diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 3d43dccbc89c2593521a0dd082c6f92518c951c8..f671edf1b222a246e3aa34cb030305a7be34c439 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "330a42f5",
    "metadata": {},
    "outputs": [
@@ -41,21 +41,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: torch in /usr/local/lib/python3.11/site-packages (2.2.2)\n",
-      "Requirement already satisfied: torchvision in /usr/local/lib/python3.11/site-packages (0.17.2)\n",
-      "Requirement already satisfied: filelock in /usr/local/lib/python3.11/site-packages (from torch) (3.16.1)\n",
+      "\u001b[33mDEPRECATION: Loading egg at /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/PyVCF-0.4.4-py3.11.egg is deprecated. pip 24.3 will enforce this behaviour change. A possible replacement is to use pip for package installation.. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
+      "\u001b[0mRequirement already satisfied: torch in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (2.2.2)\n",
+      "Requirement already satisfied: torchvision in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (0.17.2)\n",
+      "Requirement already satisfied: filelock in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from torch) (3.13.4)\n",
       "Requirement already satisfied: typing-extensions>=4.8.0 in /Users/youcefkessi/Library/Python/3.11/lib/python/site-packages (from torch) (4.12.2)\n",
-      "Requirement already satisfied: sympy in /usr/local/lib/python3.11/site-packages (from torch) (1.13.3)\n",
-      "Requirement already satisfied: networkx in /usr/local/lib/python3.11/site-packages (from torch) (3.4.2)\n",
-      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/site-packages (from torch) (3.1.4)\n",
-      "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/site-packages (from torch) (2024.10.0)\n",
-      "Requirement already satisfied: numpy in /usr/local/lib/python3.11/site-packages (from torchvision) (1.24.3)\n",
-      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.11/site-packages (from torchvision) (11.0.0)\n",
-      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/site-packages (from jinja2->torch) (3.0.2)\n",
-      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/site-packages (from sympy->torch) (1.3.0)\n",
+      "Requirement already satisfied: sympy in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from torch) (1.13.3)\n",
+      "Requirement already satisfied: networkx in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from torch) (3.3)\n",
+      "Requirement already satisfied: jinja2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from torch) (3.1.3)\n",
+      "Requirement already satisfied: fsspec in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from torch) (2024.3.1)\n",
+      "Requirement already satisfied: numpy in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from torchvision) (1.24.2)\n",
+      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from torchvision) (10.3.0)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from jinja2->torch) (2.1.5)\n",
+      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from sympy->torch) (1.3.0)\n",
       "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -75,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "b1950f0a",
    "metadata": {},
    "outputs": [
@@ -83,34 +84,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor([[-0.9795,  0.7523,  0.8343, -0.0598,  0.3667, -2.0467, -0.6454,  0.5932,\n",
-      "         -1.4877, -1.4277],\n",
-      "        [ 0.2857,  1.2235, -1.1033, -0.2778, -1.5773, -1.9997, -0.8219, -0.4121,\n",
-      "         -0.2512, -0.9691],\n",
-      "        [ 0.4293, -0.6780, -0.6763, -0.5158,  1.7610,  0.8322, -1.4890,  0.6857,\n",
-      "          0.3931, -1.7304],\n",
-      "        [ 0.2365, -0.2215,  0.0878, -1.3077, -2.2274,  0.0471,  0.4229,  0.2999,\n",
-      "          1.6608, -0.4597],\n",
-      "        [-0.7914, -0.6193,  0.3148,  0.2495, -0.8671, -0.1750, -0.2270,  0.0452,\n",
-      "         -0.8493, -0.4726],\n",
-      "        [-0.2461,  0.5174,  0.0432, -1.6498,  0.1466,  0.4093, -0.4276,  0.9521,\n",
-      "          2.0915,  1.0765],\n",
-      "        [ 0.6324,  0.6887,  0.3230,  1.0649, -1.7234, -0.5458, -0.2392,  0.3203,\n",
-      "         -0.8843,  0.7544],\n",
-      "        [-0.2075,  0.5479, -1.6141,  1.3578, -0.8545, -0.0216, -0.3450, -0.0836,\n",
-      "          0.2637,  0.8819],\n",
-      "        [ 0.3133,  1.3201, -0.6707,  0.0446, -0.1030,  0.2500,  0.9326,  0.6421,\n",
-      "          1.0294, -0.1829],\n",
-      "        [ 1.0690, -0.7387, -0.0121,  0.3317,  0.2573,  0.1225,  1.0007,  0.1241,\n",
-      "         -0.1426,  1.4330],\n",
-      "        [-0.7790, -0.7018,  0.5592,  0.0154,  0.1271, -0.4686, -0.3782, -0.0503,\n",
-      "         -1.0020,  2.3186],\n",
-      "        [ 0.7689, -1.0463, -1.6566,  0.9265,  1.0589, -0.4197,  0.0226, -0.7334,\n",
-      "         -0.8651,  0.0277],\n",
-      "        [ 1.0587,  0.8789,  0.8870,  0.9864, -0.5615,  1.2596,  1.2531,  1.2855,\n",
-      "         -0.2959, -0.1690],\n",
-      "        [ 0.3559,  0.4418,  0.1828,  0.6727, -0.0380,  0.8039,  1.0082,  0.0988,\n",
-      "         -0.5981,  0.0457]])\n",
+      "tensor([[ 0.0389, -0.5304, -1.0509, -0.3653, -0.2151,  0.8205,  0.9418, -1.8568,\n",
+      "          1.0905, -0.6958],\n",
+      "        [ 0.9869, -0.4804, -1.6483,  0.1681,  1.0701, -0.8505,  0.3314, -0.1402,\n",
+      "          1.5103,  0.6229],\n",
+      "        [-0.2329,  0.0072, -0.5153,  0.5303, -0.5038,  2.0605, -1.2592,  0.9534,\n",
+      "          0.2835,  0.1676],\n",
+      "        [ 0.7069, -1.3188,  0.0267, -0.3575,  0.0908, -0.6786, -0.9655, -0.3261,\n",
+      "          0.7367, -1.5028],\n",
+      "        [ 1.9429, -0.9112, -0.8401,  1.2664, -1.8774,  0.7245,  1.6716,  1.5497,\n",
+      "          0.5129, -0.2442],\n",
+      "        [ 2.1000,  1.7094, -0.8136,  0.5368, -0.2812,  0.5926, -0.7298,  1.9213,\n",
+      "         -0.7614,  1.2061],\n",
+      "        [ 0.0666, -0.6648,  0.3627,  0.5127,  0.0754, -0.6980, -0.1640,  1.3168,\n",
+      "          0.9687, -1.4006],\n",
+      "        [-0.5745, -0.4071,  1.1972,  0.7209, -2.5053, -0.0794,  0.7772, -0.1327,\n",
+      "          0.8022,  0.5401],\n",
+      "        [-0.0659,  1.3164, -1.7258, -1.7576,  0.0235, -1.3476, -0.0630,  1.0453,\n",
+      "          0.8747, -1.4363],\n",
+      "        [-0.2698, -1.7575, -0.7699,  1.0546, -2.1230, -0.8082, -2.2629, -2.2038,\n",
+      "         -0.0351,  0.8076],\n",
+      "        [-0.3969,  1.2511,  1.5900,  0.5894, -1.0114,  1.1888, -0.6382,  0.5760,\n",
+      "         -0.9202,  1.6992],\n",
+      "        [-0.3187, -1.8084,  0.1229,  0.4198,  1.8049,  0.6793, -0.1488,  0.1905,\n",
+      "         -0.3428, -0.3910],\n",
+      "        [-1.0298, -0.2742,  0.3822, -0.5296,  0.7767,  1.0297,  0.6338, -0.7335,\n",
+      "         -0.2740, -1.2077],\n",
+      "        [ 0.2138,  1.8621, -1.2581,  0.5853, -0.2709,  0.4013,  0.4921,  0.8036,\n",
+      "          1.6153,  0.3042]])\n",
       "AlexNet(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
@@ -180,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "6e18f2fd",
    "metadata": {},
    "outputs": [
@@ -214,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "462666a2",
    "metadata": {},
    "outputs": [
@@ -295,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "317bf070",
    "metadata": {},
    "outputs": [
@@ -359,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "4b53f229",
    "metadata": {},
    "outputs": [
@@ -367,51 +368,49 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 0 \tTraining Loss: 43.975669 \tValidation Loss: 38.347677\n",
-      "Validation loss decreased (inf --> 38.347677).  Saving model ...\n",
-      "Epoch: 1 \tTraining Loss: 34.997114 \tValidation Loss: 31.994015\n",
-      "Validation loss decreased (38.347677 --> 31.994015).  Saving model ...\n",
-      "Epoch: 2 \tTraining Loss: 31.149257 \tValidation Loss: 29.776703\n",
-      "Validation loss decreased (31.994015 --> 29.776703).  Saving model ...\n",
-      "Epoch: 3 \tTraining Loss: 28.815007 \tValidation Loss: 27.427899\n",
-      "Validation loss decreased (29.776703 --> 27.427899).  Saving model ...\n",
-      "Epoch: 4 \tTraining Loss: 27.052385 \tValidation Loss: 26.452557\n",
-      "Validation loss decreased (27.427899 --> 26.452557).  Saving model ...\n",
-      "Epoch: 5 \tTraining Loss: 25.561286 \tValidation Loss: 25.019063\n",
-      "Validation loss decreased (26.452557 --> 25.019063).  Saving model ...\n",
-      "Epoch: 6 \tTraining Loss: 24.222937 \tValidation Loss: 24.275458\n",
-      "Validation loss decreased (25.019063 --> 24.275458).  Saving model ...\n",
-      "Epoch: 7 \tTraining Loss: 23.071588 \tValidation Loss: 23.836704\n",
-      "Validation loss decreased (24.275458 --> 23.836704).  Saving model ...\n",
-      "Epoch: 8 \tTraining Loss: 22.091297 \tValidation Loss: 22.734067\n",
-      "Validation loss decreased (23.836704 --> 22.734067).  Saving model ...\n",
-      "Epoch: 9 \tTraining Loss: 21.208610 \tValidation Loss: 22.307053\n",
-      "Validation loss decreased (22.734067 --> 22.307053).  Saving model ...\n",
-      "Epoch: 10 \tTraining Loss: 20.421559 \tValidation Loss: 21.723610\n",
-      "Validation loss decreased (22.307053 --> 21.723610).  Saving model ...\n",
-      "Epoch: 11 \tTraining Loss: 19.685223 \tValidation Loss: 21.840338\n",
-      "Epoch: 12 \tTraining Loss: 19.035864 \tValidation Loss: 21.749165\n",
-      "Epoch: 13 \tTraining Loss: 18.402592 \tValidation Loss: 21.629479\n",
-      "Validation loss decreased (21.723610 --> 21.629479).  Saving model ...\n",
-      "Epoch: 14 \tTraining Loss: 17.791936 \tValidation Loss: 21.097066\n",
-      "Validation loss decreased (21.629479 --> 21.097066).  Saving model ...\n",
-      "Epoch: 15 \tTraining Loss: 17.219881 \tValidation Loss: 21.093654\n",
-      "Validation loss decreased (21.097066 --> 21.093654).  Saving model ...\n",
-      "Epoch: 16 \tTraining Loss: 16.631275 \tValidation Loss: 20.932878\n",
-      "Validation loss decreased (21.093654 --> 20.932878).  Saving model ...\n",
-      "Epoch: 17 \tTraining Loss: 16.143030 \tValidation Loss: 21.765923\n",
-      "Epoch: 18 \tTraining Loss: 15.585900 \tValidation Loss: 21.552932\n",
-      "Epoch: 19 \tTraining Loss: 15.082865 \tValidation Loss: 21.752597\n",
-      "Epoch: 20 \tTraining Loss: 14.584362 \tValidation Loss: 22.326752\n",
-      "Epoch: 21 \tTraining Loss: 14.197031 \tValidation Loss: 21.679743\n",
-      "Epoch: 22 \tTraining Loss: 13.676794 \tValidation Loss: 22.989129\n",
-      "Epoch: 23 \tTraining Loss: 13.237653 \tValidation Loss: 23.331841\n",
-      "Epoch: 24 \tTraining Loss: 12.802143 \tValidation Loss: 23.089242\n",
-      "Epoch: 25 \tTraining Loss: 12.411929 \tValidation Loss: 23.204556\n",
-      "Epoch: 26 \tTraining Loss: 11.942305 \tValidation Loss: 23.184003\n",
-      "Epoch: 27 \tTraining Loss: 11.593721 \tValidation Loss: 23.960704\n",
-      "Epoch: 28 \tTraining Loss: 11.164415 \tValidation Loss: 24.723535\n",
-      "Epoch: 29 \tTraining Loss: 10.773485 \tValidation Loss: 24.439442\n"
+      "Epoch: 0 \tTraining Loss: 42.708639 \tValidation Loss: 36.602445\n",
+      "Validation loss decreased (inf --> 36.602445).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 34.090246 \tValidation Loss: 32.836420\n",
+      "Validation loss decreased (36.602445 --> 32.836420).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 31.014456 \tValidation Loss: 29.736714\n",
+      "Validation loss decreased (32.836420 --> 29.736714).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 28.924668 \tValidation Loss: 28.046650\n",
+      "Validation loss decreased (29.736714 --> 28.046650).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 27.237106 \tValidation Loss: 26.626871\n",
+      "Validation loss decreased (28.046650 --> 26.626871).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 25.783448 \tValidation Loss: 25.650498\n",
+      "Validation loss decreased (26.626871 --> 25.650498).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 24.584218 \tValidation Loss: 24.939755\n",
+      "Validation loss decreased (25.650498 --> 24.939755).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 23.537466 \tValidation Loss: 23.884750\n",
+      "Validation loss decreased (24.939755 --> 23.884750).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 22.534044 \tValidation Loss: 23.447565\n",
+      "Validation loss decreased (23.884750 --> 23.447565).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 21.740266 \tValidation Loss: 23.582127\n",
+      "Epoch: 10 \tTraining Loss: 20.910820 \tValidation Loss: 22.604175\n",
+      "Validation loss decreased (23.447565 --> 22.604175).  Saving model ...\n",
+      "Epoch: 11 \tTraining Loss: 20.156267 \tValidation Loss: 22.095753\n",
+      "Validation loss decreased (22.604175 --> 22.095753).  Saving model ...\n",
+      "Epoch: 12 \tTraining Loss: 19.496955 \tValidation Loss: 22.177211\n",
+      "Epoch: 13 \tTraining Loss: 18.883715 \tValidation Loss: 21.548499\n",
+      "Validation loss decreased (22.095753 --> 21.548499).  Saving model ...\n",
+      "Epoch: 14 \tTraining Loss: 18.217741 \tValidation Loss: 21.207056\n",
+      "Validation loss decreased (21.548499 --> 21.207056).  Saving model ...\n",
+      "Epoch: 15 \tTraining Loss: 17.655938 \tValidation Loss: 22.966628\n",
+      "Epoch: 16 \tTraining Loss: 17.092876 \tValidation Loss: 21.748603\n",
+      "Epoch: 17 \tTraining Loss: 16.530585 \tValidation Loss: 21.922741\n",
+      "Epoch: 18 \tTraining Loss: 15.954465 \tValidation Loss: 21.682462\n",
+      "Epoch: 19 \tTraining Loss: 15.462039 \tValidation Loss: 21.682473\n",
+      "Epoch: 20 \tTraining Loss: 14.924893 \tValidation Loss: 22.205248\n",
+      "Epoch: 21 \tTraining Loss: 14.415005 \tValidation Loss: 21.790278\n",
+      "Epoch: 22 \tTraining Loss: 13.920029 \tValidation Loss: 22.615292\n",
+      "Epoch: 23 \tTraining Loss: 13.489346 \tValidation Loss: 22.974763\n",
+      "Epoch: 24 \tTraining Loss: 13.005203 \tValidation Loss: 23.479381\n",
+      "Epoch: 25 \tTraining Loss: 12.508791 \tValidation Loss: 25.127668\n",
+      "Epoch: 26 \tTraining Loss: 12.083272 \tValidation Loss: 24.255751\n",
+      "Epoch: 27 \tTraining Loss: 11.663482 \tValidation Loss: 24.039723\n",
+      "Epoch: 28 \tTraining Loss: 11.197567 \tValidation Loss: 25.445273\n",
+      "Epoch: 29 \tTraining Loss: 10.847690 \tValidation Loss: 24.944432\n"
      ]
     }
    ],
@@ -424,6 +423,8 @@
     "n_epochs = 30  # number of epochs to train the model\n",
     "train_loss_list = []  # list to store loss to visualize\n",
     "valid_loss_min = np.Inf  # track change in validation loss\n",
+    "val_loss_list = []\n",
+    "\n",
     "\n",
     "\n",
     "for epoch in range(n_epochs):\n",
@@ -467,6 +468,8 @@
     "    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",
     "\n",
     "    # Print training/validation statistics\n",
     "    print(\n",
@@ -501,25 +504,39 @@
    "metadata": {},
    "source": [
     "Yes, overfitting occurs.\n",
-    "- The validation loss consistently decreases from epoch 0 to epoch 15, showing the model is learning and generalizing well initially.\n",
-    "- Starting from epoch 16, the validation loss begins to stagnate and fluctuate, with occasional improvements.\n",
-    "- From epoch 17 onward, validation loss increases steadily, indicating overfitting as the training loss continues to decrease.\n",
     "\n",
-    "- The lowest validation loss is observed at epoch 15 (21.093654), after which performance starts to degrade.\n",
-    "- Overfitting Trend: The divergence between training and validation losses beyond epoch 15 indicates the model is overfitting the training data.\n",
+    "- ##### Initial Learning Phase (Epochs 0–14):\n",
+    "\n",
+    "    - Validation loss decreases consistently until epoch 14, showing the model is learning effectively and generalizing well during this phase.\n",
+    "\n",
+    "    - The training loss also decreases steadily, which is expected during the learning phase.\n",
+    "    \n",
+    "    - The lowest validation loss occurs at epoch 14, reaching 21.207056, indicating the optimal model performance.\n",
+    "\n",
+    "- ##### Transition Phase (Epochs 15–19):\n",
+    "\n",
+    "    - At epoch 15, the validation loss increases to 22.966628, followed by stagnation and slight fluctuations between 21.682462 and 22.205248 in epochs 16–19.\n",
+    "    \n",
+    "    - Training loss, however, continues to decrease, signaling a potential divergence between the model's performance on the training set and its ability to generalize to new data.\n",
+    "\n",
+    "- ##### Overfitting Phase (Epochs 20–29):\n",
+    "\n",
+    "    - From epoch 20 onwards, validation loss steadily increases, reaching 25.445273 by epoch 28. This indicates that the model is overfitting to the training data, capturing noise rather than useful patterns.\n",
+    "    \n",
+    "    - Training loss continues its downward trajectory, which is a common sign of overfitting as the model excessively adapts to the training set.\n",
     "\n",
-    "- Do early stopping at epoch 16 to prevent overfitting and preserve the best model."
