU+TdLEM6uqorllDvHw56VtuwVmyZI73SkRGxJXK8BcxsWUxXbzBMIwkBBFFxNXqIa9PRI4eqVWrSP3DPyx0GSKHhLnQBYiIiIiIiBzJzOHRHEYYEheLQHKmaGzbhLaN5XmjIwkBzCAY/ToyDGzPI7RtYssinihAYBiEDQ2YxSLmrl2HendERI4Ihm1jXH45UVsbVqkEAwOTLhv7PnZPD0FzM9HKlWTPOmv+ChURETlCRMPhDCOOwazto6PYNJO/dcb8jTNW5rTTyH70o4SXXUZ44olUTzgBb/lyvOXLqZ5wAuEJJxBedhnZj3yEzKmnztm+iMiBrOZmACLbxhgJY00hrlaT17fjYDY2HuryREREFgV1wBIREREREZmF+quuYugznyHI5bC7ugjr6sA08Zcswdi1C6NSwS6ViByH2DQxo4jYMDCiCCuOCR2HKJXC7+iA4bPBD2CaEEUYNYzZEhE5WuRuuIHCN7+Jt2QJ7vbt+J5H1NaGMfKhcRxDoYCzcyexbRMvWYL17GdrnIGIiMgErIYGsCwix8Eslw8Ypb6/OIowPY/AcTCmGFeYWr2a1Lvfjd/VRfE73yHq7gbA7Oig7vLLcTo65nAvRGQyqfXrKbW1ETY2Jh1k29qmXN7s6SHMZCCTIf2kJ81TlSIiIkc2BbBERERERERmwWpsJHrKUwgHB3Efe4y4s5No+XLi9na8MMTduxfD9zE9DycMk/AVEBsGMRCmUnjt7cRTjNswy2Vi1yWa4oMNEZGjjdPRgfP//h/BP/8zvmnidHVBVxfBcBDWLJexPA+/oYFo1Sqiq66i5dprF7psERGRw5JhWYRnngnd3bg7d1INQ4zJThABjL6+pANWUxPuk5887fqdjg6aXvKSuSxZRGbAsCyMZz6TaOdOnA0bCLq7ob194oWLRZz+frxlywgf/3hSq1fPb7EiIiJHKI0gFBERERERmaWm176WaP16vGOPxRoawvnLXzB27iR2HLzGRjAMTCBwXfxslmJHB35dHZFt47W2Ei9bNum6Y8/DLhSIWlvh/PPnb6dERI4AuQsuwH33uwlPPRXv5JPxli4d7SYYNjZSPfFEwhNOIH7JS2h505vU/UpERGQK6Wc9C5qbiS0Lq7Nz3Cj1seJqFWfv3mS879Kl5C64YJ4rFZGDUX/99URLl1JdvpzU7t2YO3YkowaHxWEIe/fibt5M0NgIHR2kX/ziBaxYRETkyKIOWCIiIiIiIrNkNTZSf9ttDN18M2SzGH19WP39GIODYBh47e1U6ushivCe/GTcJz6R8v/8D/a2bdj5PJ7nYbjuAeuNowh7+3bCdBpyObLXX78AeycicnjLnn02ma98heLPfkb17ruJdu0C3ydubsZ68pNpuOaaZKzS4SyOKf3hDwR79gBgLl0KHR0KjImIyLyqO/98ek87Dcpl3M2bCTdtIuzogPp6MAziMMTs7cXp7ibKZIiPOQb7pS/VzyuRI4Td3Ez2Xe+ifNNNVG0be88enA0bCF03OXHM84gtC3/JEuKlSzFf/3rqzjxzocsWERE5YiiAJSIiIiIiMgec9nZaPvEJCj/+Md43v0n84IOj10UNDXDJJeSuv56mFSsAGEyliD74QYxNm0ht3Ijf3k7U2pqM+Yhj6O/H6e7GiCL8desIn/pU0scdt1C7JyJyWDMsi9xFF5G76KKFLmVGonKZoa98hejb38bcuxd8H4DQtolaW/Evvpjor/4Kq65ugSsVEZGjgmHQdMstDLzudXimibl7N6mtW4lMEywLw/eJbZugtZV46VLiF7+YhiuuWOiqRWQG0iedhPHhD1N4//vh978nKBYxSiWIY4JUChobCVetIvXqV6u7nYiIyAwpgCUiIiIiIjJHDNum/pJL4JJLiEolgr4+jFQKu6UlCVaN0XjjjfR3dRF+5StEe/di9/Rg7tlDZFkYUQRA0NREtHQp4Tnn0PLWty7ELomIyCHid3WRv+kmzEcfxeruxh4YwAwCACLbJm5oINixg8Hf/Y6m970Pu7l5gSsWEZGjgdXYSPNHPsLABz9I/KMfEQ0NQT4PUQSuC/X1RCtXYr/oRTQ84xkLXa6IHITUmjWkPvABqlu3Urr7bsKdOyGKMJqbSV1yCU1nnqnOdiIiIgdBASwREREREZFDwMxmcbPZKZdp/vu/Z+jYYwm+9CXiHTuSDzZ8Hywr+WCjpQXjqqto+au/wrD155uIyGIRlUpJ+OrPf8bdupXItpNRL8OdroyhIezeXtyBAXxg8KabaP7whzHT6YUtXEREjgpmNkvLzTcT/t3fUbjnHsJNm6BahYYG0hdeSPassxTOEFkEUmvWkHrNaxa6DBERkUVDR/BFREREREQWUMMVV8Azn0npgQeo3n8/cT6PkclgnXgijRdfrOCViMgiNPjFL2Ju2IC7dStBYyPRihVgGIx8lB1nMpSbmnB37sTdsgXfdRn6+tdpesELFrRuERE5uli5HI033rjQZYiIiIiIHBF0JF9ERERERGShGQbZs88me/bZC12JiIgcYnEYEt9zD1Z3N5HjjIav9mcYBt7y5TibN2N0dxN+61vw/Oer44iIiIiIiIiIyGHIXOgCREREREREREREjhalX/8as7sbe2CAoK1tykCVYRgELS3Y/f2YO3dSevDBeaxURERERERERERqpQ5YIiIiIiIiIiJzwOvspHzffcSFAkYqhXPyyWRPP10di2SccPdu8DyMMISGhmmXj+vrMffsAd8n2LULTj99HqoUEREREREREZGZUABLREREREREDg9xTGXzZqK+PrAsnJUrcdrbF7oqkWkVf/tbKp//PNbvfw+VCgQBsWniZTJU1qzBuu46Gq+9VkEsASCO45ktr+eNiIiIiIiIiMhhTwEsERERERERWVBRpUL+rrsI7r4ba9u20cs9wyA86yxS111H7klPUnhFDktDd9xBdNttWD092D09WOXy6HWR4xDs2UO8eTO9f/gDLf/8zxiWtYDVyuHAWrKEwHWJTRPyeWhpmXJ5s1Agsm2wbewlS+apShERERERERERmQkFsERERERERGTB+Hv3MnTTTVibNmH39GD392P6PgBhJgM9PQS//jW9l15Ky1vegmHrz1g5fBR+/GOiD3wAc+tW7KEh/OZmgpUrIZ2GIIC+PpzeXuLBQeIoor++npabblrosmWB1Z13Hv2trQRNTdg9PQTNzZMHTOMYu7eXoKmJaOlSsk94wvwWKyIiIiIiIiIiNdGRaxEREREREVkQ4eAg+X/4B6xHHsHdto3YsvBbWiCbJY5jrMFB3N27iXp6IAzpA1rf9jZ1wpLDQxzjfeQjWLt3Y+fzeOvWQTa773rHgSVL8Nr50ODxAABwD0lEQVTacB57DHvLFsK77qL67GeTWr164eqWBWfYNsZllxHt3o29cSPGrl3Ey5Yd+N4Wx9i7d2MEAXF7O+YVV+j9T0RERERERETkMGUudAEiIiIiIiJydBr85CcxH3sMd+tW/KYm/BNPhCVLoL4eo6GBaOVKqiecAIaBvWkT1ve/T/H++xe6bBEACj/7GeaePdi9vXhLlowPX41hWBb+6tXY5TIUixS//vV5rlQORw0vehHR2rV4q1fj9PVhb9wIfX3g++D7GD09pDdvxh0awlu9muj442m48caFLltERERERERERCahAJaIiIiIiIjMu6hUwvi//8Pcu5cwkyFevnzCzi6G4+Afeyym58HAAJU77liAakUO5P3gB9DfjxHHxK2tUy5rpNME2Sxmby/85CfzVKEczqz6enLvex/RiSfiHXccUSaDu2sXqYcfJvXww7h79hBkMpTWriU8+WTqb70Vc5KQn4iIiIiIiIiILDyNIBQREREREZF5V/jBDzDyeex8nuqqVRhTjNUyHIegqQm7pwd+/WuC/n7s5uZ5rFbkQHFvL0a1SpRKYZjTn98W1dVhFgqYAwMQxxolJ7jLl9P4yU8y9PnPE3zve0S9veB5AMSuSyWXo/r0p7Pi1a/Gqa9f4GpFRERERERERGQqCmCJiIiIiIjIwYvj5P8ZhknCzk6MUgkjjjEaGqZfvqEBe2AAogh/504FsGThDYeu4lqXH3mt1BDWkqOHVV9P89/+LfErX0nxvvsIu7qSQGp7O/GaNaQsS52vRERERERERESOAApgiYiIiIiIyIwEfX0U7ryT6Pvfx9y7F6KIqKkJLryQuhtuILVmzfQriSKIY2LDqCm8ZRgGxnCAJQ6C2e2AyBwwli4lzmaxursJfB/DcaZc3ioUiLJZovZ2db+SAxi2Te7CC0e/D8OQwt69C1iRiIiIiIiIiIjMhAJYIiIiIiIiUrPB228n+vjHk/GBPT0YpRIAZipF1NlJ+a67KF5xBc033YRhWZOux2xpIU6nMaIISiWYpsOLUSwSuS4Adnv73O2QyEFKX3kl3ve+R+w4mD09xMccM+mycaGAVakQrFqFceml81iliIiIiIiIiIiIzAf1vRcREREREZGaDHzxi8S33Yb12GOkNmzALBSI6uqIcjkM3ye1aRP2xo2Y3/wm/e94x76RaxPIPv3pkM0SZLOYPT1TbziOsfv7CZqbCY87Dnf58rndMZGDkD39dKJ16/A7OnC7u2FgYMLl4nIZd/t2/MZGyOWov+66+S1UREREREREREREDjl1wBIREREREZFplR96CD7xCcytW7FLJarHHgv19aPXh0BQreJs24b92GMEhsHg6afTeMMNE67PWbKE8JxzoK+P1LZtVBsaoKnpwAXjGHPHjmRcYVsbzjXXHJodXATiIKD4858TdHZCFGG2t5N7ylMwp+kuJgcv84Y3UHn96/GCgNT27QQ9PYQtLZBOEwcBVn8/9uAgYS5HtGoVxitegd3autBlHxW8zk6Kd95JtGkT+D5GfT32E59I/WWXYQx30xMREREREREREZkrCmCJiIiIiIjItMq33445MICTz1M97jjIZA5Yxkil8Netw92wAaOri/Ab34DrrwfDmHCddX/915T/8Ae8SoXU9u34g4NEbW37xhEODmJ1d2NVq3jHHkt4wgk0PuMZh3I3j0hhocDQ5z5H/N3vYvb1QaWSXO66DP7XfxE/9anUv+xlOB0dC1zp4pM59VSif/93vLe/nWp9PWZPD+7OncloTcMgqKvDW7UqCRe+7GU0Pu95C13youd3dTH07ndjPfAARrGIPTSUdONzHKKf/pT+j38c87nPpekFL5j0vUlERERERERERGSmFMBapHzf5+c//zk//elP2b59OwMDA+RyOZYsWcL555/P05/+dBoaGha6TBEREREROQKE+TzGz3+O1duL39AwYfhqhGFZBG1t2D09xNu3U/ztb6k766wJl00fdxzhv/4r3j//M9V0Gqu7m9SmTcmVcUxsmgSNjXirVhGdcAKN730vhuMcil08YvldXQy94Q1Yjz6K1dWFPTCQhH+A2DAIGhqIenvJ33cf2fe8h/QJJyxwxYtP3bnn4nzqUxRuv534+98nyueJwxDDssC2Cc8/n8yNN5I9/fSFLnXR83bupPCa12Bt3Yq9axdWuUyYToNpYnoe7N5N0NRE9LGP0bdnDy1veINCWCIiIiIiIiIiMicUwFqEOjs7ee9738uWLVvGXd7f309/fz+PPPIId9xxB6973es4a5IPQkREREREREZUN27E8DzsQoHK6tVMF1eImpsxd++Gchn/4Ydhir876s49F+vDH6b46U/DL39JWCqB5yWhiEyGqLkZLrmExpe/HCuXm9sdO8JFlQpDb3wj1kMP4W7dSuS6eMuXEzc2JvdfPo/d20vqsceoViqU3vQmrE9+8qjqhBUHAYWf/IRgyxYIAozmZuouughnyZI53Y67bBktr3sd0ateReXhh4mGhjDSaVInnIDd3Dyn25KJxUFA4U1vwty8GXfbNoKGBvxVqzDS6eEFYhgcxNm9G3PTJoI772Rg5UqanvOchS1cREREREREREQWBQWwFpmenh7e9ra30dfXB4BhGJxyyiksXbqUoaEh/vCHP+B5HoODg/zHf/wHb3/72zldZ+GKiIiIiMgU4kolCS/EMdjT/xlp2HYSAApD4nJ52uXT69eTfuc78bu7Kf34x0R9fWDbWCtW0Pi0p2G47lzsxqKTv/NOrEcfxd26laCxkWjFCjCMfQG5xkbCxkairi5SnZ1U02nyn/oULW9+80KWPS+iSoXBT3+a+DvfwezrwyiVkuev61L86EcJzz2Xur/6K9Innjin2zXTabJnnDGn65Ta5H/wA8xt23C3b8dvaSFevnx8WNQwoKkJv64OZ+NGzF27CL7yFeIbbki6lYmIiIiIiIiIiMyCAliLzK233joavuro6OCtb30rxx577Oj1Q0NDvPe97+WPf/wjQRDwnve8h49//OPkdCa5iIiIiIhMwmxoAMMgtiyMSgXq6qZcPva8JOziOMlta+S0t9N4442zLffoEMeEd9+N1dNDZNuj4asJF+3oICgUMLu6iH/8Y6LXvhYzm53ngudPmM8z+PrXY/75z1h792IPDmKEYXL/xDFhNgt9fZR/9zv8t72N+qc85aC2E1UqFH78Y6K9ewEwlywhd9FFmFOM6JRDx//mN7F7e5P3qmXLJl/QcfCXLMHdvZuoq4vCj39M/cUXz1+hIiIiIiIiIiKyKJkLXYDMnQceeICHH34YANu2edvb3jYufAXQ0NDAW9/6VpYuXQpAPp/njjvumPdaRURERETkyJE+8USipiaCxkbs4RM+pmL29BCm05BOkz7vvHmo8OhTefRRzB07sPv7CVtaJg1fjQhaW7GHhiCfp/CTn8xTlfMvDkMGbr4Z849/TLoclct4y5ZRPe205N9xxxGlUqS2b8fcupXg3/6N8p//PKNtBP399N16K4PXXUf07/8OH/wgfPCDhP/2bwxeey197343QW/vIdpDmUjseVgPP4w1MIDf1DTt6yEeGQs5OIh///2HvkAREREREREREVn0FMBaRO65557Rr5/2tKexZs2aCZdLp9M8//nPH/3+e9/7HmEYHuryRERERETkCGXYNsallxK1tWGVyzBVCKtUwunrI2htJTz9dFL7nRQicyPYuxeiCNP3iWvpaFxfjxHHGJ5H1NV16AtcIIV778V68EGcLVsI6+oIjjsOWlrANJNQTjZLtGoVldWrcbu7MfbsofTxj9e8/ur27Qy94hWYX/kK1sMP4z78MKnHHkv+/eUvWH/5C+bttzP0ildQ2bTpEO6pjBXm8xDHGEFAnEpNu7xhmkS2nSyfz89DhSIiIiIiIiIistgpgLVIlMtl/vjHP45+f/E07fOf+MQnkhkei5DP5/nzDM/4FRERERGRo0vuxhuJ29vxli4l1dmJsWsXse+PXh9HEfT04G7eTFBfD+3tpF/4wgWseJEb0+EnjuPplx+7jLl4DwV4d96ZBASjiHDVqkk7IRmNjXgtLdjd3Vh//CPVbdumXXc4NETxjW/E3LgR97HHMHwfb9UqKiPdtVatwghD3E2bMB97jNIb30hQQ8c4mb2RsY+xaSbjJqcTxxhhSGyaUENgS0REREREREREZDqL96jrUeaRRx7BH/7wI51Oc9xxx025vOu6nHDCCaPf/+lPfzqk9YmIiIiIyJHN6ejA/dd/JVq1iuqKFdiDg6T/8hesjRuxHnuM1MMP4+7Zg9/aSrRmDfzt31J37rkLXfai5axcCaZJ5LqYQ0PTLm8MDiZhk3Qae/nyeahw/oWDg1h//CN2Xx9BUxPGNEGzqL0dq1KBUonS//3ftOsf+vKXMbduxd2+Hb+1lXDdOmhsTLZjmhiNjYTr1uG3t+Pu2IG5YwdDn//8XO2eTMHMZIiOOYYwl8MaGBgfOJxIoYAZhtDQgD3m2IiIiIiIiIiIiMjBUgBrkdixY8fo16tXr8ayrGlvs27duglvLyIiIiIiMpG6s84i/f73E55zDv5JJ1FdvZoolyOqq8Nbtgzv5JMJTzsN621vo+k5z1noche11Jo1hCecQNDait3Xl3Qgm0wcY/X2EjQ1EbW0UHfhhfNX6Dzyu7sBMD2vprGMRipFbNtQrRL39Ey5bBwExN/9LmZXF1EqRXzMMZN214qXLCFKpzG7u+H//m9cpzg5RAwD8xnPIG5rw6pWYXBw8mXjGGvvXoJcjri+ntxVV81fnSIiIiIiIiIismjZC12AzI2dO3eOft3R0VHTbdrb20e/7uzsnPOaRERERERk8cmccgqZT32K8p/+RPnuu4l374YwhOZmUpdcQt2FF2LUcEKIzJ5zzTVEDz8Mvb3YW7cSrFlzYNenOMbs7MSqVvFWrcK49FIMx1mYgg+xkeddbBgwVSBtRBzv65RkT314pPz732P29GAPDuJNEb5KCjEIWltxd+4kGhigeN995J785Fp3Qw5S7ppryH/5y3jDHci8KILm5vHjOn0fq7MTq1LBW7+e+KlPxaqvX8CqRURERERERERksVAAa5HI5/OjXzc1NdV0m7HLFQqFOa5IREREREQWs8xpp5E57bSFLuOoVv+MZ9D3f/8HQYCzeTPuX/5C0NJC1NCAYRgYhQJWXx9mEOCtWUO0fj2NL3nJQpd9yDhLl1JyXaJMBjOfJ2ppmXL5uFjEiCLIZrGmGcsYdHVBEGCEIXEuxxTxq0R9fbJuzyOapruWzA27uRnnrW8lePvb8QG3s5N4716ChgawbYxyGTufJ7ZtvHXrCE89lebXvW6hyxYRERERERERkUVCIwgXiUqlMvq167o13SaVSk14exERERERETn8GZZF8y23EJ53Hv7xxxO0t2MPDJB+7DFSGzdid3cTNjbinXAC0amnUv/+9y/qbj9mJkP0pCcRtrbiDA4Se96Uy1vd3aNj6Oouv3yalY85fFJDd614pLOWYSQduWRe5J78ZOx//Vei447DO+kkguZmzFIJa2gII47xVq7EP+kkwic+kaYPfAAzm13okkVEREREREREZJFQB6xFwhtzYNmeZnTCRMtVq9U5r0lEREREREQOLTObpeU//5P8Pffg33UX8YYNEEXEcYxhWUTLl2NeeSVN11+PmU4vdLmHXPaGG6j++MeEmQzOli0Ea9fC/iMX4xhj927sQgFv7VriCy7Abm6ecr3OypVUHYfIcTDzeeJMZsrljaEhItsG18VeuXK2uyUzkLvwQqIzzyT/rW8RfPvbWNu3QxgSZzJEZ51F5oYbyD7+8VOPkZwHcRhS+PnP8f/wBygWoa4O9/TTNcZVREREREREROQIpQDWIjG261UQBDXdZuxyY7thiYiIiIiIyJHDsCwarroKrrqK6pYt+J2dxFGE09FB+sQTFzxoMp8yp51G+eqrCb7xDezNm3EfeYSgsZGwqQkMA7NcTsYy+j7eqlVEq1dT/3d/N/16TzmF8sqVBLt3Y/f24re3T36/xjFOTw9BUxPRMcdQd+aZc7uTMi0zm6XxxhvhxhshjonDEKPGk9UOuThm8H//l/DrX8fcswczn4cwBMsiqK+nv6MD8/rrqX/Ocxa6UhERERERERERmYHD5OiTzFZ6zJnM3jRjFkaM7XqVnsMzoevq6shkMoRhOGfrlIMz9jHQ4yFydBj7WjdNTRoWWcz0c17k6FPLz3l71SrsVav23aaGcXmLTeM//AP9QUD8rW9hDAxg9faS2roVI46JHIegqQm/rY1o1Soy73oXZktLbe+jV1xBtHkz9PdjbtlCuHo17N+pKIpGOy5F7e3wjGcclY/BgotjSg88QPXOOzEeegijVCJOp4mPPx73mmuou+CChQkmxjH9//ZvWD/4Adbu3dgDAxhRRGzbGGFIbBgEu3YR7d1L70MPwd/9HRiGfs6LHCX097zI0UV/04scffSzXuTooZ/zh6dMJkM+nz+k21AAa5Gor68f/XpgYKCm24xdLpfLTbv8F77wBb70pS9NuUw+n+d5z3se1157LXv37q2pDpkfPT09C12CiIiIHCL6OS8ii04c4z34IPzoR5i9vUkXo9ZW4qc8BfeMM6YPz7z4xXinnopxzz2k//CHJOQSxxiGgbd0Kf7FF+NcfjnVdBpq/Ns1vuACwu9+l3S5TGb7duy//AWvqYmwrg4MA6tQwB0YSMI/q1ZRWbsW4ylPoaK/jedV2NMD73oX7qZNmENDWENDGGEIpknw2GNUfvlLhpYvJ7r55nkfD1n99Kepu+ce3K1bMcKQclsbYWMjhmURhyHm4CDp3l6MoSH8apVqKkXqFa/Qz3kREZFFTj/rRUREFi/9nD98XHvttXziE584pNtQAGuRWL58+ejXXV1dNd2mu7t79OsVK1ZMu3yxWKxp3aVSqabti4iIiIiIiOzPe/BB7E9+kkxnJ2Y+j1kuAxCl00Q/+xn+MccQvOxluGedNeV63DPOgDPO+P/s3XmYnWVhN/7v2WbJLMkkIQlJ2GVRoKioFP0pimBdcCvWtZevtb6v1r4urVsVtVZRUeF9a1vXX7XV2va1/YlrcYUq+mLdKriwhxBIQkgyWWYmk5k5y/P7IzoNQhYOyZzJ5PO5rlzXec7cz3m+B0JuzpPvue9MbdmS1vr1KaamUlqwILXjjkt3G6sflarVlN/61ky85z1JrZbKli2pbduWnl/dSGvVapkaGkpzaCgTp5yS8lvfmvIBXG2afWtu2ZLyW96SrjVr0rNuXcrNZqb6+9Ps6Um50UjP3XcnGzemNDqa+kUXpfGe96S6H/dDDki2bdsy76tfTde6dUlRZOfxxyfVan79O7FUqaRYuDDjg4PpXbMmXevWpfjGNzJ14YWpLFw4IxkBAAAAaJ8C1hxx1G7f2lyzZk2azWYqv7kVwm9YtWrVfZ6/J319fVmyZMlex4yOjubGG2/MF77whbz85S/f52tycDWbzelW7eLFi/f5ewI49NXr9enHtVqtg0mAg808D4efw2GeH/vOd9Lz/venfNddqW3alFKzmeavSkyVbdtSbNyY+rZtKf7X/0rpDW/IwJOfvO8XXbo0efCDD0zApUtTfOxjGfvqV9P40pfSvPXW/9pisFxOcfzxqV1wQRY+/ekpVd1ymWlb3/veVNavT/fatanPn5/68uW7inO/+vlUs5nKhg3pW78+k9Vqmn/91xn6u7+bke0It3/1qynV6+nasSMTJ5yQ2rx59z2wqyvNo45K96pVmRofT+NrX8vSV7/aPA+HgcNhngf+i8/0cPgx18Phwzw/O33sYx876NdwN3COOOWUU1Kr1VKv1zMxMZFbbrklp5xyyh7H1+v13HTTTdPHp59++j6v8fu///v5/d///b2OueyyyzI6OpqdO3f6g2SWqVQq/p3AYaD1678ATPw3D4cR8zwcHub6PD95++1pXXJJKnfemdqmTakvWZLWEUek9Kv32mg2Ux4eTtfdd6dRr6d12WWpH398eg5UuWp/VSpZ8KxnJc96ViZXr07jrrtSFEWqy5al54QTZjYL0yZuuSXV665Lde3aNAYHUxx1VMq/UawqVSopVqxIvVRK17p1qc+fn4kf/zh9v/3bBz1fcc01qWzZklZPT8p9fXsf3N+fVk9Pqlu3pvbDH5rn4TAx1+d5YM/M9XB4MNfD4ck8P3vs/NUq+weTAtYc0dvbmzPOOCM//vGPkyRXXXXVXgtY11xzzfRvsIGBgZx22mkzkhMAAGB/NUdGsuNb30pzw4akVEp52bL0n3deKgMDnY7GQbDjH/8x5U2b0rVpUyaOOSal+fOze32mVKmkWLIkU7296V69OpMDA9nxmc+k593v7ljm7uOOS/dxx3Xs+vyX8csvT3lsLJWpqdSPPz6lvaxq1Vq6NKXh4WTbtkx8/vMzUsAqbd+e0tRUWj09+7XiVrOnJ6V6PaWxsYOeDQAAAIAHTgFrDnnKU54yXcC68sorc8EFF+Too4++17jJycn80z/90/Txk570JK1LAABg1qhv2JCR//f/Tfnqq1MaG0tpYiJJUvT0ZOQjH0nxuMdl4H/8j9T2sUU6h47m6GhK3/lOyps3pz4wkNL8+XsePDCQ+vz5qWzenHz/+2kMD6e6aNHMhWVWKm6+OeXt29OYNy+lrq69ji1VKmkMDKS8fXtat9wyMwFrtV3Fq2Zzv4aXimLXeFtZAgAAABwSyp0OwIHzyEc+Mg95yEOS7Npi8J3vfGdWr159jzEjIyN597vfnbvuuivJrtWvLrzwwhnPCgAAcF8mbrkloy9/eSpf+EKqN9yQ7htuSPfq1bt+3XBDqtdfn/LnP5/Rl788E7fe2um4HCA7f/rTlMbHUxsdTXPhwn2Oby5alOqOHcnERMZ/+MMZSMhsV5qYSFqtFPv5BbOiWk2p1ZoueB5sxYoVafX1pbpjR4rdth65z7GtVqpjY2nOm5fmsmUzkg8AAACAB8bX6OaY17/+9Xn961+fLVu2ZOPGjXnta1+b0047LcuWLcv27dtz3XXXZXJyMsmu/Ubf+MY3pr+/v8OpAQAAksbwcMbf+MaUb7st3XfemfrAQCaPOy759WeW0dGUN29O16pVmZqayvgb35jq3/5tqvtR2GF2a42O7loZqCiSnp59n9DdvWtsvZ5idPTgB2TWK/r6UlQqqezcmb3Xm3YpTU2lqFZTzJt30LMlSfcznpHGD36QYsOGlDdvTrGXFfzKmzenSNJcsCCt88+fkXwAAAAAPDBWwJpjFi9enIsvvjjHHXdckqQoivz85z/PN7/5zfzwhz+cLl/Nnz8/b3nLW3LGGWd0Mi4AAMC0kc98JuW1a9N1552ZWrYsreOOSwYGdm3DVSolg4NpHX98ppYuTdfatSnfeWdG//mfOx2bA6DU05OUf3WLotHY9wm/HlOp7DqXw175oQ9NMTSUys6dyfj43gfX66mNjaW1YEGKGbov0v/Yx6Z15JFpLF2arg0bki1bdpUIf9OWLenasCH1JUvSPOKI1H77t2ckHwAAAAAPjBWw5qCVK1fm0ksvzXe/+91cffXVueOOO7Jt27b09fVl2bJlOfvss3PeeedlcHCw01EBAACSJMXUVErf+EbKGzemOW/eXleHydKlaY6MpLx5cxpf/3qK//E/UqrVZi4sB1z3gx+c8Wo1zd7elLduTdHXt9fx5a1b0+ruTrq60vXgB89QSmaz/gsvzI5/+Zc0+vpSWb8+zRNO2FXc/E1FkfK6dWnVasngYPqe85yZCVgqpectb8nk61+fqVYr3WvXprlpU5pDQym6ulKamkpl69ZUJicztXRpWitWpP7qV6e2n1sqAgAAANBZClhzVK1Wy7nnnptzzz2301EAAAD2acd//EdK27enOjKSqaOO2uf45qJF6Vq3Lq0tWzL+ox+l79GPnoGUHCxdy5dn9GEPSzZvTtddd2VyyZKUurrue3C9nurWrWksWZLmQx6SnhNPnNmwzEq1pUvTesITkp07U7v11mTVqjSXL09222Kw2LkzlQ0bUt2xI1PHH5/mwx6Wnhks8M176ENTXHJJpt7+9kzOn5/y5s2pDg+n1GymqFTSHBxM49hjUyxcmOpb35rar1Y3BwAAAGD2U8ACAACg45rDw0m9nlKrlexj9aMkKfr7d41tNNLcvHkGEnKw9Tz3uan/5Cdpbt2arlWrUj/mmHuUZ5IkO3emumZNiq6uFIsWpet3f7czYZmVFrzpTdm6YUOSpLJ2bbpvvTXN7u60arWUG41Udu5Mc968TJ1wQponn5wF73znjGfse8Qj0v2P/5ixL3whjX/7t7Q2bpz+WWvx4pSe9rQMPPvZKc2fn7G7757xfAAAAAC0RwELAACAjitVKil+9bhotXIfG4fdU7P5qxNLie0H54S+Rz86W5/3vDT/z/9JsWZNum+5JY1589Lq70+SlHfsSHXHjjT6+9M89ti0nv70LDj//A6nZjYp9/Rk6H//72y79NLkyivT3LEjpW3bUmo00uztTWPlyqSvL82zz86Ciy5KZWCgIzmrQ0NZ8Ad/kLzkJZm66660duxIua8vXUceOb1tYvPXf8YBAAAAcEhQwAIAAKDjqkcfnalfr1SzfXuKJUv2Or40MpJWrZbUaqntx5aFHBqG/viPs7WnJ63PfCaTO3akvHlzymNjSZKipyeTy5cnfX0pfu/3svCP/3i6rAK/Vu7pycK3vjX1l788Y1/8YprXXpuMjyfd3Smfemr6nv3sdK1Y0emYu5RK6Vq+vNMpAAAAADgAFLAAAADouHlnnJGJlSvTuOuuVIeHU1+8OCmX73Ns0Wqla8uWNBYuTOvYY9N76qkznJaDplTK0MtelvoFF2Ts8svT+Na3Uh4eTpK0Fi5MHv/49F94YbpWruxwUGa72hFHZOhlL+t0DAAAAAAOEwpYAAAAdF6plMoznpHi9tuTrVtTvv32NI89NqXfKGEVrVaqt9+eFEVaixen8oxnWAVpDqotW5ahV74yeeUrk+JXm1P69wwAAAAAzFIKWAAAAMwKg895TrZ897tJo5HabbelesMNqQ8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" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(123)\n", + "\n", + "###\n", + "# Actual Quantiles\n", + "###\n", + "q1 = norm.ppf(quant_sel[0], loc = 10, scale = 1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7))\n", + "q2 = norm.ppf(quant_sel[1], loc = 10, scale = 1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7))\n", + "test[\"quant\"] = np.where(test[\"y\"].values < q1, 0, np.where(test[\"y\"].values < q2, 1, 2))\n", + "test[\"alpha\"] = np.where(test[\"y\"].values <= q1, 1, np.where(test[\"y\"].values >= q2, 1, 0))\n", + "df_quantiles = test[test[\"alpha\"] == 1]\n", + "\n", + "# Lower Bound\n", + "yl = list(set(q1))\n", + "yl.sort()\n", + "yl = [yl[2],yl[0],yl[2],yl[1],yl[1]]\n", + "sfunl = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yl})\n", + "\n", + "# Upper Bound\n", + "yu = list(set(q2))\n", + "yu.sort()\n", + "yu = [yu[0],yu[2],yu[0],yu[1],yu[1]]\n", + "sfunu = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yu})\n", + "\n", + "###\n", + "# Predicted Quantiles\n", + "###\n", + "test[\"lb\"] = pred_quantiles.iloc[:,0]\n", + "test[\"ub\"] = pred_quantiles.iloc[:,1]\n", + "\n", + "###\n", + "# Plot\n", + "###\n", + "(ggplot(test,\n", + " aes(\"x_true\",\n", + " \"y\")) + \n", + " geom_point(alpha = 0.2, color = \"black\", size = 2) + \n", + " theme_bw(base_size=15) +\n", + " theme(legend_position=\"none\",\n", + " plot_title = element_text(hjust = 0.5),\n", + " plot_subtitle = element_text(hjust = 0.5)) +\n", + " labs(title = \"LightGBMLSS Regression - Simulated Data Example\",\n", + " subtitle = \"Comparison of Actual (black) vs. Predicted Quantiles (blue)\",\n", + " x=\"x\") + \n", + " geom_line(aes(\"x_true\",\n", + " \"ub\"),\n", + " size = 1,\n", + " color = \"blue\", \n", + " alpha = 0.7) + \n", + " geom_line(aes(\"x_true\",\n", + " \"lb\"),\n", + " size = 1,\n", + " color = \"blue\", \n", + " alpha = 0.7) + \n", + " geom_point(df_quantiles,\n", + " aes(\"x_true\",\n", + " \"y\"), \n", + " color = \"red\", \n", + " alpha = 0.7,\n", + " size = 2) + \n", + " geom_step(sfunl,\n", + " aes(\"x_true\",\n", + " \"y\"), \n", + " size = 1, \n", + " linetype = \"dashed\") + \n", + " geom_step(sfunu,\n", + " aes(\"x_true\",\n", + " \"y\"), \n", + " size = 1, \n", + " linetype = \"dashed\") \n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# True vs. Predicted Distributional Parameters\n", + "\n", + "In the following figure, we compare the true parameters of the Gaussian with the ones predicted by LightGBMLSS. The below figure shows that the estimated parameters closely match the true ones (recall that the location parameter $\\mu=10$ is simulated as being a constant)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:22:09.748483300Z", + "start_time": "2023-05-18T06:22:08.982134200Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 800, + "width": 1200 + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred_params[\"x_true\"] = X_test[\"x_true\"].values\n", + "dist_params = list(lgblss.dist.param_dict.keys())\n", + "\n", + "# Data with actual values\n", + "plot_df_actual = pd.melt(test[[\"x_true\"] + dist_params],\n", + " id_vars=\"x_true\",\n", + " value_vars=dist_params)\n", + "plot_df_actual[\"type\"] = \"TRUE\"\n", + "\n", + "# Data with predicted values\n", + "plot_df_predt = pd.melt(pred_params[[\"x_true\"] + dist_params],\n", + " id_vars=\"x_true\",\n", + " value_vars=dist_params)\n", + "plot_df_predt[\"type\"] = \"PREDICT\"\n", + "\n", + "plot_df = pd.concat([plot_df_predt, plot_df_actual])\n", + "\n", + "plot_df[\"variable\"] = plot_df.variable.str.upper()\n", + "plot_df[\"type\"] = pd.Categorical(plot_df[\"type\"], categories = [\"PREDICT\", \"TRUE\"])\n", + "\n", + "(ggplot(plot_df,\n", + " aes(x=\"x_true\",\n", + " y=\"value\",\n", + " color=\"type\")) +\n", + " geom_line(size=1.1) + \n", + " facet_wrap(\"variable\",\n", + " scales=\"free\") + \n", + " labs(title=\"Parameters of univariate Gaussian predicted with LightGBMLSS\",\n", + " x=\"\",\n", + " y=\"\") + \n", + " theme_bw(base_size=15) + \n", + " theme(legend_position=\"bottom\",\n", + " plot_title = element_text(hjust = 0.5),\n", + " legend_title = element_blank())\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Actual vs. Predicted\n", + "\n", + "Since we predict the entire conditional distribution, we can overlay the point predictions with predicted densities, from which we can also derive quantiles of interest. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 800, + "width": 1200 + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "y_pred = []\n", + "\n", + "n_examples = 9\n", + "\n", + "for i in range(n_examples): \n", + " y_samples = pd.DataFrame(pred_samples.values[i,:].reshape(-1,1), columns=[\"PREDICT_DENSITY\"])\n", + " y_samples[\"PREDICT_POINT\"] = y_samples[\"PREDICT_DENSITY\"].mean()\n", + " y_samples[\"PREDICT_Q05\"] = y_samples[\"PREDICT_DENSITY\"].quantile(q=quant_sel[0])\n", + " y_samples[\"PREDICT_Q95\"] = y_samples[\"PREDICT_DENSITY\"].quantile(q=quant_sel[1])\n", + " y_samples[\"ACTUAL\"] = y_test[i]\n", + " y_samples[\"obs\"]= f\"Obervation {i+1}\"\n", + " y_pred.append(y_samples)\n", + " \n", + "pred_df = pd.melt(pd.concat(y_pred, axis=0), id_vars=\"obs\")\n", + "pred_df[\"obs\"] = pd.Categorical(pred_df[\"obs\"], categories=[f\"Obervation {i+1}\" for i in range(n_examples)])\n", + "df_actual, df_pred_dens, df_pred_point, df_q05, df_q95 = [x for _, x in pred_df.groupby(\"variable\")]\n", + "\n", + "plot_pred = (\n", + " ggplot(pred_df,\n", + " aes(color=\"variable\")) + \n", + " stat_density(df_pred_dens,\n", + " aes(x=\"value\"),\n", + " size=1.1) + \n", + " geom_point(df_pred_point,\n", + " aes(x=\"value\",\n", + " y=0),\n", + " size=1.4) + \n", + " geom_point(df_actual,\n", + " aes(x=\"value\",\n", + " y=0),\n", + " size=1.4) + \n", + " geom_vline(df_q05, \n", + " aes(xintercept=\"value\",\n", + " fill=\"variable\",\n", + " color=\"variable\"),\n", + " linetype=\"dashed\",\n", + " size=1.1) + \n", + " geom_vline(df_q95, \n", + " aes(xintercept=\"value\",\n", + " fill=\"variable\",\n", + " color=\"variable\"),\n", + " linetype=\"dashed\",\n", + " size=1.1) + \n", + " facet_wrap(\"obs\",\n", + " scales=\"free\",\n", + " ncol=3) + \n", + " labs(title=\"Predicted vs. Actual \\n\",\n", + " x = \"\") + \n", + " theme_bw(base_size=15) +\n", + " scale_fill_brewer(type=\"qual\", palette=\"Dark2\") + \n", + " theme(legend_position=\"bottom\",\n", + " plot_title = element_text(hjust = 0.5),\n", + " legend_title = element_blank()\n", + " )\n", + ")\n", + "\n", + "print(plot_pred)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/Gaussian_Regression/index.html b/examples/Gaussian_Regression/index.html new file mode 100644 index 0000000..1e818e3 --- /dev/null +++ b/examples/Gaussian_Regression/index.html @@ -0,0 +1,2823 @@ + + + + + + + + + + + Basic Walkthrough - Gaussian Regression - LightGBMLSS + + + + + + + + + + +
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Documentation built with MkDocs.

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+ + + + + + + + + + + + + diff --git a/examples/How_To_Select_A_Univariate_Distribution/How_To_Select_A_Univariate_Distribution.ipynb b/examples/How_To_Select_A_Univariate_Distribution/How_To_Select_A_Univariate_Distribution.ipynb new file mode 100644 index 0000000..773f673 --- /dev/null +++ b/examples/How_To_Select_A_Univariate_Distribution/How_To_Select_A_Univariate_Distribution.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to Select a Univariate Distribution\n", + "\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/StatMixedML/LightGBMLSS/blob/master/docs/examples/How_To_Select_A_Univariate_Distribution.ipynb)\n", + "\n", + "In this example we will show how to select a distribution for a univariate target variable. We use the California housing dataset and select a distribution for the target variable `median_house_value`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-23T07:25:25.674294200Z", + "start_time": "2023-05-23T07:25:19.998524700Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "from lightgbmlss.distributions import *\n", + "from lightgbmlss.distributions.distribution_utils import DistributionClass\n", + "from sklearn import datasets\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-23T07:25:25.723162900Z", + "start_time": "2023-05-23T07:25:25.668311200Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "housing_data = datasets.fetch_california_housing()\n", + "X, y = housing_data[\"data\"], housing_data[\"target\"]\n", + "feature_names = housing_data[\"feature_names\"]\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Select Distribution\n", + "\n", + "In the following, we specify a list of candidate distributions. The function `dist_select` returns the negative log-likelihood of each distribution for the target variable. The distribution with the lowest negative log-likelihood is selected. The function also plots the density of the target variable and the fitted density, using the best suitable distribution among the specified ones.\n", + "\n", + "It is important to note that the list of candidate distributions should be chosen to be suitable for the target variable at hand. For example, if the target variable is a count variable, then the list of candidate distributions should include the Poisson and Negative Binomial. Similarly, if the target variable is on the positive real scale, then the list of continuous candidate distributions should be chosen accordingly." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-23T07:25:31.083598700Z", + "start_time": "2023-05-23T07:25:25.713194900Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting of candidate distributions completed: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:12<00:00, 1.58s/it]\n" + ] + }, + { + "data": { + "image/png": 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Documentation built with MkDocs.

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+ + + + + + + + + + + + + diff --git a/examples/SplineFlow_Regression/SplineFlow_Regression.ipynb b/examples/SplineFlow_Regression/SplineFlow_Regression.ipynb new file mode 100644 index 0000000..25b7759 --- /dev/null +++ b/examples/SplineFlow_Regression/SplineFlow_Regression.ipynb @@ -0,0 +1,1635 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Spline Flow Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/StatMixedML/LightGBMLSS/blob/master/docs/examples/SplineFlow_Regression.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Normalizing flows transform a simple distribution into a complex data distribution through a series of invertible transformations. The key steps involved in the operation of normalizing flows are as follows (from left to right):" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "
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Image source: https://tikz.net/janosh/normalizing-flow.png
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Start with a simple, easy-to-sample distribution, usually a Gaussian, which serves as the \"base\" distribution\n", + "- Apply a series of invertible transformations to map the samples from the base distribution to the desired complex data distribution\n", + "- Each transformation in the flow must be reversible, meaning it has both a forward pass (sampling from the base distribution to the complex distribution) and an inverse pass (mapping samples from the complex distribution back to the base distribution)\n", + "- The flow ensures that the probability density function (PDF) of the complex distribution can be analytically calculated using the determinant of the Jacobian matrix resulting from the transformations\n", + "\n", + "By stacking multiple transformations in a sequence, normalizing flows can model **complex and multi-modal distributions** while providing the ability to compute the likelihood of the data and perform efficient sampling in both directions (from base to complex and vice versa). However, it is important to note that since LightGBMLSS is based on a *one vs. all estimation strategy*, where a separate tree is grown for each parameter, estimating many parameters for a large dataset can become computationally expensive. For more details, we refer to our related paper **[Alexander März and Thomas Kneib (2022): *Distributional Gradient Boosting Machines*](https://arxiv.org/abs/2204.00778)**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2023-06-23T15:25:53.838277800Z", + "start_time": "2023-06-23T15:25:51.384830800Z" + } + }, + "outputs": [], + "source": [ + "from lightgbmlss.model import *\n", + "from lightgbmlss.distributions.SplineFlow import *\n", + "from lightgbmlss.distributions.flow_utils import NormalizingFlowClass\n", + "from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data\n", + "from scipy.stats import norm\n", + "\n", + "import plotnine\n", + "from plotnine import *\n", + "plotnine.options.figure_size = (20, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:12:13.097935100Z", + "start_time": "2023-05-18T06:12:03.538184Z" + } + }, + "outputs": [], + "source": [ + "# The data is simulated as a Gaussian, where x is the only true feature and all others are noise variables\n", + " # loc = 10\n", + " # scale = 1 + 4 * ((0.3 < x) & (x < 0.5)) + 2 * (x > 0.7)\n", + "\n", + "train, test = load_simulated_gaussian_data()\n", + "\n", + "X_train, y_train = train.filter(regex=\"x\"), train[\"y\"].values\n", + "X_test, y_test = test.filter(regex=\"x\"), test[\"y\"].values\n", + "\n", + "dtrain = lgb.Dataset(X_train, label=y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Select Normalizing Flow\n", + "\n", + "In the following, we specify a list of candidate normalizing flows. The function *flow_select* returns the negative log-likelihood of each specification. The normalizing flow with the lowest negative log-likelihood is selected. The function also plots the density of the target variable and the fitted density, using the best suitable normalizing flow among the specified ones. However, note that choosing the best performing flow based solely on training data may lead to overfitting, since normalizing flows have a higher risk of overfitting compared to parametric distributions. When using normalizing flows, it is crucial to carefully select the specifications to strike a balance between model complexity and generalization ability." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting of candidate normalizing flows completed: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14/14 [01:20<00:00, 5.78s/it]\n" + ] + }, + { + "data": { + "image/png": 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nllNormFlow
rank
116595.917006SplineFlow(count_bins: 20, order: linear)
216608.693807SplineFlow(count_bins: 12, order: quadratic)
316622.862265SplineFlow(count_bins: 16, order: quadratic)
416640.156074SplineFlow(count_bins: 6, order: linear)
516640.611035SplineFlow(count_bins: 16, order: linear)
616649.404709SplineFlow(count_bins: 8, order: linear)
716651.375456SplineFlow(count_bins: 8, order: quadratic)
816653.378393SplineFlow(count_bins: 6, order: quadratic)
916674.331780SplineFlow(count_bins: 12, order: linear)
1016822.629927SplineFlow(count_bins: 4, order: quadratic)
1116902.398862SplineFlow(count_bins: 20, order: quadratic)
1217538.588405SplineFlow(count_bins: 4, order: linear)
1317692.968508SplineFlow(count_bins: 2, order: linear)
1417737.569055SplineFlow(count_bins: 2, order: quadratic)
\n", + "
" + ], + "text/plain": [ + " nll NormFlow\n", + "rank \n", + "1 16595.917006 SplineFlow(count_bins: 20, order: linear)\n", + "2 16608.693807 SplineFlow(count_bins: 12, order: quadratic)\n", + "3 16622.862265 SplineFlow(count_bins: 16, order: quadratic)\n", + "4 16640.156074 SplineFlow(count_bins: 6, order: linear)\n", + "5 16640.611035 SplineFlow(count_bins: 16, order: linear)\n", + "6 16649.404709 SplineFlow(count_bins: 8, order: linear)\n", + "7 16651.375456 SplineFlow(count_bins: 8, order: quadratic)\n", + "8 16653.378393 SplineFlow(count_bins: 6, order: quadratic)\n", + "9 16674.331780 SplineFlow(count_bins: 12, order: linear)\n", + "10 16822.629927 SplineFlow(count_bins: 4, order: quadratic)\n", + "11 16902.398862 SplineFlow(count_bins: 20, order: quadratic)\n", + "12 17538.588405 SplineFlow(count_bins: 4, order: linear)\n", + "13 17692.968508 SplineFlow(count_bins: 2, order: linear)\n", + "14 17737.569055 SplineFlow(count_bins: 2, order: quadratic)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# See ?SplineFlow for an overview.\n", + "bound = np.max([np.abs(y_train.min()), y_train.max()])\n", + "target_support = \"real\"\n", + "\n", + "candidate_flows = [\n", + "\n", + " SplineFlow(target_support=target_support, count_bins=2, bound=bound, order=\"linear\"),\n", + " SplineFlow(target_support=target_support, count_bins=4, bound=bound, order=\"linear\"),\n", + " SplineFlow(target_support=target_support, count_bins=6, bound=bound, order=\"linear\"),\n", + " SplineFlow(target_support=target_support, count_bins=8, bound=bound, order=\"linear\"),\n", + " SplineFlow(target_support=target_support, count_bins=12, bound=bound, order=\"linear\"),\n", + " SplineFlow(target_support=target_support, count_bins=16, bound=bound, order=\"linear\"),\n", + " SplineFlow(target_support=target_support, count_bins=20, bound=bound, order=\"linear\"),\n", + "\n", + " SplineFlow(target_support=target_support, count_bins=2, bound=bound, order=\"quadratic\"),\n", + " SplineFlow(target_support=target_support, count_bins=4, bound=bound, order=\"quadratic\"),\n", + " SplineFlow(target_support=target_support, count_bins=6, bound=bound, order=\"quadratic\"),\n", + " SplineFlow(target_support=target_support, count_bins=8, bound=bound, order=\"quadratic\"),\n", + " SplineFlow(target_support=target_support, count_bins=12, bound=bound, order=\"quadratic\"),\n", + " SplineFlow(target_support=target_support, count_bins=16, bound=bound, order=\"quadratic\"),\n", + " SplineFlow(target_support=target_support, count_bins=20, bound=bound, order=\"quadratic\"),\n", + " \n", + "] \n", + "\n", + "flow_nll = NormalizingFlowClass().flow_select(target=y_train, candidate_flows=candidate_flows, max_iter=50, n_samples=10000, plot=True, figure_size=(12, 5))\n", + "flow_nll" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Normalizing Flow Specification\n", + "\n", + "Even though SplineFlow(count_bins: 20, order: linear) shows the best fit to the data, we choose a more parameter parsimonious specification (recall that a separate tree is grown for each parameter): \n", + "\n", + "- for count_bins=20, we need to estimate 3*count_bins + (count_bins-1) = 79 parameters\n", + "- for count_bins=8, we need to estimate 3*count_bins + (count_bins-1) = 31 parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:12:13.097935100Z", + "start_time": "2023-05-18T06:12:04.423429800Z" + } + }, + "outputs": [], + "source": [ + "# Specifies Spline-Flow. See ?SplineFlow for an overview.\n", + "bound = np.max([np.abs(y_train.min()), y_train.max()])\n", + "\n", + "lgblss = LightGBMLSS(\n", + " SplineFlow(target_support=\"real\", # Specifies the support of the target. Options are \"real\", \"positive\", \"positive_integer\" or \"unit_interval\"\n", + " count_bins=8, # The number of segments comprising the spline.\n", + " bound=bound, # By adjusting the value, you can control the size of the bounding box and consequently control the range of inputs that the spline transform operates on.\n", + " order=\"linear\", # The order of the spline. Options are \"linear\" or \"quadratic\".\n", + " stabilization=\"None\", # Options are \"None\", \"MAD\" or \"L2\".\n", + " loss_fn=\"nll\" # Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\"(continuous ranked probability score).\n", + " ) \n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyper-Parameter Optimization\n", + "\n", + "Any LightGBM hyperparameter can be tuned, where the structure of the parameter dictionary needs to be as follows:\n", + "\n", + " - Float/Int sample_type\n", + " - {\"param_name\": [\"sample_type\", low, high, log]}\n", + " - sample_type: str, Type of sampling, e.g., \"float\" or \"int\"\n", + " - low: int, Lower endpoint of the range of suggested values\n", + " - high: int, Upper endpoint of the range of suggested values\n", + " - log: bool, Flag to sample the value from the log domain or not\n", + " - Example: {\"eta\": \"float\", low=1e-5, high=1, log=True]}\n", + "\n", + " - Categorical sample_type\n", + " - {\"param_name\": [\"sample_type\", [\"choice1\", \"choice2\", \"choice3\", \"...\"]]}\n", + " - sample_type: str, Type of sampling, either \"categorical\"\n", + " - choice1, choice2, choice3, ...: str, Possible choices for the parameter\n", + " - Example: {\"boosting\": [\"categorical\", [\"gbdt\", \"dart\"]]}\n", + "\n", + " - For parameters without tunable choice (this is needed if tree_method = \"gpu_hist\" and gpu_id needs to be specified)\n", + " - {\"param_name\": [\"none\", [value]]},\n", + " - param_name: str, Name of the parameter\n", + " - value: int, Value of the parameter\n", + " - Example: {\"gpu_id\": [\"none\", [0]]}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:22:05.890475500Z", + "start_time": "2023-05-18T06:12:04.439051100Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2023-08-11 12:37:41,890] A new study created in memory with name: LightGBMLSS Hyper-Parameter Optimization\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bf06f1dd1bc942bcb4a0293ab3172ca0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/50 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ] + }, + "metadata": { + "image/png": { + "height": 500, + "width": 750 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Partial Dependence Plot of how x acts on param_21\n", + "lgblss.plot(X_test,\n", + " parameter=\"param_21\",\n", + " feature=\"x_true\",\n", + " plot_type=\"Partial_Dependence\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:22:07.960311200Z", + "start_time": "2023-05-18T06:22:07.616856700Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 700, + "width": 800 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Feature Importance of param_21\n", + "lgblss.plot(X_test,\n", + " parameter=\"param_21\",\n", + " plot_type=\"Feature_Importance\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot of Actual vs. Predicted Quantiles" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:22:08.982134200Z", + "start_time": "2023-05-18T06:22:07.960311200Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(123)\n", + "\n", + "###\n", + "# Actual Quantiles\n", + "###\n", + "q1 = norm.ppf(quant_sel[0], loc = 10, scale = 1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7))\n", + "q2 = norm.ppf(quant_sel[1], loc = 10, scale = 1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7))\n", + "test[\"quant\"] = np.where(test[\"y\"].values < q1, 0, np.where(test[\"y\"].values < q2, 1, 2))\n", + "test[\"alpha\"] = np.where(test[\"y\"].values <= q1, 1, np.where(test[\"y\"].values >= q2, 1, 0))\n", + "df_quantiles = test[test[\"alpha\"] == 1]\n", + "\n", + "# Lower Bound\n", + "yl = list(set(q1))\n", + "yl.sort()\n", + "yl = [yl[2],yl[0],yl[2],yl[1],yl[1]]\n", + "sfunl = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yl})\n", + "\n", + "# Upper Bound\n", + "yu = list(set(q2))\n", + "yu.sort()\n", + "yu = [yu[0],yu[2],yu[0],yu[1],yu[1]]\n", + "sfunu = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yu})\n", + "\n", + "###\n", + "# Predicted Quantiles\n", + "###\n", + "test[\"lb\"] = pred_quantiles.iloc[:,0]\n", + "test[\"ub\"] = pred_quantiles.iloc[:,1]\n", + "\n", + "###\n", + "# Plot\n", + "###\n", + "(ggplot(test,\n", + " aes(\"x_true\",\n", + " \"y\")) + \n", + " geom_point(alpha = 0.2, color = \"black\", size = 2) + \n", + " theme_bw(base_size=15) +\n", + " theme(legend_position=\"none\",\n", + " plot_title = element_text(hjust = 0.5)) +\n", + " labs(title = \"LightGBMLSS Regression - Simulated Data Example\",\n", + " x=\"x\") + \n", + " geom_line(aes(\"x_true\",\n", + " \"ub\"),\n", + " size = 1,\n", + " color = \"blue\", \n", + " alpha = 0.7) + \n", + " geom_line(aes(\"x_true\",\n", + " \"lb\"),\n", + " size = 1,\n", + " color = \"blue\", \n", + " alpha = 0.7) + \n", + " geom_point(df_quantiles,\n", + " aes(\"x_true\",\n", + " \"y\"), \n", + " color = \"red\", \n", + " alpha = 0.7,\n", + " size = 2) + \n", + " geom_step(sfunl,\n", + " aes(\"x_true\",\n", + " \"y\"), \n", + " size = 1, \n", + " linetype = \"dashed\") + \n", + " geom_step(sfunu,\n", + " aes(\"x_true\",\n", + " \"y\"), \n", + " size = 1, \n", + " linetype = \"dashed\") \n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# True vs. Predicted Distributional Parameters\n", + "\n", + "In the following figure, we compare the true parameters of the Gaussian with the ones predicted by LightGBMLSS. The below figure shows that the estimated parameters closely match the true ones (recall that the location parameter $\\mu=10$ is simulated as being a constant)." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 1000, + "width": 2000 + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dist_params = [\"loc\", \"scale\"]\n", + "\n", + "# Calculate parameters from samples \n", + "sample_params = pd.DataFrame.from_dict(\n", + " {\n", + " \"loc\": pred_samples.mean(axis=1),\n", + " \"scale\": pred_samples.std(axis=1),\n", + " \"x_true\": X_test[\"x_true\"].values \n", + " }\n", + ")\n", + "\n", + "# Data with predicted values\n", + "plot_df_predt = pd.melt(sample_params[[\"x_true\"] + dist_params],\n", + " id_vars=\"x_true\",\n", + " value_vars=dist_params)\n", + "plot_df_predt[\"type\"] = \"PREDICT\"\n", + "\n", + "# Data with actual values\n", + "plot_df_actual = pd.melt(test[[\"x_true\"] + dist_params],\n", + " id_vars=\"x_true\",\n", + " value_vars=dist_params)\n", + "plot_df_actual[\"type\"] = \"TRUE\"\n", + "\n", + "# Combine data for plotting\n", + "plot_df = pd.concat([plot_df_predt, plot_df_actual])\n", + "plot_df[\"variable\"] = plot_df.variable.str.upper()\n", + "plot_df[\"type\"] = pd.Categorical(plot_df[\"type\"], categories = [\"PREDICT\", \"TRUE\"])\n", + "\n", + "# Plot\n", + "(ggplot(plot_df,\n", + " aes(x=\"x_true\",\n", + " y=\"value\",\n", + " color=\"type\")) +\n", + " geom_line(size=1.1) + \n", + " facet_wrap(\"variable\",\n", + " scales=\"free\") + \n", + " labs(title=\"Parameters of univariate Gaussian predicted with LightGBMLSS\",\n", + " x=\"\",\n", + " y=\"\") + \n", + " theme_bw(base_size=15) + \n", + " theme(legend_position=\"bottom\",\n", + " plot_title = element_text(hjust = 0.5),\n", + " legend_title = element_blank())\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Density Plots" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:22:09.748483300Z", + "start_time": "2023-05-18T06:22:08.982134200Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 1000, + "width": 2000 + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred_df = pd.melt(pred_samples.iloc[:,0:5])\n", + "actual_df = pd.DataFrame.from_dict({\"variable\": \"ACTUAL\", \"value\": y_test.reshape(-1,)})\n", + "plot_df = pd.concat([pred_df, actual_df])\n", + "\n", + "(\n", + " ggplot(plot_df, \n", + " aes(x=\"value\",\n", + " color=\"variable\",\n", + " fill=\"variable\")) + \n", + " geom_density(alpha=0.4) + \n", + " facet_wrap(\"variable\",\n", + " ncol=2) + \n", + " theme_bw(base_size=15) + \n", + " theme(plot_title = element_text(hjust = 0.5)) +\n", + " theme(legend_position=\"none\")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Actual vs. Predicted\n", + "Since we predict the entire conditional distribution, we can overlay the point predictions with predicted densities, from which we can also derive quantiles of interest. " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Documentation built with MkDocs.

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+ + + + + + + + + + + + + diff --git a/examples/ZAGamma_Regression/ZAGamma_Regression.ipynb b/examples/ZAGamma_Regression/ZAGamma_Regression.ipynb new file mode 100644 index 0000000..4684a2a --- /dev/null +++ b/examples/ZAGamma_Regression/ZAGamma_Regression.ipynb @@ -0,0 +1,954 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Zero-Adjusted Gamma Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/StatMixedML/LightGBMLSS/blob/master/docs/examples/ZAGamma_Regression.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:24:10.418630300Z", + "start_time": "2023-05-18T06:24:10.403008900Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from lightgbmlss.model import *\n", + "from lightgbmlss.distributions.ZAGamma import *\n", + "\n", + "from sklearn.model_selection import train_test_split \n", + "import pandas as pd\n", + "import plotnine\n", + "from plotnine import *\n", + "plotnine.options.figure_size = (18, 9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:12:13.097935100Z", + "start_time": "2023-05-18T06:12:03.538184Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# The simulation example closely follows https://towardsdatascience.com/zero-inflated-regression-c7dfc656d8af\n", + "np.random.seed(123)\n", + "n_samples = 1000\n", + "\n", + "data = pd.DataFrame({\"age\": np.random.randint(1, 100, size=n_samples)})\n", + "data[\"income\"] = np.where((data.age > 17) & (data.age < 70), 1500*data.age + 5000 + 10000*np.random.randn(n_samples), 0) / 1000\n", + "\n", + "y = data[\"income\"].values\n", + "X = data.drop(columns=\"income\")\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n", + "\n", + "dtrain = lgb.Dataset(X_train, label=y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Distribution Selection" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:12:13.097935100Z", + "start_time": "2023-05-18T06:12:04.423429800Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Specifies Zero-Adjusted Gamma distribution. See ?ZAGamma for an overview.\n", + "lgblss = LightGBMLSS(\n", + " ZAGamma(stabilization=\"None\", # Options are \"None\", \"MAD\", \"L2\".\n", + " response_fn=\"exp\", # Function to transform the concentration and rate parameters, e.g., \"exp\" or \"softplus\".\n", + " loss_fn=\"nll\" # Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\"(continuous ranked probability score).) \n", + " ) \n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyper-Parameter Optimization\n", + "\n", + "Any LightGBM hyperparameter can be tuned, where the structure of the parameter dictionary needs to be as follows:\n", + "\n", + " - Float/Int sample_type\n", + " - {\"param_name\": [\"sample_type\", low, high, log]}\n", + " - sample_type: str, Type of sampling, e.g., \"float\" or \"int\"\n", + " - low: int, Lower endpoint of the range of suggested values\n", + " - high: int, Upper endpoint of the range of suggested values\n", + " - log: bool, Flag to sample the value from the log domain or not\n", + " - Example: {\"eta\": \"float\", low=1e-5, high=1, log=True]}\n", + "\n", + " - Categorical sample_type\n", + " - {\"param_name\": [\"sample_type\", [\"choice1\", \"choice2\", \"choice3\", \"...\"]]}\n", + " - sample_type: str, Type of sampling, either \"categorical\"\n", + " - choice1, choice2, choice3, ...: str, Possible choices for the parameter\n", + " - Example: {\"boosting\": [\"categorical\", [\"gbdt\", \"dart\"]]}\n", + "\n", + " - For parameters without tunable choice (this is needed if tree_method = \"gpu_hist\" and gpu_id needs to be specified)\n", + " - {\"param_name\": [\"none\", [value]]},\n", + " - param_name: str, Name of the parameter\n", + " - value: int, Value of the parameter\n", + " - Example: {\"gpu_id\": [\"none\", [0]]}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:22:05.890475500Z", + "start_time": "2023-05-18T06:12:04.439051100Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2023-08-11 12:13:43,049] A new study created in memory with name: LightGBMLSS Hyper-Parameter Optimization\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7fbdc3cd3fc74e5485cb645d52aa6ac8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/20 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " concentration rate gate\n", + "0 13.424025 0.149034 0.943975\n", + "1 13.424025 0.149034 0.054856\n", + "2 9.111379 0.275753 0.054856\n", + "3 9.111379 0.158935 0.054856\n", + "4 9.111379 0.275753 0.943975" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred_params.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SHAP Interpretability" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:22:07.616856700Z", + "start_time": "2023-05-18T06:22:07.020722700Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Partial Dependence Plot of concentration parameter\n", + "lgblss.plot(X_test,\n", + " parameter=\"concentration\",\n", + " feature=\"age\",\n", + " plot_type=\"Partial_Dependence\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2023-05-18T06:22:07.960311200Z", + "start_time": "2023-05-18T06:22:07.616856700Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Feature Importance of gate parameter\n", + "lgblss.plot(X_test,\n", + " parameter=\"gate\",\n", + " plot_type=\"Feature_Importance\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Density Plots of Actual and Predicted Samples" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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jXx4+eL3BSvounAkVzr5u60upWj7D7wwA4MwTIAQAAACA80S9Xs7WbfVs3dZ5gmtRFBkba2fvnkb2PtbMY481su/xZprNTv/R0XZu+8aB3PaNA91rbNhYzY4L67ngonouuLCenU8oZdVqXzMCAAAAAACwuEqlUirLK6ksr6S+4+D+ol2kNdJKa38jzX2NNPc109rfSGuk1e3THm1l4vbxTNw+fvDESin1bX3zQoV9F/anslLtCwDoLX67AQAAAIDzVKlUyuBgJYODlVy8s7Ov3S4ytL+VvY81sndvM3sfa2b/vmaKzkKFeWxPM4/taebrXzsYKly9ppILL+rLBRf15aKLO6/rN1RTKpWW4F0BAAAAAABwPimVS6murqa6upq+iwe6+4tGu7NS4eMzwcLHG2ntb6ZozBS+WkWmd09mevdk8qWD16usqabvgvmhwtqWvpQqal8AwLlJgBAAAAAA6CqXS1m7rpq166q5bGZfs1nk8b3N7N3bWanw8b2NjAy3u+cM7W9laP+B7JqzUuHy5eVccFFfLry4LxfOvG7eUku5rLAKAAAAAADA4ivVyqltrKe2sd7dVxRF2qOtg6HCfZ2AYXvs4GqFrf3NHNg/lgO7xuZcq5T6jr7UL+hP34UD3VULK8srZ/Q9AQCcDAFCAAAAAOCYqtVSNm2uZdPmWpKk1Wql0Siyf18zQ0NF9u1t5vHHmxna30x7Jlc4Pt7Ot781kW9/a6J7nXq9lG3b69l+QT3bL+jLjgvq2XFBXwZXKqwCAAAAAACw+EqlUiorq6msnL9aYXuqnea+Rlr7Gmk+3uyEC/c3kplcYdEoMvW9yUx9bzKjGeqeV11fS21HPfUL+lO7oC/LLlme2qZ6Sh6qCQCcRQQIAQAAAIATVquVsnFTLVu2Hgz/tVpFhvZ3woSzocJ9jzfTmimsTk8Xufd7U7n3e1NJRrvnrVpdyY45ocLtF/Rl67Z6ajWFVQAAAAAAABZfua+c+pa+ZEtfd1/RLtIabnZWKdzXSGtm1cL2gXa3T3NvI829jUx8Y7y7r9RXTv2CvvRd2J++CzorFfbt6E95mYdqAgBLQ4AQAAAAADgtKpVS1q2vZd36WnJ5Z1+7XWRkuJXHH29m/75m9u9rZf/+Zg6MHyysDg+1Mjw0kdtvO7haYbmcbNnaWa1w2456tm2vZ9u2ejZsqqVSESwEAAAAAABgcZXKpVTX1FJdU0t2zlmtcLJ1cJXCfY00H2+kNdRMZspfxVQ7U3dPZOruiXnXq26spe/C/tQv6O+GC6sbrVYIACw+AUIAAAAAYNGUy6WsXlPN6jXzv4qcmmxn//6ZQOG+Zvbta2ZofzPNZud4u508+MB0Hnxget551WqyeWs9W7d1QoVbt9WzdXs9mzbXUq0qrgIAAAAAALC4yv2V1LdVUt/WWa2w1WqlaHVWKyyG2jPBwmaajzdSTM5ZrXBPI809jYx/bbS7r9RfTt+OvtQvHDgYLrygL+UBqxUCAKePACEAAAAAcMb19ZezeUs9m7cc3FcURUZH2zMrFR5csXB0tJWi6PRpNpMHdk/ngd3zg4WVSrJpc60bKNw6s2Lhpi211OvlM/jOAAAAAAAAON+UKqVU19ZS2TA/+Nc+0JoXKGzum1mtcKb2VUy2M3n3RCYPWa2wtqneXamwfmHntbqhllLJAzUBgBMnQAgAAAAAnBVKpVJWrqxk5cpKLryor7u/2SwyMtzK0FAzw0OtDO1vZmiolZHhg8HCVit56MFGHnqwkXx1fM41k/UbqtmytRMm3LKlns1batm8tZ41ayuKrAAAAAAAACya8rJK6ssqqW8/uK9oFWkNNTvBwplQYXNfc95qhY1Hp9N4dDrjXxvp7isNlNM3Gyqcfd3Rn3K/h2kCAMcmQAgAAAAAnNWq1VLWrqtm7br5X2e2WkVGR1oZmhMqHN7fzPBwK+2Z+mpRJI/taeaxPc3k1vnXrfeVOmHCzfVs3lrrrIi4tZbNW2pZtmz+02EBAAAAAADgdChVSqmuq6W6rpZc2tlXFEXaE+205gQKD1utcKKdybsOZPKuA3MudoTVCi+wWiEAMJ8AIQAAAABwTqpUSlm9pprVa6rJxQdXLGy3i4yOtjK0v5XhoU6gcGS4leHhVqanim6/6akiu78/nd3fnz7s2itXVbJlay2bttSzZUstGzfXsnFTLRs31tLnKa4AAAAAAACcRqVSKZVllVSWVVLf0d/dXzSLNIeaae2bDRY20ny8mWJq9mmaSeOR6TQemc74Vw+uVlgeKM9bpbC+oy/17f2prPAQTQA4HwkQAgAAAAA9pVwuZdWqalatqiY5GCwsiiJTU0WGh1oZGemEC0dmwoUjIwdXLUzS3X/XtycPu/7qNZVs3DgnVDjzumlTLcsVXQEAAAAAADhNStVSautrqa2vdfcVRZH2gXZnhcKZQGFzXyOt4YOrFbaPtFphksraaurb+9O3o+9gsHBbf8oeoAkAPU2AEAAAAAA4L5RKpfT3l9K/uZxNm2vzjrXbRcbH2hkePrhi4eyqhQfG2/P6Du3vrG74nbsODxcuX14+GCyc87Npcy2rVldSKpUW9T0CAAAAAADQ20qlUirLK6ksrySHrVbYCRTOXbGwmCq6fVr7mpnYN5aJ28bmXbO6sZb6jv70bZ8NFvanvrWeUk2wEAB6gQAhAAAAAHDeK5dLGVxZyeDKSrbvmH+s0SgyNtpZpXB09memPT7WTnGw5prx8XbuvWcq994zddg96n2lbNhQy/qN1WzYWMv6DdWZdmd7+XKrFwIAAAAAAHByOqsV1lNbX+/uK4oixUQ7zf3NNPc30przWjQOFrmaexpp7mnkwP8bPXjBclLb0tdZrXD7zGqFO/pT21RPqeKhmQBwLhEgBAAAAAA4hlqtlDVrq1mz9vCvU9vtImOj7U64cHROwHCklZHRVtqtg32np4o8+MB0Hnxg+oj3Wba8fFiocMOGWjZsrGb9hlr6+j3hFQAAAAAAgIUrlUopLaukvqyS+ra+7v6iKNIea6W5v5nW/sbMazPNoUYyW99qJ40Hp9J4cCrJyMFr1kqpbamnvrUvtW19qW/rT31bX2pb6inX1bMA4GwkQAgAAAAAcJLK5VJWrqpk5arDVw8siiIHDrQzMjwnXDjazthoK2OjrUxOFvP6HxhvZ/f4dHZ//8gBw8GVlTkBw06ocDZcuHZdNX19CrIAAAAAAAAcX6lUSmWwmspgNbmgv7u/aBdpjbbS2teYHy4cbiYzpa2iUWR691Smd08dctGkuqHWDRTWt80GDPtSWX54LQ0AOHMECAEAAAAAFkGpVMry5ZUsX17JliMcbzSKjI91woVjo+2MjXWChaMz29NT8wOGsysb3nvP1BGulgwOlrNuQy3r1lWzdn0169ZXs25dLevWd9orV1ZSLpcW4Z0CAAAAAADQC0rlUqqrqqmuqqbv4oP7i1aR1nAzzf2NmZUKm2kNzQ8Wpkiaexpp7mnkwDdG5123srp6MFS4ta+7XVlTTamkfgUAi02AEAAAAABgCdRqpaxeU83qNUf+mnZ6uj1vxcLZkOHoTLvZnN9/dLSd0dGpfP97Rw4YVmulTrhw3Uy4cH01a2cChutnQob1ulUMAQAAAAAAmK9UKaW6tpbq2tq8/UW7SGukEybshgpnttM8+LDM1lAzE0PNTHxrfP51+8upb6mntqUvtS311Lf0pba507ZqIQCcPgKEAAAAAABnoXq9nHXrylm37vCvcYuiyNRU0Q0Wjo+3MjbWzvhYK+Nj7YyNtzI1OX8Fw2ajyKOPNPLoI42j3nNwZWVm5cL5qxiuWVfN2rXVrFpdSaXiKbAAAAAAAADMrFi4upbq6lr65uwviiLt8XZaQ43DgoXFZPtgv8l2pu6dzNS9k4ddu7KyMj9YuKWe2uZOwLDsoZgAcEIECAEAAAAAzjGlUin9/aX095ezfsOR+zSbxbxA4fhMwHBsrBM4HB9rp92ef87oSCujI62jrmJYKiWr11SyZm01a9Z2QoWz4cI1azurG65eU02tJmQIAAAAAABwviqVSqmsqKSyopL69vnH2pNzgoXDzbRGWjOvzWRO7ao10kpr5EAm7zpwyMWT6rraYSsW1jbVU9tQS6kmXAgAhxIgBAAAAADoQdVqKatWV7Nq9ZGPF0WRiYmZkOH4nHDhMVYxLIpk/75W9u9rJTlyyDBJVq6qZM2aatasq8wLF3ZDh2ur6etXvAUAAAAAADjflPvLKW/uS21z37z9RbtIe6wTJmzODRYON9Mea83pmDT3NtLc28jEN8fnX7yUVNfWUt1Y6wQK5/5srKe8opJSyYMwATj/CBDCjEajkZtvvjlf/OIXs3v37gwNDWXFihXZtGlTnvWsZ+VHf/RHs3LlyqUeJgAAAACcFqVSKcuWlbJsWTlHWcSwu4rhgQPtjI+3c2C8lQPjs9udlQwnDwkZJsnIcCsjw63c9/2j33/Z8nI3TLhmXTVr1lSyek1nBcPVqzvbK1dVUqko4gLAUlA7AwAAAOBMKpVLqaysprKymvqO+ceKZpHWaLMbKGwNt9Ia6QQNi4k5yxYWSfPxRpqPNzL57UNWLkxSXlZObWM91ZlA4dyAYXVdLSV1KQB6lAAhJHnggQfyrne9K/fee++8/fv378/+/ftz55135uMf/3je8IY35GlPe9oSjRIAAAAAzqzjrWKYJK1WkQMHOuHCg8HC+e2JiXaKQ3KGB8bbOTA+nQfunz7qtUulzmqGc0OFqwUNAWDRqZ0BAAAAcDYpVUuprqmluqZ22LH2dDutkWbaI61OyHDOa3u8lcypUbUPtDP1/clMfX/y8JtUktr6eqobaqltqKe6vtbZXj+zLWAIwDlMgJDz3t69e/OWt7wl+/btS9J58vpVV12VzZs3Z2RkJLfeemump6czPDycd7zjHXnrW9+aa665ZolHDQAAAABnh0qllMHBSgYHK0ft024XmZg4crjwwIF2Z5XDiXbarfnnFUUyPNTK8FAr9x1jDIKGAHD6qJ0BAAAAcC4p18spr68n6w8/VrSKtMcODxbOvqY5J13YShqPTqfx6HQmMn74xUpJdV0t1fW11NbXUp0JGXbDhutrKdfLi/dGAeAUCBBy3nv3u9/dLYBu3Lgxb37zm3PxxRd3j4+MjORd73pXdu3alWazmXe+85153/velxUrVizVkAEAAADgnFIul7J8eSXLl1ey4Sh9iqLI9FRnNcOJA51g4cHt1rz97fah5y48aLhisJJVqytZtaqSlasqWbWqmpVz26s7QcOVKyspl4UNATj/qJ0BAAAA0CtKlVIqq6qprDo8NlEURYrJ9mHBwvZoK62xw1cvTJE09zbS3NvIEdYvTJJUVlVnQoW1VNfXU11b7YQO13ZWMKysrqak/gTAEhAg5Lz27//+77njjjuSJNVqNW95y1ty0UUXzeuzcuXKvPnNb85v/MZv5JFHHsno6Gg+/vGP5zWvec0SjBgAAAAAelOpVEpffyl9/eWsWXv0fkVRZGqqOGLI8NB9Rwoajo60MjrSygPHHU8yuHI2ZHh42HB2W9gQgF6idgYAAADA+aJUKqU0UEl5oJLapvphx4t2kfaBmTDhbKhwbPa1mdZYK2nNP6c13ExruJmpeyaOfNNyUl1dTWU2VDgTLOy8Vjuva6opVa1kCMDpJUDIee2f//mfu9vXXnvtYQXQWf39/Xn1q1+dP/iDP0iS/Mu//Ev+43/8j6lUKmdimAAAAADAjFKplP7+UvpPMmg4MdH5mZzzOjVVHOH8ZGS4lZHhEwsbrhgsp1ZrZPnyUjZu2p9Vq2sZHCxncGUlgysrWTFYyeBgJZWKwCEAZx+1MwAAAADoKJVLqayoprKimmw+/HhRFCkm2vOCha2x+SsYFtOH1KDaSXNfM819zUzlKCHD0sxKhmurqa6ppbKmmurqzuqF1TXVzuvqWiqrqilV1ZsAWBgBQs5bExMT2bVrV7f9whe+8Jj9n/3sZ+fP/uzPMjExkdHR0dx+++255pprFnuYAAAAAMBJWGjQMElarSKTk3OChQfamZgo5gUNFxo2nG/oqPdcvrzcCRPOBAsHBysZXFk+uH3Isb5+T5oFYHGpnQEAAADAwpVKpZSWVVJeVkk2HrlP0WinNd5Oe7wTKGzNvLbHW53VDcfbKSbbh5yUtIaaaQ01M5XJYwwgqQxWUlldTWV1rbOy4ZpqqquqqayZ015dTamvnFJJ2BDgfCZAyHnrzjvvTKPRSNJ5Suqll156zP71ej2XX355br311iTJN7/5TUVQAAAAAOgBlUopy5dXsnz58VdNOixseEjQcHKinfHxZqani0xPJe32ka8zPt7O+Hg7jz7SWNAYa7VSlq/ohA6XLy9nxYpKlq+oZPmKcpavqGTF8pnXwc7rbJ96X0lBGIAFUTsDAAAAgNOrVCunurqcrD56bKNoFjNhwtacoGG7GzJsH2inPdFKDq05FUlrpJXWSCvZPXXscdRLqaysdn5WVVIZrKayqprKysrB/bPHVlZTrnuwJUCvESDkvHX//fd3ty+88MJUKsf/x0E7d+7sFkHnng8AAAAAnB+OFzZsF+2MjIwmSQYHV6TVLGVyssjUZDuTk+1MThadlQzntme2pyaLNBqHr3CYJI1GkaH9rQztP3SVw2OrVjMTNDwkZLi8kmUrylm2rJxlyypZtqycgWXlLFte7m7395dTLgsfApwv1M4AAAAA4MwrVQ+G+46mKIoUU+1OmPBAK+2J9sFw4dz2RDvFEWpNxXSR5t5GmnsX9mDLUn/5YLhwVbWz0uGKzmt5eSWVFZWUBzuvlRXVlFdUUu4TOgQ4mwkQct568MEHu9sbNx5l3ehDbNiwobv9wAMPnPYxAQAAAAC9o1QqpV4vp15PsvL4IYwkaTaLTE21Z0KGc8KFU539s6/Tc9rTU0cOHXaulwwPtTI8dGLBw874k/6B2ZBhJ1w4sKycgYFKN2h4MHhYybKBmePLyunrK6V/oBNCrFSEEAHOBWpnAAAAAHB2KpVKKfVXUu6vJGtrx+xbNDpBw9bEwYBhMdlOe7LdCRpOtlNMzoQNp49cYyom22lOttPcs7DAYZKUaqWUV1QOhgwHq6msqKS0vJxGMZHSslLGN4+kurya8vJKyssqKS8rd16teAiw6AQIOW+Njo52t1evXr2gc+b2GxsbO80jAgAAAADOd9VqKdXq0Vc4PJJ2u8j0dJHpqflBw+kjhA4nJw+GD6enixRHzx6mKJKJA+1MHGjn8VN4T/V6KX395fT3d0KFA/3lTnvg4L7+/pmf2X3d7c5rX18p9b5y+uql1OolKyMCLAK1MwAAAAA495Vq5VRWlVNZdfyoSNEqZgKF7bSyAxhBAAA0JklEQVQnWp2Q4UzQsOgGDmcCiFPtFMd4qGXRKNLa30xrf/OoffbkwJEPVEupzIYJ5wYLZ14r8/ZVUh4op9xfTqm/nHJfudtOtZRSSQ0J4EgECDlvTU5Odrfr9fqCzunr6zvi+QAAAAAAS6VcLnVDdyeiKIo0G0WmG52w4fR0kenpdhrTs9ud9vR0MWdfe9528+g14K5O31ZGR07yDR5BvV5KvW9mhce+UvpmXjv7O0HDev/M65H69ZU7wca+Umq1Umq1cqq12e3Oz2zbCorA+ULtDAAAAADOL6VKKZXllWR5JcmxVzZMkqJdpJguZsKEM6HCbrjwYMiwPTm3ffSVDruaRVojrbRGWqf2hipJub+ccn+lEy7snxM0PKxdSbmvlFK9nFK9PGe7lPLMvlLf7PbMMQ+4BM5hAoSct6anp7vb1erC/q8wt9/U1NRpHxMAAAAAwJlSKnVW86vVk+XLT+4as6sfNuaGDRsHf5qNo7fnH1tYGHHWbMAxaZ/cwE9AuZzDw4XVzp9dtTqzr15OrTo/eNh9rZRSqSaV6ux2KZVKDm5XS6lWMrP/YP/qTLt7fHZ75vxDr+eJusCpUjsDAAAAAI6lVC6l1F/qrPa3QO2inZGh4aSRLO9bnjSSYrpIMd0JFrZnXjvtdtpzjs1tL6gk1Era4+20xxepflQtpVwvpdRXTqlWSrmvfEjosJRSpZRSrXO8VC11VkWc2S5VZ451t+ceKx/Sb875lc5PKqWUKjm4XS4lakTAAgkQct6a++TU5gL/ZcrcfnOfqHqqli9fnoGBgbRap/jUhB4z98+j3S6S8uL/YyA417SLg/+/KBU+AMBc7XYxf9s8AvOYQ+DozCFwfOYROLrzbh4pJfW+pN5XyvJUTulSnRURczBg2CzmbTebRVrNIs1m5mwfud1qzT2nc7x9kv9VtNvprNA4dZyn4y6xUqkTdiyXSylXZl7LSaXcKeiXy50QYrdPeab/7L7S/PPm9avMXqvzWpq5Vqk0e9/OdkpJudQpVJfKnWOlzPyDgplj7aLdPa9arXS3S6WZ65UPP2den3IOP2fmvSedscz+eWT2/jP/UUpn3+zx2fZC+mzaXMvGTcd/+vL5amBgIKOjo0s9DE6R2tnZb+6fR9Fup31iiw/DecHnVTi6Ys6HIvMIHM4cAkdnDoHjM4/A0RXtdifo1peUV5RTKp/4RFIURdLKwWBho0jRbHdeG0WKZnFwe6adxhGON4sUM/tzMl89Nou0m0Vy4Cyr/5UzL2CY2cDhzP7jH+vUZVJOtzhSKs8UScqHbJcO9iuVM3+7dDLnzDlvzl+fs3WbTiMHX2cKN6XSkfendPC0edecc635x0uH9V3QvQ+10L/6D+1Xmv89e+ehfUe42JF2LeSeJzvWBVy8srqa6mqRtKM5G2tn/tvivNXf39/dnvtE1WOZ++TUuecfzV//9V/nwx/+8DH7jI6O5lWvelVe+tKX5tFHH13QOM5HY2NjSz0EAM5h5hEATpY5BIBTYR45eaVyUqt3fk7grByr4tVuF2m10vnphgw7AcF2K2nNvM72a7fTfW23ijnHj7+/1Sq61223Oz9F0flZLEUx895aRdJIkrM78Hiu+dH/UM2P/ocT+h/keeWlL31p3v/+9y/1MDhFamfnFr9nAHAqzCMAnCxzCACn4rTMI+UkfTM/x+149OpR0Z4JEbaStGYCiq0kzaK7rzjkeKdPkTQPOWfO8bSLpJ0U7fntzLYXQ3vm/TQK1SHOmNqPDaT24oGlHsZZ62ysnQkQct4aHBzsbg8NDS3onLn9VqxYcdz+4+Pj2bNnz3H7HThwYEH3P19ddsWpPbkcAAAAAOBs0G4XB4OGs2HGmdDibJixu69VzNk+GA7shhSbB0OPrVbRqT+3OkHCbmixPVOXbhed7Tlhxta8vsWcvjmsb3vm+kW7c89ibigynfZsQLITkiw623P7zD1ezG8f+nNW8tBwzgNqZ+eOyhVWRAUAAACAc1ExGzZsdoKIBwOJnX1F8+CxzBwripntQ0KJRSsz++aGFA85NhuUnNk+8jkzx2ZqON2fdg4WetqZ/1rMrAx5yL7uOe1DrjX7AywZAULOW9u2betuL6RQmSSPPfZYd3v79u3H7b98+fJs3LjxmH1GR0dz55135hOf+ERe//rXL2gc54tWq5W9e/emUill/fr1qVQECeFQjUaju12r+QcDMNfsPJLEPAJHYA6BozOHwPGZR+DozCNwbAuZQ4qimB8qbHcCjCk6de6iOKRPu9NudwvWB/t0rpejvxZFN+CY4mDtel67KLJyVSWrViurHc373ve+pR4Cp4Ha2dlv9veMktoZHJXPq3B0Pq/CsZlD4OjMIXB85hE4OvMIs+YGDov27HbRre10OnU7H2wfUtc5dP/B84t5++ZeqzjK/vnXPt695w5w7hs70ps9wr6j9Gk2m91dlcqRalELuOdCw5kLGtfhnY70ANDapnpqm+oLvPH552ysnal0ct7asWNHd/u+++5Lq9U67i8k99xzzxHPP5rrrrsu11133TH7vOc978no6GgmJib8QnQMlUrFnw8cQbvd7m77/wgcnXkEDmcOgYUxh8CRmUdgYcwjcDhzSG+amJhY6iFwGqidnVv8ngFH5nc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Documentation built with MkDocs.

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+ + + + + + + + + + + + + diff --git a/fonts/fontawesome-webfont.eot b/fonts/fontawesome-webfont.eot new file mode 100644 index 0000000..e9f60ca Binary files /dev/null and b/fonts/fontawesome-webfont.eot differ diff --git a/fonts/fontawesome-webfont.svg b/fonts/fontawesome-webfont.svg new file mode 100644 index 0000000..855c845 --- /dev/null +++ b/fonts/fontawesome-webfont.svg @@ -0,0 +1,2671 @@ + + + + +Created by FontForge 20120731 at Mon Oct 24 17:37:40 2016 + By ,,, +Copyright Dave Gandy 2016. All rights reserved. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/fonts/fontawesome-webfont.ttf b/fonts/fontawesome-webfont.ttf new file mode 100644 index 0000000..35acda2 Binary files /dev/null and b/fonts/fontawesome-webfont.ttf differ diff --git a/fonts/fontawesome-webfont.woff b/fonts/fontawesome-webfont.woff new file mode 100644 index 0000000..400014a Binary files /dev/null and b/fonts/fontawesome-webfont.woff differ diff --git a/fonts/fontawesome-webfont.woff2 b/fonts/fontawesome-webfont.woff2 new file mode 100644 index 0000000..4d13fc6 Binary files /dev/null and b/fonts/fontawesome-webfont.woff2 differ diff --git a/img/favicon.ico b/img/favicon.ico new file mode 100644 index 0000000..e85006a Binary files /dev/null and b/img/favicon.ico differ diff --git a/img/grid.png b/img/grid.png new file mode 100644 index 0000000..878c3ed Binary files /dev/null and b/img/grid.png differ diff --git a/index.html b/index.html new file mode 100644 index 0000000..db50f86 --- /dev/null +++ b/index.html @@ -0,0 +1,252 @@ + + + + + + + + + + + LightGBMLSS + + + + + + + + + + +
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LightGBMLSS - An extension of LightGBM to probabilistic modelling and prediction

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We introduce a comprehensive framework that models and predicts the full conditional distribution of a univariate target as a function of covariate. Choosing from a wide range of continuous, discrete, and mixed discrete-continuous distributions, modelling and predicting the entire conditional distribution greatly enhances the flexibility of LightGBM, as it allows to create probabilistic forecasts from which prediction intervals and quantiles of interest can be derived.

