diff --git a/Difflense_Aleksandr_Duplinskii/Baseline_SR_models/RCAN.ipynb b/Difflense_Aleksandr_Duplinskii/Baseline_SR_models/RCAN.ipynb
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@@ -0,0 +1,862 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b6b16ab3",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# RCAN Baseline Training (DeepLense-style)\n",
+    "\n",
+    "This notebook trains a **Residual Channel Attention Network (RCAN)** baseline on your paired LR/HR `.npy` datasets:\n",
+    "\n",
+    "- **LR file**: `lr_all_lsst_20k.npy`\n",
+    "- **HR file**: `hr_all_lsst_20k.npy`\n",
+    "\n",
+    "The code assumes *paired ordering* across LR/HR arrays. Images are **47×47**; if shapes differ, the loader will **pad/crop to 47** safely.\n",
+    "\n",
+    "**Highlights**\n",
+    "- Pure PyTorch RCAN (RCAB + channel attention)\n",
+    "- AMP (`torch.cuda.amp`) + gradient clipping\n",
+    "- PSNR/SSIM metrics (SSIM torch fallback; `skimage` optional)\n",
+    "- Checkpointing & auto-resume\n",
+    "- Simple train/val split (shuffle + seed)\n",
+    "- Optional padding to 47×47\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ab91af5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python: 3.10.18 | packaged by conda-forge | (main, Jun  4 2025, 14:45:41) [GCC 13.3.0]\n",
+      "PyTorch: 2.8.0+cu126\n",
+      "CUDA available: True\n",
+      "GPU: NVIDIA RTX A5000\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "#@title 0) Environment check\n",
+    "import os, sys, math, random, time, json, shutil\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "from torch.utils.data import Dataset, DataLoader, random_split\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "print(\"Python:\", sys.version)\n",
+    "print(\"PyTorch:\", torch.__version__)\n",
+    "print(\"CUDA available:\", torch.cuda.is_available())\n",
+    "if torch.cuda.is_available():\n",
+    "    print(\"GPU:\", torch.cuda.get_device_name(0))\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "from dataloaders import *\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "58296854",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"lr_path\": \"/home/imglab/Sasha/DeepLense/grav_lens_diff/lr_all_lsst_20k.npy\",\n",
+      "  \"hr_path\": \"/home/imglab/Sasha/DeepLense/grav_lens_diff/hr_all_lsst_20k.npy\",\n",
+      "  \"out_dir\": \"./rcan_runs/baseline_rcan\",\n",
+      "  \"target_size\": 47,\n",
+      "  \"normalize\": true,\n",
+      "  \"train_frac\": 0.9,\n",
+      "  \"shuffle_seed\": 42,\n",
+      "  \"augment\": false,\n",
+      "  \"n_feats\": 64,\n",
+      "  \"n_resblocks\": 10,\n",
+      "  \"n_groups\": 5,\n",
+      "  \"reduction\": 16,\n",
+      "  \"batch_size\": 128,\n",
+      "  \"num_workers\": 4,\n",
+      "  \"lr\": 2e-05,\n",
+      "  \"weight_decay\": 0.0,\n",
+      "  \"epochs\": 100,\n",
+      "  \"warmup_epochs\": 5,\n",
+      "  \"grad_clip\": 1.0,\n",
+      "  \"use_amp\": true,\n",
+      "  \"ckpt_every\": 5,\n",
+      "  \"val_every\": 1,\n",
+      "  \"max_keep\": 5,\n",
+      "  \"resume\": true\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "#@title 1) Config\n",
+    "from dataclasses import dataclass, asdict\n",
+    "\n",
+    "@dataclass\n",
+    "class Config:\n",
+    "    # Paths (edit to point to your files on the cluster)\n",
+    "    lr_path: str = \"/home/imglab/Sasha/DeepLense/grav_lens_diff/lr_all_lsst_20k.npy\"   # change if needed\n",
+    "    hr_path: str = \"/home/imglab/Sasha/DeepLense/grav_lens_diff/hr_all_lsst_20k.npy\"   # change if needed\n",
+    "    out_dir: str = \"./rcan_runs/baseline_rcan\"\n",
+    "\n",
+    "    # Data\n",
+    "    target_size: int = 47         # pad or center-crop to this size\n",
+    "    normalize: bool = True        # per-image mean/std normalize\n",
+    "    train_frac: float = 0.9       # train/val split\n",
+    "    shuffle_seed: int = 42\n",
+    "    augment: bool = False         # light augments (flip)\n",
+    "\n",
+    "    # Model (RCAN-ish small baseline)\n",
+    "    n_feats: int = 64\n",
+    "    n_resblocks: int = 10         # RCAB per group\n",
+    "    n_groups: int = 5             # residual groups\n",
+    "    reduction: int = 16           # channel attention reduction\n",
+    "\n",
+    "    # Optimization\n",
+    "    batch_size: int = 128\n",
+    "    num_workers: int = 4\n",
+    "    lr: float = 2e-5\n",
+    "    weight_decay: float = 0.0\n",
+    "    epochs: int = 100\n",
+    "    warmup_epochs: int = 5\n",
+    "    grad_clip: float = 1.0\n",
+    "    use_amp: bool = True\n",
+    "\n",
+    "    # Logging / checkpoints\n",
+    "    ckpt_every: int = 5\n",
+    "    val_every: int = 1\n",
+    "    max_keep: int = 5\n",
+    "    resume: bool = True\n",
+    "\n",
+    "cfg = Config()\n",
+    "Path(cfg.out_dir).mkdir(parents=True, exist_ok=True)\n",
+    "print(json.dumps(asdict(cfg), indent=2))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4fd9bb57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#@title 2) Utils: padding/cropping, metrics (PSNR/SSIM), and tiny helpers\n",
+    "import math\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "import numpy as np\n",
+    "\n",
+    "def to_chw(x):\n",
+    "    # accept HxW or CxHxW; return 1xHxW float32\n",
+    "    x = np.asarray(x)\n",
+    "    if x.ndim == 2:\n",
+    "        x = x[None, ...]\n",
+    "    elif x.ndim == 3 and x.shape[0] not in (1,3):\n",
+    "        # if HWC, convert to CHW\n",
+    "        x = np.transpose(x, (2,0,1))\n",
+    "    x = x.astype(np.float32)\n",
+    "    return x\n",
+    "\n",
+    "def pad_or_crop_to(x, size):\n",
+    "    # x: tensor [C,H,W], center pad or crop to size\n",
+    "    C, H, W = x.shape\n",
+    "    # crop if larger\n",
+    "    h_start = max((H - size)//2, 0)\n",
+    "    w_start = max((W - size)//2, 0)\n",
+    "    x = x[:, h_start:h_start+min(size,H), w_start:w_start+min(size,W)]\n",
+    "    # pad if smaller\n",
+    "    pad_h = max(size - x.shape[1], 0)\n",
+    "    pad_w = max(size - x.shape[2], 0)\n",
+    "    if pad_h or pad_w:\n",
+    "        pad_top = pad_h//2\n",
+    "        pad_bottom = pad_h - pad_top\n",
+    "        pad_left = pad_w//2\n",
+    "        pad_right = pad_w - pad_left\n",
+    "        x = F.pad(x, (pad_left, pad_right, pad_top, pad_bottom))\n",
+    "    return x\n",
+    "\n",
+    "def normalize_img(x):\n",
+    "    # per-image mean/std normalize (per-channel)\n",
+    "    mean = x.mean(dim=(1,2), keepdim=True)\n",
+    "    std = x.std(dim=(1,2), keepdim=True) + 1e-6\n",
+    "    return (x - mean) / std\n",
+    "\n",
+    "def psnr(pred, target, max_val=1.0):\n",
+    "    mse = F.mse_loss(pred, target)\n",
+    "    return 20 * torch.log10(torch.tensor(max_val, device=pred.device)) - 10 * torch.log10(mse + 1e-12)\n",
+    "\n",
+    "def ssim_torch(x, y, C1=0.01**2, C2=0.03**2):\n",
+    "    # simple luminance/contrast/structure SSIM on single-channel; average over batch\n",
+    "    # expects tensors in [0,1] (we'll renormalize for metric)\n",
+    "    def _gauss_kernel(ch, k=11, sigma=1.5):\n",
+    "        ax = torch.arange(k, device=x.device) - k//2\n",
+    "        xx, yy = torch.meshgrid(ax, ax, indexing='ij')\n",
+    "        kernel = torch.exp(-(xx**2 + yy**2)/(2*sigma**2))\n",
+    "        kernel = kernel / kernel.sum()\n",
+    "        return kernel.view(1,1,k,k).repeat(ch,1,1,1)\n",
+    "    # assume [B,C,H,W]; if normalized, shift to 0..1 for metric calculation\n",
+    "    x_ = (x - x.min())/(x.max()-x.min()+1e-8)\n",
+    "    y_ = (y - y.min())/(y.max()-y.min()+1e-8)\n",
+    "    ch = x_.shape[1]\n",
+    "    kernel = _gauss_kernel(ch).to(x_.dtype)\n",
+    "    mu_x = F.conv2d(x_, kernel, padding=kernel.shape[-1]//2, groups=ch)\n",
+    "    mu_y = F.conv2d(y_, kernel, padding=kernel.shape[-1]//2, groups=ch)\n",
+    "    sigma_x = F.conv2d(x_*x_, kernel, padding=kernel.shape[-1]//2, groups=ch) - mu_x**2\n",
+    "    sigma_y = F.conv2d(y_*y_, kernel, padding=kernel.shape[-1]//2, groups=ch) - mu_y**2\n",
+    "    sigma_xy = F.conv2d(x_*y_, kernel, padding=kernel.shape[-1]//2, groups=ch) - mu_x*mu_y\n",
+    "    ssim = ((2*mu_x*mu_y + C1)*(2*sigma_xy + C2))/((mu_x**2 + mu_y**2 + C1)*(sigma_x + sigma_y + C2)+1e-12)\n",
+    "    return ssim.mean()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8774a9b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# #@title 3) Dataset\n",
+    "# class PairedNPY(torch.utils.data.Dataset):\n",
+    "#     def __init__(self, lr_path, hr_path, target_size=47, normalize=True, augment=False):\n",
+    "#         self.lr = np.load(lr_path, mmap_mode=\"r\")  # expect [N, H, W] or [N, C, H, W]\n",
+    "#         self.hr = np.load(hr_path, mmap_mode=\"r\")\n",
+    "#         assert len(self.lr) == len(self.hr), \"LR/HR count mismatch\"\n",
+    "#         self.target_size = target_size\n",
+    "#         self.normalize = normalize\n",
+    "#         self.augment = augment\n",
+    "\n",
+    "#     def __len__(self):\n",
+    "#         return len(self.lr)\n",
+    "\n",
+    "#     def __getitem__(self, idx):\n",
+    "#         lr = to_chw(self.lr[idx])\n",
+    "#         hr = to_chw(self.hr[idx])\n",
+    "#         lr = torch.from_numpy(lr)\n",
+    "#         hr = torch.from_numpy(hr)\n",
+    "#         lr = pad_or_crop_to(lr, self.target_size)\n",
+    "#         hr = pad_or_crop_to(hr, self.target_size)\n",
+    "\n",
+    "#         if self.normalize:\n",
+    "#             lr = normalize_img(lr)\n",
+    "#             hr = normalize_img(hr)\n",
+    "\n",
+    "#         if self.augment:\n",
+    "#             if random.random() < 0.5:\n",
+    "#                 lr = torch.flip(lr, dims=[1])\n",
+    "#                 hr = torch.flip(hr, dims=[1])\n",
+    "#             if random.random() < 0.5:\n",
+    "#                 lr = torch.flip(lr, dims=[2])\n",
+    "#                 hr = torch.flip(hr, dims=[2])\n",
+    "\n",
+    "#         return lr, hr\n",
+    "\n",
+    "# full_ds = PairedNPY(cfg.lr_path, cfg.hr_path, target_size=cfg.target_size,\n",
+    "#                     normalize=cfg.normalize, augment=cfg.augment)\n",
+    "\n",
+    "# n_total = len(full_ds)\n",
+    "# n_train = int(n_total * cfg.train_frac)\n",
+    "# n_val = n_total - n_train\n",
+    "# g = torch.Generator().manual_seed(cfg.shuffle_seed)\n",
+    "# train_ds, val_ds = random_split(full_ds, [n_train, n_val], generator=g)\n",
+    "\n",
+    "# train_loader = DataLoader(train_ds, batch_size=cfg.batch_size, shuffle=True,\n",
+    "#                           num_workers=cfg.num_workers, pin_memory=True)\n",
+    "# val_loader = DataLoader(val_ds, batch_size=cfg.batch_size, shuffle=False,\n",
+    "#                         num_workers=cfg.num_workers, pin_memory=True)\n",
+    "\n",
+    "# n_total, n_train, n_val\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a5e6c789",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_ds = PairsDatasetUnified(\n",
+    "    root_dir=\".\",\n",
+    "    norm_preset=\"hybrid\",         # keep identical LR stats to diffusion\n",
+    "    percentile_p=99.99, percentile_from=\"lr\",\n",
+    "    out_range=\"[0,1]\",\n",
+    "    pad_to=None,                    # or None if your model wants native size               # RGB repeat for ViT/SwinIR\n",
+    "    noise_aug=noise_aug,          # SAME augment -> identical noisy LR per index\n",
+    "    return_order=\"LRHR\"\n",
+    ")\n",
+    "# Use the same split indices as above to keep train/val identical:\n",
+    "idxs = torch.randperm(len(full_ds), generator=torch.Generator().manual_seed(42))\n",
+    "n_total = len(full_ds)\n",
+    "n_train = int(0.9 * n_total)\n",
+    "train_ds = torch.utils.data.Subset(full_ds, idxs[:n_train])\n",
+    "val_ds   = torch.utils.data.Subset(full_ds, idxs[n_train:])\n",
+    "\n",
+    "train_loader = DataLoader(train_ds, batch_size=64, shuffle=True,  num_workers=4, pin_memory=True)\n",
+    "val_loader   = DataLoader(val_ds,   batch_size=64, shuffle=False, num_workers=2, pin_memory=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e8e75f78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Params (M): 3.944585\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "#@title 4) RCAN model\n",
+    "class CALayer(nn.Module):\n",
+    "    def __init__(self, channel, reduction=16):\n",
+    "        super().__init__()\n",
+    "        self.avg_pool = nn.AdaptiveAvgPool2d(1)\n",
+    "        self.conv_du = nn.Sequential(\n",
+    "            nn.Conv2d(channel, channel // reduction, 1, padding=0),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.Conv2d(channel // reduction, channel, 1, padding=0),\n",
+    "            nn.Sigmoid()\n",
+    "        )\n",
+    "    def forward(self, x):\n",
+    "        y = self.avg_pool(x)\n",
+    "        y = self.conv_du(y)\n",
+    "        return x * y\n",
+    "\n",
+    "class RCAB(nn.Module):\n",
+    "    def __init__(self, n_feats, reduction=16):\n",
+    "        super().__init__()\n",
+    "        self.body = nn.Sequential(\n",
+    "            nn.Conv2d(n_feats, n_feats, 3, padding=1),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.Conv2d(n_feats, n_feats, 3, padding=1),\n",
+    "        )\n",
+    "        self.ca = CALayer(n_feats, reduction)\n",
+    "    def forward(self, x):\n",
+    "        res = self.body(x)\n",
+    "        res = self.ca(res)\n",
+    "        return x + res\n",
+    "\n",
+    "class ResidualGroup(nn.Module):\n",
+    "    def __init__(self, n_feats, n_resblocks, reduction=16):\n",
+    "        super().__init__()\n",
+    "        modules = [RCAB(n_feats, reduction) for _ in range(n_resblocks)]\n",
+    "        modules += [nn.Conv2d(n_feats, n_feats, 3, padding=1)]\n",
+    "        self.body = nn.Sequential(*modules)\n",
+    "    def forward(self, x):\n",
+    "        res = self.body(x)\n",
+    "        return x + res\n",
+    "\n",
+    "class RCAN(nn.Module):\n",
+    "    def __init__(self, in_ch=1, out_ch=1, n_feats=64, n_resblocks=10, n_groups=5, reduction=16):\n",
+    "        super().__init__()\n",
+    "        self.head = nn.Conv2d(in_ch, n_feats, 3, padding=1)\n",
+    "        self.groups = nn.Sequential(*[ResidualGroup(n_feats, n_resblocks, reduction) for _ in range(n_groups)])\n",
+    "        self.tail = nn.Sequential(\n",
+    "            nn.Conv2d(n_feats, n_feats, 3, padding=1),\n",
+    "            nn.Conv2d(n_feats, out_ch, 3, padding=1)\n",
+    "        )\n",
+    "    def forward(self, x):\n",
+    "        x = self.head(x)\n",
+    "        res = self.groups(x)\n",
+    "        res = self.tail(res)\n",
+    "        return res\n",
+    "\n",
+    "model = RCAN(in_ch=1, out_ch=1, n_feats=cfg.n_feats,\n",
+    "             n_resblocks=cfg.n_resblocks, n_groups=cfg.n_groups, reduction=cfg.reduction).to(device)\n",
+    "print(\"Params (M):\", sum(p.numel() for p in model.parameters())/1e6)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6a4c6c4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/user/1015/ipykernel_2550876/1184635936.py:3: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
+      "  scaler = torch.cuda.amp.GradScaler(enabled=cfg.use_amp and torch.cuda.is_available())\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "#@title 5) Optimizer, scheduler, loss\n",
+    "optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay)\n",
+    "scaler = torch.cuda.amp.GradScaler(enabled=cfg.use_amp and torch.cuda.is_available())\n",
+    "\n",
+    "def cosine_warmup(step, total_steps, warmup_steps):\n",
+    "    if step < warmup_steps:\n",
+    "        return step/max(1,warmup_steps)\n",
+    "    progress = (step - warmup_steps) / max(1, total_steps - warmup_steps)\n",
+    "    return 0.5 * (1.0 + math.cos(math.pi * progress))\n",
+    "\n",
+    "total_steps = cfg.epochs * max(1, len(train_loader))\n",
+    "warmup_steps = cfg.warmup_epochs * max(1, len(train_loader))\n",
+    "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda s: cosine_warmup(s, total_steps, warmup_steps))\n",
+    "\n",
+    "criterion = nn.L1Loss()  # L1 for SR\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2059ea76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#@title 6) Training & validation loops\n",
+    "import json, time\n",
+    "from collections import deque\n",
+    "\n",
+    "def save_ckpt(path, epoch, model, optimizer, scaler, best_psnr):\n",
+    "    Path(path).parent.mkdir(parents=True, exist_ok=True)\n",
+    "    torch.save({\n",
+    "        \"epoch\": epoch,\n",
+    "        \"model\": model.state_dict(),\n",
+    "        \"optimizer\": optimizer.state_dict(),\n",
+    "        \"scaler\": scaler.state_dict() if scaler is not None else None,\n",
+    "        \"best_psnr\": best_psnr,\n",
+    "        \"cfg\": cfg.__dict__,\n",
+    "    }, path)\n",
+    "\n",
+    "def load_ckpt_if_exists(path, model, optimizer, scaler):\n",
+    "    if Path(path).exists():\n",
+    "        ckpt = torch.load(path, map_location=\"cpu\", weights_only=False)\n",
+    "        model.load_state_dict(ckpt[\"model\"])\n",
+    "        if optimizer is not None and \"optimizer\" in ckpt:\n",
+    "            optimizer.load_state_dict(ckpt[\"optimizer\"])\n",
+    "        if scaler is not None and ckpt.get(\"scaler\") is not None:\n",
+    "            scaler.load_state_dict(ckpt[\"scaler\"])\n",
+    "        return ckpt.get(\"epoch\", 0)+1, ckpt.get(\"best_psnr\", -1.0)\n",
+    "    return 0, -1.0\n",
+    "\n",
+    "def validate(model, loader):\n",
+    "    \"\"\"\n",
+    "    Returns: (psnr_avg, ssim_avg, mae_avg, mse_avg)\n",
+    "    - Averages are computed per-image over the whole validation set.\n",
+    "    - MAE/MSE are per-image means over pixels.\n",
+    "    - PSNR is computed from per-image MSE with max_val=1.0.\n",
+    "    \"\"\"\n",
+    "\n",
+    "\n",
+    "    model.eval()\n",
+    "    psnr_sum = 0.0\n",
+    "    mae_sum  = 0.0\n",
+    "    mse_sum  = 0.0\n",
+    "    ssim_sum = 0.0\n",
+    "    ssim_n   = 0      # how many images contributed to SSIM\n",
+    "    n_imgs   = 0\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        for lr, hr in loader:\n",
+    "            lr = lr.to(device, non_blocking=True)\n",
+    "            hr = hr.to(device, non_blocking=True)\n",
+    "\n",
+    "            pred = model(lr)\n",
+    "\n",
+    "            # Ensure [B,1,H,W]\n",
+    "            if pred.dim() == 3: pred = pred.unsqueeze(1)\n",
+    "            if hr.dim()   == 3: hr   = hr.unsqueeze(1)\n",
+    "\n",
+    "            B = pred.size(0)\n",
+    "\n",
+    "            # --- MAE/MSE per image (mean over pixels) ---\n",
+    "            mae_vals = F.l1_loss(pred, hr, reduction='none').view(B, -1).mean(dim=1)   # [B]\n",
+    "            mse_vals = F.mse_loss(pred, hr, reduction='none').view(B, -1).mean(dim=1)  # [B]\n",
+    "\n",
+    "            mae_sum += mae_vals.sum().item()\n",
+    "            mse_sum += mse_vals.sum().item()\n",
+    "\n",
+    "            # --- PSNR per image from per-image MSE ---\n",
+    "            max_val = 1.0\n",
+    "            psnr_vals = 20.0 * torch.log10(torch.tensor(max_val, device=pred.device)) \\\n",
+    "                        - 10.0 * torch.log10(mse_vals + 1e-12)                          # [B]\n",
+    "            psnr_sum += psnr_vals.sum().item()\n",
+    "\n",
+    "            # --- SSIM (handle scalar or vector returns) ---\n",
+    "            try:\n",
+    "                ssim_out = ssim_torch(pred, hr)\n",
+    "                if torch.is_tensor(ssim_out):\n",
+    "                    if ssim_out.dim() == 0:\n",
+    "                        # a single mean over batch\n",
+    "                        ssim_sum += ssim_out.item() * B\n",
+    "                        ssim_n   += B\n",
+    "                    else:\n",
+    "                        # vector per-image\n",
+    "                        ssim_sum += ssim_out.sum().item()\n",
+    "                        ssim_n   += ssim_out.numel()\n",
+    "                else:\n",
+    "                    # plain float\n",
+    "                    ssim_sum += float(ssim_out) * B\n",
+    "                    ssim_n   += B\n",
+    "            except Exception:\n",
+    "                pass\n",
+    "\n",
+    "            n_imgs += B\n",
+    "\n",
+    "    model.train()\n",
+    "\n",
+    "    psnr_avg = psnr_sum / n_imgs if n_imgs else 0.0\n",
+    "    mae_avg  = mae_sum  / n_imgs if n_imgs else 0.0\n",
+    "    mse_avg  = mse_sum  / n_imgs if n_imgs else 0.0\n",
+    "    ssim_avg = ssim_sum / ssim_n if ssim_n else 0.0\n",
+    "\n",
+    "    return psnr_avg, ssim_avg, mae_avg, mse_avg\n",
+    "\n",
+    "\n",
+    "@torch.no_grad()\n",
+    "def visualize_results(model, loader, n_samples=4, vmax=None):\n",
+    "    \"\"\"\n",
+    "    Show a few random low-res / predicted / ground-truth triplets,\n",
+    "    each with a colorbar sharing the same scale.\n",
+    "    \n",
+    "    Args:\n",
+    "        model: trained model\n",
+    "        loader: DataLoader\n",
+    "        n_samples: number of samples to visualize\n",
+    "        vmax: maximum intensity for all colorbars (vmin=0, same across all)\n",
+    "    \"\"\"\n",
+    "    model.eval()\n",
+    "    lr, hr = next(iter(loader))   # take first batch\n",
+    "    lr = lr.to(device)\n",
+    "    hr = hr.to(device)\n",
+    "\n",
+    "    # Run model\n",
+    "    pred = model(lr)\n",
+    "\n",
+    "    # Ensure [B,1,H,W] -> [B,H,W]\n",
+    "    if pred.dim() == 4 and pred.size(1) == 1:\n",
+    "        pred = pred.squeeze(1)\n",
+    "        hr   = hr.squeeze(1)\n",
+    "        lr   = lr.squeeze(1)\n",
+    "\n",
+    "    # For visualization: upsample LR to HR size\n",
+    "    lr_up = F.interpolate(lr.unsqueeze(1), size=hr.shape[-2:], mode=\"bicubic\", align_corners=False)\n",
+    "    lr_up = lr_up.squeeze(1)\n",
+    "\n",
+    "    # Select subset\n",
+    "    idxs = torch.randperm(lr_up.size(0))[:n_samples]\n",
+    "    lr_up = lr_up[idxs].cpu()\n",
+    "    pred  = pred[idxs].cpu()\n",
+    "    hr    = hr[idxs].cpu()\n",
+    "\n",
+    "    # Plot\n",
+    "    fig, axs = plt.subplots(n_samples, 3, figsize=(12, 3*n_samples))\n",
+    "    for i in range(n_samples):\n",
+    "        imgs = [lr_up[i], pred[i], hr[i]]\n",
+    "        titles = [\"Low-res (upsampled)\", \"Prediction\", \"Ground Truth\"]\n",
+    "\n",
+    "        for j, (img, title) in enumerate(zip(imgs, titles)):\n",
+    "            im = axs[i, j].imshow(img, cmap=\"gray\", vmin=0, vmax=vmax)\n",
+    "            axs[i, j].set_title(title)\n",
+    "            axs[i, j].axis(\"off\")\n",
+    "            fig.colorbar(im, ax=axs[i, j], fraction=0.046, pad=0.04)\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    model.train()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "821503d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resuming from epoch 28, best_psnr=61.663\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/user/1015/ipykernel_2550876/934354275.py:21: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
+      "  with torch.cuda.amp.autocast(enabled=cfg.use_amp and torch.cuda.is_available()):\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'epoch': 29, 'train_loss': 0.00019758629090613387, 'val_mae': 0.00016580138029530646, 'val_mse': 1.5177952827798435e-06, 'val_psnr': 63.55326846313476, 'val_ssim': 0.9982847032546998, 'lr': 4.000000000000001e-06, 'time_min': 0.7994792858759562}\n",
+      "{'epoch': 30, 'train_loss': 0.00016434669153226526, 'val_mae': 0.0001625780357280746, 'val_mse': 1.5198942046481534e-06, 'val_psnr': 63.57758697509766, 'val_ssim': 0.9982441620826721, 'lr': 8.000000000000001e-06, 'time_min': 0.7847822745641072}\n",
+      "{'epoch': 31, 'train_loss': 0.0001617517038075095, 'val_mae': 0.00016192181559745222, 'val_mse': 1.5065529096318642e-06, 'val_psnr': 63.60192010498047, 'val_ssim': 0.9980468564033508, 'lr': 1.2e-05, 'time_min': 0.779647954305013}\n",
+      "{'epoch': 32, 'train_loss': 0.0001601335817658184, 'val_mae': 0.00016319295903667808, 'val_mse': 1.5302964620786951e-06, 'val_psnr': 63.407773010253905, 'val_ssim': 0.9982028303146362, 'lr': 1.6000000000000003e-05, 'time_min': 0.7755640546480814}\n",
+      "{'epoch': 33, 'train_loss': 0.00015854550086305286, 'val_mae': 0.00015690020169131457, 'val_mse': 1.490721041591314e-06, 'val_psnr': 63.657118408203125, 'val_ssim': 0.9981181893348694, 'lr': 2e-05, 'time_min': 0.7743256290753683}\n",
+      "{'epoch': 34, 'train_loss': 0.00015742869854488587, 'val_mae': 0.00016471015347633512, 'val_mse': 1.472538627240283e-06, 'val_psnr': 63.58766864013672, 'val_ssim': 0.998032823562622, 'lr': 1.999453257340926e-05, 'time_min': 0.7745883504549662}\n",
+      "{'epoch': 35, 'train_loss': 0.00015611780804486165, 'val_mae': 0.00015839214227162301, 'val_mse': 1.4548654689860996e-06, 'val_psnr': 63.70681085205078, 'val_ssim': 0.9980843119621277, 'lr': 1.9978136272187745e-05, 'time_min': 0.7736316124598185}\n",
+      "{'epoch': 36, 'train_loss': 0.0001539021934459402, 'val_mae': 0.0001523238233057782, 'val_mse': 1.4444790376728633e-06, 'val_psnr': 63.862669158935546, 'val_ssim': 0.998015992641449, 'lr': 1.9950829025450116e-05, 'time_min': 0.7732997179031372}\n",
+      "{'epoch': 37, 'train_loss': 0.00015289980778016534, 'val_mae': 0.00014941103907767684, 'val_mse': 1.4520718959829536e-06, 'val_psnr': 63.68299255371094, 'val_ssim': 0.9981667885780334, 'lr': 1.9912640693269754e-05, 'time_min': 0.7740920225779215}\n",
+      "{'epoch': 38, 'train_loss': 0.00015104837658450152, 'val_mae': 0.00015275406301952898, 'val_mse': 1.4130058470982476e-06, 'val_psnr': 63.911955841064454, 'val_ssim': 0.997969358921051, 'lr': 1.9863613034027224e-05, 'time_min': 0.7734478155771891}\n",
+      "{'epoch': 39, 'train_loss': 0.00015228419509552855, 'val_mae': 0.00015356444800272584, 'val_mse': 1.4217390471458203e-06, 'val_psnr': 63.78040167236328, 'val_ssim': 0.9982259092330933, 'lr': 1.9803799658748096e-05, 'time_min': 0.7730762441953023}\n",
+      "{'epoch': 40, 'train_loss': 0.00016405668785855016, 'val_mae': 0.00016044307209085674, 'val_mse': 1.4136179925117175e-06, 'val_psnr': 63.711825744628904, 'val_ssim': 0.9980459952354431, 'lr': 1.973326597248006e-05, 'time_min': 0.7738110939661662}\n",
+      "{'epoch': 41, 'train_loss': 0.00015632317163890677, 'val_mae': 0.00015898883854970335, 'val_mse': 1.3913511556893354e-06, 'val_psnr': 63.79762268066406, 'val_ssim': 0.9978681211471557, 'lr': 1.9652089102773487e-05, 'time_min': 0.7734771291414897}\n",
+      "{'epoch': 42, 'train_loss': 0.00015416815227284824, 'val_mae': 0.00014285461360123009, 'val_mse': 1.3692167194676585e-06, 'val_psnr': 63.977930603027346, 'val_ssim': 0.998274534702301, 'lr': 1.9560357815343577e-05, 'time_min': 0.7730305830637614}\n",
+      "{'epoch': 43, 'train_loss': 0.00014726620088851555, 'val_mae': 0.00014807991962879896, 'val_mse': 1.3820703634337406e-06, 'val_psnr': 63.74030822753906, 'val_ssim': 0.9984212307929993, 'lr': 1.9458172417006347e-05, 'time_min': 0.7729678710301717}\n",
+      "{'epoch': 44, 'train_loss': 0.00014597996610612277, 'val_mae': 0.00015038120606914162, 'val_mse': 1.3463810291796108e-06, 'val_psnr': 64.0781787109375, 'val_ssim': 0.9984169516563416, 'lr': 1.934564464599461e-05, 'time_min': 0.7738643089930216}\n",
+      "{'epoch': 45, 'train_loss': 0.00014450252289881316, 'val_mae': 0.00014612273359671234, 'val_mse': 1.3568963904617703e-06, 'val_psnr': 63.8862053527832, 'val_ssim': 0.9984259390830994, 'lr': 1.922289754977385e-05, 'time_min': 0.7730872710545857}\n",
+      "{'epoch': 46, 'train_loss': 0.00014493302933905598, 'val_mae': 0.00015032406768295914, 'val_mse': 1.2998469537706115e-06, 'val_psnr': 64.11983770751954, 'val_ssim': 0.9984260334968567, 'lr': 1.909006535049163e-05, 'time_min': 0.7731371601422627}\n",
+      "{'epoch': 47, 'train_loss': 0.0001426080542332526, 'val_mae': 0.0001394188952399418, 'val_mse': 1.29534365987638e-06, 'val_psnr': 64.24785653686523, 'val_ssim': 0.998416660785675, 'lr': 1.8947293298207637e-05, 'time_min': 0.7737797856330871}\n",
+      "{'epoch': 48, 'train_loss': 0.00014249832827720876, 'val_mae': 0.00014038238674402237, 'val_mse': 1.299398765695514e-06, 'val_psnr': 64.11824194335938, 'val_ssim': 0.9983878674507141, 'lr': 1.879473751206489e-05, 'time_min': 0.7734704931577047}\n",
+      "{'epoch': 49, 'train_loss': 0.00014200004487272008, 'val_mae': 0.00014254038606304677, 'val_mse': 1.306706929426582e-06, 'val_psnr': 64.1752615661621, 'val_ssim': 0.9985272960662842, 'lr': 1.863256480957574e-05, 'time_min': 0.7733281215031942}\n",
+      "{'epoch': 50, 'train_loss': 0.0001443840598335394, 'val_mae': 0.00014045482943765819, 'val_mse': 1.29601062326401e-06, 'val_psnr': 64.11122674560546, 'val_ssim': 0.9985741143226623, 'lr': 1.8460952524209355e-05, 'time_min': 0.7731968442598979}\n",
+      "{'epoch': 51, 'train_loss': 0.00014206889220257454, 'val_mae': 0.00013901252555660905, 'val_mse': 1.2446906166587724e-06, 'val_psnr': 64.16609356689453, 'val_ssim': 0.9985603547096252, 'lr': 1.8280088311480203e-05, 'time_min': 0.7738610863685608}\n",
+      "{'epoch': 52, 'train_loss': 0.00014040294330478777, 'val_mae': 0.00013625481876078992, 'val_mse': 1.249679380634916e-06, 'val_psnr': 64.26320404052734, 'val_ssim': 0.9984829411506653, 'lr': 1.8090169943749477e-05, 'time_min': 0.7734921693801879}\n",
+      "{'epoch': 53, 'train_loss': 0.00013900750163626355, 'val_mae': 0.0001374688930809498, 'val_mse': 1.2823391980418818e-06, 'val_psnr': 64.15073223876954, 'val_ssim': 0.9987308549880981, 'lr': 1.789140509396394e-05, 'time_min': 0.7742460091908773}\n",
+      "{'epoch': 54, 'train_loss': 0.00013813977085379224, 'val_mae': 0.00013612616853788496, 'val_mse': 1.242427495526499e-06, 'val_psnr': 64.223951171875, 'val_ssim': 0.9985865836143494, 'lr': 1.7684011108568593e-05, 'time_min': 0.7742828249931335}\n",
+      "{'epoch': 55, 'train_loss': 0.00013883980657783147, 'val_mae': 0.00013474829797632994, 'val_mse': 1.2544221062853467e-06, 'val_psnr': 64.25694613647461, 'val_ssim': 0.9986429133415222, 'lr': 1.7468214769841542e-05, 'time_min': 0.7738146543502807}\n",
+      "{'epoch': 56, 'train_loss': 0.00013726889863791908, 'val_mae': 0.0001381910580676049, 'val_mse': 1.2207216022943611e-06, 'val_psnr': 64.34404217529297, 'val_ssim': 0.9987994990348816, 'lr': 1.7244252047910893e-05, 'time_min': 0.7737253944079081}\n",
+      "{'epoch': 57, 'train_loss': 0.00013750379423446563, 'val_mae': 0.00013902431260794402, 'val_mse': 1.2380810112517794e-06, 'val_psnr': 64.36912939453126, 'val_ssim': 0.9987122135162354, 'lr': 1.7012367842724887e-05, 'time_min': 0.7742626190185546}\n",
+      "{'epoch': 58, 'train_loss': 0.00014195917667024307, 'val_mae': 0.00013895935588516294, 'val_mse': 1.2182634509372293e-06, 'val_psnr': 64.31668615722656, 'val_ssim': 0.9987453823089599, 'lr': 1.6772815716257414e-05, 'time_min': 0.7734952211380005}\n",
+      "{'epoch': 59, 'train_loss': 0.00013712548326943663, 'val_mae': 0.0001339285697322339, 'val_mse': 1.2037584856443573e-06, 'val_psnr': 64.33533767700196, 'val_ssim': 0.9987457256317138, 'lr': 1.6525857615241686e-05, 'time_min': 0.7739108363787334}\n",
+      "{'epoch': 60, 'train_loss': 0.00013376957285283992, 'val_mae': 0.00013282449811231344, 'val_mse': 1.1869047448271885e-06, 'val_psnr': 64.44660778808594, 'val_ssim': 0.9985918684005737, 'lr': 1.6271763584735373e-05, 'time_min': 0.7741204897562662}\n",
+      "{'epoch': 61, 'train_loss': 0.0001344488296029901, 'val_mae': 0.0001348612894071266, 'val_mse': 1.1751582851502462e-06, 'val_psnr': 64.43484539794922, 'val_ssim': 0.9985371823310852, 'lr': 1.6010811472830253e-05, 'time_min': 0.7739541530609131}\n",
+      "{'epoch': 62, 'train_loss': 0.00013570467309744744, 'val_mae': 0.00013251142180524766, 'val_mse': 1.1738844059436815e-06, 'val_psnr': 64.3289997253418, 'val_ssim': 0.9986264052391052, 'lr': 1.5743286626829437e-05, 'time_min': 0.7742996374766032}\n",
+      "{'epoch': 63, 'train_loss': 0.0001339432392535642, 'val_mae': 0.0001321334899403155, 'val_mse': 1.193594576761825e-06, 'val_psnr': 64.36334161376953, 'val_ssim': 0.9988023972511292, 'lr': 1.5469481581224274e-05, 'time_min': 0.7741695761680603}\n",
+      "{'epoch': 64, 'train_loss': 0.00013239326328906093, 'val_mae': 0.00013625469384714962, 'val_mse': 1.1446940043242648e-06, 'val_psnr': 64.53116510009765, 'val_ssim': 0.998477557182312, 'lr': 1.5189695737812153e-05, 'time_min': 0.7750452041625977}\n",
+      "{'epoch': 65, 'train_loss': 0.00013349740466694118, 'val_mae': 0.00013176229619421065, 'val_mse': 1.1526980470080161e-06, 'val_psnr': 64.44134497070313, 'val_ssim': 0.9987253580093384, 'lr': 1.4904235038305084e-05, 'time_min': 0.7741690754890442}\n",
+      "{'epoch': 66, 'train_loss': 0.00013155703662902097, 'val_mae': 0.00013016205350868405, 'val_mse': 1.1455263138486772e-06, 'val_psnr': 64.44477786254883, 'val_ssim': 0.9985991735458374, 'lr': 1.461341162978688e-05, 'time_min': 0.7741188287734986}\n",
+      "{'epoch': 67, 'train_loss': 0.0001341721042165044, 'val_mae': 0.00012938388215843588, 'val_mse': 1.1482763347885339e-06, 'val_psnr': 64.52641198730468, 'val_ssim': 0.9987076201438904, 'lr': 1.4317543523384928e-05, 'time_min': 0.7735893527666727}\n",
+      "{'epoch': 68, 'train_loss': 0.00013107386061526235, 'val_mae': 0.00013593394565396011, 'val_mse': 1.1334674863974215e-06, 'val_psnr': 64.6206801147461, 'val_ssim': 0.9987128210067749, 'lr': 1.4016954246529697e-05, 'time_min': 0.7745556275049845}\n",
+      "{'epoch': 69, 'train_loss': 0.0001309556032634994, 'val_mae': 0.00013035059324465693, 'val_mse': 1.1217887076782062e-06, 'val_psnr': 64.58432192993165, 'val_ssim': 0.9986101069450378, 'lr': 1.3711972489182208e-05, 'time_min': 0.7737564643224081}\n",
+      "{'epoch': 70, 'train_loss': 0.00012995729102453394, 'val_mae': 0.00012682182341814042, 'val_mse': 1.0990916489390656e-06, 'val_psnr': 64.6913649597168, 'val_ssim': 0.9987598357200622, 'lr': 1.3402931744416432e-05, 'time_min': 0.7737411896387736}\n",
+      "{'epoch': 71, 'train_loss': 0.00012904007720256536, 'val_mae': 0.0001266953544691205, 'val_mse': 1.1038934735552175e-06, 'val_psnr': 64.65394995117188, 'val_ssim': 0.9986943931579589, 'lr': 1.3090169943749475e-05, 'time_min': 0.7742483695348104}\n",
+      "{'epoch': 72, 'train_loss': 0.00012955196081670595, 'val_mae': 0.00012894851225428284, 'val_mse': 1.119504437156138e-06, 'val_psnr': 64.53240298461914, 'val_ssim': 0.998703803062439, 'lr': 1.2774029087618448e-05, 'time_min': 0.7736289779345195}\n",
+      "{'epoch': 73, 'train_loss': 0.00012834267165979984, 'val_mae': 0.00012622391199693084, 'val_mse': 1.102163076211582e-06, 'val_psnr': 64.706580078125, 'val_ssim': 0.9985861120223999, 'lr': 1.2454854871407993e-05, 'time_min': 0.77355983654658}\n",
+      "{'epoch': 74, 'train_loss': 0.00012831579491740476, 'val_mae': 0.00012678173044696452, 'val_mse': 1.1022753278666642e-06, 'val_psnr': 64.7160443725586, 'val_ssim': 0.9987255434989929, 'lr': 1.213299630743747e-05, 'time_min': 0.7735840082168579}\n",
+      "{'epoch': 75, 'train_loss': 0.00012763471316615556, 'val_mae': 0.0001257915839087218, 'val_mse': 1.109021513912012e-06, 'val_psnr': 64.69878915405273, 'val_ssim': 0.9985253486633301, 'lr': 1.1808805343321102e-05, 'time_min': 0.7743240753809612}\n",
+      "{'epoch': 76, 'train_loss': 0.0001274463567876171, 'val_mae': 0.00012481738033238797, 'val_mse': 1.0859101539608673e-06, 'val_psnr': 64.71810705566406, 'val_ssim': 0.9981697030067443, 'lr': 1.148263647711842e-05, 'time_min': 0.7744259993235271}\n",
+      "{'epoch': 77, 'train_loss': 0.0001273948126396161, 'val_mae': 0.00012617610744200647, 'val_mse': 1.0706781913540909e-06, 'val_psnr': 64.80442706298828, 'val_ssim': 0.9982701964378357, 'lr': 1.1154846369695864e-05, 'time_min': 0.774039872487386}\n",
+      "{'epoch': 78, 'train_loss': 0.00012690787536859122, 'val_mae': 0.00012331628100946545, 'val_mse': 1.083768419448461e-06, 'val_psnr': 64.73046182250977, 'val_ssim': 0.9986898880004883, 'lr': 1.0825793454723325e-05, 'time_min': 0.7747644503911336}\n",
+      "{'epoch': 79, 'train_loss': 0.00012629918983422472, 'val_mae': 0.00012761137867346405, 'val_mse': 1.0869989937418723e-06, 'val_psnr': 64.66077587890625, 'val_ssim': 0.9978799505233764, 'lr': 1.0495837546732224e-05, 'time_min': 0.7745023608207703}\n",
+      "{'epoch': 80, 'train_loss': 0.00012655650291419323, 'val_mae': 0.000123623154242523, 'val_mse': 1.0786333568830742e-06, 'val_psnr': 64.7520851135254, 'val_ssim': 0.9981640863418579, 'lr': 1.0165339447663586e-05, 'time_min': 0.7742859204610189}\n",
+      "{'epoch': 81, 'train_loss': 0.0001267329729581169, 'val_mae': 0.00012339493259787558, 'val_mse': 1.0702826293709222e-06, 'val_psnr': 64.75344757080079, 'val_ssim': 0.9976493110656738, 'lr': 9.834660552336415e-06, 'time_min': 0.7740583181381225}\n",
+      "{'epoch': 82, 'train_loss': 0.0001254100704971034, 'val_mae': 0.00012450074800290166, 'val_mse': 1.0510757956581073e-06, 'val_psnr': 64.79287829589843, 'val_ssim': 0.9969047117233276, 'lr': 9.504162453267776e-06, 'time_min': 0.7745318333307902}\n",
+      "{'epoch': 83, 'train_loss': 0.00012513365516445685, 'val_mae': 0.0001222321424866095, 'val_mse': 1.0470532733961591e-06, 'val_psnr': 64.82542831420898, 'val_ssim': 0.9966018257141114, 'lr': 9.174206545276678e-06, 'time_min': 0.773943289120992}\n",
+      "{'epoch': 84, 'train_loss': 0.00012472765685859176, 'val_mae': 0.0001291182627901435, 'val_mse': 1.0569397891231347e-06, 'val_psnr': 64.79508715820313, 'val_ssim': 0.9970200233459473, 'lr': 8.84515363030414e-06, 'time_min': 0.77371879418691}\n",
+      "{'epoch': 85, 'train_loss': 0.0001244404842548241, 'val_mae': 0.00012292812386294826, 'val_mse': 1.0633612637320766e-06, 'val_psnr': 64.77186953735351, 'val_ssim': 0.9962393350601196, 'lr': 8.51736352288158e-06, 'time_min': 0.7742799719174703}\n",
+      "{'epoch': 86, 'train_loss': 0.00012420977447541093, 'val_mae': 0.0001227755177533254, 'val_mse': 1.043011201545596e-06, 'val_psnr': 64.84423333740234, 'val_ssim': 0.9962519164085388, 'lr': 8.191194656678905e-06, 'time_min': 0.7739297946294149}\n",
+      "{'epoch': 87, 'train_loss': 0.00012399124867404178, 'val_mae': 0.00012189070356544107, 'val_mse': 1.0551146533543943e-06, 'val_psnr': 64.74889862060547, 'val_ssim': 0.9963782553672791, 'lr': 7.867003692562533e-06, 'time_min': 0.7737677653630575}\n",
+      "{'epoch': 88, 'train_loss': 0.00012395313400870143, 'val_mae': 0.00012589690764434636, 'val_mse': 1.0511568762012758e-06, 'val_psnr': 64.84291784667968, 'val_ssim': 0.9961383357048035, 'lr': 7.545145128592009e-06, 'time_min': 0.7741914749145508}\n",
+      "{'epoch': 89, 'train_loss': 0.0001240047506582659, 'val_mae': 0.00012730149761773645, 'val_mse': 1.041666631863336e-06, 'val_psnr': 64.80084973144531, 'val_ssim': 0.9953305578231811, 'lr': 7.225970912381557e-06, 'time_min': 0.7745344718297322}\n",
+      "{'epoch': 90, 'train_loss': 0.00012333982493868757, 'val_mae': 0.00012429329275619237, 'val_mse': 1.0382358777860645e-06, 'val_psnr': 64.83639025878907, 'val_ssim': 0.9962836804389954, 'lr': 6.909830056250527e-06, 'time_min': 0.7739000121752421}\n",
+      "{'epoch': 91, 'train_loss': 0.00012303191461725862, 'val_mae': 0.00012291321583325044, 'val_mse': 1.0220403637504205e-06, 'val_psnr': 64.90552893066406, 'val_ssim': 0.994918426990509, 'lr': 6.59706825558357e-06, 'time_min': 0.7741242448488871}\n",
+      "{'epoch': 92, 'train_loss': 0.00012243253792242265, 'val_mae': 0.00012031506543280557, 'val_mse': 1.0393891443527536e-06, 'val_psnr': 64.86195330810547, 'val_ssim': 0.9951209025382995, 'lr': 6.2880275108177915e-06, 'time_min': 0.7744419972101847}\n",
+      "{'epoch': 93, 'train_loss': 0.0001221228034513365, 'val_mae': 0.00012130418926244601, 'val_mse': 1.035020387462282e-06, 'val_psnr': 64.82320904541015, 'val_ssim': 0.994407591342926, 'lr': 5.983045753470308e-06, 'time_min': 0.7739169398943583}\n",
+      "{'epoch': 94, 'train_loss': 0.00012202037130738443, 'val_mae': 0.00012181283929385245, 'val_mse': 1.0196277462455328e-06, 'val_psnr': 64.90269854736329, 'val_ssim': 0.993587586402893, 'lr': 5.6824564766150724e-06, 'time_min': 0.7738484382629395}\n",
+      "{'epoch': 95, 'train_loss': 0.00012169444199952013, 'val_mae': 0.00012369912210851908, 'val_mse': 1.0406047440483235e-06, 'val_psnr': 64.78324722290039, 'val_ssim': 0.9938664445877076, 'lr': 5.386588370213124e-06, 'time_min': 0.7740368405977885}\n",
+      "{'epoch': 96, 'train_loss': 0.00012170889267057233, 'val_mae': 0.00012043006281601266, 'val_mse': 1.0242867592751282e-06, 'val_psnr': 64.86265737915039, 'val_ssim': 0.9936711440086364, 'lr': 5.095764961694923e-06, 'time_min': 0.7750699400901795}\n",
+      "{'epoch': 97, 'train_loss': 0.00012134632419448854, 'val_mae': 0.00012215540988836436, 'val_mse': 1.0174796598221291e-06, 'val_psnr': 64.92337384033203, 'val_ssim': 0.9930575947761535, 'lr': 4.8103042621878515e-06, 'time_min': 0.7738781293233236}\n",
+      "{'epoch': 98, 'train_loss': 0.00012138958737387896, 'val_mae': 0.0001198850559303537, 'val_mse': 1.0141586453755735e-06, 'val_psnr': 64.96506896972656, 'val_ssim': 0.9937065172195435, 'lr': 4.530518418775734e-06, 'time_min': 0.7743688305219014}\n",
+      "{'epoch': 99, 'train_loss': 0.00012114267772644038, 'val_mae': 0.0001245410917326808, 'val_mse': 1.0093365081047522e-06, 'val_psnr': 64.93408764648437, 'val_ssim': 0.9932243967056275, 'lr': 4.256713373170565e-06, 'time_min': 0.7749759038289388}\n",
+      "{'epoch': 100, 'train_loss': 0.00012081175863392756, 'val_mae': 0.00012618127861060201, 'val_mse': 1.0072171580759459e-06, 'val_psnr': 64.86564733886719, 'val_ssim': 0.992709921836853, 'lr': 3.989188527169749e-06, 'time_min': 0.7740012447039286}\n",
+      "Training complete. Best PSNR: 64.96506896972656\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "start_epoch = 0\n",
+    "best_psnr = -1.0\n",
+    "ckpt_latest = Path(cfg.out_dir) / \"ckpt_latest.pt\"\n",
+    "ckpt_best = Path(cfg.out_dir) / \"ckpt_best.pt\"\n",
+    "\n",
+    "if cfg.resume:\n",
+    "    start_epoch, best_psnr = load_ckpt_if_exists(ckpt_latest, model, optimizer, scaler)\n",
+    "    print(f\"Resuming from epoch {start_epoch}, best_psnr={best_psnr:.3f}\" if start_epoch>0 else \"No existing checkpoint found.\")\n",
+    "\n",
+    "history = {\"epoch\": [], \"train_loss\": [], \"val_psnr\": [], \"val_ssim\": [], \"lr\": []}\n",
+    "\n",
+    "global_step = start_epoch * max(1, len(train_loader))\n",
+    "for epoch in range(start_epoch, cfg.epochs):\n",
+    "    epoch_loss = 0.0\n",
+    "    t0 = time.time()\n",
+    "    model.train()\n",
+    "    for lr, hr in train_loader:\n",
+    "        lr = lr.to(device, non_blocking=True)\n",
+    "        hr = hr.to(device, non_blocking=True)\n",
+    "        optimizer.zero_grad(set_to_none=True)\n",
+    "        with torch.cuda.amp.autocast(enabled=cfg.use_amp and torch.cuda.is_available()):\n",
+    "            pred = model(lr)\n",
+    "            loss = criterion(pred, hr)\n",
+    "        scaler.scale(loss).backward()\n",
+    "        if cfg.grad_clip is not None:\n",
+    "            scaler.unscale_(optimizer)\n",
+    "            nn.utils.clip_grad_norm_(model.parameters(), cfg.grad_clip)\n",
+    "        scaler.step(optimizer)\n",
+    "        scaler.update()\n",
+    "        scheduler.step()\n",
+    "        epoch_loss += loss.item()\n",
+    "        global_step += 1\n",
+    "\n",
+    "    val_psnr, val_ssim = (0.0, 0.0)\n",
+    "    if (epoch+1) % cfg.val_every == 0:\n",
+    "        val_psnr, val_ssim, val_mae, val_mse = validate(model, val_loader)\n",
+    "\n",
+    "    hist_entry = {\n",
+    "        \"epoch\": epoch+1,\n",
+    "        \"train_loss\": epoch_loss/max(1,len(train_loader)),\n",
+    "        \"val_mae\": val_mae,\n",
+    "        \"val_mse\": val_mse,\n",
+    "        \"val_psnr\": val_psnr,\n",
+    "        \"val_ssim\": val_ssim,\n",
+    "        \"lr\": scheduler.get_last_lr()[0],\n",
+    "        \"time_min\": (time.time()-t0)/60.0,\n",
+    "    }\n",
+    "    history[\"epoch\"].append(hist_entry[\"epoch\"])\n",
+    "    history[\"train_loss\"].append(hist_entry[\"train_loss\"])\n",
+    "    history[\"val_psnr\"].append(hist_entry[\"val_psnr\"])\n",
+    "    history[\"val_ssim\"].append(hist_entry[\"val_ssim\"])\n",
+    "    history[\"lr\"].append(hist_entry[\"lr\"])\n",
+    "    print(hist_entry)\n",
+    "\n",
+    "    # save latest\n",
+    "    save_ckpt(ckpt_latest, epoch, model, optimizer, scaler, best_psnr)\n",
+    "\n",
+    "    # save best\n",
+    "    if val_psnr > best_psnr:\n",
+    "        best_psnr = val_psnr\n",
+    "        save_ckpt(ckpt_best, epoch, model, optimizer, scaler, best_psnr)\n",
+    "\n",
+    "# write history\n",
+    "with open(Path(cfg.out_dir) / \"history.json\", \"w\") as f:\n",
+    "    json.dump(history, f, indent=2)\n",
+    "print(\"Training complete. Best PSNR:\", best_psnr)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f7deb232",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best checkpoint — PSNR: 64.965  SSIM: 0.9937  MAE: 0.000120  MSE: 0.000001\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "#@title 7) Evaluate best checkpoint on validation set\n",
+    "ckpt_best = Path(cfg.out_dir) / \"ckpt_best.pt\"\n",
+    "from pathlib import Path\n",
+    "import torch\n",
+    "\n",
+    "ckpt_best = Path(cfg.out_dir) / \"ckpt_best.pt\"\n",
+    "data = torch.load(ckpt_best, map_location=\"cpu\", weights_only=False)  # <— key change\n",
+    "model.load_state_dict(data[\"model\"])\n",
+    "model.to(device).eval()\n",
+    "\n",
+    "psnr_val, ssim_val, mae_val, mse_val = validate(model, val_loader)\n",
+    "print(f\"Best checkpoint — PSNR: {psnr_val:.3f}  SSIM: {ssim_val:.4f}  MAE: {mae_val:.6f}  MSE: {mse_val:.6f}\")\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f9d37ef3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LR: min 0.0 max 1.0 mean 0.08074530959129333 std 0.12246625870466232\n",
+      "HR: min 0.0 max 1.0 mean 0.002721345517784357 std 0.022611377760767937\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Inspect one batch exactly as your loader returns it\n",
+    "lr_b, hr_b = next(iter(val_loader))\n",
+    "print(\"LR: min\", lr_b.min().item(), \"max\", lr_b.max().item(), \"mean\", lr_b.mean().item(), \"std\", lr_b.std().item())\n",
+    "print(\"HR: min\", hr_b.min().item(), \"max\", hr_b.max().item(), \"mean\", hr_b.mean().item(), \"std\", hr_b.std().item())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0eed6eaa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1200 with 24 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_results(model, val_loader, n_samples=4, vmax=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7638ab65",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torchtest",
+   "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": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Difflense_Aleksandr_Duplinskii/Baseline_SR_models/SwinIR.ipynb b/Difflense_Aleksandr_Duplinskii/Baseline_SR_models/SwinIR.ipynb
new file mode 100644
index 0000000..45a3f2c
--- /dev/null
+++ b/Difflense_Aleksandr_Duplinskii/Baseline_SR_models/SwinIR.ipynb
@@ -0,0 +1,3653 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "34ef9fcb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python: 3.10.18 | packaged by conda-forge | (main, Jun  4 2025, 14:45:41) [GCC 13.3.0]\n",
+      "PyTorch: 2.8.0+cu126\n",
+      "CUDA available: True\n",
+      "GPU: NVIDIA RTX A5000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/imglab/.conda/envs/torchtest/lib/python3.10/site-packages/timm/models/layers/__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers\n",
+      "  warnings.warn(f\"Importing from {__name__} is deprecated, please import via timm.layers\", FutureWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys, os, math, json, time\n",
+    "\n",
+    "sys.path.append(os.path.abspath(\"/home/imglab/Sasha/DeepLense/grav_lens_diff\"))\n",
+    "\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "import torch, torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "from torch.utils.data import DataLoader, TensorDataset\n",
+    "from tqdm import tqdm\n",
+    "from skimage.metrics import peak_signal_noise_ratio, structural_similarity\n",
+    "from dataloaders import *\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "sys.path.append(os.path.abspath(\"/home/imglab/Sasha/DeepLense/grav_lens_diff/Baseline_models/SwinIR\"))\n",
+    "from models.network_swinir import SwinIR\n",
+    "\n",
+    "print(\"Python:\", sys.version)\n",
+    "print(\"PyTorch:\", torch.__version__)\n",
+    "print(\"CUDA available:\", torch.cuda.is_available())\n",
+    "if torch.cuda.is_available():\n",
+    "    print(\"GPU:\", torch.cuda.get_device_name(0))\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "94ceb0fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lr_path = \"/home/imglab/Sasha/DeepLense/grav_lens_diff/lr_all_lsst_20k.npy\"\n",
+    "# hr_path = \"/home/imglab/Sasha/DeepLense/grav_lens_diff/hr_all_lsst_20k.npy\"\n",
+    "\n",
+    "# LR = np.load(lr_path)   # [N,H,W]\n",
+    "# HR = np.load(hr_path)   # [N,H,W]\n",
+    "# assert LR.shape == HR.shape, f\"Mismatch: {LR.shape} vs {HR.shape}\"\n",
+    "# print(\"Loaded:\", LR.shape)\n",
+    "\n",
+    "# def compute_global_scale(hr, hi_pct=99.5):\n",
+    "#     vmax = np.percentile(hr.reshape(-1).astype(np.float64), hi_pct)\n",
+    "#     return float(vmax)\n",
+    "\n",
+    "# scale = compute_global_scale(LR, 99.99)\n",
+    "# print(\"Global scale (p99.5 of HR):\", scale)\n",
+    "\n",
+    "# def to_unit(arr, scale):\n",
+    "#     x = torch.tensor(arr, dtype=torch.float32)\n",
+    "#     # x = x / (scale + 1e-8)\n",
+    "#     x = torch.clamp(x / (scale + 1e-8), 0.0, 1.0)\n",
+    "#     return x\n",
+    "\n",
+    "# LR_t = to_unit(LR, scale).unsqueeze(1)   # [N,1,H,W]\n",
+    "# HR_t = to_unit(HR, scale).unsqueeze(1)   # [N,1,H,W]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d79968d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# vit_ds_train = PairsDatasetUnified(\n",
+    "#     root_dir=\".\",\n",
+    "#     norm_preset=\"hybrid\",         # keep identical LR stats to diffusion\n",
+    "#     percentile_p=99.99, percentile_from=\"lr\",\n",
+    "#     out_range=\"[0,1]\",\n",
+    "#     pad_to=None,                    # or None if your model wants native size               # RGB repeat for ViT/SwinIR\n",
+    "#     noise_aug=noise_aug,          # SAME augment -> identical noisy LR per index\n",
+    "#     return_order=\"LRHR\"\n",
+    "# )\n",
+    "# # Use the same split indices as above to keep train/val identical:\n",
+    "# idxs = torch.randperm(len(vit_ds_train), generator=torch.Generator().manual_seed(42))\n",
+    "# n_total = len(vit_ds_train)\n",
+    "# n_train = int(0.9 * n_total)\n",
+    "# vit_train = torch.utils.data.Subset(vit_ds_train, idxs[:n_train])\n",
+    "# vit_val   = torch.utils.data.Subset(vit_ds_train, idxs[n_train:])\n",
+    "\n",
+    "# train_loader_swin = DataLoader(vit_train, batch_size=64, shuffle=True,  num_workers=4, pin_memory=True)\n",
+    "# val_loader_swin   = DataLoader(vit_val,   batch_size=64, shuffle=False, num_workers=2, pin_memory=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f3eb6183",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vit_train = PairsDatasetUnified(\n",
+    "    root_dir=\"../\",\n",
+    "    lr_name = \"HST-HSC_dataset/train_LR.npy\",\n",
+    "    hr_name = \"HST-HSC_dataset/train_HR.npy\",\n",
+    "    resize_to=64, \n",
+    "    norm_preset=\"minmax\",         # keep identical LR stats to diffusion\n",
+    "    out_range=\"[0,1]\",\n",
+    "    pad_to=None,                    # or None if your model wants native size               # RGB repeat for ViT/SwinIR\n",
+    "    noise_aug=None,          # SAME augment -> identical noisy LR per index\n",
+    "    return_order=\"LRHR\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "vit_val = PairsDatasetUnified(\n",
+    "    root_dir=\"../\",\n",
+    "    lr_name = \"HST-HSC_dataset/test_LR.npy\",\n",
+    "    hr_name = \"HST-HSC_dataset/test_HR.npy\",\n",
+    "    resize_to=64, \n",
+    "    norm_preset=\"minmax\",         # keep identical LR stats to diffusion\n",
+    "    out_range=\"[0,1]\",\n",
+    "    pad_to=None,                    # or None if your model wants native size               # RGB repeat for ViT/SwinIR\n",
+    "    noise_aug=None,          # SAME augment -> identical noisy LR per index\n",
+    "    return_order=\"LRHR\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "\n",
+    "train_loader_swin = DataLoader(vit_train, batch_size=32, shuffle=True,  num_workers=4, pin_memory=True)\n",
+    "val_loader_swin   = DataLoader(vit_val,   batch_size=32, shuffle=False, num_workers=2, pin_memory=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "29c5098e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x2000 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Number of samples to display\n",
+    "num_samples = 5\n",
+    "# target_size = (128, 128)   \n",
+    "target_size = (64, 64)   \n",
+    "\n",
+    "# Create a figure\n",
+    "fig, axs = plt.subplots(num_samples, 2, figsize=(12, num_samples * 4))\n",
+    "\n",
+    "for i in range(num_samples):\n",
+    "    lr, hr = vit_train[i]   # tensors, shape (1,H,W)\n",
+    "\n",
+    "    # Interpolate both to target_size\n",
+    "    lr_up = F.interpolate(lr.unsqueeze(0), size=target_size, mode=\"bilinear\", align_corners=False).squeeze().numpy()\n",
+    "    hr_up = F.interpolate(hr.unsqueeze(0), size=target_size, mode=\"bilinear\", align_corners=False).squeeze().numpy()\n",
+    "\n",
+    "    # Plot low-res (condition)\n",
+    "    im1 = axs[i, 0].imshow(lr_up, cmap='gray', vmin=0, vmax=1)\n",
+    "    axs[i, 0].set_title(f\"Low-res {i} (resized)\")\n",
+    "    axs[i, 0].axis('off')\n",
+    "    plt.colorbar(im1, ax=axs[i,0], fraction=0.046, pad=0.04)\n",
+    "\n",
+    "    # Plot high-res (target)\n",
+    "    im2 = axs[i, 1].imshow(hr_up, cmap='gray', vmin=0, vmax=1)\n",
+    "    axs[i, 1].set_title(f\"High-res {i} (resized)\")\n",
+    "    axs[i, 1].axis('off')\n",
+    "    plt.colorbar(im2, ax=axs[i,1], fraction=0.046, pad=0.04)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d6b80250",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "# def show_samples(LR_t, HR_t, n=5, vmax_lr=None, vmax_hr=None):\n",
+    "#     \"\"\"Plot n random LR–HR pairs after preprocessing.\"\"\"\n",
+    "#     idxs = torch.randint(0, LR_t.size(0), (n,))\n",
+    "#     fig, axes = plt.subplots(n, 2, figsize=(6, 3*n))\n",
+    "\n",
+    "#     for i, idx in enumerate(idxs):\n",
+    "#         lr_img = LR_t[idx,0].cpu().numpy()\n",
+    "#         hr_img = HR_t[idx,0].cpu().numpy()\n",
+    "\n",
+    "#         ax1, ax2 = axes[i]\n",
+    "#         im1 = ax1.imshow(lr_img, cmap=\"viridis\", vmin=0, vmax=vmax_lr)\n",
+    "#         ax1.set_title(\"Low-res (preprocessed)\")\n",
+    "#         plt.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04)\n",
+    "\n",
+    "#         im2 = ax2.imshow(hr_img, cmap=\"viridis\", vmin=0, vmax=vmax_hr)\n",
+    "#         ax2.set_title(\"High-res (preprocessed)\")\n",
+    "#         plt.colorbar(im2, ax=ax2, fraction=0.046, pad=0.04)\n",
+    "\n",
+    "#         for a in (ax1, ax2):\n",
+    "#             a.axis(\"off\")\n",
+    "\n",
+    "#     plt.tight_layout()\n",
+    "#     plt.show()\n",
+    "\n",
+    "# # Example usage:\n",
+    "# show_samples(LR_t, HR_t, n=4)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "53171fa9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# N = LR_t.size(0)\n",
+    "# n_train = int(0.9 * N)\n",
+    "\n",
+    "# train_ds = TensorDataset(LR_t[:n_train], HR_t[:n_train])\n",
+    "# val_ds   = TensorDataset(LR_t[n_train:], HR_t[n_train:])\n",
+    "\n",
+    "# train_loader = DataLoader(train_ds, batch_size=64, shuffle=True,  num_workers=4, pin_memory=True)\n",
+    "# val_loader   = DataLoader(val_ds,   batch_size=64, shuffle=False, num_workers=2, pin_memory=True)\n",
+    "# print(\"Train/Val:\", len(train_ds), len(val_ds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1b842162",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/imglab/.conda/envs/torchtest/lib/python3.10/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4322.)\n",
+      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2.244625"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def build_swinir(scale=1, in_chans=1, img_size=48, window_size=8):\n",
+    "    return SwinIR(\n",
+    "        img_size=img_size,\n",
+    "        patch_size=1,\n",
+    "        in_chans=in_chans,\n",
+    "        embed_dim=96,                 # stronger than 60\n",
+    "        depths=[6, 6, 6, 6],\n",
+    "        num_heads=[6, 6, 6, 6],\n",
+    "        window_size=window_size,\n",
+    "        mlp_ratio=2.0,\n",
+    "        upsampler='',                 # no upscaler (same size)\n",
+    "        resi_connection='1conv',\n",
+    "        upscale=scale\n",
+    "    )\n",
+    "\n",
+    "model = build_swinir(scale=1, in_chans=1, img_size=64, window_size=8).to(device)\n",
+    "sum(p.numel() for p in model.parameters())/1e6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "869a5aa8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pad_to_window(x, window_size=8):\n",
+    "    B, C, H, W = x.shape\n",
+    "    ph = (window_size - H % window_size) % window_size\n",
+    "    pw = (window_size - W % window_size) % window_size\n",
+    "    if ph or pw:\n",
+    "        x = F.pad(x, (0, pw, 0, ph), mode='reflect')\n",
+    "    return x, H, W\n",
+    "\n",
+    "def unpad_from_window(x, H, W):\n",
+    "    return x[:, :, :H, :W]\n",
+    "\n",
+    "def forward_swinir(model, x, window_size=8):\n",
+    "    x_pad, H, W = pad_to_window(x, window_size)\n",
+    "    y_pad = model(x_pad)\n",
+    "    y = unpad_from_window(y_pad, H, W)\n",
+    "    return y\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "092b511a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/user/1015/ipykernel_1827902/424652376.py:12: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
+      "  scaler = torch.cuda.amp.GradScaler(enabled=torch.cuda.is_available())\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimizer = torch.optim.AdamW(model.parameters(), lr=2e-4, betas=(0.9, 0.99), weight_decay=1e-4)\n",
+    "epochs = 200\n",
+    "warmup_epochs = 5\n",
+    "\n",
+    "def lr_lambda(ep):\n",
+    "    if ep < warmup_epochs:\n",
+    "        return (ep + 1) / warmup_epochs\n",
+    "    t = (ep - warmup_epochs) / max(1, (epochs - warmup_epochs))\n",
+    "    return 0.5 * (1 + math.cos(math.pi * t))\n",
+    "\n",
+    "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)\n",
+    "scaler = torch.cuda.amp.GradScaler(enabled=torch.cuda.is_available())\n",
+    "criterion = nn.L1Loss()\n",
+    "grad_clip = 1.0\n",
+    "\n",
+    "# --- EMA that only tracks trainable floating parameters ---\n",
+    "class EMA:\n",
+    "    def __init__(self, model, decay=0.999):\n",
+    "        self.decay = decay\n",
+    "        # only keep params that require grad AND are floating point\n",
+    "        self.shadow = {\n",
+    "            name: p.detach().clone()\n",
+    "            for name, p in model.named_parameters()\n",
+    "            if p.requires_grad and torch.is_floating_point(p)\n",
+    "        }\n",
+    "\n",
+    "    @torch.no_grad()\n",
+    "    def update(self, model):\n",
+    "        for name, p in model.named_parameters():\n",
+    "            if name in self.shadow:  # only float trainable params\n",
+    "                self.shadow[name].mul_(self.decay).add_(p.detach(), alpha=1.0 - self.decay)\n",
+    "\n",
+    "    @torch.no_grad()\n",
+    "    def copy_to(self, model):\n",
+    "        for name, p in model.named_parameters():\n",
+    "            if name in self.shadow:\n",
+    "                p.data.copy_(self.shadow[name])\n",
+    "                \n",
+    "def load_ckpt(path):\n",
+    "    ckpt = torch.load(path, map_location=\"cpu\")\n",
+    "    model.load_state_dict(ckpt[\"model\"])\n",
+    "    optimizer.load_state_dict(ckpt[\"optimizer\"])\n",
+    "    # rebuild EMA for current model, then load matching entries\n",
+    "    global ema\n",
+    "    ema = EMA(model, decay=0.999)\n",
+    "    if \"ema\" in ckpt:\n",
+    "        for k, v in ckpt[\"ema\"].items():\n",
+    "            if k in ema.shadow and ema.shadow[k].shape == v.shape:\n",
+    "                ema.shadow[k] = v.clone()\n",
+    "    return ckpt.get(\"epoch\", 0) + 1, ckpt.get(\"best_psnr\", -1.0)\n",
+    "\n",
+    "ema = EMA(model, decay=0.999)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e5031a20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@torch.no_grad()\n",
+    "def validate(model, loader, use_ema=True, x8=False):\n",
+    "    # use EMA weights for eval\n",
+    "    backup = {k: v.detach().clone() for k,v in model.state_dict().items()}\n",
+    "    if use_ema:\n",
+    "        ema.copy_to(model)\n",
+    "    model.eval()\n",
+    "\n",
+    "    psnr_sum = ssim_sum = mae_sum = mse_sum = 0.0\n",
+    "    n = 0\n",
+    "\n",
+    "    def _aug8(x):\n",
+    "        ls = []\n",
+    "        x0 = x\n",
+    "        ls.append(x0)\n",
+    "        ls.append(torch.flip(x0, [-1]))\n",
+    "        ls.append(torch.flip(x0, [-2]))\n",
+    "        ls.append(torch.flip(torch.flip(x0, [-1]), [-2]))\n",
+    "        xT = x0.transpose(-1, -2)\n",
+    "        ls.append(xT)\n",
+    "        ls.append(torch.flip(xT, [-1]))\n",
+    "        ls.append(torch.flip(xT, [-2]))\n",
+    "        ls.append(torch.flip(torch.flip(xT, [-1]), [-2]))\n",
+    "        return ls\n",
+    "    def _deaug8(ys):\n",
+    "        outs = []\n",
+    "        outs.append(ys[0])\n",
+    "        outs.append(torch.flip(ys[1], [-1]))\n",
+    "        outs.append(torch.flip(ys[2], [-2]))\n",
+    "        outs.append(torch.flip(ys[3], [-1, -2]))\n",
+    "        outs.append(ys[4].transpose(-1, -2))\n",
+    "        outs.append(torch.flip(ys[5], [-1]).transpose(-1, -2))\n",
+    "        outs.append(torch.flip(ys[6], [-2]).transpose(-1, -2))\n",
+    "        outs.append(torch.flip(torch.flip(ys[7], [-1]), [-2]).transpose(-1, -2))\n",
+    "        return torch.stack(outs, dim=0).mean(0)\n",
+    "\n",
+    "    for lr, hr in loader:\n",
+    "        lr = lr.to(device, non_blocking=True)\n",
+    "        hr = hr.to(device, non_blocking=True)\n",
+    "\n",
+    "        if x8:\n",
+    "            ys = [forward_swinir(model, a) for a in _aug8(lr)]\n",
+    "            pred = _deaug8(ys)\n",
+    "        else:\n",
+    "            pred = forward_swinir(model, lr)\n",
+    "\n",
+    "        # [0,1] already\n",
+    "        B = pred.size(0)\n",
+    "        # MAE/MSE\n",
+    "        mae = torch.mean(torch.abs(pred - hr), dim=(1,2,3))  # [B]\n",
+    "        mse = torch.mean((pred - hr) ** 2,    dim=(1,2,3))\n",
+    "        # PSNR/SSIM (use skimage per-image for robust SSIM)\n",
+    "        pred_np = pred.detach().cpu().numpy()\n",
+    "        hr_np   = hr.detach().cpu().numpy()\n",
+    "        ps = [peak_signal_noise_ratio(hr_np[i,0], pred_np[i,0], data_range=1.0) for i in range(B)]\n",
+    "        ss = [structural_similarity(hr_np[i,0], pred_np[i,0], data_range=1.0) for i in range(B)]\n",
+    "\n",
+    "        psnr_sum += float(np.mean(ps)) * B\n",
+    "        ssim_sum += float(np.mean(ss)) * B\n",
+    "        mae_sum  += float(mae.mean().item()) * B\n",
+    "        mse_sum  += float(mse.mean().item()) * B\n",
+    "        n += B\n",
+    "\n",
+    "    model.load_state_dict(backup, strict=True)\n",
+    "    model.train()\n",
+    "    return psnr_sum/n, ssim_sum/n, mae_sum/n, mse_sum/n\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcd31594",
+   "metadata": {},
+   "source": [
+    "### Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f8d5e677",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/200: 100%|██████████| 90/90 [00:36<00:00,  2.46it/s, loss=0.0325]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/200: 100%|██████████| 90/90 [00:36<00:00,  2.45it/s, loss=0.0527]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0528]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0128]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 4\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0263]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0168]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 6\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0418]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 7\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0182]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0173]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 9\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0303]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 29.665 | SSIM 0.7065 | MAE 0.048738219 | MSE 0.015566058\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0185]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 11\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0251]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 12\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0339]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 13\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0388]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 14\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0226]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 15\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0381]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 16\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0431]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 17\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0145]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 18\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0278]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 19\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0381]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 31.166 | SSIM 0.7588 | MAE 0.045673460 | MSE 0.015248730\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 20\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 21/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0165]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 21\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 22/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0387]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 22\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 23/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0142]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 23\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 24/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0293]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 24\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0476]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 25\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 26/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0113]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 26\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 27/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0288]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 27\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 28/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0295]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 28\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 29/200: 100%|██████████| 90/90 [00:37<00:00,  2.43it/s, loss=0.0148]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 29\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 30/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0167]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 34.128 | SSIM 0.8758 | MAE 0.037562042 | MSE 0.016106235\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 30\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 31/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0103]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 31\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 32/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0310]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 32\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 33/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0296]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 33\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 34/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0302]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 35/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0149]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 35\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 36/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0112]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 37/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0261]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 37\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 38/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0146]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 38\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 39/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0195]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 39\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 40/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0142]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 34.494 | SSIM 0.8381 | MAE 0.039252535 | MSE 0.016313842\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 40\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 41/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0114]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 41\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 42/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0132]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 42\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 43/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0169]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 43\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 44/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0126]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 44\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 45/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0202]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 45\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 46/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0085]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 46\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 47/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0149]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 48/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0317]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 48\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 49/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0140]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 49\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 50/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0107]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 34.686 | SSIM 0.8106 | MAE 0.040009439 | MSE 0.016354972\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 50\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 51/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0318]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 52/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0200]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 52\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 53/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0263]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 53\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 54/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0189]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 54\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 55/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0308]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 55\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 56/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0157]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 57/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0154]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 57\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 58/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0149]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 58\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 59/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0200]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 59\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 60/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0357]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 35.311 | SSIM 0.8401 | MAE 0.038848180 | MSE 0.016245600\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 60\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 61/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0186]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 61\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 62/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0112]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 62\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 63/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0315]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 63\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 64/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0193]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 64\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 65/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0106]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 65\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 66/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0258]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 66\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 67/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0178]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 67\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 68/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0093]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 68\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 69/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0250]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 69\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 70/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0248]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 35.726 | SSIM 0.8637 | MAE 0.037925337 | MSE 0.016185392\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 70\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 71/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0140]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 71\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 72/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0174]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 72\n"
+     ]
+    },
+    {
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+     "output_type": "stream",
+     "text": [
+      "Epoch 73/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0108]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 73\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 74/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0144]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 74\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 75/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0290]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 75\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 76/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0488]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 76\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 77/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0323]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 77\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 78/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0175]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 78\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 79/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0146]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 79\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 80/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0166]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 35.979 | SSIM 0.8764 | MAE 0.037465833 | MSE 0.016197875\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 80\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 81/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0324]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 81\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 82/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0350]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 82\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 83/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0493]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 83\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 84/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0578]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 84\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 85/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0103]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 85\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 86/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0180]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 86\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 87/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0226]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 87\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 88/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0433]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 88\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 89/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0235]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 89\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 90/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0109]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 36.202 | SSIM 0.8840 | MAE 0.037191808 | MSE 0.016256535\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 90\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 91/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0226]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 91\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 92/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0100]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 92\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 93/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0450]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 93\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 94/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0113]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 94\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 95/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0141]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 95\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 96/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0142]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 96\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 97/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0103]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 97\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 98/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0183]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 98\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 99/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0089]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 99\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 100/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0296]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 36.406 | SSIM 0.8891 | MAE 0.036913246 | MSE 0.016295914\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 100\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 101/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0323]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 101\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 102/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0257]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 102\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 103/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0108]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 103\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 104/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0165]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 104\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 105/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0111]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 105\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 106/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0100]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 106\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 107/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0273]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 107\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 108/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0681]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 108\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 109/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0095]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 109\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 110/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0622]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 36.518 | SSIM 0.8894 | MAE 0.036819860 | MSE 0.016349746\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 110\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 111/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0205]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 111\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 112/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0082]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 112\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 113/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0180]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 113\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 114/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0163]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 114\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 115/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0351]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 115\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 116/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0151]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 116\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 117/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0504]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 117\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 118/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0277]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 118\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 119/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0109]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 119\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 120/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0126]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 36.570 | SSIM 0.8883 | MAE 0.036819216 | MSE 0.016411303\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 120\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 121/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0098]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 121\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 122/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0231]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 122\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 123/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0231]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 123\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 124/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0407]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 124\n"
+     ]
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+     "text": [
+      "Epoch 125/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0290]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 125\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 126/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0198]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 126\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 127/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0116]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 127\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 128/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0095]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 128\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 129/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0087]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 129\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 130/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0265]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 36.568 | SSIM 0.8866 | MAE 0.036971994 | MSE 0.016433511\n",
+      "Epoch: 130\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 131/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0105]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 131\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 132/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0166]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 132\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 133/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0309]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 133\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 134/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0108]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 134\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 135/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0166]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 135\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 136/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0142]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 136\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 137/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0078]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 137\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 138/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0167]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 138\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 139/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0236]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 139\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 140/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0404]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 36.598 | SSIM 0.8848 | MAE 0.037149232 | MSE 0.016437273\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 140\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 141/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0519]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 141\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 142/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0309]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 142\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 143/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0095]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 143\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 144/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0076]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 144\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 145/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0255]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 145\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 146/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0077]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 146\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 147/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0106]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 147\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 148/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0107]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 148\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 149/200: 100%|██████████| 90/90 [00:37<00:00,  2.42it/s, loss=0.0068]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 149\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 150/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0157]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 36.591 | SSIM 0.8804 | MAE 0.037450045 | MSE 0.016462060\n",
+      "Epoch: 150\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 151/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0115]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 151\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 152/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0076]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 152\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 153/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0123]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 153\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 154/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0075]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 154\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 155/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0373]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 155\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 156/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0094]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 156\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 157/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0113]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 157\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 158/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0081]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 158\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 159/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0116]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 159\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 160/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0101]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 36.632 | SSIM 0.8775 | MAE 0.037543230 | MSE 0.016473578\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 160\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 161/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0148]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 161\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 162/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0240]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 162\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 163/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0124]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 163\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 164/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0068]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 164\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 165/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0083]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 165\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 166/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0091]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 166\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 167/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0093]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 167\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 168/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0227]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 168\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 169/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0083]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 169\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 170/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0096]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 36.718 | SSIM 0.8802 | MAE 0.037215307 | MSE 0.016476512\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 170\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 171/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0099]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 171\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 172/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0240]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 172\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 173/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0073]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 173\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 174/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0103]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 174\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 175/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0078]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 175\n"
