notebook.ipynb

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    "id": "view-in-github"
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   "source": [
    "<a href=\"https://colab.research.google.com/github/Davidsonity/Dog_Breed/blob/main/Notebook/Dog_Breed_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gScz9Z1lsDo7"
   },
   "source": [
    "# **Dog Breed Prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nNnTK9-0svYi"
   },
   "outputs": [],
   "source": [
    "# Select the kaggle.json file downloaded frm kaggle account settting page\n",
    "from google.colab import files\n",
    "files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3T_7gkMeujzg"
   },
   "outputs": [],
   "source": [
    "# Installing Kaggle API client\n",
    "!pip install -q kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cEhjyrLRwoZp"
   },
   "outputs": [],
   "source": [
    "# creating /.kaggle because the kaggle API client expect the file to be in this format so we move it there\n",
    "!mkdir -p ~/.kaggle\n",
    "!cp kaggle.json ~/.kaggle/\n",
    "# This permissons changes avoid a warning on kaggle tool startup\n",
    "!chmod 600 ~/.kaggle/kaggle.json"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
    "colab": {
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    },
    "id": "G3fE-q4ZyITr",
    "outputId": "cbcb23f9-d043-49ee-bd97-b0e410a00b98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/dog_dataset\n"
     ]
    }
   ],
   "source": [
    "# Creating directory and changing the current working directory\n",
    "!mkdir dog_dataset\n",
    "%cd dog_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    },
    "id": "k7V8_8pczKRF",
    "outputId": "69f6a248-4662-416f-8f7b-f311b7656772"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading dog-breed-identification.zip to /content/dog_dataset\n",
      " 99% 682M/691M [00:04<00:00, 175MB/s]\n",
      "100% 691M/691M [00:04<00:00, 149MB/s]\n"
     ]
    }
   ],
   "source": [
    "# search for dataset\n",
    "!kaggle competitions download -c dog-breed-identification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3na52GTXzuGY"
   },
   "outputs": [],
   "source": [
    "# unzipping downloaded dataset\n",
    "!unzip dog-breed-identification.zip\n",
    "!rm dog-breed-identification.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PWIPhvZv0gtM"
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   "outputs": [],
   "source": [
    "# Important library imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm import tqdm\n",
    "from keras.preprocessing import image\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.layers import Input, Conv2D, Flatten, Dense, Dropout, MaxPooling2D, BatchNormalization\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam, RMSprop\n",
    "from tensorflow.keras.applications.xception import Xception as PretrainedModel"
   ]
  },
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     "text": [
      "(10222, 2)\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>breed</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000bec180eb18c7604dcecc8fe0dba07</td>\n",
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       "      <td>pekinese</td>\n",
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      "text/plain": [
       "                                 id             breed\n",
       "0  000bec180eb18c7604dcecc8fe0dba07       boston_bull\n",
       "1  001513dfcb2ffafc82cccf4d8bbaba97             dingo\n",
       "2  001cdf01b096e06d78e9e5112d419397          pekinese\n",
       "3  00214f311d5d2247d5dfe4fe24b2303d          bluetick\n",
       "4  0021f9ceb3235effd7fcde7f7538ed62  golden_retriever"
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    }
   ],
   "source": [
    "# Reading the labels.csv file and checking shape and records\n",
    "labels = pd.read_csv('labels.csv')\n",
    "print(labels.shape)\n",
    "labels.head()"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "scottish_deerhound      126\n",
       "maltese_dog             117\n",
       "afghan_hound            116\n",
       "entlebucher             115\n",
       "bernese_mountain_dog    114\n",
       "Name: breed, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of each breed\n",
    "breed_all = labels['breed']\n",
    "breed_counts = breed_all.value_counts()\n",
    "breed_counts.head()"
   ]
  },
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   },
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "['scottish_deerhound', 'maltese_dog', 'afghan_hound', 'entlebucher', 'bernese_mountain_dog', 'shih-tzu', 'great_pyrenees', 'pomeranian', 'basenji', 'samoyed']\n"
     ]
    }
   ],
   "source": [
    "classname = breed_counts.index.tolist()[:10]\n",
    "print(classname)"
   ]
  },
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      "text/plain": [
       "                                 id               breed\n",
       "0  003df8b8a8b05244b1d920bb6cf451f9             basenji\n",
       "1  0042188c895a2f14ef64a918ed9c7b64  scottish_deerhound\n",
       "2  00693b8bc2470375cc744a6391d397ec         maltese_dog\n",
       "3  0162b3e0144fb1d1ab82fbff3ace9938            shih-tzu\n",
       "4  01b36cb1b80ab8c3a7d2b7128ad21bdc             samoyed"
      ]
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   "source": [
    "labels = labels[(labels['breed'].isin(classname))]\n",
    "labels = labels.reset_index(drop= True)\n",
    "labels.head()"
   ]
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      "100%|██████████| 1141/1141 [00:03<00:00, 291.33it/s]"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Creating numpy matrix with zeros\n",
    "X_data = np.zeros((len(labels), 224,224,3), dtype = 'float32')\n",
    "# One hot encoding\n",
    "Y_data = label_binarize(labels['breed'], classes=classname)\n",
    "\n",
    "#Reading and converting image to numpy array and normalizing dataset\n",
    "for i in tqdm(range(len(labels))):\n",
    "  img = image.load_img('train/%s.jpg' % labels['id'][i], target_size=(224,224))\n",
    "  img = image.img_to_array(img)\n",
    "  X = np.expand_dims(img.copy(),axis = 0)\n",
    "  X_data[i] = X/255.0\n",
    "\n",
    "#printing train image and onehot encode shape & size\n",
    "print('\\nX Shape:', X_data.shape, 'size: {:,}'.format(X_data.size))\n",
    "print('Y Shape:', Y_data.shape, 'size: {:,}'.format(Y_data.size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j7k2_lMWLtuP"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6dmdb-aqNHbr"
   },
   "outputs": [],
   "source": [
    "#Splitting the data set onto training and testing datasets\n",
    "X_train_and_val, X_test, Y_train_and_val, Y_test = train_test_split(X_data, Y_data, test_size=0.1)\n",
    "#Splitting the training data set into training and validation datasets\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(X_train_and_val, Y_train_and_val, test_size= 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FutQ-sTyZf7n",
    "outputId": "01a6b850-9755-496f-ebe9-37efd666fba6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of classes: 10\n"
     ]
    }
   ],
   "source": [
    "k = len(classname)\n",
    "print('number of classes:', k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zRV51Ap6Qqph",
    "outputId": "949fc83e-ae32-497b-8835-608fe5c23bf1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "83689472/83683744 [==============================] - 1s 0us/step\n",
      "83697664/83683744 [==============================] - 1s 0us/step\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " block1_conv1 (Conv2D)          (None, 111, 111, 32  864         ['input_1[0][0]']                \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block1_conv1_bn (BatchNormaliz  (None, 111, 111, 32  128        ['block1_conv1[0][0]']           \n",
      " ation)                         )                                                                 \n",
      "                                                                                                  \n",
      " block1_conv1_act (Activation)  (None, 111, 111, 32  0           ['block1_conv1_bn[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block1_conv2 (Conv2D)          (None, 109, 109, 64  18432       ['block1_conv1_act[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block1_conv2_bn (BatchNormaliz  (None, 109, 109, 64  256        ['block1_conv2[0][0]']           \n",
      " ation)                         )                                                                 \n",
      "                                                                                                  \n",
      " block1_conv2_act (Activation)  (None, 109, 109, 64  0           ['block1_conv2_bn[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block2_sepconv1 (SeparableConv  (None, 109, 109, 12  8768       ['block1_conv2_act[0][0]']       \n",
      " 2D)                            8)                                                                \n",
      "                                                                                                  \n",
      " block2_sepconv1_bn (BatchNorma  (None, 109, 109, 12  512        ['block2_sepconv1[0][0]']        \n",
      " lization)                      8)                                                                \n",
      "                                                                                                  \n",
      " block2_sepconv2_act (Activatio  (None, 109, 109, 12  0          ['block2_sepconv1_bn[0][0]']     \n",
      " n)                             8)                                                                \n",
      "                                                                                                  \n",
      " block2_sepconv2 (SeparableConv  (None, 109, 109, 12  17536      ['block2_sepconv2_act[0][0]']    \n",
      " 2D)                            8)                                                                \n",
      "                                                                                                  \n",
      " block2_sepconv2_bn (BatchNorma  (None, 109, 109, 12  512        ['block2_sepconv2[0][0]']        \n",
      " lization)                      8)                                                                \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 55, 55, 128)  8192        ['block1_conv2_act[0][0]']       \n",
      "                                                                                                  \n",
      " block2_pool (MaxPooling2D)     (None, 55, 55, 128)  0           ['block2_sepconv2_bn[0][0]']     \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 55, 55, 128)  512        ['conv2d[0][0]']                 \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " add (Add)                      (None, 55, 55, 128)  0           ['block2_pool[0][0]',            \n",
      "                                                                  'batch_normalization[0][0]']    \n",
      "                                                                                                  \n",
      " block3_sepconv1_act (Activatio  (None, 55, 55, 128)  0          ['add[0][0]']                    \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block3_sepconv1 (SeparableConv  (None, 55, 55, 256)  33920      ['block3_sepconv1_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block3_sepconv1_bn (BatchNorma  (None, 55, 55, 256)  1024       ['block3_sepconv1[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3_sepconv2_act (Activatio  (None, 55, 55, 256)  0          ['block3_sepconv1_bn[0][0]']     \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block3_sepconv2 (SeparableConv  (None, 55, 55, 256)  67840      ['block3_sepconv2_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block3_sepconv2_bn (BatchNorma  (None, 55, 55, 256)  1024       ['block3_sepconv2[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 28, 28, 256)  32768       ['add[0][0]']                    \n",
      "                                                                                                  \n",
      " block3_pool (MaxPooling2D)     (None, 28, 28, 256)  0           ['block3_sepconv2_bn[0][0]']     \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 28, 28, 256)  1024       ['conv2d_1[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add_1 (Add)                    (None, 28, 28, 256)  0           ['block3_pool[0][0]',            \n",
      "                                                                  'batch_normalization_1[0][0]']  \n",
      "                                                                                                  \n",
      " block4_sepconv1_act (Activatio  (None, 28, 28, 256)  0          ['add_1[0][0]']                  \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block4_sepconv1 (SeparableConv  (None, 28, 28, 728)  188672     ['block4_sepconv1_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block4_sepconv1_bn (BatchNorma  (None, 28, 28, 728)  2912       ['block4_sepconv1[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4_sepconv2_act (Activatio  (None, 28, 28, 728)  0          ['block4_sepconv1_bn[0][0]']     \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block4_sepconv2 (SeparableConv  (None, 28, 28, 728)  536536     ['block4_sepconv2_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block4_sepconv2_bn (BatchNorma  (None, 28, 28, 728)  2912       ['block4_sepconv2[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 14, 14, 728)  186368      ['add_1[0][0]']                  \n",
      "                                                                                                  \n",
      " block4_pool (MaxPooling2D)     (None, 14, 14, 728)  0           ['block4_sepconv2_bn[0][0]']     \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 14, 14, 728)  2912       ['conv2d_2[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add_2 (Add)                    (None, 14, 14, 728)  0           ['block4_pool[0][0]',            \n",
      "                                                                  'batch_normalization_2[0][0]']  \n",
      "                                                                                                  \n",
      " block5_sepconv1_act (Activatio  (None, 14, 14, 728)  0          ['add_2[0][0]']                  \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block5_sepconv1 (SeparableConv  (None, 14, 14, 728)  536536     ['block5_sepconv1_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block5_sepconv1_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block5_sepconv1[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5_sepconv2_act (Activatio  (None, 14, 14, 728)  0          ['block5_sepconv1_bn[0][0]']     \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block5_sepconv2 (SeparableConv  (None, 14, 14, 728)  536536     ['block5_sepconv2_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block5_sepconv2_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block5_sepconv2[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5_sepconv3_act (Activatio  (None, 14, 14, 728)  0          ['block5_sepconv2_bn[0][0]']     \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block5_sepconv3 (SeparableConv  (None, 14, 14, 728)  536536     ['block5_sepconv3_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block5_sepconv3_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block5_sepconv3[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " add_3 (Add)                    (None, 14, 14, 728)  0           ['block5_sepconv3_bn[0][0]',     \n",
      "                                                                  'add_2[0][0]']                  \n",
      "                                                                                                  \n",
      " block6_sepconv1_act (Activatio  (None, 14, 14, 728)  0          ['add_3[0][0]']                  \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block6_sepconv1 (SeparableConv  (None, 14, 14, 728)  536536     ['block6_sepconv1_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6_sepconv1_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block6_sepconv1[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6_sepconv2_act (Activatio  (None, 14, 14, 728)  0          ['block6_sepconv1_bn[0][0]']     \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block6_sepconv2 (SeparableConv  (None, 14, 14, 728)  536536     ['block6_sepconv2_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6_sepconv2_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block6_sepconv2[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6_sepconv3_act (Activatio  (None, 14, 14, 728)  0          ['block6_sepconv2_bn[0][0]']     \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block6_sepconv3 (SeparableConv  (None, 14, 14, 728)  536536     ['block6_sepconv3_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block6_sepconv3_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block6_sepconv3[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " add_4 (Add)                    (None, 14, 14, 728)  0           ['block6_sepconv3_bn[0][0]',     \n",
      "                                                                  'add_3[0][0]']                  \n",
      "                                                                                                  \n",
      " block7_sepconv1_act (Activatio  (None, 14, 14, 728)  0          ['add_4[0][0]']                  \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block7_sepconv1 (SeparableConv  (None, 14, 14, 728)  536536     ['block7_sepconv1_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block7_sepconv1_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block7_sepconv1[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block7_sepconv2_act (Activatio  (None, 14, 14, 728)  0          ['block7_sepconv1_bn[0][0]']     \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block7_sepconv2 (SeparableConv  (None, 14, 14, 728)  536536     ['block7_sepconv2_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block7_sepconv2_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block7_sepconv2[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block7_sepconv3_act (Activatio  (None, 14, 14, 728)  0          ['block7_sepconv2_bn[0][0]']     \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block7_sepconv3 (SeparableConv  (None, 14, 14, 728)  536536     ['block7_sepconv3_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block7_sepconv3_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block7_sepconv3[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " add_5 (Add)                    (None, 14, 14, 728)  0           ['block7_sepconv3_bn[0][0]',     \n",
      "                                                                  'add_4[0][0]']                  \n",
      "                                                                                                  \n",
      " block8_sepconv1_act (Activatio  (None, 14, 14, 728)  0          ['add_5[0][0]']                  \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block8_sepconv1 (SeparableConv  (None, 14, 14, 728)  536536     ['block8_sepconv1_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block8_sepconv1_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block8_sepconv1[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block8_sepconv2_act (Activatio  (None, 14, 14, 728)  0          ['block8_sepconv1_bn[0][0]']     \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block8_sepconv2 (SeparableConv  (None, 14, 14, 728)  536536     ['block8_sepconv2_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block8_sepconv2_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block8_sepconv2[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block8_sepconv3_act (Activatio  (None, 14, 14, 728)  0          ['block8_sepconv2_bn[0][0]']     \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block8_sepconv3 (SeparableConv  (None, 14, 14, 728)  536536     ['block8_sepconv3_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block8_sepconv3_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block8_sepconv3[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " add_6 (Add)                    (None, 14, 14, 728)  0           ['block8_sepconv3_bn[0][0]',     \n",
      "                                                                  'add_5[0][0]']                  \n",
      "                                                                                                  \n",
      " block9_sepconv1_act (Activatio  (None, 14, 14, 728)  0          ['add_6[0][0]']                  \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block9_sepconv1 (SeparableConv  (None, 14, 14, 728)  536536     ['block9_sepconv1_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block9_sepconv1_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block9_sepconv1[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block9_sepconv2_act (Activatio  (None, 14, 14, 728)  0          ['block9_sepconv1_bn[0][0]']     \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block9_sepconv2 (SeparableConv  (None, 14, 14, 728)  536536     ['block9_sepconv2_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block9_sepconv2_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block9_sepconv2[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block9_sepconv3_act (Activatio  (None, 14, 14, 728)  0          ['block9_sepconv2_bn[0][0]']     \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " block9_sepconv3 (SeparableConv  (None, 14, 14, 728)  536536     ['block9_sepconv3_act[0][0]']    \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " block9_sepconv3_bn (BatchNorma  (None, 14, 14, 728)  2912       ['block9_sepconv3[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " add_7 (Add)                    (None, 14, 14, 728)  0           ['block9_sepconv3_bn[0][0]',     \n",
      "                                                                  'add_6[0][0]']                  \n",
      "                                                                                                  \n",
      " block10_sepconv1_act (Activati  (None, 14, 14, 728)  0          ['add_7[0][0]']                  \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " block10_sepconv1 (SeparableCon  (None, 14, 14, 728)  536536     ['block10_sepconv1_act[0][0]']   \n",
      " v2D)                                                                                             \n",
      "                                                                                                  \n",
      " block10_sepconv1_bn (BatchNorm  (None, 14, 14, 728)  2912       ['block10_sepconv1[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block10_sepconv2_act (Activati  (None, 14, 14, 728)  0          ['block10_sepconv1_bn[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " block10_sepconv2 (SeparableCon  (None, 14, 14, 728)  536536     ['block10_sepconv2_act[0][0]']   \n",
      " v2D)                                                                                             \n",
      "                                                                                                  \n",
      " block10_sepconv2_bn (BatchNorm  (None, 14, 14, 728)  2912       ['block10_sepconv2[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block10_sepconv3_act (Activati  (None, 14, 14, 728)  0          ['block10_sepconv2_bn[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " block10_sepconv3 (SeparableCon  (None, 14, 14, 728)  536536     ['block10_sepconv3_act[0][0]']   \n",
      " v2D)                                                                                             \n",
      "                                                                                                  \n",
      " block10_sepconv3_bn (BatchNorm  (None, 14, 14, 728)  2912       ['block10_sepconv3[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " add_8 (Add)                    (None, 14, 14, 728)  0           ['block10_sepconv3_bn[0][0]',    \n",
      "                                                                  'add_7[0][0]']                  \n",
      "                                                                                                  \n",
      " block11_sepconv1_act (Activati  (None, 14, 14, 728)  0          ['add_8[0][0]']                  \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " block11_sepconv1 (SeparableCon  (None, 14, 14, 728)  536536     ['block11_sepconv1_act[0][0]']   \n",
      " v2D)                                                                                             \n",
      "                                                                                                  \n",
      " block11_sepconv1_bn (BatchNorm  (None, 14, 14, 728)  2912       ['block11_sepconv1[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block11_sepconv2_act (Activati  (None, 14, 14, 728)  0          ['block11_sepconv1_bn[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " block11_sepconv2 (SeparableCon  (None, 14, 14, 728)  536536     ['block11_sepconv2_act[0][0]']   \n",
      " v2D)                                                                                             \n",
      "                                                                                                  \n",
      " block11_sepconv2_bn (BatchNorm  (None, 14, 14, 728)  2912       ['block11_sepconv2[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block11_sepconv3_act (Activati  (None, 14, 14, 728)  0          ['block11_sepconv2_bn[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " block11_sepconv3 (SeparableCon  (None, 14, 14, 728)  536536     ['block11_sepconv3_act[0][0]']   \n",
      " v2D)                                                                                             \n",
      "                                                                                                  \n",
      " block11_sepconv3_bn (BatchNorm  (None, 14, 14, 728)  2912       ['block11_sepconv3[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " add_9 (Add)                    (None, 14, 14, 728)  0           ['block11_sepconv3_bn[0][0]',    \n",
      "                                                                  'add_8[0][0]']                  \n",
      "                                                                                                  \n",
      " block12_sepconv1_act (Activati  (None, 14, 14, 728)  0          ['add_9[0][0]']                  \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " block12_sepconv1 (SeparableCon  (None, 14, 14, 728)  536536     ['block12_sepconv1_act[0][0]']   \n",