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Features

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  • Estimation of all distributional parameters.
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  • Normalizing Flows allow modelling of complex and multi-modal distributions.
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  • Zero-Adjusted and Zero-Inflated Distributions for modelling excess of zeros in the data.
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  • Automatic derivation of Gradients and Hessian of all distributional parameters using PyTorch.
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  • Automated hyper-parameter search, including pruning, is done via Optuna.
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  • The output of LightGBMLSS is explained using SHapley Additive exPlanations.
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  • LightGBMLSS provides full compatibility with all the features and functionality of LightGBM.
    • +
  • LightGBMLSS is available in Python.
    • +
+

Installation

+

To install LightGBMLSS, please first run

+
pip install git+https://github.com/StatMixedML/LightGBMLSS.git
+
+

Then, to install the shap-dependency, run

+
pip install git+https://github.com/dsgibbons/shap.git
+
+

Some Notes

+

Stabilization

+

Since LightGBMLSS updates the parameter estimates by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all distributional parameters. Due to variability regarding the ranges, the estimation of Gradients and Hessians might become unstable so that LightGBMLSS might not converge or might converge very slowly. To mitigate these effects, we have implemented a stabilization of Gradients and Hessians.

+

For improved convergence, an alternative approach is to standardize the (continuous) response variable, such as dividing it by 100 (e.g., y/100). This approach proves especially valuable when the response range significantly differs from that of Gradients and Hessians. Nevertheless, it is essential to carefully evaluate and apply both the built-in stabilization and response standardization techniques in consideration of the specific dataset at hand.

+

Runtime

+

Since LightGBMLSS is based on a one vs. all estimation strategy, where a separate tree is grown for each distributional parameter, it requires training [number of iterations] * [number of distributional parameters] trees. Hence, the runtime of LightGBMLSS is generally slightly higher for univariate distributions as compared to LightGBM, which requires training [number of iterations] trees only.

+

Reference Paper

+

März, A. and Kneib, T.: (2022) Distributional Gradient Boosting Machines.
+März, Alexander (2019): XGBoostLSS - An extension of XGBoost to probabilistic forecasting.

+
+
+ +
+
+

Documentation built with MkDocs.

+
+ + + + + + + + + + + + + + + diff --git a/index.md b/index.md new file mode 100644 index 0000000..b4fcd27 --- /dev/null +++ b/index.md @@ -0,0 +1,37 @@ + + +# LightGBMLSS - An extension of LightGBM to probabilistic modelling and prediction +We introduce a comprehensive framework that models and predicts the full conditional distribution of a univariate target as a function of covariate. Choosing from a wide range of continuous, discrete, and mixed discrete-continuous distributions, modelling and predicting the entire conditional distribution greatly enhances the flexibility of LightGBM, as it allows to create probabilistic forecasts from which prediction intervals and quantiles of interest can be derived. + +## Features +- Estimation of all distributional parameters.
+- Normalizing Flows allow modelling of complex and multi-modal distributions.
+- Zero-Adjusted and Zero-Inflated Distributions for modelling excess of zeros in the data.
+- Automatic derivation of Gradients and Hessian of all distributional parameters using [PyTorch](https://pytorch.org/docs/stable/autograd.html).
+- Automated hyper-parameter search, including pruning, is done via [Optuna](https://optuna.org/).
+- The output of LightGBMLSS is explained using [SHapley Additive exPlanations](https://github.com/dsgibbons/shap).
+- LightGBMLSS provides full compatibility with all the features and functionality of LightGBM.
+- LightGBMLSS is available in Python.
+ +## Installation +To install LightGBMLSS, please first run +```python +pip install git+https://github.com/StatMixedML/LightGBMLSS.git +``` +Then, to install the shap-dependency, run +```python +pip install git+https://github.com/dsgibbons/shap.git +``` + +## Some Notes +### Stabilization +Since LightGBMLSS updates the parameter estimates by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all distributional parameters. Due to variability regarding the ranges, the estimation of Gradients and Hessians might become unstable so that LightGBMLSS might not converge or might converge very slowly. To mitigate these effects, we have implemented a stabilization of Gradients and Hessians. + +For improved convergence, an alternative approach is to standardize the (continuous) response variable, such as dividing it by 100 (e.g., y/100). This approach proves especially valuable when the response range significantly differs from that of Gradients and Hessians. Nevertheless, it is essential to carefully evaluate and apply both the built-in stabilization and response standardization techniques in consideration of the specific dataset at hand. + +### Runtime +Since LightGBMLSS is based on a *one vs. all estimation strategy*, where a separate tree is grown for each distributional parameter, it requires training ```[number of iterations] * [number of distributional parameters]``` trees. Hence, the runtime of LightGBMLSS is generally slightly higher for univariate distributions as compared to LightGBM, which requires training ```[number of iterations]``` trees only. + +## Reference Paper +März, A. and Kneib, T.: (2022) [*Distributional Gradient Boosting Machines*](https://arxiv.org/abs/2204.00778).
+März, Alexander (2019): [*XGBoostLSS - An extension of XGBoost to probabilistic forecasting*](https://arxiv.org/abs/1907.03178). diff --git a/js/base.js b/js/base.js new file mode 100644 index 0000000..b0f4726 --- /dev/null +++ b/js/base.js @@ -0,0 +1,283 @@ +function getSearchTerm() { + var sPageURL = window.location.search.substring(1); + var sURLVariables = sPageURL.split('&'); + for (var i = 0; i < sURLVariables.length; i++) { + var sParameterName = sURLVariables[i].split('='); + if (sParameterName[0] == 'q') { + return sParameterName[1]; + } + } +} + +function applyTopPadding() { + // Update various absolute positions to match where the main container + // starts. 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Obscure keys are omitted and their use is discouraged. +var keyCodes = { + 8: 'backspace', + 9: 'tab', + 13: 'enter', + 16: 'shift', + 17: 'ctrl', + 18: 'alt', + 19: 'pause/break', + 20: 'caps lock', + 27: 'escape', + 32: 'spacebar', + 33: 'page up', + 34: 'page down', + 35: 'end', + 36: 'home', + 37: '←', + 38: '↑', + 39: '→', + 40: '↓', + 45: 'insert', + 46: 'delete', + 48: '0', + 49: '1', + 50: '2', + 51: '3', + 52: '4', + 53: '5', + 54: '6', + 55: '7', + 56: '8', + 57: '9', + 65: 'a', + 66: 'b', + 67: 'c', + 68: 'd', + 69: 'e', + 70: 'f', + 71: 'g', + 72: 'h', + 73: 'i', + 74: 'j', + 75: 'k', + 76: 'l', + 77: 'm', + 78: 'n', + 79: 'o', + 80: 'p', + 81: 'q', + 82: 'r', + 83: 's', + 84: 't', + 85: 'u', + 86: 'v', + 87: 'w', + 88: 'x', + 89: 'y', + 90: 'z', + 91: 'Left Windows Key / Left ⌘', + 92: 'Right Windows Key', + 93: 'Windows Menu / Right ⌘', + 96: 'numpad 0', + 97: 'numpad 1', + 98: 'numpad 2', + 99: 'numpad 3', + 100: 'numpad 4', + 101: 'numpad 5', + 102: 'numpad 6', + 103: 'numpad 7', + 104: 'numpad 8', + 105: 'numpad 9', + 106: 'multiply', + 107: 'add', + 109: 'subtract', + 110: 'decimal point', + 111: 'divide', + 112: 'f1', + 113: 'f2', + 114: 'f3', + 115: 'f4', + 116: 'f5', + 117: 'f6', + 118: 'f7', + 119: 'f8', + 120: 'f9', + 121: 'f10', + 122: 'f11', + 123: 'f12', + 124: 'f13', + 125: 'f14', + 126: 'f15', + 127: 'f16', + 128: 'f17', + 129: 'f18', + 130: 'f19', + 131: 'f20', + 132: 'f21', + 133: 'f22', + 134: 'f23', + 135: 'f24', + 144: 'num lock', + 145: 'scroll lock', + 186: ';', + 187: '=', + 188: ',', + 189: '‐', + 190: '.', + 191: '?', + 192: '`', + 219: '[', + 220: '\', + 221: ']', + 222: ''', +}; diff --git a/js/bootstrap.min.js b/js/bootstrap.min.js new file mode 100644 index 0000000..ca013b7 --- /dev/null +++ b/js/bootstrap.min.js @@ -0,0 +1,7 @@ +/*! + * Bootstrap v4.3.1 (https://getbootstrap.com/) + * Copyright 2011-2019 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors) + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) + */ +!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?e(exports,require("jquery"),require("popper.js")):"function"==typeof define&&define.amd?define(["exports","jquery","popper.js"],e):e((t=t||self).bootstrap={},t.jQuery,t.Popper)}(this,function(t,g,u){"use strict";function i(t,e){for(var n=0;nthis._items.length-1||t<0))if(this._isSliding)g(this._element).one(Q.SLID,function(){return e.to(t)});else{if(n===t)return this.pause(),void this.cycle();var i=ndocument.documentElement.clientHeight;!this._isBodyOverflowing&&t&&(this._element.style.paddingLeft=this._scrollbarWidth+"px"),this._isBodyOverflowing&&!t&&(this._element.style.paddingRight=this._scrollbarWidth+"px")},t._resetAdjustments=function(){this._element.style.paddingLeft="",this._element.style.paddingRight=""},t._checkScrollbar=function(){var t=document.body.getBoundingClientRect();this._isBodyOverflowing=t.left+t.right
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convenience function for configuring and constructing + * a new lunr Index. + * + * A lunr.Builder instance is created and the pipeline setup + * with a trimmer, stop word filter and stemmer. + * + * This builder object is yielded to the configuration function + * that is passed as a parameter, allowing the list of fields + * and other builder parameters to be customised. + * + * All documents _must_ be added within the passed config function. + * + * @example + * var idx = lunr(function () { + * this.field('title') + * this.field('body') + * this.ref('id') + * + * documents.forEach(function (doc) { + * this.add(doc) + * }, this) + * }) + * + * @see {@link lunr.Builder} + * @see {@link lunr.Pipeline} + * @see {@link lunr.trimmer} + * @see {@link lunr.stopWordFilter} + * @see {@link lunr.stemmer} + * @namespace {function} lunr + */ +var lunr = function (config) { + var builder = new lunr.Builder + + builder.pipeline.add( + lunr.trimmer, + lunr.stopWordFilter, + lunr.stemmer + ) + + builder.searchPipeline.add( + lunr.stemmer + ) + + config.call(builder, builder) + return builder.build() +} + +lunr.version = "2.3.9" +/*! + * lunr.utils + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A namespace containing utils for the rest of the lunr library + * @namespace lunr.utils + */ +lunr.utils = {} + +/** + * Print a warning message to the console. + * + * @param {String} message The message to be printed. + * @memberOf lunr.utils + * @function + */ +lunr.utils.warn = (function (global) { + /* eslint-disable no-console */ + return function (message) { + if (global.console && console.warn) { + console.warn(message) + } + } + /* eslint-enable no-console */ +})(this) + +/** + * Convert an object to a string. + * + * In the case of `null` and `undefined` the function returns + * the empty string, in all other cases the result of calling + * `toString` on the passed object is returned. + * + * @param {Any} obj The object to convert to a string. + * @return {String} string representation of the passed object. + * @memberOf lunr.utils + */ +lunr.utils.asString = function (obj) { + if (obj === void 0 || obj === null) { + return "" + } else { + return obj.toString() + } +} + +/** + * Clones an object. + * + * Will create a copy of an existing object such that any mutations + * on the copy cannot affect the original. + * + * Only shallow objects are supported, passing a nested object to this + * function will cause a TypeError. + * + * Objects with primitives, and arrays of primitives are supported. + * + * @param {Object} obj The object to clone. + * @return {Object} a clone of the passed object. + * @throws {TypeError} when a nested object is passed. + * @memberOf Utils + */ +lunr.utils.clone = function (obj) { + if (obj === null || obj === undefined) { + return obj + } + + var clone = Object.create(null), + keys = Object.keys(obj) + + for (var i = 0; i < keys.length; i++) { + var key = keys[i], + val = obj[key] + + if (Array.isArray(val)) { + clone[key] = val.slice() + continue + } + + if (typeof val === 'string' || + typeof val === 'number' || + typeof val === 'boolean') { + clone[key] = val + continue + } + + throw new TypeError("clone is not deep and does not support nested objects") + } + + return clone +} +lunr.FieldRef = function (docRef, fieldName, stringValue) { + this.docRef = docRef + this.fieldName = fieldName + this._stringValue = stringValue +} + +lunr.FieldRef.joiner = "/" + +lunr.FieldRef.fromString = function (s) { + var n = s.indexOf(lunr.FieldRef.joiner) + + if (n === -1) { + throw "malformed field ref string" + } + + var fieldRef = s.slice(0, n), + docRef = s.slice(n + 1) + + return new lunr.FieldRef (docRef, fieldRef, s) +} + +lunr.FieldRef.prototype.toString = function () { + if (this._stringValue == undefined) { + this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef + } + + return this._stringValue +} +/*! + * lunr.Set + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A lunr set. + * + * @constructor + */ +lunr.Set = function (elements) { + this.elements = Object.create(null) + + if (elements) { + this.length = elements.length + + for (var i = 0; i < this.length; i++) { + this.elements[elements[i]] = true + } + } else { + this.length = 0 + } +} + +/** + * A complete set that contains all elements. + * + * @static + * @readonly + * @type {lunr.Set} + */ +lunr.Set.complete = { + intersect: function (other) { + return other + }, + + union: function () { + return this + }, + + contains: function () { + return true + } +} + +/** + * An empty set that contains no elements. + * + * @static + * @readonly + * @type {lunr.Set} + */ +lunr.Set.empty = { + intersect: function () { + return this + }, + + union: function (other) { + return other + }, + + contains: function () { + return false + } +} + +/** + * Returns true if this set contains the specified object. + * + * @param {object} object - Object whose presence in this set is to be tested. + * @returns {boolean} - True if this set contains the specified object. + */ +lunr.Set.prototype.contains = function (object) { + return !!this.elements[object] +} + +/** + * Returns a new set containing only the elements that are present in both + * this set and the specified set. + * + * @param {lunr.Set} other - set to intersect with this set. + * @returns {lunr.Set} a new set that is the intersection of this and the specified set. + */ + +lunr.Set.prototype.intersect = function (other) { + var a, b, elements, intersection = [] + + if (other === lunr.Set.complete) { + return this + } + + if (other === lunr.Set.empty) { + return other + } + + if (this.length < other.length) { + a = this + b = other + } else { + a = other + b = this + } + + elements = Object.keys(a.elements) + + for (var i = 0; i < elements.length; i++) { + var element = elements[i] + if (element in b.elements) { + intersection.push(element) + } + } + + return new lunr.Set (intersection) +} + +/** + * Returns a new set combining the elements of this and the specified set. + * + * @param {lunr.Set} other - set to union with this set. + * @return {lunr.Set} a new set that is the union of this and the specified set. + */ + +lunr.Set.prototype.union = function (other) { + if (other === lunr.Set.complete) { + return lunr.Set.complete + } + + if (other === lunr.Set.empty) { + return this + } + + return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements))) +} +/** + * A function to calculate the inverse document frequency for + * a posting. This is shared between the builder and the index + * + * @private + * @param {object} posting - The posting for a given term + * @param {number} documentCount - The total number of documents. + */ +lunr.idf = function (posting, documentCount) { + var documentsWithTerm = 0 + + for (var fieldName in posting) { + if (fieldName == '_index') continue // Ignore the term index, its not a field + documentsWithTerm += Object.keys(posting[fieldName]).length + } + + var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5) + + return Math.log(1 + Math.abs(x)) +} + +/** + * A token wraps a string representation of a token + * as it is passed through the text processing pipeline. + * + * @constructor + * @param {string} [str=''] - The string token being wrapped. + * @param {object} [metadata={}] - Metadata associated with this token. + */ +lunr.Token = function (str, metadata) { + this.str = str || "" + this.metadata = metadata || {} +} + +/** + * Returns the token string that is being wrapped by this object. + * + * @returns {string} + */ +lunr.Token.prototype.toString = function () { + return this.str +} + +/** + * A token update function is used when updating or optionally + * when cloning a token. + * + * @callback lunr.Token~updateFunction + * @param {string} str - The string representation of the token. + * @param {Object} metadata - All metadata associated with this token. + */ + +/** + * Applies the given function to the wrapped string token. + * + * @example + * token.update(function (str, metadata) { + * return str.toUpperCase() + * }) + * + * @param {lunr.Token~updateFunction} fn - A function to apply to the token string. + * @returns {lunr.Token} + */ +lunr.Token.prototype.update = function (fn) { + this.str = fn(this.str, this.metadata) + return this +} + +/** + * Creates a clone of this token. Optionally a function can be + * applied to the cloned token. + * + * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token. + * @returns {lunr.Token} + */ +lunr.Token.prototype.clone = function (fn) { + fn = fn || function (s) { return s } + return new lunr.Token (fn(this.str, this.metadata), this.metadata) +} +/*! + * lunr.tokenizer + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A function for splitting a string into tokens ready to be inserted into + * the search index. Uses `lunr.tokenizer.separator` to split strings, change + * the value of this property to change how strings are split into tokens. + * + * This tokenizer will convert its parameter to a string by calling `toString` and + * then will split this string on the character in `lunr.tokenizer.separator`. + * Arrays will have their elements converted to strings and wrapped in a lunr.Token. + * + * Optional metadata can be passed to the tokenizer, this metadata will be cloned and + * added as metadata to every token that is created from the object to be tokenized. + * + * @static + * @param {?(string|object|object[])} obj - The object to convert into tokens + * @param {?object} metadata - Optional metadata to associate with every token + * @returns {lunr.Token[]} + * @see {@link lunr.Pipeline} + */ +lunr.tokenizer = function (obj, metadata) { + if (obj == null || obj == undefined) { + return [] + } + + if (Array.isArray(obj)) { + return obj.map(function (t) { + return new lunr.Token( + lunr.utils.asString(t).toLowerCase(), + lunr.utils.clone(metadata) + ) + }) + } + + var str = obj.toString().toLowerCase(), + len = str.length, + tokens = [] + + for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) { + var char = str.charAt(sliceEnd), + sliceLength = sliceEnd - sliceStart + + if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) { + + if (sliceLength > 0) { + var tokenMetadata = lunr.utils.clone(metadata) || {} + tokenMetadata["position"] = [sliceStart, sliceLength] + tokenMetadata["index"] = tokens.length + + tokens.push( + new lunr.Token ( + str.slice(sliceStart, sliceEnd), + tokenMetadata + ) + ) + } + + sliceStart = sliceEnd + 1 + } + + } + + return tokens +} + +/** + * The separator used to split a string into tokens. Override this property to change the behaviour of + * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens. + * + * @static + * @see lunr.tokenizer + */ +lunr.tokenizer.separator = /[\s\-]+/ +/*! + * lunr.Pipeline + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.Pipelines maintain an ordered list of functions to be applied to all + * tokens in documents entering the search index and queries being ran against + * the index. + * + * An instance of lunr.Index created with the lunr shortcut will contain a + * pipeline with a stop word filter and an English language stemmer. Extra + * functions can be added before or after either of these functions or these + * default functions can be removed. + * + * When run the pipeline will call each function in turn, passing a token, the + * index of that token in the original list of all tokens and finally a list of + * all the original tokens. + * + * The output of functions in the pipeline will be passed to the next function + * in the pipeline. To exclude a token from entering the index the function + * should return undefined, the rest of the pipeline will not be called with + * this token. + * + * For serialisation of pipelines to work, all functions used in an instance of + * a pipeline should be registered with lunr.Pipeline. Registered functions can + * then be loaded. If trying to load a serialised pipeline that uses functions + * that are not registered an error will be thrown. + * + * If not planning on serialising the pipeline then registering pipeline functions + * is not necessary. + * + * @constructor + */ +lunr.Pipeline = function () { + this._stack = [] +} + +lunr.Pipeline.registeredFunctions = Object.create(null) + +/** + * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token + * string as well as all known metadata. A pipeline function can mutate the token string + * or mutate (or add) metadata for a given token. + * + * A pipeline function can indicate that the passed token should be discarded by returning + * null, undefined or an empty string. This token will not be passed to any downstream pipeline + * functions and will not be added to the index. + * + * Multiple tokens can be returned by returning an array of tokens. Each token will be passed + * to any downstream pipeline functions and all will returned tokens will be added to the index. + * + * Any number of pipeline functions may be chained together using a lunr.Pipeline. + * + * @interface lunr.PipelineFunction + * @param {lunr.Token} token - A token from the document being processed. + * @param {number} i - The index of this token in the complete list of tokens for this document/field. + * @param {lunr.Token[]} tokens - All tokens for this document/field. + * @returns {(?lunr.Token|lunr.Token[])} + */ + +/** + * Register a function with the pipeline. + * + * Functions that are used in the pipeline should be registered if the pipeline + * needs to be serialised, or a serialised pipeline needs to be loaded. + * + * Registering a function does not add it to a pipeline, functions must still be + * added to instances of the pipeline for them to be used when running a pipeline. + * + * @param {lunr.PipelineFunction} fn - The function to check for. + * @param {String} label - The label to register this function with + */ +lunr.Pipeline.registerFunction = function (fn, label) { + if (label in this.registeredFunctions) { + lunr.utils.warn('Overwriting existing registered function: ' + label) + } + + fn.label = label + lunr.Pipeline.registeredFunctions[fn.label] = fn +} + +/** + * Warns if the function is not registered as a Pipeline function. + * + * @param {lunr.PipelineFunction} fn - The function to check for. + * @private + */ +lunr.Pipeline.warnIfFunctionNotRegistered = function (fn) { + var isRegistered = fn.label && (fn.label in this.registeredFunctions) + + if (!isRegistered) { + lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\n', fn) + } +} + +/** + * Loads a previously serialised pipeline. + * + * All functions to be loaded must already be registered with lunr.Pipeline. + * If any function from the serialised data has not been registered then an + * error will be thrown. + * + * @param {Object} serialised - The serialised pipeline to load. + * @returns {lunr.Pipeline} + */ +lunr.Pipeline.load = function (serialised) { + var pipeline = new lunr.Pipeline + + serialised.forEach(function (fnName) { + var fn = lunr.Pipeline.registeredFunctions[fnName] + + if (fn) { + pipeline.add(fn) + } else { + throw new Error('Cannot load unregistered function: ' + fnName) + } + }) + + return pipeline +} + +/** + * Adds new functions to the end of the pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline. + */ +lunr.Pipeline.prototype.add = function () { + var fns = Array.prototype.slice.call(arguments) + + fns.forEach(function (fn) { + lunr.Pipeline.warnIfFunctionNotRegistered(fn) + this._stack.push(fn) + }, this) +} + +/** + * Adds a single function after a function that already exists in the + * pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline. + * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline. + */ +lunr.Pipeline.prototype.after = function (existingFn, newFn) { + lunr.Pipeline.warnIfFunctionNotRegistered(newFn) + + var pos = this._stack.indexOf(existingFn) + if (pos == -1) { + throw new Error('Cannot find existingFn') + } + + pos = pos + 1 + this._stack.splice(pos, 0, newFn) +} + +/** + * Adds a single function before a function that already exists in the + * pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline. + * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline. + */ +lunr.Pipeline.prototype.before = function (existingFn, newFn) { + lunr.Pipeline.warnIfFunctionNotRegistered(newFn) + + var pos = this._stack.indexOf(existingFn) + if (pos == -1) { + throw new Error('Cannot find existingFn') + } + + this._stack.splice(pos, 0, newFn) +} + +/** + * Removes a function from the pipeline. + * + * @param {lunr.PipelineFunction} fn The function to remove from the pipeline. + */ +lunr.Pipeline.prototype.remove = function (fn) { + var pos = this._stack.indexOf(fn) + if (pos == -1) { + return + } + + this._stack.splice(pos, 1) +} + +/** + * Runs the current list of functions that make up the pipeline against the + * passed tokens. + * + * @param {Array} tokens The tokens to run through the pipeline. + * @returns {Array} + */ +lunr.Pipeline.prototype.run = function (tokens) { + var stackLength = this._stack.length + + for (var i = 0; i < stackLength; i++) { + var fn = this._stack[i] + var memo = [] + + for (var j = 0; j < tokens.length; j++) { + var result = fn(tokens[j], j, tokens) + + if (result === null || result === void 0 || result === '') continue + + if (Array.isArray(result)) { + for (var k = 0; k < result.length; k++) { + memo.push(result[k]) + } + } else { + memo.push(result) + } + } + + tokens = memo + } + + return tokens +} + +/** + * Convenience method for passing a string through a pipeline and getting + * strings out. This method takes care of wrapping the passed string in a + * token and mapping the resulting tokens back to strings. + * + * @param {string} str - The string to pass through the pipeline. + * @param {?object} metadata - Optional metadata to associate with the token + * passed to the pipeline. + * @returns {string[]} + */ +lunr.Pipeline.prototype.runString = function (str, metadata) { + var token = new lunr.Token (str, metadata) + + return this.run([token]).map(function (t) { + return t.toString() + }) +} + +/** + * Resets the pipeline by removing any existing processors. + * + */ +lunr.Pipeline.prototype.reset = function () { + this._stack = [] +} + +/** + * Returns a representation of the pipeline ready for serialisation. + * + * Logs a warning if the function has not been registered. + * + * @returns {Array} + */ +lunr.Pipeline.prototype.toJSON = function () { + return this._stack.map(function (fn) { + lunr.Pipeline.warnIfFunctionNotRegistered(fn) + + return fn.label + }) +} +/*! + * lunr.Vector + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A vector is used to construct the vector space of documents and queries. These + * vectors support operations to determine the similarity between two documents or + * a document and a query. + * + * Normally no parameters are required for initializing a vector, but in the case of + * loading a previously dumped vector the raw elements can be provided to the constructor. + * + * For performance reasons vectors are implemented with a flat array, where an elements + * index is immediately followed by its value. E.g. [index, value, index, value]. This + * allows the underlying array to be as sparse as possible and still offer decent + * performance when being used for vector calculations. + * + * @constructor + * @param {Number[]} [elements] - The flat list of element index and element value pairs. + */ +lunr.Vector = function (elements) { + this._magnitude = 0 + this.elements = elements || [] +} + + +/** + * Calculates the position within the vector to insert a given index. + * + * This is used internally by insert and upsert. If there are duplicate indexes then + * the position is returned as if the value for that index were to be updated, but it + * is the callers responsibility to check whether there is a duplicate at that index + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @returns {Number} + */ +lunr.Vector.prototype.positionForIndex = function (index) { + // For an empty vector the tuple can be inserted at the beginning + if (this.elements.length == 0) { + return 0 + } + + var start = 0, + end = this.elements.length / 2, + sliceLength = end - start, + pivotPoint = Math.floor(sliceLength / 2), + pivotIndex = this.elements[pivotPoint * 2] + + while (sliceLength > 1) { + if (pivotIndex < index) { + start = pivotPoint + } + + if (pivotIndex > index) { + end = pivotPoint + } + + if (pivotIndex == index) { + break + } + + sliceLength = end - start + pivotPoint = start + Math.floor(sliceLength / 2) + pivotIndex = this.elements[pivotPoint * 2] + } + + if (pivotIndex == index) { + return pivotPoint * 2 + } + + if (pivotIndex > index) { + return pivotPoint * 2 + } + + if (pivotIndex < index) { + return (pivotPoint + 1) * 2 + } +} + +/** + * Inserts an element at an index within the vector. + * + * Does not allow duplicates, will throw an error if there is already an entry + * for this index. + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @param {Number} val - The value to be inserted into the vector. + */ +lunr.Vector.prototype.insert = function (insertIdx, val) { + this.upsert(insertIdx, val, function () { + throw "duplicate index" + }) +} + +/** + * Inserts or updates an existing index within the vector. + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @param {Number} val - The value to be inserted into the vector. + * @param {function} fn - A function that is called for updates, the existing value and the + * requested value are passed as arguments + */ +lunr.Vector.prototype.upsert = function (insertIdx, val, fn) { + this._magnitude = 0 + var position = this.positionForIndex(insertIdx) + + if (this.elements[position] == insertIdx) { + this.elements[position + 1] = fn(this.elements[position + 1], val) + } else { + this.elements.splice(position, 0, insertIdx, val) + } +} + +/** + * Calculates the magnitude of this vector. + * + * @returns {Number} + */ +lunr.Vector.prototype.magnitude = function () { + if (this._magnitude) return this._magnitude + + var sumOfSquares = 0, + elementsLength = this.elements.length + + for (var i = 1; i < elementsLength; i += 2) { + var val = this.elements[i] + sumOfSquares += val * val + } + + return this._magnitude = Math.sqrt(sumOfSquares) +} + +/** + * Calculates the dot product of this vector and another vector. + * + * @param {lunr.Vector} otherVector - The vector to compute the dot product with. + * @returns {Number} + */ +lunr.Vector.prototype.dot = function (otherVector) { + var dotProduct = 0, + a = this.elements, b = otherVector.elements, + aLen = a.length, bLen = b.length, + aVal = 0, bVal = 0, + i = 0, j = 0 + + while (i < aLen && j < bLen) { + aVal = a[i], bVal = b[j] + if (aVal < bVal) { + i += 2 + } else if (aVal > bVal) { + j += 2 + } else if (aVal == bVal) { + dotProduct += a[i + 1] * b[j + 1] + i += 2 + j += 2 + } + } + + return dotProduct +} + +/** + * Calculates the similarity between this vector and another vector. + * + * @param {lunr.Vector} otherVector - The other vector to calculate the + * similarity with. + * @returns {Number} + */ +lunr.Vector.prototype.similarity = function (otherVector) { + return this.dot(otherVector) / this.magnitude() || 0 +} + +/** + * Converts the vector to an array of the elements within the vector. + * + * @returns {Number[]} + */ +lunr.Vector.prototype.toArray = function () { + var output = new Array (this.elements.length / 2) + + for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) { + output[j] = this.elements[i] + } + + return output +} + +/** + * A JSON serializable representation of the vector. + * + * @returns {Number[]} + */ +lunr.Vector.prototype.toJSON = function () { + return this.elements +} +/* eslint-disable */ +/*! + * lunr.stemmer + * Copyright (C) 2020 Oliver Nightingale + * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt + */ + +/** + * lunr.stemmer is an english language stemmer, this is a JavaScript + * implementation of the PorterStemmer taken from http://tartarus.org/~martin + * + * @static + * @implements {lunr.PipelineFunction} + * @param {lunr.Token} token - The string to stem + * @returns {lunr.Token} + * @see {@link lunr.Pipeline} + * @function + */ +lunr.stemmer = (function(){ + var step2list = { + "ational" : "ate", + "tional" : "tion", + "enci" : "ence", + "anci" : "ance", + "izer" : "ize", + "bli" : "ble", + "alli" : "al", + "entli" : "ent", + "eli" : "e", + "ousli" : "ous", + "ization" : "ize", + "ation" : "ate", + "ator" : "ate", + "alism" : "al", + "iveness" : "ive", + "fulness" : "ful", + "ousness" : "ous", + "aliti" : "al", + "iviti" : "ive", + "biliti" : "ble", + "logi" : "log" + }, + + step3list = { + "icate" : "ic", + "ative" : "", + "alize" : "al", + "iciti" : "ic", + "ical" : "ic", + "ful" : "", + "ness" : "" + }, + + c = "[^aeiou]", // consonant + v = "[aeiouy]", // vowel + C = c + "[^aeiouy]*", // consonant sequence + V = v + "[aeiou]*", // vowel sequence + + mgr0 = "^(" + C + ")?" + V + C, // [C]VC... is m>0 + meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$", // [C]VC[V] is m=1 + mgr1 = "^(" + C + ")?" + V + C + V + C, // [C]VCVC... is m>1 + s_v = "^(" + C + ")?" + v; // vowel in stem + + var re_mgr0 = new RegExp(mgr0); + var re_mgr1 = new RegExp(mgr1); + var re_meq1 = new RegExp(meq1); + var re_s_v = new RegExp(s_v); + + var re_1a = /^(.+?)(ss|i)es$/; + var re2_1a = /^(.+?)([^s])s$/; + var re_1b = /^(.+?)eed$/; + var re2_1b = /^(.+?)(ed|ing)$/; + var re_1b_2 = /.$/; + var re2_1b_2 = /(at|bl|iz)$/; + var re3_1b_2 = new RegExp("([^aeiouylsz])\\1$"); + var re4_1b_2 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + + var re_1c = /^(.+?[^aeiou])y$/; + var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + + var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + + var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + var re2_4 = /^(.+?)(s|t)(ion)$/; + + var re_5 = /^(.+?)e$/; + var re_5_1 = /ll$/; + var re3_5 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + + var porterStemmer = function porterStemmer(w) { + var stem, + suffix, + firstch, + re, + re2, + re3, + re4; + + if (w.length < 3) { return w; } + + firstch = w.substr(0,1); + if (firstch == "y") { + w = firstch.toUpperCase() + w.substr(1); + } + + // Step 1a + re = re_1a + re2 = re2_1a; + + if (re.test(w)) { w = w.replace(re,"$1$2"); } + else if (re2.test(w)) { w = w.replace(re2,"$1$2"); } + + // Step 1b + re = re_1b; + re2 = re2_1b; + if (re.test(w)) { + var fp = re.exec(w); + re = re_mgr0; + if (re.test(fp[1])) { + re = re_1b_2; + w = w.replace(re,""); + } + } else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = re_s_v; + if (re2.test(stem)) { + w = stem; + re2 = re2_1b_2; + re3 = re3_1b_2; + re4 = re4_1b_2; + if (re2.test(w)) { w = w + "e"; } + else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,""); } + else if (re4.test(w)) { w = w + "e"; } + } + } + + // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say) + re = re_1c; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + w = stem + "i"; + } + + // Step 2 + re = re_2; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = re_mgr0; + if (re.test(stem)) { + w = stem + step2list[suffix]; + } + } + + // Step 3 + re = re_3; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = re_mgr0; + if (re.test(stem)) { + w = stem + step3list[suffix]; + } + } + + // Step 4 + re = re_4; + re2 = re2_4; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = re_mgr1; + if (re.test(stem)) { + w = stem; + } + } else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = re_mgr1; + if (re2.test(stem)) { + w = stem; + } + } + + // Step 5 + re = re_5; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = re_mgr1; + re2 = re_meq1; + re3 = re3_5; + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) { + w = stem; + } + } + + re = re_5_1; + re2 = re_mgr1; + if (re.test(w) && re2.test(w)) { + re = re_1b_2; + w = w.replace(re,""); + } + + // and turn initial Y back to y + + if (firstch == "y") { + w = firstch.toLowerCase() + w.substr(1); + } + + return w; + }; + + return function (token) { + return token.update(porterStemmer); + } +})(); + +lunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer') +/*! + * lunr.stopWordFilter + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.generateStopWordFilter builds a stopWordFilter function from the provided + * list of stop words. + * + * The built in lunr.stopWordFilter is built using this generator and can be used + * to generate custom stopWordFilters for applications or non English languages. + * + * @function + * @param {Array} token The token to pass through the filter + * @returns {lunr.PipelineFunction} + * @see lunr.Pipeline + * @see lunr.stopWordFilter + */ +lunr.generateStopWordFilter = function (stopWords) { + var words = stopWords.reduce(function (memo, stopWord) { + memo[stopWord] = stopWord + return memo + }, {}) + + return function (token) { + if (token && words[token.toString()] !== token.toString()) return token + } +} + +/** + * lunr.stopWordFilter is an English language stop word list filter, any words + * contained in the list will not be passed through the filter. + * + * This is intended to be used in the Pipeline. If the token does not pass the + * filter then undefined will be returned. + * + * @function + * @implements {lunr.PipelineFunction} + * @params {lunr.Token} token - A token to check for being a stop word. + * @returns {lunr.Token} + * @see {@link lunr.Pipeline} + */ +lunr.stopWordFilter = lunr.generateStopWordFilter([ + 'a', + 'able', + 'about', + 'across', + 'after', + 'all', + 'almost', + 'also', + 'am', + 'among', + 'an', + 'and', + 'any', + 'are', + 'as', + 'at', + 'be', + 'because', + 'been', + 'but', + 'by', + 'can', + 'cannot', + 'could', + 'dear', + 'did', + 'do', + 'does', + 'either', + 'else', + 'ever', + 'every', + 'for', + 'from', + 'get', + 'got', + 'had', + 'has', + 'have', + 'he', + 'her', + 'hers', + 'him', + 'his', + 'how', + 'however', + 'i', + 'if', + 'in', + 'into', + 'is', + 'it', + 'its', + 'just', + 'least', + 'let', + 'like', + 'likely', + 'may', + 'me', + 'might', + 'most', + 'must', + 'my', + 'neither', + 'no', + 'nor', + 'not', + 'of', + 'off', + 'often', + 'on', + 'only', + 'or', + 'other', + 'our', + 'own', + 'rather', + 'said', + 'say', + 'says', + 'she', + 'should', + 'since', + 'so', + 'some', + 'than', + 'that', + 'the', + 'their', + 'them', + 'then', + 'there', + 'these', + 'they', + 'this', + 'tis', + 'to', + 'too', + 'twas', + 'us', + 'wants', + 'was', + 'we', + 'were', + 'what', + 'when', + 'where', + 'which', + 'while', + 'who', + 'whom', + 'why', + 'will', + 'with', + 'would', + 'yet', + 'you', + 'your' +]) + +lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter') +/*! + * lunr.trimmer + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.trimmer is a pipeline function for trimming non word + * characters from the beginning and end of tokens before they + * enter the index. + * + * This implementation may not work correctly for non latin + * characters and should either be removed or adapted for use + * with languages with non-latin characters. + * + * @static + * @implements {lunr.PipelineFunction} + * @param {lunr.Token} token The token to pass through the filter + * @returns {lunr.Token} + * @see lunr.Pipeline + */ +lunr.trimmer = function (token) { + return token.update(function (s) { + return s.replace(/^\W+/, '').replace(/\W+$/, '') + }) +} + +lunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer') +/*! + * lunr.TokenSet + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A token set is used to store the unique list of all tokens + * within an index. Token sets are also used to represent an + * incoming query to the index, this query token set and index + * token set are then intersected to find which tokens to look + * up in the inverted index. + * + * A token set can hold multiple tokens, as in the case of the + * index token set, or it can hold a single token as in the + * case of a simple query token set. + * + * Additionally token sets are used to perform wildcard matching. + * Leading, contained and trailing wildcards are supported, and + * from this edit distance matching can also be provided. + * + * Token sets are implemented as a minimal finite state automata, + * where both common prefixes and suffixes are shared between tokens. + * This helps to reduce the space used for storing the token set. + * + * @constructor + */ +lunr.TokenSet = function () { + this.final = false + this.edges = {} + this.id = lunr.TokenSet._nextId + lunr.TokenSet._nextId += 1 +} + +/** + * Keeps track of the next, auto increment, identifier to assign + * to a new tokenSet. + * + * TokenSets require a unique identifier to be correctly minimised. + * + * @private + */ +lunr.TokenSet._nextId = 1 + +/** + * Creates a TokenSet instance from the given sorted array of words. + * + * @param {String[]} arr - A sorted array of strings to create the set from. + * @returns {lunr.TokenSet} + * @throws Will throw an error if the input array is not sorted. + */ +lunr.TokenSet.fromArray = function (arr) { + var builder = new lunr.TokenSet.Builder + + for (var i = 0, len = arr.length; i < len; i++) { + builder.insert(arr[i]) + } + + builder.finish() + return builder.root +} + +/** + * Creates a token set from a query clause. + * + * @private + * @param {Object} clause - A single clause from lunr.Query. + * @param {string} clause.term - The query clause term. + * @param {number} [clause.editDistance] - The optional edit distance for the term. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.fromClause = function (clause) { + if ('editDistance' in clause) { + return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance) + } else { + return lunr.TokenSet.fromString(clause.term) + } +} + +/** + * Creates a token set representing a single string with a specified + * edit distance. + * + * Insertions, deletions, substitutions and transpositions are each + * treated as an edit distance of 1. + * + * Increasing the allowed edit distance will have a dramatic impact + * on the performance of both creating and intersecting these TokenSets. + * It is advised to keep the edit distance less than 3. + * + * @param {string} str - The string to create the token set from. + * @param {number} editDistance - The allowed edit distance to match. + * @returns {lunr.Vector} + */ +lunr.TokenSet.fromFuzzyString = function (str, editDistance) { + var root = new lunr.TokenSet + + var stack = [{ + node: root, + editsRemaining: editDistance, + str: str + }] + + while (stack.length) { + var frame = stack.pop() + + // no edit + if (frame.str.length > 0) { + var char = frame.str.charAt(0), + noEditNode + + if (char in frame.node.edges) { + noEditNode = frame.node.edges[char] + } else { + noEditNode = new lunr.TokenSet + frame.node.edges[char] = noEditNode + } + + if (frame.str.length == 1) { + noEditNode.final = true + } + + stack.push({ + node: noEditNode, + editsRemaining: frame.editsRemaining, + str: frame.str.slice(1) + }) + } + + if (frame.editsRemaining == 0) { + continue + } + + // insertion + if ("*" in frame.node.edges) { + var insertionNode = frame.node.edges["*"] + } else { + var insertionNode = new lunr.TokenSet + frame.node.edges["*"] = insertionNode + } + + if (frame.str.length == 0) { + insertionNode.final = true + } + + stack.push({ + node: insertionNode, + editsRemaining: frame.editsRemaining - 1, + str: frame.str + }) + + // deletion + // can only do a deletion if we have enough edits remaining + // and if there are characters left to delete in the string + if (frame.str.length > 1) { + stack.push({ + node: frame.node, + editsRemaining: frame.editsRemaining - 1, + str: frame.str.slice(1) + }) + } + + // deletion + // just removing the last character from the str + if (frame.str.length == 1) { + frame.node.final = true + } + + // substitution + // can only do a substitution if we have enough edits remaining + // and if there are characters left to substitute + if (frame.str.length >= 1) { + if ("*" in frame.node.edges) { + var substitutionNode = frame.node.edges["*"] + } else { + var substitutionNode = new lunr.TokenSet + frame.node.edges["*"] = substitutionNode + } + + if (frame.str.length == 1) { + substitutionNode.final = true + } + + stack.push({ + node: substitutionNode, + editsRemaining: frame.editsRemaining - 1, + str: frame.str.slice(1) + }) + } + + // transposition + // can only do a transposition if there are edits remaining + // and there are enough characters to transpose + if (frame.str.length > 1) { + var charA = frame.str.charAt(0), + charB = frame.str.charAt(1), + transposeNode + + if (charB in frame.node.edges) { + transposeNode = frame.node.edges[charB] + } else { + transposeNode = new lunr.TokenSet + frame.node.edges[charB] = transposeNode + } + + if (frame.str.length == 1) { + transposeNode.final = true + } + + stack.push({ + node: transposeNode, + editsRemaining: frame.editsRemaining - 1, + str: charA + frame.str.slice(2) + }) + } + } + + return root +} + +/** + * Creates a TokenSet from a string. + * + * The string may contain one or more wildcard characters (*) + * that will allow wildcard matching when intersecting with + * another TokenSet. + * + * @param {string} str - The string to create a TokenSet from. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.fromString = function (str) { + var node = new lunr.TokenSet, + root = node + + /* + * Iterates through all characters within the passed string + * appending a node for each character. + * + * When a wildcard character is found then a self + * referencing edge is introduced to continually match + * any number of any characters. + */ + for (var i = 0, len = str.length; i < len; i++) { + var char = str[i], + final = (i == len - 1) + + if (char == "*") { + node.edges[char] = node + node.final = final + + } else { + var next = new lunr.TokenSet + next.final = final + + node.edges[char] = next + node = next + } + } + + return root +} + +/** + * Converts this TokenSet into an array of strings + * contained within the TokenSet. + * + * This is not intended to be used on a TokenSet that + * contains wildcards, in these cases the results are + * undefined and are likely to cause an infinite loop. + * + * @returns {string[]} + */ +lunr.TokenSet.prototype.toArray = function () { + var words = [] + + var stack = [{ + prefix: "", + node: this + }] + + while (stack.length) { + var frame = stack.pop(), + edges = Object.keys(frame.node.edges), + len = edges.length + + if (frame.node.final) { + /* In Safari, at this point the prefix is sometimes corrupted, see: + * https://github.com/olivernn/lunr.js/issues/279 Calling any + * String.prototype method forces Safari to "cast" this string to what + * it's supposed to be, fixing the bug. */ + frame.prefix.charAt(0) + words.push(frame.prefix) + } + + for (var i = 0; i < len; i++) { + var edge = edges[i] + + stack.push({ + prefix: frame.prefix.concat(edge), + node: frame.node.edges[edge] + }) + } + } + + return words +} + +/** + * Generates a string representation of a TokenSet. + * + * This is intended to allow TokenSets to be used as keys + * in objects, largely to aid the construction and minimisation + * of a TokenSet. As such it is not designed to be a human + * friendly representation of the TokenSet. + * + * @returns {string} + */ +lunr.TokenSet.prototype.toString = function () { + // NOTE: Using Object.keys here as this.edges is very likely + // to enter 'hash-mode' with many keys being added + // + // avoiding a for-in loop here as it leads to the function + // being de-optimised (at least in V8). From some simple + // benchmarks the performance is comparable, but allowing + // V8 to optimize may mean easy performance wins in the future. + + if (this._str) { + return this._str + } + + var str = this.final ? '1' : '0', + labels = Object.keys(this.edges).sort(), + len = labels.length + + for (var i = 0; i < len; i++) { + var label = labels[i], + node = this.edges[label] + + str = str + label + node.id + } + + return str +} + +/** + * Returns a new TokenSet that is the intersection of + * this TokenSet and the passed TokenSet. + * + * This intersection will take into account any wildcards + * contained within the TokenSet. + * + * @param {lunr.TokenSet} b - An other TokenSet to intersect with. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.prototype.intersect = function (b) { + var output = new lunr.TokenSet, + frame = undefined + + var stack = [{ + qNode: b, + output: output, + node: this + }] + + while (stack.length) { + frame = stack.pop() + + // NOTE: As with the #toString method, we are using + // Object.keys and a for loop instead of a for-in loop + // as both of these objects enter 'hash' mode, causing + // the function to be de-optimised in V8 + var qEdges = Object.keys(frame.qNode.edges), + qLen = qEdges.length, + nEdges = Object.keys(frame.node.edges), + nLen = nEdges.length + + for (var q = 0; q < qLen; q++) { + var qEdge = qEdges[q] + + for (var n = 0; n < nLen; n++) { + var nEdge = nEdges[n] + + if (nEdge == qEdge || qEdge == '*') { + var node = frame.node.edges[nEdge], + qNode = frame.qNode.edges[qEdge], + final = node.final && qNode.final, + next = undefined + + if (nEdge in frame.output.edges) { + // an edge already exists for this character + // no need to create a new node, just set the finality + // bit unless this node is already final + next = frame.output.edges[nEdge] + next.final = next.final || final + + } else { + // no edge exists yet, must create one + // set the finality bit and insert it + // into the output + next = new lunr.TokenSet + next.final = final + frame.output.edges[nEdge] = next + } + + stack.push({ + qNode: qNode, + output: next, + node: node + }) + } + } + } + } + + return output +} +lunr.TokenSet.Builder = function () { + this.previousWord = "" + this.root = new lunr.TokenSet + this.uncheckedNodes = [] + this.minimizedNodes = {} +} + +lunr.TokenSet.Builder.prototype.insert = function (word) { + var node, + commonPrefix = 0 + + if (word < this.previousWord) { + throw new Error ("Out of order word insertion") + } + + for (var i = 0; i < word.length && i < this.previousWord.length; i++) { + if (word[i] != this.previousWord[i]) break + commonPrefix++ + } + + this.minimize(commonPrefix) + + if (this.uncheckedNodes.length == 0) { + node = this.root + } else { + node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child + } + + for (var i = commonPrefix; i < word.length; i++) { + var nextNode = new lunr.TokenSet, + char = word[i] + + node.edges[char] = nextNode + + this.uncheckedNodes.push({ + parent: node, + char: char, + child: nextNode + }) + + node = nextNode + } + + node.final = true + this.previousWord = word +} + +lunr.TokenSet.Builder.prototype.finish = function () { + this.minimize(0) +} + +lunr.TokenSet.Builder.prototype.minimize = function (downTo) { + for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) { + var node = this.uncheckedNodes[i], + childKey = node.child.toString() + + if (childKey in this.minimizedNodes) { + node.parent.edges[node.char] = this.minimizedNodes[childKey] + } else { + // Cache the key for this node since + // we know it can't change anymore + node.child._str = childKey + + this.minimizedNodes[childKey] = node.child + } + + this.uncheckedNodes.pop() + } +} +/*! + * lunr.Index + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * An index contains the built index of all documents and provides a query interface + * to the index. + * + * Usually instances of lunr.Index will not be created using this constructor, instead + * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be + * used to load previously built and serialized indexes. + * + * @constructor + * @param {Object} attrs - The attributes of the built search index. + * @param {Object} attrs.invertedIndex - An index of term/field to document reference. + * @param {Object} attrs.fieldVectors - Field vectors + * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens. + * @param {string[]} attrs.fields - The names of indexed document fields. + * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms. + */ +lunr.Index = function (attrs) { + this.invertedIndex = attrs.invertedIndex + this.fieldVectors = attrs.fieldVectors + this.tokenSet = attrs.tokenSet + this.fields = attrs.fields + this.pipeline = attrs.pipeline +} + +/** + * A result contains details of a document matching a search query. + * @typedef {Object} lunr.Index~Result + * @property {string} ref - The reference of the document this result represents. + * @property {number} score - A number between 0 and 1 representing how similar this document is to the query. + * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match. + */ + +/** + * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple + * query language which itself is parsed into an instance of lunr.Query. + * + * For programmatically building queries it is advised to directly use lunr.Query, the query language + * is best used for human entered text rather than program generated text. + * + * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported + * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello' + * or 'world', though those that contain both will rank higher in the results. + * + * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can + * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding + * wildcards will increase the number of documents that will be found but can also have a negative + * impact on query performance, especially with wildcards at the beginning of a term. + * + * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term + * hello in the title field will match this query. Using a field not present in the index will lead + * to an error being thrown. + * + * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term + * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported + * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2. + * Avoid large values for edit distance to improve query performance. + * + * Each term also supports a presence modifier. By default a term's presence in document is optional, however + * this can be changed to either required or prohibited. For a term's presence to be required in a document the + * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and + * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not + * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'. + * + * To escape special characters the backslash character '\' can be used, this allows searches to include + * characters that would normally be considered modifiers, e.g. `foo\~2` will search for a term "foo~2" instead + * of attempting to apply a boost of 2 to the search term "foo". + * + * @typedef {string} lunr.Index~QueryString + * @example Simple single term query + * hello + * @example Multiple term query + * hello world + * @example term scoped to a field + * title:hello + * @example term with a boost of 10 + * hello^10 + * @example term with an edit distance of 2 + * hello~2 + * @example terms with presence modifiers + * -foo +bar baz + */ + +/** + * Performs a search against the index using lunr query syntax. + * + * Results will be returned sorted by their score, the most relevant results + * will be returned first. For details on how the score is calculated, please see + * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}. + * + * For more programmatic querying use lunr.Index#query. + * + * @param {lunr.Index~QueryString} queryString - A string containing a lunr query. + * @throws {lunr.QueryParseError} If the passed query string cannot be parsed. + * @returns {lunr.Index~Result[]} + */ +lunr.Index.prototype.search = function (queryString) { + return this.query(function (query) { + var parser = new lunr.QueryParser(queryString, query) + parser.parse() + }) +} + +/** + * A query builder callback provides a query object to be used to express + * the query to perform on the index. + * + * @callback lunr.Index~queryBuilder + * @param {lunr.Query} query - The query object to build up. + * @this lunr.Query + */ + +/** + * Performs a query against the index using the yielded lunr.Query object. + * + * If performing programmatic queries against the index, this method is preferred + * over lunr.Index#search so as to avoid the additional query parsing overhead. + * + * A query object is yielded to the supplied function which should be used to + * express the query to be run against the index. + * + * Note that although this function takes a callback parameter it is _not_ an + * asynchronous operation, the callback is just yielded a query object to be + * customized. + * + * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query. + * @returns {lunr.Index~Result[]} + */ +lunr.Index.prototype.query = function (fn) { + // for each query clause + // * process terms + // * expand terms from token set + // * find matching documents and metadata + // * get document vectors + // * score documents + + var query = new lunr.Query(this.fields), + matchingFields = Object.create(null), + queryVectors = Object.create(null), + termFieldCache = Object.create(null), + requiredMatches = Object.create(null), + prohibitedMatches = Object.create(null) + + /* + * To support field level boosts a query vector is created per + * field. An empty vector is eagerly created to support negated + * queries. + */ + for (var i = 0; i < this.fields.length; i++) { + queryVectors[this.fields[i]] = new lunr.Vector + } + + fn.call(query, query) + + for (var i = 0; i < query.clauses.length; i++) { + /* + * Unless the pipeline has been disabled for this term, which is + * the case for terms with wildcards, we need to pass the clause + * term through the search pipeline. A pipeline returns an array + * of processed terms. Pipeline functions may expand the passed + * term, which means we may end up performing multiple index lookups + * for a single query term. + */ + var clause = query.clauses[i], + terms = null, + clauseMatches = lunr.Set.empty + + if (clause.usePipeline) { + terms = this.pipeline.runString(clause.term, { + fields: clause.fields + }) + } else { + terms = [clause.term] + } + + for (var m = 0; m < terms.length; m++) { + var term = terms[m] + + /* + * Each term returned from the pipeline needs to use the same query + * clause object, e.g. the same boost and or edit distance. The + * simplest way to do this is to re-use the clause object but mutate + * its term property. + */ + clause.term = term + + /* + * From the term in the clause we create a token set which will then + * be used to intersect the indexes token set to get a list of terms + * to lookup in the inverted index + */ + var termTokenSet = lunr.TokenSet.fromClause(clause), + expandedTerms = this.tokenSet.intersect(termTokenSet).toArray() + + /* + * If a term marked as required does not exist in the tokenSet it is + * impossible for the search to return any matches. We set all the field + * scoped required matches set to empty and stop examining any further + * clauses. + */ + if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) { + for (var k = 0; k < clause.fields.length; k++) { + var field = clause.fields[k] + requiredMatches[field] = lunr.Set.empty + } + + break + } + + for (var j = 0; j < expandedTerms.length; j++) { + /* + * For each term get the posting and termIndex, this is required for + * building the query vector. + */ + var expandedTerm = expandedTerms[j], + posting = this.invertedIndex[expandedTerm], + termIndex = posting._index + + for (var k = 0; k < clause.fields.length; k++) { + /* + * For each field that this query term is scoped by (by default + * all fields are in scope) we need to get all the document refs + * that have this term in that field. + * + * The posting is the entry in the invertedIndex for the matching + * term from above. + */ + var field = clause.fields[k], + fieldPosting = posting[field], + matchingDocumentRefs = Object.keys(fieldPosting), + termField = expandedTerm + "/" + field, + matchingDocumentsSet = new lunr.Set(matchingDocumentRefs) + + /* + * if the presence of this term is required ensure that the matching + * documents are added to the set of required matches for this clause. + * + */ + if (clause.presence == lunr.Query.presence.REQUIRED) { + clauseMatches = clauseMatches.union(matchingDocumentsSet) + + if (requiredMatches[field] === undefined) { + requiredMatches[field] = lunr.Set.complete + } + } + + /* + * if the presence of this term is prohibited ensure that the matching + * documents are added to the set of prohibited matches for this field, + * creating that set if it does not yet exist. + */ + if (clause.presence == lunr.Query.presence.PROHIBITED) { + if (prohibitedMatches[field] === undefined) { + prohibitedMatches[field] = lunr.Set.empty + } + + prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet) + + /* + * Prohibited matches should not be part of the query vector used for + * similarity scoring and no metadata should be extracted so we continue + * to the next field + */ + continue + } + + /* + * The query field vector is populated using the termIndex found for + * the term and a unit value with the appropriate boost applied. + * Using upsert because there could already be an entry in the vector + * for the term we are working with. In that case we just add the scores + * together. + */ + queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b }) + + /** + * If we've already seen this term, field combo then we've already collected + * the matching documents and metadata, no need to go through all that again + */ + if (termFieldCache[termField]) { + continue + } + + for (var l = 0; l < matchingDocumentRefs.length; l++) { + /* + * All metadata for this term/field/document triple + * are then extracted and collected into an instance + * of lunr.MatchData ready to be returned in the query + * results + */ + var matchingDocumentRef = matchingDocumentRefs[l], + matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field), + metadata = fieldPosting[matchingDocumentRef], + fieldMatch + + if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) { + matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata) + } else { + fieldMatch.add(expandedTerm, field, metadata) + } + + } + + termFieldCache[termField] = true + } + } + } + + /** + * If the presence was required we need to update the requiredMatches field sets. + * We do this after all fields for the term have collected their matches because + * the clause terms presence is required in _any_ of the fields not _all_ of the + * fields. + */ + if (clause.presence === lunr.Query.presence.REQUIRED) { + for (var k = 0; k < clause.fields.length; k++) { + var field = clause.fields[k] + requiredMatches[field] = requiredMatches[field].intersect(clauseMatches) + } + } + } + + /** + * Need to combine the field scoped required and prohibited + * matching documents into a global set of required and prohibited + * matches + */ + var allRequiredMatches = lunr.Set.complete, + allProhibitedMatches = lunr.Set.empty + + for (var i = 0; i < this.fields.length; i++) { + var field = this.fields[i] + + if (requiredMatches[field]) { + allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field]) + } + + if (prohibitedMatches[field]) { + allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field]) + } + } + + var matchingFieldRefs = Object.keys(matchingFields), + results = [], + matches = Object.create(null) + + /* + * If the query is negated (contains only prohibited terms) + * we need to get _all_ fieldRefs currently existing in the + * index. This is only done when we know that the query is + * entirely prohibited terms to avoid any cost of getting all + * fieldRefs unnecessarily. + * + * Additionally, blank MatchData must be created to correctly + * populate the results. + */ + if (query.isNegated()) { + matchingFieldRefs = Object.keys(this.fieldVectors) + + for (var i = 0; i < matchingFieldRefs.length; i++) { + var matchingFieldRef = matchingFieldRefs[i] + var fieldRef = lunr.FieldRef.fromString(matchingFieldRef) + matchingFields[matchingFieldRef] = new lunr.MatchData + } + } + + for (var i = 0; i < matchingFieldRefs.length; i++) { + /* + * Currently we have document fields that match the query, but we + * need to return documents. The matchData and scores are combined + * from multiple fields belonging to the same document. + * + * Scores are calculated by field, using the query vectors created + * above, and combined into a final document score using addition. + */ + var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]), + docRef = fieldRef.docRef + + if (!allRequiredMatches.contains(docRef)) { + continue + } + + if (allProhibitedMatches.contains(docRef)) { + continue + } + + var fieldVector = this.fieldVectors[fieldRef], + score = queryVectors[fieldRef.fieldName].similarity(fieldVector), + docMatch + + if ((docMatch = matches[docRef]) !== undefined) { + docMatch.score += score + docMatch.matchData.combine(matchingFields[fieldRef]) + } else { + var match = { + ref: docRef, + score: score, + matchData: matchingFields[fieldRef] + } + matches[docRef] = match + results.push(match) + } + } + + /* + * Sort the results objects by score, highest first. + */ + return results.sort(function (a, b) { + return b.score - a.score + }) +} + +/** + * Prepares the index for JSON serialization. + * + * The schema for this JSON blob will be described in a + * separate JSON schema file. + * + * @returns {Object} + */ +lunr.Index.prototype.toJSON = function () { + var invertedIndex = Object.keys(this.invertedIndex) + .sort() + .map(function (term) { + return [term, this.invertedIndex[term]] + }, this) + + var fieldVectors = Object.keys(this.fieldVectors) + .map(function (ref) { + return [ref, this.fieldVectors[ref].toJSON()] + }, this) + + return { + version: lunr.version, + fields: this.fields, + fieldVectors: fieldVectors, + invertedIndex: invertedIndex, + pipeline: this.pipeline.toJSON() + } +} + +/** + * Loads a previously serialized lunr.Index + * + * @param {Object} serializedIndex - A previously serialized lunr.Index + * @returns {lunr.Index} + */ +lunr.Index.load = function (serializedIndex) { + var attrs = {}, + fieldVectors = {}, + serializedVectors = serializedIndex.fieldVectors, + invertedIndex = Object.create(null), + serializedInvertedIndex = serializedIndex.invertedIndex, + tokenSetBuilder = new lunr.TokenSet.Builder, + pipeline = lunr.Pipeline.load(serializedIndex.pipeline) + + if (serializedIndex.version != lunr.version) { + lunr.utils.warn("Version mismatch when loading serialised index. Current version of lunr '" + lunr.version + "' does not match serialized index '" + serializedIndex.version + "'") + } + + for (var i = 0; i < serializedVectors.length; i++) { + var tuple = serializedVectors[i], + ref = tuple[0], + elements = tuple[1] + + fieldVectors[ref] = new lunr.Vector(elements) + } + + for (var i = 0; i < serializedInvertedIndex.length; i++) { + var tuple = serializedInvertedIndex[i], + term = tuple[0], + posting = tuple[1] + + tokenSetBuilder.insert(term) + invertedIndex[term] = posting + } + + tokenSetBuilder.finish() + + attrs.fields = serializedIndex.fields + + attrs.fieldVectors = fieldVectors + attrs.invertedIndex = invertedIndex + attrs.tokenSet = tokenSetBuilder.root + attrs.pipeline = pipeline + + return new lunr.Index(attrs) +} +/*! + * lunr.Builder + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.Builder performs indexing on a set of documents and + * returns instances of lunr.Index ready for querying. + * + * All configuration of the index is done via the builder, the + * fields to index, the document reference, the text processing + * pipeline and document scoring parameters are all set on the + * builder before indexing. + * + * @constructor + * @property {string} _ref - Internal reference to the document reference field. + * @property {string[]} _fields - Internal reference to the document fields to index. + * @property {object} invertedIndex - The inverted index maps terms to document fields. + * @property {object} documentTermFrequencies - Keeps track of document term frequencies. + * @property {object} documentLengths - Keeps track of the length of documents added to the index. + * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing. + * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing. + * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index. + * @property {number} documentCount - Keeps track of the total number of documents indexed. + * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75. + * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2. + * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space. + * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index. + */ +lunr.Builder = function () { + this._ref = "id" + this._fields = Object.create(null) + this._documents = Object.create(null) + this.invertedIndex = Object.create(null) + this.fieldTermFrequencies = {} + this.fieldLengths = {} + this.tokenizer = lunr.tokenizer + this.pipeline = new lunr.Pipeline + this.searchPipeline = new lunr.Pipeline + this.documentCount = 0 + this._b = 0.75 + this._k1 = 1.2 + this.termIndex = 0 + this.metadataWhitelist = [] +} + +/** + * Sets the document field used as the document reference. Every document must have this field. + * The type of this field in the document should be a string, if it is not a string it will be + * coerced into a string by calling toString. + * + * The default ref is 'id'. + * + * The ref should _not_ be changed during indexing, it should be set before any documents are + * added to the index. Changing it during indexing can lead to inconsistent results. + * + * @param {string} ref - The name of the reference field in the document. + */ +lunr.Builder.prototype.ref = function (ref) { + this._ref = ref +} + +/** + * A function that is used to extract a field from a document. + * + * Lunr expects a field to be at the top level of a document, if however the field + * is deeply nested within a document an extractor function can be used to extract + * the right field for indexing. + * + * @callback fieldExtractor + * @param {object} doc - The document being added to the index. + * @returns {?(string|object|object[])} obj - The object that will be indexed for this field. + * @example Extracting a nested field + * function (doc) { return doc.nested.field } + */ + +/** + * Adds a field to the list of document fields that will be indexed. Every document being + * indexed should have this field. Null values for this field in indexed documents will + * not cause errors but will limit the chance of that document being retrieved by searches. + * + * All fields should be added before adding documents to the index. Adding fields after + * a document has been indexed will have no effect on already indexed documents. + * + * Fields can be boosted at build time. This allows terms within that field to have more + * importance when ranking search results. Use a field boost to specify that matches within + * one field are more important than other fields. + * + * @param {string} fieldName - The name of a field to index in all documents. + * @param {object} attributes - Optional attributes associated with this field. + * @param {number} [attributes.boost=1] - Boost applied to all terms within this field. + * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document. + * @throws {RangeError} fieldName cannot contain unsupported characters '/' + */ +lunr.Builder.prototype.field = function (fieldName, attributes) { + if (/\//.test(fieldName)) { + throw new RangeError ("Field '" + fieldName + "' contains illegal character '/'") + } + + this._fields[fieldName] = attributes || {} +} + +/** + * A parameter to tune the amount of field length normalisation that is applied when + * calculating relevance scores. A value of 0 will completely disable any normalisation + * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b + * will be clamped to the range 0 - 1. + * + * @param {number} number - The value to set for this tuning parameter. + */ +lunr.Builder.prototype.b = function (number) { + if (number < 0) { + this._b = 0 + } else if (number > 1) { + this._b = 1 + } else { + this._b = number + } +} + +/** + * A parameter that controls the speed at which a rise in term frequency results in term + * frequency saturation. The default value is 1.2. Setting this to a higher value will give + * slower saturation levels, a lower value will result in quicker saturation. + * + * @param {number} number - The value to set for this tuning parameter. + */ +lunr.Builder.prototype.k1 = function (number) { + this._k1 = number +} + +/** + * Adds a document to the index. + * + * Before adding fields to the index the index should have been fully setup, with the document + * ref and all fields to index already having been specified. + * + * The document must have a field name as specified by the ref (by default this is 'id') and + * it should have all fields defined for indexing, though null or undefined values will not + * cause errors. + * + * Entire documents can be boosted at build time. Applying a boost to a document indicates that + * this document should rank higher in search results than other documents. + * + * @param {object} doc - The document to add to the index. + * @param {object} attributes - Optional attributes associated with this document. + * @param {number} [attributes.boost=1] - Boost applied to all terms within this document. + */ +lunr.Builder.prototype.add = function (doc, attributes) { + var docRef = doc[this._ref], + fields = Object.keys(this._fields) + + this._documents[docRef] = attributes || {} + this.documentCount += 1 + + for (var i = 0; i < fields.length; i++) { + var fieldName = fields[i], + extractor = this._fields[fieldName].extractor, + field = extractor ? extractor(doc) : doc[fieldName], + tokens = this.tokenizer(field, { + fields: [fieldName] + }), + terms = this.pipeline.run(tokens), + fieldRef = new lunr.FieldRef (docRef, fieldName), + fieldTerms = Object.create(null) + + this.fieldTermFrequencies[fieldRef] = fieldTerms + this.fieldLengths[fieldRef] = 0 + + // store the length of this field for this document + this.fieldLengths[fieldRef] += terms.length + + // calculate term frequencies for this field + for (var j = 0; j < terms.length; j++) { + var term = terms[j] + + if (fieldTerms[term] == undefined) { + fieldTerms[term] = 0 + } + + fieldTerms[term] += 1 + + // add to inverted index + // create an initial posting if one doesn't exist + if (this.invertedIndex[term] == undefined) { + var posting = Object.create(null) + posting["_index"] = this.termIndex + this.termIndex += 1 + + for (var k = 0; k < fields.length; k++) { + posting[fields[k]] = Object.create(null) + } + + this.invertedIndex[term] = posting + } + + // add an entry for this term/fieldName/docRef to the invertedIndex + if (this.invertedIndex[term][fieldName][docRef] == undefined) { + this.invertedIndex[term][fieldName][docRef] = Object.create(null) + } + + // store all whitelisted metadata about this token in the + // inverted index + for (var l = 0; l < this.metadataWhitelist.length; l++) { + var metadataKey = this.metadataWhitelist[l], + metadata = term.metadata[metadataKey] + + if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) { + this.invertedIndex[term][fieldName][docRef][metadataKey] = [] + } + + this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata) + } + } + + } +} + +/** + * Calculates the average document length for this index + * + * @private + */ +lunr.Builder.prototype.calculateAverageFieldLengths = function () { + + var fieldRefs = Object.keys(this.fieldLengths), + numberOfFields = fieldRefs.length, + accumulator = {}, + documentsWithField = {} + + for (var i = 0; i < numberOfFields; i++) { + var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]), + field = fieldRef.fieldName + + documentsWithField[field] || (documentsWithField[field] = 0) + documentsWithField[field] += 1 + + accumulator[field] || (accumulator[field] = 0) + accumulator[field] += this.fieldLengths[fieldRef] + } + + var fields = Object.keys(this._fields) + + for (var i = 0; i < fields.length; i++) { + var fieldName = fields[i] + accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName] + } + + this.averageFieldLength = accumulator +} + +/** + * Builds a vector space model of every document using lunr.Vector + * + * @private + */ +lunr.Builder.prototype.createFieldVectors = function () { + var fieldVectors = {}, + fieldRefs = Object.keys(this.fieldTermFrequencies), + fieldRefsLength = fieldRefs.length, + termIdfCache = Object.create(null) + + for (var i = 0; i < fieldRefsLength; i++) { + var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]), + fieldName = fieldRef.fieldName, + fieldLength = this.fieldLengths[fieldRef], + fieldVector = new lunr.Vector, + termFrequencies = this.fieldTermFrequencies[fieldRef], + terms = Object.keys(termFrequencies), + termsLength = terms.length + + + var fieldBoost = this._fields[fieldName].boost || 1, + docBoost = this._documents[fieldRef.docRef].boost || 1 + + for (var j = 0; j < termsLength; j++) { + var term = terms[j], + tf = termFrequencies[term], + termIndex = this.invertedIndex[term]._index, + idf, score, scoreWithPrecision + + if (termIdfCache[term] === undefined) { + idf = lunr.idf(this.invertedIndex[term], this.documentCount) + termIdfCache[term] = idf + } else { + idf = termIdfCache[term] + } + + score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf) + score *= fieldBoost + score *= docBoost + scoreWithPrecision = Math.round(score * 1000) / 1000 + // Converts 1.23456789 to 1.234. + // Reducing the precision so that the vectors take up less + // space when serialised. Doing it now so that they behave + // the same before and after serialisation. Also, this is + // the fastest approach to reducing a number's precision in + // JavaScript. + + fieldVector.insert(termIndex, scoreWithPrecision) + } + + fieldVectors[fieldRef] = fieldVector + } + + this.fieldVectors = fieldVectors +} + +/** + * Creates a token set of all tokens in the index using lunr.TokenSet + * + * @private + */ +lunr.Builder.prototype.createTokenSet = function () { + this.tokenSet = lunr.TokenSet.fromArray( + Object.keys(this.invertedIndex).sort() + ) +} + +/** + * Builds the index, creating an instance of lunr.Index. + * + * This completes the indexing process and should only be called + * once all documents have been added to the index. + * + * @returns {lunr.Index} + */ +lunr.Builder.prototype.build = function () { + this.calculateAverageFieldLengths() + this.createFieldVectors() + this.createTokenSet() + + return new lunr.Index({ + invertedIndex: this.invertedIndex, + fieldVectors: this.fieldVectors, + tokenSet: this.tokenSet, + fields: Object.keys(this._fields), + pipeline: this.searchPipeline + }) +} + +/** + * Applies a plugin to the index builder. + * + * A plugin is a function that is called with the index builder as its context. + * Plugins can be used to customise or extend the behaviour of the index + * in some way. A plugin is just a function, that encapsulated the custom + * behaviour that should be applied when building the index. + * + * The plugin function will be called with the index builder as its argument, additional + * arguments can also be passed when calling use. The function will be called + * with the index builder as its context. + * + * @param {Function} plugin The plugin to apply. + */ +lunr.Builder.prototype.use = function (fn) { + var args = Array.prototype.slice.call(arguments, 1) + args.unshift(this) + fn.apply(this, args) +} +/** + * Contains and collects metadata about a matching document. + * A single instance of lunr.MatchData is returned as part of every + * lunr.Index~Result. + * + * @constructor + * @param {string} term - The term this match data is associated with + * @param {string} field - The field in which the term was found + * @param {object} metadata - The metadata recorded about this term in this field + * @property {object} metadata - A cloned collection of metadata associated with this document. + * @see {@link lunr.Index~Result} + */ +lunr.MatchData = function (term, field, metadata) { + var clonedMetadata = Object.create(null), + metadataKeys = Object.keys(metadata || {}) + + // Cloning the metadata to prevent the original + // being mutated during match data combination. + // Metadata is kept in an array within the inverted + // index so cloning the data can be done with + // Array#slice + for (var i = 0; i < metadataKeys.length; i++) { + var key = metadataKeys[i] + clonedMetadata[key] = metadata[key].slice() + } + + this.metadata = Object.create(null) + + if (term !== undefined) { + this.metadata[term] = Object.create(null) + this.metadata[term][field] = clonedMetadata + } +} + +/** + * An instance of lunr.MatchData will be created for every term that matches a + * document. However only one instance is required in a lunr.Index~Result. This + * method combines metadata from another instance of lunr.MatchData with this + * objects metadata. + * + * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one. + * @see {@link lunr.Index~Result} + */ +lunr.MatchData.prototype.combine = function (otherMatchData) { + var terms = Object.keys(otherMatchData.metadata) + + for (var i = 0; i < terms.length; i++) { + var term = terms[i], + fields = Object.keys(otherMatchData.metadata[term]) + + if (this.metadata[term] == undefined) { + this.metadata[term] = Object.create(null) + } + + for (var j = 0; j < fields.length; j++) { + var field = fields[j], + keys = Object.keys(otherMatchData.metadata[term][field]) + + if (this.metadata[term][field] == undefined) { + this.metadata[term][field] = Object.create(null) + } + + for (var k = 0; k < keys.length; k++) { + var key = keys[k] + + if (this.metadata[term][field][key] == undefined) { + this.metadata[term][field][key] = otherMatchData.metadata[term][field][key] + } else { + this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key]) + } + + } + } + } +} + +/** + * Add metadata for a term/field pair to this instance of match data. + * + * @param {string} term - The term this match data is associated with + * @param {string} field - The field in which the term was found + * @param {object} metadata - The metadata recorded about this term in this field + */ +lunr.MatchData.prototype.add = function (term, field, metadata) { + if (!(term in this.metadata)) { + this.metadata[term] = Object.create(null) + this.metadata[term][field] = metadata + return + } + + if (!(field in this.metadata[term])) { + this.metadata[term][field] = metadata + return + } + + var metadataKeys = Object.keys(metadata) + + for (var i = 0; i < metadataKeys.length; i++) { + var key = metadataKeys[i] + + if (key in this.metadata[term][field]) { + this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key]) + } else { + this.metadata[term][field][key] = metadata[key] + } + } +} +/** + * A lunr.Query provides a programmatic way of defining queries to be performed + * against a {@link lunr.Index}. + * + * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method + * so the query object is pre-initialized with the right index fields. + * + * @constructor + * @property {lunr.Query~Clause[]} clauses - An array of query clauses. + * @property {string[]} allFields - An array of all available fields in a lunr.Index. + */ +lunr.Query = function (allFields) { + this.clauses = [] + this.allFields = allFields +} + +/** + * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause. + * + * This allows wildcards to be added to the beginning and end of a term without having to manually do any string + * concatenation. + * + * The wildcard constants can be bitwise combined to select both leading and trailing wildcards. + * + * @constant + * @default + * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour + * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists + * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists + * @see lunr.Query~Clause + * @see lunr.Query#clause + * @see lunr.Query#term + * @example query term with trailing wildcard + * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING }) + * @example query term with leading and trailing wildcard + * query.term('foo', { + * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING + * }) + */ + +lunr.Query.wildcard = new String ("*") +lunr.Query.wildcard.NONE = 0 +lunr.Query.wildcard.LEADING = 1 +lunr.Query.wildcard.TRAILING = 2 + +/** + * Constants for indicating what kind of presence a term must have in matching documents. + * + * @constant + * @enum {number} + * @see lunr.Query~Clause + * @see lunr.Query#clause + * @see lunr.Query#term + * @example query term with required presence + * query.term('foo', { presence: lunr.Query.presence.REQUIRED }) + */ +lunr.Query.presence = { + /** + * Term's presence in a document is optional, this is the default value. + */ + OPTIONAL: 1, + + /** + * Term's presence in a document is required, documents that do not contain + * this term will not be returned. + */ + REQUIRED: 2, + + /** + * Term's presence in a document is prohibited, documents that do contain + * this term will not be returned. + */ + PROHIBITED: 3 +} + +/** + * A single clause in a {@link lunr.Query} contains a term and details on how to + * match that term against a {@link lunr.Index}. + * + * @typedef {Object} lunr.Query~Clause + * @property {string[]} fields - The fields in an index this clause should be matched against. + * @property {number} [boost=1] - Any boost that should be applied when matching this clause. + * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be. + * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline. + * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended. + * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents. + */ + +/** + * Adds a {@link lunr.Query~Clause} to this query. + * + * Unless the clause contains the fields to be matched all fields will be matched. In addition + * a default boost of 1 is applied to the clause. + * + * @param {lunr.Query~Clause} clause - The clause to add to this query. + * @see lunr.Query~Clause + * @returns {lunr.Query} + */ +lunr.Query.prototype.clause = function (clause) { + if (!('fields' in clause)) { + clause.fields = this.allFields + } + + if (!('boost' in clause)) { + clause.boost = 1 + } + + if (!('usePipeline' in clause)) { + clause.usePipeline = true + } + + if (!('wildcard' in clause)) { + clause.wildcard = lunr.Query.wildcard.NONE + } + + if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) { + clause.term = "*" + clause.term + } + + if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) { + clause.term = "" + clause.term + "*" + } + + if (!('presence' in clause)) { + clause.presence = lunr.Query.presence.OPTIONAL + } + + this.clauses.push(clause) + + return this +} + +/** + * A negated query is one in which every clause has a presence of + * prohibited. These queries require some special processing to return + * the expected results. + * + * @returns boolean + */ +lunr.Query.prototype.isNegated = function () { + for (var i = 0; i < this.clauses.length; i++) { + if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) { + return false + } + } + + return true +} + +/** + * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause} + * to the list of clauses that make up this query. + * + * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion + * to a token or token-like string should be done before calling this method. + * + * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an + * array, each term in the array will share the same options. + * + * @param {object|object[]} term - The term(s) to add to the query. + * @param {object} [options] - Any additional properties to add to the query clause. + * @returns {lunr.Query} + * @see lunr.Query#clause + * @see lunr.Query~Clause + * @example adding a single term to a query + * query.term("foo") + * @example adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard + * query.term("foo", { + * fields: ["title"], + * boost: 10, + * wildcard: lunr.Query.wildcard.TRAILING + * }) + * @example using lunr.tokenizer to convert a string to tokens before using them as terms + * query.term(lunr.tokenizer("foo bar")) + */ +lunr.Query.prototype.term = function (term, options) { + if (Array.isArray(term)) { + term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this) + return this + } + + var clause = options || {} + clause.term = term.toString() + + this.clause(clause) + + return this +} +lunr.QueryParseError = function (message, start, end) { + this.name = "QueryParseError" + this.message = message + this.start = start + this.end = end +} + +lunr.QueryParseError.prototype = new Error +lunr.QueryLexer = function (str) { + this.lexemes = [] + this.str = str + this.length = str.length + this.pos = 0 + this.start = 0 + this.escapeCharPositions = [] +} + +lunr.QueryLexer.prototype.run = function () { + var state = lunr.QueryLexer.lexText + + while (state) { + state = state(this) + } +} + +lunr.QueryLexer.prototype.sliceString = function () { + var subSlices = [], + sliceStart = this.start, + sliceEnd = this.pos + + for (var i = 0; i < this.escapeCharPositions.length; i++) { + sliceEnd = this.escapeCharPositions[i] + subSlices.push(this.str.slice(sliceStart, sliceEnd)) + sliceStart = sliceEnd + 1 + } + + subSlices.push(this.str.slice(sliceStart, this.pos)) + this.escapeCharPositions.length = 0 + + return subSlices.join('') +} + +lunr.QueryLexer.prototype.emit = function (type) { + this.lexemes.push({ + type: type, + str: this.sliceString(), + start: this.start, + end: this.pos + }) + + this.start = this.pos +} + +lunr.QueryLexer.prototype.escapeCharacter = function () { + this.escapeCharPositions.push(this.pos - 1) + this.pos += 1 +} + +lunr.QueryLexer.prototype.next = function () { + if (this.pos >= this.length) { + return lunr.QueryLexer.EOS + } + + var char = this.str.charAt(this.pos) + this.pos += 1 + return char +} + +lunr.QueryLexer.prototype.width = function () { + return this.pos - this.start +} + +lunr.QueryLexer.prototype.ignore = function () { + if (this.start == this.pos) { + this.pos += 1 + } + + this.start = this.pos +} + +lunr.QueryLexer.prototype.backup = function () { + this.pos -= 1 +} + +lunr.QueryLexer.prototype.acceptDigitRun = function () { + var char, charCode + + do { + char = this.next() + charCode = char.charCodeAt(0) + } while (charCode > 47 && charCode < 58) + + if (char != lunr.QueryLexer.EOS) { + this.backup() + } +} + +lunr.QueryLexer.prototype.more = function () { + return this.pos < this.length +} + +lunr.QueryLexer.EOS = 'EOS' +lunr.QueryLexer.FIELD = 'FIELD' +lunr.QueryLexer.TERM = 'TERM' +lunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE' +lunr.QueryLexer.BOOST = 'BOOST' +lunr.QueryLexer.PRESENCE = 'PRESENCE' + +lunr.QueryLexer.lexField = function (lexer) { + lexer.backup() + lexer.emit(lunr.QueryLexer.FIELD) + lexer.ignore() + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexTerm = function (lexer) { + if (lexer.width() > 1) { + lexer.backup() + lexer.emit(lunr.QueryLexer.TERM) + } + + lexer.ignore() + + if (lexer.more()) { + return lunr.QueryLexer.lexText + } +} + +lunr.QueryLexer.lexEditDistance = function (lexer) { + lexer.ignore() + lexer.acceptDigitRun() + lexer.emit(lunr.QueryLexer.EDIT_DISTANCE) + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexBoost = function (lexer) { + lexer.ignore() + lexer.acceptDigitRun() + lexer.emit(lunr.QueryLexer.BOOST) + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexEOS = function (lexer) { + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } +} + +// This matches the separator used when tokenising fields +// within a document. These should match otherwise it is +// not possible to search for some tokens within a document. +// +// It is possible for the user to change the separator on the +// tokenizer so it _might_ clash with any other of the special +// characters already used within the search string, e.g. :. +// +// This means that it is possible to change the separator in +// such a way that makes some words unsearchable using a search +// string. +lunr.QueryLexer.termSeparator = lunr.tokenizer.separator + +lunr.QueryLexer.lexText = function (lexer) { + while (true) { + var char = lexer.next() + + if (char == lunr.QueryLexer.EOS) { + return lunr.QueryLexer.lexEOS + } + + // Escape character is '\' + if (char.charCodeAt(0) == 92) { + lexer.escapeCharacter() + continue + } + + if (char == ":") { + return lunr.QueryLexer.lexField + } + + if (char == "~") { + lexer.backup() + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } + return lunr.QueryLexer.lexEditDistance + } + + if (char == "^") { + lexer.backup() + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } + return lunr.QueryLexer.lexBoost + } + + // "+" indicates term presence is required + // checking for length to ensure that only + // leading "+" are considered + if (char == "+" && lexer.width() === 1) { + lexer.emit(lunr.QueryLexer.PRESENCE) + return lunr.QueryLexer.lexText + } + + // "-" indicates term presence is prohibited + // checking for length to ensure that only + // leading "-" are considered + if (char == "-" && lexer.width() === 1) { + lexer.emit(lunr.QueryLexer.PRESENCE) + return lunr.QueryLexer.lexText + } + + if (char.match(lunr.QueryLexer.termSeparator)) { + return lunr.QueryLexer.lexTerm + } + } +} + +lunr.QueryParser = function (str, query) { + this.lexer = new lunr.QueryLexer (str) + this.query = query + this.currentClause = {} + this.lexemeIdx = 0 +} + +lunr.QueryParser.prototype.parse = function () { + this.lexer.run() + this.lexemes = this.lexer.lexemes + + var state = lunr.QueryParser.parseClause + + while (state) { + state = state(this) + } + + return this.query +} + +lunr.QueryParser.prototype.peekLexeme = function () { + return this.lexemes[this.lexemeIdx] +} + +lunr.QueryParser.prototype.consumeLexeme = function () { + var lexeme = this.peekLexeme() + this.lexemeIdx += 1 + return lexeme +} + +lunr.QueryParser.prototype.nextClause = function () { + var completedClause = this.currentClause + this.query.clause(completedClause) + this.currentClause = {} +} + +lunr.QueryParser.parseClause = function (parser) { + var lexeme = parser.peekLexeme() + + if (lexeme == undefined) { + return + } + + switch (lexeme.type) { + case lunr.QueryLexer.PRESENCE: + return lunr.QueryParser.parsePresence + case lunr.QueryLexer.FIELD: + return lunr.QueryParser.parseField + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expected either a field or a term, found " + lexeme.type + + if (lexeme.str.length >= 1) { + errorMessage += " with value '" + lexeme.str + "'" + } + + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } +} + +lunr.QueryParser.parsePresence = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + switch (lexeme.str) { + case "-": + parser.currentClause.presence = lunr.Query.presence.PROHIBITED + break + case "+": + parser.currentClause.presence = lunr.Query.presence.REQUIRED + break + default: + var errorMessage = "unrecognised presence operator'" + lexeme.str + "'" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + var errorMessage = "expecting term or field, found nothing" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.FIELD: + return lunr.QueryParser.parseField + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expecting term or field, found '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseField = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + if (parser.query.allFields.indexOf(lexeme.str) == -1) { + var possibleFields = parser.query.allFields.map(function (f) { return "'" + f + "'" }).join(', '), + errorMessage = "unrecognised field '" + lexeme.str + "', possible fields: " + possibleFields + + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.fields = [lexeme.str] + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + var errorMessage = "expecting term, found nothing" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expecting term, found '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseTerm = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + parser.currentClause.term = lexeme.str.toLowerCase() + + if (lexeme.str.indexOf("*") != -1) { + parser.currentClause.usePipeline = false + } + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseEditDistance = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + var editDistance = parseInt(lexeme.str, 10) + + if (isNaN(editDistance)) { + var errorMessage = "edit distance must be numeric" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.editDistance = editDistance + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseBoost = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + var boost = parseInt(lexeme.str, 10) + + if (isNaN(boost)) { + var errorMessage = "boost must be numeric" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.boost = boost + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + + /** + * export the module via AMD, CommonJS or as a browser global + * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js + */ + ;(function (root, factory) { + if (typeof define === 'function' && define.amd) { + // AMD. Register as an anonymous module. + define(factory) + } else if (typeof exports === 'object') { + /** + * Node. Does not work with strict CommonJS, but + * only CommonJS-like environments that support module.exports, + * like Node. + */ + module.exports = factory() + } else { + // Browser globals (root is window) + root.lunr = factory() + } + }(this, function () { + /** + * Just return a value to define the module export. + * This example returns an object, but the module + * can return a function as the exported value. + */ + return lunr + })) +})(); diff --git a/search/main.js b/search/main.js new file mode 100644 index 0000000..a5e469d --- /dev/null +++ b/search/main.js @@ -0,0 +1,109 @@ +function getSearchTermFromLocation() { + var sPageURL = window.location.search.substring(1); + var sURLVariables = sPageURL.split('&'); + for (var i = 0; i < sURLVariables.length; i++) { + var sParameterName = sURLVariables[i].split('='); + if (sParameterName[0] == 'q') { + return decodeURIComponent(sParameterName[1].replace(/\+/g, '%20')); + } + } +} + +function joinUrl (base, path) { + if (path.substring(0, 1) === "/") { + // path starts with `/`. Thus it is absolute. + return path; + } + if (base.substring(base.length-1) === "/") { + // base ends with `/` + return base + path; + } + return base + "/" + path; +} + +function escapeHtml (value) { + return value.replace(/&/g, '&') + .replace(/"/g, '"') + .replace(//g, '>'); +} + +function formatResult (location, title, summary) { + return ''; +} + +function displayResults (results) { + var search_results = document.getElementById("mkdocs-search-results"); + while (search_results.firstChild) { + search_results.removeChild(search_results.firstChild); + } + if (results.length > 0){ + for (var i=0; i < results.length; i++){ + var result = results[i]; + var html = formatResult(result.location, result.title, result.summary); + search_results.insertAdjacentHTML('beforeend', html); + } + } else { + var noResultsText = search_results.getAttribute('data-no-results-text'); + if (!noResultsText) { + noResultsText = "No results found"; + } + search_results.insertAdjacentHTML('beforeend', '