+     ]
+    },
+    {
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+     "output_type": "stream",
+     "text": [
+      "Epoch 176/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0065]\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 176\n"
+     ]
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+     "output_type": "stream",
+     "text": [
+      "Epoch 177/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0112]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 177\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 178/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0303]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 178\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 179/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0088]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 179\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 180/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0177]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 36.726 | SSIM 0.8819 | MAE 0.037131980 | MSE 0.016474348\n",
+      "  ✓ new best, saved ckpt_best.pt\n",
+      "Epoch: 180\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 181/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0298]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 181\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 182/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0200]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 182\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 183/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0231]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 183\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 184/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0326]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 184\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 185/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0081]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 185\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 186/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0061]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 186\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 187/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0169]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 187\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 188/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0083]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 188\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 189/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0081]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 189\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 190/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0092]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 36.694 | SSIM 0.8802 | MAE 0.037253093 | MSE 0.016474830\n",
+      "Epoch: 190\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 191/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0093]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 191\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 192/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0100]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 192\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 193/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0103]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 193\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 194/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0077]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 194\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 195/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0111]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 195\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 196/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0122]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 196\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 197/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0119]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 197\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 198/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0073]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 198\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 199/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0087]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 199\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 200/200: 100%|██████████| 90/90 [00:37<00:00,  2.41it/s, loss=0.0056]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Val — PSNR 36.691 | SSIM 0.8799 | MAE 0.037270423 | MSE 0.016473586\n"
+     ]
+    }
+   ],
+   "source": [
+    "out_dir = Path(\"./runs/swinir_same_size\")\n",
+    "out_dir.mkdir(parents=True, exist_ok=True)\n",
+    "ckpt_latest = out_dir / \"ckpt_latest.pt\"\n",
+    "ckpt_best   = out_dir / \"ckpt_best.pt\"\n",
+    "best_psnr = -1.0\n",
+    "\n",
+    "def save_ckpt(path, epoch, best_psnr):\n",
+    "    torch.save({\n",
+    "        \"epoch\": epoch,\n",
+    "        \"model\": model.state_dict(),\n",
+    "        \"optimizer\": optimizer.state_dict(),\n",
+    "        \"ema\": ema.shadow,\n",
+    "        \"best_psnr\": best_psnr,\n",
+    "    }, path)\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    print('Epoch: '+str(epoch))\n",
+    "    model.train()\n",
+    "    running = 0.0\n",
+    "    pbar = tqdm(train_loader_swin, desc=f\"Epoch {epoch+1}/{epochs}\")\n",
+    "    # pbar = train_loader\n",
+    "    for lr, hr in pbar:\n",
+    "        lr = lr.to(device, non_blocking=True)\n",
+    "        hr = hr.to(device, non_blocking=True)\n",
+    "\n",
+    "        optimizer.zero_grad(set_to_none=True)\n",
+    "        with torch.amp.autocast(device_type='cuda', enabled=torch.cuda.is_available()):\n",
+    "            pred = forward_swinir(model, lr)\n",
+    "            loss = F.l1_loss(pred, hr)\n",
+    "        scaler.scale(loss).backward()\n",
+    "        scaler.unscale_(optimizer)\n",
+    "        nn.utils.clip_grad_norm_(model.parameters(), grad_clip)\n",
+    "        scaler.step(optimizer)\n",
+    "        scaler.update()\n",
+    "        ema.update(model)\n",
+    "\n",
+    "        running += loss.item()\n",
+    "        pbar.set_postfix(loss=f\"{loss.item():.4f}\")\n",
+    "        \n",
+    "    \n",
+    "\n",
+    "    scheduler.step()\n",
+    "    \n",
+    "    save_ckpt(ckpt_latest, epoch, best_psnr)\n",
+    "    \n",
+    "    \n",
+    "    if (epoch + 1) % 10 == 0:\n",
+    "        val_psnr, val_ssim, val_mae, val_mse = validate(model, val_loader_swin, use_ema=True, x8=True)\n",
+    "        print(f\"Val — PSNR {val_psnr:.3f} | SSIM {val_ssim:.4f} | MAE {val_mae:.9f} | MSE {val_mse:.9f}\")\n",
+    "        if val_psnr > best_psnr:\n",
+    "            best_psnr = val_psnr\n",
+    "            save_ckpt(ckpt_best, epoch, best_psnr)\n",
+    "            print(\"  ✓ new best, saved ckpt_best.pt\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6c3d7970",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "@torch.no_grad()\n",
+    "def show_samples(model, loader, n=4, vmin=0.0, vmax_lr=1.0, vmax_hr = 1.0):\n",
+    "    ema.copy_to(model)   # show EMA result\n",
+    "    model.eval()\n",
+    "    lr, hr = next(iter(loader))\n",
+    "    lr, hr = lr.to(device), hr.to(device)\n",
+    "    pred = forward_swinir(model, lr)\n",
+    "    pred = torch.clamp(pred, 0, 1)\n",
+    "\n",
+    "    n = min(n, lr.size(0))\n",
+    "    for i in range(n):\n",
+    "        fig, axs = plt.subplots(1,3, figsize=(10,3))\n",
+    "        im0 = axs[0].imshow(lr[i,0].cpu(), cmap=\"magma\", vmin=vmin, vmax=vmax_lr); axs[0].set_title(\"LR\"); axs[0].axis(\"off\")#; fig.colorbar(im0, ax=axs[0])\n",
+    "        im1 = axs[1].imshow(pred[i,0].cpu(), cmap=\"magma\", vmin=vmin, vmax=vmax_hr); axs[1].set_title(\"Pred\"); axs[1].axis(\"off\")#; fig.colorbar(im1, ax=axs[1])\n",
+    "        im2 = axs[2].imshow(hr[i,0].cpu(), cmap=\"magma\", vmin=vmin, vmax=vmax_hr); axs[2].set_title(\"GT\");  axs[2].axis(\"off\")#; fig.colorbar(im2, ax=axs[2])\n",
+    "        plt.tight_layout(); plt.show()\n",
+    "    model.train()\n",
+    "\n",
+    "# After a few epochs:\n",
+    "# show_samples(model, val_loader, n=6, vmin=0.0, vmax=1.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "191050a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(180, 36.726288486277646)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "out_dir = Path(\"./runs/swinir_same_size\")\n",
+    "out_dir.mkdir(parents=True, exist_ok=True)\n",
+    "# swinir_best   = out_dir / \"swinir_best_no_noise.pt\"\n",
+    "swinir_best   = out_dir / \"ckpt_best.pt\"\n",
+    "load_ckpt(swinir_best)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "230aecd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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6ILuKRcL9uDO++SPf89zn69LlsfUpk7bbdJn/s583fLOIcbPoft/9tJk19N12aG3MmL/N4bpGrmvW5yy55Z+lLc3yzXuu6/PAN50AAAAAgGgYdAIAAAAAomHQCQAAAACIZuZMp80SltVEtdc3nlLtPmdC+sSd4bxIte15r9d4WgyV6Dqceblm1uDvIbOw9089e63PY1VSZw5YdLy/doPz3J2QLFnbGWff5XHzqN3ty3fbsesuh9SXdO47af6M6aoVH/PYfK+De/3tn2vM+qR+RwIAAAAAQCAGnQAAAACAaBh0AgAAAACimTnTOc5PqnbV+5qQ/VTPcA5V25Wd3Rg/ve32+p8/2fwbR1nq+6coTqh2Mvut2bk+18JcGR5W7cTkFtZG/7fLw8EC8skQ9b/P2Z36VDMOiCG0JmTIY9vMKrad36tr/l0PW6M0ZP+h5yX0uTZ93vXNviaVrV0Zdr+1+T4Z+zw35aFd2wp9r+GbTgAAAABANAw6AQAAAADRMOgEAAAAAEQzc3Bukj9r/kWPV/dqpiQ03zdIV1Q7L9dVezyx513PQ3fN14/Jf6735rEXtXqR83seLq5amJXNBpgcbpevDVv/NLF1OiPW+ALQjbb7lD7n1LE7hN9DTdnFdr8/Cc25TS93reudRayt7/fcY2Zh+6TL+qZ2f+3X2Yyn65q0fNMJAAAAAIiGQScAAAAAIBoGnQAAAACAaGbOdMbMDi5SzbG28315oeufSqEzeWm6qh+f6EtWFmszH2vb0nSfPhaTR61dx6lzY593n6+5zZIMzDUpq7FqF4W+5l2aNNwPIvVrBrj0+7XZocTzb7RVc/28PuEao2vhn/s2X4++dQt999VmLUPfjKfvsYSY575n0ZSb9M0mdqlvmfl51t7mm04AAAAAQDQMOgEAAAAA0TDoBAAAAABEM3Oms3VT+ZjMZOR8837znC9tM5YiuWpVnlPg0/SAai8Pz1btgcmQnhp9d2pfOkto83tJYmur2hqOfhmk4UAf62i8vs2adX3OENWyAok+54N0SbXLQmc6bQbUXUfLnvfZa+DWMsL582YNnRHet3yxeXx/66OiG31+LfaKK6Ppm/nE1ux5nGc2lmvaG12+V/nmBe36PtnH9j+/Nn/+COnvY38W96l/2rZ57svV54Uei89zi/5bMFG3DgAAAADY0xh0AgAAAACiYdAJAAAAAIhmfpnOaZ65iXrmbqjaqcncFcUpvX6Lc9rttl3rO7fvOBe2Dqimly1lZ6l2NtAZz9HkOdVeHhxyH+CUotxo3L+0mMGozzNvzkH60tdJbzsz2dVhtl+1x/lxva1aBnTFsXN97EV+QrVDaowNBmc1Lq9M5hN7zyLVSe5UaJ5vF9f19OJ9Hs36rtuxzfNGhrM3QjJ2bdft7FMf6T4vzRnOpucSmskMPY8+57Xta+D73JtyvN7H5ujDQuvKzvO1Y9HDAgAAAACiYdAJAAAAAIhmftNrp75OtmUefKYDzFvosdSn6+pyMRuVLsGSJnrqsEwtT1M97dNO1R2Nn1VtW5omM9NAXVN901TfPoPBQdUOmdbsmkJtp7wW5Zp+vJmuMBjosjxlOTJtO1V4dvZYUlNGx07HtaVqrJF5/Hiir1vzNA6/qWbN07WB5qk5feqLnfo+dXL6+Hb167L5OtTLkJnyAfbc2Fsw5NzVtt3ze2aB9LkcxlzL7nmeB9/z6Lu9pm37avuze9BnyMhTg6eXBz/vWr/jV3YvCSi54vs69F2fHhUAAAAAEA2DTgAAAABANAw6AQAAAADRtJbpdJW0SBrKZ9TmO5v5zFmq83g2iyjVRDWLQrdjzsdvOwtgz5PNGpayfZmSZVMiJTU5yLzQ2dm2DW3O0lFOZpp7Xri+n2xuMjH7nky+r9oDk4VNUn2PleX69NbVsrx4wbR19nVleI5qV7V7X28vTYeNy1eXL9Drm+e6MTm2ua9KlzzJMn0sNi9tM8JL2dkC+Oh1jnO3ZPD2ULkV+9sBtr+zStF9WGX6NHV7hp6XBT6vi6bNjKdvaY7QEipW0+Ndmcu2f9PE57kE9+3mtWyzhaF8jrXr9ymfa+4a89hce+23XIyyGpv96T6x6Tr4vs5Cz+sueYcGAAAAAPQRg04AAAAAQDQMOgEAAAAA0bSW6Rxmzbm2plqcdk5xvc6hzuvZbGJTXrRtlegMna2NabOH7vqPdq61mdvtnI+/ub6tX1aarGs2OKS3bc7zIF1ufHxt3y3O17d1OAemZqi95huTZxofb+e0j6dykCIiSWrnyG+ex1qmotLXcMPkRcf5CdW2GWRbp3Mg+jwPHBmmfUvnN+xf348231TLstgs7G7JwAEiHddZtNsm/7cz5jzafFPtPbH5PZPaw4spZp1O34yn6/Ft7ju0rqbvc7Ea17f9p+9ry9X/uvrryv4+RkCe0PdY7MM7zYjqY7Wfh9NUf46bFLpOfWHaTbWM267F6sInTgAAAABANAw6AQAAAADRMOgEAAAAAETTXp1OkyVbNrULbaZzWj1Dp7dlM3RdjpVreVOT4dy3dFi1R5NnzRbWVWswOKjbZq72xOQDq2r2mqM2R2uzLTYbazOcW2xQsddhONB1Qcf5cbMBczwNuclaFtZmE01mMzP7ttLkbNXOSzvnXdd6ba4ja2unrm+z5ovLR/rxjjp0yaD5fi4acsH2PLpqo9paqhuT443rA72uy2nNNaPsynhGzICGZq9iquW27Ar2fcvU3XRkNslwLqaYGU5fMWthOmsy1g7Gfm4Ky3h6PTdXPxLcv9r8tlnss7/Y+f2IfWr9mvixn91rv+dSmrqdpo5nm3xfK3zTCQAAAACIhkEnAAAAACAaBp0AAAAAgGhay3TmJiNnM3leavUmdaZtnhkjmzexGU6b57Nspq4o7Vzr5nnjtk5o098NbJZwNNa1KpeH5+ot2VqX5jqkia1tqdl8qiS6XuX0dbX3S1K75jo3mZo57CtLOjNcq+Vmnvv6WG/fZjqb2Pn3Npdr74nS5Efr9VP1+jazmSQ6Q7q2cdQc0PavLWeew5wXVz4V6LWgHI9vxtJ3X3P8m25gTbou1TOZun+zibb6+v15LlgMMTOcbo5+x2Y+PTOevqa3Z/fl5tvHObbv7M+nlgcPAxz5Ut/8qQdnXdjaZ0QzrjAfAe3vufgIfS1QpxMAAAAA0BsMOgEAAAAA0TDoBAAAAABE01qm084DtlmxkDnw88xw1muI6rqZRaHbdXpcb2tZVraejpkfnZn8YJrqXOVkqnamzTVag4HOWNayhjJpXJ6Zx1u2Dqitb5lP3xOFzqZWzmvc/NzG+UnTPq63X5t/7/P3Fv1Ym121NZFKU5vVHsukeKFx+6vLF6m2nc+vMhiO82aX21qrC1WDEa1w5X57LWodzq7/Btvm/hYo1+io2xlcd7NHeVX0gzND5+gD232fbLd+b+ixedXtDNZmBtS1Ldd5NMtd9YQ77Vfs74CYcULZXKu4Xut4e11/BuSbTgAAAABANAw6AQAAAADRMOgEAAAAAETTWqbT2q1ZsdDnVdqajLXt6ayjzXAOB/tVezrTOc51Xm95eL5qZ7aOpoOt6WjreKZp8+1Tlnpe+XTmdJjpY0sTva3c1Lq09U/tHPbcLLe1MsXkS/2uoz4Pk/yEWa6PxWZZbQ7Ytq21jaf0P1T2PO78HnTff8BusvO/q9r+r0v+ucawXNhckcFEoLZz6r7ba3ofrdVBrGUH7Wu3+bXsfM921Pl0rd+sP7l32z+7f7ejzfPgV9/Sec1ctVjt8sRRq9h1j023O+5/+aYTAAAAABANg04AAAAAQDQMOgEAAAAA0UTLdGJr9ayAznAOBmepts062nnry0sXnPn/0fipxnVdGSWb2axlOk2G01UXtBCdH5x+fJbsU8sGJrualXr5pDil2jYvOqzVINXLC/N4H65arb61Ml3azF3a+8vmTYFe53q9c5WzZ4D8dfc32iTwklS1CJorR+baILlLdCtmPWHXtnz31bS+O883e03FrXdgXsuuPJ9dPt125ksdhxI5B9+UdXftu94nWn6ZT2dWtulYPPKgW65v75mA8x56LL74phMAAAAAEA2DTgAAAABANAw6AQAAAADRLESmM6RGUttCj8U+PhscUu1hdsBsT2fwbK4yS7avvema4263nQ30tnzn59v59k0Z0VqNJTOf3j7Poej6pK59pYnOiBZy0hztzrONoXPe286ANu17eXihao8nx3a8bWwvZv5oT2kxwyni6sPC8kquTLuL7fPUMu/bh8wlFpt/H7p5z1eO16Irt+a7fK5cfWTocp9DCcyA1vstV3/etNTW7fTNysardRz9/nLU+WzKn/p+3gw9dr7pBAAAAABEw6ATAAAAABANg04AAAAAQDS9zHTaOcJpuqza2UDnHseTZ1W7zYxnvY7mQb3czBt311jU20tNfUqbTbSaMkpLmTk2MyfdZpDSVO8rMznIJPHLPVaVfm7jpnXNsTXVXxKpZzzt+uNcZzbz/LjdQuP2Q9h7xOZ0i3Kk16/0PeLiU1vT3m/j2nkAeiR6hnPndTtrmU1HptNdJ872cU318ho3tcW2ddtmn5x1O4HI2s+O7c7vTJx1E131IZPmj/W139MIqH3pzkG6+kjf3w2ZfV3//ti183gZz9h89hc7z7w7X7UAAAAAgF5g0AkAAAAAiIZBJwAAAAAgml5mOmtzikudDpxUx6Puf3oOc5rq+pDL2TmqbbOGaxtHVbssX1DtLDtXtYcDvf3asdj6O7Y+5dT+bQbTZiwHJhs7qNW21M9lJdXZRJthGoh+/KRab9zfaDpfaKeJm9hCWeqsrGv+fWmytTZnWavh5MpNNLCPtTlfW2s1q1ZV2x6rPe/WxuT75gBmrz9VVRPV7rKmLRAupA5n87quzKZ9XdYz8X5vn7YPs/1AKZvtxBGjaarxCfSRb/3qLnNovrWzY2byvN+jXdl024/V1i+3+f+tNGc0Ezuk8KxtXOvXavUnd97veWc4fU0/18r+nopfXVjLru96fMj9GnpsLnzTCQAAAACIhkEnAAAAACCaXk6vdXJ8dd0mO8XKTqkajXW5lrI8pdp2Ou3KUE/P9fn56q1MT6mtTdM07SzR011XB+ep9sFKt5erFb25yhxroo91LdFTia1ysDl9bFyYdWs/+a/3VZtuW5s+ax+vn+vK0vmqPc5PqHZRbJZccZco0c97kOrzlA32qXZemGnHpjSNnYZsp2j7TKe1mE47H11Ox+rTvp28S6SYh3v+hP/0+rVpZna6rKOvt+Ws7HK7PTsVzPZhTTPFpqfant5Y8/tC6PvIbuU7pRPz02Y/FdoH9qrPDDZ7GZN66SW/SIL9bFN7vKP/tn2k7QeTqefiLEFlBPeR9tjt46faofdP2/dfu2Ukw46NbzoBAAAAANEw6AQAAAAARMOgEwAAAAAQzWJmOiObnv9cmOzhqdGaXrk2L1yP42uPr2UZ9eOXhuebti7H0ZRDSm1JE5MV3J/qbZ9XvlS3k7NU+1BmSqKUei73mpl//1ypb6eNVJ+rIt0s32HLuYwLnYWt5VPNn0dKe9ptzrdW/kA/F1v6ZmP6OM2x1H+OWmc+J9OlYERkYkqcZNnZqm3L5NTLw9j86s7Lu6Af+pTx3F1cP+Gfbvn/IvU+xvaX2UBntZfM69aubzNCebWh26Kz3U3lsFz5UGue5QD6bHff+3hR2yUk2uyvu3//tv1K83Kde7f9qc212z5TZzjt71nYPrJ2JKUu6VYkujyi/T2M6c9GvuVTfDOgIXr9uwqij8f3fgx9bnzTCQAAAACIhkEnAAAAACAaBp0AAAAAgGhmznRS7+r/c9VLs+fBsb6z3qTNIaU6mziYyira+fYH0gtU+4LyYtU+N9EZpcsO6Pn3/+5sfc2fHetj+bdTZr7/Kf3ci0rv/+mpTJOd6z8wOcay0nP9a/WhHJryUiIi46m6nCI6x+l9LzuucW4yn7btsmdeW4ii00xRYB1OV0aztjtHHbnpTJLNH9k+aJjp/nB/dli1DyUv0ctLnYEfmQz7yUTXcH5BvqfaZVOfFz0CZM/r4uQ0AZHmfsw349n2sTR9Xm69P7afPzz7YJV7d2Q4bR9pf59iNdO13oeJznjaHOaofF61183vYRSlzsXrbLnE5fzs3t51jJ35jLl93+fNN50AAAAAgGgYdAIAAAAAomHQCQAAAACIZuZM577li1TbZuRG46Oq3accWsxMk/9caX3ehmYOvK1zZDNLS6meQ79vsFlvcklW1bLzS33NLkh1zc9XnaMzTm9+ia4hevWrn1TtJ/7lXNX+7JP62MtKZ6RGpw6o9venMlR2Lr9vHTqbhV3KDpnlet/jXO8vN5nOPt2vALYze4ZTROc4XRnO1Uz3bz9QvUq1X72ql79sv+4zvreu3wu+eVLXAn5soGvO2drEiI+ax/MTeq7ttfOpNej7OS30WKcf79p3+7+X4uoTdf3hWk30Kfbz6FKmP0Oek12q2peVl6v2QZOjP1HojOYT6XdUe5yaGumJ6SM9agRHr13soe06sW3W1nRtu+37k286AQAAAADRMOgEAAAAAETDoBMAAAAAEM3Mmc5EBqrtyuB1yc45TlM9Dz0zc9gn+XOq7VPvKRvoOe3DTGcHLXve1k321bK15JYGevurA52jPFRt1pJbqXSm8xxTh/MVZ+ttH7lI5xr/w026RlJ685tU+4c/9T9Vu3pANeVUfr5q/9uarim6JJvXxWZVq0pnNG0NOzs/P030tq1aXc9a7Sp7/w4E2I12U26tVrfT1uk07el+Ik1NzTnzPnGWqcN51X6d4bzjlcdU+/LX6pz4V/7Hhap977f1e8WTE92XvxBc0xRYHKG5ykXqx3zypsFcfaD5TGlraw4Gm8vLUn/usp9HDwx07fWrEp17/09X6Gv80lWdY/9v/6Z/e+P493Ut5OeSJ1TbHk9znU57HvTSynymrGkxA9p1nVjf3KVP5jh03xbvegAAAACAaBh0AgAAAACiYdAJAAAAAIhm5kzn+sZ3G5fPc759ra5MOVLt3LR9jtWuWxS6lmVe6FyPrbtZmH1XlW6nydm6neqsos0drZj6kwOZyiyZvyEMzKT2H1jVc6+vvvopvW+T4Swv+QG9/D/+e9V+6UNfUe3lJ3Wmc1RNVHucbM7vL0pdr8nF5hRqc/2N0uzb5nqHyy9T7Um+eV2p4Qn0haMup9gMp35Lm+437DLbXq50X3vusn7dX/rD+rcAhlfpOshnfcn2aTpjX5rawn2qI7dX+PbloXknbGq7lmZbjxUJr3sYtD2b7Xb1C7UsePN3R4PEZDqz1W3WFKkS89sZ5vPoSnKWal9xSC+/4S2P6cdfotf/xgfM9r+vz5P9LY9ahr/hMjszm7UHOGrDe9bGbLoH26/F2vz4ppq2vvtu+/Mv33QCAAAAAKJh0AkAAAAAiIZBJwAAAAAgmpkznUtDndfbmDyj2ns192brcLprgJp6ObW6cvqSlGbe+XQuUkRkMF2HzmzbZjqfXNftr35VZ5L+w3/9e30sb7xKtTf+80Oq/Q//cqne/ppqyoboXOWo2sy/5tVY78vU3UwSfV7zQj/vyuSjrLxYM+3jqj0Y6KxBra5nRHZ+/TA7R7WXMl3fz9Z2rUp97gBsbbp/tX2KNUn06+rJNd33/v2Xdcb98Dd0hvMzT+n3yO9unFDtkeh2YV7H03kmWwe71iYP2om9+rmmC66cW0gOzpVx891Xr9jXfu00mN/2SJdV29Z+n5Sbn61sv2Lrp49Ff656yvSRDz+oaxsvZzpn+VWT4TyW6N+Ksb/1YTOe+rm7+kDPbKx57jGzjq7MZcz8s2tbsfs8vukEAAAAAETDoBMAAAAAEA2DTgAAAABANDNnOslwzsZ9XmyGc2iWNv8doFbrbWre+rDS9ZhOFTpT+cQLOtP034/q7ODk03rfL/9f/1u1v3b0MtX+h2N6f4+c0DVIn06fVO3xVC3MstTHZtn6UDb7arMHtXpOtVpW+rkXxalt9x373rbbn+TP6uXm2FeGh1VbZTx9a1Ohl3wzSDit9rpvYPNJNi90SnQe//+s6/Z/+c4h1c5MH/XUhq7v+6/po6q9MdGZTltLWPVp3pnN2OsD/eXqP9vOyLWag/OtF1nLpjfX7bT9TFHrd7b/fQzbR64V+rPKV8e6Luf7vnmxORJ9Xv5FvqPaJ3L9GbEots+5b9WOKSRT7Lo/QupqtmF6/6H5Ul980wkAAAAAiIZBJwAAAAAgGgadAAAAAIBoZs50umqcuSybOp/T+ZVFyova+c7ZQOcih5mugWTZup6DwdI2a56W2iyjyeJMZzxLs2zd1Ml8dqy3NXpOX/6Tk/2qvXJUt4+N9HP/7prOcH4neVy1T1XHzLFun12wz2uS68ylzSW4Mp6VY+5/n+4xe0+MJzo3MTbnZnl44eay/Pt6Y9TvWwg+NcBCsiV7XVM9S5ttGpXPq/aTJpP5dKHr3aWFft2uJToDum5qNk9MjrwsdR813Qf2ri7nvPePhefbj/nU1vTtA0P71FYzd4H1Il1sPeDR2NSS98jFT9f0FBE5luiM5slEf5a3v0EyKvS+Nya6z83N9uv9zlQfGdonOR7f5vtq6P3im1n2Xd4lvukEAAAAAETDoBMAAAAAEA2DTgAAAABANDNnOvevXKLadj51UW6o9mi6lqCIbEx09mwpO3vWXfeKnRudF89vs+Zpg3RFPz7R520gOtNpc4+lPc8mpzlONufAbyT6Gthp2yfttnKdUfr283r+fZro53rSXONnUj1/f606rtp2/v/0PWNzBDbfZDOcLja3YOtwLlLurX6sOjs2nYF2Pi9XXoOsVi+F5JX2klqWuyELaWu8JZXJUps+w76nWbavtuvnxahxea3mnNdr0VHbryELBSwmew9vvrd1nWHz2b53/+2Rsdxac59o+6Hl4Wb94UGqPxPaz2G2vVHo2sQbotu2HruzTyyb63Q2M/eHqz91ZGmdyz2EZoRj187sct980wkAAAAAiIZBJwAAAAAgGgadAAAAAIBoZs505sV643KbqUuSoWqvLl/UsD07VzqsJmiXbI3F3Mxxt5nPYXaeamfVPtUe5/rx1cDMzx/o2pnlVMbT5iQnSXPN0OOmnVbm8WLm26f6WHKz3OZNbR286TxAva6mzR3o+8lKE33rZgOdna0qXT/VXpdFzsk1Hbudf79v6QLzWH2/rm98t70DQ2sW+f704/d3T5tPcuWXpvucpGyu7Wv7T5smcvVZtfxTLadu6nLatqpB15xlmnvdTiCyem4y3nckMftbZ01QV5Yw+ABMv5Vs33fU6p+Xfp/LXH2ifbzt5+oZzoj9nKsP9exjm65z25li35xlm/sP3RbfdAIAAAAAomHQCQAAAACIZubptaPxU4419Ph1dflivTQ1u9LVOaLq9ueG9Vfydjrt0EyPdZUOWR8f0+sv6/UHg81pzHa667qdamZOel6Z6bN2emxp23r91Eyhttuz609PvXD9jPd48rRo+polif5pb1uaxk5rttNKm6awVJU+T32e6uia6uCaDoOWtPjz6nvL9mUQtlpemds9qfVxDdNS7Sw2c81sH1GZ/tI9vXb26bNbPV7U9Frf+yfwfuN+Rc+ETONzTmm17+mB/XdTCRVXeZXa51PfUh+OPrQeOdD90nSky5Y0qZdM8Zv27+4DbZ/ZXtmoRRZa8sfnM2vbU39d+KYTAAAAABANg04AAAAAQDQMOgEAAAAA0cyc6aznXZpzkjajNylO6Xa+mVVMZj+M2Zg577a8Rn0e+c4zfPXzoM9TLTdkf5La8bP9tf01zHmfVGu6bZa7Mku2xMmk1GVy7DUtK12GpCgcP6c9tT/7PPJizaxtzqOd426ONS9023W/2gxoMvX3F1tepW9UXsTkau39M548ax5t7rcFKk/Ua2TiTov98/92d/Z+NvGUxoxnLR/anIWq5ZWC80mujOfsj/XG/Yo9xPmZrvbabv5865ODC87MOftQz7JTpp9Sn71c+3L0G/WSVs37bsq1OwWWNGk719vEt6RJ6D0TM6cZmjflm04AAAAAQDQMOgEAAAAA0TDoBAAAAABEs+MwZZouNy7fmBy1j1CtNnOcdk6xzXAuZWerts0PhmX49LzvQXaOamfpPtV21XqzOctaLUyTs1zLNx9fljbFqS2ZGqFW4aipZGs0TXKd0x2NnzRb9Klvae+P5sf61s6sZY5Nxjhk2/O0PDy3cfnGWNc7rWq5tW5rNOG0bmsHL6565t0uN+1aZnnzfi9bzjG6Mp219ftUg466sug53xxcl/te1H2JOLLp3qfUleH0y7U7BfRToefZp/ZraF1N38d3+TkudF980wkAAAAAiIZBJwAAAAAgGgadAAAAAIBodhyszAYHGpePTU3HmPPW7bZLs+/R+CnzCL/8YJvKqjl3aTOcaaovUVnq3NB0bUybB7U1GEeT51R7eXjIrO/3Nwib8XTV1uyTpmPzmbs/b3k50u38+cb1XVlsYL5sZsdV17h5a9OZT1cNuba1nmfy2jkZTfSLK7dWf99t7guatm3N8z3de9vetY1dfWZz1lyvG9pvRK4f3HRuQo/d8Xj/+3fnfO+ZRfptDr7pBAAAAABEw6ATAAAAABANg04AAAAAQDQ7znSOJ880Lrd5wi7V517HHFs3z5+fmHqQg3RJP9pkOAuTR7U5zSZ5ofN9NsOU5zrTaefvL2UHG7c/znU900n+rGrP85qHmp4Tn5hrUlvX5HK7znhO78+V4bTPxdb1XORrtlcsdE3PkIyOiPhmPN2Pj6nFTJHrvJDZxILx7cfs57amTJ1r2755PN+6iT59snd/7tmHhucyp3Xcz3j0e6Hvi+2/r06fq+bnEVrHs01df57gm04AAAAAQDQMOgEAAAAA0TDoBAAAAABEs+NMZ5+zYLW52ibXlph547aup88cZ7tuYTKchZzQ+xqcpdqDdEW1bVZ2aXhYtZvm609MZtPOx7fXzGYya3XszL7yQucHXfdA07z1PufS7DWx56UommutdsmVPVlZOr/x8ePiZONyzF+fXyvBbH/mnfFcID5ZKzKb2OX8M56z5+B8tx0zw9nmY09vICwH7/dYI7RfsscesL3Y74v+tS9nP8++95svn+373uuhx8o3nQAAAACAaBh0AgAAAACiYdAJAAAAAIgmqapqpgm6SbKs2z3OGdk5x2lqjj3RUVabw4z53OrzofWc9jTdZ9o6X1jkOiM6XcczNGdbrwnaXCer/nh73veZNTafa0iONraYeY7Y7LFnA1N71WQqbJ3PqtLXBbNJkxX3SlMWuvbmPDnzTD1GTnPhldXIvRK2ZPvItnNsPkJrOoZsP/rnC98+ssN+qcvPVm2/xzq3N33eHec09DzErPMZWrPW9Rlygd/BAQAAAAB9x6ATAAAAABANg04AAAAAQDQz1+m02bDc1PfrUybJHovND4rML09Ynx+tx/1psqTa9QynnW/dXr3U0G3Ze2Rl6TzVzov1M/8/Gj9lHt2fuq99upd92WO3r9OwGl6IhYznjHzzRz75Jte2e5yVAhZN7FqFTfuyuqxV2Hae1Lk971rIHut69nHOY41YxzM0B+lzntvOXAbnLO3jk+nP2+a3W6q8cVuW7/3MJ04AAAAAQDQMOgEAAAAA0TDoBAAAAABEM3Omc3X5QtU+taHnWsesdemqbVnnV19yntwZvOb1e8XMx9+Y6BqQ2cCvliHalw0OzfsQ9qR51qTb09rMVZLRBFrTZY7Sd98uUT+HJTZj58hk2uWufsosbzoXtX23rJY1DKxvqbblyGR2+Z7s2vci1YYPPY980wkAAAAAiIZBJwAAAAAgGgadAAAAAIBoZs50ro2fUe0k4njVVZNmkJ3T+PiyHJn2umq3Wduy7bnYfZq77fvc8vw5s4a+R8pq/7bL+mSR5tdbzvyzb61BAAAC+Wa/QmsT+mm3fnVI9rCqiub1A3OWfaoJHfL5uO1MZmgt177u6zSb+022XRY768onUAAAAABANAw6AQAAAADRzDy91jV1ss2v6JNET39dys5V7eWhLvuQmCmD41yXHRmNj6l2VU304z2O3X71nKb7zBr6q+qy3HBssXlaxzynPmSZKa9hpnXY8i6uacu2rE6fTF9X32s6z2tk78dscFCvYF4bea7L2KAf+jxlGwBiiznl0P1Yv+9fgqbPOstleE71dZVQcRzPoupbGZKmqb++U5rbv0az39+xS8vwTScAAAAAIBoGnQAAAACAaBh0AgAAAACimTnT2WaZEas239nMSS9MCZS1Dd1O0yXVXsp0rm0wWFXtej61xRIqjvn0g8F+1U4Tfex58YLZ3s7zp8Eq+1PKYT/VPZ3VHaTmmtTyofPMHYQ9z3my1ygx0/F3S55jt+nTz9gDQNdcfWDIb2+EajMf6M6uev6uh+Mz5zxLwIVeh3kem+v+882QhohdxqRpX21nrfmmEwAAAAAQDYNOAAAAAEA0DDoBAAAAANHMnOls2/Q8YDtnOMvOVu2iXFPtslxX7aTQmcyyylV7YHKTpanD6FN30S6zx+JSmWOz8/dtnnCe2S6bs7RCjs2VD+064za9fVdt1T7l7eyx9LkWKrbXp3sKALrm2weG1Mr03ZZvnq9pf6H7cvHNIjaZZx50q/37fDYP3ZdL03mNXeuyzZxl7AynxTedAAAAAIBoGHQCAAAAAKJh0AkAAAAAiGZumc7pmo1L2blq2TDTtSwnuT7MDZOjtHOMS1PXc3npkGoX1disb3OZs9ft9J1HbvOCpcyeJw1VSWH21fw8Wz+WqXpSNns4fT+IiKTpimrX1o94ntredpe5CFeWIMv0ayHh7069QJ1OALtZl5nNtmsoxqyT2Hpfn5j39MA6nrEeu5WQWqyue8D//ivMvzR/Vuqyjqzvc4v5GdMXnzgBAAAAANEw6AQAAAAARMOgEwAAAAAQTWeZzlqecGpKcmEymGmpD2ucHzdbax4r21qY6+NnGpd3OfZuu15U87b0Oc+yc1S7yE/MvK3YkkRf8+HggGoXtZqhs+duu2av4WCgM8plqTPFVTVR7Zh5PrtvtINMJgBszzeD59KnPtb+JsW0qrLZwJb3bc+DyXC2WUM0VMxamr7H7l7fb1zQVKeza7FrbYbgm04AAAAAQDQMOgEAAAAA0TDoBAAAAABEEy/TaWoFDQdnq/Ykf+7M/xfFC2pZWYVl3uwc+qqydTjNoc5x/rWrjmJRrJkH6Dyqzm2ac56dp9pL2UHVXptjprOW5zDPyzfH2y86U5Glq6o9sZkLc3+3yZ7nek3a5hpemD/yotjNuL/3pkXKcDrrgFa2Bnqy5f9v9dhQrtdPSP3K2LnbkHPhe2yhx+7z+NBr7HveYmY0264zv0if5AEAAAAAC4ZBJwAAAAAgGgadAAAAAIBoZs50+uYu7Bz3olzbZs26stzw2pe1yJmQ0tQstZk7ex2mc5vTOVmR+jlfH89+DTpnco5lFXYPzJf+W049n6qf6zyf29Lw8Nz2jdks1r0P9Bf50f4IzeRNPz523UFb89xZK96jZqNr2yGZzK3W91pufpslcdQAdW27zddb7Ixmm3VlY+d6fTW9dnzvdV980wkAAAAAiIZBJwAAAAAgGgadAAAAAIBoZs50DrNzVNvmB62loa4ROZ48q9o+tYF2s3odRVeWUc+3HqRLm/+/dKFatjF+WrUXKc/S52Nzqdcg9aszG5Pdd25q5KIfQvIigEvbtdf6at7ZKcQT89rWM3h+OctZl522823Ptn2X6ZymPpam+qM74dvv9On12+axOOu+BvbPYZ/1/b6L9D0vfNMJAAAAAIiGQScAAAAAIBoGnQAAAACAaGbOdGaDFdXOcz1HeDg8V7Un+YmAw9q7XHO9s0yf5zQZnvn/kcnNurbta5EyoX3S5/NkM8RoR5dZlL2Sz8PO9SkbZcW8P/ucEYOfLuskhtZobLqnQ+tLhm6vbvbvnkL3FfpaD6nNOs/Xftv7XuR+jG86AQAAAADRMOgEAAAAAETDoBMAAAAAEE1SVdVMk4PT9ID+hyo3W9Lx0D7VJlxktWzBVIazvnK+/bIW9h1SexXtmb4ubZ/zshq1ur29IkmW530IM+N12k+LktNp+/6J+VsBbefn6B93zvaRvtc5Zu3itutHhmQPXfu2/POnpWl3991TaDa2af02c7ezPL5pe4vSl28l9LVTVc2/E8I3nQAAAACAaBh0AgAAAACiYdAJAAAAAIhm5jqdzrygWU5uqB2182jOc5v5Pjs3e3l4WLUHqc5kjIP2hllVUqj2MNvM1uaFqYdb2bwGgEWwqJmgvtVvbnpP7NuxYlPItYl9HUNej6HZwbYzoZXHd01t17ntsl+bZ43e0H379lv+z236c2Lz/dB2n8k3nQAAAACAaBh0AgAAAACimX16LXop5Ktu13TabLBPr8/UzU40TacVERkO9p/5/9r0WsAh9k/4o33znCrWNz7PfS+fp0Xj2680rp+Y71PMZ5e2y2ns1j5x3q+fmK/1tsvkhByL6/5pv//ffH2E3ru+j+ebTgAAAABANAw6AQAAAADRMOgEAAAAAERDphNnJCYHUZQbqr0xflqvv0tzDF2z8/ObMpwiIuvjo1MPJmcLoDuxSzzMO0eGOHxzaUGfLxzvi11mpOd9P8c8zzFzka7ttf287PbazIi6zkPU14JD21lYF77pBAAAAABEw6ATAAAAABANg04AAAAAQDQzZzr3Sl2ivgu5Dq66nDH3je3Z85jnxxvbQJt4HfePb902X66+3Kev7/p9ocv9zTuPt5u0fS5DtmfvGd/7PySrGJptra9va5C2911S6LH16Zq3zec6tl23s01d14Dmm04AAAAAQDQMOgEAAAAA0TDoBAAAAABEM3Omc2XpQtUeTdcKlMXKBc2zJo4vVw5zPJ33q/LGbdnnNZ4c09teusD/ANG56XuiT/cq+oF7Ai4hmdF5Z/u5vxdT7bqZuuAhNadd90TbtWOTZGBWKHa+Lc/7uZ7BC/vuyO/chOZH7TXWj28zKxvKN6/q02f2Seh4yPe58U0nAAAAACAaBp0AAAAAgGgYdAIAAAAAopk50znM9qt2Vels4cbkGdWeZ+7CzjFO032qvX/Z5FMnz6l2nm+2EzFz9zvmymG2aWP8tNexoBtNud5x/n2z8s5zMdg539dGnzMeQJPYNUJjP35e297zbIaz9p2HzQu2V5Pcd31nttC8z9qMZxWQ8bT6dQ82f0/ln8m0n1f0eWyzhmSfznPo8+qytmbb541vOgEAAAAA0TDoBAAAAABEw6ATAAAAABBNUlUV4SIAAAAAQBR80wkAAAAAiIZBJwAAAAAgGgadAAAAAIBoGHQCAAAAAKJh0AkAAAAAiIZBJwAAAAAgGgadAAAAAIBoGHQCAAAAAKJh0AkAAAAAiOb/AVZvaYWrLLh3AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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7REZzYPq81Su7VW2PpWtykzYP69JU9QJV2/6p840nzSP4mcZaDPOaynodkg91ZF1zkvkyv1EiRjm8sZHRBAAAa8EnUAAAAACAM0w6AQAAAADOMOkEAAAAADiz5jBZ0+QebS6oWJhVdT9YULXNmrWD+eWvy/6UXlcCvW2vquqSp3tVNvt6bGebT6m64OseowXTbzIIdSY0nuN0nYGzeb6l1lG9QlY2bB3HenbJZjbNUEyPUbhnX1elon0d6gwwHBk0w5n62k3PcGZngnXuO5HxNL2D17NvJwAA2Bz4TScAAAAAwBkmnQAAAAAAZ5h0AgAAAACcWXOms907o+pKaYeqZ6p7VH2meVjVgcmWLTSfXv5658zValnB0305C1JSta96fIpMFXfp5dNmfU/X8TypiEijrft2RrHel0HYVsvWO+OZyFuNEJnNcaTzd9XSVlUvBSavPMQejRiAzU2mvK/Y9xzf069DP+N1ad/DoqibPrYcPUc98wZF307Ya8BeIwCAzYnfdAIAAAAAnGHSCQAAAABwhkknAAAAAMCZNWc6t09doepW77SqPTN/na29QtWnF3+iat9f6b1pew2Wfd1Hs+Lp2u7LikxurW/6cNq+oNWpV6n6bPuZ5a+Djulz6JFrHAb7nNusYXbvwc0jnjF+2cr1b8/TYuvIquu+vD4/Z5p0NsNZLs6oOgz19RKa68fzbN/O4eV8s/J7ZD43n6znnMwnAGwOfAIFAAAAADjDpBMAAAAA4AyTTgAAAACAM2sOzi22X1B1q/uiqsPqXlXXSttUXSntVHUv1rfT5syKpk9nSXRtdVKXinie7utpM59h1FN1PCPV7S/odRN9O0ebPUzm/eLGK88Xz47Z81Yuzqq6YzPDmyiLaDN2tidu/Jq0z/+or0ecp9S+nbZPp36ON9NrA5OPDCcAbE58WgEAAAAAOMOkEwAAAADgDJNOAAAAAIAzaw6A2Qynna822s+m76hQU3U802nZvnI2AtIzKc5epPtw9sKW3l6oM5s2b2r3NxXLn/peSS1bbP1C1UEi4+l2Hh+FXVWXYsdSMP372t2T+rGR7YU5up852H3b82z7dm6un4/oYy/4OtMcP3cRbQ83Hft+1Q/s+53tw+muL2cW+nICAACRzfVJHgAAAACwzph0AgAAAACcYdIJAAAAAHBmzZnOrP5/NlvWaB01G9Dz24JfX1kkuo9mKIGqm3JW1d1I50Gbfd3T0eYubabJ93V+sF7UGU+X0vtqnmt9fS7Kpe2qnq1evPy1zf7ZutmxudzhSmbFbIZ05RoKTTbVZobXu99kWs5tvbOv9tibnefNGn5sXX5uNA5sdjF3L8LU59H0FTavHZvxtO8xdv1EXtphxjPrPAya+Rxmz0fypwAAuMMnVgAAAACAM0w6AQAAAADODO0exsRtfp5u35F2+6JvHhuKbnHSDnWLlDDSyz0zd7a3J4aBbqlib7/tlRqrjq3TO6PqIGyaNfS+E7fPmuMuFudUXS7O6LGaY7OmKxeuumw9WyGca3/2vFfNrcCt7rHYuvbnHev78w879lJxdtV1e/0F8z8Ztycmbg3Ot749N+t9qzEGl/t228Q1EW+LY26vFXO7rLkrNOs2d5e30+Zlz8sob3Edp7EAALDR8JtOAAAAAIAzTDoBAAAAAM4w6QQAAAAAODOysFj8z/zblin9qJ36WN/TLU8qBV33yzpLuNjXLVfCUG+/0X5G1Wo8Nk+XyGjqLGCpMKXqwORR65Vdqu72F1U9VdbLy4VpVffN9qJYXmuh/axa1umdFM1mBYf9Mwd9biLT+macpedhbf5zTtWV0hZVNzvHVF2MtQcSESmXdI630dbtYmwumLYok2fgVh7x69FsyranShrfDGeWYbZAGRQZz/UxcLshAMBE4NMsAAAAAMAZJp0AAAAAAGeYdAIAAAAAnHGW6czq4ThT27uyzMx9S57OwHVC3SfxbOuwqm2mrl7aoR9f0pnOwGQ665V9ev3eyvq93mk9ttI2VU9XL1J1taAznr6YvGmkM5lnm0+bfeu+oDumX6Nqm++Ln+eCXzHrFs26fVOrMnd2MG0sIiLt7vHU8YySHXs/WFp9ZXNc9nrbWr5E1e2ufg5thnOmrHut+ua8LLWfV3UY6r6MZDw3mUQPz5zrj5O81+44HwuGIm+GM0+2lnwoAIwPPr0CAAAAAJxh0gkAAAAAcIZJJwAAAADAmXUL2dkc2nTpguWvI9NXrhvqfN2ZxiFVByZ/1w+aZrnOwNlMXMGvqnq2fLHeX+zxXdNr0m7L5hjLnu6rWTCZzoLpMbqlfqmq5xtPqvrE0qN6rLX9qi75teWvbY/PZkf3f6xX9qi61dX9JC2bAU1my/z05XZ7EuvNOkb5TpH0nGRkXiat7ilV9/oNVQehvh5FtqrK9lqNvxZERBod+7x0BViWM+eYlYFzmntLvK5sr+D094H4e8bLMo497T1qwHxoVt/OrPNIn8/1Rw9QABgf/KYTAAAAAOAMk04AAAAAgDNMOgEAAAAAzqw5WBfZXoF+OdeObD7wROPxVdcNzbo2w5ncth5bq6uzjL7JcNYrOvvYj0zfzvL25a+7waJet6/7dgbhblUnMpwZ9daS7hEa1nuq7vb1/tOyh2GkHztV1RnOmukxajOdNp9qM6CD9vGMZ23tczRuGc+45HHYDLG5Pm2PWvOzHd/kes+0dK/W0PSRBfIY5wynb75v+F769xH7fcN+b0jkzhP7D1OW5ZR4Xec7j1mZ0M2K3CUAbA78phMAAAAA4AyTTgAAAACAM0w6AQAAAADOrDlIt3XmNao+s6T7Sdrcm61tXrDXn1/rrsWzfTXr+1Xd7encY7PznKptX85qYU7VNgtZ9OvLX2+fukItO7X0hKqDML2HYtFkOG2+zy7fVb5Sj62s+4T2Rfd4bIdnV9Y159hmOO2+s/pw1ss7JI3dXlbms1doLX/d6jxvNzYxksdpzqvpJdgw/VLLhSlV29ywfa3kzdJi/bns4TjsLOB6Zj6T3xf0t5xiQb83e1JQdWB62kpk+ySnv6dF6lAH69M5bJs145n12nB5HvLum3wpAAwPn2YBAAAAAM4w6QQAAAAAOMOkEwAAAADgzJoznbPFi1R9Rp5cZc1zS8vB2Qyb7eU2W9ufuq1275Sqi8Ut+vHVi1Udic5JWvGMZ9mf1tsyeVI7dpu5rEhd1X6UngcsiT720OYDvflzDfk/tmTzTfo47XmbqV2y6rZEkpnNfqSP7WxT95e06yc3GFs+YF9Oe95tXmuUfT/tebZ9N+fNebO9B4G4zZL1Oxf7WvLNtyz7LpDMQ8eX2a3nzHja72GJ9yCcj/W8vvNmNDfzaw8Aho3fdAIAAAAAnGHSCQAAAABwhkknAAAAAMCZNQffXlj6gctxKDaPN1XcqerF3guqDsOWaDpr0+qdUfWW6l6z9urZnF7UVLXNTRYKtVUfKyLSEf14GynxTV+6klTM2NLzp1V/Jb8aefo47GPtvqplnX214j1AX368PvZ6ZXfq48NQZxUb7V+sbMuv2NVT2axWsaCztpWSPpZG+1lVjzbjqfcdmIxncn1+FjRpsrJfmzUbZl+3to9mL9Dvj1m5cNsD1+Yqk/2h48vs2FJ3dQ5m31mvUzKfY2ezvg4BYBzw6RYAAAAA4AyTTgAAAACAM0w6AQAAAADOrDno1usvqDordzZIH0Xb1/ClpUfMxu22Sqn7bnWPqbpjMp5p2cR+oMcyV9unal/S990TmzdN15ElVdvzXDD7i9cFcx5sPrRttm0zn4m8lOGb7c+UL0xdP02z87yq82Yu+4E5lqhr1hjfn6e4zGzazJw9D+RFkcXm3vL2NtRMzjzUr1PPS79erfzfd4a5Lbt+xr7o6wkAwDI+gQIAAAAAnGHSCQAAAABwhkknAAAAAMCZNQfpBs3SbJk6oOqzjZ/Htq2HYbcVmF5ug44tML03F1tH17zt48Fi6r6y2LzqjMmI1orbVV2Wun68l963M872vOtHnfSxmYynPXbP9Pkse7pXpmX3VypOrRTpQ0k8Z4WCPg+ztVeout3VOd1W90WzxY358xWb4ZypXaJqe23b80rGE1lyZTyzcouJXplZuUlrkOX5MpnZ31cyhpLV19O+FmMnh36SAICNhk+cAAAAAABnmHQCAAAAAJxh0gkAAAAAcCZfc8SYZDZMb2pu6pWqnm88Zbaw+nx3+DkzPdZScU7VU5ULVt1/o/2SWtbtnzbbzhirydxVzb5shtP22swjkJ6qbUbTN8+R7flp+SbDabdvM5uhWd4OdG/XhebTaut52LHb66/dO2EfkWv7aSap92WxUFP19umrVH268eR6DgcbUFreMLOnZ2avyvQcZFYG1EvdvX2d5n3d5stDJzOfa8942vNIxhMAMOnG59MyAAAAAGDDYdIJAAAAAHDmvG+vTd5apG8dOqtupUzeojjKWxJtK5GSr29J9GO3uLb9Svq2ct5iVS7OqNreTtsNl8zj9S2ylcIWVRe9Smzd9Nu/bPuVWjSl6tA8h6FpiWJvr12KTqm6H+rbbQNTh7HaN+c1educrnt9favuWdNGZ5itQOy1WitfqGp7XPaWa3uruVv6OO3rDlhPg94Gmrg9174v5GyPled9IHvdvPu2y+0jsm41xrBx2zIAjA6/6QQAAAAAOMOkEwAAAADgDJNOAAAAAIAzzsJnYdhV9Ti1lbB8k6tc6r64/HWre8yunW/jJt+30Dys6qimcz3lgs5ZWv2orTcfG0/Z05nNoujcZCXS2dVqZDKeZv1SZFum6LG2ZKeqFwrzql4q6MxnGMtKLprsYcEc97apK/S+ejo32WgdVbXnlyWPZIZ0db5v2rWYVjSjZF9X9nWXtT4w2dJbqKSxrwWbxbbZ/yyRZ8Zi3mNC0VnxRCumeNzQ5kPJIgIAJhyfQAEAAAAAzjDpBAAAAAA4w6QTAAAAAOCMs0xnnuxYVo/GJJvFyTd3tuv7iVxcPGtje6/lPGUmt1Ov7lV1VobT5k1LJrdZifXSrEezel2T0axFVVXv8HUfzq0VfWzdQOeGmoHOMnqBaUQXzqnSntf4cErmuOP9Rs9l0CyizU9Vy7v09mUlv9qKZXpFRJodXSeNz89uyGxuPnnzfqPMByb6cA5s7b01ffvenfg+oJcXTf/mQiE9N66/b4j0goZZIbXU71H2NOXMeGadZzKhLyMru34ummpmrwRgQ+MTKgAAAADAGSadAAAAAABnmHQCAAAAAJxxlunMEs9xFnydNdw+faWqQ5PHa/fnVb3UPKI3njN3GZq8zCB9GCPTJ9FmOKcrF+j1E/vWtc102uxjPMdZj3RGsyT6sTsKOg+6q6qX75nS+ZaeCR0939DL/a7Jw5gMaE/08zolW5e/7hc6almjf0LVZ5eeVHWptE3VM/XLVL1o+p8m+3bqn690TN/PtHWzkKPEesrK6+XNTY51rm3AvH6arAxnvbxd1bPFPanbWwxeUnXTtMztJnKZq/cYTfydA3tcGRlPa6yeU2xKB64+OeohABgxPi0DAAAAAJxh0gkAAAAAcIZJJwAAAADAmXXLdNqMih/L3G2buiLXtqrFOVXXZq9RdbOnswON1lFVd00m9PjiD1fdV1ZfTpvhrFV07mfG1AWT0bRRnH7UVnUQ6exjyeQkCya3GVc0P1OYKRVUfbHJcG6v6NxPzdfPWcHTj3/OtqHr6LxpJ+ipuicr58qOOwj1cVr2+rHXQGCys83O86rO01+VjCbGWd6ejBMtK9toV7dZxxyvZduHc0tRv6e8KtLfp3xPn/dDBZ0J7RVbqu4Hura9g/WzasedL+Npkfl82WY5znH0sa8eGPUQADj2yb9OX86nawAAAACAM0w6AQAAAADOMOkEAAAAADizbplOm60JY1nIE4s/Tn1swfSX3Gp6NNptu8zk2RyOzXDaPpyJx5seoL7JNla8GVV3pZlal72VHFHP07lIP5xTtc31TBV1vmW2qHNBZZPp3KefBmkFOuN5ysQyS+byiuc47XNUKejj9mwfzpbuxZp1zeTJcAKu5c1dpmXPXGc4XfbtzNrWOOVTK6Lf8ObK+r26YDKdlY7OdNo+oNnfl1aWm00n+3YCADBh+E0nAAAAAMAZJp0AAAAAAGeYdAIAAAAAnBmL4FtW1iUIdI7x5OJP8m3fL2evdJ6y8qQnlx5Tdbm4RdVTlV2qtjmgoqf7cgaR7n3Z8M6sbNtkkJrekqr7JuO50NNjrRZ0PWuujtNm/WZf57Nagc679k1vuY630qfO9un0Tf/SSnFW1VFtn6qXTO/VvD8/ydPPb5Def+uxPYy/rN6aeXKTG6lPZ+6xJ/p22hVW728Zmfej0OTzw1DXC9FxVT/d2Wb2pHd+1j+hatuX0+4vn5x9O60h9vF0mfnFxvXmXa3slQBsaHzaBQAAAAA4w6QTAAAAAOAMk04AAAAAgDNDy3Qmc2p601vql6p6vvFk7LH59pXMwKXnXVxm6Oy2s86D75velV5F1SeWHlV1tbRd1TOmL2g8pxR5et++Oc7Fvs4UHV2y2Ry9/hlf9+E80ValvNjU+dKW6Lrp6SxuT1YaeYamX2nR1+ehF+r8R7U4p3euW+LJUlP38Ywkq0eePle18oWymmbnefM/g14/et8ztUtU3egcU7XNNJMBHX95MnLnWn+UfTqzpO1/nPN9ifdm8zq0GcyF7nOqfrpkXoei3x87vQVV94KGqm2m02ZM08Y6aqO+5jD5mqavN4DNh0+vAAAAAABnmHQCAAAAAJxh0gkAAAAAcGbNmc6sjInNmUUmvxLPcL68fvGcX59rX1HUVXXBn1b1zumrVN3o6X5pC41DZrBr79uZPG59nDajaXM6c3Wd14sinWWMTLbRnotG+xeqLpieo+XC1PLXPdPrslzQfTufj07psXd2qLprMheeifG0+vrYFgP9vHRE14HJePZjmc5+1NHLQl1btn+pZ39ekuiXaq+pQXrkDZcd20LzsFkjvfcrxk/ezFtWhjO+fNCc5HrmLEef/bPfO1a+TnyPyujb2e0vqjoI9fub3Z7t82nXz+qVmbbtrO9D2Rs09RAzo3nzy9ic3vZfnhn1EACMGJ9mAQAAAADOMOkEAAAAADjDpBMAAAAA4MyaM52+n56DDDPyLrYXZzzXtm3qCrXM9mg823gqddvdYEnV/cA0lMzMx6zdrMlobint1WOJdC+3IErPKtpjtVlF3/SvLKTUNX+rWlYSvW7H0/t6KTyr6umubn5ZNGNZEn1eW56u4304RUR6nq7jvTlDk/e0+Srbv7Rg8qpWubhN1bWKzqvaa8gKo17qcpdsxhObT1oubtCc5Ohzlg7Z9/LU/PPqeU8RkUh0JrMfpmc+E0Oxf4vA7i+jf3SarFx39rZsBt5uYHgZTzKcOJejT8yNeggAHHtVxnJ+0wkAAAAAcIZJJwAAAADAGSadAAAAAABn1hwm2zn9OlXbDNyJxUfMI9J7DcYzoKeWnlDLdky/Wtczr1V1s3tS1SfNvj2T/0tm5kxu0iyPZ3emq3vUsmpxTo8l0GOxmUub0bTn4fTS43r7pe2q3j6VdYf0Ctv7suPpfKnNFHV9nfHsRHNmfd1DtO+ZPnaiH2/7clrx8xqaDJF9Doom02nVSjrDaZ8X31wDkcniLjSPqLrdPR5/tFpG5hKDSuvDOWrjPLbcEhnP2KLE3xXQtc1s2vfuIGN5YigDZDjzGjjzaR8/wFjzXj9kQDeHcjHIXgnAhsZvOgEAAAAAzjDpBAAAAAA4s+b7BjvBgqqD0LYCOf+2JMVCXT/W3JbU6et9t7rHVF0ozKg6DHUrj8jcFlUszKp6a/2yVcdmW5qcavxM1f3+aVXXK/tUXSlt0WM1t2qWi3oszc5zq45FRKRYqK66zN5i2vfS27XYW1i75nbcvAqS3tYkfguYvX7srWp9036l7OlrpOjp89AzrWrs7d+Jc1O5UNXNzvPnHCcwcXJev57D2z7HS3rLFLs862eyg98UmrZ9uyzf2Cx7K3H2sQPD9eMT27NXAjDRLs1YzqdrAAAAAIAzTDoBAAAAAM4w6QQAAAAAOLPmTOf80k/1f3jpbSV8X2fugkBn7kqxLKPNVNo/XV8uTKl618zrVd0Nl1S90PqFHottiRJr1yIi0lDtMnSmr9l5UdJ4JlvY6ur1mx09lmpZZwm31HQG1OYJ7fbsn7KP/7n5sLZfLbPHnfUn/KfN2KzAtGSx2694Ols77ekMRy+W0+x4+jkr2DY3GT8PsflRz5/W+zIZT5sh7Qc6q7tZuGzbgNU5bUMyaAZ5kMeP+/UUH1/iONPHnv+1MljOMl/G027r/P+mwrBltUAZp5Y8diy0b3HnpmueGfUQAIwYv+kEAAAAADjDpBMAAAAA4AyTTgAAAACAM2vOdHp+OWMNPX+dquxW9WLrWVVvqx9Y/rof6vyd7Y1ZMX01rbniXlXvn36DqkOT3Wl487qOTq66/9nKHrVs0WQsl8xx2fPg+zVVt02PUZvF2VrTXW7OmK13erovaDyR0mibsdhcj800meVT5V2SR8uMJSrq7ZUK+tjVvnyd9/SlkLqvQHTfzVACVRdF9xy1PUhPdnV/1U7vhN6/r9d3KStvNcw+oXZfBdMTNys7i+GwWbGBcm25rw+bv0/P/2Vdf1FaTtLk8cdKVs4x73lNvL9mPcBuP/150ZvOmxfV69u/k5DZlzPje8VGQYZz/VT2rPnjJoANik+cAAAAAABnmHQCAAAAAJxh0gkAAAAAcGZoN9lHJjOy0Dikas/07WzGMnWB6ZvZCxqqnpm6SNVlT+fSZqOdqt4ebTHr67zgyVDn9wq+Pg2twsLKuM28fFtF9xS1Gu3nVW3zUjY72Oro9ctF3etye+0KVb/U/3+qDlQe1vwMIdFL1WS5RD9ntpel1e6f1XX3lKprpW1m+6vngGyfzWo0lbq86+mcb99kPK2u6JywPfZBMnV5M5n2tVEo6J6itm+sXX+YGc96Wb9WhrltrM5thjP9dW57KCfygRlDS76Ow1W+FonsticpCzj0sWY9L/Y9KVanLVsD+5wlH2/fo+wWzv9cjFMfzkFtpGMZtce+vXXUQwDg2K/81/TlfOIEAAAAADjDpBMAAAAA4AyTTgAAAACAM0PLdCayYZ7u62lzaovNwyurmrznRbPXqdpmOLeHOuO5y9MZzlfM6Nzk9orOZRxv6eU/bej84Iv+SraxF+ksoT3OLeVXqLrbW9R1X/eyTOZ8THaxrx+/ED2n6qLtsxjLa9mx9foLqrbPQTGj/6llM5y7Z35F1XVPZzZsjiieM8rKcPY8nS9ty1Lq+raPZz9Kz6fmkdXr0p7XMGyr2jfX99a6zgXPN4+ouh/oYx2EvSYWW3pfE5W5myBD7cuZkP6zQpvhLPr6ei0V9WvPXiP9wOSnA309h7KSQU5mAe3YJuj6GnL/00SWNiNbO0i+OitnnrV8lOxrY5x6Z47TWCbdmc769cIGMJ74TScAAAAAwBkmnQAAAAAAZ5h0AgAAAACcGVqmMy/PX8l8VkpzqetWRfc1tBnO12zVmbk37dQZpMvmdH/Jx0/r7KH/ot5eu7Fr+euX5Gm1LIrS+0PmZXM+7e5xVbeiQNX1ysWqnq3vjW1L/wzBZgVt77Zt9QOpY/PN9i6auVbVFU8/L1PRnNmfHnsY27+fyHsGpk7PIPVEP8c2w5n1+Hz0tkom02n7zIaB7hFaLursbCcja+uSvd5oQzcmBsjz2ee0ZPrAbqu/UtUHoterumBei09VfqrqE80nVN3tr1yvma8ze1xjnC1Myspopi8vmix3sVBL3VsYrpzX0HyfsZnM0LxnJJ6H3BnPSXpezl9WtpoMpzuzpW72SgA2NH7TCQAAAABwhkknAAAAAMAZJp0AAAAAAGdGlun0Y308t9YuVctsPmUq1Jm412zTWZnb9uiM3LXv1LV/1V5VX/Tgz1Td/p7e/0tHty9/fTp6SS070zuqattXM4hsbiHfvD6rt1ur87yq41nIqfIutWxLbZ+qi356n6yi6PNa9WbNct0bsxDpsdUinVkKzfPY9Fb6T3Y83QswS7LnZ3q2thvqXpedns715snQZeVuE9kvX/eoteu3bZ4qIyuG8TdwX860DF7G9eCb66de0e8D/7nwRlX/9mU6P+17euz/7ZDuv/tQ6QVVd4PYe94kR+AS5zU9o2nPs29e5wVTl4szujZZ28jk9Tux8xoFJg+fkeG0ufDszOb4Zjiz+nYOs6+n2366iHv9u5rZKwHY0Ph0CwAAAABwhkknAAAAAMAZJp0AAAAAAGfWnOnM6iWY6P+Xg2+ygmVP90GcjaZUvaeucxivvfKYHsuv36TqYO8rVF0//KKq9z+uswYzpZX9Fbs6B9nqnlR1u6u35XkVUw93Xp/IC3ZWMqc2xzNducA8Wo/NnveSVzPLTcbJ1HZ5ltBbGV9XdKYzzOjTaY8tMJlOm+G0PUp7/TOq9jx97HnkvdbpjbnxDZwFG6Cfpb2+6oXtqr5sVl/r1176rKo7bf347c/qDHyxb97T1vFnlVl5vbz5vzyy+nDajGfBZOYTtfneEKbk0uM9O0XOkenMqK1kxjPDGPVTzXoOB8l4urx+oPWfa4x6CAAcK2cs5zedAAAAAABnmHQCAAAAAJxh0gkAAAAAcGbN4bRa+UJVh5HOo3R6p1U9SJbRZoY6ovMqR5d0zuK5Z7ao+opHn1S1f1rn+Zb+j+6t+dMF3deu0dP5wtSxmmzgevdYjGc8212dbbX9+ixfCqm17bPpZ/R2a2X03rQ5zbhepB/bC3XO1p7XXqjXX2j9QtX9vu7LWSzqa8ReY/1gJRNKn0xMMptvPrqo3z//50/3q3q+p99PDzf04/thR9X6dTxY9m+QHouu2RxkZL4PBTkPPSyYnGaov4f2+iuZN9vvOQxt/+f0zHvu52WMMpzDlJXJJMO5fjq2vTWADYdMJwAAAABgZJh0AgAAAACcYdIJAAAAAHBmzZnOrbVLVd2P2qo+3tP9K7Pms/G+Y/OdZ9Syucp+VS95OmN0eFH38fwfz+js4q//tc5w7p77iaq/cUQfy0Mv6VzH8SCWrfFX76U2Csl+bCvn2fNsRlM/B6HJ7fS9tlmenmUtmj5zVW/a7E/vP5GbjPWl60c6J2avp7wZzqC/YB6vx1L0q7ou6J6k/f587MFZd6UDQ5YrU6fXDU3+r9HV4anv+A+r+qe/uFjVPU+/Fs+I6ePZ0/no+FgHzhI6Fs/sJfJ6dux2sXmvtUfm2VxlYDOf+nmJZzZfHpt9HlcebzOcyff9yclsrnducpDtk/F0p3IB5xLY7PhNJwAAAADAGSadAAAAAABn1nx77enWIVUnb6tKn7/a2yXjtws12s+nrlup6Ns4nw90m5Lg5Jyqj7d0XS1sVfWLTX0b6S+6uoXKGf/U8te2lcd6s+e5Utqh6m7stlJ7C9ZS5yVVJ5+D9D+7P1O9SNVFX9/WHHp6f76nLyd7+1gvWmmDYtsw2Ntnm+bvq9tt2dtpxey7YG6nrZRMyxR7K3Jh5djCwLRr8bndFulGeVuefd23e6dUfTLQr60z/tP68aLfD8PQ3iZqb8O3t3qev7wtLfIuT1s3/+229pZX/X6aaLVkzlN2I64cty1PUIuTcb5FddDrD2t35mdr/rgJYELtzljObzoBAAAAAM4w6QQAAAAAOMOkEwAAAADgzJpvsm+bP8NveV6++/Xj+RebX1lqPWtX13TnDmmKbqnyzJLJhIrO91Uj3S6j6ekMX0tMXjAmkbXJyAENzOSCZqp7VH2msTL2wKzbSbSxydiVaZmypbZP1fOtp1OXF0RnbRMtWkJ9ntMEJj/V7+u2Db6vL4LItBjwTQ6zWpwzY9Pnara2f/nrxfZzeiw242mzW9j01jW3ZrPYoq9l+x7VM9d6L8h7/aZlvzOyhRnZw1Fm5nJnPK1E5jMrZ5nnvE9uhnMjGec86qT58uP7slcCMNE+lLGcT88AAAAAAGeYdAIAAAAAnGHSCQAAAABw5rwbJ+XNcKZvK71/pO3j+UKQ3jvTPr5a3qbqbaVLVB1IL/XxcbZ35bzJHg49/2fO84LJu8aziVnn0T5nNgc5Hcs1iogstPW+bEZUpyxF5mqXqrrg6YynxPp8hpE+575Zd2v9MlUvmmugYzLGheKsqqcqF6g6cS7Mz1umijuXv+6VGmpZI9ETlL6dWEdZeb6Mno5R7p8tZq2fMp6c2UObmcub8Rz08QPtOzPzac9jjnOT8zyOsk8ssBbferGTvRKAiUamEwAAAAAwMkw6AQAAAADOMOkEAAAAADiz5mBmvaL7QzY7L6p6mL0Ls7KJWT1DbR6m0zut6n5VZ0JnzbH5sX6Tocl7VgpbVF0q1FUdBLpn6KDzensuuv3TZo3YctOPr2aOq907oeop02dzunyhqo8tfM+MRV8u9ryeMhnRgumVGc9tztXSe3Z1zXnsh229QuIa0cfe6ur8acMst5nO+HlO7mt4+eVhs8ed7Burx06PUTey8oAjzdjl7fGYZ6gj7h850X0+rQ362hw0pzuQrHNK/9N184Yd1eyVAGxoG/O7HAAAAABgLDDpBAAAAAA4w6QTAAAAAODM2jOd5R2qtplOl5I5tIy5so3WmNxGq6uzjX3T9zO+fpTRWy2w+T/H8/hEr81Yps9mOKcqu1RtM502w2l7Z2Ydi31eigWd2Wh1njPrr2Q8gzC9Z1eQ6H+qM572PIQmT9oJdaYzn9XznuMgjJ27euVitaxQ0DnapdZRVUehyYCO2bFNqmH2i8QmNUC+MOv6G/byNHnzzUN/raS+p5llWYdF5nNoepxKYNPjEycAAAAAwBkmnQAAAAAAZ5h0AgAAAACcWXOmc755xOU4nLKZvI2U/4uiYPnrSmlLyppJpxpPqnpr/bKMfelQhs1Vzlb3qrpcnFF1EDvvDZM1TOZw7HlNv1Rz534niO3FGc9x1svb1TLPK+gH1+y2CNa4sKEznANcMxv6vBij7EeZtW/Xy/Nwn+FcqbO+H0eJofD+6Mpte86OeggARmzjfDIHAAAAAIwdJp0AAAAAAGeYdAIAAAAAnFlzprOf6JM4ufPVSc7/2XzfdG3/8tdFv6KW+YkcpD7OfrCg6tPNQ6n7Tp43nX+xud+5+iWqPrX0RMq213wpjpx9DhKZN3Ms9rwlHp8zF5yW3Q1Mr9VKQedqbeYzngmGO4P0PZxkWce5mTKfVp5rYpLP02ivgaxe02Ysdiib42W6Lv7Tf79q1EMAMGKTM9sCAAAAAEwcJp0AAAAAAGeYdAIAAAAAnFlzkG6SM5wbi34e2r0z5/z63NL7bAZB0yzP6nGmt9ftz6v6VONneu+xLOOM6Qm62NJ50HHKeNoMZrW8S9W18g5VL7aeU3XfnJe66Wfa6ev+ZfZ5sMJYbrOQcZ58r6TqRM/a1EfDlbHOeA6xL6c9rknOJrq2Uc/NaI8r/VpO9i02NX2Nh6b/xf816iEAcKz8id9OXc5MEgAAAADgDJNOAAAAAIAzTDoBAAAAAM6MT3AOa2IzeVn5vzzbypvbTfaf1PmXtIxos3vCbG2cf/6hx9bt6/6m/aCt6iDUtVUu6t6Z3f5ivtHEcpo2s1kwz8nphu69GpFZGgtZWcexynimyJvXI+M5+cb/2lx5T0v03cTIHP7+3KiHAMCxKzOWj/MnfQAAAADAhGPSCQAAAABwhkknAAAAAMAZMp05Jfp6mR6OYvomuu5vOsj2bf/JQXtj5unrmbcn6LCl9Wez5yHR2zLsmkfqOsFsb6H1jBmLuYYS9P7jj/cyfm6UlS/FeBj/nNy5Teq44U5WTnfo14x9L1fv1zkz62TcnQkj3iuAzY7fdAIAAAAAnGHSCQAAAABwhkknAAAAAMCZDZHpTMvniQyWVbR5u0pph6qnKxeoeqH9nKp7/fmhjWVQ9jzNTb1K1WebTznd/3rnNuPs81gt71K1J4Xlr1vdF/WyIed0bSbUytp+1uPzbAujkac/5UbOTdK3c/IMu6fs0DOg5DLHEj1TAfCJFAAAAADgDJNOAAAAAIAzE3l7rb1NtFScVXW5OKPqRuuoqj2/nGNnel8Fv6LqVu+Mqnv9BbOB8Z3X29YdG5t+Hjq9+TWvO2yD3vLKLbMb30a+pTaO22knX97ncNi357rE9Tk8T57dMuohAHDsNRnL+fQKAAAAAHCGSScAAAAAwBkmnQAAAAAAZyYy05llyrTDCEPdLiOrJUa8vUatskctm6vuV/Xp1iGz9+G1axk2mwW0rTc2clbQHpttoZK2LpBlnHNp4yzveSNjN3428rW/kY9tvV2z+8SohwBgxPh0DQAAAABwhkknAAAAAMAZJp0AAAAAAGfWHDi0vTHHKfdme2OeNX05S8Up/QBzLInYRmx5oaB7epa82nmNcXnTdt/GIOc177bH6Tlcb5v52DF8WVlDsmHYqCap7yZG58GnLxr1EAA49uGM5XzyBgAAAAA4w6QTAAAAAOAMk04AAAAAgDNrznTOTR1Q9Xzj56pez4xcVs/F0NS14jZVBxXdn9L27ZRYb81G+3m1qNE262b05YxML8yZ+mVm36dU3Q+WzPbSz2v82LdOX6WWzTeeWnXdtWwbwNps1hzboFlW+m5Ovs167SOf461RjwDAqDHrAAAAAAA4w6QTAAAAAOAMk04AAAAAgDNrznSOd/5vuGNLP9b0Xpg2wzlV26fqWknnS22mEwDWk8s+i2Q+N5+s64m+npvT+698adRDADBi4zyTBAAAAABMOCadAAAAAABnmHQCAAAAAJzxoigiNAMAAAAAcILfdAIAAAAAnGHSCQAAAABwhkknAAAAAMAZJp0AAAAAAGeYdAIAAAAAnGHSCQAAAABwhkknAAAAAMAZJp0AAAAAAGeYdAIAAAAAnPn/jFZhBZ0IVlMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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GAAAAAGDuMegEAAAAACTDoBMAAAAAkAyZTgCYMWQZz0qdZ8Wi89cAdbRYExRxQu91Z7nvtWr7dZzl66JxDrK+feLHGZuT9D22yBxvVJb1fHj317C2asuZZH0+7dZVDuGbTgAAAABAMgw6AQAAAADJMOgEAAAAACRDphMAsKtQtmqSxwawmBq910O5xrbrdob49j/pcwmKOH7s82wlrWdq62wGjt26NrOxoTqwdujW5HhNMsAiWeRryDedAAAAAIBkGHQCAAAAAJJh0AkAAAAASIZMJwAsspYzRDHZq+jae2Q4Lzyt16DzmXZ+Ducrqm9oO+MZm1Vsa91zbe70mUab5xqSOp/aKAMayHgmFTrP0PMUOvfI7RP2e7E1RPmmEwAAAACQDINOAAAAAEAyDDoBAAAAAMnMZaazaZ04Jxswc3WTAFxIWq1R13T9Fvu/0OMiw4nG+LyeD23nKuvrh/bt9Hkt5/185xbbPweeh7nqM5vW4fS+bvZ5su15/k6t5TqfjiY1RZud2zy/KgAAAACAGcegEwAAAACQDINOAAAAAEAy85HpNPO6u50DenlkLZ+yHJj2UG+edXbfNwA0NNlcTmzmM7C8xT6ROp3ABSq2H2lSCzPz3+pmgeXO7oP5U8+5hKT+jRHv/XHD+o5NM5wLY3bqaJ5bi8ejTicAAAAAYFYw6AQAAAAAJMOgEwAAAACQzFxkOnvdi1R7Zeky1c7NfPx+rjOfZTVS7Y3RCdVeHzyl2lVtfZXvFCHjCSBa2jqcenmW2bbuHzOzfmXyHVU1Nm2bj/KcSmT/ONcZzmnmlyJ/xyC4PZBaKKs4xfdTnvX1/wicaym6j7QZuapJnc7GQseLee+3ua+2Tfk7s5jPxehzbTnz6evvm9btjry++aYTAAAAAJAMg04AAAAAQDIMOgEAAAAAycxGptPMCe53L1Ht5f7FenUzVl7pHFbtA5luD2RNtW2GydoYPrOzbrnlXRcArMZZxYichM1w5vmyavc6K6rdyZdUuzB1ikfFhmrbusaVKq15AWcDp5lJox4eFk2r76e4nHvX9JHdzj7VLkud4dwqzpjluo/Ux2+5Hmn08xJbM7JNbZ+rb922c5OReVaV4w0NrUKPs+XXrM2fTnCuPzKdAAAAAIAZwaATAAAAAJAMg04AAAAAQDIzkem08+ttHU5rn6nbeSi7QrUPlC/S7eyQ3oEpvWnrfI7G69v/HpbHzLn29MbUOwMueJOtN+nPVNgM58Glq1V7Odf94WZ5UrXXapl2EZEtp26nJ+ceqsU3zxYpR0ldT0xb0/eTpxamzXDaPrJjcu/Lff07Ivu7R1S7MLXe17Z0HzkY6T60rP0WiK177AplWWNrNjbNOu59X/be3anpHH0u08yXRmY4k34ehF7zlP2xObZza9PscS/QpygAAAAAYNYw6AQAAAAAJDMT02vt9ION4XOq/aJ916r2/uxS1bbTaZekbw6g2wdFl1QZZZuqXf+57FGhfzqbEioAGmtxao6dSmZLotiSUgcr3bZ/etzMX9D795Qf0OVTRKKnILU9bTNmeu8iTwUGZk2wJErkNNIGZaVsn7bUOajal8qLVXuc63vUcVeXlapHskREStm5T7THdqegtl3qQwsfP6XIKatRz8WkvzOLeSzTnlYcuEa8n4um7bxvz/eczuKbTgAAAABAMgw6AQAAAADJMOgEAAAAACQzE5lOO2d4VKy2uvtNGej9Z0N9+KpQ7X53Z37/1viM3rbU2062VAIA+JWlzh8NSt2fVrnubzcL/XP/henjAkeLOjdH6jIkMftfpJIobas/N2Rf0YZWs2Kh3KL/mrXrl5ludyt9q2xz805mVOXeY0ucNBWb4awvD+VstdQlUprlUUPlXpr1982yubFZ19D2sa+DZ//B7HUzfMoCAAAAAJJh0AkAAAAASIZBJwAAAAAgmdnIdBq2bufq5ndVe2m/rst5Mjc1lypdW3No6nBuVDrDNDYZpnqOc1ysqWVkOPeuEl3EL8s6tYXNcgzefbewfyDKFPOANlsyKnTNuDPDp1R7PdfdflHo2sOjQtegK01/PNk6bzi3UEan6fUYUd/UmuPrw/lc4fN+dsTUFgxc/7ZPG5rfETmdH9N7y2yf6c+9T7cWZroakbE1R2Nzk6HnTe+v3T4w7tizLvDcRGT07W/eNO0T5+lZBAAAAADMGQadAAAAAIBkGHQCAAAAAJKZyUynHQvnJoM0MHXlys5ItTekp9pO5qnUmaWN0QndHj5T21bve5EzHjbPYsU+9n7vsGpvjY7v7CvTr1FoXnmWL6t217RH4+fNFnF/T2kzbwqk5a/JVZS6LvFwpPNLTibP1qgzeadwDbB5lToXOUltn2tEXTdrjjOfi/z5PnMC/VDwOvIu1/uyvxNSVjrHPhydVu1T5lzyXN+vjAv9OyFFqfenj9205qJ/fTdnGdjciMkq2sfSds4xvL90fXJsTVDfuU7/dw/a66+dHrFhHc95+lQFAAAAAMwZBp0AAAAAgGQYdAIAAAAAktlzpnOS9asyUxNpuXeJahdmfv6WqaVplWZ+s52/X89wiugc58zVf3TmnRe7rLgX+rEs9S7X+zbLt0Y2N6kt9/X2ve5+fbTa6zYe61xuKONpMxmWPXfLzr8fmWumKM7U1o3LmwLT5OZHTEbT5jsCmZ/p51F8Qn8n9Z379PJCsdrMF7VB58QaXh+hLB8WU6OM5h6WR7D3E879gMlo2jqd9t7IvT9p85r2P+5QzrJZf27zo/7lsXnU+PX3zo4jYp8H97E20bR2aug1Dh2vCXMu9nmJfF5n51MWAAAAALBwGHQCAAAAAJJh0AkAAAAASGbPmU5fzUURkUxM9rGBytRQWt18whwrMMc9MJ/Zzr/31uKcdN4klNk059rvHdn+dyfvq2Vl6c9B2vqnS72LVLsoh/7tzZz5fvegPlXzOiz3Lt7+t64k6GY8nb+HVPpcilIv37d8rffY9lxt3nR9sLN+WeoaXDOX68UciP17XpNrKtTftbu/tJr+HXT37VPnHlOKP/e26+ft/Dttfmi2hGpXI0bTPrFB7dgAm/crKn2HUgZzkjbjGXNuNq+359vycx6rWS3NUA3QpjVE47KNcbUw/ccKPQ+x5+Z7zeNrp8Zdy6HrzxVxDdh8csMMZ4MzAQAAAAAgDoNOAAAAAEAyDDoBAAAAAMnsefL4/qUrVNvmBzeHT6l2o1qHZt2y2Nj7tuchZc3R8MHt3G+d2ex1dZbWPu/dzr5dl4Xm33fyJf+pmb9J1DOZIuH5+nb7es7S7mucL3vPxe7b5lFthtMe2+Yyu+b6XFnaqfO5uaXzyqmvPyyCWf773Sxn7po9b03ySu3uO16z+nnNslGxv4tQzyTZ+nWLnPGc6r3BgunYz3jn/kFnyUpTK9N/zYeuuVDu0X8rnLJ2cdMM5yTz3qFzDfUrzj1jg74i1KeVgdruwf3Zxxq4BkoZq1ac2Lxy0wynZ3vncZvfwDE599g+cpbvlAAAAAAAc45BJwAAAAAgGQadAAAAAIBk9jyZvJfvU+1O/zLv+jbj6dbvqWXs5rnuYaCupjPfOTLDudzX2Udbb7I+vz+UO6jnP0VEcvOa2IxnYY5VBObIV1ngdawttnP5bY1PZ9+Rc+TtY+kEHkv9uckznY0tZF21yfnAiq9nhvPRJL8Um2u0fXVQ5Gsek29qWt/OzbiHPvptBs7m61oUep55Ly2EbmdFte01Oi50bUx7P+G/BkOZOFv32+bU/NdYm/nupvtyt29vf8E+0dnY388En1fndQnUnI64D4x9nu25dE0GOTe/A2Lr2Fflzm9/NM3dNq/ref6/oePc3trX2Fk/7rHxTScAAAAAIBkGnQAAAACAZBh0AgAAAACS2XOm05mTbOYcL/Uu0osrOx9ft4ejnVqIc5WRi8xkFqXOKZSlzgeGMpzOHHnPXHE757zf2a/aHbM8k45p62P1TAajFP1Yx3ZOe6BW5kg2d103pCz1se32NuvaDdQgtZmO6CwDoNjMnH/tpJnPhpmLdqWujWmf9923tzlGN+cYyEmG8kqRr7m3vwzlbAKc/qzFHFn42o2tOYcLgb0/cWpt2/rsJsNZVU2u4WZ9YvPamJPTKMsYzIJH5uKN0H2fUx/Y9pHBrGMDTp1O3XZrw5vfbylrfWRsxtg+Tidzv3vd5L3x1eVseC1H3svM7jsHAAAAADD3GHQCAAAAAJJh0AkAAAAASGbPmc7SZOBKM0fYZu563f1mud5+qFefG7YOZ7erM5g2k2mtbT6p2jaLGJszquckljov0ueW2VqVOlOxJAdUOzZnWXT0i1iYzGdh8q71x2JrHNnrycpy/TzY7S2bN3WyCNRVRItCtdPcTJCY5XaPnutxpjKbVupzOf+MUahvdfpiZ986A2/ZjI9lPwOd5bXP2MqWd46sZxcSyt/b/cf1jw37UvriheTUNTTvl9A11uwzO9QvxdXBDfFloMP50GaZ6ND+2+wjbU7X2V/gs6k094ih56peuzWYkQ/kHp37XbM/p26sWV6Uu9eNda9V/7mEr7fQNdFinxm6v3B+34dMJwAAAABgRjDoBAAAAAAkw6ATAAAAAJDMnjOdncxf97DK7Xxnf+ZOzUG288JnKdPhzF/Wc9DtHPeOqQ95ZvM7qt01uct+76A+nJM91DmibtbX7XyltkwfOzcZpH2ij32gOqSPFaiDVWQ6g2FzmKNMv+br2cld95Xl+tzs3H7bLkL1nex8+8p//eW5rVm689hnuQYXZlNu3pdWJSYTX9l8ny9TFJnhnKPrN/xei8xwepY7eaTcn09yl/s/LkMZozKz14D5YYPa6k69Oifb5D0V9zM08Jkaqo8XhzqdcDl9nr1EZ/g6aXJP0P7vRzSrL2z7yHq/Fsps2j4x1B+HM516fzb37st8Ov2rvb7s8x6ZY3fqxBZ7z+Rboc+O8P1ASJPMcuSxGl6/83N3AgAAAACYOww6AQAAAADJ7Hl67ZHspaq9np/S7eyEau/+Y8Jn7Vu6evvfm8Nn1LJMQnOHJidUImWpp6eo2mnFS2b90JSt0HTafkeXOcllZ/pDqCTKwVKfy4tEl7U52NHH6pnpCRtmesEZM4V1WJkp2OZPGpvZquxmZMsNBH9iuhlb4qc+DSQ81cFfOgEXnn39w6ptp/Jsjc7oDUr9c+x2qvqFW8KnYakCz/Qu2/faKEQnN/1fvk+fmelfQ9PmxqZ/HMum3t4TZ3DKq9iPxMo246bPTlbkdNuGP8mfbF9oxN4rWeNCvz9Kc29UeaYvWllwKvze93V2/dB102bJlbhrNCZiIOL2W/V+LzSd1vaZdt+dwPRcJzZlSzFmZnm591J5uRnO2Ps4e66xfWKTPtQ39XZPx2693/LsL3F8h286AQAAAADJMOgEAAAAACTDoBMAAAAAkMyeM52HK51Z6ogpc2KjE87PD+u52oPhs9v/bjvDWZnAi7P/iJyH3bYo1lR7bXPD7EvP3e73LlXtXndFtW2G02Y8fRlOEV0mZdlkOA+Xl6n2JbnOcF66pPd1xYp+Xi4yVSBOb+lz++663v7Zgc6p2fn6W9lOZsOWc8ka5iSDP4cdUJ9z7+TzejoLu7l1XLWLYt0ce3YyyZiMg/2rVHur1P2EzegV5ufYU2eYZ0Vs6YHoEimecgA2j9Tt6MzmUkeXr1rOdQbNlpyyn4FbJrM5rPQ1sCH6dw9GpV5flQPImmV8gxnPFjNCodfUPVbDjKdzAM/2ZDhnxmXLr1Btm3k+PfquahdD+8sg9r2+92OHr8Gmdu+H7LHd94u/D7P506Yl3ZyMZ+3ey2Y4u7ntI03Jv0zfz/Yyk4M393Uj0feIo0r3gfZzc0t0u/5chsrihcTmekPqr4t9jptmOl0pr+fYfcX1sXzTCQAAAABIhkEnAAAAACAZBp0AAAAAgGT2nOnMTU6tG5nBs3Oa67nLphm4vKPnle9fulK1B6OTqr01el61o45v5udXpr5kz9Tl7Pd0TiiWzTraOfI9Wd7+t81wXprrjOc1+3UG6RUX6WO96pCuJXi1aX/7ef3YHu7p+fvZKd0uBzqoVtZe88LUYyrN8+jUawrlGgKZuM0t/ZqXJlNXv2byXD9Ptlarvb7WTZ1ZMp4XHqcGmLlmsJt280k2E1+vExfKcB7Kr1bta8prVfuKvv6c6eb6fX3CZNCelRf0cvORuSbPqXb9MzKUuSzFfJ4G1m+3zltsLte/vHL67shzpRbnXLi8fLFqj801POic1u1M37eVgbq4MUI1dkPLQ/vzL/Nnmt33Q8O6iLZPtLXia22b4dzXvUi1D2T6HvOyUv+WwcUm02lrvZ8qdKbzeKZz7ifzp1Xbfo76+shS9D1jbC3jWN7fDQn9FoERfy2HcvEt1om150adTgAAAADArGLQCQAAAABIhkEnAAAAACCZPWc6VzNdL+d0pjNytiaZrctZFLbmUnvKQtfK3DB1FLv5skyKr07c2eVxWdjCPI9LJqe5v7po+98XmZpJV5kM56su1pPcb7pcv4Y3/Add9yi/VtervPjTT6l29n90Bqqo9Hz+zbF+3gfjnWtgLdM182zGs2tq6tmadqHn0WaI+12d3/JmMMzfYrq5Llhq86U242mtDfTzVpWDXdbEvFrd+p5q2+tvXOjrtyx1f+hmOsilnVMgT+L0v7W8Us/klfbnuobydeV1qv3aS3Vd45su1a/hUkfnjf7ncd03f/WE7j83ZFW1h7nOzBflTv9bZqau25TruPpq0IXYLFX4Wo+s4+lD3nNmPJfrPtKp4TgydWsT9oHtZpz9+3PvNeLyd/YeMpZbw3z32vC2j9yX6d/xsDn3Hz6k76v+zcW6T1zKdcf1rdN6/W++oO9RC9PvjXN9T1q/Dwz1kcHcrv0dhtjaxplt7uzPec1M263bGZfRD/eJDa7vxH0k33QCAAAAAJJh0AkAAAAASIZBJwAAAAAgmT1PFn8he1a1bYZzWJw2bZ1XmaRirLMzhcmjtlk30e7LrQGqx/VLvUOqbWtC5s48cz1Hvis663ig2skRHezrfV29os/t3x4+pdo3vEXnyrLbX6/a5SF9rof6X1PtGz+us4pPber5/k8u6ezAC+OdbGRX9LnqmfsuOz+/LHUG1M6BD9Xdsu1OLUNq86RuLUB97nZ9m8M9uE8/L2c2v6vPlYzn3LM5cnvN2LqwbuZiXrNmcRnLWKH8oPO+NnmlTi2PbfNKB6tLVPvFK3r5rVfqz7B/95/0bwdUm/p9vvFf9Mfpo6u6n1ga6v13TD8Sm/dPyX1ea23bd2Y6827ZrJTNM5fBzCcWwbHBI97l4dy7zcHVl/k/72M1rdvZRPS5B2pCOm0n977TD9l7m5XqRaptaxXffJl+jf79fzS/+3FA7+/a/6bvKU8OdQ7+mXWdo7d9ZP38sizud2KcDKfNZAbqesbUdrXH6ph7RHtfX5hrvXDuCW0fuft74VzrO2Ku39D1GPle4JtOAAAAAEAyDDoBAAAAAMkw6AQAAAAAJLPnTOeZQmc6x6Wpn1Osq7ad77wx1DWa2sxVBrU4/74yxYCWekdUe1zq3I/lzcrsgc1C5rXncbmjn9PDff24L79IZ5Tyq3UdueKqq/TBOubyWPJndzZL/2OpP3elmdMeK5SpCM2/t3WUOrV2x9QQtbmCXqazWbbm0rAyz3MgUzHlEnxoQRmZyyW3dm5Ns1gxOqYv7eW6/+znpo8q9Tu1eEZn0I4P9f42xvo1HpkMUlnanM7e+0Tn+om+nuJqBao1bd9p8kqdjv6cKEvdP+okrEhV2nOJrVmHeTDYej68Uo39XI25DuLrHPo/o624fqpZ3Vm3zmZcH1mvVRxc1+Y9zbH65h7zkr7u0zqvNDXLD+nM5uH/8YxeP9PLLZt9rD8Xsf2CU4czsH3sPabvWLZPtH3maOwfP5Vi86sNM8a+65c6nQAAAACAecWgEwAAAACQDINOAAAAAEAye57svbF1QrXXB99R7VBGc6IZzinav3Sld3lsLTZbd24oOjO6lu3MBX9+uKyWPTPQ88YfeUbnT1f+u66pdKjzBX3w/Tq7uPZfv63aX/+Orj/5vQ39Gr8w1Omd1Vq9VFubzSoDdTctO4c+VGfL1qMqavmRrNJ55a7NeJosmM18OnlV86ed2EwGZl987oyc2rk4z6OtpRZ47zgZoNr+bP3cQa5zNM+auptffO4i1V79hO57n9m8VLW/ckKf27GxznZv5LqWtc3/1zNspTlXJ99m+7tgrcu4jFtlg+aej2+3b9W3FVlua9aax2brMXrOck8mmAvG3tlaxfG1NX3XcCg3aZcH+pEGeb697D+GU18ykPEM/Z6F21fs9Jn2vmuU69fs1Jbuhx4+petqXvPgMdVe7uv61f/43NWqfWKg++tBpvvkwqnHni73HluX0/dZFbp+ura2sRmJOTVrnTrfkdqsy9lwfXpnAAAAAEAyDDoBAAAAAMkw6AQAAAAAJLP3TOfgu6p9oWQ0bV3Ofk/XtlzqHTLr++fb2znpTt1Ok/m0GaVRped6j2Qnf3jG1E594ozOGnZNfcnq/+j59T/8n/X8+/6Snr//tSd1hvMfn9cZ0ifP6HM9ZmoPrdUyTWMxdV4rnW/aKtdU2+aAnJpLxmB0Um9vckMHlvVjr78OI6dunJbn+jVy626aGksmj7V/Wed+z2zq91YVWfMRs4CM5jSEcjb1996o1H3navacan+n0vmk4pju6//3Kd3fbZo6nE+NdGbzufxp1R6Uq6o9Njmdothp29qWzetyxto942n70sJmOJ3PtMDftu1yJ9TZrM6h/1i8bycl+LsM5nMyTijDef51ac9H/bG0XXs4mGsPZDjtrbvOvev7sg3RfdozhW7/r+cuVu2nNq5QbVP6WL6zpu8R/99I125dy3S7MPe0Mbl3R+vv/d37yCrTy2xG09bptK+pXV6Ye8Lo3LvvsYeuTye72ux65ptOAAAAAEAyDDoBAAAAAMkw6AQAAAAAJLPnTCfOLbYmkp27beelF5W/5pmtAVlkO/PY10RnKJ8Z2JqgOtO5Ver2/127RnyeWNcT9J9e1+fyvS2dwzyZ6/n59fzpsNLr2ryVW6dIz9e3z/vWSNfEG491ptPWO13d0DVHu52dbO5yX2e5LKcOXeWvverW0Yqr1Yo50DT30HJuYmEFnicn01nLRtp80DDTGcsTua49PTD96XcHur8sRPfd6/kpvX2l80/DwmQ6nT5uVPu3zXQG6nRG1+WMtbM/e25jmzcy55Ln5jPM5FUnmqskwzlHYrK8oVqVzc4kNpfZdo6zCfc3J/R9my/3vpnp+6jj5vcsNsb6Pu6JkwdUOzevy6rpI89k+ndENkt9PHs+9b4jlHt3+8i4zHB87e3djzUqNnZZ86xuR3+2ONePk8mMzbk7xeJ3F/u4I9efnXcGAAAAAGDhMOgEAAAAACTDoBMAAAAAkAyZzoacfEugHk+ovKnNHXUzvb2t/zOU2lxxs+/SVPMpB7q9Ouqr9nd6+nKwNZZODnWG6aSZb386O6XaNvNUr81p63AOxzrvFMpw2lyly//3FJvxLIqd81nbPKWWLfevUu3+sq7nF6oZGn/umHtNs2NtZs9mKF/UNie349TI3emDClNfckt0H2RrKG/lOofj9MXmWDa34+TUTdtXgy70uGIznE3ySVZmP2fM8yqZPXf9OROsIwiISJNccpvX+/nsz5/pjKuzGbpnzExeNTMZ6nB91FqdTpt7N+va5YOOzq2fzHRteGts929z7p4Mpz1+qI9suw5nzPpuhtiOE/w5+OYm+JkfeX+xuHcjAAAAAICpY9AJAAAAAEiGQScAAAAAIBnCZQG2puLWyNSeHL3g3X556QrVtvV4LJsrsnU8x5WeCz6SnTn0Nu85zkwNUJOxXC2WTXvFe25nRGeWNnKdidoSPR9/JPpcN4udGkyhDKdVFDo3VGZ6/dD2cfRr7tT+M6+JZa8Zp25dqKbYns4R2IVT02uWa4DaLIu/7qbNN9k8Uym79wM2j+RkC027MH3KVqgmqK25bLKO9vh2ucp0OnU6p5fhDO3bZjydXK3NfAb2B+zNzvsxVC891M/Ea7I//7ZNa3zG1orP6jUfzak5mcpc92HjSr+37b675jdNCns/a3Pu9j7PrF/vI517vmCdztS1jHd/3ZzPCtGPsyr9r1FI/PXv21ng2A3761m6+wAAAAAALBgGnQAAAACAZJheGxL4KrnXu0S1V5Yua3Y4czw7fcF+S75Zm2ZqS3EsZQf1vjJ/OZZ10VNe7Vf8dnrupll/aMqg2KkR9RICdjpAJ9M/qz8c6Z/iHmw9LZqewpqZeXd2imubJSjsa2KnRDtlcgw7LYTpZWjVAl9Pjabb2l/Ut6U9nOmy/rJNlp2KFppu6/YD9em1szOd1j1+uyUfgHOz15nvGg9NUQ1NtfRzpy9GbR4ldG6hfihUgsjZf61p+8TQtrbPs+dm+0Dn/rbwl0jxxqZaL70Uc73FCU6ZDkVgUvbvsftuOP2bbzoBAAAAAMkw6AQAAAAAJMOgEwAAAACQDJnOSDYraHOUlp2zHvqZ/jzX+8trJVFEzvET1bWfu+5kOls4MPPz80zvy+qY5TbzaY0rnRF1y7vsvtzJSwX//uHPcDoSzoF3ztWcSuxjy3OdZy3KwS5rYl5UpvBN8HqdpAmWUHHLa4SOFXrfnn9JFaeUh6e8iohIVer+NDN9kBXKL/kynO76k8xoNhMqoTLxv2036Pvt+xYpTS4z13bmOVSiot6O7/PiHkvTvGB9f8E+MfLY9v43tD+nzEnENTL938aov+b6Pt69Z8z9yy2zfm6Gbu7vD+jNo56b4PVq86Zx5Qr5phMAAAAAkAyDTgAAAABAMgw6AQAAAADJkOkMsBmPfvci1V7qHVJtO4d9Y/iMapelneOu979v6RrV7nb2Bc5vZ662zXvmZj68fbHtfPlxuaHbgXqTbmbJn2mqt0Nz+eeZnZ8fyn/sX7pctdfN/orxqmC+zFSGM6nEeaTI43kznjZDGdg29tyC+aRgfd7d+8D4vNI0+9P5yXBilrRXJ9F+Btv3evP3h39/8f1ajEA94cD7z9YyrvdT7vNktrX3NrE1e+1yp3hyRIYz+BrG1jZuVh/Vt2+7rX3e7G/DhO4ZbfbWnpn72eN5rNGZYP/qIXzTCQAAAABIhkEnAAAAACAZBp0AAAAAgGTIdEYqS52btHOlB1vPm/V1rUpb660SXQsuVEvTqqpi12V2FvcokMG0NUiLQs8bt+u756L3PyrWd10emvNemOd5mpz6foHnwT6W3KnRpN9241LvL5jxLNZqJ0eeCQ21WrezWcYzxM3VhHI79Y3tQpvBNMeqIjOdDTNEcc/F4rzv3fySXS5meeB6jcqFUZdzemL7md3XD/9GRGz+L5T3FtP2PZamuXX//qJrlHoyeTbv6ezLqaMZmQcM7S+wflxWtlnu1vebJOfi21/oerTLncxnboZqZneF3d7pQ/X2+vcFzj9Xe3bncdc333QCAAAAAJJh0AkAAAAASIZBJwAAAAAgGTKdATbnOC5Oq/bGlq0HZedmh4ra6O23xmf0UjOX29YBLcududn93kF97Mi52aGcZSg3URQ6h7k5/J45QkfOl30dJplldOuRjndZc5ftA/P1Lbt/m/E8szmondvsZF+xIKaY8QyJz4DGbOvvy8NiM51x28+O0Gtqs1B6aehzZZLs5zMZz5Ti3n/hvqLc5d/hfQVzwkbsNZu2TqcW26/43o/RfV7D927se79J7j11P1Pfv72+nOvRuafU93Flpu8BO1nfe2y3Lm2Dx9ogI78XfNMJAAAAAEiGQScAAAAAIBkGnQAAAACAZMh0hjg5R11HczzWGU8nwxmYH22ziqOxrvNpjYtVvbtazUibBcyzuJd3qXdItQdbJ73rOxlSJ8egH1s43+oxzdyPeVyh+fP2ebO53H7X/7zZ/duc7zQzULgAzXDGMyTmveLmcNId66zFfB+HahraTFnjLCx1OedD435kku+XdP1U+5nmuDyrpbOI7WZXq8jfv0hp8s97/VjmeXDqZtpamWYHWVxt4vg6tQ1EPo980wkAAAAASIZBJwAAAAAgGQadAAAAAIBkyHTGsnmVUE4xMo9iM6Ojsc4H2uPV17frhvnngW+N/PlSZ29m3nqjDOcU2fMuyoFp65pK3c6yXm5qLmWV/tuO3b6T6xpMo/GGag+GzwbOGNNms2Kh+n/z+t4QEd1nNa5HlzqntffzS5+VXswMZ3z+jbqcFySnr/DXd40RyhGHc457zz3ujS/fF1uvNFaT5zFuX7P0+xLpa6dGZMeDz0tc1tWpDR/stkI1SiNet5ZrzvJNJwAAAAAgGQadAAAAAIBkGHQCAAAAAJLJqsqdxX0uebYcXglzLTZ3FsrD2Bqk06y1GcWp51Sodp7vU+19/SOqbTOeznx8Y2uk63AOR8f9p5cwD1hWg/BKcGTZUrPt5znj6dNyHgR7lLKvjX5NTR3iyO2j80iex940w1lVw0bbX8jy/ID5PynvB1LXAI25pqfdB4byfTvC701/DtfN1rb72P1ZyabZ8kDGOFhntt4O5Usn+3M6/j60vWy1iEhZbniXT/vdAAAAAABYYAw6AQAAAADJMOgEAAAAACRDnU5si82VNa5ROqucXILOppblmmqPC51V6XcPenefm1qsQzmt2t2O2d5kB4rxqnf/mD8LVcezLtQHkPk8PzPdt4bybrEZT5sbsxn5vT8X1OmcpshrttW+Izbv1+b+muVF80D+z9ZXt++PUM6yvjxcX7LdWpizVOfTZR5L0s+qprn3uLqfMfnTtvGJDwAAAABIhkEnAAAAACAZBp0AAAAAgGTIdAJWsE7nimr3ezqDaefj9zJd17Nrtu/t08uHhc5s2tzDoJbxGI1PiV55ljMSF65QlmxhMpyxZul6nXa+dJaei1aZjFre1+1AZq0st3Q7VHew/jou7HN6AbDvR/ta1tvOe7dZNjG2dmGb2UQncxk8tj/D6a4fU7MxTvqMpu9cm9UPTst/rrYP7OS61nue6+X2eR4XujamWxve85oH648GRL7mfNMJAAAAAEiGQScAAAAAIBmm1wKWmS6Q53r664Hlq1W739mv2rbEyr78YtW2021H1aZql9VItYfFGdVe7u3sb+SUT2E62SyiNMMciC3REDstaZ6nekZMubLTA52pY5meXuvEE8zfwocjXVKqKgPTa+kD51Po/TTR6e/TvIbs9R23ta8kyrm1WXqm7dI01s7+mpZnCW0fu77eVrfttk4faSIHy319z7ive1i1x6WeTru+9Zxqj8a6rJ83kpB4Oq3FN50AAAAAgGQYdAIAAAAAkmHQCQAAAABIhkwnLgyBPJbO3OkSKf3OAdPWGU6rn+v1l7MXqXZXevrYmT6XTr6k2nlpM5/257Ax7yihMgdCWZZ5zmw2Evjbtel7bf+21NGZTmtcDFS7KAe7rIm50ub7pWnZh4kKlTSJWz92/3HlYJqWJYnlz4T6cpWhDKa7rX95Zj6CbdbcV5YkMzl2Z99i+8S+aes+csX8Lsgw0/eQg1zn3keZznxK5Xvsk/3cmuV3JgAAAABgzjHoBAAAAAAkw6ATAAAAAJAMmU4sJqdWlc5p5mbOfL+zk7uszBz3bkfX1bQ1j7qZ3lfHZjbN+mPRdTgL07Zs3c/sgs2OXTjqGU/ynZi6Fuu6VSYzP6629HLTV9u6xbY/xZxoO3c5U5+DMecSylS2WVfzXGIynDHbtrG+Xz23mbLOpohIbuoJu8sDdZt9Audi86Lr5QnVHtnf+Shb/J2PwONo+vsTfNMJAAAAAEiGQScAAAAAIBkGnQAAAACAZMh0YjGZeel5rnOZK0uXq3Y9tzkcnVLLNreOq7bNBnR7OtM5Fl1HLjdvM5tJGpZrernNIpi/DW1uPV9rzVKuBQA0J9Ne2Dqbp/T6pv8bm7qcFXWK51MoU9ckoxm774nW8Yw9VtPMpxH12BveT4Rew+DzHnrsO8ttPxFi+w1bS9PWRy1FZ83t+t5jif8ezj03vf5ovK7aRTnU52YynHa5+zpEXENNsqp7wDedAAAAAIBkGHQCAAAAAJJh0AkAAAAASIZMJy4Qel76QOUitcLJEOn58uuDp1TbZjz393RedCS6plJh6s7ZOnSnN5/0nk9R6AwoAEyPqcNZ2eUmf2QyQqWp02nZLJab5TLtmarfiG1t5yqbbB99LqGsoWf/oTKGzrlErm/Zx5Iyvxp6HqPfi6H1m7y3be12mw23y+32/oxn/T7Q9lGhjKfNrds6nVlhzk1sHzr2thtxXlNdR9nW7Qzhm04AAAAAQDIMOgEAAAAAyTDoBAAAAAAkk1WVO3P5XPJsOfW5ALMps3P99Zz2PF8ybfNeMfP79y9fqdqDrZOqvTU6oTc3c+azYOjj/JWVraGHvciypfBKU5LyegEc0fm4kPPPbMbmjYL7M/l+7J1zD5mybmdI6jqdvnOfaI3QvaifT+C9lriG42RzulbK12X3eqMi/nzoXjgZUifT2eLrFHiey3LDu/msXf0AAAAAgAXCoBMAAAAAkAyDTgAAAABAMtTpxGIIZA0q0TlMd059Z9dtbdtm5KpS128al/7cz5lNW3fOX6eOTB6aCGWCJ5kZblsos7coj2WeHke4jmDL+7Ort5jjnKvnfcY5/UybecDYfGhsrcuQNjOcbZ9baP++SzyU4XTOJVDPNPo19z3WtPV43Vyl/7G59YN97LZbpt30+8CIOrJNRe6LbzoBAAAAAMkw6AQAAAAAJMOgEwAAAACQDJlOLAYzrzzvrKj2vv4R1R6MdG3M8XinnWU9775DQjmgqqQWJqYnlHlru7bhNC3KY5l47jZlPb6G+5rka7oo189C8GUZQ1nDptdzkxqQjetHRu4v9nit1tpsmuH0q+csnQxly/VPbe3MPOurtq2FWcpWbVmo3mlo6BVZL9WKWT/4eyjtfvbwTScAAAAAIBkGnQAAAACAZBh0AgAAAACSIdOJheDMOzdz5pe7h1S7k+v5+eu1eezj4ozeF7XaAEzRxDOcM4Rc5WJofA37cmptX7+hLGJMLc2WM8zB5zH0XHjrTzbMZLac4XTrWUYcOzLrauty2ntIe89YVXp5Vdb3b2qxh+qXWm0/j779BY7V9mfP7H7SAAAAAADmHoNOAAAAAEAyDDoBAAAAAMmQ6cQFoTA1lbKso9p5fb5+MYkzAoC9mXidzgkis3mBmmR9wMiMXPDYKTPQNluYuB5lMF/oE5s9DOUsg1nZiH1HLre1NctyS7X1HaR/e6dOZ0jb6zeoS5v6s4ZvOgEAAAAAyTDoBAAAAAAkw6ATAAAAAJAMmU4sBDvvvBivqvba4GnV3r98RfJzAoAUWs/dtF5fD5hhgcxb8P3UJCPXsJ5k6znKlPuKzZ+2mVeNfp7M5uZ3QIrC/i5Ig3NN/BpWlf5hEu/1HMoQNzwXi286AQAAAADJMOgEAAAAACSTVVW1p98rz7Pl1OcCpONMhbDt2hSBBZpqFjsNr6wGKU9nYWXZ0rRPAdg2SyVVFqkkSlUNp30Kcyt0Dxm6TqKu6abTNJveA9SPn/p+os3ps20fK/Q6xJaD8R2vybatb++5vzyffScUukds+x6SbzoBAAAAAMkw6AQAAAAAJMOgEwAAAACQDCVTcGFw5swvTm7TZ//yi1V7Y/iUXmGB8qsAzmq9pMqCCuWXMDmtXqNtlx2JPXytZEXy917iEhe+bcM5XP/2jc4tttRM29eA93dCmu07+LxmHbPB+R+vaR8Yuz7fdAIAAAAAkmHQCQAAAABIhkEnAAAAACAZMp3AAiPDCSClec5BzvO5I0LijGeT6yi6Pmng3J08d5s5ydC5WbHPa0ytzNC+J5zxzGr7q6rdl51dPvbvq2EOuEnN29iMZ+y58k0nAAAAACAZBp0AAAAAgGQYdAIAAAAAkiHTCSwyMpwAjDZzOrNWA9T32KjLOUUxeb1zre/5LIu+nlv+XEz6HojNcM7Y+9Er8nWIeW83PZZ7AF9dTpGqtn+b4Yzed8O8arA+aouo0wkAAAAAmBkMOgEAAAAAyTDoBAAAAAAkQ6YTAABsa5KdaruuW5u5SzKcUxTKbMauX8/QNc3qOnUUC704slZmzLpNM5iNzi2483ZrWTY9Xqt51djcZWh3an9233EZzWAfmnUCZxN5fN+xAtdXVB1Y4ZtOAAAAAEBCDDoBAAAAAMkw6AQAAAAAJJNVVUXIAQAAAACQBN90AgAAAACSYdAJAAAAAEiGQScAAAAAIBkGnQAAAACAZBh0AgAAAACSYdAJAAAAAEiGQScAAAAAIBkGnQAAAACAZBh0AgAAAACS+f//AnNgQdTOtwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_samples(model, val_loader_swin, n=40, vmax_lr=1, vmax_hr = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b546449d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torchtest",
+   "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": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Difflense_Aleksandr_Duplinskii/Baseline_SR_models/readme.txt b/Difflense_Aleksandr_Duplinskii/Baseline_SR_models/readme.txt
new file mode 100644
index 0000000..a22d303
--- /dev/null
+++ b/Difflense_Aleksandr_Duplinskii/Baseline_SR_models/readme.txt
@@ -0,0 +1 @@
+Baseline models for superresolution
diff --git a/Difflense_Aleksandr_Duplinskii/Baseline_SR_models/satGAN.ipynb b/Difflense_Aleksandr_Duplinskii/Baseline_SR_models/satGAN.ipynb
new file mode 100644
index 0000000..d3fb945
--- /dev/null
+++ b/Difflense_Aleksandr_Duplinskii/Baseline_SR_models/satGAN.ipynb
@@ -0,0 +1,1032 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fc086dce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# --------- small residual block (no norm; astronomy likes no BN) ----------\n",
+    "class ResBlock(nn.Module):\n",
+    "    def __init__(self, ch, bottleneck=0):\n",
+    "        super().__init__()\n",
+    "        mid = ch if bottleneck <= 0 else bottleneck\n",
+    "        self.conv1 = nn.Conv2d(ch, mid, 3, padding=1)\n",
+    "        self.act   = nn.LeakyReLU(0.2, inplace=True)\n",
+    "        self.conv2 = nn.Conv2d(mid, ch, 3, padding=1)\n",
+    "    def forward(self, x):\n",
+    "        y = self.conv2(self.act(self.conv1(x)))\n",
+    "        return x + y * 0.2   # small residual scaling for stability\n",
+    "\n",
+    "# --------- Generator: UNetish, same H×W in/out, 1→1 channels ----------\n",
+    "class SatGANGenerator(nn.Module):\n",
+    "    def __init__(self, in_ch=1, ch=64, depth=3, res_per_stage=2):\n",
+    "        super().__init__()\n",
+    "        self.head = nn.Conv2d(in_ch, ch, 3, padding=1)\n",
+    "        # down\n",
+    "        self.down1 = nn.Sequential(*[ResBlock(ch) for _ in range(res_per_stage)], nn.Conv2d(ch, ch*2, 3, stride=2, padding=1))\n",
+    "        self.down2 = nn.Sequential(*[ResBlock(ch*2) for _ in range(res_per_stage)], nn.Conv2d(ch*2, ch*4, 3, stride=2, padding=1))\n",
+    "        # bottleneck\n",
+    "        self.mid = nn.Sequential(*[ResBlock(ch*4) for _ in range(res_per_stage+1)])\n",
+    "        # up\n",
+    "        self.up2  = nn.ConvTranspose2d(ch*4, ch*2, 4, stride=2, padding=1)\n",
+    "        self.rb2  = nn.Sequential(*[ResBlock(ch*2) for _ in range(res_per_stage)])\n",
+    "        self.up1  = nn.ConvTranspose2d(ch*2, ch, 4, stride=2, padding=1)\n",
+    "        self.rb1  = nn.Sequential(*[ResBlock(ch) for _ in range(res_per_stage)])\n",
+    "        # tail\n",
+    "        self.tail = nn.Conv2d(ch, 1, 3, padding=1)\n",
+    "    def forward(self, x):\n",
+    "        x0 = self.head(x)           # [B,64,H,W]\n",
+    "        d1 = self.down1(x0)         # [B,128,H/2,W/2]\n",
+    "        d2 = self.down2(d1)         # [B,256,H/4,W/4]\n",
+    "        m  = self.mid(d2)           # [B,256,H/4,W/4]\n",
+    "        u2 = self.rb2(self.up2(m) + d1)\n",
+    "        u1 = self.rb1(self.up1(u2) + x0)\n",
+    "        # out = torch.sigmoid(self.tail(u1))  # map to [0,1]\n",
+    "        out = self.tail(u1) \n",
+    "        return out\n",
+    "\n",
+    "# --------- Patch Discriminator (hinge), spectral norm ----------\n",
+    "def snconv(c_in, c_out, k, s=1, p=0):\n",
+    "    return nn.utils.spectral_norm(nn.Conv2d(c_in, c_out, k, stride=s, padding=p))\n",
+    "\n",
+    "class PatchDiscriminator(nn.Module):\n",
+    "    def __init__(self, in_ch=1, ch=64):\n",
+    "        super().__init__()\n",
+    "        self.net = nn.Sequential(\n",
+    "            snconv(in_ch, ch,   3, 1, 1), nn.LeakyReLU(0.2, inplace=True),\n",
+    "            snconv(ch,   ch,    4, 2, 1), nn.LeakyReLU(0.2, inplace=True),\n",
+    "            snconv(ch,   ch*2,  3, 1, 1), nn.LeakyReLU(0.2, inplace=True),\n",
+    "            snconv(ch*2, ch*2,  4, 2, 1), nn.LeakyReLU(0.2, inplace=True),\n",
+    "            snconv(ch*2, ch*4,  3, 1, 1), nn.LeakyReLU(0.2, inplace=True),\n",
+    "            snconv(ch*4, ch*4,  4, 2, 1), nn.LeakyReLU(0.2, inplace=True),\n",
+    "            snconv(ch*4, 1,     3, 1, 1) # Patch logits\n",
+    "        )\n",
+    "    def forward(self, x):  # x in [0,1]\n",
+    "        return self.net(x)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4a345f59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cell: setup\n",
+    "import os, json, math, numpy as np, torch, torch.nn.functional as F\n",
+    "from torch.utils.data import Dataset, DataLoader, random_split\n",
+    "from typing import Optional\n",
+    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "\n",
+    "try:\n",
+    "    # newer AMP API (avoid the deprecation warnings)\n",
+    "    from torch.amp import autocast, GradScaler\n",
+    "    AMP_DEVICE = \"cuda\" if DEVICE.startswith(\"cuda\") else \"cpu\"\n",
+    "    scaler = GradScaler(AMP_DEVICE)\n",
+    "except Exception:\n",
+    "    from torch.cuda.amp import autocast, GradScaler\n",
+    "    scaler = GradScaler(enabled=(DEVICE.startswith(\"cuda\")))\n",
+    "    AMP_DEVICE = \"cuda\"\n",
+    "\n",
+    "torch.backends.cudnn.benchmark = True\n",
+    "try:\n",
+    "    torch.set_float32_matmul_precision(\"high\")\n",
+    "except Exception:\n",
+    "    pass\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b8f0f136",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cell: PSF kernel (the one you saved during dataset generation)\n",
+    "\n",
+    "KERNEL_NPY = \"lsst_psf_fwhm0p7_beta3p5_pix0.110.npy\"  # <- set to your saved filename\n",
+    "\n",
+    "kernel_np = np.load(KERNEL_NPY).astype(np.float32)\n",
+    "assert kernel_np.ndim == 2, kernel_np.shape\n",
+    "kernel_t = torch.from_numpy(kernel_np)[None, None]      # [1,1,kh,kw]\n",
+    "kernel_t = kernel_t / kernel_t.sum()\n",
+    "kernel_t = kernel_t.to(DEVICE)\n",
+    "\n",
+    "kh, kw = kernel_t.shape[-2:]\n",
+    "pad_h, pad_w = kh//2, kw//2\n",
+    "\n",
+    "# --- PSF tools ---\n",
+    "def center_crop_psf(psf: torch.Tensor, target_h: int, target_w: int) -> torch.Tensor:\n",
+    "    \"\"\"psf: [1,1,kh,kw], returns center crop to [1,1,target_h,target_w] (keeps sum=1).\"\"\"\n",
+    "    _, _, kh, kw = psf.shape\n",
+    "    assert target_h <= kh and target_w <= kw\n",
+    "    top  = (kh - target_h) // 2\n",
+    "    left = (kw - target_w) // 2\n",
+    "    out = psf[:, :, top:top+target_h, left:left+target_w].contiguous()\n",
+    "    return out / out.sum()\n",
+    "\n",
+    "_psf_cache = {}  # key: (H,W) -> (kernel, pad_h, pad_w)\n",
+    "\n",
+    "def get_psf_for_size(H: int, W: int, device=None):\n",
+    "    \"\"\"\n",
+    "    Ensure reflect padding is valid:\n",
+    "      pad_h = floor(kh/2) <= H-1  -> kh <= 2H-1 (if kh odd), <= 2H-2 (if kh even)\n",
+    "    We'll choose odd kh = min(original_kh, 2H-1) (same for W).\n",
+    "    \"\"\"\n",
+    "    key = (H, W)\n",
+    "    if key in _psf_cache:\n",
+    "        return _psf_cache[key]\n",
+    "    _, _, kh0, kw0 = kernel_t.shape\n",
+    "    # target odd sizes\n",
+    "    th = min(kh0, 2*H - 1)\n",
+    "    tw = min(kw0, 2*W - 1)\n",
+    "    # force odd\n",
+    "    if th % 2 == 0: th -= 1\n",
+    "    if tw % 2 == 0: tw -= 1\n",
+    "    k = center_crop_psf(kernel_t, th, tw)\n",
+    "    pad_h, pad_w = th // 2, tw // 2\n",
+    "    if device is not None:\n",
+    "        k = k.to(device)\n",
+    "    _psf_cache[key] = (k, pad_h, pad_w)\n",
+    "    return _psf_cache[key]\n",
+    "\n",
+    "def psf_same(x: torch.Tensor) -> torch.Tensor:\n",
+    "    \"\"\"Reflect-pad + conv with size-matched PSF; x: [B,1,H,W] in [0,1].\"\"\"\n",
+    "    B, C, H, W = x.shape\n",
+    "    k, pad_h, pad_w = get_psf_for_size(H, W, device=x.device)\n",
+    "    xpad = F.pad(x, (pad_w, pad_w, pad_h, pad_h), mode=\"reflect\")\n",
+    "    return F.conv2d(xpad, k)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "30ae14a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cell: dataset\n",
+    "class PairsDatasetSatGAN(Dataset):\n",
+    "    \"\"\"\n",
+    "    Loads hr_all_*.npy and lr_all_*.npy (N,H,W), maps both to [0,1] with a *global* percentile scale,\n",
+    "    and reflect-pads to a fixed square (default 48) so the UNetish generator works (H,W divisible by 4).\n",
+    "    \"\"\"\n",
+    "    def __init__(self, root_dir=\".\", hr_name=\"hr_all_lsst_2.npy\", lr_name=\"lr_all_lsst_2.npy\",\n",
+    "                 pad_to: Optional[int] = 48, hi_pct: float = 99.99, mmap=True):\n",
+    "        hr = np.load(os.path.join(root_dir, hr_name), mmap_mode=\"r\" if mmap else None)\n",
+    "        lr = np.load(os.path.join(root_dir, lr_name), mmap_mode=\"r\" if mmap else None)\n",
+    "        n = min(len(hr), len(lr))\n",
+    "        self.hr = hr[:n]; self.lr = lr[:n]\n",
+    "        self.N, self.H, self.W = self.hr.shape\n",
+    "        self.pad_to = pad_to\n",
+    "\n",
+    "        # global scale from LR to keep DC loss meaningful (fixed across the dataset)\n",
+    "        flat = self.lr.reshape(-1).astype(np.float64)\n",
+    "        vmax = np.percentile(flat, hi_pct)\n",
+    "        self.scale = float(vmax if np.isfinite(vmax) and vmax > 0 else 1.0)\n",
+    "\n",
+    "    def __len__(self): return self.N\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def _to_tensor01(x: np.ndarray, scale: float) -> torch.Tensor:\n",
+    "        t = torch.from_numpy(x.astype(np.float32)) / (scale + 1e-8)\n",
+    "        return torch.clamp(t, 0.0, 1.0).unsqueeze(0)  # [1,H,W]\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def _pad_reflect(x: torch.Tensor, side: int) -> torch.Tensor:\n",
+    "        # x: [1,H,W]\n",
+    "        _, H, W = x.shape\n",
+    "        ph, pw = side - H, side - W\n",
+    "        l, r = pw//2, pw - pw//2\n",
+    "        t, b = ph//2, ph - ph//2\n",
+    "        try:\n",
+    "            return F.pad(x, (l, r, t, b), mode=\"reflect\")\n",
+    "        except RuntimeError:\n",
+    "            return F.pad(x, (l, r, t, b), mode=\"replicate\")\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        hr = np.array(self.hr[idx], dtype=np.float32)\n",
+    "        lr = np.array(self.lr[idx], dtype=np.float32)\n",
+    "        HR = self._to_tensor01(hr, self.scale)  # [0,1]\n",
+    "        LR = self._to_tensor01(lr, self.scale)  # [0,1]\n",
+    "        if self.pad_to is not None:\n",
+    "            HR = self._pad_reflect(HR, self.pad_to)\n",
+    "            LR = self._pad_reflect(LR, self.pad_to)\n",
+    "        return LR, HR  # condition first for SatGAN (G: LR->HR)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6bacdcb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N: 20000 | scale = 1062.732421911619\n"
+     ]
+    }
+   ],
+   "source": [
+    "# cell: dataloaders\n",
+    "BATCH = 128\n",
+    "ds_full = PairsDatasetSatGAN(\".\", \"hr_all_lsst_20k.npy\", \"lr_all_lsst_20k.npy\", pad_to=48, hi_pct=99.99)\n",
+    "n_total = len(ds_full)\n",
+    "n_train = int(0.9 * n_total)\n",
+    "n_val   = n_total - n_train\n",
+    "train_ds, val_ds = random_split(ds_full, [n_train, n_val], generator=torch.Generator().manual_seed(42))\n",
+    "\n",
+    "train_loader = DataLoader(train_ds, batch_size=BATCH, shuffle=True,  num_workers=4, pin_memory=True)\n",
+    "val_loader   = DataLoader(val_ds,   batch_size=BATCH, shuffle=False, num_workers=2, pin_memory=True)\n",
+    "\n",
+    "print(\"N:\", n_total, \"| scale =\", ds_full.scale)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "544a28b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cell: losses & metrics\n",
+    "from math import log10\n",
+    "try:\n",
+    "    from pytorch_msssim import ms_ssim\n",
+    "    USE_MSSSIM = True\n",
+    "except Exception:\n",
+    "    USE_MSSSIM = False\n",
+    "    def ms_ssim(a, b, data_range=1.0, size_average=True):\n",
+    "        return torch.ones((a.size(0),), device=a.device).mean() if size_average else torch.ones((a.size(0),), device=a.device)\n",
+    "\n",
+    "def d_hinge_loss(real_logits, fake_logits):\n",
+    "    return F.relu(1 - real_logits).mean() + F.relu(1 + fake_logits).mean()\n",
+    "\n",
+    "def g_hinge_loss(fake_logits):\n",
+    "    return -fake_logits.mean()\n",
+    "\n",
+    "def flux_loss(xhat, x, eps=1e-6):\n",
+    "    # relative sum error across image\n",
+    "    num = (xhat.sum(dim=(1,2,3)) - x.sum(dim=(1,2,3))).abs()\n",
+    "    den = (x.sum(dim=(1,2,3)) + eps)\n",
+    "    return (num / den).mean()\n",
+    "\n",
+    "# metrics: MSE, MAE, PSNR, SSIM, MS-SSIM\n",
+    "try:\n",
+    "    from pytorch_msssim import ssim as ssim_pt, ms_ssim as msssim_pt\n",
+    "    HAVE_SSIM = True\n",
+    "except Exception:\n",
+    "    HAVE_SSIM = False\n",
+    "    def ssim_pt(a, b, data_range=1.0, size_average=False):  # dummy\n",
+    "        return torch.ones((a.size(0),), device=a.device)\n",
+    "    def msssim_pt(a, b, data_range=1.0, size_average=False):  # dummy\n",
+    "        return torch.ones((a.size(0),), device=a.device)\n",
+    "\n",
+    "@torch.no_grad()\n",
+    "def eval_metrics(G, loader, device=DEVICE):\n",
+    "    G.eval()\n",
+    "    mse_sum = mae_sum = psnr_sum = 0.0\n",
+    "    ssim_sum = 0.0\n",
+    "    N = 0\n",
+    "\n",
+    "    for lr, hr in loader:\n",
+    "        lr = lr.to(device, non_blocking=True)\n",
+    "        hr = hr.to(device, non_blocking=True)\n",
+    "\n",
+    "        sr_logits = G(lr)                 # logits\n",
+    "        sr = torch.sigmoid(sr_logits)     # map to [0,1] for metrics\n",
+    "\n",
+    "        # per-sample vectors\n",
+    "        diff = sr - hr\n",
+    "        # mean over C,H,W for each sample\n",
+    "        mse_vec  = diff.pow(2).mean(dim=(1,2,3))          # [B]\n",
+    "        mae_vec  = diff.abs().mean(dim=(1,2,3))           # [B]\n",
+    "\n",
+    "        eps = 1e-12\n",
+    "        psnr_vec = 10.0 * torch.log10(1.0 / (mse_vec + eps))  # [B], assumes data_range=1\n",
+    "\n",
+    "        if HAVE_SSIM:\n",
+    "            ssim_vec = ssim_pt(sr, hr, data_range=1.0, size_average=False)  # [B]\n",
+    "\n",
+    "        # accumulate sums\n",
+    "        bsz = lr.size(0)\n",
+    "        mse_sum  += mse_vec.sum().item()\n",
+    "        mae_sum  += mae_vec.sum().item()\n",
+    "        psnr_sum += psnr_vec.sum().item()\n",
+    "        if HAVE_SSIM:\n",
+    "            ssim_sum += ssim_vec.sum().item()\n",
+    "        N += bsz\n",
+    "\n",
+    "    out = {\n",
+    "        \"MSE\":  mse_sum  / max(1, N),\n",
+    "        \"MAE\":  mae_sum  / max(1, N),\n",
+    "        \"PSNR\": psnr_sum / max(1, N),\n",
+    "    }\n",
+    "    out[\"SSIM\"] = (ssim_sum / max(1, N)) if HAVE_SSIM else float(\"nan\")\n",
+    "    return out\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9a56510b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cell: models/optims\n",
+    "G = SatGANGenerator(in_ch=1, ch=64).to(DEVICE)\n",
+    "D = PatchDiscriminator(in_ch=1, ch=64).to(DEVICE)\n",
+    "\n",
+    "opt_G = torch.optim.Adam(G.parameters(), lr=1e-4, betas=(0.9, 0.99))\n",
+    "opt_D = torch.optim.Adam(D.parameters(), lr=1e-4, betas=(0.5, 0.99))\n",
+    "\n",
+    "LAMBDA_L1   = 1.0\n",
+    "LAMBDA_SSIM = 0.0   # set 0.0 to disable if you don't have pytorch_msssim\n",
+    "\n",
+    "LAMBDA_FLUX = 0.0\n",
+    "\n",
+    "R1_GAMMA    = 10.0\n",
+    "R1_INTERVAL = 16  # apply every 16 D steps\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8349ab04",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LR stats: min=0.0000, max=1.0000, mean=0.0781, std=0.1176\n",
+      "HR stats: min=0.0001, max=0.7044, mean=0.0019, std=0.0156\n",
+      "[Sample 0] LR range=(0.00241, 0.967), HR range=(5.8e-05, 0.704)\n",
+      "[Sample 1] LR range=(0.0022, 0.924), HR range=(5.8e-05, 0.704)\n",
+      "[Sample 2] LR range=(0, 0.986), HR range=(5.55e-05, 0.704)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ==== Inspect raw batch stats ====\n",
+    "lr, hr = next(iter(train_loader))\n",
+    "print(\"LR stats: min={:.4f}, max={:.4f}, mean={:.4f}, std={:.4f}\".format(\n",
+    "    lr.min().item(), lr.max().item(), lr.mean().item(), lr.std().item()))\n",
+    "print(\"HR stats: min={:.4f}, max={:.4f}, mean={:.4f}, std={:.4f}\".format(\n",
+    "    hr.min().item(), hr.max().item(), hr.mean().item(), hr.std().item()))\n",
+    "\n",
+    "# check a few per-image ranges\n",
+    "for i in range(min(3, lr.size(0))):\n",
+    "    print(f\"[Sample {i}] LR range=({lr[i].min().item():.3g}, {lr[i].max().item():.3g}), \"\n",
+    "          f\"HR range=({hr[i].min().item():.3g}, {hr[i].max().item():.3g})\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a1b7d2df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YNGkSqlatCqPRiKioKOzevVux7KFDh9C2bVt4e3sjJCQEr776Ku7du8eV+fXXXzF27FiEhYXBx8cHNWvWRJ8+fXD+/HlZfSqVqtCtU6dOXNnZs2fjueeeQ3BwMFQqlW1VyPIGXQeCKFkKs0nz5s1zeOz+/fsLPf7nn3/mykqShISEBERERMDX1xfBwcHo0qULDh065PZzkiQJ7733HurUqQMvLy80a9YMX375pazc4MGDFfveqFEjt/fJ03HmfafEmjVr7L7b1q9fz5W/ceMG+vTpg4CAAPj5+aF79+7466+/7LZx8OBBW32pqanFOk+i/KB90B0oCrVq1UJOTo7Ly9IWl08++QQjR45Ez549ERcXhwMHDuDVV19FdnY2Jk2a5PD4wYMHY/PmzXjttdfQoEEDrFmzBl27dsW+ffvQtm1bW7kTJ06gY8eOaNy4MRYtWoTr169j4cKFuHDhAv773//ays2fPx8//fQTevfujWbNmiEpKQlLly7FY489hp9//hlNmza1lV27dq2sP0eOHMGSJUvQuXNnLn/q1KkICQlB8+bNsWvXrqJcqocCug5EeeFB2VQA6NSpEwYOHMjlNW/e3OnjX331VbRs2ZLLq1+/PpeeOHEiFi1ahAEDBmD06NFIT0/HJ598gujoaPz0009o1apV0U9A4O2338a8efMwbNgwtGzZElu3bsWLL74IlUqFfv36cWUNBgM+/fRTLs/f399tfSkLOPu+U6J9+/aK77YPPvgAv/32Gzp27GjLu3fvHp588klkZGTgrbfegk6nwwcffIDo6GicOHEClSpVktUjSRLGjRsHHx8fZGVlFf9kifLDg14isqyQnZ3NKlWqJFsu96WXXmI+Pj4sLS3N7vGHDx+WLY2ek5PD6tWrx1q3bs2V7dKlCwsNDWUZGRm2vJUrVzIAbNeuXba8n376ieXl5XHHnj9/nhkMBvbSSy85PKehQ4cylUrFrl27xuVfunSJMcZYSkqK4rLr5QW6DgRRsgBgY8aMKdKxBct5f/3113bLmc1mZjQaWa9evbj8v/76iwFgr776apHaV+L69etMp9Nx5yRJEmvXrh2rXr06s1gstvxBgwYxHx8ft7VdVnH2fecs2dnZrEKFCqxTp05c/vz58xkA9ssvv9jyzpw5wzQaDZsyZYpiXcuXL2eVKlVi48ePZwBYSkqKy/0hyicPRBYyY8YMqFQqnD9/HgMGDIC/vz8qV66MadOmgTGGa9euoXv37vDz80NISAjef/997nglfeDgwYPh6+uLGzduoEePHvD19UXlypXxxhtvwGq1FrvP+/btw+3btzF69Gguf8yYMcjKysL27dvtHr9582ZoNBoMHz7clufl5YWhQ4ciMTER165dAwBkZmZi9+7dGDBgAPz8/GxlBw4cCF9fX3z11Ve2vDZt2kCv13PtNGjQAGFhYThz5ozd/uTl5eGbb75BdHQ0qlevzu2rXbu23WPtUdx7azKZMH36dERGRsLf3x8+Pj5o164d9u3bx5WLj4+HWq3G3r17ufzhw4dDr9fjt99+K/I5FFCc60AQpUlZtKn3k5OTg9zc3CIff/fuXVgsFsV9ZrMZOTk5CA4O5vKrVKkCtVoNo9HI5d+4cQMvv/wygoODYTAYEBYWhlWrVjnVj61bt8JsNnPvCZVKhVGjRuH69etITEyUHWO1WpGZmelU/QUU3K+FCxdi2bJlqFu3Lry9vdG5c2dcu3YNjDHMmjUL1atXh9FoRPfu3ZGWlibra7du3VC1alUYDAbUq1cPs2bN4u7tmTNnYDQaZV8WDh48CI1G49QXW3u48r5zlv/85z+4e/cuXnrpJS5/8+bNaNmyJfeVo1GjRujYsaNiO2lpaZg6dSpmzpyJgIAAl/tBlG8eqOa6b9++kCQJ8+bNQ1RUFN59910sXrwYnTp1QrVq1TB//nzUr18fb7zxBn788UeH9VmtVsTGxqJSpUpYuHAhoqOj8f7772PFihVcuTt37iA1NdXhlp2dbTvm+PHjAIAWLVpwdUVGRkKtVtv2F8bx48fxyCOPcAYEgO1z5IkTJwAAv//+OywWi6wdvV6PiIgIh+0wxpCcnIygoCC75Xbs2IH09HSZAXIXRb23mZmZ+PTTT9GhQwfMnz8fM2bMQEpKCmJjY23XCMiXbERERGDo0KG4e/cuAGDXrl1YuXIlpk+fjvDwcFtZZ+51amoq8vLySuRaEERpUZZsagFr1qyBj48PjEYjmjRpgg0bNrh0zkOGDIGfnx+8vLzw5JNP4siRI9z+Av+WNWvWYP369bh69SpOnjyJwYMHo2LFityER3JyMh5//HHs2bMHY8eOxZIlS1C/fn0MHToUixcvdtiX48ePw8fHB40bN+byC+y8aL+zs7Ph5+cHf39/BAYGYsyYMU5pjQtYv349Pv74Y4wbNw4TJkzA//73P/Tp0wdTp07Fzp07MWnSJAwfPhz/+c9/8MYbb3DHrlmzBr6+voiLi8OSJUsQGRmJ6dOnY/LkybYyjRs3xqxZs7B27Vp89913AICsrCwMHjwYjRo1wsyZM21l792759QzkJGRYTumuO+7wq6J0WjECy+8YMuTJAknT56UtQPk35uLFy/a3iMFTJs2DSEhIRgxYoTLfSCIByILiY+PZwDY8OHDbXkWi4VVr16dqVQqNm/ePFv+nTt3mNFoZIMGDbLlXbp0iQFgq1evtuUNGjSIAWAzZ87k2mrevDmLjIzk8mrVqsUAONzulwGMGTOGaTQaxfOpXLky69evn91zDgsLY0899ZQs//Tp0wwAS0hIYIwx9vXXXzMA7Mcff5SV7d27NwsJCbHbztq1axkA9tlnn9kt17NnT2YwGNidO3cKLVMUOURx763FYpFJXe7cucOCg4PZyy+/zOX//vvvTK/Xs1deeYXduXOHVatWjbVo0YKZzWaunDP3WnyeinsdCKI0KYs2lTHG2rRpwxYvXsy2bt3Kli9fzpo2bcoAsI8//tjhOf/000+sZ8+e7LPPPmNbt25lc+fOZZUqVWJeXl7s2LFjXNkLFy6wxx57jOtL3bp12dmzZ7lyQ4cOZaGhoSw1NZXL79evH/P392fZ2dl2+9StWzdWt25dWX5WVhYDwCZPnmzLmzx5Mps0aRLbtGkT+/LLL23X+4knnpDZMJGC+1W5cmWWnp5uy58yZQoDwMLDw7k6+vfvz/R6PcvNzbXlKZ3LiBEjmLe3N1fOarWytm3bsuDgYJaamsrGjBnDtFot+/XXX7ljC/rvaIuOjrYdU9z3ncjt27eZXq9nffr04fILbLj4LDPG2LJlyxgA7ln47bffmEajsclSCn5fJAshnOWBOjS+8sortn9rNBq0aNEC169fx9ChQ235AQEBaNiwoUOP3gJGjhzJpdu1aydzeFi/fj1ycnIc1lW3bl3bv3NycmQSjAK8vLwc1peTkwODwaB4bMH++/9fWFl77Zw9exZjxoxB69atMWjQoELLZWZmYvv27ejatWuJfe4q6r3VaDTQaDQA8mcb0tPTIUkSWrRogWPHjnFtNG3aFO+88w6mTJmCkydPIjU1Fd9//z20Wv6xLiwii0hYWJjL50kQnkRZsqkA8NNPP3Hpl19+GZGRkXjrrbcwePBgmWTjftq0aYM2bdrY0s899xx69eqFZs2aYcqUKdi5c6dtX4UKFRAWFobWrVujY8eOSEpKwrx589CjRw8cOHAAQUFBYIzhm2++QZ8+fcAY4yJDxMbGYuPGjTh27BieeOKJQvvkrJ0HgLlz53Jl+vXrh0ceeQRvv/02Nm/eLHN+VKJ3796cA2RUVBQAYMCAAZwdjIqKwpdffokbN27Y7sH91/bu3bvIy8tDu3bt8Mknn+Ds2bO2r39qtRpr1qxBeHg4unTpgiNHjmDq1KmyWeA333wTAwYMcNjnihUr2v5dnPedEps3b4bJZJJ9kXXUzv1lgHwn2S5dusic/QnCWR7o4LpmzZpc2t/fH15eXjJJg7+/P27fvu2wPi8vL1SuXJnLq1ixIu7cucPl2TOOhWE0GmEymRT35ebm2n0JFByvJDso0BkWHF/w/8LKFtZOUlISunXrBn9/f5u+uzC++eYb5ObmlpgkBCjevf3888/x/vvv4+zZszCbzbb8OnXqyNqZOHEiNm7ciF9++QVz5sxBkyZNZGViYmKKcyoEUWYoSzZVCb1ej7Fjx2LkyJE4evQoF0XJGerXr4/u3btjy5YtsFqt0Gg0sFgsiImJQYcOHbjQqTExMQgLC8OCBQswf/58pKSkID09HStWrJDJXgq4desWgHx7ez/+/v4wGo1O2/nCeP311zFt2jTs2bPHqcG10v0GgBo1aijm33/fTp8+jalTp+KHH36Qab7vl24AQL169TBjxgxMnDgRTZs2xbRp02R9adKkiaL9tUdR33eFsX79egQGBqJLly4utXN/mU2bNuHQoUM4deqUS20TxP080MG10gCwsEEhY6xI9SmRkpLilEOOr68vfH19AQChoaGwWq24desWqlSpYitjMplw+/ZtVK1a1W5doaGhuHHjhiz/5s2bAGA7PjQ0lMsXyyq1k5GRgS5duiA9PR0HDhxw2Jf169fD398fzzzzjN1yxaGo93bdunUYPHgwevTogYkTJ6JKlSrQaDSYO3cuLl68KDv2r7/+woULFwDk6/eUEF+EhVHwgiSIskpZsqmFUTAwFB3wnKVGjRowmUzIysqCn58ffvzxR5w6dQqLFi3iyjVo0ACNGze2zZ5LkgQgf9a3sC9/zZo1A/CPnS5g9erVGDx4MEJDQ7Fv3z4wxqBSqWz7RTtfGEajEZUqVXL63Au7P47ueXp6OqKjo+Hn54eZM2eiXr168PLywrFjxzBp0iTbtbif77//HgDw999/4/bt2wgJCeH2Z2RkODXTrNfrERgYCKBo77vCuHr1Kg4cOIDhw4fLQkoGBgbCYDAU2g7wz72ZOHEievfuDb1ej8uXLwPIv14AcO3aNZhMJpf6RZRPymSc6+LSsmVLXLlyxWG5+Ph428IhERERAPJjQ3ft2tVW5siRI5Akyba/MCIiIrBv3z5kZmZyTo2HDx/m6m/atCm0Wi2OHDmCPn362MqZTCacOHGCywPy/+p+9tlncf78eezZs8fhzMHNmzexb98+DB48WPET2YNm8+bNqFu3LrZs2cK9nOLj42VlJUnC4MGD4efnh9deew1z5sxBr169OEcWQP4iLIyCFyRBEK5RFJtaGAVyFXHG3Fn++usveHl52QbxycnJAKA4+DebzbYoI5UrV0aFChVgtVodfu0SpWYFkrKIiAh8+umnOHPmDGeLRTtfGHfv3kVqamqRz91Z9u/fj9u3b2PLli1o3769Lf/SpUuK5RMSErB7927Mnj0bc+fOxYgRI7B161auzPjx4/H55587bDs6Ohr79+8H4Pr7zh5ffvklGGOKX2TVajUeffRRmbMrkH9v6tatiwoVKgDIH0Bv2LBB0bH2scceQ3h4OOdcTxBKlMvBdVH0gU899RQCAwOxfPlybnC9fPlyeHt7o1u3bra8Aq/omjVrwtvbGwDQq1cvLFy4ECtWrLB5befl5WH16tWIioqyzdb4+/sjJiYG69atw7Rp02w/+LVr1+LevXvo3bu3rR2r1Yq+ffsiMTERW7duRevWrR2e08aNGyFJUolKQopDwYzL/TM/hw8fRmJiouwT6KJFi3Do0CF899136NatG/bv349Ro0ahffv23Gdw0lwTRMlSFJuakpIiG0TevXsXixcvRlBQECIjI235SjZV6fjffvsN3333Hbp06QK1Oj8Y1iOPPAIg3/Y9/fTTtrLHjh3DuXPnbNFCNBoNevbsiQ0bNuDUqVPcIlxie4UNvrt3747XX38dH3/8MZYuXQog35YlJCSgWrVqNo14bm4uzGazzb4XMGvWLDDGuH6WBPfb2QJMJhM+/vhjWdlLly5h4sSJ6NmzJ9566y1UqlQJI0eOxBdffMGF6CuK5tqV9112djauXr2KoKAgxWhYGzZsQM2aNQuVEvXq1QuTJ0/GkSNHbHrxc+fO4YcffuAiqfz73/+WHbtx40Zs2rQJX3zxhSx0LUEoUS4H10XVXM+aNQtjxoxB7969ERsbiwMHDmDdunWYPXu27TMXACxduhTvvPMO9u3bhw4dOgDIdyjp3bs3pkyZglu3bqF+/fr4/PPPcfnyZXz22WdcW7Nnz0abNm0QHR2N4cOH4/r163j//ffRuXNnzuhOmDAB3333HZ599lmkpaVh3bp1XD1Khm79+vWoWrWqrV9KrF27FleuXLGFzfrxxx/x7rvvAgD+9a9/oVatWi5dO1d45plnsGXLFjz//PPo1q0bLl26hISEBDRp0oQLUXXmzBlMmzYNgwcPxrPPPgsgP7RUREQERo8ezcUtLarm+kFeB4IoSxTFpi5btgzffvstnn32WdSsWRM3b97EqlWrcPXqVaxdu5ZzIFeyqX379oXRaESbNm1QpUoV/PHHH1ixYgW8vb255dMjIyPRqVMnfP7558jMzETnzp1x8+ZNfPTRRzAajXjttddsZefNm4d9+/YhKioKw4YNQ5MmTZCWloZjx45hz549DuUa1atXx2uvvYYFCxbAbDajZcuW+Pbbb3HgwAGsX7/eNqhNSkpC8+bN0b9/f9ty57t27cKOHTvw9NNPo3v37i5fT1do06YNKlasiEGDBuHVV1+FSqXC2rVrZVIhxhhefvllGI1GLF++HAAwYsQIfPPNNxg/fjxiYmJsEomiaK4B5993v/zyC5588knFrx+nTp3CyZMnMXnyZO6L5/2MHj0aK1euRLdu3fDGG29Ap9Nh0aJFCA4OxoQJE2zlevToITu2YKa6S5cuDsPcEgSABxuKTwxrU9iKVdHR0SwsLMyWLixslNKxBW25ixUrVrCGDRsyvV7P6tWrxz744AMmSZJim/v27ePyc3Jy2BtvvMFCQkKYwWBgLVu2ZDt37lRs58CBA6xNmzbMy8uLVa5cmY0ZM4ZlZmZyZaKjo+2GPBI5e/YsA8Di4uLsnqO9esVzEinuvZUkic2ZM4fVqlWLGQwG1rx5c7Zt2zY2aNAgVqtWLcZYfoixli1bsurVq3NhqBhjbMmSJQwA27Rpk91+OkNxrgNBlCZl0aZ+//33rFOnTiwkJITpdDoWEBDAOnfuzPbu3Vtom/f/7pYsWcJatWrFAgMDmVarZaGhoWzAgAHswoULsuOzs7PZzJkzWZMmTZjRaGT+/v7smWeeYcePH5eVTU5OZmPGjGE1atRgOp2OhYSEsI4dO7IVK1Y4dV5Wq9Vmw/R6PQsLC2Pr1q3jyty5c4cNGDCA1a9fn3l7ezODwcDCwsLYnDlzmMlkcthGwf26f8VfxgpftXL16tUMABc+76effmKPP/44MxqNrGrVquzNN99ku3bt4q5zgT395ptvuPquXr3K/Pz8WNeuXZ26Jo5w5n1XcG5KIVEnT57MALCTJ0/abefatWusV69ezM/Pj/n6+rJnnnlG8XkRoVB8hKuoGHPCq4UgCIIgCIIgCIc80BUaCYIgCIIgCOJhggbXBEEQBEEQBOEmaHBNEARBEARBEG6CBtcEQRAEQRAE4SZocE0QBEEQBEEQboIG1wRBEARBEAThJsrlIjIEQXgmubm5MJlMLh+n1+vh5eVVAj0iCIIg7ofstGPcOrjWaPy4tErFV69R8xfVYr0Hx/CT64xZhDa9ubQk5drtg0qoT9wvMfkDo1bpubTYB6usTdc+CGg1vkL9kqyMWKd4HmbhWjq61jotf68k8boK5wwAajV/rawSf63EOmTHq8Tjcwsp+f/7rXdleVqNP58hXBfx/ovXVrwO4v0XUdqvE545kyWTS+uFa5ude9luG0Q+ubm5qFOnGpKS7K+Cp0RISAguXbpUbgx3cZA/08or2j18lNcPtfL3iedT+stviO91Qhmy085BM9cEQXgEJpMJSUlpuHxpI/z8vB0f8P9kZmajdp1+MJlM5cJoEwRBPCjITjsHDa4JgvAo/Lz18PM2OH+AhWacCIIgShOy0/ahwTVBEJ4FY/mbK+UJgiCI0oPstF3cOrjWCZprq6BfFjW2GrXjTwpMphezr38W9dGihpoJuju1Ay0zAFilbP4YQc8s6r5FvbLJks6lRb2zXltBKC/XGovXTjxPtdr+dRG1xyLiecuvOyBJ9rXmos5b1FiLunAvXSUubbHm2O2jUr+Y0Cfx3pgFPbSXPohL55pSubR4L5WeB7OFPw9RIy/uJ1xEYoDkgk5UKl9Gu/g8DBrr8qqfLgqOrpUnarLFZ5R+4x4H2Wm7kIUiCMKzkCTXN4IgCKL0KAU7vWzZMtSuXRteXl6IiorCL7/8Yrf8119/jUaNGsHLywuPPvooduzYYdtnNpsxadIkPProo/Dx8UHVqlUxcOBA/P3331wdaWlpeOmll+Dn54eAgAAMHToU9+65PmFGg2uCIDwLi8X1jSAIgig9SthOb9q0CXFxcYiPj8exY8cQHh6O2NhY3Lp1S7H8oUOH0L9/fwwdOhTHjx9Hjx490KNHD5w6dQoAkJ2djWPHjmHatGk4duwYtmzZgnPnzuG5557j6nnppZdw+vRp7N69G9u2bcOPP/6I4cOHu3x5VIy5Twjjpa/OpUVZiCMJhxJK8gR7dYqf8eWh9QRZiCCngEIYPLEOUXog8iBkIbLrJJyHLCShSgxxKJaX/90lXluLIJdxtyxErF8Jsd/i/bRa+TrcIQsRnzkxBKHsvC18G4QymZmZ8Pf3R9qldfCr4IIX+t1sBNYZgIyMDPj5+Tk+oJyjUukedBfcAM0LuY+y8OWn5CUFFIrPOUrLTkdFRaFly5ZYunQpAECSJNSoUQPjxo3D5MmTZeX79u2LrKwsbNu2zZb3+OOPIyIiAgkJCYpt/Prrr2jVqhWuXLmCmjVr4syZM2jSpAl+/fVXtGjRAgCwc+dOdO3aFdevX0fVqlWdPl+3aq5NZn4QoXIw2BIHhJKkFGNajEPN/wAMugChDuEHItgNnZbXHouxmi2SfPrfoAu024Y4CJQEwy8OpsVzyjXf4dJ6DT/YVjpGjK1s1Ffm0jmmFC6tcRDvW6ZlVvgjQ6/j+2U18QN+8RgL4/eLcavzhD6I2nalGNOO/qgQr5NauPZ5Zj42p6wNUT9tzZD1QS/8UcAk+wN8wjVUTIJK4fmzV5542PD8wbPKA7TrzC2DzrKgySYNtqdRVDudmcmPXQwGAwwGPuqIyWTC0aNHMWXKFFueWq1GTEwMEhMTFetPTExEXFwclxcbG4tvv/220D5lZGRApVIhICDAVkdAQIBtYA0AMTExUKvVOHz4MJ5//nmH52nrr9MlCYIgSgOL1fWNIAiCKD2KaKdr1KgBf39/2zZ37lxZ1ampqbBarQgODubyg4ODkZSUpNidpKQkl8rn5uZi0qRJ6N+/v20mPSkpCVWqVOHKabVaBAYGFlpPYVAoPoIgPAtXnV/IoZEgCKJ0KaKdvnbtGicLEWetSwOz2Yw+ffqAMYbly5eXSBs0uCYIwrOgwTVBEIRnU0Q77efn51BzHRQUBI1Gg+TkZC4/OTkZISEhiseEhIQ4Vb5gYH3lyhX88MMPXF9CQkJkDpMWiwVpaWmFtlsYbh1cq0VnMFEvLeibDdoALp1rlTuwibptSdDtmi3CKYhxq4U+WKy8DlhEjNUNyPXNWrV9pzfRGVAji0HNP5Ba4Rwtkjzes5euIp/W8zpwk5m/LqKzoFXKk9XJ9UEj9EHhOuWZef2xqF9XC45SZmsWl9breF24qJ/XCLG4lTT4InLnwcxCSv5/eeFeyNqQOX46dv5ScnokigFjio7FdssTZYwH/5vxBM10cXHmHIqvyxbvlSf8MUsa7AdOCdppvV6PyMhI7N27Fz169ACQ79C4d+9ejB07VvGY1q1bY+/evXjttddsebt370br1q1t6YKB9YULF7Bv3z5UqlRJVkd6ejqOHj2KyMhIAMAPP/wASZIQFRXldP8BmrkmCMLTcFVHTZprgiCI0qWE7XRcXBwGDRqEFi1aoFWrVli8eDGysrIwZMgQAMDAgQNRrVo1m2Z7/PjxiI6Oxvvvv49u3bph48aNOHLkCFasWAEgf2Ddq1cvHDt2DNu2bYPVarXpqAMDA6HX69G4cWM8/fTTGDZsGBISEmA2mzF27Fj069fPpUghAA2uCYLwNEgWQhAE4dmUsJ3u27cvUlJSMH36dCQlJSEiIgI7d+60OS1evXoVavU/X1XatGmDDRs2YOrUqXjrrbfQoEEDfPvtt2jatCkA4MaNG/juu+8AABEREVxb+/btQ4cOHQAA69evx9ixY9GxY0eo1Wr07NkTH374oUt9B9wc51qr5aUKDmUhQhg9MeYwIF+222y5I+wXwtY5sZy5PUSZASAPtSfKQmQxqAXpgSNZiIgolwDkshC1mpcriLIQEUeyELXaNfkMIJeSOJKFiCidJ7ffidCMIuK9kB3vQBYixupWqk8MrSjWIbaRZ+JXgCKUKYifeufYR/DzNTp/3L0cVHxsHMW5dhLPiHNNspDSwj3h+u7HE/+YLf45Upxr5yA77RxunbkWBxk6LT9QYSr+4TULGmu9oMEG5AvRaDQ+dvsg1y+Li6/YH/CrFGIU64XBlDg4Nhp4LbE4oBe1ykoLtNyPVi1/YMWFZTRq3sNWHByLbYj7dcJ1zBH+sHHURwAwW/iY4LLFVMQ/MsQY1Q4WFVLqg6jzFvXw4uBYrrkX44OHcuk84Y83pVjbZjFWtrhwjdn1pVKJf1BZrVBZnf+E6EpZ4kFR8oPpEh8sO2ETXacE9MzigmAOrovrg++yoMEGSIddspCdtg/JQgiC8CxIFkIQBOHZkJ22Cw2uCYLwLMhoEwRBeDZkp+1Cg2uCIDwLihZCEATh2ZCdtsuD9yohCIK4H4n9Myvi1FY0beWyZctQu3ZteHl5ISoqCr/88ovd8unp6RgzZgxCQ0NhMBjwyCOPYMeOHUVqmyAIokxTSna6rOLWmWvRAU3m4ObAYc1slS8AIjqUyRzWhL8PxIgVWuHPB1mkDqEPVoUIFWI/jXo+8HiuiXeCY4KDh+g0qdf6261frxUioACQmJlLiw6NWhWfzrWkc2lxYRrxOoh90KrkTpUaDX//snJvcmkx+otWw9chXieDlo+AYrKKTpv8vQYUnEn1vDNpdh6/QpNeWNiIMT4tXleZg6uCQ6O42I1WaMOisBgS4QKMubYwTBECHm3atAlxcXFISEhAVFQUFi9ejNjYWJw7dw5VqlSRlTeZTOjUqROqVKmCzZs3o1q1arhy5QoCAgJcbrt84N55mxJxVnTZQdH1c3LGMdyVNhxFmspvVDzI/jHitS2+g6MSD0IS4GihmfIRLabEKAU7XZYhWQhBEJ5FKWj5Fi1ahGHDhtkWJEhISMD27duxatUqTJ48WVZ+1apVSEtLw6FDh6DT5f8BX7t2bZfbJQiCeCggzbVdSBZCEIRnYbX+o+dzZnMxxJPJZMLRo0cRExNjy1Or1YiJiUFiYqLiMd999x1at26NMWPGIDg4GE2bNsWcOXNgLWfhpQiCIACUuJ0u69DMNUEQnkURZ0QyM3lZmcFggMFgkBVPTU2F1Wq1rfRVQHBwMM6ePavYxF9//YUffvgBL730Enbs2IE///wTo0ePhtlsRnx8vPN9JQiCeBigmWu7lOrgWlw0RFwFUElja5XEhWb4BV3ERUQcrfon6qFFzbaS3llc3dDhojCCtKiSb2MufS8viUvrtPYXxgHkWnK9mtf9VlDzOlGxTzLduJbXjd/Nu+GwD6LeT7wX4qJAsoVuBP2yeF11gnZZrA+QL7CTa+Z13LIVGIXnQbaojLDIkEbog9IqkSJmK79ojKurghICEnPN+eX/y9aoUYPLjo+Px4wZM9zTJUlClSpVsGLFCmg0GkRGRuLGjRtYsGABDa4BeKTG2s16Z7l+2nH9Mjvs4nUS31dy7bJ8wCKTtjq6lA4WnXHPCo+esPAMaazdShHtdHmBZq4JgvAsrC5+Qvz/steuXeOW1VWatQaAoKAgaDQaJCfzzq/JyckICQlRPCY0NBQ6nQ4ajcaW17hxYyQlJcFkMkGvl6/sShAE8dBSRDtdXqApNoIgPAuXwjv982nSz8+P2wobXOv1ekRGRmLv3r33NSlh7969aN26teIxTzzxBP78809I933aPH/+PEJDQ2lgTRBE+aOIdrq8QINrgiA8i4LPja5sLhIXF4eVK1fi888/x5kzZzBq1ChkZWXZoocMHDgQU6ZMsZUfNWoU0tLSMH78eJw/fx7bt2/HnDlzMGbMGLedNkEQRJmhFOx0WcatshCNmterWgU9q6iH1QmaXSU9mqg5E+NQa8U2maCRFfRkspjEQh9FHTAg1zuLZbwNVezuz8y9xqWN+iAurRN0xCpoIOKt5mNCW8HHZ74r3eLSfupQvk6tvE6uD4LuW/k68I+LJPF6ZoOWnyk0W7K4tKjBFuNaWy38vVDSz5vB16nX8Bp5k4XXw1sZX6dOiFEt6qXF50mrlfsBiHGsHcUMJ1ykYHECV8q7SN++fZGSkoLp06cjKSkJERER2Llzp83J8erVq1Cr/7mPNWrUwK5du/D666+jWbNmqFatGsaPH49Jkya53HbZp/jPd7E11UX6jRVPU+2Mflr06RBtplpt/5Ur2hLRBoo+IEo2UiVcWnl4YftrPZQfDTZRLErBTpdlSHNNEIRnIbmo5ZOKpuUbO3Ysxo4dq7hv//79srzWrVvj559/LlJbBEEQDxWlZKfLKjS4JgjCsyAvdIIgCM+G7LRdaHBNEIRnQUabIAjCsyE7bZcSHVyLGmtRf6bT8DrfPEEvC8hjX4txjs3WLGG/XCPLtWFO49J6bQCXVtLLitpgg9afS4u6cDHt58XH3zVLOVzaX12NS2vBa7wBQAL/SeUe43W/gWq+DR0TIhgIp5XFUrm0j7Yyl7YIWmVA3m8T+OvipRN04YJuW0yL90rUvytpGmW6b8Zrz0XNo04jtCHEVhdjr4v3zizEUVdqQ5L4Z1ClkcdKJ1ygYEUvV8oTJUhZ0Vi7N061aGvE371GLY8SI/quGARboBd8PkRyrby9MVl4Gyv6sUD0MYJcl13SGmwlXNdlkwa7zEF22i40c00QhGdBMyIEQRCeDdlpu9DgmiAIz4KW1SUIgvBsyE7bhQbXBEF4FlYpf3OlPEEQBFF6kJ22i1sH16J+1UtfiUvnmFK4tBgHW4xBnF+nGOea1+2KujgxDrZR6INYn4hZiGEMyDXWjpD3SYiLreH7JBIiyZdgzgF/XlXB67RNjNczidphSdQSq/hr7w3+HK+bjsn6oBc08mL8b1l5La83zDPzmnoxXrQYg1wJR3GpRR2kqLEXY7GrVXx5uV5R/hORP+d8THGTJV12DOEC9LnxAfMANNYux61WijHtqA4HcasdxKgWfYSMgo8JAARp63PpJqp6XLpOBd5mJuXwdvuU6TqXvqk6y6WzhPcXUxqwqOzHyna3BluJ4sfGJg22x0N22i40c00QhGfBXPzc6MTLniAIgnAjZKftQoNrgiA8C5oRIQiC8GzITtuFBtcEQXgWFgZYXJjlsJQvo00QBPHAITttF7cOrnVaXjOdnZfMpcWY1ZIQo1PUhgHyWMcWidfpippZueZWHjv7fsS4pGpJSWMrxPkUNGhaoQ96rf1YpiJiXOs8mGVl/FW8VriChj8mzSLEbxY0bpKgw/MC38d0doNLG3WBsj6Ica7Fay3GHBfvnagDF7V8ooZbrA8AvHSV7JaRxU438/G8ZXp4oU/y2Npy4yHqvMX43DqNn+wYwnmYxMBcmOVwpSzhIRRTY+1YXy0/xlHcanG/Xsf7jPjogrl0PUTIWhxek/ddefHok3Z7+GuHbVx69im+jTvqm1w6T8PHwRb9eQCAqUSbJui0hZ9L2dBgE54G2Wn70Mw1QRCeBWNKb3j75QmCIIjSg+y0XWhwTRCEZ2GRXPzcWL4cZQiCIB44ZKftQoNrgiA8C3KUIQiC8GzITtvFrYNrkyXT7n4xPrCol1ZC1FiLeldRxyvqo8X9oqZWpp/WGGV9EHW8ejWvV1YLdVRCLS5tVsl1cfdTTarKpXMUNNcBOv5aXTLx8ZsDwPfxtorXmmsZf6srMiEGuZq/d1oFrbEZoubawKV1av7a5Vj4Popxr02Wu7I27keMg55/DJ8W75+o89dree24GCdb1FlK4BsQnzcAsAq+AhrhORb3Ey5CRruUeQBxrR3iSGPtOM613Nbzv3XRZ0T0v6kgxK+vw5px6WdC5H4pL/a/zLexdz+X/vEd/n22/jJvE2+xW1zaquLfBSqVhksrrTUgqYS41rLXPL/f7RpsoATCrlHca4+D7LRdaOaaIAiPgjEXHWXKmZaPIAjiQUN22j40uCYIwrMgLR9BEIRnQ3baLjS4JgjCs6DPjQRBEJ4N2Wm7uHVwLdNUC3GtRX2rmBb1sPlleF2cSdDxeumrcGmrxOtdRd2dWi1obCW+z6LuDgA0Crq2+9HCy+5+SdCHGZkQi1nQwOVBrtH+28QL4appArh0upXXJ1dX85rqs+wvLm0RdHmVWHUufYOdlvVBjEMu3r9sIaa0ty7I7n6Djo8Ja7ZkCfsDZH0Qdd65pttcWtRdyuOiC8+gGKvbzD9fVpX8JyIJz5goB5TtJ1yjlEI8LVu2DAsWLEBSUhLCw8Px0UcfoVWrVopl16xZgyFDhnB5BoMBublyv4CHnSLpqx3GpXZNY60U57q4GmsxjnVl1OHSwRpeH30+Qz4TN2Ahb0dTzbz9uaPifVvuqi5z6RzBV8Yk8e9ExqxcWuk6iDZO9EOSGSyZxtr+ug4ONdiAw1jYxY97TRrsBw6F4rMLzVwTBOFRMAsDc2E1L1fKFrBp0ybExcUhISEBUVFRWLx4MWJjY3Hu3DlUqVJF8Rg/Pz+cO3fOllaJnl8EQRDlhNKw02WZ4ruJEwRBuJOCz42ubC6yaNEiDBs2DEOGDEGTJk2QkJAAb29vrFq1qtBjVCoVQkJCbFtwcHChZQmCIB5qSsFOl2VocE0QhEfBJNc3AMjMzOS2vDzlEJgmkwlHjx5FTEyMLU+tViMmJgaJiYmF9uvevXuoVasWatSoge7du+P0abl0iiAIojxQVDvtCsuWLUPt2rXh5eWFqKgo/PLLL3bLf/3112jUqBG8vLzw6KOPYseOHdz+LVu2oHPnzqhUqRJUKhVOnDghq6NDhw5QqVTcNnLkSJf7ToNrgiA8CysDLC5s1vwZkRo1asDf39+2zZ07V7H61NRUWK1W2cxzcHAwkpKSFI9p2LAhVq1aha1bt2LdunWQJAlt2rTB9evX3XvuBEEQZYEi2mlnKZDuxcfH49ixYwgPD0dsbCxu3bqlWP7QoUPo378/hg4diuPHj6NHjx7o0aMHTp06ZSuTlZWFtm3bYv78+XbbHjZsGG7evGnb3nvvPZf6DrhZc60RFtyQO0Lwf7qIC3YoLSojLjwiLugiMbPd/SIWK78Qik7HlxcdRgDAT8Mv8pLLeKcUE+OdVvLUfBtq4W8YPeMdIA3Cbbij4uvL75iYIV/c5H4usGtcuprEO9pkqPgFXFJUfHmrVT7r56OtzKXzrLzzjejAaJJ4B0XReZSBv9ayBYAU/tS1CI6b4iJB4jOk1fDX2mzlr63ofGg0VOPSJgt/jgCg1/rZ7adWa9/BlbAPk1yMn/r/Za9duwY/v3/ujcFgKOwQl2ndujVat25tS7dp0waNGzfGJ598glmzZrmtndKhFOZUiu3ACLv7RefF/DKuOTAahQWm/NQhXFov8b/jqxLvkJ2dJ3fAzwWfZxJsuYXxdlWS+PeX+D6zCk7kIoqOneK1Et/D4rUXfmrifsfOgkr3zrVpyuI7OBKlTVHttLPcL90DgISEBGzfvh2rVq3C5MmTZeWXLFmCp59+GhMnTgQAzJo1C7t378bSpUuRkJAAAPjXv/4FALh8+bLdtr29vRESEmK3jCNo5pogCM9CKsKGfIfD+7fCBtdBQUHQaDRITuZX80xOTnbaoOp0OjRv3hx//vmny6dHEARR5iminXZGvlcU6V5iYiJXHgBiY2PtSv0KY/369QgKCkLTpk0xZcoUZGcrTHg6gAbXBEF4FqwImwvo9XpERkZi7969tjxJkrB3715udtoeVqsVv//+O0JDQx0XJgiCeNgoop12Rr5XFOleUlKSS+UL48UXX8S6deuwb98+TJkyBWvXrsWAAQNcqgOgUHwEQXgYzMLANCUb4ikuLg6DBg1CixYt0KpVKyxevBhZWVm2T5ADBw5EtWrVbIZ/5syZePzxx1G/fn2kp6djwYIFuHLlCl555RWX2yYIgijrFNVOl6R8zx0MHz7c9u9HH30UoaGh6NixIy5evIh69eo5XU+JLiIjarc0gh5WXJTEKiktyMBrrh3pdEV9mcnCa4tFTbao2dWqjbIeiBprrYp/GLxUvAZXC37RGW/my6UN4I+3Cn2ownhtMwD4quV69Pvx0/B15lkrculUFb/YihfjNdt+4NtUaR1/1DCrxAVa+PM2mflrLy4aI94bUYMvlgeAPHOG3TLiQjSizlu+iAyftki8Xl4Ji9X1T0SE87jqWV4UL/S+ffsiJSUF06dPR1JSEiIiIrBz507bzMfVq1ehVv/zG7hz5w6GDRuGpKQkVKxYEZGRkTh06BCaNGnieuNljCItGiPD1Y+k9jXWor4akP+2tRrelhuERWC8NfxCW6LdzlDzNtME+/ppAJAgaqgFPxLx/VXMxU+UF5Hhz4Op7Lch2l3xWotrf4jh3ZV8Y2Q4WFSm+BRf9024RlHtdIFszx5Fke6FhIQUS+pXGFFRUQCAP//806XBNclCCILwLIqo5XOVsWPH4sqVK8jLy8Phw4dtRhQA9u/fjzVr1tjSH3zwga1sUlIStm/fjubNmxetYYIgiLJOCdrpokj3WrduzZUHgN27dzst9SuMgnB9rkoASRZCEIRHwayAgyAJsvIEQRBE6VHSdtpV6d748eMRHR2N999/H926dcPGjRtx5MgRrFixwlZnWloarl69ir///hsAbCvuFiwMdvHiRWzYsAFdu3ZFpUqVcPLkSbz++uto3749mjVr5lL/aXBNEIRHwZj8U7Sj8gRBEETpUdJ22lXpXps2bbBhwwZMnToVb731Fho0aIBvv/0WTZs2tZX57rvvbINzAOjXrx8AID4+HjNmzIBer8eePXtsA/kaNWqgZ8+emDp1qmudB6BizH2vJp2Wj3Os1wp6WCuvhxX1ZkY9r4ED5LpdUactxsEWNbSifswqxDUWY6F66+R6Z42oJZb4WKa11BFc+p6K1wX7Mv46GBivjw5U8zpwP538bx7xLpmFmJG5Ev9noZdaw6Wzrfx1Mwl/RpoFLeA19SVZHzSCJtECXnOYJcSBzTGncWm1mj8+O48PBi/qp5W0fGKeGLdcfD5ETaKouRePtzL++VASlYltaDW8fl3UZFut6bI6CDmZmZnw9/fH30P7wE9v38eAO85kQtXPvkJGRoZDLR8BqFSuORA5pbkudlxr+/tFjbWorwbk/jKiPRHj9HureL8Uq6CXFu2bVYhBXRTEd56owRbbkPklOdgPAFZJ6LeVt2myWNrCO1HWR9GXSmYTHdtph5oAB+Jd98S9FtsQteXKK7oSPGSnnYNmrgmC8Cxc1VGT3xJBEETpQnbaLjS4JgjCo5Cs+Zsr5QmCIIjSg+y0fWhwTRCEZyGp8jdXyhMEQRClB9lpu7h1cC1qT/Msd7i0Ru3Fp1WO9TqiBlYntCHqxyQrn1aB1x6LOjxR22dV0F2JmmsfDa/duwNeO1xLqsuls8DH7xY11j5a/jYoqeB9dHw/LYLmuqaBr+N2Hv8NRifonVXCOaWb+OsmngMAXFdf49KiBlur4u+vGGdW1Nx7G6pwaVErKB4PADl5qXbLiM+Dxcpfe7EPMv208IyaLHyMc0D+HIvx2cX9hGuURpxrwrORxbUW10xQiPuv1fC/O4OG13UaVPx6A6LGWkyL6FX8u0e0f/l5rr1SrUKMabOKtyViLG0zE/w5JAWdsCP5uzCDKMbBluu4hQqLEuda1inXfrSi7t89GmyiOJCdtg/NXBME4VFIVhUkq/OzHK6UJQiCIIoP2Wn70OCaIAiPgjEXZ0RoEosgCKJUITttHxpcEwThUTCmAmPOz3K4UpYgCIIoPmSn7VOig2u1oKkWYwznmXlNtlol17DpNXwca1FjJmqmRU21ycLHyRZjeIr6aVHTBsi1epWl6lz6nprX5ZoEUZsRfFzZCoLGOtib14XrFTRzguQaerWoQePRqPkDKgmhbf/MFOJkW/k+pFvk18Gb8RrGLFU6v1+IG2vQ8tftDrvCpWXxVoV4rEqxbB3d7zwzH2Ncp+HPK9ecwqW1GkGHKcR8VdIGymLwCj8jeQxfwhWYVQVJ7YLRLmefG0sap+