+    "- Do early stopping at epoch 14 to prevent overfitting and preserve the best model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "d39df818",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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wDA0REREZPQYaIiIiMnoMNERERGT0GGiIiIjI6DHQEBERkdFjoCEiIiKj1+rXoVGpVMjOzoatrW2Tl0gnIiIicQiCgNLSUnh4eKhv+HsnrT7QZGdnG8SN6oiIiKjpMjIy1Itq3kmrDzQNN3vLyMiAnZ2dyNUQERHRvSgpKYGXl5fGTVvvxGACzeLFizFv3jy88MIL6put9e/fX2OpbQB47rnnsGzZsns+bkM3k52dHQMNERGRkbnX4SIGEWiOHj2Kr7/+GqGhobdsmzJlCt555x31cysrK32WRkREREZA9FlOZWVlmDBhAr755hs4ODjcst3KygpyuVz94FUWIiIi0iZ6oJk5cyaGDBmCqKioRrevWrUKzs7OCAkJwbx589S3riciIiJqIGqX09q1a3H8+HEcPXq00e3jx4+Ht7c3PDw8cPr0abz66qtITk7Ghg0bbntMpVIJpVKpfl5SUqLzuomIiMiwiBZoMjIy8MILL2Dnzp2wsLBodJ+pU6eq/9y1a1e4u7sjMjISqamp8Pf3b/Q1ixYtwsKFC1ukZiIiIjJMEkEQBDHeeOPGjRg5ciRMTEzUbXV1dZBIJJBKpVAqlRrbAKC8vBw2NjbYvn07oqOjGz1uY1dovLy8oFAoOP6GiIjISJSUlMDe3v6ev79Fu0ITGRmJpKQkjbannnoKQUFBePXVV28JMwBw8uRJAIC7u/ttjyuTySCTyXRaKxERERk20QKNra0tQkJCNNqsra3h5OSEkJAQpKamYvXq1Xjsscfg5OSE06dPY86cOejbt2+j07uJiIio7TKIdWgaY25ujl27duGTTz5BeXk5vLy8EBsbizfffFPs0oiIiMjAiDaGRl+a2gdHRERE4mvq97fo69AQERER3S8GGiIiIjJ6DDTNVKcScLmgDNfKlHffmYiIiFoUA00zPb/mOAb+Xzw2ncwWuxQiIqI2j4GmmfxdbAAAF/NKRa6EiIiIGGiaKcDNFgADDRERkSFgoGmmQHlDoClDK5/5TkREZPAYaJrJx8kaplIJypS1yFZUiV0OERFRm8ZA00zmplL4uVgDAC7mstuJiIhITAw096FhHE0yx9EQERGJioHmPgRyYDAREZFBYKC5DwFyBhoiIiJDwEBzHxq6nC7llaFOxZlOREREYmGguQ8dHK0gM5VCWatCelGF2OUQERG1WQw098FEKkEnt/oVg5M504mIiEg0DDT36Z9uJwYaIiIisTDQ3KdATt0mIiISHQPNfeI9nYiIiMTHQHOfGqZuXy4oR3WtSuRqiIiI2iYGmvvkYW8BG5kpalUC0q6Vi10OERFRm8RAc58kEgkCbsx0YrcTERGROBhodCCQKwYTERGJioFGBzq53pjpxLVoiIiIRMFAowO8QkNERCQuBhodaJi6fbWoApXVdSJXQ0RE1PYw0OiAs405HK3NIQhAakGZ2OUQERG1OQw0OnDzTCeOoyEiItI/Bhod4YrBRERE4mGg0ZEA3tOJiIhINAw0OqKe6cQuJyIiIr1joNGRgBtr0WQrqlBaVSNyNURERG0LA42O2FuZQW5nAQC4mMeZTkRERPrEQKNDnXhPJyIiIlEw0OhQoBtvgUBERCQGBhodCuAtEIiIiETBQKNDgeq1aDiGhoiISJ8YaHSoYQzNtTIlCsuUIldDRETUdjDQ6JCVuSm8HC0B8CoNERGRPjHQ6Fggb4FARESkdww0OsZbIBAREemfwQSaxYsXQyKRYPbs2eq2qqoqzJw5E05OTrCxsUFsbCzy8vLEK/IeNNwC4RIDDRERkd4YRKA5evQovv76a4SGhmq0z5kzB5s3b8a6desQHx+P7OxsjBo1SqQq703ATWvRCIIgcjVERERtg+iBpqysDBMmTMA333wDBwcHdbtCocB3332Hjz/+GAMHDkT37t2xYsUK/P333zh06JCIFd+Zn4s1TKQSlFTVIq+EM52IiIj0QfRAM3PmTAwZMgRRUVEa7YmJiaipqdFoDwoKQocOHZCQkHDb4ymVSpSUlGg89ElmagIfJysAHEdDRESkL6IGmrVr1+L48eNYtGjRLdtyc3Nhbm6Odu3aabS7ubkhNzf3tsdctGgR7O3t1Q8vLy9dl31XDeNoLvIWCERERHohWqDJyMjACy+8gFWrVsHCwkJnx503bx4UCoX6kZGRobNj36sATt0mIiLSK9ECTWJiIvLz89GtWzeYmprC1NQU8fHx+Oyzz2Bqago3NzdUV1ejuLhY43V5eXmQy+W3Pa5MJoOdnZ3GQ9+4Fg0REZF+mYr1xpGRkUhKStJoe+qppxAUFIRXX30VXl5eMDMzw+7duxEbGwsASE5ORnp6OiIiIsQo+Z79c5PKMqhUAqRSicgVERERtW6iBRpbW1uEhIRotFlbW8PJyUnd/swzz+DFF1+Eo6Mj7Ozs8PzzzyMiIgK9e/cWo+R75u1oBXMTKSpr6pB5vRIdbgwSJiIiopYhWqC5F0uWLIFUKkVsbCyUSiWio6Px1VdfiV3WXZmaSOHvaoPzOSVIzitloCEiImphEqGVr/5WUlICe3t7KBQKvY6nmb32BDaezMbL0YGYOaCj3t6XiIioNWjq97fo69C0Vv+Mo+HAYCIiopbGQNNCAm+6BQIRERG1LAaaFtKwFs3lgnLU1KlEroaIiKh1Y6BpIe3bWcLK3ATVdSpcLSwXuxwiIqJWjYGmhUilEnRy+2c9GiIiImo5DDQtKNDNBgDH0RAREbU0BpoWxHs6ERER6QcDTQtqCDTJDDREREQtioGmBQXeWIvmyrVyVNXUiVwNERFR68VA04JcbWWwtzSDSqifvk1EREQtg4GmBUkkEvUCexxHQ0RE1HIYaFpYgPzGTCcGGiIiohbDQNPC1DOdOHWbiIioxTDQtDDOdCIiImp5DDQtrCHQZF6vRLmyVuRqiIiIWicGmhbmaG0OF1sZAOBSPm+BQERE1BIYaPQgkONoiIiIWhQDjR50cuNMJyIiopbEQKMHXIuGiIioZTHQ6EGAnIGGiIioJTHQ6EEn1/oup7wSJYorqkWuhoiIqPVhoNEDWwsztG9nCQC4mMeZTkRERLrGQKMnARwYTERE1GIYaPREPY6GU7eJiIh0joFGTzjTiYiIqOUw0OhJwE2BRhAEkashIiJqXRho9KSjqw2kEuB6RQ0KypRil0NERNSqMNDoiYWZCbydrAEAF3M504mIiEiXGGj0iDOdiIiIWgYDjR41DAy+xEBDRESkUww0etQwdZtXaIiIiHSLgUaP1FO3cznTiYiISJcYaPTIx9kaZiYSlFfXIau4UuxyiIiIWg0GGj0yM5HCz7l+YDAX2CMiItIdBho9U98CgTepJCIi0hkGGj0LvDF1m/d0IiIi0h0GGj1ruAUCZzoRERHpDgONnjUEmkv5ZahTcaYTERGRLjDQ6JmXoxUszKSorlXhamG52OUQERG1CqIGmqVLlyI0NBR2dnaws7NDREQEtm3bpt7ev39/SCQSjce0adNErPj+mUgl6OTKgcFERES6JGqg8fT0xOLFi5GYmIhjx45h4MCBGD58OM6ePaveZ8qUKcjJyVE/PvzwQxEr1o2GbidO3SYiItINUzHffOjQoRrP33//fSxduhSHDh1Cly5dAABWVlaQy+VilNdiAuW8SSUREZEuGcwYmrq6Oqxduxbl5eWIiIhQt69atQrOzs4ICQnBvHnzUFFRIWKVutHpplsgEBER0f0T9QoNACQlJSEiIgJVVVWwsbFBXFwcgoODAQDjx4+Ht7c3PDw8cPr0abz66qtITk7Ghg0bbns8pVIJpVKpfl5SUtLiP0NTNdzTKe1aOUqramBrYSZyRURERMZN9EATGBiIkydPQqFQYP369Zg0aRLi4+MRHByMqVOnqvfr2rUr3N3dERkZidTUVPj7+zd6vEWLFmHhwoX6Kr9Z3O0t0NHVBin5Zdh0KhsTwr3FLomIiMioSQQDu+1zVFQU/P398fXXX9+yrby8HDY2Nti+fTuio6MbfX1jV2i8vLygUChgZ2fXYnU31XcH0vDulnMIaW+HLc8/InY5REREBqWkpAT29vb3/P1tMGNoGqhUKo1AcrOTJ08CANzd3W/7eplMpp4G3vAwRKMebA9zUynOZJUgKVMhdjlERERGTdRAM2/ePOzfvx9XrlxBUlIS5s2bh3379mHChAlITU3Fu+++i8TERFy5cgWbNm3CxIkT0bdvX4SGhopZtk44WJvjsZD62Vurj1wVuRoiIiLjJmqgyc/Px8SJExEYGIjIyEgcPXoUO3bswKOPPgpzc3Ps2rULgwYNQlBQEObOnYvY2Fhs3rxZzJJ1alyvDgCA309mo0xZK3I1RERExsvgxtDoWlP74PRJEAREfRyP1IJy/HdkV4wP7yB2SURERAbB6MfQtCUSiUR9lWbNkXSRqyEiIjJeDDQii+3mCXMTKZKyFBwcTERE1EwMNCJzsDZHTNeGwcG8SkNERNQcDDQGoKHbadPJLA4OJiIiagYGGgMQ7usIPxdrlFfXYfOpbLHLISIiMjoMNAZAIpFgPAcHExERNRsDjYEYdWNw8OlMBc5kcXAwERFRUzDQGAhHa3MMDuHgYCIiouZgoDEg6pWDT2ShnIODiYiI7hkDjQHp7ecIP2cODiYiImoqBhoDwpWDiYiImoeBxsDEdq8fHHyKg4OJiIjuGQONgXG0Nkf0jcHBvEpDRER0bxhoDNC4Xl4AgN9PZnNwMBER0T1goDFAEX5O8HW2RpmyFltOc3AwERHR3TDQGKD6wcH1V2lWH2a3ExER0d0w0Bio2G6eMDORcHAwERHRPWCgMVBONjJEd6kfHLz2KK/SEBER3QkDjQFruGHlxhPZqKjm4GAiIqLbYaAxYL39nODjZFU/OPhUjtjlEBERGSwGGgMmlf6zcvAqrklDRER0Www0Bi62+43BwRnFOJvNwcFERESNYaAxcM42MgxqGBx8JEPkaoiIiAwTA40R+GdwcBYHBxMRETWCgcYIRPg5wdvJCqUcHExERNQoBhojcPPg4NUcHExERHQLBhojMfrG4OCTGcU4l10idjlEREQGhYHGSDjbyDAomCsHExERNYaBxog0dDvFHc9CZXWdyNUQEREZDgYaI/KQ/z+Dgzefzha7HCIiIoPBQGNEpFIJxvasv0qzhoODiYiI1BhojMzo7p4wlUpwIr0Y53M4OJiIiAhgoDE6LrYyDOriBgD4al+qyNUQEREZBgYaIzS9X0dIJcDmU9nYeS5P7HKIiIhEx0BjhLp62mNKXz8AwBtxSVBU1IhcERERkbgYaIzUnKgA+DlbI79Uiff+OCd2OURERKJioDFSFmYm+HB0KCQSYF1iJvYl54tdEhERkWgYaIxYDx9HTH7IBwDw+oYklFax64mIiNomBhoj93J0IDo4WiFbUYVF2y6IXQ4REZEoGGiMnJW5KT6IDQUArD6cjr9TrolcERERkf6JGmiWLl2K0NBQ2NnZwc7ODhEREdi2bZt6e1VVFWbOnAknJyfY2NggNjYWeXmcpqwtwt8JT/SuX0H41Q2nUa6sFbkiIiIi/RI10Hh6emLx4sVITEzEsWPHMHDgQAwfPhxnz54FAMyZMwebN2/GunXrEB8fj+zsbIwaNUrMkg3WazGd0b6dJTKKKvHRjmSxyyEiItIriSAIgthF3MzR0REfffQRRo8eDRcXF6xevRqjR48GAFy4cAGdO3dGQkICevfufU/HKykpgb29PRQKBezs7FqydNHtv1iAid8fgUQC/PpcBHr6OIpdEhERUbM09fvbYMbQ1NXVYe3atSgvL0dERAQSExNRU1ODqKgo9T5BQUHo0KEDEhISbnscpVKJkpISjUdb0TfABWN6eEIQgFfWn0ZVTZ3YJREREemF6IEmKSkJNjY2kMlkmDZtGuLi4hAcHIzc3FyYm5ujXbt2Gvu7ubkhNzf3tsdbtGgR7O3t1Q8vL68W/gkMyxtDguFmJ0PatXJ8vPOi2OUQERHpheiBJjAwECdPnsThw4cxffp0TJo0CefONX/l23nz5kGhUKgfGRkZOqzW8NlbmuG/I7sCAL796zJOpF8XuSIiIqKWJ3qgMTc3R8eOHdG9e3csWrQIYWFh+PTTTyGXy1FdXY3i4mKN/fPy8iCXy297PJlMpp411fBoayI7u2Hkg+2hutH1pKxl1xMREbVuogcabSqVCkqlEt27d4eZmRl2796t3pacnIz09HRERESIWKFxePvxYDjbyHApvwyf704RuxwiIqIWZSrmm8+bNw8xMTHo0KEDSktLsXr1auzbtw87duyAvb09nnnmGbz44otwdHSEnZ0dnn/+eURERNzzDKe2zMHaHO+N6IJpPx/H0vhUDA6RI6S9vdhlERERtQhRA01+fj4mTpyInJwc2NvbIzQ0FDt27MCjjz4KAFiyZAmkUiliY2OhVCoRHR2Nr776SsySjcrgEHcM6eqOP5Jy8NK6U9g062GYmxrcRTkiIqL7ZnDr0OhaW1qHpjHXypR49ON4XK+owZyoALwQ1UnskoiIiO7KaNehoZbhbCPDgmFdAABf7L2EC7ltZ10eIiJqOxho2oBhYR54NNgNNXUCXl53GrV1KrFLIiIi0ikGmjZAIpHg/REhsLMwRVKWAsv/uix2SURERDrFQNNGuNpZ4O2h9V1Pn+y6hJT8UpErIiIi0h0GmjYktlt79A90QXWtCi+vP40adj0REVErwUDThkgkEvx3ZFfYyExxIr0Yb8adQSuf5EZERG0EA00b49HOEp+OfQBSCfDLsQx8uZerCBMRkfFjoGmDIju7YeGNqdz/+/Mi4k5kilwRERHR/WlWoMnIyEBm5j9fgkeOHMHs2bOxfPlynRVGLevJCB9M7esHoP4Gln+nXhO5IiIiouZrVqAZP3489u7dCwDIzc3Fo48+iiNHjuCNN97AO++8o9MCqeW8NjgIQ7q6o6ZOwHM/JeJSHmc+ERGRcWpWoDlz5gx69eoFAPj1118REhKCv//+G6tWrcLKlSt1WR+1IKlUgv8bE4Ye3g4orarF5BVHkV9aJXZZRERETdasQFNTUwOZTAYA2LVrF4YNGwYACAoKQk5Oju6qoxZnYWaCbyb2gK+zNbKKK/HMymMoV9aKXRYREVGTNCvQdOnSBcuWLcNff/2FnTt3YvDgwQCA7OxsODk56bRAankO1uZY+VRPOFqbIylLgf+sOcHbIxARkVFpVqD54IMP8PXXX6N///4YN24cwsLCAACbNm1Sd0WRcfF2ssa3k3pAZirF7gv5WLD5LNeoISIioyERmvmtVVdXh5KSEjg4OKjbrly5AisrK7i6uuqswPvV1NuPt3Xbz+Rg+qrjEARgXkwQnuvnL3ZJRETUBjX1+7tZV2gqKyuhVCrVYebq1av45JNPkJycbFBhhppucIg73hwSDABYtO0CtpzOFrkiIiKiu2tWoBk+fDh+/PFHAEBxcTHCw8Pxf//3fxgxYgSWLl2q0wJJ/5552BeTH/IBALz46ykcu1IkbkFERER30axAc/z4cTzyyCMAgPXr18PNzQ1Xr17Fjz/+iM8++0ynBZI43no8GIOC3VBdq8KzPx7D5YIysUsiIiK6rWYFmoqKCtja2gIA/vzzT4waNQpSqRS9e/fG1atXdVogicNEKsGnYx9EmFc7FFfUYPKKoygsU4pdFhERUaOaFWg6duyIjRs3IiMjAzt27MCgQYMAAPn5+Rx424pYmpvgu0k94OVoifSiCjz74zFU1dSJXRYREdEtmhVo3n77bbz00kvw8fFBr169EBERAaD+as2DDz6o0wJJXM42Mqx8qhfaWZnhRHoxXlh7AnUqTucmIiLD0uxp27m5ucjJyUFYWBik0vpcdOTIEdjZ2SEoKEinRd4PTtvWjSNpRXji28OorlPh6T6+eHtosNglERFRK9bU7+9mB5oGDXfd9vT0vJ/DtBgGGt3ZfCobz685AQB4/bEgTO3LNWqIiKhl6GUdGpVKhXfeeQf29vbw9vaGt7c32rVrh3fffRcqFZfMb62GhnngtZj6q2//3XoBX+1LEbkiIiKieqbNedEbb7yB7777DosXL0afPn0AAAcOHMCCBQtQVVWF999/X6dFkuF4rq8fqmrq8MmuS/hwezKqa1V4IbITJBKJ2KUREVEb1qwuJw8PDyxbtkx9l+0Gv//+O2bMmIGsrCydFXi/2OXUMr7al4IPtycDAGb098fL0YEMNUREpDN66XIqKipqdOBvUFAQioq4qmxbMKN/R7w5pDMA4Kt9qXjvj/O8mSUREYmmWYEmLCwMX3zxxS3tX3zxBUJDQ++7KDIOzz7ih3eHdwEAfHcgDW/9fgYqTukmIiIRNGsMzYcffoghQ4Zg165d6jVoEhISkJGRga1bt+q0QDJsT0b4wNxUitc2JOHnQ+moqRXw31FdYSJl9xMREelPs67Q9OvXDxcvXsTIkSNRXFyM4uJijBo1CmfPnsVPP/2k6xrJwP27Zwd8PCYMUgnwy7EMvLTuFGrrONuNiIj0577XobnZqVOn0K1bN9TVGc7y+BwUrD9bTmfjhbUnUacSMKSrOz4Z+wDMTJqVmYmIqI3Ty6BgosY8HuqBryZ0g5mJBH8k5WDGquNQ1hpOuCUiotaLgYZ0KrqLHMuf7AFzUyl2nsvDcz8l8oaWRETU4hhoSOcGBLni+0k9YWEmxb7kAjz7wzFUVNeKXRYREbViTZrlNGrUqDtuLy4uvp9aqBV5uJMzVj7VC0+vPIoDKdcwecVRfD+5J2xkzZpYR0REdEdNukJjb29/x4e3tzcmTpzYUrWSkent54SfnukFW5kpjqQV4cnvDkNRWSN2WURE1ArpdJaTIeIsJ/GdzizGk98dgaKyBl3b2+OnZ3qhnZW52GUREZEB4ywnMjihnu2weko4HK3NkZSlwNjlh5BfUiV2WURE1Iow0JBedPGwx5opveFsI8OF3FKM/OpvXMwrFbssIiJqJRhoSG8C5bZYPy0Cvs7WyCquROzSv/F36jWxyyIiolZA1ECzaNEi9OzZE7a2tnB1dcWIESOQnJyssU///v0hkUg0HtOmTROpYrpfPs7W+G36Q+jh7YDSqlpM+v4INhzPFLssIiIycqIGmvj4eMycOROHDh3Czp07UVNTg0GDBqG8vFxjvylTpiAnJ0f9+PDDD0WqmHTB0docPz8bjiFd3VFTJ+DFX0/hs92X0MrHpxMRUQsSdVGQ7du3azxfuXIlXF1dkZiYiL59+6rbraysIJfL9V0etSALMxN8Pu5BeDpY4uv9l/HxzovIKKrAf0d15f2fiIioyQzqm0OhUAAAHB0dNdpXrVoFZ2dnhISEYN68eaioqLjtMZRKJUpKSjQeZJikUgnmPdYZ744IgVQCrEvMxNMrj6K0imvVEBFR0xjMOjQqlQrDhg1DcXExDhw4oG5fvnw5vL294eHhgdOnT+PVV19Fr169sGHDhkaPs2DBAixcuPCWdq5DY9j2XMjDrNUnUFFdhyC5LVY81RPu9pZil0VERCJp6jo0BhNopk+fjm3btuHAgQPw9PS87X579uxBZGQkUlJS4O/vf8t2pVIJpVKpfl5SUgIvLy8GGiOQlKnA0z8cRUGpEm52Mnw/uSe6eNiLXRYREYnAKBfWmzVrFrZs2YK9e/feMcwAQHh4OAAgJSWl0e0ymQx2dnYaDzIOXT3tETfjIXRytUFeiRJjliUg/mKB2GUREZEREDXQCIKAWbNmIS4uDnv27IGvr+9dX3Py5EkAgLu7ewtXR2LwdLDC+ukPIcLPCeXVdXh65VGsPZIudllERGTgRA00M2fOxM8//4zVq1fD1tYWubm5yM3NRWVlJQAgNTUV7777LhITE3HlyhVs2rQJEydORN++fREaGipm6dSC7C3N8MPTvTDqwfaoUwl4bUMSPtpxgdO6iYjotkQdQyORSBptX7FiBSZPnoyMjAw88cQTOHPmDMrLy+Hl5YWRI0fizTffvOeuJN6c0ngJgoAluy7hs92XAADDH/DAh6NDITM1EbkyIiJqaUY7KLilMNAYv1+PZeD1DUmoVQkI93XE8id7wN7KTOyyiIioBRnloGCiOxnTwwsrn+oFW5kpDqcVYegXB5CUqRC7LCIiMiAMNGQUHu7kjHXTI+DpYIn0ogrELv0bPyZc4bgaIiICwEBDRiRIboc/nn8Eg4LdUF2nwtu/n8XM1cdRwpWFiYjaPAYaMir2Vmb4+snuePvxYJiZSLA1KRePf8YuKCKito6BhoyORCLB0w/7Yt20hzS6oH74m11QRERtFQMNGa0HvNppdEHN33QWM1axC4qIqC1ioCGjpt0Fte1MfRfU6cxisUsjIiI9YqAho9fQBbVeqwtq5cE0dkEREbURDDTUaoR5tcMf/3kE0V3cUFMnYMHmc5j+83EoKtkFRUTU2jHQUKtib2mGZU90x/yh9V1Q28/m4vHP/2IXFBFRK8dAQ62ORCLBU33+6YLKKKpkFxQRUSvHQEOt1u26oIrKq8UujYiIdIyBhlq1xrqgIv9vH+JOZPJqDRFRK8JAQ61eQxfUhul9ECS3xfWKGsz55RQmrTiKjKIKscsjIiIdYKChNqOrpz02P/8wXo4OhLmpFPsvFmDQkv349q/LqK1TiV0eERHdBwYaalPMTKSYOaAjtr/wCMJ9HVFZU4f3/jiPkV/9jbPZvB8UEZGxYqChNsnPxQZrpvTG4lFdYWdhiqQsBYZ9cRCLt11AVU2d2OUREVETMdBQmyWVSjC2VwfsmtsPQ7q6o04lYFl8KqI/2Y+/U66JXR4RETUBAw21ea62FvhyQjd8M7EH5HYWuFpYgfHfHsbL606huIJTvImIjAEDDdENjwa7YeeLffFkb29IJMC6xExEfRyPzaeyOcWbiMjAMdAQ3cTWwgzvjgjB+mkR6ORqg2tl1Xh+zQk888MxZBVXil0eERHdBgMNUSO6eztiy38expyoAJibSLHnQj4GfRyP7w6koYZTvImIDI5EaOXX0ktKSmBvbw+FQgE7OzuxyyEjlJJfitd+S8Kxq9cBAP4u1njz8WAMCHQVuTIiotarqd/fDDRE90ClEvDrsQx8tCMZhTfuBdU/0AVvDglGR1cbkasjImp9GGi0MNCQLpVU1eCLPSlYcTANNXUCTKUSPBnhjdmRAbC3MhO7PCKiVoOBRgsDDbWEtGvleP+Pc9h1Ph8A4GBlhhcfDcC4Xh1gasKhaURE94uBRgsDDbWkvy4V4J3N53ApvwwAEOhmi7ceD8bDnZxFroyIyLgx0GhhoKGWVlunwuoj6fh450UUV9QAqF/T5o3HOsPH2Vrk6oiIjBMDjRYGGtKX4opqfLLrEn46dBV1KgFmJhI83ccXswZ2hK0Fx9cQETUFA40WBhrSt0t5pXj3j/PYf7EAAOBsY46XBgXiXz28YCKViFwdEZFxYKDRwkBDYhAEAXuT8/HelvO4fK0cANDFww4vRweiX4ALJBIGGyKiO2Gg0cJAQ2KqrlXhx4Qr+HT3JZRW1QIAeng74MVBAXjInwOHiYhuh4FGCwMNGYLCMiWW7kvFT4euQllbf+uECD8nzB0UgB4+jiJXR0RkeBhotDDQkCHJK6nCl3tTsOZIOmrq6v/X6xvgghcfDcADXu3ELY6IyIAw0GhhoCFDlFVciS/2XMK6Y5moVdX/LxjV2RVzHg1AFw97kasjIhIfA40WBhoyZOmFFfh09yXEncjEjVyDmBA55jwagAA3W3GLIyISEQONFgYaMgapBWX4dNclbD6dDUEAJBJgaKgHZkd1gp8Lb35JRG0PA40WBhoyJsm5pViy8yK2n80FAEglwMgHPfFCZCd0cLISuToiIv1hoNHCQEPG6EyWAkt2XsTuC/U3vzSVSjC6uyem9/eHtxNvp0BErV9Tv79FvS3wokWL0LNnT9ja2sLV1RUjRoxAcnKyxj5VVVWYOXMmnJycYGNjg9jYWOTl5YlUMZF+hLS3x3eTeyJuxkN4pJMzalUC1h7NwID/7cMLa08gObdU7BKJiAyKqFdoBg8ejLFjx6Jnz56ora3F66+/jjNnzuDcuXOwtq7/V+j06dPxxx9/YOXKlbC3t8esWbMglUpx8ODBe3oPXqGh1uDYlSJ8sTcF+5IL1G2Dgt0wc0BHhHG6NxG1Qkbd5VRQUABXV1fEx8ejb9++UCgUcHFxwerVqzF69GgAwIULF9C5c2ckJCSgd+/edz0mAw21JmeyFPhqXwq2nclFw/+5j3RyxswBHRHu68hbKhBRq2FUXU7aFAoFAMDRsX7l1MTERNTU1CAqKkq9T1BQEDp06ICEhARRaiQSU0h7e3w1oTt2zumLUd3aw0QqwV+XrmHs8kP417IE7E3OhwH9G4WISG8MJtCoVCrMnj0bffr0QUhICAAgNzcX5ubmaNeunca+bm5uyM3NbfQ4SqUSJSUlGg+i1qajqy0+HvMA9r3UH0/07gBzUymOXb2Op1YcxeOfH8DWpByoVAw2RNR2GEygmTlzJs6cOYO1a9fe13EWLVoEe3t79cPLy0tHFRIZHi9HK7w3oisOvDIAUx7xhZW5Cc5ml2DGquN4dEk8fkvMRE2dSuwyiYhanEEEmlmzZmHLli3Yu3cvPD091e1yuRzV1dUoLi7W2D8vLw9yubzRY82bNw8KhUL9yMjIaMnSiQyCq50F3hgSjIOvDsR/IjvBzsIUqQXlmLvuFAb8bx9+OnQVldV1YpdJRNRiRB0ULAgCnn/+ecTFxWHfvn3o1KmTxvaGQcFr1qxBbGwsACA5ORlBQUEcFEx0B6VVNfj5UDq+O3AZ18qqAQDtrMzw7x5eeKK3N7wcuUgfERk2o5rlNGPGDKxevRq///47AgMD1e329vawtLQEUD9te+vWrVi5ciXs7Ozw/PPPAwD+/vvve3oPBhpqy6pq6vDL0Qx8e+AyMooqAdTfViEyyBUTI3zwcEdnSKWcGUVEhseoAs3tppiuWLECkydPBlC/sN7cuXOxZs0aKJVKREdH46uvvrptl5M2BhoioE4lYF9yPn5IuIr9F/9Zy8bP2RpPRnhjdHdP2FqYiVghEZEmowo0+sBAQ6TpckEZfky4ivWJmShT1gIArM1NMKqbJyZGeKMT7/JNRAaAgUYLAw1R48qUtYg7kYUf/76CS/ll6vaH/J0wMcIHUZ1dYWpiEPMGiKgNYqDRwkBDdGeCICDhciF++PsKdp7LQ8PyNR72FpjQ2xtje3rByUYmbpFE1OYw0GhhoCG6d1nFlVh16CrWHs1AUXn97ChzUykeD3XHk7298YBXO95egYj0goFGCwMNUdNV1dRhy+kc/PD3FSRlKdTtXTzs8GRvbwx7wANW5qYiVkhErR0DjRYGGqLmEwQBJzOK8dOhq9hyOgfVtfWrDttamCK2myee6O2Njq42IldJRK0RA40WBhoi3bheXo11iRn4+VA60osq1O0Rfk54orc3BnVxgxkHERORjjDQaGGgIdItlUrAXynX8FPCVey58M8gYldbGcb29MK48A5wt7cUt0giMnoMNFoYaIhaTlZxJdYcTsfaoxm4VqYEAJhIJYgMcsWTEd7o48+ViImoeRhotDDQELW86loVdpzNxc+HruJwWpG63cfJChPC61cidrA2F7FCIjI2DDRaGGiI9OtiXilWHbqK345nqVciNjeVIiZEjnG9OiDc15FTv4norhhotDDQEImjXFmL309m4+dDV3Eup0Td7udsjXG9OmBUt/ZcsI+IbouBRgsDDZG4BEFAUpYCa46k4/eT2aiorgMAmJlIEN1FjvG9OqC3nxPH2hCRBgYaLQw0RIajTFmLzaeyseZIOk5n/rNgn4+TFf7dswNGd/eEiy2v2hARA80tGGiIDNOZm67aNIy1MZVKMKiLG8b16sAZUkRtHAONFgYaIsNWUV2LLadysPpIOk5mFKvbvRwtMbZnB/yruydc7SzEK5CIRMFAo4WBhsh4nM8pwdoj6dhwIgulVfVXbUykEvTt5IzR3b0Q2dkVFmYmIldJRPrAQKOFgYbI+FRW12FrUg7WHEnHsavX1e12FqYYGuaB2O6eeJB3/iZq1RhotDDQEBm3ywVl2HA8CxuOZyJbUaVu93OxRmw3T4zq1p63WiBqhRhotDDQELUOKpWAhMuF+C0xE9vO5KKypn76t0QC9PF3xujunojuIoelObukiFoDBhotDDRErU+ZshZbk3LwW2Kmxq0WbGSmeKyrHLHdPNGLKxITGTUGGi0MNEStW3phBTacyMRvxzORUVSpbu/gaIVR3dojtpsnvBytRKyQiJqDgUYLAw1R26BSCTh6pQi/Hc/EH6dzUH5jRWIA6NPRCWN6eCG6i5yzpIiMBAONFgYaoranoroWO87mYn1iJg6mFKrbbS1MMfwBD4zp4YWu7e3ZJUVkwBhotDDQELVtGUUVWJ+YifWJmcgq/qdLKkhuizE9vDDiwfZwtDYXsUIiagwDjRYGGiIC6ruk/k4txK/HMrD9bC6qa1UA6m+S+WiwG/7Vwwt9O7nAhLdbIDIIDDRaGGiISJuiogabTmXh12OZSMr65yaZcjsLxHZvj39194KPs7WIFRIRA40WBhoiupNz2SVYl5iBjSeycL2iRt3ey9cR/+ruiUHBcthbmYlYIVHbxECjhYGGiO6FsrYOu8/n49djGdh/sQCqG38zmkol6O3nhOgQOQYFu8GNN8ok0gsGGi0MNETUVDmKSvyWmIktp3NwIbdUY9uDHdphcBc5orvI2S1F1IIYaLQw0BDR/bhyrRw7zuZix9lcHE8v1tgW6GaL6BA5oru4IdjdjtPAiXSIgUYLAw0R6UpeSRX+PJeHP8/mIiG1ELWqf/769HK0RHSwHNEhcnTr4MDZUkT3iYFGCwMNEbUERUUNdl/Iw/Yzudh/qQBVNSr1NmcbczwaLMfjoe7o7efEcEPUDAw0WhhoiKilVVTXYv/FAuw4m4dd5/NQWlWr3ia3s8DwBzwwslt7BMn5dxDRvWKg0cJAQ0T6VF2rwqHLhdh2Jhdbk3KgqPxnKnhndzuMerA9hj/gAVfOliK6IwYaLQw0RCQWZW0d9l7IR9yJLOy5kI+auvq/bqUSoE9HZ4x8sD2iu8hhLTMVuVIiw8NAo4WBhogMQXFFNbaczkHciSwkXr2ubrcyN0F0FzlGPtgefTo6c7wN0Q0MNFoYaIjI0FwtLMfGE9mIO5GJK4UV6nZXWxmGhdWPt+E0cGrrGGi0MNAQkaESBAEnMooRdzwLm09no/imWy8EutliSKg7YkLk6ORmK2KVROJgoNHCQENExqC6VoV9yfnYeDILu87lo7run2ngHV1t8FiIHDFd3REkt+WVG2oTGGi0MNAQkbFRVNTgz3O52HYmFwcuXdMINz5OVojp6o7HQtwR0p7dUtR6NfX7W6qHmm5r//79GDp0KDw8PCCRSLBx40aN7ZMnT4ZEItF4DB48WJxiiYj0xN7KDP/q4YXvJ/fEsbei8Mm/H8CgYDeYm0pxpbACS/elYugXB/DIh3vx/h/ncDz9OlSqVv1vU6K7EnWuYHl5OcLCwvD0009j1KhRje4zePBgrFixQv1cJpPpqzwiItHZWZhhxIPtMeLB9ihT1mLvhXxsP5OLPRfykXm9Et/8lYZv/kqDu70FBofIERPiju7evPUCtT2iBpqYmBjExMTccR+ZTAa5XK6nioiIDJeNzBRDwzwwNMwDldV1iL+Yj61Judh9Pg85iiqsOHgFKw5egYutDNFd3BAT4o5wX0eYmoh6MZ5ILwx+Nad9+/bB1dUVDg4OGDhwIN577z04OTnddn+lUgmlUql+XlJSoo8yiYj0ytLcBIND3DE4xB1VNXX469I1bDuTg53n8lBQqsTPh9Lx86F0OFiZYVCwHIO7ytHH3xnmpgw31DoZzKBgiUSCuLg4jBgxQt22du1aWFlZwdfXF6mpqXj99ddhY2ODhIQEmJiYNHqcBQsWYOHChbe0c1AwEbUF1bUqHEy9hu1JufjzXC6u3zQV3NbCFFGd3TA4RI5+AS6wMGv871EiQ2C0s5waCzTaLl++DH9/f+zatQuRkZGN7tPYFRovLy8GGiJqc2rrVDiSVoStZ3Kw42z9lZsGVuYmGBDoisEhcgwMcuXtF8jgNDXQGNUn2M/PD87OzkhJSbltoJHJZBw4TEQEwNREioc6OuOhjs54Z1gIEtOvY1tSLnaczUVWcSX+SMrBH0k5MDeVol+AC2JC5Ijs7AZ7SzOxSydqMqMKNJmZmSgsLIS7u7vYpRARGRWpVIKePo7o6eOItx7vjNOZCmw7k4vtZ3JwpbACO8/lYee5PJiZSBDh74xHOjojwt8Jnd3tOGOKjIKogaasrAwpKSnq52lpaTh58iQcHR3h6OiIhQsXIjY2FnK5HKmpqXjllVfQsWNHREdHi1g1EZFxk0gkCPNqhzCvdnh1cCAu5Jaqw83FvDLsv1iA/RcLAAB2Fqbo5euECH8n9PZzRGe5HaQMOGSARB1Ds2/fPgwYMOCW9kmTJmHp0qUYMWIETpw4geLiYnh4eGDQoEF499134ebmds/vwZWCiYjuXUp+GfZcyMOhy0U4klaEMmWtxnZ7SzOE+zreCDhOCHSzZcChFmG0g4JbCgMNEVHz1NapcDa7BAmXC3HociGOphWhvLpOYx8HKzOE37iCE+HvhE6uNrwdA+kEA40WBhoiIt2oqVPhTJbiRsApwrErRajQCjhO1uZ4pJMzhoR6oG+AM2SmnBpOzcNAo4WBhoioZdTUqXA6sxiHLhchIbUQx64Woarmnxtp2spM8WgXNwwN9UCfjlzUj5qGgUYLAw0RkX5U16pwMqMY28/kYmtSDnJLqtTb7C3NEN3FDY+HeiDC3wlmvB0D3QUDjRYGGiIi/VOpBCSmX8eWU9nYeiZXY1E/ByszDA5xx9BQd4T7OXFaODWKgUYLAw0RkbjqVAKOpBVhy+lsbD+Ti8LyavU2ZxtzxIS44/FQd/TwcWS4ITUGGi0MNEREhqO2ToVDl2+Em7O5KL7pXlOutjI81tUdUZ3d0NPXgQOK2zgGGi0MNEREhqmmToWDKdfwx+kc7Dibi5Kqf9a8sTI3wUP+zugf6IL+gS7wdLASsVISAwONFgYaIiLDV12rwl+XCrDtTC7iLxZojLkBgI6uNhgQ6IL+ga7o4cOrN20BA40WBhoiIuOiUgk4l1OC+IsF2Jecj+PpxahT/fNVxas3bQMDjRYGGiIi46aoqMFfKQXYl1zQ6NWbTq42N8INr960Jgw0WhhoiIhaj4arN/uS87EvuQDH06/jpos36qs3/QJd0D/ABV6OvHpjrBhotDDQEBG1Xne7euPnYo3+Aa7oF+iCcF9HWJjx6o2xYKDRwkBDRNQ23Dz2Jj65AInp1zXG3liYSRHh54R+AfXdUz7O1iJWS3fDQKOFgYaIqG1SVNbg75Rr6qs3N9+KAQC8nazQ/0a46e3nBEtzXr0xJAw0WhhoiIhIEAQk55UiPrm+e+rY1SLU1P3z9WduKkW4ryP6dHRGhJ8TQtrbc9VikTHQaGGgISIibWXK2vqrNze6p7KKKzW228pM0cvXERH+Tojwd0JnuR2kDDh6xUCjhYGGiIjuRBAEpBaUIf7iNSSkFuJwWiFKb1q1GADaWZkh3NcREX5OiPB3RoCbDSQSBpyWxECjhYGGiIiaok4l4Fx2CRIu1wecI2lFKK+u09jHydocvf2c0NvfCRF+TvB3sWbA0TEGGi0MNEREdD9q61RIylIg4XIhElILcezKdVTWaAYcF1sZ+vg7IbKzG/oFusDOwkykalsPBhotDDRERKRL1bUqnM4sRkJqIRIuFyLx6nUoa1Xq7aZSCXr7OeHRYDdEdnblrRmaiYFGCwMNERG1pKqaOpzMKMa+5ALsOp+HlPwyje2d3e3waGdXRAW7oWt7e3ZN3SMGGi0MNEREpE9p18qx61wedp7Pw7ErRRq3ZnCzkyGqsxuigt0Q4efElYvvgIFGCwMNERGJ5Xp5NfZcyMeu83mIv1iAipsGF1uZm6BvJxc8GuyGAUGucLQ2F7FSw8NAo4WBhoiIDEFVTR0OXS7EznN52HU+D3kl/9x3SioBunjYo7u3A7p5O6C7twM87C3adPcUA40WBhoiIjI0giDgTFYJdp7Lxc7z+TifU3LLPnI7C3XA6eHtgGAPO5iZSEWoVhwMNFoYaIiIyNDlKCpx7Mp1JF69juPp13E2u0TjxppA/c01Qz3bobu3A7p3qA86rbmbioFGCwMNEREZm4rqWpzOVCDx6j8hp7ii5pb9/Jyt0c3bAT19HPBwJxe0b2cpQrUtg4FGCwMNEREZO5VKwOVr5Th+I+Akpl+/ZXo4AHR0tUG/ABf0DXBBuK+jUc+iYqDRwkBDREStUXFFNU6kFyPx6nX8nXoNJzOKNaaIy0ylCPdzQr8AF/QLcIa/i3Hdf4qBRgsDDRERtQWKihocTL2G+OQCxF8sQG5Jlcb29u0s0TfAGf0CXPBQR2eDvz0DA40WBhoiImprBEHApfwyxCcXYP+lAhxOK0L1TbdnMJFK0K1DO/Tt5IJ+gS4I8bCHVGpYV28YaLQw0BARUVtXWV2HQ2mF2H+x/urN5YJyje3trMwQ7uuICD8nRPg7I8BN/O4pBhotDDRERESaMooqsP9SAeKTC/B3aiHKlLUa252szdHbzwm9/Z0Q4ecEfxdrvQccBhotDDRERES3V1OnQlKWAgmphTh0uRBHrxShqkalsY+rrQy9/ZzwkL8TIvyd0MHRqsUDDgONFgYaIiKie1ddq8KpzGIkpBYiIbUQienXNcbfAICHvYX66k2EvxM8Hax0XgcDjRYGGiIiouarqqnD8fTrOJRaiITLhTiZUYyaOs3oMPfRADwf2Umn79vU729Tnb47ERERtSoWZiZ4yN8ZD/k7A6hfxTjx6vX6KziXC3E6U4EQT3uRq2SgISIioiawMjfFI51c8EgnFwBAmbIW5gZw00wGGiIiImo2G5lhRAlRI9X+/fsxdOhQeHh4QCKRYOPGjRrbBUHA22+/DXd3d1haWiIqKgqXLl0Sp1giIiIyWKIGmvLycoSFheHLL79sdPuHH36Izz77DMuWLcPhw4dhbW2N6OhoVFVVNbo/ERERtU2iXieKiYlBTExMo9sEQcAnn3yCN998E8OHDwcA/Pjjj3Bzc8PGjRsxduxYfZZKREREBkz8UTy3kZaWhtzcXERFRanb7O3tER4ejoSEBBErIyIiIkNjGCN5GpGbmwsAcHNz02h3c3NTb2uMUqmEUqlUPy8pKWmZAomIiMhgGOwVmuZatGgR7O3t1Q8vLy+xSyIiIqIWZrCBRi6XAwDy8vI02vPy8tTbGjNv3jwoFAr1IyMjo0XrJCIiIvEZbKDx9fWFXC7H7t271W0lJSU4fPgwIiIibvs6mUwGOzs7jQcRERG1bqKOoSkrK0NKSor6eVpaGk6ePAlHR0d06NABs2fPxnvvvYdOnTrB19cXb731Fjw8PDBixAjxiiYiIiKDI2qgOXbsGAYMGKB+/uKLLwIAJk2ahJUrV+KVV15BeXk5pk6diuLiYjz88MPYvn07LCwsxCqZiIiIDBDvtk1EREQGp6nf3wY7hoaIiIjoXjHQEBERkdEz2IX1dKWhR40L7BERERmPhu/tex0Z0+oDTWlpKQBwgT0iIiIjVFpaCnt7+7vu1+oHBatUKmRnZ8PW1hYSiURnxy0pKYGXlxcyMjI42LgJeN6ah+eteXjemo7nrHl43prnTudNEASUlpbCw8MDUundR8i0+is0UqkUnp6eLXZ8Lt7XPDxvzcPz1jw8b03Hc9Y8PG/Nc7vzdi9XZhpwUDAREREZPQYaIiIiMnoMNM0kk8kwf/58yGQysUsxKjxvzcPz1jw8b03Hc9Y8PG/No8vz1uoHBRMREVHrxys0REREZPQYaIiIiMjoMdAQERGR0WOgISIiIqPHQNNMX375JXx8fGBhYYHw8HAcOXJE7JIM2oIFCyCRSDQeQUFBYpdlcPbv34+hQ4fCw8MDEokEGzdu1NguCALefvttuLu7w9LSElFRUbh06ZI4xRqIu52zyZMn3/LZGzx4sDjFGpBFixahZ8+esLW1haurK0aMGIHk5GSNfaqqqjBz5kw4OTnBxsYGsbGxyMvLE6li8d3LOevfv/8tn7dp06aJVLFhWLp0KUJDQ9WL50VERGDbtm3q7br6nDHQNMMvv/yCF198EfPnz8fx48cRFhaG6Oho5Ofni12aQevSpQtycnLUjwMHDohdksEpLy9HWFgYvvzyy0a3f/jhh/jss8+wbNkyHD58GNbW1oiOjkZVVZWeKzUcdztnADB48GCNz96aNWv0WKFhio+Px8yZM3Ho0CHs3LkTNTU1GDRoEMrLy9X7zJkzB5s3b8a6desQHx+P7OxsjBo1SsSqxXUv5wwApkyZovF5+/DDD0Wq2DB4enpi8eLFSExMxLFjxzBw4EAMHz4cZ8+eBaDDz5lATdarVy9h5syZ6ud1dXWCh4eHsGjRIhGrMmzz588XwsLCxC7DqAAQ4uLi1M9VKpUgl8uFjz76SN1WXFwsyGQyYc2aNSJUaHi0z5kgCMKkSZOE4cOHi1KPMcnPzxcACPHx8YIg1H+2zMzMhHXr1qn3OX/+vABASEhIEKtMg6J9zgRBEPr16ye88MIL4hVlJBwcHIRvv/1Wp58zXqFpourqaiQmJiIqKkrdJpVKERUVhYSEBBErM3yXLl2Ch4cH/Pz8MGHCBKSnp4tdklFJS0tDbm6uxmfP3t4e4eHh/Ozdxb59++Dq6orAwEBMnz4dhYWFYpdkcBQKBQDA0dERAJCYmIiamhqNz1tQUBA6dOjAz9sN2ueswapVq+Ds7IyQkBDMmzcPFRUVYpRnkOrq6rB27VqUl5cjIiJCp5+zVn9zSl27du0a6urq4ObmptHu5uaGCxcuiFSV4QsPD8fKlSsRGBiInJwcLFy4EI888gjOnDkDW1tbscszCrm5uQDQ6GevYRvdavDgwRg1ahR8fX2RmpqK119/HTExMUhISICJiYnY5RkElUqF2bNno0+fPggJCQFQ/3kzNzdHu3btNPbl561eY+cMAMaPHw9vb294eHjg9OnTePXVV5GcnIwNGzaIWK34kpKSEBERgaqqKtjY2CAuLg7BwcE4efKkzj5nDDSkFzExMeo/h4aGIjw8HN7e3vj111/xzDPPiFgZtXZjx45V/7lr164IDQ2Fv78/9u3bh8jISBErMxwzZ87EmTNnOK6tCW53zqZOnar+c9euXeHu7o7IyEikpqbC399f32UajMDAQJw8eRIKhQLr16/HpEmTEB8fr9P3YJdTEzk7O8PExOSWEdh5eXmQy+UiVWV82rVrh4CAAKSkpIhditFo+Hzxs3d//Pz84OzszM/eDbNmzcKWLVuwd+9eeHp6qtvlcjmqq6tRXFyssT8/b7c/Z40JDw8HgDb/eTM3N0fHjh3RvXt3LFq0CGFhYfj00091+jljoGkic3NzdO/eHbt371a3qVQq7N69GxERESJWZlzKysqQmpoKd3d3sUsxGr6+vpDL5RqfvZKSEhw+fJifvSbIzMxEYWFhm//sCYKAWbNmIS4uDnv27IGvr6/G9u7du8PMzEzj85acnIz09PQ2+3m72zlrzMmTJwGgzX/etKlUKiiVSt1+znQ7brltWLt2rSCTyYSVK1cK586dE6ZOnSq0a9dOyM3NFbs0gzV37lxh3759QlpamnDw4EEhKipKcHZ2FvLz88UuzaCUlpYKJ06cEE6cOCEAED7++GPhxIkTwtWrVwVBEITFixcL7dq1E37//Xfh9OnTwvDhwwVfX1+hsrJS5MrFc6dzVlpaKrz00ktCQkKCkJaWJuzatUvo1q2b0KlTJ6Gqqkrs0kU1ffp0wd7eXti3b5+Qk5OjflRUVKj3mTZtmtChQwdhz549wrFjx4SIiAghIiJCxKrFdbdzlpKSIrzzzjvCsWPHhLS0NOH3338X/Pz8hL59+4pcubhee+01IT4+XkhLSxNOnz4tvPbaa4JEIhH+/PNPQRB09zljoGmmzz//XOjQoYNgbm4u9OrVSzh06JDYJRm0f//734K7u7tgbm4utG/fXvj3v/8tpKSkiF2Wwdm7d68A4JbHpEmTBEGon7r91ltvCW5uboJMJhMiIyOF5ORkcYsW2Z3OWUVFhTBo0CDBxcVFMDMzE7y9vYUpU6bwHx+C0Og5AyCsWLFCvU9lZaUwY8YMwcHBQbCyshJGjhwp5OTkiFe0yO52ztLT04W+ffsKjo6OgkwmEzp27Ci8/PLLgkKhELdwkT399NOCt7e3YG5uLri4uAiRkZHqMCMIuvucSQRBEJp5xYiIiIjIIHAMDRERERk9BhoiIiIyegw0REREZPQYaIiIiMjoMdAQERGR0WOgISIiIqPHQENERERGj4GGiNociUSCjRs3il0GEekQAw0R6dXkyZMhkUhueQwePFjs0ojIiJmKXQARtT2DBw/GihUrNNpkMplI1RBRa8ArNESkdzKZDHK5XOPh4OAAoL47aOnSpYiJiYGlpSX8/Pywfv16jdcnJSVh4MCBsLS0hJOTE6ZOnYqysjKNfb7//nt06dIFMpkM7u7umDVrlsb2a9euYeTIkbCyskKnTp2wadOmlv2hiahFMdAQkcF56623EBsbi1OnTmHChAkYO3Yszp8/DwAoLy9HdHQ0HBwccPToUaxbtw67du3SCCxLly7FzJkzMXXqVCQlJWHTpk3o2LGjxnssXLgQY8aMwenTp/HYY49hwoQJKCoq0uvPSUQ6pLv7aRIR3d2kSZMEExMTwdraWuPx/vvvC4JQf0fjadOmabwmPDxcmD59uiAIgrB8+XLBwcFBKCsrU2//448/BKlUqr6LtoeHh/DGG2/ctgYAwptvvql+XlZWJgAQtm3bprOfk4j0i2NoiEjvBgwYgKVLl2q0OTo6qv8cERGhsS0iIgInT54EAJw/fx5hYWGwtrZWb+/Tpw9UKhWSk5MhkUiQnZ2NyMjIO9YQGhqq/rO1tTXs7OyQn5/f3B+JiETGQENEemdtbX1LF5CuWFpa3tN+ZmZmGs8lEglUKlVLlEREesAxNERkcA4dOnTL886dOwMAOnfujFOnTqG8vFy9/eDBg5BKpQgMDIStrS18fHywe/duvdZMROLiFRoi0julUonc3FyNNlNTUzg7OwMA1q1bhx49euDhhx/GqlWrcOTIEXz33XcAgAkTJmD+/PmYNGkSFixYgIKCAjz//PN48skn4ebmBgBYsGABpk2bBldXV8TExKC0tBQHDx7E888/r98flIj0hoGGiPRu+/btcHd312gLDAzEhQsXANTPQFq7di1mzJgBd3d3rFmzBsHBwQAAKysr7NixAy+88AJ69uwJKysrxMbG4uOPP1Yfa9KkSaiqqsKSJUvw0ksvwdnZGaNHj9bfD0hEeicRBEEQuwgiogYSiQRxcXEYMWKE2KUQkRHhGBoiIiIyegw0REREZPQ4hoaIDAp7wYmoOXiFhoiIiIweAw0REREZPQYaIiIiMnoMNERERGT0GGiIiIjI6DHQEBERkdFjoCEiIiKjx0BDRERERo+BhoiIiIze/wPYnlVFBCAe+gAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -532,12 +549,21 @@
     "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.xlabel(\"Epoch\")\n",
     "plt.ylabel(\"Loss\")\n",
     "plt.title(\"Performance of Model 1\")\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e58770ea",
+   "metadata": {},
+   "source": [
+    "Looking at the Validation/training loss graph, we notice that there is indeed an overfitting at epoch 14, the validation loss starts to increase, while the  traning loss don't."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "11df8fd4",
@@ -548,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "e93efdfc",
    "metadata": {},
    "outputs": [
@@ -556,20 +582,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Loss: 21.151382\n",
+      "Test Loss: 21.184091\n",
       "\n",
-      "Test Accuracy of airplane: 66% (666/1000)\n",
-      "Test Accuracy of automobile: 80% (803/1000)\n",
-      "Test Accuracy of  bird: 59% (599/1000)\n",
-      "Test Accuracy of   cat: 49% (491/1000)\n",
-      "Test Accuracy of  deer: 55% (550/1000)\n",
-      "Test Accuracy of   dog: 44% (443/1000)\n",
-      "Test Accuracy of  frog: 78% (784/1000)\n",
-      "Test Accuracy of horse: 63% (636/1000)\n",
-      "Test Accuracy of  ship: 77% (777/1000)\n",
-      "Test Accuracy of truck: 65% (659/1000)\n",
+      "Test Accuracy of airplane: 75% (754/1000)\n",
+      "Test Accuracy of automobile: 76% (766/1000)\n",
+      "Test Accuracy of  bird: 58% (582/1000)\n",
+      "Test Accuracy of   cat: 46% (466/1000)\n",
+      "Test Accuracy of  deer: 56% (567/1000)\n",
+      "Test Accuracy of   dog: 39% (396/1000)\n",
+      "Test Accuracy of  frog: 66% (660/1000)\n",
+      "Test Accuracy of horse: 69% (691/1000)\n",
+      "Test Accuracy of  ship: 75% (750/1000)\n",
+      "Test Accuracy of truck: 69% (696/1000)\n",
       "\n",
-      "Test Accuracy (Overall): 64% (6408/10000)\n"
+      "Test Accuracy (Overall): 63% (6328/10000)\n"
      ]
     }
    ],
@@ -655,7 +681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "id": "36f1add8",
    "metadata": {},
    "outputs": [
@@ -735,7 +761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "id": "267479fb",
    "metadata": {},
    "outputs": [
@@ -743,56 +769,57 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 0 \tTraining Loss: 45.389846 \tValidation Loss: 41.777527\n",
-      "Validation loss decreased (inf --> 41.777527).  Saving model ...\n",
-      "Epoch: 1 \tTraining Loss: 39.424652 \tValidation Loss: 35.060819\n",
-      "Validation loss decreased (41.777527 --> 35.060819).  Saving model ...\n",
-      "Epoch: 2 \tTraining Loss: 35.046248 \tValidation Loss: 32.135539\n",
-      "Validation loss decreased (35.060819 --> 32.135539).  Saving model ...\n",
-      "Epoch: 3 \tTraining Loss: 32.571120 \tValidation Loss: 29.923746\n",
-      "Validation loss decreased (32.135539 --> 29.923746).  Saving model ...\n",
-      "Epoch: 4 \tTraining Loss: 30.437492 \tValidation Loss: 27.588401\n",
-      "Validation loss decreased (29.923746 --> 27.588401).  Saving model ...\n",
-      "Epoch: 5 \tTraining Loss: 28.696167 \tValidation Loss: 26.766404\n",
-      "Validation loss decreased (27.588401 --> 26.766404).  Saving model ...\n",
-      "Epoch: 6 \tTraining Loss: 27.121808 \tValidation Loss: 24.613978\n",
-      "Validation loss decreased (26.766404 --> 24.613978).  Saving model ...\n",
-      "Epoch: 7 \tTraining Loss: 25.725125 \tValidation Loss: 23.538336\n",
-      "Validation loss decreased (24.613978 --> 23.538336).  Saving model ...\n",
-      "Epoch: 8 \tTraining Loss: 24.334904 \tValidation Loss: 21.935171\n",
-      "Validation loss decreased (23.538336 --> 21.935171).  Saving model ...\n",
-      "Epoch: 9 \tTraining Loss: 23.203895 \tValidation Loss: 22.022023\n",
-      "Epoch: 10 \tTraining Loss: 22.118146 \tValidation Loss: 19.978609\n",
-      "Validation loss decreased (21.935171 --> 19.978609).  Saving model ...\n",
-      "Epoch: 11 \tTraining Loss: 21.068114 \tValidation Loss: 19.735263\n",
-      "Validation loss decreased (19.978609 --> 19.735263).  Saving model ...\n",
-      "Epoch: 12 \tTraining Loss: 20.225011 \tValidation Loss: 19.091033\n",
-      "Validation loss decreased (19.735263 --> 19.091033).  Saving model ...\n",
-      "Epoch: 13 \tTraining Loss: 19.421816 \tValidation Loss: 18.326990\n",
-      "Validation loss decreased (19.091033 --> 18.326990).  Saving model ...\n",
-      "Epoch: 14 \tTraining Loss: 18.541730 \tValidation Loss: 17.414942\n",
-      "Validation loss decreased (18.326990 --> 17.414942).  Saving model ...\n",
-      "Epoch: 15 \tTraining Loss: 17.814699 \tValidation Loss: 17.238110\n",
-      "Validation loss decreased (17.414942 --> 17.238110).  Saving model ...\n",
-      "Epoch: 16 \tTraining Loss: 16.933247 \tValidation Loss: 16.829644\n",
-      "Validation loss decreased (17.238110 --> 16.829644).  Saving model ...\n",
-      "Epoch: 17 \tTraining Loss: 16.389078 \tValidation Loss: 16.914702\n",
-      "Epoch: 18 \tTraining Loss: 15.726570 \tValidation Loss: 16.755409\n",
-      "Validation loss decreased (16.829644 --> 16.755409).  Saving model ...\n",
-      "Epoch: 19 \tTraining Loss: 15.119167 \tValidation Loss: 16.418999\n",
-      "Validation loss decreased (16.755409 --> 16.418999).  Saving model ...\n",
-      "Epoch: 20 \tTraining Loss: 14.478328 \tValidation Loss: 15.640075\n",
-      "Validation loss decreased (16.418999 --> 15.640075).  Saving model ...\n",
-      "Epoch: 21 \tTraining Loss: 13.877445 \tValidation Loss: 16.125324\n",
-      "Epoch: 22 \tTraining Loss: 13.347822 \tValidation Loss: 16.349312\n",
-      "Epoch: 23 \tTraining Loss: 12.772845 \tValidation Loss: 16.131425\n",
-      "Epoch: 24 \tTraining Loss: 12.325509 \tValidation Loss: 15.288487\n",
-      "Validation loss decreased (15.640075 --> 15.288487).  Saving model ...\n",
-      "Epoch: 25 \tTraining Loss: 11.783999 \tValidation Loss: 15.305513\n",
-      "Epoch: 26 \tTraining Loss: 11.342386 \tValidation Loss: 15.720134\n",
-      "Epoch: 27 \tTraining Loss: 10.812438 \tValidation Loss: 17.583329\n",
-      "Epoch: 28 \tTraining Loss: 10.439049 \tValidation Loss: 16.388209\n",
-      "Epoch: 29 \tTraining Loss: 9.974275 \tValidation Loss: 16.448369\n"
+      "Epoch: 0 \tTraining Loss: 46.005447 \tValidation Loss: 45.827183\n",
+      "Validation loss decreased (inf --> 45.827183).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 42.967785 \tValidation Loss: 37.902005\n",
+      "Validation loss decreased (45.827183 --> 37.902005).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 36.465591 \tValidation Loss: 33.340846\n",
+      "Validation loss decreased (37.902005 --> 33.340846).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 33.368031 \tValidation Loss: 30.396645\n",
+      "Validation loss decreased (33.340846 --> 30.396645).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 31.379963 \tValidation Loss: 28.739690\n",
+      "Validation loss decreased (30.396645 --> 28.739690).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 29.695657 \tValidation Loss: 27.057870\n",
+      "Validation loss decreased (28.739690 --> 27.057870).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 28.245915 \tValidation Loss: 25.748171\n",
+      "Validation loss decreased (27.057870 --> 25.748171).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 26.835941 \tValidation Loss: 24.020309\n",
+      "Validation loss decreased (25.748171 --> 24.020309).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 25.593168 \tValidation Loss: 22.720403\n",
+      "Validation loss decreased (24.020309 --> 22.720403).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 24.306287 \tValidation Loss: 22.299537\n",
+      "Validation loss decreased (22.720403 --> 22.299537).  Saving model ...\n",
+      "Epoch: 10 \tTraining Loss: 23.119146 \tValidation Loss: 20.951232\n",
+      "Validation loss decreased (22.299537 --> 20.951232).  Saving model ...\n",
+      "Epoch: 11 \tTraining Loss: 22.024102 \tValidation Loss: 19.703325\n",
+      "Validation loss decreased (20.951232 --> 19.703325).  Saving model ...\n",
+      "Epoch: 12 \tTraining Loss: 21.156385 \tValidation Loss: 19.277170\n",
+      "Validation loss decreased (19.703325 --> 19.277170).  Saving model ...\n",
+      "Epoch: 13 \tTraining Loss: 20.183908 \tValidation Loss: 18.797876\n",
+      "Validation loss decreased (19.277170 --> 18.797876).  Saving model ...\n",
+      "Epoch: 14 \tTraining Loss: 19.324008 \tValidation Loss: 17.757718\n",
+      "Validation loss decreased (18.797876 --> 17.757718).  Saving model ...\n",
+      "Epoch: 15 \tTraining Loss: 18.546498 \tValidation Loss: 17.704141\n",
+      "Validation loss decreased (17.757718 --> 17.704141).  Saving model ...\n",
+      "Epoch: 16 \tTraining Loss: 17.678391 \tValidation Loss: 17.114598\n",
+      "Validation loss decreased (17.704141 --> 17.114598).  Saving model ...\n",
+      "Epoch: 17 \tTraining Loss: 16.871943 \tValidation Loss: 16.832795\n",
+      "Validation loss decreased (17.114598 --> 16.832795).  Saving model ...\n",
+      "Epoch: 18 \tTraining Loss: 16.158514 \tValidation Loss: 17.014248\n",
+      "Epoch: 19 \tTraining Loss: 15.656602 \tValidation Loss: 16.450417\n",
+      "Validation loss decreased (16.832795 --> 16.450417).  Saving model ...\n",
+      "Epoch: 20 \tTraining Loss: 14.928917 \tValidation Loss: 16.496048\n",
+      "Epoch: 21 \tTraining Loss: 14.339634 \tValidation Loss: 15.770803\n",
+      "Validation loss decreased (16.450417 --> 15.770803).  Saving model ...\n",
+      "Epoch: 22 \tTraining Loss: 13.881359 \tValidation Loss: 17.007506\n",
+      "Epoch: 23 \tTraining Loss: 13.125295 \tValidation Loss: 15.496202\n",
+      "Validation loss decreased (15.770803 --> 15.496202).  Saving model ...\n",
+      "Epoch: 24 \tTraining Loss: 12.726118 \tValidation Loss: 17.043877\n",
+      "Epoch: 25 \tTraining Loss: 12.270297 \tValidation Loss: 16.089589\n",
+      "Epoch: 26 \tTraining Loss: 11.706688 \tValidation Loss: 15.889038\n",
+      "Epoch: 27 \tTraining Loss: 11.236043 \tValidation Loss: 15.671258\n",
+      "Epoch: 28 \tTraining Loss: 10.738385 \tValidation Loss: 15.497161\n",
+      "Epoch: 29 \tTraining Loss: 10.371817 \tValidation Loss: 15.715626\n"
      ]
     }
    ],
@@ -805,6 +832,7 @@
     "n_epochs_2 = 30  # number of epochs to train the model\n",
     "train_loss_list_2 = []  # list to store loss to visualize\n",
     "valid_loss_min_2 = np.Inf  # track change in validation loss\n",
+    "val_loss_list_2=[]\n",
     "\n",
     "for epoch in range(n_epochs_2):\n",
     "    # Keep track of training and validation loss\n",
@@ -847,6 +875,7 @@
     "    train_loss = train_loss / len(train_loader)\n",
     "    valid_loss = valid_loss / len(valid_loader)\n",
     "    train_loss_list_2.append(train_loss)\n",
+    "    val_loss_list_2.append(valid_loss)\n",
     "\n",
     "    # Print training/validation statistics\n",
     "    print(\n",
@@ -871,22 +900,22 @@
    "id": "bca17d68",
    "metadata": {},
    "source": [
-    "- Model 2 starts with relatively high validation losses but steadily decreases, achieving better performance in terms of validation loss. The best validation loss of 15.28 is obtained at epoch 24, but as with Model 1, validation loss becomes more unstable from epoch 24 onwards. Training loss continues to decrease with each epoch.\n",
+    "- Model 2 starts with relatively high validation losses but steadily decreases, achieving better performance in terms of validation loss. The best validation loss of 15.49 is obtained at epoch 23, but as with Model 1, validation loss becomes more unstable from epoch 24 onwards. Training loss continues to decrease with each epoch.\n",
     "\n",
-    "- Model 2 showed a stable decrease in validation loss, achieving better performance than Model 1 in the early epochs (at least up to epoch 24). However, from epoch 24 onwards, validation loss also became more volatile, which could be a sign of long-term overfitting.\n",
+    "- Model 2 showed a stable decrease in validation loss, achieving better performance than Model 1 in the early epochs (at least up to epoch 23). However, from epoch 24 onwards, validation loss also became more volatile, which could be a sign of long-term overfitting.\n",
     "\n",
-    "- Model 2 seems to work better for the first 24 epochs, with a lower and more stable loss of validation than the Model 1."