      " v2D)                                                                                             \n",
      "                                                                                                  \n",
      " block12_sepconv1_bn (BatchNorm  (None, 14, 14, 728)  2912       ['block12_sepconv1[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block12_sepconv2_act (Activati  (None, 14, 14, 728)  0          ['block12_sepconv1_bn[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " block12_sepconv2 (SeparableCon  (None, 14, 14, 728)  536536     ['block12_sepconv2_act[0][0]']   \n",
      " v2D)                                                                                             \n",
      "                                                                                                  \n",
      " block12_sepconv2_bn (BatchNorm  (None, 14, 14, 728)  2912       ['block12_sepconv2[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block12_sepconv3_act (Activati  (None, 14, 14, 728)  0          ['block12_sepconv2_bn[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " block12_sepconv3 (SeparableCon  (None, 14, 14, 728)  536536     ['block12_sepconv3_act[0][0]']   \n",
      " v2D)                                                                                             \n",
      "                                                                                                  \n",
      " block12_sepconv3_bn (BatchNorm  (None, 14, 14, 728)  2912       ['block12_sepconv3[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " add_10 (Add)                   (None, 14, 14, 728)  0           ['block12_sepconv3_bn[0][0]',    \n",
      "                                                                  'add_9[0][0]']                  \n",
      "                                                                                                  \n",
      " block13_sepconv1_act (Activati  (None, 14, 14, 728)  0          ['add_10[0][0]']                 \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " block13_sepconv1 (SeparableCon  (None, 14, 14, 728)  536536     ['block13_sepconv1_act[0][0]']   \n",
      " v2D)                                                                                             \n",
      "                                                                                                  \n",
      " block13_sepconv1_bn (BatchNorm  (None, 14, 14, 728)  2912       ['block13_sepconv1[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block13_sepconv2_act (Activati  (None, 14, 14, 728)  0          ['block13_sepconv1_bn[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " block13_sepconv2 (SeparableCon  (None, 14, 14, 1024  752024     ['block13_sepconv2_act[0][0]']   \n",
      " v2D)                           )                                                                 \n",
      "                                                                                                  \n",
      " block13_sepconv2_bn (BatchNorm  (None, 14, 14, 1024  4096       ['block13_sepconv2[0][0]']       \n",
      " alization)                     )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 7, 7, 1024)   745472      ['add_10[0][0]']                 \n",
      "                                                                                                  \n",
      " block13_pool (MaxPooling2D)    (None, 7, 7, 1024)   0           ['block13_sepconv2_bn[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 7, 7, 1024)  4096        ['conv2d_3[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add_11 (Add)                   (None, 7, 7, 1024)   0           ['block13_pool[0][0]',           \n",
      "                                                                  'batch_normalization_3[0][0]']  \n",
      "                                                                                                  \n",
      " block14_sepconv1 (SeparableCon  (None, 7, 7, 1536)  1582080     ['add_11[0][0]']                 \n",
      " v2D)                                                                                             \n",
      "                                                                                                  \n",
      " block14_sepconv1_bn (BatchNorm  (None, 7, 7, 1536)  6144        ['block14_sepconv1[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block14_sepconv1_act (Activati  (None, 7, 7, 1536)  0           ['block14_sepconv1_bn[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " block14_sepconv2 (SeparableCon  (None, 7, 7, 2048)  3159552     ['block14_sepconv1_act[0][0]']   \n",
      " v2D)                                                                                             \n",
      "                                                                                                  \n",
      " block14_sepconv2_bn (BatchNorm  (None, 7, 7, 2048)  8192        ['block14_sepconv2[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block14_sepconv2_act (Activati  (None, 7, 7, 2048)  0           ['block14_sepconv2_bn[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " flatten (Flatten)              (None, 100352)       0           ['block14_sepconv2_act[0][0]']   \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 10)           1003530     ['flatten[0][0]']                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 21,865,010\n",
      "Trainable params: 1,003,530\n",
      "Non-trainable params: 20,861,480\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Building the Model\n",
    "ptm = PretrainedModel(\n",
    "    input_shape= X_data[0].shape,\n",
    "    weights = 'imagenet',\n",
    "    include_top = False\n",
    ")\n",
    "# Freeze pretrained model weights\n",
    "ptm.trainable = False\n",
    "\n",
    "# map date\n",
    "x = Flatten()(ptm.output)\n",
    "x = Dense(k, activation='softmax')(x)\n",
    "\n",
    "model = Model(inputs = ptm.input, outputs = x)\n",
    "\n",
    "model.compile(optimizer= Adam(0.0001),\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5npqOjfofktM",
    "outputId": "54bc5e02-3736-4b46-f51a-ff68702d3c90"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "6/6 [==============================] - 22s 1s/step - loss: 0.9866 - accuracy: 0.6979 - val_loss: 0.1621 - val_accuracy: 0.9660\n",
      "Epoch 2/10\n",
      "6/6 [==============================] - 5s 639ms/step - loss: 0.1337 - accuracy: 0.9581 - val_loss: 0.1225 - val_accuracy: 0.9660\n",
      "Epoch 3/10\n",
      "6/6 [==============================] - 3s 587ms/step - loss: 0.0698 - accuracy: 0.9754 - val_loss: 0.1065 - val_accuracy: 0.9563\n",
      "Epoch 4/10\n",
      "6/6 [==============================] - 3s 588ms/step - loss: 0.0480 - accuracy: 0.9841 - val_loss: 0.1091 - val_accuracy: 0.9612\n",
      "Epoch 5/10\n",
      "6/6 [==============================] - 3s 591ms/step - loss: 0.0377 - accuracy: 0.9899 - val_loss: 0.1172 - val_accuracy: 0.9660\n",
      "Epoch 6/10\n",
      "6/6 [==============================] - 3s 582ms/step - loss: 0.0221 - accuracy: 0.9942 - val_loss: 0.1298 - val_accuracy: 0.9563\n",
      "Epoch 7/10\n",
      "6/6 [==============================] - 3s 590ms/step - loss: 0.0128 - accuracy: 0.9986 - val_loss: 0.1303 - val_accuracy: 0.9612\n",
      "Epoch 8/10\n",
      "6/6 [==============================] - 4s 650ms/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9612\n",
      "Epoch 9/10\n",
      "6/6 [==============================] - 3s 655ms/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.1115 - val_accuracy: 0.9660\n",
      "Epoch 10/10\n",
      "6/6 [==============================] - 3s 595ms/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9612\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "steps_per_epoch = X_train.shape[0]//batch_size\n",
    "r = model.fit(X_train, Y_train, batch_size = batch_size,\n",
    "              epochs = 10,\n",
    "              validation_data = (X_val, Y_val),\n",
    "              steps_per_epoch = steps_per_epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "ClrqtBOIyzP0",
    "outputId": "79ba4af6-fe31-48a1-cc52-91d1905a0855"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot accouracy per iteration\n",
    "plt.plot(r.history['accuracy'], label= 'train accuracy')\n",
    "plt.plot(r.history['val_accuracy'], label= 'val accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qZAASLyL1XSo",
    "outputId": "5e243b7b-515a-4103-c217-e9c6f4c77a22"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 105ms/step - loss: 0.1088 - accuracy: 0.9565\n",
      "Accuracy of over the test set: \n",
      " 95.652174949646 %\n"
     ]
    }
   ],
   "source": [
    "# Using test set and finding accuracy\n",
    "Y_pred = model.predict(X_test)\n",
    "score = model.evaluate(X_test,Y_test)\n",
    "print('Accuracy of over the test set: \\n', score[1] * 100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jGtcU4Ys3Tgd"
   },
   "outputs": [],
   "source": [
    "# Saving Model\n",
    "model.save('dog_breed.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "id": "vwyGlJPT08vV",
    "outputId": "1d8bee6f-8d02-415f-9cce-2cd557add0a8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yGFv8P3/6Fvsnj0gkVZ67vYM7drg4aaMlPGRDQhAD5pYqnNSOiSKXWHAo5hM4dsTFeYvSwiVUK8mNmxtcvVxhY7XM/EKFTDbFeNqiOZoysCecVnfRlDHlnAFiih+/fcqff+dDglhk/dIKP33vh0y9Htlyilsv3Oa5V25TWV4kMxfgBV0EzWMWDqmsl7h5e4FbN+coZceIgkccQODHjEcDep02n//cq/RqZ8wGfVJpncPaA5zxhJ2tLXTVwrFtXMdmeamCJPg0Gi1E0WDQn5BMJNFVhd1Hd9ne2ESTdWQ8Ys/H1DQMyWD34QG95pCttct88fVf4vLWDhfdAc16n/nyMqlshvakxcwdsn15nQd372DpMllLRZJkLqontOsnJFQfz5ngxxKl0gqV/ByhO6bbOKZYUEmlBEQl4N33fsr9hw9IJWVG3gSlmCc3v8rUERn1Agg1zutN3MBnUh9xMRgzlBS00jyBIrGwWCStwaR7wd7j+8ipDGHokMmI2G6b2ayL6065ee0ZMtkixXKR0nwOzRBwgwnudEQQuHx4533Ozy8Io5hisYIiGSiyyd7uE8LAZ+fSJeLYpV6v8d7bT9jeXEOVQnx3TDJhMh6OcG0Py8pgajKR5zAdTzBljWHPYTYTsAOJ4/MmFxcOzz2zw+0XLlMoaeQLOYqFeRQMQsdDjAM21pdodwf8/u9/h7sfPOTFG5f57Esv8t3vfY9XXn2Bk9N3yOZN1lbnyWZT1C8uOK/VyBfzLF77zU++rA3Hb77h+1NK5SzVswb9rkC+sIEgaUztBt3hGUKsEgUuvm+TMAxSKQtJlGm3O3j2DFkOmc7G2N4Y1ZSw7SGFjEGvfoY7G6BrIYNBxOe/9FtcdIaoiZhsLsFcIcFcNo838ZlNRVLpRXKFdXb36tRqYwy9gGUU0OQkiuAjKzoRGrKqcn7+iJ3NItWzffrDARNngGzMSJaLoAn4fo8bV5ZYLJW596DJu3datMYTnjx9zDhocvOFdQZOE0+IaA0mTL2QWFRpnlexhwGalEUz5zATeZ7uP+XRu0/YrqRYWF1BFiVSqSSVuQKi4DLqnTObNNh78iFawufzv3qLylya6ukF/e4UU0+SS+dQJIVMMk1xfo6F5Q1ETBKmhe86vPLS82RTFo2LOqooYOoJwiBm/8kpTw8uuHtnn2JugTjUSFol3rv3hPnyKkvzS6iGykWnSSCEbGxusLK0wvnTE3qtOq1On5vXr/Ps7euM+23efftdkpk5wggCd4ymhcjSFFWdUSqb+KFNLITMVebRtIhQkbEFjYuWTaM+YTYI6bcH+F5ItVZFjy20bB45P0fX82n2uwy6F1xdr/DKzUtARKylmJ/LkC9qzM+nWZgvksvmGA9dhqMpM3dAb1CjP2rQ7tSJI1hZ2+ba1Vu89/bHXNTPWV5eYzKx+ejDe9x+5lmy2Sz5fJZEQuP6zav8xm/8Yyb9LqenTyiXM4hSwHDQotmqYSVUOhd1HGeCriq4tsNk7LK795RL165z9dlrvPTKLeaKc/RGZ8TiiNX1dUbdGd7YY9Ifcbz/CFmRKc8tU8gVOditsrO+wahf5+/85i8RSQNefvkZFpbmOTo+4uq1KzTbLSoLFSJilq7+vU8e5x98439+wxDm0UdpHt854aOjPQpXFxAzCmEQUkinuXRZYqO8hDjScQcyH35wzMwOKaaTzBcXUZQytWqHxfll1lYWyaYTzKZj6t0mQ2/CpeJrEBgkcyLLayluP3OdG1dusv/oMULcJpKqpBJXeLB3QSq/zr/+ox+wtPkKpxdT6p0hYSRy5dIy59UL6qct3v3JLhsr22SLS0wCB8mMyFaWaPTKPHxSp5Av0232MNUcpdIOb/74CecNm4Qq8vqLFV56RiOTvSBd8kGPUEURZ9jFiiJCO0Q2Umj5Mt/49reRBJdBu8bf+7XXee25F/mXX/8WmztlbLuPogaous8kbNOwa6y/tE52uwSqgp6S2dwu8uLL28ycNnc//phOe4pIibnyPLKg0e+4SLKKFzrE8Yh0JkaXHfLzGfqTPoIsMre4Sm/sIGgJYkmnM5rwzod32PvoCM/zuXzrMrGh4sc6fiARxCJPzw9pD+usL82hiCK6JKHLKrKq0O5XSaZdcqsLSPPrZObnSSQzxC7M+jOKqTwby6uYikKpYjGdTJBClfpplVRCotV+jCpNyCVkrq2vU3dDasM+er7E3nGN5cVl+q0LyhmTF56/jqHrpIU88cxj0BqwurxDKl2m3hyzd3iO5xmo0jKF9CZzhcssVS4xXyoghj0mgz0ub6bZ3HiebmfGbDoiigLS6TInpy0UPYmZSpKfK2DKAv1Jg2KlhKwbeAF4tk/16JS5TJZRfZdqb4heWKDlJdg7qNGonrP/9Jiz0ZTeVOTBx1Wmfo31SwkCt8+jDz5i7+4jagf79C+OyQt55lNlpNilkE8Tyjl+9OERe+enYPQJ5Ku8e+eMqSeTyhZodGuYlkIYuKxc/a1PHuedd//wjTs/+ZCV/AbNixFH5w0kVSGTSLK1NMf68gKBC7t3Tzg5qlGtNclXKhhmknF/iiLoLK+skcvnMBIGM3tCvV4nn83hOz5H+ydsLy4ym3VJF00SaQtNNfjBd7/HoN1msVIgqRnI8YyFhRJHew/o9XuIUczO2grMJgwuLlDkCc9cv44u6xSzKZ4eHiBJNr7fQ1VdDENm/6jGee0EQo+0JhOOxpw9OqJ+0uY/+NKv8Zv/6POsXSojCQqaUqI/NBClArIoYKgxsjyi24/REwlOz074yudfQ8Zla32BrfVlbHfMztVLTPs2cqwx7E2YjB10LcVkEjAY+4zHDr7t4TouxAK5TJZCrkQiYaFrGpIgEosumpHg9KxDtzcgmbLI59NEoYBlpbCdgCgQEASJTq9LEDkcneyRzpj0B20+85kXKRXSzOwmr372GaLIJp1JsLxUZnm5xAsvXKJYSBI4aVKpCoOBQ+2iiee5bG6uUp7L4UQaifQKBAKPPrpP+6LFXHmBZntAtzfFShWZOG1kVWM2C1ld3+TR4/uYmsTm2gq/8sVf5nD3iItOn8OjY2ZuiGGl2H38kGwywdpCEQKXSqmMqYoMBx2WFudJGAaarJBLJpFCl9HwgqlT56K+x+J6CVkNadROaHfbTCY2iWQOVUsRhD7Hx0/IZdOoisajh7uEvs/qWoXAn5IQMkRBzMMHTxBFmSgUsCcuw+6Q6mmNYv4Shfl1rGyes9o+sjDg9s0N6s0eja5D9bTBtN9CELpkMhKrC5usLN+g2Zry5OiI9currG5tM/THnI8afPj4Pnce7rH3tElubpmtK7f42V/+Kf3uKQvlFFLkIPkR0+6Ek91znv3Kf/nJ4/zeN/+3NxbzawyaAf7MIJMus7G0hDPoEXk9us0L3n/7jEK6xPrqKivrKzT7HbrdMQmlwGzoYpoKy0uL5PM57NmMUqFExkyTUC2m/Smx26NUtlC1mGa9ycHjI979yTvIUcyz1y/jjMZc28nwg+9+G12OeP6Zm3z59ddoHB9xfX2Fl2/d5ObtZzg9vuDRwwNEFF588QUGwyaXLi1imgK+P6aQNbh1c4cr66toUUTj+Ixha8BkEoBk8uCsipWtcOeDe9y7u8vV68+RzpaZjmwkVMb9Cd/5yT6ioFAwDfKaDO6Ym1e3aXebnHeadA5DXn/pixRTcyyWVwg8gSe7R2hGHtXIsby8jTuZ4do+iiIThh7luQLlcp6FhRL2bMDp+Qmd/pR6fUSr08fxAvrDAd/9zl2uXr5J6CYZDXxOT2vML+YJGVOcS1Aoprh56zqZnMVyJcnt25t0ugcYWogihGhSSOQPIOjy8N5HvHOniSgbHBydUm+2cRwbVVVot9sMp1AobaCICs1Gk6dHJ1zUOziegCCZ5PKLDManNFotbt9+gRs3b/LM9ct0m1XqZydkEhkatRYDe4Bp6RjJBBubW3TbbV5+9hluXb9Ct90mjEGUeywuLWPqWcJQQVcT2P0ejdN9MtmA9UsJ5uYsIn+EY4/Y2z9k/+AU25OQ1SxRFCJKPtmsQTpl0e/2qZTnyGSSGKbE0mIJA4t7d+/jeT7tVo+zkyp3PviA9ZU1ZFmkvPAZvv2dH2DbU5aWLPI5D0WZMXNsXn7hNbbXFpHiLs/evsTLr3yWZGYDnxwf750yxEfIGWgpi4vBBa5qo2Ul8ksF0oUSH3x4QK0W8OJVhS9+9jblVAI1iNB9lWnDwQoL7Hzl73/yOAenH7wxHfm89+5DJMHga7/6VYbtKqE9xDQDSuUsSavCZ199no2NOUoLaQLJJZVJQRDTrNfRdBHHc+gPe4RxyGg4gFCgmC0x7A0hiNh7ckoUxXhOAJGEGARcubyK5zRYWkzjBUmOTwcoagZki9PTUxqNKvOLOZbX5miOLH76zi4Hhx16o4jK4hahpCBqJhMnQooDFi0FWZBoNbuIgkQqk6LvzjibTTl1x3SGPh/duYvMEFW1uXFrGVEVsachs3FE68KmNU2RTc1hCiaVbIWEnubrf/xNigurJLMlnlm7TTphoqoxrjPm9GyfRNpAMzVUXUcWJZRIoFgqMp1NaHebOO4EQ1NQRJAJsB0HK1kgCGRiRBBiVteW2Vxb5O5Hj/jhdz7i+LjO4sIy2XwGM6Hz4Z332dm5iqKYlMtzqILIeNxHFEKEGEwlSexHiFGEHAWUS3kG3ghF9SlXsihKRBxHyKpGGEpM7RBRkchnEtiTHu1mFV2TSGWTmAmN6WzMYFJlMplwafsqpqbQ7zZw7QFf+Oxr5NMFyoUFzlpPCUKP5158kcrcMv1ek3brnBeevYFhqCyuLvHk/BGV1R3syMAsr+EMJ9hByMnZIf1hn0y6yHxlE8vMEwYigqjh+AG6oWEkdHJpk+mkSyqp4jkzhv0hru0wHHSZjDpMpx3KhQpvvfVdUtkUn//S5/G8KUHkoOsxg0mbD+/fJWnpPD08ZK2yRuAEjCZjZA0EcUbaAkMVcJwxheICsZii3bc5a7V4//4dprGLN/ZYnlsgnzdwnQG66nF5c5FgNiZnZfjq6y8hRzILpWUef7THxVmLvJVHigU2v/Qf/5Vx/sLxPUPJc3q+S6RLpOYMSkWFlcrzBNGIv/zwOzQ7DZYWr2HmRFrnhwRigKzaDKd1wkigOG8yccf0z3vMVcpMnBGdTptKcQ5dNlhaXGLY6fHiK9vsH+wCLtOJT6VUIW2ZWKkYK2VwcpHl0d6MZEqitGATRDaLG4s4wpST1jF/9hdDvv0Xb5FMpLh+/Rme1kbYnsdZs49uCmyWdPJpgUkkMzMSJDMlgijE1RRmpS5TJcVaPotijzGjMdurFU52h9jSkFTGZGaPsIoS7/7fe5wddLi9tY0pi9z9+D4HRw5XnlN4vP+Ym3/rFn4wYTi+4Pz8iOXFDGY2yWjm0h07HB08wBu4IEX42GxfWkNP6DjeFF0SEPCZjkaMR6DLWRYrRfSEgq6JNLsdZEnGMAz+m//6H2MldY6rjxgPp1hmmVZzwMJyEte1UQUDWbUQkFEljenQYzZxKOXzBH6IHE65cklnf69Kq+kgizKIOrKuYiULDEcjnGGbfktGET2u3VjnonbG4fEd2u0+S0tLZIoyVy7v0Kidcbj3GEmKKGXTCFFAKp0Ez+NLX/ksgqTw5OCc3/3d3+Pq1QXmiln6kxaGCofn+0ipLLV+n0p+nemox8xzmfkBrqTjOEkGPZOUpZPOZMhmCnT7Do4zQdECdD1PKqmiKXmmdgcroULs02o2KZWLLC8V8cIh57Vdrt3aZm5hjaOTJ/QmHVYuLTHqNUmXDS7dXmZQdQknA2pPuqxtr4JhEshnJMwxw36TdtfG8wV6nQ6ynmUw7DG/kOPatSuEgo87iYgcjZRskZEMvLhN0ejxD3/jeRRpCcWz8GKX9rnNZCQQRzpGKk0c+X9tf7/wyfnWn3zjjePaOUpGZudymWvrOWa9E2bTDqWVMk4UUMynqB48YTadkMllCSUB13coF9O4nsPYdZg4Y5I5C1mX0DQVQzdIJ1O0mk3Ki1lENcBKmeztHmKPY1wnRkSl3elTKi3wwd0TNFVHlmSshMnVyzssL1SYTieEQcDTozaWJrKyNMfKUpnxsIMk+limRNoyMEQfHXCFXnQAACAASURBVBfHV/F9DcEXsXsDCimThCZycnCMyoBSGswAiuYCjXrIcCJxdHbOaDLBj1wMY4HDwx57Rw3uPDhFTeToDKfoWsxiJY2EBmJAs3mCpoGVELHtMZPJmOloynQwRkZETygsrFTQTAVDl5EEAUNRkYKYhJogmcihawbDQZvhsI3rDri8tU6lXOJzX3gG1+vi+EPW1lfxvABNNxClCNsbIEo+M1sgFgNGox6yJFM9rXN0cMTywiKh7+B5M1JWgtWFJa5d2mZzawvdNLlotRhMJ4RuyKDeJ6mbaIrEB+//DJGAtdUF5ufyPH/7BlY6QRwJ3PvoLrIYMz9XIJ9J4DoO9WqbKJQpLqeICDirVrlyeYnXXn+JYjGJovg022dYaZ1CroAigSj4CKJDLLgkUxrzi/Nohkmn22MwGqKbOoNRj/PqUxIJmcpclko5TcZKYk+HTKZdwsCl3+mzvblFGHiUymmSCRk/1FCNFFMn4qIzQNFMUtkcZipBOpfm/p07ZOUUJTNH62mDQPQx50xCKSB0QxRRQ5BkZrMZqqaQTGpMJy367RqZRBK7b7O8sMSlSzuM+0MkRPJZk9CbcnJUJZmYQ8PAntn86Mc/QdYttGSKzmTMs599leTqK598Wfuv/9d/+cZgOkQ1XDZWUiSkn09XzHybru2zvnMVb9LBmTqYiRQBErMgRNI0JBku6ueoukyxnCaZ0VBNAd1U6fZ6RD7kcyXG7ggzadAf9imXF5jZEZKsI8tZjo469AciZkZEUwJEXIbdBv7M4fy0jmXmkdAQGbG6aPLsjXUM0UEXZyzPGawsJNhcyRCEXQRNptVycEYi/jiic95AcV2WkmmulYsI8ZhJv0s2kSCVUkhnsjx42MAPClQvxviizs5Ojnc/eIRqWRiJZZqtGc1mj9/6rS9TKs442K8hhB7+ZEw+lSGhWYwnAdXqgLnSJuW5VbRUEt2SSWREEqaM6MfogoYhqqiCSELNsPf4DMcO6Q97ZHIWVlLFMFVkRaLRvEu7U2V5eR5Z1gjjgGzBJBZtmu06XuhyUT1nPOyjyArNZpswitFNg+F0SKvfwUxkcEYmzfMRmpQk8mIkIWY66eK7Pfr1Ln4/RghEzo7OCbyA61evsbi4QOAFzKYesRIxnIw5Pj3h0vYmqiJjKD/fOhw9rfK0ekZ+USKKPcpzRQbDAeVCnmIuze7je5QLGTQFJt0zpr0WkT1i0DpjNmowGbZw3TGh4KAlI2yvS39QZTKqk07J2JMelvnzm7UQRRiGysnhPrPxhEK+xHxlCUXVcFwPWTWQ9Tmy2Xl8F77/5k+JBIVUsUy2sMDUgerBBQvFBZaWF+mPqoyiJgOni6mlcboSvdaEkd1hZXmeSjnHoNvi/PSYtGWiyAKpjMHq9iZaIoU7DfEdsGc+jUafSEoy8xVSmkwuZ1GrnzFxxoiaRHPQ49UvfwE1f/OTx/kv/oc33theW+L5rQ1W82kce0JzMKYxnJIqLmMlcmQVn4CYw9pTpqHD0J5yelEnWSjgOTZ5TaBcTJFIigxGDYLII3AjRBIQKXTqKsf7Q2q1ActrKxxVD+hPOyysz6MlLDKFChNfxvNtRtMhi0tz9LoDYiFBf+YRCCIf3f0QVYU4gNnEQZUl8jmLQslC02NE3Ua1BCJX5qI65mmth2+atDyfg9NzDCXJ2lrIwpJFezylOb4gjkTO9gQOT4dcDGq04ieMhy1uPVvEMgOcwYyF7CKTfp//9D/7KrF6QqWUo3N+gjT1UEONs+qIs7bHwNdpdD0e7tU5G3sU5xOIdCgYGuJYwIgsRt0JoecyHobU6jZepNGf2QiqSn6uiKAq1JpdFDem33KolNdJpvN8fPdnKPqM0XTM4UGX0EtSSfrgCSSUMvXGCDOTIlIE2pMR5z0b0Szys/fucffeHlGgYYoSw3qdBD7rpSTL5TLZbJZRf8Bk5GAaSdY2V8nPlZi6IefNEVbaQk0kODo7Y21ljUl/TAIVJZZQLJGz7jGiOUA3ZCIkxFhBCBW69QHj5pC0bKIg4ugX+FMRfyghBhK5bAokH4cZ/cmQtJWmlEtiaTGFtIoqetjOBEkxmDoBgTshlU7izhzKxQrjkc1Zrc3YDtGsOTzSaAmdjK5y8OAjJMlH1gWyS/M0BxN0I4eixdhSQJiWmaoBgiYTTX2EkcdqcY5kQsNXHdIpGd+d4k5kitktkC36fovcWkght4rTD6keHXBxVkVXdI5OzjHzaXzDo6LlMTUdTZNYXyuT0ANu31yjeb7L3LVf/+RxvvkXv/vGyy9f5tVXLlGr3ieVBNvto+kCqZzJbDagkEnT7HYJBYFcrkBvOOK0eoEkyyiCSClfIFsoMJ5NqV7UUBWDTDJP4IR02m1ibcbQaTNzfFptF0gjSEmOTp6yvjWPpHvsP36KroaIeNy+cZnZeEocxGiKiOeOKRSXMI0iipRmOgkRJI1QEpkGLt3phIHdYeq2iAWD0RTq3T5O6NEbjokC0BSL+coahBmmLiQyCk+PGnQaIZo5z/rmFT64u0v9JMHCaoZbLyXRU+cUyg7lpYhrVzeIIxnZMsjlimiqiR1EPDk7Y69Ww5Nlzs5rHD89xXddKnmNWb+FEquYcpbpJOThw0Nmjo8XhMi6ipFSUJNgpZWffw8LI2q1BuFkRrmYww9mWDmTd3/6NqdHTVYXVxCiEaHb4cr2NkQKupZiOrNZWCwTRT6iEDOd2pwc1Xj37V1mE596fUC+kKTbb+OLHqIp4EUxk5mHqCgk0zlUU6O0WEQ1Fc7rNRTVYHVhh3SixPGjE7LoCGOPsD9AEmxkM6S4mGUyC/BdkX7PJmFk6bT71KundFs1LEvBSChk58uEnookiJTn06A6BKKLljSRFIN8tkgyaeL5DrIiY7suThDiBjFWOkMmaRKFHgnTxPd9ZFVl4tjEQDqXR5QkeoNzYt8mDhysvIWkS0yCKePJECHyMGNotep0223W1pYolwoUchlMXWcw6BOrAm2nTzKXYjR1GA48Uok8oe1RMjRKkkzkeLSaF4CDpMaEYowdhMhqkpXFbaRwTKdfp9tvUqqUyeTy1M5b1M7aXH79H33yOAX/3huj4Tnlokq/d8r1axu0OufohohpyVTPjxA0C92ySKRSzGyXdrdPs90BUcIwTGa2w1mtjiDJmGYKU7cY9UdYiQSpVAJHHmFHDmYqR7NjY7sCoSDR7nW4dH2TauOUw/02W5dWSaQtgiBgMguQJJ1yqUgUeSQz27SaI4JAptHqYGbSoEv0nCFDb0asRohSyNhXcAWDRDaLF9psbSxRLGU4qTUI3IgokslVCqCG9LsTivkifhyhaCDKMd3eAvXWGavrRTY3V5mOPMpzS2hyhsXFS9iCA5FI4IJppXmwd4QnQWVxAU2UWChlmC9qqHhYqoKpGBDJjCYzQhF8YjRDQ09CogjFRQtRC39+Qdoeve6Qubl1knkLT7BpNs8ZDyTu3+lSLlQolgM2t9NIUYrzsyammUPXdMCn020hSQLry2tUTy9IpStsb6+TSpm0uj3cOATDpOd6uJGAH8RoiSzt3pREOk+mkCMSwY9ikqkc+dQc9WqTUqpIOLBJSyZF08JKmzhqgCP59BoRvc4MU8twXq2jyBKaAqvLJWazHlY6QWsg0m73MRIiubJM375gZA/oT6YEnkg6VURRFWb2jOF4TK3ZIkAgmcuRzGYRhZggDBBEkcFwwGg6wwsjxrMp6VyemJjAm2CpKq5tU+s0EVSZmWfTbjUopiwKepLRoI+uyGQzFmHkYTszOr0OIRFTZ4YzmSLLGoaVJZHK4bkhw1YHaeQg9hym4wFeOENQRSJFINZkkBTiQKCQLDKddfFCn1qjxebmDufnbdyZjyprrL/8V4/v/cK3tbockEnqDIZDnlYbRFHIYNgnk00Ryj0kVaDjTlnMzuMMhkydGdPpDFGQ2djYQFdVBq0Oke2RTleQRZHpeATCjFAIiKWQbscnEjTceMraTo7JxCUKY3whx8FRF3tmEOtpdqttstkErmRweDFiY3mT84GL44SkmGEmEsRxQDpvESsugq4SByGRECCpFrJYxAGErEm2qGFaAZvzFtPZlJmoE3kN1LSHZAnsflzl9q3rWKrK3cf3Gdg2X9pZYmtng0ePRxw/FLGiVZ7c6yNLEybNGrdufJZgMiOMJ6QzSdqNFrqiIykaaU1hbXOVOLSJ1T5iDBkriRDH2PaUSA+JfA8/FBAtETMl4whDzqpd6o0hmlpibXmL9U0VzVpjEHZxgxm+F3PWE0Bd562fPuX1X1ZJVUwmoxmP9w65aHl85pXPMLHb2DOb09NjpnMuWxtLiLrG5voOve6I73/3L+kMXVxVxRckluYLWAmRZGaJx08eU2ucMPVibL+PYarcuvU8D/bf48MPP+b1l1+nvtfH1hIsLV/lqL6PsJjiuD1CnMhIsUQxPYdnR6yvL9O4OEUSAhaWKlhWkg/frhJFM7Y2ykzcAbHkIcgR3VoXQ1UYJsYEgY7ri5xctOn0+hTLJQRVIRRDRjOXXDpNv99jOHOYTGdMZx5Tx0M3dHrDEYaaRhESjEYtwshAkiw69SqaLJMzTcTAZ2GugCiLTGcjHM9lNBwTB5BL5RhVG4QnI9qdiOWbRWzBo9GqErshE1ug40tohootBsyUMWYuzeLaAnMLMf5wTL16n9LcDqmURben8NOf7aLIIpc2VnHs3l/b3y98cr797f/zjfF4Rq8zxA0EQkEFRcOOoDeZ/Pzvel1lNB6iKjKmoQMRggTbV3eIQh97PGJ9dQNDNTnce0qvN0QzTfSkQaqQxp7oSGisrVQoFgwm4za6LpFN5pHiJN36jIWVOQQ5JJPP4kUCF+0x3YGNmkhiJEykOCImIJnUSCVVCkUL1xsxHHWIooBSukSv7nIxmJIsZCnkLRayOknBRhBcrLxJuSRgZUIiFe4/qKKrCRYXJDLFKZUFHduZsLa6QFIf4zt1ynkRU4+wkiIP7z3i+tXn8fpNslqaaXdEHEGunKPRbbG0NM/Oxhq6LuAJPTwvRlWSPD05RTFFRs6AUIzQLIs4FkhmTJzQ5cl+nWbT5+hggu8FJDOQmVslnc/S7vRYXtmhWp3QaU94/+MnXL9dQZAVvJnI4fEF3Z5NhMQLr7xAHEfcf/AATUug6TrHp49ImDrZTBpRlLmotZhMPRqNHumkRSaVQVFS7O+d02z1iCKXp0936fdaiEKEPavR6jUIFYlZLGDm8rSHY3qOjZhM0OgN2KksUymXkRWR7UsbFOfK5NMpZlObjbVN/j/O3uvXljw9rFuVa1fVrp3jCfuEe8+5uW9nTk/ikBqSQ3IsihStAIq2YcCAHv3gFz+1/Wr4xRAJ2IAFC4QFg7Ik0rBEa8hpDqdnOt6+fcO554aT0845VO3Kfmg+kgMM/4gFfPV9q9bPdQJiXyaXtmis1SD2IYrwFhGRq6CRZtIbsXRdJFlGlBXSuSx2xsY0U0wnY46Pr5jNl0RRgu+H+H5IfzCm2epRra/huj55u4Q385hMFiiGiWqYeFHAarlIJZNhPOojyKBoEpKmoOsGaSuHKpssZiGKL6MvJDKZKstQpD1os8QhW8mi5fPMURlPIyZTjxCBQr6ImVJZTPt0m8dE/ph89iad1ogkUXjw2ZfIkoZp6Wgpieq93/r5x9qP/8MfvB+FEReXLd795rc473RRsxaSmaI17qLaBp3JAE2RKOSy2GkLQ9cIIp8wCYkDD2c0pFqqMO7P6DYHOMuQubekOxsQqwJaLJNSZAJnSewLTAYO8VLAmS5QRIGVWp5GJUMxr5M2ZK6al3jLkEw6R2N9lShwCByPlK5QzOUYD3us1Wt0mk1kQcaQDQxZJXAXxIZMupgmnZLICTGG5xMFMHZiRr0RjuugpE2WvoKp6xjGiGxGxltI9JsGWRVWSwa+06ZcFtjcLFKrZvjop4+4vnWfR5//lJSsk7Wz9EYjJqGLpyR0xl1ECeysSXc+4PJywWiYoKgmdt5iESwQFQ1EnclowWQ2JRZUuh3QlHUmQ4UoWqAbYyxDQwgjIjdkZ+MOl8fnzGct7tze4sbuKotJxMxdMp4G9AcOiSBz57W7ZEoFjg8P2NjYoVpbo9fuIyUqwTIka6Xx3Cn5XIqMLVGv56lUKnhuwNVlm9XVKpsbeQxjScaGcjnFZr6Ku/AJVQPRyjJZLml3W6TTMhuNEpaeYIgyzmJGKqVh2SZRkqAICvv7B/juV+ey6406RTuLO3OREHEmLstphK2WsLUcSZigqhrFUhk7l0VLaURRyHQy5uLslPEsYDJ1kSSFJBbQNYMwFkBQ8PwYUdJYKRe4Oj/DDzxu3b9NKESoKShlU3TPDsiU88RExCIUSmXq1XWSUCXxFHrNCVEgoxt5mv0F3dEEOZWwtlEkX8mhZ/M8P+vgBWBaBkIckdY0Zr0+o06XtJEik8/RbQ04OnzOxsY666sNXh2c4Pkhk5nD3e/83s8/1hZzaexcgWanSzqbI1MqYJVSaIbEKBySpGQUUWUZLblqXSBFAmIsMBz2SEsRWdNkY62OM53gTBdYpk3i+XhywDzyaI/GVASoZKvsPWoRBRatqwmmaeCHYza3Y2BM7yDm5us7zLwFlhggpXVKpRwpMWLmTGmdD9FUhcgNIAY5UojmCWGcEAgB/WmLUl2huF5jSsiwf0la1IgGLhedGVPVZjrUEKSQhTgjl8/RqFex9CsO9o/5/JMIW38HRk1q9Tpfu/Vdnh89pZot0h+PcecJvu8zGM84ODnn6d4+Z60OW6+voRTTnJ+2MEZtnMShP/boDWN0QeL113YYTw85OxvgBRFBJJPWLFxnyNrmNUxjDXduYegqqtKnsWGCe8liMma1uMWrLx6TUXw0ocnf+/YdVMXmauDRXwxIRBVRSZErlHn2/AWrGzUEWWM0naGoWeYThbVyjvOjK5bOhExWwYun5AsijU2bfKHAwfyKa9t1ypUiG5tpXh03WTgOq2sqRlulotYQiqt8efKV73p7vUYmLVJKRxhiwvNnpxSKRQbDNuV6mcOXL8mnS3huQhJpFAsraPGQjJ3jeDghcCNqpU1iO0FMUgiolAolxu4Md7FEMhR8L8T3AtyFg5UyyJVWOD29QhBVVFVBkiVIZIrFMkEkoCk6/f4FqpbQ6nY5OXvB0BmTysgkiUocjVGMDY4vj/C8r86AxDruIsRzE1oXAyw7x9yU+emLAyqlHNerOVKqjBB76IZMLAo4LBB8h5ykkHg+SgQWFkW7Sq5aRBOP0LQc+Ry4us7NmzdpdQZcHQ3+Vv5+JpyWnEIIIt5+8zZHx49YXc8jaBGiElHIyHS7TbJaicVshpXTSGJQBAUNg2XPY+7EyKbJfBgx6S1xQxBtAWe5IA5lRpcBpm4Tj5dEvkqUSNi5MqqqMWgNEVSVtc0aR8EhZ80+ipJGivKk0xqDWYe19Sw7ZoPt7TKhF0AokEnV8N0AMVGplasEUcA0GNJfLuHsEkmWsRWbuSvTPp8z8+dkVyQaaybOQuLlgwsKJZmZ5GPWapyfnlEsrGEoKueXY6ZuzEIQqF+/TXvm8vRlE6tUwxMltu68yXTU56hzRX8yoDDPkjM0DCHP/uMOa1WVYC6RkgzqNQMrHTKa+miyQiGXJhFCgiggWygjJhJKEhAkQyxzyHBxBurrKHqacq6OGInoszmrNY29L6ZMW1eYhkMhJdPppdFFCSc4Zy52OR/NaY6PmDkLKlkF3JBJr42/WifyAs5fnLBZKyNrEf5SpJcaEYkjBDlkfcMil4kRxR7VSgbELCmziqMotMZTkviERjFLxZLJWwq5rMZ8PCVtGBSLJn7kMHUCvnz6gnS6wItXJ6QUjVvXthAlgb2jE/rdzyjks+SzGSQjJF6GXJ5fki/UmM0d9l7tsYyWrG2t0theQ9dUxEj9SpsLIsa9IcFgxNKJuX33DTzfZjFyadQqNLIrHJw/ZuknIGZov2qR0hPubN1gMWoihDJHhwlnxxJ7j09Zyb2FtIDZeMH6yirBGzfQTYtQUGi11kgbadZWNyhmMigpaPXbrGQTzHIGWZLwXJ+xM6aeKSA5MkYsUtEspMImK1WFD37yIbGikcpkqeYz1KLs3w3OtC1x1Wly7fZtPn70gFzBxJ0MKRTT5FURX4qRFhHVdIG8lWUxd2g329Rr9a/MlPNzZm0HbyKwnIuIpsVsuqDvLFk6CUpi4iUKzmSGnpKIxZiVRh1NTZHKeFgZBTObIr2RZXLlsegukIQ0tUqWu7s3ub62wsGjZwTCiCSOmYw9Rt0ppXydIBSRNJNyKU/D2uXJwRPixQjP7aPbsHAlPBmqKzWMDIwPZiz6CRX5GoKbYv/LIx59MmYxHFJIR6jmgmpZRdQiEnye77/CtCt0LqcIgYAu6bx40WS+GJGgoqs6h/un/IJZwu+GDE9daikJbzGmXNVYXxfpdp8gEfC1t+8wm/dYuH0kzUCW8qhJgV7iEDlDFHnI7vUq/e6YlWqV6WJC4CzJ5tJMOks0VULRFHqjFpIkMR95vPEL9yk0RDJ1A8+f4y18CnkdBZfEnzEeDzk4PmLpuGw0tqlZaRbuBDuXI2WYeKMr0rqCoikgL/ETCUSb8cjF0LIs4gFuNCIcj1hfz1NoNFCVhPGoRSybBKLMaLIkjEUK1XWOzts0Nu7y6Yef8dadG6RSApPpECdxmUVDsppCIAocX7S5Ou9AqOIJHhN3gmYKpI0iCSK97hRnPiUKPdJmhiTRWV9fRXBdajc3GUwjFq6A70kcH5wya12yer9AxcyiSQrL7iUpISIbJBy96iFbVX7yo6f88i/9InljjX6zT/VOjsgQOL16xTLxiKMALWXx7tdv07zsMpqOWFmrQuSjC7BZTWOKCgs/ZL/XptcbEThLblY3yWUyqLKMIluIikQ6l0PNqaALdLsn5Ez77wanZMxIxAlRsGS1tMbwbIydVQmGS8TQo1EsMRtYpLTUV/qZKRDkbdzljCSBrJ3m0ccviBYiUaCSKlbwbYnJLKJUqnP35j3sZISUuEhyhO9FZOwMk+mcQighqQta3Vcsk5j1reucv2zSa3a4bW9x5/o1Hn/2ObPOCKsgIAsaaU3ji70XOKsio7mLMlog6ilsWcEfxyiKgiqqxLGLoitcr1XRzBTtzhXOQARHZxH4BJ5LrqZTzGd57Wt3SCUw7404W/SZLDyMWKKglhi2HTYKq0wv97Dx+OA/fsxv/OZ1coU8iWAxnXZRxTHX100ySpnIb5PLhLz79h2WwYAkWJCz8ww7XVQ9IWNaGIZJJl1iMZXIbq0hxDFWWqPZP2c87PDy8Rm/+K2vsV5fpX3RJJsvEyCxREDL5RmNRpTKJnvPHzBcDriWXse0NKqrBZqLC8bDS7SCQCIbzLyQxcJFiSElwdp6ncgQ6LdaaGKAnLdxAshUKuy9OKHfXjCfegxqATe2c3zz63fpD0ek0yKWITBz5riBw5KvLLCXh1cYKQstXaWQK+At56xvVrlxe52ACf3xBaoqcvPmDtmMhTOdomoa1ZUVqsUGUeKRFzX8SGM4ChhOAyZTH1BZLh1GoxGOOEdXVbJWnslizGQBT5+9pFIuEc0GvPvGN2jPDtHiCc5shBk5+KLAyWkbSTaJQps7N0wuT1/iLAasrawxmh0RJxFoInNnwWQyQZiLrK40SBQH3ZRBnOMtF2RMDUVWiaYO2XwOL45ZOi4IMcVqHruYYYmPlTcYjodYtkIqoyIbApJoIcXh387fz1oIzbv/5n0/8NEUGwWL5lmbfMbGcxxaV6fYaZv9Z5ekUgayLHN5ecl8PidtW3Q6bZIgZr3a4NbuDhAzcRwyK1XMYg5nOabZOiKlBiy9EaK4pFRIs3RnTMY9RCmgvpFD1kKOWy1ubdxGiSU2VzfY3Fqj270i8SPu7tyhVEzjuQG2XUI3cuy9OCQSBRRNZeEuGA1HyJGEIkusrtUpV3IomkAuZxEuQzwnZNAakkghQton17CobuQp5Ewyqa8qgJVCjrkXIkoGIhahLzGfzpCSiJwt0FhNk1KX/KN//F2Go2OKRZVSRUcSl0z6TZaTPlHgkDJNTEPHWSzI2SWK+TWERMO2sphGGpUE/AghVhFFjShMuLxqIiBgmSabjft4jshsHHFx1gVStNpjFMMmna9weHKJGMJoPmUZRVTqa8zGDvXSGgWrgJYoFLJlRkuBTDFPFIXcvbXLq/2nRElIulokaxUo6iXmcwfHW6CnbR49OmA8nLO+WiOXkcmmJUqlDGnbYjabEIQBo/EAQRGJpJirfgddSZErZomFiLkzY+mOeOONXbRUwNHJHi8OnlIoVRFFAZKYMIpYLn1U1cC2C4hKQmUlS6sz5uGjV8wdOD3r8uLVMXESM5pM6DsTNE3F0nVIZLxQoFxdYTTq83v/+D/DVCPcRGQym2FaIpE/ZDxsUshmOW8OaA2WSHoKw5RI2yqStCQRHEJ8MoUcbhgyd6e43gzL1DEMjdWVCoosUMhnUSSRyXBA/+IY1x1RXCmz1qizUiuTsTRUXUBNS2iWjWEZfPLpT5GUmOm0jyJFKGLC5t3/+uff1j59+AfvL4MEARVZ0un1hhjpHImk4sUq7YGDnilgZfK0+2MmsyXzqYdtFUn+OmfZ6Szo9weYaRk3DnGShFiJMS2PakUiCmKy+SwkHoWMhSrIBL5HLp9B1UW8YEmCyeHDY473jviHv/lbpE2Nz7/8BN8Publzm8tWi8FozmVrSG8yR1AUQgKiyKFStikULFIpDVlKMZl4LN0ICYHQcfBGDuE4ZugPsOsqm7ezVFY1XGeO7wsECXiiyOmoTSZfQjJM/s3/8xGtwQnf/M57zJwhKUMhX8iwu/4Oo9GSk4sWfihSWdnk4qzP1ekAjRxRoNEbuiiKiapk6TRdri4WdFsOsmQSRRJqAnIiM19EjMYOveGETr+PlbYxDJMXBwGXFwteHXRYqe8wFSmgNgAAIABJREFUmfp0+iNCBJZhyDII8ZcJdqHG0VWP/tjn6nKEZZaRQoWClSf0Iw5bZ2ztrCErEVLsIccxnu/TGY9p9zzGY0jnMiSxy7TbRwl01morWJZMvaGTIFGrruG5PvOZx3g4Ybn0kFSF4XRMu9dlc6OCbinM5xOWjoOqSGxsrNPpXjFdTLlqD/jhDy7JZRU6nS6O6xGEMYkgoRs2YRQCIidnE/aen5Ar2axvN9jcaTD3ZixDj3TBJm3plOwsGTNHo7HDBz/8AGc24t371/BnHZrtiFZrzNbWDumUQblYx87miLQ0erGCkUmzub1BIghcXrQhURgMpugpk0w2japIhF5A4PnUKhUMPYWhG6SzeWJk+v0J1YJBqWLjxj5P959iGRqrtTJR7KFaCopZRpS/8qNPjo+4ffMmK+U6WatAduNvPqX8zLFWs8q4yyl2PseXnz+juFrjuHlFgMAXT55RXauzuqXwVw8eMx1MyVlpsimb6SghXOqMhi6SrGNaGjdu76Jc9vj05TFpLUN9tYBlOERBifbVFWtlC01K8XDvEe4ypNZYIxAkYlVFJ4WoadQadRaDOZl6mlaryfrGdf7ixz8iTkLGszndwYTaWoONGzusrhZxZn2C5RTPnzMKPQQ5R7gw6XWmLJ0W9ZxCLVMkpUg4xQ2y9QwlG6LllFevznjjzV8ilGHkDCjUVlEJkRP47u/e5ubN+wx6LklK5/rGW9x/7R2sGF6d77OWrJPIAS/PXrLEoXF9nWQOmpRmupQ5PDhnY1PGMnPs7x8AMcvApb6WxijoxAiARiptILshpWqNXC6LQMzh6Qkp3WQ+m3IrEvmTP/khd+9WeOvt19BNkaPTI5ZTkeHCJZIUPv/ymLs3Nzg+a6Gv1kgWC2Qh5t69a1y2jxCTmEyxihHmscwscraAKxvISopKPsYUDBbdGUNBQM+VsGsm3eAlppVHUGyW/pyd7Ts0m18twZ4+e0K6lCalpEjZBsP+BEXRWbojUqqEQg6SOZ999ilxBDeub6BIWSRRxbZNBsMBmaxBJp9lMXFYOpAkIaWKgWZEyIZDuVZHSK1yehwym08QQ5ecoDJxF3iuiqVrqKbCR3/1Abe2ipTsdVwvR/dywWQwwFvM+P5v/Ap/+e/+mLlgUVu1ebT/BAKR29fvIscCbtzm+ZNDynWbVEpHCGA+nSKuCEiCTLc7pNkcIKCSsqtIQRff9zg5u6LfnXJz06bXnnJ5fsbadoPa5hrzhcvOW++hyCqanJASDcy09bfy97O3tXYFTc3y9MsnjOcjZn6AoObodUeo6SonVwueHLe5d7OI1x3jhQKt9hiWKilRR5GzJJLAeDIjCGWm0yWD7pRElUnfypK1JA5P57h+wsnJBb3TC+7s3ENTLUaLGZHgE8cxs4FLNAkYjcZUf2eV//Vf/QtSZQM/8pkMpnTafba2t7FzEpV6DUEGx5lRq+aZ9D3a/S7dQYtO84rd7XeJQ5l+65zI0jCUFGbG4vQ8YtyOWbVtFqMh333z2/TnEWbNRrIEUiWB/UcfkC9WufnWBuVSjuE84nf+0T8jn9lFT9Ik8WdMXh2Sys2IlRA5nOAv25TWdoimEbqoEHUsxrMZQRQTxgnpTJal54AkgASSpJJEcHx6wdwHM1sgBHqDPvVqheF0wq21Bvfu38XxZ5TKOpYtU1vJ4nhjptMrTHMDb+KgWwa/87vfx0yphO6c7qDLIvbIpnWu7d6k0z+l12pjxRFWohP4Pu3LKxwjxWnnjPdeb3B/JUMQhew0bvPp4wO+ePoUbX3JO2/fYDoPmM08FpNLIOHV8wNmsznf/we/yd7LPfL5ErJkcnbQIZ+r8+TLV9y7+TWIs+SzW1imRSaVZu72UHQF1w2IooQEgWa7xflRkySQuOieIcoRshYxdTrIM8gVC7huibjtcXt3F2kW8vDLPX7j+2/yn//OP4TYxRLGJG6bvZNT+t0Ja41v8vjzLzk/vuL0+P+isn0TQ8/RHnxO72qMqWZxJ8+xFIth75yNaxnc+YLQ8xBCjZSSQhIU2q0uSy/EtHJYVpbhYMp0PESUfAyjyK3bVWyrwrjV587r3+Lk4DlD5wXZXB7Hddm6eY8f/Lv/m+3NLQrZlb+Vv5851v7P/8M/f38ymTActjAMjVF3Tq2wQTFTYjVfJHDmhIKMEGp0zrsUMln+6W9/m4otUrZ0xrMBXadLfa1GmMScnp5A7DEa9Di76NHthyhShuZZn8kgZDaOuOoOuBx0SWyRpRGyVEMcBXoXh0xGPSoVjam8JHvrGj948IA7b77F4csjNMtEkEWC4Cv16sXzQ9rdOZ2Bh2YUSWdKGLKFKYbU8jG2OUPTXIYzl6Voo2eG1NdE5mHEySRmKlkEioTjDFmrFBieXfAXj2YYmkA0bnK9do0UdYr2CpaqIixPGI6esvQC/uOffYiYpPnk40Pe+4XXqZaLGKkUZ1ctjtsDvva1txCCAH86RoiX3L67g2lbLMKE2XzKeDrGsgvomk3gRZyfNgkCgU5nxK27ZTY3Mkwnl3/dr/VZWV3n1o0dDg+f07o4J0n3MW2NtFng2fMXLL0JYTQnW9JJFSxmoYuu+QS+T4zMy6MeaiZPdxHRHS257F5QKBuU7Rx6qBOMPQbtC2aLLpmsTi5bwXCrdC77ZCopIsWhVM0RzpZkZJuiVcSf+XjimGF7RFGt843b32C7vEr7/IR//yf/jm984ztsbmyxiCc0L/ucHQ1wJwnXN28gJjEvXnzGVbvN+VWAoFicXY2YO3B2MkOOUjRKK0w7Q4RFgVknIW1kcd0RhuawvZ0hXxCQDAG7XuVqsOQvfvg5K9UGn3z6GDOTR8kYGEWJbCVip7JCvbCJkqR5/OVL8iWNxg2bXMUknamRsuuo+RpB4JFJaaysbCBpGR4/OyJTqrG+ukLrYA9NlLi1c4dyrkLOLlJZ32A8HTGZj6lbW6RVGzuVwl9OSKQlB5evMIs22ZVf/fm/OU8ff/j+5fkV3W6He/fuYaYzeOGSlwePuPfGGrUVFUFymHT7vHH3Pr12EyFJECWB49MTZs4SUVRZzOYEXoAgQD5vU1/Ls7KWp1hK0+w85423Nrl7Z5W7txpUSll2d65haAUef3bCgw9PuGpN+O3v/gNu3byDmTZ5eXXCYfuUlWtVPvrsx3zr3ff41ne+jqYlXF0eMh02UcQly3mXce+cYa+LSI5yboPm+RhdzSKraZBNUnaFsRtycH7F1BWprO/QGc3Yf/WKs9Nzvv8rfx8Vi3/5h/+aj59OqFpZcnqKyJf48KNP2H/xJZ9++qdUKy7d/h4pU2dz+xbnF00m0wG3bm2Rz+hkLJXV1RIbmzW8eRdn3KFWyhL5Lk++fEi32yHyIiRBZjEPabUnPH9xip7KEIYi1doKl5dNhHjJk4eP2NzYYj5b8F/+V7/PfD6kWNApZHU2N+uIRkynPeLissed23dJYgfPn1LMpzh8tU8mnSHyUqysXqNUrrOx3aDdHzKcT/mlX73Pzo3b1Cq7OJOA+dAlY+Uo1ap0JiMue11mgc984hLGMwaTUzJ5hfPzAzq9K9559zViccYXX3yAnA0QCMgaGTx3iiy7fLn/U373n3yfTN4giedowpBrG2VGwxZB6PLmW+/guglPn14xHy+p5DUKOZ1O8wJVErl9c4fQDzg7uSBj58lkKjx48BmL+ZBvfutN7r2+i6wk2IUsl61L/pc/+GMi0SRXShMEI/7JP/0e917foFxJYZoimiywmDtoeprzi0uqtRWu7V4jVywyn8fUaje5eeMtUpJG0bQ4fX7IfBFRW7nOWuMGhXyF6WhOa3iGZCs8OXxFs9+hsl7l5cEes1kPSQzoTafsvXrE/uFDjIyAYog47oxMNkN+7Td/fjiT4YP3r11fwzBFHu89pVissbraYOvaJoE/Rde+6vO8cedrNE8G5NJlLi+7jCZz3ACGwxm6rDDoDsjlChimiaTEZPI6uYJGvqBhZ1OMRm1yWZWMpTAbDxn1u0SejK1V+fQnT8gWLb73K79CIiYMJkM+23tErbHOzPVoNDZ56/YbaLpOEscYmkKvc0UhbbC5UqNRr5DL5JkuYvL5CmEo8ZOPv0A10pjZLNlyjs6kzTjQ8OU0L87O8Qi46jaJo4S1lV0ODpo827/gzre+waOHn/Dd736HobPASSJCJWbmtLhxs04uLbBwQorFLXxPIIzg8OULNtZWsHSDjFWg25rzkx99jiHneOv+XaaTEdVyiTdfv0e9VsY0DIql8l9vSj10I0UiJmSyFmbGoJov8svf+XvUait0ez2+9Uvf5tn+Q+7eu87l+QFJ7JMu5eh0JszGPu99/T2W7pDrW6u0L4+JPJdeq40ofaXUISbs7R8wmiz4te/9Ar1hh71nB3zx4DlR4H/VNpJiNFNGTstkqxl2X9thpXqN/qALOKQMGS8MeP1r73B0cYSuxRg6KBkNRVIZ9WcosoIghbz97n3GszEvXr7ixYt90iQoisCtOzvkijlkXQNR5fJyTBIs2ayrmLrMzvYG0/EA3/OxrQyDwYRUKk2+WEI3ZOaLHqWyjaIkaLrK072nnJ5dsLm9wtzTubw8ZGXdpFyW8MMBsuxTrVZoXrbIFCwuLo6RlZiVtTJ7e49pdzqoqkWt2qCxukbelLCkhEohg6rpZAolUlYaL/C5ujyjNTrnydE+Y3dGoiUkUgRCQC5rMBy2McpZPMFh4Y1x/Rmu7zCbTpiMZlx//e/wy1jr4F++7/l9VjcqBJHAjz98hCikSaeL/J9/9Gfc2Nkgl0rTOl3QbfqcHreJEhUkjUJlFTNlYqU06vVViqUysSiQr2QxMzKO3yeb06nWdnEXIZEf4C7mxEmCKEt4XkDgR7z1xut87Rd3eXn8hPPeOZptsHvzFq3mGCFJE3ga11a3MQ2bJBYRIxF3uqCYLRJ6Iecnlxwen/JXnzymP1qQq+hM3TGFWhGrkCJWHOZem4/2T3nV7lNcKzMLpyyCBaPZnIWXEIk6L0+bfHz4gP/2v/svmLkzRt6SDx484LB5TrM/pNFYZ7tSQFMKGOYKg/6MycTlxo1d6qUV9h8d8PTxBR/+6AJNKfD26+/QafWQZIndm9uYlsqLl48YL/rYhTR2zubk4hhJgSDyWGlUKVazHD09Yv/ZCy6vrrj39ltsXN/i5eE+B6+e8/jR50wmU656HUQMCrk6F2cnzKZdthq1r+qF917HSmmkrIhsRqXVanJ81GJ3p87+3iskUnheiONMcRdD7t7d4tZrW9gljVhZ4osLLtpH2OnrTIdT/MUERZXJFit89OUjauvrzMZD5DAgUjWiQCal5nEWARfNCwzLYG/vFb/xG7/D5vp1hBl0e0MiSSRRBP78Lz/g5cFL3njzDa5v1RHDCasrqzQaDYyUxWzioCg6sqJz1eryZO8JG5trKCrM5gPqq1WG4xG12grt3pjJwsUwS7zzzm0ydkzaihHwMFIax8dnDAczgnhANqeyvlEgbYvcvLlNPpdlNOjza7/2S8znXaJlh0n/FHfeBTHBJ0LSJObOmH73nLPOMYmSkDJ1qqtVvMBh6c6RJIjCAMk2mbpTvGDJyvoKe0+fsrtzi8lwwo23/w6nlM9/8N+/v9YosXBd5o4A5Dg86HB12ePN12+Ty+Sw5AyHr3pcnA+4ffsuiq6imTpmOseg18VfThlNJhSrNYxshs6gxcKbgOShqDFPHl/Q6U5wnBCkFJZdIBF1Xp2coJoKF+0TPFpUtutEWkxrOuT501fcuvY6oSvTvBoTLZcUskU0SUeVNT7+6DNSepo4EjHTOUbzBVPm1Dcs6tsFUjmZh3ufoZkimh6AOKW79EAX2bpeo9s9RxBjdnZu8PDLR7w8PEROafzeP3+PF/tP6PRGLAWJG6/f5/jijM31BlKs8HqjgedJhGi02x02ttexjBSt8x6j1pLpSGW2LKCoGfaev8RIW9x/8zUUXeLw+OVXJT5bxcpZiLKEH0TMnQWqrlKslHn8+AlKJNMbjHACHz2bprF7nZ9+9hGVaoXz0wss0yZbLKOpGR492ufWzZtoSkhKFXEmA66tb7Bar1KoyQhEGIZMuZghly1Syq8wm/j0O23u3Nzgva+9Tqt1hKz5mBkFN5x+ZRFlUnz28Io//bcfcGOjzMZmg9nS54vDI1rjAW/c2MUURZxE5eKsiyrbdLsj6qs1LMvio598wf7eIelUjmghcnjeYuoFfPn8OaphoKY0JtMBuYyFIqg0WwMy6SKqYuJ7EccnFwxGE2bzKYqho6YU8lkL27botFuUyhX6oxn7z49Yb2xzcnDARqPMZqPKydEL5rM5l5ctru3cQ1FNCln5q1qHkWCZMUnos1pb4cbONTx3wHh0yWh4hqXHeO6Qi+Y5+y9f4HpL0qZOr3WGkASISUw6bVIu5fC8JaIk4fkBuzdvI6gpnu7t8+DBcyzLIG1l0dQUkqywfe9vFt9/JpzHH/3R+4tZzGiWUFm5xd7zKyRZoVC06fcPeLb3jOtrr1GtrzCZ9UhnE1KZJboVIskCGdtkfbVGvlxhGScEooxVzLEI5uze2ELWZC7ah5h2CtOu8OxgwHCuMfEMpr5EdWuL6vYWwXLOXJdpuwsSRUb0QzbzBWxV5MWrx1yev2I67fHWO/fp9FpM5hNG0zGiqiBqGmqmhJqvIZg+46hJKgeVlTKKqKIrMom/ZHPNYHfVwO9fkgXW7AK2qLBazlHK6JQKGvmUgjhWePbZAbZpY6bgxmaNWkYiE/s4gxGFUo7Hzz5hvLhgGV6QxGPcmcfTB2fMFjHH81Mu+mdUNwuM3BEfff4pnz18xGC0QEvlEVUZUVQYTXzGY59CvkEYGISBwcuXTeJgQbqQxchlCDSZ/+kP/wVeDAcH50iJThSncGZzHj9+RWNtB991SBsyigiGJDJsDSnnSiymC6IljHtjCCK8xZzLswNWqhnu37pB2S6wmE0II59StcxVq0m/P0JAZj50wUixsWZQswUEOeCoeUIzmCKnFQqmgDAb0RyO0A2TcrlKvpQhZUIseNy+fYeXL16BEFGomDw5POKnj0659fo3mDsSQSBTyBSolNaZzGReHTQx9AKzqcdoNKWxsUWukMMwU9ilIuPphK+99y7P9p7Q6w8YDr+6UfuhSpzo5PUlRdtiPJgQBgqNtZt8/Nke7cEMM5MlJxQpZrNYuo7vzjBkE2cSYqg6krBElAMypRLd5iWTwZBycY211R0KuSolu0w+ZVNUVa7X62SMFKNuB9/36Y9mPHj8guu33uTJwwOcWcTu1g1ymSqjkUN/OKG2tkZj5++QKbl88Kfvn170aFy/g2IU+fMPPiSfz5KxVX7/938d2475qz9/wta1Tbau18kVNVBmzJ0Bi7nDYjajeXXJ/ssDNCtDoV5n4kyRlYh6PU+n2yJTkNFMjc8f7mOYdWYLhcv2hPZgjKBo5EoV1CThuN9jGvjkinnSiJw+eUwcLPjN3/4e1UqJTMYim7Xo9dpcXJ6x/2Ify7ZQUyqilqY7CTDzIlLKQ1YFTCNH+6KPECSs12tU8lC2FXJ6ilqmwnplDSGIMGSZ8aDLzZ0NHn30CD20UROTtGnw2uvbjAeXTFtHaL5DqVBA0SRmyz4Tp4NuxWxvr7GcxqyUdihW6pRvW9y4vYaVTVEoZsnYGdbWNijkqiiSiabLOEuXP/2Th7RbEwaDKbncCt3+guFgSfvqgNpajUKtzMOXzzludrh+fZcf/+hLrjXWIfkqsdlqDaiU6+iawq9+95u0Wpf8f//vj5j0epi6xe6Nu7SbfYREQpUVFAXKlSylkklKNTg/ajMZT1l4Dp1el2pthWpllXKuyrg343zUYnezxus7DY7OXpGvl7C31wilkKwQcbtR56LfJwgD8oU8m9sNTs6eI4oRxwfHpE2bSrlIoaIhmWnWt2/T7i7wfBFF0FmrrWKkbATR5Ccffs6PfvQRD7/Yp9ftkxCwDFy0lIaatiiWSuiqjKaKbDY2kGWN84smi4WP4/qU7RBd1RBQefzoBZdXbXZ27yFoCo+e7OFcechSwtXlGZVygfbViBvbb9Dv9sllDYqlIomUAt/n7NUxF+c9HFfAtiuEbsKwNaBeLSITcnZ+xHg2IZ3LISgqtdVNLi57XB21qZZXGAwmHB2esLOzw3zpEiUht+//s58fzpg/fv+8fcRkNMfWsxTSaX74g7+kVBJYWy8iqiJhoPFXH35INlOl1Zzz/FkHxALLSKQ/ldCsXbR0lcrKKteu18lYEXkLcmmJZLlAmeRQPQln2MLUDF486zHqBsSSRHbVYSI8ZiQHbK/XWc3mKGgpmoM+rp7iyf4RpVSBzsVHpGSIfJnRtA2yw2gg8x/+9Dm6HnLvrRzlBnihy3DgcXLUx52FZFQdxfNZ9icIskJ7POdiPMeXVNypg7r0SYUxkueyuVajaEbY1pJqTebbX7/P5XETd5ogq6tgbDDuNrl2bQtDFVnNl8nGGXqvJuT1FIF3jCaeY/kDcvqQYt7DzohcnrU4eHaKO/ZxJ0sOzs7Ze36GLinc2t3i5q0NxHTAi/YRvqnzlz/qUKjcwHF1qoUKdxrrmJHH2zsrLHttsprMyZWIH4jMFnMW7oKPP37MD//TY77x7i8TuzY5u0E6nWHv5TkvztrYtRV27twgm1EppkXsTJlYL3PVbzOYd3n73Zs01mxUXBJ/yqu9L1BEFd9ZkM+nKRgKRrjA6LXYTikoUcjB+Tnd8ZDJ1OX67ts8frqP68/Z2tpEDtOE0wzt8wWO71NfyyNKU3Z3qqytZLHTIp98+ucYOYHm+ArSKvmtBjffe5fr79xHK2h4cY+1hsGN3TTljEjZsEjHCslyQRQtqNZyNIddpr7D937xPc4GV7zsHRNlZfrhjOZshuPrCEKGE/MpUiBRSOqs+TvMJx5Tc0p+s0jiqggzGbe5YD70GY59upMJN+/tEjDnxw/+Aledk8loqIaKamdoD8act3oMhnOCZUguY7MgxSePH1NoVDHLaV4cP2c6nSDEAu9847/5+eH86V/+j+/Hicqrwyu0VJEgkfjVX/9luoMBO7duYJUq/O9/8QHVa9t8+OBzOsM+iqIiCVDO5/HcGaLigzBDVRzyWYHR4ARF8kmpAoauYaoymazMxrUy2Wz+q9eqnIDpcM5wOMCQTQQvQA9ETGTGvR5p0yBnmazlCmxXqpCaMJktEOQUshmTyAEbmzf43q99l42tMnpqSRCEWKZFsZAnl7WxLY31lRL5nM76ehlfmuOzRNRVFo7LTmOTSbODoWl4yyWOs0AzBDLZNLvXr3Fxdk6v26WYtbm1e4uffvgTtq6X+fzLT0gIGU+mDMcu/cECWTOw7DS6ZSEZJqmsxTTwOD7vcnzcR5FsDD3N0g1YOA6T0Zy//+u/xdJxaLWalKpFsoUSrhdxa0dCVXuY1oJm8ymyMMcQEva/eMJGaZ3V0iqfPzgi9AK+/a1fQMQnCWfcvbtJ4I+4e3eLXv+c8WxCqVTk/r27VAt5nnz5BZ7vYeVzRLLMeDlhtuhiaAlZwyBeClxdzej0A0auxu07d7j/xmukVAVBjFh6LtPFnJWNTfxY5OGTZxS2biLqGV6eNsmUy8RSxIuDQxLR4M9+8BP6szHj8dVXsnwccNHs0e1OmczmZHJ5Pn/wCYISkMnYrKyu8OrVPo8efoogeEiCxze/8Q5GOsfz/VN8B+JA4JOPf4xiwHg6pFwu8dpr9xk0XyKIIgdHZ2xt7qArFv4iZP/pHqVchtfvr7GTXicbG2jzBFGSCKWESW/K5GyKM3EYBQOcYM7YHaKaMmESEkZflRcuLposFwPyBZveaERtdZ3zix7P989ZqV7n4eevuBgccf36JvlCjgSJbn9MNl+hVKxw/82/+SGjn/l47h/94fVEwKDV9Oj3Eza3XyNtZzDSOkpK5OnhM46lhOFZF90TqNlFms+PqGWymIpCb9zh2t1NtjZXCf05Ig6r9QKh79Hv9lhdWyP2XWaLDpolMl8qXF0Y9Ppwcj4nX7KRUyEbtzKYYoy7mLP0JwRCiO8v0b2YmysNDsafsXQlqrWbxIqLooOt7/Kv/9V/wrYTctmAGzu3sYt5AhFQRCbTIYG3wFQlYt9FSsX4JIgpC2caIgxDwvactWKF4XjErTu3iQwXXVEInCWhF7L/4ghBVCmvbCOpBmY1JoodJpMhhp7i4PkVmdQKQiTSbp1gZ1Ns395lMGvh4REnJs44hZrkmA0cLg5PSaVUNhvbJAEkiUsQz2ncWCNSNP63/+Pf8t57Jl9/721mswWyqNKobZLXCzRftWkd9eg0OxxPJW7fuc7B2XNyJY3rOys8fPgZr93eJfYd1uqrDIY+3nyBkIAqS5xcHrO6s46Qlvn/OXuPn1vy/D7vqRxO1amT0xvOm9+bQ/ft7pnpST3JpGiRpizSNCUa1sKyBRi2F1oYthezNOClF4YM27AIwqYokmOQxJDU5OnpHG5333zvm+PJsc6pqlPJi9aWBGb+hN/iAX7f9Hysok2v12Xa6VFSdJbzVXJ2mdY45NnxJT0/IZ4fsrtZ5+b2GrWqQ+f0iL0Xz6k0VhnNAsJU5IPTNoZh485CavUKCB7d9iX9S5cvvvINzo/PWS7puIsR3ckYSSuwtXmbdqvHwwcf89pr10AKODvvMZ8nqLLJIgi5PDumVrZp1ApEkkqjtkZOy/PD7/8FL99dYzA9I2Mb9MZzlptb5DMB9doaoWDw8SdP0DWL6XzOwvP41rffYDo/5+Pvv0s2NLlevkJ3NuJ43iFXLrOcW2WRBnh5l4uLM4KFh6GbqKrJ1sYuczeiddkjnLepVgv84OcfsohjKrUmCAbHh21y2RKlqwJJmmA5Ndq9GX/11z/h7q3bFIsm/+O//NkvH5771g//1Xd3d+8yGEe0+3OCSObJ3hlvf/AZrZ5PubLG7KJLLlUo6waJO+MLL7/EfO6CIiGpMgUSZNzjAAAgAElEQVTHpl4pEkyHeKMelqyRBBLziUK/HWLqZXQ1TxIrtC76tFototDl9S++wk9/8kP2n77g//3+ZxQqJZ7uHfPa17/GYeeSSqPCcrXK0eNHLG+VqVWXSQUVxUyYeAMODjusNa+wvbNKzpHJiCaikDJ1B7Q7x/RHl+i2gO7IoIPrT/HDgFSR6PfH2JpJNVfk9o0brG2u0Rn1OOn16Xa6bKw2MFWJrfU1rl3Z5fD4mLHrUlorMvVmmLk8v/jwAbLVoNG8Ras7pVCtIKgZHu23eXZwysbWFhkti5TotM9bHO8/Y6lepJLP0zo5x5uMCPwBk+EFuazE0fPH/NZ3vk00d5FimUnXJZ8p8uYPfsGw1efrX/giihiztlLGk895692/4bUvXaXf76BpOiv1VXJ2CTExubjoMZuPKDpZFCHBc6cs4oT2ZELL9Xn4/JzjoxHrSzvcWL/GaqmG4zg8PnzGj997C1+LyEgJpqrQbNaYzweYToat23eRBJPl5Wts7NzFEB0ygspatcSs10VeCDBPeeP1N3j7rZ+yvdXk5s07LK2vUKg2+OM/+UsePj7g+OQCUZI4Oz9HSBRW6hs0yitogoaS6piqA6FB62IGqUkSa6SxRDZrcuXaCo1Gjlq9gmPnCOcht+/tMBqO6Zx3eXT/MWKYoCQpV3fWkFjwwSfPSRJYXlthaWsFq1LlYuCysXObv/3Jz+jMpjirdda3rxKnEsfHFwx6M0r5NZr1m0RehsBT6A8XRLGEt4jZP9gnilyWGxa2taDvdhiP+pRLVYJIxMjkGI7H3LpzkzvXfgWH0F//2f/63f/vz3/K1at3ODnr0h/NOTnvMvNjZCXDZDTnn377P6Czd4QlKxw8e85sOuHqjRscnJ7Q7Y1RxQyrS02iYE6zXoI44XD/jNOzIYdHXQq5EkuNNfp9Dy+UaXVHZPJ5Lgd9LtodjGyWxCkRxXBy3sZ0cuTLJbzpHD0QcTBZW6shRTrdtst564j6co0osnj46JJM1kKWEy5OLhBUEeQUxZAxbYNFGjHxZgymU6REZOFFiKqJOw2o5stkVI29g+c8ePYQP/F5uH/JrVu3qZSKGIpGfaXJYDzBFxX+5C9+zEuv3sCycrz3yWd4Prw47IFg8977HzIc9pBUnW4noFjIoogJtmawVlumYGUo2BqKMGc6avOVL73Gt775ZYpFDV0LuXtnk42VBs8++4xcdpnuxZhRP0AWLFx3wfXrt8k5OX70s3+Ht/B46Rs32NxeY+TO2L16gzfe+DUK+Qpnxx28aUg+myfryAyHfSRJJJWh0ChTa67geh7nJz2EUKeaK2KpCtl8BoSA4bTLy69co7FS4N71m4z6HRQl5vzikMGwy2LuQaohCSZipNJYXaZRsJmPztGJyIg6V7dusrG2zmuv3OH6rWvk7DqaZbJ3sMds/vm+qqIoFAo21XIZz405Pz5nOhgT+wuyhsXJ0SlvvvkI28oThQE3r1/nF2++iZ1VqS3lMTIKcZzyzlv3cacxTs1iaWkZf+biTvqMRx1y2Qz5XJ7RYExz9w6KLlJoFDnpnfDi+JCL9hCnWORy1GLvdI9Ot8/B/j7RwkcWRVRZo1pawtByqLLNBx9+yCePHjByR1QbZZycRqVsokoBuaxExqlRrza5vGxzcHiAqqR8+cv3eProE77xtX/xy8P5v//P/8N3d3ZucHnZQ5QNytUVfvTj+zh5i+u3Xuavv//vKOd1vvK1r/De++/RbveoN0oYhsFk8nk7WxQ1Hj/6lCicktFjSD3MjElzc5vVjU1u39kgikN++uZH3L33dd65/5TTzphEsYlVB7QcaGUs3SaOBN79xbt89ctfRQsl8r6K0Pbp9c958skR7719wOtfeQl3Puedd/bJOhv0hiOCcMrScgNv4ZOIMYmQgCQhqzozP0JUMuRVB10xkWQT08yS1TIISYJqKvSnA+rrDboTkyePn7C7tUm1VuUX773PeWfAXFTZb7fJ6zqSqvPZo+cgmdx79Q0+/eQFQpKyulLG9wKO96aIccQrt6+TVWQKpo6ShNQKBpvNItevbbG6WkcSQwwtRNMiQm9AztBYrTVAkKhUqkTRgpE7Z+aFXL1xm8vegPZgyObVG7w4u6A3CZD1HB9+/JhokVKv1dloLuNYKp3WMafH+yytLePGc3reiESFdrdN97LDtc1tVqtlbt/YRtNjTs+f8d77v+DFi6fMJmNSP+Snf/szXr57ncHwlEolS5wE9LsDOq0hQmhQyi+RMiJcjHCcCDujUCouUSg00G2D9ugURQJFauCT8OCTT/jss8cM+xMypo5lKYhCQrlYwdQl/NkIYg87I3N+1mVnZxlRCtHEBCEKGQ17mJbKYHSBYRokqYSiFgCbTCUDRITBBF2Laa5W8OYzXjw/pFJZw7GrCErIpw/fpzM8ZTKc8J/89n9KvVHHLip8/ZtfYqVeY+72ScIJ1YrD9evbBIs5o+EQ28kQChPMHHznH3yVb//a12k0Sty4uos/c+mctzg98xj2XAb9DqWSSZyM8Od9Xrp5k52d3/kVjO9/9offHQ1d5t4CWVHImBmmsxHuNGRjc5u94xOSDAxnI379N3+dzd11zk5OMDWd9ZVVfD+iUKnwu7/zW5QLGlkrgnROREwqy6BIDN1DpvMpP3/rPk/2Okw8kcFoQWsYsCDLLDTot4dcHp6iCTJSkhIMx2zkqiwrWdbMEi9On2JqDhnLprJcYexNefS0zdn5nOFkRH25iKyKpLKAbmmIisTc95nOPKJEYubH6KGMikp3MCWKBR58+oDm8gqaqWI4GQ7OjxlObJorK+SdDBnT5MdvvU1/5tH3Y9R8CcX30c0M9x8+oVJrcnY+4mDvlJWlOrWSjSwoBGONOHCpFzPsrDaoOhZS7DEdXaJKAVZWY+qOOT5+garEODkNVYlQJRFLy7B/8Zj28JTGUoWzVotZ8Hk4z5MXR0iazcraLr2pQH8Q8ezZGYIgs7qyRBz6nBw/Y9g75vjoGZKg8tHD+yyEiPxSmfPuJecnp2zVV7iyvsJ6s0AYDjFtkUXsohsaW80dbu/e4cb6DV7/8j0uLg7p9s7IFS1EUizT4d4rb9A/n/LkwXPyqwayFpMKMxahx3DkIxk5Hu8/4+hyj0hIKakbxKFHsVQgly9AAqIISewxHnURSCjmTBxbYXO9QaOR5/adLXauNMnmMqxWq5weHaEoInbOQNFFFEPnswdPkaQ8SWTy/PQR165v0esd4dgS1WqRTz99wD/5/f8CUy9z8OyQx08+RtVTqlWb5XKDulMjDQPceZfT0z1ePHnEvZeusrSUJZ/XiCKXjKmgajKaJlFoSGxfq/H+h2/y6WcfoCkKC2+BLllUcnXmnsl06iEIIbYj8PJLu6SRx9s/eYdf+4f/0y8P54c//KPv3nzpVT68/wlrBZ1d2+O//y9/l9/49hf53/7kr+jGGvLMo17b4k/+9K/pDwOuXL9Jo5FHFEdMZ+cMR+c8f/KY6chj7go8eX7GwekYPbvOWSvg2BX5aH9Mf2FgWnmWi3kqWZ0knOIlcybRnJVNneXtZbLlHL//27/Hgx/8nFc3l+hNH9HRDshubdFnSm6riFTOMlcUrt27ztJ6lsZqnvryEr6YkIoxgT9BIoQoIJex0ESdQWtAIa2SV+oErki0UEF1eH7U49lel+FQYD5UmIUzNtdqQMT3vvcDhmOJy+6CcnON/cs9/LBNplAkTk16nSmhF7O+XGc+6fHqy7f58O0PMAKJ/+5f/HNevXsbMR7R6z0jiofIksLcFRnOfbqjFgghZkZDN01Uy2EmS3TTGVr+kFItj2JkOD4OqVZu8uf/9q+wdIPf/gf/MTvrV1leN/j4rcdE0xAlbXNlo4KY6Nx+7T/j5Mjl4bM9ru7eZGlrg5HnEgkRK+UG65kV1uQVHEkhSca4vT6D0xbT7gQCgebWDrIi82L/GYkSs7m9SXN5h2huMJ8oXN34Mv3ThKWrX2G5uEE4XJAOVfZ/fsCjt57xfO+Qxs4y797/gLOTHm/9/AFOSeHJ4/dI/CGnB4+wDAFJjOkPXOq1EhUnIWeXubL1Mp32lMdPHlMs6kyn59hGQng54t6Na+xeW6NQd9g7PcW06iwCnZ/94ENODzvcu9tAJkU1TM47fcZeBJqFqlkIqsj9vfv0em2KTokbG9fREPnxj3+ALGmYShHbqCKqNo8/26eabzK4mBDPUvrtDqLgE4Sdz+euqkmjvMJGYwNHzeCNRixVcnhuh/kw4h/9+m8w6PRpXwzJZWt85Svf5MruJrWV3/zl4Xz3Z3/0XSRwpwNeubPNWqPI6voae8dn/O2bH9Bc3aJsOMRxTLffwl94uMGM5fV18tUVlleb5BybQW9I4Pn43oRiKYMXTPE8lzicM+ueMW1f4qgqyzmLu7srLJctDvcfkis5fO2rr2NNRdaqVV6+doO//os/4p/9s/+IXF0mzEZ0pRnqNKRcyEMSMZ9OiMOI0A0QFgJVp8x41OO8+xTLFMlmNPrdLqpiEPgCqlLAsqokXhdFnZKrKthFCUmNiUnRtSyiaPH42Qlzd0TRlqkXTO7dukXoBpiyShIkpF6MGNSZ9CUMuYw3A0O1yVlFHn/6HFPVKTsFmg0HOyuiyD5RNEUzFCauR2c4YeJHLJKIaqWGqWQolhuYsoGpZHj66BlSJHH+qENOaWBiEc9THn7yBEu18CZd/uA//w5nx2/zozf/NeF8QqOWobkmgjhEFEOsjMnWK2s0GgnjySW37ux+rtxARIoEVpdWWN1Yp7DawMg69LpjXhwcE8YCp+eXPHr4mDCKmboeG1s7hF5CGAhk7TIrzU1EWWY0bXG09z6P997i5x/8jPG0zXqzgaTpnPYmfPDpAQgFpKTA1tp1Pnr0Aa994Qu4szmPHz/n/KJFLl9ka2ub2XSMqqjoRhbDNMk6BuWKhW4qhAvI2nXWtu+gOjkaW5sE8zk3r1zlcG+fV+69zEeffERzZ40rOxWyuTzT8YJ6dYujvR7VwjqKarP37IicLjNo98joGS47A1wvRjEcMoaNoamMJhPGMx9REJgOx1ycnaIpGqsrq1i2TbVaR1F1Bv0Rw65L4IUkifT5nafrM1+k1K5U6C86XH1phy9/7VWWKyWC0ZSnb33E1dd/hTnnO2/94Xd/+JOfUq9ZXL+yRsExebG3z9vvf8hyc4PZqIshdCmXY3au5ag0RDL5kJApQTInjhaIUUylXGc0nhAlPndfucJrr90gDqdocsSSlaNgWiTBAm8yJHT75LMa/X6LzatX2D/YoxCU2N97xJ/88fd545ubyNqEx8cfcxH00epl0tPPpc3T6Zh+v4eISClXZtKfMh1O2dt7gWwk5LNZLDODIqssFgkCGoZuE/gxphaimymLdIqZFTFtlTCCFweXfPzpIXa+wu//3j8kl1c4P3tKMJ8yG3sUnRKjzgRTMJBVDVGIiZMZ/e4Jt29usrpUoODotM7O6LROWa5r3Lm7hed1IQ0ZjKbM/JhE1klUGbuYB1Hhvfc/5uK0Q8bMIYsmmqTz2f2H1PVr2IZN4E2Zuh2ePTuiXtrgN/7DbzOe3Oey8zHXdnY5edFhu7nLfDZCQKJSWqfXTRBY0B0eYeUWyGJCwbJoFOus1NfI2DaCJTNyZ5yfDjhvDRmOPdrdPv4iRFIUoiQlihNsFSr1JfaeHSNLFiIKQtYijIYImoti+QgZgxif3Z0dPnv8lP2LLksbV5nNQE4Mzo7OuBifkLFtnFyBo6Nzfvd3/hHXrl+jUi6wu7PLbLZgMBjgeS6TaYdFNKbX61CrbbK1cQet3kQwLeL5nNH5BV6/z/VrO1xenODGLre+eANNmbOxtsPFSZ87977NlY1bnB61cXIFDMMgGQ7QtQyqZrO2exszXyNMRA72X2BoIr1+nxQDGZEkiqlX6+zsbCMIAu50xmQ6Q5FV3Mmc733vfVx3wvLyKqtrOxyedkhEgx9/9GMOz49o9/tMJxMK2QJyLPD0w4fc+c5/8yvUnD/7V9/dubLEN7/6KoqUoGVMFqKM6/lsrDf5yivXubYqs7ykUasqrDUdNG3Bxdkh/nyClkBWNZlOZ4zGLt7CR9YSFosxIgvyWZP22RQEm8u+i2HnGUwnzPwFmpPjrfuPyNWbTHsxnuTy7d98hc+evkm5pFEs2OiWQ7W+zlqpjGLouIuAXKmIJGvsHZwyHLv0hxOazS3K+RrD3oSnT/fxvRAn56BoEsFiynjcRhBtZK2IYWbxvQXTqcdwPGdpZYu1jTXsYp7W8IyR32UcDJEMkxf7F/hRwvr6Lo5lkUgdhoN9vvzVl9jYbHB0eMTB/j6jvkscSGR0nd0rWXQ9+vzbPvWZTGJMq4zh2KRqguKYxIqE4eSRDYvuaMpFt0cQJ5hmhskooNrM05oc0bxWZzALGM80xjMXs7AgSKc08qt89M4B5fwW1XKJ2SyhkNtAkqtkshaXvT1Mx8fJZJkOpwzPh1yedRANg8vpgBgVO1NCVDRcP6C5vk6cpHS6XTTdoFypks8GqHJMu9OjXl9lESf0O20ePPmUzuCS6WyCVaojKSr3P7rP1u4VDi5OWdleIYymuINznLyCl3rkHId8rkhzdRVZlPDcCaYm8Is33+X4sEO95nBxfoxhily/tomdsbFzy8QLg3cefMZb7/yCaDLFiGPOD/ZY3limudpgIYV0J22WaxlIBDQpA76A53oUS2X2Dp+jZyQunx/T7o/wQ/jbn75Jb+KSkOBkdVQFLDtLuz1grbnC7RvXuHJlB0EAWRIoloqomsYiWBD4AWvNCuViAUXPkKDiBymfPdijXtsmZ69gmRUWkYpuF+hNXJ6fHfL6t/7bXx7OWHr2XU2KqJcM3n3/HZzlNZ6ctak3mxC7jFuHHDw8I2ctMx0pTLoS/Qso6E3cTkJB1ahlNZaqy8hKhr7rE4kpCCmeO6ff6fFgb05/kUGvb3ExiZgsUgJRpzdPqG7e4afvP8KXJVJHojcfsbpWp54vMzobo6YOZwdDTi5OMJ0SvcGEKBLQNIs4FohSgcHEZTiYEg4jZFGjVm2QL5bQDI2sY6CpKVE0pT01uBiAphYZtha4g4hRP8AplQmjBaetE56cPme4mBHrNpJVJxYdpouEQtHm9PwJJ4enfOUrr3J00OPpkw7f/6tj8tklHn5yTr8X89JLL/PqF1dwbBkxDJh2A4pWk+X6Jqat8vTgPQ7aB4SSSKpoPN074OnhCfsnh4iywMybkF8tIOVhnLh0vAnZ6jKXvSETz+Ub3/46m9s3KJZDbt1+DVDJlXTOW0d88uATLMcmVdpI+pBeN+SjDx4hCQbjicdHnz3hvDvirXc/5fjknOPjAxBBVkWiKGTQ66LKCkuNBuViiWwxpdNvsbLWJBFFur0OSQq+H9K66FIqLCHIFrps0j7pMPMDBv6ItStLeH4bRRqx1sxx++YV1pZWGA2mnBydMuj3yRgKlWKGYl7k6tXK52ncc49C1iaYuRw83+Pw6SH7j14wW0ypVxxWCnm6J8cMum1II+ysRZSGtFoXRKREC4i9Bc8ePkASU8oVB8VUUEyNQnmXSNE4OrtkZ3eN2bRLxhCZTIb0xyPmvs/Ld26QcwwWwRSZgDicoagivj9lNOrx/NkLpFTEsS1KpSLzmct07OLYOb7ypa+xUrrNWv0qW+s3+T//8N/w/sMnnLkTDkY9fvs3/uUvL/iazFxMU+Pg4Cm3Xr5LPwhpXL1B6/QYPRhTyGuotTLHg4DJXGSxEJCVApKsUSwoBNMWB70TnMqEcaAiySatbh9Jslhy6njTDiedQ2obTY6OzhhPXRolh5WtXRZBwN/85F1ETMLAo17dwrJVZsNzTj2fi/0RlaUsp4MB11azJHOVzvGIWinPfDol9ny2V5rc3r2F588Z9s5JJHDKDhfdNuksQRQKEGu4QwVPnYGZcDo4Z6u0zotPn3FyPOCy+xl3X7tHvIipFgoMpmO6i5D5cIS0kFETiXKlwKQf8p3bu1y0Tjhvz5hO4atvbDN1Z6xe0Xj9S7f4xtdvoeLhu+fMRjOiucAiiZiqU5T8nFJWIp1DMh2QquCNBsRewt0bN9CJMKSEzrCDYC4xGCpM3ZDGssbdOw0qhSKrlSKDbp+nrRbBOOXj947J5QQ++ug+cayxvukTXAwwHJ8XTyZMxgsEsY8/99i/vERuz6mVVwkmU3JLAjEu0/GIfqdL1swgKALnpycs1+oswgqGVaY7COi0H6LIIv1+H1VWsA2V3uUlG9erEKYUnSpj16NS3eL5szZJADmtjkQWW5gz7ZzSPR0hSTppHDF3p1ycL5hMjqjVsjRKJZrle/jegjheULlexrFKSILJeDHj/OIMU025dec6yr3beKGPqpj4I59kEnIZpXj6jFouSyGXw5uN8eYeupplMA/4X/6P/4fW+QH5jM7vbb1GeaNEEAY4y+uM5zGlYp5CUcN2cqRxxKzdotW6ABJyxTyz2YDxqE8um8X3JvT7l7RaA/qDGcV8A284xq4tEyUJhmDzT//gt/nR2z+n0+4Se383gn8vnB9/8ogv3dllbecmg1HA5dkYQUnx3TmaGiLqKfWbOS4uRSbnIb3+AEMF3clQr1jMz2ZM+xOC8JyLKQxjGZ+QRsVC14sk8YgImYt2G7NQwnUHNG7uYmVsfvD2+1TKJfquR5rOcfsjuuczFpMJxa0tZL3CaXeC06gjzUcw85G8EDWI2NhY5vTyEiMRiMYzzlsvSNQBhpZDkG3yeZswTPFnMd50joJNxpqxkCYIicF8PkVARhNNBhd9jh7v8Y0vfJFR3OLB0z3m/ueZJmkcU6uVcfIW3/jON5gM9kgFAVXTqDfWQVaJkjmt7j637orM4geowga9/gh/OqO5ukswE5kFY8JeD13WyRoxw8mCeDFjs9bgzrfukpFlRt0Wg8tjlFyBwcWQdKESDn0SO+Kjd9/kv/rn/5hgdMpiOuEHP9jDUhKuXf06T5/cZ9DNksQyP/nhp2xfz6BYU9rtEblSlkK9wtHhGcVKk1EnoJhxKJTybFwpcHZ+ydnpEdFsjiMk3L12lW6vh6WJLNVvolkm4/4Jjz57gCILiOK/z4wZtNi5soM/GaGpGSTF5OT8gqsvv8Le0QGziU/zWpNKoUTR6qNrIstrt/nFO5/guh66mqCrGWRRpOBkkNEhUTg+OCXvVOi1uoSFkELBxpQFbm0vo2cc5qM5T5/vMXQnNFZXaF8O2VzdZa8TUSo06LTOyBk6iiRhyQ5nvR5vvfMel+0xpWKOKxtLvPzyqzx/+AGpILPUaLKRLREFMxZBl/7FgCgIMDWVWjXPaDxkNOqQL1hs76wx6I+plovMQp96yeH1L3yR0SSg2xvz6ccPWCQx0SJBUU0cEda2r5KzS78anK4vcTZOiBWTJ0/bPPvohD/43X9MX3xBIoyoLunME4n945jnB5d4bo9Xbua5e6MCSYJqNlnkS5z2JhhxhOtLkKqMui5poYA3miCbBVw/oGFEfOf1m6RJxP/9r/+Ilc1dMETydsoiSRGkBAMFS7RZjBaYukEmq+FGLRplnd01m0bhOsdH+4jShGxW5uR0n3kARklnrov4wgJJSXFUnUF3QBwtyJgpTj2DWqwynnl0vQmKBvVmhU7nnHWrzEahQF3SGMxH7CwXIJXptFvEakqzWUNxTI57M+LzFTrdY5pLJVZLWUhdUlGlZGxT0jeI/AWL2TmjYZ9MPo++mUPwfVoXp8RBiCoUke0cQjIhGIq8/vI9iqZFMB6iZrN4lzGue0HsBazUVrm7eYXFPOFTv4CwELh0n1NrlHlp5zXeeftdenkbVRVoNLZprq2i2wt6o4fcvb1BZUem0xnjBufM3Bklp8FKTuC1lxrUiiqtiwuyqsYrL9/DSmKUQQu9f0xDTlmuKRhGysXJUw5efAZ+QIyMla8z8edUmzb9YIYpKqBo9AIP2Snw/Pk+ojDnS/e22d4qoYoCSWhz2nrMdk1nZdtm0JHJqAbeZEbGMHDMFeI4QLc0nIJNvxtxdhbgRz6R0qVcrNDutrFnLnGic9JuU1td5WIwpD0ZExsKN7cbVJeWyFg2y3aVjKShzHzckwPqGY//+jdfYzybs7l9BcPeRC+n7D38jPW0wCfvfEIw61HOLig4DqZhcXzZQ5dBViQm4zFRnDAcD0lJkJOUpWINVdGZDMbMEWnN+qAWkBNQCKlkHV6/9yo/+9GPkMW/e7f97605veTZd/f3nvLuW2+z9/SAb371W7Rbp/T6Z9SXLXQNpEgiinJEvsz22go3tpaRQg8xlZCShCR2CYkYTKdECOimSeh5LGYzxFQgUWssFjGFvIUABD70exMMwyHr2MRxRL20Q/eojS4oyEnAypLN1m4Oq7ig0hCpqDqlSp6J22cejDEcHZ8Iu5KnUCuRq+bJORaLYIGmyZRKBTRTI0EkiAT6I5+OO8SLQkRFRTUt3CBkOPGYhRLtgU9/mjIfXdBslKjkMixXClRyFrvra0iRyF/+27/Ek0RGswG2rWFmTDqX5/izBb4HebtK+3LM4UGPvaenzAZzLEkjlzFQZQhTH0FPyao2daPGxd6A5foK+UKe4WzCo8OnxLZMLIsYmQxW1kaQElIppLJsIOkqplPE9VP+5heHtMd9Jos5miWSLVmghvRGZyytFsnYGjPXJ/RixEQgZ+mUCzo7W2VWly0moz7zeUCYRpi2Rr2axTZEEnlBtuKQZhW8hcjh4VNmszbrW0ts76xxcNRCVARyRYfpdEocanR6A1rtNvlSDlFMqNVKZEyDQW9KobRMvy9gWDmiMGExD8kbFrW8g60mpJHLIl4gSKDoBn4Q8/jxHtl8ho3dOm7QYzRJWcQmmVyVw5MWl+0umiojELG2VKa5XGGpktLtnSCIEUXbZD7rI6Yx2aKDkXOQpITXvvAytq2TxD65nMl4cE4aTulcHJLEPpZhEXgx4/vgaasAACAASURBVL7Hxx88otsZEsYgqxZRIuHNIzTRoZJbR5WKOPYyomQTxCmPnj9lOvfI6Hl2N1fYbK5y/6MPWGpU2Nxcp7D5K9j33nrv//quoSvMxjMqxTKKItHtnRDhYeV0XNcldid4kUK1scxas46U+qSRT5zGJMkMWfbQMipLqw1UXWfhL3CsLHEIpungBiLVWpHxsIUgxIiijKwoCKKIaSjksgajy5DEc7ENheZShlu3aohyj0TokHVSBudTvCCgPWwTSwm1tSXORz3sWolIFREkGS1RWHgeuaJNREhn0KM7mtEaBJy2XEDD0GxUzcB1Z7jzGd4iwfXh4KTDIlGpaNAoGcRBn8jvEc4njLtdkrlA52REywjQMjoX58domk4tV2bSm7Oxdp1oLvLs+SkP906REpmSYqH6MVIUoygK8yTkbNilIOZZySyz3VzHsi2QJAbehHEacDkfI4kZ4gQSIeHo9DnZYoa3P/gULzQZuQpvv7/Ph49b1NdqTLw+xRrUV2wSwSVjiiz8KYIAUqhSL9dZ+DNEXBwroeRoRPOQNDYoV5vM5i7D4QXz+RA9o5Otl/HEz+vypw+PKJUcShWTYjlHNp/lT7/3E9Y3NpEVE0PPcnpyiSRLnJztsb5Vw/MGZDIWtl3h3ivfQlaLXHZT+oM+08GA1PUpmw6OoiNHPkE0Y5r4pAL4wQJZVWluNUENSZUF2aKFblbwFgKeF2PoDuVsibpTRI8FaqZNMpxgKgP8hUs2l2HmjiEJUBSZQEgRDYO8raMoIkIaMeq3MFUBKfYgdLl5dYvrV6+wtLRGHKZ4c58kTZjOPDTDYvf6bQrlOoZqkbcbZLQGqlTE82TagwnH54egwHAw4f23HlIpZTl48Rx3OgMEVFVh6fpv/fINoemoha5ZrK+tUsrV+OSjD4gWMxqlMpqVZzqKUDMpk3DMcDJHnIj4gzar1SxmRsIxBeRYwTQssrkyYTJk1J9gGDLdyYwgjNE1gzSdIIhTDFslFRYUZJVWa4gkCiiijCB6OHmfckXj2s0qTkHCDzMIQYGZ67EQVHzBpO8miHLKaWeMZufZP71kPPNwjDyan+CFc1RLJxRCvDDBLpZZSDGHF/sUA5tMWEZMIk4PT7lydYfNis79jx+x9NIKt2/dpeaEjKYniJLEZDxlOJghMCGb2yCWRPqey2Tcp6oodDpDop7LSm0JVbV5fnyIlctw69V1solEPpZwZBXXdTm6POHSn9ANZ0RFBXlFw7EdNCFGk1KWK1UGww6CWWZ/v0Umo2EZJld2b2OaGu5M5O13X1CpxhwedVkpNxhcnPHKq8tsb5aoFEqMBy7+1OfseExB04hICFwPMRKxrBzEIZGvkMusM56nHJ5OcachuqQRBT5PD9poloWkClx0u0zOF0xHU67f2mTsCgxHHrpu8+DBCf/k93+P6lKZufunzL0p9WoWVZwT+gO8uYVQXOfkvEuxUKbWLJIeHzPteGiCTDAJcRcKplmiVHUYCUPOW33CaEilUqda09HNlF6/TxQ72NoEXViw8OZsbd9j1pnTP24z73m43RRT0enNp/iGxsHJAZPJjGs7VxEFle54ykKWqGVraIpDuJgRTEOOOvtIQsrJ4Smj9pCl1SWKFYdUCAjjGVHqU6zmyFfySIaEVcwiSQKLaUKn36aQaxDJKZ8+vk9tI8/4eMCN3TtIsU7ncshkMqJWqfDo2SGmnf87+ft74VxvVjjYb5G1KrzYf8LqZpXRqEMkhTx4soepZ/AFOJ9MmASQ0TJYdpbxPEDQVIhDdMPCCxI0P0BKI5aqeYbjAEUT8RcRrfYxlUYB3RIpNmyms5RCpoCRzaDrMu5sSLmWYGkKK0sZJv4pda1JtzXAyZfZ398njDOE4wWRbCFJC0ZTDyNrkyYCtuWgKSbuqIekGHR7HrPQYx5G6JaElS/TWC2ybJVpOCW6vRbCLKGsZ8lmDQ6VObW6TTp7QV/O4oUBk9EIO+tQ0IqoioVkGsxinziMUJFwJ3NaQUq5ucbW1escn5ySmCJB4JHLJeQVnWyiYaQqFwddzjp9PFEgV6oTpgGd0QmKVkdRcggJKImKjYSUauhI5AybQiZPybGRFYWt5hpPgz6ypFOvLmOoCbqkYisSuiByeXTKbOrhjmfYpo4sxiytlAlDk8WiTpT4qGpCVisjU0JQUkajMdEiJm8K9MZTHh900bMVavUyaZDBtmQcxyGTqRJGGp99uk+nA7adYmZM4njOzWt1Do/m5LMNrIyFvbPDydmYNAnZ33+G78/xoz6GuiCSQ4QoIYoigljAMIokesCzp0+YuxG1yhLBAobDPpE/Z9QeYaR5AnWAqSnUG8soUoxtWwjFhIpTIRm5DNtd+n1o3Fnn3R//Ma9+8WVSWaM7GvP0+T6iafHhxRGvvnITRYCcXWLcG6LrEqGf8OLymCiOyZW3cQoZZLmBU3BQNB3NskgVgUSEIA7pjwd4IWS1HEenZ5TXi8yiOddvXUeMHHZ2dvj4k/s4hRydyYj1rTWG3vRXg9PQQSBCEgRmszFWLsLMyZ8HvQx9FpFMq+NxOQswnByJK1MpVEknfTRDpb6Up9/u0uu0qZVSSFLyto2qZxA0n/7I5fyzLsPZhNe//RpWMctg3qE79rCsPKosEo8neP6A3e1NypUs4+EYyUxBSUiFz5+g5Azagy7RYsLmehVV/vxLXcxpyJpBHAnMdJlUNBhPA1rdAFGRyBKhmGMqNQVD7yGaIVkJloI6cSgipzpSknJ2cMgrr1zDi2cIMcSeQixlSFMJRbaJghBZiLi5dY2Pfv4LMqaBpEiUS2Vaw86/t9YvMPM2jpAgSzKDmYs7niLkoWYWUQ0dWdPIJy5ZImS/Q5jOCVMXOVuk22szDxOadZ3dnWWyts3CCwmjBEOAW1dW0ew8/eGY47ND3vjWa7Tae7w4POVg7xwnV8SydAQlJrEENENGEGxARJVDnHyEJiak3oBKeRVn6xbj85Tx+Yc4NqysLnPSCmidD7nWrDKVD9jerRFGMZ98esT7H+wxHMdoZsBl9xH5OILknLVlnfOjMc3KJr6vMO7so4ghguBx/+Of4888mjWZlZKKEKVEqYcrCgznAbKkkimscO36No3aKsPuKW7/kIyoUzfrhG0R1xSxVgoEicpCVTganpIvZJBSCGWXIJ2Si22sosHmjQ1kW+G4fUHZrqEoJVStyLsf/xm719eIA4+c00CUQk6PD7EslfrSLsVykSRSmLkz+l0Xw7Qo1VYRDZNYEPEWIpddj3yxij/s8eDoU7qDIcXKEg8/PkM/ybK97pEqIbU1k0I1R5hmQPQwHOVXg/P5E4fTsy5f/N3XODg7Ioolik6F/RfH5J06pp7n09MOp6cTyuUy3daQViaFYMjaqok3g8V0hKxYjKYpK80mMSluu02SzggXHQr1LIkkYOULPHhywP5Bl15vxo2rt/FmQ5Joxqzd4/qNVcaLIWlG5s13n5CVa/RaY3TDYkGPrBXiewn5QpZg4RMsAkajEYo2xzRMNC3EC1M0W6ekFpEUUGSY98Zk9AyD+YjhcEzWWEIybXrjAaIwxc4IZFQFOfRo5iWO+nOsqIAlV3l+8ISoLCKlGZJAJh9Y7NTXWapncEwFyUjpnh1RbFTp9XvMBiHF4hqxk3I2PycIAmrZBiXdxjRkEHwsX8JJbcJAIZgtUNMFnekR43Eb1a4TTk3ee+cJd+5dJ+PoOJkiKhabm9dAh62tKgPxnECL+Nu3PmFrZ43UzqDXcqi6wGQ+ImhdUi2ZBFFEEomYicJkb4SwSFhatlGMIUq6IFPMUM3e5fCoTcbzycg9It9Fkiyqyw4Tb8B8FvHh+/ch0T/XS2YWtDqH+HFCPB9jGhYffbpPtX6VyWTO8nID1fj8HnLa9/GSiIm3wCg3EMUIL4wZui7ewmU5u8vG1l0szWYx91BEjZxZRQkjzFxKf+4yiccYVo7RKCTwejx5sI+qQ6nsoBkxhasFFv0OWJ9n53z80RNMPUtHTen3fL76xqvcvbGMraVMPY/954/p9zr0hiPsUp719SbhIuXF/ph80UGyZE57I04nZ5SXV/CiOZetEwbtPjeuXMW0Leb9DqV6jShVuOi7XLYuyDg7tNsXNDeW8JIxTsEkSUIOzp7+anD++fceMfVOsXI/wZ17NOpLtE4njHoh4bxH43oFUQ2xDZ3pYMLc9WgHEU4WTvqXJHGb7XqZyTTAH/qo+RAjm2GeCvjRgulsRH2pQaneYNDvMhvPkFMDS1VQEo3j8y6los7W8jaaIRNLHoqhMJ7GHB+eYWVUrFJEdTklWymyiIromsnxWQfdMMk4ZXRTQxFisrIMqo2YydIdDzk9O2Q6nqEnFhk5w9QLmQcBCzXAc3102cfUNXZ2lxGShHDuUdAcTnwfmRy57DKz+QPoj2lUt0gWBqqbkNcMothlFoqkYo5qPkcGiQISGCoSAn44IRJnZJ0sOTVHzVlCFEKm3iX/P2dv9mRJfp7nPXnyZJ48efLse53al66qruptuqe7ZwEwGGEZgABIEAqRJgVbiqAZ4QvLtORwOMI3uBIVIftGsi05ZIuhcBDmKoAABwvBwTIzmJme6enu6rW69qpTVWffT+bJPX2BW5MRwB/xxPv7vt/7vW+n36Zr+BQyi2gJmXbvjJ5ZpzhV4uleh4X8LU6Oz1GrLRJ2iEuZPO3+COvZAeFEwPRSmbX5KQaNBktTC0S9MLOzRaJRAdsYUkqViMoRopEYjmsQCgX0Tvv0d5pE0XDrYZKVNLrbwcNHiKo0WybjgU5CFVGySXzRQbcDEpksx0ctMhkNUYwS12Jk8iHEkIxteox1n7FpUl5cpjeZEFYUFCGELIEguKwslRHCfcZGi1R5GscziIkiQauHrHsU1DzpiIYx7qMP++D+4qWgahlCoofq+jT7JrZjghOw93SbqCejiBKBHRBoIpFEDM+TqTUaJOQ0taMh46GHKHcQRYX4gy0uLq9RP6kjS2FChBEEDSniIUhxzlsDhsMxIVtk4nvU200CUcTyDLar54ytAaY3QPAEnGce89PTJNMFEvE0ETVFvrRPo69z3HYQxAyRZAXTbHN6UseaGEhC5FeDM5MLsJoTzs5PUOUQtdM+x3tVisUCE6NLTJUoFcpM9DOWl+dxHIdQYFPJq7Rqu2iSjCxHQQ6hyVHuPtimPFdh5cIKp6fH5IjgGBKe7lFvnxGyIB9Logk+k3aTrCwSFwLWLq4w1g9JJcKE8FhYyNEXbBzHQE0LEBYwHAs5EmNs6DSbTRKpFIVoHiUSIRGNMJVIE03kaQ91mo06omORiYXJqRrBxECJSghhFS1WYGJ6nJ7u8nTvkLWlCoosE1FTHJ+H8MQS9+8fISence0I9Wqf62sK7sjE00fk4zGUjERUCZGNx0iFYkQjCtHEHE7gY