JaO6R4ca3FuNmiLVB6V4j+FwY1/9tWC/43osZa3K8Fr7E2Mv79pWdy3wqDYOu1wnlKgqU2C5rrHBUfdz9XdU9I8/WbhP0AoJJyZHn3I2qkxVjbTBL3C/dGNl0pt3cqV3XZ4v0vbwLdMgjZafvQzDVBEB4FzYgQBEF4NmSn7UODa4IgPAomqcBcCNvkSlmCIAii+JCdtg8NrgmC8CgYc835pbw5yhAEQTxoyE7bx62DazE+JhPSFiuvBdMJmlxRDwsAXjpeE63T8ro3UYOtUwtxjzVin3h9maizM6h4jTcAZEu8NjxXFcila7FqXNosaNi8hRjTlY18mxUE+WBFvfwpbOrHXzur8ImlshcfHzXdxGvz0kx8I76C7vtKFr8/IDdA1oer2XydPozXJJoEDeNdNX/dgnT1uXSK+TyX1mn5+swKum+Nmu9DjomPey3GNRdjrRt0/L0TtZvi86Sk+zZb79ktY5Hk/Sacx2pVw6p2XrdutZLGvVQpAZ8CR3GtRU22UpxrvaCx1gr6Z0mwy2IbOqG8L+P9OfwYX7+fRnBkAVBBJ/rP8HZaWJ4AeVY+466FbyND4t9HGYKfyz0F7blMIi2885haSIsabCHutVwvbT/utfIxDjpZKmtjk51wJ2Sn7UMz1wRBeBQ0I0IQBOHZkJ22Dw2uCYLwKMhRhiAIwrMhO20fGlwTBOFRWCU1rJILnxtdKEsQBEEUH7LT9nHr4FrUWXnpK3FpMea0JPF6aG9DqKxOUactSrP0WiEOtqDz9tIG8PUJmlox1qmPiu8zAPiKecLnDZ2gB9QLOqTqPrwuTtRY1/HhNXAtKvE6YQCoH57GpYXTgK6KoDW+wV+ozBReH/jb31W4tJ+W79QJia8PADIFHfcdKx/fO0fFa43N4DuZy/jyXpoALp1lTubSogYbAFQqvl9ZuUlcWnwe9Fo+NrfZwuultRpB5y3oqUVdZn4f7BsJinNdPErLC33ZsmVYsGABkpKSEB4ejo8++gitWrVyeNzGjRvRv39/dO/eHd9++22R2ibsI/6GRN8ItVphTQQV/1sW/WlEzbUGfB1iHP+KQjrEi49rHeott5GhRv7lEKCzryVOM/HneTOHt7E3s/lXtC6Pb1P0MQHkmmlJLaSFl6joKyWuPyDeC8dxrwF5HHPhkGLGvRZjsTPZSg9ESUPRQuxDowCCIDyKAi2fK5urbNq0CXFxcYiPj8exY8cQHh6O2NhY3Lp1y+5xly9fxhtvvIF27doV8ewIgiDKPqVhp8syNLgmCMKjkKCCxFzYirCi4KJFizBs2DAMGTIETZo0QUJCAry9vbFq1apCj7FarXjppZfwzjvvoG7dusU5RYIgiDJNadjpsgwNrgmC8Cik/9fyObtJ/6/ly8zM5La8vDzF+k0mE44ePYqYmBhbnlqtRkxMDBITEwvt18yZM1GlShUMHTrUvSdMEARRxiiqnS4vuDnOtYlLyzXW/H61EKtU1GADSjE47cfoNFn5NlU6IZapEAc7z5rBpb21FWV9yAOvJQ5hIXwB4Q+yyl68lk8nxDp9rCJ/HQyCJq5uI15fDQCG7k34DItwHaryGmpvA6/d03y0h0s/7nODS5+5wh//d6483rdWzT8uUmYQl75t4fXxKcJvKQ+8nlkj6CgNGr5NsyTo7SGP2RrgXYdL38m6wB8gajeFZ85itR+TWtQjAoBO46tQknAXRfVCr1GjBpcfHx+PGTNmyMqnpqbCarUiODiYyw8ODsbZs2cV2zh48CA+++wznDhxwul+EYUj80tw0Y9Bq5LHuVaKSc/tF9c0YPy7wJfxayhU0vMa61q+/PGiHQeAVlV5v5HKj/J/4Kl0/HOdfpo/r2NX+XfLkTt8HzUZvK6c5cq/tYt+RFY1nxY11VaVGNuft8sSeBso90OR3ztWKnGriQcJRQuxD0ULIQjCo5Dg2pISBWWvXbsGP79/nNAMBvkiH0Xh7t27+Ne//oWVK1ciKCjI8QEEQRAPOUW10+UFGlwTBOFRWCWViyGe8mdE/Pz8uMF1YQQFBUGj0SA5mZ9lTE5ORkhIiKz8xYsXcfnyZTz77LO2PEnKf1VotVqcO3cO9erVc7q/BEEQZZ2i2unyQvkSwRAE4fEUfG50ZXMFvV6PyMhI7N2715YnSRL27t2L1q1by8o3atQIv//+O06cOGHbnnvuOTz55JM4ceKETI5CEATxsFPSdrqsQzPXBEF4FBLL31wp7ypxcXEYNGgQWrRogVatWmHx4sXIysrCkCFDAAADBw5EtWrVMHfuXHh5eaFp06bc8QEBAQAgyycIgigPlIadLsu4dXCtEhxKRGcw0ZlMdEARnREBufOEZOXr1Gp4hw+14HSXncfHrQ30rs+lzZIYnF4+mR8oVebSXoLDR57gZKcVFjio5s0/VUF63hGmaT1+IRSvoS1lfbCGh8vyXEE/tSeXNmzdy6W9vuCvaz0fubOOWeLvn0FYLMdfzTsApTNe8yo6FN21pHBpZ5xgzBbeATHPzDukigsXmS1ZXFp8JnVa3jnRZOEXumFMfh2sUq5Qp+Coq+BsRThPaTjK9O3bFykpKZg+fTqSkpIQERGBnTt32pwcr169CrWaPuw9KEQ7LDo0iotJAY4XjRH3yxaREX63FQ18m/V8+fo6NL4q64PfR724NPO1L1MS3edjlq7n0vpNgVw618o7XeZY+Pdffh7fZq6Kt4FmtbDYl8RfB5XKJKSFay/cG9miMkplZAMr0daLdZQ3hW7Zgxwa7UMz1wRBeBRWpoLVBUPsStn7GTt2LMaOHau4b//+/XaPXbNmTZHaJAiCeBgoLTtdVqHBNUEQHkXBogOulCcIgiBKD7LT9qHBNUEQHgWDa6t5sXK28hdBEMSDhuy0fdw6uLYKC3LotLz2S6Z31fD6MXFBGECuobYKC9Fo1AZhPx8Q36jn49JaxP1qXvWmFXR4AJCt5hc/0QlKOS+NuFAN/xD5aPjzMmr56+DbQNCn1agu60NxYQF8n9WB/IItjzxynUunn5LHCNaJi/4I++8K11b8LelUvD7QT1eVS+dJ/HXOtabL+mDQ+XNpi7DQjPg8iM+UqB8UFzoSy+u0AbI+yBZDIo21W8kP8eTC58ZyFuKJUPaNcfUYraDB1gm2oYKw4EtNb96+BTwfKmvD6kBj7Qg29iUu3ersp1z68iE+5OOtHLn2PN3C+77cExbHyQHvp6IRfJ8sMn27cK3d4Jgm1qn07nepPoWBG3NHR4lCITttH5q5JgjCo2BQuTTLUd5mRAiCIB40ZKftQ4NrgiA8CgrxRBAE4dmQnbYPDa4JgvAorEwNK3Nh5S8XyhIEQRDFh+y0fdw6uBbjWFusglZZ4yfs5/WyZqE8AGgYrx8zCnGMTWZeMytqtEVtl8TMXDpX4vVnOo08bqjILYlvs74+gO+Tlf8TLVfQGokhaSxpvAbbkMXHJQUAFlhJllccpCf5lej0v3zNpf108vjOejV/bYKNvN4v3cxfe7WgcbSCv/aixlojxA9nQvxwQH5/xNjYWbl8zHARUbMt4qXjr3Oe5Y6sDGmsSxaaESGKgjyuNY8zcfTvR/yIrVMJx4dWcam+omBsy6+xEHyMt6EVdPz7EQC8hHeghvF2VbSzYsxwWYxx2N9fKsh03xQH+0FDdto+NHNNEIRHQVo+giAIz4bstH1ocE0QhEdBMyIEQRCeDdlp+9DgmiAIj4JW/iIIgvBsyE7bx62Da53Gl0uLca3FGMS55ttcWq+VxwgV41rL9/OxR3Vab75NlTxu9f1ohT5ZkCcrU0WqxaW9wR+TaeK1foEGMfYo/yebl5Yvr6/O6+akGjUL7a+7YEI8Vk1l/px89Ly2DwCEsK9IzeXPQyN89qkixAPPQAqX9lLzfUg3X+XSBo38ecgy3eLSoqZer+Xjd4safLOF17OLsdbFuNdaNf88AfLnWmIU99qdMLi28ld5+9xIKOunxTyxhKgVtggabbOg482y8Hb7Vp4Q5//wOXnHWrZQ6m7RsfJ90qj4PmkV5M8aFf970LLS10yLbbiqdyc8n9Kw08uWLcOCBQuQlJSE8PBwfPTRR2jVqlWh5b/++mtMmzYNly9fRoMGDTB//nx07drVtn/Lli1ISEjA0aNHkZaWhuPHjyMiIoKrIzc3FxMmTMDGjRuRl5eH2NhYfPzxxwgODnap7+XLfZMgCI9HKsJGEARBlB4lbac3bdqEuLg4xMfH49ixYwgPD0dsbCxu3bqlWP7QoUPo378/hg4diuPHj6NHjx7o0aMHTp06ZSuTlZWFtm3bYv78+YW2+/rrr+M///kPvv76a/zvf//D33//jRdeeMHF3pMshCAID4M+NxIEQXg2JW2nFy1ahGHDhmHIkCEAgISEBGzfvh2rVq3C5MmTZeWXLFmCp59+GhMnTgQAzJo1C7t378bSpUuRkJAAAPjXv/4FALh8+bJimxkZGfjss8+wYcMGPPXUUwCA1atXo3Hjxvj555/x+OOPO91/mrkmCMKjKHCUcWUjCIIgSo+StNMmkwlHjx5FTEyMLU+tViMmJgaJiYmKxyQmJnLlASA2NrbQ8kocPXoUZrOZq6dRo0aoWbOmS/UAbp65Nlky+co1vF5VjDGtUfNaY4s1W1anWEbUxMrjWPN62DyhT946Pm6ol8p+3GMAyFDz2nCrEH9ZKwVw6b+z+D4FGvjLnC3Eg045xD91wbV3yPpg7dFVllccNHv3c+m8G7zWPClLHlc7V/iuE6AXNIwSf16XzXyMaLUQT/WexH/ekST++dBreQ0/AEg6vozJmiWk+edDjKVukfhnTC1o8kUNNoM81rZk5Z8xJjxzIM11saAQTw8/TNA3q4SPxkyIKS1J/G/MIsl9YyxqPk+r4v1IRN2vWcWXzxb8ezJM/O/4ryw+fWE7Xz8ANGx9jEtbIx+TlXGFnET+3XPXwvuhWBUGLEzIkwSfH9I/E+6gqHY6M5MfkxkMBhgM/G8pNTUVVqtVpnMODg7G2bNnFetPSkpSLJ+UZH/tC7EOvV6PgICAYtUD0Mw1QRAeBs1cEwRBeDZFtdM1atSAv7+/bZs7d+6DPZESgjTXBEF4FKS5JgiC8GyKaqevXbsGP79/vsCIs9YAEBQUBI1Gg+TkZC4/OTkZISEhivWHhIS4VL6wOkwmE9LT07nZa1frAWjmmiAID4NmrgmCIDybotppPz8/blMaXOv1ekRGRmLv3r3/tCdJ2Lt3L1q3bq3Yn9atW3PlAWD37t2FllciMjISOp2Oq+fcuXO4evWqS/UAbp65NugCuLRMJydoqtVqXsNmFcoDgEFj5NJmq/04xWIcYzHu8d28G3z9Rl7Xa2Xy+M65yODSFRmv27YIGja18MdcniDbvZzFa9Hv3uB1vy3//besDwHgddiseRM+XZGPKa2++BdfgYXvhHT6Ope+dY7vU65VjNUNhZiW/Kgm2czfX0m4Lt6Mvxc5Kv66GvT8/ntWecgd8f6LmmpRc69VG+3ut0j88RrZMymPsy6WcXU/YZ/S0ly7EkN1y5YtmDNnDv7880+YzWY0aNAAEyZMsHmfP0ww4XetEq8vU9Dsqtw7TyNqskXfB9F/BwAsLJfvkjh3JJyGCfxvP1vF26/befxL/9I9vr4fbvLvAQCwTLnMpRu2/41La9s34NJSw3pcWn2RPz4vg39FWyT+JEwKtyJXeI/mqfnzFN9xYlrUZIvX3hnNNum6H35K2k7HxcVh0KBBaNGiBVq1aoXFixcjKyvLFj1k4MCBqFatmk1WMn78eERHR+P9999Ht27dsHHjRhw5cgQrVqyw1ZmWloarV6/i77/zx1jnzuXHqg8JCUFISAj8/f0xdOhQxMXFITAwEH5+fhg3bhxat27tUqQQgGQhBEF4GFYGWFyYjVZy6nJEQQzVhIQEREVFYfHixYiNjcW5c+dQpUoVWfnAwEC8/fbbaNSoEfR6PbZt24YhQ4agSpUqiI2Ndb0DBEEQZZiSttN9+/ZFSkoKpk+fjqSkJERERGDnzp02p8WrV69Crf7nD942bdpgw4YNmDp1Kt566y00aNAA3377LZo2bWor891339kG5wDQr18/AEB8fDxmzJgBAPjggw+gVqvRs2dPbhEZV6HBNUEQHgVjKjBXVv4qguba1RiqHTp04NLjx4/H559/joMHD9LgmiCIckdp2OmxY8di7Nixivv2798vy+vduzd69+5daH2DBw/G4MGD7bbp5eWFZcuWYdmyZa50VQZprgmC8CiKuvJXZmYmt+XlycO1AUWLoXo/jDHs3bsX586dQ/v27Yt6mgRBEGUWWknXPm6duZbr5IRYpYzXr6rBa1P1Wj6GJyCPay1qrPOEWMpBvk259D3TTS6t1fBxszPMvPbYWxso64NOJcbr5s8rE7wO2NvKa6gzTLx++UQ6f9k7VOY1ckcvhsr6EPlv/jz8rqZxaXVNvt/m43xMRutdvs9Jf/Ja89v3+HO8niPXDSfz0j3cyePrFLXnWuHxuqviK8i18PfOIjwfejV/rwEg18ofoxU0+WI6z8zrusW46KImP9fEx5VV0pKKmmoxLbZBuIaVufYJsaBsjRo1uPz7P/XdT1FiqAL5q3dVq1YNeXl50Gg0+Pjjj9GpUyfnO1qOkcW1Fn4jco2u8C4RjlfyhTAL/hMqtdxvhCuv4jXa2ap7XPqOJPzOs/iZN8bkNjLTHMSlT20N4NK19qRz6fBm27m0/sNXuLTf41Fc+m7kj1z6dq48Dn+moCXPBX9eFsb/0Sl7bytp6glCoKh2urxAshCCIDyKfM9y5z8hFnihOxPiqThUqFABJ06cwL1797B3717ExcWhbt26MskIQRDEw05R7XR5gQbXBEF4FAxiHBrH5YF/Qjw5oigxVIF86Uj9+vUBABEREThz5gzmzp1Lg2uCIModRbXT5QX6fk0QhEdR0nGuixJDVbGfklSorpsgCOJhhtYjsI9bZ64tEq9h06h4TZpazeudRZS0qqLGWixjNPCxRrPM/GyUWsWfokrF6/C0Yh/B66UBINvK63BvCVI+H/AxprMlvs+pufwBJolP/5XFf77218k1b6cv8+HB/G7ymkN/I68ttgp9uJvLt5GUzWuTLwl9uJEjvxdZQtydPInvp1G4dmdUp/kKhB+XWsWXZ5KoyU6X9UGt5u+nGEtd1AuahTqMel7P7ihOtkEbIOuDRs1fK9EvQK/hddyEa5SGls/VGKpz585FixYtUK9ePeTl5WHHjh1Yu3Ytli9f7nrj5QLRhon2RNT58ntVMn8d/ncp/u4B+W9ZLbTJ1PbjNWeLX7iFLkuCvTNn8X4rAHDXzL9PbmTztv7sXf7Lyu8ZfB012nzPpf8U7PIxwSXkRh7v7wMAGWreHyePCZpriY/nbZVc02DLNNmlodEmHbjHQZpr+5AshCAIj4JBBamEF5FxNYZqVlYWRo8ejevXr8NoNKJRo0ZYt24d+vbt63LbBEEQZZ3SsNNlGRpcEwThUTAmn8l0VL4ouBJD9d1338W7775btIYIgiAeMkrLTpdVaHBNEIRHYXFx5S9XyhIEQRDFh+y0fWhwTRCER0Fe6ARBEJ4N2Wn7uHVwLTowmq28I4VGcGiUhIUAxDQA2SIeooOiv74Wl07LOselvQ38QhGi04vo5KJV84upAIBFcPjQa/gyWeAXNkkVrkOumXdwC7TyzoZaYRESvcLiB79n8HUECSF8g/T8eeRJvL7JS8M/2teyRSdLvr6r9+QOJJlm3okoTbiWt9WpfJ3C/RcdiBjj+yw6OOaY+foAQKflr52jRV+0Gt6BiIFvU3ROlD+jcscps+WeLM9unwiXcNWzvLx5oZc2THgtqpS0k6LDWTEXUpIvQMb/DkUnPEDu7G4W0pJof1RCn4Uuiw6OkvCuMDOzrA/Zebx9umPi7ctNwe5eyBD7yNvAu2b+PNMY78AoOi8CwD3G2x+TJC4iI7x3RWdR4bzE/fIFf+TIF6Ip2YVqxGeUKHnITtuHZq4JgvAoSMtHEATh2ZCdtg8NrgmC8ChIy0cQBOHZkJ22Dw2uCYLwKEjLRxAE4dmQnbaPWwfXeeYULq1W8wuVMEG7pRW0y0qLyJgsmXyGoLnOyrvF1ylopq1WXl9mtvKatQrG6lz6nummrA8BhtpcWgzKbwWvUUtX8QvZmFWitpx/zDLu8ufkLWi2AaCakdfupfByZ9Tw5bV8oioyNZdvM9fKa958dPy1/ytXuO4AUtVJQj95PbN4Hfw11bi0CfziBeLiPFbh3hj1QbI+3M29zqV1Wn4RBouVX8hIpt0UNNQWxpe3CgshMbX8XogaRMK9kJavpHG0wEvJtylbNEYwWA4XlYH8N6ikw+brFPqgFtJCG1YVb88sKr5+k0owwgCyVfw7L4PxaZ2VtycqC3/trSrBd0ZoI1fFv3tymdxOm4VFYkwSb1fF6yTaREcLc4k4o8F2DC0SU9YgO20fmrkmCMKjkKCClTm/4IArCxkQBEEQxYfstH1ocE0QhEdBMyIEQRCeDdlp+9DgmiAIj4K0fARBEJ4N2Wn7uHVw7W3g9csWideLyeJaC9pVnZqPEQooaKiFGJ1iG6I+TC2col7Lx4s2We7aPR4AzIzXsOVa0rm0ry6ES+dY+bjXfurKXPqOmtema4TYpmqJLw8AN3P4Typ6Na/VS73NP7oaQcRolkR9Ic+dbF6X5w0hkDYAb8ZrrHNV/DFifNU8idcD5pj5mKzivVCr+esg3hsAMGj9ubSoH9QIGmkvfSCXzhY0+pLwPOk0vsJ+ubZT9BUQn2u1gk6bcB4rY7C6ELfJlbJE8VGKKSyLfe1i3GvR7or+N3Jdr9xOy36rQhGZ5loWS5vXWKsFjbVViIlvVvHvBQDIU/Fx8rMF2y7zK5JpzQXdt9AnuY+IXGcuxrEWy4h+SOJ5y9YjkF1IZ2JWk4b6YYfstH1o5pogCI+CPjcSBEF4NmSn7UODa4IgPAr63EgQBOHZkJ22Dw2uCYLwKGhGhCAIwrMhO20ftw6uTVZeI6sTtKmiHjbPnM6llbRbWg2vYWOCdlgrxtIGHydUo5Zrh/n6+eNzTWmyMqLGWq3idXSiLs5LzeuCs1T88bksg0v7qPh4ztfUl2R9EPXOVay8LjtDxV/7PEGbJ+q6jUL8VQ34ONmpKl4/DQAWFa9pzIMYT5WPwapR8ddevNZqIb5unoXXcCthsfIae/H+ivf/Xu7fXFqv4XXe4jMn6jbFuNcAoBZirYuxts0W/joQrmFl+Zsr5Yni8ODjXsvbtB8XGwpxrsUaZRprlagl5m2FaNfVatEW5An7+fIAYBbiUos2zlWsgj2Sx+3n3z2A3IbJ4lgL7ytZeVG77kCr7gzyd7uDOhzE1iYePGSn7UMz1wRBeBQ0I0IQBOHZkJ22Dw2uCYLwLJjSTKX98gRBEEQpQnbaLjS4JgjCo6DPjQRBEJ4N2Wn7uHVwbbXyWlNRcy1qrL10lbi0qNkGAIuV17yKcYktVl73K8YgFo8Xdd9mQecr6oLz2+Q1aKI2T9SwmQQtcq7Ea6x1QuxuM+zrowHgFvtT6IOgVxa0fjomxmTl9YKpuMK3KZyTUh+0QuzrLInXZYvXQTxPkVwLf13kmnx+f34Z+xprlaAd16j4OmUxXYV7Kz4/4v78RvhrLz5jjmL6EvaR4FqU3KKqM5ctW4YFCxYgKSkJ4eHh+Oijj9CqVSvFsitXrsQXX3yBU6dOAQAiIyMxZ86cQsuXN8TY167HvbavsVbJ4kEr9UL8rfJtyDTZYux/lf2416JtUQnx7fP7qbabdhWZbtyJGNNyG+covrdrGmtn9NPKsa/dh1KsdaJ0KS07XVahUQBBEB4FY8zlzVU2bdqEuLg4xMfH49ixYwgPD0dsbCxu3bqlWH7//v3o378/9u3bh8TERNSoUQOdO3fGjRs3inu6BEEQZY7SsNNlGRpcEwThURQ4yriyucqiRYswbNgwDBkyBE2aNEFCQgK8vb2xatUqxfLr16/H6NGjERERgUaNGuHTTz+FJEnYu3dvMc+WIAii7FEadrosQ5prgiA8iqJq+TIzM7l8g8EAg0EeitNkMuHo0aOYMmWKLU+tViMmJgaJiYlOtZmdnQ2z2YzAwEDnO0oQBPGQQJpr+7h1cK3TBnBps6CH1qj5mNWixlrUxwKAReLrUAtlmKCzUwmT8aKGWtTA5Qm6XzWTXxIVE7R7gqYtzypo81S8Ns9LE8ClLUIM6mwLr1320fIxrAG5JjoVV7m0eK1FXbioodYKMaizLPzncK2CXlrUN4u6OpOV16+L10HUt4txY0U9tVotvxdiXp7ZfmxsvZaPay22KdMbiihpJh3pRylGa7EoaoinGjVqcPnx8fGYMWOGrHxqaiqsViuCg4O5/ODgYJw9e9apNidNmoSqVasiJibG+Y6WGUoh7rWbNdhKZUTZN2Qxo/k2RXsm00/DdT21uzXXzux3pJGWxakupsbaOX21J8S1fhDx3B9eKBSffWjmmiAIjyJ/WV3nLXFByWvXrsHP75/FlpRmrd3BvHnzsHHjRuzfvx9eXl6ODyAIgnjIKKqdLi/Q4JogCI/CKkH4RuK4PAD4+flxg+vCCAoKgkajQXJyMpefnJyMkJAQu8cuXLgQ8+bNw549e9CsWTMXekkQBPHwUFQ7XV6g7yIEQXgUDAySC5urYbn0ej0iIyM5Z8QC58TWrVsXetx7772HWbNmYefOnWjRokWRz48gCKKsU9J2uqzj1plrSYj7KYsPLcQxFsurNQp6Z5X9Loo6bqskxsW2r6kVY3GLWmUlRC2xJPGaa6twXvesfAxqL60/l9YKWuNsK6/BBgCLrI4ALu0o9raoXc9xEOtUA/kn9bSsC1zaoOPPw2ThNfSy+M8CspjVgu5OkuT3Tow1q9XY/ywvaupF3aRaeL4swvOj5AcgajlFHbhSvwnnYS6u/FWUCE9xcXEYNGgQWrRogVatWmHx4sXIysrCkCFDAAADBw5EtWrVMHfuXADA/PnzMX36dGzYsAG1a9dGUlISAMDX1xe+vr6ud+Ahx2Hca9kBxdNgA45jYYtaYtEWiHNNoiZb1h4rec21iDP6Zlc11Y7bKMqUo3s11uVtYFYWKA07XZYhWQhBEB5FaSxO0LdvX6SkpGD69OlISkpCREQEdu7caXNyvHr1KtTqfwZGy5cvh8lkQq9evbh6CnOaJAiCeJihRWTsQ7IQgiA8CqvEXN6KwtixY3HlyhXk5eXh8OHDiIqKsu3bv38/1qxZY0tfvnxZcVEEGlgTBFEeKQ07vWzZMtSuXRteXl6IiorCL7/8Yrf8119/jUaNGsHLywuPPvooduzYwe1njGH69OkIDQ2F0WhETEwMLlzgv8jXrl0bKpWK2+bNm+dy32lwTRCER+GKjq9gIwiCIEqPkrbTrq6ie+jQIfTv3x9Dhw7F8ePH0aNHD/To0QOnTp2ylXnvvffw4YcfIiEhAYcPH4aPjw9iY2ORm8vLQWfOnImbN2/atnHjxrl8fVTMjWtSqlT2Q18ZdHz8ZlEHrBRb2Wy9x6W9dJW4tEnQ1GoFDbVFjP8s6L5FjbZeK482IGqDZbGSJVFbzqttZLG2HcRLdU73Lcbe5vsg6sDF8gYNf545ljQu7VzsUh6ZZprZ16LLNY9CfQofksT7JWruxbjWeWZBcy1cB62avzd5ljt8HxSug6jTdoTZkupS+fJKZmYm/P398UzFidA5sCX3Y2Z52HZnATIyMpyKFlLeUTlhX+xT/DkZhxps2QHOtOlAIy2rw9Xywn5nrkNxNddFscMua6pFXI1j7UQfH4jG2sU2hfcVoUxp2emoqCi0bNkSS5cuBZDvdF6jRg2MGzcOkydPlpXv27cvsrKysG3bNlve448/joiICCQkJIAxhqpVq2LChAl44403AAAZGRkIDg7GmjVr0K9fPwD5M9evvfYaXnvtNafPTQmauSYIwqOwMubyRhAEQZQeJWmnC1bRvX+RLker6CYmJsoW9YqNjbWVv3TpEpKSkrgy/v7+iIqKktU5b948VKpUCc2bN8eCBQtgsbgepIAcGgmC8Chc/YRIshCCIIjSpah2OjMzk8s3GAyyBb+KsopuUlKSYvmCyE4F/7dXBgBeffVVPPbYYwgMDMShQ4cwZcoU3Lx5E4sWLXL2VAHQ4JogCA9DYi4abZq5JgiCKFWKaqdr1KjB5XtaxKW4uDjbv5s1awa9Xo8RI0Zg7ty5Lq36S4NrgiA8CgkMVpq5JgiC8FiKaqevXbvGaa6VBqxFWUU3JCTEbvmC/ycnJyM0NJQrExERUWi/o6KiYLFYcPnyZTRs2NDOGfK4dXCtFpzDRIcCK+Md2pi4cIlafpFFp0e5k5x9Bzed1rUFHsyWe7I8i4pvw5FDm+hUJy6mIi5sotfw5fU6Pg0AjAkLjQrPtOgsqBEcN8UFXsS06JSp0/rI+iAuZOPI0UV07BSvi9mSxaUdPR+AstOrvTrF65Br4n98aj3/iUiSPU/y9sRnSnxmXH3mCB6ShXg6Sr9719x33L/IjFK/hEVhhDrERWdkTcrsm7jIjPw6yJwgi+CQ6ArOOZ676thX+g6MRNmjqHbaz8/PoUPj/avo9ujRI//4/19Fd+zYsYrHtG7dGnv37uUcEXfv3m1bdbdOnToICQnB3r17bYPpzMxMHD58GKNGjSq0LydOnIBarUaVKlWcPNN8aOaaIAiPgjHXlsp1Y8AjgiAIwglK2k67uoru+PHjER0djffffx/dunXDxo0bceTIEaxYsQIAoFKp8Nprr+Hdd99FgwYNUKdOHUybNg1Vq1a1DeATExNx+PBhPPnkk6hQoQISExPx+uuvY8CAAahYsaJL/afBNUEQHgXNXBMEQXg2JW2nXV1Ft02bNtiwYQOmTp2Kt956Cw0aNMC3336Lpk2b2sq8+eabyMrKwvDhw5Geno62bdti586d8PLKVzwYDAZs3LgRM2bMQF5eHurUqYPXX3+d02E7i1vjXGs0AUIO/2lILUg2xE/wXkIcbEAed1in4T+555n5+Mx6Ld8HUZogIsopJCENACpBBuJIFuKl5//CKRFZiICrshCR0pCFiLKfkpCFiIifaUVZiJcgC8kz8zGp3SELyc697FRfyzsF8VOf8BsDrQvxUy0sDz9lLqM4105S/DjXShQvqqtnxL0uXn1Fq7N4PKyyEIpz7bmQnXYOt85ci3pnUQ8tIg62c0w35XUKg2lxURiDLpBLi4ZBHHxrNfb1sOIiM4DjRV/EQaNKpbHbJ52w0I1Gw7eZZ06X9UGn4Qe7ot5PXKjGZOYH02IfxT8QrCp+YCsulJN/DP9SVmv4OsTrIi7gIv6RIZ6D2Cfx+QCUFsvh+y3eP43w49cKf8iI90Z83pTIzUvi0iqhn+J+wjVo5rosYl+f7Ihia7ABhQG3o8VUnOjY/dU7Mf53PDB1dfBdfO2y6wuCuVjeDfrq4g+mSeNd2pCdtg/JQgiC8CjIaBMEQXg2ZKftQ4NrgiA8CqvKCpXK+RWxrLAvmSIIgiDcC9lp+9DgmiAIj4K5OCPiHn0mQRAE4Sxkp+3j1sG1UtxPvoAYZ9SxxlZ0atNreSG8zCFRKO/IGVHUEYtxtPPr4HVyovOf6Cwo6pXFPok4ckYEALOVd/5Tgdd1ixprsU1mta9v1goxykUNNwDkmnjnUg3j+ykeI9YpIjo8yq+jknOpqH8XdN+CpjHXnCK0yT9jrj6zAKARnkHxWkoK949wHgkSVC5oKCXSW3ogpazBBhxrf13UZDuKk100POFZLWYfSGNNgOy0I2jmmiAIj4L9v5rPlfIEQRBE6UF22j40uCYIwqOwqCxgLoQ0s8J53R9BEARRfMhO24cG1wRBeBT0uZEgCMKzITttH7cOrmWLq6j4i2m1CottiDGqJfnFF+MWi7GSRUSNtqjBFXVzJonX+WoVdN+OdN2ymNFCeTFGtag1FjXcor4aUIqtzV8XpUVf7kdcAEbUmos6cabg2Sueh3iMo0VhRJiFP28xTrpWI78X4v2XHLQharLF58MixGIXF42RlP7aFp4hSUiXt89f7qa0PjcuW7YMCxYsQFJSEsLDw/HRRx+hVatWimVPnz6N6dOn4+jRo7hy5Qo++OADvPbaa0Vqt3ziXg22EkWKjc1V4KomuygUd5GZUrAtHqGpFiGb6mmQLMQ+pbucFEEQhAOssMAKswub658bN23ahLi4OMTHx+PYsWMIDw9HbGwsbt26pVg+OzsbdevWxbx58xASElLcUyQIgijTlIadLsvQ4JogCI9CUkkub66yaNEiDBs2DEOGDEGTJk2QkJAAb29vrFq1SrF8y5YtsWDBAvTr1w8Gg/NL/hIEQTyMlIadLsuQ5pogCI9CglUm53JUHgAyMzO5fIPBoDgQNplMOHr0KKZMmWLLU6vViImJQWJiYhF7TRAEUX4oqp0uL5To4Noq6FlFjbUYB1kpHrQk1KFW8Vpj8RgvXSUunWfhYzOLfZLr7OSIbWiEPoh6aFFrLNNQC8+YIz01INcGS1a+T6JuWylO9f0oaarvR9RXA0B2XrLdYwy6inwbDmJri9dVcqBtB+S6fY3Gl0uLOm2z0Ae9pgKXFp8PEbFPShiE5zrPnObwGKJwlGLNO1O+Ro0aXH58fDxmzJghK5+amgqr1Yrg4GAuPzg4GGfPnnWts2USUQ/rRAxpt1M8DbYSjnS+xdZkyyosSp8fwOydW+Jz26m+RBYHKV+znGWRotrp8gLNXBME4VEU1VHm2rVr8PP7Z4Efkm8QBEGUDOTQaB8aXBME4VEwWMFcmMks+Arj5+fHDa4LIygoCBqNBsnJ/JeY5ORkclYkCIJwgqLa6fICOTQSBOFRSEX4zxX0ej0iIyOxd+/ef9qUJOzduxetW7d29+kQBEE8dJS0nS7ruHXm2mzhHYrEuMWihjbHxM8ciTGtAbnGWoxbrBZ0byYrH7faUR9ETa1J1GQDMOp5baaoHXIUe1kn6IJNwnUS94txsgF5fGatEI/ZZEnn0mYr34aITmN/hi/XLNcii9dOqxFiQgvXRSwvOj9YrNlcWtRky+KmA5DE50Go0yzUKSLq32VxsAUDID4/SmVEPbzSc0w4j5LW3p3lASAuLg6DBg1CixYt0KpVKyxevBhZWVkYMmQIAGDgwIGoVq0a5s6dCyDfCfKPP/6w/fvGjRs4ceIEfH19Ub9+fZfbJ0QcvXg9QJMtq7B8DBZKRlMtUj6u5cNEadjpsgzJQgiC8CjyZzhKduWvvn37IiUlBdOnT0dSUhIiIiKwc+dOm5Pj1atXoVb/M6D7+++/0bx5c1t64cKFWLhwIaKjo7F//36X2ycIgijLlIadLsvQ4JogCI8iX8vn/CxiUbV8Y8eOxdixYxX3iQPm2rVrg7HSmMEjCILwfErLTpdVaHBNEIRHYWUWlz5El7fPjQRBEA8astP2cevgWqfldbxM0KSJWmS9NoBLy2JQKyAL5yK0Ie4XvVlFjbWoqRXjJANAniWDS4uxkkU9tF64DmIMalFLLJ63UsgasU7x2jq8lqI23cLHYvY11uLSeWb+nAG5NtxRaB1RDy2WFzXbYmztPAXdt3iMeJ6ihlqMky7uFxGfB7VaQfct3G/xXqgVdNqE8zDm4owIK18zIu7HE+JeO8L9cbFFXNUWu6zRfkCUjmbaVcqXROBhhOy0fWjmmiAIj4KBuRg/1RMHDwRBEA8vZKftQ4NrgiA8CsYkF2dEaBaMIAiiNCE7bR8aXBME4VEwZnbJs7y8fW4kCIJ40JCdtk+JxrnWChpdeUxiMS42r6dVQtQ3O4y9LGisVSox1vI9vn6Fh0UraGjFWMmintls4et0FPfYmbjIYvxmqxjPWbgOPoKGOjvvJpfWCPdG1FgrxZgW43OL/ZYs4rUWYkgLf7lqhONFjbWSPloWG1uoQ3w+RP2zqMEW94vPqBg/XKlfYix2Z3wHiMLJf05oRuTB4ejzrSdojZ255yW7Rlp5+8ztGvSbfNghO20fmrkmCMKjkCC55Czmiu6PIAiCKD5kp+1Dg2uCIDwKSTJDpaLPjQRBEJ4K2Wn70OCaIAiPIv9zO3mhEwRBeCpkp+1Dg2uCIDwKV7V55U3LRxAE8aAhO20ftw6uxQU45M6D2cJ+x45gMoc14S8lsQ3RgVF0NhQd1rz0/OIsouMgAFhEJzhxERgrv1+8DuJDJTrEiY6g4vGA/DqoBIdEcYEe0UHRS1eZS+eYku3WZ5Hk10FcyEZ0cPTSVeLSVimPS4sLsuSaUvk+CNdFq+DoKXO0FBcREhd0Ee6/TsOfg/jMWcFfR9EpF3DsJKtiJetI9bAjMYvsd2qP8ma0HzxKM1Ce4OQo4ui5oN9p0aDfG0F22hE0c00QhEdBMyIEQRCeDdlp+9DgmiAIj4KMNkEQhGdDdto+NLgmCMLDcNUIly+jTRAE8eAhO20Ptw6ulRb9uB+DLohLi5pdcQEYQGHREKENcREZUf8qlhfry7WKC77I9c7ieYl1iIgL04i6XVEfLVuMRdgPAEwSdNsQz9N+HQz8eYm6blF7rFPQGueZeY20VtAvm6x37fZRsor3yv7zIl5HQGlRGOE8hedBRFy4SES8zhptgKyMo8VxHJ0XYR/S8pVFRB22J2qwRYr73Dwsmu3y8vspi8+o50J22j40CiAIwqNgzMUQT6x8hXgiCIJ40JCdtg8NrgmC8DCscG1WqXwZbYIgiAcP2Wl70OCaIAiPIl/a48KyuuVsRoQgCOJBQ3baPm4VjTFm4TaRPHMqt4lYrNmyTa3WcxuDxG0qlZrbRCRm4TaxPi99FW5TqbSyTUSr8eY2+X5fbhNRq/TcJkkmbhP3q1V6aNRe3Cb2UWImbhOPV0HDbVYpl9vE+sX9VikXBl0Qt4n79ZoK3KZWablNq/HiNkfPi3gdtRrf/LjW921iG47unU7jx22y66zx5jbxeWOQZP0Wn0FH50U4QLjHTm1FYNmyZahduza8vLwQFRWFX375xW75r7/+Go0aNYKXlxceffRR7Nixo0jtPpyohK0soC7m9rBQXs67LD6jHkwp2Gl322jGGKZPn47Q0FAYjUbExMTgwoULXJm0tDS89NJL8PPzQ0BAAIYOHYp79+T+X44oy78UgiAeQlgR/nOVTZs2IS4uDvHx8Th27BjCw8MRGxuLW7duKZY/dOgQ+vfvj6FDh+L48ePo0aMHevTogVOnThX3dAmCIMocJW2nS8JGv/fee/jwww+RkJCAw4cPw8fHB7GxscjN/Segw0svvYTTp09j9+7d2LZtG3788UcMHz7c5eujYm6cq9dp+WggYhQMsyWdb1wlX4FPRLZCo4OZQJn3qsJs9v3ohJlnpRUaZX1yED1EaYVFe4hRMZxZoVH0vJVFIBGurU7jw6XFFRrFGXblmWT+WpX4Co0KXwVkK3CK+4V+i9dStrqmuCqog2gzSn2QRXsR9pst8q80hJzMzEz4+/sD0EGlcvVzoxkZGRnw8/NzWB4AoqKi0LJlSyxduhQAIEkSatSogXHjxmHy5Mmy8n379kVWVha2bdtmy3v88ccRERGBhIQEp/vqCZRMNJuyOBNIc0tF4+GM+sCY+UF3oUxQWnba3TaaMYaqVatiwoQJeOONNwAAGRkZCA4Oxpo1a9CvXz+cOXMGTZo0wa+//ooWLVoAAHbu3ImuXbvi+vXrqFq1qtPnS9aFIAgPQwJjVqe3gpd9ZmYmt+Xl5SnWbjKZcPToUcTExNjy1Go1YmJikJiYqHhMYmIiVx4AYmNjCy1PEATxcFNydrokbPSlS5eQlJTElfH390dUVJStTGJiIgICAmwDawCIiYmBWq3G4cOHXbo6bp3CoBk6giCKil6vR0hICJKSklw+1tfXFzVq1ODy4uPjMWPGDFnZ1NRUWK1WBAcHc/nBwcE4e/asYv1JSUmK5YvS1wcN+QEQBFFUSsNOl4SNLvi/ozJVqlTh9mu1WgQGBrp8vhQthCAIj8DLywuXLl2CyWRf+qMEY0z2idJgMLirawRBEATITjsLDa4JgvAYvLy84OXlms+CqwQFBUGj0SA5mfc7SE5ORkhIiOIxISEhLpUnCIJ4WClpO10SNrrg/8nJyQgNDeXKRERE2MqIDpMWiwVpaWku23rSXBMEUa7Q6/WIjIzE3r17bXmSJGHv3r1o3bq14jGtW7fmygPA7t27Cy1PEARBFI2SsNF16tRBSEgIVyYzMxOHDx+2lWndujXS09Nx9OhRW5kffvgBkiQhKirKtZNgBEEQ5YyNGzcyg8HA1qxZw/744w82fPhwFhAQwJKSkhhjjP3rX/9ikydPtpX/6aefmFarZQsXLmRnzpxh8fHxTKfTsd9///1BnQJBEMRDS0nY6Hnz5rGAgAC2detWdvLkSda9e3dWp04dlpOTYyvz9NNPs+bNm7PDhw+zgwcPsgYNGrD+/fu73H8aXBMEUS756KOPWM2aNZler2etWrViP//8s21fdHQ0GzRoEFf+q6++Yo888gjT6/UsLCyMbd++vZR7TBAEUX5wt42WJIlNmzaNBQcHM4PBwDp27MjOnTvHlbl9+zbr378/8/X1ZX5+fmzIkCHs7t27LvfdrXGuCYIgCIIgCKI8Q5prgiAIgiAIgnATNLgmCIIgCIIgCDdBg2uCIAiCIAiCcBM0uCYIgiAIgiAIN0GDa4IgCIIgCIJwEzS4JgiCIAiCIAg3QYNrgiAIgiAIgnATNLgmCIIgCIIgCDdBg+sygkqlwowZMxyWmzFjBlQqVcl3iCAIgpDRoUMHdOjQwWG5/fv3Q6VSYf/+/SXeJ4IgShcaXDvJmjVroFKpcOTIkULLXL58GSqVyrap1WoEBgaiS5cuSExMLMXeli7JyckYMmQIqlSpAqPRiMceewxff/21rNyWLVvQt29f1K1bF97e3mjYsCEmTJiA9PT00u80QRAPDY7sc4cOHdC0aVMur3bt2py99vHxQatWrfDFF1+URpcfCGazGe+88w7q1q0Lg8GAunXr4t1334XFYuHK/frrrxg7dizCwsLg4+ODmjVrok+fPjh//vwD6jlBlC20D7oDDyP9+/dH165dYbVacf78eXz88cd48skn8euvv+LRRx8tUp05OTnQaj3vdmVmZqJt27ZITk7G+PHjERISgq+++gp9+vTB+vXr8eKLL9rKDh8+HFWrVsWAAQNQs2ZN/P7771i6dCl27NiBY8eOwWg0PsAzIQiivBEREYEJEyYAAG7evIlPP/0UgwYNQl5eHoYNG1akOr///nt3dtGtDBgwAF9//TVefvlltGjRAj///DOmTZuGq1evYsWKFbZy8+fPx08//YTevXujWbNmSEpKwtKlS/HYY4/h559/lv2hQhCEACOcYvXq1QwA+/XXXwstc+nSJQaALViwgMv/73//ywCwUaNGlXQ3WXx8PCvN2/ree+8xAGzv3r22PKvVylq2bMlCQkJYXl6eLX/fvn2y4z///HMGgK1cubI0uksQxEOII/scHR3NwsLCuLxatWqxbt26cXm3bt1ivr6+rHHjxiXW1wL27dvHACjaxZLgl19+YQDYtGnTuPwJEyYwlUrFfvvtN1veTz/9xNluxhg7f/48MxgM7KWXXiqV/hJEWYZkIaVAu3btAAAXL14sch1KmuuDBw+iZcuW8PLyQr169fDJJ5/Ijlu9ejVUKhVWrVrF5c+ZMwcqlQo7duwocp8A4MCBA6hcuTKeeuopW55arUafPn2QlJSE//3vf7Z8JR3i888/DwA4c+ZMsfpBEARRXCpXroxGjRoVy1Yraa6vX7+OHj16wMfHB1WqVMHrr7+OvLw8rsyZM2dgNBoxcOBALv/gwYPQaDSYNGlSkfsE5NtqAOjXrx+X369fPzDGsGnTJltemzZtoNfruXINGjRAWFgY2WqCcALP0xk8hFy+fBkAULFiRbfV+fvvv6Nz586oXLkyZsyYAYvFgvj4eAQHB3PlhgwZgi1btiAuLg6dOnVCjRo18Pvvv+Odd97B0KFD0bVrV1vZO3fuwGq1Omzb29sb3t7eAIC8vDxFOUfB/qNHj6JTp06F1pWUlAQACAoKcnzSBEEQdsjIyEBqaqos32w2O3W8xWLB9evX3Wqrc3Jy0LFjR1y9ehWvvvoqqlatirVr1+KHH37gyjVu3BizZs3CxIkT0atXLzz33HPIysrC4MGD0ahRI8ycOdNW9t69e8jNzXXYtk6ng7+/PwDYBvOivb7fVtuDMYbk5GSEhYU5PmmCKOfQ4LoEyM7ORmpqKqxWKy5cuIC4uDgAQK9evdzWxvTp08EYw4EDB1CzZk0AQM+ePRU13StXrkRYWBiGDh2Kbdu2YdCgQQgJCcGiRYu4cs2bN8eVK1ccth0fH2+bRW/YsCH27NmDK1euoFatWrYyBbMkN27csFvX/PnzodFo3HptCIIon8TExBS6T2lQaDabbYPxpKQkvPfee0hKSsKYMWPc1qcVK1bg/Pnz+Oqrr9C7d28AwLBhwxAeHi4rGxcXh61bt2L48OF44oknEB8fjytXriAxMREGg8FWbuzYsfj8888dth0dHW2LRtKwYUMAwE8//YQ6derYyjhrq9evX48bN25wg3yCIJShwXUJEB8fj/j4eFva19cX77//vtsGkFarFbt27UKPHj1sA2sgf+YjNjZWJvUICQnBsmXL0L9/f7Rr1w4nTpzA7t274efnx5Vbv349cnJyHLZft25d279feeUVJCQkoE+fPvjggw8QHByMr776Cv/+978BwG59GzZswGeffYY333wTDRo0cOrcCYIgCmPZsmV45JFHZPkTJkxQ/Cr3/fffo3LlylzekCFDsGDBArf1aceOHQgNDeXsv7e3N4YPH44333yTK6tWq7FmzRqEh4ejS5cuOHLkCKZOnYoWLVpw5d58800MGDDAYdv3z8B37doVtWrVwhtvvAFvb29ERkbi8OHDePvtt6HVau3a6rNnz2LMmDFo3bo1Bg0a5OypE0S5hQbXJcDw4cPRu3dv5Obm4ocffsCHH37olNzCWVJSUpCTk6M4IG3YsKGijrpfv35Yt24dtm/fjuHDh6Njx46yMk888YTLfWnWrBk2bNiAkSNH2o4PCQnB4sWLMWrUKPj6+ioed+DAAQwdOhSxsbGYPXu2y+0SBEGItGrVSjYQBfIHmUpykaioKLz77ruwWq04deoU3n33Xdy5c0emNy4OV65cQf369WXrDxTMJIvUq1cPM2bMwMSJE9G0aVNMmzZNVqZJkyZo0qSJS/3w8vLC9u3b0adPH/Ts2RMAYDAY8N5772H27NmF2uqkpCR069YN/v7+2Lx5MzQajUvtEkR5hAbXJUCDBg1snyefeeYZaDQaTJ48GU8++aSi4S8Nbt++bYsB+8cff0CSJKjVvD9rSkqKU38E+Pr6coa4QB/422+/wWq14rHHHrN9ilSaRfrtt9/w3HPPoWnTpti8ebNHhhgkCOLhJygoyGarY2Nj0ahRIzzzzDNYsmSJTc73ICgI5/f333/j9u3bCAkJ4fZnZGQ49ZVRr9cjMDDQlg4LC8OpU6fwxx9/4M6dO2jSpAmMRiNef/11REdHy47PyMhAly5dkJ6ejgMHDqBq1arFPDOCKB9QtJBS4O2330aFChUwdepUt9RXuXJlGI1GXLhwQbbv3LlziseMGTMGd+/exdy5c3Hw4EEsXrxYVqZly5YIDQ11uC1cuFB2rF6vR8uWLfH4449Dr9djz549AOQayIsXL+Lpp59GlSpVsGPHjkJnSwiCIEqbbt26ITo6GnPmzEFWVpZb6qxVqxYuXrwIxhiXX5itTkhIwO7duzF79myYTCaMGDFCVmb8+PFO2eoXXnhBdqxKpUJYWBjatm2LwMBA7Nu3D5IkyWx1bm4unn32WZw/fx7btm1zeaacIMozNGVYCgQEBGDEiBF47733cOLECURERBSrPo1Gg9jYWHz77be4evWqTXd95swZ7Nq1S1Z+8+bN2LRpEz788EOMGzcOv/32G6ZOnYpnnnmGm1kuiuZaiQsXLiAhIUFWf1JSEjp37gy1Wo1du3bJtI4EQRAPmkmTJqFr165YuXIlXnvttWLX17VrV3z//ffYvHmzzaExOzubW7SlgEuXLmHixIno2bMn3nrrLVSqVAkjR47EF198wYXoK4rmWomcnBxMmzYNoaGh6N+/vy3farWib9++SExMxNatW9G6dWtnT5cgCNDg2mVWrVqFnTt3yvLHjx9v97jx48dj8eLFmDdvHjZu3FjsfrzzzjvYuXMn2rVrh9GjR8NiseCjjz5CWFgYTp48aSt369YtjBo1Ck8++STGjh0LAFi6dCn27duHwYMH4+DBgzZ5SFE010C+/q93796oWbMmLl26hOXLlyMwMBAJCQlcuaeffhp//fUX3nzzTRw8eBAHDx607QsODrYbso8gCKI06NKlC5o2bYpFixZhzJgx0Ol0xapv2LBhWLp0KQYOHIijR48iNDQUa9eutYXAK4AxhpdffhlGoxHLly8HAIwYMQLffPMNxo8fj5iYGJssoyiaawDo06cPqlatiiZNmiAzMxOrVq3CX3/9he3bt6NChQq2chMmTMB3332HZ599FmlpaVi3bh1XjzMDe4Io1zzQJWzKEAUrgBW2Xbt2rdAVGgsYPHgw02g07M8//3S5fQAsPj6ey/vf//7HIiMjmV6vZ3Xr1mUJCQmyFRpfeOEFVqFCBXb58mXu2K1btzIAbP78+S73RaRfv36sRo0aTK/Xs6pVq7KRI0ey5ORkxXMobIuOji52PwiCKJ+4a4XGAtasWcMAsNWrV7vcl+joaJk9u3LlCnvuueeYt7c3CwoKYuPHj2c7d+7kVmhcsmQJA8C++eYb7tirV68yPz8/1rVrV5f7IjJ//nzWqFEj5uXlxSpWrMiee+45dvz4ccVzsGevCYKwj4oxQQhGEARBEARBEESRIIdGgiAIgiAIgnATNLgmCIIgCIIgCDdBg2uCIAiCIAiCcBM0uCYIgiAIgiAIN0GDa4IgCIIgCIJwEzS4JgiCIAiCIAg38dAPri9fvgyVSoU1a9Y86K4QBEF4JGQnCYIg3MdDP7h+kHz22Wdo3LgxvLy80KBBA3z00UcPukuljiRJeO+991CnTh14eXmhWbNm+PLLL50+fvfu3Wjbti28vb1RsWJF9OrVC5cvX5aVq127NlQqlWwbOXKkYr179uzBU089BX9/f1SoUAGRkZHYtGlTUU+TIIgioPSbValUmDdvntN1HDt2DM899xwCAwPh7e2Npk2b4sMPP+TKSJKEhIQEREREwNfXF8HBwejSpQsOHTrk7lNy2uYNHjxY8dwbNWrk9j55OocOHbLZ+ZCQELz66qu4d++ew+PWrFlT6DOkUqmwfv16rvyNGzfQp08fBAQEwM/PD927d8dff/1lt42DBw/a6ktNTS3WeRLlh4d++fNatWohJyen2EvYusonn3yCkSNHomfPnoiLi8OBAwfw6quvIjs7G5MmTSrVvjxI3n77bcybNw/Dhg1Dy5YtsXXrVrz44otQqVTo16+f3WO3bduG7t2747HHHsO8efOQmZmJJUuWoG3btjh+/DgqV67MlY+IiMCECRO4vEceeURW7+rVqzF06FB06tQJc+bMgUajwblz53Dt2rXinzBBlEEelJ0EgE6dOmHgwIFcXvPmzZ069vvvv8ezzz6L5s2bY9q0afD19cXFixdx/fp1rtzEiROxaNEiDBgwAKNHj0Z6ejo++eQTREdH46effkKrVq3cdj6u2DyDwYBPP/2Uy/P393dbX8oCJ06cQMeOHdG4cWMsWrQI169fx8KFC3HhwgX897//tXts+/btsXbtWln+Bx98gN9++w0dO3a05d27dw9PPvkkMjIy8NZbb0Gn0+GDDz5AdHQ0Tpw4gUqVKsnqkSQJ48aNg4+PD7Kysop/skT54UEvEfkwkp2dzSpVqiRbWvell15iPj4+LC0t7QH1rHS5fv060+l0bMyYMbY8SZJYu3btWPXq1ZnFYrF7fJMmTVj9+vVZXl6eLe/EiRNMrVazuLg4rqy9pYzv59KlS8xoNLJXX33VxbMhCMLdAODsgytkZGSw4OBg9vzzzzOr1VpoObPZzIxGI+vVqxeX/9dffzEAbrUFrti8QYMGMR8fH7e1XVbp0qULCw0NZRkZGba8lStXMgBs165dLteXnZ3NKlSowDp16sTlz58/nwFgv/zyiy3vzJkzTKPRsClTpijWtXz5clapUiU2fvx4BoClpKS43B+ifOLxspAZM2ZApVLh/PnzGDBgAPz9/VG5cmVMmzYNjDFcu3YN3bt3h5+fH0JCQvD+++9zxytpCQcPHgxfX1/cuHEDPXr0gK+vLypXrow33ngDVqu12H3et28fbt++jdGjR3P5Y8aMQVZWFrZv316i52wymTB9+nRERkbC398fPj4+aNeuHfbt28eVi4+Ph1qtxt69e7n84cOHQ6/X47fffivGVQC2bt0Ks9nMXQeVSoVRo0bh+vXrSExMLPTYtLQ0/PHHH3j++eeh1+tt+eHh4WjcuDE2btyoeJzJZLI7w5CQkACr1YqZM2cCyJ/NYIy5emoE4VGURTt5Pzk5OcjNzXXpmA0bNiA5ORmzZ8+GWq1GVlYWJEmSlTObzcjJyUFwcDCXX6VKFajVahiNRi7/xo0bePnllxEcHAyDwYCwsDCsWrXKqT4VxeZZrVZkZmY6VX8BBfdr4cKFWLZsGerWrQtvb2907twZ165dA2MMs2bNQvXq1WE0GtG9e3ekpaXJ+tqtWzdUrVoVBoMB9erVw6xZs7h7e+bMGRiNRtmXhYMHD0Kj0RT7K2xmZiZ2796NAQMGwM/Pz5Y/cOBA+Pr64quvvnK5zv/85z+4e/cuXnrpJS5/8+bNaNmyJVq2bGnLa9SoETp27KjYTlpaGqZOnYqZM2ciICDA5X4Q5RuPH1wX0LdvX0iShHnz5iEqKgrvvvsuFi9ejE6dOqFatWqYP38+6tevjzfeeAM//vijw/qsVitiY2NRqVIlLFy4ENHR0Xj//fexYsUKrtydO3eQmprqcMvOzrYdc/z4cQBAixYtuLoiIyOhVqtt+0vqnDMzM/Hpp5+iQ4cOmD9/PmbMmIGUlBTExsbixIkTtnJTp05FREQEhg4dirt37wIAdu3ahZUrV2L69OkIDw+3lXXmGqSmpiIvL4+7Dj4+PmjcuDF3XgWfYO1dh4J6xBcfAHh7e+Pvv/9GUlISl//DDz/A29sbvr6+qF27NpYsWSI7ds+ePWjUqBF27NiB6tWro0KFCqhUqRKmTZum+GImiLJEWbKTBaxZswY+Pj4wGo1o0qQJNmzY4NS57tmzB35+frhx4wYaNmwIX19f+Pn5YdSoUdxA3Wg0IioqCmvWrMH69etx9epVnDx5EoMHD0bFihUxfPhwW9nk5GQ8/vjj2LNnD8aOHYslS5agfv36GDp0KBYvXuywT67avOzsbPj5+cHf3x+BgYEYM2aMU1rjAtavX4+PP/4Y48aNw4QJE/C///0Pffr0wdSpU7Fz505MmjQJw4cPx3/+8x+88cYb3LFr1qyBr68v4uLisGTJEkRGRmL69OmYPHmyrUzjxo0xa9YsrF27Ft999x0AICsrC4MHD0ajRo1skxRA/kSFM89ARkaG7Zjff/8dFotF9q7U6/WIiIhw+l0pXhOj0YgXXnjBlidJEk6ePClrB8i/NxcvXrS9AwuYNm0aQkJCMGLECJf7QBAeLwuJj49nANjw4cNteRaLhVWvXp2pVCo2b948W/6dO3eY0WhkgwYNsuVdunSJAWCrV6+25Q0aNIgBYDNnzuTaat68OYuMjOTyatWqxQA43OLj423HjBkzhmk0GsXzqVy5MuvXr1+JnrPFYuGkFAXlgoOD2csvv8zl//7770yv17NXXnmF3blzh1WrVo21aNGCmc1mrpwz10C8zt26dWN169aVnV9WVhYDwCZPnlzoNbBarSwgIIB17NiRy09NTWU+Pj4MADty5Igt/9lnn2Xz589n3377Lfvss89Yu3btGAD25ptvcsf7+fmxihUrMoPBwKZNm8Y2b97MXnzxRYf9IQhPpizaScYYa9OmDVu8eDHbunUrW758OWvatCkDwD7++GOH59ysWTPm7e3NvL292bhx49g333zDxo0bxwDIbOyFCxfYY489xvWlbt267OzZs1y5oUOHstDQUJaamsrl9+vXj/n7+7Ps7Gy7fXLF5k2ePJlNmjSJbdq0iX355Ze26/3EE0/I7K9Iwf2qXLkyS09Pt+VPmTKFAWDh4eFcHf3792d6vZ7l5uba8pTOZcSIEczb25srZ7VaWdu2bVlwcDBLTU1lY8aMYVqtlv3666/csQX9d7RFR0fbjvn6668ZAPbjjz/K+tK7d28WEhJi9zqI3L59m+n1etanTx8uPyUlRfFZZoyxZcuWMQDcs/Dbb78xjUZjk6UU/L5IFkI4S5lxaHzllVds/9ZoNGjRogWuX7+OoUOH2vIDAgLQsGFDh96/BYiRJNq1aydzjli/fj1ycnIc1lW3bl3bv3Nycjgpw/14eXk5VR9Q9HPWaDTQaDQA8v9iT09PhyRJaNGiBY4dO8a10bRpU7zzzjuYMmUKTp48idTUVHz//ffQavlHY/fu3U71OSwszPbvnJwcGAwGWRkvLy/b/sJQq9UYMWIE5s+fjylTpuDll19GZmYm3nzzTZhMJtnxBbMqBQwZMgRdunTBokWLMG7cOFSvXh1A/uxKwcxewSfNnj17Ii0tDUuWLMFbb72FChUqOHWuBOFplCU7CQA//fQTl3755ZcRGRmJt956C4MHD1b8clXAvXv3kJ2djZEjR9qig7zwwgswmUz45JNPMHPmTDRo0AAAUKFCBYSFhaF169bo2LEjkpKSMG/ePPTo0QMHDhxAUFAQGGP45ptv0KdPHzDGuMgQsbGx2LhxI44dO4Ynnnii0D65YvPmzp3LlenXrx8eeeQRvP3229i8ebNDh28A6N27N+cAGRUVBQAYMGAAZ8OjoqLw5Zdf4saNG7Z7cP+1vXv3LvLy8tCuXTt88sknOHv2rO3LpVqtxpo1axAeHo4uXbrgyJEjmDp1qmwW+M0338SAAQMc9rlixYq2fxdcj8KumbPvygI2b94Mk8kkk4Q4auf+MgDw6quvokuXLujcubNL7RNEAWVmcF2zZk0u7e/vDy8vLwQFBcnyb9++7bA+Ly8vWbSJihUr4s6dO1yePUNaGEaj0TYAFMnNzbX7wrif4pzz559/jvfffx9nz56F2Wy25depU0fWzsSJE7Fx40b88ssvmDNnDpo0aSIrExMT41Sf78doNHIykQIKPtk6ug4zZ85Eamoq3nvvPVtors6dO2Po0KFISEiAr69voceqVCq8/vrr2LVrF/bv328z+kajEVlZWejfvz9Xvn///ti5cyeOHz+O9u3bu3SeBOEplCU7qYRer8fYsWMxcuRIHD16FG3bti20bIH9EH/LL774Ij755BMkJiaiQYMGsFgsiImJQYcOHbhwqDExMQgLC8OCBQswf/58pKSkID09HStWrJDJXgq4desWAMgkaf7+/jAajcW2ea+//jqmTZuGPXv2ODW4VrrfAFCjRg3F/Pvv2+nTpzF16lT88MMPMs33/dINAKhXrx5mzJiBiRMnomnTppg2bZqsL02aNFF8d9ij4HoUds2cfVcWsH79egQGBqJLly4utXN/mU2bNuHQoUM4deqUS20TxP2UmcF1wUysozwATjmoFXasSEpKilPOO76+vrbBXmhoKKxWK27duoUqVarYyphMJty+fRtVq1Z1qu2invO6deswePBg9OjRAxMnTkSVKlWg0Wgwd+5cXLx4UXbsX3/9hQsXLgDI18ApIb5MCqPgJQPkX4d9+/aBMQaVSmUrc/PmTQBweB30ej0+/fRTzJ49G+fPn0dwcDAeeeQRvPjii1Cr1ahfv77d4wteMPc78lStWhUXLlxQdG4CIBs0EERZoizZycJQ+t0qUbVqVZw+fdrhb/nHH3/EqVOnsGjRIq5cgwYN0LhxY9vseYHPxYABAzBo0CDFNps1awYg37bdz+rVqzF48OBi2zyj0YhKlSo5PPcCCrs/ju55eno6oqOj4efnh5kzZ6JevXrw8vLCsWPHMGnSJEX/k++//x4A8Pfff+P27dsICQnh9mdkZDg106zX6xEYGAjgn+tYcH3u5+bNm06/KwHg6tWrOHDgAIYPHy4LKRkYGAiDwVBoO8A/92bixIno3bs39Hq9bU2F9PR0AMC1a9dgMplc6hdRPikzg+sHRcuWLXHlyhWH5eLj4zFjxgwA+fGWAeDIkSPo2rWrrcyRI0cgSZJtf0mxefNm1K1bF1u2bOEMfHx8vKysJEkYPHgw/Pz88Nprr2HOnDno1asX5wwCyF8mhVHwkgHyr8Onn36KM2fOcDMahw8ftu13huDgYNsL1Gq1Yv/+/YiKinL4ki747H3/zFtkZCQuXLjAfR4F8l8YYlmCIJyjKHayMJR+t0pERkZi9+7dNofGAsTfcnJyMgAoDv7NZjMsFoutfIUKFWC1Wh1+qRNlcgVyuOLavLt37yI1NbXE7dD+/ftx+/ZtbNmyhftSd+nSJcXyCQkJ2L17N2bPno25c+dixIgR2Lp1K1dm/Pjx+Pzzzx22HR0djf379wPIlyVqtVocOXIEffr0sZUxmUw4ceIEl+eIL7/8EowxmSQEyJe2PProozhy5Ihs3+HDh1G3bl2bHPDatWvYsGGDomPtY489hvDwcC4wAEEoQYNrBxRFS/jUU08hMDAQy5cv5wbXy5cvh7e3N7p161YifS2gYNbi/tmTw4cPIzExUfYZcdGiRTh06BC+++47dOvWDfv378eoUaPQvn177lNyUTTX3bt3x+uvv46PP/4YS5cutfUpISEB1apVQ5s2bWxlb968iYyMDNSrV8/uQhYLFy7EzZs3uc+7aWlp8Pf352ZrzGYz5s2bB71ejyeffNKW37dvX2zcuBGfffYZZs+eDSD/D4zVq1cjMDAQkZGRTp0nQRD/UBQ7mZKSIhtE3r17F4sXL0ZQUBD3WyyINFGzZk14e3sDAPr06YN58+bhs88+w1NPPWUr++mnn0Kr1aJDhw4A/llIauPGjXj66adt5Y4dO4Zz587ZooVoNBr07NkTGzZswKlTp9C0aVOub/f3t7DBt7M2Lzc3F2azWebfMWvWLDDGuH6WBPe/IwowmUz4+OOPZWUvXbqEiRMnomfPnnjrrbdQqVIljBw5El988QUXoq8ommt/f3/ExMRg3bp1mDZtmu16rF27Fvfu3UPv3r1tZbOzs3H16lUEBQXJZE5AfmjGmjVrFiol6tWrFyZPnowjR47Y9OLnzp3DDz/8wEVS+fe//y07duPGjdi0aRO++OILm/8OQdiDBtcOKKrmetasWRgzZgx69+6N2NhYHDhwAOvWrcPs2bNtn8RKimeeeQZbtmzB888/j27duuHSpUtISEhAkyZNuDBPZ86cwbRp0zB48GA8++yzAPLDM0VERGD06NFc7M+iaK6rV6+O1157DQsWLIDZbEbLli3x7bff4sCBA1i/fj03GJ4yZQo+//xzXLp0CbVr1waQL2/55ptv0L59e/j6+mLPnj346quv8Morr6Bnz562Y7/77ju8++676NWrF+rUqYO0tDTbC3LOnDnc58vu3bujY8eOmDt3LlJTUxEeHo5vv/0WBw8exCeffKLo8EIQhH2KYieXLVuGb7/9Fs8++yxq1qyJmzdvYtWqVbh69SrWrl3LOYUvXboU77zzDvbt22cbNDdv3hwvv/wyVq1aBYvFYpsR/frrrzFlyhTbp/vIyEh06tQJn3/+OTIzM9G5c2fbH+hGoxGvvfaarZ158+Zh3759iIqKwrBhw9CkSROkpaXh2LFj2LNnj0O5hrM2LykpCc2bN0f//v1ty53v2rULO3bswNNPP43u3bu7fD1doU2bNqhYsSIGDRqEV199FSqVCmvXrpVJhRhjePnll2E0GrF8+XIAwIgRI/DNN99g/PjxiImJsV3nomiuAWD27Nlo06YNoqOjMXz4cFy/fh3vv/8+OnfuzP2R8csvv+DJJ59U/Ppx6tQpnDx5EpMnT+a+1t7P6NGjsXLlSnTr1g1vvPEGdDodFi1ahODgYG5l3x49esiOLZip7tKli+LAniBklH6AEtcoLAROYatbRUdHs7CwMFu6sBBTSscWtOUuVqxYwRo2bMj0ej2rV68e++CDD5gkSQ6PK+45S5LE5syZw2rVqsUMBgNr3rw527ZtGxs0aBCrVasWYyw/TFfLli1Z9erVuVBOjDG2ZMkSBoBt2rSpCGfNY7VabX3R6/UsLCyMrVu3TlauIIzTpUuXbHmHDx9m7du3ZxUrVmReXl4sPDycJSQkyK7hkSNH2LPPPsuqVavG9Ho98/X1ZW3btmVfffWVYp/u3r3Lxo8fz0JCQpher2ePPvqoYp8IoqxQFu3k999/zzp16sRCQkKYTqdjAQEBrHPnzmzv3r2Ftrlv3z4u32QysRkzZrBatWoxnU7H6tevzz744APZ8dnZ2WzmzJmsSZMmzPh/7J15fBXl9f8/d79ZSMKaAEZRQAFBsEEiuOASjYqttC5IVZZScAGLRq1gkVC1oqIIKppii7QKhbr81KrFYoRWC8UCWsUiLpVNSFhDINu9d+b5/ZFvrpwzk5k7Nzfhhpy3r3nJM/PMM888Mznz3JnPOSclRWVmZqorr7xSffzxx4a65eXlavLkySo3N1f5fD6Vk5OjLr74YrVw4cKYzisWm3fw4EF14403ql69eqnU1FQVCATU6aefrh5++GEVCoVsj9FwvebMmUPWr1q1SgFQL7/8Mln/wgsvKAAkfN4///lPdfbZZ6uUlBTVrVs39ctf/lK9++67ZJwbngWvvvoqaW/79u0qIyNDXXHFFTGNiR0ffPCBGjZsmAoGg6pz585q8uTJqrKy0vTceDhHperDGgJQn376qeVxduzYoa655hqVkZGh0tPT1ZVXXqm++uor2/5JKD7BKS6lJD2dIAiCIAiCICSCVpOhURAEQRAEQRCSHZlcC4IgCIIgCEKCkMm1IAiCIAiCICQImVwLgiAIgiAIQoKQybUgCIIgCIIgJAiZXAuCIAiCIAhCgpAkMoIgJA21tbUIhUKO9/P7/QgGg83QI0EQBOFoxE7bk9DJtdtFB83rzSJlj9tPyrqKWJbNcLGX7ZpeTco+Twat76L1I1o1206HgLcHAG4X7TdYmx62PawdIWU3OwbfX2N98nrSDX1QMYwNOabNWPP2/F46bnXhCkObvA5H063/2AK+TNonnfahNryf7aEb2vC4Uy2Pwc+LX1/bcWTXht9vAKArep4eN73vNb2WlCMR66xuQj21tbU4+eTuKCtzPl45OTn49ttv24zhbgr8bwIwz2jX+mnah1lXPOPi4sdk9sRmu92zgj9r3G7jI9zt8tkck8LtsK7CpMztOrd/ZjbVsE5RW67A02sYbb01iU/P4fQZ21YROx0b8uZaEISkIBQKoazsALZ+uwwZGdY/oo6msrIaPU6+HqFQqE0YbUEQhGOF2OnYkMm1IAhJRUZ6EBnpKbHvoDt96yUIgiA0BbHT1sjkWhCE5ELT6hcn9QVBEISWQ+y0JQmdXHuYJtegzWK/XLjGiWtXASAcqSBlv7cDKWsGDXYtK3MNNdO4ubmGzdgHrrvlGjZ+zIAvi5QjGt3Oz9vHxo1r0wGjLs7jDtDtTCdn2J8fk+m67fTSZm0Y9evWeuVwpIqUuV6Q9ynErn39MStJ2cP2Cfjak3JNaC87JhtbpgX0suvPzxEwXj9jnbb1Cz3h6MrZWw498frL45vjQWOd+EBXjjXWNlrmevgzjzXBDsmfmVD0GMrFdMEmfyZmfiJOUIY+84NYP8frV9pprJsKv1ZiA1ocsdOWSCg+QRCSC113vgiCIAgtRwvY6QULFqBHjx4IBoPIz8/HRx99ZFn/5ZdfRp8+fRAMBjFgwAC888470W3hcBj33nsvBgwYgLS0NHTr1g1jxozBrl27SBsHDhzADTfcgIyMDGRlZWHChAk4cuQIP5QtMrkWBCG5iEScL4IgCELL0cx2evny5SgqKkJxcTE2btyIgQMHorCwEHv27DGtv2bNGowePRoTJkzAxx9/jJEjR2LkyJHYtGkTAKC6uhobN27E/fffj40bN+K1117Dli1b8KMf/Yi0c8MNN+Dzzz/HypUr8dZbb+Ef//gHJk2a5Hh4XErxD1Xx4/N1IWWnYdHikYXYhb1zKgsxhqmyl4XoTFLh81Kpgp0shPehOWQhESZd4fIHO8kHAHg9qZZ17GQh/PpyWQg/RzNZCP8kaZCFeGm4P8eyEJtzBEw+3Rp+o9I2NSZlEcyprKxEZmYmDnz7EjLaOfBCP1yNDiffiEOHDiEjwzpcpAC4WKi21klrkYVw7ELzNbV+a5WFNPXrU9OnMRKKLzZayk7n5+fjrLPOwjPPPAMA0HUdubm5uP322zFt2jRD/VGjRqGqqgpvvfVWdN3ZZ5+NQYMGoaSkxPQY//73vzFkyBBs27YNJ554IjZv3ox+/frh3//+NwYPHgwAWLFiBa644grs3LkT3bp1i/l8E6q51thE18tiTnO9LNc3m8W59rFY2RE2WeYTNsOEnU2++HavTdxks2O6mA7OzyZ0Ie2wdZ/4efPJuSfN0Iewon3g+nW+Tyhi3YdQhF6L1EA2KRvircJoZPlElBt1u8mzMX4q3R7w0R9SgPFHAo/7ys+b95HDJ8p1YfvYnTwOecRw3xvjlAux41I6XIYHunV94Xij+T+qxhXHusnYTVytf6jH8yoslgm5dZ84bLv8/bVJ4rXTlZV07hEIBBAI0JeFoVAIGzZswPTp06Pr3G43CgoKsHbtWtP2165di6KiIrKusLAQr7/+eqN9OnToEFwuF7KysqJtZGVlRSfWAFBQUAC3241169bhxz/+se15Rvsbc01BEISWIKI5XwRBEISWI047nZubi8zMzOgye/ZsQ9P79u2DpmnIzqYv/bKzs1FWVmbanbKyMkf1a2trce+992L06NHRN+llZWXo0oUqMLxeLzp06NBoO40hofgEQUgunDq/iEOjIAhCyxKnnd6xYweRhfC31i1BOBzGddddB6UUnnvuuWY5hkyuBUFILmRyLQiCkNzEaaczMjJsNdedOnWCx+NBeXk5WV9eXo6cnBzTfXJycmKq3zCx3rZtG95//33Sl5ycHIPDZCQSwYEDBxo9bmMkeHLNHdioHlbTjY56dtjFZ+b6MMX0ZXy7y8X7RDW8sTipcIePsFbFKvDYyVT3q/H436w97oQHmMWlpv0O8XFg4xb0dSTlusghVp854cUQ95r3gTsLaho7T5trya81d9oEgLoQ8xRmMcKNDq20j36b+rwPit8fMF4vD9N18+2CQ5RypuNMnE+20GK0Qk11XA6MTkm87bDXUDtt0L69xMe1tsPsWotdaFaa0U77/X7k5eWhtLQUI0eOBFDv0FhaWoopU6aY7jN06FCUlpbijjvuiK5buXIlhg4dGi03TKy/+uorrFq1Ch07djS0UVFRgQ0bNiAvLw8A8P7770PXdeTn58fcf0DeXAuCkGw41VGL5loQBKFlaWY7XVRUhLFjx2Lw4MEYMmQI5s2bh6qqKowfPx4AMGbMGHTv3j2q2Z46dSqGDx+OJ554AiNGjMCyZcuwfv16LFy4EED9xPqaa67Bxo0b8dZbb0HTtKiOukOHDvD7/ejbty8uu+wyTJw4ESUlJQiHw5gyZQquv/56R5FCAJlcC4KQbIgsRBAEIblpZjs9atQo7N27FzNnzkRZWRkGDRqEFStWRJ0Wt2/fDrf7+69Jw4YNw9KlSzFjxgzcd9996N27N15//XX0798fAPDdd9/hzTffBAAMGjSIHGvVqlW44IILAABLlizBlClTcPHFF8PtduPqq6/GU0895ajvgEyuBUFINpRDoy2hwARBEFqWFrDTU6ZMaVQGsnr1asO6a6+9Ftdee61p/R49eiCWtC4dOnTA0qVLHfXTjIROrnl8X2Pcajq4Okuu4jJJIhPwZZEy1wL7ve1IubqOCto5hiQiNolPAPvz4tpyrnFzs6QNbjbsfHtNyPocAJPzYOgGDTYt82QrXDfOxxUA6sIHSZnHxo5oNaTMk8D4WNxzQ5xrpm+uM4np7/d1ovuwa2Eo6zyONe2TMYkQi9VtErNa06xjrRt0/IIjXJoGlxb7J0QndYVjRWL1ys0So7pFNNUJJgl+WLa8vjpW+D3C+3ks4pwfP4idtkbeXAuCkFyILEQQBCG5ETttiUyuBUFILsRoC4IgJDdipy2RybUgCMmFruoXJ/UFQRCElkPstCWtUGQmCMJxTUQDIhEHS3xavgULFqBHjx4IBoPIz8/HRx99ZFm/oqICkydPRteuXREIBHDqqafinXfeievYgiAIrZoWstOtlYS+uTYk8GDJUtwsgQu8zBGMOYoBRqe3iHbE8pheltAjwtr0se3G9o194M59SrNOuBL0tSflWuYI6POkkbKuwmy7MXsRH0uOh40tH+uITp0NuaMfd+zjzomA0XGvLkwT0eisjy6bhC58O/+t5zVxcOUOh1qEJ4lhjpo2vx95n/j9Y5aAwc4p1s7ZVLBBKWeJYeJIIrN8+XIUFRWhpKQE+fn5mDdvHgoLC7FlyxZ06dLFUD8UCuGSSy5Bly5d8Morr6B79+7Ytm0bsrKyHB+7bZCEDoyt0WFREJKVFrDTrRmRhQiCkFy0gJZv7ty5mDhxYjQhQUlJCd5++20sWrQI06ZNM9RftGgRDhw4gDVr1sDnq4/u06NHD8fHFQRBOC4QzbUl8lNeEITkQtO+z/4Vy+IwxFMoFMKGDRtQUFAQXed2u1FQUIC1a9ea7vPmm29i6NChmDx5MrKzs9G/f388/PDD0NpYeClBEAQAzW6nWzvy5loQhOQizjcilZWVZHUgEEAgEDBU37dvHzRNi2b6aiA7OxtffPGF6SH+97//4f3338cNN9yAd955B19//TVuu+02hMNhFBcXx95XQRCE4wF5c21J806ubQLcm2lqOVwDzRO6cL2zYolquE44HKGabZ+XJYjRjdpmnmCF1+FtVNXtpn12Ux2vQTesWeupAWPSF66J5tph43nxxDd0XAwJf0wkjjxZjteVQso8yYzG9PGpwVxSrg3vJ2W/l2rNIzEkY+H6Zt4mH2uu8+Ya63CETtA8bDtgTDTEk98YfAsEZ8TphZ6bS++v4uJizJo1KzFd0nV06dIFCxcuhMfjQV5eHr777jvMmTNHJtcAmvoRtHVoqhPRfoInGLGccxIkmkkOJGlMQpFoIZbIm2tBEJILpTubEPxf3R07diAj4/sfaGZvrQGgU6dO8Hg8KC+nmVDLy8uRk5Njuk/Xrl3h8/ng8Xii6/r27YuysjKEQiH4/fKDShCENkScdrqtIJprQRCSCyc6voYFQEZGBlkam1z7/X7k5eWhtLQ0uk7XdZSWlmLo0KGm+5xzzjn4+uuvoR/1afPLL79E165dZWItCELbI0473VaQybUgCMlFw+dGJ4tDioqK8Pzzz+MPf/gDNm/ejFtvvRVVVVXR6CFjxozB9OnTo/VvvfVWHDhwAFOnTsWXX36Jt99+Gw8//DAmT56csNMWBEFoNbSAnW7NJFQWEmYaW67r5TGpuZ7aoPuFUVPL6xj0ruyYitXnWuS6cAXdzjTdpjCdm8dN35AFvDTONddsa3odKfO42Lw+YIyFzbXEPL4zPwavn+LvRMpVdWVse0dDH2pCVM8c1un15n2oY/rCUOQwKfNx4ufIY3fX16HX0ywu+dHw62+nNefbhWOArhw6yjg32qNGjcLevXsxc+ZMlJWVYdCgQVixYkXUyXH79u1wu7//m8nNzcW7776LO++8E2eccQa6d++OqVOn4t5773V87NZP09/JNFljHZe+OhneJTW1D3F8Wrcbq6T4XM/7mAx9EixpATvdmhHNtSAIyYXuMGyTHt/nxilTpmDKlCmm21avXm1YN3ToUPzrX/+K61iCIAjHFS1kp1srMrkWBCG5EC90QRCE5EbstCUyuRYEIbkQoy0IgpDciJ22JKGT66CP6nRDkUOk7PYwPTSoptbvpppdwKg/5rGWFdPl8jjXuqIxpA1xsZlm2+Mzao3dbjpMbp2WuZ5ZwfrzB4+1zeF9BACvm8aU9nnTSNmgRdZpHGyfh9bn48o11hHNGGOa96GOjR0fB66p5uPCNdYhFmM64Msy9MHnpudRE6JjxWOQc82+YvpCp5p9M+LZR7DAqWd5G/NCb3mOgca6BTTV3F61BlRc8xMbXSwfB4cabLNrq9C2JlJtErHTlsiba0EQkgt5IyIIgpDciJ22RCbXgiAkF5JWVxAEIbkRO22JTK4FQUgulHL2/Tu+b+WCIAhCvIidtiShk2uuoeU6YK6pdbG412bxne1iY3ONNW+Tx17mWuSAvwvb36jDi2g1lnX4dl2n550a6EzKXFPN2+Nxs+vXWcf79nqoHpq3yfvE26sNHbTc36xfXFPN2+RxsbkWOcDie/Nrze8XM9ICXUm5Lsx0/kwvzzXX/P7hsbVDJnG0U9kxa8P0PLnvgeCQiF6/OKkvJJDWoLG2r+9cU538GmxXDMPKbZz9ebH6TdRgA8br71yDLXGvkx6x05bIm2tBEJIL5fBzY1IkwRAEQWhDiJ22RCbXgiAkF+IoIwiCkNyInbZEJteCICQXEeXwc2PbMtqCIAjHHLHTliR0ch3WaPxmrqHlMYf5dq4jBgBNcX0y3cfL2uTYxXvW9DpSNurVALfLZ9mmCzzONdP1sjZTfZ1I+UDVFlJOTznB0AcDbKz4efCY1BEwXTjbn8fN5vppM0KRw9ZdZOPANdZcY6+xmNM+b4ahTaMum/aBa6x5THGuwXeB3l+hSAUpez3phj7UhPZa9olvF5yhdAXl4C2Hk7pC85B4jbX19tj01Ilo46j6LaDJ5jbTsD2GW91Ol22vybbRYBsatJ9giQb7+EPstDXy5loQhORCvNAFQRCSG7HTlsjkWhCE5EK0fIIgCMmN2GlLZHItCEJyISGeBEEQkhux05YkdHLtcQdJmWtouWatLnzAcn/AqLE2xrWmbfIY0lw7zGNS+33tSJnHgzZrQ0EjZa7J5rpfTnV4HymnBrIt6wNAhrcbKR+OlNE+snFyuTykzGOQ+1hc7MO1u0jZG6DbzeBjHdHo9Q4wfbOdLtwXoLpvfq0Ac0083YfGpfZ6Ukk5FKFxsPk952H1+T1s1qbdMQWHyBuRFqYF4jsfA4213T62Gmq2f0toru20xC7uz2Nan/kAKf7MtO6B8eu9zaTIbJxt7HTTNdjCMUfstCXy5loQhKRCKYeOMm1MyycIgnCsETttjUyuBUFILuRzoyAIQnIjdtoSmVwLgpBcyOdGQRCE5EbstCUJnVzrunVMah5LmcewNoNrynxMz8o10rwPXC/N4ztzPZqZXppriXmsbN5HHveYHyPN38WyT2baPt4mx+um48J14QEPjRkdUVT/HPTTGNS6TjXagFFjneLvaNknPm58/0CAarJrQvtJ2U5fXV+Hjp2dHtpOw8/vH79JrG0eC9vj5seksbUFh7RQiKcFCxZgzpw5KCsrw8CBA/H0009jyJAhpnUXL16M8ePHk3WBQAC1tUZN/vGO45jW8RwjDo0238dgRx1qqG3bi2EfO7iNs41zbdhuUp/bTXa57DTV/Bzs/7xM+sDHIeGpryXu9TFHQvFZIm+uBUFIKlREQTnI5uWkbgPLly9HUVERSkpKkJ+fj3nz5qGwsBBbtmxBly5dTPfJyMjAli3fJ3xy2XmGCYIgHKe0hJ1uzbSE+7MgCELsNHxudLI4ZO7cuZg4cSLGjx+Pfv36oaSkBKmpqVi0aFGj+7hcLuTk5ESX7Gz7KD+CIAjHJS1gp1szMrkWBCGpULrzxQmhUAgbNmxAQUFBdJ3b7UZBQQHWrl3b6H5HjhzBSSedhNzcXFx11VX4/PPP4z1FQRCEVk1z22mgXrrXo0cPBINB5Ofn46OPPrKs//LLL6NPnz4IBoMYMGAA3nnnHbL9tddew6WXXoqOHTvC5XLhk08+MbRxwQUXwOVykeWWW25x3HeZXAuCkFwoh29D/k/LV1lZSZa6ujrT5vft2wdN0wxvnrOzs1FWVma6z2mnnYZFixbhjTfewEsvvQRd1zFs2DDs3LkzsecuCILQGojTTsdKg3SvuLgYGzduxMCBA1FYWIg9e/aY1l+zZg1Gjx6NCRMm4OOPP8bIkSMxcuRIbNq0KVqnqqoK5557Lh599FHLY0+cOBG7d++OLo899pijvgMJ1lxzZzGegKO6bjcpc0ewWOAOjNwp0m+TuIQ74dWFaVKR1IBRb8mdCVO8rA2tkrbBtkcUHQcPSzrjAk34EnSbONEp6iSX5u1MytzRJeiifahRB0nZj3RS1j3UgVE3+ZmpMSdIjSWmCUUOkzJ3JuWOoFV15aTMr42ZQyNPLONy0SQwAV8WKdfUMYdF5gDJ+2g4nkkSGbeLJyai18YsGZIQOyqioDzOtXy5ublkfXFxMWbNmpWQPg0dOhRDhw6NlocNG4a+ffvit7/9LR588MGEHKPlSL6kMU4dGM3qO3Vg5Hbd4MBo2N9juT0e7Jy2efIvbufNHCDtnB7tEtHYJZ2JxdHcKU1PKmN2LcTJsTmJ107HytHSPQAoKSnB22+/jUWLFmHatGmG+vPnz8dll12Ge+65BwDw4IMPYuXKlXjmmWdQUlICALjpppsAAFu3brU8dmpqKnJychz1lyNvrgVBSC70OBYAO3bswKFDh6LL9OnTTZvv1KkTPB4Pysvpj7vy8vKYDarP58OZZ56Jr7/+2vHpCYIgtHritNOxfGGMR7q3du1aUh8ACgsLLaV+jbFkyRJ06tQJ/fv3x/Tp01FdXW2/E0Mm14IgJBcqjgX10TyOXgKBgGnzfr8feXl5KC0tja7TdR2lpaXk7bQVmqbhs88+Q9euXeM6RUEQhFZNnHY6NzcXmZmZ0WX27NmGpuOR7pWVlTmq3xg//elP8dJLL2HVqlWYPn06XnzxRdx4442O2gAkFJ8gCElGc39uBICioiKMHTsWgwcPxpAhQzBv3jxUVVVFP0GOGTMG3bt3jxr+Bx54AGeffTZ69eqFiooKzJkzB9u2bcPPf/5zx8cWBEFo7cRrp3fs2IGMjO+lr429BDlWTJo0KfrvAQMGoGvXrrj44ovxzTffoGfPnjG3k2DNtbWmzTbZiolGiuvkDIloQlQTyzVqPCmM2031zn5fO1KO6FTTa9YHnqDF504hZa+L3ixcc53hop+eK9QuUvaA9hEAUlw0ycthjf4aS/dQrXhX/SRS3sPOu1ZRnTgfe4+L6gsBo+aaa6y5pjqsVZGy3f3B9e9eNq6AMRGN30uvX23ogOUxeZIYTbfebqaf1tj15L4GQtNw6lkejwR01KhR2Lt3L2bOnImysjIMGjQIK1asiL752L59O9zu7++NgwcPYuLEiSgrK0P79u2Rl5eHNWvWoF+/fs4P3spITNIYpx9J7fTPJu3Z2BeD5po/G7gvjF17ZrpvE7tphVIaK3MfEXpM7nPEn0VmdQyJ09jldPH5kU3SGaMG29AFGPTODpPKNF2DLTQ38drphi+LVsQj3cvJyWmS1K8x8vPzAQBff/21o8m1yEIEQUgu4tTyOWXKlCnYtm0b6urqsG7duqgRBYDVq1dj8eLF0fKTTz4ZrVtWVoa3334bZ555ZnwHFgRBaO00o52OR7o3dOhQUh8AVq5cGbPUrzEawvU5lQDKKzdBEJKKlnhzLQiCIMRPc9tpp9K9qVOnYvjw4XjiiScwYsQILFu2DOvXr8fChQujbR44cADbt2/Hrl31aoGGjLsNicG++eYbLF26FFdccQU6duyITz/9FHfeeSfOP/98nHHGGY76L5NrQRCSCqUDyvi127K+IAiC0HI0t512Kt0bNmwYli5dihkzZuC+++5D79698frrr6N///7ROm+++WZ0cg4A119/PYDvw7b6/X6899570Yl8bm4urr76asyYMcNZ5wG4lHIY2dsCjyeLlL0eGseaa784fk87wzqui+Nxjjlcs+ZxU/2zz0v7xOt7PUadL9cOc21xqofGZw7pNO5xBw/VP3NNdZZO9dRVLmPYF5+i+6SB9lNnmjRe9jIF0B431SbVgR7TDaN2sEZRTXSdTnXbXNPI439zPaKu89ja9P4w0zTWhPbTfrq4bpKWQxHeB6afZ9ef7293zwKAz0NjhodZ3GtNq7BtQ6gP0ZSZmYldE65Dht86/jjZLxRCt9//GYcOHbLV8gmAy+XMgSgmzXWT41Q71FibHM/OFnBNtVFzTcv82eGxaQ+wj43NNdJGjTW1iRqzPzxvg5l9Mmqure2soQ3WJ2McbHubaIyFbTOzspl5JUZzzY/BnwXmSacEitjp2JA314IgJBdOddTy5loQBKFlETttiUyuBUFIKnStfnFSXxAEQWg5xE5bI5NrQRCSC91VvzipLwiCILQcYqctSfDkmuvHrLVZHhfV63g8Rv1OdV25Yd3RuFkbXA/N4XGSDe2ZRCfk8ZZ5TGmu1ctg2z0sVmmqohpdH7sMp7hpliEACDNNWk4KPW+dSef31dKxP8LOu4tOj1EDe73ZQTcdGz/T1HPCLGY41+5FNOsY5VzzCABeFnfaTPdI26D1g36qb+extT1uOq5cMwkYdY+aomPLNdiCMyRayPFAgqO8xhDn2hDH2kZjzf/WDfkK3NS+8fwFvAwY/WnMfFeORgO1eRGeS0BRXxjNTbdzGwsAmovZcodvDHUw3xcmd1aGsY/lDzCefY7qg8S9TjrETlsjb64FQUgqdM0FXYv9LYeTuoIgCELTETttjUyuBUFIKpRy+EZEXmIJgiC0KGKnrZHJtSAISYVSLiieo9mmviAIgtByiJ22JqGT64CvAylzfTOPfck1uDw+JwAEfTSGdG2Yxjnmujmu2/V5qQY7FD5MyimBTrTPJjrxFG8WPQYTsQVANbY8ZnRXReNcpzBdXs90qu1rHzDehBr71cdrZPjompPS6bhoipYPMun5wTp6Tpvr9hj6kKufSMo73NtJ+Yii+2T4upFyrU71zVy77GbadLP7gceRtYuDzu8xrrHWdKr75veoWaztCItjzdFRa7ldsEF3QYmjzDHDNq61bUxrk12aGNfaUDaLc83jVjvUWPuZr0TQlUnLzM5z3xkACCgWG5tprnn+gTDTXNeyHAc1bppjoVbRuP3c3wcAQnZ6dxsNtuE5zW4Hfi3M3ki62D7GuNd8B9bntibQbY2InbZE3lwLgpBU6LoLugND7KSuIAiC0HTETlsjk2tBEJIK5fCNiKO3J4IgCEKTETttjUyuBUFIKpRy5vzS1hxlBEEQjjVip61J6OQ6HKFaVK535borHoM4FKF66Pp9qO42LdCVlHksbR7nmmuw/b52pMx1vyleqhsHAJ+LaqK5xjpD0djJPK51e6bt69+e6vIyWKhm3eQm7OCnY9c1SM+7nY+Wd9XQY7iY1u9/VbSPtRGqeTvJQ7XogPGPI6x1J+XdbtrHKrWPlO3G+khdGSlzzSQARDR6Pfk9Y9Ag2uj8zeLlHo1ZrHZ+H/u8GaQcjlBdpOAMTXNDc8eu69W0BMdUFo49NrpuM60xj0nP6/C4+VxjneKidrydov4+WYr+nWey5xcApPtpH4Ie+raO2/Ya5kxzOEyfTxU61WBXsHMwGwfHMA22ctnlq2DXxuSFpK3Guolxr+ND7EQiETttjby5FgQhqZA3IoIgCMmN2GlrZHItCEJSISGeBEEQkhux09bI5FoQhKRCjLYgCEJyI3bamoROrl1M/6UUDabs9VDtckSjejIz+D5cY+v1UD3z4dqdpBz0UR0d16gFmF4200N1xABQp6iWPJNp8cIuqgM+kemV2weoDo/TLUhFb/0yjdrzFKap7vWDg6RcvYvqmc7pRjVs+7+iWr3UnTmk7HHRGLBdU5kQHMCmg/Q8O7CxP6hTvXsqqH5wv4demwM1X5Oyz0uvdW2IniNg1MxX15WTcoRp/fzs+vL7pSa0l5QDXtq+mR+Ah92TOovnzrcLzmgpLd+CBQswZ84clJWVYeDAgXj66acxZMgQ2/2WLVuG0aNH46qrrsLrr78e17EFip3vgyEOdkxxrqkNM8S1dlHNdbrKIuWOrJwToPt3TzPa9a4p9Nt3ls9aS3wgRM9jdw3Vce+qoucQCNHyXpNxUG5rXyc7Xyjup2SIa82vlam+ml8vtksT417zWOwKbUxzkASI5tqatnW2giAkPQ1aPieLU5YvX46ioiIUFxdj48aNGDhwIAoLC7FnjzF50tFs3boVd999N84777w4z04QBKH10xJ2ujUjk2tBEJIKHS7oysFil1HQhLlz52LixIkYP348+vXrh5KSEqSmpmLRokWN7qNpGm644Qb8+te/ximnnNKUUxQEQWjVtISdbs3I5FoQhKRC193QHCy6Xm/GKisryVJXV2fafigUwoYNG1BQUBBd53a7UVBQgLVr1zbarwceeABdunTBhAkTEnvCgiAIrYx47XRbIaGaa51rrN3W2lOumzPTYXG9V0SvJWUeu5TX97ipljjFS/XS1WGqua1zU301YNRp14JqxTNZnGuuL8vw0z4NyKT66QHtK0i575VGLbqrHTvPnJNI2XN5AS2Xriblzu22kvL5Qap/zt1FY06/siPL0IcuKXQcdlbR6x0A1QsecR2iDbDPQu2C3Ug5rNeQcmqgi6EP1XX0sz2/x7gm26An1I1xq4+Ga7DNtJ3ct4DHgXUz/brgjHgdZXJzc8n64uJizJo1y1B/37590DQN2dnZZH12dja++OIL02N8+OGH+P3vf49PPvkk5n61LZr3wWmnyQaMf6uGONcuqlf2g9qOdEVtRycftWcnt6Ma60FZ1P4BwNndqQ9I5wH0B57LR+/ris9pn9dvo3kc1h+kffBUMn+PWvo8AwDNRe2R5qYaaq6p1pnPkJuNE7ehxmthovtukbjVwrFEHBqtkWghgiAkFTqcpZRoqLtjxw5kZHzvwBoIBMx3cMjhw4dx00034fnnn0enTsbkSoIgCG2NeO10W0Em14IgJBXxvhHJyMggk+vG6NSpEzweD8rL6VvG8vJy5OTkGOp/88032Lp1K374wx9G1+l6/aPC6/Viy5Yt6NmzZ8z9FQRBaO3Im2trZHItCEJSoekuaA70eZruzGj7/X7k5eWhtLQUI0eOBFA/WS4tLcWUKVMM9fv06YPPPvuMrJsxYwYOHz6M+fPnG+QogiAIxzvNbadbOzK5FgQhqdBV/eKkvlOKioowduxYDB48GEOGDMG8efNQVVWF8ePHAwDGjBmD7t27Y/bs2QgGg+jfvz/ZPysrCwAM6wVBENoCLWGnWzPNmkQmrFHnQJ4QxuOmzho8GYcZ3EmFO1t4PbRNTkinfUr1dSZlDdS5AwBSkUXKPlAtZwqoA0j3NFpOY6OcHaTnmd2RJipxD+hh6IN24XDDOiu0iy8gZW+7j0i5/Sm7SbniJeooelo7mtgGAL46Qh16/neY1gmwcQmDJl3grqKaTp19vC66f3Vkn6EPPAkMd1iNaPQ8FGgfNZ7whd2DPLERv6cBQCnuFMmTMlg7TQrWtMTnxlGjRmHv3r2YOXMmysrKMGjQIKxYsSLq5Lh9+3a4HSRIEJxh5ihMttsllYExgQvfx20os4QsitqSVGa/OgTpMXqmU1tyUb/thj5kzL+alFVGpqHO0bRn5Uufeon28WWq8a/V6DO0KkzPAQCqNCqNqmVO+mE3tXFuFojAxWyk4VrFMEkyBivg22G5ve0pdFsfIguxRt5cC4KQVGjKBc2BIXZS92imTJliKgMBgNWrV1vuu3jx4riOKQiCcDzQUna6tSKTa0EQkoqGpANO6guCIAgth9hpa2RyLQhCUqHgLJuXamOZvwRBEI41YqetSejkmmtN3QY9LNV+BXw0cYlZEhmNJY0xJvAIs+08kYB1QHy+f9BtDOXlZvo+n2LJCFx0e0UdPUZ/JrtL89JjZg2ggjPt/HMMfWgq2pAhpOw5vJqUu57yNSnnHMgytLE3RM+zS5BqFD11LOGPohpFj5uOW1fP6aRc56JJZMyojVSQclirImWuxTRqsNk9xu4Hrunn2+uPQc9TN7QhWt2mUO+F7uBzYxvzQhdiw8XsMrfjXJPN7Xial95XJ6RQH5GsH9OELwCg2Wis7VC/uJGUh2z5HSl/s6YXKZfXGLXnB6qpDvsw832pZc9Ebs/4M9SurMz08SZ2szlxmUzcVCzicCFuxE5bI2+uBUFIKhRcjt5ytLU3IoIgCMcasdPWyORaEISkQkI8CYIgJDdip62RybUgCEmFOMoIgiAkN2KnrUno5NrvpXplHlPY7Ukn5TCPKWyi3eIa2YC3HTsG1cEFfFmkXBOisZJTA10MxziakOLRmAGfi8ZC9nLtHgvaWRXhMaJpfY+L/oSL7KNadZ+n+X/zqFyqF9TD35ByO58x3nf3IO3XF1yLx+rr7NpFFL1We9W3pMyvZXXIGOc64KOaRp3p/LkekGvqPW6qE+ca/JB2mG03xqwO+jqSck2IptEO+Hj0WsEJEuJJSARKGWP1O8HNbquAm+mIu1o/SxJB6jk0znX2RmrP0nzUngFAgD3WPSy+N4/3baepTgoMsbYlDvaxRuy0NfLmWhCEpEK0fIIgCMmN2GlrZHItCEJSIVo+QRCE5EbstDUyuRYEIamQz42CIAjJjdhpaxI6ueYxhT0sznVYM+qZCTzGMACvO5VWsdGHhcJUM+tnGm2uwe6Ychop12oHDX1I81LdW52rjtcgpaFd6HnUMOkf/zzi68I0cOW7DX1Q2caYqk3CS/vo70L75N5s3KW8jmrHj4TpiQXcdHuVqqQNsF+uIZ3eDxHNPs41j2ut60ZNNNnONNMeUI1iRKfH9LjYPcv8BgAgFKH3GI+9zrcLzlBw5ijT1j43JidcA9vyul3un8N9PnRQexVxUdsQ0un26gg1WHvqqG3Q120xduKswTH1NWbqjL4vdriYD5BbWV8LHu/bEPea+QyZ+UYZ+gAeC1s00scbLWGnFyxYgDlz5qCsrAwDBw7E008/jSEsZ8fRvPzyy7j//vuxdetW9O7dG48++iiuuOKK6PbXXnsNJSUl2LBhAw4cOICPP/4YgwYNIm3U1tbirrvuwrJly1BXV4fCwkI8++yzyM7OdtT3JPRcEAShLaPHsQiCIAgtR3Pb6eXLl6OoqAjFxcXYuHEjBg4ciMLCQuzZs8e0/po1azB69GhMmDABH3/8MUaOHImRI0di06ZN0TpVVVU499xz8eijjzZ63DvvvBN/+ctf8PLLL+Pvf/87du3ahZ/85CcOey+Ta0EQkgylXI4XQRAEoeVobjs9d+5cTJw4EePHj0e/fv1QUlKC1NRULFq0yLT+/Pnzcdlll+Gee+5B37598eCDD+IHP/gBnnnmmWidm266CTNnzkRBQYFpG4cOHcLvf/97zJ07FxdddBHy8vLwwgsvYM2aNfjXv/7lqP8yuRYEIamIKOeLIAiC0HI0p50OhULYsGEDmQS73W4UFBRg7dq1pvusXbvWMGkuLCxstL4ZGzZsQDgcJu306dMHJ554oqN2gARrrnXF9KnsO4DO4lr7fVTL7HYbu8N13DwWMo+VHfDSOMhuN9UzB90dSLkqspf2yUP10wBQragOO0t1pn1gJ/o1kxqf0o7+YttXS3W/ezfQ3zjZJ64z9ME15HRS1nv1NtRxguvzr0l523oag/xI2HgtKsP0PFK8tN/f1dJrUeWm49bORceNSflQzWKXcr00YB+nOhyhmmweF5vXdzO9ND+mm/kNmOF22dcRYkdCPB3/8L9D7jvDNbrGsjGGNW+Tl3mc/ZCL+ltUg24/WEfzG/yviv6df/W2Mcb0afnrSVlzqsHWaZ8PraZ+KXvrqA2tNZmxhFl8b83N7CjbhWvTRR8txEK8drqykk6QAoEAAgH6t7Rv3z5ommbQOWdnZ+OLL74wbb+srMy0fllZWcx9LCsrg9/vR1ZWVpPaAeTNtSAISUZDiCcniyAIgtByxGunc3NzkZmZGV1mz559bE+kmZBQfIIgJBUS4kkQBCG5iddO79ixAxkZ32fz5m+tAaBTp07weDwoL6fZj8vLy5GTk2Pafk5OjqP6jbURCoVQUVFB3l47bQeQN9eCICQZ8uZaEAQhuYnXTmdkZJDFbHLt9/uRl5eH0tLS74+n6ygtLcXQoUNN+zN06FBSHwBWrlzZaH0z8vLy4PP5SDtbtmzB9u3bHbUDJPjNdcDbnpR5TGKfj+qduX7W4zFqV7nmmsfc5PDY2rpO44RyDbbfTTXWZu1zfV/IRft0SNFh/OoIrZ/uo3rmz0C1fJl+Oi6dvjGGmvG2+x8pu6uovtkVocdUKfQY+ORLUjzwTgUpp6XQc/hut/FafHuYavHqNFquBdXcZymqfzriosfksVC9XE9tov3jsbDrwodImWuouQabx1o3xGxl2k8e9xoANOZbYKfbFpzRUpprJzFUX3vtNTz88MP4+uuvEQ6H0bt3b9x111246aab4jp2MqOYKNfFx1eZaHJdTXtPw//W7WIpcz212TpdhS3LIVAbesRF49Pv45rrwymk/P5u5kMCQJuxnZRPu/hzUvb0pW+/1D6qP614n9qrD78+gZS3VtFxORQy2hpuh8NMS66DjoPZWJLt7PknmmwBaH47XVRUhLFjx2Lw4MEYMmQI5s2bh6qqKowfPx4AMGbMGHTv3j0qK5k6dSqGDx+OJ554AiNGjMCyZcuwfv16LFy4MNrmgQMHsH37duzatQtA/cQZqH9jnZOTg8zMTEyYMAFFRUXo0KEDMjIycPvtt2Po0KE4++yzHfVfZCGCICQVmkPPci2ON9cNMVRLSkqQn5+PefPmobCwEFu2bEGXLl0M9Tt06IBf/epX6NOnD/x+P9566y2MHz8eXbp0QWFhofMOCIIgtGKa206PGjUKe/fuxcyZM1FWVoZBgwZhxYoVUafF7du3w+3+/sfmsGHDsHTpUsyYMQP33Xcfevfujddffx39+/eP1nnzzTejk3MAuP766wEAxcXFmDVrFgDgySefhNvtxtVXX02SyDhFJteCICQVTmOixhPn+ugYqgBQUlKCt99+G4sWLcK0adMM9S+44AJSnjp1Kv7whz/gww8/lMm1IAhtjpaw01OmTMGUKVNMt61evdqw7tprr8W1117baHvjxo3DuHHjLI8ZDAaxYMECLFiwwElXDYjmWhCEpCLezF+VlZVkqaurM7QNxBdD9WiUUigtLcWWLVtw/vnnx3uagiAIrRbJpGtNQt9c10VoXGOvO9WyPtfA1dbuNdThMYT5MbjOm8PjVkeYXjasUw2vz+TnhsdFz6PCRT1SM1UW7SOoDq6WfQ/xs2Psq6Pn+OWHNDYzAJy4cycpB0/YRcqKeXVph+itvO9beg5eL9WW//V/VNv35WHjQHRmMu5PK+jYBUD17JXszylVZZCyctHt++u2kLKZ/p3r8r0e2ik7TT6/nwwafaaX1nSqr6/fhx4zolPtpt19L1jj1Enx6BBPR3P0p76jiSeGKlCfvat79+6oq6uDx+PBs88+i0suuST2jgpxY4hhbfKoNmqu6d8yj3MdVlxzXUHKBxT9O/dXU9viMvHHCOkdSfnL16jNC7ppH0M6fX6VszjW25jGekcV1T/vCxvtE9eOh0HrRFiuCEMMcRsNdiyILvv4J1473VYQWYggCEmFppyF12v47RpLiKem0K5dO3zyySc4cuQISktLUVRUhFNOOcUgGREEQTjeiddOtxVkci0IQlKhYEgiZ1sf+D7Ekx3xxFAF6qUjvXr1AgAMGjQImzdvxuzZs2VyLQhCmyNeO91WEM21IAhJRXPHuY4nhqppP3W9UV23IAjC8YzkI7AmoW+ueWzSsEZjeHItF9eu+r1ZhjY1nWqk+UcIHkuba3K5pprH1uZxkv0BqtE2w8O0xYeYVq+jonGrd1ZRbTm4NjlM++R1Ge/CLz6hb+RO306PGdHo2FeF6THKamiM1sMRqh/cH6L7V5vE2NldTTWMe1z7DHWsOOzaT4+h0XKKvxMpa7px4hKOVBvWWe3DY1L7PDZ+AFrIcrtZm1xjzbcLzqj/3OisvlOcxlCdPXs2Bg8ejJ49e6Kurg7vvPMOXnzxRTz33HPOD94m4DpeutVl+JrM3vNw3W8McbQN8ZhZG9w2hF302cBzIBx2U/8ev85yJNQY+5TqoXUOhujzqJZ20VCuCtM+V7I41gciVD990EWfsYBROx5SNLa/If63bh332q7cIhyLYwqWtISdbs2ILEQQhKRCwQW9mZPIOI2hWlVVhdtuuw07d+5ESkoK+vTpg5deegmjRo1yfGxBEITWTkvY6daMTK4FQUgqlDK+6bSrHw9OYqg+9NBDeOihh+I7kCAIwnFGS9np1opMrgVBSCqcxkSVD8aCIAgti9hpa2RyLQhCUiFaPkEQhORG7LQ1CZ1c88DxXk86KRsScjAnlYhJwg7FEgFwJ0ieBCQUpgH0OWlBGmqrXaA7KddpRgcRl4c6/0VAHWMOuw6QcidFEwlUarR+TYQOe1inWqR1B1i2FgAnp1HPl799R5MNpHjonZvupdeCq52+OkLPaccRWn9PrdEpb4+izp8eF3fsNCYBOpr9dV+RstedwsrUsTOkG6+l203HTil6D/HENDz+T9BHnU2r6spImd9f/HgAUBuijpxeH91Hs3G6FKyR5ATJhWJ/RC4z7WQcDohkd8P+vIbzJDJ2ZY059vEkMyHm8BgCre82GYd0ahJRwxwWdx6hKw5H6POtmjkXVrNnTbWLOidy50UAqGMOjNypnydS42PJHR5tHRhjcjZsXqdIfo8KzY/YaWvkzbUgCEmFaPkEQRCSG7HT1sjkWhCEpCKi6hcn9QVBEISWQ+y0NTK5FgQhqZDMX4IgCMmN2GlrmnVyzXVVbhfVR3O9tJkOK8ySyPi97Ug5FDlsuZ1TG6KJAarVHlLmiUwAowYtBKqp9bqoVni3ezcpn6xySXl7NdXAVbBEAzkpxstyKEQ10pl0FwTcVP+3uZJqHrkzwZeVtA9pHnrMKpNEKF52u2gsaYMPdBzCTC+Y6T+RlGs1ei24BtLtsr89zXT6R8MTG3GNtR1hzaif9nlpQp9QpMJyu+AM0fI1N9zOtnyiXm7rXXYaba6fNtNc2+h67bczgTTDz5J/dUsz2qdrTqT+GBv3tyflnVQOjUPMfh1x0aRotbwM2gDXVwNAWKc2K8KS5/CkMbpOdd+24xaH/t2ethZLovUjdtoaeXMtCEJSocMFTcWecMBJIgNBEASh6YidtkYm14IgJBXyRkQQBCG5ETttjUyuBUFIKkTLJwiCkNyInbYmoZNrnelTlZvqqHws7jXXy2om+laPJ5WUucaaa2o1pi/jmlm/h2qyPSy2shk8TqiPyQNDih5Dd1HtXjnoeWeC9iHgpg1Who36s8oQbTPFSzXYfqa55n0sq6Eaaq6xrtFo+2FQHR4AHHHRGOBcD1itrDXUtVqFoU1yzEiV5XbAqM3kcagjEXqtODy2doRdWx6L3WWi+zbqRXnsbdEPNgV5I3IcYBu3mmusuR6abbaJew3Yx2t26fSYustZPGcfe1x2MHl0nP4AzZtw8kufkfInfz2VlMtCtE/cT8VOY8311YAxXjcfB53ljrCLa20YF7FvAsRO2yFvrgVBSCo0paA5CIrqpK4gCILQdMROWyOTa0EQkgr53CgIgpDciJ22RibXgiAkFfK5URAEIbkRO21NQifXHhbfV9OoPoxr4vh2r8cYH9jnSSPlmhCNIe33dqBtsrjYXJPNNbu87GfHA4waNbeLxjut06kW2e/JodtBdbyHmH4wzPTU7VxGMZ/bRXeqidB9Qmx7mOniapiu7juNxmN1s3EKwNiHsItq+Q5GtpJyhMXG9rK45lwPn8KuXRhUc83rA8ax93isY6fzGK51EaoLN2iq2f1gFsOVaw497iA7pjFGuBA7mjLGZberLzQFZ3Gvlck7KJfjMFvWGmtmzozbY4lzbaPB1hXz12BaZe5LU+miz6stFcZnxbM3U1uf5TuFlPm96gHXgbNnJGifuR8LPyfAaDf5M5Fv5zaSP+9s41ybarDt6tjotkXXnfSInbZG3lwLgpBUyBsRQRCE5EbstDUyuRYEIblQJtEibOoLgiAILYjYaUtkci0IQlKhw1kyZPmALAiC0LKInbYmoZNrN9Oven2dSDkUqSDlgM1202MwfWtYo3rngI/qeEMRut3npTq5iEbjHFeF9hiO6ffSuNQ14QOk7PXQ2Ml1bqrNO6LTNrPdNNZpnYvq9Pa4jFrjU1QPUq5mWjsv0+5FuN6Q/WzkGusjrgpWNnQBVWo/bYPpnzWNxiD3GvTQVMcd0qnGml8LPq6AUSPP9+F9Cuv0WviZX4BTvWF9H+h9zvcxi40txE5LafkWLFiAOXPmoKysDAMHDsTTTz+NIUOGmNZ9/vnn8cc//hGbNm0CAOTl5eHhhx9utH5bg+uwDRpsQ9xrG123IZY8P56ZLwT7W+bb2QoN1v45taD+GXtoagHUmcTU37mTPtNSwPxzQHXcFSx3QA1oOcx03xEW15rnYACaQWPNt8egn27uWP9mun+hZRHNtTXWFk4QBKGFUUo5XpyyfPlyFBUVobi4GBs3bsTAgQNRWFiIPXuMP64BYPXq1Rg9ejRWrVqFtWvXIjc3F5deeim+++67pp6uIAhCq6Ml7HRrRibXgiAkFQ2OMk4Wp8ydOxcTJ07E+PHj0a9fP5SUlCA1NRWLFi0yrb9kyRLcdtttGDRoEPr06YPf/e530HUdpaWlTTxbQRCE1kdL2OnWjHy/FgQhqYj3c2NlJZOIBQIIBIwhJUOhEDZs2IDp06dH17ndbhQUFGDt2rUxHbO6uhrhcBgdOnSwrywIgnCcIbIQa5pVc821X24W95jrsryedNs2PeCxk/kxqMYt6OvI6lP9GY+LHI5QTRsAhCJUS8xjb0eY7o1r94LuTFKudO0l5RRQHTDX3QHAVtcOuoJpENN02sYu15e0zy6qX+a6Og/TBtaqQ4Y+VIVpv4PeLNoHXzarX07KXOtn0Eu76bWuixj74GH3EN+Hx331e+nYczTdOhY7j2ENACE2Dj4vvcfCEapNF5wRb4in3Nxcsr64uBizZs0y1N+3bx80TUN2Nr1fs7Oz8cUXX8R0zHvvvRfdunVDQUFB7B1tNTiLex0XBg02r8DizRs0vBEY4L4ONhpspfP4zTR3ANci8zj+dW6jnT7opnbazewqh8fW5s+niKFMbSa3qYBJPO8W0FjbkwxxrVvgvm5DSCg+a+TNtSAISUV9Wt3YLXFDzR07diAj4/sfmWZvrRPBI488gmXLlmH16tUIBo0/vgRBEI534rXTbQWZXAuCkFRoOtg7RPv6AJCRkUEm143RqVMneDwelJfTLyvl5eXIyclpZK96Hn/8cTzyyCN47733cMYZZzjopSAIwvFDvHa6rSDfRQRBSCoUFHQHi9OwXH6/H3l5ecQZscE5cejQoY3u99hjj+HBBx/EihUrMHjw4LjPTxAEobXT3Ha6tZPQN9cRncZr1rke2m0d95jHMAaMuly/j8acDtdRzSzXR3PNts9F9dIa09H5vKmGPnAddlWkjJTbp/Yk5QM1X5NyWqALOyaLUe2l4+CHsQ+HFD0m10gfce233M7LGmgfqnUa07VWqzD0gWv16lhca47XTXXeNZF9pJzKxuVI7S7agIkOz+dLM6wjfWT6whCPvc011OyeU2x/jd3TAODx0Hswoh2x3C44QznM/BVPhKeioiKMHTsWgwcPxpAhQzBv3jxUVVVh/PjxAIAxY8age/fumD17NgDg0UcfxcyZM7F06VL06NEDZWX1f4/p6elITzf6irR1bONeG3ZoqgYb4DpsxfZxMc2tiz3+FHu1xm0J10OHXTROP2B8hhnK/DwMemfrPhh04Saaa94mt9t8rO36YKeXNr8WidVYt7WJWWugJex0a0ZkIYIgJBUtkflr1KhR2Lt3L2bOnImysjIMGjQIK1asiDo5bt++HW739xOh5557DqFQCNdccw1ppzGnSUEQhOMZydBojchCBEFIKjRdOV7iYcqUKdi2bRvq6uqwbt065OfnR7etXr0aixcvjpa3bt1qmhRBJtaCILRFWsJOL1iwAD169EAwGER+fj4++ugjy/ovv/wy+vTpg2AwiAEDBuCdd94h25VSmDlzJrp27YqUlBQUFBTgq6++InV69OgBl8tFlkceecRx32VyLQhCUuFEx9ewCIIgCC1Hc9tpp1l016xZg9GjR2PChAn4+OOPMXLkSIwcORKbNm2K1nnsscfw1FNPoaSkBOvWrUNaWhoKCwtRW0vlnw888AB2794dXW6//XbH4+NSCcxJ6fdRT3szvaoVXB8NAC62jsfg5DpuO7gOmMcENdOPBXw0VnJtmOqT+T7pwW50O/sgYojdzWJz10WM8VNTfDRZRcBlresNKaoT5+eZ7qF65wPhb0k56MkytHkktJuU+XlwbZ8xvirtA9cjej302nB9IgBENOt7KqxRHSQ/RkSj4+L3ZlhuN7+HmXbTEL+d6vh13Rg7XTBSWVmJzMxMXNn+HvhcsYfRC6s6vHVwDg4dOhRTtJC2jstlHXvZnqa/k7HVYBt24Me074PRh8eZHpofk2838xEy9MHhWPFnhWG7jV66fqVTTTWnafVNOSYaa4fHZM8nwZyWstP5+fk466yz8MwzzwCodzrPzc3F7bffjmnTphnqjxo1ClVVVXjrrbei684++2wMGjQIJSUlUEqhW7duuOuuu3D33XcDAA4dOoTs7GwsXrwY119/PYD6N9d33HEH7rjjjpjPzQx5cy0IQlKhKeV4EQRBEFqO5rTTDVl0j07SZZdFd+3atYakXoWFhdH63377LcrKykidzMxM5OfnG9p85JFH0LFjR5x55pmYM2cOIhGTpFU2iEOjIAhJhdNPiCILEQRBaFnitdOVlfTLfCAQMCT8iieLbllZmWn9hshODf+3qgMAv/jFL/CDH/wAHTp0wJo1azB9+nTs3r0bc+fOjfVUAcjkWhCEJENXDo22vLkWBEFoUeK107m5uWR9skVcKioqiv77jDPOgN/vx80334zZs2c7yvork2tBEJIK5TDhgMTAFQRBaFnitdM7duwgmmuzCWs8WXRzcnIs6zf8v7y8HF27diV1Bg0a1Gi/8/PzEYlEsHXrVpx22mkWZ0hJbBIZQzINmgzFwxy/uAOcmYMIdwhxe2gSkJq6naQcYE6VvM1Q5JBlH9KCXcGpqqOOfAFve1LmyQXqwvQYbjcdZoOTHTtH7rwIAFUh6iEb8lDHPS9LyBPWafIdH3Pk3FW1gZR5QpeKmv8Z+sCT/iiXdcIDfp4eG+eH2hB1FE0LGv+I3Ox68SRD3Mkyols7MBqSNjBnRL83y9AHW0ddE8dcIXY0KLgcGG1NJtctjJmjmFPHvUQnmTHZxXBb8H7zhC7sHNj+BgdI1fIuS3YOj0DTHRCPhQOj0PqI105nZGTYOjQenUV35MiRAL7PojtlyhTTfYYOHYrS0lLiiLhy5cpo1t2TTz4ZOTk5KC0tjU6mKysrsW7dOtx6662N9uWTTz6B2+1Gly5dGq1jhswCBEFIKpRy+EZEZCGCIAgtSnPbaadZdKdOnYrhw4fjiSeewIgRI7Bs2TKsX78eCxcuBAC4XC7ccccdeOihh9C7d2+cfPLJuP/++9GtW7foBH7t2rVYt24dLrzwQrRr1w5r167FnXfeiRtvvBHt27c37WdjyORaEISkQhwaBUEQkpvmttNOs+gOGzYMS5cuxYwZM3Dfffehd+/eeP3119G/f/9onV/+8peoqqrCpEmTUFFRgXPPPRcrVqxAMFiviAgEAli2bBlmzZqFuro6nHzyybjzzjuJDjtWEhrn2u1OJ+VmkYUwiYVTWQiPYxyLLKS6jup47GQhXD5hJwvh5xiLLMTnSSNlp7KQw7XfkTKXhVTXGQO1252XplFJRSxxYI8mFDlMymayEC4D4WX+SZPLQnweeo/y+mGNejL7PMbPV07jt4cj+xzVb6s0xE89J2MyvA7ip0ZUHf5ZuUDiXMdI0+Ncm9E0iUTT416bYV3HLg62XX2nMawTQVuRhUic6+RF7HRsJPTNtZdNXDSePIPbIsMfoLE7fLLDNbB8Qs+Nj1EHTI8R9NIEMVW1VF9df0x6I9RFqDY4Pdid9jnCJvCsDz4v/dGh6SzpiDLGVPR7adIYrk8GS3TDUUojZTdPXMN04jxxjlm/7H748PMOaXTy7PdYJ8Ixm+BzeB/4/cLvSfskDLQ9s2vBddl8As77IDhD3ly3Rqz/juxosgYbMJlw200i+RqeHIppsg22IxnSRDifxCZk8kwabLq+uumTadF4tzRip60RWYggCEmFGG1BEITkRuy0NTK5FgQhqdD/7z8n9QVBEISWQ+y0NTK5FgQhqdBdOjSXZl+xoX4bM9qCIAjHGrHT1iRYc01jUPOYwwaHEEMMaqNWlWukwyyWtp3Dopc7VbqpU2Us8H65WRvcEY9rjbl+meulfV7qnHikzqj75g6MOnO+CGs07jU/z5BOt3N9NCeiGZ32+D61YXoedmMb9FFHUL4/1+553PTaAfaaaa5/5g6NHje9R+2cE7m+uh56z2mGOsmgxWy96NDhkjcirZwW1mAD9trfJmuyOfb3nVOnbjvs9dKx0MQ24uhD4hM9yd/8sUbstDXy5loQhKRC/Z+az0l9QRAEoeUQO22NTK4FQUgqIq4IlIM3fhqMEV0EQRCE5kPstDUyuRYEIamQz42CIAjJjdhpaxI6uQ4zvTPXXHM9NIfrYev3OcLqUB2uYnGIuR6axynWNetYzSn+zoY+1ITKDetoH6zjo/IY0l4PTejCk9Ck+Y057GuYPpnHqbY7pl0CGF7fTNvHNfVelpiG677tYo5z7R5P4FMT2m/oQ5jp330sBrlBS85Og19vrulXimqwzXTf/J7Uda65bltGJNG01OfGBQsWYM6cOSgrK8PAgQPx9NNPY8iQIaZ1P//8c8ycORMbNmzAtm3b8OSTT+KOO+6I67htk8RqsM2IKzY2acCZJjsWEpeirYEWsC1JEbeaIzY12RBZiDXieSUIQlKhIQINYQeL88+Ny5cvR1FREYqLi7Fx40YMHDgQhYWF2LPHPHFRdXU1TjnlFDzyyCPIyTFmDhUEQWhLtISdbs3I5FoQhKRCd+mOF6fMnTsXEydOxPjx49GvXz+UlJQgNTUVixYtMq1/1llnYc6cObj++usRCMSe8lcQBOF4pCXsdGtGNNeCICQVOjSDfMeuPgBUVlLJUCAQMJ0Ih0IhbNiwAdOnT4+uc7vdKCgowNq1a+PstSAIQtshXjvdVkjo5NrvaUfKdRGqE+b6aK8n3bZNronVWNxinemV/b6ObDvVw7pdVJPN+1QT2mvoA9+H31Bc/8wJ6TQOttKYRpu1V22iNVY2N2Y4UmW5vTZM2+Tach6jmuvnAaAuXEHKPnb9uMaex3/2++j9URuKsDK9X8IRejwACDI9Ou8T19gHvDS2dkSvoX1k+umArxMph0z6wHXYXO+emFi0bRlnWr4GPWZubi5ZW1xcjFmzZhlq79u3D5qmITs7m6zPzs7GF1984bSzrRCuh40hhnTCaZoG2ww7nW+TNdmGBmPpcxLYgma2R4nXVwNJMW6CDfHZ6baCvLkWBCGp0FTY0eO6IaHSjh07kJHxvYOryDcEQRCah3jtdFtBJteCICQVChqUgzeZDV91MjIyyOS6MTp16gSPx4PychoFqLy8XJwVBUEQYiBeO91WEIdGQRCSCj2O/5zg9/uRl5eH0tLS74+p6ygtLcXQoUMTfTqCIAjHHc1tp1s7CX1zbRcPmmtTudaYa3QBo663LnyAlFMCXS23c700j9XM9bERncY5NsPnSaNtsF9kGtN5e1n8br6dSwF5vGjAXs/Mt/M+8mPy2NohFj/a7zV5A+ihWuNwhOqVvWy710XP+0jNNtpHbxYp8xjVqYHuhi6EIlS/HmQaez52XPfP8XroefJ7lN8/9etoP8NMt83vWcEZXDef6PoAUFRUhLFjx2Lw4MEYMmQI5s2bh6qqKowfPx4AMGbMGHTv3h2zZ88GUO8E+d///jf67++++w6ffPIJ0tPT0atXL8fHTy6SUYPNSQJNtqHBtjFZaB5NNadtjOXxREvY6daMyEIEQUgq6t9wNG/mr1GjRmHv3r2YOXMmysrKMGjQIKxYsSLq5Lh9+3a43d9P6Hbt2oUzzzwzWn788cfx+OOPY/jw4Vi9erXj4wuCILRmWsJOt2Zkci0IQlJRr+WL/S1ivFq+KVOmYMqUKabb+IS5R48eUIlPtycIgtAqaSk73VqRybUgCEmFUg7T6raRz/OCIAjJgthpaxI6ueZxqw0DzwZXY/pmHsPabB8/0+nyeMw8Ljbvg1EPnULKuk61x4BRS8x1vHYaW35MnemlPS6uAzdqk0Ia10R3sDlmneV2Hpubx72urjPq5wO+LMs2OVzHzWNU82unRej9wGNUxwK/3lwzbbjnFL8HadnsWvD4tnZ6eMEZ9SGeYn9L3Na0fM1PMmiwOYmPi81JhLbYsW67BWgZzbRT2tZE63hE7LQ18uZaEISkQkE5eyOSlJMHQRCE4xex09bI5FoQhKSi/nOjAy1fG/vcKAiCcKwRO22NTK4FQUgqlAo78ixXqm05ygiCIBxrxE5bk2DNNdUO14b2sO00pjD/pBDwdTK0yeNWc001h/864npYXo7oNZbbzdrkWmAeW5mPA9cWe90sXjSLk8z1zwBQxzKHhpkGm+vd+RcYrmevC1P9Ex9Xt9sY31nXbfZhOm4eK9suBjmPex7SaExrAAYNPh973gbXt/Ox5XGzeX3TX9t8HdNgt5X4t81F/ZjLG5HkoTVosM1o+Rxpbe3Td+PI3+Txjthpa+TNtSAISYUO3ZFjmBPdnyAIgtB0xE5bI5NrQRCSCl0Pw+WSz42CIAjJithpa2RyLQhCUlH/aV280AVBEJIVsdPWyORaEISkwqk2r61p+QRBEI41YqetSejkmju82Tkw+lhyFp50JBYMDoseWg4zJzqeJIQ77pk5TPJ+14b3kzJPIlMXrmBtckc96sDIx6kmtNfYB+Zox50/+dhxZ8KAjyad4X3UmfNgWvBEQx+485/hvCKHSJkn3+FofBxYgiAzjRZfx8fF46bOpC52i9eFaQIg3h53iPS4jefAnR6huONU2zIiiUaMdrJj9gYqGZwcOU7vi5Z3gGydyN+bIHbaDnlzLQhCUqGriOFHjhVtzWgLgiAca8ROWyOTa0EQkgp5IyIIgpDciJ22RibXgiAkGU6NcNsy2oIgCMcesdNWJFZzrajmmicu4dSy5CoeE42uh2mouWZa11kSEVeANsA01PwYfk872iempwaMumyusdaYBpfXV2xc3EwXHBv084umM90vu3EjbGz5tfCwc+D66IhGk+sARo21h51nmOm++TF9TFMdAUuuw5LvGM8R0NhYcnS2D9dU6+z+4ePK7w9+7YB4r58QK/K5sTWSjIlmnNJWNdpt5e/neLhHkwex09bIm2tBEJIKpRyGeFJtK8STIAjCsUbstDUyuRYEIcnQ4OytUtsy2oIgCMcesdNWyORaEISkov7zoYO0um3sjYggCMKxRuy0NQkVjbldXrJ43KlkcbuDZAn6O5FFqYhh0bRqsnjcQbL4fZ3J4vNmkAUqQhbeXl3kIFncbr9h4YS1I2TxuPxk0fUQWTi6XkuW2NDJ4nH7yVJ/Kb9fvJ5UsgT9XcjC+xgK7yeL15NiWJTSyaLpIbLwaxPwdSJLRDtCFqXXkiWi0UVXEcNiew+ya+dyeclivCfZtVY6WVxwGxZ+/fhYxnd9hSjsbzamJQ4WLFiAHj16IBgMIj8/Hx999JFl/Zdffhl9+vRBMBjEgAED8M4778R13OMTF1taI26Hy/FCWznv4+EeTSJawE4n2kYrpTBz5kx07doVKSkpKCgowFdffUXqHDhwADfccAMyMjKQlZWFCRMm4MgRmpMjFlrzX4ogCMchKo7/nLJ8+XIUFRWhuLgYGzduxMCBA1FYWIg9e/aY1l+zZg1Gjx6NCRMm4OOPP8bIkSMxcuRIbNq0qamnKwiC0OpobjvdHDb6sccew1NPPYWSkhKsW7cOaWlpKCwsRG3t9y/BbrjhBnz++edYuXIl3nrrLfzjH//ApEmTHI+PSyXwXX3QfwIp8zeN8WRo5G9+vSzKBY9gweEZGnn0kFgyNNr1iUcPCbPMg7yP/G0mz9DII6IATc/QyPtgyNCo0+gg8WRoNF4rfn3pMRWvH0uGRptfvzxDo+FtN/NYtsvQaIYhQ6NhH9qmpjnPPNoWqaysRGZmJgAfXC6nnxvDOHToEDIyMmzrA0B+fj7OOussPPPMMwAAXdeRm5uL22+/HdOmTTPUHzVqFKqqqvDWW29F15199tkYNGgQSkpKYu5rMhCLjYuj1WZos6WRd02xcXxGfVAqfKy70CpoKTudaButlEK3bt1w11134e677wYAHDp0CNnZ2Vi8eDGuv/56bN68Gf369cO///1vDB48GACwYsUKXHHFFdi5cye6desW8/mKNREEIcnQoZQW89LwsK+srCRLXV2daeuhUAgbNmxAQUFBdJ3b7UZBQQHWrl1rus/atWtJfQAoLCxstL4gCMLxTfPZ6eaw0d9++y3KyspInczMTOTn50frrF27FllZWdGJNQAUFBTA7XZj3bp1jkYnoa8wakM7E9mcIAhtCL/fj5ycHJSVlTneNz09Hbm5uWRdcXExZs2aZai7b98+aJqG7Oxssj47OxtffPGFaftlZWWm9ePp67HG7uuPIAhCY7SEnW4OG93wf7s6Xbp0Idu9Xi86dOjg+HwlWoggCElBMBjEt99+i1DI6ARsh1LK8IkyEAg0UlsQBEGIB7HTsSGTa0EQkoZgMIhgsHkzYHbq1Akejwfl5eVkfXl5OXJyckz3ycnJcVRfEATheKW57XRz2OiG/5eXl6Nr166kzqBBg6J1uMNkJBLBgQMHHNt60VwLgtCm8Pv9yMvLQ2lpaXSdrusoLS3F0KFDTfcZOnQoqQ8AK1eubLS+IAiCEB/NYaNPPvlk5OTkkDqVlZVYt25dtM7QoUNRUVGBDRs2ROu8//770HUd+fn5zk5CCYIgtDGWLVumAoGAWrx4sfrvf/+rJk2apLKyslRZWZlSSqmbbrpJTZs2LVr/n//8p/J6verxxx9XmzdvVsXFxcrn86nPPvvsWJ2CIAjCcUtz2OhHHnlEZWVlqTfeeEN9+umn6qqrrlInn3yyqqmpida57LLL1JlnnqnWrVunPvzwQ9W7d281evRox/2XybUgCG2Sp59+Wp144onK7/erIUOGqH/961/RbcOHD1djx44l9f/85z+rU089Vfn9fnX66aert99+u4V7LAiC0HZItI3WdV3df//9Kjs7WwUCAXXxxRerLVu2kDr79+9Xo0ePVunp6SojI0ONHz9eHT582HHfExrnWhAEQRAEQRDaMqK5FgRBEARBEIQEIZNrQRAEQRAEQUgQMrkWBEEQBEEQhAQhk2tBEARBEARBSBAyuRYEQRAEQRCEBCGTa0EQBEEQBEFIEDK5FgRBEARBEIQEIZNrQRAEQRAEQUgQMrluJbhcLsyaNcu23qxZs+ByuZq/Q4IgCIKBCy64ABdccIFtvdWrV8PlcmH16tXN3idBEFoWmVzHyOLFi+FyubB+/fpG62zduhUulyu6uN1udOjQAZdffjnWrl3bgr1tORrGpbFlyZIl0bo9evRotF7v3r2P4VkIgtCasbPPF1xwAfr370/WcXuUlpaGIUOG4I9//GNLdLnF4c8nvkycODFad9y4cZZ1v/vuu2N4JoKQ/HiPdQeOR0aPHo0rrrgCmqbhyy+/xLPPPosLL7wQ//73vzFgwIC42qypqYHXm3yX6/zzz8eLL75oWP/kk0/iP//5Dy6++OLounnz5uHIkSOk3rZt2zBjxgxceumlzd5XQRCEoxk0aBDuuusuAMDu3bvxu9/9DmPHjkVdXR2ZbDrhb3/7WyK7mDA6d+5saqtXrFiBJUuWEBt88803o6CggNRTSuGWW25Bjx490L1792bvryC0ZpJvtnYc8IMf/AA33nhjtHzeeefh8ssvx3PPPYdnn302rjaDwWCiupdQTjnlFJxyyilkXU1NDW677TZcdNFFyMnJia4fOXKkYf+HHnoIAHDDDTc0az8FQRA43bt3J7Z63LhxOOWUU/Dkk0/GPbn2+/2J6l5CSUtLI+fawOLFi5GRkYEf/vCH0XVDhw7F0KFDSb0PP/wQ1dXVYqsFIQZEFtICnHfeeQCAb775Ju42zDTXH374Ic466ywEg0H07NkTv/3tbw37vfDCC3C5XFi0aBFZ//DDD8PlcuGdd96Ju0+N8Ze//AWHDx+OyQgvXboUJ598MoYNG5bwfgiCIDihc+fO6NOnT5NstZnmeufOnRg5ciTS0tLQpUsX3HnnnairqyN1Nm/ejJSUFIwZM4as//DDD+HxeHDvvffG3afG2L17N1atWoWf/OQnti9wli5dCpfLhZ/+9KcJ74cgHG/Im+sWYOvWrQCA9u3bJ6zNzz77DJdeeik6d+6MWbNmIRKJoLi4GNnZ2aTe+PHj8dprr6GoqAiXXHIJcnNz8dlnn+HXv/41JkyYgCuuuCJa9+DBg9A0zfbYqampSE1NbXT7kiVLkJKSgp/85CeW7Xz88cfYvHkzfvWrX9keUxAEwY5Dhw5h3759hvXhcDim/SORCHbu3JlQW11TU4OLL74Y27dvxy9+8Qt069YNL774It5//31Sr2/fvnjwwQdxzz334JprrsGPfvQjVFVVYdy4cejTpw8eeOCBaN0jR46gtrbW9tg+nw+ZmZmNbl+2bBl0Xbd9ERIOh/HnP/8Zw4YNQ48ePWyPKwhtHZlcNwPV1dXYt28fNE3DV199haKiIgDANddck7BjzJw5E0opfPDBBzjxxBMBAFdffbWppvv555/H6aefjgkTJuCtt97C2LFjkZOTg7lz55J6Z555JrZt22Z77OLi4kYjlxw4cAArVqzAyJEj0a5dO8t2Gpwd5TOjIAiJgOuEj+b00083rAuHw9HJeFlZGR577DGUlZVh8uTJCevTwoUL8eWXX+LPf/4zrr32WgDAxIkTMXDgQEPdoqIivPHGG5g0aRLOOeccFBcXY9u2bVi7di0CgUC03pQpU/CHP/zB9tjDhw+3jEayZMkSdO3aFRdddJFlO++++y72798vtloQYkQm181AcXExiouLo+X09HQ88cQTCZtca5qGd999FyNHjoxOrIH6Nx+FhYUGqUdOTg4WLFiA0aNH47zzzsMnn3yClStXIiMjg9RbsmQJampqbI/PNdZH88orryAUCtkaYV3XsWzZMpx55pno27ev7TEFQRDsWLBgAU499VTD+rvuusv0q9zf/vY3dO7cmawbP3485syZk7A+vfPOO+jatSux/6mpqZg0aRJ++ctfkrputxuLFy/GwIEDcfnll2P9+vWYMWMGBg8eTOr98pe/NNVPc6zewH/55ZfYsGED7rzzTrjd1grRpUuXwufz4brrrrM9piAIMrluFiZNmoRrr70WtbW1eP/99/HUU0/FJLeIlb1796KmpsY0fN1pp51mqqO+/vrr8dJLL+Htt9/GpEmTSBSPBs4555wm923JkiXR8INW/P3vf8d3332HO++8s8nHFARBAIAhQ4YYJqJA/STTTC6Sn5+Phx56CJqmYdOmTXjooYdw8ODBhDolbtu2Db169TLkHzjttNNM6/fs2ROzZs3CPffcg/79++P+++831OnXrx/69evXpH7F+uXwyJEjeOONN1BYWIiOHTs26ZiC0FaQyXUz0Lt37+jnySuvvBIejwfTpk3DhRdeaGr4W4L9+/dHY8D+97//ha7rhrcVe/fujelHQHp6OtLT0w3rt2/fjg8++ACTJk2Cz+ezbGPJkiVwu90YPXq0g7MQBEFIHJ06dYra6sLCQvTp0wdXXnkl5s+fH5XzHQsawvnt2rUL+/fvJ1GXgHpteSxfGf1+Pzp06GC6benSpTjttNOQl5dn2cbrr78uUUIEwSESLaQF+NWvfoV27dphxowZCWmvc+fOSElJwVdffWXYtmXLFtN9Jk+ejMOHD2P27Nn48MMPMW/ePEOds846C127drVdHn/8cdNj/OlPf4JSytYI19XV4dVXX8UFF1yAbt262Z+wIAhCCzBixAgMHz4cDz/8MKqqqhLS5kknnYRvvvkGSimyvjFbXVJSgpUrV+I3v/kNQqEQbr75ZkOdqVOnxmSrG3MqX7duHb7++uuYJsxLlixBeno6fvSjH8VwtoIgAPLmukXIysrCzTffjMceewyffPIJBg0a1KT2PB4PCgsL8frrr2P79u1R3fXmzZvx7rvvGuq/8sorWL58OZ566incfvvt+M9//oMZM2bgyiuvJPrEpmquly5dihNPPBHnnnuu5f7vvPMOKioq5E2IIAhJx7333osrrrgCzz//PO64444mt3fFFVfgb3/7G1555ZWoQ2N1dTUWLlxoqPvtt9/innvuwdVXX4377rsPHTt2xC233II//vGPJERfUzXXS5cuBQDbsHp79+7Fe++9h9GjR1tGiBIEgSKTa4csWrQIK1asMKyfOnWq5X5Tp07FvHnz8Mgjj2DZsmVN7sevf/1rrFixAueddx5uu+02RCIRPP300zj99NPx6aefRuvt2bMHt956Ky688EJMmTIFAPDMM89g1apVGDduHD788MOoPKQpmutNmzbh008/xbRp0wzaQs6SJUsQCARw9dVXx308QRCE5uDyyy9H//79MXfuXEyePNlW4mbHxIkT8cwzz2DMmDHYsGEDunbtihdffNEwWVVK4Wc/+xlSUlLw3HPPAajPlPjqq69i6tSpKCgoiH7pa4rmWtM0LF++HGeffTZ69uxpWXf58uWIRCLyIkQQHCKTa4c0GD3OuHHjLPfr1q0bfvrTn+LFF1/EN998Y2vU7DjjjDPw7rvvoqioCDNnzsQJJ5yAX//619i9ezeZXN96662oq6uLJpMBgI4dO2LhwoW46qqr8Pjjjxs81uOhwTnG7k1IZWUl3n77bYwYMcIy/qogCMKx4u6778a4ceOwZMkSW9tuR2pqKkpLS3H77bfj6aefRmpqKm644QZcfvnluOyyy6L1nn76aaxevRqvvvoqiWDy+9//Hv3798fEiRPx9ttvN6kvAPDee++hvLw8pvwCS5YsQZcuXSxDHAqCYMSluBBMEARBEARBEIS4EIdGQRAEQRAEQUgQMrkWBEEQBEEQhAQhk2tBEARBEARBSBAyuRYEQRAEQRCEBCGTa0EQBEEQBEFIEDK5FgRBEARBEIQEcdxPrrdu3QqXy4XFixcf664IgiAkJWInBUEQEsdxP7k+lvz+979H3759EQwG0bt3bzz99NPHukstjq7reOyxx3DyyScjGAzijDPOwJ/+9KeY91+5ciXOPfdcpKamon379rjmmmuwdetWUmf16tVwuVyNLr/5zW9I/Q0bNuDKK69ETk4O0tPTccYZZ+Cpp56CpmmJOGVBEGKksb/ZRx55xHZfq7/7f/3rX4b6oVAIDz/8MPr06YNgMIjs7GyMGDECO3fuTOg5xWrzxo0bZ9r3Pn36JLQ/rYE1a9ZE7XxOTg5+8Ytf4MiRI7b7LV682NL2NyQ3a+C7777Dddddh6ysLGRkZOCqq67C//73P8tjfPjhh9H29u3b16TzFNoOx32GxpNOOgk1NTVNTmHrlN/+9re45ZZbcPXVV6OoqAgffPABfvGLX6C6uhr33ntvi/blWPKrX/0KjzzyCCZOnIizzjoLb7zxBn7605/C5XLh+uuvt9z3rbfewlVXXYUf/OAHeOSRR1BZWYn58+fj3HPPxccffxzNYta3b1+8+OKLhv1ffPFF/O1vf8Oll14aXbdhwwYMGzYMvXv3xr333ovU1FT89a9/xdSpU/HNN99g/vz5iR0AQWgFHCs7CQCXXHIJxowZQ9adeeaZMe//i1/8ArQ51X0AAQAASURBVGeddRZZ16tXL1IOh8MYMWIE1qxZg4kTJ+KMM87AwYMHsW7dOhw6dAgnnHBC/CfAcGLzAoEAfve735F1bS1z7SeffIKLL74Yffv2xdy5c7Fz5048/vjj+Oqrr/DXv/7Vct/zzz/f1PY/+eST+M9//oOLL744uu7IkSO48MILcejQIdx3333w+Xx48sknMXz4cHzyySfo2LGjoR1d13H77bcjLS0NVVVVTT9Zoe2ghIRTXV2tOnbsqEaMGEHW33DDDSotLU0dOHDgGPWsZdm5c6fy+Xxq8uTJ0XW6rqvzzjtPnXDCCSoSiVju369fP9WrVy9VV1cXXffJJ58ot9utioqKbI/fq1cv1bt3b7Ju4sSJyu/3q/3795P1559/vsrIyIjltARBSBAAiH1wwqpVqxQA9fLLL9vWffTRR5XP51Pr1q2L61ix4sTmjR07VqWlpTVrf1oDl19+ueratas6dOhQdN3zzz+vAKh3333XcXvV1dWqXbt26pJLLiHrH330UQVAffTRR9F1mzdvVh6PR02fPt20reeee0517NhRTZ06VQFQe/fuddwfoW2S9LKQWbNmweVy4csvv8SNN96IzMxMdO7cGffffz+UUtixYweuuuoqZGRkICcnB0888QTZ30xLOG7cOKSnp+O7777DyJEjkZ6ejs6dO+Puu+9OiDRg1apV2L9/P2677TayfvLkyaiqqsLbb7/drOccCoUwc+ZM5OXlITMzE2lpaTjvvPOwatUqUq+4uBhutxulpaVk/aRJk+D3+/Gf//ynCaMAvPHGGwiHw2QcXC4Xbr31VuzcuRNr165tdN8DBw7gv//9L3784x/D7/dH1w8cOBB9+/bFsmXLLI/90Ucf4euvv8YNN9xA1ldWViIYDCIrK4us79q1K1JSUhycnSAkD63RTh5NTU0Namtr497/8OHDiEQiptt0Xcf8+fPx4x//GEOGDEEkEkF1dXWjbX333Xf42c9+huzsbAQCAZx++ulYtGhRTP2Ix+ZpmobKysqY2m+g4Xo9/vjjWLBgAU455RSkpqbi0ksvxY4dO6CUwoMPPogTTjgBKSkpuOqqq3DgwAFDX0eMGIFu3bohEAigZ8+eePDBB8m13bx5M1JSUgxfFj788EN4PJ4mf4WtrKzEypUrceONNyIjIyO6fsyYMUhPT8ef//xnx23+5S9/weHDhw22/5VXXsFZZ51FvnL06dMHF198selxDhw4gBkzZuCBBx4wPC8EwY6kn1w3MGrUKOi6jkceeQT5+fl46KGHMG/ePFxyySXo3r07Hn30UfTq1Qt33303/vGPf9i2p2kaCgsL0bFjRzz++OMYPnw4nnjiCSxcuJDUO3jwIPbt22e7HG2sP/74YwDA4MGDSVt5eXlwu93R7c11zpWVlfjd736HCy64AI8++ihmzZqFvXv3orCwEJ988km03owZMzBo0CBMmDABhw8fBgC8++67eP755zFz5kwMHDgwWjeWMdi3bx/q6urIOKSlpaFv377kvIYMGULGyYyGdswmvKmpqdi1axfKysoa3b9Ba8cN7AUXXIDKykrcfPPN2Lx5M7Zt24aSkhK89tprmD59eqPtCUJroDXZyQYWL16MtLQ0pKSkoF+/fli6dKmjcx4/fjwyMjIQDAZx4YUXYv369WT7f//7X+zatQtnnHEGJk2ahLS0NKSlpeGMM84wvHAoLy/H2Wefjffeew9TpkzB/Pnz0atXL0yYMAHz5s2z7YtTm1ddXY2MjAxkZmaiQ4cOmDx5ckxa4waWLFmCZ599Frfffjvuuusu/P3vf8d1112HGTNmYMWKFbj33nsxadIk/OUvf8Hdd99N9l28eDHS09NRVFSE+fPnIy8vDzNnzsS0adOidfr27YsHH3wQL774It58800AQFVVFcaNG4c+ffrggQceiNY9cuRITPfAoUOHovt89tlniEQihmel3+/HoEGDYn5W8jFJSUnBT37yk+g6Xdfx6aefGo4D1F+bb775JvoMbOD+++9HTk4Obr75Zsd9EISkl4UUFxcrAGrSpEnRdZFIRJ1wwgnK5XKpRx55JLr+4MGDKiUlRY0dOza67ttvv1UA1AsvvBBdN3bsWAVAPfDAA+RYZ555psrLyyPrTjrpJAXAdikuLo7uM3nyZOXxeEzPp3Pnzur6669v1nOORCJEStFQLzs7W/3sZz8j6z/77DPl9/vVz3/+c3Xw4EHVvXt3NXjwYBUOh0m9WMaAj/OIESPUKaecYji/qqoqBUBNmzat0THQNE1lZWWpiy++mKzft2+fSktLUwDU+vXrTfeNRCIqOztbDRkyxHTblClTlM/ni/bZ4/Go5557rtG+CEKy0xrtpFJKDRs2TM2bN0+98cYb6rnnnlP9+/dXANSzzz5re87//Oc/1dVXX61+//vfqzfeeEPNnj1bdezYUQWDQbVx48Zovddee00BUB07dlS9e/dWL7zwgnrhhRdU7969ld/vV//5z3+idSdMmKC6du2q9u3bR451/fXXq8zMTFVdXW3ZJyc2b9q0aeree+9Vy5cvV3/605+i433OOecY7C+n4Xp17txZVVRURNdPnz5dAVADBw4kbYwePVr5/X5VW1sbXWd2LjfffLNKTU0l9TRNU+eee67Kzs5W+/btU5MnT1Zer1f9+9//Jvs29N9uGT58eHSfl19+WQFQ//jHPwx9ufbaa1VOTo7lOHD279+v/H6/uu6668j6vXv3mt7LSim1YMECBUB98cUX0XX/+c9/lMfjicpSGv6+RBYixEqrcWj8+c9/Hv23x+PB4MGDsXPnTkyYMCG6PisrC6eddpqt928Dt9xyCymfd955BueIJUuWoKamxratU045JfrvmpoaImU4mmAwGFN7QPzn7PF44PF4ANT/Yq+oqICu6xg8eDA2btxIjtG/f3/8+te/xvTp0/Hpp59i3759+Nvf/gavl94aK1eujKnPp59+evTfNTU1CAQChjrBYDC6vTHcbjduvvlmPProo5g+fTp+9rOfobKyEr/85S8RCoUs9y8tLUV5eTnuu+8+wzaPx4OePXuisLAQ1157LYLBIP70pz/h9ttvR05ODkaOHBnTeQpCMtKa7CQA/POf/yTln/3sZ8jLy8N9992HcePGWUq1hg0bhmHDhkXLP/rRj3DNNdfgjDPOwPTp07FixQoAiL4JPnz4MD7++GPk5uYCAC666CL06tULjz32GF566SUopfDqq6/iuuuug1KKRIYoLCzEsmXLsHHjRpxzzjmN9smJzZs9ezapc/311+PUU0/Fr371K7zyyiu2Dt8AcO211xIHyPz8fADAjTfeSGx4fn4+/vSnP+G7776LXoOjx/bw4cOoq6vDeeedh9/+9rf44osvol8u3W43Fi9ejIEDB+Lyyy/H+vXrMWPGDMNb4F/+8pe48cYbbfvcvn376L8bxqOxMYv1WdnAK6+8glAoZPhiaXeco+sA9U6yl19+OXGGFwQntJrJ9YknnkjKmZmZCAaD6NSpk2H9/v37bdsLBoPRaBMNtG/fHgcPHiTrrAxpY6SkpEQngJza2tqYtb1NOec//OEPeOKJJ/DFF18gHA5H15988smG49xzzz1YtmwZPvroIzz88MPo16+foU5BQUFMfT6alJQUIhNpoEFbaTcODzzwAPbt24fHHnssGprr0ksvxYQJE1BSUoL09HTT/ZYsWQKPx4NRo0YZtj3yyCOYP38+vvrqq+j+1113HS688EJMnjwZV155peGHhSC0FlqTnTTD7/djypQpuOWWW7Bhwwace+65jvbv1asXrrrqKrz22mvQNA0ejydqZ84555zoxBqoH6tzzz0Xa9asAQDs3bsXFRUVWLhwoUH20sCePXsAwCBJy8zMREpKSpNt3p133on7778f7733XkyTa7PrDYCc59Hrj75un3/+OWbMmIH333/foPk+WroBAD179sSsWbNwzz33oH///rj//vsNfenXr5/ps8OKhvFobMyc+sEsWbIEHTp0wOWXX+7oOEfXWb58OdasWYNNmzY5OrYgHE2rmUU0vIm1WwcASqm42jNj7969MTnvpKenRydrXbt2haZp2LNnD7p06RKtEwqFsH//fnTr1i2mY8d7zi+99BLGjRuHkSNH4p577kGXLl3g8Xgwe/ZsfPPNN4Z9//e//+Grr74CUK+BM8NK33w0DQ8ZoH4cVq1aBaUUXC5XtM7u3bsBwHYc/H4/fve73+E3v/kNvvzyS2RnZ+PUU0/FT3/6U7jdbkO4LaD+7cP/+3//DwUFBcjOzjZsf/bZZ3HRRRcZJuY/+tGPUFRUhK1bt5q2KwitgdZkJxujYWLIHfBiJTc3F6FQCFVVVcjIyIjaGTN70KVLl6iuV9d1APVvfceOHWva9hlnnAGg3rYdzQsvvIBx48Y12ealpKSgY8eOMZ97Y9fH7ppXVFRg+PDhyMjIwAMPPICePXsiGAxi48aNuPfee6NjcTR/+9vfAAC7du3C/v37kZOTQ7YfOnQopjfNfr8fHTp0APD9ODaMz9Hs3r075mclAGzfvh0ffPABJk2aZAgp2aFDBwQCgUaPA3x/be655x5ce+218Pv90ZwKFRUVAIAdO3YgFAo56pfQNmk1k+tjxVlnnYVt27bZ1isuLsasWbMAAIMGDQIArF+/HldccUW0zvr166HrenR7c/HKK6/glFNOwWuvvUYMfHFxsaGurusYN24cMjIycMcdd+Dhhx/GNddcQ5xBAOPDpDEaHjJA/Tj87ne/w+bNm8kbjXXr1kW3x0J2dnb0wahpGlavXo38/HzTh/Sbb75p6ineQHl5uekkoOHtfmMRBwRBaJx47GRjNMhV+BvzWPnf//6HYDAYtQ8DBgyAz+fDd999Z6i7a9eu6HE6d+6Mdu3aQdM02y91XCbXIIdrqs07fPgw9u3bF/e5x8rq1auxf/9+vPbaazj//POj67/99lvT+iUlJVi5ciV+85vfYPbs2bj55pvxxhtvkDpTp07FH/7wB9tjDx8+HKtXrwZQL0v0er1Yv349rrvuumidUCiETz75hKyz409/+hOUUqa23+12Y8CAAQZnV6D+2pxyyilo164dgPoJ9NKlS00da3/wgx9g4MCBJDCAIJghk2sb4tESXnTRRejQoQOee+45Mrl+7rnnkJqaihEjRjRLXxtoeGtx9NuTdevWYe3atYbPiHPnzsWaNWvw5ptvYsSIEVi9ejVuvfVWnH/++eRTcjya66uuugp33nknnn32WTzzzDPRPpWUlKB79+5EL7l7924cOnQIPXv2tExk8fjjj2P37t2NZrtcunQpUlNT8eMf/9h0+6mnnoqVK1di//790aQBmqbhz3/+M9q1a4eePXvGdJ6CIHxPPHZy7969hknk4cOHMW/ePHTq1Al5eXnR9Q2RJk488USkpqY2uv9//vMfvPnmm7j88svhdtcHw2rXrh2uuOIKvPXWW/jiiy+iGRA3b96MNWvWRKNBeDweXH311Vi6dCk2bdqE/v37k7aPPl5jk+9YbV5tbS3C4XB0QtfAgw8+CKUULrvsMruhbBJHPyMaCIVCePbZZw11v/32W9xzzz24+uqrcd9996Fjx4645ZZb8Mc//pGE6ItHc52ZmYmCggK89NJLuP/++6Pj8eKLL+LIkSO49tpro3Wrq6uxfft2dOrUySBzAuptf4PUx4xrrrkG06ZNw/r166N68S1btuD9998nkVT+3//7f4Z9ly1bhuXLl+OPf/xjQhMOCccvMrm2IV7N9YMPPojJkyfj2muvRWFhIT744AO89NJL+M1vfhP9JNZcXHnllXjttdfw4x//GCNGjMC3336LkpIS9OvXj4R52rx5M+6//36MGzcOP/zhDwHUh2caNGgQbrvtNhL7Mx7N9QknnIA77rgDc+bMQTgcxllnnYXXX38dH3zwQVQX3cD06dPxhz/8Ad9++y169OgBoF7e8uqrr+L8889Heno63nvvPfz5z3/Gz3/+c1x99dWG4x04cAB//etfcfXVVzf66XnatGm48cYbkZ+fj0mTJiElJQV/+tOfsGHDBjz00EPHJEOdILR24rGTCxYswOuvv44f/vCHOPHEE7F7924sWrQI27dvx4svvkicwp955hn8+te/xqpVq3DBBRcAqA87mJKSgmHDhqFLly7473//i4ULFyI1NdWQPv3hhx9GaWkpLrroIvziF78AADz11FPo0KEDcXx+5JFHsGrVKuTn52PixIno168fDhw4gI0bN+K9996zlWvEavPKyspw5plnYvTo0dHJ/rvvvot33nkHl112Ga666irH4+mEYcOGoX379hg7dix+8YtfwOVy4cUXXzRIhZRS+NnPfoaUlBQ899xzAICbb74Zr776KqZOnYqCgoKoRCIezTUA/OY3v8GwYcMwfPhwTJo0CTt37sQTTzyBSy+9lPzI+Oijj3DhhReafv3YtGkTPv30U0ybNo18rT2a2267Dc8//zxGjBiBu+++Gz6fD3PnzkV2djbuuuuuaD0zp/aGN9WXX3656cReEAy0fIASZzQWAqex7FbDhw9Xp59+erTcWIgps30bjpUoFi5cqE477TTl9/tVz5491ZNPPql0Xbfdr6nnrOu6evjhh9VJJ52kAoGAOvPMM9Vbb72lxo4dq0466SSlVH2YrrPOOkudcMIJJJSTUkrNnz9fAVDLly+P46wpmqZF++L3+9Xpp5+uXnrpJUO9hjBO3377bXTdunXr1Pnnn6/at2+vgsGgGjhwoCopKWl0DEtKShQA9eabb1r2acWKFWr48OGqU6dOyu/3qwEDBqiSkpImnacgHEtao53829/+pi655BKVk5OjfD6fysrKUpdeeqkqLS1t9JirVq2Krps/f74aMmSI6tChg/J6vapr167qxhtvVF999ZXp8TZs2KAKCgpUWlqaateunbrqqqvUl19+aahXXl6uJk+erHJzc5XP51M5OTnq4osvVgsXLozpvGKxeQcPHlQ33nij6tWrl0pNTVWBQECdfvrp6uGHH1ahUMj2GA3Xa86cOWR9Y1krX3jhBQWAhM/75z//qc4++2yVkpKiunXrpn75y1+qd999l4xzw7Pg1VdfJe1t375dZWRkqCuuuCKmMbHjgw8+UMOGDVPBYFB17txZTZ48WVVWVpqeGw/nqFR9WEMA6tNPP7U8zo4dO9Q111yjMjIyVHp6urryyisbvV+ORkLxCU5xKRWDV4sgCIIgCIIgCLa0mgyNgiAIgiAIgpDsyORaEARBEARBEBKETK4FQRAEQRAEIUHI5FoQBEEQBEEQEoRMrgVBEARBEAQhQcjkWhAEQRAEQRAShCSREQQhaaitrUUoFHK8n9/vRzAYbIYeCYIgCEcjdtqehE6uvV6aedDjpoOolE7KuqIXR9eqDW263NYXQqmI9TFBj8nR9Vp6PJdxSPg6rzuVlEMRmrXL4zHPDtjYMQM+Om61oT2GfbyeDFLm5+X30DS6ugrTPrkDpFwT2kvKqYFsUnabjIPdWGoavZ4BX6Zln+rCh2j77P4wO57H7TesO5owu4e87H6I8HvMRT/e8PN2mXzcCUUqaB3WhuE+149AsKe2thYnn9wdZWXWWfDMyMnJwbfffttmDHdTMNo484x2xz/H/sOty27smW0xXjvjORhsmKFsfd46e6bC5rnN7V39StqGAk+nYf0sMWnQYX3n8LmEYI7Y6diQN9eCICQFoVAIZWUHsPXbZcjISLXf4f+orKxGj5OvRygUahNGWxAE4Vghdjo2ZHItCEJSkZEeREZ6Suw76E7fggmCIAhNQey0NTK5FgQhudC0+sVJfUEQBKHlEDttSUIn1wbNLCtrTGvMtWFuj/ETA9dQc82s35tFymGtkpR9TKvM+8C1zJpu1H37mIZa07m2uBPrA9XYct0u71NduIKUUwPdDX2w00hHtBpS5mPv86YZ2rTa3+9tZ6jDrxcfh6C/PSnXhg7SNn20zaCfas35OJjBNdUcH7uHIpq1pp6fZ214Pym7XUaNt9dGUy80EV05e8uhN78e8/jieNVYH3sNNcepppqfA7dXbuZz4jGxT9y/xuOhdbgdN/pCUe0xfzZEdNpH7kMEAIqfh8Gfhp+33d87H0f5mz/miJ22JPmskSAIbRtdd74IgiAILUcL2OkFCxagR48eCAaDyM/Px0cffWRZ/+WXX0afPn0QDAYxYMAAvPPOO9Ft4XAY9957LwYMGIC0tDR069YNY8aMwa5du0gbBw4cwA033ICMjAxkZWVhwoQJOHLEeVACmVwLgpBcRCLOF0EQBKHlaGY7vXz5chQVFaG4uBgbN27EwIEDUVhYiD17jNHUAGDNmjUYPXo0JkyYgI8//hgjR47EyJEjsWnTJgBAdXU1Nm7ciPvvvx8bN27Ea6+9hi1btuBHP/oRaeeGG27A559/jpUrV+Ktt97CP/7xD0yaNMnx8LiUUgl7V+/xZNEyk3TYyULMQ69Zy0K4ZMOpLIR/djOThXDpCZdD8PBwdrIQfk68foq/s6EPTZWFpASYdCVSRcqaXkfKZrIQHsKJj4NBYmEjC+G0hCyEE48sxCmhcFmT22gLVFZWIjMzEwe+fQkZ7Rx4oR+uRoeTb8ShQ4eQkZFhv0Mbx+XyHesuNBPJ967o+JSFUJtqKguxCeeXjKH5JBRfbLSUnc7Pz8dZZ52FZ555BgCg6zpyc3Nx++23Y9q0aYb6o0aNQlVVFd56663ourPPPhuDBg1CSUmJ6TH+/e9/Y8iQIdi2bRtOPPFEbN68Gf369cO///1vDB48GACwYsUKXHHFFdi5cye6desW8/k2q0NjOLKPlD1somuI2Wlyc/M/dD7B4xNTPhG2j51My2aTqVCkkq1hemYP1TPbHTOFTTJTA11IuarOOBkL+jqyflobSB5jmk9c0/z0mAEPq6/RGNQA4Gc/ZLygPxKqNXq9M4K5pFyr0T542Vh7WZ9qwnRyXt8HOnb8R4ELHlI26MRZjNa6CD1P/kOIx3QFAE2jP0y8Xjp2kYhx7ITYcSndoNG0qy8cb7TCibLpTnaTZx5nn02E2WTa66bRGXxe4+QmyJ6BARe1mV4XnXxHFLWhdeowKdeEWTxj9piOmPz9aYZ1if4bFQ32sSZeO11ZSedTgUAAgQC9J0OhEDZs2IDp06dH17ndbhQUFGDt2rWm7a9duxZFRUVkXWFhIV5//fVG+3To0CG4XC5kZWVF28jKyopOrAGgoKAAbrcb69atw49//GPb84z2N+aagiAILYForgVBEJKbOO10bm4uMjMzo8vs2bMNTe/btw+apiE7m36hz87ORlmZ+ZfgsrIyR/Vra2tx7733YvTo0dE36WVlZejShb7k83q96NChQ6PtNIaE4hMEIbmIaPWLk/qCIAhCyxGnnd6xYweRhfC31i1BOBzGddddB6UUnnvuuWY5hkyuBUFILpy+jZY314IgCC1LnHY6IyPDVnPdqVMneDwelJeXk/Xl5eXIyckx3ScnJyem+g0T623btuH9998nfcnJyTE4TEYiERw4cKDR4zZGQifXXN/Kh51rrLlWmetnASAUZrptL70oXqZJ40524Qh3quvItlfQ9jzGi+5jDohuNz2POnYMrqPjDoyhMNO0KepEx3W/AKCrMFtD2/R6qBaPO7Xw7SGmG0730hunDkbdMNdYc1I91GmSa/f4ednpvPk4A8ax4+cZ0anzDdee14S4wyI9RoQ5tPJrWd8mdTitC++13C44RCmDA5RtfaGVcewViXFpqEkD9udg8Cti523noMjzE3AH7HQP/YQNAO3RlZQzVRYpBxTtUzXoc3evm37+Vl5rh0f+zAWMdtMQ11rJ16ZWTzPaab/fj7y8PJSWlmLkyJEA6h0aS0tLMWXKFNN9hg4ditLSUtxxxx3RdStXrsTQoUOj5YaJ9VdffYVVq1ahY8eOhjYqKiqwYcMG5OXlAQDef/996LqO/Pz8mPsPyJtrQRCSDZGFCIIgJDfNbKeLioowduxYDB48GEOGDMG8efNQVVWF8ePHAwDGjBmD7t27RzXbU6dOxfDhw/HEE09gxIgRWLZsGdavX4+FCxcCqJ9YX3PNNdi4cSPeeustaJoW1VF36NABfr8fffv2xWWXXYaJEyeipKQE4XAYU6ZMwfXXX+8oUgggk2tBEJINkYUIgiAkN81sp0eNGoW9e/di5syZKCsrw6BBg7BixYqo0+L27dvhdn//RWTYsGFYunQpZsyYgfvuuw+9e/fG66+/jv79+wMAvvvuO7z55psAgEGDBpFjrVq1ChdccAEAYMmSJZgyZQouvvhiuN1uXH311Xjqqacc9R2QybUgCMmGcmi0JRSfIAhCy9ICdnrKlCmNykBWr15tWHfttdfi2muvNa3fo0cPxJLWpUOHDli6dKmjfprRrJNrHteaB5uvNSQhMeqdPSy2ss72cbl5YhOqmTXEIOZJaFhMUDN4bORQqIKUDbG1mdqc6+R4ApcA6yNPSmMGP4bdPjwRQIqvAynX6FQ3zvXQgFFHl4b2pFwF2kaKi27nsbi5BjvCxpnroQFjMpyaOqrJ5/twjTU/ZpjFME8NUL0iT94DAKEIjfvqYb4DfLvgDJeuw+XAaDupKxwrWl5j3fyaauN2biO55pqXnWqsUz1UI8r11QDQFVSH3T2V+qWk+2gfy6qZ30qYfr6vdlO7XuuqIGUzvxS7682vjUGT7Tguttm1Fl+M5kTstDXy5loQhORC0+oXJ/UFQRCElkPstCUyuRYEIbkQzbUgCEJyI3baEplcC4KQXOiqfnFSXxAEQWg5xE5bcuyDjQqCIBxNRAMiEQdLfJ8bFyxYgB49eiAYDCI/Px8fffSRZf2KigpMnjwZXbt2RSAQwKmnnop33nknrmMLgiC0alrITrdWmvXNtdedSsrc0Ys7E4a1I4Y2uBMkT/LCHdhczAGSJ4nhDpLGJCL0eIDRCc7joefl91GnE02jjnm1zKnO56V94I5+ZrjdPlKuC1eQcsCXRfvIzivVTx1h7FAw/iHwcTjCkt8Y6jNHl6CbJY1hSWb8burME1HGpEJhlvyGOwDxseZJGjR2fXkf6yIskY2JU6UOa8dMY+IIwRFKOUsME0cSmeXLl6OoqAglJSXIz8/HvHnzUFhYiC1btqBLF2NijlAohEsuuQRdunTBK6+8gu7du2Pbtm3IyspyfOy2QfO+t2mysyLg2GHR6Ljn3KGROzDyJFjcMd3rCliWPYo+F+p7ZT02msO/Fzc8pGw4xxjG4djkeeLjwDuRgHuoLdMCdro1I7MAQRCSixbQ8s2dOxcTJ06MJiQoKSnB22+/jUWLFmHatGmG+osWLcKBAwewZs0a+Hz1E5oePXo4Pq4gCMJxgWiuLRFZiCAIyYWmfZ/9K5bFoRd6KBTChg0bUFBQEF3ndrtRUFCAtWvXmu7z5ptvYujQoZg8eTKys7PRv39/PPzww9DamAe8IAgCgGa3060deXMtCEJyEecbkcpKGrM8EAggEAgYqu/btw+apkUzfTWQnZ2NL774wvQQ//vf//D+++/jhhtuwDvvvIOvv/4at912G8LhMIqLi2PvqyAIwvGAvLm2JKGTa11FSFljemeul+ZaY67RBgCNvVzXWaIRnjSGa9zc7iAtcw0c07zxJDMA4PXSfrmYBo0nheF6Mz9LEqPpVEvscVtr2ur7RZPAeD0phjq0DdpmRFGtcVin7XX0nkLKIRjHoVqjeuZs96mkfNhFt/P7IYJay+1K0V+2bpMPKz6WsOVwzU5S9jI9vFH7R+85N7vneMIgD7t/AMCleGIIt+V2wSFxeqHn5uaS1cXFxZg1a1ZiuqTr6NKlCxYuXAiPx4O8vDx89913mDNnjkyuAST6I+ix0FQbdrfRWJvZacPzx5BExr6No9FVmJTDitrtwy5jwiq3omNXXU1tpo89v2pAj3HETX+k8mcHt6E8oZlZHeNYH4uJlmisE4pEC7FEZgGCICQXSneWKvf/6u7YsQMZGd//gDd7aw0AnTp1gsfjQXl5OVlfXl6OnJwc0326du0Kn88Hj+f7iUnfvn1RVlaGUCgEv98+q6ogCMJxQ5x2uq0gmmtBEJILJzq+hgVARkYGWRqbXPv9fuTl5aG0tDS6Ttd1lJaWYujQoab7nHPOOfj666+hH/Vp88svv0TXrl1lYi0IQtsjTjvdVpDJtSAIyUXD50Yni0OKiorw/PPP4w9/+AM2b96MW2+9FVVVVdHoIWPGjMH06dOj9W+99VYcOHAAU6dOxZdffom3334bDz/8MCZPnpyw0xYEQWg1tICdbs00qyyE6894jGFDjGmmdwWAgLc9KYc0FhvZS3Xcus5036xNfkyuDUsLdjX0obpuLylznRxvk+uhedxrrlHjMaiP1JUZ+uB20XimPLY212Tz2KMKdHvQk0XKFdoOUm7nMX4e53Gq92Mb3Q62PfQVKaf5qAOZh8VsrY7QcTbTlXO9ut9Lx4HHwVbMiSKFjXV1HZUGGI6njHHPua6ba8d5rFrBIbpy6Cjj3GiPGjUKe/fuxcyZM1FWVoZBgwZhxYoVUSfH7du3w+3+/m8oNzcX7777Lu68806cccYZ6N69O6ZOnYp7773X8bFbP4l/J+NYY22rpzbDWdxq23jOZppr3obNWPHnj6aHLLfzGPthl9E3psZNY/X7mV+JB/RZorOcBrWKaq5rNVq266M5bUsS0CZoATvdmhHNtSAIyYXuMGyTHt/nxilTpmDKlCmm21avXm1YN3ToUPzrX/+K61iCIAjHFS1kp1srMrkWBCG5EC90QRCE5EbstCUyuRYEIbkQoy0IgpDciJ22JMGTa+vYllzD5vOmk3JEM+pbucbax/SutUyf7GbbuWab94FrdOtqDxr64HZRDS3XjruYpo3rn7lemmu0OQFfpmGdz031x3VsXIIslnZthOru0vxd6HatgpQzfSeQMtfhAUCtTtvkY6m5aLzUVF8nQxtHcyS023J7KHLYsI6PndtNyz7QmK51bBx4mzyOtVnMVk5dmMaWtYulLThEkhMkGU3XWDc5bnVMGuvExq2201ib6akNtoDbEza/0FmMaV2jZf7s4HrnsNv4LKlzUY00b8Nj8/zReH4ClhOB+73EYjOdkwxxsQVLxE5bIm+uBUFILiJ6/eKkviAIgtByiJ22RCbXgiAkF/JGRBAEIbkRO22JTK4FQUgulKpfnNQXBEEQWg6x05YkdHLN41rzss70YlxjHWHxNAHAw/TMoQit4/Vm0bKHami5ptrnoZpcHqPY56E6cMCoo/OweM9cB8x1cbqiOjquqeb6aB67uf6YNCZ00OUh5dpIBSlzjbXHRvddpx8h5ZrIfkMf0nydSdkLOtbVGt0npNE2+TlkBLrT/SNUyxwKm2iuPUxzzc6Da/S9XFNtiCtL78Egi4NdGzKOg4/FVhcSjHxuPMYkQW4xW421vd65OTTV1sezh+uT7fwz+PPJEEdbtx8HFzyW2+3gGmveJ/O01vI3edwjdtoSeXMtCEJyoRx+bhQHUkEQhJZF7LQlMrkWBCG5kBBPgiAIyY3YaUtkci0IQnKhw6HRbraeCIIgCGaInbYkoZNrxbRYXE/GYwprejUpu1g8abN9uG7X46H7cJ0u11jzuMheRdv3emg8acAsbjVtw07X7XXRYxh0dEwD5zbR+nFNNR8H3sdaF62f7suxPIbfRbXtVarc0AeuiU73ZpMy11jz+Kp1YaotP1K7i5Q9bnotzbTnvA1+j9npCbnGWldUH19Vu4O1Z/wTcYP2k19PuzjmgjUqokM50Oc5qSu0DI7jWjvUWJv/ndtorLlPUDNoqp3iNCa+XdxswL7fdudtd0xu75onzjVH4l4nG2KnrZFZgCAIyYV4oQuCICQ3Yqctkcm1IAjJhWj5BEEQkhux05bI5FoQhORCQjwJgiAkN2KnLWnRyTWPQc0x09jyGNG1YRp3OMVFYy9zvVlEZ3ppxeMkU12wmQaO1zH0UacatECAxkGuY7G5U32dSDnM+mgG7wPXK/PY2QEP7UOY6dsDHlr/cKSMlDP8Jxj6UKMdJOU6nZ6Xz23Uqx8Nv/68zzwWd3Von6ENn5fq2bkGW2Ox01MDVBfOteo1Iaot57p/M/10mOvfWWx0vl1wiLwRaWGari1ueY21cX/D36pN3GrbuNfNANcn2+mjnWqyAZM41Ay787TTUNv5VjW2TjjOEDttiby5FgQhqVBKQTkwxKqNafkEQRCONWKnrZHJtSAIyYV8bhQEQUhuxE5bIpNrQRCSC/ncKAiCkNyInbYkoZNrL9Oe8pjCLnhomenNakJ7DW0a41zTcl2Eam49TDPLdb1cH83jQ5uREqAaaV2nOvC0II0hzWNSe5kWWdPrSNnvpuMW0mm8aMCog8tNH0rKVTrVogfdVHNdrdHtHlB9czsvPYeQohptwKip5hrpWo1qsDlcN87jg9fFcm00WuSxsbnukmuyjTHGWdxzN433rbE42ADgZvegrocstwsOaaEQTwsWLMCcOXNQVlaGgQMH4umnn8aQIUNM6y5evBjjx48n6wKBAGpra03rH8841lcDCddYm8aS5/kCbOJaNzWOdTz722mR7eL28+eAWXv8PA372MWIPgZ6aX5PKbMA3gSJe33MkVB8ljS/B4cgCIIDlO58ccry5ctRVFSE4uJibNy4EQMHDkRhYSH27NnT6D4ZGRnYvXt3dNm2bVsTzlIQBKH10hJ2ujUjk2tBEJKLBi2fk8Uhc+fOxcSJEzF+/Hj069cPJSUlSE1NxaJFixrdx+VyIScnJ7pkZ2c3WlcQBOG4pgXsdGtGJteCICQVzf1GJBQKYcOGDSgoKIiuc7vdKCgowNq1axvd78iRIzjppJOQm5uLq666Cp9//nm8pygIgtCqaYk31wsWLECPHj0QDAaRn5+Pjz76yLL+yy+/jD59+iAYDGLAgAF45513yPbXXnsNl156KTp27AiXy4VPPvnE0MYFF1wAl8tFlltuucVx32VyLQhCcqHU984ysSz/p+WrrKwkS11dnWnz+/btg6ZphjfP2dnZKCsrM93ntNNOw6JFi/DGG2/gpZdegq7rGDZsGHbu3JnYcxcEQWgNxGmnY8WpdG/NmjUYPXo0JkyYgI8//hgjR47EyJEjsWnTpmidqqoqnHvuuXj00Uctjz1x4kQiAXzssccc9R1IsENjRKOOeNyxK8ScDzkBX5ZhnZ0DSIQ5TXKHNU3jzmb0lHlSEjMnlYCHJrepVRWWfeT1uZOcmx0jxdWelFPdtAwYHQzDijr78X24U0uKh273g/bpsE5vWN5HAAhptA/KRc+bO2ZWh6mDatCbRcrcoZE7n3LHT8B4fcMRes8F/R1J2ejQSO8Hfm3CGncmNd5/3HGXO1VqutEJUogdFVFQHgfxUyP1dXNzc8n64uJizJo1KyF9Gjp0KIYO/d6JeNiwYejbty9++9vf4sEHH0zIMVqOFninkmAHRsN2k/a5A6Op06NFm9zh3q5+PHWcOjQq5sFt3G6SwIWvUzbbbbB3omxbn/uFeuK107FytHQPAEpKSvD2229j0aJFmDZtmqH+/Pnzcdlll+Gee+4BADz44INYuXIlnnnmGZSUlAAAbrrpJgDA1q1bLY+dmpqKnJwcyzp2yJtrQRCSCz2OBcCOHTtw6NCh6DJ9+nTT5jt16gSPx4Pycpqds7y8PGaD6vP5cOaZZ+Lrr792fHqCIAitnjjtdCxfGOOR7q1du5bUB4DCwkJLqV9jLFmyBJ06dUL//v0xffp0VFcbo6fZIZNrQRCSCxXHgvpoHkcvgUDA2DYAv9+PvLw8lJaWRtfpuo7S0lLydtoKTdPw2WefoWvXrnGdoiAIQqsmTjudm5uLzMzM6DJ79mxD0/FI98rKyhzVb4yf/vSneOmll7Bq1SpMnz4dL774Im688UZHbQCSREYQhCRD6Q7T6saRnKCoqAhjx47F4MGDMWTIEMybNw9VVVXRT5BjxoxB9+7do4b/gQcewNlnn41evXqhoqICc+bMwbZt2/Dzn//c8bEFQRBaO/Ha6R07diAj4/s8HI29BDlWTJo0KfrvAQMGoGvXrrj44ovxzTffoGfPnjG3k9DJtWJaU43pY1MD3UmZJwnxemiSEgCoDR0kZb+X6pntNGgeD9XD2iUScLmMujufiyUWcdPPGGGdnkeatzMp1+k0uUqqmyWdUXR7wEU1vQDQESeQsl9RPXsYNLFNhYv+Wuuin0TKe9w0Rm+G2/5z+CE3PQbXKOqKbvd7qJ69jiWZ4dfySO0uUuZaZgDwuug9EmHb+T2l2D2YFqBvGqvqdpOy2yYpjRkRjSVLamJyiraOigDKWv5qqO+UUaNGYe/evZg5cybKysowaNAgrFixIvrmY/v27XC7v7+OBw8exMSJE1FWVob27dsjLy8Pa9asQb9+/ZwfvJURV9IYA87+Jox22V5PbUgaY6OpNh7DphxDEhqz54cVSllrqrmPiXE7tbmmdbiVbKIG25DYJp4wEHzsePKcJieVAUQL3rzEa6cbvixaEY90Lycnp0lSv8bIz88HAHz99deOJtcyCxAEIbmIU8vnlClTpmDbtm2oq6vDunXrokYUAFavXo3FixdHy08++WS0bllZGd5++22ceeaZ8R1YEAShtdOMdjoe6d7QoUNJfQBYuXJlzFK/xmgI1+dUAiiyEEEQkgqnMVHbWuYvQRCEY01z22mn0r2pU6di+PDheOKJJzBixAgsW7YM69evx8KFC6NtHjhwANu3b8euXfVfybds2QIA0cRg33zzDZYuXYorrrgCHTt2xKeffoo777wT559/Ps444wxH/ZfJtSAISYXSAfa13La+IAiC0HI0t512Kt0bNmwYli5dihkzZuC+++5D79698frrr6N///7ROm+++WZ0cg4A119/PYDvw7b6/X6899570Yl8bm4urr76asyYMcNZ5wG4lHIY2dsCn68LbZypTrgmzqAni+G7gcdFNbF8Hx4rORyhsZR5XGuPm4rpM31U2wwAdTqNfawpqrn2uGgbXL+sMT10e0XH6ZBrPyl31Wm8XgCoA4vPzPSD6aAa7COgOmAPq1/Dtte6aKiZOhfVLgNGbXiEtRFiMaL52HLN48FqGsasU1pfUt5fvcXQB67T5tSE6Fh6PXRceB94LG2NxcHWDHGvAR+L121HXWiXfSUBlZWVyMzMxK4J1yHDb9TbN7pfKIRuv/8zDh06ZKvlEwCXy5kDUUya6wTHtTbErI7BF8Lt8lkew65sjJNN2/PYbDc9BrM3dnGruYaa+y0ZtpvE1Nd1633sdNzGONnW28011w5jY9vF/7bVXMcCPwbXjpsnnRIoYqdjQ95cC4KQXDjVUcuba0EQhJZF7LQlMrkWBCGp0LX6xUl9QRAEoeUQO22NTK4FQUgudFf94qS+IAiC0HKInbYkoZProK89KRtjDutsO9WzBnwdDG1yDVs4QvcJ+juSMtebcd2v103jJHM9WlVkj6EPqZ5OpFzL9unopjGkfcpa05ihaBzr0/xUg30kbPyJF2ZtdglSrVNIo5q0k3z0PHfXMM22Tsc1TdH6VcqouQ65qS47BfR6B71U735Yo7G2Xey7UEqAjuuRMK3Prx0AVIf20jpMg6/pVAdujH1rHUeW+wl4PUZtmG6icxQSh0QLaXvYxbU2+u/EoHe201S7aRteZku87lS2PWBZBgAPaJtuWAcC5v44Eab75X4tPKdC2MQ3RnPRNlzM1vPP87ZxsPmcqDn+4BIe91pobsROWyNvrgVBSCqUckEZnujW9QVBEISWQ+y0NTK5FgQhqdA1QHeQdbGtafkEQRCONWKnrZHJtSAISYW8EREEQUhuxE5bk9DJdXUdzevu9VDNGte3ej1UexzRqKbXrI0A03XzOMU8rqjbzWNrU42bj2mwY9HydfBQjTWP+8l1dD5QbV57L429nOal7eekGHV6rAqYxBoZrNt7ammFAe1pH7YepuNyIEx1xD5l7EOWTuNQ73UdJOXDLF63302vb0g3xowm2yOHSTmi1RrqcO0lvzYeNx1bHvec34M8TmxEo7G804ImMcfDhyz7YB73VYgZ3QUljjLHjJjiWtviLK613d81t+PG9kzqgNsG7n9Dy9xeBVy0nALqf5Gi6LMJAALMN4bnF9CZVjjMnhU830CNi+ZpqHVT+2QW7zvEzluDTfxmGw02t2d8XM1uF7GBbQCx05bIm2tBEJIKXXdBd2CIndQVBEEQmo7YaWtkci0IQlKhHL4RcfT2RBAEQWgyYqetkcm1IAhJhVL1i5P6giAIQsshdtqahE6uU/ydSTmsUb0Yj0nMtcpuF9XLAiZ6L6a1i0SoLpe3meKhcbCD3ixS9rJjGvRkAGo0qi0OMK14pk6PcQI7hs9N22zno+VOQfqLLjtovAt7pVPdXHktFVl3S6Ha4ToW23RXDa2f6qVawO1H6DjUaEbNXGWIuvsexC5SPkHvRcpfYwMpR5g+nusFvR6qf09j8b8BoDZC9c5cc8811jyWbU0t1YVzeFzrqtrvDHW4rlvxe0b0hk1CHGWSHBO9s+MmeBs8RrVBk23tS2O2D//b9zB7w/1tgi76t99OUbuepej2TLfxeZXup/0Meui9qTPTXsOcZw6H25FyhU412JUu2ufDJuPA4VH5+TOSP2PtNNaxxb3mGnq2i+ER1xI2s+n3rfA9YqetkTfXgiAkFZrmguaO3RBrWtsy2oIgCMcasdPWyORaEISkQt6ICIIgJDdip62RybUgCEmFGG1BEITkRuy0NQmdXPM4xYrFFOZaVZ3FGOYxrQFj3GmdpQQyaPeYlsvLdHW1kQpS7uQ/lW5XNI4oAHTynELK7fQs2kem5aqIUB1wj1R6XlxjfWEXqqs7oZ0xHnTAR8/7dBc90Q45tI1QFdX+7T+QRspbD7GYrR56bXbXGONcc811H70/KVeAauwz3F1JWXPTmK6HtTJS9jCtelivMfSBX3+zWNhW9T1uqvv3eem48FjtLpM4svw+1vRay+2CMzTNDc0duz5S0+LTUi5YsABz5sxBWVkZBg4ciKeffhpDhgyx3W/ZsmUYPXo0rrrqKrz++utxHVugOI5zbaKfNezD/nZ5nGs/i2OdrrJIuSMr5wTos6R7mtFGdk2hdjnLZ60lPhCifd5dQ23Hrir6/CsLGfMwcJTbRlNtp7kGS6XH42ArnpbPeC34MRxjeK5zHTh9hir+4BeanZay062VtnW2giAkPQ1e6E4WpyxfvhxFRUUoLi7Gxo0bMXDgQBQWFmLPnj2W+23duhV33303zjvvvDjPThAEofXTEna6NSOTa0EQkgodLujKwRJHRsG5c+di4sSJGD9+PPr164eSkhKkpqZi0aJFje6jaRpuuOEG/PrXv8Ypp5zSaD1BEITjnZaw060ZmVwLgpBU6LobmoNF152ZsVAohA0bNqCgoCC6zu12o6CgAGvXrm10vwceeABdunTBhAkT4j43QRCE44HmttOtnYRqrrnmTWOa6hQfjYMd0Zwfnsc1Tg92s9zOY22n+jqRspvFT/WxOKIAEFRUl7vXvZOUe+unkfLJ6bSNdB/9xXZCKtNLB2ifT+hBYzkDQNo1PemKFKof1E8+iR5z2w5afmMT3f0Lqn/2ujuwIxrHQVNUr1xeTcduW2Q7KXvY7VWl9pFywE1137UsnjjXTALGONYcfg9yPwC+P9duBrzt2f7Ga2HUHDrbLlgTr6NMZSX1lwgEAggEAob6+/btg6ZpyM7OJuuzs7PxxRdfmB7jww8/xO9//3t88sknMfdLOBqbONYGvbTPcrvB18ZkHw8r85wGflBfmHRFY0x38tH6J7ej9m5QFo8gDZzdnfpsdB5AbbuLPQsqPqfnsX4b9VNZf5D2wVNJ+6zX0ljcAKC5qP3hvi66YmUXK4P7NTH9dAyf9/n14bpuofUjDo3WtK2fEoIgJD16HAsA5ObmIjMzM7rMnj07If05fPgwbrrpJjz//PPo1KmT/Q6CIAjHOfHa6baChOITBCGpiPeNyI4dO5CR8f3XELO31gDQqVMneDwelJfTt4zl5eXIyckx1P/mm2+wdetW/PCHP4yu0/X6R4XX68WWLVvQs2dPw36CIAjHK/Lm2hqZXAuCkFRouguaA32eptcb7YyMDDK5bgy/34+8vDyUlpZi5MiRAOony6WlpZgyZYqhfp8+ffDZZ5+RdTNmzMDhw4cxf/585ObmxtxXQRCE44F47XRbQSbXgiAkFbqqX5zUd0pRURHGjh2LwYMHY8iQIZg3bx6qqqowfvx4AMCYMWPQvXt3zJ49G8FgEP3705juWVlZAGBYLwiC0BZoCTvdmkno5JonbIloNBlKXZg6h3GHNTOnB03R5ChelqCDO6wZEwdQJzzuzFGtqBNd0GV886WB7tNOUee/IEtwEPDQX2ipbJS7B2l7uV0qaP3B1LEGAPT+1GlSZXc11DkarSt19PSlUUeYris/IeXQ+9QZ7HDYeGvsqWVjybZ31LuQcp2LOvPUuuj9UBnZRcr8+vvcRqdKnV0Ln5c5+DBnwtQAdaINR+j9FFHGRDVHY55Eho6DpltvF5zREp8bR40ahb1792LmzJkoKyvDoEGDsGLFiqiT4/bt2+F2kCBBaF6MDpDGBC4Gp0e2jwfUwTGgmOM56LOlQ5Aeo2c6Ta5yUT/qwA0AGfOvJmWVYe2A3Z6VL33qJdrHl6nGv1aj9q4qbLSRVRpzFHdTuxt2UxvoViFWpjZU6fS8Dc6KZq5bhmc5v36sumHi1dYUuq0PkYVYI2+uBUFIKjTlgubAEDupezRTpkwxlYEAwOrVqy33Xbx4cVzHFARBOB5oKTvdWpHJtSAISUVD0gEn9QVBEISWQ+y0NTK5FgQhqVBwls1LtbHMX4IgCMcasdPWJHRyzRO4BHxUm8w1tVwfyzXbAOBz0wQuXGPt9dB9lKL6MK7b5Vo9rssLm2hwPaC6XTe7SToEmHac7X9yGu3TgM77STmzD9O0deMJXQDdRmNthz5gACl7DlMdXvZX/yXlTfuMfejIIpuV19Bx6BGg+sJNIZpshydtSPFQxWFE1ZJyTfiAoQ+KafE0jeoFuQa7JkTHmt+DQT/rg0avPz8eANSFaTIcjzuVbaf6dcEZSplpMK3rC60bu6QydvVN69gknvEonmSGbk/zsuRfKfT5lvVjo03WbDTWdqhf3EjKQ7b8jpS/WdOLlMtrjNrzA9X0mXeYJQSrZcl1uJ9SIsa+pXGZTNxULNluhLgRO22NvLkWBCGp0JQbmnIQ4slBXUEQBKHpiJ22RibXgiAkFRLiSRAEIbkRO22NTK4FQUgqxFFGEAQhuRE7bU2CNde1bA2NG+pxU9GuplO9bFirMrQZZLpcYyxtqpHlum+Xj2rS3OyUI4rW97uofhYAdKa7TVG0zpEw3Z6TSj9/hFhmokM1dFx60JDU0C8YZuhDolHdskk50P0LUj5ta4Vhn+01VHuewjSJldVUO56q0km5zkWvlc9Fr2WNRmOOB7zGmOPVIap39nroWPJY6j4P1exzPSGvb4i1bhJ7nWusuS6bbxecISGeBI6dDhgA3AnWArvZbRVwM1vQlcb1bw5Sz6FxrrM30jj/aT7mCAMgwJ5xPL6321C2jg/uAtd1R9h2s3HnsbCbGLeaX38Tuyy0LGKnrZE314IgJBUKLkee5W3NC10QBOFYI3baGplcC4KQVIiWTxAEIbkRO22NTK4FQUgqRMsnCIKQ3IidtibBmutquoLppLQI1WR7PfbaVK7T5nGLM/wnknJEYzGnWdxrL4ubHXBTXbAXRg2bT6caNZ3Fz/QxcV5ZNdWD9aCHxPYjdMWAgxWk7P52q6EPql8/w7omcZjq25VGz2nnYTouAHA4TM+zHR0WfMY082EX09SD6turNXotOWZaPp+XxT0P07jnRl1/HSvTPnHNNtdcRzSqL4y1n0L86HCm5XOSyEBITpxqcmPRYNuhgeqXI+xvvzpCbeKeOj8p6+u2GBs9a3CT+0WoC9vXYbhc9O/BzUKguZmGmud+EIRYaAk7vWDBAsyZMwdlZWUYOHAgnn76aQwZMqTR+i+//DLuv/9+bN26Fb1798ajjz6KK664Irr9tddeQ0lJCTZs2IADBw7g448/xqBBg0gbtbW1uOuuu7Bs2TLU1dWhsLAQzz77LLKzs+EEmRUIgpBU6HEsgiAIQsvR3HZ6+fLlKCoqQnFxMTZu3IiBAweisLAQe/bsMa2/Zs0ajB49GhMmTMDHH3+MkSNHYuTIkdi0aVO0TlVVFc4991w8+uijjR73zjvvxF/+8he8/PLL+Pvf/45du3bhJz/5icPey+RaEIQkQymX40UQBEFoOZrbTs+dOxcTJ07E+PHj0a9fP5SUlCA1NRWLFi0yrT9//nxcdtlluOeee9C3b188+OCD+MEPfoBnnnkmWuemm27CzJkzUVBQYNrGoUOH8Pvf/x5z587FRRddhLy8PLzwwgtYs2YN/vWvfznqv0yuBUFIKiLK+SIIgiC0HM1pp0OhEDZs2EAmwW63GwUFBVi7dq3pPmvXrjVMmgsLCxutb8aGDRsQDodJO3369MGJJ57oqB0gwZprl4tp0pi+1eOm+tZQpIJ2xmPU+XJNLde3GuNc02P6ve1I2e2iQmEe57pGp7GWASDI+pWt0/imO0NH6HamC64I02HuzGTdhz6ld137jpvAcfuZ7rtXb0MdJ7i+2UHbq6Z9OBQ23ho87ivXlme46PXVmN5duWh9rpvk2sCayAGTPtB+8TYMbbLrzX9O6jrVVHPtp67o/QQAPg+Nvx3WjrDtxvtYiB0J8ZTkmMUYToAGmhyCHcMQf94EO+0wbyPsora/iv2tH6yjhvp/VfT59tXbRv+c0875mJS1M8+07JMBnfbx0GpqW/bW0VwDtSYzljCzu7qb2zS6nWNnUwUBiN9OV1ZWkvWBQACBAP1b2rdvHzRNM+ics7Oz8cUXNCdHA2VlZab1y8rKYu5jWVkZ/H4/srKymtQOIG+uBUFIMhpCPDlZBEEQhJYjXjudm5uLzMzM6DJ79uxjeyLNhITiEwQhqZDMX4IgCMlNvHZ6x44dyMj4/usvf2sNAJ06dYLH40F5eTlZX15ejpycHNP2c3JyHNVvrI1QKISKigry9tppO4C8uRYEIcmQN9eCIAjJTbx2OiMjgyxmk2u/34+8vDyUlpZ+fzxdR2lpKYYOHWran6FDh5L6ALBy5cpG65uRl5cHn89H2tmyZQu2b9/uqB0gwW+uleIxhDPY9v/P3pnHR1Flff/XazohJCEEEpYIKCgiSpQlwKjoGI2KozwqAqOCDC+4gCJxRYEw6giKIopo1BnEBQbcHlwfRowwo0MGBdxQcGXHBAIkgWzdXXXfPzJpOacq3V2dTuiQ8/VTH7xVt+69daty6vat3zmX6lvtTIPtcbUzlFlZ+yst005jY9vtVFNb6ysnaR4XuVajep82TqphS3Z0MbQhQaca2sOg8bxPcKaStMdJf80dZqFKy31UG/jTnvYkfca39NcXALirPyNp+/lULwgX7Qcbi2ONfVS/XPEe1Q999xPVKlX6jfrFXw7TUYyD/Wh1MW2en93vSr2UpNPsJ5J0ib6Flm83/tH5/PS6uKZeVyx2LdPkOx3xrA6qo/T6ucafPsNmZWg6j99OjwvWaC7NtZUYqm+99RYefvhh/PTTT/D5fOjVqxfuuOMOXH/99RHVHcsoFsffFhVNO9NQ8zTXWIP5a4TIDwChmqmzMjVQ+1Rjo7aizEvfTzuOUPv2SXGaoQ73rB9IuvvVP9MmnkDfN2o3tYllH1L78+lPXUl6eyVtQ7nXGIe/BvQ9zON5G/uS9bVFvbvVGOXC8UFT2+m8vDyMGzcOAwYMwKBBg7BgwQJUVlZi/PjxAICxY8eiS5cuAVnJ1KlTMWzYMDz++OMYPnw4li9fjg0bNuD5558PlHnw4EHs3LkTe/fuBVA3cAbqZqwzMjKQnJyMCRMmIC8vD6mpqUhKSsKtt96KIUOGYPDgwZbaL7IQQRBiCmVxNlpFMHNdH0O1oKAA2dnZWLBgAXJzc/H999+jY8eOhvypqam4//770bt3b7jdbrz33nsYP348OnbsiNzcXOsNEARBaME0tZ0eNWoU9u/fj1mzZqG4uBhZWVlYtWpVwGlx586dsNt/+7E5dOhQLFu2DDNmzMB9992HXr16YeXKlejbt28gzzvvvBMYnAPA6NGjAQD5+fmYPXs2AOCJJ56A3W7HVVddRRaRsYoMrgVBiCmaQ3N9dAxVACgoKMD777+PxYsX49577zXkP++880h66tSpeOmll/Dpp5/K4FoQhFZHc9jpKVOmYMqUKabH1q5da9g3cuRIjBw5ssHybrjhBtxwww1B6/R4PFi0aBEWLVpkpakGRHMtCEJMEenKXxUVFWSrra01lA1EFkP1aJRSKCwsxPfff49zzz030ssUBEFoschKusGJcpxrWhyPEWxncbC5Xpbrpc3O4XpWHgfbbqdt4HGRfRrV7Hp1GkfU7aCabgD41fYTSSfbqNdopUZ1uRV++hi1i6O67wNe+pvm6zLaD2Xr6TUDwKBDe0g6Yc96kra3YfGdE6kG+8hmei9+2U513vtqqL5wd7VRc+2w0e86O6up1rgMtG8P22nM8CR0IukD+g6S5nFqnTD2Q60qI2l+P7k+UGe67xofbRN/PvyGmNVGzXW1l2riue8APy5Yw6qT4tEhno7m6E99RxNJDFWgbvWuLl26oLa2Fg6HA8888wwuvPDC8BsqNEhIDbZBk218VXPtMNjfNj+Ha5FrQe3ZYbYGwr5qWt5PLuPrM+FXqqk+spS+0/xaGUnvr6H+PLurqV3mGuudR+g1lPmMcfirbfQ6fKDXwfXseig9fAj9uxmiwz7+idROtxZEFiIIQkyhKWufELX/Gu1wQjw1hrZt2+LLL7/EkSNHUFhYiLy8PJx44okGyYggCMLxTqR2urUgg2tBEGIK9d/NSn7gtxBPoYgkhipQJx3p2bMnACArKwtbtmzBnDlzZHAtCEKrI1I73VoQzbUgCDFFU8e5jiSGqmk7db1BXbcgCMLxjKxHEJyozlxzfaqP6Vc1RbVgmk41tWZxje2siTZb8N8DPK6xw0a1x/FuGjfUz9p0WDOuH9/GQXV0XMNWrmjc63QH1VBvKae64LJaqhs/rR29Jme1UWu8+Wc6o9Z5P+3bSi+9TsU+1+ytpFryGp3FS2Wxt2tNJHP8j8PPdHVtQLXHXEFfregejWkaXXbaL1V+GpsbMD4jXLdfWUPvH9dcu0w09TQ/vQazZ9KvB9f1K5t8EGoMVp1fIlF3Wo2hOmfOHAwYMAAnnXQSamtr8cEHH+CVV17Bs88+G0HtrQGumQ5+3IbgvhK6ztI2tngAAN3B/i55fGb2BdsQ99pG66hlPkNVGrUFFV6jX8oBtm/nYaqpLvXSNu6qovlL2W+1AzW0jYe89Lq5nwsA1NjoPj97X2lsLYCQ+vYQca6bhVhog0BoDjvdkpFRgCAIMYVuMcSTHkGIJ6sxVCsrK3HLLbdg9+7diI+PR+/evfHqq69i1KhRlusWBEFo6TSHnW7JyOBaEISYQilrCw5EsogMYC2G6kMPPYSHHnoosooEQRCOM5rLTrdUZHAtCEJMIZ8bBUEQYhux08GRwbUgCDFFXYgna/kFQRCE5kPsdHCadHCtmFOK25lC0rW+g0GPm6Hp1Mkk3t2eHafOGzX+MpoGTZs5rBlgfiu1oM6ER2zUEa+WtaGjrV3Q4itpN8Fh4rS5Zj91jMk4TB3zEhz0ya3SqL7Jp9P09kqa9rKflTV+41/Cd5XUIdHJgs0csB2gZbB+8jMHRp9OnU/9rN/4AkF1eeg53NGJLzLkUPT+codY/rw42YIwfJEawLiwkY31Az8uWEMWJ4gtFAuiZeOegYDR4SyE47nB4THEIjI6c8LTlfHVZXCCZH+X3JHPaaO2wc8WleEOjhrrB5/Jg+dnulKXnS1cw45z21/GPMnLmANjBXPAr7UZo9Vwh3tudw39xPuW2dSQDo4mzoZGJ8imdZLkz6jQ9IidDo7MXAuCEFOIlk8QBCG2ETsdHBlcC4IQU/hV3WYlvyAIgtB8iJ0OjgyuBUGIKWTlL0EQhNhG7HRwojq49ut0MRWub+W6Oq6x1nSqJwOAOBfVK9f4qK6Xa7e45ra6dj9Ju5xUu8xJYovMAEClRsto5+hG0lW2CpL2gNZRzNroq0khab9O29ytrXFxAq5y3FoRXNOYxqTkfFGYSvYz8hDLUOEzLtIQxx6XCraAQTnoctI1GtVou+xUJ861yjUGDT5dIAYANC/XD9J2uhxtSJrrtvkz5nYms+O0fL5ATF2dXLPoN+QRIke0fE0N17s2x0K9wes06HrB/8aC+0oAJguMsaRNUbvqV3ThLa7B9oH693jZ33m13+hbUe7ji3NR+xFnpw+rK0TX80dbYwvf+Ew0117Q9zBfKI37tnA/Jm5TDXr4EOnIaG2xJFo+YqeDIzPXgiDEFAo2KDOnuSD5BUEQhOZD7HRwZHAtCEJMoSnAb2Eiq7WFeBIEQTjWiJ0OjgyuBUGIKUTLJwiCENuInQ5OVAfXXDdnY3pVrrHlWlWeHwC8fqqZ5bGzuc6O18E1tS4n1f36NapHK6/daWhDG3dH2iamafMqGs/ZxbR7ykb7xYM0kt7mLSPpykNGrXGah/ZNaQ2L72ynn1x2sBDRLnb8sJ+e72L9eEgZ4zsfsFNNNe+Hau0QSac4MmmbfT+RtIPdbx5fld8bM/j9r/VRnTfXWLscVA9vjJvN9Yeh9dRxrhTWhrKQ5wgNI1q+2MYsprAh9nWIuNfGdwWvg8e5Zn+HUZDo8neFIWmjGm0HqEbb4TURTJfTOPllXnqOh7nTHPbRvqxmU4E1TP9cbaP2iq8lAAC1Ot1nXE+Aprl+3RhjnPV9qLjXdWeFkUdoyYidDo7MXAuCEFNoSkGzEBTVSl5BEASh8YidDo4MrgVBiCnkc6MgCEJsI3Y6ODK4FgQhppDPjYIgCLGN2OngNOng2mGn+jOfRuNBG+NcU70rAMS5qGa62stijzJNNo9zzfVkTuUJepzXZ5Znf+0Wkk5wUw31YTuLrW2jbdrO9IftVHuSPmLSDweqqL7ZzjSO7UDr8DONW6VG036miTugqA7PCWOsbQ1U/8c1iAkOeh1l2i6SdrMY1LUavXe8783ip9p02nc+P9UX2u009qxRg0/17Ierd7DjKbQ8kzZw/WBNbTFJO1kZgjVkWd3m5tjHvTbew+C+DqYK3hCyXkMsbUVjRhviOzt4TGnqv1FjM/qllHmTSHqvl9plF7OrGmt0JWgdR+wV7Dj1a6nR6XEAqGXvWa6x9mvczyRE3H5DDPJmiGstGu2YR+x0cGTmWhCEmMKvAIcsqysIghCziJ0OjgyuBUGILSzOiLQ6MZ8gCMKxRux0UGRwLQhCTKHDWqQ1+YAsCILQvIidDk5UB9dcY831rg47jTHt06he1iwWZi2VwcHtpJo2rrnlsZGdDtqmau8BkjZquulxAPAknETSLifVDrvsVFfHqdapTo5LGn12qulO1ql2GTCJla1onXtZHU52ax1M6+dj+ulDXCcOGqsbAKoVrcPONNc612SzeN9VPlpHKLy+w4Z9XA/odNBnym6jbeLKzcraX2kb2TMbMr4uAJ3Fznaw2NmaTvXxgjU0ZW01r0hX/lq0aBHmzZuH4uJi9OvXDwsXLsSgQYNM877wwgt4+eWXsXnzZgBA//798fDDDzeYv7XBY19bjXttKM/wLuBpk7/LEDsM8Zu51tjB4uwr5t9jo++aGrtR71zBbJ6Tpe3MDuugum6/ou8Cv87T1LbwGNaA0XeJ+wyF0ljztSS4TQx9b5o+rrVZrHWheWkuO91SaQ7PFUEQhLBRSlnerLJixQrk5eUhPz8fmzZtQr9+/ZCbm4t9+/aZ5l+7di3GjBmDNWvWoKioCJmZmbjooouwZ8+exl6uIAhCi6M57HRLRgbXgiDEFPUhnqxsVpk/fz4mTpyI8ePHo0+fPigoKEBCQgIWL15smn/p0qW45ZZbkJWVhd69e+Ovf/0rdF1HYWFhI69WEASh5dEcdrolI5prQRBiikg/N1ZU0M/0cXFxiIszypu8Xi82btyI6dOnB/bZ7Xbk5OSgqKgorDqrqqrg8/mQmpoafkMFQRCOE0QWEpyoDq7tNlqcUb9KtWBcP8310nX7uC47uF7MwdrA9WW8TYY42Cb66UNVP5N0WpveJF3D4oryeM9OG9X1VmlU163baRtr7fSaAaOGjeu8uUbapZje2cZin4JeN49ZXamM2nOOV2caRH8ZSdvttEzD88Dujd3OdOJ248DIyWKG89i0hli1hti2NM0129wPgB8HABvzHdC4BttuPEcIn0gXJ8jMzCT78/PzMXv2bEP+0tJSaJqG9PR0sj89PR1bt24Nq8577rkHnTt3Rk5OTvgNbTE0Pu61dQ02L4HXGSouNsB12Dr/22d+K7qN56e2Q7PR95Wf2Se7SZxrG9OScx+QUBjtGWujQS/NnJJM8oT0IwkRxzqUxjo8fXUsxLU+FvHcj19kEZngyMy1IAgxRd2yuuFb4vqcu3btQlLSbz/YzWato8HcuXOxfPlyrF27Fh6PJ/QJgiAIxxmR2unWggyuBUGIKSKdEUlKSiKD64ZIS0uDw+FASUkJ2V9SUoKMjIyg5z722GOYO3cuPvroI5xxxhnhN1IQBOE4QmaugyPfRQRBiCl0paBZ2HSLXuhutxv9+/cnzoj1zolDhgxp8LxHH30UDz74IFatWoUBAwZEfH2CIAgtnaa20y2dqM5ca0xTrWk0Jme8m2ocq7005rDdbvzEyuMQc7jGjce19rE2OFl5hrjImjFuKNdh89ijtb5yknawMrkWuY2L9kOZdwdJux00jjYAxDlYPG6NxpxOcnSmdbKYrC6wflEsXiqrj18jYNT38TiwHB6n2uWkWmS/on3t9dL8/N4CQLybxwAPHseckxBH+77KEPeax802/onY7LRdXKPIn0HBGsriyl+R2Oy8vDyMGzcOAwYMwKBBg7BgwQJUVlZi/PjxAICxY8eiS5cumDNnDgDgkUcewaxZs7Bs2TJ0794dxcXFAIDExEQkJiY2WM/xQTNoVUNosI332Eyjy/8uadrGzrEplj+EJtums/JM7JPZPtoGGudasTjXnFA+I9z2ANY11aHqjAWNtcS1jj2aw063ZEQWIghCTNEcK3+NGjUK+/fvx6xZs1BcXIysrCysWrUq4OS4c+dO2I/6EfXss8/C6/Xi6quvJuU05DQpCIJwPCMrNAZHZCGCIMQUmq4sb5EwZcoU7NixA7W1tVi/fj2ys7MDx9auXYslS5YE0tu3bzddFEEG1oIgtEaaw04vWrQI3bt3h8fjQXZ2Nj777LOg+V9//XX07t0bHo8Hp59+Oj744ANyXCmFWbNmoVOnToiPj0dOTg5+/PFHkqd79+6w2Wxkmzt3ruW2y+BaEISYQoeyvAmCIAjNR1Pbaaur6K5btw5jxozBhAkT8MUXX2DEiBEYMWIENm/eHMjz6KOP4qmnnkJBQQHWr1+PNm3aIDc3FzU1NJzuAw88gF9//TWw3XrrrZb7x6aiuCal00kXVNBZ/F8zTTU5bnMb9jnsxn2kDqYvczG9MteH+XWq83U725I0j3ttBo+/zOOKOhy0zVxjXcviYnPMdHtcC85jSHNtcIqTxvw9rBXT8th18n6JdxoXx/DqNK6rUlQvyO8FbxPXQ/N7GyrudV2Z9Lp9WiU7Ts/x82fQEAc9uG7cTJ/In1N+HRor0+ujfS+YU1FRgeTkZFzW7i64bOGH0fOpWrx3aB7Ky8vDihbS2rFZjL1spPFzMoa41yFPCKfOUJroEHpoG9dos/xhtMFwTiMJpY820y5b11RzrGqsw/jgf0w01hbrNIkZLhhpLjudnZ2NgQMH4umnnwZQ53SemZmJW2+9Fffee68h/6hRo1BZWYn33nsvsG/w4MHIyspCQUEBlFLo3Lkz7rjjDtx5550AgPLycqSnp2PJkiUYPXo0gLqZ69tvvx2333572NdmhsxcC4IQU8jMtSAIQmzTlHa6fhXdoxfpCrWKblFRkWFRr9zc3ED+bdu2obi4mORJTk5Gdna2ocy5c+eiffv2OPPMMzFv3jz4/cxJOAzEoVEQhJhCUwp2C4ZYa21u6IIgCMeYSO10RQX9ch8XF2dY8CuSVXSLi4tN89dHdqr/N1geALjttttw1llnITU1FevWrcP06dPx66+/Yv78+eFeKgAZXAuCEGPoytosR2uLnyoIgnCsidROZ2ZSyWqsRVzKy8sL/P8ZZ5wBt9uNG2+8EXPmzLG06q8MrgVBiCnUf/+zkl8QBEFoPiK107t27SKaa7MBaySr6GZkZATNX/9vSUkJOnXqRPJkZWU12O7s7Gz4/X5s374dp5xySpArpER1cM0dH5yOJHac6la44wU/Xgd1FvNpdHEUvshMtZd2LpeVc4c2r58tXGIiQ49z0QVcuMMaXzSEOzx6ddZm1oZKL/V+5ecDZn3LFrZhDo8H1TbWZurAmOBKI2kXWyjHzOmSOxNqipapacw5kK6XAI+rHUnX+OhCOPwa+SIPgHEBF+6Q6HTS67CzhSD8bFEhDn8+nGxRGcDY93whmoS4ThAiR4OCzcrnRhlcNzOhF3AJBX8ph3RwNCwyY1Yff58EP84dGI0LtLA6WHlmjufN/SRGZUEXy2VG34FRaHlEaqeTkpJCOjQevYruiBEjAPy2iu6UKVNMzxkyZAgKCwuJI+Lq1asDq+726NEDGRkZKCwsDAymKyoqsH79etx8880NtuXLL7+E3W5Hx44dw7zSOmTmWhCEmEIpizMiIgsRBEFoVpraTltdRXfq1KkYNmwYHn/8cQwfPhzLly/Hhg0b8PzzzwMAbDYbbr/9djz00EPo1asXevTogZkzZ6Jz586BAXxRURHWr1+P888/H23btkVRURGmTZuG6667Du3atTNtZ0PI4FoQhJjCqme5RAsRBEFoXpraTltdRXfo0KFYtmwZZsyYgfvuuw+9evXCypUr0bdv30Ceu+++G5WVlZg0aRLKyspw9tlnY9WqVfB46tQHcXFxWL58OWbPno3a2lr06NED06ZNIzrscIlqnGuHI4WmmWQjlCzETJLhdNDP8qFkIX52PJQsxM5iFEciC+Fxjrmsg38+tLM6IpGF8DaFioMdShbCyw9HFsJjY3NZCI/3zfs2lCzELMY5l2TUsNjZbifrF9ZGq7IQ/nyZtSGULORINV0BSjCnPn7q75Imw2khfqpf1eLfFYskznWYND7OtRmNi+raHHGvDUWELKOx5zc9x6ssROJcxy5ip8MjypprOrhSTC9t1FhzjZvxj8G4CAgrk5XhcXVgZbKFTvTg8QrN2sAXKjHATuE67lpfKUm38VBvWd4mGxcrw6jr5oNlPuDnad5P5f4dJM0H9GYD21p/OUnzRV54m3RmrPgiM3ywzV9WZgPhUINjXqdxwE77kdfp81ew48Y/EX5/eZn8uGANmbluiXC72cwabMBkwB1qMZVQrbKm0TansQPwxmuXw2tnI+qMgr668YNp0Xg3N2KngyOyEEEQYgox2oIgCLGN2OngyOBaEISYQv/vf1byC4IgCM2H2OngyOBaEISYQrfp0Gxa6Iz1+VuZ0RYEQTjWiJ0OTlQH1w4WE9joHBhcY8s1uWZlGB0Sedxqrgum2mG3sy1Jc0dAl6ONoQ1cQ2vU8QbXknvcND4id8JLiKPLcfo1qjMHjM5//Dq4LtzYBhpGRvdTbbJR081iVpsQql+cTIvscdI28Dpq2TWaPQ/8fsY5ubMp1X1zDT1vk8EpyUkdLcw03i5HIs3DfA14HHPBGjp02GRGpIUTXQ02x1STHUr7a1GTbSg+oq/asfhsNrJNEWiso7/QUyz2a+tC7HRwZBQgCEJMof6r5rOSXxAEQWg+xE4HRwbXgiDEFH6bH8pCmDMNwSMACYIgCNFF7HRwZHAtCEJMIZ8bBUEQYhux08GJ6uCaa2TtPC4o61wes9rsN5CmU80rl887QfWvoRaF4XpnHqO42rvf0AausfXpbCEbFs/bsNCJj9bJy+N6ajOtMdcW8zjUOtMv8/jMXFvO9dFc520WG5UvyBIqTjmP312rUz28n91b3maXnfYTYOwbrrHmcdG5xjrUIkRcc+/zlxnaoLE6dI0/o8d+cYmWTHN9bly0aBHmzZuH4uJi9OvXDwsXLsSgQYNM83777beYNWsWNm7ciB07duCJJ57A7bffHlG9rZPGabA5ZhreiGJjB6ORGu0WS0zErea0kr5vQYgsJDgyChAEIabQoVnerLJixQrk5eUhPz8fmzZtQr9+/ZCbm4t9+/aZ5q+qqsKJJ56IuXPnIiMjo7GXKAiC0KJpDjvdkpHBtSAIMYVm06DZ/BY260Z7/vz5mDhxIsaPH48+ffqgoKAACQkJWLx4sWn+gQMHYt68eRg9ejTi4sJf8lcQBOF4pDnsdEtGNNeCIMQUOjSTMJ7B8wNARQVduj4uLs50IOz1erFx40ZMnz49sM9utyMnJwdFRUURtloQBKH1EKmdbi1EdXDN9c5cu8VvBNe38hjVdWWyuMQ8VjZL+zT6gnU7U2h+rkUOofsFjNpinofreHl+rrHmsZXt7DZw/XRdHTSOdSgdN48hbbe5SNqvqG64lvW9MtF9c30yvw4H09AbrpO1gcM1WVzbbJbHrmjfaexeuBw09rrbEMea6acVjf/tYs8PYNRhO1iZvA2CVaxp+er1mJmZmWRvfn4+Zs+ebchdWloKTdOQnk7jy6enp2Pr1q1WG9sC4XrYEFrlJiG6GmwgwtjYQQuMQCNqIXpCsxEFDXXQ4ptkWevWpc9tmURmp1sLMnMtCEJMoSmfpdd1/Q+iXbt2ISnptx86It8QBEFoGiK1060FGVwLghBTKGhQFmYy1X8/NyYlJZHBdUOkpaXB4XCgpKSE7C8pKRFnRUEQhDCI1E63FmLwG5YgCK0ZPYL/rOB2u9G/f38UFhb+Vqeuo7CwEEOGDIn25QiCIBx3NLWdbulEV3PNtMgOB9Xg+lg8YK4jNtU7I3gsZa6xdjLtcai4x0qj2uJ4d3tDG6praexrrj1WrN08FjPXIvP4z17/QZrfYYzvzPuW/wZ02OkncGMc62qWZtpkphu2sTYDJnGs2XVyrTjvJx3Br9vp4G0wPg9cNck181wjzfuBa9E5Xj99nsziffP7o7Hn2uz+CeGjoCzGT7Wu+czLy8O4ceMwYMAADBo0CAsWLEBlZSXGjx8PABg7diy6dOmCOXPmAKhzgvzuu+8C/79nzx58+eWXSExMRM+ePS3XH1vEogab0/SabI5ljTbQ5Prm5qBpNNShaPn91tpoDjvdkhFZiCAIMUWdli98Q2y26FIoRo0ahf3792PWrFkoLi5GVlYWVq1aFXBy3LlzJ+z23wZ0e/fuxZlnnhlIP/bYY3jssccwbNgwrF271nL9giAILZnmsNMtGRlcC4IQU9Rp+cKfNYxUyzdlyhRMmTLF9BgfMHfv3h1Kta6ZF0EQhIZoLjvdUpHBtSAIMYVSFpfVPQ4+xQuCILQkxE4HJ6qDaz/TnvI41jrXP/P4zya6ulCfEuw8tjKsxV7mcUm5Nhkwxu/mZXAdL6+T674NsZiZTpjrq4HQmmquXzeeT9vE+57rpV1Oo26Ya8V5LGynncaUtttZPHA/vW63M5WWz8oz3CsYnzGDFp31tW7Sl0fD7xU3FrYwfpg7WCxtoXHI58ZjTSxosDnhvJij658fiUY0Ip12ExObWtfWNdA6HhE7HRyZuRYEIaYQRxlBEITYRux0cGRwLQhCTFH3udGClq+VfW4UBEE41oidDo4MrgVBiCmU8lmKiapU63KUEQRBONaInQ5Ok8a5rqzZSY8z3TDXx3L9tFmZPK4x1/0qnd5sl0FbzHXCNBazX6NtAkLHd+Z4/WUkzTXbdlZnODphXif/Feh00DJ5vGYe1zrORfXOhvp04zUaNdEh7g2LIc7vr6a87Dgrj+mrAeP95H3HY2v7WD+A9yPTSxtimJv82uZ1GvWDsjZTY6jrc5kRiR1iUYNtxrH/O2xtn74bRv4mj3fETgdHZq4FQYgpdOiWHMOs6P4EQRCExiN2OjgyuBYEIaaQGRFBEITYRux0cGRwLQhCTKErP2wSP1UQBCFmETsdHBlcC4IQU1g1wq3NaAuCIBxrxE4HJ7qDa7boh9OZTNJ8wQ6v/yA938T/RDc4vfEFWuhx7kjBnQtdjiRjJUdhtnAJd3qs8e5jOeg5brYoDHfcC4XLZFESvlANbyd3xAzl2Mfzc2dCs34w/HGwND/O+4E7KPLngTtM8mswy6MxJ0ruPMrbEMo5NZSDpBl8QR4zR0whfMRoxzpmTnux6ORo9bkQR+TwkL83Qex0KGTmWhCEmKLuc2P4A53WZrQFQRCONWKngyODa0EQYgqZEREEQYhtxE4HRwbXgiDEGFaNcOsy2oIgCMcesdPBiOrgmn8iMCyOAqZnZVpVM3SuqWbSPr6QCdfIcs22YeEavsCLyQIufEEWG9MKc32yT2MLlxhg2nSmsTb7hcf7gV8XXxTGsFgO62u++I7LRfXxNb5Dhjbwcwx6ZNbsWl8pSfN+05jm3mXQaNOFbwDjAjxxrjR2DtU78+eD3yvDIkQsbdYGo8b6SNDjgjVkRqQl0lIWmglGa9Vot5a/n+PhGY0dxE4HR2auBUGIKXSlWQzxJKviCYIgNCdip4Mjg2tBEGIMDdZmlVqX0RYEQTj2iJ0OhgyuBUGIKayv/NW6jLYgCMKxRux0cKIqGlPQyWa3u8nmciSRTdOOkE0pv2Gz2exkM9TJ8mt6Ddn4cYfdQza7zUk2XfkNm9uZRDalvGTTdbrx67TbPWyj/cLr49dss9kN5zgdiWTzazVk4/fCrx0hm83mJJvPX0k2p91j2Pg5/Lr5dcS50sjG+9rtTCWbUjrZHPYEw2aDnWy1vlKy8X7idXL4NfGN93NdX1eRLdRxwSLKb32LgEWLFqF79+7weDzIzs7GZ599FjT/66+/jt69e8Pj8eD000/HBx98EFG9xyc2trVE7Ba344XWct3HwzMaQzSDnY62jVZKYdasWejUqRPi4+ORk5ODH3/8keQ5ePAgrr32WiQlJSElJQUTJkzAkSNG36tQtOS/FEEQjkNUBP9ZZcWKFcjLy0N+fj42bdqEfv36ITc3F/v28QWi6li3bh3GjBmDCRMm4IsvvsCIESMwYsQIbN68ubGXKwiC0OJoajvdFDb60UcfxVNPPYWCggKsX78ebdq0QW5uLmpqfgt0ce211+Lbb7/F6tWr8d577+Ff//oXJk2aZLl/bCqKc/UuV8egx0Ot0MgjQZhhWKGRRWrg0SG4JzRfoZHPhvMV+wDAwSKKxMIKjRzuiWuIYMIinvCIFuGs0OhnkVb4Co2879xO2teGFRoNkV2s/7L1s8gsPOIIj2ATaoXGkBFRYLwOHu2FH9e0MkMZgpGKigokJycDcMFms/q50Yfy8nIkJQVfgbWe7OxsDBw4EE8//TQAQNd1ZGZm4tZbb8W9995ryD9q1ChUVlbivffeC+wbPHgwsrKyUFBQEHZbYwGjjYxKqU1QZnMjc03hcXxGfVDKd6yb0CJoLjsdbRutlELnzp1xxx134M477wQAlJeXIz09HUuWLMHo0aOxZcsW9OnTB59//jkGDBgAAFi1ahUuvfRS7N69G507dw77esWaCIIQY+hQSgt7q3/ZV1RUkK22tta0dK/Xi40bNyInJyewz263IycnB0VFRabnFBUVkfwAkJub22B+QRCE45ums9NNYaO3bduG4uJikic5ORnZ2dmBPEVFRUhJSQkMrAEgJycHdrsd69evt9Q7UZ3C8PnMp+sFQRBC4Xa7kZGRgeLiYsvnJiYmIjMzk+zLz8/H7NmzDXlLS0uhaRrS09PJ/vT0dGzdutW0/OLiYtP8kbT1WMO/zgiCIIRLc9jpprDR9f+GytOxI1VgOJ1OpKamWr5eiRYiCEJM4PF4sG3bNni91mRUQN0nR/6JMi4uLlpNEwRBECB2OlxkcC0IQszg8Xjg8YT2vWgMaWlpcDgcKCkpIftLSkqQkZFhek5GRoal/IIgCMcrTW2nm8JG1/9bUlKCTp06kTxZWVmBPNxh0u/34+DBg5ZtvWiuBUFoVbjdbvTv3x+FhYWBfbquo7CwEEOGDDE9Z8iQISQ/AKxevbrB/IIgCEJkNIWN7tGjBzIyMkieiooKrF+/PpBnyJAhKCsrw8aNGwN5Pv74Y+i6juzsbGsXoQRBEFoZy5cvV3FxcWrJkiXqu+++U5MmTVIpKSmquLhYKaXU9ddfr+69995A/n//+9/K6XSqxx57TG3ZskXl5+crl8ulvvnmm2N1CYIgCMctTWGj586dq1JSUtTbb7+tvv76a3XFFVeoHj16qOrq6kCeiy++WJ155plq/fr16tNPP1W9evVSY8aMsdx+GVwLgtAqWbhwoTrhhBOU2+1WgwYNUv/5z38Cx4YNG6bGjRtH8r/22mvq5JNPVm63W5122mnq/fffb+YWC4IgtB6ibaN1XVczZ85U6enpKi4uTl1wwQXq+++/J3kOHDigxowZoxITE1VSUpIaP368Onz4sOW2RzXOtSAIgiAIgiC0ZkRzLQiCIAiCIAhRQgbXgiAIgiAIghAlZHAtCIIgCIIgCFFCBteCIAiCIAiCECVkcC0IgiAIgiAIUUIG14IgCIIgCIIQJWRwLQiCIAiCIAhRQgbXgiAIgiAIghAlZHAdY9hsNsyePTtkvtmzZ8NmszV9gwRBEARTzjvvPJx33nkh861duxY2mw1r165t8jYJgnDskcE1Y8mSJbDZbNiwYUODebZv3w6bzRbY7HY7UlNTcckll6CoqKgZW9u8PPvssxg5ciROOOEE2Gw23HDDDQ3mLSsrw6RJk9ChQwe0adMG559/PjZt2mTIV1NTgzlz5qBPnz5ISEhAly5dMHLkSHz77bdNeCWCILRUQtno8847D3379iX7unfvTmx2mzZtMGjQILz88svN0eRjwocffogJEyagb9++cDgc6N69e4N5dV3Ho48+ih49esDj8eCMM87A3//+d9O8r732GgYPHoyUlBS0b98ew4YNw/vvv99EVyEILRPnsW5AS2bMmDG49NJLoWkafvjhBzzzzDM4//zz8fnnn+P000+PqMzq6mo4nbF5Wx555BEcPnwYgwYNwq+//tpgPl3XMXz4cHz11Ve46667kJaWhmeeeQbnnXceNm7ciF69egXyXnvttXjnnXcwceJEnHXWWdi7dy8WLVqEIUOG4JtvvkG3bt2a49IEQTjOycrKwh133AEA+PXXX/HXv/4V48aNQ21tLSZOnBhRmR9++GE0mxhVli1bhhUrVuCss85C586dg+a9//77MXfuXEycOBEDBw7E22+/jT/+8Y+w2WwYPXp0IN/ChQtx2223Yfjw4Zg7dy5qamqwZMkSXHbZZXjzzTdx5ZVXNvVlCULLQAmEF198UQFQn3/+eYN5tm3bpgCoefPmkf3/93//pwCom2++uambqfLz81Vz377t27crXdeVUkq1adNGjRs3zjTfihUrFAD1+uuvB/bt27dPpaSkqDFjxgT27d69WwFQd955Jzn/448/VgDU/Pnzo38RgiC0aELZ6GHDhqnTTjuN7OvWrZsaPnw42bdv3z6VmJioTj311CZraz1r1qxRANSaNWuavK569uzZo7xer1JKqeHDh6tu3bqZ5tu9e7dyuVxq8uTJgX26rqtzzjlHde3aVfn9/sD+Xr16qYEDBwbeA0opVV5erhITE9Xll1/eNBciCC0QkYVEkXPOOQcA8PPPP0dchpnm+tNPP8XAgQPh8Xhw0kkn4bnnnjOc9+KLL8Jms2Hx4sVk/8MPPwybzYYPPvgg4jbV061bt7B03m+88QbS09PJLEaHDh1wzTXX4O2330ZtbS0A4PDhwwCA9PR0cn6nTp0AAPHx8Y1usyAIghkdOnRA7969G2WvzTTXu3fvxogRI9CmTRt07NgR06ZNC9i8erZs2YL4+HiMHTuW7P/000/hcDhwzz33RNymejp37gyXyxUy39tvvw2fz4dbbrklsM9ms+Hmm2/G7t27idSxoqICHTt2JO+BpKQkJCYmir0WhKOITf1BC2X79u0AgHbt2kWtzG+++QYXXXQROnTogNmzZ8Pv9yM/P98wIB0/fjzeeust5OXl4cILL0RmZia++eYb/PnPf8aECRNw6aWXBvIeOnQImqaFrDshIQEJCQmW2/zFF1/grLPOgt1Of7sNGjQIzz//PH744QecfvrpOOmkk9C1a1c8/vjjOOWUU3DmmWdi7969uPvuu9GjRw/yOVIQBOFoysvLUVpaatjv8/nCOt/v92P37t1RtdfV1dW44IILsHPnTtx2223o3LkzXnnlFXz88cck36mnnooHH3wQd911F66++mpcfvnlqKysxA033IDevXvjgQceCOQ9cuQIampqQtbtcrmQnJxsuc1ffPEF2rRpg1NPPZXsHzRoUOD42WefDaDux8Qbb7yBhQsX4g9/+ANqamqwcOFClJeXY+rUqZbrFoTjFRlcN4KqqiqUlpZC0zT8+OOPyMvLAwBcffXVUatj1qxZUErhk08+wQknnAAAuOqqq0w13S+88AJOO+00TJgwAe+99x7GjRuHjIwMzJ8/n+Q788wzsWPHjpB15+fnhxW5hPPrr7/i3HPPNeyvn5Heu3cvTj/9dLhcLrz55pv44x//iMsvvzyQr3///li3bh1SUlIs1y0IQusgJyenwWOnnXaaYZ/P5wsMxouLi/Hoo4+iuLgYkydPjlqb6icPXnvtNYwcORIAMHHiRPTr18+QNy8vD2+//TYmTZqE3/3ud8jPz8eOHTtQVFSEuLi4QL4pU6bgpZdeCln3sGHDIopG8uuvvyI9Pd3wVfJoe13PU089hdLSUtx222247bbbAABpaWkoLCzEkCFDLNctCMcrMrhuBPn5+cjPzw+kExMT8fjjj0dtcK1pGv7xj39gxIgRgYE1UDfrkZuba5B6ZGRkYNGiRRgzZgzOOeccfPnll1i9ejWSkpJIvqVLl6K6ujpk/SeeeGJE7a6uriYvh3o8Hk/geD3t2rVDVlYWRo4cicGDB+Onn37CnDlzMHLkSKxevTpwjiAIwtEsWrQIJ598smH/HXfcYfpl7sMPP0SHDh3IvvHjx2PevHlRa9MHH3yATp06kXdAQkICJk2ahLvvvpvktdvtWLJkCfr164dLLrkEGzZswIwZMzBgwACS7+6778Z1110Xsu5IZ+Ct2OuEhASccsop6Nq1Ky677DIcPnwYTzzxBK688kp88skn6NmzZ0RtEITjDRlcN4JJkyZh5MiRqKmpwccff4ynnnoqLLlFuOzfvx/V1dUkukY9p5xyiqmOevTo0Xj11Vfx/vvvY9KkSbjgggsMeX73u99FrY1mxMfHGzSGAAKfNuu1eeXl5TjnnHNw1113Bbz4AWDAgAE477zz8OKLL+Lmm29u0rYKgtAyGTRokGEgCtQNMs3kItnZ2XjooYegaRo2b96Mhx56CIcOHYLb7Y5am3bs2IGePXsaZoFPOeUU0/wnnXQSZs+ejbvuugt9+/bFzJkzDXn69OmDPn36RK2NnHDtNQCMHDkSTqcT7777bmDfFVdcgV69euH+++/HihUrmqydgtCSkMF1I+jVq1fg0+Rll10Gh8OBe++9F+eff76p0W8ODhw4EIj/+t1330HXdYP2ef/+/WH9CEhMTERiYqLlNnTq1Mk0VF/9vvqwUG+++SZKSkqIJASo+7yZlJSEf//73zK4FgQhKqSlpQXsdW5uLnr37o3LLrsMTz75ZEDSdyyoD+e3d+9eHDhwABkZGeR4eXl5WF8a3W43UlNTLdffqVMnrFmzBkop8qOA2+tffvkFq1atwvPPP0/OT01Nxdlnn41///vflusWhOMViRYSRe6//360bdsWM2bMiEp5HTp0QHx8PH788UfDse+//970nMmTJ+Pw4cOYM2cOPv30UyxYsMCQZ+DAgejUqVPI7bHHHouo3VlZWdi0aRN0XSf7169fj4SEhMCn3JKSEgAwDPSVUtA0DX6/P6L6BUEQQjF8+HAMGzYMDz/8MCorK6NSZrdu3fDzzz9DKUX2N2SvCwoKsHr1avzlL3+B1+vFjTfeaMgzderUsOx1pDGms7KyUFVVhS1btpD969evDxwHGrbXQJ2eXey1IPyGzFxHkZSUFNx444149NFH8eWXXwaMUqQ4HA7k5uZi5cqV2LlzZ0B3vWXLFvzjH/8w5H/jjTewYsUKPPXUU7j11lvx1VdfYcaMGbjsssuINrGpNddXX3013njjDbz11lsB7WFpaSlef/11/OEPfwjo++rbtHz5cuI4+c4776CyshJnnnlmRPULgiCEwz333INLL70UL7zwAm6//fZGl3fppZfiww8/xBtvvBFwaKyqqjLM9gLAtm3bcNddd+Gqq67Cfffdh/bt2+Omm27Cyy+/TEL0NbXm+oorrsC0adPwzDPP4OmnnwZQN8FRUFCALl26YOjQoQCAnj17wm63Y8WKFbjxxhsDs9y7d+/GJ598EogoIgiCDK4bZPHixVi1apVhf6hwQ1OnTsWCBQswd+5cLF++vNHt+POf/4xVq1bhnHPOwS233AK/34+FCxfitNNOw9dffx3It2/fPtx88804//zzMWXKFADA008/jTVr1uCGG27Ap59+GpCHRKq5fvfdd/HVV18BqJup+Prrr/HQQw8BAC6//HKcccYZAOoG14MHD8b48ePx3XffBVZo1DQNf/7znwPl/eEPf8Bpp52GBx54ADt27Ag4ND799NPo1KkTJkyYEFE7BUEQwuGSSy5B3759MX/+fEyePDmsuNDBmDhxIp5++mmMHTsWGzduRKdOnfDKK68YQpoqpfCnP/0J8fHxePbZZwEAN954I958801MnToVOTk5ATlGpJrrr7/+Gu+88w4A4KeffkJ5eXnAXvfr1w9/+MMfAABdu3bF7bffjnnz5sHn82HgwIFYuXIlPvnkEyxduhQOhwNA3ZfUP/3pT/jrX/+KCy64AFdeeSUOHz6MZ555BtXV1Zg+fXpknSYIxyPHcgWbWKR+9a+Gtl27djW4QmM9N9xwg3I4HOqnn36yXD8AlZ+fT/b985//VP3791dut1udeOKJqqCgwLBC45VXXqnatm2rtm/fTs59++23FQD1yCOPWG4LZ9y4cQ32y4svvkjyHjx4UE2YMEG1b99eJSQkqGHDhpmuqHbw4EE1bdo0dfLJJ6u4uDiVlpamRo8erX755ZdGt1cQhOOPaK3QWM+SJUtMbVg4DBs2TA0bNozs27Fjh7r88stVQkKCSktLU1OnTlWrVq0iKzQ++eSTCoB68803ybk7d+5USUlJ6tJLL7XcFk6wdxlfXVfTNPXwww+rbt26KbfbrU477TT16quvGsr0+Xxq4cKFKisrSyUmJqrExER1/vnnq48//rjR7RWE4wmbUkwcJgiCIAiCIAhCRIhDoyAIgiAIgiBECRlcC4IgCIIgCEKUkMG1IAiCIAiCIEQJGVwLgiAIgiAIQpSQwbUgCIIgCIIgRAkZXAuCIAiCIAhClGjWwfX27dths9mwZMmS5qxWEARBCILYZkEQhOjRamau//a3v+HUU0+Fx+NBr169sHDhwrDPra2txT333IPOnTsjPj4e2dnZWL16tWnedevW4eyzz0ZCQgIyMjJw22234ciRIyTPt99+i5EjR+LEE09EQkIC0tLScO655+Ldd981LfO1117D4MGDkZKSgvbt22PYsGF4//33TfP+/PPP+OMf/4iOHTsiPj4evXr1wv333x/2tR4PWLlfZixfvhxnnXUWPB4POnTogAkTJqC0tNSQr6SkBOPHjw/09VlnnYXXX3/dtMyPPvoI559/PtLS0pCSkoJBgwbhlVdeifgaBeF4wGazmW5z584Nee4NN9zQ4Pk2mw179uwJ5D3vvPNM81x88cVRvyZd1/Hoo4+iR48e8Hg8OOOMM/D3v/897Pb37t076m2KdcJ5b5qxZMmSoM/A0qVLSf49e/bgmmuuQUpKCpKSknDFFVfgl19+CVrHp59+GijP7D0gCGY06/Ln3bp1Q3V1daOXmLXKc889h5tuuglXXXUV8vLy8Mknn+C2225DVVUV7rnnnpDn33DDDXjjjTdw++23o1evXliyZAkuvfRSrFmzBmeffXYg35dffokLLrgAp556KubPn4/du3fjsccew48//oj/+7//C+TbsWMHDh8+jHHjxqFz586oqqrCm2++icsvvxzPPfccJk2aFMi7cOFC3HbbbRg+fDjmzp2LmpoaLFmyBJdddhnefPNNXHnllaT+8847D126dMEdd9yB9u3bY+fOndi1a1eUerJlEO79MuPZZ5/FLbfcggsuuCBwD5988kls2LAB69evh8fjAQBUVFTg7LPPRklJCaZOnYqMjAy89tpruOaaa7B06VL88Y9/DJT5zjvvYMSIERgyZAhmz54Nm82G1157DWPHjkVpaSmmTZvWpP0hCKE4VrYZAC688EKMHTuW7DvzzDNDnnfjjTciJyeH7FNK4aabbkL37t3RpUsXcqxr166YM2cO2Ve/xHg0uf/++zF37lxMnDgRAwcOxNtvv40//vGPsNlsGD16NMkbFxeHv/71r2RfcnJy1NsUy4T73jTj3HPPNZ2keOKJJ/DVV1/hggsuCOw7cuQIzj//fJSXl+O+++6Dy+XCE088gWHDhuHLL79E+/btDeXouo5bb70Vbdq0QWVlZeMvVmg9HOMVIpucqqoq1b59e8PSt9dee61q06aNOnjwYNDz169fb1jqvLq6Wp100klqyJAhJO8ll1yiOnXqpMrLywP7XnjhBQVA/eMf/whaj9/vV/369VOnnHIK2d+rVy81cOBApet6YF95eblKTExUl19+eWCfpmmqb9++Kjs7W1VVVQWt63jGyv3i1NbWqpSUFHXuueeS/n733XcVAPXUU08F9j366KMKgCosLAzs0zRNDRw4UGVkZKja2trA/gsvvFB17txZ1dTUBPb5fD510kknqTPOOKNR1ysILRkAavLkyVEr75NPPlEA1F/+8hey32xJ9KZg9+7dyuVykWvSdV2dc845qmvXrsrv9wf2jxs3TrVp06bJ2xTrNOa9aUZVVZVq27atuvDCC8n+Rx55RAFQn332WWDfli1blMPhUNOnTzct69lnn1Xt27dXU6dOVQDU/v37LbdHaJ1YkoXUz7r98MMPuO6665CcnIwOHTpg5syZUEph165duOKKK5CUlISMjAw8/vjj5HwzXd8NN9yAxMRE7NmzByNGjEBiYiI6dOiAO++8E5qmNeJnQx1r1qzBgQMHcMstt5D9kydPRmVlZYPyinreeOMNOBwOMpvs8XgwYcIEFBUVBWaFKyoqsHr1alx33XVISkoK5B07diwSExPx2muvBa3H4XAgMzMTZWVlZH9FRQU6duwIm80W2JeUlITExETEx8cH9n344YfYvHkz8vPzER8fj6qqKkv9V38fdu7cicsuuwyJiYno0qULFi1aBAD45ptv8Pvf/x5t2rRBt27dsGzZMnL+wYMHceedd+L0009HYmIikpKScMkll+Crr74i+caNGwePx4MtW7aQ/bm5uWjXrh327t0bdpvNCPd+mbF582aUlZVh1KhRpL/r+2P58uWBfZ988gk6dOiA3//+94F9drsd11xzDYqLi/HPf/4zsL+iogLt2rVDXFxcYJ/T6URaWhq5h4IQKS3RNh9NdXU1ampqGl3OsmXLYLPZyJejo/H7/SHlBnv27MGf/vQnpKenIy4uDqeddhoWL14cVv1vv/02fD4fed/YbDbcfPPN2L17N4qKigznaJqGioqKsMqvp/5+PfbYY1i0aFFAYnjRRRdh165dUErhwQcfRNeuXREfH48rrrgCBw8eNLR1+PDh6Ny5M+Li4nDSSSfhwQcfJPd2y5YtiI+PN3xZ+PTTT+FwOML68huMxr43zXj33Xdx+PBhXHvttWT/G2+8gYEDB2LgwIGBfb1798YFF1xgWs/BgwcxY8YMPPDAA0hJSbHcDqF1E5HmetSoUdB1HXPnzkV2djYeeughLFiwABdeeCG6dOmCRx55BD179sSdd96Jf/3rXyHL0zQNubm5aN++PR577DEMGzYMjz/+OJ5//nmS79ChQygtLQ25VVVVBc754osvAAADBgwgZfXv3x92uz1wvCG++OILnHzyyeQPHwAGDRoEoO6TFlA3+PT7/YZ63G43srKyTOuprKxEaWkpfv75ZzzxxBP4v//7P/IZC6jTCq5atQoLFy7E9u3bsXXrVkyePBnl5eWYOnVqIN9HH30EoO4z44ABA9CmTRskJCRg9OjRBqPaEJqm4ZJLLkFmZiYeffRRdO/eHVOmTMGSJUtw8cUXY8CAAXjkkUfQtm1bjB07Ftu2bQuc+8svv2DlypW47LLLMH/+fNx111345ptvMGzYMDJgfvLJJ9GhQweMGzcuYMSfe+45fPjhh1i4cGHgM62u62Hd69LSUvh8Psv3y4za2loAMB3wxsfH44svvoCu64G8ZvkSEhIAABs3bgzsO++88/Dtt99i5syZ+Omnn/Dzzz/jwQcfxIYNG3D33Xc32B5BsEpLss31LFmyBG3atEF8fDz69Olj+OEeLj6fD6+99hqGDh2K7t27G47/8MMPaNOmDdq2bYuMjAzMnDmT2A6gzo9i8ODB+OijjzBlyhQ8+eST6NmzJyZMmIAFCxaEbMMXX3yBNm3a4NRTTyX76+0Pfw9UVVUhKSkJycnJSE1NxeTJk8PSGtezdOlSPPPMM7j11ltxxx134J///CeuueYazJgxA6tWrcI999yDSZMm4d1338Wdd95Jzl2yZAkSExORl5eHJ598Ev3798esWbNw7733BvKceuqpePDBB/HKK6/gnXfeAVD33rrhhhvQu3dvPPDAA4G8R44cCesZKC8vD5wTyXsznD6Jj48nkkld1/H1118b6gHq7s3PP/+Mw4cPk/0zZ85ERkYGbrzxRsttEARLspD8/HwFQE2aNCmwz+/3q65duyqbzabmzp0b2H/o0CEVHx+vxo0bF9i3bds2BUC9+OKLgX3jxo1TANQDDzxA6jrzzDNV//79yb5u3bopACG3/Pz8wDmTJ09WDofD9Ho6dOigRo8eHfSaTzvtNPX73//esP/bb79VAFRBQYFSSqnXX39dAVD/+te/DHlHjhypMjIyDPtvvPHGQJvtdru6+uqrDTKVkpISdcEFF5DrS0tLU+vWrSP5Lr/8cgVAtW/fXl177bXqjTfeUDNnzlROp1MNHTqUyBzMqL8PDz/8cGBf/T202Wxq+fLlgf1bt2419HNNTY3SNI2UuW3bNhUXF2e4t//4xz8UAPXQQw+pX375RSUmJqoRI0YYzg3nXgNQa9asCZwX7v0yY//+/cpms6kJEyaQ/fXXC0CVlpYqpZS69dZbld1uV9u3byd5R48erQCoKVOmBPYdOXJEXXPNNcpmswXKSUhIUCtXrmywLYJghZZom5VSaujQoWrBggXq7bffVs8++6zq27evAqCeeeYZy31QL98yO/dPf/qTmj17tnrzzTfVyy+/HLCX11xzDck3YcIE1alTp8DfeT2jR49WycnJISV3w4cPVyeeeKJhf2VlpQKg7r333sC+e++9V91zzz1qxYoV6u9//3ugv3/3u98pn88XtJ76+9WhQwdVVlYW2D99+nQFQPXr14+UMWbMGOV2u4k0zexabrzxRpWQkEDyaZqmzj77bJWenq5KS0vV5MmTldPpVJ9//jk5t779obZhw4YFzonkvRmMAwcOKLfbbbiv+/fvN32WlVJq0aJFCoDaunVrYN9XX32lHA5HQJZS//clshAhXCJyaPx//+//Bf7f4XBgwIAB2L17NyZMmBDYn5KSglNOOSWkJ249N910E0mfc845BkeFpUuXorq6OmRZJ554YuD/q6ur4Xa7TfN5PJ6Q5VVXV5PP+UefW3/86H8bymtWz+23346rr74ae/fuxWuvvQZN0+D1ekmehIQEnHLKKejatSsuu+wyHD58GE888QSuvPJKfPLJJ+jZsycABGY7Bg4ciFdffRUAcNVVVyEhIQHTp09HYWGhwfnHjKPvbf09/Omnn3DNNdcE9p9yyilISUkh9/bo69Y0DWVlZUhMTMQpp5yCTZs2kTouuugi3HjjjXjggQfwxhtvwOPx4LnnniN5MjIywo7w0a9fv8D/h3u/zEhLS8M111yDl156Caeeeir+53/+B3v27MGtt94Kl8sFn88XOP///b//h4KCAlxzzTV44oknkJ6ejtdeew3/+7//a6gnLi4OJ598Mq6++mpceeWV0DQNzz//PK677jqsXr0agwcPDus6BSEULck2A8C///1vkv7Tn/6E/v3747777sMNN9xgSTa1bNkyuFwuYqvq+dvf/kbS119/PSZNmoQXXngB06ZNw+DBg6GUwptvvolrrrkGSikSGSI3NxfLly/Hpk2b8Lvf/a7BNlixP9y5cvTo0Tj55JNx//3344033jA4P5oxcuRI4gCZnZ0NALjuuuvgdDrJ/r///e/Ys2dP4B4c3beHDx9GbW0tzjnnHDz33HPYunVrwK7a7XYsWbIE/fr1wyWXXIINGzZgxowZhlngu+++G9ddd13INrdr1y7w/5G8N4PxxhtvwOv1GiQhoeo5Og8A3Hbbbbjkkktw0UUXWapfEOqJaHB9wgknkHRycjI8Hg/S0tIM+w8cOBCyvPqQZ0fTrl07HDp0iOwLZtQaIj4+3jBgraempiak8Y6Pjw/IBfi59ceP/rehvGb19O7dOxB2aezYsbjooovwhz/8AevXrw9ofkeOHAmn00nC9F1xxRWBEHsrVqwg9Y8ZM4bU8cc//hHTp0/HunXrQg6uze5DcnIyunbtSjTI9fuPvj+6ruPJJ5/EM888g23bthHdnpkX9mOPPYa3334bX375JZYtW4aOHTsa2hLOjwFOuPerIZ577jlUV1fjzjvvDHxGve6663DSSSfhrbfeQmJiIgDgjDPOwLJly3DTTTcFnsuMjAwsWLAAN998cyAfAEyZMgX/+c9/sGnTJtjtdUqsa665BqeddhqmTp2K9evXW75OQTCjJdlmM9xuN6ZMmYKbbroJGzduDBndp54jR47g7bffDkhYwuGOO+7ACy+8gI8++giDBw/G/v37UVZWhueff94ge6ln3759AIDi4mKyPzk5GfHx8Y22P9OmTcPMmTPx0UcfhTW4NrvfAJCZmWm6/+j79u2332LGjBn4+OOPDZrvo6UbAHDSSSdh9uzZuOuuu9C3b1/MnDnT0JY+ffqgT58+Idt8NJG8N4OxdOlSpKam4pJLLrFUz9F5VqxYgXXr1mHz5s2W6haEo4locO1wOMLaB9SFRoqkPDP2798fliNNYmJiYHDTqVMnaJqGffv2kQGc1+vFgQMHQoZi6tSpE4mXWs+vv/4K4LdQTp06dSL7ed5wQj5dffXVuPHGG/HDDz8EZpZWrVplMPSpqak4++yzyaxPffnp6ekkb/0185ehGQ3dh3Du7cMPP4yZM2fiT3/6Ex588EGkpqbCbrfj9ttvD+iUj+aLL74IvKi++eYbw48CTdOwf//+kG0G6vqj/utEuPerIZKTk/H2229j586d2L59O7p164Zu3bph6NCh6NChA3Fsufrqq3H55Zfjq6++gqZpOOuss7B27VoAwMknnwyg7jn729/+hrvvvjswsAYAl8uFSy65BE8//TS8Xm+DX1cEwQotyTY3RP3AMFxfEQBYuXIlqqqqDDOWVuqpt1PXXXcdxo0bZ3rOGWecAeA3e1/Piy++iBtuuAGdOnXCmjVroJQiExLh2p/4+Hi0b98+7GuP1GaXlZVh2LBhSEpKwgMPPICTTjoJHo8HmzZtwj333GNqsz/88EMAwN69e3HgwAFkZGSQ4+Xl5WHNNLvdbqSmpgKIznuznp07d+KTTz7BpEmTDCElU1NTERcX12A9wG/35q677sLIkSPhdruxfft2AAgEGti1axe8Xm+ThHAUji+aNc51Yxk4cCB27NgRMl9+fj5mz54NAMjKygIAbNiwAZdeemkgz4YNG6DreuB4Q2RlZWHNmjWoqKggTnL1s4315/ft2xdOpxMbNmwgnyW9Xi++/PJL00+VnHrDVD9rUFJSAgCmLy2fzwe/3x9I9+/fHy+88IJhYFnvTMhnn6LNG2+8gfPPP9/w+bWsrMwwa1ZZWYnx48ejT58+GDp0KB599FH8z//8D/Hi3rVrF3r06BFW3WvWrMF5550HIPz7FYoTTjghMCtUVlaGjRs34qqrrjLkc7vdpN31jqX1s+4HDhyA3+9v8B7quh71yAuC0NxEYpsbol6uYsVmLV26FImJibj88svDPofX06FDB7Rt2xaapoX8asYla6eddhqAOvvy17/+FVu2bCGzuOHan8OHD6O0tLTJ7fXatWtx4MABvPXWWzj33HMD+492Uj+agoICrF69Gn/5y18wZ84c3HjjjXj77bdJnqlTp+Kll14KWfewYcMCkxDReG/W8/e//x1KKdMfWHa7Haeffjo2bNhgOLZ+/XqceOKJaNu2LYC6d8+yZctMHWvPOuss9OvXL6hjvCAALWxwHYmu7/e//z1SU1Px7LPPksH1s88+i4SEBAwfPjywr96b+YQTTghEfbj66qvx2GOP4fnnnw/IBGpra/Hiiy8iOzs7MPuRnJyMnJwcvPrqq5g5c2bgD/WVV17BkSNHMHLkyEA9fBYdqBtovfzyywGPeQDo2bMn7HY7VqxYgRtvvDEwE7J792588skn5JPpFVdcgalTpwZmUOpnSesXKLjwwgtD9ltjcDgchpmw119/HXv27Anowuu55557sHPnTvznP//BKaecgsLCQowbNw5ffPFFQBMXqeY63PsF1M10VFVVhVwRbfr06fD7/SEXe/nxxx9RUFCAyy67LDBz3bFjR6SkpOB///d/8cADDwRmqI8cOYJ3330XvXv3lnB8QosnEtu8f/9+wyDy8OHDWLBgAdLS0tC/f//AfjPbfHQ5H330EcaMGWM4BtSFe4uLiyN6W6UUHnroIQB1emqgzoZdddVVWLZsGTZv3oy+ffsa6qlvb0OD7yuuuALTpk3DM888g6effjpQV0FBAbp06YKhQ4cCqJMi+Hy+wHuingcffBBKqSZZOfJo6me2j7bZXq8XzzzzjCHvtm3bcNddd+Gqq67Cfffdh/bt2+Omm27Cyy+/TEL0RaK5tvLerKqqws6dO5GWlmaYsAHqNPcnnHBCg1Kiq6++Gvfeey82bNgQ0It///33+Pjjj0kklXq/maNZvnw5VqxYgZdffhldu3YNeY2C0KIG15Fqrh988EFMnjwZI0eORG5uLj755BO8+uqr+Mtf/hL4PAUATz/9NP785z+TmdDs7GyMHDkS06dPx759+9CzZ0+89NJL2L59u2GW9i9/+QuGDh2KYcOGYdKkSdi9ezcef/xxXHTRRcRY3njjjaioqMC5556LLl26oLi4GEuXLsXWrVvx+OOPBz6bdujQAX/605/w17/+FRdccAGuvPJKHD58GM888wyqq6sxffr0QJkZGRm4//77MWvWLFx88cUYMWIEvvrqK7zwwgsYM2YMmV1tCi677DI88MADGD9+PIYOHYpvvvkGS5cuNTgwffzxx3jmmWeQn5+Ps846C0DdJ9XzzjsPM2fOxKOPPgogcs21lfs1duxY/POf/yQvmLlz52Lz5s3Izs6G0+nEypUr8eGHH+Khhx4y9GGfPn0wcuRInHDCCdi2bRueffZZpKamoqCgIJDH4XDgzjvvxIwZMzB48GCMHTsWmqbhb3/7G3bv3h1wPhWElkwktnnRokVYuXIl/vCHP+CEE07Ar7/+isWLF2Pnzp145ZVXiFTKzDbXs2LFCvj9/gYlIZs2bcKYMWMwZswY9OzZE9XV1fjf//1f/Pvf/8akSZMCdgio+/tfs2YNsrOzMXHiRPTp0wcHDx7Epk2b8NFHH4WUa3Tt2hW333475s2bB5/Ph4EDB2LlypX45JNPsHTp0sCgtri4GGeeeSbGjBkT+HH/j3/8Ax988AEuvvhiXHHFFZb70wpDhw5Fu3btMG7cONx2222w2Wx45ZVXDBMkSin86U9/Qnx8PJ599lkAde+vN998E1OnTkVOTk5AIhGJ5hoI/7352Wef4fzzzzf9+rF582Z8/fXXuPfeew3+QfXccssteOGFFzB8+HDceeedcLlcmD9/PtLT03HHHXcE8o0YMcJwbv1M9SWXXGI6sBcEA1ZCizQUjqahlab4qlgNhXsyO7e+rmjx/PPPq1NOOUW53W510kknqSeeeMIQnq6+zqNDuylVt8LfnXfeqTIyMlRcXJwaOHCgWrVqlWk9n3zyiRo6dKjyeDyqQ4cOavLkyaqiooLk+fvf/65ycnJUenq6cjqdql27dionJ0e9/fbbhvJ8Pp9auHChysrKUomJiSoxMVGdf/756uOPPzbk1XVdLVy4UJ188snK5XKpzMxMNWPGDOX1ekP2T7j3sJ5u3bqRVS9ramrUHXfcoTp16qTi4+PV7373O1VUVKSGDRsWCL1UUVGhunXrps466yxDqKlp06Ypu92uioqKQrY1FOHer2HDhhmesffee08NGjRItW3bViUkJKjBgwer1157zbSe0aNHq8zMTOV2u1Xnzp3VTTfdpEpKSkzzLl26VA0aNEilpKSo+Ph4lZ2drd54441GX6sgKNUybfOHH36oLrzwQpWRkaFcLpdKSUlRF110EVn1lNfJbbNSSg0ePFh17NiRrH54NL/88osaOXKk6t69u/J4PCohIUH1799fFRQUmIYoLSkpUZMnT1aZmZnK5XKpjIwMdcEFF6jnn38+rOvSNE09/PDDqlu3bsrtdqvTTjtNvfrqqyTPoUOH1HXXXad69uypEhISVFxcnDrttNPUww8/HJa9rr9fR69Eq5RSa9asUQDU66+/Tva/+OKLCgAJn/fvf/9bDR48WMXHx6vOnTuru+++OxAmtb6fn3zySQVAvfnmm6S8nTt3qqSkJHXppZeG1SehCOe9WX9tPJyjUnVhDQGor7/+Omg9u3btUldffbVKSkpSiYmJ6rLLLlM//vhjyPZJKD7BKjalwvBqEQRBEARBEAQhJBGt0CgIgiAIgiAIghEZXAuCIAiCIAhClJDBtSAIgiAIgiBECRlcC4IgCIIgCEKUkMG1IAiCIAiCIEQJGVwLgiAIgiAIQpRoUYvICIJwfFNTUwOv12v5PLfbDY/H0wQtEgRBEI5G7HRoojq4djhSSNpmoxPjdpubpP1aBctPj5vWYac3RtNrgtZps9FL1LQjrDy6XK6u/IY67XbaLjsrUymdtakqaBt4P/A2+/xlhjbwvuFtcrJ+qfWVkrTLmcLqoH0f50pFKDTdy9L0Ovl1OR3GpYiPxtBvipbvMHke+P3m/WBjH2P4/eT3jve9l/W905EYsg2hnkm/P/iqbkIdNTU16NGjC4qLrfdXRkYGtm3b1moMd2Pg9ggwX9Hu+EM+1B479NBZoo61JTyUybtfMCJ2Ojxk5loQhJjA6/WiuPggtm9bjqSk4D/Mjqaiogrde4yG1+ttFUZbEAThWCF2OjxkcC0IQkyRlOhBUmJ8+Cfox2JWTBAEofUidjo4MrgWBCG20LS6zUp+QRAEofkQOx2U6Gqu7cGn+j3udiRdWUO1qWYaXQfT1Oo61UXpITTWBo2tIylo+bU+o46I/+Cy2WmdKoSejGuReZu41tjtNOqfnQ76C9GvVdMymB7awbTCLkcbkuZaZH6+mf6Mn8P16iG16Cp4HVwv7XDEhWwDPyeUHpqfr+vcKYP7CRj/RHyGvtJZWrR7jUJX1mY5dGvaSuF40FiLfrplEep+NcWsJn/OuZ04Hv4OjiFip4MiFkoQhNhC161vgiAIQvPRDHZ60aJF6N69OzweD7Kzs/HZZ58Fzf/666+jd+/e8Hg8OP300/HBBx8Ejvl8Ptxzzz04/fTT0aZNG3Tu3Bljx47F3r17SRkHDx7Etddei6SkJKSkpGDChAk4cuQIryokMrgWBCG2kMG1IAhCbNPEdnrFihXIy8tDfn4+Nm3ahH79+iE3Nxf79u0zzb9u3TqMGTMGEyZMwBdffIERI0ZgxIgR2Lx5MwCgqqoKmzZtwsyZM7Fp0ya89dZb+P7773H55ZeTcq699lp8++23WL16Nd577z3861//wqRJkyx3j00pFbW5ercrI+hxoyzkV5KORBbiDxn2LrhUIRxZCC+TSw24LEQ3SBOCyyfCCUEXShbC5Q68TXHOZJKu9ZeTNJdXhCcLof1gJqE4GquyEDdrMwD4tMqgdVqWhRjaRPvN7aQyIsD4jPDng1+Xrlv/1dsaqaioQHJyMg5uexVJbS14oR+uQmqP61BeXo6kJOP9Eig2m+tYNyEKyLzQ8UVz/EAOLgtRytcMbWj5NJedzs7OxsCBA/H0008DAHRdR2ZmJm699Vbce++9hvyjRo1CZWUl3nvvvcC+wYMHIysrCwUFBaZ1fP755xg0aBB27NiBE044AVu2bEGfPn3w+eefY8CAAQCAVatW4dJLL8Xu3bvRuXPnsK83qpprHveYD1S4xpoPXLjuFwD8GosZzGJj84ErH5ga9MyOtiStsz8ojzvN0AajppalmWGwM70zH/DxwRcfAPIfDGZ18DYYYoqDDzprSdrFfsgYr9Hk0eAxpkMMpvlAltdpZy95r/8wSfOBdDjwH2h+jfUl1+hzjbWdx2I33otQca1D+R4IwbEpHTYV/svWSl6hpXB8Dp5trUTnqyzGmDbe7+bQYAuNIVI7XVHB1tiIi0NcHPWv8nq92LhxI6ZPnx7YZ7fbkZOTg6KiItPyi4qKkJeXR/bl5uZi5cqVDbapvLwcNpsNKSkpgTJSUlICA2sAyMnJgd1ux/r16/E///M/Ia8z0N6wcwqCIDQHIgsRBEGIbSK005mZmUhOTg5sc+bMMRRdWloKTdOQnp5O9qenp6O4uNi0OcXFxZby19TU4J577sGYMWMCM+nFxcXo2LEjyed0OpGamtpgOQ0hofgEQYgt/FrdZiW/IAiC0HxEaKd37dpFZCF81ro58Pl8uOaaa6CUwrPPPtskdcjgWhCE2MLqbLTMXAuCIDQvEdrppKSkkJrrtLQ0OBwOlJSUkP0lJSXIyDD37cvIyAgrf/3AeseOHfj4449JWzIyMgwOk36/HwcPHmyw3oaI6uDaybTG3FnQ56eOXVzny53LAKMm1sniVBucx1ia64K5xtrrp/ofM6dKn78saBucDqqxtcFB0n6NXTdX47DrjnNSx08A8OvUgZFrsI3aYXrdvO+59pjrobmGu65Men+5Hp73NXdgrfVRJ0q7g2qu+fPCnw/AqEc39INi7WbXwTX5PJZ2tZc62bqcKYY2GJxied+J2qpxKAVY0VFHzydbaDaO/d9Ia9E/W8bE9gfF5G81VN+G1mQ3hwZbaBRNaKfdbjf69++PwsJCjBgxAkCdQ2NhYSGmTJlies6QIUNQWFiI22+/PbBv9erVGDJkSCBdP7D+8ccfsWbNGrRv395QRllZGTZu3Ij+/fsDAD7++GPouo7s7Oyw2w/IzLUgCLGGzFwLgiDENk1sp/Py8jBu3DgMGDAAgwYNwoIFC1BZWYnx48cDAMaOHYsuXboENNtTp07FsGHD8Pjjj2P48OFYvnw5NmzYgOeffx5A3cD66quvxqZNm/Dee+9B07SAjjo1NRVutxunnnoqLr74YkycOBEFBQXw+XyYMmUKRo8ebSlSCCCDa0EQYg3RXAuCIMQ2TWynR40ahf3792PWrFkoLi5GVlYWVq1aFXBa3LlzJ+xHrZY9dOhQLFu2DDNmzMB9992HXr16YeXKlejbty8AYM+ePXjnnXcAAFlZWaSuNWvW4LzzzgMALF26FFOmTMEFF1wAu92Oq666Ck899ZSltgMyuBYEIdZQFmdEJBSfIAhC89IMdnrKlCkNykDWrl1r2Ddy5EiMHDnSNH/37t0RzrIuqampWLZsmaV2mhHVwTXXO/t9xhjB9ASq2eWLrwBAnIvGneZaYL6oDI9rrTFdsCEetCGusXHBj4S4LiRd7aWiea775breOFcqbROL523oN6avBgC3k8bn9vlpDGjjQjbBtegOG9WJ+1g8ZzPdMNeWuxxtSNqr0TjVNd5DQdvIMdxbk4VsuB7d4WBxzTXat1x7XuM9ELQOG4+TbvJMelxUp8UX5OH3SrCGTddhs2C0reQVjhVNr7FuFRpqq3roqBCiTtM2hbD1IQZaRk22aLBjDbHTwZGZa0EQYgtNq9us5BcEQRCaD7HTQZHBtSAIsYU4NAqCIMQ2YqeDIoNrQRBiC13VbVbyC4IgCM2H2OmgHPtgo4IgCEfj1wC/38IW2efGRYsWoXv37vB4PMjOzsZnn30WNH9ZWRkmT56MTp06IS4uDieffDI++OCDiOoWBEFo0TSTnW6pRHXmmi8iAlvwzwBmDmscvsiLiy1UwxcVMRxn5WnMcc/lZE56zPkQAGp91DHPzRYW8Zs4vQWDO7xx50PuOAgADjtd7MSvMadHxZPMydJOy+RtMJRnAl8chzsgOlkdvM18AR9+vs9v3amSL2RjLJM6fvJFggwL1diNfc/x+qnjJveCNhwXrKGUtYVhIlhEZsWKFcjLy0NBQQGys7OxYMEC5Obm4vvvv0fHjh0N+b1eLy688EJ07NgRb7zxBrp06YIdO3YgJSXFct2tg+jO2zSLs+IxcRY8FgS/TrMFxKycXwe1iQY7y28nO87vtzg4xiDNYKdbMiILEQQhtmgGLd/8+fMxceLEwIIEBQUFeP/997F48WLce++9hvyLFy/GwYMHsW7dOrhcdSuLdu/e3XK9giAIxwWiuQ5Ka/mpLghCS0Gp3/R84WwWZ0S8Xi82btyInJycwD673Y6cnBwUFRWZnvPOO+9gyJAhmDx5MtLT09G3b188/PDD0FqZB7wgCAKAJrfTLR2ZuRYEIbao1+hZyQ+gooJKyOLi4hAXF2fIXlpaCk3TAit91ZOeno6tW7eaVvHLL7/g448/xrXXXosPPvgAP/30E2655Rb4fD7k5+eH31ZBEITjgQjtdGshuovIsMVRbEyDbbYgx9HYTfSuXHfLtcR2tuiHQdvF0k6myebU+soM+7iOm2uJQ8HbxPXNTkc8SVfV0kVqAONiN3wBF65ntttcJO3TqPaYt8HtohrsWh9dGAUANL2WtsHZhh2n99+wmAq7NVxr7nHRBWJ4v5i1gWuw+fOhNL7AD9OuK9oPxufL+CfCFxriz7muh1g8SQhOhF7omZmZZHd+fj5mz54dnSbpOjp27Ijnn38eDocD/fv3x549ezBv3jwZXAOISY111DXUx+eHXqPG2h70uMEXxqyf2TtP2bgGm9p+xb2jGq3BBkSH3cRItJCgyMy1IAixhdKtLZX737y7du1CUlJSYLfZrDUApKWlweFwoKSE/ogtKSlBRkaG6TmdOnWCy+WCw/GbU++pp56K4uJieL1euN1u0/MEQRCOSyK0062F4/OnuCAILRe/Zn0DkJSURLaGBtdutxv9+/dHYWFhYJ+u6ygsLMSQIUNMz/nd736Hn376CfpRTjk//PADOnXqJANrQRBaHxHa6daCDK4FQYgtrDjJWP00+V/y8vLwwgsv4KWXXsKWLVtw8803o7KyMhA9ZOzYsZg+fXog/80334yDBw9i6tSp+OGHH/D+++/j4YcfxuTJk6N22YIgCC2GZrDTLZmoykJ4DOFa7z6SdjDtstuZTNJezRgf2ME01VyHW+un2mBNUd1vHNPxcu2xw2591onHMeZacq7T5Xppri3mbXI5jbpwft1cr8zTbjfVO/M22+1MD6/RfnPajXpnBfrLk+uyuQ68svZXko53d6Dlsc9E/F4oZfyly+NWx7vTSNrJ9PD8uvw61Vjze8XbYKav574FPF479wMQLKIriyGerBvtUaNGYf/+/Zg1axaKi4uRlZWFVatWBZwcd+7cCbv9t7mHzMxM/OMf/8C0adNwxhlnoEuXLpg6dSruuecey3W3fBo/J9NoTXVU9NTRn1sKHSPaGgYfogiwGrfaoLHmNpLZN/4uMYP74xhsKMsfSoMtxADNYKdbMqK5FgQhtmgmR5kpU6ZgypQppsfWrl1r2DdkyBD85z//iaguQRCE4wpxaAyKDK4FQYgtdA2wEj9ab11aPkEQhGOO2OmgyOBaEITYQmZEBEEQYhux00GJ6uDax+L/ulypJM21qVz/ymNfAoCf7ePaY43V6bBT3TfXGnNNLdeCmcU15ho0xXRGca4Ukub6Z67b5Vpl3i9mGjlDrGym805gema/Ro/zeNC8jTY7rdMQoxpmenV6XbyNSfHdSdrFdNz8Or2sfF4fALT1dGF10l/DPAYrj4ud6OlM0pU1xSTN44ObxRx3sHjsbuZrwJ8pwSKyrG6M0VI11sHPibY+Opw6Q8PiOx+DNnIbyt+J/B0cz3yIAOO7ocZfRtPeQyTN36kGrTl7fGw8jrYh7jVgvE5uJySeQ6MQOx0UmbkWBCG28Ot1m5X8giAIQvMhdjooMrgWBCG2kBkRQRCE2EbsdFBkcC0IQmyhVN1mJb8gCILQfIidDkpUB9c8vq9fqyJprlXlWi4b00sDxhiaPK6105FE0ppO63SxOrku3MnqDCeuKNekcX1ZKC2yx90u6HEzzS7XtfmYXj3RRZdt3l/5LWsTvTdcy8f7ucZ30NAGri2v9ZXRNrH7zetwszjY1ayOFHc32gY7vdeAURPd1tGRpMu0XbTMhB4kzTXY/F5wPTyPzQ0AlTXbaZl2el26btSKCxaQz43HmGOgsbasLQ6d37pe+VhotIO3IRpxrkNh9br4uyLJ2cWQ5zT0IemdLurbsh2fkXRVLfUBUjauwRY/lphD7HRQZOZaEITYQln83CgLTAiCIDQvYqeDIoNrQRBiCwnxJAiCENuInQ6KDK4FQYgtdFg02k3WEkEQBMEMsdNBiergWtN5bGWqZzbGIKY6KjPtV423lKRdTqqx5mW63en0fN8Bkk6Io8c5Pr9RL2vQJzPdbyh4zGhd+YKW73JSDS8AVHnpdcS5kkm6tHILSSd5ugY/351C0jxetIfFKAeMccm5tpzHP+XHucbabqf9qIH2i8tEg3/ER7V7CUwzzXX88XZ6vEzbSesM8Qz6NXrNAOBxdwpahps9o4I1lF+HsqDPs5JXMOMYxPttFo114zTU/N0SEbwOi5/GDWssRDBC4brtxmrHw9GB39SLDrpS3PR9cu231BeGr0ehM421UqzNIeJeA2axryWudTQROx0cmbkWBCG2EC90QRCE2EbsdFBkcC0IQmwhWj5BEITYRux0UGRwLQhCbCEhngRBEGIbsdNBiergmseiVIrHmK4gaQfTZCudxroEjLGxdaZvTYyn2mKumebne31U2+V2UT001w3X1Unbxc/hemW/RrXnXGPNY1L7WGxuHsMaAFxOo/74aOLj0kjaq9F+4Bptrt3jOjqfSaxmrh3n+Fiduk6vm/ety07TvB/c9kRDHfHO9iTNddpt7LQfahSNW811lPzeulgsbuUwGgQeC1spWobO9YGCNWRGpMVhOa51SOjfUHg6YWvnGDTVUdBgh6rTqmaa5ze0IRwNdyNvjc7sG18r4Ii2z3DOe3uzSPqp7ReRdNf29J15wPEjSRvXTGBxsFtZWLeYROx0UGTmWhCEmEIpBWXBEKtWpuUTBEE41oidDo4MrgVBiC1kRkQQBCG2ETsdFBlcC4IQW4iWTxAEIbYROx2UqA6uHQ6qkeV6MR6jmuun41w0JrEZ1d79JF1RtZ2kPS6qyU30dCbpyhoaJ5lrbrluuK5dVK/stlNdbi3XktvdhjJofqrZ5XGz3Q5jnGuuwzbTIx+N30Y1ax4H7dtanbbZz7R88Q5jnGuD9o5pyeOd9Byjrpvq7Gr8ZSQd52DPB9NTA0Acu26fotdZ7qextFNdNJ6q3071grqT1sFjc5dV/mBog9uZQtLKRq+Tx9oWLNJMIZ4WLVqEefPmobi4GP369cPChQsxaNAg07xLlizB+PHjyb64uDjU1NSY5o9tGucTEJG+OqRm2qrG2nicnxNKU82Phzw/RH1hEaLruJY4lEbb7LhBpx3lCUMe17+a2VwA+KjyW5KeRs2woY3cZkYlxrjQtEgovqDIEywIQkyhdOubVVasWIG8vDzk5+dj06ZN6NevH3Jzc7Fvn9E5q56kpCT8+uuvgW3Hjh2NuEpBEISWS3PY6ZaMDK4FQYgt6j83WtksMn/+fEycOBHjx49Hnz59UFBQgISEBCxevLjBc2w2GzIyMgJbenrw1V4FQRCOW5rBTrdkZHAtCEJM0dQzIl6vFxs3bkROTk5gn91uR05ODoqKiho878iRI+jWrRsyMzNxxRVX4Ntvv20wryAIwvFMc8xcL1q0CN27d4fH40F2djY+++yzoPlff/119O7dGx6PB6effjo++OADcvytt97CRRddhPbt28Nms+HLL780lHHeeefBZrOR7aabbrLcdhlcC4IQWyj1myd6ONt/tXwVFRVkq62tNS2+tLQUmqYZZp7T09NRXFxses4pp5yCxYsX4+2338arr74KXdcxdOhQ7N69O7rXLgiC0BKI0E6Hi1Xp3rp16zBmzBhMmDABX3zxBUaMGIERI0Zg8+bNgTyVlZU4++yz8cgjjwSte+LEiUQC+Oijj1pqOxD1RWSMi8AcDV80xs8CxZs5E2o6dRhyMadJTXGnSOp8yB0Y+UImXj9dVCbeTR0iAaODoddkgZWjSXTTl3YNc3iMc9A2csdAu4mjTJKzE0n7QPvFp6jDo9MR3Dk0zk6dB204QtLc2RAwOvtx/IoOZrhzDl9Mhy8I42eLyHg1o0Oj38bqCOEYU6UfstRG4/Ng/PTPn1NZRCbK6P/drOQHkJmZSXbn5+dj9uzZUWnSkCFDMGTIkEB66NChOPXUU/Hcc8/hwQcfjEodxxUROCRayW/mTBjKgTGU05zBodHg4OgI3YZQi8iEmL4zOjRqIY6HdmgMlQ45pRiiTr5wGwDst1NH8A+OUFuu24NflxGxqTFHhHY6XI6W7gFAQUEB3n//fSxevBj33nuvIf+TTz6Jiy++GHfddRcA4MEHH8Tq1avx9NNPo6CgAABw/fXXAwC2b98etO6EhARkZGQEzRMKeWIFQYgplF9Z3gBg165dKC8vD2zTp083LT8tLQ0OhwMlJSVkf0lJSdgG1eVy4cwzz8RPP/3UuIsVBEFogURqp8MhEuleUVERyQ8Aubm5QaV+DbF06VKkpaWhb9++mD59OqqqqkKfxJCYYYIgxBbqv5uV/KiL5pGUlBQ8LwC3243+/fujsLAQI0aMAADouo7CwkJMmTIlrCo1TcM333yDSy+91EJDBUEQjhMitNMVFexLflwc4uLoV/Fg0r2tW7eaFl9cXGxJ6tcQf/zjH9GtWzd07twZX3/9Ne655x58//33eOuttyyVI4NrQRBiCqVbXFY3gpW/8vLyMG7cOAwYMACDBg3CggULUFlZGfgEOXbsWHTp0gVz5swBADzwwAMYPHgwevbsibKyMsybNw87duzA//t//89y3YIgCC2dSO10U8r3osGkSZMC/3/66aejU6dOuOCCC/Dzzz/jpJNOCrucqA6u7Ta6eEqoAPhOpsE2g+vifBrVBrvZwjRcD+t2taVtUsE1umaauRo/XfTF7aRluhw0rbM6nDb6q6yNjem62cIC8cq4iEwZ6CfsJNWBpGttVHPdWe9K0vvtdPGdatBfj252L1x2471x2ZheXR0x5DkaO+jiOJV+1gb/gaDnJ7qMn+h5X1ZptAwHW5CHaxb5QjhcY83vP1/gxwyH3cPqbF0hh6KN8gPKETrf0fmtMmrUKOzfvx+zZs1CcXExsrKysGrVqsDMx86dO2G3//YsHDp0CBMnTkRxcTHatWuH/v37Y926dejTp4/1ylsYES0aY7UOg92NYIGXEBrrULae/63b7dY02nX7LDy4MC6sFUpTzRc9434sZmXoYH8gbDyk+O0NpQvnbTL5A6z1lZH0QWwjad6XfGGa0DaU970xP39uFbvw5niuj2citdO7du0iXxj5rDUQmXQvIyOjUVK/hsjOzgYA/PTTT8ducC0IgtBomthRpp4pU6Y0KANZu3YtST/xxBN44oknIqtIEATheCNCOx2OfC8S6d6QIUNQWFiI22+/PbBv9erVxBE9EurD9XXq1Cl4RoYMrgVBiCmsxkRtbSt/CYIgHGua2k5ble5NnToVw4YNw+OPP47hw4dj+fLl2LBhA55//vlAmQcPHsTOnTuxd+9eAMD3338PAIGFwX7++WcsW7YMl156Kdq3b4+vv/4a06ZNw7nnnoszzjjDUvtlcC0IQkyhdIB9LQ+ZXxAEQWg+mtpOW5XuDR06FMuWLcOMGTNw3333oVevXli5ciX69u0byPPOO+8EBucAMHr0aAC/6b7dbjc++uijwEA+MzMTV111FWbMmGGt8QBsSlmM7B0ElzONpLmm2m+IWU2Pm8W6DKVB4zo6l5PFpPZRTW1CXEeSdhh0eEYRkU+nemauk3Oz2Ns8hnQ7dCZpro9OVCkknaKohhsAXCzG6hEW57raRtMJivZtqZ16zLbTqWbbz3R5NTZj6JlD2EvSPkXzOG1Ue+zVg2uy/TqNOc01jGbPg5PF2ub3ht/vth7W9xo9rukhYnPrRj1hqDjXNvZMeX3WvJVbKxUVFUhOTsbeCdcgye0OfUL9eV4vOv/tNZSXl4cVLaS1Y7MFj1dvyB+ONtViXOtGa6xN6gulsQ6lqXbY6TPH83PbY6b7DsdH42i4Zpq/37iN5Pm5VhkIrcs2rj9A80fDZ4T3fai+5XaYjxV0rsnmOu8wHC9Caa51ResUzBE7HR4ycy0IQmzRTJprQRAEIULETgdFBteCIMQUorkWBEGIbcROB0cG14IgxBRKs0Fp4YfJspJXEARBaDxip4MT1cE112oZ44jS6gyxLU1+2mg61fW6HFSrw7VZykfLMGiwWVxjp4PqhM00c247LaOtg8ZNrNRprOVkGz2erLcj6STQkC4+puGuhjF2aZKD6v3iQXXeLhvtl3gn7fuuOtVxb/MeJOk2oDGsK200DjYAxLE6E2z0uo6ofSTNtek6ez643p3HE3c5jPG+DbFmWdrlpFrzSi9vE9cb0r7m95/HyQaM+kG/rgc9LlhDZkQEA2HEueb7+N+yw0H/Lh1MQ+1k6zQ4Wax/HmPfbTOuBeBgsf15m/g7UmO23q+Y9thO32/cx8Rvo/kBQGP7NJ3HlKbH7WwYEA0NNrez/F3PY28bYmUb/qjljzzWEDsdHJm5FgQhplDKBmVY2SJ4fkEQBKH5EDsdHBlcC4IQU+gaYBKkJWh+QRAEofkQOx0cGVwLghBTyIyIIAhCbCN2OjhNOrj2+YPHOeaYabscTPem8fiXimq54tzJtEwm9AmlsXab6nxpjOk40DYl22jMaD1EbO42DtrtndvQNh3xGfuhUwLTzbHo5DpLx7Fw3TXsV2N8dXuS3lZN71Wynmpogx30nO22b1kbWF+ze8f1yzzmdBs3jUHOtYF1ZfA6eNzr4MIuHttW89M6vIY42MbYp6Fi8Po0a8+9wNBtULoFQ2wlrxCSsOJahyRU3GtWZ4i/KaOe2vjq4n/bxjjW1Fa47NTPxG1n6xXYaDoezK9FGTXXcYrW4WDvDp3FWvYxzTVfX6DaRmPq19ipL0ytjdorAPCFjDlO4XbWoGdXPBlaPGvIE6KMUOmQmF1zaxP5Njdip4MiM9eCIMQUStVtVvILgiAIzYfY6eDI4FoQhJhC1+zQ7eHPvumatZk6QRAEoXGInQ6ODK4FQYgpZEZEEAQhthE7HZwmHVy7nFSzxjXYTgfVrJlpVbkWj2uwXU6qd+Yaa57mujtDTE8TnZaL1XlE0bjWuo1qidvrVDuc4aQxppPc9JpSaZPQL8X4FNpYHZ08VGtepVFtn8dOr2N/LdWWV7Wh+qeEMtrGUi7SBlCj0TK9+skkrWz0+D7sMJRxND4bjdnKY7gaYp8C8PtpHo+TauxrWPzuOBc9Xu2l947HpOb5NZ3q4c3wa1SX7XKEPkdoGHGUiXEsanoBox0PqckOUYexPKP/DNdl8zjWXHPtYWsFtFXUxyRVUdvA1x4AgLYuaofddvpsct+YWuY8U+6jdric+XyU22ibD9uNr3DuI2R4J3J9cwhtMo9JHRUNtiGDtTZJ3Otjj9jp4MjMtSAIMYWm2aDZwzfEWitb+UsQBOFYI3Y6ODK4FgQhppAZEUEQhNhG7HRwZHAtCEJMIUZbEAQhthE7HZwmHVzrIZbvMcQ2NYldyjWxvMwa7yGSdrE41Tyuda2vnKQT3Gm0fEXjjgKAzmKRJtsySPoIqI43ESeQNNfhnZBIr3tAO6qrc9uNerKTUstIun06jX/KpXcV+6kesLSC9su2Cqrtq0miekWHiZavwkvbVVpNz6kEjdHK72+1Ru+V02bULB6N10RzzZ+HGj+9n4Y6mcaaw/WJPj/tV4+7neEcXqbfX8baYIwRLoSPptmhWfBC1yL0Ql+0aBHmzZuH4uJi9OvXDwsXLsSgQYNCnrd8+XKMGTMGV1xxBVauXBlR3YI1jO8K4z3n++x2btOovXGzONaJKoWk27N0RhzVO3dpwxYTANApngqSU1zBtcEHvbTNv1bT99XeSnoNxV5ap91Eu67Y+4O/00JprhVbSs+maB2WY1DXFRr8cIgyQ2qwJaZ1s9Ncdrql0rquVhCEmKfeC93KZpUVK1YgLy8P+fn52LRpE/r164fc3Fzs27cv6Hnbt2/HnXfeiXPOOSfCqxMEQWj5NIedbsnI4FoQhJhChw26srBFsKLg/PnzMXHiRIwfPx59+vRBQUEBEhISsHjx4gbP0TQN1157Lf785z/jxBNPbMwlCoIgtGiaw063ZGRwLQhCTFGv5bOyWcHr9WLjxo3IyckJ7LPb7cjJyUFRUVGD5z3wwAPo2LEjJkyYEPG1CYIgHA80tZ1u6URVc811UTxOMddU1/rKSDreTWNW15VB9WJcN2dnl6DptTS/g2p027hpDGq/ovlTnJmGNlTpVCvMf4CdgX4k7XbS3yzdmMb6pDa0X05IpPG9u/eisZoBoM2FnekOO425ikyqA29TTXXcHf/1A0m3XUev+4Qqqid02FIMbdhxhF7HoVqqWaxlfclJdNC+P6wVk7SN/dbjcWgBwM/urwYa79vjonpnP4udzTXVPo2mrWq2AcDtorr9iDSJQgBdt0PTLaz89d+8FRUVZH9cXBzi4oy6/tLSUmiahvT0dLI/PT0dW7duNa3j008/xd/+9jd8+eWXYbdLCB/+tx/qOPeVAEw014Y411TP7AG1X21ZHOsObmp/erSldZ7VjtoeABjUuYSWcTq1VzYXfXmUfUvbvGFHJ5o+RNvsqKBrLug1xm/tPhttl2an71D+TtVtTJPN1ivgaX4vQsekNhI61rbY0FgnUjvdWmhdVysIQsyjR7ABQGZmJpKTkwPbnDlzotKew4cP4/rrr8cLL7yAtLS00CcIgiAc50Rqp1sLEopPEISYItIQT7t27UJS0m+r7JnNWgNAWloaHA4HSkroLGNJSQkyMjIM+X/++Wds374df/jDHwL7dL3uVeF0OvH999/jpJNOCru9giAILR0JxRccGVwLghBTaLrN0udGTa8z2klJSWRw3RButxv9+/dHYWEhRowYAaBusFxYWIgpU6YY8vfu3RvffPMN2TdjxgwcPnwYTz75JDIzjVIyQRCE45lI7XRrQQbXgiDEFLqq26zkt0peXh7GjRuHAQMGYNCgQViwYAEqKysxfvx4AMDYsWPRpUsXzJkzBx6PB3379iXnp6SkAIBhvyAIQmugOex0Syaqg2vuUMIX/PBrdJERp4M6Z5g5Rvg16pjHnVQMCwO4qBMKX3TGZad18gD5tYo6FwJAV9uptEwWsNGraND9rm3odbvZj7uTk6gTXVICdXoxOC8C0EZcathnBVvvk0m6S/XbJF32b9pvHdz0mgDgK7bggcNGf4l2QXuSPgQaM7haUcfQJAe9ziM6y+8zOnYaHFrZ81DDzuHPD19kSLfR58PtZIvrmDg0csdcv06fGQd7xgRrNMfnxlGjRmH//v2YNWsWiouLkZWVhVWrVgWcHHfu3Am7hQUShOgSysExnHMcNraIDGjapagtaQNqt9vH0fJ6JVKbeN6pOw1tSFp4NUmrxOBfUvgSVRc9vZSk41ZQm1qjUdtS6TM6fVdqtM4aO7VPPjt9D9sVdYA0BiJg94INkszulTh1H/+ILCQ4MnMtCEJMocNaTNRI46dOmTLFVAYCAGvXrg167pIlSyKqUxAE4Xiguex0S0UG14IgxBR1Wr7wDXFr0/IJgiAca8ROB0cG14IgxBTK4oyIamUzIoIgCMcasdPBiergWtdZUH0W55/rsLheli8AAxj1XgY9GNNM634aED/ORRcGqNao7tfjoPo0l82oYdundpB0T3UaSTtYGyt9VJSWFhdcyd++K9VgqzMGBc0fCSqVavdcl51B0unf0sUz9lQadcNd29D7daCW3gs/08x7bHSRhgTQvt5R+xlJt3VTDTbXVwOA00Z1kdV6pSHP0cS76XVr7BnlesGqWhqeLc6VErR8wKjT5os0CNZQqm6zkl8QQmHwCVJUg+220xdWG7bgS1fmG5NyZRdDHVoIjXUo1JRrSXrQlr+S9M/repJ0SbVxMZ2DbEGww6DpGqZF5+9h3k98wR7DIjKir26ViJ0OjsxcC4IQU2jKDk1ZCPFkIa8gCILQeMROB0cG14IgxBQS4kkQBCG2ETsdHBlcC4IQU+jKBt1C2CYreQVBEITGI3Y6OFGOc83j/9Y0kPO/x0PEvQaMcYm5PszrP0zSXP/q89M64uKoJs4JD0nX6BWGNrSxU91upaLXlcS0wR3j+ecPqkmr1ejx+LM7GOpsarT+Z5F0csaXJF27y/iHcHoy1SuXVFPt3vZK2i8JOtVcH7DvJWmusa7y05jSLrtR/871flynz3XaXD/IY6lzDX84sdf5OcoWvEzBGpqyQbNgiK3kFYRw4U+Vx8Fi/3c1rkcQbRJ+l0bS6ZuoP0cbl9EvJY691nl8b7shzTXWrevzvRAZYqeDIzPXgiDEFAo2S57lrc0LXRAE4Vgjdjo4MrgWBCGmEC2fIAhCbCN2OjgyuBYEIaYQLZ8gCEJsI3Y6OFEdXBviZXLtFjvO85vpW7mmOhR+jep+uSa3yldK0tW2gyQd70w1lBkHqsPlGjXOr1VUm+dx0Dihp7alD1n1p/tpG7KaPk6y/acfgx5PdGqGfesPUg10pd+Y52iq7EdI2gOqwa4FPc7x6dWGffyZ8rjp/fL5adxrQ1xrpsHmmn6vRp83HicbAGq8NFa6IS5sKws5FG10WNPytbZldY9HjLGSGx87WSlqnzQWf14DTfvY+6eambfSWhpjX20ysaFnnmmxlSGoDf4uMHvybTa61x5le8TtHSKYkTS+6/UQx4VYozns9KJFizBv3jwUFxejX79+WLhwIQYNangdkNdffx0zZ87E9u3b0atXLzzyyCO49NJLA8ffeustFBQUYOPGjTh48CC++OILZGVlkTJqampwxx13YPny5aitrUVubi6eeeYZpKenW2q7jAIEQYgp9Ag2QRAEofloaju9YsUK5OXlIT8/H5s2bUK/fv2Qm5uLffv2meZft24dxowZgwkTJuCLL77AiBEjMGLECGzevDmQp7KyEmeffTYeeeSRBuudNm0a3n33Xbz++uv45z//ib179+LKK6+02HoZXAuCEGMoZbO8CYIgCM1HU9vp+fPnY+LEiRg/fjz69OmDgoICJCQkYPHixab5n3zySVx88cW46667cOqpp+LBBx/EWWedhaeffjqQ5/rrr8esWbOQk5NjWkZ5eTn+9re/Yf78+fj973+P/v3748UXX8S6devwn//8x1L7ZXAtCEJM4VfWN0EQBKH5aEo77fV6sXHjRjIIttvtyMnJQVFRkek5RUVFhkFzbm5ug/nN2LhxI3w+Hymnd+/eOOGEEyyVA0TbodHGYxBT/bPdTjVrhpjFiupjAaPmlccxrq6lGmoFKpRr66GxSP2sDq7L8yuq0QaASlCNLf9J0tWWTNJuO/2F1paGFcXuahpbewBNAl9+b2gDevWkaUfjbp0q3ETSNWW0PDPnAyfbleiiHXHYR7Xldp0er7TRGOLVGu1XHtfaTHPNNfS1vnLaRgctg+v6ddAY1HY7iwmr02e0srbE0IY4J73ftf7yoMcFa0iIpxjHTA/LdbjRrpJrcmH09+Dx5TWW1pnGutZG7csRZlsOMY31T0doevtK42ih+5DvaJ19+hjyWKF8LfVL2V9L10SoNhmx+Ng7Tbez+9XIH6OR6KGNmnqriPgr1ojUTldU0HFAXFwc4uLouK60tBSaphl0zunp6di6datp+cXFxab5i4uLw25jcXEx3G43UlJSGlUOIDPXgiDEGPUhnqxsgiAIQvMRqZ3OzMxEcnJyYJszZ86xvZAmQkLxCYIQU8jMtSAIQmwTqZ3etWsXkpJ+Wymbz1oDQFpaGhwOB0pK6JfjkpISZGRkmJafkZFhKX9DZXi9XpSVlZHZa6vlADJzLQhCjOHXrW+CIAhC8xGpnU5KSiKb2eDa7Xajf//+KCwsDOzTdR2FhYUYMmSIaXuGDBlC8gPA6tWrG8xvRv/+/eFyuUg533//PXbu3GmpHCDKM9eGuNYMnWmwHQ4WN5Rp5ADA66NxhxWovpVrarkmu8p7gKSdDipwTnRTjc4Rr1Fjqxz07V0BGgrmsNaV1qnRX3MJR2ibTmxDv2N/8El3kr5U325oQ1zlW3THOf1IUu/Rg6QdX28maeyieqHq72k86IOHkkh6RxW9NwBwhN2eCi/tl4Ma1TBW2Gn8bv58OGxUjK6zOLQ+jbYRMGquecxVB9P11/iorpvrMrk+2uNuR9KVNb8a2sDbpbNY2mbtFsKnuWaurcRQfeutt/Dwww/jp59+gs/nQ69evXDHHXfg+uuvj6juWEYxUa4tCl8GuE7XZiiSx07mGuvQcZD5Pm5P/Nxfw0H1zEdsNH2glr4rfjlM7fhHu41xb383jepBTz57PUk7+9HZL3WA1ln2MbUdn/5E3y3bK2k/lXuN78waMHsEet1ce64b9Ozya1UITVPb6by8PIwbNw4DBgzAoEGDsGDBAlRWVmL8+PEAgLFjx6JLly4BWcnUqVMxbNgwPP744xg+fDiWL1+ODRs24Pnnnw+UefDgQezcuRN79+4FUDdwBupmrDMyMpCcnIwJEyYgLy8PqampSEpKwq233oohQ4Zg8ODBltovshBBEGIKZVFHrSLQXNfHUC0oKEB2djYWLFiA3NxcfP/99+jYsaMhf2pqKu6//3707t0bbrcb7733HsaPH4+OHTsiNzfXegMEQRBaME1tp0eNGoX9+/dj1qxZKC4uRlZWFlatWhVwWty5cyfs9t9+bA4dOhTLli3DjBkzcN9996FXr15YuXIl+vbtG8jzzjvvBAbnADB69GgAQH5+PmbPng0AeOKJJ2C323HVVVeRRWSsIoNrQRBiCk1ZW/nLSt56jo6hCgAFBQV4//33sXjxYtx7772G/Oeddx5JT506FS+99BI+/fRTGVwLgtDqaA47PWXKFEyZMsX02Nq1aw37Ro4ciZEjRzZY3g033IAbbrghaJ0ejweLFi3CokWLrDTVgGiuBUGIKZp65a9IYqgejVIKhYWF+P7773HuuedarF0QBKHlIyvpBieqM9c6iyHtdCTQ4wZtKtWbuZ1U91tXpp/laUvL8FONGtfkxrlSguav8h8kaa7hBQCNadQSbFSXW6yorru3qxOrg34P+a6Cao37JdN+2bCBxuYGgJP30njeiV98QtLuznRQcJCGW4XdRR/tz3/IJOkdVVRPWOE39sOBGnodZV7aL+W2MpLuoGgdB21U9+2w0TqVjcZn5TGrAaPGXtP5M0fPiWf30+unGn5OVe3+oMcBo66bp4XGYTW8Xn3ecOKnApHFUAXqVu/q0qULamtr4XA48Mwzz+DCCy8Mv6GtCv4qDT6PY9Rk0/zhaK65xlpX1FbwNQx8qoqkK5n92q9TO+2oTCFpTRn/7ktr00i660r6rkj9gMfipsdLWBzrHUxjvauS2siDPuO6DEds1MZ5Qa9TM/QTbRPvW7OY4iGJIBa20LKI1E63FmTmWhCEmEJFsAFNHz+1bdu2+PLLL/H555/jL3/5C/Ly8kw/TQqCIBzvRGqnWwuiuRYEIabQlDV9nvZfqx1O/FQgshiqQJ10pGfPupVSs7KysGXLFsyZM8egxxYEQTjeidROtxZk5loQhJgi0pW/womfCkQWQ9W0nbqO2lrjZ3lBEITjHVlJNzhNGueaa7m4Bpvr6mxwGMtkeXjc4lBxrmt9ZSTtcrQhaT+LzcyPAyYaNaYN5pq0r/2/kHRvrTtJ/1rFf+1R7Z7bRMO7rZJqiVN30L5N3Uy1xzUa7cvdTFPd1kXbzOOnltYY/xKqNRbXWrF4zzZ6vNS2hx7nzwPTXB/x05lEpYxaP66ZtrNY2VW1NAZ5KPw6vf88DjrXdAOAX6Px2rkeNFS8dyE4Vp1fIlF3Wo2hOmfOHAwYMAAnnXQSamtr8cEHH+CVV17Bs88+G0HtQigMf1PMHOkwxne2Kfb+0WkeP+jfupf9ndoc7P3DQ28zHXHNEaOP0KFaatO2u2mZcY7g/hnVzD+H+7WU+emPuUM26mcAAEeYdtyrqG8T90vSdVqHQXMdKuZ4GH+BRo18aA198ALDqDOEECHUcSE4zWGnWzIiCxEEIabQLYZ40iMI8WQ1hmplZSVuueUW7N69G/Hx8ejduzdeffVVjBo1ynLdgiAILZ3msNMtGRlcC4IQUyhlbcGBSBaRAazFUH3ooYfw0EMPRVaRIAjCcUZz2emWigyuBUGIKeRzoyAIQmwjdjo4MrgWBCGmqPNCt5ZfEARBaD7ETgcnqoNr7tjgdiSStNdPnS+cdurgqGB0HuPOYS7uFMmcILkTC19UpNZXTtJtPA2H3qpHY4sP+EHTZTbqiJek6EIA+3Tq+HfwCL2mCi91osxMNDp2ltbQfV3b0DLKDlFHmmrmC8g9dTVFyyurpfeuym/8nXnET/vWDqqhqmLONdyB8YgW3NmQO6fW+IwLvpgtLHM0Ph/ta8OiQxo9bnDC1Y2OUpxQDozhOPgIDSOLE8QW3PHLBhPtJHcwMyzGxR3YWHZWpPF46L8xbm+4gyNP+5gzswFmhjUbdfyrBXVsBoAKH33nxfuoXXaZOO2TNjHn+Gr2rqmyHWFpo0NjjaL7+HX62WJvvN/4YjyhHBzNaLwNFBsa64idDo7MXAuCEFOIlk8QBCG2ETsdHBlcC4IQU4iWTxAEIbYROx0cGVwLghBTiJZPEAQhthE7HZzoaq6ZFqvWd5BWxjTYHK79Aow6XK6J1RXVvWk6TcchhaS5do8vSmK2iAxfJKZKO0DrsNPFBCpZEH+PojpxH9PobmMLBRw8RPMDQKqLLj5w2MeuQ6d9X8000/FOmv9ALa3Tx+5dlTLq37nGuti+i6Q10DL9TJPoslO9tJfpn/mCLi6nsR+4Zp4/c/z+8fvLF4nx+Y+w41zTb1wQhj9jrcxmNDmi5Wtq+BxS7C96FGpRGbM84fhPBENn5fnt9O/ea6synFPJ/IiciGNpuuiVsU76rvEhuL+PTxnbwDXWPG1cRIb2k+VFY6wuACMcF4idDo7MXAuCEFMo2KDMnOaC5BcEQRCaD7HTwZHBtSAIMYWmAJNgNUHzC4IgCM2H2OngyOBaEISYQsGa1KaV2WxBEIRjjtjp4ER1cO1immquofb6qFbZ7WpP0lxfDRj1Xw4H1R4bYpkyTTXX1CbEpdM67VQDx/VoAOByUq1wla+UpN1x9LprFa3zV9tPtA22drQNTPPoU8mGNth99JyDPvqouriWXKfavSN+etzOAsseVlSXV2Gj2mbAqCV3MP2gj5XBY5DH2ajeWdm5Rp/WqdvMNPi0Tp3rvJm+kGuseZxsrjc0avKNcWQNOmwe01c0iI1CtHyxDY97DZjEvm7iuNemX5h5vGZ+PMSfZah4zvx9ptmM7wovi0PN7ZXDFlxzzf17dNYGvuaCxmJSAyaaat5ujce59rF0cA02v7dmMa2NsbCtx8oOVqdw7BE7HRyZuRYEIaZQ//3PSn5BEASh+RA7HRwZXAuCEFNIiCdBEITYRux0cGRwLQhCTCGfGwVBEGIbsdPBiergmsf/tTENtcMkhnSw8wGjRrrGe4ikFdOH8fxcx8vTdnvoLuCasnhXKjtOdXLV/jKSbuPsQNKVimrPnTYaC1W3GfVltSyeN9dp8y8ublCtscZjdYNpiVlxNaDaQcCoxdNttMxaxWKG22jM1yqNatWrvLQffCzutdvZ1tAGfr/s7BHmca255rGqtoSknSwuLddYm8W55rGwfRrtK+57IFhDltVtbpoh7nVIDTbLzvKH1GADRh12CA22YmsDKFtwXbDdYP+Memfus8HtT6j8IWNMK67JNvqlGLTihrUhrGmsQ8W5NtdPW9VIN05T3dokB7GA2OngyMy1IAgxhV8BDguG2N/KjLYgCMKxRux0cGRwLQhCbGFxRkQmrQRBEJoZsdNBkcG1IAgxhQ5rH4klSJcgCELzInY6OFEdXNvtNAY113JxDTbXanH9K2DUi3GNNde/ck2t20ljRvM42Q471TvX+soMbeDtrPYdpOdwbTkrs9K/n7aJac+9OtXs+uxVhjaUM50c1zPbWUxpm51q+RJBY4rzGNXlqpi2QRnbEGejGuhy326SjnemkHRZ7XZaJ3s+Ety0TdW+4PpDwKjL5jjt8UGP8+eHwzXW/PkBgFo/1f3z55prsAVrNJcX+qJFizBv3jwUFxejX79+WLhwIQYNGmSa94UXXsDLL7+MzZs3AwD69++Phx9+uMH8rQ2ueTXEvQ5JcN13KA12XR62I4QGW4XQlhvjXFMbbKan5hpqm81ryBMsf6j4z6FicQMmsbL5OzSEZtp4PETc6zAIFffaeEJrG4q1PCRaSHCawHNFEAQhcpRSljerrFixAnl5ecjPz8emTZvQr18/5ObmYt++fab5165dizFjxmDNmjUoKipCZmYmLrroIuzZs6exlysIgtDiaA473ZKRwbUgCDFFfYgnK5tV5s+fj4kTJ2L8+PHo06cPCgoKkJCQgMWLF5vmX7p0KW655RZkZWWhd+/e+Otf/wpd11FYWNjIqxUEQWh5NIedbsnI4FoQhJhCRbABQEVFBdlqa43LUwOA1+vFxo0bkZOTE9hnt9uRk5ODoqKisNpYVVUFn8+H1NTU0JkFQRCOMyK1062FqGqudZ3qy3jcaofDqKkm55voYWt8NBZyKN0218jy8z1Me8zjZuvKqJFL9HQhaY1dJ9eoOVgbazUae5nn9zhSSNprotlt4+xI0of9VCPd1plB0lwPfdhG8/N+czGtcpWPxqQGAN1F7w+PGV7DtOiuEHHNeZxrrhU0i3Ot+5j23Enr4PeTY7e5gx7nz6CZxps/gzp7zu12GmNcsIZft/ar3//fRzkzM5Psz8/Px+zZsw35S0tLoWka0tNpTPz09HRs3bo1rDrvuecedO7cmQzQjx8aH/c6pAY7ZNxraxpsIIxY2Oy4jR3XwXyEWJ08HY722KjBttaXVjXYgHVNNb8X4cWxPhqTNjSxZlriWh97IrXTrQWJFiIIQkxRN8sR/suzPueuXbuQlJQU2B8XF2d+QiOZO3culi9fjrVr18LjkR9SgiC0PiK1060FGVwLghBTRLqsblJSEhlcN0RaWhocDgdKSmhkoZKSEmRkZDRwVh2PPfYY5s6di48++ghnnHFG+I0UBEE4jpDlz4MjmmtBEGIKXSloFjbdohe62+1G//79iTNivXPikCFDGjzv0UcfxYMPPohVq1ZhwIABEV+fIAhCS6ep7XRLJ6oz11yr5WZxj31aBUl7XB1ImscPBgAH069yXbfdwfSvPBYpi63M60iIo7pLr4/qowGgqpbGqY5n8Zk51T5aB9cF2w2abNovcQ7j7FuZdwdJc01blY3ql912Wie/NxqCx2z1mMR39uvUQYzHqfYzvfrh6p0kHe+m99vpoPe2hvUb73cASIijZXCNNS+T95NPo23kGus4VwrN7zeJWc10k6Kxji7K4spfkdjsvLw8jBs3DgMGDMCgQYOwYMECVFZWYvz48QCAsWPHokuXLpgzZw4A4JFHHsGsWbOwbNkydO/eHcXFdT4MiYmJSExMtN4AgdJIDTYQOhY2f064TeSaaoPOO4y5KEMZrE6rmmtOOFrmUJrqUPmtaqzD01fHQlzrxvsSCL/RHHa6JSOyEEEQYormWPlr1KhR2L9/P2bNmoXi4mJkZWVh1apVASfHnTt3wn7UQkzPPvssvF4vrr76alJOQ06TgiAIxzOyQmNwZHAtCEJMYXXBgUgXJ5gyZQqmTJliemzt2rUkvX379ojqEARBOB5pLjvdUpHvIoIgxBR+pSxvgiAIQvPRHHZ60aJF6N69OzweD7Kzs/HZZ58Fzf/666+jd+/e8Hg8OP300/HBBx+Q40opzJo1C506dUJ8fDxycnLw448/kjzdu3eHzWYj29y5cy23Pbqaa6ab4npWHmPYy+I/61qV5Tp1ncUm5XFF+e8Hpnfmml1lEmvbw7TFPL4z11Tzfqj1lZG0w05DhPF4zn7d2A+8TKeDxqWu9VPdNq+Dx2vmMah5jGkegxoAHA56/3TWlzz+N48PzjXUiZ7OJG28V4YmGOB6Qb9GY07z++lyUH0sbzPXWJvFXufPMa+Dx8EWrKFgUcvXZC0R6miGuNeGE6xqsI3tCqXBNlRpWZNtRvA84ei2gxcQgeaaH48gbrW180OXYVVjLXGtY4+mttMrVqxAXl4eCgoKkJ2djQULFiA3Nxfff/89OnbsaMi/bt06jBkzBnPmzMFll12GZcuWYcSIEdi0aRP69u0LoM4p/amnnsJLL72EHj16YObMmcjNzcV3331Hwqo+8MADmDhxYiDdtq1xzY1QyMy1IAgxhQ5leRMEQRCaj6a20/Pnz8fEiRMxfvx49OnTBwUFBUhISMDixYtN8z/55JO4+OKLcdddd+HUU0/Fgw8+iLPOOgtPP/00gLpZ6wULFmDGjBm44oorcMYZZ+Dll1/G3r17sXLlSlJW27ZtkZGREdjatAm+IJ4ZMrgWBCGmsBLeqX4TBEEQmo9I7XRFRQXZamtrDWV7vV5s3LiRrIBrt9uRk5ODoqIi0/YUFRUZVszNzc0N5N+2bRuKi4tJnuTkZGRnZxvKnDt3Ltq3b48zzzwT8+bNg99v/IIdCvl+LQhCTKEra7McrS1+qiAIwrEmUjudmZlJ9ptFXCotLYWmaYHoTfWkp6dj69atpuUXFxeb5q8Pm1r/b7A8AHDbbbfhrLPOQmpqKtatW4fp06fj119/xfz588O80jpkcC0IQkyh/vuflfyCIAhC8xGpnd61axdZSTcuLq6hU44JeXl5gf8/44wz4Ha7ceONN2LOnDmW2hrVwbXbSRc/MTg+MEcvu52mzRxGuHOGrtcEPa4xhzaOzhY64c5n3OENAKq9fBEZupCJX6umadbGBJaft7naW0rS3MkOADyudiTNHTF5X3rsKSRdVbuP1UE/xfA28cVYAKNzX7WPOj3GuejCMz4/dczUmKMmb5PxXhv7odrE0ZKUwR0QmSOU0cmWOWXy42wRorpKaDtDOTgK1tCgYLNgtDUZXDczoZ0JQxF9B8eG2nVUESEeE6sOj6Z5uBMkb2eUF0sJz7mQEw2HxfDLqyv0WDgwNs5xUwhOpHY6KSmJDK7NSEtLg8PhQElJCdlfUlKCjIwM03MyMjKC5q//t6SkBJ06dSJ5srKyGmxLdnY2/H4/tm/fjlNOOSVou49GNNeCIMQU9fFTrWyCIAhC89GUdtrtdqN///4oLCwM7NN1HYWFhRgyZIjpOUOGDCH5AWD16tWB/D169EBGRgbJU1FRgfXr1zdYJgB8+eWXsNvtphFKgiGyEEEQYgqrnuUSLUQQBKF5aWo7nZeXh3HjxmHAgAEYNGgQFixYgMrKSowfPx4AMHbsWHTp0gVz5swBAEydOhXDhg3D448/juHDh2P58uXYsGEDnn/+eQCAzWbD7bffjoceegi9evUKhOLr3LkzRowYAaDOKXL9+vU4//zz0bZtWxQVFWHatGm47rrr0K5dO9N2NoQMrgVBiClkcC0IghDbNLWdHjVqFPbv349Zs2ahuLgYWVlZWLVqVcAhcefOnbDbfxNfDB06FMuWLcOMGTNw3333oVevXli5cmUgxjUA3H333aisrMSkSZNQVlaGs88+G6tWrQrEuI6Li8Py5csxe/Zs1NbWokePHpg2bRrRYYeLTUXxm6rLmUbSXN+q63QhE6fT2i+BujKoDtdhp9pgrut1O1NImuuh3Q6+gAvVTwOAjy3QkhhPvV255toGB0lz/TJfhMbtom3w+Wk/AcaFZmp8hwx5joZriflCN/w410dzDbcZBt0209Vx7bjfX0bSbhd9XvjCOLx8wHj/+HVwuI7fy9vAng+Na/JNlFNGnTZ7JpkGu8a7O2gbhToqKiqQnJyMIUm3wGkL33HEr2pRVPEMysvLQ2r5BMBmczVDLY1THIbUYIdViNU2BM9v0E9HRGPLaLxOuEk01YZKYmGRGIttUL4maMPxh9jp8JCZa0EQYgqZuRYEQYhtxE4HRwbXgiDEFPp//7OSXxAEQWg+xE4HRwbXgiDEFLpNh2bTws/fyoy2IAjCsUbsdHCiO7hmmjTFYk47mL6Z61l5DGrAGDPY5aBaHYc9uOaHa2x5TOIaH41hzTW4AOBwJNAyfYdJ2s903k47zW+mHT6aqloam9Hjam/I4/XTOp12qk/2aVSn7XJRjbWmMS2xg/Z9rZ/FzbaZxHdm8HvD9e8uB20D1yxyTX6tn2rRzeDPjF87QtKGmNPsDzreTVdn8vI6+TNsErPab/Kc0uMS57ox6NBhkxmRFg6/J42Lg80JS5MdVmzso2lcnOzwCBEHOwSRxbUOhcUyo9CG6GusxQY0N2KngyMz14IgxBTqv2o+K/kFQRCE5kPsdHBkcC0IQkyh23TYbDIjIgiCEKuInQ6ODK4FQYgp/PBDWZARaBAZjiAIQnMidjo40R1cMy2WjWlw490dSJprje320DpfHlOY/xjica5thnjPiSw/Lc/HNLyAUUvMcTlomX4teBt4nOQ4Fu+b66sBICGO9l219wBJ81jaXGPNdd9ca2xog2ZsA7+/TqZFt7P4ubzOOFcyrYNp17kG20yPyDWHTkPf8/tHy6iu3UPSLleqoQ7SJhODoDNfgtBtEKzQXJ8bFy1ahHnz5qG4uBj9+vXDwoULMWjQINO83377LWbNmoWNGzdix44deOKJJ3D77bdHVG/rpHEabI6ZZjekDjuUVriRmuzw4D4djS2zGWYDRWMtmCCykOBEIyq+IAhC1NChWd6ssmLFCuTl5SE/Px+bNm1Cv379kJubi3379pnmr6qqwoknnoi5c+ciIyOjsZcoCILQomkOO92SkcG1IAgxhWbToNn8FjbrRnv+/PmYOHEixo8fjz59+qCgoAAJCQlYvHixaf6BAwdi3rx5GD16NOLiwl+VTBAE4XikOex0S0Y014IgxBQ6NNNl54PlB+qW5T2auLg404Gw1+vFxo0bMX369MA+u92OnJwcFBUVRdhqQRCE1kOkdrq1ENXBtaZTLWoc07NW1lC9aygtM2DUM/O41TY7vbn2EGX6mB6aYxbf2cG04H4ev5udw7XjPCY1j60N0NjdbmdbcGp9VCPNH2p+Dtdtc70Tv1dejZYX7zbG2vZr1axNZSQd50qhbWQaRn7/DfeKaftM48ryONQ61UTz+8ev02YPftzwTJroDV1Oer+4xpofF6xiTctXr7/MzMwke/Pz8zF79mxD7tLSUmiahvR0GvM8PT0dW7dutdpYISKiq8EGohAb26q22GKM6jpiQHca5VjZ0ddTmxED/SYwIrPTrQWZuRYEIabQlM/S61pXPgDArl27kJT02w8bkW8IgiA0DZHa6daCDK4FQYgpFDRLIZ7Ufz83JiUlkcF1Q6SlpcHhcKCkhEYrKikpEWdFQRCEMIjUTrcWxKFREISYQo/gPyu43W70798fhYWFv9Wp6ygsLMSQIUOifTmCIAjHHU1tp1s6UZ65pp1niBltiGNMq9d5DGsT3M4UkuaxlXl8Zq5N5nU47TRWs183arI1nem6Wbv9TLfrcdGY0VyrrELEc67x0RjWZvC+45psHr+bX7fHnUbSPj+9VzwGNQA47PQzu8dFddn8Onx6paGMo+H9wO8Fj3sNGPvep9N2c800f174M8mfj3DizvJ2cZ23WbuF8FFQFuOnWtd85uXlYdy4cRgwYAAGDRqEBQsWoLKyEuPHjwcAjB07Fl26dMGcOXMA1DlBfvfdd4H/37NnD7788kskJiaiZ8+elus/tvD+CqFFbhZC3e8Y0GQbCmwdg4Xm0VRzWkfftmSaw063ZEQWIghCTFGn5QvfEEfyY2bUqFHYv38/Zs2aheLiYmRlZWHVqlUBJ8edO3fCfpSz9N69e3HmmWcG0o899hgee+wxDBs2DGvXrrVcvyAIQkumOex0S0YG14IgxBR1Wr7wZxEj1fJNmTIFU6ZMMT3GB8zdu3eHMg1fIwiC0PpoLjvdUpHBtSAIMYVSFpfVbSWf5wVBEGIFsdPBiergmuuAuYbWbogPTLXKum7UXPtANbJcp+txdQh63Mb0sFyTyz9V6Ew/bb6P6v94mTXe4JppB4vdzbXK4cT/5n3ldMQ3kLMOpdHrrPUdZHXSe8XjPwNGvbLTQc/h+mUeHxyg1+3z00U/9BAxqgGjvp2j2DMIluZ96/MfoscdbYKWDwA2NoHJDQw/LlhDPjc2N7GoweZEPy42x6om1LJGO0aIDe1r6xpoHY+InQ6OzFwLghBTiKOMIAhCbCN2OjgyuBYEIaao+9xoQcvXyj43CoIgHGvETgdHBteCIMQYmsU5jtblKCMIgnDsETsdjCYdXHONjeaneln+ScHlNK6uxrXFNqaZra7dQ9IOg66b6npdLO4xj//MjwPWf3FxrTDX+fpDxlo2ie/M9Mtc78zjXPPrcofQu7udbdn5xqVKa31ltA12qvP2+nmsbXo+7weHg+u8aYxxrqcHjHHMedxr/sx5faU0P6vT6Uw21BEKQ5xzdj+5pl6whq77YbMgXFeqdRntpqclarDNaNo10lrbZ+7G0bpmLVsDYqeDIzPXgiDEFDp0S85iVnR/giAIQuMROx0cGVwLghBT1H0pEi2fIAhCrCJ2OjgyuBYEIabQlR82iZ8qCIIQs4idDo4MrgVBiCmsGuHWZrQFQRCONWKngxPVwbXdsAgIdVBzOahTnd1Oq/dpNL8ZGsvDHdS4MyBfHMXPzueOgWaBzg11MudC7pBodLKjzoXckY/n95kslOK0JRj2keMOtjgK8ya0wUHTbOGaWh9dTMUMF3PUq/aW0OMmDqlHo7F+APtjcxucTY1OlVbhDq4uR/B+5Ivr8IWRAMDPnGxDPWOCNcRoxxrhOC21RKfHpnV4bD3I319rROx0cGTmWhCEmMKq40trc5QRBEE41oidDo4MrgVBiCnqQjyFP6vY2mZEBEEQjjVip4Mjg2tBEGIMq0a4dRltQRCEY4/Y6WA06eCaa7D9TINt09mvHpNfQaE+JXCNNdfI8oVLnKwOro+Nc7Uz1FHD6uC/wLgGmy8iwxdw0Zhml2uRzX4Nev10MRzDojLsOvniOz5UkrTZQjW0EaF/kfK+NSwaFEJ7zLXnPrYYC78GAHAy3TfXu3MdN9fU877nC8BwHaahfBj73u8vo3WaLEQkhI9o+VoiLWHhGY7V56a1aLSP17+nlviMxi5ip4MjM9eCIMQUutIshniSlfIEQRCaE7HTwZHBtSAIMYYGa7NKrctoC4IgHHvETgdDBteCIMQU1lf+al1GWxAE4Vgjdjo4TSoi05WXbE57AtlczkSymWGDnWyG4zYn2TiaXkM2r7+CbHa7m2y1/nLDputestlsdrIp5Seb3eYkm087QjYFnWwOm5tsSumGze1MIpvT7iEbbxO/LpejDdn4cQNKN24h+pZft8uZRDaHI4FsvHyXI5Fsca5Uw8b7jm+w2cnm12vI5rC7ycbLr9Mb/raZ1cGv2+lMIRs/LlhE+a1vEbBo0SJ0794dHo8H2dnZ+Oyzz4Lmf/3119G7d294PB6cfvrp+OCDDyKq9/jExraWgN3i1lo4XvulJT6jMUwz2Olo22ilFGbNmoVOnTohPj4eOTk5+PHHH0megwcP4tprr0VSUhJSUlIwYcIEHDnCfbNC05L+MgRBaAWoCP6zyooVK5CXl4f8/Hxs2rQJ/fr1Q25uLvbt22eaf926dRgzZgwmTJiAL774AiNGjMCIESOwefPmxl6uIAhCi6Op7XRT2OhHH30UTz31FAoKCrB+/Xq0adMGubm5qKn5bRLs2muvxbfffovVq1fjvffew7/+9S9MmjTJcv/YVBTn6l3ONJLmKxNGtEIjmzHlM4Gms65HwWe7eUSLplihkcMjnhgimrCoKjxqBmCMOMLhEUZ4X7odbUnar1eTtFlkDg6PDuJlUTJCrdBo6NsQkT3M8PP7b1gNM/iv42is0Mi9nnm/8GdU08qC1inUUVFRgeTkZAAu2GxWPzf6UF5ejqSk4M9gPdnZ2Rg4cCCefvppAICu68jMzMStt96Ke++915B/1KhRqKysxHvvvRfYN3jwYGRlZaGgoCDstsYCZs90FEptgjKbGplbig7HRxQIFYUVgVsDzWWno22jlVLo3Lkz7rjjDtx5550AgPLycqSnp2PJkiUYPXo0tmzZgj59+uDzzz/HgAEDAACrVq3CpZdeit27d6Nz585hX69YF0EQYgxlKo1qaKt3lKmoqCBbbW2taelerxcbN25ETk5OYJ/dbkdOTg6KiopMzykqKiL5ASA3N7fB/IIgCMc3TWenm8JGb9u2DcXFxSRPcnIysrOzA3mKioqQkpISGFgDQE5ODux2O9avX2+pd6I6heHzl0azOEEQWhFutxsZGRkoLi62fG5iYiIyMzPJvvz8fMyePduQt7S0FJqmIT09nexPT0/H1q1bTcsvLi42zR9JW481IWPcC4IgNEBz2OmmsNH1/4bK07FjR3Lc6XQiNTXV8vVKtBBBEGICj8eDbdu2wesNLVHiKKUMnyjj4uKi1TRBEAQBYqfDRQbXgiDEDB6PBx6PJ3TGRpCWlgaHw4GSkhKyv6SkBBkZGabnZGRkWMovCIJwvNLUdropbHT9vyUlJejUqRPJk5WVFcjDHSb9fj8OHjxo2daL5loQhFaF2+1G//79UVhYGNin6zoKCwsxZMgQ03OGDBlC8gPA6tWrG8wvCIIgREZT2OgePXogIyOD5KmoqMD69esDeYYMGYKysjJs3LgxkOfjjz+GruvIzs62dhFKEAShlbF8+XIVFxenlixZor777js1adIklZKSooqLi5VSSl1//fXq/7N33uFVFVsbf08/KSQhlCRA6EgVAgEiKAYkEooKXmoEqZeAEkQCSBEIAgJKEaRFUEQpgoKChYsCwlUkF6UpKEURCC0hAVJIO22+P/LlyMzeOS0n4YSsn89+ZGbPnpldztqT2e9aM23aNGv5n376ianVarZkyRJ29uxZlpCQwDQaDTt9+vSDOgWCIIiHltKw0YsWLWIBAQFs9+7d7LfffmO9e/dm9erVY3l5edYy3bt3Z61bt2ZHjx5lhw8fZo0aNWIxMTFO958G1wRBVEhWrlzJateuzbRaLWvfvj373//+Z90XGRnJhg0bxpX/9NNP2SOPPMK0Wi1r3rw5++abb8q4xwRBEBUHd9toi8XCZs2axYKCgphOp2Ndu3Zl58+f58rcvn2bxcTEMF9fX+bn58dGjBjBsrOzne67W+NcEwRBEARBEERFhjTXBEEQBEEQBOEmaHBNEARBEARBEG6CBtcEQRAEQRAE4SZocE0QBEEQBEEQboIG1wRBEARBEAThJmhwTRAEQRAEQRBuggbXBEEQBEEQBOEmaHBNEARBEARBEG6CBtflGIVCgTlz5tgtN2fOHCgUitLvEEEQBCGhc+fO6Ny5s91yhw4dgkKhwKFDh0q9TwRBlB40uC4BGzduhEKhwLFjx4otc/nyZSgUCuumVCoRGBiIHj16ICkpqQx7++A4fPiw9fzT09MfdHcIgqgA2LPPnTt3RosWLbi8unXrcvbax8cH7du3x8cff1wWXX7gZGRkoHr16lAoFNixY8eD7g5BlFvUD7oDFYWYmBj07NkTZrMZFy5cwJo1a9ClSxf88ssvePTRR12qMy8vD2q1Z99Ci8WC8ePHw8fHBzk5OQ+6OwRBEDYJCwvDpEmTAAA3b97E+++/j2HDhqGgoACjR492qc7vvvvOnV0sNWbPno3c3NwH3Q2CKPfQzHUZ0aZNGwwZMgTDhg3Dm2++iU8++QQFBQVYu3aty3Xq9XqPH1yvW7cOV69exb///e8H3RWCIAi71KxZE0OGDMGQIUMwZcoUHD58GL6+vnjnnXdcrlOr1UKr1bqxl+7nzJkzWLt2LaZOnfqgu0IQ5R4aXD8gOnXqBAC4ePGiy3XIaa4PHz6Mdu3aQa/Xo0GDBnjvvfckx3344YdQKBTYsGEDl79gwQIoFArs2bPH5T7dz507dzBz5kzMnTsXAQEBbqmTIAiiLKlWrRqaNGlSIlstp7m+du0a+vTpAx8fH1SvXh0TJ05EQUEBV+bs2bPw8vLC0KFDufzDhw9DpVK5dSA8YcIEPP/889Z3E0EQruPZ054PMZcvXwYAVK5c2W11nj59Gt26dUO1atUwZ84cmEwmJCQkICgoiCs3YsQIfP7554iPj8fTTz+N0NBQnD59Gm+88QZGjRqFnj17WsvevXsXZrPZbtve3t7w9vbm8mbNmoXg4GCMGTMG8+bNc89JEgRBOEFmZqasr4fRaHToeJPJhGvXrrnVVufl5aFr165ITk7GK6+8gho1amDTpk34/vvvuXJNmzbFvHnzMGXKFPTr1w/PPfcccnJyMHz4cDRp0gRz5861lr137x7y8/Pttq3RaODv78/lffbZZzhy5AjOnj1rfTcRBOE6NLguI3Jzc5Geng6z2Yw///wT8fHxAIB+/fq5rY3Zs2eDMYYff/wRtWvXBgD07dtXVtO9fv16NG/eHKNGjcLXX3+NYcOGITg4GMuWLePKtW7dGleuXLHbdkJCAjeL/ttvv+G9997Dnj17oFKpSnZiBEEQLhIVFVXsvubNm0vyjEajdTCekpKCt99+GykpKRg3bpzb+rRu3TpcuHABn376Kfr37w8AGD16NFq1aiUpGx8fj927dyM2NhaPP/44EhIScOXKFSQlJUGn01nLxcXF4aOPPrLbdmRkJBeNJC8vD5MnT8bEiRNRt25dGlwThBugwXUZkZCQgISEBGva19cXS5cuddvg2mw249tvv0WfPn2sA2ugcOYjOjpaIvUIDg7G6tWrERMTg06dOuHUqVPYt28f/Pz8uHJbtmxBXl6e3fbr16/PpV955RX06NED3bp1K8FZEQRBlIzVq1fjkUcekeRPmjRJ9qvcd999h2rVqnF5I0aMwOLFi93Wpz179iAkJISz/97e3oiNjcVrr73GlVUqldi4cSNatWqFHj164NixY5g5cybatm3LlXvttdcwZMgQu22LM/CLFi2C0WjEjBkzSnBGBEHcDw2uy4jY2Fj0798f+fn5+P777/Huu+86JLdwlLS0NOTl5aFRo0aSfY0bN5bVUQ8aNAibN2/GN998g9jYWHTt2lVS5vHHH3e6L9u3b8eRI0dw5swZp48lCIJwJ+3bt5cMRIHCQaacXCQiIgLz58+H2WzGmTNnMH/+fNy9e9etDolXrlxBw4YNJesPNG7cWLZ8gwYNMGfOHEyZMgUtWrTArFmzJGWaNWuGZs2aOdWPy5cvY/HixVi9ejV8fX2dOpYgiOKhwXUZ0ahRI+vnyWeeeQYqlQrTpk1Dly5dZA1/WXD79m1rDNg//vgDFosFSiXv45qWlubQHwG+vr5W4zxlyhT0798fWq3W+okxIyMDAHD16lUYDAbUqFHDfSdCEAThJqpWrWq11dHR0WjSpAmeeeYZrFixwirnexAUhfO7ceMGbt++jeDgYG5/ZmamQ18ZtVotAgMDARRKCWvWrInOnTtbbXVKSgqAQtt/+fJl1K5dW/JeIAjCNvSLeUC8/vrrqFSpEmbOnOmW+qpVqwYvLy/8+eefkn3nz5+XPWbcuHHIzs7GwoULcfjwYSxfvlxSpl27dggJCbG7LVmyxHrM1atXsXXrVtSrV8+6rVixAkBhSML7HSYJgiA8mV69eiEyMhILFixwW6z+OnXq4OLFi2CMcfnF2erExETs27cPb775JgwGA8aMGSMpM2HCBIds9b/+9S/rMcnJyfjrr79Qv359q62OiYkBALz88suoV68esrKy3HLOBFGRoJnrB0RAQADGjBmDt99+G6dOnUJYWFiJ6lOpVIiOjsauXbuQnJxs1V2fPXsW3377raT8jh07sH37drz77rsYP348fv31V8ycORPPPPMMp090RXP9xRdfSPZv27YN27dvx8cff4xatWq5cooEQRAPhKlTp6Jnz55Yv349Xn311RLX17NnT3z33XfYsWOH1aExNzcX69atk5S9dOkSpkyZgr59+2LGjBmoUqUKxo4di48//pgL0eeK5nr+/PkSacyZM2cwa9YsvPbaa+jQoQN8fHxcPU2CqLDQ4NoNbNiwAXv37pXkT5gwweZxEyZMwPLly7Fo0SJs27atxP144403sHfvXnTq1Akvv/wyTCYTVq5ciebNm+O3336zlrt16xZeeukldOnSBXFxcQCAVatW4eDBgxg+fDgOHz5s/Qzoiua6T58+krxTp04BAHr06IGqVas6f3IEQRAPiB49eqBFixZYtmwZxo0bB41GU6L6Ro8ejVWrVmHo0KE4fvw4QkJCsGnTJkk4U8YYRo4cCS8vL+uCY2PGjMHOnTsxYcIEREVFWSV2rmiun3jiCUle0ZoE7dq1k7XlBEHYhwbXbqC4VRaHDx9u87gaNWrghRdewKZNm3Dx4kU0aNCgRP1o2bIlvv32W8THx2P27NmoVasW3njjDdy8eZMbXL/00ksoKCiwLiYDAFWqVMG6devQu3dvLFmyROKxThAEUZGZPHkyhg8fji1btti17fbw9vbGgQMHMH78eKxcuRLe3t4YPHgwevToge7du1vLrVy5EocOHcLOnTu5CCYffPABWrRogdGjR+Obb74pUV8IgnA/CiaKvgiCIAiCIAiCcAlyaCQIgiAIgiAIN0GDa4IgCIIgCIJwEzS4JgiCIAiCIAg3QYNrgiAIgiAIgnATNLgmCIIgCIIgCDdBg2uCIAiCIAiCcBNlOri+fPkyFAoFNm7cWJbNEgRBEDYg20wQBOE+KszM9QcffICmTZtCr9ejUaNGWLlypcPHFhQUYOrUqahRowa8vLwQERGBffv2yZY9cuQInnjiCXh7eyM4OBivvPIK7t27x5W5d+8eEhIS0L17dwQGBtp9qVksFqxduxZhYWHw8vJClSpV8NRTT+HXX3+VlL148SJeeOEFVK9eHV5eXmjUqBFef/11h8/1YcCZ+yXHtm3b0KZNG+j1elSrVg2jRo2SLBEMAAqFQnZbtGiRy3USREXCmd+QyKFDh4o9/n//+x9XtnPnzrLl7l+wxV1YLBa8/fbbqFevHvR6PVq2bIlPPvlEUm748OGyfWrSpInb++TpOPLelGPjxo3FPgMKhQJbtmzhyl+/fh0DBgxAQEAA/Pz80Lt3b/z999822zh8+LC1PrLZhKOU6QqNderUQV5eXomXjnWW9957D2PHjkXfvn0RHx+PH3/8Ea+88gpyc3MxdepUu8cPHz4cO3bswKuvvopGjRph48aN6NmzJw4ePMgtH3vq1Cl07doVTZs2xbJly3Dt2jUsWbIEf/75J/7zn/9Yy6Wnp2Pu3LmoXbs2WrVqhUOHDtlsf+TIkdiyZQuGDh2KuLg45OTk4OTJk7h16xZX7tSpU+jcuTNq1qyJSZMmoUqVKkhOTsbVq1edu2DlHEfvlxxr167Fyy+/jK5du1rv4YoVK3Ds2DEcPXoUer2eK//0009j6NChXF7r1q1LVCdBlDUPyjYDjv2GbPHKK6+gXbt2XF7Dhg0l5WrVqoWFCxdyeUVLh7uT119/HYsWLcLo0aPRrl077N69Gy+88AIUCgUGDRrEldXpdHj//fe5PH9/f7f3yZNx9L0px5NPPolNmzZJ8t955x38+uuv6Nq1qzXv3r176NKlCzIzMzFjxgxoNBq88847iIyMxKlTp1ClShVJPRaLBePHj4ePjw9ycnJKfrJExYE95OTm5rIqVaqwXr16cfmDBw9mPj4+7M6dOzaPP3r0KAPAFi9ebM3Ly8tjDRo0YB06dODK9ujRg4WEhLDMzExr3vr16xkA9u2331rz8vPz2c2bNxljjP3yyy8MAPvwww9l29++fTsDwD7//HOb/TSbzaxFixYsIiKC5ebm2iz7MOPM/RIpKChgAQEB7Mknn2QWi8Wa/9VXXzEA7N133+XKA2Djxo1za50EUZFw5DdUHAcPHmQA2GeffWa3bGRkJGvevLlL7TjDtWvXmEaj4c7JYrGwTp06sVq1ajGTyWTNHzZsGPPx8Sn1Pnk6jr43HSU3N5dVqlSJPf3001z+W2+9xQCwn3/+2Zp39uxZplKp2PTp02XrWrt2LatSpQqbMGECA8DS0tKc7g9RMXFKFjJnzhwoFApcuHABQ4YMgb+/P6pVq4ZZs2aBMYarV6+id+/e8PPzQ3BwMJYuXcodL6frGz58OHx9fXH9+nX06dMHvr6+qFatGiZPngyz2ezq3wxWDh48iNu3b+Pll1/m8seNG4ecnBx88803No/fsWMHVCoVYmNjrXl6vR6jRo1CUlKSdVY4KysL+/btw5AhQ+Dn52ctO3ToUPj6+uLTTz+15ul0OgQHBzvU/2XLlqF9+/Z4/vnnYbFYiv3r+bvvvsOZM2eQkJAALy8v5ObmOnX9iu5DcnIynnnmGfj6+qJmzZpYvXo1AOD06dN46qmn4OPjgzp16mDr1q3c8Xfu3MHkyZPx6KOPwtfXF35+fujRo4dEujJs2DDo9XqcPXuWy4+OjkblypVx48YNh/ssh6P3S44zZ84gIyMDAwcOhEKhsOYXXY9t27bJHpeXl4f8/Hy31kkQzlAebfP92PoNOUJ2djZMJpPdciaTya7c4Pr16xg5ciSCgoKg0+nQvHlzbNiwwaF+7N69G0ajkXvfKBQKvPTSS7h27RqSkpIkx5jNZmRlZTlUfxFF92vJkiVYvXo16tevD29vb3Tr1g1Xr14FYwzz5s1DrVq14OXlhd69e+POnTuSvvbq1Qs1atSATqdDgwYNMG/ePO7enj17Fl5eXpIvC4cPH4ZKpXLoy68tnHlvOspXX32F7OxsDB48mMvfsWMH2rVrx33laNKkCbp27Srbzp07dzBz5kzMnTsXAQEBTveDqNi4pLkeOHAgLBYLFi1ahIiICMyfPx/Lly/H008/jZo1a+Ktt95Cw4YNMXnyZPzwww926zObzYiOjkaVKlWwZMkSREZGYunSpVi3bh1X7u7du0hPT7e75ebmWo85efIkAKBt27ZcXeHh4VAqldb9xXHy5Ek88sgj3A8fANq3bw+g8JMWUDj4NJlMkna0Wi3CwsLstiNHVlYWfv75Z7Rr1w4zZsyAv78/fH19Ub9+fYkx2L9/P4DCgXvbtm3h4+MDb29vDBo0SGJUi8NsNqNHjx4IDQ3F22+/jbp16yIuLg4bN25E9+7d0bZtW7z11luoVKkShg4dikuXLlmP/fvvv7Fr1y4888wzWLZsGaZMmYLTp08jMjKSGzCvWLEC1apVw7Bhw6xG/L333sN3332HlStXWj/TWiwWh+51eno6jEajtX5H75ccBQUFAAAvLy/JPi8vL5w8eRIWi4XL37hxI3x8fODl5YVmzZpJ/uhwpU6CcJXyZJuLsPcbsseIESPg5+cHvV6PLl264NixY7LlLly4AB8fH1SqVAnBwcGYNWsWZzsAIDU1FY899hj279+PuLg4rFixAg0bNsSoUaOwfPlyu305efIkfHx80LRpUy6/yP6I74Hc3Fz4+fnB398fgYGBGDdunENa4yK2bNmCNWvWYPz48Zg0aRL++9//YsCAAZg5cyb27t2LqVOnIjY2Fl999RUmT57MHbtx40b4+voiPj4eK1asQHh4OGbPno1p06ZZyzRt2hTz5s3Dpk2b8OWXXwIAcnJyMHz4cDRp0gRz5861lr13755Dz0BmZqb1mNJ4b27ZsgVeXl7417/+Zc2zWCz47bffJO0Ahffm4sWLyM7O5vJnzZqF4OBgjBkzxuk+EIRTspCEhAQGgMXGxlrzTCYTq1WrFlMoFGzRokXW/Lt37zIvLy82bNgwa96lS5ckEohhw4YxAGzu3LlcW61bt2bh4eFcXp06dRgAu1tCQoL1mHHjxjGVSiV7PtWqVWODBg2yec7NmzdnTz31lCT/999/ZwBYYmIiY4yxzz77jAFgP/zwg6Rs//79WXBwsGz9tmQhJ06cYABYlSpVWFBQEFuzZg3bsmULa9++PVMoFOw///mPtexzzz1nLTt48GC2Y8cONmvWLKZWq1nHjh05SYIcRfdhwYIF1ryie6hQKNi2bdus+efOnZNc5/z8fGY2m7k6L126xHQ6neTefvvttwwAmz9/Pvv777+Zr68v69Onj+RYR+41AHbw4EHrcY7eLznS0tKYQqFgo0aN4vKLzhcAS09Pt+Z37NiRLV++nO3evZutXbuWtWjRggFga9ascblOgnCF8mibGXPsN1QcP/30E+vbty/74IMP2O7du9nChQtZlSpVmF6vZydOnODKjhw5ks2ZM4ft3LmTffzxx1Z7OWDAAK7cqFGjWEhIiOQ3OWjQIObv729XcterVy9Wv359SX5OTg4DwKZNm2bNmzZtGps6dSrbvn07++STT6zX+/HHH2dGo9FmO0X3q1q1aiwjI8OaP336dAaAtWrViqsjJiaGabValp+fb82TO5cxY8Ywb29vrpzZbGZPPPEECwoKYunp6WzcuHFMrVazX375hTu2qP/2tsjISOsiv+92AADexklEQVQxrr43i+P27dtMq9VK7mtaWprss8wYY6tXr2YA2Llz56x5v/76K1OpVFZZStHvi2QhhKO45ND473//2/pvlUqFtm3b4tq1axg1apQ1PyAgAI0bN7briVvE2LFjuXSnTp0kjgpbtmxBXl6e3brq169v/XdeXh60Wq1sOb1eb7e+vLw86HQ62WOL9t///+LKOtJvkaIZjNu3b+N///sfIiIiAADPPfcc6tWrh/nz51u93YvKtmvXDps3bwYA9O3bF97e3pg+fToOHDiAqKgou23ef2+L7uFff/2FAQMGWPMbN26MgIAA7t7ef95msxkZGRnw9fVF48aNceLECa6Nbt26YcyYMZg7dy527NgBvV6P9957jysTHBzscISPVq1aWf/t6P2So2rVqhgwYAA++ugjNG3aFM8//zyuX7+O8ePHQ6PRwGg0csf/9NNP3PEjR45EeHg4ZsyYgeHDh8PLy8vpOgmiJJQn2ww49hsqjo4dO6Jjx47W9HPPPYd+/fqhZcuWmD59Ovbu3Wvd98EHH3DHvvjii4iNjcX69esxceJEPPbYY2CMYefOnRgwYAAYY1xkiOjoaGzbtg0nTpzA448/XmyfnLE/onPloEGD8Mgjj+D111/Hjh07JM6PcvTv359zgCx6RwwZMgRqtZrL/+STT3D9+nXrPbj/2mZnZ6OgoACdOnXCe++9h3PnzlntqlKpxMaNG9GqVSv06NEDx44dw8yZMyWzwK+99hqGDBlit8+VK1e2/tvd780dO3bAYDBIJCH22rm/DFDoJNujRw9069bNqfYJogiXBte1a9fm0v7+/tDr9ahataok//bt23brKwpPdj+VK1fG3bt3uTxbRq04vLy8YDAYZPfl5+fbNN5Fxxd92hePLdp///+LK2uvneLaBoB69epZjSYA+Pr64tlnn8XmzZthMpmgVqutZWNiYrg6XnjhBUyfPh1HjhyxO7iWuw/+/v6oVasWpxcuyr///lgsFqxYsQJr1qzBpUuXON2enBf2kiVLsHv3bpw6dQpbt25F9erVJX1x5I8BEUfvV3G89957yMvLw+TJk62fUYcMGYIGDRrg888/h6+vb7HHarVaxMXFYezYsTh+/Lg1MklJ6iQIZyhPtlmO4n5DjtKwYUP07t0bn3/+OcxmM1QqVbFlJ02ahPXr12P//v147LHHkJaWhoyMDKxbt04ieymiKEJTSkoKl+/v7w8vL68S25+JEydi1qxZ2L9/v0ODa7n7DQChoaGy+ffft99//x0zZ87E999/L9F83y/dAIAGDRpgzpw5mDJlClq0aIFZs2ZJ+tKsWTM0a9bMbp/vx93vzS1btiAwMBA9evRwqp37y2zfvh1HjhzBmTNnnGqbIO7HpcG1nMEqzogxxlyqT460tDSHHGl8fX2tA5aQkBCYzWbcunWLG8AZDAbcvn3bbiimkJAQXL9+XZJ/8+ZNAP+EcgoJCeHyxbKuhHwqOiYoKEiyr3r16jAajcjJyYG/v3+xZYvOWXwZylHcfXDk3i5YsACzZs3CyJEjMW/ePAQGBkKpVOLVV1+V1RTfH0rw9OnTkj8KzGYz0tLS7PYZAAIDA61fJxy9X8Xh7++P3bt3Izk5GZcvX0adOnVQp04ddOzYEdWqVbPr2FL0Urtf517SOgnCUcqTbS4Oud+QM4SGhsJgMCAnJ0fie2GrnSI7NWTIEAwbNkz2mJYtWwL4x94X8eGHH2L48OEICQnBwYMHwRjjJiQctT9F6xg4eu6u2uyMjAxERkbCz88Pc+fORYMGDaDX63HixAlMnTpV1mZ/9913AIAbN27g9u3bEqf8zMxMh2aatVotAgMDAbj3vZmcnIwff/wRsbGxkpCSgYGB0Ol0xbYD/HNvpkyZgv79+0Or1eLy5csACq8XAFy9ehUGg6FUQjgSDxdlGue6pLRr1w5XrlyxWy4hIQFz5swBAISFhQEAjh07hp49e1rLHDt2DBaLxbq/OMLCwnDw4EFkZWVxhvro0aNc/S1atIBarcaxY8c4CYXBYMCpU6e4PEepUaMGgoODZQeLN27cgF6vR6VKlQAUOmiuX79eUrbImVCcfXI3O3bsQJcuXSSfXzMyMiSzZjk5ORgxYgSaNWuGjh074u2338bzzz/PeXFfvXoV9erVc6jtgwcPonPnzgAcv1/2qF27tnVWKCMjA8ePH0ffvn3tHlf0qV3uertaJ0F4Oq7Y5uKw9RtyhL///ht6vd7uIF5sp1q1aqhUqRLMZrPdr2aiZK158+YACu3L+++/j7Nnz3KzuI7an+zsbKSnp5e6vT506BBu376Nzz//HE8++aQ1/34n9ftJTEzEvn378Oabb2LhwoUYM2YMdu/ezZWZMGECPvroI7ttR0ZGWtd2cOd785NPPgFjTCIJAQqlLY8++qiss+vRo0dRv35967v06tWr2Lp1q6xjbZs2bdCqVSubjvEEAZSzwbUrur6nnnoKgYGBWLt2LTe4Xrt2Lby9vdGrVy9rXpE3c+3ateHt7Q0A6NevH5YsWYJ169ZZP+kXFBTgww8/REREhHX2w9/fH1FRUdi8eTNmzZpl/aFu2rQJ9+7dQ//+/V0654EDB2LFihXYt28fnn76aWs/d+/ejaeeegpKZWHAl969e2PChAnWGZSi/KIFCoqOLS1UKpVkJuyzzz7D9evXJQs6TJ06FcnJyfjf//6Hxo0b48CBAxg2bBhOnjxp1cS5qrl29H4BhTMdubm5dldEmz59OkwmEyZOnGjNS0tLk7wAs7OzsXz5clStWhXh4eFO10kQ5RVXbLMzvyE52yx3/K+//oovv/wSPXr0sNrArKws6HQ6Tm/LGMP8+fMBFOqpgUIb1rdvX2zduhVnzpxBixYtuLrvb6+4wXfv3r0xceJErFmzBqtWrbK2lZiYiJo1a1o14vn5+TAajdb3RBHz5s0DY6xUVo68n6KZ7ftttsFgwJo1ayRlL126hClTpqBv376YMWMGqlSpgrFjx+Ljjz/mQvS5orl25r2Zm5uL5ORkVK1aVTJhAwBbt25F7dq1i5US9evXD9OmTcOxY8esevHz58/j+++/5yKpfPHFF5Jjt23bhu3bt+Pjjz9GrVq17J4jQZSrwbWrmut58+Zh3Lhx6N+/P6Kjo/Hjjz9i8+bNePPNN62fpwBg1apVeOONN7iZ0IiICPTv3x/Tp0/HrVu30LBhQ3z00Ue4fPmyZJb2zTffRMeOHREZGYnY2Fhcu3YNS5cuRbdu3STGctWqVcjIyLDOLH/11Ve4du0aAGD8+PFWjdz06dPx6aefWleX9Pf3R2JiIoxGIxYsWGCtLzg4GK+//jpmz56N7t27o0+fPvj111+xfv16xMTESFYwczfPPPMM5s6dixEjRqBjx444ffo0tmzZInFg+v7777FmzRokJCSgTZs2AAo/qXbu3BmzZs3C22+/DcB1zbUz92vo0KH473//y71gFi1ahDNnziAiIgJqtRq7du3Cd999h/nz53PXcPXq1di1axeeffZZ1K5dGzdv3sSGDRuQnJyMTZs2cU60jtZJEOUVV2yzM78hOds8cOBAeHl5oWPHjqhevTr++OMPrFu3Dt7e3tzy6SdOnEBMTAxiYmLQsGFD5OXl4YsvvsBPP/2E2NhYqx0CCn+rBw8eREREBEaPHo1mzZrhzp07OHHiBPbv329XrlGrVi28+uqrWLx4MYxGI9q1a4ddu3bhxx9/xJYtW6yD2pSUFLRu3RoxMTHWP+6//fZb7NmzB927d0fv3r2dvp7O0LFjR1SuXBnDhg3DK6+8AoVCgU2bNkkmSBhjGDlyJLy8vLB27VoAwJgxY7Bz505MmDABUVFRVomEK5prwPH35s8//4wuXbrIfv04c+YMfvvtN0ybNk3iH1TEyy+/jPXr16NXr16YPHkyNBoNli1bhqCgIEyaNMlark+fPpJji2aqe/ToITuwJwgJzoQWKS4cTXErTYmrYhUX7knu2KK23MW6detY48aNmVarZQ0aNGDvvPOOJDxdUZv3h3ZjrHCFv8mTJ7Pg4GCm0+lYu3bt2N69e2Xb+fHHH1nHjh2ZXq9n1apVY+PGjWNZWVmScrZCV126dIkre/HiRfb8888zPz8/5uXlxZ566ilulakiLBYLW7lyJXvkkUeYRqNhoaGhbObMmcxgMNi9Po7ew/v7f/+ql/n5+WzSpEksJCSEeXl5sccff5wlJSWxyMhIa+ilrKwsVqdOHdamTRtJqKmJEycypVLJkpKS7PbVHo7er8jISMkz9vXXX7P27duzSpUqMW9vb/bYY4+xTz/9VHLsd999x55++mkWHBzMNBoNCwgIYN26dWMHDhyQlHW0ToJwlfJom535DcnZ5hUrVrD27duzwMBAplarWUhICBsyZAj7888/uWP//vtv1r9/f1a3bl2m1+uZt7c3Cw8PZ4mJibIhSlNTU9m4ceNYaGgo02g0LDg4mHXt2pWtW7fOofMym81swYIFrE6dOkyr1bLmzZuzzZs3c2Xu3r3LhgwZwho2bMi8vb2ZTqdjzZs3ZwsWLHDIXhfdr/tXomWs+FUrP/zwQwaAC5/3008/sccee4x5eXmxGjVqsNdee80aJrXoOq9YsYIBYDt37uTqS05OZn5+fqxnz54OXRN7OPLeLDo3MZwjY4VhDQGw3377zWY7V69eZf369WN+fn7M19eXPfPMM5LnRQ4KxUc4i4IxB7xaCIIgCIIgCIKwi0srNBIEQRAEQRAEIYUG1wRBEARBEAThJmhwTRAEQRAEQRBuggbXBEEQBEEQBOEmaHBNEARBEARBEG6CBtcEQRAEQRAE4SbK1SIyBEE83OTn58NgMDh9nFarhV6vL4UeEQRBEPdDdto+bh1cazTVubTFwl98tcqbS5vM97i0UqGFPcQ6DKYsvg4lX4dSwZ+i2ZLPpRXC5L1CIb0kFsafh3iM2KZa6cX30ZzNpRkzcWmt2o9LFxilq4CpVb5cWjwPEY1QnjEL36aGX3bXaMrh08K9AaT3R+yD2KZYh0Jh+1qL98oiXCcAMAt1atQB/DHCMye2oRLuVYExnd8vnIMc4v03W3KFNoRn1Jhit06i0GDXq1cTKSm2V8GTIzg4GJcuXaowhrskSG2c/Ip2Dx8V9UOtxX6RB86DX25DfC8T8pCddgyauSYIwiMwGAxISbmDy5e2wc/P2/4B/09WVi7q1hsEg8FQIYw2QRDEg4LstGPQ4JogCI/Cz1cPP18v+wWLsJSHmTmCIIiHB7LTtqHBNUEQnoXF4pwhrmBGmyAI4oFDdtombh1cq5X8VL9B0L+KelhRmyqnIxZ1uPZ0vPaOt4i6OzvHA4CXthqXNph4DbWo47UwI5fWqnh9s6jBNgvXRSvoiOWwCOcl6oDVKv4vSvE6iRpse+UBwGgS9M6CPlnUSIv6ePH+2tPDi/XLHSOeh05TmUvnGVKFPvLX2p5mWywPAErhORefY1YuNI4ejNkCmM3OlSec4GHQWHumflpRxteWOaRVtnetPOH3Y++6PXhNNiFAdtomnmmhCIKouBTNiDizEQRBEGVHGdjp1atXo27dutDr9YiIiMDPP/9ss/xnn32GJk2aQK/X49FHH8WePXus+4xGI6ZOnYpHH30UPj4+qFGjBoYOHYobN25wddy5cweDBw+Gn58fAgICMGrUKNy7Jw3wYA8aXBME4VnQ4JogCMKzKWU7vX37dsTHxyMhIQEnTpxAq1atEB0djVu3bsmWP3LkCGJiYjBq1CicPHkSffr0QZ8+fXDmzBkAQG5uLk6cOIFZs2bhxIkT+Pzzz3H+/Hk899xzXD2DBw/G77//jn379uHrr7/GDz/8gNjYWKcvj4Ix5rbvLV662lxaEiZPlDLYkQXIHWPvk7tYpyhVMZn5sGmiLESUVwCATuPPpe3JQkSpglKh4Y8XZCHiOYppOUx2QgqKfbYnCxExWwokeaIsRCVcW1EWIrYp3l/xeEdkIWIbkhCDal6CI8pCpKH5hHNwQRYiXnvxGTUa5Y0BwZOVlQV/f3/cubQZfpWc8ELPzkVgvSHIzMyEn5+f/QMqOArBHpVPPHNeyDNlIfYoD3+clr4shELxOUZZ2emIiAi0a9cOq1atAgBYLBaEhoZi/PjxmDZtmqT8wIEDkZOTg6+//tqa99hjjyEsLAyJiYmybfzyyy9o3749rly5gtq1a+Ps2bNo1qwZfvnlF7Rt2xYAsHfvXvTs2RPXrl1DjRo1HD5ft2quxcG05GG1M2i0WKRT7woVfxPEwRQTB2x2Yi2LMIvtASEA5ORf59I++ppcOt9412YbXlofvo8W2/G8JX8AQDooFHXcDLz2STKwNdsO+C4O+DUq6Y9GGmM8QziGv1dmMT64qJ8Xnhe9tip/vEXaZ/HaiM+YySzWycdezy/gY06Lg2vxjy2lQhoySByAM8l52o/XThSPglmgsPPHn1ieeNh48INnlwbKdn14nD0vO5NJcpl2fg/SAXl51GSTBvtB46qdzsri39E6nQ46nY7LMxgMOH78OKZPn27NUyqViIqKQlJSkmz9SUlJiI+P5/Kio6Oxa9euYvuUmZkJhUKBgIAAax0BAQHWgTUAREVFQalU4ujRo3j++eftnqe1vw6XJAiCKAtIFkIQBOHZuGinQ0ND4e/vb90WLlwoqTo9PR1msxlBQUFcflBQEFJS5BdlS0lJcap8fn4+pk6dipiYGOtMekpKCqpX5yfk1Go1AgMDi62nOCgUH0EQnoWFFW7OlCcIgiDKDhft9NWrVzlZiDhrXRYYjUYMGDAAjDGsXbu2VNqgwTVBEJ6FyVS4OVOeIAiCKDtctNN+fn52NddVq1aFSqVCairvM5Wamorg4GDZY4KDgx0qXzSwvnLlCr7//nuuL8HBwRKHSZPJhDt37hTbbnG4dXAt6nzVogZX4rDIf87Va6VicVFja7bw2mCtho9BbTKLTne8Tlit4jW0opOeqAsGAKWK19CKDo06tW2HR4NRdIDk/1KTxMUW6gMAjVqMGW0Q0rzm2mjKEY7ndd/2HCDlHBol560JlJThEL7WazWis6Gdw2UcTESNtF0HV6j4tOB8Kj6TOjUfJ1vUosv1wWQSdf8P/9KupQpjdnWjkvJEOaPsFYlOa6hd0E9LfXZs12FvnQap47nFzn7IyJMtwm6+gH2nSLGPniDDIg32A6cU7bRWq0V4eDgOHDiAPn36ACh0aDxw4ADi4uJkj+nQoQMOHDiAV1991Zq3b98+dOjQwZouGlj/+eefOHjwIKpUqSKpIyMjA8ePH0d4eDgA4Pvvv4fFYkFERITD/Qdo5pogCE+DVv4iCILwbErZTsfHx2PYsGFo27Yt2rdvj+XLlyMnJwcjRowAAAwdOhQ1a9a0arYnTJiAyMhILF26FL169cK2bdtw7NgxrFu3DkDhwLpfv344ceIEvv76a5jNZquOOjAwEFqtFk2bNkX37t0xevRoJCYmwmg0Ii4uDoMGDXIqUghAg2uCIDwNk7lwc6Y8QRAEUXaUsp0eOHAg0tLSMHv2bKSkpCAsLAx79+61Oi0mJydDqfznq0rHjh2xdetWzJw5EzNmzECjRo2wa9cutGjRAgBw/fp1fPnllwCAsLAwrq2DBw+ic+fOAIAtW7YgLi4OXbt2hVKpRN++ffHuu+861XeABtcEQXgazMkZEQrFRxAEUbaUgZ2Oi4srVgZy6NAhSV7//v3Rv39/2fJ169aFI8u6BAYGYuvWrU71Uw43a6756kT9s17QRxtMmUI6w26dOg0fasVk4TXZosZa1OSKC7pIFxWRxiguMPACd50QO1mptF2H2KaIqMEW9dGFddi+VV6C/jnPeIdLi9o8lcq27luplPZZrfLijxFihIt1iIg6cFGrnmdIE9qTxtrWa3hNtBhjXNRpi8+Y+HyYhedH1FibZWKOa9S8L4Fexzs6yMXnJhxHYbFA4YTRdqYs8aAofY11aWuqHdFT21soTbrf9uJe4vtLuoiW1C9FzJP6pQgabEmbD4MGGyAddulCdto2NHNNEIRnYTYXbs6UJwiCIMoOstM2ocE1QRCeBTk0EgRBeDZkp21Cg2uCIDwLWkSGIAjCsyE7bRNa/pwgCM+ijJY/X716NerWrQu9Xo+IiAj8/PPPNstnZGRg3LhxCAkJgU6nwyOPPII9e/a41DZBEES5pozsdHnFvTPXgmOEVh3ApY1m3qFNKTj+WcxSRzCNypdLi4vKiAt6iA4f4mIp+cbbXFotOLiZJAvdAFpNVS4tOqxpwDsginVoVHwfRAfGQF0DLn2n4KKkD6IjTIC2DpcuYLwjnuh8KB4vOs6IzohyC7iIiAvNiMfYc94RF6UREe81AGSbsri0Wng+xPstPmNmZtvZ0CLcO/H4wn7dE9Ke6OBTjjFbnNTyOX+9t2/fjvj4eCQmJiIiIgLLly9HdHQ0zp8/j+rVq0vKGwwGPP3006hevTp27NiBmjVr4sqVKwgICHC67YqBe+dtnHZWlK3EWQdG286IcrZBdGYX7bBayaftOTSKNtZk5u2TyZIn6YNFeD9ZwKclAROES1tyB0c5HoRNtLfQjBueqYpMGdjp8gzJQgiC8CzKQMu3bNkyjB492rogQWJiIr755hts2LAB06ZNk5TfsGED7ty5gyNHjkCjKYykU7duXafbJQiCeCggzbVNSBZCEIRnwdg/ej5HNieXPzcYDDh+/DiioqKseUqlElFRUUhKSpI95ssvv0SHDh0wbtw4BAUFoUWLFliwYAHMFcwDniAIAkCp2+nyDs1cEwThWZhMhZsz5QFkZfGSIZ1OB51OJymenp4Os9lsXemriKCgIJw7d062ib///hvff/89Bg8ejD179uCvv/7Cyy+/DKPRiISEBMf7ShAE8TDgop2uKLh3cC3ox0S9mUHQy4p6ai8t/7IDpJozezo4EVHDJmpyRQ2uuLAJIKMVlyyWw+veNMLiJ6LGWquuxKXvmflFagJ0dSV9yDbe4NJ5Fn7xFJ2SX9gkR6jTR1j4xkvJL8aSYUyWtCnCGD9Lx1TCwjSC3lDUVIuLzojlRf2hqB0EAKVwbUVdtlZY4EXUv4v3X1xwQVxkRnzeAKl6UKrbl2rFCSdw0Qs9NDSUy05ISMCcOXPc0yWLBdWrV8e6deugUqkQHh6O69evY/HixTS4BuARGms3Lwoj2nnRv0d8vwGATsO/P7xVVbi0r4JP6xhfR4GCf5fcY7yPUI6JX2gLMut2GSXvPNFiiQvR2P5cL94L+xpsOTzBL4U01m6FooXYhGauCYLwLJjFuaVy/7/s1atX4ef3zx9XcrPWAFC1alWoVCqkpqZy+ampqQgODpY9JiQkBBqNBiqVyprXtGlTpKSkwGAwQKuVOrcRBEE8tLhopysKpLkmCMKzcEbHd9/siZ+fH7cVN7jWarUIDw/HgQMH/mnSYsGBAwfQoUMH2WMef/xx/PXXX7Dc55Rz4cIFhISE0MCaIIiKh4t2uqJAg2uCIDwLk9n5zUni4+Oxfv16fPTRRzh79ixeeukl5OTkWKOHDB06FNOnT7eWf+mll3Dnzh1MmDABFy5cwDfffIMFCxZg3LhxbjttgiCIckMZ2OnyjFtlIXoNr+PNM/D6MFHDJsZFFvXVcmVEnZyouVULmlxRZmUUYmmLGlypDk+q/dUJ52kvNqlOE8ClVQoNl9YIsU9zzbzODgC0gj5dEnMVKi4t6gFFjbWIXsX3UYx7DQBM0MmJ96bAmMn3SWn7fosxX80m/jrLxZGVxNIW41gL98osxKQWNdSixloaJ136KUt8HkSNo6jjJpzEwpwM8eT8jMjAgQORlpaG2bNnIyUlBWFhYdi7d6/VyTE5ORlK5T+2IDQ0FN9++y0mTpyIli1bombNmpgwYQKmTp3qdNvlH/fPyTitsbarrwZKqrEWbYtoU0V9NQD4qWtw6SALvx5BbRVvh301fB/SC3i7mwy+TYua3y//zuTLMIton8TflnBdJOGhRQ33w6LBJkpEGdjp8gxprgmC8CzKyFEmLi4OcXFxsvsOHTokyevQoQP+97//udQWQRDEQwU5NNqEBtcEQXgWFrNzK39ZKtbnRoIgiAcO2Wmb0OCaIAjPgmZECIIgPBuy0zZx6+A6J+8KlxZjEot6aFEfK+pdAcAkaGb1mmpcWqp3ta0LVip4HZ0Yc1qMxQxI45uKmjajoPsW41znG+7wber5eLwWoc++Kj4mNQAYGN+G2Ae9EOdao5HGYL2fexY+DrZBiOUt6qsBqS7bXnxUUWtuNAltMNtxskU9vRyiLtJoyhBLcCm1oF0XNfcW4XmTQ6cJ5NJiLG21cC8IJ6FldT2MkmusXYpbzVVQ+hprqT+GqLnm3wNaJW9LAMAbvA7bV9BMixprvZq/LsoCPq0QfGnEtFLJ++8A0vO271ckxv4Xfk9ieTsabMAVHTZpsMsdZKdtQjPXBEF4FiZL4eZMeYIgCKLsIDttExpcEwThWdCMCEEQhGdDdtomNLgmCMKzYKxwc6Y8QRAEUXaQnbaJWwfXKkHPKmqVjXZiDitluiPGIWbgPU5F3Zz0eF43p1H7cGkxNrNc7FJR3yzqssX43mI8VDGOtdGSJ9SfxfdBK9XyiRK2Sip+meZcdpdLeyn48zCA1y/7Knldd6blKpcuMGdLupCPDC4tnqd4XSRac0Hfbi8utlycaxEx5rRSeOa8dUFc2mDkz8tksa3rFp9RQOorID6D4n7CSchR5gHzADTWDmmq70da3p6WWLJfYSfOtbAegULB653lKBDsbLqCX7MgN4+PS20RtMW5Cv4dmS0cL/r3WCzS9QhKTsn1zyWPhU0abI+H7LRNaOaaIAjPwuzkal7OhIMiCIIgSg7ZaZvQ4JogCM+CZkQIgiA8G7LTNqHBNUEQnoUFThrtUusJQRAEIQfZaZu4dXBtFvSrYqxkaVxj+82LOjijybZuW4xFKnIvj9cWa9UBXNpsluplRe2dUiX2yXb8ZouS18WJWuVK6hrFd7ioX6zA5n6TsN+s4NvUgteu57B0m/X5aWtJ8gwW/tqL2nHx2ucb+f0mMx8PWkTUbIt66sI2+PPQankdt8HEa6rFe2MPJsSslrMH4jMpxikX48oSzsFMFjAnwjY5U5bwEEqosZbqq+2Xkfj4SOJc2z6eMf6ztpy/xj3BJyhfwfvTSOoULIxZ8O8RY/2LNlfOv8Pe+gNSnNQ324l7TVQMyE7bhmauCYLwLMgLnSAIwrMhO20TGlwTBOFZkJaPIAjCsyE7bRMaXBME4VmQ0SYIgvBsyE7bpFTjXKuFmMOidtlg4uMcy+nwLIIGVq+pxqVzC5KFNvi4xqLGTSVodi2M1/mqFbweGpDGX1YpdZIy9yPGc5a2Yft4Oc1ugCqUS4uaaVGTfdvwF5f21/DHeyn42NxmQRduYlJ9dK6Bb1M8T/Fai/tF/bOoPRcxmrMkeRbGx74WNdYmIZa6UiHGOeefUYtJOE+FeK+lGn6TKYMvIzz3ZqEPhJPQsrplzAOIa20Xexpr+3GuRTtqNy3RWAu+M4IdF/XPAGBivAbaANs+H1K/JF6zLbYp8Uth0jjXYr/FNsTzluyXXAexBeH3Jqd/l/ihUNzrhw6y0zahmWuCIDwKxhiYE7McrIJp+QiCIB40ZKdtQ4NrgiA8C/rcSBAE4dmQnbYJDa4JgvAs6HMjQRCEZ0N22iZuHVyLcYlNkv0ZXFoSY1omZqdG5celRZ22XhviVB9FHbioLysQdeAAvLRVuLTJzGvtRG2xUtCHaVUBXFqv5M8pz3KX76OMJjvbcotL+ymDuXSO8ja/X8XHzi5gvA5YBV7/rlbw18UoE8NVp/Hn0mYLr/PWKHkNtaj9E7XrBca7wn5eH13Jq46kD3kG/jzt6du1KkH3beY1kGLsW0f0p1pNVS5tFvwCxP2Ek5RRiKfVq1dj8eLFSElJQatWrbBy5Uq0b99etuzGjRsxYsQILk+n0yE/33bs9ocRl/TVduNaO6exlotzLfFVsRO3WqrRVtnpI49oawBA4WTMZ3trQUh130ab++XqdF5jXUINNmA3FjZpsB8CKBSfTWi1C4IgPApmcX5zlu3btyM+Ph4JCQk4ceIEWrVqhejoaNy6davYY/z8/HDz5k3rduXKlRKcJUEQRPmlLOx0eYYG1wRBeBZFWj5nNidZtmwZRo8ejREjRqBZs2ZITEyEt7c3NmzYUOwxCoUCwcHB1i0oKKjYsgRBEA81ZWCnyzM0uCYIwqNgJub05gwGgwHHjx9HVFSUNU+pVCIqKgpJSUnFHnfv3j3UqVMHoaGh6N27N37//XeXz5EgCKI8U9p2GiiU7tWtWxd6vR4RERH4+eefbZb/7LPP0KRJE+j1ejz66KPYs2cPt//zzz9Ht27dUKVKFSgUCpw6dUpSR+fOnaFQKLht7NixTvedBtcEQXgWzMnZkP/X8mVlZXFbQUGBbPXp6ekwm82SmeegoCCkpKTIHtO4cWNs2LABu3fvxubNm2GxWNCxY0dcu3bNvedOEARRHnDRTjuKs9K9I0eOICYmBqNGjcLJkyfRp08f9OnTB2fOnLGWycnJwRNPPIG33nrLZtujR4/mJIBvv/22U30H3OzQyIQA+loV7wiYLzh+iY4Sei2/sAkAGIz8IiFeOn4RGXFBl3v5N7i06MCo1wZy6Zx84WUqIwwSHULsLX6iU/GOf3oF78CYYxGcD5W8U6YZ0oUBfJT8tfRiPlzaoJA6IHLlFXyfRKdLjbB4jkrJOzzK9UtceCbPxJ+XeH/FRYTEe2k2889PgVHqXGpvUQWNsEhQvjGNS4tOtGoV/3yITrUOOQwJTpHifsJJLHDOP+n/y4aG8gslJSQkYM6cOW7pUocOHdChQwdrumPHjmjatCnee+89zJs3zy1tlB1lMKfipAOj09XLHS+xN/zv0t6iMfbSDvWhlBGdLuVsjT0HRnvOhs47C8pdB+dsYMkdHIkyx0U77Sj3S/cAIDExEd988w02bNiAadOmScqvWLEC3bt3x5QpUwAA8+bNw759+7Bq1SokJiYCAF588UUAwOXLl2227e3tjeDgYJtl7EEz1wRBeBSufm68evUqMjMzrdv06dNl669atSpUKhVSU1O5/NTUVIcNqkajQevWrfHXX3/ZL0wQBPGQUZqyEFeke0lJSVx5AIiOjrYp9SuOLVu2oGrVqmjRogWmT5+O3Fzbk5dyUJxrgiA8C/b/mzPlURjNw8/Pz3ZZAFqtFuHh4Thw4AD69OkDALBYLDhw4ADi4uIcatJsNuP06dPo2bOnEx0lCIJ4SHDRTmdlZXHZOp0OOh2vQLAl3Tt37pxs9SkpKU5J/YrjhRdeQJ06dVCjRg389ttvmDp1Ks6fP4/PP//cqXpocE0QhEfBLE4uq+uCF3p8fDyGDRuGtm3bon379li+fDlycnKsnyCHDh2KmjVrYuHChQCAuXPn4rHHHkPDhg2RkZGBxYsX48qVK/j3v//tdNsEQRDlHVftdGnK99xBbGys9d+PPvooQkJC0LVrV1y8eBENGjRwuB63Dq6Vgr7ZYOb10qI21WjiFzYxmqVT76JuTlzARdRk6zW8blvU5BYYM7i0r55fbEUlWVREilnQ/Qao+YdFD18uncGuC23w2mN/xuup9Uyq6c5X8OetYLyix0fBn7cX47XH9xS8flkpaPc04O+dRWGW9EEDvl9G8H26Z7nJ99GOhlGyYAL4NuUWFRI19JJnTFzAR9BYi20azfwzqFTwC9nIIVk8QnIepLYqCcwEMCfW85BZy8MuAwcORFpaGmbPno2UlBSEhYVh79691pmP5ORkKJX/3Me7d+9i9OjRSElJQeXKlREeHo4jR46gWbNmzjdeznBp0Rhn23B20RgHFpER0+IiVqIPiLhferzG5n457Ns8cdEY3gaKtkbiAyKjY7UIvjGSfkrGQ+K1FYoL5RWS/Q6Iae3qvEtKyXXfhHO4aqevXr3KfWEUZ60B16R7wcHBJZL6FUdERAQA4K+//npwg2uCIIgSU8qOMkXExcUVKwM5dOgQl37nnXfwzjvvuNYQQRDEw4aLdtoR+Z4r0r0OHTrgwIEDePXVV615+/bt4xzRXaEoXF9IiJOrgZeoVYIgCDfj7GpeFW3lL4IgiAdNadtpZ6V7EyZMQGRkJJYuXYpevXph27ZtOHbsGNatW2et886dO0hOTsaNG4VR5c6fPw8A1oXBLl68iK1bt6Jnz56oUqUKfvvtN0ycOBFPPvkkWrZs6VT/aXBNEIRnweDcjAhF7SIIgihbStlOOyvd69ixI7Zu3YqZM2dixowZaNSoEXbt2oUWLVpYy3z55ZfWwTkADBo0CMA/um+tVov9+/dbB/KhoaHo27cvZs6c6VznASgYczKytw10Wl6/bBL0rColrwOWaHJldFOiDk6MMS3GRhbRaipxaYuF16OJmjedii9fmMfHiDZYBK24hdceB2t4HWY242Mt+yt4DVCIhf/ckK7g40UDgC/j+xUk9DNd0KurJfpDXihXAF7Ld0eZJuyX6t8LGH/eorZPJNvAa7DFmOQi+YY7XNokxEUHpJp68RnKK+DPQy3EvZbEyRbS4jNotkivg+g7YC+urMnEnxchT1ZWFvz9/XFj1AD4ae1r363HGQyo8cGnyMzMdChaSEVHobD9O5SUd0Rz7WRca2c11tIY1dJ5IdE/x1mNtWifxPpEXxlxf2E/VUIZvt8WO34moj0yM2FtAQu/MJLE/wPSd6JFqMOejpuJ+yW6cHFEJbMWgANl+ANs73dP3Gvb8bwZk190iuAhO+0YNHNNEIRnUUaaa4IgCMJFyE7bhAbXBEF4FKS5JgiC8GzITtuGBtcEQXgUzKwAMzse/s2ZsgRBEETJITttG7cOrs0yGlmuMRUfo9hk5stLdXiAwcSv5iPq5kQ9mUbtI7TB66G1al6rnG+4y6XzLHwaACzC9wy1oFkMVbfm0t4WPs61XsGnfYX9oj66pa6WpA9G4a8+USrfRM9rmHJM/AFZRl5Hlyto9ypZAri0XLxvUXOYw3htuI+Cj9dtUPEabTE+uKjlE7WAOjWvdQekcctF3aR9faBYH/8MalT8dVSo+HslV6dGxT9zRnOOzTYJ29CMCCHBTsx8uTxnNdYaJe/PoxLsvFbB+2+I7wEAUMF2LGym4B9Ws+C3YhJ0vybw9skorHdgskh1wlK/EeFaSUJli3ZZuNaC3Fm0sY4h3i/n6hB1/+7RYBMlgey0bWjmmiAIj4IxBRhzYkbEibIEQRBEySE7bRsaXBME4VHQjAhBEIRnQ3baNjS4JgjCo7BYFLA4oc+zWCrWjAhBEMSDhuy0bdw6uBa1XmpBv2oUYjGbhTjYWnWgpE6Vktdpi3E+Rf1rgTFTOJ6Pw2gQwoLqNLyu10sdIOmDRtDaieTDtsa2Bgvi0vV8eW1fvpnXj2mU0oewsT+fJxyCXOG86lXi463mm/n0pWxBP23i462aLFK9820h5vMFdpXvk3C/tYJeWYzZKsYHVwuaRzlEXaWoqRefQVFXKcaVVSoC+P3C82QUNP8AoBJiZ5uE83BNk0hYsSjAnDHEFcxolzYOxbW2i72410Kb9uJa20nL1eGsxlqr5O2VTvCV8QJv37yY9L2gY3wbKiHutUXQChsFzXW+grexeQr+3ZKv5O1RgSJb0gejnZjj9vxQRERNtkRHLiN/VgiPkN02xT5XtGnO8gjZaZvQzDVBEB4FY/IvbFvlCYIgiLKD7LRtaHBNEIRHYTErYVE6PvNpMTs3S0oQBEGUDLLTtqHBNUEQHgXNiBAEQXg2ZKdt49bBtT2tqajVUgp6akeQxELWSLXBXHkxLrIQv1nUcIs6YAAwQYgJrQrm0lUtIVzaD7yWTy3oydTCH3BBWj6jvq/0OgZo+PPwVfPaYa2SP8YgxDa9VcBrrCFoAdPzeT1UtlH6S7AY+PMKYU24dJ6gPdcKmsU8xscQFzWPFuEcxXsjh1jGS1eN3282COX5tKgFFJ8PUV9NlD4U4snDsaPplT1EcoydOuzEtZaLcy2Jay38ltUK3v9G1FjrFbymuhLj4/YHMH5/gEr6/vJR83ZVr+KfTYtgVvME55lsI78OQ6YQhz9TwdvMbKX9V7ho4yTvaf5VIikv1bcLaZmfn31dd8niXrtGxZo5LW3ITtuGZq4JgvAozGYFzDJOvbbKEwRBEGUH2Wnb0OCaIAiPgmZECIIgPBuy07ahwTVBEB4FGW2CIAjPhuy0bdw6uNZreI1agZHX2NrTZJuZQZIn6uZE/ZeoqbUIsZS9tFVt9kGr9OHSaiEWKgAYhHjcGvBlKiv4Oiqp+T5X8+J1ePV4qR981XyfmlbiY50CQJ1APn63tzd/3j7B/HkrNPyDfPFUZb4+b167dyKD1xbfypfq08yMP68qBXxc8juCjlvU4hkVvJ7dYOGvqyOxbEXNvaihVln4eyNqM6Vxz/m4seLzppHRXIvx2i1CH1zRpBL/YGEKWJwwxM6UvZ/Vq1dj8eLFSElJQatWrbBy5Uq0b9/e7nHbtm1DTEwMevfujV27drnUNlEy5DTXkjjXggZbEuda0C/7sgAuXUVIB+v48jV9BHsHIMSL11AHaGy/8+4Y+D7fzON13Ddy+HNIMUjbFGGC/409zbW4X3yHSuJai3ZZVl8tauSFQ0oY91qMxc5QwbzlPICystPlFRoFEAThUVjMCqc3Z9m+fTvi4+ORkJCAEydOoFWrVoiOjsatW7dsHnf58mVMnjwZnTp1cvX0CIIgyj1lYafLMzS4JgjCo7BAYZ0VcWhzYUXBZcuWYfTo0RgxYgSaNWuGxMREeHt7Y8OGDcUeYzabMXjwYLzxxhuoX79+SU6RIAiiXFMWdro8Q4NrgiA8iiItnzObMxgMBhw/fhxRUVHWPKVSiaioKCQlJRV73Ny5c1G9enWMGjXK5XMjCIJ4GChtO13ecavmOt94m0uLWlQvbZDN8hqZuKFqFa9zE+MaSzS1Cl5XZy9WskIlxKCGtA9+6mBJ3v3kCxq1SsJl9VHzD1V1HR9YtFElXnvcunOapA1NU147Dv/qXNLSugWXVl64yKWb1L3C9/Fb/rrcMfDaPqOF1yYDwG0hFrZW0CeLcWDzFfx5MSGgqo+Kj0mdp+A1+nkOxLkWNdImM6/rFjXaauEZUwqxb00WXk+tVfLnBEifa62aLyPquAnnsFiUMFsc/7vf8v9ls7L4667T6aDTSX0o0tPTYTabERTE26OgoCCcO3dOto3Dhw/jgw8+wKlTpxzuF+E4cv4VtvYrRP8O2I+FrVbwv309eOeXSoxfM6Gqhi9frxLfZliA1EfosZqpXLrao8L7SvCFyfid7+OxK/yaCcfu8n1QZfE+IJZ83s8JAMwKwS9Fyb+fRE21RcGnRa2603GvYd+/iij/uGqnKwoV62wJgvB4LC5sABAaGgp/f3/rtnDhQrf0Jzs7Gy+++CLWr1+PqlWr2j+AIAjiIcdVO11RoFB8BEF4FK6GeLp69Sr8/P75iiA3aw0AVatWhUqlQmoqP8uYmpqK4GDpV6qLFy/i8uXLePbZZ615Fkvhq0KtVuP8+fNo0KCBw/0lCIIo71AoPtvQ4JogCI/CwpwL21S0pLSfnx83uC4OrVaL8PBwHDhwAH369Cmsw2LBgQMHEBcXJynfpEkTnD59msubOXMmsrOzsWLFCoSGhjrcV4IgiIcBV+10RYEG1wRBeBRmiwJmixPL6jpRtoj4+HgMGzYMbdu2Rfv27bF8+XLk5ORgxIgRAIChQ4eiZs2aWLhwIfR6PVq04H0aAgICAECSTxAEUREoCztdnnHr4Fp09FIJC3AUmHiHNR8977xhNOVI6sw38E6Pei3vwCE6LIrOGKJDm6+Wb1MpOL2ICwsAgEVQC4mLyNxW3OHSjfW1JXXcTxUt70Dy6KP852lN65rSPjz1OJdmvrZn6MxVeWdBZTCfDrn7E5duncRfZ6VC6igjOv+ZGX8dMoQFDpIZ71zoq+CdMDMt17m0wczff6WSv5cAwCzCvVDzC/hInG+E+ys6PJqEBYLEZ9Zi4Z+fwn7ZWYhGKXUGJRynLD43Dhw4EGlpaZg9ezZSUlIQFhaGvXv3Wp0ck5OToVSSS8qDwp6DoyPHqMRFZMCnNYL98gH/uw3U8/asgS/vkP1Us2RJH/xW9OXSzM9fUuZ+Kgvpbu9u5tK6z3iNf76Zt085Run7KscsOJYreRtnVPJO20ph8TaF8B6XLNjjwAykeIzdRWMk97uiKXTLHyQLsQ3NXBME4VFY4FxMVFfjp8bFxcnKQADg0KFDNo/duHGjS20SBEE8DJSVnS6v0OCaIAiPgj43EgRBeDZkp21Dg2uCIDwK5uSMCKtgMyIEQRAPGrLTtnHr4FrUq0LQWYmaXYMxm0uLC3gAUh2dqIHVaQK4tL1FY8Q2FApeV6dWSheR0Sn489IxXudWT1gMxSzIxRr48hmhvrwGTkLVAEmWPY21BEEvamnYiEurO6Zw6aAb57l0cDavZQaAP7L4+5dv5sV3dwQ9c23w4cn+VvzBpTXCdc2z8Np1Ocxm6cIN9yMuVGMy5XNpcdEZlXC/xedNqZT5iVjEJN+GSkGa65LAWOHmTHmCsIdkwTHGa7DVwv5KwoIvtb35d0vA87z/DgCY7Wis7cFeGcKl259/n0tfPNKQS6fmSRfTuZPLv5+ywafzBS26ZIEeO4vxSPTUcvp4icZarEMobleTbRuFzMCNOSIOJ1yG7LRtaOaaIAiPwsyUMDPHHdqcKUsQBEGUHLLTtqHBNUEQHkVh/FTnyhMEQRBlB9lp29DgmiAIj8LCFE4uTlCxtHwEQRAPGrLTtnHv4FrUWAvxfsWY02YxvqZC2h17+lW52Nj3o1VX4utT8rFNC8y87ruaite0AUAuMrm0RoiHqhP0zVpBBif+wWYw8wV09XndLwuVxrl2N5am/Hnqm/AxW/XneO0yAAgSa3ip+B9LoIrX9l1gV/gDhONzTGl8m+oALi3GLAeAXPDHmIWYrGJcdLXgByA+c146Xi+fk39TqJ/XUwMy2k0l34ac7wDhOAwKp5xfKpqjTEWAlUKc45LqejUK4fiQ6vIF3Yj343yc66AT/BoJPhr+fQYAOuG1Lsb3Vgppe5pqj0ASa5viYD9oyE7bhmauCYLwKMxMAbMTsxzOlCUIgiBKDtlp29DgmiAIj4K0fARBEJ4N2Wnb0OCaIAiPgrR8BEEQng3Zadu4dXAtaqotZj6tVfOxmsXyjmjiLIzXnIk6XrEOMe61UcVrtH21fKzSLHZL0qZO4culayGIS2cY+T75Cjq4eyb+odKphVjMaXwf1ZUDJH0obXJP89ri6zn29YRKO7+Vqha+jlwlr12vrKnDpe+ZpdfeHqI+0EfP388CI9+mt5bXWOcWpHJp8RmVw2DK4NJM8BVgwnNNOIcFzn1urGjL6j6MiBprMfayuF+MZy9bhvFlLMIxRgVvd00W/vhcE/9yuVUg+BAd5dcGAAC0ayvNKwkFRpu75Z58hRBEWmknBJpSLk4114ZKSHugJpsoc8rCTq9evRqLFy9GSkoKWrVqhZUrV6J9+/bFlv/ss88wa9YsXL58GY0aNcJbb72Fnj17Wvd//vnnSExMxPHjx3Hnzh2cPHkSYWFhXB35+fmYNGkStm3bhoKCAkRHR2PNmjUICgqCM9CvhCAIj8LiwkYQBEGUHaVtp7dv3474+HgkJCTgxIkTaNWqFaKjo3Hrlvwk3JEjRxATE4NRo0bh5MmT6NOnD/r06YMzZ85Yy+Tk5OCJJ57AW2+9VWy7EydOxFdffYXPPvsM//3vf3Hjxg3861//crL3NLgmCMLDYEzh9EYQBEGUHaVtp5ctW4bRo0djxIgRaNasGRITE+Ht7Y0NGzbIll+xYgW6d++OKVOmoGnTppg3bx7atGmDVatWWcu8+OKLmD17NqKiomTryMzMxAcffIBly5bhqaeeQnh4OD788EMcOXIE//vf/5zqPw2uCYLwKGjmmiAIwrMpTTttMBhw/PhxbhCsVCoRFRWFpKQk2WOSkpIkg+bo6Ohiy8tx/PhxGI1Grp4mTZqgdu3aTtUDlLJDo0rJx282mfn4v2IcbJVSGtNajGMsxr02CXGIlYL+VdSHqYVYzGbG6+4qKaVaYzN43dstxut4g5UBXNpHI2islbx271wGr+v1+4M/h5o7v5P0AX27cUnma18bfD+KdD4+NNu0j0vfSeFjNetVUk2jj5o/jwyDoGEUNIu3lelc2iRcazHOtYhC0EQCgMnMXyuVin8e8g13ubRG5cOlRQ22+IxKnjeZZ1LU9YvPpJlRnOuSQCGeHixMcGRRiFpJOd8YD4iNLPG3EXwfRPsjaq5zwP/27xbwvjN/5/C/8z+/kcaYbtzhBN+H8DY2emyfzEP3uHRaAe8zkmeShmAwClpzs9K2b5PFzrBH1LeXRgxyovzhqp3Oysri8nU6HXQ6/reUnp4Os9ks0TkHBQXh3LlzsvWnpKTIlk9JSXG4jykpKdBqtQgICChRPQDNXBME4WEUhXhyZiMIgiDKDlftdGhoKPz9/a3bwoULH+yJlBIUio8gCI+CVv4iCILwbFy101evXoWf3z9f3sVZawCoWrUqVCoVUlP5aF6pqakIDg6WrT84ONip8sXVYTAYkJGRwc1eO1sPQDPXBEF4GCaL8xtBEARRdrhqp/38/LhNbnCt1WoRHh6OAwcOWPMsFgsOHDiADh06yPanQ4cOXHkA2LdvX7Hl5QgPD4dGo+HqOX/+PJKTk52qByjjmWtRYy3GuZaLn6lW8ZpYpULDpbXqSlxa1MyK+jKTOY9L61UBXDrDfFXSBy9lZb6MktcKGxivA0YW/xeOSsE/PEbGn0ONu7x+WvVVhqQPwWr+oVEG831ij9TnDxBijLM9vKerMYW/Tndy/Ll0WoFUa3yelytDp+TvV7LpDpfWgD9vtXAddCr+vPNMt7m0xSKNFy3GtRafGZ2GPw+RAhOvyVYrea25qNk3mnnNIwBo1QE2y4j7Cecoq5lrZ2Kofv7551iwYAH++usvGI1GNGrUCJMmTcKLL77oUtsVDdEOKyS3TLD9QnlpDGvpX1QSLbGwJoLoX1MA/nebreAN3G2DoLnO5t9F39/k9c8AYJp+mUs3fvJXLq1uXZPv851sLp25n9ej/nShFpe+nMNfp0yD1EbmC9pxI/jzlmioHVhfgiBESttOx8fHY9iwYWjbti3at2+P5cuXIycnByNGjAAADB06FDVr1rTKSiZMmIDIyEgsXboUvXr1wrZt23Ds2DGsW7fOWuedO3eQnJyMGzduACgcOAOFM9bBwcHw9/fHqFGjEB8fj8DAQPj5+WH8+PHo0KEDHnvsMaf6T7IQgiA8Cuakjpq5oLkuiqGamJiIiIgILF++HNHR0Th//jyqV5c6NQcGBuL1119HkyZNoNVq8fXXX2PEiBGoXr06oqOjne8AQRBEOaa07fTAgQORlpaG2bNnIyUlBWFhYdi7d6/VaTE5ORnK+yb4OnbsiK1bt2LmzJmYMWMGGjVqhF27dqFFixbWMl9++aV1cA4AgwYNAgAkJCRgzpw5AIB33nkHSqUSffv25RaRcRYaXBME4VGURbSQ+2OoAkBiYiK++eYbbNiwAdOmTZOU79y5M5eeMGECPvroIxw+fJgG1wRBVDjKwk7HxcUhLi5Odt+hQ4ckef3790f//v2LrW/48OEYPny4zTb1ej1Wr16N1atXO9NVCaS5JgjCoyjtONeuxFC9H8YYDhw4gPPnz+PJJ590snWCIIjyD61HYBu3zlyLelgxRrCcfvV+mELaHTFO8b3861zaV89r2ESdnRjXWi/oYU0WIfa2oOkGAF9FFS4tatiqs6pcOoVlcOmAPH6/UqHi0uey+HOspOU1cwBw5yP+m8ojERe5tOLoZS6tqunL92k/f/yl2yFc+mouf52O3pbeC52KryMtXxoL+36yFbyGOtN4jUuL98pg4vWHop4eABSCzluMW61U8v1WKXndpEJ4xsT7L2qwxfKF/cwQ+hlgcz/hHM6G1ysq60j8VMC1GKpA4epdNWvWREFBAVQqFdasWYOnn37a8Y5WKMRXqe15HKkmmy8vaq5Ffx0AUDClUEb8rQuaawX/Pron2Ip0JqzDkMP7uZiZ9Nm6a+BtfegX/DGB3/D9NjPeRyS1gH/XXBE01ldzeJubbhT8fQDcU/B21ADexonXwZ6enTTZhByu2umKAs1cEwThUTAXNqD046dWqlQJp06dwi+//II333wT8fHxsp8mCYIgHnZctdMVBdJcEwThUZiZc/o88/9bbUfipwKuxVAFCqUjDRs2BACEhYXh7NmzWLhwoUSPTRAE8bDjqp2uKNDMNUEQHoWrK385Ej8VcC2Gqmw/LRYUFBTYL0gQBPGQQSvp2satM9eiXlWio4NtHZ1RRqsq6rbFWNmSPggaa7XSq5iShWTmJXNpX30NSZkMJa/zVoLXZV9UXuDSPiyAS6cV8Bpqs0UrpHkN9jVBow0ALf35On75htfyNfbjdXVp+fzAopKG1/pdEGK23jEI2r57vB4aALLNfB/uKniNa66S1zAqwZ+XVtDPF5h5baBOE8ClLRZpH0xmXmMoaqxFzIK+UHwGRe2mmfHnqFHxzzQAmAXfAfGYiue64V6cdX5x5Wo7G0N14cKFaNu2LRo0aICCggLs2bMHmzZtwtq1a11o/SFE1OUqSjZvI74bFMz2fkD6vjEL9kr87ZsUvM3ME/os+sZYhFhi+Tl8nH4AyCjg7eolLV+nl9r2+yvPxLeRYeBtYIaJt2eiDQaAe4oMLm1kguZaiPct2llxfQG7Guyy0GST7tvjKAs7XZ4hWQhBEB6FxckQTxYXQjw5G0M1JycHL7/8Mq5duwYvLy80adIEmzdvxsCBA51umyAIorxTFna6PEODa4IgPArGnFtwwJVFZADnYqjOnz8f8+fPd60hgiCIh4yystPlFRpcEwThUdDnRoIgCM+G7LRtaHBNEIRHQfFTCYIgPBuy07Yp1cG1NPA/72CiEhwgvb34RR0AIK8gTThGL6R5xz2jOYdLmyx5Qh945w29lncMFMsDgJnxjnhGJtSpFBauUfB9yhbqZAb+KVOI5Y1SZ6BKGt6JUnxQf0rnF1wxCH8m5pt5R5pswVcwNY9fnMAiE5UyV1g8RycsolAA3tkwV8Ev8CJ1jOHbFBf4ybWkS/ogLi4hWXhGxV8Huft5P0qFbQcjs0W6oI9SeAaZ4BQp7iecozDEk3PlidKDCbZAAVe0k+Jvn9+rEKqUfEKWNCmdB5NbWOZ+zBbedhjtOV3y/owwK3ijWaCQ2pYsE794l5eJtwUaoVKlcGJG8DYxT7C5ucLCN7kyDo35jM8zCjZQdPIWrxsT+iBZZMaBOUjpwjOluzCN+IwSpQ/ZadvQzDVBEB4FafkIgiA8G7LTtqHBNUEQHgVp+QiCIDwbstO2ocE1QRAeBX1uJAiC8GzITtumVAfXYtB+UWOtVPDNi/pqAFALi3iIGticghQurdfwGuoCI6/79dZV59KShQdkNLb5wuI2Ado6fBsWYVERQZt3TfkXl65u4Y+3FPBPncog1QKm5vFaPZUgUqyq569ljok/L281X2eusN9o4dM5ZukCLgbweTcU/OI5egW/qIK4iIxa0MerldW4dL6Z1wqKixkAgMnM6we9tFW4tHi/xUWERD2huCiN1E/Ato4TsL84EuEc5ChT2ojPZyks1FviRWXs9FFGs2uB7d+qggmLyPCmQPIusAh9MAu+NQZhERoAyBPecWrwNk8pnIdoI02CjTULadHfx2CR9sFkEReNERcx49OinbW3iIx47d1j78hmljfITtuGZq4JgvAoGBRgTjjNOVOWIAiCKDlkp21Dg2uCIDwKBudmOSrYhAhBEMQDh+y0bWhwTRCER0FaPoIgCM+G7LRt3Dq4FrVeYgxhs0XQtyr5/QqF/e6IsbKVsB2nWIyLLMbBFnXfXoJmGwAKzHws5TsFF7m0j4aPz51t5nXgWiUf+zRDyWvLjULsbR/G6/YAQAU+75aFP4/MHF7bJ8b9FHXdYrzVDPC68TTlVUkf9ODPQ8X42Nv3LLckx9xPjoHfr1Hx8cNF7Z4YwxqQxiU3mvjrIGqsxbjm4vMgSYv6aRltp1rFXweDideKa9W89pxwDtLyeTZyMYWdj31tL+61nd+hTHMKoQ7RnohaY6awrfO1539hlIlzbRDiUCsVGiHtnPbcLNgvsQ8mIWZ1YRlBt21PYy2UdzautXzM6tKNa008eMhO24ZmrgmC8CjY///nTHmCIAii7CA7bRsaXBME4VHQ50aCIAjPhuy0bWhwTRCER0GfGwmCIDwbstO2cevg2hFd3P1o7MSwBqSaNbUQR1TU5uUb7wpd4PebBY2aqLvLETTZAKBW8TpevTqASxcI8Zk1gu5XjGWaLWiTcxV8n3OVfOxmALglaNjE885lvH5Zy/RcWjzPW7jCpX0VfJtyOrt77DaXtggxWEVtulrQ3PtqQ7h0Rt7ffJvC86NVV5L0QYxjLWoQNSrbmmpRbyg+HyLi8YBUY62xo8EmnIOW1S1rPCHutT0NNmzuLywktinGveZfd5K42GI4ZzHutWA7lErp69Os4N8vCgVv++3ZGxGJ/pnxwbnl4vDb01RL4ljbi+3/IOJak0bb4yE7bRuauSYIwqMwMUDlhCE2VTCjTRAE8aAhO20bGlwTBOFZODkjUsH8ZAiCIB48ZKdtQoNrgiA8CgucWwyZPiATBEGULWSnbePWwbVa0FCL2i2VktcBG01CTFClNGZ1gYnXI4uxs8W/hpioFxO0fRolr00W4ySrlHy8aAAwGHktsRif2WTh452qhNjZmUY+ZrRW0OiaGa/T0wn7ASCf8VpjMf5pgdKfr0PB16EBf14WQbt30/g7l/ZWS3XfJsbHKRc11hKtuXAdsvKvCvt5Pb2o4ZbTNILvNtTCMyXRVNuJa20y87HZReRir4saazF+u7ifcI6y0vKtXr0aixcvRkpKClq1aoWVK1eiffv2smXXr1+Pjz/+GGfOnAEAhIeHY8GCBcWWr2iIYbacj3ttp34m+pzIlREy7GiwmZ2Y9qKfisR/Q7DBhU0KGms79scekj5J9M+CQXTgGPG9LNFQ29FYS2NWS4dNpR3XuqKFdfNESHNtm1LwXCEIgnAdM2NOb86yfft2xMfHIyEhASdOnECrVq0QHR2NW7fkF0I6dOgQYmJicPDgQSQlJSE0NBTdunXD9evXS3q6BEEQ5Y6ysNPlGRpcEwThURSFeHJmc5Zly5Zh9OjRGDFiBJo1a4bExER4e3tjw4YNsuW3bNmCl19+GWFhYWjSpAnef/99WCwWHDhwoIRnSxAEUf4oCztdnqHBNUEQHgVzYXMGg8GA48ePIyoqypqnVCoRFRWFpKQkh+rIzc2F0WhEYGCgk60TBEGUf0rbTpd3SjXOtah/tgj7RY21nP5V1GmLsY9NZl7vbGJ8rGy1wjlNrkbN68YBwGTg2xBjLYt15AmxtsU+i1pkUQOXZZJ+arYXLzXHxH/Ovmu+xKVFLbmoj1YL+0VNNyDVWIsYzLyGXtJne3pD4deXZ7gtKaLXVObbNPF9sgix0kW9oKjZFzXVJiFmuVYtHTyZmTQeuzP7CduYLM791W/6/1uclcXfO51OB51O6kORnp4Os9mMoKAgLj8oKAjnzp1zqM2pU6eiRo0a3AD94aHkca/tarCdjHst9kFO02s3FrZEg227DbO4tgCzr58W7bKzGmt7OKJltqeRdrfG2jF9tSfEtS6DeO4VCFftdEWBni6CIDyKwlkOZ/4rJDQ0FP7+/tZt4cKFpdK/RYsWYdu2bfjiiy+g1+vtH0AQBPGQ4aqdrihQKD6CIDwKV5fVvXr1Kvz8/Kz5crPWAFC1alWoVCqkpqZy+ampqQgODrbZ1pIlS7Bo0SLs378fLVu2dLyTBEEQDxG0/LltaOaaIAiPoijEkzMbAPj5+XFbcYNrrVaL8PBwzhmxyDmxQ4cOxfbr7bffxrx587B37160bdvWredMEARRnnDVTlcUSnXmWtSfWYQ4yWB883L6NFH/JWpsRY2sjz6US4txrA1ibGY7sbkBQKfhY0ibzc5pas0WPo51vqBn1qsDuHSeiT8nANAKsbWNjNeB61SCrlvUnguaavG6GoVY3SbwfQYAvYrvZ2b+Zb4NFa/jNpt4Db0YF1aMcy2mFZBe53xBzy7q10UNtSizFMsbBZ24t64mlxY1/YA0trYkXreMXp1wHIuTYZssLljt+Ph4DBs2DG3btkX79u2xfPly5OTkYMSIEQCAoUOHombNmlZpyVtvvYXZs2dj69atqFu3LlJSUgAAvr6+8PV92OOae74GG7AfC1uqDRbqEMorxDUURK2xTB/Ed569b+HiO8/Z+NAS/bRsITuaa7G4B2qsK56owPMpCztdniFZCEEQHkVZrPw1cOBApKWlYfbs2UhJSUFYWBj27t1rdXJMTk6GUvnPwGft2rUwGAzo168fV09CQgLmzJnjQg8IgiDKL7RCo21ocE0QhEfBGANzYpbDmbL3ExcXh7i4ONl9hw4d4tKXL192qQ2CIIiHkbKy0+UV0lwTBOFRmBhzeiMIgiDKjrKw06tXr0bdunWh1+sRERGBn3/+2Wb5zz77DE2aNIFer8ejjz6KPXv2cPsZY5g9ezZCQkLg5eWFqKgo/Pnnn1yZunXrQqFQcNuiRYuc7rtbZ65Ngn5VreK1iCohLerTNCppWCsxLrWoFxPjEIsxqCUIWi+jEFtbbA+Qxi0WddqiLlctxJAWddxmc4HN/XKIMaOZxcylM3L5uNb+XnW49L2CFC4t6shFbXq+US7GdBUuLWqsxTq8ddW5dHbeNS5tAq/B1wq6cY2gMwekemYxNrpauDdmIe61ycKXFzWPoqZfJcRil2tTfCYlukvCKRicc36hofVDiNMabJkq7OqdbZeX/K7FPsjqhp377ZfG34Ul1VTbLy/ivO7bbnG3/Krd0G+iWErbTm/fvh3x8fFITExEREQEli9fjujoaJw/fx7Vq1eXlD9y5AhiYmKwcOFCPPPMM9i6dSv69OmDEydOoEWLFgAKndLfffddfPTRR6hXrx5mzZqF6Oho/PHHH1xY1blz52L06NHWdKVKlSTt2YNGAQRBeBQWMKc3giAIouwobTu9bNkyjB49GiNGjECzZs2QmJgIb29vbNiwQbb8ihUr0L17d0yZMgVNmzbFvHnz0KZNG6xatQpA4az18uXLMXPmTPTu3RstW7bExx9/jBs3bmDXrl1cXZUqVUJwcLB18/GRTvTZgwbXBEF4FOb/90J3ZiMIgiDKDlftdFZWFrcVFEgjkxkMBhw/fpxbAVepVCIqKgpJSUmy/UlKSpKsmBsdHW0tf+nSJaSkpHBl/P39ERERIalz0aJFqFKlClq3bo3FixfDZLKvLhAhh0aCIDwKC3NulqOihXgiCIJ40Lhqp0ND+XDJchGX0tPTYTabrdGbiggKCsK5c+dk609JSZEtXxQ2tej/tsoAwCuvvII2bdogMDAQR44cwfTp03Hz5k0sW7bMwTMthAbXBEF4FM4ulksxcAmCIMoWV+20oyvpPiji4+Ot/27ZsiW0Wi3GjBmDhQsXOtVXNw+uxWDztp0RRWczs+BsBgAqpeigxjvBKUWHM8lCAvwp6jSVheOFRUUEpzwAUNq5TPacA00m3uHRS8s7BprM/DnJOZDkmG9xaa2GF9hLnAuFRWFER0CVZPEVXiGkU/PXCZA694n9FsnKS+bS4gIu4u9S7EOeIU1Sp3i/7d1PQ34G36RwbVVKqRPt/YjOi4X95Nuw2HsmCadgcM7ViIbWZY0bHPnsLSojOcCBJ8LJhWfsLToj6YK9RWhg35lQbqE0Z3B2kZlCnHQmfAAOjET5w1U7XbSCri2qVq0KlUqF1NRULj81NRXBwcGyxwQHB9ssX/T/1NRUhISEcGXCwsKK7UtERARMJhMuX76Mxo0b2+z3/ZDmmiAIj8LMLE5vBEEQRNlRmnZaq9UiPDwcBw4csOZZLBYcOHAAHTp0kD2mQ4cOXHkA2Ldvn7V8vXr1EBwczJXJysrC0aNHi60TAE6dOgWlUikbocQWJAshCMKjcNaznKKFEARBlC2lbafj4+MxbNgwtG3bFu3bt8fy5cuRk5ODESNGAACGDh2KmjVrYuHChQCACRMmIDIyEkuXLkWvXr2wbds2HDt2DOvWrQMAKBQKvPrqq5g/fz4aNWpkDcVXo0YN9OnTB0ChU+TRo0fRpUsXVKpUCUlJSZg4cSKGDBmCypWlX/NtQYNrgiA8ChpcEwRBeDalbacHDhyItLQ0zJ49GykpKQgLC8PevXutDonJyclQKv8RX3Ts2BFbt27FzJkzMWPGDDRq1Ai7du2yxrgGgNdeew05OTmIjY1FRkYGnnjiCezdu9ca41qn02Hbtm2YM2cOCgoKUK9ePUycOJHTYTuKgrlxTUqthtfC2FtUxiIszqJUSLWqom5bXHBF1LBp1byWR9SPiTpvsQ9yGlxxURixTbOFDyVjEjS44qIioi7PIvTJS1tN0gdxoRpRcy2ep1heqdAIfeAXoRGPFzXcAJBv4BeW0ap5rblKxd8/g5FfkMXeYivSeykN3J4n9EG8fxpx4SIl74AgXhdxURlxoRw53bf4jIhacvEZNRj5BXwIebKysuDv748Ofi9DrXDcccTECpCUtQaZmZl2tXwEoBBsQengXsWhXU227EHO9sG58iXVT5cVzuu0nSzvBllWyZ2S3dAHwS+JkIfstGPQzDVBEB4FzVwTBEF4NmSnbUODa4IgPArL///nTHmCIAii7CA7bRsaXBME4VEwBQNTOG6IKc41QRBE2UJ22jalOriWxAMW9LGi5lbcD0h12EqFGKvU9rKUoi5Or+U9PnPyr3NpMZYzABhMmVxao+Z1vaKmTS/EXhY12UYTr0UXdb6iLhiQaqzlyvBtClpytaCNEp9zQdKYb7wr7YOgsRZ12xYLfy+M5iy+D0LMco3ah0sXCNdZYZZqGiXxuFX8tc438hpp8RlUC30QNfpiH8R7AwAmSQxx234AhHOYYQaEZ8t+ecKzsB8T2hmcjosN2NcC242LLWI7Tnb5oYT9duG83T+wKq/X/uGB7LRtaOaaIAiPgv2/ms+Z8gRBEETZQXbaNjS4JgjCo7AoLFA48bmxomn5CIIgHjRkp21Dg2uCIDwKE0xgTsgIzLAtDSMIgiDcC9lp25Tq4NpePGBRm5pv4NeFBwCFSowpzceQlsTOFnS/ZkHHXWDMKL7DAIzmXEme+DlD1EyLWmTxPAvMvI5XreJ1v6KGVw4xZrQYh1p6LW9xaVFLLsaQFjXWoh4ekGrPxXuh0wQKbQRwaTH+t6hF16oq2dwPSGOCFwh1MmG/RsNrqkV9NLPY/mta1HADUq2lqOu25wdA2KasPjeuXr0aixcvRkpKClq1aoWVK1eiffv2smV///13zJ49G8ePH8eVK1fwzjvv4NVXX3Wp3YpJ6WqwARdiYbtdk/2Q4BFxq0UqyLUvR5AsxDbkeUUQhEdhgdnpzVm2b9+O+Ph4JCQk4MSJE2jVqhWio6Nx69Yt2fK5ubmoX78+Fi1ahODgYNkyBEEQFYWysNPlGRpcEwThUZgVZpgVJic25432smXLMHr0aIwYMQLNmjVDYmIivL29sWHDBtny7dq1w+LFizFo0CDodI6vSkYQBPEwUhZ2ujxDmmuCIDwKC8yysiRb5YHCZXnvR6fTyQ6EDQYDjh8/junTp1vzlEoloqKikJSU5GKvCYIgKg6u2umKglsH16KuV9SmSvSuwn6Nmo9ZDEi1xGIdou7XIpQXbz4TNNhqlajJlcbaFvvJIMZzzuHSBSZBv6ywfZnVSl4/LcaPlsNg4jXYGhUfM1olaNFVSn6QUWDk9dMicvoo8TzEtKipFjXUGkE/L+rj8023+eOFGNRyiLp+s5m/LiYLr6EX68wruMml1UKfRU0/IPMMCjp9laCpJ5zFOS1fkR4zNDSUy01ISMCcOXMkpdPT02E2mxEUFMTlBwUF4dy5c852thxiJ8h9meBcTGlHsKfzdbsmW7YRD/wYXMrxuEtncZCKpc8tn7hmpysKNHNNEIRHYWFmODO4KiwPXL16FX5+//zxRPINgiCI0sFVO11RoME1QRAehQVGl8r7+flxg+viqFq1KlQqFVJT+ehEqamp5KxIEAThAK7a6YqCB37DIgiiImNx4T9n0Gq1CA8Px4EDB/5p02LBgQMH0KFDB3efDkEQxENHadvp8o5bZ65NgvbUbObjQasEvasYD1ih4DXbgFTHLWqoDaYMLi1qqEVNtlYdaHO/WinVy4q6XZ26ilBHgZAW69QK+3ldtxjLW9QiA1INtKhFl8TKFnR2BUKMajEWt5gWdeCFddyV5NlC1KKL91K8TuI5ienCTPFaCddSoRPS/HmJMcqVghZdvM5i/XJ1StL0N2uJYGBOxk91XvMZHx+PYcOGoW3btmjfvj2WL1+OnJwcjBgxAgAwdOhQ1KxZEwsXLgRQ6AT5xx9/WP99/fp1nDp1Cr6+vmjYsKHT7XsWnqDBFnFvXGzA+efEaY02UOr65rKgdDTU9ij/162iURZ2ujxDshCCIDwKMzM6ZYhFB1NHGDhwINLS0jB79mykpKQgLCwMe/futTo5JicnQ6n8Z0B348YNtG7d2ppesmQJlixZgsjISBw6dMjp9gmCIMozZWGnyzM0uCYIwqNgMIM5MWvoSHQdOeLi4hAXFye7Txww161bF4xVrJkXgiCI4igrO11eocE1QRAeBWNOLqv7EHyKJwiCKE+QnbaNWwfXkml/QYtqkWhs+f1aNR9jGAByhTjEIlp1gO0+CIj6aVEfaxR04gCgEWIdG4RYyqLOzktbjUvnGdK4tFLQHosaa1GLXNhRO/G7hYdcjP9sNPMLbFiE48X4z6KOvLCfYoxw/rpIdOCCBl/UdYvnxIT6RX10YRt8HWYhLrn4PIj7RVSCxl7so5zuWzwvsYzoS0A4R6Hji+OGuKI5ypQ+5UGDLYd7fR1c0Yi6pNMuZTxT60q/2fIO2Wnb0Mw1QRAeReEfyM5o+SrW50aCIIgHDdlp29DgmiAIj6Lwc6MTWr4K9rmRIAjiQUN22jY0uCYIwsMwO/khu2LNiBAEQTx4yE7bwq2Da6mmlk/b0wWL2uRC+GP02upcusCYwaVFfbSohxU12RoVr7k1mHhtshySOMZCG3kGfuU3L20QlxZjTou64EpetSVtZuVd5psUYoKL52EUdMHOIqdd12urOlWHwXiLS6sFLbrFxLchXieDSdC2A1Cr+GdGfOZEvbqobzeaM7i0StTTG9Nt7perU9Sii/sJ57BYTFAoHDfbrIJ9bix7PFGDLYf7Y2M7i2fqmx8EFWuWsiJCdto2NHNNEIRHYYHFKccwZzzWCYIgiJJDdto2NLgmCMKjKNTmkZaPIAjCUyE7bRsaXBME4VFYmAkKip9KEAThsZCdtg0NrgmC8CicNcIVzWgTBEE8aMhO28atg2txgQ+L4KinFJzwjMY7fGeEBUAAwGTiHdTMkoVMeEc+yfHCojDioiGiA6PcAyA6HEoc2ITzlpynOcfm8aIT5r38G5I+iNdWdNQUz0N0FlWp+EViRMSFUcTjAfuL3YiaKrU6kEvbW0xHXLhGbgGXAuGZkVwXwYlJrfTi+6ji+yg+o1oN77QppxMTn1vR6VHcTzgHGW1PR86JyROdHJ19LsreAbJ8Qr83guy0PWjmmiAIj8JZx5eK5ihDEATxoCE7bRsaXBME4VEUhnhyfBaxos2IEARBPGjITtuGBtcEQXgYzhrhimW0CYIgHjxkp21RqoNriTZZJSyuIfzVI/fZQCFof8U6xcVORK0wk1kMhd/PtylZCEcGOT0y3ye+jyo7i47YO76wX4KO25TBpcVFZUStuUbQXBuE48VzEvXUAKBV+/NlhH6K8eRF/bNWzffBKPRR1FMzZpT0oZJ3fS6dV8DruCX3X1gVSry24v0Xz1tuMR3xmTRbcm3uJ5yDtHzlkfKy0IwtKqpGu6L8fh6GZ9RzIDttG5q5JgjCo7Aws5MhnmhVPIIgiLKE7LRtaHBNEISHYYZzs0oVy2gTBEE8eMhO24IG1wRBeBTOr/xVsYw2QRDEg4bstG1KVTSmVGq5TQKzcJsCSsnGLPncJtapVnlzm1Kh5jaFsIkoFEpuszCTZBMxW/K5jTETt4nnYLYYuM1e/UqFVrIxWLhNow7gNvE81CpfbhPRqgO4TUSl1Es2s6WA28T7J/ZR3AymLG4T741OE8htWk0VyZaTf53bxDbEe6OAitvE6ypeN/GcxedLrfKWPJMqpTe3ifsJZ7G4sDnP6tWrUbduXej1ekRERODnn3+2Wf6zzz5DkyZNoNfr8eijj2LPnj0utftwohC28ojSye1hoaKc98PwjHoSpW+n3W2jGWOYPXs2QkJC4OXlhaioKPz5559cmTt37mDw4MHw8/NDQEAARo0ahXv3eP8wRyjPvxSCIB5CGDM7vTnL9u3bER8fj4SEBJw4cQKtWrVCdHQ0bt26JVv+yJEjiImJwahRo3Dy5En06dMHffr0wZkzZ0p6ugRBEOWO0rbTpWGj3377bbz77rtITEzE0aNH4ePjg+joaOTn/zMJNnjwYPz+++/Yt28fvv76a/zwww+IjY11+voomBvn6rWaYC5tb4VGkxCxQn6FRr6MRhMoKXM/YoQKo1lcuZBfodFe9AhAupKgvRUa7R0vroYortAoN5stRlIR2xSjXIhRM8TrImLveEeOEfsoWbFTvG7CdVGLkWFkroMYBcXeCo1ihBPJapnC/VcLz4crKzSahT5aGM1eO0JWVhb8/f0BaKBQOPu50YjMzEz4+dleibSIiIgItGvXDqtWrQIAWCwWhIaGYvz48Zg2bZqk/MCBA5GTk4Ovv/7amvfYY48hLCwMiYmJDvfVE5D7gueGWkuhzrKG5poc4+GM+iAXnYqQUlZ22t02mjGGGjVqYNKkSZg8eTIAIDMzE0FBQdi4cSMGDRqEs2fPolmzZvjll1/Qtm1bAMDevXvRs2dPXLt2DTVq1HD4fMmaEAThYTAwZnF4K3KUycrK4raCggLZ2g0GA44fP46oqChrnlKpRFRUFJKSkmSPSUpK4soDQHR0dLHlCYIgHm5Kz06Xho2+dOkSUlJSuDL+/v6IiIiwlklKSkJAQIB1YA0AUVFRUCqVOHr0qFNXx61TGAZjijurIwiiAqHVahEcHIyUFOftiK+vL0JDQ7m8hIQEzJkzR1I2PT0dZrMZQUFBXH5QUBDOnTsnW39KSopseVf6+qCxF/ufIAiiOMrCTpeGjS76v70y1atX5/ar1WoEBgY6fb4ULYQgCI9Ar9fj0qVLMBhsL7IkB2NM8olSp9O5q2sEQRAEyE47Cg2uCYLwGPR6PfT60l3hsmrVqlCpVEhNTeXyU1NTERwcLHtMcHCwU+UJgiAeVkrbTpeGjS76f2pqKkJCQrgyYWFh1jKiw6TJZMKdO3ectvWkuSYIokKh1WoRHh6OAwcOWPMsFgsOHDiADh06yB7ToUMHrjwA7Nu3r9jyBEEQhGuUho2uV68egoODuTJZWVk4evSotUyHDh2QkZGB48ePW8t8//33sFgsiIiIcO4kGEEQRAVj27ZtTKfTsY0bN7I//viDxcbGsoCAAJaSksIYY+zFF19k06ZNs5b/6aefmFqtZkuWLGFnz55lCQkJTKPRsNOnTz+oUyAIgnhoKQ0bvWjRIhYQEMB2797NfvvtN9a7d29Wr149lpeXZy3TvXt31rp1a3b06FF2+PBh1qhRIxYTE+N0/2lwTRBEhWTlypWsdu3aTKvVsvbt27P//e9/1n2RkZFs2LBhXPlPP/2UPfLII0yr1bLmzZuzb775pox7TBAEUXFwt422WCxs1qxZLCgoiOl0Ota1a1d2/vx5rszt27dZTEwM8/X1ZX5+fmzEiBEsOzvb6b67Nc41QRAEQRAEQVRkSHNNEARBEARBEG6CBtcEQRAEQRAE4SZocE0QBEEQBEEQboIG1wRBEARBEAThJmhwTRAEQRAEQRBuggbXBEEQBEEQBOEmaHBNEARBEARBEG6CBtcEQRAEQRAE4SZocF2OUSgUmDNnjt1yc+bMgUKhKP0OEQRBEBI6d+6Mzp072y136NAhKBQKHDp0qNT7RBBE6UGD6xKwceNGKBQKHDt2rNgyly9fhkKhsG5KpRKBgYHo0aMHkpKSyrC3Zcv953z/tmjRogfdNYIgKgD27HPnzp3RokULLq9u3bqcvfLx8UH79u3x8ccfl0WXHwidO3eWtdXdu3d/0F0jiHKL+kF3oKIQExODnj17wmw248KFC1izZg26dOmCX375BY8++qhLdebl5UGt9txb+PTTT2Po0KFcXuvWrR9QbwiCIOwTFhaGSZMmAQBu3ryJ999/H8OGDUNBQQFGjx7tUp3fffedO7vodmrVqoWFCxdyeTVq1HhAvSGI8o/njsweMtq0aYMhQ4ZY0506dUKPHj2wdu1arFmzxqU69Xq9u7pXKjzyyCPcORMEQXg6NWvW5OzW8OHDUb9+fbzzzjsuD661Wq27ulcq+Pv7k60mCDdCspAHRKdOnQAAFy9edLkOOc314cOH0a5dO+j1ejRo0ADvvfee5LgPP/wQCoUCGzZs4PIXLFgAhUKBPXv2uNwnkby8POTn57utPoIgiLKkWrVqaNKkSYlstZzm+tq1a+jTpw98fHxQvXp1TJw4EQUFBVyZs2fPwsvLS/IF8PDhw1CpVJg6darLfRIxmUy4d++e2+ojiIoMzVw/IC5fvgwAqFy5stvqPH36NLp164Zq1aphzpw5MJlMSEhIQFBQEFduxIgR+PzzzxEfH4+nn34aoaGhOH36NN544w2MGjUKPXv2tJa9e/cuzGaz3ba9vb3h7e3N5W3cuBFr1qwBYwxNmzbFzJkz8cILL7jnZAmCIBwgMzMT6enpknyj0ejQ8SaTCdeuXXOrrc7Ly0PXrl2RnJyMV155BTVq1MCmTZvw/fffc+WaNm2KefPmYcqUKejXrx+ee+455OTkYPjw4WjSpAnmzp1rLXvv3j2HJjI0Gg38/f25vAsXLsDHxwcGgwFBQUEYPXo0Zs+eDY1G454TJogKBg2uy4jc3Fykp6fDbDbjzz//RHx8PACgX79+bmtj9uzZYIzhxx9/RO3atQEAffv2ldV0r1+/Hs2bN8eoUaPw9ddfY9iwYQgODsayZcu4cq1bt8aVK1fstp2QkMDNonfs2BEDBgxAvXr1cOPGDaxevRqDBw9GZmYmXnrppZKdKEEQhINERUUVu6958+aSPKPRaB2Mp6Sk4O2330ZKSgrGjRvntj6tW7cOFy5cwKeffor+/fsDAEaPHo1WrVpJysbHx2P37t2IjY3F448/joSEBFy5cgVJSUnQ6XTWcnFxcfjoo4/sth0ZGclFI2nQoAG6dOmCRx99FDk5OdixYwfmz5+PCxcuYPv27SU/WYKogNDguoxISEhAQkKCNe3r64ulS5e6bXBtNpvx7bffok+fPtaBNVA48xEdHS2RegQHB2P16tWIiYlBp06dcOrUKezbtw9+fn5cuS1btiAvL89u+/Xr1+fSP/30E5ceOXIkwsPDMWPGDAwfPhxeXl7OniJBEITTrF69Go888ogkf9KkSbJf5b777jtUq1aNyxsxYgQWL17stj7t2bMHISEhnP339vZGbGwsXnvtNa6sUqnExo0b0apVK/To0QPHjh3DzJkz0bZtW67ca6+95pBuWpyB/+CDD7j0iy++iNjYWKxfvx4TJ07EY4895uzpEUSFhwbXZURsbCz69++P/Px8fP/993j33Xcdkls4SlpaGvLy8tCoUSPJvsaNG8vqqAcNGoTNmzfjm2++QWxsLLp27Sop8/jjj7ulf1qtFnFxcRg7diyOHz+OJ554wi31EgRB2KJ9+/aSgShQOMiUk4tERERg/vz5MJvNOHPmDObPn4+7d++61SnxypUraNiwoWT9gcaNG8uWb9CgAebMmYMpU6agRYsWmDVrlqRMs2bN0KxZM7f0b9KkSVi/fj32799Pg2uCcAEaXJcRjRo1sn6efOaZZ6BSqTBt2jR06dJF1vCXBbdv37bGgP3jjz9gsVigVPI+rmlpaQ79EeDr6wtfX1+bZUJDQwEAd+7ccbHHBEEQpUvVqlWttjo6OhpNmjTBM888gxUrVljlfA+ConB+N27cwO3btxEcHMztz8zMdOgro1arRWBgoM0yZKsJomRQtJAHxOuvv45KlSph5syZbqmvWrVq8PLywp9//inZd/78edljxo0bh+zsbCxcuBCHDx/G8uXLJWXatWuHkJAQu9uSJUvs9vHvv/+29pUgCKI80KtXL0RGRmLBggXIyclxS5116tTBxYsXwRjj8ouz1YmJidi3bx/efPNNGAwGjBkzRlJmwoQJDtnqf/3rX3b7R7aaIEoGzVw/IAICAjBmzBi8/fbbOHXqFMLCwkpUn0qlQnR0NHbt2oXk5GSr7vrs2bP49ttvJeV37NiB7du3491338X48ePx66+/YubMmXjmmWc4faIrmuu0tDSJUc7Ozsby5ctRtWpVhIeHu3qaBEEQZc7UqVPRs2dPrF+/Hq+++mqJ6+vZsye+++477Nixw+rQmJubi3Xr1knKXrp0CVOmTEHfvn0xY8YMVKlSBWPHjsXHH3/MhehzRXOdlZUFnU7HOUYyxjB//nwAhTP3BEE4Dw2u3cCGDRuwd+9eSf6ECRNsHjdhwgQsX74cixYtwrZt20rcjzfeeAN79+5Fp06d8PLLL8NkMmHlypVo3rw5fvvtN2u5W7du4aWXXkKXLl0QFxcHAFi1ahUOHjyI4cOH4/Dhw1Z5iCua69WrV2PXrl149tlnUbt2bdy8eRMbNmxAcnIyNm3a5PELKhAEQdxPjx490KJFCyxbtgzjxo0rcYi60aNHY9WqVRg6dCiOHz+OkJAQbNq0SRLOlDGGkSNHwsvLC2vXrgUAjBkzBjt37sSECRMQFRVlXUnRFc31iRMnEBMTg5iYGDRs2BB5eXn44osv8NNPPyE2NhZt2rQp0XkSREWFBtduoMjoiQwfPtzmcTVq1MALL7yATZs24eLFi2jQoEGJ+tGyZUt8++23iI+Px+zZs1GrVi288cYbuHnzJje4fumll1BQUGBdTAYAqlSpgnXr1qF3795YsmSJxGPdGR5//HEcOXIE77//Pm7fvg0fHx+0b98eGzZswFNPPVWicyQIgngQTJ48GcOHD8eWLVvs2nZ7eHt748CBAxg/fjxWrlwJb29vDB48GD169ED37t2t5VauXIlDhw5h586d3NfADz74AC1atMDo0aPxzTffuNyPOnXqoFOnTvjiiy+QkpICpVKJpk2bIjExEbGxsSU6R4KoyCiYKPoiCIIgCIIgCMIlyKGRIAiCIAiCINwEDa4JgiAIgiAIwk3Q4JogCIIgCIIg3AQNrgmCIAiCIAjCTdDgmiAIgiAIgiDcBA2uCYIgCIIgCMJNlOng+vLly1AoFNi4cWNZNksQBEHYgGwzQRCE+6gwM9cffPABmjZtCr1ej0aNGmHlypUOH1tQUICpU6eiRo0a8PLyQkREBPbt2ydb9siRI3jiiSfg7e2N4OBgvPLKK7h37x5X5vfff0f//v1Rv359eHt7o2rVqnjyySfx1VdfSepbv349IiMjERQUBJ1Oh3r16mHEiBG4fPmypGxqaipGjBiB6tWrw8vLC23atMFnn33m8Hk+LFgsFrz99tuoV68e9Ho9WrZsiU8++cTh4/ft22e9h5UrV0a/fv1kr3d+fj4WLlyIZs2awdvbGzVr1kT//v3x+++/c+V++OEHPPfccwgNDYVer0dwcDC6d++On376qaSnShDlGoVCIbstWrTI4TpOnDiB5557DoGBgfD29kaLFi3w7rvvcmU6d+4s2879C7a4C0ftz/Dhw2X71KRJE7f3ydNx5L0px8aNG4t9hhQKBbZs2cKVv379OgYMGICAgAD4+fmhd+/e+Pvvv222cfjwYWt96enpJTpPouJQpis01qlTB3l5eSVeOtZZ3nvvPYwdOxZ9+/ZFfHw8fvzxR7zyyivIzc3F1KlT7R4/fPhw7NixA6+++ioaNWqEjRs3omfPnjh48CCeeOIJa7lTp06ha9euaNq0KZYtW4Zr165hyZIl+PPPP/Gf//zHWu7KlSvIzs7GsGHDUKNGDeTm5mLnzp147rnn8N5773ErY508eRL16tXDc889h8qVK+PSpUtYv349vv76a/z666/WpW+zsrLwxBNPIDU1FRMmTEBwcDA+/fRTDBgwAFu2bMELL7zgxivq2bz++utYtGgRRo8ejXbt2mH37t144YUXoFAoMGjQIJvHfv311+jduzfatGmDRYsWISsrCytWrMATTzyBkydPcqukDR48GF9++SVGjx6NNm3a4MaNG1i9ejU6dOiA06dPo06dOgCACxcuQKlUYuzYsQgODsbdu3exefNmPPnkk/jmm29K5QVPEM7woGwzADz99NMYOnQol9e6dWuHjv3uu+/w7LPPonXr1pg1axZ8fX1x8eJFXLt2TVK2Vq1aWLhwIZdXZD/diTP2R6fT4f333+fy/P393d4nT8bR96YcTz75JDZt2iTJf+edd/Drr7+ia9eu1rx79+6hS5cuyMzMxIwZM6DRaPDOO+8gMjISp06dQpUqVST1WCwWjB8/Hj4+PsjJySn5yRIVB/aQk5uby6pUqcJ69erF5Q8ePJj5+PiwO3fu2Dz+6NGjDABbvHixNS8vL481aNCAdejQgSvbo0cPFhISwjIzM61569evZwDYt99+a7Mdk8nEWrVqxRo3bmz3nI4dO8YAsIULF1rz3n77bQaAHThwwJpnNptZu3btWHBwMCsoKLBb78PAtWvXmEajYePGjbPmWSwW1qlTJ1arVi1mMplsHt+sWTPWsGFD7nqdOnWKKZVKFh8fz7UDgE2ePJk7/vvvv2cA2LJly2y2k5OTw4KCglh0dLQzp0cQDxUAuN+qM2RmZrKgoCD2/PPPM7PZbLNsZGQka968uUvtOIMz9mfYsGHMx8en1Pvk6ZTkvSlHbm4uq1SpEnv66ae5/LfeeosBYD///LM17+zZs0ylUrHp06fL1rV27VpWpUoVNmHCBAaApaWlOd0fomLilCxkzpw5UCgUuHDhAoYMGQJ/f39Uq1YNs2bNAmMMV69eRe/eveHn54fg4GAsXbqUO15O1zd8+HD4+vri+vXr6NOnD3x9fVGtWjVMnjwZZrO5BH82FHLw4EHcvn0bL7/8Mpc/btw45OTk4JtvvrF5/I4dO6BSqbjZZL1ej1GjRiEpKQlXr14FUDhzvG/fPgwZMgR+fn7WskOHDoWvry8+/fRTm+2oVCqEhoYiIyPD7jnVrVsXALiyP/74I6pVq4annnrKmqdUKjFgwACkpKTgv//9r806S3pvDQYDZs+ejfDwcPj7+8PHxwedOnXCwYMHuXIJCQlQKpU4cOAAlx8bGwutVotff/3V7vnbYvfu3TAajdz9VigUeOmll3Dt2jUkJSUVe+ydO3fwxx9/4Pnnn4dWq7Xmt2rVCk2bNsW2bdusednZ2QCAoKAgro6QkBAAgJeXl81+ent7o1q1ag7db4KwR3m0zfeTl5eH/Px8p47ZunUrUlNT8eabb0KpVCInJwcWi8XmMSaTya7c4Pr16xg5cqRVite8eXNs2LDBoT65Yn/MZjOysrIcqr+Iovu1ZMkSrF692iox7NatG65evQrGGObNm4datWrBy8sLvXv3xp07dyR97dWrF2rUqAGdTocGDRpg3rx53L09e/YsvLy8JF8WDh8+DJVK5dCXX1uU9L0px1dffYXs7GwMHjyYy9+xYwfatWuHdu3aWfOaNGmCrl27yrZz584dzJw5E3PnzkVAQIDT/SAqNi5prgcOHAiLxYJFixYhIiIC8+fPx/Lly/H000+jZs2aeOutt9CwYUNMnjwZP/zwg936zGYzoqOjUaVKFSxZsgSRkZFYunQp1q1bx5W7e/cu0tPT7W65ubnWY06ePAkAaNu2LVdXeHg4lEqldX9xnDx5Eo888gj3wweA9u3bAyj8pAUAp0+fhslkkrSj1WoRFhYm205OTg7S09Nx8eJFvPPOO/jPf/7Dfca6n9u3b+PWrVs4duwYRowYAQBc2YKCAtkBnbe3NwDg+PHjNs+zCFfvbVZWFt5//3107twZb731FubMmYO0tDRER0dbrxEAzJw5E2FhYRg1apR1gPrtt99i/fr1mD17Nlq1amUt68i9Tk9PR0FBgfWYkydPwsfHB02bNuXOq+h+2brfRfUUdx1v3LiBlJQUAECDBg1Qq1YtLF26FF999RWuXbuGn3/+GWPHjkW9evVk5SdZWVlIT0/HuXPnMGPGDJw5c6bY+00QrlCebHMRGzduhI+PD7y8vNCsWTNs3brVoXPdv38//Pz8cP36dTRu3Bi+vr7w8/PDSy+9JDtQv3DhAnx8fFCpUiUEBwdj1qxZMBqNXJnU1FQ89thj2L9/P+Li4rBixQo0bNgQo0aNwvLly+32yVn7k5ubCz8/P/j7+yMwMBDjxo1zSGtcxJYtW7BmzRqMHz8ekyZNwn//+18MGDAAM2fOxN69ezF16lTExsbiq6++wuTJk7ljN27cCF9fX8THx2PFihUIDw/H7NmzMW3aNGuZpk2bYt68edi0aRO+/PJLAIXvreHDh6NJkyaYO3eutey9e/ccegYyMzOtx7jy3nTkmnh5eeFf//qXNc9iseC3336TtAMU3puLFy9a30dFzJo1C8HBwRgzZozTfSAIp2QhCQkJDACLjY215plMJlarVi2mUCjYokWLrPl3795lXl5ebNiwYda8S5cuMQDsww8/tOYNGzaMAWBz587l2mrdujULDw/n8urUqcMA2N0SEhKsx4wbN46pVCrZ86lWrRobNGiQzXNu3rw5e+qppyT5v//+OwPAEhMTGWOMffbZZwwA++GHHyRl+/fvz4KDgyX5Y8aMsfZZqVSyfv36FStT0el01rJVqlRh7777Lrd//PjxTKlUssuXL3P5gwYNYgBYXFyczfMs6b01mUwS6cndu3dZUFAQGzlyJJd/+vRpptVq2b///W929+5dVrNmTda2bVtmNBq5co7ca/F56tWrF6tfv77k/HJychgANm3atGKvgdlsZgEBAaxr165cfnp6OvPx8WEA2LFjx6z5R48eZQ0aNOD6Eh4ezm7evClbf3R0tLWcVqtlY8aMYXl5ecX2hyAcpTzaZsYY69ixI1u+fDnbvXs3W7t2LWvRogUDwNasWWP3nFu2bMm8vb2Zt7c3Gz9+PNu5cycbP348AyCx6yNHjmRz5sxhO3fuZB9//DF77rnnGAA2YMAArtyoUaNYSEgIS09P5/IHDRrE/P39WW5urs0+OWN/pk2bxqZOncq2b9/OPvnkE+v1fvzxxyW2UKToflWrVo1lZGRY86dPn84AsFatWnF1xMTEMK1Wy/Lz8615cucyZswY5u3tzZUzm83siSeeYEFBQSw9PZ2NGzeOqdVq9ssvv3DHFvXf3hYZGWk9xpX3pi1u377NtFqt5L6mpaXJPsuMMbZ69WoGgJ07d86a9+uvvzKVSmWVpRT9vkgWQjiKSw6N//73v63/VqlUaNu2La5du4ZRo0ZZ8wMCAtC4cWO7nrhFjB07lkt36tRJ4qiwZcsW5OXl2a2rfv361n/n5eVxn/jvR6/X260vLy8POp1O9tii/ff/v7iycu28+uqr6NevH27cuIFPP/0UZrMZBoNBth//+c9/kJ+fj7Nnz2Lz5s0S54p///vfSExMxIABA/DOO+8gKCgIn376Kb744guuf/Zw9d6qVCqoVCoAhbMEGRkZsFgsaNu2LU6cOMG10aJFC7zxxhuYPn06fvvtN6Snp+O7776DWs0/jsVFZBFp3ry59d+O3i85lEolxowZg7feegvTp0/HyJEjkZWVhddee816X+4/vnLlyggLC0P//v3x2GOP4a+//sLChQvRv39/7Nu3z9pmEYsWLcKkSZNw9epVfPTRRzAYDDCZTA6dI0E4QnmyzQAkEXNGjhyJ8PBwzJgxA8OHD7cpr7p37x5yc3MxduxYa3SQf/3rXzAYDHjvvfcwd+5cNGrUCEBhtKj7efHFFxEbG4v169dj4sSJeOyxx8AYw86dOzFgwAAwxrjIENHR0di2bRtOnDiBxx9/vNg+OWN/ROfKQYMG4ZFHHsHrr7+OHTt22HW+BoD+/ftzDpAREREAgCFDhnD2NCIiAp988gmuX79uvQf3X9vs7GwUFBSgU6dOeO+993Du3DnrV0SlUomNGzeiVatW6NGjB44dO4aZM2dKZoFfe+01DBkyxG6fK1eubP23K+9NW+zYsQMGg0EiCbHXzv1lAOCVV15Bjx490K1bN6faJ4giXBpc165dm0v7+/tDr9ejatWqkvzbt2/brU+v13NRGIDCH+Ddu3e5PFtGrTi8vLyKHbDm5+fb1cZ6eXlxsoP7jy3af///iysr106TJk2sYZeGDh2Kbt264dlnn8XRo0ehUCi4sl26dAEA9OjRA71790aLFi3g6+uLuLg4AEDLli2xdetWjB071nqdgoODsXz5crz00kvw9fW1eZ5FlOTefvTRR1i6dCnOnTvHfW6tV6+epJ0pU6Zg27Zt+Pnnn7FgwQI0a9ZMUiYqKsqhPt+Po/erOObOnYv09HS8/fbb1nBg3bp1w6hRo5CYmGi9jpmZmejUqROmTJmCSZMmWY9v27YtOnfujA8//BAvvfQSV3dYWJj130OGDEGbNm2skWgIwh2UJ9ssh1arRVxcHMaOHYvjx49z0ZhEin7LMTExXP4LL7yA9957D0lJSdbBtRyTJk3C+vXrsX//fjz22GNIS0tDRkYG1q1bJ5G9FHHr1i0AsMrDivD394eXl1eJ7c/EiRMxa9Ys7N+/36HBtdz9BoDQ0FDZ/Pvv2++//46ZM2fi+++/l2i+75duAIUyuDlz5mDKlClo0aIFZs2aJelLs2bNZO24LVx5b9piy5YtCAwMRI8ePZxq5/4y27dvx5EjR3DmzBmn2iaI+3FpcF00Q2kvDwAYYy7VJ0daWppDjjS+vr7WQVBISAjMZjNu3bqF6tWrW8sYDAbcvn3bbiimkJAQXL9+XZJ/8+ZNAP+EcipyZCvKF8s6EvKpX79+GDNmDC5cuIDGjRsXW65BgwZo3bo1tmzZYh1cFx3/3HPP4ddff4XZbEabNm1w6NAhAMAjjzxit33A9Xu7efNmDB8+HH369MGUKVNQvXp1qFQqLFy4EBcvXpQc+/fff+PPP/8EUKi7k0N8gRVH0YsNKLwPBw8eBGOM+wNFvF/FodVq8f777+PNN9/EhQsXEBQUhEceeQQvvPAClEolGjZsCADYuXMnUlNT8dxzz3HHR0ZGws/PDz/99JNkcC2289xzz2HRokXIy8tz+iVCEHKUJ9tcHEUDQ9EBT6RGjRr4/fffJU7FRXZe/APAXjtFzpBDhgzBsGHDZI9p2bIlgH/sfREffvghhg8fXmL74+XlhSpVqtg99yKKuz/27nlGRobVVs2dOxcNGjSAXq/HiRMnMHXqVFnH0O+++w4AcOPGDdy+fRvBwcHc/szMTIdmmrVaLQIDAwG4571ZRHJyMn788UfExsZKQkoGBgZCp9MV2w7wz72ZMmUK+vfvD61Wa13foMjx/OrVqzAYDKUSwpF4uCjTONclpV27drhy5YrdcgkJCZgzZw6Af2YLjx07hp49e1rLHDt2DBaLhZtNlCMsLAwHDx5EVlYW59R49OhRrv4WLVpArVbj2LFjGDBggLWcwWDAqVOnuLziKDJM4qxBcWXl/grXarWcN/T+/fsBuDYL7Aw7duxA/fr18fnnn3MvlYSEBElZi8WC4cOHw8/PD6+++ioWLFiAfv36cQ4ogPQFVhxFLzag8H68//77OHv2LDeLIt4vewQFBVlf2mazGYcOHUJERIR1YJCammrddz+MMZjNZofkHnl5eWCMITs7mwbXRLnGFdtcHEVyFXHGXCQ8PBz79u2zOjQWcePGDYeOF9upVq0aKlWqBLPZbNdeipK1ImlaSe1PdnY20tPT7fa9pBw6dAi3b9/G559/jieffNKaf+nSJdnyiYmJ2LdvH958800sXLgQY8aMwe7du7kyEyZMwEcffWS37cjISOukjzvem0V88sknYIxJJCFAobTl0UcfxbFjxyT7jh49ivr166NSpUoACgfQW7dulXWsbdOmDVq1asU56ROEHOVqcO2Kru+pp55CYGAg1q5dyw2u165dC29vb/Tq1cuaV+TNXLt2bWuUjX79+mHJkiVYt26d1du6oKAAH374ISIiIqyzH/7+/oiKisLmzZsxa9Ys6w9106ZNuHfvHvr3729tR5xFBwCj0YiPP/7Y6jEPFIaNys7O5jRqAPDzzz/j9OnTdheG+fPPP5GYmIhnnnnG4ZlrVymaKbl/xubo0aNISkqSfLpctmwZjhw5gi+//BK9evXCoUOH8NJLL+HJJ5/kPl+7ornu3bs3Jk6ciDVr1mDVqlXWPiUmJqJmzZro2LGjtezNmzeRmZmJBg0a2Fw8Y8mSJbh58ya3qmfR9dy2bRs3WPjyyy+Rk5PDLYIhd78zMjKwc+dOhIaGSvYRRHnDFduclpYmGURmZ2dj+fLlqFq1KsLDw635crZ5wIABWLRoET744AMuBOn7778PtVqNzp07AyiM0qPT6Ti9LWMM8+fPB1CopwYKbVjfvn2xdetWnDlzBi1atOD6dn9/ixt8O2p/8vPzYTQare+JIubNmwfGWKkvLHW/vS7CYDBgzZo1krKXLl3ClClT0LdvX8yYMQNVqlTB2LFj8fHHH3Mh+lzRXDvz3szNzUVycjKqVq0qkTkBhaEZa9euXayUqF+/fpg2bRqOHTtm1YufP38e33//PRdJpchP6X62bduG7du34+OPP0atWrXsniNBlKvBtaua63nz5mHcuHHo378/oqOj8eOPP2Lz5s148803rZ+nAGDVqlV44403cPDgQathjoiIQP/+/TF9+nTcunULDRs2xEcffYTLly9LnGTefPNNdOzYEZGRkYiNjcW1a9ewdOlSdOvWjTOWY8aMQVZWFp588knUrFkTKSkp2LJlC86dO4elS5daZ0fv3buH0NBQDBw4EM2bN4ePjw9Onz6NDz/8EP7+/hLdW7NmzdC/f3/Url0bly5dwtq1axEYGIjExESnr5uzPPPMM/j888/x/PPPo1evXrh06RISExPRrFkzLrTU2bNnMWvWLAwfPhzPPvssgMKQUGFhYXj55Ze5eKOuzLbXqlULr776KhYvXgyj0Yh27dph165d+PHHH7Flyxbuc+n06dPx0Ucf4dKlS9bY4Zs3b8bOnTvx5JNPwtfXF/v378enn36Kf//73+jbt6/12GeffRbNmzfH3LlzceXKFatD46pVqxASEsI5kPXo0QO1atVCREQEqlevjuTkZHz44Ye4ceMGtm/f7vQ5EoSn4YptXr16NXbt2oVnn30WtWvXxs2bN7FhwwYkJydj06ZNnCO6nG1u3bo1Ro4ciQ0bNsBkMllnRD/77DNMnz7d+un+xIkTiImJQUxMDBo2bIi8vDx88cUX+OmnnxAbG4s2bdpY21m0aBEOHjyIiIgIjB49Gs2aNcOdO3dw4sQJ7N+/365cw1H7k5KSgtatWyMmJsbqd/Ptt99iz5496N69O3r37u309XSGjh07onLlyhg2bBheeeUVKBQKbNq0SSIVYoxh5MiR8PLywtq1awEUvr927tyJCRMmICoqynqdXdFcA46/N3/++Wd06dJF9uvHmTNn8Ntvv2HatGkSf6UiXn75Zaxfvx69evXC5MmTodFosGzZMgQFBXF+M3369JEcWzRT3aNHD9mBPUFIcCa0SHHhaIpbaUpcFau4cE9yxxa15S7WrVvHGjduzLRaLWvQoAF75513mMVikW3z4MGDXH5eXh6bPHkyCw4OZjqdjrVr147t3btXtp0ff/yRdezYken1elatWjU2btw4lpWVxZX55JNPWFRUFAsKCmJqtZpVrlyZRUVFsd27d3PlCgoK2IQJE1jLli2Zn58f02g0rE6dOmzUqFHs0qVLkrYHDRrEQkNDmVarZTVq1GBjx479v/bePryq8sz3/661XxMgCRBJgKZilRbxhSgUjOPUnjFtPHbmlDmtRcYKw8XBmanMWNN21I4Fp3bEFotYZZqxLW1nWi4Y53hsj/pjhoky/bWkWEF/VitUrQKKCSAvgbzsl7We3x8pkftei7X3SnbCCvl+eq3LPns/a61nveTeD2t97+9tOjo6ijo/g722ruuae++915x77rkmlUqZyy67zDzxxBNm8eLF5txzzzXG9FmDffjDHzbve9/7hH2UMcY8+OCDBoDZtGlTUeMNwnGc/rEkk0lz0UUXmR/96Eeefieto049l9u3bzcf+chHzPjx4006nTazZs0yLS0tnnvFGGMOHz5sbrvtNvPBD37QpFIpU11dbW644Qbzu9/9TvR7+OGHzVVXXWWqq6tNPB4355xzjvmTP/kTX/spQgbCSIzN//Ef/2E+9rGPmdraWpNIJExVVZX5+Mc/LqrM6n3q2JzNZs3dd99tzj33XJNIJMwFF1xgHnjgAdHnd7/7nbn++uvNtGnTTDqdNuXl5Wb27Nmn/bvu6Ogwt9xyi6mrqzOJRMLU1taaa665xjzyyCNFHVcx8efIkSPms5/9rLngggtMeXm5SaVS5qKLLjL33nuvyWazBfdx8nqdWjnYGGOeeeYZA8A8+uij4vPvf//7BoCwz/vFL35hrrjiClNWVmamTJli/vZv/9b8+7//uzjPJ+Py//7f/1tsb+/evaaiosJcd911RZ2TQhTzu3ny2LSdozF9toYAzIsvvhi4n3379plPf/rTpqKiwowdO9b88R//sXn11VcLjo9WfCQsljFFZLUQQgghhBBCCjKgCo2EEEIIIYQQL5xcE0IIIYQQUiI4uSaEEEIIIaREcHJNCCGEEEJIieDkmhBCCCGEkBLByTUhhBBCCCElgpNrQgghhBBCSsSIqtBICDm76e3tRTabDb1eMplEOp0eghERQgg5FcbpwpR0cp1KThHtvHNCtGN2uWg7brdoW5Z3OPqzmJVU38uH744bfMETsTGinXO6Ar8HAANH7hMx0Y7F5JjK4hNEO21XinbKGivaDnKifdxp94yhzp4l2m+blz19xDbdjGhnnOOB/fNOT+D3AJDJHRXtREweh4Er28YN/B4Fvo+r+wUA8gXuGdftFW3bln/I+fwxuY+4vDaF1gcAY/Ih9xlcMpn00dvbi/POm4r29vDnq7a2Fm+88caoCdyDwRtn/ctFn1mCX6pafmNWvwX6OPXfZUr97Y9N1Yp2nXWxaF9eLr//aI38XQCAyoSMDc8dkft8/l35+/Sq2Sfa77p7RLs7d1C09e9V3pGxBwBcI/eh45WOyygYlwvVmdPbGwqGvtadPk/EH8bp4uCTa0JIJMhms2hvP4w3X9+AigrvP6xOR2dnN6ad/2fIZrOjImgTQsiZgnG6ODi5JoREioqxaVSMLSt+BXc4npwRQgg5CeN0MCWdXLtKkqFfwxV6PZVKVHi2qV97JRPjRLsnI1+b6VeBWmqgX6vFY3KMWuIBAOWJatHWkgtX7aMsNl5uEwnRThuv9ESOKeX5rMO8LrehpCaOkdIS15LnOmbLbepj0GjpC+CV4OSU7Ccek/+KjdnecynHoCQ8RbyW895Dsh1XUhWNVwai7lklO9IyJADIOt3qE3le9DZJSBwXcLyv3AP7kxBEQQYSLpfeIwPxkRDaKt7E1O9PWXKiaE9KXijaV6j2Fy86Ktoz/+Nqub/vP+oZw7q1k0Q7rAykJ/+uaOvfPx0z/aQMXtnHUON3LUs9Bn3PDr1MhBSAcToQuoUQQqKF64ZfCCGEDB/DEKfXrVuHadOmIZ1OY968eXj22WcD+z/66KOYMWMG0uk0LrnkEjz11FP93+VyOdx+++245JJLMGbMGEyZMgWLFi3C/v37xTYOHz6MG2+8ERUVFaiqqsLSpUtx4sQJvauCcHJNCIkWnFwTQki0GeI4vWnTJjQ3N2PlypXYuXMnZs2ahaamJhw4cMC3/7Zt27Bw4UIsXboUzz//PObPn4/58+fjpZdeAgB0d3dj586d+MpXvoKdO3fisccew+7du/E//sf/ENu58cYb8fLLL2PLli144okn8LOf/Qw333xz6NPDyTUhJFpwck0IIdFmiOP0mjVrsGzZMixZsgQzZ85ES0sLysvLsX79et/+Dz74IK699lp86UtfwoUXXoh77rkHl19+OR5++GEAQGVlJbZs2YLPfOYz+NCHPoQrrrgCDz/8MHbs2IG9e/cCAF555RVs3rwZ3/3udzFv3jxcddVVeOihh7Bx40bPE+5ClFRz7ShLMq150yTiUmOdzXd6+mg7tu5Mh2gn1Ta0Rk1rZrVmO5eXGmzbR8vXnTsk2lqDbSuNW85ouzhlvaf00K6y+svDq4cut6SOu8vIMWnNdXlM6gtzthxTVumlLfXvLI9tHoCYq/TpSuNoW1Jb7rWNkmPQ94e2ZvQbg9ZEuwV0/F47SLlPrdNMxuT9kVXWfYBXe651j36WkqR4LDcPyyneFstyaaEVfUJa61m6v2zrv1vAaw1alpRxenLiItGuj39AtC+fKPextV1aqj78/v8S7Ve6vLkx++0XRFvbqmbUb5yOkVpT7Y1vhWz1AK13Lmi9NyKhBvtMM9A43dkp/wZSqRRSKfm3lM1msWPHDtx55539n9m2jcbGRrS1tfluv62tDc3NzeKzpqYmPP7446cd07Fjx2BZFqqqqvq3UVVVhTlz5vT3aWxshG3b2L59O/70T/+04HH2j7fonoQQMhzwyTUhhESbAcbpuro6VFZW9i+rVq3ybPrQoUNwHAc1NTXi85qaGrS3e+uAAEB7e3uo/r29vbj99tuxcOFCVFRU9G9j0iSZlByPxzFhwoTTbud08BEbISRauKZvCdOfEELI8DHAOL1v377+ySwAz1Pr4SCXy+Ezn/kMjDH49re/PST74OSaEBIt8vm+JUx/Qgghw8cA43RFRYWYXPtRXV2NWCyGjg4pA+7o6EBtba3vOrW1tUX1Pzmx3rNnD55++mkxltraWk/CZD6fx+HDh0+739NR0sm11sEVKocdg9S/+vkD59Q6eh85reP18SU+Fa0tTsSl53TC9pqiZ5UubrA+ohkEa4u73SOedcptqblOW9KvudN5W7SP5N4Qba3d07rhTE6VBfc5D/oz7QnuOPL65ZS+0HN/eDyq5R+cn092b/YduU5ceYp79qF13bpUuezfq8oN+ymn9PW3rODjIiExxlOCuWB/EjGGVmOtc3EAIJ2UGunKxPtEe4wr48v+rIzr/7Jf5mcctmQCU5cjY0Nv/qhnDHmnR7RLo6EWWwjZ37uOhyHxxdbXc6ilW37e7YwLQ8oQxulkMonZs2ejtbUV8+fPBwC4rovW1lYsX77cd52Ghga0trbi85//fP9nW7ZsQUNDQ3/75MT61VdfxTPPPIOJEyd6tnH06FHs2LEDs2fPBgA8/fTTcF0X8+bNK3r8AJ9cE0KiRlgdNTXXhBAyvAxxnG5ubsbixYsxZ84czJ07F2vXrkVXVxeWLFkCAFi0aBGmTp3ar9m+9dZbcfXVV+Ob3/wmPvGJT2Djxo147rnn8MgjjwDom1h/+tOfxs6dO/HEE0/AcZx+HfWECROQTCZx4YUX4tprr8WyZcvQ0tKCXC6H5cuX44YbbsCUKVNCjZ+Ta0JItMg7fUuY/oQQQoaPIY7TCxYswMGDB7FixQq0t7ejvr4emzdv7k9a3Lt3L2z7vTckV155JTZs2IC77roLX/7ylzF9+nQ8/vjjuPjiiwEAb7/9Nn76058CAOrr68W+nnnmGXz0ox8FAPz4xz/G8uXLcc0118C2bXzqU5/Ct771rVBjBzi5JoREDRPyiciwl3smhJBRzjDE6eXLl59WBrJ161bPZ9dffz2uv/563/7Tpk2DKUKaMmHCBGzYsCHUOP0Y0sl1Sulhe3PvirbWn2nfaz+0xqw8dY5odykf7ERMavO0D7b2vdb6asCrm8s4yqdRaYVdNcZeI/vHlB+0Jml59YS9Rmqiu/OHRVtrxWO2ysBV97U+D4mY1J5rTTbg1WVrj1atdy5LSlucrHPcs81T0T7XfsSUD3UhfXMyLrXprpHnNpNV1Z7Ucft5VifjVaLt8XdXfrskHJbrwgoRtMP0JUNFSI11gfW1xlr70+tcGcAbA3NG6p/ftl4Rbe31n1HxqaB+2idHqFCeUSHtcfh8ngHc+yH3oa+doZaZgHG6EHxyTQiJFsaES1JkQiMhhAwvjNOBcHJNCIkWtOIjhJBowzgdCCfXhJBowSIyhBASbRinA2H5c0JItBim8ufr1q3DtGnTkE6nMW/ePDz77LOB/Y8ePYpbbrkFkydPRiqVwgc/+EE89dRTA9o3IYSMaIYpTo9USvrkWid/6YS3dEIadmfzMkHObzg60U4nReokE52skcvLpJVUQiZZZnMyiUUXRulbRybF6eNyTU60u1SS3Pj0B0TbRky085AJcSccbxEZx814PhNjcmXyja2uhW3LJMoxKnn0eK8sQpNTyT5925TnRp8XnYyjx6wL/Ni2ut4q+VRfO8BbBEgnPnmK4+TludTrx1Vyok5I8kv+cYy85/I6UdMnGZSEwHEBJ4RtkxM+aG/atAnNzc1oaWnBvHnzsHbtWjQ1NWH37t2YNGmSp382m8XHPvYxTJo0Cf/2b/+GqVOnYs+ePaiqqgq975FPCe5vFZ/0b4du62Rpv4RrnaCoi7zoIlc6Pum/68IFXgrfd4MtODb0xVeGi+EuKgN4C8voJ6eFkmxJIMMQp0cylIUQQqLFMBSRWbNmDZYtW9ZfkKClpQVPPvkk1q9fjzvuuMPTf/369Th8+DC2bduGRKLvH6rTpk0LvV9CCDkrYLGvQPiIjRASLYx5T89XzBIyCz2bzWLHjh1obGzs/8y2bTQ2NqKtrc13nZ/+9KdoaGjALbfcgpqaGlx88cW499574YR5ckMIIWcLQxynRzp8ck0IiRYDfCLS2an851MppFIpT/dDhw7BcZz+Sl8nqampwa5du3x38bvf/Q5PP/00brzxRjz11FN47bXX8LnPfQ65XA4rV64sfqyEEHI2wCfXgZRWc631sEo3l1e6YF0YQBfjALy67KTSCmfzUu+qNdVa16sLAxSDRzusCrQ4SpuntcjHc/sD1y+PSS16b9arudZacM+5LXBc+nt9pvWY/LSCuuCORl+LvNJA6uIrrivPmy4qNLbsfZ59nOiV51LfM3rcriML09hK1x23vRr7U8kq3SYA2Ja8b9OqWE7e5z4mIRhgWd26ujrx8cqVK3H33XeXZEiu62LSpEl45JFHEIvFMHv2bLz99ttYvXr1KJhch3/B6Ska4ynOFPxbodsav3jn+dtX8UQXfRn+gi9nB6UpKhMFDTYZFENc/nykwyfXhJBoYdxwVeR+33ffvn2oqHjvH99+T60BoLq6GrFYDB0dspprR0cHamtrfdeZPHkyEokEYrH3kpEvvPBCtLe3I5vNIpkM/kcaIYScVQwwTo8WqLkmhESLMDq+U7xWKyoqxHK6yXUymcTs2bPR2tr63i5dF62trWhoaPBd5w/+4A/w2muvwT3l1eZvf/tbTJ48mRNrQsjoY4BxerTAyTUhJFqcfN0YZglJc3MzvvOd7+CHP/whXnnlFfzVX/0Vurq6+t1DFi1ahDvvvLO//1/91V/h8OHDuPXWW/Hb3/4WTz75JO69917ccsstJTtsQggZMQxDnB7JlFQWkndkQlE8VnGann04prA2VXsr+/kvn0paaa61J3UyLnXDubzU5Grtcd8Y5GnSGmytNY7HpCZ3rNLkHuvdJ/dpSQ9qrdkGvHp1j75Q6ZcN5I08NiVfd3dnpV94Ii79xP00jZmc9iWXaM19mTpu7/aktjyu9NDdGekXDnh1k3qdQj7W2i9XH7cmm+/0fOa9H7KB35OQuCZkokz4JyILFizAwYMHsWLFCrS3t6O+vh6bN2/uT3Lcu3cvbPu9e6murg7//u//jttuuw2XXnoppk6diltvvRW333576H2fjRTSWHuf46i26u/1zNce1N77w+NLPUhNdSH8vLYLEV6nXWgfAzgGPe5R9rqelIhhiNMjGc4CCCHRYpjK6i5fvhzLly/3/W7r1q2ezxoaGvDLX/5yQPsihJCzCpY/D4STa0JItHCdcJW/3NH1upEQQs44jNOBcHJNCIkWfCJCCCHRhnE6kJJOrm3lW601b1oPq9vJuFdrrDXTltKzJmNSQ631z9rrVGv3tD5ajwnw+k5rrZ3Hs9WWGupepUXXOt/u7CHRHpOc5BmD1nnnHalX137eltKq9yq/cH3cWmPtpyfUesFC+nZ9LbTeWV9v1+TUmKQevm8d7XOuNdFyjFqzn1Uae1vp3TN5ea3TCelBDgC9OalX92qw5T1HQsLiBGeYYJ2vR19dBIViptb9Gkt/HaynBorRM5/5+2QgOu1T8R5jMdsrcNyFxlTgvPrdD+G9r8+E7zUZFIzTgfDJNSEkWvCJCCGERBvG6UA4uSaERAtW/iKEkGjDOB0IJ9eEkGhhTN8Spj8hhJDhg3E6kJJOrrVeOab0rh4NtvpeezkDgONKbbH2NS7k/1yWlJpZrVU2luyvdcJ9+ywTba0d1tuMa29uNUbtpa2315s/6hmDXkfrunN5qXdOKz1zxpHHpfXR+hj99M5pdS69+vWywHY2J8fQm5faZa2nTsTGesagNdZlyXNEO6O05dr7VucFZNV50fv0uyc1fvpPMgj4unGYKUEtsbC+1grP31ABnW9xftED0ScPjkKa6vA+13r7hfuE12WH1GQXcQxah00N9lkI43QgfHJNCIkWTsjXjWHsoAghhAwexulAOLkmhEQLPhEhhJBowzgdCCfXhJBo4SJk0B6ykRBCCPGDcTqQkk6uHVfqdPOO1L/attbkSn2r9qQGgFSiSu1D6nyTcelzrfVmmZwegzrkIu4NrcOOq+NIJbz+3KdiWTHR1r7Zev240lcDgKM8oF1XtsuT1aKtdd7ai1mfNz3GsWXv845BeW3rtkc2qa6F1o0X0ip7rhWAtC1131pjrXX+WqNtK82+xqM199FQ2kojr/fpGHmPkpDwiUikKIWvtWaw2uPiKODXHdJz2q8GQlgKnpcCMbGYnLBCuuzCmmxqsEkRME4HwifXhJBIYRwXxin+xzNMX0IIIYOHcToYTq4JIdGCT0QIISTaME4Hwsk1ISRaMGgTQki0YZwOpKST62R8QuD3WouaiI0RbVfpigEgpzSwyZjUCucdqS3WWmL9vW1Jf2i9T+3N7Ifeh9aKa09prXFLJ8fLfVrKw1qNEQCyzgk5BqVX784eEu2xqVrRTlhSa3wsu1eOSW3Pz2tb+1Qn4vL66eOMx6SntPbWTifkeSiGnqz0xtZ6v2xengeN18da+Z6ra6n11IBXlx2LyT6uS831oMi7fUuY/iQEg9QO++qGh95DOiyF9M0eDXXY/kNC8L1sqe/9NdpynDouF9ZkB3/vGaPfeSugwx68BpuccRinA+GTa0JIpDDGwIR4ymFGWeUvQgg50zBOB8PJNSEkWvB1IyGERBvG6UA4uSaERAu+biSEkGjDOB1IiX2upX5Va7Fc9X3Okbv3eAzDTyMb7N+sNWhaD6011lrfrLfnt810rEK0e5Q+Wfs5J+1gbbluJ215zAAwJnGOaGsf64np6Z51TuVodo9oVySlj3VOeZT7+dCOL79AtE9k3wlcR3uMaxxLapO1JtvvWujPcuqe0j7WMTut2vLa6HuukCbbb5s5p1N9H+ylTQpgTHGGvqf2HwDr1q3D6tWr0d7ejlmzZuGhhx7C3Llzffv+4Ac/wJIlS8RnqVQKvb3e+2OkMxBf68KE/WENr2/WGutCmupCGuqC2ytinULomFnQ59rzvU9/HbvV5Sz056I12QPyxR6AF3Y46Ht9xhmmOD1SiV4WCiFkVGPc8EtYNm3ahObmZqxcuRI7d+7ErFmz0NTUhAMHDpx2nYqKCrzzzjv9y549e07blxBCzmaGI06PZDi5JoREi5NavjBLSNasWYNly5ZhyZIlmDlzJlpaWlBeXo7169efdh3LslBbW9u/1NTUDOYoCSFk5DIMcXokw8k1ISRSmLwJvYQhm81ix44daGxs7P/Mtm00Njaira3ttOudOHEC5557Lurq6vDJT34SL7/88oCPkRBCRjJDHaeBPunetGnTkE6nMW/ePDz77LOB/R999FHMmDED6XQal1xyCZ566inx/WOPPYaPf/zjmDhxIizLwgsvvODZxkc/+lFYliWWv/zLvww9dk6uCSHRwoR8GvJ7LV9nZ6dYMpmM7+YPHToEx3E8T55ramrQ3t7uu86HPvQhrF+/Hj/5yU/wox/9CK7r4sorr8Rbb71V2mMnhJCRwADjdLGEle5t27YNCxcuxNKlS/H8889j/vz5mD9/Pl566aX+Pl1dXbjqqqvw9a9/PXDfy5YtExLAb3zjG6HGDpQ4oTHvyAS2eFwWCUmrpDyd/BGzvQU7dAKa48ofTJ1s4TgySU4XbPEa6ivDfZ/EiJQqXNOVO+jpcypl8YmibXuSXFIIosKa5PnshFHFU9Qm05ZMssxAFp2pSp4rvzeq0I06bl0op6+P4zvek6QSlaLd2fOmaI9JTRZtXRDGU3QmL48BABJxmXBoG3nPhC30oBNedWEcyK8BAI4qEqMLz5BB4iJcftLv+9bV1YmPV65cibvvvrskQ2poaEBDQ0N/+8orr8SFF16If/qnf8I999xTkn2Q4vFLHAybwKiToz0JjJ71YwXHEBa/xPFT0cnuOk77/V4VSnosVIhGz4HCJzh691kowXHwRWWKGAMpLQOM08VyqnQPAFpaWvDkk09i/fr1uOOOOzz9H3zwQVx77bX40pe+BAC45557sGXLFjz88MNoaWkBANx0000AgDfffDNw3+Xl5aitrQ3sUwg+uSaERIqBvm7ct28fjh071r/ceeedvtuvrq5GLBZDR0eH+Lyjo6PogJpIJHDZZZfhtddeG9zBEkLICGQoZSEDke61tbWJ/gDQ1NQUKPU7HT/+8Y9RXV2Niy++GHfeeSe6u71OdoWgzzUhJFqY3y9h+qPPzaOioiK4L4BkMonZs2ejtbUV8+fPBwC4rovW1lYsX768qF06joNf//rXuO6660IMlBBCzhIGGKc7O6V1bSqVQiol3+YHSfd27drlu/n29vZQUr/T8Wd/9mc499xzMWXKFLz44ou4/fbbsXv3bjz22GOhtsPJNSEkUhg3ZFndAWShNzc3Y/HixZgzZw7mzp2LtWvXoqurq/8V5KJFizB16lSsWrUKAPDVr34VV1xxBS644AIcPXoUq1evxp49e/C//tf/Cr1vQggZ6Qw0Tg+lfK8U3Hzzzf3//5JLLsHkyZNxzTXX4PXXX8f5559f9HZKOrnWGmutadMFYDSu0rICgGPLz3TRj8ryD4h2b+6waGudXSYvdeF6jGNT3tfCGUfqkxMxqcu11T60Tq7Klnpnx0/IewppM8bzWa8l9cfjIPXr5a7UIhtLXotutb5fgZZTyRnvtdKFZvR50Iwre79oxy2pj87a8rxqTWM6KbXrAOC6SiOtisbkHVnUQ2uq844qQqR0/rrwjV8egBeqq0rKEGv5AGDBggU4ePAgVqxYgfb2dtTX12Pz5s39Tz727t0L237vuh45cgTLli1De3s7xo8fj9mzZ2Pbtm2YOXNm+J2PNEqgLS5MCQq6WAU01VpzbcvvdUExvc9CGu2+z2Kez4IwxlFtXQRN7lPHP788GN1Hx0CjNNSWnh8VKDpTWIM9eAavwSZDzgDj9L59+8QbRv3UGhiYdK+2tnZQUr/TMW/ePADAa6+9duYm14QQMlhMHjAh5igDzSddvnz5aWUgW7duFe0HHngADzzwwMB2RAghZxkDjdPFyPcGIt1raGhAa2srPv/5z/d/tmXLFpGIPhBO2vVNnjw5uKOCk2tCSKQIW81rtFX+IoSQM81Qx+mw0r1bb70VV199Nb75zW/iE5/4BDZu3IjnnnsOjzzySP82Dx8+jL1792L//v0AgN27dwNAf2Gw119/HRs2bMB1112HiRMn4sUXX8Rtt92Gj3zkI7j00ktDjZ+Ta0JItDAI97qRb4wJIWR4GeI4HVa6d+WVV2LDhg2466678OUvfxnTp0/H448/josvvri/z09/+tP+yTkA3HDDDQDe030nk0n853/+Z/9Evq6uDp/61Kdw1113hRs8AMuYkM7eAcRi8lF/PCZ1wI4r9bBaq5WMe18V6D7a91rruLXXstbYap2w1s0l7DLPGLSOTmv3krY8zrglx5iA9G/W6493pX7asbzvuccb7WMt+zgFPKg7rWOqLb26J5r3ifa7lrc4hsdTHFJb7iiteV5ptPW101r23qzSy/toGrUuUusmszm5TX3P6XbMltdGY/lo07UOW+cBxJUOvCezN3AfpI/Ozk5UVlZi/9LPoCJZjNb99+tls5jyvX/FsWPHinILGe1YVrDPvta7Fqe5DvaIHuz6hTysAa8m2tsOjh26v45XsQLbAwp7Y2uNtFdjrWOqivOqzoPWUwN+muucagdrsvUjRq8PdrCPdrF9gvbp+bok/4IO9uc2xr/oFJEwThcHn1wTQqLFMCQ0EkIIGQSM04Fwck0IiRTUXBNCSLRhnA6Gk2tCSKQwxmv/Vag/IYSQ4YNxOpjS+lwrjbXWoibU91rr5adhy7ldoh2D1MFpLZ7Hy7SAd2lP9l3Z38fnWnt9lcel//J4TBFtV73/sI3cZ6WRHtSVltT9ViW956HXkdtMKa1wdZnU9r3TJcc8zpFa8kpXjiEDqTeL+VwLrSXvMvLcjbMnye+tQ6Ld68jKTFo/qPXyvdkjnjFYtjzO7oz0tYzbUu+sSSUmiLbWBmZzcswxnzwAj0ZR3VN+OkhSPCZvwdhW4Y6n9CcDx6OxHhZCarKL8LkuFPu1xlrnTuh8Gx1LdPzTbQCIQe0TwV5lOm8lr3S/WSN/Qx1bfp/zqR3hWEo7HJyO48FV+TzaB1trsItDX69w26DvdfRgnA6GT64JIZHCGAtGV7oo0J8QQsjwwTgdDCfXhJBIQS0fIYREG8bpYDi5JoRECte14DrFP+Vw3dH1RIQQQs40jNPBlHRyrTXW2kNYa1Edjw+y1zPR77NTScSlb3U2L32OtVep1uFVpKW/c1LpwgHAVf/k0lo7R2nUxrtSkz1W+Vy7Si9WHpOXoTzu1RNeOkFq945l5fdVSd2W28w4sr37mNyeDXkeJ7lS/wwAb0PqkcvUOu8a6Y1dZkldd7eRPtbpeJUcY15qsvW1A7w67XRCnuucIzX62qc6mz8q2kk1hpi6/tr7FgDyzgnR9uYayO9JSFwLJkwgHmVB+2ykkK+1p+3nc619q0NqrHXsT1syBqYhvy833t+KlFHe2EpzrWN/Tmmuey35m9hjy3jWa2SM9ItP2UJ69gIabJ2Hot/m62vhl6hmqXW8vtd6BTXm0faYcyTCOB0In1wTQiIFs9AJISTaME4Hw8k1ISRSuI4N1y7eTcJ1wlYDJIQQMhgYp4Ph5JoQEin4RIQQQqIN43QwJZ5cK62WaseVBltrU7VmGwBspZPT2i2tsZ0wZrpoa41ut/K1Lk9Kza5jpAYOAMYq/2ate9P6vwm21CIn1b/uxiWkDu+CCvn9mLj3Lry4QvmdKiFcRUKOe/dxOYaEMisdl5Dndc8J+X133vuvzGxGaqj32HtEe6KR+vUTOCra6ViV3J6rtctSA6mvLeDVUer7IRGTx92bk9db66Mzytc6HvP6Wmv0PnUuQUF9IQmEFk/Eo8FV+GmNLaVv1n10DofWWOsckXFG/jZUGRkbKtXvGQCMTcoxpGPy3nRVaO9x5AfHc+NE+6jKSzrqySEqwU+40mAbKzi+eTyrff789ETKq8HWawyH7/XoenI61DBOB8Mn14SQSOG6VqjM8tGWhU4IIWcaxulgOLkmhEQKBm1CCIk2jNPBcHJNCIkUfN1ICCHRhnE6mJJOrm0fDdqpaI/hWKxcDsZn/XRygmhncnIbZYlq0T7eu1+0UwnpVZqIy31mla43rXywASBnekS7Gu8X7WmYLNpxW95EKaW5fv9Y2a5KSL3YrCqvT/IFNdIjOpGUQrnySVIXd3mZHMNvtsvzND4p9c1j4lLL9/pxrz6tXJ27XJfUWB+0D4p2xsjjKLelplFrrvOOPM9agw0AriuP0+OPq9oJpavUeuh0Uurp9fa1xhsALOWNrfcZs4K92UkwrrHghgjEYfqeyrp167B69Wq0t7dj1qxZeOihhzB37tyC623cuBELFy7EJz/5STz++OMD2jcJR6G/c8DP51rGco+vtSVjw1hTJdoTVbs2JdefOkbqqwFgcpmM5VWJYC3x4aw8jnd65G/g/i55DKmsbB/0OQ/GVrlP2rda50Z5vpe/LcbV3w+DJzV9ryPPcMXpkQoV/oSQSOE6VuglLJs2bUJzczNWrlyJnTt3YtasWWhqasKBAwcC13vzzTfxxS9+EX/4h3840MMjhJARz3DE6ZEMJ9eEkEjhwup/KlLU4mdXUIA1a9Zg2bJlWLJkCWbOnImWlhaUl5dj/fr1p13HcRzceOON+Pu//3t84AMfGMwhEkLIiGY44vRIhpNrQkikOKnlC7OEIZvNYseOHWhsbOz/zLZtNDY2oq2t7bTrffWrX8WkSZOwdOnSAR8bIYScDQx1nB7plFRz7bq9op1U2lSjtKha+5VX6wNAd0a+pk0npG5Xa7DTCanR1vsoi0vvUq37tX001xlzXO4D54t2Tu1DZ8WeXyG3ed4YqWl7X5n04r7iv0vvZQBAXG4zduWHRNucWyf7/787RPPitNRsT9yh9e0TVTsFze5jsl2TUBrsnLw2vbY8t1qDPTYm9c5HnDdE2yri334x5YPu9WQNxnGzgd/nHe89qe+phC3Pg999TIrHhNTynQzanZ3Sfz6VSiGV8t7Hhw4dguM4qKmpEZ/X1NRg165dvvv4+c9/ju9973t44YUXih4XKZ5Cf+vFxAKtw/b4XKvYnoT8ux1rpMd0dULGyPPGSY11fZU3dlwxtUO0z7lExnYrIe/roy/LMT+3R+bvPHdEjiHWKcfs9uq4DTiWjIGOLWsguKqWg2upNlReizr33mvhc208vtbUTJ9tDDROjxb45JoQEikcY4VeAKCurg6VlZX9y6pVq0oynuPHj+Omm27Cd77zHVRXVxdegRBCznIGGqdHC7TiI4REioFaPO3btw8VFe9V0fN7ag0A1dXViMVi6OiQTxk7OjpQW1vr6f/666/jzTffxJ/8yZ/0f+b+3kEhHo9j9+7dOP/88z3rEULI2Qqt+ILh5JoQEilcE8626WRJ6YqKCjG5Ph3JZBKzZ89Ga2sr5s+f37cN10VrayuWL1/u6T9jxgz8+te/Fp/dddddOH78OB588EHU1dV51iGEkLOZgcbp0QIn14SQSOG4FpwQ1bzC9D1Jc3MzFi9ejDlz5mDu3LlYu3Yturq6sGTJEgDAokWLMHXqVKxatQrpdBoXX3yxWL+qqgoAPJ8TQshoYDji9EhmSCfXumiMUclmZUmZvJFTBV0Ab/JEJiez6nRymU7WsCyZhKIN8tMx+aQr78oEFACoisknU7Yrx+SoZI3qlExCGa9qiqjcRMyeLgvfxK651DMG58NzZNvTQ3GDLPBiH5CvwCf/01Oibe2USZSvnZjq2WRFUg78YK/8p2ga8kAnuPIV+0F7n2gfy70l2mVxmYzak5dJmACQzcvkUsfnep1KzJbSAE9/9a/pvNst2roIDQDkHdlH7yOX9xYBIsUzHK8bFyxYgIMHD2LFihVob29HfX09Nm/e3J/kuHfvXtg2U1LOFIUTHL0FXPQ6tqetCrIYWRSmXCVxT0jLfZw/VkbdP5q51zOGigc/JdqmotLT51TGq/bHv/UjOcZHpca/15EJjV05b6GtLkf+punE8pwt45dtsqotf6c9v6nqvHqKygA+RV90ESDV3fNUkwmQUYeykGD45JoQEilchPNEHah/6vLly31lIACwdevWwHV/8IMfDGifhBByNjBccXqkwsk1ISRS8HUjIYREG8bpYDi5JoREChPyiYgZZU9ECCHkTMM4HcyQTq7LU1JT3Zs7ItrZvNRPj0nL/n19pMY2ERsj2j3Zd0W7cFGQHtEuU0VntL4aAPLIqLbUpI2NSy1fQknQkrYUlF1UJYtdlE2S35vxwTq9gWAmyYIZ8aaLRHt8+4uifUGHV8u8q1NqEs9Jy9vH7pV/PLm81Cjqog0TEueJ9ru510Vba5kBb2GIZFwWftBFXzLqnovHylVbaha15lrrqwGvzl/f1/p7Eg5j/DSYwf0J0eh8Gxu6LQN1UvUfo5JjdLGvqj/1/l45BTTWhTB/81nRnrv7u6L9+rYLRLujx6s9P9wtY9pxyHavKqajY6ouxqPbOk8lClg+EzcTxYGeRTBOB8Mn14SQSOGGrPwVpi8hhJDBwzgdDCfXhJBIEbaa12ir/EUIIWcaxulgOLkmhEQKPhEhhJBowzgdTEkn15bSbuXy0re6kLYrl/fqW7WmVmuqY7b0VnZdqYfW2LYcQ8ySut4eI3XgAFCLD4h2t/INzbpVaptyH9VJqcHV2qP8CflB/ILpnjGUGlMzSbS17+ihjNTlAcCHJ8jjeEZaZ6PXlRrrDOS1ciD9Ug/n3hDtpC319L3KJx3w6rD1PaR9zMtS5wSu3505INq2lVRt75+I1lQ7rtR5x2zpc07CYWCFSn4ZbYkyIwGjfI49ut1hGUPBagCB2Oq2Stkql2KyjKFDQfkfSJ/rmp0yho5JePNSUupnPab8vbXft9aeax9r7SleyIPcr8+g81A8um/mtZxpGKeD4ZNrQkik4OtGQgiJNozTwXByTQiJFK7pW8L0J4QQMnwwTgfDyTUhJFJQy0cIIdGGcTqYkk6utc7ONXn1vWzbSi+t9bKAV4fdk3lbtCvGBOuTtdep1oJpX+Ok8kEGgIPWPtF+nyu9RruVlrjXkZq2Pd1yDBdWyjGUfUhqdK09b3rG4J47zfPZoOiROuFkrRxjXbn8HgB2H5d+qdoHtjcvt5Ex0hdW6+7GxKUe2lXnMRWv8IxBf9adPSTahXzOe7PSkzrvSP18TF1/raf2Q9/XboExkGAMwlnpjrIHIiVH+wF7PIO1vnUY9NM6Vnjb3t8K3cf1tOU6eUv+3WZVzkh3Xp6XAxmV37N9t2cM+PAc72eDIZML/NpvumKpBBrbyOul/b71b2RY/DTY9Po/+xmOOL1u3TqsXr0a7e3tmDVrFh566CHMnTv3tP0fffRRfOUrX8Gbb76J6dOn4+tf/zquu+66/u8fe+wxtLS0YMeOHTh8+DCef/551NfXi2309vbiC1/4AjZu3IhMJoOmpib84z/+I2pqahCG4c8yIYSQABxYyJviF2eUJcoQQsiZZqjj9KZNm9Dc3IyVK1di586dmDVrFpqamnDgwAHf/tu2bcPChQuxdOlSPP/885g/fz7mz5+Pl156qb9PV1cXrrrqKnz9618/7X5vu+02/N//+3/x6KOP4r/+67+wf/9+/M//+T9DjR3g5JoQEjGMsUIvhBBCho+hjtNr1qzBsmXLsGTJEsycORMtLS0oLy/H+vXrffs/+OCDuPbaa/GlL30JF154Ie655x5cfvnlePjhh/v73HTTTVixYgUaGxt9t3Hs2DF873vfw5o1a/BHf/RHmD17Nr7//e9j27Zt+OUvfxlq/JxcE0IihTuAhRBCyPAxlHE6m81ix44dYhJs2zYaGxvR1tbmu05bW5tn0tzU1HTa/n7s2LEDuVxObGfGjBl4//vfH2o7QIk111pD7Sg9czw2VrS13tU1Xn/gsuREuY14lWhnctKX2ra8/synMiYlvUkz+U7RTqoxAoBlpCbtgL1ftCe7daKdUP9k6VHywP3dUrv8gWffFe3x1S94xzBJjtuUebXhYbDebhftzNvy1o9bXoXUO73ywHoduc7RvNRYl0Ee52HI75OWPIZ3s6/JMfpoAbV3rfY5z+Tl/RBXY9D6aK2xjiuP6ryf5lppUGO23IbeBwkHLZ5GIvqnM9xzG63RLcZL2bMN9Xep23mVA5K1ekS7W8WnIxkZC17vkrHmt094f69mzHtOtJ1BarCPbZW/kQczMk8l43jjtOPRyA9qCIT4MtA43dkp51ypVAqplPRrP3ToEBzH8eica2pqsGvXLt/tt7e3+/Zvb2/37X+6bSSTSVRVVQ1qOwCfXBNCIsZJi6cwCyGEkOFjoHG6rq4OlZWV/cuqVavO7IEMEbTiI4REClb+IoSQaDPQOL1v3z5UVLzn+qWfWgNAdXU1YrEYOjpkGeiOjg7U1tb6br+2tjZU/9NtI5vN4ujRo+LpddjtAHxyTQiJGHk3/EIIIWT4GGicrqioEIvf5DqZTGL27NlobW3t/8x1XbS2tqKhocF3PA0NDaI/AGzZsuW0/f2YPXs2EomE2M7u3buxd+/eUNsBSvzk2nGkxlrrWTVJpZ/WvtgAkHd6PJ+JfSpPYTsmNdeW8mTNKp23Jud695eypbdyuRkn2kesw6LdlZe67W51WL89IbV7728fLzs8Jb2YAaBy76Oibf/3etE2F5wvv//V855tnEq+7Q3R3vM7OYa93V49YU5NYsqUz3VCaaRdpf3T18JRvtZaL5+MjfGMoTsnfa2zueOiXZ5U3tn6nlJjSMbktcw5XZ59FsK25J+RQ831oBiuJ9dhPFQfe+wx3HvvvXjttdeQy+Uwffp0fOELX8BNN900oH2TAmj9tPa51rpin89cI+OLozXXkL9XJywZSw4pzfUbyuf/6XeqPWPI/Z2siXDhH/5atOP1U+SYD8t9Hntatn/x2/eJ9t5uGb+OZr1+3xnI+OMoP29X5a3oPJaw0NN6dDLUcbq5uRmLFy/GnDlzMHfuXKxduxZdXV1YsmQJAGDRokWYOnVqv6zk1ltvxdVXX41vfvOb+MQnPoGNGzfiueeewyOPPNK/zcOHD2Pv3r3Yv78vb2737j6v+traWtTW1qKyshJLly5Fc3MzJkyYgIqKCvz1X/81GhoacMUVV4QaP2UhhJBIYULqqM0ANNcnPVRbWlowb948rF27Fk1NTdi9ezcmqeRhAJgwYQL+7u/+DjNmzEAymcQTTzyBJUuWYNKkSWhqago/AEIIGcEMdZxesGABDh48iBUrVqC9vR319fXYvHlzf9Li3r17Ydvv/WPzyiuvxIYNG3DXXXfhy1/+MqZPn47HH38cF198cX+fn/70p/2TcwC44YYbAAArV67E3XffDQB44IEHYNs2PvWpT4kiMmHh5JoQEinC2jYN5LnZqR6qANDS0oInn3wS69evxx133OHp/9GPflS0b731Vvzwhz/Ez3/+c06uCSGjjuGI08uXL8fy5ct9v9u6davns+uvvx7XX3/9abf353/+5/jzP//zwH2m02msW7cO69atCzNUD9RcE0IixUmLpzBLGAbioXoqxhi0trZi9+7d+MhHPhL6+AghZKQz1HF6pFPaJ9dKa+rRoirPYFd97+dtmlU+1AnlQ52IS12u40gNttZspxKVcgxqzEnb63OdM1Kbd8R6R7Snmumi/VZW6uZiltT12kqb/OsjUtP9/6k2APzh0YOi/b7j0k/VTu8U7cxBqaPrbJdJAz29Uj94LCO/P5rzekwf6pXvdQ70SE3jESP1yr2W0uBDaqq15lprrLM++ueU0ki7rtYTynY2r6+F1LvnlcZe37M5H41+THlh27ZcxzV8ITQYwtrrnexbjH8qMDAPVaCvetfUqVORyWQQi8Xwj//4j/jYxz5W/EDPFnz0zjqXwbOKJ/9Cf682p7ZXjOZaa6z132HelZrrjCX/tk/Y8l55FzJWxLpUzojx3ltHc7Iuw69/In9vJvw/Sg9t5PcdGanj1hrrfSfkcXfm5DEDQA/k72xexVl9Ll19bj1tJ/B7P6jDPvsZaJweLfDJNSEkUpgBLMDQ+6eOGzcOL7zwAn71q1/hH/7hH9Dc3Oz7apIQQs52BhqnRwt8xEYIiRSOCVd18WSRumL8U4GBeagCfdKRCy64AABQX1+PV155BatWrfLosQkh5GxnoHF6tMAn14SQSDHQyl/F+KcCA/NQ9R2n6yKTyRTuSAghZxmspBtMSZ9cx5Qe2qOzU3P5QppsAIjb0is7pnRxubzU5WqtXjwmtcXdmQOiPTYln1RpfRng9VauSE2W30Nq96ZCajm78nKbNqSe+a0e2R6X8N6Fbe3Sv3lmjzwP49LyR74nJ/XNXVmld1b/4nylU+qd3zjh/ReppV7snNAe48rH8oR1VLS7XenfnXXleYtb8pj8fK47e/d5PjsVrbnXGuy40kvnPJp+5Wmekteybx15z+W0v7vSdZNwDEcWelgP1VWrVmHOnDk4//zzkclk8NRTT+Ff/uVf8O1vf3sAe482Rv2dWwPyEddXJdxzHK3Z1b8dfjURLKP6qHwMBzJG5lVOSK/y2T+mcmP0e22nS+qlAaArJ2PYO91yG2MSOj9Drt+rHu91ZrXGWh5Tr/Fqrh1L/t7o3BZHraN/pwu1zwhRGAMRDEecHslQFkIIiRTGhPNEHYjPdVgP1a6uLnzuc5/DW2+9hbKyMsyYMQM/+tGPsGDBgvA7J4SQEc5wxOmRDCfXhJBI4Ya0bXIHaPEUxkP1a1/7Gr72ta8NaD+EEHK2MVxxeqTCyTUhJFLwdSMhhEQbxulgOLkmhEQK+qcSQki0YZwOpqSTa8vSyRoyuUwX39BJKfp7AEgmZNGQnuy7op1OjBft3pxMmitPykRAXXRGFyrRCZMAUJu6RLQ7XVlERufq7FPJFxWOTHxJdcmkub2qTsl5Fd7LciQuE2MOZKpEuyYlk1hSMXknJ205pt3HVVEZlcf5bsb770z9UkcnPh2w98sxQCajdkFeO01P/rBo64QkAEioJEdPwqJKYNX3i7e/TMLVCbGZ3LGAEfeRVIVtcj7Fb0jx9Fk8hetPRhahi8rowiY+b5j137ZOcNSPznIFCt+ovHO4KlEw75P8nsnK2N7tyN+0cQm50Zg6cD0ByRaYkfglm+qiMDqh0VtsR7VV3C2Y4FhUsuHQJknq3yIy9DBOB8Mn14SQSMFEGUIIiTaM08Fwck0IiRTU8hFCSLRhnA6Gk2tCSKTg60ZCCIk2jNPBlHRyrY3/dQGYQoUBdFEZAOjqfSewj+PKwgBaY+2oQifZ/HHR1jq98oRcHwC6jCwiU2ZLnXePkbrchCqGUm5ke3e+XbSnolq0j2aU2A9AryO1dcdzqmBLXp6XpJYbqhv7hJIzv9srO+R8tH7tmR65TU+xCbnTd903RTtuSf1h0pZ655gq4pBRBV4A7/X0fi/vh0QsuAhR3lHH5MkD8BaEyeaPirb3npTFKUg4mCgTLYoqKqM1tB49c4mLyvhcc93Hk7Ohdum4IQsU67DscxriqlO5K+PHmLiMcVPK5UYqVQGxXjXGfV2y/bvj3ueBOUvGwJyRMS7nyraOqbqQmj6vhdrAQDTVo+255siHcToYPrkmhEQKAwsmRFXAMH0JIYQMHsbpYDi5JoRECoNwTzlG2QMRQgg54zBOB8PJNSEkUlDLRwgh0YZxOpiSTq4dRxo2O1pTrfSrtiXbfnrahPIhzql95Bypb80qna7eh/ZJTiWkB/XRnt95xlCWlJpofdYspbPLQWre3lb+z2Nc6YV6DL2i7XR778IJCXkcWhM9MS3HkIrJVzAJJS/UtjgnclLzdiwnvU8BIKW0xQcgPcW19q7Snirah/Py3Gr9c1ZdWz8tn/Ypz+Wlp3Qmd1T2V/dPQc9WhdZg921TXj99T+rvSTio5RtqBqd/LoqCGmzVPaTvtf8bZqW51n+7IWW9liVjquXKv3PL9h5Tj6V8843Mz/nAODnwL9wqc4rcZQtE+7X//m+i/fArMifoiPF66p+wpLd/xpE5QXmP5lr+XhXyuR6IrzU5+2CcDoZPrgkhkcL8/n9h+hNCCBk+GKeD4eSaEBIp+LqREEKiDeN0MJxcE0IiBV83EkJItGGcDqakk+tEvEq0te+xY6Sm2nGl1jhmSx9kP1IJuY+8I7eRjEuNWyEyOalHS8bHefpojVqvI49LeyXDa40s6LQOiPZkc4FoH1NaZgAw2RrR7oHURHd1Sf1yTIkWe5WOrtyWfqvHle6uS+nAASBnyX0etTrkOq70A9e+1U4BDaTW3OtrA3h12DFL6/jlLW0prafX01V748rv48onu28b+ty4Bb4nYWBZ3Wjj93rX1/s6kLC6b50r4e1h6T5qSIU02Dp3Jg8V1z3789Fcx2TOz2FTJdq/OSrj1f0PTpb7fOBnov3ykYmi/ducrJFwwNrjGcOJvPx9yTlSl+04MsZpjbU+T0a3PdeisL7a26fAOqF9sslwwzgdDJ9cE0IiBcvqEkJItGGcDoaTa0JIpHBdqBpxhfsTQggZPhing+HkmhASKfhEhBBCog3jdDAlnVxrvWsmd1i008ovOl+Ev6pty23m8tJrNJWQGmvdX+vJtKen1uRq72XAR3OdlZroZELqtLWvaNyWut24JffRZUudnvbJBoC3LOmHWmbkNm0lMByjhN9HLLkP15Vjzqtbv8P2avlspUnsNeo4LamZ73HkedLnsUd5VOtz7+dzHS+gy/fTSJ+Kq3T/+tpo71p9TwNA3khv9bjy0s4r73USjuHS8q1btw6rV69Ge3s7Zs2ahYceeghz58717fud73wH//zP/4yXXnoJADB79mzce++9p+0/shi877XWYXs02AV9r4M11TpO+/1Ua62wrX7ewmqwTajncn2cgNQ7520Zy49kqkR7R7uMeTlL9u+BjNs9roypWdfrc61zgAr5WA9eY+29FsXosAfDaLN1iyLUXAczBNUDCCFk4DjGhF7CsmnTJjQ3N2PlypXYuXMnZs2ahaamJhw4cMC3/9atW7Fw4UI888wzaGtrQ11dHT7+8Y/j7bffHuzhEkLIiGM44vRIhpNrQkikOGnxFGYJy5o1a7Bs2TIsWbIEM2fOREtLC8rLy7F+/Xrf/j/+8Y/xuc99DvX19ZgxYwa++93vwnVdtLa2DvJoCSFk5DEccXokw8k1ISRSmAEsYchms9ixYwcaGxv7P7NtG42NjWhraytqG93d3cjlcpgwYULIvRNCyMhnqOP0SKekmmvtIZyIV4i21mElYmNEW/txAl6NtdbM6nVMXvkg21J7XMjnWHudAl5NtfYJNUZq87py8tVymdKadznSHzqfkJq4pOXVDWcgz4OxJqrvpbfy25Y8L0mUBfZPKw23R48IIKd8X7OO0r/HKkXbVv92q0i+T7S77INye7njckwJr2e51hMW0vZpz+mU8kHXusoYpAayN/euZ5ta1633UUj3TYIZaHGCzk6pT02lUkilvDkUhw4dguM4qKmR3vE1NTXYtWtXUfu8/fbbMWXKFDFBP3sYvAa7ICXXYHvX0THM40ut8ikKarBNIe0x4CiNdUbluhy3pE+1xuu7L2sL6DFqPXXfOsGaac9x6uMagMa6MFHwtR6G+3oUwSIywfDuIoREir6yumG0fH3r1dXVobKysn9ZtWrVkIzvvvvuw8aNG/F//s//QTpduPAVIYScbQw0To8WaMVHCIkUA30ism/fPlRUvPe2zO+pNQBUV1cjFouho0O+Qero6EBtbW3gvu6//37cd999+M///E9ceumlxQ+SEELOIvjkOhg+uSaERIqTFk9hFgCoqKgQy+km18lkErNnzxbJiCeTExsaGk47rm984xu45557sHnzZsyZM6ekx0wIISOJgcbp0UKJNdfS3zemPIQzSoucSMhkID9f47LUOaKtdbla7+rRUBvtYx2swdY+2YBX95aIy+PK5eVxpxJVon285y3RPmfsTNHOax9Sy+uvqo+rC9LvVNvKan1ht5H9yy2pPT6C/aKdM16vZq2968kpH2ulh9djOJbZK/ehfK7jMakL1/pqwKtfP94jt6m1mx79oNJYa02/Vy/tvSf1cRVqk3C4IW2b3AFE7ebmZixevBhz5szB3LlzsXbtWnR1dWHJkiUAgEWLFmHq1Kn90pKvf/3rWLFiBTZs2IBp06ahvb1POzt27FiMHTv2tPsZrRT0vfasMDgNNlDYC9u7js4rkes7BX5L/PJSbFf+fvhrw4unkM7b7zezYJ8Sa6z9815Kq7Gmr3X0GI44PZKhLIQQEimGo/LXggULcPDgQaxYsQLt7e2or6/H5s2b+5Mc9+7dC/uUgkLf/va3kc1m8elPf1psZ+XKlbj77rsHMAJCCBm5sEJjMJxcE0IihTEGJsRTjjB9T2X58uVYvny573dbt24V7TfffHNA+yCEkLOR4YrTIxW+vyaERIq8MaEXQgghw8dwxOl169Zh2rRpSKfTmDdvHp599tnA/o8++ihmzJiBdDqNSy65BE899ZT43hiDFStWYPLkySgrK0NjYyNeffVV0WfatGmwLEss9913X+ixl/TJdSImfa29Glqpb9V66ZjttbXqyUgvZMvSmjbZNkr36ypNdVla+kP3ZKWPsdYBAz5+qUpHZ6sxZHJHRTudlPrmY737PPsQ68c6A7/3G1MqNu40PfuwLOnffTT7pmiPSU4SbT8tn8arB1R6ZuVBrs+bbmeUhtvvfjje+1ZgH31eylLSy1hfb1tp8G0robZfhGf1IHWVRGIQLvmFU+uhZvD+wEOtwQYK3zOW2qXu7615oPep8zm856Ggt3ZICsbhIrTLhTXVmsH19x/EmdBYl2Dc5LQMdZzetGkTmpub0dLSgnnz5mHt2rVoamrC7t27MWnSJE//bdu2YeHChVi1ahX++I//GBs2bMD8+fOxc+dOXHzxxQD6ktK/9a1v4Yc//CHOO+88fOUrX0FTUxN+85vfCFvVr371q1i2bFl/e9y44PmVH5wVEEIihQsTeiGEEDJ8DHWcXrNmDZYtW4YlS5Zg5syZaGlpQXl5OdavX+/b/8EHH8S1116LL33pS7jwwgtxzz334PLLL8fDDz8MoO+p9dq1a3HXXXfhk5/8JC699FL88z//M/bv34/HH39cbGvcuHGora3tX8aMGeOzx2A4uSaERApaPBFCSLQZaJzu7OwUSybjrTKazWaxY8cOUQHXtm00Njaira3NdzxtbW2eirlNTU39/d944w20t7eLPpWVlZg3b55nm/fddx8mTpyIyy67DKtXr0Y+73UGKgQTGgkhkSJvXFghXtnmh6V0MiGEkJMMNE7X1dWJz/0clw4dOgTHcfrdm05SU1ODXbt2+W6/vb3dt/9J29ST/w3qAwB/8zd/g8svvxwTJkzAtm3bcOedd+Kdd97BmjVrijzSPji5JoRECvP7/4XpTwghZPgYaJwutpLumaK5ubn//1966aVIJpP4i7/4C6xatSrUWEtcREYmKGbzR0VbJx/ali7oItf3Q6fB6KS4VEwmD2byMklOJ2fohEfffXrGrZMo5TZ1YZq0KpaThyyO4rrylYNfAolODo0h+CJ3q8S9MSmZAFCVnibaPY48T9m8LNYDAMm4FPXHYvL65R15/TL5Y6JdlpTJpN7tV4q2LvgCwJOD4qgE1oQqAtObVcV29ObUtcqqtl9SpU4QctV9a8f4b9bBYBAu1YhT6+HG7+qEUxiWPsHRb1wqAVFtQyc4enYZOuHRZxsFewyOwsmGfgykKEzx6/dthG+TznYGGqdPVtANorq6GrFYDB0dHeLzjo4O1NbW+q5TW1sb2P/kfzs6OjB58mTRp76+/rRjmTdvHvL5PN5880186EMfChz3qVBzTQiJFI5xQy+EEEKGj6GM08lkErNnz0Zra2v/Z67rorW1FQ0NDb7rNDQ0iP4AsGXLlv7+5513Hmpra0Wfzs5ObN++/bTbBIAXXngBtm37OpQEwUdshJBIETaznG4hhBAyvAx1nG5ubsbixYsxZ84czJ07F2vXrkVXVxeWLFkCAFi0aBGmTp2KVatWAQBuvfVWXH311fjmN7+JT3ziE9i4cSOee+45PPLIIwAAy7Lw+c9/Hl/72tcwffr0fiu+KVOmYP78+QD6kiK3b9+O//bf/hvGjRuHtrY23HbbbfjsZz+L8ePH+47zdHByTQiJFJxcE0JItBnqOL1gwQIcPHgQK1asQHt7O+rr67F58+b+hMS9e/fCtt8TX1x55ZXYsGED7rrrLnz5y1/G9OnT8fjjj/d7XAPA3/7t36Krqws333wzjh49iquuugqbN2/u97hOpVLYuHEj7r77bmQyGZx33nm47bbbhA67WCxTwpqUMVVEplDBF0cVlbF99K2auCrqkXe7A/trXbfWzcWL2GdeaWrLk+eIti4c4NVgS6uZRFx6JjqO1Pn6aY1zjjzOlNInFyrQUkhHl4jJMfXmDnv6WJCFaPQ+tCb7RO/+wH3o9V2TE+1s3ltMR1/PeExeP32cehupRJVoa524d0xeCx5Hj0vd11DruKZwLgHpe0VXWVmJP6i4BXGr+MSRvMngF53rcOzYsYJaPgJYqlDS0DA4xWFBDXZRGyk0hnBj1LGhOEqtvBy8BKokmmqxwRKMadD/QC7FeckV7kQYp4uET64JIZEiDxdhfizzrLRGCCHDCuN0MJxcE0Iihfv7/4XpTwghZPhgnA6Gk2tCSKQwloGxig/E9LkmhJDhhXE6mJJOrrVmWvtB55RWNe7RaHv1aXFb+jtnlXeyx2tUbSPvyH1q3TfUmLW+GgCSMaklLqTT7c1Jj+lUXHlv5+QxpBJSP+3nzaw11nlHemXHbKl90vpl7ZOdy3cFfm/76DJzjlwnZkv9s/bG1selj1uvXwyee0pp0fX3Hi260o3r/l5vdu89GYuNldtQx1GMdzo5PQ4cwM/jPLA/iRbBntOFKPRDXJQmu6A3drgnaQPLTori07pBjmkAGuvST6yieF5HF4zTwfDJNSEkUpjf56GH6U8IIWT4YJwOhpNrQkikcC0XVojXjaNNy0cIIWcaxulgOLkmhESKPPIwIWQEDrx2iYQQQoYOxulgSjq5dpT+1Smgu3ON1Ka6jlfvnLfkNhNK76p9rvU2EvGq0w0XAJDJHSrYX/tUe72ypV45pnTcfr7Vp5LNSa2ynxZZa6htW3mGF/DSdl15Y2s9tF6/J3vQMwatP86r662PW/te6+ttGXU/KK2ynx+rn+900DraF9uj2Vea61RiQsEx5JSOP24p73Uj/dtJOIbrdeO6deuwevVqtLe3Y9asWXjooYcwd+5c374vv/wyVqxYgR07dmDPnj144IEH8PnPf35A+x2dDE6DrfHT8BbUYRfSChf0sR6KJ29hz8MwPP2LhG+1ZnQ99RwJUBYSTKkd7gkhZFC4cEIvYdm0aROam5uxcuVK7Ny5E7NmzUJTUxMOHDjg27+7uxsf+MAHcN9996G2tnawh0gIISOa4YjTIxlOrgkhkcLA9D8VKW4J/5RszZo1WLZsGZYsWYKZM2eipaUF5eXlWL9+vW//D3/4w1i9ejVuuOEGpFLFVyUjhJCzkeGI0yMZaq4JIZEib+VgQlS/dhCubHE2m8WOHTtw55139n9m2zYaGxvR1tYWaluEEDIaGeo4PdIp6eTa4yGNYP2r1uD46Z21DtcxwbrcdLJatDO5w6IdV5rtdHKSaGu/aADozUrfau1rHI9JrbH2g9ZezDF1HrRuXGuXAcCG1ljL86D9mguNWWu2tZ7abwxaC67X0bpufR4811/ro/V5VVpmwOtDru8PvQ2dB6DvDz1mfV61ThzwHof3WoT37yanEk7LdzLOdHZKLXwqlfJ9ynzo0CE4joOamhrxeU1NDXbt2hV6tCMP/QQpxC9kySitBhsogTd2CbTGhXXbmpGhoQ7c/JA8kRxd+tyRycDi9GiBshBCSKRwjRN6AYC6ujpUVlb2L6tWrTrDR0IIIWcnA43TowXKQgghkcIN+frwZP99+/ahouK9qq+n00ZXV1cjFouho6NDfN7R0cFkRUIIKYKBxunRAp9cE0IihTuA/wFARUWFWE43uU4mk5g9ezZaW1vf26frorW1FQ0NDcNyjIQQMpIZaJweLZT0ybXW6WpNbjandcDSH1rrZ/s2oub/Hh9jeQjZvNRdah14TmmwoXyNtX62b5xKr6y22ZV5R7T1ecjnle+xLY+hLHmOHGNeapUBr7+z1gI7SoucSlSpbZ5QbaWHLqDB9huDcYOvhW4X0mdpPb32DweAmKoIZSw1JjVGfe30edBjctV5tHz00/rc6HOvj5uE42QWepj+YWlubsbixYsxZ84czJ07F2vXrkVXVxeWLFkCAFi0aBGmTp3aLy3JZrP4zW9+0///3377bbzwwgsYO3YsLrjggtD7jxZR1GBrIqDJLmonI38CcWZcHUb+eRttDEecHslwFkAIiRTGODAhJjpmAFq+BQsW4ODBg1ixYgXa29tRX1+PzZs39yc57t27F7b93oRu//79uOyyy/rb999/P+6//35cffXV2Lp1a+j9E0LISGY44vRIhpNrQkikcJAL9ZTDHWBZ3eXLl2P58uW+3+kJ87Rp02DM6HryQgghp2O44vRIhZNrQkikMCZkWd2z4FU8IYSMJBingynp5FrrXZPx8aKdU3porYfV6/d9KC+IpbV3Sv9qlGY6ZkuvZNfy0XWLMRS+AfS/wLTGWmtwYzE1BjXGnuzBwO35jSuuj8toj2np71zI5zqTOyq3H/N6TOt9xG3t7y31zAnlKV5oe5ps/pjnM62h1/eDPk7P/aLQHuPaaz2vzqPfGPQevH7vJAx9iS/FB+LRligz9ERBg60pvS+2phSa0JLotktMNLWu/Jsd6TBOB8NZACEkUvT9oyvE68ZRpuUjhJAzDeN0MJxcE0IiRd/rxjCJMqPriQghhJxpGKeD4eSaEBIxnJAvskfXExFCCDnzME4HUVrNtdLUZHJHRDtVwFPa9fmXTSGvZK2pTcQqRFvrgPUYMtkDoh2Ly/UBwHG0Llf28YzRKkJLfgqF9NOA11u5UCKBPi8en2wn2A/az3Nc71N7iuvjzFtKe67OS8yjuVfX1i7ste24wd7brrp2yUS1Z5uivzpuP+15Ln9UtG2tuXfkeSHhcN08LKv4sD3aLJ6Gn5GgwfZj+GukRVPffCYYXU8pRyOM08HwyTUhJFK4cEMlhoXJWCeEEDJ4GKeD4eSaEBIp+t5gUMtHCCFRhXE6GE6uCSGRIuzrw9H2upEQQs40jNPBcHJNCIkUrnFghdCujrYnIoQQcqZhnA6mpJNrbxKdTliUCWwao4qvAIDxSSg7FZ1M6Kh9liXPEe3e3LtyfbV9vb2+D2XCWtwuk2NUWbA6+TDvyONK6kIlqpBJearGM4QT3W/IfehiKWrcuhCN0Yl/Sv+kC774/SFk84flOuo4NLpokGvp6yvPk2fMPmNwVIKq5UmSDN6Gvj/0cXuTbL3JpTG1juspGhRcPIcEEzYIj7agfebx+0GNQpKjJux9MfwJkCMT/r0RxulC8Mk1ISRShE18GW2JMoQQcqZhnA6Gk2tCSKTos3gq/iniaHsiQgghZxrG6WA4uSaERIywQXh0BW1CCDnzME4HUVrNtdL9JmPBxVHyqhhH3EfDm8vLQjTJxETR1hpprfvWGmtdVMQqQuerx53NH5PrqJtG6361xlr31/vsznR4xqC14fpc60I3egy6GEpe6YR1W5/Hvm3KbTh6HTWmdFIWbMnlpV5aF67R2/ND65l1YZqsuqcKFS7S/fUx+hUA0oVmPMehrgUJB7V8I5EoFpoJy2jVaI+Wv5+z4R6NDozTwfDJNSEkUlDLRwgh0YZxOhhOrgkhkaLvbUGY4gQsOU0IIcMJ43QwnFwTQiJF+MpfoytoE0LImYZxOpiSisaMyYslmz8qFk08XiWWfP6oZ4nFxojFcXvF4pq8WByTFUs6MVEsdqxcLJZlF1xsKy6WZLxSLRVi0ejzYMGWi9pfearGs7hOt1zcXrHYdlIsmrzTLRZ9reJ2Wiz6mG0rDsftFkvMTotF9+/NHhKLa7Ji0WOyraRYLCvuWRznhFhyaknEK8SSzXeKxcAVSzJeJRaN3xhisXKx6HOpvydhcQewhGfdunWYNm0a0uk05s2bh2effTaw/6OPPooZM2YgnU7jkksuwVNPPTWg/Z6dWGoZidghl7OF0XLcZ8M9GiWGPk6XOkYbY7BixQpMnjwZZWVlaGxsxKuvvir6HD58GDfeeCMqKipQVVWFpUuX4sQJmS9WDCP5L4UQchZijBN6CcumTZvQ3NyMlStXYufOnZg1axaamppw4MAB3/7btm3DwoULsXTpUjz//POYP38+5s+fj5deemmwh0sIISOOoY7TQxGjv/GNb+Bb3/oWWlpasH37dowZMwZNTU3o7X3PTOHGG2/Eyy+/jC1btuCJJ57Az372M9x8882hz49lSvisPhGX7hC6QmO8QBVA7R4CeF0yNNoVQzt7pOKVoq3dQwptD/C6QyTVNnWFRu1IoSsVnokKjfoYzkSFRq8nZoEKjT7/0i1UodHjiqKcO/RT/bjap752xSRh6AqNttpmLucfDIiks7MTlZWVABKwrLCvG3M4duwYKiq8b478mDdvHj784Q/j4YcfBgC4rou6ujr89V//Ne644w5P/wULFqCrqwtPPPFE/2dXXHEF6uvr0dLSUvRYo4COFSXa6hBsc7jhs6biODsT04zJnekhjAiGK06XOkYbYzBlyhR84QtfwBe/+EUAwLFjx1BTU4Mf/OAHuOGGG/DKK69g5syZ+NWvfoU5c+YAADZv3ozrrrsOb731FqZMmVL08TKaEEIihgFMiMW3HPfpyWaz2LFjBxobG/s/s20bjY2NaGtr812nra1N9AeApqam0/YnhJCzm6GL00MRo9944w20t7eLPpWVlZg3b15/n7a2NlRVVfVPrAGgsbERtm1j+/btRY8fKHFCYy5/qJSbI4SMSgxMyAkz0PdE5VRSqRRSqZSn36FDh+A4Dmpq5Buimpoa7Nq1y3fb7e3tvv3b29tDj/NM4+fdTggh4Ri6OD0UMfrkfwv1mTRpkvg+Ho9jwoQJoWM9n1wTQiJBMplEbW0tACf0MnbsWNTV1aGysrJ/WbVq1Rk5DkIIOVthnC4OWvERQiJBOp3GG2+8gWw2W7izwhjj0f/5PbUGgOrqasRiMXR0yEqoHR0dv//R8FJbWxuqPyGEnI0MR5weihh98r8dHR2YPHmy6FNfX9/fRydM5vN5HD58OHSs55NrQkhkSKfTqKioCL1UVlZ6Pjvd5DqZTGL27NlobW3t/8x1XbS2tqKhocF3nYaGBtEfALZs2XLa/oQQcrYy1HF6KGL0eeedh9raWtGns7MT27dv7+/T0NCAo0ePYseOHf19nn76abiui3nz5oU7SYYQQkYZGzduNKlUyvzgBz8wv/nNb8zNN99sqqqqTHt7uzHGmJtuusnccccd/f1/8YtfmHg8bu6//37zyiuvmJUrV5pEImF+/etfn6lDIISQs5ahiNH33XefqaqqMj/5yU/Miy++aD75yU+a8847z/T09PT3ufbaa81ll11mtm/fbn7+85+b6dOnm4ULF4YePyfXhJBRyUMPPWTe//73m2QyaebOnWt++ctf9n939dVXm8WLF4v+//qv/2o++MEPmmQyaS666CLz5JNPDvOICSFk9FDqGO26rvnKV75iampqTCqVMtdcc43ZvXu36PPuu++ahQsXmrFjx5qKigqzZMkSc/z48dBjL6nPNSGEEEIIIaMZaq4JIYQQQggpEZxcE0IIIYQQUiI4uSaEEEIIIaREcHJNCCGEEEJIieDkmhBCCCGEkBLByTUhhBBCCCElgpNrQgghhBBCSgQn14QQQgghhJQITq4JIYQQQggpEZxcE0IIIYQQUiI4uSaEEEIIIaREcHJNCCGEEEJIifj/AZwOwJIwmneEAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x1560 with 24 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ==== Visual sanity check: show LR/HR that actually go into training ====\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "\n",
+    "@torch.no_grad()\n",
+    "def show_lr_hr_batch(train_loader, n_show=6, vmax_mode=\"batch_p99\"):\n",
+    "    \"\"\"\n",
+    "    Display n_show samples from a single training batch.\n",
+    "    - Inputs are taken exactly as the DataLoader yields them (LR first, HR second).\n",
+    "    - Assumes LR, HR are in [0,1].\n",
+    "    - vmax_mode: \"batch_p99\" (percentile-based) or \"per_image\" (each image gets its own vmax).\n",
+    "    \"\"\"\n",
+    "    lr, hr = next(iter(train_loader))  # what the training loop actually uses\n",
+    "    lr = lr.cpu()\n",
+    "    hr = hr.cpu()\n",
+    "    B = lr.shape[0]\n",
+    "    n_show = min(n_show, B)\n",
+    "\n",
+    "    # choose indices to show\n",
+    "    idxs = torch.randperm(B)[:n_show].tolist()\n",
+    "\n",
+    "    # pick display vmin/vmax\n",
+    "    vmin = 0.0\n",
+    "    if vmax_mode == \"batch_p99\":\n",
+    "        vmax_lr = float(torch.quantile(lr.flatten(), 0.99).item())\n",
+    "        vmax_hr = float(torch.quantile(hr.flatten(), 0.99).item())\n",
+    "    else:\n",
+    "        vmax_lr = None\n",
+    "        vmax_hr = None\n",
+    "\n",
+    "    # figure\n",
+    "    fig, axes = plt.subplots(n_show, 4, figsize=(8, 2.6*n_show), gridspec_kw={\"width_ratios\":[1,0.04,1,0.04]})\n",
+    "    if n_show == 1:\n",
+    "        axes = np.array([axes])  # ensure 2D indexing\n",
+    "\n",
+    "    for row, idx in enumerate(idxs):\n",
+    "        lr_img = lr[idx, 0].numpy()  # [H,W]\n",
+    "        hr_img = hr[idx, 0].numpy()  # [H,W]\n",
+    "\n",
+    "        if vmax_mode == \"per_image\":\n",
+    "            vmax_lr_i = np.quantile(lr_img, 0.99)\n",
+    "            vmax_hr_i = np.quantile(hr_img, 0.99)\n",
+    "        else:\n",
+    "            vmax_lr_i = vmax_lr\n",
+    "            vmax_hr_i = vmax_hr\n",
+    "\n",
+    "        ax_lr = axes[row, 0]\n",
+    "        im_lr = ax_lr.imshow(lr_img, cmap=\"magma\", vmin=vmin, vmax=vmax_lr_i)\n",
+    "        ax_lr.set_title(f\"LR  idx={idx}\\nmin={lr_img.min():.3g} max={lr_img.max():.3g}\")\n",
+    "        ax_lr.axis(\"off\")\n",
+    "        \n",
+    "        plt.colorbar(im_lr, cax=axes[row, 1])\n",
+    "\n",
+    "        ax_hr = axes[row, 2]\n",
+    "        im_hr = ax_hr.imshow(hr_img, cmap=\"magma\", vmin=vmin, vmax=vmax_hr_i)\n",
+    "        ax_hr.set_title(f\"HR  idx={idx}\\nmin={hr_img.min():.3g} max={hr_img.max():.3g}\")\n",
+    "        ax_hr.axis(\"off\")\n",
+    "\n",
+    "        # one shared colorbar per row (for HR by default)\n",
+    "        cax = axes[row, 3]\n",
+    "        plt.colorbar(im_hr, cax=cax)\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "# call it (tweak n_show and vmax_mode if you like)\n",
+    "show_lr_hr_batch(train_loader, n_show=6, vmax_mode=\"batch_p99\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b8b4dc51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_fingerprint(model):\n",
+    "    with torch.no_grad():\n",
+    "        s = 0.0\n",
+    "        for p in model.parameters():\n",
+    "            s += p.float().abs().sum().item()\n",
+    "    return s\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "39a5b792",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ep 1] Val: MSE=0.000209469  MAE=0.001044627  PSNR=36.7907 dB  SSIM=0.957682\n",
+      "[ep 2] Val: MSE=0.000209781  MAE=0.000858122  PSNR=36.7842 dB  SSIM=0.957819\n",
+      "[ep 3] Val: MSE=0.000191025  MAE=0.000686998  PSNR=37.1915 dB  SSIM=0.962740\n",
+      "[ep 4] Val: MSE=0.000171990  MAE=0.000778791  PSNR=37.6482 dB  SSIM=0.966969\n",
+      "[ep 5] Val: MSE=0.000179997  MAE=0.000640347  PSNR=37.4513 dB  SSIM=0.965259\n",
+      "[ep 6] Val: MSE=0.000171641  MAE=0.000618091  PSNR=37.6592 dB  SSIM=0.967204\n",
+      "[ep 7] Val: MSE=0.000158906  MAE=0.000594523  PSNR=37.9956 dB  SSIM=0.970596\n",
+      "[ep 8] Val: MSE=0.000152345  MAE=0.000541415  PSNR=38.1798 dB  SSIM=0.971728\n",
+      "[ep 9] Val: MSE=0.000135810  MAE=0.000509335  PSNR=38.6787 dB  SSIM=0.974726\n",
+      "[ep 10] Val: MSE=0.000099967  MAE=0.000533898  PSNR=40.0130 dB  SSIM=0.981821\n",
+      "[ep 11] Val: MSE=0.000066768  MAE=0.000406452  PSNR=41.7834 dB  SSIM=0.987645\n",
+      "[ep 12] Val: MSE=0.000022120  MAE=0.000355830  PSNR=46.6922 dB  SSIM=0.994673\n",
+      "[ep 13] Val: MSE=0.000008753  MAE=0.000339571  PSNR=51.0525 dB  SSIM=0.996464\n",
+      "[ep 14] Val: MSE=0.000004129  MAE=0.000265059  PSNR=55.0909 dB  SSIM=0.997270\n",
+      "[ep 15] Val: MSE=0.000003721  MAE=0.000249492  PSNR=55.9653 dB  SSIM=0.996817\n",
+      "[ep 16] Val: MSE=0.000002212  MAE=0.000195824  PSNR=59.6541 dB  SSIM=0.997745\n",
+      "[ep 17] Val: MSE=0.000002357  MAE=0.000238033  PSNR=58.6970 dB  SSIM=0.997644\n",
+      "[ep 18] Val: MSE=0.000002349  MAE=0.000205020  PSNR=59.3816 dB  SSIM=0.997472\n",
+      "[ep 19] Val: MSE=0.000001969  MAE=0.000183632  PSNR=60.9889 dB  SSIM=0.998020\n",
+      "[ep 20] Val: MSE=0.000002267  MAE=0.000190698  PSNR=59.8026 dB  SSIM=0.997782\n",
+      "[ep 21] Val: MSE=0.000001870  MAE=0.000183410  PSNR=60.9713 dB  SSIM=0.997998\n",
+      "[ep 22] Val: MSE=0.000002370  MAE=0.000188584  PSNR=59.3002 dB  SSIM=0.997760\n",
+      "[ep 23] Val: MSE=0.000002108  MAE=0.000172090  PSNR=60.3692 dB  SSIM=0.997951\n",
+      "[ep 24] Val: MSE=0.000001882  MAE=0.000184866  PSNR=61.0855 dB  SSIM=0.998169\n",
+      "[ep 25] Val: MSE=0.000002083  MAE=0.000173143  PSNR=60.4295 dB  SSIM=0.998207\n",
+      "[ep 26] Val: MSE=0.000002206  MAE=0.000170291  PSNR=59.8070 dB  SSIM=0.998114\n",
+      "[ep 27] Val: MSE=0.000001759  MAE=0.000145480  PSNR=62.0186 dB  SSIM=0.998301\n",
+      "[ep 28] Val: MSE=0.000002438  MAE=0.000265911  PSNR=57.7159 dB  SSIM=0.997845\n",
+      "[ep 29] Val: MSE=0.000001586  MAE=0.000140542  PSNR=62.5589 dB  SSIM=0.998391\n",
+      "[ep 30] Val: MSE=0.000001614  MAE=0.000182862  PSNR=61.8479 dB  SSIM=0.998372\n",
+      "[ep 31] Val: MSE=0.000001606  MAE=0.000155336  PSNR=61.8571 dB  SSIM=0.998424\n",
+      "[ep 32] Val: MSE=0.000001543  MAE=0.000156100  PSNR=62.6973 dB  SSIM=0.998499\n",
+      "[ep 33] Val: MSE=0.000001962  MAE=0.000185687  PSNR=59.3258 dB  SSIM=0.998359\n",
+      "[ep 34] Val: MSE=0.000001632  MAE=0.000192814  PSNR=61.0791 dB  SSIM=0.998343\n",
+      "[ep 35] Val: MSE=0.000001588  MAE=0.000135641  PSNR=62.2566 dB  SSIM=0.998496\n",
+      "[ep 36] Val: MSE=0.000001599  MAE=0.000161379  PSNR=62.0613 dB  SSIM=0.998538\n",
+      "[ep 37] Val: MSE=0.000002004  MAE=0.000175297  PSNR=59.8839 dB  SSIM=0.998273\n",
+      "[ep 38] Val: MSE=0.000001578  MAE=0.000137287  PSNR=62.2518 dB  SSIM=0.998525\n",
+      "[ep 39] Val: MSE=0.000001408  MAE=0.000162363  PSNR=63.0625 dB  SSIM=0.998628\n",
+      "[ep 40] Val: MSE=0.000001430  MAE=0.000142644  PSNR=62.3319 dB  SSIM=0.998633\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- pretrain G (no GAN, no DC) ---\n",
+    "LAMBDA_L1   = 1.0\n",
+    "LAMBDA_SSIM = 0.0     # start at 0; add later if you want\n",
+    "LAMBDA_FLUX = 0.0     # add small like 0.01 later\n",
+    "\n",
+    "E_PRE = 40\n",
+    "EPS = 1e-6\n",
+    "\n",
+    "G.train()\n",
+    "for ep in range(1, E_PRE+1):\n",
+    "    for lr, hr in train_loader:\n",
+    "        lr = lr.to(DEVICE, non_blocking=True)\n",
+    "        hr = hr.to(DEVICE, non_blocking=True)\n",
+    "\n",
+    "        opt_G.zero_grad(set_to_none=True)\n",
+    "        with torch.enable_grad():\n",
+    "            sr_logits = G(lr)                                   # logits\n",
+    "            t_logits  = torch.logit(hr.clamp(EPS, 1 - EPS))     # targets in logit space\n",
+    "            loss_G    = F.smooth_l1_loss(sr_logits, t_logits)   # or F.mse_loss\n",
+    "\n",
+    "        loss_G.backward()\n",
+    "        opt_G.step()\n",
+    "\n",
+    "    # eval: map to [0,1]\n",
+    "    metrics = eval_metrics(G, val_loader)  # make sure it does sigmoid(G(...)) inside\n",
+    "    print(f\"[ep {ep}] Val: MSE={metrics['MSE']:.9f}  MAE={metrics['MAE']:.9f}  \"\n",
+    "          f\"PSNR={metrics['PSNR']:.4f} dB  SSIM={metrics['SSIM']:.6f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6fbc0409",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ep 1] MSE=0.000001811  MAE=0.000192308  PSNR=60.3254 dB  SSIM=0.998272\n",
+      "✓ new best PSNR 60.3254 — saved ckpt_best.pt\n",
+      "[ep 2] MSE=0.000001972  MAE=0.000176328  PSNR=59.7602 dB  SSIM=0.998424\n",
+      "[ep 3] MSE=0.000001560  MAE=0.000147063  PSNR=61.6359 dB  SSIM=0.998513\n",
+      "✓ new best PSNR 61.6359 — saved ckpt_best.pt\n",
+      "[ep 4] MSE=0.000002109  MAE=0.000198209  PSNR=58.9694 dB  SSIM=0.997934\n",
+      "[ep 5] MSE=0.000001297  MAE=0.000124956  PSNR=63.5325 dB  SSIM=0.998760\n",
+      "✓ new best PSNR 63.5325 — saved ckpt_best.pt\n",
+      "[ep 6] MSE=0.000001552  MAE=0.000162996  PSNR=60.8941 dB  SSIM=0.998690\n",
+      "[ep 7] MSE=0.000001560  MAE=0.000172183  PSNR=60.6532 dB  SSIM=0.998633\n",
+      "[ep 8] MSE=0.000001360  MAE=0.000149723  PSNR=62.3460 dB  SSIM=0.998666\n",
+      "[ep 9] MSE=0.000001283  MAE=0.000122823  PSNR=62.9634 dB  SSIM=0.998814\n",
+      "[ep 10] MSE=0.000001527  MAE=0.000152832  PSNR=60.8116 dB  SSIM=0.998715\n",
+      "[ep 11] MSE=0.000001367  MAE=0.000152357  PSNR=62.4432 dB  SSIM=0.998761\n",
+      "[ep 12] MSE=0.000001264  MAE=0.000125164  PSNR=62.6956 dB  SSIM=0.998867\n",
+      "[ep 13] MSE=0.000001180  MAE=0.000115373  PSNR=63.8748 dB  SSIM=0.998861\n",
+      "✓ new best PSNR 63.8748 — saved ckpt_best.pt\n",
+      "[ep 14] MSE=0.000001159  MAE=0.000123778  PSNR=63.7939 dB  SSIM=0.998871\n",
+      "[ep 15] MSE=0.000001287  MAE=0.000159101  PSNR=62.4171 dB  SSIM=0.998571\n",
+      "[ep 16] MSE=0.000001190  MAE=0.000118014  PSNR=63.6277 dB  SSIM=0.998849\n",
+      "[ep 17] MSE=0.000001219  MAE=0.000136934  PSNR=63.2794 dB  SSIM=0.998702\n",
+      "[ep 18] MSE=0.000001140  MAE=0.000118388  PSNR=63.8582 dB  SSIM=0.998916\n",
+      "[ep 19] MSE=0.000001134  MAE=0.000115455  PSNR=63.7821 dB  SSIM=0.998942\n",
+      "[ep 20] MSE=0.000001167  MAE=0.000121092  PSNR=63.2288 dB  SSIM=0.998916\n",
+      "[ep 21] MSE=0.000001191  MAE=0.000130806  PSNR=62.9597 dB  SSIM=0.998872\n",
+      "[ep 22] MSE=0.000001096  MAE=0.000110493  PSNR=64.3727 dB  SSIM=0.998919\n",
+      "✓ new best PSNR 64.3727 — saved ckpt_best.pt\n",
+      "[ep 23] MSE=0.000001275  MAE=0.000132676  PSNR=61.9349 dB  SSIM=0.998979\n",
+      "[ep 24] MSE=0.000001089  MAE=0.000114099  PSNR=64.0553 dB  SSIM=0.998920\n",
+      "[ep 25] MSE=0.000001097  MAE=0.000118145  PSNR=63.6622 dB  SSIM=0.999000\n",
+      "[ep 26] MSE=0.000001099  MAE=0.000115384  PSNR=63.6397 dB  SSIM=0.998988\n",
+      "[ep 27] MSE=0.000001108  MAE=0.000119049  PSNR=63.7818 dB  SSIM=0.998941\n",
+      "[ep 28] MSE=0.000001573  MAE=0.000147431  PSNR=60.4317 dB  SSIM=0.998760\n",
+      "[ep 29] MSE=0.000001042  MAE=0.000106032  PSNR=64.5572 dB  SSIM=0.999018\n",
+      "✓ new best PSNR 64.5572 — saved ckpt_best.pt\n",
+      "[ep 30] MSE=0.000001053  MAE=0.000120429  PSNR=64.0631 dB  SSIM=0.999001\n",
+      "[ep 31] MSE=0.000001191  MAE=0.000135411  PSNR=62.2320 dB  SSIM=0.999009\n",
+      "[ep 32] MSE=0.000001040  MAE=0.000108628  PSNR=64.4511 dB  SSIM=0.999008\n",
+      "[ep 33] MSE=0.000001043  MAE=0.000126548  PSNR=64.0834 dB  SSIM=0.998994\n",
+      "[ep 34] MSE=0.000001043  MAE=0.000111852  PSNR=63.9316 dB  SSIM=0.999056\n",
+      "[ep 35] MSE=0.000001029  MAE=0.000101954  PSNR=64.5758 dB  SSIM=0.999012\n",
+      "✓ new best PSNR 64.5758 — saved ckpt_best.pt\n",
+      "[ep 36] MSE=0.000001032  MAE=0.000114812  PSNR=64.2621 dB  SSIM=0.998987\n",
+      "[ep 37] MSE=0.000001043  MAE=0.000112029  PSNR=64.0713 dB  SSIM=0.999024\n",
+      "[ep 38] MSE=0.000000964  MAE=0.000111653  PSNR=64.5863 dB  SSIM=0.999088\n",
+      "✓ new best PSNR 64.5863 — saved ckpt_best.pt\n",
+      "[ep 39] MSE=0.000000926  MAE=0.000106880  PSNR=64.7509 dB  SSIM=0.999142\n",
+      "✓ new best PSNR 64.7509 — saved ckpt_best.pt\n",
+      "[ep 40] MSE=0.000000990  MAE=0.000106116  PSNR=64.7464 dB  SSIM=0.999042\n",
+      "[ep 41] MSE=0.000001080  MAE=0.000117248  PSNR=63.2185 dB  SSIM=0.999071\n",
+      "[ep 42] MSE=0.000001185  MAE=0.000121989  PSNR=62.5531 dB  SSIM=0.998958\n",
+      "[ep 43] MSE=0.000001028  MAE=0.000109187  PSNR=64.0606 dB  SSIM=0.999050\n",
+      "[ep 44] MSE=0.000000971  MAE=0.000103746  PSNR=64.5845 dB  SSIM=0.999085\n",
+      "[ep 45] MSE=0.000000973  MAE=0.000120800  PSNR=64.4300 dB  SSIM=0.999047\n",
+      "[ep 46] MSE=0.000000932  MAE=0.000109591  PSNR=65.0595 dB  SSIM=0.999059\n",
+      "✓ new best PSNR 65.0595 — saved ckpt_best.pt\n",
+      "[ep 47] MSE=0.000000977  MAE=0.000108979  PSNR=64.4347 dB  SSIM=0.999049\n",
+      "[ep 48] MSE=0.000001052  MAE=0.000121106  PSNR=63.4216 dB  SSIM=0.999091\n",
+      "[ep 49] MSE=0.000001073  MAE=0.000127346  PSNR=62.9376 dB  SSIM=0.999074\n",
+      "[ep 50] MSE=0.000000938  MAE=0.000104087  PSNR=64.8522 dB  SSIM=0.999113\n",
+      "[ep 51] MSE=0.000000971  MAE=0.000117128  PSNR=64.1739 dB  SSIM=0.999123\n",
+      "[ep 52] MSE=0.000001145  MAE=0.000141988  PSNR=62.5431 dB  SSIM=0.999044\n",
+      "[ep 53] MSE=0.000000870  MAE=0.000100285  PSNR=65.3331 dB  SSIM=0.999186\n",
+      "✓ new best PSNR 65.3331 — saved ckpt_best.pt\n",
+      "[ep 54] MSE=0.000000913  MAE=0.000097776  PSNR=65.2110 dB  SSIM=0.999121\n",
+      "[ep 55] MSE=0.000000991  MAE=0.000113765  PSNR=64.2496 dB  SSIM=0.999105\n",
+      "[ep 56] MSE=0.000000971  MAE=0.000131014  PSNR=63.8557 dB  SSIM=0.999095\n",
+      "[ep 57] MSE=0.000001294  MAE=0.000131220  PSNR=60.8726 dB  SSIM=0.999159\n",
+      "[ep 58] MSE=0.000001040  MAE=0.000109152  PSNR=63.9786 dB  SSIM=0.999021\n",
+      "[ep 59] MSE=0.000000911  MAE=0.000100352  PSNR=64.6887 dB  SSIM=0.999168\n",
+      "[ep 60] MSE=0.000000993  MAE=0.000108312  PSNR=63.6806 dB  SSIM=0.999155\n",
+      "[ep 61] MSE=0.000001022  MAE=0.000120550  PSNR=63.1574 dB  SSIM=0.999142\n",
+      "[ep 62] MSE=0.000000913  MAE=0.000099791  PSNR=65.0342 dB  SSIM=0.999152\n",
+      "[ep 63] MSE=0.000000878  MAE=0.000102821  PSNR=64.9347 dB  SSIM=0.999170\n",
+      "[ep 64] MSE=0.000000874  MAE=0.000105396  PSNR=64.8529 dB  SSIM=0.999206\n",
+      "[ep 65] MSE=0.000000877  MAE=0.000101411  PSNR=64.9697 dB  SSIM=0.999166\n",
+      "[ep 66] MSE=0.000001041  MAE=0.000127705  PSNR=63.5402 dB  SSIM=0.999048\n",
+      "[ep 67] MSE=0.000000886  MAE=0.000101271  PSNR=65.1689 dB  SSIM=0.999169\n",
+      "[ep 68] MSE=0.000000854  MAE=0.000099868  PSNR=65.1559 dB  SSIM=0.999208\n",
+      "[ep 69] MSE=0.000001007  MAE=0.000118805  PSNR=63.4009 dB  SSIM=0.999127\n",
+      "[ep 70] MSE=0.000000843  MAE=0.000095242  PSNR=65.3518 dB  SSIM=0.999206\n",
+      "✓ new best PSNR 65.3518 — saved ckpt_best.pt\n",
+      "[ep 71] MSE=0.000000908  MAE=0.000097657  PSNR=64.9874 dB  SSIM=0.999171\n",
+      "[ep 72] MSE=0.000000854  MAE=0.000100188  PSNR=64.8528 dB  SSIM=0.999245\n",
+      "[ep 73] MSE=0.000000890  MAE=0.000123536  PSNR=64.5090 dB  SSIM=0.999161\n",
+      "[ep 74] MSE=0.000000836  MAE=0.000097794  PSNR=65.4877 dB  SSIM=0.999230\n",
+      "✓ new best PSNR 65.4877 — saved ckpt_best.pt\n",
+      "[ep 75] MSE=0.000000869  MAE=0.000104913  PSNR=64.9110 dB  SSIM=0.999162\n",
+      "[ep 76] MSE=0.000000818  MAE=0.000099366  PSNR=65.4723 dB  SSIM=0.999235\n",
+      "[ep 77] MSE=0.000000894  MAE=0.000107254  PSNR=64.1235 dB  SSIM=0.999238\n",
+      "[ep 78] MSE=0.000001342  MAE=0.000139407  PSNR=60.4580 dB  SSIM=0.999154\n",
+      "[ep 79] MSE=0.000000794  MAE=0.000098535  PSNR=65.7375 dB  SSIM=0.999266\n",
+      "✓ new best PSNR 65.7375 — saved ckpt_best.pt\n",
+      "[ep 80] MSE=0.000000855  MAE=0.000121710  PSNR=64.3081 dB  SSIM=0.999144\n",
+      "[ep 81] MSE=0.000000811  MAE=0.000097459  PSNR=65.7310 dB  SSIM=0.999195\n",
+      "[ep 82] MSE=0.000000964  MAE=0.000119322  PSNR=63.1608 dB  SSIM=0.999213\n",
+      "[ep 83] MSE=0.000000839  MAE=0.000118488  PSNR=64.4459 dB  SSIM=0.999248\n",
+      "[ep 84] MSE=0.000000815  MAE=0.000092242  PSNR=65.4963 dB  SSIM=0.999226\n",
+      "[ep 85] MSE=0.000000768  MAE=0.000096376  PSNR=65.7152 dB  SSIM=0.999286\n",
+      "[ep 86] MSE=0.000000777  MAE=0.000093117  PSNR=65.7133 dB  SSIM=0.999251\n",
+      "[ep 87] MSE=0.000000792  MAE=0.000100645  PSNR=65.6610 dB  SSIM=0.999251\n",
+      "[ep 88] MSE=0.000000819  MAE=0.000094339  PSNR=65.3220 dB  SSIM=0.999250\n",
+      "[ep 89] MSE=0.000000776  MAE=0.000100204  PSNR=65.9810 dB  SSIM=0.999254\n",
+      "✓ new best PSNR 65.9810 — saved ckpt_best.pt\n",
+      "[ep 90] MSE=0.000000777  MAE=0.000097693  PSNR=65.5054 dB  SSIM=0.999286\n",
+      "[ep 91] MSE=0.000000747  MAE=0.000097594  PSNR=65.7624 dB  SSIM=0.999318\n",
+      "[ep 92] MSE=0.000000726  MAE=0.000097596  PSNR=66.0724 dB  SSIM=0.999286\n",
+      "✓ new best PSNR 66.0724 — saved ckpt_best.pt\n",
+      "[ep 93] MSE=0.000000772  MAE=0.000111621  PSNR=64.9802 dB  SSIM=0.999304\n",
+      "[ep 94] MSE=0.000000747  MAE=0.000100880  PSNR=65.3207 dB  SSIM=0.999328\n",
+      "[ep 95] MSE=0.000000785  MAE=0.000092918  PSNR=65.4849 dB  SSIM=0.999284\n",
+      "[ep 96] MSE=0.000000713  MAE=0.000090408  PSNR=66.1819 dB  SSIM=0.999333\n",
+      "✓ new best PSNR 66.1819 — saved ckpt_best.pt\n",
+      "[ep 97] MSE=0.000000819  MAE=0.000098663  PSNR=64.7309 dB  SSIM=0.999308\n",
+      "[ep 98] MSE=0.000000728  MAE=0.000089293  PSNR=66.0877 dB  SSIM=0.999324\n",
+      "[ep 99] MSE=0.000000724  MAE=0.000092949  PSNR=66.0527 dB  SSIM=0.999333\n",
+      "[ep 100] MSE=0.000000727  MAE=0.000093382  PSNR=66.2612 dB  SSIM=0.999319\n",
+      "✓ new best PSNR 66.2612 — saved ckpt_best.pt\n",
+      "[ep 101] MSE=0.000000860  MAE=0.000115994  PSNR=63.9174 dB  SSIM=0.999268\n",
+      "[ep 102] MSE=0.000000744  MAE=0.000089730  PSNR=65.8092 dB  SSIM=0.999307\n",
+      "[ep 103] MSE=0.000000817  MAE=0.000101512  PSNR=64.4285 dB  SSIM=0.999332\n",
+      "[ep 104] MSE=0.000000750  MAE=0.000098536  PSNR=65.2939 dB  SSIM=0.999325\n",
+      "[ep 105] MSE=0.000000694  MAE=0.000090734  PSNR=66.2369 dB  SSIM=0.999348\n",
+      "[ep 106] MSE=0.000000870  MAE=0.000112447  PSNR=63.3857 dB  SSIM=0.999320\n",
+      "[ep 107] MSE=0.000000777  MAE=0.000114787  PSNR=65.1461 dB  SSIM=0.999256\n",
+      "[ep 108] MSE=0.000000727  MAE=0.000101354  PSNR=65.5348 dB  SSIM=0.999338\n",
+      "[ep 109] MSE=0.000000737  MAE=0.000090966  PSNR=66.1238 dB  SSIM=0.999307\n",
+      "[ep 110] MSE=0.000000903  MAE=0.000118558  PSNR=63.6168 dB  SSIM=0.999213\n",
+      "[ep 111] MSE=0.000000706  MAE=0.000094539  PSNR=66.1524 dB  SSIM=0.999345\n",
+      "[ep 112] MSE=0.000000931  MAE=0.000115582  PSNR=62.9965 dB  SSIM=0.999264\n",
+      "[ep 113] MSE=0.000000701  MAE=0.000086698  PSNR=66.2798 dB  SSIM=0.999343\n",
+      "✓ new best PSNR 66.2798 — saved ckpt_best.pt\n",
+      "[ep 114] MSE=0.000000711  MAE=0.000097382  PSNR=65.4590 dB  SSIM=0.999358\n",
+      "[ep 115] MSE=0.000000693  MAE=0.000102235  PSNR=66.0706 dB  SSIM=0.999342\n",
+      "[ep 116] MSE=0.000000749  MAE=0.000097452  PSNR=64.8691 dB  SSIM=0.999347\n",
+      "[ep 117] MSE=0.000000648  MAE=0.000090523  PSNR=66.2196 dB  SSIM=0.999398\n",
+      "[ep 118] MSE=0.000000872  MAE=0.000111934  PSNR=63.4716 dB  SSIM=0.999287\n",
+      "[ep 119] MSE=0.000000832  MAE=0.000093521  PSNR=64.1480 dB  SSIM=0.999336\n",
+      "[ep 120] MSE=0.000000680  MAE=0.000097019  PSNR=66.1358 dB  SSIM=0.999362\n",
+      "[ep 121] MSE=0.000000676  MAE=0.000096814  PSNR=66.2603 dB  SSIM=0.999366\n",
+      "[ep 122] MSE=0.000000687  MAE=0.000092776  PSNR=66.0369 dB  SSIM=0.999353\n",
+      "[ep 123] MSE=0.000000731  MAE=0.000091496  PSNR=65.4951 dB  SSIM=0.999320\n",
+      "[ep 124] MSE=0.000000746  MAE=0.000094597  PSNR=65.1254 dB  SSIM=0.999327\n",
+      "[ep 125] MSE=0.000000699  MAE=0.000094018  PSNR=65.8724 dB  SSIM=0.999356\n",
+      "[ep 126] MSE=0.000000802  MAE=0.000097899  PSNR=63.8545 dB  SSIM=0.999388\n",
+      "[ep 127] MSE=0.000000663  MAE=0.000091119  PSNR=66.2819 dB  SSIM=0.999389\n",
+      "✓ new best PSNR 66.2819 — saved ckpt_best.pt\n",
+      "[ep 128] MSE=0.000000826  MAE=0.000099620  PSNR=63.9092 dB  SSIM=0.999345\n",
+      "[ep 129] MSE=0.000000631  MAE=0.000083828  PSNR=66.7623 dB  SSIM=0.999402\n",
+      "✓ new best PSNR 66.7623 — saved ckpt_best.pt\n",
+      "[ep 130] MSE=0.000000637  MAE=0.000089740  PSNR=66.3537 dB  SSIM=0.999418\n",
+      "[ep 131] MSE=0.000000682  MAE=0.000110130  PSNR=65.4324 dB  SSIM=0.999327\n",
+      "[ep 132] MSE=0.000000632  MAE=0.000084825  PSNR=66.9425 dB  SSIM=0.999393\n",
+      "✓ new best PSNR 66.9425 — saved ckpt_best.pt\n",
+      "[ep 133] MSE=0.000000673  MAE=0.000088598  PSNR=65.8880 dB  SSIM=0.999382\n",
+      "[ep 134] MSE=0.000000629  MAE=0.000087946  PSNR=66.5538 dB  SSIM=0.999391\n",
+      "[ep 135] MSE=0.000000622  MAE=0.000095786  PSNR=66.6231 dB  SSIM=0.999378\n",
+      "[ep 136] MSE=0.000000648  MAE=0.000096769  PSNR=66.0100 dB  SSIM=0.999398\n",
+      "[ep 137] MSE=0.000000653  MAE=0.000087947  PSNR=66.6276 dB  SSIM=0.999372\n",
+      "[ep 138] MSE=0.000000654  MAE=0.000088382  PSNR=66.0633 dB  SSIM=0.999407\n",
+      "[ep 139] MSE=0.000000646  MAE=0.000091640  PSNR=66.1368 dB  SSIM=0.999388\n",
+      "[ep 140] MSE=0.000000685  MAE=0.000093674  PSNR=65.2679 dB  SSIM=0.999397\n",
+      "[ep 141] MSE=0.000000672  MAE=0.000111858  PSNR=64.8199 dB  SSIM=0.999393\n",
+      "[ep 142] MSE=0.000000788  MAE=0.000107858  PSNR=63.6894 dB  SSIM=0.999366\n",
+      "[ep 143] MSE=0.000000731  MAE=0.000107171  PSNR=64.1462 dB  SSIM=0.999394\n",
+      "[ep 144] MSE=0.000000661  MAE=0.000088808  PSNR=65.7872 dB  SSIM=0.999371\n",
+      "[ep 145] MSE=0.000000627  MAE=0.000103558  PSNR=65.9585 dB  SSIM=0.999394\n",
+      "[ep 146] MSE=0.000000648  MAE=0.000099377  PSNR=65.8359 dB  SSIM=0.999393\n",
+      "[ep 147] MSE=0.000000622  MAE=0.000087678  PSNR=66.3531 dB  SSIM=0.999407\n",
+      "[ep 148] MSE=0.000000750  MAE=0.000097541  PSNR=64.0333 dB  SSIM=0.999420\n",
+      "[ep 149] MSE=0.000000711  MAE=0.000096896  PSNR=64.8599 dB  SSIM=0.999367\n",
+      "[ep 150] MSE=0.000000619  MAE=0.000085756  PSNR=66.6956 dB  SSIM=0.999411\n",
+      "[ep 151] MSE=0.000000580  MAE=0.000084935  PSNR=66.8865 dB  SSIM=0.999448\n",
+      "[ep 152] MSE=0.000000644  MAE=0.000086905  PSNR=65.3639 dB  SSIM=0.999455\n",
+      "[ep 153] MSE=0.000000577  MAE=0.000083983  PSNR=67.0285 dB  SSIM=0.999443\n",
+      "✓ new best PSNR 67.0285 — saved ckpt_best.pt\n",
+      "[ep 154] MSE=0.000000648  MAE=0.000087321  PSNR=66.2391 dB  SSIM=0.999381\n",
+      "[ep 155] MSE=0.000000955  MAE=0.000109159  PSNR=62.3658 dB  SSIM=0.999292\n",
+      "[ep 156] MSE=0.000000612  MAE=0.000087022  PSNR=66.7794 dB  SSIM=0.999419\n",
+      "[ep 157] MSE=0.000000643  MAE=0.000090618  PSNR=65.2562 dB  SSIM=0.999455\n",
+      "[ep 158] MSE=0.000000615  MAE=0.000086162  PSNR=66.4826 dB  SSIM=0.999436\n",
+      "[ep 159] MSE=0.000000604  MAE=0.000085110  PSNR=66.8018 dB  SSIM=0.999407\n",
+      "[ep 160] MSE=0.000000642  MAE=0.000113234  PSNR=65.2987 dB  SSIM=0.999400\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- Adversarial training (hinge GAN + logit-space pixel loss) ---\n",
+    "\n",
+    "use_amp = True  # optional; works fine here\n",
+    "scaler  = torch.amp.GradScaler('cuda', enabled=use_amp)\n",
+    "\n",
+    "# weights (good starting points; tweak to taste)\n",
+    "LAMBDA_LOGIT = 1.0       # pixel loss in logit space (Huber/MSE)\n",
+    "LAMBDA_SSIM  = 0.0       # add later if you want; uses [0,1] images\n",
+    "LAMBDA_DC    = 0.05      # PSF/data-consistency on [0,1]\n",
+    "LAMBDA_FLUX  = 0.0       # or small like 1e-2 if you use it\n",
+    "LAMBDA_ADV   = 1e-3      # hinge-G adv weight for G (typical 1e-3 ~ 5e-3)\n",
+    "\n",
+    "R1_GAMMA     = 10.0      # common choice for R1 on real\n",
+    "R1_INTERVAL  = 16        # lazy R1 every N steps\n",
+    "\n",
+    "EPS = 1e-6\n",
+    "\n",
+    "E_ADV = 160\n",
+    "step = 0\n",
+    "\n",
+    "out_dir = Path(\"./out_satgan_gan\")\n",
+    "out_dir.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "best_psnr = float(\"-inf\")  # put this BEFORE the GAN loop\n",
+    "\n",
+    "def _pack_state(epoch, best_psnr):\n",
+    "    return {\n",
+    "        \"G\": G.state_dict(),\n",
+    "        \"D\": D.state_dict(),\n",
+    "        \"opt_G\": opt_G.state_dict(),\n",
+    "        \"opt_D\": opt_D.state_dict(),\n",
+    "        \"scaler\": scaler.state_dict() if scaler is not None else None,\n",
+    "        \"epoch\": epoch,\n",
+    "        \"best_psnr\": best_psnr,\n",
+    "    }\n",
+    "\n",
+    "for ep in range(1, E_ADV + 1):\n",
+    "    G.train(); D.train()\n",
+    "\n",
+    "    for lr, hr in train_loader:\n",
+    "        lr = lr.to(DEVICE, non_blocking=True)\n",
+    "        hr = hr.to(DEVICE, non_blocking=True)\n",
+    "\n",
+    "        # -------------------------\n",
+    "        # 1) Discriminator step\n",
+    "        # -------------------------\n",
+    "        with torch.no_grad():\n",
+    "            # fake image for D in [0,1]\n",
+    "            sr_fake_01 = torch.sigmoid(G(lr))\n",
+    "\n",
+    "        with torch.amp.autocast('cuda', enabled=use_amp):\n",
+    "            real_logits = D(hr)          # hr is already [0,1]\n",
+    "            fake_logits = D(sr_fake_01)  # [0,1]\n",
+    "            loss_D = F.relu(1 - real_logits).mean() + F.relu(1 + fake_logits).mean()\n",
+    "\n",
+    "        # Lazy R1 on real\n",
+    "        if step % R1_INTERVAL == 0:\n",
+    "            hr.requires_grad_(True)\n",
+    "            with torch.amp.autocast('cuda', enabled=False):  # full precision for R1 is safer\n",
+    "                r1_logits = D(hr)\n",
+    "                grad = torch.autograd.grad(\n",
+    "                    r1_logits.sum(), hr, create_graph=True, retain_graph=True, only_inputs=True\n",
+    "                )[0]\n",
+    "                r1_pen = grad.pow(2).flatten(1).sum(1).mean()\n",
+    "                loss_D = loss_D + (R1_GAMMA * 0.5) * r1_pen\n",
+    "            hr = hr.detach()\n",
+    "\n",
+    "        opt_D.zero_grad(set_to_none=True)\n",
+    "        scaler.scale(loss_D).backward()\n",
+    "        scaler.step(opt_D)\n",
+    "        scaler.update()\n",
+    "\n",
+    "        # -------------------------\n",
+    "        # 2) Generator step\n",
+    "        # -------------------------\n",
+    "        with torch.amp.autocast('cuda', enabled=use_amp):\n",
+    "            sr_logits = G(lr)                       # logits (unbounded)\n",
+    "            sr_01     = torch.sigmoid(sr_logits)    # [0,1] branch for adv/DC/SSIM/flux\n",
+    "\n",
+    "            # (a) adversarial term (hinge-G)\n",
+    "            g_fake_logits = D(sr_01)\n",
+    "            loss_adv = -g_fake_logits.mean()\n",
+    "\n",
+    "            # (b) pixel/logit term (no sigmoid in backprop path)\n",
+    "            t_logits = torch.logit(hr.clamp(EPS, 1 - EPS))\n",
+    "            loss_logit = F.smooth_l1_loss(sr_logits, t_logits)\n",
+    "\n",
+    "            # (c) data-consistency / PSF term (works in [0,1] image space)\n",
+    "            # make sure psf_same is differentiable (conv/downsample, no .detach/.data inside)\n",
+    "            loss_dc = F.l1_loss(psf_same(sr_01), lr)\n",
+    "\n",
+    "            # (d) optional perception/flux terms (also on [0,1])\n",
+    "            loss_ss = (1 - ms_ssim(sr_01, hr, data_range=1.0)) if LAMBDA_SSIM > 0 else 0.0\n",
+    "            loss_fx = flux_loss(sr_01, hr) if LAMBDA_FLUX > 0 else 0.0\n",
+    "\n",
+    "            loss_G = (\n",
+    "                LAMBDA_ADV   * loss_adv +\n",
+    "                LAMBDA_LOGIT * loss_logit +\n",
+    "                LAMBDA_DC    * loss_dc +\n",
+    "                LAMBDA_SSIM  * loss_ss +\n",
+    "                LAMBDA_FLUX  * loss_fx\n",
+    "            )\n",
+    "\n",
+    "        opt_G.zero_grad(set_to_none=True)\n",
+    "        scaler.scale(loss_G).backward()\n",
+    "        scaler.step(opt_G)\n",
+    "        scaler.update()\n",
+    "\n",
+    "        step += 1\n",
+    "\n",
+    "    # ---------- validation (use [0,1]) ----------\n",
+    "    metrics = eval_metrics(G, val_loader)  # must do sr = sigmoid(G(lr)) inside\n",
+    "    print(f\"[ep {ep}] \"\n",
+    "          f\"MSE={metrics['MSE']:.9f}  MAE={metrics['MAE']:.9f}  \"\n",
+    "          f\"PSNR={metrics['PSNR']:.4f} dB  SSIM={metrics['SSIM']:.6f}\")\n",
+    "    \n",
+    "        # ---------- SAVE CHECKPOINTS ----------\n",
+    "    state = _pack_state(ep, best_psnr)\n",
+    "\n",
+    "    # always save latest (can overwrite)\n",
+    "    torch.save(state, out_dir / \"ckpt_latest.pt\")\n",
+    "\n",
+    "    # save best by PSNR\n",
+    "    if metrics[\"PSNR\"] > best_psnr:\n",
+    "        best_psnr = metrics[\"PSNR\"]\n",
+    "        state[\"best_psnr\"] = best_psnr\n",
+    "        torch.save(state, out_dir / \"ckpt_best.pt\")\n",
+    "        print(f\"✓ new best PSNR {best_psnr:.4f} — saved ckpt_best.pt\")\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e2df480a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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efPBB+73f+z379re/bV/96lftjW984+R7+6xnPcte8YpX2Cc+8QkbGRmxU045xf7lX/5F/sT6ve99r91111128skn26tf/Wo74YQTbPv27fazn/3M/vmf/9m2b9/e1Xm89a1vnYz/v+aaa2z58uX2uc99zh544AG74447Jv/K0oknnmjPf/7z7W1ve5tt377dli9fbl/60pfcP7ZtZvac5zzHvvzlL9ub3/xme97znmeDg4NTJrDUOmD2nXPOOfaMZzzD/u7v/s6uvvpqq9frst8f/dEf2UknnWTXXnutrV+/3o4//nj7p3/6p8laUuY3PmVrndned/u9732vXXXVVfbc5z7Xvv/979tvfvOb/T7Pbuqnsr815KqrrrLXvva19rKXvcz+4A/+wH7xi1/Yt771LVu5cqUbX5Zl9r73vc9GRkas0WjYi1/84q7/dgOeRGV5kOjaU0XPPvTQQ5Pxre9///vlPi6//PKQZdlk3KsSi8vfN9Y2hL1xs0+Mkn582y9+8YvhbW97W1i9enXo6+sL5513XtiwYYPb/oMf/GA49NBDQ6PRCC984QvDT3/6UxchHUIIX/3qV8MJJ5wQarUacdLANJT5ZzdC8HH5IeyNnn/Tm94UDjnkkFCv18Oxxx4b3v/+94eiKKb0Gx8fD9dcc01YsWJFGBgYCOeff3546KGHZCTzY489Fq6++upw+OGHh3q9HtasWRN+//d/P9x0002TfcrG5YcQwn333RcuuuiiMDw8HHp7e8NJJ50U7rzzTtnvrLPOCo1GIxx00EHh+uuvD9/5zndc7duzZ0+49NJLw/DwcDCzyXpHrQOe3FPVGvW9QtWdx918882l3oktW7aESy+9NAwNDYWlS5eGyy+/PPzrv/5rMLPwpS99abLfunXrwsDAgNxH2Vo3NjYWrrzyyrB06dIwNDQULrnkkrB58+ZoXP6WLVumbK/+6Y5u6qcSqyGx73Eh7P0nBa677rqwcuXK0N/fH84+++ywfv16+U99fPrTnw5Pe9rTQpZlU+pl7N6pOoe4JARW5GHm3H333XbmmWfabbfdNq1ofgCYz6h1wMLxla98xS688EK755577IUvfGHVwwGiWGMGAACARWF8fHzKn/M8t49+9KO2ZMkSe/azn13RqIByWGMGAACAReEv/uIvbHx83F7wghdYs9m0f/zHf7R/+7d/s/e85z1dx8UDc42JGQAAABaFF7/4xfbBD37Q7rzzTpuYmLBjjjnGPvrRj9rrX//6qocGPCXWmAEAAABAxVhjBgAAAAAVY2IGAAAAABUrvcasXvf/eFwI/h/jNDNLxHyvlvXLvq3OztL7rWVLXFtejIl+g3L7Tr7HtWWpHpcaQxFasm+SlF+ql6W94liFa0sj+1TXq1FfHum7y+837ZF91fE6ub+2PTV/D8zMOsWEbC+rJq5LbL/q+TIzy8S5qWvbFs+BmX5GY/eh2drs2nrqK0VPfc/UcxB7jjq5uo/6eqlrE7vnhXjGo++0GFu77a9BFbp5/wAsfrE6NteoTQCeqExt4jdmAAAAAFAxJmYAAAAAUDEmZgAAAABQMSZmAAAAAFCx0itTCxHCEAsgMBG40E04hAr5MNOL5tQYVAiEmVkRIuMVcjHeLBJgkouQDBXuYKbDKNR55eb77e3s+zbb22XXJPHz7tjCQ3W0Rn1Yj0FQ4R0qXGLvGLp4PkTfPBLCoqj7GAuiUeONnUNf41DX1uqMyL4qjEYFyWSRheLR90xQi83j98G3xxarz5fF9JhNSdUDQFf4J0iBqahh3aOOzDf8xgwAAAAAKsbEDAAAAAAqxsQMAAAAACrGxAwAAAAAKsbEDAAAAAAqVjqVUcnzPbI9Eyl0Kl1vLz83TCPJcGoPqm+7o8cVxB56asv0qMR+0zSSmif6dkRS496+/nxVal5vbYXcvi2ulzqv+Lj0tVFpfOo69jVWye3Hm1tcWyyZUiUSmrgucfp8a5k/nkpljF0DlZ4YkxdN19ZNomGa+OTQ2H0sCn+90kjyqDrfEEmxzFKRThl5TxN+hrNAkVK2eM2He0uiG/bXfHh+sfDvw+KrQXzbAgAAAICKMTEDAAAAgIoxMQMAAACAijExAwAAAICKTSv8I42EOyixYIRGzQcuxMI7lHbhQzZi48pE4MJE24dWmJn11Ib9sSKBHiqcoZaJYIWIkPhwiGC57JuIkIwQCWxQ7eq8zMw6IjRCBUy02rsj4xJBI+LemJnVRchGLCwlFoihFIW/jiqAJBYuk4uQjEZtqezbzkf98SMhG4oK5AiFPtdGfblra7a3R3YsrkFtieyqroMKDzGLB7lgNi30RdlY/Lp5RhffIn3si5qFuTYTz9z8qk38xgwAAAAAKsbEDAAAAAAqxsQMAAAAACrGxAwAAAAAKsbEDAAAAAAqVjqVMRWJhoVIgDMzS0RIikoTNDObaG12bZlI7TMzq4lkuFZHJBpGx+XPIUt1eqIcbyT9UCUwxpL/1H5VqmOM2m8s0VClKsbuWS72kUUSHMuOK6bV2eXaVEqhmX4WYgmfWdpwbZ2OP6/Ysxh7bhR1vmnkuVXXXN3zLNPJhypdsxY5lpKJ58DMrJ379FOVmBnri5lEmhkWO/WMz680NCjUJix286s28RszAAAAAKgYEzMAAAAAqBgTMwAAAACoGBMzAAAAAKhY6cSGYD6AIBbYUBS+b099le6blQ/vUKERigoqMTPLxXhjoRVF4cfQqC/T+y2afvvIObRb211bb2ON7Cu3FyEMfT362qpx5ZGgkbJBIbHgjETM8WuRMAulCHq/MmQjcs+C5a6tUfP3rFOMy+3VdWx1dsu+6nxjVDhLJoJsYufVKSZKHz8v/LG6CWbBTGLRPKowGz9v1cFX0xd7RwgFmXvUK+B/VVeb+I0ZAAAAAFSMiRkAAAAAVIyJGQAAAABUjIkZAAAAAFSMiRkAAAAAVCwJIZSKGEnTQdeWZf2yr0pVTCLJcN2k+TXbPtEwS/0YYsmBShFJKVTpdrFzSLtI2FPnq9IiY/pFcuBEe4fsW0v7XFuW6cTKdme01PFjqY6N+tJS25vpxMv48XyyZEfcGzOzeuaf0dh4lZ7aUKnjm+nEyno2IPu2c39t1fWKXZfx1mOurTeScpqm/rkre2/NzHKRAGmmUyQnWhtL73c2xd7LuUWi2YH3c77ZSCpcDNdwthIcY19V/LsXQnuWxtCd+VGblAOtXi2G96obs/UOKgvp2i6M2rSQrigAAAAALEpMzAAAAACgYkzMAAAAAKBiTMwAAAAAoGKlV6bKoI+gF9KliQ+YKCIBF4XYhwoPiVH7rSWRUBK18C8SFJKIc0hTHZyRib61zAdvmJkVYuFfnvtziIWHNNsj/viRcXUTgqLGG8S9ydKG3F4FV3SKcdlX7Td2z2viueupLZF9O7kPrlDXRh3fTN+bTq6DRtS4mpEQFvXcqDHEgkZ6asOuTY117//hm2LPYquzW+xXv6dJmK+L2OfaYl40z8/purNwrlcyS89tkIveY9dlugvvF/O7N5sWw3VbOO/a/LDwr9d0a9ZCrk0L/+4BAAAAwALHxAwAAAAAKsbEDAAAAAAqxsQMAAAAACpWekV/XYQdtDq7Ir39QjoVCGJmFsyHPhSRIIhGfXmpMcQCKtr5qGtLupmbRkIjsswfr5Pr4ItchCs0aktdWyxIQoVOpEld9lUBEyo8xMxsoLHGtelwCB06ocI/YgEmtZoPo4hdLyWxTLYP9h7i2tQ9j1HhG32NVaW3j4WK9PYsc23q2vb1rCw9LhV0YqYDX7oJJYn9rKabIJnFg0Xzc2l2FnsfeLq6jvK9Vm26tskjRepg6GK/2B8LrV4tnNoUo9416lDc9GtTF5tH6pAy32rTwn8zAAAAAGCBY2IGAAAAABVjYgYAAAAAFWNiBgAAAAAVY2IGAAAAABVLQgilImTq9dWldxpEqmI8ldEnn+T5mOybiUTCTOw3lupYiETE2LhUCl0t9WmCZmatjk86bNR9Et/eMehUw33FEv5U4mQt6y3dd3TiUX08cR/U+QbL5fYqKbFe8/fLTJ+bOn6sr0opNIunU5al9ttN0mKsr6Ker9j4x5qbXVtPfUj2VUmYKgHSzCwv/PtQiDYzneDYbD0i+861JJL+OQN7nqX9Tle1P0+bbnriYlF1+tpMJJzpd6eb50vU8sjnbwj6s6PsfrsRSn7OzjZq09yiNu21GGqTepa6SYfW38di3zPnV23iN2YAAAAAUDEmZgAAAABQMSZmAAAAAFAxJmYAAAAAULHSK1OLYsK1pWksdMK399R0WMF4a4vfXoR8mJn1ZH4fE22/faO+XG5v5sfVjgSNqGPFQhS6OV8V5KAWNDbqS+X2SeJDNtqdUdlXjVeFVpjpwJREzNtVoIhZPJBDiQV9KOrcYuEuA73D/lhiUacKvTAza+d7XJu6t3v3K8JSMh0Oo8bbkw74fUauS19jpWvr5OOyb0e8pzH1zI+hFQkwUX0Xj/m6YHxuf242Kwvnu1isPae6COqJmY3rFVu0P/3F9OUX2NfE56/6LDAzy0W9ySPXNlFBIRWHFMx/1CazRVybZqAOKdO9Xt28l9O/N+WDiVQIWaw2qbC/WLjZfKtN8+DJBAAAAIADGxMzAAAAAKgYEzMAAAAAqBgTMwAAAACoGBMzAAAAAKhY6VTGbuZwqUhTiaXIpYlPWQmR1L1WZ8S19dSGXVtR6O1V6l0mjm+mUwZjSYvN9g7Xtntio+y7pO8IPy6RzBNLgFRpjWr7vX2HXVs91cmB4+3trm2ocbBrm8h3ye3Vte10dEJgO/dJi/2NVbJvvSbSCyPnq54x9SzUMp202Kj5xMp6TSeENrIlfr+RxMqxjr+2iroHZmZLGoe6ttHg00jN9P1tiettZtZs+/epr2dF6b4L04GVcjbtxKw5TS6b7rG6SDibifOahUS1WUmfe7LjieugEliX9h4ut98xfr9rK9rlP39NpOZivjnQatNs7DdSK2brHKZZm2atDnVxvqo2qbT0WNK4SkBvhfLfX6usTfzGDAAAAAAqxsQMAAAAACrGxAwAAAAAKsbEDAAAAAAqVjr8Qy3Ei4V0NNtbxfY6ZCNLfRCDaovJi5Y4lp5vqkXNsb4qNCIW/qHCKGILEovINdtXlurrJcM7dNfy25tZ2rPatXVUAEo6qLcXc/yeHt1XBYjEwiVU+EdRtGXfTu7DRlTQR5rW5fYmtm93xmRXdc9rWeTaijCcduGDSgbEPTAzG+/sdG0qfGTvsfy57Wk+KvtmIqxkz8TDsm9vXYeCoFvzdCG9WRcLs6d/DrG6Oz16n7GwoPJmYOH+LASFzBZ1b15UO1X2/ZeePa6t3fFtZmZ58J/VceraLpxruH+qDiaiNpnNTm0KYcZ3+f87sGqT+j73fy89X/a9beRrrm1nrr/PzV5t2r9nid+YAQAAAEDFmJgBAAAAQMWYmAEAAABAxZiYAQAAAEDFmJgBAAAAQMVKpzIqhUhENDPL0n7Xlohkur3tfm6o0uLMzILlrq2W+CS8Tu4T72LtsWOlqR9vLDkwy3wsYi4SDc3MWp3drk2l+TUynQCZWOba+jOdmJeJhL4+02l+e2yb75sudW2FuAdmZs3EX8dm4dMXzcyW1A5xbbqnlmbTemyj90ZRKUBmOtlRXW8zs0T8/KNjfgyNRN9zccujfbeM3+vaYmmRSqM+LNvV+zC/VZ1wNnu6SjnrKmGsXN/uUsuq/9mfHm/5JLLuEtWmmZI2xwlpKrFSfU5uao/K7XsykbwbPVffnkSuV7BuLnr1zxj2ojZp8XFN732ftdo0T5MaVQp7Hhnq/KhN+4eKBgAAAAAVY2IGAAAAABVjYgYAAAAAFWNiBgAAAAAVS0Iot3ywVlvu2npqOkiinY+5tly0mZnVxAK9LPVhGmZmRej4/RYTpcelgj4mWj70wsxsoPfgUsc30wuoY4ELKtSjnviwlG6CJEJkkWLNdLBJWYNh2LX1Bj9WM7M9qY/vGI9Eeqhz2JU/Ivt2RFBHI9P3Vxnv+PurFpCamfXWfNhJu9BBMuqeq0AQM7PB2kGubSLfIfsqagxJIhJBTAeb1FL9LMbCbMoanbhvWtvPFB0sNB/CP6b3c6+5XkhffuH8dLfXNWC2xOqj7CsXvU93++72oXfcxfbd3Afx7qjwroHeNXpYwYdBjU5skn3VZ7XFPlPlAvvYNRCfiaF8wNNsojYZtelJzFZtitchZeHUJjVfWNp3pNy+2fHfP8eaj8m+eSHmJ5HzmovaxG/MAAAAAKBiTMwAAAAAoGJMzAAAAACgYkzMAAAAAKBiTMwAAAAAoGIqMkgqRKpiUlsm+2aJT1VMa/pQqUheiaUfqnQ51VelL+5t9+Pqb/jEvBg1VjOzLCufflhLekWb3z41nbrXY/4aLCtWyb5jyR7XNhh8KmRMIdJn6pFHplb4Z6HffIKOmVlIfIJNnrVl305aPl1rT8cn7qhnoa+2Qo/LfMJYM98t+6p0zZpIMzMz2932iZODdZ9y1sx1SqJKYKyJd8zMrFH349o2+mvZt5b6Z7GjktMiffFEc5hy1hU/rnhCWbm+0dSybvqqzbtKbytPJZTF0tAS0R7rq/abRG5juexjs2i61xxemyL41Nqx5ma5fTcpwdNOf8Mic+DUpplJa/XfdWN1RY83VsdU6zSTFrtKhSyvEInauyceln11bdNzC7m9TF+cG/zGDAAAAAAqxsQMAAAAACrGxAwAAAAAKsbEDAAAAAAqVjr8o7/3cNc23toi+9YzH/oQW/zYCT5soJ7pEIUi+ICIRm1p6WONt7a5tp5aJAxDrPuLLQDtTYf1PkoaCj6MYiIZ1X1FyMZh4hqYmaWJb9/SHpd9D+3113y07a9jPdWr2xOx6n1HSwd37Azi3CKL5rOkLrqW/3lCb7bEtY13dsq+dREu01dbLvuOd7a7tnahr616HsfzHaX6mZl1cr/fkPqgErNIUEgkuCMXC2l7RKiJmVk7188jKiDr0PR/xlZ2MX0SCUFS28cCk/SxdOBRWSq8xywS/hF51wqxwD6+EL6bxfjlxhW/j3MXnBHEAvk8Egokwz+6WGCPuTCHwUTUJqmb2hQLqAgyaCfWV9U8PTZdm1TPaQaCdEleG3G+HRFMaBYLIVoYAUT8xgwAAAAAKsbEDAAAAAAqxsQMAAAAACrGxAwAAAAAKlY6/KPZ9mEFsTAMvfBQL1Ks13xQSKuzS/btrYuQjLYP9GjUfECGmQ76iC0AzdKGa2u2R2TfPPMhFwO11bJvw/z59hY+eGNVWCm3X1bzQQ4revXC1IGaX9X5tEi4w4C4DLvbfr+tLtZODvf4MA0zs10tf23N51CYmVkqfnawNd0k+66oH+3aOubvzUSin69W4QMu8kIHmKjnY7o6kfCQLO1xbbHwgj1NfW0U9Z7GzlcvpJ0vulicvpDMwAJqXaP1fssupo/VzDT17bH3RO0jTX3QT2xcigqH2tvuP3uKQn8eqec/thg/EtUjW9Vi+vKBILOpbEhAF4EEXZxDUClbiwq1KbqLimtTKoLFzPTnbey7rgrbUopi+rVJ1bci6HHpmjXd2iQ3t9kL1Cg3jyi6OH60NnVVd2er7//iN2YAAAAAUDEmZgAAAABQMSZmAAAAAFAxJmYAAAAAUDEmZgAAAABQsdKpjFnq0wBjSTXtfI/oqw+lEmh6aktkX5WoopIaY+NSx+prLJd9OyKdq1FfKvuqdJ9YktjSwo93ufmkxEP6/PU2M5lhtbpPJz8d0e+vV7PQfZfVc9m+r4Ga7rez5e/vQ+M6sWiDSBJa0dZpkXuCvw91K5+I2Az+WVRJj2ZmplKesshzK9KBWu3dsm9vj08JVdvHEoM6xUTpvvVswLXlhY68VO9piLynsVTVA8/0fpaVzEhKm0ooi42ri76iXaWcxZIWs8ynmdVSnczaI57TWqJrXmY6PW1fHfPviZmu5SqB1cwsyf01iCWVqsCteAaXet/F8SOPh05Em8s0tPJ9o+Oa88RJdIPatFddtNdi6bKiNsm040hibKcYc23tSDqz+hzv5Lqv0l1t6uZdVfds7t717sa6MGoQvzEDAAAAgIoxMQMAAACAijExAwAAAICKMTEDAAAAgIqVDv9QYqECKuhDLdQ0M8uDWNDY8Qsizcxqab9rU8EK7cj2hViA2ezskn3TVCzqDDr4Ik18WElqOvgiT/w+OmLx4q62PtbvLffjGu7RK7N/Z9Bfh4MG9aL3VQf54Iqi7efto3v0Itgk8WP4n606WKUu7uOSul74/8Bufx3bud7vrnSna2skg76t5tvMzHblj7i22KJdpZbpc1BBH2rRbuwdaYugAhU4E9PO9T1X71NMN30xf6jF9LFgIvX8palYYC8W0pvp4Jm+zNdnM7P+xLcPhGHZty/oRfr7Gk/0QvjRbKdvS7fKvhOJ/zxoi0CQqMja8kItUBdZC9NfdP8kg5iWuV40v/gW9M++hf9z9rmsTb2ZDpnrE7WpP+i+vcF/LoZEhK4lOphotLbTtY0VO2TfZq6/qyrq+0W8tvhrHmQwi966u5o1XdM8VmSsQcbqVWfhv8kAAAAAsMAxMQMAAACAijExAwAAAICKMTEDAAAAgIoxMQMAAACAipVOZVTJciGSkKKSdfJCp9KoRJd6plPzVIJjq+PTBFUCz14+NU+dl5lZLfFJYD3ZsOy7PD3c940kia02kUZW87fhmCU6de/Ifp/W+MxlOq3n2OO2+GOdvlr2tV5xzQ9e5ZqWb/L7NDPL1/uUs+J7Ol1oe8ufW7vQ5zuRi0d0TCckNXK/j0fSpmurm06W3COSn2JJnFnq7+9E4Y9lZjbe9NdGvQ/LB46T23dy31clUpmZjbe2ubYs1UlVHTGGIvKepqlOnMQCJJIazXTdVumfWarfH5VyNpT4GmJmtro42LUdXNd1e2WvT2bNUh8RtqOp39WHJ5a6tkcj5zCS6TRdRX12xRLKVFJbEJ9nsZ+VqkS0uU1D68ICST5DtVRi7GzVpp7Uf78ZTPR3oVXFGte2pjYk+y5v+M9hFV440tK1aVPbJ0xvTjfJvrvEtYl9B++mNuViH+p6h8jrq2uT7junCarztT6WwG/MAAAAAKBiTMwAAAAAoGJMzAAAAACgYkzMAAAAAKBipcM/Ovke15ZGQgVMhCgkos3MrKfW79paHR1mMdR3hGvbM/GIHoOgFj8u6fXBHWZmRWi7tkwsNjUza9qYa1sa/KJOM7M95gMijmj4YIX+yJ0ZqvtFpE9b6wMfzMwG/uhpri3//TP0jtPpzdHTh37r2o444j9051s2u6bNzUNk1y0TfmVpb6bH2sz9PauZv2fbig1y+3Yx7tpiC2ZriQ8JKAodJNNT9wuH2x1/DmNtHxJiphdJjzd1CEtvzwrXlkdCSWoi0CMk+p2WC7Uxc6Z9fcsvmldtZvoeq5CZWuQZ6Un8AvtlhQ7/WNvr34nnrlTL5s3OPMjXt+VLfM396cMHye3v3uyPle3U16BIfX3tpDoQJxfBQrF3TV3bJJRfzD9vLeAF9pgr5WtbN7VJtcVqU0OEfywt/Gelmdnahg8Leu5KPa4XrvTfVYcavgb8v1v198F/2+oDk+7dpY+Vp/77RUfUIDOzIvXfhdR3WjOzIlffWxZQbVqENYhvWwAAAABQMSZmAAAAAFAxJmYAAAAAUDEmZgAAAABQMSZmAAAAAFCx0qmMmUhwK4JOoTPz7bFUxnbHpz2qY5npxJ4+kULXbI/I7Zf0Hub75joBsifzKT6dEEnnSvpc23jiE/7MzI7N1ri2obpKHpSb29OG/PXqO1T3LU45yTdOM30xpjjcJ2ZmDz0s+x7yXJ8oeOKOUdn3t6P+PkRCGW2J+fugngSV0GRm1pOIhNDg09/MzDqFb69l+rltd3zf3h6f0tTJ9TOj0qca9WWyr0qFy4uW7JuKdzL2TmeRtCssHjL5TNTcLG3I7XvMvz9LxTtlZrZ2yO/35Sc8IPsu++I62b6v8zbphN7GRb90bTuavlaYme0c9+/VWLJT9m0lvmalkeTeQnwmqiTOWCpdLB1WU/uYZnLZIkw+Q7Viz3rp7VWKbOT9U7VpiQ3IvocN+C9fLzt6o+x78P9z6ZMNcdLv/OevZHv/lY+6tl0tPa6dTf+dYSzdIfu2Ul+bklx/qVTXMXSR5juntWmadShY6KJ3dTWP35gBAAAAQMWYmAEAAABAxZiYAQAAAEDFmJgBAAAAQMVKh3+kqV/8n5oOBMgLH5KRd3TIRpr5RZkqlMDMrNXZ7dpUYEJskWIhFvPFFouOt7e7tpW9x8m+mfl99AW9uHxXxwcx1FJ/vrVEL1Kc6PgFnIkIDzEzCwM65GLONHRIQL7H34eeVC+0HBCPwkBN398k8ddhTeHDVu5PdTjMaL7ZtfVmkZCNxIds9NaGZV8V/qEUhQ7eSMXzMd56TPatZ0tEm15MLN8dsRDYrNsFvuiaur6RezGXdCCIXkReN/++D2S6li+t+/q24g91UEjZJy8cfIhsP+UZ33Bt39tyrOz723E/hm2JrmOZ+JyKvT/yOga16D5mFgI9Zkl3C+xxoAri+dXfZKZPfSesJ7qOqe8cBz1LB7+VVZz4DNl+ylH/7tp+sOUo2bd/wtemWiSISX2HjtYmfj8z73BHAAAAAKBiTMwAAAAAoGJMzAAAAACgYkzMAAAAAKBipcM/OrkPMKhnOlxCteeJDgrphgogyMTix9gix0wFfURWm6pFlf22VPath/LnVoiF0WkXK16X9/vAhm3/oW/j6gfv98df+7TyB5uupg/IMDMrxv01aBWR0AnRlkfWlqfiZu5K9ri2uvXK7RuZv7/toIM78tB2bZ1Cn68K76inPhxmvNgqt1d6IkEjRfABIio0x8wsiL71mn6n2x1/HTGf6CCI6S6w7yb0pR58zWyk+r1uFX4U27+j37XhK0oPQRob8XU/VkNUfZ6vi+NnLahnToN+5meACWZS7B6r8JvpPQ+x7XPzn9ftkMu+E6I5VYkgM2DnLv89oNNFdk43tSlWL6ZvfgYTLeQQovn5iQMAAAAABxAmZgAAAABQMSZmAAAAAFAxJmYAAAAAUDEmZgAAAABQsdJRM7Ws37XlxYTsm6U+9U4lwJmZJcn00m5ykYQXS59RSXrN9ojsO9Cz2rVtav+n7Lu0fphrGwo6wVGlfo2JS7Mx19lpD+1a4tr+r9WPyL7Jf673bQ2dSBgOPkS2l5Xs3OEbR32CpJnZyCaf3rZxTI9rsOavV6fQaTs9IgGuU+jkJWUgWeHaRm2b7Jubf+72tDbLvio5dE9zk2uLJUoF8+cQe8ZT8UoXpt+9ukihbHX0+9BT08/z/KCeh26yB7tRPmFsvoo+ZyKNT/VVz6OZTj4b7ei+D+z21+tzv1wr+571B19xbb/zez7BdPQRkbprZl//zRGu7dExfQ3GxXvdEW1mZsU8SB7DfEdtmgmqNhWF/1yLJSM3U58qPJLotOINe3xS4gc+u1b2/cNvfNW1Hff0La5t82912vE3Nx7s2raMR9IiE/99qhP0+cpaPqdpq5iOhf/GAgAAAMACx8QMAAAAACrGxAwAAAAAKsbEDAAAAAAqVjp5oyharq2vsUr2HW/5wIQ07ZF969mAa2tGAgjSxC/2rKV+oWYswGC85ReMJ5bJvq1i1LUN1g6SfZVN6QbZvrLwIRsbR304xNFL9EL2/97tz7f4Hx3ccerdv3VtPSNjsm+6Ysg3rl7umkKfP76ZmX3v567psX/xYQBmZo/u8AEmaWQ99CNj/v8Yz/Ui1o2Ff+4mEr/ot4iEF+wpfCDHRGen7FuIMJtYIIdadJsm/v7W6vratjv+WYwt5K1lfh/ttr8GsX2o4B4zs3bux4DuBRkGYJbMUiCAusdJJCRAPdN57ut+M9WL5rfXNvp9Ro61eXTYtW0a1wvkfzOyzLWt2OBrU64vrW0a8+/7hgl9DjtSv3C/Wej3R10vFUhgVj5YpRvzYTF/7HnGwjOva5MIsCpEmJsKgzMzaxa7XNv27FHZ9zdN/zm+a/Ow7PvAbt8+/Ftfr9qRV1XVpo0tXZtGUv/9pl3o73Pq2sTqxXTrUDx0ZprmtL5VX0ufiN+YAQAAAEDFmJgBAAAAQMWYmAEAAABAxZiYAQAAAEDFmJgBAAAAQMVKpzImie/aauv0mFT07amJ1D8za3X8Pnoy3Vcl7tQynyLXKcbl9iqxLppUE3xaTiy9ZkIk/ixPD5d95fa5P9amMT1nLoJPkTysTydL/p//49Maj/i1H6uZ2arDfWJl3vTJklldJzc1d/sxPLR1hey7frdP4rx/VD+Kj477a6Oul5nZkuBT3R6xX7u21HTipXq+6iL108wsSfz5Tsie+hlTSYsD9TVy+2bbp4zGnttO7p/9NNGJqLlIdGo0DpZ9x5o+sRKzLJZKJULSQiQcTyWFxuqYeqbUO9HJ9bs6Zj41rJPpt2JPutS1jZhPMzMze3TUp7j2jer0UDmuxD/nu9Idelxhs2trRVIZ1bUJkcRXec27Sh2bpb4HcPIZpmFGapPoG31GfLtKQO1EPoVVHdxj/l03M8szn2g4avp7k6pNDZGyHaNq0+50u+w7Gvx3tFYkLTkXKeoqqdEs8l1CpcjOSK2Y+RqwGJNh+Y0ZAAAAAFSMiRkAAAAAVIyJGQAAAABUjIkZAAAAAFSsdPhHEfxiQrWg0kwvEhxvbdH7FYsUsx4dVtDO9SJsN67IfDPPy5+DCncY6/jF7WZmy+pH+r7mAxvMzNqpCJgofBhFZ0Ivkhxt+779Nb3YdJm4jjtb+toO7Rh2bT2pX8ieB7Fi18weGPUhGe1C9/3PEX/Nxzp6AefWpr9eRWSx54b0ftc2YCtd23jQ96aW9ru2Pa3yoRcqyMbMbKChQz32NdbUi5HVO1Kv+aATM7PE/HObRl5zFdITO4d65hc544nU+zoffu6lFnHrnsH8Ynp1VipgZu9+xQL9yILzXNTBZqrr+67E17eaaIvpBH+stgi+2dvuzy12vurzRAUSmJkVQbfva2YW2M+8xbjA/sAxX2uTF3v+ExX+IepVLFsiyUUIUixAS9SLiVR/Z9iV+O8MWaLDxcoeq1PoABMV9BELulPBRLEapAJX4iEsmAvz8+0EAAAAgAMIEzMAAAAAqBgTMwAAAACoGBMzAAAAAKgYEzMAAAAAqFjpVMYs7XVtsVQblfamUh3NzOo1n/bWznViVj3zSXRp6o+lkunM4gmMZdVSnQS2J/dpenWR8GdmttIOdW3bU59YORYG5Pa7cj+GZIdOzFvV66/Nw2M6lXGgptv3Ne6DGs3MbIW4NPft0kle47nfSZboBMdEtG+znbJvx3wSUS5S4QrTSXETud9vX3257Dve3u7a6pm+Zyo5SaUmNepL5fa5SGVMI8lPKkEulmaq3gf1npuZdSIpdvNXLEVOP2dVU6l3SWysqu7GuorLEHnVdBKXSPLKuwjsin1G5KlI4011wphKyFWfMd2MIZoWKd61WNJiN8ln6p51k3w2X9MasT+oTWbd1Sb1Xqlvc7G3RH0uxt4pdaxOomtTM/Upxt3UJnWsotC1SfVVNWjvPnzfaA1RtUn21dtPuzZ1sf3002EXRh3lN2YAAAAAUDEmZgAAAABQMSZmAAAAAFAxJmYAAAAAULHSqxTbnV2uLRamUYjFjz0i5MNML7ZOIvNFtVhabZ8XE3L7NPEBF7GFmlnq+yapDncYa291bbHQiEfT+11bj/mgkCLRKRt9YbVr29bRC1NHR31AxNKaDo1oFv7atkRbb6qDVTbV/T3b3tSLWFvBn1vb9PnuTPxzN5rslH2VnsRf22aIhGGI564pnnszveA1GnSQ6+CbfY02H5PthQokCDqkQ70jsXdP6UTeHRW8g6einoe5/lmYek5jfUVwhaqPkYALFQpSJLpvGvx+YwvZVZjTdIOcYuEfOihEn4OuAZG+8j6UX2CvdZPCwgJ7PNHCqk0qFKRsIIiZWa6OJb7fmOnaFKs3Sb44a9P0UQOmg9+YAQAAAEDFmJgBAAAAQMWYmAEAAABAxZiYAQAAAEDFmJgBAAAAQMVKpzLWRbKbSoszM8tSnxjXiqTbyWNFEuDSxCcKtvNR0c8nKu4dl2+vZX2y73hrW+m+SrvQSYmt3CcC9ojz7c9WyO3X20OubUl6sOw7FHwy5I62TmUcNH9uDZHItj3X57Up90lCzUSnrPWLFMrH0o2ybyHSGkc6/hqY6euYJL5tV0sfK0sbYnv9s4v+npWubU9zk+yrEpJU+mE989fFzCwX90GN1Szynon3MTauWqSvSj/F7Iql4yUmIspiqXtdJITplECf5BUiP89TrbG+6ljdpJnFknvLiiWRyTSzWMJYF33LJjDGkl1nK4ER84l638W7Pg/MXm2KvT9i85JJjXvH5Y9VRMYVEmrTfKhNB3I6LL8xAwAAAICKMTEDAAAAgIoxMQMAAACAijExAwAAAICKJSGoZZVeb89hri0XAQbdj8DPDae7eLKnNiTbm50R19ZbX6aH1cViz6LwC06L4MMwzHRow9LGEa6tluhwhx4RnNE2fR8y80EfDbG9mb7mbfPhHWnk3jSCDw/Zk+yUfQfDsGvbar+VfdV1aIcx2bdT+PGqBa9Z5NpO5Dt9W2u7PlbuxzDY598RM7Px5tZS44o996pvLHhH9a2l+p63RRBNKgJyYsfLxfWqQiLCUbrcw4yMY3qmV/Pkovto59ixyo0hXhtFLZ+JRfNd7KO0rhahd7NoPqabxfTlto9igb2FyOfvXKM2zVRtUrqpN7NUm2TnadarWatNXex3mtvPRADRwqpNsbH6Z79MbeI3ZgAAAABQMSZmAAAAAFAxJmYAAAAAUDEmZgAAAABQsdLhH7XacteWpb2ybyoWvMb+RfZMhA20Orsiff3x1KLM2KJOFdhQrw1GjiVCJzqjsq9agNnXs0L2bXf8GHrqPqykkUUCTPLdrm1F/WjZd3f+qGuLXZvBdLVra4dx1xYLJcnNL2iMLW7viLCSPLIgcqztgzPq2YDs2+r4a9NbW+ra1DWM6eZYndxfrxj1Pqj3xsysUffnMN7aJvuqZzx2z/W7o8cQxHjbHX9vqrA4Ftgr0/+52ewsvI88T9NcuB/T3X7L6S54I2YOF9PLnc5lyEfMwl1gPxemX5uie56l/Za1sGqT3OU0Q5C63285s1Wb5kPYkNx8QYV8xBD+AQAAAACLChMzAAAAAKgYEzMAAAAAqBgTMwAAAACoGBMzAAAAAKjYtCKDVALcXj4lJY0kOIbc9x1oHCz76iQ8P4ZUJD2amdWyfn/8SKJMUYjUvFRfrsQy35b4NjOzTuGT+4qWT2lJG/pYKrlvV/6I7FtP+2S7ohIce1KfWNkK+p5P5DtcWyPzaYJmZoW45m1xXczMal2cQ1/dJ4dOdHa6tlh6okrinGj58zIza3Z0u9JTW+Lairzl2tqFTiNNk7prU4mKZvFnX1FJi7GcLJU8uniUT1RaaFTiVTQNTdVCmTCmn4Vy+b7xfcTSzGYmpWz/zWmC4wwca3YSGBfz+48qzGVtkruMvibla5Pe79y9KwdebTow6hC/MQMAAACAijExAwAAAICKMTEDAAAAgIoxMQMAAACAipUO/0hE6EQIPsAgRgUN7N2vDysYaz4WGYSfR6pxxQIjWrkPD4ktaOxrrHJt461tum/PCn+stjiWmTXqPhBDhU6081G5fW9t2B8r3yP7KioMw8ysr+aDM7aPr3dtA43VcnsVUDHW0ddLnVtvTQeFJKnfb0ynaLo2tTi2ng3I7VW4TL2m+9aDD0bJC/0+qGujnuXenpV6XOK5VSEwZjoUJLZwuS2Cc5JM77eInNviphYqz2UgSGyh8/R+nhZbgC0X3nez4Hual6a78JD5YJoL0ae5mH52Qj7M5scC+27ObcE9ODOA2tRdbZqtoJCqzdK7Om9r03ww+7WJ35gBAAAAQMWYmAEAAABAxZiYAQAAAEDFmJgBAAAAQMWYmAEAAABAxUqnMmYiPbGraV0k5UUnxulhqWTHIFJpmp0dcvta2u/a2oVONOzk465NXoMuqZTAdsenFKrjx9ob9WHZt5u0xlbhx6ASJGNGW5tL961lvaX7jrW2urae2pDs22yPlNpnSMo/i7H7UMt88mc7cr1V2qNMT7RMbq8SGGOpjIX5dyQvJmTfWqaSJX1SY6zvgSmWsjQfEtGU8kW6bJKWTEgzm9OUtHlrmmlm0d0u6gRGzIz5Wpum//P/Oa1NescLyyzUoblPWqy6NlWXLMlvzAAAAACgYkzMAAAAAKBiTMwAAAAAoGJMzAAAAACgYqXDP1SAQCxUwERIR5r54I3YPlRIh5lZkvrwDRmmke/Sxwot11aPhBp0s980LX0ZrRDrGVXoRCGuoZlZEDvIi2bp43dDhWm0I+eaiXuTF/56m+lz2zWxUfbtrS9zbZ1CB3Ko66juTVHoa9vulA9LUW9OEgnkUNemI577du4DWGJU6I2ZWUcEkKSpDlvR16t8XzyRWig8H1aMz/xi/G4Wgc/aYvxFYG4X08/X613dAvsDR9W1aXbCipQZqU1yx/P1/Zm+uQ/12Nfivbb7i29bAAAAAFAxJmYAAAAAUDEmZgAAAABQMSZmAAAAAFAxJmYAAAAAULHScYIqSa+v5yDZt5XvLj2AQiT3xRIJUzFcleoYItsnYh5ay/pk307uk/9i5zve2uLaGvVh2beW+dS78dY2sf3S0uOqpfocRpuPurae2pDsO97c6vvWfd9GprffI44VTfJTyZSRnxGoZMdYeqF6bpb0HeHa9rQfkdunIj0xdr1aHf+Md3MOqm8s5TQTSYmxvjWRMhrrW6ikqcg9U+8pnkrVaWgxsRSsmf853aylpM0D1aeZxczXlLP5er0ORNSmmXh/q65Z1KCZMr+uI78xAwAAAICKMTEDAAAAgIoxMQMAAACAijExAwAAAICKlQ7/UIv5mp0dsmea+BCFWCCHDjYYk307IqygXlviO0YCDNR+88KPde+4Gq5NBT6Y6ZCLTq4DF1qdXa6tlvaLcTVLHyvWN0387VVtZmbt3I9L9uvo4I00qZfa3kwHmITIYtGgAioi6pm/jhMt/4zGQjpU2Ika6952/yypZ9lMh2/01lf4cUWe206+x7Wpd8xMh3TEzldd81BEgnNiQS7oUmyR8XxeeL+v2XkW5u9C9vlgIS2m5z4uTNSmbh1YNWsh1aCYhXG/+LYFAAAAABVjYgYAAAAAFWNiBgAAAAAVY2IGAAAAABVLQgilVsMliQ/DqNeWyb5F8AEEsfAPNTeMBVSosAIVbJCmOhghFoJQ9lhF7BxEQEUszEIFfSgqMMIsHjBRVi7ujZlZb93fy0IEQaSpvjcTbREEEwnuUOdQrw3IvipsJHYfMnHfdViKvga1tE/01cEqOhxmRPZVYTQqpCP23Mq+kXdEHSv27qkAEfXuxvq22o/qMcyxJHYtFrz5sOh+uvjZXxyL6WdL/PvG3KI2LUQHWs1aDHVIWbi16UB7AgEAAABg3mFiBgAAAAAVY2IGAAAAABVjYgYAAAAAFWNiBgAAAAAVKx0Z1Nc42LW1OrumPQCVmhdNpxNpJoVKLwx6vtlNKqNKRVRj7VY73+Pa1PnWMp3eON0xhEIn8HTycdE2VnpcKoGxXhuUXVWioUx1NLOaSHAMkbTHjrhnmUgTjD0HncJfg1jfZsePt6e2RPdtb3dttcxfm9h5qWuukhrNdFqjui5mZom6tpHnI0n5Gc7ci6VKLaREtNlK/JrL53GxppZ1Y34mnKEqi6E2xfC+LyyLrzbxbQsAAAAAKsbEDAAAAAAqxsQMAAAAACrGxAwAAAAAKlY6/KOT6wABJVWBCyJowEwHeqjgDTOzWiqCJxK/UFMFPpiZtTo+hKFRXyn7KkXQgQuZODd1XmZmqQjvUOdbiBAHM7Mi12OQxxKhIiocIkbfm/LXIBZmMTrxkN9ehGGYmaVJ3bXVIj9OUMEqbTHe2DUIYtGvepbNdOhMS99yuY+2CM6Jhd6oQI5YX7l9LCxFXC8VShLri6qoxc6LYdF9N1igP3sW32J6zJVunp0DrWahOwduHeI3ZgAAAABQMSZmAAAAAFAxJmYAAAAAUDEmZgAAAABQMSZmAAAAAFCxJIRQKvoky5a4tlhinUroyyOJhkoRSf5TSXSqbyKSD810Ol2sr95ex+4lIuUvicx5VV+VBhg7Vm99hWsrQlv2VUma6lix4/U3DnJt480tcnt5XpFzUCmUPbXh0n3VsWLHi/VVVPJgbHv1jMeSCxv15a6tJVIZQ+QdSSLvmaLekVokEVWNoRt5vnNa28+Ubu4xzEhDw/9anMlnsc+euUZt6ha16cCzOGtQTJnaxG/MAAAAAKBiTMwAAAAAoGJMzAAAAACgYkzMAAAAAKBi01qZWkQWsdVTsVudOWGtzk7XpoIVzHSgRj0bdG2xoJEiF2EHkWOpEIVYoIe6DrGQjbIz4dgCQRVgEgtxUOEs8VAS31cGfcSCVcT51mv+3piZWe730Y4EZ6hwltjyYHnNu7g36rlTz4GZvue9PStl33Y+5vcrFoVHXhE5riLyjKvnpiOOHxtDJ98t+9ayocjosPB0s9iaxfgLz4G1mB6LyXSfXerV/EANmg5+YwYAAAAAFWNiBgAAAAAVY2IGAAAAABVjYgYAAAAAFWNiBgAAAAAVS0IIpeJTkqTu2rJIUptK/oulyNVEqmInktCn9NSG/fbFhO4sEg1j41Jz1lhCn9pvT22p7NopxsUYdAJjWbVIsqTaby3tk33b+ajY3l+bntoSub1KhowlS2Zpv+8byyQU1zaWDNkQ17zZ3uHaYsmDg31rXdtEa5vsm4hEw9g5FIW/jioRMbZ9TVyvblIsYymnrbZP3Uwsk32D5b4ttGXfuabuBeYLUtK6Q5rZTIh99sw1atNCRM2iDs2eMrWJ35gBAAAAQMWYmAEAAABAxZiYAQAAAEDFmJgBAAAAQMVKh3/U66tdmwo1MDOrZSKsoLOz9KBqmQ6YUMEGKihEhUvE++pgBBUwoQIbzMxCF6EiajGwuo6xBYK9PStd20TzUX0scW6xABN1vDTxfeNhKV7s2qrzjQWgqHGpwBczs7zk2GL3sZtxKbHzzYsx0VcEoMSuQSwYRVDBO9H7II4XO5bab6utn7u5xgJ7PLluFvOz6H0xIPwDwHxE+AcAAAAALABMzAAAAACgYkzMAAAAAKBiTMwAAAAAoGJMzAAAAACgYqVTGZOk7tqybEj3FfO9WJpfLRt0bSo9MUYl9HWKCd25i/RENWeNJRqq/fbUlsqunWJcjGF6CVK1LlL3ammf7NvOR8X2/tr01HRiZquzy7XF0mdkImEseVBcW5WYaWbWENe82d7h2jr5brn9YN9a1zbR2ib7qrSt2DmotEeVDBnbviauVzvyjqjk0lgqY6u9xW9vmewbLPdtoS37zjWSz+azbhIRQSrkzCCVEfuPmkUdmj2kMgIAAADAAsDEDAAAAAAqxsQMAAAAACrGxAwAAAAAKlY6/AMAAAAAMDv4jRkAAAAAVIyJGQAAAABUjIkZAAAAAFSMiRkAAAAAVIyJGQAAAABUjIkZAAAAAFSMiRkAAAAAVIyJGQAAAABUjIkZAAAAAFTs/wOaIDvgnIibswAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 900x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import torch\n",
+    "\n",
+    "G.eval()\n",
+    "vmax_lr = 0.7\n",
+    "vmax_hr = 0.05\n",
+    "\n",
+    "# grab 5 batches (or just iterate)\n",
+    "num_examples = 5\n",
+    "lr_batch, hr_batch = next(iter(val_loader))\n",
+    "lr_batch, hr_batch = lr_batch.to(DEVICE), hr_batch.to(DEVICE)\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "    sr_batch = torch.sigmoid(G(lr_batch))\n",
+    "\n",
+    "for i in range(min(num_examples, lr_batch.size(0))):\n",
+    "    lr_img = lr_batch[i,0].cpu().numpy()\n",
+    "    sr_img = sr_batch[i,0].cpu().numpy()\n",
+    "    hr_img = hr_batch[i,0].cpu().numpy()\n",
+    "\n",
+    "    fig, axs = plt.subplots(1,3, figsize=(9,3))\n",
+    "    axs[0].imshow(lr_img, cmap=\"magma\", vmin=0, vmax=vmax_lr)\n",
+    "    axs[0].set_title(\"LR input\")\n",
+    "    axs[1].imshow(sr_img, cmap=\"magma\", vmin=0, vmax=vmax_hr)\n",
+    "    axs[1].set_title(\"Model output\")\n",
+    "    axs[2].imshow(hr_img, cmap=\"magma\", vmin=0, vmax=vmax_hr)\n",
+    "    axs[2].set_title(\"HR ground truth\")\n",
+    "    for ax in axs: ax.axis(\"off\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee20fd99",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torchtest",
+   "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": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/dataloaders.py b/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/dataloaders.py
new file mode 100644
index 0000000..9b269c2
--- /dev/null
+++ b/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/dataloaders.py
@@ -0,0 +1,505 @@
+
+import os
+from typing import Optional, Tuple, Union
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.utils.data import Dataset
+from dataclasses import dataclass
+from scipy.ndimage import gaussian_filter
+
+@dataclass
+class NoiseConfig:
+    # Background in counts (pre-normalization, preserved DC)
+    bg_enable: bool = True
+    bg_peak_counts: Union[float, Tuple[float, float]] = (800.0, 1500.0)
+    bg_sky_counts:  Union[float, Tuple[float, float]] = (80.0, 300.0)
+    bg_read_std_counts: float = 3.0
+    bg_prob: float = 1.0
+
+    # Optional post scaling noise (in [0,1])
+    post_mode: str = "none"          # "none"|"gaussian"|"poisson"|"poissongauss"
+    post_level: Union[float, Tuple[float, float]] = 0.0
+    post_read_std: float = 0.0
+    post_prob: float = 1.0
+
+class LRNoiseAugment:
+    """
+    Deterministic per-index LR noise. Same idx -> same noise every epoch.
