diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 3c72dadef65cb59134beeb799a164fb6b7cf7ba9..cc519cef9e54fea486e76cb0f2bcb4a2345960c0 100644
--- a/TD2 Deep Learning.ipynb
+++ b/TD2 Deep Learning.ipynb
@@ -33,18 +33,32 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "330a42f5",
"metadata": {},
"outputs": [
{
- "ename": "",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31mRunning cells with 'deeplearning' requires the ipykernel package.\n",
- "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
- "\u001b[1;31mCommand: 'conda install -n deeplearning ipykernel --update-deps --force-reinstall'"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: torch in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (2.1.0)\n",
+ "Requirement already satisfied: torchvision in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (0.16.0)\n",
+ "Requirement already satisfied: filelock in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from torch) (3.13.1)\n",
+ "Requirement already satisfied: typing-extensions in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from torch) (4.8.0)\n",
+ "Requirement already satisfied: sympy in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from torch) (1.12)\n",
+ "Requirement already satisfied: networkx in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from torch) (3.2.1)\n",
+ "Requirement already satisfied: jinja2 in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from torch) (3.1.2)\n",
+ "Requirement already satisfied: fsspec in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from torch) (2023.10.0)\n",
+ "Requirement already satisfied: numpy in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from torchvision) (1.26.1)\n",
+ "Requirement already satisfied: requests in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from torchvision) (2.31.0)\n",
+ "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from torchvision) (10.1.0)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from jinja2->torch) (2.1.3)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from requests->torchvision) (3.3.2)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from requests->torchvision) (3.4)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from requests->torchvision) (2.0.7)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from requests->torchvision) (2023.7.22)\n",
+ "Requirement already satisfied: mpmath>=0.19 in c:\\users\\amaury\\.conda\\envs\\deeplearning\\lib\\site-packages (from sympy->torch) (1.3.0)\n",
+ "Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
@@ -63,7 +77,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 14,
"id": "b1950f0a",
"metadata": {},
"outputs": [
@@ -71,34 +85,34 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "tensor([[-1.6108, 0.1317, -0.3513, -0.3216, 0.3727, 0.3612, -1.2889, -0.3386,\n",
- " 0.0120, -0.4465],\n",
- " [ 0.4556, -1.1400, -2.2728, 0.8573, 0.5658, 0.1817, -1.0059, 0.9475,\n",
- " 0.9408, -0.1243],\n",
- " [ 0.0147, 0.1123, 0.4914, 1.2003, -0.5135, 0.3748, -0.4284, -1.8824,\n",
- " 1.2185, 0.2435],\n",
- " [-1.2637, -1.0916, -2.2031, 0.6197, -0.3888, 1.1088, 1.4107, -0.5090,\n",
- " 1.3829, 0.7859],\n",
- " [-1.2325, -0.1772, 0.4179, -0.1563, -0.3375, -0.2674, 0.5254, 0.0358,\n",
- " 1.0495, 1.3925],\n",
- " [-0.2975, 0.9450, -1.4389, -1.2755, 0.0556, -0.8547, 1.6859, 1.7961,\n",
- " 0.7077, -0.7942],\n",
- " [-0.4796, -0.0267, 0.4084, -0.5886, -1.1128, 0.3938, 1.0752, -0.5991,\n",
- " 1.1073, 0.7135],\n",
- " [-1.1897, 0.4010, -1.1109, 0.4708, 0.0985, 0.6087, -0.0313, -2.0060,\n",
- " -0.2365, 0.8436],\n",
- " [ 0.1849, -0.9080, 0.7707, -1.0415, -1.0695, -0.1611, -1.3508, -1.3483,\n",
- " -0.5158, 0.0991],\n",
- " [ 1.5166, -0.2918, -1.5908, 1.2440, -1.5634, 0.1577, 2.2259, -0.2295,\n",
- " -0.2859, 1.0919],\n",
- " [ 0.8850, -0.8469, 0.2788, 1.1428, 0.1166, -1.4135, -0.3392, 0.3397,\n",
- " -0.1095, 1.3038],\n",
- " [ 0.9079, -1.5653, -1.1905, -0.3896, -0.4266, -0.3319, -2.2913, 0.8935,\n",
- " -0.9540, -2.5985],\n",
- " [ 1.2186, 0.3123, 1.0443, -0.2062, 2.0841, -1.1471, -0.7396, 0.0058,\n",
- " 0.1896, -0.5264],\n",
- " [ 1.3109, 0.0959, -0.2179, 0.1682, 0.4997, -1.3812, -0.6915, 1.9026,\n",
- " 0.7823, 0.2988]])\n",
+ "tensor([[ 1.2385, 0.9770, 0.1490, -1.3107, -0.8918, -2.0424, -1.1429, 0.7795,\n",
+ " -0.5968, -0.2449],\n",
+ " [-2.7734, -1.1905, 1.6390, -1.1704, -2.1379, -0.1500, -1.9140, -0.3900,\n",
+ " 0.1169, -0.9136],\n",
+ " [-1.7608, 0.2259, -1.5386, 0.7663, 1.0607, -0.3491, -2.0062, 0.7766,\n",
+ " -1.4132, 0.2602],\n",
+ " [-1.6190, 1.0832, 0.0241, -0.9802, 0.4896, 0.3704, 0.5288, 1.3101,\n",
+ " -0.3294, -1.7523],\n",
+ " [-1.1176, -0.8717, 1.5522, 2.9196, 1.0902, 0.6930, 0.7241, 0.7357,\n",
+ " 0.0796, 0.0333],\n",
+ " [-0.0392, 0.1984, 0.2830, 1.2385, 0.2719, -0.0432, 1.8082, -0.4086,\n",
+ " -0.4255, 0.4032],\n",
+ " [ 0.6927, -1.6535, -0.9071, -0.5867, -2.0941, -0.7682, 1.1010, 0.4465,\n",
+ " 0.4099, 0.9255],\n",
+ " [ 0.8534, 0.2541, 0.0213, -2.3995, -1.9529, 1.8424, 1.8093, -0.9751,\n",
+ " 0.9278, 0.3308],\n",
+ " [-0.6209, -0.6411, 0.6847, 1.4290, -1.7673, 0.3594, 1.3432, -0.0562,\n",
+ " 0.8164, -0.6377],\n",
+ " [-1.0142, 0.0808, -1.0360, 0.5007, 0.1061, 0.3094, -0.2928, -1.1348,\n",
+ " 0.0736, -2.1213],\n",
+ " [ 0.4499, -0.0123, -1.5131, -1.0491, 1.7004, -0.5377, 1.5895, -1.7753,\n",
+ " 0.2538, -1.0109],\n",
+ " [ 0.9338, -0.8571, 0.5836, -1.3999, -0.8808, -0.6557, 1.3292, 0.5451,\n",
+ " 2.3717, 0.1854],\n",
+ " [-0.9255, -0.4112, 0.1366, -0.1515, -0.0390, -0.2112, -0.8927, -0.2451,\n",
+ " -0.3226, -0.0927],\n",
+ " [-2.0905, -1.5615, 1.0275, -1.6315, -1.3136, -0.6393, 0.6415, -2.1767,\n",
+ " 0.0942, 0.3116]])\n",
"AlexNet(\n",
" (features): Sequential(\n",
" (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
@@ -168,7 +182,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 15,
"id": "6e18f2fd",
"metadata": {},
"outputs": [
@@ -202,7 +216,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 37,
"id": "462666a2",
"metadata": {},
"outputs": [
@@ -210,21 +224,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data\\cifar-10-python.tar.gz\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100.0%\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting data\\cifar-10-python.tar.gz to data\n",
+ "Files already downloaded and verified\n",
"Files already downloaded and verified\n"
]
}
@@ -297,7 +297,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 17,
"id": "317bf070",
"metadata": {},
"outputs": [
@@ -361,7 +361,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 21,
"id": "4b53f229",
"metadata": {},
"outputs": [
@@ -369,1957 +369,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 16, 5, 5])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
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- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n",
- "torch.Size([20, 16, 5, 5])\n",
- "torch.Size([20, 400])\n"
- ]
- },
- {
- "ename": "KeyboardInterrupt",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32mc:\\Users\\Amaury\\Documents\\ECL\\ECL S9\\Intelligence Artificielle\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 15\u001b[0m line \u001b[0;36m2\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Amaury/Documents/ECL/ECL%20S9/Intelligence%20Artificielle/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Amaury/Documents/ECL/ECL%20S9/Intelligence%20Artificielle/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m \u001b[39m# Forward pass: compute predicted outputs by passing inputs to the model\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Amaury/Documents/ECL/ECL%20S9/Intelligence%20Artificielle/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=23'>24</a>\u001b[0m output \u001b[39m=\u001b[39m model(data)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Amaury/Documents/ECL/ECL%20S9/Intelligence%20Artificielle/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m \u001b[39m# Calculate the batch loss\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Amaury/Documents/ECL/ECL%20S9/Intelligence%20Artificielle/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=25'>26</a>\u001b[0m loss \u001b[39m=\u001b[39m criterion(output, target)\n",
- "File \u001b[1;32mc:\\Users\\Amaury\\.conda\\envs\\deeplearning\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
- "File \u001b[1;32mc:\\Users\\Amaury\\.conda\\envs\\deeplearning\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
- "\u001b[1;32mc:\\Users\\Amaury\\Documents\\ECL\\ECL S9\\Intelligence Artificielle\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 15\u001b[0m line \u001b[0;36m1\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Amaury/Documents/ECL/ECL%20S9/Intelligence%20Artificielle/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Amaury/Documents/ECL/ECL%20S9/Intelligence%20Artificielle/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpool(F\u001b[39m.\u001b[39;49mrelu(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconv1(x)))\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Amaury/Documents/ECL/ECL%20S9/Intelligence%20Artificielle/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv2(x)))\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Amaury/Documents/ECL/ECL%20S9/Intelligence%20Artificielle/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m \u001b[39mprint\u001b[39m(x\u001b[39m.\u001b[39mshape)\n",
- "File \u001b[1;32mc:\\Users\\Amaury\\.conda\\envs\\deeplearning\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
- "File \u001b[1;32mc:\\Users\\Amaury\\.conda\\envs\\deeplearning\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
- "File \u001b[1;32mc:\\Users\\Amaury\\.conda\\envs\\deeplearning\\Lib\\site-packages\\torch\\nn\\modules\\pooling.py:166\u001b[0m, in \u001b[0;36mMaxPool2d.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 165\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor):\n\u001b[1;32m--> 166\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mmax_pool2d(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mkernel_size, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstride,\n\u001b[0;32m 167\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpadding, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdilation, ceil_mode\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mceil_mode,\n\u001b[0;32m 168\u001b[0m return_indices\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mreturn_indices)\n",
- "File \u001b[1;32mc:\\Users\\Amaury\\.conda\\envs\\deeplearning\\Lib\\site-packages\\torch\\_jit_internal.py:488\u001b[0m, in \u001b[0;36mboolean_dispatch.<locals>.fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 486\u001b[0m \u001b[39mreturn\u001b[39;00m if_true(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 487\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 488\u001b[0m \u001b[39mreturn\u001b[39;00m if_false(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
- "File \u001b[1;32mc:\\Users\\Amaury\\.conda\\envs\\deeplearning\\Lib\\site-packages\\torch\\nn\\functional.py:791\u001b[0m, in \u001b[0;36m_max_pool2d\u001b[1;34m(input, kernel_size, stride, padding, dilation, ceil_mode, return_indices)\u001b[0m\n\u001b[0;32m 789\u001b[0m \u001b[39mif\u001b[39;00m stride \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 790\u001b[0m stride \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mjit\u001b[39m.\u001b[39mannotate(List[\u001b[39mint\u001b[39m], [])\n\u001b[1;32m--> 791\u001b[0m \u001b[39mreturn\u001b[39;00m torch\u001b[39m.\u001b[39;49mmax_pool2d(\u001b[39minput\u001b[39;49m, kernel_size, stride, padding, dilation, ceil_mode)\n",
- "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+ "Epoch: 0 \tTraining Loss: 21.559226 \tValidation Loss: 24.017923\n",
+ "Validation loss decreased (inf --> 24.017923). Saving model ...\n",
+ "Epoch: 1 \tTraining Loss: 20.753776 \tValidation Loss: 23.070274\n",
+ "Validation loss decreased (24.017923 --> 23.070274). Saving model ...\n",
+ "Epoch: 2 \tTraining Loss: 19.989041 \tValidation Loss: 22.612180\n",
+ "Validation loss decreased (23.070274 --> 22.612180). Saving model ...\n",
+ "Epoch: 3 \tTraining Loss: 19.239937 \tValidation Loss: 21.968531\n",
+ "Validation loss decreased (22.612180 --> 21.968531). Saving model ...\n",
+ "Epoch: 4 \tTraining Loss: 18.627505 \tValidation Loss: 21.374375\n",
+ "Validation loss decreased (21.968531 --> 21.374375). Saving model ...\n",
+ "Epoch: 5 \tTraining Loss: 17.969620 \tValidation Loss: 21.638193\n",
+ "Validation loss increased. Stopping training.\n"
]
}
],
@@ -2331,6 +392,7 @@
"\n",
"n_epochs = 8 # number of epochs to train the model\n",
"train_loss_list = [] # list to store loss to visualize\n",
+ "val_loss_list = []\n",
"valid_loss_min = np.Inf # track change in validation loss\n",
"\n",
"for epoch in range(n_epochs):\n",
@@ -2374,6 +436,7 @@
" train_loss = train_loss / len(train_loader)\n",
" valid_loss = valid_loss / len(valid_loader)\n",
" train_loss_list.append(train_loss)\n",
+ " val_loss_list.append(valid_loss)\n",
"\n",
" # Print training/validation statistics\n",
" print(\n",
@@ -2406,13 +469,13 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 22,
"id": "d39df818",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -2424,8 +487,8 @@
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
- "plt.plot(range(n_epochs), train_loss_list)\n",
- "plt.plot(range(n_epochs), val_loss_list)\n",
+ "plt.plot(range(len(train_loss_list)), train_loss_list)\n",
+ "plt.plot(range(len(val_loss_list)), val_loss_list)\n",
"plt.xlabel(\"Epoch\")\n",
"plt.ylabel(\"Loss\")\n",
"plt.title(\"Performance of Model 1\")\n",
@@ -2442,10 +505,31 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"id": "e93efdfc",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Test Loss: 21.444182\n",
+ "\n",
+ "Test Accuracy of airplane: 70% (705/1000)\n",
+ "Test Accuracy of automobile: 79% (791/1000)\n",
+ "Test Accuracy of bird: 47% (474/1000)\n",
+ "Test Accuracy of cat: 36% (365/1000)\n",
+ "Test Accuracy of deer: 47% (472/1000)\n",
+ "Test Accuracy of dog: 59% (594/1000)\n",
+ "Test Accuracy of frog: 76% (766/1000)\n",
+ "Test Accuracy of horse: 69% (695/1000)\n",
+ "Test Accuracy of ship: 77% (771/1000)\n",
+ "Test Accuracy of truck: 66% (661/1000)\n",
+ "\n",
+ "Test Accuracy (Overall): 62% (6294/10000)\n"
+ ]
+ }
+ ],
"source": [
"model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n",
"\n",
@@ -2544,31 +628,37 @@
" (fc2): Linear(in_features=512, out_features=64, bias=True)\n",
" (fc3): Linear(in_features=64, out_features=10, bias=True)\n",
")\n",
- "Epoch: 0 \tTraining Loss: 46.028273 \tValidation Loss: 45.934397\n",
- "Validation loss decreased (inf --> 45.934397). Saving model ...\n",
- "Epoch: 1 \tTraining Loss: 43.720598 \tValidation Loss: 40.231559\n",
- "Validation loss decreased (45.934397 --> 40.231559). Saving model ...\n",
- "Epoch: 2 \tTraining Loss: 36.971662 \tValidation Loss: 34.566339\n",
- "Validation loss decreased (40.231559 --> 34.566339). Saving model ...\n",
- "Epoch: 3 \tTraining Loss: 32.990292 \tValidation Loss: 32.461610\n",
- "Validation loss decreased (34.566339 --> 32.461610). Saving model ...\n",
- "Epoch: 4 \tTraining Loss: 30.651598 \tValidation Loss: 30.284392\n",
- "Validation loss decreased (32.461610 --> 30.284392). Saving model ...\n",
- "Epoch: 5 \tTraining Loss: 28.931517 \tValidation Loss: 28.455610\n",
- "Validation loss decreased (30.284392 --> 28.455610). Saving model ...\n",
- "Epoch: 6 \tTraining Loss: 27.371597 \tValidation Loss: 27.639403\n",
- "Validation loss decreased (28.455610 --> 27.639403). Saving model ...\n",
- "Epoch: 7 \tTraining Loss: 25.946868 \tValidation Loss: 25.942862\n",
- "Validation loss decreased (27.639403 --> 25.942862). Saving model ...\n",
- "Epoch: 8 \tTraining Loss: 24.772974 \tValidation Loss: 25.217402\n",
- "Validation loss decreased (25.942862 --> 25.217402). Saving model ...\n",
- "Epoch: 9 \tTraining Loss: 23.679380 \tValidation Loss: 24.196019\n",
- "Validation loss decreased (25.217402 --> 24.196019). Saving model ...\n",
- "Epoch: 10 \tTraining Loss: 22.531621 \tValidation Loss: 23.777050\n",
- "Validation loss decreased (24.196019 --> 23.777050). Saving model ...\n",
- "Epoch: 11 \tTraining Loss: 21.674077 \tValidation Loss: 22.732419\n",
- "Validation loss decreased (23.777050 --> 22.732419). Saving model ...\n",
- "Epoch: 12 \tTraining Loss: 20.725845 \tValidation Loss: 22.909202\n",
+ "Epoch: 0 \tTraining Loss: 45.389665 \tValidation Loss: 43.113908\n",
+ "Validation loss decreased (inf --> 43.113908). Saving model ...\n",
+ "Epoch: 1 \tTraining Loss: 40.321355 \tValidation Loss: 39.247467\n",
+ "Validation loss decreased (43.113908 --> 39.247467). Saving model ...\n",
+ "Epoch: 2 \tTraining Loss: 35.781713 \tValidation Loss: 34.307430\n",
+ "Validation loss decreased (39.247467 --> 34.307430). Saving model ...\n",
+ "Epoch: 3 \tTraining Loss: 32.749098 \tValidation Loss: 31.964787\n",
+ "Validation loss decreased (34.307430 --> 31.964787). Saving model ...\n",
+ "Epoch: 4 \tTraining Loss: 30.853479 \tValidation Loss: 30.444714\n",
+ "Validation loss decreased (31.964787 --> 30.444714). Saving model ...\n",
+ "Epoch: 5 \tTraining Loss: 29.242304 \tValidation Loss: 28.374242\n",
+ "Validation loss decreased (30.444714 --> 28.374242). Saving model ...\n",
+ "Epoch: 6 \tTraining Loss: 27.812293 \tValidation Loss: 27.470855\n",
+ "Validation loss decreased (28.374242 --> 27.470855). Saving model ...\n",
+ "Epoch: 7 \tTraining Loss: 26.506239 \tValidation Loss: 26.082626\n",
+ "Validation loss decreased (27.470855 --> 26.082626). Saving model ...\n",
+ "Epoch: 8 \tTraining Loss: 25.186847 \tValidation Loss: 25.075025\n",
+ "Validation loss decreased (26.082626 --> 25.075025). Saving model ...\n",
+ "Epoch: 9 \tTraining Loss: 23.985566 \tValidation Loss: 23.826247\n",
+ "Validation loss decreased (25.075025 --> 23.826247). Saving model ...\n",
+ "Epoch: 10 \tTraining Loss: 22.821817 \tValidation Loss: 22.718001\n",
+ "Validation loss decreased (23.826247 --> 22.718001). Saving model ...\n",
+ "Epoch: 11 \tTraining Loss: 21.659117 \tValidation Loss: 22.270342\n",
+ "Validation loss decreased (22.718001 --> 22.270342). Saving model ...\n",
+ "Epoch: 12 \tTraining Loss: 20.717419 \tValidation Loss: 21.737828\n",
+ "Validation loss decreased (22.270342 --> 21.737828). Saving model ...\n",
+ "Epoch: 13 \tTraining Loss: 19.833583 \tValidation Loss: 21.019663\n",
+ "Validation loss decreased (21.737828 --> 21.019663). Saving model ...\n",
+ "Epoch: 14 \tTraining Loss: 18.959761 \tValidation Loss: 20.095673\n",
+ "Validation loss decreased (21.019663 --> 20.095673). Saving model ...\n",
+ "Epoch: 15 \tTraining Loss: 18.225648 \tValidation Loss: 20.296886\n",
"Validation loss increased. Stopping training.\n"
]
}
@@ -2675,6 +765,103 @@
" break"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Test Loss: 20.563462\n",
+ "\n",
+ "Test Accuracy of airplane: 72% (729/1000)\n",
+ "Test Accuracy of automobile: 83% (834/1000)\n",
+ "Test Accuracy of bird: 55% (553/1000)\n",
+ "Test Accuracy of cat: 47% (474/1000)\n",
+ "Test Accuracy of deer: 50% (507/1000)\n",
+ "Test Accuracy of dog: 50% (509/1000)\n",
+ "Test Accuracy of frog: 69% (695/1000)\n",
+ "Test Accuracy of horse: 72% (722/1000)\n",
+ "Test Accuracy of ship: 77% (777/1000)\n",
+ "Test Accuracy of truck: 69% (699/1000)\n",
+ "\n",
+ "Test Accuracy (Overall): 64% (6499/10000)\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = Net2()\n",
+ "model.load_state_dict(torch.load(\"./model2_cifar.pt\"))\n",
+ "\n",
+ "# track test loss\n",
+ "test_loss = 0.0\n",
+ "class_correct = list(0.0 for i in range(10))\n",
+ "class_total = list(0.0 for i in range(10))\n",
+ "\n",
+ "model.eval()\n",
+ "# iterate over test data\n",
+ "for data, target in test_loader:\n",
+ " # move tensors to GPU if CUDA is available\n",
+ " if train_on_gpu:\n",
+ " data, target = data.cuda(), target.cuda()\n",
+ " # forward pass: compute predicted outputs by passing inputs to the model\n",
+ " output = model(data)\n",
+ " # calculate the batch loss\n",
+ " loss = criterion(output, target)\n",
+ " # update test loss\n",
+ " test_loss += loss.item() * data.size(0)\n",
+ " # convert output probabilities to predicted class\n",
+ " _, pred = torch.max(output, 1)\n",
+ " # compare predictions to true label\n",
+ " correct_tensor = pred.eq(target.data.view_as(pred))\n",
+ " correct = (\n",
+ " np.squeeze(correct_tensor.numpy())\n",
+ " if not train_on_gpu\n",
+ " else np.squeeze(correct_tensor.cpu().numpy())\n",
+ " )\n",
+ " # calculate test accuracy for each object class\n",
+ " for i in range(batch_size):\n",
+ " label = target.data[i]\n",
+ " class_correct[label] += correct[i].item()\n",
+ " class_total[label] += 1\n",
+ "\n",
+ "# average test loss\n",
+ "test_loss = test_loss / len(test_loader)\n",
+ "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+ "\n",
+ "for i in range(10):\n",
+ " if class_total[i] > 0:\n",
+ " print(\n",
+ " \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
+ " % (\n",
+ " classes[i],\n",
+ " 100 * class_correct[i] / class_total[i],\n",
+ " np.sum(class_correct[i]),\n",
+ " np.sum(class_total[i]),\n",
+ " )\n",
+ " )\n",
+ " else:\n",
+ " print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n",
+ "\n",
+ "print(\n",
+ " \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n",
+ " % (\n",
+ " 100.0 * np.sum(class_correct) / np.sum(class_total),\n",
+ " np.sum(class_correct),\n",
+ " np.sum(class_total),\n",
+ " )\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This model has improved by 2% in accuracy compared to the previous one, but shows significant improvement on several classes, notably bird and cat."
+ ]
+ },
{
"cell_type": "markdown",
"id": "bc381cf4",
@@ -2794,37 +981,37 @@
"output_type": "stream",
"text": [
"Test accuracy for regular model\n",
- "Test Loss: 22.562925\n",
+ "Test Loss: 20.527149\n",
"\n",
- "Test Accuracy of airplane: 66% (660/1000)\n",
- "Test Accuracy of automobile: 70% (709/1000)\n",
- "Test Accuracy of bird: 57% (575/1000)\n",
- "Test Accuracy of cat: 46% (465/1000)\n",
- "Test Accuracy of deer: 42% (422/1000)\n",
- "Test Accuracy of dog: 50% (502/1000)\n",
- "Test Accuracy of frog: 68% (685/1000)\n",
- "Test Accuracy of horse: 65% (650/1000)\n",
- "Test Accuracy of ship: 86% (867/1000)\n",
- "Test Accuracy of truck: 59% (593/1000)\n",
+ "Test Accuracy of airplane: 68% (681/1000)\n",
+ "Test Accuracy of automobile: 81% (810/1000)\n",
+ "Test Accuracy of bird: 47% (477/1000)\n",
+ "Test Accuracy of cat: 38% (387/1000)\n",
+ "Test Accuracy of deer: 70% (703/1000)\n",
+ "Test Accuracy of dog: 45% (453/1000)\n",
+ "Test Accuracy of frog: 72% (726/1000)\n",
+ "Test Accuracy of horse: 77% (770/1000)\n",
+ "Test Accuracy of ship: 78% (787/1000)\n",
+ "Test Accuracy of truck: 73% (734/1000)\n",
"\n",
- "Test Accuracy (Overall): 61% (6128/10000)\n",
+ "Test Accuracy (Overall): 65% (6528/10000)\n",
"\n",
"\n",
"Test accuracy for quantized model\n",
- "Test Loss: 22.787941\n",
+ "Test Loss: 20.557350\n",
"\n",
- "Test Accuracy of airplane: 65% (650/1000)\n",
- "Test Accuracy of automobile: 69% (695/1000)\n",
- "Test Accuracy of bird: 58% (586/1000)\n",
- "Test Accuracy of cat: 49% (491/1000)\n",
- "Test Accuracy of deer: 40% (401/1000)\n",
- "Test Accuracy of dog: 48% (487/1000)\n",
- "Test Accuracy of frog: 67% (674/1000)\n",
- "Test Accuracy of horse: 63% (630/1000)\n",
- "Test Accuracy of ship: 84% (849/1000)\n",
- "Test Accuracy of truck: 58% (588/1000)\n",
+ "Test Accuracy of airplane: 68% (684/1000)\n",
+ "Test Accuracy of automobile: 80% (803/1000)\n",
+ "Test Accuracy of bird: 49% (498/1000)\n",
+ "Test Accuracy of cat: 38% (383/1000)\n",
+ "Test Accuracy of deer: 69% (698/1000)\n",
+ "Test Accuracy of dog: 45% (457/1000)\n",
+ "Test Accuracy of frog: 71% (715/1000)\n",
+ "Test Accuracy of horse: 78% (781/1000)\n",
+ "Test Accuracy of ship: 79% (794/1000)\n",
+ "Test Accuracy of truck: 74% (741/1000)\n",
"\n",
- "Test Accuracy (Overall): 60% (6051/10000)\n"
+ "Test Accuracy (Overall): 65% (6554/10000)\n"
]
}
],
@@ -2952,6 +1139,13 @@
")\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The quantized model obtains near identical results to the base model, thus proving the great benefits of quantization."
+ ]
+ },
{
"cell_type": "markdown",
"id": "201470f9",
@@ -2976,9 +1170,7 @@
"c:\\Users\\Amaury\\.conda\\envs\\deeplearning\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
" warnings.warn(\n",
"c:\\Users\\Amaury\\.conda\\envs\\deeplearning\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
- " warnings.warn(msg)\n",
- "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to C:\\Users\\Amaury/.cache\\torch\\hub\\checkpoints\\resnet50-0676ba61.pth\n",
- "100.0%\n"
+ " warnings.warn(msg)\n"
]
},
{
@@ -3092,7 +1284,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -3127,43 +1319,6 @@
"print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
]
},
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Predicted class is: Golden Retriever\n"
- ]
- },
- {
- "data": {