' + noResultsText + '

'); + } +} + +function doSearch () { + var query = document.getElementById('mkdocs-search-query').value; + if (query.length > min_search_length) { + if (!window.Worker) { + displayResults(search(query)); + } else { + searchWorker.postMessage({query: query}); + } + } else { + // Clear results for short queries + displayResults([]); + } +} + +function initSearch () { + var search_input = document.getElementById('mkdocs-search-query'); + if (search_input) { + search_input.addEventListener("keyup", doSearch); + } + var term = getSearchTermFromLocation(); + if (term) { + search_input.value = term; + doSearch(); + } +} + +function onWorkerMessage (e) { + if (e.data.allowSearch) { + initSearch(); + } else if (e.data.results) { + var results = e.data.results; + displayResults(results); + } else if (e.data.config) { + min_search_length = e.data.config.min_search_length-1; + } +} + +if (!window.Worker) { + console.log('Web Worker API not supported'); + // load index in main thread + $.getScript(joinUrl(base_url, "search/worker.js")).done(function () { + console.log('Loaded worker'); + init(); + window.postMessage = function (msg) { + onWorkerMessage({data: msg}); + }; + }).fail(function (jqxhr, settings, exception) { + console.error('Could not load worker.js'); + }); +} else { + // Wrap search in a web worker + var searchWorker = new Worker(joinUrl(base_url, "search/worker.js")); + searchWorker.postMessage({init: true}); + searchWorker.onmessage = onWorkerMessage; +} diff --git a/search/search_index.json b/search/search_index.json new file mode 100644 index 0000000..2b71285 --- /dev/null +++ b/search/search_index.json @@ -0,0 +1 @@ +{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"LightGBMLSS - An extension of LightGBM to probabilistic modelling and prediction We introduce a comprehensive framework that models and predicts the full conditional distribution of a univariate target as a function of covariate. Choosing from a wide range of continuous, discrete, and mixed discrete-continuous distributions, modelling and predicting the entire conditional distribution greatly enhances the flexibility of LightGBM, as it allows to create probabilistic forecasts from which prediction intervals and quantiles of interest can be derived. Features Estimation of all distributional parameters. Normalizing Flows allow modelling of complex and multi-modal distributions. Zero-Adjusted and Zero-Inflated Distributions for modelling excess of zeros in the data. Automatic derivation of Gradients and Hessian of all distributional parameters using PyTorch . Automated hyper-parameter search, including pruning, is done via Optuna . The output of LightGBMLSS is explained using SHapley Additive exPlanations . LightGBMLSS provides full compatibility with all the features and functionality of LightGBM. LightGBMLSS is available in Python. Installation To install LightGBMLSS, please first run pip install git+https://github.com/StatMixedML/LightGBMLSS.git Then, to install the shap-dependency, run pip install git+https://github.com/dsgibbons/shap.git Some Notes Stabilization Since LightGBMLSS updates the parameter estimates by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all distributional parameters. Due to variability regarding the ranges, the estimation of Gradients and Hessians might become unstable so that LightGBMLSS might not converge or might converge very slowly. To mitigate these effects, we have implemented a stabilization of Gradients and Hessians. For improved convergence, an alternative approach is to standardize the (continuous) response variable, such as dividing it by 100 (e.g., y/100). This approach proves especially valuable when the response range significantly differs from that of Gradients and Hessians. Nevertheless, it is essential to carefully evaluate and apply both the built-in stabilization and response standardization techniques in consideration of the specific dataset at hand. Runtime Since LightGBMLSS is based on a one vs. all estimation strategy , where a separate tree is grown for each distributional parameter, it requires training [number of iterations] * [number of distributional parameters] trees. Hence, the runtime of LightGBMLSS is generally slightly higher for univariate distributions as compared to LightGBM, which requires training [number of iterations] trees only. Reference Paper M\u00e4rz, A. and Kneib, T.: (2022) Distributional Gradient Boosting Machines . M\u00e4rz, Alexander (2019): XGBoostLSS - An extension of XGBoost to probabilistic forecasting .","title":"Home"},{"location":"#lightgbmlss-an-extension-of-lightgbm-to-probabilistic-modelling-and-prediction","text":"We introduce a comprehensive framework that models and predicts the full conditional distribution of a univariate target as a function of covariate. Choosing from a wide range of continuous, discrete, and mixed discrete-continuous distributions, modelling and predicting the entire conditional distribution greatly enhances the flexibility of LightGBM, as it allows to create probabilistic forecasts from which prediction intervals and quantiles of interest can be derived.","title":"LightGBMLSS - An extension of LightGBM to probabilistic modelling and prediction"},{"location":"#features","text":"Estimation of all distributional parameters. Normalizing Flows allow modelling of complex and multi-modal distributions. Zero-Adjusted and Zero-Inflated Distributions for modelling excess of zeros in the data. Automatic derivation of Gradients and Hessian of all distributional parameters using PyTorch . Automated hyper-parameter search, including pruning, is done via Optuna . The output of LightGBMLSS is explained using SHapley Additive exPlanations . LightGBMLSS provides full compatibility with all the features and functionality of LightGBM. LightGBMLSS is available in Python.","title":"Features"},{"location":"#installation","text":"To install LightGBMLSS, please first run pip install git+https://github.com/StatMixedML/LightGBMLSS.git Then, to install the shap-dependency, run pip install git+https://github.com/dsgibbons/shap.git","title":"Installation"},{"location":"#some-notes","text":"","title":"Some Notes"},{"location":"#stabilization","text":"Since LightGBMLSS updates the parameter estimates by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all distributional parameters. Due to variability regarding the ranges, the estimation of Gradients and Hessians might become unstable so that LightGBMLSS might not converge or might converge very slowly. To mitigate these effects, we have implemented a stabilization of Gradients and Hessians. For improved convergence, an alternative approach is to standardize the (continuous) response variable, such as dividing it by 100 (e.g., y/100). This approach proves especially valuable when the response range significantly differs from that of Gradients and Hessians. Nevertheless, it is essential to carefully evaluate and apply both the built-in stabilization and response standardization techniques in consideration of the specific dataset at hand.","title":"Stabilization"},{"location":"#runtime","text":"Since LightGBMLSS is based on a one vs. all estimation strategy , where a separate tree is grown for each distributional parameter, it requires training [number of iterations] * [number of distributional parameters] trees. Hence, the runtime of LightGBMLSS is generally slightly higher for univariate distributions as compared to LightGBM, which requires training [number of iterations] trees only.","title":"Runtime"},{"location":"#reference-paper","text":"M\u00e4rz, A. and Kneib, T.: (2022) Distributional Gradient Boosting Machines . M\u00e4rz, Alexander (2019): XGBoostLSS - An extension of XGBoost to probabilistic forecasting .","title":"Reference Paper"},{"location":"api/","text":"API references LightGBMLSS - An extension of LightGBM to probabilistic forecasting datasets LightGBMLSS - An extension of LightGBM to probabilistic forecasting data_loader load_simulated_gaussian_data () Returns train/test dataframe of a simulated example. Contains the following columns y int64: response x int64: x-feature X1:X10 int64: random noise features Source code in lightgbmlss/datasets/data_loader.py 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 def load_simulated_gaussian_data (): \"\"\" Returns train/test dataframe of a simulated example. Contains the following columns: y int64: response x int64: x-feature X1:X10 int64: random noise features \"\"\" train_path = pkg_resources . resource_stream ( __name__ , \"gaussian_train_sim.csv\" ) train_df = pd . read_csv ( train_path ) test_path = pkg_resources . resource_stream ( __name__ , \"gaussian_test_sim.csv\" ) test_df = pd . read_csv ( test_path ) return train_df , test_df load_simulated_studentT_data () Returns train/test dataframe of a simulated example. Contains the following columns y int64: response x int64: x-feature X1:X10 int64: random noise features Source code in lightgbmlss/datasets/data_loader.py 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 def load_simulated_studentT_data (): \"\"\" Returns train/test dataframe of a simulated example. Contains the following columns: y int64: response x int64: x-feature X1:X10 int64: random noise features \"\"\" train_path = pkg_resources . resource_stream ( __name__ , \"studentT_train_sim.csv\" ) train_df = pd . read_csv ( train_path ) test_path = pkg_resources . resource_stream ( __name__ , \"studentT_test_sim.csv\" ) test_df = pd . read_csv ( test_path ) return train_df , test_df distributions LightGBMLSS - An extension of LightGBM to probabilistic forecasting Beta Beta Bases: DistributionClass Beta distribution class. Distributional Parameters concentration1: torch.Tensor 1st concentration parameter of the distribution (often referred to as alpha). concentration0: torch.Tensor 2nd concentration parameter of the distribution (often referred to as beta). Source https://pytorch.org/docs/stable/distributions.html#beta Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/Beta.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class Beta ( DistributionClass ): \"\"\" Beta distribution class. Distributional Parameters ------------------------- concentration1: torch.Tensor 1st concentration parameter of the distribution (often referred to as alpha). concentration0: torch.Tensor 2nd concentration parameter of the distribution (often referred to as beta). Source ------------------------- https://pytorch.org/docs/stable/distributions.html#beta Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = Beta_Torch param_dict = { \"concentration1\" : response_fn , \"concentration0\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) Cauchy Cauchy Bases: DistributionClass Cauchy distribution class. Distributional Parameters loc: torch.Tensor Mode or median of the distribution. scale: torch.Tensor Half width at half maximum. Source https://pytorch.org/docs/stable/distributions.html#cauchy Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/Cauchy.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class Cauchy ( DistributionClass ): \"\"\" Cauchy distribution class. Distributional Parameters ------------------------- loc: torch.Tensor Mode or median of the distribution. scale: torch.Tensor Half width at half maximum. Source ------------------------- https://pytorch.org/docs/stable/distributions.html#cauchy Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = Cauchy_Torch param_dict = { \"loc\" : identity_fn , \"scale\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) Expectile Expectile Bases: DistributionClass Expectile distribution class. Distributional Parameters expectile: List List of specified expectiles. Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". expectiles: List List of expectiles in increasing order. penalize_crossing: bool Whether to include a penalty term to discourage crossing of expectiles. Source code in lightgbmlss/distributions/Expectile.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 class Expectile ( DistributionClass ): \"\"\" Expectile distribution class. Distributional Parameters ------------------------- expectile: List List of specified expectiles. Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". expectiles: List List of expectiles in increasing order. penalize_crossing: bool Whether to include a penalty term to discourage crossing of expectiles. \"\"\" def __init__ ( self , stabilization : str = \"None\" , expectiles : List = [ 0.1 , 0.5 , 0.9 ], penalize_crossing : bool = False , ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if not isinstance ( expectiles , list ): raise ValueError ( \"Expectiles must be a list.\" ) if not all ([ 0 < expectile < 1 for expectile in expectiles ]): raise ValueError ( \"Expectiles must be between 0 and 1.\" ) if not isinstance ( penalize_crossing , bool ): raise ValueError ( \"penalize_crossing must be a boolean. Please choose from True or False.\" ) # Set the parameters specific to the distribution distribution = Expectile_Torch torch . distributions . Distribution . set_default_validate_args ( False ) expectiles . sort () param_dict = {} for expectile in expectiles : key = f \"expectile_ { expectile } \" param_dict [ key ] = identity_fn # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = \"nll\" , tau = torch . tensor ( expectiles ), penalize_crossing = penalize_crossing ) Expectile_Torch Bases: Distribution PyTorch implementation of expectiles. Arguments expectiles : List[torch.Tensor] List of expectiles. penalize_crossing : bool Whether to include a penalty term to discourage crossing of expectiles. Source code in lightgbmlss/distributions/Expectile.py 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 class Expectile_Torch ( Distribution ): \"\"\" PyTorch implementation of expectiles. Arguments --------- expectiles : List[torch.Tensor] List of expectiles. penalize_crossing : bool Whether to include a penalty term to discourage crossing of expectiles. \"\"\" def __init__ ( self , expectiles : List [ torch . Tensor ], penalize_crossing : bool = False , ): super ( Expectile_Torch ) . __init__ () self . expectiles = expectiles self . penalize_crossing = penalize_crossing self . __class__ . __name__ = \"Expectile\" def log_prob ( self , value : torch . Tensor , tau : List [ torch . Tensor ]) -> torch . Tensor : \"\"\" Returns the log of the probability density function evaluated at `value`. Arguments --------- value : torch.Tensor Response for which log probability is to be calculated. tau : List[torch.Tensor] List of asymmetry parameters. Returns ------- torch.Tensor Log probability of `value`. \"\"\" value = value . reshape ( - 1 , 1 ) loss = torch . tensor ( 0.0 , dtype = torch . float32 ) penalty = torch . tensor ( 0.0 , dtype = torch . float32 ) # Calculate loss predt_expectiles = [] for expectile , tau_value in zip ( self . expectiles , tau ): weight = torch . where ( value - expectile >= 0 , tau_value , 1 - tau_value ) loss += torch . nansum ( weight * ( value - expectile ) ** 2 ) predt_expectiles . append ( expectile . reshape ( - 1 , 1 )) # Penalty term to discourage crossing of expectiles if self . penalize_crossing : predt_expectiles = torch . cat ( predt_expectiles , dim = 1 ) penalty = torch . mean ( ( ~ torch . all ( torch . diff ( predt_expectiles , dim = 1 ) > 0 , dim = 1 )) . float () ) loss = ( loss * ( 1 + penalty )) / len ( self . expectiles ) return - loss log_prob ( value , tau ) Returns the log of the probability density function evaluated at value . Arguments value : torch.Tensor Response for which log probability is to be calculated. tau : List[torch.Tensor] List of asymmetry parameters. Returns torch.Tensor Log probability of value . Source code in lightgbmlss/distributions/Expectile.py 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 def log_prob ( self , value : torch . Tensor , tau : List [ torch . Tensor ]) -> torch . Tensor : \"\"\" Returns the log of the probability density function evaluated at `value`. Arguments --------- value : torch.Tensor Response for which log probability is to be calculated. tau : List[torch.Tensor] List of asymmetry parameters. Returns ------- torch.Tensor Log probability of `value`. \"\"\" value = value . reshape ( - 1 , 1 ) loss = torch . tensor ( 0.0 , dtype = torch . float32 ) penalty = torch . tensor ( 0.0 , dtype = torch . float32 ) # Calculate loss predt_expectiles = [] for expectile , tau_value in zip ( self . expectiles , tau ): weight = torch . where ( value - expectile >= 0 , tau_value , 1 - tau_value ) loss += torch . nansum ( weight * ( value - expectile ) ** 2 ) predt_expectiles . append ( expectile . reshape ( - 1 , 1 )) # Penalty term to discourage crossing of expectiles if self . penalize_crossing : predt_expectiles = torch . cat ( predt_expectiles , dim = 1 ) penalty = torch . mean ( ( ~ torch . all ( torch . diff ( predt_expectiles , dim = 1 ) > 0 , dim = 1 )) . float () ) loss = ( loss * ( 1 + penalty )) / len ( self . expectiles ) return - loss expectile_norm ( tau = 0.5 , m = 0 , sd = 1 ) Calculates expectiles from Normal distribution for given tau values. For more details and distributions see https://rdrr.io/cran/expectreg/man/enorm.html Arguments tau : np.ndarray Vector of expectiles from the respective distribution. m : np.ndarray Mean of the Normal distribution. sd : np.ndarray Standard deviation of the Normal distribution. Returns np.ndarray Source code in lightgbmlss/distributions/Expectile.py 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 def expectile_norm ( tau : np . ndarray = 0.5 , m : np . ndarray = 0 , sd : np . ndarray = 1 ): \"\"\" Calculates expectiles from Normal distribution for given tau values. For more details and distributions see https://rdrr.io/cran/expectreg/man/enorm.html Arguments _________ tau : np.ndarray Vector of expectiles from the respective distribution. m : np.ndarray Mean of the Normal distribution. sd : np.ndarray Standard deviation of the Normal distribution. Returns _______ np.ndarray \"\"\" tau [ tau > 1 or tau < 0 ] = np . nan zz = 0 * tau lower = np . array ( - 10 , dtype = \"float\" ) lower = np . repeat ( lower [ np . newaxis , ... ], len ( tau ), axis = 0 ) upper = np . array ( 10 , dtype = \"float\" ) upper = np . repeat ( upper [ np . newaxis , ... ], len ( tau ), axis = 0 ) diff = 1 index = 0 while ( diff > 1e-10 ) and ( index < 1000 ): root = expectile_pnorm ( zz ) - tau root [ np . isnan ( root )] = 0 lower [ root < 0 ] = zz [ root < 0 ] upper [ root > 0 ] = zz [ root > 0 ] zz = ( upper + lower ) / 2 diff = np . nanmax ( np . abs ( root )) index = index + 1 zz [ np . isnan ( tau )] = np . nan return zz * sd + m expectile_pnorm ( tau = 0.5 , m = 0 , sd = 1 ) Normal Expectile Distribution Function. For more details and distributions see https://rdrr.io/cran/expectreg/man/enorm.html Arguments tau : np.ndarray Vector of expectiles from the respective distribution. m : np.ndarray Mean of the Normal distribution. sd : np.ndarray Standard deviation of the Normal distribution. Returns tau : np.ndarray Expectiles from the Normal distribution. Source code in lightgbmlss/distributions/Expectile.py 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 def expectile_pnorm ( tau : np . ndarray = 0.5 , m : np . ndarray = 0 , sd : np . ndarray = 1 ): \"\"\" Normal Expectile Distribution Function. For more details and distributions see https://rdrr.io/cran/expectreg/man/enorm.html Arguments _________ tau : np.ndarray Vector of expectiles from the respective distribution. m : np.ndarray Mean of the Normal distribution. sd : np.ndarray Standard deviation of the Normal distribution. Returns _______ tau : np.ndarray Expectiles from the Normal distribution. \"\"\" z = ( tau - m ) / sd p = norm . cdf ( z , loc = m , scale = sd ) d = norm . pdf ( z , loc = m , scale = sd ) u = - d - z * p tau = u / ( 2 * u + z ) return tau Gamma Gamma Bases: DistributionClass Gamma distribution class. Distributional Parameters concentration: torch.Tensor shape parameter of the distribution (often referred to as alpha) rate: torch.Tensor rate = 1 / scale of the distribution (often referred to as beta) Source https://pytorch.org/docs/stable/distributions.html#gamma Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/Gamma.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class Gamma ( DistributionClass ): \"\"\" Gamma distribution class. Distributional Parameters -------------------------- concentration: torch.Tensor shape parameter of the distribution (often referred to as alpha) rate: torch.Tensor rate = 1 / scale of the distribution (often referred to as beta) Source ------------------------- https://pytorch.org/docs/stable/distributions.html#gamma Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = Gamma_Torch param_dict = { \"concentration\" : response_fn , \"rate\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) Gaussian Gaussian Bases: DistributionClass Gaussian distribution class. Distributional Parameters loc: torch.Tensor Mean of the distribution (often referred to as mu). scale: torch.Tensor Standard deviation of the distribution (often referred to as sigma). Source https://pytorch.org/docs/stable/distributions.html#normal Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/Gaussian.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class Gaussian ( DistributionClass ): \"\"\" Gaussian distribution class. Distributional Parameters ------------------------- loc: torch.Tensor Mean of the distribution (often referred to as mu). scale: torch.Tensor Standard deviation of the distribution (often referred to as sigma). Source ------------------------- https://pytorch.org/docs/stable/distributions.html#normal Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = Gaussian_Torch param_dict = { \"loc\" : identity_fn , \"scale\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) Gumbel Gumbel Bases: DistributionClass Gumbel distribution class. Distributional Parameters loc: torch.Tensor Location parameter of the distribution. scale: torch.Tensor Scale parameter of the distribution. Source https://pytorch.org/docs/stable/distributions.html#gumbel Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/Gumbel.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class Gumbel ( DistributionClass ): \"\"\" Gumbel distribution class. Distributional Parameters ------------------------- loc: torch.Tensor Location parameter of the distribution. scale: torch.Tensor Scale parameter of the distribution. Source ------------------------- https://pytorch.org/docs/stable/distributions.html#gumbel Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = Gumbel_Torch param_dict = { \"loc\" : identity_fn , \"scale\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) Laplace Laplace Bases: DistributionClass Laplace distribution class. Distributional Parameters loc: torch.Tensor Mean of the distribution. scale: torch.Tensor Scale of the distribution. Source https://pytorch.org/docs/stable/distributions.html#laplace Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/Laplace.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class Laplace ( DistributionClass ): \"\"\" Laplace distribution class. Distributional Parameters ------------------------- loc: torch.Tensor Mean of the distribution. scale: torch.Tensor Scale of the distribution. Source ------------------------- https://pytorch.org/docs/stable/distributions.html#laplace Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = Laplace_Torch param_dict = { \"loc\" : identity_fn , \"scale\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) LogNormal LogNormal Bases: DistributionClass LogNormal distribution class. Distributional Parameters loc: torch.Tensor Mean of log of distribution. scale: torch.Tensor Standard deviation of log of the distribution. Source https://pytorch.org/docs/stable/distributions.html#lognormal Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/LogNormal.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class LogNormal ( DistributionClass ): \"\"\" LogNormal distribution class. Distributional Parameters ------------------------- loc: torch.Tensor Mean of log of distribution. scale: torch.Tensor Standard deviation of log of the distribution. Source ------------------------- https://pytorch.org/docs/stable/distributions.html#lognormal Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = LogNormal_Torch param_dict = { \"loc\" : identity_fn , \"scale\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) NegativeBinomial NegativeBinomial Bases: DistributionClass NegativeBinomial distribution class. Distributional Parameters total_count: torch.Tensor Non-negative number of negative Bernoulli trials to stop. probs: torch.Tensor Event probabilities of success in the half open interval [0, 1). logits: torch.Tensor Event log-odds for probabilities of success. Source https://pytorch.org/docs/stable/distributions.html#negativebinomial Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn_total_count: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). response_fn_probs: str Response function for transforming the distributional parameters to the correct support. Options are \"sigmoid\" (sigmoid). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). Source code in lightgbmlss/distributions/NegativeBinomial.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 class NegativeBinomial ( DistributionClass ): \"\"\" NegativeBinomial distribution class. Distributional Parameters ------------------------- total_count: torch.Tensor Non-negative number of negative Bernoulli trials to stop. probs: torch.Tensor Event probabilities of success in the half open interval [0, 1). logits: torch.Tensor Event log-odds for probabilities of success. Source ------------------------- https://pytorch.org/docs/stable/distributions.html#negativebinomial Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn_total_count: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). response_fn_probs: str Response function for transforming the distributional parameters to the correct support. Options are \"sigmoid\" (sigmoid). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn_total_count : str = \"relu\" , response_fn_probs : str = \"sigmoid\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" ]: raise ValueError ( \"Invalid loss function. Please select 'nll'.\" ) # Specify Response Functions for total_count response_functions_total_count = { \"exp\" : exp_fn , \"softplus\" : softplus_fn , \"relu\" : relu_fn } if response_fn_total_count in response_functions_total_count : response_fn_total_count = response_functions_total_count [ response_fn_total_count ] else : raise ValueError ( \"Invalid response function for total_count. Please choose from 'exp', 'softplus' or 'relu'.\" ) # Specify Response Functions for probs response_functions_probs = { \"sigmoid\" : sigmoid_fn } if response_fn_probs in response_functions_probs : response_fn_probs = response_functions_probs [ response_fn_probs ] else : raise ValueError ( \"Invalid response function for probs. Please select 'sigmoid'.\" ) # Set the parameters specific to the distribution distribution = NegativeBinomial_Torch param_dict = { \"total_count\" : response_fn_total_count , \"probs\" : response_fn_probs } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = True , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) Poisson Poisson Bases: DistributionClass Poisson distribution class. Distributional Parameters rate: torch.Tensor Rate parameter of the distribution (often referred to as lambda). Source https://pytorch.org/docs/stable/distributions.html#poisson Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). Source code in lightgbmlss/distributions/Poisson.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 class Poisson ( DistributionClass ): \"\"\" Poisson distribution class. Distributional Parameters ------------------------- rate: torch.Tensor Rate parameter of the distribution (often referred to as lambda). Source ------------------------- https://pytorch.org/docs/stable/distributions.html#poisson Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"relu\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" ]: raise ValueError ( \"Invalid loss function. Please select 'nll'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn , \"relu\" : relu_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function for total_count. Please choose from 'exp', 'softplus' or 'relu'.\" ) # Set the parameters specific to the distribution distribution = Poisson_Torch param_dict = { \"rate\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = True , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) SplineFlow SplineFlow Bases: NormalizingFlowClass Spline Flow class. The spline flow is a normalizing flow based on element-wise rational spline bijections of linear and quadratic order (Durkan et al., 2019; Dolatabadi et al., 2020). Rational splines are functions that are comprised of segments that are the ratio of two polynomials. Rational splines offer an excellent combination of functional flexibility whilst maintaining a numerically stable inverse. For more details, see: - Durkan, C., Bekasov, A., Murray, I. and Papamakarios, G. Neural Spline Flows. NeurIPS 2019. - Dolatabadi, H. M., Erfani, S. and Leckie, C., Invertible Generative Modeling using Linear Rational Splines. AISTATS 2020. Source https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.transforms.Spline Arguments target_support: str The target support. Options are - \"real\": [-inf, inf] - \"positive\": [0, inf] - \"positive_integer\": [0, 1, 2, 3, ...] - \"unit_interval\": [0, 1] count_bins: int The number of segments comprising the spline. bound: float The quantity \"K\" determining the bounding box, [-K,K] x [-K,K] of the spline. By adjusting the \"K\" value, you can control the size of the bounding box and consequently control the range of inputs that the spline transform operates on. Larger values of \"K\" will result in a wider valid range for the spline transformation, while smaller values will restrict the valid range to a smaller region. Should be chosen based on the range of the data. order: str The order of the spline. Options are \"linear\" or \"quadratic\". stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\" or \"L2\". loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/SplineFlow.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 class SplineFlow ( NormalizingFlowClass ): \"\"\" Spline Flow class. The spline flow is a normalizing flow based on element-wise rational spline bijections of linear and quadratic order (Durkan et al., 2019; Dolatabadi et al., 2020). Rational splines are functions that are comprised of segments that are the ratio of two polynomials. Rational splines offer an excellent combination of functional flexibility whilst maintaining a numerically stable inverse. For more details, see: - Durkan, C., Bekasov, A., Murray, I. and Papamakarios, G. Neural Spline Flows. NeurIPS 2019. - Dolatabadi, H. M., Erfani, S. and Leckie, C., Invertible Generative Modeling using Linear Rational Splines. AISTATS 2020. Source --------- https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.transforms.Spline Arguments --------- target_support: str The target support. Options are - \"real\": [-inf, inf] - \"positive\": [0, inf] - \"positive_integer\": [0, 1, 2, 3, ...] - \"unit_interval\": [0, 1] count_bins: int The number of segments comprising the spline. bound: float The quantity \"K\" determining the bounding box, [-K,K] x [-K,K] of the spline. By adjusting the \"K\" value, you can control the size of the bounding box and consequently control the range of inputs that the spline transform operates on. Larger values of \"K\" will result in a wider valid range for the spline transformation, while smaller values will restrict the valid range to a smaller region. Should be chosen based on the range of the data. order: str The order of the spline. Options are \"linear\" or \"quadratic\". stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\" or \"L2\". loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , target_support : str = \"real\" , count_bins : int = 8 , bound : float = 3.0 , order : str = \"linear\" , stabilization : str = \"None\" , loss_fn : str = \"nll\" ): # Specify Target Transform if not isinstance ( target_support , str ): raise ValueError ( \"target_support must be a string.\" ) transforms = { \"real\" : ( identity_transform , False ), \"positive\" : ( SoftplusTransform (), False ), \"positive_integer\" : ( SoftplusTransform (), True ), \"unit_interval\" : ( SigmoidTransform (), False ) } if target_support in transforms : target_transform , discrete = transforms [ target_support ] else : raise ValueError ( \"Invalid target_support. Options are 'real', 'positive', 'positive_integer', or 'unit_interval'.\" ) # Check if count_bins is valid if not isinstance ( count_bins , int ): raise ValueError ( \"count_bins must be an integer.\" ) if count_bins <= 0 : raise ValueError ( \"count_bins must be a positive integer > 0.\" ) # Check if bound is float if not isinstance ( bound , float ): raise ValueError ( \"bound must be a float.\" ) # Number of parameters if not isinstance ( order , str ): raise ValueError ( \"order must be a string.\" ) order_params = { \"quadratic\" : 2 * count_bins + ( count_bins - 1 ), \"linear\" : 3 * count_bins + ( count_bins - 1 ) } if order in order_params : n_params = order_params [ order ] else : raise ValueError ( \"Invalid order specification. Options are 'linear' or 'quadratic'.\" ) # Check if stabilization method is valid. if not isinstance ( stabilization , str ): raise ValueError ( \"stabilization must be a string.\" ) if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Options are 'None', 'MAD' or 'L2'.\" ) # Check if loss function is valid. if not isinstance ( loss_fn , str ): raise ValueError ( \"loss_fn must be a string.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss_fn. Options are 'nll' or 'crps'.\" ) # Specify parameter dictionary param_dict = { f \"param_ { i + 1 } \" : identity_fn for i in range ( n_params )} torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Normalizing Flow Class super () . __init__ ( base_dist = Normal , # Base distribution, currently only Normal is supported. flow_transform = Spline , count_bins = count_bins , bound = bound , order = order , n_dist_param = n_params , param_dict = param_dict , target_transform = target_transform , discrete = discrete , univariate = True , stabilization = stabilization , loss_fn = loss_fn ) StudentT StudentT Bases: DistributionClass Student-T Distribution Class Distributional Parameters df: torch.Tensor Degrees of freedom. loc: torch.Tensor Mean of the distribution. scale: torch.Tensor Scale of the distribution. Source https://pytorch.org/docs/stable/distributions.html#studentt Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/StudentT.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 class StudentT ( DistributionClass ): \"\"\" Student-T Distribution Class Distributional Parameters ------------------------- df: torch.Tensor Degrees of freedom. loc: torch.Tensor Mean of the distribution. scale: torch.Tensor Scale of the distribution. Source ------------------------- https://pytorch.org/docs/stable/distributions.html#studentt Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : ( exp_fn , exp_fn_df ), \"softplus\" : ( softplus_fn , softplus_fn_df ) } if response_fn in response_functions : response_fn , response_fn_df = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = StudentT_Torch param_dict = { \"df\" : response_fn_df , \"loc\" : identity_fn , \"scale\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) Weibull Weibull Bases: DistributionClass Weibull distribution class. Distributional Parameters scale: torch.Tensor Scale parameter of distribution (lambda). concentration: torch.Tensor Concentration parameter of distribution (k/shape). Source https://pytorch.org/docs/stable/distributions.html#weibull Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/Weibull.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class Weibull ( DistributionClass ): \"\"\" Weibull distribution class. Distributional Parameters ------------------------- scale: torch.Tensor Scale parameter of distribution (lambda). concentration: torch.Tensor Concentration parameter of distribution (k/shape). Source ------------------------- https://pytorch.org/docs/stable/distributions.html#weibull Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = Weibull_Torch param_dict = { \"scale\" : response_fn , \"concentration\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) ZABeta ZABeta Bases: DistributionClass Zero-Adjusted Beta distribution class. The zero-adjusted Beta distribution is similar to the Beta distribution but allows zeros as y values. Distributional Parameters concentration1: torch.Tensor 1st concentration parameter of the distribution (often referred to as alpha). concentration0: torch.Tensor 2nd concentration parameter of the distribution (often referred to as beta). gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). Source code in lightgbmlss/distributions/ZABeta.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 class ZABeta ( DistributionClass ): \"\"\" Zero-Adjusted Beta distribution class. The zero-adjusted Beta distribution is similar to the Beta distribution but allows zeros as y values. Distributional Parameters ------------------------- concentration1: torch.Tensor 1st concentration parameter of the distribution (often referred to as alpha). concentration0: torch.Tensor 2nd concentration parameter of the distribution (often referred to as beta). gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source ------------------------- https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" ]: raise ValueError ( \"Invalid loss function. Please select 'nll'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = ZeroAdjustedBeta_Torch param_dict = { \"concentration1\" : response_fn , \"concentration0\" : response_fn , \"gate\" : sigmoid_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) ZAGamma ZAGamma Bases: DistributionClass Zero-Adjusted Gamma distribution class. The zero-adjusted Gamma distribution is similar to the Gamma distribution but allows zeros as y values. Distributional Parameters concentration: torch.Tensor shape parameter of the distribution (often referred to as alpha) rate: torch.Tensor rate = 1 / scale of the distribution (often referred to as beta) gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150 Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). Source code in lightgbmlss/distributions/ZAGamma.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 class ZAGamma ( DistributionClass ): \"\"\" Zero-Adjusted Gamma distribution class. The zero-adjusted Gamma distribution is similar to the Gamma distribution but allows zeros as y values. Distributional Parameters -------------------------- concentration: torch.Tensor shape parameter of the distribution (often referred to as alpha) rate: torch.Tensor rate = 1 / scale of the distribution (often referred to as beta) gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source ------------------------- https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150 Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" ]: raise ValueError ( \"Invalid loss function. Please select 'nll'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = ZeroAdjustedGamma_Torch param_dict = { \"concentration\" : response_fn , \"rate\" : response_fn , \"gate\" : sigmoid_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) ZALN ZALN Bases: DistributionClass Zero-Adjusted LogNormal distribution class. The zero-adjusted Log-Normal distribution is similar to the Log-Normal distribution but allows zeros as y values. Distributional Parameters loc: torch.Tensor Mean of log of distribution. scale: torch.Tensor Standard deviation of log of the distribution. gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150 Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). Source code in lightgbmlss/distributions/ZALN.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 class ZALN ( DistributionClass ): \"\"\" Zero-Adjusted LogNormal distribution class. The zero-adjusted Log-Normal distribution is similar to the Log-Normal distribution but allows zeros as y values. Distributional Parameters ------------------------- loc: torch.Tensor Mean of log of distribution. scale: torch.Tensor Standard deviation of log of the distribution. gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source ------------------------- https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150 Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" ]: raise ValueError ( \"Invalid loss function. Please select 'nll'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = ZeroAdjustedLogNormal_Torch param_dict = { \"loc\" : identity_fn , \"scale\" : response_fn , \"gate\" : sigmoid_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) ZINB ZINB Bases: DistributionClass Zero-Inflated Negative Binomial distribution class. Distributional Parameters total_count: torch.Tensor Non-negative number of negative Bernoulli trials to stop. probs: torch.Tensor Event probabilities of success in the half open interval [0, 1). gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution. Source https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150 Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn_total_count: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). response_fn_probs: str Response function for transforming the distributional parameters to the correct support. Options are \"sigmoid\" (sigmoid). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). Source code in lightgbmlss/distributions/ZINB.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 class ZINB ( DistributionClass ): \"\"\" Zero-Inflated Negative Binomial distribution class. Distributional Parameters ------------------------- total_count: torch.Tensor Non-negative number of negative Bernoulli trials to stop. probs: torch.Tensor Event probabilities of success in the half open interval [0, 1). gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution. Source ------------------------- https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150 Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn_total_count: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). response_fn_probs: str Response function for transforming the distributional parameters to the correct support. Options are \"sigmoid\" (sigmoid). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn_total_count : str = \"relu\" , response_fn_probs : str = \"sigmoid\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" ]: raise ValueError ( \"Invalid loss function. Please select 'nll'.\" ) # Specify Response Functions for total_count response_functions_total_count = { \"exp\" : exp_fn , \"softplus\" : softplus_fn , \"relu\" : relu_fn } if response_fn_total_count in response_functions_total_count : response_fn_total_count = response_functions_total_count [ response_fn_total_count ] else : raise ValueError ( \"Invalid response function for total_count. Please choose from 'exp', 'softplus' or 'relu'.\" ) # Specify Response Functions for probs response_functions_probs = { \"sigmoid\" : sigmoid_fn } if response_fn_probs in response_functions_probs : response_fn_probs = response_functions_probs [ response_fn_probs ] else : raise ValueError ( \"Invalid response function for probs. Please select 'sigmoid'.\" ) # Set the parameters specific to the distribution distribution = ZeroInflatedNegativeBinomial_Torch param_dict = { \"total_count\" : response_fn_total_count , \"probs\" : response_fn_probs , \"gate\" : sigmoid_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = True , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) ZIPoisson ZIPoisson Bases: DistributionClass Zero-Inflated Poisson distribution class. Distributional Parameters rate: torch.Tensor Rate parameter of the distribution (often referred to as lambda). gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution. Source https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L121 Parameters stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). Source code in lightgbmlss/distributions/ZIPoisson.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 class ZIPoisson ( DistributionClass ): \"\"\" Zero-Inflated Poisson distribution class. Distributional Parameters ------------------------- rate: torch.Tensor Rate parameter of the distribution (often referred to as lambda). gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution. Source ------------------------- https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L121 Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"relu\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" ]: raise ValueError ( \"Invalid loss function. Please select 'nll'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn , \"relu\" : relu_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function for total_count. Please choose from 'exp', 'softplus' or 'relu'.\" ) # Set the parameters specific to the distribution distribution = ZeroInflatedPoisson_Torch param_dict = { \"rate\" : response_fn , \"gate\" : sigmoid_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = True , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn ) distribution_utils DistributionClass Generic class that contains general functions for univariate distributions. Arguments distribution: torch.distributions.Distribution PyTorch Distribution class. univariate: bool Whether the distribution is univariate or multivariate. discrete: bool Whether the support of the distribution is discrete or continuous. n_dist_param: int Number of distributional parameters. stabilization: str Stabilization method. param_dict: Dict[str, Any] Dictionary that maps distributional parameters to their response scale. distribution_arg_names: List List of distributional parameter names. loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. tau: List List of expectiles. Only used for Expectile distributon. penalize_crossing: bool Whether to include a penalty term to discourage crossing of expectiles. Only used for Expectile distribution. Source code in lightgbmlss/distributions/distribution_utils.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 class DistributionClass : \"\"\" Generic class that contains general functions for univariate distributions. Arguments --------- distribution: torch.distributions.Distribution PyTorch Distribution class. univariate: bool Whether the distribution is univariate or multivariate. discrete: bool Whether the support of the distribution is discrete or continuous. n_dist_param: int Number of distributional parameters. stabilization: str Stabilization method. param_dict: Dict[str, Any] Dictionary that maps distributional parameters to their response scale. distribution_arg_names: List List of distributional parameter names. loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. tau: List List of expectiles. Only used for Expectile distributon. penalize_crossing: bool Whether to include a penalty term to discourage crossing of expectiles. Only used for Expectile distribution. \"\"\" def __init__ ( self , distribution : torch . distributions . Distribution = None , univariate : bool = True , discrete : bool = False , n_dist_param : int = None , stabilization : str = \"None\" , param_dict : Dict [ str , Any ] = None , distribution_arg_names : List = None , loss_fn : str = \"nll\" , tau : Optional [ List [ torch . Tensor ]] = None , penalize_crossing : bool = False , ): self . distribution = distribution self . univariate = univariate self . discrete = discrete self . n_dist_param = n_dist_param self . stabilization = stabilization self . param_dict = param_dict self . distribution_arg_names = distribution_arg_names self . loss_fn = loss_fn self . tau = tau self . penalize_crossing = penalize_crossing def objective_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Function to estimate gradients and hessians of distributional parameters. Arguments --------- predt: np.ndarray Predicted values. data: lgb.DMatrix Data used for training. Returns ------- grad: np.ndarray Gradient. hess: np.ndarray Hessian. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Weights if data . weight is None : # Use 1 as weight if no weights are specified weights = torch . ones_like ( target , dtype = target . dtype ) . numpy () else : weights = data . get_weight () . reshape ( - 1 , 1 ) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate gradients and hessians predt , loss = self . get_params_loss ( predt , target , start_values , requires_grad = True ) grad , hess = self . compute_gradients_and_hessians ( loss , predt , weights ) return grad , hess def metric_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ str , float , bool ]: \"\"\" Function that evaluates the predictions using the negative log-likelihood. Arguments --------- predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns ------- name: str Name of the evaluation metric. nll: float Negative log-likelihood. is_higher_better: bool Whether a higher value of the metric is better or not. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate loss is_higher_better = False _ , loss = self . get_params_loss ( predt , target , start_values , requires_grad = False ) return self . loss_fn , loss , is_higher_better def loss_fn_start_values ( self , params : torch . Tensor , target : torch . Tensor ) -> torch . Tensor : \"\"\" Function that calculates the loss for a given set of distributional parameters. Only used for calculating the loss for the start values. Parameter --------- params: torch.Tensor Distributional parameters. target: torch.Tensor Target values. Returns ------- loss: torch.Tensor Loss value. \"\"\" # Transform parameters to response scale params = [ response_fn ( params [ i ] . reshape ( - 1 , 1 )) for i , response_fn in enumerate ( self . param_dict . values ()) ] # Replace NaNs and infinity values with 0.5 nan_inf_idx = torch . isnan ( torch . stack ( params )) | torch . isinf ( torch . stack ( params )) params = torch . where ( nan_inf_idx , torch . tensor ( 0.5 ), torch . stack ( params )) # Specify Distribution and Loss if self . tau is None : dist = self . distribution ( * params ) loss = - torch . nansum ( dist . log_prob ( target )) else : dist = self . distribution ( params , self . penalize_crossing ) loss = - torch . nansum ( dist . log_prob ( target , self . tau )) return loss def calculate_start_values ( self , target : np . ndarray , max_iter : int = 50 ) -> Tuple [ float , np . ndarray ]: \"\"\" Function that calculates the starting values for each distributional parameter. Arguments --------- target: np.ndarray Data from which starting values are calculated. max_iter: int Maximum number of iterations. Returns ------- loss: float Loss value. start_values: np.ndarray Starting values for each distributional parameter. \"\"\" # Convert target to torch.tensor target = torch . tensor ( target ) . reshape ( - 1 , 1 ) # Initialize parameters params = [ torch . tensor ( 0.5 , requires_grad = True ) for _ in range ( self . n_dist_param )] # Specify optimizer optimizer = LBFGS ( params , lr = 0.1 , max_iter = np . min ([ int ( max_iter / 4 ), 20 ]), line_search_fn = \"strong_wolfe\" ) # Define learning rate scheduler lr_scheduler = ReduceLROnPlateau ( optimizer , mode = \"min\" , factor = 0.5 , patience = 10 ) # Define closure def closure (): optimizer . zero_grad () loss = self . loss_fn_start_values ( params , target ) loss . backward () return loss # Optimize parameters loss_vals = [] for epoch in range ( max_iter ): loss = optimizer . step ( closure ) lr_scheduler . step ( loss ) loss_vals . append ( loss . item ()) # Get final loss loss = np . array ( loss_vals [ - 1 ]) # Get start values start_values = np . array ([ params [ i ] . detach () for i in range ( self . n_dist_param )]) # Replace any remaining NaNs or infinity values with 0.5 start_values = np . nan_to_num ( start_values , nan = 0.5 , posinf = 0.5 , neginf = 0.5 ) return loss , start_values def get_params_loss ( self , predt : np . ndarray , target : torch . Tensor , start_values : List [ float ], requires_grad : bool = False , ) -> Tuple [ List [ torch . Tensor ], np . ndarray ]: \"\"\" Function that returns the predicted parameters and the loss. Arguments --------- predt: np.ndarray Predicted values. target: torch.Tensor Target values. start_values: List Starting values for each distributional parameter. requires_grad: bool Whether to add to the computational graph or not. Returns ------- predt: torch.Tensor Predicted parameters. loss: torch.Tensor Loss value. \"\"\" # Predicted Parameters predt = predt . reshape ( - 1 , self . n_dist_param , order = \"F\" ) # Replace NaNs and infinity values with unconditional start values nan_inf_mask = np . isnan ( predt ) | np . isinf ( predt ) predt [ nan_inf_mask ] = np . take ( start_values , np . where ( nan_inf_mask )[ 1 ]) # Convert to torch.tensor predt = [ torch . tensor ( predt [:, i ] . reshape ( - 1 , 1 ), requires_grad = requires_grad ) for i in range ( self . n_dist_param ) ] # Predicted Parameters transformed to response scale predt_transformed = [ response_fn ( predt [ i ] . reshape ( - 1 , 1 )) for i , response_fn in enumerate ( self . param_dict . values ()) ] # Specify Distribution and Loss if self . tau is None : dist_kwargs = dict ( zip ( self . distribution_arg_names , predt_transformed )) dist_fit = self . distribution ( ** dist_kwargs ) if self . loss_fn == \"nll\" : loss = - torch . nansum ( dist_fit . log_prob ( target )) elif self . loss_fn == \"crps\" : torch . manual_seed ( 123 ) dist_samples = dist_fit . rsample (( 30 ,)) . squeeze ( - 1 ) loss = torch . nansum ( self . crps_score ( target , dist_samples )) else : raise ValueError ( \"Invalid loss function. Please select 'nll' or 'crps'.\" ) else : dist_fit = self . distribution ( predt_transformed , self . penalize_crossing ) loss = - torch . nansum ( dist_fit . log_prob ( target , self . tau )) return predt , loss def draw_samples ( self , predt_params : pd . DataFrame , n_samples : int = 1000 , seed : int = 123 ) -> pd . DataFrame : \"\"\" Function that draws n_samples from a predicted distribution. Arguments --------- predt_params: pd.DataFrame pd.DataFrame with predicted distributional parameters. n_samples: int Number of sample to draw from predicted response distribution. seed: int Manual seed. Returns ------- pred_dist: pd.DataFrame DataFrame with n_samples drawn from predicted response distribution. \"\"\" torch . manual_seed ( seed ) if self . tau is None : pred_params = torch . tensor ( predt_params . values ) dist_kwargs = { arg_name : param for arg_name , param in zip ( self . distribution_arg_names , pred_params . T )} dist_pred = self . distribution ( ** dist_kwargs ) dist_samples = dist_pred . sample (( n_samples ,)) . squeeze () . detach () . numpy () . T dist_samples = pd . DataFrame ( dist_samples ) dist_samples . columns = [ str ( \"y_sample\" ) + str ( i ) for i in range ( dist_samples . shape [ 1 ])] else : dist_samples = None if self . discrete : dist_samples = dist_samples . astype ( int ) return dist_samples def predict_dist ( self , booster : lgb . Booster , data : pd . DataFrame , start_values : np . ndarray , pred_type : str = \"parameters\" , n_samples : int = 1000 , quantiles : list = [ 0.1 , 0.5 , 0.9 ], seed : str = 123 ) -> pd . DataFrame : \"\"\" Function that predicts from the trained model. Arguments --------- booster : lgb.Booster Trained model. data : pd.DataFrame Data to predict from. start_values : np.ndarray. Starting values for each distributional parameter. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns ------- pred : pd.DataFrame Predictions. \"\"\" predt = torch . tensor ( booster . predict ( data , raw_score = True ), dtype = torch . float32 ) . reshape ( - 1 , self . n_dist_param ) # Set init_score as starting point for each distributional parameter. init_score_pred = torch . tensor ( np . ones ( shape = ( data . shape [ 0 ], 1 )) * start_values , dtype = torch . float32 ) # The predictions don't include the init_score specified in creating the train data. # Hence, it needs to be added manually with the corresponding transform for each distributional parameter. dist_params_predt = np . concatenate ( [ response_fun ( predt [:, i ] . reshape ( - 1 , 1 ) + init_score_pred [:, i ] . reshape ( - 1 , 1 )) . numpy () for i , ( dist_param , response_fun ) in enumerate ( self . param_dict . items ()) ], axis = 1 , ) dist_params_predt = pd . DataFrame ( dist_params_predt ) dist_params_predt . columns = self . param_dict . keys () # Draw samples from predicted response distribution pred_samples_df = self . draw_samples ( predt_params = dist_params_predt , n_samples = n_samples , seed = seed ) if pred_type == \"parameters\" : return dist_params_predt elif pred_type == \"expectiles\" : return dist_params_predt elif pred_type == \"samples\" : return pred_samples_df elif pred_type == \"quantiles\" : # Calculate quantiles from predicted response distribution pred_quant_df = pred_samples_df . quantile ( quantiles , axis = 1 ) . T pred_quant_df . columns = [ str ( \"quant_\" ) + str ( quantiles [ i ]) for i in range ( len ( quantiles ))] if self . discrete : pred_quant_df = pred_quant_df . astype ( int ) return pred_quant_df def compute_gradients_and_hessians ( self , loss : torch . tensor , predt : torch . tensor , weights : np . ndarray ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Calculates gradients and hessians. Output gradients and hessians have shape (n_samples*n_outputs, 1). Arguments: --------- loss: torch.Tensor Loss. predt: torch.Tensor List of predicted parameters. weights: np.ndarray Weights. Returns: ------- grad: torch.Tensor Gradients. hess: torch.Tensor Hessians. \"\"\" if self . loss_fn == \"nll\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ autograd ( grad [ i ] . nansum (), inputs = predt [ i ], retain_graph = True )[ 0 ] for i in range ( len ( grad ))] elif self . loss_fn == \"crps\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ torch . ones_like ( grad [ i ]) for i in range ( len ( grad ))] # # Approximation of Hessian # step_size = 1e-6 # predt_upper = [ # response_fn(predt[i] + step_size).reshape(-1, 1) for i, response_fn in # enumerate(self.param_dict.values()) # ] # dist_kwargs_upper = dict(zip(self.distribution_arg_names, predt_upper)) # dist_fit_upper = self.distribution(**dist_kwargs_upper) # dist_samples_upper = dist_fit_upper.rsample((30,)).squeeze(-1) # loss_upper = torch.nansum(self.crps_score(self.target, dist_samples_upper)) # # predt_lower = [ # response_fn(predt[i] - step_size).reshape(-1, 1) for i, response_fn in # enumerate(self.param_dict.values()) # ] # dist_kwargs_lower = dict(zip(self.distribution_arg_names, predt_lower)) # dist_fit_lower = self.distribution(**dist_kwargs_lower) # dist_samples_lower = dist_fit_lower.rsample((30,)).squeeze(-1) # loss_lower = torch.nansum(self.crps_score(self.target, dist_samples_lower)) # # grad_upper = autograd(loss_upper, inputs=predt_upper) # grad_lower = autograd(loss_lower, inputs=predt_lower) # hess = [(grad_upper[i] - grad_lower[i]) / (2 * step_size) for i in range(len(grad))] # Stabilization of Derivatives if self . stabilization != \"None\" : grad = [ self . stabilize_derivative ( grad [ i ], type = self . stabilization ) for i in range ( len ( grad ))] hess = [ self . stabilize_derivative ( hess [ i ], type = self . stabilization ) for i in range ( len ( hess ))] # Reshape grad = torch . cat ( grad , axis = 1 ) . detach () . numpy () hess = torch . cat ( hess , axis = 1 ) . detach () . numpy () # Weighting grad *= weights hess *= weights # Reshape grad = grad . ravel ( order = \"F\" ) hess = hess . ravel ( order = \"F\" ) return grad , hess def stabilize_derivative ( self , input_der : torch . Tensor , type : str = \"MAD\" ) -> torch . Tensor : \"\"\" Function that stabilizes Gradients and Hessians. As LightGBMLSS updates the parameter estimates by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all distributional parameters. Due to imbalances regarding the ranges, the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve convergence might be to standardize the response variable. This is especially useful if the range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the standardization of the response are not always advised but need to be carefully considered. Source: https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173 Parameters ---------- input_der : torch.Tensor Input derivative, either Gradient or Hessian. type: str Stabilization method. Can be either \"None\", \"MAD\" or \"L2\". Returns ------- stab_der : torch.Tensor Stabilized Gradient or Hessian. \"\"\" if type == \"MAD\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . nanmedian ( torch . abs ( input_der - torch . nanmedian ( input_der ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) stab_der = input_der / div if type == \"L2\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . sqrt ( torch . nanmean ( input_der . pow ( 2 ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) div = torch . where ( div > torch . tensor ( 10000.0 ), torch . tensor ( 10000.0 ), div ) stab_der = input_der / div if type == \"None\" : stab_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) return stab_der def crps_score ( self , y : torch . tensor , yhat_dist : torch . tensor ) -> torch . tensor : \"\"\" Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples. Parameters ---------- y: torch.Tensor Response variable of shape (n_observations,1). yhat_dist: torch.Tensor Predicted samples of shape (n_samples, n_observations). Returns ------- crps: torch.Tensor CRPS score. References ---------- Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. 102. 359-378. Source ------ https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549 \"\"\" # Get the number of observations n_samples = yhat_dist . shape [ 0 ] # Sort the forecasts in ascending order yhat_dist_sorted , _ = torch . sort ( yhat_dist , 0 ) # Create temporary tensors y_cdf = torch . zeros_like ( y ) yhat_cdf = torch . zeros_like ( y ) yhat_prev = torch . zeros_like ( y ) crps = torch . zeros_like ( y ) # Loop over the predicted samples generated per observation for yhat in yhat_dist_sorted : yhat = yhat . reshape ( - 1 , 1 ) flag = ( y_cdf == 0 ) * ( y < yhat ) crps += flag * (( y - yhat_prev ) * yhat_cdf ** 2 ) crps += flag * (( yhat - y ) * ( yhat_cdf - 1 ) ** 2 ) crps += ( ~ flag ) * (( yhat - yhat_prev ) * ( yhat_cdf - y_cdf ) ** 2 ) y_cdf += flag yhat_cdf += 1 / n_samples yhat_prev = yhat # In case y_cdf == 0 after the loop flag = ( y_cdf == 0 ) crps += flag * ( y - yhat ) return crps def dist_select ( self , target : np . ndarray , candidate_distributions : List , max_iter : int = 100 , n_samples : int = 1000 , plot : bool = False , figure_size : tuple = ( 10 , 5 ), ) -> pd . DataFrame : \"\"\" Function that selects the most suitable distribution among the candidate_distributions for the target variable, based on the NegLogLikelihood (lower is better). Parameters ---------- target: np.ndarray Response variable. candidate_distributions: List List of candidate distributions. max_iter: int Maximum number of iterations for the optimization. n_samples: int Number of samples to draw from the fitted distribution. plot: bool If True, a density plot of the actual and fitted distribution is created. figure_size: tuple Figure size of the density plot. Returns ------- fit_df: pd.DataFrame Dataframe with the loss values of the fitted candidate distributions. \"\"\" dist_list = [] total_iterations = len ( candidate_distributions ) with tqdm ( total = total_iterations , desc = \"Fitting candidate distributions\" ) as pbar : for i in range ( len ( candidate_distributions )): dist_name = candidate_distributions [ i ] . __name__ . split ( \".\" )[ 2 ] pbar . set_description ( f \"Fitting { dist_name } distribution\" ) dist_sel = getattr ( candidate_distributions [ i ], dist_name )() try : loss , params = dist_sel . calculate_start_values ( target = target . reshape ( - 1 , 1 ), max_iter = max_iter ) fit_df = pd . DataFrame . from_dict ( { self . loss_fn : loss . reshape ( - 1 ,), \"distribution\" : str ( dist_name ), \"params\" : [ params ] } ) except Exception as e : warnings . warn ( f \"Error fitting { dist_name } distribution: { str ( e ) } \" ) fit_df = pd . DataFrame ( { self . loss_fn : np . nan , \"distribution\" : str ( dist_name ), \"params\" : [ np . nan ] * self . n_dist_param } ) dist_list . append ( fit_df ) fit_df = pd . concat ( dist_list ) . sort_values ( by = self . loss_fn , ascending = True ) fit_df [ \"rank\" ] = fit_df [ self . loss_fn ] . rank () . astype ( int ) fit_df . set_index ( fit_df [ \"rank\" ], inplace = True ) pbar . update ( 1 ) pbar . set_description ( f \"Fitting of candidate distributions completed\" ) if plot : # Select best distribution best_dist = fit_df [ fit_df [ \"rank\" ] == 1 ] . reset_index ( drop = True ) for dist in candidate_distributions : if dist . __name__ . split ( \".\" )[ 2 ] == best_dist [ \"distribution\" ] . values [ 0 ]: best_dist_sel = dist break best_dist_sel = getattr ( best_dist_sel , best_dist [ \"distribution\" ] . values [ 0 ])() params = torch . tensor ( best_dist [ \"params\" ][ 0 ]) . reshape ( - 1 , best_dist_sel . n_dist_param ) # Transform parameters to the response scale and draw samples fitted_params = np . concatenate ( [ response_fun ( params [:, i ] . reshape ( - 1 , 1 )) . numpy () for i , ( dist_param , response_fun ) in enumerate ( best_dist_sel . param_dict . items ()) ], axis = 1 , ) fitted_params = pd . DataFrame ( fitted_params , columns = best_dist_sel . param_dict . keys ()) fitted_params . columns = best_dist_sel . param_dict . keys () dist_samples = best_dist_sel . draw_samples ( fitted_params , n_samples = n_samples , seed = 123 ) . values # Plot actual and fitted distribution plot_df_actual = pd . DataFrame ({ \"y\" : target . reshape ( - 1 ,), \"type\" : \"Actual\" }) plot_df_fitted = pd . DataFrame ({ \"y\" : dist_samples . reshape ( - 1 ,), \"type\" : f \"Best-Fit: { best_dist [ 'distribution' ] . values [ 0 ] } \" }) plot_df = pd . concat ([ plot_df_actual , plot_df_fitted ]) print ( ggplot ( plot_df , aes ( x = \"y\" , color = \"type\" )) + geom_density ( alpha = 0.5 ) + theme_bw ( base_size = 15 ) + theme ( figure_size = figure_size , legend_position = \"right\" , legend_title = element_blank (), plot_title = element_text ( hjust = 0.5 )) + labs ( title = f \"Actual vs. Fitted Density\" ) ) fit_df . drop ( columns = [ \"rank\" , \"params\" ], inplace = True ) return fit_df calculate_start_values ( target , max_iter = 50 ) Function that calculates the starting values for each distributional parameter. Arguments target: np.ndarray Data from which starting values are calculated. max_iter: int Maximum number of iterations. Returns loss: float Loss value. start_values: np.ndarray Starting values for each distributional parameter. Source code in lightgbmlss/distributions/distribution_utils.py 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 def calculate_start_values ( self , target : np . ndarray , max_iter : int = 50 ) -> Tuple [ float , np . ndarray ]: \"\"\" Function that calculates the starting values for each distributional parameter. Arguments --------- target: np.ndarray Data from which starting values are calculated. max_iter: int Maximum number of iterations. Returns ------- loss: float Loss value. start_values: np.ndarray Starting values for each distributional parameter. \"\"\" # Convert target to torch.tensor target = torch . tensor ( target ) . reshape ( - 1 , 1 ) # Initialize parameters params = [ torch . tensor ( 0.5 , requires_grad = True ) for _ in range ( self . n_dist_param )] # Specify optimizer optimizer = LBFGS ( params , lr = 0.1 , max_iter = np . min ([ int ( max_iter / 4 ), 20 ]), line_search_fn = \"strong_wolfe\" ) # Define learning rate scheduler lr_scheduler = ReduceLROnPlateau ( optimizer , mode = \"min\" , factor = 0.5 , patience = 10 ) # Define closure def closure (): optimizer . zero_grad () loss = self . loss_fn_start_values ( params , target ) loss . backward () return loss # Optimize parameters loss_vals = [] for epoch in range ( max_iter ): loss = optimizer . step ( closure ) lr_scheduler . step ( loss ) loss_vals . append ( loss . item ()) # Get final loss loss = np . array ( loss_vals [ - 1 ]) # Get start values start_values = np . array ([ params [ i ] . detach () for i in range ( self . n_dist_param )]) # Replace any remaining NaNs or infinity values with 0.5 start_values = np . nan_to_num ( start_values , nan = 0.5 , posinf = 0.5 , neginf = 0.5 ) return loss , start_values compute_gradients_and_hessians ( loss , predt , weights ) Calculates gradients and hessians. Output gradients and hessians have shape (n_samples*n_outputs, 1). Arguments: loss: torch.Tensor Loss. predt: torch.Tensor List of predicted parameters. weights: np.ndarray Weights. Returns: grad: torch.Tensor Gradients. hess: torch.Tensor Hessians. Source code in lightgbmlss/distributions/distribution_utils.py 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 def compute_gradients_and_hessians ( self , loss : torch . tensor , predt : torch . tensor , weights : np . ndarray ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Calculates gradients and hessians. Output gradients and hessians have shape (n_samples*n_outputs, 1). Arguments: --------- loss: torch.Tensor Loss. predt: torch.Tensor List of predicted parameters. weights: np.ndarray Weights. Returns: ------- grad: torch.Tensor Gradients. hess: torch.Tensor Hessians. \"\"\" if self . loss_fn == \"nll\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ autograd ( grad [ i ] . nansum (), inputs = predt [ i ], retain_graph = True )[ 0 ] for i in range ( len ( grad ))] elif self . loss_fn == \"crps\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ torch . ones_like ( grad [ i ]) for i in range ( len ( grad ))] # # Approximation of Hessian # step_size = 1e-6 # predt_upper = [ # response_fn(predt[i] + step_size).reshape(-1, 1) for i, response_fn in # enumerate(self.param_dict.values()) # ] # dist_kwargs_upper = dict(zip(self.distribution_arg_names, predt_upper)) # dist_fit_upper = self.distribution(**dist_kwargs_upper) # dist_samples_upper = dist_fit_upper.rsample((30,)).squeeze(-1) # loss_upper = torch.nansum(self.crps_score(self.target, dist_samples_upper)) # # predt_lower = [ # response_fn(predt[i] - step_size).reshape(-1, 1) for i, response_fn in # enumerate(self.param_dict.values()) # ] # dist_kwargs_lower = dict(zip(self.distribution_arg_names, predt_lower)) # dist_fit_lower = self.distribution(**dist_kwargs_lower) # dist_samples_lower = dist_fit_lower.rsample((30,)).squeeze(-1) # loss_lower = torch.nansum(self.crps_score(self.target, dist_samples_lower)) # # grad_upper = autograd(loss_upper, inputs=predt_upper) # grad_lower = autograd(loss_lower, inputs=predt_lower) # hess = [(grad_upper[i] - grad_lower[i]) / (2 * step_size) for i in range(len(grad))] # Stabilization of Derivatives if self . stabilization != \"None\" : grad = [ self . stabilize_derivative ( grad [ i ], type = self . stabilization ) for i in range ( len ( grad ))] hess = [ self . stabilize_derivative ( hess [ i ], type = self . stabilization ) for i in range ( len ( hess ))] # Reshape grad = torch . cat ( grad , axis = 1 ) . detach () . numpy () hess = torch . cat ( hess , axis = 1 ) . detach () . numpy () # Weighting grad *= weights hess *= weights # Reshape grad = grad . ravel ( order = \"F\" ) hess = hess . ravel ( order = \"F\" ) return grad , hess crps_score ( y , yhat_dist ) Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples. Parameters y: torch.Tensor Response variable of shape (n_observations,1). yhat_dist: torch.Tensor Predicted samples of shape (n_samples, n_observations). Returns crps: torch.Tensor CRPS score. References Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. 102. 359-378. Source https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549 Source code in lightgbmlss/distributions/distribution_utils.py 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 def crps_score ( self , y : torch . tensor , yhat_dist : torch . tensor ) -> torch . tensor : \"\"\" Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples. Parameters ---------- y: torch.Tensor Response variable of shape (n_observations,1). yhat_dist: torch.Tensor Predicted samples of shape (n_samples, n_observations). Returns ------- crps: torch.Tensor CRPS score. References ---------- Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. 102. 359-378. Source ------ https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549 \"\"\" # Get the number of observations n_samples = yhat_dist . shape [ 0 ] # Sort the forecasts in ascending order yhat_dist_sorted , _ = torch . sort ( yhat_dist , 0 ) # Create temporary tensors y_cdf = torch . zeros_like ( y ) yhat_cdf = torch . zeros_like ( y ) yhat_prev = torch . zeros_like ( y ) crps = torch . zeros_like ( y ) # Loop over the predicted samples generated per observation for yhat in yhat_dist_sorted : yhat = yhat . reshape ( - 1 , 1 ) flag = ( y_cdf == 0 ) * ( y < yhat ) crps += flag * (( y - yhat_prev ) * yhat_cdf ** 2 ) crps += flag * (( yhat - y ) * ( yhat_cdf - 1 ) ** 2 ) crps += ( ~ flag ) * (( yhat - yhat_prev ) * ( yhat_cdf - y_cdf ) ** 2 ) y_cdf += flag yhat_cdf += 1 / n_samples yhat_prev = yhat # In case y_cdf == 0 after the loop flag = ( y_cdf == 0 ) crps += flag * ( y - yhat ) return crps dist_select ( target , candidate_distributions , max_iter = 100 , n_samples = 1000 , plot = False , figure_size = ( 10 , 5 )) Function that selects the most suitable distribution among the candidate_distributions for the target variable, based on the NegLogLikelihood (lower is better). Parameters target: np.ndarray Response variable. candidate_distributions: List List of candidate distributions. max_iter: int Maximum number of iterations for the optimization. n_samples: int Number of samples to draw from the fitted distribution. plot: bool If True, a density plot of the actual and fitted distribution is created. figure_size: tuple Figure size of the density plot. Returns fit_df: pd.DataFrame Dataframe with the loss values of the fitted candidate distributions. Source code in lightgbmlss/distributions/distribution_utils.py 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 def dist_select ( self , target : np . ndarray , candidate_distributions : List , max_iter : int = 100 , n_samples : int = 1000 , plot : bool = False , figure_size : tuple = ( 10 , 5 ), ) -> pd . DataFrame : \"\"\" Function that selects the most suitable distribution among the candidate_distributions for the target variable, based on the NegLogLikelihood (lower is better). Parameters ---------- target: np.ndarray Response variable. candidate_distributions: List List of candidate distributions. max_iter: int Maximum number of iterations for the optimization. n_samples: int Number of samples to draw from the fitted distribution. plot: bool If True, a density plot of the actual and fitted distribution is created. figure_size: tuple Figure size of the density plot. Returns ------- fit_df: pd.DataFrame Dataframe with the loss values of the fitted candidate distributions. \"\"\" dist_list = [] total_iterations = len ( candidate_distributions ) with tqdm ( total = total_iterations , desc = \"Fitting candidate distributions\" ) as pbar : for i in range ( len ( candidate_distributions )): dist_name = candidate_distributions [ i ] . __name__ . split ( \".\" )[ 2 ] pbar . set_description ( f \"Fitting { dist_name } distribution\" ) dist_sel = getattr ( candidate_distributions [ i ], dist_name )() try : loss , params = dist_sel . calculate_start_values ( target = target . reshape ( - 1 , 1 ), max_iter = max_iter ) fit_df = pd . DataFrame . from_dict ( { self . loss_fn : loss . reshape ( - 1 ,), \"distribution\" : str ( dist_name ), \"params\" : [ params ] } ) except Exception as e : warnings . warn ( f \"Error fitting { dist_name } distribution: { str ( e ) } \" ) fit_df = pd . DataFrame ( { self . loss_fn : np . nan , \"distribution\" : str ( dist_name ), \"params\" : [ np . nan ] * self . n_dist_param } ) dist_list . append ( fit_df ) fit_df = pd . concat ( dist_list ) . sort_values ( by = self . loss_fn , ascending = True ) fit_df [ \"rank\" ] = fit_df [ self . loss_fn ] . rank () . astype ( int ) fit_df . set_index ( fit_df [ \"rank\" ], inplace = True ) pbar . update ( 1 ) pbar . set_description ( f \"Fitting of candidate distributions completed\" ) if plot : # Select best distribution best_dist = fit_df [ fit_df [ \"rank\" ] == 1 ] . reset_index ( drop = True ) for dist in candidate_distributions : if dist . __name__ . split ( \".\" )[ 2 ] == best_dist [ \"distribution\" ] . values [ 0 ]: best_dist_sel = dist break best_dist_sel = getattr ( best_dist_sel , best_dist [ \"distribution\" ] . values [ 0 ])() params = torch . tensor ( best_dist [ \"params\" ][ 0 ]) . reshape ( - 1 , best_dist_sel . n_dist_param ) # Transform parameters to the response scale and draw samples fitted_params = np . concatenate ( [ response_fun ( params [:, i ] . reshape ( - 1 , 1 )) . numpy () for i , ( dist_param , response_fun ) in enumerate ( best_dist_sel . param_dict . items ()) ], axis = 1 , ) fitted_params = pd . DataFrame ( fitted_params , columns = best_dist_sel . param_dict . keys ()) fitted_params . columns = best_dist_sel . param_dict . keys () dist_samples = best_dist_sel . draw_samples ( fitted_params , n_samples = n_samples , seed = 123 ) . values # Plot actual and fitted distribution plot_df_actual = pd . DataFrame ({ \"y\" : target . reshape ( - 1 ,), \"type\" : \"Actual\" }) plot_df_fitted = pd . DataFrame ({ \"y\" : dist_samples . reshape ( - 1 ,), \"type\" : f \"Best-Fit: { best_dist [ 'distribution' ] . values [ 0 ] } \" }) plot_df = pd . concat ([ plot_df_actual , plot_df_fitted ]) print ( ggplot ( plot_df , aes ( x = \"y\" , color = \"type\" )) + geom_density ( alpha = 0.5 ) + theme_bw ( base_size = 15 ) + theme ( figure_size = figure_size , legend_position = \"right\" , legend_title = element_blank (), plot_title = element_text ( hjust = 0.5 )) + labs ( title = f \"Actual vs. Fitted Density\" ) ) fit_df . drop ( columns = [ \"rank\" , \"params\" ], inplace = True ) return fit_df draw_samples ( predt_params , n_samples = 1000 , seed = 123 ) Function that draws n_samples from a predicted distribution. Arguments predt_params: pd.DataFrame pd.DataFrame with predicted distributional parameters. n_samples: int Number of sample to draw from predicted response distribution. seed: int Manual seed. Returns pred_dist: pd.DataFrame DataFrame with n_samples drawn from predicted response distribution. Source code in lightgbmlss/distributions/distribution_utils.py 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 def draw_samples ( self , predt_params : pd . DataFrame , n_samples : int = 1000 , seed : int = 123 ) -> pd . DataFrame : \"\"\" Function that draws n_samples from a predicted distribution. Arguments --------- predt_params: pd.DataFrame pd.DataFrame with predicted distributional parameters. n_samples: int Number of sample to draw from predicted response distribution. seed: int Manual seed. Returns ------- pred_dist: pd.DataFrame DataFrame with n_samples drawn from predicted response distribution. \"\"\" torch . manual_seed ( seed ) if self . tau is None : pred_params = torch . tensor ( predt_params . values ) dist_kwargs = { arg_name : param for arg_name , param in zip ( self . distribution_arg_names , pred_params . T )} dist_pred = self . distribution ( ** dist_kwargs ) dist_samples = dist_pred . sample (( n_samples ,)) . squeeze () . detach () . numpy () . T dist_samples = pd . DataFrame ( dist_samples ) dist_samples . columns = [ str ( \"y_sample\" ) + str ( i ) for i in range ( dist_samples . shape [ 1 ])] else : dist_samples = None if self . discrete : dist_samples = dist_samples . astype ( int ) return dist_samples get_params_loss ( predt , target , start_values , requires_grad = False ) Function that returns the predicted parameters and the loss. Arguments predt: np.ndarray Predicted values. target: torch.Tensor Target values. start_values: List Starting values for each distributional parameter. requires_grad: bool Whether to add to the computational graph or not. Returns predt: torch.Tensor Predicted parameters. loss: torch.Tensor Loss value. Source code in lightgbmlss/distributions/distribution_utils.py 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 def get_params_loss ( self , predt : np . ndarray , target : torch . Tensor , start_values : List [ float ], requires_grad : bool = False , ) -> Tuple [ List [ torch . Tensor ], np . ndarray ]: \"\"\" Function that returns the predicted parameters and the loss. Arguments --------- predt: np.ndarray Predicted values. target: torch.Tensor Target values. start_values: List Starting values for each distributional parameter. requires_grad: bool Whether to add to the computational graph or not. Returns ------- predt: torch.Tensor Predicted parameters. loss: torch.Tensor Loss value. \"\"\" # Predicted Parameters predt = predt . reshape ( - 1 , self . n_dist_param , order = \"F\" ) # Replace NaNs and infinity values with unconditional start values nan_inf_mask = np . isnan ( predt ) | np . isinf ( predt ) predt [ nan_inf_mask ] = np . take ( start_values , np . where ( nan_inf_mask )[ 1 ]) # Convert to torch.tensor predt = [ torch . tensor ( predt [:, i ] . reshape ( - 1 , 1 ), requires_grad = requires_grad ) for i in range ( self . n_dist_param ) ] # Predicted Parameters transformed to response scale predt_transformed = [ response_fn ( predt [ i ] . reshape ( - 1 , 1 )) for i , response_fn in enumerate ( self . param_dict . values ()) ] # Specify Distribution and Loss if self . tau is None : dist_kwargs = dict ( zip ( self . distribution_arg_names , predt_transformed )) dist_fit = self . distribution ( ** dist_kwargs ) if self . loss_fn == \"nll\" : loss = - torch . nansum ( dist_fit . log_prob ( target )) elif self . loss_fn == \"crps\" : torch . manual_seed ( 123 ) dist_samples = dist_fit . rsample (( 30 ,)) . squeeze ( - 1 ) loss = torch . nansum ( self . crps_score ( target , dist_samples )) else : raise ValueError ( \"Invalid loss function. Please select 'nll' or 'crps'.\" ) else : dist_fit = self . distribution ( predt_transformed , self . penalize_crossing ) loss = - torch . nansum ( dist_fit . log_prob ( target , self . tau )) return predt , loss loss_fn_start_values ( params , target ) Function that calculates the loss for a given set of distributional parameters. Only used for calculating the loss for the start values. Parameter params: torch.Tensor Distributional parameters. target: torch.Tensor Target values. Returns loss: torch.Tensor Loss value. Source code in lightgbmlss/distributions/distribution_utils.py 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 def loss_fn_start_values ( self , params : torch . Tensor , target : torch . Tensor ) -> torch . Tensor : \"\"\" Function that calculates the loss for a given set of distributional parameters. Only used for calculating the loss for the start values. Parameter --------- params: torch.Tensor Distributional parameters. target: torch.Tensor Target values. Returns ------- loss: torch.Tensor Loss value. \"\"\" # Transform parameters to response scale params = [ response_fn ( params [ i ] . reshape ( - 1 , 1 )) for i , response_fn in enumerate ( self . param_dict . values ()) ] # Replace NaNs and infinity values with 0.5 nan_inf_idx = torch . isnan ( torch . stack ( params )) | torch . isinf ( torch . stack ( params )) params = torch . where ( nan_inf_idx , torch . tensor ( 0.5 ), torch . stack ( params )) # Specify Distribution and Loss if self . tau is None : dist = self . distribution ( * params ) loss = - torch . nansum ( dist . log_prob ( target )) else : dist = self . distribution ( params , self . penalize_crossing ) loss = - torch . nansum ( dist . log_prob ( target , self . tau )) return loss metric_fn ( predt , data ) Function that evaluates the predictions using the negative log-likelihood. Arguments predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns name: str Name of the evaluation metric. nll: float Negative log-likelihood. is_higher_better: bool Whether a higher value of the metric is better or not. Source code in lightgbmlss/distributions/distribution_utils.py 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 def metric_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ str , float , bool ]: \"\"\" Function that evaluates the predictions using the negative log-likelihood. Arguments --------- predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns ------- name: str Name of the evaluation metric. nll: float Negative log-likelihood. is_higher_better: bool Whether a higher value of the metric is better or not. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate loss is_higher_better = False _ , loss = self . get_params_loss ( predt , target , start_values , requires_grad = False ) return self . loss_fn , loss , is_higher_better objective_fn ( predt , data ) Function to estimate gradients and hessians of distributional parameters. Arguments predt: np.ndarray Predicted values. data: lgb.DMatrix Data used for training. Returns grad: np.ndarray Gradient. hess: np.ndarray Hessian. Source code in lightgbmlss/distributions/distribution_utils.py 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def objective_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Function to estimate gradients and hessians of distributional parameters. Arguments --------- predt: np.ndarray Predicted values. data: lgb.DMatrix Data used for training. Returns ------- grad: np.ndarray Gradient. hess: np.ndarray Hessian. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Weights if data . weight is None : # Use 1 as weight if no weights are specified weights = torch . ones_like ( target , dtype = target . dtype ) . numpy () else : weights = data . get_weight () . reshape ( - 1 , 1 ) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate gradients and hessians predt , loss = self . get_params_loss ( predt , target , start_values , requires_grad = True ) grad , hess = self . compute_gradients_and_hessians ( loss , predt , weights ) return grad , hess predict_dist ( booster , data , start_values , pred_type = 'parameters' , n_samples = 1000 , quantiles = [ 0.1 , 0.5 , 0.9 ], seed = 123 ) Function that predicts from the trained model. Arguments booster : lgb.Booster Trained model. data : pd.DataFrame Data to predict from. start_values : np.ndarray. Starting values for each distributional parameter. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns pred : pd.DataFrame Predictions. Source code in lightgbmlss/distributions/distribution_utils.py 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 def predict_dist ( self , booster : lgb . Booster , data : pd . DataFrame , start_values : np . ndarray , pred_type : str = \"parameters\" , n_samples : int = 1000 , quantiles : list = [ 0.1 , 0.5 , 0.9 ], seed : str = 123 ) -> pd . DataFrame : \"\"\" Function that predicts from the trained model. Arguments --------- booster : lgb.Booster Trained model. data : pd.DataFrame Data to predict from. start_values : np.ndarray. Starting values for each distributional parameter. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns ------- pred : pd.DataFrame Predictions. \"\"\" predt = torch . tensor ( booster . predict ( data , raw_score = True ), dtype = torch . float32 ) . reshape ( - 1 , self . n_dist_param ) # Set init_score as starting point for each distributional parameter. init_score_pred = torch . tensor ( np . ones ( shape = ( data . shape [ 0 ], 1 )) * start_values , dtype = torch . float32 ) # The predictions don't include the init_score specified in creating the train data. # Hence, it needs to be added manually with the corresponding transform for each distributional parameter. dist_params_predt = np . concatenate ( [ response_fun ( predt [:, i ] . reshape ( - 1 , 1 ) + init_score_pred [:, i ] . reshape ( - 1 , 1 )) . numpy () for i , ( dist_param , response_fun ) in enumerate ( self . param_dict . items ()) ], axis = 1 , ) dist_params_predt = pd . DataFrame ( dist_params_predt ) dist_params_predt . columns = self . param_dict . keys () # Draw samples from predicted response distribution pred_samples_df = self . draw_samples ( predt_params = dist_params_predt , n_samples = n_samples , seed = seed ) if pred_type == \"parameters\" : return dist_params_predt elif pred_type == \"expectiles\" : return dist_params_predt elif pred_type == \"samples\" : return pred_samples_df elif pred_type == \"quantiles\" : # Calculate quantiles from predicted response distribution pred_quant_df = pred_samples_df . quantile ( quantiles , axis = 1 ) . T pred_quant_df . columns = [ str ( \"quant_\" ) + str ( quantiles [ i ]) for i in range ( len ( quantiles ))] if self . discrete : pred_quant_df = pred_quant_df . astype ( int ) return pred_quant_df stabilize_derivative ( input_der , type = 'MAD' ) Function that stabilizes Gradients and Hessians. As LightGBMLSS updates the parameter estimates by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all distributional parameters. Due to imbalances regarding the ranges, the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve convergence might be to standardize the response variable. This is especially useful if the range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the standardization of the response are not always advised but need to be carefully considered. Source: https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173 Parameters input_der : torch.Tensor Input derivative, either Gradient or Hessian. type: str Stabilization method. Can be either \"None\", \"MAD\" or \"L2\". Returns stab_der : torch.Tensor Stabilized Gradient or Hessian. Source code in lightgbmlss/distributions/distribution_utils.py 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 def stabilize_derivative ( self , input_der : torch . Tensor , type : str = \"MAD\" ) -> torch . Tensor : \"\"\" Function that stabilizes Gradients and Hessians. As LightGBMLSS updates the parameter estimates by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all distributional parameters. Due to imbalances regarding the ranges, the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve convergence might be to standardize the response variable. This is especially useful if the range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the standardization of the response are not always advised but need to be carefully considered. Source: https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173 Parameters ---------- input_der : torch.Tensor Input derivative, either Gradient or Hessian. type: str Stabilization method. Can be either \"None\", \"MAD\" or \"L2\". Returns ------- stab_der : torch.Tensor Stabilized Gradient or Hessian. \"\"\" if type == \"MAD\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . nanmedian ( torch . abs ( input_der - torch . nanmedian ( input_der ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) stab_der = input_der / div if type == \"L2\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . sqrt ( torch . nanmean ( input_der . pow ( 2 ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) div = torch . where ( div > torch . tensor ( 10000.0 ), torch . tensor ( 10000.0 ), div ) stab_der = input_der / div if type == \"None\" : stab_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) return stab_der flow_utils NormalizingFlowClass Generic class that contains general functions for normalizing flows. Arguments base_dist: torch.distributions.Distribution PyTorch Distribution class. Currently only Normal is supported. flow_transform: Transform Specify the normalizing flow transform. count_bins: Optional[int] The number of segments comprising the spline. Only used if flow_transform is Spline. bound: Optional[float] The quantity \"K\" determining the bounding box, [-K,K] x [-K,K] of the spline. By adjusting the \"K\" value, you can control the size of the bounding box and consequently control the range of inputs that the spline transform operates on. Larger values of \"K\" will result in a wider valid range for the spline transformation, while smaller values will restrict the valid range to a smaller region. Should be chosen based on the range of the data. Only used if flow_transform is Spline. order: Optional[str] The order of the spline. Options are \"linear\" or \"quadratic\". Only used if flow_transform is Spline. n_dist_param: int Number of parameters. param_dict: Dict[str, Any] Dictionary that maps parameters to their response scale. target_transform: Transform Specify the target transform. discrete: bool Whether the target is discrete or not. univariate: bool Whether the distribution is univariate or multivariate. stabilization: str Stabilization method. Options are \"None\", \"MAD\" or \"L2\". loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/flow_utils.py 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 class NormalizingFlowClass : \"\"\" Generic class that contains general functions for normalizing flows. Arguments --------- base_dist: torch.distributions.Distribution PyTorch Distribution class. Currently only Normal is supported. flow_transform: Transform Specify the normalizing flow transform. count_bins: Optional[int] The number of segments comprising the spline. Only used if flow_transform is Spline. bound: Optional[float] The quantity \"K\" determining the bounding box, [-K,K] x [-K,K] of the spline. By adjusting the \"K\" value, you can control the size of the bounding box and consequently control the range of inputs that the spline transform operates on. Larger values of \"K\" will result in a wider valid range for the spline transformation, while smaller values will restrict the valid range to a smaller region. Should be chosen based on the range of the data. Only used if flow_transform is Spline. order: Optional[str] The order of the spline. Options are \"linear\" or \"quadratic\". Only used if flow_transform is Spline. n_dist_param: int Number of parameters. param_dict: Dict[str, Any] Dictionary that maps parameters to their response scale. target_transform: Transform Specify the target transform. discrete: bool Whether the target is discrete or not. univariate: bool Whether the distribution is univariate or multivariate. stabilization: str Stabilization method. Options are \"None\", \"MAD\" or \"L2\". loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , base_dist : torch . distributions . Distribution = None , flow_transform : Transform = None , count_bins : Optional [ int ] = 8 , bound : Optional [ float ] = 3.0 , order : Optional [ str ] = \"quadratic\" , n_dist_param : int = None , param_dict : Dict [ str , Any ] = None , target_transform : Transform = None , discrete : bool = False , univariate : bool = True , stabilization : str = \"None\" , loss_fn : str = \"nll\" , ): self . base_dist = base_dist self . flow_transform = flow_transform self . count_bins = count_bins self . bound = bound self . order = order self . n_dist_param = n_dist_param self . param_dict = param_dict self . target_transform = target_transform self . discrete = discrete self . univariate = univariate self . stabilization = stabilization self . loss_fn = loss_fn def objective_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Function to estimate gradients and hessians of normalizing flow parameters. Arguments --------- predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns ------- grad: np.ndarray Gradient. hess: np.ndarray Hessian. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Weights if data . weight is None : # Use 1 as weight if no weights are specified weights = torch . ones_like ( target , dtype = target . dtype ) . numpy () else : weights = data . get_weight () . reshape ( - 1 , 1 ) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate gradients and hessians predt , loss = self . get_params_loss ( predt , target , start_values ) grad , hess = self . compute_gradients_and_hessians ( loss , predt , weights ) return grad , hess def metric_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ str , float , bool ]: \"\"\" Function that evaluates the predictions using the specified loss function. Arguments --------- predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns ------- name: str Name of the evaluation metric. loss: float Loss value. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate loss is_higher_better = False _ , loss = self . get_params_loss ( predt , target , start_values ) return self . loss_fn , loss . detach (), is_higher_better def calculate_start_values ( self , target : np . ndarray , max_iter : int = 50 ) -> Tuple [ float , np . ndarray ]: \"\"\" Function that calculates starting values for each parameter. Arguments --------- target: np.ndarray Data from which starting values are calculated. max_iter: int Maximum number of iterations. Returns ------- loss: float Loss value. start_values: np.ndarray Starting values for each parameter. \"\"\" # Convert target to torch.tensor target = torch . tensor ( target ) . reshape ( - 1 , 1 ) # Create Normalizing Flow flow_dist = self . create_spline_flow ( input_dim = 1 ) # Specify optimizer optimizer = LBFGS ( flow_dist . transforms [ 0 ] . parameters (), lr = 0.3 , max_iter = np . min ([ int ( max_iter / 4 ), 50 ]), line_search_fn = \"strong_wolfe\" ) # Define learning rate scheduler lr_scheduler = ReduceLROnPlateau ( optimizer , mode = \"min\" , factor = 0.5 , patience = 5 ) # Define closure def closure (): optimizer . zero_grad () loss = - torch . nansum ( flow_dist . log_prob ( target )) loss . backward () flow_dist . clear_cache () return loss # Optimize parameters loss_vals = [] tolerance = 1e-5 # Tolerance level for loss change patience = 5 # Patience level for loss change best_loss = float ( \"inf\" ) epochs_without_change = 0 for epoch in range ( max_iter ): optimizer . zero_grad () loss = optimizer . step ( closure ) lr_scheduler . step ( loss ) loss_vals . append ( loss . item ()) # Stopping criterion (no improvement in loss) if loss . item () < best_loss - tolerance : best_loss = loss . item () epochs_without_change = 0 else : epochs_without_change += 1 if epochs_without_change >= patience : break # Get final loss loss = np . array ( loss_vals [ - 1 ]) # Get start values start_values = list ( flow_dist . transforms [ 0 ] . parameters ()) start_values = torch . cat ([ param . view ( - 1 ) for param in start_values ]) . detach () . numpy () # Replace any remaining NaNs or infinity values with 0.5 start_values = np . nan_to_num ( start_values , nan = 0.5 , posinf = 0.5 , neginf = 0.5 ) return loss , start_values def get_params_loss ( self , predt : np . ndarray , target : torch . Tensor , start_values : List [ float ], requires_grad : bool = False , ) -> Tuple [ List [ torch . Tensor ], np . ndarray ]: \"\"\" Function that returns the predicted parameters and the loss. Arguments --------- predt: np.ndarray Predicted values. target: torch.Tensor Target values. start_values: List Starting values for each parameter. Returns ------- predt: torch.Tensor Predicted parameters. loss: torch.Tensor Loss value. \"\"\" # Reshape Target target = target . view ( - 1 ) # Predicted Parameters predt = predt . reshape ( - 1 , self . n_dist_param , order = \"F\" ) # Replace NaNs and infinity values with unconditional start values nan_inf_mask = np . isnan ( predt ) | np . isinf ( predt ) predt [ nan_inf_mask ] = np . take ( start_values , np . where ( nan_inf_mask )[ 1 ]) # Convert to torch.tensor predt = torch . tensor ( predt , dtype = torch . float32 ) # Specify Normalizing Flow flow_dist = self . create_spline_flow ( target . shape [ 0 ]) # Replace parameters with estimated ones params , flow_dist = self . replace_parameters ( predt , flow_dist ) # Calculate loss if self . loss_fn == \"nll\" : loss = - torch . nansum ( flow_dist . log_prob ( target )) elif self . loss_fn == \"crps\" : torch . manual_seed ( 123 ) dist_samples = flow_dist . rsample (( 30 ,)) . squeeze ( - 1 ) loss = torch . nansum ( self . crps_score ( target , dist_samples )) else : raise ValueError ( \"Invalid loss function. Please select 'nll' or 'crps'.\" ) return params , loss def create_spline_flow ( self , input_dim : int = None , ) -> Transform : \"\"\" Function that constructs a Normalizing Flow. Arguments --------- input_dim: int Input dimension. Returns ------- spline_flow: Transform Normalizing Flow. \"\"\" # Create flow distribution (currently only Normal) loc , scale = torch . zeros ( input_dim ), torch . ones ( input_dim ) flow_dist = self . base_dist ( loc , scale ) # Create Spline Transform torch . manual_seed ( 123 ) spline_transform = self . flow_transform ( input_dim , count_bins = self . count_bins , bound = self . bound , order = self . order ) # Create Normalizing Flow spline_flow = TransformedDistribution ( flow_dist , [ spline_transform , self . target_transform ]) return spline_flow def replace_parameters ( self , params : torch . Tensor , flow_dist : Transform , ) -> Tuple [ List , Transform ]: \"\"\" Replace parameters with estimated ones. Arguments --------- params: torch.Tensor Estimated parameters. flow_dist: Transform Normalizing Flow. Returns ------- params_list: List List of estimated parameters. flow_dist: Transform Normalizing Flow with estimated parameters. \"\"\" # Split parameters into list if self . order == \"quadratic\" : params_list = torch . split ( params , [ self . count_bins , self . count_bins , self . count_bins - 1 ], dim = 1 ) elif self . order == \"linear\" : params_list = torch . split ( params , [ self . count_bins , self . count_bins , self . count_bins - 1 , self . count_bins ], dim = 1 ) # Replace parameters for param , new_value in zip ( flow_dist . transforms [ 0 ] . parameters (), params_list ): param . data = new_value # Get parameters (including require_grad=True) params_list = list ( flow_dist . transforms [ 0 ] . parameters ()) return params_list , flow_dist def draw_samples ( self , predt_params : pd . DataFrame , n_samples : int = 1000 , seed : int = 123 ) -> pd . DataFrame : \"\"\" Function that draws n_samples from a predicted distribution. Arguments --------- predt_params: pd.DataFrame pd.DataFrame with predicted distributional parameters. n_samples: int Number of sample to draw from predicted response distribution. seed: int Manual seed. Returns ------- pred_dist: pd.DataFrame DataFrame with n_samples drawn from predicted response distribution. \"\"\" torch . manual_seed ( seed ) # Specify Normalizing Flow pred_params = torch . tensor ( predt_params . values ) flow_dist_pred = self . create_spline_flow ( pred_params . shape [ 0 ]) # Replace parameters with estimated ones _ , flow_dist_pred = self . replace_parameters ( pred_params , flow_dist_pred ) # Draw samples flow_samples = pd . DataFrame ( flow_dist_pred . sample (( n_samples ,)) . squeeze () . detach () . numpy () . T ) flow_samples . columns = [ str ( \"y_sample\" ) + str ( i ) for i in range ( flow_samples . shape [ 1 ])] if self . discrete : flow_samples = flow_samples . astype ( int ) return flow_samples def predict_dist ( self , booster : lgb . Booster , data : pd . DataFrame , start_values : np . ndarray , pred_type : str = \"parameters\" , n_samples : int = 1000 , quantiles : list = [ 0.1 , 0.5 , 0.9 ], seed : str = 123 ) -> pd . DataFrame : \"\"\" Function that predicts from the trained model. Arguments --------- booster : lgb.Booster Trained model. start_values : np.ndarray Starting values for each distributional parameter. data : pd.DataFrame Data to predict from. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns ------- pred : pd.DataFrame Predictions. \"\"\" # Predict raw scores predt = torch . tensor ( booster . predict ( data , raw_score = True ), dtype = torch . float32 ) . reshape ( - 1 , self . n_dist_param ) # Set init_score as starting point for each distributional parameter. init_score_pred = torch . tensor ( np . ones ( shape = ( data . shape [ 0 ], 1 )) * start_values , dtype = torch . float32 ) # The predictions don't include the init_score specified in creating the train data. Hence, it needs to be # added manually. dist_params_predt = pd . DataFrame ( np . concatenate ( [ predt [:, i ] . reshape ( - 1 , 1 ) + init_score_pred [:, i ] . reshape ( - 1 , 1 ) for i in range ( self . n_dist_param )], axis = 1 ) ) dist_params_predt . columns = self . param_dict . keys () # Draw samples from predicted response distribution pred_samples_df = self . draw_samples ( predt_params = dist_params_predt , n_samples = n_samples , seed = seed ) if pred_type == \"parameters\" : return dist_params_predt elif pred_type == \"samples\" : return pred_samples_df elif pred_type == \"quantiles\" : # Calculate quantiles from predicted response distribution pred_quant_df = pred_samples_df . quantile ( quantiles , axis = 1 ) . T pred_quant_df . columns = [ str ( \"quant_\" ) + str ( quantiles [ i ]) for i in range ( len ( quantiles ))] if self . discrete : pred_quant_df = pred_quant_df . astype ( int ) return pred_quant_df def compute_gradients_and_hessians ( self , loss : torch . tensor , predt : torch . tensor , weights : np . ndarray ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Calculates gradients and hessians. Output gradients and hessians have shape (n_samples*n_outputs, 1). Arguments: --------- loss: torch.Tensor Loss. predt: torch.Tensor List of predicted parameters. weights: np.ndarray Weights. Returns: ------- grad: torch.Tensor Gradients. hess: torch.Tensor Hessians. \"\"\" if self . loss_fn == \"nll\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ autograd ( grad [ i ] . nansum (), inputs = predt [ i ], retain_graph = True )[ 0 ] for i in range ( len ( grad ))] elif self . loss_fn == \"crps\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ torch . ones_like ( grad [ i ]) for i in range ( len ( grad ))] # Stabilization of Derivatives if self . stabilization != \"None\" : grad = [ self . stabilize_derivative ( grad [ i ], type = self . stabilization ) for i in range ( len ( grad ))] hess = [ self . stabilize_derivative ( hess [ i ], type = self . stabilization ) for i in range ( len ( hess ))] # Reshape grad = torch . cat ( grad , axis = 1 ) . detach () . numpy () hess = torch . cat ( hess , axis = 1 ) . detach () . numpy () # Weighting grad *= weights hess *= weights # Reshape grad = grad . ravel ( order = \"F\" ) hess = hess . ravel ( order = \"F\" ) return grad , hess def stabilize_derivative ( self , input_der : torch . Tensor , type : str = \"MAD\" ) -> torch . Tensor : \"\"\" Function that stabilizes Gradients and Hessians. Since parameters are estimated by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all parameters. Due to imbalances regarding the ranges, the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve convergence might be to standardize the response variable. This is especially useful if the range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the standardization of the response are not always advised but need to be carefully considered. Source --------- https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173 Arguments --------- input_der : torch.Tensor Input derivative, either Gradient or Hessian. type: str Stabilization method. Can be either \"None\", \"MAD\" or \"L2\". Returns --------- stab_der : torch.Tensor Stabilized Gradient or Hessian. \"\"\" if type == \"MAD\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . nanmedian ( torch . abs ( input_der - torch . nanmedian ( input_der ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) stab_der = input_der / div if type == \"L2\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . sqrt ( torch . nanmean ( input_der . pow ( 2 ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) div = torch . where ( div > torch . tensor ( 10000.0 ), torch . tensor ( 10000.0 ), div ) stab_der = input_der / div if type == \"None\" : stab_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) return stab_der def crps_score ( self , y : torch . tensor , yhat_dist : torch . tensor ) -> torch . tensor : \"\"\" Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples. Arguments --------- y: torch.Tensor Response variable of shape (n_observations,1). yhat_dist: torch.Tensor Predicted samples of shape (n_samples, n_observations). Returns --------- crps: torch.Tensor CRPS score. References --------- Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. 102. 359-378. Source --------- https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549 \"\"\" # Get the number of observations n_samples = yhat_dist . shape [ 0 ] # Sort the forecasts in ascending order yhat_dist_sorted , _ = torch . sort ( yhat_dist , 0 ) # Create temporary tensors y_cdf = torch . zeros_like ( y ) yhat_cdf = torch . zeros_like ( y ) yhat_prev = torch . zeros_like ( y ) crps = torch . zeros_like ( y ) # Loop over the predicted samples generated per observation for yhat in yhat_dist_sorted : yhat = yhat . reshape ( - 1 , 1 ) flag = ( y_cdf == 0 ) * ( y < yhat ) crps += flag * (( y - yhat_prev ) * yhat_cdf ** 2 ) crps += flag * (( yhat - y ) * ( yhat_cdf - 1 ) ** 2 ) crps += ( ~ flag ) * (( yhat - yhat_prev ) * ( yhat_cdf - y_cdf ) ** 2 ) y_cdf += flag yhat_cdf += 1 / n_samples yhat_prev = yhat # In case y_cdf == 0 after the loop flag = ( y_cdf == 0 ) crps += flag * ( y - yhat ) return crps def flow_select ( self , target : np . ndarray , candidate_flows : List , max_iter : int = 100 , n_samples : int = 1000 , plot : bool = False , figure_size : tuple = ( 10 , 5 ), ) -> pd . DataFrame : \"\"\" Function that selects the most suitable normalizing flow specification among the candidate_flow for the target variable, based on the NegLogLikelihood (lower is better). Parameters ---------- target: np.ndarray Response variable. candidate_flows: List List of candidate normalizing flow specifications. max_iter: int Maximum number of iterations for the optimization. n_samples: int Number of samples drawn from the fitted distribution. plot: bool If True, a density plot of the actual and fitted distribution is created. figure_size: tuple Figure size of the density plot. Returns ------- fit_df: pd.DataFrame Dataframe with the loss values of the fitted normalizing flow. \"\"\" flow_list = [] total_iterations = len ( candidate_flows ) with tqdm ( total = total_iterations , desc = \"Fitting candidate normalizing flows\" ) as pbar : for flow in candidate_flows : flow_name = str ( flow . __class__ ) . split ( \".\" )[ - 1 ] . split ( \"'>\" )[ 0 ] flow_spec = f \"(count_bins: { flow . count_bins } , order: { flow . order } )\" flow_name = flow_name + flow_spec pbar . set_description ( f \"Fitting { flow_name } \" ) flow_sel = flow try : loss , params = flow_sel . calculate_start_values ( target = target , max_iter = max_iter ) fit_df = pd . DataFrame . from_dict ( { flow_sel . loss_fn : loss . reshape ( - 1 , ), \"NormFlow\" : str ( flow_name ), \"params\" : [ params ] } ) except Exception as e : warnings . warn ( f \"Error fitting { flow_sel } NormFlow: { str ( e ) } \" ) fit_df = pd . DataFrame ( { flow_sel . loss_fn : np . nan , \"NormFlow\" : str ( flow_sel ), \"params\" : [ np . nan ] * flow_sel . n_dist_param } ) flow_list . append ( fit_df ) fit_df = pd . concat ( flow_list ) . sort_values ( by = flow_sel . loss_fn , ascending = True ) fit_df [ \"rank\" ] = fit_df [ flow_sel . loss_fn ] . rank () . astype ( int ) fit_df . set_index ( fit_df [ \"rank\" ], inplace = True ) pbar . update ( 1 ) pbar . set_description ( f \"Fitting of candidate normalizing flows completed\" ) if plot : # Select normalizing flow with the lowest loss best_flow = fit_df [ fit_df [ \"rank\" ] == 1 ] . reset_index ( drop = True ) for flow in candidate_flows : flow_name = str ( flow . __class__ ) . split ( \".\" )[ - 1 ] . split ( \"'>\" )[ 0 ] flow_spec = f \"(count_bins: { flow . count_bins } , order: { flow . order } )\" flow_name = flow_name + flow_spec if flow_name == best_flow [ \"NormFlow\" ] . values [ 0 ]: best_flow_sel = flow break # Draw samples from distribution flow_params = torch . tensor ( best_flow [ \"params\" ][ 0 ]) . reshape ( 1 , - 1 ) flow_dist_sel = best_flow_sel . create_spline_flow ( input_dim = 1 ) _ , flow_dist_sel = best_flow_sel . replace_parameters ( flow_params , flow_dist_sel ) flow_samples = pd . DataFrame ( flow_dist_sel . sample (( n_samples ,)) . squeeze () . detach () . numpy () . T ) # Plot actual and fitted distribution flow_samples [ \"type\" ] = f \"Best-Fit: { best_flow [ 'NormFlow' ] . values [ 0 ] } \" df_actual = pd . DataFrame ( target ) df_actual [ \"type\" ] = \"Data\" plot_df = pd . concat ([ df_actual , flow_samples ]) . rename ( columns = { 0 : \"variable\" }) print ( ggplot ( plot_df , aes ( x = \"variable\" , color = \"type\" )) + geom_density ( size = 1.1 ) + theme_bw ( base_size = 15 ) + theme ( figure_size = figure_size , legend_position = \"right\" , legend_title = element_blank (), plot_title = element_text ( hjust = 0.5 )) + labs ( title = f \"Actual vs. Fitted Density\" , x = \"\" ) ) fit_df . drop ( columns = [ \"rank\" , \"params\" ], inplace = True ) return fit_df calculate_start_values ( target , max_iter = 50 ) Function that calculates starting values for each parameter. Arguments target: np.ndarray Data from which starting values are calculated. max_iter: int Maximum number of iterations. Returns loss: float Loss value. start_values: np.ndarray Starting values for each parameter. Source code in lightgbmlss/distributions/flow_utils.py 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 def calculate_start_values ( self , target : np . ndarray , max_iter : int = 50 ) -> Tuple [ float , np . ndarray ]: \"\"\" Function that calculates starting values for each parameter. Arguments --------- target: np.ndarray Data from which starting values are calculated. max_iter: int Maximum number of iterations. Returns ------- loss: float Loss value. start_values: np.ndarray Starting values for each parameter. \"\"\" # Convert target to torch.tensor target = torch . tensor ( target ) . reshape ( - 1 , 1 ) # Create Normalizing Flow flow_dist = self . create_spline_flow ( input_dim = 1 ) # Specify optimizer optimizer = LBFGS ( flow_dist . transforms [ 0 ] . parameters (), lr = 0.3 , max_iter = np . min ([ int ( max_iter / 4 ), 50 ]), line_search_fn = \"strong_wolfe\" ) # Define learning rate scheduler lr_scheduler = ReduceLROnPlateau ( optimizer , mode = \"min\" , factor = 0.5 , patience = 5 ) # Define closure def closure (): optimizer . zero_grad () loss = - torch . nansum ( flow_dist . log_prob ( target )) loss . backward () flow_dist . clear_cache () return loss # Optimize parameters loss_vals = [] tolerance = 1e-5 # Tolerance level for loss change patience = 5 # Patience level for loss change best_loss = float ( \"inf\" ) epochs_without_change = 0 for epoch in range ( max_iter ): optimizer . zero_grad () loss = optimizer . step ( closure ) lr_scheduler . step ( loss ) loss_vals . append ( loss . item ()) # Stopping criterion (no improvement in loss) if loss . item () < best_loss - tolerance : best_loss = loss . item () epochs_without_change = 0 else : epochs_without_change += 1 if epochs_without_change >= patience : break # Get final loss loss = np . array ( loss_vals [ - 1 ]) # Get start values start_values = list ( flow_dist . transforms [ 0 ] . parameters ()) start_values = torch . cat ([ param . view ( - 1 ) for param in start_values ]) . detach () . numpy () # Replace any remaining NaNs or infinity values with 0.5 start_values = np . nan_to_num ( start_values , nan = 0.5 , posinf = 0.5 , neginf = 0.5 ) return loss , start_values compute_gradients_and_hessians ( loss , predt , weights ) Calculates gradients and hessians. Output gradients and hessians have shape (n_samples*n_outputs, 1). Arguments: loss: torch.Tensor Loss. predt: torch.Tensor List of predicted parameters. weights: np.ndarray Weights. Returns: grad: torch.Tensor Gradients. hess: torch.Tensor Hessians. Source code in lightgbmlss/distributions/flow_utils.py 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 def compute_gradients_and_hessians ( self , loss : torch . tensor , predt : torch . tensor , weights : np . ndarray ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Calculates gradients and hessians. Output gradients and hessians have shape (n_samples*n_outputs, 1). Arguments: --------- loss: torch.Tensor Loss. predt: torch.Tensor List of predicted parameters. weights: np.ndarray Weights. Returns: ------- grad: torch.Tensor Gradients. hess: torch.Tensor Hessians. \"\"\" if self . loss_fn == \"nll\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ autograd ( grad [ i ] . nansum (), inputs = predt [ i ], retain_graph = True )[ 0 ] for i in range ( len ( grad ))] elif self . loss_fn == \"crps\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ torch . ones_like ( grad [ i ]) for i in range ( len ( grad ))] # Stabilization of Derivatives if self . stabilization != \"None\" : grad = [ self . stabilize_derivative ( grad [ i ], type = self . stabilization ) for i in range ( len ( grad ))] hess = [ self . stabilize_derivative ( hess [ i ], type = self . stabilization ) for i in range ( len ( hess ))] # Reshape grad = torch . cat ( grad , axis = 1 ) . detach () . numpy () hess = torch . cat ( hess , axis = 1 ) . detach () . numpy () # Weighting grad *= weights hess *= weights # Reshape grad = grad . ravel ( order = \"F\" ) hess = hess . ravel ( order = \"F\" ) return grad , hess create_spline_flow ( input_dim = None ) Function that constructs a Normalizing Flow. Arguments input_dim: int Input dimension. Returns spline_flow: Transform Normalizing Flow. Source code in lightgbmlss/distributions/flow_utils.py 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 def create_spline_flow ( self , input_dim : int = None , ) -> Transform : \"\"\" Function that constructs a Normalizing Flow. Arguments --------- input_dim: int Input dimension. Returns ------- spline_flow: Transform Normalizing Flow. \"\"\" # Create flow distribution (currently only Normal) loc , scale = torch . zeros ( input_dim ), torch . ones ( input_dim ) flow_dist = self . base_dist ( loc , scale ) # Create Spline Transform torch . manual_seed ( 123 ) spline_transform = self . flow_transform ( input_dim , count_bins = self . count_bins , bound = self . bound , order = self . order ) # Create Normalizing Flow spline_flow = TransformedDistribution ( flow_dist , [ spline_transform , self . target_transform ]) return spline_flow crps_score ( y , yhat_dist ) Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples. Arguments y: torch.Tensor Response variable of shape (n_observations,1). yhat_dist: torch.Tensor Predicted samples of shape (n_samples, n_observations). Returns crps: torch.Tensor CRPS score. References Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. 102. 359-378. Source https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549 Source code in lightgbmlss/distributions/flow_utils.py 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 def crps_score ( self , y : torch . tensor , yhat_dist : torch . tensor ) -> torch . tensor : \"\"\" Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples. Arguments --------- y: torch.Tensor Response variable of shape (n_observations,1). yhat_dist: torch.Tensor Predicted samples of shape (n_samples, n_observations). Returns --------- crps: torch.Tensor CRPS score. References --------- Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. 102. 359-378. Source --------- https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549 \"\"\" # Get the number of observations n_samples = yhat_dist . shape [ 0 ] # Sort the forecasts in ascending order yhat_dist_sorted , _ = torch . sort ( yhat_dist , 0 ) # Create temporary tensors y_cdf = torch . zeros_like ( y ) yhat_cdf = torch . zeros_like ( y ) yhat_prev = torch . zeros_like ( y ) crps = torch . zeros_like ( y ) # Loop over the predicted samples generated per observation for yhat in yhat_dist_sorted : yhat = yhat . reshape ( - 1 , 1 ) flag = ( y_cdf == 0 ) * ( y < yhat ) crps += flag * (( y - yhat_prev ) * yhat_cdf ** 2 ) crps += flag * (( yhat - y ) * ( yhat_cdf - 1 ) ** 2 ) crps += ( ~ flag ) * (( yhat - yhat_prev ) * ( yhat_cdf - y_cdf ) ** 2 ) y_cdf += flag yhat_cdf += 1 / n_samples yhat_prev = yhat # In case y_cdf == 0 after the loop flag = ( y_cdf == 0 ) crps += flag * ( y - yhat ) return crps draw_samples ( predt_params , n_samples = 1000 , seed = 123 ) Function that draws n_samples from a predicted distribution. Arguments predt_params: pd.DataFrame pd.DataFrame with predicted distributional parameters. n_samples: int Number of sample to draw from predicted response distribution. seed: int Manual seed. Returns pred_dist: pd.DataFrame DataFrame with n_samples drawn from predicted response distribution. Source code in lightgbmlss/distributions/flow_utils.py 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 def draw_samples ( self , predt_params : pd . DataFrame , n_samples : int = 1000 , seed : int = 123 ) -> pd . DataFrame : \"\"\" Function that draws n_samples from a predicted distribution. Arguments --------- predt_params: pd.DataFrame pd.DataFrame with predicted distributional parameters. n_samples: int Number of sample to draw from predicted response distribution. seed: int Manual seed. Returns ------- pred_dist: pd.DataFrame DataFrame with n_samples drawn from predicted response distribution. \"\"\" torch . manual_seed ( seed ) # Specify Normalizing Flow pred_params = torch . tensor ( predt_params . values ) flow_dist_pred = self . create_spline_flow ( pred_params . shape [ 0 ]) # Replace parameters with estimated ones _ , flow_dist_pred = self . replace_parameters ( pred_params , flow_dist_pred ) # Draw samples flow_samples = pd . DataFrame ( flow_dist_pred . sample (( n_samples ,)) . squeeze () . detach () . numpy () . T ) flow_samples . columns = [ str ( \"y_sample\" ) + str ( i ) for i in range ( flow_samples . shape [ 1 ])] if self . discrete : flow_samples = flow_samples . astype ( int ) return flow_samples flow_select ( target , candidate_flows , max_iter = 100 , n_samples = 1000 , plot = False , figure_size = ( 10 , 5 )) Function that selects the most suitable normalizing flow specification among the candidate_flow for the target variable, based on the NegLogLikelihood (lower is better). Parameters target: np.ndarray Response variable. candidate_flows: List List of candidate normalizing flow specifications. max_iter: int Maximum number of iterations for the optimization. n_samples: int Number of samples drawn from the fitted distribution. plot: bool If True, a density plot of the actual and fitted distribution is created. figure_size: tuple Figure size of the density plot. Returns fit_df: pd.DataFrame Dataframe with the loss values of the fitted normalizing flow. Source code in lightgbmlss/distributions/flow_utils.py 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 def flow_select ( self , target : np . ndarray , candidate_flows : List , max_iter : int = 100 , n_samples : int = 1000 , plot : bool = False , figure_size : tuple = ( 10 , 5 ), ) -> pd . DataFrame : \"\"\" Function that selects the most suitable normalizing flow specification among the candidate_flow for the target variable, based on the NegLogLikelihood (lower is better). Parameters ---------- target: np.ndarray Response variable. candidate_flows: List List of candidate normalizing flow specifications. max_iter: int Maximum number of iterations for the optimization. n_samples: int Number of samples drawn from the fitted distribution. plot: bool If True, a density plot of the actual and fitted distribution is created. figure_size: tuple Figure size of the density plot. Returns ------- fit_df: pd.DataFrame Dataframe with the loss values of the fitted normalizing flow. \"\"\" flow_list = [] total_iterations = len ( candidate_flows ) with tqdm ( total = total_iterations , desc = \"Fitting candidate normalizing flows\" ) as pbar : for flow in candidate_flows : flow_name = str ( flow . __class__ ) . split ( \".\" )[ - 1 ] . split ( \"'>\" )[ 0 ] flow_spec = f \"(count_bins: { flow . count_bins } , order: { flow . order } )\" flow_name = flow_name + flow_spec pbar . set_description ( f \"Fitting { flow_name } \" ) flow_sel = flow try : loss , params = flow_sel . calculate_start_values ( target = target , max_iter = max_iter ) fit_df = pd . DataFrame . from_dict ( { flow_sel . loss_fn : loss . reshape ( - 1 , ), \"NormFlow\" : str ( flow_name ), \"params\" : [ params ] } ) except Exception as e : warnings . warn ( f \"Error fitting { flow_sel } NormFlow: { str ( e ) } \" ) fit_df = pd . DataFrame ( { flow_sel . loss_fn : np . nan , \"NormFlow\" : str ( flow_sel ), \"params\" : [ np . nan ] * flow_sel . n_dist_param } ) flow_list . append ( fit_df ) fit_df = pd . concat ( flow_list ) . sort_values ( by = flow_sel . loss_fn , ascending = True ) fit_df [ \"rank\" ] = fit_df [ flow_sel . loss_fn ] . rank () . astype ( int ) fit_df . set_index ( fit_df [ \"rank\" ], inplace = True ) pbar . update ( 1 ) pbar . set_description ( f \"Fitting of candidate normalizing flows completed\" ) if plot : # Select normalizing flow with the lowest loss best_flow = fit_df [ fit_df [ \"rank\" ] == 1 ] . reset_index ( drop = True ) for flow in candidate_flows : flow_name = str ( flow . __class__ ) . split ( \".\" )[ - 1 ] . split ( \"'>\" )[ 0 ] flow_spec = f \"(count_bins: { flow . count_bins } , order: { flow . order } )\" flow_name = flow_name + flow_spec if flow_name == best_flow [ \"NormFlow\" ] . values [ 0 ]: best_flow_sel = flow break # Draw samples from distribution flow_params = torch . tensor ( best_flow [ \"params\" ][ 0 ]) . reshape ( 1 , - 1 ) flow_dist_sel = best_flow_sel . create_spline_flow ( input_dim = 1 ) _ , flow_dist_sel = best_flow_sel . replace_parameters ( flow_params , flow_dist_sel ) flow_samples = pd . DataFrame ( flow_dist_sel . sample (( n_samples ,)) . squeeze () . detach () . numpy () . T ) # Plot actual and fitted distribution flow_samples [ \"type\" ] = f \"Best-Fit: { best_flow [ 'NormFlow' ] . values [ 0 ] } \" df_actual = pd . DataFrame ( target ) df_actual [ \"type\" ] = \"Data\" plot_df = pd . concat ([ df_actual , flow_samples ]) . rename ( columns = { 0 : \"variable\" }) print ( ggplot ( plot_df , aes ( x = \"variable\" , color = \"type\" )) + geom_density ( size = 1.1 ) + theme_bw ( base_size = 15 ) + theme ( figure_size = figure_size , legend_position = \"right\" , legend_title = element_blank (), plot_title = element_text ( hjust = 0.5 )) + labs ( title = f \"Actual vs. Fitted Density\" , x = \"\" ) ) fit_df . drop ( columns = [ \"rank\" , \"params\" ], inplace = True ) return fit_df get_params_loss ( predt , target , start_values , requires_grad = False ) Function that returns the predicted parameters and the loss. Arguments predt: np.ndarray Predicted values. target: torch.Tensor Target values. start_values: List Starting values for each parameter. Returns predt: torch.Tensor Predicted parameters. loss: torch.Tensor Loss value. Source code in lightgbmlss/distributions/flow_utils.py 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 def get_params_loss ( self , predt : np . ndarray , target : torch . Tensor , start_values : List [ float ], requires_grad : bool = False , ) -> Tuple [ List [ torch . Tensor ], np . ndarray ]: \"\"\" Function that returns the predicted parameters and the loss. Arguments --------- predt: np.ndarray Predicted values. target: torch.Tensor Target values. start_values: List Starting values for each parameter. Returns ------- predt: torch.Tensor Predicted parameters. loss: torch.Tensor Loss value. \"\"\" # Reshape Target target = target . view ( - 1 ) # Predicted Parameters predt = predt . reshape ( - 1 , self . n_dist_param , order = \"F\" ) # Replace NaNs and infinity values with unconditional start values nan_inf_mask = np . isnan ( predt ) | np . isinf ( predt ) predt [ nan_inf_mask ] = np . take ( start_values , np . where ( nan_inf_mask )[ 1 ]) # Convert to torch.tensor predt = torch . tensor ( predt , dtype = torch . float32 ) # Specify Normalizing Flow flow_dist = self . create_spline_flow ( target . shape [ 0 ]) # Replace parameters with estimated ones params , flow_dist = self . replace_parameters ( predt , flow_dist ) # Calculate loss if self . loss_fn == \"nll\" : loss = - torch . nansum ( flow_dist . log_prob ( target )) elif self . loss_fn == \"crps\" : torch . manual_seed ( 123 ) dist_samples = flow_dist . rsample (( 30 ,)) . squeeze ( - 1 ) loss = torch . nansum ( self . crps_score ( target , dist_samples )) else : raise ValueError ( \"Invalid loss function. Please select 'nll' or 'crps'.\" ) return params , loss metric_fn ( predt , data ) Function that evaluates the predictions using the specified loss function. Arguments predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns name: str Name of the evaluation metric. loss: float Loss value. Source code in lightgbmlss/distributions/flow_utils.py 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 def metric_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ str , float , bool ]: \"\"\" Function that evaluates the predictions using the specified loss function. Arguments --------- predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns ------- name: str Name of the evaluation metric. loss: float Loss value. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate loss is_higher_better = False _ , loss = self . get_params_loss ( predt , target , start_values ) return self . loss_fn , loss . detach (), is_higher_better objective_fn ( predt , data ) Function to estimate gradients and hessians of normalizing flow parameters. Arguments predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns grad: np.ndarray Gradient. hess: np.ndarray Hessian. Source code in lightgbmlss/distributions/flow_utils.py 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 def objective_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Function to estimate gradients and hessians of normalizing flow parameters. Arguments --------- predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns ------- grad: np.ndarray Gradient. hess: np.ndarray Hessian. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Weights if data . weight is None : # Use 1 as weight if no weights are specified weights = torch . ones_like ( target , dtype = target . dtype ) . numpy () else : weights = data . get_weight () . reshape ( - 1 , 1 ) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate gradients and hessians predt , loss = self . get_params_loss ( predt , target , start_values ) grad , hess = self . compute_gradients_and_hessians ( loss , predt , weights ) return grad , hess predict_dist ( booster , data , start_values , pred_type = 'parameters' , n_samples = 1000 , quantiles = [ 0.1 , 0.5 , 0.9 ], seed = 123 ) Function that predicts from the trained model. Arguments booster : lgb.Booster Trained model. start_values : np.ndarray Starting values for each distributional parameter. data : pd.DataFrame Data to predict from. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns pred : pd.DataFrame Predictions. Source code in lightgbmlss/distributions/flow_utils.py 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 def predict_dist ( self , booster : lgb . Booster , data : pd . DataFrame , start_values : np . ndarray , pred_type : str = \"parameters\" , n_samples : int = 1000 , quantiles : list = [ 0.1 , 0.5 , 0.9 ], seed : str = 123 ) -> pd . DataFrame : \"\"\" Function that predicts from the trained model. Arguments --------- booster : lgb.Booster Trained model. start_values : np.ndarray Starting values for each distributional parameter. data : pd.DataFrame Data to predict from. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns ------- pred : pd.DataFrame Predictions. \"\"\" # Predict raw scores predt = torch . tensor ( booster . predict ( data , raw_score = True ), dtype = torch . float32 ) . reshape ( - 1 , self . n_dist_param ) # Set init_score as starting point for each distributional parameter. init_score_pred = torch . tensor ( np . ones ( shape = ( data . shape [ 0 ], 1 )) * start_values , dtype = torch . float32 ) # The predictions don't include the init_score specified in creating the train data. Hence, it needs to be # added manually. dist_params_predt = pd . DataFrame ( np . concatenate ( [ predt [:, i ] . reshape ( - 1 , 1 ) + init_score_pred [:, i ] . reshape ( - 1 , 1 ) for i in range ( self . n_dist_param )], axis = 1 ) ) dist_params_predt . columns = self . param_dict . keys () # Draw samples from predicted response distribution pred_samples_df = self . draw_samples ( predt_params = dist_params_predt , n_samples = n_samples , seed = seed ) if pred_type == \"parameters\" : return dist_params_predt elif pred_type == \"samples\" : return pred_samples_df elif pred_type == \"quantiles\" : # Calculate quantiles from predicted response distribution pred_quant_df = pred_samples_df . quantile ( quantiles , axis = 1 ) . T pred_quant_df . columns = [ str ( \"quant_\" ) + str ( quantiles [ i ]) for i in range ( len ( quantiles ))] if self . discrete : pred_quant_df = pred_quant_df . astype ( int ) return pred_quant_df replace_parameters ( params , flow_dist ) Replace parameters with estimated ones. Arguments params: torch.Tensor Estimated parameters. flow_dist: Transform Normalizing Flow. Returns params_list: List List of estimated parameters. flow_dist: Transform Normalizing Flow with estimated parameters. Source code in lightgbmlss/distributions/flow_utils.py 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 def replace_parameters ( self , params : torch . Tensor , flow_dist : Transform , ) -> Tuple [ List , Transform ]: \"\"\" Replace parameters with estimated ones. Arguments --------- params: torch.Tensor Estimated parameters. flow_dist: Transform Normalizing Flow. Returns ------- params_list: List List of estimated parameters. flow_dist: Transform Normalizing Flow with estimated parameters. \"\"\" # Split parameters into list if self . order == \"quadratic\" : params_list = torch . split ( params , [ self . count_bins , self . count_bins , self . count_bins - 1 ], dim = 1 ) elif self . order == \"linear\" : params_list = torch . split ( params , [ self . count_bins , self . count_bins , self . count_bins - 1 , self . count_bins ], dim = 1 ) # Replace parameters for param , new_value in zip ( flow_dist . transforms [ 0 ] . parameters (), params_list ): param . data = new_value # Get parameters (including require_grad=True) params_list = list ( flow_dist . transforms [ 0 ] . parameters ()) return params_list , flow_dist stabilize_derivative ( input_der , type = 'MAD' ) Function that stabilizes Gradients and Hessians. Since parameters are estimated by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all parameters. Due to imbalances regarding the ranges, the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve convergence might be to standardize the response variable. This is especially useful if the range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the standardization of the response are not always advised but need to be carefully considered. Source https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173 Arguments input_der : torch.Tensor Input derivative, either Gradient or Hessian. type: str Stabilization method. Can be either \"None\", \"MAD\" or \"L2\". Returns stab_der : torch.Tensor Stabilized Gradient or Hessian. Source code in lightgbmlss/distributions/flow_utils.py 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 def stabilize_derivative ( self , input_der : torch . Tensor , type : str = \"MAD\" ) -> torch . Tensor : \"\"\" Function that stabilizes Gradients and Hessians. Since parameters are estimated by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all parameters. Due to imbalances regarding the ranges, the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve convergence might be to standardize the response variable. This is especially useful if the range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the standardization of the response are not always advised but need to be carefully considered. Source --------- https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173 Arguments --------- input_der : torch.Tensor Input derivative, either Gradient or Hessian. type: str Stabilization method. Can be either \"None\", \"MAD\" or \"L2\". Returns --------- stab_der : torch.Tensor Stabilized Gradient or Hessian. \"\"\" if type == \"MAD\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . nanmedian ( torch . abs ( input_der - torch . nanmedian ( input_der ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) stab_der = input_der / div if type == \"L2\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . sqrt ( torch . nanmean ( input_der . pow ( 2 ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) div = torch . where ( div > torch . tensor ( 10000.0 ), torch . tensor ( 10000.0 ), div ) stab_der = input_der / div if type == \"None\" : stab_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) return stab_der zero_inflated ZeroAdjustedBeta Bases: ZeroInflatedDistribution A Zero-Adjusted Beta distribution. Parameter concentration1: torch.Tensor 1st concentration parameter of the distribution (often referred to as alpha). concentration0: torch.Tensor 2nd concentration parameter of the distribution (often referred to as beta). gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py Source code in lightgbmlss/distributions/zero_inflated.py 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 class ZeroAdjustedBeta ( ZeroInflatedDistribution ): \"\"\" A Zero-Adjusted Beta distribution. Parameter --------- concentration1: torch.Tensor 1st concentration parameter of the distribution (often referred to as alpha). concentration0: torch.Tensor 2nd concentration parameter of the distribution (often referred to as beta). gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source ------ https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py \"\"\" arg_constraints = { \"concentration1\" : constraints . positive , \"concentration0\" : constraints . positive , \"gate\" : constraints . unit_interval , } support = constraints . unit_interval def __init__ ( self , concentration1 , concentration0 , gate = None , validate_args = None ): base_dist = Beta ( concentration1 = concentration1 , concentration0 = concentration0 , validate_args = False ) base_dist . _validate_args = validate_args super () . __init__ ( base_dist , gate = gate , validate_args = validate_args ) @property def concentration1 ( self ): return self . base_dist . concentration1 @property def concentration0 ( self ): return self . base_dist . concentration0 ZeroAdjustedGamma Bases: ZeroInflatedDistribution A Zero-Adjusted Gamma distribution. Parameter concentration: torch.Tensor shape parameter of the distribution (often referred to as alpha) rate: torch.Tensor rate = 1 / scale of the distribution (often referred to as beta) gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py Source code in lightgbmlss/distributions/zero_inflated.py 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 class ZeroAdjustedGamma ( ZeroInflatedDistribution ): \"\"\" A Zero-Adjusted Gamma distribution. Parameter --------- concentration: torch.Tensor shape parameter of the distribution (often referred to as alpha) rate: torch.Tensor rate = 1 / scale of the distribution (often referred to as beta) gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source ------ https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py \"\"\" arg_constraints = { \"concentration\" : constraints . positive , \"rate\" : constraints . positive , \"gate\" : constraints . unit_interval , } support = constraints . nonnegative def __init__ ( self , concentration , rate , gate = None , validate_args = None ): base_dist = Gamma ( concentration = concentration , rate = rate , validate_args = False ) base_dist . _validate_args = validate_args super () . __init__ ( base_dist , gate = gate , validate_args = validate_args ) @property def concentration ( self ): return self . base_dist . concentration @property def rate ( self ): return self . base_dist . rate ZeroAdjustedLogNormal Bases: ZeroInflatedDistribution A Zero-Adjusted Log-Normal distribution. Parameter loc: torch.Tensor Mean of log of distribution. scale: torch.Tensor Standard deviation of log of the distribution. gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py Source code in lightgbmlss/distributions/zero_inflated.py 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 class ZeroAdjustedLogNormal ( ZeroInflatedDistribution ): \"\"\" A Zero-Adjusted Log-Normal distribution. Parameter --------- loc: torch.Tensor Mean of log of distribution. scale: torch.Tensor Standard deviation of log of the distribution. gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source ------ https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py \"\"\" arg_constraints = { \"loc\" : constraints . real , \"scale\" : constraints . positive , \"gate\" : constraints . unit_interval , } support = constraints . nonnegative def __init__ ( self , loc , scale , gate = None , validate_args = None ): base_dist = LogNormal ( loc = loc , scale = scale , validate_args = False ) base_dist . _validate_args = validate_args super () . __init__ ( base_dist , gate = gate , validate_args = validate_args ) @property def loc ( self ): return self . base_dist . loc @property def scale ( self ): return self . base_dist . scale ZeroInflatedDistribution Bases: TorchDistribution Generic Zero Inflated distribution. This can be used directly or can be used as a base class as e.g. for :class: ZeroInflatedPoisson and :class: ZeroInflatedNegativeBinomial . Parameters gate : torch.Tensor Probability of extra zeros given via a Bernoulli distribution. base_dist : torch.distributions.Distribution The base distribution. Source https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L18 Source code in lightgbmlss/distributions/zero_inflated.py 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 class ZeroInflatedDistribution ( TorchDistribution ): \"\"\" Generic Zero Inflated distribution. This can be used directly or can be used as a base class as e.g. for :class:`ZeroInflatedPoisson` and :class:`ZeroInflatedNegativeBinomial`. Parameters ---------- gate : torch.Tensor Probability of extra zeros given via a Bernoulli distribution. base_dist : torch.distributions.Distribution The base distribution. Source ------ https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L18 \"\"\" arg_constraints = { \"gate\" : constraints . unit_interval , \"gate_logits\" : constraints . real , } def __init__ ( self , base_dist , * , gate = None , gate_logits = None , validate_args = None ): if ( gate is None ) == ( gate_logits is None ): raise ValueError ( \"Either `gate` or `gate_logits` must be specified, but not both.\" ) if gate is not None : batch_shape = broadcast_shape ( gate . shape , base_dist . batch_shape ) self . gate = gate . expand ( batch_shape ) else : batch_shape = broadcast_shape ( gate_logits . shape , base_dist . batch_shape ) self . gate_logits = gate_logits . expand ( batch_shape ) if base_dist . event_shape : raise ValueError ( \"ZeroInflatedDistribution expected empty \" \"base_dist.event_shape but got {} \" . format ( base_dist . event_shape ) ) self . base_dist = base_dist . expand ( batch_shape ) event_shape = torch . Size () super () . __init__ ( batch_shape , event_shape , validate_args ) @constraints . dependent_property def support ( self ): return self . base_dist . support @lazy_property def gate ( self ): return logits_to_probs ( self . gate_logits ) @lazy_property def gate_logits ( self ): return probs_to_logits ( self . gate ) def log_prob ( self , value ): if self . _validate_args : self . _validate_sample ( value ) zero_idx = ( value == 0 ) support = self . support epsilon = abs ( torch . finfo ( value . dtype ) . eps ) if hasattr ( support , \"lower_bound\" ): if is_identically_zero ( getattr ( support , \"lower_bound\" , None )): value = value . clamp_min ( epsilon ) if hasattr ( support , \"upper_bound\" ): if is_identically_one ( getattr ( support , \"upper_bound\" , None )) & ( value . max () == 1.0 ): value = value . clamp_max ( 1 - epsilon ) if \"gate\" in self . __dict__ : gate , value = broadcast_all ( self . gate , value ) log_prob = ( - gate ) . log1p () + self . base_dist . log_prob ( value ) log_prob = torch . where ( zero_idx , ( gate + log_prob . exp ()) . log (), log_prob ) else : gate_logits , value = broadcast_all ( self . gate_logits , value ) log_prob_minus_log_gate = - gate_logits + self . base_dist . log_prob ( value ) log_gate = - softplus ( - gate_logits ) log_prob = log_prob_minus_log_gate + log_gate zero_log_prob = softplus ( log_prob_minus_log_gate ) + log_gate log_prob = torch . where ( zero_idx , zero_log_prob , log_prob ) return log_prob def sample ( self , sample_shape = torch . Size ()): shape = self . _extended_shape ( sample_shape ) with torch . no_grad (): mask = torch . bernoulli ( self . gate . expand ( shape )) . bool () samples = self . base_dist . expand ( shape ) . sample () samples = torch . where ( mask , samples . new_zeros (()), samples ) return samples @lazy_property def mean ( self ): return ( 1 - self . gate ) * self . base_dist . mean @lazy_property def variance ( self ): return ( 1 - self . gate ) * ( self . base_dist . mean ** 2 + self . base_dist . variance ) - self . mean ** 2 def expand ( self , batch_shape , _instance = None ): new = self . _get_checked_instance ( type ( self ), _instance ) batch_shape = torch . Size ( batch_shape ) gate = self . gate . expand ( batch_shape ) if \"gate\" in self . __dict__ else None gate_logits = ( self . gate_logits . expand ( batch_shape ) if \"gate_logits\" in self . __dict__ else None ) base_dist = self . base_dist . expand ( batch_shape ) ZeroInflatedDistribution . __init__ ( new , base_dist , gate = gate , gate_logits = gate_logits , validate_args = False ) new . _validate_args = self . _validate_args return new ZeroInflatedNegativeBinomial Bases: ZeroInflatedDistribution A Zero Inflated Negative Binomial distribution. Parameter total_count: torch.Tensor Non-negative number of negative Bernoulli trial. probs: torch.Tensor Event probabilities of success in the half open interval [0, 1). logits: torch.Tensor Event log-odds of success (log(p/(1-p))). gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution. Source https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150 Source code in lightgbmlss/distributions/zero_inflated.py 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 class ZeroInflatedNegativeBinomial ( ZeroInflatedDistribution ): \"\"\" A Zero Inflated Negative Binomial distribution. Parameter --------- total_count: torch.Tensor Non-negative number of negative Bernoulli trial. probs: torch.Tensor Event probabilities of success in the half open interval [0, 1). logits: torch.Tensor Event log-odds of success (log(p/(1-p))). gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution. Source ------ - https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150 \"\"\" arg_constraints = { \"total_count\" : constraints . greater_than_eq ( 0 ), \"probs\" : constraints . half_open_interval ( 0.0 , 1.0 ), \"logits\" : constraints . real , \"gate\" : constraints . unit_interval , } support = constraints . nonnegative_integer def __init__ ( self , total_count , probs = None , gate = None , validate_args = None ): base_dist = NegativeBinomial ( total_count = total_count , probs = probs , logits = None , validate_args = False ) base_dist . _validate_args = validate_args super () . __init__ ( base_dist , gate = gate , validate_args = validate_args ) @property def total_count ( self ): return self . base_dist . total_count @property def probs ( self ): return self . base_dist . probs @property def logits ( self ): return self . base_dist . logits ZeroInflatedPoisson Bases: ZeroInflatedDistribution A Zero-Inflated Poisson distribution. Parameter rate: torch.Tensor The rate of the Poisson distribution. gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution. Source https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L121 Source code in lightgbmlss/distributions/zero_inflated.py 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 class ZeroInflatedPoisson ( ZeroInflatedDistribution ): \"\"\" A Zero-Inflated Poisson distribution. Parameter --------- rate: torch.Tensor The rate of the Poisson distribution. gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution. Source ------ https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L121 \"\"\" arg_constraints = { \"rate\" : constraints . positive , \"gate\" : constraints . unit_interval , } support = constraints . nonnegative_integer def __init__ ( self , rate , gate = None , validate_args = None ): base_dist = Poisson ( rate = rate , validate_args = False ) base_dist . _validate_args = validate_args super () . __init__ ( base_dist , gate = gate , validate_args = validate_args ) @property def rate ( self ): return self . base_dist . rate model LightGBMLSS LightGBMLSS model class Parameters dist : Distribution DistributionClass object. start_values : np.ndarray Starting values for each distributional parameter. Source code in lightgbmlss/model.py 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 class LightGBMLSS : \"\"\" LightGBMLSS model class Parameters ---------- dist : Distribution DistributionClass object. start_values : np.ndarray Starting values for each distributional parameter. \"\"\" def __init__ ( self , dist ): self . dist = dist # Distribution object self . start_values = None # Starting values for distributional parameters def set_params ( self , params : Dict [ str , Any ]) -> Dict [ str , Any ]: \"\"\" Set parameters for distributional model. Arguments --------- params : Dict[str, Any] Parameters for model. Returns ------- params : Dict[str, Any] Updated Parameters for model. \"\"\" params_adj = { \"num_class\" : self . dist . n_dist_param , \"metric\" : \"None\" , \"objective\" : \"None\" , \"random_seed\" : 123 , \"verbose\" : - 1 } params . update ( params_adj ) return params def set_init_score ( self , dmatrix : Dataset ) -> None : \"\"\" Set init_score for distributions. Arguments --------- dmatrix : Dataset Dataset to set base margin for. Returns ------- None \"\"\" if self . start_values is None : _ , self . start_values = self . dist . calculate_start_values ( dmatrix . get_label ()) init_score = ( np . ones ( shape = ( dmatrix . get_label () . shape [ 0 ], 1 ))) * self . start_values dmatrix . set_init_score ( init_score . ravel ( order = \"F\" )) def train ( self , params : Dict [ str , Any ], train_set : Dataset , num_boost_round : int = 100 , valid_sets : Optional [ List [ Dataset ]] = None , valid_names : Optional [ List [ str ]] = None , init_model : Optional [ Union [ str , Path , Booster ]] = None , feature_name : _LGBM_FeatureNameConfiguration = 'auto' , categorical_feature : _LGBM_CategoricalFeatureConfiguration = 'auto' , keep_training_booster : bool = False , callbacks : Optional [ List [ Callable ]] = None ) -> Booster : \"\"\"Function to perform the training of a LightGBMLSS model with given parameters. Parameters ---------- params : dict Parameters for training. Values passed through ``params`` take precedence over those supplied via arguments. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. valid_sets : list of Dataset, or None, optional (default=None) List of data to be evaluated on during training. valid_names : list of str, or None, optional (default=None) Names of ``valid_sets``. init_model : str, pathlib.Path, Booster or None, optional (default=None) Filename of LightGBM model or Booster instance used for continue training. feature_name : list of str, or 'auto', optional (default=\"auto\") Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default=\"auto\") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. keep_training_booster : bool, optional (default=False) Whether the returned Booster will be used to keep training. If False, the returned value will be converted into _InnerPredictor before returning. This means you won't be able to use ``eval``, ``eval_train`` or ``eval_valid`` methods of the returned Booster. When your model is very large and cause the memory error, you can try to set this param to ``True`` to avoid the model conversion performed during the internal call of ``model_to_string``. You can still use _InnerPredictor as ``init_model`` for future continue training. callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. Returns ------- booster : Booster The trained Booster model. \"\"\" self . set_params ( params ) self . set_init_score ( train_set ) if valid_sets is not None : valid_sets = self . set_valid_margin ( valid_sets , self . start_values ) self . booster = lgb . train ( params , train_set , num_boost_round = num_boost_round , fobj = self . dist . objective_fn , feval = self . dist . metric_fn , valid_sets = valid_sets , valid_names = valid_names , init_model = init_model , feature_name = feature_name , categorical_feature = categorical_feature , keep_training_booster = keep_training_booster , callbacks = callbacks ) def cv ( self , params : Dict [ str , Any ], train_set : Dataset , num_boost_round : int = 100 , folds : Optional [ Union [ Iterable [ Tuple [ np . ndarray , np . ndarray ]], _LGBMBaseCrossValidator ]] = None , nfold : int = 5 , stratified : bool = True , shuffle : bool = True , init_model : Optional [ Union [ str , Path , Booster ]] = None , feature_name : _LGBM_FeatureNameConfiguration = 'auto' , categorical_feature : _LGBM_CategoricalFeatureConfiguration = 'auto' , fpreproc : Optional [ _LGBM_PreprocFunction ] = None , seed : int = 123 , callbacks : Optional [ List [ Callable ]] = None , eval_train_metric : bool = False , return_cvbooster : bool = False ) -> Dict [ str , Union [ List [ float ], CVBooster ]]: \"\"\"Function to cross-validate a LightGBMLSS model with given parameters. Parameters ---------- params : dict Parameters for training. Values passed through ``params`` take precedence over those supplied via arguments. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None) If generator or iterator, it should yield the train and test indices for each fold. If object, it should be one of the scikit-learn splitter classes (https://scikit-learn.org/stable/modules/classes.html#splitter-classes) and have ``split`` method. This argument has highest priority over other data split arguments. nfold : int, optional (default=5) Number of folds in CV. stratified : bool, optional (default=True) Whether to perform stratified sampling. shuffle : bool, optional (default=True) Whether to shuffle before splitting data. init_model : str, pathlib.Path, Booster or None, optional (default=None) Filename of LightGBM model or Booster instance used for continue training. feature_name : list of str, or 'auto', optional (default=\"auto\") Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default=\"auto\") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. fpreproc : callable or None, optional (default=None) Preprocessing function that takes (dtrain, dtest, params) and returns transformed versions of those. seed : int, optional (default=0) Seed used to generate the folds (passed to numpy.random.seed). callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. eval_train_metric : bool, optional (default=False) Whether to display the train metric in progress. The score of the metric is calculated again after each training step, so there is some impact on performance. return_cvbooster : bool, optional (default=False) Whether to return Booster models trained on each fold through ``CVBooster``. Returns ------- eval_hist : dict Evaluation history. The dictionary has the following format: {'metric1-mean': [values], 'metric1-stdv': [values], 'metric2-mean': [values], 'metric2-stdv': [values], ...}. If ``return_cvbooster=True``, also returns trained boosters wrapped in a ``CVBooster`` object via ``cvbooster`` key. \"\"\" self . set_params ( params ) self . set_init_score ( train_set ) self . bstLSS_cv = lgb . cv ( params , train_set , fobj = self . dist . objective_fn , feval = self . dist . metric_fn , num_boost_round = num_boost_round , folds = folds , nfold = nfold , stratified = False , shuffle = False , metrics = None , init_model = init_model , feature_name = feature_name , categorical_feature = categorical_feature , fpreproc = fpreproc , seed = seed , callbacks = callbacks , eval_train_metric = eval_train_metric , return_cvbooster = return_cvbooster ) return self . bstLSS_cv def hyper_opt ( self , hp_dict : Dict , train_set : lgb . Dataset , num_boost_round = 500 , nfold = 10 , early_stopping_rounds = 20 , max_minutes = 10 , n_trials = None , study_name = None , silence = False , seed = None , hp_seed = None ): \"\"\" Function to tune hyperparameters using optuna. Arguments ---------- hp_dict: dict Dictionary of hyperparameters to tune. train_set: lgb.Dataset Training data. num_boost_round: int Number of boosting iterations. nfold: int Number of folds in CV. early_stopping_rounds: int Activates early stopping. Cross-Validation metric (average of validation metric computed over CV folds) needs to improve at least once in every **early_stopping_rounds** round(s) to continue training. The last entry in the evaluation history will represent the best iteration. If there's more than one metric in the **eval_metric** parameter given in **params**, the last metric will be used for early stopping. max_minutes: int Time budget in minutes, i.e., stop study after the given number of minutes. n_trials: int The number of trials. If this argument is set to None, there is no limitation on the number of trials. study_name: str Name of the hyperparameter study. silence: bool Controls the verbosity of the trail, i.e., user can silence the outputs of the trail. seed: int Seed used to generate the folds (passed to numpy.random.seed). hp_seed: int Seed for random number generator used in the Bayesian hyper-parameter search. Returns ------- opt_params : dict Optimal hyper-parameters. \"\"\" def objective ( trial ): hyper_params = {} for param_name , param_value in hp_dict . items (): param_type = param_value [ 0 ] if param_type == \"categorical\" or param_type == \"none\" : hyper_params . update ({ param_name : trial . suggest_categorical ( param_name , param_value [ 1 ])}) elif param_type == \"float\" : param_constraints = param_value [ 1 ] param_low = param_constraints [ \"low\" ] param_high = param_constraints [ \"high\" ] param_log = param_constraints [ \"log\" ] hyper_params . update ( { param_name : trial . suggest_float ( param_name , low = param_low , high = param_high , log = param_log ) }) elif param_type == \"int\" : param_constraints = param_value [ 1 ] param_low = param_constraints [ \"low\" ] param_high = param_constraints [ \"high\" ] param_log = param_constraints [ \"log\" ] hyper_params . update ( { param_name : trial . suggest_int ( param_name , low = param_low , high = param_high , log = param_log ) }) # Add booster if not included in dictionary if \"boosting\" not in hyper_params . keys (): hyper_params . update ({ \"boosting\" : trial . suggest_categorical ( \"boosting\" , [ \"gbdt\" ])}) # Add pruning and early stopping pruning_callback = LightGBMPruningCallback ( trial , self . dist . loss_fn ) early_stopping_callback = lgb . early_stopping ( stopping_rounds = early_stopping_rounds , verbose = False ) lgblss_param_tuning = self . cv ( hyper_params , train_set , num_boost_round = num_boost_round , nfold = nfold , callbacks = [ pruning_callback , early_stopping_callback ], seed = seed , ) # Extract the optimal number of boosting rounds opt_rounds = np . argmin ( np . array ( lgblss_param_tuning [ f \" { self . dist . loss_fn } -mean\" ])) + 1 trial . set_user_attr ( \"opt_round\" , int ( opt_rounds )) # Extract the best score best_score = np . min ( np . array ( lgblss_param_tuning [ f \" { self . dist . loss_fn } -mean\" ])) return best_score if study_name is None : study_name = \"LightGBMLSS Hyper-Parameter Optimization\" if silence : optuna . logging . set_verbosity ( optuna . logging . WARNING ) if hp_seed is not None : sampler = TPESampler ( seed = hp_seed ) else : sampler = TPESampler () pruner = optuna . pruners . MedianPruner ( n_startup_trials = 10 , n_warmup_steps = 20 ) study = optuna . create_study ( sampler = sampler , pruner = pruner , direction = \"minimize\" , study_name = study_name ) study . optimize ( objective , n_trials = n_trials , timeout = 60 * max_minutes , show_progress_bar = True ) print ( \" \\n Hyper-Parameter Optimization successfully finished.\" ) print ( \" Number of finished trials: \" , len ( study . trials )) print ( \" Best trial:\" ) opt_param = study . best_trial # Add optimal stopping round opt_param . params [ \"opt_rounds\" ] = study . trials_dataframe ()[ \"user_attrs_opt_round\" ][ study . trials_dataframe ()[ \"value\" ] . idxmin ()] opt_param . params [ \"opt_rounds\" ] = int ( opt_param . params [ \"opt_rounds\" ]) print ( \" Value: {} \" . format ( opt_param . value )) print ( \" Params: \" ) for key , value in opt_param . params . items (): print ( \" {} : {} \" . format ( key , value )) return opt_param . params def predict ( self , data : pd . DataFrame , pred_type : str = \"parameters\" , n_samples : int = 1000 , quantiles : list = [ 0.1 , 0.5 , 0.9 ], seed : str = 123 ): \"\"\" Function that predicts from the trained model. Arguments --------- data : pd.DataFrame Data to predict from. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns ------- predt_df : pd.DataFrame Predictions. \"\"\" # Predict predt_df = self . dist . predict_dist ( booster = self . booster , data = data , start_values = self . start_values , pred_type = pred_type , n_samples = n_samples , quantiles = quantiles , seed = seed ) return predt_df def plot ( self , X : pd . DataFrame , feature : str = \"x\" , parameter : str = \"loc\" , max_display : int = 15 , plot_type : str = \"Partial_Dependence\" ): \"\"\" LightGBMLSS SHap plotting function. Arguments: --------- X: pd.DataFrame Train/Test Data feature: str Specifies which feature is to be plotted. parameter: str Specifies which distributional parameter is to be plotted. max_display: int Specifies the maximum number of features to be displayed. plot_type: str Specifies the type of plot: \"Partial_Dependence\" plots the partial dependence of the parameter on the feature. \"Feature_Importance\" plots the feature importance of the parameter. \"\"\" shap . initjs () explainer = shap . TreeExplainer ( self . booster ) shap_values = explainer ( X ) param_pos = self . dist . distribution_arg_names . index ( parameter ) if plot_type == \"Partial_Dependence\" : if self . dist . n_dist_param == 1 : shap . plots . scatter ( shap_values [:, feature ], color = shap_values [:, feature ]) else : shap . plots . scatter ( shap_values [:, feature ][:, param_pos ], color = shap_values [:, feature ][:, param_pos ]) elif plot_type == \"Feature_Importance\" : if self . dist . n_dist_param == 1 : shap . plots . bar ( shap_values , max_display = max_display if X . shape [ 1 ] > max_display else X . shape [ 1 ]) else : shap . plots . bar ( shap_values [:, :, param_pos ], max_display = max_display if X . shape [ 1 ] > max_display else X . shape [ 1 ] ) def expectile_plot ( self , X : pd . DataFrame , feature : str = \"x\" , expectile : str = \"0.05\" , plot_type : str = \"Partial_Dependence\" ): \"\"\" LightGBMLSS function for plotting expectile SHapley values. X: pd.DataFrame Train/Test Data feature: str Specifies which feature to use for plotting Partial_Dependence plot. expectile: str Specifies which expectile to plot. plot_type: str Specifies which SHapley-plot to visualize. Currently, \"Partial_Dependence\" and \"Feature_Importance\" are supported. \"\"\" shap . initjs () explainer = shap . TreeExplainer ( self . booster ) shap_values = explainer ( X ) expect_pos = list ( self . dist . param_dict . keys ()) . index ( expectile ) if plot_type == \"Partial_Dependence\" : shap . plots . scatter ( shap_values [:, feature ][:, expect_pos ], color = shap_values [:, feature ][:, expect_pos ]) elif plot_type == \"Feature_Importance\" : shap . plots . bar ( shap_values [:, :, expect_pos ], max_display = 15 if X . shape [ 1 ] > 15 else X . shape [ 1 ]) def set_valid_margin ( self , valid_sets : list , start_values : np . ndarray ) -> list : \"\"\" Function that sets the base margin for the validation set. Arguments --------- valid_sets : list List of tuples containing the train and evaluation set. valid_names: list List of tuples containing the name of train and evaluation set. start_values : np.ndarray Array containing the start values for the distributional parameters. Returns ------- valid_sets : list List of tuples containing the train and evaluation set. \"\"\" valid_sets1 = valid_sets [ 0 ] init_score_val1 = ( np . ones ( shape = ( valid_sets1 . get_label () . shape [ 0 ], 1 ))) * start_values valid_sets1 . set_init_score ( init_score_val1 . ravel ( order = \"F\" )) valid_sets2 = valid_sets [ 1 ] init_score_val2 = ( np . ones ( shape = ( valid_sets2 . get_label () . shape [ 0 ], 1 ))) * start_values valid_sets2 . set_init_score ( init_score_val2 . ravel ( order = \"F\" )) valid_sets = [ valid_sets1 , valid_sets2 ] return valid_sets def save_model ( self , model_path : str ) -> None : \"\"\" Save the model to a file. Parameters ---------- model_path : str The path to save the model. Returns ------- None \"\"\" with open ( model_path , \"wb\" ) as f : pickle . dump ( self , f ) @staticmethod def load_model ( model_path : str ): \"\"\" Load the model from a file. Parameters ---------- model_path : str The path to the saved model. Returns ------- The loaded model. \"\"\" with open ( model_path , \"rb\" ) as f : return pickle . load ( f ) cv ( params , train_set , num_boost_round = 100 , folds = None , nfold = 5 , stratified = True , shuffle = True , init_model = None , feature_name = 'auto' , categorical_feature = 'auto' , fpreproc = None , seed = 123 , callbacks = None , eval_train_metric = False , return_cvbooster = False ) Function to cross-validate a LightGBMLSS model with given parameters. Parameters params : dict Parameters for training. Values passed through params take precedence over those supplied via arguments. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None) If generator or iterator, it should yield the train and test indices for each fold. If object, it should be one of the scikit-learn splitter classes (https://scikit-learn.org/stable/modules/classes.html#splitter-classes) and have split method. This argument has highest priority over other data split arguments. nfold : int, optional (default=5) Number of folds in CV. stratified : bool, optional (default=True) Whether to perform stratified sampling. shuffle : bool, optional (default=True) Whether to shuffle before splitting data. init_model : str, pathlib.Path, Booster or None, optional (default=None) Filename of LightGBM model or Booster instance used for continue training. feature_name : list of str, or 'auto', optional (default=\"auto\") Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default=\"auto\") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify feature_name as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. fpreproc : callable or None, optional (default=None) Preprocessing function that takes (dtrain, dtest, params) and returns transformed versions of those. seed : int, optional (default=0) Seed used to generate the folds (passed to numpy.random.seed). callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. eval_train_metric : bool, optional (default=False) Whether to display the train metric in progress. The score of the metric is calculated again after each training step, so there is some impact on performance. return_cvbooster : bool, optional (default=False) Whether to return Booster models trained on each fold through CVBooster . Returns eval_hist : dict Evaluation history. The dictionary has the following format: {'metric1-mean': [values], 'metric1-stdv': [values], 'metric2-mean': [values], 'metric2-stdv': [values], ...}. If return_cvbooster=True , also returns trained boosters wrapped in a CVBooster object via cvbooster key. Source code in lightgbmlss/model.py 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 def cv ( self , params : Dict [ str , Any ], train_set : Dataset , num_boost_round : int = 100 , folds : Optional [ Union [ Iterable [ Tuple [ np . ndarray , np . ndarray ]], _LGBMBaseCrossValidator ]] = None , nfold : int = 5 , stratified : bool = True , shuffle : bool = True , init_model : Optional [ Union [ str , Path , Booster ]] = None , feature_name : _LGBM_FeatureNameConfiguration = 'auto' , categorical_feature : _LGBM_CategoricalFeatureConfiguration = 'auto' , fpreproc : Optional [ _LGBM_PreprocFunction ] = None , seed : int = 123 , callbacks : Optional [ List [ Callable ]] = None , eval_train_metric : bool = False , return_cvbooster : bool = False ) -> Dict [ str , Union [ List [ float ], CVBooster ]]: \"\"\"Function to cross-validate a LightGBMLSS model with given parameters. Parameters ---------- params : dict Parameters for training. Values passed through ``params`` take precedence over those supplied via arguments. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None) If generator or iterator, it should yield the train and test indices for each fold. If object, it should be one of the scikit-learn splitter classes (https://scikit-learn.org/stable/modules/classes.html#splitter-classes) and have ``split`` method. This argument has highest priority over other data split arguments. nfold : int, optional (default=5) Number of folds in CV. stratified : bool, optional (default=True) Whether to perform stratified sampling. shuffle : bool, optional (default=True) Whether to shuffle before splitting data. init_model : str, pathlib.Path, Booster or None, optional (default=None) Filename of LightGBM model or Booster instance used for continue training. feature_name : list of str, or 'auto', optional (default=\"auto\") Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default=\"auto\") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. fpreproc : callable or None, optional (default=None) Preprocessing function that takes (dtrain, dtest, params) and returns transformed versions of those. seed : int, optional (default=0) Seed used to generate the folds (passed to numpy.random.seed). callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. eval_train_metric : bool, optional (default=False) Whether to display the train metric in progress. The score of the metric is calculated again after each training step, so there is some impact on performance. return_cvbooster : bool, optional (default=False) Whether to return Booster models trained on each fold through ``CVBooster``. Returns ------- eval_hist : dict Evaluation history. The dictionary has the following format: {'metric1-mean': [values], 'metric1-stdv': [values], 'metric2-mean': [values], 'metric2-stdv': [values], ...}. If ``return_cvbooster=True``, also returns trained boosters wrapped in a ``CVBooster`` object via ``cvbooster`` key. \"\"\" self . set_params ( params ) self . set_init_score ( train_set ) self . bstLSS_cv = lgb . cv ( params , train_set , fobj = self . dist . objective_fn , feval = self . dist . metric_fn , num_boost_round = num_boost_round , folds = folds , nfold = nfold , stratified = False , shuffle = False , metrics = None , init_model = init_model , feature_name = feature_name , categorical_feature = categorical_feature , fpreproc = fpreproc , seed = seed , callbacks = callbacks , eval_train_metric = eval_train_metric , return_cvbooster = return_cvbooster ) return self . bstLSS_cv expectile_plot ( X , feature = 'x' , expectile = '0.05' , plot_type = 'Partial_Dependence' ) LightGBMLSS function for plotting expectile SHapley values. pd.DataFrame Train/Test Data feature: str Specifies which feature to use for plotting Partial_Dependence plot. expectile: str Specifies which expectile to plot. plot_type: str Specifies which SHapley-plot to visualize. Currently, \"Partial_Dependence\" and \"Feature_Importance\" are supported. Source code in lightgbmlss/model.py 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 def expectile_plot ( self , X : pd . DataFrame , feature : str = \"x\" , expectile : str = \"0.05\" , plot_type : str = \"Partial_Dependence\" ): \"\"\" LightGBMLSS function for plotting expectile SHapley values. X: pd.DataFrame Train/Test Data feature: str Specifies which feature to use for plotting Partial_Dependence plot. expectile: str Specifies which expectile to plot. plot_type: str Specifies which SHapley-plot to visualize. Currently, \"Partial_Dependence\" and \"Feature_Importance\" are supported. \"\"\" shap . initjs () explainer = shap . TreeExplainer ( self . booster ) shap_values = explainer ( X ) expect_pos = list ( self . dist . param_dict . keys ()) . index ( expectile ) if plot_type == \"Partial_Dependence\" : shap . plots . scatter ( shap_values [:, feature ][:, expect_pos ], color = shap_values [:, feature ][:, expect_pos ]) elif plot_type == \"Feature_Importance\" : shap . plots . bar ( shap_values [:, :, expect_pos ], max_display = 15 if X . shape [ 1 ] > 15 else X . shape [ 1 ]) hyper_opt ( hp_dict , train_set , num_boost_round = 500 , nfold = 10 , early_stopping_rounds = 20 , max_minutes = 10 , n_trials = None , study_name = None , silence = False , seed = None , hp_seed = None ) Function to tune hyperparameters using optuna. Arguments hp_dict: dict Dictionary of hyperparameters to tune. train_set: lgb.Dataset Training data. num_boost_round: int Number of boosting iterations. nfold: int Number of folds in CV. early_stopping_rounds: int Activates early stopping. Cross-Validation metric (average of validation metric computed over CV folds) needs to improve at least once in every early_stopping_rounds round(s) to continue training. The last entry in the evaluation history will represent the best iteration. If there's more than one metric in the eval_metric parameter given in params , the last metric will be used for early stopping. max_minutes: int Time budget in minutes, i.e., stop study after the given number of minutes. n_trials: int The number of trials. If this argument is set to None, there is no limitation on the number of trials. study_name: str Name of the hyperparameter study. silence: bool Controls the verbosity of the trail, i.e., user can silence the outputs of the trail. seed: int Seed used to generate the folds (passed to numpy.random.seed). hp_seed: int Seed for random number generator used in the Bayesian hyper-parameter search. Returns opt_params : dict Optimal hyper-parameters. Source code in lightgbmlss/model.py 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 def hyper_opt ( self , hp_dict : Dict , train_set : lgb . Dataset , num_boost_round = 500 , nfold = 10 , early_stopping_rounds = 20 , max_minutes = 10 , n_trials = None , study_name = None , silence = False , seed = None , hp_seed = None ): \"\"\" Function to tune hyperparameters using optuna. Arguments ---------- hp_dict: dict Dictionary of hyperparameters to tune. train_set: lgb.Dataset Training data. num_boost_round: int Number of boosting iterations. nfold: int Number of folds in CV. early_stopping_rounds: int Activates early stopping. Cross-Validation metric (average of validation metric computed over CV folds) needs to improve at least once in every **early_stopping_rounds** round(s) to continue training. The last entry in the evaluation history will represent the best iteration. If there's more than one metric in the **eval_metric** parameter given in **params**, the last metric will be used for early stopping. max_minutes: int Time budget in minutes, i.e., stop study after the given number of minutes. n_trials: int The number of trials. If this argument is set to None, there is no limitation on the number of trials. study_name: str Name of the hyperparameter study. silence: bool Controls the verbosity of the trail, i.e., user can silence the outputs of the trail. seed: int Seed used to generate the folds (passed to numpy.random.seed). hp_seed: int Seed for random number generator used in the Bayesian hyper-parameter search. Returns ------- opt_params : dict Optimal hyper-parameters. \"\"\" def objective ( trial ): hyper_params = {} for param_name , param_value in hp_dict . items (): param_type = param_value [ 0 ] if param_type == \"categorical\" or param_type == \"none\" : hyper_params . update ({ param_name : trial . suggest_categorical ( param_name , param_value [ 1 ])}) elif param_type == \"float\" : param_constraints = param_value [ 1 ] param_low = param_constraints [ \"low\" ] param_high = param_constraints [ \"high\" ] param_log = param_constraints [ \"log\" ] hyper_params . update ( { param_name : trial . suggest_float ( param_name , low = param_low , high = param_high , log = param_log ) }) elif param_type == \"int\" : param_constraints = param_value [ 1 ] param_low = param_constraints [ \"low\" ] param_high = param_constraints [ \"high\" ] param_log = param_constraints [ \"log\" ] hyper_params . update ( { param_name : trial . suggest_int ( param_name , low = param_low , high = param_high , log = param_log ) }) # Add booster if not included in dictionary if \"boosting\" not in hyper_params . keys (): hyper_params . update ({ \"boosting\" : trial . suggest_categorical ( \"boosting\" , [ \"gbdt\" ])}) # Add pruning and early stopping pruning_callback = LightGBMPruningCallback ( trial , self . dist . loss_fn ) early_stopping_callback = lgb . early_stopping ( stopping_rounds = early_stopping_rounds , verbose = False ) lgblss_param_tuning = self . cv ( hyper_params , train_set , num_boost_round = num_boost_round , nfold = nfold , callbacks = [ pruning_callback , early_stopping_callback ], seed = seed , ) # Extract the optimal number of boosting rounds opt_rounds = np . argmin ( np . array ( lgblss_param_tuning [ f \" { self . dist . loss_fn } -mean\" ])) + 1 trial . set_user_attr ( \"opt_round\" , int ( opt_rounds )) # Extract the best score best_score = np . min ( np . array ( lgblss_param_tuning [ f \" { self . dist . loss_fn } -mean\" ])) return best_score if study_name is None : study_name = \"LightGBMLSS Hyper-Parameter Optimization\" if silence : optuna . logging . set_verbosity ( optuna . logging . WARNING ) if hp_seed is not None : sampler = TPESampler ( seed = hp_seed ) else : sampler = TPESampler () pruner = optuna . pruners . MedianPruner ( n_startup_trials = 10 , n_warmup_steps = 20 ) study = optuna . create_study ( sampler = sampler , pruner = pruner , direction = \"minimize\" , study_name = study_name ) study . optimize ( objective , n_trials = n_trials , timeout = 60 * max_minutes , show_progress_bar = True ) print ( \" \\n Hyper-Parameter Optimization successfully finished.\" ) print ( \" Number of finished trials: \" , len ( study . trials )) print ( \" Best trial:\" ) opt_param = study . best_trial # Add optimal stopping round opt_param . params [ \"opt_rounds\" ] = study . trials_dataframe ()[ \"user_attrs_opt_round\" ][ study . trials_dataframe ()[ \"value\" ] . idxmin ()] opt_param . params [ \"opt_rounds\" ] = int ( opt_param . params [ \"opt_rounds\" ]) print ( \" Value: {} \" . format ( opt_param . value )) print ( \" Params: \" ) for key , value in opt_param . params . items (): print ( \" {} : {} \" . format ( key , value )) return opt_param . params load_model ( model_path ) staticmethod Load the model from a file. Parameters model_path : str The path to the saved model. Returns The loaded model. Source code in lightgbmlss/model.py 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 @staticmethod def load_model ( model_path : str ): \"\"\" Load the model from a file. Parameters ---------- model_path : str The path to the saved model. Returns ------- The loaded model. \"\"\" with open ( model_path , \"rb\" ) as f : return pickle . load ( f ) plot ( X , feature = 'x' , parameter = 'loc' , max_display = 15 , plot_type = 'Partial_Dependence' ) LightGBMLSS SHap plotting function. Arguments: X: pd.DataFrame Train/Test Data feature: str Specifies which feature is to be plotted. parameter: str Specifies which distributional parameter is to be plotted. max_display: int Specifies the maximum number of features to be displayed. plot_type: str Specifies the type of plot: \"Partial_Dependence\" plots the partial dependence of the parameter on the feature. \"Feature_Importance\" plots the feature importance of the parameter. Source code in lightgbmlss/model.py 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 def plot ( self , X : pd . DataFrame , feature : str = \"x\" , parameter : str = \"loc\" , max_display : int = 15 , plot_type : str = \"Partial_Dependence\" ): \"\"\" LightGBMLSS SHap plotting function. Arguments: --------- X: pd.DataFrame Train/Test Data feature: str Specifies which feature is to be plotted. parameter: str Specifies which distributional parameter is to be plotted. max_display: int Specifies the maximum number of features to be displayed. plot_type: str Specifies the type of plot: \"Partial_Dependence\" plots the partial dependence of the parameter on the feature. \"Feature_Importance\" plots the feature importance of the parameter. \"\"\" shap . initjs () explainer = shap . TreeExplainer ( self . booster ) shap_values = explainer ( X ) param_pos = self . dist . distribution_arg_names . index ( parameter ) if plot_type == \"Partial_Dependence\" : if self . dist . n_dist_param == 1 : shap . plots . scatter ( shap_values [:, feature ], color = shap_values [:, feature ]) else : shap . plots . scatter ( shap_values [:, feature ][:, param_pos ], color = shap_values [:, feature ][:, param_pos ]) elif plot_type == \"Feature_Importance\" : if self . dist . n_dist_param == 1 : shap . plots . bar ( shap_values , max_display = max_display if X . shape [ 1 ] > max_display else X . shape [ 1 ]) else : shap . plots . bar ( shap_values [:, :, param_pos ], max_display = max_display if X . shape [ 1 ] > max_display else X . shape [ 1 ] ) predict ( data , pred_type = 'parameters' , n_samples = 1000 , quantiles = [ 0.1 , 0.5 , 0.9 ], seed = 123 ) Function that predicts from the trained model. Arguments data : pd.DataFrame Data to predict from. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns predt_df : pd.DataFrame Predictions. Source code in lightgbmlss/model.py 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 def predict ( self , data : pd . DataFrame , pred_type : str = \"parameters\" , n_samples : int = 1000 , quantiles : list = [ 0.1 , 0.5 , 0.9 ], seed : str = 123 ): \"\"\" Function that predicts from the trained model. Arguments --------- data : pd.DataFrame Data to predict from. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns ------- predt_df : pd.DataFrame Predictions. \"\"\" # Predict predt_df = self . dist . predict_dist ( booster = self . booster , data = data , start_values = self . start_values , pred_type = pred_type , n_samples = n_samples , quantiles = quantiles , seed = seed ) return predt_df save_model ( model_path ) Save the model to a file. Parameters model_path : str The path to save the model. Returns None Source code in lightgbmlss/model.py 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 def save_model ( self , model_path : str ) -> None : \"\"\" Save the model to a file. Parameters ---------- model_path : str The path to save the model. Returns ------- None \"\"\" with open ( model_path , \"wb\" ) as f : pickle . dump ( self , f ) set_init_score ( dmatrix ) Set init_score for distributions. Arguments dmatrix : Dataset Dataset to set base margin for. Returns None Source code in lightgbmlss/model.py 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def set_init_score ( self , dmatrix : Dataset ) -> None : \"\"\" Set init_score for distributions. Arguments --------- dmatrix : Dataset Dataset to set base margin for. Returns ------- None \"\"\" if self . start_values is None : _ , self . start_values = self . dist . calculate_start_values ( dmatrix . get_label ()) init_score = ( np . ones ( shape = ( dmatrix . get_label () . shape [ 0 ], 1 ))) * self . start_values dmatrix . set_init_score ( init_score . ravel ( order = \"F\" )) set_params ( params ) Set parameters for distributional model. Arguments params : Dict[str, Any] Parameters for model. Returns params : Dict[str, Any] Updated Parameters for model. Source code in lightgbmlss/model.py 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 def set_params ( self , params : Dict [ str , Any ]) -> Dict [ str , Any ]: \"\"\" Set parameters for distributional model. Arguments --------- params : Dict[str, Any] Parameters for model. Returns ------- params : Dict[str, Any] Updated Parameters for model. \"\"\" params_adj = { \"num_class\" : self . dist . n_dist_param , \"metric\" : \"None\" , \"objective\" : \"None\" , \"random_seed\" : 123 , \"verbose\" : - 1 } params . update ( params_adj ) return params set_valid_margin ( valid_sets , start_values ) Function that sets the base margin for the validation set. Arguments valid_sets : list List of tuples containing the train and evaluation set. valid_names: list List of tuples containing the name of train and evaluation set. start_values : np.ndarray Array containing the start values for the distributional parameters. Returns valid_sets : list List of tuples containing the train and evaluation set. Source code in lightgbmlss/model.py 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 def set_valid_margin ( self , valid_sets : list , start_values : np . ndarray ) -> list : \"\"\" Function that sets the base margin for the validation set. Arguments --------- valid_sets : list List of tuples containing the train and evaluation set. valid_names: list List of tuples containing the name of train and evaluation set. start_values : np.ndarray Array containing the start values for the distributional parameters. Returns ------- valid_sets : list List of tuples containing the train and evaluation set. \"\"\" valid_sets1 = valid_sets [ 0 ] init_score_val1 = ( np . ones ( shape = ( valid_sets1 . get_label () . shape [ 0 ], 1 ))) * start_values valid_sets1 . set_init_score ( init_score_val1 . ravel ( order = \"F\" )) valid_sets2 = valid_sets [ 1 ] init_score_val2 = ( np . ones ( shape = ( valid_sets2 . get_label () . shape [ 0 ], 1 ))) * start_values valid_sets2 . set_init_score ( init_score_val2 . ravel ( order = \"F\" )) valid_sets = [ valid_sets1 , valid_sets2 ] return valid_sets train ( params , train_set , num_boost_round = 100 , valid_sets = None , valid_names = None , init_model = None , feature_name = 'auto' , categorical_feature = 'auto' , keep_training_booster = False , callbacks = None ) Function to perform the training of a LightGBMLSS model with given parameters. Parameters params : dict Parameters for training. Values passed through params take precedence over those supplied via arguments. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. valid_sets : list of Dataset, or None, optional (default=None) List of data to be evaluated on during training. valid_names : list of str, or None, optional (default=None) Names of valid_sets . init_model : str, pathlib.Path, Booster or None, optional (default=None) Filename of LightGBM model or Booster instance used for continue training. feature_name : list of str, or 'auto', optional (default=\"auto\") Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default=\"auto\") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify feature_name as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. keep_training_booster : bool, optional (default=False) Whether the returned Booster will be used to keep training. If False, the returned value will be converted into _InnerPredictor before returning. This means you won't be able to use eval , eval_train or eval_valid methods of the returned Booster. When your model is very large and cause the memory error, you can try to set this param to True to avoid the model conversion performed during the internal call of model_to_string . You can still use _InnerPredictor as init_model for future continue training. callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. Returns booster : Booster The trained Booster model. Source code in lightgbmlss/model.py 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 def train ( self , params : Dict [ str , Any ], train_set : Dataset , num_boost_round : int = 100 , valid_sets : Optional [ List [ Dataset ]] = None , valid_names : Optional [ List [ str ]] = None , init_model : Optional [ Union [ str , Path , Booster ]] = None , feature_name : _LGBM_FeatureNameConfiguration = 'auto' , categorical_feature : _LGBM_CategoricalFeatureConfiguration = 'auto' , keep_training_booster : bool = False , callbacks : Optional [ List [ Callable ]] = None ) -> Booster : \"\"\"Function to perform the training of a LightGBMLSS model with given parameters. Parameters ---------- params : dict Parameters for training. Values passed through ``params`` take precedence over those supplied via arguments. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. valid_sets : list of Dataset, or None, optional (default=None) List of data to be evaluated on during training. valid_names : list of str, or None, optional (default=None) Names of ``valid_sets``. init_model : str, pathlib.Path, Booster or None, optional (default=None) Filename of LightGBM model or Booster instance used for continue training. feature_name : list of str, or 'auto', optional (default=\"auto\") Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default=\"auto\") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. keep_training_booster : bool, optional (default=False) Whether the returned Booster will be used to keep training. If False, the returned value will be converted into _InnerPredictor before returning. This means you won't be able to use ``eval``, ``eval_train`` or ``eval_valid`` methods of the returned Booster. When your model is very large and cause the memory error, you can try to set this param to ``True`` to avoid the model conversion performed during the internal call of ``model_to_string``. You can still use _InnerPredictor as ``init_model`` for future continue training. callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. Returns ------- booster : Booster The trained Booster model. \"\"\" self . set_params ( params ) self . set_init_score ( train_set ) if valid_sets is not None : valid_sets = self . set_valid_margin ( valid_sets , self . start_values ) self . booster = lgb . train ( params , train_set , num_boost_round = num_boost_round , fobj = self . dist . objective_fn , feval = self . dist . metric_fn , valid_sets = valid_sets , valid_names = valid_names , init_model = init_model , feature_name = feature_name , categorical_feature = categorical_feature , keep_training_booster = keep_training_booster , callbacks = callbacks ) utils exp_fn ( predt ) Exponential function used to ensure predt is strictly positive. Arguments predt: torch.tensor Predicted values. Returns predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 def exp_fn ( predt : torch . tensor ) -> torch . tensor : \"\"\" Exponential function used to ensure predt is strictly positive. Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" predt = torch . exp ( predt ) predt = torch . nan_to_num ( predt , nan = float ( torch . nanmean ( predt ))) + torch . tensor ( 1e-06 , dtype = predt . dtype ) return predt exp_fn_df ( predt ) Exponential function used for Student-T distribution. Arguments predt: torch.tensor Predicted values. Returns predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 def exp_fn_df ( predt : torch . tensor ) -> torch . tensor : \"\"\" Exponential function used for Student-T distribution. Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" predt = torch . exp ( predt ) predt = torch . nan_to_num ( predt , nan = float ( torch . nanmean ( predt ))) + torch . tensor ( 1e-06 , dtype = predt . dtype ) return predt + torch . tensor ( 2.0 , dtype = predt . dtype ) identity_fn ( predt ) Identity mapping of predt. Arguments predt: torch.tensor Predicted values. Returns predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 def identity_fn ( predt : torch . tensor ) -> torch . tensor : \"\"\" Identity mapping of predt. Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" return predt log_fn ( predt ) Inverse of exp_fn function. Arguments predt: torch.tensor Predicted values. Returns predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 def log_fn ( predt : torch . tensor ) -> torch . tensor : \"\"\" Inverse of exp_fn function. Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" predt = torch . log ( predt ) predt = torch . nan_to_num ( predt , nan = float ( torch . nanmean ( predt ))) + float ( 1e-06 ) return predt relu_fn ( predt ) Function used to ensure predt are scaled to max(0, predt). Arguments predt: torch.tensor Predicted values. Returns predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 def relu_fn ( predt : torch . tensor ) -> torch . tensor : \"\"\" Function used to ensure predt are scaled to max(0, predt). Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" predt = torch . relu ( predt ) predt = torch . nan_to_num ( predt , nan = float ( torch . nanmean ( predt ))) + torch . tensor ( 1e-6 , dtype = predt . dtype ) return predt sigmoid_fn ( predt ) Function used to ensure predt are scaled to (0,1). Arguments predt: torch.tensor Predicted values. Returns predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 def sigmoid_fn ( predt : torch . tensor ) -> torch . tensor : \"\"\" Function used to ensure predt are scaled to (0,1). Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" predt = torch . sigmoid ( predt ) predt = torch . nan_to_num ( predt , nan = float ( torch . nanmean ( predt ))) + torch . tensor ( 1e-06 , dtype = predt . dtype ) predt = torch . clamp ( predt , 1e-03 , 1 - 1e-03 ) return predt softplus_fn ( predt ) Softplus function used to ensure predt is strictly positive. Arguments predt: torch.tensor Predicted values. Returns predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 def softplus_fn ( predt : torch . tensor ) -> torch . tensor : \"\"\" Softplus function used to ensure predt is strictly positive. Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" predt = torch . log1p ( torch . exp ( - torch . abs ( predt ))) + torch . maximum ( predt , torch . tensor ( 0. )) predt [ predt == 0 ] = torch . tensor ( 1e-06 , dtype = predt . dtype ) predt = torch . nan_to_num ( predt , nan = float ( torch . nanmean ( predt ))) + torch . tensor ( 1e-06 , dtype = predt . dtype ) return predt softplus_fn_df ( predt ) Softplus function used for Student-T distribution. Arguments predt: torch.tensor Predicted values. Returns predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 def softplus_fn_df ( predt : torch . tensor ) -> torch . tensor : \"\"\" Softplus function used for Student-T distribution. Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" predt = torch . log1p ( torch . exp ( - torch . abs ( predt ))) + torch . maximum ( predt , torch . tensor ( 0. )) predt [ predt == 0 ] = torch . tensor ( 1e-06 , dtype = predt . dtype ) predt = torch . nan_to_num ( predt , nan = float ( torch . nanmean ( predt ))) + torch . tensor ( 1e-06 , dtype = predt . dtype ) return predt + torch . tensor ( 2.0 , dtype = predt . dtype )","title":"API Docs"},{"location":"api/#api-references","text":"LightGBMLSS - An extension of LightGBM to probabilistic forecasting","title":"API references"},{"location":"api/#lightgbmlss.datasets","text":"LightGBMLSS - An extension of LightGBM to probabilistic forecasting","title":"datasets"},{"location":"api/#lightgbmlss.datasets.data_loader","text":"","title":"data_loader"},{"location":"api/#lightgbmlss.datasets.data_loader.load_simulated_gaussian_data","text":"Returns train/test dataframe of a simulated example. Contains the following columns y int64: response x int64: x-feature X1:X10 int64: random noise features Source code in lightgbmlss/datasets/data_loader.py 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 def load_simulated_gaussian_data (): \"\"\" Returns train/test dataframe of a simulated example. Contains the following columns: y int64: response x int64: x-feature X1:X10 int64: random noise features \"\"\" train_path = pkg_resources . resource_stream ( __name__ , \"gaussian_train_sim.csv\" ) train_df = pd . read_csv ( train_path ) test_path = pkg_resources . resource_stream ( __name__ , \"gaussian_test_sim.csv\" ) test_df = pd . read_csv ( test_path ) return train_df , test_df","title":"load_simulated_gaussian_data()"},{"location":"api/#lightgbmlss.datasets.data_loader.load_simulated_studentT_data","text":"Returns train/test dataframe of a simulated example. Contains the following columns y int64: response x int64: x-feature X1:X10 int64: random noise features Source code in lightgbmlss/datasets/data_loader.py 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 def load_simulated_studentT_data (): \"\"\" Returns train/test dataframe of a simulated example. Contains the following columns: y int64: response x int64: x-feature X1:X10 int64: random noise features \"\"\" train_path = pkg_resources . resource_stream ( __name__ , \"studentT_train_sim.csv\" ) train_df = pd . read_csv ( train_path ) test_path = pkg_resources . resource_stream ( __name__ , \"studentT_test_sim.csv\" ) test_df = pd . read_csv ( test_path ) return train_df , test_df","title":"load_simulated_studentT_data()"},{"location":"api/#lightgbmlss.distributions","text":"LightGBMLSS - An extension of LightGBM to probabilistic forecasting","title":"distributions"},{"location":"api/#lightgbmlss.distributions.Beta","text":"","title":"Beta"},{"location":"api/#lightgbmlss.distributions.Beta.Beta","text":"Bases: DistributionClass Beta distribution class.","title":"Beta"},{"location":"api/#lightgbmlss.distributions.Beta.Beta--distributional-parameters","text":"concentration1: torch.Tensor 1st concentration parameter of the distribution (often referred to as alpha). concentration0: torch.Tensor 2nd concentration parameter of the distribution (often referred to as beta).","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.Beta.Beta--source","text":"https://pytorch.org/docs/stable/distributions.html#beta","title":"Source"},{"location":"api/#lightgbmlss.distributions.Beta.Beta--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/Beta.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class Beta ( DistributionClass ): \"\"\" Beta distribution class. Distributional Parameters ------------------------- concentration1: torch.Tensor 1st concentration parameter of the distribution (often referred to as alpha). concentration0: torch.Tensor 2nd concentration parameter of the distribution (often referred to as beta). Source ------------------------- https://pytorch.org/docs/stable/distributions.html#beta Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = Beta_Torch param_dict = { \"concentration1\" : response_fn , \"concentration0\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.Cauchy","text":"","title":"Cauchy"},{"location":"api/#lightgbmlss.distributions.Cauchy.Cauchy","text":"Bases: DistributionClass Cauchy distribution class.","title":"Cauchy"},{"location":"api/#lightgbmlss.distributions.Cauchy.Cauchy--distributional-parameters","text":"loc: torch.Tensor Mode or median of the distribution. scale: torch.Tensor Half width at half maximum.","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.Cauchy.Cauchy--source","text":"https://pytorch.org/docs/stable/distributions.html#cauchy","title":"Source"},{"location":"api/#lightgbmlss.distributions.Cauchy.Cauchy--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/Cauchy.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class Cauchy ( DistributionClass ): \"\"\" Cauchy distribution class. Distributional Parameters ------------------------- loc: torch.Tensor Mode or median of the distribution. scale: torch.Tensor Half width at half maximum. Source ------------------------- https://pytorch.org/docs/stable/distributions.html#cauchy Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = Cauchy_Torch param_dict = { \"loc\" : identity_fn , \"scale\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.Expectile","text":"","title":"Expectile"},{"location":"api/#lightgbmlss.distributions.Expectile.Expectile","text":"Bases: DistributionClass Expectile distribution class.","title":"Expectile"},{"location":"api/#lightgbmlss.distributions.Expectile.Expectile--distributional-parameters","text":"expectile: List List of specified expectiles.","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.Expectile.Expectile--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". expectiles: List List of expectiles in increasing order. penalize_crossing: bool Whether to include a penalty term to discourage crossing of expectiles. Source code in lightgbmlss/distributions/Expectile.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 class Expectile ( DistributionClass ): \"\"\" Expectile distribution class. Distributional Parameters ------------------------- expectile: List List of specified expectiles. Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". expectiles: List List of expectiles in increasing order. penalize_crossing: bool Whether to include a penalty term to discourage crossing of expectiles. \"\"\" def __init__ ( self , stabilization : str = \"None\" , expectiles : List = [ 0.1 , 0.5 , 0.9 ], penalize_crossing : bool = False , ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if not isinstance ( expectiles , list ): raise ValueError ( \"Expectiles must be a list.\" ) if not all ([ 0 < expectile < 1 for expectile in expectiles ]): raise ValueError ( \"Expectiles must be between 0 and 1.\" ) if not isinstance ( penalize_crossing , bool ): raise ValueError ( \"penalize_crossing must be a boolean. Please choose from True or False.\" ) # Set the parameters specific to the distribution distribution = Expectile_Torch torch . distributions . Distribution . set_default_validate_args ( False ) expectiles . sort () param_dict = {} for expectile in expectiles : key = f \"expectile_ { expectile } \" param_dict [ key ] = identity_fn # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = \"nll\" , tau = torch . tensor ( expectiles ), penalize_crossing = penalize_crossing )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.Expectile.Expectile_Torch","text":"Bases: Distribution PyTorch implementation of expectiles.","title":"Expectile_Torch"},{"location":"api/#lightgbmlss.distributions.Expectile.Expectile_Torch--arguments","text":"expectiles : List[torch.Tensor] List of expectiles. penalize_crossing : bool Whether to include a penalty term to discourage crossing of expectiles. Source code in lightgbmlss/distributions/Expectile.py 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 class Expectile_Torch ( Distribution ): \"\"\" PyTorch implementation of expectiles. Arguments --------- expectiles : List[torch.Tensor] List of expectiles. penalize_crossing : bool Whether to include a penalty term to discourage crossing of expectiles. \"\"\" def __init__ ( self , expectiles : List [ torch . Tensor ], penalize_crossing : bool = False , ): super ( Expectile_Torch ) . __init__ () self . expectiles = expectiles self . penalize_crossing = penalize_crossing self . __class__ . __name__ = \"Expectile\" def log_prob ( self , value : torch . Tensor , tau : List [ torch . Tensor ]) -> torch . Tensor : \"\"\" Returns the log of the probability density function evaluated at `value`. Arguments --------- value : torch.Tensor Response for which log probability is to be calculated. tau : List[torch.Tensor] List of asymmetry parameters. Returns ------- torch.Tensor Log probability of `value`. \"\"\" value = value . reshape ( - 1 , 1 ) loss = torch . tensor ( 0.0 , dtype = torch . float32 ) penalty = torch . tensor ( 0.0 , dtype = torch . float32 ) # Calculate loss predt_expectiles = [] for expectile , tau_value in zip ( self . expectiles , tau ): weight = torch . where ( value - expectile >= 0 , tau_value , 1 - tau_value ) loss += torch . nansum ( weight * ( value - expectile ) ** 2 ) predt_expectiles . append ( expectile . reshape ( - 1 , 1 )) # Penalty term to discourage crossing of expectiles if self . penalize_crossing : predt_expectiles = torch . cat ( predt_expectiles , dim = 1 ) penalty = torch . mean ( ( ~ torch . all ( torch . diff ( predt_expectiles , dim = 1 ) > 0 , dim = 1 )) . float () ) loss = ( loss * ( 1 + penalty )) / len ( self . expectiles ) return - loss","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.Expectile.Expectile_Torch.log_prob","text":"Returns the log of the probability density function evaluated at value .","title":"log_prob()"},{"location":"api/#lightgbmlss.distributions.Expectile.Expectile_Torch.log_prob--arguments","text":"value : torch.Tensor Response for which log probability is to be calculated. tau : List[torch.Tensor] List of asymmetry parameters.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.Expectile.Expectile_Torch.log_prob--returns","text":"torch.Tensor Log probability of value . Source code in lightgbmlss/distributions/Expectile.py 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 def log_prob ( self , value : torch . Tensor , tau : List [ torch . Tensor ]) -> torch . Tensor : \"\"\" Returns the log of the probability density function evaluated at `value`. Arguments --------- value : torch.Tensor Response for which log probability is to be calculated. tau : List[torch.Tensor] List of asymmetry parameters. Returns ------- torch.Tensor Log probability of `value`. \"\"\" value = value . reshape ( - 1 , 1 ) loss = torch . tensor ( 0.0 , dtype = torch . float32 ) penalty = torch . tensor ( 0.0 , dtype = torch . float32 ) # Calculate loss predt_expectiles = [] for expectile , tau_value in zip ( self . expectiles , tau ): weight = torch . where ( value - expectile >= 0 , tau_value , 1 - tau_value ) loss += torch . nansum ( weight * ( value - expectile ) ** 2 ) predt_expectiles . append ( expectile . reshape ( - 1 , 1 )) # Penalty term to discourage crossing of expectiles if self . penalize_crossing : predt_expectiles = torch . cat ( predt_expectiles , dim = 1 ) penalty = torch . mean ( ( ~ torch . all ( torch . diff ( predt_expectiles , dim = 1 ) > 0 , dim = 1 )) . float () ) loss = ( loss * ( 1 + penalty )) / len ( self . expectiles ) return - loss","title":"Returns"},{"location":"api/#lightgbmlss.distributions.Expectile.expectile_norm","text":"Calculates expectiles from Normal distribution for given tau values. For more details and distributions see https://rdrr.io/cran/expectreg/man/enorm.html Arguments tau : np.ndarray Vector of expectiles from the respective distribution. m : np.ndarray Mean of the Normal distribution. sd : np.ndarray Standard deviation of the Normal distribution. Returns np.ndarray Source code in lightgbmlss/distributions/Expectile.py 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 def expectile_norm ( tau : np . ndarray = 0.5 , m : np . ndarray = 0 , sd : np . ndarray = 1 ): \"\"\" Calculates expectiles from Normal distribution for given tau values. For more details and distributions see https://rdrr.io/cran/expectreg/man/enorm.html Arguments _________ tau : np.ndarray Vector of expectiles from the respective distribution. m : np.ndarray Mean of the Normal distribution. sd : np.ndarray Standard deviation of the Normal distribution. Returns _______ np.ndarray \"\"\" tau [ tau > 1 or tau < 0 ] = np . nan zz = 0 * tau lower = np . array ( - 10 , dtype = \"float\" ) lower = np . repeat ( lower [ np . newaxis , ... ], len ( tau ), axis = 0 ) upper = np . array ( 10 , dtype = \"float\" ) upper = np . repeat ( upper [ np . newaxis , ... ], len ( tau ), axis = 0 ) diff = 1 index = 0 while ( diff > 1e-10 ) and ( index < 1000 ): root = expectile_pnorm ( zz ) - tau root [ np . isnan ( root )] = 0 lower [ root < 0 ] = zz [ root < 0 ] upper [ root > 0 ] = zz [ root > 0 ] zz = ( upper + lower ) / 2 diff = np . nanmax ( np . abs ( root )) index = index + 1 zz [ np . isnan ( tau )] = np . nan return zz * sd + m","title":"expectile_norm()"},{"location":"api/#lightgbmlss.distributions.Expectile.expectile_pnorm","text":"Normal Expectile Distribution Function. For more details and distributions see https://rdrr.io/cran/expectreg/man/enorm.html Arguments tau : np.ndarray Vector of expectiles from the respective distribution. m : np.ndarray Mean of the Normal distribution. sd : np.ndarray Standard deviation of the Normal distribution. Returns tau : np.ndarray Expectiles from the Normal distribution. Source code in lightgbmlss/distributions/Expectile.py 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 def expectile_pnorm ( tau : np . ndarray = 0.5 , m : np . ndarray = 0 , sd : np . ndarray = 1 ): \"\"\" Normal Expectile Distribution Function. For more details and distributions see https://rdrr.io/cran/expectreg/man/enorm.html Arguments _________ tau : np.ndarray Vector of expectiles from the respective distribution. m : np.ndarray Mean of the Normal distribution. sd : np.ndarray Standard deviation of the Normal distribution. Returns _______ tau : np.ndarray Expectiles from the Normal distribution. \"\"\" z = ( tau - m ) / sd p = norm . cdf ( z , loc = m , scale = sd ) d = norm . pdf ( z , loc = m , scale = sd ) u = - d - z * p tau = u / ( 2 * u + z ) return tau","title":"expectile_pnorm()"},{"location":"api/#lightgbmlss.distributions.Gamma","text":"","title":"Gamma"},{"location":"api/#lightgbmlss.distributions.Gamma.Gamma","text":"Bases: DistributionClass Gamma distribution class.","title":"Gamma"},{"location":"api/#lightgbmlss.distributions.Gamma.Gamma--distributional-parameters","text":"concentration: torch.Tensor shape parameter of the distribution (often referred to as alpha) rate: torch.Tensor rate = 1 / scale of the distribution (often referred to as beta)","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.Gamma.Gamma--source","text":"https://pytorch.org/docs/stable/distributions.html#gamma","title":"Source"},{"location":"api/#lightgbmlss.distributions.Gamma.Gamma--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/Gamma.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class Gamma ( DistributionClass ): \"\"\" Gamma distribution class. Distributional Parameters -------------------------- concentration: torch.Tensor shape parameter of the distribution (often referred to as alpha) rate: torch.Tensor rate = 1 / scale of the distribution (often referred to as beta) Source ------------------------- https://pytorch.org/docs/stable/distributions.html#gamma Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = Gamma_Torch param_dict = { \"concentration\" : response_fn , \"rate\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.Gaussian","text":"","title":"Gaussian"},{"location":"api/#lightgbmlss.distributions.Gaussian.Gaussian","text":"Bases: DistributionClass Gaussian distribution class.","title":"Gaussian"},{"location":"api/#lightgbmlss.distributions.Gaussian.Gaussian--distributional-parameters","text":"loc: torch.Tensor Mean of the distribution (often referred to as mu). scale: torch.Tensor Standard deviation of the distribution (often referred to as sigma).","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.Gaussian.Gaussian--source","text":"https://pytorch.org/docs/stable/distributions.html#normal","title":"Source"},{"location":"api/#lightgbmlss.distributions.Gaussian.Gaussian--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/Gaussian.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class Gaussian ( DistributionClass ): \"\"\" Gaussian distribution class. Distributional Parameters ------------------------- loc: torch.Tensor Mean of the distribution (often referred to as mu). scale: torch.Tensor Standard deviation of the distribution (often referred to as sigma). Source ------------------------- https://pytorch.org/docs/stable/distributions.html#normal Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = Gaussian_Torch param_dict = { \"loc\" : identity_fn , \"scale\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.Gumbel","text":"","title":"Gumbel"},{"location":"api/#lightgbmlss.distributions.Gumbel.Gumbel","text":"Bases: DistributionClass Gumbel distribution class.","title":"Gumbel"},{"location":"api/#lightgbmlss.distributions.Gumbel.Gumbel--distributional-parameters","text":"loc: torch.Tensor Location parameter of the distribution. scale: torch.Tensor Scale parameter of the distribution.","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.Gumbel.Gumbel--source","text":"https://pytorch.org/docs/stable/distributions.html#gumbel","title":"Source"},{"location":"api/#lightgbmlss.distributions.Gumbel.Gumbel--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/Gumbel.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class Gumbel ( DistributionClass ): \"\"\" Gumbel distribution class. Distributional Parameters ------------------------- loc: torch.Tensor Location parameter of the distribution. scale: torch.Tensor Scale parameter of the distribution. Source ------------------------- https://pytorch.org/docs/stable/distributions.html#gumbel Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = Gumbel_Torch param_dict = { \"loc\" : identity_fn , \"scale\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.Laplace","text":"","title":"Laplace"},{"location":"api/#lightgbmlss.distributions.Laplace.Laplace","text":"Bases: DistributionClass Laplace distribution class.","title":"Laplace"},{"location":"api/#lightgbmlss.distributions.Laplace.Laplace--distributional-parameters","text":"loc: torch.Tensor Mean of the distribution. scale: torch.Tensor Scale of the distribution.","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.Laplace.Laplace--source","text":"https://pytorch.org/docs/stable/distributions.html#laplace","title":"Source"},{"location":"api/#lightgbmlss.distributions.Laplace.Laplace--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/Laplace.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class Laplace ( DistributionClass ): \"\"\" Laplace distribution class. Distributional Parameters ------------------------- loc: torch.Tensor Mean of the distribution. scale: torch.Tensor Scale of the distribution. Source ------------------------- https://pytorch.org/docs/stable/distributions.html#laplace Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = Laplace_Torch param_dict = { \"loc\" : identity_fn , \"scale\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.LogNormal","text":"","title":"LogNormal"},{"location":"api/#lightgbmlss.distributions.LogNormal.LogNormal","text":"Bases: DistributionClass LogNormal distribution class.","title":"LogNormal"},{"location":"api/#lightgbmlss.distributions.LogNormal.LogNormal--distributional-parameters","text":"loc: torch.Tensor Mean of log of distribution. scale: torch.Tensor Standard deviation of log of the distribution.","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.LogNormal.LogNormal--source","text":"https://pytorch.org/docs/stable/distributions.html#lognormal","title":"Source"},{"location":"api/#lightgbmlss.distributions.LogNormal.LogNormal--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/LogNormal.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class LogNormal ( DistributionClass ): \"\"\" LogNormal distribution class. Distributional Parameters ------------------------- loc: torch.Tensor Mean of log of distribution. scale: torch.Tensor Standard deviation of log of the distribution. Source ------------------------- https://pytorch.org/docs/stable/distributions.html#lognormal Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = LogNormal_Torch param_dict = { \"loc\" : identity_fn , \"scale\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.NegativeBinomial","text":"","title":"NegativeBinomial"},{"location":"api/#lightgbmlss.distributions.NegativeBinomial.NegativeBinomial","text":"Bases: DistributionClass NegativeBinomial distribution class.","title":"NegativeBinomial"},{"location":"api/#lightgbmlss.distributions.NegativeBinomial.NegativeBinomial--distributional-parameters","text":"total_count: torch.Tensor Non-negative number of negative Bernoulli trials to stop. probs: torch.Tensor Event probabilities of success in the half open interval [0, 1). logits: torch.Tensor Event log-odds for probabilities of success.","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.NegativeBinomial.NegativeBinomial--source","text":"https://pytorch.org/docs/stable/distributions.html#negativebinomial","title":"Source"},{"location":"api/#lightgbmlss.distributions.NegativeBinomial.NegativeBinomial--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn_total_count: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). response_fn_probs: str Response function for transforming the distributional parameters to the correct support. Options are \"sigmoid\" (sigmoid). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). Source code in lightgbmlss/distributions/NegativeBinomial.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 class NegativeBinomial ( DistributionClass ): \"\"\" NegativeBinomial distribution class. Distributional Parameters ------------------------- total_count: torch.Tensor Non-negative number of negative Bernoulli trials to stop. probs: torch.Tensor Event probabilities of success in the half open interval [0, 1). logits: torch.Tensor Event log-odds for probabilities of success. Source ------------------------- https://pytorch.org/docs/stable/distributions.html#negativebinomial Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn_total_count: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). response_fn_probs: str Response function for transforming the distributional parameters to the correct support. Options are \"sigmoid\" (sigmoid). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn_total_count : str = \"relu\" , response_fn_probs : str = \"sigmoid\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" ]: raise ValueError ( \"Invalid loss function. Please select 'nll'.\" ) # Specify Response Functions for total_count response_functions_total_count = { \"exp\" : exp_fn , \"softplus\" : softplus_fn , \"relu\" : relu_fn } if response_fn_total_count in response_functions_total_count : response_fn_total_count = response_functions_total_count [ response_fn_total_count ] else : raise ValueError ( \"Invalid response function for total_count. Please choose from 'exp', 'softplus' or 'relu'.\" ) # Specify Response Functions for probs response_functions_probs = { \"sigmoid\" : sigmoid_fn } if response_fn_probs in response_functions_probs : response_fn_probs = response_functions_probs [ response_fn_probs ] else : raise ValueError ( \"Invalid response function for probs. Please select 'sigmoid'.\" ) # Set the parameters specific to the distribution distribution = NegativeBinomial_Torch param_dict = { \"total_count\" : response_fn_total_count , \"probs\" : response_fn_probs } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = True , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.Poisson","text":"","title":"Poisson"},{"location":"api/#lightgbmlss.distributions.Poisson.Poisson","text":"Bases: DistributionClass Poisson distribution class.","title":"Poisson"},{"location":"api/#lightgbmlss.distributions.Poisson.Poisson--distributional-parameters","text":"rate: torch.Tensor Rate parameter of the distribution (often referred to as lambda).","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.Poisson.Poisson--source","text":"https://pytorch.org/docs/stable/distributions.html#poisson","title":"Source"},{"location":"api/#lightgbmlss.distributions.Poisson.Poisson--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). Source code in lightgbmlss/distributions/Poisson.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 class Poisson ( DistributionClass ): \"\"\" Poisson distribution class. Distributional Parameters ------------------------- rate: torch.Tensor Rate parameter of the distribution (often referred to as lambda). Source ------------------------- https://pytorch.org/docs/stable/distributions.html#poisson Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"relu\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" ]: raise ValueError ( \"Invalid loss function. Please select 'nll'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn , \"relu\" : relu_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function for total_count. Please choose from 'exp', 'softplus' or 'relu'.\" ) # Set the parameters specific to the distribution distribution = Poisson_Torch param_dict = { \"rate\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = True , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.SplineFlow","text":"","title":"SplineFlow"},{"location":"api/#lightgbmlss.distributions.SplineFlow.SplineFlow","text":"Bases: NormalizingFlowClass Spline Flow class. The spline flow is a normalizing flow based on element-wise rational spline bijections of linear and quadratic order (Durkan et al., 2019; Dolatabadi et al., 2020). Rational splines are functions that are comprised of segments that are the ratio of two polynomials. Rational splines offer an excellent combination of functional flexibility whilst maintaining a numerically stable inverse. For more details, see: - Durkan, C., Bekasov, A., Murray, I. and Papamakarios, G. Neural Spline Flows. NeurIPS 2019. - Dolatabadi, H. M., Erfani, S. and Leckie, C., Invertible Generative Modeling using Linear Rational Splines. AISTATS 2020.","title":"SplineFlow"},{"location":"api/#lightgbmlss.distributions.SplineFlow.SplineFlow--source","text":"https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.transforms.Spline","title":"Source"},{"location":"api/#lightgbmlss.distributions.SplineFlow.SplineFlow--arguments","text":"target_support: str The target support. Options are - \"real\": [-inf, inf] - \"positive\": [0, inf] - \"positive_integer\": [0, 1, 2, 3, ...] - \"unit_interval\": [0, 1] count_bins: int The number of segments comprising the spline. bound: float The quantity \"K\" determining the bounding box, [-K,K] x [-K,K] of the spline. By adjusting the \"K\" value, you can control the size of the bounding box and consequently control the range of inputs that the spline transform operates on. Larger values of \"K\" will result in a wider valid range for the spline transformation, while smaller values will restrict the valid range to a smaller region. Should be chosen based on the range of the data. order: str The order of the spline. Options are \"linear\" or \"quadratic\". stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\" or \"L2\". loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/SplineFlow.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 class SplineFlow ( NormalizingFlowClass ): \"\"\" Spline Flow class. The spline flow is a normalizing flow based on element-wise rational spline bijections of linear and quadratic order (Durkan et al., 2019; Dolatabadi et al., 2020). Rational splines are functions that are comprised of segments that are the ratio of two polynomials. Rational splines offer an excellent combination of functional flexibility whilst maintaining a numerically stable inverse. For more details, see: - Durkan, C., Bekasov, A., Murray, I. and Papamakarios, G. Neural Spline Flows. NeurIPS 2019. - Dolatabadi, H. M., Erfani, S. and Leckie, C., Invertible Generative Modeling using Linear Rational Splines. AISTATS 2020. Source --------- https://docs.pyro.ai/en/stable/distributions.html#pyro.distributions.transforms.Spline Arguments --------- target_support: str The target support. Options are - \"real\": [-inf, inf] - \"positive\": [0, inf] - \"positive_integer\": [0, 1, 2, 3, ...] - \"unit_interval\": [0, 1] count_bins: int The number of segments comprising the spline. bound: float The quantity \"K\" determining the bounding box, [-K,K] x [-K,K] of the spline. By adjusting the \"K\" value, you can control the size of the bounding box and consequently control the range of inputs that the spline transform operates on. Larger values of \"K\" will result in a wider valid range for the spline transformation, while smaller values will restrict the valid range to a smaller region. Should be chosen based on the range of the data. order: str The order of the spline. Options are \"linear\" or \"quadratic\". stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\" or \"L2\". loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , target_support : str = \"real\" , count_bins : int = 8 , bound : float = 3.0 , order : str = \"linear\" , stabilization : str = \"None\" , loss_fn : str = \"nll\" ): # Specify Target Transform if not isinstance ( target_support , str ): raise ValueError ( \"target_support must be a string.\" ) transforms = { \"real\" : ( identity_transform , False ), \"positive\" : ( SoftplusTransform (), False ), \"positive_integer\" : ( SoftplusTransform (), True ), \"unit_interval\" : ( SigmoidTransform (), False ) } if target_support in transforms : target_transform , discrete = transforms [ target_support ] else : raise ValueError ( \"Invalid target_support. Options are 'real', 'positive', 'positive_integer', or 'unit_interval'.\" ) # Check if count_bins is valid if not isinstance ( count_bins , int ): raise ValueError ( \"count_bins must be an integer.\" ) if count_bins <= 0 : raise ValueError ( \"count_bins must be a positive integer > 0.\" ) # Check if bound is float if not isinstance ( bound , float ): raise ValueError ( \"bound must be a float.\" ) # Number of parameters if not isinstance ( order , str ): raise ValueError ( \"order must be a string.\" ) order_params = { \"quadratic\" : 2 * count_bins + ( count_bins - 1 ), \"linear\" : 3 * count_bins + ( count_bins - 1 ) } if order in order_params : n_params = order_params [ order ] else : raise ValueError ( \"Invalid order specification. Options are 'linear' or 'quadratic'.\" ) # Check if stabilization method is valid. if not isinstance ( stabilization , str ): raise ValueError ( \"stabilization must be a string.\" ) if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Options are 'None', 'MAD' or 'L2'.\" ) # Check if loss function is valid. if not isinstance ( loss_fn , str ): raise ValueError ( \"loss_fn must be a string.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss_fn. Options are 'nll' or 'crps'.\" ) # Specify parameter dictionary param_dict = { f \"param_ { i + 1 } \" : identity_fn for i in range ( n_params )} torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Normalizing Flow Class super () . __init__ ( base_dist = Normal , # Base distribution, currently only Normal is supported. flow_transform = Spline , count_bins = count_bins , bound = bound , order = order , n_dist_param = n_params , param_dict = param_dict , target_transform = target_transform , discrete = discrete , univariate = True , stabilization = stabilization , loss_fn = loss_fn )","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.StudentT","text":"","title":"StudentT"},{"location":"api/#lightgbmlss.distributions.StudentT.StudentT","text":"Bases: DistributionClass Student-T Distribution Class","title":"StudentT"},{"location":"api/#lightgbmlss.distributions.StudentT.StudentT--distributional-parameters","text":"df: torch.Tensor Degrees of freedom. loc: torch.Tensor Mean of the distribution. scale: torch.Tensor Scale of the distribution.","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.StudentT.StudentT--source","text":"https://pytorch.org/docs/stable/distributions.html#studentt","title":"Source"},{"location":"api/#lightgbmlss.distributions.StudentT.StudentT--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/StudentT.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 class StudentT ( DistributionClass ): \"\"\" Student-T Distribution Class Distributional Parameters ------------------------- df: torch.Tensor Degrees of freedom. loc: torch.Tensor Mean of the distribution. scale: torch.Tensor Scale of the distribution. Source ------------------------- https://pytorch.org/docs/stable/distributions.html#studentt Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : ( exp_fn , exp_fn_df ), \"softplus\" : ( softplus_fn , softplus_fn_df ) } if response_fn in response_functions : response_fn , response_fn_df = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = StudentT_Torch param_dict = { \"df\" : response_fn_df , \"loc\" : identity_fn , \"scale\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.Weibull","text":"","title":"Weibull"},{"location":"api/#lightgbmlss.distributions.Weibull.Weibull","text":"Bases: DistributionClass Weibull distribution class.","title":"Weibull"},{"location":"api/#lightgbmlss.distributions.Weibull.Weibull--distributional-parameters","text":"scale: torch.Tensor Scale parameter of distribution (lambda). concentration: torch.Tensor Concentration parameter of distribution (k/shape).","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.Weibull.Weibull--source","text":"https://pytorch.org/docs/stable/distributions.html#weibull","title":"Source"},{"location":"api/#lightgbmlss.distributions.Weibull.Weibull--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/Weibull.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 class Weibull ( DistributionClass ): \"\"\" Weibull distribution class. Distributional Parameters ------------------------- scale: torch.Tensor Scale parameter of distribution (lambda). concentration: torch.Tensor Concentration parameter of distribution (k/shape). Source ------------------------- https://pytorch.org/docs/stable/distributions.html#weibull Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" , \"crps\" ]: raise ValueError ( \"Invalid loss function. Please choose from 'nll' or 'crps'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = Weibull_Torch param_dict = { \"scale\" : response_fn , \"concentration\" : response_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.ZABeta","text":"","title":"ZABeta"},{"location":"api/#lightgbmlss.distributions.ZABeta.ZABeta","text":"Bases: DistributionClass Zero-Adjusted Beta distribution class. The zero-adjusted Beta distribution is similar to the Beta distribution but allows zeros as y values.","title":"ZABeta"},{"location":"api/#lightgbmlss.distributions.ZABeta.ZABeta--distributional-parameters","text":"concentration1: torch.Tensor 1st concentration parameter of the distribution (often referred to as alpha). concentration0: torch.Tensor 2nd concentration parameter of the distribution (often referred to as beta). gate: torch.Tensor Probability of zeros given via a Bernoulli distribution.","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.ZABeta.ZABeta--source","text":"https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py","title":"Source"},{"location":"api/#lightgbmlss.distributions.ZABeta.ZABeta--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). Source code in lightgbmlss/distributions/ZABeta.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 class ZABeta ( DistributionClass ): \"\"\" Zero-Adjusted Beta distribution class. The zero-adjusted Beta distribution is similar to the Beta distribution but allows zeros as y values. Distributional Parameters ------------------------- concentration1: torch.Tensor 1st concentration parameter of the distribution (often referred to as alpha). concentration0: torch.Tensor 2nd concentration parameter of the distribution (often referred to as beta). gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source ------------------------- https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" ]: raise ValueError ( \"Invalid loss function. Please select 'nll'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = ZeroAdjustedBeta_Torch param_dict = { \"concentration1\" : response_fn , \"concentration0\" : response_fn , \"gate\" : sigmoid_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.ZAGamma","text":"","title":"ZAGamma"},{"location":"api/#lightgbmlss.distributions.ZAGamma.ZAGamma","text":"Bases: DistributionClass Zero-Adjusted Gamma distribution class. The zero-adjusted Gamma distribution is similar to the Gamma distribution but allows zeros as y values.","title":"ZAGamma"},{"location":"api/#lightgbmlss.distributions.ZAGamma.ZAGamma--distributional-parameters","text":"concentration: torch.Tensor shape parameter of the distribution (often referred to as alpha) rate: torch.Tensor rate = 1 / scale of the distribution (often referred to as beta) gate: torch.Tensor Probability of zeros given via a Bernoulli distribution.","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.ZAGamma.ZAGamma--source","text":"https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150","title":"Source"},{"location":"api/#lightgbmlss.distributions.ZAGamma.ZAGamma--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). Source code in lightgbmlss/distributions/ZAGamma.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 class ZAGamma ( DistributionClass ): \"\"\" Zero-Adjusted Gamma distribution class. The zero-adjusted Gamma distribution is similar to the Gamma distribution but allows zeros as y values. Distributional Parameters -------------------------- concentration: torch.Tensor shape parameter of the distribution (often referred to as alpha) rate: torch.Tensor rate = 1 / scale of the distribution (often referred to as beta) gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source ------------------------- https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150 Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" ]: raise ValueError ( \"Invalid loss function. Please select 'nll'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = ZeroAdjustedGamma_Torch param_dict = { \"concentration\" : response_fn , \"rate\" : response_fn , \"gate\" : sigmoid_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.ZALN","text":"","title":"ZALN"},{"location":"api/#lightgbmlss.distributions.ZALN.ZALN","text":"Bases: DistributionClass Zero-Adjusted LogNormal distribution class. The zero-adjusted Log-Normal distribution is similar to the Log-Normal distribution but allows zeros as y values.","title":"ZALN"},{"location":"api/#lightgbmlss.distributions.ZALN.ZALN--distributional-parameters","text":"loc: torch.Tensor Mean of log of distribution. scale: torch.Tensor Standard deviation of log of the distribution. gate: torch.Tensor Probability of zeros given via a Bernoulli distribution.","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.ZALN.ZALN--source","text":"https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150","title":"Source"},{"location":"api/#lightgbmlss.distributions.ZALN.ZALN--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). Source code in lightgbmlss/distributions/ZALN.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 class ZALN ( DistributionClass ): \"\"\" Zero-Adjusted LogNormal distribution class. The zero-adjusted Log-Normal distribution is similar to the Log-Normal distribution but allows zeros as y values. Distributional Parameters ------------------------- loc: torch.Tensor Mean of log of distribution. scale: torch.Tensor Standard deviation of log of the distribution. gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source ------------------------- https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150 Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential) or \"softplus\" (softplus). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"exp\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" ]: raise ValueError ( \"Invalid loss function. Please select 'nll'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function. Please choose from 'exp' or 'softplus'.\" ) # Set the parameters specific to the distribution distribution = ZeroAdjustedLogNormal_Torch param_dict = { \"loc\" : identity_fn , \"scale\" : response_fn , \"gate\" : sigmoid_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = False , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.ZINB","text":"","title":"ZINB"},{"location":"api/#lightgbmlss.distributions.ZINB.ZINB","text":"Bases: DistributionClass Zero-Inflated Negative Binomial distribution class.","title":"ZINB"},{"location":"api/#lightgbmlss.distributions.ZINB.ZINB--distributional-parameters","text":"total_count: torch.Tensor Non-negative number of negative Bernoulli trials to stop. probs: torch.Tensor Event probabilities of success in the half open interval [0, 1). gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution.","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.ZINB.ZINB--source","text":"https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150","title":"Source"},{"location":"api/#lightgbmlss.distributions.ZINB.ZINB--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn_total_count: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). response_fn_probs: str Response function for transforming the distributional parameters to the correct support. Options are \"sigmoid\" (sigmoid). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). Source code in lightgbmlss/distributions/ZINB.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 class ZINB ( DistributionClass ): \"\"\" Zero-Inflated Negative Binomial distribution class. Distributional Parameters ------------------------- total_count: torch.Tensor Non-negative number of negative Bernoulli trials to stop. probs: torch.Tensor Event probabilities of success in the half open interval [0, 1). gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution. Source ------------------------- https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150 Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn_total_count: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). response_fn_probs: str Response function for transforming the distributional parameters to the correct support. Options are \"sigmoid\" (sigmoid). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn_total_count : str = \"relu\" , response_fn_probs : str = \"sigmoid\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" ]: raise ValueError ( \"Invalid loss function. Please select 'nll'.\" ) # Specify Response Functions for total_count response_functions_total_count = { \"exp\" : exp_fn , \"softplus\" : softplus_fn , \"relu\" : relu_fn } if response_fn_total_count in response_functions_total_count : response_fn_total_count = response_functions_total_count [ response_fn_total_count ] else : raise ValueError ( \"Invalid response function for total_count. Please choose from 'exp', 'softplus' or 'relu'.\" ) # Specify Response Functions for probs response_functions_probs = { \"sigmoid\" : sigmoid_fn } if response_fn_probs in response_functions_probs : response_fn_probs = response_functions_probs [ response_fn_probs ] else : raise ValueError ( \"Invalid response function for probs. Please select 'sigmoid'.\" ) # Set the parameters specific to the distribution distribution = ZeroInflatedNegativeBinomial_Torch param_dict = { \"total_count\" : response_fn_total_count , \"probs\" : response_fn_probs , \"gate\" : sigmoid_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = True , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.ZIPoisson","text":"","title":"ZIPoisson"},{"location":"api/#lightgbmlss.distributions.ZIPoisson.ZIPoisson","text":"Bases: DistributionClass Zero-Inflated Poisson distribution class.","title":"ZIPoisson"},{"location":"api/#lightgbmlss.distributions.ZIPoisson.ZIPoisson--distributional-parameters","text":"rate: torch.Tensor Rate parameter of the distribution (often referred to as lambda). gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution.","title":"Distributional Parameters"},{"location":"api/#lightgbmlss.distributions.ZIPoisson.ZIPoisson--source","text":"https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L121","title":"Source"},{"location":"api/#lightgbmlss.distributions.ZIPoisson.ZIPoisson--parameters","text":"stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). Source code in lightgbmlss/distributions/ZIPoisson.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 class ZIPoisson ( DistributionClass ): \"\"\" Zero-Inflated Poisson distribution class. Distributional Parameters ------------------------- rate: torch.Tensor Rate parameter of the distribution (often referred to as lambda). gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution. Source ------------------------- https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L121 Parameters ------------------------- stabilization: str Stabilization method for the Gradient and Hessian. Options are \"None\", \"MAD\", \"L2\". response_fn: str Response function for transforming the distributional parameters to the correct support. Options are \"exp\" (exponential), \"softplus\" (softplus) or \"relu\" (rectified linear unit). loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood). \"\"\" def __init__ ( self , stabilization : str = \"None\" , response_fn : str = \"relu\" , loss_fn : str = \"nll\" ): # Input Checks if stabilization not in [ \"None\" , \"MAD\" , \"L2\" ]: raise ValueError ( \"Invalid stabilization method. Please choose from 'None', 'MAD' or 'L2'.\" ) if loss_fn not in [ \"nll\" ]: raise ValueError ( \"Invalid loss function. Please select 'nll'.\" ) # Specify Response Functions response_functions = { \"exp\" : exp_fn , \"softplus\" : softplus_fn , \"relu\" : relu_fn } if response_fn in response_functions : response_fn = response_functions [ response_fn ] else : raise ValueError ( \"Invalid response function for total_count. Please choose from 'exp', 'softplus' or 'relu'.\" ) # Set the parameters specific to the distribution distribution = ZeroInflatedPoisson_Torch param_dict = { \"rate\" : response_fn , \"gate\" : sigmoid_fn } torch . distributions . Distribution . set_default_validate_args ( False ) # Specify Distribution Class super () . __init__ ( distribution = distribution , univariate = True , discrete = True , n_dist_param = len ( param_dict ), stabilization = stabilization , param_dict = param_dict , distribution_arg_names = list ( param_dict . keys ()), loss_fn = loss_fn )","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.distribution_utils","text":"","title":"distribution_utils"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass","text":"Generic class that contains general functions for univariate distributions.","title":"DistributionClass"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass--arguments","text":"distribution: torch.distributions.Distribution PyTorch Distribution class. univariate: bool Whether the distribution is univariate or multivariate. discrete: bool Whether the support of the distribution is discrete or continuous. n_dist_param: int Number of distributional parameters. stabilization: str Stabilization method. param_dict: Dict[str, Any] Dictionary that maps distributional parameters to their response scale. distribution_arg_names: List List of distributional parameter names. loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. tau: List List of expectiles. Only used for Expectile distributon. penalize_crossing: bool Whether to include a penalty term to discourage crossing of expectiles. Only used for Expectile distribution. Source code in lightgbmlss/distributions/distribution_utils.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 class DistributionClass : \"\"\" Generic class that contains general functions for univariate distributions. Arguments --------- distribution: torch.distributions.Distribution PyTorch Distribution class. univariate: bool Whether the distribution is univariate or multivariate. discrete: bool Whether the support of the distribution is discrete or continuous. n_dist_param: int Number of distributional parameters. stabilization: str Stabilization method. param_dict: Dict[str, Any] Dictionary that maps distributional parameters to their response scale. distribution_arg_names: List List of distributional parameter names. loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. tau: List List of expectiles. Only used for Expectile distributon. penalize_crossing: bool Whether to include a penalty term to discourage crossing of expectiles. Only used for Expectile distribution. \"\"\" def __init__ ( self , distribution : torch . distributions . Distribution = None , univariate : bool = True , discrete : bool = False , n_dist_param : int = None , stabilization : str = \"None\" , param_dict : Dict [ str , Any ] = None , distribution_arg_names : List = None , loss_fn : str = \"nll\" , tau : Optional [ List [ torch . Tensor ]] = None , penalize_crossing : bool = False , ): self . distribution = distribution self . univariate = univariate self . discrete = discrete self . n_dist_param = n_dist_param self . stabilization = stabilization self . param_dict = param_dict self . distribution_arg_names = distribution_arg_names self . loss_fn = loss_fn self . tau = tau self . penalize_crossing = penalize_crossing def objective_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Function to estimate gradients and hessians of distributional parameters. Arguments --------- predt: np.ndarray Predicted values. data: lgb.DMatrix Data used for training. Returns ------- grad: np.ndarray Gradient. hess: np.ndarray Hessian. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Weights if data . weight is None : # Use 1 as weight if no weights are specified weights = torch . ones_like ( target , dtype = target . dtype ) . numpy () else : weights = data . get_weight () . reshape ( - 1 , 1 ) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate gradients and hessians predt , loss = self . get_params_loss ( predt , target , start_values , requires_grad = True ) grad , hess = self . compute_gradients_and_hessians ( loss , predt , weights ) return grad , hess def metric_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ str , float , bool ]: \"\"\" Function that evaluates the predictions using the negative log-likelihood. Arguments --------- predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns ------- name: str Name of the evaluation metric. nll: float Negative log-likelihood. is_higher_better: bool Whether a higher value of the metric is better or not. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate loss is_higher_better = False _ , loss = self . get_params_loss ( predt , target , start_values , requires_grad = False ) return self . loss_fn , loss , is_higher_better def loss_fn_start_values ( self , params : torch . Tensor , target : torch . Tensor ) -> torch . Tensor : \"\"\" Function that calculates the loss for a given set of distributional parameters. Only used for calculating the loss for the start values. Parameter --------- params: torch.Tensor Distributional parameters. target: torch.Tensor Target values. Returns ------- loss: torch.Tensor Loss value. \"\"\" # Transform parameters to response scale params = [ response_fn ( params [ i ] . reshape ( - 1 , 1 )) for i , response_fn in enumerate ( self . param_dict . values ()) ] # Replace NaNs and infinity values with 0.5 nan_inf_idx = torch . isnan ( torch . stack ( params )) | torch . isinf ( torch . stack ( params )) params = torch . where ( nan_inf_idx , torch . tensor ( 0.5 ), torch . stack ( params )) # Specify Distribution and Loss if self . tau is None : dist = self . distribution ( * params ) loss = - torch . nansum ( dist . log_prob ( target )) else : dist = self . distribution ( params , self . penalize_crossing ) loss = - torch . nansum ( dist . log_prob ( target , self . tau )) return loss def calculate_start_values ( self , target : np . ndarray , max_iter : int = 50 ) -> Tuple [ float , np . ndarray ]: \"\"\" Function that calculates the starting values for each distributional parameter. Arguments --------- target: np.ndarray Data from which starting values are calculated. max_iter: int Maximum number of iterations. Returns ------- loss: float Loss value. start_values: np.ndarray Starting values for each distributional parameter. \"\"\" # Convert target to torch.tensor target = torch . tensor ( target ) . reshape ( - 1 , 1 ) # Initialize parameters params = [ torch . tensor ( 0.5 , requires_grad = True ) for _ in range ( self . n_dist_param )] # Specify optimizer optimizer = LBFGS ( params , lr = 0.1 , max_iter = np . min ([ int ( max_iter / 4 ), 20 ]), line_search_fn = \"strong_wolfe\" ) # Define learning rate scheduler lr_scheduler = ReduceLROnPlateau ( optimizer , mode = \"min\" , factor = 0.5 , patience = 10 ) # Define closure def closure (): optimizer . zero_grad () loss = self . loss_fn_start_values ( params , target ) loss . backward () return loss # Optimize parameters loss_vals = [] for epoch in range ( max_iter ): loss = optimizer . step ( closure ) lr_scheduler . step ( loss ) loss_vals . append ( loss . item ()) # Get final loss loss = np . array ( loss_vals [ - 1 ]) # Get start values start_values = np . array ([ params [ i ] . detach () for i in range ( self . n_dist_param )]) # Replace any remaining NaNs or infinity values with 0.5 start_values = np . nan_to_num ( start_values , nan = 0.5 , posinf = 0.5 , neginf = 0.5 ) return loss , start_values def get_params_loss ( self , predt : np . ndarray , target : torch . Tensor , start_values : List [ float ], requires_grad : bool = False , ) -> Tuple [ List [ torch . Tensor ], np . ndarray ]: \"\"\" Function that returns the predicted parameters and the loss. Arguments --------- predt: np.ndarray Predicted values. target: torch.Tensor Target values. start_values: List Starting values for each distributional parameter. requires_grad: bool Whether to add to the computational graph or not. Returns ------- predt: torch.Tensor Predicted parameters. loss: torch.Tensor Loss value. \"\"\" # Predicted Parameters predt = predt . reshape ( - 1 , self . n_dist_param , order = \"F\" ) # Replace NaNs and infinity values with unconditional start values nan_inf_mask = np . isnan ( predt ) | np . isinf ( predt ) predt [ nan_inf_mask ] = np . take ( start_values , np . where ( nan_inf_mask )[ 1 ]) # Convert to torch.tensor predt = [ torch . tensor ( predt [:, i ] . reshape ( - 1 , 1 ), requires_grad = requires_grad ) for i in range ( self . n_dist_param ) ] # Predicted Parameters transformed to response scale predt_transformed = [ response_fn ( predt [ i ] . reshape ( - 1 , 1 )) for i , response_fn in enumerate ( self . param_dict . values ()) ] # Specify Distribution and Loss if self . tau is None : dist_kwargs = dict ( zip ( self . distribution_arg_names , predt_transformed )) dist_fit = self . distribution ( ** dist_kwargs ) if self . loss_fn == \"nll\" : loss = - torch . nansum ( dist_fit . log_prob ( target )) elif self . loss_fn == \"crps\" : torch . manual_seed ( 123 ) dist_samples = dist_fit . rsample (( 30 ,)) . squeeze ( - 1 ) loss = torch . nansum ( self . crps_score ( target , dist_samples )) else : raise ValueError ( \"Invalid loss function. Please select 'nll' or 'crps'.\" ) else : dist_fit = self . distribution ( predt_transformed , self . penalize_crossing ) loss = - torch . nansum ( dist_fit . log_prob ( target , self . tau )) return predt , loss def draw_samples ( self , predt_params : pd . DataFrame , n_samples : int = 1000 , seed : int = 123 ) -> pd . DataFrame : \"\"\" Function that draws n_samples from a predicted distribution. Arguments --------- predt_params: pd.DataFrame pd.DataFrame with predicted distributional parameters. n_samples: int Number of sample to draw from predicted response distribution. seed: int Manual seed. Returns ------- pred_dist: pd.DataFrame DataFrame with n_samples drawn from predicted response distribution. \"\"\" torch . manual_seed ( seed ) if self . tau is None : pred_params = torch . tensor ( predt_params . values ) dist_kwargs = { arg_name : param for arg_name , param in zip ( self . distribution_arg_names , pred_params . T )} dist_pred = self . distribution ( ** dist_kwargs ) dist_samples = dist_pred . sample (( n_samples ,)) . squeeze () . detach () . numpy () . T dist_samples = pd . DataFrame ( dist_samples ) dist_samples . columns = [ str ( \"y_sample\" ) + str ( i ) for i in range ( dist_samples . shape [ 1 ])] else : dist_samples = None if self . discrete : dist_samples = dist_samples . astype ( int ) return dist_samples def predict_dist ( self , booster : lgb . Booster , data : pd . DataFrame , start_values : np . ndarray , pred_type : str = \"parameters\" , n_samples : int = 1000 , quantiles : list = [ 0.1 , 0.5 , 0.9 ], seed : str = 123 ) -> pd . DataFrame : \"\"\" Function that predicts from the trained model. Arguments --------- booster : lgb.Booster Trained model. data : pd.DataFrame Data to predict from. start_values : np.ndarray. Starting values for each distributional parameter. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns ------- pred : pd.DataFrame Predictions. \"\"\" predt = torch . tensor ( booster . predict ( data , raw_score = True ), dtype = torch . float32 ) . reshape ( - 1 , self . n_dist_param ) # Set init_score as starting point for each distributional parameter. init_score_pred = torch . tensor ( np . ones ( shape = ( data . shape [ 0 ], 1 )) * start_values , dtype = torch . float32 ) # The predictions don't include the init_score specified in creating the train data. # Hence, it needs to be added manually with the corresponding transform for each distributional parameter. dist_params_predt = np . concatenate ( [ response_fun ( predt [:, i ] . reshape ( - 1 , 1 ) + init_score_pred [:, i ] . reshape ( - 1 , 1 )) . numpy () for i , ( dist_param , response_fun ) in enumerate ( self . param_dict . items ()) ], axis = 1 , ) dist_params_predt = pd . DataFrame ( dist_params_predt ) dist_params_predt . columns = self . param_dict . keys () # Draw samples from predicted response distribution pred_samples_df = self . draw_samples ( predt_params = dist_params_predt , n_samples = n_samples , seed = seed ) if pred_type == \"parameters\" : return dist_params_predt elif pred_type == \"expectiles\" : return dist_params_predt elif pred_type == \"samples\" : return pred_samples_df elif pred_type == \"quantiles\" : # Calculate quantiles from predicted response distribution pred_quant_df = pred_samples_df . quantile ( quantiles , axis = 1 ) . T pred_quant_df . columns = [ str ( \"quant_\" ) + str ( quantiles [ i ]) for i in range ( len ( quantiles ))] if self . discrete : pred_quant_df = pred_quant_df . astype ( int ) return pred_quant_df def compute_gradients_and_hessians ( self , loss : torch . tensor , predt : torch . tensor , weights : np . ndarray ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Calculates gradients and hessians. Output gradients and hessians have shape (n_samples*n_outputs, 1). Arguments: --------- loss: torch.Tensor Loss. predt: torch.Tensor List of predicted parameters. weights: np.ndarray Weights. Returns: ------- grad: torch.Tensor Gradients. hess: torch.Tensor Hessians. \"\"\" if self . loss_fn == \"nll\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ autograd ( grad [ i ] . nansum (), inputs = predt [ i ], retain_graph = True )[ 0 ] for i in range ( len ( grad ))] elif self . loss_fn == \"crps\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ torch . ones_like ( grad [ i ]) for i in range ( len ( grad ))] # # Approximation of Hessian # step_size = 1e-6 # predt_upper = [ # response_fn(predt[i] + step_size).reshape(-1, 1) for i, response_fn in # enumerate(self.param_dict.values()) # ] # dist_kwargs_upper = dict(zip(self.distribution_arg_names, predt_upper)) # dist_fit_upper = self.distribution(**dist_kwargs_upper) # dist_samples_upper = dist_fit_upper.rsample((30,)).squeeze(-1) # loss_upper = torch.nansum(self.crps_score(self.target, dist_samples_upper)) # # predt_lower = [ # response_fn(predt[i] - step_size).reshape(-1, 1) for i, response_fn in # enumerate(self.param_dict.values()) # ] # dist_kwargs_lower = dict(zip(self.distribution_arg_names, predt_lower)) # dist_fit_lower = self.distribution(**dist_kwargs_lower) # dist_samples_lower = dist_fit_lower.rsample((30,)).squeeze(-1) # loss_lower = torch.nansum(self.crps_score(self.target, dist_samples_lower)) # # grad_upper = autograd(loss_upper, inputs=predt_upper) # grad_lower = autograd(loss_lower, inputs=predt_lower) # hess = [(grad_upper[i] - grad_lower[i]) / (2 * step_size) for i in range(len(grad))] # Stabilization of Derivatives if self . stabilization != \"None\" : grad = [ self . stabilize_derivative ( grad [ i ], type = self . stabilization ) for i in range ( len ( grad ))] hess = [ self . stabilize_derivative ( hess [ i ], type = self . stabilization ) for i in range ( len ( hess ))] # Reshape grad = torch . cat ( grad , axis = 1 ) . detach () . numpy () hess = torch . cat ( hess , axis = 1 ) . detach () . numpy () # Weighting grad *= weights hess *= weights # Reshape grad = grad . ravel ( order = \"F\" ) hess = hess . ravel ( order = \"F\" ) return grad , hess def stabilize_derivative ( self , input_der : torch . Tensor , type : str = \"MAD\" ) -> torch . Tensor : \"\"\" Function that stabilizes Gradients and Hessians. As LightGBMLSS updates the parameter estimates by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all distributional parameters. Due to imbalances regarding the ranges, the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve convergence might be to standardize the response variable. This is especially useful if the range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the standardization of the response are not always advised but need to be carefully considered. Source: https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173 Parameters ---------- input_der : torch.Tensor Input derivative, either Gradient or Hessian. type: str Stabilization method. Can be either \"None\", \"MAD\" or \"L2\". Returns ------- stab_der : torch.Tensor Stabilized Gradient or Hessian. \"\"\" if type == \"MAD\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . nanmedian ( torch . abs ( input_der - torch . nanmedian ( input_der ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) stab_der = input_der / div if type == \"L2\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . sqrt ( torch . nanmean ( input_der . pow ( 2 ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) div = torch . where ( div > torch . tensor ( 10000.0 ), torch . tensor ( 10000.0 ), div ) stab_der = input_der / div if type == \"None\" : stab_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) return stab_der def crps_score ( self , y : torch . tensor , yhat_dist : torch . tensor ) -> torch . tensor : \"\"\" Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples. Parameters ---------- y: torch.Tensor Response variable of shape (n_observations,1). yhat_dist: torch.Tensor Predicted samples of shape (n_samples, n_observations). Returns ------- crps: torch.Tensor CRPS score. References ---------- Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. 102. 359-378. Source ------ https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549 \"\"\" # Get the number of observations n_samples = yhat_dist . shape [ 0 ] # Sort the forecasts in ascending order yhat_dist_sorted , _ = torch . sort ( yhat_dist , 0 ) # Create temporary tensors y_cdf = torch . zeros_like ( y ) yhat_cdf = torch . zeros_like ( y ) yhat_prev = torch . zeros_like ( y ) crps = torch . zeros_like ( y ) # Loop over the predicted samples generated per observation for yhat in yhat_dist_sorted : yhat = yhat . reshape ( - 1 , 1 ) flag = ( y_cdf == 0 ) * ( y < yhat ) crps += flag * (( y - yhat_prev ) * yhat_cdf ** 2 ) crps += flag * (( yhat - y ) * ( yhat_cdf - 1 ) ** 2 ) crps += ( ~ flag ) * (( yhat - yhat_prev ) * ( yhat_cdf - y_cdf ) ** 2 ) y_cdf += flag yhat_cdf += 1 / n_samples yhat_prev = yhat # In case y_cdf == 0 after the loop flag = ( y_cdf == 0 ) crps += flag * ( y - yhat ) return crps def dist_select ( self , target : np . ndarray , candidate_distributions : List , max_iter : int = 100 , n_samples : int = 1000 , plot : bool = False , figure_size : tuple = ( 10 , 5 ), ) -> pd . DataFrame : \"\"\" Function that selects the most suitable distribution among the candidate_distributions for the target variable, based on the NegLogLikelihood (lower is better). Parameters ---------- target: np.ndarray Response variable. candidate_distributions: List List of candidate distributions. max_iter: int Maximum number of iterations for the optimization. n_samples: int Number of samples to draw from the fitted distribution. plot: bool If True, a density plot of the actual and fitted distribution is created. figure_size: tuple Figure size of the density plot. Returns ------- fit_df: pd.DataFrame Dataframe with the loss values of the fitted candidate distributions. \"\"\" dist_list = [] total_iterations = len ( candidate_distributions ) with tqdm ( total = total_iterations , desc = \"Fitting candidate distributions\" ) as pbar : for i in range ( len ( candidate_distributions )): dist_name = candidate_distributions [ i ] . __name__ . split ( \".\" )[ 2 ] pbar . set_description ( f \"Fitting { dist_name } distribution\" ) dist_sel = getattr ( candidate_distributions [ i ], dist_name )() try : loss , params = dist_sel . calculate_start_values ( target = target . reshape ( - 1 , 1 ), max_iter = max_iter ) fit_df = pd . DataFrame . from_dict ( { self . loss_fn : loss . reshape ( - 1 ,), \"distribution\" : str ( dist_name ), \"params\" : [ params ] } ) except Exception as e : warnings . warn ( f \"Error fitting { dist_name } distribution: { str ( e ) } \" ) fit_df = pd . DataFrame ( { self . loss_fn : np . nan , \"distribution\" : str ( dist_name ), \"params\" : [ np . nan ] * self . n_dist_param } ) dist_list . append ( fit_df ) fit_df = pd . concat ( dist_list ) . sort_values ( by = self . loss_fn , ascending = True ) fit_df [ \"rank\" ] = fit_df [ self . loss_fn ] . rank () . astype ( int ) fit_df . set_index ( fit_df [ \"rank\" ], inplace = True ) pbar . update ( 1 ) pbar . set_description ( f \"Fitting of candidate distributions completed\" ) if plot : # Select best distribution best_dist = fit_df [ fit_df [ \"rank\" ] == 1 ] . reset_index ( drop = True ) for dist in candidate_distributions : if dist . __name__ . split ( \".\" )[ 2 ] == best_dist [ \"distribution\" ] . values [ 0 ]: best_dist_sel = dist break best_dist_sel = getattr ( best_dist_sel , best_dist [ \"distribution\" ] . values [ 0 ])() params = torch . tensor ( best_dist [ \"params\" ][ 0 ]) . reshape ( - 1 , best_dist_sel . n_dist_param ) # Transform parameters to the response scale and draw samples fitted_params = np . concatenate ( [ response_fun ( params [:, i ] . reshape ( - 1 , 1 )) . numpy () for i , ( dist_param , response_fun ) in enumerate ( best_dist_sel . param_dict . items ()) ], axis = 1 , ) fitted_params = pd . DataFrame ( fitted_params , columns = best_dist_sel . param_dict . keys ()) fitted_params . columns = best_dist_sel . param_dict . keys () dist_samples = best_dist_sel . draw_samples ( fitted_params , n_samples = n_samples , seed = 123 ) . values # Plot actual and fitted distribution plot_df_actual = pd . DataFrame ({ \"y\" : target . reshape ( - 1 ,), \"type\" : \"Actual\" }) plot_df_fitted = pd . DataFrame ({ \"y\" : dist_samples . reshape ( - 1 ,), \"type\" : f \"Best-Fit: { best_dist [ 'distribution' ] . values [ 0 ] } \" }) plot_df = pd . concat ([ plot_df_actual , plot_df_fitted ]) print ( ggplot ( plot_df , aes ( x = \"y\" , color = \"type\" )) + geom_density ( alpha = 0.5 ) + theme_bw ( base_size = 15 ) + theme ( figure_size = figure_size , legend_position = \"right\" , legend_title = element_blank (), plot_title = element_text ( hjust = 0.5 )) + labs ( title = f \"Actual vs. Fitted Density\" ) ) fit_df . drop ( columns = [ \"rank\" , \"params\" ], inplace = True ) return fit_df","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.calculate_start_values","text":"Function that calculates the starting values for each distributional parameter.","title":"calculate_start_values()"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.calculate_start_values--arguments","text":"target: np.ndarray Data from which starting values are calculated. max_iter: int Maximum number of iterations.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.calculate_start_values--returns","text":"loss: float Loss value. start_values: np.ndarray Starting values for each distributional parameter. Source code in lightgbmlss/distributions/distribution_utils.py 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 def calculate_start_values ( self , target : np . ndarray , max_iter : int = 50 ) -> Tuple [ float , np . ndarray ]: \"\"\" Function that calculates the starting values for each distributional parameter. Arguments --------- target: np.ndarray Data from which starting values are calculated. max_iter: int Maximum number of iterations. Returns ------- loss: float Loss value. start_values: np.ndarray Starting values for each distributional parameter. \"\"\" # Convert target to torch.tensor target = torch . tensor ( target ) . reshape ( - 1 , 1 ) # Initialize parameters params = [ torch . tensor ( 0.5 , requires_grad = True ) for _ in range ( self . n_dist_param )] # Specify optimizer optimizer = LBFGS ( params , lr = 0.1 , max_iter = np . min ([ int ( max_iter / 4 ), 20 ]), line_search_fn = \"strong_wolfe\" ) # Define learning rate scheduler lr_scheduler = ReduceLROnPlateau ( optimizer , mode = \"min\" , factor = 0.5 , patience = 10 ) # Define closure def closure (): optimizer . zero_grad () loss = self . loss_fn_start_values ( params , target ) loss . backward () return loss # Optimize parameters loss_vals = [] for epoch in range ( max_iter ): loss = optimizer . step ( closure ) lr_scheduler . step ( loss ) loss_vals . append ( loss . item ()) # Get final loss loss = np . array ( loss_vals [ - 1 ]) # Get start values start_values = np . array ([ params [ i ] . detach () for i in range ( self . n_dist_param )]) # Replace any remaining NaNs or infinity values with 0.5 start_values = np . nan_to_num ( start_values , nan = 0.5 , posinf = 0.5 , neginf = 0.5 ) return loss , start_values","title":"Returns"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.compute_gradients_and_hessians","text":"Calculates gradients and hessians. Output gradients and hessians have shape (n_samples*n_outputs, 1).","title":"compute_gradients_and_hessians()"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.compute_gradients_and_hessians--arguments","text":"loss: torch.Tensor Loss. predt: torch.Tensor List of predicted parameters. weights: np.ndarray Weights.","title":"Arguments:"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.compute_gradients_and_hessians--returns","text":"grad: torch.Tensor Gradients. hess: torch.Tensor Hessians. Source code in lightgbmlss/distributions/distribution_utils.py 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 def compute_gradients_and_hessians ( self , loss : torch . tensor , predt : torch . tensor , weights : np . ndarray ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Calculates gradients and hessians. Output gradients and hessians have shape (n_samples*n_outputs, 1). Arguments: --------- loss: torch.Tensor Loss. predt: torch.Tensor List of predicted parameters. weights: np.ndarray Weights. Returns: ------- grad: torch.Tensor Gradients. hess: torch.Tensor Hessians. \"\"\" if self . loss_fn == \"nll\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ autograd ( grad [ i ] . nansum (), inputs = predt [ i ], retain_graph = True )[ 0 ] for i in range ( len ( grad ))] elif self . loss_fn == \"crps\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ torch . ones_like ( grad [ i ]) for i in range ( len ( grad ))] # # Approximation of Hessian # step_size = 1e-6 # predt_upper = [ # response_fn(predt[i] + step_size).reshape(-1, 1) for i, response_fn in # enumerate(self.param_dict.values()) # ] # dist_kwargs_upper = dict(zip(self.distribution_arg_names, predt_upper)) # dist_fit_upper = self.distribution(**dist_kwargs_upper) # dist_samples_upper = dist_fit_upper.rsample((30,)).squeeze(-1) # loss_upper = torch.nansum(self.crps_score(self.target, dist_samples_upper)) # # predt_lower = [ # response_fn(predt[i] - step_size).reshape(-1, 1) for i, response_fn in # enumerate(self.param_dict.values()) # ] # dist_kwargs_lower = dict(zip(self.distribution_arg_names, predt_lower)) # dist_fit_lower = self.distribution(**dist_kwargs_lower) # dist_samples_lower = dist_fit_lower.rsample((30,)).squeeze(-1) # loss_lower = torch.nansum(self.crps_score(self.target, dist_samples_lower)) # # grad_upper = autograd(loss_upper, inputs=predt_upper) # grad_lower = autograd(loss_lower, inputs=predt_lower) # hess = [(grad_upper[i] - grad_lower[i]) / (2 * step_size) for i in range(len(grad))] # Stabilization of Derivatives if self . stabilization != \"None\" : grad = [ self . stabilize_derivative ( grad [ i ], type = self . stabilization ) for i in range ( len ( grad ))] hess = [ self . stabilize_derivative ( hess [ i ], type = self . stabilization ) for i in range ( len ( hess ))] # Reshape grad = torch . cat ( grad , axis = 1 ) . detach () . numpy () hess = torch . cat ( hess , axis = 1 ) . detach () . numpy () # Weighting grad *= weights hess *= weights # Reshape grad = grad . ravel ( order = \"F\" ) hess = hess . ravel ( order = \"F\" ) return grad , hess","title":"Returns:"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.crps_score","text":"Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples.","title":"crps_score()"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.crps_score--parameters","text":"y: torch.Tensor Response variable of shape (n_observations,1). yhat_dist: torch.Tensor Predicted samples of shape (n_samples, n_observations).","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.crps_score--returns","text":"crps: torch.Tensor CRPS score.","title":"Returns"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.crps_score--references","text":"Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. 102. 359-378.","title":"References"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.crps_score--source","text":"https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549 Source code in lightgbmlss/distributions/distribution_utils.py 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 def crps_score ( self , y : torch . tensor , yhat_dist : torch . tensor ) -> torch . tensor : \"\"\" Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples. Parameters ---------- y: torch.Tensor Response variable of shape (n_observations,1). yhat_dist: torch.Tensor Predicted samples of shape (n_samples, n_observations). Returns ------- crps: torch.Tensor CRPS score. References ---------- Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. 102. 359-378. Source ------ https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549 \"\"\" # Get the number of observations n_samples = yhat_dist . shape [ 0 ] # Sort the forecasts in ascending order yhat_dist_sorted , _ = torch . sort ( yhat_dist , 0 ) # Create temporary tensors y_cdf = torch . zeros_like ( y ) yhat_cdf = torch . zeros_like ( y ) yhat_prev = torch . zeros_like ( y ) crps = torch . zeros_like ( y ) # Loop over the predicted samples generated per observation for yhat in yhat_dist_sorted : yhat = yhat . reshape ( - 1 , 1 ) flag = ( y_cdf == 0 ) * ( y < yhat ) crps += flag * (( y - yhat_prev ) * yhat_cdf ** 2 ) crps += flag * (( yhat - y ) * ( yhat_cdf - 1 ) ** 2 ) crps += ( ~ flag ) * (( yhat - yhat_prev ) * ( yhat_cdf - y_cdf ) ** 2 ) y_cdf += flag yhat_cdf += 1 / n_samples yhat_prev = yhat # In case y_cdf == 0 after the loop flag = ( y_cdf == 0 ) crps += flag * ( y - yhat ) return crps","title":"Source"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.dist_select","text":"Function that selects the most suitable distribution among the candidate_distributions for the target variable, based on the NegLogLikelihood (lower is better).","title":"dist_select()"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.dist_select--parameters","text":"target: np.ndarray Response variable. candidate_distributions: List List of candidate distributions. max_iter: int Maximum number of iterations for the optimization. n_samples: int Number of samples to draw from the fitted distribution. plot: bool If True, a density plot of the actual and fitted distribution is created. figure_size: tuple Figure size of the density plot.","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.dist_select--returns","text":"fit_df: pd.DataFrame Dataframe with the loss values of the fitted candidate distributions. Source code in lightgbmlss/distributions/distribution_utils.py 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 def dist_select ( self , target : np . ndarray , candidate_distributions : List , max_iter : int = 100 , n_samples : int = 1000 , plot : bool = False , figure_size : tuple = ( 10 , 5 ), ) -> pd . DataFrame : \"\"\" Function that selects the most suitable distribution among the candidate_distributions for the target variable, based on the NegLogLikelihood (lower is better). Parameters ---------- target: np.ndarray Response variable. candidate_distributions: List List of candidate distributions. max_iter: int Maximum number of iterations for the optimization. n_samples: int Number of samples to draw from the fitted distribution. plot: bool If True, a density plot of the actual and fitted distribution is created. figure_size: tuple Figure size of the density plot. Returns ------- fit_df: pd.DataFrame Dataframe with the loss values of the fitted candidate distributions. \"\"\" dist_list = [] total_iterations = len ( candidate_distributions ) with tqdm ( total = total_iterations , desc = \"Fitting candidate distributions\" ) as pbar : for i in range ( len ( candidate_distributions )): dist_name = candidate_distributions [ i ] . __name__ . split ( \".\" )[ 2 ] pbar . set_description ( f \"Fitting { dist_name } distribution\" ) dist_sel = getattr ( candidate_distributions [ i ], dist_name )() try : loss , params = dist_sel . calculate_start_values ( target = target . reshape ( - 1 , 1 ), max_iter = max_iter ) fit_df = pd . DataFrame . from_dict ( { self . loss_fn : loss . reshape ( - 1 ,), \"distribution\" : str ( dist_name ), \"params\" : [ params ] } ) except Exception as e : warnings . warn ( f \"Error fitting { dist_name } distribution: { str ( e ) } \" ) fit_df = pd . DataFrame ( { self . loss_fn : np . nan , \"distribution\" : str ( dist_name ), \"params\" : [ np . nan ] * self . n_dist_param } ) dist_list . append ( fit_df ) fit_df = pd . concat ( dist_list ) . sort_values ( by = self . loss_fn , ascending = True ) fit_df [ \"rank\" ] = fit_df [ self . loss_fn ] . rank () . astype ( int ) fit_df . set_index ( fit_df [ \"rank\" ], inplace = True ) pbar . update ( 1 ) pbar . set_description ( f \"Fitting of candidate distributions completed\" ) if plot : # Select best distribution best_dist = fit_df [ fit_df [ \"rank\" ] == 1 ] . reset_index ( drop = True ) for dist in candidate_distributions : if dist . __name__ . split ( \".\" )[ 2 ] == best_dist [ \"distribution\" ] . values [ 0 ]: best_dist_sel = dist break best_dist_sel = getattr ( best_dist_sel , best_dist [ \"distribution\" ] . values [ 0 ])() params = torch . tensor ( best_dist [ \"params\" ][ 0 ]) . reshape ( - 1 , best_dist_sel . n_dist_param ) # Transform parameters to the response scale and draw samples fitted_params = np . concatenate ( [ response_fun ( params [:, i ] . reshape ( - 1 , 1 )) . numpy () for i , ( dist_param , response_fun ) in enumerate ( best_dist_sel . param_dict . items ()) ], axis = 1 , ) fitted_params = pd . DataFrame ( fitted_params , columns = best_dist_sel . param_dict . keys ()) fitted_params . columns = best_dist_sel . param_dict . keys () dist_samples = best_dist_sel . draw_samples ( fitted_params , n_samples = n_samples , seed = 123 ) . values # Plot actual and fitted distribution plot_df_actual = pd . DataFrame ({ \"y\" : target . reshape ( - 1 ,), \"type\" : \"Actual\" }) plot_df_fitted = pd . DataFrame ({ \"y\" : dist_samples . reshape ( - 1 ,), \"type\" : f \"Best-Fit: { best_dist [ 'distribution' ] . values [ 0 ] } \" }) plot_df = pd . concat ([ plot_df_actual , plot_df_fitted ]) print ( ggplot ( plot_df , aes ( x = \"y\" , color = \"type\" )) + geom_density ( alpha = 0.5 ) + theme_bw ( base_size = 15 ) + theme ( figure_size = figure_size , legend_position = \"right\" , legend_title = element_blank (), plot_title = element_text ( hjust = 0.5 )) + labs ( title = f \"Actual vs. Fitted Density\" ) ) fit_df . drop ( columns = [ \"rank\" , \"params\" ], inplace = True ) return fit_df","title":"Returns"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.draw_samples","text":"Function that draws n_samples from a predicted distribution.","title":"draw_samples()"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.draw_samples--arguments","text":"predt_params: pd.DataFrame pd.DataFrame with predicted distributional parameters. n_samples: int Number of sample to draw from predicted response distribution. seed: int Manual seed.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.draw_samples--returns","text":"pred_dist: pd.DataFrame DataFrame with n_samples drawn from predicted response distribution. Source code in lightgbmlss/distributions/distribution_utils.py 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 def draw_samples ( self , predt_params : pd . DataFrame , n_samples : int = 1000 , seed : int = 123 ) -> pd . DataFrame : \"\"\" Function that draws n_samples from a predicted distribution. Arguments --------- predt_params: pd.DataFrame pd.DataFrame with predicted distributional parameters. n_samples: int Number of sample to draw from predicted response distribution. seed: int Manual seed. Returns ------- pred_dist: pd.DataFrame DataFrame with n_samples drawn from predicted response distribution. \"\"\" torch . manual_seed ( seed ) if self . tau is None : pred_params = torch . tensor ( predt_params . values ) dist_kwargs = { arg_name : param for arg_name , param in zip ( self . distribution_arg_names , pred_params . T )} dist_pred = self . distribution ( ** dist_kwargs ) dist_samples = dist_pred . sample (( n_samples ,)) . squeeze () . detach () . numpy () . T dist_samples = pd . DataFrame ( dist_samples ) dist_samples . columns = [ str ( \"y_sample\" ) + str ( i ) for i in range ( dist_samples . shape [ 1 ])] else : dist_samples = None if self . discrete : dist_samples = dist_samples . astype ( int ) return dist_samples","title":"Returns"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.get_params_loss","text":"Function that returns the predicted parameters and the loss.","title":"get_params_loss()"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.get_params_loss--arguments","text":"predt: np.ndarray Predicted values. target: torch.Tensor Target values. start_values: List Starting values for each distributional parameter. requires_grad: bool Whether to add to the computational graph or not.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.get_params_loss--returns","text":"predt: torch.Tensor Predicted parameters. loss: torch.Tensor Loss value. Source code in lightgbmlss/distributions/distribution_utils.py 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 def get_params_loss ( self , predt : np . ndarray , target : torch . Tensor , start_values : List [ float ], requires_grad : bool = False , ) -> Tuple [ List [ torch . Tensor ], np . ndarray ]: \"\"\" Function that returns the predicted parameters and the loss. Arguments --------- predt: np.ndarray Predicted values. target: torch.Tensor Target values. start_values: List Starting values for each distributional parameter. requires_grad: bool Whether to add to the computational graph or not. Returns ------- predt: torch.Tensor Predicted parameters. loss: torch.Tensor Loss value. \"\"\" # Predicted Parameters predt = predt . reshape ( - 1 , self . n_dist_param , order = \"F\" ) # Replace NaNs and infinity values with unconditional start values nan_inf_mask = np . isnan ( predt ) | np . isinf ( predt ) predt [ nan_inf_mask ] = np . take ( start_values , np . where ( nan_inf_mask )[ 1 ]) # Convert to torch.tensor predt = [ torch . tensor ( predt [:, i ] . reshape ( - 1 , 1 ), requires_grad = requires_grad ) for i in range ( self . n_dist_param ) ] # Predicted Parameters transformed to response scale predt_transformed = [ response_fn ( predt [ i ] . reshape ( - 1 , 1 )) for i , response_fn in enumerate ( self . param_dict . values ()) ] # Specify Distribution and Loss if self . tau is None : dist_kwargs = dict ( zip ( self . distribution_arg_names , predt_transformed )) dist_fit = self . distribution ( ** dist_kwargs ) if self . loss_fn == \"nll\" : loss = - torch . nansum ( dist_fit . log_prob ( target )) elif self . loss_fn == \"crps\" : torch . manual_seed ( 123 ) dist_samples = dist_fit . rsample (( 30 ,)) . squeeze ( - 1 ) loss = torch . nansum ( self . crps_score ( target , dist_samples )) else : raise ValueError ( \"Invalid loss function. Please select 'nll' or 'crps'.