+    """
+    def __init__(self, cfg: NoiseConfig, base_seed: int = 12345):
+        self.cfg = cfg
+        self.base_seed = int(base_seed)
+
+    @staticmethod
+    def _u(rng, v):  # sample scalar or (lo,hi)
+        if isinstance(v, (tuple, list)) and len(v) == 2:
+            lo, hi = float(v[0]), float(v[1])
+            return float(rng.uniform(lo, hi))
+        return float(v)
+
+    def _rng(self, idx: int):
+        seed = (self.base_seed * 1000003 + idx * 9176) & 0x7fffffff
+        return np.random.RandomState(seed)
+
+    def _background_counts_then_scale01(self, lr_np: np.ndarray, rng) -> np.ndarray:
+        # robust scale to [0,1] without removing DC
+        scale = np.percentile(lr_np, 99.5)
+        if not np.isfinite(scale) or scale <= 0:
+            scale = 1.0
+        lr01 = np.clip(lr_np / scale, 0.0, 1.0).astype(np.float32)
+
+        peak = self._u(rng, self.cfg.bg_peak_counts)
+        sky  = self._u(rng, self.cfg.bg_sky_counts)
+
+        src  = lr01 * peak
+        mean = src + sky
+        counts = rng.poisson(mean).astype(np.float32)
+        if self.cfg.bg_read_std_counts > 0:
+            counts += rng.normal(0.0, self.cfg.bg_read_std_counts, size=counts.shape).astype(np.float32)
+        counts = np.clip(counts, 0.0, None, out=counts)
+
+        denom = max(1.0, peak + sky)
+        return np.clip(counts / denom, 0.0, 1.0).astype(np.float32)
+
+    def _post(self, img01: np.ndarray, rng) -> np.ndarray:
+        m = self.cfg.post_mode.lower()
+        if m == "none" or rng.rand() > self.cfg.post_prob:
+            return img01
+        if m == "gaussian":
+            sigma = self._u(rng, self.cfg.post_level)
+            out = img01 + rng.normal(0.0, sigma, size=img01.shape).astype(np.float32)
+            return np.clip(out, 0.0, 1.0, out=out)
+        if m == "poisson":
+            peak = max(1.0, self._u(rng, self.cfg.post_level))
+            lam = np.clip(img01, 0.0, 1.0) * peak
+            counts = rng.poisson(lam).astype(np.float32)
+            return np.clip(counts / peak, 0.0, 1.0)
+        if m in ("poissongauss", "poisson+gaussian", "pg"):
+            peak = max(1.0, self._u(rng, self.cfg.post_level))
+            lam = np.clip(img01, 0.0, 1.0) * peak
+            counts = rng.poisson(lam).astype(np.float32)
+            if self.cfg.post_read_std > 0.0:
+                counts += rng.normal(0.0, self.cfg.post_read_std, size=counts.shape).astype(np.float32)
+            counts = np.clip(counts, 0.0, None, out=counts)
+            return np.clip(counts / peak, 0.0, 1.0)
+        return img01
+
+    def __call__(self, lr_np: np.ndarray, idx: int) -> np.ndarray:
+        rng = self._rng(idx)
+        if self.cfg.bg_enable and rng.rand() <= self.cfg.bg_prob:
+            lr01 = self._background_counts_then_scale01(lr_np, rng)
+        else:
+            # simple per-image min-max fallback
+            mn, mx = float(lr_np.min()), float(lr_np.max())
+            lr01 = ((lr_np - mn) / (mx - mn + 1e-8)).astype(np.float32)
+        return self._post(lr01, rng)
+
+
+
+# --- helper to accept (N,H,W), (N,1,H,W) or (N,H,W,1) ---
+def _ensure_nhw(x: np.ndarray) -> np.ndarray:
+    if x.ndim == 3:
+        return x
+    if x.ndim == 4 and x.shape[1] == 1:
+        return np.squeeze(x, axis=1)    # (N,1,H,W) -> (N,H,W)
+    if x.ndim == 4 and x.shape[-1] == 1:
+        return np.squeeze(x, axis=-1)   # (N,H,W,1) -> (N,H,W)
+    raise ValueError(f"Expected (N,H,W) or (N,1,H,W)/(N,H,W,1), got {x.shape}")
+
+
+class PairsDatasetUnified(Dataset):
+    """
+    Unified HR/LR dataset with shared LR noise (index-deterministic),
+    model-specific normalization/resizing/padding, and flexible output range.
+
+    Returns:
+      - If out_range == "[-1,1]": tensors in [-1,1]
+      - If out_range == "[0,1]":  tensors in [0,1]
+
+    Args:
+      norm_preset: "minmax" | "percentile" | "hybrid"  (HR=minmax, LR=percentile when "hybrid")
+      hr_norm, lr_norm: optional overrides ("minmax"|"percentile")
+      percentile_p: percentile for global LR/HR scaling (default 99.99)
+      percentile_from: "lr" or "hr" — which array to compute percentile from
+      out_range: "[-1,1]" or "[0,1]"
+      resize_to: int or (H,W) or None — if set, interpolate both LR and HR to this size
+      resize_mode: "bilinear" | "bicubic" | "nearest" | "area"
+      align_corners: bool or None — used for bilinear/bicubic
+      pad_to: int side length for square padding (or None for no padding)
+      pad_mode: "reflect" | "replicate" | "constant"
+      constant_pad_value: used only when pad_mode=="constant"
+      channels: 1 or 3 — repeats channels if 3
+      noise_aug: LRNoiseAugment or None. If given, used to produce lr01 in [0,1].
+      return_order: "HRLR" or "LRHR"
+      return_mask: if True and pad_to is not None, also returns a [1, pad_to, pad_to] mask of valid region
+    """
+
+    def __init__(self,
+                 root_dir: str,
+                 hr_name: str = "Mejiro_dataset/hr_all_lsst_20k.npy",
+                 lr_name: str = "Mejiro_dataset/lr_all_lsst_20k.npy",
+                 mmap: bool = True,
+
+                 # ---- normalization controls ----
+                 norm_preset: str = "minmax",
+                 hr_norm: Optional[str] = None,
+                 lr_norm: Optional[str] = None,
+                 percentile_p: float = 99.99,
+                 percentile_from: str = "lr",
+
+                 # ---- output / shape controls ----
+                 out_range: str = "[-1,1]",                # "[-1,1]" | "[0,1]"
+                 resize_to: Optional[Union[int, Tuple[int, int]]] = None,
+                 resize_mode: str = "bilinear",
+                 align_corners: Optional[bool] = False,
+                 pad_to: Optional[int] = 48,
+                 pad_mode: str = "reflect",
+                 constant_pad_value: float = 0.0,
+                 channels: int = 1,
+
+                 # ---- LR noise (index-deterministic) ----
+                 noise_aug: Optional[object] = None,
+
+                 # ---- API shape ----
+                 return_order: str = "HRLR",
+                 return_mask: bool = False):
+
+        # Load arrays
+        hr_path = os.path.join(root_dir, hr_name)
+        lr_path = os.path.join(root_dir, lr_name)
+        if not (os.path.isfile(hr_path) and os.path.isfile(lr_path)):
+            raise FileNotFoundError(f"Expected {hr_name} and {lr_name} in {root_dir}")
+        self.hr = np.load(hr_path, mmap_mode="r" if mmap else None)
+        self.lr = np.load(lr_path, mmap_mode="r" if mmap else None)
+
+        self.hr = _ensure_nhw(self.hr)
+        self.lr = _ensure_nhw(self.lr)
+
+        n = min(len(self.hr), len(self.lr))
+        self.hr, self.lr = self.hr[:n], self.lr[:n]
+        self.N = int(n)
+        self.H = int(self.hr.shape[1])
+        self.W = int(self.hr.shape[2])
+
+        # Preset → defaults
+        preset = norm_preset.lower()
+        if preset not in ("minmax", "percentile", "hybrid"):
+            raise ValueError("norm_preset must be 'minmax'|'percentile'|'hybrid'")
+        preset_map = {
+            "minmax":     ("minmax", "minmax"),
+            "percentile": ("percentile", "percentile"),
+            "hybrid":     ("minmax", "percentile"),  # HR=minmax, LR=percentile
+        }
+        def_hr, def_lr = preset_map[preset]
+        self.hr_norm = (hr_norm or def_hr).lower()
+        self.lr_norm = (lr_norm or def_lr).lower()
+        if self.hr_norm not in ("minmax", "percentile"): raise ValueError("hr_norm invalid")
+        if self.lr_norm not in ("minmax", "percentile"): raise ValueError("lr_norm invalid")
+
+        # Global percentile scale computed once
+        self.percentile_p = float(percentile_p)
+        src_choice = percentile_from.lower()
+        if src_choice not in ("lr", "hr"):
+            raise ValueError("percentile_from must be 'lr' or 'hr'")
+        src = self.lr if src_choice == "lr" else self.hr
+        flat = src.reshape(-1).astype(np.float64)
+        vmax = np.percentile(flat, self.percentile_p)
+        self.global_scale = float(vmax if np.isfinite(vmax) and vmax > 0 else 1.0)
+
+        # Output / resizing / padding
+        self.out_range = out_range
+        assert self.out_range in ("[-1,1]", "[0,1]")
+
+        # normalize resize_to + validate mode
+        if resize_to is None:
+            self.resize_to = None
+        elif isinstance(resize_to, int):
+            self.resize_to = (int(resize_to), int(resize_to))
+        else:
+            h, w = resize_to
+            self.resize_to = (int(h), int(w))
+
+        self.resize_mode = resize_mode.lower()
+        if self.resize_mode not in ("bilinear", "bicubic", "nearest", "area"):
+            raise ValueError("resize_mode must be 'bilinear'|'bicubic'|'nearest'|'area'")
+
+        # align_corners only valid for linear/bicubic
+        self.align_corners = align_corners if self.resize_mode in ("bilinear", "bicubic") else None
+
+        self.pad_to = pad_to
+        self.pad_mode = pad_mode
+        self.constant_pad_value = float(constant_pad_value)
+        assert channels in (1, 3), "channels must be 1 or 3"
+        self.channels = channels
+
+        # Noise & API
+        self.noise_aug = noise_aug
+        self.return_order = return_order.upper()
+        if self.return_order not in ("HRLR", "LRHR"):
+            raise ValueError("return_order must be 'HRLR' or 'LRHR'")
+        self.return_mask = bool(return_mask)
+
+    def __len__(self) -> int:
+        return self.N
+
+    # ---------- helpers ----------
+    @staticmethod
+    def _minmax01(x: np.ndarray) -> np.ndarray:
+        x = x.astype(np.float32, copy=False)
+        mn, mx = float(x.min()), float(x.max())
+        return ((x - mn) / (mx - mn + 1e-8)).astype(np.float32) if mx > mn else np.zeros_like(x, np.float32)
+
+    def _percentile01(self, x: np.ndarray) -> np.ndarray:
+        s = self.global_scale
+        return np.clip(x.astype(np.float32) / (s + 1e-8), 0.0, 1.0)
+
+    @staticmethod
+    def _to_tensor(x01: np.ndarray) -> torch.Tensor:
+        return torch.from_numpy(x01).unsqueeze(0)  # [1,H,W]
+
+    def _pad(self, x: torch.Tensor, side: int) -> torch.Tensor:
+        # x: [C,H,W]
+        C, H, W = x.shape
+        ph, pw = side - H, side - W
+        if ph == 0 and pw == 0:
+            return x
+        l, r = pw // 2, pw - pw // 2
+        t, b = ph // 2, ph - ph // 2
+        if self.pad_mode == "reflect":
+            try:
+                return F.pad(x, (l, r, t, b), mode="reflect")
+            except RuntimeError:
+                return F.pad(x, (l, r, t, b), mode="replicate")
+        elif self.pad_mode == "replicate":
+            return F.pad(x, (l, r, t, b), mode="replicate")
+        elif self.pad_mode == "constant":
+            return F.pad(x, (l, r, t, b), mode="constant", value=self.constant_pad_value)
+        else:
+            raise ValueError("pad_mode must be 'reflect'|'replicate'|'constant'")
+
+    def _map_out_range(self, x01: torch.Tensor) -> torch.Tensor:
+        if self.out_range == "[0,1]":
+            return x01
+        return (x01 - 0.5) * 2.0
+
+    def _repeat_channels(self, x: torch.Tensor) -> torch.Tensor:
+        # x: [1,H,W] -> [C,H,W]
+        return x if self.channels == 1 else x.repeat(self.channels, 1, 1)
+
+    # resizing helper (x is [C,H,W])
+    def _resize(self, x: torch.Tensor, size_hw: Tuple[int, int]) -> torch.Tensor:
+        x4d = x.unsqueeze(0)  # [1,C,H,W]
+        kwargs = {"mode": self.resize_mode}
+        if self.resize_mode in ("bilinear", "bicubic"):
+            kwargs["align_corners"] = self.align_corners
+        # antialias when available (for downsample in newer PyTorch)
+        try:
+            x4d = F.interpolate(x4d, size=size_hw, antialias=True, **kwargs)
+        except TypeError:
+            x4d = F.interpolate(x4d, size=size_hw, **kwargs)
+        return x4d.squeeze(0)
+
+    # ---------- main ----------
+    def __getitem__(self, idx):
+        hr_np = np.array(self.hr[idx], dtype=np.float32)
+        lr_np = np.array(self.lr[idx], dtype=np.float32)
+
+        # LR: apply shared noise if provided; else chosen normalization
+        if self.noise_aug is not None:
+            lr01 = self.noise_aug(lr_np, idx=idx)  # expected to return [0,1]
+        else:
+            lr01 = self._minmax01(lr_np) if self.lr_norm == "minmax" else self._percentile01(lr_np)
+
+        # HR normalization by selected mode
+        hr01 = self._minmax01(hr_np) if self.hr_norm == "minmax" else self._percentile01(hr_np)
+
+        # → tensors [1,H,W]
+        HR_t = self._to_tensor(hr01)
+        LR_t = self._to_tensor(lr01)
+
+        # Resize both (if requested)
+        if self.resize_to is not None:
+            HR_t = self._resize(HR_t, self.resize_to)
+            LR_t = self._resize(LR_t, self.resize_to)
+
+        # Optional padding (after resize)
+        mask = None
+        if self.pad_to is not None:
+            if self.return_mask:
+                _, H, W = HR_t.shape
+                mask = torch.zeros((1, self.pad_to, self.pad_to), dtype=torch.float32)
+                ph, pw = self.pad_to - H, self.pad_to - W
+                l, r = pw // 2, pw - pw // 2
+                t, b = ph // 2, ph - ph // 2
+                mask[..., t:t+H, l:l+W] = 1.0
+            HR_t = self._pad(HR_t, self.pad_to)
+            LR_t = self._pad(LR_t, self.pad_to)
+
+        # Map to output range and repeat channels if needed
+        HR_t = self._map_out_range(HR_t)
+        LR_t = self._map_out_range(LR_t)
+        HR_t = self._repeat_channels(HR_t)
+        LR_t = self._repeat_channels(LR_t)
+
+        if self.return_order == "HRLR":
+            return (HR_t, LR_t, mask) if self.return_mask else (HR_t, LR_t)
+        else:
+            return (LR_t, HR_t, mask) if self.return_mask else (LR_t, HR_t)
+        
+        
+#---------------------------- Model I --------------------------------------------------------
+
+class PTImageDataset(torch.utils.data.Dataset):
+    def __init__(self, pt_path, downsample_size=None):
+        self.data = torch.load(pt_path)
+        self.data = (self.data - 0.5) * 2  # normalize to [-1, 1]
+        self.downsample_size = downsample_size  # e.g., (75, 75)
+
+    def __len__(self):
+        return self.data.size(0)
+
+    def __getitem__(self, idx):
+        img = self.data[idx]  # shape: [1, H, W]
+        if self.downsample_size:
+            img = F.interpolate(
+                img.unsqueeze(0),
+                size=self.downsample_size,
+                mode='bilinear',
+                align_corners=False
+            ).squeeze(0)
+        return img, 0  # dummy label for uncond model
+
+
+
+#---------------------------- Model II --------------------------------------------------------
+
+class NpyDiffusionDataset(torch.utils.data.Dataset):
+    def __init__(self, root_folder, downsample_size=None):
+        self.image_paths = []
+        self.labels = []
+        self.class_to_label = {'axion': 0, 'cdm': 1, 'no_sub': 2}
+        self.downsample_size = downsample_size
+
+        # Walk through folders and store paths + labels
+        for class_name, label in self.class_to_label.items():
+            class_folder = os.path.join(root_folder, class_name)
+            if not os.path.isdir(class_folder):
+                continue
+            for fname in os.listdir(class_folder):
+                if fname.endswith('.npy'):
+                    self.image_paths.append(os.path.join(class_folder, fname))
+                    self.labels.append(label)
+
+    def __len__(self):
+        return len(self.image_paths)
+
+    def __getitem__(self, idx):
+        path = self.image_paths[idx]
+        label = self.labels[idx]
+
+        # Load and parse based on label (e.g., axion = [image, intensity])
+        data = np.load(path, allow_pickle=True)
+        if label == 0:
+            img = data[0]  # axion: [image, intensity]
+        else:
+            img = data     # others: plain image
+
+        # Validate and normalize to [0, 1]
+        if not isinstance(img, np.ndarray) or img.ndim < 2:
+            raise ValueError(f"Invalid image at {path}, got shape {getattr(img, 'shape', None)}")
+
+        img = img.astype(np.float32)
+        img_min, img_max = np.min(img), np.max(img)
+        if img_max - img_min > 1e-8:
+            img = (img - img_min) / (img_max - img_min)
+        else:
+            img = np.zeros_like(img)
+
+        # Add channel dim if needed
+        if img.ndim == 2:
+            img = img[None, :, :]
+
+        img = torch.tensor(img, dtype=torch.float32)
+
+        # Resize if needed
+        if self.downsample_size:
+            img = F.interpolate(img.unsqueeze(0), size=self.downsample_size, mode='bilinear', align_corners=False).squeeze(0)
+
+        # Normalize to [-1, 1]
+        img = (img - 0.5) * 2
+
+        return img, label
+
+
+class NpyDiffusionDatasetHighLowRes(torch.utils.data.Dataset):
+    def __init__(self, root_folder, downsample_size=None, blur_sigma=3):
+        self.image_paths = []
+        self.class_to_label = {'axion': 0, 'cdm': 1, 'no_sub': 2}
+        self.downsample_size = downsample_size
+        self.blur_sigma = blur_sigma  # standard deviation for Gaussian blur
+
+        for class_name in self.class_to_label:
+            class_folder = os.path.join(root_folder, class_name)
+            if not os.path.isdir(class_folder):
+                continue
+            for fname in os.listdir(class_folder):
+                if fname.endswith('.npy'):
+                    self.image_paths.append(os.path.join(class_folder, fname))
+
+    def __len__(self):
+        return len(self.image_paths)
+
+    def __getitem__(self, idx):
+        path = self.image_paths[idx]
+        data = np.load(path, allow_pickle=True)
+
+        # Handle axion case differently
+        if isinstance(data, np.ndarray) and data.shape[0] == 2 and data[0].ndim >= 2:
+            img = data[0]
+        else:
+            img = data
+
+        if not isinstance(img, np.ndarray) or img.ndim < 2:
+            raise ValueError(f"Invalid image at {path}, got shape {getattr(img, 'shape', None)}")
+
+        img = img.astype(np.float32)
+        img_min, img_max = img.min(), img.max()
+        if img_max - img_min > 1e-8:
+            img = (img - img_min) / (img_max - img_min)
+        else:
+            img = np.zeros_like(img)
+
+        # Original (high-res) target
+        x0 = torch.tensor(img, dtype=torch.float32).unsqueeze(0)  # [1, H, W]
+
+        # Resize if needed
+        if self.downsample_size:
+            x0 = F.interpolate(x0.unsqueeze(0), size=self.downsample_size, mode='bilinear', align_corners=False).squeeze(0)
+
+        # Create low-res conditional image by blurring
+        img_blur_np = gaussian_filter(img, sigma=self.blur_sigma)
+        x_cond = torch.tensor(img_blur_np, dtype=torch.float32).unsqueeze(0)
+
+        # Resize low-res image as well if needed
+        if self.downsample_size:
+            x_cond = F.interpolate(x_cond.unsqueeze(0), size=self.downsample_size, mode='bilinear', align_corners=False).squeeze(0)
+
+        # Normalize both to [-1, 1]
+        x0 = (x0 - 0.5) * 2
+        x_cond = (x_cond - 0.5) * 2
+
+        return x0, x_cond  # taret, input
+
+
+cfg = NoiseConfig(
+    bg_enable=True,
+    bg_peak_counts=(800.0, 1500.0),
+    bg_sky_counts=(80.0*10, 300.0*10),
+    bg_read_std_counts=3.0,
+    bg_prob=1.0,
+    post_mode="none"
+)
+noise_aug = LRNoiseAugment(cfg, base_seed=42)
+
+
diff --git a/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/evaluation.py b/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/evaluation.py
new file mode 100644
index 0000000..694ca64
--- /dev/null
+++ b/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/evaluation.py
@@ -0,0 +1,248 @@
+from typing import Tuple
+import torch
+import torch.nn.functional as F
+
+@torch.no_grad()
+def psnr_torch(pred: torch.Tensor, target: torch.Tensor, eps: float = 1e-10) -> torch.Tensor:
+    """pred, target in [0,1], shape [B,1,H,W] -> [B]"""
+    mse = F.mse_loss(pred, target, reduction='none').mean(dim=[1,2,3])  # [B]
+    psnr = -10.0 * torch.log10(mse + eps)
+    return psnr  # [B]
+
+def _gaussian_window(window_size: int = 11, sigma: float = 1.5, device='cpu', dtype=torch.float32) -> torch.Tensor:
+    coords = torch.arange(window_size, dtype=dtype, device=device) - window_size // 2
+    g = torch.exp(-(coords**2) / (2 * sigma * sigma))
+    g = (g / g.sum()).unsqueeze(0)  # [1, W]
+    window = (g.t() @ g)           # [W, W]
+    return window
+
+def _ssim_components(x, y, window, K=(0.01, 0.03)):
+    """x,y in [0,1], shape [B,1,H,W], window [W,W] normalized."""
+    C1 = (K[0] ** 2)
+    C2 = (K[1] ** 2)
+    pad = window.shape[0] // 2
+    w = window.expand(x.size(1), 1, *window.shape).to(x.dtype).to(x.device)  # [C,1,W,W]
+
+    mu_x = F.conv2d(x, w, padding=pad, groups=x.size(1))
+    mu_y = F.conv2d(y, w, padding=pad, groups=y.size(1))
+    mu_x2, mu_y2, mu_xy = mu_x * mu_x, mu_y * mu_y, mu_x * mu_y
+
+    sigma_x2 = F.conv2d(x * x, w, padding=pad, groups=x.size(1)) - mu_x2
+    sigma_y2 = F.conv2d(y * y, w, padding=pad, groups=y.size(1)) - mu_y2
+    sigma_xy = F.conv2d(x * y, w, padding=pad, groups=x.size(1)) - mu_xy
+
+    # Luminance, contrast, structure terms
+    l = (2 * mu_xy + C1) / (mu_x2 + mu_y2 + C1)
+    c = (2 * torch.sqrt(torch.clamp(sigma_x2, min=0)) * torch.sqrt(torch.clamp(sigma_y2, min=0)) + C2) /         (sigma_x2 + sigma_y2 + C2)
+    s = (sigma_xy + C2 / 2) / (torch.sqrt(torch.clamp(sigma_x2, min=0)) * torch.sqrt(torch.clamp(sigma_y2, min=0)) + C2 / 2 + 1e-12)
+    return l, c, s  # each [B,1,H,W]
+
+@torch.no_grad()
+def ssim_torch(pred: torch.Tensor, target: torch.Tensor, window_size: int = 11, sigma: float = 1.5) -> torch.Tensor:
+    """Single-scale SSIM. Returns per-sample SSIM [B]."""
+    window = _gaussian_window(window_size, sigma, device=pred.device, dtype=pred.dtype)
+    l, c, s = _ssim_components(pred, target, window)
+    ssim_map = l * c * s
+    return ssim_map.mean(dim=[1,2,3])
+
+@torch.no_grad()
+def msssim_torch(pred: torch.Tensor, target: torch.Tensor, window_size: int = 11, sigma: float = 1.5, levels: int = 5) -> torch.Tensor:
+    """Multi-scale SSIM (Wang et al. 2003) with standard weights for 5 levels."""
+    weights = torch.tensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333], device=pred.device, dtype=pred.dtype)
+    weights = weights[:levels]
+    window = _gaussian_window(window_size, sigma, device=pred.device, dtype=pred.dtype)
+
+    mcs = []
+    x, y = pred, target
+    for i in range(levels):
+        l, c, s = _ssim_components(x, y, window)
+        if i < levels - 1:
+            mcs.append(c * s)  # contrast*structure for intermediate scales
+            x = F.avg_pool2d(x, kernel_size=2, stride=2, padding=0)
+            y = F.avg_pool2d(y, kernel_size=2, stride=2, padding=0)
+        else:
+            ms_ssim_map = l * c * s
+
+    mcs = torch.stack([mc.mean(dim=[1,2,3]) for mc in mcs], dim=1) if len(mcs) else torch.ones(pred.size(0), 0, device=pred.device, dtype=pred.dtype)
+    s_l = ms_ssim_map.mean(dim=[1,2,3])  # [B]
+    if mcs.numel() > 0:
+        out = torch.prod(mcs ** weights[:-1], dim=1) * (s_l ** weights[-1])
+    else:
+        out = s_l
+    return out  # [B]
+
+
+def mae(pred, target):
+    """Mean Absolute Error"""
+    return F.l1_loss(pred, target).item()
+
+def mse(pred, target):
+    """Mean Squared Error"""
+    return F.mse_loss(pred, target).item()
+
+
+@torch.no_grad()
+def evaluate_sr(
+    ema_model,
+    sample_epsilon_conditional,
+    test_loader,
+    DEVICE='cuda',
+    MAX_SAMPLES: int = 1000,
+):
+    ema_model.eval()
+    n_done = 0
+    psnr_sum = 0.0
+    ssim_sum = 0.0
+    msssim_sum = 0.0
+    mae_sum = 0.0
+    mse_sum = 0.0
+
+    for batch in test_loader:
+        high_res_batch, low_res_batch = batch
+
+        if n_done >= MAX_SAMPLES:
+            break
+        remain = MAX_SAMPLES - n_done
+        if high_res_batch.size(0) > remain:
+            high_res_batch = high_res_batch[:remain]
+            low_res_batch = low_res_batch[:remain]
+
+        high_res_batch = high_res_batch.to(DEVICE)
+        low_res_batch = low_res_batch.to(DEVICE)
+
+        preds = sample_epsilon_conditional(ema_model, x_cond=low_res_batch)
+
+        # Map to [0,1]
+        preds = preds.clamp(0.0, 1.0)               # predicted HR
+        high_res_batch = (high_res_batch + 1) / 2.0 # GT HR
+
+        # Ensure [B,1,H,W]
+        if preds.dim() == 3:
+            preds = preds.unsqueeze(1)
+        if high_res_batch.dim() == 3:
+            high_res_batch = high_res_batch.unsqueeze(1)
+
+        # Per-image metrics (assume psnr_torch/ssim_torch/msssim_torch -> [B])
+        psnr_vals   = psnr_torch(preds, high_res_batch)     # [B]
+        ssim_vals   = ssim_torch(preds, high_res_batch)     # [B]
+        msssim_vals = msssim_torch(preds, high_res_batch)   # [B]
+
+        # MAE/MSE per image: reduce over pixels per sample, then mean over batch
+        # reduction='none' -> [B,1,H,W] -> collapse spatial dims per sample
+        mae_vals = F.l1_loss(preds, high_res_batch, reduction='none') \
+                     .view(preds.size(0), -1).mean(dim=1)            # [B]
+        mse_vals = F.mse_loss(preds, high_res_batch, reduction='none') \
+                     .view(preds.size(0), -1).mean(dim=1)            # [B]
+
+        psnr_sum   += psnr_vals.sum().item()
+        ssim_sum   += ssim_vals.sum().item()
+        msssim_sum += msssim_vals.sum().item()
+        mae_sum    += mae_vals.sum().item()
+        mse_sum    += mse_vals.sum().item()
+        n_done     += preds.size(0)
+
+        # live progress
+        if n_done % 100 == 0 or n_done == MAX_SAMPLES:
+            print(f"[eval] {n_done}/{MAX_SAMPLES} — "
+                  f"PSNR: {psnr_sum/n_done:.3f}  "
+                  f"SSIM: {ssim_sum/n_done:.4f}  "
+                  f"MS-SSIM: {msssim_sum/n_done:.4f}  "
+                  f"MAE: {mae_sum/n_done:.6f}  "
+                  f"MSE: {mse_sum/n_done:.6f}")
+
+        if n_done >= MAX_SAMPLES:
+            break
+
+    psnr_avg    = psnr_sum / n_done
+    ssim_avg    = ssim_sum / n_done
+    msssim_avg  = msssim_sum / n_done
+    mae_avg     = mae_sum / n_done
+    mse_avg     = mse_sum / n_done
+    print(f"\nFINAL ({n_done} images) — "
+          f"PSNR: {psnr_avg:.3f}  SSIM: {ssim_avg:.4f}  MS-SSIM: {msssim_avg:.4f}  "
+          f"MAE: {mae_avg:.12f}  MSE: {mse_avg:.12f}")
+    return psnr_avg, ssim_avg, msssim_avg, mae_avg, mse_avg
+
+
+@torch.no_grad()
+def evaluate_sr_cfg(
+    ema_model,
+    sample_epsilon_conditional_cfg,
+    test_loader,
+    DEVICE='cuda',
+    MAX_SAMPLES: int = 1000,
+    guidance_scale = 1
+):
+    ema_model.eval()
+    n_done = 0
+    psnr_sum = 0.0
+    ssim_sum = 0.0
+    msssim_sum = 0.0
+    mae_sum = 0.0
+    mse_sum = 0.0
+
+    for batch in test_loader:
+        high_res_batch, low_res_batch = batch
+
+        if n_done >= MAX_SAMPLES:
+            break
+        remain = MAX_SAMPLES - n_done
+        if high_res_batch.size(0) > remain:
+            high_res_batch = high_res_batch[:remain]
+            low_res_batch = low_res_batch[:remain]
+
+        high_res_batch = high_res_batch.to(DEVICE)
+        low_res_batch = low_res_batch.to(DEVICE)
+
+        preds = sample_epsilon_conditional_cfg(ema_model, x_cond=low_res_batch, guidance_scale = guidance_scale)
+
+        # Map to [0,1]
+        preds = preds.clamp(0.0, 1.0)               # predicted HR
+        high_res_batch = (high_res_batch + 1) / 2.0 # GT HR
+
+        # Ensure [B,1,H,W]
+        if preds.dim() == 3:
+            preds = preds.unsqueeze(1)
+        if high_res_batch.dim() == 3:
+            high_res_batch = high_res_batch.unsqueeze(1)
+
+        # Per-image metrics (assume psnr_torch/ssim_torch/msssim_torch -> [B])
+        psnr_vals   = psnr_torch(preds, high_res_batch)     # [B]
+        ssim_vals   = ssim_torch(preds, high_res_batch)     # [B]
+        msssim_vals = msssim_torch(preds, high_res_batch)   # [B]
+
+        # MAE/MSE per image: reduce over pixels per sample, then mean over batch
+        # reduction='none' -> [B,1,H,W] -> collapse spatial dims per sample
+        mae_vals = F.l1_loss(preds, high_res_batch, reduction='none') \
+                     .view(preds.size(0), -1).mean(dim=1)            # [B]
+        mse_vals = F.mse_loss(preds, high_res_batch, reduction='none') \
+                     .view(preds.size(0), -1).mean(dim=1)            # [B]
+
+        psnr_sum   += psnr_vals.sum().item()
+        ssim_sum   += ssim_vals.sum().item()
+        msssim_sum += msssim_vals.sum().item()
+        mae_sum    += mae_vals.sum().item()
+        mse_sum    += mse_vals.sum().item()
+        n_done     += preds.size(0)
+
+        # live progress
+        if n_done % 100 == 0 or n_done == MAX_SAMPLES:
+            print(f"[eval] {n_done}/{MAX_SAMPLES} — "
+                  f"PSNR: {psnr_sum/n_done:.3f}  "
+                  f"SSIM: {ssim_sum/n_done:.4f}  "
+                  f"MS-SSIM: {msssim_sum/n_done:.4f}  "
+                  f"MAE: {mae_sum/n_done:.6f}  "
+                  f"MSE: {mse_sum/n_done:.6f}")
+
+        if n_done >= MAX_SAMPLES:
+            break
+
+    psnr_avg    = psnr_sum / n_done
+    ssim_avg    = ssim_sum / n_done
+    msssim_avg  = msssim_sum / n_done
+    mae_avg     = mae_sum / n_done
+    mse_avg     = mse_sum / n_done
+    print(f"\nFINAL ({n_done} images) — "
+          f"PSNR: {psnr_avg:.3f}  SSIM: {ssim_avg:.4f}  MS-SSIM: {msssim_avg:.4f}  "
+          f"MAE: {mae_avg:.12f}  MSE: {mse_avg:.12f}")
+    return psnr_avg, ssim_avg, msssim_avg, mae_avg, mse_avg
diff --git a/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/models.py b/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/models.py
new file mode 100644
index 0000000..84509d0
--- /dev/null
+++ b/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/models.py
@@ -0,0 +1,839 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import datasets, transforms
+from torch.utils.data import DataLoader
+import numpy as np
+import matplotlib.pyplot as plt
+import copy
+from torch.utils.data import random_split
+from einops import rearrange
+import os
+import random
+from typing import Optional, Tuple, Union
+
+
+
+def cosine_beta_schedule(timesteps, s=0.008):
+    steps = timesteps + 1
+    x = torch.linspace(0, timesteps, steps)
+    alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * np.pi * 0.5) ** 2
+    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
+    alphas_cumprod = torch.clip(alphas_cumprod, 1e-8, 1.0)
+    
+    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
+    betas = torch.clip(betas, 1e-5, 0.999)
+    return betas
+
+
+def cosine_beta_schedule_custom(timesteps, beta_start=1e-4, beta_end=0.02):
+    """
+    Returns a cosine-shaped beta schedule scaled to [beta_start, beta_end].
+
+    Args:
+        timesteps (int): Number of diffusion steps.
+        beta_start (float): Minimum beta value.
+        beta_end (float): Maximum beta value.
+
+    Returns:
+        torch.Tensor: Tensor of shape (timesteps,) with the beta schedule.
+    """
+    x = torch.linspace(0, 1, timesteps)
+    cosine = (1 + torch.cos(np.pi * x + np.pi)) / 2
+    cosine = (cosine - cosine.min()) / (cosine.max() - cosine.min())  # Normalize to [0, 1]
+    betas = beta_start + cosine * (beta_end - beta_start)  # Scale to desired range
+    return betas
+
+
+
+# --- Noise Schedule ---
+# betas = torch.linspace(1e-4, 0.02, T).to(DEVICE)
+betas = cosine_beta_schedule_custom(T).to(DEVICE)
+# betas = cosine_beta_schedule(T).to(DEVICE)
+alphas = 1. - betas
+alphas_cumprod = torch.cumprod(alphas, dim=0)
+
+
+# --- EMA Update ---
+def update_ema(ema_model, model, decay):
+    with torch.no_grad():
+        for ema_param, param in zip(ema_model.parameters(), model.parameters()):
+            ema_param.data.mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+# From Pranath code:
+#-------------------------------------------------------------------------------
+
+class Residual(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+
+    def forward(self, x, *args, **kwargs):
+        return self.fn(x, *args, **kwargs) + x
+
+class Rezero(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+        self.g = nn.Parameter(torch.zeros(1))
+
+    def forward(self, x):
+        return self.fn(x) * self.g
+        
+class LinearAttention(nn.Module):
+    def __init__(self, dim, heads = 4, dim_head = 32):
+        super().__init__()
+        self.heads = heads
+        hidden_dim = dim_head * heads
+        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
+        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+        qkv = self.to_qkv(x)
+        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
+        k = k.softmax(dim=-1)
+        context = torch.einsum('bhdn,bhen->bhde', k, v)
+        out = torch.einsum('bhde,bhdn->bhen', context, q)
+        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
+        return self.to_out(out)
+
+
+#--------------------------------------------------------------------------------------------
+
+
+
+# --- Timestep Embedding ---
+def get_timestep_embedding(timesteps, dim):
+    half_dim = dim // 2
+    freqs = torch.exp(-np.log(10000) * torch.arange(0, half_dim).float() / half_dim).to(DEVICE)
+    angles = timesteps[:, None].float() * freqs[None, :]
+    return torch.cat([torch.sin(angles), torch.cos(angles)], dim=-1)
+
+def get_time_embedding_continuous(t, dim):
+    """
+    Continuous sinusoidal time embedding for flow matching.
+
+    Args:
+        t: Tensor of shape [batch] with values in [0, 1]
+        dim: embedding dimension (must be even)
+
+    Returns:
+        Tensor of shape [batch, dim]
+    """
+    half_dim = dim // 2
+    freqs = torch.exp(
+        -np.log(10000) * torch.arange(0, half_dim, dtype=torch.float32, device=t.device) / half_dim
+    )
+    # multiply continuous times with frequencies
+    angles = t[:, None] * freqs[None, :]
+    return torch.cat([torch.sin(angles), torch.cos(angles)], dim=-1)
+
+# --- Forward Diffusion ---
+def q_sample(x0, t, noise):
+    sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod[t]).view(-1, 1, 1, 1).to(DEVICE)
+    sqrt_one_minus = torch.sqrt(1 - alphas_cumprod[t]).view(-1, 1, 1, 1).to(DEVICE)
+    return sqrt_alphas_cumprod * x0 + sqrt_one_minus * noise
+
+# --- Ensure shapes match before adding ---
+
+def center_crop(tensor, target_height, target_width):
+    _, _, h, w = tensor.shape
+    start_h = (h - target_height) // 2
+    start_w = (w - target_width) // 2
+    return tensor[:, :, start_h:start_h + target_height, start_w:start_w + target_width]
+
+# ---  U-Net ---
+class ResBlock(nn.Module):
+    def __init__(self, in_ch, out_ch, time_emb_dim):
+        super().__init__()
+        self.conv1 = nn.Conv2d(in_ch, out_ch, 3, padding=1)
+        self.conv2 = nn.Conv2d(out_ch, out_ch, 3, padding=1)
+        self.time_mlp = nn.Linear(time_emb_dim, out_ch)
+        self.act = nn.SiLU()
+        self.norm1 = nn.GroupNorm(1, out_ch)
+        self.norm2 = nn.GroupNorm(1, out_ch)
+
+        self.res_conv = nn.Conv2d(in_ch, out_ch, 1) if in_ch != out_ch else nn.Identity()
+
+    def forward(self, x, t_emb):
+        h = self.norm1(self.conv1(x))
+        h += self.time_mlp(t_emb)[:, :, None, None]
+        h = self.act(h)
+        h = self.norm2(self.conv2(h))
+        return h + self.res_conv(x)
+
+    
+
+class SelfAttention(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.norm = nn.GroupNorm(8, channels)
+        self.q = nn.Conv1d(channels, channels, 1)
+        self.k = nn.Conv1d(channels, channels, 1)
+        self.v = nn.Conv1d(channels, channels, 1)
+        self.proj = nn.Conv1d(channels, channels, 1)
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        h = self.norm(x).view(B, C, H * W)  # Flatten spatial dims
+        q, k, v = self.q(h), self.k(h), self.v(h)
+
+        attn = torch.bmm(q.transpose(1, 2), k) / (C ** 0.5)  # [B, HW, HW]
+        attn = F.softmax(attn, dim=-1)
+        out = torch.bmm(attn, v.transpose(1, 2)).transpose(1, 2)  # [B, C, HW]
+        out = self.proj(out).view(B, C, H, W)
+
+        return x + out
+    
+class AttnBlock(nn.Module):
+    def __init__(self, time_emb_dim):
+        super().__init__()
+        self.block1 = ResBlock(512, 512, time_emb_dim)
+        self.attn   = SelfAttention(512)
+        self.block2 = ResBlock(512, 512, time_emb_dim)
+
+    def forward(self, x, t_emb):
+        x = self.block1(x, t_emb)
+        x = self.attn(x)
+        x = self.block2(x, t_emb)
+        return x
+    
+    
+class UNet_512_mix_attn(nn.Module):
+    def __init__(self, time_emb_dim=EMBED_DIM):
+        super().__init__()
+
+        self.time_mlp = nn.Sequential(
+            nn.Linear(time_emb_dim, time_emb_dim * 4),
+            nn.SiLU(),
+            nn.Linear(time_emb_dim * 4, time_emb_dim)
+        )
+
+        # Downsampling
+        self.enc1 = ResBlock(1, 64, time_emb_dim)
+        self.attn_enc1 = Residual(Rezero(LinearAttention(64)))
+        self.enc2 = ResBlock(64, 128, time_emb_dim)
+        self.attn_enc2 = Residual(Rezero(LinearAttention(128)))
+        self.enc3 = ResBlock(128, 256, time_emb_dim)
+        self.attn_enc3 = SelfAttention(256)
+        self.enc4 = ResBlock(256, 512, time_emb_dim)
+
+        # Bottleneck with attention
+        self.middle = AttnBlock(time_emb_dim)
+
+        # Upsampling — updated in_channels due to concatenation
+        self.dec1 = ResBlock(512, 256, time_emb_dim)              # mid only
+        self.attn_dec1 = Residual(Rezero(LinearAttention(256)))
+        self.dec2 = ResBlock(256 + 256, 128, time_emb_dim)        # d1 + x3
+        self.attn_dec2 = SelfAttention(128)
+        self.dec3 = ResBlock(128 + 128, 64, time_emb_dim)         # d2 + x2
+        self.attn_dec3 = Residual(Rezero(LinearAttention(64)))
+        self.dec4 = ResBlock(64 + 64, 1, time_emb_dim)            # d3 + x1
+
+    def forward(self, x, t_emb):
+        t_emb = self.time_mlp(t_emb)
+
+        # Encoder
+        x1 = self.enc1(x, t_emb)
+        x1 = self.attn_enc1(x1)
+        x2 = self.enc2(F.avg_pool2d(x1, 2), t_emb)
+        x2 = self.attn_enc2(x2)
+        x3 = self.enc3(F.avg_pool2d(x2, 2), t_emb)
+        x3 = self.attn_enc3(x3)
+        x4 = self.enc4(F.avg_pool2d(x3, 2), t_emb)
+
+        # Bottleneck
+        mid = self.middle(x4, t_emb)
+
+        # Decoder with concatenation
+        d1 = F.interpolate(self.dec1(mid, t_emb), size=x3.shape[2:], mode='nearest')
+        d1 = self.attn_dec1(d1)
+        d2_input = torch.cat([d1, x3], dim=1)
+        d2 = F.interpolate(self.dec2(d2_input, t_emb), size=x2.shape[2:], mode='nearest')
+        d2 = self.attn_dec2(d2)
+        d3_input = torch.cat([d2, x2], dim=1)
+        d3 = F.interpolate(self.dec3(d3_input, t_emb), size=x1.shape[2:], mode='nearest')
+        d3 = self.attn_dec3(d3)
+        d4_input = torch.cat([d3, x1], dim=1)
+        out = self.dec4(d4_input, t_emb)
+
+        return out
+    
+class UNet_512_lin_attn(nn.Module):
+    def __init__(self, time_emb_dim=EMBED_DIM):
+        super().__init__()
+
+        self.time_mlp = nn.Sequential(
+            nn.Linear(time_emb_dim, time_emb_dim * 4),
+            nn.SiLU(),
+            nn.Linear(time_emb_dim * 4, time_emb_dim)
+        )
+
+        # Downsampling
+        self.enc1 = ResBlock(1, 64, time_emb_dim)
+        self.attn_enc1 = Residual(Rezero(LinearAttention(64)))
+        self.enc2 = ResBlock(64, 128, time_emb_dim)
+        self.attn_enc2 = Residual(Rezero(LinearAttention(128)))
+        self.enc3 = ResBlock(128, 256, time_emb_dim)
+        self.attn_enc3 = Residual(Rezero(LinearAttention(256)))
+        self.enc4 = ResBlock(256, 512, time_emb_dim)
+
+        # Bottleneck with attention
+        self.middle = AttnBlock(time_emb_dim)
+
+        # Upsampling — updated in_channels due to concatenation
+        self.dec1 = ResBlock(512, 256, time_emb_dim)              # mid only
+        self.attn_dec1 = Residual(Rezero(LinearAttention(256)))
+        self.dec2 = ResBlock(256 + 256, 128, time_emb_dim)        # d1 + x3
+        self.attn_dec2 = Residual(Rezero(LinearAttention(128)))
+        self.dec3 = ResBlock(128 + 128, 64, time_emb_dim)         # d2 + x2
+        self.attn_dec3 = Residual(Rezero(LinearAttention(64)))
+        self.dec4 = ResBlock(64 + 64, 1, time_emb_dim)            # d3 + x1
+
+    def forward(self, x, t_emb):
+        t_emb = self.time_mlp(t_emb)
+
+        # Encoder
+        x1 = self.enc1(x, t_emb)
+        x1 = self.attn_enc1(x1)
+        x2 = self.enc2(F.avg_pool2d(x1, 2), t_emb)
+        x2 = self.attn_enc2(x2)
+        x3 = self.enc3(F.avg_pool2d(x2, 2), t_emb)
+        x3 = self.attn_enc3(x3)
+        x4 = self.enc4(F.avg_pool2d(x3, 2), t_emb)
+
+        # Bottleneck
+        mid = self.middle(x4, t_emb)
+
+        # Decoder with concatenation
+        d1 = F.interpolate(self.dec1(mid, t_emb), size=x3.shape[2:], mode='nearest')
+        d1 = self.attn_dec1(d1)
+        d2_input = torch.cat([d1, x3], dim=1)
+        d2 = F.interpolate(self.dec2(d2_input, t_emb), size=x2.shape[2:], mode='nearest')
+        d2 = self.attn_dec2(d2)
+        d3_input = torch.cat([d2, x2], dim=1)
+        d3 = F.interpolate(self.dec3(d3_input, t_emb), size=x1.shape[2:], mode='nearest')
+        d3 = self.attn_dec3(d3)
+        d4_input = torch.cat([d3, x1], dim=1)
+        out = self.dec4(d4_input, t_emb)
+
+        return out
+    
+
+class CondPyramid(nn.Module):
+    """
+    Lightweight LR feature pyramid producing c1 (H×W), c2 (H/2×W/2), c3 (H/4×W/4).
+    Channel sizes match your encoder scales: 64, 128, 256.
+    """
+    def __init__(self, in_ch=1, chs=(64, 128, 256)):
+        super().__init__()
+        c1, c2, c3 = chs
+        act = nn.SiLU()
+        self.block1 = nn.Sequential(
+            nn.Conv2d(in_ch, c1, 3, padding=1),
+            act,
+            nn.Conv2d(c1, c1, 3, padding=1),
+            act,
+        )
+        self.down1 = nn.Conv2d(c1, c2, 3, stride=2, padding=1)  # /2
+        self.block2 = nn.Sequential(
+            nn.SiLU(),
+            nn.Conv2d(c2, c2, 3, padding=1),
+            nn.SiLU(),
+        )
+        self.down2 = nn.Conv2d(c2, c3, 3, stride=2, padding=1)  # /4
+        self.block3 = nn.Sequential(
+            nn.SiLU(),
+            nn.Conv2d(c3, c3, 3, padding=1),
+            nn.SiLU(),
+        )
+
+    def forward(self, x_cond):
+        c1 = self.block1(x_cond)          # [B, 64,  H,   W]
+        h  = self.down1(c1)               # [B, 128, H/2, W/2]
+        c2 = self.block2(h)               # [B, 128, H/2, W/2]
+        h  = self.down2(c2)               # [B, 256, H/4, W/4]
+        c3 = self.block3(h)               # [B, 256, H/4, W/4]
+        return c1, c2, c3
+
+
+class UNet_512_mix_attn_conditional(nn.Module):
+    def __init__(self, time_emb_dim=EMBED_DIM, use_cond_pyr: bool = True, use_global_cond: bool = True):
+        super().__init__()
+        self.use_cond_pyr = use_cond_pyr
+        self.use_global_cond = use_global_cond
+        
+        if self.use_cond_pyr:
+            self.gamma_c1 = nn.Parameter(torch.tensor(0.0))
+            self.gamma_c2 = nn.Parameter(torch.tensor(0.0))
+            self.gamma_c3 = nn.Parameter(torch.tensor(0.0))
+
+
+        # Time embedding MLP (unchanged)
+        self.time_mlp = nn.Sequential(
+            nn.Linear(time_emb_dim, time_emb_dim * 4),
+            nn.SiLU(),
+            nn.Linear(time_emb_dim * 4, time_emb_dim)
+        )
+        
+        
+        
+           # global conditioning path (GAP + tiny MLP), gated at 0
+        if self.use_global_cond:
+            self.cond_global_pool = nn.AdaptiveAvgPool2d(1)  # GAP over x_cond
+            self.cond_mlp = nn.Sequential(
+                nn.Linear(1, time_emb_dim),
+                nn.SiLU(),
+                nn.Linear(time_emb_dim, time_emb_dim)
+            )
+            self.gamma_cond = nn.Parameter(torch.tensor(0.5))
+            self.mix_emb = nn.Linear(2 * time_emb_dim, time_emb_dim)
+            # init so mix_emb([t, c]) ≈ t at step 0
+            with torch.no_grad():
+                self.mix_emb.weight.zero_()
+                self.mix_emb.weight[:time_emb_dim, :time_emb_dim] = torch.eye(time_emb_dim)
+                if self.mix_emb.bias is not None:
+                    self.mix_emb.bias.zero_()
+
+        # Encoder (unchanged, input has 2 channels: [noisy, LR])
+        self.enc1 = ResBlock(2,   64, time_emb_dim)
+        self.attn_enc1 = Residual(Rezero(LinearAttention(64)))
+        self.enc2 = ResBlock(64,  128, time_emb_dim)
+        self.attn_enc2 = Residual(Rezero(LinearAttention(128)))
+        self.enc3 = ResBlock(128, 256, time_emb_dim)
+        self.attn_enc3 = SelfAttention(256)
+        self.enc4 = ResBlock(256, 512, time_emb_dim)
+
+        self.middle = AttnBlock(time_emb_dim)
+
+        # Decoder — channel sizes depend on whether we concat cond features
+        if self.use_cond_pyr:
+            # d2: [d1(256), x3(256), c3(256)] -> 128
+            self.dec1 = ResBlock(512,              256, time_emb_dim)
+            self.attn_dec1 = Residual(Rezero(LinearAttention(256)))
+            self.dec2 = ResBlock(256 + 256 + 256,  128, time_emb_dim)
+            self.attn_dec2 = SelfAttention(128)
+            # d3: [d2(128), x2(128), c2(128)] -> 64
+            self.dec3 = ResBlock(128 + 128 + 128,   64, time_emb_dim)
+            self.attn_dec3 = Residual(Rezero(LinearAttention(64)))
+            # d4: [d3(64), x1(64), c1(64)] -> 1
+            self.dec4 = ResBlock(64 + 64 + 64,       1, time_emb_dim)
+
+            self.cond_pyr = CondPyramid(in_ch=1, chs=(64, 128, 256))
+        else:
+            # Original decoder sizes (no cond features)
+            self.dec1 = ResBlock(512,        256, time_emb_dim)
+            self.attn_dec1 = Residual(Rezero(LinearAttention(256)))
+            self.dec2 = ResBlock(256 + 256,  128, time_emb_dim)  # [d1, x3]
+            self.attn_dec2 = SelfAttention(128)
+            self.dec3 = ResBlock(128 + 128,   64, time_emb_dim)  # [d2, x2]
+            self.attn_dec3 = Residual(Rezero(LinearAttention(64)))
+            self.dec4 = ResBlock(64 + 64,      1, time_emb_dim)  # [d3, x1]
+
+            self.cond_pyr = None  # not used
+
+    def forward(self, x, t_emb, x_cond):
+        """
+        x:      [B, 1, H, W]   noisy image at step t
+        x_cond: [B, 1, H, W]   LR conditional image
+        t_emb:  [B, EMBED_DIM]
+        """
+        t_emb = self.time_mlp(t_emb)  # unchanged
+        
+        # ✅ inject global LR conditioning into time embedding (FiLM-style)
+        if self.use_global_cond:
+            c_global = self.cond_global_pool(x_cond).flatten(1)   # [B,1]
+            c_emb = self.cond_mlp(c_global)                       # [B, EMBED_DIM]
+            t_cat = torch.cat([t_emb, self.gamma_cond * c_emb], dim=1)  # [B, 2D]
+            t_emb = self.mix_emb(t_cat)                                  # [B, D]
+
+        # Concatenate condition at input (unchanged)
+        x_in = torch.cat([x, x_cond], dim=1)  # [B,2,H,W]
+
+        # Encoder
+        x1 = self.enc1(x_in, t_emb)                 # [B,64,H,W]
+        x1 = self.attn_enc1(x1)
+        x2 = self.enc2(F.avg_pool2d(x1, 2), t_emb)  # [B,128,H/2,W/2]
+        x2 = self.attn_enc2(x2)
+        x3 = self.enc3(F.avg_pool2d(x2, 2), t_emb)  # [B,256,H/4,W/4]
+        x3 = self.attn_enc3(x3)
+        x4 = self.enc4(F.avg_pool2d(x3, 2), t_emb)  # [B,512,H/8,W/8]
+
+        # Bottleneck
+        mid = self.middle(x4, t_emb)
+
+        # Multi-scale LR features (only if enabled)
+        if self.use_cond_pyr:
+            c1, c2, c3 = self.cond_pyr(x_cond)      # [B,64,H,W], [B,128,H/2,W/2], [B,256,H/4,W/4]
+            c1s = self.gamma_c1 * c1
+            c2s = self.gamma_c2 * c2
+            c3s = self.gamma_c3 * c3
+
+        # Decoder
+        d1 = F.interpolate(self.dec1(mid, t_emb), size=x3.shape[2:], mode='nearest')
+        d1 = self.attn_dec1(d1)
+
+        if self.use_cond_pyr:
+            d2_input = torch.cat([d1, x3, c3s], dim=1)
+        else:
+            d2_input = torch.cat([d1, x3], dim=1)
+        d2 = F.interpolate(self.dec2(d2_input, t_emb), size=x2.shape[2:], mode='nearest')
+        d2 = self.attn_dec2(d2)
+
+        if self.use_cond_pyr:
+            d3_input = torch.cat([d2, x2, c2s], dim=1)
+        else:
+            d3_input = torch.cat([d2, x2], dim=1)
+        d3 = F.interpolate(self.dec3(d3_input, t_emb), size=x1.shape[2:], mode='nearest')
+        d3 = self.attn_dec3(d3)
+
+        if self.use_cond_pyr:
+            d4_input = torch.cat([d3, x1, c1s], dim=1)
+        else:
+            d4_input = torch.cat([d3, x1], dim=1)
+        out = self.dec4(d4_input, t_emb)
+
+        return out
+    
+class UNet_512_mix_attn_conditional_cfg(nn.Module):
+    def __init__(self, time_emb_dim=EMBED_DIM, use_cond_pyr: bool = True, use_global_cond: bool = True):
+        super().__init__()
+        self.use_cond_pyr = use_cond_pyr
+        self.use_global_cond = use_global_cond
+
+        if self.use_cond_pyr:
+            self.gamma_c1 = nn.Parameter(torch.tensor(0.0))
+            self.gamma_c2 = nn.Parameter(torch.tensor(0.0))
+            self.gamma_c3 = nn.Parameter(torch.tensor(0.0))
+
+        # Time embedding MLP (unchanged)
+        self.time_mlp = nn.Sequential(
+            nn.Linear(time_emb_dim, time_emb_dim * 4),
+            nn.SiLU(),
+            nn.Linear(time_emb_dim * 4, time_emb_dim)
+        )
+
+        # 👇 NEW: tiny MLP for the cond flag (scalar -> emb)
+        # cond_flag convention: 1 = condition present, 0 = dropped (uncond)
+        self.flag_mlp = nn.Sequential(
+            nn.Linear(1, time_emb_dim),
+            nn.SiLU(),
+            nn.Linear(time_emb_dim, time_emb_dim)
+        )
+        self.gamma_flag = nn.Parameter(torch.tensor(1.0))  # scale for flag injection
+
+        # global conditioning path (GAP + tiny MLP), gated at 0
+        if self.use_global_cond:
+            self.cond_global_pool = nn.AdaptiveAvgPool2d(1)  # GAP over x_cond
+            self.cond_mlp = nn.Sequential(
+                nn.Linear(1, time_emb_dim),
+                nn.SiLU(),
+                nn.Linear(time_emb_dim, time_emb_dim)
+            )
+            self.gamma_cond = nn.Parameter(torch.tensor(0.5))
+            self.mix_emb = nn.Linear(2 * time_emb_dim, time_emb_dim)
+            with torch.no_grad():
+                self.mix_emb.weight.zero_()
+                self.mix_emb.weight[:time_emb_dim, :time_emb_dim] = torch.eye(time_emb_dim)
+                if self.mix_emb.bias is not None:
+                    self.mix_emb.bias.zero_()
+
+            # 👇 NEW: learned null for the global-cond embedding path (optional but helpful)
+            self.c_null_emb = nn.Parameter(torch.zeros(time_emb_dim))
+
+        # Encoder (unchanged)
+        self.enc1 = ResBlock(2,   64, time_emb_dim)
+        self.attn_enc1 = Residual(Rezero(LinearAttention(64)))
+        self.enc2 = ResBlock(64,  128, time_emb_dim)
+        self.attn_enc2 = Residual(Rezero(LinearAttention(128)))
+        self.enc3 = ResBlock(128, 256, time_emb_dim)
+        self.attn_enc3 = SelfAttention(256)
+        self.enc4 = ResBlock(256, 512, time_emb_dim)
+
+        self.middle = AttnBlock(time_emb_dim)
+
+        # Decoder — channel sizes depend on whether we concat cond features
+        if self.use_cond_pyr:
+            self.dec1 = ResBlock(512,              256, time_emb_dim)
+            self.attn_dec1 = Residual(Rezero(LinearAttention(256)))
+            self.dec2 = ResBlock(256 + 256 + 256,  128, time_emb_dim)
+            self.attn_dec2 = SelfAttention(128)
+            self.dec3 = ResBlock(128 + 128 + 128,   64, time_emb_dim)
+            self.attn_dec3 = Residual(Rezero(LinearAttention(64)))
+            self.dec4 = ResBlock(64 + 64 + 64,       1, time_emb_dim)
+
+            self.cond_pyr = CondPyramid(in_ch=1, chs=(64, 128, 256))
+        else:
+            self.dec1 = ResBlock(512,        256, time_emb_dim)
+            self.attn_dec1 = Residual(Rezero(LinearAttention(256)))
+            self.dec2 = ResBlock(256 + 256,  128, time_emb_dim)
+            self.attn_dec2 = SelfAttention(128)
+            self.dec3 = ResBlock(128 + 128,   64, time_emb_dim)
+            self.attn_dec3 = Residual(Rezero(LinearAttention(64)))
+            self.dec4 = ResBlock(64 + 64,      1, time_emb_dim)
+            self.cond_pyr = None
+
+    def forward(self, x, t_emb, x_cond, cond_flag=None):
+        """
+        x:         [B, 1, H, W]   noisy image at step t
+        x_cond:    [B, 1, H, W]   LR conditional image
+        t_emb:     [B, EMBED_DIM]
+        cond_flag: [B, 1]         1 = condition present, 0 = dropped (uncond). If None, assume 1.
+        """
+        B = x.shape[0]
+        device = x.device
+
+        if cond_flag is None:
+            cond_flag = torch.ones(B, 1, device=device)
+
+        # helpful broadcasters
+        def b11(flag):  # [B,1,1,1] for feature gating
+            return flag.view(B, 1, 1, 1)
+        def b1(flag):   # [B,1] for embedding blending
+            return flag.view(B, 1)
+
+        # Time embedding base
+        t_emb = self.time_mlp(t_emb)
+        
+        t_emb = t_emb + self.gamma_flag * self.flag_mlp(cond_flag)
+
+        if self.use_global_cond:
+            c_global = self.cond_global_pool(x_cond).flatten(1)      # [B,1]
+            c_emb = self.cond_mlp(c_global)                          # [B,D]
+            # Blend with learned null when dropped
+            c_emb_eff = b1(cond_flag) * c_emb + (1.0 - b1(cond_flag)) * self.c_null_emb.unsqueeze(0)
+            t_cat = torch.cat([t_emb, self.gamma_cond * c_emb_eff], dim=1)  # [B,2D]
+            t_emb = self.mix_emb(t_cat)                                      # [B,D]
+
+        # Concatenate condition at input, gated
+        x_in = torch.cat([x, b11(cond_flag) * x_cond], dim=1)  # [B,2,H,W]
+
+        # Encoder
+        x1 = self.enc1(x_in, t_emb)
+        x1 = self.attn_enc1(x1)
+        x2 = self.enc2(F.avg_pool2d(x1, 2), t_emb)
+        x2 = self.attn_enc2(x2)
+        x3 = self.enc3(F.avg_pool2d(x2, 2), t_emb)
+        x3 = self.attn_enc3(x3)
+        x4 = self.enc4(F.avg_pool2d(x3, 2), t_emb)
+
+        # Bottleneck
+        mid = self.middle(x4, t_emb)
+
+        # Multi-scale LR features (only if enabled) — gated
+        if self.use_cond_pyr:
+            c1, c2, c3 = self.cond_pyr(x_cond)
+            c1s = b11(cond_flag) * (self.gamma_c1 * c1)
+            c2s = b11(cond_flag) * (self.gamma_c2 * c2)
+            c3s = b11(cond_flag) * (self.gamma_c3 * c3)
+
+        # Decoder
+        d1 = F.interpolate(self.dec1(mid, t_emb), size=x3.shape[2:], mode='nearest')
+        d1 = self.attn_dec1(d1)
+
+        d2_input = torch.cat([d1, x3, c3s], dim=1) if self.use_cond_pyr else torch.cat([d1, x3], dim=1)
+        d2 = F.interpolate(self.dec2(d2_input, t_emb), size=x2.shape[2:], mode='nearest')
+        d2 = self.attn_dec2(d2)
+
+        d3_input = torch.cat([d2, x2, c2s], dim=1) if self.use_cond_pyr else torch.cat([d2, x2], dim=1)
+        d3 = F.interpolate(self.dec3(d3_input, t_emb), size=x1.shape[2:], mode='nearest')
+        d3 = self.attn_dec3(d3)
+
+        d4_input = torch.cat([d3, x1, c1s], dim=1) if self.use_cond_pyr else torch.cat([d3, x1], dim=1)
+        out = self.dec4(d4_input, t_emb)
+        return out
+
+    
+def filter_by_shape(target_model, old_state):
+    """Keep only tensors whose keys exist in target and shapes match."""
+    new_sd = target_model.state_dict()
+    filt = {k:v for k,v in old_state.items() if (k in new_sd and new_sd[k].shape == v.shape)}
+    return filt
+
+
+def get_state_dict(ckpt_path, key_candidates=("model", "state_dict", "ema_model")):
+    ckpt = torch.load(ckpt_path, map_location="cpu")
+    if isinstance(ckpt, dict):
+        for k in key_candidates:
+            if k in ckpt and isinstance(ckpt[k], dict):
+                return ckpt[k]
+    # fallback: assume the file itself is a state_dict
+    return ckpt
+
+def strip_module_prefix(state):
+    # remove leading "module." if present (from DataParallel)
+    return { (k[7:] if k.startswith("module.") else k): v for k, v in state.items() }
+
+@torch.no_grad()
+def seed_widened_conv_from_old(new_conv, old_conv):
+    """Copy old conv weights into the left slice of new_conv's input channels; zero the rest."""
+    w_new = new_conv.weight
+    w_new.zero_()
+    in_old = old_conv.weight.shape[1]
+    w_new[:, :in_old, :, :].copy_(old_conv.weight)
+    if new_conv.bias is not None and old_conv.bias is not None:
+        new_conv.bias.copy_(old_conv.bias)
+
+def transplant_decoder_block_(new_block, old_block, extra_in_channels):
+    """
+    Seed widened decoder block (your ResBlock) from old one.
+    Handles conv1 (in_channels changed) and res_conv (1x1 projection) if present.
+    """
+    # conv1 widened: [out, in_old + extra, k, k]
+    seed_widened_conv_from_old(new_block.conv1, old_block.conv1)
+
+    # conv2 usually same shape — copy directly if it matches
+    if new_block.conv2.weight.shape == old_block.conv2.weight.shape:
+        with torch.no_grad():
+            new_block.conv2.weight.copy_(old_block.conv2.weight)
+            if getattr(new_block.conv2, "bias", None) is not None and getattr(old_block.conv2, "bias", None) is not None:
+                new_block.conv2.bias.copy_(old_block.conv2.bias)
+
+    # residual 1x1 projection may also be widened
+    if hasattr(new_block, "res_conv") and hasattr(old_block, "res_conv"):
+        if new_block.res_conv.weight.shape[1] != old_block.res_conv.weight.shape[1]:
+            seed_widened_conv_from_old(new_block.res_conv, old_block.res_conv)
+        else:
+            with torch.no_grad():
+                new_block.res_conv.weight.copy_(old_block.res_conv.weight)
+                if getattr(new_block.res_conv, "bias", None) is not None and getattr(old_block.res_conv, "bias", None) is not None:
+                    new_block.res_conv.bias.copy_(old_block.res_conv.bias)
+
+
+class UNet_512_mix_attn_conditional_flow(nn.Module):
+    def __init__(self, time_emb_dim=EMBED_DIM, use_cond_pyr: bool = True, use_global_cond: bool = True):
+        super().__init__()
+        self.use_cond_pyr = use_cond_pyr
+        self.use_global_cond = use_global_cond
+        
+        if self.use_cond_pyr:
+            self.gamma_c1 = nn.Parameter(torch.tensor(0.0))
+            self.gamma_c2 = nn.Parameter(torch.tensor(0.0))
+            self.gamma_c3 = nn.Parameter(torch.tensor(0.0))
+
+
+        # Time embedding MLP (unchanged)
+        self.time_mlp = nn.Sequential(
+            nn.Linear(time_emb_dim, time_emb_dim * 4),
+            nn.SiLU(),
+            nn.Linear(time_emb_dim * 4, time_emb_dim)
+        )
+        
+           # global conditioning path (GAP + tiny MLP), gated at 0
+        if self.use_global_cond:
+            self.cond_global_pool = nn.AdaptiveAvgPool2d(1)  # GAP over x_cond
+            self.cond_mlp = nn.Sequential(
+                nn.Linear(1, time_emb_dim),
+                nn.SiLU(),
+                nn.Linear(time_emb_dim, time_emb_dim)
+            )
+            self.gamma_cond = nn.Parameter(torch.tensor(0.5))
+            self.mix_emb = nn.Linear(2 * time_emb_dim, time_emb_dim)
+            # init so mix_emb([t, c]) ≈ t at step 0
+            with torch.no_grad():
+                self.mix_emb.weight.zero_()
+                self.mix_emb.weight[:time_emb_dim, :time_emb_dim] = torch.eye(time_emb_dim)
+                if self.mix_emb.bias is not None:
+                    self.mix_emb.bias.zero_()
+
+        # Encoder (unchanged, input has 2 channels: [noisy, LR])
+        self.enc1 = ResBlock(2,   64, time_emb_dim)
+        self.attn_enc1 = Residual(Rezero(LinearAttention(64)))
+        self.enc2 = ResBlock(64,  128, time_emb_dim)
+        self.attn_enc2 = Residual(Rezero(LinearAttention(128)))
+        self.enc3 = ResBlock(128, 256, time_emb_dim)
+        self.attn_enc3 = SelfAttention(256)
+        self.enc4 = ResBlock(256, 512, time_emb_dim)
+
+        self.middle = AttnBlock(time_emb_dim)
+
+        # Decoder — channel sizes depend on whether we concat cond features
+        if self.use_cond_pyr:
+            # d2: [d1(256), x3(256), c3(256)] -> 128
+            self.dec1 = ResBlock(512,              256, time_emb_dim)
+            self.attn_dec1 = Residual(Rezero(LinearAttention(256)))
+            self.dec2 = ResBlock(256 + 256 + 256,  128, time_emb_dim)
+            self.attn_dec2 = SelfAttention(128)
+            # d3: [d2(128), x2(128), c2(128)] -> 64
+            self.dec3 = ResBlock(128 + 128 + 128,   64, time_emb_dim)
+            self.attn_dec3 = Residual(Rezero(LinearAttention(64)))
+            # d4: [d3(64), x1(64), c1(64)] -> 1
+            self.dec4 = ResBlock(64 + 64 + 64,       1, time_emb_dim)
+
+            self.cond_pyr = CondPyramid(in_ch=1, chs=(64, 128, 256))
+        else:
+            # Original decoder sizes (no cond features)
+            self.dec1 = ResBlock(512,        256, time_emb_dim)
+            self.attn_dec1 = Residual(Rezero(LinearAttention(256)))
+            self.dec2 = ResBlock(256 + 256,  128, time_emb_dim)  # [d1, x3]
+            self.attn_dec2 = SelfAttention(128)
+            self.dec3 = ResBlock(128 + 128,   64, time_emb_dim)  # [d2, x2]
+            self.attn_dec3 = Residual(Rezero(LinearAttention(64)))
+            self.dec4 = ResBlock(64 + 64,      1, time_emb_dim)  # [d3, x1]
+
+            self.cond_pyr = None  # not used
+
+    def forward(self, x, t_emb, x_cond):
+        """
+        x:      [B, 1, H, W]   noisy image at step t
+        x_cond: [B, 1, H, W]   LR conditional image
+        t_emb:  [B, EMBED_DIM]
+        """
+        t_emb = self.time_mlp(t_emb)  # unchanged
+        
+        #  inject global LR conditioning into time embedding (FiLM-style)
+        if self.use_global_cond:
+            c_global = self.cond_global_pool(x_cond).flatten(1)   # [B,1]
+            c_emb = self.cond_mlp(c_global)                       # [B, EMBED_DIM]
+            t_cat = torch.cat([t_emb, self.gamma_cond * c_emb], dim=1)  # [B, 2D]
+            t_emb = self.mix_emb(t_cat)                                  # [B, D]
+
+        # Concatenate condition at input (unchanged)
+        x_in = torch.cat([x, x_cond], dim=1)  # [B,2,H,W]
+
+        # Encoder
+        x1 = self.enc1(x_in, t_emb)                 # [B,64,H,W]
+        x1 = self.attn_enc1(x1)
+        x2 = self.enc2(F.avg_pool2d(x1, 2), t_emb)  # [B,128,H/2,W/2]
+        x2 = self.attn_enc2(x2)
+        x3 = self.enc3(F.avg_pool2d(x2, 2), t_emb)  # [B,256,H/4,W/4]
+        x3 = self.attn_enc3(x3)
+        x4 = self.enc4(F.avg_pool2d(x3, 2), t_emb)  # [B,512,H/8,W/8]
+
+        # Bottleneck
+        mid = self.middle(x4, t_emb)
+
+        # Multi-scale LR features (only if enabled)
+        if self.use_cond_pyr:
+            c1, c2, c3 = self.cond_pyr(x_cond)      # [B,64,H,W], [B,128,H/2,W/2], [B,256,H/4,W/4]
+            c1s = self.gamma_c1 * c1
+            c2s = self.gamma_c2 * c2
+            c3s = self.gamma_c3 * c3
+
+        # Decoder
+        d1 = F.interpolate(self.dec1(mid, t_emb), size=x3.shape[2:], mode='bilinear')
+        d1 = self.attn_dec1(d1)
+
+        if self.use_cond_pyr:
+            d2_input = torch.cat([d1, x3, c3s], dim=1)
+        else:
+            d2_input = torch.cat([d1, x3], dim=1)
+        d2 = F.interpolate(self.dec2(d2_input, t_emb), size=x2.shape[2:], mode='bilinear')
+        d2 = self.attn_dec2(d2)
+
+        if self.use_cond_pyr:
+            d3_input = torch.cat([d2, x2, c2s], dim=1)
+        else:
+            d3_input = torch.cat([d2, x2], dim=1)
+        d3 = F.interpolate(self.dec3(d3_input, t_emb), size=x1.shape[2:], mode='bilinear')
+        d3 = self.attn_dec3(d3)
+
+        if self.use_cond_pyr:
+            d4_input = torch.cat([d3, x1, c1s], dim=1)
+        else:
+            d4_input = torch.cat([d3, x1], dim=1)
+        out = self.dec4(d4_input, t_emb)
+
+        return out
diff --git a/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/readme.txt b/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/readme.txt
new file mode 100644
index 0000000..1496e38
--- /dev/null
+++ b/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/readme.txt
@@ -0,0 +1,8 @@
+models.py - contains the NN architectures for conditional diffusion (super-resolution task)
+samplers.ipynb - contains different samplers to apply an already trained model
+dataloaders.py - contains dataset classes that are used to load train and test sets
+
+train_res_imp.ipynb - is used to train the NN that is used for superresolution
+
+evaluation.py - metrics for evaluating the images quality
+sr_eval_mejiro_diffusion.ipynb - evaluates the results using metrics from evaluation.py
diff --git a/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/samplers.py b/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/samplers.py
new file mode 100644
index 0000000..3eb9404
--- /dev/null
+++ b/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/samplers.py
@@ -0,0 +1,449 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import datasets, transforms
+from torch.utils.data import DataLoader
+import numpy as np
+import matplotlib.pyplot as plt
+import copy
+from torch.utils.data import random_split
+from einops import rearrange
+import os
+import random
+from scipy.ndimage import gaussian_filter
+from typing import Optional, Tuple, Union
+
+from models import get_time_embedding_continuous
+from torch.amp import GradScaler, autocast
+
+# --- Hyperparameters ---
+T = 1000
+BATCH_SIZE = 256
+IMG_SIZE = 64
+EMBED_DIM = 128
+DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+
+def cosine_beta_schedule(timesteps, s=0.008):
+    steps = timesteps + 1
+    x = torch.linspace(0, timesteps, steps)
+    alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * np.pi * 0.5) ** 2
+    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
+    alphas_cumprod = torch.clip(alphas_cumprod, 1e-8, 1.0)
+    
+    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
+    betas = torch.clip(betas, 1e-5, 0.999)
+    return betas
+
+
+def cosine_beta_schedule_custom(timesteps, beta_start=1e-4, beta_end=0.02):
+    """
+    Returns a cosine-shaped beta schedule scaled to [beta_start, beta_end].
+
+    Args:
+        timesteps (int): Number of diffusion steps.
+        beta_start (float): Minimum beta value.
+        beta_end (float): Maximum beta value.
+
+    Returns:
+        torch.Tensor: Tensor of shape (timesteps,) with the beta schedule.
+    """
+    x = torch.linspace(0, 1, timesteps)
+    cosine = (1 + torch.cos(np.pi * x + np.pi)) / 2
+    cosine = (cosine - cosine.min()) / (cosine.max() - cosine.min())  # Normalize to [0, 1]
+    betas = beta_start + cosine * (beta_end - beta_start)  # Scale to desired range
+    return betas
+
+
+
+# --- Noise Schedule ---
+# betas = torch.linspace(1e-4, 0.02, T).to(DEVICE)
+betas = cosine_beta_schedule_custom(T).to(DEVICE)
+# betas = cosine_beta_schedule(T).to(DEVICE)
+alphas = 1. - betas
+alphas_cumprod = torch.cumprod(alphas, dim=0)
+
+
+# --- EMA Update ---
+def update_ema(ema_model, model, decay):
+    with torch.no_grad():
+        for ema_param, param in zip(ema_model.parameters(), model.parameters()):
+            ema_param.data.mul_(decay).add_(param.data, alpha=1 - decay)
+            
+# --- Timestep Embedding ---
+def get_timestep_embedding(timesteps, dim):
+    half_dim = dim // 2
+    freqs = torch.exp(-np.log(10000) * torch.arange(0, half_dim).float() / half_dim).to(DEVICE)
+    angles = timesteps[:, None].float() * freqs[None, :]
+    return torch.cat([torch.sin(angles), torch.cos(angles)], dim=-1)
+
+# --- Forward Diffusion ---
+def q_sample(x0, t, noise):
+    sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod[t]).view(-1, 1, 1, 1).to(DEVICE)
+    sqrt_one_minus = torch.sqrt(1 - alphas_cumprod[t]).view(-1, 1, 1, 1).to(DEVICE)
+    return sqrt_alphas_cumprod * x0 + sqrt_one_minus * noise
+
+# --- Ensure shapes match before adding ---
+
+def center_crop(tensor, target_height, target_width):
+    _, _, h, w = tensor.shape
+    start_h = (h - target_height) // 2
+    start_w = (w - target_width) // 2
+    return tensor[:, :, start_h:start_h + target_height, start_w:start_w + target_width]
+
+
+def predict_x0_from_noise(x_t, t, pred_noise, alphas_cumprod):
+    sqrt_alpha_bar_t = alphas_cumprod[t].sqrt().view(-1, 1, 1, 1)
+    sqrt_one_minus_alpha_bar_t = (1 - alphas_cumprod[t]).sqrt().view(-1, 1, 1, 1)
+    x0_hat = (x_t - sqrt_one_minus_alpha_bar_t * pred_noise) / sqrt_alpha_bar_t
+    return x0_hat.clamp(-1, 1)  # Optional clamp to valid range
+
+
+def sample_epsilon(model, n_samples=1):
+    model.eval()
+    x = torch.randn(n_samples, 1, IMG_SIZE, IMG_SIZE, device=DEVICE)  # Start with x_T
+
+    with torch.no_grad():
+        for t in reversed(range(T)):
+            t_batch = torch.full((n_samples,), t, device=DEVICE, dtype=torch.long)
+            t_emb = get_timestep_embedding(t_batch, EMBED_DIM)
+
+            eps_theta = model(x, t_emb)
+            # print(f"[t={t}] eps_theta stats — mean: {eps_theta.mean().item():.4f}, std: {eps_theta.std().item():.4f}")
+
+            alpha_t = alphas[t_batch].view(-1, 1, 1, 1).to(DEVICE)
+            alpha_bar_t = alphas_cumprod[t_batch].view(-1, 1, 1, 1).to(DEVICE)
+            beta_t = betas[t_batch].view(-1, 1, 1, 1).to(DEVICE)
+            
+            assert eps_theta.shape == x.shape, "Prediction shape mismatch"
+            assert alpha_t.shape == x.shape[:1] + (1, 1, 1), "alpha_t shape mismatch"
+            assert not torch.isnan(eps_theta).any(), "NaNs in prediction"
+            assert not torch.isinf(eps_theta).any(), "Infs in prediction"
+
+            mu = (1.0 / torch.sqrt(alpha_t)) * (
+                x - ((1 - alpha_t) / torch.sqrt(1 - alpha_bar_t)) * eps_theta
+            )
+
+            noise = torch.randn_like(x) if t > 0 else torch.zeros_like(x)
+            x = mu + torch.sqrt(beta_t) * noise
+    return (x + 1) / 2
+
+def sample_epsilon_conditional(model, x_cond, T=T):
+    """
+    Conditional sampling from diffusion model.
+
+    Args:
+        model: trained conditional diffusion model
+        x_cond: conditioning image [B, 1, H, W] (e.g. blurred low-res image)
+        T: number of diffusion steps
+
+    Returns:
+        Generated high-resolution image [B, 1, H, W] in [0, 1] range
+    """
+    model.eval()
+    n_samples = x_cond.size(0)
+    x = torch.randn_like(x_cond)  # Start from noise, same shape as condition
+
+    with torch.no_grad():
+        for t in reversed(range(T)):
+            t_batch = torch.full((n_samples,), t, device=DEVICE, dtype=torch.long)
+            t_emb = get_timestep_embedding(t_batch, EMBED_DIM)
+
+            eps_theta = model(x, t_emb, x_cond)  # 👈 Conditional input
+
+            alpha_t = alphas[t_batch].view(-1, 1, 1, 1).to(DEVICE)
+            alpha_bar_t = alphas_cumprod[t_batch].view(-1, 1, 1, 1).to(DEVICE)
+            beta_t = betas[t_batch].view(-1, 1, 1, 1).to(DEVICE)
+
+            mu = (1.0 / torch.sqrt(alpha_t)) * (
+                x - ((1 - alpha_t) / torch.sqrt(1 - alpha_bar_t)) * eps_theta
+            )
+
+            noise = torch.randn_like(x) if t > 0 else torch.zeros_like(x)
+            x = mu + torch.sqrt(beta_t) * noise
+
+    return (x + 1) / 2  # Return in [0, 1] range
+
+
+
+def sample_v(model, n_samples=1):
+    model.eval()
+    with torch.no_grad():
+        x = torch.randn(n_samples, 1, IMG_SIZE, IMG_SIZE, device=DEVICE)  # x_T
+
+        for t in reversed(range(T)):
+            t_batch = torch.full((n_samples,), t, device=DEVICE, dtype=torch.long)
+            t_emb   = get_timestep_embedding(t_batch, EMBED_DIM)
+
+            # Predict v
+            v_pred = model(x, t_emb)
+            # v_pred = model(x, t)
+
+            # Get per-sample scalars
+            alpha_bar_t = alphas_cumprod[t_batch].view(-1, 1, 1, 1)  # [B,1,1,1]
+            sqrt_ab     = torch.sqrt(alpha_bar_t)
+            sigma_t     = torch.sqrt(1. - alpha_bar_t)
+
+            # Recover predicted noise ε̂
+            eps_theta = sqrt_ab * v_pred + sigma_t * x
+
+            alpha_t = alphas[t_batch].view(-1, 1, 1, 1)
+            beta_t  = betas[t_batch].view(-1, 1, 1, 1)
+
+            mu = (1 / torch.sqrt(alpha_t)) * (x - (1 - alpha_t) / sigma_t * eps_theta)
+
+            if t > 0:
+                noise = torch.randn_like(x)
+            else:
+                noise = torch.zeros_like(x)
+
+            x = mu + torch.sqrt(beta_t) * noise
+
+        # Final output should be in [-1, 1]
+        x = torch.clamp(x, -1., 1.)
+
+        # Optional: convert to [0, 1] if needed for display
+        # return F.interpolate((x + 1) / 2, size=(150, 150), mode='bilinear', align_corners=False)
+        return (x + 1) / 2
+    
+@torch.no_grad()
+def sample_v_conditional(model, x_cond, T=T):
+    """
+    Conditional sampling with v-parameterization.
+
+    Args:
+        model: trained conditional diffusion model that outputs v_pred
+               signature: model(x_t, t_emb, x_cond) -> v_pred
+        x_cond: conditioning image [B, 1, H, W] (e.g., LR/blurred)
+        T: number of diffusion steps
+
+    Returns:
+        Generated high-resolution image [B, 1, H, W] in [0, 1].
+    """
+    model.eval()
+    B = x_cond.size(0)
+    x = torch.randn_like(x_cond, device=x_cond.device)  # x_T
+
+    for t in reversed(range(T)):
+        t_batch = torch.full((B,), t, device=x_cond.device, dtype=torch.long)
+        t_emb   = get_timestep_embedding(t_batch, EMBED_DIM)
+
+        # --- predict v (conditional) ---
+        v_pred = model(x, t_emb, x_cond)  # same API as your epsilon-conditional model
+
+        # --- convert v -> epsilon_hat ---
+        alpha_bar_t = alphas_cumprod[t_batch].view(B, 1, 1, 1)          # \bar{alpha}_t
+        sqrt_ab     = torch.sqrt(alpha_bar_t)                            # sqrt(\bar{alpha}_t)
+        sigma_t     = torch.sqrt(1.0 - alpha_bar_t)                      # sqrt(1-\bar{alpha}_t)
+        eps_hat     = sqrt_ab * v_pred + sigma_t * x                     # <- from your unconditional code
+
+        # --- DDPM step (same as epsilon-param) ---
+        alpha_t = alphas[t_batch].view(B, 1, 1, 1)
+        beta_t  = betas[t_batch].view(B, 1, 1, 1)
+
+        # mu_t = (1/sqrt(alpha_t)) * (x_t - ((1 - alpha_t)/sqrt(1 - \bar{alpha}_t)) * eps_hat)
+        mu = (1.0 / torch.sqrt(alpha_t)) * (x - ((1.0 - alpha_t) / sigma_t) * eps_hat)
+
+        noise = torch.randn_like(x) if t > 0 else torch.zeros_like(x)
+        x = mu + torch.sqrt(beta_t) * noise
+
+    x = torch.clamp(x, -1.0, 1.0)
+    return (x + 1.0) / 2.0
+
+@torch.no_grad()
+def sample_v_conditional_cfg(model, x_cond, T=T, guidance_scale=2.0, timesteps=None):
+    """
+    v-parameterized conditional sampling with classifier-free guidance.
+    model: predicts v; signature model(x_t, t_emb, x_cond)
+    x_cond: [B,1,H,W] in [-1,1]
+    guidance_scale: s; 1.0 = no guidance; 2-3 gives stronger adherence to condition
+    timesteps: optional list/1D tensor of descending ints; default uses all 0..T-1
+    """
+    device = x_cond.device
+    B = x_cond.size(0)
+    x = torch.randn_like(x_cond)
+
+    if timesteps is None:
+        t_schedule = torch.arange(T-1, -1, -1, device=device)
+    else:
+        t_schedule = torch.as_tensor(timesteps, device=device).long()
+
+    zeros_cond = torch.zeros_like(x_cond)
+
+    for t in t_schedule:
+        t_batch = torch.full((B,), int(t), device=device, dtype=torch.long)
+        t_emb   = get_timestep_embedding(t_batch, EMBED_DIM)
+
+        # predict v for uncond and cond
+        v_uncond = model(x, t_emb, zeros_cond)
+        v_cond   = model(x, t_emb, x_cond)
+        v_guided = v_uncond + guidance_scale * (v_cond - v_uncond)
+
+        # convert v -> eps_hat
+        alpha_bar_t = alphas_cumprod[t_batch].view(B,1,1,1)
+        alpha_t = torch.sqrt(alpha_bar_t)
+        sigma_t = torch.sqrt(1.0 - alpha_bar_t)
+        eps_hat = alpha_t * v_guided + sigma_t * x
+
+        # DDPM update
+        alpha_t_scalar = alphas[t_batch].view(B,1,1,1)
+        beta_t         = betas[t_batch].view(B,1,1,1)
+        mu = (1.0 / torch.sqrt(alpha_t_scalar)) * (x - ((1.0 - alpha_t_scalar) / sigma_t) * eps_hat)
+        noise = torch.randn_like(x) if t > 0 else torch.zeros_like(x)
+        x = mu + torch.sqrt(beta_t) * noise
+
+    return (x.clamp(-1,1) + 1) / 2  # [0,1]
+
+@torch.no_grad()
+def sample_epsilon_conditional_cfg(
+    model,
+    x_cond,
+    T=T,
+    guidance_scale: float = 2.5,
+    timesteps=None,
+    to_image=True,
+):
+    """
+    Classifier-free guidance sampler for ε-prediction models (DDPM update).
+
+    model:  predicts epsilon; signature model(x_t, t_emb, x_cond, cond_flag)
+    x_cond: [B,1,H,W] in [-1,1]
+    T:      total diffusion steps (len of schedules)
+    guidance_scale (s): 1.0 = no guidance; ~2–4 typical
+    timesteps: optional 1D list/tensor of steps to run (DESCENDING). If None, uses T-1..0.
+    to_image: if True, map from [-1,1] -> [0,1] at the end.
+
+    Requires global (or outer-scope) 1D tensors on device:
+      - betas            (len T)
+      - alphas           (len T) where alphas = 1 - betas
+      - alphas_cumprod   (len T) ᾱ_t = ∏_{i<=t} α_i
+    """
+    device = x_cond.device
+    B = x_cond.size(0)
+    x = torch.randn_like(x_cond)
+
+    # default schedule: T-1, ..., 0
+    if timesteps is None:
+        t_schedule = torch.arange(T - 1, -1, -1, device=device)
+    else:
+        t_schedule = torch.as_tensor(timesteps, device=device).long()
+
+    # handy indexer: take v[t] per-batch and shape to [B,1,1,1]
+    def gather(v, t):  # v: [T], t: [B]
+        return v[t].view(B, 1, 1, 1)
+
+    for t in t_schedule:
+        t_batch = torch.full((B,), int(t), device=device, dtype=torch.long)
+        t_emb   = get_timestep_embedding(t_batch, EMBED_DIM)
+
+        # ------- CFG: cond & uncond in ONE forward -------
+        x_cat      = torch.cat([x, x], dim=0)
+        t_emb_cat  = torch.cat([t_emb, t_emb], dim=0)
+        xcond_cat  = torch.cat([x_cond, x_cond], dim=0)
+        flags_cat  = torch.cat([torch.ones(B,1,device=device),  # cond
+                                torch.zeros(B,1,device=device)], dim=0)  # uncond
+
+        eps_cat = model(x_cat, t_emb_cat, xcond_cat, flags_cat)  # ε(x_t, t, cond_flag)
+        eps_cond, eps_uncond = eps_cat[:B], eps_cat[B:]
+        eps_hat = eps_uncond + guidance_scale * (eps_cond - eps_uncond)  # ε̂_guided
+
+        # ------- DDPM update (use posterior variance \tilde{β}_t) -------
+        alpha_t       = gather(alphas, t_batch)               # α_t
+        beta_t        = gather(betas, t_batch)                # β_t
+        alpha_bar_t   = gather(alphas_cumprod, t_batch)       # ᾱ_t
+        prev_t        = torch.clamp(t_batch - 1, min=0)
+        alpha_bar_tm1 = gather(alphas_cumprod, prev_t)        # ᾱ_{t-1}
+
+        # mean μ_θ(x_t, t)
+        # μ = (1/√α_t) * ( x_t - (β_t / √(1-ᾱ_t)) * ε̂ )
+        sqrt_one_m_ab = torch.sqrt(1.0 - alpha_bar_t)
+        mu = (x - (beta_t / sqrt_one_m_ab) * eps_hat) / torch.sqrt(alpha_t)
+
+        # posterior variance \tilde{β}_t = ((1-ᾱ_{t-1})/(1-ᾱ_t)) * β_t
+        tilde_beta = (1.0 - alpha_bar_tm1) / (1.0 - alpha_bar_t) * beta_t
+
+        # sample next x
+        noise = torch.randn_like(x) if t > 0 else torch.zeros_like(x)
+        x = mu + torch.sqrt(tilde_beta) * noise
+
+    if to_image:
+        return (x.clamp(-1, 1) + 1) / 2.0
+    return x
+
+def cosine_timesteps(steps, device, eps=1e-3):
+    s = torch.linspace(0, 1, steps + 1, device=device)
+    return torch.cos((s * (1 - eps)) * (torch.pi / 2))  # 1 -> ~0
+
+
+
+@torch.no_grad()
+def sample_flow_conditional(
+    model,
+    x_cond,
+    steps=100,
+    solver="heun",       # "euler" or "heun"
+    return_range="0_1",  # "0_1" or "minus1_1"
+):
+    """
+    Conditional sampling for a flow-matching model (linear path).
+
+    Args:
+        model: trained conditional flow model (could be ema_model)
+        x_cond: conditioning tensor [B, C, H, W]
+        steps: number of ODE steps between t=1 -> t=0
+        solver: "euler" (explicit Euler) or "heun" (RK2 predictor-corrector)
+        return_range: "0_1" to map from [-1,1] -> [0,1], or "minus1_1" to keep [-1,1]
+
+    Returns:
+        x: generated sample [B, C, H, W]
+    """
+    device = x_cond.device
+    model_was_training = model.training
+    model.eval()
+
+    # Initial condition: prior at t=1 (standard normal)
+    x = torch.randn_like(x_cond)
+
+    # Time grid from 1.0 -> 0.0
+    # ts = torch.linspace(1.0, 0.0, steps + 1, device=device)
+    ts = cosine_timesteps(steps, x_cond.device)
+
+    for k in range(steps, 0, -1):
+        t_curr = ts[k]          # scalar tensor
+        t_prev = ts[k - 1]
+        dt = t_curr - t_prev  # negative step size (since we go 1 -> 0)
+
+        # Embed continuous time
+        t_batch = t_curr.expand(x.size(0))
+        t_emb = get_time_embedding_continuous(t_batch, EMBED_DIM)
+
+        # with autocast(device_type='cuda'):
+        v = model(x, t_emb, x_cond)  # predict velocity
+
+        if solver.lower() == "euler":
+            x = x + dt * v
+
+        elif solver.lower() == "heun":
+            # Predictor
+            x_pred = x + dt * v
+
+            # Corrector: evaluate at (x_pred, t_prev)
+            t_batch_prev = t_prev.expand(x.size(0))
+            t_emb_prev = get_time_embedding_continuous(t_batch_prev, EMBED_DIM)
+            # with autocast(device_type='cuda'):
+            v_pred = model(x_pred, t_emb_prev, x_cond)
+
+            x = x + dt * 0.5 * (v + v_pred)
+
+        else:
+            raise ValueError("solver must be 'euler' or 'heun'")
+
+    # Restore training mode if needed
+    if model_was_training:
+        model.train()
+
+    if return_range == "0_1":
+        return (x + 1) / 2
+    return x
+
+
diff --git a/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/sr_eval_mejiro_diffusion.ipynb b/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/sr_eval_mejiro_diffusion.ipynb
new file mode 100644
index 0000000..d15b5a6
--- /dev/null
+++ b/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/sr_eval_mejiro_diffusion.ipynb
@@ -0,0 +1,525 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "379ce42b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Super-Resolution Evaluation — Mejiro LR → Diffusion HR\n",
+    "\n",
+    "This notebook evaluates a trained diffusion model that upsamples **Mejiro** low‑resolution (Roman WFI–like) images into high‑resolution predictions.\n",
+    "\n",
+    "**Assumptions** (already satisfied in your environment):\n",
+    "- `ema_model`: EMA-wrapped diffusion NN (PyTorch `nn.Module`)\n",
+    "- `sample_epsilon_conditional(model, x_cond=...)`: sampling function that returns HR predictions given LR inputs\n",
+    "- `test_loader`: PyTorch `DataLoader` yielding `(high_res, low_res)` batches shaped `[B, 1, H, W]` in `[-1, 1]`\n",
+    "- `DEVICE`: `'cuda'` or `'cpu'`\n",
+    "\n",
+    "The metrics are computed purely in PyTorch (no extra deps): **PSNR**, **SSIM**, and **MS‑SSIM**.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "28989ca3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import math\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib import gridspec\n",
+    "from model_grav import *\n",
+    "\n",
+    "# Ensure your model and loader are in scope:\n",
+    "# ema_model, sample_epsilon_conditional, test_loader, DEVICE\n",
+    "# assert 'ema_model' in globals(), \"Please define `ema_model` in this kernel.\"\n",
+    "# assert 'sample_epsilon_conditional' in globals(), \"Please define `sample_epsilon_conditional` in this kernel.\"\n",
+    "# assert 'test_loader' in globals(), \"Please define `test_loader` in this kernel.\"\n",
+    "# assert 'DEVICE' in globals(), \"Please define `DEVICE` (e.g., 'cuda').\"\n",
+    "\n",
+    "# Small helper for clean plotting range\n",
+    "model = UNet_512_mix_attn_conditional(use_cond_pyr=True, use_global_cond=True).to(DEVICE)\n",
+    "\n",
+    "ema_model = copy.deepcopy(model).to(DEVICE)\n",
+    "for param in ema_model.parameters():\n",
+    "    param.requires_grad = False\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b67d525a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total: 20000, Train: 18000, Test: 2000\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dataloaders import *\n",
+    "\n",
+    "full_ds = PairsDatasetUnified(\n",
+    "    root_dir=\".\",\n",
+    "    norm_preset=\"minmax\",\n",
+    "    out_range=\"[-1,1]\",\n",
+    "    pad_to=48,\n",
+    "    noise_aug=None,               # skip extra noise\n",
+    "    return_order=\"HRLR\"\n",
+    ")\n",
+    "\n",
+    "n_total = len(full_ds)\n",
+    "n_train = int(0.9 * n_total)\n",
+    "train_ds, test_ds = random_split(full_ds, [n_train, n_total - n_train],\n",
+    "                                 generator=torch.Generator().manual_seed(42))\n",
+    "\n",
+    "train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True,  num_workers=4, pin_memory=True)\n",
+    "test_loader  = DataLoader(test_ds,  batch_size=BATCH_SIZE, shuffle=False, num_workers=4, pin_memory=True)\n",
+    "\n",
+    "print(f\"Total: {n_total}, Train: {len(train_ds)}, Test: {len(test_ds)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cd8fdb43",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# model.load_state_dict(torch.load(r\"grav_model_200_res_imp_mejiro_lsst.pt\"), strict = False)    \n",
+    "# ema_model.load_state_dict(torch.load(r\"grav_ema_model_200_res_imp_mejiro_lsst.pt\"), strict=False)\n",
+    "\n",
+    "model.load_state_dict(torch.load(r\"grav_model_200_res_imp_mejiro_lsst_pyr_glob.pt\"), strict = False)    \n",
+    "ema_model.load_state_dict(torch.load(r\"grav_ema_model_200_res_imp_mejiro_lsst_pyr_glob.pt\"), strict=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8586533",
+   "metadata": {},
+   "source": [
+    "## Quick sanity-check visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6321ae20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/user/1015/ipykernel_2513924/1283954906.py:46: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+      "  plt.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x2500 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Number of samples to visualize\n",
+    "vmax = 0.1\n",
+    "num_rows = 5\n",
+    "n_samples = num_rows\n",
+    "\n",
+    "# --- Get a batch from the test loader ---\n",
+    "(high_res_batch, low_res_batch) = next(iter(test_loader))\n",
+    "high_res_batch = high_res_batch[:n_samples].to(DEVICE)\n",
+    "low_res_batch = low_res_batch[:n_samples].to(DEVICE)\n",
+    "\n",
+    "# --- Generate model predictions ---\n",
+    "with torch.no_grad():\n",
+    "    ema_model.eval()\n",
+    "    preds = sample_epsilon_conditional(ema_model, x_cond=low_res_batch).cpu()\n",
+    "\n",
+    "# Move inputs and labels to CPU and map to [0,1]\n",
+    "low_res_batch = (low_res_batch.cpu()+1)/2\n",
+    "high_res_batch = (high_res_batch.cpu()+1)/2\n",
+    "\n",
+    "# --- Plotting ---\n",
+    "fig = plt.figure(figsize=(16, num_rows * 5))\n",
+    "gs = gridspec.GridSpec(num_rows, 4, width_ratios=[1, 1, 1, 0.05], wspace=0.1, hspace=0.3)\n",
+    "\n",
+    "for i in range(n_samples):\n",
+    "    # Low-res input\n",
+    "    ax1 = plt.subplot(gs[i, 0])\n",
+    "    ax1.imshow(low_res_batch[i, 0], cmap='magma', vmax=vmax)\n",
+    "    ax1.set_title(\"Low-Res\")\n",
+    "    ax1.axis('off')\n",
+    "\n",
+    "    # Model output\n",
+    "    ax2 = plt.subplot(gs[i, 1])\n",
+    "    ax2.imshow(preds[i, 0], cmap='magma', vmax=vmax)\n",
+    "    ax2.set_title(\"Predicted High-Res\")\n",
+    "    ax2.axis('off')\n",
+    "\n",
+    "    # Ground truth\n",
+    "    ax3 = plt.subplot(gs[i, 2])\n",
+    "    im3 = ax3.imshow(high_res_batch[i, 0], cmap='magma', vmax=vmax)\n",
+    "    ax3.set_title(\"Ground Truth\")\n",
+    "    ax3.axis('off')\n",
+    "\n",
+    "cbar_ax = plt.subplot(gs[:, 3])\n",
+    "plt.colorbar(im3, cax=cbar_ax)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5731c63c",
+   "metadata": {},
+   "source": [
+    "## Metrics: PSNR, SSIM, and MS-SSIM (Torch-native)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "26984b84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from typing import Tuple\n",
+    "\n",
+    "@torch.no_grad()\n",
+    "def psnr_torch(pred: torch.Tensor, target: torch.Tensor, eps: float = 1e-10) -> torch.Tensor:\n",
+    "    \"\"\"pred, target in [0,1], shape [B,1,H,W] -> [B]\"\"\"\n",
+    "    mse = F.mse_loss(pred, target, reduction='none').mean(dim=[1,2,3])  # [B]\n",
+    "    psnr = -10.0 * torch.log10(mse + eps)\n",
+    "    return psnr  # [B]\n",
+    "\n",
+    "def _gaussian_window(window_size: int = 11, sigma: float = 1.5, device='cpu', dtype=torch.float32) -> torch.Tensor:\n",
+    "    coords = torch.arange(window_size, dtype=dtype, device=device) - window_size // 2\n",
+    "    g = torch.exp(-(coords**2) / (2 * sigma * sigma))\n",
+    "    g = (g / g.sum()).unsqueeze(0)  # [1, W]\n",
+    "    window = (g.t() @ g)           # [W, W]\n",
+    "    return window\n",
+    "\n",
+    "def _ssim_components(x, y, window, K=(0.01, 0.03)):\n",
+    "    \"\"\"x,y in [0,1], shape [B,1,H,W], window [W,W] normalized.\"\"\"\n",
+    "    C1 = (K[0] ** 2)\n",
+    "    C2 = (K[1] ** 2)\n",
+    "    pad = window.shape[0] // 2\n",
+    "    w = window.expand(x.size(1), 1, *window.shape).to(x.dtype).to(x.device)  # [C,1,W,W]\n",
+    "\n",
+    "    mu_x = F.conv2d(x, w, padding=pad, groups=x.size(1))\n",
+    "    mu_y = F.conv2d(y, w, padding=pad, groups=y.size(1))\n",
+    "    mu_x2, mu_y2, mu_xy = mu_x * mu_x, mu_y * mu_y, mu_x * mu_y\n",
+    "\n",
+    "    sigma_x2 = F.conv2d(x * x, w, padding=pad, groups=x.size(1)) - mu_x2\n",
+    "    sigma_y2 = F.conv2d(y * y, w, padding=pad, groups=y.size(1)) - mu_y2\n",
+    "    sigma_xy = F.conv2d(x * y, w, padding=pad, groups=x.size(1)) - mu_xy\n",
+    "\n",
+    "    # Luminance, contrast, structure terms\n",
+    "    l = (2 * mu_xy + C1) / (mu_x2 + mu_y2 + C1)\n",
+    "    c = (2 * torch.sqrt(torch.clamp(sigma_x2, min=0)) * torch.sqrt(torch.clamp(sigma_y2, min=0)) + C2) /         (sigma_x2 + sigma_y2 + C2)\n",
+    "    s = (sigma_xy + C2 / 2) / (torch.sqrt(torch.clamp(sigma_x2, min=0)) * torch.sqrt(torch.clamp(sigma_y2, min=0)) + C2 / 2 + 1e-12)\n",
+    "    return l, c, s  # each [B,1,H,W]\n",
+    "\n",
+    "@torch.no_grad()\n",
+    "def ssim_torch(pred: torch.Tensor, target: torch.Tensor, window_size: int = 11, sigma: float = 1.5) -> torch.Tensor:\n",
+    "    \"\"\"Single-scale SSIM. Returns per-sample SSIM [B].\"\"\"\n",
+    "    window = _gaussian_window(window_size, sigma, device=pred.device, dtype=pred.dtype)\n",
+    "    l, c, s = _ssim_components(pred, target, window)\n",
+    "    ssim_map = l * c * s\n",
+    "    return ssim_map.mean(dim=[1,2,3])\n",
+    "\n",
+    "@torch.no_grad()\n",
+    "def msssim_torch(pred: torch.Tensor, target: torch.Tensor, window_size: int = 11, sigma: float = 1.5, levels: int = 5) -> torch.Tensor:\n",
+    "    \"\"\"Multi-scale SSIM (Wang et al. 2003) with standard weights for 5 levels.\"\"\"\n",
+    "    weights = torch.tensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333], device=pred.device, dtype=pred.dtype)\n",
+    "    weights = weights[:levels]\n",
+    "    window = _gaussian_window(window_size, sigma, device=pred.device, dtype=pred.dtype)\n",
+    "\n",
+    "    mcs = []\n",
+    "    x, y = pred, target\n",
+    "    for i in range(levels):\n",
+    "        l, c, s = _ssim_components(x, y, window)\n",
+    "        if i < levels - 1:\n",
+    "            mcs.append(c * s)  # contrast*structure for intermediate scales\n",
+    "            x = F.avg_pool2d(x, kernel_size=2, stride=2, padding=0)\n",
+    "            y = F.avg_pool2d(y, kernel_size=2, stride=2, padding=0)\n",
+    "        else:\n",
+    "            ms_ssim_map = l * c * s\n",
+    "\n",
+    "    mcs = torch.stack([mc.mean(dim=[1,2,3]) for mc in mcs], dim=1) if len(mcs) else torch.ones(pred.size(0), 0, device=pred.device, dtype=pred.dtype)\n",
+    "    s_l = ms_ssim_map.mean(dim=[1,2,3])  # [B]\n",
+    "    if mcs.numel() > 0:\n",
+    "        out = torch.prod(mcs ** weights[:-1], dim=1) * (s_l ** weights[-1])\n",
+    "    else:\n",
+    "        out = s_l\n",
+    "    return out  # [B]\n",
+    "\n",
+    "\n",
+    "def mae(pred, target):\n",
+    "    \"\"\"Mean Absolute Error\"\"\"\n",
+    "    return F.l1_loss(pred, target).item()\n",
+    "\n",
+    "def mse(pred, target):\n",
+    "    \"\"\"Mean Squared Error\"\"\"\n",
+    "    return F.mse_loss(pred, target).item()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2585da39",
+   "metadata": {},
+   "source": [
+    "## Batched evaluation over ~1000 samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1d69df46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "@torch.no_grad()\n",
+    "def evaluate_sr(\n",
+    "    ema_model,\n",
+    "    sample_epsilon_conditional,\n",
+    "    test_loader,\n",
+    "    DEVICE='cuda',\n",
+    "    MAX_SAMPLES: int = 1000,\n",
+    "):\n",
+    "    ema_model.eval()\n",
+    "    n_done = 0\n",
+    "    psnr_sum = 0.0\n",
+    "    ssim_sum = 0.0\n",
+    "    msssim_sum = 0.0\n",
+    "    mae_sum = 0.0\n",
+    "    mse_sum = 0.0\n",
+    "\n",
+    "    for batch in test_loader:\n",
+    "        high_res_batch, low_res_batch = batch\n",
+    "\n",
+    "        if n_done >= MAX_SAMPLES:\n",
+    "            break\n",
+    "        remain = MAX_SAMPLES - n_done\n",
+    "        if high_res_batch.size(0) > remain:\n",
+    "            high_res_batch = high_res_batch[:remain]\n",
+    "            low_res_batch = low_res_batch[:remain]\n",
+    "\n",
+    "        high_res_batch = high_res_batch.to(DEVICE)\n",
+    "        low_res_batch = low_res_batch.to(DEVICE)\n",
+    "\n",
+    "        preds = sample_epsilon_conditional(ema_model, x_cond=low_res_batch)\n",
+    "\n",
+    "        # Map to [0,1]\n",
+    "        preds = preds.clamp(0.0, 1.0)               # predicted HR\n",
+    "        high_res_batch = (high_res_batch + 1) / 2.0 # GT HR\n",
+    "\n",
+    "        # Ensure [B,1,H,W]\n",
+    "        if preds.dim() == 3:\n",
+    "            preds = preds.unsqueeze(1)\n",
+    "        if high_res_batch.dim() == 3:\n",
+    "            high_res_batch = high_res_batch.unsqueeze(1)\n",
+    "\n",
+    "        # Per-image metrics (assume psnr_torch/ssim_torch/msssim_torch -> [B])\n",
+    "        psnr_vals   = psnr_torch(preds, high_res_batch)     # [B]\n",
+    "        ssim_vals   = ssim_torch(preds, high_res_batch)     # [B]\n",
+    "        msssim_vals = msssim_torch(preds, high_res_batch)   # [B]\n",
+    "\n",
+    "        # MAE/MSE per image: reduce over pixels per sample, then mean over batch\n",
+    "        # reduction='none' -> [B,1,H,W] -> collapse spatial dims per sample\n",
+    "        mae_vals = F.l1_loss(preds, high_res_batch, reduction='none') \\\n",
+    "                     .view(preds.size(0), -1).mean(dim=1)            # [B]\n",
+    "        mse_vals = F.mse_loss(preds, high_res_batch, reduction='none') \\\n",
+    "                     .view(preds.size(0), -1).mean(dim=1)            # [B]\n",
+    "\n",
+    "        psnr_sum   += psnr_vals.sum().item()\n",
+    "        ssim_sum   += ssim_vals.sum().item()\n",
+    "        msssim_sum += msssim_vals.sum().item()\n",
+    "        mae_sum    += mae_vals.sum().item()\n",
+    "        mse_sum    += mse_vals.sum().item()\n",
+    "        n_done     += preds.size(0)\n",
+    "\n",
+    "        # live progress\n",
+    "        if n_done % 100 == 0 or n_done == MAX_SAMPLES:\n",
+    "            print(f\"[eval] {n_done}/{MAX_SAMPLES} — \"\n",
+    "                  f\"PSNR: {psnr_sum/n_done:.3f}  \"\n",
+    "                  f\"SSIM: {ssim_sum/n_done:.4f}  \"\n",
+    "                  f\"MS-SSIM: {msssim_sum/n_done:.4f}  \"\n",
+    "                  f\"MAE: {mae_sum/n_done:.6f}  \"\n",
+    "                  f\"MSE: {mse_sum/n_done:.6f}\")\n",
+    "\n",
+    "        if n_done >= MAX_SAMPLES:\n",
+    "            break\n",
+    "\n",
+    "    psnr_avg    = psnr_sum / n_done\n",
+    "    ssim_avg    = ssim_sum / n_done\n",
+    "    msssim_avg  = msssim_sum / n_done\n",
+    "    mae_avg     = mae_sum / n_done\n",
+    "    mse_avg     = mse_sum / n_done\n",
+    "    print(f\"\\nFINAL ({n_done} images) — \"\n",
+    "          f\"PSNR: {psnr_avg:.3f}  SSIM: {ssim_avg:.4f}  MS-SSIM: {msssim_avg:.4f}  \"\n",
+    "          f\"MAE: {mae_avg:.12f}  MSE: {mse_avg:.12f}\")\n",
+    "    return psnr_avg, ssim_avg, msssim_avg, mae_avg, mse_avg\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "69a8b38c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[eval] 1000/1000 — PSNR: 53.625  SSIM: 0.9946  MS-SSIM: 0.9995  MAE: 0.001060  MSE: 0.000006\n",
+      "\n",
+      "FINAL (1000 images) — PSNR: 53.625  SSIM: 0.9946  MS-SSIM: 0.9995  MAE: 0.001059687227  MSE: 0.000005567043\n"
+     ]
+    }
+   ],
+   "source": [
+    "psnr_avg, ssim_avg, msssim_avg, mae_avg, mse_avg = evaluate_sr(\n",
+    "    ema_model=ema_model,\n",
+    "    sample_epsilon_conditional=sample_epsilon_conditional,\n",
+    "    test_loader=test_loader,\n",
+    "    DEVICE=DEVICE,\n",
+    "    MAX_SAMPLES=1000,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1d63dc1",
+   "metadata": {},
+   "source": [
+    "### (Optional) Save per-image metrics to CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d03535b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# If you need per-image metrics (not just averages), run this cell.\n",
+    "import pandas as pd\n",
+    "\n",
+    "@torch.no_grad()\n",
+    "def evaluate_sr_per_image(\n",
+    "    ema_model,\n",
+    "    sample_epsilon_conditional,\n",
+    "    test_loader,\n",
+    "    DEVICE='cuda',\n",
+    "    MAX_SAMPLES: int = 1000,\n",
+    "):\n",
+    "    ema_model.eval()\n",
+    "    rows = []\n",
+    "    n_done = 0\n",
+    "    for batch_idx, batch in enumerate(test_loader):\n",
+    "        high_res_batch, low_res_batch = batch\n",
+    "        if n_done >= MAX_SAMPLES:\n",
+    "            break\n",
+    "        remain = MAX_SAMPLES - n_done\n",
+    "        if high_res_batch.size(0) > remain:\n",
+    "            high_res_batch = high_res_batch[:remain]\n",
+    "            low_res_batch = low_res_batch[:remain]\n",
+    "\n",
+    "        high_res_batch = high_res_batch.to(DEVICE)\n",
+    "        low_res_batch = low_res_batch.to(DEVICE)\n",
+    "\n",
+    "        preds = sample_epsilon_conditional(ema_model, x_cond=low_res_batch)\n",
+    "        preds = preds.clamp(0.0, 1.0)\n",
+    "        high_res_batch = (high_res_batch + 1) / 2.0\n",
+    "\n",
+    "        if preds.dim() == 3:\n",
+    "            preds = preds.unsqueeze(1)\n",
+    "        if high_res_batch.dim() == 3:\n",
+    "            high_res_batch = high_res_batch.unsqueeze(1)\n",
+    "\n",
+    "        psnr_vals   = psnr_torch(preds, high_res_batch).cpu().numpy()\n",
+    "        ssim_vals   = ssim_torch(preds, high_res_batch).cpu().numpy()\n",
+    "        msssim_vals = msssim_torch(preds, high_res_batch).cpu().numpy()\n",
+    "\n",
+    "        for i in range(preds.size(0)):\n",
+    "            rows.append({\n",
+    "                \"index_global\": n_done + i,\n",
+    "                \"batch\": batch_idx,\n",
+    "                \"index_in_batch\": i,\n",
+    "                \"psnr\": float(psnr_vals[i]),\n",
+    "                \"ssim\": float(ssim_vals[i]),\n",
+    "                \"ms_ssim\": float(msssim_vals[i])\n",
+    "            })\n",
+    "        n_done += preds.size(0)\n",
+    "\n",
+    "    df = pd.DataFrame(rows)\n",
+    "    return df\n",
+    "\n",
+    "df_metrics = evaluate_sr_per_image(\n",
+    "    ema_model=ema_model,\n",
+    "    sample_epsilon_conditional=sample_epsilon_conditional,\n",
+    "    test_loader=test_loader,\n",
+    "    DEVICE=DEVICE,\n",
+    "    MAX_SAMPLES=1000,\n",
+    ")\n",
+    "csv_path = \"/mnt/data/sr_metrics_per_image.csv\"\n",
+    "df_metrics.to_csv(csv_path, index=False)\n",