- "image/png": 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bTBT2Dgu3KVTd8fJ+HhvTEIEQIEWojBWbHOdyMV7brDcX7E+8PjMykza5NY7DeIB2hLIP+pb+XhCjwh4kocVwKS9uzTQbSNnz0kko0MIAKpC0Gez40o4hCtop09R1IlOBaAGq5FyNUOXVUpFDHz9Py7UwhtBNUj9n4hA/EBnRdEQ1IVnQeOYQB8bRfOJJBR98MUDtI+o3UuNdtV8/9sJuVDsP7zasVHS07/WeSbfGO6b9l+hJ68ZqNoja632e2uYrr8g9g6nbO+94o96ba8h4e/bHHbfatuluzVcsiIlmU/Hl3Wazkk/mr5ujl0JNEzUMu1GM1M2852eoCr6kIXgPqTA1QxQOX1SENj9tXXbRqDiS7h4axBrUXHNiPp2Bl1GYZo7KKAk0FmFujMK7XIRexnUekb92fR6jKJu2J5xGFvLCkn/DN7pvKgtX3QOiiivpNs+gLXv37K4nSreYTWMpzKJtgqwG3ark79Qau8cgK7oTpDEaW6Rc2c+NAa+MrxKaFGnZQ3bhxufuvMcPB5JmcvKoDAiJGCcjLp8QD07Nd1+DeWp7zQ6S97mpBsiXjKJaVywyr9uISjfu+q8imlo+jc2da1AY7fn0i/fc0Gpdi9x9/iOIpzeY3jxb16dTOes817l2HrR4HCozarRkFNFQhVbFybB4jZS0EPYd/SgvEaLc/t67FrWJom6u9/9W5ttQQJNKe1i1VlzYDLM7I5WuL9bzKrqK8fJma1RksPfjdufQgrp60FFVtyYY9G/EKFonqhRq8/pS5PQLUK3j3TB6MfWJ3/0l4ffveDkdn/Sskwp7X+vrG5Nqg9AqEqmZUhZOnlt47Y5MuhnvkMRtT3VvvmZZqoUQ+zCiWcni0RyATMoO1UBWCMUtBwZVq8JR28gl4KfOu1Mh1SxbqjeiI94qxbuNRGUgO8wyhNOMYvVHdjtE3QjdjtrRQ12X2taPr8ouAPp/b6eu479FxTN8J6ZwmgFSC7GzI9H67y0a6Pvcr+GnG6zd103LPlRHC/aSHWX2uSVaJq43BdXhNgQhdS13pFX4YlFjtDGLrF2sh/bRy/sEteC/MpefIL5mb+rfbc4A0WwpKvwNGUXjRLJbjEX2iauh1G062n924tgtyWXCmkV2z/uwz8sLXvahEV2vM9umr1KvQdv2fV1Ye0v7OOfWsyYtDIpAZRQ3wqtu/t2AuTvAdlTSPKlFDRK1OH4dRtR5chos9FZBcaCelDOkiMvO8kdEbnhoLm1V1JP74ZXPnXQyrpOcXSvdYGrPc2FmRQJjOQ7SsHmVpNotR2UU+4a0zS37t/su3NdNlU+p2u6RlkYuBW0LTh2pMkcqkzRjotZoRM1ksQTCPfnOQrSrSpxzan1wTro+uVsSKUiy0qarzK8yms4ulnPDw92M2G/aGDNUyN8CA5sQKBa4Ps6h84IpFs+jZVydqOp53w1C2fdYHZ8iMoCutFD0FIuHBPRvxShySg1e7n74vYP1qoT1Eq7uBFalRiV8a6JuVinsdDfK1m92K628+PdmS9xYc18up+6xHB1A2e3JRT9uMFrocyZd+Vu71jWXVOebDaS7BFJMcksmeMh4Uo8zhWKos0jB6lqrdTlcET81arAOxUmtS1D6TPMVtPuBpm7kbFxcBNOlKWHO5SkfAqpDN9+fbmotkvTmsz8DJV4mf/3o543Z9DEJpZOq+Bt1RUFy4dO+0aJXRUJN8S3sT4QUi1Gzro0YlHf9at/A48r07b9Oerfrvra76rKvdY21uB1sFTSue0f9vsZitjsbLbzY9/u8dU3Li//WNU7lfdZeCUhUB3lDNTbU6l8uyl+5Ps89mjYkb0U67IRu4+4swxUNdNbhKs3rqksdQDNG/gi76Ra+v/bItt6sYy38GOPq23BV0lbppuyz3G7VXVq15K8mf+mBn4EZNWWlMojCqPeoPGHPUTYmVXM+rLfFZZm5oadma+m7Vd5saCZbNF+bYml6b63p0NSnHukIkCuMznu9C4m0IDQ8Nyn6L+ax/+t2DYoZttIAJcqwzfetKa7B/mJv2XnCviWkzNVtTQoagzNVTZtnC1UMqBUJXssAoFg4uGtCqqaGt7gSoK+xYpGt5k5ukcbonrVa16UbQ/tCaHRaUSoFbYjsiHjnC/kW5fTTSiOf0k4xrdagMKnzYX9UkhUBTY6k9bNMToZW499M9dCEaCqxC1X9kLbf2ma9IZ06YzsnrhtDOz2jcnjavzuj0L6tBpHr+yqbqZ98KgV5eZdWv7RWX1RhfLnB21o1KaVKUF1LL4jd+rlHIFYDfks/b7v9dvytX/W7olLQPdIzi9sxV1isxTVeGKeaZb8mPVXjauENtPVp+va+PaVE+uVcia8yGmugaXQ3496vPVpyRwFtQ75Yi9oT2nyXvhRyaSoqvcq1S/D6szOlSnc1jtXqiNT1SjWzsqO0mvncMpzbOLX1oc6na/NHJefdwFj72jGxfp705q0/fu1hhLcBi/3T9b2t8R59lEdc91RlsrkZU3faNXr48/15eX0Wo/CSceQSr753ScWh0nkxpAdVbRiFOcBeo0JvpTm0wjEVMtrc6z4jzncZei+jN2t0vL2tNd0IxFDCTcKTjYxdBKQmJjRnUsrd60uvO+hpv1TOfOsmtoAsae1VYla0plm05+pYa8Zim4+KGDq6acFfolS1o2KXStxtQ6mpHLthr6op4LztBillCZ2TUs9ACkTNpUqZ3Kzp7ZxXBt7xjW7jaAkp1m6Zd2dN71buN0Y3h2VOtaldpbxPHyfRmGndbNKEkABpB7I2v1IYaOExVbL7zn6hqi2rVzKFrnYPW6OhF/aw2+xS9j7+yNXmQ6T7KW74F3PSmGjNWti3GrtL2sawh2uXe9xeYyc1IpEbL9pfuz7TmKklZl3aAu2h0WaIsZDTH4v42qFhQ5ANldT2u/ArhZt6T2W3qgpkIXeKnO4N3CSF+pvF2nXgdkNlyyXoRUTRBLUUWkUIO+TbF6hXR/6SpCjikF4K7s/fMqCdLKS7r6VQ3EqoJtGqqlAZQs1SrapTL71rdSQITvjl29e8eji2gilZleCqK1F5Os/M80bKtcBLnQttqAe3M6YdecnN/Nk4e/G3e2925vJS928z0ObxRopXxv/JZO+/1/gFyMjoSMkSxVLKtnkKithDpmtGpxRDouV2uPJOdb7UonB7ta9aw6LMcS3/+LJnf+kyNcT63FfD/gSJOLmlgZtWCsuuc9irvVhimRCspZToBdpPuT4PUThfsu8q97ut/OSlEBmFFHaRW54xqqpy4QaQ6p7xCALO47KgxUvRDbkjJKX6p4HObWQ3V8gorUe1FWn/0MriVaKuzKGLR9g7aYyKaujcv9s5fEE6mgt0h1pgpz5hlvhOKlXorZUhfOpqvCnFIdXq7wqSMI+H3+GT5UwIDM4RvOcwDIzTiPee4D3D4Pji9T2PDycrVJYTKcXGUFWVV3eeZbNCtikp66Zcl63Nia8GklZj0phsjLkxF7KyxWz1KQsjyZq7fL5bw/AeYUnjIk0vxxj+rQu0GiT3B+uciGCRkM6To+U3iLO0KIuQLFShlV7UPBRO8H6nFlVFMiaBRYp4rsyxhFgrO+rau/fJ9YldotDLHq3Zj7V77sXvu+u+MMzufYYG666wfJKsHtQM4Lkgsr8dougg0m2ny9YXZzEIVTWQ/i67uij+2mjjAv3mUyjBUn2EYp8DUv59MVmN46ruuZJl893CuVuAW3/qIuzj+nOTuRNLCwRrQy5SiCpqurwJKPCXNkdSmNo+Fbc6/g2Z3FQD0ps+9qhDSnHZcQwcxoGH04HT3YEhBHxwBC8cD4FpKupQhuR2OKpqANi3zeRI2XM8eIvnkJIHQUTwbW5VlZi0SG/D/GtUYjI1bouZZdtaUdtud++roP3YXhpSd0N2v3Y530rJT+/pNqnjJiy7MhZjFNryMyoOzRhqc0LLAbq9arBVT/K38v5238i+Xu1zI4S+6Vt7Q/dNpZu6b0pbVQVpzLPOXmVgzuHUgbfs2b92lkd/fV7AldSOV51pH7BZlJMNXPeiutqIvzzbjbeV0Ws4tN5k/5iduGMWvaejcuK+c6UfFVkY4+iI3cAhFRHUTV01ukrsFS1JHVt1CUsf3CIdYXbEXq3oZXw71683UOrxiNX8rON6Ab2lVvWhupFpTKXtqjaq+s6afGRVlo/TwOk4cnecePV45P7+SAi1hoESt5VtuxpzUEpZ/D2tOcUVzRER8D5wOB45Hcf2XidKjgveecSZl8S67IsR0KRWVCEnJcbE03nlw5NwXSPrVmNJaFJZXxDBjhcrkNZ9YQAawtP2U+s3WB+0xGK4vfhzs72Ud5cq4FKK0mTtg+2KXHaGblVNHblBkztJN4RUBVsLr+4ZQn9JZRDSCgnV61a8AE21q0Qi7a72ZxFzTfWTqhIWRkGw9XeOdf0bVbiqQTh1irqULWoIklm4dz+fNFLemUpft6mpLs2y99JlI+299e8bhNKhkF36SruzSSiRYoY1yN4vw41NRVJZWGebVV5CtH0c/dt20vkxV++LsajueSJiAUYBaWdK0DZDYWDiblrUQghCtUkIIQiH0XMYR07HA68f73jz5o5xdPiiWuQ0sy2ZFBMpJguHrvELavUXWi3JcpaGbSJAInGLtHgHzVghjUQYBpz3O1PFoeLBDdbtZLU4DkE4vBq4n+D5uvF08TxdI9clvmAS+2XrW8vTV9FQWUe/9vW4B9eea3QqDrxSjU2pqptSrGKdYUsxdOc7WrJaD1oYqhn0fenGbiTd3c/9Vc/XaGpFt5Yt0raMo6rrUtpTdA/BaE/Voru37Kl5NQBTSbvvKrMTATcgeLLL+CF+2uE/c/1nJIVJqVGi5Aqd2DfL/nvCgmIqx9uHa9l2dr/DYvT7+ITm7qvEo1UMVylSxSv0W7TVkCx92pGJslexl9bZfNOM7NW6XS66QCYILQy3CrK9iEr5KT7NHTZbtmYNwunVh2ahfoFQ6mtwFp5dbTnW5R5O7Nuj2jScg+A9Yxh583jP61d3vHp9IgQrvhq3RI4bmmJjRjkltmW9MTJq3IwxamESOHKMTSKui3kMXC1Dnzd8cERdC5R3LMtieRnOI6UOpQ8jznmyA9HEEITX9yMPp4F1S7w/bzxfNq5LYo0WeGbLXtdW2xwhO2RuRkQRnIRmvLXANFtLAZzr7BjQlQAskreUaNhRzQt6bcLBUtrRVhMJiuE3Nomne+6MmMrW6L2td1lPX2m8UU9HI+W+LLuKUplb00mkId12aSn/J2UMzrV5gMKnzHmMCyM/9frsXI+9epz0U4i9fufiNljLmGgl7dA/85Q1bIGD0iZNhRZUZzNaHeOVYbAvfod0brn0DgerBG/6v+5P7MRQdqsk8x6I3GbK9v+WxbBqacY16iLnEl8iBfbtmnbnObhJtP/rdvIaoi3UqZCCJjz3x4k3r068fjxyfz8wBMh5I8fNCudskZy2xgSMYaRi+DIDnsZIzhuqqVW5TjHtgy4i04VgY8qJPAYjzgLx12VFQkmXF0cYDozHQAj1QCiP83Za2wCMQ0ScMHgYAzxftRlQm/Spc94ZAivUrjNrEnhXB9vBUr1s7Rn8/qlJYOm2a6mLWvAFNTm/pWaL9P44EMFJ3oskF3qouKeTE/BylStt2YtvBt3oh11I3SKJ/a9b2u88HgqUehU3IQudwPsp1+dFZnaMrnHU8kFdQqFXHswnbN+UwKKWD9+aovK8Gkuv2qkk1aLcd+Am0qV6PipR9FNXM+X2T7T6GiuxqPWuIQalY2d7gk6VAtL1QwuZym13CnzdA51a3Q46RkHtjLMNWXrdz2n/aw1+6ovHeCeMIXCYBt68OvGLLx84HD3BCzktxHUjbhs5RXJMVhcjx8Z9VYVtXW1czkq7b+uM5mjznhIpbjvzz4bsXDA9VzTj00SKazmyL5KS4kKgWAw53Q+EqVKI4PyI9zYxUjIZ748QXGYMiiPxXoWYzDNkU1rPyLAZ6gsU132VtAS/1rWUzrZDlcj19+rVqlRSGEth9oY07ZwPGvOp3j5XNnBu27G276QoSVUgITWtjBaXkSv99wysLnMX71Lf2P7c903LZM3VWL9HfUIRgC0uJ4ME6pGEFYX+Van04vpMRlEhnLysbrdvzx9ncTfXbfiqDSiVAi3VNJf6gexU0jV+ixukf3GhDvcj775t5/aGBlzqRpZPbukmWNraGDOQViez3lddfe08D2FnHrVx/ZF3AFU566M498Kw9pK7w8QXb+754s0db16PJYgrkbaNdb6QUiZta2ESqZz9sEKOUAKh0mqbW7Gj7fK6GvLIiZSVuFwQH3A+IOKJ2wbrQs6Rbblyun9gma/EZJJ3PBxJ14VhPHC8u+f+8ZEwBoYxMAyDJUFpasSeELZtxenGXUgcHoUQBt4/rcxrNs9V2S26Ly0h2N8pZbYu8/w2Gzl3hNltyD7PqMyxo/EJYwA5lU3ldiOtGlKsp9L0hu6qHdt7BLqKZagWT2AdwO2i1zivT0geoR5LeCMksPemqp72qRIAhQF7IPYfv/jlMyK4P78UXl87qkrgnWG8pPpqVLHPajGVXcrSpLBHid297eoSvCorqTCRCu92aEDbwDcoSwsc3EVMRSE3r2r7fM/icyJkZ3UQ9roUNpqdKWgbkx1b2DWmQqrFcbQip8owdtRx0w924mnSxdEKjYg47o5H/uFXr3l8GDkeHY6VtGzE5cI6X1jmMzmupS6GBaplHDku5GQoQ8QR15V13YgxmrQPkx3gqwkfgtWaVG2HBMdk8RYWF+E4Xxc0O5wLjIMnx43j3SuOpxN39ycGnwkS8XlDVyXmvTALiLk1UyqHBtt7HyeHZM/ZC+c5sqS6oWkBUbHEZtR8lp7ODC3t7lfVgmbdbR5FXQetaORGyFUkm/ebcdDRUGMQBfVmoEcEfZesDzU2xz6rRW60Jes59lwPo8EWktDRh2razyBVqAZSV8/VbSX9lVBHUPqVsRifuk9+6vWZjGJ3/dyyBCnf99K4Skxpz+6zxm3A0U07Nim+wLjKWCr8poP6+iNjrWjlFn/Yezqwt6OH9ly9uS6aNErqg7r2hefFL/Zl1StvO9CFZTc92UScqt54gRpvEIpqszMMEZiGgePxwBdv7nj1MDBNFlqPZnJcWC8fmc8fWK7PRc8vTDpbKLPZKYr6kxPbcmZbN9YtgxsYxqltMh8GlmW2E7d8KLxZiXGzugnDgZwtOMmX+pDhcOB4GJlGj5cEeSOvqxXtQVE34v1oUYZl/LkdDlxF3crkIY8Qk7BGbROjaoy4hmW/zCKpdouaQFbbd802tbu+e7WhftY+7yB67soK2NzlRut0bfZRCb2aXg3y2lp5CVZLDJDmmnvexlHpD7R539pp63uR14ZAXbFuN5e+c5/Q/249+enXf8ZJYdrBq+5qC9kZDhsGeLFvgJaroXsDnW2RUs2rceCm8XU7qt/47be64L2C2vpe7u2ppDzUtM3euix7rMOnDLiPo6jPUWol0HMe+1pvyNC+0T2ytMxGYwr64m4njnEIPNwfefPqxFdvj4yDpa6TzFMRlyvL5QPz8w+s80wWg/qWFJWIsQRBYUbFtC3E+QPbFolRiOoZx5mqX4sLxG3DhQmfaTamFCMqVtpN1aS8L5GO0+HAOHqCU2MSUQsKSRYpO4JMVgUqAzFBjBvtfBHJpLjhgckLcfDMi5gNgnK8QZXkt6txw/Sr16zt6BIodeOq1J126kbfoxKaw39nDjXYqoLFou/UNfNAzw70JY2U73dU2e0O3e+olLLf0zGoThr39LgzntqfgipEimexZ0uV/vjJ12efZl5ln0U+0ow47R6RHWIRaQkuZWB2GE2FQMXcqLDLVJvi3I+mbb68T5DcMqEbFa307+VEtk9u9zefzHpDQbff1UWuz7WIzO4NiOJLUdVckFOmBDl1PB0s5N0CpOxT14iiY67lj2mc+PWv3vD29YmH+4DLM3G9kFM9FCRz/fAtl3d/Yrk+I8M9iJ1vkdLGuqxc1xK9KA6RwDpf2S7PBfoOLNvGupzxISBuICYIIViimFjCWNoWYrJ09Lw9gwxk58EPjIcDXhI5zmzZEaVItpJq7pxD1wtbOrNta2FQiTDdlY2dzYBafncivDoEkAPfP1smb0MErjLfsnNfMGbzPLrizq1r9lLV6wzLReqXhbB8Iq31V3rhV+IgGuM3m0VFsTXQ7uVmrPul7gcntbhtfWdVN0u+hzjkBqNQUISWm+vhV/u+lJIJ3QSV9MkL0pI2pfTnb4YonNS4RrrZrkpFJ9Xr/UCNgtTySalFhMKeI18WX6HUSbjFCm0ztyV94erpMIv2C14/f6k31rY//aibyo5HN2gqHbLYGUaNrW8fd7/68o6Wcl4uLzXDXZuUrArOrvUI0zTwcDryj//wJY+PBuc1nklxsbgHtcOF5udnPnz/Dcu8kdKI8560XchxJW4L6zJzXSIpexCrYPX89M7WSEF1swI8DmIaEG+2jZw2BjziJ/wwsVw+sszPpBTxfuDx8UsrZrttbHohXiAMAz6MSJgsx4B9k5IT2zazrSsxZcSNqH5HcFYucBjvCdNgGysl4nrhLsxwOvG8BZ5m63BjqgWFtTT0bm3Fg50ERqsQ1uimk+BabDDVIFoF0b6Ou2fN3lG9Kn4nK+1oVYqTX3dakXJMwCfvoNZkqQCnjyC29+RqH6NDqyKYg7k5iBtN1uOw7Xcph1rdXqaS/a3qUUg3yJu30lWB2meu31A1TaVK2Mo8en1p11FpT9bN3HhFr+P3XdA+FHs3GlUDUW13J6TdmLm3TTej0pagwyI39o+KAv/sfMGNJdw2Zc2T6BlVxyCgpXzfnw68fjzx5dt7Xj0OONnQNJO3GY2r2UtTJG8L23IhbgsxbqQMEle2+SNxndmWmXm+skQh5gDY2RIxJpPiOaIp4mUgOcGrQyjoww/U+AEvSk6RbdssA1NGRCCuV/KW2CTj/UTOGZ8SEjcSQ2FMGdHIulzNbZtSyXa9ssxnnKiFnZ8emO4ecJ3EJCcmD26YGP2B50VY45741Na7MvPKDqR8VxBCL9xyFWC66/43NFLd+HTqJyXehHImSIMn1U1eFrvQoj3jWiCf9GGWhTaqGoLUMz52D0djAWKBWzaP1em/0/Velj/v54nuXGtXq+o+1QQ5oWn788T74vr8yMzur7bRu03W44tboG0TnGH3j3eQHn25+bszKLhVI27e2do3+N2DLXsqtxv3jb+/82Zwuv9yiyxeMKzbB+z3zhK+95P2bP9cZRR93dDdfWoEMw2BN48n3r694+2bI4O3SMq8LeRtQXRDEHJc2ZYr6+Uj2/xE3DIxQ44Ly/kD23wxNDHPJAaiHkCC1fDM2TwgcUXTxjg4c+aUI+oU8H4oZKkIyWIyUj3Xk+K1mNG0omTCaHPgU0ScY1OH+AnRiKaZy/mJlLSka9sszdcrmha8U9brMw9pww8jPgScH8koXpUxKMPoyTqiCjHZppRKOy8kWCsaDLdVzbt0+Z3mbqREY+5VujdLUql9mTGp3rssqyqqUquducYMtLeNdC/ZDdZ7irkr6kmXxVFsbsZopCuaSxOKxaaiJYWixZ4I4nbVx1Y1I1gRqp96fXZkpivb1wJLdojRJ1uhhQuWibDj0AxKZW3nGLXJAkrI8B43cMuU9gXUtpnr5y+3dJ2o3f6h1cNSNuxure5a14I2ZV+eyh32MOfdPVpdYznvkPfTHH/72zd8vzN6pRLvbgxrLNUJrx9OfP3VI/f3I94n4nIhzhdyXGxTe4U0s81Xrs8f+fDd73h+90cSIxHHvCxcr1eW65llWVjWlTA+wjjY2ZyayXFt56JqjgQfASVrQlIEPzAMgEbQBU2RFNeWYZnzwjI/Q5rRtJFSJEQzPA7R4UhclytueiBvV7b5A8/nGcUqTuXi3XNh4PLhe9I2c3c8kVNknAaG6cAw3SPDhGyRYYiMx8yX928QP3C+RuZltfKGQjHY3eLUG29b+TY3+0JX/MZ24Cdr5xojkA7yC07qSeDSCYISPl5yLXpvmbLTX9d6bYKqbBi9VSFWrBqFPrQyEimisOlCfX5RVYPVGIvmYvwoxf8V1Fk/w09PHv18r0c5X7nZGnbns5QSai+l8afxCntrFRIpCfMzt/PJS80FV42STVXoA8X7y7g9VYdsqGaPiehVib84Tr3Ztp9ARrpFF+nt7eXhbgb2J6Qk+OwIpaKUqoY5cYxj4PF05O+/fsPdncfJSlxmtrPZBZxGvEaIK8t15vzxHe+++4Z/+c1v+P6b36Hi8eOJ6f4t13lj24SkI/hAJqDbhiZFhoAyWpi3CkhASazLih8OjCEwDkcGb8cMrPOF67YiLuACaIys14XBHyyPQyEmz7ZAkoR3EZdnPr7/FvVPrOuF6+Udlw/vLHDMDYgP+DBCjoh4gh8QLzw/vWdYBsZxZDpemY73JHWsYWKLKyfg7fTI0Q989Cc+ni87oy/zmfTlpt//lnJM343dAOXWeGjP2EHBRQo3wWEEIrIfCFUN7t7VIwD38HyjJ2mZ1Ohuu7J134MDe5Z2IyyFvSZoeVCL0bQGr7UGVFGN5WGF5HcPSlVHvMf/rdLMq1sqV44mndEk70pHja5sJketnLz3lde7bDy+iNv6tEhRVHT3NFhT9l3PgKqREfbd176r+1bq0/WN9a8XKghU50qnitDt6hcaS2Fgt8cM0IMd62E7Sq62WR+wJDAnwjAEjoeR1w8H7u4CjkjaZuJytYCoPBe0lJnXxId37/jw/gfev/vAh0vmmg92indayf5CdgPKBijOD+BsucUJzge2dSaliOaEkFnWhPgR50ecGwhhYiiJZesWyWrp2UPwpeqT9WXbMioOGUZyiizriiNCWrjOG4SF+Xrl8nTl/Hxt/TE7hBK8IzhP8o48Z4Yv/54w3ZEZWeaIk6tFNsaVuFoC2qjCEE68Ph1ATjxfl3Z6uFZhonWFexvTTh3SXKZ1RQs6LJLipqwhhnpbEV4pqeploWtMT13fqlaWqPeunU7IdDTUC44WUXpDaFXI7BzFVA5DGtWLYepPtT1lQ4diUbUt4EzMnR2qe+4nXJ+neijFY9GCkttYWi2FnsN2m+IWle/btZ+s25npISRt4916V25lds8E9o9vGU3b6Nr+fAkhmopA7cGNmrKzp9rvnTV1EEH2e4BWuLXDFO1r54TghHHwHKfA3WlgGBTdFnNHbit2UE8EjcSYOT+def/+Bz68f8/HpwtL9GycSKy4nAnrioyu6a7igsU+lHBscBadSdV/hZzspHUfTD0ZhgBYJGZM9dyMTE3priHjtWivBTVl0raBJjRvLGtE0sK6bCxLIiXY4oZz2dylOTL40KhqDMLxYWG684gbETLbuoFTlBVlQVHuEMaTMoyO++OJLQaWLRHTHhxVN339Xx9V2w6lekEMLXKyPVuooBrLq8FREqjb6baikk6uqBY1oRMut/aq7rMbQtdGgx0O6vrf3dp6WOiuDUQxo6Vl/2pBGjWb9CXK+mvX5yEK8WQ8uWZxthJu3UtvZuXWV9sk6k2lVfbn2lXb2m8y8Od56VsuDVv/2uM9UXQYr/W1n6LeR65tzdtpluKKsexlH3fm5kXIlkNNxVXNki7yoi+dqqI0Di9eGIPjMAWOxwHRlbheSetKTplQjE9pXblernz3pz/w3Xff8vx85nJdSW4guhOJA56NmC7IfAUSSAAC3jn8dELEkzZjONM4IDqgyYq3hDDg/UDwgXF0zNcLMUFWz+idFbNB0JyRHMnRwrcR0LyheSNtV+p0bVtCY2JZN2J2uHAgLQsxFmicE1E2M2hm5e50z+HVmbtXkRAGDtOB56fvgA3UwrbPT+9BlJwj00m5ux+IpxEuGykvJTu22o9sw1Q3amdushXpNtcNk6CS8i6wsipaPFK+rmd/gLFZMncb1A0FVrGoL0iyv6txqPaRvGhhJ9V6b/HelEzgpgoXREFOdpgUCuJw2bf9mtPKT70+U/XYVY3a4ZsCqjdcSm43VmWxt5pX4+L7RAjVDmE1LSz4pOp8t6pFLW9foeRLhNNnn+gn/32JQW75tYCWWDt9MZaXN6ulgN9UqKrvfsmwfuTKOTMNR7768pHXrw4cj8J2nombJSYNzuF0Y7le+OZff8fvf/cbvv3hA0ue2LJn2zzz5Zmn80cLknLw9l559XAAcYgLDMMBNwzgDrbRveM4viW4SIqRdd3YkhCGkWEYCMET40pMinMjwQWEzWJfstW1iIwch8GyR5fNKlallXm9Ijh8GBinA9frGS/CNE7McWZdN1LaTMD5QFwiUsLH3ekVGxOXOTIcVsbDiS+++Jrr5T3btuBSIsaN737/Gw733/Pw5he8Qni4+xrwZAKX64p3xuyzZnLqse++lr5blqy6o74q7ITm2qwHQlmavqlvprJ1Qklzu981D4fu+6RDk320kDQ7GjsD+NGrZz+0ceQciSmaGokixa7STgKTUnnOCal4roRMjH8jRqHdD1Di6e3HULXe3ql78nR16bz0adzogbJvst5HvTOIWo7/xzfdC3nfMY38o9/cMra+leo/h5Yg1hhBdZPtqGCXAqVdlSbBgAbtd47k2ri9d5ymkV//3RtePY4MQUkl1kCK9JK8kuPG9999yx+/eceffli5xntSVq7LynleOD/NfPhwISUrDOMZeHycGIJ5XbxsqAZEV5tLDyIZp5lYXKTDcE8YDuaWLL5750ZCKAFcqaAGFZwbOAwDOW8sS42t2FiXZ7Z1RpzHj1OLQDQvmXlVnJixMScrVJNyILgBYWCJyrplPn58R9rObNd3fPHFl0yHI2GYWJar1eTMifU68zH9ES+eO3GM4ZGH08iyRrbNUgydmOGuRwaurF8ffNVwQUMcfcCU3ZtqDQ+1AK1xGNndphSEqAXqa2tvZxBFeGkXYQyYUazCy44SVVtAXK12plSD/U66PTpugleE/VAut5cq1ErdP4rN/+z1mYxi31p9R+ss7Yhpx3a9NO8ZYoPobdR7W7vxx7WJhhqoVW0Dt33r9bcq16W2390ruo9C6rkhLy5pwTZ0763PUFyodSB9C501pOuP7Byj/a1ioe/TGHh8OPL4cGAcFNSIXCm+Jd3QtLBcznx4/57zZWHLnsvlwrv373j/fObpPHOdN5bFzpicgidw4BdfZbwrx/LlQJaMY7OakuIRL6QllrJ4wuF4xzQdy0axaEDvLebCOUoItUOcwzmHHwbW64ZKAEmoLsRtJqfNNlq0wC4Lg04W2JWjSW6FGDPXTYnq8Ckz5ERiYzzMxG3hehHOzx8A+OKrrxkGQx3bthGSktLGtq5cn9/hxhPTA0zja07TyFOqkZayC7LG6DFGUfpR1eEm4TtGUSlKFVxO1O2qpb6owk1RmBpWXo2WL0HATuy3dL8Hi90QYtd3CvPZT4xv+63U/mjn01TPBlBDwcW5xiDUFb+g+xvVzFQqKtM9JFZk9/NqncY6AP8JlKoF5u3//cTIJwwD2XMpXsr+PZ6iHuijfDLPlbXJiwfVnLs1QKsyn2aR3lcAwWL+aXH2NmZpz+z39uh2V8duhkMzHgoMg+fuOPL21ZFpEsi2aVPCIuziBY0X8nLh6f0PfHj/kXXNOBf44bvf8t//j/+Rb9898/GyoQjDdMKJZxoGND7wD79eCURkMGSSNDGGjIjHixWgXbZITuDcgYfHtwzB2QaMGyklxsGXOJCEknDel+K1VlA3qyOMJ3COlJ7JaSkAq6Sjq4dYArXyRtxWEEfKMG+Zp3NiVcF58CFxmA0zToMUJCSkJPjhxKvXrzgcJsK6MmbHJp6UNubLM7h/xYlwfDXw6v4N1zW28nT12IS96nep6lW9DT2Bd6QoWlfY4LxLGXUdA9GSm14ldTN+9JJpjxLtafCGlm9omt2VWZBPx6+aEGymwPLePZxcLeejfOmat62EkSNotpgnF/5GkZkK7eBd6BWI7oMbXcvgba2S/JKLNgtCxxBeXrd1bbW120+1dFy0daP1GEvfLVLAzl6MJuFqNFutjIQxvfiiwG+KpVKGlLMstADOGz53Qwkdr7hFFju6gMe7I29fnXjzegJdrRpVLEle+QLbM2l+5vr0gT/9/rdc18z3737gn37zG/7P/9f/G396v3CYXnGaHonbD6zv/oSIcDyeGMeJZV1ZQgIVwuBAHYz3DOOBwzSQ1pngjxwejxzv3/Dq9VtSWkhpZEiJuC14Mtu6sMWI+sA4nahoDecYT6/IaUHzhZysHobzDlVvCWv+SE6edc2W0bouZIXnOfL9+5l3z5kcFFwEBI/y7fsf+OXbB948PvD48Iqnq+OPf/yGGDd++ctfME4nYnrG4cgS2LYFuVxw7ls0Kw+/OPDq7sjzdWPZtrYCNyVXG330m7qnmv6rQhveUwrGmgojUkpBKGQTlNXT1y3zj171812VrnE+Sk042wO2bgVwbqil/m2oIgzGgFu6QKVF2WlPykSIOrz7G7lHVff4+u7DNpAbyFMHd4MoqitN9zvUUQNG6tkWdlU2cttuf15ip4bRMj4r76FTezpJYsk/0eI+SrJNbhtaWlnOxmQq7uvHnR3qdl/5LdaRG+TxyVRhBPuLN4989cUdD/cDPmS2VcnJ+ufIlstRqlVdrleerol3T1f+x9/8nv/Hf/3f8v37C//hf/m/xbuB+fKO73/3PcPdF8ScyJr59rvv+NMf/0h+c2IaR5JmXr2943g3MARh8hvueOQw3uHCRBhGPCZhxAleHCEc2ZYr+IEgA37wODeQyklTinA8jCzXhU21BA041utsEs8FsizEKCzLzHw9s5zfs8bM5RJZYuIaYV6vbPUckFIQ6I8/fODx7sDXbx/59//Vf8nT6PDhieCFL7/8CieppLaPJBms4ta6sszPhKc/8nD3K3I2b9S6baV8fx+yvQshERMmNSDppTJZ6cg5qxWqLuDQ4vnIBSHsxxz0Ze5qKYE+XbzW4TA6LaEGJXEN2emmUX1Dq/qCZezJ8A2tuj3nRQrT2d2v2pCxlr781OszDynORToXWN+gVvnJFVrXcXUGwN4n9WNDfYG/mtGzhbM2s9ENc5KW7LPDvqbrSWU3FT7ebngtfWpMpb5yxzoUwdm8O00H/RFVp+//3oey6FIL4TorYff2xN0pEIKarptSmx5BLQcjJ9Z15vnpIx/PV/71mz/y+z/+iT9995EhDPzqV79A3ZEPH+5Yzu8hDGzrTFzPzPMHrteZyxTs9GqAHBlcJJAhbqjzjNOxBFm5G8LRgq4sLMEXu4RtDbM7lDT6tHXCQBgnC8Fe180CulhZlsy8LMzzleePMzEp51W5bpk5ZpaU7MCgbn3OcybGRIyRV6cTSCY4U50eH05Q1MGcHCKWeKbqSDGxXD4SpgcO/kSePDFCLdZjS7QLql5ZcC8WdA8btGW1codVnc4tx+SGATUoqU0t6N/ZmETpQHOYdD3Sm7dDrarV97f7srtTqYdN7VrQjv9bP6u35EfI989dn80onNYKJl08fe/5gGZZvQ1tftEWdCpHZ2CsTxUvya3MrpV/CmfMehMWjZaw3NJ2K9HfqykFhqjTwsiEdvJSgSLNk2vNNNeYvaL9cjOO/jvp/1PadeII3nEYB754c8/rV0ecxGLgS2iMHSTOUDwR1/nMu/ff8/37d/zmt7/lD3/6lvMceXX/wOM9JD8Securr/8dKV0sDfysXK7fk2JmWTbMVekY3cokMz5DXBdkPBAOHl+iNi3SNlldSAUhlSpZVnpfxJFLuLWIxSms87Wcw2l68uF4R4orW0xs88ISI+fLyrJsXOeFd08bOQuXBOeYuW6x0Y4XZ2eciDGhNSrfvLtwF/7AYXIMLhFc4vz6nnG0YwLjlnDj0apmiSNn2OaZ7fqe4ei5m+6YF88S477J2NPO63/2xIM90L9+o2XD7QJF2CvCd1molDBwsb/dn2MSOx9p9C3QGfEr2u7+6oTI7ly0vKYdLXTM70aQ7UziFlH9jRCFuTu3NrBWO+Lmfb00b1qDPd4X3CxZHSat6wlVXStuz/GoC1lVhArtVLVkO2pbcNFdCvQTZZNqHzjnjbDV5IgqpTQbJb+VVhhXVHHiSrm2tPezHKunP2JIVWhwsx4AAzCOgfvTgbdvTgjWVj0xPacFcIha4RfRhfn6xLsfvuU3v/0N/93/6/f8N//9b3j3dOV0OvHll3/Pf/pv/u/IFHCHI4+v/47r88p2/oikjzwcRw7TA6iwbQmRC2+OJ3RZOc8Dazrx+ItXKAO4ETccAIh5wXmbi7hdcT5YKTxnGaXeT0iJ1kzbagbNtJmBTAJZlcNxwntDHt/9/nueLhaZua3GJL47wzUltlwNlyckeHzwjIMnLWdEE1vMXFb4/bsrr779gEVvbrx/PPL2izdG+Hljft4YxwE5PqLDCAxcnp84EhiOwpuHB759/9w2xo9J0grzfwwk3lx10+uOO524dkp4O6ZC7Ni+Spo1EKrG+tQ6ljWS2dyVXci51nTyjp5vKMxSHGqcZUXNe4T0Hj29o37YCfXlvv3L1+fZKErgUzvghCKN6+8CrUAo3T21Yw0hWDapUiy1UjI+qz+5zkWx5tbJLOfhAqmoQZE9W87aFWeZfXteRYV/xVJcXVkFyqXsbow9VvFYq6OpwFtPDTHOmokN+1FtWw1mVitzbyhSVU6Hia/ePvD2zYnDQUjLlQYzlWJo3Sxde31G4sy7b3/PP//Tf+L/+d/+M//xdwkJXzCM77len1iXC8fTwPL0ket330D4PTlnlusVTZm7uwd+/Q+/gnzFs3F3cASnLGnCT2+4f/t33D9+wTgc8IOFdasmvPekGIkpoUyMox1s7IshLyUlbYriGccJFxfWxU5Xm46PXLaFZZ2BwN39PV984bguP7BxRklE9fgArp26BfePv2ScRkJhFk9P38P1B5xLuMnztCS+efdcaiisvL4/cXc3MQyjpcu7kXXdyPpkuS4PgmNkm8+Ic9w9HFlOB87XhTUmi6RtcS09arWN1guonRi734rKoIB4K7jrKk3duFHK2kptbv+iTyarhKTNQNbw821DNJHZdpDsXzXhqPUeca32y6fXT2KL7frMwjVWPKNpUmo6EUXS26a3JADrQr7pSl/rsEvTsQ7XY7IKs6gGmLogWot0WJ6steB6b4lQszydc5aEBc14tDOyglCKuuScTeuOWzrO2xs6q6FLWo0v609lfkWiWNFgG7t3lux0OAx8+eaeV48ThxHydiHHDXwAxOpJ5ghpg7QgaSGuF7750x/507fvOS8TX339Jcd5ZXp/4v27b7lcrmi8GrPMliqeUsKrYzo+8Hdf/z1vXt8jGvBsnEbFhSOH6Q3j6Q2n+zccjnctmEqca+eOZhUkC2H0kLXEUQj18CCCFbPJORPXDS+Z7AS8w4WBmL1tWDKv7gbOrx5MVUE55sglrpZiL2KxLPmKE2fZjLoR40ZQS4P2wEeF6xp5nleeLwNP55n5erHN4EarpjV4VDPbthLWmYAQtwW3BNL4xN30QEpGuzEp3hVRVuRcrsVqCxNwzlcS6MK898tAqGWf2jkaBd73N3RysheZdc9UtbtXwRvtQTnwqSrjdfNkasZVT3dGpjtz24+sLKpI/bw3tL8wtv+l6zPrURij2AduBTpaAJKUsk1lw+7dFm5KeZW5qtceel2fqA5wqFW193mXdl+rXNQXgBFpFZ5pTKJOWGPt7S37hGtXPbucNalWZGcHbza2dghyGYe0LlTK2g8KrofzfPn2xDgoogvr9RnFF8YmBuOjMQmXZsgL1/NHfnj3xHUW7l/9Hf/wy/+CH94/cTqcCG7gm29+x9PlzBgcg3doSjh1TId7Xr1+y6//7u+4P1nORpDIMWT8+IbT41dMpwemg7lQfRgbgzX/OsWbY8zBqiaZumeZpsWY6SBudliQF8jeztgQZyHjaTU7xd1RefPqRNJMTInBbzytCYnJmKwqcXsibYrIWCqDX/Hk/UR4YEuJ6xo5XzeeLgvXeSYExzB4HEIYD8RttQpc6wzOF3TpWMNHjg8Tp2kAHJdy1mm1g6txihIvUwWidhK9t1vITrwCUqJ2pR5b2BkyX7CHRh4mLPUG/dfd0QK1Kk2izWZWC+y6sjf2lDVu39HEHt2//9Ouz2QU3SYtf0opiAGFizUDDtzUcdDK1+wny233peeGVB3jNlrCtLgaT++s+1KrCIF0Sf75BrM0TeZmKEKJ99dcXNfF5anFot8ZJyvx7GzmNmimBbYgHKeJu9PEw539PD4MiEuk5Uycz2zzFX+4N7UKiOtKnM+4vKI6I+mJ3//udyRe82/+8R/4D69/gQvCb//0A396c8erV3fc3R/4/R9/y+V84fm64VX4xVe/5Jdffskvv3zF3391wDvl7jgyDicGHzg8/ILj6UgYDoQwEEIgBF8roaDeEYJlsZoxjrYOWTPLainhaGZdletzJKvHyYbTiG5X4jpzPB3wHq7PieenD9zfvWZLR2KMDKxcNuW8wdNslZi26zNP67kUlYW0KYwWobtF68caM8/XjeBmPj5duc7K4QAhRObLe6bTa8Rb0eBtW7EKxyWoThwhBB7vvuTucODdc+DjeWlVs3MljHJAtIgBW3GhMMmSO5HLYcmNVnYBId7KBZDzzgioasAeMr0nLu4bvNZlqwdU7zKnSn/anoJqXN3RUCXoXA8qqvutwfHKZLQ9oJ9Q8F++PotReMn47sSf5mGuBpQSqaZFomaVfWOJYOVpOlWFqm7UJqv0d02StxqCNz0xi4FNjqf6grLVb2ZvqRo+q7rRcWv21G9z5OwoYT87u46yShApELycPK5gZ10JQTwheMYQmKaB093E/d3A3UnQfCFenlkvz2zLTFZh9AccloOQtivL5ZkxZHK+MD/9wHV1fP2rX+P8kSzCdVO+fnvkLqy8GWcex7d8+frA9bKQc+L14yN//3dfoNszPs8cQuTh7oGHuxOH48RwOHE8vsYFK3wbwtgk4+7aE4SA84JqRHICsVPDRDNjAHWJuK0EFzkeD4yTJ6WJdT3ihwPrZhLdCbgwEtMj4hwP9yd8GHn33Td89fqOTCD4kfP1yrpdcSnj8l7R6bJapugSlYzjMHqc88yL8pt//YavvzxxHGEcH0GvzBct6fFmnLUzU1dyNhdrxpOz43C65+/e3jONBz6eV5YtIdmQsR10pOaqHm6ry1vHquVqn69eWchZuwBeQ5a9Ct3TZX222hByUSEakm4vzU3KKYKm3iZX0B0l7SFTKsuVZrRqJjvaqQJTs50V+1Ovz7RRlLpWL3yzlI5VLmWTZvUGasBHuaVB+BsE0bPMItW1cEdPFwnRYF31tnQMS2qlwMqf93do9yPlMQFyLY5T+rDDuzreIml1Rws4Z4Ql1Uq9G2ilxEncn0Yejp5pUERX0naxQKOrJXupBNy44Z3p1etyJm4zHiWvFy4f3yG64mQF8Wh2xTW9chiE149HcjrxcPTkdI/3gTdvXiHOMT8vaMyc7h55eHzD3WliHAfCdORw94D3Yzl53LIOrd+l/843dSunjOqKYbOdoHKbD8c4OeKmoIlxCDg5sT2+4nIO4AaQoagimeAcRwLPw4GwXbmbPOl+5H6CD2e4ritbLKetq+5oThx3w8D9YWQKgSDC+brx/bsnHu4n7u5GxI+kaEzB+cRw8ORUhIRmO4Xs6ftm4BYyj8c7gvNcV8dlTixrJqkjy352Rq6iu+306posAoZdhZCO/tt89a7IbMim20z2c1N13tDMfq5v/cb+cihWRK6qLaX0njis/N4tRthV/V0I2olltXt/I0ZhL89tp/Wmv92QU0NZtS1MPeuxU+ZpUY1l4LWwxq5X1amq7VP0s0K4zQ3aBauW0OodOHzKKCpH2+uWyO7irLCuN0p16taeBbpH2lmMhMH1wxg4HQce7gaOEwSJaFxIy5n18sQ6L5aAJQPhGBFvLte4LmYYjBvb9Znzh++Ja8aPZxufHC3vAWUMDjlNaDzycAh4NzBMEw8PdzydZwZOiB549eZL7u7vmabAEIxRDNPRYiYEk5qipW5GZcClEHGRQCknar4uhTkUezZefPEeKD4nRBx+GHnIr6w61jgThhkfPCnOgMN5mI53zNcrx1HMlpEc3gkfzo7zvLBsteKWVWGagufV3YG748Q0BLwTPj5nni8Lz+czy3LgcAwWy5EiLkd8CJYdm6D65Ndla4WC0czD4Lk/HBiC5b14URYRthL8hcjtRpJ6ZseO9/eyzbsQ2w2P1G/qL2VP0OBFQ8w1BqMIH1e/LNyoj4UW4ZaOtbKmfOMseJnHsq+yNm/+345R1PDPKmmpBr8+DLpAJy1W8lKJuLqd6tFnLZS7Qf4Kg7tNSc8oilWawiwqty/P1IrOVd82gFZrWexWZaXq3oUjN2Z3O2l78JQUOOe4BZplsZ1jGgfe3B94uJ94uJ84HgTR1QrKLhfWyweWyxPbupBiJhOYHr8sZ27aieO4wBbPXC7vef/uD1yujiwjh4cD090bxtLnbVsJ24J/uCcmNXvDMOKccrpzvHn9wGE6cP/whtPB3LRhmJiOD2aPqNQjgYAjrXM79zPGCARSzhamnTaEhIjHeU8YJ9Pr1SRZSuCSEGQo+rsVvjncPbDMpbjvfGG+fCSlxBYT6wbPH7/nqDAOnk2totfjceDj88h3Hy8oicE7huCYguft63vujrapnRMOk1UQP5+vfPzwgXGYUBU7lDkCqgynNyZRs63V5WoFbdI2Y8ggcffqLfeHIw/HwHke+P7jwmVemdnIVJVXSyzNLnag4KwC528DtKDK8nrGZ4X7Rru9iXQHFv1xPvsOF+oJZ/ZVEbzZQglcYYJJ7YyOlC20vuZ+OFc8SaU/dYvUHn6OmfMzy/X3ksftTCJbZe16nqjFEWBHkle3QH8GYrPClNyRLkikhKWUAdUIwU7qdb2xcetNLU4tNpI+DLvmqFCfKKc9N72xpFTTmEMNzxVwid3S4iwQTGCQAe+EMXhePxx5vB8ZRyEEy9PI65m4PLPNzyzn9yznDyUzVFkSPPJfknMs8N/sNNvlA/PTR9bZIf4LRAKkC3n9juHwlmm6IwwnUrYCMpIyw2C2kZQix8OR+7s7TscDhymg28pwuGM83jMd78EFRLQwpyvLMrf8EtXEFhdyKkywEKUXxzBN+HFCCXZkQLZowGEceAiPbFti26IZEdcLwSVkdHgJBDexza54SBKv377l+z/dkcIzkNiyVS17dXfgl18kfvHxwNN8ZXCe4D3DMHC6P3F/GBmC4Fzm8U7QvCGa+fDumYfTvVVxUgDHqhk3HhAd2/kg27pwPT/x/PyBmCJxfoMiHE4bYTxyDANvHgbC4OEcuCzrTi/ShWgVxGli6FOarIX2s+it3QGHqJAl7965Vi/ihZCqTKJrvX0kJTERD7nENOUEkjErXRG+aoWmhFIvUz6v/sTL6zMRhZZw5t1nXFUCG1AtjEGzCNumlIIAXZPsNg1mNtw5syEAKaczV1uEVu4nBe41b0o99s2eN66/25gNSdQcCmNI0sVp7CjxNry19bHVnVCTBGL+9eA8h9EzDoEpeO7uRoYBnCRIkZSu6DaTtpW0LWzzMzmtrMvMtiWi3FkIcS6nNYkVe4jLAuo4PX5NOP0CHwKqkTg/M4wPDINDsqKbErwnaclgRBAZOByOTIcDw+DRvBKmA+PhyDgdcMECpuI6WzbouoIEMqupPOtslaq2zfiEc4RhRIaAywmXMy443DCxLTNp3ch5MaYqHucdg5vQFO0QomRCxDlhGAdiGsl5xQ/w6tVrLs+JZX3GaS6hN4ofhS/f3HGcB7y3qtzDOHI4HQkCQkTzwhjEUj3iyvUceXr6wOn+oSDLhANSXEuVKbN5zfOV6/MT3gtoJHztCecBgAOOJCtDmLifPF4CzplLtpU0fAHTO2WgoWFHp1qoKynp2m6WTudVKBv8ZVu79+Nm61FryXfvd7vqrRR7Xq5FcHYU047VKHun7s+X7/hL12eWwtNWbbu9onHM3WjZ+3Pawa5ogWu0eAWRnUnQbc7aZtaqAmg/m1SHE9IPuCwSNXGrqg2lSxUiVrdrz9B6JtGNt0FDQJy2k5y8gyF4pmKXGAaHl4hoRHQjx8UCoOJafjZSXFjnM/MaYZwsbqKqZE7QvFiJMjdyfHzFeHxtdovtWtLPZyTUsZXCtC437BqGgePxyOCFGh4+HE4M4wE/DMaLts1O6oqRrIL3AdWVlLOVUotbKY+miPeEYbB4hGaEE1wIuKTEqCzrQopXg7g+WGVt76nHBeUsaE5l01uVbdGNh8cHcr6S4kzStZR1sOjPYQyI2OE/3g8Mw8B0HE1hTUraAI1kNmJeWePC+ek902FC8WbcA3LcyG4ALfkf68oyz2iyALWHh7cM44R3gxl4vWdwwiADMgZidlyWev7IiwJHu5miEmT7vFVl54Zk2x894ym2+4be6nbad9SLVxaVvkVI1Jgg54pCL0W1qSXw6t4zu6IhmdLyp7zoL16fxyiq66cbwa5jFZel7F4AZbcgKxQIVyCi5rIJq0TcDTRVJdFcD3Ytr+zCxXevSV/ot6KHMplitRgqU9nb151B/Mg47S22HBYxSLNGN2LQ4gEQO35P2HC6QVrJ29rQhOnEEJdnrpePXObMeP/Aej0zyallZcbtjOaMH44cTl8j4qxSFBYItC0fIQarJqWWe6Feildh4HQ6cTpOSLpYiroEDsd7wjjhvANNbNczy3VGxTMcTggWkZhRqLkJZYzOCcMwMo4HfBjKuSCKD57J34MbucyJp6cP5HTBe+FwOHJ39xrxwUqvIaQYcaL44HDJkZczD6/vyWm2kn/LhcsaS8XvgKgyeMF7xYeMd5ngImPwaBK2rGzxGXQlx5m4rXx8v3K6P+HCBHhSTgxxw7sNHMScLVZlTWzLzHp94tXbXzBMxxKZGhiO97As+CExhJHHY0DVMa+ZzU4XavTRgwu92dVCHxGJQm/83PeRFltCLraEW5vcriTvpK1CszXfKiVFWDhT4QZX8IpqKdefW9ervaWi+MZQfsL1me5Rb2Xfcd0oqksSqm94P6jElc1e7BHNrWPfScsG7Qav6ZMNXEHHbQUhecG9LUPDGMSO56rK0tqhW9wfrScB1R5BUTyCd9xNgcPkOE4e7w1RBO+sPqVeyWkmbwtptfDr+fqBuFyt3H7eWOYry7yxriDbwjo/E4ZAGEecF+J6YRjuEH9HOH1BONwzP/9A1o+EvLHOV9K2taP81J8QNzId7piO9xwnxzgq2zUBntPjV4zHIwrEbSNukeRGhvsjWZWYVp4+vmNdVrPyZ1AdwIH3hhzEO5TN/O2SCYPDBxjCyOF0x+nVW/AjP3zzB+b5wrotiFyw2ozCui2YephxXjhMgcsiBNl4fHzEiedy/kDIlkFKzqgkS22XjPeZMXhIhoI0rqT1StqeLes2bUiOrOvK+++/4Xh6ZBjvAGGbLuYlcwNZhcv1yho3O/tD4cO777l7eMU4WjxJRoyppogLG8N4x8NxYAzmQr1e400avNFRxwRU9gN+0BcJkLsQNENwIqVEKmq592YsprinW5vS0Wf5qDUrnW2j0LOT/X0KqPOwCU5qCb/aVk0m+xupHjiHii/IofSxbLjd81nDowwiW3h3tQD/2FUsslqiJNun5ZUCfby3FH1B+zu1LF/Jw6huqzZv1aaiVTvsp6ge4tKFlzezjxloY0rMmyLiGbxwGJTJbXgybJFtubCtFpUYlyvrema+PJt6oZkQgtkn4sIWlfXDd1yfPzAd7whDQLcZGPCHI86fEGeniIv3uDDAOpDlQNqeSXkzo3EW/BgYguNYmFha3wEQpiPH+wc7PBiP4iFMbMvGhw/PXGc7YvBy3axQrTpSdqxzDcKxHJjDMJPzCmL2H+cE74Xj6Y7T6Z6HV694+4u3PLy6Y1tWtmVF0xVzPZiqul6fYDPIn7ZIFs88Lzg/cbi749WbL0k//Knku5iaYhstk7bIysJhGlGNxYYyg0YTC84h3s4e2ZaZECwk3fmBbbtaTUjv2ZIZW814mzhMEzlGtnVhXS4lzciOWQzDgSDOivM6IbjAcfSgwhJLpm8peJQq/TexQomL2Kmp0mCHR+xzJzg1qGpHbgovbaYVCuS+LVdV7IqU9YYJVLWb4mX0DlIuJ8NHY1DO2+E/8mf35KfXZ5bCcy2DtP9U90qYTe2oBh35ZPT7U/0U7iBAGrRvTXU2iHYQrO7xEs1mAV1MRH/ZwtbzTevytQoDIg2uVUBSmdNNVJ1A8MrgI07XcpL4yno9sy5XtnVmW65s64XlejbVxTlCyKVe5MxyjSzbhfnyTIwbox4smCocEXdC/AEXRlNpnLc0bz/hQkS2S2F6ibReGKY7y3cInsErSzJCHw8nnHdsMYELJIR5Xnn//iPPzzPXJbJsmcuiLJulc69b4nqx07YoatbgzVtRM3uFbDU1DmdOpyfuH5/55S/ecjcdGA4HDscjcfakbcF5Q4s5bqXQjZ3DEUJgWYrz2sHp/oHnpx9YtMLhapTOFj0YM8llO9MkFRtKQR+ixiycCJrUUIZGxE3lMGVz98ZtY12tmI7khIZQEtoWtuVq6dohlApWHvGBTDIJ78zD573gcwkGrAGBWhMFLBDKvVBnFe1qotAMm+Is6c5LPezYdTS874tKxq2JPgGz0ToNIuvNzV0kUmEU1RXv1TfvzE+9Pj8prISE1E5UBpaVEjhya9SsEe5SJX9DBGVjFsbgXPF6VJgltLZ2n28fxNVYzJ7T3+bNos9cXbRmwOy4T7Gn7GCywsMSZl5tL14IXpgGx2GA4xgZZCYv5tXYloX1emZZZtZ1YVsWtu1K3FYz8LlALtWn43rlej7zfE5crs92AlhWRBxhvCPriLgJPxwNZcVy1udwwOdM3iYyZpTc1ifuBYYgDIPgJeFkYDrcM57uSDGSOCDhyLKsfPP9O77507ek7InZMyfH8yVxvsxcrzPX65Xz+QxIqbztEVHStrBHH0bG6UDwF3x4jwvf8Hfffs+vfvGWr754zZdvXzE+nNgWTxgCg/ekdWa5DqxFbRynUE4zT6gm7u7uTMInSxpzZMSXUGU1xr7OF5wTcjm7wtBzsXF5x+AnYtpaZWwRT07Zkth0ZVujrc264jQSPZBfEdeF5fpMTjNhMONpdp7kAzAUpplBzIXtXRFQWL6LSD1/hiYob34vH3RhiU01cN05udr2VVV26zbQnajZ1fY+A1oKvfdeyJ2aS2/sNKQSt7PiZCS7zGcAis+tmVmFdeWk9U2Ve1UJ3Xpa/jZLOE5LOmYuvmDLnWh6RmnOApyawkfd5M2rUbwPuegY7i8aZfqla60BqUC73CXqSLvHyZ5V+nhwvLm7cvIzbn1ifr+Wk7ZW1m1mvV5ZNjtoxuJKLLowA8kJ3oOKZ91W5suZeXZcrxe2uIEIYTyxXs4QjrjhSAiBbXkib+di0ATNkTCecGHEh80SrIJnDMLgM3m9cvfqLePR7rmugjs98O7de3744Qe+/fZbMp6nxfH+aeGb7594/+4HluePphatV+brmRDsECDnA8t1IZXS+4jghpFXr7/gcLCw8PEw8v/+T3/iX37/LY93I7/44oF/92//kce7E8EH/CHw+s098BU+eJ4/vuPju2/MhpEhi+dw98j9/SOaNuYSu+AUUrqQc0K8NyOqM1fw4Efw9WDj4m0JnlgQWlxnhqMRRtpWNiKXOTIvV5Z1xpEtxiSvrOsFJLJtlkdiFBnxPkIY8ViBo5SVLSsxleCmaquodstuc/Z7BUrx3I5rCBWlGt17w2vUIAD6dqRvcSfnXGwbdn5MDUQE0apGy82z3gXzPjpFQ8Z5j/dSyhz+tOuzGEVK0aoZUbnaDs+t6IZ08KgOuXS4pCzjnGXZ1ZhINS66c+PabgmS0vpPCTcuk2yT7fjEo3GLzvZ+aOlbd1/LLszdR2rZhILDBcf9JDwOH3HrzJKtSnZakx1Os11Y5yfm5yeWTYmpWLKdVaqyU75Hgr9DgS1G5nVF5UBclyI1DTJv2TH4YIfveI/KDo+XdWHbMtN0QOMF8kLwFmvgnRAc6DBwuDsREyxLIg+PPH0886//+q+8//DEnDx/erfwzXcf+PjxiaePH3j//TcQV1QjmiLz1fR1V4xrOSnzPBvjU1Mxv/3TvzAdj9zdP/D2i6949eYt4gfePV34+PEDToR/+PXX3E1WB8Nr5u7VG3CeGJWP778vaoEZLnOcuXt8zXz5yDLb4TqpGCpVE1GTQXW1kG/vAooSDlPJfjXVzA9H0jabYbYE8aS0EnNmvSa2LbFuieDKybkirPOZFBdLxR8OIIGYMjltHARk+grEVAR1iayOnExaikLKe1xvo/SGAEpIdd7LKILipVgXGio2T5226jYvSzTUJ7stVei0Um2uElPs0E1Eiier2P2Cw8uICwGfBxS1imXxtqrcX7o+MzLTdMWCd8pndUgVMvUh0U1LamOscKhma768s56l0G1dGvvoGQLVJ7wDNu361feabmH2AmNadMyeu1TkYuHDdxM8HhNeMpodKQ9GLGrW8xQTcctmoFwjMVl4u9VzvLIpyLrhw8iyLqzrSowJ8Y4UtwK/1WIahiN+mGyDljMxMt6iKV1mmCaQxZBKyoQwMZ0eCcNgtpPDHSlnljUTVXCj4/2Hd3x8vvJ0iZw3x29//0d++O57Lucn1uuF6/mjVQrDoGlOkVjrTzg7c3VZrtaXnOykLE0Ml5HrxapspRR5fPVI8AJx4be/+WeOx4Ht/sggkVES4zQRwsDx7oHpcMf1+V0JhFO25WoH+0wT/uKJywU7ADhR3euDK/EOxWaT81psN5Yt6sPE0QdW78l5MwVWHDFtxDWyRWVbzZjnQ6WhSNwSMQqbeMJ0xOS7KQEhODwDDA+ov0PwhFKf1QuUw/tu44AakdUNrHvcBHuQn6olJAq1YM5Op5WQC8soIrOoHWqu0L2kQS9cOx5S8bMW+YgFvqlzoIOpIp95fSajsCQhqfpT+dTqKmjT+YFOjShbMddMzUxN7ipDaJzY1c3aWi6jrYbFvi9S7zQ8siORTutp95q7rgabaIdW+mSePcJTOAxwf4C7aYPoyHIw6JZX4FLK/UPWQNSRLWViUnCOgxtZoxWUjeoJ04HL+cKybqQMIbiiU6cyQiGMR4P8IqR1NtuAOvCTeTdcYL1cLKlMPYfDPaf7V4TBxj8c7jk/X5hXJYojbIkfvv+O5+vK0zXx7ftnfvvP/4nz++/Z1qsV79VSDcwWiJoIhoJkY64pm5qTYmRLqx3lt87M85nr8wfStpHWrzkeD3gS//L0Da/fPLAtjxyCMjrl4f7IEAaOxxN396+4nt/ZnGtmW2bGhwfG6cAwHrhePuJchyZRvB+NkTqzm5BSOSLPgwy4MBKclTZIabXyAc6zrquFlyfPskbLX/FFTOSt5ShpFvzwZCUUAVTxkpkAOSRkFPAngphRM5UdnxViLvFFJaO4UrCqklIt4Q8i1bBZ3l/lqu40WyMpd9d8F++jCXLGlf3mapEopWUhtf2hfU2LZhYx5iOu9e8TmfoXrs8zZuZkdQvLMHDuljnQczmzA6juk2GsIbe4C9ve1Znp9/DsjjcK5fCd9lYpeSM7eGgRbt1C1P/Wz/cMXzFbgu4uWy3RUzXmYvDC4DKOxLpsHIZAIKOyEdOVqBeT7EVipKRsUc3Xr0qaHOv1I5fLwhI94uHD+wsxjvjgSm3LGcTjw0SKcyEEC2nP24qmlbieUQUvgbw9sywJ5MDh4YG3X3zN8TjixSI7wTMviSXBklaePvwr//qn73h3znz3w0f++Z/+iX/9zX/Hen0u3gA4HO8YxztUpdRvsMQii7QsJQ/FWcBXXol5MdUsl2rdeeVffvc/8OH7P/Dq4YG3b1/jBH77z//M+e0rXj2eODjY1oPVxZgOPL79Jc9P71jmC3HbjDGhHE/3dobJ8webg2z1UC3OYCQMk23EvCFuQiQYg5hGhukEJAYnhBxAIzKO6DwTNRPzwLJuJadHICfW+YofQrGfec7PCym+Z7nOLKcTmh7JaWM6XQmnZ/LpH4kqpGxFnpPaUQbVXVqFp3nlLLXdilHLTreFptvfoviW1lBVEmk02aKHkzG5um6jjoRhwAdHkBrYWAVst/vLrxkMwWsJsCs1LXL6G6ke5mKxQqo43x0l6PZNWXrYp7C6Cn8orq/ijtRc/BnV1Vl9kVKzNRKOvViI1MYwhFAyRfZZKe/fU8DLonTe3JtKP5WJiOBKdSw79dmzJbgskPxgYcp5gTjbhv3wOy4f33G5nDmfZz6cV5bNjJKn+3t8+oHl/I5lVuYYuP7rmes1goyEIfDxw3tS8XCI88T1ShitrmRKiXVb2baVFq/iMnldORyP+HBgOpx4eLxDtydkPBKGIxlhjRvn68b788Zv//SRf/qPv+HdhzPv33/g2z/9ge3ygXVdTZXQzLLOnI4L43DEh8nqUGjxuWdDjts6s21Xts1OC6PYYLz3DMGC757Oz6zLheX8A7/4+u/5/ocLcUusl3e8eThC8sT5yN3pnuPdK073j+TCCD2RbRaGIXD3eMf9+Z7leiFrKYCrDi9Gaz54nAzGSNXOQtGUywZ1+PGAo3hAikE8ZWFeE5d54zDYZonbhes1ce9f4cNIFk8ikxS2pMgSeeJSUv8zhxgJCnL4R1QPRHXmVcmGjhvy7aSXA4YS7UqHGroaCEXoVbrcyZh9G1i+TN1TYiqE8+xqt/Y0XfZpUy06CVpvVyUVAfnyRLy/dH0eonhhYdFccjkadO8kdP+QUqRebUP2Ta+37SpF02inQRcvxwt7x94Pw1fVxVlVkhYe3ve3HKSyP1p0zMZYijdFMcmRQb2Q1RnDElfsn2rBP+vMcr1wvUZUJg7BcXccyOt3xsi2jXVeWGNkjooPx+anH6c7xDnTUfGE8WQMLmWTcm7EuZXq2vXhwDiemA4nDscDPiRSlFamNGZhjcLznHn/NPPh/Xt++P4d795/4OnpI8v12WwyumfbqmZiXK0cvw/tszr9MUZz9caVlKO5S1WRVonKCvWgFpR2vibO54/44YDGgTzDIfwCL8GMkSqM44FhKO5XhJwyS5o5nA54L0yHE/PlubiNjYHbOacBsBJ9Ks5sFMEK5GhRxrXQVHATyzJbUczsWJbIum2WTIYZlVMUNEeyJmJZ52W+ksspaN45ljW0c1bH6YwL3+HCK7w7ksWRshKTeUVuikXpHhXRXPQ/tpc6taQGUe1fSWMWqIUi+nKfVE+haqn8oD+y7zq43X/S7SXXS9C/cn12ZCbl7M16LqIZmIuykXf40w7iaVcbdfm+DrazKVTbxwt1oepvN0PWosUVdePHFuOld2lPpimtFibR6rUUZpNLYI0tVk0LDqBjMS5a9eyccjGIZYbDwDSNnA6e9f0VVWfoYD5zXRJzguloacwijmE6lsNkFZwZ5FSTGRG9xU5I0aU1Z8bpxHR6xeF4ZDoEcnyye4tPPWYhEpijcJ4Tz89PnJ+fOT9/5Hp5NjfnC+uNiJCyBTKlNBgTKYhRsaK/W1yJaSup5VJcm9ryFbwYXWjOLDFyuXxkCCPEEd0GvvgiW/r9lhn8ZgFOzhPCUJLSFlKKxG3DeTgcjnx0HjQhUsObnQVDlYpg4rAjBsKAKxXBUbMTZIEQpmJENKQ7L1tRC4Mhipggl8zjFFlTRlRIcbECvTm2cGgtJ5JNgzJKJEwzbnhDcq8a/dXjAH9c5ZduHxhNtdqyrXpStZ/dbpWWjt7Qbt07xdv3gjnVvXJL7fuyVxvdngn9YoP8hevzamZ6bzUJwQxJ2kU3QtHJrGdedim/s0aooWr1yep1aIkrhWPcIAEqt+xRRYV0NVKTyqAtUg+rvtTyHsUaF+8QfCmC2gXLFGChWsLKxNyOY3C4auATj4RDsZHkUiwkkxI8Hu85TAcc0c7RTFtJMb9ynTfmRRA5oFg4t3NWMt6gpEPVDtP13jNOBwRYyeQoiCuBSccJFwQ0EWMhGBnADcQE/nCHHxV1T1wuT7Y5amCSMyNW0lz88BC8Z4tbMdqb7cZcZxahmKLZEFK2sYYQGJ1jI2Iox0LMNStZrMZlnC+k6YkUXhHlgSV7ZDjhxoB6x/nyzPFoByWPxxNbymZwXFcrBnx/z6s3v+DyfCHlzBCM3oZxwg8HC7XeZlA7T1RcyWhViKtl2vp7Z0chhIHEyvN5LuE7ZouKcTOEqEpKmXU1b06OSs4X9MM77u8+cppGDtPI5TByfT/w8OCZ7l/j779me/hfMef7cjBUpnckVETQjq1QbQdKUTd/qXROMfDXZC0nIOWsGe+tvoVVExey+pt3VOTsavHfuofqdqx7qLdo9vvZ/fTt/1mMIoSRIYzUaDJFipFELYoNAxxOtJzT2J9jtPcZ9pCtG7ikSoufsKHQ1+ERtBx3p+3HgkZ6Dl0K1zSOvOtydXqlVO7Gmd2l5fa0iVaiwqYOdQFls+CotCLxiiwfcFlw4UQ4jjyMjtPDPWHwbGnBOVjmJ66XC9fLxvPzhcTIGBM+ZXIO+HBANZrvf7PYlJw2UtxK3YqI855pOFndi9ORuF5Zr+ZWHcaJw3QijAfUedK88Pz0sZzIFfnh3YUfvv+mtLWxbQvrZmd/pGSzn+YZzUoYBOc3k4qJkk8QSXktLtyisqSMOlNdRAxRSUluSqV2w7wpCYeEiTDescWVlFZEzCCZUyJFQ07jNBG3xWxUGURM5//iF18zTBfmeSVpwntT9SAThskSw+JsNTDGkcEL4XBvcSvbSpyt9J66QCRwuZzxwegwZyVlIRY0SBDCMJUDhCycPcUE6cI2bszjzDwFttmzXOH09J7j/Q+MW4KH/w1RD6QcaJnSGIaotVGA5tbvRWTdssayXQmeSjhVnBTvRoLewLa7+Nk3fntpOV4B3dX8sqfa0YHtWAt7PPxYQNefuT4ze7RpHs28qGqS2+o9uHZPH3Gp0M7JMHuC4bXKbbtRQT1KEGMisn+DqiW4NF26tinF/oC5jqp/pZBG4bAVkbTVLBF10v6u/9xUIJKAF8HLgnABZpKfwC2obsTiBbpezzgGhkNG48xyXbheFs7XhWVTJCjrtiLlRC5bUyMaV2o5VjOY+InBTQRv+RYhOFTE4jTUAqKCE2tLPDFLcf8JMQl+uOPv/ot/z2/+4z+zrs+s22zwPkWCHwjebApVpfCFiVqsREJyLLaaanMqa0i2Q6HLhykn1m0rqcxGETF7MgEkgHMsy8blfGb0MHhl9EKMK6oZ7wPT4UTOCzFixX3WlYP3nE4jzgnPz08WmNbcmto8YzkrMeZy6peFnotayLcPgZxTCd/e8KHYFKKiXrnOK36Y8QSSC6S4liC2CdUBBy34DfE2Hjwpe7Yt4c9/wh/fIe4rVKZWB6Puk1obolJvjbmoQqn6B2s9WWMe2jGchnVpoL1DvY3uq52NygCk/btv3BfbrBLbT+cTn18KrxpequxHQLKUittFK3QUWFz4aDU7KFRXTgu0yDszqJzOmn05OtAsxfddAqOaEQfqLNbNVnQQSk27Yh7Z768FTZ3spQaqhuSKSuCdWC0FVkQviJ5RXVHx5ExRO8xQljKMPsHoiNvKPK9crwvXeWWLFsadcirxE7HMUfGJ+51NmAFvwDnPMJj64xxsW7SKiy4QPAV2BxRvxtekVqvCZYbpxNe/+keOj7/kfHkizWdiMjQRxgEQPObSda6EL4tHk3lEdjddhbP2e9aOtsSOLFi1hJiXu8SPiBssR8U5Uk4sy8KyBLZpILiBVNOcm6FwBDJbjpbhuK2EcGAaPWtwbNtKFod3wZ4JHkmltKL4EuNrYxEfLBPZWVHfdV2IMRGCIyUlOssIvs6RYVoY3YQOiWVZrUqZswLCitUBSdm81ikJyIjKQMqOeH1PWL/FTw8kNzUnf0fNNiciHaNlB7cdojYPn9Yd1oo9GZOoaKCsg2qb69pW+6qwm+5EjbIbLFJzz0Ltxe9Puz67FN6+eW/P0PCuGmUKsUv3SOOOmGfkBhFYe75IcdHdl9yUllY+rKt6Vf+tt1CLrli2X+XCmilGQ7vRjE/a0EpRJRszs7HAODimQQgyI9s3uPgEaSFlS2lelgvrOhPXjQ8fzrx6M5Rxbcxz5nKeOV9mLteNTQfGoahNWYph0IMPiLfK0prN9QjWH8sKDUiJV4gx4ccDXjJBUll6iwEwQgmEQZgmz51khqPw63/3v2berszLlW1+AoRtW20+xObvdPeKIQwIWipfpWIs2ydFhFZOhCKtlBJD0Cz2do3HB/w44YNVrAqjLy5XMxLHDEMYULXM0m2bGadTmXwlbwvrsjJiWbGvXt3z/bcX4rrhfOAwjeCVnAdEPH6YjFmkhARnB/+IJxGYl5Xr5VzoRokZtgQxwrLCMG1I2AhBeb4sxPUjTrTMvTAMA6P3bMGhK0zHr/FywDOwLh9wz/+MD28Ywh2ohZZXW1o73a4EHNbi0zta6HKM6t7oa1iUea3n6FRULt39lBABWxNDJ/VdcsNg6ibR/X1lH/zU6zPTzNt+a5CnVw+qgbKI853IAEm2AS12ArJKPQ4IyiSYNVbauaE3yKizH7QS5dXSrBXtUDiDMZeci2TIqWM+tynsVhrdkEp9h+XvK+RseRSMxOTJK8RlI65WXfp6OXN+njlfV16/zQiJuG7ktHE+P3M+z8yLkMPAwY+ktDHPm+Vt3D3i/WCME4eqN6gvlvjkg5Rj6oRMsKIxziGlqKz3IwxTYT4ZcVaSbxzh+frM73/3O968OfFv/u1/YJqO/P4//tecr+/ZUsRlZQwTv/6H/8DDw2vO5w/88N3vS6lD8yrUyXfOFYSR2WJqc1+rLjmknHZuwkHjZpW9szGM6Xgi6JmsiXl+QpMj6IkhDPhwwIeFnDLDcMT7I+IPpOXJNpoIfhC++vW/5cP335PSynz5wHB4ZDiNu+DSaow2xHV4+IJlM0/Hsm7ELIxZkeIeXSPMqzA8r6T8xLBpKQcQzHMSMymDJ5JRknjWBX744295/fV/hX/zNRBYPvwOP/wC5++Q8DVaEvhgZ6o50QnEvfZJIb89QrN7sEr+RJF71cNIX22ley6XdmQvvVC/rFWtmkdSaPkq8TOywj6zZqa0079Ub3liDaqqE9AjAfus7vT6gEl5yfumb3DNMFu5vx7RVlWGfMtVb64C6nLeGYjstgrghmFIyUgEKahiz1BNObFGxxodjiNZF5JuxPjEcnmy8OCoRALjFBjHUE6xiyzXa0vUceLIqlbbYTFbQTi8ZjrelfqSlNOecoHsrqhxGyJT0cc9+ITkZCqLBvx0IuZaE8FgNxpJaWNdLnx89z0fnhecU169/YJt/nf88ff/RNgWxmnk1au3vPnql3x89x3z+cNew7OuQZtPy/vwxaiWXhCXLyXhg7M07JwWVB8QcXjnGQeP1wAF8i/zQihjHIaBYTiwrUsLqBr0iKbNIjNzghwJB8/dq0dL6V9nOyzJD4ZgsejenE1lUZ/BD8TzR1Lc0N7Oku2sumsq6rCaMMjrxvNzKmpemX9vtiA/DIRpZLq7YxxAwoS6wPD498g6WJaxzgzBsRXhlFVLgdzdftDQdPmr0XDnkWjGSi3G/ixkX0Vm2zhlPC+oX+29SBeNXHaUa4K13FNL8f3tEEVXv6EKYOmJqkKgNjXtWZuLXZSX7UlN3awD0QzZlQMDi/2gGS0brym96LnnTT9vYVfuJlXq+aEiJYu12jHK95VZ5ExOEKPgdARG7Fg6LFlLRjsIx0eOwcrKe28SNm4WauuDIwx2aK+dn2lS7vAwErw3gqyzly1/wbqdbIwlOKZ6GMjR+u4CuLHUZ0jkGG/IyA4WF3KcyZslfR3uHjieHnElCSuMB1JcuJw/sMxncgmo6larCOs9rd9K398WLbIEpUIdYuHLYbDq2SF4SyKkMmgxO0GMpJwJCM6PoFesbJ1nGEZSPCDJUvW11J+wmppKTMk8HpSYDoBSRCYXfV/Ek+Na1DmjlVQ9ZKV40SGYultHsywbqVR+8gGCN7tHO+MkJ5yMZsfZruBHJLyxdUrW/55J2Kbd61VUC0a12VWDfkatLmvVgwtzMaSgIIpzuVUU7+2Qjap7d4jWsAR7hytf9SxBq6rzI3vnz12fl2YOdp4EvWpQNl7jjLU3e0yFlAHUvyk2DM3FGVohvxUpsPtdsRSjzaZQoVRlQu1M4uIavZmM6l2ppckqh5XqlVHLCJXcwIu5krJVsjbIRM4ebWcjBNQN+Okt4+nIlC9M+RnnA2MYGQMMzg6PGYJnGjPJORjuzJWbLPV4Gscyb/a/nCOaVpwqVjVa8cPYyEJzJi4rklfCMOCGkaSm46fNjHWa7XCcIXhOxwNffPUV13nlj3/4A8/PT8SUOZzuzA6QE+enD7z/7l+4zhfzfrRDM/d10noUAhXW+1KqztbdvrdNlKph1nmOd/ccTyeG4IjzBRciGiYkDIia4TcmZVAz3JqU2xARhnBkGw6EsRyJo5l1/gg6I6IM08i2Xiz5y7ni5SooyA+44WC0kKzGZS6q5hoTOBhK8YjBgfcO7x0uCEkdcYl4lxgHjxRvV46RNF/w23vCq1d4PePZ2B5/wTA8GkNbLyzDxrpVQ/0eeFh02xu6NXVYjQmKIdubA74RyJhqrpEQXKkCv6egV1lcabsikvra9q5clRUTiF5rUtsO2n/K9Zlej6oOVFiT67Bu7rr9q+AQ7YyMJERdqzNYkQOaSYDLdSDSGEQl4v6gnoaxbiuZ7pfxia4KMY3Dmv0it0ekqE5OlHEIjMExBBCxAjHCgcyd1YPw70l5JqVn8vbEuoC8vrOUZzdxenjgiy9XsjyzfVjxo0AITPlg1Z2Uki4dieuleBqUMICXwUrfOSvhlrMhBlQJ4xE/DkgxBi5bQhjxg8eTCJuwbhfWLfHw8MBhGnEetvXCx/ffs62pJDEllhRZ5mdiWtnT7aXN0w3PL4Zn5wIhDICWk8WsqI13nmkcuTve8erxgeAEjVfiLAzTgGAeC+8GkhzYFNYcCDlwdzri/EdSWkl5KZJ0IanD+ZFxukecI87vEdnwAeJ6Yrl+xM56HdA5EcYM0wmcsJ3fk9YLaV0tcjbBIObGT1jMnwhMB1MZ15QITnhetBjVgdUm4TgFwuBxo7cSAzGStyu6vSdOX4E6yILThaze0Ec7cKqEmvdxDXWX1witYhfSFmXbSNf2Q05I8gjmHasIqIRpvdh3tXF98fntf2sG6WcEZn4mo9AaZlU7kz/pRlNASnEa46AWSKOFI+w817icdJ3uVZS+dF1f8BiR/bBYel2wupX2y4qUVKkorb3WfqYUAK5MyxKBpsGqbHsXcXlB8wXdPhCv33F9/pan99/x8enM+RqRcLDsRudZ44Xj3QNvvoCYPVt64po30rLgVPDjgcP9G8bxACKGokSsgIxzlmznpBRD3Qrstci9YRqL4XAgxsQw3pFdIGW4PH3Hh6cL62YFfVUzW1w53Z14WB45P73nup1LwFEilkzEW7ZgcN6MmqlLLqqqR0E/aswmp4RzMI0Dh6JqTIcDPlgin91bdOq8WnKsaDlxPGBh4hk3nIqKaVXBcs5QTvhKyapc+emEREu/nw6HUpdjK1Bb8eMdOSXW8wfickHFqlHFbMInaEkJL0OOOdsxBk5YV1hSYktKdhBUCWppN8tmNH4YMqdjiULdZuL5Ww6v/z3iRvBHhIwvyWW5JIuZ3ctU63Y2SiFkix6uhuKymyoKpqrihiyqHcY5bbYiudWY+11KB05MuJd1rKjCtkx300+4Pts92krS6c127yBQ7Yf0QKDou7UGxU2jVHvD7ateDuL2uTq5N9par97U/3aGHYBPmm0dtl+yQky5HQlvxlAFjZDtLAmrqB2JCQtw8g7nLTlp3RKH4ytQz+maOV0S6aqc1411yUj2eD8UA6ZtCsmpHP4zoKGEoGvN1jU0UdW3eoiUAi6UOIpUCuSua0lqyqzbgip2gM40MY6Wpp3Saof9ZCsMs8+ivJyMbuaroc2gcN0Its6WSeqDLxC5GohBs8VLqGZS3NjEzPPTYAFmVs0q44fJTiOPBfGoq7nRDY06P4Am0rYgJVckFYblxhPiBnJa0Tg3ZlYT4EwFocXd5BJb46UakqVFbIK5UEMGn009tZKTamqOA1VHmp8QEuIm1I9N62+mhk4YqaoZ/nWPmqx5Pk29LOvb4wEtBJszZGd9yIK1oWV+2gbUfelk70u/RZTdPre/+6ddn39SmNa4dm19UupBPdbTmjBWPzImIezT0E1PQwY0l9I+Ommbv+V6aGUSO4Kob+7ZhDUr+7y17zolr5unLq6OLVm6cUzKYXC2qIVgtbhOlcli+f1m3gpxxLxxXRNf3r9FJHC8ixzvNra8cr4szPOCLmWhyuJqyuQ4k2LC+Z05ZcXCp7OpJUgxyqZMotQ5EEFLEdmcXTM+ppy4XC6W1yLCEAKn0z3npyfm+UKM6x51Wdq2kJ/cjJdFUaPnsrUil21C+9iXIyBr6rlq2qVd4Wo5Z7atGGNVOZyOoCVXJmWGMOK8J0W1g5OyoBpt3cKIxo1x3IO40IgPHs0WXBUOj9anFMlFHcoxkQszy6okrcdBFFcqWoLqrGoWXFEsC9dF8D7jvbOqVlqK+2BxLzhIywVJVxgfwE02TarNQJ07wqy5MABe90S+nTVXobsb7XeSLdHP2dz2zt8el32LKSqz6JhE87rovi+6ffJTr8/zelQ42sU5vPTSiHRWXq0buFp/X3A69syOznME3URWBrMHV2krZlpvv2UT1LeXNqQkovXWZ7oGbqdcKGXUU8bKL9RzpgPIiPoJDSPiVqyEXGIcRrbVCrnG5JhOr1mTMpwi0wMc9QPD0zP3d3cMx0e++OKNFVHJqUFVBMuS9IGM5/L0nSGEIsmG4JFgJ1i7rDgPKV5J6pAsHI4n7hPE/IHt6YkP797hNBHXlZzh8fE167xa1aXLR67LubmRXTFC5txPTLfuWusyVkZW3aKew+FQLP15dy2q5YVsW+KH9898/cu3hCB2LIFIsdPkJkSc9wzjkRQ3zh//aDVClxVxnsPpnuF4B3LHEAbG01vW5Y+IC4TjESee5ek7gi9QX9SOYYwLOZrnKmdt510YijMIL5rxDo6jnSc7DRYCgBPmBMslcgiJbRNG57hfLoxTqWGRNiS+J4Rfw+E1qK3RdcusMbU0+RqYXemsLPWfzdyU/vRywEr5J2MUyWKErNLXjRVp34xdSf+/jBi6PfUTrs9GFJVetEi0fX/uNSAqvq/eiJsBUVFSiW6vEZroLs3quF0H3+rgKqNp54sW30gxcrYDjG8msqhB2dxRtwhD2ddRiZh1eSzenawbmhY020/aZkgR5+wkK+8i03Swoi4kOyDJecQPDMc7jo+ZZbnwxVe/ZF5XJEwMox3Pl1JCiObK04SEr1AJXJ8/8Pz+eygE4bzj8PAV42FCwgGVgXVb0exQsboMx9PA8/nCtq6kmHj95gu+/fYbptM9uIGzPoGDMIwMw4GUY7Fn1NyJasPZUWGd+/5yzpezNBzeGVoZp4khBHKGbUtcrjPOOU6nI84JMVoM6eCFYRz3tHLnGYbAukacjIThhHMjInbQcdyeuJ7fMU4PTKd77h/f8PD6S+7e/gOX8wfWy3vW6zvmyzvu7t+g6m2Ncw3la34zijmqkYbFfMRSvt4TguCiqXwihiRjsvyay5KZh0San8kH0PEef3gknb/h7s0Tw+EXbO6e8zUSilxJBUkoVdiVwLhubl9siyJbhZpN2qPcrBmNpVSTy83jU8FpVfX7/fjn+YTe2EN+yvX5BwCV6lJtZO2yJZFWTbjvNN39txK8ga6iY7XOdwzDjJfaGOZt47de4Z2wi27Rcha0eUBuet/bW7T7sKg3Fl9kUZeQELH0cC8ZwQ6cyWljXmZ8ECtbp4KVuRuYpgP39494d2H7YSZui1nqyxkUWjwfYTxZTAHCMl9Y1plcysSFMPDmy7+3NGs/kPFIGpBhKCqKMcHr9WLFcX1gGOwdrhghFcUPAy4EwjAy6gmkBDaVuU1pK8x6n89ag8LC8k09cQVNDCXuYwgD4zia8bfYLGpa+/F0V4yXFHeDBdqllM2tq2bQxFmBxKzCtiWQAfGWqJYZ2KKyrBvTujCMU8m6nUu8RCJuC86FgmaK21FqSnc3HmAQCKGsb0okXZkCnIvE8MU1NnlnG16VeSvQX5QQhOFwhx9OdnhQXnC+pnzv85RrsF+Rp/Ugvx3b3P533xpFd9sdI2UIanRYPNSuFM3YbQ79ruhRd2eP0F2ofgag+M9kFLAzg5veVd3700IyN8FRAqKCyfeds7U9W5uue31/rCku1eKBaCkCVTtSZrWhmg7F5J3LVzW8RmvcdLgaqLv+iNQyfq6cSZLQZCeWL9dn/AjTYSKMB6uYhMO7gWk84B5fIbryXjfiahsr50jOAtlCwofDq3aGxbpeWdeF+foRFA6HO4sRCCMqHlHB+wHxg5W222bWxWwgiCMMI3q+WJCWDZqcEn4YS4l7SwwTCeaWLMf55eItUd29SMYUQkESnphXLI3aFYZXAoZ8sMrVJcnLkMTGdDhY0Vqn4Mw9GZwVyUklVNrC70txX4V12RA/4txkG2A8kFRZloXL83seHh9JyzNpmwsCCMR1NkZRYh9adG+hU1cW1IkSnJrkxwzXW944BNoGckBOmWnyeC3ejwixCMHgPePhyHD8EiVYRKjbaGfpGiho6rltCbGDjQpR2T/9tu5oXAyFVKdBq0iWseMLpNIkSHcQp+wtGG1XhlD3kEorONTB6J90ffYBQEDJxXgZQtKPubMGyN79fdq6e7QbHNU4tm/6T9HLDsesBx4LiNemolSjnnT+o8ooXoyoMALZJ7/cK84RvHAcMgOQNkNSWZUt2VF0y/zM5fyRy7zhp39D8IHk7VSqJSYIB47DicHNXD84Xr1+y4MbOYzZ3MR5Q3UjU+IqotkktpSZr9/z/XffMoz3jKcvm9sSEVQCfjD32ratnJ+fuM6RV2+/wl9n1h9+4Id3H6gVnnIW0pIIbiiRj1Yzc2AwVCPacghsnsx24Z0nDJPlmJQZa+XTOs6/rhs5ZWZR3rx+ZfaXuLKkmev5mbu7E+IH8J5lXXDHI95ZHfbL9crgwR8Plm7vDygLOdkp8FmF9TqjeWN53ri8y4R/848M45G4LcyXD4zjVKJUt+JhSaS0NkTmRQjFdubEvB1jiczMWdmWyBiM5moE5+gz67oxBM9xsILI7z8s3B0D9/cbwzjA4Qs2/4ZN79A1l4K7uannPXKwDFGrzmdq8AsU0aToS0nf0b8YyKiHRjuw2sBlX2l/X92rReeq6Radqe8zKmb+Z9TM3M/7vB0G3Wf93zUUe/+kRyIG0Y2hGDiz23pOq4339BP/4/2TNgN2iGz1H7sfNezcgLXCoKqKk+LGtmZWZrx+z3b5nvn8juePH3j/7jt++OF7ni8Liud4nBgGix1Y15WnpyfET3si13Ti7tUdy/rEsq7lTNESqpvqnOwu0bRcuZyvHA9vrWQeG+v80VK4Q2jQc12vqEIYj4xq8RN+hWkMvP3yLesW0bjgYiAcJj58967kdFhNyLisxLSWitEZ7wJRNijJVfX0rObtwCpO+YIoRBw+HDic7szYqsnyI0IwOwYZ1Vj0aYPdwziWkncHSy/XTN6uxXMyMBzuTNW4fiQuF9b5QvbBJPg44ccj83XheHqFOE+KkSVdccNUEKKlBahM+MExjMrknhic0eLo4BBgCOaaXhPMi3A6CE6VFJVryhyO5mHJCkuC4+BYI7z/cMH7zHT/wJtX/4jT10BEJTK6QPDGmLdkqf+poNicy4FAspP3reLeKL3pGoYi8o0h2b7Wxsy11uyTznhZNmkDDsl+MbRjnCoXpvhTr888e/TlVXBCgUE3xTvaj3YqxG4BbqE8ctteS5LRHSFU28XOKDqGk/e3SWMs9X8Vx/Sopvulopn29+7PjgnWLZPcDMzkFNm2levlIx8/fmSNgoQTh8kqa2spMYcK5+vK6TSSkzEri5sQpsORMDqmcbSZ0NwWPKWtFI6xOpVDOLJuG5fn91zOH/nVv/lfMKSIyGbSSIQcLSs2DAPrtuFKLkMuQVppm5nnmWVZUMHUlHYeST253MqxGbAqMkksESyU4i91TWrNkZZaLhbPIM5ZMR0/ME5WySo4wanV35RykLClsq8MwdyoqFrkIhPOeTRFak7JcHjE+RPqLlyWK+tq8RtoJqVHK5aTEzGuCBteKcY9i+gMzuMdeGacqNklHIwepsFQ8ZaFZbOgqtMoLeYCVZYtc5CMC76UoRNwEGNmvkSe3n3P4fG3TArD3YKf3jAe3hAZWBFi3choqQXRSOyFkO1U307cV2bQaLQ9Ywc+q3LjbfpEcpeNo/l2z+zlHzvG9BOuz2YUzY5ATRbae9cHYt16f7SDItqeh/LxC11NUVo6eeWK1OCufphS3F57P6Qoh4LrdSWaTtZx9MYjbtovi5tNkpKvqNjhuOu6cD0/cb2uZDkyTEeGw9Rg8v+Htn95liRL0juxn56Hmbn7vTci8l1Zj+5Cgxg0QQAEyZkRIYUywqEMl+SfyQVFuOKWK1KE5IILEAMO2EBXd3VXZ1ZmZDzuw93tcc5RLvQcM7uRhe6MFimriowbft3NzY6p6tHHp5/mnI05KsEwNHwDhN5AVv1wQAn0fW/fUlpvAAbXzqXuIAa+mqcHzuczKRlxStGC5IWiCXGhutZV3Goewpiz5/XPPE6Voj+T8sYKVrKtr68cqKXS3rckYJvtYfM1KrmK+K1iVcMg78OqDOKcJUydzbb04iqTmwGOUYy8NkarONTZolbucxt1Qcm40IEf8G7A6SMln8nF5r2WnCurVnPzqyvubECPcxEkIIyQi7F2OOiD/ancPSxZmBJMs8Jxq7wpsORCpxmP3QcqRu6LsYhdz1fGx9c2uCl4XNcR3avaXS1r+LtudvIsKl/lvWFO6kM0sXxmJNojXjuD1reWoqjTLUO/T9zWl0pp3oSwYWSaBv3Yy/7PHf844poP4qnmFRhNnl3sdgntbssqZM/ciN3iwLartZ/3VtWM7nbuQoN/7wgUxFkGeneZVrK13ah5DO2vWtQAGnuXCXxwiSGM9PJE0cJ0feDy8Ianx0fE39J3N0YkEyPjORG7nnmaeXi453T7VTUYNg2s8wPH21eUPLGkhdBFgx+3eFVgma6V0MZ238eHN7x+/QbcwItPfkERg9loWchJEU/1chLzYnRvl+vE+enMpZZJiwR87HApMU8Xy6FoQwlCCIFhGEhp4XJ9YlkmnBgWwlXD4MSbJK+w+dY/s81Z1VLqtPCRED3ROcKhJw4dDmUaz1BLqA5lmSa6zpiy03glk/H9AcVRSuby+AOqM+oiJdzS3XyKl09xuuCZyKkg6vD+QOhekPMF3/WmsM3Y5JF5fGKezgRn3sTQwaGDvrN7WLJVM64LiGZCTVjnYoZFtOA0ExC6YEbI2s4jvr/BxRPqjPUKf2AugTEpUyrGOKYYERNN7lkVWMRyJybnjSZPNh2ru9g+q2d/e5qf0pKca9NU25Chomf1mXHYE0J5HP5ZJ/Dff3xcr8f+gtgs1D7UkWeW7dmnaUbDrKJb/YfVy2iQXTbDuAUU1RJKs5Lswhlou952rZaZ2C7DrddlhDA7a79ehv3LixJdYfAg7kAZH0nzFS1KHF7RL1eD7TpPyQXvei6XM09PI49PC1/94hXX8w8s84xI4JNPPmV+/C0C9P0tMXqu939HPNwh3ozGnGZul8kSes4Th5d8+fNPWJaReX7Df////r/yP/2v/luG0wvEi7nECF6N70EkkxDCcOBwU+inhZvbzNxPIPD+/Q8VCZlQIHYdLILzgSBCXwbG6Wp0c0LNQQgx9m15KNmwD77SxeeUWJaREA50XSR4A209PF0A5XjoazpFzHAtY+X6tBA05QS6UJaJMl8B6w+J/Q3zcrVQ7+k1pbyj63u6vmc4DBB6e9rB44eefL0yXh4IwRO7iPdC8D19Fxg6TxdaAlPoO+g74bo4liLMWbkmZclNdivewpkMeQchKHgF79AQUN/hwoFwuKU7vSIcPiHLics1MWfB0k5bLk50o6xZ+yI3EX6mOxs+aU8ItQuPnwXUJr+pjRpEauK5oaNNkUS2UqqdoYVDPz2d+ZFVD6WR4q5cALvb3RLhtbYr+6qFPndG1huVncF4vnCrPd3zJKhYqW09mS1sC0jWMzfvhe2BtXkkrSgLpbaw513waNecimMpkeI7uwrfI+GACxMik5USi9XrixauTyM5O47HO9JUh+1gIcE0XhFVuu6AhIHp/Jqi3uZpiqA5Gy3cfKUbAsPxUw6niYeHdzw8vObN97/DSc+v/uxf8lnoCN2BnGbEWedoFE+YE6ojIh4fOvr+SIxnrpcnlnnBuUh/ONJ1vSVqlwkRZ5T9OaFFOfRHYtdbfiMnci5WUSjZwGG1rOpdAIFcSXJNLsRYw/tI9J6u68gF5pToQkBzIqWJJIaNCN6ShZoTHq1NZktdO2VZCvNiz2KpCVjRhGdi6SH3HfPlgevTDzQ4eClKydCFA4qxWUdX6LzxcwSvxjMhsGRnFQ6tjOuloDgbXaOGeVCcMcmLEKISulgrM8Y4FocTxDuSO5GzYymlssRvG9S+9C97GV1h/Jtoi1ATj83O6Pbe3Za4xTGOVPI6wbx1ha7funrOhpRtHkWheoR/LIarjfpuF+tsXhKbwWjKudvvtRnKTXm3xWy3/9w7ASrR6M62ttLps8HFsss9bMXaRj/WPqKra7Z35pqg63pBpagZCvUUiTbpqg2gEUNdaoZSOTeWZWGeEs739H3HdH2i65ztuHNiHK+cvEe8R6UwPr3G959SslHyU4pxRc4jvr+lO74gxO+ZpyeeHt7w7u1rhIH7d2+4uXvJKUbSkkxwKzmv88EShmIKG2OP93YNOSW8D8TY46KSFkcuC50YgY4WS7j2sed4OpGLVW8u5yf63kITMM6KxksBClXZ116PEOlipIuhTll3lQ+igsuycX/kZSEtMyF4NCV8F7DxfTPz+ERKmXnOzClTZ63bCD9ZKClXEmCb1LZcn2xgsdu6XsGMkZBxUghSyXw8OK+oOOZkQpEr70PTGSfW5NcwQ6X+cd5wI84HEFeJhkBdROlWw1NUtj3nWUJCn//zA3TmcyOiu/fvDE39yE6F1g1RV13ZvH2tm/NeZ5t33mgPf+rxkYbCADmmX9VXE4foLtaRZ3av+VP7ZQCM4Xhv8VoO4nkX43Mjsq7tOutgF6bs/l3KruZRB8UqLaNtSEMzwhtV2eaSWMXDxsQ5VDoIx7prTZSi9IcX5OuVtEzktHC9XMENII5SJq5XJcYTTw/vuX/3jpevPuH21UDJV5bpwjRNHF7eQl5s2AuFeTwzTiPxBDeHGzTPXB7fcj0/UorQxcj9+x948eoV/TCwzAlchzi7ThFHjB3TYlOuuj6Y91RMWIILeLEdsySHFCX2PUN3IMaBEAdOQ+Tm5kjB5nW+f/sO581oOJm4ziNdf0Cz4RNiiHSxp+s6+qHnMByspwOrkMRuoAsDJV0REXzXEX2dWauJPF/RNCHdS3LOLNPE5fGe6zRxnSamJaEihNjZpuOM6CZ0B3tcRaEIJc/4rq+5KCXNM1kLmrMBQqvI1JnaFOA6Gw4hpdZhalLmnKcTIzwuImQcWRyFHh8GxAm5FK7nJ/L1Df70S+iqrNQu082bfW4PnsHhV8uwf4Oxvq8E0tsHN+95/WDzSDzKBlk3CW4bH1UfoEEPpE0zLxjO/CceH131aFbKLrumaaSsD0nrDYPbYS6qO2SlBBry6XnL92Ys9keLqtYSklSui2cWWX/0CdTiMC3N/NYr0QKadpWo5w+2maA5CZdZOMbIDY4Qext+VB64nmckREIcKHhuP3nJ47u3pHnGS0937Hl8+x3nh7csy2IClFt4YxO5pvM7Yt+DKCkvhHhricNSez9CYLh5wd2UEOk4P038xV/8e5ZScLHnk5efsMxXlIVCQLQQnOLFXO7OwTD03NzdAcLVeXCOy+M9pWSG4cDtyxcMsSPnzDh2vHnzLe8fvq1VEuHly885ne4MFTmO5DevCU5qD4ojdj3Hw6EmD23N+/6Ec8ad4buIiBL8AacLjmQ5ClU0TyQdEYSlhjBJC2OC+6eJy5hIOVfSW3B9xEVv3lIILPNo/JlpIUaH5GSVEhe4jhdECl4yXTA5E9e6nW3DKWqx/dyoOYEiYh5QgaFS7zXpmxM8nC8cOo9GeMgL7779Sz6JL+j8QO5umJKB1rbq3+blNllzDUvwweb2XH5tA0bb+fTZ+56V/WXnTbCzKTsfxSD8fj2F2Rz9kVfz9x0faSjMAOxUf5cJrzHQOk+xXXFb7LK599UtQp+HKW1bb2otsgsj2BKnP7Ivq/F6HqbsfbRmoQ288ofr09TQKBXLXHuBqRMOPq7ndU7oDwPjNNW4zzL1oevx3mjo+q7w+O5MzoUQDzZiMBxxuYBeSOMjUwr0N6+I/RGHo7/5lKKFeb4Q5yP9cMfh9JLL05MhOEmk5Hh6Gnn/7i2fvLyjZMHMYaYUG+N46I0b4TJOTOMFh1rjVRfJl7Ndf28hR5TC+7e/ZxxHruOV1z/8HrAJWkWVeVm4vdwyHI6EEHn58hObsZEL4oQuGCRcxIYUiTj6oceJcU6ixabGOajDR2itWTkVVJTYRZbpwvXxHdfLmSWD728N5USxSkbwhNDRdZF+sHBnvDwyTdd16nrnFeNfVUQCeTlDgztjVQxDtQJt2txa7WqvV/StSas1jmH9IK4slKRc00L2SjwWpjExPb7B9d/Rxc+Z3HHNDezd/nWHNwGqDV9t33sertum1eTyOT/ptqlRw3o2naubnWVadl72mqLbvsdGSsofb/bodmydas0PaAu0ukVsRsG8Dl0f0Pa+5zfRqh7rAtCMr+7Kp9upV9v5gbHYX+d27L5zNTa7VFPNNOeilQdC8Qjj4kl10E5LFJK1gpqsRfp6fkTVsBIxOspiRsL5SPAD02QcC60HpuRMEsM8lFKM+i505DwbH2SazW3vBmLXE2Kk64174v27t/zub/+GP/mTP9nWGZsT4r3DZ0s2N3YsUwRL2GrJ1qdSIKeJ8zTy7u1rruOVcZq4XJ4Ac60b6UuaJ26WW46nG4bDLcF7G/JbgV6IWMUjVMo45/A1Fdjg4a45gIJdK65ejzGypLxweXrg6emJWSP4QOyPeB/xvlhlJgRitD8ijnmazGiVNinMDDaSgGj9K/X+baBTi1iN66OBztbcWd2edP1TI+u639gMV5OLrMZJkrNWcuMZXya8O9jAIKA1Ia6Got7+NptjL6a1gvihVnzgOD+Tb7Xg2ooZG2hOdQ26f6QBW5LDfv5RF+vfc/wjGK5+9OLeNOxeq09jZxTWhrJ14WT9l6zVlD/gEWkNIURZa7S6BSrr+q0PoX6nyPqg23nawu67UNuDzVpIebEhtjX+e5o8d9EAPD50dP2R5ekHUilAIHjPw7vXxOElfXS4Aue33yNifIziO54eH5mmO3rXUI5GwpLTQloWhuEz0Jk0X4BMXl4Zq3eIxOHI6eUnhFF5uH/Pt797y+vvv+e//K//10aigi1aEBu0cx4npmmsFPi2I5WcbIp4yTgP03Th/u1rni5PPJ3PTMvCnBIinpTT6n2N88hU/8zzxOc+crp9RapxuBMhl4UQDUQWQzB6PIw52yDWmT742kjnjZFK6t5XEnm2JObleuX+6cplvnB8ccfp9hWoMk+PxGDhRgyeEGzAzzgtLEtquwTW02K4FecCOSlLsmqNNattLGolS90Q7HMi5klsU8YNum2doJY3SKXQezEshTMCZMSDO4Ab0GyUA0U960Q7dhtaizbKRsdgPKSsz3ANGVr00QwB5bne7fNqq8LX3IO2/FwzNKYAG5v97kQ/3U58JB9FTmhe6oJubtEf6rG33WJjZ9K1dLNuLc+utNnTNf+xTjpqvha0pEdjD/I7v6CsBmEbyrqeXVo852nTlPYr72BtILKztfZnmJbEkgpRIorjen7P5enCcHpFUTg/vUMLHIcDhz4SWBjHiet15PHpwvk64/uXfPayQ/sFKRdymZmv73gUY3/2wwuGPiLlSp4T4/Utpxc/oxsOHE8vUBXGaeGv/tO/5fFp4ebVL7lMC4fj0aoF2dCT45KYpivzdEGXCdFMXq7k+YrXTOcSl8d3PN6/4/27t7y/Xs21994a33Je12RfWh7nmXT/jsvlwldf/Yzbu1fE/kDJNhFbi7FLibdBuZILTiC6Sr03zuZZoOQ0oZIN2CUCaeS7777ju/vM46WQ5kxa3nD3SjndveLV5/+M6fEH+pgZYmCIkcf3r8lpqU1rA1K9FDQB1mg3Z2FOBvMeukD0xvFQspAyNg+20vb10VeYuYHFmkXJuY48rS76shQIRtpTXCTPGdEFEZjlhjFvuYdnHK87mbdE4uYdVzT+j3wAq1I0FdiQnvtDam+Eva+272OG3mo2DQ6wNYU9Oz/Pz/f3HR9Z9chVoWpM30bT1UfVGk6Mmr66vfx4EWA1hrvEqL07q7Kn8lrjrabgGwvcGkxUZ2JzuWRvvDa/xeJSc733meXmejonpjh1Wby42uBjngKqeFfohxtKKUzTlcvTPc513N3cELlyfvcN79++YVoK41woxfPiZkDLQqlowm7wPPzwBtwNvl84P76j6z6rqMJCmq92LbGnP9xQcDw+/RUx9vS9gE78D//+/8O//p/9LykK4ziTUjEez2mubrJwfXrP0/t3TOOE5kLvA49LYpkXsipdHY5cUCOjFcWJX9vNgTp+0IRtyYm3796SU+Z0uuN4e7fj3ug4no50ncMXJTolBKwdfr5S0kQpM2kZLXzwnlTg4eHMf/jtey45kLKQ54kYOsbrmRgjnO64/eRz/PJgYYgI49N7lvGMIoQQQBZyTtVrEHK6UtRTklCSErzacCa1kYbzDHNSYjAO3+CNgKeFRyI26XtliW/y6prn0iochTSfKfN7BnlklE/J1M7NXe5hH040edvyZ80IbN60sH3e9G7zFMxR3qp+G21h8+NNhyxvvgU9f4iI6I/GcLWmYBSQUlm3WoaikTi0HX0fFsj6+62F3C50jz1fcw+6W4Tqun2Y0VBtUdnzrEQ1EfW7Nq/H+vtrK3W9hzUBur7L2eZR+x9AKDgyERUT7uA9TpRpmZmnkbRkbo43dG4iXR843781Nuh5QogcDx13tz1pnshxqA1iBdQZhHmZuZ4fuLk9ruujZUHIdF1PGoxwllK4uX2BcmbJib/97W/41Z/+M4bjLaqsw3iNW8L6RZbpwjIZH2ff9TS4e+OYaENuzLDY/TZpleq9+TousE13H8cRw4TC4TggvrdhznV6vSUvKyhOjHLObKzxd5ScIArztPB0mfntN/d8++6K+NoYlhKpODNWeSHPZ8IxEvse7zJaJkoaUU1QQ4aVwAUAIxlGj0avX8Qg/VVmUoZxgVSUDqnYE1eh6lsfxQqIaknDVdLcqs1t4JFqxulCcEoprn6XVg94I6/ZKdLzF1q3Z/vy9W367KcWxuyRms/SFjRdabxeTYrbs4VdzP1Rx0cZihXBvX5hYQMwbQnNLaGy/XebSKDPFHsLt3ZGaP3dBze7Tkiv4ca6erKzrEJrV22Viu0K28NR9iZu9SwQbLq4W5NLVkvvUN9ZziAEVK/GxD0vOPG8enmA9J7x/APnpydC6KFkhqHn9sWRu1Nkmq70vTFBgeEsnHOUNDOe75kuB7rhBu89TjNaFvp+sDLeNOFd4OXLT6EI797f883f/Q2/+c1/5Gc//xU3ty9t+lY2Q5FTZpkn8mzeRQie0+nE8ljoYlexDzYiMWtlTdJKTdgWXywW70Ksg2dstZZl4XI94xzc3Z3oDrFyVRZSmiBERMoaL9dkRVUErcoN9+cL33z/yH//V+94mjKHQei7QHSQy4L31lC2zI8wObrbO8vDjFeDQDlqU9hSjbcHsRzQdJ2QcETVkyujtxiujSXBZbGBxVLvseW91k1MMV7YNgVNpE7AbIAzez1UoJuIh6KEqCQ10FXbiFZ9eRYO7xS9etAr0VLzwuuHpSlJS7zCBjvQRt23E+vmpUj1KGSTf1OhrdP0j5bMFBdxLrA2zTaLuWYgmwWteAXqCDrZX2pVwNUIVEXfWc+2Nutvd5/fkjJbd91W5tkZix/FX1uY4QSb21LJAewB6O6TW/kqqzImOErdKcqM5kxaEtE7Pv38jht/5vc/PKLF8+lXP2d8eMNw+Jw4HOgPPXm+IPjKeuQgO4bbT8hpocxXVCPj0xMhngjRmeCUQoyGivReq5GwBqvLRbjkkf/z/+n/yH/xz/8F//Jf/Wt+/st/wjTOpHkmTRPz9YrvDnz2+S1913PsB36YRvo4MPRHUiqIjIxLgpQraMtwiAXzCo79kS70WwWkJLrY47HGrt9/83f87GcwkfF6hHgErfiDnMkpkTRVA9zjIwxR+f7tI//hby781bePvLsmnAhzSkSv3B4iQwhE76w3Izry9EiJC9IPdMMNfX9iymeWZLT8aZkZDjd470gLPJ0Th0MiFyVjCdUQTKnmyla1D3kdjiUXvKiVRHdK3fZE28XD6iH3weNjV3tjHAnHlIxjszRj0YzlzkNporzm+dkAgi3v12QR1/Jnz72MBk1c/ZwfhRDmHa3YCtomuKmJNmX4icc/Akdh3oGiKw7eLF+ziPU9O3zE88txu5ulmUxEW1+c2Z9SFxM2ldfCtkvtjuc142Z9m9fQHvL2qXY9rn7XGqCoLW7Doqx+R4GkHinBXHcKL296olNiUL799onD3aecesfgJ36fF5w7VaZqa3v23gQnFcX1t8i0mCucEpouTPOBQzKXOYaBEI9kjGIu9pHj8cA0HeieBnzsyFflm+9e8+b+/8Fv/uov+Vf/8t/wi59/TcrJhgOFwNdf/4xDd8LhmK5XK7fGnq470BdFJaJ6BTVQWHY2VT2ESAgdd4dbRLB5n7kQtbNdlQKaSXNifnpk6AZUtaIcjV7Ok/FUcJ0z9O6yKH/73fd888PMt2/OPI2z4RvqQ3QYT+WhH+ij9YxYwlIgz5Ql48LM8dXPGOffgU5brkkXnESci5QcmWYh41HJzPNC33lyKaRsT74N1XZS8G7BApGtVTE6Gztp2AvDHgTvTfZV0JKJQ0c4fYEbvqxqkCl4ct14VumtbsVK/IwlUjX/ASOwk9NcdB1O9aG8/yEowApTaNKtSlLDA1ky2XSxIolIfyxDobRBKlUF1+/ZYsQfRUCqz5WR/Zt2GLNmV565v9v791l42BvDPxTfbcZEtxPQ8BhbOXkDjG1mpf5mZ6VTccza4eVAN5x4ceeYpol5nnh4mlB/5O7ujs4tpPFCkcFgx5opanT+7Zyq4OORcn23NuekeWKeE8uy0KWl8lTYrXkndCHSD8faPXnkcLzhcRopRXn37t4g4YeX5ALn8yMC/JNf/xk/+8Wv8OKN+j4rsR/o+wP9nOrO55mXYsYrZ6SxVvmOvjtwON5YOTMlcqq5D7VhxqIeKUKeZygLTpNNBskZoeEwMuiMAy7jlYf7B37zzXvOVxjntBNrI7QNzhFD+2OuvXcGfFJNpGVG85XQvcDHiKQFzQkXenxlDEupMBcheo/kSgpUjUku1kKuKF2w0qPDODStdG5KhVqg3BzV5iFolVPvrLksBGe4mnBg4oakDX+x956rVsj+32vQ+0xV5IO/Wx5uZYnT7XXQ5zqwO1vTqWZLiuzkXyqORMUS+z/x+LghxVo5CHa39jyoeK6c672tXsDuwrTQuku3+93x/lF39WceRCPLkefn2n0nz86w/dZO0/DuH7yhoTm3C97doQ2Fmeno3Inh8IK77HjzZmacZt4/jLx4+Tm3N0eW6YGn80gqkShYg1JJ+DCspUfBIb6rE7HMTV2mmXlcWKaJ1M+UvNT5KSYMIRjpTR97DocTp9sJ/7DgQ2S5XLh/uvD779/SDTe8fv17gnf8+he/5uuvf8V4vXJ+eGQ+j8T+QD+c6Odi4/PwTF1myRlZZit7IwQf6LsDw+FUafUT2Rl5T8pVwUTxGqyDNs1IniEvLLOCFMsXkHFlAi08Pj7wd9+95W+/fyRGA0Q5EVIxOQlOiMHVuB+Cx7o9xVqztCRymcmaOHRHQuyQcSTnRIg2FiBn4+aYMtwGj0vW99KkohRZuST7aPiIVsY1fohaZhfbdxsdvkJNjDrEGzFvjNZ450KguI6RW5aSaN607IVQd1K5qsEuFKhKv5fLhgp4loPY25xnElrP8eylJvO13wfTN0GsL0VtaPZPPT5ySDGsI/bqxW1HM3fFyFXrijRaVmnNZLukWasNqxiLUKlJUJXniroagTXEkTUXAjtjUle12dfnpkmfvSpYCQyBxAb9FlEaO1b7REHJ7oB0Lzncfcm7+9e8/uH3PJ1n+uGOV5++YJ6vvH93z3dvzgzHz7i/f0sXAqfBiGW9j7juCKFnSUZnp3mhLFeWyyPXp9dcTwPd0DOUW5wP5DRSkoUoXd/TDwdOpxumeUH1O5wEgo847zjeviAXx3WcQTP3D1dubk+Ukhl9wMeB2B3oDzccMhTnYRqZijCmxHJ+ZJrPiBhL92G44XC843q9Im4heAuql+WKlmTDc3ygl4DOE+P7N0zn9wB0MXA4HDjdHLk5KExPPN6/55vX98ZFUVItwW7mPXo4RIhBicFVD0OIEfPMSrJmtFKY7r/lcPyElDLjeGYaH+lCTyFQ6JkX88qCKJ2H2bWRgaBFKpGNw2upnoONXvCuju8TZcmZ0NVqCMZslbP9zolwOHT0t79Cuk8prkfI+MrStcnsjpCmvuZ2P1un76asW5GuIXiVlcCihkoIqxf8o5DkmZdif6dkvKWqDSdknyoFrteRn3p85JDiTfG3i/vwogXqFCapF7+ixT7wRjb3yGjct5M2TMa+yNM+swFi2hWY0amP5oM08J7Opp3LQC5uNSgiLTatZkjZZkTWCCu7AwuKyhOSrpyGAyI9Lgws04WHp9fcv7vn6WlkXl5zPb/nxd0L/OnWrtpB6AZ8f2NksmVhud4zXh64jo+U95HDzaccbzO4jvHyDtFk5cSc6bobbo63lGVmvDxyd4jcnowVSnFM1wtv08LT0zv66A0arsZP2fUDwzDTH24Jw4ifF/ySCAXm5QfmyWDjqBBCx/H0ghevPud0c2uJvnmyqkrJOO8oywwlmYK5hVj5Lt893fO7v3vNL774hK//5E/57NVXcPmOy+MDT0+Zy2yhQKo5pDZVzq/uvE3tagOPnSuIGqWfsXs5NGXmKRHSPRI8x5dfcH3/e/JyJS2wXIVhCIyXK8Oh59TdMucnlnkENaaqPgS8U3ICT8FLwkmkiCEvVYwctyTFBZvgVtSRtVR4u6MbBuTFnzAPPye5hifZGQEw4qRdzqHNm5WK0VBVfKlARN2BorThwM04Nx7PbVjWxm7lduG2Ukl8awuC1hGYbX6MAD7EOhZRSHnmpx4fmczcdoEti7v5TGbxqmJqbR5bXXlbvn0o0ZSy3Sjt1Rpy7PMO5slsWI0tTLFv3oO3trPpzjOAta1c9t+nq6W2cxlu3qo529eB2KRxMfq4vrsyLYm8FEpOvH97z+PDkzVU5ScawYHBD3pcjDYwxgVyupDSyFJZppeUCBmgoxRn9G1Pb/Ah2L0heLUpX11/4DAceXFrf2xiuliC8nAidu9AF8bpEUrGu0iM1i/SD/aeaU7MUyKp2CwM52nM2113YjjccTzdGclNb7kL5xdSmknZ4aG2xyeCQIiOJRkdnzhhuLnlcHNL7Dvmh8zj08zTdWFMxZK7q3tuW4kTR3Se3rsK17YchZNCrueNMeC9XWcuhWm+otIZ23Z/S87vbMcvwjQvTEvilcDh1HPz4o7p7Ei5EPoTN5/9EtLE09u/oyxnvAfvPXNu/qZScKSMdZ06w984kTVE6voI8Y4sA0m9heUrv0M7CyvtIEIdgrz9blX+Omtjnauyo7Gzt7XqSX1vo15SO9Pes855mw+7lQ61ht2KqsNhJV3nfrr6f+TsUbsJ2kPehfa2PDt05k4ZV5VWgxorpTZ5PUtd7t+5GYldTLf9pi74WtmQ7bWdcWrnkN3n93Gh/bc8u/4P3gTUkizVC5HIMBzx/gHBcgnT5cz9+0eu1wkQ0jIxxEDwHeIP+P4TfBdxYQDNLOMDab4yj2fm6WqzMV1nPI7LzHh5wDtHiEaU4n0PWjb+h/7A6XTg9ubIeRZYHMebF/THG/r3t+TpkevliZxsp/Te+ka6/sDQH5n6mambSEDXHwixR3zAuUiMB/r+yDAc6TpPmhdbElfBaFgfjNSZpxEhBOpg4cLpMHD74iXHmxPBwzXPPJwnnsaFOWeGyo7VvEbB5mtYKdQTveUpgrPcRFoW5iXhQyA4w7jIMpHG2dTFCS4MlMXK3UUd0ziyTAtDL3S9YzgMaB5wKTHcveKzX/05ZZ6MV/MxI8x1UHHaebGepOAKuFIqpsIRvaOPnq4/UFxPUkcqNXHbypxs+bXGdGai2BR6/++y7v65UfNrfpZMbx5Hq+Y11Cy7d6yedaX233PGtoYSoc6M9b6Gw/zk46MNRSkZca1V1XgVWzPNhqQ01mLzONTchpZ/gOpt2NE8kpZP1LWTZhemrHMKqmGQBhop6wK15bIfmoXeHpqs+Q0zDi3nsX5ub1zqT1tXagufHBpuiN2NDZspM/1w4u/+5j/y8O6JXJQYPY6F4eZIf/qM7uU/Zfj8n4CeIb1jPn/L49vfMs+e6+Mj18sFN9zR37zicn5DKU+U9DnDcGS8vMe5SHd4STfcEruO2HfEGOiHnhcvjjxODpaOz372J8zzmeF4YCwzD08j16dE6EAwiHWM1o3aDweGm0K5XLl78SnjPHE+P1KyJUhjI6LpInmaLLgRIVVnUIrgtP7BEnuqmcFHji96bm5P9L3H6YTLV94+nrm/Tsy5cIhtQABAhc1XhuwhQgiO4BVHRnNmmiaWlOlKT9FK96bWz7DMF5Z8puuPgCereVdpsXF+47wQn5R+KbUjteP08hWf/fJ/hMQXiBQev/0fGN/9DcVFHLm6/daUoMWTc1Uwr0SX6SOcTj397acsGeaykNT6mqrorQbgmUTVv7ZJXbWqsnqtdePTBn5r73Hbxlw/s1LGis0iQXO1vZUhTty2eTavv53Pe1Qi4jtstNVPOz7OUFQYbysTbSiFD/IWOydh30IOrlrD/Rv2nzWY9d6atjyHVUlaXqPstPt5Z53o1g1atLY4r9ex90q2s8PeRMDK5KW6ovAa52L0mct8Tx8dwcH9/RvefP+WkgXnFKcLp9sTX/6z/w13P/8XHD/9FUEWJJ8Z37zm8vgNT+++ZSqWfAzDS06f/oz3v/8LxHtuXnzG4fAJ4/mdGWZZuKYMPnB7+9Ko3aLjMBz5/JOXnOcLcvG8urulO37N4/u3TOcnCAcuTxMvPnlB30WucTblPwzMy8xlWpCYOZ5ecjfNTOOVN8tMPxw5HG4YjicCWkcL1PV0Qh+CcS1mI6AJzhNcQGLkxc2BBc/x9pYuOpjvmS9XfjhfuSzLipmIzp5HKZavuB06DgEj3gngdaEsdcbJMuFwkEaWMjOVxHQdWZbZmK6LMp3f0wdhmRfGa0EFuuCIMRgJsVMeHh7oDz1pfGQ8/8DdV1/w2Z/91xxOr3j/V/Du4T1TtvBExVi7U65kNs56fYJP9MHR9wF3+hlFjDBIqky3cLwhky339UFObZ8kr5vq3iNuobEIKxv6HlvR6A6bnJrHLGvILzVHuIn66pKb3hSbaKYipJ9Owv2xk8LqBe2SMlseoYE5NqWWDxSzcfexMvbUxZGapGwpINmUXypQsdkFXUOO9r07t6u5Z2wLW9Mmq5fSrOz6cZph2L5kzyFQihJ8a223h9APA945lmni/t17xAmhLETvOdzc8dV/8d/w4k/+5/R3n+FigDRRlmsdqrtQMoTuyHBzYEkL14ffM08jsT9RstZReQ/4EFEpaBGu5yeCN+7Ovr9h6A+8epl4+1SYUiYtIwd3w+F4g4893/ztXxtVng9rSLgOHMaDOqsiaKYLnpvjDdPxli72hNjhfYfXhEhAXKr9IY6c6ngE53GaLOmrhegdt8eed4+Z5enCzIIy8uZ+4nFMLFlXKn2pIwqdA7JyMziGznAT5h5Hcm3315Lpgs33TFpIOXOdCmnReg5jglqW+icVlmLYiW4xQFwX6vyRXNBkFRs00x1P5BefM7z8mn4yxq0xmYxGv+m3YcaUQyccjx398YgSQGyGSdDdrr0KnxoMfDUUBv1uMryK25rfa8rfvGCb+9ryOPswWqTJaf1sLXtu+lb1skHDV9G2vKFoxvhA0j+s8/X4SEOx4eJZ16MmgOqO0xKW+wtf9+ydS7UquVgD0gplEEErnJj2TWtKQtfzbWfeXLv979vHZLMLNOTbLnrbjjV5pD/+1ZoysgcfYweaLM9wPRvLs48MN6+4++LXfPqn/yXHT3+JeAc6225cFkQV7yLd8ILSH9FpIucr4/UdSm9rVwrzdGEaoRtuEOfIaebCAyEE+uMtsRvou47TEDn2ni5YA5jmZRXu6Xq2cYTOV45LsT6SCjkWMA64nHEYqGvoDkQf8M7jnMdpMeJaaRTwVD6JjYTWjLolKY+HgaenC+P9e8Ic6EPh7ePENWVyqQzYJsFVESB64TR4uqg2vEdAJFAKtRku40WYl8VmpeTMnDxLMjBWJw7wpKx1UBK0qfVLUpZFKZ0Zp5IzaZ7RZazNf4F4uKG/+4rj9RH35kKZkk1gw7ybGksQRDkMwuHmRHd6gfqhJre9SUWdgL5ZAGhsOW2X33d9qrLOkl3zZztdAepayKo32gxFe88qxpuBWPfED85lv636hmJUCs9qjX/v8ZFt5obmaqVDG6jTZiOKuVlU66rCTtVZ9/kV81AVtwqgGT/lgwZzWr9nu+VNmTdzsS6T7KnydHsG0txAvzvPbgnXN249++Yt1cy1ymq9ffOolgukM9FlulgYXv6Su5//Kz79s/8VL37+Tyl5RIv1ImQJ1mUZbzicPiP8vOfx+sR0/pbl+hapE8hdEHKZuJ7f0nUOFw+ILsZGdXkw5fUd8RTpOk8g0btM7zJ5uZDmC+P5njJPfP2zX3B7e4ciLEumZOj7AyFerZGJ6tjlDClBHU3nXcCpM8yUc/jgcHN91nVUYRETYueltqUUJDoOxxN3l4mH198wx8DN6cT3D2fmXHZrrSAFp4ZjCX3gpo/0sVj1oXadaiWA0ZShs0FHUyosGfBCIoCa5+AQUs4277NsApGLkdfMs5oBSQvz9cz48D0hmKcVugPDy5/jQ8fduyvLnHmaHpmBzin4TJBM7+H2eOB09ynDy18ih88RF+pUMl/zhQVyNsl0Dpsg3FwAt1UA1TwGKXVS+R/YnKjOgLDJewtLVqRm3ZQ3b0Ofa4TuJFp1N5Fvw4f+1OPju0eluf+Q1VzXpryqm3vblA1Y/6Zh/3ULLVQzIsXcYee3pGY7J7s8RTvH+q+dW/UBtqLR9EujX/7gt5snYg8WJ4haOXIzcnZeBZaiXBbHG3pi9+fc/jLy1fB74qdvcTe/4sUv/g3d3eeE4UAuY7WJNrC2EIn9CzjcwnxCS6a8+UucFmJ3CyXg4skgwSh5GUkpkeYRxJOWwni9Mh3vWOYbcj/YDj70/OyLzzjceC7acz5/T57PfPrJJ/xv/7v/PcfjwMPTlfE6cbmMhOhWbmOp+RtX3dhGHTf0R7p+wIdgbN4+oiGiITCpWs+FKklAfCG62kNQQFLhiy97yB3TWHjzdOab90+AQbQFIWcDtCkF75SXN4HDIXAYHEMn9FGYr++Nxq4UVIWn88ichVSaVycEb4Ke6/OfM6RaEfRsCtKSi6KVOGc68+7b/8if/pcO8QPeB3yI5NMNv3i64qQjL78xti5jRUbJRFe4e/Eph7tP8acv0f5zy1FUD231eqtn0a5T20TpvSO9/3t3re2FVZ11e20NUGTb6MxQ7CALyocE9duxmwusruKcPqLl/KPp+i0rK6DZEJjVLVqtWXW/ClIzttuirNZRd6q+JmYaoESefd+WB7G24nam9pndmwGpsx/te4ta6W1ltaoffTZbpAptg5Kt8xD234NZ/rkI93OH8DPyJzcMp5HPvl6guyMON4irw4oLtH6HZsSceHBQmFkub8hLXsfOBR+Mes1FK12BEc6KIhLIanMz5nHicj7jfU8IR27uPHSCDJmH794jc+J//Gd/zqeffMUvvvw5v/vrv+Eyjsb05DwPjxOPj4/r0GJDE9kfkUBwgb4/MPQDfexsByrF5pSWOgy5mW1Vck6gmd4HbBygsowjiPCUFn7/MDLmvLrLRZWgYk1KWJjxagApFzR7NHvIwQYFLcs6lHjOjmkxziInNq1dBHIq1gDnWnOVyWHwwlyrFc5hIQ1U45cZ77/n/P4bTi9/iY89URUpiZdf/ZrL5cL9m98zp8XwI6oEJwxdput7fH+DxBtyTezvyBdoJqqJcOsg3dHRV1mtf7UqRTMOz8qnNdnpVlOz+93+fB8UCHTrq2ob+jOPXDBPpzhEfnp99CM5My0etXWoGVc+VNb9fWglQNkbh20x1mTR+vEfO2EfttBq9SJMWLdXt+yu25iW62XaTNGdx7HjKdtb+O2hrfHLs1yIAnNxqPZIDEhQ+pOgEtb1sLq57EKbthgJ0hVdzuQ8ksvGbZC1rBmQZlnTPFk4EHokHkAK0/WC84943/Hqky84xAPZJy55RLPy8y//lLvDS07Dicvb9zw8PXKdJkMcHk5cpoVxmpmmiZxSZWKqE8DFEXwkxo4QOnwIlHm0CoeCuDa53KgDvChBhC4Kzgs4wxLcP428P8+8OU/8cB4pKKF6d16E6KrxdtbfceoDngWPri3VOVlfSa4eRSpWLC9FKAIxqwHXMPo+0efNhcHVkMRbQ5lzsuY/oFCmM+e3v6M/fU6shDkSOk6vPufu06948epz3r39Dhx4dfResEmGPS72iO9sZEH9vibIW6Ok7jan50LuVknaS75UZu5dNk1tLQTrpHb7d8ve22hhs2vCuybmVtktTYLbNRrXx57Y6R86Po4zs+g2XaixAcHqrtt8D2octSUot1zCtssar1/9XcNi7I69EbDQbEv67I1EQ6xRBW2lXa8Pq0h5VlJa8RQ/Mly6+1KlTYhev7XmMZwrpHlZIbT2CWNbeka5rnVNqJRq6ZEyviWP7wATZGOxVqbzZLNAq0elpZDmkbwkusEz9IElj4yXB/NWfOTzn/1TvBfG8oTIRKDnf/Fv/lsub97y+u/+ht/89b9jWmbG8Up2jnB7i/YHJERKKXXaua2/kzpj01t7eQg2dSzlYiTC3hOkAymE7oAXiN5x6Bynw0Cer0iZUB355vWVb98/cT8tnFNaB/FGJ3TO0Qfr63DOUJjHoceL0MdIF906vtAMBswJcDaYx/IthWlKHA423lAV5iUR6n2Ic8Rgz6ULQghGOjP0ro7RA82Zx+/+ktOnf4YLNyYCzjPcvuLVl79kenzLdH2P8XkXDiFwPAm+O9TZsDYtrbRu0SrHLaO2KuszOW7asgul22ap+nzGje62psoNVXZZ+WYYW7FgpXyUPV1U+77mbVTj0dICztmUup94fDxdvzZM+mYNm7HQ2jxjltZtnXu70qRWl8p23Vau24yA7L+I3Y7/I1+jvt5Cn3oVjlwp8iwEMXbtLb4zc1WvfPWItHo9pRqven87tzBnGz3nZIen14aSq9cvbk30aqWkUy10MuOX7wk+4YcDl6eZ29MdT+czuUx4HxC3xYxFHfOUOR6PhhjUheV6psgB5wJDf2tJzWFgKMLdovz660A533N5eM/D+3c8vP4d58uZIjZuYHm4J2nBHQ640Fm5kG1IUww9pYOus+pLmhN5MdKZvu9B4PqU+fKrn/PZy0+4ORxxTpnGK9f3r0njPfNU+OZp5N24VCKYxt9o3sdNJxyihZDHPnJ3DAydgnp8DIir+ZmcLcdaHIsKWmwwsvOOwUfGccb7ZFUDV7FO3ioPrS09BOg7z/HguTkULnMhSK38BM/4/V8z/fIt4fApLh7ARRQ4fvIzvv7zI5eH15zffQM50XUdruuR0FeIgoVcWoGDz6sMP/55/68PpdjmnG6vl+aVFtMZ+697lq+wEMsMYNtsN0ChARJN71pCip2ewRa6P9+c/77jI/kopOIljXVjtYyrR+DWKditVt5oMpr11coqZfGjKVjtuN9yBM+ima1s2Zh/nq9Zy22UlT+gxY3KBr/e9P656W6GprUca7vm+iVF2jfZC06kxs77bHXbV1r4UNb3CgYgcmWiVOEK3nNJiXlO5Cx0/VDzL8bxWMpikzHUXOt5sQSijwHIzOM979/8li++/qd0XcfpdIP7pOf9735D1o6+H5C8QFnImilrr4KynJ9QcYjvicNxpUYLPkA0QmHNxWaOqKPrBkIX8cFzOp744pNP6EPEoczTTLo+8vT6G673b1BN3B3vePs0koq1kXtxDF3gEOrsTwHEMUTHaXBIUKLvEBJ5mZmX2QYmFY/WIcr4DpurUsFvMVJpRI16PyulNpgZQbJw8MLQQ/CFcc5EH+pjEaMgnB5Zzm9Yxq+I8QQu1BkrnsOLV/ziX/03fPcf/l/MD9/j0oWcHZLFKkJiU7eMsr/1UNTtov7HFLt2IWvb0JrHq6vUtHGGDVuxsl2tf2Q9P+08rnmtJnHe7zfCKpGlrAanYTzs9yZX2uDiP/H4SEPRVqJ94eZV7EMIqbv2llStr+kaqLS7rq9ZXN86Rk0JWzZ3Q0k2s9KupblvqtY7kquiaiXRbQNezEDtHb96BmH9vAqoa4nTXf9HFc5tt6/nkOZFbaHJxpVRUWI1tgzFISWRl4llHklpYhzPtcfDV6yDrNR8uSgu2DDcolAWyycEJ5QyM17e8/jO8cnnv8DHA4f+QHYLv/+b/0T34ms8gtOM5FzBRVjC0oGmTKlJOJdznf0hiPPELhjEVws5LSBiHBYVNh69cHd7Zym7lNGslGVkShPjPMIy8/nLl3zz9i1zMgBaFzw3nSNWbglEGfrA6dBxHCLeO2IXICcbRFyUnCtcHIfzsXo/vj49JYRQ+Twsd5G1YRKqoRDoo804behNJza0R6SGKDqTL29Il3f44+dsw6cE8Z7bz37O8ss/Z3z/knJ9RxwE7V6SZUB2vBZahato3d3XJouquKvONwMg6weboVgBWE0Wn0lq2zibrNtmK/XfpeYrthzcVgl51li5wjs3DMeeXesfOv4Rs0frHewZhtvr9afqNdHmC2zTmTecu9Xfc2VobMpVb6qWUbfxf5uL79vAF60kOruqCXVXZv/a84tjU/IPYku1zsP1ftrzdjwr+Ta3j52RWPMfUOc8bhUaJ47gApom0nhmvDwwXh65np8Qb9OwLESxAT5GAy90oQdnlPYl59qUpOTlymW+orowjo+cugND33Oer7z5m//Ey19GwukFXhVfrENUBCgLKivZIIo1IRkYyCNe8H1vSqnFZno6b30hw8DQRYbg6GJA1CoQ3nmWNJI6xxIcXCa+fvUl/7H7HUta6GPg2HtOUfG1+ako3J16bk8Dx6Ez5GT0ZBwlCVocqQhLsVAu+kCQYDkKVZt2Ftxa5bANYWOWcl7wQAyOJSkpa503mrbdVeAoQjr/nnT+kvjqT42urz5VKZk4nPj0T/8l8+PXLOc3BGZ0CCR/Qynedn6RlZ6xtJxWNRTShAxZN6Mf6VKFCTRDsXnHulb7Vq+9qUY7HzV0rpuTQ1HXQHHb5gqsm/e2eWoFpf2RDEXJxUbXu71ZaLu1jZmnGS6h5geaN1BwuLUd1yjimzbuQ4IKDKmaui5w/crcFm9136rhWd2XWmJ9Fn7tUjyVeGS1+PX3red/jT+keRNmEKmVgf0592GQhS+yIunaZxBFx0eu998wnt/a1PLLA20qV04jRTsIjjJNtovHnuF44jLNNsC3FF68PBoEvOU+KJzvv6ePHTEcWPIDvLqF3iOa6ABXFcMI05WkmdwMXLGd1oce5x1OLVFps0Rb1scbZLzrOQ4DQxCGGHGhYx5Hnt78QHh1wD32lLMnP1mi8vObnlNUuhigXGmxtjghOs+Xnxw5DJEQhCCJvoMx2z0pHhccIdtgwlSEkgpddHV9PUoCL7WDFWLoEJlpV60K01LIRcjZCGdUoQtmyIuC94789A3p6XPIM2iH1jk1glBKwseOw6c/Z/jsF7ZxqTKqbV5SNxXbqGX9PfU158AwQu5ZuLtvHTdx1G33322kzVj8+NBVwZoGGPGxoivkUZ/JdlOGsnokf+C0/8DxcVR4aSYnA5g0rJ2F8OYFuDZwZPWudh6EXX61nnX3rwjO7ffPaWZadPCHjlUh16N2su7du7paK0t3BVKJk3WZAcyvsQX1SMXoS4umaEEPdbrVVvlpb2idpvY+o4I3haPM6PyAlNnmZ3YHHA9QzBUumlGXyYuQUiLEjnjsuV4W7s9noHAYIqTMlBcUqS3R1v8xHs/I4Mi6IL/4Aje8ws2ebjjB5QlyXe9SkDpyoPFPiDOAl6utyTH2RnKSbc3c8UCMHbHr6fqeQxT6Phrye5m5XN4SXngezo88PD5yQLk/P+AlEWRB0wKu1NKmPf3PbjuCFCTbBHP8hTTZFHehsCTFZrwasS4+rh5DqWue1VVvWvFBEDKaGuTZcZkLMbgKKlOyCEtReueIwbpVnSplek++fIdef49/9edomaqzbMqtRVGxXJw8k2Uzvs41pVPEyToBTNH6s7DNktnkdvNaN91wNfG7d38/9Nf3G1NLWqjW0EvM83RiDYzeOZzfkJtNVdZwQ/jPGKI/fHycoSg2kalBpdtEr9boVbbwrOqP7u973f3XWnLV6eYWrRdufiTslF5aT26Lu2DXb9u+V9Y4c72IGibtwa3N+W4/t4yE+T32fldjP6031GY1UL9nTVJh4UIrAzbi0gaRs48Uuv6ILk9rgrMl5gQLwVJWWpScSuFy/0DSTAx2nnGqeYoY8N6SydP1ien6hHceIZOne3T4At8f6W5fIg9vrWSm7TuN1RtvZDUudmaMncOHUGdc1DBPxZrDQqy9HzY4SFwgTSPzdGVJZ7w4Ltcz9+cnrqnw+O4tc7I28FSlMxclODhEz20fDObc5sK4mnCj4Gr36LzUZHitIrWgT+pTK7Wfw5LmtohZt/wDTrZns27AbgVfGeDLgGTp/APTd/+O0+0vEN/Tkn1rJWK/FwkfyPP+F3slbj/r8w+J7Bip6lufheOb77F2SkmFGrT7YfOy97kLkzLDkJjnq6xh+GpU6nrUS/oQo/T3HR/JR5EolUB0c+XtTxvlvuIN6p2std56odJajOtn90otzUR/aC/WRXNVqOoNu62bRNaL2RaoXgqlyBqOrEai7VK7B4BW27NiLiwGdYIFVtl6EbxsQ1/t/KydvQ2/sT08wXlPd7ghje8RUZwXXACPQzM2GHkRfOhRhXFeuH985HQ84CRY9+iUCCESWyVAC+P1kcvlHu89wcNy/5o0fI3evGR4+Tn++29wruBUkWy7njgHPiAhGmENYlPXO4OF44KFkRWAFbx1SDZWKsQzjyPT9ZHEiCY4Xx55d35CCvhyoRfL+SzFjGYphUPw3A2RQ/TmtWil/UfW3c45oevgPFX3HfMaWkXKAAWsAk8zDIKRzNRd3vmaw9BWbqzDepzJkRV6MqgjXd5x/f2/4/Dlv8Tf/Qn4fjX+e3928+S3zalFmPacN/lrxum5+TANldZO8AfCjVVvaBuJrJtg86jkR8nP59/hFNRt3vu6LWudN8JWofmYCOQjAVc2e9StiZ/t27TdHLKWSMNqA3beQmPggXXXhR35TdXmtnayc5EUEFcXVvQ5SL5ZBSpke+fFlRpybAa0WaKWeWZ7uGLeQdl/np0xodCHWiauT2qLU7edJOcq7FIYugEXreUbH+lPJzIXuKo1My3Weu58ZF4WputkkGNvuJRpyQjWdBW7aDF8iDw+vDG8CvDpy88ZLyOv374myQu++PrP+Oab36HLjOREWqylOOeCdxB9TwgRVw1G19lowDgcrceiFEjJeiGqF+S8p6jj8e33PL7/hnhSvvvuW354944fns7gPMfkuOuF6CypuBTl2EU+ve354jZiIY63eaBihfHcZm044ebguYzKuFhptzPmnZoYN7Lb4D3XlFaAlUiHlsm6qKVOH2+GXO15HjoLDXKGoVspnykpMT38wONf/F+4/fP/A/72F4gb8E6fK6Luw82dc7kqR5XjKgNrxWEXjrSdqEWsH+7qKxVAPbN5TO09duKmeQV9JqPmGtrXlVLIuW2Om2/zgfT/8TwKV8fONybhtg23xMuW+JPtavavAdRxfexusmExbC2qC18/buHM7ny0B/H82loUsvcIoO5oPMdTrEaO7TJWm6xbHLcSkdTfVigZqWxhjLD7YLt2kxAcitcZr09kLcThhtOLhJaFy+NT3VmtsS5loSwjKp5SjKIupULWBCL0w5F+6Oi6iFB4uH9gnmdOx08I3obbvPzkS354/54p/ZYv/vU/5Vf/4r/i9be/4/7xHpYJWWYD7/hABSDTD0ecs3l7cTiZ8RAz9jFGQ1QGTx8jSODN3/4179/9jim/5dB5/uIv/pLHxwud89wdB8ZlwddcgACyJL66Cbw4eEK0AcjeBePOFNA8mxGWGho5x80hUYCpFJY00/WeXMwzcRREIsH7yt1RrBzsW9gkz3AJTqCPynUpBKm5F2+JP5uoFVHXsdx/z/W3/3eGL/4F/ef/kiQ2i+XHO+9zldv2nq38/9wHeP5ZXeVo25g2b6V+cq/87Vtkl2dfne7NpdmpR6VrLasHv3Wutjb+5rj/sQyFyDphqXmC5n7Js/cVLbseD1s4i6vt5p55S+uxd9Z2Jnd3O/r8GcEOGdeMR24r2R5YdTxaRroBX9b0RftuaafdL5/uyuJrJmV3H/Y/61FYbZ15NCiBRGBC0pnQ3aBpYZnOpOVqRKpSS7+lUIqDUmN39djErWQJOe8Z+h7vPUtaWKaR8TpxOBzoepst4WLPFz/7BaV/4jx7/uqbv+Dzmy+4/fRLZDgRLg+48Wr9EwjiAr4/0HVdbTcvdHFgOBzx3llfRvQchsHmrZbM05u3vPnuN0zlkRwSjw9PfPfdGzMywdlOVqxsvSRb81AKkhekVlCCOAykVKpRtvBjHTonheitpK5qHlAsIHVNjJrOYPnmDWbwhrgUKWSsJLpt+cZ7cZls9rSTltx2qASKenIS+s6hT9+RuyM5HnAv/qz28DRJ2ODRa3J7J3sWSuyUtglDhQZYPn3Lt5jHYJ7K6jU3papy2uZwtF6plgtsYVlwmwfT9KSFJ20ur+T1gnaK83FGAv5RQ4q3LtHnXymrK2/Jw7K6jHtXq1303rTsf94qDM9/s8Zku0+3bpH2oD4s/5it2qjxaDtNPfPq/u2+ahsutHMJ93/X3EeLk0W0dqzaShg0ve4AJIKOSJnw8UDyj2jJpGW2ur1Yi70WSx424mLEQFd5STgPvQ/0XUfJmWkeuV6e0Cy8eNkRuogLAR96Pvn8S0Z/YHr3yDevf8NNf6K/ucEPB1wX8dcL87KsxsJ3PdGGbYI6uq7neLB29+CMv7L3VpGY5itP999z//QtHBKlTJzv73l6uhrdvheW2qNRipCw1vUgNjbRCG+UEAOaF9qTaAA2356HlIrgtAeZs2XzfZ1cnrKts6+4krYpOFfRkprJyno+qeuZEmikGqTabiCBUpxNEFNHmc+Up9+TYk93/ByJNzROibLKxiZbTS73IYQ82+GrHInJ7N5ItM3GOq+3jasNuRBp7QVup2FNdu2cXmyDWlszV/luirCHJ27qJ6s+/JE8ij1dHLpBQ1VlI53ZuwsVjroaCdw6IVoaTRjPP9JiqtWUNG9h94+WONK1fmILVyoIseHlywcG6dm9wDMij/Vo8XgbSqxrrpmWmnVO6ji2iqKrT2Afn3rvCMxEnqx2DyzzhXl8rPBpR0pPOFnM+/A7g4Z1j2b19IOn6zqCdzw+vGUcr6SUOR3vyGpj+cR5JPR0dwfkktFwBj/ym2//v/zq63/O3c0r+vgJp/mWcZqZl4Ulpcp9ac/JiXBzPHE69AxdJIQKcDrf8/TwPefrO2Y3svSJ5XLPPD7xdD5zjJ3N98yFccm1lOlskpgUG/K8aJ0wZizisybEmQrk4s1VVmij/LyXOk5QWZLtmmnlMPHkoqv/7Z0hEZ2YN2mU+GZEXK2YZHM66lPe2gVEAirGtn0eE10f8fMTcv/XdDdfEl7+CfgDxXlErAmtrIagKq3slLfKT1WWTah1U1BzNNrv0npFTROUssqRJXBz25pp2yjVq0ftfCY3zVhs3vGPN/AKtKqxR/pjTQpbk1zCTonqkjUvYluSdbGsQqG2e1rGiVJs11DZFlTwbB1w9sqaH6jWsqwEHM9dKUvcbD0a9cXdtMDNGDyLC9erle09+5xKPZ9bIdxKKUYE6wQoNlVKtIGtLAPf4Sz00BnFMBISeoabT02Z3r8hTRMuLPiYWGZqrd7Kdtc5E/ueLsAhCo8P73l8eoeqDcs9P70GfeTm5oaXr4xt6fbVJ/D9G5YlcXt6yVIKb6d7JgcvugNMDwxdYBh6xL9AcczXK6h1sr58eYfPCR2fGNPIPN3zt//p35KiEu+OvPrqC97+h2/4/ptvOJ8vlOA4DoH7a+YyZa5L5sUhcHeMvOg9R+9Imihprl5YqfM+MyUvZC0ktRGGYIOQLR5Jpjwq5AVKZ0OOrCvY/O6cE06sizMtCymZnBVtvl1LrNtPzpnhaHuZUtCSceLpo2fJmXEuiMuEOFPu/xMcXiCHgJfOmLad4FdZ3zIQrchlhqzpQP25Ju/Xyde0/ISFXy0kWZP3gG94pLVpa/dLbe3yDf7Nc3e7bY+yMWqVXM+x84iKVjDkTzw+mrhGy0bM0a5e95q3lm9qEXT9VXut+gCq4BtIxRbCSqqmpGsIsqIsG3hEny2q/fA8VGinaZZ8/7m1u3P3+RZSOWkPfTN+pRo7sx26JjsLijg13sadz0MFBIHiNBPIhDjggiDlFskjaXo0UtiKFbChOEouhVxgTg5UGDpH9AZ0WyoRLlhYIoi5zEnJSfFx4HA6cTqeGPoDl8uZ8XIFd8bHA8PpjilELm9fU1ImxgPBB+brGSeOrj9w/8OMC463r/+ON9//jqQL33/zN8Sh43h3w3h94P2798ypkHDMi5ItKUQfPYdOuOkj0UEIgeNpwDsoZeQwBLouUHJiSXl1ohF5tr2UUgwingslCxRPXsB3BhDTSkvYlMQ8VjPSRlnYQsJtAwAqI7sBkUpVzLYfOOeQMGCwdmFJSri+wz/+liCKHAPOH5sU03JvezyQrD/L+trqOVRYdnG7EZvrdrfbUJ9p1SanbbPdZF5XI9DCkPX79+ERZqdKnS3SNjPXdOKnRx4fH3q02Ebh2UU9J4aBFba7f012NWOBXMpev2r7ejMOtQ0d2UKb9caaF9HCgebi7WLCmkz9wCbXa99dd/3txqIsNaEo6/2uRqbtBprrszVjEf1WPDXj4vBS8GSCFILvrWMzH9HlaDBpZ9R/RtDqcGQWrQ1RqRCCo/O2+y7zRMo2nAZnrr24QC5Gk7csCcXhQ+Tu7gWfvPqEaZqZpgX8mdjfkELksXgeriNlnui7BaeZ6+N7UCWGSLzvCaeB77/9W7775rfghKfHR/qu43K9cD0/8v7hiSVn8g7zEoKji44hWG7AicPHyHA60nkHRKK3pOKSEkuGBqbyrra660bNoqrWcFY3vJwhUMFVWh1HaZ4DazhbH68Z8X0EUHVRajWnAg2gJuedE7rDTR2QLKhATgvl8h3Fd3gX8YcvrcOUjYEezGA1+StN0nS9k+d/iq79GMrGRPV862pJUX2WcJf1ZvYmpW5izVKs+bMKZq+bWm75EyowsMn1Hy9HsasetBekLlrrK1jvvFlO1oWhZnLXe177OBo4Sg2/vy6DVu9icymFLSzIartwq8bYetlgmmeZhXpdZY1zf4yjX1Glzdtpnf5iXo/lG01KvRgjVbud3lfprfeCgCfRuUwUCMEQf8SeHHskdIjvEdcjbsG5GZFUmaft0XfR4Vwhp5l5ntAKu/YSKxHvQMrKZZx5ejozzxdSKXzx5Zf42HF/vjL//geWx3tc6HmB8PoxcSkHnA9M4ri8/443r79lvjxSlhGHcnj5knEcGc+TcUAQGRflOl/4/s1brtMT4mwQ8c2hJ4mnV29doN7b/Iz+yHA4cnNzCyjBdZQ0MS8j16VQ6HBSUYSh8niSaKpfyLVU1Z5NodQOUATE02ZKAQZoI+0UwB6c7eK1x6gpt3Uobx2zTgzGfrx7STx+jmiC+YGiV9L1Cad/g89nohTk8BnFdZTKu7JTbdbqV9v4qmFq+bNtq9LNdmBiYefZm43NI9nrVAtt2r23KWQtabs1K27NannHT2vnaOOv9hfxDx8fD7haZ3bANtykGoV2wcDaGLVfnNXBbK8H2qptnsbOkMi2GPY7o2GD3elosZpBc4E1ZrQQoZ1s+9Zqk9adQKuBacnYUiyf4pwQvWPWOhlNFe9qeaoa8VwU9cLABcEwAd69wDlPLw4vXf3GKvnO0x0/px9mYrzgXKlANlenSFmMOnRCySPLUjP/rjceBoUlF0Rmog/cv/sOpHD78jN8f8fp9lM+++wLfv3rC3/xH/9/PDxdeXj6G96+fUe8+xmUxMP7d7z5/rfcv3/NNJ8r96Xxhrr7R0MwOqHzRnk3ziPzMlleAMvXxBm0jNYkRqGkxLgIr159wtdffsnt6UDXeWJ0nB+/5zJdmOaRGCLBN+JjXeemaPHW/1IWcu1ZCIDmggt1tGGtCw5DvxpmVUF8h4gHsZDGieFCpPbm1JGl5pE447lobdZOCsEL+fIDx09+SXd4iRdHmt7D9I7MDOMj7v1viOVKHF5CvCUxWE+Is5DHyHbyPqDAkvds8rzTzVUzim6s9jv51KZDbN56wz/Yhri6Lbv32XcadmjnSciqZVA9jT9EO/n3Hf8Ihiut8bxj7asQLPNe71J3r9sG3rb7DbXfTM1WOcitotO+qArvFps00hPdvtl6BeoCbL0iLXPcXDk739ZUukZ09ZtkxUu0RS87qx6dtpnDlmythqL9KThEFJ+vkB5x8oTv7ojMlqBsYZNEnD/gw4yPPd55vIBzeT1n8I0MNrPMSlbLeQSPua6V/zNEG/0Xux4nwvs333L76bcIEPobvvz8C/70V3/Ct9/+nvfvH3j35jvC44WUC9M0UspCCJ6UfK27CzkZuKuoQlbSojUvZTT9TitGxpmCLklRMp0zQNbxeOQXX3/N3c2BGKNVe8rINS3konjfVcYuRcTyLVvpuuZgtOZdSiGr8WRqRTO2kIQOQxW17ds7nFd8EZt4XpG5Nn5Aq+EH8a03pGYXilIcqPO4MpMefkeURLj7Cn/4BWW6RfIVKSNZM24+g4s4DDKvIZjRsVh37QsyuW+5ilU62RoZN4tRnYTV82jvae/Yy2Q7ijYuDt0R00hbvhW2XtO5NE0A88IpdRr6H424prXY0mIo2UKP5jHtbsqs4rN/bT/WxW2f2RyKNQagTW1upqV9yb46LFtou5npffFjR6S7GeHNrFcb0vwJW1pXERFVOKPT1Zq7uhO24mxcdwwBTfjyhCsPeLfgvK/lsxrKiEecEdQ67w0e7UOlsrfEqNimR87F6PdUrP25Gr6W0Xbe12nnyjJPXM8PXJ/eEnzkgHJz8xm/+tWvmOfENM3cP7xnnn5gnhdSTgbmoljZ0AlSoEiL7U2Ich3FsALsUJuMFRzeN0pBT4gDh+ORly/uuLu9oe88zpvBmcbRynAuEF3z2jLW7Vuf8TNDUSquodbQxKpArlIb5KKUVFqWzta2JKtuSH2faYvN3xHryrRmvB08eieR7bkyP1Gme0q6IRzu8OFTa0HPI6SzSV5e0HStG+WAEeq46mk1oTMJ3ecG9vk8WuiwvpNNf9h2+53kbh9VtbCinWQ9n+lNaTQL9fPuufhXI1ON5B+LuMaSeGXTTlmj+upcVwtaL3o1Eu2exBRmI5uxGnhLkDpAHRu+QWtdGUOFqjhwZbXYm+nY1H91sbSyZ+1ilPV9uksAte9RMwDBOaKzIFip80u91eGN+Ha7fScQnX1jaa3PuiDzGxwL0t1AOGDiEGjGAgEfhNB1xNjjXMC5RIyshCLTUsE3lpYnqdHKUSd6G7O0Y1lGm55+vGF8emsVFAc3L7/g17/+M57OV54uF969f8vD/TvGy5llmQ0R6RwiCSEjlSbA13tTZzvUxrEBJSeGbuAwmHFLKXEcBu5u73hxe8OnL28rg721z6dl4vHxPTlDHzu6GBinCaX2AzmpTU51hkcxQ5GKGg8Fxn+Zc8F5S3oWNZo+V8mNVAuk1jmaKWTrgnWsvCmlgBePM5baLbdRNTvnYkzcQJkvLE/fEw63xNPXgENzQvMEy4OF3mk2L0bj2pnrsAY11SZ/7GSNzVDIhneo2wfPTMYuybh6F203Yzsn7FDDjfmdRt2/M4li/ymtma4aiP08kJ9yfDSOwgaiKDhoPI9aCrJrfik7a7V6G8Jud2+1YLFKh67qvXZ1rrMKWijgpNK6NXdpc6xWt3Q1CDxzZ7bl2My2Sm0yqi83d61gNfouGujHyknGrCUUijOjsQ2P1dp/IHUYjLedMV+R3CGhR9WR07UyD2WC74ghVnShZd37o4e5MC3KPJsnkTH8gGZrsoo+VIH2+NAzTRcTUhd4vH/H4/0bm1+q1mz22Wef8fkXX/D+/p739++tx0I8l/Mjl8tjdfONGr/Uen+ppcdSOzzXZ4gp5dBHThXmneaRX//qV7x8cbKhxBVIFbue8fLIuzff8ebt7zkc7+j7Ad8diAiaJ7wUU+xkYWtG1uFEWbF1SOZxFAxNWrdMSsm1actCkSknCwUq9Z8IBK+VRc2kwnkbizClhCtK3/XVo3MmRZpwXutQ44Be3qJxQLpbXH/AuyNLflWJgxLChMzZaAwFUuWF2Ot02zwV62x9VrqsRiCzZsfYfvv8WHMTf+D1XXzCxmO7nUkItOYyFEoRnJa1e/unHh8/UrAuvnNqiluPjVmb1bVZ76TqewNUASshiK53a30CWzVCnwtqKTbwq3FAVuIVu9/mKZTVeJTa9l3yLi6pNstmZsqaZymqz7v0oFKdYROxRayejnEwDrHFRqZYgYTHAEASbiy8YHsPLKAJMH5M53ydn+HwQYhdx2W6MM/KvMCSTWmMpt7iau+9YVicrtkp5wxB2XUDh9MrxEVympgv7znff4vvBj7/4iuu48z3r7+n5IU53VheQBw5LYib0FlZyoIXV3MDufJMVowBioijCwHVTCkLx8OBr774GV999ZkN9CVXLiJlfHrH/ft3vHv7FrIn+ljRrEudj+rw3ohpSl5qt6PBrCnKssCchCUbIa9Zh6pK6sip1Lmg5qTmUmjDsUUU78qmO8UMu8mg0CRE1WDfNq1eqsFPBGdsWt4rpLP1vXgBDEchzlHomYuRB0mRFY/xLLpQ1k1mK0WuMfX2B2rP1GYsVi/CtQxDO+ceT7FVVLbEf1XEHSzAPMZgOT6E4GwDcmAl7p94fHR5tMXJq4vVqsfNE6grsblRu4XDMtGwlX20xsTNAIjsl2GLXRqeQtZqytp1smZxmzlvC77V0tuVSXXN3UogU83b9rnKfWAoSQONeVxNXNrs0QZPVpqFFrxmKpQHcRHa94ugaamGs/FkWEkvdD2xPxC7K04u2zJUIVsBQRV/UopxkCLW/xCCI4SO2B/oDjeIj5ScmKcnrk8/cLj7ipvTDZ99/gWff/4FT4+PDIfjCnFPi9/miqrtjsuy4NU8xuA8beCTc+Cd43A48vLlC169uOXVqzuOxyNCgpLQAmm5cn58x+V8T1pmnIursSmaLbwQAV8nmntvRL2lWF4mGdw7a82GaQ1FGx8qamCsClJrGperB2vIT9uotl14LydS54JKnc9hcqkoWpKFjsx4f7I2eE1IHlEcQQKFUNs4/YbB1K0jY5c2WH/HJprrdWzXpaunrTWcqD+Zx/HsRM1zfvbp9b2bV77eck0D1LyFbh6sKhs3xk84Ps5QiKyxEEXqrt08BrX4e32zrgkbrYuhNWO+LpNst8rOO2m8FlBWbd+mOrc3tc5RXQVgX/BpcaM6o5zT3bkbgUnzZDZDYeGGay5QNRautkQ7MXCR5gUvVvcvzqMEXFYcC05ny0ZWg2ZTtBdzcSptvhmKQBxu6I8j3dOZ4IRQkZ71bZaYa4zipRK9WL23hkddnZ3Z4UIPEkjZmK8Qx3R95ObVLZ988im//OWf8PtvvwNfmbbLQp6DfV/NTRhMWlG1HMgQO8ZpMr0Wm7b16uUnfP3zn5mR6APBiYWLBTQnpuvE0+N7xuuZtd9C7NmXDFpT81onHYuznI2KdZ3OS2Ep1lPRRu45c1NtpxRlKYVOXa0A1fzYJio4Cktphr/JGGhxUPzqirvWdySVv1UzmkfIZ4L/lBg8SkHzhGohdqfKeh2s8U1dxSTsmMB3FkGkyVCTv7ap7rVqS6zChqK07FxTj7b9PvuPbW27D2u1OfvAw+47s6akq2fRpo/91OOjDIVUmvf1WssOttHiWNlw6y3DCmCTxHWdJSD2Yo1O9p5J8z5sCV3NEzQDY+XGUhOrLRX0HHvf1k8wD4B9FQN7cHvAVWiVidoX0HWBlEtt/xYOB7eS73rUGKs1m2EgszAgPlSwWKLkEYisDyrPlOKrNHsQzzC8IKdMXhbO3RtCdPTddv/L3nYKRmdPWNfGjKhjSYV0vnKZvmPOyul45HgYcG5kfPiW2A30wwv+2T//F7h44He/+1u+++4bqwbMM+M1kJaBtCxcr2dLUuYEKIdhwPuuGkbl5ubIp59/zu3dHcNwIDhQcp1qdqYsF+blTOgCIUVyyYQIqjMl+Yp4TMRofrJmXT3E4KEEZXZKyS00tKeYclrzQDhYEiTf+m0KTjzFGhpqvqlCtqsh8FBBXYVGAq1FQTu8c3Q+MAwDXYgVubjQeyF2niyOrIKnIOUKTvHi6GOHUxuMnLKSKqP5sy1oF16wvtpyDrs4RdgIaao+rOIpm0ysBkaaTsl6vr2Xv5/Xux7r+rSmOGEjpP6Hj49LZtYMvF2DVoVsdwGqblPasgNRtXhqZ+vavzbBf/41re7cGnlQjMC0eh8WttQEZsVpbA252k5SF8XVSkq162tXqyXtnDeqtFBzAtFDdK6yOBdyznTBjJVmxTPhJON1IcqVyJlS6gzPcDTiA7FxgSVPLPMZ5FiRnYYnCN2Brj/QH44MN7ccny7kfKaQKAK+CEvW2pdgO0EI5ipryWTxXOZEHhOqVw6ngoT3hh0IkZgSj++/Iy0z8fASd3jJ1198yvHQ8fnnn/Dmqy8Zxyvz1eaLFFUe37/hfD4zjRfm+UqaRkJ3REvCO3j54o7PPvuM0+lAjAY7n89PLOMDOY1oWZBc6KJH+65WNAqugtVsd10gm79XFLSk2mgYEC84v9BVRTdIe92MioHdfD3PvCgEoQs19+QsNHTOoWTjxvSGvFRVltna3JuEhhAs8amWs4oxMBxONawodIcjLga8ROOl8B2pRNAAGPZkSY0XokpfzR21UFhh5WzZq6TJ9g7yvZoXduE4eGT1UraQpIl3ve8tvq6VwU0HtL6u61Q7rBLU8Ex/tKpHPbaOTF0TNc1daurYcgatLt+O5iltRkJXj6TlFdZCyboI7YtN8LZF3ZuYuuC6hUiglWm6rDlQMKFTzOVUNTCVjaIT+tCSV7YLJTEaeREhoBTJOC34ivq3wudIqchABIp4QwuCua1pwUUrLxa15JLvLGSIceAw3NLF98Q4kbK1ZmuqLdMKrQu7PfScDOdQUguNPGmZWaYZLbl6TsL1+oRSiOmKH5/w/SuCJk6dwN2RcQgsx25luT4deqZ5trmk88R4OeN8xInR550OPcehw3urXFwuD5RlBDUYvdEDmtfnHMQukJfFdjPntgphlQ8r1RWKceEh4gihJ5fZ+md02wj3MhVc9RpK6wexdbDwRWsKxBKmzhnPavHWIRtiIIS+jnHENLDiLWLfg1glrqQz3XBayXvtSzsrh6qwZKke1dY42KS5eQxr6N3KFjujYSC/VSLX5/uM3X79+4OwuirJj1i0d3mJsrNOZZevkdXSfODs/APHP8pQrKnGXWxUlNoy/oGh+sBqmVHYfrf+u1pJkbbgm0Ct9eSm0WvEtYEa1ghGwSjO2rUC4lC3VVFaGNFQ743wBqCv8yNAwUN2tsl4Cl7MbbWwI1dD4XA61zRbc20F5wJooSRTXoCSF1JKIJ1NxXYdMQz0/dGqIDESUiaqJfZc23W0XmMNhQpKTpmi2focAqSlDripxlkRlmXGuwL5ysw9ob/aKIGk9JIRn4h9LcGKow+3tBF3uSjj9VKNgOlJkMIynsnLlXl84vL4FocwdB7vbIfO4moYYNBsiiCa7bpqEplaZcg1gdliaDAQmc8LQa3ys7RQsckahl7NVfhLrbTA1tshqoRaXfLemyHAG5I1dvjY4b3D+YL4bWNxPljviRbSdE+4+wzv2xQ5pTibASrFytexhtmGIM3r1C4zAlL7h6pQrgaitRxUea961IxN0VKTv9uOXx1jO++zkGTnpzePo23azThoZZBfDdkWHn2Mqfh44pp6kYJbm6zW3MD6veZaWlu2CY1uPlO9gboIsnkGWbfKg+DIdTq61JAgq+LWykdrd2+V6p3FVY+I31GZNcyDHd7bFLKiRiMPVIpze0B93LwZwQhhAwuuJKSMUGa8LNWVreXiMq5Z+ISVflOaWKYL1Bh6mZ+YpyupOHx/AxiASl0gHA7ESkqTSyaGzJJtbJ8BkMAFwWWQoixpsR0PgdppORxsB8xpQXPHcRhwLkGZKXlmnq/MKbHMM9M4sWQrxYrvcN0A4tGU8KEnhp7hFM0Al4WSFtIy8fvf/SWXx7dQFk7HAyknYnhF6AJeHFF7ymOpHBRq0HNyewpGiFONhObCsmSUUgl8xVwI30NZzEMjoTTgV+UFEcWFrbStaohR0xapTYIB5wM+BJx4usGMmfc2ENl5y4uE0EBmSlpGhnBgGHp0uRLKIyGeUNcxixA0IRU050PEo6SkFTnqav+PW73U5h2r2nzUFje0xLhtgEYhiEIy+q7V+2h61d5r0g6tUND0Kn9ACNskFxrK3W9himwm448XenyQJFn1/Ufft9rO9kG2SUpuW4z20YppaP+ulIaWTa+ZY6PEN9y/umZpq6PWskZCbfstJmQu2YmsYMYWDyp1I64MSZulNte0GC5CE5SM6AyaEJ0JXBAKUiYrnbXW+UZBL0I3vKLgYZkp2XpY0jKRlkxOBeWRNN0Qu46uP9lcz3hkOBWKepblDZquaPFI8dVzKWhZTJnFoGLLvBikOnr6vqOkkXdvzty/89zcvuLlqxtCqJ2d0ePKCDlBnpB8gRk0WU8FF093uCVfnyihx4WenGe8Czw+PPLw/p63737gh/sHpvHKceh58fIOElwvj6TFczj2lsiMgZIdpSy1wrOVIRshXK4bTVaoY0TW7lDreQloNK/iOs6rAlTRWJPmqoUlZ2sVD3affWejBrxzFTEJMVZErChBinFsuurleCMSbmVYqUpV8gSc8OGAy4VURrKcKC7Sk23co4OgEL3xiuTq5eQKYEsZllKsR4bG4GUzUpY6azVl86zA7rt50qVunI135kM1a/f/4S+2XIVUr9nCumdVRtaA5ycd/4jZo9WNevYdsin/6nVsg1JN0bcKyBoWtHOImtsmsrrN6K7FthmmaiRWGPl6DTWUqFbLXi5IqZDpuqPZWwWcmgA5Mer6YANtvRO0FIKzTlQpBcqIlDNogrIgZcSVpRqOgttGIwN1mI3rDZJs2SO71JRqnT4juqBlRtwB3x3ohiOx67lxHf3xFadXP+Ph3Xtef/+acrmSU6YPHV0/WGkT4Xq92vm8r9OyEtend4ZEDBHnO2NrCpGui5yOHVFqGKQ2WFCc4sUwHiULmvvax6D44BBmG648nbme73l4/5aSilVrciGnxNB1lvycZkQzNzc3xNiRlpGUMiF2VUhtJGOpxDP2KIKJjrdnl6vbnlJd0wrMMk+iFsy18mf7Bv5zBATvAyFYp2jwvlZNTEyc36pnrVfHS6kKa4C21nawclaIIUApCaczgQx+IEhaUZWCEMQhwa4vYyMQc8Fkrypy688o2cBcJrpbsl9oOBUzXm1YczMUzw7ZAoctS7oq0hZY1DetU/Jo3s0zrf3Jx0dOM2+x1i6UqBfwzEq137VEZ2lJT2CDqexuUthp/XbOXflmjVNFa9mw9iooqzHarOV2zg/P3DpEpGbIvVOG6FZDYT0PrjYZFZAFp1dzhTVBmXBlrNdTEDLPCHlbHVxzFTwPzqHlUt+bcZIoealZ6EiIB0Lo8F3H4HskHBhuryT+Et6+pjw+WIzt63QvEeZ5xqjvbZc0vocZJBL6IynNXM4F7wNp6Iw+3yfaJEznPb4kiiu4OhncmrKStV47XV8TXSh5Ji0TTnwNRwrLvHBzOjJPyjLPlGXmeDiZW+4DGxrRNFbE0VjD2jQ1dV1lwTIjq6WQa07HPL1aKq09QFpHJbhKQOIcSLKQJngb0GQiaAAtJ21DcKuhcB4bjCQ1XJBdWN2kRFydtTsjeayDkivYikxWQaikQ868pLGNcZDWH9NQmUJxApVsWNVCSa3zS43hzJtHUkpN0lYDs1dnoRIy/VhfmtrtQ2xD1Faj3PSk7fIrVumnHR85KUxrf8fOSrF3JnSLR+rfmnNlfaYKSzMSuxpJc/21vafFU5v3slrJXKybEtiwF3ae5pbWL1jjsGf2SLzFr1V4umAkrofo6WPDQSS8JBzGko0riM5ImUEXKBNSDGuwVldsa6xgK8N5OBF86MhZKTqD5IrFMsVy84zzHbgOqmKJZLpO+MU/+Z9w+vRP+f6bv+Z3f/lvuX/zlnmeiGJDel68fMWyjJSSyMvIeRqZS+Z4esHgbWrHPCW8T9ZqnQdGNdYs74Suc6gm0mKekHlvuXaHBmIMLLpAKYTOMRw6DoeB6zjj1Lo353nBOZtcNpfE0/mR4XgwYuHYEUKsk9gtBFBx1siWlzWzhA8QHEEESiHPMyEX5iXV9nC3omHBStSGa7BkqZPAmAwVa3Brx5LMePtqTIILhIoQtbGC9lylVdCqZqVkOZMQMyF48jKT5ivBO6MzlEeUgEqkuIE+gDhLYc/FZqXUCNjCLTHvBpQ5JQ6dgs7MizIu4LqhejWWC1rSwjQt+4D9g5+qi1S9CpP7RitoN9JaD5rxWrVh53EDa1L5px4f71H8odfXfMPmOVgyL5nr1dpZHSCB1rmp2lxBqbtvczE/vIHacqw1QdSCWrM+6/n2x7N/7gxJy1FIXeTOd/TRmwKjBIHIjNMZ0cU8iWK5Clqjmuvs55LYV8OpU8YJlgCVmnVP+YoPHah1XM7zRJ7fkLIifmCaroyXC7iezvU4cbz+7f+Nw93X/PLnX/DF5/87/v2/+3/y8OZ7E6bLIz5G+m6gFE9KM9eUOPY9QwApI+P5Dcu8MByPQCLlzOE48PmrG2IM1phVUZ1KQnKBMhOi0f+rWNbe4ei7ntPpwOkwgBau02yUdinhdCF4oYuBJXqeHt5yOt1YOTl0TMtEG/ZbULyLlSGqIWuNcNkcDIfrAqEYmc9SEmkeUescswjfVYuvDdinHAdHykZQnERsuDGFtMx4Sm0YKWuo2VRISytHCmlRgi+kZWEar4STY55KNYSWfyhE2wtE8C7hnKO4OoWu5liydxaaCMwzTLXnqe8ikcLDw8hSQCQQqrfTqoZLxoB7aznT3J9tgpg8Cz20VRL2VApgz24NQrZek73XVLv0f/LxkQxXluFdjdEa8+jqbWjNwKqwtp1bgqjRkbG66iuwyrGecx9/6Qq93lyK7d5+bFDMidm9Q6nJRtmZierWAZqEydsO7sRt8O1SQDNakuUjtrrO7sTmhq69C9Sbw4FzeOltGK/aLo1ECoWcZ9J8ZpoXSnFIODLPMylbUxKqhHgkdrcs4xPiF8Qd+Nkvfw2qPD68Y7xcyCWTloTz1m5+PPQ4Hyg5M+aLEe24QMg9UcGHwGnobJet1HLiIs4ZOMnieb+WjnPKNW+Qcd7RddYmPgcb6Veq0QNTzBACXYykZbZmM7FdtcwCFfehXiF4GkGuUgU2FUsGeisrO68WGlAQ5vXZrknGUqd11+ftPHTek3Kdh1IHEjlnQ4fabI4mh8bjUarMAsVmkOSc8cWvGA20kFJmXjK+g+g3rI+TYt5lrqCy9XW1XIViSWcnljtxQk6B0A94Ne9TxLHU67USvcnPXke0ekW6bqA1D6d1TTWvaGcTQVeN6N6b2Kgcarz7n9mQ//PHR4cez+i5tOUknlusFn3srn5NFNXbZjUJNYEpbv+7doP1Z2npCq3dpvVbqrC1EGX1eerHKr8ubchMTXFQUJxao8yUEv0iRAfaGJDad9cFlvXzLc4uVtYs1RVcyzQtEGuWT2kDf+10rUKSyGliWa5IhrQsGJeALZwZz568jJR8RWWh73uON7fM02h/lkwqBa+WEIzR5nDknEm5GoIIy5KIybAdMdTZFK2F3XnEJaOg946CIjnb86wGMJeCOJt1GmOki4EYPEuy72rsUd4JIQSW+UpKcx3xZ+uRakm3qOUGKuk9VDTjknPNCVnyMgQhuYzIUhWzhpFts9CmsK1aZXR2sO5LNqzYOeslQaqymGwY/4c3oJc2O6+Vm7PUblZrXc05s6RElxXvLQEsrYmOnZ+6A0CV1duRlTpRRdBiXcPNKORSCYq0YSt2XC3NxYZqJJqumVerpVRUa15zQQ2IpWzeNs/yidu1Pt+V/+Hjo0OPBoBam3DaNdFy/5uit+yxitaWWVkfSjsaWEaa+7QzBCs5qb1zl2sw1zO3TPU+hmsCVbvlWlSmgFYyFKnsR15BZ3vg1ozjif3Omq+eiAB+TXrZNZtgq1NwVrOnuuu51sNLsRxNyUpOM6oJ7wohemLuMDyBISHFdZWOHpbpiWW+Iu5ASgvX8YHrZcJ7R384Mc0LuTytcN1pziypsDXGGXw5p5nr5QoSOZ4KuTiWEi2zjnkFllZRxAnTNNbOSrs/7wIpzfV8BnFOOdHHCdVkSrSMVm6UmrUH8jJbA1YdR7BkW8cAhFKfnzgUa1Ar1yeWZPJj1YuIDwXxS+UNacbCnnGuxj9U8h5DdmZLCoY2TYxa/rTAVYsNS1JnOaoYD2RXaK3pzguNcCcvM4t3SD9QsvXjzPNs8q0GI5cCEqzV3Ako1bCqUNSj6ip7WB2646wCpzjmpRglYcosa6K/PTu3aUdLBtd8nkgrk5u3qzkbqEus+dCYzc0jQqRC6HdGQ4DWOb1uaj/t+MeR62oDKNVyjJOV+GQ9mjfgNlbtPww5bWam0IqM+3et+IpdqUhrr4Zb55mum039z95wtP7Q9qcZDVYUooiSS2ZaMvMcuOuhq4Jd/Q/2lHZFXSVIaVbfWetz3Y1K3TGzerL0KFd87ECUTKIBv1PKLGlhniaWlOgPL8EdER/J05Vxeo/4jr6/ZRod8+UBNHM49PgglgRzViu/XJ4YrzMiRrPnfMR3Rw43rzieXhAPryjxBZMKmgpQ2+K9lVbRRNcPlGLeDeKNX4KtdNj3kWnqiH0ko/ggjNPIcYhIiAStoV2eKKWWN8Xo/nJx5CzMqSBSKBRUlNg5QuzNG8JbGzeKVPg1mYqyrljcYhtWKoovlcoPR0pV4cVCBEqy0NWpIWznGQkWYi6j0QAaR6jDh47iAjk1f1BZ5sW6cp3ilswynuu92f3EruAItTLmwEdSDqRSPTOSMZJ5SAmbllaUcSnMS2ZKee1jyblU6Llf97pnIXRVbqO1M+nJ1IBYHN5bCbqSylY2tuZka92AmuRTDdNC0R1s4B84Pnr26IetqU2xpd5EDUy2m9yXDtcX2f7dwpedgivyzKjIeh6enePDktaPLKRgFHU7I7GH1TZXf5HNqnvNBAxI0wlEpHLo15BAQg212nVrLaXZfM2ck8WN4lcwzZIyhUDRRNaAjzf0TnBJkVTwoUemEaWwLCPTeMHFgeXxCXEZHwLRTxwPkRAcyxLwkzVVSU12RR8Z+pF5ntehPNEFPIBmUpq4jlfAmaDHjsJCkIz3PT4crAoyXetW7Cs7txpoLG+NVkjraVCWJbF4V3k0LdwouQBlzawHF7FWbPMuvNQkorNNJ/qOeZmNxbpCn1dXWtf6yBZqVte7ZCPxiwEzTDvRc1IbASsxMH5zVlXFqAeqi1pyoUjtCcmJkqxS42fzVMDBbM13OURCjCjgccY94iJaPMmZQfX1OhKOOSlTMsauVGDOsNREoiLVSLT8xH4rs2szGLqVgR1GnGRYZzMMIsHC4F0V43lTaNOtDSquOa3NhT/1+Lg2c6qSraVNVi8AWr876+vrT/snuDvXs3uB7ZyyLVU7TzvHaiWfLQY7yn228GR3bev7dE/Ya35MThu6bUTpPEgQc9MASsNMVFIbMfdcKxmsoegahVyFtYvU5K+xN6mCSsD5HsQZDXy20lZuXaI5scxXJu/xcTAui5LIy4UYDIjk6u5vZdwM2chdLQdhjWgpJ3I20pWyXEmTkGJgvAZ8tGFETo35yVfBjzFS0tWMYs2x5NpurmrKZi6u7Wy+5iXysrB4AQI+WOhQ0sIa8GnCSVqbxop6KAtZs3lDHZbsVa1jC6xsuSXrnj9oLbJiZ3LlnAjhg+1L2PIXNd/SwoQmp7oKVe0RKdl2dDXTkJMZeMPMe3CWaxLxiCuWy8nGFSV4Y/NuuTqxa5uLcp2VOSlLbrNR2cnkVt1oCczmU0st8TUOVNO7stukpMrhVgZtG187r13PBiGw9WwJUOCPxcJdfaJnJmsDUgmNNKJ5GBv4ajvWfX9N2tirqwFou9V6/nrq1eJWEWzGwhmQ24THEidSa8hlt+tvl1+TQRZQsHW+2nVMqlwX46XEGyFrUAHNOLUuSO/F3LwidfETNhfMmbUvlglvD86w/sXCgdChaaKMV9IyMY0z56eRIjZdTHNCinL32c+IQ890XZiuT9y+esn16ZE0XZlHAz/NUyFlNUp7dQy3t8RoCMVlnknpiZyNmt/5SCmO4TbigpLygo8QhxN91xGcJ1WeBnP4ClMNy8CEiyqcHogVMr0sM62npxdP1/XkeTLDggIGfbcOj0ghGu1dSSCZvhzxwVxj88oWM+Bpqd22ZUvwqXkO5ilinCGqdLUZThEzJLUb1NUeIeO4NK+mqCOrw++9Vi2UPOPDobr/9ryWxUITFQfeE6JxU4iCq52rst/JgVSUpRg/xXVJPC1mHCrXor1PDM9RasLYwWqATWZkfY/zbq1sqJaaSzMtssYxC9vbDtmg381orT5Yk33NIHlnlH7a8XGGwrl1UdYwQ/ZhxXNMg9u9t1Skov37g/Pua7xt8V07o27/la3z0766rWxzhWvip2Va1ypLY/ip06bWEKl+pRbIbTE9j5pIxZFCh4SBI5OZAMmUMlsTUC1vqXTVUADikHAg5ZHz4w+I6wjxRDzcUqbzyiY1LUrOC8syW2m0FG5Pt6ALWhLLMnN5uieEnsVdOT+8wXeB4D2dd8yaSNWlLrUDsxR492ZEvCVWu2jJ1dAFkMCyFDIT4ZjwOaDOcffiK24GD5pq0jLQdQ6wCWWal0pYa4lKQqCkd6CJ4ODYDVzOT0zjlVwc4ga8F/ohkhclV89C8wgsOIK1cCMstfnN+wun0w2abZhxHq1FPM1X0mxrLbuKRlk3k/rvpMxLqc15toH44PF9MQU394KUZ6JEitT5JTYoBQMsFcT1luOInhh7UKPck2UxXk7xzDKSSyaXDpWAuurVFOvDKTKSiWT1pGLJ4s7VEqcY4Y54c9rWHAM2IU7UStjBb5WPkjOkhVQKKedKjmPPwsqbjtJ6OZqe0byHuv1V3k2aquDq5x1z+SMlM700XHzDSNjrKy7CVYRiMxYq7K3o9oH1P/XQ6oLsSqy5eVXPq73PyrPVdWmhylom2oUwruI3VhdvNRjbNbiaB2qw+FSEMQmox3HEuZmo2XwGKQZpBmO0Eiszkit1fBF8f0tc6r1I5YZ0wcIINZYrCSeyLuRsyaZhODLPZ5aykMrEskxoyYzjzMN5ZtYnXtydiH3HkI/GVTFbGzilkLPUDtVUOxcFHwP90RGTwy1w98mXiAj9cODli1ecjgNlGSl5IueFYehJaSYtiSVPaJ7Msa5zRlULeZmt49Z5vIPgDeykqc7bEEV8JDhP6CIlTUjl1kg5k9PZBgFhvR3LPJK7aB5EWkgsiHPk+UpOE1lrnojmB1YJa5GNQkpKkC0HNV0WRCP+YCFk8JgRVmsqTHlm9s4IdaQgzpjUW4UDhRAHaoqEnG1EAM7h1Rn3pyxoDbecVo+38ls4Ih4bNdl76/+Ys1UhSsqkVNYStnkTtql2dVRlrmjmxma/ynZFXW5BekOWbviOFnY1IBeoPb9V+4yhy4kgLv1Ezf/oHIVsarj+x1zrBqha0wNrbKmb09EigBZ/bCd6dqzGQGtY0c7J5uatKC22BVidmbqYW3l0y5/Is/e2i3ge4ypW456Bq/NEBuxfCUh4F7ZvFezzDshlJZD1oSMXc+VzttZu6++wuH9eMrkILvQch97Kc9TmuaIGXFoS85JYspDOE4fjwVq5Y6AfDizZchULhUyhYhdrkkvw0Uqu4jyx6zmdjpxOR47HgaGP5PlKSRNoqcKk1Q03pV3p6HYw5Jxm6zlxghTjxKBhTlIiS510VkulDgOdKYorSnBKUrcmxjXPlDSSkwG1cvX68jKTciEVsVEubv98NvlqXkWudK2lmOFYlkIZfA0/rNFKi4UyCEzTSBBDanoPhWTl2ApWKmRE/UarX3MSOI/kjMtWHkYSqkawTBBUCkhZCZFTHeS0LLBkZUmlAqwq/oS2odWwuuZpSp1sv28OfcZgpU3Oq7/d8BO016Cx7G6FZVZrUvgjIjPXSKL+u6G9zDho3d0bKky3m6sfsg1fVwXdZTp2hmU7dymyAbF2iZ4WVrSJ1s2Jc7UU2wq19r0mrlu01q5h++62E6EmOIg1KS21ASfKgJMRYUZYaivwGquwNjxJQnUhpwtULgwbhJMYr1dKWaA2kV3HkYIn9gO3t3fk5QmlgmcKpHlmmmbm2fIf43XidsoMnVUY+uOxoiYTbs7okpClEGq/iasJUec74nDk5u4lL17ecXN7wzD0OMlcL/e2k4VADLFS5y/VUKSKbPR4EUvQXc+UnOi6gHfOGsUanyUOiu2EXuJaihTXoWoYEnWGA8jJRgMYP2Qhz9ZXkZe5Ttcq1neRCnMRXNE6V7YKecHOVfNNuYK+VLdJgyUlUF+rQtUolQxSwAXmaUS9Q72DynVqXcGWsDTYuFBV2ZKslY2riLMepmzIVGr/johHXa4DpS2HMS3KlGxjWIrBtJsyOdea7rdqx1Lh63YfO++czXNvu/Hef26Vwj2lf/OiVTfdhcbL+ayn+h88PspQ5GYU6jNremKZ3rLek9Ayspvh0/o/+/8+bGn/ft5sth6tJFa2Trj1aHZi+6Eebb/yq8HYwo/1xNbc1DLstPgOsJwl6gRXHJcF1J/ITjhS0OWK98F2AZ1N2Cs4y4XIMp+5XB/No1Cs7dw5YhhAlMvTGfEHDr2Bi1wIpFy9ABESUjEHwjQrl0siKZwvC2jg0FkY2B/ucCET+2w+z5zNGXUOF3rjuDjeMBxOHG5uOA4Rl0fm85VZla4fOA4Hu1dNzPNIWWZQiCGiabbn6CNoYrw8MgyRED2lFK7jzOW6cBwCMQQOvefh/p6lzLjjkf7mjhA8ZbkyjyPjOHK+XvGS6Lxl9OcFpqWQ0lKBRMqcClO2EHCTnZrhtxQKbR5QgyepOuZkyd3oYRgghKYKvgKvHCoeVQ94liVbHiCqJXvxlOLIGaIkk2nfgYvWAZxtfTPK0gieQ4eGSOgGNDuEQKGVQotxVjRNleoI13DOVc+3UEFZBQNgIdXr/iA8Xzko/5CeVPq7atR01a/9lsjaL1VUaxn7px0fB7jSLbT4wAFgb7Z2XlEt5ezCgzW2NHQkpZU6dTUs6+zEFsK0lEe9cakEJ/vgwa5v56XILqhpVkLNWm+8FWUdFsN6zZWjoFrCnDMLxpgkckKc58BbaxYTgyObsamSKwYg6gvkYu5pWmY6DyHY3FFOBRcCwUeKKpenB8brBe8CLg4EdSgRzhfKMoEuDJ2RwqhzTNl6IQphBRRF74lDMICa8ygeH3u66AlOIU1MUyZ2PTH2dN2BEHtyySxpJqeRnBLBe4KLFFFSWoj9wJIMcu7JDIeOUgrznHk8z4xLYhgiEgPhMODOT5SSmKcriDAMXS3DBmLXIZcLbbBuEAdemOZslPyiSFGuS7FSsnN0lcY/57q8CnPWdVpb1TzEO7yCk8xhEA6Dq1PAamt5cLg4AJ6UapkyG7v6vCih8xS1fEJZlLQseC+EVPCxEDtvIUop5BrneOewociBXASvNvcjqyOpshRX53IYT4bzgY6tIpGbYahKkqsO7bNyH25w77IgEwABAABJREFUUgF+a5KGXSGgKs1ePxuSeDMcroYdTWZ/2vGREO52AbpmmNeLY1du+lBJ2w87twi0wtYbA08rVcrmcbQE0TNPxngG9tkJsyerz1Kv01xYKjVZS8appmqTTDA3egzzXHY8H2tEZPkKQfB41xPlhHAGVVyd7wHs6v7bDE+wkXjig5GtACUvlXPLrslq+B0+eiRUKPCSUV1QcmXW9nT9wc6RZq7jSCoJqSXXNszF12YoXLQserShwsYyFUxQSmFeLIPvKiwYKvmss1GByzyhUqd5pStpupjHEyLLODHOmfPVMvG4du5QocTGV+nEkodFavMVzpi4JK/di6JajUQjOlYjro1GdGOVHSPrac5jygUNFhZKS+mLdaw7EQ6D1HWQVRYzugKMjAfCvL+WzrLZsdUfrgmR/z9tf9LkyLZsZ4Lf7swMgLtHxGlux9ewSWZRJCulOKhp/feSGtYoJQeZQlaRj+TtTheNuwOwZjdaA9VtQJx3+d4JSl3cGyciPOBwmGFv3apLl66lLVhRVzuX6X4ngOEIOv+zm18ZWUx/3dZmfyhhLdJqI5fCXGykXPozdQ+1vpbbPVHADk9LC/ZAAHC/D20DOKQ3BPtZa/ei2br+awYKuW3Inauxl1H9D7czftenuF3n/rix7u5ev3+PfcPnmcn9nbkPFp+/QX2Nym2AOOoNkc5tqHRXvg7x9BuqAadHObu7Jk5azNLO+8SQjjjTAsUcwjRhaSZ9l3Ho9KNgsnphUC2FZmIoonVwrfreYpqIKeBMA2PJr2CvE4O27MbpiENYSuH19UqVwHR8IMRx/+BFbtOH3vUej4JnXnQOpaBqWzGqZkSMgRQTwxChbtSysa2z4QtiMn4LPiSqFLbcmJfKsjVCMhakCzTTKlULxGg6lv0UNo3SYSBKoZSmVHKpphSmjMPclFyebPKy2ubbr6tjEri9K+a9jp+rP2tgHG/KVTgLg6LcDB3oUm8V1YIIOPydzL2tTesaiHhaheKy4k5OpQl7oGhih6QFB7U31M+h59b930LwpBiprhopzng/7hYM+h7QZlYPAB3J3XPnz9f8vn5vkeGGVUhfxhYg3L6ffk5m+6ceXyyu+4/9OvpbF5z4vbqwa9A3urc43d0OFW7OYj162texDMNqt1uwUB8GHTazCU7/ecC43dL+LrJFUn3fYW+r9u+4pT9KMxakyq54FaMqcAuQmyOvnsGPECxYSUPKi6kv6cBOLqtqU3QFLISQBjtlNqYhkWtgywXJK5I3BhN8USQ8MUs2/QTl7j+9/YbD6cS2Lqy58f0PLxyPB/1MigrYHB4ead5ROq6zZb0eUeYoTnUVfNAuyNuvf81pOjAMI9F7YOX19T3rPKsD+enIp/d/Zl1mmgTSOHL9+J6Xl4XzecX7wJuHyRzHC7k0XIjUsuJqJtSbFmqphXXLLJtQWkCMpqjDWrBWpTdX0SlXbw5iOGM67Ol50zTeHMhCcMQhga+MwTEFxzhEoBC91/IAVUdvTayroCu21kZMKjEYwoB0xMOWiA+WGZm+hWIoDpGISNBsqlaaKzSKcvBELR204hB0i9lAJFiHA6L3ynW520c6Ik/veH++mvc12jfYPWjZ96SgHrd32ETfcp+3Gj/77y95fDEz83OqkuzpeaeVNbEJzR1klH2o57MswPWbpplFNYn3/ZVF7soR+xYHNNHWldcPInw2Jervspt+I2TvA91H7rsUwmKF8UPkzhuiCUIgDZBC1yAwH0cXUDPiA14KtVwVDLXUmxBodVMdSD9Yx0OQsvLp03cEP7FthVLMh6Mt4CYtEUIiRDXyOT4MPH71yK//9l/y+vIDnz6+56cffsLFxNt37wjR0Woh5wyXmatoiRCHSdN7S7e984TpRDg8MB2eeHjzNW+/+hXTqEBl3i5cnn9gmy/08e/nn/5A3jbwERc855dnLueFdc2ICNMYd2WqVgqtrhyPJ67nyrZtzOuFt2/fEkJQijiRHz9daVI4xMYQIGfPvFkF6B0Bp21jrx/bUrX0c5YRCTo1qiP82hlIUWXqxhRIURmUIlkHuoKOv9daqKXRnDqI1aqkt2bEtXAM4CPSPKUKQ/RQGsXa2T54XFTh22JLq3N5FIh0BL9Yky2hqmXdvlA1ImptOFduOhh3j31lui7JaBmF+3w736BA3Vs7AC/3z1FhJt0/7P/Y5R+/ID7sjy/GKPQnWoC4yyikNXvTXV0HQ1h1895mQ/UiRXReoF+08059O3722APLXsbYa7Rex/UXsVTPuR0cvkXeHnod3emsk7Q+A4+cw4tZArieCWkdChoLUxSGUIiuGUlL25A4cFVbi/jC3r71Ee9HWt2gqRHwOB3xLrHlDFIVWBwiaZzUmEYKIQwM45EggTg9kLeVbZ4p2woIh8NJW6BBNRyapZx5WzTVF29Tm81OJ0eKlcl74jAwHg7EaAzIfGWbX1mXK84F5TPkjbJtiIu0UijbSqtKFlI8wGvJYhhICHp6j9PAtia2WqhZ8EENhnItzCtcF53lCAIuempVh/ses0vruIIG7Fx7s9sZAOpoJpRbmmYlh2a5gBjt2Rv7sfbPvuHDqGWS9I5JwVXFq1oTtnVmAJAIEmjO4eNgfAibl+hlqmjJWVtFZ9or3iubFRsfEDdQDJ/ZAf0Gueh1lcauM9HTGOedRUVd+DoZvC9r2w3N/qxfdHt5csur75KJfevs+0Fk/9pfrfTooeHu3Ec3vX6ldx32f5d7yne/Cn2X933dv/T4RxUC9+WEpTG1qSnLXs5odtG/qYvcyM/v5X34Fk1j90Xgeoooe8ajviKeGGBMMIbCoIA9Ho/3g47al0B1bhd9iUGFYkKc2JZXJWLhmI4PSAUfZnyoDOOgNgEpqa5Fs8DqI8HQ/3W+UGyGwnuvwKHzhJCslodasgFwFXJR4MzSWecdIY2M04FxnEgxqiDvciavF/J6odVqKllFwUxM36CtilPUYuIznhA1WAxDIlnQUAn8oCrgvigHxke2tTKvjetSWbMqYGczuanGXemnbK2Q0i1Q1KbeK32KugcLTWQM/CyVENGdaArtDgUFa58HCaN2nIzFqINW0I1ySsn6/m2Nl+J1yM4FywgU9RYb9Gu+4qqKJTvfaFF5Fs61PYmtBpRrE942Zms2HHaPLdwduH2H7VWC7EHic46SFfO939mzhp6q72Dqvbfe/UNuG+MXPL4sUOydjp/vOI3S9R6gsBPbtRtui+tU6RtbswM2/XTvr6okGftZ1trUm9LtXNkri8/6ytKzHcxVjH1O/2aA4ndpsX2AyPWAJ7aQujK3Z0qBMUXG5JmS4xAdsSkPw1HxYqVGGAhh3G9WCCM+DIpVeGVc1lq0JXq9kJLWxmkciF69PKU2Sl65nF/YtqbaF+tKGo/mzam4zrpcCV+9I40T3jfm+UV7/SGyqzs72GoGHON45He/+y2Pb75mGCYAXj7+wLZeoVW8V6AxL4sGGh+Jw5G8XlFl7Mb1esU5lcUL0mhSSMPANI3EqBOg6yqIJEIURq+g5etlYV425kUJWs5Drqbq6NRjZc1NZyFcUIq4OJPv96RgWpdeP8sk6jLuTWRn3TbGmLSDVCtSVcOzVDFbgUzNyosQtKWJ+Wx0Qp/3TtuwrapmRS4IypcJIeBD1OBVq86lNE8iWgRTCn+MA/hRx86dSfXZYVQrqgEqd5tdOjfo/pTvMyv/ePs5p+tyxyRE/Uaba7d1uz+3M0y6MJN9/e6F/xId47/3+GKFq7ZfnP0w+smtKbsmaY3OjGlWB1hZSQduekD5TKBjBz3B7W2cXl/1592XJ73scNZlvctUFPrHqxpq/2EK7AF9Qi/6+5ur70tnGBxDDJwmx9tjZLCZgRiEFCOhHREZaE29RUHNeCX0fnvFhwHvI9t6IfgRaUoq8unEONnJta3kdaE6IZRNA0ETynqlbODjRJoOPL55x8tzNt5D4fT4loentzhZWecred2Yqy4yNcNR9aevv/014/GB6fSWNB44n5+p5SdqLXgXOI6JkLTyzttGrg2fjsRpIG+Z15dXrpdPrMuMIEyHA9uWyTnTxHE8nhjHgVoL18sr26b8Ax/Aec+WF+blqgpWTtW0g3dE14heqc7bdo9FVUJX32qaORyGiPMFceomX1FXrRADhxiptbJtmehhiJ7ShChOs60QjA7d7JBQl3pn+pneWKwOLRMbjq02Qs2GHVVq0TM/jQMxqCoXom3uWiOEZtOkVQ9G74BAdQOlesPtbL3Zudd6V+Kud9lsrqSXOv+IYIjsGh+afX2Ovbk9i4BOO+hQ3K0S+Lwm+KWPL+569JrPWYrf0/v7Zka/KH2Td2T1PRf4vKDo/7JDBb2OunvODuL0YHOHb/yj92k3U9tcfu+Lq9mwBrT9Z/fsw/VMAssaYEqN0yAcfUfQNb32iA3dBBwDLfRxJXBBaMEruCeqnCU4lvnKts5UEabjIyXP1n/3pOFAswnUVitOhDQosSlOR05P74jDSMmFklXcJRjlGlR1rNSN80XT7jEl/HFg3TYaZwUAhyPiHNuiMnbRw3SMOJQUJq0BgTgeyLlwPV84v75wvTwjtRBjwOMYhmhpujPilhn7SrOWqyPEri+hn0EyMDG3SnDCGANjEpJHuSDudkj0Cd/OZgzesxVIQUgm2Re8sBnp0nnHISVq3mitUqojWcnio7ZrvSmQ7ek6qvNxv3RCiPSpCBHYSqbRlFtihsa1ZDvmzGlMuuS/pgFONrwMOEngCviEdyq02wf1ZJ9fcjR/41voHuqtUsdnnUU7WVu7BYqeQfTSpRfmcvvPZzvMOWf7qs9OuX8kQvVPPb6s9Nh7sPYf5+5OfH1Lfi+hxMRtRf+M+9mWtou7Sy96PqLf3m9Ev1Fdp+LGcd9fhp9FX+f4bIpoXyC37KaHov55dDGWIcAxwmloHKJwiKJ04+AIwdikrpvI9ncT7yousfoZ8zQpVLHJSa2H8HFCXPdDN1MfSdSq4KaIMB4fKW0lpEQYBhyNbV0pWW0SdVCp2OmdQGBbswKYIepAVV6t2+JI44XY1OXLIcoTaIWtFptQ1OBTC1znhcv5hevrC7SiKLyP4FUGrmM3h8NRZ0RQzQgftKXYA7NvahsYUySUhndVT/2k9zm4m5BLP1iD2/FmMO5Krip5J1YmdOd50I2WUrTnomWeC+AaNwKYw9Vio9d6sjtrzevpbviCCF0MRjCsx3ubIfLGB7Hs0QPEvSwWU21Xe4cNmoLY4kS1WW1XV9mX7D4H1dc3Jrl4X9or3HArTbqYcUcDbyPkrq/oG4Jxl107gwzukIG/iFz89x5fmFG4nUiirWi5AxK1POgCurJTTD97BTp40csY3awW6QyH6FG9L7gbUXv/QT9LQe4zlf5h3xZO1/e0F9tfrb8952x2IjoeR+FNKkwxMwZhiJGQDsris566OL9zJLDv7TwNRMB7GtlarUGFU8KBMT3qpvYRFXGJur7DpPqKXgfHahMe3v6apfykswV5RVxlvl7IOSPOk/OqegdxxLlACBPIrOBrE86vZ6JfkaA+KueXZ46nRhyGvc59eX4lZ2N3ppEowvOn98znF9blTHDCw/Gw4z5xmKhNyzzvPI8Pj2oE7MG5itSEGjJbN2jwXK+v1h2JtFJpQ2BKotyUpiVqESzYwBQdMei8Sz+IijRL3TVI+OCZkk5jFhtem4aAd3rixpB0/LvrbuKJrZG3olhnSMQ0ci+zV5tOxnqfSGnCx4lSVXXGuQKo1UFr6mhfvSPIpKxSsx6spSBu1fXqArjJlEk9t+FE2YcWbwNdttHdbc3f9kXPRAB0otQjvY637EDzhG5B2PG+fnYG0X8Xdys6OsD6Sx9f2PXorcJbEJA9WqJOSXaRP9vanyVCziZIP4ton6E3/c8qStEl9P/RP+vRpj/NgsXtRusHA5oWd2u5O82f/eVS8jwNjjcjvEkbQ7gqgBYjLg7EYcLHAbyKwUhTDwgA5yMuDDdQShp5W4jphDO7PlygZGXjlbxBKVyuBR8fGIZAc8J6fWYYBmIaCVLJy0e2+YwQSOnAEPtiUM5CSg8cH94wjJGSF8QHYhhA9CSsrXJ4OFByZplntvKRl5dPJjvXEMlKNpre4nxDZOFy+SPbciEEzzAmHo4T02CIvwuMxyd++v5P4ArDeODh7deq9kXGrZnNqY5njMk6CB4fjzRZVaSnVE5TADbm3NRJ3UcaMESlk49Jy5t1C1AEjMIeoxBiw0ed0hySB1R9fFkKbx9VeMZJo9Ssn4m/bbYQEtWLGQUtpPGosv3oye5DoIaBVrO6g6VKq42yNjbgeHpgmgZEvP5qqv7dWqGJmfnESIgHiAdamBB0lqQZVtc6Ea7jc7ZK75e+Kl/1rFk61KYli+3sfgj3tmuwM1K/l9tavNthP2dA/XUDhfu8PboL1tifu0owYumS3L9dwyysDrtPfW5JSect9Of2i2T/Ob1dqZmBpYnGWdAb7u/uT3favB/ZtXan/dxDdLwZhYehcUrCmDwpPuhUp520avvn9YNHGZgumCaFs+AkFfrAmXOafRig62Uijg1voNq2bYzTE2k64Dxs8wtpPOgizSZztxXicNDFJYK4hAsjMRUIcDw9Ke9iCDRzVa+o7kJEeDyNSjTKmVIyWzkT40R16nDl04gLJ5a1sK2vLOuFJsJxmpjGkWkaOB0npG3EOOl0Zc3k5co0HTk+vCHGSByiao7aBzWMx9tnYGIK58vKh5eNy1z56iGRs564wXsOMah9Y9Tux5Ci3lvnwAk+iOFDTYWT6O5dwTpoej9bGxiTdSh847qspNjsc9T74oInkig50+qKSMRpr0lZnjFpS1RApBIlU01d6nJ+xvFIGhIu2WSyQhOqH9IasWw4v2mWxYAE2/Rd+Fcqnf3Zy/dGn/lx+3q9QXr3x+wtaNxtitvWtOf7vSy5bR757Fl3Ueqv1h61seye7th7owuZOnf3pizT2PMGsSziPlB81j3prMqfx8FboNijx/4GrMF+V4bI/ry777HvF9RyfojagksBHgbhTapMURiiWJo8EtKgcu0hainRb6wA3lDyvYfttXXc1aVdMBl8Bbu8iAYcAdcauQrD4Yk4mIR/WRUvaKoluZVGk6icBDtdeqDz3pO8qmEr9RhqDnQ4tVb1PI0xmvO5ToGueSPEZmK62s7MubCuC+s6k/PMMB1Iw8g4jUyj8iNEBmIcEBzL5YUYPIfjicPpAR91YRZjf4Y0kNKk4Gj/7ETYSmUtja2o5+aWHWPyDCb7ppiD34HQPQibGI8yFW9lqBLfbhmiisNUJOk1hzjgSjFNzUJE5e+8TwZfmY6mQMeyWnFqBek9wWQOQ+ybN1PLqsZG3hF8pMVb+arcimr08Kp4B71U/3wL3W1Vm+C0uYz7k/HzTbevavauoIWCu/3SD1j32SaEPpt1e63P99YvfXxhe1Tly7WtpHWud8a71zTCUhqx/vQtqt0UnP3tbt2lSNJrLuNJIPo6e9vMNn5jz2HuXv8WDMzcrR/2e6bSM4kxeR6nxHHwHAbHQ9wYXd0zlRAH4ngiDKMBd87IMVVbWpam/lxyqTlTQ7IhI0ef8yg4L8Q4IqL4QYgNnwJinYJhOKovSmnm0RlvqtEWjErVjMU7MbRPmA5HkMoynxUUlN5jhxDCTrjacuE6F2BjOj4wuIQEoW7PLMtVS7MQOByOTNPEYRo4DBHvhTScANjWheX6zPHhkdPjE+PxaH7MmVo3BGE4PGjWslzs/mtrspvUhOAM0HWk6JkGLYNC8AxJ27nOew3OVMWXgC6yfaPo63Su855GZN1UHas1tW5MwxGRyjovlG3D+2D4AUZW0/XRTJzG+YCUShGhuUDziZgG9SrxBR8jeVUgs9Zg9oOFKJHuwI5pQPQBPBqUoiIxsmeeNsQgOrNSzYKzl8IdS5PujIbsnYr97JP7DX7TrhK6XMO+/O0ZRnXnVq58tnB/4eMLMQp9a844CCGoR4NHpbsE9N/2OGetyTuM4R6dkN5aunvLdUdn/X7RmkDohdb+ZBHz0ZT9VcXdhtN0qExTYo8oUHlM/PrNkaeT1t5j0lOpd2zAFnTUE02k0UqmFhUskTuAyLnAbuHWqoYGJzjXfR61H167MngakGrTqwaU5aKWeYfjO5Z1oRStZcfDI3l9ZjqMlFJptbDMF+LgOYQjPo58/e3veHzzNS+ffuD8+sLL6woNUgwcpoHj6RFPMfKP2g4eDqoDmdcL23Jlmia894zpwHQ44XGk4DhMBx4ejjrbguN8fmVZZg6PjxwfvtJSDB2w6q3TNDwS08hyvtDKqnqPLrHlQik6ip9iJEU4jQPjoH4VtWwMQ9KWbnBUo7mXnClZPUTVt6NSbLI0WGkbgycMgexXqKrnWZMHVzkcj4CwLAvLstpW2hAfCFGFhDqvR9czdOPjbMzUlJJR1COeg64+H6ni2MqGX3WIz1v2iBSkbDS34vyKTyviBwSbokWzCM0MHQFvFO+emdqzWi81NEDsbKm9+3GXZex/v8/EuZX/+068vcz/yOMLpfBUIyD4oAKdPYtojdo6mutuKZLTul7EW1TrUVEzECfs05yCgsf75nM93bSYeFda9UAhTW7C4FaOeLQzoTinZ4zwMEZOo+fxEHgzwTigegcx4qPSpnVK0E6bVnSgq65KftlzTL0+H5M9tyeG9UagwWtXI1Qgag/fVVzL4DTTisNIXRsuqOt5Mx6C/gInnuPjN3oqbasK8Dr1Aa3iSMMDb3/1L2jSuFxeeX5+JpeCSGEcIodpYBgP1DwTUiKOEwMQh2CswmriMxsNDc5FKqdp4t1XX3E8Hkgp0NrG5eVsZsgjaTxCiIRxIsaoY9pS9BStVYOaNMbjScuaZdsnPHubb8uN4KPKT9piH5PTzoKoLN8GFKnKY4iBlDzzwm5LgJ0PDqdTmDHuAbrVgmuFMBwZRqgtkMtVD7N+RDlHTEd1SpemmFccVArAaRnZirq3NbFyNEQc0bK5BuIpZcVXb4I1ELziSM6rSnfDcW/GXEsz8x5d4+1u56qYj3qqOO7KhZ2mecMdetnez7euMdH6v7q7TELsz/KzUkP4R944/9Tjy5zCvAJQGvyqXahukHonZNsvpqf8+82y1+nwQbeJ09dw+lFKf47spKreddxrlp2QJXdf12xGFbX1tBmC43HyPI6e4wCHWIg0XHNGR1fzHDPghF1tWmvOlgs1Z3WVcsohdkQLRFYWiBKV9p44aGDwUU8qqZaW9nkS5T8409lsrRoYd8NjGo6UjgzTgRCC9v+dsK0J3zR9XteF6/mFTx/ec71ecGi3YJpGpuPRiFNXq7+UrTh5dfFuTf0z6tYQpyPnh8PEV1+942jj7sVO1W3LYB2VNJ5Iowri9HXgAOnuaKXgnSeOR3Ab25opRa/f9cUqWDrdLMuA4O3k8zozsmz6EXuEaN4coBIAtZlPqJhkshPzBdFl0JrpTqBWezEKzqmAcA/stVaiV/yp22TuUx6mSdfXss5sKKDqw57/7xuvVdVEDTETRiXL7QeZLVWVupO79XpfGvT1vSMXd8/p70Lufi7/HSyjv6bs5cyu+Lb/sLtGxP3Xf8HjCwOFqTI7VZvWQNEHcO4Dhdu/fgMZ+ymwf8k6CbfL3MVmpNPE234T7xWDbk3O/icraEQvKDhhCI3HMfDu4HlIjSFUoqvQHLU43fQh7qPLYpmRa4WaV8qmYq9t2xQ/8JZ9dAUpbqI73kftUZtWh45JR01lGziyGus2xTu2bVVZPJwpbq/00XW6lkFIhOGgAjA4mlTma0KybvJPP33H5fzC6/NH1nnRwDgkTg8KNIY4GlimiuLzWjgdxl2VWlom1wJhIKbE27dv+fbbb/EOct4M5FwQHGmYSOORcTgwHQ/cRHL09UtVH0/doA7vB0JQtmPO2XgGOrDlcASnGzN4GAZRCwSnKlwhBurc9tJyDxROeQKlNqNQd96K4jEqOacbIeeNSfRzCVH0s2h1319qxqQ6mTg1SwqtIc68Y6uyRb3XnKdVoTrtmuw8IQScp1XRQBFW7XqEjPcF9Zu4sSX30YG7R0foxGwtOq9o3yBi5Kq7A3PfTz3q9ZLpLuWu7fMM4i8FhS+tQr4MzGxNh5pMO+IWFbXEwExWd4BFbi0dx2dQhelS3k5RDZRxB2+kaf/554+OV/QxcH2oe7S6rFdigKN3vBsyD7IxNG8gqkeY9mGtvlD6hhbRUeG8btS80vJGyaojEUYtUUJK7O5VTssMhyYX3aMzRt1MlbyzAWsV1mVh21bwQXkZpdjJGEjDkaHo/IVDyEXw8QGCziUcnwYul5ktv7KuV8rlrEIvpZj828Tj2zccj4/ENDDPZ7qje82V6/nCJRbePB1I00BwlfNl5quvnvjN737Fb3/7KwT4+PEj83WmlMbx8R2P33zFME06HYqexq307KBpmWauXiKN1+czpX4gDANhGIiDJ3jZM6sUImNsHJJjSNp5cli3w6v/57qq6VAyFqd35h9S1H5RM9G2n5jDmHTGxGvwKWVjXS+E4ag6Ek41LlR1SzUjt3wmRV0D4+GRkjdV9XZOS0cp1FJNgVx1xktVnCWEgI96ZPmgcoGCdlacX8Ap+7ZLO3exwb+AJNCTBS3R612pqwOLvlMve1TY55buNvvPwYdebuir7O/iH/30v5yY/MXHl4nrmmlJT9F6gHNGae6di05X1bf5WTi8XYuwb/Q9La1Zb6uzGkz8PiK75ykBFCTt32niIJa/TNHzODae0sZYz3hXoB1x4YALE/HwSByPSo0OGtjU70Hr27ItLPMLNW+aUtNIMe3vtTXBRzWGvflbNmrT904IGjRKAdc0gjgzyXEQx5FhfGRdZ4Dd9HeTBC7iQiP4wPW64ER9RJCCD6r0PE46AVnrwpA8dUhmOqt6FmkcdA6ExrYmclMZvxh0xLrhGIeR0+Mb/vZfv+V0eiIOI9fryuX1E/P1DGFkPLzh6Zu/4TAlHGp1uOYr6zwTvXUojJS2rRoEr68vPH/6kdoqx4c3vJ1+xW9//Sskb6TQ2LLwcAwchkp0fW4BG0fX03vrZjtGk05RQergHNV5Oy3VfhHUgU4dtiKdyBd8Ii8zuISPI9PhxKUsNCkg2lkJIZJLxTXUMzVNgDfxZWVARh8s1gqlVcYQdh3N6B1DsoGypMQ88VHBS5c0P5aqYKXtaqmi4Latf5yjOX9fTVuWplvc4/By03MR9LDen3wDyegGQfvmt2DRsbNevoS+L90XxYn/AXFdum5DV6vqoi52Q9xN28HtHYW/8OgI5B5FNV3T8ONt49tIubMiw4LD7Xe3v4zKi8FphFMsjG4lygpVaC6DT1rS+IiPndprE4A2+VdLIa8zebnuJ6R6PyrC32pRLwdvWovO7zHQuYDWMaJpZ4/3ztNl1GM0TQRnJYkxA0vb1IUmRHuPAVhZ1gUnlWjTnTidpYhxIIRMzoXgPMMw4pwjBk8aR9IwEdLA8/NHSlXLgBB1UbfaKLnivepuQiCum27SdUMIxHQgTQ+G9jfqNlO2K8t6xdvgWEzRMjCVsnM+EYcD0+mRVlULI68rQ/JK/nKVnAPDGHQyZseZbObB1nlreiB5JzrHYie6D4KrZqjT0HvidDM6m/PYdUJd140oOp9i5DnMt9Pd+YC21mg544awH1i9rC21WhvWSmkXzKtEHdurr6pIJuhnFob9l7hI79w5W5/evq/1y7dDpmN59+Cio7c7O64h+164qzn0HoDhD/dpxr5hbwFB+nfeOC6/9PGFhCvjKNxlDPp/Z5v1PsG5J2nf3tAti+h0VuUo0IrxT+35XSegv9rdDerdJBFU7syCxJQcj6ly8CuDW3CSkaau3OJN0cjwDASaVErJplmgXP28zeq7uSsUGz/fXKF09jugxte26Jxtbp0EY69G3a22DjHi/QA+kLcVUE5Js0DifNyHu/RmerYt46jKELUT1AdPTIkQPOuiuprDMALV2pQj4+GIXzdyqbucWgjeRq6hFAUfn58v5G1hmg6kYVI2aUykQYlXnkpdF/L6Sl6vOt5+ekscRkII5E3JWpoBONIwcuQNpWzUUpmvV/zDyOGQSH6kZqeaENWc3vvHbVhNbQ1pQTUhbNRfP3tPCILzTX04BQYDk8XWjx42nXAhyoFrBWpWzCSONm1722S64dWw2ftIN5DqXbtaqy1F7fJ1Ls6+4a3b5wXEeZVGjANECxZG+bfzE7+f4n2XuL016ui/305WB9yL4HYxJfxtNFJEM56+J+9boo67nWcbpuN5nwOo//zjyzIKF7QnrziuRSu3b4idWIJHvN8j3GcX7+4vQdCRdbHx+U7T9kp/dj1fMaBTrNrbX8ODeFJ0HJPj3QGe/CupXfBt01STiFDxLiN5Ia4zPiQl60hj26znbw5NKoEfCD7tbVZwSo9uhbY13fDRQQjWDXG4VtgnZr0yBGtbqXUDGoeHr2itkXOm1Kr/XqqqYufMcHhkGDPZCXldEO8Vx2iqKqX2gAEf1DsyJE1v9cQc8a6RpqOpckc+/vgd81zxYWQYhXlZrZ2cDJSu0K5czjrS/vbpCRcmctZU3rMRypXnD3+i1A0fE0/vfss4PVLzwuX1mfOnH1nni2o92CdSqorybNtKKZmcH3lzHPBDJresw2yla0Y6cFEdwHPVTedVseowwpCERmEIkRQda1EQr2SB0Vi5aHlXqs7fxBaMPh8VYG2qxJWGiZLZxXzxycBVZc7mbdUA6NWX00dUSBfNYoYh2qZUglaKSU2FXKSZUrfzXgHvNCLxiDBSq+w6rM3KDc9t5GCH8y0T7/oZvaujYtZ6SATfMybrLYrQnDp+7Zl7z3C5NVptC+luE9lh+PZXyyjuTEf0/97ikzcCoVGbuVFuf/7QAGoXb1LtGrbZPc7U3j1by7HjD2LByZk6ttaIhyFyTI6DL5zcmVFeQTakVnWAlkpwIylpXQo2cWhKUTo+vCoFWJqe3sPBUj8xazm7lgZ4p87fecZLVQWkqECuCwEfBs1OtrO6eYswHd4heEqZVdPAOke1ZXJZVSvBB3wccbVQ24V13ezHGXdlmGguqr5FFQ7HRy6vnyh5obTMeDjy8PTOMIOF55cXXl4/cjg8cDieGJJnPb8gUqlNy6lil7blxpobf/e7v2GZPyJ1g7by8eMnAKajit54AvPlhfX6rBqb1wvrdlWRHu9Jw0iInoeHB0qZyFkD5etlw1PwTRmX3mv3o4k6oi2lGo9FWaS1FaKPRK9YTzo45hVqcVQz7ekHkJZylZQGNVfOlRAbUCzgqTFyLV3oNigYLxXvojJpI4rfGFPS+0B0nsM06gECVBpbaTplGiPTdCAOB0JMu0AOtUDJ4DKQlZuxk/J0U2oppbtINT3R8mM/8W2diSnGIewiNA21UtSEk32c332Ws/8sE+kZFDbDsv+VXx4mvrjrcfvhKlBqdaY0uofDHiD7f34GUojVp2LRvM+xeQfN9xewQNTMLdxqsRQ9p2ngMCWmQVWXI4VYV6IsxHaFthltWFR81Q90yXXng6b5O3XX34BQi/AIxiRUeXZq0ZPCrqcBeZuJeSVNB/zhEedPOKeUbCUzZb0+H7TH7zxlXSl5s/o9KPmsByBLT/W01yGCvC20WozYFZAGw3CiboVtm5lOT0zHE8v1SqtCSiM+6BDYOs9s66xTrtuKN/GV8PRkupPKEB0PR702u75tfaHkq57ENvXqnGptdBPhdTkrC9cH0nTExaAcilYpWX8PQfU/UxooZSEvr9SWaVJ3Cn8zvKEUVaIKXoHKLVfG2ElaXaxFy5Kuf1mlHzg6+9KMNOVDtBanEvH0NG+2q5otK6dTwKI+qYIZDe1ZbsdLmrWSTWrfMAQRR62wZcHFBk5lDjEAXjd1wcuGyEBzUZnCaBejR4mbW52teFFGZnPu1lEShTB7/tzbpw5MFKhpUn2fsfcE4me1R9+GOx3grwlmttY1BPoN7W/ciEqfvTMLXXt91cNYu2ET3C5cn3Prjf+8lgzB83gc+erpwMNh4DAGohNkXWnrCnnByaqLVm4eCSl2b4bO8TcijP5EdIw97KQowFzI1SRHcjaNz44U98ym6lDSeKQLsEpPE7tEvtf5j2rj5U1zX0IcdeLS+CLeqOg3ANdpB6j1RR5QVasBnNMOisDh+IQ0WNdVhWWctujUCX1VpahaaM4RTLw3Rb9ndN53py99j5fzx92UaB8Xd93LslLzqm3HeNDyLaioTquZnDdKXeyajL0bo8oC5plGpgvRdO2E2tTeIdkkaK1qTDwm05sUXUcO0760Eqe1vmp6EFUxHh+sBS5Nqfytl8f6WTfb6G4vKZuR2fpqM5VWUX0MwXgT+4ySZcou6PoqyqZ03ukQ336Caw6iXauw74mbrkRf8D1I9DXZMZfuYyKGa9ywvr172i9MfnYWW3nUf563HdaFaz6PDr88VHxZRlEzraVdH6LP82kiIIjZxO8dEbldxU2cZv/kLMC0PcvQJ964E/0yhhR4PA387a/e8vWbg85oUMjzmW07U+RKldVat8E8PxVs6gCUoIHO442yrYpNeItpTWwWQ99KraIYwrpQixoKeu8IUYVbO9Wr9QhtRJ1W1r391kxRquRF5fpBf3YYKeWFUrMBnYlOP5amp2P0ntxkHy0TH5F22e/p+fzC269+DS7S+GhqVs1wDA81k4KyCZ0Tcs7M88w3337L8fiAD5Hz+YVtUzD3+eWZXDOn0wPBe5BCWV5pweGnA8455ssnQlRLAR916Xh/YL48gwscH56IMeloe97IeSVFx8PTO1oZKduFvM1QOsfGM04R71VDI9eqKtxWpzvRMXOHyvaXJhbkHOr2pc5djkqrm+IL0enMTPeJMfasD4PhUGphOA4DSCO3wpaz8jic3y37eldMN7IGihCTTaCaeJOIOp5RCH6jjdXCgteuh+jwVxPZTaw1cZB9nMFQlhvIeGcw3PdOFwDWPeZuB51he+E+mW83jk/vAO2ZlDUEmlld/GVw4C8/vrA9qifpji73WOcUcXbeVI5/nu/s5UTt8y7WOtWugRiY6C193C/aO949Hvj6zZFv3h756nFijA6pK2W7IMtPyPyM5IyYZV2pjWyUaHWbarS24QpUdEq0xaSYwF4XGq5iad+6LCpjv82UVQNQTEENf9O49+xFMi1fKOvBzGEcMU2UbdYMoxYjcM24oAAYOEpZcE5HqwkqhydSdUzdGT4TIskwoFobLo7qUD4MDDFwuZzZ5hMxJQ4Pb3j++IHWPIeHR3wceHjzR17OV110IXKYRi7njcvLJ6Q1nr76Fe+++Rvm61nl3Rwcj284TEdaUaWtdDiQpom8XFmXmTQceHj3LzT9r5l1XVk+fre3m0WEbVtIMTGlI44jZbsClRAmQgxEP7BxJQ6qmq1TsZl1c1wXWEpBcGrkExzjIAhVzXuqGAlJVaNKtZO7z0ZaTh1CsqCiC0mJWRUhIQ5yrbi8EdNIChHx2YBy/QYFDyOEQdmY/fDzyh25abDelnctlW09a/vaOf3+Ydg3tzH6LGOwEOFu3OVdAc9ridPfi4ddPwVhH768P1tb7dQBub2hO2wCd78Vu7mS8CWR4guHwm7cBa3rbxOeHejsU5630kNrLW8isHtx0twemaUqh8FFBQRjCAxD5M3jxDdPR56OA6cpElylbBtlPVPWV8p8oWbVfFQ+vUqz45yd3MkYWjrO3aqjlBVXVtU88EG/t2b129wWtnXhen6m5oVWNlpRSIk44fxg2pHYPVAfUZFK8CPO9Q8t0NBR5Fo3vaY4KQ+jZGrNxBCp6KBXE+1OBGuXevtkQ1RNRifK+AzpwDCeqOOVeb6wrVdCmnQOIw3M52dta8aB49uviT/8xLpVqOqsNgyDZkG1sC0X3ZBDJNH1I4uWFq5p1uAi6+UFh5CGA+P0lmCy9TgItTIeH9htEAz8VdJSpc+zaEaVNSOtWVF7A+68g7V6Xq6Fl6u2c6cIyatb15A8ram8f22N5HRaVzFwHeCqtapmhbPa2wVlglpq30TboCHI/l6bBRDnPCklSq77ZtO13Yl8WppGH9j3ohM6x2dHHsE0M7VkdaahqUEhwF0Q0qxCaeudSNX3Re+IYK/tb99pX7njM/9MKa5D/j1pvz2vP+uGw3zeiP3nH188Zg4do3Q33MF9ntq4Pdw1CxSmY3F3EbfobTwKA0SnIXGYBk6Hka/fHnh7mpiSI3lB6kZZLEisZ+q26gfuujKQaD/bBWtRqriqPuSGi4imoyqI2lRZqmjrbl0uurBLF2NVmEC9HcJdsLxhLnScoX8EPiBZhUwEIcRBAcyaaU0BSgz4arWoAG08sHeQrNPhfAQpOAtkPqi5ThoPxKSKU4oVDMQ0kPPKti4kJg4Pb0njyFYWOpoe06CbVUTLqTwbeatL1mPgnKbaCmBuDOOBYToxHB6Uk7KfjIK3LEnsfrKDwmKfO5Yi1531eHvoIn5dGuelsm6VMapwbgiqnRm8Z6vadtXhLGegsQ4bOrTU8MnAQgtizvv9xHUWkL2XfY2KKVThVAfCuXb7/Kz0+ExkyXfOTF/zd9nyDj7eQEiklz99q++iGvT69tZUvq0dOmHsDqvr0If0p3e4YZei78Ftv923+66fzp697M8XPvP4+OceX6yZ+dlFYwCNu4tOlnF41+t9bUW5zlq8ex1Noyq4Rkiew5j4+u2JN48H3jwceDoOxIC2UGumbAvr5Zmaz7SyqK+Bs7656ARrSAPQpzt7QOtKWMFaWXFHqJsUSsnm2N1VjAIu2UKqhRCdejpEzU6wTawAYlDvEP1k9kVWs6azPgzEOFLyuntdpvHEui2UslDLrK/R++c9GzLyk67DjbxewDgQ8fDAOD2zzqqMhWksVIH5om3Zw/EN4/GRNTfDXoSQEltTOTbvA62uLHkjpoHpcGAaJ+IQcOKQkrlePjE+nBhObxinR7yHnAu5qK/Hui67SlQTDbhOKrEbDBuop0NcAaQgIShb1NaRiOPH50WtBkUVz4fkVWwqajuxNbUPVOp0lzXwVlJoEMjVkbwz8yDBubSn6D44srU2nfNW46tqmdcPWfef9PUbjFgF+/g6nuhV6Uu5MrYWOhOi3YGOnWncmpGhLOO41Uc7rqglj+6JTg/SA4+bDOIejO4P6BvWd/eHWzC5f8jPvtargi9IKb44o9hp0/YD96tDDE3u2D3gBAnQW0I9qOxBVV+EFBPHQ+Lvf/WONw8ThzEyJE9wFSmqnSC1UDZN5WuttGpx1A+qiu3A2QCQiJJwwi6m1Wm5aIo/aL3vnAMTolUVpoFxUr5/KZom96HO1jrQpjMXPiRrHXqlXhuSLs6RL59oIvg0EX2ilY11NZet6ZEQEq/bVVNUGxATqRpIRFWxxboq3o8QPNu24sCmNDcOxzes83c4u05kI+dBuQCuEofKm7ffkLeN+XIlb43hpECcksQSMXnW64Xz9crzh488Pp04nQ4a2KUwnY68/fbvVAqvFl4+/si2bdTaKCajF9PIfP5ErVk7HcOAtKrTrClqq7isSN30z01Zr00cW3Z8eK68nDemKEyjY0jCYcK8TDUzhC4wJFSpBK+pvIgzfY5EzptpaWoAqq03FnV5huAMH3N4H9hKZXR9GloQpx4sRvWjVkdMytbsGWCrWQ8Ipx2mEAacNBX4Cc5KHBU/9n4l+qQOci5S8Krb6b36ykhXa/s53+H+IL5hDv0ZPbD8fJPfT1fz2Xfb97ZqGYXfn3+vh/HPPf4HCVd+7zHv6ZnpOIa9L983p5Ygmko5qw2xEkRIyXMYI28eJt6cEocRUqh4qdRs6ZsItKadg70/tJO7lTEKKHBZ9lHqG03BwKhgvH+vvp1NhJJVwQqx8iIqxdo5E5pxSvWureGbR9yAcwNSNbj4FEhpIMakQ2VFEf8YR42PrZLzTPARZ5L/ZdvobdmAUys61wFVpVr3LFetDSPVb4qlbAslzzgqw3DShdAK0zhxnV8p2bGtiZpVNv5wfETEMZ/PxPgAToHenAvTdOJqWZ1zjXm+4BCOpxPHx7ccHo6kYSCvV9brmbJelVBWwfnEeBhYrx9oZdYZlRDZlisxBiqBtQnI7TPTGaDIlgvr1ljWymURYlS/0SHCOPTNXu2E1895ip4SNYhjTl+2AwhBuwke7Vz5GPaOA2j5EnyySsjYjWgL3Xkh+UCKt4xAxFS5q9jnlBSjcA5MhKlWE/ntcz/B0Qjabq6F2CoD+vrVRhuKYSp9BPyuisD2tK5Xbpu+szI9mCyDu68tPt+fe7bid6xCbL6lv54q29+/xi97fJkehdXiIejYbwxB/+4d3lSAorlaK69G9t5yR2ub3ShEa/8hBQ5j5OmYmAZH9LJH2ZuDlRjPofT8UG+dBQDs5mmd3HvXPYuxm99r/j6UZe3IUjad48DdOjFiH6ZFNGna6fF3XZ3WqrXjxpt0XlMAs5dDYnLuiNxasnYy7UHBOWIa1d26A2POgpbvLS4brG5Zf1UdoPMh2HsTwpDwvpG3RstlBxW986p9YVhNjInaGuu68ebtGz3hLECO48B4GJkOR8bDiZQStazkdabkZb/vmqFbiVU2nYC1rylJSLMAsTxYA4RHaiBvlXUTtixsRenaXeg4RRPNdbpObiCDI6VAKs38WHWEQDeCnsDe0vEmDYUIvYKGiE6I7tmmBgR/h3UQlAFrMhe211SYuIm2U5XvEoBA5wuJ9IFNx+dHo9DzGW8ZKSiWRulZwW3uQh9iZabcIsieJfRS4abEch9iepZxo2sL95yM3UBIX4x92PIL9v4XBYoYnGkYaqtwTBoYYvSMg9bwMfjbAu8ZhZhi1B3Zyoly11NSVexp8AyxA6EgTQNFt0ITKiLZovHedlABGhErFwo3HrjWoKGDWs6b/kSwYKVBouSNPpjjTUhHqby3X7Woia/zjYrQDNXGTcTxAR8j1TAIadqdqLXSJFsaGgEV5NX3uZr+psMFT0gHnVhtPYNqKhtvrlwaOLWT0GcbWmlGxjGgymtms24zuWhAqeuiw0noDEIphWGc2LaN5XpV4Z44El0gRs9XXz9xmA6k4UAIWkKsyyt5XdTpfJjIBcN7NHujNdKkmU3ZVEDHuT5Z62gtGt9AKFtjvhYljNlWqmYzmILqmqbk9vL7hht47c6U20xIF+VRncsubKv3qYkH153cb2BkP1R0Xbjd57OJWkTGGDXb3Y2t9LVrzUgIQNzneIKzMrB1HKaX4d4OJcU3ghcIqHSkBHuf7aai1W7nfZ/BuIMxftZZvA1I9OsFo4Wz1/LQw9Wedd3KkB5fPoMQfsne/+VPhTcPI0+PB+1MjIlxiAwxaOCIlu7bG+pTGvuGq33O3mjbJkGWYiQMA2mMOk2J9b09+GHQ2NeKYhWgFFfnd9cTER2qqrUgVVtyrTQaVTU+XMAnwy1QwE1M/FUXLMa3sHfeCiKmylSrtkvnM2lIHF2j5QlXE2JjxXE8Ij3zcQEfD6pl0Yqevz5SaiakpJ2VsqHpMITDAQ0gXT5ef7VaCUlbuyq24uD8gR3oag3nI3l50YWSRlI6kOKFtn0iXy4wTsSUuF7ObFtBXOR8eeXt178hCGz1A8uSiXFiKQuvl8wwCbWuHMQzTpjYjM5hSIyIn5BtxVFpbWG9vnJ4+ka7OTUrc1Q5xeSc2TZ1L1+WzPk8M89Xnh48D4cjueqMScNxmjynSRhNR6g5IQTd1MUYrEHUoDh4yPq2kHjLtrz3FjQc0roCmZrmaIek6untTNou6GYWgTXXm2s50GUJ985W6/oi0T4jiCkyjhPcZYEaIO3zNOk9sBIyNDxqI+myIxfTQDV5pnvbwP0AvGtj3Mvutxs9k949vK9COgO5f3vXoN4hCctavqT4+MJAceDt45HDqIFiiJquBQfeVV1YvdwwKnNPgXuAuCkjVYiD+mckbWXq7LbpZooqErW6aZpp9n37Q0y2TNRkt3cMVNJON6kLwXCGG8hZtllRfeftw/F2UgMokUhEx5H3BFEqtXQ1KU8TT4oT0XQf+olOtfkQ+7AUWhFcGEz9quCkEeNAjUrGaU3YtuveXnNoWSfFKaYSVDbfB/ClA1ma/dQQKHmhLoU4Xnl48w2X1xeW8zN5fiU9PgHacYo+cV3PNJOESyFwfvnIw5u3uOAo+UrOD7jTCVyiitOpyqLLRPDkTcV4ar7QataNEpwuI+dxTkFF7SBp8E7RsZKZRmEcJh4fRp0ezUqfjtHx5hRIvnNwdMPVHb3TIThHM8k8RxEoTWhWb8cQ6IpXSg3H3g/gHKWBq2ItUM0w9Sk3/9FcGz4qgJqiUtpvmL3f12aTjIi6gw1pwFsLVxW6wu7t0ppqZdAKrhSQLpfY9pIE583mz+n31dZ/4F59IHew3B000ZXe+/ORHjT2d20zKhowartlIvuB89dqj54OI6fjwDREphR3PMG7CpKV294vQIoqRFk93TfBnlcBLqgCkfPa0uw+B1q/O0sjdTG4oLP+sq5ahgAueCR3PKHtv4NFUzPD0R8rlKJCNh51oxLLeNR4lj3VvFnFlT0d9EE1M7uLlYsDPo5G9e0YjM2EeE9Xuu5VZlcu6tRlnffYdszBOxD9D71MCSHZSZmJcSCv2+3IsVIqNH2v6/zKw/TIeDgwHEbm89kQe7cL/4aQkCYEHzgcHliuZ46Pb1EqvWO+zkzTZJR380VzFtAQ6L4u3hFIhDAqNVkytYo5cClOI3bKS6sqhTfc5m2qZZPOCafJMSbV3ZQqRNcReV3ywT4TjxCcaFyyANxav5+Y43E/KJp6jlh63poQ91TdwD5h158QNMso1bQ7rX18AxW7Z6jsXb1aC8UJkUBwES/eBu4+xxwMSNESpqMSztq+loV0yQbdIJYp216RO0zvPiHYfW4/exhS0enP0nENbnvuS4CJu8cXBorEcUoMKZC8wxtHQkVnVgUSTURVWkaKEoxum7cPyHgICZcOuDhpO9BhbksAHufFGHhqQquj35CXFcj6KvHWK9/1PGs20MkGj6QREEuPC7hKdINO0Xk1EM4G1Dnb9KptkKlNFzzeqNvpgPNJP2zT3VQKus2VGAimiLpOS4rleD276L1w7wdy2/TkkqZq2xJsg2ovPyb1z6jrql4U7mwxQls6zidCElx1zPMLp7e/ZphUZWq5ngk+EX2kukxtlZhGOrfj+PCOH//8XyhbsSndyOvrKzF6/cwOk3mQHvXvUvBBtOMQRlxUcLhuhVKUNr9ts6bulub7ENjWlRAcyaZ3S842tanZxGFUzkUujVqtjVmb6kg4nd2QDiY6Bc7FMjGFc3RTOG8dBVSXQrzsa6MZ8r/PGznNKALs06xFGsV8VtSsyJtptqObQHnpRCyvQHjNeNfU5Mf7/Xv3kxtL++0Ebz0YWFOghwRXb9Ru2YEZ+611HgUmqGvdDG6xxRvGhvRrvpHa7mLGZw/HHWnwFzy+KFAcxsSUgkX1DamrZg5Na1a64IaIUXbXPa13Lho6ayfh+ATjEyH1IbNGdIGSjSuRxZSckrIH4wBDIgyTajQY0Fe2lfX6yjq/cj0/k+eLDfgAruJcoBQFLLWdCT4VqJ5mQiqlrHpCixKDRDDPyUzdNrO7GwghGeCoJCe83T7RLoV4Na7N6wJmkuS89vTX5UxnLgafqHW1kgx8GmxE2iOuUKQwnEbSoLMSpa5I8wQ/kKLgnJ7gVVcPfZZgub6CNIakTM2QImk8UCss8xWJk4qwhKRK3d//A68ffwQXqLVwfvnAcn3h8emRN2/f8PD4DuGqMy7BU6sjTSedzSnqC7LOF9ZZgdgQomEzllEZNT9EzSS6SEsIkRg9McAhNv7841X9PoIw2hi3B1zs/AfNRjtjs9qJajCSTrqGRAjasl5rJjajWVtU6MNkzUA+36xNGhzBeaILqnhWO2lONTTxbidf1VJxeGLyTOO0d4I6HbxJIfSRctz++br9V6MR+oXpxGzb9mnn+/LiVhqIAaYY/mEZtJXnIHeUb/teUT1SjzNof09X9h/wZQTuL5brFxwFaZlWVmh5xyHEwJve9hGwuf9g0VDTuRAGfJzww0E3liHnIm1XW3YYkFaEdNJefoyJNDxq2m8zBB291QW5sS0X1usLra60slKzqjrlnG10WnBuoBUlDYnzSL1HhgWk4JyCiLRKs9O45JW8Jeo40Jhu78EAMoenVmUt1ropiao2dRO/fkLwhHQk+EjJr7S64hzqk+l1erTWWdPk8YD3yoUoeSFvWSXlo2YE6qbVaNuiWZd1ac6fftCAWgtlu5ggjgfvaZKZXxeCz4xDguMTpzdf8/ryrCx0H0kpMR2OHI6PjOMjMSV8imriK5DSiVIzW57VTHnLLPMrQj8h5cZE9Ko3om3eSK2VnDV7CcFrwKPx8Vx4f24MEabgWDMMQYWJQvA7UhSjDciJY83azq6iU6UKeSlnwodIqNYa7aew55aV2PchQunrOmlAiZ3R2xo1Z1MuUzxoGDSLarWR1xUGYZiOZi1hW7uf/g6d4KVLKDbEoQHODVrOObNNRLtZrcnuKmevpplDVWzPOUfA/GGqlfcOtR+8wyf051vwsoWto/Ruxz76nEf4gmDxhcI1RScxW6aVTbsRKPnFdQKUzmxbkPD7W3FOF4yPg24iO43bjmlomu6k4lGTF3GRNIwMw0BMCuw579X7MW/kvO0syZCOTGEkTQ/k9cx2/UTZZkTQsmGw1hieXBQJbwI0bWliXqk75fvOYNYZoauJajbqVNidvkVfJHdpYWui49bbakHioN0D6YK0TlF074lpYp1fLNvyeK9YQt5W8rZqezYMODQTEsF4/n2Barrd8lWVvXJWanfP7CQTo6fmC3UbdLBuywyHN4xZRWocQTGDh0cOhyPDqH4fMSacmD1iLVZiqP5E3XQKto9kO+nEOuh1dhPPlq0caOq6FXq3YROeL1WVngyf6foc0fg5Hd/xTteEYi4GBvbTvOmJ3w+RYDKD0LP42+CVwhm6ifXfdTOHGHpVYHW9KIPSysoaguJUWCApWcWHu1CzqufuJc6+FqTdQH4tNFRZ3jLBJjaK3tR+0od4Ky1EwyQot4fKDSuh5wgaAOwy6WGgyS0j6Q/95y8ZLr89vixQiE5gupZ3dyhDTLQ1tPPfm5JMLIp3sorvArIh7YHi7hLoPhEOsQGoSBoGyyg8il1ESs7kdeby/EwYT6TDyQalJlI6gPM7P6KWTIhHPWlipLhAcw2qaFuzWivU2lq7UaywR2JvrElNL7UN6rjhIM4AtC6lLk21LErZKGXDBXUQb03Hzmu1YOM93kdCGqmXpl0Om1bc8rJ3D0RQanS1OtbAsRAiTarW9LXq9ZpWpTPgTQxfiDGAaClQNg1gcThyOGZTtBJChGkciSlqOejVSU03k1iQWLXtmTdaKfpc6zNKrfvH6Zye5q3C1r1RvNPMoFXW3LgujcvcDRn0visPpwcE88MQS4wcSsvfQUOdA2ntNgkK6onbmqiwe6/9bYBKE4zb6aukKwjSZ5bc/v57p0w/z7z7ggiOVjfVwDBldWfG1X1rth1w1MzKOTExHu3U7QClrZfOMdpJW4ZrKcZlTMsOVt62DHt3BPuDcTk0n7iBE7deyK0E+Tlu8U89vlDhyuqlfUHcjHpdiLhuK2+LVKne+itYSSFOue/OJXvbphzTT3dnJ+RWmIZHUtIN7nxQhWMX8K2Bn5mvrywfPzIc3zE9vuPNV99Qy0LwI+PxG/yvhB//2//B5XzGhZHx+AY/PlKa6T/URtlWfZ9WD2tgE8ueenKqrTVn79OHhOCpTWvL4DXbUCWrmVY28wytBoQekFYp20zOKxCodSWmSUVrmg4xRa3SqKZb34qmv2E6AlByMX9PaGVmOh4p2SGonkSMk/b7pRG96jnEECk+qXL1NJJLZlmvjHkmugdiOoFbgYUQBi7nK+uWGbeNY4XFO/K27BtFzGoQGtPhgThGZaCWQnWevGmQ1A3sWZcV7wLDmBjGSMmFn55nttwoam2uJWnU2j8mHezqniutinqleAUfY+i8F2NGNgtEXtU7OhfPh6BAoNEcvA/GeHC7O7ozRTNtZYrNbmlKH7zyerRsEw2mIe/Yj0se2oYUZ6WGZouO7mbvjUGr68Z5SA6WeaFW9VpR2cP+rrBhumAlQzPgWxW77pEO3WF9StXKlJ0gFuh2Q59Rt13PzvSeV6D8tdqj+rrJPCi0VbVnDDFpve3U9yA4U1fa5/arGeTKXjv5kIxYI0j1tNyoPoJTwVrnHaVUhKJt7FLwMVBqpLoD/vgtKc3M88paPjIcH9mun8irqkxNh0cevvlXzH/8z1zOZ8rzzPS4UpYL3qFO1aO2ObXtqYuidTc00U5NiIqtBBf3KH/TNdQFW4q6dtciVPHKn0D78iKiGhp52zGFaAEQp+ZAmmVb65XC9fqB2vKerZXtCk3NgUiJxsA4DDgqeU2UvBBCQichVTV321a6vHzZLowpqUL24tjmMyFOLMvVwOAzKUVyUaFeEcdy+Z5tfeH09BWH0xPShHWb1ZV80GlTqOQitKYgZGueJtolaLUSU2AcT4g45jnzw4cz25oNzPSsTrkWo7nLR99U0Mfq7FJvabZK+LMP+3VsoOEsSzOF632D6LGsjbgbG7SIeonulb3T9x471bpC8YJPWLtUX6OYepn3Ydc62asYUeC8+4k4HHEcEVF+UXSV5nSOqVQFUJokxUvMrpA+s2T4xP5wTn1OOg/Jyo+ebWmfrA9k3nKFXQoAZ4eRWMZs+/kL9v4XCtckxRj2ckKBHhWRVZMV39M7h6avoiSk7jJWO7BppYC357RmKZeRTprzlFpp17OdIEbLRScKRXQIqa6RXDekrLx8/Aiyqdy9CNu6MaZIrnCdZy6XlccqlO3MkBLu9MDjw9eMx0f74J2CdOuVus1aZnVdRtDyYucx2KJC9OS6q0trrdx0OJs5jpnXqVHKffBWrpS9dmw0WqnkbWFbVPau62q0WhGnLeGQAhJ7K1eVq1UcJ1KlUrwDKazXF615a9YysdkmE+3MpGkx4R29pm1diMOgHiBOZyqmw8OeSYg0pmlSzChqybGtiqPotG3dg6x3yl7EOWoR1i1zXVbWNZO8kpTAAkDQTZqiI4U+G9K0q1Ha3RSFKbB79snHndgmaj7dA3l1rjfh9lS+g3nS5CYuaydxaxUJnTimAHm5G3SMSR3ZNNAXnSI1TEUxAstOpEJV3E5SQCRqSdqq+p065YZ41H2sdv6PoJkHd29YbgxR5zAsMO/zJQ6VVtAbIXti3nVgeolyU+9iDzTe9ZvyVwgUPqiRivd6UvpgwrVBdR5c6ICQQjYaJAoNJR+V1myja0QOMeoNte/pm6rZVB8hUNdZa/MGLibEDaTxaEKz3uzbAqVmzi8vmt7WhtRK3jI1NubrzPV85uXlVVO5MtPGiZBGfDowTE+kccQFTy2Zss26cEDduFFiVnPViFS6Gbq8e2fciQUG0H57xzS6IrfvNa9tEEW7G8HEX6phGnlTKTrv4t7rllbpWpwheFqGZbmQS1GORBxu7UmnNWpZteRSIFExDK3xRWX+82ynn44/l1wYwmSUX3VJOxyOJtiji+twOBCjKYXnwrpad6kWC6TFnut2Sv6WC/OyMS+qkD4MymatppYdoidGFarxPuwlXbVSt3f/nBGuooNswF0HKjtPRd3DxIyV3H5qim2tHSS0D6EP3alyesdD3D53o/L6bgcZ906GKHdjV/fSf6LDjdoeTkhLeu9rBa9CNp0Tgv0M2Xd0u4EOHePQC7/bhV0MR7jJN/ThS3sTO8P1dv3Qg6Xc/vLXChRxOBCGEz54Uhp0dLqPlftoV6h0bT2ptG1VirUNm8P5SPBJN4cz8owxMFUbUVH+Jp4AbNcXluuVbc2Mhzcc33yLqiAr3hHHA0MTZJ2Z1wX8EVzCxQiS+fG7/8xPP/yB508fOJ9nzYKkkXOluZGnrXKQhPMHhnEiPiaG6ZHh8CeeP37Py8eflMhlg1YheNb1lbgc8CESoxrx1LLR6kpnUZaSrVWop81tWlFbYXm7WrqqG0paIS+vChIKDGnSwSzn9yna4XBgnA7EkDi/zszzmZwXnHMcjydAqeTOMrC6qUJ5sc/AG2tU52OubMtBO1eiw3PFO7BWsNBU/GYYCd5O/TQSo82srCvX65l1OeuMj/bycK4xDEFBzCKsW+U6F9ZN3dNjDKQUlafStH06DZFxcMTkdk5ArkUDvgih42BOcEFU2ds2fUOFkNUCweZMmm6YDmDquvQ30FU0de8eGxoYvHqROh1y9L5PETfEHPLiMNLqpmS8shKmB8PjZMcLuvB0a+pjG/phGhPNZUpNNHG7Nmbv0WrGqvtHjB+xx4wOatJZu7d/cHZtn6OWqrQOBvoKWk/ZEdVvyZdwKb4sUMSJNBwtgwg2urtPgnFjzBUFt0o2UFCoMjAenxgOB1KKxCBs61UdpfJKy5liitDburItK26eKa2wLAs5N05fv+Xx3a9V0g3HZV4IAY4npS6vy5Vmi2uZLzz/9Aeev/8HXp9/5P2PP/DD9z+B6Mk1HirNH3l+/oRLD2xNGIvWx2V5QZowDBMxDlwvr4gUdYdCbgbGhlHgVGZOB8AKm3mXtlKVihyT6Xlu+z3xpvzkTPr98vIjeVvxPjAeHqhl0Xtas0rvh8AwqZiuSGW5nlnnK2VT493xeGBZNioFcZ4waHa3LtbVKFk9SXE6ASuNss7EMSmzTyqHw4HpeKCrQC3nF7ZlZjwcGceJVDa29cWMfdS7ZOgS0PTRb8e8VLZc2LaGd5Fl1Xkc7xxDUJWuXI1a7WEa1e0NtIxq4smlWdtWuao0bT8G73Ch2fAW2l7wNj7urYyojWrEqQ7itdb2iWYnDSfhTo9SwchSMqU6Qg0k61J11etWtBUfgnqC9CCghME+CFahKYYR49EyM/N5KZkQxVi8bg/OPkbl9OzYnW3gbm+4ByuhOcE7bRh4w8juyyd3h7v0tGSnre/5FHu59AVx4ktLj5uOoAYG/Y+m3rKfmFKrSaZj0djjY2CYjkyHSRdFy5RWcXWDvFJWPUkFZShWacg2k6sgJMbTkcd3v+Lw+KQYRRPEJ90IrUDx+KB07FI2rpcXPr7/M+fnV+Jw4vjwhunlldeXF46PjyTntbQxI5qcCzDT6srzD/+Fur4idVWlJcp+8ux2bpZvNmmkoCPN4oxvYEQvbfHqIs/m06HlwwiiuhW1VpbrB8p6tvax0txDCEpLN01PHwfttojiKHm9GjMQQkyWNWgbMqbEYXpgrWeKLwzR4/0A3FTQaaJZUvC2wXQx5U0NjxzKRJSqStnLciV6Ty0rXc4+Jl0+tWZKUa5EKU2NhO1XEWVoOmkErwByT/9BhYvGFCyQtn1oStqtPOhtS9vzBH9ja0I3AVIMppcDzYhQvW6/Zdk3ToWeqvoV79VZ3BmvgthMS9M2lXem4mVzIN5raW3j7k7CTl0XMywKyQYdgSaF2FaG4GjiqZZJ6MyH2zEGbqtL95hFMhGhKK9zf1++E1Lg1hXt39Qzin7dnj1T6UHjrwZm9sduUmLUUi13qpI8qhJ4am9NGRiYhpFhHIkx4J1tgLIpSzKv1JypRKUmm+BIXleWtRCnJw7jI9PpwZiMWi9GHLkIUjEevuoJ5G1lXa9s68JWhPF04vjk+apUrucZDB5r0kz7UeX1W82UfOX1+UfW83uoG6fHR1JSRNoH9fPove++mPs5gNwAz07JdkDJK7SbKrXytBTMrLWoHibK0gwxgaggb89MpFVCPCCd/bmtZleogsTOqcsWdL1NcHLQrK4JPlQGvAbRav1CRFvE7VbzOufI67Ij+8pmVKOb1grNOxAjJ3WeQyumo6mt29pM58F+TDZDZG+b0Qe3Gwx5ZwxM78hFOxwas24noXRk/Geb2jdovvMePGIjV3Q+hbttg94JsJGsz09TW8vB+Bv7Z9h6J6LjAH3KuAetzgxm75wojnU/Z9GDIjYLlIl+2hXDWzOcYX+X0FdTZ5I6wzw6+1YDgtvXke8HlwPcjWtx/+jVR9+rHST9kscXBYramtKHpYNMt8xC0zQxVSdTIRLBO20tTscHhnEATDBmXdQvYr2St9V8HSvZ0vPW4Hq9cn69cno78fT1qKh+rSpm65QMhIFRqpeoojTLrEEipJE4niA+8nD6irdf/4o//MN/xKFdhG2Ztazwgbyupi+wseWF15ePbNdnlsuJ3/zLf40PkwaX1oNkNlDTBHbsw0VEQdI4Ka9im8nrCz4cdPrUR03bnVoH5O2K1I1heiCNijPkvBDjxHX7QMmLpsZe+/I1L6yL+nWkOFoWppssjJMyFEVd2w8h4oIK1jgXiJtXslQplCIq9WamODgVEbpez9pBSSPeO8ZhtGFWZ85fo7JHnQaNbV1Z+2tW/byRDhw6ctZ5Hx880