+    "- Model 2 seems to work better for the first 23 epochs, with a lower and more stable loss of validation than the Model 1."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "id": "18dcef12",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -899,6 +928,7 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "plt.plot(range(n_epochs_2), train_loss_list_2)\n",
+    "plt.plot(range(n_epochs_2), val_loss_list_2)\n",
     "plt.xlabel(\"Epoch\")\n",
     "plt.ylabel(\"Loss\")\n",
     "plt.title(\"Performance of Model 2\")\n",
@@ -907,7 +937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 15,
    "id": "489f9382",
    "metadata": {},
    "outputs": [
@@ -915,20 +945,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Loss: 15.510981\n",
+      "Test Loss: 15.866069\n",
       "\n",
-      "Test Accuracy of airplane: 82% (820/1000)\n",
-      "Test Accuracy of automobile: 80% (803/1000)\n",
-      "Test Accuracy of  bird: 64% (641/1000)\n",
-      "Test Accuracy of   cat: 48% (480/1000)\n",
-      "Test Accuracy of  deer: 70% (701/1000)\n",
-      "Test Accuracy of   dog: 66% (667/1000)\n",
-      "Test Accuracy of  frog: 77% (776/1000)\n",
-      "Test Accuracy of horse: 80% (804/1000)\n",
-      "Test Accuracy of  ship: 86% (860/1000)\n",
-      "Test Accuracy of truck: 80% (808/1000)\n",
+      "Test Accuracy of airplane: 82% (823/1000)\n",
+      "Test Accuracy of automobile: 89% (898/1000)\n",
+      "Test Accuracy of  bird: 57% (574/1000)\n",
+      "Test Accuracy of   cat: 52% (520/1000)\n",
+      "Test Accuracy of  deer: 69% (696/1000)\n",
+      "Test Accuracy of   dog: 64% (643/1000)\n",
+      "Test Accuracy of  frog: 81% (818/1000)\n",
+      "Test Accuracy of horse: 76% (762/1000)\n",
+      "Test Accuracy of  ship: 83% (830/1000)\n",
+      "Test Accuracy of truck: 80% (801/1000)\n",
       "\n",
-      "Test Accuracy (Overall): 73% (7360/10000)\n"
+      "Test Accuracy (Overall): 73% (7365/10000)\n"
      ]
     }
    ],
@@ -947,7 +977,7 @@
     "    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",
+    "    output = model_2(data)\n",
     "    # calculate the batch loss\n",
     "    loss = criterion(output, target)\n",
     "    # update test loss\n",
@@ -1002,13 +1032,13 @@
    "source": [
     "- Test loss (Loss):\n",
     "\n",
-    "Model 2 has a significantly lower test loss (15.51) compared with Model 1 (21.15). This suggests that Model 2 is better at generalizing to test data.\n",
+    "Model 2 has a significantly lower test loss (15.86) compared with Model 1 (21.18). This suggests that Model 2 is better at generalizing to test data.\n",
     "\n",
     "- Overall accuracy:\n",
     "\n",
-    "Model 2 has a better overall accuracy of 73% versus 64% for Model 1. This shows that Model 2 is better at classifying the test data.\n",
+    "Model 2 has a better overall accuracy of 73% versus 63% for Model 1. This shows that Model 2 is better at classifying the test data.\n",
     "\n",
-    "Model 2 excels particularly in classes such as Airplane (82%) , Ship (86%), Horse (80%), and Truck (80%). It also has better accuracy than the Model 1 in all classes. \n",
+    "Model 2 excels particularly in classes such as Airplane (82%) , Ship (83%), Automobile (89%), and Frog (81%). It also has better accuracy than the Model 1 in all classes. \n",
     "\n",
     "- Conclusion : \n",
     "\n",
@@ -1032,7 +1062,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 16,
    "id": "ef623c26",
    "metadata": {},
    "outputs": [
@@ -1043,34 +1073,34 @@
       "Evaluating Initial Model...\n",
       "\n",
       "Accuracy per Class:\n",
-      "Class airplane: 82.00% (820/1000)\n",
-      "Class automobile: 80.30% (803/1000)\n",
-      "Class bird: 64.10% (641/1000)\n",
-      "Class cat: 48.00% (480/1000)\n",
-      "Class deer: 70.10% (701/1000)\n",
-      "Class dog: 66.70% (667/1000)\n",
-      "Class frog: 77.60% (776/1000)\n",
-      "Class horse: 80.40% (804/1000)\n",
-      "Class ship: 86.00% (860/1000)\n",
-      "Class truck: 80.80% (808/1000)\n",
+      "Class airplane: 82.30% (823/1000)\n",
+      "Class automobile: 89.80% (898/1000)\n",
+      "Class bird: 57.40% (574/1000)\n",
+      "Class cat: 52.00% (520/1000)\n",
+      "Class deer: 69.60% (696/1000)\n",
+      "Class dog: 64.30% (643/1000)\n",
+      "Class frog: 81.80% (818/1000)\n",
+      "Class horse: 76.20% (762/1000)\n",
+      "Class ship: 83.00% (830/1000)\n",
+      "Class truck: 80.10% (801/1000)\n",
       "\n",
-      "Initial Model Overall Accuracy: 73.60%\n",
+      "Initial Model Overall Accuracy: 73.65%\n",
       "\n",
       "Evaluating Quantized Model...\n",
       "\n",
       "Accuracy per Class:\n",
-      "Class airplane: 82.10% (821/1000)\n",
-      "Class automobile: 80.40% (804/1000)\n",
-      "Class bird: 63.80% (638/1000)\n",
-      "Class cat: 48.20% (482/1000)\n",
-      "Class deer: 70.50% (705/1000)\n",
-      "Class dog: 67.10% (671/1000)\n",
-      "Class frog: 78.00% (780/1000)\n",
-      "Class horse: 80.30% (803/1000)\n",
-      "Class ship: 86.10% (861/1000)\n",
-      "Class truck: 80.90% (809/1000)\n",
+      "Class airplane: 82.20% (822/1000)\n",
+      "Class automobile: 89.80% (898/1000)\n",
+      "Class bird: 57.40% (574/1000)\n",
+      "Class cat: 51.90% (519/1000)\n",
+      "Class deer: 69.80% (698/1000)\n",
+      "Class dog: 64.30% (643/1000)\n",
+      "Class frog: 81.30% (813/1000)\n",
+      "Class horse: 76.20% (762/1000)\n",
+      "Class ship: 83.00% (830/1000)\n",
+      "Class truck: 80.20% (802/1000)\n",
       "\n",
-      "Quantized Model Overall Accuracy: 73.74%\n",
+      "Quantized Model Overall Accuracy: 73.61%\n",
       "model:  fp32  \t Size (KB): 2330.946\n",
       "model:  int8  \t Size (KB): 659.806\n",
       "\n",
@@ -1131,27 +1161,14 @@
     "model.load_state_dict(torch.load('model_cifar_2.pt'))\n",
     "model.eval()\n",
     "\n",
-    "# Evaluate the accuracy of the initial model\n",
-    "#initial_accuracy_per_class = evaluate_model_classwise(model, test_loader, num_classes=10,class_names=classes)\n",
-    "#print(\"Initial Model Accuracy per Class:\")\n",
     "print(\"Evaluating Initial Model...\")\n",
     "initial_accuracy_per_class = evaluate_model_classwise(model, test_loader, 10, classes)\n",
     "overall_initial_accuracy = sum(initial_accuracy_per_class) / len(initial_accuracy_per_class)\n",
     "print(\"\\nInitial Model Overall Accuracy: {:.2%}\".format(overall_initial_accuracy))\n",
-    "#for i, acc in enumerate(initial_accuracy_per_class):\n",
-    "    #print(\"Class {}: {:.2%}\".format(i, acc))\n",
-    "#print(\"Initial Model Accuracy: {:.2%}\".format(sum(initial_accuracy_per_class)/10))\n",
     "\n",
     "# Quantize the model\n",
     "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
     "\n",
-    "# Evaluate the accuracy of the quantized model\n",
-    "#quantized_accuracy_per_class = evaluate_model_classwise(quantized_model, test_loader, num_classes=10,class_names=classes)\n",
-    "#print(\"\\nQuantized Model Accuracy per Class:\")\n",
-    "#for i, acc in enumerate(quantized_accuracy_per_class):\n",
-    "#    print(\"Class {}: {:.2%}\".format(i, acc))\n",
-    "#print(\"Quantized Model Accuracy: {:.2%}\".format(sum(quantized_accuracy_per_class)/10))\n",
-    "\n",
     "print(\"\\nEvaluating Quantized Model...\")\n",
     "quantized_accuracy_per_class = evaluate_model_classwise(quantized_model, test_loader, 10, classes)\n",
     "overall_quantized_accuracy = sum(quantized_accuracy_per_class) / len(quantized_accuracy_per_class)\n",
@@ -1201,7 +1218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 17,
    "id": "0f245b97",
    "metadata": {},
    "outputs": [
@@ -1211,7 +1228,7 @@
      "text": [
       "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
       "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-      "100 97.7M  100 97.7M    0     0  38.0M      0  0:00:02  0:00:02 --:--:-- 38.1M\n"
+      "100 97.7M  100 97.7M    0     0  29.8M      0  0:00:03  0:00:03 --:--:-- 29.9M\n"
      ]
     }
    ],
@@ -1221,10 +1238,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 18,
    "id": "b4d13080",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/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",
+      "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/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"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -1317,36 +1344,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 58,
    "id": "d996d331",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Predicted class for car.webp is: race car\n",
-      "Predicted class for pizza.webp is: pizza\n",
-      "Predicted class for ball.webp is: soccer ball\n",
-      "Predicted class for Basketball.jpeg is: basketball\n",
-      "Predicted class for Siamois.jpeg is: Siamese cat\n",
-      "Predicted class for bottle.jpg is: wine bottle\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "list_Img = [\"car.webp\", \"pizza.webp\",\"ball.webp\",\"Basketball.jpeg\",\"Siamois.jpeg\",\"bottle.jpg\"]  \n",
     "\n",
-    "def testing_image(test_image, model):\n",
+    "def testing_image(test_image, model_name):\n",
     "    \n",
     "    image = Image.open(test_image)\n",
     "    \n",
     "    image = data_transform(image).unsqueeze(0) \n",
-    "    \n",
+    "    model=model_name\n",
     "    model.eval()\n",
     "    out = model(image)\n",
-    "    predicted_class= labels[out.argmax()]\n",
-    "    print(f\"Predicted class for {test_image} is: {predicted_class}\")\n",
+    "    return labels[out.argmax()]\n",
+    "    #print(f\"Predicted class for {test_image} is: {predicted_class}\")\n",
     "\n",
     "for test_image in list_Img:\n",
     "    testing_image(test_image, model)\n"
@@ -1354,7 +1368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 59,
    "id": "fa768e57",
    "metadata": {},
    "outputs": [
@@ -1371,7 +1385,7 @@
        "102523238"
       ]
      },
-     "execution_count": 138,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1382,7 +1396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 60,
    "id": "9fa7dce0",
    "metadata": {},
    "outputs": [
@@ -1399,7 +1413,7 @@
        "96379996"
       ]
      },
-     "execution_count": 137,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1411,23 +1425,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 61,
    "id": "39b49798",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Predicted class for car.webp is: race car\n",
-      "Predicted class for pizza.webp is: pizza\n",
-      "Predicted class for ball.webp is: soccer ball\n",
-      "Predicted class for Basketball.jpeg is: basketball\n",
-      "Predicted class for Siamois.jpeg is: Siamese cat\n",
-      "Predicted class for bottle.jpg is: wine bottle\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for test_image in list_Img:\n",
     "    testing_image(test_image,quantized_model)"
@@ -1440,7 +1441,7 @@
    "source": [
     "The Initial and Quantized versions of the Model yield identical results. However, the storage savings are minimal, decreasing only slightly from 103 MB to 96 MB.\n",
     "\n",
-    "After quantize it, the model is still able to correctly classify the other images."
+    "After quantize it, the model is still able to correctly classify the other images which  suggests that we can decrease the model size without significantly compromising performance or accuracy."
    ]
   },
   {
@@ -1453,7 +1454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 93,
    "id": "4248df6e",
    "metadata": {},
    "outputs": [
@@ -1463,109 +1464,238 @@
      "text": [
       "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
       "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-      "100 49.7M  100 49.7M    0     0  25.8M      0  0:00:01  0:00:01 --:--:-- 25.9M\n"
+      "100 49.7M  100 49.7M    0     0  29.5M      0  0:00:01  0:00:01 --:--:-- 29.6M\n",
+      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+      "100  103M  100  103M    0     0  32.0M      0  0:00:03  0:00:03 --:--:-- 32.1M\n",
+      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+      "100 28.4M  100 28.4M    0     0  26.6M      0  0:00:01  0:00:01 --:--:-- 26.8M\n"
      ]
     }
    ],
    "source": [
-    "! curl -o ~/.cache/torch/hub/checkpoints/googlenet-1378be20.pth https://download.pytorch.org/models/googlenet-1378be20.pth"
+    "! curl -o ~/.cache/torch/hub/checkpoints/googlenet-1378be20.pth https://download.pytorch.org/models/googlenet-1378be20.pth\n",
+    "! curl -o ~/.cache/torch/hub/checkpoints/inception_v3_google-0cc3c7bd.pth https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth\n",
+    "! curl -o ~/.cache/torch/hub/checkpoints/shufflenetv2_x2_0-8be3c8ee.pth https://download.pytorch.org/models/shufflenetv2_x2_0-8be3c8ee.pth"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 94,
    "id": "f63893af",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Predicted class for car.webp is: race car\n",
-      "Predicted class for pizza.webp is: pizza\n",
-      "Predicted class for ball.webp is: soccer ball\n",
-      "Predicted class for Basketball.jpeg is: basketball\n",
-      "Predicted class for Siamois.jpeg is: Siamese cat\n",
-      "Predicted class for bottle.jpg is: wine bottle\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "model3_path = \"/Users/youcefkessi/.cache/torch/hub/checkpoints/googlenet-1378be20.pth\" # I have download it.\n",
+    "import pandas as pd\n",
+    "from torchvision import models\n",
     "\n",
-    "model3 = models.googlenet(pretrained=True)\n",
     "\n",
-    "model3.load_state_dict(torch.load(model3_path))\n",
     "\n",
-    "for test_image in list_Img:\n",
-    "    testing_image(test_image,model3)"
+    "### GoogLeNet\n",
+    "googlenet_path = \"/Users/youcefkessi/.cache/torch/hub/checkpoints/googlenet-1378be20.pth\" \n",
+    "\n",
+    "googlenet = models.googlenet(pretrained=True)\n",
+    "\n",
+    "googlenet.load_state_dict(torch.load(googlenet_path))\n",
+    "\n",
+    "googlenet_Quantized = torch.quantization.quantize_dynamic(googlenet, dtype=torch.qint8)\n",
+    "\n",
+    "### inception\n",
+    "inception_path = \"/Users/youcefkessi/.cache/torch/hub/checkpoints/inception_v3_google-0cc3c7bd.pth\" \n",
+    "\n",
+    "inception = models.inception_v3(pretrained=True)\n",
+    "inception.load_state_dict(torch.load(inception_path))\n",
+    "\n",
+    "inception_Quantized = torch.quantization.quantize_dynamic(models.inception_v3(pretrained=True), dtype=torch.qint8)\n",
+    "\n",
+    "### densenet\n",
+    "shufflenet_path = \"/Users/youcefkessi/.cache/torch/hub/checkpoints/shufflenetv2_x2_0-8be3c8ee.pth\" \n",
+    "\n",
+    "shufflenet = models.shufflenet_v2_x2_0(pretrained=True)\n",
+    "shufflenet.load_state_dict(torch.load(shufflenet_path))\n",
+    "\n",
+    "shufflenet_Quantized = torch.quantization.quantize_dynamic(models.shufflenet_v2_x2_0(pretrained=True), dtype=torch.qint8)\n",
+    " "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
-   "id": "5a72b00c",
+   "execution_count": 95,
+   "id": "dd5a1753",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pre_TTT_models = [googlenet,googlenet_Quantized,inception,inception_Quantized, shufflenet,shufflenet_Quantized]\n",
+    "\n",
+    "dfmodels = pd.DataFrame(index = ['googlenet', 'googlenet_Quantized','inception' ,'inception_Quantized','shufflenet', 'shufflenet_Quantized'], columns = list_Img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "485c793c",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "model:  fp32  \t Size (KB): 26654.254\n"
-     ]
-    },
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>car.webp</th>\n",
+       "      <th>pizza.webp</th>\n",
+       "      <th>ball.webp</th>\n",