+    "print(\"Saved:\", csv_path)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torchtest",
+   "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": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/train_res_imp.ipynb b/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/train_res_imp.ipynb
new file mode 100644
index 0000000..b84a045
--- /dev/null
+++ b/Difflense_Aleksandr_Duplinskii/Conditional_diffusion/train_res_imp.ipynb
@@ -0,0 +1,589 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d648a530",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "from torchvision import datasets, transforms, models\n",
+    "from torch.utils.data import DataLoader\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import copy\n",
+    "from models import *\n",
+    "from samplers import *\n",
+    "from torch.amp import GradScaler, autocast\n",
+    "import matplotlib.gridspec as gridspec\n",
+    "import random\n",
+    "from dataloaders import *\n",
+    "\n",
+    "T = 1000\n",
+    "IMG_SIZE = 64\n",
+    "EMBED_DIM = 128\n",
+    "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "\n",
+    "weights_dir = \"weights\"\n",
+    "\n",
+    "print(\"torch:\", torch.__version__)\n",
+    "print(\"torch cuda build:\", torch.version.cuda)\n",
+    "print(\"cuda available:\", torch.cuda.is_available())\n",
+    "print(\"CUDA_VISIBLE_DEVICES:\", os.getenv(\"CUDA_VISIBLE_DEVICES\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5d6c297",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_ds = PairsDatasetUnified(\n",
+    "    root_dir=\".\",\n",
+    "    norm_preset=\"minmax\",\n",
+    "    out_range=\"[-1,1]\",\n",
+    "    pad_to=48,\n",
+    "    noise_aug=None,               # skip extra noise\n",
+    "    return_order=\"HRLR\"\n",
+    ")\n",
+    "\n",
+    "n_total = len(full_ds)\n",
+    "n_train = int(0.9 * n_total)\n",
+    "train_ds, test_ds = random_split(full_ds, [n_train, n_total - n_train],\n",
+    "                                 generator=torch.Generator().manual_seed(42))\n",
+    "\n",
+    "train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True,  num_workers=4, pin_memory=True)\n",
+    "test_loader  = DataLoader(test_ds,  batch_size=BATCH_SIZE, shuffle=False, num_workers=4, pin_memory=True)\n",
+    "\n",
+    "print(f\"Total: {n_total}, Train: {len(train_ds)}, Test: {len(test_ds)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a7bfc0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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A4ME70QAAADgkvPc3krdv3y4/+9nP5Oyzz+YCGkBi+Do3AAAADgmTJk2S8847T0488URpaGiQ+++/X5qbm+Xmm2/Od9MAHEK4iAYAAMAh4eMf/7g88sgjcu+990pBQYF86EMfkvvvv1/OOeecfDcNwCGEr3MDAHLuqaeekqlTp8oRRxwhBQUF8pvf/OZ9P7N69Wr50Ic+JMXFxfLBD35QHnjggdTbCaBvmT9/vrz66qvS1tYmra2tsmbNGqmtrc13swCkJF/jCS6iAQA519raKmPHjpXFixdHqv/mm2/KJz7xCfnwhz8s69atk2uvvVYuv/xy9TfeAQBAGPI1niCdGwCQVwUFBfLrX/9apk2bZta5/vrrZfny5fKXv/wlU/bZz35WGhsb1d+lBwAAYcnleIJ3ogEgcHv27JH29vZY09i/f78UFBR0KysuLo79W+4HrF27NusrmVOmTJFrr702kekDAIB4QhpPRL6IHjBggGvCAIBkdHR0pDbtPXv2yJgxY6S+vj7WdMrLy6WlpaVbWV1dndxyyy2xpntAfX29jBgxolvZiBEjpLm5WXbv3i2lpaWJzAfp08YTnZ2dat33DqRERPr1i/4mWldXV+S62rxE3hnQRa3rme6+fftiTdfDs84sWtus7Rb182nW1ZbZ2h+0bZzEOtPmZ30B1LPMnvbG3aes9mrLZs1Lq+tpr+fYtHjOBZ55aeXWT6l5ls2z72gXl/3765d4zc3NankSQhtP8CQaAALW3t4u9fX1smnTJqmsrOzRNJqbm6W6ujprGkndNQYAAL1baOMJLqIBAFJRUSEVFRU9+uyBu+OVlZU97jjfz8iRI6WhoaFbWUNDg1RWVvIUGgCAXiKU8QQX0QAA2b9/v+urce/9bNomTZokjz76aLeylStXyqRJk1KfNwAAiCaU8QQX0QCAnHd6LS0t8vrrr2f+/80335R169bJkCFD5KijjpI5c+bI5s2b5ac//amIiHzlK1+RO++8U6677jr513/9V3niiSfkF7/4hSxfvrxHbUbvksS7wJ53CD3SeqdUeyfUmpdW7lm2uO/bWuVJTDfuNDzvlHrei7Xqxn1fO6137y3adD3bwrNPWtKqq0liu3neOU/ros+zLXqbUMYT/E40ACDn/vSnP8npp58up59+uoiIzJ49W04//XSZO3euiIi8/fbbsnHjxkz9MWPGyPLly2XlypUyduxYuf322+VHP/qRTJkyJS/tBwAA+Zev8UTk34kmnRsA8iPNdO7m5mapqqqS7du3xwoCOeyww6SpqSm1d5hw6NDGE54nhNYTobhPopN44ubhefLYG55Ep1U37tPaXKe194b9IZffCvA86ffMz5Oq7pluWk+ik9gWaSWPa+fUfKRzhzae4OvcAIBe/w4TAADo/UIZT/B1bgAAAAAAIuJJNAAgmDvHAAAgPaGMJ7iIBgAE0+kh/+K+l5rEu8Bx37vM9TvGceeXywThNKfh2R+0/Syt7ZZE+nQut5vFsxzaMeRJdvdsN08ytnV+iXvMW9Lap+ImyXd2dkb+fNJCGU9wEQ0ACKbTAwAA6QllPME70QAAAAAARMSTaABAMHeOAQBAekIZT3ARDQAIptMDAADpCWU8wUU0ACCYTg99SxKhXFHrekKRPCFFlt4QkuUJh0orQCkt2rJ5lsET7JTE8qa1P3j21bj7macNaS2b5zi2aPPzBMIlUVfjCVjr6OiIPN2khTKe4J1oAAAAAAAi4kk0ACCYO8cAACA9oYwnuIgGAATT6QEAgPSEMp7g69wAAAAAAETEk2gAQDB3jgEAQHpCGU9wEQ0ACKbTw6HLk0LsSeeO+nmr3GqXJ5U3l6x2xW2vJ7HYMw1rup2dnbGm69mfrDZoSdOepHRLYWFh5LqaJBLYtbrWOtfaa6WfexK3o35exNdejbXOPdvYk8AeN1U9n/1yKOMJLqIBAMF0egAAID2hjCd4JxoAAAAAgIh4Eg0ACObOMQAASE8o4wkuogEAwXR6AAAgPaGMJ7iIBgCISN/qvNB3xQ3fsgKJos7Lmp9nupa4gU+WuOssbniXNQ1PCJnVXk8oUhIBU1Gn6wmHsuYVdz/z7L9JtDeJcD1NEsdW1DYksf9qda0QsrSC8eLuk/kWwniCd6IBAAAAAIiIJ9EAgGC+fgUAANITyniCi2gAQDCdHgAASE8o4wm+zg0AAAAAQEQ8iQYABHPnGAAApCeU8QQX0QCAYDo99H2eRGhLWqm+GiuZOG5Sr2e6lrjLZombqu6RVoJ1WuvBs/969geLJ+U57rw868xKso+bSu1ZZ54Ed2v/9exnueRZtqSFMp7gIhoAEEynBwAA0hPKeIJ3ogEAAAAAiIgn0QCAYO4cAwCA9IQynuAiGgAQTKcHAADSE8p4gotoAEAwnR7yTwv+sfahuAFgFi0kyAriidtej7SOJU97kwgA86yztAKQtOXwBFRZ7fKsM0+YVVr7etxgsCT2yVz2EWnNK4ngwbgBgUkEGuZCKOMJ3okGAAAAACAinkQDAIK5cwwAANITyniCi2gAQDCdHgAASE8o4wkuogEAwXR6AAAgPaGMJ3gnGgAAAACAiHgSDQAI5s4x8k9LJ851kmzcRGgrJdeTbhw3IdziWba4Kc9WuzzbM62kaU0S68azHjzT1SSR5K2VexKhLdo0kjgu4u4PnnWWxL4Xd5/0JHl70uVJ504fF9EAgGA6PQAAkJ5QxhN8nRsAAAAAgIh4Eg0ACObOMQAASE8o4wkuogEAwXR6AAAgPaGMJ7iIBnIkbhgJkKZQOj3kX9xgsSTCrLQ2eIKDPAFVVnvjTsMTWJZE8FXcMCvv/OJ+Pm4bkuiftWkksW7iTsMTYJdEG7S6ngAwS2dnZ1ZZEueStMQ9hpIIKcyFUMYTvBMNAAAAAEBEPIkGAARz5xgAAKQnlPEEF9EAABHpW50XAADonUIYT3ARDQAI5s4xAABITyjjCd6JBgAAAAAgIp5EAwmzkhb7988+3Do6OlzTANISyp1j5F/c9GgtkdfiSfK2Un09KblpHQuelGdtOTwJy0nU9UgraToua9k8ye5x11kSyc2e6WrTSGI9aOXWcZzWPhl3nSWRUh53X42biJ4roYwnuIgGAATT6QEAgPSEMp7g69wAAAAAAETEk2gAQDB3jgEAQHpCGU9wEQ0ACKbTAwAA6QllPMFFNBCDJ+SkoqIiq6y5uVmtu2/fvqwywsaQplA6PfROnqAjS9zAJ6sNcafr4Qlm8tT1BBrlOqhLK08rQMkKj/PwtNezn2nTLSwsjFw3iUAtbexh7Q9aubV+466zXPcxcdevtc7SWg5tfmkdx1GEMp7gnWgAAAAAACLiSTQAIJg7xwAAID2hjCe4iAYABNPpAQCA9IQynuAiGgAQTKcHAADSE8p4gneiAQAAAACIiCfR6PXSSkFNQnFxcVaZlaaptXfgwIFq3Z07d0aeLpCEUO4cI//i7i+e1Nm0Up6tNmjT6OzsjN2GtFLKPfPyJE1H/byInuhstUFbNs90k0iE9ux/nlRqbbr9++vDdM8+pbXBStGOu2yeVGpP8ngS+5kncdszXY0npdzi2dd7w3j43UIZT/AkGgCQ6fR6+tcTixcvltGjR0tJSYnU1NTIs88+e9D6ixYtkuOPP15KS0ulurpavv71r8uePXt6NG8AAJC8fIwn8oGLaABAzi1btkxmz54tdXV18vzzz8vYsWNlypQpsmXLFrX+Qw89JDfccIPU1dXJyy+/LPfff78sW7ZMbrzxxhy3HAAA9Cb5uCnPRTQAIOd3jhcuXChXXHGFzJw5U0466SRZsmSJlJWVydKlS9X6zzzzjJx11llyySWXyOjRo+X888+Xiy+++H07SgAAkDu5Hk/k66Y8F9EAgJx2eu3t7fLcc89JbW1tpqxfv35SW1sra9euVT9z5plnynPPPZe5aH7jjTfk0UcflY9//OM9X2gAAJCoUG7KEyyGXkU7eEpKStS6e/fuTbs5GVZYRlVVVeS6+/btyyqzwiCKiooiff5g0wA8kggCaW5u7lZeXFyshu9t27ZNOjs7ZcSIEd3KR4wYIa+88oo6j0suuUS2bdsmZ599tuzfv1/27dsnX/nKV/g69yEubniRRxKhPZ7worQC0nL5TqE1r7iBWhZP2JJnuhrP/mAFSXnC2OKGZyURsObhCSyzyuPW1aS1bHH3J2sanhAyqw1a0FwS7e2pXAaLHbgpP2fOnExZlJvyDz74oDz77LMyceLEzE35L3zhC6558yQaAJCI6upqqaqqyvwtWLAgsWmvXr1a5s+fL3fddZc8//zz8qtf/UqWL18u3/72txObBwAAyL/m5uZuf9aDs4PdlK+vr1c/c8kll8i3vvUtOfvss2XAgAFyzDHHyHnnnee+Kc+TaABAIneON23aJJWVlZly7Sm0iMjQoUOlsLBQGhoaupU3NDTIyJEj1c/cfPPN8oUvfEEuv/xyERE59dRTpbW1Vb70pS/JN7/5zbzedQcAAO9IYjxRXV3drbyurk5uueWWuE0Tke435WtqauT111+Xa665Rr797W/LzTffHHk6XEQDABLp9CorK7tdRFuKiopk/PjxsmrVKpk2bZqIvPOVtlWrVsmsWbPUz7S1tWVdKB/4ndG+9JMYAAAcykK5Kc+tewBAzoNAZs+eLffdd5/85Cc/kZdfflm++tWvSmtrq8ycOVNERGbMmNHtHaepU6fK3XffLQ8//LC8+eabsnLlSrn55ptl6tSpmYtpAACQX0mMJw7clD/wZ11Ev/um/AEHbspPmjRJ/UxSN+V5Eg0AyLnp06fL1q1bZe7cuVJfXy/jxo2TFStWZN5r2rhxY7dO7qabbpKCggK56aabZPPmzTJs2DCZOnWqfOc738nXIgAAgDybPXu2XHrppTJhwgSZOHGiLFq0KOum/KhRozI5LVOnTpWFCxfK6aefnvk6d09uynMRjV6vNzxlsu5Mtba2ZpVZ7S0tLY08v4EDB2aVNTY2qnVJ50ZScv216FmzZplf3169enW3/+/fv7/U1dVJXV1dDlqGNHn2M+38piXRWtO1vpanlSeRNJ1Wqm/cY9P6vCfl2bPOPG2Iu86s9vbvnz3Etdqg7VNJrDMPbX7Wr3JoPCnPSegNSfRxP+9JNM/1/uDhSYzPlVyOJ/J1U56LaABATn+SAgAAHJryMZ7Ix0153okGAAAAACAinkQDAHgSDQAAYgtlPMFFNAAgmE4PAACkJ5TxBBfR6FW0cISWlha1bhJhLVFZB7XWNk+gRFFRkVq+Z8+eWNMFvELp9NA7eQJ+vNPQaEFS1ue18rjhaNY0kpiuh2ca2rawPq+tX6vP9rTBs360+SURvpVWyJtWbgWVerZF3LGStc60+Vnz8gR1RZ2XxRMAlkQQWlr9X9yAtHyOGUMZT/BONAAAAAAAEfEkGgAQzJ1jAACQnlDGE1xEAwCC6fQAAEB6QhlPcBENAAim0wMAAOkJZTzBO9EAAAAAAETEk2j0erlM4fbS2mbdRdu1a1dWWXFxsVpXS+fuzesBfV8od46Rf2mlxmrnyCRSv+MmC1vn7rgJwJ7U7yT6D08idP/+2cPLJJLWPYnm+/bti9wGz/pNizY/zzrzpFJ7joskEqw9y+ZJKfckbsdNYE8i9Tvq5716w/77bqGMJ7iIBgAE0+kBAID0hDKe4CIaABBMpwcAANITyniC74cCAAAAABART6IBAMHcOQYAAOkJZTzBRTSC5QnssHiCbDRagJg1XSBNoXR66J08YUBxA7lE4ofueD6fRACYJ+go6uctSaxfbRppbbfOzs7I00iib417vrPa4BmTeEJN44ZcWaFenqA5rTxuMJm3btxwviTa4JHW8Z0LoYwnGKkDAAAAABART6IBAMHcOQYAAOkJZTzBRTQAIJhODwAApCeU8QQX0QCAYDo9AACQnlDGE7wTDQAAAABARDyJRrDKy8vVck8Kamtra1aZlWSpSSLBEUhCKHeO0Tv1hpRnT9200netNniSmz19kDZda15xE6E92zitc0oS2zhu2nUS+2Raae3atreOt96w3TRppWgn8YsuaZ1LcrktoghlPMFFNAAgmE4PAACkJ5TxBBfRAAAR6VudFwAA6J1CGE/wTjQAAAAAABHxJBoAEMzXrwAAQHpCGU9wEY1gFRcXq+WecA+NFjYm4gt7AXItlE4P+aftL1a4TtzQHU8ImcUT4hQ31CjXx5K2fqwAJc928/BMN26AUhLrN24b0goUtaYbNxAr14FwaQW3pbX/phVkFnddEiyWPr7ODQAAAABARDyJBgAEc+cYAACkJ5TxBBfRAIBgOj0AAJCeUMYTXEQDAILp9AAAQHpCGU/wTjQAAAAAABHxJBo94kkrjZuOGjdZ0uJJV7USFcvKyiLXbWlpySqz1k1a6Z2AJZQ7x8i/uCm5SSRja6zzsdYHpdWGJMRdv9Z66Ozs7HGbDtYGrdyzLSzaL2IkkaLtaa/GaoO2bJ6Ucmv7eMY0Gk/atXcaGk/itmedxU3Rtn5hxTMe9uy/fblfDWU8wUU0ACCYTg8AAKQnlPEEF9EAgGA6PQAAkJ5QxhO8Ew0AAAAAQEQ8iQYABHPnGAAApCeU8QQX0Tgoa2cuLS3NKuvfX9+dtEAtK1xBm19lZaVa1xM+EZcnkEILGxPxBYHs3r07VhsAr1A6PeRf3KAjS1rhRbmcrtU3aqFGaYVDWSFZnjakFfjkCfBKK2jOUzfu+vWMldI6hpIIYY3bR3iC5tIaK1nbIq3zWdxp5HPMGMp4gq9zAwAAAAAQEU+iAQDB3DkGAADpCWU8wUU0ACCYTg8AAKQnlPEEF9EAgGA6PQAAkJ5QxhO8Ew0AAAAAQEQ8icZBWXeEtCTuAQMGqHXLy8sjz09LEywuLo5cN9d3sDzzKykpySprb29X67a1tWWVkc6NNIVy5xj550kWjptCbCXqamm/njRmiydxO+50k6Ats6e9SSRup5XO7fm8J5Vam0Zay5tWyrNnG3vWmacNnmM+ieT8uJI4R6X1qwC9TSjjCS6iAQDBdHoAACA9oYwnuIgGAATT6QEAgPSEMp7gnWgAAAAAACLiSTQAIJg7xwAAID2hjCe4iEZirB1fCxxLK4zECsDwhFp0dnZGboMnRMbThlyKGw6CQ0MonR7yL5fnEU8QlWc/ts7znpAhrW2eNiQRSBS3X0qrv7Pqxg3ESmLsUVhYGPnz2njC2j5x11la5+Ekphv3uIg7LrPm55luWgFraQUPavtproQynuDr3AAAAAAARMSTaACAiPStO8AAAKB3CmE8wUU0ACCYr18BAID0hDKe4CIaABBMpwcAANITyniCd6IBAAAAAIgoZ0+irfQ5T3Imcs+z3TxJgEmks8ZN57ZoaeJJJLHGbVcStPaWlJSodbV12dbWptYltbvvC+XOMfJPO18kkUqdyzYkQetXkkgA1nhSqT3pxkn8Ikbc/sOT8pxE/xx3umnt61YbtGl4xt5WgnXc7ZZEKnXc7enZxknI5a+h5LNfDmU8wde5AQDBdHoAACA9oYwnuIgGAATT6QEAgPSEMp7gu9QAAAAAAETEk2gAQDB3jgEAQHpCGU+kchGtvQw/aNAgtW5TU1OkzyN9WnhEVVWVWreoqCirzBNGYoWQeba9No3+/fVd2jNdLVDCEyy2d+/eyPOy1kNaJxFtPbS3t0euy7F56MpHp7d48WL5/ve/L/X19TJ27Fi54447ZOLEiWb9xsZG+eY3vym/+tWvZMeOHfKBD3xAFi1aJB//+Md7NH/khyeQUaubRPBV3Dakde72LJunvR6eUCVrXnGDmdIK9UoifNSzn2nlVlCXNs5Ios/VppvEdosbrGrNK+56SCt40DMO9ATteta5tR7S2nd6iotoAABSsmzZMpk9e7YsWbJEampqZNGiRTJlyhRZv369DB8+PKt+e3u7fPSjH5Xhw4fLI488IqNGjZK33nrLvEELAACQFi6iAQA5v3O8cOFCueKKK2TmzJkiIrJkyRJZvny5LF26VG644Yas+kuXLpUdO3bIM888k/kZutGjR/eovQAAIB2hPIkmWAwAkOn0evrn0d7eLs8995zU1tZmyvr16ye1tbWydu1a9TO/+93vZNKkSfK1r31NRowYIaeccorMnz/f/GokAADIvVyOJw5YvHixjB49WkpKSqSmpkaeffbZg9ZvbGyUr33ta3L44YdLcXGxHHfccfLoo4+65smTaABAIneOm5ubu5UXFxdLcXFxVv1t27ZJZ2enjBgxolv5iBEj5JVXXlHn8cYbb8gTTzwhn/vc5+TRRx+V119/Xa688krp6OiQurq6HrUbAAAkK9dPovP1ehhPogEAiaiurpaqqqrM34IFCxKbdldXlwwfPlzuvfdeGT9+vEyfPl2++c1vypIlSxKbBwAA6Fve/XrYSSedJEuWLJGysjJZunSpWv/A62G/+c1v5KyzzpLRo0fLueeeK2PHjnXNN5Un0dpdhJaWFrUuab9+cdNKLeXl5VllJSUlsdugsdIIPYnbWt0D70q+l5YmbiUt7tu3Ty2Pylrne/bsySrrDe9+WG3oDW1D7iRx53jTpk1SWVmZKdeeQouIDB06VAoLC6WhoaFbeUNDg4wcOVL9zOGHHy4DBgzodtyfeOKJUl9fL+3t7eoxjt5JO/cmkVDr4dnX4/ZtaaUFe9KN0zrPexLC00rGtj7v+WUQrdyarlY3rf7Smq72GotnPVjT9aQ8a9OwxlVpjfXj7jtJpGh7jjcPzzj7UEznjvrNtgOvh82ZMydT5nk97Le//a0MGzZMLrnkErn++ut9+0TkmgCAQ1YS7zBVVlZ2+7MuoouKimT8+PGyatWqTFlXV5esWrVKJk2apH7mrLPOktdff73bYOHVV1+Vww8/nAtoAAB6iSTGE1G/2Xaw18Pq6+vVz7zxxhvyyCOPSGdnpzz66KNy8803y+233y7z5s1zLSfvRAMAcv4O0+zZs+XSSy+VCRMmyMSJE2XRokXS2tqaSeueMWOGjBo1KtNxfvWrX5U777xTrrnmGrnqqqvktddek/nz58vVV1/dozYDAIDk5fKbbT3x7tfDCgsLZfz48bJ582b5/ve/78pY4SIaAJBz06dPl61bt8rcuXOlvr5exo0bJytWrMjcTd64cWO3r89VV1fL448/Ll//+tfltNNOk1GjRsk111wj119/fb4WAQAApODAN9reTz5fD+MiGgCQl991nDVrlsyaNUv9t9WrV2eVTZo0Sf74xz/2aF4AACB9uRxPvPv1sGnTponI/74eZo0vzjrrLHnooYekq6src7O+J6+H5ewi2gobgM1aZ2VlZVllVgCYFui2d+9eta4nEEWr6wm1sOpqIWJWsJi2o1dUVKh1tXVmrV9t/WihYCIiHR0dWWVWKIE2P+s3buMGtxHYB698XEQjTJ4QMY0nSDOtALHeEPDjGVd5+nJrulrdtALhrLqesYdnPOGp69nPtD7eWmfa2COJoC5PKF3c4FpPu3Ldb3iC0NIKmvMcs9r8PPtDXw8W88jX62E8iQYAcBENAABiy/V4Il+vh3ERDQAAAADok/LxehgX0QAAnkQDAIDYQhlPcBENABCRvtV5AQCA3imE8US8dA8AAAAAAALCk+g+yJO0GDedz5qu9qPnnnl5ErdLS0vVuoMHD84qO+yww9S6Wjr3vn371Lo7d+7MKmtublbrtrW1ZZVZ6efa+rHWw5AhQyJ9XkSkvb09q6y1tVWtGzcVF4euUL5+hfzT0mytXyrw/GqExqqrlSfxCxNppYGn8XmLZ9k8Sd6exG1v2zTafmb9fI02prF+9UTrt61toY0Hdu/erdbVjgHtF0BE9GXzJDdbx5tn/VrTiDrdJH61R5uGZzxsLa82XeuXVzzi/oKAtWxae/PZL4cynuAiGgAQTKcHAADSE8p4gotoAEAwnR4AAEhPKOMJvtsJAAAAAEBEPIkGAARz5xgAAKQnlPEEF9EBsnZQLTTBCr7yhGRpQQhWXS1ETAsQExEZNmxYVll1dbVad/jw4VllVvjW3//+d7Vco4WTWcEP1jJrPGE6nrALbZkJG4NIOJ0e8s8T6qVJItTLwxMGFDfM0xN0lESfoE3DE17kCWPzsNqglXv6XC1ATERk0KBBWWUjRoxQ62qBY1rIqIjI9u3bs8qsUFMtJNQK39ICx6y6nuPNcwx5trE2Xc8xlNbxZu07nvbGPe94jqFcBhfGEcp4gotoAEAwnR4AAEhPKOMJHkEBAAAAABART6IBAMHcOQYAAOkJZTzBRTQAIJhODwAApCeU8QRf5wYAAAAAICKeRB8irDs35eXlWWWVlZVq3QEDBmSVxU2UtqahzUtEZODAgVllQ4YMUetqSdzHHHOMWve4447LKmtoaFDrasthpV7u3bs3q8xK/daWubOzU62rsRIctURPqw0kccMSyp1j9E6eVOokkrw9ybeepF5PG+LWtcRNY/akfnvWr5UartX1TNfq17T5aWMiEX08ceqpp6p1tSTvv/3tb2pdrX9ubm5W68btn5NIj46bCJ1WWrvVBs8vmcRdDmscqLHGzp42eObnWQ+5EMp4gotoAEAwnR4AAEhPKOMJLqIBAMF0egAAID2hjCf4bicAAAAAABHxJBoAEMydYwAAkJ5QxhNcRPdBWuCBFWLgCeHQpmFN1xMwos2vpKRErasFi2khHiIiH/jAB7LKxo0bp9Y94YQTsso2bNig1u3o6Mgqa2xsVOtq4WSeMDbPOtu9e7dat6mpKdLngYMJpdND/mn7ixWi4zmXeaZrhVxpPP2oJ6jLE6ilSWKdxW2DhzXdtLZFUVFRVpkVLDZ69OissilTpqh1tTHJ8uXL1braOMMaI8QNh/KEx3nqWvtZ3H0nrWMoibqe9e7Zbmm1N5fHcRShjCe4iAYABNPpAQCA9IQynuBxFQAAAAAAEfEkGgAQzJ1jAACQnlDGE1xEAwCC6fQAAEB6QhlP8HVuAAAAAAAi4kl0H+RJp0wrPVFL09SSMEX0JEorjVMrr6ioUOseddRRWWXHHnusWreysjLS50VENm7cmFXmSUH1JI1adbX59aW7c32RtY2rqqqyygYMGBB5utZ227lzZ+Rp5EIod46Rf3ETh5NILPbwpBvHnW5nZ6daV+tHPenGHp7kZovWNs8vg3jWr9WPJjENjfYLHvv27VPrauVWX+NZv559UlvvSbRB21etNsQ9Xqx2aW2Im07vbYNnnK21zaqrbSPr/NDbhDKe4CIaACAifavzAgAAvVMI4wkuogEAwdw5BgAA6QllPME70QAAAAAARMSTaABAMHeOAQBAekIZT3ARnSJtRygrK4tc19qRSktLs8o8wVee0AWrrhYiZi2bVtcKUtACRqzAjq1bt2aVvfrqq2rd4447LqvsxRdfVOtu2LAhq6ylpUWtu2fPnqwya1vEldZ08Q7PPllcXBx5Gu3t7ZHr5rPzCKXTQ/7FDVCyJBH2FXW6SYQMeZZNO/97ljeJNsRdv9bntVAvT+imZ0xjnY///ve/Z5WtXLlSrauNaV577TW1rjZ2sMY0cUOjrM9r2ziJYDHPeveMXzzjVs/YOeq8RHzHmydg1hMspm1Paz32tvFhKOMJLqIBAMF0egAAID2hjCd4JxoAAAAAgIh4Eg0ACObOMQAASE8o4wkuogEAwXR6AAAgPaGMJ/g6NwAAAAAAEfEkOgHWXRMtrXrgwIGRp2El9mnlViqjlgJpJQsPGDAgq8xKLiwpKckqKy8vV+taKYVRtbW1qeVvvPFGVpm1znbs2JFVpqVwi4j87W9/yyrbsmWLWre1tTWrzJOSaKV0asu8a9cutW7c9Xsos47NysrKrDLr2NT2da1MRE9rtxx22GFZZdu3b4/8+aSFcucY+edJ9dXO6Z5EXWvf9KTvxk3qtWhtsz4fNxnbWg9xt4VnPVjz8ow9PLRl3rt3r1r37bffziqzfpVDW2arf9bKrYTwtFLrNZ7t5jmGvPOLKolj3tMGz3rQ6moJ7iL6+Ntaj9q+6klg59c+0sdFNAAgmE4PAACkJ5TxBBfRAIBgOj0AAJCeUMYTvBMNAAAAAEBEPIkGAARz5xgAAKQnlPEEF9FO2sYtLS1V62pBW0mEOWjBAlZdrdwTBmGFI2ghTFZgmVbuOUisYLFt27ZllVmhC3//+9+zyqwQp4aGhsht0ILBrHCpjo6OSJ8X8YXp4B3afq0FiInooX9WWNigQYMilYmINDc3Z5U1NjaqdbX2DhkyRK2bC6F0esg/z/6infesPswzXU9IlqeuJyRUC4VMog1auafft8RtgyeYyTofa6x+VGMFizU1NWWVacGhFmuMoM1PGwuI6NvI2m5x90nP+DJuIJc1vySO47g8IWT9++uXTNo4efDgwWpdrY/fvXu3Wlcb41r7jjX2zZdQxhNcRAMAgun0AABAekIZT/BoCwAAAACAiHgSDQAI5s4xAABITyjjCS6iAQDBdHoAACA9oYwnuIgGAATT6QEAgPSEMp7gItpgbUQtabqiokKtq6X7DRgwIPJ0reRCLZ3PSubTpqElglrlVtKiVtdK59aSkC3aerfaoKVebtmyRa2rTcNK09TStT0pkp6kUGtbaGmPVVVVal0t/Zkk73dYx7GWsmkleR922GFZZUOHDlXraomy1r7T3t6eVWYlbwKHkrjJwmmx+lHtfJpWez1J3p6+3KKdc5JI8tZY60ZrgzVu0M6xVp+rnWOtNli/qqHx9Pue9Rv3l1cscdParX1Sm4Z1DHmO+bR42uAZO2tj3yOOOEKte9JJJ2WV/eMf/1Draunwnl+KSes4xv/iIhoAEMydYwAAkJ5QxhNcRAMAgun0AABAekIZT/CdTwAAAAAAIuJJNABARPrWHWAAANA7hTCe4CLa4An3sEKctBCx8vJyta4WGmWFGGjBAlbYgBa+Ze3YWgiBJwDDE5JlBaxp5da20MqtYCYtdMETamGtB20a1v6g1bXWmTa/EE5ISbO2hRYsZh2bQ4YMiVxX2567du1S6+7YsSOrLNdBK+8Wytev0Pd59jdPXU8gl8VzDHv6BK0fLS0tVetq5dZ60MYOWiCXiC8kyxM6pfXPVhu0c68WNmZNVxsTWW2z1plWN4lztzYNT7CYJ+zO6hs940CN1QbPmDFuf+JZZ1ZdT5igVtcTImxti7RCCnMhlPEEF9EAgGA6PQAAkJ5QxhO8Ew0AAAAAQEQ8iQYABHPnGAAApCeU8QQX0QCAYDo9AACQnlDGE3ydGwCQ6fR6+tcTixcvltGjR0tJSYnU1NTIs88+G+lzDz/8sBQUFMi0adN6NF8AAJCOfIwn8oEn0aLf9dCSMEVEBg4cmFVmJQxqKZLDhg1T62rl1nQbGxuzyrSkXxE9nc9Kp9RYqYytra1ZZdY686RpVlRUZJVpaYYHm59GSxXdunWrWldLkfSkiXuSQrUyEX27eZIhrZNQPtMa0+ZJINWOLWtbaCmxVl3tuLDSubVpWOnyh6Jly5bJ7NmzZcmSJVJTUyOLFi2SKVOmyPr162X48OHm5zZs2CDf+MY3ZPLkyTlsLZIUN4U4rTYkwZOarJ2HrH5f61es8cRRRx0VqV0iIhs3bswqs/pGz69GaOXWeEI77+3Zs0etq6WJV1ZWqnW18ZpWZrXNk85ttdc6/0dtg6dP8GyLJJLoPcem1hd7fvXE4km49xybUT8vom+jhoYGta62zE1NTWrd3bt3R/q8iD3WQbpY6wCAnN85XrhwoVxxxRUyc+ZMOemkk2TJkiVSVlYmS5cuNT/T2dkpn/vc5+TWW2+Vo48+Os7iAgCAFITyJJqLaABAIp1ec3Nztz/rWy/t7e3y3HPPSW1tbaasX79+UltbK2vXrjXb+K1vfUuGDx8uX/ziF5NdeAAAkIhQXg/jIhoAkIjq6mqpqqrK/C1YsECtt23bNuns7JQRI0Z0Kx8xYoTU19ern3n66afl/vvvl/vuuy/xdgMAgL7pwOthdXV18vzzz8vYsWNlypQpsmXLloN+Lu7rYVxEAwASuXO8adMmaWpqyvzNmTMnkbbt2rVLvvCFL8h9990nQ4cOTWSaAAAgeaG8HkawmJP2Un///vpqHDRoUFaZFZjz3icyInY4ghZm5Qk68oRPWF/H1EIMrAANLfSstLRUraspKytTy4cMGZJVpoWYiejLpoWYWaz1q+0PVsCIJ/hBC7uwgtS0UJXm5ubI8zpUaOtMCwUT0beRJ5zP2sZamIgVGqKFkezcuVOtmwtxvkZ14HOVlZVmyM+7DR06VAoLC7PWV0NDg4wcOTKr/t/+9jfZsGGDTJ06NVN24Njr37+/rF+/Xo455pgetR29gxXw49kntWl4zrueoCMtXMqan9WGuCFkVVVVal1tQGiNU1paWrLKrHOWNh6w1pnG2pbaNKxztzbOsNavtszW+UkbD1hjD6291jdoNNa+o61fa3/w7JOe40LbRp5j0GqDxmqDtn49x7FnjJvEeUfry63xhHa8Wfu6FizmGYvmUxLjiagOvB727pv23tfD1qxZ06O2chENAMhpp1dUVCTjx4+XVatWZd5D6urqklWrVsmsWbOy6p9wwgny4osvdiu76aabZNeuXfLv//7vUl1d3aN2AwCAZCUxnnjvw6Di4mL113oO9nrYK6+8os7jwOth69at61EbD+AiGgCQc7Nnz5ZLL71UJkyYIBMnTpRFixZJa2urzJw5U0REZsyYIaNGjZIFCxZISUmJnHLKKd0+f+CbPu8tBwAAfdt7b47X1dXJLbfcEnu6Sb4exkU0ACCnT6JFRKZPny5bt26VuXPnSn19vYwbN05WrFiRuZu8ceNGfvsSAIA+JonxxKZNm7q9fqE9hRbJ7+thXEQDAHJ+ES0iMmvWLPXr2yIiq1evPuhnH3jggR7NEwAApCeXGSv5fD2Mi2gAQF4uogEAwKEl1+OJfL0exkV0AqxUPG1HsBKWS0pKssqsdG4t2dqTtKglCVo8aaVWe7WUQispVJuGlbitLbO2HkX0RHMreVxLT7TSrrXyJNITrcRIjfYVF2udaamr1nbrazyJ8dr+YK3zrVu3ZpVZKdpaareV5N3W1pZVxteXEYK4idtptSGJpF7tGPYsg9VPaAm+jY2Nat3NmzdnlVljD6sf1MS9WeZJ57b6Ue18aq1fbZ1Z09XGJFY/qvUf1phGS6u2xmBa3SSOFW2ftNqrTcNqr7bdktjXPdPQls2zn1m0aVjJ49o+pSVrW+VWu7TpehLYQ5Kv18O4iAYA8CQaAADEFsrrYVxEAwC4iAYAALGFMp7gu4MAAAAAAETEk2gAQDB3jgEAQHpCGU9wEe2kvZhuBVVooR9///vf1bpaKIAWXiEism3btqwyLQxLxLczekIXtHCPtMJiPEFdVkiWFr6lBZ6J6Ot3+/btal1tG2thJiLxg8WsdaaVWwEKWphIEuEevZW1P2jbyAoC0ep69nVPgF0+13konR7yz3N+0/Ytz/5mBfForPOFJ4TM0wZtflZd7Tyk9VUiehCUNZ7QzntWGKLWtiSCpLRyz3bzhLFZy6adj7ds2aLW1fpRK3w0rTGCxhMslsS+7gn1ijsW9QSgesZK1jr3LJt2vCVxjvIcA3HPUUkLZTzBRTQAQET6VucFAAB6pxDGE7wTDQAAAABARDyJBgAE8/UrAACQnlDGE1xEAwCC6fQAAEB6QhlPcBENAAim0wMAAOkJZTzBRbToKXxWkqXGSvfTUiB37twZeRpWG7QUSSsZ0kq21mhJgJ6kRSuFWEsItKartddattbW1qyypqYmtW5ZWVlWmZW4vWHDhqyyt99+W627a9eurDJrPWjLFjex25pGUVGRWldbD9oyHGx+fYm1DNq+Y/Gkgmqs5PxDYf0CPZHWr0Z4Ur8984qbtGv9WoJW15MWbJ3HtD4oiTZ4xhPaNJLY7lq55xc8rGRsbZ1pydoi+j5ltUFLbrbqauWesZLVV3nWgza/JJLSPWnXnjGj5/ygTcM6Ljw868yzbHETt/vSxWhfxUU0ACCYO8cAACA9oYwnuIgGAATT6QEAgPSEMp7gJ64AAAAAAIiIJ9EAgGDuHAMAgPSEMp4I6iLa2jD9+2evBitsQAvysALAtJAHK2RICyGz2qAFQmjhFSL6MluBElpAgxXaoIUbeAIeLNp68Kyzbdu2qXW1bdTW1qbW1cJErMAyLdzD2hbaeveEcFh1tUAUK3xCa2+IAVfasWntD3ElEVySC6F0esg/7fhL4lwYlyfgx+IJDvLMyxNM6QkA01ihSHFD3qxzoWc84emvtL7YE7DmCUu1eLZbEkGjUdvgYe2T2rq01q82Dc90kwgW01jr0RPq5QkW84wHPOc+zzrLhVDGE0FdRAMAdKF0egAAID2hjCf6xiMSAAAAAAB6AZ5EAwCCuXMMAADSE8p4gotoAEAwnR4AAEhPKOMJLqIBAMF0egAAID2hjCeCuoi2kvW0BEcrwVrbuEVFRZHrWknTWkJ4SUmJWldbDitZWEuXLC4uVutqiamendlKI/SkMmrLZm0LLXFSS9a2pmulHGqp31YbtGX2JKVbtGWztvHu3buzyqx9XSsPMZ1b01dStIG+zuorNJ7zU284l3nOsZ4EYI21HrVyz3Q9/bNnGp4kZU9yc65/RSTqvER860Er9/RLSaSqez6f1sWOJ2nacy6J294k0vtz/csCSFdQF9EAAF0od44BAEB6QhlPcBENAAim0wMAAOkJZTzB9xcBAAAAAIiIJ9EAgGDuHAMAgPSEMp7gIlrihy1pIVAiIk1NTVllVqiAFvalhYKJ6KELVuiUFohlBTFowWJWaJoWhOYJn7DqekJDrPUelzZda16ekAhPyElra2tWmRZ4JqJvN8ArlE4PfZ8VtuQ5x2r7rCfEyVM3iaAubTmSCN+KG26WRGBZ3MCxJM4/nmloy+xZtiSCqOK2wSOtwD5rummNqzSeYDJPGyxa26wxnOfYTOu46KlQxhNcRAMAgun0AABAekIZT/BONAAAAAAAEfEkGgAQzJ1jAACQnlDGE1xEAwCC6fQAAEB6QhlPcBENABCRvtV5AQCA3imE8QQX0Qmw0vK0civRU0t/7ujoiN0GLUXbSv3WUgNLS0vVutpylJSUqHW15EFPimQSKdye5MK4aZqepEYrGVJL4iaFG8ChIG7ar3U+9qTZ5jpxWJPWIDPudK10Y2084dkW1vgnblq1Nd24Kc/Wtkwi0VmT1naL2wbPMZREEr3nF13i/rqOxbONtbFZEucBT8q+Vjet/RT/i4toAEAwX78CAADpCWU8wUU0ACCYTg8AAKQnlPEEF9EAgGA6PQAAkJ5QxhP8TjQAAAAAABHxJLqXiBuOkETQihaO4AkhswLAKioqsso8ASNJhCN4gsU01jrT1sPu3bsjT9cTEgGkKZQ7x8g/TyCRZ9/ynOe1c6wnFMnTJ3jO50n0S2kFrHn6Yk8oUlo8bfCEWcXdRmkFlSYxDvRM17M9PcdbWjzbQmtvrkMK445902pXFKGMJ7iIBgAE0+kBAID0hDKe4HEXAAAAAAAR8SQaABDMnWMAAJCeUMYTXEQDAILp9AAAQHpCGU9wEQ0ACKbTAwAA6QllPMFFdB+k7WAlJSVqXS3dr6OjQ63rSSDVkgs9idueFFSLJxkybkqh5/Otra2Rp5HrtEcA6I2SSCyOmzjsSbv2JOcmkW7s+bz2SxtJDEzj9rnWOvOkl8ddNs829kzDs4094ypLWknnaaWJpzUOTCvpP26aeBJ1c9le9AwX0QCAYO4cAwCA9IQynuAiGgAQTKcHAADSE8p4gotoAEAwnR4AAEhPKOMJficaAAAAAICIeBJ9iEgimCAtnmCNuKEWSdDa0NLSotbV2maFgxAWht4slDvHyL+09pe4YT7W59MK6vKEAXn6mqift8pzHdzmmVfcbWytM608rfAui2efTCLsK2pdTwBYWuMc7Rj0ihuMl9YxlNa2zKdQxhNcRAMAgun0AABAekIZT/B1bgAAAAAAIuJJNAAgmDvHAAAgPaGMJ7iIBgAE0+kBAID0hDKe4CIaABBMpwcAANITyniCi+g+SEvn27t3b6zPJ9GG3bt3q3VLSkqyyqyDJG6SdxLLpiVx79mzJ6dtAIBDVdxBUhLJzVrabxJpzNo0PL/YkMSyxf18Euncns97Uqk9Y4SonxfRt5GVCK1Nw7PvJLFsnkRoTwp8b6Ath7V+444ZLbk83ixae61zSa6T5PEOLqIBAMHcOQYAAOkJZTzBRTQAQET6VucFAAB6pxDGE/zEFQAgc+e4p389sXjxYhk9erSUlJRITU2NPPvss2bd++67TyZPniyDBw+WwYMHS21t7UHrAwCA3MvHeCIfuIgGAOTcsmXLZPbs2VJXVyfPP/+8jB07VqZMmSJbtmxR669evVouvvhiefLJJ2Xt2rVSXV0t559/vmzevDnHLQcAAKEr2B/xkn/AgAFptwV9nLUraftOeXm5WlcL8tCCvg42v7i0EDErzAHIhY6OjtSm3dzcLFVVVXL66aebQTrvp7OzU1544QVpamqSysrKSJ+pqamRM844Q+68804ReScYpbq6Wq666iq54YYbIs1z8ODBcuedd8qMGTN61G7kh7afec6xuX5SETcAzGpvWtONOi+r3Aop8pwfPAFrcUOyPDzrLIkQJ08ImWd/iBuS5fl8Z2dn5DYkEfKmTSOtoC6rvWkFdXnWT9wgP2udHYrjiXzhnWgAQCJBIM3Nzd3Ki4uLpbi4OKt+e3u7PPfcczJnzpxMWb9+/aS2tlbWrl0baZ5tbW3S0dEhQ4YM6VGbAQBA8kIJFuPxGgAgEdXV1VJVVZX5W7BggVpv27Zt0tnZKSNGjOhWPmLECKmvr480r+uvv16OOOIIqa2tjd1uAADQd+UjY4Un0QCARO4cb9q0qdvXr7Sn0En47ne/Kw8//LCsXr1a/R16AACQH7l+En0gY2XJkiVSU1MjixYtkilTpsj69etl+PDhWfUPZKyceeaZUlJSIrfddpucf/758te//lVGjRoVeb48iQYAJJKmWVlZ2e3PuogeOnSoFBYWSkNDQ7fyhoYGGTly5EHb+YMf/EC++93vyu9//3s57bTTkll4AACQiFyncy9cuFCuuOIKmTlzppx00kmyZMkSKSsrk6VLl6r1f/7zn8uVV14p48aNkxNOOEF+9KMfSVdXl6xatco1Xy6iAQA57fSKiopk/Pjx3TqsAx3YpEmTzM9973vfk29/+9uyYsUKmTBhQo+XFQAApCOX44kDGSvvfrUrVxkrfJ0bibGSBLUkQCtxW0vD3Lt3b7yGOZHEDaRv9uzZcumll8qECRNk4sSJsmjRImltbZWZM2eKiMiMGTNk1KhRmfeqb7vtNpk7d6489NBDMnr06My70+Xl5WbaP3qnuOdY6/OeVF9P+q5nup4BoFY3iQTruME8nnXmSdGOu25E9KToJBKPtflZ+0jc9ZtE4rZnW8RNjPfUtcTdfz3HhUcSCeGe9astR2/+ZYJ8ihpUerCMlVdeeSXSvHqascJFNAAg5+8wTZ8+XbZu3Spz586V+vp6GTdunKxYsSLTEW7cuLHb4OLuu++W9vZ2ufDCC7tNp66uTm655ZYetRsAACQrifFEdXV1t/K0+vo4GStcRAMA8vKTFLNmzZJZs2ap/7Z69epu/79hw4YezQMAAOROLoNKk8hY+e///u8eZazwvVUAQM6DQAAAwKEnl0Gl+cxY4Uk0AAAAAKDPyVfGChfRSJ0W0KCFjXk+DyBZ+fg6N9ATSQRUpRUcFHVeIiKFhYVZZZ7AM48kAsDSCiyL28f3hv3B2m7a+k0i3EybRhIhWZ6QK21+SYT+aTx1tfA5kfiBhp5tbIm7nyURxpYLoWSscBENAOAiGgAAxBZKxgrvRAMAAAAAEBFPogEAPIkGAACxhTKe4CIaABBMpwcAANITyniCi2gAQDCdHgAASE8o4wkuopEXJG4DQJg8qbOeFGKt3NPXWGm2WrmVAJzL1O8keFJ9tWWz1kPcbeFJIU5iPBE3KdrTXo+4yc8ivm0Rd/1ay+tpg7bMntT6JNaZthxamr5VN4l1FldaSf/4X1xEAwCCuXMMAADSE8p4gotoAEAwnR4AAEhPKOMJLqIBAMF0egAAID2hjCf4nWgAAAAAACLiSTQSY4UYeEIeAORPX7oDjL5L6xOsIB4tuCqJ/dQzDa0NVn+nhQ8lEXTkEbfPzfV5QFtmT+CT1d60Qt48YXfatvCEb1nbMm44XxLBV571G3cbe9pmfd6zLdIKP8zlsZXv/jzf888FLqIBAMF8/QoAAKQnlPEEF9EAgGA6PQAAkJ5QxhN8zxYAAAAAgIh4Eg0ACObOMQAASE8o4wkuogEAwXR6AAAgPaGMJ7iIRo9oyYNVVVVq3dbW1kifBwAc+uImIVviJlt7+iVrXp6EZU97tel60oLjJh4n0QZPGnMS6dEecfczT4p2EtPVePZfa7pxx2bW5+NuT88xFHede6eb1v6QVl0kh4toAEAwd44BAEB6QhlPcBENAAim0wMAAOkJZTzBRTQAIJhODwAApCeU8QQ/cQUAAAAAQEQ8iUaPaKEJbW1tal1CxIDeL5Q7x8g/z/5SWFgY+fNxg6+svsoT7qSx2usJ1NLakET4ljZdT+iUJ2DN4pluWmFLaW1jj7ht8KwzT0iWZ//1tC2J9nqOC20/80w3ieBBz3Q9577e1geHMp7gIhoAEEynBwAA0hPKeIKLaABAMJ0eAABITyjjCd6JBgAAAAAgIp5EAwCCuXMMAADSE8p4gotoAEAwnR4AAEhPKOMJLqL7IE9yocaTVurR2dkZ6/MAALxb3P7Ok0Js0dJzrSRlT7pxXJ4U4lynfnvSmOOuM2tbaNPI9TglbtJ0EtPVWInQnsR4jee48E5D49nX4/KkiXv2Sc+vDXiktR7wv7iIBgAEc+cYAACkJ5TxBBfRAIBgOj0AAJCeUMYTXEQDAILp9AAAQHpCGU/wE1cAAAAAAETEk+hezAoVKCsryyorKSlR62p3dFpaWtS6HR0dWWWegAcAfVcod47RO1khOJ4gqN4QSJTLMCBrXp5QL08o0oABA7LKrO2jlScRqBVXEts4idCzqNO15uWpq203T7BYEuFxGs+xEvfYFvG1N+5xbNHmZy2bdgx5juN8BouFMp7gIhoAEEynBwAA0hPKeIKLaACAiPStzgsAAPROIYwn+K4uAAAAAAAR8SQaABDrrnEId5wBAMD7C2U8wUU0ACCYTg8AAKQnlPEEF9G9hJYEOHDgQLWutoPt3LlTrVteXp5VVlFREbldzc3NarknNRAAgAO0NFpPom7ceYn4Bmpx29DX0o0LCwvV8v79s4eMWpmI/msfWplI/EGzJ8E61+OUuMtmtVcrT2KfTiIFO+50PfPSppvE8eZZNm0anvZaPMuG/OAiGgAQzJ1jAACQnlDGE1xEAwCC6fQAAEB6QhlPcBENAAim0wMAAOkJZTzBT1wBAAAAABART6J7CS1AYM+ePWpd7S6NJwTBCg3RpltVVaXW1QLHtLAxAH1DKHeO0bd4Qnu0/dATMmTV1fo2qx/1BFelFRzkGSNobbCWoaioKKvMWg/W+EWjrd+0trHFM920wrfSCgvztMGzHjy0NljtTSsQzrMcaR2baQXb9bYQslDGE1xEAwCC6fQAAEB6QhlPcBENAAim0wMAAOkJZTzBO9EAAAAAAETEk2gAQDB3jgEAQHpCGU9wEQ0ACKbTAwAA6QllPMFFdC9m7UhayuHAgQPVumVlZVllVpqmNl1P4nYSCZkAgENbWoMkLaHWk/Js9VVp9WGe9RA3sdiaV9y0a2s84Umwjjovkd6Rzh2XZ7rW/ht3n0xrX/fsZ542ePYHb9viSmvZtG3fly4wQ8BFNAAgmDvHAAAgPaGMJ7iIBgAE0+kBAID0hDKe4CIaABBMpwcAANITyniCn7gCAAAAACAinkT3YlagRHl5eaQyEZH+/aNv4rjBJQD6rlDuHKN3svo7LaDK0y8lEb6ltSGtACWLtX6iTtcT9LVv3z61fM+ePZHbpYWSWm3wBJhq0gpYs9qrzc9zDkxi/407P08Ym6cNnv3MM11rH/HMz7POPMe8Z15x15lnfnGPqzhCGU9wEQ0ACKbTAwAA6QllPMHXuQEAsn///lh/PbF48WIZPXq0lJSUSE1NjTz77LMHrf/LX/5STjjhBCkpKZFTTz1VHn300R7NFwAApCMf44l84CIaAJBzy5Ytk9mzZ0tdXZ08//zzMnbsWJkyZYps2bJFrf/MM8/IxRdfLF/84hflhRdekGnTpsm0adPkL3/5S45bDgAAQlewP+Il/4ABA9JuC94j1+9Ea+9PWO9U7Ny5M6vMeo+K96qBeDo6OlKbdnNzs1RVVYlIz4/VA91IU1OTVFZWRvpMTU2NnHHGGXLnnXeKyDvnu+rqarnqqqvkhhtuyKo/ffp0aW1tlf/6r//KlP3TP/2TjBs3TpYsWdKjdiM/tPFEEu88xn1XNdfvRHuWTVs/nnetCwsLI8/LaldxcXFWWVFRkVpXO2dZYwStrrV+tXGR5/1ez3St9eDZHzzvsmty/e593PfL03rf23N+SOvY9EzXoo3JPVkBnv3JWmdpPunN13giX3gSDQAQkZ5/BeuA5ubmbn979+5V59Pe3i7PPfec1NbWZsr69esntbW1snbtWvUza9eu7VZfRGTKlClmfQAAkB+H+le5RRzBYmk+CQEA5EdRUZGMHDlS6uvrY02nvLxcqquru5XV1dXJLbfcklV327Zt0tnZKSNGjOhWPmLECHnllVfU6dfX16v147Ybucd4AgAOPUmNJ0aOHGl+y6U3IZ0bAAJWUlIib775prS3t8eazv79+7O+vqV9/RMAABx6khpPFBUVSUlJSUKtSg8X0QAQuJKSkpx2WEOHDpXCwkJpaGjoVt7Q0CAjR45UPzNy5EhXfQAAkFu5Hk/kE+9EAwByqqioSMaPHy+rVq3KlHV1dcmqVatk0qRJ6mcmTZrUrb6IyMqVK836AAAAaeFJNAAg52bPni2XXnqpTJgwQSZOnCiLFi2S1tZWmTlzpoiIzJgxQ0aNGiULFiwQEZFrrrlGzj33XLn99tvlE5/4hDz88MPypz/9Se699958LgYAAAgQF9EAgJybPn26bN26VebOnSv19fUybtw4WbFiRSY8bOPGjd1++uPMM8+Uhx56SG666Sa58cYb5dhjj5Xf/OY3csopp+RrEQAAQKAi/040AAAAAACh451oAAAAAAAi4iIaAAAAAICIuIgGAAAAACAiLqIBAAAAAIiIi2gAAAAAACLiIhoAAAAAgIi4iAYAAAAAICIuogEAAAAAiIiLaAAAAAAAIuIiGgAAAACAiLiIBgAAAAAgIi6iAQAAAACIiItoAAAAAAAi4iIaAAAAAICIuIgGAAAAACAiLqIBAAAAAIiIi2gAAAAAACLiIhoAAAB9zujRo+Wyyy7r8Wc/+clPJtsgAMHgIhqJeeCBB6SgoED+9Kc/5bspvcqyZcvk85//vBx77LFSUFAg5513Xr6bBABAr/J+Y4jzzjtPTjnllBy3qndav369fP3rX5czzzxTSkpKpKCgQDZs2JDvZgFB6Z/vBgCHurvvvluee+45OeOMM2T79u35bg4AAIeE9evXS79+4T0PWrt2rfzHf/yHnHTSSXLiiSfKunXr8t0kIDjhnXkAEdm/f7/s3r07J/P62c9+Jk1NTfLEE0/IEUcckZN5AgBwqCsuLpYBAwbkuxkZra2tOZnPBRdcII2NjfLiiy/K5z73uZzME0B3XEQj51544QX52Mc+JpWVlVJeXi4f+chH5I9//GPm3xsbG6WwsFD+4z/+I1O2bds26devnxx22GGyf//+TPlXv/pVGTly5PvO88C7T48//rhMmDBBSktL5Z577snM79prr5Xq6mopLi6WD37wg3LbbbdJV1dXt2k8/PDDMn78eKmoqJDKyko59dRT5d///d/fd97V1dVB3ikHACBN2jvRf/7zn+Xcc8+V0tJSOfLII2XevHny4x//2PzK89NPPy0TJ06UkpISOfroo+WnP/1ppHnfcsstUlBQIC+99JJccsklMnjwYDn77LMz//7ggw/K+PHjpbS0VIYMGSKf/exnZdOmTd2m8dprr8mnP/1pGTlypJSUlMiRRx4pn/3sZ6Wpqemg8x4yZIhUVFREaieAdPB1buTUX//6V5k8ebJUVlbKddddJwMGDJB77rlHzjvvPPnDH/4gNTU1MmjQIDnllFPkqaeekquvvlpE3unkCgoKZMeOHfLSSy/JySefLCIia9askcmTJ0ea9/r16+Xiiy+WL3/5y3LFFVfI8ccfL21tbXLuuefK5s2b5ctf/rIcddRR8swzz8icOXPk7bfflkWLFomIyMqVK+Xiiy+Wj3zkI3LbbbeJiMjLL78s//M//yPXXHNN8isKAIAANTU1ybZt27LKOzo63vezmzdvlg9/+MNSUFAgc+bMkYEDB8qPfvQjKS4uVuu//vrrcuGFF8oXv/hFufTSS2Xp0qVy2WWXyfjx4zPjjPfzmc98Ro499liZP39+5ib/d77zHbn55pvloosukssvv1y2bt0qd9xxh5xzzjnywgsvyKBBg6S9vV2mTJkie/fulauuukpGjhwpmzdvlv/6r/+SxsZGqaqqijR/APnBRTRy6qabbpKOjg55+umn5eijjxYRkRkzZsjxxx8v1113nfzhD38QEZHJkyfLI488kvncmjVr5Oyzz5ZXXnlF1qxZIyeffHLmgvpLX/pSpHm//vrrsmLFCpkyZUqmbN68efK3v/1NXnjhBTn22GNFROTLX/6yHHHEEfL9739f/u3f/k2qq6tl+fLlUllZKY8//rgUFhYmtToAAMC71NbWmv/2fhe2t912m+zcuVOef/55GTdunIiIzJw5M9O/v9f69evlqaeeytyMv+iii6S6ulp+/OMfyw9+8INI7R07dqw89NBDmf9/6623pK6uTubNmyc33nhjpvxTn/qUnH766XLXXXfJjTfeKC+99JK8+eab8stf/lIuvPDCTL25c+dGmi+A/OI7psiZzs5O+f3vfy/Tpk3LXECLiBx++OFyySWXyNNPPy3Nzc0i8s5FdENDg6xfv15E3rmIPuecc2Ty5MmyZs0aEXnn6fT+/fsjP4keM2ZMtwtoEZFf/vKXMnnyZBk8eLBs27Yt81dbWyudnZ3y1FNPiYjIoEGDpLW1VVauXBl7PQAAAN3ixYtl5cqVWX+nnXba+352xYoVMmnSpMwFtMg7X3223hs+6aSTuo0hhg0bJscff7y88cYbkdv7la98pdv//+pXv5Kuri656KKLuo0rRo4cKccee6w8+eSTIiKZJ82PP/64tLW1RZ4fgN6BJ9HIma1bt0pbW5scf/zxWf924oknSldXl2zatElOPvnkTKe2Zs0aOfLII+WFF16QefPmybBhwzJ3h9esWSOVlZUyduxYERFpaWmRlpaWzDQLCwtl2LBhmf8fM2ZM1nxfe+01+fOf/9yt3rtt2bJFRESuvPJK+cUvfiEf+9jHZNSoUXL++efLRRddJP/yL//Sw7UBAADea+LEiTJhwoSs8gM3uw/mrbfekkmTJmWVf/CDH1TrH3XUUep8du7cKSLv3PzfunVrt38fMmSIFBUVZf7/vWOL1157Tfbv328+/T4QhDZmzBiZPXu2LFy4UH7+85/L5MmT5YILLpDPf/7zfJUb6AO4iEavdMQRR8iYMWPkqaeektGjR8v+/ftl0qRJMmzYMLnmmmvkrbfekjVr1siZZ56ZCe36wQ9+ILfeemtmGh/4wAe6hYiUlpZmzaerq0s++tGPynXXXae247jjjhMRkeHDh8u6devk8ccfl8cee0wee+wx+fGPfywzZsyQn/zkJwkuOQAAyAXr9awD7zZv2rQp6yL5ySeflPPOOy/z/+8dW3R1dUlBQYE89thj6vTLy8sz/3377bfLZZddJr/97W/l97//vVx99dWyYMEC+eMf/yhHHnlkTxcLQA5wEY2cGTZsmJSVlWW+ov1ur7zyivTr10+qq6szZZMnT5annnpKxowZI+PGjZOKigoZO3asVFVVyYoVK+T555/vdtE8Y8aMbsmY2kXzex1zzDHS0tJy0HewDigqKpKpU6fK1KlTpaurS6688kq555575OabbzbvcgMAgNz4wAc+IK+//npWuVYWxciRI7Ne4zrw7TfLMcccI/v375cxY8ZkbsQfzKmnniqnnnqq3HTTTfLMM8/IWWedJUuWLJF58+b1qM0AcoN3opEzhYWFcv7558tvf/vbbk+IGxoa5KGHHpKzzz5bKisrM+WTJ0+WDRs2yLJlyzJf7+7Xr5+ceeaZsnDhQuno6Oj2LtPRRx8ttbW1mb+zzjrrfdt00UUXydq1a+Xxxx/P+rfGxkbZt2+fiIhs376927/169cv837W3r17o68EAACQiilTpsjatWtl3bp1mbIdO3bIz3/+8x5Nr6SkpNu4ora2VgYPHnzQz3zqU5+SwsJCufXWW7v9JKfIO0+4D4wnmpubM2OMA0499VTp168f4wqgD+BJNBK3dOlSWbFiRVb5NddcI/PmzZOVK1fK2WefLVdeeaX0799f7rnnHtm7d69873vf61b/wAXy+vXrZf78+Znyc845Rx577DEpLi6WM844I1Zb/+///b/yu9/9Tj75yU9mftaitbVVXnzxRXnkkUdkw4YNMnToULn88stlx44d8s///M9y5JFHyltvvSV33HGHjBs3Tk488cSDzuOpp57KBJRt3bpVWltbM3eYzznnHDnnnHNiLQMAABC57rrr5MEHH5SPfvSjctVVV2V+4uqoo46SHTt2SEFBQeptOOaYY2TevHkyZ84c2bBhg0ybNk0qKirkzTfflF//+tfypS99Sb7xjW/IE088IbNmzZLPfOYzctxxx8m+ffvkZz/7mRQWFsqnP/3pg86jqalJ7rjjDhER+Z//+R8REbnzzjtl0KBBMmjQIJk1a1bqywmEjotoJO7uu+9Wyy+77DI5+eSTZc2aNTJnzhxZsGCBdHV1SU1NjTz44INSU1PTrf7xxx8vw4cPly1btnT7mvaBi+uJEyeav/0YVVlZmfzhD3+Q+fPnyy9/+Uv56U9/KpWVlXLcccfJrbfemgn3+PznPy/33nuv3HXXXdLY2CgjR46U6dOnyy233JJ5J9vyxBNPdPvauYjIzTffLCIidXV1XEQDAJCA6upqefLJJ+Xqq6+W+fPny7Bhw+RrX/uaDBw4UK6++mopKSnJSTtuuOEGOe644+SHP/xhpv+vrq6W888/Xy644AIReedr4VOmTJH//M//lM2bN0tZWZmMHTtWHnvsMfmnf/qng05/586dmXHEAbfffruIvPOVdi6igfQV7H/vd00AAACAQ8S1114r99xzj7S0tJhhYgDgwTvRAAAAOCTs3r272/9v375dfvazn8nZZ5/NBTSAxPB1bgAAABwSJk2aJOedd56ceOKJ0tDQIPfff780Nzdnff0ZAOLgIhoAAACHhI9//OPyyCOPyL333isFBQXyoQ99SO6//37yRwAkiq9zAwBy7qmnnpKpU6fKEUccIQUFBfKb3/zmfT+zevVq+dCHPiTFxcXywQ9+UB544IHU2wmgb5k/f768+uqr0tbWJq2trbJmzRqpra3Nd7MApCRf4wkuogEAOdfa2ipjx46VxYsXR6r/5ptvyic+8Qn58Ic/LOvWrZNrr71WLr/8cvU33gEAQBjyNZ4gnRsAkFcFBQXy61//WqZNm2bWuf7662X58uXyl7/8JVP22c9+VhobG9XfpQcAAGHJ5XiCd6IBIHB79uyR9vb2WNPYv3+/FBQUdCsrLi6O/VvuB6xduzbrK5lTpkyRa6+9NpHpAwCAeEIaT0S+iB4wYIBrwsCh5L0H8wGDBw/OKrO+3NHZ2ZlV1tjYqNbt1y//b1poy1FaWqrWraioyCqzlqGrqyvSvKxpWNP11O3fP/r9w46OjqwybRlE9OXYt2+fWlfbH3bu3Bm5DUnZs2ePjBkzRurr62NNp7y8XFpaWrqV1dXVyS233BJrugfU19fLiBEjupWNGDFCmpubZffu3ea+id5HGwhZx1Tcc6F17vbU1Y5rz5f4rGXztEH7aSarDdr8rDZo8/OcN61zkzZdq73aNKxztNY2a7paG6z1q52PrZ/D0so969fi2dc9+6SnDRrPsnnqeo43T9us65W4bYi7Hq1pePp3a5/UxhnWetyzZ0/k+XmFNp7gSTQABKy9vV3q6+tl06ZNUllZ2aNpNDc3S3V1ddY0krprDAAAerfQxhNcRAMApKKiQv1GQRQH7nhXVlb2uON8PyNHjpSGhoZuZQ0NDVJZWclTaAAAeolQxhNcRAMAZP/+/a6v0b33s2mbNGmSPProo93KVq5cKZMmTUp93gAAIJpQxhNcRAMRWAe19g7roEGDXNPIJc97alrdtrY2ta72Lld5eXnsNkT9vNUG6726kpKSrDLrvSTP+0qe9wu1d/DyKdedXktLi7z++uuZ/3/zzTdl3bp1MmTIEDnqqKNkzpw5snnzZvnpT38qIiJf+cpX5M4775TrrrtO/vVf/1WeeOIJ+cUvfiHLly/vUZvRu1jHtec9RE/egsZzrHreX/XkOHh41pk1L8/68bx3qc3Pam9RUVHkNsR9v9w673q2hTYNz7u1Sbwj76nr2caed4G1dZbEu/ee9961+VnvDcflOd4869x6h9szRvC8750LoYwn8p9eBAAIzp/+9Cc5/fTT5fTTTxcRkdmzZ8vpp58uc+fOFRGRt99+WzZu3JipP2bMGFm+fLmsXLlSxo4dK7fffrv86Ec/kilTpuSl/QAAIP/yNZ6I/DvRpHMD2bQ7fdaTaO0uflNTk1o3rXTuuHdMrbvM2lNn60m0Z7raevAkpibxJFpLsvQ8SbDuJmvzy0c6d3Nzs1RVVcn27dtjBYEcdthh0tTUlNo7TDh0aAExnieaFs9x6ZlX3CfR3vnF5Xkir5Unsc49TwPTSjr3PCn1PDnXxP3GhIi+zpJ4mpjLJ9HWr1HE/aUNz5Noz69vWDzrIe64yuL5xoPnqbX1jcIkhDae4OvcAIBe/w4TAADo/UIZT/B1bgAAAAAAIuJJNAAgmDvHAAAgPaGMJ7iIBmLQDvYdO3ZE/nwS78RpbbB+lF57X1t751dEpLGxMavMes954MCBkdol4nvP2VNXW2ZrPZSVlWWVtbe3q3W1d42sd5Q971H1NqF0euidrHOhJ2FZOzdY+6Y2jSRSk7VppJWwHPf9YGsanvenPe+JWtstbrJwEttNK/dMN4l3wLX5WW3Qcoo8+3oSae2efV1bP3EzDET095+TSPr35EB5tptW7jkuklhnuRDKeIKLaABAMJ0eAABITyjjCd6JBgAAAAAgIp5EAwCCuXMMAADSE8p4gotoAEAwnR4AAEhPKOMJLqKBhPXmICmtbSUlJWrdoUOHRp6udtKzglY84T9ae7UgERE9RKyyslKtqwWk7d27V62rBXns3r1brauFtO3bt0+t29uE0ukh/+KGF3mCeJIIb9TOWdZxrdX1hJB5Ap88dT3r3GqvNj9PgJIV1qStyyT6BG1bWKGQmiTWb9wgKE9YmCfUyzqGtGW2tps2Dc968BzHFk/YnbafWPuONi4qKipS62rL1tbWptbVxghJ7Du9LdQ0lPEE70QDAAAAABART6IBAMHcOQYAAOkJZTzBRTQAIJhODwAApCeU8QRf5wYAAAAAICKeRAMAgrlzDAAA0hPKeIKLaCBHPMmmcVknofb29qwyK5V6165dWWWlpaVq3YqKiqwyT2qrlSKppYJaqd9lZWVZZQMHDlTrWqndUVnJvFrypichM4kk4Z4KpdND/mnHhCeh1krUjfp5Efs8EnUa1nHtORbiHu+eBOskpquxliFuwrKH1QZtu1nbPe4yW5/3/BpFWvuDVu4Ze1h1PevBsy08YyVtP0vi2NTGE4cffrhaV0vtbmhoUOtu2bIlq8z6tQ9tbGbtI56E+1wIZTzBRTQAIJhODwAApCeU8QTvRAMAAAAAEBFPogEAwdw5BgAA6QllPMFFNAAgmE4PAACkJ5TxROSL6FyGIgF9mRWWoYVcNTY2qnU9ASNaiIYWICYism3btqwyKyysvLw8q6ylpUWtqwX9aMEcIr7zhrYuOzs71bodHR2Rp6utXyuMRFsOTyiRNV2tvKqqKvJ009CXOi8cWqzjRDvWrLpxxySeY9VT13NcJXEMaudIT/CVtWyefskTZnUorF+LZ515+jBP36jV1UI7RfT1k8T61dZD3HGOdxra/metM226hx12mFp3yJAhWWVayKiIyPbt2w/WxG48x4W2ja0A1FwJYTzBO9EAAAAAAETE17kBAMF8/QoAAKQnlPEEF9EAgGA6PQAAkJ5QxhN8nRsAAAAAgIh4Eg0ACObOMQAASE8o44nIF9GDBg3KKrNS5qz0OCAEVtqjlsTtSZb08CQ47t27N/J0Kyoq1PKSkpKssiQSTLU0TStxcvfu3VllnnOUtc527dqVVWYtW9wU03ymaYbS6aF3SiJxWzu/WfumJz1aq5tEez3T9fCc3zy085N17vakaGt1renGTY/28EzX+sUGra/wrDMP6/OedaZNw9qnPcnuHlrb4qZwi/j2SS1du76+Xq2rjRGamprUutox5NkWnvWb1vgyilDGEzyJBgAE0+kBAID0hDKe4J1oAAAAAAAi4kk0ACCYO8cAACA9oYwnuIgGAATT6QEAgPSEMp6IfBHd0dGRVUaAGBBdLkMerGNTK7fCrMrKyrLKSktL1bra+cFiBbBoPMFiWhCItR62bNmSVWZtHy2wzBMa4gkaam5ujlw3aaF0euhbPGFA2rHmOe96whA9gWXWOc+zbJ7pasvsma7F039obUsiHCoua3k9bfOMfT0Bdp6+UdtXkxiTa22z1plnG2vTsI63uGOEJLZxa2trVtnGjRsjf769vV0t9xzHnn1Hk89rtFDGE7wTDQAAAABARHydGwAQzJ1jAACQnlDGE1xEAwCC6fQAAEB6QhlPcBENAAim0wMAAOkJZTzBO9EAAAAAAEQU+Um0lkaby7RhJMe6y0Paet+kbc/i4mK1blFRUVaZlQitHd/WPqIlY1vKy8uzyjzJsVaip5ammURdLXncc6fUSi7XpkGaJkJlpTxr+1b//tG/RGftm9rx7kkW9iQWWzwp2tr6iZsSbZVb042b5G0lC2vrwdoW2jSSSDeOm5RurTNtOaw2eNLlPds+btq19XmtrtUuT6q0J3E77v7r+VWOvXv3Rp6uRTt3WetGO0dZqd+e/TcXQhlP8HVuAEAwnR4AAEhPKOMJHiUDAAAAABART6IBAMHcOQYAAOkJZTzBRTQAIJhODwAApCeU8UTki2hCxPombWccMGCAWlcLN/CEQcAvblCLiH5sWsE72va06mrhZFb4jxaAYe1nHp7gku3bt2eVWQFrAwcOjDxdDy30wwpu84Sc5EIonR56J+v4ixue5QnS9ISFecKLPMtg1dXaYIUhxpXrZfP0S55zZNyAKkvc/SyJ87w2DWt/8ASsaf22VdfTh3kCy+IGzXnOJVbwZ9yQN2udaWMoT/ihJ+Qtn0IZT3BlDAAAAABARHydGwAQzJ1jAACQnlDGE1xEAwBEpG91XgAAoHcKYTzBRTQAIJg7xwAAID2hjCd4JxoAAAAAgIh4En2I0+7oWInFe/fuzSqz0h7jJhl7kiwPFdqyWQnW2vqxUiSLioqyykpKStS6u3btyirTEqVFRFpaWtRyjbbveFIkrbpa26yEzNLS0qwyaxm0baEldlus/VRbv0mkDudCKHeO0Tt5jhNPerRFO49Yvz7gSaX2pH7nMuXZSu/1JCF71oNn2azzf1SeZbN4kqbjppRbPPuOJz3ak2CtsaarjQ89x6C13bS2eZLHPb9k4mmbJw0/iWNT4zmX5HOMEcp4gotoAEAwnR4AAEhPKOMJvs4NAAAAAEBEPIkGAARz5xgAAKQnlPEEF9EAgGA6PQAAkJ5QxhNcRB/itHADLfxIxBfg4qEdEFoYloge7NKXDqiD0bbFoEGD1Lp79uzJKrMCwLRQCitQS5uuFeqi1bWC0LSwut27d6t1PWF32nKUlZWpdcvLy7PKrHAP6xjQaIFjWpCaiC8YprcJpdND/nmCjrRj2BNIZNGm6wlDtOalTSOJUCStbhLt1dZDWsGfnuCrJKbrOS/FPXd7Ap/SaoMnzMqzzqy6ngAwTxu0/cFaD55gPI+4fVoSfaK2HGmF/SYtlPEE70QDAAAAABART6IBAMHcOQYAAOkJZTzBRTQAIJhODwAApCeU8QQX0QCAYDo9AACQnlDGE7wTDQAAAABARDyJDlASyYUe2l2liooKtW5jY2OkzyfRhlynGWpt0BKwRXxt6+joiFQm4kuk1RKzS0pKIrfLSsDWkq2ttGtPUqhWt39//RSnpYy3traqdbVtYSWP92Wh3DlG72Sd8zxpv56UZ21+1vlC+9UISxLpxBrtPO1J8raWTWOtR0/qd9R2edugzc9T17ONPdvSmq4ncduTRK+Jm3Iuoi9z3NR7a7pJpMBr0/AkpVvb2HNsppXAHned5Xqs/26hjCd4Eg0AyHR6Pf3ricWLF8vo0aOlpKREampq5Nlnnz1o/UWLFsnxxx8vpaWlUl1dLV//+tfNG1EAACD38jGeyAcuogEAObds2TKZPXu21NXVyfPPPy9jx46VKVOmyJYtW9T6Dz30kNxwww1SV1cnL7/8stx///2ybNkyufHGG3PccgAAEDouogEAOb9zvHDhQrniiitk5syZctJJJ8mSJUukrKxMli5dqtZ/5pln5KyzzpJLLrlERo8eLeeff75cfPHF7/v0GgAA5A5PogEAwchlp9fe3i7PPfec1NbWZsr69esntbW1snbtWvUzZ555pjz33HOZi+Y33nhDHn30Ufn4xz/e84UGAACJCuX1MILFkBdWMFNad6C0QCyrDWmFMWghES0tLWpdLdTLCtvwhHBowSOlpaVq3crKyqwyTwiHFW6mLbMnjMSihZNZ4WbafmYFrGmBY/kM7EhLEkEgzc3N3cqLi4vVfXnbtm3S2dkpI0aM6FY+YsQIeeWVV9R5XHLJJbJt2zY5++yzZf/+/bJv3z75yle+wte5+yDtWPOEASURdOQJGfIcF9r8PEFSlriBWp71a03XU1dbNmt5Pe2N2i6LtY3j7pPWevBM1xNgZ/VXGm1+nnl5tptnGkmMaTxjBE8ImUfc/dfalp79rLfJdbDYgdfDlixZIjU1NbJo0SKZMmWKrF+/XoYPH55V/8DrYUuXLpUzzzxTXn31VbnsssukoKBAFi5cGHm+h95IEACQF9XV1VJVVZX5W7BgQWLTXr16tcyfP1/uuusuef755+VXv/qVLF++XL797W8nNg8AANC35Ov1MJ5EAwASuXO8adOmbt9g0J5Ci4gMHTpUCgsLpaGhoVt5Q0ODjBw5Uv3MzTffLF/4whfk8ssvFxGRU089VVpbW+VLX/qSfPOb3zwkvx0AAEBfk8tvth14PWzOnDmZsiivhz344IPy7LPPysSJEzOvh33hC19wtZVRBwAgkXeYKisru/1ZF9FFRUUyfvx4WbVqVaasq6tLVq1aJZMmTVI/09bWlnWhfOBrcH0piAQAgENZEuOJqN9sO9jrYfX19epnLrnkEvnWt74lZ599tgwYMECOOeYYOe+889yvh/EkGgCQ83eYZs+eLZdeeqlMmDBBJk6cKIsWLZLW1laZOXOmiIjMmDFDRo0alek4p06dKgsXLpTTTz9dampq5PXXX5ebb75Zpk6d6no/EAAApCeX32zriXe/HnZgPHHNNdfIt7/9bbn55psjT4eLaABAzk2fPl22bt0qc+fOlfr6ehk3bpysWLEiczd548aN3Z4833TTTVJQUCA33XSTbN68WYYNGyZTp06V73znO/laBAAAkIID32h7P/l8PYyLaCTGuuukpT+3tbWpdT2pjB5RDsQDtJTnJHgSHLX1Y60bT3qtVm6laGvJ1lZ7tem2t7dHrmtNV0tVLyoqUuu+9/2Zg003iXTfQ1GuvxY9a9YsmTVrlvpvq1ev7vb//fv3l7q6Oqmrq8tBy5AmT8KsJ904bmKxdV7o3z97qJREGnNaSbueJG9PGzyJ2555ae21pqtNw9M3WttC66+s/UnbHzxJ3pa4qeqe6WrLIOJLu9Z4fmnDs+95UuDT6t890/VsH896SCu5PA25Gk+8+/WwadOmicj/vh5mjS+Sej2Mi2gAQM6/zg0AAA49obwexkU0AAAAAKDPydfrYVxEAwB4Eg0AAGLLx3giH6+HcRENAOAiGgAAxBbKeCLyRbQnzCGtcCj0btaOP3DgwKyyffv2qXXTCkJobW3NKisvL1fr7tmzJ6ss1/u0FvqhhWyJ6OvSCkfzBItZwWBRec4P1r6jhX5Yy+Y5RyFbKJ0eeqckAvw8gVoeWtus6Wrn0yRCIbW6VhviBl9Z28JzPo0bkmW1wRPcFrcN1vKmNU7RljmtYDFPiJ7Fs0965qWt3wEDBkSerrVsWts829Kzn6UVSJpEoGEuhDKeCCd2FgAAAACAmPg6NwAgmDvHAAAgPaGMJ7iIBgAE0+kBAID0hDKe4CIaABBMpwcAANITyniCd6IBAAAAAIgo8pPosrKyrDIrAa+trS1yXRw6rG2sJWMnkZjq0dLSklVmpTKGtq/2hpR9a15aUronyRvRhXLnGPmnnf+t49eTmuxJyfUkC1u/YBB1ukmcYz3T1XgSt6314DnOtbpagrA1P6sNnmX27GeeVGpPorMnaVpbZ9Y619al1a644y1r3Wjr0mqDZ/+NmzRtLa9Wbu2T2nLETacXid9XWp/3LFsuhDKe4OvcAIBgOj0AAJCeUMYTXEQDAILp9AAAQHpCGU/w/UcAAAAAACLiSTQAIJg7xwAAID2hjCciX0QPHDgwq0wLjBIJL5gJ7/CEQ+V6H9ECMLQAPJH8hjEcoIVEaOFoIvq6PFSOwUN52XqbUDo95J8nxEnjCe3xhHp5Aoms6XpCnLR+yROo5QkZSiKEzLNsWts84WZWe7XAJ6vP9qwzz76jLYcnAMziCQvzzEtbjn379ql1teXwhIVZ4gbYpRU05zk/eJbXE1ybRKiiZ53lQijjCb7ODQAAAABARHydGwAQzJ1jAACQnlDGE1xEAwCC6fQAAEB6QhlPcBENAAim0wMAAOkJZTzBO9EAAAAAAEQU+Um0lgyspS6LkJ7bm8RNT0xCb9gftJTC8vJyta6W2u1Zj1bSYlxpTdejN+xPSEcod46Rf9r+kut9SOsTPAnLVvKtdp72nAuTaIMnGVvjma7Fs8xpJTdr68xKpY76eUsSSchaorNnPSRxDHmSx+PuU550+STGYHGTsS3adD3p3EmcHzxJ/7kQyniCr3MDAILp9AAAQHpCGU9wEQ0AEJG+1XkBAIDeKYTxRP6/HwoAAAAAQB/Bk2gAQDBfvwIAAOkJZTwR+SJaCxEjOKj3sHa6srKyrLKOjg61rlae1jb2BB4kEYiihYiVlJSodXfv3p1VVllZqdbVQkq0YDKRvne8aOuyuLhYrast2969eyPXRf6F0ukh/zzhRR7aNKyAn7jTtWjnN+vzngAwra4VfBW3vdY52hOaFjfoyDNdi2f9erabtt6t9npCsjzbLe6yWeK217NPWtONG0rn4dkne0PArOe4yGe/HMp4gq9zAwAAAAAQEV/nBgAEc+cYAACkJ5TxBBfRAIBgOj0AAJCeUMYTXEQDAILp9AAAQHpCGU/wTjQAAAAAABFFfhJNom7fpKVHW2mEcVNFLdp0Bw4cqNbVEgZbWloit0FL4RbRU6WbmprUulp7tZRzEZH29vasstbW1sjT7Q2s/WHAgAFZZdb61dL7k0jeRO6EcucY+efpa7Tzk5UA7KFNwzoXagnfnjZ4EoutNHFtnXmSx/v314d7npRnbf3ETcu2pmu119N/aPOzPq/NTxs/WdO1eFLKPennaSVua9P1HBcWbdms9Rs3ndtql2df1dqWVuK2RZuftWzaWNSTPJ60UMYTfJ0bABBMpwcAANITyniCi2gAQDCdHgAASE8o4wneiQYAAAAAICKeRAMAgrlzDAAA0hPKeIKL6EOEFcSghQ1YdbXAAisATAudsqbb3NycVVZUVKTW1QJYrOlqoQmlpaVqXe2g1MLGRPQgs+3bt6t1NbkOn/DQ1oO1LbRt7wnIgR9BIAiBJ6hLOyaSOE60fdYKs/K0V6ubRJhn3EDGJEJCtWWzgo48gWXauvSEkGnjkYNNI+p0PfukJ8zKM0aIG6TmlVaQnyeUTltmaz149jMPz3rX5ufZfz0BbVa7tG2Uz7FoKOOJ3jvaBwAAAACgl+FJNAAgmDvHAAAgPaGMJ7iIBgAE0+kBAID0hDKe4CIaABBMpwcAANITyniCd6IBAAAAAIiIJ9GHOC3Jz0oC1NKYrXTu1tbWyNPV7ip5EkhLSkoiT9eipRRaSd5a3X379ql1tSRvK8kyrXTVuPOy2qslnu7evVutu3fv3qyy3pxS3ltVVVXlbd6h3DlG/nnStT3pu1rduOddEf1c6Enctupqy2G113M+9SSEazz9s1XXs2ye84fWX3mSsa02xE2E9vT7nukmkc6tlVv7gyfl2TNdz3EcN63as0+mlYbvOV57QxuSFsp4gotoAEAwnR4AAEhPKOMJLqIBAMF0egAAID2hjCf4viUAAAAAABHxJBoAEMydYwAAkJ5QxhN96iJaCyzozeFFaQVBxW2DFRZWVlaWVaYFiImI7NmzJ9K8RPTt5glP8YRlJBHmYAWOabTQMy1kS8QXQuYJLikuLo40LxHf+vGE0nV0dGSVpRWwdqjQtrG2LXMllE4P+ec5B8QNqPLMywqQ1M5lnvAi67zrGdNodT2hkJ5gMUvc/sMTZuWZrieYyTP28ASWWcvgGf+kNWbU6nq2pXVcxA316t9fvwTRpmFtC229JxFu5tkWWtvSCprzSOKY76lQxhO99woUAAAAAIBepk89iQYApKcv3QEGAAC9UwjjCS6iAQDBfP0KAACkJ5TxBBfRAIBgOj0AAJCeUMYTvBMNAAAAAEBEvfJJtJWWV1FRkVXW3Nys1s1lArB110RLbraWzUrB1sRN99OSO61ptLe3R56uRdtuRUVFal1t/VjJkHHb5UkKtbablqZspTJq02hqalLrauunsrJSretJNtX2M2sb79ixI6vMStMcNmxYpHmJ6MnuvTllPy3a8dbY2Jj7hvz/hXLnGPmn9UFW+q62b3l+CcLzCwjWdLU+yJPq60kstljrJ24bNHGTn0XibwtL3F/7sGjT8CQse5KQPfuOtWyeZOy452dP8rg1Rkirj/esd88vjngS+aPOy+JJE/fI57gqlPFEr7yIBgDkViidHgAASE8o4wkuogEAwXR6AAAgPaGMJ8L7DiUAAAAAAD3Ek2gAQDB3jgEAQHpCGU/0yoto6yX7tra2rLJcBohZrDZ0dHRklXlCyMrKytS6WmDT3r171bpasMCuXbvUuloAmFYmoge6acsrogdNeMIgPMFZnhAObZ2L6NvICp/QlsMKTdPKrbra/Kz1oJVb4XEaa5/UtqcWpGaVWwEjGmv/7Q3Hd1q0fcc6hnIhH53e4sWL5fvf/77U19fL2LFj5Y477pCJEyea9RsbG+Wb3/ym/OpXv5IdO3bIBz7wAVm0aJF8/OMf79H8kR+e/cUTjuMJ1PL0K57AJ+24tvo7TzhU3JAs63yshaZZbfCc06O2y+IJj7P6O22dJbHvecJd4waOeUKnPAFrVmCrZ7oaTwCeZ9msfS+t8C2tbUkcm56QQm2/9mw3z76XNC6iAQBIybJly2T27NmyZMkSqampkUWLFsmUKVNk/fr1Mnz48Kz67e3t8tGPflSGDx8ujzzyiIwaNUreeustGTRoUO4bDwAAgsZFNAAg53eOFy5cKFdccYXMnDlTRESWLFkiy5cvl6VLl8oNN9yQVX/p0qWyY8cOeeaZZzJ36EePHt2j9gIAgHSE8iSaYDEAQKbT6+mfR3t7uzz33HNSW1ubKevXr5/U1tbK2rVr1c/87ne/k0mTJsnXvvY1GTFihJxyyikyf/5819cHAQBAunI5nsgnnkQDABK5c/zerITi4mL1fflt27ZJZ2enjBgxolv5iBEj5JVXXlHn8cYbb8gTTzwhn/vc5+TRRx+V119/Xa688krp6OiQurq6HrUbAAAkiyfRAAA4VFdXS1VVVeZvwYIFiU27q6tLhg8fLvfee6+MHz9epk+fLt/85jdlyZIlic0DAAAgij71JLqvfW3Pk9JZWlqaVWalEZaXl0duQ3t7e1aZdZdHK7dSA7XUbk8Kqif1Uls3Vl2LlsRttUFLgdfWozUNT+prEinae/bsySrT0tNFfCmSWl1t3Yjo22LgwIGR6x7KrO2mBWJZ2y0XkrhzvGnTJqmsrMyUW2nuQ4cOlcLCQmloaOhW3tDQICNHjlQ/c/jhh8uAAQO6HV8nnnii1NfXS3t7u5l0j97H09dorIRabRqeX2yweFKTo37emoYn5dmqq/VBVnvjpp9bn9fa4Dm/eMYTnm1p9c+evtyTAq+N4zy/9mEtW9zxsDW+9LTBk+SttdezDJ7j2Do/ePYdrdw6hrTlsOpq6z3uuUik9z29DeXXPngSDQBI5B2mysrKbn/WRXRRUZGMHz9eVq1alSnr6uqSVatWyaRJk9TPnHXWWfL66693G5y8+uqrcvjhh3MBDQBAL5Hrd6IP/NpHXV2dPP/88zJ27FiZMmWKbNmyRa1/4Nc+NmzYII888oisX79e7rvvPhk1apRrvlxEAwBy3unNnj1b7rvvPvnJT34iL7/8snz1q1+V1tbWTFr3jBkzZM6cOZn6X/3qV2XHjh1yzTXXyKuvvirLly+X+fPny9e+9rXE1gEAAIgn1+OJd//ax0knnSRLliyRsrIyWbp0qVr/wK99/OY3v5GzzjpLRo8eLeeee66MHTvWNd8+9XVuAMChYfr06bJ161aZO3eu1NfXy7hx42TFihWZsLGNGzd2++padXW1PP744/L1r39dTjvtNBk1apRcc801cv311+drEQAAQAqiBpUe+LWPd9909/zax29/+1sZNmyYXHLJJXL99de7XsnkIhoAkJd3mGbNmiWzZs1S/2316tVZZZMmTZI//vGPPZoXAABIXxLjierq6m7ldXV1csstt2TVz+evfXARnWNWKEBra2tWmXU3RAsx6OjoUOtqgQVJBBBo4QiegBErUEubhlVXa4P1DqYWLGaFWmhtaGlpUeta6z0qT6iFNS8tjCqt8C4rLEPbp6x1tnfv3qyy0MLGROxgl3wJ5Scp0DtZ5+O4+5b1ee1c5ulzPQE/Vhu0814SQUdptcETpuYJnfKEj0b9vDUNqw3a+dgz3bjr5mBt02jz84TGWu31jBnjhut5QsiSGCN42ut6GqnsO9a8tHJP8KBnG+ezX85lUGlPvPvXPgoLC2X8+PGyefNm+f73v89FNADAh4toAAAQVxLjiQMBpe8nn7/2QbAYAAAAAKBPyeevfXARDQDIeZomAAA49ITyax98nRsAICJ8LRsAAMSXy/FEvn7tg4toAAAAAECflI9f++AiupfQkgetdONc8qRTWgmDnrpayqGWrC0iUlVVlVVWVlam1tXK9+zZo9ZtampSyzVtbW1ZZe3t7ZE/71lnnlTGtNKurfbu3r07lfkdynbu3JnvJnRDsBhyRfulAauv8ZzLPAnWnqRez3Rzfe7V7Nu3L3Jdbb1bSemaJI59zzS09WD90oFW13rfUfsVEGu62jSsdebZFp5f+9D2P8928xxvae3rnrT2JKYbNxnbWg9ae602eFK0tfl50vs954ykhTKe4CIaABBMpwcAANITyniCi2gAQDCdHgAASE8o4wnSuQEAAAAAiIgn0QCAYO4cAwCA9IQynoh8Ee1ZqLSCNQ4F1nrU1llv2JE8wVdJBB6UlpZmlQ0aNEite9hhh2WVjRw5Uq07ZMiQrLLm5ma1bkNDQ1bZ22+/rdbVlsOarhYwYq0zz/r1BFVokjhe44b0eHiOoVzT2mbtv54Au1wIpdND/mlBPFaYkLZvWQFK2jnAE1Jknce0Nlh1PeduD0/QkTa/JNqQ1rlbK7e2mxb25RmnaGMMEZHy8vKsssGDB0dugxUg1tramlVmrUetrhU2pq2fJALAPPuZJ6jL0y5tf/BsY89xbLXBE9Kmzc/TJ3rOZx4Ei6WPJ9EAgGA6PQAAkJ5QxhO8Ew0AAAAAQEQ8iQYABHPnGAAApCeU8QQX0QCAYDo9AACQnlDGE3ydGwAAAACAiCI/idbSja27BTt37swq6w3JuWmx1oOWAulJZcxnsl5PWMumJVmWlJSodSsqKrLKtBRuEZGjjjoqq+yUU05R644aNSqr7K233lLrau21aImc2ra06noSzYuKitS62rFpTXfv3r1ZZVZ7tf3P2tcHDhyYVWYlZLa1tUWalzWNqqoqte6ePXuyytrb29W6uTwfebZxPu/AhnLnGPnn+UUBT3KzZ7qe87E2vwEDBqh1tWkkkfrtOWdpda0EYO3c60k0t9rlSW7W5mf1w1pdqw2eX/sYMWJEVtmRRx4ZebrWry1s3rw5q0zrA0VEdu/enVXmObda+6S2r1tp4p79LO6viHjm5Tk/eI5jz3Q9+6+1ftNaD9o0rDbkQijjCb7ODQAIptMDAADpCWU8wUU0ACCYTg8AAKQnlPFE3/q+MAAAAAAAecSTaABAMHeOAQBAekIZT0S+iD6Ug8FySQtgEtF3Gi1kQiS9bRE37MUKXdBCxKyQrLKysqwyLRRMRGTs2LFZZePHj1frHnHEEVll1rbQAlGsIJCGhoasMis0TQt28YR7aKFgIvp+YoWnxN13PKEWVhs01r7uaUNlZWVWmRX20tHRkVWWxHGlTaOxsTH2dHMhlE4PvZMVqKWVe4KvrH1T6688bbBCnDzBQZ7wxrg8bfBOQ+MJAIsbZlVcXKzW1cYTVjDlBz/4wayympoata42vz//+c9qXS1o1xr/aGFq1v6g7ZOe9WgFt2njFM90rWMorf3MCsyL2wbPOcYT4OWZbtxzn7UtciGU8QRPogEAwXR6AAAgPaGMJ3gnGgAAAACAiHgSDQAI5s4xAABITyjjCS6iAQDBdHoAACA9oYwn+Do3AAAAAAARRX4SrSXgWamzoSV5W8u7Z8+eSGXe6cZlJRTu2rUrq8xKAtRSL63pasthJVhric7V1dVq3ZNPPjmrTEvYtOZ3/PHHq3W3bduWVfbqq6+qdbWUTk9apKW9vT2rzEqa1ta7ti1F7ETZqKxt3NraGqnMYu3rcZNuvfMLWSh3jtE7Wcek1gdZqbPaNKzpxk03ttrgORY87fWkJmvlnvOj51cYPHU953mrH9X2B8/Yw0rGHj58eFbZ6aefrtbVxhNbtmxR67700ktZZZ59xxqDedaZxnMMedrr2Sc96dGe9ZDE/hs39TuJRH5tGla7etuYJpTxBF/nBgCISN/qvAAAQO8UwniCi2gAQDB3jgEAQHpCGU/wTjQAAAAAABHxJBoAEMydYwAAkJ5QxhORL6K1ELHe9iJ7yKyAhqqqqqyy0tJSte727dsjz69//+xdRyuzyisqKtS6WmDH7t271bpbt26NVCYiMnjw4KyyN998U6379ttvZ5VZQV1a+b59+9S6mrghNFa5FkxmlXsCZ3LNE5rW3Nwcebra/mBNN4mguL4glE4P+af1CUmcC7VpeEKnrH7UEwbkCUvSpusJULLmpZVb5zGtDVb4VtR2WW3wbDdrPWjlVlCpNtax2tvW1pZVtmHDhshtsMYeLS0tWWWefcTi2caeY8tzDGlt8MwriXCzuGGAnnVmjXHjBgQmEYqq7ZP5HLuEMp7gSTQAIJhODwAApCeU8UTvfQQFAAAAAEAvw5NoAEAwd44BAEB6QhlPcBENAAim0wMAAOkJZTzB17kBAAAAAIgo8pNokrh7Dy1hsLKyUq1bVlYWebpDhgyJXFdLE7QSBrXUQCtpsaOjI6tsx44dat0///nPWWXWHSwtDfyll15S67744otZZX//+9/Vuq2trVllVjq3Vm6lJ2opkFrSuoieSm1ti1ze4UvinBE3VdRaZ1bya8hCuXOM/It7XHvOLda8tHOkJ/nWMz/PLyt4UoittGCtH/Usg9WHaW2w+hpP4rDWNs90i4uL1bpaare2bkT0JO69e/eqdbV+W/tVDxG9f7Z+PSNuardnnVvHhbbtk0hg1+p6kvMtWt20+qMkziVx2+tZN/kc54QynuDr3ACAYDo9AACQnlDGE1xEAwCC6fQAAEB6QhlP8E40AAAAAAAR8SQaABDMnWMAAJCeUMYTXEQnIIkAgbg7jSf4wcMT/JDEjq9No6WlRa3b0NCQVWaFhmgBLFZYmBYw0tjYqNbVQjisUBZtW1h1tcAyK+Rk2LBhWWW7d+9W6+7atSurzBNGYu1PAwcOzCqzQu08wTtae631oC2HFupiCT08MZROD/nnCRnynC+0uta+6enDtPbGDUezpuvpcz1BaFbIkGfZPMvsCZ3yBItpda3pDhgw4GBN7Gbr1q1ZZVaoqTbO0Poqq3zPnj1qXW08YI1pNNY6i7uNPSFvnuPNE3zlOT8kcRxr8/OMpz3L5jmfWWG0nu2WC6GMJ7iIBgAE0+kBAID0hDKe4J1oAAAAAAAi4kk0ACCYO8cAACA9oYwnuIgGAATT6QEAgPSEMp7gIhoAEEynBwAA0hPKeCKoi2hPiraVwqdt3JKSErVueXl5VpmVNK2lKXvSgq32ppVGqCVGWqmBxcXFkafb1taWVWalHHqSvLX1u3PnTrWuJ01Tm661Hjzp3FobSktL1bq5lERybNzjzSP0xG2gN9LO6Z4+zJPcnNYvYiTB0z97zpueulp/lcS5W6ubRHq0Nvaw+n3tVyMqKirUulpfrI1HRHxjBO3XJNrb29W61tghal1ru2l1rfUb9xjypGhb+472aypW0rpnncVNl/ckbnt4toW1frX1EPfXefD+grqIBgDoQrlzDAAA0hPKeIKLaABAMJ0eAABITyjjCX7iCgAAAACAiHgSDQAQkb51BxgAAPROIYwn8n4Rrb0kbwUFeEKCtI1nBRNowVfNzc2R22DtKFrYhRVUYYUFxKWtS084ghVMoAVjWMugrUstOELEF1yiBYxoZSJ6e7VwEBE9IMSarqe9npAHT7hHWrT2akEtIiJlZWVZZZ5tvH37drWuFsqS1rESulC+foX884QBaeeRJAKUNNa5xTNO8YQtxWX15dq52+p/tLZZ61ebRhJ1NVZdT5/Q1NSUVVZUVKTW1fYzK/hTa4MnWMwT8mbtZ3ED4ZI4hrTyuOFd1jQ8dT3ttdaD55jV6iYRaOhZD55tnAuhjCfyfhENAMi/UDo9AACQnlDGEzzSAQAAAAAgIp5EAwCCuXMMAADSE8p4gotoAEAwnR4AAEhPKOMJvs4NAMh0ej3964nFixfL6NGjpaSkRGpqauTZZ5+N9LmHH35YCgoKZNq0aT2aLwAASEc+xhP5kLMn0VZKXHl5eVaZlmot4ks31uanJTRb5Z5kPiuVUZNESqc2DSv1W0ugrqioUOuWlpZmlXnSHj1phFpipYhIY2NjVllJSYla19pPNFq6trXddu/enVWWRDKkNg1rf6isrIzULhF9/00i4V6ra01XSzH1rAdP4iQODcuWLZPZs2fLkiVLpKamRhYtWiRTpkyR9evXy/Dhw83PbdiwQb7xjW/I5MmTc9haJEk7j1jnAO3XMyyeX+XQzlnW+c2TYB318webX9RpeBLCPX25xdPfacnhniRvz1jJM56wfmlDG/9YY0Zt37Hqam2zfrlCW2ZrPXh+GcTzax/aNJJImtbaa6XLa9O1ktK17ZnEvq6td6sNcRPCPecHaxniJqWjZ3gSDQDI+Z3jhQsXyhVXXCEzZ86Uk046SZYsWSJlZWWydOlS8zOdnZ3yuc99Tm699VY5+uij4ywuAABIQShPormIBgAk0uk1Nzd3+7OeELW3t8tzzz0ntbW1mbJ+/fpJbW2trF271mzjt771LRk+fLh88YtfTHbhAQBAIriIBgDAobq6WqqqqjJ/CxYsUOtt27ZNOjs7ZcSIEd3KR4wYIfX19epnnn76abn//vvlvvvuS7zdAAAAHqRzAwASSdPctGlTt/f5Pe+zHsyuXbvkC1/4gtx3330ydOjQRKYJAACSF0o6d84uoq2V0tzcnFWmhSqJiDQ1NWWVxQ1KSkJa07XCBjwBI1rbrEAurW5ZWVnkuh7W1zw1VgCYZ4CuTcNaZ1bwiEZbDuvznjA2LQzHWufacWGJe7xY280Trhd1XgcrR/KS6PQqKyvN8/e7DR06VAoLC6WhoaFbeUNDg4wcOTKr/t/+9jfZsGGDTJ06NVN2IACnf//+sn79ejnmmGN61HbknnZcW8e6dp62Aom08riBpN5paKxAIu087wkbs9obN4TMOg9o07VCsrT5eaZrrXNtXXrClqz+ubW1VS3XaNOw+kZtPVghZJ4AsLjhrhbPMaQtm7WvFxUVRZ6uJ3wrbtid1QbPMe+p69nGnqA5bZ/K5/gplItovs4NAMjpO0xFRUUyfvx4WbVqVaasq6tLVq1aJZMmTcqqf8IJJ8iLL74o69aty/xdcMEF8uEPf1jWrVsn1dXVsZcfAADExzvRAACkZPbs2XLffffJT37yE3n55Zflq1/9qrS2tsrMmTNFRGTGjBkyZ84cEXnnZ+5OOeWUbn+DBg2SiooKOeWUU9SnHAAAIAyLFy+W0aNHS0lJidTU1Mizzz4b6XMPP/ywFBQUyLRp09zz5J1oAEDOv341ffp02bp1q8ydO1fq6+tl3LhxsmLFikzY2MaNG12/FQsAAPIv1+OJZcuWyezZs2XJkiVSU1MjixYtkilTpsj69etl+PDh5uc2bNgg3/jGN2Ty5Mk9aisX0QCAvLzDNGvWLJk1a5b6b6tXrz7oZx944IEezRMAAKQn1+OJhQsXyhVXXJH5JtuSJUtk+fLlsnTpUrnhhhvUz3R2dsrnPvc5ufXWW2XNmjXS2Njoni+3+QEAwbzDBAAA0pPEeKK5ubnbnxXe197eLs8995zU1tZmyvr16ye1tbWydu1as43f+ta3ZPjw4fLFL36xx8sZ+Um0NkhKK/nNuhtwKH+1T0v3GzhwoFq3pKQkq0xLORfxJfZ5ElM9tCRLT5qmlcTqGbjHTaW20jTjplJbtGXTklxFRCoqKrLKrP0h7vbMdYp2Ls87AHLDk/Iclyft2upTPNPQlsPTV3nGOZ5UX0/yuNUGrS/29OUWT2qylv5sjRG0utZ0tXGKtU9q07X6Z21dWgnhnm2hrXervdq2iJs4b7XB2hba/JJIpdba4NnGnjGN1Ya455gkUsrj/jJBb/TewNC6ujq55ZZbsupt27ZNOjs7M6+CHTBixAh55ZVX1Gk//fTTcv/998u6detitZGvcwMAgvlJCgAAkJ4kxhObNm3q9pOZnp+1PZhdu3bJF77wBbnvvvtk6NChsabFRTQAgItoAAAQWxLjicrKym4X0ZahQ4dKYWGhNDQ0dCtvaGiQkSNHZtX/29/+Jhs2bJCpU6dmyg48te/fv7+sX79ejjnmmEhtPXS/Hw0AAAAAOCQVFRXJ+PHjZdWqVZmyrq4uWbVqlUyaNCmr/gknnCAvvviirFu3LvN3wQUXyIc//GFZt25d1tfID4Yn0QAAnkQDAIDYcj2emD17tlx66aUyYcIEmThxoixatEhaW1szad0zZsyQUaNGyYIFC6SkpEROOeWUbp8fNGiQiEhW+fuJfBGthSZo4QpJOJQDxCxaiIEVVtDU1JRVZgVfaesyiTASjTVdrXzXrl1qXW0/8wR2WGFspaWlWWUtLS1qXa08iYuEuNOw9gdPWIZHLkO9rHWjbXtrPWghGn1tPeQTF9HIFS0Ep6ioSK2r7VvWOUAbk1jjCc9x7Qmz8oh73KQVDuVhhWR5wuM828ITvuUJq9P2P+urpFpdK2RUG09YY2fP9tTWmZVerK0Hz3a32qUdh57jzaK1zVpnnlAvT2CZxrMMSfSJnusCbX5phTVGkevxxPTp02Xr1q0yd+5cqa+vl3HjxsmKFSsyYWMbN25M5dqSJ9EAABHhYhgAAMSX6/HErFmzZNasWeq/rV69+qCffeCBB3o0z/Ae+QIAAAAA0EM8iQYA8HVuAAAQWyjjCS6iAQDBdHoAACA9oYwnuIgGAATT6QEAgPSEMp6IfBFdVVWVVbZt2za1bm9Iro2bBJhrWht2794d+fOe1Dlrea2UTU3cnbytrU0tLy8vzyrzpHNbaZplZWVZZVZyoZYCaSVvaqz1G3edWdtHW5ee5FirrrZ+rIRMrTyJZHdtu1lJoa2trZGn62mDlsRqrYe+dPIH8kU7p1vnIe1Ys44/rU+wpqul3FrJt9q50OpztflZ5yxP0rQnCVljnY+1cus8prXBWjZPsrAnYVnjaa+1P2hjj2OPPVatW1FRkVW2ceNGta6WmO1ZZxatrrVscfulJKarHUPW57Vls9aZJ4Fa+xUbT7K755dtPInm1vrVpmFNV2uDNXZGcngSDQAI5s4xAABITyjjCS6iAQDBdHoAACA9oYwn+IkrAAAAAAAi4kk0ACCYO8cAACA9oYwnIl9E94bwLY3VruLi4qwy64V8LWygNyxvWm2wpqsFmVkBYB7aej/ssMPUuiUlJVllVvCDVm7V1QIWtH1ERA+UsPYd7WDX9idrfkmcLOIGeFnrTNsWVtidFfQTlbUMWiiL1i4RPZzDCmPzhOloITKNjY1q3b508n+vUDo95J92PrXOm3HPLdbntYCfJIKOPCFDGs+xZJ27Pe2Ne+xa/ahnu2nLYQVGaed0T7CYZzyhhUpabfOEx3nCR6316JmuVm7V1eZnbQvPdLVya1vEDdTyHMeeID/PdD37pFVXG7emdd5JWijjCZ5EAwCC6fQAAEB6QhlP8E40AAAAAAAR8SQaABDMnWMAAJCeUMYTXEQDAILp9AAAQHpCGU9wEQ0ACKbTAwAA6QllPBH5Irq1tTXNdvSYlQSopSpaSb255Nk5cp0Q7klaTIsnlVFLkWxqaoo8Xatuc3NzVplnu3nSuS1pbQttGlZ7tWRsT/Jm3HaJiOzZsyerzEoIj7vOPKn1fekkD/Q2cVNjk0jJ9aT6epJv4yYLW7SkXu0cbc3PSljWftXAaq+2frW0YS9PWrtWV1sGEV97tX7/r3/9q1pXW5daXyWiL4e1ftNKhI46L4u1n3r2HU+auDa28/Tl1ljf84su2naz1q92vaEdr9Z0PcvmSTRH+ngSDQAI5s4xAABITyjjCS6iAQDBdHoAACA9oYwn+IkrAAAAAAAi4kk0ACCYO8cAACA9oYwnIl9Eay/6l5SUqHWtsIs0WC/Ta6FRnlCkJALAtGlY4R4abZ175ToYLGobrBAOLajCWoa2trasMmt/aGxszCqz1q8WSmG1QQuPqKqqUutqQR5We7U2eMJePNIKC0tCLsPurOlqoYqeQKC+IpROD/mnnQutcCit3Dr+tGPYmq7WBmu6nmCxqJ8X0Y8b61jyBGpp/ZLnPG+FTnnaoPEEM3n6RitISuvjPfuONU7ReLabh2e7WXU9gWWekDdtGp6QN2vdeI55bRt79kmLtk9a68wzbvW0TVs/nqA5K9wsF0IZT/AkGgAQTKcHAADSE8p44tB7nAIAAAAAQEp4Eg0ACObOMQAASE8o4wkuogEAwXR6AAAgPaGMJ7iIBgCISN/qvAAAQO8Uwngi8kV0ZWVlVpmWjiyir7hcJ/160nM9CYNauZVGriXjVVRURG5Dc3Nz5DZYKYeehO+0Eoe1bb979+7Ida19Z+DAgVll1vJaaaNRWSmdZWVlWWWetHarvbt27coq86ReIjmHYhI3kE9a32gNuLRyzzHp6QOtfsKT8qz1xVb/7Em71n7Zwzr3a22z1oM2PyvVV9sWVjK2VldLPBbR17u1bNp6sNaZlipt/UKKJ+VZq2vtO9q28PxKi8UzzvZczGht8/wyiKeuJ2nacxx79l+rDXGvIZLgSefW9tUkfuEHB8eTaABAMF+/AgAA6QllPMFFNAAgmE4PAACkJ5TxBBfRAIBgOj0AAJCeUMYTvOwHAAAAAEBEkZ9Et7a2ZpVZwWK9NYjHuruhBSlYAWDasnkCUSzFxcVZZeXl5WpdT7CYFqxh0epa043L2kesfUqjBXl49r3S0tLIbbCC0LRtrIWNeXkC1jSefdKaLoFlYQnlzjHyTwuj8pyzkggD0vp9TziUJ9TL4gk11eZnhXppda12aWFfnqAua51py+YZT1jt9QTNaeMqK2xJK7fGCJ6+0ROa5un3PaFeUT8vom97az/zjIe1utZ2i3sMedaD1d64YbQWzzrzhAlq7U1rGaIIZTzB17kBAMF0egAAID2hjCd65yNjAAAAAAB6IZ5EAwCCuXMMAADSE8p4gotoAEAwnR4AAEhPKOMJLqIBAMF0egAAID2hjCciX0Rr6cS9NYU7CUkkFmuJhrt27VLrDhgwIKuspKREraulaVq0dEpLY2NjVpmV7pdWcrO2T1lJi1obtBR5a7rWgerZ17W6VqKnRktftKaxd+/e2NPV9gcreVNLYiWxG0Bc2jndc26xzm/adD0JwFZ/6fnlCi3d2OprPHW19aONG0T0ZbMSt63yqG2w1q+2fjyDY0+ysCcROom0a63cs09a0/WkMXuSvLX1YKWUe9LPo87rYG3TaMebZz/1HEOefdJaBm17etpr7evaNKw2ePYdJIcn0QCAYO4cAwCA9IQynuAiGgAQTKcHAADSE8p4gotoAEAwnR4AAEhPKOOJQ/elZgAAAAAAEhb5SbR2Z6CvvbRutVcLWGhqalLrai/6ayEIIr4wB20a1nQrKiqyyqywMW2Z29ra1Lpa23K9jbX9bODAgWrdsrKyrDIr3EMLHNuzZ49a1xOi4Qn38Mwr7p04K9SivLw8q6ylpUWtqwWZ9bVjHtGFcucY+ec5x3rCt7TpWqE9nrBJbRrWudATiKWx2qCNB6xgMa0vt8KstPl56lo820KbrtWHebabJ2jOM7aL2w8mEVzrCcnS9gdr/XpC/zxj3Lhhgh6eYz6JMVhay6a1wVq/mnyO10IZT/B1bgBAMJ0eAABITyjjCb7ODQAAAABARDyJBgAEc+cYAACkJ5TxBBfRAIBgOj0AAJCeUMYTXEQDAILp9AAAQHpCGU9EvojWEqG1xOO+SEuws9L9tPK4ac4ielKilcKnpSlbqdTa/LTUZZHcpid6WEmh2nqIm26ZJm05du3apdbVUkE9KZLWsml1+9IJC8ChyZN2bZ2ztGlYKcQdHR1ZZVY/qqVgexKArbqe9GhtmT3jFGv9xm2DtX618Ys1pvEksGvLYbVXG+tY4wltulZdrb2e8YS13bTpesaXnuTmJBLuPfPzjHG19W61N+6YxjNW8hzzngR2T3ututr6jftLAXh/PIkGAARz5xgAAKQnlPEEF9EAABHpW50XAADonUIYT/ATVwCAzJ3jnv71xOLFi2X06NFSUlIiNTU18uyzz5p177vvPpk8ebIMHjxYBg8eLLW1tQetDwAAci8f44l84CIaAJBzy5Ytk9mzZ0tdXZ08//zzMnbsWJkyZYps2bJFrb969Wq5+OKL5cknn5S1a9dKdXW1nH/++bJ58+YctxwAAISuYH/ES/5Ro0ZllTU0NOgT7QVhVIeyuHdp+tr2SeKuVFrLrE1XC6ER0ZfDCnnzhIlEbZeISHl5eaR2iYg0Nzcn3i70jBaClJTm5mapqqqS008/3QwJej+dnZ3ywgsvSFNTk1RWVkb6TE1NjZxxxhly5513isg7YTLV1dVy1VVXyQ033BBpnoMHD5Y777xTZsyY0aN2Iz9KSkqyyqxzlhY+5AnoTCLUyxM65Qk3ixuaVlRUpNbVzheec7cnLMwSd51ZdbXwN6td2vrt6Tnu3Tz9vrYtrG2sLZvVXm17WuMJT3CbJm6AmEj8MZi1P3hoy5zEMe8JHPaE/nlC9LS61nTb2trU8iTkazyRL7wTDQBIJAjkvTdeiouLpbi4OKt+e3u7PPfcczJnzpxMWb9+/aS2tlbWrl0baZ5tbW3S0dEhQ4YM6VGbAQBA8kIJFuOxEgAgEdXV1VJVVZX5W7BggVpv27Zt0tnZKSNGjOhWPmLECKmvr480r+uvv16OOOIIqa2tjd1uAAAAD55EAwASuXO8adOmbl+/0p5CJ+G73/2uPPzww7J69Wr1q8EAACA/QnkSzUU0ACCRTq+ysjLSO0xDhw6VwsLCrFyNhoYGGTly5EE/+4Mf/EC++93vyn//93/Laaed1qP2AgCAdIRyEc3XuQEAOf1JiqKiIhk/frysWrUqU9bV1SWrVq2SSZMmmZ/73ve+J9/+9rdlxYoVMmHChB4vKwAASEcoP3EV+Ul0Y2NjVpknbc9KtSPt16+vpWvH1ZuX15O4rUlr2azjrbW1NavMk/bYG1gnWK3ck3qJ3Jo9e7ZceumlMmHCBJk4caIsWrRIWltbZebMmSIiMmPGDBk1alTmverbbrtN5s6dKw899JCMHj068+50eXm5mjqPvsU6D2nlVl3tHGClc3sSi+OOUzzjH6u92jnLk9rvWWee9lp1PQNhz7lbm5+1HrRtbE3Xs4219u7Zs0etq7XXSlXXptve3q7W1drrScZOInFbm4Y1XU/StCftWlu/nvR+D097kziGPInb1nkD6eLr3ACAnH/9avr06bJ161aZO3eu1NfXy7hx42TFihWZsLGNGzd2G3Dcfffd0t7eLhdeeGG36dTV1cktt9zSo3YDAIBkhfJ1bi6iAQB56fRmzZols2bNUv9t9erV3f5/w4YNPZoHAADInVAuovkuNQAgmHeYAABAevIxnli8eLGMHj1aSkpKpKamRp599lmz7n333SeTJ0+WwYMHy+DBg6W2tvag9S1cRAMAAAAA+pxly5bJ7Nmzpa6uTp5//nkZO3asTJkyRbZs2aLWX716tVx88cXy5JNPytq1a6W6ulrOP/982bx5s2u+BfsjXvL375/9zW9PaM/AgQPVurt27Yo8XaAv00ItrN+4bW5ujvT5JFingN5wHGqBG9a5RPtNYu38ItL3Qjg8AUJezc3NUlVVJSeeeGKP97HOzk55+eWXpampKdJPXCFs2nnPCtfx7JPaucw6v3kCibTxj9VejbUMWtusYCZtGtZ5wRM6FTfczLN+LVpdT3s9YVbWdLU2WEFS2v5gbQtt/XhCzDxBUp7gK2sba9PwjAU8dbX1KKKHqQ0YMECt6zmOPcebJ9QrbiidZ515jjerXZ7wW698jSdqamrkjDPOkDvvvFNE3lkf1dXVctVVV8kNN9wQaZ6DBw+WO++8U2bMmBG5rbwTDQAI5h0mAACQniTGE+99mFRcXKw+LGlvb5fnnntO5syZkynr16+f1NbWytq1ayPNs62tTTo6OmTIkCGutvJ1bgAAAABAr1BdXS1VVVWZvwM/d/le27Ztk87OzswvexwwYsSIzE9hvp/rr79ejjjiCKmtrXW1kSfRAACeRAMAgNiSGE9s2rSp29e5tafQSfjud78rDz/8sKxevdp8xdLCRTQAgItoAAAQWxLjicrKykjvRA8dOlQKCwuloaGhW3lDQ4OMHDnyoJ/9wQ9+IN/97nflv//7v+W0005zt5WvcwMA+IkrAAAQWy7HE0VFRTJ+/HhZtWpVpqyrq0tWrVolkyZNMj/3ve99T7797W/LihUrZMKECT1azshPouOmx1lpcL0hARjIBS090UrI1O6+tbS0qHXjHkO9+RjU2maloGrr0pMQCyA3POcc7Rj2DLKSOL952uBJC9bOWZ5lsxKLo7YrCZ5fDrDWg5bi69luVgqwp0/QkoytbaEts2e7Wf2+J8nbk0SvlVvrTCtPor2eZHetDVaKtjYN67jQ2lBUVKTW9YzXPMe8Vtf6mrK1zBpPQvihaPbs2XLppZfKhAkTZOLEibJo0SJpbW2VmTNniojIjBkzZNSoUZn3qm+77TaZO3euPPTQQzJ69OjMu9Pl5eVSXl4eeb58nRsAwNe5AQBAbLkeT0yfPl22bt0qc+fOlfr6ehk3bpysWLEiEza2cePGbjca7r77bmlvb5cLL7yw23Tq6urklltuiTxfLqIBAFxEAwCA2PIxnpg1a5bMmjVL/bfVq1d3+/8NGzb0aB7vxUU0AICLaAAAEFso44mwv0QPAAAAAIBDzp5Et7e3q+W9OdToUJDLABZPYEeItG2xZ88etW7//nxJRMQXLBb187D1pTvA6Lu0PsE6rrVj2NOnWPt03LDUJMLCPOd5Txs8QUee6WrttbaFFoqU622hlXvm5dnGVl1tPSSxfuMG0FnjNa3cc7wlsZ9p5VYQmrZ+rQAwz3S1beEJQrPOZ9q69ASIWaFp2nbzTDcNIYwnGKkDAIL5+hUAAEhPKOMJLqIBAMF0egAAID2hjCf4ni0AAAAAABHxJBoAEMydYwAAkJ5QxhNcRAMAgun0AABAekIZT+TsIpqU3HRZSYsVFRVZZVZSulbuSVqsrKxU67a1tWWVWe3ta+ImWVo8iZ6hYT0AfZunr9GSbz39h3U+9iTqav2d1QZPqq/Gaq82PyuFWEvwtdrgWZdaXWu62nJY7dWma6Uma6wU4rh9cRJ9jbY/eJbNEjeB3dpuWrlnn7T2J61tnu3mSdG2eI5DT7q8Z/161pnG2hb8Ck5+8CQaABDMnWMAAJCeUMYTXEQDAILp9AAAQHpCGU9wEQ0ACKbTAwAA6QllPMGX6AEAAAAAiIgn0YcIK1Rg9+7dWWXWXR5PiIY2DS3MxDvd3spahkGDBmWVNTU1uaaBQ0daQXO5EMqdY+SfFjBlBQR5QhY9x5onDCjq5y1WKJInqEtbP54+xRPiZAUdacFrnmAnz3qweJbZs2zaPukJALOmGzdEzNoftOl6gq8s2jrzjBk984o75hTRx53Wvp5WqJfWNmu7e7abtk96jmPPsZm0UMYTXEQDAILp9AAAQHpCGU9wEQ0ACKbTAwAA6QllPNE3vmcIAAAAAEAvwJNoAEAwd44BAEB6QhlPcBENAAim0wMAAOkJZTzBRXQf5EkV1cqTSInWkgCtVGpP+mFvZa3flpaWrDJSuA991v4wePDgrLLm5mbXNIBDXVFRUeS6nvOpVteTNO1J/bbqehK3taReLZHXmq4nqTeJXwnQ2mutX08ytqdtnmR3TRJJ0566Wrm1jTWedG9rn9TK9+7dq9YtKSnJKvO015NgbdHqaserNT9Pwr1nu1n7b9wxubXOPPvZoTDO7ou4iAYABHPnGAAApCeU8QQX0QCAYDo9AACQnlDGE1xEAwCC6fQAAEB6QhlP8BNX+P+xd/dxXtV1/v+fw8BcATMwDMwojqKm5gWBYRAqatvklGXLbheoFcqmlYpi1JqkgpVhbcXSKoWWdO1N0rWLXRVjKVJX+rkpfsv1OkUInYEBZgaGiwFmfn/4Zb6Nn9cLzptzzuczw3ncb7e53fTN+3PO+1y+3+ecz3l+AAAAAAAR8SS6D/OCAgYPHpxTVlFRYdbdunVrTpkXKBE3ECuLwQZZXGb4x4p1vPWXu6pZuXOMwrPOmyHhOiGhXl5dK2TIO5+HhDtZIUwhgWVeG6wQMW+dhYQtWYFNIdP16oYErFnlXliYtS286VrrLIlws5D1MGjQoMhtsNrrhXpZ0/WC5kL2SWsaXt2QoLmQkCyLtz9Y+29I+JbXXmt+IcFtnpCgOWuf8s5F1nJ4YWz5kJXxBBfRAIDMdHoAACA9WRlPcBENAJDUvzovAADQN2VhPME70QAAAAAARMSTaABArLvGWbjjDAAADiwr4wkuogEAmen0AABAerIynuAiug/z0h537tyZU+Ylbls7Y9wUbgA20tqBA7P6pZBE3ZBEaI/VD1qJx17bQhKLvfTdkPUQN904iYFpSIJ1SIq2Ve4lTYckmkf9vGSvX6+u1TYvNbmzszOnLCQROkTINg5ZDyGp394xFHcbeceQtX5DjuOQbeyx1lkS2zhkrM64vjC4iAYAZObOMQAASE9WxhNcRAMAMtPpAQCA9GRlPMFFNAAgM50eAABIT1bGE/zEFQAAAAAAEfEkuh8KCSMBgCiycucYhRcSUBU3UCvE7t27zXKrvV5AUNxgsZB1k0SQYUlJSeS61nJ4AUzW+vHWWUgwU0jIW0hYmDVdrw3WNLw2hGzjkPZG/bxkL0fIOvO2W9yxqLfvWG3wgttKS0sjT9eaRsh2ixv85s3POz9YvPUQso3zISvjCS6iAQCZ6fQAAEB6sjKe4CIaAJCZTg8AAKQnK+MJ3okGAAAAACAinkQDADJz5xgAAKQnK+MJLqIBAJnp9AAAQHqyMp7gIhqJ8Xb8QiYEAgD6lpDEbSu5dtCgQWZdK6HWS1iOm6K9a9cus27UeXnT9YQkQoewltlrV0jStJVknFaysLeNrXJv2dJKjA9J3LZSpb31YB0D3nRD9h2rPIltETLduPtOyPHmtSEkyTutVHVrGt45KiRVHclhDQMAMnPnGAAApCcr4wkuogEAmen0AABAerIynuAiGgCQmU4PAACkJyvjCX7iCgAAAACAiHgSjf3yQiKqqqpyyrwQg7a2tsjTBVAYWblzjMKzQnC8wJyQcJ2Q4KCQfdaahhdetHv37shtsJYtJGAtJEjK63OtgCqvrrVsXhus9eMtmzW/kGAmL2jOCuqyyrz5JRGWGhKoFRJQZe0PJSUlsdol2cvsHW/WuvTaYE0jJCTLqxsSQhY3iM/a/735efuZta967QoJxrMUsl/OyniCi2gAQGY6PQAAkJ6sjCf4OjcAQN3d3bH+DsaiRYs0ZswYlZWVadKkSXr88cf3W/+ee+7RW9/6VpWVlWns2LF64IEHDmq+AAAgHYUYTxQCF9EAgLxbunSpZs+erXnz5unJJ5/UuHHj1NjYqA0bNpj1H3vsMV144YX65Cc/qdWrV2vq1KmaOnWqnn766Ty3HAAAZF1Rd8RLfu+dExzaeCcaKDzvXawktLe39xzPB/vO2L5upK2tTZWVlZE+M2nSJL3jHe/QbbfdJumNc0J9fb2uuuoqXXfddTn1p02bpo6ODv3nf/5nT9k73/lOjR8/XosXLz6odqMwysvLc8q8dwhDhLwTHfLecMj7udax6r2jGTLdkM9b5d76tfrtJN6JLi0tzSlL4n3vkG0c8k60NcYNeU/fE/Kes9W2kHl57yNb0+0L70SH7JOekLp99Z1o73iztoVXN2TZOjs7I9cNVajxRKHwJBoAIOngv4K1T3t7e6+/Xbt2mfPp7OzUE088oYaGhp6yAQMGqKGhQatWrTI/s2rVql71JamxsdGtDwAACuNQ/yq3FBAsluaTEABAYZSUlKiurk5NTU2xpjNkyBDV19f3Kps3b55uuummnLotLS3au3evamtre5XX1tbqueeeM6ff1NRk1o/bbuTfjh07Ct0EAEDCkhpP1NXVBaXNFwrp3ACQYWVlZXrllVdif8Wru7s75+tb1lc6AQDAoSep8URJSYnKysoSalV6uIgGgIwrKyvLa4dVU1Oj4uJiNTc39ypvbm5WXV2d+Zm6urqg+gAAIL/yPZ4oJN6JBgDkVUlJiSZMmKAVK1b0lHV1dWnFihWaPHmy+ZnJkyf3qi9Jy5cvd+sDAACkhSfRAIC8mz17ti6++GKddtppmjhxohYuXKiOjg7NmDFDkjR9+nSNHj1at9xyiyRp1qxZOvvss/Wtb31L73//+3X33Xfrj3/8o+64445CLgYAAMggLqIBAHk3bdo0bdy4UXPnzlVTU5PGjx+vZcuW9YSHrV27ttfPu5x++um66667dMMNN+iLX/yijjvuOP3yl7/UKaecUqhFAAAAGRX5d6IBAAAAAMg63okGAAAAACAiLqIBAAAAAIiIi2gAAAAAACLiIhoAAAAAgIi4iAYAAAAAICIuogEAAAAAiIiLaAAAAAAAIuIiGgAAAACAiLiIBgAAAAAgIi6iAQAAAACIiItoAAAAAAAi4iIaAAAAAICIuIgGAAAAACAiLqIBAAAAAIiIi2gAAAAAACLiIhoAAAAAgIi4iAYAAAAAICIuogEAANDvjBkzRpdccslBf/YDH/hAsg0CkBlcRCMxP/zhD1VUVKQ//vGPhW5Kn7Fp0yZ94xvf0FlnnaWRI0dq2LBheuc736mlS5cWumkAAPQZBxpDnHPOOTrllFPy3Kq+6b777tO0adN0zDHHqKKiQieccII+97nPqbW1tdBNAzJjYKEbABzKVq1apeuvv17nnXeebrjhBg0cOFD//u//rgsuuEDPPPOMvvSlLxW6iQAA9EvPP/+8BgzI3vOgT33qUzr88MP18Y9/XEceeaT+/Oc/67bbbtMDDzygJ598UuXl5YVuInDI4yIamdTd3a2dO3em3tGcfPLJevHFF3XUUUf1lF1xxRVqaGjQ17/+dV177bUaPHhwqm0AAOBQVFpaWugm9NLR0ZGXPv3ee+/VOeec06tswoQJuvjii/Wzn/1Ml156aeptALIue7fvUHCrV6/W+973PlVWVmrIkCF697vfrT/84Q89/97a2qri4mL927/9W09ZS0uLBgwYoBEjRqi7u7un/PLLL1ddXd0B57nv3aeHHnpIp512msrLy3X77bf3zO+aa65RfX29SktL9Za3vEVf//rX1dXV1Wsad999tyZMmKChQ4eqsrJSY8eO1be//e39zvfoo4/udQEtSUVFRZo6dap27dqll19++YBtBwAAuax3ov/0pz/p7LPPVnl5uY444gjdfPPN+sEPfqCioiKtWbMmZxqPPvqoJk6cqLKyMh1zzDH68Y9/HGneN910k4qKivTMM8/ooosu0vDhw3XmmWf2/PtPf/pTTZgwQeXl5aqurtYFF1ygdevW9ZrGiy++qA996EOqq6tTWVmZjjjiCF1wwQVqa2vb77zffAEtSf/wD/8gSXr22WcjtR9APDyJRl797//+r6ZMmaLKykpde+21GjRokG6//Xadc845+v3vf69JkyZp2LBhOuWUU/Twww/r6quvlvRGJ1dUVKTNmzfrmWee0cknnyxJeuSRRzRlypRI837++ed14YUX6tOf/rQuu+wynXDCCdq+fbvOPvtsrV+/Xp/+9Kd15JFH6rHHHtOcOXP0+uuva+HChZKk5cuX68ILL9S73/1uff3rX5f0Rkf13//935o1a1bwemhqapIk1dTUBH8WAIBDVVtbm1paWnLKd+/efcDPrl+/Xu9617tUVFSkOXPmaPDgwfr+97/vPrF+6aWX9OEPf1if/OQndfHFF2vJkiW65JJLNGHChJ5xxoF85CMf0XHHHaf58+f33OT/6le/qhtvvFEf/ehHdemll2rjxo269dZbddZZZ2n16tUaNmyYOjs71djYqF27dumqq65SXV2d1q9fr//8z/9Ua2urqqqqIs1/H8YVQJ51Awn5wQ9+0C2p+3/+53/cOlOnTu0uKSnp/stf/tJT9tprr3UPHTq0+6yzzuopu/LKK7tra2t7/n/27NndZ511VveoUaO6v/vd73Z3d3d3b9q0qbuoqKj729/+9gHbdtRRR3VL6l62bFmv8q985SvdgwcP7n7hhRd6lV933XXdxcXF3WvXru3u7u7unjVrVndlZWX3nj17DjivA9m0aVP3qFGjuqdMmRJ7WgAAHAr2jSH293fyySf3+sxRRx3VffHFF/f8/1VXXdVdVFTUvXr16p6yTZs2dVdXV3dL6n7llVd6fVZS98MPP9xTtmHDhu7S0tLuz33ucwds77x587oldV944YW9ytesWdNdXFzc/dWvfrVX+Z///OfugQMH9pSvXr26W1L3Pffcc8B5RfHJT36yu7i4OGc8AyAdfJ0bebN371795je/0dSpU3XMMcf0lB922GG66KKL9Oijj6q9vV2SNGXKFDU3N+v555+X9MYT57POOktTpkzRI488IumNp9Pd3d2Rn0QfffTRamxs7FV2zz33aMqUKRo+fLhaWlp6/hoaGrR37149/PDDkqRhw4apo6NDy5cvj7UOurq69LGPfUytra269dZbY00LAIBDzaJFi7R8+fKcv7e97W0H/OyyZcs0efJkjR8/vqesurpaH/vYx8z6J510Uq8xxMiRI3XCCScEvWr1mc98ptf/33ffferq6tJHP/rRXuOKuro6HXfccfrd734nST1Pmh966CFt37498vwsd911l+6880597nOf03HHHRdrWgCi4evcyJuNGzdq+/btOuGEE3L+7cQTT1RXV5fWrVunk08+uadTe+SRR3TEEUdo9erVuvnmmzVy5Eh985vf7Pm3yspKjRs3TpK0bds2bdu2rWeaxcXFGjlyZM//H3300TnzffHFF/WnP/2pV72/tWHDBklvhIH9/Oc/1/ve9z6NHj1a5557rj760Y/qve99b9A6uOqqq7Rs2TL9+Mc/7mk3AAB4w8SJE3XaaafllO+72b0/r776qiZPnpxT/pa3vMWsf+SRR5rz2bJli6Q3bv5v3Lix179XV1erpKSk5//fPLZ48cUX1d3d7V7MDho0qOdzs2fP1oIFC/Szn/1MU6ZM0Qc/+EF9/OMfD/oq9yOPPKJPfvKTamxs1Fe/+tXInwMQDxfR6JMOP/xwHX300Xr44Yc1ZswYdXd3a/LkyRo5cqRmzZqlV199VY888ohOP/30np+3+OY3v9nrJ6OOOuqoXiEiVhJ3V1eX3vOe9+jaa68123H88cdLkkaNGqWnnnpKDz30kB588EE9+OCD+sEPfqDp06frRz/6UaRl+tKXvqTvfOc7+trXvqZPfOITUVcFAABIQXFxsVne/X/fbV63bl3ORfLvfve7XsFebx5bdHV1qaioSA8++KA5/SFDhvT897e+9S1dcskl+tWvfqXf/OY3uvrqq3XLLbfoD3/4g4444ogDtv///J//ow9+8IM65ZRTdO+992rgQIb1QL5wtCFvRo4cqYqKip6vaP+t5557TgMGDFB9fX1P2ZQpU/Twww/r6KOP1vjx4zV06FCNGzdOVVVVWrZsmZ588sleF83Tp0/vlYwZ5eerjj32WG3btk0NDQ0HrFtSUqLzzz9f559/vrq6unTFFVfo9ttv14033uje5d5n0aJFuummm3TNNdfoC1/4wgHnBQAAwhx11FF66aWXcsqtsijq6upyXuM60LfIjj32WHV3d+voo4/uuRG/P2PHjtXYsWN1ww036LHHHtMZZ5yhxYsX6+abb97v5/7yl7/ove99r0aNGqUHHnig18U5gPTxTjTypri4WOeee65+9atf9XpC3NzcrLvuuktnnnmmKisre8qnTJmiNWvWaOnSpT1f7x4wYIBOP/10LViwQLt37+71LtMxxxyjhoaGnr8zzjjjgG366Ec/qlWrVumhhx7K+bfW1lbt2bNHkrRp06Ze/zZgwICe97N27dq133ksXbpUV199tT72sY9pwYIFB2wTAAAI19jYqFWrVumpp57qKdu8ebN+9rOfHdT0ysrKeo0rGhoaNHz48P1+5h//8R9VXFysL33pS71+klN64wn3vvFEe3t7zxhjn7Fjx2rAgAEHHFc0NTXp3HPP1YABA/TQQw+5r6QBSA9PopG4JUuWaNmyZTnls2bN0s0336zly5frzDPP1BVXXKGBAwfq9ttv165du/Qv//Ivvervu0B+/vnnNX/+/J7ys846Sw8++KBKS0v1jne8I1Zb//mf/1m//vWv9YEPfKDnZy06Ojr05z//Wffee6/WrFmjmpoaXXrppdq8ebP+7u/+TkcccYReffVV3XrrrRo/frxOPPFEd/qPP/64pk+frhEjRujd7353Tkd++umn9wpZAwAAB+faa6/VT3/6U73nPe/RVVdd1fMTV0ceeaQ2b96soqKi1Ntw7LHH6uabb9acOXO0Zs0aTZ06VUOHDtUrr7yiX/ziF/rUpz6lz3/+8/rtb3+rmTNn6iMf+YiOP/547dmzRz/5yU9UXFysD33oQ/udx3vf+169/PLLuvbaa/Xoo4/q0Ucf7fm32tpavec970l7MYHM4yIaifvud79rll9yySU6+eST9cgjj2jOnDm65ZZb1NXVpUmTJumnP/2pJk2a1Kv+CSecoFGjRmnDhg29vqa97+J64sSJ7m8/RlVRUaHf//73mj9/vu655x79+Mc/VmVlpY4//nh96Utf6gn3+PjHP6477rhD3/nOd9Ta2qq6ujpNmzZNN910U8872ZZnnnlGnZ2d2rhxo/7pn/4p599/8IMfcBENAEAC6uvr9bvf/U5XX3215s+fr5EjR+rKK6/U4MGDdfXVV6usrCwv7bjuuut0/PHH61//9V97Xjurr6/Xueeeqw9+8IOS3vhaeGNjo/7jP/5D69evV0VFhcaNG6cHH3xQ73znO/c7/f/zf/6PJOU8fJCks88+m4toIA+Kut/8XRMAAADgEHHNNdfo9ttv17Zt29wwMQAIwTvRAAAAOCTs2LGj1/9v2rRJP/nJT3TmmWdyAQ0gMXydGwAAAIeEyZMn65xzztGJJ56o5uZm3XnnnWpvb9eNN95Y6KYBOIRwEQ0AAIBDwnnnnad7771Xd9xxh4qKivT2t79dd955p84666xCNw3AIYSvcwMA8u7hhx/W+eefr8MPP1xFRUX65S9/ecDPrFy5Um9/+9tVWlqqt7zlLfrhD3+YejsB9C/z58/XCy+8oO3bt6ujo0OPPPKIGhoaCt0sACkp1HiCi2gAQN51dHRo3LhxWrRoUaT6r7zyit7//vfrXe96l5566ildc801uvTSS83feAcAANlQqPEE6dwAgIIqKirSL37xC02dOtWt84UvfEH333+/nn766Z6yCy64QK2trebv0gMAgGzJ53iCd6IBION27typzs7OWNPo7u5WUVFRr7LS0tLYv+W+z6pVq3K+ktnY2KhrrrkmkekDAIB4sjSeiHwRXVJSEjThqKyfG6itrTXr7tq1K6dsy5YtZl1rGgMH2ovb1dWVU7Zt2zazrjW/AQPsb8VbD/mHDBli1q2qqsopa21tNet2dHTklL15Z0P/4O2TI0aMyCnz9rOQbR9St729Paesra3NrOu1DcmI2yHtz86dO3X00Uerqakp1nSGDBmSc96cN2+ebrrppljT3aepqSnnvF5bW6v29nbt2LFD5eXlicwH6bPOe1Y/LNnnLO88Zk0j5JznnccGDRoUqcybnzV2kaQ9e/bklHlfDgz50qA1rgqZblpt8OzduzenzNtuIdszZH+wli1kbBfSrpD1m8TYzjrevLGHtcxe/2NttxAh2yKJL81a80trW3jTDTmfhawHa7t5deNut/3J2niCJ9EAkGGdnZ1qamrSunXrVFlZeVDTaG9vV319fc40krprDAAA+rasjSe4iAYAaOjQoRo6dOhBfXbfHe/KysqD7jgPpK6uTs3Nzb3KmpubVVlZyVNoAAD6iKyMJ7iIBgCou7v7oL8yl498ysmTJ+uBBx7oVbZ8+XJNnjw59XkDAIBosjKeKPhFtPXd/Ndee82sa63YJN7VTuv9C+sdBet95v2VW0LeqQjR34La03oPPK31YLXXezdl8+bNOWXDhw8361r7mfcuV8j7/9b7z957bv1t37Fk8Rj6W/nu9LZt26aXXnqp5/9feeUVPfXUU6qurtaRRx6pOXPmaP369frxj38sSfrMZz6j2267Tddee63+6Z/+Sb/97W/185//XPfff/9BtRmFE/Kuatx3mkPetfbqWu8ue+0KeTcxLm+61nKEtKEvZKyEvFOaxPunlpB9xxOy3tPKFrHGGSHrzBun5Hu/jlo3rXet09rPkjg2vX21ULIyniANCACQd3/84x916qmn6tRTT5UkzZ49W6eeeqrmzp0rSXr99de1du3anvpHH3207r//fi1fvlzjxo3Tt771LX3/+99XY2NjQdoPAAAKr1Djici/E51WOneIkCfRNTU1OWVeGqF1l817OmclZoekJufzKWeo/vYU7VB4Eu2x9tUknkRby8aT6Df05WMozXTu9vZ2VVVVadOmTbGCQEaMGKG2trbU3mHCocM6PyXxJNqSxNNE67znpXNby7Z7926zbsgTwrSSei0hT/I8IU9VQ9KC87lsaSUsh7YtDSHj1rSeRCexztJK0Q7Rl7+5YUnzqXXWxhMF/zo3AKDw+vo7TAAAoO/LyniCr3MDAAAAABART6IBAJm5cwwAANKTlfFErIvokHeYkvgOftz3HLz3OkLeSwqR1o5grcvq6mqz7uDBg3PKWlpazLo7d+7MKevL73qMHDkyp8x7Zzdkuhs2bIhcN4TVNuvdfUnasWNHTpmXWm9t47KyMrOulfrtrbOQd9pC3lPrCydI6xjytoW1fl9//XWzrpXi219kpdND4YW8UxqSuB11XqGs+Xk5BSHjH6s8iYyVtN4pDTnPh2yjkOnGzZrpC4nSaUnrvdgkxvohx2E+84RC8mPSOt48cVO/00p7jyIr4wmeRAMAMtPpAQCA9GRlPME70QAAAAAARMSTaABAZu4cAwCA9GRlPMFFNAAgM50eAABIT1bGE6lcRI8YMSKnzAprkuKHfuzevdss94J/4rJCmPrCBk8iNC3utkgioCFkus3NzTllVtiYNw0vUMvafzdu3GjWjRvyYIW5SXYA2KBBg8y6VsCNF3oTErxmBYRUVVWZdYcNG5ZTZi2DJG3bti2nLN8Bdtb88h082NdkpdND/xK3/4gbSJpE3ZCgrpD5hfS5Xhv6QihXSMBaiJD1kNb4x+o/QsLjkgjqsiQx3bj7Tsj+6wkJIYsbWBay7+S7T4wbwJi0rIwneCcaAAAAAICI+Do3ACAzd44BAEB6sjKe4CIaAJCZTg8AAKQnK+MJvs4NAAAAAEBEPIkGAGTmzjEAAEhPVsYTsS6ivQW1kriTSOGzeGl5ISnEFq9dhUy728datvb2drNuW1tbTllIMmRIymFNTY1Z10pp3rNnj1nX4rXBWo6WlpbI0/XaayVxe/uT1baBA+3DasiQITllmzZtMuta8/P2vfLy8khl3vxC9gcvwdpKA/fq9gXWMre2tpp1t2zZEunz/V1WOj30TUn0uXGPy5AEa69d3vk/DSHHXRJpzNY0kvhlhbRSk/PJW79xE6zTWg99YZ2FHPNJJNGnJa3+ry8k5x+srIwneBINAMhMpwcAANKTlfHEofc4BQAAAACAlPAkGgCQmTvHAAAgPVkZT3ARDQDITKcHAADSk5XxRCoX0XFDIkJ4IR5WaJQX/GAFFmzfvt2s2xdChkKCFEICFkLCU0aMGBFrup6QUJa4wVVeCJkV6hWyDF5omhXyNmrUqMht89pglXvrJmR/sPbrjo4Os+62bdsifd4rz/dJM61jqL/rT50X+q+0gqRCgpnihjB55zerzwxpQxLttYT0HyFhk0m0wZpfyDYOqRvatqhCgtuSCKiN294kwn7jhnrlO5Ar7vGWVoieJ2Q/6YvjlCyMJ3gnGgAAAACAiPg6NwAgM1+/AgAA6cnKeIKLaABAZjo9AACQnqyMJ/g6NwAAAAAAEfEkGgCQmTvHAAAgPVkZT/Sri+iQ5M2QhMyQuv1N3BRUK6lakjZv3hx5uiFpzMOGDcsp81IHvXTtqJJI9IzLm1dlZWVOWWtrq1l3165dOWU7d+4064Zsi3ymwKPwstLpoX8J+fWLuOncSdSN+vlQcaeRRBpzPhOAvWTiuGniaSV2J7F+4043ie0Tsn7j/pKJJ+5y5Hs9xF3mfKfW50NWxhP96iIaAJCOrHR6AAAgPVkZT/BONAAAAAAAEfEkGgCQmTvHAAAgPVkZT3ARDQDITKcHAADSk5XxRJ+8iPZehh80aFBOmRd8ZW2Ezs7OyPPbu3dvUNsKzWuXFc5hrUevrhfuYc0vZN14AQ0bNmyIPY2+ytqnvOW11mUSASP96eSE/MpKp4e+Ka1zVki/lETduEFdaQVf5TsUMq0xQkgQbNzwOE/IsoW0Ia3zaEjgniWtcaA33bTEXb8h6yF0GlF587KOl0KOkbMynuhfVyEAAAAAABRQn3wSDQDIr6zcOQYAAOnJyniCi2gAQGY6PQAAkJ6sjCe4iAYAZKbTAwAA6cnKeIJ3ogEAAAAAiCjyk2grEa6kpMSsG5KMbfESt2tqanLKvBTtjo6OnLK2tjazrpVgF5KA1xfumnhtsNbl8OHDzbpbt27NKdu5c2e8hjm89va3xO246aohy9sX9jOE89LwrX0n5DyZtKzcOUbh5XN/SSu5OYlU65BfYbCEJFiHpAJ7043b34VIYrpxpxHyixghkviVF2t7JpEQHrdt3n4Wd2yX1n6WRMp+yC+khLQh5LxjjTMGDizcl42zMp7g69wAgMx0egAAID1ZGU/0r8d+AAAAAAAUEE+iAQCZuXMMAADSk5XxBBfRAIDMdHoAACA9WRlPxLqIHjJkiFluBQvs2rXLrBsSeGBNd/fu3WZdK0TMC/iJGyDgiRvCERJMsGfPHrN82LBhOWVeIFxIGEncoJW+zFoOb9+x9uu0wkjQ91nbuLq62qxr7SebN29OvE1RZaXTQ+HlM6AqiYAfq21phYUlEYQWIm7gUxIBa2mxxi9phaaFjJVCpBXUlcR2CwnUCgm2iztO7gv7ZBJj/bj7ZCH75ayMJ3gnGgAAAACAiPg6NwAgM3eOAQBAerIynuAiGgAgqX91XgAAoG/KwniCi2gAQGbuHAMAgPRkZTzBO9EAAAAAAEQU+Um0dWdg48aNkWdUXFwcebohyXpeOreVXJjv1EtrGl7Ks8VbtpDpWsvmpSRaqd0h28JLYI+7LvOd+m3Nb+TIkWZd6xjo7OxMvE3oH6x9Z8eOHZHrVlRUJN6mqLJy5xiFF7ffTysJ2RPS3r7ahiQSgK3yuCnR3nTznVicz23stcFal0nsT/lMww+RxJg8ZF8PSdmP+vkkJLFs1i/0hCSiJy0r4wm+zg0AyEynBwAA0pOV8QRf5wYAAAAAICKeRAMAMnPnGAAApCcr4wkuogEAmen0AABAerIynoh1ER0SKJHEy/sdHR05Za2trWZdKyRr4EB7cdMKgoobLLZz506z3JrG8OHDzbrWevBUV1fnlO3du9esay1be3u7WdebRlReMFNarGXbvn27Wbe8vDynzAtYy3dAGvLP2sbeOaqvyUqnh74p5PzYF8ImPVaYT0jQkTeuCjnG4oaFJTHdqJ8PFRKWGhLMFPXzoXXjjn2TmG7IcvSF6YbMKyQsLO7+l8SxGVLX2q9Drrvijr3jyMp4gneiAQAAAACIiK9zAwAyc+cYAACkJyvjCS6iAQCZ6fQAAEB6sjKe4CIaAJCZTg8AAKQnK+MJ3okGAAAAACCiPvkk2kuUs9KfvfTpysrKnDIvGbu5uTmnLCQBz2NNw0oYl+w7L6WlpWZdK4nbWzZrulaitCQVFxfnlHnJ5Xv27Mkpq6qqMuuGsNbZ5s2bzbrbtm3LKUsi/dNaZ5s2bYr8eWs9etPFoSUkbbevycqdYxReSMJyCOtYi5uGK4X1K3F/tcSbV1rrLCTBOq0k77R+6SVkf+gLSdMhrGXL9/4Qd35J7Gdx+9d8bvfQ+cVtWyH75ayMJ/rH6A4AkKp9nd7B/h2MRYsWacyYMSorK9OkSZP0+OOP77f+woULdcIJJ6i8vFz19fX67Gc/6/4UIAAAyL9CjCcKgYtoAEDeLV26VLNnz9a8efP05JNPaty4cWpsbNSGDRvM+nfddZeuu+46zZs3T88++6zuvPNOLV26VF/84hfz3HIAANCXFOKmPBfRAIC83zlesGCBLrvsMs2YMUMnnXSSFi9erIqKCi1ZssSs/9hjj+mMM87QRRddpDFjxujcc8/VhRdeeMCOEgAA5E++xxOFuinPRTQAIK+dXmdnp5544gk1NDT0lA0YMEANDQ1atWqV+ZnTTz9dTzzxRM9F88svv6wHHnhA55133sEvNAAASFRWbsr3yWCxkPCJkBCO7du3R64bIiQcwVs2K6ChoqLCrGuVewEPVvDa4MGDzbpWoJsX2mAFjnkhZNZ0vfWwY8eOnLKQwDJvG8d9xyKt4BOgr0giCOTN4Y+lpaVmQGJLS4v27t2r2traXuW1tbV67rnnzHlcdNFFamlp0Zlnnqnu7m7t2bNHn/nMZ/g6dz8UEkiUz+CfuMFZ+yu3eCGUlpDxREg4VIiBA6MPGeNuNy9gNmReIW2w9skk1pkliTFjWuIGX3nt7QvjorTOD3HD+ZLYH0K2RT7kM1hs3035OXPm9JRFuSn/05/+VI8//rgmTpzYc1P+E5/4RNC8eRINAEhEfX29qqqqev5uueWWxKa9cuVKzZ8/X9/5znf05JNP6r777tP999+vr3zlK4nNAwAAFF57e3uvv127dpn19ndTvqmpyfzMRRddpC9/+cs688wzNWjQIB177LE655xzgm/K98kn0QCA/ErizvG6det6/byg9zN9NTU1Ki4uzvl5webmZtXV1ZmfufHGG/WJT3xCl156qSRp7Nix6ujo0Kc+9Sldf/31/eanxAAAOJQlMZ6or6/vVT5v3jzddNNNcZsmqfdN+UmTJumll17SrFmz9JWvfEU33nhj5OlwEQ0ASKTTq6ys7HUR7SkpKdGECRO0YsUKTZ06VdIbX6dcsWKFZs6caX5m+/btORfK+74O2xe+KggAALJzU55b9wCAvAeBzJ49W9/73vf0ox/9SM8++6wuv/xydXR0aMaMGZKk6dOn93rH6fzzz9d3v/td3X333XrllVe0fPly3XjjjTr//POD3i0FAADpSWI8se+m/L4/7yL6b2/K77PvpvzkyZPNzyR1U54n0QCAvJs2bZo2btyouXPnqqmpSePHj9eyZct63mtau3Ztr07uhhtuUFFRkW644QatX79eI0eO1Pnnn6+vfvWrhVoEAABQYLNnz9bFF1+s0047TRMnTtTChQtzbsqPHj26J6fl/PPP14IFC3Tqqaf2fJ37YG7KF/wi2npk7t1tsJKbvQTHTZs25ZR5CXhWG0IS8Lyk0UGDBuWUlZWVmXWt+Vmf93hJ3tZXK72vW1ov7VvrXLKTuDs6Osy61o+Xe9utra0tp2zPnj1m3ZCk0LRY2837GghfOe2fQo75kP2hL8r3Pjpz5kz369srV67s9f8DBw7UvHnzNG/evDy0DH1FSJptSMKydVyGHKtJJDeHLJtV7rU3JC04pK41P2+cYg1EvfOLNZ4I+SWTECHnuLTSmEMSrJPYz6x1FvIrOEn0CyH9aNxlTqK9afWF+Uw/L/SYM5/zL9RN+cJfhQAACi6fP0kBAAAOTYUYTxTipnz/eUQCAAAAAECB8SQaAMCTaAAAEFtWxhNcRAMAMtPpAQCA9GRlPFHwi2grxMALvrKCq7xgjZCAkLhBEyUlJWbdYcOG5ZSVl5fHboMVqDVkyBCz7ogRI3LKDj/8cLOuFfb15t9d28cKAPNCIqzpenWtMBIvsCyfIU7edK3tuW3bNrNuEgEhyD9rXx08eLBZ19pPtm7dmnib0pCVTg+Fl89zYdwgKil+wE9IqJcnn8eY199ZwajDhw8361p94/bt2826W7ZsySkLCW/0hATNhWzjkLpRP+9NI2Q/C5lfSHBbEoF7IceFNb+Q4zjf4a5xx6IhIW/eOrPG2YUcc2ZlPME70QAAAAAARFTwJ9EAgMLLyp1jAACQnqyMJ7iIBgBkptMDAADpycp4gotoAEBmOj0AAJCerIwneCcaAAAAAICI+uST6CymGIckIlpp4FVVVWZdK4n7iCOOMOta8xs0aJBZd/fu3TllO3fujDzdkGTIJJJN+5u4abD5Tqc8lFnrzEvDt44Xb1tYaZqFlJU7xyi8uKnU3jFl9R9JpBuH9EtxhSRCp9UGb/2WlpbmlNXU1Jh1rV9Zef3118267e3tOWVppVJ7QtKjQ+pG/bwnrTaEjBGSOL/HbW/IdJM4hvpCGr411vfau2fPnpyyJH6Z4GBlZTzRJy+iAQD5lZVODwAApCcr4wkuogEAmen0AABAerIynuCdaAAAAAAAIuJJNAAgM3eOAQBAerIynij4RbT14ntbW5tZ13v5Pp+sl/p37dpl1u3o6MgpKy8vN+sWFxdHboO1zrwAsKFDh+aUVVRUmHWtZbPCQSRp2LBhOWVbtmwx61rt9ZbXmt/WrVvNumkdaFZ7vYCGbdu25ZQlEepl1R040D5crW1vtcubRn86YfUVIYEd1jEo2UEghQzLy0qnh74pbtCXN42Q4KAkxhhphVlF/Xwoa34h68ELFLWm69W1QhbTCtTK9/qN24a0glVDwrdC6nrHZsh+llbIW9zA1iS2hVXXG79bQX4h8+rs7IxcN2lZGU8U/qoUAAAAAIB+ouBPogEAhZeVO8cAACA9WRlPcBENAMhMpwcAANKTlfEEF9EAgMx0egAAID1ZGU/wTjQAAAAAABEV/Em0leS3fft2s66V5OfdsQhJz7WSouMmKXttsFIovTaEpBHu2LHDrLtp06acsrKyssjT9ZLSrXIrjdybrpfObSUZt7e3m3Xj8tZvVVVVTpm3P1nLnMRdtJB07urq6pwyL/XSSu0uZCJ0fxWSXhuS/lnIO7BZuXOMwgvpn0P6/RDWeS+kzw2ZrickITzu+SIkWdgbp1jp2hs3bjTrWn28l869e/dus9wSsj/ETVsPSaVOS0jadRKp32nt63HnFSKtBHZvf7KOlyT2nZDpWr/2EXKeTVpWxhMFv4gGABReVjo9AACQnqyMJ7iIBgBI6l+dFwAA6JuyMJ7gnWgAAAAAACLiSTQAIDNfvwIAAOnJyniiT15Eey/eWy/ZewFVVtiSF15hhWR507U2rhfUVVlZmVPmBXZYgQVeG6xptLa2mnWtMKrOzk6zrlXe0tJi1t2wYUNOmRcaYq0zbz1Yda2gL8leZm/fsQIWvLqDBw/OKfPaG7LvhASiWOXe/mutB28bW+vBCyyz2hCyfkPCXpIIOUmL1V7veBsxYkSs6RZSVjo9FF7cc2ESrHNOSBhQSFBXiHyHAYX0z1a/YgUaSWGBZVZ53PA5T1rbOKRv9JYtrT4hpC+3pBVYllZwW8i5JIkQsrjbzTuGQsZV1jQIKk1f3xrFAQAAAADQh/XJJ9EAgPzKyp1jAACQnqyMJ7iIBgBkptMDAADpycp4gotoAEBmOj0AAJCerIwneCcaAAAAAICI+uSTaC/dePjw4TllXrJeeXl5TpmXiGjNb9euXWZdK53SapckDRo0KKfMS6e02haSerl161azrjU/L0XbKvdSiL31E1VIIuKQIUMiT2Pz5s1m3ZB9xyr3EqxrampyykJS4EtKSsy6Q4cOzSnzls3ad4YNG2bWtea3bds2s661HrxjyEqot45BSdq+fXtOmZcm3hdY68Hb/zdt2pRT5iV2e8dWoWTlzjEKL276bsh0Q/bNtH4lIIkkb6sN3vk45NwdkpRujSe8MU3Uz3vSSsZOImk6ZBohv0YRMq+00q5D6oas37jHpifu+g1JgU/rF0dC0sT7S1+blfFEn7yIBgDkV1Y6PQAAkJ6sjCe4iAYAZKbTAwAA6cnKeIJ3ogEAAAAAiIgn0QCAzNw5BgAA6cnKeCLyRXTIy/cWLywsJCzDCmyqqqoy61oBFt6L/tY0vAAMK3zLC52ylsMLALPa5gVqWSEGXnutcDIvWMziTTdku1nbPon9oaKiwiyPWjduOIhkB4B5gXDWfuJN1woA8/Yzq9xbN1aIWHt7e+TpevuDtT0HDx5s1rWOYy+oK60AohDWPultN2s5vEA4q24hO4+sdHoovLj9R77DwkLCt6z5eeeLuMFB3nQt3voNmV9IkFTcc0ISIVkh6yfkPB8SkmWVe+OfuJIIqIobxhay3dKabhL9Uch0rXUWcsx7rLpphcQlLSvjCb7ODQAAAABARHydGwCQmTvHAAAgPVkZT3ARDQDITKcHAADSk5XxBBfRAIDMdHoAACA9WRlP8E40AAAAAAARRX4SPWLEiJyykLS8lpaWyHW9VDsrWdhLQh4+fHjk6VrlgwYNMuuGJBZby1ZeXm7WDUk/b2tryynz0o0tw4YNM8u9NGVLSNpj3LRRL2HQ2vbe+rV4283aV700Teu48PYzK23d23+tZfYSt6227dixw6y7ZcuWSJ+X7H3S20cqKytzyjo6Osy627dvzynrCyncIbx92tr2Xhp+3LTSpGXlzjEKL2Tfj5sI7dWNm+rrTdc6n3p9gtUHJZFCHLIe4qYFJyFu4nASKdohY7C45+6QfTKkXUlsn7iJ8WklbsdNWvcksd2sbeSNL63l8LZx3GVmPJE+vs4NAMhMpwcAANKTlfEEF9EAgMx0egAAID1ZGU/wTjQAAAAAABHxJBoAkJk7xwAAID1ZGU9Evoi2QoJqamrMulawhhdeZAUrWeFHkh3q5YUtRW2XJyQszGPtCF5gmVXXCxuwlnn37t1mXSvEaePGjWZdS0hQV0hwibd+LV64QloHmhUA5rH2X28bh4REWNve+7wVIrZp0yazrrXeQ9rl7WfWMevVTSv0JoQ1v5AgEE9fC/cIkZVOD/1fEoFacY9LbzxhjVOSOAdYfUK+g75C5metn5AgKa8NIedjS0gbQoTsZ0mE3cUNHEsiwC5k2eIG2CURABbShpAxWEjb4gYlhihkv5yV8QRf5wYAAAAAICK+zg0AkNS/7gADAIC+KQvjCS6iAQCZ+foVAABIT1bGE1xEAwAy0+kBAID0ZGU8wTvRAAAAAABEFPlJdGdnZ06Zl/JsJc15qXbDhg3LKfPuQljTKCsrM+vGTfdL4k5I3DRNK+VTkrZt25ZTtmvXLrPu8OHDc8q8xO3W1tacMq+9XgK1JWSZrW3spVDGTen09ofBgwdHnpdV7tUNSWW07Ny50yy3tltIunxIUrq3LUOmEZL+mRZrW4wYMcKs297enlMWd3lDp5EPWblzjMKzzpEhidueuOeWJJK8Q8Ye1npI4twSN+U5pG8NWTdx+2wp/q87JJGwnMbnPSHrLIk+JSRx2xJyHCeREB5XyPVGiJB90hvjhvyiUF/7ZZCsjCf4OjcAIDOdHgAASE9WxhNcRAMAMtPpAQCA9GRlPME70QAAAAAARMSTaABAZu4cAwCA9GRlPBHrInr37t2xG9DS0pJT5r3Qv2fPnpyyzZs3m3Wrq6sjt8HaYF5Ag1V3x44dZl0r8Gno0KGRp9vR0WHWDbF9+/acMi+EzNqe3vq1Asu8sDFre5aWlpp1LSHhEyHBGl5wW4i4wWJeoIS1Lby61vb0toUVxOftkxZvn7TKQ4I18s1q29atW8263nqPyvv8YYcdllNWyHVTiE5v0aJF+sY3vqGmpiaNGzdOt956qyZOnOjWb21t1fXXX6/77rtPmzdv1lFHHaWFCxfqvPPOO6j5o+9LKxA0JFArbihSEuGNIdI6Z4WELeXzXBYS/OmxQpyS2B8sIcGfSazzuNsiZLohYXfefhYSLBayLaxtnNZ+6i1bSNhd1M/3RVxEAwCQkqVLl2r27NlavHixJk2apIULF6qxsVHPP/+8Ro0alVO/s7NT73nPezRq1Cjde++9Gj16tF599VXzFx4AAADSxEU0ACDvd44XLFigyy67TDNmzJAkLV68WPfff7+WLFmi6667Lqf+kiVLtHnzZj322GM937QYM2bMQbUXAACkIytPogkWAwD0dHoH+xeis7NTTzzxhBoaGnrKBgwYoIaGBq1atcr8zK9//WtNnjxZV155pWpra3XKKado/vz5fe73tgEAyLJ8jif2WbRokcaMGaOysjJNmjRJjz/++H7rt7a26sorr9Rhhx2m0tJSHX/88XrggQeC5smTaABAIneO29vbe5WXlpaa+QctLS3au3evamtre5XX1tbqueeeM+fx8ssv67e//a0+9rGP6YEHHtBLL72kK664Qrt379a8efMOqt0AACBZ+X4SXajXw3gSDQBIRH19vaqqqnr+brnllsSm3dXVpVGjRumOO+7QhAkTNG3aNF1//fVavHhxYvMAAAD9y9++HnbSSSdp8eLFqqio0JIlS8z6+14P++Uvf6kzzjhDY8aM0dlnn61x48YFzbfgT6KtxG2PdXfC+3xIYp81DS9Zz0ribmtrM+ta0/C+emglJHsp2lYbvEREK+U5JFU9iYTB8vLynLKQJG9v2ULSNEOSsUP2M2t7em2wlsNKy5akkpKSnDJvnVnt3bRpk1nXapu1faTcp4qSnfYuhSXo9lUh56IQ3rqxttHIkSNTaUMUSdw5XrdunSorK3vKvRT+mpoaFRcXq7m5uVd5c3Oz6urqzM8cdthhGjRoUK/j/sQTT1RTU5M6OzvNYwZ9k3XuDdn3Qs43IWnBIQnAVv8j2X1CSJK3JyRxOyQ1OUTcRHNPSF9uLYe3bNY0vF/liJvOHfJaiTfdfP66SMjYOUQSidshQvaHECH7ulUesh6SaG9a6/dg5fObbfteD5szZ05PWcjrYb/61a80cuRIXXTRRfrCF77gntstPIkGACTyDlNlZWWvP+8iuqSkRBMmTNCKFSt6yrq6urRixQpNnjzZ/MwZZ5yhl156qdfg5IUXXtBhhx3GBTQAAH1EEuOJqN9s29/rYU1NTeZnXn75Zd17773au3evHnjgAd1444361re+pZtvvjloOQv+JBoAUHj5fodp9uzZuvjii3Xaaadp4sSJWrhwoTo6OnrSuqdPn67Ro0f3dJyXX365brvtNs2aNUtXXXWVXnzxRc2fP19XX331QbUZAAAkL5/fbDsYf/t6WHFxsSZMmKD169frG9/4RlDGChfRAIC8mzZtmjZu3Ki5c+eqqalJ48eP17Jly3ruJq9du7bX1+Tq6+v10EMP6bOf/aze9ra3afTo0Zo1a5a+8IUvFGoRAABACvZ9o+1ACvl6GBfRAICC/K7jzJkzNXPmTPPfVq5cmVM2efJk/eEPfzioeQEAgPTlczzxt6+HTZ06VdL/ez3MG1+cccYZuuuuu9TV1dVzs/5gXg/r9xfRIaEhXqCWFQCQROBHyDSsYIKqqiqz7pAhQ3LKvBfh44Z7eOETVl0vJMsKxPLaa4VleMFX1vy8NuzcuTOnzAvxsNaZt+9Y0/XWmVUeElThbUurrjddKxistbU18nS9fd2bH3zW/rBhw4YCtOQNhbiIRjbFDb7K9/4Wcj4OCTWNO92Q8K0Q3udD1ntIe+POy2uv9fVPb4AcEmpqCRlPeHWtcUbcMZw3Da+u1S8lcbzFPebTCggMCaXzxF0/aZ37Chn4mpXXw/r9RTQAID4uogEAQFz5Hk8U6vUwLqIBAAAAAP1SIV4P4yIaAMCTaAAAEFtWxhNcRAMAJPWvzgsAAPRNWRhPkAYEAAAAAEBEBX8SHZLgGJKavHnz5pyy6urqyO1KItXOmoaVjuyVDx061Kw7ePDgnDJvnVlpyt7dISut0UrL9nhtCJnusGHDcsqsNHJJqqioyCmzksC9tlnJ2pKdTtnR0WHWtbaxN12rrpfkbW03r66V9BlyB3Dr1q1mudXeJNI0s3B38mB5qa35kJWvX6FvSut84X0+bh8fMt2QMU3IdNOqm9b4xxM3udlbv9Z4wPsFD2tMEpJ+HvKrHF5da35p/dqH19eklegcN0U7pG5IG5JIxg7Zf0OWLe4vBxVSVsYTBb+IBgAUXlY6PQAAkJ6sjCe4iAYAZKbTAwAA6cnKeIJ3ogEAAAAAiIgn0QCAzNw5BgAA6cnKeCJvF9Fe4MHw4cNzylpaWsy6IS/U79q1K6fs9ddfN+uWlpbmlIWEkIUI2TlaW1vNciuoywtYa29vzymz1rlkB3h5oRYhrO1WXl5u1rXC1Kqqqsy61nJ4280KGGlrazPrWqF0SYRPWMElnZ2dZl0raM6brhWw5rH2qZBQOi/szmqbF1hm7b/96aR5qMpKp4fCixt8FTJd6zzmSSIALETc6eY7jC2kbtwAJO/zVv8REqjlhZpa4x8r6FSy+1wvNNYaz+7YscOsawkJnUoimDLuMeRJYr+OO68kxnFxp2tJYv1aChlClpXxBE+iAQCZ6fQAAEB6sjKe4J1oAAAAAAAi4kk0ACAzd44BAEB6sjKe4CIaAJCZTg8AAKQnK+MJvs4NAAAAAEBEeXsS7aXEpZUeF5J6mVaSZciyWamKlZWVZl0rGdJKlPbaYCV2S9KWLVtyyry068GDB+eUeQmZJSUlOWVWWrZkp3Z7baivr88pO/bYY826VsL3yy+/bNb9y1/+klPmbWMrFd1L/7Sm4a0zi5U4L0lNTU05ZVaCaej8Qo4La/16iZNWimkhUyTxhqzcOUb/EpJca+2HIQnWaSVNe5JIwbaEnOfjzsvra0LWr9XekG3hLa9V12uv9csT48ePN+uefPLJOWWvvPKKWffxxx/PKfvrX/9q1rXGE0kksIess5DjLeS4sKYbsmxxU/q96YbIdxJ9f+5XszKe4OvcAIDMdHoAACA9WRlPcBENAMhMpwcAANKTlfEE70QDAAAAABART6IBAJm5cwwAANKTlfFE3i6ireAsSdq0aVNOWV8OGbJCvbwghra2tpwyb9msUAsvWMwKALPCmrz5edtixIgROWXW8kpSaWlpTllIuIcXvmUFjtXW1pp1jzvuuJwyL1hs9OjROWVWiJlkb09v/Vohbd6yecFgFmsaXhibFepl7XtSWOCMVbejo8Os65Vb+vLxnWVZ6fRQeHHDwrzzWEggkdUGbz+2wqhCwlKTCCTKZ7hZ3GAyyW5vWsubRHCt1eda4aWSdNZZZ+WUWf2wJL344os5ZVYYqGTvk96xErL/xt2e3uetsWTIOg8J+w0ZX4ZMN25AW+g0QqYbErjX12RlPMGTaABAZjo9AACQnqyMJ3gnGgAAAACAiHgSDQDIzJ1jAACQnqyMJ7iIBgBkptMDAADpycp4gq9zAwAAAAAQUcGfRPfVOw4h6X6DBw+OXHfr1q1mXas8iYRMK8GxpqbGrOulVVtCUg6tcivtVLLTHr26w4YNyymzUrglO+n8hBNOMOu2trbmlK1du9asG5JSvnPnzpyyPXv2mHWtZS4rKzPrWvPz0h6tZQvZz/rq8Yr4snLnGIUXsr+EJNRa5d6vUYSkRyeR4Bt3uiGfD0kpD0k3jvp5KWy7xU3iDlk2b3+wfmHihRdeMOv+/ve/zylrbm4263q/7GGx1o+Xzh13u4W0IWS7JZEib+07Ienc3rgq7rKllSbuSSv9PB+yMp4o+EU0AKBv6E+dFwAA6JuyMJ7gIhoAkJk7xwAAID1ZGU/wTjQAAAAAABHxJBoAkJk7xwAAID1ZGU9k6iI65IV8L8zBKvc2eEVFReS627ZtyynzQsgs3nSt8C0voCokdMEKbhg0aJBZ11pnu3btMutaIRxWGJYkrVmzJqfMC0cbM2ZMTtlLL70UebrettiyZUtO2Y4dO8y6Iax91QtYs4TUBaTsdHrom0ICDpMILwqZbtwgzSRY/WhIoFYSgURxx1BxA+W8aYSMUzo7O8261jhj9erVZt1XX3010rwke4xghYxK9joLCRbzto/VtiS2cci2CJlXSFhqyHqIewylFTAYsh5CpuHtO/mQlfFEpi6iAQC2rHR6AAAgPVkZT/BONAAAAAAAEfEkGgCQmTvHAAAgPVkZT3ARDQDITKcHAADSk5XxBF/nBgAAAAAgoshPoq2UuEImvx0MLwHPSopuamrKaxus9RtyN8ZLY7YSJwcOtDe7ldqdRHqitRxeQqaVWvnaa6+Zda3l8BIyX3nllZyyjRs3mnWt5E2vrpcybrHWWch68JbNWr8hx2YSKajo/7Jy5xiFl0RSdNTphqQbh0zXY7U3iXml1d60xF0PIdP1+saQdGNrP9m7d69Z1xpXecu2e/funDJv3GCNB5JIhI6bLh+S5J1WsnsSx0XccWsS6yHq571y73zW1/rgrIwn+Do3ACAznR4AAEhPVsYTXEQDADLT6QEAgPRkZTzBO9EAAAAAAETEk2gAQGbuHAMAgPRkZTwR+SL6sMMOyylbv359oo1JWxIv+qclpG1WsMCwYcPMuuXl5bGm6wVzWEEeVhiWZIeeecEaHR0dOWUhIW9tbW2R64aEhnjTtUJDvG1phYZ4IREhwWDWOtu0aZNZt6qqKqdsxIgRZl1rmbds2WLWDQlwQd+UlU4PhRc3iCetvjzf+3Fa44yQ9WuVe+dzq1/Kd3CbNT8vWCzutvem6wW5Wqxxhjf26Kv7dV8IqktiPwuZriUkhNWbbtwQ4b58PnvzvLMwnuBJNAAgM50eAABIT1bGEzw+AgAAAAAgIp5EAwAyc+cYAACkJyvjCS6iAQCZ6fQAAEB6sjKe4CIaAJCZTg8AAKQnK+OJyBfRXkoh+oaQnc5LhrTSBL3plpaW5pQNHGjvTiFtsxK+vZRDazm89GivbRYrRXvr1q1mXau927dvN+uGHENx02srKirM8qFDh0aehpUi6aWSWu0NSb0EkB3WucFLnY2bvpvWgMxrV8j8QpJ6rfKQlOgkzsf53BZJ7A/eWCdqXS+NOWS7hYyrLCH7mTfGiNsGr25aydjWdJNIpc7nL/SEbLck6kb9PJLFk2gAQGbuHAMAgPRkZTzBRTQAIDOdHgAASE9WxhP8xBUAAAAAABHxJBoAIKl/3QEGAAB9UxbGE5Evol9//fWcMi/UIgsrLh9CQi1CQji2bdtm1rUCsbxgjsMOOyynzAuzssIjvACM3bt3R2qXV9cqk6RBgwZFKpOkXbt25ZR56ywkCC0k3CwkhKy8vDynzNsfNm3alFPmhY1Z+9nIkSPNutb66ejoMOvGDexAOrLy9Sv0TUkEdeVTvoOOLGmFhaUV+OTVDVmOuMFM3joPGVeFiDsNbxxorTNvvBayHkJCyEKC7ULaEBKiFzcsLImAtRAhbYjbtkKeO7MynuBJNAAgM50eAABIT1bGE7wTDQAAAABARDyJBgBk5s4xAABIT1bGE1xEAwAy0+kBAID0ZGU8wde5AQA9nd7B/h2MRYsWacyYMSorK9OkSZP0+OOPR/rc3XffraKiIk2dOvWg5gsAANJRiPFEIUR+Em0lBPanBe3rrERELzV5yJAhOWVeKqM13crKSrOulRpopU9L9v7gtSEkIdxSVlZmlu/YsSPyNELSubdu3ZpT5q2HkITMkBTUzs7OnDIrNVyyU7+9bWwdsxs2bDDreqmgFmvZvM9z3oAkLV26VLNnz9bixYs1adIkLVy4UI2NjXr++ec1atQo93Nr1qzR5z//eU2ZMiWPrUWSkkh0toQkN4fMKySxOETc9oaIm6QsxW9byHS9fjSkXwpJLLbmF/KrJyFCpuutB6vc+1UPbxpR25BWunzIegwZV3nijmmSEDdxO2RcxS+hpI8n0QCAvN85XrBggS677DLNmDFDJ510khYvXqyKigotWbLE/czevXv1sY99TF/60pd0zDHHxFlcAACQgqw8ieYiGgCQSKfX3t7e68/79kRnZ6eeeOIJNTQ09JQNGDBADQ0NWrVqldvGL3/5yxo1apQ++clPJrvwAAAgEVl5PYyLaABAIurr61VVVdXzd8stt5j1WlpatHfvXtXW1vYqr62tVVNTk/mZRx99VHfeeae+973vJd5uAADQP+17PWzevHl68sknNW7cODU2NrqvK+4T9/UwLqIBAIncOV63bp3a2tp6/ubMmZNI27Zu3apPfOIT+t73vqeamppEpgkAAJKXldfDIgeLWSFMu3fvPqiZZkVIQIPFC52ypuGFLlh1W1tbzbrl5eU5ZW9+UrS/6Xr7Q3t7e06ZFYbllXvTtdaPt84qKipyyrywMK/cEhJUYX211QoQk+xwsy1btph1rWWrrq4261r7iRc4Y+2/IeE0IWEmKLw4X6Pa97nKyko31O5v1dTUqLi4WM3Nzb3Km5ubVVdXl1P/L3/5i9asWaPzzz+/p2zfsT5w4EA9//zzOvbYYw+q7ci/kMAn6zzkBeaEhOvE7Z9D2hDST4TML2SMEHdeodNNK3wr7hgqidC0fAaLeaz+NWTZQsLC0gqoSmIbh3zeO14sIeedkP0sJKQ5rf0sH5IYT0S17/Wwv71pH/p62COPPHJQbeV3ogEAee30SkpKNGHCBK1YsaLnPaSuri6tWLFCM2fOzKn/1re+VX/+8597ld1www3aunWrvv3tb6u+vv6g2g0AAJKVxHjizQ/gSktLVVpamlN/f6+HPffcc+Y89r0e9tRTTx1UG/fhIhoAkHezZ8/WxRdfrNNOO00TJ07UwoUL1dHRoRkzZkiSpk+frtGjR+uWW25RWVmZTjnllF6fHzZsmCTllAMAgP7tzTfH582bp5tuuin2dJN8PYyLaABAXp9ES9K0adO0ceNGzZ07V01NTRo/fryWLVvWczd57dq1iXwNFgAA5E8S44l169b1ej3MegotFfb1MC6iAQB5v4iWpJkzZ5pf35aklStX7vezP/zhDw9qngAAID35zFgp5OthXEQDAApyEQ0AAA4t+R5PFOr1sMgX0SNGjMgp837PE2+wdgQv3dhKP/cSrK1Uxra2NrOulQjttcGaRklJiVnX+lrFjh07zLqbNm3KKbP2J69tXjq3Ve4lQu/ZsyenzPuqqJW0GPK1Umte3nS9dG4rQd3bFtZ6eO2118y61nKErIeysjKzrmXbtm1meSETIwH0TUkkFqclbjK2d45NK3m8r8pnurc3P2+7xX11JIn9N2T9hCTch9SN264kUs77wr5u7Sdp7ZMhvDFu1l99KtTrYTyJBgDwJBoAAMSWldfDuIgGAHARDQAAYsvKeCLbz/8BAAAAAAjAk2gAQGbuHAMAgPRkZTwR+SK6Py1UX2GtMy8cqqKiItLnJWnLli05ZV5AlRVO5k03JNTLsn37drPcCiHzXvDfuXNnTll5eXnkNnjttcIYvOA2S0j4lrctrLpW8Jtkb4uQY9ALj7N407WW2QsLi/r5/c0PhZWVTg+FFxLUZZ1H8h3iFBIyFBJek1aAUkhgWV8Q9/wRsmwh/ZLXrpD1a5WH7CMhAXYh6yFkuiHSCjcLCSFLaxt76yxkvJZWSFtIiF4+ZGU8wZNoAICk/tV5AQCAvikL4wneiQYAAAAAICKeRAMAMvP1KwAAkJ6sjCe4iAYAZKbTAwAA6cnKeIKLaABAZjo9AACQnqyMJyJfRFupydg/KyHQS7Du6OjIKfN2pLKyspwyL/V7z549+2tiL1Z7t27data10q6HDx9u1h06dGhOmZcauGHDhpyyww8/3KwbktpqCUll9NIerbrWupHs1O7W1laz7qBBg3LK8n1iiZv+2Z9OhAAKKySx2BOSqBv1856QX7kISepNIlE37noIEbLd0koID0l298Rtm7eNrV8BscZwkj12CBl7p7XveOvGml/Ir32EJG6H1PWEjO1Cphs3eTxkuiGSOKdi/3gSDQDIzJ1jAACQnqyMJ7iIBgBkptMDAADpycp4gmf9AAAAAABExJNoAEBm7hwDAID0ZGU8EfkiesuWLTllVoCG1L9WQJpCwhGscm89lpaW5pR5AWK7d++O3IaQ9lrBV15IljXdwYMHm3WHDBkSuQ3WdHfs2GHWrayszCnz9l8rCMQLbovaLsneRkmEWgBJyEqnh8Kz9hfvfBwSXhQ3DNGrGxLQEzdkKIn+2VpncZchiboh8j3duPMLGdt528IKFvPCwkL2s5C6VttCQlhDJLGfhSxbyDg7JGAtrX3H2vZJnKPyISvjCZ5EAwAy0+kBAID0ZGU80bduXQAAAAAA0IfxJBoAkJk7xwAAID1ZGU9wEQ0AyEynBwAA0pOV8QQX0QCAzHR6AAAgPVkZT0S+iA5J7EMyvLS9rVu3xppGEtvNmoaVai1JbW1tkdtQXV0dua6VXLh582azrpUGbiWMe+VecqzFWw87d+7MKYubbonCCEnTDJ0GcKhLq19KKy3YKs/3Lyukdb6ImxyeRMJyyHStvjiJdROyHCFJyJ2dnTllXj9hlXu/vJJE4ntUSaRzh6SUhyTyxz2XJLFPRv18Em3whCR5Izk8iQYAZObOMQAASE9WxhNcRAMAMtPpAQCA9GRlPMFPXAEAAAAAEBFPogEAmblzDAAA0pOV8UTki+i+EKzRF4SEDVjl3ov+IdO11rsXVBHShrjbzfu8FbTV3t5u1rXaFhKO4O2TVrjZ9u3bI09j2LBhZt1t27bllFlBIpK9zF64WdztlkRIStz9ISR8K4njIp+8NgwZMiSnzFu2jo6OyNPNh6x0eii8kHNWPsNxkgg6ymcAmDcvK3xr7969Zt2QPjet4zyt/s4q90JC447tQup62yJEWiFvcYWErqUVABYiiX09nyFvccPG8iUr4wmeRAMAMtPpAQCA9GRlPME70QAAAAAARMSTaABAZu4cAwCA9GRlPMFFNAAgM50eAABIT1bGE1xEAwAk9a/OCwAA9E1ZGE9EvogeOnRoTpmVTHyos3YKL2G5rKwsp2znzp1m3d27d+eUeSl8VrJjeXm5WXfPnj2RyiQ7VTEkEdFLArTKvQRHL7U7qpB07oqKiljzkqSmpqbIbbD2kyQSIK30c29/sJLDvX0yboqk117ruPD2Heu46AtCtkU+04UBRJPPFO20phvyqwYhv8LgLUPILyuEiJuMHcLra9L6JYi4yc0hnw9JYw7ZxiH7Tsj+kMQ2TutXZeL+Ck4Sx1vcX6tB+ngSDQDIzNevAABAerIynuAiGgCQmU4PAACkJyvjCS6iAQCZ6fQAAEB6sjKe4HeiAQAAAACIKPKT6Hy+zB4S/BDy8n6+WeFOXnu3bNmSU+atc2u61dXVZl0rRMyal1fXY22jwYMHm3WLi4tzyrz1sHXr1pwyK0hNCtvGw4YNyymrrKw061rL5gVcWcthhUtJ+d0nvfXbF45jK9DNCjzzytNaBm+6IWEt3rFl8bZRoWTlzjEKL+SYsuQ7zCqkDSEhQyHrIWSZ4waAJRF0FBJUGlfI+vXWecj5OO5yhPQ1IcFXIds4if4u7jEUsp8lcWymVTckaDduGJunr/XBWRlP8HVuAEBmOj0AAJCerIwn+tajEAAAAAAA+jCeRAMAMnPnGAAApCcr4wkuogEAmen0AABAerIynuAiGgCQmU4PAACkJyvjicgX0e3t7TllSSRjW9PwUpOthOTt27ebdfOZ4OslC1tJvd46i5tk6dXN587ozcvant52s6bh7Q8WK93b09bWZpaHpFMOHTo0p8xbtri8fTokgT2fCfdee631E5LInxavDdb+F5IuHzeJGDjUhCQhx01NTqIPjNsXJ5HCHbJscROAQ1KIkxinhNQNSUKO+nlvfmklQnvyvS4taf1qRF+4MIqbUJ/WrwKEtCFkPVq/jINk8SQaAJCZO8cAACA9WRlPcBENAMhMpwcAANKTlfEEF9EAgMx0egAAID1ZGU/wO9EAAAAAAEQU+Ul03ECJEN6L91bAT0dHR9A04rKW2QtisILQvHVmTcNbhh07dkSe7t69e3PKrCAqrw0h7fW2hcVaBm9+XmjIsGHDcsq2bdtm1rWC8UJ427iuri5yG+IGdqQVDpLWcey1YefOnTllaQXZJCEktKY/3UF9s6zcOUb/EjcQK2TfTOJYz2eoaRKBjCHhRSHjlL4QzBTy+bSCNNMKJ4sbsOYJGePmM4wt38FtccU9BqWwMbk1P2v8ny9ZGU/wdW4AQGY6PQAAkJ6sjCf4OjcAAAAAABHxJBoAkJk7xwAAID1ZGU9wEQ0AyEynBwAA0pOV8QQX0QCAzHR6AAAgPVkZT0S+iLZS/4qLi826cdMwvSTlzs7OnLK0kjBDJJFGGDINa73v2rUr8vxCkhY9Vl1vf9i+fXukdnlt27p1q1k3JIkyrf2ktbU1pyxuCvehIon02nyeTL3t5qWtW/rC+Qjoj7xjxzrPp5XmH3LO8s4XIe0NaUNaadeWtNqbxPys9RuSbuyJ294kxiMh2zgkGTvkuIh7DOU7gT3uOsvnL/l480vifJbPZcP/w5NoAEBm7hwDAID0ZGU8wUU0AEBS/+q8AABA35SF8QTfOwUA9Nw5Pti/g7Fo0SKNGTNGZWVlmjRpkh5//HG37ve+9z1NmTJFw4cP1/Dhw9XQ0LDf+gAAIP8KMZ4oBC6iAQB5t3TpUs2ePVvz5s3Tk08+qXHjxqmxsVEbNmww669cuVIXXnihfve732nVqlWqr6/Xueeeq/Xr1+e55QAAIOuKuiNe8o8YMSKnrK2tzaxrBUwlcWfBmkYSIVn9TUioRcg6ixsokZaQwJm4AW2h02CfRD5YoYpJaW9vV1VVlU499VT3mDiQvXv3avXq1Wpra1NlZWWkz0yaNEnveMc7dNttt0l643iur6/XVVddpeuuuy7SPIcPH67bbrtN06dPP6h2ozCs/SwkDCikD9u7d29g66K1wZPP4KAQafU/IeFFIX15iCSCxdIK34q7jUPGE0mEecaVRChdWgGBIYG4IdMNqRtyfkjLnj17Upt2ocYThcI70QCARIJA3vzLCqWlpSotLc2p39nZqSeeeEJz5szpKRswYIAaGhq0atWqSPPcvn27du/ererq6oNqMwAASF5WgsX4OjcAIBH19fWqqipYQ2cAAMMjSURBVKrq+bvlllvMei0tLdq7d69qa2t7ldfW1qqpqSnSvL7whS/o8MMPV0NDQ+x2AwCA/qsQGSs8iQYAJHLneN26db2+fmU9hU7C1772Nd19991auXKlysrKUpkHAAAIl+8n0fsyVhYvXqxJkyZp4cKFamxs1PPPP69Ro0bl1N+XsXL66aerrKxMX//613Xuuefqf//3fzV69OjI8+VJNAAgkTTNysrKXn/eRXRNTY2Ki4vV3Nzcq7y5uVl1dXX7bec3v/lNfe1rX9NvfvMbve1tb0tm4QEAQCLync69YMECXXbZZZoxY4ZOOukkLV68WBUVFVqyZIlZ/2c/+5muuOIKjR8/Xm9961v1/e9/X11dXVqxYkXQfLmIBgDktdMrKSnRhAkTenVY+zqwyZMnu5/7l3/5F33lK1/RsmXLdNpppx30sgIAgHTkczyxL2Plb1/tylfGSuSvcw8ePDjyRLdu3RrUiKj6anq0JyRF2+KlMlrJet72qaioyCnzts/u3bsjty2fQlIOq6qqIk8jif20v+2TQF8xe/ZsXXzxxTrttNM0ceJELVy4UB0dHZoxY4Ykafr06Ro9enTPe9Vf//rXNXfuXN11110aM2ZMz7vTQ4YM0ZAhQwq2HAgXN43W60dDkm9D+uK4CdRenxDyCxPWeCDffU1I2rUliRTiuNPoy6nJcccTIdsniX3nUEj9DhE3pT+J6YbI9/6btKhBpfvLWHnuuecizetgM1Z4JxoAkPd3mKZNm6aNGzdq7ty5ampq0vjx47Vs2bKejnDt2rW9BoXf/e531dnZqQ9/+MO9pjNv3jzddNNNB9VuAACQrCTGE/X19b3K0+rr42SscBENACjIT1LMnDlTM2fONP9t5cqVvf5/zZo1BzUPAACQP/kMKk0iY+W//uu/DipjhXeiAQB5DwIBAACHnnwGlRYyY4Un0QAAAACAfqdQGSuRL6JLSkpyyrxgjje/DC71/xfc98dbtr179+aUlZeXR56GF3xlbWAvUGvnzp2R2nWosILUPGkF4AH9USG+zo1ssvYXrx8NCXyypptEUFda+3fIegipG/XzSUw3hDdmzGdomhe+FRIEGzdgKq1tHNKGJEKy8hmsmkQgnDUNbxsXFxfnlHn7TtxlTuLY7GvXWFnJWOFJNACAi2gAABBbVjJWeCcaAAAAAICIeBINAOBJNAAAiC0r4wkuogEAmen0AABAerIynuAiGgCQmU4PAACkJyvjicgX0SFJdVnjpftZSdHDhg0z6+7ZsyenzEpEl6RBgwbllA0caG/KXbt25ZR56dx9Ld3vYGzevLnQTcAhxjouQhLuOU8CBxYycApJ3E6iDSF9Y9y6+e6HrfmFJFgnkWjujaEsVtuSSLuOO3APSU0OWd4kUrSTmEYabfDqWusniWPQakNI4nYSKdr5TNzuTxej/RVPogEAmblzDAAA0pOV8QQX0QCAzHR6AAAgPVkZT3ARDQDITKcHAADSk5XxBC/rAQAAAAAQUeQn0Z2dnTllmzZtMuseCgFVIbzltdbZzp07zbplZWU5ZV7ggVXe1tZm1rWCxQ7l7WOt81AhoR9W4F5/uouGA7P2h+rqarOutT9s2bIl8nQLjX0X+WD1Yd7xkFZoT0jIUEifaS1HWsdVEmFLccPC0gpjC9kf8h0OZUkr+CpEEtPNZxibJ60xash089kXhhxD3r7OeKIw+Do3ACAzX78CAADpycp4gotoAEBmOj0AAJCerIwneCcaAAAAAICIeBINAMjMnWMAAJCerIwnuIgGAGSm0wMAAOnJyngi8kX0hg0bcsp2796daGP6Ky/xz0rL85J6q6qqcsoqKioiz2/v3r2R23Aop3OH8NbDqFGjcsr27Nlj1rW256GyfkMSUy0hial9mbUeOjo6Itftb8sLpC3kmIh7Pk1iXn0h5dmarnc+ts69aSU3J1E3rW2c1vk4bnvT2hYh+2TIdJMY06S1P4S0N2R/iFs3CWml4SM5PIkGAGTmzjEAAEhPVsYTXEQDADLT6QEAgPRkZTzBRTQAIDOdHgAASE9WxhP8xBUAAAAAABFFfhJNiJgvJMxh8ODBZl0rGGzHjh2Rp7tz506zLmED4drb23PKhg0blv+GFJgVWjNkyJDIn9+2bZtZfijsk4fi+TArd45ReHFDhtIK9fKCuvpCSFZI3bjtDQmQ9HjBklENHGgPT0PCliwh2ydkGZLYJ0PmZ40ZQ4Lm0gqJ85YhZD+LGxYWIonQP+TKyniCr3MDADLT6QEAgPRkZTzBRTQAIDOdHgAASE9WxhO8Ew0AAAAAQEQ8iQYAZObOMQAASE9WxhNcRAMAMtPpAQCA9GRlPMFFdIqsJD8vRdsSsiN5KYdWG7yEwf6040rppT12dnbmlG3atClyGw4V1jorLi6O9Xnp0F5nAA4sboq2VzeJVOmo80srITyJpOm4ycve55NIq45aN2ReIesh3ynPcdPEk2iDJYn9N2QMFnIMRW2XN90k9ocQIcuWz2MI6eMiGgCQmTvHAAAgPVkZT3ARDQDITKcHAADSk5XxBBfRAIDMdHoAACA9WRlP8BNXAAAAAABEFPlJdEjwQ3+6i5BvXuiCVV5VVWXW3b17d07ZoEGDzLpWSJYXbmZtz768La221dTUmHWtQKy9e/eadVtaWiLXPZRZ+8O2bdtifR59V1buHKPwQsJ8rL4xJOioLwcShUgr+CokjC1kPYRst6jzCq1rzc/rl0LqhoSFWeXeeCKJMDVL3GMoCUkE5lmsZfMCUEO2W8j5wdpuIeeSkEDctILJkpaV8QRf5wYAZKbTAwAA6cnKeIKLaACApP7VeQEAgL4pC+MJvm8JAAAAAEBEPIkGAMS6a5yFO84AAODAsjKe4CIaAJCZTg8AAKQnK+OJyBfRI0eOzCmzUoxxcKzEvY6ODrOulexYWlpq1h0xYkRO2aZNm8y6Vmq3lwQYN+3RSyMMSVe1eMs2evTonLKQBEeS6N8QkuQKABbr3BuSUJvEedeaX0hyc0iqbxLi/npGEu0K6RtD5heShBx3POG11/qFE2+MYI3B9uzZY9aNu/+GLFuIJPbfuPtfEmn4feG4CNnPrH0nrXNJ1sanhcCTaABAZu4cAwCA9GRlPMFFNAAgM50eAABIT1bGE1xEAwAy0+kBAID0ZGU8wU9cAQAAAAAQUeQn0VboQn8TEmIQ8qJ/yHRDhARKtLa2mnWtcIOQsAIrBEGSampqcsra29vNup2dnTllVlCdZAeDWeFokjRwYO7u6+2nVl1v/Vpt27hxo1nXCh7pT3fRDnVJHPNJzK8/yMqdYxSe169YQoKkLGmFhaXV7+d7PBHSBktax34SgVpWXS8szApntcYNXtt27dpl1k0rsDWfgVppBfkl0YaQ49jb9lHn5003JCwsJDQt6ueTmG7SsjKe4OvcAIDMdHoAACA9WRlPcBENAMhMpwcAANKTlfEE70QDAAAAABART6IBAJm5cwwAANKTlfEEF9EAgMx0egAAID1ZGU9Evojuz6mz+3gbxkplHD58uFm3o6Mjp6ytrc2s6yUwpsHbPl5qd9RpeOssbuq3Z9SoUTllXqJiSUlJTtngwYPNutZybN++3axbUVGRU2alkUt2mvihcKwcKrwU4Orq6pyy8vJys661r3vJsc3NzZHrAlkV0teEpM7mM/k2RFrJ/yEJ1t56SCt5PGQbpyVk2axf9rDGAt40vPGEtY2sXyyR4u/rIdI6hkL6u5DxZb7TrqO2y5uGN/aIe+5D38KTaABAZu4cAwCA9GRlPMFFNAAgM50eAABIT1bGE1xEAwAy0+kBAID0ZGU8wU9cAQAAAAAQEU+iZYc8WAFBnnwGiIXqC3d0rPXT0tJi1rWCxawAMUkaNmxYTllZWVlY4wy7du2KPN24oRYoDGufDDmOQ0J6+ous3DlG4aW1v8Tti9M6fvMdDhV3Xl7duPMLmW5a2yKkDUOHDjXrWgGmXojr7t27c8q80KmQdRZSN+rnJTsYzDuurLppBfmlta97QWhxw808IcsREiYY0t58yMp4gotoAEBmOj0AAJCerIwn+u4jVABA3nR3d8f6OxiLFi3SmDFjVFZWpkmTJunxxx/fb/177rlHb33rW1VWVqaxY8fqgQceOKj5AgCAdBRiPFEIXEQDAPJu6dKlmj17tubNm6cnn3xS48aNU2NjozZs2GDWf+yxx3ThhRfqk5/8pFavXq2pU6dq6tSpevrpp/PccgAAkHVcRAMA8n7neMGCBbrssss0Y8YMnXTSSVq8eLEqKiq0ZMkSs/63v/1tvfe979U///M/68QTT9RXvvIVvf3tb9dtt90Wd9EBAEBCeBINAMiUfHV4nZ2deuKJJ9TQ0NBTNmDAADU0NGjVqlXmZ1atWtWrviQ1Nja69QEAQGEc6hfQUkCw2Jo1a1JsBgCgv2tvb+/1/6WlpSotLc2p19LSor1796q2trZXeW1trZ577jlz2k1NTWb9pqammK1GvnnpxAAA9Bc8iQaADCspKVFdXV3s6QwZMkT19fWqqqrq+bvlllsSaCEAAOjrkhpP1NXVuT9v25fwE1cAkGFlZWV65ZVX1NnZGWs63d3dOb9raT2FlqSamhoVFxerubm5V3lzc7PbAdfV1QXVBwAA+ZPUeKKkpERlZWUJtSo9XEQDQMaVlZXltcMqKSnRhAkTtGLFCk2dOlWS1NXVpRUrVmjmzJnmZyZPnqwVK1bommuu6Slbvny5Jk+enIcWAwCAA8n3eKKQuIgGAOTd7NmzdfHFF+u0007TxIkTtXDhQnV0dGjGjBmSpOnTp2v06NE9XwmfNWuWzj77bH3rW9/S+9//ft1999364x//qDvuuKOQiwEAADKIi2gAQN5NmzZNGzdu1Ny5c9XU1KTx48dr2bJlPeFha9eu1YAB/y+24/TTT9ddd92lG264QV/84hd13HHH6Ze//KVOOeWUQi0CAADIqKLu/pYnDgAAAABAgZDODQAAAABARFxEAwAAAAAQERfRAAAAAABExEU0AAAAAAARcRENAAAAAEBEXEQDAAAAABARF9EAAAAAAETERTQAAAAAABFxEQ0AAAAAQERcRAMAAAAAEBEX0QAAAAAARMRFNAAAAAAAEXERDQAAAABARFxEAwAAAAAQERfRAAAAAABExEU0AAAAAAARcRENAAAAAEBEXEQDAACg3xkzZowuueSSg/7sBz7wgWQbBCAzuIhGYn74wx+qqKhIf/zjHwvdlD7ls5/9rN7+9rerurpaFRUVOvHEE3XTTTdp27ZthW4aAAB9woHGEOecc45OOeWUPLeqb/rFL36hxsZGHX744SotLdURRxyhD3/4w3r66acL3TQgMwYWugHAoe5//ud/NGXKFM2YMUNlZWVavXq1vva1r+m//uu/9PDDD2vAAO5lAQAQ6vnnn89kH/rnP/9Zw4cP16xZs1RTU6OmpiYtWbJEEydO1KpVqzRu3LhCNxE45HERjUzq7u7Wzp07VV5envq8Hn300ZyyY489Vp///Of1+OOP653vfGfqbQAA4FBTWlpa6Cb00tHRocGDB6c+n7lz5+aUXXrppTriiCP03e9+V4sXL069DUDWZe/2HQpu9erVet/73qfKykoNGTJE7373u/WHP/yh599bW1tVXFysf/u3f+spa2lp0YABAzRixAh1d3f3lF9++eWqq6s74Dz3vfv00EMP6bTTTlN5ebluv/32nvldc801qq+vV2lpqd7ylrfo61//urq6unpN4+6779aECRM0dOhQVVZWauzYsfr2t799UOtgzJgxPfMGAADhrHei//SnP+nss89WeXm5jjjiCN188836wQ9+oKKiIq1ZsyZnGo8++qgmTpyosrIyHXPMMfrxj38cad433XSTioqK9Mwzz+iiiy7S8OHDdeaZZ/b8+09/+lNNmDBB5eXlqq6u1gUXXKB169b1msaLL76oD33oQ6qrq1NZWZmOOOIIXXDBBWprawteF6NGjVJFRQXjCiBPeBKNvPrf//1fTZkyRZWVlbr22ms1aNAg3X777TrnnHP0+9//XpMmTdKwYcN0yimn6OGHH9bVV18t6Y1OrqioSJs3b9Yzzzyjk08+WZL0yCOPaMqUKZHm/fzzz+vCCy/Upz/9aV122WU64YQTtH37dp199tlav369Pv3pT+vII4/UY489pjlz5uj111/XwoULJUnLly/XhRdeqHe/+936+te/Lkl69tln9d///d+aNWvWAee9Z88etba2qrOzU08//bRuuOEGDR06VBMnTjyItQgAwKGpra1NLS0tOeW7d+8+4GfXr1+vd73rXSoqKtKcOXM0ePBgff/733efWL/00kv68Ic/rE9+8pO6+OKLtWTJEl1yySWaMGFCzzjjQD7ykY/ouOOO0/z583tu8n/1q1/VjTfeqI9+9KO69NJLtXHjRt16660666yztHr1ag0bNkydnZ1qbGzUrl27dNVVV6murk7r16/Xf/7nf6q1tVVVVVUHnHdra6t2796tpqYmLVy4UO3t7Xr3u98dqd0AYuoGEvKDH/ygW1L3//zP/7h1pk6d2l1SUtL9l7/8pafstdde6x46dGj3WWed1VN25ZVXdtfW1vb8/+zZs7vPOuus7lGjRnV/97vf7e7u7u7etGlTd1FRUfe3v/3tA7btqKOO6pbUvWzZsl7lX/nKV7oHDx7c/cILL/Qqv+6667qLi4u7165d293d3d09a9as7srKyu49e/YccF6WVatWdUvq+TvhhBO6f/e73x3UtAAAONTsG0Ps7+/kk0/u9Zmjjjqq++KLL+75/6uuuqq7qKioe/Xq1T1lmzZt6q6uru6W1P3KK6/0+qyk7ocffrinbMOGDd2lpaXdn/vc5w7Y3nnz5nVL6r7wwgt7la9Zs6a7uLi4+6tf/Wqv8j//+c/dAwcO7ClfvXp1t6Tue+6554Dz8pxwwgk962bIkCHdN9xwQ/fevXsPenoAouPr3MibvXv36je/+Y2mTp2qY445pqf8sMMO00UXXaRHH31U7e3tkqQpU6aoublZzz//vKQ3njifddZZmjJlih555BFJbzyd7u7ujvwk+uijj1ZjY2OvsnvuuUdTpkzR8OHD1dLS0vPX0NCgvXv36uGHH5YkDRs2TB0dHVq+fPlBLftJJ52k5cuX65e//KWuvfZaDR48mHRuAADeZNGiRVq+fHnO39ve9rYDfnbZsmWaPHmyxo8f31NWXV2tj33sY2b9k046qdcYYuTIkTrhhBP08ssvR27vZz7zmV7/f99996mrq0sf/ehHe40r6urqdNxxx+l3v/udJPU8aX7ooYe0ffv2yPP7Wz/4wQ+0bNkyfec739GJJ56oHTt2aO/evQc1LQBh+Do38mbjxo3avn27TjjhhJx/O/HEE9XV1aV169bp5JNP7unUHnnkER1xxBFavXq1br75Zo0cOVLf/OY3e/6tsrKyJ4Vy27ZtvS5Mi4uLNXLkyJ7/P/roo3Pm++KLL+pPf/pTr3p/a8OGDZKkK664Qj//+c/1vve9T6NHj9a5556rj370o3rve98badkrKyvV0NAgSfr7v/973XXXXfr7v/97Pfnkk6RoAgDwf02cOFGnnXZaTvm+m9378+qrr2ry5Mk55W95y1vM+kceeaQ5ny1btkh64+b/xo0be/17dXW1SkpKev7/zWOLF198Ud3d3TruuOPMeQ4aNKjnc7Nnz9aCBQv0s5/9TFOmTNEHP/hBffzjH4/0VW5JvZb1ggsu0IknnihJPeMkAOnhIhp90uGHH66jjz5aDz/8sMaMGaPu7m5NnjxZI0eO1KxZs/Tqq6/qkUce0emnn97z8xbf/OY39aUvfalnGkcddVSvEBEriburq0vvec97dO2115rtOP744yW9Edjx1FNP6aGHHtKDDz6oBx98UD/4wQ80ffp0/ehHPwpevn/8x3/UJz7xCd19991cRAMAUADFxcVmeff/fbd53bp1ORfJv/vd73TOOef0/P+bxxZdXV0qKirSgw8+aE5/yJAhPf/9rW99S5dccol+9atf6Te/+Y2uvvpq3XLLLfrDH/6gI444ImhZhg8frr/7u7/Tz372My6igTzgIhp5M3LkSFVUVPR8RftvPffccxowYIDq6+t7yqZMmaKHH35YRx99tMaPH6+hQ4dq3Lhxqqqq0rJly/Tkk0/2umiePn16r2TMKD9fdeyxx2rbtm09T4n3p6SkROeff77OP/98dXV16YorrtDtt9+uG2+80b3L7dm1a5e6uroOKoETAADkOuqoo/TSSy/llFtlUdTV1eW8xnWgG9/HHnusuru7dfTRR/fciN+fsWPHauzYsbrhhhv02GOP6YwzztDixYt18803B7d3x44djCuAPOGdaORNcXGxzj33XP3qV7/q9YS4ublZd911l84880xVVlb2lE+ZMkVr1qzR0qVLe77ePWDAAJ1++ulasGCBdu/e3etdpmOOOUYNDQ09f2ecccYB2/TRj35Uq1at0kMPPZTzb62trdqzZ48kadOmTb3+bcCAAT3vZ+3atcud/r7kzDf7/ve/L0nmV9YAAEC4xsZGrVq1Sk899VRP2ebNm/Wzn/3soKZXVlbWa1zR0NCg4cOH7/cz//iP/6ji4mJ96Utf6vWTnNIbT7j3jSfa29t7xhj7jB07VgMGDNjvuEL6f6+a/a01a9ZoxYoVjCuAPOFJNBK3ZMkSLVu2LKd81qxZuvnmm7V8+XKdeeaZuuKKKzRw4EDdfvvt2rVrl/7lX/6lV/19F8jPP/+85s+f31N+1lln6cEHH1Rpaane8Y53xGrrP//zP+vXv/61PvCBD/T8rEVHR4f+/Oc/695779WaNWtUU1OjSy+9VJs3b9bf/d3f6YgjjtCrr76qW2+9VePHj+95B8mycuVKXX311frwhz+s4447Tp2dnXrkkUd033336bTTTtPHP/7xWO0HAABvuPbaa/XTn/5U73nPe3TVVVf1/MTVkUceqc2bN6uoqCj1Nhx77LG6+eabNWfOHK1Zs0ZTp07V0KFD9corr+gXv/iFPvWpT+nzn/+8fvvb32rmzJn6yEc+ouOPP1579uzRT37yExUXF+tDH/rQfucxduxYvfvd79b48eM1fPhwvfjii7rzzju1e/dufe1rX0t9GQFwEY0UfPe73zXLL7nkEp188sl65JFHNGfOHN1yyy3q6urSpEmT9NOf/lSTJk3qVf+EE07QqFGjtGHDhl5f0953cT1x4kT3tx+jqqio0O9//3vNnz9f99xzj3784x+rsrJSxx9/vL70pS/1hHt8/OMf1x133KHvfOc7am1tVV1dnaZNm6abbrqp551sy9ixY/Wud71Lv/rVr/T666+ru7tbxx57rObOnat//ud/7hVOAgAADl59fb1+97vf6eqrr9b8+fM1cuRIXXnllRo8eLCuvvpqlZWV5aUd1113nY4//nj967/+a89rZ/X19Tr33HP1wQ9+UNIbXwtvbGzUf/zHf2j9+vWqqKjQuHHj9OCDD+qd73znfqd/+eWX6/7779eyZcu0detWjRo1Sueee66++MUvauzYsakvHwCpqPvN3zUBAAAADhHXXHONbr/9dm3bts0NEwOAELwTDQAAgEPCjh07ev3/pk2b9JOf/ERnnnkmF9AAEsPXuQEAAHBImDx5ss455xydeOKJam5u1p133qn29nbdeOONhW4agEMIF9EAAAA4JJx33nm69957dccdd6ioqEhvf/vbdeedd+qss84qdNMAHEL4OjcAIO8efvhhnX/++Tr88MNVVFSkX/7ylwf8zMqVK/X2t79dpaWlestb3qIf/vCHqbcTQP8yf/58vfDCC9q+fbs6Ojr0yCOPqKGhodDNApCSQo0nuIgGAORdR0eHxo0bp0WLFkWq/8orr+j973+/3vWud+mpp57SNddco0svvdT8jXcAAJANhRpPkM4NACiooqIi/eIXv9DUqVPdOl/4whd0//336+mnn+4pu+CCC9Ta2mr+Lj0AAMiWfI4neCcaADJu586d6uzsjDWN7u5uFRUV9SorLS2N/Vvu+6xatSrnK5mNjY265pprEpk+AACIJ0vjicgX0QMHcr3dV1hfHhg8eLBZ19rhtm7datbds2dPvIYBSEWax+bOnTt19NFHq6mpKdZ0hgwZom3btvUqmzdvnm666aZY092nqalJtbW1vcpqa2vV3t6uHTt2qLy8PJH5IH319fU5Zbt37478eW8g9eZBlyR3MGf91NGQIUPMuuvWrcsp8/rcnTt35pRVV1ebdcvKynLKvLHWli1bcsq884K1Hrxl6+rqilTmte3Nx/w+1rK1t7ebdQcNGpRT5v0U1YABuW8heuvMaoP35cuhQ4fmlG3YsMGs29ramlPm7Q8Wb5+01oN3XFjLtmvXLrPu4YcfnlNmLYMkbdy4MafM28+sc+6RRx5p1t2+fXtO2Zt/imyfkpKSnLKQ80NbW5tZbq3fqqoqs661n1jLINnHi3e8WdOoqKgw61rrx2uvte298+SLL75olicha+MJrowBIMM6OzvV1NSkdevWqbKy8qCm0d7ervr6+pxpJHXXGAAA9G1ZG09wEQ0A0NChQ82nMVHsu2tfWVl50B3ngdTV1am5ublXWXNzsyorK3kKDQBAH5GV8QQX0QAAdXd3u191jPLZtE2ePFkPPPBAr7Lly5dr8uTJqc8bAABEk5XxBBfR/ZD1vpP3roZXDgB/K9+d3rZt2/TSSy/1/P8rr7yip556StXV1TryyCM1Z84crV+/Xj/+8Y8lSZ/5zGd022236dprr9U//dM/6be//a1+/vOf6/777z+oNqNwrPcFrXeJPd57uNa7piFPQzo6Osxy651m7x3NmpqanLK9e/dGnp/3fqQ1De89XOtdSu+9VuvdY289WG2zxiOSvT1D3mUfPny4Wdd6r/W1114z61rvNHvTtbJivG1sPR3z1pn1Dqv3pMvaf73tZrXNew/X2hbePmnNz3r/2iv33nO2jkPv3fuQ9lrvTw8bNsysa/VT3j5pnWO8NljL5r33bpV777Jb+1nI+/9xw73iyMp4gt+JBgDk3R//+EedeuqpOvXUUyVJs2fP1qmnnqq5c+dKkl5//XWtXbu2p/7RRx+t+++/X8uXL9e4ceP0rW99S9///vfV2NhYkPYDAIDCK9R4gifRAIC83zk+55xz9vu5H/7wh+ZnVq9eHTwvAACQH1kZT3ARDQDo8+8wAQCAvi8r4wm+zg0AAAAAQEQ8iQYAZObOMQAASE9WxhOxLqK9VMaQuiErqz+tWADoT7LS6aHwrCRkL0XbSuodMWKEWddKSPbSbK19tq2tzaxrJQB7v19qtddLebbatnHjRrOuNYby0net6Xq/1GEth5dYbK1fL2naSl72Uoit6XpJyFYiubd+rXXmpWhbqd+bN28261oJ7FYKt8dLsLb2SS9x20qPDtkfvGT30aNH55R5ifEDBuR+mbW1tdWsG3K94G1Pi7Wfeanf1nrwfhXAaq+3T1rz8xLNrTZ4x4XVBu98ZvGmmw9ZGU/wJBoAkJlODwAApCcr4wneiQYAAAAAICKeRAMAMnPnGAAApCcr4wkuogEAmen0AABAerIynoh8EW0FCHjBGsXFxTllSYQKeKEfAIB4stLpofCsECcvFMkK8/HGE1aYz549e8y61jjFapenvb09chu8oCMr+McKMZPsgCkvOCgkbCkkNM0K2vJCyKzAJm8MFzI+tNp71FFHmXW3bNmSU/bqq6+ada2ANC80LSQYz9r/vJA3a+zr7b9W2JdX1woy87abNd2Q/dfrC6zjxdvXrWPIOzattnlhYdYye9cb1nnHa6917gpZNq8N1jmqpaXFrGudH0KOq6RlZTzBO9EAAAAAAETE17kBAJm5cwwAANKTlfEEF9EAgMx0egAAID1ZGU/wdW4AAAAAACLiSTQAIDN3jgEAQHqyMp6IfBFdVVWVU+Ylv8VNvbQ+7wlJ7LYSxkNZG7c/bXAAsGSl00P/YqXkekneIanUzc3NOWVWcq5kJ996ScjWNAYOtIdar7/+ek6ZNdaS7PGLN66y0n69xG1rXXrp0Va6sbVuvLa1traada116SVj//Wvf80pq66uNuta68xbv1a5t36tfcdbNmudecnY1r7jrV9rG23bts2sO3jw4Eifl+xka289WNOw5uXVHT58uFnXOua9xG1rmb3j2KrrbYuQXxmy1o+XuB1yHWKlqnvnPqtt3rkvH7IynuBJNAAgM50eAABIT1bGE7wTDQAAAABARDyJBgBk5s4xAABIT1bGE1xEAwAy0+kBAID0ZGU8Efki2loo7yV7q9wLJrACN3bt2hW5DUOHDjXrWi/ke8EaIRvMaltHR4dZ11s/ANAX9afOC/2X1e97QV1WkE5FRYVZ1woU8gKURo8eHbkNVrk39rDqekFHtbW1OWVecJA1zvDCt6zpestmjZW8MZhV7oVZWQFK3jqzlsMLkrLGcV6IkzXurKmpidwGa91I9v7rhTiFhLFZ2zgkoMrbH6z5tbe3m3UtVtCXZO/XXh9i1fX2ya1bt+aUeePpYcOG5ZR52y1kXy8pKYlUJtnb3mtvyL5jrR8vuM06Xrztli9ZGE/wTjQAAAAAABHxdW4AQGa+fgUAANKTlfEEF9EAgMx0egAAID1ZGU/wdW4AAAAAACLiSTQAIDN3jgEAQHqyMp6IfBEdkjhZXFycOyMjkS6UtWK96VoJdl7qpcVL1it02h0ApCErnR4Kz0rEraqqMutaKcTWGEOSRo4cmVPW1tZm1t28eXOkeUn2+Mcbe1gpuV4SsjWe8H7JxEoW9sYpVmK2lyxsbQtvPYSM46zpep+3EqxbWlrMutXV1Tllw4cPN+tu3Lgxp8xLQra2hZc8bo19t2zZYta1xp1WorRk7zteOrc1DS+d2zpevP3Mquv9so21Lr3EbYvXXmu9W/uI1wbvuLDOMd76tY4Xb7rWLwB4faK13r1fG7D2B+/XgCzecZwPWRlP8CQaAJCZTg8AAKQnK+MJ3okGAAAAACAiLqIBAD13jg/2DwAAoBDjiUWLFmnMmDEqKyvTpEmT9Pjjj7t1d+/erS9/+cs69thjVVZWpnHjxmnZsmXB8+QiGgDARTQAAIgt3+OJpUuXavbs2Zo3b56efPJJjRs3To2NjdqwYYNZ/4YbbtDtt9+uW2+9Vc8884w+85nP6B/+4R+0evXqoPkWdUdsrRU2MGrUKLOuFbrgBYFYYQ5eMIFV7tW1why8cI+Ql++tYAEvbICBJRCPdWx6x6t1LjlUjsGQsJZQ7e3tqqqq0rPPPquhQ4ce1DS2bt2qE088UW1tbW4IDbCPFQDmBYtZIaHeOcDa96xwHskOA/KCpKyQIS+YaevWrTllXiCpNw2LNQ1rGSR7TOKFWVnr0goxk+xxnLdsIevMmp/XBm85LFawmBdQZY1bvf3MWr/WdpfsbeSFvFn9lTd2tgK1ampqzLrWfu2FWVlBW14AmBWm5h1v1jS8bWHx+iYrONDbbiEhZFaf67XBWmfe/mDtZ97+YF2zeNO1tqe3HqzjIimFGk9MmjRJ73jHO3TbbbdJemNb19fX66qrrtJ1112XU//www/X9ddfryuvvLKn7EMf+pDKy8v105/+NHJbeRINAAAAAOhXOjs79cQTT6ihoaGnbMCAAWpoaNCqVavMz+zatSvnhkZ5ebkeffTRoHmTzg0AyEyaJgAASE8S44k3P4kvLS11fw5v7969qq2t7VVeW1ur5557zpxHY2OjFixYoLPOOkvHHnusVqxYofvuuy/4Z4x5Eg0A4J1oAAAQWxLjifr6elVVVfX83XLLLYm179vf/raOO+44vfWtb1VJSYlmzpypGTNmBP+2Nk+iAQA8iQYAALElMZ5Yt25dr3eivXfoa2pqVFxcrObm5l7lzc3NqqurMz8zcuRI/fKXv9TOnTu1adMmHX744bruuut0zDHHBLWVJ9EAAAAAgD6hsrKy1593EV1SUqIJEyZoxYoVPWVdXV1asWKFJk+evN95lJWVafTo0dqzZ4/+/d//XX//938f1MbIT6KtBLtdu3aZda00wZDEbU9I+q5VNyRpMQRPYYDorOPQSs2U7CRLLym0o6Mjp8w7R3HM5uJJNPLFShH29iHruPbOAevXr88ps5JzJfuc49UdMmRITpk3drGWIySd2xsoWmnVI0aMMOtaicVWmRT2ayrW+tm8ebNZt7q6OqfMG2tZCdZekvemTZsitUuyt5tVJtnrwZqXZG9Pb5+01uXAgfbQ26rrpV1b7fXS2q314+2/1vy8/cHaV71UdWudef2+Ve6dH6w2eP2+Vddaj5KdaO7tZyHHsbXevaR0axpe4nXINVo+5Hs8MXv2bF188cU67bTTNHHiRC1cuFAdHR2aMWOGJGn69OkaPXp0z1fC/7//7//T+vXrNX78eK1fv1433XSTurq6dO211wbNl69zAwC4iAYAALHlezwxbdo0bdy4UXPnzlVTU5PGjx+vZcuW9YSNrV27ttcDnJ07d+qGG27Qyy+/rCFDhui8887TT37yk6Cf0JO4iAYAAAAA9FMzZ87UzJkzzX9buXJlr/8/++yz9cwzz8SeJxfRAACeRAMAgNiyMp7gIhoAkJlODwAApCcr44nIF9FWEEJra6tZNyQUIG6olxeOYAUseOEI/WmDAX2RdQx5QX5WiIYXvGMds945Y/DgwTllSQQaZkVWOj0UnnX8eSFDVlDR1q1bzbqdnZ05Zd75wgrtKSkpMeta8/OCpKywLy+gyhpXeecmK2Dtb3/+5W9Z4UPW5yU7SMqbrsULRbKWIyToyNsW1vy80DRr23vhW+3t7Tll3rawgqSsfU+yl9nrG63gKm//tfpGb51ZvGPIOi5CQvS8NoSMEaxlfv3118261r7uLZs1zvDaa9W1xhiSfY7ywuOsIDMvsMzinSdDziX5kJXxBD9xBQAAAABARHydGwCQmTvHAAAgPVkZT3ARDQCQ1L86LwAA0DdlYTzBRTQAIDN3jgEAQHqyMp7gnWgAAAAAACKK/CR6yJAhOWXbtm0z61oph3FTuENZyYX96e4G0Bd5aZpWuZXyKdnJpl7SrVXXO46tJEovIdNK3sz6+SErd45ReNbYwUvJtY5h7zxkjVO81GRrul7yrXUe8n6dpK6uLqfMOz6sxGwrLdtrgzXWkvzEbEtIYraVLOz9soI15vPWmbV+rHlJ9vrx1q+1ja0kZY/Xf1j7qrcerH3Km66V0uwtm7UcIcnYXiK0tT8MHz488nS9fc9aP96xaaXDhySPe22w9ikvrd1L4rZs3rw5p6yqqsqsax2z3nnHOs+FpH5v2bLFrJsPWRlP8HVuAEBmOj0AAJCerIwn+Do3AAAAAAAR8SQaAJCZO8cAACA9WRlPcBENAMhMpwcAANKTlfFE5ItoKyTCevlfsl+yz7f+tBGAvsgKtfDCNkKCVqzpeqEhXriMxQqcGTp0qFnXCiGzyrIkK50eCs8K8/GCjqwgHS/oyArosQK5JPt49wJ+rP3bC+R69dVXc8q885s1rrJCUb26XrCY1TavvdY50juerfUzYsQIs67FC5KytoW33UIC1kJCbq1t5O0PVrm3fq193QuHsgI2Q7ax19