fwplq3uqfeYVDEqV9WpnFKk9LUE6HcoONtxuhQ9tTWVC9iP0c6K7Juj20UaBiGN9hcmPHt24/Z7rK5kuWYGo8w3hMStQ6dBxGwq7WQPPlC2propRl+v5YadqDZLJUQjlTUhl97kvL0XSx/0T/ZPytsy3VgnhNCFfwHareUpIM5o4Jax9fykF3E9WwOjdv/Cx5cFilopu9rTDRjax7utE6CIr154NK2JcRwJMVC2TF4z6zKz5sI8mzP2y5nTm1+RDkfidCBvC/Lpg0q4V21JzZcXznNjy9q3n6Z0ew9oG3a9fGA5f2C9nqkVHr/+DeCpUsiL+kGezx9wr59Izx+Zn585vv2Gw+kNx+Mjj2+fiOlEmk6syws/fv8nStn4+jd/x/HpK9Kgteu2vNK1OE9P7zS6+2D6l2lvfbZWNFihBsPNdA6a6MxM8IHjm9+A9+T5dR80yssrODUUUgCysFyftVyojcPDt4zTE/PlleXySr4+M339W6bDEe8923ABNzAcrszXM+eXjzx+82/YlhdtYxK4nD+Sl6IO66XaFG2l5kzeFpDAmmYOxyPDdGA/9Rraht1W2qoYSG1aavogdp0dY4AheFJSkLC2xrIVxYlSJMWRnIWaNXf23lO2RTUlcET8Xr6KZQneNnUVMdp4318953C73kOv2XtmfjO+xoBmyyBwlimp+1cuFZMaQVqjlI1p7NSA/nUx7U7FSdLhRF5malV5RUeiOpAaaSWSvCOlE9HpnEUXuO1t3P3m7gIUmgrsLnRts/KzmkSfw3mlgO9ZjuhM1l5u9Y4RyjFpTajbbIdLJxT+/zlQKOW56gfU+jBVpy9XS5H07602fBwZpyPHhwfGw0jNM8XmF7Q8WNiWmbyulFpxYWSYHihlZZ0/8vH9n/nDf/vIVyuEwyMfP/7I73//I8tS8X7gq9/8ltPpyHhIDEMixUSperrV1nShuKDDTW6kpkTBafZSNvy6Is1xeHqnKXTZyFtlGA5MhyfKttByZcuV88snROD09AbnHBsqrjoc3qD2AB038zinLULNdKJOf6pwhi7WOCKqKYe4gPNptyL0PtCkl0PRCD0K/DoaTgrBQTg+MJ/NbDlEDo9f67BdnADHVmbOlxculyvX85nr6ystnDgd3zDGuH+fPArbujJfNbtzKUHTcmO+ZrI0zeKuV8Q5G1U3hp8rRBOMwSkLtAv3Vhu2i8aZ6FoU66bixzEFreG9Z1lWSikELwgBabosO7u3gwq9y6EWow7f7fW64Ittrip6dnpuLcKbZYO/pd72u7dvDjaC0J208raRUiLY6H0pzrp9SfE6bke+Uuo9aZyQGlRwye6LM4nFbbuQ6kyKicM4spZC3e5LQTP8cft/rFRsOC9KYvR+L3d0xqiPwvtbCbJHCaHPdnTDJUH5G8rb+StlFK3rLvQf0EGxTizq/2vKloshkYaRcVLuxVbK3oPeL8BF4nDg6EdCTDpLMF+YL6/M1zOvnz7g0oHp8QmpC3/6hz+wZdVEeHn5yOO7d7x994aHpweOhxPdyVw/Iy1PnDMzoJCI0wnii0rzoVN9quKtyPi2LhwOiTiemE7vlHiTF7ZcCMuVNOhIeEW0W2PCvOLub7olfK4DX2GX6ev/1iya33ALTRdrVcfwkldCGpV/sq2I6ECVOGgUUwRTzYeQEq15avOsmw6dvXx6Zdlm1rWwlcqWG9fLhXEaiV4Fg4ZTIMbEmDPxOJOuL7g6Q6vmS/LKsmi7uhYjwZVm/ATFL6JnV3nSQ0Jb4nLXZdA5Hy0Tcm02s2Gj21Vd0DHx+lqbjXq7/ZfYWhNrZ97dYbuHst936buln6a91NgR/o5/2PcaHqL8l7DnHw7TRwlKx+6tfGkN8Y1d9u5nJY/OgkR7zbxjLXivdgx1w8VGMnFqHQ0AJ/16+09nL+HYwdybAVXffnopVq4YctuFh+0C9+263wKjptN++YDYF2MUSm1mR2J3YO/uI+hU2RAH0jiQBi0Rmp2iLiRCHAlxIE4n/HDilAZKqTx/fM98eWE+axsuL2fOn97z43dHQshcX35U5N4HPv34Zw7vvmX53e/4avuG+uYdKbl9wMb5gNjMgQ7uJMaHt4TzGUF9Q9LhAHGgWZtyWa6k4RGfjownBQovrx9prrFtmev5YiZDzoR2jFcvtwXYP0jn9dQOIkjbDFdxtKLTl84YqspijeRaycuF6/mD3j+fKNvCfHnm8PgVwziizuEKnHkcPkZcTZSLgpvzfGGez7z/7o8cTiq0E5Oqc23XV9bTqAHTH/HjWw6nJxqOtG1MywVZPkJZaHkmRs+PP/60T2J6D8WIaNjmyE0YTXIw16ptTJv21AZOsw2lzy+lMkRVW/c08qqiyMk0V3Mu5Nq0jod98+z8IOeNa1Fva5DuI6Mbo+swSEcbcbf1abtLpOts9m7d54HeG6ekA5vBPHG1rIYWtCzUAGLtW6m3TekitWWjjkeCU4/dUjOxFeWldFsJKyGCqYnvAQDNnlrrg4h3Qe8uc9AgAx1B3t3YTFTJ3V+Z8/u4ws9Bz3/q8YUZhdbc/QerYMd9o+UW/aQ5BRPToH3zbcGlAzEBeeV6vVIl0PxkdaLnev3I+eU9tRbieKBJZDwd8Ckg4vj2d/+KIqPSlreV8/N/5ON3F0q+cr2e8X/fGJLn+npmW611OAXWWii5si6bArLVgVNH7pBGlutVSXHTRAwjORfNhI4T0/GBSoSmxsjff/dHtlJ59+2vGR8ceFVqktZMKm2/EzZxb8NdDlpT39G8qD9GSKOpccH1/Cfml+8peeH48Bb8yHp9ASmMh5G3v/obpZWXFeeDMiKrcHm9cn75RKvC4QTP7//Mp/c/8Pr6yttv/z2OSHQLdbyqOEuunJ9fWPKZ6APnhxM4dROP6UjygypSxYFfv/2Gh7fveP74nvPLC+ta8FJphiUMUcWDCPsKoXaauO3R4CNDHMilsWSV8htT2IOIkpyUhSnFmR5Jn6Ux0xqCgbbOaNPWdbHUeydQWTeAKrgG3Z1OENvQf2FrOIcjWldD9rXcJQ2RSitCCNO+SRvNAOOClkaKIeS16hSpHSItrxRXdZbISsJaMr6uuLDt39vZod38u+8ocaIljo0s3CG1+lvP4u3A3s2T9xaHBukOd/TiLcZk7eP7EPJPP74QzGz78M3th3O7+/sbVZBvmnTR6WitnvAKlK1sy4WX5498+vSJdc16wvvMy8uLshNb4fV1Znz6huHwlunxHYenb/mqjdTm2JaV5fXMT9/9kfX8wiVFXt4eGUJkWRaWdWFZVpq/MAwDry8v/OkPv2e5nkmycTok3r555N3bbximCTxs26pAnPM8DEeG4UT0ldObr3d9i5wz5/MLLgz4+MDjOxtP9g5vo+ca6Bvc0aPF6NmtZONLGHtOiokAbYynt8TaKKWxrRdlnR4f8XGkFuUziEBzgZxXzi8fyPMrwcPjm99RZcO5inMZ7z3P778npRHvYTomTo/fUEvhcj7z8uEjwRUuzyPDODFMR8KhslRlnw4pMU0jb77+W6bjVzy9O3M5v/Dx4wdF67Xwx3vt+lRLy4P5u/ST2uOUyFQU3J26wY+YQK4Y58AmvbSb4vbyRH+GWgpapYvKDThw6me7Kz3ZklQpfttYOhixl3b2tvd903VUdAkrKKht37prXmi8LzgX7XtuGeMur2fjCpDouIq6x+kUq05cR8UqS4GQCf5A9I5alC7gXNARyl4y3XVwxAmfi+GKidbILjGxU9O5bc5dk+JnEVIp9n+lQKEIrOwl4N1kDXr77ttT98Qsx65i3Nq+6Nd15np5YVk2xEWGAXXpXq7UvLKuK3484uKo7bowcnp4qzLvLSjTM/1Eq4VtvnA5v7D6wHVema8z58uZeamMh5HL+ZXvv/8j6/XMV6eJ4/iOOBwZj29I00G5HyVTcybkQs6VNIjNtCSlCjdBnGeZZ3x4IQ4fmE7f8+ab3zAkTfOq4TBi9agGAaAZocY5G7tPO52aVrRMEatB/S2t7Z2VWnXMu4F5pAjD9ADOmfZGZr58Iq/zrk3RaiGeHpiOJ9I0EdOR5XrBzwtIIefZWpaJFOBwGKnV2rCtsmyVMQz4eGA8BMRFtk3nO3LOOmeSy2cZZdeMxOnnrieeYhDByT5HgXO7BBxWXnSjZ1VO71yBrvthw4J3+39fa7bGpAHelKz2WsQkEPn5ptgBDH2/9y0CbIM5ZYV+vsdkDzgixqXYr1329L/XDztvwajyrSm5LEhToNdauNoy8siN6Hyfp99wC+wQ6kW+s2tzt5mXzy/V5Cl7FsKekKgV5S98fDHhql94c71IsqBgN3SfhPP25y4w7MzItollJur90Lsg4pV1uCxX1suzLfiNum2k0hDUpWmaErWt2odPehqu60JeF84vz4Dn9fXK+Xzl9fzC68sr4zSwbgsv5w+EUgiPB4bDienpa6bHr0lD0vkGgVI2qk1khnXDk2hVKKVSWsOHxLqtcDmD+wnnJ7757d/z+HjAUfVaaqUZ9bqWTRdbVeDMd0nANOr9bGLODm4fNHMe0nhE1bDyzsAcphOF1TQ5PY/vfk3OM/Plmdf333P++APzfLHR/EZKiePDG05vvmY6PbEsMz5nfIzEAPPWSEm1ItIwcDidEIR1nVmXhXndqFKtT+8J8cDx+EifmdCAURWYDH0iVIexep7fUSvvxXhTjdpuuEGnVoc7tfA71O1WEvQA4XVPdWKbdiat0yGCVCVxee/2f/+8Mu5r9t6t3EBTueFsgM2v3AUGy146aCvCzqXQDSg2O9O7IW3/flVwU9n8YLhB6AI9O7LXduB0L+/396L7bBdHkpt85D/KC+6ChXqO3hHN8JaFONW+/YWPL2uP3r1xvWeaJnZketdvFJUb23Jmqgma0VhbY8sb67aRC8r3x5Nr5Xq54oBtWXS8nMZ4Gvnhuz9xXSrNP/B3/5Ma6Eo7U7YLkje6AkEpK+/f/8Tl9cL7j5+4XmdrucE8awbjmvA//9t/w9ff/o7T09dMb76CdGJ8eGCUxpRXtuWK95FcKvX8Ql4DZXtVJD5OhOkBWXTzl7zy+ulP/MP/+f/md7/9mmlwuLZCa+oGJtmmA6NOofoJlx4I09dmTFyoFbbSmK9XwjARhxEHWuZsqy0Gz3h8JKRpd173YeDh3W9Y5mdKWaDMsJ4py0zOhRAjb3/1K46nJ6TCpx9/4vnjHynrzDZfkLxScmMtCVcGJCf8OfP47htOh684tI1PP33H5fJMWxek5L3d14rS0b2YzID170VQv1ing1wpBEJ02lJv2matVRhSb0gaDRmry1vbO0OdebiPS6MqVbVCcUoo6zMQHeTrGEQXxOkZmfvvEIt6x8lbsFg3Xd3BnO520VoRpOqUr3faBRFRcH83xrb3GMKgAb4VAw178PHE8QgUGxyTW2XkIHqroPp+tp1u3fG7Pdi7jArXNroUobPg03FDDXSftXCbjcGDEiF/8c7/wkCRq56qu82ZXWSrKu8F7BH6RhJpdM+LLWfmeeZyvXKdZ7ZcuFyvnF9fmWcF6q4vn1jni6oqtcbL8wv+ApkHfvrhR56eHgjBMQ6qDuRpxODY1sJ3P/7I+TKzbDqwlIInRZ0KnJLndDiwbEIRpTyXbeH1ww8E54hJa0sXRhzC9VXfh3dwfHrCuQGCI46CT2fWbdGF34QPP/wBKa88nAYeDh6aMitF1A2qlDNlW8ibOrHjRqZ3f89wfIc4mM8/qI2fyfOXspHzokxOF/REl8DzT79nm1/ZljOvz+/Z1plaM/P5E88fvuc6X6m1e6Im25g61Xr+9IHXj+/ZrmflsOTKkivLD98T/E+EoHyTaToyHo4M00SIajrs6oqXTV26cgEHwTem0cGmClOd3emtvReCivdIvrlmu460i01omtubqnbdRFluqXYv1P2+eQQVqQ02CNaa7JwGv+cvmAnQrSzQg72/uj2kj7ArWFqymiO5qE5it1REI1AXv1WeqF2fORWpK3rVVry1xPM2k+tGw9FcJBGUVVobUjI1KY9GOzg6on8zU7L3bV1G6W/DDmN9W5pF7dmUGGYR/G1OhZ4JYSlZsWrEU8tfiXBV7YNxrtc8sqOxzdKZfWCvqz9ZS7XWxrosXC4z58uV1+uV50/PvD6/cL1cdBR9vTJfF+Z5ZpkvzLMK6Lr1hcqf+O73/0D+9a9w6Jh0iMrpd1Skrszzhcu8IWgrTxDmLRO87GPe799/ZBgfgEhMIzkt5OWM1EHnPZoGva7knWsmpGjqUTqVWC21aobWr7nw8vpCyZG8JB5Pkfn6iT7m3ES5ECVvlJKp5Uxp4F++AxcRAtNXX+ODDlghWUlJwwHngtLN11ekbvig487eC68f/qgiN/OZbVvJ5unpfWAYj4Q44XyCrp9RtOtS7IT3Tg2nvdfSAFmpW+Gar1zPyoFRS79M9I3DGE1VWpef7yIyXnW1xGr9KtoBapZddnl9RPUwgjl+4dg7E/3xczS+d0D2mGGCNqGXDr4DZm5/jia6dzL62Kh679CJU98P6XJzmHDSLYx4nJZQdsKro4QCiN3VS0Kz0snjo99xgg54BiOgITbX5BeaC4SWcNR9TqMLF2vZKQaCWqCUyr3wjKePtYtygO54TF3PwpPuhKNUF+UGE6hnjkadyi99fFl7tKdK9JSoI7HGcJO7G2UQRmta32fjILy+vvL8/Mzz80fev//A+eXVNBihbBu5FNY1c7nOnM+LfWpncnV89/v/TG2LtSOVleijh1yQuirHoFbtwZvQ6bJtnKYECFvJvLxcOBweiHFgGA6k6NmWi9Kpgw6VNXstUMr1Ol9MRNVZO04XXbP0M1dhWdVKb7s6puEbrpernlhm1rOu6+6ita2zqliXjHOR4fRrHr7++9uClGp+JiPiPE0WWl2VYRr0tIop8fL+B9b5oq1g53eAz/lACKMGmcYuXEMr9uHpohuiZzwkq7OVlRhQ7GGZZ9Y5gNd7F7x+203mVUVnoocWdXahNRWjuQ3Ndf6C3idpjeY90TZURLsmuoB1R9bm9m6DQwOB1H44ibqB0aDPO3inU6Md5ddoQrC5jT1Q9FXa9MDQH6u5Qedn3OZKLVjtA1vYDE2HHPRndI6FeOM5+Eh3pPc4A6x7cLKuSLOCwelAVxcJUse4igvKet6nVndOxA1X0a9bqQcmoFQ1qzJym/e9vQtYRk//937Af8HjizUzm6nq1H3y0BBqryPb+qGrrKYg1FpY18L1fOb9+5/44fvv+PGHH/j+uz9z/vQ9QxSi1WzOq6JTa411yzivBrJio8sfvv+vlPKRbduQ5vjVb/6e4eGBZf5AqQvHMXFdqonksrNIvdeFuqyZYUj4unL5+BPbPFPyv2AcIo4jRHXe3uazuZHDMCWWywu4REgDw6TWAbmo10YuDfzAND2xrWdezx8Yj294OD4hbVW1rLrx8vxJO0YiLKsOddVtU42K9l9JD7/i7bff7hofcTyp74iBV9PpK7yPbOuFcv6AMID3xHFSa8M0sawL122m5IZPK/PrR67tJ8q2slxfCD6TkvmsVCGNI9NhUI3LCqfDowKS/kpuV3LOHFJA3cgrHz7OPD4cLItTVmYKniSN0tNj54iGM/S0OEWd96jNxGyyidYUwbvGw/GI8z0DUDEbp20QIOJd0c3TNxnarvSuj5jr4uzpepNqsnH9FDZlsWZhzqY2b21SY806oE9/Km1Uu3Q4SAY2moiND+pZ2Gqm0qjeMR7Uo8XBrjgljPQ+pw8JFyacn3BOT/1A2x33RApUh5FAdhEc772ZXnU3Mk/bMwnLgry220OIBCtZ4RbrXIcM+t9FCDX94r3/hRiFApA4bb3RepBwdmMMvXV6I3NphFwITij5yvs//0f+y3/8//KnP/6Zn376yHScePfuER88ta7aLy/qBD5EveFK6VXx2h9//A7CylZVFOXT88zf/O5bXi4rn86F17nu2pedQqv29A0JWheneOTx6Q3JO1qbefnh95wGyI+PDNOoA0DbatRoHRXftpWadRQ8DolpSrg0meAL1FzVYbsJOS/85//wv/Gv/92/5/HxLafDI5eX9zjv2NaVshXTwNhYt4V1ndmuF9r/9v/k3/77/wdvv/kNaThqABIdLirbwuvze05vvmFbLlxf3vPxhz/y+M3fU3Lm8vyR5z//H7y+zKxLxofEmzExjZFtXfGDcHz4hvn1RbOFsICrpOlArl4D3raSi3CcDvgQOBwGcsmsW+YwRQ5TYtucSfQHA/8KkqwnH9B0s1gd7x3BB9YtqxuC/f00OkvHRU9XA71jVEnFMUU69ujMTOjW2LMT30q+3mlxuL3e9tzYl/1kt66pYgr2v9b83gLVb7BJzqZlizNx6NagNsWihkHTCmmFWr2qqBkPI9dKKJnYJSFjRDGGQrerCGEEl5Dmdo7NNCaqjIgT1R1t3ko4vQfefY43YBmFD52xKjeRG3uO4kV1/5qDXdfi9rDS5hc+vqz0qI1ciqU6OhSln2WzNLt/pA7nKvOy4D0MSVPMUi6UPJO37lnhmZeVJqrGXa21FIJnHJIas/hbStikcLlcIehX1uWF777PnC9nrutGaWLalLZQLHuspqUYYiCGgZgODCmAbNSyMs+vSrgq2WzjIA4DkYBvQmk6addaYcsrtMOt5w84l5kvr9AWKo75/JEfv/sTtXzLmzdPpOMTp1yAT9Tyqp2dXMlFZfHwnk8//Te+/8N/QgTeffutmtg0lf+fL590YOv8TF6vrOsC3nF5eSavmfl6UcBMlMTkELbiiONb4nAEqk5bbqo0NdSIDEIuG/Oi+iGeBk3p4SF60hB5eDhwuSw6wh4cp+OBkvOeDqcUyXlV4M9B8EJzPR3Wk/u2JvSh1Om+cpV7oIQ8t7cA3Q2QAMmI9PajPd9S8q4J4tEUtrMUvb8FiyaNKn0Gw8rn1qhB1auaAE3vj9gUae0yCfRSWv1Dut6ldjlMpMnfOgxSK817aw3emJIddOxFuhP1tpWaEVTrdBxGnIvkbNqj7q6c6mWCAbT7XJFlUVo63TZ+0/aG3nfriNwDot0iQ/5agaJUXdzVaNwBIwQ1+5EGujgHrlTWLTMM0YZfLBh6b6BhYdsW1kUdu2MctCMA9ndTPw5BgWCnRrbblvEpaFvNFV7Pz6y56LxC/FzuPDjsg7wh02E/LaIKiIj6Z5bW8LMNfwV1KFP6uWe+zkac0lOA1khD2gVIvPNsi8P7gvhALZnnjz+qDWKaeHg6MB2f9HSfr7dZFOfxYSAmuDz/yIcffs8wPfD2m18R4kCeZ7b1zDq/KM4xn1nnV5bLCyKN6/MH1nVjW1fERW2LiaNV4ToviBsZp4Nu4JrteoLpalbyoiY+zqkxTzBhmg7EHQ4HZc2KnuApmRRgzbpBnWdrUG3isoOjrSrPANr+ubN/LrY+rNzsWGRX0VbMwncwQOtr0TTaSnMDADVetdrMk7SLGYutQ8FZ0FRc3ZAVyyxUMrFZrd/ssNPnNfrwI7fN1xTwVB/VXvpXRLz9MmCzNcQ5o7K7ff9qMDR17qYizV2WQd3NBnyrULp/TPts8/fORRe02b/2s4fYPrwxRt3+fj/PIH55kIAvDBRrKZDVizPFaJFTw1y4vRu8g+abGf02SvMEHOPhDcPhiTg8I1K4XmZCHBhGnXnYatEsxevgzxCDWbYJeBVqXUwJKSAcToltLbqAvSoUzdcLy6aovotaFtWqEunRq6xd3VaqB5884zDwfLmwPp/JTVi2gognxmgbfeR6+cSQ1HdiSlC2hePxqOZEQQGjEMOeXqaUWOczr88fiGlgGH8DfsQHxRO6tkOX4asls15fOX/6gefHr8j1/8bxeGR5/3uur+9Z1wtPX/095+fvmF++4/WnP1OKM3HbVecl/KCnngu02vjw4/eczy9Mh69JYySv1ZSkHaU01qWxzpnjSVXHQvAErzWrGMYwDANvHlSLUzkwSu9OQbO9vBUTe1FkPgZHCrf6uTQhdeWou8pBf2/7PmjidJ5K2k7M6kPK3gVq/6bePu0teAsM3hlIZ2bNHZyMFjhLFxbqqmTNNDUl3hibTQWRqmAqa9UOFc0YSiv6Ot4Toie4LqdQ7aIipVYd0hMMxATvbZK0FVqZCUnNjxW/Myd0zec0wO1ll0bYe06F/cseHvrvN8uA/Zt3jMTdZ75y9wHIl4WKLwsUy0Y3xBH8bs7inaMawOodhOA5nU48no4MQyR4oTaPT086Cr0sLOdXAOrRuAWo94N2D8yHIsFpOuDQdDFGhy+NLWuLbhwPHCbVPyi5UvLGmAJpSCrSm1dKaaTgCC7YtJ4jr2eCb6R0JAyB5+8+8eHlynnezPnLM40jYxoIMbKuM6fTCIdEdI5aZqRVxnFiGAfE640fj0emaVRT5Lxwvcy8f/+RKo7f/Iu/I06ZtG6kstn7VR0OH0a+/dv/C59++jOX5x/46Y//ia9//RsuH/6AiPD05reAY5uv2uXYVnAjy3plma+U3Gws/sjjG82Ualk5f/gvjMnB268ZDw8s6x/IDbYM87wxJEXI0zAxHk44HNuymD+HtqBTHElJR+rLtiAxMI6J4FWcJ9d21zFoQGFIRsfOsFVhtI7Jfj5a9qGZha5bBUz1lB6iNWa8Uw8YtBXd9T69AxcNTDQJQKngYyD6ZDqjheQ9LsDmCk6adYCMe2C/O8MwdMoy3JUZfRu5PSOqreJbgKbmRc4JXabOWZct50xtjciAC87KCIcQCChxS5zoIJ1Usz/Uksp5iEOk1T5Dok2CZtyJXsSJmIhP/9pdeScCHu20fZ5x/CwyfFnT40uHwgwtxltm1AkwguRqcmmJGKKetl71CLrTeRMdMy7ZDHT3NKqxbbNuAEt7xYNvznrKHjXI8rQipKGBE4YxgRTTYFzZsnZDXEjgPMMw4C1EB7za7CV1IfNO0/HrdeHDy4XX62paCVruhKDoc94W63crDiMuIbVxeX1lW1eODw8wDjjXcNdMy55cNj49X9i2SowvvHn3huPpCcjgI+JG0qjmxA0HfqBuF0R0GOz86U+kVIxpGFiXhdIuXF8/Kts0TZS10PKCR2nY4xDwFdJ4IiZ1EKtNeP/+O9ac+fv/6d/x9O4b1m0Bf0acMIyjgb2ZdZ55ePpa4b+snI9ajfkXPZHYt5SSnDxq0rtmm4ERPcXDLSWuAoZ5q8G513mMrsrRf3lvixtu4/MdhDMHLv3HjkFY27TrALrbnIU0Z5wXaKjre+pQwR3tW3UzhBbE1qbaINbqqc1Rct5dzvdePxrIXIXmVXUd+txFw/uRzhAtZVPJSFewpgwVIUYBF3AtKYtXDspctlIZ4zloCdQ7QdyVHRieq9mju8+wfrb7Xb9mvXr9mr9hH1/y+ML2qNhNU2TW2de68GdwikV4Q2G7kKv35sJg47hSq/WPFdmuNTNfM+uaAWdIb1C7NRxdTFTrWyEmb7qBXgOD9aNj8tRN00adMMSGuiLRB1JMxJDwsftaZra6smxZ36vTxRyCBq/aGiVXUlJj3VY9rakDWF5Xct7ACdGfCF5wToNWyZnLdaEWmA4JEWGer8r/EE1TfUwErxaMzY1c1gutNVzJ1HxlvXzcF3RZVnJrbMtZpfScI+cr3kMaEupb6jRr8wEfRtKQyHllfnlmvryyrRuPX/2Gy/XMvCzE60xKCvyqt+ZGzsXusd6z1p3eMWyp9ToZRJzRmTGcwfRE/S019pZJNFGODdzwip4E9xXdM2Qd1Vex245j7PiWc3cbw15s52FgGXWzoKRdBy9iQLl8tpHEQE1RCoQFmWqzSF4VyIPr8lfa2QOw2ZzmK763QukB7faesYDU/WrV+UxZlt5HJPXJ134IAagI8Z35F13bxWC2W5fGuTtH+z641g9m+fzm9iEyB7dBzvsP4J9/fKGl4OdBQjX/NE0NTnAuGsHIkYtShMfoGROkJECBmqEVTd0CiCus28L5srFcs3p+RJ0TqNJ0kMquSclKhTQOxEE1BDTzjYwjHA4Ty2XjMm9sZaOJY0ieYVApvnFM2qduSr/RLkbWkskW5mi96myj7rU1YhioWcg0VteIvrulV2qdGaNjTMlUyFe2dWNdG8N45PHNtzx9+zeW7naHqUATR/RJOQOiDu21VHzSmr6VK1IqOW+qMuWS6lFk1aicLx8Yxgd8UGHHsm4glbptODcyTE+EFBiHBdrG+z//kX/zv/7fWbOWV9syE7zsIHLJmdfn9/h4MMwiEUXI5gq/Z67ilY/gvG5Kp7Z7zbQl6maZA8ps1IGtTtZzDFGDn6DdEe2qmvystVXxHfPRQcI+T+Q8ePF0AQxty9ugVLhlHa1VQtQT37mgYrlrtmCjGZHDxKLF4fGGixRrmwZqVRMjB0qCupPkQ/S5YjoWAGqwYybFRnpyzpsLnFojrKbGXkMg1Ingwg60O+Nk9FgmfSObgLD+6IJOF/avgw553XIEZ8Ctbdjba/WX65+lv5VUv+TxxUNhfWpNHbltCAyUjGQGKbXB62xoeQo4EsfDieg9La/UTfUfv/76ERcby3bm8nwBCl4irnqL8rqvO/03LyvruuH8A8M0Mh1OzE2Vn/3oOBwH6sPC9Hrmel2Z18K8COPDgeHhG95+/Q6ZXzg/n5GS96RxSFpqeKeA15qv+zV3D4nSGpRMm0uH24lBGAjUfKXJkeQS4h2fzleWreLHBOMT01f/M4e4UdYX1usztbwS0kiVgJgy9Dq/QtuQCvN14c2737KcXykVcm28PP8BCmzrzLJcdXbg+E6XT1Zvk/N5pslKGld14DqeCI9fMV9f+a//6X/n+bLy23/xt7z9+u9wLrFeP+nGqoVSVrZt43L+yOoCKQ4czGe0lkyrAkTtBhjXQH1QB7y5W3nv2TYVDK5oar9VVXJCFLj1IVhJer+qAv1464rSexYhmBUllnLbH8w9XFolxaRZi/c2Oy3UthJ81PswHXZ1tmIdmeDVt6R6IZl4kNKhq+EXiVwbg/dEvAUyIz+ZtqbUjEsHnI80Ktt6JQ0T3g2IdW48jhhGYjrR3KBrBVFxZQw4LSZQXDYNcCZ51xAk5510dxvJ1CxPxOnkcdPyvLuC7dq8dAxIHc6dNDDNjT7Y9ksfXxgoZJ9w6+2j7iuhFOeoF29yaWFvxQVtOaZEGiAE9Qb9l3/3W47HQF5n3r+HZVUi1bpVtq3g/EAaJ/sQC2n0jHit5aoy+B5OR7ZVqcnDdGQ4nfA+EuOFcF1obWM+v9CzsadjAFfNrMeBi9RSyaZCtc8kBE/wnhD8jsO4ZiPBxvzDUusU/d45aAXWrTKvmTBktq2yZJiGAXEJUDHdXAUXI2odoKddiIEqheePP/Du29+yNUdukIuAG1i3M1tWla6YDpR1Vcr7tpHXlW3L1iKsLJfINAUlmwmMh0c+/Pn/gy9nhmFUU59rZhyjOrWniWEUgn9hvl5YlwtSC8fTwURwIa8rIJRSdK1mzxATXfY9WBlQbECs62jWqlmGd9qBEndXa3MTwe0zpapCV3DiDQsJ6EiIZrNaXnkVfLG1qROUvRTWz65b9IUYGdNIK4t+1raTanEUDzV0GX9rqdPI24Z3leojwQUV/m2YfFfPGixgoN6yIo1SM17AtwGkUgRIkMwisuaz0bVX3dC+S/Y3Uxfv4/bOeEF9g+s9Cz2o2XW7him6K9grxpLey5WmQVrFd52uXYe2cf9acv1YLOofSGeceR8IVhM2Q146E++mwOzVAChoXex94Ouv3nKcYFuDTji+zJzbSjZiUMmVMDldKFFLEryewq5pLZqS6XEKBJ+YjoMCTkbk2dbKeV65vj7rInmaVK68aQ25bitb0UEpXShuV/9RPoe5gImZ6nagzWpm1Zdwe5oLfu+Zr9vK6+srH376SGwJykzLCkb2cXqcXov3Ko3XtoXl+sK6bkp53iqXlwvXeWGbF2PvCXHwrNtVweGq04c42dPr2opiMU0dy8bpyLacWS6fqFkzv7zMhHAgDaO2Rr1nOukhUEyyvwyRmKLeh1psPsXAttpowSjhzUBro2Jzt5xLU2PhXVGpt/IsO5C7Eryn1F3IVjkG3sQEer1uKlRW/kAHxfVXbZ2+rd00kUqMgRi8igHTjGylQayUSgyAAakhOBzV+BZC80K3AtBBqgBmIN13hP7XgpNUkAzGtlTsQg8IPVgNZ4C7U70DuDvNC48QnJ5wwm3N9QCrP1fUS8RIYjhVNr8RpW+FhxZQfq8CvmTe48swCvoIeT9Bgv4KNqPRhToQor/5k/Z5+el4YpgmhnHi4eGBt2/foDbuwjg+MLsCbaNVIZfK63Uhjif8cVD16DiSkgaS1oRWKmEYDGxU5CSNB44oaaXVzDZnliVzvr7w8eMnPn4cmZLfZwTWZWHNZsUXtO2lJ4XWmDFEqjOLegHXHEO8UXx7LeqIGHNfQbwA6zbz4f2P/P4f/gGWE2MoDKFxfHjkcn4l45UV6RohToR0JOfMtl64ns/ENLCtmecPH/j0cla/U5Q4lEpmWa+6wLxnGAJh0Ho1RsWKwnDAt0W7IQTevHlDXhdqmQl+ouQLedNhJh+wqdMn3Ry1cHn5yLbOeH8kDSOHw8TlUmzsXdhaoeYNR9901RSsnA2oaYdiqxoUvAHVzhBOEbHWIXTdS4d1JGDPOlJw6tqFp+Eo2QKBCepotmgnqYDUVfEt64SUsnE4PO7BIhjrU/UtGrmoYExfQ9EHm2rtMglK+U5RR8JFin3edgju6jF+53aImDWjZDIgXnkpPgzK+/iMI2GyiF5VecTSGyeV6MSU0O6yCHcLE953kFKJgK57hRtzFWNQ060K0MNDMHLaXyNQaHanKZgPXgdQbICnj7o6hwWIsLeKmvlXvnn7Ff/uf/m/8u23b3CsfPv1Oz59+KDCNBLIuWqZ4gM02PJK3hZitInJ8cQwHHFxIeeNbVsYBjWUdZjGRBZCPDCdQFpmy6rX2Gpl3hY+vMyEHpltgQaPtkSt4+GDdlgEWFdd/LXqSZtR3crjYP18L4QYESqlOkrzjIcDw1X5Hsv8kf/wv/+/OH//Db/+5olvvn3L4fiAk0JZMtk+bKUZjwQ/ktsrP/z+/+S3f/e/cDgeeXh34B/+83+gcGA1wZ/jYWaaNKMKzunYuh+sPtVycH5VEZt5vnB5+cjxOOBoxJQYj0ce5CuVypdKCFou1LwRw8Dp4WscopyXRWncj2/echRYlyu1VYYUVQvT+AydTOmclofSlMMyb5VcdC3UpMzIFPVe12bkKu8RBwE1dhbnaU5FmqUUEn3zdpWnQHCOFlTfoQUtG5z3TMcDl/MzISTtevnIti6klPDuoJYIqyqoN9ShTPYNpxyGISTNKgFXq2peOjsEvGiXyzIi7yMhaSbbN6d68JoNY9MyR5IeBt41syQVPeG98ix2wRkLA5phWbYorjPDQfr8h5Zb3pS1m9eWqhht1aOZdfAO8ZgQj4PmNGOvf6Ux89pECULeM4SBPtrs0I3o9ywimtKvMjM3KvNVOB4n/tW//V/527//l5xffuTlw08094kqjdIKzXnSMJK2SvBwnCIpQc4L61qIw0g8japgLT3yB9Tj0UMI5CKMgzIk4/TAeFw4bpVcC0vWduj/j7Z/W5IkSbIEscMsoqpm7hGRWVlVXX2Z+9DQYoFdAtEQ4Q1v+P8XEAiPwGIxl97qqsy4uZupiggzHg6zqHlNDzqiadOKojLTw9xMVVSEL4cPH35jSDOiBNHl4YrWwiCC4d9Sw6J7jCuoWYZkGbHEhO5hHcd+h6BiWxe03tGGYykdt/srjrZShbuT6bjvX2E2cFk3tBsnh7fjjhEDkP78x/83nj78Fr/9w7/Dv/8/7fjzH/+E/egwKD48XbC3Ng1aO+7kr6wrSkwru91fUNZ3KJcnyO0zhg1slwuk8tp++N3f4vPP/xXuA613fPjN79DuN7x8+YTb7ZW4hfucR/Hy8gXP13dRFbpjtI5aC/qDbqaFihLp3IIqQC0Ig+AEa9uAmWJbFLV6COfGPFhoVBAGqhP7ycKmCrVGx2h8dIGD3PcdS61RGlVslyvW5YJ2NAw3rOuK1l9RjJWU6+UC91scRJ9d0EVYNnehTJxWmRGzhlw/8a8VtdZ5kN0EwIKyXGHd4Oqo7jA0goiiwRMRDpsWxDBqTkH3MAKucRyjy1OKQmy8iWwQcoM+SVX5K2zft5meRWphxiqUMMVjhZuYiP9aehSZ06Tw6aMeReaJHFKioeswYDLgA6jieHrasF6eqTp13AH9BcM5D2I/OhycSs0Zp45tWwFxKnd3irpen1ZGF5WYhGoJKyqA07vsu0GEtOZlvWC7HLgeDdd9wcveceSk9bhmc1rZrPen9LkIQ14tCzsJh+PYj5mCiSqknGPvqXFJevBSK2p1SOt43e+47htabzEIecewRlJTb1B37PeXIDkd0QkpuL98QkrD/fS7v4EbcL/d0XoHxmAVyQl6HUejLsSycOTAiJL7YN769P4n+PFK9XBlG7JoxeX5RxyhLm7mWC9POI4b7veYL1vouZMH0BrVqGvdACdXxpNC7Bohr3GAsHMzpxpVpgjuQKeDDpUrTLTeIqweFgi9EVcAMr1I+bxkBWOG++Ls7+hjEDjUEIQxA1DZV4GYUtbKFJWhuEukOAEkzv1clN2ggUFJRCipPuYOIMq4Vdg1zerJyUkRIaYx0wepkMD0EqSks0+eTaAeIhQeQkpMerS587Cn0hX3X65QYh/8shSYSmUQdSM/Kf/8GoYijUTm8NNQuE/AMjc2yUoH4A1WgEstkWtx9oM7F7n1gfvecLsdcBHm6K2hdcPl3crcN9p87/dXjP7MGnstJCxVhQxDznHsY+DYSYRa1gpV9pJs14brfcdWC/rg7GtzCa6/EJ0WQfEEKYP2XSqbqSAYYhiNqLymUdQanAKuO6/XUeqCbXMcY+DT5xte7he2q/cjooYjOmhpGNr9BUlIG+xtxrG/Ekk/7vjxr/89fvxNw+v6Ba9fv+Djpy+ohWs4huO+D2gfWC4FLgv21lC9oLeGUhc8vf8t7l/ADRqEqt4HLu9+gr98xrH/jON2w/vf/Bbr9Qnr/gW31xeIcBIZ3NF6w32/Y9tWDnASQb/dIvcFZp9F7IvkURQRdpXOtVWqXnWnJJ6AkZGzvq9SwhsCYo7K8WjpgNlUGGGzpKEIwVoRCu9syxO0hJ7EoPzAsA6RgVI0IqERICGjFtHY23LicBCBayGIjjI9uZYFWhdM4V+eU3JKhI7OLKohIDZgBqghCITBBQlBXRWhStjj2VXO9KA2hoCU7VDSikjIPUFinYaDj8BmwYHDwp009ogm4ua++dx/N+Eq68jDUsYrI4psnhkwONBJy1YMKBi+rcsCQccQglIve8PPf/oFf/rjz/jllxe8/7Dhtt+w33fmisMgxrbhZRGMtuP1dsfz83uG0KWirCvUHGgdt9evABT9eIWNjttr4QwJXVGXC7Zrw7I21EZrPGaOeYZwRSUYnRxGsy4L1lJgdgBquG6Fk64KKz0qC9wrvakCdVmxXS4kVK0bLtcLhv2M+37D6+2Vc0W2T7i/fMXx+oL9/orX+w3b6tiWOlmJzHcb+mEY7UD5+R8w0NH2G+6vnOUxBvD55cDn1x333bDWgXfvuQn7uOPeBkSMOMXLnWCarjAt6BD0w1EvG8r6HnVr+PM//Gfo+oxSn/D8wx8w+v9Go2X0euta4KOze9QVy3rFfr9DTz/GJCFyYbdA6CVBPu4UVZLphrEdfq3ZWs5nMACmlUIHcAyyXjUOcjJWUwkLWtB6x7KQAHXcbljqU4TuJaoCHe4jcDbgctnwMu7kL6lQBFiFk9xDwKgPh5uiGKt5re8Y5hgokK1g0wVFFBkSiFZGG5Fi7LcvGJ3T3CGIhjD2h5gPloPTyoRGx1SxF05Ihy4QXSEFEGcLACXuLNKmB/2NWUnxM9rIdGqK7OZc8xOn+1UMhTsHlgjGuUAiULdQhy6AcmF7a6gKyLJgu2woRdCOgdvtjs+fPuMf/ut/wp//+EfcXu+4vvsBdbFoUY5h9mNgRGMQRHHsBz59egH0Al2fcdnWCAkNagMqhteXV87TGExXVAtUmBKIa4isnANuJXJpZn70CKIB1GqJnpAXwHcUHdgua0znrii1AlrQXbE35uDdgHV7ZsgbXZW/eX/Fxy9k1R3Hjv3+BT//6e9x7C8YfcfwA9ftPRDgU60rEHgP9QQKPv35P2G9vketKz785ne43+/45eePgB1YZODuzh6B8BhrIUV9XS8YY+Dl859xrCvqwsHR1+f3aO0G+RQlOVc0c/zx7/9XvP/ht3h6egetF/T9Ro8EQMv2gBGwfLnUFT72kIQLnkOEu+YxZ7QU6BjAYMVGC3s+hg32wygxnuwIdbGJWbgbWqNeBscUssQ3QsMCUKy14H50aKGYjcFx7HeOaywber9BLJS7TYLXwTkmnOI+gEhhmyn6ASzLirpQQ3W/H9DCsifxsldY7yi//Rss2zLnwHLLDiC0UqVu1Knwjt7uEAHMVlQjN0iWhuGC4QPmCngqWAlSC3QOOg5g1a3BvSDF7RA2g3b2L8E36peq6wRIp9CxyuSe/O9uKGjJLXL7uIHIpXpYsGIc7JrvLbViW1dsG8HN+77j5etXfPzlZ3z6+RPu+4DLgvXyDrX2AAZ3IFF4AxC5o6Cj7RxHWCqnkBXhQxcRLEvBcWf9v/WG3jtEFnrm3nHco9HJT9a/qgR3guU7gSBK19yYMlCkB9pNXshSCxvLojnJHKccvaWoywZT9mX4dYOWinfPG7QW7PdXfP36Ce4NKjZLsgmYAhw8VOpKxS5Z0Mcr2nFjw5su0LLg+vTEPpalQUvI6fuADUOtBa3vUH2CCFCrU3cjmH6iivWyoY+D6c5xx+iNzMqSGMaK3pkSDTgwOkHDCPNRWJKMXRrqz5iRpkRFAMaUQDV7f4itJCendcNSdbKwzWTOz5UA5oZ5yBY0FGWzH8uoI3AFjzImhyIf7Y7q6yRnWT9jnhGt4eRokJx09BYVEj7nPjoW2QAQWKSuazSQ2UC5fcH6+gSDYVmfCLRH+72NESLKLVIifhdZnwcd0ajUEeVFRao1gsMTHA3x2cYgTvDRbAASojqOcCQZiTxEFLm/i8w1PPW9JqTxza/vMxRR9on/wmR7CYJjDpQRYjNBJ13qgsu6oUROfL/d8OXLF3z85Wd8/fqC1hzQld4SO2cnlAJRC3kwGoKlFIgPHL2jHzv2/YZtp+5grSSxLMuKUkiwGb2hHw0Go0L10dCOTlWpTD+RBgExUp4rR+KVo4ihKrUhSymz9l1LkqyYnhPE7JEusN6ndYHKwqALGy4XY3myKPbbC/b9leXYVaP8G/qYoZdQ4IHyr5BywWKG/fbCQ1oEQMH18kTNjLVhXe+432l43AbqWtDbDQIqhl2uF9xffwbccAQxa91+x4FH7UA/9mgtp1qXSMGyLDHGkA/XRvTFeDb1VYjQmzLXp0dHDgAyD6EiQIozP3d+NwlTjCD6sLmembbkM0qSlj8Y46IUITKEQE68x/wczdf7AThQKjVDYgxGhOcOGyEnp4IqBffjgNtAKTROzR0C8lFEBGN0ZPemm+Aor7i9fIyPFEipWJbLFHX23mJcBbUwRLMtvlObwjlnhvlTmsyokEimVWkoRjTIBDciJpjNjjU8YBKxTtzHTKfmTNW/wC6/w058/+zR3vssq4jUfEYYozN0F8UoHCL7w/MTni4XrOtCzYg+8Pr6FZ8/f8Sf/vzn6LQcqOuGp/c/we6/QKWGZ+d8i6MrLnXBVlfIUoBDcLmuuF4XuDfcbkwv6lLw/Lzi/bt3KOgo6PiyH7jtN3z+8oL96HMwjCI2YGxYdaBWqjwtC5ekKst6S2gcEDQzpAZA0F2gqBTIDc8jArTOYbxaQu9hWVCUv9eOHV+/fqRsfUrCOXNvs4FhA/3ogN8hUuGyoBbD87sfMAbHLPbjQF0+ALKiLAuui+P5+Rn321d6C20o+gwtinb/imW54Mcffo92fwWnu3fcv/6C0RoxCsmqPQ/4/fYFvR149/4H9iBkuIqC/b5jKZxUfhyOdb0G6t6nR0vPS9CO6UK1U9AmR2aYKSxUsMcgUFeX5BA8VtQUiPdZ5t8aepZRyUi6eO8dVSuK6pyJeq0XSK0BrjsWOQdSpeLau6crbi939KNzsPBS0fsN67JgWVYs6xX322ce1khX7y8v8GEYbSflHIr1csVyuaCsK4oK7i8fYf3AgEN8RdVl+vWIwZlmuc99NU+wAz44YsEz1Y+UD4KZngAIMZ54Bh6pi5DQZ+M0HEWjoV/l18MoxmiwXpEj8NyPsGREl1V0su+QegGRnqgWPD1RJq7td9xvDT//+U9orWB794ynd+/xX/7+f6GakoN55+hRlxdACi7XFc3p1VrjA3p5/UyJOS34zU+/w48/rNiu76F1QV1X9H/4hKqKUZKai2gM4oPZFuD9+43phLK9vdYauSK5gPAAw4ISjIik3BztOLBuqfeZINKB3ndUVJRoAS91w2h3HHdOOVOlfBwnh2+cO5peQgWuit53ILgD9foOP/zmr3G/fcXr18+4vfwCkwtDGlUsywXP7/9qhruM/Ar6QSGaUjeoUkwlfDRu91fYuEPLQnJS2dCV9Pl9vzFNEVY7SJK6wEdDU0ctwLZplE7pOCyavgBqO7gLmg8MC2DTleF2bORSDDF1AR5RRB+OpeQVAi7kYmTZXaWcVYMwBJT9j7GCxiUspQJC7sHtdsO6LgGqUsqx1JVp9KA2imjB07tntNZxv93QjzuWunKswuhY1xVPTx/Q2o2RlRnMOnUn9lDJdkNvT6jrE+r2jOv736GuV/T9K/r+GbABkRVaNkh9B/MLRgeyCsEUgftqCPddO/aQTqRB00g/3IxgfzXYbPAqADrc2oy0PHdV8EFQch6K/pqGwtAt+fOYG5JlGp8ddgLm/PfR8SV47aUoarngdmt4ednx9dNHfPnTRzz/9AeIFnz65WdKq9WCZS1AH7D2QD0VYdXEiPrv9xuWUvB6+4L7/R6e2eD+A8NvN7QerLlaoXZa1qIIabuCp63i+nSZNPTUytBsw41GBBFALHtZEPqHA+JKgk2MpnfrRKbFUUQjchCMdkPbX9GPG6jRSDboUitKpE5zFiaT/IjWuCFHP6B1xdPzj7hc3uHr5SNevnLamjXgMIOW95CyMtcfUQ6zjjE6Xr/+zEMyfJYoS6nweA+btwyjR67vVBTPlMjcsY8bfBi6AkvlYV1KyyMd7d5Z4otKmGf5kqpSvXcMY3t9rZXYw3g7DcvcSZDWB4MBRF4eIKd5RLhMxajdivgekgKVlp4jKgdnqLpSqKdEWTE/28xw3w8ALKubkQKukl3FHaLAUlegcoDP+eJcjmEH5Cjk5AyBr6E/UVaU5R183GkklA2C3eVNmpCO1gA+u9FhznYFpnZLUN4djkh122CaqySTDSunIfGImIRGJjvf5DtAzHx9pwp3w+gnh2KMHkbifNAZPrkK4AO3G/97XSu2BXh5vePzlxd8/vnPaHcO/hmj4ePPPwf/oGCMClhHd58LgACeCugRzBq6Cdpxw2gH3IHb62eWkTJEc0q1SYj0WjDuSlFsGyXotxJsuaCcjy6QQOvlITVwL/MA846S8EJDKTVH1rNKcUq/0Xj044be7hjhwVTZR7LU1DTIDRPXP1k4EVZ2yvAv6xV6XSBLBeQXlNuOY2/o0bBVlhQOVujOw9JHw3H7Ci1BMgJ5DeQL8J4cY4KxHJxj6KNjxsEPpTuyLJlWrEvBVE3yzJVPPkA+C3MPdD95JtH3IRIKWD7FbYIDdS71PBx5qDGBSx8OE0OtdX4PD7kEFoLoWh0h8kxDw0awnMhFgL73EI1WivUKwmEkTcSNLM1Utn5YF4OxwmAD0jvc7wBsivuQYLXEOAlOhzPEFC+RmHTPj1RnR6mFA2YTV/A7wmmyopr95DW2S+Ajosj5JNzH+ni58/WrNYW1Y0c2o4zRAXDTn9+em12xgtzzfae+5LoVLMXw8y+f8Od/+BN++S//GZfLFe244fjUMIbjw/snSC3wpmgmOExRayyuFAwD1lXwbltQlgWvr3fcboZtpcWs1fDl80fsjSFnTYRaHduiKLXi+VLwfH3C5XrBsq3UnOxBOdYCrBXW7rHZ6PXdyBi1ISx3WZSr1AEM9PsrZOmodUGppObSww3YONDuUbJNJRQ41mXFsq7QWulFR6QLHiBmpRCuGVCCOj5aA6Aoonh+/we8e/oJt9fPeHn5hC+fP6P3Hf0Y0HrB9d1PsLZDxdEOxKQyQHUJTsAxd6bODl/27Axj5WQM45jCqOuzKSqutbPzsi4aZWqmQDb8baItCjILQ6NSwoj4ROymoUhmLJXNOEt0iSiraIr0GsYo0XujgJDvsCwBLhsrJLWe27KUgvuxg01cBaIX3PdXXDaqarsWFKEhagebCC+XFdu2TjC15DgAkCekZUGqaI9hKKm1GakRhKnxgE2HIQJKKQqNBVAhyjT+zALCASVlO3gQcDDlD2wtTCZTigfHolLgRafkQynh2AVINqf7Cf5+6+s7DcUdOR3ZH0Omh1CGQCEC2CunR+kN99uOz7/8GZ/+/Pd4ffkF0A8Ynw7U7YrnDx9g445+7LjdDw7zUTLf9vsrMA789OPv8B/+x3+L53fvoGXBx4+f8O6//D1++eULXl7uuN17iJvysG2Xir/76x9xfb5wQwDwTpXtUlaUsqIWDsCZ8xpsoFuFFzL1lrpwXgMMZgVWlYN6gemxgIHRXiG+QOXCunpvGONAazfsr58QHd9wZ9VkWSujg7Jg9A6PvgCIRHmO3A+NFudSr+hoJAHtB75+Iulqu17w42//Gj/89g+4f73h65ePuO93tPtHrE8fkPMxVAX73jEGDQSHNxNgJpVZUdc1wFyfQCDFZjmPdW+Gp7VGBMuo4/X1jqLRlVkoIUdvxsoMIl1g4hBq7eBAXjci/DADJKjPQoKducfHcFShVIlO5Oht6R37Qf2SZZE4NIFVjI7WHbXU4NEIFpGYo+FY1hVuC0vBVnG5XuEwLAsJU3UpGPuNB24JFTLBFNXJKLMul1myHMeBqge60qmtla0GbiOqHI4S4LRhATwqg6gRNUU1JkY/5lxUskBjerqnzSgRYfB6SH60xDopeZBq9lFpSRoDwnkOMzQ2Nf3vbyh8dPgoCWXOwzWrB+FJNTZEFM2yUoqj7dhfPuF4+Qy1huP2iu3HD1hXwOyOr18PfPl8w8ttx+ve0PsdpRT8/vc/4V/+i7/B/+F/+Nd4en6HumwQLXj/4Y6/+sO/wJdPH/H161d8+fqC/b5Di2JbK969W7EW6iGMwc1/3F4wjIuppWDRdZa0srIx2h0wQBGdqQsX22yBrytk36MlOcI6T6KQEMOxDh8HZevaMY1KUQGCrluqTtKLVIrnSClMakaPUYMk6zQRLGMHaTYtGvGAZg3t2LDcrtguF5Sy4Pr8AXW9UjdUBL5uNH59x7IW2J3oOGUaI4qK5r37/ZURkbBSM8w49xMZHwzcm0dFSLAUiVkvPViJqTNxpk0W+IZ5CuNS59QdUa1gOojp3RLZB5W3BaFX6nAFpNTQCqHRbd1gDmwbjWEpJFQhojCBQOOw98b0DABq5TwOM5bsl8salQdKHS6XK7L1fGCwbSBz/ZCmMzNoyihMLgdL8weAWmP8QUTdpUY5GSex7LHEmfcvWljm7Fld1HBiCEHqE19pvaE6xaeh2f9S8BCiRLR4/myMTjVw+5UiilM6RPKW4p+ZSp4kGs+atZztx73t6PdXjP0VYobr8zv89d/+AWVb8OnLZ3z85Qs+vxCgcweenzb85qff4l/8i7/Bv/nXf4c//OGvIOUajTKKy2UAavjxx59IhX6lkjdVkQTbKthvr7jdGo7WcRwNPjqKIYREKqwDZQ2ADA64oRWW7bLLrxaWQD1UrQCOjM8czyyFhAE1A9CJjPcGD51MIEVuiLek8pdE2D9Go4dwRhX5mC1ARorh0DvlSIHRD7ZY9xBlVa5bKRxibFZgvrF0t1Mgpyg3XA/NxxTeEUFENoELhRq5DX5vAgQjysOZp7sTLCxDg6na87EjYIsTT/CH/fvAk6AjidQky6JhWIY4qiOoysHVmJqU/JjeY/jO/HlgGiHb6HBoqZBOtuzoDl3ZRp49SVoipHc2l5VyNm3NvooAG1kO1tkMF0SRE3SdIHKWIOMAx4Bpl4c+KWT0lYcGM1KAWExPIymMWRxni2hR+AjRZzUKOXFBYx/7PJjZH8Kv8Ic/3372v0+PQnLWI69DHwxDPnzKj/DBMrrwGNHmlCJrN3gjiPmv/t1/wP/0H/9HmDT8P/8f/3f8L/+f/4T73qCieP90xb/7d/8K/8f/+X/GH/769/jw/hm32wHIhuzIm7z97V14bNKCe2d58ri/QuRnOO4QZciteCaZJxau+4CUDdkx6DbQtxX92MFRfMCyXSN/79DK/o77fmOXpxmGD3y5N8AFa1FU5bCYFFsoJCRiKoJFNyJLVpyU1o79BK58YKs1SnkI8hE3nQV46kr6Oqd4Hdh3wX7cg1PyhKcPv5/SaGYNVRe0/opa6RHHEe3VAZ6WokCXwGscpRSsSw3MIgyVZMUnqz/sDGW0BshSIUJwO7E5BcVhckCzZtcxVxs5COi8dyD16U1oJBFUaot2blVK3VUVFAX2w4IBSQM3lbQixRmjUN+zkgPU2k4wVTVwIMN+eyV1Pg+TIUqqYHTSs51cUYtgvTyFDmfI7QgxJcSzgsSIQiFhUEuB1AVSalDds739rEJ4/L/EykFr7MokVzEtm+3lpcc2PlkZbsLRCaoQJ17GalKMKjB7MFLf/vouQ1ErUIpzxD3irMUzsWB/ZZMuQ6XOfMoLQ+He0MZB9eXrT/iP/9f/G/7Fv/wDthX49//m3+P/8h//X/j4y2doWfH+x5/w27/+13AI7vdXvLx+hQ5HWbLf3qcXgRPV3vsBSIHqiloWlOctMuVPDLGlYN2e+EAjf7vbnwEpkMIuvd5ueHp6hq8F8A7RhqoGWSp6Fxy7UUC1OToOtH7g08uOnz+9YnRDUcW7pxWcT+FYFHheSKUtRVGWEhuyQEDpPDOgtY5aK4ou3Cpu2BZFG4Ot3E4NB9KOiRe5hy5pUfT2CgXYkr/fcPv8C+r2BNeN5cZaAVvRjwNFgHVZIX28obTXqmiNsoCcChac6nzOD1EA0zXOSgHY4bt3Knb3BGRFGYUYNUdBvDM2NAFTCfr9BHCCBSqS0QVFW0TofS06JiN9R1VgD6LVUgtSxnBuTDhs7LDOlJTjDZycBpXEU+HG4T0aFQ/4wNEG1nXFstCA9H5g319xP+7Y+sDz+x9R1JCivFIC1BXlsxWBCLGTUitQLkAJ8VwHuncAy4yeEBHtCM4IdAljohOApJMk4UxKQS2UMMz2+NMIEHhHMHXTQLx5/VpVj7BzgDJSmJ7hIXoC8k3xkJwoemsDMozzEqTg8v6v8MNPv4WUhXM/6zv87vd/h9/8+Ae4FKBe4V5xHBRIYf9FgfUD7Ofn4SaHvoeyEB6QVJJgal1RtysWKDZdQjNQYtCtYzGWLE0WuBCo29Qh2Bjmd7IlERWI237DS5R1Wx/4+rrj85cbtSv7wN04yHkphqfFcd0c2+WCdVnI5yg18my2erM8yE3LA+ABOBnxAhW4BHgKouCiCkELFJvRm04vZczF2wEbB3S5wkSpdQlSmjEYHZXQUUCUSItSMYrpT2AZsU4qbL/BQ6pAbxpMv9CrSF4A4qD3yZPw0yh4hujkeqgi8BgPr5ydjqGj4DmygfwJH8QeJMJxERK1Rh9RHSHYLpIpEis1mfuUSlavKt+zeMHhbBoDAnuovC+mnAPbdsW6XdEHAeDj9pUdxuuVfTrWYb3zWQUJTuuFEWhEEygXcigcgA8KBkuWkz1AXVBtK9SyzUakftnyRYCbDFGm0PYG38FcO4+KCyKFjtCCeIo7SlV86+v7Ug+crb0TBvHzn28MOXDmnMZeiEVZNxatePeb3+Hp3XOE9A6XirpcUIvDRTGwoJlz8S3mPqQOgRqZjNlrEtbK3cm0Q5CYorGqLhsWA8zJcuReFiLdeAfvB7oLmglsKLbLJdrHB/b7Hd4QCDsrKrf7HSKC1jvuRzs78iJlHTZwqYalUPKt1oqysDFMNbtTF24eUOpNVeeDFCEWUJDpic+QNhWac/p2hG8QFUgMXEK0MR/tzgeslZsu8vVMNyYoJhbtGTJJSQ4AswHwBObnv8/H7hNToXw+3kQfjIYCyIScILhniE9POpkSHliYAznYiQf8nCSewQfLgQjcxaYkP9XGss+EESdwzCueQrwz7QKKpTcmN4FGOyQeYWhNsa7srREpFB5ujGBLZc+R9QOeGASbYQLML6BYEHU2Idzj4nloE9M7z83j62T8xhpZ4IOBc6G3EOXBjGgQTkfgnFmDE2COJBtlfPvx/35x3fgyBZt6SDn9S2BTHm6WlnL0jutTRSkVl+sTfv+3f4fr9YkkrpD374n7gVbVo4FGnERUEcXAOAewTvnmDFmzdyRVkIC6bKijh1hOjPGzM9Sr6w9Qa9hbx+04sN8d6xYGy6OBrDR8/vKK235goOB+tBBl5QChUqlZoFKwoEBl4MOFuprbgogiltNQiEDrFvJqDhykHLs9LADIKxDR6DURSPpVAXL4jUiIAUeuKpETi1IEyAWodUPRC5od03slJ6dEBJbitumB49FynokE1gECfQkYpj2YhsN8DrKCMHLrY5wez889QjwIGBKdyPPlMyBlO8CZTyf12KLNHKCh4xhSRkBjAGtZ2FshOoHANEUSxpgMT0RTIccIsu+Kjs2Gz/mmGMDeX6GhmLZsGaUMVrXcocsKHw02ahhtodMqnGuTKle5DoLQv4h1FPEw0IlzxM/4dDDBWTDWSF0Y1YaBFiVYQZH1AfMBTA2LBqlPHtcf0PIrSeEZTtIMgOi1fzCFKMix9vMVB9mtA3LFf/if/s9Yq+Lv/u7fIiXYVbmYWJ+QY93MDN52joKHzzFrpZTY7APD2rwOcQVTT1JcDBR4LXVDrQfcHEUWrJdntM66tgpwWZ45obzveHn5ivt9x3q5Yq28Y60LtrXSCP1vd/znP34FRPBy75TUHw3XhSW7tSreXRXvrwVLTQ2FoCILSTDshXCUZQNQKI03YgRi9LeLU+cBCO9C5Iosvhy6FNWQomWuDTcZE0RVMlkZ+3VAHcu2YL/fZ7+FqsRgn6SODx4mZXOfiWNRjYYtCuUAAcpKGimy/gxI1DUAQoeITcXsbISKB0apRKfyUnZLRnAR1O3AKWJ/i8dzjirB6D1SI5uA3rCBo3mMF4iZG7ZTkn+QFk7/YROMjdYaXLYLu1M7S6jDBqyzwoIwTF++fMK67dguT7hcPuDYv4R0o2DBBqmXSbEm2YTeXGsF6kbNCTsVxEU4cU2duJ6NKAQ8nJ1Tnp9OIxYwnDGg9QLpHZh8jYoy5fSYzYzQZcloI4Kxhy/6p1/fKa47oo03vVBedHgW8Wmt5j/5hAFnWeqH3/4N3r97wtP79zQGEX6JLtC6wfsBtwPed8jYAecAlJzDKNGl6W6wfg+PgZmbkx3YMZpAdUXZFkhZwH4YYeiYAitasG2KbS3YrDEHNcd+71gWllgvz++g7vi7v3V8eHfB++c/4s+f7tiK475X9OZ4flpxWTasxVGLxexSmTly6iyWZJlCUJYV/WgYrcHdUMtCNejou9AYKGMeYWqt1CSwB1FUJ3nG4VCT2X3KnhZGOOWB2apYUHTAx4FuJLSpGChEFaF65PYiQHGFmYDy8owM3DJiQT51hvniKEIOQymMfMbwiDbTuCAIZGwPH8QpUWpU0yS5Fhk1Ue06N1NK3vWI9Dyi2Zp5ugEmQG8d61rmTqxV0Ufj8CHLaPMkzUGB4zgoJaACVGUPRRgspmxclNYP9FBY39YLAM5nbe0eH1Yj3WHVwnSB6gIIJROFbYaYJyf3d0SDNBT+xmCchMYHqbyAAKhtcQ3dWILJFmmdOQ2im6EUo3xkrTxD9l124jsjCo+uy2mwTrMgmfcCD7eYgSk9GPscBKlhkLV1TAtbYTg7UtnSfea4w4zIcpjYOanZg0loA7pskWPSYzBdYgOU1BBFdX6/CFHwdSuAk5Tzww8NX/SFLeeVG2fcX6HbBn3/DuID18sdX9/v2HcK4qxrRfGO7PsYxilXEsAjlExQLQtEuZEcEiInBghnVA7rkVcKPDaU5RrGDuEminw8KgQahzxZgAkUJP5jPiByAKhnecwIWMLPKlXAHRO/EJQ3/+2gcLDMkDnSGPEA2+T0ViDJLTe1ZGSF/BI6EQvil4iAJOko2YY+RdFIP+Bz/92PTgLWA7g6s5e8N08JOIRn1gDWo2KXUU6mAUAIHSE6W/Vhi6eqVmS5Y2DIDqs19lekyaNR/6HYGyeI0Nk8MX95OKVneoEw2DMamMsX+31G60l05PuoOtYByYlhYYw9U5YAuv2RqwLg1yJceYSLSaaZKYhEMCQP+WfmgnBIVAx6b2iNf0YfKDWaYpDEm/nbZ6gaSA+JR40T07VGDtwiZGN3oMOgpQbjjCBSMkVRKkq9AFORieHzsmxYFoV4xdCCH34A3EsQrQqKG259B2yBXK5YlwWXK3UhWuskWjnnXxzHgaMdwHEPlmqoL6uiLGuAWhQG7n1gdNJ/03jMbSNhKPw0+54GehqKVJem11PQcM5hucIy6+gDjgFiPJw14SGCYinbFkOAs6KVnawqnKWh5pxPIYAo06QZSbrN5q7TQ8lp2AMLIQcnN3oI6gqNGEHUvJN0OowsEn5xIYYyhuF+Oxh1II0n5j7MdUiFLcTvINIyc4N3NqVJNq+FHshx7BABFtGoEowJoKpW9hClS7cB6wdKXWZkwGiwzsiHKTENBSeTTRsJVoTtYcUEObBA0qhYJFrTMwfYm6lsnr8gcokoBx3H7BlOhs94P2IVS+PhsF/TUHgavGS8SSpvZ7n0DQVrWjYRRx8tSpkjrDKHzwAgsDOXrARCfEwABwHjcE6nRK7FlMbASkitK9WllCFWne3bvMa6rHAIOwCFnopDcxlxrHLB5foBy/aBMz7bjnH7jKKGen2C2RXHsaP2VzgKysJI6f5yQ43BNqMfwcngnNGcu+EDKNvCa3BBPz7DrAEgzjB66CKk+/VxVgbc0PuYUnEwCWp1aCobgeVhnWGtMNUZUQHI6MmtgSF3Nmk1mBeWo1VRwHx5enQpnDY+LBqwFCLxHekwAEaHc8wBbU+qUJeooFDIWqEasvWBTZjK9JSqPiOSWjigXLODl1suUsMjekv4O9ktmnM7iwIkfdEQtGZgvwmfxfABtWBTepZDV5QxYL3huB9Y14VRQOImRbCtlRPbh4V2BglTrhxynXuZaR/jWcCDLGYY6BRm0pNAxRvLVNUyZAkDw+ubwHEYKQtnQLFi554Xclp2v4cUBD+zFDYdMjPjGXJxygWMX6nXI/dwwGanh8vSTd4w4gYekqDUTDSLuQ9pgYPBCaVxkLJAzaBU9Ig9kp8bltX5X3XZiHpnWBqgZlmuqOuKuixwOwAQSFzWFUnyyKlgDkepG5b1glIXPlRUmP0M2w/AD8jyngYFBqnUrTR7HyInO3rrMDswFFBxjqzKbSAE3moMfskKkA+2NENjavVos+UYAFKKnaBetLOHxFwyybNTIMlIWgQ+It0YJy9AgOAqMPwv5MviaMnoy1ZogUgI3UZfisBjPgZp3GVZKWxrHDRNsEJmOlqKRoVJojzJvp9a2B8CwSwnE1tiT4frGZGICMqyoFZA0NEbMYJhgt4NR2tANEXNKlzgJmy9YS7iHmSv1EvRQs8tUeVJZiQEww5GmbUCrhTEeRD5gXdoXVlBGE5ZxcborFYaZvMSYHzDsAUGiaFCOp8E8WiHyIiKU0bfjAtlcDK9OdBR4VLnsynJNxmRQsVzKJXrqrVCDg3jnK3wgbEM0AnAw2m8PZ//1OufkXpkzpQZaeSZ7kDOKpC0fgluTlADyNJkhNWnx+cQV9EGaCPbTAtkynsxzzzTkWh+8RHMOIa6pZ5y7uaTO41SK9b1Ahs9ogpScQFg2Z5DEUkCO0jku2Ogom5PqJWSIkULlvWCdnRAbmhHY3OUkAhTawmqcSGyb/HzotF9KLFBiStQbCWirPz5mcOltZn0f7xZT5nvPbEe/iLxjqjRG6JkEdtVom1bbEYCCGmoCehpzLiI8ijPHnkFMB4uy++f2EQacj6j3AdMJfL++e/Q2MqCiT8kG5NOKPgWYO4yDDGTNmZTSJlYyMz8Aw4g/yEMUZYJw1hM/MWZNpHyLJzHYdkbwoWnpKXgsXdEpHLf1OSIWKT6oTORWA8cUOpUaDhNRtfnPBJGDelgI6oOFuUwJ5u1AKl8VlSgnkSqALaNXc3pSotqAP+KxG887jUz1bluj6H/P/H67klhCSDNVGP+kbl/I9CI/3sol6YRm3VxXi09TyUxaP7JsfIR0hlmFgeA9XyJ7sQxMDo7IAmUGqAdavmgSXBalg2dgTBKZdOTqnLegoSBGAPW9xjEIzC94HJ5h6UAAsMoFQz7XqBHQ47OU6EHwloDQKQ2w8AIMDMnqBH4yqlq3Cs8WCliK3Nu6wlsJQb0CGnO2mEuLpKURGlCpgTRlNVHeCX+DpvBMlr2aYxLISWcVOZKHMNI5hIMtrkPzFOZrcyIq0RwEdLgufn09BrsU03iXRzcE+U/jccE9eT0wn04ereH+/3HXCLXrQ9iLdknwzmjoXJdKPgLd4bhrud3xh7mEKJgP8rJmmXnKiOTKQUZGzs5MgncnoI35/kZD4OL9MFQuEiUTz2caQrpBo7nRt4Mgjkbf9wNPiK9jLOk6VyicW2MjPizGOF/QXP/p1//zJGCAHsUBs49knMYIhQEiDVkHVxYsM4qACRzL+aKorTIpayMErzDxxWlUx2ZaDiZbScq76xBR6cmu6cLFgBSHEDhSDxLRmRFXa+88cqmp+y74EQpw3H/zJF/vgC14lIrPrz/wJKtD+j1Ga+vr3D/CkcM91Vg3RbqIviGoxv2RrCyjAKUBcDCTWtMO0pdYe2YXi7ZdALEQyzogxLxJTZm4gxuDRCPcNceDArzdFqfhhwOQ/l4IvoeCk0igqqI8hk9DoSTrepSQ6puwRgO7R1DO4YebDiSNBzRbo0xDcQY3MASzVmqgJQsf/L9BWxtd4l0yXOPcINrzdQHkUoBvRl6RBQZwpv5ZGZOxvCg0clILqMcHuxseTfUNbRSnGlSaxwZoCJk9C5PuL9+jL9PghUmL6Ysa4jZRsThA2IDReoU2amFIye9VFjcezJVVTyPASfNTdyiBgYU4E5ndyhTNEHRBbHojKRHhwYAOvcROC09u1o1lMUez7Hrucbf8vo+QzG72MrMSZM6jXmRsdkhEM2wM/PIWBlJWvDpDYcdyCxVykqhEXNovxOHaB29Jy1XISU7jBagdigGanHUteDy/I74hA+0xjkVupBnLzigwingddli0xmsN/Rjx/32gi9fSYKqy4Lf/PA71IXUbrMeQ2tuEG+oYmiq2C4LLtuHCYzVo2MJQzHMqI84BL1xapQWAEWgPQ7nsmA/QjNQcsQ9Q35VRV0KBKF14OQ9IN+TKQcQnakVqpTAb8cNQHSoSoFZhxSmZDY8vBNmWM3PWLEuG+rK92+1oBdB7wXaK1R3zqyQgeFRzjWFSZRrbdBbBlgp0RD1ZlMKqMdhEnNWM4OnYanKNMXNcex9VpeSsa6xNxhdRwr50JWaOMjclmFAFTHxPnJ86n4C2eo9BhhNQWFtx1orlc3cAESnbsgDllqwrk8Y46BTkwG3e0TclViHIprnknauMLAr2H2Q5g2BSYGhwFyhMWqTQs7kD2GMGaUYdKqhUW8j8uwiM4Wn0NLg+oQifkFS1MPYwed5+5bX94GZbhP8QuaBYZEnSQb55wybgbPunpDk/LsE7yK8mqU2UYjeIXVlKOUFavskq3iMHBwZojEhhQibX8YYMZ2Jor4UqlHOKwViuhMZl6M3dgW+fsWnLy+43RquT094evcO63qBj/v0YP046FpiF9a6oNYfISJscW8dunTUvU0NyoEKbYySzDhtuzWmAkkQc+/I3hWLkJLlRAU5CvaAQxQaW6SXFsySWFQA6rKgtxvxkCjR8fPTMPCzCIBGPh7hPDVGF1ioQ3HI3wCZDoaSqZKxjDga9U0NPj2uKECZt6CY5zPGgyd7cBiJd83UAATujsa5s/PWNeDohEPEZzo64hlN0HvusyjTI1Igj14aJ3uXzixb4GPaV1LLY9/OtBmBfQ2mjxywUwOQ571p0TDYy8RfkpVKjJgRI/J3hArsKoE4Zw9TcUag05gLrI8YvMz1KpGOUoCXFT9LVZ9M/xM3kSRsJVbzK0UUb/KiBLYekHryK04rNc2Cn1FFRhhZBpRHtBtJmQ1cQpP2DMDY8j0FbCXnc2auXiPULDEvkx69LmQn1hgEU2cNOiz3MLS+Y9/vuN1veL1Tl3LdLrhen1GKorew4GNQYMZHhP4ENkvZQDISr7HYgMoehsI57QwHeisYhR5+tK9QUZY1o0tT5JywRqakTJLSAwE6Qv6I7kSDD0CGJszgSRiSNCB5GqY95WNTemdLIxvPR5W04xzgDAmVLgADC5JCXjy6VkeG7I6SqWHCJyITx2B6k1WxeTMP+hHn9wNhmIfNFOMRhDuhGYJ+AgsdzTAIWUqe33SmIYmVZAQycZHA3iz2Oen2Z5qc72O+z4pPttxnA5pGo1YpTKNHeHKLXpQEeakQrlFpynY5GrukV2gJ0tjIURAc7tNG8kTietQhEW2VEoQ+y+a2PB+nQT5/9msZCgRRx3oYK/LWNTzcHBcmONWAEk9Q5sGkEsVipUR+PHwNqnM+RPYSLFzMIhilwF3hhZRYwx6fvZEGHN6W498AXQq2yzMulyu2baNoS8y4sFA2Gv3A7faC2/2O+0Ec5P2793j3/AGX9QIfHZzf0GDjALxjONWo6nqhFHu94rh/BaRDyxroOHUv1RzSG4ZRKdpthegFx+srW83RYaOj1gtSNwBBVNK/eJYawJa583uoTAPqStLjJ0mrlOgrGYmS5+wNqmC7gfquGQXEASCEVKBl47BeM4w07mrosgSWwkNi/QhMAtFfIfNsaoa+ErwNGA11RIFiHlJ8sbfESfBSmRUIDrBx4haPW1ESi5BYLwnpf3apatHT66YwL28xxGjDYBoo7QkCiqxGZCqWKldpVKPkmL9vxqYv1Rj2FN2+SlW0ZX0HN0UbHAp13wdUg8hX64xiNNZ39BDrbRQxulyfYypctE+E4+EWCYalODA8msQqgfWyoDunkXG/J0MZ5/VDiIN84+u7hxTnakt4wHxoGeLlw+CkegkElj/TmOWwLAtq6DLAzvbYOccSAENWgmqUgONfJVqe9JF84qMfeHn5gna/4+nDT7i+/wGX5/d49/4HPD09QRVo99cYxLMz3287Xl6+4OMvH/F6u6P3jsvlih9/8wMu2wbAqIfRD9BIDox2g44BKVegbtD1CWINoitKzIzsvaGuQuk5c7jvKGVg3S4QVRyHQWoFjhu8H5wzuizozZnWCNcjOR8AzigutA6J95DfkO3mHobcDWhjYMzUMPbGDJ9pbChZTw9UIiwtdUHdNqzbhqRVFwXHGMiBTVZ0VwxRDBEM9Eg9gz9hp3dmuzcjhGQcCgCooQhJYmE3Ii0IgE+dhLzRIx93GouSey42WHQP9xG/43l3wQBJY+CcbJaQjiF4NMrqBwOuEySn0G/2NOn8DM0KjzPEJ4eLHI+ilYON6yVmxhZAHd4NoxmOg3tYjG0MbgpYgYVokmi0nbdXeNsxDGhqWLYrUyIBmnX0Tho9wvAmCkz/QGo/RyV6yDBqVG9OXgX3w4lLfcvr+5mZ8XUqM7ablur83kwtMtRlaFRrxbIuHLFXF1rERB0cQS/NhqdAzrVEz0OPUhbQG6eJjdEhD33666p4fv4J1w+/w/b0Hst2xbKt6OOADF4VBhWl27HjdnvBx5//AUd3bNuKH378ET/85nfYNlZeUlLPQ+R2jI6eMnTLEzv36oZx9Gja4oYrUjhTrBO0ktJRlwrzDcMBtDuKgs1WBVjq9TSYiNBbY44D2LsgwJxqhQA2Ebm1h8iJPITNUwglngarGx57JULs4eiIhi4VIAxWDQ0PFc5ckWikm6plJVKconAvKE4CkQyFxVzQ6VQi9QCC8BPt5w7MVCVl5MmhYCUsK4C5KBYfx1600ysayNas8++zz4G8A6a2XDNI0pezuxUsORpQQq9QoPE7yfmg0TMbEFkwuzklzoDK+by0QMoSg6Rz8hs3t4RQTTY4mgj2wwGwRC9hBMcELqkxazZCvxNQt6iUENTk9HN/6KFh5KGFWqhFBKM3Pqd4HhZ7KRvHvvX13YYCcRjmE8xIMwCXx9DmtO+0MOuyYF0WzuoQAbw/7Aac3HPJ34vvmvhG6i4kTsJwXiPXc7mi1guWdZu9E72zIsLQucJGR2sH9vsNt/sL4MDT0ztcrs+4Pj1h267TKFgMmoWn1gFFaqReofWJmyLo4VrXsKIxVnEMTuTOvolSocWg2pGCslIUxQpQqNHJNIohYUZrSYEvucbxENw7a/0zEs51PpmvFiDvxCXSE8cip3CMlIgQE4QrJWjwcYhVIaaAGYdAGzseIQXwGviGQEIMVgOHiLOZwAOyUc8jhdLg4IiCXZ0T3CQRLfP5CX46o4QHStPcd544mMjcqFkpYDbMMF1iT7oTlFQ/wfU0YFz/0K/Iy2fujJy8hTQUwn2ZDYDJA0qx3TQktVSOIsiIOIDvEQ0g5Hakc8hTY9xzGW0LUxrSLRKQtgeMJ+474ADXgo49WLo8SzlvVWdD5re9vhPMTHIVS1s5zekNfClZV87ej1hsBbZ1xbZesNYFIhZoP/+SXi9KZbkvZnsv68Glbtzs0pl7FcXT8w80DKXCoNhvO46jAZ1cBVXB09OFE9adWAPVul9g444ffvNbfPjxrzlfYxi+fP6CpTR6SO8BjFJabvQO84Jl+QCtW2zKHuMIc4YF10S18+AI81IvK1Q51q4KsMNm+7dIpaHQAgUJZHMmhHvkwqmbGB7SWtjp9JRGok72FQRb1ewBBnUgZaqYIhlEguWoQpp63osoyrJEyZOcByyGYh1sbnOgDZitcRDJKXFECB3fquNUzyJkQHRfnExa9eBjSFYl0mnQiOnj2QfABq5Mg/NIxe47s9ZZYTmTjTASwhRnpCEK40AfxRBf4zoi/oCAfUI8cKd6+uyWDnBdA8xUqRBUVoFKjMhUluHRD/jolBfoA/txhH9RSiSW0/hzsttAM45I3LZnLOtlrg9xHJ3rkGLWEgCXBDPU247C2i+GG+qy0rH+Wk1h+TI3jqy38yFp5kp+0rZthG5hUVwuF3x4/wFP1ycs2wpBg7fBSxAK8Gps+CnMEh5I9YK6VHRpEN/PIMWBT59v0HLEBtuDFcNuItOKD+/fo3e2g/fe8fLlK8Y4sCwrLk8/ol5+gz/+6c94fXnhPNMF+N1P71FDYLX3HfCG3u4wB8ryDro+B8OS+EDRhSh7eujU9NQsSdKi5rzP2QNRQugVQInBO94MbgdqLejWmA/P3pGGkxLN0BSx1lIvGP3OZ5OpR4TzwIlV2OD7AZ0UYNiAIFrvY+1cFHW5kugzOkw6avSB9NYIBDvH55W6AoWAiGUuD0aIww+UUAtPR0NxWaYo3enhEKlhrTHyb9gcgQCkxsVpFiY+BUHREMqNwzvm9G4amjEj4fBBQW7Mvu8csFPDONgIrQ49q1ClLFi26zygJaIG0Qd8QwsVtiMKlsF5JzSsAR7WiPYi8ta4XwjTKHP2MBHvqNQOOXZ4zAupbecewl+YSZbwAFCLReYaLzjGneJDopBS0HuHFY+mtm97fV/qIZi5DqI7jw9ETiwCeNPcRHae4rJtuFyeot2a9GbSjLmBJPQOJeris7+DLiA0Mjk9+ri/4NhvOPaGYYK6XrCsK6sa7hA1lGXBVgtGP9COA6017Pc7gIKnp2cKx3TFH//+v+Dl9QvaQfR++eEZMPYTeFQ77q+foKWiLk/Q5T2AmMMBagFYtLszxXAUVzTlxtFasZix6Us1QkzlRisW9+uoyxqGpsMiJ01FMRXlv6dLdEQpMGdsCsV0rU2CkMdbT/YhKwIZrjo8CHFn+qhao5LDlKrUimGceKW+QNzQW8OwWxh5IviYJW4aIAAxi6Rjjscw58wTpeFM6b1SSSZzJ/p/9lzEJ+pZfkxcxbWcBlMJuGbJUUUh9VQL83gmc2x67smq06AApxapiEwDkamFGXD0gevKxj4JJwHldHXyJiQ4EQGIOjkOQYmKyLRxH7TOxkABn3vok4xBBW5zIVVeDFUqKgebIEH7pIgm70PSSDjL99aduitaUevGzzXnWkxJA5nn91te3xlRnLnQY4vxDLnPHz7EipRLu6wLqcHhbcYYUc8NVxdMtDNvegDEfH4NS2bGduDegoDVmc+5VYgCVVci9gKi5xYb17hx9mMAxx29D3z9+hn3+wvcBst7/jQbiDiUmXJ3Wq8cPlQuaGPM2r6LUtVJ2C9AVWWfHZKnt9Ezwgjvx/kLzLurL7DeJrsS6f0kNRMipPQsG4ZsXYTHFi38jCYQeWpGEv7f/hOCVB/L6yuFG4sCO8y3S3WYcLIUa6oWbekVpa4hbZ+fTaBwjAH1qP1nWoQQSBbm2jnTIv8IkoJcCMA5ryunq6ULZTGIZL/cHqqnYYBk825+NgBY1H8zVQCknAeF0xtCIi6a4UhDoFEjqMtpZWyao7xcRnY6jVSOCjj3KZTp4Mjp8n1MGT+DzwhOUNiLEpUcM4N3UJVqNo1Qen+4QSz2FIgdIdmeRgOdhmRiaJqGIqjrE2f8ttd3GooES07/wQP9NgzKrFjBEtlSC67XC5bQh+DCkYshDzkkcPYt8FBRBp0AU0QeaTFEJ0vPbHAqlxlKJVmK2prUUUhhXS0bugEfP31FP+7oxx1HO8jgVMF22QBkWzIHDFvnYOZSn6DLOzgK9v0e5DqBOCXptUQrcRoys9mQxA2hOAcXabAyBWIhaFuiFFwUxeosx6UQMMPdMsVezDpZlXBWcnqboOU0spJGITyxJwmHERvLjREJxTAilvYydYo5nDgwvMEGImRWaF2wKBvLJFiyfXSM1mF+cNoWov1/7hRAdQk2Ko17goTEqegRuDd84iYM/1nlKcFmhWSV5Cy/AnRWRSWcS3IH+PkTqnFACyndGbloCaJetAdoVhWCncrGwzGnmbv02cgtCAB39k+wKsXQfpD52oM/2we6dYyMyJ1ON5vPJCp/ZswTPWgmZCunEaFEQaqopTwiFd8YXbgpXCPqSaYmIj2LKH1WS77h9c/CKHLFackxDVMB0WzOdnCUpeDd0wW/+fEH/Pa3P+FyWVk/D8WhuVDwCaCZHSSK2EApy5yROPEQodCKEXbGfjSIGGoxrPUdyVWXZ4bNvePjpzv24459v2O/v8KsY7+9YISWgCxUx163SzA4SyDKlLTzcUDKE6AXmJXJ4rxcNmoG3DuZhxLTrk0wBxMhNjhiA4bHqYV1drEgzAhJOqXvcGMVoQ+bczYpXEPGqKhClwUSwsKp7wEgwD8WGEeQjXjIfNKgp0EPD120oi4r6sKUAxNMPA0dTbjhaAfMBFovnGcRacJoB3prcOzMu1UgXRgCO0uLXgpQeSBZRg+o0CgMmzRnsxxrGIVS5XEcgjAeBLarJNgbIw5j/R+2Zxg7iRI6kBoV5sYxiCVZrYEXRJ40BrCuV/TuEz8oC+Xm+oi5LOuVWNr0zrGkUWmiuhXlCtpt4P5yj88PYeNClbYxPIxrQ1ZrMKMMwdgHdAlcq3D0AMvugfmgB/OX6wI/4IhoZvTYbyEsJBpDnZL/9O0n/jurHvFH3gZ2GWF0I+FjOBl4H97/iN//9rf46Te/wbt371FLwWg7Wr+T6egUT1VlSOXRt5DlJw92mcV4eThQdcG6rPBxgcOx6QVZfx82sN8P3PdfImrZcXv5itY5SLf1zo06ODlcCpt7PLsgw0B0qrAGoAqYF6gJpHfs+32yIzEcvRmWjcg/cY0RDy6puwzHM7QmQM5wmiEhwmsprC5ItqvZHSqOAQKktSzsa0l6sSrcB1K0hi0CwaaIbM6BEE9i5DHs5BQUEUS5n9WOuqHUDf1oSHXt3jq0AKlIZv2YojQmAo3utB6DgrWsAAQmnSChCPsdyhJ4CasJx7GzVBgRhMf0Kw/d0WwkYzSUfIbTcFAflNwK99TW9JmiMSKN/cMoPJiu8VnKvSLBSUjDpZP7QMyg1MANBgcLQTkvZYyGbb2EUYh0OaJFRBrYnY2H47jh/vUVXz5+xfL0HpfrJbx6lGBDqGbEXFQtHH+YHAzKf5CgN5nMs+IT0WEhsMrobInoimtbRIAabOcZdyGEo36tqocz90Lmk/NieRE5gEXgWOqGd0/vcL0+Y12vIKWZYGRrB/rghsww2MXxiNKf35WeMQ5nAmbCByo4+zCmWG3MMRBJPkF/+EPGnwgBxdSIyPCb4N+JxTgEKCtH7/UD99sN16crbBi6Edxb1g1E/E9RVcvDysQXM8GOB63Rw2KW9FuG8yXURbQdLKs6vVQOjMkVyJJhfpeGAcqfzWdjkjhYXIvMkmNWXySk87SsHAEwsrRK1N4T7QQb6LKUqDpmFyNS40DIOXCeXkhZAXlgPojN+0WAhnzYp9uRaKvPQw9H9J3Q0CS3hiU+kH+hQtzMfUYZyKV/eCV2haCgS4AaIpFuRO4+tUBjH3BwVeIPuVeFUcqjy/QoUXsPkSR2i7a+w/sFi18x5RdU2PdlRoKePxi7KAjkoCcfAWDWFdnJylg8dFsky848E5nGkSoexp2PkB3NVHb65qP/nW3mQZBh1o3k0TmyrkuVpqKCbSFnouoCd4Rs2I5xHHNuAjUOA2Cb2IefYMebGwmDQEfOUXXmobXAcl3bb9j3G+pyRV1YCdFagW4AKOXWe4tyVwFKjWiCmzfJWwzhIhR0RSkb+mC789EOvC/v0Xqjwesd796da0NDkc86ALTIESXq7XyAZJxyVH12HBZ4pVajlpzP0CY3YtLm3RE7axqFLB+OMFQimOxGs/QxZEI+GgrEnEuWzhaCkebQkU1uZeIeEGD0/azfS8VoPa5dOQgUCY7Su+p6iV/1GDfAIdRFFVJrzGGJDleLNjRVpFxdnhsJ3IVGkvR25vbcN3oitzzUeJA4SMB8/rdM8Dnz9qIxxSvuk+mSzEjInBWIdJCM5nQarYnchaFilLeTpVk4I6XbQDeJ/hecGqKh4zEC4J0NX4WCSxTtJR1biwKycK8RPTufn6QW1rlXtERPDsBWLIxolY9n+o2v75wUlssxYUw+MAgAgw+W+0qIdtxuLwAG7vsLLpcNSz3TFA7opQ4iRFF1BbzDRgvKcjwkFCAqCWN09DbQjx1tv+P++pmf4axqtONOKrQPnE1QaanjEMcDQISv0ILL5Yrr03tsl3e8Q61Q4/WxulFxv7+g94HrlVOsj4ORkTtQ6wKzxhQjN1N6daFX9cFGMoan7GiFasjuh0PVAinkGdRlYYTTABshkIM1OliB4RrzNHTK+vXW46DSJBgcvRPvmP0UEVGoAjWiKBehRiQEUjYam/1Ock80+vFuWHWRKMV5b/DugF7ivnkADAe0sGxdtnexyQfgHf3o3COhotVFgCEABiskBg5q9pAFzHB5TtmKcLmUaL9h6sWKCniVrkBUWuasmUdg07MikIaAx0s15BDpjOfBVS3wEc5l5nP5mTp/34ahiAOgdD+6oFzeY1k3LJdnYnCgjddSoMuKKjUqXQK5U9gGInAtqNs7Rgv9jpOOwAjCnOzYAsC1TEORW4/GuGBZUlmNzrUPDrUC8Njo/U++vlNc9wE1zdoQAJ8W/Ewbxuj4+vUTbrcXrOuC9x8+4N3zFctSUYRevA+qCuUDZGTCFAIQ1LIxtDM+tN47jkYw8dhvaPsL3Gu0clPGTqUG3mDTG41BFWtLA5R1Zxm4XDaILBjdcOwHBFdqMfidzUPLEz5/+oT7voeyd8Xr6ytury8QAa5PT4AKWusQp9pQtpabESgrpUIt+zFiSK6tbB+OWSC1brDKTewYWNYLehswF5QBmN+R1YFksqoqG84lIzpuJC6nox+YNO4ZpYW3KYWRTorMmjUOgxZS3QHHse/QGjKBUEjZsD79GLk8ADPc7/fATsg2VFUs2xW1LmTMSnBOrAMmGGgkaEUEpmbksIgDpkGZZ2s8MyVl1BndW4wyBg9pTKB361F9ic3n0S0bPS0QiUa65EWMiYVlW3iClhKRxujRYJWNY7GAWh6qBZGR5XO1MXA4TVsZO6xXIh7LivX6HoZC0efKGTJM1ylboKqo68Y0QyqoH7uEEtUSFQ48VAwZIXncG6MalobdHB3cW9oUS11ImwcwZAnBHETX7Le9vrt7NHvnRc8wLfZlgGkAS1w5T5SDeUpdcFkXtvzWDO/H9PaOByMUeVsXYhcjZOp44Dv6IMbRe2IO/DvOiwQQ+bZIzFjwE8fgviG4yI1ksLFD1wXLwmrEGIN0aAhad7y+fsayPmFZNrTGhrLeg915eSJnf2Re6AR1M9+MDWnpiYQlNykr1BWOTiBKF5TSZ5pRlgsop8/D3kdnngoi/1oUYgUJQMypYvEsLHUXEy9BjFZ4YDDOcQKGKLGxulKXlRvZuN58PytCtVwiNaAnLReuxRgHvDdMgaBS5/BeYcBASrMqsKyxabjJrXMmi4N9EFoyHeXaMYM7e19Sg4R7RWGwACyzx2SwezSmqJEQdu5Z1TCgks8jjRCmM1TF3AMkg5WZSk5jndc0q1ck6SnSSWVaWHHZrhjuWJcaBh6kcg9Sun0Y1D16OSgLCcR1RooBYVRg2ZOCmBciQI41Msln75HuGkbMLYGW2UUqD2v6KxgKhlge0FJkUoDEZUo22PgcPMyFNOz3G3ojl8KDnXgqgUQun/mep3HogMQUsECf2ZwVnZzDYb1NI2Fu0XQZit5SYc5GrjQWj1iBBC12qSu2bcPl+gRSgFuw7wT7fscYHZdSUbRi33cc7T4JWtt2wXEcAYjy3nuUdGmUAr329ElhbHWBRC2fSD95C6Id0BK055RRc8hOvUpRm5hGajme/Igs/50szICC57PIHD2VsXIEAI0oKxteYqiGk74tYAm5lHUaGea/C+r2hNbvGH3H6DvHJUSDEw2/h0OIQxSCxhM07AOtvFJYVgtHGtYCKn3zWXqmCXGv7snwzOiwzP4Ppe1HMivYik2BoLMPSYgxTFCeVTZzkPAkBVrlnHkoBHzTeCWYngxTRsKcNcsUClGBjGRBC9atoo+B5UE42a1RN9VOomE2c2XjHG8iUgqTALTzC2jExpvn+9ZQUCAnNDr9pNefqdi3vb6bR5EXNML6xuU9/D8CNOwoGjx+GG73Vxy3BWtV1Kqo9YIpG19ScZufQq0ENnY5KD1P4LARCB0jDoIwPA9psBMs5CIyRw9l7REeK4Cp0aj9WErBX//Nv8b7H3+L7XLBcf8Fo++oypD85esnvHv/tzATvN5e0dqO3m64XJ+xXZ6wris+fvyZmhfCQT5tPyayL1ICeLXYQAqMGDug9QFpjwUUgoQoC2pZgVphAuj9hQh/KiJ5ATSIXTFFSyKSG8PQx1kBycgmKzEAWIrOooIZrHdY3wFrGA2AD2zXH/jeqObQUNQzCrQY4rwtsPUJvR/w40bCUhxuMw5sykansmzs9Yi0p+E+WasOZxkQC3N8BEYVXZUS5Wcv7dRXcPYziARD0j3EXohPcMPSeCHYm+JRShSdITmvNaEljdbuHSkxOPd+rKWjYjhicDJFbj01LFXgsgRjEtEYtsBb44EH06feR/AbapQ8lc7NFAMSvU4IUH+63Wjck5lu+Lw6n3wjtwREBVJOsDM8PFJ4+Vtf/wxmpgUGoJCRyHJSujlYxpwyaYhQMsEfsw5Yp2VLoorkIDUEJCpBBR+BsNM7UrADMalJ4LKiXgpuLy8TpNTwkOZEmKUPHINVDgTQqFro+dSxbCv+9t/+D/jwmz+g94GPHz9hq4ZSKsbY4Q58+OG3uHfB7esX9LYTgLMD757/Gut6wS+//IJf/vQnbH/z15ClYvQRICVOw+U9JNcR4GZlWDsJPRq03lDniJ4VaCERShX9+IomjtEVfSggA1IHZBC0KyqwB4xIwJQjbVPsWTiiKjAMWhxiXGvHgd6+oh7X+aTx+gXXH34/QbijHZHXy1R7MiMNmdqkCtlkfk8Wc0UI5LoSxS9lm9gIhkGUsgCKAVdWw/ohc4hRqRzsjBBgMaSoDF8CEpIm4m8GR2eHc9Da03kkTwIhDJypBEFhTIMEFKzrOqnbifsE3zjshT401TlSWLhMNqdF7w0wjA6jGYKHowCuFL/xBO7jhPkZBTItjLMUaedpN8LwpbnwM+3UqKqUwvGYabhZAfE3TuNbXt8NZqYVl0cKLRBfnhsjwkA9691LEWwrUViqITkMHYtwurKFLgDlxBaoHmhGNuDoht4NbbDjLfkY/ci5CmFmBCczdBgkJntPVmcYrGVb8fzuA3786a/w/v1PVMZqDe4Da1kwOlt/XejJXj79A1rbWY1RxdO7D1hWyrV//vQL6kKVaTOjITOHdcrhqUs0SEUebIBHHR2RB0M0iF8rxV9GY28FANGKUiq26/sZTSkArRXmNZM/qANqHcVCCQtExIcBYkwPS5YXI9JgNcNYPYi2/3G8AAiGijS0/RXLxma+Ag/sqQAT1Mt08zSMmZpK5M5D7jNSUs0tF7+rsZkBuFG8l/NY+bMUA0rqPi+7Rvj6gPJrln2VZVhXgqOWWNhDiptlUA9QVOr87ATqU07h5PIkQM4xjuKAmtPgm3Nwjw+QOGaQxg5YHYYGwzGAZpmYA+5pcGgesiAAjWOWMz9gk0iX15/7mM8u5rvO9CP2euBRElKDDj+Na77l10s9EkvIL3mgmjxkIel1MpetqtgWxbpQCk9L5FWTYBJWU5kf5vyHYYMpR+thKIDW2Ana24F23MMYR86oBL7MbKokJ0iXvQN1WfD07h1++PEn/PDj7+FmuL18QbeBUgRjCL1SIWOztzv226fgMSiKrHh+/gCA+MXt9QuuT89gaE8cJaeWqTGysqRPBxDnYMOVKq/L3VkzT6As8lePQ6KlYL1+wLHfoorh9KK+YVLfISiefjyiCUFgHFxj1fO5TZ/vFpFYsEr7jtmTUipGv6PUGoOBKg9KDqrJDZrkIwA0MamoJICOGTnKZC/GNQRmoLHWUI/DKFAdmFO3ooyLvK9SwlkFfuBMyVS5hRxCoNiU9XGjQMx52OKATiOUXBGf0cWZsmEapfTYSCOb7EZLsaAOJesLY2hUvwxDcgTggwuXU+krl+Px69IAMls4jcQjmY7XcZavAUwJQBqJBGrj7eazI3l+6Te+vjuiSMSbKxY9BkjQJum2mKy3Wjho52kLrczKad5ADMXNFluj2pDbWeVorZOvcDQcrWEP7kJvxCrcesiRkQdfSgyWHckq9AcjoShLxfsffsQf/vbfYKkbehv4+R/+M4DAUrzgfnvF5ekKBT377eUj4Ee0FVMi7v2Pv8PXL5/w9ctnHMcdz+/fs5fCqaFhUZ2gKC9RsrMM2QHlPJFSVnh0S861hbLM6WCfCwCHYn36EVfrkFJx6BdgGAo2htb1wOh1sv3odAxuNMjiodktkeQJuy1TSTooS2Bp4oB1PlWtKzAGxv4CsYb6/BONvBlGJ+FNxbEEQMkDvQSgGmkkyB4EClTGeUAEyKG+Ums8KyrUq1Z0QQCOwb5Ulr3FOkqJXgec3ZoiYMl3ek2HS5/OChIiSUnRtgaRlPgrGJZK6DHT1NloNQ2aFiThhVt/nP+0bPHuMyJgZOTRscz1lDemICICgClVnrFpPDzwl7zmRyMROhIxUiCZqCxtU65Agq35KHc3rzdSlslJ+YbXdxKukr/PUpHM1nBWOFKXYg78qQWlMt1Y1w3bRtVqlQotC1Q5FJj8iIPKTrBgW1oQrBqO48Bx7NjvN7Qjc0sAodBNxWl+ptk4UyAwl748vcO6XfD0/B6/++mvcLvv+Lp/xX7sU5K91gXrumFZVxz3HQ5OBmOz1A4TYN2u+PDTX6G1hl9+/jNur18hItjWS0RF/Nak0PPeGrsZRSPSGKhFUOoF7FDtmL+QoXxMz9KSwsOC/dghy3tcf3jG5f2B4/UTvLVQCd/R9hcspaIvO1o/0I6CfvD5FGWH66MDsUiAcxyiwkBpwgJ4gY8Ddv+IIQK9PMO04vXzz7i+/wGiJOyM4w6HY8wqB6XaNJrkJpFaLxS2EQCuAaRGyuhsMsuc283ghaQ9goNRIlSBmAX4+wR4i25JgoW1PIJ1Avce8ANl39QrUqDYnZ3FZ2gcTAjJ+6jE0oqm1jHASycO5B7t4DarReoc+ZdOohRBVQoqsTfljPPfpgjx3LPCMVV2ApeQt6xPReCBE/9CBDthkONjUnKP4lFR7Yn1cUudzV+pPJo3OC1bgGeSKIWfqUh6dlfmeHUhZTprudn0MkbHvt/w+ctHtP0eoBH760cD500OGpLWevQV8IFS6o5gTbYDY4yonwOiBZfLBU/XC9btirpe8OV1x+316+R5SFFs2xWqFcOAjx8/Q7Gj1BJzOhdIuaJWxXp5h+v1Pb58+Yz9oMDvslAg5GhRsu0de6NxUCOW07Vg0RmxErPIfFoK6rKi7XuspcyDJrowXYBD5WD0MDorEOs7SBkssfWVh6lskLpB+j2eSgMwqBqeWM3swmXD26wWCMhodUYdHN4zMI6X6bXKesXt62ce7PDwsA4MSuOVwupIrcvcwyMxAalwiTVH5s0SpeAyoy5oOm4SqQCBBAmLjpUqW7ACF1Kbl3qJOSbcnyNGF7hmGofAV3qUrcPZRbRArCQHbBNTmyl29s/E00GkaSNLJMlShUfWeOIAJ1mAf0gt5+9PDfmQsvM374zfn9HR6fl5bjyBDpzl2njrQ4kmU41sfc874HXMPPSbXt9d9eCDfsxtsh4bee70DAysPEDKWpiXz/q5k1Ld2oHb7Qu+fP4TXj59pNUMzyQqMXzYA7UvkWhHyaxsMTItmrniYS2V3m1dN6zbE9aVdOjegdv9QB9say+loOgCMyExrHfcbjdc1gCQhGkRdRnYWm1Q3G63yRNJbskYR0xwIoAqKLMPwQZQlrMHhK3hHJibwrs89MlIZARCNSIBEKxCHfRMDriWCHOjdX1wpqk8DIAhB+GANIHo4BpmzV4EMLBiFaK2BANZRTkxjAbr9zhPwijOnIQgKfG8ABSPTY9oruOGjhY3GgeEJ89Dml5cUks/zIiklF45AcbBFEzCA2eKEOou0VkZbM84FuGU4zkEf4JNMjG671wLkcQ9AqOI/F7yUEb5PQ//HBLknCw+T4PGeUgMw6MD9IHbkLjUf3OyAvc5I5x8CnFDPObnZ4ARgvzliczIIX8twA7a0UxhfsWI4vxqnwDPNEqeHtMCsANEB7ZlQ1HEpK5lApXUoxy4317w+uUXfP75v+Af/uv/F9YHlrrhcn2P9ekDQUVXDNngtRKTyN571TcahYBRn3Nbcble8PT0HgaSpG63A/v9gAuwrZcA6Jivvr684Dju6IONYJfLJWrbCjNGJtuFzMzb7Y5j36OScvYFWA5oGWG5Iz8cPtCHYVVMPUvYgPQDHmI1ogvKIrAjNS+BlH9HEIRqWWBQQBp7M447hnGAjpYL1gvQjjvEa+geANAbI5ciKL1wOntqPziglmpYYQOC8yKBayAQd3iDNXaJ6uVDeFCuXSlbHPCYXmYafQj8YHNQEt8LBAvcD2RDobvQ4HjyXwj0qlaoAy41GJQBgCaAmQdAClMZFXiMfxCnpP3ZeJx7dkGBsBIW5eok4DlkCh2RrBdSe6VCZUSlKqINCBIEJnU8Y3/eL8N84j9mnRwasOnLzCbNXDL1AU7D4c7GuMSZ8nNxHnQPJ8PnW2Jtsp8ljYScBi+MRvCpp8E5zem3vb7PUBgexgbKTD3cg9hkfVpKFYU6qbBLLViWjc0+eYDGwH2/4euXn/H505/w9eOf8fL5zzjuX6EQrOsT3v32X6JefwpZfNJ+y3qZIaZ7R84qVlVs6wVPlw2i7P/4+eNXfP36Gs09FGhd1g3H0WH3A30M7Pc79QwCCHMA7eC2hw0cveMSgsBmjo8ff8HL508Yo6HWim0lMAkE1boPqK4oteA4DtzvBNHausQDEs52SFwlAeBSoXWj+M+xR2qgYfcV0IXRRNzvsgqrPm4Yjin2qlohhWMOpXyhunjPDlSPqozNUq95piTCdCeISYIOgIcPWqLxqMOOT5ByYWQhFT4GHKkmLsBwWCfOITH9iXyOEuyDDmsh9KoXuHUMzwiGM0HVFIY14hEHpLL+6x4szcA/pEG9QWrBgHLwc3hztTtTlqDTQyuKFB7Q0QDfQzHKOKRKaxw2RnPiFaVcYOgwsPRJtS2HjyScObpz8tgyo9oe6RtgLpM8BU/mJu+JkREjKUtlqvDwOX+FQZk/GLu3TjoNzUxxgo8x/y4xmwdy1rAHBuq324nvrXogyFA+0eZHWrW7Rd2bkUZRYF0qtm2NbklyDUbIpu37K+63r7jfXrDvd3g/OEkJBLJ6G9AtQtt49d5jLQKQyZkKIjiaYd9fIwUIsksPMEcV4op2NAyLVukgK/lAKELxIb2OAbcF28YoqC4r2tFxv9/x+uUjjtsNEMNSqYzVDvadHMeO+/2Gpa6oS0HvA8dxYFkW9BEH3gU2dpTl3ex+HuYwiZJpAeqKEMtleuMKaF05X9IyLaqoyzbLv6R193NfFB4KFcWoNODW2ZI/bMREqSUiQYKIJw0//0cVdZ9eKraktYgYHBJt5PwMgRUJ6nPQ7y3D5WzUiqhzbu4M5zMkV/S46QwKFAAn0VswGVfyQ5DGXaAx/9V8gIOVlggyibtAF0YnFuSrmB97OuwSQ4a4dxUVUnIclbNUG9U+BaAxzwRRGRlwVFH+nhigHaVyohqTL5/EtxP/iEMrkU6KzMZUOl87q1Vx7knbz+qOZFbEl57PKa46iHH89zybkzX7kDL9U6/vL48+hDCJM4xQhJIIDfPPuhRs64J1Y8dcLoC5RTdoQ2s7Wrtz3mK2mGvoRDyQZCCIm456tJHCDRdYzAVyICZzp9FKa8wSm3nHgEyP6maRfhAdJrlGMJqjVkFdKi5b6GkcO/bbK479hjEaSiW/odYaBLAeYwEaRCpZlsMweme5r42oMBR6M+vEGeY1goYi/K6N8dANyUYgKRWaB2o0Vh9kQAJY07JE+Ex2oISnkkKxGU1h3NEgNiIq4DV4pCSJLUhsXJ2GIqoK6dA8+hqswxDhvwHSDS4NEKYnwzN8jpRGklNyhvMqaUwQKUkeFhoLVkDyUMbBEI3tm41vTEl5JFkaL+pxuAwo9TS0poCvnMtqZ86eOEwaIEWNVFECT6IEvoFpNTTbzmOWShgTFc7MRdngqGeqlRhL3PeZGiUeAWR0OqURMAOHPIRIAzHPIeYRwUnVzs/hd2X6xIFL345N5Ou7m8KmoTDMjkwPtJxVwOiOBLCtCy7bFphA0HAjv2OY3tk7cewY7R4DgZgzSgzGSSKVCMVeUp3HjErEw8fM6YCToVfkBJ3MkwM/GLI6cFb5Y9OmunFoMXYDhim0rKxk3G847jeMY4dZx6KXwBcUbTT00U4w0wOv6AQ2xzDse8e2UuYMAvZAeA1iEJuc0guaOxCT25PUY4OdhSWM8Bg2o6TsWpS6IAZ3kNG5AAaDj3BdRRi6D2U6Ak61yoNvcf/cfNHLESrdKVOIBxDNMYCxY3biikJGGIlQVDKctXwasCiP5lYP6nVGMkkOk8zR43BZ4BfZJZtRIrwAaJwCjjr3YXbKVgjMO1DZ8j3MgS4AKGFgMbt1ROhC/k/QZgUQO5vg0qgqyOswZQRNndQWA4G5DlTwumCgYnjJ3ljIlAFAVD/ysGf3Ks6UwLmsA0LmbW5yeTj0kV4k+DlLofFGM59zZRMLqVpn1P+tr+/kUdBqm2dfnkU+ezLsTjBFsK4Ltu3kTzgEYkH6MUPbdxypLdFieI8Z89EHywmRmJSNmWPDZoGJWIgEZTw89IhwGjgXyDLO9JCfi7HyvR3RNAZkK7K7wlyxHw33l0+BwRzT8CzrBi0Ft9sdqoJ2dLRm6J1d1PfXl2jRJvBoKlBhT0OtF3aoFk4+M3NUiWHDKBBZoHWFdc4aFTVIO9BGyLPpBihFafOgs0xKowHngKa6XCgPpMucQyJaUcYKKzuOMQCwhVoi0uByp5ENwhGCX6HXGPRzrj3cYO0FgMJlgcvlbDgLpDep8JIgWjbBxZGk+gj/Nr8RxsNeSzbVnb8D6YimAaYE5YKMNA1s09a6Q6M4i8R6otwMEZgPCC5QYbXIPSavaVZhFDIQFbiF/TmgijZD+mCT5t7P/iRL9SiFScWQQmGlAJBhZ9sDD3yCoTjX7Awy+e9J2npwfudaPPzrw8mnA8w1QQx7Zjt+KZxm5uVXqnpM1D5NXsQ8GgNFWE6ayQLTjjXwiSysA3AfaMcdx+0L9ttXUpN7o+XPfEwFqHXmxeQf9BmSze5F1Zn3EfuJCCLCQaLa8XLD8EHdiYhSmPockecpig3UbWWT52j4+OevcG8zaqlLxTiA7fqMUla8vrzg/fsPGCHHxylaC2A7I4puUUVUtNYg4lguK/p4RfHZFcEQ03JAbYFagStTAoaNGikIjZ2UBeeOEizLE8xfY1cZYB1eOLiHO7PCbKCqwWulkO+jgYRjCZ2ISA5QUE+RF1CyzlFBxZMs2z1sTmen6DASjMwlxiamM8k9Y9MTOuSNQc9zk31C3R29xexYcNwD9YQ7CyW1xijEsxmt1oK1FIgtEaE0ICe3AahS4bYwDZUQCh7BN5As5QoGHKoLZGGE4OYwCeBRog2cn8KDGl6/iMRoioo+HM1J4c6B2vDklzCPyzJqtqyfcg0ynzEjjzEzVUGcESAqVGfKNEu48Vmjj5ijkwS7TH2+PaT4/mnmb+q/SabxN+8BQK2GdeXk8pLDV31WPDhN/CuO+wv6sQdGwFU4c60T6El+Quyo8/vMASHINbJO7Y/6E4+GzU+FIvEJeo7ZcmvBJCXwtd8puSeK0H/gA1nXjbjDGLi9vuDp+RkpEgMPvGNQ62L0gYoKpuoN7oZ1ScVkXqM7+SJUcM/8MiIqR0jt588togyBIDQnzdCd0QJ8MC3M0lhK/ptBY+AyBy9VsO3qwMl/SXzpfJZTvyBIdcM9ogKZD/uh44cVnTNpQPZ+xM4O43KmIrlu3Bvxac4OYqb/Ng2SA9EproBH1DISW9C573sfgCmqrByDMB2YB+5Ckh7iWuEFghhpljwfswkKI/e4CKMu4YWQUBl7K1KWpEdTYrCSQ/GAQ53b1+f+TQPxBqV4WN9INELBbPqGGWrIw5aRjDIcEPHA8jhfRCWUxjU+89eicLvgzCEzkcwLR9JjuYDLUnDZNix1mVoTE8gcA8dxx+v9K/bb7TQUuSg59SmXzQ3WaWA0Hnpu7DQMEGroKoLZN9lzDbmVVQRVFwhoeHrwHszPfE+U/Setd7TWYP2I0XphhIbj8nSBu6MdVOXuPVqZ48GOzjSidxoL7hyHxz20bcXTtkRU1CG6obfG8XuxSbOMRaCefRWlnC3OAho9OFWPWh+hGh3rFwdcC/kmajSmSRcX5bAkOILtSR/3BugSBzzUmCBQDFDOrsLD684SeaRrrJwzxCWFm8Nx0+jN7pWIME8B3QcSkgRtW/xUooqNNvNylIdDZhgexksA+GCPUV2w6oJaBOJ8DgIhFlHI+UgX7VKRrEoHMMWMgxpuwXvICpso4EXgg7KOQHrpxNgUjoVVGvMwShI6GrxP/tjOtcYZjedQoHlB7mdF5OH/xITlxUxNZgricZTYAj+nmcX9yPyMb3t9Z9UjLshzypfM0AmgbqWAi/v8vGBbV5RKwdJkI/Z2YL+/4nb7jP3LJ7T7a5QCOazEk8MeFo9lTp8b2ebC5qaP8lcYh2YhVgtEqBdiu8p+jgKWHrul3D4fjxbO2lwvF3z+8oXCOWaoRdFan1L0Zh0ffvodjvuOY79BYejHgeMYMVh3oPVXFGHFo3dKtSfQVGvFcQw8PT0TAPOOUkKX0vj5x9FipEBEFAO43e64XC4UYQWFgZct2raFBstESMDSAnGFeQv8QclpKAKXwIEGoLoCi0D0IAgZrc3JcEwHANFpnhZlaXTAMGQFZnkz/V5uKx5gGMcysBSpk0zmD+rRc89GdYViv8ShJrvyMRh3INMfRoQN0p1VJCkoUrBUiZ6avJ6OlLV3G6ilEvMBD/8Y99M3uWN4R102XqsL1B0uFI3xvsBQoIW6Fzmsmk4sBkJpQccTZ6nMyECiuzN/Nk4mJ/Mw5J1mXQORIsy0LM7F+YnBKpWzlyM8OX/d2JgpMbG+1oyc8CYT+Kde/3xmJjBD7VxcCLAsiqfLip/eP2PbVo69i9y69YZjf8F++4TXL79gv79wfqhxwcwQfNdIGaTEjE2Z+ZVHWZTWeECtQ3IUrADW+mn9NTQlY4NwKtMrmltoDwIqK3KyeNGK+8sL9hs1GVQJSNngFJkccde7o+8Hpeq1YD94D711HDvpzt2N/IpGFS2RFJIRtOHYtitqJYtvHDvLaZCItnbcbq8oWtlq3w/qemqhejX4kHUU1KCqW2+47w02ZFaIiiSDkkN0x0iZtkDGB0t8gpXg3KAyV86tYBQSkZIwmQCAQgoRBB0N27llFfBJ1R58VoktMPCngrfZw3OhI+E9hTFQgXiUx80n3wVAFFfK6Q1FABAHIeeAk+GVIt3og4KyGwU7kOVZDIJ6LgqoYVhoUlhUkMqJaTgQUnpBqy8DErocBHxzohmrRiXo3+bnsWce93iOIgrBGTxnAmL5c/fZlcz9mOlcpDgzGsvCsJzHMxxTiZSZe6dybZSG41ebZi4ZfmcIOP8mbkSULeXXDdft8jAnAxGKN7Rjx3FESTRKqwxSz7wccaAAwHpHluMMzPdycaw3WN+hUep0SKDLDE8N4LRtIRDbh8MHabUIqm6WON1BIHK/ATEXNXPC3jveffgR16dnrNuC++uB2+2VepIljFmAiB4hM0AKb2sH7q8d67rOvT1s4NOnJ7z/cMW6VupNxsZJD3m/vwKujFDajnWtISYcAi0KSB/wEHjRuqB0RjRmDgyNaO7cNI+9CxNvGqdRFSxn3ioK1Ticj3l4kI7EI1ITI3gJPIBrZ8rkcNKcU4w5nh2xYYK1mriMC1ILM+d62Dw2p/c7s9IE7s501UGM6GgcDViUBkyXAi0exCfeA9WrI3ISmZEonMYoQfozRI3BRjPlPv26AycfBGCUEvRq4m55sH1GB7PcGQebBnrg7BiVE6MBAtQOsD+ijfnveWJC9yLTo2xyq6Wi1gVaOVZhGrBvfH3npLDTUKQ1EzkfoapgrRXXbSXgVyqyt8Pd0FsP4ZkdvfeHhUaslIbhEEjk0wMd0f00QS/E51k/YO2OEuQqAnhBOEkASyWoyxYHzWCg7FkJcRYXwANP6G2HBInHY8P3MVDqhuvzD3h+d8XXz/8rjv3OKsJ2QW8NcKYaFFkFXB05fm6/3fC40cdo+PzpE9atoFSFGWXwPMyd2cCx7zgODh0a1iB4j1LaHOjCPHRgKFuyS3bRBseECmIFyeqfimPgffl0W9EMLg6gYipXw6ent1BLZ6WiAjagyYKEoeNBCAY4fz/SQRrRECQSel4EJ8fSas0OyDNXn5iDzJ+cSc48IPxpMpLFWQocxknoqhzGvBbB6mEsI39xCX6GAA+0yehuxuwoJRZgk3iWqUOC2/ncyuN1a4VpJdiaiZOHQfOHO8l1fTxb8RmJwUx7kvYql0YYkc7ZJY5JB2d0wcXjgKY65RgQ6vSZ9n3L6zurHsac9A0aGzft7KXY1oqny4ZlXYJhWeAmzNcbI4q2H2g9iEESNV30XAJQSUowjld4vYL1RcmmAXCzWwjDdDTrU5wFUJRlDVBSqd7dSLFuo8NQQnaOq18Km8Z6OyiBN5jKcOapo+8cDvz65Ya63LFennnfMpBTl15fX2LaNccWlsJ6vfXQKyiO/Xjl+RBFXS94+fwzLluFCnC9VLSx4/U4ppdwa/j4y59xtB5t2Ny0NaQE2VvGTaZFsC5kWZbqgHQa5X5BrTy6nFHB8XIjNA9KrTAhXT7TKz7LbJ0ONqhlaTaetcbz8IGCHQrhQKJRKPeJjBQHBWDGgEsNAwdgpiK8NuKV/mAPDHhsWsqAIlIgD4DTH3qNgozx+GbK1LugetoACvYw+si8HjBh2bv3UOOKxr0ckOwgIc4sCFmi6HHQXJLySK4ED+UCXa4osqDqqeaWJlRyDwf3enaWzq7dSLlSRWyCj2FEhevFqzOoEKvrBrgx4gj1wyiFpgoaExQN4SIOLP621z9jmnkyvx6MRLyua8HTdcXlugGl8B2B2HczHH1HOw4SnGyglA2jHCH2QcUqjwdv3qG2Q7EwWLOMKDCRbgCQskRplASkigNqIJX7IDjYOhmZIkvkmcQDWh+4//IzeosSoQ14pxCMA6wcCHUcv778Cd127PsLbl+/YHDgOlp7xeiGulQ4BgfU9gNwYL+94vb6CoOjFmUp0x1yHEzTtguWdcH16TdQ7ZBBmTmHkn8iBmt3HJ2A6bb9DdyZHvTW4e+u05P0ZQEQepoqqHVBHx2QNTbE4OdJieapEhuphMaHnFgMBPBMqTyeOXtr2O7POEUL4KNjkQMCoLuiG9+T0dwkV7nRAGVEEbm2Rlp5ChEzTbXkDMQeOkuxwRR9dK/A/G+PSCjXhQA4sQpKEDIVcjeYsjdjNjkOJ9lPSkAodcrXaZQ5KfEf1SetgbcETcxpSEqpqHVFU0eK6TsQeI1EWvag5TqPkQCp1RF7nQhTtMnP90ZkaB0iB60CBNllyzUDIJRdmHNcZtk9iGTRVvEtr+9MPQgqPRoJj4dTVfH8dMX16Yplu0CUizxiQE87DrR9R2v7RPQ18QEwvKX60xLpQg9v1Oi5DXPT5jog5okwzahINWNzg7eDc07BWZoemMTAwOg3bpRhsJgmnSGyxyzTcBJwAHVZsa4Dox/4/MufcNxvNAqWQ4k5UkDCG2pRUr6POydBAdhbkl6oKl204POnTxAtuDw9oajPWSXDyQ5c1hV6u6Ed+wRxyUG5o7eOui3BM3GMdsQG58i+dbtAayETVohhGPpcQ50VLP59ERCAG50pYDSfeShhT4k2AXkO4FhDFYrWVKeIzjAasjAvkeZ0wCVkRGog/ZJbCKRm40wHYrQCZuVF3gQLniAH5ATuH/3W7KLMA5eq15nTh6fVkqYqKPHy8LtxnYbTeKbclUT1AgUmNYSJR1TZKjKbskEFeQoECVIhfg4xQkYHiNQtIiycDlEDv5PMN/L+0/CGhOBfvidNAhnL8echGj8jr297ff+Q4jdG4swnL1ulobhcsSykKqc+Q2sDbb9xg3dK2ZFNlxiGz05JSosLyMsYHAvvMg2Fzp4DnLlirrgyvPKRvzswgmrrAmAcE+y06Hp1T7Zn4BLBr0jNBgJeJXpTiDe0o83HmTlmO2LYbS241AqzPnN7N4vKRf6OQdcN9/sN+uUL3n1+wQ8/XKlp0TnItgQAlToJo7cgVzk7bQeVyUmm6rB2nC3l4N+tlw1ZvuTYvDgs+T/JQFc4/yE8sZlz80sCtHh48pn+kamYyIoIh1OLhbcD4FHWM6ciOkQpcDOj0MzxM2AIXMhs/ns+2gQQ/9tN+fhZD/8tcWV+9vPws5KgxOdFjEQBb5FKhPcOjjVxT+ISEj05qc6W5+BxLdjN6zMVMQ9+EXTeZ2IPj9ciiUlAzouN+5ql0vi6M00xCuxIxhx5/E/eEPueolFtZgJnQeJbX99pKCS31bwkEWApit/8cMUP75/x9PSMZbnA3NEHJ363fcf99Sva/Wug3AXrsqBr5QAgAG6D0nalkBSigA+WBrshhq3gFGUBmHellNjcEAqLOaMZ7UjhgJY+eMDOdvceeXei0qcHmlUBsJrT+kAfd9xeP2Pf94eQnJfCVKpi257wdH2ielbhhKojGo+yr7z3hu1ygVnD/fYVf/6HP+H5/b+ih/AG7wKrNfoMCpZa0Lrhvt8A50zQWjkvtYVytvedJU7nd+2vL1i2Jzy/d/iFs1Brjb4OCFwMNRReJENvpbqUGvkJ0Ghzj8iJlPjAkvKg9YY0IwJgUUX3Eu3giXmwUYwtWuldc41jOPFDdDozk390D8ajT7swLcD5DncLRSyZmA/fE6VjJU6j5FwnhjmbGeE+rzN7f4Y5Z6HENDcLDCEnl0s4uz4My+OUPJEgnjnO9OjhJqJ5Le8hI6iM9FJmcuIzjH8w0Wjd4A9T1nMpQkYIpaRaXMEMp/Lavt1OfGevB1fzvCgvWNeC9+82/P53P+Hd+x+wrFdAKu77jtajEjAOuN2gBVi3C0