\" ) else : dist_fit = self . distribution ( predt_transformed , self . penalize_crossing ) loss = - torch . nansum ( dist_fit . log_prob ( target , self . tau )) return predt , loss","title":"Returns"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.loss_fn_start_values","text":"Function that calculates the loss for a given set of distributional parameters. Only used for calculating the loss for the start values.","title":"loss_fn_start_values()"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.loss_fn_start_values--parameter","text":"params: torch.Tensor Distributional parameters. target: torch.Tensor Target values.","title":"Parameter"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.loss_fn_start_values--returns","text":"loss: torch.Tensor Loss value. Source code in lightgbmlss/distributions/distribution_utils.py 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 def loss_fn_start_values ( self , params : torch . Tensor , target : torch . Tensor ) -> torch . Tensor : \"\"\" Function that calculates the loss for a given set of distributional parameters. Only used for calculating the loss for the start values. Parameter --------- params: torch.Tensor Distributional parameters. target: torch.Tensor Target values. Returns ------- loss: torch.Tensor Loss value. \"\"\" # Transform parameters to response scale params = [ response_fn ( params [ i ] . reshape ( - 1 , 1 )) for i , response_fn in enumerate ( self . param_dict . values ()) ] # Replace NaNs and infinity values with 0.5 nan_inf_idx = torch . isnan ( torch . stack ( params )) | torch . isinf ( torch . stack ( params )) params = torch . where ( nan_inf_idx , torch . tensor ( 0.5 ), torch . stack ( params )) # Specify Distribution and Loss if self . tau is None : dist = self . distribution ( * params ) loss = - torch . nansum ( dist . log_prob ( target )) else : dist = self . distribution ( params , self . penalize_crossing ) loss = - torch . nansum ( dist . log_prob ( target , self . tau )) return loss","title":"Returns"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.metric_fn","text":"Function that evaluates the predictions using the negative log-likelihood.","title":"metric_fn()"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.metric_fn--arguments","text":"predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.metric_fn--returns","text":"name: str Name of the evaluation metric. nll: float Negative log-likelihood. is_higher_better: bool Whether a higher value of the metric is better or not. Source code in lightgbmlss/distributions/distribution_utils.py 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 def metric_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ str , float , bool ]: \"\"\" Function that evaluates the predictions using the negative log-likelihood. Arguments --------- predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns ------- name: str Name of the evaluation metric. nll: float Negative log-likelihood. is_higher_better: bool Whether a higher value of the metric is better or not. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate loss is_higher_better = False _ , loss = self . get_params_loss ( predt , target , start_values , requires_grad = False ) return self . loss_fn , loss , is_higher_better","title":"Returns"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.objective_fn","text":"Function to estimate gradients and hessians of distributional parameters.","title":"objective_fn()"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.objective_fn--arguments","text":"predt: np.ndarray Predicted values. data: lgb.DMatrix Data used for training.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.objective_fn--returns","text":"grad: np.ndarray Gradient. hess: np.ndarray Hessian. Source code in lightgbmlss/distributions/distribution_utils.py 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def objective_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Function to estimate gradients and hessians of distributional parameters. Arguments --------- predt: np.ndarray Predicted values. data: lgb.DMatrix Data used for training. Returns ------- grad: np.ndarray Gradient. hess: np.ndarray Hessian. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Weights if data . weight is None : # Use 1 as weight if no weights are specified weights = torch . ones_like ( target , dtype = target . dtype ) . numpy () else : weights = data . get_weight () . reshape ( - 1 , 1 ) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate gradients and hessians predt , loss = self . get_params_loss ( predt , target , start_values , requires_grad = True ) grad , hess = self . compute_gradients_and_hessians ( loss , predt , weights ) return grad , hess","title":"Returns"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.predict_dist","text":"Function that predicts from the trained model.","title":"predict_dist()"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.predict_dist--arguments","text":"booster : lgb.Booster Trained model. data : pd.DataFrame Data to predict from. start_values : np.ndarray. Starting values for each distributional parameter. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.predict_dist--returns","text":"pred : pd.DataFrame Predictions. Source code in lightgbmlss/distributions/distribution_utils.py 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 def predict_dist ( self , booster : lgb . Booster , data : pd . DataFrame , start_values : np . ndarray , pred_type : str = \"parameters\" , n_samples : int = 1000 , quantiles : list = [ 0.1 , 0.5 , 0.9 ], seed : str = 123 ) -> pd . DataFrame : \"\"\" Function that predicts from the trained model. Arguments --------- booster : lgb.Booster Trained model. data : pd.DataFrame Data to predict from. start_values : np.ndarray. Starting values for each distributional parameter. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns ------- pred : pd.DataFrame Predictions. \"\"\" predt = torch . tensor ( booster . predict ( data , raw_score = True ), dtype = torch . float32 ) . reshape ( - 1 , self . n_dist_param ) # Set init_score as starting point for each distributional parameter. init_score_pred = torch . tensor ( np . ones ( shape = ( data . shape [ 0 ], 1 )) * start_values , dtype = torch . float32 ) # The predictions don't include the init_score specified in creating the train data. # Hence, it needs to be added manually with the corresponding transform for each distributional parameter. dist_params_predt = np . concatenate ( [ response_fun ( predt [:, i ] . reshape ( - 1 , 1 ) + init_score_pred [:, i ] . reshape ( - 1 , 1 )) . numpy () for i , ( dist_param , response_fun ) in enumerate ( self . param_dict . items ()) ], axis = 1 , ) dist_params_predt = pd . DataFrame ( dist_params_predt ) dist_params_predt . columns = self . param_dict . keys () # Draw samples from predicted response distribution pred_samples_df = self . draw_samples ( predt_params = dist_params_predt , n_samples = n_samples , seed = seed ) if pred_type == \"parameters\" : return dist_params_predt elif pred_type == \"expectiles\" : return dist_params_predt elif pred_type == \"samples\" : return pred_samples_df elif pred_type == \"quantiles\" : # Calculate quantiles from predicted response distribution pred_quant_df = pred_samples_df . quantile ( quantiles , axis = 1 ) . T pred_quant_df . columns = [ str ( \"quant_\" ) + str ( quantiles [ i ]) for i in range ( len ( quantiles ))] if self . discrete : pred_quant_df = pred_quant_df . astype ( int ) return pred_quant_df","title":"Returns"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.stabilize_derivative","text":"Function that stabilizes Gradients and Hessians. As LightGBMLSS updates the parameter estimates by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all distributional parameters. Due to imbalances regarding the ranges, the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve convergence might be to standardize the response variable. This is especially useful if the range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the standardization of the response are not always advised but need to be carefully considered. Source: https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173","title":"stabilize_derivative()"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.stabilize_derivative--parameters","text":"input_der : torch.Tensor Input derivative, either Gradient or Hessian. type: str Stabilization method. Can be either \"None\", \"MAD\" or \"L2\".","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.distribution_utils.DistributionClass.stabilize_derivative--returns","text":"stab_der : torch.Tensor Stabilized Gradient or Hessian. Source code in lightgbmlss/distributions/distribution_utils.py 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 def stabilize_derivative ( self , input_der : torch . Tensor , type : str = \"MAD\" ) -> torch . Tensor : \"\"\" Function that stabilizes Gradients and Hessians. As LightGBMLSS updates the parameter estimates by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all distributional parameters. Due to imbalances regarding the ranges, the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve convergence might be to standardize the response variable. This is especially useful if the range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the standardization of the response are not always advised but need to be carefully considered. Source: https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173 Parameters ---------- input_der : torch.Tensor Input derivative, either Gradient or Hessian. type: str Stabilization method. Can be either \"None\", \"MAD\" or \"L2\". Returns ------- stab_der : torch.Tensor Stabilized Gradient or Hessian. \"\"\" if type == \"MAD\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . nanmedian ( torch . abs ( input_der - torch . nanmedian ( input_der ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) stab_der = input_der / div if type == \"L2\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . sqrt ( torch . nanmean ( input_der . pow ( 2 ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) div = torch . where ( div > torch . tensor ( 10000.0 ), torch . tensor ( 10000.0 ), div ) stab_der = input_der / div if type == \"None\" : stab_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) return stab_der","title":"Returns"},{"location":"api/#lightgbmlss.distributions.flow_utils","text":"","title":"flow_utils"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass","text":"Generic class that contains general functions for normalizing flows.","title":"NormalizingFlowClass"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass--arguments","text":"base_dist: torch.distributions.Distribution PyTorch Distribution class. Currently only Normal is supported. flow_transform: Transform Specify the normalizing flow transform. count_bins: Optional[int] The number of segments comprising the spline. Only used if flow_transform is Spline. bound: Optional[float] The quantity \"K\" determining the bounding box, [-K,K] x [-K,K] of the spline. By adjusting the \"K\" value, you can control the size of the bounding box and consequently control the range of inputs that the spline transform operates on. Larger values of \"K\" will result in a wider valid range for the spline transformation, while smaller values will restrict the valid range to a smaller region. Should be chosen based on the range of the data. Only used if flow_transform is Spline. order: Optional[str] The order of the spline. Options are \"linear\" or \"quadratic\". Only used if flow_transform is Spline. n_dist_param: int Number of parameters. param_dict: Dict[str, Any] Dictionary that maps parameters to their response scale. target_transform: Transform Specify the target transform. discrete: bool Whether the target is discrete or not. univariate: bool Whether the distribution is univariate or multivariate. stabilization: str Stabilization method. Options are \"None\", \"MAD\" or \"L2\". loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. Source code in lightgbmlss/distributions/flow_utils.py 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 class NormalizingFlowClass : \"\"\" Generic class that contains general functions for normalizing flows. Arguments --------- base_dist: torch.distributions.Distribution PyTorch Distribution class. Currently only Normal is supported. flow_transform: Transform Specify the normalizing flow transform. count_bins: Optional[int] The number of segments comprising the spline. Only used if flow_transform is Spline. bound: Optional[float] The quantity \"K\" determining the bounding box, [-K,K] x [-K,K] of the spline. By adjusting the \"K\" value, you can control the size of the bounding box and consequently control the range of inputs that the spline transform operates on. Larger values of \"K\" will result in a wider valid range for the spline transformation, while smaller values will restrict the valid range to a smaller region. Should be chosen based on the range of the data. Only used if flow_transform is Spline. order: Optional[str] The order of the spline. Options are \"linear\" or \"quadratic\". Only used if flow_transform is Spline. n_dist_param: int Number of parameters. param_dict: Dict[str, Any] Dictionary that maps parameters to their response scale. target_transform: Transform Specify the target transform. discrete: bool Whether the target is discrete or not. univariate: bool Whether the distribution is univariate or multivariate. stabilization: str Stabilization method. Options are \"None\", \"MAD\" or \"L2\". loss_fn: str Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\" (continuous ranked probability score). Note that if \"crps\" is used, the Hessian is set to 1, as the current CRPS version is not twice differentiable. Hence, using the CRPS disregards any variation in the curvature of the loss function. \"\"\" def __init__ ( self , base_dist : torch . distributions . Distribution = None , flow_transform : Transform = None , count_bins : Optional [ int ] = 8 , bound : Optional [ float ] = 3.0 , order : Optional [ str ] = \"quadratic\" , n_dist_param : int = None , param_dict : Dict [ str , Any ] = None , target_transform : Transform = None , discrete : bool = False , univariate : bool = True , stabilization : str = \"None\" , loss_fn : str = \"nll\" , ): self . base_dist = base_dist self . flow_transform = flow_transform self . count_bins = count_bins self . bound = bound self . order = order self . n_dist_param = n_dist_param self . param_dict = param_dict self . target_transform = target_transform self . discrete = discrete self . univariate = univariate self . stabilization = stabilization self . loss_fn = loss_fn def objective_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Function to estimate gradients and hessians of normalizing flow parameters. Arguments --------- predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns ------- grad: np.ndarray Gradient. hess: np.ndarray Hessian. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Weights if data . weight is None : # Use 1 as weight if no weights are specified weights = torch . ones_like ( target , dtype = target . dtype ) . numpy () else : weights = data . get_weight () . reshape ( - 1 , 1 ) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate gradients and hessians predt , loss = self . get_params_loss ( predt , target , start_values ) grad , hess = self . compute_gradients_and_hessians ( loss , predt , weights ) return grad , hess def metric_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ str , float , bool ]: \"\"\" Function that evaluates the predictions using the specified loss function. Arguments --------- predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns ------- name: str Name of the evaluation metric. loss: float Loss value. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate loss is_higher_better = False _ , loss = self . get_params_loss ( predt , target , start_values ) return self . loss_fn , loss . detach (), is_higher_better def calculate_start_values ( self , target : np . ndarray , max_iter : int = 50 ) -> Tuple [ float , np . ndarray ]: \"\"\" Function that calculates starting values for each parameter. Arguments --------- target: np.ndarray Data from which starting values are calculated. max_iter: int Maximum number of iterations. Returns ------- loss: float Loss value. start_values: np.ndarray Starting values for each parameter. \"\"\" # Convert target to torch.tensor target = torch . tensor ( target ) . reshape ( - 1 , 1 ) # Create Normalizing Flow flow_dist = self . create_spline_flow ( input_dim = 1 ) # Specify optimizer optimizer = LBFGS ( flow_dist . transforms [ 0 ] . parameters (), lr = 0.3 , max_iter = np . min ([ int ( max_iter / 4 ), 50 ]), line_search_fn = \"strong_wolfe\" ) # Define learning rate scheduler lr_scheduler = ReduceLROnPlateau ( optimizer , mode = \"min\" , factor = 0.5 , patience = 5 ) # Define closure def closure (): optimizer . zero_grad () loss = - torch . nansum ( flow_dist . log_prob ( target )) loss . backward () flow_dist . clear_cache () return loss # Optimize parameters loss_vals = [] tolerance = 1e-5 # Tolerance level for loss change patience = 5 # Patience level for loss change best_loss = float ( \"inf\" ) epochs_without_change = 0 for epoch in range ( max_iter ): optimizer . zero_grad () loss = optimizer . step ( closure ) lr_scheduler . step ( loss ) loss_vals . append ( loss . item ()) # Stopping criterion (no improvement in loss) if loss . item () < best_loss - tolerance : best_loss = loss . item () epochs_without_change = 0 else : epochs_without_change += 1 if epochs_without_change >= patience : break # Get final loss loss = np . array ( loss_vals [ - 1 ]) # Get start values start_values = list ( flow_dist . transforms [ 0 ] . parameters ()) start_values = torch . cat ([ param . view ( - 1 ) for param in start_values ]) . detach () . numpy () # Replace any remaining NaNs or infinity values with 0.5 start_values = np . nan_to_num ( start_values , nan = 0.5 , posinf = 0.5 , neginf = 0.5 ) return loss , start_values def get_params_loss ( self , predt : np . ndarray , target : torch . Tensor , start_values : List [ float ], requires_grad : bool = False , ) -> Tuple [ List [ torch . Tensor ], np . ndarray ]: \"\"\" Function that returns the predicted parameters and the loss. Arguments --------- predt: np.ndarray Predicted values. target: torch.Tensor Target values. start_values: List Starting values for each parameter. Returns ------- predt: torch.Tensor Predicted parameters. loss: torch.Tensor Loss value. \"\"\" # Reshape Target target = target . view ( - 1 ) # Predicted Parameters predt = predt . reshape ( - 1 , self . n_dist_param , order = \"F\" ) # Replace NaNs and infinity values with unconditional start values nan_inf_mask = np . isnan ( predt ) | np . isinf ( predt ) predt [ nan_inf_mask ] = np . take ( start_values , np . where ( nan_inf_mask )[ 1 ]) # Convert to torch.tensor predt = torch . tensor ( predt , dtype = torch . float32 ) # Specify Normalizing Flow flow_dist = self . create_spline_flow ( target . shape [ 0 ]) # Replace parameters with estimated ones params , flow_dist = self . replace_parameters ( predt , flow_dist ) # Calculate loss if self . loss_fn == \"nll\" : loss = - torch . nansum ( flow_dist . log_prob ( target )) elif self . loss_fn == \"crps\" : torch . manual_seed ( 123 ) dist_samples = flow_dist . rsample (( 30 ,)) . squeeze ( - 1 ) loss = torch . nansum ( self . crps_score ( target , dist_samples )) else : raise ValueError ( \"Invalid loss function. Please select 'nll' or 'crps'.\" ) return params , loss def create_spline_flow ( self , input_dim : int = None , ) -> Transform : \"\"\" Function that constructs a Normalizing Flow. Arguments --------- input_dim: int Input dimension. Returns ------- spline_flow: Transform Normalizing Flow. \"\"\" # Create flow distribution (currently only Normal) loc , scale = torch . zeros ( input_dim ), torch . ones ( input_dim ) flow_dist = self . base_dist ( loc , scale ) # Create Spline Transform torch . manual_seed ( 123 ) spline_transform = self . flow_transform ( input_dim , count_bins = self . count_bins , bound = self . bound , order = self . order ) # Create Normalizing Flow spline_flow = TransformedDistribution ( flow_dist , [ spline_transform , self . target_transform ]) return spline_flow def replace_parameters ( self , params : torch . Tensor , flow_dist : Transform , ) -> Tuple [ List , Transform ]: \"\"\" Replace parameters with estimated ones. Arguments --------- params: torch.Tensor Estimated parameters. flow_dist: Transform Normalizing Flow. Returns ------- params_list: List List of estimated parameters. flow_dist: Transform Normalizing Flow with estimated parameters. \"\"\" # Split parameters into list if self . order == \"quadratic\" : params_list = torch . split ( params , [ self . count_bins , self . count_bins , self . count_bins - 1 ], dim = 1 ) elif self . order == \"linear\" : params_list = torch . split ( params , [ self . count_bins , self . count_bins , self . count_bins - 1 , self . count_bins ], dim = 1 ) # Replace parameters for param , new_value in zip ( flow_dist . transforms [ 0 ] . parameters (), params_list ): param . data = new_value # Get parameters (including require_grad=True) params_list = list ( flow_dist . transforms [ 0 ] . parameters ()) return params_list , flow_dist def draw_samples ( self , predt_params : pd . DataFrame , n_samples : int = 1000 , seed : int = 123 ) -> pd . DataFrame : \"\"\" Function that draws n_samples from a predicted distribution. Arguments --------- predt_params: pd.DataFrame pd.DataFrame with predicted distributional parameters. n_samples: int Number of sample to draw from predicted response distribution. seed: int Manual seed. Returns ------- pred_dist: pd.DataFrame DataFrame with n_samples drawn from predicted response distribution. \"\"\" torch . manual_seed ( seed ) # Specify Normalizing Flow pred_params = torch . tensor ( predt_params . values ) flow_dist_pred = self . create_spline_flow ( pred_params . shape [ 0 ]) # Replace parameters with estimated ones _ , flow_dist_pred = self . replace_parameters ( pred_params , flow_dist_pred ) # Draw samples flow_samples = pd . DataFrame ( flow_dist_pred . sample (( n_samples ,)) . squeeze () . detach () . numpy () . T ) flow_samples . columns = [ str ( \"y_sample\" ) + str ( i ) for i in range ( flow_samples . shape [ 1 ])] if self . discrete : flow_samples = flow_samples . astype ( int ) return flow_samples def predict_dist ( self , booster : lgb . Booster , data : pd . DataFrame , start_values : np . ndarray , pred_type : str = \"parameters\" , n_samples : int = 1000 , quantiles : list = [ 0.1 , 0.5 , 0.9 ], seed : str = 123 ) -> pd . DataFrame : \"\"\" Function that predicts from the trained model. Arguments --------- booster : lgb.Booster Trained model. start_values : np.ndarray Starting values for each distributional parameter. data : pd.DataFrame Data to predict from. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns ------- pred : pd.DataFrame Predictions. \"\"\" # Predict raw scores predt = torch . tensor ( booster . predict ( data , raw_score = True ), dtype = torch . float32 ) . reshape ( - 1 , self . n_dist_param ) # Set init_score as starting point for each distributional parameter. init_score_pred = torch . tensor ( np . ones ( shape = ( data . shape [ 0 ], 1 )) * start_values , dtype = torch . float32 ) # The predictions don't include the init_score specified in creating the train data. Hence, it needs to be # added manually. dist_params_predt = pd . DataFrame ( np . concatenate ( [ predt [:, i ] . reshape ( - 1 , 1 ) + init_score_pred [:, i ] . reshape ( - 1 , 1 ) for i in range ( self . n_dist_param )], axis = 1 ) ) dist_params_predt . columns = self . param_dict . keys () # Draw samples from predicted response distribution pred_samples_df = self . draw_samples ( predt_params = dist_params_predt , n_samples = n_samples , seed = seed ) if pred_type == \"parameters\" : return dist_params_predt elif pred_type == \"samples\" : return pred_samples_df elif pred_type == \"quantiles\" : # Calculate quantiles from predicted response distribution pred_quant_df = pred_samples_df . quantile ( quantiles , axis = 1 ) . T pred_quant_df . columns = [ str ( \"quant_\" ) + str ( quantiles [ i ]) for i in range ( len ( quantiles ))] if self . discrete : pred_quant_df = pred_quant_df . astype ( int ) return pred_quant_df def compute_gradients_and_hessians ( self , loss : torch . tensor , predt : torch . tensor , weights : np . ndarray ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Calculates gradients and hessians. Output gradients and hessians have shape (n_samples*n_outputs, 1). Arguments: --------- loss: torch.Tensor Loss. predt: torch.Tensor List of predicted parameters. weights: np.ndarray Weights. Returns: ------- grad: torch.Tensor Gradients. hess: torch.Tensor Hessians. \"\"\" if self . loss_fn == \"nll\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ autograd ( grad [ i ] . nansum (), inputs = predt [ i ], retain_graph = True )[ 0 ] for i in range ( len ( grad ))] elif self . loss_fn == \"crps\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ torch . ones_like ( grad [ i ]) for i in range ( len ( grad ))] # Stabilization of Derivatives if self . stabilization != \"None\" : grad = [ self . stabilize_derivative ( grad [ i ], type = self . stabilization ) for i in range ( len ( grad ))] hess = [ self . stabilize_derivative ( hess [ i ], type = self . stabilization ) for i in range ( len ( hess ))] # Reshape grad = torch . cat ( grad , axis = 1 ) . detach () . numpy () hess = torch . cat ( hess , axis = 1 ) . detach () . numpy () # Weighting grad *= weights hess *= weights # Reshape grad = grad . ravel ( order = \"F\" ) hess = hess . ravel ( order = \"F\" ) return grad , hess def stabilize_derivative ( self , input_der : torch . Tensor , type : str = \"MAD\" ) -> torch . Tensor : \"\"\" Function that stabilizes Gradients and Hessians. Since parameters are estimated by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all parameters. Due to imbalances regarding the ranges, the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve convergence might be to standardize the response variable. This is especially useful if the range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the standardization of the response are not always advised but need to be carefully considered. Source --------- https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173 Arguments --------- input_der : torch.Tensor Input derivative, either Gradient or Hessian. type: str Stabilization method. Can be either \"None\", \"MAD\" or \"L2\". Returns --------- stab_der : torch.Tensor Stabilized Gradient or Hessian. \"\"\" if type == \"MAD\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . nanmedian ( torch . abs ( input_der - torch . nanmedian ( input_der ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) stab_der = input_der / div if type == \"L2\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . sqrt ( torch . nanmean ( input_der . pow ( 2 ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) div = torch . where ( div > torch . tensor ( 10000.0 ), torch . tensor ( 10000.0 ), div ) stab_der = input_der / div if type == \"None\" : stab_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) return stab_der def crps_score ( self , y : torch . tensor , yhat_dist : torch . tensor ) -> torch . tensor : \"\"\" Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples. Arguments --------- y: torch.Tensor Response variable of shape (n_observations,1). yhat_dist: torch.Tensor Predicted samples of shape (n_samples, n_observations). Returns --------- crps: torch.Tensor CRPS score. References --------- Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. 102. 359-378. Source --------- https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549 \"\"\" # Get the number of observations n_samples = yhat_dist . shape [ 0 ] # Sort the forecasts in ascending order yhat_dist_sorted , _ = torch . sort ( yhat_dist , 0 ) # Create temporary tensors y_cdf = torch . zeros_like ( y ) yhat_cdf = torch . zeros_like ( y ) yhat_prev = torch . zeros_like ( y ) crps = torch . zeros_like ( y ) # Loop over the predicted samples generated per observation for yhat in yhat_dist_sorted : yhat = yhat . reshape ( - 1 , 1 ) flag = ( y_cdf == 0 ) * ( y < yhat ) crps += flag * (( y - yhat_prev ) * yhat_cdf ** 2 ) crps += flag * (( yhat - y ) * ( yhat_cdf - 1 ) ** 2 ) crps += ( ~ flag ) * (( yhat - yhat_prev ) * ( yhat_cdf - y_cdf ) ** 2 ) y_cdf += flag yhat_cdf += 1 / n_samples yhat_prev = yhat # In case y_cdf == 0 after the loop flag = ( y_cdf == 0 ) crps += flag * ( y - yhat ) return crps def flow_select ( self , target : np . ndarray , candidate_flows : List , max_iter : int = 100 , n_samples : int = 1000 , plot : bool = False , figure_size : tuple = ( 10 , 5 ), ) -> pd . DataFrame : \"\"\" Function that selects the most suitable normalizing flow specification among the candidate_flow for the target variable, based on the NegLogLikelihood (lower is better). Parameters ---------- target: np.ndarray Response variable. candidate_flows: List List of candidate normalizing flow specifications. max_iter: int Maximum number of iterations for the optimization. n_samples: int Number of samples drawn from the fitted distribution. plot: bool If True, a density plot of the actual and fitted distribution is created. figure_size: tuple Figure size of the density plot. Returns ------- fit_df: pd.DataFrame Dataframe with the loss values of the fitted normalizing flow. \"\"\" flow_list = [] total_iterations = len ( candidate_flows ) with tqdm ( total = total_iterations , desc = \"Fitting candidate normalizing flows\" ) as pbar : for flow in candidate_flows : flow_name = str ( flow . __class__ ) . split ( \".\" )[ - 1 ] . split ( \"'>\" )[ 0 ] flow_spec = f \"(count_bins: { flow . count_bins } , order: { flow . order } )\" flow_name = flow_name + flow_spec pbar . set_description ( f \"Fitting { flow_name } \" ) flow_sel = flow try : loss , params = flow_sel . calculate_start_values ( target = target , max_iter = max_iter ) fit_df = pd . DataFrame . from_dict ( { flow_sel . loss_fn : loss . reshape ( - 1 , ), \"NormFlow\" : str ( flow_name ), \"params\" : [ params ] } ) except Exception as e : warnings . warn ( f \"Error fitting { flow_sel } NormFlow: { str ( e ) } \" ) fit_df = pd . DataFrame ( { flow_sel . loss_fn : np . nan , \"NormFlow\" : str ( flow_sel ), \"params\" : [ np . nan ] * flow_sel . n_dist_param } ) flow_list . append ( fit_df ) fit_df = pd . concat ( flow_list ) . sort_values ( by = flow_sel . loss_fn , ascending = True ) fit_df [ \"rank\" ] = fit_df [ flow_sel . loss_fn ] . rank () . astype ( int ) fit_df . set_index ( fit_df [ \"rank\" ], inplace = True ) pbar . update ( 1 ) pbar . set_description ( f \"Fitting of candidate normalizing flows completed\" ) if plot : # Select normalizing flow with the lowest loss best_flow = fit_df [ fit_df [ \"rank\" ] == 1 ] . reset_index ( drop = True ) for flow in candidate_flows : flow_name = str ( flow . __class__ ) . split ( \".\" )[ - 1 ] . split ( \"'>\" )[ 0 ] flow_spec = f \"(count_bins: { flow . count_bins } , order: { flow . order } )\" flow_name = flow_name + flow_spec if flow_name == best_flow [ \"NormFlow\" ] . values [ 0 ]: best_flow_sel = flow break # Draw samples from distribution flow_params = torch . tensor ( best_flow [ \"params\" ][ 0 ]) . reshape ( 1 , - 1 ) flow_dist_sel = best_flow_sel . create_spline_flow ( input_dim = 1 ) _ , flow_dist_sel = best_flow_sel . replace_parameters ( flow_params , flow_dist_sel ) flow_samples = pd . DataFrame ( flow_dist_sel . sample (( n_samples ,)) . squeeze () . detach () . numpy () . T ) # Plot actual and fitted distribution flow_samples [ \"type\" ] = f \"Best-Fit: { best_flow [ 'NormFlow' ] . values [ 0 ] } \" df_actual = pd . DataFrame ( target ) df_actual [ \"type\" ] = \"Data\" plot_df = pd . concat ([ df_actual , flow_samples ]) . rename ( columns = { 0 : \"variable\" }) print ( ggplot ( plot_df , aes ( x = \"variable\" , color = \"type\" )) + geom_density ( size = 1.1 ) + theme_bw ( base_size = 15 ) + theme ( figure_size = figure_size , legend_position = \"right\" , legend_title = element_blank (), plot_title = element_text ( hjust = 0.5 )) + labs ( title = f \"Actual vs. Fitted Density\" , x = \"\" ) ) fit_df . drop ( columns = [ \"rank\" , \"params\" ], inplace = True ) return fit_df","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.calculate_start_values","text":"Function that calculates starting values for each parameter.","title":"calculate_start_values()"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.calculate_start_values--arguments","text":"target: np.ndarray Data from which starting values are calculated. max_iter: int Maximum number of iterations.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.calculate_start_values--returns","text":"loss: float Loss value. start_values: np.ndarray Starting values for each parameter. Source code in lightgbmlss/distributions/flow_utils.py 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 def calculate_start_values ( self , target : np . ndarray , max_iter : int = 50 ) -> Tuple [ float , np . ndarray ]: \"\"\" Function that calculates starting values for each parameter. Arguments --------- target: np.ndarray Data from which starting values are calculated. max_iter: int Maximum number of iterations. Returns ------- loss: float Loss value. start_values: np.ndarray Starting values for each parameter. \"\"\" # Convert target to torch.tensor target = torch . tensor ( target ) . reshape ( - 1 , 1 ) # Create Normalizing Flow flow_dist = self . create_spline_flow ( input_dim = 1 ) # Specify optimizer optimizer = LBFGS ( flow_dist . transforms [ 0 ] . parameters (), lr = 0.3 , max_iter = np . min ([ int ( max_iter / 4 ), 50 ]), line_search_fn = \"strong_wolfe\" ) # Define learning rate scheduler lr_scheduler = ReduceLROnPlateau ( optimizer , mode = \"min\" , factor = 0.5 , patience = 5 ) # Define closure def closure (): optimizer . zero_grad () loss = - torch . nansum ( flow_dist . log_prob ( target )) loss . backward () flow_dist . clear_cache () return loss # Optimize parameters loss_vals = [] tolerance = 1e-5 # Tolerance level for loss change patience = 5 # Patience level for loss change best_loss = float ( \"inf\" ) epochs_without_change = 0 for epoch in range ( max_iter ): optimizer . zero_grad () loss = optimizer . step ( closure ) lr_scheduler . step ( loss ) loss_vals . append ( loss . item ()) # Stopping criterion (no improvement in loss) if loss . item () < best_loss - tolerance : best_loss = loss . item () epochs_without_change = 0 else : epochs_without_change += 1 if epochs_without_change >= patience : break # Get final loss loss = np . array ( loss_vals [ - 1 ]) # Get start values start_values = list ( flow_dist . transforms [ 0 ] . parameters ()) start_values = torch . cat ([ param . view ( - 1 ) for param in start_values ]) . detach () . numpy () # Replace any remaining NaNs or infinity values with 0.5 start_values = np . nan_to_num ( start_values , nan = 0.5 , posinf = 0.5 , neginf = 0.5 ) return loss , start_values","title":"Returns"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.compute_gradients_and_hessians","text":"Calculates gradients and hessians. Output gradients and hessians have shape (n_samples*n_outputs, 1).","title":"compute_gradients_and_hessians()"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.compute_gradients_and_hessians--arguments","text":"loss: torch.Tensor Loss. predt: torch.Tensor List of predicted parameters. weights: np.ndarray Weights.","title":"Arguments:"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.compute_gradients_and_hessians--returns","text":"grad: torch.Tensor Gradients. hess: torch.Tensor Hessians. Source code in lightgbmlss/distributions/flow_utils.py 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 def compute_gradients_and_hessians ( self , loss : torch . tensor , predt : torch . tensor , weights : np . ndarray ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Calculates gradients and hessians. Output gradients and hessians have shape (n_samples*n_outputs, 1). Arguments: --------- loss: torch.Tensor Loss. predt: torch.Tensor List of predicted parameters. weights: np.ndarray Weights. Returns: ------- grad: torch.Tensor Gradients. hess: torch.Tensor Hessians. \"\"\" if self . loss_fn == \"nll\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ autograd ( grad [ i ] . nansum (), inputs = predt [ i ], retain_graph = True )[ 0 ] for i in range ( len ( grad ))] elif self . loss_fn == \"crps\" : # Gradient and Hessian grad = autograd ( loss , inputs = predt , create_graph = True ) hess = [ torch . ones_like ( grad [ i ]) for i in range ( len ( grad ))] # Stabilization of Derivatives if self . stabilization != \"None\" : grad = [ self . stabilize_derivative ( grad [ i ], type = self . stabilization ) for i in range ( len ( grad ))] hess = [ self . stabilize_derivative ( hess [ i ], type = self . stabilization ) for i in range ( len ( hess ))] # Reshape grad = torch . cat ( grad , axis = 1 ) . detach () . numpy () hess = torch . cat ( hess , axis = 1 ) . detach () . numpy () # Weighting grad *= weights hess *= weights # Reshape grad = grad . ravel ( order = \"F\" ) hess = hess . ravel ( order = \"F\" ) return grad , hess","title":"Returns:"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.create_spline_flow","text":"Function that constructs a Normalizing Flow.","title":"create_spline_flow()"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.create_spline_flow--arguments","text":"input_dim: int Input dimension.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.create_spline_flow--returns","text":"spline_flow: Transform Normalizing Flow. Source code in lightgbmlss/distributions/flow_utils.py 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 def create_spline_flow ( self , input_dim : int = None , ) -> Transform : \"\"\" Function that constructs a Normalizing Flow. Arguments --------- input_dim: int Input dimension. Returns ------- spline_flow: Transform Normalizing Flow. \"\"\" # Create flow distribution (currently only Normal) loc , scale = torch . zeros ( input_dim ), torch . ones ( input_dim ) flow_dist = self . base_dist ( loc , scale ) # Create Spline Transform torch . manual_seed ( 123 ) spline_transform = self . flow_transform ( input_dim , count_bins = self . count_bins , bound = self . bound , order = self . order ) # Create Normalizing Flow spline_flow = TransformedDistribution ( flow_dist , [ spline_transform , self . target_transform ]) return spline_flow","title":"Returns"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.crps_score","text":"Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples.","title":"crps_score()"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.crps_score--arguments","text":"y: torch.Tensor Response variable of shape (n_observations,1). yhat_dist: torch.Tensor Predicted samples of shape (n_samples, n_observations).","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.crps_score--returns","text":"crps: torch.Tensor CRPS score.","title":"Returns"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.crps_score--references","text":"Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. 102. 359-378.","title":"References"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.crps_score--source","text":"https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549 Source code in lightgbmlss/distributions/flow_utils.py 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 def crps_score ( self , y : torch . tensor , yhat_dist : torch . tensor ) -> torch . tensor : \"\"\" Function that calculates the Continuous Ranked Probability Score (CRPS) for a given set of predicted samples. Arguments --------- y: torch.Tensor Response variable of shape (n_observations,1). yhat_dist: torch.Tensor Predicted samples of shape (n_samples, n_observations). Returns --------- crps: torch.Tensor CRPS score. References --------- Gneiting, Tilmann & Raftery, Adrian. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. 102. 359-378. Source --------- https://github.com/elephaint/pgbm/blob/main/pgbm/torch/pgbm_dist.py#L549 \"\"\" # Get the number of observations n_samples = yhat_dist . shape [ 0 ] # Sort the forecasts in ascending order yhat_dist_sorted , _ = torch . sort ( yhat_dist , 0 ) # Create temporary tensors y_cdf = torch . zeros_like ( y ) yhat_cdf = torch . zeros_like ( y ) yhat_prev = torch . zeros_like ( y ) crps = torch . zeros_like ( y ) # Loop over the predicted samples generated per observation for yhat in yhat_dist_sorted : yhat = yhat . reshape ( - 1 , 1 ) flag = ( y_cdf == 0 ) * ( y < yhat ) crps += flag * (( y - yhat_prev ) * yhat_cdf ** 2 ) crps += flag * (( yhat - y ) * ( yhat_cdf - 1 ) ** 2 ) crps += ( ~ flag ) * (( yhat - yhat_prev ) * ( yhat_cdf - y_cdf ) ** 2 ) y_cdf += flag yhat_cdf += 1 / n_samples yhat_prev = yhat # In case y_cdf == 0 after the loop flag = ( y_cdf == 0 ) crps += flag * ( y - yhat ) return crps","title":"Source"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.draw_samples","text":"Function that draws n_samples from a predicted distribution.","title":"draw_samples()"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.draw_samples--arguments","text":"predt_params: pd.DataFrame pd.DataFrame with predicted distributional parameters. n_samples: int Number of sample to draw from predicted response distribution. seed: int Manual seed.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.draw_samples--returns","text":"pred_dist: pd.DataFrame DataFrame with n_samples drawn from predicted response distribution. Source code in lightgbmlss/distributions/flow_utils.py 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 def draw_samples ( self , predt_params : pd . DataFrame , n_samples : int = 1000 , seed : int = 123 ) -> pd . DataFrame : \"\"\" Function that draws n_samples from a predicted distribution. Arguments --------- predt_params: pd.DataFrame pd.DataFrame with predicted distributional parameters. n_samples: int Number of sample to draw from predicted response distribution. seed: int Manual seed. Returns ------- pred_dist: pd.DataFrame DataFrame with n_samples drawn from predicted response distribution. \"\"\" torch . manual_seed ( seed ) # Specify Normalizing Flow pred_params = torch . tensor ( predt_params . values ) flow_dist_pred = self . create_spline_flow ( pred_params . shape [ 0 ]) # Replace parameters with estimated ones _ , flow_dist_pred = self . replace_parameters ( pred_params , flow_dist_pred ) # Draw samples flow_samples = pd . DataFrame ( flow_dist_pred . sample (( n_samples ,)) . squeeze () . detach () . numpy () . T ) flow_samples . columns = [ str ( \"y_sample\" ) + str ( i ) for i in range ( flow_samples . shape [ 1 ])] if self . discrete : flow_samples = flow_samples . astype ( int ) return flow_samples","title":"Returns"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.flow_select","text":"Function that selects the most suitable normalizing flow specification among the candidate_flow for the target variable, based on the NegLogLikelihood (lower is better).","title":"flow_select()"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.flow_select--parameters","text":"target: np.ndarray Response variable. candidate_flows: List List of candidate normalizing flow specifications. max_iter: int Maximum number of iterations for the optimization. n_samples: int Number of samples drawn from the fitted distribution. plot: bool If True, a density plot of the actual and fitted distribution is created. figure_size: tuple Figure size of the density plot.","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.flow_select--returns","text":"fit_df: pd.DataFrame Dataframe with the loss values of the fitted normalizing flow. Source code in lightgbmlss/distributions/flow_utils.py 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 def flow_select ( self , target : np . ndarray , candidate_flows : List , max_iter : int = 100 , n_samples : int = 1000 , plot : bool = False , figure_size : tuple = ( 10 , 5 ), ) -> pd . DataFrame : \"\"\" Function that selects the most suitable normalizing flow specification among the candidate_flow for the target variable, based on the NegLogLikelihood (lower is better). Parameters ---------- target: np.ndarray Response variable. candidate_flows: List List of candidate normalizing flow specifications. max_iter: int Maximum number of iterations for the optimization. n_samples: int Number of samples drawn from the fitted distribution. plot: bool If True, a density plot of the actual and fitted distribution is created. figure_size: tuple Figure size of the density plot. Returns ------- fit_df: pd.DataFrame Dataframe with the loss values of the fitted normalizing flow. \"\"\" flow_list = [] total_iterations = len ( candidate_flows ) with tqdm ( total = total_iterations , desc = \"Fitting candidate normalizing flows\" ) as pbar : for flow in candidate_flows : flow_name = str ( flow . __class__ ) . split ( \".\" )[ - 1 ] . split ( \"'>\" )[ 0 ] flow_spec = f \"(count_bins: { flow . count_bins } , order: { flow . order } )\" flow_name = flow_name + flow_spec pbar . set_description ( f \"Fitting { flow_name } \" ) flow_sel = flow try : loss , params = flow_sel . calculate_start_values ( target = target , max_iter = max_iter ) fit_df = pd . DataFrame . from_dict ( { flow_sel . loss_fn : loss . reshape ( - 1 , ), \"NormFlow\" : str ( flow_name ), \"params\" : [ params ] } ) except Exception as e : warnings . warn ( f \"Error fitting { flow_sel } NormFlow: { str ( e ) } \" ) fit_df = pd . DataFrame ( { flow_sel . loss_fn : np . nan , \"NormFlow\" : str ( flow_sel ), \"params\" : [ np . nan ] * flow_sel . n_dist_param } ) flow_list . append ( fit_df ) fit_df = pd . concat ( flow_list ) . sort_values ( by = flow_sel . loss_fn , ascending = True ) fit_df [ \"rank\" ] = fit_df [ flow_sel . loss_fn ] . rank () . astype ( int ) fit_df . set_index ( fit_df [ \"rank\" ], inplace = True ) pbar . update ( 1 ) pbar . set_description ( f \"Fitting of candidate normalizing flows completed\" ) if plot : # Select normalizing flow with the lowest loss best_flow = fit_df [ fit_df [ \"rank\" ] == 1 ] . reset_index ( drop = True ) for flow in candidate_flows : flow_name = str ( flow . __class__ ) . split ( \".\" )[ - 1 ] . split ( \"'>\" )[ 0 ] flow_spec = f \"(count_bins: { flow . count_bins } , order: { flow . order } )\" flow_name = flow_name + flow_spec if flow_name == best_flow [ \"NormFlow\" ] . values [ 0 ]: best_flow_sel = flow break # Draw samples from distribution flow_params = torch . tensor ( best_flow [ \"params\" ][ 0 ]) . reshape ( 1 , - 1 ) flow_dist_sel = best_flow_sel . create_spline_flow ( input_dim = 1 ) _ , flow_dist_sel = best_flow_sel . replace_parameters ( flow_params , flow_dist_sel ) flow_samples = pd . DataFrame ( flow_dist_sel . sample (( n_samples ,)) . squeeze () . detach () . numpy () . T ) # Plot actual and fitted distribution flow_samples [ \"type\" ] = f \"Best-Fit: { best_flow [ 'NormFlow' ] . values [ 0 ] } \" df_actual = pd . DataFrame ( target ) df_actual [ \"type\" ] = \"Data\" plot_df = pd . concat ([ df_actual , flow_samples ]) . rename ( columns = { 0 : \"variable\" }) print ( ggplot ( plot_df , aes ( x = \"variable\" , color = \"type\" )) + geom_density ( size = 1.1 ) + theme_bw ( base_size = 15 ) + theme ( figure_size = figure_size , legend_position = \"right\" , legend_title = element_blank (), plot_title = element_text ( hjust = 0.5 )) + labs ( title = f \"Actual vs. Fitted Density\" , x = \"\" ) ) fit_df . drop ( columns = [ \"rank\" , \"params\" ], inplace = True ) return fit_df","title":"Returns"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.get_params_loss","text":"Function that returns the predicted parameters and the loss.","title":"get_params_loss()"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.get_params_loss--arguments","text":"predt: np.ndarray Predicted values. target: torch.Tensor Target values. start_values: List Starting values for each parameter.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.get_params_loss--returns","text":"predt: torch.Tensor Predicted parameters. loss: torch.Tensor Loss value. Source code in lightgbmlss/distributions/flow_utils.py 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 def get_params_loss ( self , predt : np . ndarray , target : torch . Tensor , start_values : List [ float ], requires_grad : bool = False , ) -> Tuple [ List [ torch . Tensor ], np . ndarray ]: \"\"\" Function that returns the predicted parameters and the loss. Arguments --------- predt: np.ndarray Predicted values. target: torch.Tensor Target values. start_values: List Starting values for each parameter. Returns ------- predt: torch.Tensor Predicted parameters. loss: torch.Tensor Loss value. \"\"\" # Reshape Target target = target . view ( - 1 ) # Predicted Parameters predt = predt . reshape ( - 1 , self . n_dist_param , order = \"F\" ) # Replace NaNs and infinity values with unconditional start values nan_inf_mask = np . isnan ( predt ) | np . isinf ( predt ) predt [ nan_inf_mask ] = np . take ( start_values , np . where ( nan_inf_mask )[ 1 ]) # Convert to torch.tensor predt = torch . tensor ( predt , dtype = torch . float32 ) # Specify Normalizing Flow flow_dist = self . create_spline_flow ( target . shape [ 0 ]) # Replace parameters with estimated ones params , flow_dist = self . replace_parameters ( predt , flow_dist ) # Calculate loss if self . loss_fn == \"nll\" : loss = - torch . nansum ( flow_dist . log_prob ( target )) elif self . loss_fn == \"crps\" : torch . manual_seed ( 123 ) dist_samples = flow_dist . rsample (( 30 ,)) . squeeze ( - 1 ) loss = torch . nansum ( self . crps_score ( target , dist_samples )) else : raise ValueError ( \"Invalid loss function. Please select 'nll' or 'crps'.\" ) return params , loss","title":"Returns"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.metric_fn","text":"Function that evaluates the predictions using the specified loss function.","title":"metric_fn()"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.metric_fn--arguments","text":"predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.metric_fn--returns","text":"name: str Name of the evaluation metric. loss: float Loss value. Source code in lightgbmlss/distributions/flow_utils.py 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 def metric_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ str , float , bool ]: \"\"\" Function that evaluates the predictions using the specified loss function. Arguments --------- predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns ------- name: str Name of the evaluation metric. loss: float Loss value. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate loss is_higher_better = False _ , loss = self . get_params_loss ( predt , target , start_values ) return self . loss_fn , loss . detach (), is_higher_better","title":"Returns"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.objective_fn","text":"Function to estimate gradients and hessians of normalizing flow parameters.","title":"objective_fn()"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.objective_fn--arguments","text":"predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.objective_fn--returns","text":"grad: np.ndarray Gradient. hess: np.ndarray Hessian. Source code in lightgbmlss/distributions/flow_utils.py 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 def objective_fn ( self , predt : np . ndarray , data : lgb . Dataset ) -> Tuple [ np . ndarray , np . ndarray ]: \"\"\" Function to estimate gradients and hessians of normalizing flow parameters. Arguments --------- predt: np.ndarray Predicted values. data: lgb.Dataset Data used for training. Returns ------- grad: np.ndarray Gradient. hess: np.ndarray Hessian. \"\"\" # Target target = torch . tensor ( data . get_label () . reshape ( - 1 , 1 )) # Weights if data . weight is None : # Use 1 as weight if no weights are specified weights = torch . ones_like ( target , dtype = target . dtype ) . numpy () else : weights = data . get_weight () . reshape ( - 1 , 1 ) # Start values (needed to replace NaNs in predt) start_values = data . get_init_score () . reshape ( - 1 , self . n_dist_param )[ 0 , :] . tolist () # Calculate gradients and hessians predt , loss = self . get_params_loss ( predt , target , start_values ) grad , hess = self . compute_gradients_and_hessians ( loss , predt , weights ) return grad , hess","title":"Returns"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.predict_dist","text":"Function that predicts from the trained model.","title":"predict_dist()"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.predict_dist--arguments","text":"booster : lgb.Booster Trained model. start_values : np.ndarray Starting values for each distributional parameter. data : pd.DataFrame Data to predict from. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.predict_dist--returns","text":"pred : pd.DataFrame Predictions. Source code in lightgbmlss/distributions/flow_utils.py 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 def predict_dist ( self , booster : lgb . Booster , data : pd . DataFrame , start_values : np . ndarray , pred_type : str = \"parameters\" , n_samples : int = 1000 , quantiles : list = [ 0.1 , 0.5 , 0.9 ], seed : str = 123 ) -> pd . DataFrame : \"\"\" Function that predicts from the trained model. Arguments --------- booster : lgb.Booster Trained model. start_values : np.ndarray Starting values for each distributional parameter. data : pd.DataFrame Data to predict from. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns ------- pred : pd.DataFrame Predictions. \"\"\" # Predict raw scores predt = torch . tensor ( booster . predict ( data , raw_score = True ), dtype = torch . float32 ) . reshape ( - 1 , self . n_dist_param ) # Set init_score as starting point for each distributional parameter. init_score_pred = torch . tensor ( np . ones ( shape = ( data . shape [ 0 ], 1 )) * start_values , dtype = torch . float32 ) # The predictions don't include the init_score specified in creating the train data. Hence, it needs to be # added manually. dist_params_predt = pd . DataFrame ( np . concatenate ( [ predt [:, i ] . reshape ( - 1 , 1 ) + init_score_pred [:, i ] . reshape ( - 1 , 1 ) for i in range ( self . n_dist_param )], axis = 1 ) ) dist_params_predt . columns = self . param_dict . keys () # Draw samples from predicted response distribution pred_samples_df = self . draw_samples ( predt_params = dist_params_predt , n_samples = n_samples , seed = seed ) if pred_type == \"parameters\" : return dist_params_predt elif pred_type == \"samples\" : return pred_samples_df elif pred_type == \"quantiles\" : # Calculate quantiles from predicted response distribution pred_quant_df = pred_samples_df . quantile ( quantiles , axis = 1 ) . T pred_quant_df . columns = [ str ( \"quant_\" ) + str ( quantiles [ i ]) for i in range ( len ( quantiles ))] if self . discrete : pred_quant_df = pred_quant_df . astype ( int ) return pred_quant_df","title":"Returns"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.replace_parameters","text":"Replace parameters with estimated ones.","title":"replace_parameters()"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.replace_parameters--arguments","text":"params: torch.Tensor Estimated parameters. flow_dist: Transform Normalizing Flow.","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.replace_parameters--returns","text":"params_list: List List of estimated parameters. flow_dist: Transform Normalizing Flow with estimated parameters. Source code in lightgbmlss/distributions/flow_utils.py 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 def replace_parameters ( self , params : torch . Tensor , flow_dist : Transform , ) -> Tuple [ List , Transform ]: \"\"\" Replace parameters with estimated ones. Arguments --------- params: torch.Tensor Estimated parameters. flow_dist: Transform Normalizing Flow. Returns ------- params_list: List List of estimated parameters. flow_dist: Transform Normalizing Flow with estimated parameters. \"\"\" # Split parameters into list if self . order == \"quadratic\" : params_list = torch . split ( params , [ self . count_bins , self . count_bins , self . count_bins - 1 ], dim = 1 ) elif self . order == \"linear\" : params_list = torch . split ( params , [ self . count_bins , self . count_bins , self . count_bins - 1 , self . count_bins ], dim = 1 ) # Replace parameters for param , new_value in zip ( flow_dist . transforms [ 0 ] . parameters (), params_list ): param . data = new_value # Get parameters (including require_grad=True) params_list = list ( flow_dist . transforms [ 0 ] . parameters ()) return params_list , flow_dist","title":"Returns"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.stabilize_derivative","text":"Function that stabilizes Gradients and Hessians. Since parameters are estimated by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all parameters. Due to imbalances regarding the ranges, the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve convergence might be to standardize the response variable. This is especially useful if the range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the standardization of the response are not always advised but need to be carefully considered.","title":"stabilize_derivative()"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.stabilize_derivative--source","text":"https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173","title":"Source"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.stabilize_derivative--arguments","text":"input_der : torch.Tensor Input derivative, either Gradient or Hessian. type: str Stabilization method. Can be either \"None\", \"MAD\" or \"L2\".","title":"Arguments"},{"location":"api/#lightgbmlss.distributions.flow_utils.NormalizingFlowClass.stabilize_derivative--returns","text":"stab_der : torch.Tensor Stabilized Gradient or Hessian. Source code in lightgbmlss/distributions/flow_utils.py 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 def stabilize_derivative ( self , input_der : torch . Tensor , type : str = \"MAD\" ) -> torch . Tensor : \"\"\" Function that stabilizes Gradients and Hessians. Since parameters are estimated by optimizing Gradients and Hessians, it is important that these are comparable in magnitude for all parameters. Due to imbalances regarding the ranges, the estimation might become unstable so that it does not converge (or converge very slowly) to the optimal solution. Another way to improve convergence might be to standardize the response variable. This is especially useful if the range of the response differs strongly from the range of the Gradients and Hessians. Both, the stabilization and the standardization of the response are not always advised but need to be carefully considered. Source --------- https://github.com/boost-R/gamboostLSS/blob/7792951d2984f289ed7e530befa42a2a4cb04d1d/R/helpers.R#L173 Arguments --------- input_der : torch.Tensor Input derivative, either Gradient or Hessian. type: str Stabilization method. Can be either \"None\", \"MAD\" or \"L2\". Returns --------- stab_der : torch.Tensor Stabilized Gradient or Hessian. \"\"\" if type == \"MAD\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . nanmedian ( torch . abs ( input_der - torch . nanmedian ( input_der ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) stab_der = input_der / div if type == \"L2\" : input_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) div = torch . sqrt ( torch . nanmean ( input_der . pow ( 2 ))) div = torch . where ( div < torch . tensor ( 1e-04 ), torch . tensor ( 1e-04 ), div ) div = torch . where ( div > torch . tensor ( 10000.0 ), torch . tensor ( 10000.0 ), div ) stab_der = input_der / div if type == \"None\" : stab_der = torch . nan_to_num ( input_der , nan = float ( torch . nanmean ( input_der ))) return stab_der","title":"Returns"},{"location":"api/#lightgbmlss.distributions.zero_inflated","text":"","title":"zero_inflated"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroAdjustedBeta","text":"Bases: ZeroInflatedDistribution A Zero-Adjusted Beta distribution.","title":"ZeroAdjustedBeta"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroAdjustedBeta--parameter","text":"concentration1: torch.Tensor 1st concentration parameter of the distribution (often referred to as alpha). concentration0: torch.Tensor 2nd concentration parameter of the distribution (often referred to as beta). gate: torch.Tensor Probability of zeros given via a Bernoulli distribution.","title":"Parameter"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroAdjustedBeta--source","text":"https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py Source code in lightgbmlss/distributions/zero_inflated.py 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 class ZeroAdjustedBeta ( ZeroInflatedDistribution ): \"\"\" A Zero-Adjusted Beta distribution. Parameter --------- concentration1: torch.Tensor 1st concentration parameter of the distribution (often referred to as alpha). concentration0: torch.Tensor 2nd concentration parameter of the distribution (often referred to as beta). gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source ------ https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py \"\"\" arg_constraints = { \"concentration1\" : constraints . positive , \"concentration0\" : constraints . positive , \"gate\" : constraints . unit_interval , } support = constraints . unit_interval def __init__ ( self , concentration1 , concentration0 , gate = None , validate_args = None ): base_dist = Beta ( concentration1 = concentration1 , concentration0 = concentration0 , validate_args = False ) base_dist . _validate_args = validate_args super () . __init__ ( base_dist , gate = gate , validate_args = validate_args ) @property def concentration1 ( self ): return self . base_dist . concentration1 @property def concentration0 ( self ): return self . base_dist . concentration0","title":"Source"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroAdjustedGamma","text":"Bases: ZeroInflatedDistribution A Zero-Adjusted Gamma distribution.","title":"ZeroAdjustedGamma"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroAdjustedGamma--parameter","text":"concentration: torch.Tensor shape parameter of the distribution (often referred to as alpha) rate: torch.Tensor rate = 1 / scale of the distribution (often referred to as beta) gate: torch.Tensor Probability of zeros given via a Bernoulli distribution.","title":"Parameter"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroAdjustedGamma--source","text":"https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py Source code in lightgbmlss/distributions/zero_inflated.py 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 class ZeroAdjustedGamma ( ZeroInflatedDistribution ): \"\"\" A Zero-Adjusted Gamma distribution. Parameter --------- concentration: torch.Tensor shape parameter of the distribution (often referred to as alpha) rate: torch.Tensor rate = 1 / scale of the distribution (often referred to as beta) gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source ------ https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py \"\"\" arg_constraints = { \"concentration\" : constraints . positive , \"rate\" : constraints . positive , \"gate\" : constraints . unit_interval , } support = constraints . nonnegative def __init__ ( self , concentration , rate , gate = None , validate_args = None ): base_dist = Gamma ( concentration = concentration , rate = rate , validate_args = False ) base_dist . _validate_args = validate_args super () . __init__ ( base_dist , gate = gate , validate_args = validate_args ) @property def concentration ( self ): return self . base_dist . concentration @property def rate ( self ): return self . base_dist . rate","title":"Source"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroAdjustedLogNormal","text":"Bases: ZeroInflatedDistribution A Zero-Adjusted Log-Normal distribution.","title":"ZeroAdjustedLogNormal"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroAdjustedLogNormal--parameter","text":"loc: torch.Tensor Mean of log of distribution. scale: torch.Tensor Standard deviation of log of the distribution. gate: torch.Tensor Probability of zeros given via a Bernoulli distribution.","title":"Parameter"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroAdjustedLogNormal--source","text":"https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py Source code in lightgbmlss/distributions/zero_inflated.py 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 class ZeroAdjustedLogNormal ( ZeroInflatedDistribution ): \"\"\" A Zero-Adjusted Log-Normal distribution. Parameter --------- loc: torch.Tensor Mean of log of distribution. scale: torch.Tensor Standard deviation of log of the distribution. gate: torch.Tensor Probability of zeros given via a Bernoulli distribution. Source ------ https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py \"\"\" arg_constraints = { \"loc\" : constraints . real , \"scale\" : constraints . positive , \"gate\" : constraints . unit_interval , } support = constraints . nonnegative def __init__ ( self , loc , scale , gate = None , validate_args = None ): base_dist = LogNormal ( loc = loc , scale = scale , validate_args = False ) base_dist . _validate_args = validate_args super () . __init__ ( base_dist , gate = gate , validate_args = validate_args ) @property def loc ( self ): return self . base_dist . loc @property def scale ( self ): return self . base_dist . scale","title":"Source"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroInflatedDistribution","text":"Bases: TorchDistribution Generic Zero Inflated distribution. This can be used directly or can be used as a base class as e.g. for :class: ZeroInflatedPoisson and :class: ZeroInflatedNegativeBinomial .","title":"ZeroInflatedDistribution"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroInflatedDistribution--parameters","text":"gate : torch.Tensor Probability of extra zeros given via a Bernoulli distribution. base_dist : torch.distributions.Distribution The base distribution.","title":"Parameters"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroInflatedDistribution--source","text":"https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L18 Source code in lightgbmlss/distributions/zero_inflated.py 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 class ZeroInflatedDistribution ( TorchDistribution ): \"\"\" Generic Zero Inflated distribution. This can be used directly or can be used as a base class as e.g. for :class:`ZeroInflatedPoisson` and :class:`ZeroInflatedNegativeBinomial`. Parameters ---------- gate : torch.Tensor Probability of extra zeros given via a Bernoulli distribution. base_dist : torch.distributions.Distribution The base distribution. Source ------ https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L18 \"\"\" arg_constraints = { \"gate\" : constraints . unit_interval , \"gate_logits\" : constraints . real , } def __init__ ( self , base_dist , * , gate = None , gate_logits = None , validate_args = None ): if ( gate is None ) == ( gate_logits is None ): raise ValueError ( \"Either `gate` or `gate_logits` must be specified, but not both.\" ) if gate is not None : batch_shape = broadcast_shape ( gate . shape , base_dist . batch_shape ) self . gate = gate . expand ( batch_shape ) else : batch_shape = broadcast_shape ( gate_logits . shape , base_dist . batch_shape ) self . gate_logits = gate_logits . expand ( batch_shape ) if base_dist . event_shape : raise ValueError ( \"ZeroInflatedDistribution expected empty \" \"base_dist.event_shape but got {} \" . format ( base_dist . event_shape ) ) self . base_dist = base_dist . expand ( batch_shape ) event_shape = torch . Size () super () . __init__ ( batch_shape , event_shape , validate_args ) @constraints . dependent_property def support ( self ): return self . base_dist . support @lazy_property def gate ( self ): return logits_to_probs ( self . gate_logits ) @lazy_property def gate_logits ( self ): return probs_to_logits ( self . gate ) def log_prob ( self , value ): if self . _validate_args : self . _validate_sample ( value ) zero_idx = ( value == 0 ) support = self . support epsilon = abs ( torch . finfo ( value . dtype ) . eps ) if hasattr ( support , \"lower_bound\" ): if is_identically_zero ( getattr ( support , \"lower_bound\" , None )): value = value . clamp_min ( epsilon ) if hasattr ( support , \"upper_bound\" ): if is_identically_one ( getattr ( support , \"upper_bound\" , None )) & ( value . max () == 1.0 ): value = value . clamp_max ( 1 - epsilon ) if \"gate\" in self . __dict__ : gate , value = broadcast_all ( self . gate , value ) log_prob = ( - gate ) . log1p () + self . base_dist . log_prob ( value ) log_prob = torch . where ( zero_idx , ( gate + log_prob . exp ()) . log (), log_prob ) else : gate_logits , value = broadcast_all ( self . gate_logits , value ) log_prob_minus_log_gate = - gate_logits + self . base_dist . log_prob ( value ) log_gate = - softplus ( - gate_logits ) log_prob = log_prob_minus_log_gate + log_gate zero_log_prob = softplus ( log_prob_minus_log_gate ) + log_gate log_prob = torch . where ( zero_idx , zero_log_prob , log_prob ) return log_prob def sample ( self , sample_shape = torch . Size ()): shape = self . _extended_shape ( sample_shape ) with torch . no_grad (): mask = torch . bernoulli ( self . gate . expand ( shape )) . bool () samples = self . base_dist . expand ( shape ) . sample () samples = torch . where ( mask , samples . new_zeros (()), samples ) return samples @lazy_property def mean ( self ): return ( 1 - self . gate ) * self . base_dist . mean @lazy_property def variance ( self ): return ( 1 - self . gate ) * ( self . base_dist . mean ** 2 + self . base_dist . variance ) - self . mean ** 2 def expand ( self , batch_shape , _instance = None ): new = self . _get_checked_instance ( type ( self ), _instance ) batch_shape = torch . Size ( batch_shape ) gate = self . gate . expand ( batch_shape ) if \"gate\" in self . __dict__ else None gate_logits = ( self . gate_logits . expand ( batch_shape ) if \"gate_logits\" in self . __dict__ else None ) base_dist = self . base_dist . expand ( batch_shape ) ZeroInflatedDistribution . __init__ ( new , base_dist , gate = gate , gate_logits = gate_logits , validate_args = False ) new . _validate_args = self . _validate_args return new","title":"Source"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroInflatedNegativeBinomial","text":"Bases: ZeroInflatedDistribution A Zero Inflated Negative Binomial distribution.","title":"ZeroInflatedNegativeBinomial"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroInflatedNegativeBinomial--parameter","text":"total_count: torch.Tensor Non-negative number of negative Bernoulli trial. probs: torch.Tensor Event probabilities of success in the half open interval [0, 1). logits: torch.Tensor Event log-odds of success (log(p/(1-p))). gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution.","title":"Parameter"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroInflatedNegativeBinomial--source","text":"https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150 Source code in lightgbmlss/distributions/zero_inflated.py 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 class ZeroInflatedNegativeBinomial ( ZeroInflatedDistribution ): \"\"\" A Zero Inflated Negative Binomial distribution. Parameter --------- total_count: torch.Tensor Non-negative number of negative Bernoulli trial. probs: torch.Tensor Event probabilities of success in the half open interval [0, 1). logits: torch.Tensor Event log-odds of success (log(p/(1-p))). gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution. Source ------ - https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L150 \"\"\" arg_constraints = { \"total_count\" : constraints . greater_than_eq ( 0 ), \"probs\" : constraints . half_open_interval ( 0.0 , 1.0 ), \"logits\" : constraints . real , \"gate\" : constraints . unit_interval , } support = constraints . nonnegative_integer def __init__ ( self , total_count , probs = None , gate = None , validate_args = None ): base_dist = NegativeBinomial ( total_count = total_count , probs = probs , logits = None , validate_args = False ) base_dist . _validate_args = validate_args super () . __init__ ( base_dist , gate = gate , validate_args = validate_args ) @property def total_count ( self ): return self . base_dist . total_count @property def probs ( self ): return self . base_dist . probs @property def logits ( self ): return self . base_dist . logits","title":"Source"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroInflatedPoisson","text":"Bases: ZeroInflatedDistribution A Zero-Inflated Poisson distribution.","title":"ZeroInflatedPoisson"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroInflatedPoisson--parameter","text":"rate: torch.Tensor The rate of the Poisson distribution. gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution.","title":"Parameter"},{"location":"api/#lightgbmlss.distributions.zero_inflated.ZeroInflatedPoisson--source","text":"https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L121 Source code in lightgbmlss/distributions/zero_inflated.py 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 class ZeroInflatedPoisson ( ZeroInflatedDistribution ): \"\"\" A Zero-Inflated Poisson distribution. Parameter --------- rate: torch.Tensor The rate of the Poisson distribution. gate: torch.Tensor Probability of extra zeros given via a Bernoulli distribution. Source ------ https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/zero_inflated.py#L121 \"\"\" arg_constraints = { \"rate\" : constraints . positive , \"gate\" : constraints . unit_interval , } support = constraints . nonnegative_integer def __init__ ( self , rate , gate = None , validate_args = None ): base_dist = Poisson ( rate = rate , validate_args = False ) base_dist . _validate_args = validate_args super () . __init__ ( base_dist , gate = gate , validate_args = validate_args ) @property def rate ( self ): return self . base_dist . rate","title":"Source"},{"location":"api/#lightgbmlss.model","text":"","title":"model"},{"location":"api/#lightgbmlss.model.LightGBMLSS","text":"LightGBMLSS model class","title":"LightGBMLSS"},{"location":"api/#lightgbmlss.model.LightGBMLSS--parameters","text":"dist : Distribution DistributionClass object. start_values : np.ndarray Starting values for each distributional parameter. Source code in lightgbmlss/model.py 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 class LightGBMLSS : \"\"\" LightGBMLSS model class Parameters ---------- dist : Distribution DistributionClass object. start_values : np.ndarray Starting values for each distributional parameter. \"\"\" def __init__ ( self , dist ): self . dist = dist # Distribution object self . start_values = None # Starting values for distributional parameters def set_params ( self , params : Dict [ str , Any ]) -> Dict [ str , Any ]: \"\"\" Set parameters for distributional model. Arguments --------- params : Dict[str, Any] Parameters for model. Returns ------- params : Dict[str, Any] Updated Parameters for model. \"\"\" params_adj = { \"num_class\" : self . dist . n_dist_param , \"metric\" : \"None\" , \"objective\" : \"None\" , \"random_seed\" : 123 , \"verbose\" : - 1 } params . update ( params_adj ) return params def set_init_score ( self , dmatrix : Dataset ) -> None : \"\"\" Set init_score for distributions. Arguments --------- dmatrix : Dataset Dataset to set base margin for. Returns ------- None \"\"\" if self . start_values is None : _ , self . start_values = self . dist . calculate_start_values ( dmatrix . get_label ()) init_score = ( np . ones ( shape = ( dmatrix . get_label () . shape [ 0 ], 1 ))) * self . start_values dmatrix . set_init_score ( init_score . ravel ( order = \"F\" )) def train ( self , params : Dict [ str , Any ], train_set : Dataset , num_boost_round : int = 100 , valid_sets : Optional [ List [ Dataset ]] = None , valid_names : Optional [ List [ str ]] = None , init_model : Optional [ Union [ str , Path , Booster ]] = None , feature_name : _LGBM_FeatureNameConfiguration = 'auto' , categorical_feature : _LGBM_CategoricalFeatureConfiguration = 'auto' , keep_training_booster : bool = False , callbacks : Optional [ List [ Callable ]] = None ) -> Booster : \"\"\"Function to perform the training of a LightGBMLSS model with given parameters. Parameters ---------- params : dict Parameters for training. Values passed through ``params`` take precedence over those supplied via arguments. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. valid_sets : list of Dataset, or None, optional (default=None) List of data to be evaluated on during training. valid_names : list of str, or None, optional (default=None) Names of ``valid_sets``. init_model : str, pathlib.Path, Booster or None, optional (default=None) Filename of LightGBM model or Booster instance used for continue training. feature_name : list of str, or 'auto', optional (default=\"auto\") Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default=\"auto\") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. keep_training_booster : bool, optional (default=False) Whether the returned Booster will be used to keep training. If False, the returned value will be converted into _InnerPredictor before returning. This means you won't be able to use ``eval``, ``eval_train`` or ``eval_valid`` methods of the returned Booster. When your model is very large and cause the memory error, you can try to set this param to ``True`` to avoid the model conversion performed during the internal call of ``model_to_string``. You can still use _InnerPredictor as ``init_model`` for future continue training. callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. Returns ------- booster : Booster The trained Booster model. \"\"\" self . set_params ( params ) self . set_init_score ( train_set ) if valid_sets is not None : valid_sets = self . set_valid_margin ( valid_sets , self . start_values ) self . booster = lgb . train ( params , train_set , num_boost_round = num_boost_round , fobj = self . dist . objective_fn , feval = self . dist . metric_fn , valid_sets = valid_sets , valid_names = valid_names , init_model = init_model , feature_name = feature_name , categorical_feature = categorical_feature , keep_training_booster = keep_training_booster , callbacks = callbacks ) def cv ( self , params : Dict [ str , Any ], train_set : Dataset , num_boost_round : int = 100 , folds : Optional [ Union [ Iterable [ Tuple [ np . ndarray , np . ndarray ]], _LGBMBaseCrossValidator ]] = None , nfold : int = 5 , stratified : bool = True , shuffle : bool = True , init_model : Optional [ Union [ str , Path , Booster ]] = None , feature_name : _LGBM_FeatureNameConfiguration = 'auto' , categorical_feature : _LGBM_CategoricalFeatureConfiguration = 'auto' , fpreproc : Optional [ _LGBM_PreprocFunction ] = None , seed : int = 123 , callbacks : Optional [ List [ Callable ]] = None , eval_train_metric : bool = False , return_cvbooster : bool = False ) -> Dict [ str , Union [ List [ float ], CVBooster ]]: \"\"\"Function to cross-validate a LightGBMLSS model with given parameters. Parameters ---------- params : dict Parameters for training. Values passed through ``params`` take precedence over those supplied via arguments. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None) If generator or iterator, it should yield the train and test indices for each fold. If object, it should be one of the scikit-learn splitter classes (https://scikit-learn.org/stable/modules/classes.html#splitter-classes) and have ``split`` method. This argument has highest priority over other data split arguments. nfold : int, optional (default=5) Number of folds in CV. stratified : bool, optional (default=True) Whether to perform stratified sampling. shuffle : bool, optional (default=True) Whether to shuffle before splitting data. init_model : str, pathlib.Path, Booster or None, optional (default=None) Filename of LightGBM model or Booster instance used for continue training. feature_name : list of str, or 'auto', optional (default=\"auto\") Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default=\"auto\") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. fpreproc : callable or None, optional (default=None) Preprocessing function that takes (dtrain, dtest, params) and returns transformed versions of those. seed : int, optional (default=0) Seed used to generate the folds (passed to numpy.random.seed). callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. eval_train_metric : bool, optional (default=False) Whether to display the train metric in progress. The score of the metric is calculated again after each training step, so there is some impact on performance. return_cvbooster : bool, optional (default=False) Whether to return Booster models trained on each fold through ``CVBooster``. Returns ------- eval_hist : dict Evaluation history. The dictionary has the following format: {'metric1-mean': [values], 'metric1-stdv': [values], 'metric2-mean': [values], 'metric2-stdv': [values], ...}. If ``return_cvbooster=True``, also returns trained boosters wrapped in a ``CVBooster`` object via ``cvbooster`` key. \"\"\" self . set_params ( params ) self . set_init_score ( train_set ) self . bstLSS_cv = lgb . cv ( params , train_set , fobj = self . dist . objective_fn , feval = self . dist . metric_fn , num_boost_round = num_boost_round , folds = folds , nfold = nfold , stratified = False , shuffle = False , metrics = None , init_model = init_model , feature_name = feature_name , categorical_feature = categorical_feature , fpreproc = fpreproc , seed = seed , callbacks = callbacks , eval_train_metric = eval_train_metric , return_cvbooster = return_cvbooster ) return self . bstLSS_cv def hyper_opt ( self , hp_dict : Dict , train_set : lgb . Dataset , num_boost_round = 500 , nfold = 10 , early_stopping_rounds = 20 , max_minutes = 10 , n_trials = None , study_name = None , silence = False , seed = None , hp_seed = None ): \"\"\" Function to tune hyperparameters using optuna. Arguments ---------- hp_dict: dict Dictionary of hyperparameters to tune. train_set: lgb.Dataset Training data. num_boost_round: int Number of boosting iterations. nfold: int Number of folds in CV. early_stopping_rounds: int Activates early stopping. Cross-Validation metric (average of validation metric computed over CV folds) needs to improve at least once in every **early_stopping_rounds** round(s) to continue training. The last entry in the evaluation history will represent the best iteration. If there's more than one metric in the **eval_metric** parameter given in **params**, the last metric will be used for early stopping. max_minutes: int Time budget in minutes, i.e., stop study after the given number of minutes. n_trials: int The number of trials. If this argument is set to None, there is no limitation on the number of trials. study_name: str Name of the hyperparameter study. silence: bool Controls the verbosity of the trail, i.e., user can silence the outputs of the trail. seed: int Seed used to generate the folds (passed to numpy.random.seed). hp_seed: int Seed for random number generator used in the Bayesian hyper-parameter search. Returns ------- opt_params : dict Optimal hyper-parameters. \"\"\" def objective ( trial ): hyper_params = {} for param_name , param_value in hp_dict . items (): param_type = param_value [ 0 ] if param_type == \"categorical\" or param_type == \"none\" : hyper_params . update ({ param_name : trial . suggest_categorical ( param_name , param_value [ 1 ])}) elif param_type == \"float\" : param_constraints = param_value [ 1 ] param_low = param_constraints [ \"low\" ] param_high = param_constraints [ \"high\" ] param_log = param_constraints [ \"log\" ] hyper_params . update ( { param_name : trial . suggest_float ( param_name , low = param_low , high = param_high , log = param_log ) }) elif param_type == \"int\" : param_constraints = param_value [ 1 ] param_low = param_constraints [ \"low\" ] param_high = param_constraints [ \"high\" ] param_log = param_constraints [ \"log\" ] hyper_params . update ( { param_name : trial . suggest_int ( param_name , low = param_low , high = param_high , log = param_log ) }) # Add booster if not included in dictionary if \"boosting\" not in hyper_params . keys (): hyper_params . update ({ \"boosting\" : trial . suggest_categorical ( \"boosting\" , [ \"gbdt\" ])}) # Add pruning and early stopping pruning_callback = LightGBMPruningCallback ( trial , self . dist . loss_fn ) early_stopping_callback = lgb . early_stopping ( stopping_rounds = early_stopping_rounds , verbose = False ) lgblss_param_tuning = self . cv ( hyper_params , train_set , num_boost_round = num_boost_round , nfold = nfold , callbacks = [ pruning_callback , early_stopping_callback ], seed = seed , ) # Extract the optimal number of boosting rounds opt_rounds = np . argmin ( np . array ( lgblss_param_tuning [ f \" { self . dist . loss_fn } -mean\" ])) + 1 trial . set_user_attr ( \"opt_round\" , int ( opt_rounds )) # Extract the best score best_score = np . min ( np . array ( lgblss_param_tuning [ f \" { self . dist . loss_fn } -mean\" ])) return best_score if study_name is None : study_name = \"LightGBMLSS Hyper-Parameter Optimization\" if silence : optuna . logging . set_verbosity ( optuna . logging . WARNING ) if hp_seed is not None : sampler = TPESampler ( seed = hp_seed ) else : sampler = TPESampler () pruner = optuna . pruners . MedianPruner ( n_startup_trials = 10 , n_warmup_steps = 20 ) study = optuna . create_study ( sampler = sampler , pruner = pruner , direction = \"minimize\" , study_name = study_name ) study . optimize ( objective , n_trials = n_trials , timeout = 60 * max_minutes , show_progress_bar = True ) print ( \" \\n Hyper-Parameter Optimization successfully finished.\" ) print ( \" Number of finished trials: \" , len ( study . trials )) print ( \" Best trial:\" ) opt_param = study . best_trial # Add optimal stopping round opt_param . params [ \"opt_rounds\" ] = study . trials_dataframe ()[ \"user_attrs_opt_round\" ][ study . trials_dataframe ()[ \"value\" ] . idxmin ()] opt_param . params [ \"opt_rounds\" ] = int ( opt_param . params [ \"opt_rounds\" ]) print ( \" Value: {} \" . format ( opt_param . value )) print ( \" Params: \" ) for key , value in opt_param . params . items (): print ( \" {} : {} \" . format ( key , value )) return opt_param . params def predict ( self , data : pd . DataFrame , pred_type : str = \"parameters\" , n_samples : int = 1000 , quantiles : list = [ 0.1 , 0.5 , 0.9 ], seed : str = 123 ): \"\"\" Function that predicts from the trained model. Arguments --------- data : pd.DataFrame Data to predict from. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns ------- predt_df : pd.DataFrame Predictions. \"\"\" # Predict predt_df = self . dist . predict_dist ( booster = self . booster , data = data , start_values = self . start_values , pred_type = pred_type , n_samples = n_samples , quantiles = quantiles , seed = seed ) return predt_df def plot ( self , X : pd . DataFrame , feature : str = \"x\" , parameter : str = \"loc\" , max_display : int = 15 , plot_type : str = \"Partial_Dependence\" ): \"\"\" LightGBMLSS SHap plotting function. Arguments: --------- X: pd.DataFrame Train/Test Data feature: str Specifies which feature is to be plotted. parameter: str Specifies which distributional parameter is to be plotted. max_display: int Specifies the maximum number of features to be displayed. plot_type: str Specifies the type of plot: \"Partial_Dependence\" plots the partial dependence of the parameter on the feature. \"Feature_Importance\" plots the feature importance of the parameter. \"\"\" shap . initjs () explainer = shap . TreeExplainer ( self . booster ) shap_values = explainer ( X ) param_pos = self . dist . distribution_arg_names . index ( parameter ) if plot_type == \"Partial_Dependence\" : if self . dist . n_dist_param == 1 : shap . plots . scatter ( shap_values [:, feature ], color = shap_values [:, feature ]) else : shap . plots . scatter ( shap_values [:, feature ][:, param_pos ], color = shap_values [:, feature ][:, param_pos ]) elif plot_type == \"Feature_Importance\" : if self . dist . n_dist_param == 1 : shap . plots . bar ( shap_values , max_display = max_display if X . shape [ 1 ] > max_display else X . shape [ 1 ]) else : shap . plots . bar ( shap_values [:, :, param_pos ], max_display = max_display if X . shape [ 1 ] > max_display else X . shape [ 1 ] ) def expectile_plot ( self , X : pd . DataFrame , feature : str = \"x\" , expectile : str = \"0.05\" , plot_type : str = \"Partial_Dependence\" ): \"\"\" LightGBMLSS function for plotting expectile SHapley values. X: pd.DataFrame Train/Test Data feature: str Specifies which feature to use for plotting Partial_Dependence plot. expectile: str Specifies which expectile to plot. plot_type: str Specifies which SHapley-plot to visualize. Currently, \"Partial_Dependence\" and \"Feature_Importance\" are supported. \"\"\" shap . initjs () explainer = shap . TreeExplainer ( self . booster ) shap_values = explainer ( X ) expect_pos = list ( self . dist . param_dict . keys ()) . index ( expectile ) if plot_type == \"Partial_Dependence\" : shap . plots . scatter ( shap_values [:, feature ][:, expect_pos ], color = shap_values [:, feature ][:, expect_pos ]) elif plot_type == \"Feature_Importance\" : shap . plots . bar ( shap_values [:, :, expect_pos ], max_display = 15 if X . shape [ 1 ] > 15 else X . shape [ 1 ]) def set_valid_margin ( self , valid_sets : list , start_values : np . ndarray ) -> list : \"\"\" Function that sets the base margin for the validation set. Arguments --------- valid_sets : list List of tuples containing the train and evaluation set. valid_names: list List of tuples containing the name of train and evaluation set. start_values : np.ndarray Array containing the start values for the distributional parameters. Returns ------- valid_sets : list List of tuples containing the train and evaluation set. \"\"\" valid_sets1 = valid_sets [ 0 ] init_score_val1 = ( np . ones ( shape = ( valid_sets1 . get_label () . shape [ 0 ], 1 ))) * start_values valid_sets1 . set_init_score ( init_score_val1 . ravel ( order = \"F\" )) valid_sets2 = valid_sets [ 1 ] init_score_val2 = ( np . ones ( shape = ( valid_sets2 . get_label () . shape [ 0 ], 1 ))) * start_values valid_sets2 . set_init_score ( init_score_val2 . ravel ( order = \"F\" )) valid_sets = [ valid_sets1 , valid_sets2 ] return valid_sets def save_model ( self , model_path : str ) -> None : \"\"\" Save the model to a file. Parameters ---------- model_path : str The path to save the model. Returns ------- None \"\"\" with open ( model_path , \"wb\" ) as f : pickle . dump ( self , f ) @staticmethod def load_model ( model_path : str ): \"\"\" Load the model from a file. Parameters ---------- model_path : str The path to the saved model. Returns ------- The loaded model. \"\"\" with open ( model_path , \"rb\" ) as f : return pickle . load ( f )","title":"Parameters"},{"location":"api/#lightgbmlss.model.LightGBMLSS.cv","text":"Function to cross-validate a LightGBMLSS model with given parameters.","title":"cv()"},{"location":"api/#lightgbmlss.model.LightGBMLSS.cv--parameters","text":"params : dict Parameters for training. Values passed through params take precedence over those supplied via arguments. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None) If generator or iterator, it should yield the train and test indices for each fold. If object, it should be one of the scikit-learn splitter classes (https://scikit-learn.org/stable/modules/classes.html#splitter-classes) and have split method. This argument has highest priority over other data split arguments. nfold : int, optional (default=5) Number of folds in CV. stratified : bool, optional (default=True) Whether to perform stratified sampling. shuffle : bool, optional (default=True) Whether to shuffle before splitting data. init_model : str, pathlib.Path, Booster or None, optional (default=None) Filename of LightGBM model or Booster instance used for continue training. feature_name : list of str, or 'auto', optional (default=\"auto\") Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default=\"auto\") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify feature_name as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. fpreproc : callable or None, optional (default=None) Preprocessing function that takes (dtrain, dtest, params) and returns transformed versions of those. seed : int, optional (default=0) Seed used to generate the folds (passed to numpy.random.seed). callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. eval_train_metric : bool, optional (default=False) Whether to display the train metric in progress. The score of the metric is calculated again after each training step, so there is some impact on performance. return_cvbooster : bool, optional (default=False) Whether to return Booster models trained on each fold through CVBooster .","title":"Parameters"},{"location":"api/#lightgbmlss.model.LightGBMLSS.cv--returns","text":"eval_hist : dict Evaluation history. The dictionary has the following format: {'metric1-mean': [values], 'metric1-stdv': [values], 'metric2-mean': [values], 'metric2-stdv': [values], ...}. If return_cvbooster=True , also returns trained boosters wrapped in a CVBooster object via cvbooster key. Source code in lightgbmlss/model.py 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 def cv ( self , params : Dict [ str , Any ], train_set : Dataset , num_boost_round : int = 100 , folds : Optional [ Union [ Iterable [ Tuple [ np . ndarray , np . ndarray ]], _LGBMBaseCrossValidator ]] = None , nfold : int = 5 , stratified : bool = True , shuffle : bool = True , init_model : Optional [ Union [ str , Path , Booster ]] = None , feature_name : _LGBM_FeatureNameConfiguration = 'auto' , categorical_feature : _LGBM_CategoricalFeatureConfiguration = 'auto' , fpreproc : Optional [ _LGBM_PreprocFunction ] = None , seed : int = 123 , callbacks : Optional [ List [ Callable ]] = None , eval_train_metric : bool = False , return_cvbooster : bool = False ) -> Dict [ str , Union [ List [ float ], CVBooster ]]: \"\"\"Function to cross-validate a LightGBMLSS model with given parameters. Parameters ---------- params : dict Parameters for training. Values passed through ``params`` take precedence over those supplied via arguments. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None) If generator or iterator, it should yield the train and test indices for each fold. If object, it should be one of the scikit-learn splitter classes (https://scikit-learn.org/stable/modules/classes.html#splitter-classes) and have ``split`` method. This argument has highest priority over other data split arguments. nfold : int, optional (default=5) Number of folds in CV. stratified : bool, optional (default=True) Whether to perform stratified sampling. shuffle : bool, optional (default=True) Whether to shuffle before splitting data. init_model : str, pathlib.Path, Booster or None, optional (default=None) Filename of LightGBM model or Booster instance used for continue training. feature_name : list of str, or 'auto', optional (default=\"auto\") Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default=\"auto\") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. fpreproc : callable or None, optional (default=None) Preprocessing function that takes (dtrain, dtest, params) and returns transformed versions of those. seed : int, optional (default=0) Seed used to generate the folds (passed to numpy.random.seed). callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. eval_train_metric : bool, optional (default=False) Whether to display the train metric in progress. The score of the metric is calculated again after each training step, so there is some impact on performance. return_cvbooster : bool, optional (default=False) Whether to return Booster models trained on each fold through ``CVBooster``. Returns ------- eval_hist : dict Evaluation history. The dictionary has the following format: {'metric1-mean': [values], 'metric1-stdv': [values], 'metric2-mean': [values], 'metric2-stdv': [values], ...}. If ``return_cvbooster=True``, also returns trained boosters wrapped in a ``CVBooster`` object via ``cvbooster`` key. \"\"\" self . set_params ( params ) self . set_init_score ( train_set ) self . bstLSS_cv = lgb . cv ( params , train_set , fobj = self . dist . objective_fn , feval = self . dist . metric_fn , num_boost_round = num_boost_round , folds = folds , nfold = nfold , stratified = False , shuffle = False , metrics = None , init_model = init_model , feature_name = feature_name , categorical_feature = categorical_feature , fpreproc = fpreproc , seed = seed , callbacks = callbacks , eval_train_metric = eval_train_metric , return_cvbooster = return_cvbooster ) return self . bstLSS_cv","title":"Returns"},{"location":"api/#lightgbmlss.model.LightGBMLSS.expectile_plot","text":"LightGBMLSS function for plotting expectile SHapley values. pd.DataFrame Train/Test Data feature: str Specifies which feature to use for plotting Partial_Dependence plot. expectile: str Specifies which expectile to plot. plot_type: str Specifies which SHapley-plot to visualize. Currently, \"Partial_Dependence\" and \"Feature_Importance\" are supported. Source code in lightgbmlss/model.py 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 def expectile_plot ( self , X : pd . DataFrame , feature : str = \"x\" , expectile : str = \"0.05\" , plot_type : str = \"Partial_Dependence\" ): \"\"\" LightGBMLSS function for plotting expectile SHapley values. X: pd.DataFrame Train/Test Data feature: str Specifies which feature to use for plotting Partial_Dependence plot. expectile: str Specifies which expectile to plot. plot_type: str Specifies which SHapley-plot to visualize. Currently, \"Partial_Dependence\" and \"Feature_Importance\" are supported. \"\"\" shap . initjs () explainer = shap . TreeExplainer ( self . booster ) shap_values = explainer ( X ) expect_pos = list ( self . dist . param_dict . keys ()) . index ( expectile ) if plot_type == \"Partial_Dependence\" : shap . plots . scatter ( shap_values [:, feature ][:, expect_pos ], color = shap_values [:, feature ][:, expect_pos ]) elif plot_type == \"Feature_Importance\" : shap . plots . bar ( shap_values [:, :, expect_pos ], max_display = 15 if X . shape [ 1 ] > 15 else X . shape [ 1 ])","title":"expectile_plot()"},{"location":"api/#lightgbmlss.model.LightGBMLSS.hyper_opt","text":"Function to tune hyperparameters using optuna.","title":"hyper_opt()"},{"location":"api/#lightgbmlss.model.LightGBMLSS.hyper_opt--arguments","text":"hp_dict: dict Dictionary of hyperparameters to tune. train_set: lgb.Dataset Training data. num_boost_round: int Number of boosting iterations. nfold: int Number of folds in CV. early_stopping_rounds: int Activates early stopping. Cross-Validation metric (average of validation metric computed over CV folds) needs to improve at least once in every early_stopping_rounds round(s) to continue training. The last entry in the evaluation history will represent the best iteration. If there's more than one metric in the eval_metric parameter given in params , the last metric will be used for early stopping. max_minutes: int Time budget in minutes, i.e., stop study after the given number of minutes. n_trials: int The number of trials. If this argument is set to None, there is no limitation on the number of trials. study_name: str Name of the hyperparameter study. silence: bool Controls the verbosity of the trail, i.e., user can silence the outputs of the trail. seed: int Seed used to generate the folds (passed to numpy.random.seed). hp_seed: int Seed for random number generator used in the Bayesian hyper-parameter search.","title":"Arguments"},{"location":"api/#lightgbmlss.model.LightGBMLSS.hyper_opt--returns","text":"opt_params : dict Optimal hyper-parameters. Source code in lightgbmlss/model.py 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 def hyper_opt ( self , hp_dict : Dict , train_set : lgb . Dataset , num_boost_round = 500 , nfold = 10 , early_stopping_rounds = 20 , max_minutes = 10 , n_trials = None , study_name = None , silence = False , seed = None , hp_seed = None ): \"\"\" Function to tune hyperparameters using optuna. Arguments ---------- hp_dict: dict Dictionary of hyperparameters to tune. train_set: lgb.Dataset Training data. num_boost_round: int Number of boosting iterations. nfold: int Number of folds in CV. early_stopping_rounds: int Activates early stopping. Cross-Validation metric (average of validation metric computed over CV folds) needs to improve at least once in every **early_stopping_rounds** round(s) to continue training. The last entry in the evaluation history will represent the best iteration. If there's more than one metric in the **eval_metric** parameter given in **params**, the last metric will be used for early stopping. max_minutes: int Time budget in minutes, i.e., stop study after the given number of minutes. n_trials: int The number of trials. If this argument is set to None, there is no limitation on the number of trials. study_name: str Name of the hyperparameter study. silence: bool Controls the verbosity of the trail, i.e., user can silence the outputs of the trail. seed: int Seed used to generate the folds (passed to numpy.random.seed). hp_seed: int Seed for random number generator used in the Bayesian hyper-parameter search. Returns ------- opt_params : dict Optimal hyper-parameters. \"\"\" def objective ( trial ): hyper_params = {} for param_name , param_value in hp_dict . items (): param_type = param_value [ 0 ] if param_type == \"categorical\" or param_type == \"none\" : hyper_params . update ({ param_name : trial . suggest_categorical ( param_name , param_value [ 1 ])}) elif param_type == \"float\" : param_constraints = param_value [ 1 ] param_low = param_constraints [ \"low\" ] param_high = param_constraints [ \"high\" ] param_log = param_constraints [ \"log\" ] hyper_params . update ( { param_name : trial . suggest_float ( param_name , low = param_low , high = param_high , log = param_log ) }) elif param_type == \"int\" : param_constraints = param_value [ 1 ] param_low = param_constraints [ \"low\" ] param_high = param_constraints [ \"high\" ] param_log = param_constraints [ \"log\" ] hyper_params . update ( { param_name : trial . suggest_int ( param_name , low = param_low , high = param_high , log = param_log ) }) # Add booster if not included in dictionary if \"boosting\" not in hyper_params . keys (): hyper_params . update ({ \"boosting\" : trial . suggest_categorical ( \"boosting\" , [ \"gbdt\" ])}) # Add pruning and early stopping pruning_callback = LightGBMPruningCallback ( trial , self . dist . loss_fn ) early_stopping_callback = lgb . early_stopping ( stopping_rounds = early_stopping_rounds , verbose = False ) lgblss_param_tuning = self . cv ( hyper_params , train_set , num_boost_round = num_boost_round , nfold = nfold , callbacks = [ pruning_callback , early_stopping_callback ], seed = seed , ) # Extract the optimal number of boosting rounds opt_rounds = np . argmin ( np . array ( lgblss_param_tuning [ f \" { self . dist . loss_fn } -mean\" ])) + 1 trial . set_user_attr ( \"opt_round\" , int ( opt_rounds )) # Extract the best score best_score = np . min ( np . array ( lgblss_param_tuning [ f \" { self . dist . loss_fn } -mean\" ])) return best_score if study_name is None : study_name = \"LightGBMLSS Hyper-Parameter Optimization\" if silence : optuna . logging . set_verbosity ( optuna . logging . WARNING ) if hp_seed is not None : sampler = TPESampler ( seed = hp_seed ) else : sampler = TPESampler () pruner = optuna . pruners . MedianPruner ( n_startup_trials = 10 , n_warmup_steps = 20 ) study = optuna . create_study ( sampler = sampler , pruner = pruner , direction = \"minimize\" , study_name = study_name ) study . optimize ( objective , n_trials = n_trials , timeout = 60 * max_minutes , show_progress_bar = True ) print ( \" \\n Hyper-Parameter Optimization successfully finished.\" ) print ( \" Number of finished trials: \" , len ( study . trials )) print ( \" Best trial:\" ) opt_param = study . best_trial # Add optimal stopping round opt_param . params [ \"opt_rounds\" ] = study . trials_dataframe ()[ \"user_attrs_opt_round\" ][ study . trials_dataframe ()[ \"value\" ] . idxmin ()] opt_param . params [ \"opt_rounds\" ] = int ( opt_param . params [ \"opt_rounds\" ]) print ( \" Value: {} \" . format ( opt_param . value )) print ( \" Params: \" ) for key , value in opt_param . params . items (): print ( \" {} : {} \" . format ( key , value )) return opt_param . params","title":"Returns"},{"location":"api/#lightgbmlss.model.LightGBMLSS.load_model","text":"Load the model from a file.","title":"load_model()"},{"location":"api/#lightgbmlss.model.LightGBMLSS.load_model--parameters","text":"model_path : str The path to the saved model.","title":"Parameters"},{"location":"api/#lightgbmlss.model.LightGBMLSS.load_model--returns","text":"The loaded model. Source code in lightgbmlss/model.py 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 @staticmethod def load_model ( model_path : str ): \"\"\" Load the model from a file. Parameters ---------- model_path : str The path to the saved model. Returns ------- The loaded model. \"\"\" with open ( model_path , \"rb\" ) as f : return pickle . load ( f )","title":"Returns"},{"location":"api/#lightgbmlss.model.LightGBMLSS.plot","text":"LightGBMLSS SHap plotting function.","title":"plot()"},{"location":"api/#lightgbmlss.model.LightGBMLSS.plot--arguments","text":"X: pd.DataFrame Train/Test Data feature: str Specifies which feature is to be plotted. parameter: str Specifies which distributional parameter is to be plotted. max_display: int Specifies the maximum number of features to be displayed. plot_type: str Specifies the type of plot: \"Partial_Dependence\" plots the partial dependence of the parameter on the feature. \"Feature_Importance\" plots the feature importance of the parameter. Source code in lightgbmlss/model.py 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 def plot ( self , X : pd . DataFrame , feature : str = \"x\" , parameter : str = \"loc\" , max_display : int = 15 , plot_type : str = \"Partial_Dependence\" ): \"\"\" LightGBMLSS SHap plotting function. Arguments: --------- X: pd.DataFrame Train/Test Data feature: str Specifies which feature is to be plotted. parameter: str Specifies which distributional parameter is to be plotted. max_display: int Specifies the maximum number of features to be displayed. plot_type: str Specifies the type of plot: \"Partial_Dependence\" plots the partial dependence of the parameter on the feature. \"Feature_Importance\" plots the feature importance of the parameter. \"\"\" shap . initjs () explainer = shap . TreeExplainer ( self . booster ) shap_values = explainer ( X ) param_pos = self . dist . distribution_arg_names . index ( parameter ) if plot_type == \"Partial_Dependence\" : if self . dist . n_dist_param == 1 : shap . plots . scatter ( shap_values [:, feature ], color = shap_values [:, feature ]) else : shap . plots . scatter ( shap_values [:, feature ][:, param_pos ], color = shap_values [:, feature ][:, param_pos ]) elif plot_type == \"Feature_Importance\" : if self . dist . n_dist_param == 1 : shap . plots . bar ( shap_values , max_display = max_display if X . shape [ 1 ] > max_display else X . shape [ 1 ]) else : shap . plots . bar ( shap_values [:, :, param_pos ], max_display = max_display if X . shape [ 1 ] > max_display else X . shape [ 1 ] )","title":"Arguments:"},{"location":"api/#lightgbmlss.model.LightGBMLSS.predict","text":"Function that predicts from the trained model.","title":"predict()"},{"location":"api/#lightgbmlss.model.LightGBMLSS.predict--arguments","text":"data : pd.DataFrame Data to predict from. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution.","title":"Arguments"},{"location":"api/#lightgbmlss.model.LightGBMLSS.predict--returns","text":"predt_df : pd.DataFrame Predictions. Source code in lightgbmlss/model.py 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 def predict ( self , data : pd . DataFrame , pred_type : str = \"parameters\" , n_samples : int = 1000 , quantiles : list = [ 0.1 , 0.5 , 0.9 ], seed : str = 123 ): \"\"\" Function that predicts from the trained model. Arguments --------- data : pd.DataFrame Data to predict from. pred_type : str Type of prediction: - \"samples\" draws n_samples from the predicted distribution. - \"quantiles\" calculates the quantiles from the predicted distribution. - \"parameters\" returns the predicted distributional parameters. - \"expectiles\" returns the predicted expectiles. n_samples : int Number of samples to draw from the predicted distribution. quantiles : List[float] List of quantiles to calculate from the predicted distribution. seed : int Seed for random number generator used to draw samples from the predicted distribution. Returns ------- predt_df : pd.DataFrame Predictions. \"\"\" # Predict predt_df = self . dist . predict_dist ( booster = self . booster , data = data , start_values = self . start_values , pred_type = pred_type , n_samples = n_samples , quantiles = quantiles , seed = seed ) return predt_df","title":"Returns"},{"location":"api/#lightgbmlss.model.LightGBMLSS.save_model","text":"Save the model to a file.","title":"save_model()"},{"location":"api/#lightgbmlss.model.LightGBMLSS.save_model--parameters","text":"model_path : str The path to save the model.","title":"Parameters"},{"location":"api/#lightgbmlss.model.LightGBMLSS.save_model--returns","text":"None Source code in lightgbmlss/model.py 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 def save_model ( self , model_path : str ) -> None : \"\"\" Save the model to a file. Parameters ---------- model_path : str The path to save the model. Returns ------- None \"\"\" with open ( model_path , \"wb\" ) as f : pickle . dump ( self , f )","title":"Returns"},{"location":"api/#lightgbmlss.model.LightGBMLSS.set_init_score","text":"Set init_score for distributions.","title":"set_init_score()"},{"location":"api/#lightgbmlss.model.LightGBMLSS.set_init_score--arguments","text":"dmatrix : Dataset Dataset to set base margin for.","title":"Arguments"},{"location":"api/#lightgbmlss.model.LightGBMLSS.set_init_score--returns","text":"None Source code in lightgbmlss/model.py 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def set_init_score ( self , dmatrix : Dataset ) -> None : \"\"\" Set init_score for distributions. Arguments --------- dmatrix : Dataset Dataset to set base margin for. Returns ------- None \"\"\" if self . start_values is None : _ , self . start_values = self . dist . calculate_start_values ( dmatrix . get_label ()) init_score = ( np . ones ( shape = ( dmatrix . get_label () . shape [ 0 ], 1 ))) * self . start_values dmatrix . set_init_score ( init_score . ravel ( order = \"F\" ))","title":"Returns"},{"location":"api/#lightgbmlss.model.LightGBMLSS.set_params","text":"Set parameters for distributional model.","title":"set_params()"},{"location":"api/#lightgbmlss.model.LightGBMLSS.set_params--arguments","text":"params : Dict[str, Any] Parameters for model.","title":"Arguments"},{"location":"api/#lightgbmlss.model.LightGBMLSS.set_params--returns","text":"params : Dict[str, Any] Updated Parameters for model. Source code in lightgbmlss/model.py 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 def set_params ( self , params : Dict [ str , Any ]) -> Dict [ str , Any ]: \"\"\" Set parameters for distributional model. Arguments --------- params : Dict[str, Any] Parameters for model. Returns ------- params : Dict[str, Any] Updated Parameters for model. \"\"\" params_adj = { \"num_class\" : self . dist . n_dist_param , \"metric\" : \"None\" , \"objective\" : \"None\" , \"random_seed\" : 123 , \"verbose\" : - 1 } params . update ( params_adj ) return params","title":"Returns"},{"location":"api/#lightgbmlss.model.LightGBMLSS.set_valid_margin","text":"Function that sets the base margin for the validation set.","title":"set_valid_margin()"},{"location":"api/#lightgbmlss.model.LightGBMLSS.set_valid_margin--arguments","text":"valid_sets : list List of tuples containing the train and evaluation set. valid_names: list List of tuples containing the name of train and evaluation set. start_values : np.ndarray Array containing the start values for the distributional parameters.","title":"Arguments"},{"location":"api/#lightgbmlss.model.LightGBMLSS.set_valid_margin--returns","text":"valid_sets : list List of tuples containing the train and evaluation set. Source code in lightgbmlss/model.py 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 def set_valid_margin ( self , valid_sets : list , start_values : np . ndarray ) -> list : \"\"\" Function that sets the base margin for the validation set. Arguments --------- valid_sets : list List of tuples containing the train and evaluation set. valid_names: list List of tuples containing the name of train and evaluation set. start_values : np.ndarray Array containing the start values for the distributional parameters. Returns ------- valid_sets : list List of tuples containing the train and evaluation set. \"\"\" valid_sets1 = valid_sets [ 0 ] init_score_val1 = ( np . ones ( shape = ( valid_sets1 . get_label () . shape [ 0 ], 1 ))) * start_values valid_sets1 . set_init_score ( init_score_val1 . ravel ( order = \"F\" )) valid_sets2 = valid_sets [ 1 ] init_score_val2 = ( np . ones ( shape = ( valid_sets2 . get_label () . shape [ 0 ], 1 ))) * start_values valid_sets2 . set_init_score ( init_score_val2 . ravel ( order = \"F\" )) valid_sets = [ valid_sets1 , valid_sets2 ] return valid_sets","title":"Returns"},{"location":"api/#lightgbmlss.model.LightGBMLSS.train","text":"Function to perform the training of a LightGBMLSS model with given parameters.","title":"train()"},{"location":"api/#lightgbmlss.model.LightGBMLSS.train--parameters","text":"params : dict Parameters for training. Values passed through params take precedence over those supplied via arguments. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. valid_sets : list of Dataset, or None, optional (default=None) List of data to be evaluated on during training. valid_names : list of str, or None, optional (default=None) Names of valid_sets . init_model : str, pathlib.Path, Booster or None, optional (default=None) Filename of LightGBM model or Booster instance used for continue training. feature_name : list of str, or 'auto', optional (default=\"auto\") Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default=\"auto\") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify feature_name as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. keep_training_booster : bool, optional (default=False) Whether the returned Booster will be used to keep training. If False, the returned value will be converted into _InnerPredictor before returning. This means you won't be able to use eval , eval_train or eval_valid methods of the returned Booster. When your model is very large and cause the memory error, you can try to set this param to True to avoid the model conversion performed during the internal call of model_to_string . You can still use _InnerPredictor as init_model for future continue training. callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information.","title":"Parameters"},{"location":"api/#lightgbmlss.model.LightGBMLSS.train--returns","text":"booster : Booster The trained Booster model. Source code in lightgbmlss/model.py 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 def train ( self , params : Dict [ str , Any ], train_set : Dataset , num_boost_round : int = 100 , valid_sets : Optional [ List [ Dataset ]] = None , valid_names : Optional [ List [ str ]] = None , init_model : Optional [ Union [ str , Path , Booster ]] = None , feature_name : _LGBM_FeatureNameConfiguration = 'auto' , categorical_feature : _LGBM_CategoricalFeatureConfiguration = 'auto' , keep_training_booster : bool = False , callbacks : Optional [ List [ Callable ]] = None ) -> Booster : \"\"\"Function to perform the training of a LightGBMLSS model with given parameters. Parameters ---------- params : dict Parameters for training. Values passed through ``params`` take precedence over those supplied via arguments. train_set : Dataset Data to be trained on. num_boost_round : int, optional (default=100) Number of boosting iterations. valid_sets : list of Dataset, or None, optional (default=None) List of data to be evaluated on during training. valid_names : list of str, or None, optional (default=None) Names of ``valid_sets``. init_model : str, pathlib.Path, Booster or None, optional (default=None) Filename of LightGBM model or Booster instance used for continue training. feature_name : list of str, or 'auto', optional (default=\"auto\") Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default=\"auto\") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. keep_training_booster : bool, optional (default=False) Whether the returned Booster will be used to keep training. If False, the returned value will be converted into _InnerPredictor before returning. This means you won't be able to use ``eval``, ``eval_train`` or ``eval_valid`` methods of the returned Booster. When your model is very large and cause the memory error, you can try to set this param to ``True`` to avoid the model conversion performed during the internal call of ``model_to_string``. You can still use _InnerPredictor as ``init_model`` for future continue training. callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. Returns ------- booster : Booster The trained Booster model. \"\"\" self . set_params ( params ) self . set_init_score ( train_set ) if valid_sets is not None : valid_sets = self . set_valid_margin ( valid_sets , self . start_values ) self . booster = lgb . train ( params , train_set , num_boost_round = num_boost_round , fobj = self . dist . objective_fn , feval = self . dist . metric_fn , valid_sets = valid_sets , valid_names = valid_names , init_model = init_model , feature_name = feature_name , categorical_feature = categorical_feature , keep_training_booster = keep_training_booster , callbacks = callbacks )","title":"Returns"},{"location":"api/#lightgbmlss.utils","text":"","title":"utils"},{"location":"api/#lightgbmlss.utils.exp_fn","text":"Exponential function used to ensure predt is strictly positive.","title":"exp_fn()"},{"location":"api/#lightgbmlss.utils.exp_fn--arguments","text":"predt: torch.tensor Predicted values.","title":"Arguments"},{"location":"api/#lightgbmlss.utils.exp_fn--returns","text":"predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 def exp_fn ( predt : torch . tensor ) -> torch . tensor : \"\"\" Exponential function used to ensure predt is strictly positive. Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" predt = torch . exp ( predt ) predt = torch . nan_to_num ( predt , nan = float ( torch . nanmean ( predt ))) + torch . tensor ( 1e-06 , dtype = predt . dtype ) return predt","title":"Returns"},{"location":"api/#lightgbmlss.utils.exp_fn_df","text":"Exponential function used for Student-T distribution.","title":"exp_fn_df()"},{"location":"api/#lightgbmlss.utils.exp_fn_df--arguments","text":"predt: torch.tensor Predicted values.","title":"Arguments"},{"location":"api/#lightgbmlss.utils.exp_fn_df--returns","text":"predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 def exp_fn_df ( predt : torch . tensor ) -> torch . tensor : \"\"\" Exponential function used for Student-T distribution. Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" predt = torch . exp ( predt ) predt = torch . nan_to_num ( predt , nan = float ( torch . nanmean ( predt ))) + torch . tensor ( 1e-06 , dtype = predt . dtype ) return predt + torch . tensor ( 2.0 , dtype = predt . dtype )","title":"Returns"},{"location":"api/#lightgbmlss.utils.identity_fn","text":"Identity mapping of predt.","title":"identity_fn()"},{"location":"api/#lightgbmlss.utils.identity_fn--arguments","text":"predt: torch.tensor Predicted values.","title":"Arguments"},{"location":"api/#lightgbmlss.utils.identity_fn--returns","text":"predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 def identity_fn ( predt : torch . tensor ) -> torch . tensor : \"\"\" Identity mapping of predt. Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" return predt","title":"Returns"},{"location":"api/#lightgbmlss.utils.log_fn","text":"Inverse of exp_fn function.","title":"log_fn()"},{"location":"api/#lightgbmlss.utils.log_fn--arguments","text":"predt: torch.tensor Predicted values.","title":"Arguments"},{"location":"api/#lightgbmlss.utils.log_fn--returns","text":"predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 def log_fn ( predt : torch . tensor ) -> torch . tensor : \"\"\" Inverse of exp_fn function. Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" predt = torch . log ( predt ) predt = torch . nan_to_num ( predt , nan = float ( torch . nanmean ( predt ))) + float ( 1e-06 ) return predt","title":"Returns"},{"location":"api/#lightgbmlss.utils.relu_fn","text":"Function used to ensure predt are scaled to max(0, predt).","title":"relu_fn()"},{"location":"api/#lightgbmlss.utils.relu_fn--arguments","text":"predt: torch.tensor Predicted values.","title":"Arguments"},{"location":"api/#lightgbmlss.utils.relu_fn--returns","text":"predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 def relu_fn ( predt : torch . tensor ) -> torch . tensor : \"\"\" Function used to ensure predt are scaled to max(0, predt). Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" predt = torch . relu ( predt ) predt = torch . nan_to_num ( predt , nan = float ( torch . nanmean ( predt ))) + torch . tensor ( 1e-6 , dtype = predt . dtype ) return predt","title":"Returns"},{"location":"api/#lightgbmlss.utils.sigmoid_fn","text":"Function used to ensure predt are scaled to (0,1).","title":"sigmoid_fn()"},{"location":"api/#lightgbmlss.utils.sigmoid_fn--arguments","text":"predt: torch.tensor Predicted values.","title":"Arguments"},{"location":"api/#lightgbmlss.utils.sigmoid_fn--returns","text":"predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 def sigmoid_fn ( predt : torch . tensor ) -> torch . tensor : \"\"\" Function used to ensure predt are scaled to (0,1). Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" predt = torch . sigmoid ( predt ) predt = torch . nan_to_num ( predt , nan = float ( torch . nanmean ( predt ))) + torch . tensor ( 1e-06 , dtype = predt . dtype ) predt = torch . clamp ( predt , 1e-03 , 1 - 1e-03 ) return predt","title":"Returns"},{"location":"api/#lightgbmlss.utils.softplus_fn","text":"Softplus function used to ensure predt is strictly positive.","title":"softplus_fn()"},{"location":"api/#lightgbmlss.utils.softplus_fn--arguments","text":"predt: torch.tensor Predicted values.","title":"Arguments"},{"location":"api/#lightgbmlss.utils.softplus_fn--returns","text":"predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 def softplus_fn ( predt : torch . tensor ) -> torch . tensor : \"\"\" Softplus function used to ensure predt is strictly positive. Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" predt = torch . log1p ( torch . exp ( - torch . abs ( predt ))) + torch . maximum ( predt , torch . tensor ( 0. )) predt [ predt == 0 ] = torch . tensor ( 1e-06 , dtype = predt . dtype ) predt = torch . nan_to_num ( predt , nan = float ( torch . nanmean ( predt ))) + torch . tensor ( 1e-06 , dtype = predt . dtype ) return predt","title":"Returns"},{"location":"api/#lightgbmlss.utils.softplus_fn_df","text":"Softplus function used for Student-T distribution.","title":"softplus_fn_df()"},{"location":"api/#lightgbmlss.utils.softplus_fn_df--arguments","text":"predt: torch.tensor Predicted values.","title":"Arguments"},{"location":"api/#lightgbmlss.utils.softplus_fn_df--returns","text":"predt: torch.tensor Predicted values. Source code in lightgbmlss/utils.py 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 def softplus_fn_df ( predt : torch . tensor ) -> torch . tensor : \"\"\" Softplus function used for Student-T distribution. Arguments --------- predt: torch.tensor Predicted values. Returns ------- predt: torch.tensor Predicted values. \"\"\" predt = torch . log1p ( torch . exp ( - torch . abs ( predt ))) + torch . maximum ( predt , torch . tensor ( 0. )) predt [ predt == 0 ] = torch . tensor ( 1e-06 , dtype = predt . dtype ) predt = torch . nan_to_num ( predt , nan = float ( torch . nanmean ( predt ))) + torch . tensor ( 1e-06 , dtype = predt . dtype ) return predt + torch . tensor ( 2.0 , dtype = predt . dtype )","title":"Returns"},{"location":"dgbm/","text":"Introduction The development of modelling approaches that approximate and describe the data generating processes underlying the observed data in as much detail as possible is a guiding principle in both statistics and machine learning. We therefore strongly agree with the statement of Hothorn et al. (2014) that ''the ultimate goal of any regression analysis is to obtain information about the entire conditional distribution \\(F_{Y}(y|\\mathbf{x})\\) of a response given a set of explanatory variables'' . ''Practitioners expect forecasting to reduce future uncertainty by providing accurate predictions like those in hard sciences. However, this is a great misconception. A major purpose of forecasting is not to reduce uncertainty but reveal its full extent and implications by estimating it as precisely as possible. [...] The challenge for the forecasting field is how to persuade practitioners of the reality that all forecasts are uncertain and that this uncertainty cannot be ignored, as doing so could lead to catastrophic consequences.'' Makridakis (2022b) It has not been too long, though, that most regression models focused on estimating the conditional mean \\(\\mathbb{E}(Y|\\mathbf{X} = \\mathbf{x})\\) only, implicitly treating higher moments of the conditional distribution \\(F_{Y}(y|\\mathbf{x})\\) as fixed nuisance parameters. As such, models that minimize an \\(\\ell_{2}\\) -type loss for the conditional mean are not able to fully exploit the information contained in the data, since this is equivalent to assuming a Normal distribution with constant variance. In real world situations, however, the data generating process is usually less well behaved, exhibiting characteristics such as heteroskedasticity, varying degrees of skewness and kurtosis or intermittent and sporadic behaviour. In recent years, however, there has been a clear shift in both academic and corporate research toward modelling the entire conditional distribution. This change in attention is most evident in the recent M5 forecasting competition (Makridakis et al., 2022a,b), which differed from previous ones in that it consisted of two parallel competitions: in addition to providing accurate point forecasts, participants were also asked to forecast nine different quantiles to approximate the distribution of future sales. Distributional Gradient Boosting Machines This section introduces the general idea of distributional modelling. For a more thorough introduction, we refer the interested reader to Rigby and Stasinopoulos (2005); Klein et al. (2015); Stasinopoulos et al. (2017). GAMLSS Probabilistic forecasts are predictions in the form of a probability distribution, rather than a single point estimate only. In this context, the introduction of Generalized Additive Models for Location Scale and Shape (GAMLSS) by Rigby and Stasinopoulos (2005) has stimulated a lot of research and culminated in a new research branch that focuses on modelling the entire conditional distribution in dependence of covariates. Univariate Targets In its original formulation, GAMLSS assume a univariate response to follow a distribution \\(\\mathcal{D}\\) that depends on up to four parameters, i.e., \\(y_{i} \\stackrel{ind}{\\sim} \\mathcal{D}(\\mu_{i}, \\sigma^{2}_{i}, \\nu_{i}, \\tau_{i}), i=1,\\ldots,N\\) , where \\(\\mu_{i}\\) and \\(\\sigma^{2}_{i}\\) are often location and scale parameters, respectively, while \\(\\nu_{i}\\) and \\(\\tau_{i}\\) correspond to shape parameters such as skewness and kurtosis. Hence, the framework allows to model not only the mean (or location) but all parameters as functions of explanatory variables. It is important to note that distributional modelling implies that observations are independent, but not necessarily identical realizations \\(y \\stackrel{ind}{\\sim} \\mathcal{D}\\big(\\mathbf{\\theta}(\\mathbf{x})\\big)\\) , since all distributional parameters \\(\\mathbf{\\theta}(\\mathbf{x})\\) are related to and allowed to change with covariates. In contrast to Generalized Linear (GLM) and Generalized Additive Models (GAM), the assumption of the response distribution belonging to an exponential family is relaxed in GAMLSS and replaced by a more general class of distributions, including highly skewed and/or kurtotic continuous, discrete and mixed discrete, as well as zero-inflated distributions. While the original formulation of GAMLSS in Rigby and Stasinopoulos (2005) suggests that any distribution can be described by location, scale and shape parameters, it is not necessarily true that the observed data distribution can actually be characterized by all of these parameters. Hence, we follow Klein et al. (2015) and use the term distributional modelling and GAMLSS interchangeably. From a frequentist point of view, distributional modelling can be formulated as follows \\[\\begin{equation} y_{i} \\stackrel{ind}{\\sim} \\mathcal{D} \\begin{pmatrix} h_{1}\\bigl(\\theta_{i1}(x_{i})\\bigr) = \\eta_{i1} \\\\ h_{2}\\bigl(\\theta_{i2}(x_{i})\\bigr) = \\eta_{i2} \\\\ \\vdots \\\\ h_{K}\\bigl(\\theta_{iK}(x_{i})\\bigr) = \\eta_{iK} \\end{pmatrix} \\end{equation}\\] for \\(i = 1, \\ldots, N\\) , where \\(\\mathcal{D}\\) denotes a parametric distribution for the response \\(\\textbf{y} = (y_{1}, \\ldots, y_{N})^{\\prime}\\) that depends on \\(K\\) distributional parameters \\(\\theta_{k}\\) , \\(k = 1, \\ldots, K\\) , and with \\(h_{k}(\\cdot)\\) denoting a known function relating distributional parameters to predictors \\(\\eta_{k}\\) . In its most generic form, the predictor \\(\\eta_{k}\\) is given by \\[\\begin{equation} \\eta_{k} = f_{k}(\\mathbf{x}), \\qquad k = 1, \\ldots, K \\end{equation}\\] Within the original distributional regression framework, the functions \\(f_{k}(\\cdot)\\) usually represent a combination of linear and GAM-type predictors, which allows to estimate linear effects or categorical variables, as well as highly non-linear and spatial effects using a Spline-based basis function approach. The predictor specification \\(\\eta_{k}\\) is generic enough to use tree-based models as well, which allows us to extend LightGBM to a probabilistic framework. Normalizing Flows Although the GAMLSS framework offers considerable flexibility, parametric distributions may prove not flexible enough to provide a reasonable approximation for certain dataset, e.g., for multi-modal distributions. For such cases, it is preferable to relax the assumption of a parametric distribution and approximate the data non-parametrically. While there are several alternatives for learning conditional distributions, we propose to use Normalizing Flows for their ability to fit complex distributions with only a few parameters. The principle that underlies Normalizing Flows is to turn a simple base distribution, e.g., \\(F_{Z}(\\mathbf{z}) = N(0,1)\\) , into a more complex and realistic distribution of the target variable \\(F_{Y}(\\mathbf{y})\\) by applying several bijective transformations \\(h_{j}\\) , \\(j = 1, \\ldots, J\\) to the variable of the base distribution \\[\\begin{equation} \\mathbf{y} = h_{J} \\circ h_{J-1} \\circ \\cdots \\circ h_{1}(\\mathbf{z}) \\end{equation}\\] Based on the complete transformation function \\(h=h_{J}\\circ\\ldots\\circ h_{1}\\) , the density of \\(\\mathbf{y}\\) is then given by the change of variables theorem \\[\\begin{equation} f_{Y}(\\mathbf{y}) = f_{Z}\\big(h(\\mathbf{y})\\big) \\cdot \\Bigg|\\frac{\\partial h(\\mathbf{y})}{\\partial \\mathbf{y}}\\Bigg| \\end{equation}\\] where scaling with the Jacobian determinant \\(|h^{\\prime}(\\mathbf{y})| = |\\partial h(\\mathbf{y}) / \\partial \\mathbf{y}|\\) ensures \\(f_{Y}(\\mathbf{y})\\) to be a proper density integrating to one. The composition of these transformations is invertible, allowing one to sample from the complex distribution by transforming samples from the base distribution. Image source: https://tikz.net/janosh/normalizing-flow.png Our Normalizing Flow approach is based on element-wise rational splines of linear or quadratic order as introduced by Durkan (2019) and Dolatabadi (2020) and implemented in Pyro, since they offer a combination of functional flexibility and numerical stability. Despite this specific choice, our framework is generic enough to accommodate the use of other parametrizable Normalizing Flows. Gradient Boosting Machines for Location, Scale and Shape We draw inspiration from GAMLSS and label our model as LightGBM for Location, Scale and Shape (LightGBMLSS). Despite its nominal reference to GAMLSS, our framework is designed in such a way to accommodate the modeling of a wide range of parametrizable distributions that go beyond location, scale and shape. LightGBMLSS requires the specification of a suitable distribution from which Gradients and Hessians are derived. These represent the partial first and second order derivatives of the log-likelihood with respect to the parameter of interest. GBMLSS are based on multi-parameter optimization, where a separate tree is grown for each parameter. Estimation of Gradients and Hessians, as well as the evaluation of the loss function is done simultaneously for all parameters. Gradients and Hessians are derived using PyTorch's automatic differentiation capabilities. The flexibility offered by automatic differentiation allows users to easily implement novel or customized parametric distributions for which Gradients and Hessians are difficult to derive analytically. It also facilitates the usage of Normalizing Flows, or to add additional constraints to the loss function. To improve the convergence and stability of GBMLSS estimation, unconditional Maximum Likelihood estimates of the parameters are used as offset values. To enable a deeper understanding of the data generating process, GBMLSS also provide attribute importance and partial dependence plots using the Shapley-Value approach. References Hadi Mohaghegh Dolatabadi, Sarah Erfani, and Christopher Leckie. Invertible Generative Modeling using Linear Rational Splines. In The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 4236\u20134246, 2020. Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios. Neural Spline Flows. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019. Nadja Klein, Thomas Kneib, and Stefan Lang. Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data. Journal of the American Statistical Association, 110(509):405\u2013419, 2015. Alexander M\u00e4rz, and Thomas Kneib. Distributional Gradient Boosting Machines, 2022b. Alexander M\u00e4rz. XGBoostLSS - An extension of XGBoost to probabilistic forecasting, 2019. Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The M5 competition: Background, organization, and implementation. International Journal of Forecasting, 38(4):1325\u20131336, 2022a. Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos, Zhi Chen, Anil Gaba, Ilia Tsetlin, and Robert L. Winkler. The M5 uncertainty competition: Results, findings and conclusions. International Journal of Forecasting, 38(4):1365\u20131385, 2022b. R. A. Rigby and D. M. Stasinopoulos. Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(3):507\u2013554, 2005. Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, and Fernanda de Bastiani. Flexible Regression and Smoothing: Using GAMLSS in R. Chapman & Hall / CRC The R Series. CRC Press, London, 2017.","title":"Distributional Modelling"},{"location":"dgbm/#introduction","text":"The development of modelling approaches that approximate and describe the data generating processes underlying the observed data in as much detail as possible is a guiding principle in both statistics and machine learning. We therefore strongly agree with the statement of Hothorn et al. (2014) that ''the ultimate goal of any regression analysis is to obtain information about the entire conditional distribution \\(F_{Y}(y|\\mathbf{x})\\) of a response given a set of explanatory variables'' . ''Practitioners expect forecasting to reduce future uncertainty by providing accurate predictions like those in hard sciences. However, this is a great misconception. A major purpose of forecasting is not to reduce uncertainty but reveal its full extent and implications by estimating it as precisely as possible. [...] The challenge for the forecasting field is how to persuade practitioners of the reality that all forecasts are uncertain and that this uncertainty cannot be ignored, as doing so could lead to catastrophic consequences.'' Makridakis (2022b) It has not been too long, though, that most regression models focused on estimating the conditional mean \\(\\mathbb{E}(Y|\\mathbf{X} = \\mathbf{x})\\) only, implicitly treating higher moments of the conditional distribution \\(F_{Y}(y|\\mathbf{x})\\) as fixed nuisance parameters. As such, models that minimize an \\(\\ell_{2}\\) -type loss for the conditional mean are not able to fully exploit the information contained in the data, since this is equivalent to assuming a Normal distribution with constant variance. In real world situations, however, the data generating process is usually less well behaved, exhibiting characteristics such as heteroskedasticity, varying degrees of skewness and kurtosis or intermittent and sporadic behaviour. In recent years, however, there has been a clear shift in both academic and corporate research toward modelling the entire conditional distribution. This change in attention is most evident in the recent M5 forecasting competition (Makridakis et al., 2022a,b), which differed from previous ones in that it consisted of two parallel competitions: in addition to providing accurate point forecasts, participants were also asked to forecast nine different quantiles to approximate the distribution of future sales.","title":"Introduction"},{"location":"dgbm/#distributional-gradient-boosting-machines","text":"This section introduces the general idea of distributional modelling. For a more thorough introduction, we refer the interested reader to Rigby and Stasinopoulos (2005); Klein et al. (2015); Stasinopoulos et al. (2017).","title":"Distributional Gradient Boosting Machines"},{"location":"dgbm/#gamlss","text":"Probabilistic forecasts are predictions in the form of a probability distribution, rather than a single point estimate only. In this context, the introduction of Generalized Additive Models for Location Scale and Shape (GAMLSS) by Rigby and Stasinopoulos (2005) has stimulated a lot of research and culminated in a new research branch that focuses on modelling the entire conditional distribution in dependence of covariates.","title":"GAMLSS"},{"location":"dgbm/#univariate-targets","text":"In its original formulation, GAMLSS assume a univariate response to follow a distribution \\(\\mathcal{D}\\) that depends on up to four parameters, i.e., \\(y_{i} \\stackrel{ind}{\\sim} \\mathcal{D}(\\mu_{i}, \\sigma^{2}_{i}, \\nu_{i}, \\tau_{i}), i=1,\\ldots,N\\) , where \\(\\mu_{i}\\) and \\(\\sigma^{2}_{i}\\) are often location and scale parameters, respectively, while \\(\\nu_{i}\\) and \\(\\tau_{i}\\) correspond to shape parameters such as skewness and kurtosis. Hence, the framework allows to model not only the mean (or location) but all parameters as functions of explanatory variables. It is important to note that distributional modelling implies that observations are independent, but not necessarily identical realizations \\(y \\stackrel{ind}{\\sim} \\mathcal{D}\\big(\\mathbf{\\theta}(\\mathbf{x})\\big)\\) , since all distributional parameters \\(\\mathbf{\\theta}(\\mathbf{x})\\) are related to and allowed to change with covariates. In contrast to Generalized Linear (GLM) and Generalized Additive Models (GAM), the assumption of the response distribution belonging to an exponential family is relaxed in GAMLSS and replaced by a more general class of distributions, including highly skewed and/or kurtotic continuous, discrete and mixed discrete, as well as zero-inflated distributions. While the original formulation of GAMLSS in Rigby and Stasinopoulos (2005) suggests that any distribution can be described by location, scale and shape parameters, it is not necessarily true that the observed data distribution can actually be characterized by all of these parameters. Hence, we follow Klein et al. (2015) and use the term distributional modelling and GAMLSS interchangeably. From a frequentist point of view, distributional modelling can be formulated as follows \\[\\begin{equation} y_{i} \\stackrel{ind}{\\sim} \\mathcal{D} \\begin{pmatrix} h_{1}\\bigl(\\theta_{i1}(x_{i})\\bigr) = \\eta_{i1} \\\\ h_{2}\\bigl(\\theta_{i2}(x_{i})\\bigr) = \\eta_{i2} \\\\ \\vdots \\\\ h_{K}\\bigl(\\theta_{iK}(x_{i})\\bigr) = \\eta_{iK} \\end{pmatrix} \\end{equation}\\] for \\(i = 1, \\ldots, N\\) , where \\(\\mathcal{D}\\) denotes a parametric distribution for the response \\(\\textbf{y} = (y_{1}, \\ldots, y_{N})^{\\prime}\\) that depends on \\(K\\) distributional parameters \\(\\theta_{k}\\) , \\(k = 1, \\ldots, K\\) , and with \\(h_{k}(\\cdot)\\) denoting a known function relating distributional parameters to predictors \\(\\eta_{k}\\) . In its most generic form, the predictor \\(\\eta_{k}\\) is given by \\[\\begin{equation} \\eta_{k} = f_{k}(\\mathbf{x}), \\qquad k = 1, \\ldots, K \\end{equation}\\] Within the original distributional regression framework, the functions \\(f_{k}(\\cdot)\\) usually represent a combination of linear and GAM-type predictors, which allows to estimate linear effects or categorical variables, as well as highly non-linear and spatial effects using a Spline-based basis function approach. The predictor specification \\(\\eta_{k}\\) is generic enough to use tree-based models as well, which allows us to extend LightGBM to a probabilistic framework.","title":"Univariate Targets"},{"location":"dgbm/#normalizing-flows","text":"Although the GAMLSS framework offers considerable flexibility, parametric distributions may prove not flexible enough to provide a reasonable approximation for certain dataset, e.g., for multi-modal distributions. For such cases, it is preferable to relax the assumption of a parametric distribution and approximate the data non-parametrically. While there are several alternatives for learning conditional distributions, we propose to use Normalizing Flows for their ability to fit complex distributions with only a few parameters. The principle that underlies Normalizing Flows is to turn a simple base distribution, e.g., \\(F_{Z}(\\mathbf{z}) = N(0,1)\\) , into a more complex and realistic distribution of the target variable \\(F_{Y}(\\mathbf{y})\\) by applying several bijective transformations \\(h_{j}\\) , \\(j = 1, \\ldots, J\\) to the variable of the base distribution \\[\\begin{equation} \\mathbf{y} = h_{J} \\circ h_{J-1} \\circ \\cdots \\circ h_{1}(\\mathbf{z}) \\end{equation}\\] Based on the complete transformation function \\(h=h_{J}\\circ\\ldots\\circ h_{1}\\) , the density of \\(\\mathbf{y}\\) is then given by the change of variables theorem \\[\\begin{equation} f_{Y}(\\mathbf{y}) = f_{Z}\\big(h(\\mathbf{y})\\big) \\cdot \\Bigg|\\frac{\\partial h(\\mathbf{y})}{\\partial \\mathbf{y}}\\Bigg| \\end{equation}\\] where scaling with the Jacobian determinant \\(|h^{\\prime}(\\mathbf{y})| = |\\partial h(\\mathbf{y}) / \\partial \\mathbf{y}|\\) ensures \\(f_{Y}(\\mathbf{y})\\) to be a proper density integrating to one. The composition of these transformations is invertible, allowing one to sample from the complex distribution by transforming samples from the base distribution.","title":"Normalizing Flows"},{"location":"dgbm/#gradient-boosting-machines-for-location-scale-and-shape","text":"We draw inspiration from GAMLSS and label our model as LightGBM for Location, Scale and Shape (LightGBMLSS). Despite its nominal reference to GAMLSS, our framework is designed in such a way to accommodate the modeling of a wide range of parametrizable distributions that go beyond location, scale and shape. LightGBMLSS requires the specification of a suitable distribution from which Gradients and Hessians are derived. These represent the partial first and second order derivatives of the log-likelihood with respect to the parameter of interest. GBMLSS are based on multi-parameter optimization, where a separate tree is grown for each parameter. Estimation of Gradients and Hessians, as well as the evaluation of the loss function is done simultaneously for all parameters. Gradients and Hessians are derived using PyTorch's automatic differentiation capabilities. The flexibility offered by automatic differentiation allows users to easily implement novel or customized parametric distributions for which Gradients and Hessians are difficult to derive analytically. It also facilitates the usage of Normalizing Flows, or to add additional constraints to the loss function. To improve the convergence and stability of GBMLSS estimation, unconditional Maximum Likelihood estimates of the parameters are used as offset values. To enable a deeper understanding of the data generating process, GBMLSS also provide attribute importance and partial dependence plots using the Shapley-Value approach.","title":"Gradient Boosting Machines for Location, Scale and Shape"},{"location":"dgbm/#references","text":"Hadi Mohaghegh Dolatabadi, Sarah Erfani, and Christopher Leckie. Invertible Generative Modeling using Linear Rational Splines. In The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 4236\u20134246, 2020. Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios. Neural Spline Flows. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019. Nadja Klein, Thomas Kneib, and Stefan Lang. Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data. Journal of the American Statistical Association, 110(509):405\u2013419, 2015. Alexander M\u00e4rz, and Thomas Kneib. Distributional Gradient Boosting Machines, 2022b. Alexander M\u00e4rz. XGBoostLSS - An extension of XGBoost to probabilistic forecasting, 2019. Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The M5 competition: Background, organization, and implementation. International Journal of Forecasting, 38(4):1325\u20131336, 2022a. Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos, Zhi Chen, Anil Gaba, Ilia Tsetlin, and Robert L. Winkler. The M5 uncertainty competition: Results, findings and conclusions. International Journal of Forecasting, 38(4):1365\u20131385, 2022b. R. A. Rigby and D. M. Stasinopoulos. Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(3):507\u2013554, 2005. Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, and Fernanda de Bastiani. Flexible Regression and Smoothing: Using GAMLSS in R. Chapman & Hall / CRC The R Series. CRC Press, London, 2017.","title":"References"},{"location":"distributions/","text":"Available Distributions LightGBMLSS is built upon PyTorch and Pyro, enabling users to harness a diverse set of distributional families and to leverage automatic differentiation capabilities. This greatly expands the options for probabilistic modeling and uncertainty estimation and allows users to tackle complex regression tasks. LightGBMLSS currently supports the following univariate distributions. Distribution Usage Type Support Number of Parameters Beta Beta() Continuous (Univariate) \\(y \\in (0, 1)\\) 2 Cauchy Cauchy() Continuous (Univariate) \\(y \\in (-\\infty,\\infty)\\) 2 Expectile Expectile() Continuous (Univariate) \\(y \\in (-\\infty,\\infty)\\) Number of expectiles Gamma Gamma() Continuous (Univariate) \\(y \\in (0, \\infty)\\) 2 Gaussian Gaussian() Continuous (Univariate) \\(y \\in (-\\infty,\\infty)\\) 2 Gumbel Gumbel() Continuous (Univariate) \\(y \\in (-\\infty,\\infty)\\) 2 Laplace Laplace() Continuous (Univariate) \\(y \\in (-\\infty,\\infty)\\) 2 LogNormal LogNormal() Continuous (Univariate) \\(y \\in (0,\\infty)\\) 2 Negative Binomial NegativeBinomial() Discrete Count (Univariate) \\(y \\in (0, 1, 2, 3, \\ldots)\\) 2 Poisson Poisson() Discrete Count (Univariate) \\(y \\in (0, 1, 2, 3, \\ldots)\\) 1 Spline Flow SplineFlow() Continuous \\& Discrete Count (Univariate) \\(y \\in (-\\infty,\\infty)\\) \\(y \\in [0, \\infty)\\) \\(y \\in [0, 1]\\) \\(y \\in (0, 1, 2, 3, \\ldots)\\) 2xcount_bins + (count_bins-1) (order=quadratic) 3xcount_bins + (count_bins-1) (order=linear) Student-T StudentT() Continuous (Univariate) \\(y \\in (-\\infty,\\infty)\\) 3 Weibull Weibull() Continuous (Univariate) \\(y \\in [0, \\infty)\\) 2 Zero-Adjusted Beta ZABeta() Discrete-Continuous (Univariate) \\(y \\in [0, 1)\\) 3 Zero-Adjusted Gamma ZAGamma() Discrete-Continuous (Univariate) \\(y \\in [0, \\infty)\\) 3 Zero-Adjusted LogNormal ZALN() Discrete-Continuous (Univariate) \\(y \\in [0, \\infty)\\) 3 Zero-Inflated Negative Binomial ZINB() Discrete-Count (Univariate) \\(y \\in [0, 1, 2, 3, \\ldots)\\) 3 Zero-Inflated Poisson ZIPoisson() Discrete-Count (Univariate) \\(y \\in [0, 1, 2, 3, \\ldots)\\) 2","title":"Available Distributions"},{"location":"distributions/#available-distributions","text":"LightGBMLSS is built upon PyTorch and Pyro, enabling users to harness a diverse set of distributional families and to leverage automatic differentiation capabilities. This greatly expands the options for probabilistic modeling and uncertainty estimation and allows users to tackle complex regression tasks. LightGBMLSS currently supports the following univariate distributions. Distribution Usage Type Support Number of Parameters Beta Beta() Continuous (Univariate) \\(y \\in (0, 1)\\) 2 Cauchy Cauchy() Continuous (Univariate) \\(y \\in (-\\infty,\\infty)\\) 2 Expectile Expectile() Continuous (Univariate) \\(y \\in (-\\infty,\\infty)\\) Number of expectiles Gamma Gamma() Continuous (Univariate) \\(y \\in (0, \\infty)\\) 2 Gaussian Gaussian() Continuous (Univariate) \\(y \\in (-\\infty,\\infty)\\) 2 Gumbel Gumbel() Continuous (Univariate) \\(y \\in (-\\infty,\\infty)\\) 2 Laplace Laplace() Continuous (Univariate) \\(y \\in (-\\infty,\\infty)\\) 2 LogNormal LogNormal() Continuous (Univariate) \\(y \\in (0,\\infty)\\) 2 Negative Binomial NegativeBinomial() Discrete Count (Univariate) \\(y \\in (0, 1, 2, 3, \\ldots)\\) 2 Poisson Poisson() Discrete Count (Univariate) \\(y \\in (0, 1, 2, 3, \\ldots)\\) 1 Spline Flow SplineFlow() Continuous \\& Discrete Count (Univariate) \\(y \\in (-\\infty,\\infty)\\) \\(y \\in [0, \\infty)\\) \\(y \\in [0, 1]\\) \\(y \\in (0, 1, 2, 3, \\ldots)\\) 2xcount_bins + (count_bins-1) (order=quadratic) 3xcount_bins + (count_bins-1) (order=linear) Student-T StudentT() Continuous (Univariate) \\(y \\in (-\\infty,\\infty)\\) 3 Weibull Weibull() Continuous (Univariate) \\(y \\in [0, \\infty)\\) 2 Zero-Adjusted Beta ZABeta() Discrete-Continuous (Univariate) \\(y \\in [0, 1)\\) 3 Zero-Adjusted Gamma ZAGamma() Discrete-Continuous (Univariate) \\(y \\in [0, \\infty)\\) 3 Zero-Adjusted LogNormal ZALN() Discrete-Continuous (Univariate) \\(y \\in [0, \\infty)\\) 3 Zero-Inflated Negative Binomial ZINB() Discrete-Count (Univariate) \\(y \\in [0, 1, 2, 3, \\ldots)\\) 3 Zero-Inflated Poisson ZIPoisson() Discrete-Count (Univariate) \\(y \\in [0, 1, 2, 3, \\ldots)\\) 2","title":"Available Distributions"},{"location":"examples/Expectile_Regression/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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tr:hover{background:rgba(66,165,245,.2)}table{width:max-content} /*# sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Expectile Regression \u00b6 Imports \u00b6 In [2]: Copied! from lightgbmlss.model import * from lightgbmlss.distributions.Expectile import * from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data import plotnine from plotnine import * plotnine . options . figure_size = ( 20 , 10 ) from lightgbmlss.model import * from lightgbmlss.distributions.Expectile import * from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data import plotnine from plotnine import * plotnine.options.figure_size = (20, 10) Data \u00b6 In [3]: Copied! # The data is a simulated Gaussian as follows, where x is the only true feature and all others are noise variables # loc = 10 # scale = 1 + 4*((0.3 < x) & (x < 0.5)) + 2*(x > 0.7) train , test = load_simulated_gaussian_data () X_train , y_train = train . filter ( regex = \"x\" ), train [ \"y\" ] . values X_test , y_test = test . filter ( regex = \"x\" ), test [ \"y\" ] . values dtrain = lgb . Dataset ( X_train , label = y_train ) # The data is a simulated Gaussian as follows, where x is the only true feature and all others are noise variables # loc = 10 # scale = 1 + 4*((0.3 < x) & (x < 0.5)) + 2*(x > 0.7) train, test = load_simulated_gaussian_data() X_train, y_train = train.filter(regex=\"x\"), train[\"y\"].values X_test, y_test = test.filter(regex=\"x\"), test[\"y\"].values dtrain = lgb.Dataset(X_train, label=y_train) Expectile Specification \u00b6 In [4]: Copied! lgblss = LightGBMLSS ( Expectile ( stabilization = \"None\" , # Options are \"None\", \"MAD\", \"L2\". expectiles = [ 0.05 , 0.95 ], # List of expectiles to be estimated, in increasing order. penalize_crossing = True # Whether to include a penalty term to discourage crossing of expectiles. ) ) lgblss = LightGBMLSS( Expectile(stabilization=\"None\", # Options are \"None\", \"MAD\", \"L2\". expectiles = [0.05, 0.95], # List of expectiles to be estimated, in increasing order. penalize_crossing = True # Whether to include a penalty term to discourage crossing of expectiles. ) ) Hyper-Parameter Optimization \u00b6 Any LightGBM hyperparameter can be tuned, where the structure of the parameter dictionary needs to be as follows: - Float/Int sample_type - {\"param_name\": [\"sample_type\", low, high, log]} - sample_type: str, Type of sampling, e.g., \"float\" or \"int\" - low: int, Lower endpoint of the range of suggested values - high: int, Upper endpoint of the range of suggested values - log: bool, Flag to sample the value from the log domain or not - Example: {\"eta\": \"float\", low=1e-5, high=1, log=True]} - Categorical sample_type - {\"param_name\": [\"sample_type\", [\"choice1\", \"choice2\", \"choice3\", \"...\"]]} - sample_type: str, Type of sampling, either \"categorical\" - choice1, choice2, choice3, ...: str, Possible choices for the parameter - Example: {\"boosting\": [\"categorical\", [\"gbdt\", \"dart\"]]} - For parameters without tunable choice (this is needed if tree_method = \"gpu_hist\" and gpu_id needs to be specified) - {\"param_name\": [\"none\", [value]]}, - param_name: str, Name of the parameter - value: int, Value of the parameter - Example: {\"gpu_id\": [\"none\", [0]]} In [5]: Copied! param_dict = { \"eta\" : [ \"float\" , { \"low\" : 1e-5 , \"high\" : 1 , \"log\" : True }], \"max_depth\" : [ \"int\" , { \"low\" : 1 , \"high\" : 10 , \"log\" : False }], \"num_leaves\" : [ \"int\" , { \"low\" : 255 , \"high\" : 255 , \"log\" : False }], # set to constant for this example \"min_data_in_leaf\" : [ \"int\" , { \"low\" : 20 , \"high\" : 20 , \"log\" : False }], # set to constant for this example \"min_gain_to_split\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 40 , \"log\" : False }], \"min_sum_hessian_in_leaf\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 500 , \"log\" : True }], \"subsample\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"feature_fraction\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"boosting\" : [ \"categorical\" , [ \"gbdt\" ]], } np . random . seed ( 123 ) opt_param = lgblss . hyper_opt ( param_dict , dtrain , num_boost_round = 100 , # Number of boosting iterations. nfold = 5 , # Number of cv-folds. early_stopping_rounds = 20 , # Number of early-stopping rounds max_minutes = 10 , # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials = 30 , # The number of trials. If this argument is set to None, there is no limitation on the number of trials. silence = False , # Controls the verbosity of the trail, i.e., user can silence the outputs of the trail. seed = 123 , # Seed used to generate cv-folds. hp_seed = None # Seed for random number generator used in the Bayesian hyperparameter search. ) param_dict = { \"eta\": [\"float\", {\"low\": 1e-5, \"high\": 1, \"log\": True}], \"max_depth\": [\"int\", {\"low\": 1, \"high\": 10, \"log\": False}], \"num_leaves\": [\"int\", {\"low\": 255, \"high\": 255, \"log\": False}], # set to constant for this example \"min_data_in_leaf\": [\"int\", {\"low\": 20, \"high\": 20, \"log\": False}], # set to constant for this example \"min_gain_to_split\": [\"float\", {\"low\": 1e-8, \"high\": 40, \"log\": False}], \"min_sum_hessian_in_leaf\": [\"float\", {\"low\": 1e-8, \"high\": 500, \"log\": True}], \"subsample\": [\"float\", {\"low\": 0.2, \"high\": 1.0, \"log\": False}], \"feature_fraction\": [\"float\", {\"low\": 0.2, \"high\": 1.0, \"log\": False}], \"boosting\": [\"categorical\", [\"gbdt\"]], } np.random.seed(123) opt_param = lgblss.hyper_opt(param_dict, dtrain, num_boost_round=100, # Number of boosting iterations. nfold=5, # Number of cv-folds. early_stopping_rounds=20, # Number of early-stopping rounds max_minutes=10, # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials=30, # The number of trials. 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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t&&null!=t.key?s.escape(t.key):e.toString(36)}function i(t,e,n,o){var p=typeof t;if(\"undefined\"!==p&&\"boolean\"!==p||(t=null),null===t||\"string\"===p||\"number\"===p||\"object\"===p&&t.$$typeof===u)return n(o,t,\"\"===e?l+r(t,0):e),1;var h,d,v=0,g=\"\"===e?l:e+f;if(Array.isArray(t))for(var m=0;m 0.7 )]) tau_lower = np . array ([ lgblss . dist . tau [ 0 ]]) tau_upper = np . array ([ lgblss . dist . tau [ 1 ]]) # Calculates exact expectiles assuming a Normal distribution expectile_lb = expectile_norm ( tau = tau_lower , m = y_loc , sd = y_scale ) . reshape ( - 1 ,) expectile_ub = expectile_norm ( tau = tau_upper , m = y_loc , sd = y_scale ) . reshape ( - 1 ,) test [ \"expect\" ] = np . where ( test [ \"y\" ] . values < expectile_lb , 0 , np . where ( test [ \"y\" ] . values < expectile_ub , 1 , 2 )) test [ \"alpha\" ] = np . where ( test [ \"y\" ] . values <= expectile_lb , 1 , np . where ( test [ \"y\" ] . values >= expectile_ub , 1 , 0 )) df_expectiles = test [ test [ \"alpha\" ] == 1 ] # Lower Bound yl = list ( set ( expectile_lb )) yl . sort () yl = [ yl [ 2 ], yl [ 0 ], yl [ 2 ], yl [ 1 ], yl [ 1 ]] sfunl = pd . DataFrame ({ \"x_true\" :[ 0 , 0.3 , 0.5 , 0.7 , 1 ], \"y\" : yl }) # Upper Bound yu = list ( set ( expectile_ub )) yu . sort () yu = [ yu [ 0 ], yu [ 2 ], yu [ 0 ], yu [ 1 ], yu [ 1 ]] sfunu = pd . DataFrame ({ \"x_true\" :[ 0 , 0.3 , 0.5 , 0.7 , 1 ], \"y\" : yu }) ### # Forecasted Expectiles ### test [ \"lb\" ] = pred_expectile . iloc [:, 0 ] test [ \"ub\" ] = pred_expectile . iloc [:, 1 ] ### # Plot ### ( ggplot ( test , aes ( \"x_true\" , \"y\" )) + geom_point ( alpha = 0.2 , color = \"black\" , size = 2 ) + theme_bw ( base_size = 15 ) + theme ( legend_position = \"bottom\" , plot_title = element_text ( hjust = 0.5 )) + labs ( title = \"LightGBMLSS Expectile Regression - Simulated Data Example\" ) + geom_line ( aes ( \"x_true\" , \"ub\" ), size = 1.5 , color = \"blue\" , alpha = 0.7 ) + geom_line ( aes ( \"x_true\" , \"lb\" ), size = 1.5 , color = \"blue\" , alpha = 0.7 ) + geom_point ( df_expectiles , aes ( \"x_true\" , \"y\" ), color = \"red\" , alpha = 0.7 , size = 2 ) + geom_step ( sfunl , aes ( \"x_true\" , \"y\" ), size = 1 , linetype = \"dashed\" ) + geom_step ( sfunu , aes ( \"x_true\" , \"y\" ), size = 1 , linetype = \"dashed\" ) ) np.random.seed(123) ### # Actual Expectiles ### y_loc = np.array([10]) y_scale = np.array([1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7)]) tau_lower = np.array([lgblss.dist.tau[0]]) tau_upper = np.array([lgblss.dist.tau[1]]) # Calculates exact expectiles assuming a Normal distribution expectile_lb = expectile_norm(tau=tau_lower, m=y_loc, sd=y_scale).reshape(-1,) expectile_ub = expectile_norm(tau=tau_upper, m=y_loc, sd=y_scale).reshape(-1,) test[\"expect\"] = np.where(test[\"y\"].values < expectile_lb, 0, np.where(test[\"y\"].values < expectile_ub, 1, 2)) test[\"alpha\"] = np.where(test[\"y\"].values <= expectile_lb, 1, np.where(test[\"y\"].values >= expectile_ub, 1, 0)) df_expectiles = test[test[\"alpha\"] == 1] # Lower Bound yl = list(set(expectile_lb)) yl.sort() yl = [yl[2],yl[0],yl[2],yl[1],yl[1]] sfunl = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yl}) # Upper Bound yu = list(set(expectile_ub)) yu.sort() yu = [yu[0],yu[2],yu[0],yu[1],yu[1]] sfunu = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yu}) ### # Forecasted Expectiles ### test[\"lb\"] = pred_expectile.iloc[:,0] test[\"ub\"] = pred_expectile.iloc[:,1] ### # Plot ### (ggplot(test, aes(\"x_true\", \"y\")) + geom_point(alpha = 0.2, color = \"black\", size = 2) + theme_bw(base_size=15) + theme(legend_position=\"bottom\", plot_title = element_text(hjust = 0.5)) + labs(title = \"LightGBMLSS Expectile Regression - Simulated Data Example\") + geom_line(aes(\"x_true\", \"ub\"), size = 1.5, color = \"blue\", alpha = 0.7) + geom_line(aes(\"x_true\", \"lb\"), size = 1.5, color = \"blue\", alpha = 0.7) + geom_point(df_expectiles, aes(\"x_true\", \"y\"), color = \"red\", alpha = 0.7, size = 2) + geom_step(sfunl, aes(\"x_true\", \"y\"), size = 1, linetype = \"dashed\") + geom_step(sfunu, aes(\"x_true\", \"y\"), size = 1, linetype = \"dashed\") ) Out[12]:
","title":"Expectile Regression"},{"location":"examples/Expectile_Regression/#expectile-regression","text":"","title":"Expectile Regression"},{"location":"examples/Expectile_Regression/#imports","text":"In [2]: Copied! from lightgbmlss.model import * from lightgbmlss.distributions.Expectile import * from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data import plotnine from plotnine import * plotnine . options . figure_size = ( 20 , 10 ) from lightgbmlss.model import * from lightgbmlss.distributions.Expectile import * from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data import plotnine from plotnine import * plotnine.options.figure_size = (20, 10)","title":"Imports"},{"location":"examples/Expectile_Regression/#data","text":"In [3]: Copied! # The data is a simulated Gaussian as follows, where x is the only true feature and all others are noise variables # loc = 10 # scale = 1 + 4*((0.3 < x) & (x < 0.5)) + 2*(x > 0.7) train , test = load_simulated_gaussian_data () X_train , y_train = train . filter ( regex = \"x\" ), train [ \"y\" ] . values X_test , y_test = test . filter ( regex = \"x\" ), test [ \"y\" ] . values dtrain = lgb . Dataset ( X_train , label = y_train ) # The data is a simulated Gaussian as follows, where x is the only true feature and all others are noise variables # loc = 10 # scale = 1 + 4*((0.3 < x) & (x < 0.5)) + 2*(x > 0.7) train, test = load_simulated_gaussian_data() X_train, y_train = train.filter(regex=\"x\"), train[\"y\"].values X_test, y_test = test.filter(regex=\"x\"), test[\"y\"].values dtrain = lgb.Dataset(X_train, label=y_train)","title":"Data"},{"location":"examples/Expectile_Regression/#expectile-specification","text":"In [4]: Copied! lgblss = LightGBMLSS ( Expectile ( stabilization = \"None\" , # Options are \"None\", \"MAD\", \"L2\". expectiles = [ 0.05 , 0.95 ], # List of expectiles to be estimated, in increasing order. penalize_crossing = True # Whether to include a penalty term to discourage crossing of expectiles. ) ) lgblss = LightGBMLSS( Expectile(stabilization=\"None\", # Options are \"None\", \"MAD\", \"L2\". expectiles = [0.05, 0.95], # List of expectiles to be estimated, in increasing order. penalize_crossing = True # Whether to include a penalty term to discourage crossing of expectiles. ) )","title":"Expectile Specification"},{"location":"examples/Expectile_Regression/#hyper-parameter-optimization","text":"Any LightGBM hyperparameter can be tuned, where the structure of the parameter dictionary needs to be as follows: - Float/Int sample_type - {\"param_name\": [\"sample_type\", low, high, log]} - sample_type: str, Type of sampling, e.g., \"float\" or \"int\" - low: int, Lower endpoint of the range of suggested values - high: int, Upper endpoint of the range of suggested values - log: bool, Flag to sample the value from the log domain or not - Example: {\"eta\": \"float\", low=1e-5, high=1, log=True]} - Categorical sample_type - {\"param_name\": [\"sample_type\", [\"choice1\", \"choice2\", \"choice3\", \"...\"]]} - sample_type: str, Type of sampling, either \"categorical\" - choice1, choice2, choice3, ...: str, Possible choices for the parameter - Example: {\"boosting\": [\"categorical\", [\"gbdt\", \"dart\"]]} - For parameters without tunable choice (this is needed if tree_method = \"gpu_hist\" and gpu_id needs to be specified) - {\"param_name\": [\"none\", [value]]}, - param_name: str, Name of the parameter - value: int, Value of the parameter - Example: {\"gpu_id\": [\"none\", [0]]} In [5]: Copied! param_dict = { \"eta\" : [ \"float\" , { \"low\" : 1e-5 , \"high\" : 1 , \"log\" : True }], \"max_depth\" : [ \"int\" , { \"low\" : 1 , \"high\" : 10 , \"log\" : False }], \"num_leaves\" : [ \"int\" , { \"low\" : 255 , \"high\" : 255 , \"log\" : False }], # set to constant for this example \"min_data_in_leaf\" : [ \"int\" , { \"low\" : 20 , \"high\" : 20 , \"log\" : False }], # set to constant for this example \"min_gain_to_split\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 40 , \"log\" : False }], \"min_sum_hessian_in_leaf\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 500 , \"log\" : True }], \"subsample\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"feature_fraction\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"boosting\" : [ \"categorical\" , [ \"gbdt\" ]], } np . random . seed ( 123 ) opt_param = lgblss . hyper_opt ( param_dict , dtrain , num_boost_round = 100 , # Number of boosting iterations. nfold = 5 , # Number of cv-folds. early_stopping_rounds = 20 , # Number of early-stopping rounds max_minutes = 10 , # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials = 30 , # The number of trials. If this argument is set to None, there is no limitation on the number of trials. silence = False , # Controls the verbosity of the trail, i.e., user can silence the outputs of the trail. seed = 123 , # Seed used to generate cv-folds. hp_seed = None # Seed for random number generator used in the Bayesian hyperparameter search. ) param_dict = { \"eta\": [\"float\", {\"low\": 1e-5, \"high\": 1, \"log\": True}], \"max_depth\": [\"int\", {\"low\": 1, \"high\": 10, \"log\": False}], \"num_leaves\": [\"int\", {\"low\": 255, \"high\": 255, \"log\": False}], # set to constant for this example \"min_data_in_leaf\": [\"int\", {\"low\": 20, \"high\": 20, \"log\": False}], # set to constant for this example \"min_gain_to_split\": [\"float\", {\"low\": 1e-8, \"high\": 40, \"log\": False}], \"min_sum_hessian_in_leaf\": [\"float\", {\"low\": 1e-8, \"high\": 500, \"log\": True}], \"subsample\": [\"float\", {\"low\": 0.2, \"high\": 1.0, \"log\": False}], \"feature_fraction\": [\"float\", {\"low\": 0.2, \"high\": 1.0, \"log\": False}], \"boosting\": [\"categorical\", [\"gbdt\"]], } np.random.seed(123) opt_param = lgblss.hyper_opt(param_dict, dtrain, num_boost_round=100, # Number of boosting iterations. nfold=5, # Number of cv-folds. early_stopping_rounds=20, # Number of early-stopping rounds max_minutes=10, # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials=30, # The number of trials. 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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t&&null!=t.key?s.escape(t.key):e.toString(36)}function i(t,e,n,o){var p=typeof t;if(\"undefined\"!==p&&\"boolean\"!==p||(t=null),null===t||\"string\"===p||\"number\"===p||\"object\"===p&&t.$$typeof===u)return n(o,t,\"\"===e?l+r(t,0):e),1;var h,d,v=0,g=\"\"===e?l:e+f;if(Array.isArray(t))for(var m=0;m 0.7 )]) tau_lower = np . array ([ lgblss . dist . tau [ 0 ]]) tau_upper = np . array ([ lgblss . dist . tau [ 1 ]]) # Calculates exact expectiles assuming a Normal distribution expectile_lb = expectile_norm ( tau = tau_lower , m = y_loc , sd = y_scale ) . reshape ( - 1 ,) expectile_ub = expectile_norm ( tau = tau_upper , m = y_loc , sd = y_scale ) . reshape ( - 1 ,) test [ \"expect\" ] = np . where ( test [ \"y\" ] . values < expectile_lb , 0 , np . where ( test [ \"y\" ] . values < expectile_ub , 1 , 2 )) test [ \"alpha\" ] = np . where ( test [ \"y\" ] . values <= expectile_lb , 1 , np . where ( test [ \"y\" ] . values >= expectile_ub , 1 , 0 )) df_expectiles = test [ test [ \"alpha\" ] == 1 ] # Lower Bound yl = list ( set ( expectile_lb )) yl . sort () yl = [ yl [ 2 ], yl [ 0 ], yl [ 2 ], yl [ 1 ], yl [ 1 ]] sfunl = pd . DataFrame ({ \"x_true\" :[ 0 , 0.3 , 0.5 , 0.7 , 1 ], \"y\" : yl }) # Upper Bound yu = list ( set ( expectile_ub )) yu . sort () yu = [ yu [ 0 ], yu [ 2 ], yu [ 0 ], yu [ 1 ], yu [ 1 ]] sfunu = pd . DataFrame ({ \"x_true\" :[ 0 , 0.3 , 0.5 , 0.7 , 1 ], \"y\" : yu }) ### # Forecasted Expectiles ### test [ \"lb\" ] = pred_expectile . iloc [:, 0 ] test [ \"ub\" ] = pred_expectile . iloc [:, 1 ] ### # Plot ### ( ggplot ( test , aes ( \"x_true\" , \"y\" )) + geom_point ( alpha = 0.2 , color = \"black\" , size = 2 ) + theme_bw ( base_size = 15 ) + theme ( legend_position = \"bottom\" , plot_title = element_text ( hjust = 0.5 )) + labs ( title = \"LightGBMLSS Expectile Regression - Simulated Data Example\" ) + geom_line ( aes ( \"x_true\" , \"ub\" ), size = 1.5 , color = \"blue\" , alpha = 0.7 ) + geom_line ( aes ( \"x_true\" , \"lb\" ), size = 1.5 , color = \"blue\" , alpha = 0.7 ) + geom_point ( df_expectiles , aes ( \"x_true\" , \"y\" ), color = \"red\" , alpha = 0.7 , size = 2 ) + geom_step ( sfunl , aes ( \"x_true\" , \"y\" ), size = 1 , linetype = \"dashed\" ) + geom_step ( sfunu , aes ( \"x_true\" , \"y\" ), size = 1 , linetype = \"dashed\" ) ) np.random.seed(123) ### # Actual Expectiles ### y_loc = np.array([10]) y_scale = np.array([1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7)]) tau_lower = np.array([lgblss.dist.tau[0]]) tau_upper = np.array([lgblss.dist.tau[1]]) # Calculates exact expectiles assuming a Normal distribution expectile_lb = expectile_norm(tau=tau_lower, m=y_loc, sd=y_scale).reshape(-1,) expectile_ub = expectile_norm(tau=tau_upper, m=y_loc, sd=y_scale).reshape(-1,) test[\"expect\"] = np.where(test[\"y\"].values < expectile_lb, 0, np.where(test[\"y\"].values < expectile_ub, 1, 2)) test[\"alpha\"] = np.where(test[\"y\"].values <= expectile_lb, 1, np.where(test[\"y\"].values >= expectile_ub, 1, 0)) df_expectiles = test[test[\"alpha\"] == 1] # Lower Bound yl = list(set(expectile_lb)) yl.sort() yl = [yl[2],yl[0],yl[2],yl[1],yl[1]] sfunl = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yl}) # Upper Bound yu = list(set(expectile_ub)) yu.sort() yu = [yu[0],yu[2],yu[0],yu[1],yu[1]] sfunu = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yu}) ### # Forecasted Expectiles ### test[\"lb\"] = pred_expectile.iloc[:,0] test[\"ub\"] = pred_expectile.iloc[:,1] ### # Plot ### (ggplot(test, aes(\"x_true\", \"y\")) + geom_point(alpha = 0.2, color = \"black\", size = 2) + theme_bw(base_size=15) + theme(legend_position=\"bottom\", plot_title = element_text(hjust = 0.5)) + labs(title = \"LightGBMLSS Expectile Regression - Simulated Data Example\") + geom_line(aes(\"x_true\", \"ub\"), size = 1.5, color = \"blue\", alpha = 0.7) + geom_line(aes(\"x_true\", \"lb\"), size = 1.5, color = \"blue\", alpha = 0.7) + geom_point(df_expectiles, aes(\"x_true\", \"y\"), color = \"red\", alpha = 0.7, size = 2) + geom_step(sfunl, aes(\"x_true\", \"y\"), size = 1, linetype = \"dashed\") + geom_step(sfunu, aes(\"x_true\", \"y\"), size = 1, linetype = \"dashed\") ) Out[12]:
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In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: 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tr:hover{background:rgba(66,165,245,.2)}table{width:max-content} /*# sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Gamma Regression (Boston Housing Data) \u00b6 Imports \u00b6 In [2]: Copied! from lightgbmlss.model import * from lightgbmlss.distributions.Gamma import * from sklearn import datasets from sklearn.model_selection import train_test_split from lightgbmlss.model import * from lightgbmlss.distributions.Gamma import * from sklearn import datasets from sklearn.model_selection import train_test_split Data \u00b6 In [3]: Copied! housing_data = datasets . fetch_california_housing () X , y = housing_data [ \"data\" ], housing_data [ \"target\" ] feature_names = housing_data [ \"feature_names\" ] X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0.2 , random_state = 123 ) dtrain = lgb . Dataset ( X_train , label = y_train ) housing_data = datasets.fetch_california_housing() X, y = housing_data[\"data\"], housing_data[\"target\"] feature_names = housing_data[\"feature_names\"] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123) dtrain = lgb.Dataset(X_train, label=y_train) Distribution Selection \u00b6 In [4]: Copied! # Specifies Gamma distribution with exp response function and option to stabilize Gradient/Hessian. Type ?Gamma for an overview. lgblss = LightGBMLSS ( Gamma ( stabilization = \"L2\" , # Options are \"None\", \"MAD\", \"L2\". response_fn = \"softplus\" , # Function to transform the concentration and rate parameters, e.g., \"exp\" or \"softplus\". loss_fn = \"nll\" # Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\"(continuous ranked probability score). ) ) # Specifies Gamma distribution with exp response function and option to stabilize Gradient/Hessian. Type ?Gamma for an overview. lgblss = LightGBMLSS( Gamma(stabilization=\"L2\", # Options are \"None\", \"MAD\", \"L2\". response_fn=\"softplus\", # Function to transform the concentration and rate parameters, e.g., \"exp\" or \"softplus\". loss_fn=\"nll\" # Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\"(continuous ranked probability score). ) ) Hyper-Parameter Optimization \u00b6 Any LightGBM hyperparameter can be tuned, where the structure of the parameter dictionary needs to be as follows: - Float/Int sample_type - {\"param_name\": [\"sample_type\", low, high, log]} - sample_type: str, Type of sampling, e.g., \"float\" or \"int\" - low: int, Lower endpoint of the range of suggested values - high: int, Upper endpoint of the range of suggested values - log: bool, Flag to sample the value from the log domain or not - Example: {\"eta\": \"float\", low=1e-5, high=1, log=True]} - Categorical sample_type - {\"param_name\": [\"sample_type\", [\"choice1\", \"choice2\", \"choice3\", \"...\"]]} - sample_type: str, Type of sampling, either \"categorical\" - choice1, choice2, choice3, ...: str, Possible choices for the parameter - Example: {\"boosting\": [\"categorical\", [\"gbdt\", \"dart\"]]} - For parameters without tunable choice (this is needed if tree_method = \"gpu_hist\" and gpu_id needs to be specified) - {\"param_name\": [\"none\", [value]]}, - param_name: str, Name of the parameter - value: int, Value of the parameter - Example: {\"gpu_id\": [\"none\", [0]]} In [5]: Copied! param_dict = { \"eta\" : [ \"float\" , { \"low\" : 1e-5 , \"high\" : 1 , \"log\" : True }], \"max_depth\" : [ \"int\" , { \"low\" : 1 , \"high\" : 10 , \"log\" : False }], \"subsample\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"feature_fraction\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"boosting\" : [ \"categorical\" , [ \"gbdt\" ]], } np . random . seed ( 123 ) opt_param = lgblss . hyper_opt ( param_dict , dtrain , num_boost_round = 100 , # Number of boosting iterations. nfold = 5 , # Number of cv-folds. early_stopping_rounds = 20 , # Number of early-stopping rounds max_minutes = 20 , # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials = 30 , # The number of trials. If this argument is set to None, there is no limitation on the number of trials. silence = False , # Controls the verbosity of the trail, i.e., user can silence the outputs of the trail. seed = 123 , # Seed used to generate cv-folds. hp_seed = None # Seed for random number generator used in the Bayesian hyperparameter search. ) param_dict = { \"eta\": [\"float\", {\"low\": 1e-5, \"high\": 1, \"log\": True}], \"max_depth\": [\"int\", {\"low\": 1, \"high\": 10, \"log\": False}], \"subsample\": [\"float\", {\"low\": 0.2, \"high\": 1.0, \"log\": False}], \"feature_fraction\": [\"float\", {\"low\": 0.2, \"high\": 1.0, \"log\": False}], \"boosting\": [\"categorical\", [\"gbdt\"]], } np.random.seed(123) opt_param = lgblss.hyper_opt(param_dict, dtrain, num_boost_round=100, # Number of boosting iterations. nfold=5, # Number of cv-folds. early_stopping_rounds=20, # Number of early-stopping rounds max_minutes=20, # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials=30, # The number of trials. 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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tr:hover{background:rgba(66,165,245,.2)}table{width:max-content} /*# sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Basic Walkthrough - Gaussian Regression \u00b6 In this example, we model and predict all parameters of a univariate Normal distribution. Recall that distributional regression models and predicts all parameters $\\theta_{ik}, k=1, \\ldots, K$ parameters of a distribution $\\mathcal{D}$ as a function of covariates: \\begin{equation} y_{i} \\stackrel{ind}{\\sim} \\mathcal{D} \\begin{pmatrix} h_{1}\\bigl(\\theta_{i1}(x_{i})\\bigr) = \\eta_{i1} \\\\ h_{2}\\bigl(\\theta_{i2}(x_{i})\\bigr) = \\eta_{i2} \\\\ \\vdots \\\\ h_{K}\\bigl(\\theta_{iK}(x_{i})\\bigr) = \\eta_{iK} \\end{pmatrix} \\quad ,i=1, \\ldots, N. \\end{equation} where $h_{k}(\\cdot)$ transforms each distributional parameter to the corresponding parameter scale. For the univariate Normal case, we can specify the above as $y_{i} \\stackrel{ind}{\\sim} \\mathcal{N}\\bigl(\\mu_{i}(x_{i}), \\sigma_{i}(x_{i})\\bigr)$. Since $\\mu_{i}(\\cdot) \\in \\mathbb{R}$ and since the standard-deviation cannot be negative, $h_{k}(\\cdot)$ is applied to $\\sigma_{i}(\\cdot)$ only. Typical choices are the exponential or the softplus function. Imports \u00b6 First, we import the necessary functions. In [2]: Copied! from lightgbmlss.model import * from lightgbmlss.distributions.Gaussian import * from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data from scipy.stats import norm import plotnine from plotnine import * plotnine . options . figure_size = ( 12 , 8 ) from lightgbmlss.model import * from lightgbmlss.distributions.Gaussian import * from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data from scipy.stats import norm import plotnine from plotnine import * plotnine.options.figure_size = (12, 8) Data \u00b6 The data is simulated as a Gaussian, where $x_{true}$ is the only true feature and all others are noise variables: $\\mu(x_{true}) = 10$ $\\sigma(x_{true}) = 1 + 4 * \\bigr((0.3 < x_{true}) \\& (x_{true} < 0.5)\\bigl) + 2 * (x_{true} > 0.7)$ We first load the simulated dataset, filter for the target and covariates and then create the lgb.Dataset . LightGBMLSS is designed to closely resemble the usage of LightGBM, ensuring ease of adoption and full compatibility. In [3]: Copied! train , test = load_simulated_gaussian_data () X_train , y_train = train . filter ( regex = \"x\" ), train [ \"y\" ] . values X_test , y_test = test . filter ( regex = \"x\" ), test [ \"y\" ] . values dtrain = lgb . Dataset ( X_train , label = y_train ) train, test = load_simulated_gaussian_data() X_train, y_train = train.filter(regex=\"x\"), train[\"y\"].values X_test, y_test = test.filter(regex=\"x\"), test[\"y\"].values dtrain = lgb.Dataset(X_train, label=y_train) Distribution Selection \u00b6 Next, we specify a Gaussian distribution. By modifying the speci\ufb01cation in the following, the user can specify alternative distributional assumptions. This includes the option to choose from a wide range of parametric univariate distributions, as well as to model the data using Normalizing Flows. The user also has different function arguments for each distribution: stabilization : specifies the stabilization method for the Gradient and Hessian. Options are None , MAD and L2 . response_fn : specifies $h_{k}(\\cdot)$ and transforms the distributional parameter to the correct support. Here, we specify an exponential for $\\sigma_{i}(\\cdot)$ only. loss_fn : specifies the loss function used for training. Options are nll (negative log-likelihood) or crps (continuous ranked probability score). For additional details, see ?Gaussian . In [4]: Copied! lgblss = LightGBMLSS ( Gaussian ( stabilization = \"None\" , response_fn = \"exp\" , loss_fn = \"nll\" ) ) lgblss = LightGBMLSS( Gaussian(stabilization=\"None\", response_fn=\"exp\", loss_fn=\"nll\" ) ) Hyper-Parameter Optimization \u00b6 Any LightGBM hyperparameter can be tuned, where the structure of the parameter dictionary needs to be as follows: - Float/Int sample_type - {\"param_name\": [\"sample_type\", low, high, log]} - sample_type: str, Type of sampling, e.g., \"float\" or \"int\" - low: int, Lower endpoint of the range of suggested values - high: int, Upper endpoint of the range of suggested values - log: bool, Flag to sample the value from the log domain or not - Example: {\"eta\": \"float\", low=1e-5, high=1, log=True]} - Categorical sample_type - {\"param_name\": [\"sample_type\", [\"choice1\", \"choice2\", \"choice3\", \"...\"]]} - sample_type: str, Type of sampling, either \"categorical\" - choice1, choice2, choice3, ...: str, Possible choices for the parameter - Example: {\"boosting\": [\"categorical\", [\"gbdt\", \"dart\"]]} - For parameters without tunable choice (this is needed if tree_method = \"gpu_hist\" and gpu_id needs to be specified) - {\"param_name\": [\"none\", [value]]}, - param_name: str, Name of the parameter - value: int, Value of the parameter - Example: {\"gpu_id\": [\"none\", [0]]} In [5]: Copied! param_dict = { \"eta\" : [ \"float\" , { \"low\" : 1e-5 , \"high\" : 1 , \"log\" : True }], \"max_depth\" : [ \"int\" , { \"low\" : 1 , \"high\" : 10 , \"log\" : False }], \"num_leaves\" : [ \"int\" , { \"low\" : 255 , \"high\" : 255 , \"log\" : False }], # set to constant for this example \"min_data_in_leaf\" : [ \"int\" , { \"low\" : 20 , \"high\" : 20 , \"log\" : False }], # set to constant for this example \"min_gain_to_split\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 40 , \"log\" : False }], \"min_sum_hessian_in_leaf\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 500 , \"log\" : True }], \"subsample\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"feature_fraction\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"boosting\" : [ \"categorical\" , [ \"gbdt\" ]], } np . random . seed ( 123 ) opt_param = lgblss . hyper_opt ( param_dict , dtrain , num_boost_round = 100 , # Number of boosting iterations. nfold = 5 , # Number of cv-folds. early_stopping_rounds = 20 , # Number of early-stopping rounds max_minutes = 10 , # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials = 30 , # The number of trials. 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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t&&null!=t.key?s.escape(t.key):e.toString(36)}function i(t,e,n,o){var p=typeof t;if(\"undefined\"!==p&&\"boolean\"!==p||(t=null),null===t||\"string\"===p||\"number\"===p||\"object\"===p&&t.$$typeof===u)return n(o,t,\"\"===e?l+r(t,0):e),1;var h,d,v=0,g=\"\"===e?l:e+f;if(Array.isArray(t))for(var m=0;m 0.7 )) q2 = norm . ppf ( quant_sel [ 1 ], loc = 10 , scale = 1 + 4 * (( 0.3 < test [ \"x_true\" ] . values ) & ( test [ \"x_true\" ] . values < 0.5 )) + 2 * ( test [ \"x_true\" ] . values > 0.7 )) test [ \"quant\" ] = np . where ( test [ \"y\" ] . values < q1 , 0 , np . where ( test [ \"y\" ] . values < q2 , 1 , 2 )) test [ \"alpha\" ] = np . where ( test [ \"y\" ] . values <= q1 , 1 , np . where ( test [ \"y\" ] . values >= q2 , 1 , 0 )) df_quantiles = test [ test [ \"alpha\" ] == 1 ] # Lower Bound yl = list ( set ( q1 )) yl . sort () yl = [ yl [ 2 ], yl [ 0 ], yl [ 2 ], yl [ 1 ], yl [ 1 ]] sfunl = pd . DataFrame ({ \"x_true\" :[ 0 , 0.3 , 0.5 , 0.7 , 1 ], \"y\" : yl }) # Upper Bound yu = list ( set ( q2 )) yu . sort () yu = [ yu [ 0 ], yu [ 2 ], yu [ 0 ], yu [ 1 ], yu [ 1 ]] sfunu = pd . DataFrame ({ \"x_true\" :[ 0 , 0.3 , 0.5 , 0.7 , 1 ], \"y\" : yu }) ### # Predicted Quantiles ### test [ \"lb\" ] = pred_quantiles . iloc [:, 0 ] test [ \"ub\" ] = pred_quantiles . iloc [:, 1 ] ### # Plot ### ( ggplot ( test , aes ( \"x_true\" , \"y\" )) + geom_point ( alpha = 0.2 , color = \"black\" , size = 2 ) + theme_bw ( base_size = 15 ) + theme ( legend_position = \"none\" , plot_title = element_text ( hjust = 0.5 ), plot_subtitle = element_text ( hjust = 0.5 )) + labs ( title = \"LightGBMLSS Regression - Simulated Data Example\" , subtitle = \"Comparison of Actual (black) vs. Predicted Quantiles (blue)\" , x = \"x\" ) + geom_line ( aes ( \"x_true\" , \"ub\" ), size = 1 , color = \"blue\" , alpha = 0.7 ) + geom_line ( aes ( \"x_true\" , \"lb\" ), size = 1 , color = \"blue\" , alpha = 0.7 ) + geom_point ( df_quantiles , aes ( \"x_true\" , \"y\" ), color = \"red\" , alpha = 0.7 , size = 2 ) + geom_step ( sfunl , aes ( \"x_true\" , \"y\" ), size = 1 , linetype = \"dashed\" ) + geom_step ( sfunu , aes ( \"x_true\" , \"y\" ), size = 1 , linetype = \"dashed\" ) ) np.random.seed(123) ### # Actual Quantiles ### q1 = norm.ppf(quant_sel[0], loc = 10, scale = 1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7)) q2 = norm.ppf(quant_sel[1], loc = 10, scale = 1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7)) test[\"quant\"] = np.where(test[\"y\"].values < q1, 0, np.where(test[\"y\"].values < q2, 1, 2)) test[\"alpha\"] = np.where(test[\"y\"].values <= q1, 1, np.where(test[\"y\"].values >= q2, 1, 0)) df_quantiles = test[test[\"alpha\"] == 1] # Lower Bound yl = list(set(q1)) yl.sort() yl = [yl[2],yl[0],yl[2],yl[1],yl[1]] sfunl = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yl}) # Upper Bound yu = list(set(q2)) yu.sort() yu = [yu[0],yu[2],yu[0],yu[1],yu[1]] sfunu = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yu}) ### # Predicted Quantiles ### test[\"lb\"] = pred_quantiles.iloc[:,0] test[\"ub\"] = pred_quantiles.iloc[:,1] ### # Plot ### (ggplot(test, aes(\"x_true\", \"y\")) + geom_point(alpha = 0.2, color = \"black\", size = 2) + theme_bw(base_size=15) + theme(legend_position=\"none\", plot_title = element_text(hjust = 0.5), plot_subtitle = element_text(hjust = 0.5)) + labs(title = \"LightGBMLSS Regression - Simulated Data Example\", subtitle = \"Comparison of Actual (black) vs. Predicted Quantiles (blue)\", x=\"x\") + geom_line(aes(\"x_true\", \"ub\"), size = 1, color = \"blue\", alpha = 0.7) + geom_line(aes(\"x_true\", \"lb\"), size = 1, color = \"blue\", alpha = 0.7) + geom_point(df_quantiles, aes(\"x_true\", \"y\"), color = \"red\", alpha = 0.7, size = 2) + geom_step(sfunl, aes(\"x_true\", \"y\"), size = 1, linetype = \"dashed\") + geom_step(sfunu, aes(\"x_true\", \"y\"), size = 1, linetype = \"dashed\") ) Out[13]:
True vs. Predicted Distributional Parameters \u00b6 In the following figure, we compare the true parameters of the Gaussian with the ones predicted by LightGBMLSS. The below figure shows that the estimated parameters closely match the true ones (recall that the location parameter $\\mu=10$ is simulated as being a constant). In [14]: Copied! pred_params [ \"x_true\" ] = X_test [ \"x_true\" ] . values dist_params = list ( lgblss . dist . param_dict . keys ()) # Data with actual values plot_df_actual = pd . melt ( test [[ \"x_true\" ] + dist_params ], id_vars = \"x_true\" , value_vars = dist_params ) plot_df_actual [ \"type\" ] = \"TRUE\" # Data with predicted values plot_df_predt = pd . melt ( pred_params [[ \"x_true\" ] + dist_params ], id_vars = \"x_true\" , value_vars = dist_params ) plot_df_predt [ \"type\" ] = \"PREDICT\" plot_df = pd . concat ([ plot_df_predt , plot_df_actual ]) plot_df [ \"variable\" ] = plot_df . variable . str . upper () plot_df [ \"type\" ] = pd . Categorical ( plot_df [ \"type\" ], categories = [ \"PREDICT\" , \"TRUE\" ]) ( ggplot ( plot_df , aes ( x = \"x_true\" , y = \"value\" , color = \"type\" )) + geom_line ( size = 1.1 ) + facet_wrap ( \"variable\" , scales = \"free\" ) + labs ( title = \"Parameters of univariate Gaussian predicted with LightGBMLSS\" , x = \"\" , y = \"\" ) + theme_bw ( base_size = 15 ) + theme ( legend_position = \"bottom\" , plot_title = element_text ( hjust = 0.5 ), legend_title = element_blank ()) ) pred_params[\"x_true\"] = X_test[\"x_true\"].values dist_params = list(lgblss.dist.param_dict.keys()) # Data with actual values plot_df_actual = pd.melt(test[[\"x_true\"] + dist_params], id_vars=\"x_true\", value_vars=dist_params) plot_df_actual[\"type\"] = \"TRUE\" # Data with predicted values plot_df_predt = pd.melt(pred_params[[\"x_true\"] + dist_params], id_vars=\"x_true\", value_vars=dist_params) plot_df_predt[\"type\"] = \"PREDICT\" plot_df = pd.concat([plot_df_predt, plot_df_actual]) plot_df[\"variable\"] = plot_df.variable.str.upper() plot_df[\"type\"] = pd.Categorical(plot_df[\"type\"], categories = [\"PREDICT\", \"TRUE\"]) (ggplot(plot_df, aes(x=\"x_true\", y=\"value\", color=\"type\")) + geom_line(size=1.1) + facet_wrap(\"variable\", scales=\"free\") + labs(title=\"Parameters of univariate Gaussian predicted with LightGBMLSS\", x=\"\", y=\"\") + theme_bw(base_size=15) + theme(legend_position=\"bottom\", plot_title = element_text(hjust = 0.5), legend_title = element_blank()) ) Out[14]:
Actual vs. Predicted \u00b6 Since we predict the entire conditional distribution, we can overlay the point predictions with predicted densities, from which we can also derive quantiles of interest. In [15]: Copied! y_pred = [] n_examples = 9 for i in range ( n_examples ): y_samples = pd . DataFrame ( pred_samples . values [ i ,:] . reshape ( - 1 , 1 ), columns = [ \"PREDICT_DENSITY\" ]) y_samples [ \"PREDICT_POINT\" ] = y_samples [ \"PREDICT_DENSITY\" ] . mean () y_samples [ \"PREDICT_Q05\" ] = y_samples [ \"PREDICT_DENSITY\" ] . quantile ( q = quant_sel [ 0 ]) y_samples [ \"PREDICT_Q95\" ] = y_samples [ \"PREDICT_DENSITY\" ] . quantile ( q = quant_sel [ 1 ]) y_samples [ \"ACTUAL\" ] = y_test [ i ] y_samples [ \"obs\" ] = f \"Obervation { i + 1 } \" y_pred . append ( y_samples ) pred_df = pd . melt ( pd . concat ( y_pred , axis = 0 ), id_vars = \"obs\" ) pred_df [ \"obs\" ] = pd . Categorical ( pred_df [ \"obs\" ], categories = [ f \"Obervation { i + 1 } \" for i in range ( n_examples )]) df_actual , df_pred_dens , df_pred_point , df_q05 , df_q95 = [ x for _ , x in pred_df . groupby ( \"variable\" )] plot_pred = ( ggplot ( pred_df , aes ( color = \"variable\" )) + stat_density ( df_pred_dens , aes ( x = \"value\" ), size = 1.1 ) + geom_point ( df_pred_point , aes ( x = \"value\" , y = 0 ), size = 1.4 ) + geom_point ( df_actual , aes ( x = \"value\" , y = 0 ), size = 1.4 ) + geom_vline ( df_q05 , aes ( xintercept = \"value\" , fill = \"variable\" , color = \"variable\" ), linetype = \"dashed\" , size = 1.1 ) + geom_vline ( df_q95 , aes ( xintercept = \"value\" , fill = \"variable\" , color = \"variable\" ), linetype = \"dashed\" , size = 1.1 ) + facet_wrap ( \"obs\" , scales = \"free\" , ncol = 3 ) + labs ( title = \"Predicted vs. Actual \\n \" , x = \"\" ) + theme_bw ( base_size = 15 ) + scale_fill_brewer ( type = \"qual\" , palette = \"Dark2\" ) + theme ( legend_position = \"bottom\" , plot_title = element_text ( hjust = 0.5 ), legend_title = element_blank () ) ) print ( plot_pred ) y_pred = [] n_examples = 9 for i in range(n_examples): y_samples = pd.DataFrame(pred_samples.values[i,:].reshape(-1,1), columns=[\"PREDICT_DENSITY\"]) y_samples[\"PREDICT_POINT\"] = y_samples[\"PREDICT_DENSITY\"].mean() y_samples[\"PREDICT_Q05\"] = y_samples[\"PREDICT_DENSITY\"].quantile(q=quant_sel[0]) y_samples[\"PREDICT_Q95\"] = y_samples[\"PREDICT_DENSITY\"].quantile(q=quant_sel[1]) y_samples[\"ACTUAL\"] = y_test[i] y_samples[\"obs\"]= f\"Obervation {i+1}\" y_pred.append(y_samples) pred_df = pd.melt(pd.concat(y_pred, axis=0), id_vars=\"obs\") pred_df[\"obs\"] = pd.Categorical(pred_df[\"obs\"], categories=[f\"Obervation {i+1}\" for i in range(n_examples)]) df_actual, df_pred_dens, df_pred_point, df_q05, df_q95 = [x for _, x in pred_df.groupby(\"variable\")] plot_pred = ( ggplot(pred_df, aes(color=\"variable\")) + stat_density(df_pred_dens, aes(x=\"value\"), size=1.1) + geom_point(df_pred_point, aes(x=\"value\", y=0), size=1.4) + geom_point(df_actual, aes(x=\"value\", y=0), size=1.4) + geom_vline(df_q05, aes(xintercept=\"value\", fill=\"variable\", color=\"variable\"), linetype=\"dashed\", size=1.1) + geom_vline(df_q95, aes(xintercept=\"value\", fill=\"variable\", color=\"variable\"), linetype=\"dashed\", size=1.1) + facet_wrap(\"obs\", scales=\"free\", ncol=3) + labs(title=\"Predicted vs. Actual \\n\", x = \"\") + theme_bw(base_size=15) + scale_fill_brewer(type=\"qual\", palette=\"Dark2\") + theme(legend_position=\"bottom\", plot_title = element_text(hjust = 0.5), legend_title = element_blank() ) ) print(plot_pred)","title":"Basic Walkthrough - Gaussian Regression"},{"location":"examples/Gaussian_Regression/#basic-walkthrough-gaussian-regression","text":"In this example, we model and predict all parameters of a univariate Normal distribution. Recall that distributional regression models and predicts all parameters $\\theta_{ik}, k=1, \\ldots, K$ parameters of a distribution $\\mathcal{D}$ as a function of covariates: \\begin{equation} y_{i} \\stackrel{ind}{\\sim} \\mathcal{D} \\begin{pmatrix} h_{1}\\bigl(\\theta_{i1}(x_{i})\\bigr) = \\eta_{i1} \\\\ h_{2}\\bigl(\\theta_{i2}(x_{i})\\bigr) = \\eta_{i2} \\\\ \\vdots \\\\ h_{K}\\bigl(\\theta_{iK}(x_{i})\\bigr) = \\eta_{iK} \\end{pmatrix} \\quad ,i=1, \\ldots, N. \\end{equation} where $h_{k}(\\cdot)$ transforms each distributional parameter to the corresponding parameter scale. For the univariate Normal case, we can specify the above as $y_{i} \\stackrel{ind}{\\sim} \\mathcal{N}\\bigl(\\mu_{i}(x_{i}), \\sigma_{i}(x_{i})\\bigr)$. Since $\\mu_{i}(\\cdot) \\in \\mathbb{R}$ and since the standard-deviation cannot be negative, $h_{k}(\\cdot)$ is applied to $\\sigma_{i}(\\cdot)$ only. Typical choices are the exponential or the softplus function.","title":"Basic Walkthrough - Gaussian Regression"},{"location":"examples/Gaussian_Regression/#imports","text":"First, we import the necessary functions. In [2]: Copied! from lightgbmlss.model import * from lightgbmlss.distributions.Gaussian import * from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data from scipy.stats import norm import plotnine from plotnine import * plotnine . options . figure_size = ( 12 , 8 ) from lightgbmlss.model import * from lightgbmlss.distributions.Gaussian import * from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data from scipy.stats import norm import plotnine from plotnine import * plotnine.options.figure_size = (12, 8)","title":"Imports"},{"location":"examples/Gaussian_Regression/#data","text":"The data is simulated as a Gaussian, where $x_{true}$ is the only true feature and all others are noise variables: $\\mu(x_{true}) = 10$ $\\sigma(x_{true}) = 1 + 4 * \\bigr((0.3 < x_{true}) \\& (x_{true} < 0.5)\\bigl) + 2 * (x_{true} > 0.7)$ We first load the simulated dataset, filter for the target and covariates and then create the lgb.Dataset . LightGBMLSS is designed to closely resemble the usage of LightGBM, ensuring ease of adoption and full compatibility. In [3]: Copied! train , test = load_simulated_gaussian_data () X_train , y_train = train . filter ( regex = \"x\" ), train [ \"y\" ] . values X_test , y_test = test . filter ( regex = \"x\" ), test [ \"y\" ] . values dtrain = lgb . Dataset ( X_train , label = y_train ) train, test = load_simulated_gaussian_data() X_train, y_train = train.filter(regex=\"x\"), train[\"y\"].values X_test, y_test = test.filter(regex=\"x\"), test[\"y\"].values dtrain = lgb.Dataset(X_train, label=y_train)","title":"Data"},{"location":"examples/Gaussian_Regression/#distribution-selection","text":"Next, we specify a Gaussian distribution. By modifying the speci\ufb01cation in the following, the user can specify alternative distributional assumptions. This includes the option to choose from a wide range of parametric univariate distributions, as well as to model the data using Normalizing Flows. The user also has different function arguments for each distribution: stabilization : specifies the stabilization method for the Gradient and Hessian. Options are None , MAD and L2 . response_fn : specifies $h_{k}(\\cdot)$ and transforms the distributional parameter to the correct support. Here, we specify an exponential for $\\sigma_{i}(\\cdot)$ only. loss_fn : specifies the loss function used for training. Options are nll (negative log-likelihood) or crps (continuous ranked probability score). For additional details, see ?Gaussian . In [4]: Copied! lgblss = LightGBMLSS ( Gaussian ( stabilization = \"None\" , response_fn = \"exp\" , loss_fn = \"nll\" ) ) lgblss = LightGBMLSS( Gaussian(stabilization=\"None\", response_fn=\"exp\", loss_fn=\"nll\" ) )","title":"Distribution Selection"},{"location":"examples/Gaussian_Regression/#hyper-parameter-optimization","text":"Any LightGBM hyperparameter can be tuned, where the structure of the parameter dictionary needs to be as follows: - Float/Int sample_type - {\"param_name\": [\"sample_type\", low, high, log]} - sample_type: str, Type of sampling, e.g., \"float\" or \"int\" - low: int, Lower endpoint of the range of suggested values - high: int, Upper endpoint of the range of suggested values - log: bool, Flag to sample the value from the log domain or not - Example: {\"eta\": \"float\", low=1e-5, high=1, log=True]} - Categorical sample_type - {\"param_name\": [\"sample_type\", [\"choice1\", \"choice2\", \"choice3\", \"...\"]]} - sample_type: str, Type of sampling, either \"categorical\" - choice1, choice2, choice3, ...: str, Possible choices for the parameter - Example: {\"boosting\": [\"categorical\", [\"gbdt\", \"dart\"]]} - For parameters without tunable choice (this is needed if tree_method = \"gpu_hist\" and gpu_id needs to be specified) - {\"param_name\": [\"none\", [value]]}, - param_name: str, Name of the parameter - value: int, Value of the parameter - Example: {\"gpu_id\": [\"none\", [0]]} In [5]: Copied! param_dict = { \"eta\" : [ \"float\" , { \"low\" : 1e-5 , \"high\" : 1 , \"log\" : True }], \"max_depth\" : [ \"int\" , { \"low\" : 1 , \"high\" : 10 , \"log\" : False }], \"num_leaves\" : [ \"int\" , { \"low\" : 255 , \"high\" : 255 , \"log\" : False }], # set to constant for this example \"min_data_in_leaf\" : [ \"int\" , { \"low\" : 20 , \"high\" : 20 , \"log\" : False }], # set to constant for this example \"min_gain_to_split\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 40 , \"log\" : False }], \"min_sum_hessian_in_leaf\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 500 , \"log\" : True }], \"subsample\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"feature_fraction\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"boosting\" : [ \"categorical\" , [ \"gbdt\" ]], } np . random . seed ( 123 ) opt_param = lgblss . hyper_opt ( param_dict , dtrain , num_boost_round = 100 , # Number of boosting iterations. nfold = 5 , # Number of cv-folds. early_stopping_rounds = 20 , # Number of early-stopping rounds max_minutes = 10 , # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials = 30 , # The number of trials. If this argument is set to None, there is no limitation on the number of trials. silence = True , # Controls the verbosity of the trail, i.e., user can silence the outputs of the trail. seed = 123 , # Seed used to generate cv-folds. hp_seed = 123 # Seed for random number generator used in the Bayesian hyperparameter search. ) param_dict = { \"eta\": [\"float\", {\"low\": 1e-5, \"high\": 1, \"log\": True}], \"max_depth\": [\"int\", {\"low\": 1, \"high\": 10, \"log\": False}], \"num_leaves\": [\"int\", {\"low\": 255, \"high\": 255, \"log\": False}], # set to constant for this example \"min_data_in_leaf\": [\"int\", {\"low\": 20, \"high\": 20, \"log\": False}], # set to constant for this example \"min_gain_to_split\": [\"float\", {\"low\": 1e-8, \"high\": 40, \"log\": False}], \"min_sum_hessian_in_leaf\": [\"float\", {\"low\": 1e-8, \"high\": 500, \"log\": True}], \"subsample\": [\"float\", {\"low\": 0.2, \"high\": 1.0, \"log\": False}], \"feature_fraction\": [\"float\", {\"low\": 0.2, \"high\": 1.0, \"log\": False}], \"boosting\": [\"categorical\", [\"gbdt\"]], } np.random.seed(123) opt_param = lgblss.hyper_opt(param_dict, dtrain, num_boost_round=100, # Number of boosting iterations. nfold=5, # Number of cv-folds. early_stopping_rounds=20, # Number of early-stopping rounds max_minutes=10, # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials=30 , # The number of trials. 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return 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t&&null!=t.key?s.escape(t.key):e.toString(36)}function i(t,e,n,o){var p=typeof t;if(\"undefined\"!==p&&\"boolean\"!==p||(t=null),null===t||\"string\"===p||\"number\"===p||\"object\"===p&&t.$$typeof===u)return n(o,t,\"\"===e?l+r(t,0):e),1;var h,d,v=0,g=\"\"===e?l:e+f;if(Array.isArray(t))for(var m=0;m 0.7 )) q2 = norm . ppf ( quant_sel [ 1 ], loc = 10 , scale = 1 + 4 * (( 0.3 < test [ \"x_true\" ] . values ) & ( test [ \"x_true\" ] . values < 0.5 )) + 2 * ( test [ \"x_true\" ] . values > 0.7 )) test [ \"quant\" ] = np . where ( test [ \"y\" ] . values < q1 , 0 , np . where ( test [ \"y\" ] . values < q2 , 1 , 2 )) test [ \"alpha\" ] = np . where ( test [ \"y\" ] . values <= q1 , 1 , np . where ( test [ \"y\" ] . values >= q2 , 1 , 0 )) df_quantiles = test [ test [ \"alpha\" ] == 1 ] # Lower Bound yl = list ( set ( q1 )) yl . sort () yl = [ yl [ 2 ], yl [ 0 ], yl [ 2 ], yl [ 1 ], yl [ 1 ]] sfunl = pd . DataFrame ({ \"x_true\" :[ 0 , 0.3 , 0.5 , 0.7 , 1 ], \"y\" : yl }) # Upper Bound yu = list ( set ( q2 )) yu . sort () yu = [ yu [ 0 ], yu [ 2 ], yu [ 0 ], yu [ 1 ], yu [ 1 ]] sfunu = pd . DataFrame ({ \"x_true\" :[ 0 , 0.3 , 0.5 , 0.7 , 1 ], \"y\" : yu }) ### # Predicted Quantiles ### test [ \"lb\" ] = pred_quantiles . iloc [:, 0 ] test [ \"ub\" ] = pred_quantiles . iloc [:, 1 ] ### # Plot ### ( ggplot ( test , aes ( \"x_true\" , \"y\" )) + geom_point ( alpha = 0.2 , color = \"black\" , size = 2 ) + theme_bw ( base_size = 15 ) + theme ( legend_position = \"none\" , plot_title = element_text ( hjust = 0.5 ), plot_subtitle = element_text ( hjust = 0.5 )) + labs ( title = \"LightGBMLSS Regression - Simulated Data Example\" , subtitle = \"Comparison of Actual (black) vs. Predicted Quantiles (blue)\" , x = \"x\" ) + geom_line ( aes ( \"x_true\" , \"ub\" ), size = 1 , color = \"blue\" , alpha = 0.7 ) + geom_line ( aes ( \"x_true\" , \"lb\" ), size = 1 , color = \"blue\" , alpha = 0.7 ) + geom_point ( df_quantiles , aes ( \"x_true\" , \"y\" ), color = \"red\" , alpha = 0.7 , size = 2 ) + geom_step ( sfunl , aes ( \"x_true\" , \"y\" ), size = 1 , linetype = \"dashed\" ) + geom_step ( sfunu , aes ( \"x_true\" , \"y\" ), size = 1 , linetype = \"dashed\" ) ) np.random.seed(123) ### # Actual Quantiles ### q1 = norm.ppf(quant_sel[0], loc = 10, scale = 1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7)) q2 = norm.ppf(quant_sel[1], loc = 10, scale = 1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7)) test[\"quant\"] = np.where(test[\"y\"].values < q1, 0, np.where(test[\"y\"].values < q2, 1, 2)) test[\"alpha\"] = np.where(test[\"y\"].values <= q1, 1, np.where(test[\"y\"].values >= q2, 1, 0)) df_quantiles = test[test[\"alpha\"] == 1] # Lower Bound yl = list(set(q1)) yl.sort() yl = [yl[2],yl[0],yl[2],yl[1],yl[1]] sfunl = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yl}) # Upper Bound yu = list(set(q2)) yu.sort() yu = [yu[0],yu[2],yu[0],yu[1],yu[1]] sfunu = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yu}) ### # Predicted Quantiles ### test[\"lb\"] = pred_quantiles.iloc[:,0] test[\"ub\"] = pred_quantiles.iloc[:,1] ### # Plot ### (ggplot(test, aes(\"x_true\", \"y\")) + geom_point(alpha = 0.2, color = \"black\", size = 2) + theme_bw(base_size=15) + theme(legend_position=\"none\", plot_title = element_text(hjust = 0.5), plot_subtitle = element_text(hjust = 0.5)) + labs(title = \"LightGBMLSS Regression - Simulated Data Example\", subtitle = \"Comparison of Actual (black) vs. Predicted Quantiles (blue)\", x=\"x\") + geom_line(aes(\"x_true\", \"ub\"), size = 1, color = \"blue\", alpha = 0.7) + geom_line(aes(\"x_true\", \"lb\"), size = 1, color = \"blue\", alpha = 0.7) + geom_point(df_quantiles, aes(\"x_true\", \"y\"), color = \"red\", alpha = 0.7, size = 2) + geom_step(sfunl, aes(\"x_true\", \"y\"), size = 1, linetype = \"dashed\") + geom_step(sfunu, aes(\"x_true\", \"y\"), size = 1, linetype = \"dashed\") ) Out[13]:
","title":"Plot of Actual vs. Predicted Quantiles"},{"location":"examples/Gaussian_Regression/#true-vs-predicted-distributional-parameters","text":"In the following figure, we compare the true parameters of the Gaussian with the ones predicted by LightGBMLSS. The below figure shows that the estimated parameters closely match the true ones (recall that the location parameter $\\mu=10$ is simulated as being a constant). In [14]: Copied! pred_params [ \"x_true\" ] = X_test [ \"x_true\" ] . values dist_params = list ( lgblss . dist . param_dict . keys ()) # Data with actual values plot_df_actual = pd . melt ( test [[ \"x_true\" ] + dist_params ], id_vars = \"x_true\" , value_vars = dist_params ) plot_df_actual [ \"type\" ] = \"TRUE\" # Data with predicted values plot_df_predt = pd . melt ( pred_params [[ \"x_true\" ] + dist_params ], id_vars = \"x_true\" , value_vars = dist_params ) plot_df_predt [ \"type\" ] = \"PREDICT\" plot_df = pd . concat ([ plot_df_predt , plot_df_actual ]) plot_df [ \"variable\" ] = plot_df . variable . str . upper () plot_df [ \"type\" ] = pd . Categorical ( plot_df [ \"type\" ], categories = [ \"PREDICT\" , \"TRUE\" ]) ( ggplot ( plot_df , aes ( x = \"x_true\" , y = \"value\" , color = \"type\" )) + geom_line ( size = 1.1 ) + facet_wrap ( \"variable\" , scales = \"free\" ) + labs ( title = \"Parameters of univariate Gaussian predicted with LightGBMLSS\" , x = \"\" , y = \"\" ) + theme_bw ( base_size = 15 ) + theme ( legend_position = \"bottom\" , plot_title = element_text ( hjust = 0.5 ), legend_title = element_blank ()) ) pred_params[\"x_true\"] = X_test[\"x_true\"].values dist_params = list(lgblss.dist.param_dict.keys()) # Data with actual values plot_df_actual = pd.melt(test[[\"x_true\"] + dist_params], id_vars=\"x_true\", value_vars=dist_params) plot_df_actual[\"type\"] = \"TRUE\" # Data with predicted values plot_df_predt = pd.melt(pred_params[[\"x_true\"] + dist_params], id_vars=\"x_true\", value_vars=dist_params) plot_df_predt[\"type\"] = \"PREDICT\" plot_df = pd.concat([plot_df_predt, plot_df_actual]) plot_df[\"variable\"] = plot_df.variable.str.upper() plot_df[\"type\"] = pd.Categorical(plot_df[\"type\"], categories = [\"PREDICT\", \"TRUE\"]) (ggplot(plot_df, aes(x=\"x_true\", y=\"value\", color=\"type\")) + geom_line(size=1.1) + facet_wrap(\"variable\", scales=\"free\") + labs(title=\"Parameters of univariate Gaussian predicted with LightGBMLSS\", x=\"\", y=\"\") + theme_bw(base_size=15) + theme(legend_position=\"bottom\", plot_title = element_text(hjust = 0.5), legend_title = element_blank()) ) Out[14]:
","title":"True vs. Predicted Distributional Parameters"},{"location":"examples/Gaussian_Regression/#actual-vs-predicted","text":"Since we predict the entire conditional distribution, we can overlay the point predictions with predicted densities, from which we can also derive quantiles of interest. In [15]: Copied! y_pred = [] n_examples = 9 for i in range ( n_examples ): y_samples = pd . DataFrame ( pred_samples . values [ i ,:] . reshape ( - 1 , 1 ), columns = [ \"PREDICT_DENSITY\" ]) y_samples [ \"PREDICT_POINT\" ] = y_samples [ \"PREDICT_DENSITY\" ] . mean () y_samples [ \"PREDICT_Q05\" ] = y_samples [ \"PREDICT_DENSITY\" ] . quantile ( q = quant_sel [ 0 ]) y_samples [ \"PREDICT_Q95\" ] = y_samples [ \"PREDICT_DENSITY\" ] . quantile ( q = quant_sel [ 1 ]) y_samples [ \"ACTUAL\" ] = y_test [ i ] y_samples [ \"obs\" ] = f \"Obervation { i + 1 } \" y_pred . append ( y_samples ) pred_df = pd . melt ( pd . concat ( y_pred , axis = 0 ), id_vars = \"obs\" ) pred_df [ \"obs\" ] = pd . Categorical ( pred_df [ \"obs\" ], categories = [ f \"Obervation { i + 1 } \" for i in range ( n_examples )]) df_actual , df_pred_dens , df_pred_point , df_q05 , df_q95 = [ x for _ , x in pred_df . groupby ( \"variable\" )] plot_pred = ( ggplot ( pred_df , aes ( color = \"variable\" )) + stat_density ( df_pred_dens , aes ( x = \"value\" ), size = 1.1 ) + geom_point ( df_pred_point , aes ( x = \"value\" , y = 0 ), size = 1.4 ) + geom_point ( df_actual , aes ( x = \"value\" , y = 0 ), size = 1.4 ) + geom_vline ( df_q05 , aes ( xintercept = \"value\" , fill = \"variable\" , color = \"variable\" ), linetype = \"dashed\" , size = 1.1 ) + geom_vline ( df_q95 , aes ( xintercept = \"value\" , fill = \"variable\" , color = \"variable\" ), linetype = \"dashed\" , size = 1.1 ) + facet_wrap ( \"obs\" , scales = \"free\" , ncol = 3 ) + labs ( title = \"Predicted vs. Actual \\n \" , x = \"\" ) + theme_bw ( base_size = 15 ) + scale_fill_brewer ( type = \"qual\" , palette = \"Dark2\" ) + theme ( legend_position = \"bottom\" , plot_title = element_text ( hjust = 0.5 ), legend_title = element_blank () ) ) print ( plot_pred ) y_pred = [] n_examples = 9 for i in range(n_examples): y_samples = pd.DataFrame(pred_samples.values[i,:].reshape(-1,1), columns=[\"PREDICT_DENSITY\"]) y_samples[\"PREDICT_POINT\"] = y_samples[\"PREDICT_DENSITY\"].mean() y_samples[\"PREDICT_Q05\"] = y_samples[\"PREDICT_DENSITY\"].quantile(q=quant_sel[0]) y_samples[\"PREDICT_Q95\"] = y_samples[\"PREDICT_DENSITY\"].quantile(q=quant_sel[1]) y_samples[\"ACTUAL\"] = y_test[i] y_samples[\"obs\"]= f\"Obervation {i+1}\" y_pred.append(y_samples) pred_df = pd.melt(pd.concat(y_pred, axis=0), id_vars=\"obs\") pred_df[\"obs\"] = pd.Categorical(pred_df[\"obs\"], categories=[f\"Obervation {i+1}\" for i in range(n_examples)]) df_actual, df_pred_dens, df_pred_point, df_q05, df_q95 = [x for _, x in pred_df.groupby(\"variable\")] plot_pred = ( ggplot(pred_df, aes(color=\"variable\")) + stat_density(df_pred_dens, aes(x=\"value\"), size=1.1) + geom_point(df_pred_point, aes(x=\"value\", y=0), size=1.4) + geom_point(df_actual, aes(x=\"value\", y=0), size=1.4) + geom_vline(df_q05, aes(xintercept=\"value\", fill=\"variable\", color=\"variable\"), linetype=\"dashed\", size=1.1) + geom_vline(df_q95, aes(xintercept=\"value\", fill=\"variable\", color=\"variable\"), linetype=\"dashed\", size=1.1) + facet_wrap(\"obs\", scales=\"free\", ncol=3) + labs(title=\"Predicted vs. Actual \\n\", x = \"\") + theme_bw(base_size=15) + scale_fill_brewer(type=\"qual\", palette=\"Dark2\") + theme(legend_position=\"bottom\", plot_title = element_text(hjust = 0.5), legend_title = element_blank() ) ) print(plot_pred)","title":"Actual vs. Predicted"},{"location":"examples/How_To_Select_A_Univariate_Distribution/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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tr:hover{background:rgba(66,165,245,.2)}table{width:max-content} /*# sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); How to Select a Univariate Distribution \u00b6 In this example we will show how to select a distribution for a univariate target variable. We use the California housing dataset and select a distribution for the target variable median_house_value . Imports \u00b6 In [1]: Copied! from lightgbmlss.distributions import * from lightgbmlss.distributions.distribution_utils import DistributionClass from sklearn import datasets from sklearn.model_selection import train_test_split from lightgbmlss.distributions import * from lightgbmlss.distributions.distribution_utils import DistributionClass from sklearn import datasets from sklearn.model_selection import train_test_split Data \u00b6 In [2]: Copied! housing_data = datasets . fetch_california_housing () X , y = housing_data [ \"data\" ], housing_data [ \"target\" ] feature_names = housing_data [ \"feature_names\" ] X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0.2 , random_state = 123 ) housing_data = datasets.fetch_california_housing() X, y = housing_data[\"data\"], housing_data[\"target\"] feature_names = housing_data[\"feature_names\"] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123) Select Distribution \u00b6 In the following, we specify a list of candidate distributions. The function dist_select returns the negative log-likelihood of each distribution for the target variable. The distribution with the lowest negative log-likelihood is selected. The function also plots the density of the target variable and the fitted density, using the best suitable distribution among the specified ones. It is important to note that the list of candidate distributions should be chosen to be suitable for the target variable at hand. For example, if the target variable is a count variable, then the list of candidate distributions should include the Poisson and Negative Binomial. Similarly, if the target variable is on the positive real scale, then the list of continuous candidate distributions should be chosen accordingly. In [3]: Copied! lgblss_dist_class = DistributionClass () candidate_distributions = [ Gaussian , StudentT , Gamma , Cauchy , LogNormal , Weibull , Gumbel , Laplace ] dist_nll = lgblss_dist_class . dist_select ( target = y_train , candidate_distributions = candidate_distributions , max_iter = 50 , plot = True , figure_size = ( 10 , 5 )) dist_nll lgblss_dist_class = DistributionClass() candidate_distributions = [Gaussian, StudentT, Gamma, Cauchy, LogNormal, Weibull, Gumbel, Laplace] dist_nll = lgblss_dist_class.dist_select(target=y_train, candidate_distributions=candidate_distributions, max_iter=50, plot=True, figure_size=(10, 5)) dist_nll Fitting of candidate distributions completed: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 8/8 [00:12<00:00, 1.58s/it] Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } nll distribution rank 1 23596.791908 LogNormal 2 23632.597656 Gamma 3 23899.039920 Gumbel 4 24083.658916 Weibull 5 25690.867630 StudentT 6 25796.219456 Gaussian 7 25925.138312 Laplace 8 27559.623077 Cauchy","title":"How to Select a Univariate Distribution"},{"location":"examples/How_To_Select_A_Univariate_Distribution/#how-to-select-a-univariate-distribution","text":"In this example we will show how to select a distribution for a univariate target variable. We use the California housing dataset and select a distribution for the target variable median_house_value .","title":"How to Select a Univariate Distribution"},{"location":"examples/How_To_Select_A_Univariate_Distribution/#imports","text":"In [1]: Copied! from lightgbmlss.distributions import * from lightgbmlss.distributions.distribution_utils import DistributionClass from sklearn import datasets from sklearn.model_selection import train_test_split from lightgbmlss.distributions import * from lightgbmlss.distributions.distribution_utils import DistributionClass from sklearn import datasets from sklearn.model_selection import train_test_split","title":"Imports"},{"location":"examples/How_To_Select_A_Univariate_Distribution/#data","text":"In [2]: Copied! housing_data = datasets . fetch_california_housing () X , y = housing_data [ \"data\" ], housing_data [ \"target\" ] feature_names = housing_data [ \"feature_names\" ] X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0.2 , random_state = 123 ) housing_data = datasets.fetch_california_housing() X, y = housing_data[\"data\"], housing_data[\"target\"] feature_names = housing_data[\"feature_names\"] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)","title":"Data"},{"location":"examples/How_To_Select_A_Univariate_Distribution/#select-distribution","text":"In the following, we specify a list of candidate distributions. The function dist_select returns the negative log-likelihood of each distribution for the target variable. The distribution with the lowest negative log-likelihood is selected. The function also plots the density of the target variable and the fitted density, using the best suitable distribution among the specified ones. It is important to note that the list of candidate distributions should be chosen to be suitable for the target variable at hand. For example, if the target variable is a count variable, then the list of candidate distributions should include the Poisson and Negative Binomial. Similarly, if the target variable is on the positive real scale, then the list of continuous candidate distributions should be chosen accordingly. In [3]: Copied! lgblss_dist_class = DistributionClass () candidate_distributions = [ Gaussian , StudentT , Gamma , Cauchy , LogNormal , Weibull , Gumbel , Laplace ] dist_nll = lgblss_dist_class . dist_select ( target = y_train , candidate_distributions = candidate_distributions , max_iter = 50 , plot = True , figure_size = ( 10 , 5 )) dist_nll lgblss_dist_class = DistributionClass() candidate_distributions = [Gaussian, StudentT, Gamma, Cauchy, LogNormal, Weibull, Gumbel, Laplace] dist_nll = lgblss_dist_class.dist_select(target=y_train, candidate_distributions=candidate_distributions, max_iter=50, plot=True, figure_size=(10, 5)) dist_nll Fitting of candidate distributions completed: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 8/8 [00:12<00:00, 1.58s/it] Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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tr:hover{background:rgba(66,165,245,.2)}table{width:max-content} /*# sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Spline Flow Regression \u00b6 Normalizing flows transform a simple distribution into a complex data distribution through a series of invertible transformations. The key steps involved in the operation of normalizing flows are as follows (from left to right): Image source: https://tikz.net/janosh/normalizing-flow.png Start with a simple, easy-to-sample distribution, usually a Gaussian, which serves as the \"base\" distribution Apply a series of invertible transformations to map the samples from the base distribution to the desired complex data distribution Each transformation in the flow must be reversible, meaning it has both a forward pass (sampling from the base distribution to the complex distribution) and an inverse pass (mapping samples from the complex distribution back to the base distribution) The flow ensures that the probability density function (PDF) of the complex distribution can be analytically calculated using the determinant of the Jacobian matrix resulting from the transformations By stacking multiple transformations in a sequence, normalizing flows can model complex and multi-modal distributions while providing the ability to compute the likelihood of the data and perform efficient sampling in both directions (from base to complex and vice versa). However, it is important to note that since LightGBMLSS is based on a one vs. all estimation strategy , where a separate tree is grown for each parameter, estimating many parameters for a large dataset can become computationally expensive. For more details, we refer to our related paper Alexander M\u00e4rz and Thomas Kneib (2022): Distributional Gradient Boosting Machines . Imports \u00b6 In [1]: Copied! from lightgbmlss.model import * from lightgbmlss.distributions.SplineFlow import * from lightgbmlss.distributions.flow_utils import NormalizingFlowClass from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data from scipy.stats import norm import plotnine from plotnine import * plotnine . options . figure_size = ( 20 , 10 ) from lightgbmlss.model import * from lightgbmlss.distributions.SplineFlow import * from lightgbmlss.distributions.flow_utils import NormalizingFlowClass from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data from scipy.stats import norm import plotnine from plotnine import * plotnine.options.figure_size = (20, 10) Data \u00b6 In [2]: Copied! # The data is simulated as a Gaussian, where x is the only true feature and all others are noise variables # loc = 10 # scale = 1 + 4 * ((0.3 < x) & (x < 0.5)) + 2 * (x > 0.7) train , test = load_simulated_gaussian_data () X_train , y_train = train . filter ( regex = \"x\" ), train [ \"y\" ] . values X_test , y_test = test . filter ( regex = \"x\" ), test [ \"y\" ] . values dtrain = lgb . Dataset ( X_train , label = y_train ) # The data is simulated as a Gaussian, where x is the only true feature and all others are noise variables # loc = 10 # scale = 1 + 4 * ((0.3 < x) & (x < 0.5)) + 2 * (x > 0.7) train, test = load_simulated_gaussian_data() X_train, y_train = train.filter(regex=\"x\"), train[\"y\"].values X_test, y_test = test.filter(regex=\"x\"), test[\"y\"].values dtrain = lgb.Dataset(X_train, label=y_train) Select Normalizing Flow \u00b6 In the following, we specify a list of candidate normalizing flows. The function flow_select returns the negative log-likelihood of each specification. The normalizing flow with the lowest negative log-likelihood is selected. The function also plots the density of the target variable and the fitted density, using the best suitable normalizing flow among the specified ones. However, note that choosing the best performing flow based solely on training data may lead to overfitting, since normalizing flows have a higher risk of overfitting compared to parametric distributions. When using normalizing flows, it is crucial to carefully select the specifications to strike a balance between model complexity and generalization ability. In [3]: Copied! # See ?SplineFlow for an overview. bound = np . max ([ np . abs ( y_train . min ()), y_train . max ()]) target_support = \"real\" candidate_flows = [ SplineFlow ( target_support = target_support , count_bins = 2 , bound = bound , order = \"linear\" ), SplineFlow ( target_support = target_support , count_bins = 4 , bound = bound , order = \"linear\" ), SplineFlow ( target_support = target_support , count_bins = 6 , bound = bound , order = \"linear\" ), SplineFlow ( target_support = target_support , count_bins = 8 , bound = bound , order = \"linear\" ), SplineFlow ( target_support = target_support , count_bins = 12 , bound = bound , order = \"linear\" ), SplineFlow ( target_support = target_support , count_bins = 16 , bound = bound , order = \"linear\" ), SplineFlow ( target_support = target_support , count_bins = 20 , bound = bound , order = \"linear\" ), SplineFlow ( target_support = target_support , count_bins = 2 , bound = bound , order = \"quadratic\" ), SplineFlow ( target_support = target_support , count_bins = 4 , bound = bound , order = \"quadratic\" ), SplineFlow ( target_support = target_support , count_bins = 6 , bound = bound , order = \"quadratic\" ), SplineFlow ( target_support = target_support , count_bins = 8 , bound = bound , order = \"quadratic\" ), SplineFlow ( target_support = target_support , count_bins = 12 , bound = bound , order = \"quadratic\" ), SplineFlow ( target_support = target_support , count_bins = 16 , bound = bound , order = \"quadratic\" ), SplineFlow ( target_support = target_support , count_bins = 20 , bound = bound , order = \"quadratic\" ), ] flow_nll = NormalizingFlowClass () . flow_select ( target = y_train , candidate_flows = candidate_flows , max_iter = 50 , n_samples = 10000 , plot = True , figure_size = ( 12 , 5 )) flow_nll # See ?SplineFlow for an overview. bound = np.max([np.abs(y_train.min()), y_train.max()]) target_support = \"real\" candidate_flows = [ SplineFlow(target_support=target_support, count_bins=2, bound=bound, order=\"linear\"), SplineFlow(target_support=target_support, count_bins=4, bound=bound, order=\"linear\"), SplineFlow(target_support=target_support, count_bins=6, bound=bound, order=\"linear\"), SplineFlow(target_support=target_support, count_bins=8, bound=bound, order=\"linear\"), SplineFlow(target_support=target_support, count_bins=12, bound=bound, order=\"linear\"), SplineFlow(target_support=target_support, count_bins=16, bound=bound, order=\"linear\"), SplineFlow(target_support=target_support, count_bins=20, bound=bound, order=\"linear\"), SplineFlow(target_support=target_support, count_bins=2, bound=bound, order=\"quadratic\"), SplineFlow(target_support=target_support, count_bins=4, bound=bound, order=\"quadratic\"), SplineFlow(target_support=target_support, count_bins=6, bound=bound, order=\"quadratic\"), SplineFlow(target_support=target_support, count_bins=8, bound=bound, order=\"quadratic\"), SplineFlow(target_support=target_support, count_bins=12, bound=bound, order=\"quadratic\"), SplineFlow(target_support=target_support, count_bins=16, bound=bound, order=\"quadratic\"), SplineFlow(target_support=target_support, count_bins=20, bound=bound, order=\"quadratic\"), ] flow_nll = NormalizingFlowClass().flow_select(target=y_train, candidate_flows=candidate_flows, max_iter=50, n_samples=10000, plot=True, figure_size=(12, 5)) flow_nll Fitting of candidate normalizing flows completed: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14/14 [01:20<00:00, 5.78s/it] Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } nll NormFlow rank 1 16595.917006 SplineFlow(count_bins: 20, order: linear) 2 16608.693807 SplineFlow(count_bins: 12, order: quadratic) 3 16622.862265 SplineFlow(count_bins: 16, order: quadratic) 4 16640.156074 SplineFlow(count_bins: 6, order: linear) 5 16640.611035 SplineFlow(count_bins: 16, order: linear) 6 16649.404709 SplineFlow(count_bins: 8, order: linear) 7 16651.375456 SplineFlow(count_bins: 8, order: quadratic) 8 16653.378393 SplineFlow(count_bins: 6, order: quadratic) 9 16674.331780 SplineFlow(count_bins: 12, order: linear) 10 16822.629927 SplineFlow(count_bins: 4, order: quadratic) 11 16902.398862 SplineFlow(count_bins: 20, order: quadratic) 12 17538.588405 SplineFlow(count_bins: 4, order: linear) 13 17692.968508 SplineFlow(count_bins: 2, order: linear) 14 17737.569055 SplineFlow(count_bins: 2, order: quadratic) Normalizing Flow Specification \u00b6 Even though SplineFlow(count_bins: 20, order: linear) shows the best fit to the data, we choose a more parameter parsimonious specification (recall that a separate tree is grown for each parameter): for count_bins=20, we need to estimate 3*count_bins + (count_bins-1) = 79 parameters for count_bins=8, we need to estimate 3*count_bins + (count_bins-1) = 31 parameters In [5]: Copied! # Specifies Spline-Flow. See ?SplineFlow for an overview. bound = np . max ([ np . abs ( y_train . min ()), y_train . max ()]) lgblss = LightGBMLSS ( SplineFlow ( target_support = \"real\" , # Specifies the support of the target. Options are \"real\", \"positive\", \"positive_integer\" or \"unit_interval\" count_bins = 8 , # The number of segments comprising the spline. bound = bound , # By adjusting the value, you can control the size of the bounding box and consequently control the range of inputs that the spline transform operates on. order = \"linear\" , # The order of the spline. Options are \"linear\" or \"quadratic\". stabilization = \"None\" , # Options are \"None\", \"MAD\" or \"L2\". loss_fn = \"nll\" # Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\"(continuous ranked probability score). ) ) # Specifies Spline-Flow. See ?SplineFlow for an overview. bound = np.max([np.abs(y_train.min()), y_train.max()]) lgblss = LightGBMLSS( SplineFlow(target_support=\"real\", # Specifies the support of the target. Options are \"real\", \"positive\", \"positive_integer\" or \"unit_interval\" count_bins=8, # The number of segments comprising the spline. bound=bound, # By adjusting the value, you can control the size of the bounding box and consequently control the range of inputs that the spline transform operates on. order=\"linear\", # The order of the spline. Options are \"linear\" or \"quadratic\". stabilization=\"None\", # Options are \"None\", \"MAD\" or \"L2\". loss_fn=\"nll\" # Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\"(continuous ranked probability score). ) ) Hyper-Parameter Optimization \u00b6 Any LightGBM hyperparameter can be tuned, where the structure of the parameter dictionary needs to be as follows: - Float/Int sample_type - {\"param_name\": [\"sample_type\", low, high, log]} - sample_type: str, Type of sampling, e.g., \"float\" or \"int\" - low: int, Lower endpoint of the range of suggested values - high: int, Upper endpoint of the range of suggested values - log: bool, Flag to sample the value from the log domain or not - Example: {\"eta\": \"float\", low=1e-5, high=1, log=True]} - Categorical sample_type - {\"param_name\": [\"sample_type\", [\"choice1\", \"choice2\", \"choice3\", \"...\"]]} - sample_type: str, Type of sampling, either \"categorical\" - choice1, choice2, choice3, ...: str, Possible choices for the parameter - Example: {\"boosting\": [\"categorical\", [\"gbdt\", \"dart\"]]} - For parameters without tunable choice (this is needed if tree_method = \"gpu_hist\" and gpu_id needs to be specified) - {\"param_name\": [\"none\", [value]]}, - param_name: str, Name of the parameter - value: int, Value of the parameter - Example: {\"gpu_id\": [\"none\", [0]]} In [6]: Copied! param_dict = { \"eta\" : [ \"float\" , { \"low\" : 1e-5 , \"high\" : 1 , \"log\" : True }], \"max_depth\" : [ \"int\" , { \"low\" : 1 , \"high\" : 10 , \"log\" : False }], \"num_leaves\" : [ \"int\" , { \"low\" : 255 , \"high\" : 255 , \"log\" : False }], # set to constant for this example \"min_data_in_leaf\" : [ \"int\" , { \"low\" : 20 , \"high\" : 20 , \"log\" : False }], # set to constant for this example \"min_gain_to_split\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 40 , \"log\" : False }], \"min_sum_hessian_in_leaf\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 500 , \"log\" : True }], \"subsample\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"feature_fraction\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"boosting\" : [ \"categorical\" , [ \"gbdt\" ]], } np . random . seed ( 123 ) opt_param = lgblss . hyper_opt ( param_dict , dtrain , num_boost_round = 100 , # Number of boosting iterations. nfold = 5 , # Number of cv-folds. early_stopping_rounds = 20 , # Number of early-stopping rounds max_minutes = 1000 , # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials = 50 , # The number of trials. If this argument is set to None, there is no limitation on the number of trials. silence = False , # Controls the verbosity of the trail, i.e., user can silence the outputs of the trail. seed = 123 , # Seed used to generate cv-folds. hp_seed = None # Seed for random number generator used in the Bayesian hyperparameter search. ) param_dict = { \"eta\": [\"float\", {\"low\": 1e-5, \"high\": 1, \"log\": True}], \"max_depth\": [\"int\", {\"low\": 1, \"high\": 10, \"log\": False}], \"num_leaves\": [\"int\", {\"low\": 255, \"high\": 255, \"log\": False}], # set to constant for this example \"min_data_in_leaf\": [\"int\", {\"low\": 20, \"high\": 20, \"log\": False}], # set to constant for this example \"min_gain_to_split\": [\"float\", {\"low\": 1e-8, \"high\": 40, \"log\": False}], \"min_sum_hessian_in_leaf\": [\"float\", {\"low\": 1e-8, \"high\": 500, \"log\": True}], \"subsample\": [\"float\", {\"low\": 0.2, \"high\": 1.0, \"log\": False}], \"feature_fraction\": [\"float\", {\"low\": 0.2, \"high\": 1.0, \"log\": False}], \"boosting\": [\"categorical\", [\"gbdt\"]], } np.random.seed(123) opt_param = lgblss.hyper_opt(param_dict, dtrain, num_boost_round=100, # Number of boosting iterations. nfold=5, # Number of cv-folds. early_stopping_rounds=20, # Number of early-stopping rounds max_minutes=1000, # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials=50, # The number of trials. 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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t&&null!=t.key?s.escape(t.key):e.toString(36)}function i(t,e,n,o){var p=typeof t;if(\"undefined\"!==p&&\"boolean\"!==p||(t=null),null===t||\"string\"===p||\"number\"===p||\"object\"===p&&t.$$typeof===u)return n(o,t,\"\"===e?l+r(t,0):e),1;var h,d,v=0,g=\"\"===e?l:e+f;if(Array.isArray(t))for(var m=0;m 0.7 )) q2 = norm . ppf ( quant_sel [ 1 ], loc = 10 , scale = 1 + 4 * (( 0.3 < test [ \"x_true\" ] . values ) & ( test [ \"x_true\" ] . values < 0.5 )) + 2 * ( test [ \"x_true\" ] . values > 0.7 )) test [ \"quant\" ] = np . where ( test [ \"y\" ] . values < q1 , 0 , np . where ( test [ \"y\" ] . values < q2 , 1 , 2 )) test [ \"alpha\" ] = np . where ( test [ \"y\" ] . values <= q1 , 1 , np . where ( test [ \"y\" ] . values >= q2 , 1 , 0 )) df_quantiles = test [ test [ \"alpha\" ] == 1 ] # Lower Bound yl = list ( set ( q1 )) yl . sort () yl = [ yl [ 2 ], yl [ 0 ], yl [ 2 ], yl [ 1 ], yl [ 1 ]] sfunl = pd . DataFrame ({ \"x_true\" :[ 0 , 0.3 , 0.5 , 0.7 , 1 ], \"y\" : yl }) # Upper Bound yu = list ( set ( q2 )) yu . sort () yu = [ yu [ 0 ], yu [ 2 ], yu [ 0 ], yu [ 1 ], yu [ 1 ]] sfunu = pd . DataFrame ({ \"x_true\" :[ 0 , 0.3 , 0.5 , 0.7 , 1 ], \"y\" : yu }) ### # Predicted Quantiles ### test [ \"lb\" ] = pred_quantiles . iloc [:, 0 ] test [ \"ub\" ] = pred_quantiles . iloc [:, 1 ] ### # Plot ### ( ggplot ( test , aes ( \"x_true\" , \"y\" )) + geom_point ( alpha = 0.2 , color = \"black\" , size = 2 ) + theme_bw ( base_size = 15 ) + theme ( legend_position = \"none\" , plot_title = element_text ( hjust = 0.5 )) + labs ( title = \"LightGBMLSS Regression - Simulated Data Example\" , x = \"x\" ) + geom_line ( aes ( \"x_true\" , \"ub\" ), size = 1 , color = \"blue\" , alpha = 0.7 ) + geom_line ( aes ( \"x_true\" , \"lb\" ), size = 1 , color = \"blue\" , alpha = 0.7 ) + geom_point ( df_quantiles , aes ( \"x_true\" , \"y\" ), color = \"red\" , alpha = 0.7 , size = 2 ) + geom_step ( sfunl , aes ( \"x_true\" , \"y\" ), size = 1 , linetype = \"dashed\" ) + geom_step ( sfunu , aes ( \"x_true\" , \"y\" ), size = 1 , linetype = \"dashed\" ) ) np.random.seed(123) ### # Actual Quantiles ### q1 = norm.ppf(quant_sel[0], loc = 10, scale = 1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7)) q2 = norm.ppf(quant_sel[1], loc = 10, scale = 1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7)) test[\"quant\"] = np.where(test[\"y\"].values < q1, 0, np.where(test[\"y\"].values < q2, 1, 2)) test[\"alpha\"] = np.where(test[\"y\"].values <= q1, 1, np.where(test[\"y\"].values >= q2, 1, 0)) df_quantiles = test[test[\"alpha\"] == 1] # Lower Bound yl = list(set(q1)) yl.sort() yl = [yl[2],yl[0],yl[2],yl[1],yl[1]] sfunl = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yl}) # Upper Bound yu = list(set(q2)) yu.sort() yu = [yu[0],yu[2],yu[0],yu[1],yu[1]] sfunu = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yu}) ### # Predicted Quantiles ### test[\"lb\"] = pred_quantiles.iloc[:,0] test[\"ub\"] = pred_quantiles.iloc[:,1] ### # Plot ### (ggplot(test, aes(\"x_true\", \"y\")) + geom_point(alpha = 0.2, color = \"black\", size = 2) + theme_bw(base_size=15) + theme(legend_position=\"none\", plot_title = element_text(hjust = 0.5)) + labs(title = \"LightGBMLSS Regression - Simulated Data Example\", x=\"x\") + geom_line(aes(\"x_true\", \"ub\"), size = 1, color = \"blue\", alpha = 0.7) + geom_line(aes(\"x_true\", \"lb\"), size = 1, color = \"blue\", alpha = 0.7) + geom_point(df_quantiles, aes(\"x_true\", \"y\"), color = \"red\", alpha = 0.7, size = 2) + geom_step(sfunl, aes(\"x_true\", \"y\"), size = 1, linetype = \"dashed\") + geom_step(sfunu, aes(\"x_true\", \"y\"), size = 1, linetype = \"dashed\") ) Out[25]:
True vs. Predicted Distributional Parameters \u00b6 In the following figure, we compare the true parameters of the Gaussian with the ones predicted by LightGBMLSS. The below figure shows that the estimated parameters closely match the true ones (recall that the location parameter $\\mu=10$ is simulated as being a constant). In [26]: Copied! dist_params = [ \"loc\" , \"scale\" ] # Calculate parameters from samples sample_params = pd . DataFrame . from_dict ( { \"loc\" : pred_samples . mean ( axis = 1 ), \"scale\" : pred_samples . std ( axis = 1 ), \"x_true\" : X_test [ \"x_true\" ] . values } ) # Data with predicted values plot_df_predt = pd . melt ( sample_params [[ \"x_true\" ] + dist_params ], id_vars = \"x_true\" , value_vars = dist_params ) plot_df_predt [ \"type\" ] = \"PREDICT\" # Data with actual values plot_df_actual = pd . melt ( test [[ \"x_true\" ] + dist_params ], id_vars = \"x_true\" , value_vars = dist_params ) plot_df_actual [ \"type\" ] = \"TRUE\" # Combine data for plotting plot_df = pd . concat ([ plot_df_predt , plot_df_actual ]) plot_df [ \"variable\" ] = plot_df . variable . str . upper () plot_df [ \"type\" ] = pd . Categorical ( plot_df [ \"type\" ], categories = [ \"PREDICT\" , \"TRUE\" ]) # Plot ( ggplot ( plot_df , aes ( x = \"x_true\" , y = \"value\" , color = \"type\" )) + geom_line ( size = 1.1 ) + facet_wrap ( \"variable\" , scales = \"free\" ) + labs ( title = \"Parameters of univariate Gaussian predicted with LightGBMLSS\" , x = \"\" , y = \"\" ) + theme_bw ( base_size = 15 ) + theme ( legend_position = \"bottom\" , plot_title = element_text ( hjust = 0.5 ), legend_title = element_blank ()) ) dist_params = [\"loc\", \"scale\"] # Calculate parameters from samples sample_params = pd.DataFrame.from_dict( { \"loc\": pred_samples.mean(axis=1), \"scale\": pred_samples.std(axis=1), \"x_true\": X_test[\"x_true\"].values } ) # Data with predicted values plot_df_predt = pd.melt(sample_params[[\"x_true\"] + dist_params], id_vars=\"x_true\", value_vars=dist_params) plot_df_predt[\"type\"] = \"PREDICT\" # Data with actual values plot_df_actual = pd.melt(test[[\"x_true\"] + dist_params], id_vars=\"x_true\", value_vars=dist_params) plot_df_actual[\"type\"] = \"TRUE\" # Combine data for plotting plot_df = pd.concat([plot_df_predt, plot_df_actual]) plot_df[\"variable\"] = plot_df.variable.str.upper() plot_df[\"type\"] = pd.Categorical(plot_df[\"type\"], categories = [\"PREDICT\", \"TRUE\"]) # Plot (ggplot(plot_df, aes(x=\"x_true\", y=\"value\", color=\"type\")) + geom_line(size=1.1) + facet_wrap(\"variable\", scales=\"free\") + labs(title=\"Parameters of univariate Gaussian predicted with LightGBMLSS\", x=\"\", y=\"\") + theme_bw(base_size=15) + theme(legend_position=\"bottom\", plot_title = element_text(hjust = 0.5), legend_title = element_blank()) ) Out[26]:
Density Plots \u00b6 In [27]: Copied! pred_df = pd . melt ( pred_samples . iloc [:, 0 : 5 ]) actual_df = pd . DataFrame . from_dict ({ \"variable\" : \"ACTUAL\" , \"value\" : y_test . reshape ( - 1 ,)}) plot_df = pd . concat ([ pred_df , actual_df ]) ( ggplot ( plot_df , aes ( x = \"value\" , color = \"variable\" , fill = \"variable\" )) + geom_density ( alpha = 0.4 ) + facet_wrap ( \"variable\" , ncol = 2 ) + theme_bw ( base_size = 15 ) + theme ( plot_title = element_text ( hjust = 0.5 )) + theme ( legend_position = \"none\" ) ) pred_df = pd.melt(pred_samples.iloc[:,0:5]) actual_df = pd.DataFrame.from_dict({\"variable\": \"ACTUAL\", \"value\": y_test.reshape(-1,)}) plot_df = pd.concat([pred_df, actual_df]) ( ggplot(plot_df, aes(x=\"value\", color=\"variable\", fill=\"variable\")) + geom_density(alpha=0.4) + facet_wrap(\"variable\", ncol=2) + theme_bw(base_size=15) + theme(plot_title = element_text(hjust = 0.5)) + theme(legend_position=\"none\") ) Out[27]:
Actual vs. Predicted \u00b6 Since we predict the entire conditional distribution, we can overlay the point predictions with predicted densities, from which we can also derive quantiles of interest. In [28]: Copied! y_pred = [] n_examples = 8 q_sel = [ 0.05 , 0.95 ] y_sel = 0 samples_arr = pred_samples . values . reshape ( - 1 , n_samples ) for i in range ( n_examples ): y_samples = pd . DataFrame ( samples_arr [ i ,:] . reshape ( - 1 , 1 ), columns = [ \"PREDICT_DENSITY\" ]) y_samples [ \"PREDICT_POINT\" ] = y_samples [ \"PREDICT_DENSITY\" ] . mean () y_samples [ \"PREDICT_Q05\" ] = y_samples [ \"PREDICT_DENSITY\" ] . quantile ( q = q_sel [ 0 ]) y_samples [ \"PREDICT_Q95\" ] = y_samples [ \"PREDICT_DENSITY\" ] . quantile ( q = q_sel [ 1 ]) y_samples [ \"ACTUAL\" ] = y_test [ i ] y_samples [ \"obs\" ] = f \"Obervation { i + 1 } \" y_pred . append ( y_samples ) pred_df = pd . melt ( pd . concat ( y_pred , axis = 0 ), id_vars = \"obs\" ) pred_df [ \"obs\" ] = pd . Categorical ( pred_df [ \"obs\" ], categories = [ f \"Obervation { i + 1 } \" for i in range ( n_examples )]) df_actual , df_pred_dens , df_pred_point , df_q05 , df_q95 = [ x for _ , x in pred_df . groupby ( \"variable\" )] plot_pred = ( ggplot ( pred_df , aes ( color = \"variable\" )) + stat_density ( df_pred_dens , aes ( x = \"value\" ), size = 1.1 ) + geom_point ( df_pred_point , aes ( x = \"value\" , y = 0 ), size = 1.4 ) + geom_point ( df_actual , aes ( x = \"value\" , y = 0 ), size = 1.4 ) + geom_vline ( df_q05 , aes ( xintercept = \"value\" , fill = \"variable\" , color = \"variable\" ), linetype = \"dashed\" , size = 1.1 ) + geom_vline ( df_q95 , aes ( xintercept = \"value\" , fill = \"variable\" , color = \"variable\" ), linetype = \"dashed\" , size = 1.1 ) + facet_wrap ( \"obs\" , scales = \"free\" , ncol = 4 ) + labs ( title = \"Predicted vs. Actual \\n \" , x = \"\" ) + theme_bw ( base_size = 15 ) + theme ( plot_title = element_text ( hjust = 0.5 )) + scale_fill_brewer ( type = \"qual\" , palette = \"Dark2\" ) + theme ( legend_position = \"bottom\" , legend_title = element_blank () ) ) print ( plot_pred ) y_pred = [] n_examples = 8 q_sel = [0.05, 0.95] y_sel=0 samples_arr = pred_samples.values.reshape(-1,n_samples) for i in range(n_examples): y_samples = pd.DataFrame(samples_arr[i,:].reshape(-1,1), columns=[\"PREDICT_DENSITY\"]) y_samples[\"PREDICT_POINT\"] = y_samples[\"PREDICT_DENSITY\"].mean() y_samples[\"PREDICT_Q05\"] = y_samples[\"PREDICT_DENSITY\"].quantile(q=q_sel[0]) y_samples[\"PREDICT_Q95\"] = y_samples[\"PREDICT_DENSITY\"].quantile(q=q_sel[1]) y_samples[\"ACTUAL\"] = y_test[i] y_samples[\"obs\"]= f\"Obervation {i+1}\" y_pred.append(y_samples) pred_df = pd.melt(pd.concat(y_pred, axis=0), id_vars=\"obs\") pred_df[\"obs\"] = pd.Categorical(pred_df[\"obs\"], categories=[f\"Obervation {i+1}\" for i in range(n_examples)]) df_actual, df_pred_dens, df_pred_point, df_q05, df_q95 = [x for _, x in pred_df.groupby(\"variable\")] plot_pred = ( ggplot(pred_df, aes(color=\"variable\")) + stat_density(df_pred_dens, aes(x=\"value\"), size=1.1) + geom_point(df_pred_point, aes(x=\"value\", y=0), size=1.4) + geom_point(df_actual, aes(x=\"value\", y=0), size=1.4) + geom_vline(df_q05, aes(xintercept=\"value\", fill=\"variable\", color=\"variable\"), linetype=\"dashed\", size=1.1) + geom_vline(df_q95, aes(xintercept=\"value\", fill=\"variable\", color=\"variable\"), linetype=\"dashed\", size=1.1) + facet_wrap(\"obs\", scales=\"free\", ncol=4) + labs(title=\"Predicted vs. Actual \\n\", x = \"\") + theme_bw(base_size=15) + theme(plot_title = element_text(hjust = 0.5)) + scale_fill_brewer(type=\"qual\", palette=\"Dark2\") + theme(legend_position=\"bottom\", legend_title = element_blank() ) ) print(plot_pred)","title":"Spline Flow Regression"},{"location":"examples/SplineFlow_Regression/#spline-flow-regression","text":"Normalizing flows transform a simple distribution into a complex data distribution through a series of invertible transformations. The key steps involved in the operation of normalizing flows are as follows (from left to right):","title":"Spline Flow Regression"},{"location":"examples/SplineFlow_Regression/#imports","text":"In [1]: Copied! from lightgbmlss.model import * from lightgbmlss.distributions.SplineFlow import * from lightgbmlss.distributions.flow_utils import NormalizingFlowClass from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data from scipy.stats import norm import plotnine from plotnine import * plotnine . options . figure_size = ( 20 , 10 ) from lightgbmlss.model import * from lightgbmlss.distributions.SplineFlow import * from lightgbmlss.distributions.flow_utils import NormalizingFlowClass from lightgbmlss.datasets.data_loader import load_simulated_gaussian_data from scipy.stats import norm import plotnine from plotnine import * plotnine.options.figure_size = (20, 10)","title":"Imports"},{"location":"examples/SplineFlow_Regression/#data","text":"In [2]: Copied! # The data is simulated as a Gaussian, where x is the only true feature and all others are noise variables # loc = 10 # scale = 1 + 4 * ((0.3 < x) & (x < 0.5)) + 2 * (x > 0.7) train , test = load_simulated_gaussian_data () X_train , y_train = train . filter ( regex = \"x\" ), train [ \"y\" ] . values X_test , y_test = test . filter ( regex = \"x\" ), test [ \"y\" ] . values dtrain = lgb . Dataset ( X_train , label = y_train ) # The data is simulated as a Gaussian, where x is the only true feature and all others are noise variables # loc = 10 # scale = 1 + 4 * ((0.3 < x) & (x < 0.5)) + 2 * (x > 0.7) train, test = load_simulated_gaussian_data() X_train, y_train = train.filter(regex=\"x\"), train[\"y\"].values X_test, y_test = test.filter(regex=\"x\"), test[\"y\"].values dtrain = lgb.Dataset(X_train, label=y_train)","title":"Data"},{"location":"examples/SplineFlow_Regression/#select-normalizing-flow","text":"In the following, we specify a list of candidate normalizing flows. The function flow_select returns the negative log-likelihood of each specification. The normalizing flow with the lowest negative log-likelihood is selected. The function also plots the density of the target variable and the fitted density, using the best suitable normalizing flow among the specified ones. However, note that choosing the best performing flow based solely on training data may lead to overfitting, since normalizing flows have a higher risk of overfitting compared to parametric distributions. When using normalizing flows, it is crucial to carefully select the specifications to strike a balance between model complexity and generalization ability. In [3]: Copied! # See ?SplineFlow for an overview. bound = np . max ([ np . abs ( y_train . min ()), y_train . max ()]) target_support = \"real\" candidate_flows = [ SplineFlow ( target_support = target_support , count_bins = 2 , bound = bound , order = \"linear\" ), SplineFlow ( target_support = target_support , count_bins = 4 , bound = bound , order = \"linear\" ), SplineFlow ( target_support = target_support , count_bins = 6 , bound = bound , order = \"linear\" ), SplineFlow ( target_support = target_support , count_bins = 8 , bound = bound , order = \"linear\" ), SplineFlow ( target_support = target_support , count_bins = 12 , bound = bound , order = \"linear\" ), SplineFlow ( target_support = target_support , count_bins = 16 , bound = bound , order = \"linear\" ), SplineFlow ( target_support = target_support , count_bins = 20 , bound = bound , order = \"linear\" ), SplineFlow ( target_support = target_support , count_bins = 2 , bound = bound , order = \"quadratic\" ), SplineFlow ( target_support = target_support , count_bins = 4 , bound = bound , order = \"quadratic\" ), SplineFlow ( target_support = target_support , count_bins = 6 , bound = bound , order = \"quadratic\" ), SplineFlow ( target_support = target_support , count_bins = 8 , bound = bound , order = \"quadratic\" ), SplineFlow ( target_support = target_support , count_bins = 12 , bound = bound , order = \"quadratic\" ), SplineFlow ( target_support = target_support , count_bins = 16 , bound = bound , order = \"quadratic\" ), SplineFlow ( target_support = target_support , count_bins = 20 , bound = bound , order = \"quadratic\" ), ] flow_nll = NormalizingFlowClass () . flow_select ( target = y_train , candidate_flows = candidate_flows , max_iter = 50 , n_samples = 10000 , plot = True , figure_size = ( 12 , 5 )) flow_nll # See ?SplineFlow for an overview. bound = np.max([np.abs(y_train.min()), y_train.max()]) target_support = \"real\" candidate_flows = [ SplineFlow(target_support=target_support, count_bins=2, bound=bound, order=\"linear\"), SplineFlow(target_support=target_support, count_bins=4, bound=bound, order=\"linear\"), SplineFlow(target_support=target_support, count_bins=6, bound=bound, order=\"linear\"), SplineFlow(target_support=target_support, count_bins=8, bound=bound, order=\"linear\"), SplineFlow(target_support=target_support, count_bins=12, bound=bound, order=\"linear\"), SplineFlow(target_support=target_support, count_bins=16, bound=bound, order=\"linear\"), SplineFlow(target_support=target_support, count_bins=20, bound=bound, order=\"linear\"), SplineFlow(target_support=target_support, count_bins=2, bound=bound, order=\"quadratic\"), SplineFlow(target_support=target_support, count_bins=4, bound=bound, order=\"quadratic\"), SplineFlow(target_support=target_support, count_bins=6, bound=bound, order=\"quadratic\"), SplineFlow(target_support=target_support, count_bins=8, bound=bound, order=\"quadratic\"), SplineFlow(target_support=target_support, count_bins=12, bound=bound, order=\"quadratic\"), SplineFlow(target_support=target_support, count_bins=16, bound=bound, order=\"quadratic\"), SplineFlow(target_support=target_support, count_bins=20, bound=bound, order=\"quadratic\"), ] flow_nll = NormalizingFlowClass().flow_select(target=y_train, candidate_flows=candidate_flows, max_iter=50, n_samples=10000, plot=True, figure_size=(12, 5)) flow_nll Fitting of candidate normalizing flows completed: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14/14 [01:20<00:00, 5.78s/it] Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } nll NormFlow rank 1 16595.917006 SplineFlow(count_bins: 20, order: linear) 2 16608.693807 SplineFlow(count_bins: 12, order: quadratic) 3 16622.862265 SplineFlow(count_bins: 16, order: quadratic) 4 16640.156074 SplineFlow(count_bins: 6, order: linear) 5 16640.611035 SplineFlow(count_bins: 16, order: linear) 6 16649.404709 SplineFlow(count_bins: 8, order: linear) 7 16651.375456 SplineFlow(count_bins: 8, order: quadratic) 8 16653.378393 SplineFlow(count_bins: 6, order: quadratic) 9 16674.331780 SplineFlow(count_bins: 12, order: linear) 10 16822.629927 SplineFlow(count_bins: 4, order: quadratic) 11 16902.398862 SplineFlow(count_bins: 20, order: quadratic) 12 17538.588405 SplineFlow(count_bins: 4, order: linear) 13 17692.968508 SplineFlow(count_bins: 2, order: linear) 14 17737.569055 SplineFlow(count_bins: 2, order: quadratic)","title":"Select Normalizing Flow"},{"location":"examples/SplineFlow_Regression/#normalizing-flow-specification","text":"Even though SplineFlow(count_bins: 20, order: linear) shows the best fit to the data, we choose a more parameter parsimonious specification (recall that a separate tree is grown for each parameter): for count_bins=20, we need to estimate 3*count_bins + (count_bins-1) = 79 parameters for count_bins=8, we need to estimate 3*count_bins + (count_bins-1) = 31 parameters In [5]: Copied! # Specifies Spline-Flow. See ?SplineFlow for an overview. bound = np . max ([ np . abs ( y_train . min ()), y_train . max ()]) lgblss = LightGBMLSS ( SplineFlow ( target_support = \"real\" , # Specifies the support of the target. Options are \"real\", \"positive\", \"positive_integer\" or \"unit_interval\" count_bins = 8 , # The number of segments comprising the spline. bound = bound , # By adjusting the value, you can control the size of the bounding box and consequently control the range of inputs that the spline transform operates on. order = \"linear\" , # The order of the spline. Options are \"linear\" or \"quadratic\". stabilization = \"None\" , # Options are \"None\", \"MAD\" or \"L2\". loss_fn = \"nll\" # Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\"(continuous ranked probability score). ) ) # Specifies Spline-Flow. See ?SplineFlow for an overview. bound = np.max([np.abs(y_train.min()), y_train.max()]) lgblss = LightGBMLSS( SplineFlow(target_support=\"real\", # Specifies the support of the target. Options are \"real\", \"positive\", \"positive_integer\" or \"unit_interval\" count_bins=8, # The number of segments comprising the spline. bound=bound, # By adjusting the value, you can control the size of the bounding box and consequently control the range of inputs that the spline transform operates on. order=\"linear\", # The order of the spline. Options are \"linear\" or \"quadratic\". stabilization=\"None\", # Options are \"None\", \"MAD\" or \"L2\". loss_fn=\"nll\" # Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\"(continuous ranked probability score). ) )","title":"Normalizing Flow Specification"},{"location":"examples/SplineFlow_Regression/#hyper-parameter-optimization","text":"Any LightGBM hyperparameter can be tuned, where the structure of the parameter dictionary needs to be as follows: - Float/Int sample_type - {\"param_name\": [\"sample_type\", low, high, log]} - sample_type: str, Type of sampling, e.g., \"float\" or \"int\" - low: int, Lower endpoint of the range of suggested values - high: int, Upper endpoint of the range of suggested values - log: bool, Flag to sample the value from the log domain or not - Example: {\"eta\": \"float\", low=1e-5, high=1, log=True]} - Categorical sample_type - {\"param_name\": [\"sample_type\", [\"choice1\", \"choice2\", \"choice3\", \"...\"]]} - sample_type: str, Type of sampling, either \"categorical\" - choice1, choice2, choice3, ...: str, Possible choices for the parameter - Example: {\"boosting\": [\"categorical\", [\"gbdt\", \"dart\"]]} - For parameters without tunable choice (this is needed if tree_method = \"gpu_hist\" and gpu_id needs to be specified) - {\"param_name\": [\"none\", [value]]}, - param_name: str, Name of the parameter - value: int, Value of the parameter - Example: {\"gpu_id\": [\"none\", [0]]} In [6]: Copied! param_dict = { \"eta\" : [ \"float\" , { \"low\" : 1e-5 , \"high\" : 1 , \"log\" : True }], \"max_depth\" : [ \"int\" , { \"low\" : 1 , \"high\" : 10 , \"log\" : False }], \"num_leaves\" : [ \"int\" , { \"low\" : 255 , \"high\" : 255 , \"log\" : False }], # set to constant for this example \"min_data_in_leaf\" : [ \"int\" , { \"low\" : 20 , \"high\" : 20 , \"log\" : False }], # set to constant for this example \"min_gain_to_split\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 40 , \"log\" : False }], \"min_sum_hessian_in_leaf\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 500 , \"log\" : True }], \"subsample\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"feature_fraction\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"boosting\" : [ \"categorical\" , [ \"gbdt\" ]], } np . random . seed ( 123 ) opt_param = lgblss . hyper_opt ( param_dict , dtrain , num_boost_round = 100 , # Number of boosting iterations. nfold = 5 , # Number of cv-folds. early_stopping_rounds = 20 , # Number of early-stopping rounds max_minutes = 1000 , # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials = 50 , # The number of trials. If this argument is set to None, there is no limitation on the number of trials. silence = False , # Controls the verbosity of the trail, i.e., user can silence the outputs of the trail. seed = 123 , # Seed used to generate cv-folds. hp_seed = None # Seed for random number generator used in the Bayesian hyperparameter search. ) param_dict = { \"eta\": [\"float\", {\"low\": 1e-5, \"high\": 1, \"log\": True}], \"max_depth\": [\"int\", {\"low\": 1, \"high\": 10, \"log\": False}], \"num_leaves\": [\"int\", {\"low\": 255, \"high\": 255, \"log\": False}], # set to constant for this example \"min_data_in_leaf\": [\"int\", {\"low\": 20, \"high\": 20, \"log\": False}], # set to constant for this example \"min_gain_to_split\": [\"float\", {\"low\": 1e-8, \"high\": 40, \"log\": False}], \"min_sum_hessian_in_leaf\": [\"float\", {\"low\": 1e-8, \"high\": 500, \"log\": True}], \"subsample\": [\"float\", {\"low\": 0.2, \"high\": 1.0, \"log\": False}], \"feature_fraction\": [\"float\", {\"low\": 0.2, \"high\": 1.0, \"log\": False}], \"boosting\": [\"categorical\", [\"gbdt\"]], } np.random.seed(123) opt_param = lgblss.hyper_opt(param_dict, dtrain, num_boost_round=100, # Number of boosting iterations. nfold=5, # Number of cv-folds. early_stopping_rounds=20, # Number of early-stopping rounds max_minutes=1000, # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials=50, # The number of trials. 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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DataFrame ({ \"x_true\" :[ 0 , 0.3 , 0.5 , 0.7 , 1 ], \"y\" : yl }) # Upper Bound yu = list ( set ( q2 )) yu . sort () yu = [ yu [ 0 ], yu [ 2 ], yu [ 0 ], yu [ 1 ], yu [ 1 ]] sfunu = pd . DataFrame ({ \"x_true\" :[ 0 , 0.3 , 0.5 , 0.7 , 1 ], \"y\" : yu }) ### # Predicted Quantiles ### test [ \"lb\" ] = pred_quantiles . iloc [:, 0 ] test [ \"ub\" ] = pred_quantiles . iloc [:, 1 ] ### # Plot ### ( ggplot ( test , aes ( \"x_true\" , \"y\" )) + geom_point ( alpha = 0.2 , color = \"black\" , size = 2 ) + theme_bw ( base_size = 15 ) + theme ( legend_position = \"none\" , plot_title = element_text ( hjust = 0.5 )) + labs ( title = \"LightGBMLSS Regression - Simulated Data Example\" , x = \"x\" ) + geom_line ( aes ( \"x_true\" , \"ub\" ), size = 1 , color = \"blue\" , alpha = 0.7 ) + geom_line ( aes ( \"x_true\" , \"lb\" ), size = 1 , color = \"blue\" , alpha = 0.7 ) + geom_point ( df_quantiles , aes ( \"x_true\" , \"y\" ), color = \"red\" , alpha = 0.7 , size = 2 ) + geom_step ( sfunl , aes ( \"x_true\" , \"y\" ), size = 1 , linetype = \"dashed\" ) + geom_step ( sfunu , aes ( \"x_true\" , \"y\" ), size = 1 , linetype = \"dashed\" ) ) np.random.seed(123) ### # Actual Quantiles ### q1 = norm.ppf(quant_sel[0], loc = 10, scale = 1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7)) q2 = norm.ppf(quant_sel[1], loc = 10, scale = 1 + 4*((0.3 < test[\"x_true\"].values) & (test[\"x_true\"].values < 0.5)) + 2*(test[\"x_true\"].values > 0.7)) test[\"quant\"] = np.where(test[\"y\"].values < q1, 0, np.where(test[\"y\"].values < q2, 1, 2)) test[\"alpha\"] = np.where(test[\"y\"].values <= q1, 1, np.where(test[\"y\"].values >= q2, 1, 0)) df_quantiles = test[test[\"alpha\"] == 1] # Lower Bound yl = list(set(q1)) yl.sort() yl = [yl[2],yl[0],yl[2],yl[1],yl[1]] sfunl = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yl}) # Upper Bound yu = list(set(q2)) yu.sort() yu = [yu[0],yu[2],yu[0],yu[1],yu[1]] sfunu = pd.DataFrame({\"x_true\":[0, 0.3, 0.5, 0.7, 1], \"y\":yu}) ### # Predicted Quantiles ### test[\"lb\"] = pred_quantiles.iloc[:,0] test[\"ub\"] = pred_quantiles.iloc[:,1] ### # Plot ### (ggplot(test, aes(\"x_true\", \"y\")) + geom_point(alpha = 0.2, color = \"black\", size = 2) + theme_bw(base_size=15) + theme(legend_position=\"none\", plot_title = element_text(hjust = 0.5)) + labs(title = \"LightGBMLSS Regression - Simulated Data Example\", x=\"x\") + geom_line(aes(\"x_true\", \"ub\"), size = 1, color = \"blue\", alpha = 0.7) + geom_line(aes(\"x_true\", \"lb\"), size = 1, color = \"blue\", alpha = 0.7) + geom_point(df_quantiles, aes(\"x_true\", \"y\"), color = \"red\", alpha = 0.7, size = 2) + geom_step(sfunl, aes(\"x_true\", \"y\"), size = 1, linetype = \"dashed\") + geom_step(sfunu, aes(\"x_true\", \"y\"), size = 1, linetype = \"dashed\") ) Out[25]:
","title":"Plot of Actual vs. Predicted Quantiles"},{"location":"examples/SplineFlow_Regression/#true-vs-predicted-distributional-parameters","text":"In the following figure, we compare the true parameters of the Gaussian with the ones predicted by LightGBMLSS. The below figure shows that the estimated parameters closely match the true ones (recall that the location parameter $\\mu=10$ is simulated as being a constant). In [26]: Copied! dist_params = [ \"loc\" , \"scale\" ] # Calculate parameters from samples sample_params = pd . DataFrame . from_dict ( { \"loc\" : pred_samples . mean ( axis = 1 ), \"scale\" : pred_samples . std ( axis = 1 ), \"x_true\" : X_test [ \"x_true\" ] . values } ) # Data with predicted values plot_df_predt = pd . melt ( sample_params [[ \"x_true\" ] + dist_params ], id_vars = \"x_true\" , value_vars = dist_params ) plot_df_predt [ \"type\" ] = \"PREDICT\" # Data with actual values plot_df_actual = pd . melt ( test [[ \"x_true\" ] + dist_params ], id_vars = \"x_true\" , value_vars = dist_params ) plot_df_actual [ \"type\" ] = \"TRUE\" # Combine data for plotting plot_df = pd . concat ([ plot_df_predt , plot_df_actual ]) plot_df [ \"variable\" ] = plot_df . variable . str . upper () plot_df [ \"type\" ] = pd . Categorical ( plot_df [ \"type\" ], categories = [ \"PREDICT\" , \"TRUE\" ]) # Plot ( ggplot ( plot_df , aes ( x = \"x_true\" , y = \"value\" , color = \"type\" )) + geom_line ( size = 1.1 ) + facet_wrap ( \"variable\" , scales = \"free\" ) + labs ( title = \"Parameters of univariate Gaussian predicted with LightGBMLSS\" , x = \"\" , y = \"\" ) + theme_bw ( base_size = 15 ) + theme ( legend_position = \"bottom\" , plot_title = element_text ( hjust = 0.5 ), legend_title = element_blank ()) ) dist_params = [\"loc\", \"scale\"] # Calculate parameters from samples sample_params = pd.DataFrame.from_dict( { \"loc\": pred_samples.mean(axis=1), \"scale\": pred_samples.std(axis=1), \"x_true\": X_test[\"x_true\"].values } ) # Data with predicted values plot_df_predt = pd.melt(sample_params[[\"x_true\"] + dist_params], id_vars=\"x_true\", value_vars=dist_params) plot_df_predt[\"type\"] = \"PREDICT\" # Data with actual values plot_df_actual = pd.melt(test[[\"x_true\"] + dist_params], id_vars=\"x_true\", value_vars=dist_params) plot_df_actual[\"type\"] = \"TRUE\" # Combine data for plotting plot_df = pd.concat([plot_df_predt, plot_df_actual]) plot_df[\"variable\"] = plot_df.variable.str.upper() plot_df[\"type\"] = pd.Categorical(plot_df[\"type\"], categories = [\"PREDICT\", \"TRUE\"]) # Plot (ggplot(plot_df, aes(x=\"x_true\", y=\"value\", color=\"type\")) + geom_line(size=1.1) + facet_wrap(\"variable\", scales=\"free\") + labs(title=\"Parameters of univariate Gaussian predicted with LightGBMLSS\", x=\"\", y=\"\") + theme_bw(base_size=15) + theme(legend_position=\"bottom\", plot_title = element_text(hjust = 0.5), legend_title = element_blank()) ) Out[26]:
","title":"True vs. Predicted Distributional Parameters"},{"location":"examples/SplineFlow_Regression/#density-plots","text":"In [27]: Copied! pred_df = pd . melt ( pred_samples . iloc [:, 0 : 5 ]) actual_df = pd . DataFrame . from_dict ({ \"variable\" : \"ACTUAL\" , \"value\" : y_test . reshape ( - 1 ,)}) plot_df = pd . concat ([ pred_df , actual_df ]) ( ggplot ( plot_df , aes ( x = \"value\" , color = \"variable\" , fill = \"variable\" )) + geom_density ( alpha = 0.4 ) + facet_wrap ( \"variable\" , ncol = 2 ) + theme_bw ( base_size = 15 ) + theme ( plot_title = element_text ( hjust = 0.5 )) + theme ( legend_position = \"none\" ) ) pred_df = pd.melt(pred_samples.iloc[:,0:5]) actual_df = pd.DataFrame.from_dict({\"variable\": \"ACTUAL\", \"value\": y_test.reshape(-1,)}) plot_df = pd.concat([pred_df, actual_df]) ( ggplot(plot_df, aes(x=\"value\", color=\"variable\", fill=\"variable\")) + geom_density(alpha=0.4) + facet_wrap(\"variable\", ncol=2) + theme_bw(base_size=15) + theme(plot_title = element_text(hjust = 0.5)) + theme(legend_position=\"none\") ) Out[27]:
","title":"Density Plots"},{"location":"examples/SplineFlow_Regression/#actual-vs-predicted","text":"Since we predict the entire conditional distribution, we can overlay the point predictions with predicted densities, from which we can also derive quantiles of interest. In [28]: Copied! y_pred = [] n_examples = 8 q_sel = [ 0.05 , 0.95 ] y_sel = 0 samples_arr = pred_samples . values . reshape ( - 1 , n_samples ) for i in range ( n_examples ): y_samples = pd . DataFrame ( samples_arr [ i ,:] . reshape ( - 1 , 1 ), columns = [ \"PREDICT_DENSITY\" ]) y_samples [ \"PREDICT_POINT\" ] = y_samples [ \"PREDICT_DENSITY\" ] . mean () y_samples [ \"PREDICT_Q05\" ] = y_samples [ \"PREDICT_DENSITY\" ] . quantile ( q = q_sel [ 0 ]) y_samples [ \"PREDICT_Q95\" ] = y_samples [ \"PREDICT_DENSITY\" ] . quantile ( q = q_sel [ 1 ]) y_samples [ \"ACTUAL\" ] = y_test [ i ] y_samples [ \"obs\" ] = f \"Obervation { i + 1 } \" y_pred . append ( y_samples ) pred_df = pd . melt ( pd . concat ( y_pred , axis = 0 ), id_vars = \"obs\" ) pred_df [ \"obs\" ] = pd . Categorical ( pred_df [ \"obs\" ], categories = [ f \"Obervation { i + 1 } \" for i in range ( n_examples )]) df_actual , df_pred_dens , df_pred_point , df_q05 , df_q95 = [ x for _ , x in pred_df . groupby ( \"variable\" )] plot_pred = ( ggplot ( pred_df , aes ( color = \"variable\" )) + stat_density ( df_pred_dens , aes ( x = \"value\" ), size = 1.1 ) + geom_point ( df_pred_point , aes ( x = \"value\" , y = 0 ), size = 1.4 ) + geom_point ( df_actual , aes ( x = \"value\" , y = 0 ), size = 1.4 ) + geom_vline ( df_q05 , aes ( xintercept = \"value\" , fill = \"variable\" , color = \"variable\" ), linetype = \"dashed\" , size = 1.1 ) + geom_vline ( df_q95 , aes ( xintercept = \"value\" , fill = \"variable\" , color = \"variable\" ), linetype = \"dashed\" , size = 1.1 ) + facet_wrap ( \"obs\" , scales = \"free\" , ncol = 4 ) + labs ( title = \"Predicted vs. Actual \\n \" , x = \"\" ) + theme_bw ( base_size = 15 ) + theme ( plot_title = element_text ( hjust = 0.5 )) + scale_fill_brewer ( type = \"qual\" , palette = \"Dark2\" ) + theme ( legend_position = \"bottom\" , legend_title = element_blank () ) ) print ( plot_pred ) y_pred = [] n_examples = 8 q_sel = [0.05, 0.95] y_sel=0 samples_arr = pred_samples.values.reshape(-1,n_samples) for i in range(n_examples): y_samples = pd.DataFrame(samples_arr[i,:].reshape(-1,1), columns=[\"PREDICT_DENSITY\"]) y_samples[\"PREDICT_POINT\"] = y_samples[\"PREDICT_DENSITY\"].mean() y_samples[\"PREDICT_Q05\"] = y_samples[\"PREDICT_DENSITY\"].quantile(q=q_sel[0]) y_samples[\"PREDICT_Q95\"] = y_samples[\"PREDICT_DENSITY\"].quantile(q=q_sel[1]) y_samples[\"ACTUAL\"] = y_test[i] y_samples[\"obs\"]= f\"Obervation {i+1}\" y_pred.append(y_samples) pred_df = pd.melt(pd.concat(y_pred, axis=0), id_vars=\"obs\") pred_df[\"obs\"] = pd.Categorical(pred_df[\"obs\"], categories=[f\"Obervation {i+1}\" for i in range(n_examples)]) df_actual, df_pred_dens, df_pred_point, df_q05, df_q95 = [x for _, x in pred_df.groupby(\"variable\")] plot_pred = ( ggplot(pred_df, aes(color=\"variable\")) + stat_density(df_pred_dens, aes(x=\"value\"), size=1.1) + geom_point(df_pred_point, aes(x=\"value\", y=0), size=1.4) + geom_point(df_actual, aes(x=\"value\", y=0), size=1.4) + geom_vline(df_q05, aes(xintercept=\"value\", fill=\"variable\", color=\"variable\"), linetype=\"dashed\", size=1.1) + geom_vline(df_q95, aes(xintercept=\"value\", fill=\"variable\", color=\"variable\"), linetype=\"dashed\", size=1.1) + facet_wrap(\"obs\", scales=\"free\", ncol=4) + labs(title=\"Predicted vs. Actual \\n\", x = \"\") + theme_bw(base_size=15) + theme(plot_title = element_text(hjust = 0.5)) + scale_fill_brewer(type=\"qual\", palette=\"Dark2\") + theme(legend_position=\"bottom\", legend_title = element_blank() ) ) print(plot_pred)","title":"Actual vs. Predicted"},{"location":"examples/ZAGamma_Regression/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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tr:hover{background:rgba(66,165,245,.2)}table{width:max-content} /*# sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Zero-Adjusted Gamma Regression \u00b6 Imports \u00b6 In [2]: Copied! from lightgbmlss.model import * from lightgbmlss.distributions.ZAGamma import * from sklearn.model_selection import train_test_split import pandas as pd import plotnine from plotnine import * plotnine . options . figure_size = ( 18 , 9 ) from lightgbmlss.model import * from lightgbmlss.distributions.ZAGamma import * from sklearn.model_selection import train_test_split import pandas as pd import plotnine from plotnine import * plotnine.options.figure_size = (18, 9) Data \u00b6 In [3]: Copied! # The simulation example closely follows https://towardsdatascience.com/zero-inflated-regression-c7dfc656d8af np . random . seed ( 123 ) n_samples = 1000 data = pd . DataFrame ({ \"age\" : np . random . randint ( 1 , 100 , size = n_samples )}) data [ \"income\" ] = np . where (( data . age > 17 ) & ( data . age < 70 ), 1500 * data . age + 5000 + 10000 * np . random . randn ( n_samples ), 0 ) / 1000 y = data [ \"income\" ] . values X = data . drop ( columns = \"income\" ) X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0.2 , random_state = 123 ) dtrain = lgb . Dataset ( X_train , label = y_train ) # The simulation example closely follows https://towardsdatascience.com/zero-inflated-regression-c7dfc656d8af np.random.seed(123) n_samples = 1000 data = pd.DataFrame({\"age\": np.random.randint(1, 100, size=n_samples)}) data[\"income\"] = np.where((data.age > 17) & (data.age < 70), 1500*data.age + 5000 + 10000*np.random.randn(n_samples), 0) / 1000 y = data[\"income\"].values X = data.drop(columns=\"income\") X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123) dtrain = lgb.Dataset(X_train, label=y_train) Distribution Selection \u00b6 In [4]: Copied! # Specifies Zero-Adjusted Gamma distribution. See ?ZAGamma for an overview. lgblss = LightGBMLSS ( ZAGamma ( stabilization = \"None\" , # Options are \"None\", \"MAD\", \"L2\". response_fn = \"exp\" , # Function to transform the concentration and rate parameters, e.g., \"exp\" or \"softplus\". loss_fn = \"nll\" # Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\"(continuous ranked probability score).) ) ) # Specifies Zero-Adjusted Gamma distribution. See ?ZAGamma for an overview. lgblss = LightGBMLSS( ZAGamma(stabilization=\"None\", # Options are \"None\", \"MAD\", \"L2\". response_fn=\"exp\", # Function to transform the concentration and rate parameters, e.g., \"exp\" or \"softplus\". loss_fn=\"nll\" # Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\"(continuous ranked probability score).) ) ) Hyper-Parameter Optimization \u00b6 Any LightGBM hyperparameter can be tuned, where the structure of the parameter dictionary needs to be as follows: - Float/Int sample_type - {\"param_name\": [\"sample_type\", low, high, log]} - sample_type: str, Type of sampling, e.g., \"float\" or \"int\" - low: int, Lower endpoint of the range of suggested values - high: int, Upper endpoint of the range of suggested values - log: bool, Flag to sample the value from the log domain or not - Example: {\"eta\": \"float\", low=1e-5, high=1, log=True]} - Categorical sample_type - {\"param_name\": [\"sample_type\", [\"choice1\", \"choice2\", \"choice3\", \"...\"]]} - sample_type: str, Type of sampling, either \"categorical\" - choice1, choice2, choice3, ...: str, Possible choices for the parameter - Example: {\"boosting\": [\"categorical\", [\"gbdt\", \"dart\"]]} - For parameters without tunable choice (this is needed if tree_method = \"gpu_hist\" and gpu_id needs to be specified) - {\"param_name\": [\"none\", [value]]}, - param_name: str, Name of the parameter - value: int, Value of the parameter - Example: {\"gpu_id\": [\"none\", [0]]} In [5]: Copied! param_dict = { \"eta\" : [ \"float\" , { \"low\" : 1e-5 , \"high\" : 1 , \"log\" : True }], \"max_depth\" : [ \"int\" , { \"low\" : 1 , \"high\" : 10 , \"log\" : False }], \"min_gain_to_split\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 40 , \"log\" : False }], \"min_sum_hessian_in_leaf\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 500 , \"log\" : True }], \"subsample\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"feature_fraction\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"boosting\" : [ \"categorical\" , [ \"gbdt\" ]], } np . random . seed ( 123 ) opt_param = lgblss . hyper_opt ( param_dict , dtrain , num_boost_round = 100 , # Number of boosting iterations. nfold = 5 , # Number of cv-folds. early_stopping_rounds = 20 , # Number of early-stopping rounds max_minutes = 5 , # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials = 20 , # The number of trials. If this argument is set to None, there is no limitation on the number of trials. silence = False , # Controls the verbosity of the trail, i.e., user can silence the outputs of the trail. seed = 123 , # Seed used to generate cv-folds. hp_seed = None # Seed for random number generator used in the Bayesian hyperparameter search. ) param_dict = { \"eta\": [\"float\", {\"low\": 1e-5, \"high\": 1, \"log\": True}], \"max_depth\": [\"int\", {\"low\": 1, \"high\": 10, \"log\": False}], \"min_gain_to_split\": [\"float\", {\"low\": 1e-8, \"high\": 40, \"log\": False}], \"min_sum_hessian_in_leaf\": [\"float\", {\"low\": 1e-8, \"high\": 500, \"log\": True}], \"subsample\": [\"float\", {\"low\": 0.2, \"high\": 1.0, \"log\": False}], \"feature_fraction\": [\"float\", {\"low\": 0.2, \"high\": 1.0, \"log\": False}], \"boosting\": [\"categorical\", [\"gbdt\"]], } np.random.seed(123) opt_param = lgblss.hyper_opt(param_dict, dtrain, num_boost_round=100, # Number of boosting iterations. nfold=5, # Number of cv-folds. early_stopping_rounds=20, # Number of early-stopping rounds max_minutes=5, # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials=20, # The number of trials. 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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DataFrame ({ \"age\" : np . random . randint ( 1 , 100 , size = n_samples )}) data [ \"income\" ] = np . where (( data . age > 17 ) & ( data . age < 70 ), 1500 * data . age + 5000 + 10000 * np . random . randn ( n_samples ), 0 ) / 1000 y = data [ \"income\" ] . values X = data . drop ( columns = \"income\" ) X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0.2 , random_state = 123 ) dtrain = lgb . 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See ?ZAGamma for an overview. lgblss = LightGBMLSS ( ZAGamma ( stabilization = \"None\" , # Options are \"None\", \"MAD\", \"L2\". response_fn = \"exp\" , # Function to transform the concentration and rate parameters, e.g., \"exp\" or \"softplus\". loss_fn = \"nll\" # Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\"(continuous ranked probability score).) ) ) # Specifies Zero-Adjusted Gamma distribution. See ?ZAGamma for an overview. lgblss = LightGBMLSS( ZAGamma(stabilization=\"None\", # Options are \"None\", \"MAD\", \"L2\". response_fn=\"exp\", # Function to transform the concentration and rate parameters, e.g., \"exp\" or \"softplus\". loss_fn=\"nll\" # Loss function. Options are \"nll\" (negative log-likelihood) or \"crps\"(continuous ranked probability score).) ) )","title":"Distribution Selection"},{"location":"examples/ZAGamma_Regression/#hyper-parameter-optimization","text":"Any LightGBM hyperparameter can be tuned, where the structure of the parameter dictionary needs to be as follows: - Float/Int sample_type - {\"param_name\": [\"sample_type\", low, high, log]} - sample_type: str, Type of sampling, e.g., \"float\" or \"int\" - low: int, Lower endpoint of the range of suggested values - high: int, Upper endpoint of the range of suggested values - log: bool, Flag to sample the value from the log domain or not - Example: {\"eta\": \"float\", low=1e-5, high=1, log=True]} - Categorical sample_type - {\"param_name\": [\"sample_type\", [\"choice1\", \"choice2\", \"choice3\", \"...\"]]} - sample_type: str, Type of sampling, either \"categorical\" - choice1, choice2, choice3, ...: str, Possible choices for the parameter - Example: {\"boosting\": [\"categorical\", [\"gbdt\", \"dart\"]]} - For parameters without tunable choice (this is needed if tree_method = \"gpu_hist\" and gpu_id needs to be specified) - {\"param_name\": [\"none\", [value]]}, - param_name: str, Name of the parameter - value: int, Value of the parameter - Example: {\"gpu_id\": [\"none\", [0]]} In [5]: Copied! param_dict = { \"eta\" : [ \"float\" , { \"low\" : 1e-5 , \"high\" : 1 , \"log\" : True }], \"max_depth\" : [ \"int\" , { \"low\" : 1 , \"high\" : 10 , \"log\" : False }], \"min_gain_to_split\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 40 , \"log\" : False }], \"min_sum_hessian_in_leaf\" : [ \"float\" , { \"low\" : 1e-8 , \"high\" : 500 , \"log\" : True }], \"subsample\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"feature_fraction\" : [ \"float\" , { \"low\" : 0.2 , \"high\" : 1.0 , \"log\" : False }], \"boosting\" : [ \"categorical\" , [ \"gbdt\" ]], } np . random . seed ( 123 ) opt_param = lgblss . hyper_opt ( param_dict , dtrain , num_boost_round = 100 , # Number of boosting iterations. nfold = 5 , # Number of cv-folds. early_stopping_rounds = 20 , # Number of early-stopping rounds max_minutes = 5 , # Time budget in minutes, i.e., stop study after the given number of minutes. n_trials = 20 , # The number of trials. 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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'.' : '/search/'; +var allowSearch = false; +var index; +var documents = {}; +var lang = ['en']; +var data; + +function getScript(script, callback) { + console.log('Loading script: ' + script); + $.getScript(base_path + script).done(function () { + callback(); + }).fail(function (jqxhr, settings, exception) { + console.log('Error: ' + exception); + }); +} + +function getScriptsInOrder(scripts, callback) { + if (scripts.length === 0) { + callback(); + return; + } + getScript(scripts[0], function() { + getScriptsInOrder(scripts.slice(1), callback); + }); +} + +function loadScripts(urls, callback) { + if( 'function' === typeof importScripts ) { + importScripts.apply(null, urls); + callback(); + } else { + getScriptsInOrder(urls, callback); + } +} + +function onJSONLoaded () { + data = JSON.parse(this.responseText); + var scriptsToLoad = ['lunr.js']; + if (data.config && data.config.lang && data.config.lang.length) { + lang = data.config.lang; + } + if (lang.length > 1 || lang[0] !== "en") { + scriptsToLoad.push('lunr.stemmer.support.js'); + if (lang.length > 1) { + scriptsToLoad.push('lunr.multi.js'); + } + if (lang.includes("ja") || lang.includes("jp")) { + scriptsToLoad.push('tinyseg.js'); + } + for (var i=0; i < lang.length; i++) { + if (lang[i] != 'en') { + scriptsToLoad.push(['lunr', lang[i], 'js'].join('.')); + } + } + } + loadScripts(scriptsToLoad, onScriptsLoaded); +} + +function onScriptsLoaded () { + console.log('All search scripts loaded, building Lunr index...'); + if (data.config && data.config.separator && data.config.separator.length) { + lunr.tokenizer.separator = new RegExp(data.config.separator); + } + + if (data.index) { + index = lunr.Index.load(data.index); + data.docs.forEach(function (doc) { + documents[doc.location] = doc; + }); + console.log('Lunr pre-built index loaded, search ready'); + } else { + index = lunr(function () { + if (lang.length === 1 && lang[0] !== "en" && lunr[lang[0]]) { + this.use(lunr[lang[0]]); + } else if (lang.length > 1) { + this.use(lunr.multiLanguage.apply(null, lang)); // spread operator not supported in all browsers: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Spread_operator#Browser_compatibility + } + this.field('title'); + this.field('text'); + this.ref('location'); + + for (var i=0; i < data.docs.length; i++) { + var doc = data.docs[i]; + this.add(doc); + documents[doc.location] = doc; + } + }); + console.log('Lunr index built, search ready'); + } + allowSearch = true; + postMessage({config: data.config}); + postMessage({allowSearch: allowSearch}); +} + +function init () { + var oReq = new XMLHttpRequest(); + oReq.addEventListener("load", onJSONLoaded); + var index_path = base_path + '/search_index.json'; + if( 'function' === typeof importScripts ){ + index_path = 'search_index.json'; + } + oReq.open("GET", index_path); + oReq.send(); +} + +function search (query) { + if (!allowSearch) { + console.error('Assets for search still loading'); + return; + } + + var resultDocuments = []; + var results = index.search(query); + for (var i=0; i < results.length; i++){ + var result = results[i]; + doc = documents[result.ref]; + doc.summary = doc.text.substring(0, 200); + resultDocuments.push(doc); + } + return resultDocuments; +} + +if( 'function' === typeof importScripts ) { + onmessage = function (e) { + if (e.data.init) { + init(); + } else if (e.data.query) { + postMessage({ results: search(e.data.query) }); + } else { + console.error("Worker - Unrecognized message: " + e); + } + }; +} diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 0000000..e3d5e02 --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,53 @@ + + + + https://github.com/StatMixedML/LightGBMLSS/ + 2023-08-28 + daily + + + https://github.com/StatMixedML/LightGBMLSS/api/ + 2023-08-28 + daily + + + https://github.com/StatMixedML/LightGBMLSS/dgbm/ + 2023-08-28 + daily + + + https://github.com/StatMixedML/LightGBMLSS/distributions/ + 2023-08-28 + daily + + + https://github.com/StatMixedML/LightGBMLSS/examples/Expectile_Regression/ + 2023-08-28 + daily + + + https://github.com/StatMixedML/LightGBMLSS/examples/Gamma_Regression_BostonHousing/ + 2023-08-28 + daily + + + https://github.com/StatMixedML/LightGBMLSS/examples/Gaussian_Regression/ + 2023-08-28 + daily + + + https://github.com/StatMixedML/LightGBMLSS/examples/How_To_Select_A_Univariate_Distribution/ + 2023-08-28 + daily + + + https://github.com/StatMixedML/LightGBMLSS/examples/SplineFlow_Regression/ + 2023-08-28 + daily + + + https://github.com/StatMixedML/LightGBMLSS/examples/ZAGamma_Regression/ + 2023-08-28 + daily + + \ No newline at end of file diff --git a/sitemap.xml.gz b/sitemap.xml.gz new file mode 100644 index 0000000..e531124 Binary files /dev/null and b/sitemap.xml.gz differ