+FjN+twDJvnVnbwutHrfVQXV1t1rXWjzeesJZj1KhRZl2rvS0tLWbdmpqanDIvlC4k9Mw6Brxws2HDhuWUefuONV2vbj5kZTzBO9EAAAAAAETE17kBAJm5cwwAANKTlfEEF9EAgMx0egAAID1ZGU9wEQ0AyEynBwAA0pOV8QTvRAMAAAAAEFHkJ9Fe8iCAQ5OVom2VhbLSZ710S6vcu0tppXd66arIlZU7xyg8K3XfO1atc4CXZmulG3tjl4qKipyy0tJSs651bvHSja35eec3qw1tbW1mXSsh2fq8ZCede8tmJTp753nrl1e2bdtm1rXWj/frDlZ7vSRvK3F4yJAhZl0rKdr6FQcprP9obW3NKfOSsUeOHJlT5iWPW+dRL5Xa2qe8hHvruPDWr7WfeNvYWuaQ9HMvedxaD9626OjoyCnzls07XizWcoSky3vrwRr/ePuOlc7tJYR7bSuUrIwneBINAOjp9A7272AsWrRIY8aMUVlZmSZNmqTHH398v/UXLlyoE044QeXl5aqvr9dnP/tZd1AMAADyrxDjiULgIhoAkHdLly7V7NmzNW/ePD355JMaN26cGhsbtWHDBrP+XXfdpeuuu07z5s3Ts88+qzvvvFNLly7VF7/4xTy3HAAAZB0X0QCAvN85XrBggS677DLNmDFDJ510khYvXqyKigotWbLErP/YY4/pjDPO0EUXXaQxY8bo3HPP1YUXXnjAp9cAACB/eBINAMiMfHZ6nZ2deuKJJ9TQ0NBTNmDAADU0NGjVqlXmZ04//XQ98cQTPRfNL7/8sh544AGdd955B7/QAAAgUVm5iI4cLGa9vO/xXqgH0Pd4x6sVUuIFjFihIV54Stzzgxe0YpX3p5NxoSURBPLmoJjS0lIzqKalpUV79+5VbW1tr/La2lo999xz5jwuuugitbS06Mwzz1R3d7f27Nmjz3zmM3ydux+y9gkvzMoKo7KCnbxyL5DICgDzwsKsc4t3LrSOIS/QyHqf3wsAswKFvHNsyPrdunVrTpkV3rW/8qjTtUKVJHsbee21xqLeOrMC7Dwhy2ZtN29/sAKfvIA1qw1eXavcC9GzAry8ulYbvOPC6su9cDMr/M0L0bP2h5Bj09uW1nbz2vvaa69Fapc3P68N1nrwcj2s47ulpcWsa+1nXuBePhAsBgBAgPr6elVVVfX83XLLLYlNe+XKlZo/f76+853v6Mknn9R9992n+++/X1/5ylcSmwcAAEAU0W+9AQAOWUncOV63bl2vJwbeE6KamhoVFxerubm5V3lzc7Pq6urMz9x44436xCc+oUsvvVSSNHbsWHV0dOhTn/qUrr/++kR+fg0AAMTDk2gAQGYk8Q5TZWVlrz/vIrqkpEQTJkzQihUresq6urq0YsUKTZ482fzM9u3bcy6U9309tD91ugAAHMp4JxoAkBn5vnM8e/ZsXXzxxTrttNM0ceJELVy4UB0dHZoxY4Ykafr06Ro9enTPV8LPP/98LViwQKeeeqomTZqkl156STfeeKPOP/98911LAACQXzyJBgAgJdOmTdM3v/lNzZ07V+PHj9dTTz2lZcuW9YSNrV27Vq+//npP/RtuuEGf+9zndMMNN+ikk07SJz/5STU2Nur2228v1CIAAIA+YNGiRRozZozKyso0adKkA/785cKFC3XCCSeovLxc9fX1+uxnP+uGvHkiP4kmcRs4NHl3/ax3TL1kbCsx1UuOtabrvc9qtc1rg5Wy6dWFLd93gGfOnKmZM2ea/7Zy5cpe/z9w4EDNmzdP8+bNy0PLkKaQxGLr3OLtp9u3b88p81J9rbojRoww61pJ09YyeHWHDh1q1t2zZ09OWUhSr5e+a7XBOxdaYzuvDdYyDx8+3KxrJRl768xqmzfmtFKPreRyr9xrr9UHedO1fo3CS+e2tpH1ecleNq8ftdZZyLbwEuOtfjQkudzbxlZ5eXm5WddKDreOFck+vkMS+b261jnGOq68uh5re3rrzPsVAou1Pb19J1/yOZ5YunSpZs+ercWLF2vSpElauHChGhsb9fzzz2vUqFE59e+66y5dd911WrJkiU4//XS98MILuuSSS1RUVKQFCxZEni9PogEAmXmHCQAApCff44kFCxbosssu04wZM3TSSSdp8eLFqqio0JIlS8z6jz32mM444wxddNFFGjNmjM4991xdeOGFB3x6/WZcRAMAAAAA+oT29vZef95vm3d2duqJJ55QQ0NDT9mAAQPU0NCgVatWmZ85/fTT9cQTT/RcNL/88st64IEHdN555wW1kWAxAEBmgkAAAEB6khhP1NfX9yqfN2+ebrrpppz6LS0t2rt3b0+eyj61tbV67rnnzHlcdNFFamlp0Zlnnqnu7m7t2bNHn/nMZ/TFL34xqK1cRAMAuIgGAACxJTGeWLduXa933r2fzDwYK1eu1Pz58/Wd73yn59c+Zs2apa985Su68cYbI0+Hi+g888IyrJ3N2wGtaXjTtUITCIlDFCEnQOsnhqyAEskO9/CCS0KCway6XkCOFyaSZVxEo5C84CDrGPZ+0qyqqiqnzAtQss4BXhtCQpysMB8vkMiahteGYcOG5ZR5gUTW+vHCrKz5eefjkOPcWu9eG1paWiK3wQpp877mac3PG4hv2rTJLLeUlZXllHmhdNY689ZDW1tbTpkVrOfxguas8C1vnVnt9fZJK0zN225WuRcmaO3XGzduNOtax7HXBm9MYrH2M+sYlOx9x5uX1TbvHGUF23lBidZ6KGSwWBLjicrKSnd5/1ZNTY2Ki4vV3Nzcq7y5uVl1dXXmZ2688UZ94hOf0KWXXipJGjt2rDo6OvSpT31K119/vRt2+2a8Ew0AAAAA6FdKSko0YcIErVixoqesq6tLK1as0OTJk83PbN++PedCed/Nx5CLf55EAwB4Eg0AAGLL93hi9uzZuvjii3Xaaadp4sSJWrhwoTo6OjRjxgxJ0vTp0zV69GjdcsstkqTzzz9fCxYs0Kmnntrzde4bb7xR559/vvtNJwsX0QAALqIBAEBs+R5PTJs2TRs3btTcuXPV1NSk8ePHa9myZT1hY2vXru315PmGG25QUVGRbrjhBq1fv14jR47U+eefr69+9atB8+UiGgDARTQAAIitEOOJmTNnaubMmea/rVy5stf/Dxw4UPPmzdO8efMOal778E40AAAAAAAR8SQ6RVYKtpcKbKXoecl6Vuqfl9JpJVF6bQhJQgb+lnXn0EuBj5p6KNn7ZMhdStK5o+NJNPLFSjL2EqytY9hLWLaOaytBWLJTcrdv327WtdKJrYRmyU7atdKnJTtN2Xsfz2qDlwBsrQfvGLXWZWtrq1nXSiz2xg3W9vRSqUeOHBnp85K9Pb3zudUHbdmyxaxr7Q/etrCW2atrjeO8utY+5a0za8zn9XfWvuPVtdrmHRdWcriXNB3185K9n3kJ4dZ+4o0xrDRwb+xszc/bz7w0cIuVuO2N9a1j1puX1V7v3JcPWRlPcBENAMhMpwcAANKTlfEEF9EAgMx0egAAID1ZGU/wTjQAAAAAABHxJBoAkJk7xwAAID1ZGU9wEZ2ikFCA8vLynDIvbMAKEPDqWkEKXkgEwWLpsvYHLwCjP51EPF6w2K5du3LKQsIyvDAda36HwnrMl6x0eig8Kwyovb3drGsFHYUEPnn7phUSZAV9SVJlZWVOmddea9mOOOIIs651LrPmJdnnwpD+w1sP1rnXCnby2uCFLVnzs8Y5kr0evLrW+CVkul7/YS2z1VdJ9njL68OsfdUbr1nr0huXWWM7L3zLmq63n1nz89ZDdXV15DZYAXZeaJoVOObtZ1ZQl9cGaz/xtpsVQOetB2tbWGWSvcxeuJl1fHthbFa5tx7yISvjCb7ODQAAAABARDyJBgBk5s4xAABIT1bGE1xEAwAy0+kBAID0ZGU8wUU0ACAznR4AAEhPVsYTvBMNAAAAAEBEPIlOkXU3xUtltBICvTRCK93PS+m0EotD0jQ9XvIy/HVTUVGRU+YlLR4K69dbBisd1VsPIWmlIW1ArqzcOUbf5B2r1jnAS9S1pmGlAkv2Puul2ba1teWUeYm6Vv/spRBby+G1wUoL9lK0rWX2koUtIe211o0kHXbYYTllO3fuNOuGpAhby+GtBysh2WuDNYby+hovHd5ipYl7+7o1XW8/s6ZrrXPJXr9en2tt47KyMrOudWx66efW+g1JVd+2bZtZNyQh3BqDedO1eOcdS2trq1lurUtvP7OSx61l8BTyF3eyMp7gIhoAkJlODwAApCcr4wkuogEAkvpX5wUAAPqmLIwneCcaAAAAAICIeBINAMjM168AAEB6sjKe4CI6RVZ4hBcSYQVKeGEOIWEkVrBASEiEFzBiBT8UMsSgL/GCH6xtfygHX3knQitgJCRY5lBeZ4WUlU4PhTd8+PCcMitER7KDoLzzhTUNrw+z+lwvoNPihSJZx4IXvmUFYnnBTCHhoxYvYM1av9451gqj8o59K7DJG/9YAWCempqanDKvvS0tLTll3vjHCn31wtis9eCNf6x9yttuQ4YMidQub35eWFjIOHDDhg05ZZWVlWZdi3dshvTxFu/8YO1TVVVVZl1rnwwJFvPGdtY29rabtRxWSJxkb6OQYLGQMMGkZWU8wde5AQAAAACIiCfRAIDM3DkGAADpycp4gotoAEBmOj0AAJCerIwnuIgGAGSm0wMAAOnJyniCd6IBAAAAAIiIJ9F55iU4WsmF3t0YK9kxiSRkaxpee+PeKfLSNEPSRvtCSrOVpuklIlopkl4abH+6ExfqUF62/iwrd45ReFZatZcA7KVgW6w+wesnrHOvl6Idkh5tpfKGfN5bXiu12+svrWX20rmt8YCVBC7Zqcch/Z13nli/fn1O2bBhw8y61rr0EpatZfNSk61yL9l95MiROWVeerQ1hvLGYNa69JbNSnb3WPuDtx6s6XrHkJVw703X2m7e/tve3p5T5o1FraR/b18PSZe3lsNKsvfaZrVLso9jbxxoJXF7x5vVNmv75EtWxhNcRAMAMtPpAQCA9GRlPMFFNAAgM50eAABIT1bGE7wTDQAAAABARDyJBgBk5s4xAABIT1bGE1xEp8gKY/CCFOKGennBBFYIwfbt22O3IW6ol7cerKAub15W4EYSB581Py/0xgpusEIxJHsb9aeTBQ5tWen0UHhWv1RTU2PWbW1tzSnz+jsrvMgK8pHsPsgL4rECpry+0WpvWVmZWdcKDgoJfPKCjqx+1JuuFabmrTNrvXvrzFpmL3zL4gUoWdP1wtisaXiBZda69EK9QsZ21tjBC26zxg5WmJvH227W/rt161azrrVsXsCaxdsfrDA1L2DNapvXx1gBdt6xaW0jb51ZwWDe2M7ad7zpWsemV9daDi/8MGTcmg+FGE8sWrRI3/jGN9TU1KRx48bp1ltv1cSJE82655xzjn7/+9/nlJ933nm6//77I8+Tr3MDAAAAAPqdpUuXavbs2Zo3b56efPJJjRs3To2NjdqwYYNZ/7777tPrr7/e8/f000+ruLhYH/nIR4Lmy0U0AKDnzvHB/gEAAOR7PLFgwQJddtllmjFjhk466SQtXrxYFRUVWrJkiVm/urpadXV1PX/Lly9XRUVF8EU0X+cGAPB1bgAAEFsS44k3f32+tLTU/A3wzs5OPfHEE5ozZ05P2YABA9TQ0KBVq1ZFmuedd96pCy64wHwlaH94Eg0A4Ek0AACILYnxRH19vaqqqnr+brnlFnNeLS0t2rt3r2pra3uV19bWqqmp6YBtffzxx/X000/r0ksvDV5OnkQDAAAAAPqEdevW9QpHs55CJ+HOO+/U2LFj3RCy/eEiOkXW0xlvJ/DSOy1WCp+X0mklcXtJllZiZMgTJi8x1eIlWVrrx5uul3AZlbdsVrmXOGklJXpp4lY5T/DQV/B1buSLdS70knqtfctLs7XqeqnUVtKuV7elpSWnzErLluy+zUrh9up6ycLer2pYOjo6csq89Wstc3V1tVnXSqv22uUth8Ua/3hjBCuN2RsLWKnS3nqweNvN2s+8tGtrPXjrN2QMZiWPW8nPkj1Osdaj14aQixevvdb29I4ha5152806F3h1rf3BS2u3puutX2t8uHHjRrOulZjtJbB7+5TFGieHjMmTlsR4orKyMlLCeE1NjYqLi9Xc3NyrvLm5WXV1dfv9bEdHh+6++259+ctfPqi28nVuAABf5wYAALHlczxRUlKiCRMmaMWKFT1lXV1dWrFihSZPnrzfz95zzz3atWuXPv7xjx/UcvIkGgDAk2gAABBbvscTs2fP1sUXX6zTTjtNEydO1MKFC9XR0aEZM2ZIkqZPn67Ro0fnvFd95513aurUqRoxYsRBtZWLaAAAAABAvzNt2jRt3LhRc+fOVVNTk8aPH69ly5b1hI2tXbs25+vtzz//vB599FH95je/Oej5chENAOBJNAAAiK0Q44mZM2dq5syZ5r+tXLkyp+yEE06IPXbhIjpF1sbxAjCsIAQvWMMK4fCCFKy6XrBYSPCVFVjgBWpZ0/CWLWq7JDvswqsbtV2SHWrhhXB469JihbJ4gWVclCDfuIhGvljnWC8c0zrPW8FZkn3+Dwnt9IJ8QsKWrGXbsWOHWdcKOgpZNi9gzarrtdcq97aFFVDl9WHWGMFaXq8NXmCZFYTmjaus8C1vrGSFiHnTtfpyb/1awUhegF1ra2tOmRfQZrXX28+saXjHhVXXC6iy1qUXkmUF+XnjQGt+3u/3hoQJWsv85jCqfawxnxeEZu1n3tjQOra8fcc6vr1tbM3PO47zISvjCYLFAAAAAACIiCfRAABJ/esOMAAA6JuyMJ7gIhoAkJmvXwEAgPRkZTzBRTQAIDOdHgAASE9WxhO8Ew0AAAAAQEQ8iU5ASCp1SCJ0yN0YL+XQSsMMSYT2Uhmt8pD2hqRoe3Wt8iSmay2bl9IZkjxu1bVSPr02eNuiP921Q9+VlTvH6F+s856XsGz1bd4vK4T0YSHnY4vX11gJ1F57rWRgLwk55Nc+rOXwUn2t9eMlhIekXVvL7I1TrLZ5ScjWeghJmvZY6yEkedzbz7yUZkvImNFKpfbqNjU1RW6Dt8xR63oJ7FZdb/+1yr1tadX19rMhQ4bklHn7utVebz+zEthDrgus40qyU+ALKSvjCS6iAQCZ6fQAAEB6sjKe4CIaAJCZTg8AAKQnK+MJ3okGAAAAACAinkQDADJz5xgAAKQnK+OJfnURHRJ8ldZGsAJCvMCDQYMG5ZR5QQxWe70QDmu6XsCIJSQQxVuPcUO9QsLNvLpWeUjIWxIhZBYvGGbw4ME5Zd6yWcEYXgiHNY3+dBJC31CITm/RokX6xje+oaamJo0bN0633nqrJk6c6NZvbW3V9ddfr/vuu0+bN2/WUUcdpYULF+q88847qPmjMKxzVkiwkxWUJNnBP96+afWvVpCPZIcMee21yr26VpDUsGHDzLo7duyI9HnJXj+bNm0y61rjCS9YzFoP3jjF2sYdHR2R63rbwuqLvX60rq4u8nStftsLa7LWb3t7u1nXCj3bunVr5OlaQVReXW+61jqztqUkHXHEETllIWNcrw3eNrLs2rUrchtGjRoVuQ0Wb7vFHVN7IWTWtrDWozcN73xm7Sfevp4PXEQDAJCSpUuXavbs2Vq8eLEmTZqkhQsXqrGxUc8//7w5MOrs7NR73vMejRo1Svfee69Gjx6tV1991b3oAAAASAsX0QCAvN85XrBggS677DLNmDFDkrR48WLdf//9WrJkia677rqc+kuWLNHmzZv12GOP9dy5HzNmzEG1FwAApCMrT6IJFgMA9HR6B/sXorOzU0888YQaGhp6ygYMGKCGhgatWrXK/Myvf/1rTZ48WVdeeaVqa2t1yimnaP78+QX9yhoAAOgtn+OJQuJJNAAgkTvHb36/rLS01Hxvs6WlRXv37lVtbW2v8traWj333HPmPF5++WX99re/1cc+9jE98MADeumll3TFFVdo9+7dmjdv3kG1GwAAJIsn0QAABKivr1dVVVXP3y233JLYtLu6ujRq1CjdcccdmjBhgqZNm6brr79eixcvTmweAAAAUfTJJ9FeErKVJhiSRpgE6w6Jl6xnpTF7qaJWwqWXom3V9RKhvfKoQtaj196QFG2Lt35D0h5D7myFpJdbiZEh8/L2h5CkWiu121sGa733p7t+SE8Sd47XrVvXK9HWSw+uqalRcXGxmpube5U3NzebibqSdNhhh2nQoEG9jvsTTzxRTU1N6uzsdH/5AH2Pde720myt9Fyv/7D6XO/r/tY5NiSV2ksLtpKFQxLCvbpWX+GtB2v9WutG8vvXuEJesxg6dGhOmZcmbh3n3rysZOxt27aZda3t6f0iRtxfJwk5V1VXV5vl1n7ipZ9b+5mXqm6tS288YtX19l9rrO6ln1tt88ZK1vlh8+bNZt2jjjoqp8xbv9ayedcbVrill+5tTcNK3pfs/cSrax3HXv+bDzyJBgBkRhLvMFVWVvb68zrxkpISTZgwQStWrOgp6+rq0ooVKzR58mTzM2eccYZeeumlXoPdF154QYcddhgX0AAA9BFZeSeai2gAQN47vdmzZ+t73/uefvSjH+nZZ5/V5Zdfro6Ojp607unTp2vOnDk99S+//HJt3rxZs2bN0gsvvKD7779f8+fP15VXXpnYOgAAAPFk5SK6T36dGwBwaJs2bZo2btyouXPnqqmpSePHj9eyZct6wsbWrl3b67WE+vp6PfTQQ/rsZz+rt73tbRo9erRmzZqlL3zhC4VaBAAAkFFcRAMACvIO08yZMzVz5kzz31auXJlTNnnyZP3hD384qHkBAID0ZeWd6IJfRFsBSF4AhhUIkVaAmMfauF6IkxWEUFFRYda1Aha8upbW1laz3Apd8AJcLN7ObK13L6giJADMmob3vmNIaEJIe60QDW+dWeVe4EzIicGq6y2vFSjhBYFYQSn5PobQN2Wl00PhWefekDBFr8+1zr1W0Jdkh256Yw9r//b6peHDh0eal8dbNivczKv75p+Ok/xj1Fo/VlCSZAeveX2jFXIVEqw6YsQIs25IIJwVMOW1wVpmb5+01mV9fb1Z1wqo8sLcrHGct92sciugTbL3HS9gzdpXrYA2yd5u3nStcaA33ZDgQes49LZbW1tbTpm1bjzeMW9tT2+ftALHvDZY5zNv/VrXC4Xsl7Mynij4RTQAoPCy0ukBAID0ZGU8QbAYAAAAAAAR8SQaAJCZO8cAACA9WRlPcBENAJDUvzovAADQN2VhPMHXuQEAAAAAiKjgT6KtOxVWqqNXN9+sdD4v0dNK7LNSuCXpsMMOyykbPXq0WXfHjh05ZX/961/NutY6sxK7pbB0VEsS6dzW+vWSQq1kRy+t1Gqbl/ZoJVh7y2CVe20IYaUyeinaScwPyMrXr1B4VmKxl3wbkuRtnY+9JOSQX9qwpmsl/Up20q73ywrWevCOJavfrqmpMetafaZX11qXr732mll3/fr1OWVeP2rNz9tuViq1N66yeNvN6jO9ulaqurfvWKnfLS0tZl1rObx1Zo0PvV/72LJlS06ZlzwewmqDNSaS7PXjpeFb690bM1p1vXVmHS/V1dVm3ZBxoLUuvcRta0zuTdc6F3jp/dayeeNA6xjyxs75UIjxxKJFi/SNb3xDTU1NGjdunG699VZNnDjRrd/a2qrrr79e9913nzZv3qyjjjpKCxcu1HnnnRd5ngW/iAYAFB4X0QAAIK58jyeWLl2q2bNna/HixZo0aZIWLlyoxsZGPf/88xo1alRO/c7OTr3nPe/RqFGjdO+992r06NF69dVXg288cBENAOAiGgAAxJbv8cSCBQt02WWXacaMGZKkxYsX6/7779eSJUt03XXX5dRfsmSJNm/erMcee6znGxVjxowJni/fAwUAAAAA9CudnZ164okn1NDQ0FM2YMAANTQ0aNWqVeZnfv3rX2vy5Mm68sorVVtbq1NOOUXz5883X7HZH55EAwB4Eg0AAGJLYjzx5hyI0tJS853ylpYW7d27V7W1tb3Ka2tr9dxzz5nzePnll/Xb3/5WH/vYx/TAAw/opZde0hVXXKHdu3dr3rx5kdta8ItoayX3hQGZ1wYr8MALn7DCSKqqqsy6Rx99dE6Z99UCK3jNC5+wAiG84DZrGt50LV7ggVXurTPrfQQr8EOS+Z5DRUWFWdcKNNm6datZ1wposIIjJDtEI4mgL+tumHeHLCSELCSoAtnCRTQKKeT85vVLVr8SErYU0n94bdi2bVtOmReaZrXN62uswCcrkFSyl8N7189aDi9YzApN88KWrCC0oUOHmnVDtpHVXi9IylpmL+TN6re9cDNrTLJhwwazrsVbZ1bwlbc/WP22N11r//NC3qxyb8xojX9CjiGPNT8rSFayl80LLLP2X8+mTZsitUuy10NIMJ4XSmddL3jLYPXB3r6TD0mMJ+rr63uVz5s3TzfddFPcpkl64zwyatQo3XHHHSouLtaECRO0fv16feMb3+hfF9EAgMLjIhoAAMSVxHhi3bp1vW4eejfAampqVFxcrObm5l7lzc3NqqurMz9z2GGHadCgQb0edp544olqampSZ2ene+PzzXgnGgAAAADQJ1RWVvb68y6iS0pKNGHCBK1YsaKnrKurSytWrNDkyZPNz5xxxhl66aWXen2z5YUXXtBhhx0W+QJa4iIaAKD/d+f4YP8AAADyPZ6YPXu2vve97+lHP/qRnn32WV1++eXq6OjoSeuePn265syZ01P/8ssv1+bNmzVr1iy98MILuv/++zV//nxdeeWVQfPl69wAAL7ODQAAYsv3eGLatGnauHGj5s6dq6amJo0fP17Lli3rCRtbu3Ztr9yD+vp6PfTQQ/rsZz+rt73tbRo9erRmzZqlL3zhC0Hz5SIaAAAAANAvzZw5UzNnzjT/beXKlTllkydP1h/+8IdY8+QiOlBImriVnuglelrT8FKerXIrCdxrgzddbxpRp+uxpuulcVrpnW+Ord/n8MMPzyk78sgjzbrWuxRr1qwx67788ss5ZVZit/TG79O9mZc8bvH2HWs/8dItrWl4745Yda0Ed4+33axETm+6VoIoCeGFx5No5It1frLOpZLdX4X0o96vMFj9kjddqw3eL21Y0/XOx1YiuZdubGlrazPLrfTokHTkkF8c8daZtT29dOOQNljr0ktCthKdrV/1kOz0cy9N3Fpmb+zx6quv5pRt3LjRrOvtJ1HrevuOlR7tnbOtfdJaN5K0e/funDJvfGlNw2tDU1NTTpl3vFlp1VZCvmQfA950rbGON/6x2uu1wdqvvWPI2m5eG6x1Gfqbx0nKyniCi2gAQGY6PQAAkJ6sjCe4iAYAZKbTAwAA6cnKeIJ0bgAAAAAAIuJJNAAgM3eOAQBAerIynuAi2uEFHVkv+re3t5t1rRCOlpYWs64VZuWFDVhhDlawgSTt2LEj8nQtAwfau0hIEJT1w+UhAVUjR4406x577LE5ZaeccopZd8SIETllXhiJxdo+kh2U4q2bkFA6axt7wW9WeIQ33ZDgkpAQGPR/Wen0UHhbtmzJKfOCxayQLCtMSLLPm96+aZ3fvLpWX+G116obEvDjtcGanxdeZIVmhoSmeWOaTZs25ZRZ/bs3P288YW03LwjNCoLyQsisEEtvPHHYYYfllB1xxBFmXWvZvKBSK5TUGpdJ9nqwyiR7nOGNq6x9x9t/q6urc8qsca9kb0+vbsjY2ToGvH3d2sZeEJpV11sP1ljUa4O1LaxQO8ned7xxmVXu1bXa5h1v+ZCV8QQX0QCAzHR6AAAgPVkZT/BONAAAAAAAEfEkGgCQmTvHAAAgPVkZT3ARDQDITKcHAADSk5XxBF/nBgAAAAAgIp5Ep8hKl/TSCNetW5dT5qUyWqy0U8lOIPXSCK3UQC/10kuKjsr7vJX06aVpvuUtb8kpO/744826Vpqrl6JtJWdaCZtSWOK2td699WslpnrrzFoOL4k1JFXdqhuSvBkyXRReVu4co/Cs85vX31nnXq9PCEmattJsvXRji5cebbXXS7C2+iXvPG/18d6vfVipvGvXrjXrWm3z1pnVr3h9TcgYwUpN9j5v9TVe+nltbW1OmferE9a2f+tb32rWtfpt7xzY1taWU+alc1vr3dsnrXJrXpK9zryUZ2v/9davtY287WYdm16CtbX/WmnZHmtekr1+vbrW9vTWr9U2b5xj1d26datZ15qGl7htrctC/sJKVsYTXEQDACT1r84LAAD0TVkYT3ARDQDIzJ1jAACQnqyMJ3gnGgAAAACAiHgSDQDIzJ1jAACQnqyMJ7iIDmS96G+FYngqKyvNcivwwAtasUItvLAwK8DCC4mwdtyQcDMvoMEKYPFCWaz2bty40az7+uuv55TV1NSYdY866qicMi90wdrGXjCMVe6t35D9xNoWXlCFtd69sBerDV7ICQFg2ZKVTg+FZ52fvECi6urqyNO1+kGvXxo+fHhOmXfOs/or7xxr9c9WgJhkHzebN28261p9jdeG9evX55R563fUqFE5Zdu3bzfrWuvSCzqytoU3XWu9h/Q/gwcPjtwGL4zNCtSyAl8lO8TJC4ey2uaNU6wxydChQ8261ra3wvIkext5Yztrul64mbV+vX3Sml8Sy2YdW9542NrGVVVVZl1rmUP6OW9cZZ1LvHOUNV7btGmTWdc6T8YNAI4jK+MJLqIBAJnp9AAAQHqyMp7gnWgAAAAAACLiSTQAIDN3jgEAQHqyMp7gIhoAkJlODwAApCcr4wm+zg0AAAAAQEQ8iU6AlyIZksZsJQyGpHN7iYhWuVfXuvsTUjdkPXipgVb5mjVrzLpeKqilra0t8nRfeumlnDIvGdLaRt5237VrV06Zl1JulXvr11pn3nYjcRuerNw5RuFZydjeryVY6cZeXet87J3zrP4jJN3Y68OsVF6vL7f6BC/B2jqnWynRkt1eLwHYmp/Xhw0bNixyXSsF20tat5bDGud48wtJ5/baayVxeynPVnu9VPXm5uacMu8XPEJSqa31642JQn4hxUqBLy0tNetax4C3bFbbrF+lkcISt620am89VFRU5JSF/KqMlyZunWNCflXA6z+9bR+Vl6qeD1kZT3ARDQDITKcHAADSk5XxBBfRAIDMdHoAACA9WRlP8E40AAAAAAAR8SQaAJCZO8cAACA9WRlPcBGdIiuwwAodkexgAi8sIyTUK25db2e2AjC88BRrfl64hxUIsX79erOu1TYrqEWyw8K8ABcrqMKrawU/WCFmkh2i4a0Ha51528Iq98IyrHLCxiBlp9ND4Vn9oBcGtGHDhpwyL1DL6pe8fdM6FyZx3rTa5rXXCmHy6lpBWzU1NWZdq18K6Wu8/s5aD15AlRWs5AUdWWFWXqiStY3/+te/mnWt4Csv8MmaxtatW826VvCVtx62bNmSU+YF41nT8PbJkDGYFQzmbYuQfcfaJ73pWsvmHfPWNKx9RLLPJV4onTWNkLGoFyYYsmxWuXddYAm5LvDamw9ZGU9wEQ0AyEynBwAA0pOV8QTvRAMAAAAA+qVFixZpzJgxKisr06RJk/T444+7dX/4wx+qqKio19/B/CQYT6IBAJm5cwwAANKT7/HE0qVLNXv2bC1evFiTJk3SwoUL1djYqP+/vfuPrqq68///SgJJ+JUAIoliKmirSFWwUCha1H4aZdqOHWbGKbWdQrOUmVap2LRTtVXAVkVbpfixVBSlHdu6ZNqxzg8ValMZ9SNdTEFW7Q+pWhHGmhBACAQhmOT7h1/uNN73G8/JPefem+znY62sJdt9z9nnxz1773Pufd0tW7Zo9OjR5muqqqq0ZcuWzL9789VGnkQDADKdXm//AAAA8j2eWLp0qebNm6eGhgZNmDBBK1as0ODBg7Vq1Sr3NSUlJaqtrc381dTUxF4vk2gAAJNoAACQsyTGE21tbT3+vODgjo4Obdy4UfX19Zmy0tJS1dfXa/369W4b9+/frxNPPFF1dXX6q7/6K/32t7+NvZ18nDtFcZKxraRFK7nTk0TidhzWMrz2eie+Jc7HKbZv355V5iVje4mclo6OjqwybxusFFMvVdRKYPQSPa39a50jkr3fvf1ofefDOx+sBFGSvAHkyrrueUmygwcPjrxcK514xIgRZl3rmu4l6lri1N2zZ49ZbvUfXhqzlSzc2tpq1rX6BG//Wm3z2hAnTdzqa3bu3GnWtZZhJWBLdpqy149a5V4/au1Lb0zzpz/9KavMSsCW7OPmpZ9b6/PaYG2b1z9b5d75W1VVlVVmjYkke7zlJU1b55933Kz94yWPW08PvSRvq71xEqyHDRtmlu/evTurzDvXhw8fnlXmHQsrfd/bD9a+9M6zvqKurq7HvxctWqTFixdn1du5c6c6OzuzzoWamho9//zz5rJPPfVUrVq1Smeeeab27t2r2267TWeffbZ++9vf6oQTTojcRibRAAC+Ew0AAHKWxHhi+/btPW7qeDeqemP69OmaPn165t9nn322TjvtNN199936xje+EXk5TKIBAEyiAQBAzpIYT1RVVZmfjHi7UaNGqaysTC0tLT3KW1paVFtbG2mdAwcO1FlnnaUXX3wxVlv5TjQAAAAAoE8pLy/X5MmT1dTUlCnr6upSU1NTj6fNR9PZ2annnntOxx13XKx18yQaACCJJ8oAACB3+RxPNDY2au7cuZoyZYqmTp2qZcuWqb29XQ0NDZKkOXPmaMyYMVqyZIkk6etf/7o+8IEP6N3vfrf27Nmjb33rW3rllVd02WWXxVovk+g88wI74oRvWUEraQWLxVmuF4BhLcOrGyc0xAqwsF4v2QELXviEtT4vWMM6bl5dqw1eyInVhjghZHGOMRMnSHycG/lj9QkDBw4061pBOl7YmBVm5fUJVl2vv/MCKy1W+JDX31nf8fPqWv2HF0hkbZsX1GUt1xuPWP2VV9cKpvRCkaxleP2ddTy942adJ975YAWCeu21xg5eXSu4ygvfisM69l4brOuzF3Z3zDHHZJV54xSr3DsnrfOvra3NrGsZMmRI5LretlmsoC/JPne89lrvWe96Zu0Hb15gndfWe1uyj7E3Fs2HfI8nZs+erdbWVi1cuFDNzc2aNGmS1qxZkwkb27ZtW4/9/Prrr2vevHlqbm7WiBEjNHnyZD3zzDOaMGFCrPUyiQYAMIkGAAA5K8R4Yv78+Zo/f775/9atW9fj39/+9rf17W9/u1fr+XN8JxoAAAAAgIh4Eg0A4Ek0AADIWSjjCSbRAIBgOj0AAJCeUMYTfJwbAJDp9Hr71xvLly/X2LFjVVlZqWnTpmnDhg2RXvfggw+qpKREs2bN6tV6AQBAOgoxnigEnkTnmXdyWOl8XoqkZdCgQZGX60kitTtqXS/t0Uo59OpaSZReGqG1L5NIObQSROMkkHr73NoPXmprX7rgAEesXr1ajY2NWrFihaZNm6Zly5Zp5syZ2rJli0aPHu2+buvWrfryl7+sGTNm5LG1SJKVWBynb/TESWO2runeuqzrfHV1tVl35MiRWWWvvfaaWddan5c8bvXxr7zyilnXShz2lmtth5XYLdnJy97Yw+r3vYRwK63aS0S39pl1Pkn2OeWNXaz+ddeuXZGX640n4vw6ibV/4vyCh/cesvall6JtvV+8BPYRI0ZklVkp517brPeKFC+93No/XrK7de54+8xK+PbaZSVxW8n7kn3d8dob5z0UJw0fyeFJNAAg73eOly5dqnnz5qmhoUETJkzQihUrNHjwYK1atcp9TWdnpz796U/rhhtu0EknnZTL5gIAgBSE8iSaSTQAIJFOr62trcef9wSjo6NDGzduVH19faastLRU9fX1Wr9+vdvGr3/96xo9erQuvfTSZDceAAAkgkk0AAAx1NXVqbq6OvO3ZMkSs97OnTvV2dmpmpqaHuU1NTVqbm42X/P000/rvvvu08qVKxNvNwAAQBx8JxoAkEia5vbt21VVVZUp974XFte+ffv0mc98RitXrtSoUaMSWSYAAEheKOncTKKLhHXSeGEZVvCDV9cL/chVnJPcCn7wwj28gIWovCAFq9z7qKkVuOGFe1jLiLMNcQLWvH2e67HwQk4QliQ6vaqqqh6TaM+oUaNUVlamlpaWHuUtLS2qra3Nqv/SSy9p69atuuiiizJlR87lAQMGaMuWLTr55JN71XbknxXE47HOJ+86HycszGqDd/5bdb1rd1tbW1aZF+Jk9dteX24FVw0bNsysa/EC1qy2eQFKVgiZN8awwsm8fWaNB4YMGWLWtY6Rd9ysNngBa954wFJeXp5V5h1jazxgvV6yz19vn1nlVriUV9fbv9ax8M4d673p7Yc4Y484YWzW+9sLgrXa4B136z3ghXpZ1wdvHGhthxUE6C3DC9yLE8aWD6FMovk4NwAgr99hKi8v1+TJk9XU1JQp6+rqUlNTk6ZPn55Vf/z48Xruuee0efPmzN/HP/5xfehDH9LmzZtVV1eX8/YDAIDchfKdaJ5EAwDyrrGxUXPnztWUKVM0depULVu2TO3t7WpoaJAkzZkzR2PGjNGSJUtUWVmp008/vcfrj9y9f3s5AABA2phEAwDy/vGr2bNnq7W1VQsXLlRzc7MmTZqkNWvWZMLGtm3bFus3ggEAQOGF8nFuJtEAgIJ0evPnz9f8+fPN/7du3bqjvvb73/9+r9YJAADSwyQaABCMUDo9AACQnlDGE0yii5h3Ilnpnd7HHnM9GeO8Pk56tFfXS+2OytsPVlqjl/BqJWd6yZBxkq2tbUtin1nl3n609oOX6milbPalixuA4mSl5HrXbisZ2Eqqluzrk5fUm+v1zUp+luxrr9fXxEkA3r9/f1aZl4RvjRG8lGdr/44YMcKsa6UQW2nkcetaKcTHHHOMWddKld61a5dZ1zpGr732mlnXOqe8ftQ6T7zE+Tip33v27Mkq89KurbZ5x9hqm5e4vXv37qwyLzHeWp/3vrDOa68N1vng/VyitwyLtR+sfS7FS763ttm7nu3bty+rzEuMt/avdz2zrjFeAjuSwyQaABDMnWMAAJCeUMYTTKIBAMF0egAAID2hjCeIPgUAAAAAICKeRAMAgrlzDAAA0hPKeIJJdBHzggmsEAIv1CJOgIsVVGEFPEh2oJbXhji/9RonGMaq6wXOxAn1srYt18Azj7d/cw108wLPrJCSOOcO+q9QOj0UnhXa411jrUAtL6DK6mu8/iMOK8DLW64V5uMFi1nb7PVh1n7wQqeam5sj17XGE3GCjrxgSqu9XjiUddzi9EteOJS1zVZwqCS1t7dnlXmhVccff3xWmRcgZoVOeedDnG2z9o93jK1yb1xm1R00aJBZ1zr23pjG2r9eeJw1fvH2r3WMhg8fbta1QtO8/VBdXZ1V5gWsWcuwtley3/NeMJ51LLzrpHVee+/NfAhlPMEkGgAgqW91XgAAoDiFMJ7gO9EAAAAAAETEk2gAQDAfvwIAAOkJZTzBJBoAEEynBwAA0hPKeIJJNAAgmE4PAACkJ5TxBJPoImGdNF7SopUQ6J10VpKflwxpsZIlvWVYqaSSn2gYlZXyKdnb7O0HKz3RSwqNw0rT9JKxrX3m7RurrpfKaKVhesfYOkZeCqqVeumdk9Z+J90bgMVKyfVSk63rv5eaHKdvtK7/3rXQSrn1EoDjXPesfmnfvn2Rl+td54cOHZpTG3bs2BG5rrfP4vSvVp/pjT2sftAbe1ip0t4+s8qthGbJTlC3EtEle/94546V6OydT1a51z9bdb3jFic533pfeOMf69xpbW0161rL8MZK1rnuXUusc9JLord4Y1HreuTtX+uc3Lt3r1nX2g9xzvXXX3/drIvkMIkGAARz5xgAAKQnlPEE6dwAgEyn19s/AACAQownli9frrFjx6qyslLTpk3Thg0bIr3uwQcfVElJiWbNmhV7nUyiAQAAAAB9zurVq9XY2KhFixZp06ZNmjhxombOnOl+NeWIrVu36stf/rJmzJjRq/UyiQYA8CQaAADkLN/jiaVLl2revHlqaGjQhAkTtGLFCg0ePFirVq1yX9PZ2alPf/rTuuGGG3TSSSf1ajv5TnSRsEIXDh06ZNa1TjAvxMAKhLDCICQ7oMpql2QHWHjLzZUXamGVW8EcHi+wwwpo8FjHwtqPkn08rYASyQ6w8I6FxdtnVmiNV9fiBXZY54N3LAgcK06hfIcJhRcnDNG6PtXU1Jh1reubdz23+kyvDda117t2W4FY3rXbCkuygp0ku0/o6Ogw61rL8Pol6707cuTIyG3w3vtWn+DtB2sZbW1tZl2rH/UCwKxgJq8N1vH0+jtrud55Zp2/e/bsMetafaMXQmYdTy9Iytq2OAFrXp9tLdcLlLOW4Y0ZrWPknetWMFic95sXFmaVe4F91jZ772Pr/PXGYNZyvbrWe8i7RuVDPscTHR0d2rhxo6699tpMWWlpqerr67V+/Xr3dV//+tc1evRoXXrppXrqqad61VYm0QAAJtEAACBnSYwn3n4jraKiwryxsHPnTnV2dmbdXK2pqdHzzz9vruPpp5/Wfffdp82bN/eqjUfwcW4AAAAAQFGoq6tTdXV15m/JkiWJLHffvn36zGc+o5UrV2rUqFE5LYsn0QAAnkQDAICcJTGe2L59u6qqqjLl3lcFRo0apbKyMrW0tPQob2lpUW1tbVb9l156SVu3btVFF12UKTvydYMBAwZoy5YtOvnkkyO1lUk0AIBJNAAAyFkS44mqqqoek2hPeXm5Jk+erKampszPVHV1dampqUnz58/Pqj9+/Hg999xzPcquu+467du3T3fccYfq6uoit5VJNACASTQAAMhZvscTjY2Nmjt3rqZMmaKpU6dq2bJlam9vV0NDgyRpzpw5GjNmjJYsWaLKykqdfvrpPV5/JMDv7eXvhEl0kbBOGi9h0EpP9JILLV66X5z0Z6u9cVKe0xKnDV56dJxts8q9j5xYaaVWyqdkp4J6deOIs3+sul7Cq5V6aZVJpHMDobOuLd61ybrmtLa2mnWt9FwvYTlOArCVcutdCy3ecq1roferHF6is8VKdPba6/2yh8Xal95yreRlr0+wxjReerR1jL19ZrXt4MGDZl0rXdtLbrYSob1+v7m5OavMS3m2zgevv7S2zRpjSPZ7yzvuVvnevXvNula5lwhtjV+8yZJ17L32Wsfe2w/W+rxkd2t86C3X2jYrpV+y3wPeOWkd49GjR5t1rf3jJdz3R7Nnz1Zra6sWLlyo5uZmTZo0SWvWrMmEjW3bti3WHCcqJtEAAJ5EAwCAnBViPDF//nzz49uStG7duqO+9vvf/36v1skkGgDAJBoAAOQslPEEP3EFAAAAAEBEPIkGAARz5xgAAKQnlPEEk+giFudEKuZQrzhBUsWwHV4ghCVOQI4VyuLVtYJLvJCItPaZtVwv7MUKzokTxtaXLpr9VSidHgpv5MiRWWXetcXihVlZ1yGvrhXm44WQDRkyJKts165dZl2rv7NCq7z1ef2P9VMvr7/+ulnXCiEbNWqUWdcKRfL22b59+yK9XrIDpry6VuCPF+JkBZZZZZIyoUJ/zgsAs7bZ2l6Pt3+tY+wFHFn7x6sbJ/jTCt/yfjrI2u9eG6z3hXfc4rzfrPGP9x6KM7609q8XWGZtszcGyzWwatiwYWa5FdxWDGPkKEIZTzCJBgAE0+kBAID0hDKe4DvRAAAAAABExJNoAEAwd44BAEB6QhlPMIkGAATT6QEAgPSEMp5gEg0AkNS3Oi8AAFCcQhhPMIlGYrw3jJXS6aUZWgmOab0RvbRHqw1x7N+/P/L6vG2zEifjpDJ6y42TJp5WOnwIF1YAPitV2kuobW9vzyrzriFW8rJ3Pbb6oDhJv14brKToOL/CYJVJdlK01bdKdl9jJf0ebRkW6xjFSVX3+lxrn3mpydZ+95ZrHfuhQ4eadcvKysxyizVG+NOf/mTWjXOeVVdXZ5V5CeFWsrW3DdY+885fK9k9TkK4x0oD95Lo4ySPW9vs7QdrGd6xsH5BwHsPWcfIe19Y56rXXuu96f0qwIgRI7LKrHR6JItJNAAgmI9fAQCA9IQynmASDQAIptMDAADpCWU8wSQaABBMpwcAANITyniC34kGAAAAACAinkSjILwQmUOHDmWVJXFXKk6gVlq8EI2ovPZaoRRWwI5kh13ECYZB/xXKnWMUnhWYs3v3brPukCFDssq8a5YVRuWFTlnBTN411rp2W0FJkn09jhMgOWCAPSyztsMLnbKCurw+wWqbt1wr9CxOqJcXoGSVewGfcY6xFUrX2dlp1u3o6Mgq8wK1rPV5xy1O/2pts9de6xiPGjXKrGstwxprSXbwmrcN1jnlLdeq6wVfWcGD3nKtY+G11zpG3nszzrjKuj5456S1f71ts4LmvLrWMfbC2PIhlPEEk2gAQDCdHgAASE8o4wk+zg0AAAAAQEQ8iQYABHPnGAAApCeU8QSTaABAMJ0eAABITyjjCSbRAIBgOj0AAJCeUMYTTKKRGC/Z1EqR9Oqm9eaxluulZaeV2p3rcvvShQUAPNa10ErhluzEYi9910rU9RKhrTZYqeGSnXJrpTlLdrK1116rb/T2g5Xq6yUAx/mVC6ttXl2r3EuwtpbrJQtb+zLOMfaWax1j73ywktK9bbPOE+98sFLKvWNsteHYY48161rnmddeK5XaOvcku71WIrrXBu+ctNL3vf1gLcM7xtY+8xLN46SfW+uL8wsp3rXES/i2WMctzjEuZDp3KJhEAwCCuXMMAADSE8p4gkk0ACCYTg8AAKQnlPEEk2gAQDCdHgAASE8o4wl+JxoAAAAAgIh4Eo3EeHePrNAFL9QirVCvQq8rLmufeeEe1r70AiWsbc53yBuKUyh3jlF47e3tWWVWMI5kn1txrm8jR46M3K49e/aY5VbbysvLI7fBC1saPHhw5OXu2rUrq8wLyRo2bFhW2d69e826Vtu89lrbZoU1eby6Vt/m9UtWEFSc/evtMyuwKU54nBf4ZK3Pq2ud616/b+0HLyzVer9Y70HJDvuywsakeOe6tc3eONDaZ14glxUA5oXSWdcCr671fvGuUVa5d+5Y7bXOU8kOTfNYdb39mw+hjCeYRAMAgun0AABAekIZT/BxbgAAAAAAImISDQDI3Dnu7R8AAEAhxhPLly/X2LFjVVlZqWnTpmnDhg1u3YceekhTpkzR8OHDNWTIEE2aNEk/+MEPYq+TSTQAgEk0AADIWb7HE6tXr1ZjY6MWLVqkTZs2aeLEiZo5c6Z27Nhh1h85cqS+9rWvaf369fr1r3+thoYGNTQ0aO3atbHWyyQaAMAkGgAA5Czf44mlS5dq3rx5amho0IQJE7RixQoNHjxYq1atMuuff/75+uu//muddtppOvnkk7VgwQKdeeaZevrpp2Otl2AxpM56QxRzMnYxsPaZl9SY675kAgQgn6qrq7PKvPRoKz131KhRkeu+9tprZl0rBdu7xnrpxBYrTbmystKsa6VoewnAVsKyl8ZspWB7idBemrIlzj6z1mclE0t2H+alMVsp5d5yrfPES3aPk8Bu9ZlewrKVou0lhFvb8eqrr5p1rXPHWpdkJ3F7x816D8XZZ945adX19oPVXi9F29rvXt22trasMu99MXr06MjLtfalt21WYrb3qwDWcr20dmv/xrlu9WUdHR3auHGjrr322kxZaWmp6uvrtX79+nd8fXd3t37xi19oy5YtuvXWW2Otm0k0ACCYNE0AAJCeJMYTb7/pUVFRYd5c27lzpzo7O1VTU9OjvKamRs8//7y7nr1792rMmDE6dOiQysrK9N3vflcXXHBBrLbycW4AgKTefwQLAADgiFzHE3V1daqurs78LVmyJNH2DRs2TJs3b9Z///d/66abblJjY6PWrVsXaxlMogEARZ+muXLlSs2YMUMjRozQiBEjVF9ff9T6AAAg/5IYT2zfvl179+7N/P35x7X/3KhRo1RWVqaWlpYe5S0tLaqtrXXbWFpaqne/+92aNGmSvvSlL+niiy+OPVFnEg0AyLu4aZrr1q3TJZdcoieeeELr169XXV2dLrzwQvc7gwAAoG+qqqrq8eflJJSXl2vy5MlqamrKlHV1dampqUnTp0+PvL6uri43X8HDd6IRhL4WbmYFSnR0dJh1re3gI7aIK9/fif7zNE1JWrFihR555BGtWrVK11xzTVb9H/3oRz3+fe+99+pf//Vf1dTUpDlz5vSq3SgMK1zHO4es65sXdGQNgLy6Vrk3SLNCgry6cYLFrP3ghVlZAUheeJEVLOYt12qbF5LV2toauQ3efrfEOcZv/97j0epafaYXzGTtMy98y1qfdSw9Xl9uBVd559m+ffuyyrxgMWv/evthyJAhWWVDhw4161oBYFVVVWZd673pBV9Z5/rw4cPNutY+895vcepa57V3jbL2u3csrG0bM2aMWdfaP9Y+l+zjGXdCmKR8jycaGxs1d+5cTZkyRVOnTtWyZcvU3t6eGV/MmTNHY8aMyTxpXrJkiaZMmaKTTz5Zhw4d0qOPPqof/OAHuuuuu2Ktl0k0ACCvQSC5pmlKb6XAHj58WCNHjuxVmwEAQPLyPYmePXu2WltbtXDhQjU3N2vSpElas2ZN5qbbtm3betzAaG9v1+WXX67/+Z//0aBBgzR+/Hj98Ic/1OzZs2Otl0k0ACARdXV1Pf69aNEiLV68OKteb9M0/9zVV1+t448/XvX19b1uLwAA6Pvmz5+v+fPnm//v7YFhN954o2688cac18kkGgCQyJ3j7du39/g4n/dRxFzdcsstevDBB7Vu3Tr343gAACD/QvnJTCbRAIBEOr0jASDvpLdpmpJ022236ZZbbtHPf/5znXnmmb1qLwAASEcok2jSuQEAef2Jq96maX7zm9/UN77xDa1Zs0ZTpkzp9bYCAIB0FOInMwuBJ9EIgpU4eeDAgQK0JHl96YIDHBE3TfPWW2/VwoUL9cADD2js2LFqbm6W9FZyrJcei+JkJcla6ciSnRTtXfOsRF3r2i/ZacpWcq5kpxsPGGAPn3bv3p1VNmLECLOulZ7rpfpa/ZX3CxPW+8HbNms7du3aZda12jto0CCzbpzk8Ti/lGElW3vbZqUbe8fNOkZeOrcVZrhnzx6z7t69e7PKvO2trq7OKvPaa32NxUu7tvaZ117r/ea1wdrvVgK2V+4dtzhfA7KuG15ifNTXS/a5aiWiS9KwYcOyyrz9EOectM4z7/1mHU+vLpLDJBoAUPRpmnfddZc6Ojp08cUX91iOF14GAADyL5SPczOJBgAUpNOLk6a5devWXq0DAADkD5NoAEAwQun0AABAekIZTxAsBgAAAABARDyJRhCsoIr+EiwGJCGUO8coPCsUyQvUsoJ/rGAyyQ4D8gJ+rDZ4YUtW8I8VsuUtt7293axrBSu9+eabZl0rLMn7jXSrvd62WUFoXvCVVd7W1mbWtUK5rNAqjxe2ZO0f7xhb7fXClqzAp9GjR5t1rW3z2mCdq1bommSfJ8cff7xZ1zr/vDGN9b4YPny4WdfaDu9ct85f7zyztvnYY48161rH2ApHk+xzyqtrHYs4QX7e+83iveetn4D06sYJSLP6YC9UMR9CGU8wiQYABNPpAQCA9IQynuDj3AAAAAAARMSTaABAMHeOAQBAekIZTzCJBgAE0+kBAID0hDKeYBINAAim0wMAAOkJZTzBJBpB2LdvX6GbAACQnXwbJ2HZSwseNmxYVpk3ILNSmr1kbCu5OYmkaetXI6zkZ8lO6vXSe63t8PaDtQwvnduq6+0z6xhZSeuSnVjsJSxbbbDSpyX7GHvbZnn11VfNciuV2muDxTpPveXu2rXLrGvtX+t88up6x+KYY47JKhs6dKhZ13rPesnj1jZbx12KN16z3i/eMbbSqr39YG2bd41qbW2N1C7JPsZeG6x9FufXBrzkcSSHSTQAIJg7xwAAID2hjCeYRAMAgun0AABAekIZTzCJBgAE0+kBAID0hDKe4HeiAQAAAACIiCfRCIIXfgLgf/WlO8Dou7Zv355Vdtxxx5l1q6urs8ra29vNurt3784q885pK2xpxIgRZl0roMcLW/ICsSxe+JAlTh9mtTdOCJkXzGTtSysoSbJDnLzttdo7cOBAs26c42YFwnkBYNZyvf1gHWMvxMkKlfOWax0Lb/9a+8cLsLPOVSvcz2uDF2b1+uuvZ5VZYW5eeVtbm1nX2g7vfLDe895+2LNnT1aZdyysa4wXJui1zWKtz7uWWAFr3rZZx6jQwWIhjCeYRAMAgvn4FQAASE8o4wkm0QCAYDo9AACQnlDGE3wnGgAAAACAiHgSDQAI5s4xAABITyjjCSbRAIBgOj0AAJCeUMYTTKIBAEDeWEm9XuqslSrtJRZXVFREbkNlZWVWmZes/cYbb0R6vcdLE7eSor1ts/aDl248bNiwrDIvhdhKLB41apRZ1+Ltcyvl2UsIt/al1S7J3g7r+Eh2Orc3QLfOP2s/SnaytZXuLdmJ5Fa7JDtN2Tsfoq4r7jKsBGovGd5arpdUbS3Da6/1PvTOX4t3nlnnqlfXSsa2filAspPoveuZtVwv9du6blRVVZl1rfOPX6VJH5NoAEAwd44BAEB6QhlPMIkGAATT6QEAgPSEMp5gEg0ACKbTAwAA6QllPMFPXAEAAAAA+qTly5dr7Nixqqys1LRp07Rhwwa37sqVKzVjxgyNGDFCI0aMUH19/VHre3gSDSAyL9wjzp1Daxl96c5jfxXKnWMUnhXEY4U1SXZolBfaY5V756YVuuOFIg0fPjyrzLsWWqFIXkCVtW1eUJe1f7ygI2s7vHApa9u8UC8rCMoLLzrmmGMivd4rt0LXJOnw4cNZZV6olxVYZr1esvevd55ZgU/WOS3FC9Sy2uudZ1YYlRVaJdmBZXFCyKzXS/Y2e8fCKve2zSofMWKEWddqm3dOWtscZ9u8fWZtmxd2Z70341x3vOuZdf5a78F8yfd4YvXq1WpsbNSKFSs0bdo0LVu2TDNnztSWLVs0evTorPrr1q3TJZdcorPPPluVlZW69dZbdeGFF+q3v/2txowZE3m9PIkGAGQ6vd7+AQAA5Hs8sXTpUs2bN08NDQ2aMGGCVqxYocGDB2vVqlVm/R/96Ee6/PLLNWnSJI0fP1733nuvurq61NTUFGu9PIkGAPAkGgAA5CyJ8cTbf8KvoqLC/KROR0eHNm7cqGuvvTZTVlpaqvr6eq1fvz7SOg8cOKDDhw9r5MiRsdrKk2gAAAAAQFGoq6tTdXV15m/JkiVmvZ07d6qzs1M1NTU9ymtqatTc3BxpXVdffbWOP/541dfXx2ojT6IBADyJBgAAOUtiPLF9+3ZVVVVlyr28iFzdcsstevDBB7Vu3Tozl+BomEQDAJhEAwCAnCUxnqiqquoxifaMGjVKZWVlamlp6VHe0tKi2trao772tttu0y233KKf//znOvPMM2O3lUk0AJOVkBknVdTjpXcCCIOVKn3w4EGzrpV27SXqWsnLXgKwdS3z6lpJ0d4A0Wqbl9xs8VJ9raRor7379+/PKvNSqa1rt5eMbW2zlRIt2cfTS0229pl3jK3vLFrniBQvcds6J8vKysy6Vpqyt1zr2Hv7wTJ06FCz3FqG1w/HSaW2kse99Og4Y4Q47wHr/PXS+61z1RtjvP07tpKdgC3Z2+Gl1luJ5t75YNW1tleyj5H3NNbaD157+5vy8nJNnjxZTU1NmjVrliRlQsLmz5/vvu6b3/ymbrrpJq1du1ZTpkzp1bqZRAMAeBINAABylu/xRGNjo+bOnaspU6Zo6tSpWrZsmdrb29XQ0CBJmjNnjsaMGZP5XvWtt96qhQsX6oEHHtDYsWMz350eOnSoe+PKwiQaAMAkGgAA5Czf44nZs2ertbVVCxcuVHNzsyZNmqQ1a9Zkwsa2bdvW41MAd911lzo6OnTxxRf3WM6iRYu0ePHiyOtlEg0AYBINAAByVojxxPz5892Pb69bt67Hv7du3dqrdbwdP3EFAAAAAEBEPIkGYLKCNbzgEis0xAsCse4yegE5yB+eRCNfrNAd79pihTvFCfXygq+80ChLnJAsa9u8ECerDV6oV5wwKitEzAuHstpmhR9J9n7w9q+1XC88zuKl8lrr2717t1l33759WWVv/y3ZI6w+7O1pv0dY4W9eH2YFQXmhU17AlMU6xt7+tc4zL2jO2u9W2JgkjRgxIqvM2zZrn3khZIMGDcoq8/avFQjnXUus/eNdS6zj5u1fq73e92qt/e4F41lt8/aZdX0oZL8cyniCSTQAIJhODwAApCeU8QSTaACApL7VeQEAgOIUwniC70QDAAAAABART6IBADndNQ7hjjMAAHhnoYwnmEQDAILp9AAAQHpCGU8wie7nrERD7wQlNRl/zjr2XoqklTjpnWecU0DYrDTl4cOHm3WttF8vqdpKC/aSha1rmbdcq653HbNSeb1EXSvB2kvRtpbhJSFb+9JLCLeSuL26Vtv27t0buQ1eYnGcBHbrVx+sdGRvfd55Zp0nXkq5leR96NAhs67VN3rttc4pL0XbKh82bJhZ1zp/vTZYy/XOBytx2zsnrXKvvdZ+2LNnj1nXOk+8dG4rrd17b1pJ3N57s62tLavM22fW+eu9L6y2eeeZtX+9bUNymEQDAIK5cwwAANITyniCSTQAIJhODwAApCeU8QSTaABAMJ0eAABITyjjCX7iCgAAAACAiHgS3c9ZwQJeiEFFRUVWmRWYIPkBLOjf4oTSoW8J5c4xCs8Kd4oT6lVdXW3WtZbhBYBZIUFeXavcC+2xwqisMCFvGV4okhX45IU4WdvmBVRZffyoUaMi1/VYYWHePrO2wwqBkuxxSpwwtv3795t1rcAmb/9aYyhvuda10Qqtkuzz19tnccLYysvLs8q888w6xt5+sMKzvPPMaoNX19oPVVVVZl1rX3qhXtb+iROE5p1nVkCady2xwti8utb70AtYi3Pu5EMo4wkm0QCAYDo9AACQnlDGE0yiAQDBdHoAACA9oYwn+E40AAAAAAAR8SQaABDMnWMAAJCeUMYTTKIBAMF0egAAID2hjCeYRAfISwK00jBJ4QYApM1LybXSd3fv3m3WtforL4XYSvu1kp8lO7G4vb3drGuVWwnjUrzBotVeK+lXipfObdX12mvtS+9YtLS0ZJV544kRI0ZklXn7xko6P/HEE8261nZ4ydjWuMhKWveWayU0S/a4ykuPttrmLdfaD14a85AhQ7LKvDRx6xgNHjzYrGslmlup4R7r9ZJ9LOKMRa1zT7LfA8ccc4xZ1zr23j6z0tq99sZJNLeSuK1j6S3X+1UAJIdJNAAgmDvHAAAgPaGMJ5hEAwCC6fQAAEB6QhlPMIkGAATT6QEAgPSEMp7gJ64AAAAAAIiIJ9H9nBXQ4AWi9KW7PwCSFcqdYxSeFXjjBV6OHDkyq8wLDrJCsqqrq826O3fuzCrzAp+s0DOvveXl5ZHrWrxAIivcyQtN+5//+Z/I67OCirzAJ6sN1vZKdkibdXwke5u9ACUryOyVV14x6x577LFZZd4+s0K99u7da9a1zkkvUMvaP7t27TLrWueZt3+tMCovhMwKyfKu2VaAnXf+WsfICzezAta89sZhnQ9eSKF1Xnv7wdoOb9viBHhZ+9dbbhzWe2jQoEE5L7e3QhlPMIkGAATT6QEAgPSEMp7g49wAAHV3d+f01xvLly/X2LFjVVlZqWnTpmnDhg1Hrf/jH/9Y48ePV2Vlpc444ww9+uijvVovAABIRyHGE4XAJBoAkHerV69WY2OjFi1apE2bNmnixImaOXOmduzYYdZ/5plndMkll+jSSy/Vs88+q1mzZmnWrFn6zW9+k+eWAwCA0DGJBgDk/c7x0qVLNW/ePDU0NGjChAlasWKFBg8erFWrVpn177jjDv3FX/yF/umf/kmnnXaavvGNb+h973ufvvOd7+S66QAAICE8iQYABCVfHV5HR4c2btyo+vr6TFlpaanq6+u1fv168zXr16/vUV+SZs6c6dYHAACF0d8n0FKMYDEvMRIAAElqa2vr8e+KigozpXfnzp3q7OxUTU1Nj/Kamho9//zz5rKbm5vN+s3NzTm2Gvm2ffv2QjcBAJCS7du3m0nkUbS1tamuri7hFqWDdG4ACFh5eblqa2tznowOHTo0q+NbtGiRFi9enNNyAQBA8Tsynsh1ElxbW+v+vFsxYRINAAGrrKzUyy+/rI6OjpyW093dnfV7otZTaEkaNWqUysrKsn7vt6WlRbW1teZramtrY9UHAAD5k9R4ory83P1N92LCJBoAAldZWZnXDqu8vFyTJ09WU1OTZs2aJUnq6upSU1OT5s+fb75m+vTpampq0lVXXZUpe/zxxzV9+vQ8tBgAALyTfI8nColJNAAg7xobGzV37lxNmTJFU6dO1bJly9Te3q6GhgZJ0pw5czRmzBgtWbJEkrRgwQKdd955uv322/Wxj31MDz74oH71q1/pnnvuKeRmAACAADGJBgDk3ezZs9Xa2qqFCxequblZkyZN0po1azLhYdu2bVNp6f/+gMTZZ5+tBx54QNddd52++tWv6j3veY8efvhhnX766YXaBAAAEKiS7r6WJw4AAAAAQIHwO9EAAAAAAETEJBoAAAAAgIiYRAMAAAAAEBGTaAAAAAAAImISDQAAAABAREyiAQAAAACIiEk0AAAAAAARMYkGAAAAACAiJtEAAAAAAETEJBoAAAAAgIiYRAMAAAAAEBGTaAAAAAAAImISDQAAAABAREyiAQAAAACIiEk0AAAAAAARMYkGAAAAACAiJtEAAAAAAETEJBoAAAB9ztixY/XZz36216/9y7/8y2QbBCAYTKKRmO9///sqKSnRr371q0I3pWi99NJLqqysZD8BAPBn3mkMcf755+v000/Pc6v6hgsuuEAlJSWaP39+oZsCBGNAoRsAhOSLX/yiBgwYoEOHDhW6KQAA9GlbtmxRaWnYz4MeeughrV+/vtDNAIIT9pUHweru7tYbb7yR13WuXbtWa9eu1Re/+MW8rhcAgP6ooqJCAwcOLHQzMtrb2/O6voMHD+pLX/qSrr766ryuFwCTaBTAs88+q4985COqqqrS0KFD9eEPf1i//OUvM/9/z549Kisr0//9v/83U7Zz506VlpbqmGOOUXd3d6b885//vGpra99xnUe++7R27VpNmTJFgwYN0t13351Z31VXXaW6ujpVVFTo3e9+t2699VZ1dXX1WMaDDz6oyZMna9iwYaqqqtIZZ5yhO+64I9I2Hz58WAsWLNCCBQt08sknR3oNAADwWd+J/vWvf63zzjtPgwYN0gknnKAbb7xR3/ve91RSUqKtW7dmLePpp5/W1KlTVVlZqZNOOkn3339/pHUvXrxYJSUl+t3vfqdPfepTGjFihD74wQ9m/v8Pf/hDTZ48WYMGDdLIkSP1yU9+Utu3b++xjBdeeEF/+7d/q9raWlVWVuqEE07QJz/5Se3duzdSG775zW+qq6tLX/7ylyPVB5AcPs6NvPrtb3+rGTNmqKqqSl/5ylc0cOBA3X333Tr//PP1X//1X5o2bZqGDx+u008/XU8++aSuvPJKSW91ciUlJdq9e7d+97vf6b3vfa8k6amnntKMGTMirXvLli265JJL9I//+I+aN2+eTj31VB04cEDnnXeeXn31Vf3jP/6j3vWud+mZZ57Rtddeq9dee03Lli2TJD3++OO65JJL9OEPf1i33nqrJOn3v/+9/t//+39asGDBO6572bJlev3113XdddfpoYce6sWeAwCg/9u7d6927tyZVX748OF3fO2rr76qD33oQyopKdG1116rIUOG6N5771VFRYVZ/8UXX9TFF1+sSy+9VHPnztWqVav02c9+VpMnT86MM97J3/3d3+k973mPbr755sxN/ptuuknXX3+9PvGJT+iyyy5Ta2ur7rzzTp177rl69tlnNXz4cHV0dGjmzJk6dOiQvvCFL6i2tlavvvqq/vM//1N79uxRdXX1Ude7bds23XLLLVq1apUGDRoUqa0AEtQNJOR73/tet6Tu//7v/3brzJo1q7u8vLz7pZdeypT96U9/6h42bFj3ueeemym74oorumtqajL/bmxs7D733HO7R48e3X3XXXd1d3d3d+/atau7pKSk+4477njHtp144ondkrrXrFnTo/wb3/hG95AhQ7r/8Ic/9Ci/5pprusvKyrq3bdvW3d3d3b1gwYLuqqqq7jfffPMd1/V2r732WvewYcO677777u7u7mj7CQCAkBzpG4/29973vrfHa0488cTuuXPnZv79hS98obukpKT72WefzZTt2rWre+TIkd2Sul9++eUer5XU/eSTT2bKduzY0V1RUdH9pS996R3bu2jRom5J3ZdcckmP8q1bt3aXlZV133TTTT3Kn3vuue4BAwZkyp999tluSd0//vGP33Fdlosvvrj77LPPzvxbUvcVV1zRq2UBiI+PcyNvOjs79bOf/UyzZs3SSSedlCk/7rjj9KlPfUpPP/202traJEkzZsxQS0uLtmzZIumtJ87nnnuuZsyYoaeeekrSW0+nu7u7Iz+JHjdunGbOnNmj7Mc//rFmzJihESNGaOfOnZm/+vp6dXZ26sknn5QkDR8+XO3t7Xr88cdjb/fVV1+tk046SZdddlns1wIAEJLly5fr8ccfz/o788wz3/G1a9as0fTp0zVp0qRM2ciRI/XpT3/arD9hwoQeY4hjjz1Wp556qv74xz9Gbu/nPve5Hv9+6KGH1NXVpU984hM9xhW1tbV6z3veoyeeeEKSMk+a165dqwMHDkRenyQ98cQT+td//dfMp+UA5B8f50betLa26sCBAzr11FOz/t9pp52mrq4ubd++Xe9973szndpTTz2lE044Qc8++6xuvPFGHXvssbrtttsy/6+qqkoTJ06UJO3fv1/79+/PLLOsrEzHHnts5t/jxo3LWu8LL7ygX//61z3q/bkdO3ZIki6//HL9y7/8iz7ykY9ozJgxuvDCC/WJT3xCf/EXf3HUbf7lL3+pH/zgB2pqago+QRQAgHcydepUTZkyJav8yM3uo3nllVc0ffr0rPJ3v/vdZv13vetd5npef/11SW/d/G9tbe3x/0eOHKny8vLMv98+tnjhhRfU3d2t97znPeY6jwShjRs3To2NjVq6dKl+9KMfacaMGfr4xz+uv//7vz/qR7nffPNNXXnllfrMZz6j97///W49AOliEo2idPzxx2vcuHF68sknNXbsWHV3d2v69Ok69thjtWDBAr3yyit66qmndPbZZ2cmp7fddptuuOGGzDJOPPHEHiEi1neGurq6dMEFF+grX/mK2Y5TTjlFkjR69Ght3rxZa9eu1WOPPabHHntM3/ve9zRnzhz98z//s7sdX/nKVzRjxgyNGzcu05Yjg4DXXntN27ZtMztxAACQrrKyMrO8+///bvP27duzJslPPPGEzj///My/3z626OrqUklJiR577DFz+UOHDs389+23367Pfvaz+rd/+zf97Gc/05VXXqklS5bol7/8pU444QSzbffff7+2bNmiu+++Oysobd++fdq6datGjx6twYMHu9sNIHdMopE3xx57rAYPHpz5iPafe/7551VaWqq6urpM2YwZM/Tkk09q3LhxmjRpkoYNG6aJEyequrpaa9as0aZNm3pMmufMmdMjGTNK0MbJJ5+s/fv3q76+/h3rlpeX66KLLtJFF12krq4uXX755br77rt1/fXXu3e5t23bpldeecV8Cv7xj39c1dXV2rNnzzuuGwAAHN2JJ56oF198MavcKouitrY262tcRz795jn55JPV3d2tcePGZW7EH80ZZ5yhM844Q9ddd52eeeYZnXPOOVqxYoVuvPFGs/62bdt0+PBhnXPOOVn/7/7779f999+vn/70p5o1a9Y7rhtA7zGJRt6UlZXpwgsv1L/9279p69atGjt2rCSppaVFDzzwgD74wQ+qqqoqU3/GjBm6//77tXr1an3kIx+RJJWWlurss8/W0qVLdfjw4R7fZTrppJN6fNc6ik984hNavHix1q5dm/V96T179mjo0KEaMGCAdu3apWOOOSbz/0pLSzPfzzp06JC7/HvuuSfru06/+MUvdOedd+q2227T+PHjY7UXAADYZs6cqeXLl2vz5s2Z70Xv3r1bP/rRj3q1vMrKykg32f/c3/zN3+jaa6/VDTfcoB/+8IcqKSnJ/L/u7m7t3r1bxxxzjNra2jR48GANGPC/Q/EzzjhDpaWlRx1XfPKTn+zxne8j/vqv/1of/ehHNW/ePE2bNi1WmwHExyQaiVu1apXWrFmTVb5gwQLdeOONevzxx/XBD35Ql19+uQYMGKC7775bhw4d0je/+c0e9Y9MkLds2aKbb745U37uuefqscceU0VFRc7fB/qnf/on/fu//7v+8i//MvOzFu3t7Xruuef0k5/8RFu3btWoUaN02WWXaffu3fo//+f/6IQTTtArr7yiO++8U5MmTdJpp53mLv/CCy/MKjvy5Pm8884zv/cFAADi+8pXvqIf/vCHuuCCC/SFL3wh8xNX73rXu7R79+4eE9q0nHzyybrxxht17bXXauvWrZo1a5aGDRuml19+WT/96U/1D//wD/ryl7+sX/ziF5o/f77+7u/+TqeccorefPNN/eAHP1BZWZn+9m//1l3++PHj3Rvw48aN4wk0kCdMopG4u+66yyz/7Gc/q/e+97166qmndO2112rJkiXq6urStGnT9MMf/jDrzumpp56q0aNHa8eOHT0+pn1kcj116lT3tx+jGjx4sP7rv/5LN998s3784x/r/vvvV1VVlU455RTdcMMNmXCPv//7v9c999yj7373u9qzZ49qa2s1e/ZsLV68mMAwAACKQF1dnZ544gldeeWVuvnmm3Xsscfqiiuu0JAhQ3TllVeqsrIyL+245pprdMopp+jb3/525mtndXV1uvDCC/Xxj39c0lsfC585c6b+4z/+Q6+++qoGDx6siRMn6rHHHtMHPvCBvLQTQO+VdB9JTwAAAAD6mauuukp333239u/f74aJAUAcPEIDAABAv/DGG2/0+PeuXbv0gx/8QB/84AeZQANIDB/nBgAAQL8wffp0nX/++TrttNPU0tKi++67T21tbbr++usL3TQA/QiTaAAAAPQLH/3oR/WTn/xE99xzj0pKSvS+971P9913n84999xCNw1AP8LHuQEAeffkk0/qoosu0vHHH6+SkhI9/PDD7/iadevW6X3ve58qKir07ne/W9///vdTbyeAvuXmm2/WH/7wBx04cEDt7e166qmnYv9MFYC+o1DjCSbRAIC8a29v18SJE7V8+fJI9V9++WV97GMf04c+9CFt3rxZV111lS677DKtXbs25ZYCAIBiVajxBOncAICCKikp0U9/+tOj/r7p1VdfrUceeUS/+c1vMmWf/OQntWfPHvN36QEAQFjyOZ7gO9EAELiDBw+qo6Mjp2V0d3erpKSkR1lFRUXOv+V+xPr167M+kjlz5kxdddVViSwfAADkJqTxRORJND8LAACF0dnZmdqyDx48qHHjxqm5uTmn5QwdOlT79+/vUbZo0SItXrw4p+Ue0dzcrJqamh5lNTU1amtr0xtvvKFBgwYlsh6k7+2DI69Memswlcty0+K1K59tQPFI4vzNVYjnpLXNcbY3zj7L9/7N9drnvT7NczK08QRPogEgYB0dHWpubtb27dtVVVXVq2W0tbWprq4uaxlJ3TUGAADFLbTxBJNoAICGDRumYcOG9eq1R+5sV1VV9brjfCe1tbVqaWnpUdbS0qKqqiqeQgMAUCRCGU8wiQYAqLu7u9cf88rHRxanT5+uRx99tEfZ448/runTp6e+bgAAEE0o4wl+4goAkOn0evsX1/79+7V582Zt3rxZ0ls/ObF582Zt27ZNknTttddqzpw5mfqf+9zn9Mc//lFf+cpX9Pzzz+u73/2u/uVf/kVf/OIXE9l+FKeSkpKsv2JgtSuJtiX1/uqvivV88KTVXu/862v7J1dpbW+c92Ca/WQa7c2HUMYTTKIBAHn3q1/9SmeddZbOOussSVJjY6POOussLVy4UJL02muvZTpASRo3bpweeeQRPf7445o4caJuv/123XvvvZo5c2ZB2g8AAAqvUOOJyL8TTTo3ABRGmuncbW1tqq6u1q5du3IKAjnmmGO0d+/e1L7DhP4jTjq3pT+nEPfnbUtCnBTifEri/M11fcWwH/qafO+zfJ8n+VyuFN54gu9EAwCK/jtMAACg+IUynuDj3AAAAAAARMSTaABAMHeOAQBAekIZTzCJBgAE0+mh70vr+8HF8H3k/vzd5/6ybbl+r9V7fZzraFrLLdbvWifx3oyzz3JVDPuskEIZTzCJBgAE0+kBAID0hDKe4DvRAAAAAABExJNoAEAwd44BAEB6QhlPMIkGAATT6QEAgPSEMp5gEg0ACKbTQ+H1l4ApS67vhb62b+KEWRVDcFsS4gRU5bptSezfOMstVnH2Q1rHIq1wM0+xhrxFEcp4gu9EAwAAAAAQEU+iAQDB3DkGAADpCWU8wSQaABBMpwcAANITyniCj3MDAAAAABART6IBAMHcOQYAAOkJZTzBJBoAEEynh8KLk6hbrG3oL0nTfU2xJlAncT6kVTeOtPZvWn1EPo9xMRyfvnJ9CWU8wSQaABBMpwcAANITyniC70QDAAAAABART6IBAMHcOQYAAOkJZTzBJBoAEEynBwAA0hPKeIJJNABAUt/qvNC/pBXUlcQ53VfCfNKWzzArb13FeiyKtV1por+IL6RAwxDOD74TDQAAAABARDyJBgAE8/ErAACQnlDGE0yiAQDBdHoAACA9oYwn+Dg3AAAAAAAR8SQaABDMnWMAAJCeUMYTTKIBAMF0eihOXrpsrmm2cZbrCe38TiLp11pGaPuxL0oiKbo/iLNt+UzGTuJ6lg+hjCeYRAMAgun0AABAekIZT/CdaAAAAAAAIuJJNAAgmDvHAAAgPaGMJ5hEAwCC6fQAAEB6QhlPMIkGAATT6aHw0griKdYwoDiKIdjJW26u+6xY9znwdnGC8XJ9H6b1Pi7k+y2U8QTfiQYAAAAAICKeRAMAgrlzDAAA0hPKeIJJNAAgmE4PAACkJ5TxBJNoAEAwnR4AAEhPKOMJvhMNAAAAAEBEPIkGAARz5xh9S5yE2TiJusUqifbG2Q/FkJhdDInkcfSH8ywJuZ47Ie4zi7cf+/L+CWU8wSQaABBMpwcAANITyniCj3MDAAAAABART6IBAMHcOQYAAOkJZTzBJBoAEEynBwAA0hPKeIJJNAAgmE4PYSmG4Kwk9LXwrTis9nrbm9bxzHWfJdGuYjhucY5Frsvta/K9H/rytSuU8QTfiQYAAAAAICKeRAMAgrlzDAAA0hPKeIJJNABAUt/qvAAAQHEKYTzBJBoAEMydYwAAkJ5QxhN8JxoAAAAAgIh4Eg0ACObOMcLSX1KtiyHBuj8rhn2WaxuSOH/T2g/Wcov5/ZaWfJ5nhdy/oYwnmEQDAILp9AAAQHpCGU/wcW4AAAAAACLiSTQAIJg7xwAAID2hjCeYRAMAgun0AABAekIZTzCJBgAE0+mh7yvmEKg474V8BkkV8z7LlbcfimGb8Za0+ohiCCyLE/qXz+UW8vwPZTzBd6IBAAAAAIiIJ9EAgGDuHAMAgPSEMp5gEg0ACKbTAwAA6QllPMEkGgAQTKcHAADSE8p4gu9EAwAAAAAQEU+iAQDB3DlG32IlzKaVfJvP1yehGNKn+3Mbkkhgz6c4+yGJbUgrlbqvyef511eS6EMZT/AkGgCQ6fR6+9cby5cv19ixY1VZWalp06Zpw4YNR62/bNkynXrqqRo0aJDq6ur0xS9+UQcPHuzVugEAQPJCGU8wiQYA5N3q1avV2NioRYsWadOmTZo4caJmzpypHTt2mPUfeOABXXPNNVq0aJF+//vf67777tPq1av11a9+Nc8tBwAAxaJQ4wkm0QCAvN85Xrp0qebNm6eGhgZNmDBBK1as0ODBg7Vq1Sqz/jPPPKNzzjlHn/rUpzR27FhdeOGFuuSSS97xbjMAAMifUMYTTKIBAHnt9Do6OrRx40bV19dnykpLS1VfX6/169ebrzn77LO1cePGTCf3xz/+UY8++qg++tGP9n6jAQBAokIZTxAshtT1pZAAT7GFNgBJSyIIpK2trUd5RUWFKioqsurv3LlTnZ2dqqmp6VFeU1Oj559/3lzHpz71Ke3cuVMf/OAH1d3drTfffFOf+9zn+Dh3PxHnGhsn8CmJcKi0Apv6QzBTvoOO+tr+iRMOlca6vPX1h/2Ioyvk2DuU8QRPogEAiairq1N1dXXmb8mSJYkte926dbr55pv13e9+V5s2bdJDDz2kRx55RN/4xjcSWwcAACi8vjCe4Ek0ACCRO8fbt29XVVVVpty6ayxJo0aNUllZmVpaWnqUt7S0qLa21nzN9ddfr8985jO67LLLJElnnHGG2tvb9Q//8A/62te+ptJS7gkDAFBooYwnGHUAABL5DlNVVVWPP6/TKy8v1+TJk9XU1JQp6+rqUlNTk6ZPn26+5sCBA1kdW1lZWabtAACg8EIZT/AkGgCQyJ3jOBobGzV37lxNmTJFU6dO1bJly9Te3q6GhgZJ0pw5czRmzJjMR7guuugiLV26VGeddZamTZumF198Uddff70uuuiiTOcHAAAKK5TxBJNoAEDezZ49W62trVq4cKGam5s1adIkrVmzJhMOsm3bth53iq+77jqVlJTouuuu06uvvqpjjz1WF110kW666aZCbQIAACiwQo0nSrojTvm504/ess6dYk5atN4SnZ2dBWgJ8JY0z7+2tjZVV1erqalJQ4YM6dUy2tvb9eEPf1h79+7t8R0mwBLn++tWX5FE/2Fd55NI7O4PSchxeNsWJ5W6P++fXCWxz9L6ukuuy03rfODrPW/x9kOa+ye08QRPogEAef/4FQAA6H9CGU8QLAYAAAAAQEQ8iQYABHPnGAAApCeU8QSTaABAMJ0eAABITyjjCSbR/VyuJ2MS4RUDBw7MKosTLBMnfCKJgBIrxOnNN9+M3AZPWhcGQlmQhFA6PfR9+b6WphWgFEc+r/NpBbflu69Ka31xtq0YxmBpiRMeF/X1niTGgfRT+RPKeILvRAMAAAAAEBFPogEAwdw5BgAA6QllPMEkGgAQTKcHAADSE8p4gkk0ACCYTg8AAKQnlPEE34kGAAAAACAinkT3E96dmwEDsg9xnGTsw4cP59yGfCZZJsFqm7UfJTu129u28vLynNrV1dWVcxsATyh3jtH3FUMKcT5fn4RiaIOnGNpWDMe4Pyd5x1EMye7FKt+/VtNboYwnmEQDAILp9AAAQHpCGU8wiQYABNPpAQCA9IQynuA70QAAAAAARMSTaABAMHeOAQBAekIZTzCJ7uesk7GsrMysa5V7IWQHDx7MKhs4cGDk5cYJPMh3OIK1Pi8UzNo/Xnu9cLKoOjs7zXIrWAyIK5ROD31LnHMrn4FEubZL6nthScUQvpXP5eZ7fbkuN9/X4bTWl8/925/7rkJuWyjjCT7ODQAAAABARDyJBgAEc+cYAACkJ5TxBJNoAEAwnR4AAEhPKOMJJtEAgGA6PQAAkJ5QxhN8JxoAAAAAgIh4Et1PeGmGVnKzlxJtJU17Sd4VFRU5LTfOnaZiSN702muldiexbX3pThz6h1DuHKP/inPtjtOv5Jr67bUhrTTxYk3RLoY2FMO2JSHXa24SY48k3gNRl5tEGn5/VmzbHMp4gkk0ACCYTg8AAKQnlPEEk2gAgKS+1XkBAIDiFMJ4gu9EAwAAAAAQEU+iAQDBfPwKAACkJ5TxBJPofsI76QYOHJhVZgV9HW0ZFitELM5yvUAKq7wYwre8NnR1dUVehlXXCn6T7OA2TzGHtKHvCKXTQ1jiXPPS6muK4bqb7340if2er9fne7mefJ5naQXueeKEhRXDfigGaQUP5kMo4wk+zg0AAAAAQEQ8iQYABHPnGAAApCeU8QSTaABAMJ0eAABITyjjCSbRAIBgOj0AAJCeUMYTfCcaAAAAAICIeBLdB1l3aawUbq88iXQ/qw1x0h69JO846dy5bkdnZ2dOr0+iDd5+sJSVlZnllZWVWWXeth0+fDinNqD/CuXOMSDFSwu2JJFunE9ptbeYk7HjjCfyKYljkev5m1ZaezGkvXvS+iWTYt7mQgllPMEkGgAQTKcHAADSE8p4gkk0ACCYTg8AAKQnlPEEn+MEAAAAACAinkQDAIK5cwwAANITyniCSXQfZIUNeEFSVhiVF1AVdV1euVfXCq7Kd7BYriER3pva2o6uri6zbq7Hwttn5eXlkdtgscLGjrY+9E+hdHooPOt86cshOoXS1/ZZWn15En1VWvsyzngi12C7JMKw4ozBrGV4Y49c2xunDWn1R/Rz0YUynmCUDAAAAABARDyJBgAEc+cYAACkJ5TxBJNoAEAwnR4AAEhPKOMJJtEAgGA6PQAAkJ5QxhN8JxoAAAAAgIh4Et1PxElE9MRJZbR4CZlWeZwk7yQSPS1eMnacxEmr3NsP+by7NmCA/daOs8+s1G4Su/uvUO4cA1Lu52x/ScZOaxm5/tJGWnU9uY5/4iRux6nrjT2stsU5p5P4hRSrbUn0BWm9N/OZEB66UMYTTKIBAMF0egAAID2hjCeYRAMAgun0AABAekIZT/DZTAAAAAAAIuJJNAAgmDvHAAAgPaGMJ5hE93OHDh3KKvPCEQYOHBi5riVOuIcX6mUFYiURQmYFYHiBHZ2dnWa5xVpfEiFvcQIw4gSBxNlnljfffDNyG9C3hNLpofC4XsRXrPss36FeVnmcwMu0+sYkwsLyGVTqLdcag3l1rbGS165cQ8jy3cfkGkIWulDGE3ycGwAAAACAiHgSDQCQ1LfuAAMAgOIUwniCSTQAIJiPXwEAgPSEMp5gEg0ACKbTAwAA6QllPMF3ogEAAAAAiIgn0UUsTtpjeXm5WddKwfaSseOw0hq9BEdrfVYCpFceJ008Di8hs6OjI6vMS6X2lmGxjpt3jHNNK/XqWuvzzoeKigqz3GLtH5Is+5ZQ7hwDcRXDtSzXtOA4fU1a0vqFCW/sESfJO6107ji/9mGJM8aII046tzdeO3z4cFZZEmOlXKXVH9HPRRfKeIJJNAAgmE4PAACkJ5TxBJNoAEAwnR4AAEhPKOMJvhMNAAAAAEBEPIkGAARz5xgAAKQnlPEEk+giYZ00XnCWF/JgyTWgKk4IhxdQFSeoIq26VnutADEpXpiIF6JhyTVgJIkQsjjhZnECXND3FaLTW758ub71rW+publZEydO1J133qmpU6e69ffs2aOvfe1reuihh7R7926deOKJWrZsmT760Y/2av1Ab6UV1FUMIWZptSGJsUda4xSr3Fuudey94Cyrvd64wVqu14a0grrSOn+t8iQmS2lNuNJqbyhCmUQzIgYA5N3q1avV2NioRYsWadOmTZo4caJmzpypHTt2mPU7Ojp0wQUXaOvWrfrJT36iLVu2aOXKlRozZkyeWw4AAIrJ8uXLNXbsWFVWVmratGnasGHDUevv2bNHV1xxhY477jhVVFTolFNO0aOPPhprnTyJBgDk/c7x0qVLNW/ePDU0NEiSVqxYoUceeUSrVq3SNddck1V/1apV2r17t5555pnMp3TGjh3bq/YCAIB05Hs8ceSm/IoVKzRt2jQtW7ZMM2fO1JYtWzR69Ois+kduyo8ePVo/+clPNGbMGL3yyisaPnx4rPXyJBoAkOn0evsXR0dHhzZu3Kj6+vpMWWlpqerr67V+/XrzNf/+7/+u6dOn64orrlBNTY1OP/103XzzzTl/TQIAACQnn+MJqedN+QkTJmjFihUaPHiwVq1aZdY/clP+4Ycf1jnnnKOxY8fqvPPO08SJE2Otl0k0ACCRTq+tra3H36FDh8x17dy5U52dnaqpqelRXlNTo+bmZvM1f/zjH/WTn/xEnZ2devTRR3X99dfr9ttv14033pjsjgAAAL0Wyk15JtEAgETU1dWpuro687dkyZLElt3V1aXRo0frnnvu0eTJkzV79mx97Wtf04oVKxJbBwAAKLy+cFOe70QXMS/l0EprTCLNLk7qpZVk6aVeWsvwUrQrKyuzysrLyyPXHTRokFn38OHDWWUHDhww61ri7F9rXZK9f+MkbMZJ3PaWa91lS6IN6PuS+A7T9u3bVVVVlSmvqKgw648aNUplZWVqaWnpUd7S0qLa2lrzNccdd5wGDhzY4zpz2mmnqbm5WR0dHe51AmGLc9309KW0WE+c1OS01pfEunL9ZRDvV0+scm+cYvWZXuK29ysgUXlPxnJNj/bqWtvhtSHOeKI/v4fyuW1x1lXI8VoS44m6uroe5YsWLdLixYtzbZqknjfly8rKNHnyZL366qv61re+pUWLFkVeDpNoAEAinV5VVVWPSbSnvLxckydPVlNTk2bNmiXprU6tqalJ8+fPN19zzjnn6IEHHlBXV1dmIP2HP/xBxx13HBNoAACKRCg35fk4NwAg70EgjY2NWrlypf75n/9Zv//97/X5z39e7e3tmbTuOXPm6Nprr83U//znP6/du3drwYIF+sMf/qBHHnlEN998s6644orE9gEAAMhNEuOJIzflj/x5k+g/vyl/xJGb8tOnTzdfc8455+jFF1/s8cmJ3tyU50k0ACDvZs+erdbWVi1cuFDNzc2aNGmS1qxZk/le07Zt23p8dLOurk5r167VF7/4RZ155pkaM2aMFixYoKuvvrpQmwAAAAqssbFRc+fO1ZQpUzR16lQtW7Ys66b8mDFjMjktn//85/Wd73xHCxYs0Be+8AW98MILuvnmm3XllVfGWi+TaABA3n/XUZLmz5/vfnx73bp1WWXTp0/XL3/5y16tCwAApC/f44lC3ZRnEh2gOGFhSQSLWR+NsELBJDsYzPuO5ciRI7PKhg0bZta1QsT27Nlj1t27d69ZHpV3AbDK48Tpe4EdccIjvPATixWQ5oWkEDjW9xViEg28k1yvLWmdm8V8zcu1bXFen8R+SCvU1Pr4pxc+Onjw4Kwyb5xi9dvt7e1m3TfeeMMst1jnqnf+xgkEjbouyR4jxBnT5NqufEsrlDff151i64NDuSnPJBoAwCQaAADkLJTxBMFiAAAAAABExJNoAEAwd44BAEB6QhlPMIkGAEjqW50XAAAoTiGMJ/g4NwAAAAAAEfEkOs+8OzMDBw6MVHa0ZVjSSum0ygcMsE8nK2XTS720krhra2vNumPHjs0qO+6448y6LS0tWWXbtm0z68bZZ1YSpZeAbSV6egmkVt0kEjKtul57Dx06lFVWzIm0yE0oH79C4aV1Helr5yHX07dY+8HbN1af6Y2VrCRu61c9JGn06NFZZcOHDzfrWr9csWPHDrNua2trVpn3qxxWudeXW/shzhghTup3nPdVnDZ44qRd55qMXQzvwSQSt4thO/5cKOMJJtEAgGA6PQAAkJ5QxhNMogEAwXR6AAAgPaGMJ/hONAAAAAAAEfEkGgAQzJ1jAACQnlDGE0yii0ScUIC06ub6ei8kq6KiIqts6NChZl0rROykk04y655++ulZZRMmTDDrvvTSS1llZWVlZl3rDWyFbElSe3t7Vpm3H6x96bXB4oWRxAkNsULEvG0rtqAKpCuUTg/FKc71Jk7IUBxJBCjluh1JXHfjLDetNsQJC8s1WMwLNR0yZEhWWU1NjVn3zDPPzCrzxh5tbW1ZZc8995xZt6OjI6vs4MGDket6wZ8Wb5/FCQuLM56IUzct/aHv6Q/b8HahjCeYRAMAgun0AABAekIZT/CdaAAAAAAAIuJJNAAgmDvHAAAgPaGMJ5hEAwCC6fQAAEB6QhlP8HFuAAAAAAAi4kl0nnnpiVYqo2fgwIGRlxtHrnd/vFRqq71WaqZkJ2eOHz/erHvWWWdllXlpmsOGDcsq89KuDxw4kFW2a9cus255eXlWmZcUaq3PSs2U4h0L69h7xwLwhHLnGMUpTtp1nKTpOOtLIiG8P0gieTytXwaJk849aNCgrLI46dzvf//7zbqvvfZaVtnu3bvNutu3b88q89pr/VpHnERzj1W3mM/fYm1bsbarGIUynmASDQAIptMDAADpCWU8wSQaABBMpwcAANITyniCz3wCAAAAABART6IBAMHcOQYAAOkJZTzBJLpIWMEPXtiYVdcK70qCF77lBWNYrLAMr71WUNfQoUPNulY4mbdcK1jMCzezllFRUWHWjRMsdujQIbM8qr50YUHfE0qnh8KLE+qV67mVa9iYFC/cLFdJhHrFkda2WaGZ1lggCV6QplXujRHijGnihITGkc/zrBj0l36jrwW35UMo4wkm0QCAYDo9AACQnlDGE3wnGgAAAACAiHgSDQAI5s4xAABITyjjCSbRAIBgOj0AAJCeUMYTfJwbAAAAAICIeBJdJKw7L1bys2SnSMZJ9PTqxrn7YyVReumUVpLlG2+8YdbdtWtXVtkLL7xg1h08eHCkdUnSyy+/HHm5e/bsibxca58Vw120JI4xwhLKnWMUpyT6sDjpxmklhOcztbuvJYTH6ZdyHY9I9i+c7Ny506z7u9/9Lqusra3NrGuNU1599VWzrjXWKebrZVptSyIlPw3FfCz6slDGE0yiAQCS+lbnBQAAilMI4wkm0QCAYO4cAwCA9IQynuA70QAAAAAARMSTaABAMHeOAQBAekIZTzCJ7oNyPcHihIOUlkb/sIIX7nH48OGssvb2drNua2tr5DZY+6G5udmsu3v37qyyl156yaxrBY94QWhvvvlmVpkXQmaVxwks8/SlCw6KVyidHvqWfF4LiyFALIlwsziKYZvTCjU9ePBgVpk3RrCW8Yc//CHycq0xhiTt378/q8waN0j2eCCJMLY4da1j7O3fXM+dtM7pYg4IDEUo4wkm0QCAYDo9AACQnlDGE3wnGgAAAACAiHgSDQAI5s4xAABITyjjCSbRAIBgOj0AAJCeUMYTfJwbAAAAAICIeBJdJKwkQCvVWpI6OjqyyioqKsy6AwcOzKld3h2hOAmZ1na0tbXl3AZrP2zdutWsa6Veemmae/bsiVQm2andXvKmtR1xEiCTSN4EPKHcOQaKWb5TuK0+qFhTuL1yb6x04MCByOuz+vLy8vLIbbASu702HDp0yKwb5xc80pLP8y/OOZlWSn8SY7A4y81nX1nIfjmU8QSTaABAMJ0eAABITyjjCSbRAIBgOj0AAJCeUMYTfCcaAAAAAICIeBINAAjmzjEAAEhPKOMJJtF55p0cVoCFF6xRWpr9AYIBA+xDmWsYQ5xQCy9QywoA81iBY95+sIJA4gSpeaEj7e3tketaYSLefrD2ZRJhYd7+AeIIpdMD4koraKuvtSHX97m3DVYfZo1zJLsf9YLFor5esvvysrKyyMv1+n0rRMwbE1nLSGKMUKxhVsUQYJeWYmhDIa8ZoYwnmEQDAILp9AAAQHpCGU/wnWgAAAAAACLiSTQAIJg7xwAAID2hjCeYRAMAgun0AABAekIZTzCJBgAE0+kBAID0hDKeYBKdZ15anpUuGSfJO4lExDhpj1bCZVpJgPv37zfLrYRLL6XcSv/0EjKt8iTqWm1IIlnb2u9xkkK9FFQA6Ovi9HeefCYOx2lvnHXFqettb67LiLNcr2/0+jaLtQwvndvqt+Nsb5z2xvkFD6+9/flXOUJLE0ffxSQaABDMnWMAAJCeUMYTTKIBAMF0egAAID2hjCf4HCcAAAAAABHxJBoAIKlv3QEGAADFKYTxBJPoPijXwA6vbpzlWrygCysYI074llfXCljzQrKs7YgT7uHVtdrm7Ydc928SCMaAJ5SPXyEsaYVuprW+Yn4vpRXCFKdvtPpXr3+2luH1z7kGbMbp9+OEhaU1niiG8yytNiQRuFcM+6cvC2U8wSQaABBMpwcAANITyniC70QDAAAAABART6IBAMHcOQYAAOkJZTzBJBoAEEynBwAA0hPKeIKPcwMAMp1eb/96Y/ny5Ro7dqwqKys1bdo0bdiwIdLrHnzwQZWUlGjWrFm9Wi8AAEhHIcYThcCT6CLmpQYePnw48jLKysoiL9cSJ8nSW26uidADBtin6cGDB3Nabpw0cS/901pGnPTzJMRJIAWKxerVq9XY2KgVK1Zo2rRpWrZsmWbOnKktW7Zo9OjR7uu2bt2qL3/5y5oxY0YeW4sQJHGNTusXEOIkeedaN63E4iTaa4nT38VpQxxx+v18J27nuoy0xjS5JrjHZS0jzri1L03uQrR8+XJ961vfUnNzsyZOnKg777xTU6dOfcfXPfjgg7rkkkv0V3/1V3r44YdjrZMn0QCAvN85Xrp0qebNm6eGhgZNmDBBK1as0ODBg7Vq1Sr3NZ2dnfr0pz+tG264QSeddFIumwsAAFKQ7/HEkZvyixYt0qZNmzRx4kTNnDlTO3bsOOrrcr0pzyQaAJBIp9fW1tbj79ChQ+a6Ojo6tHHjRtXX12fKSktLVV9fr/Xr17tt/PrXv67Ro0fr0ksvTXbjAQBAIkK5Kc8kGgCQiLq6OlVXV2f+lixZYtbbuXOnOjs7VVNT06O8pqZGzc3N5muefvpp3XfffVq5cmXi7QYAAMWjL9yU5zvRAIBE0jS3b9+uqqqqTHlFRUUibdu3b58+85nPaOXKlRo1alQiywQAAMlLYjxRV1fXo3zRokVavHhxVv2j3ZR//vnnzXUcuSm/efPmXrXxCCbRfZAVeOCFjXV0dGSVeQPbXANGvPCtqK/3yvMdkmUFi1llUv6DMSxW26zjLtmBcICUTKdXVVXVYxLtGTVqlMrKytTS0tKjvKWlRbW1tVn1X3rpJW3dulUXXXRRpuzIdWHAgAHasmWLTj755F61HfkX5zzLNSSrv4gTipRrXU9a/Z3Vx8cJfPL6tTjBn1HXFXcZcV6fa0BavoOv0grf6g8BXmm93/rKvgnlpjyTaABAIp1eVOXl5Zo8ebKampoyP1PV1dWlpqYmzZ8/P6v++PHj9dxzz/Uou+6667Rv3z7dcccdWXesAQBAYYRyU55JNAAg7xobGzV37lxNmTJFU6dO1bJly9Te3q6GhgZJ0pw5czRmzBgtWbJElZWVOv3003u8fvjw4ZKUVQ4AAMJQyJvyTKIBAHl9Ei1Js2fPVmtrqxYuXKjm5mZNmjRJa9asyXyvadu2bXz9AACAPibf44lC3ZRnEg0AyHunJ0nz58837xRL0rp164762u9///u9WicAAEhPKDflmUQDAAoyiQYAAP1LKDflmUT3E3FSM+OkXsZJGPROfCs9Ok4KZVlZWeS6njj7J06iZ5w3e5x9FudYxKkLAIWWa+os17e35DulPE7qd67LjcMbTxRr3xhnPJFW2jUp2unK93ErhvM6REyiAQA8iQYAADkLZTzBJBoAEEynBwAA0hPKeILoUwAAAAAAIuJJNAAgmDvHAAAgPaGMJ5hEB8gL4SiG0JC0ghTi1I3ThjgBaXFeb5V725BrGwApnE4PhZdWX5OWYm6bJdeQtjh9bjGEm8WpWwzHspjDwtJSrG0r1nb1daGMJ5hEAwAk9a3OCwAAFKcQxhN8JxoAAAAAgIh4Eg0ACObjVwAAID2hjCeYRAMAgun0AABAekIZTzCJBgAE0+kBAID0hDKeYBLdz1lJlB0dHZFfP3DgwMjL9cRJmraklc7tvd5aX2dnZ6y2RW2Dl6z9xhtvRHp9nHUBQDHoz9enYk2ETkKcbUtrIJxWmng+pZXOne+6xaqvnQ9x9JdrSX/BJBoAEMydYwAAkJ5QxhNMogEAwXR6AAAgPaGMJ/iJKwAAAAAAIuJJNAAgmDvHAAAgPaGMJ5hEB6i01P4AQpzAsfLy8sh18/mGSCKwzAr7SiLcLM7r4wShAUkIpdMD4ooTqJVreFFa1/l89x9pXRNCC25L4vX9OVisGALs8imtsWjSQhlPMIkGAATT6QEAgPSEMp7gO9EAAAAAAETEk2gAQDB3jgEAQHpCGU8wiQYABNPpAQCA9IQynmASDQAIptMDAADpCWU8wSQaGVZqd5zE7oEDB5rlcVIO33zzzZzXF1WcJMskkg+t5R4+fDjn5QJAX5dWgnWcNFtvuflMAE5ruX0l1TdJfWkwLuW3vX1t33jinOv9IaW8P79f+yIm0QCAYO4cAwCA9IQynmASDQAIptMDAADpCWU8wU9cAQAAAAAQEU+iAQDB3DkGAADpCWU8wSQaR2WFjUm5B2J5oWDWmyeJcDNruWkFrXj7xlqfF6RGeATyLZROD4WXVohYrpIIHiqGa3da4ZhR1xW3bpxwqFyXm4Qkgu3SkO/rcLFe99MKgu1rCrkNoYwnmEQDAILp9AAAQHpCGU/wnWgAAAAAACLiSTQAIJg7xwAAID2hjCeYRAMAgun0AABAekIZTzCJBgBI6ludFwAAKE4hjCeYRKNXrPRDL5W6rKwsq8x7c5WXl0dal5RMarclTvJ4nDRxazuKIckVAPqrfF9jc03GLtbk8v6iGBKskzgf0tqOfCa7JyGt/ZBrYnwScr0WFPNx6y+YRAMAgvn4FQAASE8o4wkm0QCAYDo9AACQnlDGE0yiAQDBdHoAACA9oYwn+J1oAAAAAAAi4kk0Uldamn2vJk7ggRU2JtkBYF6oV5w7W3GCxSzW9gLFLpQ7xyi8OKE9xRq05bUrzrYVQ3stfS2MLc5yk5DrcuO8PonzLNc2eIohfCstuW6Hd/72l/3zTkIZTzCJBgAE0+kBAID0hDKe4JEZAAAAAAAR8SQaABDMnWMAAJCeUMYTTKIBAMF0egAAID2hjCeYRAMAgun0AABAekIZTzCJRmK8NEIr7TqttNI4bYi7DABA7uKkMRfrgKqY06NzlVZycxJtSGu5aawrrnyma8d5vxXreSoVb0J4MbyHkD4m0QCAYO4cAwCA9IQynmASDQAIptMDAADpCWU8wSQaABBMpwcAANITyniC34kGAAAAACAinkQjdVY4wptvvpnKcpOoC4QolDvHKLxcw4D6y/W8WEORPMW634s5xClOiF6u50NaYWzFoJjfF8UgznmWD6GMJ5hEAwCC6fQAAEB6QhlP8HFuAAAAAAAi4kk0ACCYO8cAACA9oYwnmEQDAILp9AAAQHpCGU8wiQYABNPpAQCA9IQynmASjYLoD2mRAID48jlISiuxOM7rk0geL4aBZa5tSKLfT2s/FMM5mSvGVUeXzwT2fOPYFwaTaABAMHeOAQBAekIZTzCJBgBI6ludFwAAKE4hjCf4iSsAQObOcW//emP58uUaO3asKisrNW3aNG3YsMGtu3LlSs2YMUMjRozQiBEjVF9ff9T6AAAg/woxnigEJtEAgLxbvXq1GhsbtWjRIm3atEkTJ07UzJkztWPHDrP+unXrdMkll+iJJ57Q+vXrVVdXpwsvvFCvvvpqnlsOAACKSSFuyjOJBgDk/c7x0qVLNW/ePDU0NGjChAlasWKFBg8erFWrVpn1f/SjH+nyyy/XpEmTNH78eN17773q6upSU1NTrpuOPCspKYn8F0c+n2jk+6lKrvumGCSxf/ryU6tC6WtPAK12pXV9SOKvWCWxz3or3/u0UDflmUQDABLp9Nra2nr8HTp0yFxXR0eHNm7cqPr6+kxZaWmp6uvrtX79+kjtPXDggA4fPqyRI0fmvvEAACARodyUZxINAEhEXV2dqqurM39Lliwx6+3cuVOdnZ2qqanpUV5TU6Pm5uZI67r66qt1/PHH95iIAwCAcBTypjzp3ACAnD6aduR127dvV1VVVaa8oqIikba93S233KIHH3xQ69atU2VlZSrrAAAA8SUxnmhra+tRXlFRYY4pjnZT/vnnn4+0zt7elOdJNAAgkY9fVVVV9fjzJtGjRo1SWVmZWlpaepS3tLSotrb2qO287bbbdMstt+hnP/uZzjzzzGQ2HgAAJCKJ8UTUT7bl6shN+Z/+9Kexb8rzJBoAkMid46jKy8s1efJkNTU1adasWZKU+T7S/Pnz3dd985vf1E033aS1a9dqypQpvWorAABITz4/2ZbETfmf//znvbopzyQaAJB3jY2Nmjt3rqZMmaKpU6dq2bJlam9vV0NDgyRpzpw5GjNmTObu86233qqFCxfqgQce0NixYzPfnR46dKiGDh1asO1AuuKkyfa1xGprkOltQ5wBaTHsB6sN3jbE2Q9x5JqcXAz7sZjTn9MS59yJU7dYxdm2JJbbVxz5RNs7KeRNeSbRAIC8PomWpNmzZ6u1tVULFy5Uc3OzJk2apDVr1mS+17Rt2zaVlv7vN47uuusudXR06OKLL+6xnEWLFmnx4sW9ajcAAEhWvscThbopzyQaAJD3Tk+S5s+f794pXrduXY9/b926tVfrAAAA+RPKTXkm0QCAgkyiAQBA/xLKTXnSuQEAAAAAiIgn0QAAnkQjb4o1JCutgJ8k2pDP5RZDqFec1yexbbmuL61zJM5yi/k6XAzndbEqhgC7pIUynmASDQAIptMDAADpCWU8wce5AQAAAACIiCfRAIBg7hwDAID0hDKeYBINAAim0wMAAOkJZTzBJBoAEEynBwAA0hPKeIJJNAAAgCOfacxxpNWGYkgpT0Ku7U0iITyf+yyJ5PG0WG1LKzE+re0t5v1rKdZ29SdMogEAwdw5BgAA6QllPMEkGgAQTKcHAADSE8p4gkk0ACCYTg8AAKQnlPEEvxMNAAAAAEBEPIkGAEjqW3eA0b8Uc3BQMSiG/RNHMYSxFet+iKOYz8lcg8HS2ra03itJnE+57rM4AXaFPneK4f2XNibRAIBgPn4FAADSE8p4gkk0ACCYTg8AAKQnlPEE34kGAAAAACAinkQDAIK5cwwAANITyniCSTQAIJhODwAApCeU8QSTaAAA0Gckkb6ba/JtvsVJu05r2/rS4LaYFcP5FEda74t8L7cY9LVjj6NjEg0ACObOMQAASE8o4wkm0QCAYDo9AACQnlDGE0yiAQDBdHoAACA9oYwn+IkrAAAAAAAi4kk0ACCYO8coTmmFDPXnIJ8k3nf94b0bZxuK4XxIYp8XQ/hWnLC7tM6z/nD+xlEM528UoYwnmEQDAILp9AAAQHpCGU8wiQYABNPpAQCA9IQynuA70QAAAAAARMSTaABAMHeOAQBAekIZTzCJBgAE0+kBAID0hDKeYBINAAAKqhhSZ+O0Ia008f6iGNKj+/OxKNaJBonxySmG9HMcHZNoAEAwd44BAEB6QhlPMIkGAATT6QEAgPSEMp5gEg0ACKbTAwAA6QllPMFPXAEAAAAAEBFPogEAwdw5RljSCgDrz6FVSYQXFWsoUhLnQ5xty1WcfZNEG6xlcH2PL63rTl85FqGMJ5hEAwCC6fQAAEB6QhlPMIkGAEjqW50XAAAoTiGMJ/hONAAAAAAAEfEkGgCQ013jEO44AwCAdxbKeIJJNAAgmE4PAACkJ5TxBJNoAABQlHJNJ+7PKdppSWsQm+/BcVpp4vlM4k4rKT2JurkqhrT2OOIkbnPdCQOTaABAMHeOAQBAekIZTzCJBgAE0+kBAID0hDKeYBINAAim0wMAAOkJZTzBT1wBAAAAABART6IBAMHcOUZxihPak+/lxgl8wlvS2r+5Hou0QrbSCslKor1W24rhml0Mbegviu16FMp4gkk0ACCYTg8AAKQnlPEEk2gAQDCdHgAASE8o4wm+Ew0AAAAAQEQ8iQYABHPnGAAApCeU8QSTaABAMJ0eAABITyjjCSbRAACgX0oitbYYEsLTklZyc7GmUhdDinYcSSSP96VJSTFL6/3K8em7mEQDAIK5cwwAANITyniCSTQAIJhODwAApCeU8QSTaABAMJ0eAABITyjjCX7iCgAAAACAiHgSDQAI5s4xCi9OmJVVnkTAT67LTSLMqhjE2Q997X3e19prSWIb0gqPQ/FI6zrZW6GMJ5hEAwCC6fQAAEB6QhlP8HFuAIC6u7tz+uuN5cuXa+zYsaqsrNS0adO0YcOGo9b/8Y9/rPHjx6uyslJnnHGGHn300V6tFwAApKMQ44lCYBINAMi71atXq7GxUYsWLdKmTZs0ceJEzZw5Uzt27DDrP/PMM7rkkkt06aWX6tlnn9WsWbM0a9Ys/eY3v8lzywEAQOhKuiNO+cvKytJuCwDA0NnZmdqy29raVF1dLan336E60o3s3btXVVVVkV4zbdo0vf/979d3vvMdSVJXV5fq6ur0hS98Qddcc01W/dmzZ6u9vV3/+Z//mSn7wAc+oEmTJmnFihW9ajcKo7Q0+/59nKcPfe070X1Nf/lONN7Cd6KLW1rHwnsfd3V1pbI+qXDjiULhSTQAQFLvP4IVV0dHhzZu3Kj6+vpMWWlpqerr67V+/XrzNevXr+9RX5Jmzpzp1gcAAIXR3z/KLcUIFkvzSQgAoO9ra2vr8e+KigpVVFRk1du5c6c6OztVU1PTo7ympkbPP/+8uezm5mazfnNzc46tRr6l+SQEAIB84Ek0AASsvLxctbW1OS9n6NChqqurU3V1deZvyZIlCbQQAAAUu6TGE7W1tSovL0+gReniJ64AIGCVlZV6+eWX1dHRkdNyuru7s74DZT2FlqRRo0aprKxMLS0tPcpbWlrcDri2tjZWfQAAkD9JjSfKy8tVWVmZUKvSwyQaAAJXWVmZ1w6rvLxckydPVlNTk2bNmiXprY/4NjU1af78+eZrpk+frqamJl111VWZsscff1zTp0/PQ4sBAMA7yfd4opCYRAMA8q6xsVFz587VlClTNHXqVC1btkzt7e1qaGiQJM2ZM0djxozJfCR8wYIFOu+883T77bfrYx/7mB588EH96le/0j333FPIzQAAAAFiEg0AyLvZs2ertbVVCxcuVHNzsyZNmqQ1a9ZkwsO2bdvW46eQzj77bD3wwAO67rrr9NWvflXvec979PDDD+v0008v1CYAAIBARf6daAAAAAAAQkc6NwAAAAAAETGJBgAAAAAgIibRAAAAAABExCQaAAAAAICImEQDAAAAABARk2gAAAAAACJiEg0AAAAAQERMogEAAAAAiIhJNAAAAAAAETGJBgAAAAAgIibRAAAAAABExCQaAAAAAICI/j8AQjTx1ET3sAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x2000 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vmax_lr = 1.0\n",
+    "vmax_hr = 1\n",
+    "\n",
+    "num_samples = 5\n",
+    "\n",
+    "fig, axs = plt.subplots(num_samples, 2, figsize=(12, num_samples * 4))\n",
+    "\n",
+    "for i in range(num_samples):\n",
+    "    x0, x_cond = train_ds[i]  # x0 = high-res, x_cond = low-res\n",
+    "\n",
+    "    # Convert from [-1, 1] to [0, 1] for display\n",
+    "    x0 = (x0 ).squeeze().numpy()\n",
+    "    x_cond = (x_cond ).squeeze().numpy()\n",
+    "\n",
+    "    im1 = axs[i, 0].imshow(x_cond, cmap='gray', vmax = vmax_lr)\n",
+    "    axs[i, 0].set_title(f\"Low-res {i}\")\n",
+    "    axs[i, 0].axis('off')\n",
+    "    plt.colorbar(im1, ax=axs[i,0], fraction=0.046, pad=0.04)\n",
+    "\n",
+    "    im2 = axs[i, 1].imshow(x0, cmap='gray', vmax = vmax_hr)\n",
+    "    axs[i, 1].set_title(f\"High-res {i}\")\n",
+    "    axs[i, 1].axis('off')\n",
+    "    plt.colorbar(im2, ax=axs[i,1], fraction=0.046, pad=0.04)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0dcedb0",
+   "metadata": {},
+   "source": [
+    "### Load weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bc35f34d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = UNet_512_mix_attn_conditional(use_cond_pyr=True, use_global_cond=True).to(DEVICE)\n",
+    "ema_model = copy.deepcopy(model).to(DEVICE)\n",
+    "for param in ema_model.parameters():\n",
+    "    param.requires_grad = False\n",
+    "\n",
+    "# model.load_state_dict(torch.load(f\"{weights_dir}/grav_model_100_res_imp_mejiro_lsst_pyr_glob.pt\"), strict = False)    \n",
+    "# ema_model.load_state_dict(torch.load(f\"{weights_dir}/grav_ema_model_100_res_imp_mejiro_lsst_pyr_glob.pt\"), strict=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "744af967",
+   "metadata": {},
+   "source": [
+    "### Training loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63f3561b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1 | Train Loss: 0.42900304\n",
+      "Epoch 1 | Test Loss: 0.11367296\n",
+      "current lr = 0.0001\n",
+      "Epoch 2 | Train Loss: 0.05892310\n",
+      "Epoch 2 | Test Loss: 0.05793835\n",
+      "current lr = 0.0001\n",
+      "Epoch 3 | Train Loss: 0.03358138\n",
+      "Epoch 3 | Test Loss: 0.03367980\n",
+      "current lr = 0.0001\n",
+      "Epoch 4 | Train Loss: 0.02299875\n",
+      "Epoch 4 | Test Loss: 0.01817473\n",
+      "current lr = 0.0001\n",
+      "Epoch 5 | Train Loss: 0.02284420\n",
+      "Epoch 5 | Test Loss: 0.04650907\n",
+      "current lr = 0.0001\n",
+      "Epoch 6 | Train Loss: 0.01731437\n",
+      "Epoch 6 | Test Loss: 0.01543930\n",
+      "current lr = 0.0001\n",
+      "Epoch 7 | Train Loss: 0.01557422\n",
+      "Epoch 7 | Test Loss: 0.06619114\n",
+      "current lr = 0.0001\n",
+      "Epoch 8 | Train Loss: 0.01191889\n",
+      "Epoch 8 | Test Loss: 0.02318055\n",
+      "current lr = 0.0001\n",
+      "Epoch 9 | Train Loss: 0.01219830\n",
+      "Epoch 9 | Test Loss: 0.02182707\n",
+      "current lr = 0.0001\n",
+      "Epoch 10 | Train Loss: 0.01079337\n",
+      "Epoch 10 | Test Loss: 0.02504417\n",
+      "current lr = 0.0001\n",
+      "Epoch 11 | Train Loss: 0.01051510\n",
+      "Epoch 11 | Test Loss: 0.01472986\n",
+      "current lr = 0.0001\n",
+      "Epoch 12 | Train Loss: 0.00921762\n",
+      "Epoch 12 | Test Loss: 0.01505435\n",
+      "current lr = 0.0001\n",
+      "Epoch 13 | Train Loss: 0.00803795\n",
+      "Epoch 13 | Test Loss: 0.00769374\n",
+      "current lr = 0.0001\n",
+      "Epoch 14 | Train Loss: 0.00771805\n",
+      "Epoch 14 | Test Loss: 0.00622446\n",
+      "current lr = 0.0001\n",
+      "Epoch 15 | Train Loss: 0.00811967\n",
+      "Epoch 15 | Test Loss: 0.01223205\n",
+      "current lr = 0.0001\n",
+      "Epoch 16 | Train Loss: 0.00825960\n",
+      "Epoch 16 | Test Loss: 0.02514930\n",
+      "current lr = 0.0001\n",
+      "Epoch 17 | Train Loss: 0.00736006\n",
+      "Epoch 17 | Test Loss: 0.00952818\n",
+      "current lr = 0.0001\n",
+      "Epoch 18 | Train Loss: 0.00691260\n",
+      "Epoch 18 | Test Loss: 0.00609062\n",
+      "current lr = 0.0001\n",
+      "Epoch 19 | Train Loss: 0.00621782\n",
+      "Epoch 19 | Test Loss: 0.01586251\n",
+      "current lr = 0.0001\n",
+      "Epoch 20 | Train Loss: 0.00584781\n",
+      "Epoch 20 | Test Loss: 0.06759196\n",
+      "current lr = 0.0001\n",
+      "Epoch 21 | Train Loss: 0.00596123\n",
+      "Epoch 21 | Test Loss: 0.03668334\n",
+      "current lr = 0.0001\n",
+      "Epoch 22 | Train Loss: 0.00638608\n",
+      "Epoch 22 | Test Loss: 0.02118204\n",
+      "current lr = 0.0001\n",
+      "Epoch 23 | Train Loss: 0.00622708\n",
+      "Epoch 23 | Test Loss: 0.09056871\n",
+      "current lr = 0.0001\n",
+      "Epoch 24 | Train Loss: 0.01086238\n",
+      "Epoch 24 | Test Loss: 0.02988869\n",
+      "current lr = 0.0001\n",
+      "Epoch 25 | Train Loss: 0.00765712\n",
+      "Epoch 25 | Test Loss: 0.01881708\n",
+      "current lr = 0.0001\n",
+      "Epoch 26 | Train Loss: 0.00631964\n",
+      "Epoch 26 | Test Loss: 0.03907193\n",
+      "current lr = 0.0001\n",
+      "Epoch 27 | Train Loss: 0.00624793\n",
+      "Epoch 27 | Test Loss: 0.04043196\n",
+      "current lr = 0.0001\n",
+      "Epoch 28 | Train Loss: 0.00527542\n",
+      "Epoch 28 | Test Loss: 0.00498842\n",
+      "current lr = 0.0001\n",
+      "Epoch 29 | Train Loss: 0.00589334\n",
+      "Epoch 29 | Test Loss: 0.00276468\n",
+      "current lr = 0.0001\n",
+      "Epoch 30 | Train Loss: 0.00561177\n",
+      "Epoch 30 | Test Loss: 0.01924751\n",
+      "current lr = 0.0001\n",
+      "Epoch 31 | Train Loss: 0.00529169\n",
+      "Epoch 31 | Test Loss: 0.00439632\n",
+      "current lr = 0.0001\n",
+      "Epoch 32 | Train Loss: 0.00494330\n",
+      "Epoch 32 | Test Loss: 0.01175788\n",
+      "current lr = 0.0001\n",
+      "Epoch 33 | Train Loss: 0.00471563\n",
+      "Epoch 33 | Test Loss: 0.01203718\n",
+      "current lr = 0.0001\n",
+      "Epoch 34 | Train Loss: 0.00450669\n",
+      "Epoch 34 | Test Loss: 0.00529115\n",
+      "current lr = 0.0001\n",
+      "Epoch 35 | Train Loss: 0.00454591\n",
+      "Epoch 35 | Test Loss: 0.02007844\n",
+      "current lr = 0.0001\n",
+      "Epoch 36 | Train Loss: 0.00497532\n",
+      "Epoch 36 | Test Loss: 0.02172481\n",
+      "current lr = 0.0001\n",
+      "Epoch 37 | Train Loss: 0.00497586\n",
+      "Epoch 37 | Test Loss: 0.06341146\n",
+      "current lr = 0.0001\n",
+      "Epoch 38 | Train Loss: 0.00503796\n",
+      "Epoch 38 | Test Loss: 0.00954998\n",
+      "current lr = 0.0001\n",
+      "Epoch 39 | Train Loss: 0.00438461\n",
+      "Epoch 39 | Test Loss: 0.00421542\n",
+      "current lr = 0.0001\n",
+      "Epoch 40 | Train Loss: 0.00439958\n",
+      "Epoch 40 | Test Loss: 0.02065178\n",
+      "current lr = 5e-05\n",
+      "Epoch 41 | Train Loss: 0.00414909\n",
+      "Epoch 41 | Test Loss: 0.00227085\n",
+      "current lr = 5e-05\n",
+      "Epoch 42 | Train Loss: 0.00425261\n",
+      "Epoch 42 | Test Loss: 0.01599596\n",
+      "current lr = 5e-05\n",
+      "Epoch 43 | Train Loss: 0.00433684\n",
+      "Epoch 43 | Test Loss: 0.02605721\n",
+      "current lr = 5e-05\n",
+      "Epoch 44 | Train Loss: 0.00382685\n",
+      "Epoch 44 | Test Loss: 0.02180831\n",
+      "current lr = 5e-05\n",
+      "Epoch 45 | Train Loss: 0.00361329\n",
+      "Epoch 45 | Test Loss: 0.00595118\n",
+      "current lr = 5e-05\n",
+      "Epoch 46 | Train Loss: 0.00398587\n",
+      "Epoch 46 | Test Loss: 0.00524762\n",
+      "current lr = 5e-05\n",
+      "Epoch 47 | Train Loss: 0.00381636\n",
+      "Epoch 47 | Test Loss: 0.00361109\n",
+      "current lr = 5e-05\n",
+      "Epoch 48 | Train Loss: 0.00392805\n",
+      "Epoch 48 | Test Loss: 0.02659656\n",
+      "current lr = 5e-05\n",
+      "Epoch 49 | Train Loss: 0.00412995\n",
+      "Epoch 49 | Test Loss: 0.00238452\n",
+      "current lr = 5e-05\n",
+      "Epoch 50 | Train Loss: 0.00369909\n",
+      "Epoch 50 | Test Loss: 0.00787644\n",
+      "current lr = 5e-05\n",
+      "Epoch 51 | Train Loss: 0.00365068\n",
+      "Epoch 51 | Test Loss: 0.00133727\n",
+      "current lr = 5e-05\n",
+      "Epoch 52 | Train Loss: 0.00381349\n",
+      "Epoch 52 | Test Loss: 0.00987355\n",
+      "current lr = 5e-05\n",
+      "Epoch 53 | Train Loss: 0.00453348\n",
+      "Epoch 53 | Test Loss: 0.00279402\n",
+      "current lr = 5e-05\n",
+      "Epoch 54 | Train Loss: 0.00410263\n",
+      "Epoch 54 | Test Loss: 0.00368487\n",
+      "current lr = 5e-05\n",
+      "Epoch 55 | Train Loss: 0.00352485\n",
+      "Epoch 55 | Test Loss: 0.00710688\n",
+      "current lr = 5e-05\n",
+      "Epoch 56 | Train Loss: 0.00366288\n",
+      "Epoch 56 | Test Loss: 0.00924322\n",
+      "current lr = 5e-05\n",
+      "Epoch 57 | Train Loss: 0.00393396\n",
+      "Epoch 57 | Test Loss: 0.01162726\n",
+      "current lr = 5e-05\n",
+      "Epoch 58 | Train Loss: 0.00401648\n",
+      "Epoch 58 | Test Loss: 0.00586950\n",
+      "current lr = 5e-05\n",
+      "Epoch 59 | Train Loss: 0.00413230\n",
+      "Epoch 59 | Test Loss: 0.00158431\n",
+      "current lr = 5e-05\n",
+      "Epoch 60 | Train Loss: 0.00379609\n",
+      "Epoch 60 | Test Loss: 0.00913000\n",
+      "current lr = 5e-05\n",
+      "Epoch 61 | Train Loss: 0.00390712\n",
+      "Epoch 61 | Test Loss: 0.01813034\n",
+      "current lr = 5e-05\n",
+      "Epoch 62 | Train Loss: 0.00327499\n",
+      "Epoch 62 | Test Loss: 0.04012494\n",
+      "current lr = 2.5e-05\n",
+      "Epoch 63 | Train Loss: 0.00378786\n",
+      "Epoch 63 | Test Loss: 0.00721027\n",
+      "current lr = 2.5e-05\n",
+      "Epoch 64 | Train Loss: 0.00329165\n",
+      "Epoch 64 | Test Loss: 0.00554116\n",
+      "current lr = 2.5e-05\n",
+      "Epoch 65 | Train Loss: 0.00392308\n",
+      "Epoch 65 | Test Loss: 0.00646896\n",
+      "current lr = 2.5e-05\n",
+      "Epoch 66 | Train Loss: 0.00353238\n",
+      "Epoch 66 | Test Loss: 0.00249719\n",
+      "current lr = 2.5e-05\n",
+      "Epoch 67 | Train Loss: 0.00332705\n",
+      "Epoch 67 | Test Loss: 0.00137197\n",
+      "current lr = 2.5e-05\n",
+      "Epoch 68 | Train Loss: 0.00355443\n",
+      "Epoch 68 | Test Loss: 0.01531933\n",
+      "current lr = 2.5e-05\n",
+      "Epoch 69 | Train Loss: 0.00344020\n",
+      "Epoch 69 | Test Loss: 0.02239899\n",
+      "current lr = 2.5e-05\n",
+      "Epoch 70 | Train Loss: 0.00321830\n",
+      "Epoch 70 | Test Loss: 0.01580239\n",
+      "current lr = 2.5e-05\n",
+      "Epoch 71 | Train Loss: 0.00415271\n",
+      "Epoch 71 | Test Loss: 0.00712157\n",
+      "current lr = 2.5e-05\n",
+      "Epoch 72 | Train Loss: 0.00315840\n",
+      "Epoch 72 | Test Loss: 0.01908068\n",
+      "current lr = 2.5e-05\n",
+      "Epoch 73 | Train Loss: 0.00317792\n",
+      "Epoch 73 | Test Loss: 0.01131393\n",
+      "current lr = 1.25e-05\n",
+      "Epoch 74 | Train Loss: 0.00345208\n",
+      "Epoch 74 | Test Loss: 0.00173112\n",
+      "current lr = 1.25e-05\n",
+      "Epoch 75 | Train Loss: 0.00345180\n",
+      "Epoch 75 | Test Loss: 0.01603254\n",
+      "current lr = 1.25e-05\n",
+      "Epoch 76 | Train Loss: 0.00407560\n",
+      "Epoch 76 | Test Loss: 0.00234396\n",
+      "current lr = 1.25e-05\n",
+      "Epoch 77 | Train Loss: 0.00318310\n",
+      "Epoch 77 | Test Loss: 0.11368799\n",
+      "current lr = 1.25e-05\n",
+      "Epoch 78 | Train Loss: 0.00306725\n",
+      "Epoch 78 | Test Loss: 0.01866102\n",
+      "current lr = 1.25e-05\n",
+      "Epoch 79 | Train Loss: 0.00311515\n",
+      "Epoch 79 | Test Loss: 0.00838620\n",
+      "current lr = 1.25e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Setup optimizer, scheduler, and scaler\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
+    "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
+    "    optimizer, mode='min', factor=0.5, patience=10, min_lr=1e-7\n",
+    ")\n",
+    "scaler = GradScaler('cuda')\n",
+    "\n",
+    "train_losses = []\n",
+    "test_losses = []\n",
+    "\n",
+    "for epoch in range(200):\n",
+    "    model.train()\n",
+    "    epoch_loss = 0\n",
+    "\n",
+    "    for x0, x_cond in train_loader:  \n",
+    "        x0 = x0.to(DEVICE)\n",
+    "        x_cond = x_cond.to(DEVICE) \n",
+    "\n",
+    "        t = torch.randint(0, T, (x0.size(0),), device=DEVICE)\n",
+    "        noise = torch.randn_like(x0)\n",
+    "        x_t = q_sample(x0, t, noise)\n",
+    "        t_emb = get_timestep_embedding(t, EMBED_DIM)\n",
+    "        target = noise\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "        with autocast(device_type = 'cuda'):  # AMP forward pass\n",
+    "            pred = model(x_t, t_emb, x_cond)  \n",
+    "            loss = F.mse_loss(pred, target)\n",
+    "\n",
+    "        scaler.scale(loss).backward()\n",
+    "        scaler.step(optimizer)\n",
+    "        scaler.update()\n",
+    "\n",
+    "        update_ema(ema_model, model, decay=0.999)\n",
+    "\n",
+    "        epoch_loss += loss.item() * x0.size(0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "    epoch_loss /= len(train_loader.dataset)\n",
+    "    train_losses.append(epoch_loss)\n",
+    "    print(f\"Epoch {epoch+1} | Train Loss: {epoch_loss:.8f}\")\n",
+    " \n",
+    "    if (epoch + 1) % 100 == 0:\n",
+    "        torch.save(model.state_dict(), f\"grav_model_{epoch+1}_res_imp_hsthsc_pyr_glob_cond_dropout.pt\")\n",
+    "        torch.save(ema_model.state_dict(), f\"grav_ema_model_{epoch+1}_res_imp_hsthsc_pyr_glob_cond_dropout.pt\")\n",
+    "        print(f\"✅ Saved models at epoch {epoch+1}\")\n",
+    "\n",
+    "    model.eval()\n",
+    "    test_loss = 0\n",
+    "    with torch.no_grad():\n",
+    "        for x0, x_cond in test_loader:  \n",
+    "            x0 = x0.to(DEVICE)\n",
+    "            x_cond = x_cond.to(DEVICE)\n",
+    "\n",
+    "            t = torch.randint(0, T, (x0.size(0),), device=DEVICE)\n",
+    "            noise = torch.randn_like(x0)\n",
+    "            x_t = q_sample(x0, t, noise)\n",
+    "            t_emb = get_timestep_embedding(t, EMBED_DIM)\n",
+    "            target = noise\n",
+    "\n",
+    "            with autocast(device_type = 'cuda'):\n",
+    "                pred = model(x_t, t_emb, x_cond)\n",
+    "                loss = F.mse_loss(pred, target)\n",
+    "\n",
+    "            test_loss += loss.item() * x0.size(0)\n",
+    "\n",
+    "    test_loss /= len(test_loader.dataset)\n",
+    "    test_losses.append(test_loss)\n",
+    "    print(f\"Epoch {epoch+1} | Test Loss: {test_loss:.8f}\")\n",
+    "\n",
+    "    # --- LR scheduling ---\n",
+    "    scheduler.step(test_loss)\n",
+    "    print('current lr = ' + str(optimizer.param_groups[0]['lr']))\n",
+    "\n",
+    "# --- Plot ---\n",
+    "plt.plot(train_losses, label='Train Loss')\n",
+    "plt.plot(test_losses, label='Test Loss')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('MSE Loss')\n",
+    "plt.yscale('log')\n",
+    "plt.title('Loss Curves')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c112b6c",
+   "metadata": {},
+   "source": [
+    "### Sampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f51206c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/user/1015/ipykernel_3216137/2739588192.py:47: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+      "  plt.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x2500 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vmax_lr = 1\n",
+    "vmax_hr = 1\n",
+    "# Number of samples to visualize\n",
+    "num_rows = 5\n",
+    "n_samples = num_rows\n",
+    "\n",
+    "# --- Get a batch from the test loader ---\n",
+    "(high_res_batch, low_res_batch) = next(iter(test_loader))\n",
+    "high_res_batch = high_res_batch[:n_samples].to(DEVICE)\n",
+    "low_res_batch = low_res_batch[:n_samples].to(DEVICE)\n",
+    "\n",
+    "# --- Generate model predictions ---\n",
+    "with torch.no_grad():\n",
+    "    ema_model.eval()\n",
+    "    preds = sample_epsilon_conditional(ema_model, x_cond=low_res_batch).cpu()\n",
+    "\n",
+    "# Move inputs and labels to CPU\n",
+    "low_res_batch = (low_res_batch.cpu()+1)/2\n",
+    "high_res_batch = (high_res_batch.cpu()+1)/2\n",
+    "\n",
+    "# --- Plotting ---\n",
+    "fig = plt.figure(figsize=(16, num_rows * 5))\n",
+    "gs = gridspec.GridSpec(num_rows, 4, width_ratios=[1, 1, 1, 0.05], wspace=0.1, hspace=0.3)\n",
+    "\n",
+    "for i in range(n_samples):\n",
+    "    # Low-res input\n",
+    "    ax1 = plt.subplot(gs[i, 0])\n",
+    "    ax1.imshow(low_res_batch[i, 0], cmap=\"magma\", vmax=vmax_lr)\n",
+    "    ax1.set_title(\"Low-Res\")\n",
+    "    ax1.axis('off')\n",
+    "\n",
+    "    # Model output\n",
+    "    ax2 = plt.subplot(gs[i, 1])\n",
+    "    ax2.imshow(preds[i, 0], cmap=\"magma\", vmax=vmax_hr)\n",
+    "    ax2.set_title(\"Predicted High-Res\")\n",
+    "    ax2.axis('off')\n",
+    "\n",
+    "    # Ground truth\n",
+    "    ax3 = plt.subplot(gs[i, 2])\n",
+    "    im3 = ax3.imshow(high_res_batch[i, 0], cmap=\"magma\", vmax=vmax_hr)\n",
+    "    ax3.set_title(\"Ground Truth\")\n",
+    "    ax3.axis('off')\n",
+    "\n",
+    "cbar_ax = plt.subplot(gs[:, 3])\n",
+    "plt.colorbar(im3, cax=cbar_ax)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torchtest",
+   "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": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Difflense_Aleksandr_Duplinskii/Mejiro_dataset/mejiro_dataset_generation.ipynb b/Difflense_Aleksandr_Duplinskii/Mejiro_dataset/mejiro_dataset_generation.ipynb
new file mode 100644
index 0000000..7bb24b7
--- /dev/null
+++ b/Difflense_Aleksandr_Duplinskii/Mejiro_dataset/mejiro_dataset_generation.ipynb
@@ -0,0 +1,558 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b0d96164",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ptxas: /usr/local/cuda-12/bin/ptxas\n",
+      "ptxas: NVIDIA (R) Ptx optimizing assembler\n",
+      "Copyright (c) 2005-2024 NVIDIA Corporation\n",
+      "Built on Thu_Sep_12_02:14:55_PDT_2024\n",
+      "Cuda compilation tools, release 12.6, V12.6.77\n",
+      "Build cuda_12.6.r12.6/compiler.34841621_0\n",
+      "Generating 10000 pairs with substructure=True\n",
+      "Image shape: 47x47, dtype float32\n",
+      "[###########################---] 9001/10000   90.0%  ~1.609s/it  ETA  26.8 min\n",
+      "Saving final arrays...\n",
+      "Done in 270.4 min  (~1.622s per pair incl. warmup)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os, time, sys, copy, json, shutil, subprocess\n",
+    "import numpy as np\n",
+    "\n",
+    "# ====== CONFIG ======\n",
+    "N_SAMPLES = 10000\n",
+    "OUT_HR = \"hr_all_lsst_regr_2.npy\"\n",
+    "OUT_LR = \"lr_all_lsst_regr_2.npy\"\n",
+    "PARAMS_JSONL = \"params_all_lsst_regr_2.jsonl\"\n",
+    "PARAMS_JSONL_TMP = PARAMS_JSONL + \".tmp\"\n",
+    "\n",
+    "WITH_SUBSTRUCTURE = True\n",
+    "LOG_EVERY = 1000\n",
+    "SAVE_CHECKPOINT_EVERY = 2000\n",
+    "BAND = \"F129\"\n",
+    "FOV_ARCSEC = 5\n",
+    "EXPTIME_SEC = 146  # typical Roman single exposure\n",
+    "\n",
+    "# ====== ENV ======\n",
+    "os.environ[\"ROMAN_TECHNICAL_INFORMATION_PATH\"] = \"/home/imglab/Sasha/DeepLense/grav_lens_diff/Mejiro_dataset/mejiro/roman-technical-information\"\n",
+    "\n",
+    "# Point the kernel to CUDA 12 toolchain & libs (optional but kept from your original)\n",
+    "os.environ['PATH'] = '/usr/local/cuda-12/bin:' + os.environ.get('PATH','')\n",
+    "os.environ['LD_LIBRARY_PATH'] = (\n",
+    "    f\"{os.environ.get('CONDA_PREFIX','')}/lib:/usr/local/cuda-12/lib64:\"\n",
+    "    + os.environ.get('LD_LIBRARY_PATH','')\n",
+    ")\n",
+    "os.environ['XLA_FLAGS'] = '--xla_gpu_cuda_data_dir=/usr/local/cuda-12'\n",
+    "os.environ['JAX_PLATFORM_NAME'] = 'cuda'\n",
+    "\n",
+    "# Sanity-check what this kernel sees\n",
+    "print(\"ptxas:\", shutil.which('ptxas'))\n",
+    "subprocess.run(['ptxas','--version'], check=False)\n",
+    "\n",
+    "# ====== IMPORTS (from your code/libs) ======\n",
+    "from pyHalo.preset_models import preset_model_from_name\n",
+    "from mejiro.exposure import Exposure\n",
+    "from mejiro.instruments.roman import Roman\n",
+    "from mejiro.galaxy_galaxy import GalaxyGalaxy, SampleGG\n",
+    "from mejiro.synthetic_image import SyntheticImage\n",
+    "from mejiro.utils import lenstronomy_util\n",
+    "from mejiro.engines.galsim_engine import GalSimEngine\n",
+    "from mejiro.engines.stpsf_engine import STPSFEngine\n",
+    "\n",
+    "# ====== HELPERS ======\n",
+    "def to_numpy_force(img):\n",
+    "    if hasattr(img, \"get_array\"): return img.get_array()\n",
+    "    if hasattr(img, \"array\"): return img.array\n",
+    "    if isinstance(img, np.ndarray): return img\n",
+    "    raise TypeError(f\"Unsupported image type: {type(img)}\")\n",
+    "\n",
+    "def _clip_e(kwargs_obj, e1='e1', e2='e2', m=0.6):\n",
+    "    e1v, e2v = kwargs_obj.get(e1, 0.0), kwargs_obj.get(e2, 0.0)\n",
+    "    mag = float(np.sqrt(e1v**2 + e2v**2))\n",
+    "    if mag > m and mag > 0:\n",
+    "        s = m/mag\n",
+    "        kwargs_obj[e1] = float(e1v * s)\n",
+    "        kwargs_obj[e2] = float(e2v * s)\n",
+    "\n",
+    "def _safe_float(x, default=np.nan):\n",
+    "    try:\n",
+    "        v = float(x)\n",
+    "        if not np.isfinite(v): return default\n",
+    "        return v\n",
+    "    except Exception:\n",
+    "        return default\n",
+    "\n",
+    "# ====== RANDOMIZED LENS/SOURCE MODEL ======\n",
+    "class RandomGG(GalaxyGalaxy):\n",
+    "    def __init__(self):\n",
+    "        base = SampleGG()\n",
+    "        p = copy.deepcopy(base.kwargs_params)\n",
+    "\n",
+    "        # Lens variations\n",
+    "        p['kwargs_lens'][0]['theta_E'] *= np.random.uniform(0.5, 1.5)\n",
+    "        for k in ['e1','e2']:\n",
+    "            p['kwargs_lens'][0][k] += np.random.uniform(-0.15, 0.15)\n",
+    "        _clip_e(p['kwargs_lens'][0])\n",
+    "\n",
+    "        # Source variations\n",
+    "        p['kwargs_source'][0]['center_x'] += np.random.uniform(-0.6, 0.6)\n",
+    "        p['kwargs_source'][0]['center_y'] += np.random.uniform(-0.6, 0.6)\n",
+    "        p['kwargs_source'][0]['R_sersic'] *= np.random.uniform(0.5, 2.0)\n",
+    "        for k in ['e1','e2']:\n",
+    "            p['kwargs_source'][0][k] += np.random.uniform(-0.15, 0.15)\n",
+    "        _clip_e(p['kwargs_source'][0])\n",
+    "        p['kwargs_source'][0]['n_sersic'] = float(np.random.uniform(0.5, 4.0))\n",
+    "\n",
+    "        super().__init__(name=\"RandomGG\",\n",
+    "                         coords=None,\n",
+    "                         kwargs_model=base.kwargs_model,\n",
+    "                         kwargs_params=p,\n",
+    "                         physical_params=base.physical_params,\n",
+    "                         use_jax=[True, True, True])\n",
+    "\n",
+    "# ====== SHARED INSTRUMENT + PSF + ENGINE ======\n",
+    "roman = Roman()\n",
+    "kwargs_numerics = dict(SyntheticImage.DEFAULT_KWARGS_NUMERICS)\n",
+    "kwargs_numerics['supersampling_factor'] = 1  # speed\n",
+    "\n",
+    "instrument_params = roman.default_params()\n",
+    "\n",
+    "kernel = STPSFEngine.get_roman_psf(\n",
+    "    band=BAND,\n",
+    "    detector=instrument_params['detector'],\n",
+    "    detector_position=instrument_params['detector_position'],\n",
+    "    oversample=kwargs_numerics['supersampling_factor'],\n",
+    "    num_pix=51,  # 51 for speed (use 101 for broader PSF)\n",
+    "    verbose=False\n",
+    ")\n",
+    "kwargs_psf = lenstronomy_util.get_pixel_psf_kwargs(\n",
+    "    kernel=kernel,\n",
+    "    supersampling_factor=kwargs_numerics['supersampling_factor']\n",
+    ")\n",
+    "\n",
+    "engine = 'galsim'\n",
+    "engine_params = GalSimEngine.defaults(roman.name)\n",
+    "\n",
+    "# ====== RECORD EXTRACTION ======\n",
+    "def extract_record(idx, strong_lens, *, with_substructure, sub_info, exptime):\n",
+    "    p = copy.deepcopy(strong_lens.kwargs_params)\n",
+    "    lens0 = (p.get('kwargs_lens') or [{}])[0]\n",
+    "    src0  = (p.get('kwargs_source') or [{}])[0]\n",
+    "\n",
+    "    ein = _safe_float(strong_lens.get_einstein_radius(), default=np.nan)\n",
+    "    mhost = _safe_float(strong_lens.get_main_halo_mass(), default=np.nan)\n",
+    "\n",
+    "    rec = {\n",
+    "        \"index\": idx,\n",
+    "        \"seed_render\": (idx * 8635) & 0x5fffffff,\n",
+    "        \"seed_noise\":  (idx * 9176) & 0x7fffffff,\n",
+    "        \"with_substructure\": bool(with_substructure),\n",
+    "        \"substructure\": sub_info,\n",
+    "        \"band\": BAND,\n",
+    "        \"fov_arcsec\": FOV_ARCSEC,\n",
+    "        \"exptime_sec\": exptime,\n",
+    "        \"targets\": {\n",
+    "            \"einstein_radius\": ein,\n",
+    "            \"main_halo_mass\": mhost\n",
+    "        },\n",
+    "        \"lens\": {\n",
+    "            \"theta_E\": _safe_float(lens0.get(\"theta_E\")),\n",
+    "            \"e1\": _safe_float(lens0.get(\"e1\")),\n",
+    "            \"e2\": _safe_float(lens0.get(\"e2\")),\n",
+    "            \"gamma\": _safe_float(lens0.get(\"gamma\", np.nan)),\n",
+    "            \"center_x\": _safe_float(lens0.get(\"center_x\", 0.0)),\n",
+    "            \"center_y\": _safe_float(lens0.get(\"center_y\", 0.0)),\n",
+    "        },\n",
+    "        \"source\": {\n",
+    "            \"center_x\": _safe_float(src0.get(\"center_x\")),\n",
+    "            \"center_y\": _safe_float(src0.get(\"center_y\")),\n",
+    "            \"R_sersic\": _safe_float(src0.get(\"R_sersic\")),\n",
+    "            \"n_sersic\": _safe_float(src0.get(\"n_sersic\")),\n",
+    "            \"e1\": _safe_float(src0.get(\"e1\")),\n",
+    "            \"e2\": _safe_float(src0.get(\"e2\")),\n",
+    "        },\n",
+    "    }\n",
+    "    return rec\n",
+    "\n",
+    "# ====== ONE SAMPLE ======\n",
+    "def make_pair(idx, with_substructure=True, max_attempts=4):\n",
+    "    # Seed controls RandomGG parameter draws\n",
+    "    np.random.seed((idx * 8635) & 0x5fffffff)\n",
+    "\n",
+    "    attempt = 0\n",
+    "    sub_info = {\n",
+    "        \"used\": False,\n",
+    "        \"cone_opening_angle_arcsec\": None,\n",
+    "        \"two_halo_contribution\": None,\n",
+    "        \"sigma_sub\": 0.055,\n",
+    "        \"LOS_normalization\": 0.0,\n",
+    "        \"log_mlow\": 6.0,\n",
+    "        \"log_mhigh\": 10.0,\n",
+    "        \"log_m_host\": None,\n",
+    "        \"r_tidal\": 0.5\n",
+    "    }\n",
+    "\n",
+    "    while True:\n",
+    "        attempt += 1\n",
+    "        strong_lens = RandomGG()\n",
+    "\n",
+    "        # --- optional substructure ---\n",
+    "        if with_substructure:\n",
+    "            er = _safe_float(strong_lens.get_einstein_radius(), default=1.0)\n",
+    "            if not np.isfinite(er) or er <= 0: er = 1.0\n",
+    "\n",
+    "            CDM = preset_model_from_name('CDM')\n",
+    "            cone_candidates = [max(3.0 * er, 3.0), 5.0, 10.0, 20.0]\n",
+    "\n",
+    "            realization = None\n",
+    "            for angle in cone_candidates:\n",
+    "                for two_halo in (False, True):\n",
+    "                    try:\n",
+    "                        realization = CDM(\n",
+    "                            strong_lens.z_lens, strong_lens.z_source,\n",
+    "                            sigma_sub=sub_info[\"sigma_sub\"],\n",
+    "                            cone_opening_angle_arcsec=float(angle),\n",
+    "                            LOS_normalization=sub_info[\"LOS_normalization\"],\n",
+    "                            log_mlow=sub_info[\"log_mlow\"], log_mhigh=sub_info[\"log_mhigh\"],\n",
+    "                            log_m_host=np.log10(strong_lens.get_main_halo_mass()),\n",
+    "                            r_tidal=sub_info[\"r_tidal\"],\n",
+    "                            two_halo_contribution=two_halo\n",
+    "                        )\n",
+    "                        sub_info.update({\n",
+    "                            \"used\": True,\n",
+    "                            \"cone_opening_angle_arcsec\": float(angle),\n",
+    "                            \"two_halo_contribution\": bool(two_halo),\n",
+    "                            \"log_m_host\": float(np.log10(strong_lens.get_main_halo_mass()))\n",
+    "                        })\n",
+    "                        break\n",
+    "                    except Exception:\n",
+    "                        realization = None\n",
+    "                if realization is not None:\n",
+    "                    break\n",
+    "\n",
+    "            if realization is None:\n",
+    "                if attempt < max_attempts:\n",
+    "                    continue  # retry with a fresh RandomGG\n",
+    "                else:\n",
+    "                    with_substructure = False  # fall back gracefully\n",
+    "            else:\n",
+    "                strong_lens.add_realization(realization)\n",
+    "\n",
+    "        # ---- render HR (no PSF) and LR (PSF + exposure/noise) ----\n",
+    "        sim_hr = SyntheticImage(\n",
+    "            strong_lens, instrument=roman, band=BAND, fov_arcsec=FOV_ARCSEC,\n",
+    "            instrument_params=instrument_params, kwargs_numerics=kwargs_numerics,\n",
+    "            kwargs_psf=None, pieces=False, verbose=False\n",
+    "        )\n",
+    "        sim_blurred = SyntheticImage(\n",
+    "            strong_lens, instrument=roman, band=BAND, fov_arcsec=FOV_ARCSEC,\n",
+    "            instrument_params=instrument_params, kwargs_numerics=kwargs_numerics,\n",
+    "            kwargs_psf=kwargs_psf, pieces=False, verbose=False\n",
+    "        )\n",
+    "\n",
+    "        # Apply finite exposure + detector/sky noise via GalSim engine\n",
+    "        local_engine_params = dict(engine_params)\n",
+    "        local_engine_params['seed'] = (idx * 9176) & 0x7fffffff  # stable but varied noise\n",
+    "        exposure = Exposure(\n",
+    "            sim_blurred,\n",
+    "            exposure_time=EXPTIME_SEC,\n",
+    "            engine='galsim',\n",
+    "            engine_params=local_engine_params,\n",
+    "            verbose=False\n",
+    "        )\n",
+    "\n",
+    "        hr = to_numpy_force(sim_hr.image).astype(np.float32)\n",
+    "        lr = to_numpy_force(exposure.image).astype(np.float32)\n",
+    "\n",
+    "        record = extract_record(\n",
+    "            idx, strong_lens,\n",
+    "            with_substructure=with_substructure,\n",
+    "            sub_info=sub_info,\n",
+    "            exptime=EXPTIME_SEC\n",
+    "        )\n",
+    "\n",
+    "        return hr, lr, record\n",
+    "\n",
+    "# ====== SIMPLE PROGRESS BAR ======\n",
+    "def prog(i, n, t_per, start_time):\n",
+    "    done = i\n",
+    "    total = n\n",
+    "    frac = done/total\n",
+    "    barlen = 30\n",
+    "    fill = int(barlen*frac)\n",
+    "    bar = \"#\"*fill + \"-\"*(barlen-fill)\n",
+    "    elapsed = time.time() - start_time\n",
+    "    rem = (total - done) * (t_per if t_per > 0 else 0.0)\n",
+    "    msg = f\"\\r[{bar}] {done}/{total}  {frac*100:5.1f}%  ~{t_per:.3f}s/it  ETA {rem/60:5.1f} min\"\n",
+    "    sys.stdout.write(msg); sys.stdout.flush()\n",
+    "\n",
+    "# ====== DRIVER ======\n",
+    "def main():\n",
+    "    print(f\"Generating {N_SAMPLES} pairs with substructure={WITH_SUBSTRUCTURE}\")\n",
+    "    t0 = time.time()\n",
+    "\n",
+    "    # Warmup + shape\n",
+    "    hr0, lr0, rec0 = make_pair(0, with_substructure=WITH_SUBSTRUCTURE)\n",
+    "    H, W = hr0.shape\n",
+    "    assert lr0.shape == (H, W), f\"HR {hr0.shape} vs LR {lr0.shape}\"\n",
+    "    print(f\"Image shape: {H}x{W}, dtype float32\")\n",
+    "\n",
+    "    # Preallocate\n",
+    "    hr_all = np.empty((N_SAMPLES, H, W), dtype=np.float32)\n",
+    "    lr_all = np.empty((N_SAMPLES, H, W), dtype=np.float32)\n",
+    "    records = []\n",
+    "\n",
+    "    hr_all[0] = hr0; lr_all[0] = lr0\n",
+    "    records.append(rec0)\n",
+    "\n",
+    "    # Loop\n",
+    "    t_per = 0.0\n",
+    "    tick = time.time()\n",
+    "    start = tick\n",
+    "    prog(1, N_SAMPLES, 0.001, start)\n",
+    "\n",
+    "    for i in range(1, N_SAMPLES):\n",
+    "        hr, lr, rec = make_pair(i, with_substructure=WITH_SUBSTRUCTURE)\n",
+    "        hr_all[i] = hr\n",
+    "        lr_all[i] = lr\n",
+    "        records.append(rec)\n",
+    "\n",
+    "        if (i % LOG_EVERY) == 0:\n",
+    "            dt = time.time() - tick\n",
+    "            t_per = dt / LOG_EVERY\n",
+    "            tick = time.time()\n",
+    "            prog(i+1, N_SAMPLES, t_per, start)\n",
+    "\n",
+    "        if (i % SAVE_CHECKPOINT_EVERY) == 0:\n",
+    "            np.save(OUT_HR + \".tmp.npy\", hr_all[:i+1])\n",
+    "            np.save(OUT_LR + \".tmp.npy\", lr_all[:i+1])\n",
+    "            # JSONL checkpoint\n",
+    "            with open(PARAMS_JSONL_TMP, \"w\") as f:\n",
+    "                for r in records:\n",
+    "                    f.write(json.dumps(r) + \"\\n\")\n",
+    "\n",
+    "    print(\"\\nSaving final arrays...\")\n",
+    "    np.save(OUT_HR, hr_all)\n",
+    "    np.save(OUT_LR, lr_all)\n",
+    "\n",
+    "    # Final JSONL write (atomic via replace)\n",
+    "    with open(PARAMS_JSONL_TMP, \"w\") as f:\n",
+    "        for r in records:\n",
+    "            f.write(json.dumps(r) + \"\\n\")\n",
+    "    os.replace(PARAMS_JSONL_TMP, PARAMS_JSONL)\n",
+    "\n",
+    "    total = time.time() - t0\n",
+    "    print(f\"Done in {total/60:.1f} min  (~{total/N_SAMPLES:.3f}s per pair incl. warmup)\")\n",
+    "\n",
+    "    # cleanup temps\n",
+    "    try:\n",
+    "        os.remove(OUT_HR + \".tmp.npy\")\n",
+    "        os.remove(OUT_LR + \".tmp.npy\")\n",
+    "    except Exception:\n",
+    "        pass\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6712c2a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_json(\"params_all_lsst_regr.jsonl\", lines=True)\n",
+    "\n",
+    "# each row's \"targets\" is a dict\n",
+    "df[\"targets\"].iloc[0]  # {'einstein_radius': ..., 'main_halo_mass': ...}\n",
+    "\n",
+    "# extract Einstein radius column\n",
+    "df[\"einstein_radius\"] = df[\"targets\"].apply(lambda t: t.get(\"einstein_radius\", None))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.hist(df[\"einstein_radius\"].dropna(), bins=50)\n",
+    "plt.xlabel(\"Einstein radius\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "065db212",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   index  seed_render  seed_noise  with_substructure  band  fov_arcsec  \\\n",
+      "0      0            0           0               True  F129           5   \n",
+      "1      1         8635        9176               True  F129           5   \n",
+      "2      2        17270       18352               True  F129           5   \n",
+      "3      3        25905       27528               True  F129           5   \n",
+      "4      4        34540       36704               True  F129           5   \n",
+      "\n",
+      "   exptime_sec  substructure.used  substructure.cone_opening_angle_arcsec  \\\n",
+      "0          146               True                                3.675302   \n",
+      "1          146               True                                4.189703   \n",
+      "2          146               True                                4.881895   \n",
+      "3          146               True                                3.528246   \n",
+      "4          146               True                                3.000000   \n",
+      "\n",
+      "   substructure.two_halo_contribution  ...   lens.e2  lens.gamma  \\\n",
+      "0                               False  ...  0.038350         NaN   \n",
+      "1                               False  ... -0.092230         NaN   \n",
+      "2                               False  ...  0.071898         NaN   \n",
+      "3                               False  ... -0.000114         NaN   \n",
+      "4                               False  ...  0.154530         NaN   \n",
+      "\n",
+      "   lens.center_x  lens.center_y  source.center_x  source.center_y  \\\n",
+      "0      -0.007876       0.010633         0.356843        -0.442115   \n",
+      "1      -0.007876       0.010633        -0.290794        -0.746928   \n",
+      "2      -0.007876       0.010633         0.698617        -0.054102   \n",
+      "3      -0.007876       0.010633         0.204397        -0.169680   \n",
+      "4      -0.007876       0.010633         0.784338        -0.546111   \n",
+      "\n",
+      "   source.R_sersic  source.n_sersic  source.e1  source.e2  \n",
+      "0         0.242599         3.872820  -0.082232   0.033324  \n",
+      "1         0.319927         3.657190  -0.080430   0.015202  \n",
+      "2         0.312891         2.513513  -0.158148  -0.090483  \n",
+      "3         0.147935         0.736615   0.022718  -0.078270  \n",
+      "4         0.231739         2.717847  -0.013404  -0.138688  \n",
+      "\n",
+      "[5 rows x 30 columns]\n",
+      "['index', 'seed_render', 'seed_noise', 'with_substructure', 'band', 'fov_arcsec', 'exptime_sec', 'substructure.used', 'substructure.cone_opening_angle_arcsec', 'substructure.two_halo_contribution', 'substructure.sigma_sub', 'substructure.LOS_normalization', 'substructure.log_mlow', 'substructure.log_mhigh', 'substructure.log_m_host', 'substructure.r_tidal', 'targets.einstein_radius', 'targets.main_halo_mass', 'lens.theta_E', 'lens.e1', 'lens.e2', 'lens.gamma', 'lens.center_x', 'lens.center_y', 'source.center_x', 'source.center_y', 'source.R_sersic', 'source.n_sersic', 'source.e1', 'source.e2']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Load JSONL into list of dicts\n",
+    "with open(\"params_all_lsst_regr.jsonl\") as f:\n",
+    "    records = [json.loads(line) for line in f]\n",
+    "\n",
+    "# Flatten nested dicts into columns\n",
+    "df = pd.json_normalize(records, sep=\".\")\n",
+    "\n",
+    "# Inspect\n",
+    "print(df.head())          # first 5 rows\n",
+    "print(df.columns.tolist()) # see all available columns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "be9095ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   index  seed_render  seed_noise  with_substructure  band  fov_arcsec  exptime_sec  substructure.used  substructure.cone_opening_angle_arcsec  substructure.two_halo_contribution  \\\n",
+      "0      0            0           0               True  F129           5          146               True                                3.675302                               False   \n",
+      "1      1         8635        9176               True  F129           5          146               True                                4.189703                               False   \n",
+      "2      2        17270       18352               True  F129           5          146               True                                4.881895                               False   \n",
+      "3      3        25905       27528               True  F129           5          146               True                                3.528246                               False   \n",
+      "4      4        34540       36704               True  F129           5          146               True                                3.000000                               False   \n",
+      "5      5        43175       45880               True  F129           5          146               True                                3.490930                               False   \n",
+      "6      6        51810       55056               True  F129           5          146               True                                4.107413                               False   \n",
+      "7      7        60445       64232               True  F129           5          146               True                                3.245845                               False   \n",
+      "8      8        69080       73408               True  F129           5          146               True                                4.837932                               False   \n",
+      "9      9        77715       82584               True  F129           5          146               True                                4.711684                               False   \n",
+      "\n",
+      "   substructure.sigma_sub  substructure.LOS_normalization  substructure.log_mlow  substructure.log_mhigh  substructure.log_m_host  substructure.r_tidal  targets.einstein_radius  \\\n",
+      "0                   0.055                             0.0                    6.0                    10.0                     13.0                   0.5                 1.225101   \n",
+      "1                   0.055                             0.0                    6.0                    10.0                     13.0                   0.5                 1.396568   \n",
+      "2                   0.055                             0.0                    6.0                    10.0                     13.0                   0.5                 1.627298   \n",
+      "3                   0.055                             0.0                    6.0                    10.0                     13.0                   0.5                 1.176082   \n",
+      "4                   0.055                             0.0                    6.0                    10.0                     13.0                   0.5                 0.770112   \n",
+      "5                   0.055                             0.0                    6.0                    10.0                     13.0                   0.5                 1.163643   \n",
+      "6                   0.055                             0.0                    6.0                    10.0                     13.0                   0.5                 1.369138   \n",
+      "7                   0.055                             0.0                    6.0                    10.0                     13.0                   0.5                 1.081948   \n",
+      "8                   0.055                             0.0                    6.0                    10.0                     13.0                   0.5                 1.612644   \n",
+      "9                   0.055                             0.0                    6.0                    10.0                     13.0                   0.5                 1.570561   \n",
+      "\n",
+      "   targets.main_halo_mass  lens.theta_E   lens.e1   lens.e2  lens.gamma  lens.center_x  lens.center_y  source.center_x  source.center_y  source.R_sersic  source.n_sersic  source.e1  source.e2  \n",
+      "0            1.000000e+13      1.225101  0.069416  0.038350         NaN      -0.007876       0.010633         0.356843        -0.442115         0.242599         3.872820  -0.082232   0.033324  \n",
+      "1            1.000000e+13      1.396568 -0.131966 -0.092230         NaN      -0.007876       0.010633        -0.290794        -0.746928         0.319927         3.657190  -0.080430   0.015202  \n",
+      "2            1.000000e+13      1.627298  0.048921  0.071898         NaN      -0.007876       0.010633         0.698617        -0.054102         0.312891         2.513513  -0.158148  -0.090483  \n",
+      "3            1.000000e+13      1.176082  0.119024 -0.000114         NaN      -0.007876       0.010633         0.204397        -0.169680         0.147935         0.736615   0.022718  -0.078270  \n",
+      "4            1.000000e+13      0.770112 -0.138803  0.154530         NaN      -0.007876       0.010633         0.784338        -0.546111         0.231739         2.717847  -0.013404  -0.138688  \n",
+      "5            1.000000e+13      1.163643  0.090485  0.035096         NaN      -0.007876       0.010633         0.017210        -0.378090         0.092507         0.822396  -0.146107   0.056081  \n",
+      "6            1.000000e+13      1.369138 -0.026879 -0.053528         NaN      -0.007876       0.010633         0.882599        -0.214804         0.117726         0.511122  -0.123034  -0.111485  \n",
+      "7            1.000000e+13      1.081948 -0.004317 -0.101226         NaN      -0.007876       0.010633        -0.133808        -0.157991         0.164063         1.069657   0.000776  -0.046727  \n",
+      "8            1.000000e+13      1.612644  0.038526 -0.076479         NaN      -0.007876       0.010633         0.264350         0.009935         0.219336         1.367262  -0.171453  -0.049772  \n",
+      "9            1.000000e+13      1.570561  0.018804  0.035596         NaN      -0.007876       0.010633        -0.067549        -0.237718         0.297885         3.124051  -0.063371  -0.093422  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "pd.set_option(\"display.max_rows\", None)  # show all rows\n",
+    "pd.set_option(\"display.max_columns\", None)  # show all columns\n",
+    "pd.set_option(\"display.width\", 200)         # avoid wrapping too soon\n",
+    "\n",
+    "print(df)  # or just df in Jupyter\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8525cf9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torchtest",
+   "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": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Difflense_Aleksandr_Duplinskii/classifiers.py b/Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/classifiers.py
similarity index 100%
rename from Difflense_Aleksandr_Duplinskii/classifiers.py
rename to Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/classifiers.py
diff --git a/Difflense_Aleksandr_Duplinskii/evaluation.ipynb b/Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/evaluation.ipynb
similarity index 100%
rename from Difflense_Aleksandr_Duplinskii/evaluation.ipynb
rename to Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/evaluation.ipynb
diff --git a/Difflense_Aleksandr_Duplinskii/model_grav.py b/Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/model_grav.py
similarity index 100%
rename from Difflense_Aleksandr_Duplinskii/model_grav.py
rename to Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/model_grav.py
diff --git a/Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/readme.txt b/Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/readme.txt
new file mode 100644
index 0000000..4542247
--- /dev/null
+++ b/Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/readme.txt
@@ -0,0 +1,8 @@
+model_grav.py - contains the NN architecture that is trained to denoise, as well as the noise schedule and the sampling function to generate images using the trained model
+train_grav.ipynb - is used to train the NN that is used for denoising steps
+sampling.ipynb - is used to sample images based on the 
+
+classiers.py - contains different NN architectures that are used to classify the images into 3 classes depending on the assumed dark matter structure
+train_classifier.ipynb - is used to train the classifier, it is used to calculate the FD and assess the performance of the diffusion model
+
+evaluation.ipynb - loads images from the original dataset and the generated ones to compare them and evaluate the FD
diff --git a/Difflense_Aleksandr_Duplinskii/sampling.ipynb b/Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/sampling.ipynb
similarity index 100%
rename from Difflense_Aleksandr_Duplinskii/sampling.ipynb
rename to Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/sampling.ipynb
diff --git a/Difflense_Aleksandr_Duplinskii/train_classifier.ipynb b/Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/train_classifier.ipynb
similarity index 100%
rename from Difflense_Aleksandr_Duplinskii/train_classifier.ipynb
rename to Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/train_classifier.ipynb
diff --git a/Difflense_Aleksandr_Duplinskii/train_grav.ipynb b/Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/train_grav.ipynb
similarity index 100%
rename from Difflense_Aleksandr_Duplinskii/train_grav.ipynb
rename to Difflense_Aleksandr_Duplinskii/Unconditional_diffusion/train_grav.ipynb
diff --git a/Difflense_Aleksandr_Duplinskii/readme.txt b/Difflense_Aleksandr_Duplinskii/readme.txt
index 651ef85..228339d 100644
--- a/Difflense_Aleksandr_Duplinskii/readme.txt
+++ b/Difflense_Aleksandr_Duplinskii/readme.txt
@@ -1,8 +1,4 @@
-model_grav.py - contains the NN architecture that is trained to denoise, as well as the noise schedule and the sampling function to generate images using the trained model
-train_grav.ipynb - is used to train the NN that is used for denoising steps
-sampling.ipynb - is used to sample images based on the 
-
-classiers.py - contains different NN architectures that are used to classify the images into 3 classes depending on the assumed dark matter structure
-train_classifier.ipynb - is used to train the classifier, it is used to calculate the FD and assess the performance of the diffusion model
-
-evaluation.ipynb - loads images from the original dataset and the generated ones to compare them and evaluate the FD
\ No newline at end of file
+Mejiro_dataset - code for generating the benchmark dataset based on the Mejiro simulation package
+Baseline_models - superresolution models used for evaluation
+Unconditional_diffusion - image generation pipeline
+Conditional_diffusion - resolution improvement (low-resolution conditioning)