+s+ncmEjBIjKceZSaZRERmM+uj9AaYQoBUSWDKcd07pDOo0Bw1QTXq+TGv7faKaw2nzAGk0Ye3CIkLcom2dEhWiuJ0JK/kUOi6br71Eu9FkorcQJjYKsLEwy0i32H90zsLyAk4wYTBuEI9ICOaERvUZvXECJanih3zMoIVjW6ixX3zhpNJJxLDPcc1BEEyOTk5ZXl1AkaPMz03j+yMUJeCsdojt2ShKmNnZPFFVQg5LbG8/I6apqGoUKapwdnxGKq2w92yfixsXMG0LbxIQjyRJSBH8Xgt71AfPxrRNhsYANAlXDjj1O4x9uPPgKXEly6PdZ1xcv0h5IYtudnn0bIts5SaJ2DR7tTNq5w00NUP16IzFlSUSyQxnZ3U21j/Bk+13sS2DXDJOLpMkInlokgoTEy0cQlbDhCIey+kSJ9UGrVaLZruPGAmTLWbIF8p0Wl3uP3jK0sICZ9UOtiMwV57DMkAXREpTJe49rzLonLE8XeBk75zLq5u/Gpxq1OXK5hrRiMTB82OEyhSLUzdptc84O+tg3bCIRkNcvlwmk7bRhz0UAtJygJZIUT1sM3IdVtaWCEkR5gsiriWAJeOZCsM+jEYGMS1CRNUwnCGKIpBOpuk02+RzMUKCSbs3otXqsBwtsbxURI3INCd1tESSrtXkqHWKLEdQIiatZgdZkhC8ACUURg4EBMsn7Evk4jlq1TYhw2F9do7AG5NQFGQUYpqGGktxfDpkZmaGgXlO3xLoeAGi6VMu5Pj2X/6cXCGPkk1gBhaSGGZkuoi2w8X5KcKTIclMlGwmjiB6aFGRsBMgCwG+7+M6BqJjowkCqpQgHpaxujUM/ZTBeMTe8TGOJpEJSUw8myDskinlGQawtVunORJo1s75wusbeIGH6oZpnJ0hZwTqzTapwgqCLPGotYdt+Mj9GguVaUZtl1wmQzYzhVZcwO9NSFsGsVCU48Ypkg/ZZArD0xn1+9T6deS4RnG6TKlSoKT6jF0d1xfQtATtps6zx11C4R7Ly7NsrM+iRERK+STHBz2ssUtcytJp93CUgKSWQhITKGqUucVlBNFnOOxiTX4xNnzm9Vdw3QnWWMd1XKKCCI5N/ewYRQ3j42LYBo1WHU/wOXp6TDyZwQ0HTESHVClJRI4ydbFCvJKgrXd58PAegujRqo9Jl+eZm1UJCU2mZpe5fVsnECNUT2s8evqc//ubP+If/86XuH/3Qz5++oDXXrnGi7ducLT/iFxUISTLTAILMeIzmvSxxi0UIURgWqyvXqRQyrJ9uI9AmGy2RK3eZf/5AWo0Tj4/xfrCIsejEU8/fkQmraFFMmhaiZsvz2Pr9q8G58L0Ag8e3GO2ssDp4YibG2vs7+xQPalx+eoG+sBl2K+xtFhEkUz8sE1o4iP6CZZym4QGVZACYoIMfoiiEqVaa+J3x6TEGB0nzIfPd1i6OE9lIYcTAWNsIykupjhhqlJiOGzyfK9Ox2zz8HGdf/r1V5iKBbjWmMW5ZYSmh6sIBAH0uyMc02J5YZ5UMkUiphKPx6ifVqlPfPKZWXwTBDuEbwYgQnfQIyxI5ItFzInF3Y/vouVOSJfTTEnTiFIEUYzSM11Wb1zFMDv0Rk0sIcW1G6vUTzQk0SKThOtrt7CiY7aO75DMavjk8HyXeqOBL/mMjR5aJEw6nyESjSL7Pv3+OY3zGp4gUSyXcBWFsByQiQtk4wWUaJra2ZhCYoZGp8HG7Uv0dYvw2ECNRekd9th+csxEslgWIrgGHB+f8MqLr2CNLAy9TTodJZXWqMzOEJaShC0ZsHFdA8fWkQWfiT7BmQjYhsRoYoDbZ255jemZDSYMmbT2sRwHY+zy9GGN/ecNvvwbr7G5OUMuLxNVwshh6Pe7hPwYriOjDxRaNZ1nj+/yxV/7NTw/zNz8ZWRNYvvJQ3R9xLUXE6gxAfwIgecyGQ6on52jRmKk8ivUrD6WNcGxJpzUT9CiUXBhUKuR0BJE4zLhpIwUkZhLzJNPl9h9vEf1tM/Lt26zUH4RXW/S6rTJF1OMTIuwLPL+Rx+iRDUuX5vjnfdPuH7rMpWZFP3OJuVcjHxSQfA7xCIihmkTFlWS2QhKv80456CPusxOTVEplqnXawxabSpzsyhKDMu0iMU1TNPi+c5jMuk4keGQnOcyn0xiOAaKJNPq92l0O78anNc2b6GEVb735ne5urlJTJE4PT7i/Oyc11+7QrfRo5jOgxMioiiYnok5GiOlFJrnHVw/YDDq8/MPn/DijRewrA716hGzsyVSsRSqCElNxbEMDGNASPIpVHLIkQSW7zAJ+dhhAcO2UGNZzH4bWdB48uBjXrt1g3gsSqU8x/GTMwb9Abl0kY1PXiSfLrD/bAc/EAiJEuV0lla7Q+28zqBnsLfTQNICLl6ZwfQNLMfjRz9/gDl2GBg+rWqNQA0hqyLJRAJ8kUcPnlBIzuEzJB33SBWhlEiRTWro9Q5LSzMA3Lt/j/x6HkH2GbkWrh8iX6lweHrEu3e2uHF1lvmNDUIhGWfcx8NBiomIYRVbjCEEYfLxCJPeEX7IJxpRSYgRwq7LS9dv0JROSVsaih9H7Iw4e7DPrfUVTBXmU1OEPJlbC7PYzQ7jkUlcjSD5HnFPIGj20QOFsBhn5+CUR3s7KOGAoKfjnOtogkoqN0eqkMNSRoyCPn3DIpZOY3si4/GQwPEZ903myiqaZHNy+IxifhNRkAiFIuTy0/zkJx/RaY9JxePMza1yVmtw/942561zEimNWCrCF774ee5+dIdkFOKpNLXTc3qtLvggINPrDtG6Q+SchuU4jMYTCoUylXwRRYxwfnzOvY92OPdMlLRGNpshm06zs33MbGGW3/ntr5OP51CEODtH75HKpjCsHnc/vsfU1AyRKOQLKbxA5Eufu8KPf/gnXNrcYGGpRK5cxGjUafYtmudnZLNFdBf2jk5ZvTjH9IxGWMpTPemwv7fH3Pw0U1M3qMxN4wYhqudpNi+vE1M1Mpkko+GA+z+5jxOL0Do5hkiYA1MnXykzVZn6O/kL/X1wvv2zd7h65RJf+crraHELY3JMJuPz+uvXWFoqMex1iAoZCqkFHt7b52CvRkRRCUc9opkJZ8N97p3s4qcDxCwkpxOUFtJ09TpSzCddVEnLIWTTQpzozBVyuKbOyd5zBr0evg+NehfLH6BEwxQKGu1mjdu3LhOLuTTrB0wmI0I+VEpl4kqUjeULZKIaouUxbnQYNztkNY1EMsbe/h7vf7DF42cneIKKGUrRNCVOBj4dU6ZnyphCjIiWodcf4rsOcUUmGgJVFGj3GwyNBpWFDJ5g0B+NaHb6tNo6pal5tp48oliZxgkJ1Lpd+qaFFE8QUmNEkkm+8BtfZP7CIvVOl5CSwJci+GGB4kwZSwg4bHQQowmCEKSzKlFVwHHGRBWZDz7cIySLCJJLr91ADUQqchLjuEZRCchKAbOxGGK7z9nDIwb1MXP5Cqokk0poxJMabljgj/7kj/nz73yPwThBuxeiMwwYWiaV5Twza3mGXp0+R+TWPKxYi4PmPl44RH88xMcinVG5efMSq8txVhbjXFqbQwxCBJ7IeORhmCHuPzpAtwNK5SzXX9jgs597jVDYZzQeUD2vsvXwIX/z1t+ye3jE4vJV7m8d0mg6nFQNjg5HDLohYuo0k17A6LCNOPSZTpbJJwp4Toh2Xwc1Tt/zaLfi1E7iVI8EvvOdh1TPOli2TiEfQ5EsRr1jnm8fUCxNkU7muHhxg48+vMfy0jrJRJ5ifoacEpBRBMxhm1GvB4FMjwRv/u0W+1Wfb7+5xd7emHrDpdW26Q8tzmpNzus1BsMeEUVmaaaIYA6Iyj4CE548u8fT3ftsHzyAiM4rn/8clbU1Hu03EOU4mxeuofoa4cHfzd/fa9/7o//rf/1Gr3vGtWsLZNIRpLCDGPK4cf0inc45tm3QrE84P61j6DovXn+BxYUS7eYellUnOVMit3qJjRc2SORitLp14imNbClHqpglU8wyl5llZXaWidHB9xyePHlOu9YmHksz6g85Oz2lXE5z4/ZlpmcLVA8ecPvqIqpko2hRRpbNR3fvkkokCFxYnF3ENSxUKUJCVcFxse0JjWGH/f1zao0+giQzvThPJJ3ipN3Dl+NktCL62CEQJBKpBKm0yrBfZ65cJh6NMeoNSOWnKFZiROMQFhQm/TDbjw+5ffVlolIExzIQEyKnw3Me7xyQSMfpNPtcXL/EyBhRPT/i6bP7GJbA1NQ81njIoF/HdExsQaY58DAtH2NcI58RiERCjHWH7YMW7YnF3955TnE5RNYXWYjnGT7bZ2E2zUTqk86nCMYerYNzKBa4++ghUlKlsjrFJOJCUuXjk3P+n+9toQvgd2XWL20SjUaISi43Li+TySrkp5NklkRCmRGDkU1/CIlUlqfPnjAx2nTbTQRPYmE2TDoZJpPKIKtJRgODsRGwvXPCUbXO4soqv/bGTcQwhKQQrmcjRUQSmTgXLq4QlgUq0xXK+Qvs7Z7hWGHCQpyD3TMS8QJaNEswcUgIILkgCSF8L2DryVM+evgYPxolmkmiD+fQBypbW7ssLhaZm82SigkszZVIaSLf++6foURz2LbHw4dPcP0QF1bWufbCKzh2mEHfpqAGjEYjotEY3/v+3zI7t8q7b9/j6eMTNG0KSUoTDsdQNeUX9k/Xw7I8Mtk8fuAzHg+p5GOkUypSNEJUiyFERHRzRBCy6Q9aRDLLyMk0l65e5f0PPuZ49wR3YJORNG6/8St0pfzszt984yc/e49qtc2FC+sMhm1kVeas2eb+o3NKlWv88Ccf4Ao2hWKaZDJD4Mfo9UNEE3NkClO45ph+s0XrrI3oKUwXFjGHFlg+hUSS/eoOulenUJHJF+MkYjkKuSniagYtGSGqCVxcX0ZQBvRGJ0iyQCm5yEzuCpKkcVY75uBRm1HHIgiHOescEo4MmUxqDPQRR40uO9UBtinQ6PYxBRtJE9HSKmJYxDVtNpZXqVSmSBWSaAmfQgHCQp+kGiLke5jmGN8zkWI+hlHD1nVGTQuj5bJQniWu+GxsFDgzt1m/MM1aeZr1coWEJNNoVxHlCdN5FdeYMBglebD1kMlkgBsI7B10qJ7rTEyflaVpyokkew/3efbomFrXYue8y/FohJLWmJsp0u/XqcyUiJdipFeSmJLB3OwCiXAaKVBBENipGxzunqBGYrzw0suIvosSTdLvWgz7Jgtzi8QyDifVR4BFLBJH1wV8ScWVXHTPwjRlctEiw7MuR9t7SLJEPKPhugapuEUsNiae0WhPdGrjDj2nhxka8+YPf8JLN19gbXGVXCKF75pIYo9k0sGyh8ixNO9+9JTdsx6NjoFRb/Hxxw+4uLmE5wY8e7KLY1t4zpCNF5aYvnoRPznFz97b4c0fPWTvSGc8spkr5ViZzbGxfoFB/whNm1DMRYjKUEjHccwJTx89IJ9PE3J1lEQGC4V0togQEjk/q3J36wF2WKJ/3mfr2SOSuQQ7R9s0m0MyyRkc20OWbNI5mc0rS+RLCRBcZubnmJ6dR5BUXSk6/QAAIABJREFUPnqwy6PtOkuzsyzOTaMqEXLZFMfPnzFXLCE6UN0/Z/f5HUpZkbA44cbNTS5srhDJaPz4vXf47a//T7+8t/b1V2+AYfBsa5d7Pz/kq1/7PKe1Kk+6O4yNHq2+w2uf/4eUSyAJbe49eoBjjVlfmycimtj9DqOOzWA4wbEEYuUiNjHqvRZep4ktRnFVGTfksN+uUZamyM8U6e9UGY463HjpMoYZYX/nHLvfBcHhyuoq9sQjHIrSn4zYPThmNOiyWF4gFA7jIvHR1jNWZsvI8Th6u0c/sGn3JsgZlc3yPGPboVo956x5xoWVdaKCRyapEo0KBE6TcMhh1B+zOFNGRCDwQBLiWKGAUDhKp9NhrOtcvbjK8vwCP/nhd1l5IYIn+EiyRMT1KGdTnHVNlhdmaZw38Pom455H48xl0A44O+7QqevEo1lCgUohW2Z+eonmyQGJQhZJzCMrMeJ+CMUZYhEgZmOsR2bBt5Ell/PugGZrxNzllxgOJxj9ARPbpuXt40ZtPrq7wxc/JaEVr+Ac7KGetPhHF7MkchFORwLjeBRVlsjE0ihiGsf1iMhxUrkKkWQUwQwIL2YxHY8nx8+IxjRmFyrIwYSwrTLWfc7aXRqjMclshnqjwSdfeYG1+Wv8x3//J3zik69y7foqgWhTKOSJFzf4/d//12TKKUqVWS5tbpD3J7zyX14hJkeoC0PMzpDK6gpqRuLDh0/4/OxFKnMVbv8DiUa7w+LiFBtr0yjShEI2SiBFubj6GYRohH67RTKuElMUTH3EoFEnCERiiQr9sU9YSaKbDrvbjykWMrQ7TYZBmLyromXKvPm37/LaZ16j2bL5z9/9a776ld+gUkrzfPcR9x68x+fe+Ac02n3u3LvDzVsvsXFtlYFp0O9PSGVTmI5LMhYjcHx2nu7xqdc/xcAfs7p4gaF9SkJ1OTrdYWp6GTWh8ekrVyiU/m4E/17l/NM/+p+/8clbL9FrjPnwvft8+ctfxXQcmu0Oy+sXaHY67J4eMjbOKZc1cvk4yysV8uUEk6CDJIUR3CjpTJHzRp9ac0x+epH98zqn7TbZ6WmiKRnLtZmZXUQ3HJ49fUZYDCjmouQTMF2IElY0FhZm2VxfZS4/RRwZJRzGCize+fgOC9NT6BMLM5BIZGeYTAJULUm6WGFge4wDl3gmwdrlS4jRGKFwBFWN4zkBx/uHtGsN1KiMJPj0u3XG4y62NUIMPCxDp9tuEddiaOEYcUUjnciysryGY5nE4mGiqoDt6ayuXGUyGlOrVul0W8iqQqZUJhpJklJLzC9cYTIJ026fcXlzlVQ8RbvZwzZdlpdWmJ6qYBs9xKzGWI0x6DhkxDSVIIYz7DKmz3opwsbKEmEhxHd/9AGTUIq9sx6zK2uUyiXCioRYlDEMl1goTU7JI/S6nB3sMJePUsxIoDdJpWOI3i8iN8Bjdn6RiKYxcW2CsEginkGNJPAsFyEcpjPuImsyQghCQUBKm+LoqE27ayIICnJY4fnTHT77qc/xrT97kxevvcK3v/8WeweH3PrEK9h+iHbbZvvJEeEgyj/93X/CpDdkOishOwYJQcDqDXj55k3UZJSz0TkH9SphMcrSUgW8AetrRTbXK2hR+NafvU0mmUGLO8RiHmJgEMIE30WNRFEjGr3umGfbh3h+mvL8Kj9+++dkMhoJTcWxdNqDIYOJye6zQz79uc8xu7jMO+/fZWX9Mu++d4d2p83FjTVWLyyRyUaxXBPDMejrfUQ5xM8/eJfD412EkM2o2/jFvWc8TrPVYmwYTAyTTCaHKIpk8lEEwWeqUiESjaJpGo+27pLPJVhc/s1fXjn7/ec49iq3bq3RqJ0ihExMt40f7lPvNnn743ewJZn1K5vEUi7tRhej6TGpGqSLGlJSwQgczvZ3eLJ7jj4RSZYWebZ/wtrmMo3hkJcXpzFiCrv7x/TaXV66/CJROSCwOoTsAVlP4IdP72NbDrIocnV5jaKskQyH0EWBWKbEk61dtFSeQc/GCdtYhkwqleTgzh4GARYy3UGPSGJIs9FDllU6jS7mSGe6OMPe9iHCFQvf8qmdHpMtxPE80HUTmRCZVJ6IGGF4NKDdGRDPZiFjEgq5NE/OiccUlmYvYAsih6c1ptJpvOGQseFx+GiPhbkVkvEMIdenVNC4emWZzc05amenFDKLnFWbCGGTiAIxJcSjD7c4I8rlymUyJAjax8ieQFyRuDA3RSgcprA0y3/3B1f4i+9/wPbuPrfdEPmoTK+us7/dATPKydk+F36/gDU6YWV+BnPUptmxsOw4YUPEtAISuSRSTGMk9mmNDe4/eYAxNlidX+XlF67j+x4f3b3Hs+oxSlJjYWGehKTwn3/6Dm984RZhuuztHJHJp7i8uo4WjbA4V6F6sgeE+MGPdvn8r9fJFXNIkTRSWOHFSzdICmEuLyzgmE8I9D6SFCKhBbiShz7oUpjJU1qbIpuUOKttIWDj+iPcIMn7H3yMh0u96TIR6uQLKnEtznvv3eH69ZcQUzEEOcZoEua8aXJ4fMggiFCrnaMpJrevX0IpRtk9PuTKpWvcaX1IfzSkUC4TjcUxLZ3bL21gjAzefvtHvP6pV1icrTA0dHRHJ5HM4vgQi6tEZIFMJkkxlqU3GkJNZHd3j25vzIsvrjIejbAtC0kJM+7/4oB8MNAplDJc2bzG8+1nv5pyPrj/p9/4+P4WkajMF379UxyefwhqC09sISdMfvvrn2f5qoYWNUhrYVbmFihm5zg7G9Ds6sQLWdLTRVBiiFGN1z/zBcZjnempPN32Md3mMUslGUUIs71VJRUrcmn9ApVCBsHqofoe2Uick/4ZihshHU5weeoCGcFnMuoysB1Oezb7O12OzgbMLG9y7+E2yYSGbZgM+xbbz884qTZJaVEM3WbYMxh2x7i2RTIms3FhmsX5NJNRjVGvTiymoGoaMTXBhcWLdGoDPFPANwVOnlX55Ctfot8PQBDJ5jUm+i9ySp9uHfD29nvohkOjbnB6btDqBTx4ts/E0hFDE3Z3ttjb2aZQlEilfAoFBc8ZE9ciiEwY9c9JxhTmZ+fpdQaIiESjCuXFOerGkONWmwvzFRBjRNPTaPESb37vb3h0fw8tZjE7myUU9jjft1GkCFPTcdau5TnpPKFtdfjw0SHTy6/THmm898HbLG6ukK7kERMyviIhqyqO7yILIRRRIqYoSGKIXDFP1zDYPqwjinF2t8/53GcvMT1dAODVl68zPZVlqqwSCbscHe5SyCdZmF4jFjX4+OEdCpUK3/zmX/KJm6+w//gen3n1OnJK4uxoC8MesFc9oq47PKu2ee/BNj0DTus92r0x3b7N2+9sce/jY/b2TQgv4UnLnHaj7J72aHQEJnaSn/50i+tXX+edt5/wr/7lv+PSlddwvASNzhlrF1c4OnrIV9+4SUQYMeyccvvWLd59/0N+97d+jalyEUmEqWKBQk6iWduinJXIqhKt6hGHO8/5+O599g5PUaIp0ukCgi8gE7C+uIAQCvFg6xF7+yfEYkkurK6jRuM8efQY2zGxDIuIpODYAdl0GTWaJPDCqFKcyuIbv/xC6PnenW8EqDx++pTVi7NcvDZNpiiRyqjIqsT9h/eJZ5NkYioqEkfbR5we1CiXK1y+fonycoWjVo3jsxqWE7C7fUghnSWwB8SjDtc2Flm9kMFyPcJykUbHwAlMRCVEKGyR0OKIokq1tUdKyrFSWSJqBiwV0vhYhBJpnp91UGM5tHSKneN9CpUU+axMt3lMMV+gWq2xsrTI2kqBWDSKEpGRJYFcJsrcTBJVnTA1pZKMSmhqlGwhi2E5JBJ5JEHh3bc+4PmjI2QhQno6jporUZxbIjWV5KixjR1McAWPIKwSpD1cK4QxDNNqO3znzQ8I5BCfeeNlIvKIdFLlwtwVllcqTMw2g14bUZCQRYVMOosQBPSaLQ6e7jDq9Gi223znrQ841tucDvocHdRYWJojW5wlFIoixRL8+Htv8j/8we+RScmoSsDR8S7NfgdfMJk4Jpsbl6ifN3CMELWDHnPZVcz+BCVpISoWtqeTTCWJSilkL4lGloig0e0MUOUoE33IefsENR9haiFHvqxRPz8jlwlh2wbJeAx7MmB2Jkc4bKGPW+RyWVKpON/+f/8ac9JnbFoMzT5f/crXcE2DfFxh5dICk+Ypb/3kHVYuXcKNJjHFDFvbHbpDiaX5G4hCieEozdbdJieHHoaewwvK1LphWkaIs75Fve3SG4RpNExsU8J1ZR5u7dFs6bz5g7dJpsrEExbZvMYnX72EPTgGa0A2HuHxsz1e/9yXSMVdjOGIEDDqdZAkg0opzHw5RSWbwxyO6bcH9Ps6ji+SyU6xs33IoNPn+uYV8sk0ju9Qb7UZDnUmhoVre1y7epULK0toqsLe7kMa9VMCoFguIUkRfD/g5PCQtav/6JeH8/f+6//iG/rY5l/+4R9yWt9HSwZ0B6eIksBQn5AvVPDFKEoA7sik3xzQaej0RxM+9bmb9N0B2yfH1BtthEAiKsUQPZ+Xr1/iM598kYWZIj2jyvP9E+wgyV+9+WPy0zly5RRRBWzT5vi4Tn90ysbCNcZdg927j4gIDsubK7z545/R9yI8evKc3rDPtdtXEWWLRMwj5OlMxjrpdI6ZmSkcu4njWJjmGFUNk89rZDNhknEfXa8RCUn0e0Pq7Q6qlkKWYwieRL85YHN9g4ioMhCbFOeXCcc07JDBUWObVv+c6cUFQGGn/oRkrMCjuwccHbT47Bu/ztd++zdx/Q5qVEcWBUrpddzAYNivEQIGfQNVSZJIZfnwziNkWeHShRVc3cSLhJlaryDkUxyfN5lOzXJeO+bWy68SlhREwjy69xHzsyWOD7fZenSXZ893ufLKBQ6O9jANn2Hb5M6PP6S6Vydiiezde07Itlm4nKZciaMbXeq1OvOVizy+W+V7f/IW3faA3qiPOdbRx12UhEC8FCWejxCNwfJynogQMBwMmJ2dZjTqMpn0CNDp9pvMzszQbLcoanPcfvkGuakUw8kESVbBdsnHI3zz3/8b9F4TQckSTaXoOQF3H53S7IBtqzROdU6ODB497lM7tfGdNAI5hoZEa2hxcF5nYFsIoSRPn52ws32IJIQZ9sekMwVOqnWUaIruwKCQtXi+85C15TKB2UKRPczhkM984SsMDJiMT/nTb36H8ajP9NQUojThhZsXMLtNHMNmqlTh8OCcTn/M4UmT57vHNBpthv0BT7bu4VkmmUKWZq+DPp4wU5nhhz94HykMyYSKPhqSyUg0Wuf4+GjxBFoiTrlYpN1ssrjxtV8eTrv/8Bu/81uf5t7H38I3+ywUrrG/5fDWdx9TTM1xZf0isRA8/aDHX33zAc8fD+m0RpQKcTZWKtT2qtTOB7QNk/h0hdW1i0yJMSbHLf7izR/wFz99h51dnRu3XqXV7dHrRRl0HF79xEuMjCfE5Cz/57/9GfH8bYyBjDEW2ds7JZIuMhbC6IFNt39CPD1FOZ+nd1wn6WrMpheZLS9z1jhjYneJaCOEiE4mW2Zmao656QJRxcI22nhjkVxsA9cdYns6SAkOaz1ShSztbhVj2EQftggLHhGxjOA1uLAQ5eH7jzh6MMEeJDjc2yZd6hHTSnS7fbREjNJ0CsHtU447zGclNNGmcXJASpGxApux7aHrI3JqiLQsILsBxtBi73AXKadyNGlxb+8ZsbjIdDpGUQlj603ycZ+EphJPpni8X2drt85b792l1msxO1fi8tV17NMDrl65TFiLcVitc/HCdQJPIplM07Za5Fcz7Ozv8LP3Pua83WNkKLh+DkhzfNrCDiQIaTQ6fVK5FC++eJmkCmGnjxKaIIk62USaufI0ghUi6qVR/Qw79864snSVbq1G83QXK2bxbO859++d88bnv8TskszA2uGb33qLVPEFDLHIyO8Tik2ztT1iMMly2gpoDwKQs7iiStvQcUIBZsjFVwQEJYRpTTBGY6q7Rzza3kJWFVxPptNxqdd1klqKXDbP2soUC5U4b390iG8GXFleI+RCRM6gJStYrkI8lkCwJW7evsJo3OHdDz7m05/+XaKRS1i+hpzxGboG+1WbiZLg9a/9Fp//8pe4dmWT6kGDR4/P6RsajlLn8vVZdnarVGtdEqkkxakCjtCn1n9OpfACpeIqP/7Je8xOL5BNpSkvLdKtNaksf+mXb7b+j3/49eDuozs4EYfP/saXEWWNnacP8Kwun3p5gbjqIPlJ7t5vkJvaZDCx0F2d09MdTk8OyIsiMVlAnCoRLlYAjfloCc0O09cHWGLARx/dQQzZXFm5zEx6k//wb/8dvtDmf/nf/xn//H/817TqHvGCyJWVC6RUlduXrvDxvfdZ2Vxlr3ZM1xphhETSyRTl/DTvv/0BiqKgajJrl5YJy9Afj0CW8KgTCk8Y9E1Ev0AqPoMkT1heS1DWKjiTEB/fu4fjj9HSAYbVQRJV8pkyf/nnP8UcuvxX//hl5gtZ/vKP3+K0KpHOzpCZL2FHBvS7AQhj3vjsdS5tLjBqHzHp9SilZ6gdd0hIUfTOKbXBiFS5wPRUhu7JEaILnq8wHIfZOtomu1Cgb+kc108ZGwYRMcLnX3uDXr3HTDqF7UC13uLdu9usbF7m53feZqaicfvFZTLJKHmxjY7E0Ff4+QfPWJ+9RPWwxp0PDli9WuD6zWvgDqg1mkhhheOjOlIoRjk3Ra/RJBR2KM9kWVmaY246jyBYhIQAHxHbDQgEmZRo8vTBYwrpWT58+wFnp+fEFIVMUeP6zXX64zbJ+UUs+xdLGSkm0R6dMhqZ6N08IXOJ8/MuiYLGyVmHJ89P0OIlKtMrOLbLoDvE9j1M38b3fSzLwjRNJpMJk8mEUCiEoihYno3luAiegOALhPHwHZ1UUsE0WlzcWEZUHJ7e+5h/869+H398QkTySWoStutQPTun15tw5YUN0vkUaipBfxCw+7zGeNjh069fx3cNfALOhhPqQ4Net0W7fkq7VqMys8hxrYcxjDJXucCtl24hhODNH3ybVreKbvd48fYGB4cB1ZMq8YSK7xpcWFnENi22nzzlW3/l//82W/+9cH5hQwi+8tu/TqhY4s/+5i3+m3/23/LRO9+nlFXISl1eunYBZxTlBz/7GCesIcdk5pfLhAML0fZRDQvHGNEJhXj/8IT62Cct5cjJKeq1Gr1Bl1dee5mn9+/y5Vc/R+3JOdgOG1eX6Tl1fv5gi+mlyyytxGienmMOxxTTWRKxBG+9+zbEIsiJGEpBIpvOcnrSYKa0yN7OPsZkTCKloBt9FpYWqDe7TKway8txWs0BijxNKJQglhLJlgQS4Vk0KYMsemw/e59WZ4fPfPEzbO+0OTnpsbPbJpNO8dlPrCFZOj/+3s8ZjxMQjnPtU68h5aJ84toGjt2jfvoMy+wwV0mSVCT2nx4xU1zgeHsPq93CjcaYXlkhm9Wo7jynfnxOd+Cwc9ih7RmkZ7PEcxqlShHPdTk+OOSVGy/x+P4j4n6Eie2hpbIMXYGzZp3STI6E5mMaDVJJlZtzCaK5Iv/p299jZuESspCg3xkTSxbYq9dpdJuszxYY9Gz0kY2pO6iSSDmfoXFaxzX7vHTrArdu3qB+eoAWFYlEFJLpHHJEwwnCbG+9TVQMs7b6AlIQxTVdvvPdbzO7PMXa5SW+/Z2/5DO/+buEZZWxFbBf3ccPBTzaOqVfy2P3p/H9CB17QK3eoFCYwrQ8RkMDWZaJqSpDw6Cnj9F1Hdd18TzvFzEmloVt2yiKghgWCYdlRiODECKe5xOLKYTEgKgawrR0rl1ZRPT7LBbCfPn1a3z/r/+c5fkcxqhNMhVHTaZxAwctGWX98kUOT+q06kM0JcGrr34CXzTZefI2z08bnHb7FAopXrpxiclkhCOIhOMZ3vyTbe6+t41u95GiAisbS9jY3Ht8wh/897/Hu+8+Z3//AEkMiMUkvvi519l5+oRETONf/Isf/vK187XtP/5GaWYWV0qRqWzSGTk4E6geNlmdvczB8z5/9J++S7/vMFUs4xo95rMSQfuUcKdLXk3S6+uMTAfDCmh1dPo9nUQ8yWylQiKeoN465daNG0QRCHtjpJDDZKKzvXfGdvWEytIUg/4BF5bnqTXOiChR9o4OGVomixc3KU7P8ODphwyGQ2Zm5xmMdSzboVY7JZmM02m3ubQ5wys3p5kvThFxVeYKFZbn0ujDfVxnhGuCLMYwJjqSFDAzXWBuZo50fJaP3j3jvZ8cYHQznDYnPNw6YDzyiGl51q++yFmvye3PfpILV1YZN+7g2iPCSChikggprJFHv9fE97ucHD5FsiS0VJ5saRYpEqNRa3N4Uqc78kgU55AzWWKpFGFJBgRiUZW52Xm6nQ72xCIqanhBiFyxhOFNuLCxQqdfJ5uJEQ55iKLA+swl3r2zRaY4R7E8B14EORKn2TXQAxEtW2R76ymupZLWSlQPWzQbXeJaCBGJuXyJT37i00zGBpPaGTlNIRZ2aTeOqB4+RR80kGUPLaagKjJB4IBos7w2z9bze9S7VV78xHVCkQyGaRHRFDqjAc2mwXiogV2hVvXoDSwsPGIxDddxmBhDCGxCoo8o+tiuw2BsMB6P8X0f27YZDocEQYDjOJimiWlMKBdLOJaL64MnQK5UZGxY+KLEyHAp5KYJXImdZ/voI5tEosT33/w5xkTnyuUXODw75OC4ysgwsb0QvX6fTDrN7u4+7719h5liBUWNsLx2lXgyj9Ef0Dw7QfB9bNfj4dNtAtPDdYZsXpnhyrUluqMWhmmzeekaP37rLt3mgFQ8iRgIpLQ421sPWZ5dxBpOePXT/+SXnzm/9c1//o2br7zGo+1zmh2Tt3/6i/za+nGbt3/0mDvvH5EuVrh9/SWq20+4tFhhfSZJ1LNIyCohJctBx+TwuIUsJfAdicAVMCcGeDa5TAJRMqkeH7O0UKHReYqaFjlp1onEprFDHn3jGHfSBs9lZqbCVLnMk+1tNi5fRYxE0E2T8myRcmmGwWhCpzPk8PCI9dVVLizN8eILVxEZEw6NSEUqXJy/Rl6LYwyPmZmRKefzpGJTpFIa+UKc0XiAJ0A4rKD3fO7/9IDqkw4Jv4Tl+bTOemSzs4wcj0avxvb+Y05OHhKPOniTZwi+z/bDY473alxee5lCukipEEeSJqwszXBh+hKSmmTkh4nE0+zuHrF7cEbPFHClJIlMkUazhTEyOD2pcl49Z3q6QkyLUWvW0Ts6qXSKkTGCCIQUyJUy/MWf/wRFdvFcMDoyR+d9zlt9ZqbmUIIwC9MzPH2yw1s/+4ijwwafuLxGXErgGyYrs0leuJJjeirNqG/jDX0SDkiWw8r8HAk1hKG3cMwhUUVgMGjiOA65bAFJDOG4Dq12Hd0ccdQ4YuPGZfyQR6m0SlgM0ekNGI18DvbG1E98zo8dBn0H1/WIRCERk4mIHnI4oFLJk0ioNDoNGu0Ori8CMBqNGA6HCIKALMskEgmWlpaQxDDnp6eIokg6kyQe12h32oRCAme1GtGoysn+OTElSTKe5fGjp5xUm8zOlbFsh6nKFIagY7kSI1Pk/oMDas0OqUyaGy/c4O2fvs/e/gnLa9fIF5cRhRhGX8cajCnny8S1NOOxhzFs8PLtNdbX57l2ZYPZmVlu33yVmaklnj/dR3BsIiJsrK5w7857TOVzqBGF471jfu1rv0KR0WT0/jf0iYA9kfk//rc/4x/+5ifptE9QFYWRDrYXISy75FWZr3/1DdbmSlh6l+cnNd59ds633nnMYcsgFIozaE0QDMglUpiTEbreJZ2KIskmcS2G7vRJzwl4qkM4keTB0yYTa0QiN+Frb3yB7WdPcW2HzY3LTE3PcPf+fWKJBI7rMuiPePJ4l1x2inBYYefZHmsXlskkY6T+P87eI9ayNT3Pe1aOO4ezT64T6lSuunXzvX0vO5MtN0nbsmBAJgzIAYbgMLANe+CJe2B4ookTKBMiKVOyZBo2aYmx2exmh5v7prqVw8lp57T2Xjl5UDThgdhAc/yv4f+s73vf/wuWQZKk/OTDexQLLS5fvESaRDizKf3ekOHEwS4VUOUEsoi7jx6we3qErMp4wwlblUVKOcijCc1Kxle+9Cbt/pBPPr/D9naVm1cu8PaNDUrpHOSUSrFKs9rg5du3qNUrxL7DaDig2lrFG3kEY487jw+YoeIlAp98+gXDWUSATrG1xtHuCfuP9hCB9bVldFVBlOCkfUJ/PODVay8hqSKlWomBM6DSKHJwtM83vvYGX3x2j5XVC2R+mb2jUy5e2UHOI65tLKPmKZtri/T7R6ws2lysF1mpljBlj9vXC6wsx6ytVNCVKh+/94RnT3axSyaNxTquN2fuB5y0++wetilVlygXF1ioLxOFOZZVIkkE3v/kU3auPa/C0otVipqN7/k8fHyAKDQY9TVmIxMxtxGzjDwL0FWJPAyxdZ1apUyn3aPXG6GaJdJcwZm5eJ6HoiiUSiUMw8CyLEqlEv1+H01RsG0bXVeIk4DRqI+qylimhqpINGpVbF1nPO4935JdK6GaCpKccv3WDncffUaqxly6+jq2vcze4Zy5l+K6DsWSzfqFNbwwZToXSLH48Q8/hDDh+vY2vjPn/ffuUG8s82/8O3+H3ukpo/aEv/jeOzy8u8vv/58/4M/++If8d//tf8XhyTMUFdLM4+aNyzx68gBF19g9OubX/t1/9Wbrn6k5/+H/+PU8iyze+cFnfPMbX2dpu8q9Zw/44sE+BycphrFAU5nx9tUdWkaGrkQoJZHHXsxj3wa9TtTuoM4TgtMBbncASoZWE2muFUGKefX1i9h2g73uPqG2T280ompfYe9znWsXF5g473F9+TJ5Bp4XEIYZC0srHLf7PNk/5O69E1Y3l3HnPq9+6Us8fPwEUch4+7XbbKzU+ejdH3LUntG48hZvvbpIOj8i90Ok1EZRDIqtjL56ZcyNAAAgAElEQVT3gAom3Y5DL0gpra1g6ALRyTkH372P6cb4p3DhUpHapdeYmUv84r/9t0nSMxJ/iBV0aN/7gmD7RaLY4e4X7yASc+PKJVYWtxGiMrrUIg8CTnff4Z27zzhzM5Y3tpk6DogSg1mEl2tI0wjRm1MsK/Qnx7jhhK9+6xfYuHSB3ZMDxG7A+voaH9/5hFhJuf7abebzOZ9++CmrzRWuXr5KUdnm8d5d/vR7f8Z//B9+jc1aAUOSOT47J1DU56sHqlWO945IwjGvvNkikTrkYo04uMnpkUCSxTQbFQxNYH/3Mb7voagm05mPollcWirTalbxXQ/NMomzjH/x3T+lvtriyfEelVqVX3r7FXq9EftHDp22wbBXhqzJydEpspQgiQK2XkcWc8ajIYKsUFlcYTjzmSUibhxxeLyL4zhIkoQkSRSLRSRJQpZlxuMxWZzgey5ZFpFmCaoq0+l3aTZbLC0v485cfuXb3+DDDz5kOhmwslLCc0f8g//+v6RYCOicP+LP3v1Thh2V46M5urpOFA25dNni+rUFzo4OiCOFp7tjeoMJywtNvvH6dS6vVVhab2KUDKI8J5yH/ON/9PvcfOFFrl65hqJpxHmIFw6Zznr0ZYXW8gq//uu/zuWrV9nbPeLW7Vf55JP7/NPf2P35Nedv/m//7Dv3Hz7lpdvruNNd6mWbcm2NWSBy0PPJRIU3KmVu3nqD9nmP3vEzFnQXWQ05BGIlZacQ8coLK9QqAuWmjdasItcKbFxZZX1rCVUSePb0Mcf7Z1zdeJvxac6s63LjSo1XXlzn4uZFioWEnesXWViqsvvwIXaisFhaZDqL6XozRmcD1laLlOyYSilgZUUnjBwySUXUy9i1BvWGgBw6iNEMzYiZxG1CbY4fZERji7if0j/10As7/N9/8h61ehPND6m6Aqork5ymXLrwKj/8yft869/8BuULJX7jt/9nfvuf/h6TcMbmC6/wG7/3fzCZTugdHbPWXGKlsoCSK4iagViyoWDy2cMz/uCdj0mKEt14SqQnqAUBKZsgeWfokkSiSuRljUKjzJXrV1lrLdPtDPnkzl0urH+FeRIiGilx3sOSJWpynVcuXkOOznF792lGCV++vcnX3rxElgR4ScrQDUllm/v3DtCVEuur1/jwg3fQdRVdKxLMCxTK15CtKu7gCZK/h63FuPMpQZQymXrMJxPKhoopRmCuUW42iJ0D9GJIb97lXAS5sYClCXj9E0r1ZWaeyo9+cIYkXuT8dIKQpvQ6beRMwTYL+HGfTMgxKiV6M4dHB/sk5DSadQq2xag/ZDwYkqUpBdvGNE1msxmiKOJ6LrOZhx/GyKpJJihoRpFqYxlJMRFEDbNYxYtzdo+P6I2HzGYevXaPt17aYa06Y63WRy41+PjDu/ROfYpanciLEBRwopDcqDCa5SRzhYq5iBhr9DtDjk7PCHyf9dYKplbl0f09fM+nUq7SaC7Q7pzzO//kdzBtk5dfeZlSnvDDf/kveeulV1iqL3N23MWfJRzunvBrv/av7kr5mXD+1j//n75jGCIri0WEyGFrfQnCiMyb8+DjT2jaBrdeeYkgS/G9AWIyZa2ukKYBB2cDGrUm24sVTClFxiUMR0gGrG8vsr93j90nX1BRRV68fo1Ws0Eah1y7cpErlzbYXFskzwLyNMAZRvzWP/7ntDtDLu/c4PH9p5yfD3h4cEysKlxYL1KtVbHtArNZgKYVqNebfP7Zp9i2xmKrzMN7PwV/xMX1GmniMptPCIMANRWJRzMeHeySGQYfPrgLeopuhdhWxGKrwPqVFb76r7+FVqnSDXq88Ytvcv/xp9y8eYWXX7rMhfVlfv0f/hZvvPIVVEHm0s4W9cUWfWfIYDZlfWOLPBMZTBw+uvsAQZNQTQNNV7FMA8vSKVbLmOUCVqUOtoZYECk3bDRT5nD/MYeHj7CLKm9eWaSguKiJR7NQZKVUp6wqVGWNQiZzcXGH1eYKUehSXCySxi5JkmOZNR7dnfJP/tF9Lq3dpKJG9DsDVpe2sIwFzroh3//xZxQri1TrZbLMpT0aklsGx8MusQrnsx6TzEMq6Qwcke99989ptorEZHz02X22r7zMZz+9jxAIrFSX+YsfPeVob87a0pvs7zk4k5gszYn8hMXWCnGcopoSuSDx6MlTuoMxoqQydeYcnZwwmTpomk690aBWe76kdjqd4jgzOp0OURSjqSpZliMIAkEYcP36dURJYjabsbS8/DzKSiqOMyWMYnRZoVIq0Ts/YLVlsrJUobG0yO1rt9neWOfevXsUygaDcRc3CIljBXeW0uuMWVnbxvVChqMe5VqB8WyEXS7z9Ok+H31xnzuP9pi6Cb3hjO//6H3mfsZndx7y1a98m9Afs3Vxh1//X3+Xb/7SV7n38D6qrlJvlPnmN//+zw/nh+/84+8s1TSWqgpfevEaQhxStYsUFI1pd8ioN8JXZO7dv8Nqs8jrL1+hrCcE4ZzKQosLa1s0SuXnK8qrOjkzFEtEkDOu7myzvrTIZq2MnKfIQsb3//z73LyxRaNWIE886mWbNPL5H/7B7/Hlr/4Si8sbHB91iKMcSbWYpxnXX3mFZrOArGq0z0c06uvEEayvrlKtGgi45LHDgmXx+o0rFA2FJAkoFks0yk26u+dMj/ssX9lCsGWWVheoVjXWVyr47phcFhGLJqmtcz6ZcPONWyRiTBz7vPfeO4wGQx48esp5d86vfulX+epXvkahXqbQqPDnH/6IIE9QNB1ZVPjJX/yYnx6cIRkaqiwhkSKKCYom0h52OBt1KJZsVF1ilkyplnX82Ygr60tsLtf5ypsvgnPErDuiqFWRgiK9kw7Nosw/+80/5EKjjilWsEoakiwymc7Ryy3Oz32++92f8sMfPmBleZvVlQ0OT95D1go4bk5/ENHpeuzunXPv/mMkIeTChSaT2YxZ4DKLXfrugHnuEUgxqSkyncKP3zni8vUW7cE5x2fnrK1v4I48bmxe597HD5CVHYYDkftf9JiModVapn1+RLlcZGlxAdM0GDpjnu7u0+4NUHQDwyxgmhaqrlOt1rh69RqtxUVycsbjMbPZjDhJKBQLaLpGkmQEfkCxWMQuFMjzHFGSEASB8XiMrutYVgndNDk4PMI0bJIox1QVbEvixrUdonDA2ckpcexRqZU4650znkasrV/k/Xfvs7/bwS7YFAtVPD9i5jsMnT5bl7aYzOf89LM7XLh+nXHos3d6ytT1qdQXOD7pkOYq5VKLpVYRRdV5/PQL/uBP3qW1XECQY6pliy+99R/9/HBupn/2Hb/zkBd2Vggjn9WNyxx3XeaxyuLGLcZuwmq1zmLZpmSKLC7X6c6HtP0Zqa2xvLxDq7BJybAQ84wkT4mRUBQbfxLy6PMnLJZ1dFUgyQKqdYNO75g8C1ldXkDOc7I4pHNisnhhi9//wz/ivN+j1mzw7k8fs3njEts3b6IqAusbOzSaF6hWlume95hNerz80jalgkBdV7jduonoZzSqLQyzim7VILc4e9wGR+DizXUULaSsw2qphHPmMD6DvV2Pu3d7nPYT9LpJebGOrKnMZy6PHz3j6LBNkugst9b59hu/BIrANJnx+ZP7HAzPSWWJ6WjGvY/v4vgJ/Vzj6bMniFnM9nqDxWYJWU5RVQFRyqmLOUbqo8sZJUUmHU9YUFR2GjWsNKI3TBiNLGaTEj/4/jNO2yMuXFrkxu0WdqXJw6d7OPoMqbTEf/Hf/DbPjlSOOyof3dkjlBP+/n/9S5hLQ5o7NZSSxWm/y3weMekNWS4XuFA2WK4VKZVK1MoViraG740oWAqNRoU8jxEFAUNRuHy9hJdNqDRMLu6sgj9hwS4hJhqqXObdD04ZjwNqlQvIqsbqWgNR8qhUdKbjLoNRh64TcunaNVbXNqk3W0RZ9nySom6g6yZRnDAcDhiORsxdF0EQkGSZMArxfI/QD8lzkGWZRrOBqqpIsozruvT7fcIwZLG1hqyo2IUi+wdn5KioqsV4MGY4nlCzEzIB5t4MtWBx78k+hWKL48MximSz1Fpgdb1OFEbEacTY6aFZMHWH9CY9br/+Gp3ZGVZd4YVXr3LzpSu44ZRMiCmVi7z3wXuMRl3SXEAxdda3a9x45TKFiobvD3jz9f/s528Z+/iHf0KtuYBl6fT7Hl/sneAEBiftET/6yY+wzCLjLz7nl//Wl/GDIfcPjjErRWZGjCTppJlMEoCiaMhShcn0hPYwBBE+e/8e096If+0bryJrKUVVZPnSBqqmMJ1O6fcHXN7a4cff/zEv3PoFfuf/+t9J1YSrVzbJ1Zy/+/e+jVpa4M6zp5zsPcN3fW5de4Evv/ELfPzhj2lUC6iijGTYuAOP3btPqLVqJFWZXJcRdYnx8ByrVoPc4+jpAVZdpljRCYM5OxtXGJzvU9aaXFpf5NGTRzTfqDOcjDgPXT54/31uXHmBRS+GWOPazi0yC9LUZ+5O0XWVSqlCsVjG6Xp0JwGFQglVTGnUq9x6cZtaWUYmoKpr+G5KXSiwqhsYuo4jyIzGLoPjHm9uXqGoV1CVnNXNHb7/zh/xzjvfw7brTKYDzHcN/r2/9ysoRKwrCp/u3uPcOeHxcc48O+Rr39zglddf5dHej3m0+0eIsk+9eJEkEDDMjCByWF4zePHyDiYxfpoQRjIL1TItGyxTJEwDetMZepyhWXWe3n9GTMbC+hKaIaPIKZIYEkYZ3UGCE5ioqowgyXR7Z5RqC4hSiiCkeP6Mfn9AEKYsbt8iE2TMgoHTG5DnAmESM5+PiZM+pl2iWqtRKBQIwpAojonjiCRJSJIUURRRFAVZlgmCgF6vh10q4jgOlmWRJAmFgk2Y+SyZyxwenWMbFiga3bHL3olP++wDXn/7DSStyOHJkME4pWBrrK8usf/0AZ4z5jzusLlxAV3OMfUqZjFnYanE1sU1Tk5OqJQ1qs0SmqIQBl3sQkwmjFm/cAndyokEgf/nu9/lW7/8ddqjAx4+e0gQz6kUS38tfz8zcp49/ePvlJc2GScyRnOd9+88wotlDMNm68ImN29cpbJaoTM85cYrL6AUikSyhaCVySOR4VkPRRRQZIEwinl6eMoP3/+UTn/A229c4StvX6W+2kIvl0nEnFTK6Q36iJJEvVIncVN01eLBwx611Qqbl9fZubmJKGecnp9zPpww8QKccUi1usD16zcwDJE4HHHz+ibNehExz8CJkYYxtUaVzBBxhJDIEHl2uk/gO1QrFppeJE7gws4qo2CKVqzR7jrYBRPTEnnjtatEzjkj5wwnGbN6eYWEkIWlKi9cvsqs0yWRY1Il5tnhQw4O9wj8gKOjDtNZhmIuoGhlyqZNTkCaO8hqiJx7aElAIcnYLlZoajKCkKPYRQYTF0XRubpzA9MuEwEHvTbvfvwhkpHxyptXmfkRolDBD0SuXL1Ie9Dm/DzmD/7gfZaWdd5++ya26WIbM77x9g5XN5YooqJOFB5/dhcpjbl4cY3L1zdpLlWpLdUR7CKCVUazDAQlI0td3OkYZzTBn/k8+OwMLRC4de0603lAs1lHTFN6B4foUgU/bXA8yMkiicnE5fRkRBCmKIrMcDjE1EoUzQVMs0p3NufO3XscHZ3QGwxwnBmarrG8tExzoYXnB5yenTGbz8iBOEmYzWdEcYwgCiiy8pdrBQvkeYYgCLiehyiKJEnC8vIyG+sXSIgxbBPDLBLHcLB/iO86aJrI9vYCAydAKzb53d//AV5QZNhPIQnYuVhhc73AclPmxZvXuHn9EsWiwKVLLXYuLzManxNFcy5tbZH5AYoIaegjiwIbGxt4noesKMzCmFyW+PTuJ1x98RJh5lOulSgUS9y++u///JGzHVf5yfu7LFxYo3fvE3RdZWWtymgw5OjgiE/vjFh/5Rans1PizwIq5SZhIDMdzShpMquLRcbBKX5fgVxAMRW2djYplS0WV+u06haCLNDu9UHKOD07ZjaZsrq8gkaZ/ukxQgZf+sZrvHfvJ5wPT7kgl+n09li/sMNJz0PJMkplnVarxPpGDU3MeePNV2jVCoz7J8xmLmkc4ccjMidDL5ZRiyWiNMQqyWRCge7ZgKqxgSTbdEcxT47bLAs2sTZnZaXM2nKN2O8xeHRCZuUUFguM3S6teotySWU4PSQVA54cTTl6/5DF9RWiKEMUTMIoAEkjTlLGTo/teoO1Rh2zFFEupRSkhGQyo2YtYEYK7vx5ZG5LU7qzBDGX2O11WJQaDN0xvdERV16sIkgCSTTmlTeu8IM/uYOpKwRfv0anc8Lhk0N+5Zu38YMRgX+XWrlKFAa0tOv4JwHdZw7KJGfTaKJXTCwVUkJSU2YqiTzonOPMAi5fXKViJHjzOVGSMJ/NKReq/MJb62yVttg779A7HdCol1luNRiKHTxXYdJPEZICaehj6iWqVRVJtYjDnEp5hfPuiDgKkFSVvtdDlmUs67lm3NjaQhAEOt0eSR5imCa6rjP3XBLfJ01TZFkmjmNkRUGRVFqtBXTD4PDoCM/30S2TMAyx7ALlSoUkjdA1Dc00WFlZYnNzi2a1zLC7hxP6PHo64YXXrtLuB5y1I1QJCpqEpsdksQ+KwFtvXUYUYDA5ZGuziVWS6Q5OSKIZly6uUtAM/ImLLpkoho6bB9SaC7QWVnn46An1pUXsQoEPPnmPbrdPa63CcNTB9/2/lr+f+c753g++k3/+6DEH3VMqDZO1RYOyLTN1XaaJTJAqDM8GKLnLV994FQUdTaxycj7hi4cPKFQTWksCOQJpnLG2cgFZ1Nh9+oxmo8HKygqrBZmPP/yY/jigurBBuzNG03ROjw7ZXFtmNhlSe/ki88k5qpRAMoY8RVRMpr5IkMqoUoxpmsiijJgYrCxcxTIKtLsHOH6bwOujoiFLKZ43RxRgcWGRIJC58/khnbbDSnWJ5cUWfjznsPuM1mqV1moNRRfIspT53CE4EBCkmM7wkOu3trl07TJREjOZzun3J8xPp8SiTM9NUewinZnPaDZBVnJEQi4stFikwEqlwEJZpFjL6Tsdnj49RIsqXGy+iDCdsOeOuZ/kjAKHZklnsSAwz6f0pJhsKhF6CYZmQiKhiTJ1W8WWQ7bXKliGQtlWSYOEZBay0lhk1B9ydHKCYhloRYuK3SB6JtFNT1BqIpdeuIQgmISxxL39fd796KfUq0VeuX2FelFDynxif47r+yRJysiZoeQXyISMIJ8zdMcU7DKt8ibjw4zHP+1iSw3unvVRbZNREOIFMapokmYSe8MB7XBOGPi8srzE1tZFJM1kHkRM5y5zL6Q3GHF2fk6apmRZRp7nWJb1PL0NAkajEc1mE8M2SbOM1bV19g+OiOIIRTfJ8xxZVqnV62yutRBFEUF8Xq2EAKpqkGQZw/4AsfOYf+vX3sKPhvzovY+plKoUCw63rhW4tHYRf+4gKyPs0gJeKON4Ce1OF2c25NLldVoLZZy5i6qViEOZJJH44s590ixmaa2BooGhqjQbi5TKdU5Oz8hkl8OzB+iWyn/wd7/4+d857zz5X74TZGOWFqtstMosWTq2oqCICqNZzHAa4g4y3n79FWpFG0NVSJOIXIxZulAhyz3iKIFcZGlxBcs0GfS7FGyDSsWmP2hTKdp89ukXTCYpWVbEtps4U4/hqE8ueFy5doFhOqHfPmA2G1ItGHiuh6ya+AHMvRQpNXCdgDSJ8XyH8/Yxvj/H9UdAwHjgIKd1RkOP999/yMMHp6hqgfPTmPNTj4L5fIyiIkVIagJSjFmQqdWLONMBWRLgexOQGkwDF8U0WNvaoVBoIIgW9784ZO7A4aOHKJpOmGZIikySp2iqiCanrLZqCLHL1kKR6xfX0KWMMPQZDMZIgoalGdRLBUy1xIOjHn/87n2G4zHRfM5qvcZyq0UuKGSuSM0ust5ooiYRiTNm1G6jaRaZbOMmOpKWEoQpjWaLJMnZPzjkuNPlzV/4MqgqEQKpYdDOesRGRrlRp1JYREksck9gNg1YXd3i2pVrmJqIIHgMBmecnp7gzBwCL+TDuyfEAlQrBYpGkaJWJHByhu0IPzRIBZ1HR8eM5nPQNJI4o1Vr4Xs+giQxCzy8uUurVOLJs13Gzpyj41Pu3L3P7u4uk6kDgvBXxQeCIKDrOgBxHKNpGpqmkWQpG5sbVKo1VE0lh7/6dnFpkWq1hqnJiKKIrChIskyeZUwdh/l8ThAErFxaYRqmFGtNti5W2bms8/JrNSRlTCgco5oBmSfghzG5IFGqlrFLFpqpoOkSo1EfWfQQsoBSocTtW7fRtQKPHx8xnWTU6uvMgjlPdg8xrDIPHz+msWCjmRGK7HP98n/y86e1mS5w2j3npWuv8uyTz3l5ZwOjoKFECY1ykScHj1msbZJGAZqs40xGtM96pILMLPbo9LokQcbOzja1SpPZbEjgO5CnTCdtJtMxgudwYWuHmaPz4H4PRUtIs4hvfPNvcefe99CKEtkoQpU0CkaR08MOtWqDu5/u4ycaF6+8gDsJOW+fI2kzimWJ1dVVBHWON5o+jyQlm2g+Q5ADLlwskKQmmp2zf3gCokq5KmOoOWHgosgahWKV4XBIkp7TqBVZXGjQPj3i8dmcLBUJw5jjgxGtyg7D/pgf/NFnbG9eZn11HT/PkYQc4gRbExFkDQkBg5hSrUAup7T7HVbry0jGAu5RzP7BPuuLZSRLB8XATwTq1RLNlQqtqk6ve86l7UV00+Dzoz2W6mVqtsXrW03aZyc8erzHea9LqBTJxAQ3mlHQDZJsSDCa4sYJVq2GYNjEQczd3YdkuYhUAa1qM/ACLClHTUzGAzg5mDOfD7C1M1qLkGcumqlx6fIWkqAABlnV4+OPH6FrAgt2BcmUkEKFycSl5+QMR1POh0Mc3+Nao4EqquRZRrlQoFawGUceuizjhSlBnBENx5x3eyiqhqKqz9M6QUCWn19RUXw+/zwMQ9I0Jc9zfN+nUi2TxjEiOdeuXuHsvM29ew8QAduyKNgmtiVj20XCMGI8GqFoGs1GDT+IGAwHHM/b1PNVgsgldp5Q0M+JryzxyU93Wd+qcHnHpmHqnPcGoLo0dQWrYJMKFkHggShTNFTSMKGg6TjjMWQ5L7zwEr/3L37AeJ7z9V+9xebVa7z7448I/QBJLrLW2OH+vff/Wv5+Jpzf/ZMT4qhATZe581kf2cn41V95m2DUYaEg89ZL2wzO54h5ynQ8Ig4SdEXiwZMuISqeLzF35izUgucVEcEQAUjiEN93EYHT7oCCKeDPU5ZXVjGtCr1Bh0K5zNL6OlqpTHo+RUyLaEKBVrWCIuicPHvG2J3z2ssbjE4fkQQCN27fwE875HKC4w7J8ow8F1lfXqLTHjBpT1jfXkZSZLJUolzXiOcqg14ftVSmUqnTnziUFhocHXWpuwqWZOBrOt5AQwg1Rr0Z1VKJ/qHDw+QZh/snyK6G6unIioaaJyTzCUHsEEsJrVYVTRYomSq97oCJXOE0n/Po6Ri7cIEPPnUYTxT8TGZjp4ZVSMl0n5s3FtFLMpLgo5dUJMnnQrnJ9i+/wb07H/PswT6twgVM0+X1L13iw3uHtCdd3CClc3zMt77+FYLQQzE1FoolkhQCNHpOxt2HJ6zWKmRhRpIraKLIp8cnRHOVOw92eXDYRjk8wo9m/OrffotiZQNnoqBIEqSgqQWC/CnLmxWCKCCKM6aOTxaqTJyQ7sAnwaJYqZHIKkEUU7HKIOTIksDcmaJLAqptEXgJUQqKKJBmGUKaoakaqqaBIIIgkGXZX/V0RlGELMt/BavnufieS+C7CHnG0mKL4+Mj/CCCLEEWcor2c4gajQUC3yNDwPV9Hj+8z4XNCxwPXXpPn7HeWuW1Gzdp1RpU5CLzsz3OAo8SDs2bDSyzysid8ejxM4xikXqjCpLJeOgh+SKj7pyTk0NEqY2gGjh+yCuvX+HPfvAur8c77D08JsxEzjseTx85aErE/rO/Xlb+TDjH3UXmYcjBwS6LpUuEuoJq11lQBGRbQsg6qA2JpaZG2VaRMOh3Be7dc7j/4JxSRefGtVss1Bs8fXQfXY+oVg1EQUYWDPwwJRIFVARKtQL9zhgdEUH0COKQay+8iR/P+OjdP+SN2y8xGwUslOv0uyMaxWU8b8RH731Gr3NKqQKz6YxIShEVhYODNmVdoWhYKJKGoZSR5TK9QYyoCCiSiaauMprMiKYidcPii3sdhoGD0YtIxSrjSU5flyiqJrFfZNT1GfVilmorpMmcQX9C4Iesri9Ta1Y4bR+TCAlH/TOMgo5RknDnAlbJoFmqcnZ0yjTxaJVN+s6ATx52uXN/zNxLOW4PqDdL1CpzWhsGkzAmEmfkYkCc+4zmPbzBmJX1FW5cvcjORQ/H7eMTEU7ahIHMrc0Ks5lPEi9TUXKWV5cRMxG72CSIRe4/O+X9z54wGAtUU4E0lhgMB5yfCLSPPA4PemBoFBYsTAG2b2xhVZZo98+483mfW1cv0qiUUA0Nd/YBpi5SKJWYuzGWWiDNZCTdpFaxOD2d0KgvIMgqWZpTKBQR/Ig4jJm5Mwolm3ZvQC6YCIpGLkkIkkKeg6qq1KpVXN8niGKyLCMIgucmkCzTarUIwxBFUUgTn9Gwz/LyEr7nYpgWaysrzFyXarlEvV5DJEYSIA49VpaXiOKEDz76iPnModfuIPkWFV3Ddz0+/LzH7ZsVChcN3vjyJkvNhCxJaJ+EOKGPUSvijHrcfXwX0zZZWLhA+7xPTWkgZQrFSERSczQr4/N7D9AMg3JZ4e69e5yfjqgW1th7NmZ49oCirRL7fz2CPxPOYb+HmwtM4oBIEXl54QqJYSMSkXpz0knMdORzeXMZiRhDM1CVhEa9wIX1BlGSY2g2vhuRxjmJlOGM5yRJRJaICGjIBZMkB+SYYlVAEuZYRTg42ufVlS/hRTJCnqApAqPeGCPPMHSVl1++jvTgMY7bQySkWCoyGA65cPkCn915BLHGUgnTS/sAACAASURBVLlEngrMnAGikmAVU0iT526a43Jy7jDsBtzcfgnVDDkb9DCqRbwkZezMUfOEiqHR04f4c5/P7u0jKwl2f0CzaTKKZ0xFl7VLK4zmY1J05oFDo7lCJsVISkSr1WK5ViCNAxbqFRqKzc6FCpqQ88Xd57Weg1FMyYLZ6AhFyVANDVUWmAUhw3mfUkln5EZcLCwyHfkkYkZvPGAYTElIWW4ukMUeywUbxS5SbS3gz+YkzhDLLJOFIVEgcPeLR7S7M3KxQKo18NOAwWyMf9ZGDAxMwSRNc2xLZGmxRLFhMJz7vP/JPu/8aJeCtU5rYYEHDz+npMjkco6oCejFKtNRhGlpxGTosoEtSjheiJgJSLmI7/kUFQ2toHEyHWDWa9hxRBpZeEFElqaYlk2SxCR/aQKJoogsyyRJwv/fuPz/IE2ShDgM8bw5vW4HwzCQZQVRAMsw0FQFRZYIvefacjgaIisa87lHuVTC0DWEHL71tW8ziXr4SUgsFnjQmeDic2Vlg72jhxBkCNkQP3fRXA+1qLG5uYUzjzk+GhKGMkvrVbYubKAZGlGS0Gw2iTKNR4/3sLRlnt47p1ypc7w/pGQvkiYJnh8yd0d/MzglRMQsxFBzgijkZODz+dMhQjhme9lmNvXwuhnR1GIau4SBT7sT8ehxlySXkE2Dw24fVfKpVXUQIwIxJ5YtZMUm9GPSSCZyXPwwo2apeO4Ey7A4OnqC+qUvMRs6vHzzFlNnzGDUI019GvUamysN9vcDbl/dwm4s8vjpI54e3efitQ3qi2Vs26ZZsbGEkKLpYdkGqaBwcO4hqiJxGBJGLtduXUOXVPrOjPryAgurDabhmIXFIiXDpLN/Qrkg8+jhM8yCxnAyJ1ENIlPi4PQ+1aqNvSATZDmjcczG5gpLCxpZNsMZH1GXM2a9OQcHU8rlEkmUcSbOKFdSqpWQl3Zy8sBEzkUWCxpFOaM9HOPLKqgKulVida2FlsDB6Jx54FEXLXpHbQJFwy0YvPXaa7zejIlGA/zIx85MdNOmPxkxz1JGnS5RqnDcH5EJMHI8juMiZsVFTVMqVo1UgFASGLkzdMmm2WgynfV5fLTLo9M57TBjf9znthiz++SU5oJEpuUMY4fN9W1UPeXo83PcUUDkGyilJczEwwsTJuMZcSoQlqqsra4THJ8gBaAIOqqhYJoa7nyOIotoqo5hmhSLNpPJjMBNyOKMOIxRVQVRkphMphSsIq7rgZiRJBmTqUt17hPGOZZVYDafEwYR7sxlFjgEbs7xQY+CZeP6M0Q5wjZlNC3j5ku3macdeqMOnVHC6fGcB/d99r/Y49bFGocHz5C0nHqjTCGQif0RzVaFq1sbfNDdw+nkSBsm5XqVQqWKIIlohsw31r/M4nITfx5x6u7yxd17yJaFVSmzv3+CrahYtcrfDE5ZNJGiEDWPEBD5/LM9tpfW2VheYZ5MMYs2djdkOoo5aI/49POnuIGOoVfQNYnRfErBklhfbVEvxcw9n7koMg9EJLuEbObEw4het4MuCJR0iSQJmExchMzn2YNPCOKMmTNF0mXUok5iCcyFOY+P7yNKLl976xbPOhPKtsibL12mYgnUqxqD6QBtpYCW5iThjEzLKahltExl0hFwXYlLF69BovP08S4nnTaGraDXIBeGXLmyjTNwaTRqeH5Gu+dRXyxhFRYpFIsEsUN9cYHlVh3btHjWPWSWBiyuXkQKRyQzh2Wrgt9xODyfEuRlUkshnLvc+aLDzrU1mpZJXZ1StSUMTSPJ5kiuS+7NoNIiVwzCICQIFI6OT8kSBdnIKXoJdaFEkJtMggwxL1BW4Mg5ZhQ6LK6v0OkdM4wFTLnIg/MBh8cdPrkXsXOxjG0ZyKLGQjlGyiTEWKU9GpGIDlpBwXcS3E6AJk6RUodarUZzuUJ7csT9ZxZpJFDQi4hlmTB9HulMRSfsz8hdifP+BE8uYCoxVqFI5Lq0ByMqrTWmSUa1ucz+7gGGaVCsiliGSp6qSGJOo9H4q86TLEnJwgxJkFD+Umfm5KRZRpqDIMgkOUiqghckjCdzKhWFctl6fpYkuK5PqqR4YU69sY4/d4jCnPbxLmZBQtMFfvTO9zg+3yUIPbIsZTpyGZ2dsrWq8Mobb+JmIw56M8RQxqRAGg4Zto+wFAlTyjAlnST08HwHs1RE1TW8dMrJ4QlmSaHRqLK9+BLFusCzvS694QyjoePOEsr2wt8MzsZiFcYRbpyiaDobq2sMRlOmwzPqpTlba1Xqmzpd95RpPEQtxSxsrFOvrnJ+fsqkF7DaXOLlG5eJZ8cM0zG567O2tkm11mI2nSFoDge+TUxEkugkikKYxTTWajx6+sHzv6rgsLa6RXPnIhPPodtts1irI0YWR16XeXrIygbsrG8zHnkcdny00GZ2qhELMm1niG73EYRlphOVTz/fZXV9FU2NOTp8jFWxuLlRRdEFKhWVLCkhijIzJ6fTSdl9/JhBL6VSCNjaqRNHp9Qra8xGFs9+OuZcOeHqxR2ik3eYDjvMxgLzqUbRLLG2XKJUechL20UkyWXqhAR7Dkd7Z/QpsF1fpFLRUI2ATHboT02moUEvUMgElSAy+PTjY4Kxw/bGFeKBT6Y3sAoG550unXjIu5/eQb26TdqysESFsV7gJBI4d0JG5095/HRG4CkMpzK2tf1cy2ZjjIrEdOgy6PaxihZ+MsMwDYLBFG/gUTENlso2sevwWt1iZaVA0G1z3u9hV4tU9QamVuH8sUM0GTPuBeSxipi6zIYneEaVpZUVoukETdfJBXi6u0u5VPnLmUAZpmUzGk3IctA0/S/dWo0wHOIGPoKukYQRuSiQCTnFUgnLLvC1r/8iQZBwdLjH6ckxrutyenrKrVu3EASBNE05PDykUCigFzLiOEJTI7rTPfr9AXNvxsQXGU5Cps4fc3o8QJUtlltNRoNH3Lwms960mXfOuNha57T7iEF7iCXAwkINSZEIE5eF1ZzGClQrE/q9z/HjDrptMfHG9AYdtjevYBZqKOLzTFSVZAqGSj8LEZARRPVvBqekiRi2RuKrBGHI3PX4+NN9/FmHb/3iddrTGUE8ASmiumKydukFZKlBtzMjSAdsby9w89I6qTtCCn0WTBs5l7FyUJwxdSGnqEQIpsD+ZM54nBDrMqqhMonGpAyRZZ2r11bRiwrn3Q7j+QyjpJPKGRtXNjkanBAncyxF4+Sky6wbMTtN2G37hG6N6XRApSwhDUL6nTP290POz33ybIYkS9glnXJFRa+BGzgEcchio4WMiiLrtHtnyIaOYmW8eHMJrTpCsQVOj485uC9QtBr0vFP+8//079D93U+RBZmDsz7n3RmqMscj5NZ2g8ubOpE/pSPqyNc0DttjVGJWt5dpVUuIao4TOHzw5IwP7vfJKwlrWyoFXcQqhFSbBZrNlOMHKZvXr2ArNt+785CJEHHcn/F773/E1kqZJPJ49pP79CZnpGrI0J0iCAWi3OfFNxbYO7uPJgrcvFxlFp5jlE1KsUWSgGbIaAWJXtfneCYwOw0p9+dcXFxitVHAKgsMZyHplsVhu4do12ifnhJPFYp6DVKZUsXCKpcojyT6cRXN1BFEkVKxwNzz6PS7mKZNpVpFEEX8MMZxPYIgxDQN4jRjOJ6AJJNLEpkgkEQZoixSLNosLbcQZZUXX30Z0y5y9OwZv/2bvwlAlmXs7e0xnU4Zj8dMp1PSNGV9dYmFlSqO20HUc1DA9QTyTCQ3JFwmlAyJwHd47fZ14mDMt7+5DYFL7MxIxITVpkw3jRGJEMUCSarjuCHbl1rM/THe7JSCXWcymZGMRRIhQ5DBix3m6XN/ZqG2wNF+H12UcEcjglBmbvz1FUI/E86n+3vISo5mKEDC3bsPKNsKt25do7Gywk8++FPWtxrYBRFDUUEOyfIZsuKzuFIkT2NOj54gNQpsLVXIQw1BCmj3B/QGHSolixVRwvOmmJZOrCroJRvNVOicd1lsljENkctra4Q5SBmosk6KgJhliHGEmKYoxRtokkz/pMsnH93jzv0hUqXMgmriSjLZ/8vZmz1Zct5nek+uJ/Ock2dfa9+6qrurFzQaaADEQgAkBUmkJIrUPlLEyLInZCt84wuHwyF74InxhSccnhhrHJqQfaOxZ0aiqIUiCZEESIAAQRBoAI1eq7trr1NVZ99PntwzfdGKuZIYQf4P+Xz5Lb/3eW0Pdxwy6ET0+xFhoFE/6VKaiaMlVbZ3ayxr82h6FtecUspUOdyukUnFWT+bZWJ6LK2nWVyDRDGGqIo0TrrMzMwRU2I82Gmyd3qT2cpV6r0hA2tAfs4gnlTomrsUildQBBXHnlLKptEJmfgeJ7UGx70E6UKBfL7M4eEeupGjXHBQ8jLlpEsq7pMq6+iKRMCIYkGhF5zwyX4LKaugjkM+vLFNfiZDKOgoboRjWhwfHmBUZNIZHVkVUWWdcGrTOD1laXWBpOYzs1DEtSPGPRsRhUJOJ1NMctpP0nZEpkKcvdMWly9nObMSp13fQ8dmcVHnaKzSaPXoHnfJKCX8YIKRjVMszeJOA+qtY/wwYGKaBESIkogf+CCKBGGAqmmIkki328eyXaIQbNvD9UICP0RVdYIoYmgOKReypJMJYorEytIikSTjRx6xpIaiKoRhyHQ6xfd9fvjDH6KqKpZlkclkCIKA2l6f49MaqAFBpGJbIlcuPk+3VaffOcTqT7l8qUo6G/HMNYnduyaSG7BYncfOdKlEAQsXL/CDd6/T7XWxXYGJ6RD3RTw3hoBCwtDR4hJW3wJkRFnASMRxPJPt/QdoRMRUlaW5Bd77wSdUMilaTROr/1NeCPWGJvGEQiQ8gqJSqbCxscDSco7j9iHZmRLJXBUtZtPutAjSEZmESrt5iJ7QMbJpNBSUmEyEQNzIUB82OW63aPfqTDwdJVul4XhMPZ+pJ7K8VAIZKrOzZJIxJqM+o76JkSswV85gJB1GkwndRp1+q8ml8xuMhRl0NYaSM9ivv0vHdZnNpollkhS1PMPuEZnyDI5rMbuYxLFsBqMTkimRse2QK5WYm1mGKESMewiuzNH2Lk89/TSJ5Ayj6YThZEKmImG5IAcJxpMG/XGdfn+b4qzBg9ohnrfO9U9u0hx2mI+JPPPsJglxnnq9R2N7zOdefBKPLt5UwnI09g4DLNPm4eFdZpZbnA7bpBWBixc1zNAin1VIxRSSSp5my2RoqhSrBp8cfszADknNV5A7If1GGwuZu/cOqIgaG4sB1atptJKKIymMpjF6HYv+0MOIZYhsUAOLtfllOq0x06xPPJEhUpokkj4Lcwa9A4u+oyJpOR60D1kpGSAIVKurvLP7gEQ+R0LQKK7OwBSCyKWjQMNssne/iSTrDHpDcopCo9kkLwhkCjqqquL4HiERvh+gxmLENI3JZAJBgO8/mp1V1BhGysAJfM6ur7O2uEDtYJ+YKNKfTBiPhqQLeVrtFoqi4Lruf77dnUwmeJ5Hv99/5BdSdAbTDtViCceJMe4NmHRGhGaHhaLHU9eeZHlZJpGcsDTnYbUVTg6OeHDrIU8+U6A8WyaUklzarHL34QHtfo9Wx2IptYhlyYiyjhSLmEwjxiOHeFJDkxVkUSQR12i2u/TGLr12l/nqInFJpjybZ60U48OPb/90cGp6Ds+3qZ92WVyYZ3FpDj+02d5/iCccUZ3L0Gh6XL28SHtqIUUS5qDHTCn7aCVJpvFtaHYaHO1vIyJiItF0HMJ0nIkWcs+OSC6ucXapwmjQ4OHDW9ieixTT6Bo59GQaKVXm4619+qMpyVQWXYvhuBGyrDBXreCbEbFYxEhUyORE5uJ50gUDQ5Nx7QhRExhETUorKc6trOPZE0ajBNligt5Y5LDWptM5xIjr5GMpnNEYxReR3JDAsokrMslKno/uHrC58TJ/+WfvcXQski8nkQyZ2fV13nrvPq5v4YoprLFLfVvmO81trmzOMerUuXi+jCvkiUUlorDDyNqmvJphMrbZe3BMyRTx5TxzekRKcwjUGJIUJ1/OYltDTqYWDRPu7+ywdG2TVqtDTM/jdlposk29v8/T5QLnEnHmJiHpmSIDp4upRbjTAXHfJ13JcRoOefzyCps5jebOfUwzpBifZzwZUlmUSGciFrNlNg2Tw26CsVDBi+5hCgky2XU+2bN4555Ipmyxtl5mNZFAiUI6oylfefMTSvNPEiVTjFuQyxsMR0NmZmdxiPACnzAKkWSZQiGPpsaYeh7D0ZipZRPTdUaTCYaRxBoOmUxMoiBgfWWFL33h8/z1V/+Cne1tzl+5QkqPs7+9w3AwIJ/P/+c/p6ZpyLJMEASMx2Pi8Tia4HFxY5Fmv4VtOvjWBHt4zOPnZJ58Is/aZQvHDNEklUF9zHyljKqFSGqW9fPr9NoO77x2k7m1PD/7yos4xHFcndt3t6g3T5md15g4CkaiSqEIYeDTa52gGSJhqLJQLXG3W0MQNW5/codrF6+geDKHD4+4vFj56eCMu20SRg5PXUYOM/Q6PumChON7zC5UOXh4n4sLBrN6nnx1A1kTOWidoGgqXgSiZVM7bvNwZx8EhWQ8Q0yK0PQYqXKMUJmiCg5rc4sY4gr3fnDCtStXmQYNPMWk3gmQxQQfPtzh9LDO6XGbzQtPIleyvHvrHZ69UsYVaiSNCelkCdMUWShsUBZSeJLHqH7MZNLFE6dk5+KUywZddxdJC4kbEnY4IV9JkcrGUSOdmFDg5vWHJFSJ02YaWT5HKnWXgAnxWJ6u0GT3/pSPrg+ZWS7jhh08b8L+7T06RyqUYtjdCOdEIJVVsHybWw+2cOmgttN8NlVhNK5x9+4ekTVgY03htAv3DzW++9422dkqwnwRRZdJxlR2HvY5PBwhaR2awzZTp0q5kEBxLYRBl5ELI3VCtRpnJozxTz//88RDAfUbPtt3b+KVPbxYSMEusBAvs1PvoOtplJxCwUiQjv0KYWnCNLjJ1Aw5OkzQGydZXs+Syk05eu8BQswgcy5DkF3gzY8afO/Nh6xe2CA+2GYlmSaXiyFqNt4w4MJmHFGAfirLft1hYk9oddssrZxBcjzUSCTyIiajKb3uiFw2y/xcGVUMSeoqakxhOrUZj0YoiooAaLJEo3mK5VvomTitYZtqr43o2HT39tl5eIdG+5j+oE0ibtDrtpElCTWmoAgBxVycO3feR4qfpddyyKViiDmXRPSAcwvneX7zAiPtDh/98D7nz6wghRLVcox8XqdUWCUhrZLIi6xuDjkdnHJl5QlCXyERy2EPWtx8eIjpRwhuFkggIJDLJ4lkH0V3GQy64AosFVMoMzkmlTieXcebyrjOiGpx9h/l78cOvt+58R9fPbM6x87DXUAmpmoMey3azQblfIbICbhyfpP9Bzvks1m6wyGBIjFxbARkAgtajSGdoYUTKtgu+JaDTMjIHFKqzmBoMdYWzvGV//cbDDp9nn3uErlsHDEWcePmA4xkDsQJciRSzhaZn7nAn//Fm9y7WyeYCGyuXqRc1jmqtbl/r0O3F+J5EVNrSCafACGiPFNBluDgYA/fs9FiKrKsIkQynhNSzJU52mnzZ//fxzROOuSLSe7eOmRuboZkdkA2q3F/a5/trSml4hx9s09/2GEysjFHLovVGYoFhdXLMxixgIWyQSYXUV2UWL6Q4NMvX2JlsUBCA2s6ZP/BMWvzVbIG5NIzuFYKywbNENE1SBoSohQhy0kcxyeZlbA8C1WsMFspMhpbyKLGsG9hJDKUtSxzSobPXXuRca1NNFQ5cWooZeFRBrINmBC6AX37mLWNHAupFJXZZ4lndCS1RlxTaTUNbm6fMjInVHKLjManJFNjLHPMTOkKr397h6PDIwoFj89c2WR+poqRStAeNun0O4iCjCRkqB85RKHOabcPgkBM03HdgKWlFY6P67iez/7BIUEQUCpkCQKf4WiEIErkc3kiIqaWha5pyKJIt9em0+3g+i7xhI41nWJOJjiWjeVMkGWBXCZDq9VElhQEIBZTHiWJHItEQmIyEpDFJHEF8imFF59eYyYrcWX9DG998m2efuoaxXyR1ZUlFDmkUMiiKVnwU9hTn0RCIVfMEAQ+sUgh5isYShzPsikU8lw8t8n5s+cYm2M+uf0xlvNIfl3IVDAHPqtn1kgkEsRUCVVTicVSNFtjSuUZrr30Wz+5pqRc3Hp1/ew85VKRb33zW7iWh+BFqKhMu1MWqmvs1/Yo58vs7Oxx0myRrpSR9Tiu7dFvTOg2pgiSiu24yCLkkgb9Vh/PlxDFFEuLq9z4+Ba9dpNCIcnzzz9DGApsbe/TaLfJFgwUT8EfwVOXn8O1MvzlV9/Hnqbp1X1qO112D26ysHiW23dr9IcWU3eEL0zIZBPYjoOu6HimjRSKPH7xMoaWQAgEVFnHd2V0LYMUZum1pnS7TdbPl1hZXmJ5tcL9ne8zmQy4c+eID94xKc8VSeVlPv3iS7z86V/g8ceukc5Ae3CdsbmNZdXJFmUCuc/m1Vk+8/IlYsEQcTpEjQLqrR7BROT8/DKz6RzjlkPrZITvW8STPvGUSrFi0Ok1Oa21yeXz+OEYczplMoiz9fCIsQlhoCOHCfpHA4Y7Lf7gy79L8+Ye4sDjvtxke/QQR5hQjmdI2hqy43Lh7CxnL6YQxTb93hDTTpDOKijaCNcO+dF7DQ4aQ+KaQn13m7X1gPnFkHsfdandh1JB46lr86ytptlYXiBjpIkEkXqzRUyLs7y8zslxH9eT2dndY2hrRJFAo9UhputIkkqz1SIIIxzHJqHHmUwGAIRhRDZfYDye4AchQRiiKsrfx8O6DIcDnnzicTKZNLMzsywtLDMzM8PqYoVrVy9TLZRonNYxJyaiIKLGVARRxPVccrkyhfwig+4IKZoS2H1+9qULFOIecdlFLQXMzy6Q0JLkciUQBTwvIAhkwlBF0w0yRpF8Kokmg+T46KGC3TUxOxO8scf8SoZItimUC8zOzrC3v0cslsQei3zwzhZ26IIoksllMDJpPry5RSY/Q6pY5Oq1n6LZehJFTEY9lLRKazhGT9o8cf4JZqolqsUcJycHGNkYRDqep4Gq8nC3QXWpgiTK6KkEmYyPklRRhx7jYZfpdIyuqUhKmkbN4q3eXcJgQGFGJpuEMIiI/BQHD0xiUhIhsji+k6ScqtDa8/njP/m/UAQBS7RZvjCDng+YO3uJr7/5LoEvU14rk0gk8YIxjtMnmUww7g+ZK5ZIGrOojksoOJhmH5QYvd6I/miAESvz1AvrXLpWxPMPcScO33pjm1d+9hnMsc3a0hU+/uhveeP1H3H2/OPcufVNYnGTFz59jeXFBf7NH/X5X//wC7THAf/Pn/8d6WySL37pOf74X/4pv/CpJ5DdHM2Uwsd2g4KcpTkIyRoryD6cWVJQ8wJCUcYNffIZm2DskC6lEM0xg9Mas9UCN49ayPEMOWMWIQpYmpmFosNjK6v8x7/9Kna/S7vZJfvSEqlqnosLyxgjEbkMtjMlngtIzciMhBi9Xoav/N1fsbG+DP4JCSnF1JbpdkLG030eO1cEeYn79w9YPbeGkfVIZSGVSvM3f3GHarpE8ewFnE4XP0pQSGfQEmmefrZKdc7DtEyM7nlu3byDkdEoFCscn9ZJJg0KpRLr6+scHh5g2zbj8YTRxGQwnpDLFlBEAdtxcBwXWVJR5BiWZfPnX/kKv/SLX2Dz7FkK+RK6pjPq7rG0MM8Hb3+fVExmHI+hxg2mtkOxWEZLaIyGfVRZpJBRiSsDpGDMS88v89pXv87TVz6NkT2DHk+hKgaIGruHIzzXYWkxQ1wb0jo9IS7E8WITzLCH6EZofhwdg1xcI68ZvP3GD1i5tMHs/FkyiTSJmMGw1cMaB5w/u85yJcf9e3dJKSuQVDiznCXwZfIV7R/l78fC+b/9H1+jWJ5jfeMKUUzis1/4LLoic3vrOq998wHPPHOV9YUKi9UKX/jCZxh4E1774ev0R0MGnTqpMKJayDANB1TmNS5cvoDbt7jxwR2sSZ9Evkh/7JHQArR4kv2DXYLQhxDajT6S4RM4Mkbeo1iKaLZv0h+1ePyFx7hw7Slu33sPQZnSsgZk5krUT1sk8jEss4XnDpibmUOKdEpZHc+HXCGOGrPp9FooyhQnmiKrHm4U0hj0CByNYiGLOeyzd2QxU7jMoK/xl1/9Hutzr/CH/8v/zB/+T/8CQelQP9nnM5/5Ge7f6XP9RwdcffICM4U5/v1f/CnXPvUUuVyB737rLTJiDMEUWT97kbe3PqElQRCzObl1m9ZExHEd2tM2bsxHVXREcwTTIYbgISkSChpnNy/jhw7hssru2OX2xzeQJYjsMZ3WMTfuvUlltsTmi49TMseEQQz3tMd85iwntW2yhSy+L3J7uM2ZZJWJ6/GDjx5w0rcQj+NoQkTv+BiiLKOJBV6cd66b1NpTZhcKrDwRkiuCb+XZ3mpjVBRO+y3m221+9O57nDm3RiKXR1BEUkYJa+eIg9oJgX4RWdNpt7usJQ1Gu/uk01mmpsn+3j6iIOC45iMJ1+w83f4QwzCY2jaiMOX4+JSUkSZppHHsMbY15cMPP6SYzzE/N086baBEeT7++AatdotGq0E8niH6+zOnKAkcHR2hKB5RIY+iTnGtE1aXUoRSlxdfeZyRV0cPlrBNE0eKkPU4f/bV11lcWiKUVGRlxMnBPmbfpLJRQk4pJOQUehSRkkSClE88rXBt/hrvvn+Xo12XlTNnictJCgsGtaMDqiUFf9Tm8to8mujQPd0nHktgZFJ4busf5e/Hbmtf/+53X33/R7eplGdZXpzn777xNeKqxfJ8hueeOcdsNYnltliZLzMedXm4u0V1uYIb2eiaiCq5hAypLpZR4jFcz6fT7JBIxCktlpC0iLmlAuVSnpgcZ9gZUSxkGZs9ypUCt+5uI5IjvzwllXaJ6RbPvXgZJWnw0e0HxPQM1tRnv3EHy/PYOPcYmppCkRV6jyZN2gAAIABJREFU7Q5n184yGTr81d+8znajgai6aPGQZFpBi8u4BPRNC9Pzcd1Hw9HJZAJJhnFfJwrSrK2tMBo5/NVf/YDayZjf+d0XsPxtjFiRd7/XZveBg2U5hNohpRTc2j3g0rWLLC6UWUzFmI0FzFYSZFbzPOzVEVJFtg8esLV/wifbNfY6TSrrZRLZFFt3T5HDgPlqnnJ+kcOdIa3mGDUhk8rmuf5RjY7jYWSTXLq8Sat5gKR6XHv+Mbp+j/kLi6jFOPufnDJpjri4ch439Hgw2qOnmpxGXTaeeIzXvvU28cQcixvr/PD96wx7LufWS8gxGU8S6A4ijo6mDMZtls6GrF+MoSo5pv0Z7t4+5vyVJIVilYnlcNJucnf3Dtc/+QDL83D9JI5VoNOWOWxFdPtDdD3BxDSJJw3qJ6cYSYN2u42qqownQxRFZWZ2jr39AwwjxXA0xLJswgDiWoJMOsXq6grplEGr1eAXv/iLLC4uIksiYRQysTyUmEEQyYiSipHOYqQNBv0+CBGjUYszK6vMlTPEYzaj8Sm/9usvEopTEEHwFWzbpT+YMHFl9g7H1NsT0nmNbCEil4+xtFRFKySxBIndwy5HRwOy+TJ6NsY47KEpaYbdkDe/9wH3t/boD3rMz1fpNE9p1xtkUxlarQZ6XODc+WWiKKR2eIrv+ly59l/+5GfOv/nKH7/63/3BP+Mzz1/j515+it/60s+wWIiT0SIyCnz7b/+SbFUgE1eYTkeMrQEPDx9y/vJZ9JiIIj7aQjb6A8SYwcSMOD3qUK1UkTSBUAswUiGZVJ6EUqFx1AXGjKenJA2V4VDim1+7zb3jFtcef4wgkLl96wGCluaDm3cYjvtcu3YZNSMR07L0uxHf+voP2b57xJnl82QTRXqtCVu7B+gzVbKFLKbZJ50x8Hn0jutFMbL5OWJqkslYJG1UadTbFLNnef+HN1lYMsjm47TbA95774T9/bv80i/8PN5UZXe7hZFKMpjWeOypOTKJAnIyy8iEZq2N2Bjw6fPnGVsj7HySPiIHdxuMrTqVlTmmUZLTXptAmJJQM9TumQSBwMLaIr0BvP7dLZwoyW79mNffvc/QMTgc94nF4ygxlYQq8+VfeIV6bZ+rj10gmdDpt9p8fGePMAi4sLnBaz/4OvveHkN1iGiImO0xCdFgEvkc1neYXchRLemcXa9QmUvgyQ6ibPLss/N85mcWuHx5lqKxylz+MXrNU5L6hLThIuolBEUjUHziWZlM0cAOYDTSuXPTotOOc/+gTvj32srhYEQiHieu60zGYzrNFq7rYKSTKIpKPp+nXm/gOC4Rj0LWU9NiajoEgUe73aDba6HGJC5dPM+5jXWSRoJEtoDlQ6c/ZnX9HH4k0B8MmIzHBIHH1ByT0OKIgUSn3iCX1VlenkdRfd5+9x1+9pd+Fb/fxw9DBFmlO44YmjG6A4vVMxXSOYdkHOKRTCRqSGqVwz2bH/3gAeOhSzyhkC3r6IqGb0u02hOKxVka9QahH/DZF38GKdKZ2ziLHQnICYWRPWFt4wJ/+9qbpDJVnvrUTwHnk5upV+ereVJJgQd3PiaY9pjLpaimktTu3+P5T10ls6DimiaaphFJERPf4uj0kGG/jSz6KKpMqzdm6kj0Wi6JWAZJFumbbRKZBCEDCBRuvX/Ex+9voWoj9ITPrbs3yaQ3mC1dYfnSNfa2Tuk2LHxXxo4iijN5rjy5wnh8yNb+MbJiYA5leg0PTcpgjSzK+TL1WovljYtkz6zi+RZaTMAwdCZjk6kTYHkyyWwVTUnhTBTisQJ/+Rd3wBd57lPP4EUnzM3nIBJ4+NAnsGRuXt/ht//Jb7O7/xGC0qM0G+OlVz6H4pX52mvvsbvbRbI1els1zlcWmV1d473DQ967tc3ksE91MUmt22TsxBBkkUJOx+rZNB86JHNZ8nMF7m2dsndg4RHHlQKqq/OYfgYvEcPybAaDARfPrNA+3GfSaiF4Hg9u3yOaeri5PLIgYE8HxPIC6dUEak5Gjyu4LZNRfQhZHSUORlrj0uYalYJOrqRSWayyuhEnld6lWPAJTI2UcBVN0Kk3Xmdu3qVkzIFeAVVhYveozGYolHK0O0NcO8dpTeHB1pCRa6NrOtOJiee6DAcjZElkd3uHREJnaplERNi2zdSykVWVQX+IZT8qyFUVjXQqy9SaoOsxstkUrmvx2OVLnDt7FsdxkHQD0wlIpfPYTkAQhiwuLOD5LjsPHqCqEp1mn5ee/yydVpvf+93fYmNjjVRG5ez5c+zu1VgtpvCFiFjc4MbdI27ePWH/4BQ96XN2M0tM8JAmEa2+jZ5cYDLQ2X/QRpM0UmmFylyKTDJOOpnHnATU6y3KpSrJpIHvikhCkn/1x1+hP+mwdn6NXLWIZmS4fuM+8wsbXL36U9zWDmvfffXWze+we/B9Wt0t7j64z7/6o//At9+7zotfvkZXaNBsddlrtQkSMZrjPrG4xpnVZVaXFhGmDnnVIJp45CSdWS1JKgww+230lIagyWgCOKbJ3OIMetqgMHOR+eVn+eCjIxrdOhNvny9+7hLZ5IiY3qdUidhcn6G+X+ff/7sPadVkWl2bxq7JoDbm0sYKF64VeOzlMo7WRU4JKDGfXu8u2UQEnkmv08WaukxMD1lJIAkCSVFn1O7x/vfe42effZwziyqa2qB2WCN0VQRRQ5NDDndPkKU8X3/ta/zef/Nl9o5usnnlUWHvM1dn6Q4GPPXUZ3nlC5/ns19+nteu/zmLl5eQhTTf/9ZHNEc+R60mhiHw5LkK54pFNFuFSKF6tszt+/uEkcLQGxElIipLZdZWLiGMVaYnJ+zWdviv/ukvcmFzjfdv3MCP6Thaklp3yHF3gJxI8/ObZ5CtAYrgcencOURLpBwr0qn1WFhbZRyaXN1cY2MuRSkBycglsiLK+UVySQPH7RIIIdZEZtT38Nweg/ExcaPI3PyTIFdIyWkSooIURYwHHs26xezsC3zta9vEk5fZr1tYrklvNCSmxyhXKnR7HcqlElEUocW1R/pKMSISRRzPw/cj/CAgCALS6QyFQoZsLkGlWuLo+JhcvkQuX6R2eMTa4gK5ZBzbDUgm4iR1haQeMVNKcefmhxRzaXK5PEQCoiZgJFv8/M+tcHpwC9+y+cp/eoMffrDDcWtIOKyxtHCJ0Dcw4jPghfzqlz9H1hCxTYtsah5dW+D69UO+8/p1VF1g42IRSeuTSklUMjOIskaz2Sajx0nGIsRgRKliYAseO40O9/cV2l2Nt9+4i9OJaO22SalxPv7gI3751/7HnxzO+sE3XlXj0B816PR7DEdTZhcLfOEXP83U7eJGHlv3T+mPLM6sb9Jq98nnCtgTk8jzSEg6mKBICWJyEjVSkd2QuXKRhBHDnI7J5FLMzi4BKnfv7vLscy/xnTfeeBQNijwef+Iyfjjh+++8RSabQ1F0BgOHt9++zRe//AJeMELP+gjChKsX19g8N0soDfAEE89z6NQ77DzcJV/Jk9QTRKFATI3jOAG+D8lEmkKuRDCZILgW+bRGvbFF3HAxUjGUWIqj4zal0iKRO8YaOczPr6LGNG7d+4jf/2//gDff/R6mPeJnPvtL5CpL/MKv/hqmP8DzW6wuF9GUOLXDCX/7zXcww4AnX7jI5uMrCIJNt9Hi/t17qDp0Rw0uX15nc3OTTKZC4OnUa12uv/sBou9xZmUONT5hdbnKrRvv0x/us3GugqR4qLpGvrRAq2vy3de+R2V+lkIlR6GSZevBbXb3d+n0hzx+9VO0m2N0KcFMeYaMkSeKFO7cOSCbnyEUoDka87DWw/J0QuIc1E74xrdvMLV9FpbOosaqBP4se/tT3nl3h4mZo9NO8PHHA3T9DN978zaJZA7bfSTRIoq4cP48k/EIIxEnCkOm00e7rVCICIKQMIzI5fJMJhOiCHzfZzgY0W53cD2fbDZHs9nCsafIYoSRUDi7sULcyDCd2hA+Mis2myecWV2k12tQqz0k9EbU7t/m93//N9g/fMhgbHNQaxG4SSIrhmzHUMQu5y9cBVHltNVh+2CHw+M9xpMJiqJjTcG0IrZ2Tmj2x3g4JNMyRFOm4y6WOWJijzBNmyjQKObnOKm3yZcqZIolxvaUm7d9RgOXVDxNStNIxjSGnQG6muSVX/6Hje8/Fs5kfOvV/rDD4XGNuYUlMoU8hVKBiT1A0UVufHKTZz/9iywsnuE7r7+JObFJaDoba2sUclnqx03ubR1y4842O/s1FuaXWVlaQo8rHDYPsBwTJJF0Js+DrX2ajR5379wjnU7TH7Q5s75CGPq8e/0tXnjhJTxXQFVS3PzkHp967iqi6nDmXJV8NcPSbJHD3XskkzLFaonaaR3PFei1xoiRRK6Y5/SkQS5fRFF0QCYIIizTpt1qM6g38U2TtdU5llfKIASUqov0+rCz02Dz3DN8+Pa3ePrpT3N01GVr+xAppnPz9kMuXDqL7Zo8de0VZtbOcNo/xWPCZFxDDGz2d055/Y0PUfUMmWqKZElBSth4/phOs4NjWXj+iMpcmmKpgOsIvPvOLbrNKaqsMxkNGHZPyeYT5LNJRv06+7s7/Nznr6AbNqISECJzd+uEft9m/ewai2ur7B/t4EdT+sMOjutiTnyOjzpUy8sQPKpol6Q49fqQT27usV9rI8Z0hr7MuzcOsD0VPZEnkcpy2uhQnVsmX1hmNEnwF1/5Ef/m3/45N2+1eOv7e2zvmHz0UY1my0FSY3Q6bfzAQdM0RAECz8OcjPE9F13TSKUMTk9Psb1Hwi7TnOK5Hp73aARP0zTSqQyJeArTtOj2+gShTxT6xFSBwLM5u36GVLaA5/m8++67TKcTOq06k3GLcjFF6PeRmPDM5iqOP8CVHPbrR4ydMbY1onOwR0qA006b3/gvfpfT0wb/5x//EaZnsnpmDc+D6x/cRoulyFfmCJUED/f3SecTrJ9dIJfVKGTipOM6mUKK8cSmcWKix/No8RQjy8QJPcypRWvgE2Ii0GM6POCpa2uo8pRnrp3nzGO/9pPD+dpr//xVRY2h6Rn2DupcufoEdmBRmSnz9W9+A9sLOGmMMJIpAtdnfm4ex54iiiH1xjHvfXSDUSBx5solMjMztEcjZEXGyCZIl1KohopleezvHBJ4AoqkkcvkWFpc4OGD+3zqmaeYjMc899IzjAYWH16/RS47S6E4w9HJIaFsM7Y77O23CH2fF599Fk3R2D2qs3PQYf3MFdJGkW6vy3HjgBCBubkFJFmlUW9imRbZbAZZEDBbJsVsiVw+RaFSZHvnhLff2eLhVpcL558icBWeOL/Cn/2nv+L8pat0Rx6Nzoip5fJzP/cKf/XX3+JLv/wlBE3GEccMzSayZOObU2KyQXfggqKQyIukqypaNiRhqMQ1hXRK58Jj57j0+Hm++Y27jHohOw9OCTyBVEJjMm6QyWmc31hnaXaOyahDdTZFviRSqKjkihlu3LyPkZohly+hKBK2ZxGLCTz73DWymTR6IkkimUGPp0mn8iRUnY8+vkEQSIxNOG3a3N7qcdKwiRkZCrObDAcBb3znPRLxPKsra+Rzi7zzwy2+9+YWt++eUquPkOQ8opJh0PfxQ4WpazMxeyRTCp73yMAeUxWEKCKfy1I7PCSmqPR63UdpEsfi2Wef5eTkhKlpI0kysiwjywq2ZTMcPmqzVhQVVVXJpVMkNA3XmvLk41fIZrO0Ww3MqYUkSXz9639NIRdn1G8g+gOefGyNpCLw/o0f8dwrz/Ly55/mV37zFT7z8jl+7uXzvPjMOne3D7Adi5t375DMpfj1f/IbBCF4LhCqhEjkSjlu3LxDPJmkMptHUTx8d0Ipl2W+Ok+908W0RO4/OOXO3W0EWSKZTSDKAo7rctwa0eu3UGWHM2sVDg/ucH5jiULOYPnCTwHnN7756quymqfeDLn02HN0B332j3e5//ABnq/y8mc+z+bZZd59+y1Wl+YgdNE0GTuw6U16dC2TqSzSNAeMA5dcMU8xl8a1xtx/cJtOt4noq0SegBhKrC2ssrq0wmQ4YG97m+ODA9ZXVqg32tQOm8xWFohpBtu7R+zUjhESBgtnLiCJWTQ9yVtvvUkgwGF9RLqyRqM9RlIkLl9eI65LlMplTMvloHZMPBHHsad0GyfocoQfKDQ6XTwxYurLFMoX+frf3OT3fvcPGI9GrK1UuHfzBjOrOWxlwHbtLpl8hupMjsbJHr/z659jZWGGO/evIygWk0kHszfENUO+88YPeHBQ5/NfepKEOiSWdol0m1CI0GIpEskcY9tja2+fpfksTz/+FIvVVXYe3KTe2OKLv/Iprj11kbe+/wG3PnxItVIllUpz9twKguyRNAy+//3brK3OEwYWn3x0nSefOMf19z5g0Bljmx4nx21m52eIpxVMq4s/GSPHZJrdPju1Iadtge4gZDRxuXljl1sfPqRbs8hpS2T0FRqnHn/37Q85rbs02gG10y6SGmdsTh8tuOkEYeQhhD6OOSR0bWQtiSiKtJoN4ppGt9MmCgNkSWJlZRnHdnj62Wfo9/ukjDSCIJLN5piZmUEUJWIxhVQ6gaLK+IGPpmqIokSn1SafyfHeOz/CnHQolQssLZ+h0x1x6+YnHG7fw7eGfObZx9hcm6PnOHz1b99me38Xc9rku298ldHwLqtrWRJpiaX1S3h4NPtdjpttFD3NRx/dIpPNcXZjA10TGXVrzM1UiCdUdne3GAzbLM7OogpxzAFIapE7908wMgWW1xe5/OQGhUqSdDrNcGCzs+UiY5BQc8xUV5kMBNqdiJs3Tvml3/yHvbU/Fk5Vu/vq9sM63WbEW9//kPWza9zfucvc3CI//4Uvc3hYZ3/7R5xZXWJleYHZShHEiFrjCE8MiKcSGEaKcrmEEIUUUxmEscnhnbs8efkCm2tn6HdHZIwsgR0w6g9ZXlhADEPM0QjPsrEmE/YOTnCmDoqsEkSwevY8aHE+/bkvMvVVup0mtdoBL77wFAgBaAl6po0dmUiqhee1MbQYjWaT3f19HNclGddRxYiZUo7NjVVShTzNYRs9q6Hoad577wGOLXB8tMPuzsfMzaksLszx+PPrGJWA5bMVJClgMDhBk6b86pde5rWvfRVRcvnkzkc06nUmfYvWSQ9FiVjYSLK7/wmVtERx3sBRXIamxXgYYE0jBElDTaYZ90d4k4hOo48e9zh3rsT6uSIPHj7k3r0GcaVItlDA9X3ub98ljHwGgz6f/czzNE9PcJ0Rv/nlzyJGPnIYQwo03nvvOq1mn+pcDiccYqRlnOGIVC6FoiU4bdv0hzKjkcxoFLC5fpYnNy9gyDlGLagfjtnfbeL5ElMbascthkML23LwAw9ZCJlMeni2gxCJRJ6MrmZQDe1R65coIIsSWkx9JNyybUqlIp7rgiTw4MEDiqUStuVi2zaxmPb37dUirmcyHg3RdR1N13EtD0mQEAKR8WDCw+2POb95lt2DGjHNIJfNs3f/NiI+yzNZVDHgf//T/4AeLzBTWmWuPMO51U227txj616N2ZkrCMoUN/BpdboosTS2IxBGIrZtUjvaJh6LKBoioe9TqpSpzBYwEjGKuSJZo0L7dMLrb37ExI4IxYBGu8b+8R0iwWY4GGOOQhw/IJtVQZgQ00MKlTR6XCYQXT7/xf/6J4fz5u0/fVUgQ6m4wXA45YmnH6Myk6dUmWE0dIjrCfJZH0WWkCKwplOmjsXUt8hVCxTzeXRZZXZuFvwQfzhFdTzKiQSDVoODnV2yxQqiqNDpdEjEDb7z7e9QOzpC13SefuppRAnWNy6wu7ePkUgyHI349hvfI54qkqsu0umbjEen9PttZNEinU6gZ7K4okxMF5EUC0WcklR1huMxw4nHdOpSreRJaAoLMyWEwKM57HPSOqXRaTG/tMEnNw64sHGZvZ1bPH5lldUzGfLFCie9LfLzKnEjhmf7bJxd4V/+8/+Bdn2bXDLNDz54m5mFWVQ1xt7DGsmYQSaXZHalQLWaxJBCRANcKUBQ4qTiJRqnQ7b3avRHEyLb5eGtHXLpArbTo3a6S76kMhhM8d00MSVHu91m/2CPlz/7KXIFgyBwmUxGHB7scenCeSJ3yg/efJszy5sc7p5SKc/y0sufJl81iGSTidljcNrhtNOiVFnAj1J4fprRCJqNHgoBw0aD+7f30OUivqvh+SKBKHJSb5AtFHEdEVVREfCIIh9zPIYQZFFHCDU0xcCTHcIwhChEFiVGwwGiAFEYousavu/T7nZotdpYlsVoOH70UUoyYRjieTaiFGI7DmEIluXg2R6u5TDsD9BUDT3u0x/1uHXnAe+8/T6XL19m5/5tiBw6p7scPLxLlJ+h3XJI62Um/RHdVo+ZyhzTqcLu7oRQqFFvNRmbNvXGGN+XmJmZYXf3PmEwJZ/SiIUWuUIJUVExp2PiCY0oFDitdQhclcPTIdlCicpcDt0Qsdwu7c4JipTE0Mu0BjtYzglTr4FuuMgxB0VzGIxO+KUv/vc/OZyHne++OhgoXLn0NFevLhM5EZXUCySldZJxCS3ZwzDyrOsLLMoVIhM61gQpG0NSHUqZCEObILlDZNtCE2IMhw6n3QGCniBfnSc9I2AUEvhRjO7QRtJF1KTA6sVZjrt7nHZPsFMWkWBjpDVS8QLNUxMvEpg7m4F0g6PxIamERL7nkGyMkVybWFYmW8qi+irhUMR3IkQBCjmVUl7FMweIjkvvuE8pNcdbb+3hmhVuvefy1tf3OX/mPO3eLX7nn73MzGqcSdCmOdyBsMS//hevIUwLNGs1Zktp5ufjBOKUD/dqtEZDnn7+BTRd5/zlM1SXM2RLMooUIEsKhflljrttjtsdTtoTOgORTl9gMpKQwxyV/Bp6NsEgrLF+pcr5x1col4qsz88hOR0SsQ2Ojm+Sydu89Nk1Qj+iVRP4xl9/wOa5NRQ5wO8NiKsSyUTI8y9dZv38PLGkyr/7v7/CdJrm4ZbHyWmZN97a4fCky8xsBiEYUdA1NqpnGNRthv2I6uwZIlWl75ok81lO6g30WBx3aoEXIYQCMTkOkYyqJBAkleF0RCh5eJJFziiQS+eJgoh2o025UGQ8mRAEEaKk0BuMIJJwbA9ZVBgOhgS+jzWdIgoCnuvT64xJxtOEQYjvOUSCh49LIPmEKnhiko9vPaRYKiESsLezjRdMcUKLvl/nmc8/gSGMsCYmhdIMclJG1EQKVQPf8WjXO3hTFXyNdLpAdS6NnvbxhBGZQpLKwjyRqBL6MRJGCdeJkKII15oQ+gGZdBFR0jkdBxx27nPuvMTyvETBMAjdFFNHxY35tPYGpONl4okiTqAxdlU+2Tpm5cxVXvnMb/+DcP7YIqM/+cavRNakj4bK4YMOV85fo1bbIp5SH0WalBiKIhCPdEJf4uHhAUNcFpZmKOgKnjUiU56l3bSwxzHGvZDTk0M+9cIZZhZ8xmaNZDfN7OwFXv3Xf0JXiNh4bhMbk+7glI2NDSzTIeEnccwOiZiCNZlij/n/OXuTXluy80zvWbFiRR+x+9Ofc9u82TA7MpkpMiWTkqpULstVkgXbgAFDVTCMmvg3eKCBDQ888NDwVIDLsKukkg2XZZGURIlkUkyS2Tc389687Wn3Prvf0ceK5cFJa0YC4mDP9yDe+OJr3uelNhYvf+MV3nrvh1jZdbLJlCBd00sk1p7Cu9UjLdd4teb2aICRJbYdsFq1rJYVbaMoFg0OLqZuCZOI5Srj9MkSrQXP3Nkn6Qvee//v+Pa3v83FxYR7D6Z8/dVv8ef//s9585sv0OtlDPou2zs+08sJk6XHyfkZw51dgk6M7bn4gU1ZbRCmJnQTHLZ4Mv6cZTtjWVQs5prNssa3fVzbIkhato4ivMDguVd0fLuWqNbHbSP++jtjvvHrr2LMktPzz/jr777Hs9ef59q1fbZ2a07PP2O/c8Szzz2H48f86O/e4//93o957oWXmS5brt94iaZp+cu//H/Y29qmTFNef+nryMJlfprz5N4UL4oRnmC+2nAxWyG9iMU6Y5NeGaElMJ9vsIRkvlwQRhFZkSIsi7quqVuN7/v4XsxgMKCpKpqmIgw8dN0QBR6r1ZqiKOj0en9PLgDo9/torRmPx9R1TRCEFEVBWRZfAuANbauR0kIpG1AI0xCFCiUFw96AMICkI/ni4U/57/77/waRfc7DhwXvfXDCfLXh8Pouxmz4vX/2u9z95D7DowbHVZRlxf0HXxD3BkymK1rjYFku6/mMoYIXX3iN9Srn7PyYvFjT6w0Y9a/RtiF/89H7uF3Dq1/ZxzYNp4/G7B88z9GtZ/jg8/eYXz6lriq2tg7pDvZ4593PeXx/zPRszV/9zdN/eJDRn37/j//ICRtmi3N+89f/ABqL/k6OF6Y0taHOEt5592022vDjjz8m2dsl6PR4/NlDzKxEEVOakPWq5r33PmC2uOCFl25w/fYO48tz3v/wE+TG52yS8lfv3CO8tYu3l+APIm49d4N0scFTPm//8DFCtvRGfRq94Y1fe42jgwMe3PuMxA2oLzRdzyEMJAfPH5Dc6HO+PqfKlkS2xBea3ihkMlnw6PGY9aambQWrzYanp49IBgEvPn8Di4rNesydO3uEUUUcCW7fuMP5ySXjsw0qdlAODEceSadiNGrZP4xRoqbf6fHJR09J4i4np+co5RJ3utiOi+d55HnB9PKc1kzxk5KtXZe9vT4WLTQl56ePSWKLvdsDhA9VUwCS3d0j3v/gCz7/7II0s2maDn/+F9/l//iTtwncmBsHr7KaWcwnK775jdd4/OiE19+4yul879OP+N7f/pxX33gNv9tjucn5wVtv8ende7hqhze//luoJiY9LbA2FhcPT/GlxdbOiE1VsVqt8cOY2WLN5svBj6cchGkJgxDHU8xmU+I4wnNd8jzH9VyMBtdxmU2nhEFAGPqEvkuWZuTphvl8SlNVzKZTkm6XoiiI45ibN29Gdh7tAAAgAElEQVSyWq3Isoy6rkmShCzLvoRF22AMcRLhOIqtrRFSSuqqZXu0hWVpyiJne2sHKQz9fgdb5dy4fkjijrlx7YiXXnyBXifCFpogUJTlhu2dLrPqIYv1jFZo6kajjaRubD765AGTecpqlRG6IX4wZJM1HB7tc3i0SxDatLpks1nS3etz994Dnjw4xRYR3c4hQdDl088+xfEsUBl+5OL6iixb0osDfAVnx/f5w3/53/7DP2v/x//5f/ijne0Rt4+eQ6cJ69WauJMxn13QFAnf/96nfOPNF/nxhx/wzGuv0BkOWU0X+I3g9tY+67Ri3WjiRPHCq7u88NUBlbnk5HzM337/My4vXJLdPpknWDuGnWcOuf38AbZVY+VrwloQ1JJ11eeTz9/n5OIpr7/5KkZAHCd0gojNdEqxeorj5vhDw1rMyMSGi/MT9vsjXC0Zxn0Wq4zVukQpjxbNcn3JJjvjq69dY7RjURULXMfQG4Y4fsPBtS6O2+J5HkIIxuMpa+tTRtse/8G3vsq//ZN/Q5y0DLeGbG0d8v2/foeLU41yOnz4ySNsO8b1B4TRkLqS+H4X1wnJijkGCIIB04uGn/7wIXc/HFNuFAfbz7F1eIOsAlFHmDohX7s41j4//uEDblz/NeLwGg/uX5BuKp699evcvv4NqDvc/eQhn3/2hNnU0OmEeMEBH90dczFt6A7u4Do7/O3fvMf+8DaePaBr3+bR+09Quc2t/j71fE6oNM89v09WF9y9/5SL8wsmlzOk8hiNtmmbq4qYpylKSZIkZnxxwcHBHlJKprM5BwcHLGYLTHsFtJpNpxR5Rq/bpd/t4CjJ3s4uabqmaRqwJEqpv0+hns/neJ5HGIZ4nkcQhERRiOu6bG1voXXDZrPGdR2effYOSoVMxmOMqaBtkZbk2uEhRZGRZTMW8wk3D2ocW9JLEs7PzzFty2q55MnpA248s0vrlYRhxGq5pq4E0gS0rYvBw4+6aC3x1JDHT6Z88MEnVHVGlEikXTIaekhZMFnPGQ538O0u82mK5waYtqSTuFRlihs5ZGlG6PsU6YLjh59xdBjxxhu3ePmVf/kP93Pe+/Axf/BPf5/8Ev7k3/07/uD3vsliXuB7u3z4s1OUFSGwsZREi4b7n3/EM4Ndtg52qdKUUmf4gUPDnKLR9DpdJvcu+Pj9KbPLDnUe8ZeffsTR7QPsrsfJ8WOeuTlgx404Pz1H5JK//N4P+PEjw7/4V/8YL4TGCWiDgHVREYUBi9UlL33jkCAMuPv4C4xs6XcTZqfAuqQ/2CUMd/jw5++xTOfsH444POhgyzUHu11Cb0OVFqwziW4USsZXZ2QiZTjsXkGLvZbhjs8/+93f4eR4w8Xkkp29Q54cGxAznvRdfvr+jOdu/TYnZ1N+41v/JWme893vvI8bPMbzFNcO98g2a4Qc4Pk259+fUKQudf46IycAI1icxnxGwarSLKYL2srQ1qAriza9yZ/9mw9ZXa4wDdT1iLd+8IgffPcLkihgMSt4/LTGloKf/+SnbIqK1vYwdsynHz7FVZdQ7TPdRPTjDr5l8+rtF9ntBFjphDi06O9eZ9MWbNIlyrLQWpNlJXkN6012xYNtGtq2RdmCy/EpQejSSSIc18eyJFXdYts2juNR1RnKkVjA6fETRqMhN64dcbi/R+grPr93j+lqze7uLuPxVYhuWZYA5HlOGIZsbW2z2awZj8fs7GwTBAFgkNLi6dOnRMEWg8GIPJ9iO1c70jTNyLIU3drMZxlJfJvL2Zgib/Fcm7oyBEHM9s0hlUyJogjX9gm9hNUk59OPH2Esj+1Rj1IIOp2Q04dzvvj8Md0wIKtq1mlGFPm0pkKpBlNlNJXNsLvDVncbZVdIuebi7JwwjEnnhl4yoFhv2BsOePtv3uPZ2z2UV/9C/f1ScVLk/N2ff8zPfnyXILhga+s3OJ8Yfv7zTzHNiBdevkGZzznY32N+fsLAD/DKFNFUCAd2d7rUEiwZXIXmfprxkx9uqMoQ2w1I8wzP9SkrQd/pkdQef/HH/xdStHz99dcYLyp2br7Mrx9WDLb6pPWaeycXuMLj2YPrCGpef/M13rv7MUPhoN2YwJFk4zUjOyIWNrPxBXkGcXzA0c2bjLYVrt/QTSzaomJ2OuPG/vNkCSw3FbNJRZFrptM1xnh4nkZYFs+/fIvJ8Yp0NuStn11w8+Z/xqNHD9DtNU4uKm48/8958qRmsY55///8EEs5WPYO9+4dE/ke9z/6Amm5FLVHVa0RaNJVSjf2UMqm1gX1/WMKlqTZkirNCZSHhUWW10gnoqhb8mVKFMZYls2qLBCyoTUFNRLLdGkam7ZIvowEiCmqBmkcLG1h64KuHeCXNV+50eHFgwidThFygTMKmKUFj8dLLs5T1usNnuOy5SdYbsQqzZnNpuRpSrcTM59Nrgh6ZUGarvE8nzRNkZZDr9On0+lQNzmTyZit0ZCLizOEMUwuLkhCH1pDN+ky35RsNhuSJOHi4uLqpK9tGQwGFEXBxcUFrusShiFgEMKi0+kgpeDy8pKqWFCXGXESoOsMBDRNSxDE7B3s8OknP+GDjx9y49oWjmuTpkvOLxbMM83WMzcZr57QtRy05aFwCF3B4U7MZLXGDTYgakxliHfgjd3nmJ8tSPOMshwgTEiVW4gmYLcbUNQ+plUoJWlZ0R+0BI5HmbXsHx4ibZv7d895PL3kH3/7ZQLHpaqaX02cNw+3+ODnnzGIdvi9P3iNDz/4IcLqMOxe50//9K/YOXzE117bpZGaG9cO8RqNv7liBDlxyMPJMWmluXP7q9z9cMK7PzvBVtfpJIpKTxHqksA6YPEo5Zk7t8gLeHHvZSaLCfONw1lh+IN/8V/xzlvf5dHjJ2ztj/jxW+/yW9/+HT74+AHPHfQ4/uIdou3nOblYYTtdZk9PGSmbZ/ZuslrMQdRs0pRHT6ZcTGtu33mDOBa0Rc7pccrZwwavsMmdnMYSTOeCw/0X8XyXk/PP+eabryBEy/37X/DhewvufXbKxYniO/rnFE2GVCdk9RxtKkJ7C4SFZbsIUWNMiiTk0d0zksBHtyvsKAJymnqNoOVyekJrapTbgJ1j6T2q1MMRCa2taA3oSmL5iroocSyfKmtwXQuUIc/nlLVBCB9dK4xxkWaKJR20LhBOi2VpXCU5HCjeeG6L7dDnZtejXn1BEtu0juD9T+5xvJA8WSqspMvOlkMQhKzzirPZCs9z8HwPJS3+0W9+i7jj8W//9E/p9jtsNit2dvdIohDXDVjlBXtH+4xPn3B4sI8tJb7r0e12WMyn/PCHP+K5559lvpiRZ9lVpJ8QPHP7NpeXl6RpygbDcDCiqmtWqxWDQR/Pc6nqgqqqWCzm9Hpd4mDAvc8njLa3qK0W1/PwfY/lasp6CUcHN9nedji6fsT773zAz3/+iEordq/f4MnZMXt3XOJAIeqWji+J+iG7fZ95tuH++WP82GW2rrn3cEI36tDd6mFyl9W65eRpjovF9PySZBCxczC6yvAUNlgrijwlDn2ubd/iz/7633Pt+nXqqqRct6h+h+l4A86vmDL20WlI4C7Zv9lw7c4r/OC7hsXinOU64+DokH/6e7/JR2fv8fh8wu6t22S1ZrlqEIXGWTbMRZ95bSg/njI7HiPZ4Ecul+spdVrjii3Gp1ckuOMHX/DVF2/x1ocnTKqGNCs4evY67336A976y/s06oKHdz+jJy1GaNa1zdP3H3G4u89wF8qBx3S+whvGHD85wzguw6BDz3EJKptjOSFbl5weX3J4bcCm0Cx0Tvf2AB0bykWF6wTsXhuwyBrOnuTkm23e+p/eZXxxgZI2KryOa2/j9dc8efQYo1swBlc5uNJjtVjhuy5KXTFUTauvAFOqpakWSClxao0xYGoLZXtI41LXDUJLjOEKuCyvYu9qc3X+1lJQ5AWOEDRCYkkLbUAXDbaVYLWAbrGtHMsqqes+rmVD0zCMPZ7Z69J1JS8ME7ZswZ7rses1WP3rjJcZf/XO+ywKj9qV2N4VPzZxA7Z9B78b0OYLPs7XiNAmUDZyM8ZSfZ6/fYN5XSAdl4vLY565ts/jp8cgNfN6hkWNJS2uXz/ii/ufYisLy5Yo36U/GpF99BGR76HLgs5wgMRgmprt4QAA11GEUYQxLev1GiktlsslnU5Cp9NBCEFeXBBE0BpIC01Rz9jd6zPwOngOJHHM3h7MpnPOxykPnszx44TntgN2bo/YmKcE1g0uZ0+RYUUyUAi9wROafjCgEj6t7SFSn4/uPsR1Vtw4uM4Ld55nPT1FOiuefyVgdpqj1AbVbXn7nU8InIiv3EmwOjZ+5ya58jhLlwyTff72rY9Ihj0Cp6Bts19NnIPDhq+/8hU6XsW//r//d978+psc/+SnyG2Lb/2jN1hX54RKcXt7Cz1d01UBKuwyq5aM1ysebTasKkO7yfGMRAUemWiwYxfLlVBrtrtbPLn3CaMbQ5TlIXGp84zlJOXat27xr/+3P0Zi8a/+6/+Epp0zX53S5gVFPsd3DdoqmFs9HpycU7cCrSXu1iEXeY1xbaZZzdMvnuKpIbee3SErM8bTC1bZEi/0aesA1+tCeJ3NyvD46Zy7955wfLzAlj4CgatG1HWFTjPm2RR0i++6V3zUqgHTIqUkjCLqqiIJY2hbLAs2rcb3FYYWx3HQzZXnMHQ8hJC0lEgkbdsSBD6gadsGKSUAVV1Rt1cx6wIBrYWwJUVREHg+UlyR5mxpURQ5SkqCXogShoHnc30U8exWl8Sx6FgFkScZbic0dc34bMIn9x+yXMyQbkQchfQjn/kqRRuNaxpGcZf9/oC5cHlyOaPVhtl0SWB73N7Z58H4nCDpUFgZzx4ccXLvMd3Qg7SibBqEEEwmE0ajEUmSsL29zUcffcR0OuXw8Ii6aplMJgghGAwGKKUwxtC2LZfTGdJxCYKr4VBZlRRFARjAUBQ5SRRwuH8AlkVdVWw2cy4nY7aGMWWZ4w48Hj6coBzNbDHltW9s8fKrXycc9FjqKbZwcDqK/DTn8uQpu6MXaTFYQiKMpM5blrM5QSR45vl9ijJnkc350ds/w5QN2XzCG197gds3QrBXrGaC00eSs5Mlm5XkjTe7/PCd73D2eIUrbAgkv/sfv0mWTrGTiMU8/YX6+6XT2tP0O390+1YPy6yxZYvnuzz38rOM9hIa+5KWFVHbpU1LxKamXZeEwgWpyF3Fsm6pNprteEBs+ziWJO7F2I7FtYNdBt0IKofAsug6EldafPzZAxbrkuWmxhYO07MJuyOXfr9BsCbyLebnM3aH23ztlTvcvfczTqqMylyNwMtasLd3jbxoKHWDpRSD0ZCbN6/jJwItMoxlyAtBnofY8jpPHmnuflDy+d01n38+Y72WtG1AmjaYGmgtLGzapmbQ6dI0V9M/gYFW47gOrutQ1C3GGJI4wXEVSRzTak2SxNjSxnWvIgiktLGVg+f5KKWQtiIIQ1zfR1gtQeQRhj5Ne/VwO64ijEI838XxQppGoywbP/AIg4BOJ8H1XFqtsaSkv9OFYslLB0Ne3enxTNdD5Uu6rmZ3t0sQeYxnJT995wNm8yVbo21E29KNfAJloesKJQV932EQhZTaMM0qTidTJIJACPa3trBsiwZDGITEQchOf4vxxQWXizm94YDFfE7TNEyn0y97Rq5E9mVCte/7GHMVOuQ4Dtvb2+zu7mKMYbFYgBB/T+qrqoqyKpFSUFUVQhi01swmY3TdsEkz+r0+ra65eX0HZWuml4/YHsacnDzgn/xHv8nekcezXznk4NqQtFxTm4rxZEwy6jHc6pJlG2zREvk+xjgsVg2PHl9wdj5D+QH7RwO8SLJcL5nM5uRVi7B90lITdTJ6Q5fZVPHwQcVi0+NkPmO8fEwSB2wlDo/unePaEVgOaZ3R3+3QGXV56bk//IevUp48/l//qB+0HG5H3NzfIbAVVBm2KfDtgkHoopouZVYwSHrUacVissQNE2Z5QZMbwjXseR32/Bi/BbttabINrm4w6xV1akhsidNmLGZjtvaOCKMuO7tHfOfff4f0csk//73X2N9xcawSU2Y4rcfsbEqSKPJqTDwMGcYhomzQqxK9qrg8nvCVOzcY9RNWm3OK5gJtMmzls15ZTM5sHt1zuPexzSfvV1ycFuQZNI3ECBvLurIldeMAoyusVrM32sIyBqM1SkpC3wcMnSRCWhau4yMt2N3ZwsKgpMC1JZHvIQUEvovrKCLfx1X2VfwDhiSKUJYkCn0cJRj0E5RtQdsQRwGjYR/bEuzvbCOEjfiyyjq2xdaoj2VabEuQxCGeqxCy4vbuiFcOd/DzJV1ZY5uKmzeO0JZhsljzyWcnPHr4mCRJiHwX1zL0kxDaBiOgaTVd3yNxXcqm5cHFJUXTEnoeO50Q6VhoJVjrirPLMVgQRBFBJ+Hh+SmZqdnuD1FK4XlXp3rn5+dYlsXJyQlxHCOlDVhIKdFao5TCcRzm8zlZljGZXDKdz/8+zTrNUpS6msgul0uktAhdxcH+AZeTGUmUYAnNV547Yns7Zja5j2Wl7Ozs0ulbNGZKt+9Q6wIj4HK2YDDYYlVXYAxKKWLfJ/Qi8hLy2qbGZZNV2I7ECwSaEiMFW3t7dLd6aLtk3WTM5gv2dwcsZimz2YYHZ2tSq8UPPPajG5h0xu5oi7t3T6laWNUZH3/xEUe3jvjaLxDnL/2s3emNKIolsq2o65rIDegHfWwpqKo1URLwo3uPsEOPZH+H89kC6aorQ3NtMZIeKnBwigq9zog9hWothO2R2D5COJSrDGUbdrdiFsuK/laXHbfDd3/0Mzqeg9VqtvpdpmefU5QLPMvCt3dZTjecnV7g+wGutEk3Kc/vXuOkWJKtBOMV3BodsMmXpJ6PFRjaxiNdREzONdMLuHhiWEw1Ze7iKRBUtLrAtm2KOqcXecSuomwbBr0+ruOQNRobQ9xJWKcpnqOIovBqWS4U3UDhypYwDqiqAiwb2ubqYdYVcRQipU1ZlGhlYdorO1RZlkgJgXJJAo/Vqsa4V/u/tm0J4pBO4FFmNYQOUkqiIKBtaizr6nY1zzcErkccJRzt7DKeXjAQFSR7DONdxmnJuix59OgpOmsZ9Hsc7GyTb5YMeh28wKeqG5CSkoqy1VfVyhJIWpLQoyxTOp1tpISqLjk9PaZuBUWaE9g+tu0ipcD1XPb29vjss8+u4uG/JByEYchms/mSRzsnDBKqqgJgNpvR7/ev7mxXK/KiIIoiwjBgPp9T5AV1U9Dr9YjjmLquiAOf1XLB9miLpm7wXIfvffcvGPRt+r0S3bZMl4a3f/6Ua9cDDo52WaUFs8ma8dklzw62qXXL5fkMXwmi/oiqrDi9OOfpZU6Jz9beiCgQGNvCLhQXF5rJOKOpBWkxBxSm9VmlmvHFU46fXqDZpmhs6iLgycdn7G5rru/v09zucL5Z8sydF5gsHRbz9S/U3y8V58W0ZLmZ8tKLBxh7Sb7IWFys6XWG7AyOePz0CYfXr9E9PKA0ku4m4/LzM6qqQaUtjrCukofzDM92kQLydIPTCbGEjVIO3agicFpuXd9ilXqUrs2ySHGslltH+/TCkM1ixeOHj/Fc2L/9LHbbRYkL2kaQLjMCI/C9mK7Xx9/b4axdMfXWfPD2XUqT43YUduPSlAn3Pkk5eVSSrm3K3EY3JXESoyyDblqEZ1+FwTo2oRsgKkMgI3wRYAtD3ra40ib0fbIsQ9kSz3EoiwIHTbfToW01geeyqVv80KUqC0wrKFtN7NpX+8K8QdkW0pL4nkuKpigLfMfDbg2+lARxjOu4FGVBFEbYwiJ0JdQWQXBVpYuyohOHTCcTAtdh0AkJlEexTGnLGj+2OS5KTrMcaWyOn16wuFzz1dv7OKMuSejzaHZBHOyAtLFtB20ZGuWQG0NluBKoMF+eyCl8F3wh2NvaZb3eMFuuKdICs8lorJpbuwcQulRVheM4uF9eDw2HQ0ajEdvb22itadurajmbzdBaU5Yl4/GY5XJJ0zQopeh0OiilODs7ZWt7hJQCKS0sS1AUOXmW0e/26Q+GzOdLLs4vGPZ9olCyNfLw3Iqt3ZjWTMmLhstxinQCTo6f4DsxZV5ydp6SLxcIXdCXAUkYUGrBw+MTgsE+oQ8HkUCLiNVa8ujzFe++P6NqBFHX8Dv/4dd47dVDLH2MaFvKjaCtQoTtUa7XNFZKdK3Pap4yn60oNIR+zIuv/BaYX9xz/lJxHq/O6XRCnlzMWF3OyWcap0mYTS55eHfJKt3w5u9/i0oZmrKhbkouV0sqofGEot8dsJltqEyBti1KCtoQnI6kaCtqYTG87SN0waJdECQBq/mSbtQndip02/Lmb3yDdz79Pt0oZNjps7isCL2Mw4MR89UlYX+bveFtsjTl7PiS/eE1svUKoRVNbeEGEUIrZscBD764ZHzeYnSExMazW5Sr6YQtjYYKialb6qahk3QIlEPVpGwNB+iyAqvFdUAIC0e0jDoRmywjVBLhuRTpEteKr/pDV1EvG3pBTCU0ZVHiKUnXs6m1prDaLz2LNq4SGAWqFSglkGg828J1XQBcz8OVFpaAxLMxpaAbuizmMwLXxkHjiIbdwQhH2VRlQYHFcragzG3qCnpJwunTY6bjGbePjkjCqzO49XIBCALfoWpBC0FVN8SuR55mLGVFWjd0owCT5wTKQRcVrufim5a94YC2aZFhwq2ja8xnczpeh0oaTi8X+EoSBT6L2SVHh4fkeUan26FuNHGnQ+AGLFfzK1SJaVmnKdJRuL6PG/jQNriOj5I2pjHkRYXWNVEUXBHh05LwIGazmaBsi6O9I5S1Ynvoc+u2oTdM2Tvc58c/vGQ5C3nv3QkGg/Jt+v0Olm6oqzX9fky5EXz6yX12hiPipIcwLUpWSEvSifc4PU2ZPJrjt5Lnru3SOh61U5IazXg1pr+bEI8OQK3Q0wZPSF782jO8dFhxMIjww4jaqmlXa9brc5rGI/J/Raj0vJww8G7z+WfHzJ+sGEU7+NGAy8mcx4++QHkOX60FvgSaBllV7O/tk601NYa50Wg7YHRrhKsaCjNHRprWk+SbnFZr8qjB6IZsM2fkQRR5CLHmK88m4FmoZEIrStq6w+mjlF6YIPsVTZ3x6SdnfP3NN1AmgUZDI3j65DGVXjLc8Qh7AY7vk+YBx19suDwTCCtAeQ62kFiNxsHCbmqUsrEQVxYndfU5HTiCsCPZGjrMp3M2lcZVAgtQNLiuQufgaI3reRTUKNNc/VoLxzQEgKcUaVHgxwHdwGWV5awxtEYjWo0tNK7V4roWlhJYtkRb4CqLdJPiex7lZk0UBniWgMAj8R2KlcB3bEATOgpfSfJ0hfIcAj9kbWAYjfAth3RRcnpyhuM6GGVoLclkXXA5XeLaCsexqfKSoilpyxLVXKFDppZLg00vSqizAoNEFwY7MGT5itV6RaUbukkC0kIbTatLlqsFnuNzvlqgbEkchlRlQVlVWEIS+C5SSizRXvXJtsQgcQMP5btoIZAC1vMpcRhydHDIcrGCRoAWuLZDHESkjUualyiVgzZQu0SdPjQlh9cSDm87ZOsVtoy4HAe0oiYrL3n5tT5CrAhVwmhksHVNpFwenZ6w19+mzSoOhx16WyGWtCiyimyWUU4Lrg92GCYOouPgHkrqcMnZ+AnpWY/Q2mX3es7+EQg9ZytRrBsLgY2ybJ67M+Kat8XZasbDR18QRxG8/iuIcxBtU60E+VIhmh6i7rBcVCAkL738LJfLCT//yXv89m//Gqvi6g1sK0UQSIo852Q6RgVdWgyJsgg6EX5kqIVmtVpSpwXajVitcuxK4/YVdqxpTcnODY9NPWdd3+P2s8/w9MNzjj8/4T//3df59JN36WwnOGpIUds8ePQ2y0XBsHMbC59kAOfzU6RJmE1cHt5bsJzWdKIBQthUVU3baGyjr5wgjkeuNaVusIXAdhSOtGh1zbCboOvqauiCQVoSz/fQukYg8B1FVRaMBkPQOcoSCK1pmwYlJRKDsiWFMHTjCCVBmhZlCVpL0OoGYxxsW2Jag21LWmFQ0sICjG5wbEmrLKQQSKCbRESBz0rZuMr+ks3qoCzIdE3Pi/GlxCQJgeuQbtas10v8KCQrMmphSE3DZZ5xtl7TdSUVkk2e01Y1jqNojcFYgvlqiQpi3CCiESAQNBiKRuNaNovliqqGyeUUu7HwHBcDbLIC2YkQfsDD4xP29w4Yj2d0u11Ea+FKFzDopoG2pa0NQfTloExYrMyKzXqNMS1pukE3hjQrwIBua+rap2lqiqoizUo8z1AXJVquiOOAui1JU0mZ15SZYnfniE8WMxqREXVdlG9o2aCxCTGgK9BwuN1nux9S1g1HO0Okr9AiY5U+xAliXLfh4nxJY8UgKvLTgjZKUZXieKxR7ZzdwxEvvtSQxCUXj4/54j0HJ4voD/t0dgbce/oE4ytsDZvV5a9WOUP6zE42TJ4UvHDtVRbjOXW1JogklmMQVs348SXf/94PqHTGYpFSZYrt4XUsoVCex7JOWV9e4loV190RUeSTpxs8C9qqJI52KKocx2uxI8mqXlBWG6TWNBQYqbHMgPOzCy4nFR2/g6gERV5xeH1ITcv4yQXKDpi2EwbDbRbrNU9OVgTLgs3aJU9jutFVr7darqjSDa6yCXwHRQu6oS1L2qqgE3fQWoNpqbKUaKuLrgsGnQ5ivqLWmtD3qeqKPC+waKmrCse1qIucXjQk8D2auqafhHhKQquJPZdO4NOYEkcaOpFP3QryskZytbmTUqJsi7ppULaiKnIso/EcG9cOsC2JKVuG3S7KVpybqyFNk2+wAN+xyQT0PBejNdL30GUGbU2rNW7gsczX5LqmloLj2YzJKkUOukyzCm3AsSUmr0Ep3MDn6eMTrKzg6EYHI6A1LZayWa3WbB8e4fsBWrbMpwseL59y49o1NllO4EfcPz1Da818vaaTZ6xXa7JVbmsAACAASURBVNrG0I07VGmBZQkqnRIFAVme4ymbdLnCfPlgCmOuCPyWRVkW1HVJq1u6vQQhwPNcykCSFtXVbrRuOThIQKyQdkMY9MnTlH50kz9766/JtE9nW5HWGTUORZaRRH12ohBLQ1XWBN0ex48fsL23h9A5lDWrzRw/GdDdHtCNHJ4cT5nM56g4ppQeF/Oasg6JtwOMmeAOLDJbIhub0f4u/cRFr2Im0znFdMV6XbE32saNPC5nk19NnI8+GjM9X3EwvMn0dEVTaoS0kNJhOp2z2eTo2uGj9+4iXI3rhUT+FiCZjKdUAiwBo05CEkqqzYY1OdlmhV8L3FKzWZ7TH4a0ZUFarTFNg64F5aZh/+Aa62xDll1y/foOXb/l6fgpB3cO8IeKt+9/xOzRE3asayhh6PZ9ajGn09vnqHmGzSqgFg0qlNi1YLWYo7MNoQWOaAksgW3ZpGlK21T4ysamxXMdHCUpVjWy1VfEuCjAtyzaqqLju1yka6QA27LAlqAbfCXxlcQRBlta2L6HY1vUZYUShjhwyYochcZXAlODritM66CbGunYiFaDrgmjgCZPcWwL0Ta4Sl0JTApCT9E0DYFj4wjY6nW4vBxjNTWetHBli9YNSgnKosLCICUI62qK6joOldZcLjcURrIoDafTJX3XohOHrMuGeVnheh6tMWxW6yu7mO9f/QfXJZ/PScKQ0WCAWKbUeclmsqQpS87PTwn6CW1z5SDxHJfA9ZgDom2Jw5DNakMUhqzSlND3SNdrqrxgvVwhLIuk0yVLIY4jBoMtLs4nSGl/6VZxWCxnxEmIbluyNEMYyDdzTJviuTmBZ+jFHqEXsd074vPPjpFxj+1btxFOS20sbDskSy26gSGJQ2qnhhaQFVgVTZPT721R6Zo0F+xtHzIzG6LulNG1BCf0EHbMfNnh3XszjDcn7rVIKViWA+ZTh55XMRou6ISHrMsUYcNyMWe32UMaC0c4v5o48wuX15//Nvfu3sWxLAb9ENsN8UKH+aoliWMoAqTSpGaOcn3COGE2nVMVLRKLvU6HoRvjOYZxuuRkcUl/0KXOalpt4RgPUTvML2cMuz0cpUiLjMTfxqoGqLJLJ1zi9Xe5yxPKfsiDp494/cU77NQx8UZxs/NVWircyOCGEcdPDFbh05UhnpcyXjzlcrqirQtCWxB7LqHno/XVIMiVDr6raIVBKoVt20gL4l6CpStEXVGuayJHonDp+i5nRU4YxQghMLpG0mIbjSfBkxZFXeHaNkLXKNOiTYvVVF/uiWtcS1ELg2U0bVPT6hqjBdgCC42kxbFgtDWgyHNsx6ZpGyzd4ggwbUM3CGjyjO2tIbOLUwJlI3yXuryqUINej3ldoJSD0Q6NEJgoYtTpIusa1w4QtmKZ5Zxeboh2EwJpYRmNqWosx2fY6zNdpdhYhK5HXdc4lgRb4NDQ8RRN7UHuo/yc20f7FPWa6WbJ68+/xPl4zORizFduXsfVzRWOk4ad7R66blBJgiUlvqPwbJvKvepFkyikaQr8wCNJIjbrDUo5pOmG2XxNo0vm84L1IiUOO5RphSssTDXl1mGXKC4IREEg4MH9d4hi+PpvvcTodkKQbHHvi5/w6699g4Gzx6N3v0N05zbKU0yXM9yex0antEqQNRUyUJSbFfefznj7R08xpmbnQHH9to+050S2y2vPjXhynvJ3f/OYMOzyzV97gffv/h2z0wv+099/EceZsb3vkHR6XExsjrZ6fPr5fY5PLn41cV7f/gqiUnSCAMMaKTO2dw5ZrjPKwiYMuwgLBt0RkfLI6ooHjx7S9XYJ/ZhYBcSOh8kb0qbA8jwarSgdh2QQ0d0JSEuP9Tjn5GFB54UQIwzLecnhs7fpx0Pee/g+1+/ssCZm6/AZxHCbh5+/jbn/CV+9c0i7SvGtgvF4xfhJiIVidpljmw355pL5eMF0fIm2NbGrCKTFIFYYXaNbC4yFwUbrEmG1eEqi1P9/DZTQ5Bs820IYTeLHrJo10oAjwGo12rS0TYnWFY4QOJaFLaApC1wvpsxrXFtiey62aLFFi7KuFt6VbnCUvFoNCIFlWUgB5ku3h+cq+t2Es2yDrktsaYGGOssoi5zId5lnK6oiIwmuVj2DbkKajSmynJvXD8izFLShVgosG2EsAsvGSnM6XsI0q68I85uSqm6p7BJajY11dQwexqRZTVvV+LaDpQ02Fq7roPMNdtvQjzxE7pG3DUfbI8p6g33WctTvoqqSajZllISkgx5nl2OUMvi+pLI0RjuUVUUvScCykJ0ES1jwZfvg+w7j8TnL1QKMIMtSGl0RBC6NrtgaJjiWxzzNeenZZ8jTj3nhzh06SU0vkFg0JN01/8UfvsH1V77CVE+o2zXWSc58OSHqDKkbwyrLCOIA40gq25A2OfgOxlEUuWG2sslXG7LaZXt7yO07e9TVgul5zifvnoO+ZO/wDuL0kkm14UeXP6SbWITa4ad/8Yhv/pai24vodPq88uJ1ItfwG1//Gv/Lu3/8C/Vn/TJxxn6fbF0xHO1iOz67hwcYCVmeYSvJ3s4OrrDYjfu8fvtVrodHeOuImAhXVfjBnG53Q6dT4DoFkW0xUCGbyzVpC5dGU08FSTngZudZVO5isoqDfp/0dEr+NGerPWJ8nBM6A/KNYTEt+elbp7RVlygcolyH+19ILi5CJk9bpg9WDOhirQzLswWLyZy2roikwDWGyPVoS0262oDVUoucQqyo2wpbCHyhCdqcUKdEsqXVNbYT0AgXYwoiW2CXOaGycQOH2Xpxta/ThkbU2BIoczyjoW1orQYj9VWsutYEQhJKSVvk6CzHFCV226IMiKpGaPBdn7aqCJWFQ4NCI0xD29YIS1PVGVm6QtcZO1sDXGNw2hq9XtPzvKuktdbCqSv8tsHCRloetvJRtJgiZVPX+K5AmQJftNRZSVNBm9dEQtBxbDDQCIUMQhxHEAcWg46H5/5/nL3Zr6XZed73W2t987DHM5+qOtU1dnVXd7MndkhRgyXLQqzJliEkThBnVK6C5CL/QO6CJEAGJEgMBPCNA8iyYzuRBdFiJJImZVIkm+y5qmuuOnXms+f9zd+3vpWLXeEdCaQvzu0+GwfnXe9a7/s8v8fG6oRURuNIg2oKFBXSNZTkeIGDIyWRchBFxTAMiWyLMpsRupJ+z0WpisAHqyywLYMXe5RFhtO2+Eoi25ZWt5wcnnF+NkYoi6Qo0UISdrrYjkNZ5DRJhWMk/a6PEypQFvcfHOG7m4gmZhhdZGezx5UrfSRTukozkLBlBwwjj9xMkeuX0L6HG1uEYU0UWPQHW1jKJ1AC2WQM+hE2Es842C34tsJSFmfnNcq9jh1uMktGDIeG61f62DQIHbO2e5vDRY+Pn5zy3Q8/h9gw2HFIsimW6vHbv/X3vljnFAJczyFJZkRxl9l8SRjFRHEH22opi4L1rSHKTXGEja983rj1Gq7voqWLMVN0k1FVmtksRVg+qvUJtcegu0EyzaFqEG5N6HtkyzFbWyG+Z1POWuqipR9tkNo5H3/8EbmRPLp3lxsvXSCwYk4P51zd2eP9k4JBd4B0KnzXp1gkHD874eT8lFrXK0xEo5HKxncCbKmom9VOTStBK1oG3QGB4+BbgshVjE8XuFJgmhbpKXw/pKmm+EohdIMlBQ0tQRhQFzXSGDzfwlard1VoK2SraXWNUBC4Lq6SFMYgDAgDbaOxlYU0q+GHJQS60fi+izEtvuPiWApbroZE0rYRAtpW47o2RZ4RuR7dbky+nEHTQNNgjMKxbETd4EhJpQVS2QhlYSu1cstUK2JfUxdIVhF4WoNE4TsuDYZKK5ASEBijkWJ1PQcwSlI2DZaU2OiVEMJzVuL0PAfdIgSEgU+aLRmPTlEYumGAqSuKqiTyAxwhqAXYjkWnE+FiI4ygKUsCz6OsK+bzOUk2wQ0CNra2wNRURUocdWjnGaHvEMcO48kY3bScnk25c/c++mpAWSgSe04dDCnSHD9wOXh+zu7WVdxgyNOTGXuXrhHZCY1eMuhvUjUVd5+uBjfBxZjh5pCB63D6eIVXscUa9+9+Rm+tQ1U3zOZLglAT9UIuXAwpS43WiuPTUw7PHuH6klvuFcpiyjLzkaJhkc+oTo549PzoixWnrucsFmNcD9bX1wkClyTN0crC9hVlWbNoJ1xcH3LnyT2ioEMjF5wdn9LpgXJbqlaiLB8VDKAM6RDSB/TTlIHj4F+y8Ds2eVViOUPWegPqsiF0PBZnFY3MOTw/plYwmo14++3b3Lz9KuU84cFHd0ifTNgNtsimcwLpU+UJz54ecTYaU+qKlhZbOgR+QOj7TOZzqrLEdh3KpqbIK4wUtHaJMOC7EUWSYCub2WSGqGtc16fW9WoCajvQGjzbITEGS0q0EJR5QV0lWKamqTIsKVGsdp6utHCloa0qsrKhlRau71KNU6SyadsWSym63S6LLKHVLdJAU9cr5ZGz8nIa3eJaFlWeYykLrTVSrFKgG61ptWa6nJNXFb5UlGWOY1kk8yU1FpZ0CQIPS630rHmWY9suRW1Iq4pJkrDeGeDZNrWoSAqDwDCbTUl7PmvdkLTJV4eHHfN8/4i9vctUtHSCgNgLyGYLbMC3HcomxwtdxASODw/oRhHdbo/JdEJdaqRq6XZiTs+OaMucwPKw5erw6vg2uA5ZssAyGkdBW1ecHh4QRj5FtmTY6+GtCzQFZQZSSGzpY7mSte0u8UbJ44OfULtd3M2IjSsv88/++R/T68Z4TsO7ly/ynR+9zxt7C65dWkfVFQ+nBf/sT+7hDNZI2pzPT+a8fLPLjSshm7sO2VxzenZG9vyU975ynWHfYXKWYLWKre5VZnPNybN90sqwsdblZPSc3/nddxGhg1sK7j49pm0y9p+c8uDzT/mt3/7tL1acYWSjcRBCc3R4xO6FXbKspKwaMBZJkZOJKcdTzbOjQ6Jgxub6gCvX1mmaBfN0yXCwS7+3xdlZQl0ofMfHUQ11uaTXdahDiAc2AxlzfnJGUdUY7VLmgqPRiLZpWLu8CV5LlHlImfCXf/F1yBzeufE1JgdLfG3wLIeiqEjSnPHoFITAdiRGCALPJQpijGlZJCm61XiWwgiJbqHT6SDblq21NbI0YTye0e92aJsWUzeriEPfozbmp0oVy7KwBIzPR2xsbBF4Hrpq6Uch0+kC2WqUUThK4FkS6oo0L1nkOW7YwViKsm5wLQchBEIIlJTQroT1lpSAQbQGiaAuKmxXIi2Bo1Z7SEcq8jxHBh55VeLaq0NOIvE9F2FaHMvBlhIhVj+OrRCsLG++71PUhqpeHVDzNKdC4jkKT4PTNFh1S1XklEWKu9alVALHcRC2xf7Hn3Fh9zJFXlGVDXEYUb7Ai5iqoq5yinyBFAZlKy7u7nKw/xzfcgi7HsvFAm+tS1UW6FYAJWARhxFKQFnUuMLQj0PWvYDzyZzZcokUBileCDS6Fr1BzPHBhI4fUxZjPNfHcm2EW1CrDGV8PEtwfnTM7uYOTVFwYX2HR3cesL22yen4iF4kGUYdBBFSeqSFT640wVpE3mrm0xbLCXC8mq0dheNu0OlabO30kNYYWboU2RnzGWRpxWSeUtSGr375ZdYHHk/Su7SqIWk36Pc3yB6PufnGO/wff/Rt/vM/+ALF+cnnnyCkwXVtrlx7ifPJjNl8ief7+IFNaLsYLZiMpnSiARvrfXzPYDsa1w0pMkM6KdHVGOXZ2EOFsWfIsEI2ExJRUC66dNxd6mKJ1hU/eP9DXrn+ZYqloTdcx3E1VlcjnZaoH/LZnU9JJxm7netMDueQOuhiSqtb0kXCfJ6hpGG+WLC2uY5lKbpxh6bWnI1G5GUBUpDp5YoUZwR5WrLe88jS5IWFy2GRZKgXwgGlBLopEazI5UYa8izDBB55nmMrizIvVkWlLKSU+K4LloCqRQpFXdXYtoMrLSoNZV2gESt1S6NxHQutG3TdkGtNf2NA6NpIAbayCDyfZZoSuS5CCDAGWoOUEsuxCYKA5WJB3TTEUUSRJqjYQ0qwpCArSuIwQFCTZylGrq7M8zRDSItSG/LWMCtKZCvxlFoVct4QhQFKSsyLyXJTFeRlg0ZSNJqirCnLkk4nYrFYsL6+jmPbuGr1Ntte67P//IB8uaQTx0zGU4RlY7sebWuoywKDxPVs+mFImWVYrkNv0MNVCifJcKMueVGS5RkKQRxGSGlwfQsjYNDr4Uob1+6ipIOQEa2UbO+9zm63iw52+Bff/ICyaDg/Pubta3t88PBTTFqiI8Xzo2Oabsnl9Wt0fcmShkuXdxj0akJPcD4WLMYto2nGlb0NLFtQtyXYC4L+DK+JeHL/lI8/O+XkrOb6zTdZJBNGpxlV4vHGq7/I6eiMv/zOj9jbs1nfvIwUHsejg59Zfz93IJQ3DfMs42w654M7d2mkpJEtVmAzXp7TqoqtQZdskhA5PeaTlLt3PqfISgb9ixjd4eQwYbEsaaUmGBiIEkprgd/3aG2FtFym0zlxGGLjMD5IOH+e01YevhexSKZkTcn5ZMZkkhA6A9pM8fLlWwyDiMCCdDGlylOKLEWJ1SK9343ohT7DOCJwLPI8x7Jt4l4X2/com5osX6H/Qz8gSRakWUJRV7RKUSFYFiWtFBRlQZ6nJMuEXqeDEIJ+t8doNMKxbPIkfWGOVj8FJkuhwChqDW0LVaVRtocXdilqzSLJibtdHNchz3MEgixJaOoa3TTUVUVdVuhGI8yqCI0xWFLRVPULM7ekLEts2yaIQizHoW41xmhsW+I6FgJNnq8E+nVdIg0s53OEWXlPdaupWk2pNVlVUxpohcC0Gse2ES+ECZaSYAy6rmmqCiyHVjlkTYtWCunYaCHJ6wojDH7oEwpYC0ICpegGAXmaYlkW4/kMYSlaIRAC0mRJHEY4tuLi1iZvvPIy/ciHpkLoith3sTGEjsOg08FzXRzHJoo6FEXDwfNjHMfGUobAdXn69IRv/Msf8viRZjwektU2VePw2ScPOXp2TKAcPv3RD7h5cYNXX9rg5Nzw7NmExw+fY+maDV/y6+++wluXNvmFa1e41B9SGg/hx2jL4cnBiKxysZxNgmiDK9euUdY5QcdBeZBUOceTU4abW4SdTe7dPeMf/f2P+Phbmns/Vnzyo4Q/+/q/ZveCy+/9/rWfWX8/t3M+fH7EzVs3uX//PnEvYLCzwTib0/qwLKeshQHnT57gtz7HD89B1bx04yUEDvvP5jx8ltFZ38P4DtpuaTXcf/8Rr++9TjP1yU8yio2KV3/lFp99+JdMD3KGwSUOHszY29umrM5YlDPa9QEnJ1Ns5bE8SXjr5tdoU81yfI6DoNfrkGQ5YeQzPZtg2oq1bpetbo8sTUknc5RyaE3J6dkZ0+WCnd0dQj9Y0d60YXtnSL/X5fGz5yT5ipkaBh5n0xGWMTjKYEmBpSzyJCVvNUoILl24QFNppqMJw+116taiMYq8MjRSs0gLhoN1pC3IyoaFaai0YbpYUjcG21ktoYUAy1a4eiU2kAiqskSYVSd2bZu6LPFdF8XqGvx8dI6UkjDyqZoa23Oo05pksWSw3mM2nSDMiy7uOJS6oZWrd6RtWZi2xVLWSqjguCR1zclkRmcQkc5miHiA69gMB308x4JWY79YNc0rTeu4jBYL8iwl8F2qPMX2PJ4eHdBiKEZjXNejWS7oRRFO3GGSpKRNjVjMKIqSKlny9ttvk1YZsWUzPj+mcD06oUvZtswnNUkyJ6k0wloVpW87dLoxAKUxRIMu+/tPefu1GwgTkmY94miNjz9qOD074d/6g1fY2nmZabvG+uYOO5s9ek7O2fgMKwppy5vMx/fZdmKefvyYIM949v3v4fgW1T1F3Zcs+jbKi9i4vIaqIpKs4Yc/nHN08hndXsDlwRqu0+EXf+0V3vwafONb36WUS9qy4vz8HM/qcP+jj/jStddJ6hSvO8BqayiTL1ac790ecfnKGnu7axQmoDEpUccljj3mk5Jnjw/Y6FygOfwJb760QyZ9iqDHIh6yf/AAuVkg3H36cYhTZxx/eIZZGGrhc/lL61xoU75z/3vcOzdEF7Z5dHiPjbdi9p+d8oSS5aSh098EXSMCj+UsJUsKrl16ncXBAlPkdIcOTyeHLJcJ6701Pj++w+WLewSdHossJWsqCDyWi5yyqjBVw3a3z9XNbeqqIm9b2rYC1+PB0RFNU9MWKcKRYFxGozF+4LOxtYVqS54nM2a2oGkduvEajTF4XsAo00RuwLIqOcpzBsMh08mUspFkZYMjYTmfUpcllbJILcOyaomrllA6BI4PpiKpM/qDHpYt0MsMz41XE9u2JbZtIs9COwqNoNEGjCBJapSy8IRDS01VjkENSLTCFopB32UynREHPkmeUjQaz1Z4tiJwbcQshbpG2T5N0WLcHond0tashBmuRdJoBpaHLUsudkOK85ye77NcLGilIF0sGfYHNFrjCJ90OcO/vkael9hBiK4a2kq/WBV5NNowXy5RGq5t7bA4nVDaLXld0jaSPG/YXB8gujGOlMgs48npCXany3Q+4eDkMbdevsma36UpS65de4nTxRRpDHZjMz2ac3HL4WoguHP0bY7NKd6g5dNHBxyPG65dv8q3P7iDv5OxGcNwfUBOzYN2Rv+mT5TnmDTB8fssZhmiDbB8SdipAE0zNyTjBaOTGlX0qeIhtqmxioLIDrD8Hset5pe+skVUnnPp2gXORj7PZ89pqwY7GPI//OHX+fL1135m/f3ca+3ejXdxOxeY54bPnzwjqTIsT5AWc9768qsU9ZynT4+5cu06ypbEoU9ZpExGJ2xtdHjl+iVeubzLrYvbvLyzze29l/j1r/0yr924hWjg4MlzAndIFO7w8MEY399gvszxo4DR/DmXrvWx/BlPnj5ekfTahhs3rnF+cspsPMOxbHQDi+USIwX7hwesbawjlKCoK5p2hbUcTydMp2PaVnPpwi43r12lG4UIo8mSBbYSHB8dcvhsnyLNcB2HXm9AUZUYZdFKxbPDIyw/5vHzQ3KteXZwiPviepVnKaapmS/mnI1GaASjyQyNIC8rRpM5nf6QrMip6xW8a5GkNLohSVPa1mAwFGVJ3dRYlkXd1BghyNJstXKxHaSUP6WhN02DUha249C0La6zeos6joMxYNkuWV5R1hqMROuWuqpX3KLWsFgu6XRier0enc7KN6obTaM1i2VK3RjKul59r6ygrjR13cCLq6h+AR9DCFpjsJTF+WhEUZQIIZknCW6wktcdHZ2wWCzwPQ8/CPB9H2Vbq9/d77FIEoIoZP/wOUmWkRYFca8HUiGkQiqLMOrg+eFPV3lCSMajMUhBQ0NWZDiOx9nZiCTNEdi8tHeFg2cHdMUVAnY5PcywZR9HdfnBdz7g9Omc55+fczp/hjNQqK5E9AOii9sMr13D276A6e8gOhsEYZew02WWLGloWdscsnNxm729Xd5+8zV8O0Bpwej5Az77wZ/z9o0h2clj1lxF3/U4fvKY9W5M11W8efMKFHN+/dffpRJf0Gz9zR8c43hTjBIsc0F5eMKVy5v0B31+8pPvEccx44lhZ/cqs9OnWJ6PHk0olgXb/S3cpkYvE54czghssKyAZLRPq12e7z/n4f0HRNdv8a//4hlSR2TLjM21dbSV0usvCKKCpDwlCkJ8Aa6R+I5ClxnT0YSrl3Y5OnqEFwYIoTg/H+P7EW6nQ17V1AKyqqLRGlsYZKvxXZvtrQ1OT45ZziZI03Jhe5uj42MGcRfdNMRhTFHVJFWNtmwKBGnb8vh4hBCStm5Xn2taHEsR+g66zPHjLo+fPmN75yJ5vuLetEaihaRBvLiWKdIkxXFdsqzBsZ0XkGYXrSvqWtPULTjWSurmuNieS9VC1RryssBxPbQRK7G3XhWUblvKpkYIcFyfRq9cHMMLa3iej1+UFPXKUaTslc2qtRUGQRgGTOZz5lnKoBuyTDOU0SjlUVcNZVnS63RojUBZDmlRIpSkqmssy8W0LUEQkGUZWZZRhCVNq1ksc57sH+I4Lo7rM18mGMtCSEW/E7NcpKRVzTLL6cURvfU1ZuMx69tbJGXBk2dPiP2AwkCFoKga2rxkMp0zHK6T5SmLfIltsVrFuRZFrtmMeuRpTVXCr/zq3+D+pye8/8d/yfb2azzfP8bUC/7d3/97fH7vU9YurlHuHtKJA7JlSpMXzIqGxu1wZ3KG0TW+5xLYEguHqDvEYDFbzllbX6OZtTzfv4MyLocPPuPSRs17rw7ZuGKz0d1gYOb4bo+8XKCTipubF3h2fMwr2wNaRxPciL9YcY6WDW6R8Uu/+BW++60/5/b12wz8CCuVuIsO6XlDL17ncJzR6W0wmZ/RC6FnK+rpc3JbYWtDL4pZTmYMNztYMuKbf/Ujzs9LLPrc/d4pltPhow8/ZnN4Ac87YZlMSetjZjPJzu4WoSPJxidsbGywHoac3HvC5tolDg6foyzJbLHk7HTEcGOdTrfPbJGtulajKfTK1Lu9scl8NiUKfcoiYzI6J0uWdLsd7t+7y6XdPZIkJYj7TOczbD9gtMhpbEnVlJyNJ0xSiDyXV7Yu0h6PKPVqAd0NAqRpOJrMyVvJ50/2CcOYKIrIi5qilXxw5x4Kget7FCZf/aPWYIce2xubtK0mL0uqsqFtDVWtcS2HujUI26WtGqaLBb5v0bUsam1oBWAMaZYBmk7o4zkux4cL8lojbBfp+CR5hbBd6rKiAaL+kMl4AtQs0xLLdojikNb3qIWiFJJO4KFbEO1KmGBakNLC8W0K3eJ1Imbpkr7nrLi0noeSEmlbVEbjd2OeHJ3R37pE22hA8OzgiPXdXaazOdsX93jw6Amn0wkXtnfwBOQCelub3N/fRwmBaGo8x2d9a5tn5+eA4NGjJ/T6A0anYzbWh6RNvhJa1BVu5dPtrKFbhbBtnhwd4zgJP3h+zsQuef/jv+JXfuVNHCG4d/R9tF3wrocSXgAAIABJREFU0cd36Fdb/On73yWOYnYvXOTzu3cZDHpsb2/i+Q6mqlnM5syLnKJtiOIeexevsH/nKU2V8vbrrzJOXC6/vMn1SwlxOCeKMtZ3bepWcnIasPbyLeaLOf/df/8N/sN/53Ve3bqMcDXnyRfU1n753VucHh/w5POfsNPz6RpJc55zcjojOzZ4fof98weU5RzbN8Sh5JXrmxyfPuPa9RtYYcD56JyHD/cRtY1v91gYmBnJk/OCZJJSFgatJ/huh2VaM50XtK2i27vO0zslx4+X3Hoz5o2rbzEMffY/uofIbZy+wLYVtanw3YBLFy8yzzKk7SDsmtHZOX4Qo6SNkjbaQFk1lFXDaDxdIRc7XZTt0PdDTs9HGANIGz/scDadrvIyfe+Ff9GgpcUkKXhycIL0A57sH2J0ycarr+LVBfvThLxsEJZF3rRYLRTaUAmFbiX9bhdtJElWI6WFH3kMel3iTkhZZivekFg5ZcIwQBQ5lhLUbUsrBWW7SoRL0hTLdmkxBGHA2dkZg34H13UpyhI/7pDXDXndYNnuav3leczTDC8KSYoSY7m0ZqXy8aKQbq/LbDZnnuV0Ip/N9SHLJF1xeRyPTtTFcTwsZUjTOZO8ZZ4mRN0OcRghXggzbNclKXKqpsZ1PEazBYHzQrxiYLpYEoYxeVW+cJ/02djaRBrQLRT16u8npUA3FYvlFBUGpIsFvuOwu7lJHHdYzmc4xqwUWMJdMYqalsFgwGx0TNZkuEOL2NZ85e+8zkk54m/d+GucHe/jCsHh87vcvHWL4Rs3ccuXmO9INta3OD48ZG/9FfJigk4y2jalrQqcRjNNKs6zgrXXNgm8gLXhOkF0gWyWUhooZcUH54cEyZTNumYY93l8/4BS73J4eEZVa9791T0+f3ZIFHi8fHWXDbf3M+vv5745x4sZ0pbceuUam1tdPvn0r2hFyWR+hhv72IHLjWs7PD7cJ9GGcLiOsl3isMsnd57xwb1DHpwkPDouSHWP87ngZFLw9HDE8WlBXnaJvA2moznz+ZinT35M3pxxOnnInc8+pt/Z5PR5wsc/fEYxFySjGl9E7G7sUOcFUgmSdEEv7KDLhrX+Gov5kiIv8NwAXTdYYkUbP58uORnPODwdkdUtk3nCeLokLWqk42EshRawzFOmyyXT+RIjbJR0qYqWtrU4OTnFC3wePn6CkYqHT5+xyCuOz8eM04Kkqmkth8kyI9OGu/cfcTKZschLamHRCMV4kdEqC2U5eJ7zgrhn0el0sWwbKS2UsjAtNK15sYoR1FpTtw3SUuRFQZJnOJ5HXhbUuqFpNfPFYqU2sp1VQJDjcT6dkWQ5WDZVo8nLCqFskrLm9PwMoSRB4KFbTa01SVFgLJtSNxRVje8HKGHRVKtdZt1o6qYlLXMcz2U6nQJg9IrdWxQFi2RJd23ALClZ5DXTpMAPO9h+gO3YVE3904i/KIq4f/8hjx8/JklSTo/PKPKK87Mxg06PjUGPyHfoxT5rvQi7bWjzJbauiJSk4/qkswVNVkCjydKEjQtrxJsRJtK8+dffwN9NiIc1kWf47K9+zPJoytb6BVQ34v3n9zg4O+KtL7+3IrWbgMVpQ2j66AnUZzV+Ibi+FfPqS+tcWo8JXR+M4uhwxMGzM2J/gG+BsRS9q1/C3XuTmdriw/unpLXDcPsKkaXouzZ7l3a4/fZrTOqUT57dJ2uKL9Y5C9nHj7t8/PgpNy71uNG/xkcf/YDtK3tsbm1x/PyUYjnBDXwu3bjJtRsXkekxkR5iFT4H5xmHB0c8u3vAbF0ynwrmeonTBuytb6CTDkkypxOtczK+x8tvb/PLv/Ye169f4yff/4T7nxyyt9chzWpODzOctQ6mEmgaAjdgOh2xc2GL4wcjItdHSmsVM1c3NBpMDYN+n9YoFmlB0UCpBUdnE9IkJ3Bd7CDm6HyCbQk836XIKpSU2I6HQGH7AXnZoKhQrmK5XCIti/2DI4S9egtOkgKMoagNebkgiHoUjWaZl4RxjFEWQRhQaUHdKnQrVmZt0zKTE1TVcD4Z4UQhvh+SpwVOqwkshZKSptUkWUHLSnDQliW6qoCVQmiVHaKYz6bsbG4xns2Jg5D+2joP7z+gE8XYSYpyXJZJhhd3sF2Per7ANobFYo4Uq0TprKgptaFsNLbrsNLVrqbCVVUjpSGvKk4nC2qzGjL1ul3aRq/enbMpdavJ6wrleqsBm2tTNJrZYsma7zMZT3GKAkvZVEVNW2u0EZycHiG0Zn04xHUc8ixBBRJbNnQCl0ZreqGLbdv4nQjRtijXZTkD1/YRLUSdgFpmbO9t0L2QQLdByJTbV68xelhwK3idox8fc+mXbnO4nzPOLOL4ELey6L3Up7Uc5rOSfrxFOjmmH4WsRSUXeiNKJ6AygvF4zHLaUuYtqrJ4/OgU6eZ8+9MP2Rm/xtXbl7i68yZf/6dP+E/+7lf41jc/4qPvfcp7723Q2R0ymeYUqsTu9VmEPzvI6Od3zlRy5eW3mFcaE7h4w4Bf/d1fZu+NbZ7OPuf2V6+yttbhK1/7Cmtbm/zhH/0T/uwb3+IP/+iP+eGPP+X0bImjIkJnwPQ0xTEBFzf2GAYD9LKFDIpli6U8brx6lXe/ehM7nHI6/pi8ecZ//J/+DmnxnJdvvMFslNAUhmSeE/oRy+WcK1eukqRL2rql3+1z9PyAMi8Jg5AqK+nEMcNenzzNWGQ5KIskKyjKmrppWaQZj5/ug7SxfI9ZmlCblslsjrJWEjmFQpjVEMj3bLRedZDj42O2dnbxoy5OEFLULUmecT6eslwmHBwesb61SRiG1LqlqCqOT085H085n0wxQrJYLjk9O+XR48cU+eoEdWwHz/PQWrNYLhlNxmRZjlQSx3PJsgzPdQmjCD8MVoxZ3fyUAq91i7IcmhaCMGY2W5CVFWdnI5RymC1TsrykaQ2e72FZFlmWYQBhKWrdoCyFZVsYBFIpOp0ujuPQNM2qm9cNjusSx/GKrue4VGWJ73pMJhMQAj8M6PUHdAcDlLRZLBZYtsPZ2RllVfHo0SO6vS69Tgfdrg6IizsX2Nt7CSkkO9u7eI7DfDbB1A15sqCpc4b9LpHnETg2se+ynM0ps5xOENJUJY8e30e5AmNrBjs9RAij0RkmbymPCi761xmYCyRHkucPE/rDq+h4SWpNqZyUJ6fPODg954MPHzI61Rw9zZmcZKgmox84eJagzHJEK6jLlrLUCGNz+OAhrmk4PV3ghRcp6g5XrqzzzT/7Nh/+6FP+5tsx/9Fv/RpfvX2FK3tDvJ6FtxEwauc/s/5+LlT6e//P//5fqfYu7761x5du/S6hfYv/84/+Idl8ypvX3+ThJyO+/JtvkGcLHn18h37YJVkK0lLh2A6vv36TeOBiBQW/+Btv8so712mM4OSs5GSWk7QVOjlmmR3yH/xnf5fT6R1uX+/y7qvb/PYvvoUqa/70T77Oo4czXr95g9gP8Wjp+YootEiWC+azBONYPD3cJ29q4l6P4+MTJHBxd4N0OSXL5ggpUEqwvr1DoVtmac4szSlrje152JZFnZd0oogwdFDK4IUOk9kU02rKquLClUvkZUEQBCjbYbC2znSxQBs4Ojlj2bhE8ZAkyRBGYUuBLSVZlnJycvpi6tqQ5zmba1tYlocTRGgJXuiyNuyy1rEQ0rAoW4xSVE2JEjVJllJLB1E0uLa/CnltV84PpSTSgG/7hGGMrhKWiyW1Ujw7GzHOC2QQolyP8XRCFMVUTYtSNq1R1K1BSgvP9cjTlMj16XgBdbUiOPiehbIVy7yixqLEY5bOCVwHWWl6XsjmYINknnE6noDjIV2f9Qg81bJcTBBSEMQ9DsdT5nXLomyZpxU3ehG2ZVFpg9+JuP36bYaDAabMiKIOdLosXwQCuWgiS9LvxauoB9tnKRa4gUu2qOiHEa/fvkB/WOO6FbdefZ33f/iQ0aNdHjyZ8sf/6jt88Pkz5rVidJhzZWOH9vSQoe/RaTpkhyk3N69jFgXdyAOZMi1OIFjgrTVkzUWOjrs8eTzBtSX9bsiDzw/49JMJqnuVUVbTmppPP3yf0dmM/YOS+XnI4ljx7/2X18n9M3BrOl6P7MDnj/7n73N+L+Lf/7f/i///UOmbt24w3JIIBf/Nf/tfEzg91tc2ePnGBRbzGbdvXePo8ylG+pgoQjg2wi5588uvce3qFr7dZzETDLrrnJ0eMT7/lGRpmE4nKEtiu4aFXVGhGc0O+Oo7N/jalzbwRUUvtBlHmkpALwpWiEXPpuut0dYZwrJIspyyalgsFijLphd0WOYljTa4riIvS7KiIIwjcgqk3ZCmKaPRiLKscByXXidGN5r5YonrWMyXCXHoYzsuJ6MRQdDh4PCE7Z0d0iTh5OSEQbe3kr5pzWKxoG0ajATXdknTlQSv2+0SdTrM5lM6oY+0HaaLhCzL8cOI8WxOlias9TpYwtAYA3Jl7Tofz1jkDXWREdjQ73goFFBTNQ1u4OMISMsCqRWO65IuU7phRN1qqloTxBGmBdv1yPKCrCixHQfdGISykG1D0zRUdYPWGiEVnu/T6XTQpmWaJhitycucMPAwSBolsF2XNJuha00Y+4QbHrVu8KMAxuMXXb+lbRpaDeoFUNoIiZSGThQySzMcS9LqCmkpLMvi0qULfPjhRzR5xtZwgKxLyirDtxVrnQhD++KmIFnb2kC5S0bzBYNOD9Mq0jLDMoIqLQgGDn7g8Ojhc548OuLoiUuw4RN7F3Clj107nB2Pce5YeBsQ9MZsX1pnYzDg4SefU56PuXzxJeZtzXavg7Jn6KylbiX1coRdjkgOjumt7XItcMkmJ/TfHnD9r7/C/skBdrhGGEcEsSZfJiTFOeNlzG40IC8MYbhOkVdkyx51+QXRmCcnT5knDvfv36dpCryO4PXbX2J3Z8jdjz7gT/7vf0QsfBJbwbDPrM7YuLSBtg3z5Yz/61/+OU8fVvQ6ERJNUZTE0RqCmLJMyfOKzctXODiFe3ce8svvfAWZaRpd8eRwn0dPG7QlWA9Dttb6OEpQpikYjWVZ5EVJi0Xgh5RlRVFVZFmO47hEnS6f3buH59j0B2torXFdj9HZiKpapYCFQbCyXSlBXmrqWjMcdBhPZ7z6yqss85zRdEEQ+LiOQ5KluMolDjrkxWodUtYNsqgAwWIxIwxDbMdCKcX5eILv2hyejfAcB/OCT4SSjEZjHMcmK0tcBZ5tMU8ymkJQVJpW2RRaIwWkZYXnhhTZAlsp6tXQ9oWWtcELY9JywmCwhkESd7poVvvVMAwpyoaiqFgb2KxtrNOa9gVlXWDM6q1pWTZVtSL7z5IllmethPKtRrgui0VC0bRo22W8XGJJSV2U9PtrzMcTJrOVXrbf67EoS2ha6rrBth0sawUQ11oz7HeZzBdUdUHTaNIiZ2d3hwePH3Nhc5PQcWjKF6b2qmajF+B7Dm7go4FZltEbDnF7PdzxhMPzM9pKIPwQq22ABq2h0RJdlnx+7zlWuUlvq0cUduh2QyYnY9565xaLbE6Ta/b2Nuh1bKo85+KFLtV8wef33md3b5fKlHSDDlltUwtJ1Hd4JdoitBtIGvzIprnYo3cpZ/1KzsW9HdKs4q/e/zEvv34Lt9/lnd/YYjk7p6xiwKaqHJKl4KW9Vzg/E1+sOI+efMY77/4SJ0/HWK6LMAm3brzG8/3HPHn0jJs3L3AlHPJwumDrldvc3d/n6cExelbwcD5lcSZZ610l8FwC32U+m1GWLUJ5rK/1yTJN0basr13m448/57OPZvyrf/59Nnshi6RBDW5ixCa3b1zBEQ35MqFvgSMVaZZRNi1GKrJkyeHJMRvbF/E8H101TOcLhLKJen0m8zl5qfEiQVGsEP1SCjzPQ2JoqpLGQFE0uFkF0kLaDnleUdcl/eEaeZ6RZzlREJEmGVJJTk/OsC2HoqyxbZs0TfE8D4ShaCraVuMpn7SqccNoZUx2beZJSislRimMslasWtemahrqvMGL+zzaP2QxH/HSzjpVa/CEoG0aTqczBhtDkJKsyDFSsMxT5knCFc9lNpvRDRyWy4SgO8SyHVzfx/M8FkmCY1mUZU1VVQRBQJpmSNtG6galVglfURzj+D5ZVa/cLY5NoWvKxpCNx8yTlLWOT7/bQyqJkJJlnoFSKMdG1jW6qmlfDJO63S7T6RxpGTY3tnj6fJ9Wr8zWca9PkqQrvXDbsrU2xFEtpi5wLBvaik5nAycMsYKIQLd0hgOctmW0XOC6DqPxmNiJ6AQBRo7w4gDLq7Fin3/zt77Ch995Slqd0MgG/JL1Pc2yuE9WVPz2b/4+j5Z/irBsLm1f5gefvY/04Fd/8z3SpuRkdo4JHB6dZSTZPm+8ucegu4FrSpKTBaHyOT+dcvcnz7Di9/jSa+/hDro8+lHJ3e9OeOMX3iDeihHmgMMDQRzHfPPb3+Zbf/6Uq5ffQlrVFyvOP/i9v8bhvuSl+CIqLvlbv/c3+d/+x39Ar7tOb3iRS1djZqdz5mnGD//xvwAZsLVzBT0SdJ0+7rrg6cl4RaKTCtcJcRyLNG9XwwVdU07mvP7WO/w4NfzJN85Yj69yPDY0Tc3d797hy1/929zc9SmnY0SV4XohbVVTty11KzibzlF5ybUr15lnJWejCXYQkZYlvcGQWbqkqms8L6AsV8Zlq1qd6LZtIzEs5zNqaeEHAWVjqPKS07MRnU4HIySz6QxlOURuwOn8DNf38aOVksZ2HCxPsFwu6fS7lE1Fp7PK/xDK4nA0xnI8KmnhRyHz2Yg0y/GjGGlZVFrT1DXKEgjAExZnk5Ra2LidHrVSnE9nNFWLMoogillkOY7n4QQhyWxKmqRoBNJ2qFqD64U084zJfIHleKxthKssFgFt22CkwvM8giCg0YYWg5CrgdL6+hq11iyXKXVZgm5Q0kJKi8PDfZTtEXZ6tFW5KvSqpNA1+eicOO6uUsSKmqasSNOE2nFwpU3baMbjY/rDAUUyY297CzA8evaUK3tXeOPV25wc7NOkS4xo2VzvEgUxly5ssb57iQqLoDfgZJYwSTMCP6AR1soK14/pByG0c/pbDlde6yFjQepoqp5gc9/Qv7xDZbe8984NttcjnKzmO9/4kH/wD/9Xfun33mV383XqrGExqtBFyV/8+V/w1b/xy3idgKyo8DsdCArOszP8tR7CcclDQVvPeON3LvCu+xJep8dHH/wpTw7GGNVFmZLv/+l3OD444fLFGyTJgt/4zV/h6EDz3r/xHicnGbb3s4vz505rOyLn6//4n5CeTPiFN9/g7/8v/xOeExP4Qzyvy3d/9D4fjguezSEpPCyrx2yUUuQFpamoRUJ/aOgONGVzxmz5nFrPECoDkSBUSmSlnDy9x61XX2f76jvk9kUS6wJO9wp7114lTQuePXpAU+QE/sqDGAY+TV1TNTXaQBxGuLbHZDLF9wOkXOlH03wldg+jaEXUUwrP9fBeeCLbtkW3K+yG54UE4eo6WFQVi+UCgCAIMMawsbmGkqsczSzL0FrT7/dXn6H16ru8iLIrioK6aWjalrwoQCoWScbzo+OV2Ntx0a2hRVDWDUZIyqohK3JaVoqnqNtj88IFUDbLNCPPC7RusNzV+w+paI1YKYWMwHJctAGD5Gw0QihJnmer7JMWLMsBIXEclyxZpUlX/x9AujWkabYSrTsuddUwny6oXqxJbMtBoQj9AKUUbaOp8hLPdtC6pdPvYQSkeYZSiv+Xszfrke3Kr/x+Z+995hNjRs6ZN+/MO5AsslisYg2qEroFt1tquS3ZRjf84Cf7w/g7tIGGDcONRtuW1Gi7NZRUqpJqZLE4X5J3zDEyMuaIMw/7+OHcIvwiPfA1EUAiI88+e+//Wuu3XNfB93yUaWGZNnVd4790Ly1mU157eB9LGvQ6HTr9PnESs1jM8VyHjW6XrUEfKaHT72C0XWS7RWd/j0hIVqWmVjaZhtlyzXy9pDQq+ttd3LZBf9+ltS3wBhpvo8LspAQ9h2W84NqdQ15cPeGnv/4bJtEpv/N77/CHf/QHPPrpmPjU5vmvppx9OOPu4HW27QNEaHP22RXnn59z+tkntE1J1/UYnl0SrjMOjq5z4941KmeJ08qJihHOtubBD24wMS7oX+/hOg7XN66jdUBNix/9za8RRgetffa3X0HUna+2ONNpRBXGfOONhxi64HBvj+V0xemLc85Pr4jimkenC8brmhIPpdrNAMAoWaYrFvGMKJ4RJXOKKkKaFY4n6G96mE6J5RRsb7UJwykfvvceT54e8+J8jLADgv4mruMRLZfYjsVGt4MtJHWpicKo2ZkQKCVxbJuyLDCVyd7eHlLJ5kGva7QGy2weQK1rdK3RWje5yapseLFSoKRAVxV5XpBmBccnpzx//qIJSFuK0cWQMIqaktai0aYc16U2muOxbdmkaYptNaU9lW6KeiyrqWuv64Zyb4jmfleVmqrSTe1cu4Xv+3hB071iuQ6n52esVuvG/J7nxFmDuLQdl7zSjUE8jpFCYNAAqQutUUoxHI3Iy+aemZVNr2VNzWq9otCarMwxhCDPc6Rs3DhSiOY+XGuqIieJYyxlMtgY4Fg2yhBsb22xu7VFHMWkSUpdQ1GUBEELy7JJ0oS6rnFdF891KfLGoL9YLNB1jWkpdFXS7XbxPAetS7q9Hqv1GtuycUyLLEuJXkK/g3aLztYWuTCQrosWklJDLSRPn58wGc/I0hzbtnB9k42dFrfu7lMZCetkjlJwMTzm4Poepc7RwM7uEeNpyGy+YLGa89EHn9CVA/7dv/m/GL8Iceo+6Rr67QHD0yFb3S3u37jLm3fv4taCMsyxDQuha6g13X6XWgouR7COumBukVctjm5/nSh3EGKbq3PF7Tu3sByF7biMpyGffvKc5SpnY2P/H1x//+ix9vhdzav373FwV/HjX/2Mxx+PONx+wNVkSGb1KOmwYUtGkwxRWKyNNcIOmaczgsEBdeGgSpP5PCevNO1+lyjX+J5BZ7ODsVoR08MiYWsekY8+YbaY8yQ+5YUysSu4c3ANyzXJ1zE90yaoBGWhkYagTGPayqLtOsRZzs5mn+F0wmodYZsOdaaxDRPLsIjyhDiKkbaLKQS1qKjKDM8xKZVGlXmTsHdtqLsURf5yESo6foc8STldR1RSEpcl6yRlp9PFNG2qqsYyHco8py5qHNPGkFDVFVWZkOeCoowxLYMihVIZIBS6rKmVQRpnZFVG5SqWWUVeg2drWrZBEq6olEVsCPI6RRlNV2eWJPRaDjqM8F2bRZZzMRySpCmG32YSJUhLEUaNjqaUizQNVtEKLQSrKMMQBkWckGc5tqnQZU6exVRFRJKtse0BuirJkhpLSmzboqw1Z3qNthzmSUYtTSzbo9+H0eiKsleyubnJcDhEFyn9VoeZrnDdgDxcYtmK89EFYRqRJhlCBrSCNstwRdtWbAx6CJ3jK