+       "      <th>Basketball.jpeg</th>\n",
+       "      <th>Siamois.jpeg</th>\n",
+       "      <th>bottle.jpg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>googlenet</th>\n",
+       "      <td>race car</td>\n",
+       "      <td>pizza</td>\n",
+       "      <td>soccer ball</td>\n",
+       "      <td>basketball</td>\n",
+       "      <td>Siamese cat</td>\n",
+       "      <td>wine bottle</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>googlenet_Quantized</th>\n",
+       "      <td>race car</td>\n",
+       "      <td>pizza</td>\n",
+       "      <td>soccer ball</td>\n",
+       "      <td>basketball</td>\n",
+       "      <td>Siamese cat</td>\n",
+       "      <td>wine bottle</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>inception</th>\n",
+       "      <td>race car</td>\n",
+       "      <td>pizza</td>\n",
+       "      <td>soccer ball</td>\n",
+       "      <td>basketball</td>\n",
+       "      <td>Siamese cat</td>\n",
+       "      <td>wine bottle</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>inception_Quantized</th>\n",
+       "      <td>race car</td>\n",
+       "      <td>pizza</td>\n",
+       "      <td>soccer ball</td>\n",
+       "      <td>basketball</td>\n",
+       "      <td>Siamese cat</td>\n",
+       "      <td>wine bottle</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>shufflenet</th>\n",
+       "      <td>race car</td>\n",
+       "      <td>pizza</td>\n",
+       "      <td>soccer ball</td>\n",
+       "      <td>basketball</td>\n",
+       "      <td>Siamese cat</td>\n",
+       "      <td>red wine</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>shufflenet_Quantized</th>\n",
+       "      <td>race car</td>\n",
+       "      <td>pizza</td>\n",
+       "      <td>soccer ball</td>\n",
+       "      <td>basketball</td>\n",
+       "      <td>Siamese cat</td>\n",
+       "      <td>red wine</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "26654254"
+       "                      car.webp pizza.webp    ball.webp Basketball.jpeg  \\\n",
+       "googlenet             race car      pizza  soccer ball      basketball   \n",
+       "googlenet_Quantized   race car      pizza  soccer ball      basketball   \n",
+       "inception             race car      pizza  soccer ball      basketball   \n",
+       "inception_Quantized   race car      pizza  soccer ball      basketball   \n",
+       "shufflenet            race car      pizza  soccer ball      basketball   \n",
+       "shufflenet_Quantized  race car      pizza  soccer ball      basketball   \n",
+       "\n",
+       "                     Siamois.jpeg   bottle.jpg  \n",
+       "googlenet             Siamese cat  wine bottle  \n",
+       "googlenet_Quantized   Siamese cat  wine bottle  \n",
+       "inception             Siamese cat  wine bottle  \n",
+       "inception_Quantized   Siamese cat  wine bottle  \n",
+       "shufflenet            Siamese cat     red wine  \n",
+       "shufflenet_Quantized  Siamese cat     red wine  "
       ]
      },
-     "execution_count": 142,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "print_size_of_model(model3, \"fp32\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "2ba4a6be",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quantized_model3 = torch.quantization.quantize_dynamic(model3, dtype=torch.qint8)"
+    "for test_image in list_Img:\n",
+    "    #testing_image(test_image,model3)\n",
+    "    dfmodels.loc['googlenet'][test_image] = testing_image(test_image, googlenet)\n",
+    "    dfmodels.loc['googlenet_Quantized'][test_image] = testing_image(test_image, googlenet_Quantized)\n",
+    "    dfmodels.loc['inception'][test_image] = testing_image(test_image, inception)\n",
+    "    dfmodels.loc['inception_Quantized'][test_image] = testing_image(test_image, inception_Quantized)\n",
+    "    dfmodels.loc['shufflenet'][test_image] = testing_image(test_image, shufflenet)\n",
+    "    dfmodels.loc['shufflenet_Quantized'][test_image] = testing_image(test_image, shufflenet_Quantized)\n",
+    "\n",
+    "dfmodels"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
-   "id": "ca22e557",
+   "execution_count": 99,
+   "id": "5a72b00c",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Predicted class for car.webp is: race car\n",
-      "Predicted class for pizza.webp is: pizza\n",
-      "Predicted class for ball.webp is: soccer ball\n",
-      "Predicted class for Basketball.jpeg is: basketball\n",
-      "Predicted class for Siamois.jpeg is: Siamese cat\n",
-      "Predicted class for bottle.jpg is: wine bottle\n"
+      "model:  fp32  \t Size (KB): 26654.254\n",
+      "model:  fp32  \t Size (KB): 108949.97\n",
+      "model:  fp32  \t Size (KB): 29811.148\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "29811148"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "for test_image in list_Img:\n",
-    "    testing_image(test_image,quantized_model3)"
+    "print_size_of_model(googlenet, \"fp32\")\n",
+    "print_size_of_model(inception, \"fp32\")\n",
+    "print_size_of_model(shufflenet, \"fp32\")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 100,
    "id": "0b5e40ef",
    "metadata": {},
    "outputs": [
@@ -1573,22 +1703,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "model:  int8  \t Size (KB): 23583.076\n"
+      "model:  int8  \t Size (KB): 23583.076\n",
+      "model:  int8  \t Size (KB): 100503.486\n",
+      "model:  int8  \t Size (KB): 23667.906\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "23583076"
+       "23667906"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "print_size_of_model(quantized_model3, \"int8\")"
+    "print_size_of_model(googlenet_Quantized, \"int8\")\n",
+    "print_size_of_model(inception_Quantized, \"int8\")\n",
+    "print_size_of_model(shufflenet_Quantized, \"int8\")"
    ]
   },
   {
@@ -1596,11 +1730,17 @@
    "id": "fd486d30",
    "metadata": {},
    "source": [
-    "The Initial and Quantized versions of GoogleNet yield identical results. However, the storage savings are minimal, decreasing only slightly from 26 MB to 23 MB.\n",
+    "- The Initial and Quantized versions of GoogleNet yield identical results. However, the storage savings are minimal, decreasing only slightly from 26 MB to 23 MB.\n",
+    "\n",
+    "- The Initial and Quantized versions of Inception yield identical results. However, the storage savings are minimal, decreasing only slightly from 108 MB to 100 MB.\n",
+    "\n",
+    "- The Initial and Quantized versions of Shufflenet yield identical results. However, the storage savings are minimal, decreasing only slightly from 29 MB to 23 MB.\n",
     "\n",
-    "After quantize it, the model is still able to correctly classify the other images.\n",
+    "- Shufflenet classifies the image of the wine bottle as red wine, which is quite similar.\n",
     "\n",
-    "I have the same results with ResNet50 and GoogLeNet."
+    "After quantize it, these models are still able to correctly classify the other images.\n",
+    "\n",
+    "I have the same results with ResNet50, GoogLeNet, Inception and ShuffleNet."
    ]
   },
   {
@@ -1621,13 +1761,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 104,
    "id": "be2d31f5",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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9hdf3oQ99SAB51ate9Wnf26Ox7S7wyZ73IQ7xF0GJ/CUcYYc4xOcwXvrSl/J7v/d7vO9978Nau0wB/ZuCZzzjGVxzzTX80i/90iN9KZ91/Pmf/zk333zzJyys9jcBb3zjG/mBH/gB7rzzTo4fP/5IX85nHZcuXSKlxNGjR/me7/mepXJziEP8ZXAY4jnE3wjcd999HD16lJtvvpk/+7M/e6Qv568Me3t7fPCDH+Q//If/8Ehfyl8L3vnOd/JFX/RFfyPJCeT7+77v+77HBDmBbMT9bC5tcIi/2ThUUA7xqMef//mf88ADDwDZvPfMZz7zEb6iQxziEAB/8Ad/sDTqXnXVVTz+8Y9/hK/oEI8mHBKUQxziEIc4xCEO8TmHRzTN+A1veAPXXnstVVXxhV/4hfzv//2/H8nLOcQhDnGIQxziEJ8jeMQIyi/+4i/yyle+kh/5kR/hfe97H5/3eZ/H85///GVJ7UMc4hCHOMQhDvHYxSMW4vnCL/xCvuALvmDp6k4pcdVVV/G93/u9vOpVr/oLP5tS4oEHHmBlZeUvrPR4iEMc4hCHOMQhPncgIkwmE06ePPkp1616RLJ4uq7jve99Lz/4gz+43Ka15su//Mv5kz/5k4ft37btZRULT58+zZOe9KS/lms9xCEOcYhDHOIQf7W47777PuWaXI8IQblw4QIxxoel2h0/fpyPfvSjD9v/J37iJ3jNa17zsO0vuPUUpWkYlBZFpK0DnU+UlcGVGqUV87miaTqUSsQIEYVP0LbQdZACgMLHRAxCTIAClCIJJNGgFDEGtDZYpyidZlAVSAysrw2pmzlV4RiMLE0dMFqjteVjH98Bk1BaIRGiTxirQWuQ/jxJQCm0VjhXYK1Bi8WaSFlqtGl51lMCa8PAtNGsjBSf9zg4fRbe8J8jU68gCUoBCYZDQ1UoSgdIYjwa4juhaWpcCRI0ZZEYDiF0ipSEqlSMR455HdlrEm2XUCisyZdojaKuQRvQCuoQUEkjSfAYqlIRm8juXiKhSAjWQlHk+6qKfLtdEGLsBbulbqfoq2ihFPlfBKUVm1tbFEVJwvGcL3wuX/v4xxPSjDioiEePw3gdFQ2u0RS1Q0kBkg+SUKSgSK0hzSGEOUF2UbJDMtskfYkg2zRhl9DUpKbCpONU7grK4QZuOMAMFGIjkTkSOmKnSI1GdRYVxyhVoswAKFFoCA1dd5Hp7v1s755he+8C89keShSVG2AE6tgxTQGqAePNk6wcuZLxxhrD4YByUDFcWaUarSAR6uk2Z87cxekLDzBJGj1cZ2PzKKeOnWToBuxO5py+56N87AO/zenb3k8380gQCq3RzmFdBVGwwKCqIAGxwRih8y2tbxAiw0GJD+B9QiEYnb9zo0JuhxZGTjGqFIUBaxxBOaIbIq6kHG4xGmziinVEWUIINH5K2+0Sw0U6P0ViIKiEdQ5bWHRlCM7T0jElcCHM2PYtkyRcWdzCF1z5PE6dfBw+JroYaNoOHzz1fMr53Qe4NL/AZN6gWhiMDWVREOuaZndG6hJaW4qyYjgaI0aR2kCSyLCssIVBk0vjW1eiUCQlWGUwRYGxhhQ7umaOn09RvqVwFcPxOqPVNVxVEn1DM53S1TOKcsDK5iblaBVlDCkEfNvRzmbU8x1S14ArKYoV7GCAKE1AUFpjjCMGoWtmnDpxkiuPbVFajZKASR5XObRxpK4hdAEfBB8ldxt2gPINSVqki4SkaNsZjU9c2qvZmc3YmUXO7DTcfMMJrrryFOV4TDUckcTyrvd5zpwdo42jnjeEGHPXp/vvyRW03Zx2tsdoZZ1yUBFj5PR9d3Lmwp9j9YDR8ATOOtbWFbU1+M6wubXF8RNHsXKWi/efofNHceUqSRSSBHQEgSRC8JETo9/hOV/yZLaOHef0HR/nf779tzh/4TybaxXPfPotXH/DjZTVmIQmicKWQzCGzgecKxmNVjCuwHeRonAY53LHJYDkflxE6FqP95GmaZnuTSEmovek0EKMKFtBMcjXiUdCi/dz2npK287Z3b3Efffdzb333MF89wJGhKJwjAZDxitrDIdjqmqIcwUBaHygbjt25lMm7ZRpM6PuZiQCSoRl7KK/1sXvCoWQ/56WA8VBCAqFOhD7SOwPKSJC6o+pOPAoYHkSEXV5N7w4R//3xbH73Ugiyys7cMkHoEBU7n7l8ivOn83XPBgO+dKnfwlv+Hf/hZWVFT4VHhV1UH7wB3+QV77ylcvf9/b28uqqYU7yNShD3XX4ACjo6oRJiqLSjEvFuAISNLUwbxW+SyQfKY0hkohREUUISUhRESOEmBAUgkaAkBLGKExQqFRS2sDqaICKAS1w7uIe6YJHk0mA90LXClpnYhNFQAkSdG58fQtS/agsAnU9I6aEVhVH1kquOLLF0c2CU1tnWB1WXJwJvpsyn4EzAklom0wyrNVUhaFpGqJP6IGldDCf1MSY78G3Hi0gJtHMwbewOnRUTrG719CGDi0KHQw+RjoNs6lgjTAsLUagKIRJG9FKo1IiRs3eXOEbmNUBdCYH1km+Lm1QSRFjoguJJHB5895vykoptOqfulZcPNeysrLC+uZxRoMBV25t4qMjjse0R44QxmNMKqlqx6gboVAgBqUNCU0ICpmXpAmEbpcYSyQZgoFkAl63ND7gZw7cGB03GZRHqNa2KMYlDEFsR0wDpImkWkGhwTtIq4gMMbpCUSHiiGGKbw1DP8E0O7hiQO0jBkVVDiiUofOeva5mr25ozp2mTTBEoVVFNdCMnWZQlpjBiJXVNcrRGqpcZWdnGwqHLQALq1ubrG2eYGPjKOWwohwaHvzT98JegzUFSRUYHCkGtEQKpdEuEZKgfSDUNUoCkgI+QBsjMQrOCkIkGYvRkcIkBhFWtGYcI0NXULkVkqlobEkqRwxHa4wHmwwGKyhtqUPNpNbMdKBpdnExEFODx6PQWFVglcOqhLGeTnVYU6PoCD6iXWI0HLG6skHrE22IGNsym++yHc8zCVMm84Z6nqhsCakgNoJqNTYpvAgxBbQSCmPxJEQrBrZiOByglEIBReEoXIkoRRLBaI0rytyHpIhJCSUJqw2jcsBgMGA8HFJUBaHTmNBRahiNVllZ26IYrQBC2zbMg6eTgJMIrqIcjBmsbCDG0MaApIgxjsINwUUGheOaUye54sgmTgtGG0yYYYsKiKQu0NYNSSCESLQFxgxIzYzQTRBRhJCoG8ukSdS10DnYM56q0BwbDVkrCqqyoqiGzLqCzpcU1SZRNKg5xkSUBrRgXIUrHJ3fxrgRRbVJNRyyuzun8WepG8NosEGSDZIuUZXBe4MYRzG6gmq8hW5noDYwxSlssQJJESUhJARBScJLpCiGHDl6jCNHj3HHn/4Zs6Zm49S12NTg2zkj0zE0LRGNT4bhqMBVI4KCshoxHKwQUiL4xHA0wpUl1hWklOjarp8UCdFHQhDaLjAdzujqmqauSV2Lloh2maB4AaFDxZboC9rSMJ8p2nqKUoL3NU0zwyQhdRodPYWKFHiitOArolKEmPA+4GNN62e0fkodamIMILIkIdmm0DMUUSilQRZPif6/+72mWnwmz+wQpRCRywiKqEXPqvpzHVylWy23Z9KxIDD5OAty0s95ESBJAqV68pFZiPTX23feBxhSf67l2LagRwrlNGVRLPv7T4VHhKAcOXIEYwxnz569bPvZs2c/4fLgZVletjrpAvMGBi5iY/5CyoFCRNE1inmX6ESIIvgGvM8PSJuEVqAlKw8aQCesgRg1IQgK0z9nQRERAaM11lrKyjIaVoyGJVrD+saIe+5rmExblAYlCpGOKGBKg006f5kIIvlxK0lAyu2xb2CgSDGCCFF1XNxpiDHR+oKbrhCuOrFCqz3nz+8yncN9ZzRNVFiniT6BCMZoJCbaRjAiyED1jTQSomCtonKJolBYXVBUEbTigXOR1kfEQEyK2EWGAzAqsTqypJiIKaKHisZrUoKyFEKnQCJKNMZqTGFougRK0KIIAZRNhAAxZcKSUPsvzj6vXzbWlBJGZzYefWBve4fhcDXvrx0iJbVEdqbbpNRSygCaEUMzQple4ulfQENBEoMQsTpC7IgSUaIgWRQVGo9RmkSJ1iWiNaITon0+lHgyYzUQEhI0OlUgBVosOhq0NnSiiaKIKERrlFFURYGWEVppnC0xojCmwuoKK7tc6OZMHniQblIz39thPj3OfNowWJ9Qjce4Yh3tRmwcPclotIZKiZnfJk4eZOYMg9ER1tZXecLNtzIer3FbUXLmIx9EeYskRYjgNRA1og22csRU45xGtWo5uxyMB0x2dkla0FYRo8KIxqAQLaA8CoVWBcas46rjaDNEmYJQOawtUCZircdYTTKWJlmslNhY4YMlpIBPLSpl8q87MMohyuEsjLWl0Q2NChhJRBG6GGlDoItC5zsu7Z3jgfP3cW7nArPdKaJLhhtrKO2QrkGW7UtDihgRYsjvIgqcK0BrYvBobdFYtLEkSSQJGPJCg52v6WYzfDOlCC26KEHnaaES0GLQOLQxlNWIYjDGFBVoQ+cb2nrGZG+b2c55RAJVtY7WBZGEShrvPa1vcHaAxgGJYVUyGlQY5dES0GaEwaCMIjaBLkDAoa1F0WFslWfRKmHEk3SFiM8qcQwkDF1MzGuPSx2h3ma+rWjnu1A6zuxtMJleh7aa0CZIeShaqphGEYHOd1hjUNqiNNTTKd5PSALWlmhlMVrQRohNQmuNdUXuN8OM6MmkXimS0ojEvg9Uff8a+vMaJru7fPzjdzDY2OKKG29m++7bqNtIXU8Is128DyTl0Ahq7ShmNMYYCKGhbluUsmg1IEWPqyqSMXRdR9c1SEw4Y6kKh1WGUHTMp1NiyH2uiOB9i9EWjMFZg2Ag6b4/EmIMhOjpoicgpBiRFLFaMzeglZBihzIFyTg6oA6RppvTdg0+ekRSP6arBT84KCb3faGQRPa3k7tJ2f/xsoU6Fz/Lwc88/IflPou+Vuh5xYIb9VRowZcWBCk3fM0+zVgoLIu+ln1S0p8u7Z/wsmv4dB2vjwhBKYqCpz3tabzjHe/ga77ma4A8ML3jHe/4tBafWl01OONoG49WmrYJzOeK6RRQgiugaYX5LFENNFrngdu6BQNUhKDp2kQU8KEPA8XMFo1WJMmEJqlEjIGuhZmaIzFy6oqjnLvYsDupIaWe1WaJfTzWHNtSVFp48ELk3m2BENAogqS+I8gGIaUVSikERdL5S0kJ2s7zwNnAu97fodRZVoaJtcpy74OJP3qv0LTSd8cGraGpI9FHrM3XO29yg6gqRSLhW3AonNUgIUvaRhgODX6a2NtTlEPFYAW6WlE5GA0F3ypqL+zNoG0CGiEajSs1UZEHMS1IK6SUO56UEkSwCpLOXVHu4NVyxrCYPSxemMXP/cSABYGJIeTPWksSy8XpDvdP7kWXQ0o95qicYG11DW1MJpeiUEmhgiNG2xPEnhCSSb+oPGMAm19InTsjrEJMIBpNIqFSgihIUKSo0alApMQkhxKTw38IogJCIKaIYLHFkHKYKKzLxxXdq6ARJzBKI0jCnq/Z2znLdL7DZO8ca9snGW8eYbi+SjGaoYdDrK0oVo9SaMWwrWh9A11HKHYwTrG6dZSri2exsjbkY+u/wfnbb6fd6ZhN5wxLRYieQGClXKOZ7qBsbjOQKO0AlQwhCs4oBm6IF0/lCgoDiojTgAqMVtcp1DrWHsWYMdH4vu2WeaiRCDoiIjk8qC3eVHSlywSuFdqYUF1kIOSBBqhQJOXwpiI4qExWNLsQ6UKkDR07k3Pcf+Fuzu6cYTqdM28aRqsDymGFRRO7hpQ6RCSTUBRJFF30REk4W6C1IqZIiIECA0ajjIXoUSmRlNC1NfP5hHa6jepqnCtRShNFo5VCGwNGIGXCY1SBKwYo44gS8V3HbD6lm0+RENB9mNTjSd5gtKAkQkwkFUgxYIxma22DwaBEpfxMCS1KdUhUWR1IeVKDtogxoDQpgg8eq8t+cNJEZWjbjhADdRuZzDrGaZedCztIOwMUdYzcV99MCI/DWU2KHaJkOQAZpTFKE1MkxYit8mRMJDGb1bTdNtoqtC2y6qlz3xWToixMDrOokL+PCDmgdnDG3qsDKe2PWCI8cO89XLh0nlOP/3zK8Rqnp1PGj7ueYrxOmOzS+Sm+28NohXOWajSkMHmeXxSOFCFGT2GgLDRBTCatMRK6jnJgMQp8CmiVg9GQ+2LvPSHBoBxRlgVKRYSIRI3SGgXE4PGxw6dA6vsQRGiih1Zy+0kebR1YR9CWJgSatqYLLTHlMONlysMSiqWacUDBuIy8HAwD6X1ColCfmLAs+tSHnGe/b+2PIbkvTJ+EPSRZ9Jf5c5Jy+u8nU0DSZZv3ry2LLJ8eQ3nEQjyvfOUreclLXsLTn/50nvGMZ/Cv//W/Zjab8e3f/u1/6WM0jeb8VAhB2FpzFEZjTMwNxSuakEhJcKXC2gTY/CWrmGXdpBBlER1JMeSYaNqXriRl8mCMBkm90gFN4xkUBZPpjDPndvHeY00eCLWKHNuAG682DEo4dizyecny9j+O3H06nzcqhRJBqdwlGGUyEdJgXUHsAqXTVBbmdcud9yZSClxzEqIy3H6H4sFLGm0SyQdEW4y1dF2HMprR2DIoIfpI5yMhJKoSRiua0glNm5jOEqYQYlB0QdNFhVeCTgntFY2HpAzNNNHVEWM0pjdcx6SYzoThMFI4RQsE3zfa/oW3FsoSCqdQhvws1QHG3ePgi5VJm+pnwnk2JgebtNEEn7g0Oc+Z7TspR2s4t4IrLe3welyZ6CcnaNGkZFFJ5xdYJVARtKCtI1FmRUs0KI3SLsurOs+EkABJkKSJ3iE+Ex6oUGIQpVFK9y92RCQgaU5MHaI1rhxhjCbqFhUTKUIXA0lFogqQOgai8iw+duzM9thrG6a7U8qdCwzXj1MMVxiMHMPxmPH6Bmo8xFhLVQzQzuCImNjh9IzxaoWxT0L9Pwlnfo/7P/oRpKlRVUlq+lDN2ODrAt92qELjdMGwKGmTQWnHeFgxthWdClgSA6UwErHKoLB0vsJVK+higNYOhyAaRAWCzIhSodKIiCVKg9c1jZ0z71pa1RB0VvJ0iKjYoYhURuGMxtgCZcAphVUaESHESAie+fQSpy/czoMX72NSN9RhSqc9q0MhWo8PCiQRQg5ZhRRxKg+DIXpS/tayVO090beIdvn7kwSSUCJ0TY3vWrp6SmhanAClydFzlQlulIiJJg/kpswzSwNd6ogh0jZzurYlpIBxFmscQqJtawhZsZR+9ikpISlQVmOOHNmiMAodAsq4PIiLBjE5JOBb0ArfJkRpJMbl4BIQkrKE2OJ9oOk8re9oQsSHFum2mV2CbtaQRKg7uGSfhh6X+d1K0t9jnr2jde4fQ4dSEWMdSmuCz89o3jZYXWCUBaWwVtGlhFYW6yyFczjbErQiJYNWuh+dct+QJa3YhxTyq922Dfd8/ONUq+tc+8Sbue8jH0FJx4mrb2SwcQQZrKJ3h+xdPMtk9zzaGoqqwlmDK4aUtshqWz2HWFDbCUkZQjtHoocU6dqG+WzWT0Rj77WClHK7sbbEOYch989RCdaarEynQNfV1M2MKAHRoG1uP0EJTfIkL3QEtHWI1ng0XYrMfUuXIiHFZbhDLTqqXkJ5aDKtuoyd9D+qhwTHF/IH+2ThoEJy2cE+Afk4GPK5jOAsemnF0quiltIKBy7iwNWofaKklhcq/YRTZ0VIyOrmp4FHjKB84zd+I+fPn+ef/tN/ypkzZ3jqU5/K29/+9k9rjYq1VYPSmsnEMK9bihVLUcFQNLIXUQqqMst1SnVYnahbxXSuaDuAgLEhP3el0EbjRIBsABWl0Nrkh9ubZxUJlKb1kbMXdpm3DboP4VhlsNbwuBsMR1Yctz8QuVR33Hyl4YYrC+56oCVI2v9OzX6jWswCSHkGo0jMu5aI4uQRy5UnE5tbiiiBkycs4/EaF5qOsxcmaF1RlDaXlFaRpobKlZSFQiuIIUuREoSozZII1LXQtoqQOkQURhlCmxBPP8fJITOjoXTZ5zIYaLogtG2ibRVtI7RJEZJgDAxHGm0M1kScSViTO6Gk0oGw2TIiuf8mkIlhkoMvWwKt0Atiowwxwd7eDufOn2Ewm1FWm4xXTxB8NviJFogJJRoiEPJLoyXfTxRIvgUbEONAVhHd5ZmpZEKURCCGfGliSV6yFC8FBJNfWp1nCkIkSUCSR1KLUoliOCKZhjgXTKmJviPiUZItb5GI0grtDCYJA63okhCabfZmFzi3fQ7KexkMV9na2qQYDFhdP8LG0WOMVlcZrAwZjkaoArTxlDS4wqCVozh1HRK/EB8DbWhRGIyDzdWS1fGI2E6ZbnuGzmALxbAa4pJmdVYydNkgWmjBBYVKkaoosS5Rlg6lLMqBNgrRCmKJFsEnT5CONgZSSnjR1FIzlz2auEsdprTR04gnJYULAUdAfETbEmcN1iqcjQxSQacsKWXlrGn2OLd9L/dfuI/ZrMZ3HW3cQ0oIZUuQOToYJLZIyn4siYGkLTFm0q+NRRlDlI4UEhJzOCOSMJLyIONbuqYhhIBKicKVOJPfg+A9xijatsa0JVH33azJ/UTXdb0fK1A3c7rQEbWhcIrClgStsxfBzxAxKGUwVlECJjlWVkasDCyaBMaiTIFWEdSA6MPSqyApEUNEu1H+uZnhfYu1OVQXkxC6mhgVjU/Mu4Bva+r5JXaDIek96s4T1DHaI5sMbZGfc+oF+d5pr5RGKYUPHRqFMQ5YqAw13s8YFKso5VAKtE10XvVhNIt1hqpINOWgf9/18o3Ps/4DakH/rl+6cI57776LrauvY3N1lT87fQ9ra2O2TpzCDIeoomJgKkKCZvtBJnsX0A9oYmgpBmsU43WUctkEO5vQNQ3VYIiEADFgENpmSgjZQI0kNBEVPSqFXpVxWGMgCp2fo5WH1JGCp+sa6mbGvJ4Skifp/MxUUv34n9trK4JJkaDAJwiSaFMkKMlRNGShJy2nXnngXmDxdHK/KAhR9rnBgd0WAZnLNh8kKkvl4rK/HfS97BNExQFis1S1+s8eVHQuI00Hjn9geybXLM2y0kvi6lOkFH8iPKIm2Ve84hWfVkjnodjdrjEqcmQtGyNjyFLtxsCwMQDvFTEJimzm9AmiZEUkpURd57ha4bI5U2vJEqxKaJNfoEg2dyoM2ubZXULybDjk1ywG6JRmoARtYH0o3PKkY0Qj3Hv6PuZtdqsbo0iikdiLnP23nWJm1FZplDV0+Ky0xMTmiuaZT9GMRhA62FhRXPsFhtvvMtS3K9YGhkldM9kRqjKrONZaqmGFb+aUhSIag9IwqMBowSiFW9HMa4210HSa5KFLWSVyJs8Yuy57LkYDRVkqUojZv5M0o8owbyPe51BZ7rDBoNAmonWOp4TQ64H9DE0vKEpv9+67LWDxoipQ6cC3LPvBUm3xQbE3adjdbdlrYFAWbKoZwXeklH0zBAPRINFmmqU8SEdSecafEIJEYlKIHkIxyERGVyQFKSboekUMwaQSVIVYhxKLdNnEGzVI7AipAx0JpqPtQh4ExaCsBWUyaZIWEwWMQUuFchYfI8korDQMVX7WLjRM64vU03O0E0XrTiL1mAuT+2kmx6hWNzh66kqsO8nQrFIUBhUbCkdWIKrIiRNHSPH/QVnh4gO3Y8KQI5vrjIdDRNeEJFSra+jQMChKhsbiuzmDckiRFF0zQ1tD5UYUWjA6oKsCdNnH1jsMJVGXBB3x0iEhIbolEPEqMk8TpvEiU9lhYmY0ffw968XQRk0RS8oEFYlCOZS2rBaGnbiGpEjXzLi49yD3XPw429MpXfDUcUKnpuhSkdQu0ZekUKATaKNzKE72O2GtS1xR9pk5kRAjJuXMgpQgxUjsWtq2IYaAUY6yGgCBFFuSb4m+IRjwIdJ4jyoqrHMgiSiB2LXoGIgSCV1DSD4rdbpAjMNoh5NIaDN5QQQbDU4UwXrWRkMKlZAYoSjRtkSZPClKbQ0SEVsivs3qhgSIWckkdUiq8L4jJsH7QOs9s7qm7RLdfMpkPiV6QxOFeefR48cztBtobfBdQogolSde2uTsJg0k36G1RhmDUZpZ64lSE6PH2DI3OK0wLtGJRWMysbOawiZaYxEl6N5cSeY/y8kPLMginLn/NOfPn2Pzmht44PbbufDAvXze029hbX0jh8VJmMEqo62AVZHZ9hkmOw8SY0c5WmfYHmMw3iBh8D7myUAM+BDwPhNOJQlDQkvKXjeJlFbhe9+OhI56OqXzHmSOc4EUAvO9XfZ2L7E32WbW5HYoKS7JlfSKC72pWiVF0tnDE0QyOSFPtBYDvO6N2ctBvm+vvZa1n0XTUxnVh8ik7yiXyRWLXlIEzeJ69rftk46DI2f2A9JbDLLqcVmPe9lvS0Pu/pFRSi0NtA9TZ4T8POhJzDKK17//nwYeFVk8nwzF0DDf9bS1YjC0jMYF3iumk8hwJAxHifkEQoB5va8eDAYKbS2SPG2nCF4oS4UxgtU6F4+R3nhKDgVFElqbJXGwKIxSjCvFlccNa2t5Rrazqzh9VnjyDRc4tmKZryq2d+GeBxTKKHSMJNXP9A58V0ZrfIgMK5NNhP1s+/jRyLWnLPNmwNnJlKRhOOq4eGkPouXqE6tMZ5ELey1dFzBAaANnz25jDVSl4BQU1rK6FnjitYoj6wpF4M574bbTUM8hiaXzniSahMYZh9MgYpg3bd4uka4RJAnKZDMxJPq+ug+fydJbszRtHZygLWZOyIHGrZZzgUWDXsQrF+YtFCRt8MlQzzTzbU00ET9qaKoO3/j8lan+q/MJ6WLuBFRHKiIx5SwWpRzKVKTQoZ0hVSuowiLREqPCp0QZFudUIDltF+vwCKYwePFISqSuJqQGVCLSorRAEFSyOXRkJXfmkkixwyIkVZIIoFsoDHQOGo2KGi0WYwIrWoguUmnDcG0FZQKubIhhwsVz95FoOFVdhXUlgiXOW5QBUktp4MjWKeKNEasCTjVcecUJ5rMdjhYn8EkYVGtML56jtJpmMqUclYxGY0LrKQ3ZUFgNUGGGpsVWBcY6wsBS3HQ9s7sv5LCKeEJqSBJyphs1wcyZhm1m3UX2UsssdnRJMKKxKRPalAIpWoLPxKykZGDWsOUW3m9S+47t6UXuOX8H53bP47tI5xsaJkTXIlbwMqMLU2wYobEYYwk6tyatssrjioKyLEApQoiEENBao03e1sVA1zV4n42z1WCMKxwxtDTzQAgRoicYk2eF2qJthTaOGJvsOZKEESGpiNYKg0apEqwlaZNfDKOxdoBPNaGtkSh41VAxZDyq0LaAFLDFIHtQSJkoiyH6CcqtZ3Ov0kiYE1MmXIqE0pqYwIeWKIouRpq2o20DYb5NXXfMu6zSxaQYuquxdjWHJ2NYvqdGJRRZAU0qkZKncA6lDcpA03R03QQQbP/80IIyEDuD1hZXFlgLVgdizKqn9DJ/ljRzH0Dfv0nv0Lx04QKz2YyzZ85x5v7ztF3N1dddz2AwJAVPSpaYLLpapVrPWVCznXNMdy/gmyni5+Br3GgdayqCr6nnORxTuAHJKGIUuqZDUk6pT6HDSkAkG7hj3RG7FpGI1R3Bd8yahsnkErPZJfbmu8zaOYm078nQOVilFyGORc8mmihClF5tVIBKea7WD/YLWrAordDHeqCfBO8rFPuERfV+EellaLUgLwvy0x9b9WPKweyexbFyfkhW7ZeRm32TSSZKB9QXUYsJpFoeo99wObFZhpv0spdfHHRx3/uU6i+HRzVBqZzCrBj2diOzuaCNpnCK8dgwnwurw8jaCGaNYtYKSTwGTejywFeUkl/GmEuTZAlK5Y6B/AWnpPoUK8EWBdFHikJjtaNwgcfdIHz+Ew2FEq6/XnNxB/7fXxbe9rsTjm0YLuwJD5yNnJsYlErYqAh9ul1Ki0adwwVtGxHlccagYmR1pNgYK6pCYZzn0l6+znmruPvBwOnziesLAxHGpaYzCmMs83li3kZiAq1yJsBoJHz+Eyw3Xx/xMTIaKm55guIXf1P4wEcSroyMB2CLbBQurUNSzMTMaoKHFHJmjisS3oPvFKBz3YS+HxaEGPv4Mgclx4XHJH93Si3I9OUMf/9lOihVgk+R882U3eSJgxXM6BShCbSNZd5A3bSEzqOcEEPCd4bUCkEUSbWoQkgDjR6MaRuPmBVKO8bXgdHKBl3UhEawQUMd6LoOWxSYagTiaCWiDXgfmHYtg9UhMou0Zoo3La50+Lqlo8U4R0gqtysW6dYlMqigi8QQENVirEZTEidgQ8Q5T0GBUpYcIAexJcVaQTnawFWOkBI+7FHXHW0Lg+EWsStBNSjjM2HywqAYcWRtleHjbsZ35yiLiDYFzXyblfWS2WSbwUaBVYkuKrbWr6aqVpicexDnCgbVkBS7HIIxK5SjEqNblExpJxcQk5A0R9mEtY6mbRHIWUAIbZzTpZY2RhqJeAlYcljTiusHJw3RQSywWAZ2naq8ioti2fZzdqYXOLNzhq5JpBho05To5kgRQRnabk4tM0xw2GRJkv1WKXvcM1Ew2QybgBg7Qgi4skJbQ5RE8A1dU5N8ZDRcpRgMMc4R65QNqq1HSYstBmhTULgxrhxnFbZLoDqyd8lgdSYwZVGR+lTSJClnU8VIEo2SPFCGVONDw7Hjpxg51dcYUpBC7hNS7GOIHtoOMfntEOmIKddsyrqCgG+IscN3uZZJJ4YuZVWs0g1iNEEgxEDUA8zgKqwr8vWFHHLWWmVy3Q+mcZkKPewF0ETX1tR+gjYOY4usxJqsMkZRFEZTFCXWCBiP9ykTlD7Esz8hWbzsvfKqFPPZHJ+Euq6Z7uwwXBlw7MQJCueyCqEdQXeZ9BdjitUjdCHQTC/hu5rJ9jliCBwtLYNRRdMm5tMJRVFgjSCpIyWhaab4usWags57Qj0jNS2xneOKAqtjVgZSR9u1dPMZXT1hMpuwN9ujDVmJRWeVYDFEp2U3tk9SFv3dIuVX+r8f9JcsVY4DqcKX+e4OYEGAloRiMZlb9JpLsVkdmOjtqx8Hj6n1QTFjcaEL2qSWXsCD3fM+AXlovImHbV8eWi4PQ3169ORRTlB8KxhtWVtX+KAQArM6IKIonBCTRmmwWqiszbMcEWIAydltQA69XMYaFwOpyhwz9b6U4AVnypyamiJXntRcc0XBZDJiPpuhCVx1ynJ0LfHhu+Ces+BKRRsMSCTFPlMHyTOhvkFqnU1ESgkheI6tFXzBEyquuTZwfEtzxZHEpe3IuFD4JHzkNqibElsFTp/dAzTHjw65/sQ65y9OSCmwujqgmbd0wROUcPUJwxOvVkg0TGeBWYCrjglXHdPce9oSdM4+MspSDMA4ePB8ZF7DoNQ4IySjKfvwjzE59dgHgajQRlGUWcpsGpXjrepg45Tly7iIf2rVFyNKB95Ycvwyu8Q1i2S3+WzO/3nvn9JomMXI+MRRRmJzKubqKjuxYdRMqaQidonYzIm1IvpILGbEsA2xww4qis0VUm/+MytgSodWmm7e5vRbpwnTSNs2GBSmGKPGlk7XDI6N2Dt9DimzXN+FOW0zQ/kChQXV4ZJDnNC5SKkNZqox1pGsIaoWbfLzQgrqeo+oElEbUjnEVQV0c5IzDDaPEF1iZW2dWCjcsGJYaLp2QkgT2m4H40Y0k2200SivsidBjxAlVMOEc6vUTUcKO9TbE5QOuNLgOpdnkrGG0RBsgfcTtImUK5sU1Qr1znkMlmrlOFU1JDVnsHpO0XnMypimqzE2d4aFKyltiY+7ZNPpJbRUFErjyF6RQEdpNEoJpXKU1mILR+HGlHaLgdliZNcojGcyucD9u2eZNx6JijZNMxksAjZngqJFSF1LDC0BR0xZhZQoJBcJJJwkSBpRkRgTkgStDAAxdHT1nHY+y7VZnANrSApCCoTY4WOHpEiBRluLKSps4VC51BpaG7Sx2UgpQlj4LBIgQggds67FByFF6Y3rNTEEKldwZGuT0hokeJS1Of1WGVJsQBeolNDGIkZnw3YQlMpqUfTTnBrtW4KP+JDoguC90IZcaG9jpcSaIY3vSClhi+MUg5No54i9/0QWc2KJKBwKCCETL2NtztQyirab47sJhS4xqsj+Jq3wQt7X2PyMTEKLp208RlUYteAlvS6wrJ2xPxTPZjO0LYhRmEy2efxNV7Oyuoa1FokRYw06aJLWRG0RV1GsbGYj+nyH0M6Zbp/FGUVlDaUpseJxukCTCW6KWTWR2FEWjnbWsLd9iTDfBV+zsjKiZCX3BSln/sQu1zzZm+2yN5vQRQ9GIUkvPXOLMSPPtzRyQDJe+E0S+2HsPJrs94wpLWhOr3Y8dBRXB3weepFtsyAP+9O5RabkspbJ4rn317LwgiALL8gBsrg8j9onFYsMn54QffIw0AGiRV87RXGZWrQ4wcOSlz4FHtUE5eJeQkVPVRiKMktR80Yzm+U6HqPC4Fxk1iS6oBkO+qqwArM6ZVc1C2aYMy3oGaox+Xi9AglAjJHCWAaDEkJOOzu+WnHqxmdx5523ceHCnVgLQXqvxuI9tIBASrqvUhj25cDezCQIVis0iRuuCzz180zO+0dRjQxuovnw7cLpC5pZM8S5irFpmewFIBFqhREYV449atZXSuYGLm5HfBPpWsFZw2BccX5vD+bCxYswnSfGQ0A0g1KxtlaA1VzYaVA2gRESufKtLQWjIl2tEQUGQRc9kYgJo8lpfDp3QsZkIpJin1Hfkz7dv1hpyfjz7/lZLExn+btIKqeL1ntTPvh/PkoaFYTKokYj1GCIjEZMzR53bd/PJZkyHFZo7VB9mCe2gWHh0NajdGLNavaahs0Tx7MvIUU0Gq1jX99BoUThihFpEpjXnmqYGB0bMBgOScmzWqxy/swZBpsDVFlgZk32KohHCqGb1lhXkjT4NmBNQkmuIpgMRG1JWhGaGp8iSWtUpVFNQleOav0I7XRC4+cMNzYpV7cYH91kd+88SSdWNlZRyaFSmwmFmxDilBANKRaUw1zEKqmAKCHWTZ49K4U2BTG0CImo8vONkkjdhKoYEAoHAwc6oaoCXIFZHSO2QFgFu0o0A7wkMI6yWMGi8aElhQ4jAwZmwEB3xGIF3XrGY8PZ2YO0aptKlWyaNTbMFiOzxagcslatMCpWGbhNhmYNxw51O2det6jgCHFCk/aIRYtyEeUM2mhsslgcKmh86tBickaVFqIyxCj4mDAp5iyMEJEYiMRsXE6Bdj7Ftx1uWIGhNzt6vO/w3RzlcwggpkiXEoHUe1wCIjGHP3TuP3xb0zU1TTfvjeZC5xtmkymTuiZEsMZgtMUaw8raFkc3NjAmD/RGW7IJ3wAKnQLRWNRgjBid+5xeYTHOoqOla1u8gA85VO0jtD7gY2RjVbGydozR3oD5ZM6OmtJU1+CqDYy2tF1AJPX+B9BOASYTLd/lcJV2oGVZ7bbrZlTVBlpblFJYk4hZ3sFag7MOqzpU8rR1yP4Vk/vc/YCE9GGf/WFuPpthqhJtDBIDm1tbDKsBSfbDEEar3B/oPjykDGJLVDkCEbp6ztkzDyAxoU1B7WF98xim6GhDIiWF8hEjMRtjU4BuzmzvHDo2lKaD1GKKCq0N+IZ2ts10ss3OdIdZOyMtycc+IVgkHSwmYUuZWPpQyjIistAnDijFizTgxeSNxWCvUbJPbFL/9A6IGssKucsxf9Gf9tsOJOkcmP7th2ku/wb2j7EgFftKTO64LxNOHpZ1pB6WQXS5+Vctz/rp4FFNUOaN4NtAWQiuhbJQuMJhO2F3FpnOszu7Cdk0ZV0O5+QITsQY+ronuZUVJhc+EyIgGGsyEwzZUFe4gsGgRAhoBzuTxJmLHSev+Dirdgc9VPzZ7XDmYjZH+hDoEoQ+kTxJymEXsp9j6bGQXhgUcEZz1Zai0MJuq5nXwrCCYaU4t5v4+IPCsGwJaZbDTKUjxJbtac3ggnDqSMna9QZrJ1Sn4J7Tmo/embj3TOL2eyNPvKFhNMjVc+8/q7j3TKRtBW0Us4nmge2argWlBFsYSgezecIaYTBIaKUZjhRdq4kRjEtoLYQOnFHYwmRjLLA07+tFrDW/0CnlujeLYkTZk7AvIbIgbymXXRcBrTxlvJvpBZgER2uHKFcxKEbE4Zj56r2MNjawK0Pc2hpmWOGqCjcoWa3GbG2u4xxMlcaYITu7NYMVQ+EcdTulstC1MxBDEIW1BW5zQNNoausZV45qdTUXYnKwCVhTMFYRug3Onz6N7wKx88TUkUJAvIAt0UOLnyec9CExlSBBwuc7DZGUPKqAOkzBjcGVhOixgxEUGmUTR67YYLJ7AW00g+GQclTQtdt0cY7C5LRZFZnMdkA0IQpt64l+Lw8IShNUopOWuZ9QVkPQJaTYGxGBymJGjuAFPR5QlkNUZSjKiukc1o5eSZjPsCT0cJzriPgWZweIOIwcxceWcakobMdq5ZhLLm6lqxVW9DG2iqvZ0EcZuDFDO2BoHVWhGTpH6VaxpiGlhMPgVaSRhk53iAsoo3Iotvd6aKVzFkqIGKUwyqLK3jTf175JMZKiJ/gOlQK+88yZEGJHbOY4lT1GUStC6pWVriV0mdCgci0SyQcjRp8btmQ9FJ9o/ZzJbIdmOqFtGvqEDZqmYz6bMms6ECirimpUUZZjrrjiquw/MRYFeVAkIL5BJY9oQ2x30dVmTvMl9z1KKQwdXpkc2vGaxgfaAJ1YmpAnyceuGLFCyfqwYj6aUxYV580NuGKM0tlLp20/yKbUh6ryZCT5FmtzMTpjNJPJnBCmpNTitM3mWa3QFrzkWiGFKyisRcmcGLMnblntpg9JaPLET5GTAxa2lM53DNbWcvgNxfr6Bq7I1WCV6sMl/SApIZJCzhgLva8sKkeyJc1kwl133YGkRFkOkbbBlCUhgStHaO0IPocdiQ3SToj1DjE1zEyga2bYokAVlqaume9dYnfnPLvTHRrfkmc+aqmwL/WLRRhnQRhkoaiwrPSa+0AWLCL/Lmqf4Oyn1OyrKAsPiVKIymZcvTxr1mr2wzgLUpCPlfpjLf+3jArt97NL5YX9/eSy/+cbXRCvxSfTQ8jIMoQlB9Sk5VaWR/t01yZ+VBOUYqBp61w5tvOCD4mNVWFrzVJ3BTt7np1ZNmsNSugiOcMC+lhrRBuFReMKRWkE6yTHxkUTEiABIaGM5cSx44zHQ86dfZAQWzCG93xYccfdH2F1RWg6xfmLBUkKtGnpgs5VHfsqqpAHfpGFFKeWYSTTx5WUUhgF6+sjfILdnV2aVjhzPrE3E6yO+BCXjNr7XGckSuKe+yccW215wvXCvBVWRoon3ahwpebPPir85h9E7jsD44GmruHB7cTuVGMVoCMpRarCMa4sTguBbADUA0VRJSQaJGlMEbGiKJXKywtooXAarXKp9PFIM50m6i5nBhi9uK/cYL3kejPxQLKO9OE31f+zEJ6lr4gkWgjjiJ97uumcrpsRG+hUgZmOMBcd4YEKZYdIOYZqhBuvwNoK1cY6G0e32Di2xermCkVVMNIVbR0x80CMiYu+xsaSrm5pWk9VBHzcY7R2hK0jG7hhhS0rYjIUqsPNL1GWmrpu8CpSrY9QswZfWbw0aB/pZnOCn5HUKs6WeZkBK3kdIyXQ12ER40ECMbboQYEMFc1sj4BQd3tsja7CjSvG44JjJ44SfENd7zIYaFZXHW03ItSeixfPMxodpfaRpp2QQsy1N2JAVRXVKKddtlJiRopqbY1pE1GVZby6Rucj9fYu6sgVTLd3KEUh1QqxyKqVHg3x0aOsxg3XsMMVZrMpyhQMhysoA2WxzmxvmyoO6IKlGm5x5uJ9KLGslFsM9TGGdouxWaHSBVYbCgUDHVkpFVWxglG7WG0Z2oqgWpwJFFrwNhMwVEKpIhMUIRfSSwltBONsT4g1RhcYk83Koa/6mUKgbWs6r1Ap4ZSmGK5SDteycZqYvRxtTeorD2tbZV+QJCR5UvBAJMWW0DVE76nne+ztXaKd1kjKlYQTQtdFvE9IzNNJqyxFUbC6usqpk8coq1xJFrGovv6O9EZiQsx1fKTrSXyf9qtBSUcKHSFEmral9Y4uCU2ENgi2CBw9tsLIJ6IpmDlHUmN2/LXYoiBJXl9MJKENRA1ObC7QJoEoidK4/N5aaOYNtd8FEsYNAJuX/nC5ArezBlu6XMBNarpmTuiyV2jhR1vIDSIKUswm8z5jTxnNqWtvoChXuHTPR9nY3MIVBUYvEhSyLStIVsNSDJA8KQQ6H2jqNle8VjnMl3xH1wWa+ZyqLFlb36LSgKnAR3xX00z3qCfnaefbFIWha2bMu0sUzqDLkkndcn77Ig9sX+LSbEpcVMFd/jctlYPcv+8P2P1dIQeG/1wnZNHfLejFQjXuGY/0oW+1r1wsBvnF4B4PHHMRAViuknMgQvMwHCAHi+9DFmRJZUVGH/AM9gaEXv3uj98zkMUqxAeLwi2OrPKQcGDrUvP5NKugPMoJyshl42PTRVLU1FOFhJb1VcOwcjhnGa0I072IVgprFBghRqgbTRdyvNBYcAWkYGnabE5LKWU5TzTGWMYrK5RVyfr6OikFLl48T93MmdWJ0+fp1RfL6voqm1sbXDi3S9Nt96GdXAQuN74DBqY+JrfMWRdFCMJt9xged2ODRnCF4ux5zbveE9me5JRd6WObMeX1U8rKUWiNGxpuvA7KAs7tGGaNsD4SnvFEzV33RJSD7Wng4kRYH+ZCHiEpfBRGWtgc5YJ2Udqs5gRNGICUeU2TqMAYx+5uL9hJoio0qITRgi36MvpWL8vaJ1GZlC0arskxHkGh+1lpjHkWoNVinaLEQndeNO0uCOe3PR4IFmJf8yXomoY6V9P1GloIuwbNANSQTixlMWZndYMHtjao1teptjYZH9lifHSNwdqIlfUxpdsiFoIeBNSkISpLO5+wd26X0fpRtsYbaKsxJEQco9EWKTUURUHT7GILR9c1aBGGa1tIN0c5odnbYz7fo9AriM4LR+omk7NgIBVCCLljMlYRrcIUA9ZOHKcNNaoo0c5SDCpiyiXn3aDgyKnHsX3hPubtnPFoyGB1jcI6Uswp3sPBEaZzT2uF0HjCvKVwAy7gMBubbK06qpUhZe1pu5yZMrBDzMouW8dPMjzWEHyTMxDEM95a5YIbUY5GtBfPYMoCVQSq0tC2ETUy2GpINVrDrQ4IbYeva8Q4jslVHFl7HKkb4eyAgVuhYsBAF1glVApKo3DWYnWJ5p5s7laCNR2rFpwbMreGTrVEsvkbMaQAJkQUBq1VLpIlEURjbUnhSozTBG8QibmYmjSgIk5ZxuN1hivrFKMx2lm8b/FdzuDwKWG0IypFVJqQUs6Ucy1KWrpmTttkj8J8d4eu7VBSYKtcSE9SRJmA1gVF8KSUqMoS6zSr6ytsraxibQmxRZdDtHQQI7lMoUW6GlGaJJpY7xKbDk2ufptEE0JWD+ou0MmQGBuarqPuWsYrlsHYMWg1xFz2f6cdoefHsabozcR97SFUX/E2KyEx5sX8tM3kwlqD9x31/FImhsYBKldj1UBQ/eKMJVoLKtV0bUsMCtOX0s00Y+HOyJPAdDD2oDWlK5AQsEYxHo8pipwanGJu97muZcpEM3li7EipI8YOJJBSJIkiqYKAEHzDbDahNAans4lX2Q5JQts07F26wGT3LCm2KIZ0bUM922Ea52AL6mTYmTecn9dMm4bFgqaXDceXKQKL+IjaVxHkwD593GWfQGQCAPRrsOVQWTZ59yEf2SchPORMl/8uSy6TJ3j7n1mEkj7hBw8wnYMhpUX4Z7+k/b55dnmnB6/pYeTn4In2T/KXWX/nIB7VBAUim5slnY9MJx2tV0RRzBshpQ6jhfWhY6XSuWMxEKNj2qWcZiaQksH0L2VoFULEWN2XEoYoklcxto7dvR2qqmB9Y4Mk0J45Q+s7kghdgsKNOH7iKo6dOMawPMddIbC7uwOi99tHb3DK8cY+bglIgqigjsKf3hGZtoErjxti0tx5f+DMRUXoV+rVOtdxkUUZfu+hcDhjAc+VVx9nr5tx8dJFLk4dA5N4wnXwpOs1Xas5tx258rjiCTdq3vEnge1dISTNtM7Xc90VmpPHE9YkPnqf4o67BQYKVUS6piFFTYwKUYJxOQMgRcnVUltAJZyFqgAfIMRcMG7hSRFJvZFwoZAspM/9l3fZIfXVdiUKs+0GryCgcspwX9ExmcjEggGMsnndILWLEtsLzAO67ZJir2RYjJgWYy6Vq7jxJmq0gVlbZ3R0k9HRNVaOb7C2NsYNHOubqxTFkEkQqjqyog3iAyko7GDMbLelblqcLZjOd4GsgihTgStxJBId89gS/JzOJrTOKx8nH7OM7wN2UMJ4gE0BowA3YLixyebaGgFhMBgxqnJatI91DlvOEuvrK8z2Os6cvo+tI0dY3dwghkC4tE0IEWccF6e7pAi2WKUYrbF93z1IWRGMI1FQjdYIBLQd5GqyY2F96wp03VKtbKFHYwbMWSsSDK6gns2JqsBojzNzhoMhyoOo7CGIukZZTekUmyc36Johs0LDrCAUBeVgFZeGDFihwGRjNhqT9ZA825YCnzraOEGpXFSu0AMGlMxomEuN8oI0CWlUXkqgJ385nVtyKMhaXFHijKYzc5C8jEX0LUiiGKxhywpbjTDW5fTL6CHmwU4LKFPmLBilCDHQNvN+MGxpu4ZmOqGebtM2Hc4McOUQVw5wpUMDgUQc5aUfgs/Lp1un2do6wmA0zKGSxFJdFGWga4CUM4ishVD3NU8Ws25D8C0hQhc0QfJSA0ksddMwbz1XnFQonYgqh4TQikYdgWIVtCF2Pmcnshg+BPpMntB2GJ1Jh6BwhaZtZ7TtBGcqrC7zhIIs0IFC9YXOlEpAl0NiDPvMyLxsRJ6YJRZTbCX5nVUITdNy+t57me/VpOAZjse5knRMHExnzf9PKIlI7PqViD2KmFXIFPBJCGJAlfm8nWf70kVC1zIaruJcQTPZY/vcA0xnuyhr0ZLXCdqrp/j5JYxxpGqdThmSGxCZLEMf+1jEsKX3zR0IbCwNGIs6ImpJIhYkR0Qvi5klFonGkk3Ji+DKoi/sv6J9ArF4HurANrWI8SyhFl0oHNwr/664rC79MoAj+2Gjg8GgfKx8/H0vC8sQ1ENJysFti/DPpxfgeZQTlOlcGNqO1RXNuDLMGs1snui6CFicFdS8r3Ba5ge6XUeaLi9Gpo2hGigKq0lBwCWs7WP5KaGiEBRIjNT1HOeyo91YiysqVte2MLM96tmc5D3OFBw7cpwrj59gdbDKfN7QNC0p1bBf1iYXuVnS6/y/pIAkKC3sNfDBO4XbPh4RBd5oDP0Kwn1jtyZXuM1mXEUrHd2O57bbhBtPTDg6bDGA+MhdZxNHNg1XHV+llau49+zHuP9MzUZbsLsHTZfrmhgF1xzXPPMpiqIE7zWnThqOrSk+fGfHbCoYK4SQF3wbrWWFKfocYouxN/r1xe5EBGcVzqblekOx9/M4Y9ACXqRfejx/P8tCbgcjo4sXuvet1AI+SV51VGCuQn4OWmERRKtsYIwBDbg2UNg5Q6cJA00VLS6VSKzQu0O6+4dMzZhWRrR2hfGRTdaPHWX15BajY0co1o9wZiKMhiWbKxbV5QHMui3mnUKHhiC5Y6ybOcOi7T0MLSiDHTpS6wlWY9EoKuzKmNTNUFrQpkSVBW61om1q9NBhhpqNKzZJCmzhcFWBlgG+2UOFDvGaOiaq8Roxas6cPcOVJx1luc60Kzh79hLHjx1jPt0mtLuslJs0sxotHm0q1lZX2N6tsQ5821HqElTHYGjYm+2hyorkFJUzEC2d0ayfuh5tSmbbZ5lv303XPIjWgXI8oK13SLKba2eMRnlBNpWoRg5pDAmDyIBCjxmZdUwaYYPKadWi8uKdESQIEi1t6JiFXaxucAJjKXL7MrnQYUwRvMqKggZn8rowIQZCSFhlsTZ7jFAJUiIEj+9qUugodYEZ6ezLkYSKgdSnnfqmJvmEtcOcpuwcWIvvAtPZhLoWYmzouppmOmNWzzBiUIMBlpTTnaPC2lzq3hpLUoLtSkI7xznH0a2jVGUJsds32iqLRE/qGpLkuitGIhICMdpeZUmk2BBjk6vzRvKz9TO6ZGlDVjPHY42XbAbWTcNkHrnUbqFs1Ye8BNWny2WyoVHaICRC26CVwRiL0okkibbZxYc5o+oY9MsEaBPwokj9voUtcIS83AgGsH3WRvZcKQSlF1l7WSlarAEUYqJtO+bzKauDksFwhO7ru2itszqmdT95WWTl+Bxyi57oQ29u9nQx0q8hjnIFXZwyaVq67gL1bIfSWiaTKXuzGdMu0XnBhQadPPM60NShL7SXaLTL2sEiNMW+GJFJxv6Q+1BPhnqIunJ5FEgvM22ULAhO6iMoahnCP/DxfNzL+ceSNC1pxwGfyJJUIPspw+ryYy2P0xOPxfENeql47ZMNlur/Q0nQwfsXDj6Lg3fxGPOglEXJZLdGSeTYEZtfXp/9FU0njMeRJIkVZ1Ba5fTDKKSY148YjDRaIrHNS6oPRglryWWkY17zglrThp7Fq1zu+eLFi4go1jfWc82EKMxDg3UKYw22GHLs+Aantre5ePES3oes0ISwbOhaLeTOrC4svjYJELVQoPAKxOrcebPQWhb76j5VORFQaB8xAn/2cWjDHlee0KTouOfBxKTWHN1I1Fd3DFfOsT7y+KS472yuqIiGeaOpKrjqlGJlbSUvgjhrGI0SVxxznDnvqAa5bL4zwrwRulot442F1TSS8CnmFG4lfRZArtCrdZYtMbmTKskLC/oo+CCEmAvqpbjvfFf9s1mYa5XOlQilXzPHCCitc8YQAIakdI4VJ90PTOTVnol0ESatopCEc5ZiaBhUMB4WlEXF0FiGvsJeqpifK9n94BrzMKYr1xkeP87GqWNcee0xyvGQzg1wayu4qkJ5wYnG2QEexTyETHhDyNdZlIjqcNUwVzpVieQ9alBgZIjCkIyiOnGU1eEY384YrJVoJQxXBiijkdhSDBzajqi3z9G1LYP1TerdGSTDxd3AfH43N15/Ax0Ftz84Y7J3D7sXLqKVxpptipFmMFznzIUJXe2x5Qr47KfQfopNlknQbK1YhkVB6KZE6TAOxOksw5dryMoazl6FzEtC+wCuEDSeYVUQUkTrBi8dld6gi3sk6zClAwoMeVG10mjU1KCUQ0VyBxpAQoIITaq5lC4xLGGUDEVrcLGiBCod8OQslrxQpsW4nHkkMRElYdFY49B9ga4UA75rmddzrAjVoCRJxPuWrp0SQ0Pwc+r5lNg0ObxYFpSuRLsqD6Khxcc8w8+hoDaHz0LAG4iqpo1CqidYXVANssnYuL7KrSS66NlaO8bm+ipWJ8QntDUQ5ogtyBQ9l+4nWUi2z9YAXVaI94S2JgRDjDn8E/pCcxPvmbee1QGsDCs6JXntoWnNpT3FpNvElbn+SYz9ejJK5XfT9OEdSYQYKIsSpTVlCdNpy7zZyWEIN8gTJQ3WCp2yGPrwnLUUrs7LW8SctWgxKNGLoEMepLVCxRz+ld5nYYzBx0TXtVQbQ6pqiEYhfeFMrXoz6WL6kkJe0iBmX1EMnuBz9lWIKpM9Y7MnCUP0UybNLru75zEpkLDMpWAPx27jccFTSEeKink01F2g8TtMdMFuPc/ZTkrtqx/7HfFluLyUwlIfhwXJOahSHPic6jdosr0ALic8i2DQwVL2+XP5v70AndvtkgR8gvMuzwf7HpqlrMMiz2iRWZrD7AfGnQNh94NY1LQ6mIG5vAbZJy2PqSyerXXQAboO9vYiq2uGJIq9SSApRdsopIRpE6mSYWVsWF/PBZPyV5fTbysHVZmNp7HLVQZFQNl++XCrcIWlqiqUUqytrbO5tclovMK5CxewBu6b7dB2HZP5hOFKxXi8wrFLWxw9eoTJZIr3ixL7AP26DIt8dvLP2XWtUJJyGXalIOWqhc7YXj7NX3CMaUmIUy/7IokmCn9+L9x+mix3q4TG0Engf314xpGNWc7YqRXntyODoQavaJssH1otbF7xRcyY8eCFd4N0dL4jxQLfQduCKIOPMJ1HSpcVEqezrB8kkXovjVLZi+IjmEhWUXr2vTSWpXxOYxSF0/gAMeY6EoIcYPmKZKpcXl4kr+2jLGhLYQx59VGFLRTKaiQkYggU1jAqhcL5vNaJgPYgaPZmgd06MWwCg1mDsZquiYyqgrWRwzjDUFW42hDvGLNz3yazD6xm0+TaBjNfMg8K1oasXXOCE9ce5ciRU4ysReYzTKhR3S46zTCqg35GnZRQz/ZQqkYNS9xgDTeqwCjWThxB6U2MTpSDAlEtWrXE2LG357GFQpcV9c42kwfuY/PoKS5cvMC5C3s00wlra0eZ7cL5C1MuXDDsPfAg47Li8TdtcnG+R+Qo9fxBVoZH2d2dMRwp2m7CYFTQNf1KrNqwk4SV1Q2Gx0+gPPiYGI8qqjhl3m3jdI1bq/DNMZrpabq2Bukoh2O8zLKoLzNQFpxHvEWnAlNOSY0ldkMKSvAR6XT2a8SIxDyTTKajLifEglzMrYPK50J12D4eHjVW8vpXzijCYmaYcpFFpfL6ND50tN7Teo8EQTuHtXl9megDzXQPRPAhLySoUbhiSFGVFGUB2vYEJ9DWNXUzJQQPYjBKoZTDh8S82WbWdky7mnE5YHPzKGtrWwyqIdYYYmjxXc2RjS0Gro90WIcyBpWaHMKRANogPi+sJykRdQVksruoySTaEaXJ/gwBH6FpE74VrjwxYHU4Yk+1TGKkqWu2p1t4t46zlhBzWX3VF6vQOmGVwyhFFxJIwphcG6gsNBcenFDXuzn93hQondfCRgsJ04fAs8m3slnNDB1ITNnDpCGvHm8Qlfq1y/ZVUkUmcDEJwXvKcsCgLPvaMpmc5AkLy2cCqS9O1xKDz+pJ53MGYXJgc5G+pByUFmUdiCYElev3uIqkR3iVaMOMvWaGinmV8C4k5iGyO58yl2xYXfbNBwcgBcuCaQe30YdeHupP6UPZ6UC3v6AFixCR6knK8lP7EsllpGix6vt++CvLH7L4MfUhlcWzXqQPHYyzLOJGiyFokWW5CMvIguDAomDm5bnGB254QcWW/1lcp15+zwdDRn9ZPKoJSgodmxtQ14bpNBLwDAcVm+tC3QltkwdeNVDEecR3QjFQVGX+TNd5ykEON3i/X4FSa41zCmUVqc3vl0b1K542uK1NTpw4wdXXXMv4nnsQH3jwvruIvmN9vMIVJ44jaK666mouXdzl/PmLNE2N1pJlPDTOaXwIfcywj/GILFtkWqTlxX5xtkU9AHJmntZ9BFSZXBZfVL9SqmSJOeXFC1E5c6mZKu57UNidalYGhiiehMLHhM2lDmhbwwOn4ezd78XYVYZOkYLjY/ck7nrQk0yCoFH9fQzHFt9FvEDshLwOt2CsxjqNEUPXBWbzrIw4lxhWKledJad4IyqneUs2DBvdd10q+4lksZCZVsigIIZMHrOBCJRRSOFI4ogx1zoojCNZRT2Z51h6kct3V33V053JHF0YxqMS8YGBA1KulbM7FyaThuk0MqoSZTXBOMf6+h6uOE9KlrY2+AsVZSgogkbqMbt3Oj66rUlHrmR09UmuvOkqrrn6GMe2rmFgc2XgNswppKPb20GqMUpXaEpEa8xohXJsMS5ngJSDIWVl82J0bY3EDomRdu5RRYlHc/HSJdxgjaYJNBPPmXNzzp6rue+BCzx4/hI77ZD5ucTA7GGHgSff8kQ+evclXLXK+e1LHD95Mi9Db4XdC9vECMeOH2NtUNA2u5QVbF86g9OOwiqGgwE7O3OirxG/zfb8IkZFUn2Brm1QumB9fJQQPO38NKmdQYoYcSRjMcWQGGYUrCE+QOiQVoPvxWTRmZijGK9usLl6nEl3jlndEVtoTSKl7H/SYtHJoBVYk2f0LNLVIa8uTq5d1LUt3kesLtBDGA+GrIxWKQcDsDoPGP0HrSkz4SkGuWy7NgSJ+DBnPp8wmzQE73Fa4wq7SC0hhTld2zGZTpk2HWmkKco5pSuzcZlESpHhcMTJE1fkEvJK0OjsP9EOZQvwTU5lTiBGY1DQV4NOvoPUIH2dJetKUpqTENoIXYgoA1cdXWelGlD7RIdiHhOzdBztVlHaEhqfDbJaUMogBBIahSaEDq2kX509l1mYTSY07S7GFBhTkUM3ObMuRYNxGuMcVitc4VES6VqfyUU/YOVx7cAAn2dWiMphnoj0YbiOQVVRlEU/m98nMVmWCagUQPLSFTFKJjYp17WiV1W1smAKUAVJ9cucjCxKVRA7knakpNHS4LrArG7Ym3uCb4gp0QZoJR4gBfvhi33dggMhk8w6ErmA4D7yDjlrh2UyBNCH6BUova8MsSzmi0i2Fu+HRvIYcbAI2v6Z5PKaKwtCoxRxadLtxxjVk6eHEIaHml5FFtd+YL+HhmgWpSJYrC20CG/tX9flOtBjKMQzb3KxMMi1OOZTIfiW4UhTVYq5S+xOsyqirWZ3kjBNYpHuVZQKq3PYYrHqq1KC7VPajDOEqDE6EmMgBE1bz2nmMyY7OwxuKjh5xQma2Yw/H1aExlNPp4SuY33rGMNqzHQy58KFS0wmE+q6zmQjZhnQGosQkV4ZES/EBVPN/fWSs0hSOTyy8KIsMoL6mgJaxX1iHHVm4YVCAgiRLsH2JM+0osCgEEYDCJLDMJ3LSsxHH4CVPz3HxvoFOq84c85y8WLHykghRlFZRWVzI5+2lrMXc2l5TC/fJkNqyfK1yd4T60CMUFiFc+Bsvh+NwiCIzS9rSIkgWd2AhTcl35OgCGKIGCSFTJQw5JTTXN/GLNYQ0XlmqosKbJ7FFTbXmPE+EhKEKQycMLAai4B1iA5snhzT+pArWvrIvEloOvbmLeXAUhbk82KJOhGVIswsKhpuOr6CXas5e/+9/J///W7e4R2bJ05y41Mfz3U3neLUlcdYGW/x8XtmHNu6gbFz6BjQdYNyClMWJJ0YDCu0s7jBAIXGGGjrHer5NhfOn2e8NqQNmjooPvbRj7K1uYUPHWfOb3P6wQdY31jhqq0RszOJ655wM/fc/jHe/cEzRIErr3oSd975MU6ddBxZqblwcUYnMF4d09Qzoq/Zm8zACucuXGJlOGR7eg5ix/3338vmkU00MwY2sjfZYX19ldHqBnVznmKwxaXzd+GKklG5Sb19P0rNUHpAoVcoKo3DoqcRZh5pDbEzqJgN65KzUpEkDAfrXDl+CjvtA+zs3UeXZnTMUK1DxQIbDSYajDJYbTNjD3nQMtrkGWby+GZO6DzOWDbXj1I6x6AqqYoSXbgcZ08xp/ZGAa1wy7CCEKKn8y3tfEo9qwne50HIOqyrgEjQuWCbVpaqHFCUAwaDMcNyCMrkEERKJPGcOHI9mxtjrK3QaY5yQ1TKKxarXo1JoqBYzUXLiEiYo3SVSWpoIMZ+QUpF6wPzrqWLmnnTMnCK4xuraGuwcY51BbZYwZsrsW6AVjnkhWS/m1YJhc3eMQUhdhhjc+VjlQe9erqH73ZxdoAzrg8LxX6OkLPvnLGZ2KiWGBraOhOUxQwc8mx+MbNXRh0Y4RXrmxvMu6xUucL231/qJ1wpL+oYPDF0y/AO/XpmixBYFpcsWju0KdDakZQhqZwEgLXIqIAQSAmijxincNajbU3UlpmonB21MPP28y7Ugiyp/YF+IYksQyCL7B3ZH8iVYsFJHiIuAPtqxcJuu08a9r0blxdA65WStKz/u39uFueQyz67NKoePLnKn/mklpClQnN5KOrh+y326VWgfoKg1P71XU6C/qKDPRyPaoLiW0G0yR4HoyBAioH5VFNVhmpoUEZo50Lj8+w+hZwGa10mBTHsr0ORjZ2pXzPDEBqPb7NM5aqC8cqY+XSKpIBJnovnTuOqMWtrq2ysrXN2fobTD5wmxMhoPKata644eQU33/xEZrMZd9xxB13wFFYRk8dZSJKwRlEUiq6D2SyvALwfzulfgoXEiWBSNtXJIiO+l9GyopBT7XxQWN1ilM4zMJVd9LM6pyevrsDqCijRxFZYGSRKA3uN8Of3C/p0omvyrCwEYVQZUmsICA25Gq5zwtq4YGeey7fTZTXIaLKU2uq+qnLKYTKX4935+5Lsw+nNs6pP4/Q+r6cSoqINPTfvZUqvyBK45BVCjbGUxQApHdHlfTW5IJ5SinI8IrRzTKGwA8PK+hBtEpzfYWfbowvNytoApyJKGabTHAYotCUBXllyqamGtom0IXfivvNY3WFUltu9T7Qzz9RdpJqcZ220yucftXz8vpr7P/hn/PEH/pg/rtZYueIkV15/NVdee5yUhJNXnUStbxHbiNcG7XINnNREhsbRNf0yCMmCWsEWFqVn3HXbvRy74gRC4NLelOADPnQYpfjA+97P333RC7lm1nL/2btw4rnxmjEf/uBp3vEHH+JLnztideC59upT/OkH/pS61hw5cYqksh+hbmvm7VlWN9ZxtuP+B84R2o7NzTUuXTrP0a0VUltzZu8ixmlIMJtETKy5+MCHGBSaWJYkU6Ik4Kp1DIKpKnRpMSEvPREjpC5gVTZIiuwXjUpEQFHYDY65dVbcMXbc/Uz2HqSbtuiZQJv9DUbnwRWlSbHLxlKTq46KQNvVpOgZVAXjwZjhaAVnbV5ITUGI/ZoxEhHJCp5ILlTW+gbfNvjO08w7YpvL5Sf6YoMS+/auqYqc2VINB9jeRG9tNokG35FCLmdw5RVXMLAahc/lWhT53VR5sUB0XrvGDofZFB5z7ZdcWTSgXUWK+Z5ChEnt2Z021MFQ156T6yWltbQhIpKz7FR1hGCPU7kSJKtKyuR3LnOIrNopCcSuoXRFJsg2V9du212SzCndBqJdX3k5kFT2d1ilsdahVCSEBroG3wa0suhlSe2UlyYgD155IcS0DBlsbm3SnpmQUi5rL6L6AnsxLxYYcsZOrv3S9gbZBFGyhwlFTpO2oBzaODCWbEbNGUe5tpJBSTYDaw3W2fyvzaRM9YsbLgMXB1OEl14OWYZcFt6Ry3My9yGL0P2nwKKw38Hoy8PqvMrBbfuhlZhlo8u2Xga1MMsuNIyFArQ4y74ys88gLtc9eNjW/ftb/GURgjt4pss+8emyEx7lBCUEhbKCtgIqYZ1eVkJsmohTmmEBEoQ25EeoF+GHvndo29DP8jUmaVLqwAjWCM5lFll7oW07hCkGqOuG+WzKfLrL+nDA6uoKV5w8ybkHzzKb7rK7sw0pURSOra11Lm1tcvToEe6//37iPFENK3xbc2Tdc3zDMBwYLuxEzm5rum6/XPKCNCuVFZ383RvQklUGkwmOSimXnZccKsn3CfS1BkSysz8lhTIK7zW7U6H1ipUSRkOhrDSVU7giEZKha2MOwei8roc0kapSJNF0EaJYmkmk6/06Kddkw2qNdYI3iRD7QnKAcblxxpBnUdbmlMkkQvKglOlnoWBN7DsDTejNtpmgVHgTiEogJsS43LGLRYIHrXO9iiZRKkVVGJJ2hJBoGs+ls3sUFWgig8ox88K0iayOCmwRGRYlIzUm+BZnK/bqwPaFXepp4uRVxzlycp2jW2PKStNGz87OhNAmYhfwXWT34pR2z1NPtnF0bIxhfcWwVtWEeJGd9gHOfuBDfPQPDcmtsHHFKW648Xqe8KSbOHHNFeyurOTsg5gYGlgvhMrAeGWEHWxSrFtODq+mlTuY1ntsn52zd1EzOmKY79XU0wYdPf/fH72LJz71KRxZdbzr3X9MVRiuveoq7jvt+f/+z4e47rjhz963w267QtMJZz58J+trq1SDAZi8NEFROPSgwrctKUXmsz0sHfff9T4Kq7HlCn46576d9zEqhc2tNXwzZe34LbnM+c6DkALVYIhPkcpAM3uAslBovQG+I6KRUueViZuARoOLRN0tSStKGJXHKDfHrFYn2B2epbs4hW3yIoHaZiOspFxRln3pOvkO71sgMRyuMhqtMhyNMTab5lOM4DUigRBAUiZ6KURCOyf6jrbt8jIBMVdRds7lmh8m1zkxWmGcQSeDK/5/8v7zV5Y0z+/EPo+LiHTH3nNt+a420+M5wx2Sw+WSXELSUgIISJDeCpD+NEHACpAEYUFCi6UTl2M4PbanXXV32WvquuPShXmsXvwi85xbPdzteVnYKFTVyXMyIyIzI57n93x/X1NjjMVZQWZCiAxDSwgBMhwfn/HowQNcVcuiqFhU9lBkDCsx7tEAeeuagnA6it8AHtSEohypRHIY2PaJZR8oJRNi5t7cQBI5eiw9ylqiPaHoI6wxYliXdjAG4+ds0EoTkifniDFTrFJULrNZd/TdkpLBmFq+IyWIbSoOrc2IuFhUGchhSxoGgi/iMqyVoMFFLBwYuWc7lGH0mWW5XDMMAyVFZtOpFCk5j6ZsgZw80XeEYUMYWqIfxK05JfGsihnx6DaghTiNNuxw2qwgk4iAdYqs5FxSSVjrqKyjMk6K3cIYBDhOu+amGJD14g1ttOxAlP3j2+2UN+ercqtXIgXTzVNyebMVcqtZgtkf+jbldrzG98fa7fs/VyrdvIXdSe9UQ3K+t7pV+yf+zdjJ7eftXWzLeGS1+wx2KNPNbm7E07/89rUuUJTR9H2mnhhs0SQ1asuVQqlMt81Uc41VhsqNA1gpKB3QxrDdiKW9M4boE7kYXKWZTBTWaPKYL1NbxXrd4ruB2XRGP3iurq94lO6jTObweMFb77zFT374A7brJZ/9/Kf86nd/naIVKUZOTo745ocf8OTJY7onHUPX8t79xO9+t3DnBN59K7NqHf+Pf5W4vNwxxIt4JCgh/FmnhchRpEWUSGgMTkdBtwtUE5hODX0v2RwoRcqieFFaY7RGq0xV1zhryKlj0yFeG1OZDGtTsBkqPRY7RuNsYbWMZCc3dFUZjg4c7rgQAnSD5WrlZT+VRStDXSWaOtN2RYjIU0NTi7kTGeGSZFnlygo6iUoH9r0txQ1zviA+Mdo5WQV6sQIfikFneX8Se25QFkKKmGxp5g3a9FQTjaNwvdnSd1DVlhQym75QdKAKiiF2FB2opxXGaeqq4tBOWV8FDt45ZXF6irYFVxt8u8HYir73hFTow4A7tDTHUwyaMmzRvkMh18DEKU6mie82meg1q2XL82c/4uM//DHf+9dT1PSQR+++xTe//S7vf/Mdjs/usJlM0aZhEjN161m0lsXsgG//1j9gMq0JQ8/l63M21xcY9yld/ymb7TM++/wJk+Yj3rp/hHOJVy83/OyLV1S2QpHYrCwH9RrvZkQM1ta4ekIojuvra46mFn+2JPorZtMpV9c9Zu4Y4ob6YIarJ5x/+YLf/N3/kh9+719B6AnDK2xTs72+YHn+jHe+9dtcvfiUo4NT6jxl+fpL7hw+IPkt1RSiMZgGbBXxvadUhVQg2i3ZDqLCEqs/0GDVjMVizmRygq+u6PQa9TrvhFrjalvIn2XkUnQl4LstWmkmriYmzxB6TKn2nJAhDHTbjq7vRD1RPDkkUuyRy1HalHVVoRspokU2m0ghSligtljnMJXF6EoKgRwIaaAkcV12leHdt97h6HAhoYTFi+FiCeSisFl4IKQghZkfwDWUEtE5kEIL2kiWE5YQMzFmuqQYfMQYkVQfzxwpDISwJtoWpR1tuINyMzAaP0iG0E6toRGfJ4wai0SxURDTO8Pl5Yp+uKJgsKpGjVOGUomEFFrGaiprMKkl+a0ElGaDUgZTxhbHDv+91eoQFAUokhKutCBbi8UCY0b+XIqkGEl+wPdb+nbDsN3guw4/9AxxEFlxypQyfj9Ky+tFnkRRlpQhaQ1JS+TIyP2wuWBNhbPVmC8kRVXJWtQwY0yH5pZHk9qpXEZl0VggSGvj1s/jY3Xrb7td7nkaI+JgduXQvni4tX929eSN4rPsHNzG/e8cYHe/Fgxyh0nKMW6XN7t4Fcbn7f+yO//C/tW/oAG6dd7jVDVeE7vCqIwL5THF+m8PnOy3r3WB4lwhDoroM5WzaJXwIeEHw3SuaLTm4kJxfKypCjJZq4wqmnab9hdfjAmrC1VdaCbCS1Epo0umcloyfoxAdc5Zhq4XQtxqxWF7yNH8gPfff5fDo2POX77kpz/9Mb/1+HPefu99jFIcLGYcHs55cP8e282W7eYVHzwqaKf48jwzmRR+41cKHzzS/PjjhNaGnfuqHnuxOcLBVHP/jiA6L17JSigjq61vva35rV+z3DtLUCzf+3PPX/4UXFVTO02Kg7gy5kzf9wxjDLpWmhAKlsLxocHqRNUotMlYV/ChYK14LRgFThfquuB7T3ZCJp6iSLPCdlAEn4CMsYrJRDFfKFZLxdBHgoKmllWxNsAYLujcbhATCXhKYtCkM8K3Qb6qRmkSCiqLT4WIlu/JIO27UR7ulKEkcVAtKNo4YIZEbSJO12wGz2bZoo2i7zO+VThbCMGjraXbZC6Lx2lNSoF+k/jiR1+wfLDk7XfuUJSj3V5DMnStZ2gD9XzGarnFWsO0rknKk01ChYRPeZReJlLscI3mrcMFH377EDc7ZnVRePJkw08+/kv+w3/3Pf5tPefOg3f54Dsf8O47j3jr0X2KqxiKwzU1h7Mpd8+OeHC24O6jd3n7vW/yzoe/yu9sOj776Mf89KPv021fcL284psfvE3afIzvC5t+YNv1XF0rptOBwwOYzg85f/WKn/7kU1xdcXL/AYfvnvHJz37E7GjGw0dvE6Pn4VvvsJg94qff/0+EtefuyZTL1x9TVOHg6CEXF8+5c3yXTERZw/r6S4JPvH51RWU3THSiW1+hyimmF/8ec1BIiE+QIpNUjy+vyWbDW3e+xXB2yNOXP8LHFlQGZXDVjPp0wqwaSHVHvNoQe0+MgiBqZFWefEcfPH3XSkJwTITgaeYHVHWDVaByoe1brrcb4iCFvtWWODqU1tpSu0pawlo4QVZZou/o261MGNpSVVNsPUE5i92ph/qINpq6djjtmE3nfOP9b9I0DaAkVdmIzb22kx1QT/YbAEoOKN1AEc6cHn2PYvTEaChFlFXOaJyr6aNhYiMTC3EYSMFTSiLpOe1wTGUn6KIocRckp/ZtCj22B2LwaKUxVibpwSc219e0wzXsHGR37QJdAMnksa5CKQN5S/RbAHIRv5WdOdvemVRJG3jn1L1bUSvtMCZRaZhOF4JyIdBsiSKt9t2Wvm3puo6h6+n7lsFHUszErBBVFWhnUMrK+9JG7CKU+KgYLa1khYYScc7inMMYh9FuL2kWie0OQ/nFGfYW9YSbwD5BO8QQ/4b0WshjSrW6RYC9caXdtYn2raMRpVE7REbdKhbKTTtmHwp9W100VjN6LFT0vvjYLXz1WCqqcRH4lVZMgZvC4ysFCKN1w55sewt+KewVQztZsyqiwtwFQ+7O42+zfe0LFDsXIzZnEiFnaqcgZ/peUdeS19P5zHxeaLuMHhU7MY8XDiOBslK4WipWZxXTRq6KttOUtmCssFZzjnRtz9DP2K5XXL1+xbSa8uj+A9559x1ev3zBq/OXfP/7f8n9B29R1xXX1xfE4Hn7rYd0bcf1ZcfJceLb3/1NPvv0U66WL7i8Ft/JHaNKxgw1QoqFd+8rfudXFGcnmsMDzedPE//DHw2sO8Vbdx3/+PcUZ0eCODy8mzg7MDx9mblaRXxWmLEoSEmBlkFtOpnhKsfx3LK6fsn5lSh0jg7FCn/RZJKXALXZDHyf2LRKWj9ZMUSFCoVpU2gaIRn3PtP10LYQgmK+gOlEqukUIcUdDCi3gdEikU5JJKE57dCVm/dPAacUjyaW10Pgsh/okwRA1jqTu0w2GrIQdWOJ6ALZaHRUOA3bGPA2UEogp0Ih4oMmFZFKNtpgXEOMkRpFTIUOUUtUizm6RK6fXnH17IKTO1OOT6bEEGmXHW2fuFpvCQlc3dDFTG0m1LOGOGypbZbMEpGMoS2YxqGd4/DOnHvvTPiV333IP9eO68stH//kBX/9/U/49I8+4k//rWV2dMKd+/d458Fd7t49Y/PgIV/8DExVcXh0wAfvPKCqDQXD0bvvUb14ReNqDtIZl9c/4Lf/7m/z8589pnKF7Kb86V9/ypAydz/8Dn/v9/4B8+mEq+WGH/7VD/js5x/x85895u7ZhD4EnHX4IfDlk5/wa7/+HY7u3OPLT37E7GBGzq85Ppxw/uJzqrKmPQ8Y9y1Kfcjs9A4PPvy7fP7zj6Bf4apjXK5wZUqMiuag0BwnNpvn6NqTUiaUFZvuS4Z4zYf3fptf+/3/Dd///Lf568++x/PXPyVmj0hsNSwm6KqmOqjIr5eoiw0ua6yeoLQi+kjXtaw2KxkPhpZNt8VNLpk0DbW1lJTYtB1t73GuYjaboY2suq2qxGTNWnRlcHZC3cxQKku+ltZkrWhsjaummGaCdW5fiOYw0LgapR11PeH0zl3unp2hrYwhRu/4DhblnPi/xEAOA1QzSmhJfSs8ESXBfGkIpJQYBjGXG7xHqUTVTFhdDdxvwCrhbRAzxRYG3zAMhxIqmgsxSbaYGLTJ5CYUEXHYNdqhjcU62GwTXbfFhy3OztC6QisthHylKDsP4FFiLLJycUvWqhoLlF0VhBRIRQvHSO1M3m9W8Xls0dV1LY+T5IPF4BmGjqHvGPqe0Ht87+k7Tz94sWzQDq0R8mgZlZdKvHvKmA4tE39mF/gHQrB1lcPZSng0mHEiljlmryK8tf1NZmPl5o97NEN+X/bDGOx4MLuSYFcAjJ/B6EmyQ5jKuGOpXaQ98qb/yc1+b8t7v9JZ2f/ipvEjJUpWt4rV/Tm9+a6kXpGTyaNz+W7vO57wbudlzJu7+Sx2nlY3ZdDfsj75ehcoE2cICEfBVYqSFD6DbQqhg22rqOqCMYW+LZwcKjZtIZpM7N2YY5MkIDBrcio0U5hXmqoutL1i08rq3emMD5m+6yhkXl9eMj+cMp3NuDx/xfHJKb/667/BD378E/phw6cff8Tnn3zIo3cfEdJA5Rynxyec3b1ktWzYrK8p7edUak2M8IOfRH78iUjuVFEYJVCkLjCpFf/otw2LWeTZ60LjMv/s9zQffWb40aeZ77yvuHtquLzSDGnAx8K9Y8NbdxVXq0jOuyTlkQ2eEkF1DD4RvGZoNT5AKYHZzPDiPOPnhYdnhqoGnQVh0aMEtNuCDxIgqDJ0XcJZRWVFWeSMYnDS/um3CusS85mkp47hsHveCmixyPeFvi8MoyOtrOR2A0TBaMW80Vx5kVvGsIM4xfFQW4FhhS8saFfSmeg0ptb4pGlcje8H8VnBEnUmeEUaCs5Kfks/FEFjVCH0BkxF1gFdpP1Vl8zm1ZbV6y2giUNkOySSBmsty/4F2z6RkqGuxehq2sDh1OCawHbSczBpBEFQjs11SyoaW1vqJtEcwK//F4/44LunfPHJmh/8yROef/FjXp//jI9/OAU74/D0mId37/Dg7XdQuuKP/6NicXLE/funfPDeI07u3me9mrB5/ZzZ7C1S7Hnv/ZpptWXx4Js8+PbvcfnyOX/y14+Z3b3k7/3e3+fsjuP/9Ot/h6ef/px/+f/+b/n86UsePTjh7GSgXy158STx1sMTPvrBD/j2d97ncN6g8Fwtz5kvZrSrjvNnTwnGcXrUoMp9chkgBrqL57ThBe88+G18amnqDndSoQ8816sfo0xEacOmPccPS4zJVFbx3umMe4ff5VfffY8//fgj/urjP+B6+URsznUh1wpzsqCeVJj5jHLRUraeHAaxcs+ZMoYlJsAXxeA7/LpC60IIPW3vKRhOj+8yq6Y00wlVEpWIYiQOapHRCvk7MwTxBjJFY1yFcQbnHFVVoZQ4r1ZVjTYKnTVVU3Pv4dvMZjMUVpKC6xqKh5xQaRClSuhJRYJDS87kHCF5DIGS02jTn0hJ44fAkAUZQtX0/pqzOxXWiKW+D5FoYbuakrK0THyQ4kU4LjtEwwKKlBI5R5pa7OmdU6zXHb1fkZJn0txBaScEUh1IIyqhtKJylTg504/3tgJdYSozTm5pdDIVT5EytgeE4J8FUSnQd9Jmq+paPvuRIOuHjqGTfJ/oA34YaNuebdfjQ8CgxYdltC8oIVFMQpkRgdYGjSKN48WYYYDShpwVRru966+0ljS/4G9ye/q+cSN7Y9shKbdbKtLKGdtb7HgYO7ShjJFjN0hFgVGuO+5J3SpF3pAB36AiSr15rrsW1O6cgP155X0hscN2doXLV9/Hmw6wuyep3Ynuqy7FLo5k/+wdsrR/wa0//y37PV/rAuX02HJ5bsV7Q0WaSSF1FqWjtCE8DL4QVc1kkgUliZmXzxVDCMymisYVEY1aOJoZapfAJrad4uoKlErMpoqYLdseTFU4OJyxWQ88f/Faeq8G/Ec/YH5wxr3793jy+ZoXL7/k3//7f83v/6N/xL17b9FvPavra6xW2GrOX3+6Ydm94GwR2Gw1P/mi8PS1rCxKGrMZtAWdqHTh+LBmsnjA8PwVT156pjMkiA+oq8TRnfeoT97ip9//Q1aVx6wK1514BGglN0nO4uqakiAYXidqI/fibFYxDJ6uzYQc8LGw7RXziUMrsX2mKOqqcHCguLqAkgpVtbvtzHi7ZKwpmIkiVHLHWSMkXzuVnuTQK0IQ3XxKZi+1U1qKwTKy/o0CYwvWCM9gmxRtENhTpchQLFEljJH3Z7XCKkWMnqwVOSrSJkHUTJyl6MLcVTgyq20gDoVYEikqQnSYRlN0Zhs9Tln5PKLZT3TTSuNc5uS05vR4MrarFM9frGn7RLNY0F1ecjSb4AcFIdFtA+vLxHMfqRvFpFJM6oipBly1pK4rqqrB1JbmcI6bGeoYKL7l+P4J/+ifvYXqz7hoE1++yLx6uuazxz/jR88/4S9//NdMqob3v/EO2+tjPvuZ48///IhJM+XXfv1d7r31AfXJIVfPX/L65z/g9OiQj7+4YPHgA379Nx/yF3/2Az76/l9QcNy7+5DvDy2/+s23+bv/6B/yR//uf+DFy9d888M7YKbcf/u7/OWf/ZCrly9YnU1oL9e8++4jrs+fcv5FSzOb8PCDh7zzrQdcn1+gzJYvH/8lm4s17Ytz7p1+QNf2YM45On0PN/cst5+wDY/3DsGrzUuEKWT318Os0nx4NuNs8Xd4cOdt/uD7/4YnT/+MrAVNyabArMbVNRxOUdcd8fWarvOUnKmUoqkqqrrCOMeQC3409wrB04ZA5SagCsZobFXhipOsm+hFHpygSpkhBPquY7W5JrQDs2aK0QarFLokSgojMqBw1mGMRWuo6oa7d06wzr0xSck8N6CiIAtp6EHVsopWhqIMKhtKFnVSUY445gQlJJsrpETvA64kTuYOXRQ+ePzgSWbC+voIo0TuHEMvBX+5aV0oJVyNEFpZ/RsriiGd6bY9MVyPqHKD1RajR4KsEqRBjCyNjL824IxiO5SxJawpShZHktAcRw7KboUiUEcBcs5470WwYK2YJKVA8gOh6xnajr7t6buBtutZbzvWXUcKkdo6JgivSJP3BQomo4xk2zBKz0f7JLQpqChCAOeq8V+H1ZWQk8tYG6hbTZ4i5pNwazreT8A36MFtFGKH5gCjgolbEz27fs5NkVEYW0xvzuVCQOXmPBhdZ8fC5YYp8iYG8tVN7d+DIPV5PBlVRt7LrSJL35zSeD+OmM+uCL1dNN1CbcYu0543s8ufkwTk/+nz++r2tS5Q6iZydJjZbsVszVbiwNp5jTOK4iQ1dvAJbbN4kCAfWlUXbCWEN2cKR4cwqyWjp20167XIYmdTmdjjFKpNpq4NTVWxpqXdbri8uqSqDeeXFzx6O3N8OOVLoxn8wOXla149f8Hh4gRNJsUB3/ccHhxxdb3kz3/UYxUMQ6GPanRaVWNQ4ZhnUQStGHrLW29ZjqaalBM/+7zw4rUiRc3HnxZ+5zc8ixNPUxdU1nzyheb8QkiGOYsUUi4Q6SEWBSEkSghYo4jB0EwqKBkfoBsUvQefEkcLsd6XMLCIqwuHd6SNU1BonQk+kfMoIzYFaxRaGYKH4CMehfayMhk8kgWiC5RIAYxVVONdkbPo/I3WqKLHCHhYRujz2Oc0GlcKQvGFHNUYHCi3afIFtKg7VCq4maZUGt3AdKowjcYvPaGTm3u9jWy3PURDUuLeG7OVFVVRuKai2EIqhuevAutt4O6Rw1kpiro+ctles9wkjhaGFAOzymCsZrLI5LXn/XeOOTqpODmdEehZHB5xcnJE3UyYTqacHE8xqtCvO9brjsePzxnaiHMVlU5847059w4Ub92PvLiKfP/jC9aXke9fPCPbmvnBAadn97l3/x5/9odPuG43PHj3Pd798EPe/s3f4wff+0N+8uOfcXzvMYSeiRq4ev2cj/7y+7y495rje/dQTy4o3nD3wV2efraiaMf9d+7y8c+fsrlekoYJV89fk4fX1EWxXW2orePRe29z+uAAY2d0ObDpKmJoiUA3BHzKvDp/zNuPjjBzT1Ytz1/8Je3wJXkQmXGOG2BCDPW+tYcSVO6wKXz7/l2ulr/P5bNPOV/9DFNpTD1FS9YCOEeeaFSt0WxhEPfZg8oxaab0OdEPA13oiIOoQ1JMZG0JQ6DvPbbqsc6CkkmzjwGTM6bIBLFtWzbrFToXaCbC84gRiKRosVZ4DEXvuv8wnc05Pligdo7PxshkoxSYmqI0JWsS0u6hBLATjK7IxYAWsqjSkZQUGT1KlyO5KAYfOJooDmc1miiZQ6UQ/Ix2e4SrBLFLMQnvY4ydUEphlEGrQgqinNLW7RSr5Ojx/mr0PREpt1agbCLRYLWWaA/jsMZLS91arleC9hChGrlhsp7OY2LuDrFJYoMwtjVyLlgzOvz6AMNAv93Sbjds1hvW6y1t27HpBtbdwHLdk2LksNE4k9CkkZScKTqhnXxvkjdhJC1YS9GUYkYhxH/rpL1jjcOMqiSdd/yZHQ/kdlWh9sXLTSEjvCJ5SXkDxdi9SnRGt3YFtxCS3cMytnPkE5MidveCnZT4Bpa4dUrsSSm7v5Yb/5Q3XWHFhdwofZOWPO5C7reRA6iE0wWKhBQlanyBuvWZlNGZnf3n9eZZqt2j2xa6v+T2tS5QYl+oNES3axloUAmFoAQpCURmq0LbFvo2o5ylmUEYDCY7cg40k0LtxJK976HrCot5oakNJEXSicWkMK8V29ZjVU9TV1TWslpuoCSmswnD4PFDx2w2pd12bNoVP/no+6zX19w9e8R8tmAxn3F5dSU2zqpiiJZQBpQt5CSVrbZF+shZoMB1r/g3/2nFl69XKFW42MKTp5nNtoBR/OzLwv/rXz3jw/eeYurCi880n3xeWG/yOBCJ94hSYxWrxCXQGUPtZNCRjCKxMy9kuiGQs2PbFqzJnB1a5jNxqB16BVphTSHGwmSiyFZaQFIliO+JqQq1y/jo8CHifaZrhWuiDeJhwG5BNwKj4/kWtVtgCbwYi+K1h2wqWbmVKKoNMsZqKFnUTQn8uHJzFIxVdLFQukRRkdnEQJYBSqeCVYWUAinITWhVkjwm7dBRkzAEVQgpUzySr1MKQ5+oUqEyirbviQFQltQVrro1hxPDsm2FwGYydaU4v9xwcOcUbRVT7XBYhquBVCcG47l4uRZkaOPxUbH0htXlFWHbgkqgE4oGqop7J4r/5p/+Ki+ev+TVVc9nT1d012seX7/myRcfM50vmLiaZ598zp/8hz/g6M4x9++d8MG3v8X68opZBe/8xnf56ScXXKxecHD3lM8+/4JPP3/MXK2Z5RX3H97jyRcXZP8pxUf0ZM4/+Ad/jx/84f+PDz/4VXoq5g8mOP+aLz55yrI74q0PD/jGNx7w8uWWy+crfvLXP+Ttuw959voLDuYNyXSsN19wuXnO8y9/wnb1jGpSo62hpqKqEmoXVFbK2POWlW9BYdQE186IT1v6SY89nGFnE+pqJh4YtaLcqZk098kLRXl6Td1lHIrWZ/oQ6TpPDEEcmMclYsqRIXZUg2WXG+NzJgwej3BaKIGhbSlDxNbiBBv9gFaKjMVkOedcOZSSJGCVE4eHxzR1teddaSPOsCQwGHLoycWhTAW2ocRuLLo7UEpURcogHk2QiyHkRMpSMPdh4N2jCU7DEDxdAp8tbT8l5EOm1gq/K+d9+wHUPiOrqEzwA9YYrBnbIakw9Cvafg2I6VnBjCiAQtQ8WtojzuCqSFUZdLH07UAKBVPl0QOk7MecPTGzjC0OlfeTcoqRujJM6oYSIn27ZXV1xeXrV5xfnLNeXrPdbliuN1xeX7PZbKm1YebqkZmaQSWKSmAzavx8