pdY47nHfsrH1jRAl3fkSqtBa4VR3fsrRGkgUCkQrs93LCje0zwnocboHhq1rUF3iilPqzRq/e30dD8v0CkKVVPg6gq2K7XqNQcbOYSYJYRY7GXhUjyfv+KPj6g9w6goK4XRj6HT+9hwyKaAmANn37+I3754QOenxYapeMGxROkrCjrFWW5Q+pA73tUYvilbX/FGA2jUU9TBVEjZ6Xny+dfWF59foend+9hluXgAlVBSSIPADx6SQlQLhqsmBZ2oDfY4GRvBAtW6mUqRbGs6SglaNgIVmM0+WWFdNg4MQZkWfJsL58YxF+8zi5UnRFgbMnZRp0YGuQx/AZaG+ijwFChRaAj08ZoU4eTUxP7wMZ9Vu1EAE6r1wdLhSmkCxAXcghMFM2A0joakmHL1O1NpP+QGhHTic8J58uvip96iuKk4Q0npQWQBVlxOpNlsO2/KKAF3SWqx9nnoW/W81te3z2k+MxtJKocK54vT7henlHWC1wKGY9jACgodQNEsbrBZEXrDm8dfQgGFgy5wIoB2wVDN4zIgTE4ILgZMEIinW3Vp1UkefZhw0yj+jY9yrb2YWmR6WlSKiFVkSboFIItooJS1xgbcMfoDSdWj7D64bXiv+GG2+sLSl1gfqAdBz2WBzvVKd5yv9/j3x0iBV8+fgLsGaXQY/R2xxisqS/rSsGZmGk62sC9s2uVGh7sMrWQjpdItwSC0cjadBRslw3bZeFQou6oW508E3dPCgAEOTyai1qiBb6UgnaMaFaLwdDm1OkMjKcAZFRKbttIcRxMuXA6tTedBpmrv3l+uefebuqsIJz/fb5vGoq0HPFzTucKmrnKjBiScUuCHwV13KIxL6pEcwhQpBLmHKXYj/3cDwJICUqAboBUShfOfXHe5+Mr9xEEVJ0KQ5HRw/l2Lqg9pCKJYJTsT3lIKcnOqjApIUUYC5y1Ev8VI4o3AEg8nFLYRFWWCyAF3YCjDRxHj1RAMEZBt4o2Oo7ecTTH/XDsreDwC7oqvBp6RAHJ0+jmGDN0w4Ml4MKbAI8bhtdn57+CltSQG5ULlROuHPGAc0s74GIQ6IkWq6Adx5STE5XHbTw3ogfbUGDYby94evcOqXRMDCYbwCyuXaZUv2rF68sXljnXCk91qMBKtBYUZ08GcR+W7nK2yfRsPpDCJYmfjDEg7QDkTpq8PqNUMlZ3AfS6xnMyOBQq/Ccmd4L3STGxCisCE4OJAnZeSyLqjuiABWZalxvGM4R+eKUxyn6STPv4uP87+cdDiP1YgZsp8YwsMZ8xsY8wFEEdJ6Hs8XmeaUyqu08SXTRXWURD2ROTRktif065gyA1pYFLm6VypiFzyHFs6yhUTIAzt3egCpj4xUxDdMYQ01HRw8WfoKxP04Jz32ea942v72dmWjT2OMU0aMHZKdcGcLSO+23Hy+tOLkQ0LO17x+vLjn0/qPFw3/Hyqtj7FQMbvDhapyw7c+KY3yHZap7kkjxsCWnq9F6PRoQhZHT9efaQgPXjaYExH1pmHrl2GpRtN8Nx3GeEgfmWTFX4X8NoJFQcx/6Cdb1AVLEsK0bMPyWYyc2p+d+R576+fKFRihb3bV2DIEaa9bIUToTvWVoOopeFylTQ4EshOEygNtegw/2G/T5QasG2bRABXtsNdV0oJpPuPYVsxeFgtWLesZCKzsVgKHb0EZqlgmzUohdOzCEOxoNHnl2ksZqa6wmQOJcRQla53hgMbu8iOL0nCErOzZ+H02VeBytwjE6pS1Hm4aMgr87PZ09OvCczzHhOHu0F7KqV2U/Emx6BDawEj51ycw52+7LUmd+Sew9cl7AK4nEoJf8+mZnx256EOJkph4axUiBEgVh4Zhn1LQtzzsDNff+Nr+/Wo+iWZJoCO4DXW8Mv5QaTXyCq2PcDr693fH29YQQA1kfH7U6ilZnHH04CS0YeJdxYcoJEfd9TnyHGBRo3f7a2MyRMtSSAYGCQwlRRFw6ZhZ2ovJb0PJjenf8OItsanH+wy3W0Y2IL+YgT1z8rLukBsoToeHn5gmVZURZOFysfCvbXVxz7Tm/g4INUbqJ9f0VdCkZnqmLvnqC1YqkVxYD9do/UJSKFfQfKkU8GwIBYzqbgdjQbsIMpwroCfRx4/fIFbo6n52e83r6iLhXrukRnKjdgRlIyGKGdE63AsrcNaoXVwrQwwGaGsxw2LBGhDAh6zwUG1tWAh4leDlClLMqX5/N4ICe5xLPOwIVU/Qz7ywP/4mzZfnzxzu7NcR0Cl4paK8Q1hjx31LrMFIbRAv9ItGYbMDuLBTTMKoMYmRvWyhA/gKLIJsYkJ04DkdPRApOJugsiPqK9maEEy/aptD35Gihn1BApYlLiSRTMKhb3ctGY7odzD3O9fiVDcXQDp5UnG81wPwxfXw788ukVEGoBHK2hdYZo7Px0tGaRx/JC3R8OLMDNmF5Cz1bejBhEHFIENtJLGLIDec70SDZfSJGpKAbG9BbmjqIP0dvUSeAPPPU/caYjpQhJUJaeNUqSj9ifJLsu5O/LgjGo7szpUQYbgmVjReh2e5n3OLkhInNuqQ16rGXjyDs4Qu1qnApFmYvHWtoAKEEX6l0hd8a/bBiNhr4dO2qtaAvz6eN2h7ihXC8YY6A7JQKWWrnhLYVZHNAaG13nQc9Wd48Yv/fB1G0GAsRhMnrqSqZpOjP34DklMzOeU1axCFwOTOZmhN9poAGK2+RfPbZYZ8qX/IXj6GiREudEefgAxbPKjIy45vIQZQC1rqywSZ8iimStknlnJljrGmBhBbRCXVDy0MeMl7Ncg0j50kT4jJxz5AVvaBLDwepHyAEAEPVZzXDBTNGTASvik2eTPTPyENP8aqlH6w/17agPA4a9DdxbCJ9Gx+FULfLI48JA8Lqzjpw5Hl9vclLR6SH5OSeI6DjzujdIeaRB2TBzDoWVyBRk1pJduWiP8x2QCwxMzwaR0KXMT+JnZ345m4oy9M3sxz2IZQqooh8H1IP4UipOcDZ4A8IhRAJGYcexw5wlV41oChMMfKwK+Ayv83A4fLZOnFhFY/jdD7SjYr/fsGwrG9K6zmiFBz0bltISM8xmRYUbMCMEUcVgWYrRgflcw/yMfKweHk/eXDseNm985WO09hjyPUR1+X3I8xTPKp8zYm9ZvtERw6TYB7MUCvNmmRPuYbxTIzPDRO6nuiwxOErTOyGFdP0xZ0WGBBKVpYf9gfmQ4v06DYdENEUuhiNFkvMZ8kNSoBgPfx7T4fORZT9Q6mfOk/UXQPC3vr7LUIzOUqZb5oPxRxzN0mJlODhvnxeq5wPPKUupewDwxiwW6uGX5qZBRBBpgsXBeCs6+3we9GjScQBjzAeABDKTVKPKzs/oAQG46BrDX/g/pjFARHphkQmq87rnRsvN7QjCF3kk5o7tcsWwjjHyO9gJmog7vaOQcg1eazbO1VqxrEswJtnQ4JCTSfjoYZGGWfA4mQpI4g8NBjEXwXb5HQHjzhZ5VfbkpCHf1lj/GYazfTuaCuBO4dbuUeKTpDlzrdiqcCLxKgQEWZnJd4Xhyb0Sz/b0GXZ60EfPEOvkCD7EjDf4fPMezjK4Yo6KMMe2Ru7eTzB4dthKjnWMZ18Ey7Li2M+hxyKsJNBwpFIW90vuHw5gP6tuZmn6HA+XzIggtryNkxKuouczDKc0AdG/QDwzooOf/SjCvvT5Or/l+03Fd2MUnkQThLUO3/HojfUfuYr5wMIy5kTmtIo2ggWXzDbkZiGImaSY04rE1kop91yAN0HESZwSSJC1ZE5dJ8V2UBkmfsnndwDUVfCpEiSRgiKunpz9uD8LfxHRVg5Kduu43254enrGsd+IUWhh5CUMdZeFczxnX0GEi20cE6cBGka/h5F+hMXiyqMJLpJ6pF7o6YR5odY7G5p8oB0fQF0C5yjF5ytEC1ofOPrAslwh3mKqOieZJo9GKCoaZDCfBr6oTmlB1kDy+/MyGsQW8kgixMcE3hDXLPN6p79MIxxGA5rgdBjwM9c5HXasC40Spf+7AT06Wc3uGG5wFWiQ/UopAXTixMcAHEYsSZRRqdnJTXjc+zR8jGxV2B2bmNwjJuBIfKJAUGYEnAK4J5gejhUOmbKCBCkxo1rJLIxrouyKLrI8fBvfME9PpNrf+vp+HkU8V1hEDXoukmPMFIwAVDYVPXi2uGhOio56e7ID7eGWIidl30eU/CY6zc9i+ZDh+mmcdW4YCcueQ3ZhbEW3pCS7w3o/FZIBZPtz4hai57Caee+xixiVh+jqY7g8gyeG2r13LOsFpSxYF0roYYaN3GgKOYEviTIbGJ20fcf1uoTnDpwnd6VHWTcMmQ3mqyL2YGh5vUXqaVAguL98wtPze7hTRXy5EMHnFHbD5y87PjzX8IgHhhlKXTG6UUIQLPPm8pzpVDoTGqs0Wox8DCiBowh7faan9fynnenJxGESqAyikU/WAN5Kz59RR+bq6UaGAW0QqxiXZb6HVR+LtnSZ5Km5Vg4Ijng/+RWGFr2eZ3TlRWKHxzxST66JnecApzPzNOo+ZjQwDQ3IGUqQP1MacyBn2qrHeIV5QKMFoqwAlJU4FzzOQ824y6Pq9q2v7zMURfnHUtpezmeZ4dR8ZVXizMlmb32CbA/nj+vgM0yFp39PYCZLcHGjTqMgPt5+DoDMi/IhSojAv6H75teLzIMjouwpQZXjBzsAACWFSURBVHqpqOxLVvl5H4/BjWjwBc5AB5MqiDMtasdBabZS0eSY+WN6O8/QF9nvxMatHJZkFp7OmFacmefjQc0dGD+ee0gfcuuzH/E47mweK1SZ6kd/EIx17PuBY1VKMkr0lIyIAjyHP729+dRIyId6YinTsoWXBLEEPI5RoMF7DAsn4J16oJBznfHwPQ/7Lg9ERhux+0i66wPHMTCch2niGnkYM0JAYDIRfc6UI0R2LM7AGx5FgKbK/JOFyakpinPfY/qycFjntn38OR4+2/NnMCBng8wHz3cxSkl5weCXRNSTOEnuk3+8OvTff32noUgyCQLAySPz0JIyI8AzJ8ubN095epzh0nTTAGQgJ1Wlim/y9dkafOIAbymtsQHPVYtFyRTHzr8XVkdO1N7hRhKWiALdiKLHJlNPwxDXE1beHw7o+dmIvPVEoMOi4jh26MaZpOyYfcyRhCXcXKv8fgnCmLNZjENnOiXsgHP9wpI+bMn4xyPwJnOTe7y39479OLBs5A0ce8O61PnrozXc94LLpqhFAW9sxc88fAyM7qFkhbffnw89uCwTZXDMZ+iGYNaezR2Zu58h/RnCemgwvCFyOWDBlZGk4CMiyVwbz2fHKHQ/Gsyv85rm+2I9BeTdkOquM3UleJnVqiRrnbuY9xQh/RjQOkJkGLPjlnv3XKk3UajPAjt//gYkBc7SagobZ7qGeccup1PIb0kjkafEZgr07abiuwyFgiFtdq+NCQIG0216SV5cKWXKoQFJBor5pM4Gr1Tk4XtixB4k/DZbsrOC0FtGBlwuswcvOlfM49BHyZBBGhIIAspULnIPZL7wfdxkifo/+MlJ8ZdY5ASzYroWBILKaxaaFY0DacgfOdogWaYsC6wd85LJ8xAgwV13dirGZYmBM0zXFakULXqG3ed4hsyL8ABhkAI0p8CDa2ZG6vhxNJbydMXoB2rVWaIFBF9f7oCvuF4qy82dc0v7AHqMYaT8HvDYyZkGgdfKdM4N0AB6zcYZ5UzQNb05Hg5ufuJ54Au4rglwZ7ieB6Nopq0Amb75IYLWOz6/3PC74wmXAuQcTs2SetGYYl4xwesQ0illgRaOiORE8aiCeIHbgY6Q/RvUcvVxQBGYRkY22aOSYTOy6iLTx/nEPM4I+NFkmIEp/wy+8n0KUU5/m9qnOJ8Do5fEfuS/WeP/f6/v6x5tJDxRpe7hwDxGBTPEHOHN/LzDeJ/NBbIYERgRhJ/hXGoFUkMBIW+Wn57ehpv9RLfP4cmzcmGxLKLzeiF4+J2QUU+cQrJYx0V9g19MkxdpBfh+0YICg5nComR8WnkuTY4ysJH5eY1ogx+jM1Q3DOsoSsDs0eqPPibxRxCaEg9/z7Ql1iUwLy2k8j747BmM0/AZ+tEgrqGj0dg0JYIOeuDX2w4bHe+eV4CQJtyBTqEF5vQhv+fzSh7OQl5fGE6zA2IckKMlgVnDmwVJNCsMAeJg8tAUvP1oy0mKdBCZSliG8ABCNU2Nwsi9NXhdIKWglIX3VULmDhRb5kAqIX8EiLGFcfDiC9MhTFvkCV4K2igPQLiij1A8iwtV9QeAU3A6Ii6JqkSp9zGqyIDZptYInWNB0TLHTuZnTmxnYiQeUd73xBP/nAFAjz8QncmHMOkkKux4c3GCmNjtedQyDJJ5CKlf8QD8ZGUl8YaTY4yJvMu52fFweB9bxU9wEg+YAN8vYYoec1XEbIpz8Ew+mDSAj5sZ8zo8Lw2ClJJnnMl7zUrVCSpFDT3+eCxwGheWG8MsanyvPMzYyLBUIo6Yof55TbM6IYqsGOjjJgIA84gMBVVWAI7iikKwAu6cEm8GlDKwLfSwKg5xalMKGMW5n1Ph+Q2nUZ4uMZ2ADLharBXA2C4wodwnD57wTHNPQzydkj0AfvFZqYWRj+uRhAQ3qp75QlanCs6BBIKsJjy2mWeJPqtyUgrMOjCvOtSu47LNwEnunucm09/8gUxpwdSTSKeG3IoZMb9x/EL1NtZw5nCjuUWnM4tfk9ShPWOSBLm/x1J8f1NYemxkrvj4UB0pIfgQ78ViUeCWC5NhoZwPXKKUlOzGuAuzh89+XABkJGPBjQCyejI35kNVQebXyImjZuyWHuhhc/Hz4n7fWsfznvPPNCZx3xFSnxmsszdBci14N9MPnft53tls55fsZfG5mXndTGNmdDW9eB4IRhzy0K85c/KHjeLwKXknj97dJbot2a9jZrjdO8uace0KR+/OMBg0hmNGQSfc+peNU8j2bhtwr+eyRjT21lDQSDxGyXy+iWs9Ylb8IPfHlfSwp4mT8DNbo2qalog2w5jmKtkYKDVSpumoToxMS4X1I5pUw7gg9Dty3cK+pdPKfZmr444JpOfey2f3cLLO/Rc/NjurfAVc+NyPk8wYa61gJJQHQB4+6E3W/k+8vnMAEBckv8EevmkePvf5UATkVPChswIxPyMeYpamWmt4szfC+2d0QhcboFYId0jIrk/vKZSyH+OR3CLzs6axkeQMemz3xDHoRc07acSC837nHnuoeqSHjCa103Dk8N/04A55gERPA8vDyvQJUAnpei0PmyUPmvAgO5DTt7NClPeUdjyJY+ehkcAokqyTl8vvMBB8G3fgenmaXbRmDuhZ47/diEldNkYcSwX25mRmCsuMQ/GAHXEnzGjaQfajkTxmNqAzUsyD9Hb3ekSL00/G/skKUQKD3A+PEaNMpwDw4GvlvhvDcN/Ztr9UVpO6jWmMPNjFWtYp2jNTnzgHEhT5mRmIQmRAETL/onPc44kVyBTzkYg+RStSQWw64fmvZ2qZBuhxj5k5uhgWXZBjFIAotwrHODDF0YcIH2+d+De+/lkKV+aCYckS9Pl9w+U0EPEbFkaj6AmmnU8k1Z4NRULFKi8/9sWUpleJprAzzAcwS7R5EY+kJQE35vAzHAOC/yHZdkxNw1zkMSwin39kETOccYFkaCoFrgp1C3m9yEEfHsaMvB8+krYwRG6lALPXhSlRzvycvAlg6lNMJH2ujz58z7kZVTjDYSrGzBgmoyjMaCTt3NE6lkpDkN8/+Qjm+PRlx/1YcN0Knq4LnvzA691D6zMnYT3cp4cRcwv/UKMY/Ja+/IZFKUCmkrNCkJtiPsg8TfllCSrH7sv9IW+dmAf62fZ24mylAOOcWgYfnBynDoDduYBHeT5Sp8j9Z6oiBsGSm3LuQZvXnusvOJfHJ+kpDQfmWkRkkTfwsB+nEwkV44wgSnY3G0+e5e8/rNLjc/mekOK7Uw/CBw+bH+c95A3w2UbXoTwcCj1vOhFYEqGSskqFoKSapkWcB3xZ0PvbNIKhrc3wcHrf+N03Xkoy5HsAzrQyPHQCn2TQ2VzZ/14r7kSlYxGyfFYCuMwSqLtDNKsMZ1nWg3EZrSCzPXtudVWO1ctDAczSq0jMkJwXIkGdPsPoYLZHY1qGuMCbjtls3op7MnOK6brCSkzwhr9BB9yd5UUzuKzYakGtYWRtMHJhdhGMwngaM6oDeSnm0Uxl0STm05VmD42EMciWAeT9xdXzV2Rqh+RVnilbRoh2nrd4z3403PeBZQUuS4XqSV3noasQ4dBnEUDqElLejhxlgADS+Qr0SAhuS3IwhkY6HOhyAPa0Vz4Fb5leyhluAnF+ZsktzEgaH66XoMNKjcrJjOEAT41On2uTTYj/bWTxT7++O/XgMp7h4lnUkBzWhDQE6QEec65sKHrb9MM/RPLl/HTJfDo3G2Y3JQTTwiPnO85OsdP+zspDfArCOKUp8geLmwd7XpY8hHoP78p/y9/Jv9OYfZmTmoiAR6SQZbikj8fP2bGYEY/Mz0WGtLkO+b/EPx6JPPNt3IyzruEeBKDTq53hq0xDktfvcLJXkcYceOzHyfTGzHA0APeB8pRlxahg+T+yPg+AMuJa3oCDkS44TnIR5ncKsmI0sZWMgOS89kc/nQZFHiKnx05biKCFuFLvBlmpZj4ySgSiAS7K6l5Q6grgmCuT4Gleq3vQoNJIhTbIGBoAb4pC02Ll5c9tFg/zsZExrz//7tFwWqwf3KEPgkjzzW/2bP7z3Jdv3/NPv75/rsdDyJ/kmBmA+aO3TkHZ3DL5oDys/FlKO6dHRUydx+/BUHiEjbMbLjdNHoKH+05DcHrc+B5kzZoJUeJuaSqKCFwVPVH0COvPktZ5J8naPF+ncpLEYKBcJxqRbP/OvJOVEZUojSbegvBNk6ymU0NjmjQRZPPRWVWJFCbKwHMtLCA2yR6Ks2kJYtE+n58cuJCcgB75GnGAw+AhorLb/Y6iG9Ya0U2Uk80x105ibU4Xklw6bnIffYq2zEoVkj3qEe3J/Iw0tmdVMasSmF7ZXfFAbH6jaJZbgsSrA62tMOdQ5Xn/s+cDkKC9lwGI3OZeTn7EQPZxEBeYjgyObVswjBgOG5QzXXlz/qcj5TgHhLGKMzCxNv6+eNCv4xmaOEo/YKU8GLB0vPG8PJ9dPEvBuQe+8fXdhoKbmlYxD+v0ELNEyZigqM6DbvnQkR4F4SliY2v8RUrRpe22h1LYxCMwQyh/KKDH0p23L48b1M8QNkVSHgDD5FeQT1BikWmoRnjR9PpnndpDMCRoIM7eExqGoDoDsNFDWi3EZkOJXCLCSk+hkij96UkSzJ15fH4vsq0uoiIJQDZwoMm3AuZaIj1SXO/JcuRnipwN3zMaMCc+FPdsliO5uAdeXnaMyxb9EQmYRrTmJ8FrOolYVxqFEbiTPjgYzW+euERAVGFoy3QgE92P+8gITMq5l96+0ujxjT2iimGGUhbMaXWiqAu1QNwdGNyRIw6ogB3AacAk/t39gPcdUgVrAWRZ8HILYDzj5IyqJA3zY8PYiTedYfED5uVZFo1ickZJIbNok+Ga7MzHXXCugaoiRyJ86+v7KNyaYa88pBDxdxKh40O5SuIGT0NyhsB8ZIbHtCRBsFmizHb0MB3T0MxY5Lz5aaYMk2YrIrO9Og0TmZkn2DUJLuHV8urOH2bIxm9hydHm3+U7Ne479S1OzxhrkTiZA5ACjoE7jSA3vs11lrm2J/zlD98qISozPerDSswwH+fpMTv/e+JLjujjePuc3eip3D1KnzRAY3BCeU6eygPfeo/xBAXW7fHBcE0eunvnXcQFuDXAQpE8Dd58Ow8BqxkJhj88k3RSSTzKyN+MEwbPYPLRXUQaU3C0HfuxoI0nLFuyXuMZayVl3gbcOSTKAtScCzbpuwZ4g6AjMQwBOPV+hGhPGP8HfDH2TcDXcu4m94yaA7dLI2F0OP5gAEQqI+DhKIPK7wCJfzOixlvHwe84z+63vL6zezRs8sNNPcb9b0UyHkOmNB5/8SsPjy9xC6YCWVOOkp/L/L1gup7//fh3MeMjeQznqjx8nwhOcczHlXr4d8nSW/7JcD8BucdE4OFQZpfkm8+MEFwevyFLVjjd4ANkeJb1zt+Y6ll6ht8K56ZK3YQ8RBkdSYTsf7kWj/863/4IEsbzigOd0aLbye8Ezk1uRu+dBiQjH3rLTEfn0k7Qes4odQ/tBEZhjodLjmeRy2Hmb4zu3DvT+YQBMUSTIdc4PyVBTRGK9PQ+4jMLRPpcn9Qb9ehFoQZp4E3RAn7elIPU/3Nfkdr+EEXlo/5HjFfutdMInvd+np/sicm5LAibxSqkOSt25oacfpapyDSi+VM//3zr6zvLo5gH93x8Do/uzCnqIpi1Yc9Iwf9/7V3bkhw3jj1gZkveiPUPOOz//zA7/DwRtltdlUliHnAOgCx5rO4HRezGFGctrbqrMkkQxPUAlIAIQsyMRlXc4oKwMzClyt/rli0FVJ2t05l/CjrL3FKwNN4Rr+1WiExfL1MwLSNL4kqmjBGbAcmY2smqLkRo4aQTghmBAtWUpoiHC2MRbo0l/Qy9OxeQt5KRytETYaAavtDSa9W1wdBVZailCqojbStBWMD1lYcPiBJ6uUbLCyKvwGq4L2E6rxkZjNnjUE4cAtc9BgFbQ25H1UwYQU+DQrWnB+ugGIaru3S8x3yh+niTpiloyxcfI1KI7tFqYE6WWmdpgIrUgOmLBXlhIc7zDqzqSyK0rIRDHfR441on5lI6u7hOG3GNbyElo3Hdsm6ql4XKG+JD4Xp4+zzLHM4TtocFWGe0guEufitt/a7xIUGx2cA+RhbkOH2vRK1hJtOWHCkgSJBHCDgh3ULrhQAIPEViA7hR8FrSzlvE4MDbrQUt2/CEZwPAYJywazbm6PPzGiEktg30f52b0lKLemO/EBYIRh9h+E3vLoWyOCFUBkFh2xg8RGwgbCP7K66cZyHs4mAiUHjdQqCqivbzFcKzB6sqS7nTWqocS8/ta65yAVb8QRfE+lYWhbkhISg4T0cGZLvbuQ1ZQCHUBx7h94wxpXndpLP+WhOOkzwWfBcCjdbb9sL6oaoPMTPYHldOdgV1zhBWgYtxGpu8+WtOzDNuu5v3G9Z5UHHsGCP445xxdaUo7wjA05xvWMcNtug2W9FBZ0e0vx5XCtd0l9W9HQAig2JbHXJHnEtzwI8Dx9ufwP/8L3umDuzbXvVPFBKycr92SP7z+GAwc0WlI+cpE1HmnCaTsGFbpbEpeQXRlil9HdXGPa0I78FJalL31Lg6SABz0o4KCKmrk0vqC9lpMHb79kTk8dBkwA1ojy6MXB4S6pLlqQF7cDYCjHWrWnS5Vncoo9+60pqK28k2bJth7IbXP/9iBacEk/ptRvGP0xT2C0xbUN6WLuTaB4zNiD0XJfCWWe2c+og6Lba0H6TJ1EHb6huZyYJHLWBWAROilwJrsOTaQ1iMl2gk3Kaa7eTtWr+RF/WIFTSvQVcn09Da94Vov0h6ZGdtgewWNgz2pzgvdDNEnGvOE+f9jvN+i5VuA7YGbE7M+xFNbCycjnMRyEf5dB7AOXRTGNL0F4o33Bh26073KjAQ3RoGaL3wc7p9LfnaZbGHYDtuN4yXz7CXEbfCgZDv9PulNL+n65EvWSkoih0KaefurYGqBlOgeJykmMGvKEvRqd8rActuScK7ZwbCmBRJiSXRNC8WCfRItM/Wby5CT4xpOYV4SQVbdaDqM4sBU8iFGaxabE0SfTnvAan3u9woG/CphCSLy6hVo61bMNdKq4bM8zcpyLoepukubxYFYydJq/a33IVLvKY9H4Aq11K3OQI+v+XFOl3o0sLJvYhGvQtnovMl9B8BQQ4e8YslVcVQZo4RF33QDI+isGw8hJG0gymOUXgW3V7fJks9E/gEgwT0jjUcpzFe8RD/yc5cLd62xB9ajtxsoJ7huAbyFVvhxw1W78nXWZ0oBQAt1rPmCd+rDZ7ObZ/nd3U9JAmTiUAtmgvx/ExvRuLSSCCUN1gZMs1rMD0oJjMrVc41xbV6bEuP0m4psW1F0RHfUUxXx7IHT9s2xwzanihYpkNisiTk43Fec61Lrl6xAbVZ27e4/UtBwjmjp4PcLFX4necEJtjZCjCiHGmLpQaKdJviMRQBJrFg/HRo1jrk0Tot9aZt1F6jGIlMvVwZLLbGzbhRBVmLdl7CQpZixngElCIGopu6co3GxpZtnp291JcjBZCHVtXc4/9ajMKIeWCg1xcrQVWZy8Bk39gQIuH7n+eZLBZTGMTbTAxzbPuGfe7Ytxes5djGWXyTwXRaCoiyBGOQVrG6VECrFbixiDE7UskKG3EW4IPWau3NRXnxXGVGhdm8OSf2dPtQ7gYAuQPf16JY85Kauarefug6+sua4KKASGFRpqnhgl7Nx18qD6EIrkzneObIA7Py4Hov4slvcwZnxVV0L4XWkreBaW2G1AiVBUAehLXA+oHSaACq/NcRAaYkWzxgkxtlBnZpSDcrrIEt+n64QzCs5RUXGWCsRhOiAMkAIOkbOI8F+BlizDaoaXD0xvBs6moGnK22ZN/prij9OJRa5Bo3lWY/8olnStOsyNt0GpvWDphtWPOM/qkyN2SttWeuVRieKhEA5VRPqYKCRHshC1A0CloPAG6G84wCseNceKF7F987MTbD5nGt4MSMFPDcssDOUmhO+DwxAewj3rjhjOAn4wcE2kAFjLABaE25W+Rxv1K0fucXhVe2eKx1010xwlWsrtQZ+Ew+/I44isjOdBMoDvpmdfZlXumgReAuvrOWJJ9FOXJqnpYi6gxF31PPUvfs+B4LqUBty7stoi3+mT0qNnbeTgGwgH6b85pFrJD6xmanqc9o/sV/YwCgwEz5ksKEzEAUljZ4rHhPBKlCWw8bGLuu8ztx3g5WjYalEJtctBgj+loKkUc9kx9Ir9bVUDVcHmMrwKBzXF68rNLYcyk7Udq0x2xEJ1lQqkQckuxyFUyOyWj0XQ3Nqee0uhMohjRh8wybY+wpxCXex6ialMKwDOi2Alk0igGrmXK3VhcPaTzRsWxhEyAMAy/bjs2Ik8g3O4XsxMunz7ifiz5Qxb7CUtTdr0xXe2QgfD/ha0cGi/1qfYXwb9Z5PJVuU9FfUlBnBRLWY8Ami/ZgbC0IgLEVwwh8iFo1WJyhJbV3seb/eXwYmSlS98nHj5pvmaedLM0mFdLCwRDcDGuWgUx3+cpm8GHAQ6enzA03DEW0RZckLUEl7ZbWiCSprJjEPrS55ukrE1hSW81Fuiy2LAu/WjAXUkAuWjD9YFlwgJiiHb51V4IWQw/c5iOXUrEtc3EJ+irFmCcJEbFgSb3mZD1kFiO2pQPrUmZC5kFZ8F9rJEvTttEjlQoytSvaBKx8IS4AVsZMxVLFzF8xtSwtQx4COa66TiJIVLmdnIqRHjS+9mEBPKMls5ZjJyDOaJWtbiWbaMe0Nee/jT342uo+DglY9iPmQY/U5sUMsr4P2lNHJ3Eaixnr8fq3STl5xinWPAB85n4wrrhCYQx8Z0GhZcRlsyX13eJgo0nIGOGnJSHMWAVJa6IVmF04llLTbcCH/Mz6SNCqUq+K0s9ZSExtkD0SpfFwbSgFhsk35++zaCmnVb+L09Yi8vFgCcOELue8KbDYyFfX7MEdbmFNVKeqwCz4iqsY9b5sfZE0EJXLutG63JSPF9JTjW/4h6yAJGrhJELzKQaUkgVC5V5FN4U7hWXhMBiXaG7cGBJunoweWxCHzXkrW7/pqojf9kE80jZVzooEa9Hdy/pAW8eIIOiLalXgNNsd8LhJTMnjaJrL+iXyiWJx7hGc/rQpQMz1JRPWrIMsFB6ynC5Fc6X8HuQItyWUSaCQmZVi6qoyGzHXRSRt/E9JAN6/a01gv2N8OD1qabgsJD+kicpDuhwqqU2V5Q17OMqDGa6nKSOC5s/yzoWkGpIZetbEaXeueVwPkxM4M6KfJUCJzMPZ75uQsFBWRyhDvQPwqgrlTypeEp+RZQQ0aDjqETJofDpONvGNRzQGo9DZTcVbtABkWThQXcSVmlRcofz6uaJKIAOIQnZyynHjG7LvRaSyLeeKMphTU4NzjZqLdmh7jMBr7aUBR641v1Nb2uimClIKtxLJkJsaRWxtWzhnldWn8JCg6VZvyTvAwUDzhn2PeJBP8UQILAMD42vifvuCeZ447wfmccRzV3REF0T7drzhZX9hZfgLCmvU1idoNa4ICs8/rP2jWUxo8YxLpahFO78mUEXPNQ8cxyvxO6RJljldcTffGh9vhQexYx6X0maXkqLOeOSYBwhwhPHKdw+alM885FN2t4aHxlGHVnEEAFG1yQO97Rubtyp4Ka28HiwIzpnFSeusJqipQZvETvQmckoXiwHNSvoqBWnthIg2AGHBJeFXE1z1UQFljGhRmbbtnaQPOO+yhmSROQVoBDJVWt1rRpoIvvRGtmEYo28rv3cRUlZ9KGR5nDN/O7YtD3D4+tHItlf8boppgFmhLmLsSj79MAFlgJrZ5zNkaXXP2CyEy3EuvL6+4fbXK/aX4JmNd8MO7HA/oEt9fB3sfhYIzfN8xTzvhLZvWOuoAOJy/PCyMOE4luFRKF4zbkX0tU4IpVqrs+Jx8THkyvaTGEMxHZjD5x379kME14c1gVFxu/eMDwsKI3S4ufZkaBmjfjFVLzuG5LA8/MmEfm2+K/G/pAZkUShu4Bm+y4kMEdiCWNv2EuAmXUIMBYRoUDrYFJamIOLQ8ZxpquiiAKhgnGgSggn5hTCf612jHx79wWBbxD1Z56CVu9KbWqNoGCZk3Q4VUYe474JgHlhq+LWcB5v7BvntnvuGdOma8COtk5Gcv4dBUXcmaDMmIU7PZjgm0tR1jVqDGpeY+GkM3tcqbInV+2rp+XdShHwhz49OQgrcrDnJGabZAUfcJHmeJ3EUE9v4nNkqs0idzhk3vYW1eiL7maz42ZwT54zWjJvAXh5C5dO2sLlhnlEgVlanlE6JZM03PyNMUruDo4zoUWuyOCODeA9YuEkqDb7gYWwkT4JW23vHx4vCHszxMpnCtEp4dJ6IbiU9SrCmv0yMLr0kAdSJmZIpD3Z/okA1YVYN3jWaNkfpS0FeE/3XcBGQ1pYVAaC1+EtjPPne6mDwu7okN0MX+k6RBIBd/HAFu3p+O83z9rv4fuAfUvDV00FvFOWyxNu6q6DLcurw1Hou9LQuNMsR0fwWHFu6a0iL5fqspgwYkAhtaiWwTYhcHYayVLv5oAY2l8NvcUnTZX76PL//dUSF8fGlSt/AM2zDrgJ3BYT7PO6Y55FCACuKxNac/G/RXRVvxud2m9iH4UyhcD2c2oHrqej/Uvbmuq4epI0lLnhyAeujFoCx4NgSJVwMyc++36D4uKCQpo8J5h882H//vS4pU5pJazVhYfUFGB5KwNv3snAJIrbBxgYnfFiISOczFjWiikTGQN7XoUyEE9ocuegSUBl8a3KqNs2S9t2kA62VDtEehnQD4ruyCqx+fiEg32CGaGtWVyeGexa1DoX+BIusgn6JFkWHUKdEw0WYc276Zwa5VGAnMWkbvx9KwT26ww2UD5xzTwkzKkCafONQi/oSyKNpz6JFCWTph/j57JcNyZUhqxQSslk7TlpGgIbfDessbgTbuVYJjoG5DhzHG47bK477K5HhDizHeZ447gevmHDS3OE7A6Dm2G3iZQPuc8R+G1qMohanS4iTPlqXrgbI9HLbRgiJjMBrDCPQL85D2aIsxGOz5eWW7tx/Oq9/Nz6Mo/gnnHjwqJhLzJRHKv7uPDOJXsPX1oGESxKQ6cCZsQNA6BGlQxVFl6SHHYiisH5A+FpaZAIaDRvAtqWmy6vtzRK8IpPZPZhFjBdxFqeb0Q6mV4GXukZ1LU/CNCulNJ+vxQvMIpWWgTaNQSBPxjYMGJ8b7iQwAEPClVq8kN49AFYH2x9oC7PGw30v2hIELQfC1UiV7lnA19cLsFM0D28hE0NTb+Ol3CBE5qRc0nqO5EBQrawwwNG3PPbGAZVfL6SwMIv0qK+JcwI7hcZcE/O4wc87/IzYw3ncmam64bz9ifO4JQ7H4BHPcFp7PnDe3+BjwhD4FYNuYKNy33pMr0mBi3XLdHn2V+h7xuDktmexoJSaDcPYduwvnwEfvK9IBCOw7P2ex0drPZqAcGm2loqENopblwaBw8aiWYfUsgXkcWoyALyJzGCYIxAk8i0DE1G9EAFDlTBH7GBl0xcn73u6Ijr08v0lih5h5GPsiZqEhMCIxV1dLwAY7PrkKakLG0BIkQfuoUxFzY1PSheEvLDA99fnzYiE7GdFIexsAx9gLTNndSML3vq8YBe21Pu1qdZN1CbUlA0y7o0OMUz7Xge19h1xMRRpY8yWRDdC4VmQjONgChK6FrJZbKhEet1IlpwZqeSM6UjtlAWXKVkS3nxkDGauuAj6E2t0FjzRogG3P7HOCZ8L61Sh2MmOaVVFPZJgDtjCnBH7WOqM5fFzObFXA7IClqrbU3nCXFeeMe7DGAPb+BRnQncCk5abbdh2Nd+JxEK60ijh/N7xQUERZpaYRJMvYIg3xgI/BfKTpysQGrlDOdsw5DVqBgFTyt/WvaL9HZ3to9P3uAgQ4/uTbS6h/PwDImAvmHJpPdRhMwaM0hymcFyId1ujQQpPCq3+tlxyxh7az7JjOddlI668E82Xt3npS/zXsAhutVLt/ByFcS2bc7IriKysiNK8j1sVW26sDfP2+Wb+Einqo1V69Px1c0HD4lKcq0BZOvMmRXO10duH+g87AJA7TzobBa8SUFOZCtHAQ1DM887/jrRSz+NOS2Iyy+6Iln4Ly6Q8icBdE240Xx0wVVN/RUT+v15KIW24RqYq9GtZtXE99Ekbpj8z/vNAi68V5D+PdwkKEfz29saqzTA1Fy9KkXndEZFaaADVHJM+rb4DBCuobZ0o0nXF0iZ60/4eTKW1B5JupYUS81DFqCyL87JB51mxg7BkVhJbzWRcvQrR18QJ8GHZVCT3IFqSxdoipaZGKLExynxUYNQArGEA4p7LfhCiElEFWlGGHhZMS++ihGFMKg3M/Plj3r0IXPuUfvDopo4OaxcypL/jorVzQ7k+mCzHePdmA2tF7cdkS7kur6V1fTlu9xv2fV5iJRJkq0HO5WIJZVh9KTkv4TkIqVc8bJhhjGgqg7eFP/eFL1++RF9T432stz/w+te/cH97xf32BffDcdwO3G+vuN1e8Xa/4T4jsBlIyIlJo27ZgWV3YLvjvt9weMRUnJuSzYKVESIvZj/MQWQtwVJhUYQiiqXcwzr2PWjjjB11oWOGOQZedMRTfgSIb82Jc+oe2QcN8DfD/B2f+u233/DLL79882HP8RzP8f9v/Prrr/j555//8TPvEhRrLfz+++/48ccfP2SuPMdzPMf/3eHu+OOPP/DTTz89YIO+Hu8SFM/xHM/x3z0+kCB5jud4jv/W8RQUz/Ecz/HN8RQUz/Ecz/HN8RQUz/Ecz/HN8RQUz/Ecz/HN8RQUz/Ecz/HN8RQUz/Ecz/HN8W8IoaV7aMwjagAAAABJRU5ErkJggg==",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Load the image\n",
- "\n",
- "image = Image.open(\"./dog.png\")\n",
- "plt.imshow(image), plt.xticks([]), plt.yticks([])\n",
- "\n",
- "image = data_transform(image).unsqueeze(0)\n",
- "\n",
- "# Get the 1000-dimensional model output\n",
- "out = model(image)\n",
- "# Find the predicted class\n",
- "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
- ]
- },
{
"cell_type": "markdown",
"id": "5d57da4b",
@@ -3182,10 +1337,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 40,
"id": "be2d31f5",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import os\n",
"\n",
@@ -3218,21 +1384,29 @@
" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
" ]\n",
" ),\n",
+ " \"test\": transforms.Compose(\n",
+ " [\n",
+ " transforms.Resize(256),\n",
+ " transforms.CenterCrop(224),\n",
+ " transforms.ToTensor(),\n",
+ " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+ " ]\n",
+ " ),\n",
"}\n",
"\n",
"data_dir = \"hymenoptera_data\"\n",
"# Create train and validation datasets and loaders\n",
"image_datasets = {\n",
" x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
- " for x in [\"train\", \"val\"]\n",
+ " for x in [\"train\", \"val\", \"test\"]\n",
"}\n",
"dataloaders = {\n",
" x: torch.utils.data.DataLoader(\n",
" image_datasets[x], batch_size=4, shuffle=True, num_workers=0\n",
" )\n",
- " for x in [\"train\", \"val\"]\n",
+ " for x in [\"train\", \"val\", \"test\"]\n",
"}\n",
- "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n",
+ "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\", \"test\"]}\n",
"class_names = image_datasets[\"train\"].classes\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
@@ -3274,10 +1448,93 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 41,
"id": "572d824c",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\Amaury\\.conda\\envs\\deeplearning\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+ " warnings.warn(\n",
+ "c:\\Users\\Amaury\\.conda\\envs\\deeplearning\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
+ " warnings.warn(msg)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n",
+ "----------\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\Amaury\\.conda\\envs\\deeplearning\\Lib\\site-packages\\torch\\optim\\lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
+ " warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "train Loss: 0.6980 Acc: 0.6189\n",
+ "val Loss: 0.3289 Acc: 0.8431\n",
+ "\n",
+ "Epoch 2/10\n",
+ "----------\n",
+ "train Loss: 0.4541 Acc: 0.7828\n",
+ "val Loss: 0.1939 Acc: 0.9412\n",
+ "\n",
+ "Epoch 3/10\n",
+ "----------\n",
+ "train Loss: 0.5170 Acc: 0.7828\n",
+ "val Loss: 0.1902 Acc: 0.9412\n",
+ "\n",
+ "Epoch 4/10\n",
+ "----------\n",
+ "train Loss: 0.5624 Acc: 0.7787\n",
+ "val Loss: 0.9477 Acc: 0.6275\n",
+ "\n",
+ "Epoch 5/10\n",
+ "----------\n",
+ "train Loss: 0.5317 Acc: 0.7787\n",
+ "val Loss: 0.2647 Acc: 0.9085\n",
+ "\n",
+ "Epoch 6/10\n",
+ "----------\n",
+ "train Loss: 0.5587 Acc: 0.7582\n",
+ "val Loss: 0.1927 Acc: 0.9477\n",
+ "\n",
+ "Epoch 7/10\n",
+ "----------\n",
+ "train Loss: 0.3477 Acc: 0.8607\n",
+ "val Loss: 0.1847 Acc: 0.9412\n",
+ "\n",
+ "Epoch 8/10\n",
+ "----------\n",
+ "train Loss: 0.3008 Acc: 0.8566\n",
+ "val Loss: 0.1822 Acc: 0.9477\n",
+ "\n",
+ "Epoch 9/10\n",
+ "----------\n",
+ "train Loss: 0.2957 Acc: 0.8811\n",
+ "val Loss: 0.2329 Acc: 0.9346\n",
+ "\n",
+ "Epoch 10/10\n",
+ "----------\n",
+ "train Loss: 0.3519 Acc: 0.8402\n",
+ "val Loss: 0.2553 Acc: 0.9020\n",
+ "\n",
+ "Training complete in 7m 4s\n",
+ "Best val Acc: 0.947712\n"
+ ]
+ }
+ ],
"source": [
"import copy\n",
"import os\n",
@@ -3315,21 +1572,30 @@
" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
" ]\n",
" ),\n",
+ " \"test\": transforms.Compose(\n",
+ " [\n",
+ " transforms.Resize(256),\n",
+ " transforms.CenterCrop(224),\n",
+ " transforms.ToTensor(),\n",
+ " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+ " ]\n",
+ " ),\n",
"}\n",
"\n",
"data_dir = \"hymenoptera_data\"\n",
"# Create train and validation datasets and loaders\n",
+ "# Test dataset used was a copy of the validation dataset, ideal solution would consist in the use of a completely different dataset\n",
"image_datasets = {\n",
" x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
- " for x in [\"train\", \"val\"]\n",
+ " for x in [\"train\", \"val\", \"test\"]\n",
"}\n",
"dataloaders = {\n",
" x: torch.utils.data.DataLoader(\n",
" image_datasets[x], batch_size=4, shuffle=True, num_workers=4\n",
" )\n",
- " for x in [\"train\", \"val\"]\n",
+ " for x in [\"train\", \"val\", \"test\"]\n",
"}\n",
- "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n",
+ "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\", \"test\"]}\n",
"class_names = image_datasets[\"train\"].classes\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
@@ -3456,7 +1722,8 @@
"exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
"model, epoch_time = train_model(\n",
" model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
- ")\n"
+ ")\n",
+ "torch.save(model.state_dict(), \"model_transfer1.pt\")\n"
]
},
{
@@ -3475,6 +1742,213 @@
"Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model accuracy on test dataset: 0.9477124183006536\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "tensor(0.9477, dtype=torch.float64)"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def eval_model(model):\n",
+ " model.eval()\n",
+ " running_corrects = 0\n",
+ " \n",
+ " with torch.no_grad():\n",
+ " for inputs, labels in dataloaders[\"test\"]:\n",
+ " inputs = inputs.to(device)\n",
+ " labels = labels.to(device)\n",
+ " outputs = model(inputs)\n",
+ " _, preds = torch.max(outputs, 1)\n",
+ " running_corrects += torch.sum(preds == labels.data)\n",
+ "\n",
+ "\n",
+ " accuracy = running_corrects.double() / dataset_sizes['test']\n",
+ "\n",
+ " print(f\"Model accuracy on test dataset: {accuracy}\")\n",
+ " return accuracy\n",
+ "\n",
+ "eval_model(model)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Classification with a set of two layers:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n",
+ "----------\n",
+ "train Loss: 0.6585 Acc: 0.5615\n",
+ "val Loss: 0.4243 Acc: 0.9020\n",
+ "\n",
+ "Epoch 2/10\n",
+ "----------\n",
+ "train Loss: 0.5632 Acc: 0.7008\n",
+ "val Loss: 0.2866 Acc: 0.9412\n",
+ "\n",
+ "Epoch 3/10\n",
+ "----------\n",
+ "train Loss: 0.5807 Acc: 0.6844\n",
+ "val Loss: 0.2944 Acc: 0.9477\n",
+ "\n",
+ "Epoch 4/10\n",
+ "----------\n",
+ "train Loss: 0.4571 Acc: 0.7828\n",
+ "val Loss: 0.2206 Acc: 0.9346\n",
+ "\n",
+ "Epoch 5/10\n",
+ "----------\n",
+ "train Loss: 0.4628 Acc: 0.7992\n",
+ "val Loss: 0.2162 Acc: 0.9477\n",
+ "\n",
+ "Epoch 6/10\n",
+ "----------\n",
+ "train Loss: 0.3780 Acc: 0.8279\n",
+ "val Loss: 0.2164 Acc: 0.9346\n",
+ "\n",
+ "Epoch 7/10\n",
+ "----------\n",
+ "train Loss: 0.3275 Acc: 0.8648\n",
+ "val Loss: 0.2042 Acc: 0.9477\n",
+ "\n",
+ "Epoch 8/10\n",
+ "----------\n",
+ "train Loss: 0.4218 Acc: 0.8115\n",
+ "val Loss: 0.2140 Acc: 0.9412\n",
+ "\n",
+ "Epoch 9/10\n",
+ "----------\n",
+ "train Loss: 0.3325 Acc: 0.8730\n",
+ "val Loss: 0.2088 Acc: 0.9477\n",
+ "\n",
+ "Epoch 10/10\n",
+ "----------\n",
+ "train Loss: 0.3635 Acc: 0.8443\n",
+ "val Loss: 0.2166 Acc: 0.9412\n",
+ "\n",
+ "Training complete in 6m 49s\n",
+ "Best val Acc: 0.947712\n",
+ "Model accuracy on test dataset: 0.9477124183006536\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "tensor(0.9477, dtype=torch.float64)"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Download a pre-trained ResNet18 model and freeze its weights\n",
+ "model = torchvision.models.resnet18(pretrained=True)\n",
+ "for param in model.parameters():\n",
+ " param.requires_grad = False\n",
+ "\n",
+ "# Replace the final fully connected layer\n",
+ "# Parameters of newly constructed modules have requires_grad=True by default\n",
+ "num_ftrs = model.fc.in_features\n",
+ "final_layers = [\n",
+ " nn.Linear(num_ftrs, 256),\n",
+ " nn.ReLU(),\n",
+ " nn.Dropout(),\n",
+ " nn.Linear(256, 2)\n",
+ "]\n",
+ "model.fc = nn.Sequential(*final_layers)\n",
+ "\n",
+ "# Send the model to the GPU\n",
+ "model = model.to(device)\n",
+ "# Set the loss function\n",
+ "criterion = nn.CrossEntropyLoss()\n",
+ "\n",
+ "# Observe that only the parameters of the final layer are being optimized\n",
+ "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n",
+ "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
+ "model, epoch_time = train_model(\n",
+ " model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
+ ")\n",
+ "torch.save(model.state_dict(), \"model_transfer2.pt\")\n",
+ "\n",
+ "eval_model(model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This model with an extra fully connected layer achieves slightly better results. However, one could argue it isn't worth using this bigger model to attain this small improvement."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "model: int8 \t Size (KB): 45304.25\n",
+ "model: int8 \t Size (KB): 44911.014\n",
+ "Model accuracy on test dataset: 0.9477124183006536\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "tensor(0.9477, dtype=torch.float64)"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
+ "print_size_of_model(model, \"int8\")\n",
+ "print_size_of_model(quantized_model, \"int8\")\n",
+ "\n",
+ "eval_model(quantized_model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The quantized model doesn't lose in accuracy, however the size decrease is clearly not significant. The model thus loses in clarity for the user and the small gains in terms of size do not justify quantization in this case."
+ ]
+ },
{
"cell_type": "markdown",
"id": "04a263f0",