4lEUdQmO4NtTMul5RlQXLKYL1lcjijCiEC06XsdOu2m5dx2K9LM5PJ0zfbhFr3iBmavYJDlyKjg62/8t/z0/37B3z59zHe/7rIYnnN7Z8nhG/f54ukZY8Pk0+ySnVslt+/s88n7xwxfrLm3u0+40oiVyf4dC8+JEGbNMtrio48Vf/6jT+gNSr7/O7e4efsWIQuCmyVH37nLn8UTIvEpr74z4OxFQjoy0QKkv8ZS5VdbnDPexd+4xp/8h4+58+AdjFsfYtvn+N2UWTLBkX1Ohz6GthBlRToL6XRq7g62cbwADZRVRKEFWrgoT5EbBt2eT1JVSFdRlGsCu8TpdZiPHHJRM5lNaQcOlqi4dnCXllmy3ekikpT1eoWwFfEyIUvXXL9+m9FwTFYURKUmzgriOKLVbmMUFW2vxXo6p6w1vu8jbY+saI6hUINBEwjOK0wlvvzcOmqkksl8hiUVlhdg5yXZuvGpzmYzNjc3qaqKOI4RQmKagjSLMaSBrmoQYAhBljWIEmlU6CylFjY1giSrqesSUdvURYUyJGGYE7R7KGkgahdT1ijbxQ981ssFw6srWr6DaSrmqzU1BrZQFHXNbNEI/egSQwiqvCBOEizLwrJBKZvVakWr1aKqwDYtyiJvnECGQV1r6lpw7eg627rCFJKyrFgu5w16peUT+AF7e/tMJgvW6zUbGxsN/0jKL68OWZZh2zaX0wmd3gZxkqHsjCTJCdoGaVoQuAFJmBJGC9qug+uYBIHFqw9fIY1XLKcT2oMNTL+N7bYwhElNQ4aYTqfEUYRSTRu6pUAYCtvZ5Pwk5WIy48NPh7S/KHnrm9+ms/eCZHjARz/8iIcbt3jtIEDVr3H2wXP++z9+lYdv76H1IY8+XfCf/tMjbt58wNGtPk+ffkZVbFLqglSt2Ty6xnCyYDSvCA2DZZ6APuG1714jcgyefTbk6qLkaN/g4Z1bjKYZZrLBd94+4uefTNCFC3XG/mFJ0E7Z6D1mvci+2uIM0+s8OR/R3fbpbszY2epA6RLHNfNHC8ZTjTI1nmtw/XoX3+5x/84ee3tbnF2O+Nmvf0mwafPgtXuYrs8XJ+dcrWI6A+hYPhUCnZXIXLC+KJlNY1xfsiF9bFnx4NouopwTWC2oEqQ0sFyLqyh8GX+CusgxXRNsRbSO0YbGdi3SMGJ/axtXmRhaU2UZWVmhi7zR4mqNUg171HIFqS5wHIvJYk4tGxSlNC3GkxmWZeI5PmVZUv//kCFp2rh6qpcVBXmeNMxLBFVV4LUC0lQ10o1sUB/SElR1QZwWUAtcp0NZ1RRZiSka3pHjOdRVTbRO0VVD5ouTnKKusW3FKo5oBT5CGWR51iRBypoKTaFLTGmQJkkDqTYtpGlT6aZpIC+rlxA1TaErqqrENhVlkeN7boNbEYLAcymznOVyQZUXWC+lkDxvjpG+33wfv82nmqbZ9I6+LMkFKDFANsVEZVUjTIui1E0xTG3QbnUQSlLXBWURYymbLI9wA5dabFA3HUW4AAAgAElEQVRJRctvE2cFru+iMZjO51xdXbFaLFDU5DpFKIiSJkjfVh3++q/+gquJwWBrhqeG/P6DQ375458gyhY/+et3GV28YLNzjW994w/54umfcO1rfV48+QStN3jw4BWevThnsbhkGY3Iq5TtgcX1Q5+syClLjyhfIisH8ox2x8RgguUuOTo65O7OdUQJL754zNVIsdv3cVTCd373gPlyxb3+LvOp5vDQ5OjQYzG9/GqLc//mN/nJJ/8L//z3vs32Rof3fnROWxwgpIulSw52Dc5Wc3qOwde/ts3ORpd+x8MwUhZuxJ1XXG69cZ3BVp9SKAqnSz9rs8xWhPmCtCjpmtDyfS7DS6RZsb3Txnb7eKqmT8VW20HlGcoyqcuaMEoZXk3JDJBIouUKtx1QRDG2Y0GcUNcayzZR6iUe0jIxtSZKMhzHxxRgliZQo+uaIs8IgoB2y2edhGRaY9oWBpIoTlGGZBUnRFmCbTfeToDFYkFd11/uHFBR1zWtwCeOa4qiaAYotdk0iNkWtgQMSVmljWZZFNRFDroiSmJsUqTV9MWIWuF7Pp7rMJuPsB2FMBXhcv4ST2lSVDWd/gaX0yVxnqN1ju9IpJQYQiCkQtea+LeIzbIkKwqUNImzHMe2SPOykVYCHyUlymom2UXWHO1NKRvTRVlR6YS6BsdxqGu+fGE14e/mc47TgM/CLEVLg7jIkWWB53kslku67TZpmiGFpN/rEi4mUNVIWWPaCi0FbreL19uglibz1Zq0NBrrYFmSvPwfGwKE1birtDA5Pp+zePSCcG3x2sNXaLX6nDydMh/dJFv3+d63/xn/9n/9n3nt9Zv4Tpf/+FcfkJRrxN9ekIRNpG6j9wqH2wM+/uKX1NaCW/d2uHWty/Vdl9kC8iTl+tFN/K6F19IE7RKlCkx/k6cf18i0ZjWas5jOePr5Evden+1NiMoRabZCFz3+7odPuX6ti/dPH9Dtbn+1xfn48j1+55+9xRtvv8l//g9/y6NfLzjodmn1cl57rcOyHtKZufzu925x7/YA17IRhsfT52OG02PsrsX5/JLhakotLVZpySzMsVt9oiTk4nJE//p99rYPWO1GWHbCtVu3uBpP6CgbP65wDYMN18esJMs4ZLYKEbbPYjJhEHSazpGixFUSQ5o4VkylDSzLIa9yongFtSAuqubNriviJENIAVSUZYEwmrq/9XqBaUryQiOkYL5cY9pOs8Ojse0mmiSlJM9zlGq+PsNo9LyNQZflYoVhNOCssixxHJcsDwla7YbZY5mUZYVSFlIqwjhEUtF2bdrdFhubitooCBclluqgTINVuCJMYpTdIq8L2v0ObsujLHKiNCUrKsraYLZYY5oOnq2wbbeRq8pmSFQUBa1Wh7yoMEtNqQuoa1TdSCm1EC/bwRqHj5QSJSUGjTZXFiW2Y1NqTbScY9sBWteNRPPyJaOUamJraUpZlgjL4mo2RyOoDUmWZiRxiud4CEOSlwXLRYSFQdtvXgyW47Cxtc3VMsRt9xFu0BwlJys2N7dZhxFhGGKiMYSk02vR2e4zmS94ejZmNJ6SlzbDq5AXL2aYUvL5ry7Y3+jxZ//+/+D1V96gzBJQNfNkjZbbhNN9LGlR5hmWLvFkxm7fYvfmDV576xqdlgXrkKvxCbNlRlSUePM2vb7P5paNQU4v2OLWoSYdrlC6jS73qO/02b/WZbOzydmzpyQrh1nucG37e/zmZ+8TzU55481t/vj3v8LiPJtc8Pbd71LrTR4++Bpb0mJ59Zyd/Q3uf/M+i7TD+CSF9KIpoUk0wyvN45M1otWFlk0qaspSIw2bq9mY6TLm4e59LscpyvB5+tmIyYsVhzsbxNmS+foZjifp+S10HLHZ2kTHU6azJWlWkWY1cQ1SuQRBF7cCo47xbPflvc0gaPkvs4NLal0ijOb4WtYluqqaBwkT27EQRkWRJk33pBAv+yAjMG0c18OgwVpatkOex1/uDqZpkmUZSil6vR5BEGA7gjiJEdLA89wGrylNlLKoNVQVRGEzNdS1QW0YSFNi6BJp1fQHLV5/YxPHhcefjVgvDfIsIUpDHM9mPJ2wd7iPFIooTaAqKXTFYrUiLzXCtLE9jzTPaHU6lHn+ZTu2UIo0zym1bmYBZYllmQjTQgNVUTQcXQdW4RrTah6NVquF67pkRoZlWeRxTFVplGmyXCwRQrBer9Fa0+l0GvqDbnRshOD5i2N8zyOMInzXazpNHY8kjnEdlyjKMZTGlBau7ZHEBVK57B3uMEsyAs/Asj0eP/2Ms/NLxpNp0zze8mn7Lhs7bUzf4+zxE6LSIDdstvYPWC9XVGWJHwSsnpzyu18/Ytve5M69t/j82aeczz/mv/qj3+WHf/OEX//9CbUO2Rz4bPdsLCPmn3z3LTYOApSf0el2WYmK0llT2Guk0yURXR69f4ySJTeub+EHIdn0HLGK2Wzvc23nNlZrRCWmfP6k5sWzMd3uJoYLtlvw2ltHJNmMH//ss6+2cx7t3OKD9x7zxfMZe9sZ+w8jfvAHd5hczciLJaurjGhxzMZ2m/P5mCi1+Ol7l5RGm97WgFqATiGtDObjBMfa5JWdiuL8BC+vaG0dsXg8w9IG0XDKwFXoIgFt4LsFeA6mb7MchlxNltSGRV47OLZDpWUTK1ouCDouYZIShzGOslhlJeP5HMu2kEpBbdBqtyCMyCuN79rUNdjKxECRVxlep4PruSTTafNWzwss2yfNcqqqJCk1wqiABgZt2i5p3OiFpoTXHtxmb7PN3+mc8/G8AUVLi3UYY1smVRo3i9+wyLKma6Zh0poIXWObJkIUXA1PuHFji1u3thldhsRxQbvXJc9KbKdC1IrpeAIUbG50qCWM50vKWuB4NqYt0TlkRY7rOhi6Jo4T+v0+55dDEJK0KBFSYJomplIYlSYtK0zLRgjJOkoo6gWG1mx0OqAUhmqGTHnRHE9rQ5AWBb7jkOcplmmy0e8wmzXVkDWwWEWcHJ9w/+494pdpIUNJkAbL9YJut48WIByPMCsxDIVrNxqw7QY4ysN1PbJsyAe/eZ88yynSFKg4vL5PK3CwO5Lx1Zjx5RhDSwwtuDi9Io5iNro9pPR58uQTuu2KOl2xunxMYMTU8QRRzrl1dMDPHz1B2T6rrOTHv/wlUpQ4vW9itzyqZUUaVpxOQpS3y15vA9P3OD+POBmu+PyjFzy4F/HK/UMkGX3XZFbniGqN27d5cXzB6TNBuKgosxmrxYK7D64x2N3kT//jzwlL9x9cf/+oztkPDG7f2uJnH/wMtb3D51PBVb7NvBxwuYpRLcn+nV1Ol2t+/mTMT5/NOE4t3L1dnL7CMua4+ZRsNSZP19jlglfba37/ruC/eBBwe6NgXxYEUciusngwOGCrcjgwO2wql07gME+mrIuKRVkRCwm2S12BLSRFmVKZFct1yHK1bpwstSRaRojKIHBa2Mql3e5gyBqhQFAROCaOkhhVjcRESRfLaVFoSVEaCCRSC6SukXVNVeRQN4dbKRXSdEgzjWnZzZ2rSggczfe+dpfb+5u0PQ8QKNujpLHdObIp/KEqMGWNMEqMWiNrE9dqowwJOsdIa+oIKAtK1gg3Y2PXobNhcf1oi64r2OoqHr6yQ80cPzAI45BK11hSIQxNVpWUvwV6VRrXNMmiCMuycP0AbYBlW1BDEsaUaQ5lTVVqoighqeByETIOExJdk9UVlaHRtaYGTNMmTDIsx0PXNVTly9rAhG7gIQyoygIhLHZ2Dym1QZgk+K0WmzvbLMIFSZkxD+eUqgLXZbhYM1+tMOoKXeYUuqbEoMxysiimLkp0nlPmCbYjObp9wCuv38WUJuF0hcw1RAnVKiacrMjXBfG65NNPXvD5QvF//tU573264OJySKvt8p03v8HP/+bXPLizxWx1xeV0wmSd4w1uM88t3v9sSFkFTIc57/38EU8/OyZeGghZU8kTupsLvvGN29zcvUU+q6D0MJzbTNnipNJ8PH/CVR4T1W0y00OxzdmLFNfeoSxcolBR06Uz2PpqO+fj8ZwUxTe++Q7d1h5ES1ax4HJZ8nx4xZvfeAdPKkwRoJcLhCF49dVdDm4coNUSVgkqrjBnCUGYYhchLVvRtjRRHrJjCsxWRe0prt3ZpFCand4O7Vafycmctt0jXqQs0xLL8ZDKYh1FlDWYpqCoNEmSoYSiFqK5HyZrXNfFcl2UkghT4bkO89WasihRyqTGwBCCsqqoDQlCEsUJeZGRF8VLgl0BGMiX8kBVVZiyMSkLIZryWschzwpu3LhBlqakVY3pOGxtbbI6OacbBCRxRFnphpguGkeNIY1mUiwUzci5pq5pJAg8ijDhMk0ZriNyBAPRpogNzMLg6AgQHhsDD/1ZyWya0W93mM8yHLuLrkqomgqHLMlwg+Cl0b8pRHIci9U6w5EWEkGNJi8KCl2j6sYOWdSaJEnY6LTRL4/5jimbkmGt0QjKvCDwfbJwjSUkrmkhjBrTcbkcTaiKAs9z2N7eZHJ19bJ8ycG2TEajS7qdHuswpNVyCaOYfB0zX4WMJ1P8/iauMJgMr3g+n5DnBYvFgtl0imHUvPP2m/R6G5jKpNXp8/6Hn7FYhRjCxLJNdlstlLKwLJvz8wtWuY8WKcrI6O10MH2Da0d98GIsMeL7rx0wupoiTIuB9LlcG4jE5MUXx+xutxjXBbmoWYSKZBbQ82usVkZ/L8Df9KmSDnYqCC9HuP0Au91iup5Q73vsv77FwryEwsQLPa6ulpyPLnn7W28ijRZZGn21xbmWEXklkYbP+PKMcDLBNCKuohUrE0ZGgeP5DI5eoTgfk4ymzOdrzGCCP7DJIov1i0u08tnubLManTO8yjBSkxyFLVwG3gi/44C9ZKUL/O4WVtdGDiV1WCKWBYZySMM14WJCnGss28VXLvNVgiUleW3gd7oUdWO8trXRYC11iTQVZVkQRwlag1KSNMvRCNK8QlZGg/mIQoq8GW54vk8YXjUPrwFKKbQuSdMEoQSGsiiKgjAssS2B53kkacr7jz5FOSbf+tYbfP74EdePrjE6PSZOC2rXRQkTpfjyvCKlxKigKArsjsNg0GJvu0scr1knJetSgOny2YtLWGXoeUIhXY5ubfF8cs63f+97/N1P3ucouMHzpxNsxybLNd1igziKcTwX5VoYumxeKGmKKSSuLcnzphhJiGaqWwnFLE5YpTlJElMkEVsbfRzbYTm5QgVeIwsbBlmaYioLJSTT9ZrujSPiOERXBX7QbgDYRUG77aN1iZAGURRh2zaj4ZDAbz4znszoCMl4scTCYLlOuRrPaQ/mWJshush5/zfvI4TEshw8N6DbDbh95w5pknFy8pzpIub56YjpZIayTHhp3Hddh/V6hWnXmNJD6wjTq3E7AbPFgvTTK8K8psxG/Ks/+j7/+7/7UywnIPBaZLniapZy8pNf8PobR/QGNj0DPv7gmI39TdJ4QVyds72hcaycJ08uMdYTbENgjEJqU2N2YdWP2O/X3LjTY7DfZzra4cc//gWrVc6TJ2N03sYy/a+2OHd2DJICxvPnZIWm3a6p6zWtwMDe2mJy9QK5tmgHh1zNViRZw4SZjWaoeptspViHBcpOGQws8qDPLAlJQsXuzi6+F+AXORU5o3hE1e6gzZpSgFA2ZVpRp7BYJQyvxhS6pt3fwLJtzkaXDeyp1yVJE0zX53J4RZzmlLop5wlsF9tuprlV0WQWQSIkxGnGKk5QliYvSwJbQV3TAO1qlGlSlI3BHhot01ImCANe6px5XtBp9zg5OWbQ88nrbfb2tzjc3+Zwu8VOW6GKENMwKUpN9dvyXNsAGk+urBVCSqAZMlVCUiqbysi5uroizqeUWU5HKQyRYnbvYbQ2ObjucjKb4W71yXWOtVmwteWTF5LVixhDNe19pt+0i9VaowxFXiaYjmK+CKEGaUgs00Y5JqtVSC0ENeB6Ht1uQ0AQQoABYFADnudT1IoojgnaLZbhCtAURU5elpi2TctUZFozHI4IvAApBbrSrFZrgqDVyDC5JlzHFJWB47os1glRnDG+miCCc4L2BlKafPLJp5RFRbfb5+joAM8NODk+5osnj4gKSYVC2j5J0lAiZvMl9XTBfDEl8H1qOUHaC/YOO3z86SfkkeDVu9/jlz/5FdIa8y//y/+Gk6sLXD/nsHfINM0YtLuUecYo03Qsj33H4qPlKZ88/4D9OymRPmPjzV02PZ9nxRmjuMBSLsloia4yun2DQVeReSOoKnZf3eHewwck5Yq/+PN3efbsFM/t0fP/YcDXPz4Q6tqsS7Btn1ZHstseQNpAcC+nEVWUEU4zUsdgtaxAQhRPULpAzAR+mbB51Ma0bUw3QxqSQmxRoUhcSV7HKE/QH+zQKRIyYaFLg9UsJItz8kSyXoRcTWdoIdkY9DA9l+PTc7rdHlpIoiRHKsXZ5YjRdIbtB82u+fJhV1JQ5DWmNKkwwBAIJZBKo6yGNqArDYaBbdvol0L6b80Guiy/3OVsW1JUguKlyO55HqbZGMihZmd7wM2jQ1wleePuNfb39/j7H2lULUmRSGUj6gLDqBp9tSyoazCtxlC/WCz4KJ3j+D5xUuAJRb/vYRg5g77D0cEW7Y2bIHN2drc5Pp7w2qu3+eLRM27fHtDt2mxvHTK/FvOLX/ymwYMaMakoSZOEXrtNuJxRUiADgUJSZCVpGaNMB6WgKGocx2Kz16Pd7lCUJabZHL91pdFVxdbmNqdXM9ZpzNH+Lut4zd07t5lcjYjyhEoY5GXFcrnAMKDV8tnb2+Xk9IQwjPD9DmVR4PktdKUxTQfT9sizJaPxFGkqhOuTjKZMpzMWixXROqJ/r08cJTx+/JTJ+Jzh8JJZajSusCgCXVNVAt91qesaiYsuLX7n9QNu3LnD7dst/t8//XvG44xP9SlC9ViFUz785IIwEQhH0N3cIK1iJsspOTk3vQFZ6vDhx8/wzTabbdDRiDS2efZxzHJpYghBomG2DHEqjWcKup5FNl8yLHJKTP787Kf4QYt+dxchJWVVUlYpWfIVAV8P9/b5/GJMYgmiKMQatBh4m6wmcw5Um76qyMqS0UpQ5TXdnRaOu2JDGRxYMHwyp626BLZLlUlmq5yFNpBBh5/94n3KZMG33zlEWSZBv0cxC6nCmuViSb6qUbnDMi2I04zdwwM6vQ7jxQzX9/D8gDQtqIqaOFszns4Iun2kaZOtVrRbAaapENJASoHnB4RJwmq5AqnItaY2GjlDG1BWFaLWmC+lh7rWKGUjiqKp27MshKgp0oK0bHZT13WJopjN3gbjyYQgcNjb3iCajbm222dvs82t/U2mqcXFoqnZQzd31uaXCExhopRACIiimGlR4hmSrtfm5q6F51l0Bza1WlNZU/JE0eu5jM9GPLi5xWw85vZel4O9a4xHZzhFxtvfvctoeUZ34wZ/+cNfEwTbGLpknSfkuqAdBDjKwDIUZVaShznSEFi5AYZEK0G302n00Tx/GRrQ2LZFUVXN1aAsiLKUi/EV925fJylzJssZeVGhHJ+oyFgs5nQ6HdI0ZW9vjy8efYFtuvT7fSajCYHfpqRiFiaY0qQTtFmHUy5HI7BtUtForlWpX4bBCy6GQ2bTEYZRkOcFUVyyTgp0VSMRlJWgyBsYWV3aVEIxUAZv3tpi77pN9Xvf4Id/9SG+Z2O4Lmac8if//keQ+2QhxOs1gaO4ujjh8OiI54/OONM18XBJGi3YO9ogiW1Gly2Gw4yirkmp6PhtWpZNsZyw2XPY321huxUvLka88uAdfvnhEMfLKcsvGGzs8eTxF3T2HGznK9r31t4Bs6KxpP3q798lvkz4n/71v2YsL/jhu39O3+9QzJa0vID5dMjR4AZvPbjDeHrBIim4qkomzycU0QXSkBieR93qMLyaUBc5lukwiysWz4bsDnaJJwl25ZHONVUmiJKEs9mIg70Dgl6HMA2pipTDfhtLGKSuRViWJKsmr9kKfLKipBv4BIFPTU1eVsxmC5T08T2Xoq6JiwKhBKLWSFFjSIOiKkiynLalqMsKJ/BI0wzTkhRlSXOilRRlTFFUKLMR0bOsYDaPkIYmWsyp0hjSkP1eQNvSfOfr97mKFb/85DnLdYWRdYmrlFKkCEOjrAKlBMs053S+oDJKPHNFy3axXQvlSDbLHrZdUucpBw8turaDF/QhMbFqj8vRCRsdH8eB0dUZXUvzz7//Td57csorr/V5442vgZb8xV/+guNTk1JZ9IIMX0nKXBMMbJzAxlvUxFc5gWqzvdUlcG3IctKiaRvzvZeQ7CxjuVqxt7/P2fFzVtE2p+dDkqSgQtJptwhXGXlS4G0FWKbAsQVBy6YVdJgv503Ur9OlEDHrOKTKIkaJZqPXYhyVLE8viauay8mStIrJspizYY1jBQghUZZmmWRYwqLjmKyXa9q+TydoE69XlGHErZvXcB2bMLb4u5884RvxNeYXBb7ostXrko6G3L67SXqVcufWPrnOcUg43PBodft4QZfj0wtWq4wqVdQiI6+WtLs2B2aPg2s3MUyfz55pwrggT1MODg95cOcml6ePOT+ZoQ2b/d4+W2JOnWmenZ2yTjOk0CxWU9qtr0h8/+jD98gKzZ17r+NaPq/cvMU6rci14BvffJMyXOHttqgdj7fsI9AZ9cWcbJZzOk7pObsMFKyqeVOzLhVhkaGU5P7925iGwrreIqsLRKHIKk0eaURtow2Yrcb43RbdzQGTcEaUxniuhSsUnmmhEMRJgmm5mPbLEX8UE/gBZVHhBT6L5ZKsrDBomK/a0CzXS7xWGyUMbLPR+XIE1I2vNE0SgiAgyzOUkOi6QilBVdSNLZQaKQWWbWPQZCXzrGAxHRMu5zjUbHRbGHXNwwe3uE6Lq3XI8emYfBkTZhGGUhiGpCprhDKpheDajfvcfKWNIuXs+RWO12vymth02z0ef/A+494l588SDg4OMU2b8eQKpWqeP37O/sEOEpPVbE6sTZIk5WB/j0cfvYfv+KAjtCg4Hl3Q37rGnTsPm3vV7IR5eInbtth2BojExrMVhi6aK7YhSOIEQ4YYUnJ8ckJZlphKcevWbeaLJdPprBnUtTukueb47JKW43N6fMobb75KmiYYhkG/3+Pk+JI8rxiOLtk66iOUQREVLNcRUZpieTb5fE6uNYtVjFQVCAjjCMtsk2caigwpTYQyKeKCoOVhmwLPgc12hzSVDDo108kxU3mE7fo8/2JN4A2QVUQ8ixm0N5iPL6iymsHWAF2X2EpxtLfP5tY20/maDc+n57YxLQfbKel0Dbyg5mqWYBinXDt6wO61N/j5zz7l7OSClr/NbDrm+fNjHBOiOGJ0/hSdjInimO++fZ9KCbpbXT769BHRavrVFueWscTZ3OTpZ+/z7DKkwODZ8y8wspBXr23S3bRxlY02Wzx69ITjx08wSwPP2aTfv8P1vQOq5QnZaskyjrmYrFmbkt3rN5hcDklmS24MXuP2/fu40uOvn/wYmTrUZUVdSpIk4/6dezwbXjELlwStRqMTsqG4l0lKkTUT40pXLJYrwijBDdpUZQWGJMlyhLLIqxpRVOSFJssLrKpCiObP13Vj7YPmbvnbZMVvDe11Xb/8Wd5U7VkWhkFzbKbCthVUBuF6CZSUOqfVayEwKO0MR7bZ2e0znl8hCVHLlKJ2oXIQhonUEqVqosWYxcLgxq0bZE8WVKuUskiwDUEWnbNzNME2Xao048XTT+l1B3Q7PTY2+gQtly+++Iyg5XKyHqHCjMthzOnpFdd3dhBuyq6jOM+uuHuwgykEn378ERvtFp22gyAgnK0xdIljBphCURUlUZw04fGmZBSNwXy5orOxycXFBd9551t8/tmnJElCu91lOp0yXzfmjINrO5ycHHN8csrGYIOyqonTjNliQa+7weX4CmaKbtCjzNds7jZwMWkpJpcLilIjaoWha0zlvCRpuKBz+n2PVqeP9js8Oz5nPZuhRY60cvp9H0WL7c2AcPY5vc1XsV3JKi54fnLCZ58+Zf9Acf3mdXruFqfLK6KoxHEdkqhgPlmiDIvVYkm6WiOUpNOpcdwa24K93U3aXYG0PS4vnnA1izCKktsHG6g6ZDo6oRVUFGXEH/7xDyjrFBlojLoi02u6rRbUK+7d2eT46fFXW5yeLbh+44hfPf0V1sYWv/r8OTpd4YucKBxz9/oOYZGTE5IqB7V/RDVdcjlecqsH2gTr2gbzxTnHccjE00SOYjg95lrQp7Xb5fL8gkF3A4Itjnauc/Z0gjIspus5+wf7zFdL5nGEYbsI1yWPIgoBQlgUeYw0JKs0JytyJpMJW1tbZGVFUTdgLEOalLqkyDVFGrNKIhCNA0iKJheqhMCyrZcJibqBKEMzUFKKsmzKfGrZFP9UWtNptxESZC25deOI42efk0RLqDVCSVzfIY2TxrCvBNs7fT56VDQPnzQRlQXawHUF3UBQi5hlMeL4yZDTF0+5Ol/ha4PdXouu73J/r8srb3+HZGowupwQrmMMYG+/g2WZbG72uBjavPPOWzz67Mc8O36BW3V5dWubbDwip+DhrX26r+2QCAPXTjFdhe/XfPL0I44e3sbdv0Y2VcTjCqqasmh6W1ANRM1xPUZXYyzLJQwjbNvi+PiYMAybsLjvE2fNfdwwDFzP5c7dO7z77q+RysIwLGazFa/ce4WL80vu3L3DcDV72ZJd0+73GI1GrFdLNAohajzXIy8j0jzGrA3yosR1LDqBi2mEjFYrpGw8vYGlXhIWfBxTQxkx6DpcLE+4iiSmUkjp4Wxvkqqc8/EZEk0SpYyLEttRBG0bJQ2MuqQuMm4c7pKXKYZZsD3oUVURjz78GL/jIyyXKMxJVhG9oEUvcBE6Z3w+4Tvf/hppuWJjUxBlmr27R+RFxXI1o1zn+NoiTWN+8E/e+WqL893Hp4i9B8TSIZcWlePRavksrs4Z3LzHZ+fHZMrncr1gteQVgicAACAASURBVA4powVbCrodyenyCYsvhgiVYygTxwtQeYFE8Oqrr/Jw74CTjx+RVynxfE3X3KTr9RnLmDQqieKcrcM9zk/PKCrw2x626zOfr7DbPqUGbUjiLCVKC8qqRCgbLRoGalmBqGqiNKdCUAuJxkBaDpKm3s5zZLOYDAPLNBFKfrlb6pcSSpqmRC/dNcv1GmmqpmJPSXRZ0Gn5CKPiYG8HaWQYhoFpuRimRWVkGLZFkRdUVcKgP+B06VBXMXUlGuqeSvCdmI0dg5sPH6I8k9PLJaK4jhc7eLlgv9/mw/d+TikEXj+nVBXepsl8tmaej7Fqh8X5hEl8ycn4BYvpBYdbm9S5x1F3m2JcUxURqBU3H+4iWwHVfEq+0kTxlLJvsppdoISDln2EkoTjBVmeIS2rOYLbFlGeE+c5ludiYOA5TULHtm06nQ7tdpt1XOD7Td/JOlrTbrVptbqUlcHN67f52c9/hh90sD2L8+EZlWkTFxFlmhJHa3r9DhpNsS4RpkTQDNAc10PKpsrBNJrnKF6OmC8qpBPgCc31/W1kmTA8P+XenSOuHd1gb6fPf/7kA7A6pDWEqyGreElQXHFymvP9t18nyzRZvuLyfIa3ULz9rTdZrBYMNl0sS2MLhTIlvmlyerkgWqTsbu4zXYacvbigvbFF27fY7FvE65hrey0GfZuKNslqgmFq/uC//u/49W/e5713r9BpydHRLtFa8av3fs3/+D98hcUZ3H+Hv/zNI+7df5WTkzPu3NkhjxIO37hLx/douV3+7Ic/IvMCWru7pGubymiiSVVaE2YhxizFM31aieTazg1ufO11Ng92cGXN/kOPn7/397z49DHRqCReGKSRyWK2RhomL07OcGwTz5Ks5iGrVUiZxOgdi+U64XQy5XS5IEkqlKk42D9gHUc4nsd0vsJybPJCU9eKrNDUQlBqg+Jl6gRoWqRfOn4KXTXQLyGIooiyLL+Mg4VhSJyVmJbNzs4OZZ7wg+9/l/FwyKDfYXq54muvfR3HaYOo0YYFlqaMU8q6yU2mSYohArJiBTKnv+Fy0Jd07YytrksVLnn+XPD8KubgmosuRnRci26/5gf/4iHPnAXTUHA5n+J7baTrM50tUEoxHo/odjt8MDzFN9t4ro/nKjYGmnUWogyN3enTuXmHF9MFdeDT7XTxk5LB7RtMioTnlyviKGc1qRG5jVELkjQnzDPy5YqyaDo5+0ELp9Wi3QoYnp9hCEGn08E0TSyryXpmZYnf8il0xd37D3n/vY+wnQlVLXj64jm9bhuhBLYyydKIg70BRRoRxU18zrUkSZzidNvoAizXxRAWge1ThhPKfMyNfZ/f/e4RV1cTjk/muJxxPBxx4+Y24+VzCuOKN9/6Gv9y5zar2KezcZM//8v/h55b8q/+xe+gpzNUkjPse3z86TN6+4qbd69x85UWlrfFx5895sNPPmR7MODh9evsbvf5+teuk8QpwrSYzNeAgXQ8dg867Gx1OHsaUqaSlmOyv3fIbD7Bdm2cxZDZx7/g9+5f5/atI549fcqD73yTf/Nv/7evtnMuaxMsl3Q9ZlOEvLV3ncDbZrmMCGyL0rHZaQvqfpv23jbbWw/IxkMWz57zrW9+h/agz7sf/IpPfvkRG84W69mCrVriSsXz+QVnw2PiNOVwe4/FLCZw97g8u6QsDSaTOYeH+8RxiKN8kiIjyRKSKGE4HLG7uUOma5Tv4hhNLGoya2SWF8fHWI5NWpYvY16arCzRjZKB5biYSuA6Tb1brTV5npHkecP8cZwvM4pCiC8jYoPBFhj/H2fv0Wtbfp/pPSunnePZ++R7zzk3V91KrEiqmEllCeqmYKsBe2D0wEDD8CfgyDDsnhjwxBPDbsd2tyC1JLYohiKryCIr3bp1cz457JzWXjl5sIsaUQTY+wPs2W/90/s+j4iu64ydGc1mk/2nj9jYfIHQHbOyvI4oKAhCRpbJCKKOoouk4RxJWnRAo3SOkcvIlJhiOaVey1FScySez+5xD3GpyYXXznPuwjLVOEAaTBh0R7Trz6OUzvHoJz+DtIDtKVRKFWzfJfRCYq1EYOSZiRozL8ARUv7461/g8NP3uHC+BUnC+zefMDz0karrPDjo0JDnZHMXEQ9B9dnevkhV1Xl8MiKIZQRhwVyazR0GkwlWbmET297aWVjCEBAEgelkQrPRwHXdhZho94C1zU2Ojg9ot5bxPJdef8SVq9eRJIWNjU2m0xH5fJ4wSHBnUyZyQnupSSaYTKZzyEJ8UhQFsihh5kwxjDyRLGFaMrWyzKuv7FBUbd5+8SJ67lW+/6Of8sorL/PVb3+DH7/7E8I04s7uDb56sUlQqpCKCm9f38HQPArJKbo4wVAUCpvraFKLWqvGxoUtPr1/Fy0sUqgpvPmVl/CmU4bDx9SrAkkUIooZkmhwfrPOhSt/yPd+8ENOTncx9DXeeOML5DQdORWQBYHMyxgMB5TiR7y93WJ9tc7J3n3O6QrGpMe68U+P4G8czuB4wHKryfmdDdL5lJJVxj7uc/Jkj2J5wtWXX8U01nk8mtMdnzGQJqSBz1vf/Dbnds4TDs4QwpBiqUiYiiSazD/c+gDuZaiRgzPosbO8yfHAYToBe3qAY4toikGzfZFcocBsdoxpQac/JI5CvNmUUd5EDnSq7QK652NrGaPRGMuymIyHFIsFZvYcTTOQBAnZVJl5U4Jwgc8wNYMoWrROUjllPB6Txi6aruP5IaXSEqORB2j4gUuWxSRZjDd3qFUrrDVqtIoa88Ep4XzIeqtE5rTI12aQSohJBWIFSxVJkhFZNEROMzJbxBhrWEsVcmshcnJCqyYjeAHzNKa+0SBniAhZH2UeoykKquSxtl1nGvdIYxGNOZVmjcePn7JZNsnbM87VVvmbX75H5+omvXGXN7YuM9q7S/fez9lpWdiKzLP+nE+ShOXVCpHoM1zTSEc+gjenaWqsbG4ToVBSy9TyAv0oxJ3HxKIFSkYizjAsmaVajXA+wSbBDmzqtRKWBEVVZTAcfZ7UsbEKJt3DhNnIZnVljfWVGqdHj1GlmPlkwFK9zmg8QjR9rJy0kEohY+kWNbPC6VkXMRLQ1RL5VETyXdRMxVItFEEgVRJCTcVvr2IbMSan/LNvXURvXCIur3P9rT+kWChStnQmN/bYnX/C3uwHqMsy02nI9evfIowSRvMBm2qDNFfAyzLCUh1htcbd/ftsnTtPs1zn6FRGvXCRwDQRCgXO7t9BCDu0oyHNzOQ7X2qTCW0eP+tiktHrjig12yyvb6O0dC7m15AGD+me7FLLBxgbE/bPDri5e8Yf/Rd/8Z82nO3NFZ7sP2GWTHjz+nVu33uCOPLQ1Bz7u2ek0gOqcpm2oSIkKTlBohc6vPvhz/jRT79HNacTqzAzwFQlFF1m7swpFYqYQolUiAkjkeFghmMLqHKDK5d2EFEY9QeMhkNMXWU2H1MulplOp1Q2qsxsm8APMHMWorD4T1VT8AN/EWiPF51NTTNIMiBOCMLFY++vODcLgoG46F5aFikKQeijKBq9Xg9ZVkgzIBJwPZdczkKUNcbDPremA7799a9wfLjH6vIS1UqZaalAnGaookgSRaRZgiQnON6c6WRGHIsoZp5eckLm+KwLGtevX+KFtRZ2b8D+WQ+l1qBcz3MwOiRWRerLLaqCQufJHmGiIKPyjTffoLzU4stffpubt++xvnWeFbPGf/PFF9hLHG7uPmY0HKHGEaVqEVkDQdaIBJH1c9tU6g32Dw4pD4a0hDylZg1ZTZiEfRJUJp5PqIgkacjUtknUElG4YPGq8uLmVNVUIn+OwAKHGYUBxWKB8cTGCTxm9gxREhHFRcH65OSYZrNBv99na+s8jx8/XnwkDQOrkEMMYyzVYDKZUCmUieIMQZaIk4jxeIwgp6iKhGXoRL5PmrnkrTKubcPIIYgGXLjUwul38SIFUymxXDRptqpIkYeyucTugYahFLFyeeJ0xr0HewtKQ2bzzf/8z6id9jjudPno3m3KSw3+4p+9gj0cc//GQ6bdGUou4/Krr5ITZF55/nXGgzMqZYO8JRDHDmLkUSsVUWWBWrXIaNInXyyy0r6IlbOYh5dZL+zgdh+ytHOZxrmQC4JOef3aPzl/wq9uJn/d73/8m/8sOzw95uT4FGUe80Jti288/yUSNybwEvwgwR653D67gyX6JM6UPjPkly+wNxnjjAPWV9aIpmOurLT5yvVrMPO4/fEdel2HOBaZzRSiSEIRckSBSLO6QhhEBL5DlsVkxMRRSpItSASlaoWbN2+iahpWPsd0ZjMLF1nXIAgQZZXheITj+Fj5/CK/qmokmUAcx4vGiqoShiFJknxeOlbJiImigDBI8PwARVZJkpAw8gnDgEq1sigXk3H92gUsVeTC1jqlUoGlegVRhO0LTbJMJPFD4jggSR06vR63751y586Qw+4IJ++y3LZoVTJkp0tJTlnfbJGYBmKtgiPCKHIolU2i4xPUmcObV59nr3PGo3mfzeo655+/ysPjfW4dPaM/GPDqhauEvQmaomJaFruzmHLmsm6GPHd+nc7IZRaB1d7k1qPHjAcD1qoldNkkIcYoS6h5gXA6R+yB7jd59nTG090+Y1tBMUuISkbOVBdpLz9lLMQIooQzHnL5/HmEDA6OjnHChMNOn4tXrnHzg18QRRHf+ta36PV63LhxgzfeeIPZbEa/3yeXs1iqFUjDmMlwzNJSCz8ImXsu3fGY0dRGN02snEYUh2iyQlE3kVOH115ZpbUkEYRH7KwWMcMh0WSAbJX49Mkpilbgz77z59iTMW5rlTSX8aN338EdJdTNGvE4RDPg2dkjavVVNM3g4pXLyJqGLMoU9dwirur5RImArJXYv3WHJ08eky/laa23mQUz5qmNEzs08xK90y4r65doNtqcdk/I0pgkEJiPUvZzKRcvbLNTLyHO5wiijpRr0JtOuXz5v/q1TobfuHI+PXxGnGZYhSJCmtIb2CSpSpQtwMuZpHI0OEA3VNo5mXLTwmhf4nFeojObIQkmacejXSxRjlLkg2OsoUPlQQf7xEcrNKHSIJBkpHRBFT/bP0DVNErlPL3RkHqjhizlcF0PRVH48IMbKJpKLl+iWCzRG05x/QU1W9dN4jQhDGLCJMFEXOBCgpAoSf+RCuB53j+iRWx7kRXO53QUWcKPPUp5E9/3yUjJ4gRNVnCmNq7ncPXiFlub6xzvPeTN11/h7t27rKyu8snNm6xurBNFNpoaAxHu3GE88+h0Z3R7I3zHpmbOeL7SYLNpYWolJv6QcTjHWsozExwko8hSpU3/dJ+KYrFzaYu9vSNqjSb92ZxR4nP/779HEscUJZmr7R2ur23xUecDNFkhC+esL21Qz0k0rIT7u89oNdcRpy7NYpUvfeEVPnnwGaexh+vPF+fj7gxld8RL9VWeb53n6CigsVYmllUmt84YjYZomkIl30ZTDfwwQBFFxtMxQRDhhhGyJBEkKWfdLoVCBc/xaLVa7O7ukiQJlmXhui79fp9Go8HR0RG6ppEGCZEfLtov0wUHqVApITpTUhJMXcWxJ2i6RujHdCcTVmoWZ0eHVPJVlJzGyE14/9M9fvftl+h3j/mD3/0aqpljMumQL5To2gcISZmC1GC9WaWsF8nXBCQ5ZL1ZoTfs0Rue8ssfPUGQZL7y9T/i2VGfJ9Fj2s0Sw14P3ITIDSB1iJ2YeKzSKOfoPD1jYA+R19vEaoknR33e++guV65tsdyqkvkC9UqBD+5/n/vf+wXPL6+wXmpweecFpCRk0DmAy79+/n6jAvC/+9f/8rvnls8jJTLB2CceeODF9Do96q0m/8df/t+YrQrDoE//4CnzQY+dnS0iTeXxwz0M0aK9sUl5qcJk1ufxnZukk4C8XKPSvkCslZhgMpy4CImIgkQ+l8d3PVzfob60xMie4gcZp50OeweHzByHYrXKua1tOv0+nW4PI1eiXK4iiDK+HxAmi6BBmmVomoGqasiKTJoubht/ddHzKwZQlmXEUbjwVGYpcRwiKxLFYh4BgUKhyHzukJCxtbmONx/TXqoxGY9orSyTSTKTuY8oyHiBy3TWZTwdo8gmh4cjPvnsEcPplGpO5At1lUvlElboMx33MZol1GaJaepTW12iWW6gxXBlbZ2LK+soqYCcShwcnJA3K5waCXsPn7JqVXmrdp69H7zPS+cu8+Wvf40P7t7ms4cP+P1v/TF3737Go6f3aLXaXN15ga21bUqFEken+3z0yQcYZp1IUIkEGUSZeqlCtdzALDY5d/UF8tU6u4cdDg7GmEaFYrEELMDcoqgQCQKCKGHPpuQLRbqDAfZ8ztwNEESZarlOqWgxGAwoFotMJhPCMMT3fc6dO8f+/j6FQp7AdbGs3IJnpMhkwgLOV23UcF2PJEowDA1ZEtFUHRGBLA2olFW+/JXXaF86hysrpGaOgRdiFUoUqmVUy6S2vIGdibz3zvfZXL3Il1/5Njs7L7GaLzM/PqFRK7Nz5Rpba8tMe4estCpsbKzQH0x5/uU3eHL8jHF0xpl9iI+LH8/oHu/RzBuMTvbxJ32unl/h6kab+soGW1dfJl9aolipMZiMCKKY+tIyldoKjpcQT0P2P33AW9dep3c05qc/+ZjDgw6vv/3nv1YB+BuHU7P3vvvks4fIkczzm5d569pLXNna5uqVbfbOnhFLIc+/dp3mepO3XnmV5VqLew+e8ov797l+/WWWV9cYiAG9dE4ndTidzqg1zpHpTe6PXB45Ht2JRxCmKKKMkCbYkym1Rh03iAhJCZOMj27coj8akyuWqDaXKFarRGnG3YcPKdVqGGYB07IWD+KeT5ymgECagqKopMIiyP6r0nQYhmRZhuu6pOmiWOw7DrIkEcchcexjmTpREFCrNYjCCNfxmUchS806aeRRqxTZuXiJ5bU1umObVNHJmyauN6PTOwFB5uRkysNHXTr9IU40JGd4XK1qvPr8VQxDJbUUZjr4MqysrCC6AYLro4QRXn/IbDTBDQJam1vsXHkJPVfhg717fPXF1ziXb3DzL3/I5fY2O9eeIzYsXnjrS3zxS1/lgx+/S0pIZanGzvkLyL5EUSuiCzKD4TFpFJCbwlprg2Dm4jsu1VIJL0u50TvjdDpncDJm73CAbUuEoQKZwNyZkpISZwluGDEYjlA1FUEW6Q0HCJJELlfm4cPHFApFPGf2j0a3JEnQdR3XdWk0GhiGget61Ov1hRlOlhdnWGeOospkpMiigjv1yFkmQRii6QaaYeD5NlZRZOL16EYTup6HUGpQarXxPZvlVo3BeEyu3oJcnZaVY9SL8KYpP/r3/46DJ/d4793v8/6NT1jffo5KpUi5UcIq5ugOeownCe+9e5OpPcJ2T3n07B65SoGd8+cpahpXdra5snOexJ0gpB6NdgXN1LCKVWSgYGgU8yaZkLC/f8zFC9dpLwnM+rt8+dVtCrpNc1ml0Na58+wjfufL/+q3H073F+99Nx1FXN+6RkEyeHrrM/7o976KZgncfvIB7dUCpzc+I0hT5pOAg/0BjmyRv7hNokkUwhh6Q+zJhEKpydbGdfZvdrn1s6c4noKgFelPbYIoRJVlcrqOYehMpzb1lRXe++AT7j5+xhfffptaq0WUZZQbdY7Pzrj98AGKYTCdz5FllclsRm8wwDBN8vniwkEpK4u2iShiWSau6+I4zgJH+flTSRAsggOR56OqMvm8wc72Ji+//CJRGKKrBoEfkjNznNo2+XyOVqPC9eevUapUGM89JLPArUf7uKMu9x/cwnVdfvbux6hSi48/foyoJdTXBDYu5ZFKAuXNFq2r29wddVBbdS5ducySYHBFLhF6Dp/d+ZQsSZiEPqfunJ89fcZRkKC31ni53eZKc52TR3v8y3/133Lt975J/uUXmQoyBYpUKfPyW69wcPAEKafy05+8z2s7L6EnCu6gT7FqUF8u4aoBT0/2qFcKbJXKNLyASpQSOQGeL7J3MOTkdI7vGpBZjGczFEMiV85z0j8jZxbp9XpkAuRLBURJ4snTXVZWVkkiaFQb5PM6W1tbjEYjXNcljmNyudw/nvVr9Rqd0ZAoiomjGMPQFpc8aYJlGBiySeBk+IFPsVwmySS8KMEoWuRqGqWlPLPkjGe9Y4R8mfd++T7Xzi/jDY6ZjPt4kcA0hIJUwA01llY3ePXtF2mfq7D6XJsDz+Pf/PgDxnqVn975jGf9AX4k8YP/cAOvI1GTc7x1dZvXrl3HVCvogYziZGTzgM7RMbKlY7bKpJaMODjkzn/8K7zuKVbmM+sfoUsJTx884uql5yiZ50h9nUaxRBza9Nwp79y6i2/V+PJr/+VvP5yf3fx/vvvW11/jwaNP+Off+Rb2pMPdGx9z4733acR5wqMp1UaeszE8PZ0jigrx8ITx/mPmskpQLTGazyjk6lTlJfZv9ul3Y+xUQrJ0xvYIULAnNnldwTJ1giRA0lUeHR0y9AKa586zVsrxwfs/R5FkFEnm/sPHeH6EF8REmcR47tPpDQjjmOXlFtPZFNt18eOEqeMjKimKOEGSs8+bKhmpIJOJMn6akUgyshGj5DIqdYtXvvAcL7z4/GIbFviLFULXif2YSiFHnEVUqkUyIcJ3ZihCghy69KIB/VObKyuvIiVl7j58hFT2CLQ9XnuzTSHnk4gJ79zaZz/Jk5R1TGVMMR6zJhYRj1Xi1OTYHnP97dcxKiaVaoGirpJkKT+//Qk/vnOb/+/7f4sjx5zOumitOvM4xB/2ufsf/x15+5Ts9qfoGfzgww/Zee1N8mubDMk4SUL+57/+G969t0fJPE+UiaTulNLoiPbojMJ4RtkoMZkI7D3p8fjeMxJEUFIUI6NaLpBTTVJbQFAVprMZlVKVOEzZXD9PvzugXqujGyopESNnwNid8/OPPuTS8y9gz10qlQqh6xI4M3RRpF6qoGg6954+wyiVSTKQRJl6roBl6pzYi6xunKkIgkw5r6NkEypmynPb52lsVLBTlVBbRxarDJ89Ip6eYs9HuIU8j7yAmdehUlc4efYp46N9iobFv/2//oa5HbC52uaDTz7An7roYo433vodjkd76JWA/v4Tdswc/tAhVnOcX3qVlfp1RAlGsz2QfOqlddxeiehggu30UVoGcknjbHfAZuEq7khDLVY5dg7IciJ/+8Ev2Y9UlNUrPO7NKDaWeePKH//2w3l69LPveknIzQd3CNOY+48e8cZrb3Hu/AXazRXaq+v8r3/9bznyoL1zlVpzCUNRMM0SQ1fgwaNDaoUGCkVGZwGTfkTiypQLDcaDCYq4aJHEcUStXGYyGtBstsgV8ty+/4AYEatYZNbpsLK6Rnt5mbsPHmDkcpQrVWaOg+8HnyvTNZIkot1aQhJE0gyiNCMTBEQxI018ojjFD2KSOF0Uh9OELI5J4xBF8LFUkVdefJELW1sYus6jh0/IMpk4zphMZqiaiefNF9S7NKKQL9Ab9CkVyuzvHXLz/iN2H+wx6Y9Jkojj4SG19QIvvn4RUQqQ5ZRitcBSa52T02Pyps9aW+Hcap3ZZE6+tMQ4i8gtV9nvHNLpd0mSjGathecljCYOk84h33z9C2zUy9QMnePDA/aePYPQYX66T+hOyGsWg8Tn0WRAb+7w6Sef0e126A0H9OwJjY1VFFlCdkLaRoGyIhNLGYM0Qi7W2Ny+jqyVEAWVnFlBUUx8zyGNInw/xHdjIiIUTUbVFAQB7t67w8bGOp3OGY+fPOLqtSsM+z1WllcWVH1VJ/B8ZEXC0FTIEpBEAi9A0lQkRV2kiwydLI4RophUSAkEgbkbgCB8TmSI8QMbx5lSyFu01jISKeDps0O2V1rUBYdzdZMrOxcJJYv3PryFM59ysnuInmn4Yxc509havYgQqbQrK5QNkWvtZTZLZc4ePeJPfu/b/M6br/P1b3yJs06HykqLlcuXyOXrnJx0efzsDkZRpL3WxvYETnsB3eEUJ0noDSdkocjmyg5/+e//jqdPHhP7c4ZSwONnTzkbDtnY3uIXH31IqVJBAN689qe//XD++Gf/+3ePemeY5QIHZ6e88NJLqJJJmgnEQYZpFnAUkYlo8P33P+bmnYfc/OQOk2GM52gU86tErszRkzGjToSaFpETg7PDHhvLmzSqTTqDLpqm4DkucZxQb7S4ffc++8cdllbXmNo2eU2jXKnheB4gki8VWV1bR9d1Op0zLl+9iqLIeHObpUYTz3dBEEiSDN8PkBUZRVcJwpQ0EdA1gyyJIQnQ5QxTFViu5tnZWOWNV19BVzXGkxl7+ycYepEohl53SKFYxDBUkjhEkiQqlTJJIiCLMk+f7vLsYEyz0uDk9JCpNyK3JLN8ucbOlTWyeKFvt/0hg7MOl1brNC0fMe6SiQGZbtKPISkWCHWZ43GPerOFZ3s8uPWQi+cvk89XuNQuITpjCkLKUrnAOz/6Aa12i3NryzSrBgkesZth1OsMfA+jXGZ5dR2EjM6gh1ayiMWMyXzMcpKjFGm0mytMDJlH8yFTx8OZRggYRH5G92yKgELkB5CBZZiEYUz2eXqoXC6jqipHR0fk83nOzs6wbZuNjQ1C28b3fERRQhTkz7WDPkEYIAgLFIwmq9iuS2OpRbfbRQSKOQshjoizhIPuGQji56pGgSCMkCURUQZNN/Ddz3jh+hZZIjA82mc9r7LZqFAqlLj35BDPF9i6tMWFrYu89+Of8fW3voGUaeDLrFQ3MVOTNy4UiE6OaMkipTDmo5/+FCfw6M1nLF04T2SqJKbGT3/xEUN7Rr5hIudknDRBNKtsvvZ1Ln7tD2nUl2lKOapKkcbWJR7sPmE06fClFy/z2WxIdzqh0qwzcV1ufHaTRrPJcmuFF7a++dsP5+3P/u67cZYxcRfEO00zkTKFUqGCFIpokoqpSvy/f/UfaG9u8NJLL1Mptxj0PcQsh5CYRJ6MpZYglEn8jGDuU7JykMacnhxj8axofgAAIABJREFUFkzsuY0gSORzJR4+esJJp4eez2O7HvlCkZKZo9dfSGcFWcG2HURFptvpsdRqoao6s9l0oSdXZQLPRxRlBEkmiCIyAeIsJUkg9EOiIESTBepFk0bFYHtjiavbG6w2GuSsHBkig7FNFGYEQcJwNML3IlRVZDQckKUZtVoFSVIJo4izsw7T2QzbU+h2zsgVBUbuGV/42jXkUkyYzAltm9jzuXbhIn/xB3/Ca+c3OFfSmPZPGE9nTBMFv1Cn5wbcfvoIX0h58ugpD+88YtabIiUKneMeF5arvPz8NXRdRVZVYlTGU48gitg72aXrTHn8y7u4Ucw3f/d3F9FDz2PQ61Gv13ny6BHT0ZClYo6GVGa1scnrb36NtGBx/2APWdWw9AJxmDEe2fTObObTgHqtTj5ncXxyuqi5SSAKGYaucf/eXVrNJu58TntpieX2EtPxmFa5wsnhCec3z3F82sFxHWRFZjqdoGoKpmmSBhEJ0OsPCMKQSqmIpeusNJeI4oBUXKS/QEKWFVRdR5IXdcIkAcvw0DWLi5df5qNffIRvj2gt1TjqDjib2CxvnieOHMTPBcE3PrtFIEB/7mFHKQ8P9rDUE6qWQWxPEOaLLu8kjujFIX//4S951u/gZAl2lOCmMUfDM1JTobi8Sm+e8h/+5of8/c/e46N3fkx6dEg0GjHxptwaHHH+hUuYSsovTk446vcYzWxOuh2+8MYb+EGEPZ3x9kvf+bXD+RvfOSVHpX94SmoKjD2H9ZbIB5/c4IULV6lJecQgQu/1+K+/+RbC8hJHQ5fd7hHnL13C1Ev4Tsh0PGfc7+F8rhZXZYFyvY3vORj5jMf7z5jaDgWrQK5YZxaJjN2ES+dXuH3vNoKqIPgJvX6fYrFIxuLc8uTxU4qlAqVigenMJmeomGYBfz5HFDJsx8YJIpIoJMpAUhZuT1WWkdKEgqlwaWuNrXMtVtt1KoUyoqRw3BtwcnzKuYtXSTKVX77/IWkUULQEWss1HHuAIKTEcUqn08V15wzGXYLQB6uEVk2onytixRGeOqCU1+mePuV3X3yDL7/wBkqWI68oEE7J4jpWvMGj/QOiUkoyHTAjojufEacRiqhSqi7xys41hBj8bo9nZw5n7gGT+Qg/CtloX2ajss7x2TF3e0dUl2qUrAmnR2f8b//D/8Tv/9mfsraxyZWVVcaeR9Qd0KzUaJRMzNVLtFa2meoG1UmV77z2B9zu7+PFKTlXINg/5ax/Sk5r4zs+cerjuS6VtSZxuPB3qopEs15lfWON/f19LHNxI7vUrJOXdarlMp7rISHQqNUXJPkkYtDr4TsuW8sbZFHE8eMnbJ47hyIvigcze0Y5l6PaqJIGEbuHZ5iGhiLrC6u3XiMRRM56O4S3Kzw8OKHcfBGMgB8fThCEiGK9ga2EWDMHW0gI8wlcqvCxvU++ssxZ7wijZtGfddnONWmvLpGbZQyPTkmNAvu9HmdTj8yPOUt2AYHhaIQiyexFc+4OHYRY5WA2xNTmfP3VJl9oNskbBkeigCSvc9cPGEkGviiyfv48uWKRw7NTbty6RalYoWj90/Lc37hy/i//+r//7j/86D3MapnqRpuzUZ+L167gBC4/efcdDrvHpIoAFgzGfURBYXn5Mr4n4c09JqMRtu0CCVHsY5oy9WaFfMHi6d4TeqP+ohDtRaSIuEHCcDRFUFWq9Tp+4LO2tkocQJzEaLpOmqbM5w6GoYOw0AUMBkOSOEIRJU5PDtFVlUyAMIrx/AWxXUwjkjBAzjIsXeWF567y9pdeZ3trnbXVFoKsk4kagSCSqy+RCiKmZTLsnZG3dFQxpVLNMRkPET5XPoiySG/QBQlsd4rRjNnYrlNfzlFdKtI+12RtrY48n/K1567RMPJoapXAd5n4HruDGf1IRim1mDkBJ4f7CJoI2oLIUC2UkUUJVZLpj4ZsP3cZP4Kx7yHlTCZ+wPbGVd7563fonw3ZvnqJUqOJKsWc39lGEwVUXeGDWzfQCiYz12W1ucxX3voibuDyzqef8fD4kN2jPabjLqmUMUpT9npnuIFNlCk4tkQULhi7URyhaRqyrJHFIflcjtAPCIKARr2OJAjs7e5Rr9ZIohjLMHD9gN39fbZ2LjKzbcQ0Zb29TKVYQEZg2B9iFgp0en0URSGNI8QsI6epCGmy2MIKEvZsRpYlqJpMELqkSQSkkOUQBYN83qI37tOZThBzFfK1VcZTn/HYIycIlNor+IrC4WSClMvhphnt1Q0GoynTacxsmuC6AkEgczSesTuZYq20MSpVCtU6vfGcTBaptRvkKhWmbsjci+kPJ5z1z0gSG1GJuXP0mBsnuww1lRtP9jkaTslMi1q+TJam3H/wkEarDZLC5vY29aUmr21/67dfOR/tdim318m1V3E0hePJGffe+TsaxTL5usYH+0/pNV+imkac31jnaH+Mawekc4lkGuKPHfwU0iQmSTLibIG87A2nVBurqIbNaDRkebVIpzsmFSRsLyAh4879hySRy9mZThpIWKaJKCkULIvjowN8X2Cp1VxwUQUYDAe0mnXyuRyGoUEQQ+oiSwKqIiMRg6RSyudpLzV46cXrbG1vEYUzJFVDSjLyxRyOpBKKCrbrMJ9OyOU0It9BMARWVxv0Bx3mbsRkOiYTUhzXJs4Cmq0q5uqcr37jRTzfxQl89MJiS9nM5xDsMZEgMTEzRrHH+w8e8PhsjKoVCB2X0ckBRS3GnyXMRYnmyipiLPDKF14hdQLq7YizWRd7OuPclS06oy6qrjA6O+GNq89RL1WYaDbVdp2/ffYxe3t9vnzpMhN3zvZLVzgYdOmOpnz55d/h1pOHfHz8DCNvMhx32T9+RH2lRjxOiecipi6TaimV9SXk+3PiUYCkahCnFC0DBJG9kw7r6yscHBwsINqzKYZpUK6UWF5pc3p6ihMFeHFATIYfBMiCSBKEqIKEbuRQEuicdGnrOs1mk1arhakqZIFPEATUatVFZ1bTObe8zNiZk0gJIiBILJyeegrBgMHREbIl4noR3/veL2m1VlhfanKutUzPHzHcn+FJCVFoousF5t0pD+/cpm5VcOc5Ds8G9LHZqNaxmg3MosLesIdgyPT6Y3wf5DQiUx3CKCGLZXRFodIsYFkaplXEL6vsjZ6iyhn5oU3BalAuqoiCyO6TXUzD5M3X3+TxwQFWIcdJt4PjePCfIjK69voXyC8VifIJjuohlS2uXTvP7r0H/Nmf/hkfvv8RYXWVtWaRu+9/jORbjLpTiMpEboKQyniui+PYGKZOM1cm8BMEQeXo8IDhcMi15y7RH4zI5YscHR1TKleJogh7PsG0dOyZzdF+j6XWErIsUi0XmTlzdGVBKHAdF0mWSOKYnGUhfE4q+NVb2q+K1AoZqqLSqFXYWFtldWWZfKFIFIikWYBsmKhGASMRkBAYjEfMHZu11Tbj/hnzLMSyVOq1Ks7+MVkKY3vM3LMxTJE333yJtH2KZqYcdXqkggCywWw24FqtwsOH9zjTc3SUpzwddbGtHMa5DYRM5/j+A8yySdnKCEQZSVRYW24z6o34+S/fp5or0h/1UIs5XrrwHFEWkgkxpqHQPT3i8vbrPL7/kKWrbZ7cu4PWajDzD/nw2WMqOZ3xnSM2L13kxZ0LKKbF2XjCg/4JLalMwTQx6iZjK2Zih2iJhJKIzLwpz07HOFGE7fosVVsE/pw4Tmg0a/R7BeIkpdfrs729zZPHTxAlkTRJFlT4NOWs16U3HFGp1wmikFwux7BzRq/ToVbMU7By6IaBYZistJeZO3NyWgmrkEeJQjzXIfJ8nDDAMnTCNGLiO0iquHibDn2GzoClmgVyRBpK9M/6/MnvfwffS3j/H95hUpnwh//iiwxnA6bdYwzTgFHM5eYmt+5/RNRNSbM8ZfKIcczBsxP84wNyF9o0L61yMuqiaCZJlGIaEu2lOopucHzUJXAipv4UiZjpPENbWWI861Apm/TPJmzodaqFAh8++IhXrr6EbhgcHB/TrDWY+i5ZmjIcjf7J+fuNw8nzKaLiUpMkxs92ubxzjonns33pKp/cfcr25RcIplNGj2dkgwazUQiuRCR4zMSQQeSQxBGVUgnf89h7+JScaVHMF9hstjEykXv396gvtQiDOUu1CpVKkZxp4gc15tMpcRyzurGycG6qCk7g02y3iOOQo26HUqlIOHMxjRxHZz1CZOYTl0wE2dAgjjFVkYqRp1QusbK6ytVrlyhUSnhxhlFoEMUL65gTRBTMHL43pyJlrG5vcny0hz0UuXjlCoPRKUuFEqdRl4ljE4oRZlXhi2+c461Xyvzw5h657RLj8W2qtQrHDx5yrtUm0GrcV3w0o0CWGEwEj83GCnN7zMHhIVKUIuWK9ESFZtFAjlz27t+hubJCa7PO4dERWkklThx23QM6Z33SREQVDTJF4N88/DFB6LJjJyiKSHwY03E0jgsq5TjlkpyjHsqURxE/eOenqMvLbC2f592f/5ztnYuYao6Cn6MiF/AtlzO/T26tiqFmSJ0ehZqGBBSsMn7oMRz3aaydY+yESFqOOBTw5wEXL2zz8Y2PGQ0GqJqCfeJRKBQJgoRK1cB1HWb+HFUX0FOZzPVwkZjZLoOTLs1amXg+Q6kUyFc0VFmibi0xdTzGtkvP8bCdGEuwEFh0eMWszkk3RKsv9IiKVeSTn/0EgpQVy2StUKP3w095/lyLr+aXSGWHMAsQ4gHX37yEFMlM8DEVHeIMFYVme4MT2+ajg10016VYLnHgDJn2xjx/aQc/tNloFIjiFEU10FSTaDBhKZ3x51/7FpVSGd8RCDOFhwePqQ92+ejTh/iuR7vexMpSTm8/oVgps6SY/+T4/cYz5ztPv/9dez4jjgLMssXQnVBsVJjZM+rlMpVcEWyVg7vHnO32yCKRglUmzlLm3hxRkhAQ8VyXKI7JshRdN7Asi5k949neLqmqs7u3vzB3qRrNVovhaMR0NiNDoNZcotlcIvB9yqUi9myKpmnUalVm0wmu4xIkKSkCcZKSpBmu42LqJiQgpKCIAkvNMmvra9TqNar1KvlCHkESSdKYMApBlImThXqeLAViDEPFnc9oNhsIAgSpg6GbBFHI05NdNi+t8vwLF7hydZ2zg8fU11ocH+1z/coVSqrOZrmBP5kzC3x8TWFvMmQixJRWGhx2T/HCCE3RESMBS9QpiiajwYSpE5Av1+iNRoRpgu3MsfIm9WaDp91jlLyJoMk4UUCuVqCx2mbi24RSzOmwj0xCpVEjnM1piRobWo6CqHHr9kNmmc57N27T6fdZXt0EUUGQVbSchRsEhEmME3oMByNMTPIUicYZpmRBBkkaYeYMrFwex14oE3KGztyeo2gq+/t7FMolxpMphm4xHA2Jk4xcLo8kiUzHI4p5C0PXmEymTP0A07To9/ukSYSuyESRjyYJFKwczaVlipUKmmYwGI0JonDh51Rk0iTGExJSXUAyQS8IFBsWgikgFWWEisbu7BRFzzg+6TAe+yhajU9u7TOdpMSugBgJ3AsdXFHBjlNGXsiz0zP+6oc/5O7TQ4y8SnWpQXmlSRT7GKbKaNBDBExNw3Mc3NmUnKQSzKZ89snH3PjwUy5dvE4QSXx48zbj+RxnHjOzXcx8nuDzllNjqYYkZXzj7X/xa8+cv9Eytn92jJLTOX/1ApKl4SQBT/efEaUxw36fw90Dzp4MmQ9DslhEl3UkcbHdkIUUIU3QVJVSqfS5+VhG1VW6gz6pICDIMp1uj0yUkDUdq1QkzjKiLGV5fQPbWwCy5vMZrusiCuB5LrmcSRJH6JoGJJTqFURVZuraiLKIbhhkyUKIqUkSS40GOxd2WFlbpdZskC8UyARIsxQv8EizFEgRxQWJDyHF0HWiMGRtbZXNzXXiOKZaK1NrFhnNTkH2OH9plUyJePeX7yEbJjN7TOw7PLjxCbOTU57cvMvRk12ePjtgGkYU6jVk26H7ZJfI9gicEDFVKeVqyLHK8eNjzo4nDIYemaCiGhZ+FOHFIXGWctrpUG/UF2dax0bRBAQp5eNPP6BQKzCcTpFMlX46xcejWalQy5WZjl1+9N4HDPyYSZqxun0ZWS1QrCwRxAJ+khFm0LOndOcTZEmlKFkokQhRimUaGOYi5CEIoKoqvuMQeg6kCXN7DoAzn9NaWeGs26NQLi8ie79S0McxURwtBMZxjGmaFPJ5NFXGsHQUTcYPffLFApIoE/sRsihj2zPiKKK9vMxrr79GpVzBsqyF3FeWQcwIY58kcsnJAkrm88W3X+bKS9ssX2lz/dsvUzlfJc6rPOyN+auffcZYKnKrO+H/fPddDrKAfUnm56en3HIdDmQBu16kdPkcz335RQrLDXwhJhYiTjon3Lt/DxCxdAtdVYkcByEK6RwdMxmOmU3nWGaOQX/C9773Q3L5Ko6b4vkxqxub1BpNHu/uYpbyJGKKkdP/yfn7jdtazRCJk5D7j+4RxhGVSp3xaEa70sY5HDBzp6SnCaZoIpkiqqTguQ5CEmNpOqKkEiXiIvwsCAiSiO04JGmCn0TEYoZVsIjjDEURMA0dgZR2q4mQQZYlzOc2oR+QJSEioKsyvjMnTkIUSaBSLjJ35ximhigkqKqESIqQpAhiiq5rtFoNVtaWaS8vk5IhqSJe6BI5MYK0wI5En8tthSwmS0NUWVxkbS0dz50jSQtcRqlqstQsEZjLZFpMb9DHiUPuHx6xvFbFC4MFVsN16QwXhdvlzVVOhZimrLGSWAw9l2EYoxQLaFIOWdCY+EN6wynD+ZzcUhlRlLHHU2behHarSZqmSFJGTjLp2V2WlpokSUzqOpRzJmKc0ijXmDtzPEugaKiMzlzKRZOPnzxGlAS02GNl8xrz8ZzBszFmr0+URch5nd5oxHA8IWcZC7EwKnIo4nkhQpYRpQETe0ylUiJvWXS6HZI4IAx8NE0lTVP8IMTKFUhFGT+McRyHUrHIeOrQ6XRoLjUQxYX2IgwCDEPH1FQmoyFmzmI+ndAdDimZOpOJS70WLDSIYUx3OMEJQpI0Rdd10iRiMhqSKVCsWqhk5HUVpagT2jPKpslgOMQQRFQxpXZuldxOhU8fPmUgppRfvICxJnPP9BBbG/hyxtQw8WQJ15TJ1pscdDusLC9j+x6ym3LhwkV0VcO2Z+wfnf3/nL3Jr2XrfZ73rL5fa/dn7326qlN91e3I2/GSImlbiqQgiK0MEkAIECNA4EH+Cv4JsRPEDjLwwDNZSCIHsp0YlCVFHXnFe3mbqlvdOXXafXbfrb7P4BAZhQTE+RotrBe/b/3e93sfNEXCtU3CYEtOScNpsGMbOJbDf/iTPwGlQZJk3Lt/D/Weymw6o91t8/F3P8ZtuYzHl6jKL5+Pv/JY+8c//Zc/SrYbbu/tMxnN6LaHOFKDw8YhyXmEupVJZiFCUaJJIoos0Wi4N5eZ04wakbysmU4mxHGMYRqkeUa722ET+uRViWkYNFyb/d0BQl0gS6ApEnWVEW5XKJKAJCnUVYVp6oxGI3RNJUsTXNemqgpUVaTKEzzXQtck4tgnjLeUQsGTdx7x27/7WyRxgNdqYLv2DVWrrtj6G/zQR5BEsvzmY0KoMAyVLAnRdQVNlSiLHFWVGb08IVyuaHYccr2iaqiYbRe726ZARBNq0HQkx6Y2dboHA6xOA7lhINgy0cUV355XyClEYYHudtDtNls/JfBjFEmn0+vS67dotAyQUjzXQKhrdEUnjQqKXKAqaoLNTR3lbDblrbfeZTyeM5usyLKaTREiodF2+pSSSunp2AdDKtdkG2cossJg2GAVrqjFiiSL8f2AMsmwNZOB1carDXI/R600tvOAuqjJ85wkTdA0jSgI0RSF5XxOu9UkTTPu3X/Aq+MT/DihRMTWde7dv8/5xRXT6Yy7d4/I04QsiamrEkkQaOg3DQi265IUGXGSoCg3bRVZmiCIAnGacTm65vTsHESBoixQJBld15AkgfZOk0zKqS2JRCgZX88JVgFtuUEbF6/SyDcZ7Wab2pIRLAVtr4m318PsdqjKAsu1KcWKpC7RWi7bPKEUBbqD4S8YpDfTb77YUlYicVZxdjUiqwpKWaSWVWpZRFJltlHAqzfn2I0GpZhjuBLTk1Pm4ysc20AyJKabBZKhUkkSv/Px7//drRRdAM1x2evuEG0SHNWhyGUIJdiKiKGAJgn/35SzLQvNUG4iVqJEneVMxhNGoxGNVvOmyU5WiNIEQRJRNI08SbAbNoYmI+syWZ5QZhV1XbI/2GGxXCCbBmVVADWe5yD+gvJVljeQoYNBj8VigW6qhFGEKFfIuojtOhRizmI7xw+2NKMQRPCDAFmRCaKAOEluoLDVDY3Mc23EukSVIIpCZNFAURSarQZ77QF//G//D+x+g71PHvL5/Bhvp8ts7ePpJsvFglwX2X98F93SmV5fIJQpriQhCRVSnRPEKdMwZlUJ6HHKenPJcr7CEFXalker76I1RES1oGd5TBdjqGEx9bE0j6QE02viBxtG8zmoKq8vz0HXWcQLiqKi37dJZyGRFdPY30UlJM4jBAnqbYTXsrjyZ6iGiCTLVJUAVYVpOuRBSlQGFFGFpTlIrkXo5virGzhUlt/gGEShxjZMDEOnyHLSLKHVahHGyQ0JW5JpeS5wcwzWNI3NZku73eL85DWqLND2GtRBiq3pN6cU1yXPUrK6Qqlr1kGArMokZUWa3DQhJklCVdfomoogCSRCSkhJ7VnQa2I1LeLzMUlaYxguRmYSrWvWoxm3ugrbqxloEuIyQVZV6rqgymeolklV3TT7F0FAsg2wFJ3Z5TV5mpKEAabh0O702fgBZSnQHu6hmhI5GX6QYTZswiIhSWOGB0OSPCINYoxmg+1sjCZKuJaG5FlM/QWCINFsd36p/n6lOA96e1QFjEc3TePjywvqWCBiQrfVYeXP0XST7XqDrhsUtcB2uWG1DgiChHWUEKc5e/uHZEVBnObkVclkuaTd6SCIMlWR4dkWZZaBJOFvbqJWraaLpovUnkNSg67fYOxs06SucjxDo20bINQcDDsYCkR5wdVkQm93iG4a1HXJxl/z5Zef0TQtnvobhru71IKArGj4m5CiqJDISYvoBpyriIiUiFWOrolkuoAi3/QfvfPdH9Df3eef/S//Ez/7gz9CedAFTUV3DGzHwRRBbZlsgw1RGZFUFVGUMA98nKaFqaiUrs5yk7CtZUYXY0pBRABKSUQw5Jt002yG1TXYZhu2mwBBksmTEtOzCKsERdPx9A75Yg5izWyxYDjcB6CqKuRZSLuzy+uzE2pdJJVSZEUi3PgMWjucnr2i0iuSNMVrNGk2OxRqyXqxIQ4TMrkk8n1kzaRWa5xek8VshGN7UFU3FSzFzRLOdVyCYEsah+RpTNO1yUuo0hRV1cjTDKEuMXWFMotx233SOCVWVPKsokpzDFVHkzViEpIsJy1KDMuirFICf0taCVxNlgRJht1oYFkmdVFAVaCqMnF5Y4VVlkwuC0iKxuRijLTSqDY5smhza3CPz766JCwknLbD9PmEZsMhDUIGhxqvvnyBu9PBGXTZbEOSIMDwFCRJurFfqpLb9+9yeT1F1hQGnRZJFuGnazIRNmlMMZrQbdiIskqz08PUXdIiYbGa4XY6DHd3yUVI85yoKDk7Peee8sv/OX918P0n/+ZHltnFsHu0uz10BXqehaNrUIoslgH+OicuapZ+QpBWLLYx821EUkIlqVSCTFFBWQuEUUJeVqR5SV0LxHGGroqoikKelawWW7IoRULAUGR0+aZ1XlagYWmYqsjdwz3kOqdlK/Q7Fq4FtusQRhG5KHG52vLgvW+xszsgiwI0ocKfXeKZEuvlhM1miSTd1DnOpwtEZLKkIMpDsiJBFKDKcvz1CtvSbqotSVn7S7zbD+i3OnQsi4vzC2RPQ9FEgmTNZHSKItmcXJxQCRmiLBBEBVEAttYgnK3xFJMsVXm5ysj0Nl5zh3bDosxDkjpjWSYkozG3vRZNt8nl1TVhmJEXArJqUNYiiegDIv4mIg0rdEVFlwQsWWE9XdGxm3hRgeHYaB2XVeIz7PZZjecUaUGlCmRShSkapGGGa7jE25iG1UAWFIRapNlqoDcM9K7DPPFZLEP8SYKSqUgIZHVMlkGNwKDbYzWf4VgmZZbQbbW4eHOKKaskRUFd1WzWc2QKjg73cW2b9XyDKKjIsk5R3jxjaDrBeo2EgFBBXQsYhoZQZaRZga4b2JaFpWuIZYZSZ9i6hFhVyKqA1pCRzBqlKkmnPl2zRbqt2Pg5suJwPlsTiSbd/TukhYy/2DC/OqPj6ihCzU6nR39nQFwUqLbBOvCZL+c4novlWDTaHtssJqtzNFkkXi8p4gBBKpEtBde0USWNEglJN8kFkYySQqjRbBc/K6g0Da3d5PjqilUYoug67U6X33nvv/i7H2vHm4TWXpN1lCMZCo4zIBgtiOYJy5MNwUKgSG9eXo3ANgxJsxLNNBBFidliRZIkWJaFLonkZU5RlejcIA+qqkRQDdZhQq/RJE8X2JpEv+Vy9uYp/+CH30VRQK9qTMfBD0I++f53+ezp51RSie5ozNdTJtOQs+sZvdu3afZa1EJOngW4loykqlSigb+9ZjKdY1oeeR5SIREkGU5HJylD4jhFV3UUsaLOU8S6JAkD6ipC0UVUVWI9f0VdiTR3dH7/9/8Rf/j//DuEqGDub/A8l7OLORgSQg7bVYCpucSbiAf2gCoJsCWB59mKYtikED2CSmB2PScLQgzLRhE0DF2hFMQbK6m6sYIkSSZJM3TLJdymmF2dzWqLhIZsmxR1QZrU7A2PMFSHNedouo6tmahlQn3poy0LdveGLPIQQdKI4oyyFAiCBFWQqZICW1QwdJu0TCmsinU8QZQlijykSEJqQcewPRbrNYaoU6b5DeJAENBklfFozNGde+wOBkiKxjqNSdIY27EJgzWGoSPLEpZtkaZG8IXrAAAgAElEQVQ5YRhgahKqqpCkMUVZohs6gigiSiIlEkUhYpkKnqVCmWKZMhs/JslrZF3CjiVkRWcTZrQlF0moObl8g6lZ7A/vcLU6oe9YLPwZde6C0qLpOiwjBd1uM68z7OEO5/MZdiSTKwJKUaOUIi3VwSllik1MrInotk2dl6TbACMvUYQaobrBfEiGRyWreI0GQRSy3W7JKwVN1yjqAtSaSi54/eYl4/kMq+EhSSJRvP2l+vuV4vQ6HoVQsQlWTKZneIrAwOhCDZGfYhtN1nmBoimEcUJRC4jqTS1IURX4YQBIN1RjamRZosgKROmGnqyo8k3vT5aRZSmOqaDWCZqY4RhwuNem2XQQs5juYMh8veH2oz1Ot6eIjkJcJ5SCzOYiZp1GqLHPxeScVEx47/F9prNz9LrC0yTm8wWSqLK3e4uNH1MKIIgKaVUgqBK9Zhtd0Qi3PmWcYhs6omzhRz5lkKFqMrPtlqZhs1os6Pb73Bs+4s3oEnlrohgerlqRCynJJqESUjTHwMMgv1yyJ+qYgsaLcst2s6bT7TC9GLF8/Yod20LcVGgWyD2T68WcYlOiOCpdxyShQgZkTafaSqRxgVQrOHaTIitYLrbs73tUlYSm2AgNl3kUUS0CimnAwPAwconrV+cIQwdRFdFKCVHWUAXpJqcsC6xDH1XTkSwJDMiCDKEGzRQxLRWtVFgtV0iKgq4oKAjkSUyR3ixPmp5DnsTs9vtMZnNsU6OoCvYGXb76+gpNFbFNDV0VbyjbaYooaJRFilCJUGZQSWiaAQKUZQ4IKIpI09UwVQVVrtjp7iBoDoWooZc6uQInyTViKeFHKx6++whJFDl6fJ+LaowjCdSzLYODPc5GL3n85BGHjw54ffoNaV3h3Bry+eUxdgDdXpfTs1NiP+Tbj9+iYTnMl0uuJnP0WsHIwJQMqs2KjmMgSSVBHhIIKbVuIdQVvU4bhJrVdku4jXAbHr1BD2qQo4C8yNA1mThOuDz/NUFGm8WK+WSJrMioSkVS5syjjPXUp8wl4iIizkoQBKbzJTUCgiiimyZhGJIVGaZuIYrg+z5FkVNXJXVdounaL6ZngWsbyEJBt+ch5iLdloFQNtjf7/+icaBAMS3UXoNMg1gGURF4eXmJ1XSI6xUJMdPtCLOtMt5eENUDmgOPeL1ik8SEiDi6zcX1iuVii2qaFGJN56C+SYyows0WzZ8hVaBZCkmVo3s2YeJzdj1CU10+u/4GUZB5R2swaN8hDTXOz79gKwu4DQ1DkiklmfV2TRytUXKVTZDy8GifYbfH20j85OQ1dTDFm8c0a5MhFkoho8QivURBLAWMTpNSlojKnFkaMssi4rhCERXqSsAybWaTOYoCAjLhNuL0eIJjL7jOrnn04BHHr14irhJ6Q4u6LJlNpyhyhuwYdGQLrWGjmjphnmCYCpu4wGpb+MEayhKxqEAUUGwNsyVSzgvqukAoCgQ5R9d0hCrDMhXu3zlkMpmSJz66JSJRIKsKUZShijrDbpOdtosqybQ8gzKrSFMJx1Sp6urGB/cMFE1FVn5xf7MWESoZy9SQNJnWToPBsIOsaHT6+yiaA6LGtsoQL57zen7GxXhGs+1gODpX4TX0ZDTA7IgcPtghfhkR11uyOqd10OLi+oxNHXHryV2yNEXSRGRVxLY0bNsASsS6pK05rC+nOKaJVNToqGilhFSJ5JXCeBtCJZKVFX4UoWgq1DW/sNCJwwhN0zjY38cPAsosx1Q1GtovTwj9SnGuLresN1vavSattk5aRKxmE7I1KEWb9XJJkGWkWXaDdAfyPCeLAtI0JSsyxFS8Ac1mKaIkUVYFgvALEFBVkcYBpqOxv9OkqQsIhcD+bhdVyXBbDmgysmaimDZUFXEOs6WPnKu8enFBb9DDborctYf4RcKgt4PhGpRZjiBXFCn4SUwgFyAWjGfXTK/mdDs9dM9k7S/RXI3j6xFWYBFFIYakYQkO08vX6IbCNtyQpjGP7gwZHW+JsoLgq6+IJxskQaIuRF4dn3P0YIeGqmHIKk63z+sXr/E6ewhti6hncFJvWakS9U6LsNIoqgyv0UL2TIo8Zx1FLLcBjq7SNwQ8yaKraniVwH2zxXKz5epui+tVQFlCHIYonkm32ybY+uRZShxFaLJAHARImkz/aB+v2abKM4ZaybwMaJgmwXiO5Lo0dBWpLLGSjDROaQURwiZjullTGQKKZ2N0HMLxlmC8xbNtjLyEIka1ZBRZ4off/5hep82P/8MlaVYgOSaOrZDXOZop42gC+rDDbsdhtdxwsNNkvdpSmzL7uzc1mZKpk+Y57XaLsq6I4pg8SdFkhcdPHvHovSeUUo2oioiCgG7ZdJptpkmMKpsM9Tu8Wo6RJYv1MiAtMpSWzsZf4z66w6F4gOZJrMMpsg+mZ4KcM7jdwY9XZGVMo+1xfXFFq2XRbR0gGwKji0tmkwVNq4du6GRlzXi1QYwTwsKApKJ12IPtBj9J6TkecRJjWg6DnV1EUSSKIrIgpUgK8rTg7Udv3/jEWcGg0/v1xFnMdfY7A65Hl2ynK/pdkyIWkdDJc4X1KiAoAyzLRgLyoiDOInRJB+nGj8rqhM2mIM1v2gPyPEOUZcqyoCxLDBnyaM3t/UfYYkGnMWRvf8itaB9B0xA0DU21qQsZQ9X4F//zv2S+WZOUOT/84Q84uzxF25WpsMk1kWm8RW2IREHJerZCt1Vcu42UhkipjJjD/fbtmzC8LlOwxl+H1K7LloRKg5fPvyEoEvztCkGqmc7G3Do65M30DdrQZj6Z8WZ1RkfScd0Gm+MZ7d02o2TLTvceriuiKxIdf4u108FoW7xSIy4vz0kmArnp4jY6NG7vEF0fMyoC0GsCNaZWTGQJrqUtZuTjSTYVFbpsMal99EojCn2Wy4C333mP6eQSTZFQGi5bN6bXHVAJJit/i9N06O7u8fLFMY5uotoaahRhqBKpWmPoMv70mlutLgM0NrFId5FTN4a8kptspJiplCG5GlsiCiHFMBukswirafHo0RHhZsXHH73D5fk5/X6TJM0pqhzXUZElhXa7hSILJElI1zOo4wC1YdLQFYq84Nagy07DZjS+xjANXK+BbuiIkoS/WJBGIa1Wg9tvvQu2yyzYIlUFb77+OW9OzwgUWOYpomEyPh+TzhP2hh3K4gaz11JMVsUWxVOJqxjVFrE8Dbuhsk5D8tKnyhUif4VQpdiGTKvpUFQpl4sVtVGjtk1KQcZqNvHDGKfdoggiUhFCYgrX4eL6mqblkNcCvZ1d0iSh1+4RhxFZmdFquMznC/b29/jq6dccHt7iyy++YHM5g1+HMuYMVK5nFzgdh9HlmGG/QZyEuJrGZLVAUDRsTUZRVOIwJy9KiiQhqXKqskaWFbKqJE8j4jxDVG7+P3VZoCgyRARcTURXajptD1OtObp9QLvdpJMXRGlGHdboQsmnX3zJeL7kP/74z3CaPR4/esJR8y5aoFEqKblQ8mZ7ySqIWFOQxDFlnuM0O7imy/L6gt5ul0ycIokypqljmQqWpXD94g17jk6n0eTLF8/Zv7fDm9k5tuOgKRotfZda1fnq+XN6dw6oyWj1Xd787DmbyOG9jx7y/OoCM00ZtNqkSsoyCcgMmVzlJj9qexyfrbAWEnZDo+speKXGPNTRFZVcKlAdh0iEtCqoXZOT7YI6K1AsjcO9HivBp7qYUosiYRZh7RgcuLsQhGR+giPLpP6Sew926JUl2yxB1jLW5RLPM3EcGykSEIsMURXY5AG6rlBqElka0zI11CpErhQOdZlA0hh4HVaFzNI2GQkrNvESocrYtZrsDTv0P34Lu+GwfbWl2zZRqppN4FOJcH/3FqphEJYFZd2m5bgUQYSuiXi9IdPJGMuCB28/Rn0uEvoRiljhuRatvSGieIv1ZEIoyGz8kIbVwtE8hDziYNDny5/8OU+jOQePH/GXf/6nZNstuzsDkjREMmRmM58yFYnCFNN2OJ6dI7YNAinF0h2iRYplOMhpScswuLw45dHjB6zXI1TVQFFMqBQEsUBxFaJsi65r9Ls9hBr8cIu/uCbOYmzbuqEJaBqSLJEXGUkeczW+xHNd5ssrDvb3iDYLhCDk6ulLNufXfPDBB7/e5PzNh3tcOAqm0yTba3E1esPhg9sUicR2FBPUGYIist4GFIiIioohmaRZRC1I1LVIXYnUtYCqKiiaQlGkiBXURYGu6dSpz53bd1jMxtx6/x0ERUY1DJJ0S6/TY71cc/36Nc+//ILPn73gW+9/G7XZZe/giHEQE9QSBTZep8F2OUFSXXx/gyapiBUYkkXT6nJaLdlsU1TbxrR0bNtAlWts1+J2eR9PVmgaCg+HA/wwIaxqgsmC3YePSfP8hp3iV4SLAPUXntrg3SOW0xkfvnefUbHFkCUkR+Hp8XPCLKbTcPAaNp5q8+nffIZbmRhdA0UEUUgZjyaoiswy2CA6KrkAQR7huiaiIrCz28doe8zXSzRB4rbbZpP7+GXK7rDLeHGJJcv4kzF7rSGPn9xj7ft0u13W/pasSKCIsV0dt22ymE+xVJ3b+4dMLxSmmxsw8TYa0RJlHu72KbMAkjX99gBH1chyicLPKfOSVbQmJaYpmdR1SqNtMDwasJjN2Luzy9ejcw4HAxqJyXS14PG9XWpFYRIl6KZH5odAgdNQEc0cxYM0y3F6Td5x3+fy5ILNbI1u28iOw85BH3fYJcklDNNEKHOy7ZbXx1/hWBWVA6ZjE5Yh08UIW7NRbZn9gwdERYzoygTnKaPLKbK5RbMt1rFPkMe0Wk2mVxMcWWe4e8PtaagGm+mENIto9l0ss0ES1zgDD0GXmF7PEYqUMrrBQ1LltKwbVqnWbFCWFbalI4jVTZM+Jaous9wsaLoqs+0EXdTRdIXHdx5jqhqzyfjXE+cjz8EOY5ydHvN4TZpYHF8+RxQNJumKx9/+kBdfn1CrClleIFBTUqEYGoqkk/gVcqWiGRp5mbFczei4NsNeh9VsjCTU7HpNpCLj+uKUxV4H174FQoVuGgiKRJDGfPbppwx3dthGCU9HY7773vtIpsE4XJMKOV+dvcR/6iPKGZvtlF7Xw5QEkm3FfHNJ750ObcOiLHMMw2Cws8N6M2cdBtTUlILEIgmJspSuauOuSoplTVTLjD9/wTTyefzR++iZxHK6JO3IZGaN6Qjcu/0WXx8/ZR3MUHeHnMwuEFwFOSvQbJXp5IowBSNNaLZcYq1Gl1SuVyc3UUHLpHUwQNBlgiJCzgUMWaIuc1zXJctzqrVPKeikqwhTlqhlAVM3MQ2V9XxOWSb0Bk0mqzUf/eAjXj99SZTmxElOlaR86+23MHSd67M33NkfUlUZ4TbhzsE9jt+corseZrfDV6sFYRhhpDnTcERzuMvVJmSd1iR1BlqF55oIUcbOvofhSWyTJWEZMDjsMznZYffWEUVZweiS7kEfVBU5KZAVm2wTEUU+hZox2y5I65srermoUMoCD99+j81sjaTpaL026AbUGY5o0mi6xHHG9fiMTbbir198hqgXWHu7pFXCzq0eaRAzvLPDbD0jKVMUWSMtIpKsoNOyME2LVpGzXCxoOzaepvHo1iGthsvl6JzVMuCw28UvBZqqhSIqzOfXWJ4HpULTciEXiDchWZaxCTa4LQfHtoiLlDRPqcqUspbY2x1w8vqYbreLL0pYTYPFfM6de4eEUcb1asbl9Iq6Kn89cf50/YzK1MjWEctgg7dn09PbLOdbvvsP3ub1V6/QswhT9HDMG1yebluoisRsuubewSG9VpPJdIzruhjGDp5roMg1qyVARVs1GfZ3kDWR7XaDKEnkZYlqWcR5yXyz5U8/+ym/9bv/OR9+50PO/+j/Ql6tmT5/jSpJNDyPznRBM8+RKakyhXKzQfccLiYLZMchnq046LSZzCb0PI94uyGLY/KiZLFcM1tM+c733scUJIxRSLyY0pylOKrO9XLK3Qd3OP7ZNxiJyP333+Uvjn9GFC75rXe/xb7uUeg5+uI1lhCzmqzoHjbIVYVytWbQ2eHq2SsGrSaKqiAqYAsSqSoimhp6y2Phr8i2Kbe6PYpEIFuvabd7SIXI33z5BY1uG1MQiYocRSq5ezhgFfuMp1d8+N67bEYzJrNLnH6f69WI2/ce8of/27+m22lCdbN1tRSV/UGf/k6bzz79GXmQctt9ROfWPms/QAdUw6ZRSKw3VxxPr3jUtMjSgPHlFUIt8K13usSLJQ8eH/D4W7e53J4x2JE521zw7PQbWl2bW99+zGg85zvfekQe58R5SWvvLklQo4kFdx96nC9OSLBpmwbzsyWVbbCczNAsh+6tQ5BkVmVGGIWE2w1ka5SqJgoCphfP2ZZLUjHkennN4uoVDx+9zSyecHR0yOniNUEcgiQgbWQURUCwTfr9Ls9ePscwdCxN4+XXX/H+W0+wZZmyrOg1OuiChBCBWWhYhYrrWoiDPtPViiTPyOMcQzEpsuKGcqeqKIJAneeURYxlqIhCSb/XRUSi12yw19tBHe7z4uo5jucyWy1o9NusJkv6twf8itz7rxbnyfScg917mJqBKav44xEfDw4wujqT4xlNw+W1meJ5bebLBKPTo9lzeXXymqarsbvrYLs19x8/pOk5lFlEGK4JwjW9fpf+sE+8gAd3H2A5BqIkECYJeSUgIfHy9Rs+++IZv/eP/2umowX1asntnQ7bN6/R4xQ5zzEjl4dpTrfbZTDo8+binEwSOVnMEPKCIstIk5Adq0lV2hy/OuH5yxfcub/LrTu3qIUKVRUJfR+v3eH16Ruy0RipLAm3AU/uPGbmR/zuJ79JWsR8fv4N/mjM+/fv8LG3S3QxYSc1cOUe41cb+gdNxtOY1JaoPIWr01M0VUE3FIo4w80UktEVapLj9pt0hj3MQmWz8WlONji2jU/J5nSCtlNyt9OndCwGgx0mecXlyStkWyEXSvqtFoUfIJYFH3/wPn/w7/8d7334IePFlLsP7rKYjDk63OfN6xekOztYusl8OufD9z9g/OI1yfU54XjKcjxD77YZDg94dHCbiaXzbFpRplt6DRN/IyOKsNc1KRoVirhF7+mcnE748idnNFtdCqUgiVKeTU7RXRfFkUiTGsmwmGwy2vYQWagxzSZix6Vcn1AIOWIzoLJFim2NX8dkcU6UZsyiABRwNZFgu+Xliy/J0pRtvEFuyNimSjL2mcy3DA985tsFh8ouii3SclxkRWGxWNJttbBNA9szefjoPicnx6i6QsNrswkC5puQRbTlP/vd3+Enf/GXTEYT2q6DhsLlySm1KKBKApKpkNQlqiTQbXfp9/uEccBkfk1aJEhyRZxuKZIcRahZz9fs9/dINls2cUq82ZLmGdM44+joHrqt0uq45JH/S/X3K+N7//eP/9cfrSYbym1FV3MRVgHKMsTNBJbfXFEtQypDoMhKXM/lnW89YXirz9yfMvc3dPda/Cf/6fd4+OQOWRYiihW2Y4BUc/v+EWEas/FzdMul2esg6RpZUZHmFaPRjH/6T/85Dx++RaNt88f/5v/k66++xJRlyiSgaUsYWkYQTljMxnhNi/PZJcfTK4S2zWenp7y4XlLpBXN/jtdU2dvvc/feAbdvD2k1beoqR1clbh3sYeoqr549Q9cMln7Em+mcTJJpdG+wfqM3V9x5csDxm+fsODbbl2fUbyY8+7OvULYheq0iRwJRVXD7gyf4eYLWtImCgKZp07Qc3n38Hstrn+h6xZ2dAYO9HdxhG93SkZMcL6nJNY0VNbUsE1MhOxarKGC+XnD7YB9dU0nyBMMymc2nCEXJuw+eMJlMMRyP1WbD/tFtRBGm4xFFnNBrtnFtG03RSKIUw7AZnZzTKEUcUca2DAZ3Dugf7KOUInEas8x8ZFWl0enS2ulxdPcOtVBxeOc217MxF/M1oVhxtV0yD7fojsXTb56yd3TAz4+/5mI9pam76J5NXsk0vS5VWjBfjrlcnSE2ZF6fv2IbL7leT1BMhdcvn6MbMpVcIZgSURkQxEvW6ZZITJkkS45n55xNL27SQZZFrRgEcUyWJ2w3KxzXQhBAFETefvQ2z7/+hrfevsNXz77C8RzW/pZmuw2IzCZLHLdF8/YA1TS5mkwItyFHB7c4OT7hgw/eZzy9xk8CEunGAjQNHahYLGckeUROgh9vUE2R9WpOFAS0Gw3Cjc/hcI8yzRmdXdDybN568JDZ9RTP8xiNrkjyBNc1+cE7/9Xfvbd2Wr/8EbJBlIkgKQiKREnF+WzKX//t5+iNBkkVIhsWhufRGrYojZpCB73dIKtz+oM2+3t7+L7ParEmCCIqRLZxyjqMiRKB569PmK5WXFxe8fSbF/zt337Oj3/857x+dUp/Z4/nX37K977zCa5pISsC08Ultx/vgVMhtUQ++J2/hzL0eJMuqffaZD0XY7+D1WuwDCN+44cf4jY0JBmKMqHd9pAksC2dq/MzFvMZmigSbHyCLOPo3XcZBT6T9ZYyq1GQuHp9zvX8mH/y3/23+NfXvL17SL3Ycnd/n+989zf46dOnJElB7607XEkJ7aM9vjp9gSKKWILMnf4+o6spq0JGyCpOj18hehrqsEljt4uGRDie80ooeJEFZLKE2nSpVJltHJInMU/u3OXHf/5n2M0GWVkwGPRZTSY0dBdTt1iHEYgipVyzXi3pNZskmy2T0ZgizdE0i7ICz20xu1piFzJiJSLYJn97fcaoSBgvN1zN5ljtFrO1z8++/gbRcHjng4/IBZFPf/4ViyBiGQSIpsHlek5Y5qCIXF9fUwkVlSYxDVYU6zWnF284unOP2fWUlmPz4uVn/MUX/5FNPSfI1yxWU+IqZjS+4uLilOV8RlYliKbMszffMAum1K5K3dS5TtZcBnMq9Qbf2O/12UYZk9mMXqfF/v6QIPQxVR1TMymSgu9/8n2+fPZXfPTJR/zFT/+aXn+IqhnYpsvh/h129w5Qhy5xnhGEEeQVpqIxm05Yb1Y0mi6SIYMlI8kisiSTZSmVUFKRo9satmcSxj55ltFrd5hNppiqgSoqZHHKcrbknQf3MRSFyA+ZzKa0uh0Eqca0NL7/5L/8u4vzf/jD//FHIQW+FBGbCVslRug0eL1aMq1qxmXOwdsPsPeHlK7BxXbOs7NjrKZNo2tTegkzcc7p9JTPvvyM1xcXmDtdatdktl2x9rfUYYQi1nzx1ee8OT/n518/5fTqksvrEV6zSVEX5IrI9dWYnZ0BhVrzZ0+/5u3f+ZA/f/opWtej9GRad/rsvXOEueui9TRWxRzRyWkf2sRWxqwOWV6NCM/HmJWIqukUosD46gohSjl0Gww7TQpLIDdKBv02jq5xuZqzlkvmNlyPL3nyyX0G920ePGhRB1PefXLEt5+8z7/65/8a951dvh694u63HrLcLhANERTYGfbwoy1FkWPoOq/Xlzz6zY8xOw0IIsRlgFpWCKrA+XhFnpYoSk1SbWl2DHb3Wty/d0AQb7G6NkkW47oOq+mGVnuXs/GMSbABQ+Ts8hS3LlhNrlGoSLMMWddQDJ3+wT5ZUaHpKp2mjtbvsNZlVrKC2elRUWC4ImbbQnM9LmZz0lIiC2sOOweMzq84PjvF3uugeR7j0Zh7g0PGby55ePshfpZzNp0xHOyzupqzEAsiseR//8N/hZWvOfn8T5HqFYGwQe87jLdzxqScTabcuvuARRSxKBKmZciXV685nV4hKzov35xwen6OJIms1wsG/T6qrqBaGlJZ0mzajObX7B7s8eqbV/QbOzhOk59+/YxZmvJmco5qeLTcIW+endOymtiqxXg0ohJK/GRKXeRIQkV/p8Pl9Iq3Pnyf2jaYRjGCImOLEt3mDrJqsfJj+rsHNwu3rIC0RKkrWo0WyAaXkyXIJobpkAQBUpHT7A+oBJlKBEWX6XQaBNst52cj/tEP/8mvATKa/ORHsq5QSQUVOXt7e/z008/YPzjinXe/zfH5GU++8xBrx+PTb37G8fiY1l6T5n6DQk0Q9IIo2vD866/JsxS300BrWby5PqEuY94cf0M1mTJoOVi2ys+/+Tlm1+NicUVhiuiDBoOHB8yKM9776BHNXYfungt2wbc+eosffPIho5MTFnnB3tFtXp2d8NMvPufRO08YDHfQVIlup3FDrI5jlKxkb2eH/aNDNnXMhgSv06QocyrbITB1Nios4w1tR+eo3+Hy9DnvvXuHWgj47d/7Pb589RV37t/js09/xiff/fuM/YC/+PJL/ub8JZKR89/89/+Yf/9XP8YbNPGGHVb+mlcvXtJutFAVlW2wojFscfft+/jhhq7XQAI2YUBlKHj7QzAVnB0bs2UQlQmlVLMJI7589oyirJEFDcNwaHkd2u0Ow70B3xy/wGs5LNcLbu/tIysyq82aTr9PLYgUVU0F9HcHRFGIKuZM12skXWNn7wDdspjNJuRFiqxpzFZroqxgsH+LD9//CFWUOL04wc9DtI5NUZS0HI+u16Df7txE045uYZkm45NTsvUWsWUzmoz4zm98zBdPP0PS4KvjpyyEmMbRLq8uT3Eciwd37nD55oz1asVwd0Cz7RGnAS3XZafZIQx8VqsFCNBoehwc7rHerIniiHu37rD8RT2LrGtIiLRbXbKi5ouXL1hGIUd7bRqmQ7/dZ71YksQRiiqTFTG6pbIYn7KZzaHMGfb7mLqJ12pzfHaOpBskRY7neoRpzmLj0xsM2GzXiEKNrkhkSUjb836RiquRZJX9/QPqomSn0+Lo1i3iqiSrShqdBuPJFbp2c+/57p27fPTgH/7dxflXp3/yo+lszOHtvZvbBLqJZze5feuI41enfO8H3+Mvv/ozCrngu3//E5yOzSpe0tlvkogRdZFhlgKb5Zxbtw946+N3OZ68oRQTinBJxzX4hz/8HrJUINkiO3eHDB/ss//kFu5+m1vv3meWLvnt3/s2p+fPuF6d4Wcr9u7vcXpxyuHePrZicBXH/JL/MBMAABE1SURBVM1nn9HfP2Tpryirkun1FVfnpwTLOaakUCYZHc+hO+igNnSiOuFnn/8UfzGnoenMo4hVHDJfTlCEiof7Q8Q05Nvv3OfB/V2C9Qi53ebZ82+QsxqjUPgX/+yP0G2Ry3TDg9/+gI4hoDQ1tJ7L+eyKUhUJ0xhdN5AECa/pYtoitQFBHjK6vrzp1hFqRNvEFyuMjs3e0ZC0TqgUgShPiZOcrKjRLJvYL9msA/KsxHM9FFnm1fFLhoc7FHXGeHLFRx9+zHy1QtUNvn72jMV6g2Fb+HHA7v4eSRazml2wd3hAkhecXl7SaDZRFAVdN9istyzmS6qqxjAt2l6T+fiavEi4fe+IQqlot5pUeYYlKaRhxJc//5zvfe8TwtUKKUp5+85dXl+/YXR9TkzG6fyKdRFiD1p07u4jWCqiJkLkQ1aQ+AF3bx1y/OoFkb/BtTXEoiCcr2i3m2i6immbmLbJertmdD3Cti0iP0WUFV5fnOF6DdIwYbVc8/rkDcvtlrtPHtPMIj58933SKOLps2/QdZWdQYcsj5lMLhk4BqYoYqkqR4e3ODu74no2Y//oDq1uF7fRohBEZMMgK0s2mzWCUFGXGYNeC8fW8RyH2WxOo9lCQKC/08OxDF6//AZBrBFMnavpNXmVcTW6wHYshKoiTRJ+8O7/P47hV4rz3/78D350+/Yhk6trvv/Jb/Dp3/yUx4/eosxLTo7fsHuwy/VsxHK+gLomS1Ncz8YPtwiyhCHKOLLKTreHpIlEdUzvcIfJ9AIxifAMFcUWOZ694XR5xTRaMt5MeX3+CtvRkYSCKo9R8g3haoFnW3QGHYxOi8vliten17S6++zcvc2Dx0+I05jHbz3B99f/b3t3siNZepZx/H/meYo5coisrMqsyhraVa6u7uoBsBssIWGEJcwgsWDJDXADvWODxD2wYsHkhUEIATbGtN3d7i7XmFWVlVNkRMY8nhPTiROHRW4Byd64F/m7hU/P4pveB00SKWcyTIYDLN1AFFUUVUZRwNIlZp0Ou26GimgQvjolPjnmXj7LvUIRcxwx6/axTRPL95itEr548pgHb99hOeijDyLsTsRv3twmqte4vbeFqE559OwLOpMBIQteVY/Z2ruB4/kUiyU0TUWWIBUnxFJCIqbEi5hyuYxqWUQCjEQIRx3mUZ9Ou0WtWmM5X9KsdyARubVzC8vMkM8VkCSFw4NX3H/7Lq5n0eicY7kmw9GAFIV5HCNKMvPlika3RbvbQ9Y1BFlkf/8ZN3cqTJdLao0682RFKkjIigqpQLfWJNAtNFlFWK5IoymeomAbOslyTr1RZyXA12+/xevHz7A0Hce2UBSZo9cHjM6bFCybZDnn/t2v0Ym6pLqI5VkIkoiqXazFeDSEaMzzx0/Z290jXswJx0Om0QhLUxDjmHgUcVY7xQ0cusMegiKwWq0wdI35bA6xwNWdXURdBiHFkGTu7Oyxub5OJpelUCpx3dHxHY92u02+kGMw6KHpMoFvIUsp7nKJa5iUCkVqZ3UM2yaaz0hFCd20aHU6rBSFYRiSywZkMx7Dfodw1KPfbxH4Dqs4wXU9Tk6PCQKXSTRmPgsp5rLYlsFZr8t8teC8Wcf1bcqlIsVSgfrpGb/1zp/+4uH8/PTfPj45OkKXZF48eopnOWTdDPE8ZhJNWCYrpsM5nuFjiCpRf8xiusB3M0wmc2RJQZdVXNdhEoUYps5kGiElKYHioK9kxLJPazlFCRxWisiVyhZ//N3f58lPfooxm1O2HXazG9joXKtsI2o6P93fZ4jAzfvvkUg6jqXS7rSJZhPOqidkPJdus4mpyGyWSxiaie75+K6Nka64U94gGM+ZPTnE7c14f2uPh76FdnJEMJjgJwq65nLaHRApBi+Pa3z4rW8jtA749Vt7vP7BD3hYLLKrqWSTGfp8xFpgUvngbRaiQCwrLBFZ27jCcDzhrHrKsN9lfb1EHEecnteQdR1N1ZlFMxBlhosFjXCEK6wIZIXzsyab61cwZJP37r9P7bDGzuYVvnzynGgc0m63MHWFtbUiP/rxDymUi+iWyWAwolzaoNVq0+n2cT2P63s3iCYTKpUKrw5eI4kChSBgMp8jaRq2HxBOIkBgNBpRdAMWnSHD3gDPtpl0+wwbDdqNGvlshmk8Q9EMhu0+OhKu5VAurfH85QvGwyGjdocbV6/BfArJkvXtzYtP9uGEnfUKjdMavu2hyyofvPMOkqTQbHc5PT1DlCRAwLFshGXCZqnMSlzRHfWRdIV8sYBtmwSOQzKZ0WsPyOQvTrzzgUf3rIqepIzabeaTKYFtcvjZZ1TWNxlHE6pnNe68dYtPP/mEfNbn6kaZDSeg1ewwHAy5trvLbBmjaDrLJCaOY1zPoT+JSOIFtqFhGcpFQ52uYOo61bMaruuzSlNOT48Jx0M8x2Bne5t6tcrJ0RGSrTNbzPA8k7OzY0zr4leWpqi8f+uXOBD66+/95celTJZVOCcwHCxRR0LGsR0836M/6OMEBbKBi8SKOJ6xXM4wXB3R0IiSlGqzSziMMFON4XEbdSpTMNc43W+SdSs8rZ7h5zexjAzJAs5Pzih5WRoHh3SPT9ktFzHFAjk/g6Eb6J5Oe9pFVCXW83mGjRYCc2bTMWfVQ2zHYj6dsYoTJqMJ8WTJ2XGDUWeKMJ2xrmqsLwVyg5i72Q2yboYX1VNiDUzfIxonpIJFfwln45BnL1+xVihyp3KV77z7Ho6p8Du/9xGSHLFWyWDmLV43ztm6fY+J52K4Of7m7/6FW7fvAjo/+uGPkEXIZ3wkESRRRTVsRFFFXSkU3ByqrCEqCufdFvJ8jqPZLJYSqhmQJCqul8W2XR4/foLl27Rb57ieyY3dbeJldPEw3nQZDGdcvXqD04Nj7n7tHkmc0Gw2WCuVuXvvLvPFjDRJeOftBySxwOvDEwT5ojbesA2WqyX9QY/Tk1N+/uQZmfUimmewtlGk2T5H9y2Wukx3OUNJNAaNHrZssrG2iR9kGM2nLJYJd27dRtV0rm5XsHyXJy/2kQUZO9XY29yh3mjRCyecn7cQRZX2IALN4vpbXyeVDRBkqrVzTEUm42r0pyOagzaqZaAaOrNxiDRbsrd+BT8IGIQDHn35KYPzM3777Qeo/RHuasV3P/oWx48e8Qff+UOePNun2emzUanQ7w95++5dhOkMX9MxsVmlItvXrhPFc46rJ4TRgHzGYxH2aDVqaJpBpZQnnYUMWg1MRWY0DDlvdEkElUSSCechsgK2qXJlvcjx/j4aKXdu3KTaqlMu5jmvVVFVAUkSWKxi4jThG7f/5BcP5/nk5x+fvDlGXsIimlMpb+K5LuPBEFVXUHQVs+AzjDqIcowTaIwXfTrTIQtlhZnLIykOUiIgRwlJZ0zUiUgXMt32hEkIHzz8gMOnB8zDOY8/f8xvfPABL/f3ub67w8OH7/D68ADVyvPq+BXtUYtO1CK/4SOsZgyOjkmabTTPYDQeYDs2cRyTJAmWYRKNJ7iOTylTJuqMScIJWUEkj0xeNRkPh/zzl5/T9jTapSyrrW0i2eKz/QPEnE93NuTduzeo+DYfXr9GsjRRMiavO4cM5IiDUYufNarYt+7weatPikAmX6JU2uTG3h1+8tmXWLZ1sSjnJ5imjiwauEGW6kmNoxcHCLMltm7R7nZQTB1TU+gPR2A4DKYx1/Zu8fzVK3rhiKCQRdc1KpU1wnBEp11nsVjQ6XbRTIcUlbOzJv12g70bN5hMJpi6gaZfzA5OVytcx+bV/ktu3riL6/tMZlMePb2YUN/tdXFdh3sPHvDeR99Ez7gM4oiT+inrVzbIbJToJ3NiW8UWbD794Sf0Gm3GYcjLN4e8/9E36AwHqIpCOA55cfrm4q7WtFjOlzy4e5+//9t/wMpkkF0XxbLpNDo02n1ky2X/9RGqYXF0fMpaaZ31Yp5+p0amnCdbLmEHLqPxmLAzYG/zKvuPHmP7PqqloKsSpcDlaibDFS9DJZvH03VOXr7i8csjloKI7riIisL+i31qR8dsZPMU/YDnTw5wgwDNMHmy/wLbs5HEFFUSCByDfDaHaboswhFpPKVVr7GYzXAcH8MNCKdLJskcxzdZxVPajTqz0QhHV2nXmxQyOaL0oqZClWUEIWUQjljbWCeVJH7t+i9xz/lXf/HnHxMCC5WtrQqN7ilP9j9nOOqiyDbTacpx94QomiKrOqrh0RtMGQ0nuJaPnAqE7SFplGJrAZrlIbgKL4+fo0oCwjJm584VPvniE4pXNmlMxijZgPLuLrEq8cXLFxg5n+L9e+wPagQ727ysnpD3CkxrXdL2CDsVST0NYbmivn/CtrPJ8E0bVzBwDQNFSjELNoaj8NAyub+Wpy/P+Om4y/deHtALHIJ3bvGqV+N43GHgGxS/+R7P5j1GwpQrJZdvf/QhOSUhNeBF/ZTX3QH7Rz2k1OPhw4/YCPKo8YSZMGT/6BhvvcKjZ8+Zz0dMhw2W8RgnCDB0l87BG2aLKbZvkYophVIZN8gSLhJMv4CR32IcTtktZXHTFSf7+1iGyY3rO6TLKc2TI/qdHqqq0u6GZEoblNe3iMIhQhKR93X+7Lt/xH9///vc3drCNVW8Up5BFHHa7iFgk3MLxOMO7UYdVRQpZvKoooIsKdiWwzSJaQ7ayIrEWqmMIEqoto1omsyXCaqkkAx6LMcDNvNZtrbWyeWzzKSUdhTyaP8Fpp9F8SxeHR2TDbL4ns/jZ0+5+/Bt/uvTH7O9XWHQaSGuJLJekeZZg7Wyz429NdYrGfYPX/Ozp08RNZ1crsxqluArFup0iS3LHJ684bzfYD6fkK5AERTySCQHB9z0XUq+wz/++39Q/No9ws4x1ZMW1zdv0X1Z5XauTEGWmYQjDD+HZ2volsZg0KM/6HNWO2M8DHFMl9k0IZMr0plFLNKUyWJFrrRBKhosE4GMk8GUVCxZRDVSOqMmoiISKD7NpzV+99e+Rad+yLWNHTb8AnKcYik2O1u71I/qJMMZ3/xl9pzVxmcf27aHICocnRxSWs9RKOexLYdkJZMtlKj267iWjanrKIpMNIlYxAtWQkp3MCCfKbJVqtCptRl2e6RCjO2Y6KLO5loF3dMZz6YMwpDSxjrl9TK2bZPxHNbLRWaTCEF3UVUJx3WIFxOOD17z1vXrBIaNIavUJ0MyfgZbc/CtAMe0kSSBwloBL3DRPBtT09lSNNRVTG3Y5XwUIsgG4/mcN40qqSwgSSKd7uBi3IoicXNnm4IuI88XTNptwmXK4XkNQVfJZ/NcKa5jmwb9ZovyWpGnb56SKW7wT//6nyCCa1uQTNi5WiGVLkqdbl/dZfPaFTKFHPlsjuViSaPeZCVKFDY2OW+1sVUJR4J+p0N/MGIYTam3mkhiiqXr3L7zFsVSCcfzUVSFZJVcvI6xTURBYBXOMLWLEqhZsqQ3mzCZL1iJF1P8zqtnsJziBwG247BKYWd3F8d1OTmtMhgN8YOAKIqYRROqpydIokQKJKsVru9RdnwKfoaH9x+gqipPnz/HzPkImsokiui1ujRbdfK5PN12B01R0TUNL/AJslk2KpuYpsliGjOfJdiOi+0aLJIp9fMaT5684Ob1m7x7/x7xYoEkXnyytgydTDaDaZm4nkMmkyVZgaIoKPGCoqqgJwmT2ZzzcMJElCh6NltX9vjZ54/58N2HzMIRgWOSylDY2OT48IBUAEVVGYxH5EtFRFFEFEUsy0SzLA7rNWRJZrmIWS+tYZkm/d6A1TIhl81iWjq6IyOpAlk/YBUu2MyU6bfbbF/bpNkbEycJvcGA07Mq08WcZquFYzt848H/XscgpGn6f4bz0qVLvzr/b1fKpUuXfnUuw3np0lfUZTgvXfqKugznpUtfUZfhvHTpK+oynJcufUX9D6iJs9leZCNHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Originally:  afghan_hound\n",
      "Predicted:  afghan_hound\n"
     ]
    }
   ],
   "source": [
    "# Plotting image to compare\n",
    "plt.imshow(X_test[1,:,:,:])\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "\n",
    "# Finding max value from prediction list and comparing original value vs predicted\n",
    "print(\"Originally: \", classname[np.argmax(Y_test[1])])\n",
    "print(\"Predicted: \", classname[np.argmax(Y_pred[1])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iLkGbi_vH6xm"
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "x4jGSybPICTW"
   },
   "outputs": [],
   "source": [
    "# loading the model\n",
    "model = load_model('dog_breed.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ms6zQzH1ISi8",
    "outputId": "16d83ea8-150f-45dd-d128-32f9738caaeb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 1s 105ms/step - loss: 0.1088 - accuracy: 0.9565\n",
      "Accuracy of over the test set: \n",
      " 95.652174949646 %\n"
     ]
    }
   ],
   "source": [
    "Y_pred = model.predict(X_test)\n",
    "score = model.evaluate(X_test,Y_test)\n",
    "print('Accuracy of over the test set: \\n', score[1] * 100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hukBEvSQIem2"
   },
   "outputs": [],
   "source": [
    "img = cv2.imread('/content/Scottich_deerhound.jpg', -1)\n",
    "opencv_image = cv2.resize(img, (224, 224))\n",
    "opencv_image = image.img_to_array(opencv_image)\n",
    "opencv_image = np.expand_dims(opencv_image.copy(),axis = 0)\n",
    "opencv_image = opencv_image/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LG7Y8CgHJWbh",
    "outputId": "bb97dbe5-1a3f-4ae7-c4a3-521d6ffc944d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 224, 224, 3)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opencv_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1U_P8uRrIyDU"
   },
   "outputs": [],
   "source": [
    "prediction = model.predict(opencv_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "79Tp4RyZWdXi",
    "outputId": "3e81a264-ce25-4ac0-ec25-1fdb967223d0"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'scottish_deerhound'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classname[np.argmax(prediction)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gBMBTrTk4kn_"
   },
   "source": [
    "### The End!!"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "Dog_Breed_Prediction.ipynb",
   "provenance": []
  },
  "gpuClass": "standard",
  "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}