jbEU61DGgTH79q9SmpAyyliUFlKtMRaFHdU3oxR4BxtwUzTIg7GMuIUe3LRcvtpqkQlf3X7ire3NlkwB9MgRgl1kCLcKDtnNiO2oEZPbWd3vCpddITX+/AZ/pchYe6MKYt/u2RUq+1NVCl3KqBzatZVu0JF9K2f3OagbA7g36q83pNS/3Pa1LlCMQaSqKTEkCyMBMwTDpou0naKaGEpW+CD+FyYXMU0yCqM9RwuYNIV2kIDBEhVNpahNwmkJsNMKGgcnR4r2VeH6uuP0tKHkxHLbk5Ln5PgAozJDt0Ehg8G27fny2RP6dosfAg8evE0pkeS3OCK6RPH4sJqQk1Sruow9yDEgS4u89sdP4emrzKRStFERo8IayQxNxfCjLzKffwnOwMYnQsp7O3zGwd6MF6MZ2ezOGnJOKF0w1pBipN8WSiw4rTAVxFxYreUCTSkxnUjLJg6KeS2md6iC3zlLJoixUIqokWT1VXBGUVfy2YYgN3XKu0JyXGGNhD0Z1MYVjE6gM6EYXg9TlF9j0aQsxDejBMI1ylBUIVIoYxto57LrnKaQGaJiuVXoHMklMZ9qhuUgkfUktLIUs0OXC10RSSZoAgL7ahKVEdlo1AY/DAxDIeIIpXA4U9ROMXEWdIWtJuiYsASuly2ffbHi4qqwaEDrHlNPqGeKxjpm05rFgUW7LQeTwjR0nLnIzx5fs9pmdJkxaTJvvf8eZXrE45cbuv5LcrfkoMq8fX9OW6Y8fXZJ3nja6YxmNsPmltwlug1MakOtPPOjO3Sl4uDOKY+f/TUvnvyUIVWEzWs6O9BWnjR0nN27h80bTu88BOPIqef59cDffXRG113z4P0HvPPo1/nzP/j3WJP59Kcf4QP81Z89YX15xZAyzeU5ByZzePg2F8vnDMOWZep5fnGBGjr88pzDg0NytSAFRd87hqhYecXESFbTNmiWbaLbtvTLFd2zJV1uUYdL6rMp05MD6tkCV03QxuEOG+bNAzhaoC5WDBct4dKTyTIRI1QEoxVN01BZJeGTFFCjZYHSxFKIoccXSGEgB0+lFVYXyOIyq42iMgpqM17DIlErKaGM5ez0CDc6tVIkmFR6QeLWmgqobovWlQRdFkVJMsEWLREcMXhCjBQMwXekKCaKQ0xEHziqLV3fsul6NkPBmwmb4RBtDrDOMgx5j2TsJg2FRWktPi8xUNUTjDVoU1gtW/p+iQ8tVXWI1kJ61Qi6qEaWrdGSguxMROtMjolhkDT5nQJPFRkP9Q6NoDCWhux6CHUtKM+kbqirhqHruLq85Pmz5zx79pTl8pxtu6bdbrlcXrFcS36Sm82EXxejuEwrsVIgZVRW2CJuw8pVaFdLPg9KFHZaM8SwlyarUTKu9ZvTohrRiZ3b7/60v8pG1QqVb79O7YuUXZtHWih6/+53sl644axIAVDeKGJ2RcMuE2dfBKF23Zr98/7G4uArb+im/IA334a0fXYyZgVvFDb7teT+3NSbLasdXrIrSN847P/CfFD8kKmtqCJyzPhBUTc12zbweimExzrIyrqpYTLRWDLGFLRV1DYxmUA/wHaj0bowmxlRAqlC8OJQW9lCyVA7KWb8NjKpG5SG9dZLMbJecnh0iC4BVaLI2VJk6APL6yt+7n/IxdUr5rMDcQQ1CqMVKUUxjVOWKL706GxHeZbk7xiAnBkChGyklzteSLpCjJcwbENBh0JWCY20R8SFUi6aUgoqF7TVMhDUUpSgxPI6x8KkMhw3E84vt/Qhjh4qik0PXVQseiUZNTsPpiKtnRBErqwNKKNkYPeQgmLodwWIEJoZCxFbCWs9RbGXJhSyhpxvEbTUrrNaqJxFmQaVPCoVckjjzWaIqVAM+JxQVmGyDJ7ovFdEpahpfeHAyRrdVIp7dxtevOpZt4qihYCYSkGTaYA+iqFdUUgRqaDUjrIttG3HrIJJLcmr15ctxhYq3RAo+NTRbyKrzcCBg55Ed30pVvH1HI2iqhKL+R3mUziZg9YZPWlIxTMUxXXXcX15zfm2YXpyh9O7Z7zqDNvVhs16zdVVS5unvOqXtFctWQViGjh0iX4zcLVdsTg+IaXEi2fPKCXz4Qfvsu0GPv7sE3pfMLnn/LMfcHTvLT589x7t5hVDH0EZ0uDpfWB59THVbEK3viIun/HDv/hTvvmdh1y93PBrv/VbHD26z8nM8uKv/oJPY03XV6w2A6okzocLDt/7Bt4rLl485mVKLJfnDNtX5OSlheE18aBCqUDUB2x84cWqMHGistv2hddXHRevnrE8f8Z6uabrO/SlIl0NcNcTzjqawymumWJcha015XROnjrKosE0hoU1HGwkB0hlUKN8eDo7pJlNcZMGaytQBRsDWSViChSfyVHcg6UdUI1Or56ca5msbSOpysaRsriq3jk8487BAbZ2Ii1WZo8gCH807vlhyoKKvZDaw4Cu5+gSCChSTPSDJxVBc4YQCDHSd4EmdzS6YbtJLLuBzkyJbsF6eYyrJnI/xnQDsStR6+04ASlGShmRSKMwFpaXWwa/pJSMtROMFpKwVYGsDUWLi6+tHM4oKhPQSNCn99LMEL6JTLuF3Tw8KvT20mO5t6xzqJJpRrO77WbNyxcv+fSLL3j69Au22xWDbxmGlrZriTEIr2g856yykGSzIMElAkmhssYph3ETVFVjdu6+MZIKmFGpJU6yVkISx88l785+XyvsGyEyNO1qi11D5lbr5qtKn12hspuk9wrdfX9lPM54iFJuH3dXyNw83u9/12P6yvF2LR29Q3HG4lDeG/sCSRyRZSzXShaNhV0hJaTaN853d87svtuR01LY4Tn707r9//1bzf8LavFoO0KDRZHQrFpwXqGNZtsmitVkXyghEZP83k1kJe+UopkWhqDZrqTNMZ1p5lNR0aSE2N9rmdhQhWkD85li1Ur7aNJUe8h22w3kcknTOBQFo0XFEnKh156iEi9eeBaLY0BjjZHckSQXgzYaWzQ+inmcMYxBUXqU42cCinokhJ4eJN6/q3CN4dkFvHyVx4tSQr8kpSvvpYR5Dw+Og123gWyEXOoMTVOTVcTowrTS3DuZcLnqiEkkidpauiGKisHDvFEwL6QgqwOxjZaLNoQivgQoUpSMHTNmb4S4I+lmMXnaNXMx+7gBrUCZ8W5XYvwWYuFynbAqM3eWSQ3BFlKIkuZsLVYpjJHqXSUhyMWoiClL0KMK5B6c1tw9bnBO02023D9UqBxxlaOkQsCy2nZQFE4rtFN0IZNiFnKi75g6qGvDqlOsOo/RhY2PhL5QVRUX6xY0VI1i5T0+j/kuSREnhaFrmR3MxL1VJ9Y9VPUBdWPZbi/xbU8MkW0bqLSh3wycd1cczSdcbtZYFK+ffErKms020A0ei2Y+E9RrOwSUMcTtwDIGVtdXoDRWwQ8323GlXDFsV4IwrTzXz75gNVE8enRGyYkQAmcH8PIVvH71GjVklD6mHWqeffGEe0eKk4dHXD3/HBM1y/OXzBcnXG0LXYbztsf0PXEGyzzDrzeozSt8cqQYUH0g+oCZOoptUOpYrlE7ZdV2fP7qmkXTUFtH23u+/PJLXj7+OdevXjB0nhyKtErWilQ8aZ3wR1vMcUVzOKeZTlB2ip7UWGdppjX2ZI299JhrD12gGIdr5kwWh7hJLeiisWiliCngjCPS45On5EJVWZytxrwYMYU01mCqBl3VaFsBosAzSvPo3n1msynKWkxO2LoCJHQox07aS3EQhUkSBCJrQTZLEFVMDIGSB7z3xGLwKdMPnj5GOh84rTJOBTYh0RVFqeZ4FrT9IfWsomRFjEFWxewmE9j5mcYwYIDKWZwqOKtoNx3DsJKkYjNBq7HtYTNF1YKOKLDW4WyhMh6jMyEXIYhjhZtQCrtGz24S302umlHJV5CsM++pqykKxWq54svnr/j4syc8fvqEYdhiVMBoaQk556grh3OWqrLUk4rKWXJQo1MuqKyxylLpSuTT1mGc2yNARke0MqMlv3BUjKywdvjGiFy8iWbcTNDq1n958zm/xLZHUXa/UPsjfuVwO2yjvNF++crpjOfwxpnfFEDcoC5fPYdy69HuRG6Qop3UmDeLlP3T1f4YglqzLzp3XJu/SZb9y25f6wJlGMDHwosL2PaR6zXMZ5qjw4qmLmxD2n94MSpWS1BZYWaZyTSjtWG7lRbHfK6ZzxQ5itW6c4WSFTmNt7KW3JGDaWEzK1xcrpiHitlUcXZ2yHLZ4X3HdJJYzOcMfo21hegThAw6UxXD9fJSIOBiaSpBMNTYPlCj02vWYzrxGNCQAaOtrFBi4t1Hmr//a4aTueLoCJqF4v/53w389KkkGQuAfDMEqSLIhVZSdFmjsEZRV078UVRhWhWUE9Rlu91yelxxOKvp+sJ6SPSxQK0JMbNthfg6BMVsommqMpo9ZUIqxKDwSQFSbDgLlRuNHVGkDDGLbX6OY9GSiwxWSmFM2d/wxhQmtZhYeVWRikLHltnckkd5L6pgkN65tqBVxDpFAHofMEaxqCVq3efIaijo657DhSMTmS8sSjuW60G8Hkpm6oyYuKWMz4KkRC+uoo0tTJ3joNFYm9EEQhSTr8tt5GJ5hUbRFTC54BMMQcwE7axiOQROteXg4ICzu29Tzw/ZblY8ff0KZysGH3j1akkYPKHPdINjGQP90OLqwnS7RqOY1Yb2ao32mYlVrPueZetRWuNToaSAtRCStCYa57CVw29eE5Lid//e3+eHP/gBy+2WlBJaJ9brFYdH3+TJky84mi84uHtGmwwffvgBf/3Dn7BsPf/r/+P/jipfM/Svscrzvf/x3/Dzn53TrgPN7Jg+Z4ZhS7ttaSqNxnLx9GcczS2lXzL4gveeyayhtzMmzV2iOyKqYw6OzujWHd31Oemzn3E8P6bWFUO35dWXn/P8sx+wWZ5TSqK2lqpyTFyFSZDWkDc9w/kGf6cj3z2gmntMLSZj7qDBTirM0YBZedR1RLcK4yY0szmmEjKb0ho93nNGOzG7yhmlJUpDWSPpvbbCugrXTLFNg60ayU4q0p6ZTGai3rFGTMKMw7oKsgdlUEFRjCRqF+NkHtQOciKHOK5MZZmSYhb0JHhiUQxDYBgi3kfu3LEUZ0FNcVVFnh7RbxakvEBZS05FEMqShIivx5X8aGoRvBdOiXJgNIOPtO2Grr9CGzcSf6XwN6bgtUNpi9UG5wxWJQwDKmdiDPihiFcNkj4tCp7yRn8gFwlGHZXBpJSJKWKtIYXI8nLJi9fnfPbsFY+fnmPwLKaGxcwyqQx15Zg0jsmkYjKtmTQ1laugMgytJpU02vqDSgpdlLTWtHjK6JTRZixGR0t8jZaCZR82ya63MtYEOwfWm8Jrr8jaoyhfmYxHhGOXyVN2bUR9C2W4NV7zi2DIHuG43ZbJ47i+a+/INCftO1Vue7/uCgzZsbT5bqNAcuQ8IkVa7UwzuUG9bhUu+xO+1R7aebvsa5U3WkA3m1Lq9qF/qe1rXaB0UVQv28Gw3oqL6PUyEUJmPgW/ysQoLPGsYIiZTSdZDPOZJQT5YOczmE8MMY6cEA1J5PmjvFagwJQLB5MMdxRfXnimM4vTFZqB2dwR2kJda95/7y3+/K8+5WqTUEmRsnioKAK2SCKE1gXnNJUzxJgoIxHKGjEJy0hRkcqOT6FIWkzHfv/XC3dPLT/9zFBfdPzTfwj/1d9zfPb/CcRwk3hQiMKbGHvBksmjsVZ6nIMPaCVk120nLPy6UkxdRcka4wyd74AK6GHM/MlKCsN+XfBRc7TQ1E6UNykp+TeKogeTR/fD0adESctIq9ENU2W0VcQoOUQpZNJYmDW14mSROT2CoDSFGoxjuW7JSTGdzom5x1UycFqVUSVTsiAdKWRc0XQ+obLY8B/ONY3OqGxZLT2FjDaZ3meysnQ+sWojnUdky8agyAQiwRTcyN1pS0GnxMLWlOw5PZ0SAHfd0/vMZZ9ZbiL0EVUMnU+Ax6dMrTXJNtx750MevvMhyRiMfcVqveF6vaEfOvqgubjsMRjWW8PFtiOFwpMvLnF5xfnrnvNWMakKD+YargsxJaZHB0yMocQWp7O4FV90uKZi2tRYq1mtO4pV/Oj7f8EwROaTyWj9Xri6uOTf/ut/z52zIwxb3n34NpvzRB6u+dYHj3j8yRMOJpHf/K3f4OrynKF9zY8+/5KfPNXkpHGrjhTXqBwEeatmtCVztxKOlVcV682SanaPYfaQPDmguf8eJSiMa9gMmUxic/WSy5/+GZfNIU4bSt/z8suPef7Zjxi6Fmcck7qmqWtcZSgqYxJED2VrqKjQMROO1viFwsymwk9xFcVOYTJDHxXUKuC8QPtOGfJuVhoXDMpIwVGURo/Xg7EWYydUkyn1ZEbVTLD1FGMtOSdyDhgKpycnHC8Wcv/FgJk4cgqYHaJpxmIkI8m7OYGOkDPaakHAfKEUTVQ1iUgqkZgKMRf6II3ek4Ma5WaS4GsqdLOgPV+AmWKtoetFMSPPlhgLYyxGGwqRFHqqqkJbi3WK6+s1IWwYwhprGpyrpKACis4UZTBa4ZyVdlgZKGlLIBF8JMSCqkFY/iNNtgiiKxyKTEpjgvQNOYMUPM45wuBZrzdcXK44v1yz7QLOwGT0urLa0NRyPU8nE5q6FsdeY8BaUpCF4dB7um2Pqz160kBT0JUmjedktMUai1FaXIiVQqmR+3dL/bIrTm62sp+Mx0dSpNzyLNnn0dzadvvJe1nwrmC5taOv/LhPamZXMMhcAKNSiJtWi7xOffVM2RHB2R1PgVJaso7YEVplX3r/ovGJOx24uoWESEWze6fy2tF8c0ekfePD2RGAf+ET+Z/fvtYFyuW6kH1ku4XOK7JWlCgusosjS907chTTrd0XHLNi8Jl+sOhScJWiaRR+SBgrq4S+l/9XVhQp9RS8TzRWHFXrGmLUtKFweHZA8p5p1XDvwQnnl0tmLvHW3RmX6xXGKsiOUhIhFiBirSWGHqUstVP7vlwso4eCMSNDX8rj8RKQgcwqFo3m7r3vsM4LfvBXf8wPf5qpraYau7oBMXhjXAnurhFjNDmDsxWVEzJnSoqciqQ+h8IQgDxQV5bpLHPv4R1Wq8Ty2QZloDaOZCNdUMQEq1b8UQ7mCmsYIW812urLCklbJRmHdqz+02h1naSVlkYTuZQLIcl7bmrNnSPFg7PC/AieJ/Gq0aqiWZxyfXXO0PXM5xrvFZebLScnC5zVtMOALgWNxvsBazQkKFkymyon8mvfJtbbQogRlQp1pUkkijIkZALIg8c5hWtqfNcTFPgQcQlqO+UieBSwKRFnM9NGM2TYdj05GWIE7z1+V/wCuTjejROyWbDxPVZnYuklIE3B4Hu69QqdDJfLwItVT+czztZ89HzDyUwRteP4uEIPHa1XbEJPnwr9dUtTFR4+OEb1PdpqDheOpnZMD894+uQJPibu3n9IbXqMWVDNj9hslpQIy22LaSq+8+0PuTh/zqcff0y73fBb/+Wvc73cEi4/4eKTP+NH/hNK/ZB/+S//kovLjm3ncZUm2CBXYVaYEnBD4PTeHYYghmSrbc9KL5jPP2BIU2bNPa79goPphKgcTa2J60x7dcX6+jO6ekqFIvcbXn35CZvlFSA5SYvplMl0dI5NhawzTTWhrqaYqkKVRHfdMWx70nyLOoyY6QTlGmyzQDWQGsPQgwlgk4FgJIhyN8hmmdwlxNJhbEVVTakmc5rpnGrSCN/FOMgRlRM6J5zR3L1zymTaYJzFIvJNSqBgyTFhlCWnAFiU8oCgqZk8cgUK4saqiUVs8QuGIXQMKEIsHNaG6bQhm4ZiJlhXEXXNtptgXSPjyhAkKXlc/csqWuTLPkRKymL1bgw5RTbX18S4IuWe2s3RI+FVj/J7kHBBbSXzRseWOCzJeIa+JycnrkhFIYGgad9+KOO4kFMZUQW5J1IK5OAxOIZ24OLyivPXlwxePGBizKSsUUqUOM4amlqQk7p20nrTBj9Ecgn4CCUPmG2HrQfspGDrjLJ5JM8JIq6LFCdmbPGMvYo9IvA3wQG3GiHjD1+FC0Y579745PYLb5GU994nIzYyBu1J0XCDgvxNVvZyamVsC41/2BUyu91+9azUjapnp775SpPpzWKrjPwTJX9JZffe1I2iZ3/6NwdV40ndkINv0Ja/bbPna12gGG15fZnpOkE9LBpdi6yz6MR0Jv4U2iiMspIqqmRSHoZM5SKV0XS9gpxplJLEXz0GaSlFVWnCIOTPxVT4Fc7AvdPA05dwvd5yMK25ulgxn1astoHVuuXsdMa95TFtO7BeDpQi3h9SnWeMkbydGERaF0IeDdFgl5OhlMLHNLZnNJaC94UffQKPHm2xJrOYgY6Fi6tEQFYkumjxBMgj7HfLIEdrRd95NI6DiaWZFiptqBtHInNx7SlMiSVxfhHYbi9xDhYHjlJEJbTtRBGE1eSsGAKstqLUsVai4VMuxDiGFhaFNsJRocjgb/TIlUGTciBGaQ1RCtMp3DuR3KGT44ypC2VVqCcNxinCtkc7zZAU7fmaUgKTieXOnQOy7+k2GzatKHByTJyeGBbTmna1hfmMmAy10RwsHMYNrNewWUdSKNTOYqzCxEgxBp/BKxi2kco4tFL4FLEOlFFs28QQoA6epq4YaOiSx4eOtpOMmJ1ddsmFFBLXq8hPPz7ng3deY2pD9hui79l2icuLa3LomM1qvnzV8sWrNVsv10QpHderLfnenAeHC7Zecb7ccH9ek1WhspaEZtt7QjC0G897H5zRqw7rZuSq4qpPVErx3r0Z9x89pD56m8dPvuRb33pAjJ7XVy05K376kx+Dj5xOLPfPKs4ff8rp/bscn93l8vyaz55+zrPLx7y8hs3G48NAVUFdaayxxBSZWcm1Ol4UGu3I0fL0ZYevZvRYlm1C9YasA/PFEcd371M5x/OrV/jtluV6TZnMmBpD9kv6zTUqiTqscpa6qanqWlZvPuKsYzY7YLZYYLQh+AHTGvRWkVqP2vbkxYA6rMkzKFUNlcZbRc4J7zuqzmC8I/tITIEUEqpIa8dpQ+0cVdPgpjPcVMi4UnjGkXEoZNHpdMrZyTFWG8kHMnqcyCQnSuVMcYakDMYWSo6UInJ5QgRtyLmQUiSlgC8ivQ4UhlToQqaLibcPrOQ06Zpc1Vg3pesaumFOPXXSTh15bSj2WUViBqZIw4BCkNUdqrpZtgxxNSI7NShLQaOUFDaqaEoW7p0xGqs7/LAlp46+9eQ0xTKitjtSpNopQ8Rpt5Q0FitAKeLrkhNV3dBtOy5eX7Jeb8aJemdKV4gFFAZrLLVzWGvk35E3lLMnJo+PEHIDucdVA64ZUM5SaYOqLWQRPuSYyGk3sxfJN9ulPf9Ct+YWglBGmh9foYSMBcTu97fbNVJU3HA2oIwLyLIvCm6M0m5aMzs05rYR2xsIz1cO/zcVJ/vn3jonpZREgnCrMNk/ef+1sfNiEUmynLwqI7pzq8i5aXXdOuYb53qj3fplt691gdI4g3YaFYVH0RhpG8SsSYOWCfhRw2qb6FqPMgYfMgZNSJpmYlDaEfooyp2xCtUKKquomkQOYI1mPmd0YtU0TaFREGLm5eWaTUlkZbhYXjGbOVZty+HigAd3j7lerllf9+LnoAw+SDS6NUZkiOLlJxP4WFGnsb+klMZqcWLUWmOVwJPf/2lEm8+oa3AOnr9WfPlqNELKCq1FcqvLLefDkUBrjYWSSCEz9BLlbmpF13bMZ5Z37tU01TGrTcezl0v67YA5sBwcLLi6XONTpLIGZzWbrtCHhNfC8bEWkVsinjK5yEogjyoFycBABvx9oSZhfxpoKs18Vjg7g7vHhYNpYToDqVsSeVgRNj25W+PIDFEmxQcPH6BKYHl5zfVyxeXlRnI8dgPvMjFER4w1M12z2Q688/Y7WOtxVy85VYWqDKyHAesMC1vRDh2+L6yHwGw2BZNBaw7mDdfXG0LSZDsl4JnNNT4Wnl/1TIJ4YPhsCLHfI2C7QcaHgLWa9Tbw6edfcjA3kDu6fsPVpuNildluNa9fr1hthedTisKnRIiCNLVPVyy3mtV6y4GONMc1i2ZKM7WY6YQXz1/x5atzFlNDNIU+QIqBzz/9iKO5RTPh+XLN7//zf8zHP33Ns59/wcn8fT78tXeZP73A6sjEHvHRT16iiqf3DRN3xnq15upyzXqt6fQB2RkWB4rr6y8kYLOIggJVWEwsi0nmzv0Zk5kjtRvOr+DLpWKwcFAF4cq0a4yB168vaAd4+Pa73Lt3l6ef/5T1+RIzm0OtUGmAXHDG4qxm0syoaydtlZRRJeGqCfPFIbPFAZpMpyHGBlUSMSjSdUtYduRVRzor5MMpqm5AO5I2dFoxmEDtE7pNpG0ilQBaUamKSVMzmU5o6hl1PcNWE4xWlCxFhDh+i9/F0ckJs0mNKgmdjUjxUiKPPX6VBU0oOYMxpKQopkKVXhYn2pKzpDIPIROLJcRATBofxPiuJDg9mJHMjGxrTD3FVlO2lxWpTNHWkkKUdgqyiNvB+2hJ6/VhQBmNriypCNcmpETfL1FqR5Ctxvs1kVQtqE4GZw1OQ+UiOWaGfkPX5304X9m1BUoe3+voE0ORdhajQ7USInBOhcZNWF+t2Sw3eC8SYKVEEeljISQFiFeJ3rWdsnyOGcg5ElKQz6zfkjpHpWc0k0A9SeRaxpuCFouKzEhOFoJySXkERG7aNcL4vGmryG9vkWNRt8bY8VW3ioDdlou8fzUi5W/UO/uiZkQulFgj7NsuSib/3eL1q9vtffxP/W7/1sZW5u54sCtGFLxRRtz8tMsYKlnmFVWEhZNvFWS79/7mccutQu1v1+T5WhcoOYtqo3aKLhViycwnNYfHDYZIHDJaOc67QIpFViQFfNasNkJadLbgXB6lfuBcklWgVqRBU09gUsvxUlFMJhIm2PmCU4UHR4nXqw2z+SG105weTKgqeH15hdU1bz08obIzHj99ztBHlB4JUzFSVRUpCpmrqae0ncc5g1IRYiZTaCoZmLQSFUhWiTYovveDhDVj0nAuDEljtWa+UMQIfW8oBJQSDkpBvElygem0huzpS6HKDc7WWGs5XwU2w5aZeykqIjVgK0vymb6sUCVgnRRspSSMzTTJMAyafojkBLuepfBopLjKSWTAe8gQheRviEqpdobDeeH+PXh4V3F2CpNGCsXaKrqsxMOlv2JoO1LOGJWxNtG2HS+/fM3JyZTZbEIu0iKxRlOLUQ5hSHy5uqCeTFCvDJPG8fjlincfHlIf3CGFwtGxZf34MT5nopoynYOqFdNc4YPieuWh0rw4XxFC4uBgyoN3HzA9t/jQ8/KLa1abnutVj7WGEMKu/b7fdre+Anyf+eSLK1atp9DjCMSouN4Unr5Ystp6Kg2Ns3gfCElg7lIySRleXlxDgXt3JqyTY/ArTJPJ28zJwZzrzYarlefV919QisE6TV1bvvmNU957732urjb8xZ/8Bad3ptx/MOdP/uRjPv7iNWdHU6om8+jRA1SZ8957d+iD4rOPHzNVWzyWZcr40LDpAtvtGh+jBHY2DqWlxdbYKTNnqMvAsFZsesUPHnd8udRU84Htsxe4Zibf5Wpgshg4sTVnKTAxomLphxWtDtjsxHRvzH0x1lDVFmMrlGJ0JS1UxlLbisoaUiqgRJpuVENQin7w+DbheoPLHpUs6QBildCmQhlFdtCbjLYy4E5Ujb4qaK+YVA1V3eCaGuesyNiJlBzFWJGMUQpra+6cnNK4Cm01ZVTtpODpQsJ3HYvpFOV3XATF0AdMNRGCY05AoiCKoFQ0OWVCzMRSCNGQsvggzQ9m5GqCnRzgmilaT1huDNrUaG3o+kjKEhC4m/RQDoUi5kQMg7hwG4MqBdeIRLob1lhdYbTdR1Aok8iMib/GYlyNMQlrvCA7PtD1kYIdbdJH4nveeTvl/dxYUgGVsGbnTjq6R+PYrlr61hPHBeEOAY4ZfFT4pIlZzORyyqSYCMqjCoQYGfxA2yniVlNUzdx5UhfJfabUowhBF3QuqKwgj2Z4Xq4juOna7NoV8Gaxse/+jNubgX9vohpv5OHc4qbcNnJ7ExHZtUjeHDtu/jsiKn+DOqaUm1bO7b8rtQtj3B1Lj22em5Jh//72raebUeuNTQkaopSkN+92shM77eXSt97/vhX0t1T0fK0LlO9+Y8KvfFBjXMN6Ewkh0AXLF89avrzs8UOimQiZq+16MgpXabSBNiSeXyRmW8O0lkyexVRhrfQhYxK5nTUCQ5Y8oipWSK8xGFAwn4gGfztsSENF11o+eO99+uEplbOcnTa8ff8Oisgnn78QUK8kIpJeCmA10osuaZy+M9bt0n1HqbDOI/sc9uFaIaOTtKLunsG/+C8t33zPkLXie99P/Os/0EJYU2msfgshRLbbwuGB5c7RFN8HzpftKBesuTOfczDNtB58BlUkzhwyi5lIunPOMJJcnVHYiaxEUoaY8lgQje6fkk4l17xSAq2ON0ZGUTs4Pco8vAsPT+HoECZVoXGaugJnM2lAoOGwoXYa58bWUILD2YJ7j95lMlGoYUPoKiwLghfCno+a5daTAVdE/rxarunWBb9a0rUb7j16QAo9jbZg4MnzS+qqIuuKkAur5YZpnTk7WfD0+ZJJrTk9PWZ53TN0nufnazrvAXEkLTGTcvrFwYcickbruFh3DLnw8qqlqMLEBk4OF/QhIiOAovXQ+iBtP6TAds5R1RUpRkKKrJOhW0eGLjBLGWUi3RAxRmFnDd06oIg8unNKVTU8fbri5cVP+dY7d3jy6QW6vM3Liy1+6Ll42jGspvzmf/FdPvnoU+7cucfHn71CFYsf1riJ4bKtWXWRl69e8O77b/PkyWMKCqUN201kUisODxrh7VQ1XR+42sCXryIvVj1JzQg+CxSPwccIbGnmC/KQGNY9XfIonYnZM3QJzwRlJdTOWUvjamnFjT5CIfTiMaIl86nkSPA9yXsoRUzGCLKCDyJzb7YVTT0lZEXnBlIdURMrSjarSUaDsdSTGfbQojeaylusrQX9VElCC3ck9tG7R2lFXdUcHhxQNRNBM3OgZMXLi3P+6qdfcH5+zj/5+7/D3eNDlK7p2y3dZstkoVFDCyhCSQTSiH4kKJFcND54hiIWBgfziuroDNUssJMZTTPDD5pVW7C2GS3d454MqUpBqySOtUpQtZwTupqAVhgnJUQftoTQUrkZWksxgxbjx6gsGoMxBmccRge06slF3GiHHnaOswVJwN1l8dygKnk0iMz4oEdk2uK0QytD3w54H/Yr892WUiGERO8zPsjPYYgkHQnjjD4MA4P3DAMkX+FKIHaR2EZiG0iVeFRlnckhUkIe2zpyf+3Hp/2xZb9S4L3J1bh9b/+ChPerFQyiBJXvYlyoFPVGkbI/HEoWIiWjxlbZzVHH443dwDeLiHEJ9DcUPW8cYyf74YZSe7s18+Zzb+mBbnEi9wjSrjgZS23hpYwo0K19/e1wk5vta12gfPqsp11HTs9mHB1VUFo214GYM7aasmnX+E3LfD6TFVHRWG0IwQuKUQyphdU20dSFmEEbS50sRgUoSNqtzVgLziliNLQehpBoakPtMkdWo03Ah0IKPZvtlqODQ4xJVBbqyvO7v3kfo3o+e7yiKAs5EIMXuFVlCgPGyIpMJRlslRLWPbvkyzG0r4wQoB77g3UF//t/ovmnv2t5fq7BBP4P/0LxxXP46ONdanARQtt4VfVd4FoPVEZxejRBKQhDxxAj2GN+7YND2k3L5XVg3a6JKYBVhBBoWwVaE1OiHzIxpfEbkRshjb3lfQ91hAMlU0JyG7QqHM40Z8eF+3fg7inMJ1nk3QgPyLmCc9JdyTmz3Yh1fN0Yjk/vYJTi7KSmlDUXTy95572HzIeG9WrNat3hQ2I2WzCdOLbdlnazJfQ9h/OaZuoYUuTuwxMKEd97FrOKy6uet48qtn3hshXOx1uPTuh9YN3vpF2Oi+WW8voSUzwpSKGZUhYYN6e/ceASmV0heDH3W5fCduupGktz1AgRMEZUiRzOLUMQJVBhB/tKInQIMumootl2AWsiIYq9/8RkfIocziYcnZyw7S54eFaTfcc2DBzMK15fr9n4Ax6884BPP79i6Ae+8eFDtHGQt0Tf8e1v3efq8hq6lq7tCKmwzIdcrz29T/T9wI9+8BE5J6bTmpQzTSMr58PDe6Q4sG43HDY1q2XkxcVAsRMOJodcL7eUoWBzYbluqaYz8mrDJlRE1fDhcRx9LipKiMTgsUVMo4wxGCfS0JwzMQSi91gsumRSifRdpO9b4jBIK0NBKoGQA5SMUeIkayLktUYlSJXHHBXUTJMajTIO5TLJGKga1FShBo2KjohckymHvdQTJcV4yorZdMpsMpqCkRm2HUkN/PBnT7koJ/zkasXdnz3m6Le/Q06ZuO2wTiTOMUhxCRofesnViZkQPLFk+mEg5Uwoijv37jO58whTzamahspa1m2gbRPVxAkRNY9W8mNKXjEKg5hSxjCgS0Ybcb61DpbrgWFYUbJHm2NxWWV0eNYalMYocNYKiqJ6FJ7gvZDBfQ04GJVKOWdKiuz4NymL2kyaA3pEtuUjtFpjiib5hI8yhojrtSGNrTEfoR8S/RAZvGNSy2eeMug8JqancbEUEjlm0pBJ24BvAsZFjIJkxs+095RQIBR0lPYFt3gzOyThP+eAejvvZvcYpHh4kwy7Q0duNY+0FLbj0HnTxhGcC/HTKfvWym38puSb5+4bTWp8VnmzSbOXTO8x3HHi2Gfo3GpRjZdzHpVMxcgv9L5Qk+PpkRuk9kjLiJKwc9x98zMTd9o338Uvs32tC5Q//8hTiud4e82kghB7Nq0XkmyqhN0fE6v1BqUMWssEK9bGhVQU3ouz4RCVMMMbRUmRw4Ws9pWymBKxGlLSdF1myApXKYxKUGS9MJ8YUmVAZ548fsL9+/c4u3OINpEUAirDvbMa9CGPH1+B1XgPIUWcFbIZOWOdod8VwwmqRngz3ku+TU6alMbgw2JIKTGp4b37C/pwlx9/9oKX54lf/3bheGagRIwxIyQnUK3V4lsyDAP1bOfcKF4Y86lhvVryp395iXWRqhIb8HYb2HQZrBZeSQhoCrWRNkwqGh/GPq66cUzMWVo5WkmhVIokIJ8ewqMzuHucODxQzKbilSIqIHCuYJ1Ioo3ZwaZpVOM4ri+vOVjMGbwlthtOTo64uF6zaSN37p6hrGa1Hmi3LUobZtMGbcRky1YFUzXUTcNy2WHSikWtiF3h4dmU5Xqg7yNX7ZaplQH06YslWYHVhlgM28tryTfKmT5GMkag6FEBsu/ljj/vghpvbK7F+6ZoIeVdrTraXsLsdAGrBupmitWRQiFl+f6cE8OsogXtq2xm0lSsUkTpQs4a6wx+KJy/PMfqxD/8/d/m+9//nJwMp3cPsYtD6hqaauA7Hx7x0cctzy9W/J3f/ibdFkgD603hxaueO8dTBl9oZg0vXndcX/X0IdMPHZXV1E3FP//n/yu+972/RBvNYjJlPp/z7PGn4AyrVNNulgTd0G9bNuuXaGtwtqCLo4Qev60oqqOUjquLC66NonYV88mCIa9EZYVG71oRWlGUIsRI8hEiGCtmiyVEhtTR98OIGFhizoQwkHJCW0szaWjqKaDxvSf5hImWRleorOj9QK4jurIUY8gW/LQQq0yJPTmBzhpdLDc6uZGIXgqnx8dUVozc2qslodtwsV7x5OULHnz7LSrnuLq44vpiia0aKltwthJkASmg4uiU7L0nJsgxErKiDZnWZ4yGh+9/wPzwDOtqbCUE7svlipQtxhpCTPvFGAjqqm/xKULsBDmROHCUheXlin64BkCrGjBjyyBQlAR3GqUxo/uqUz05tPihJfYtMU9Am9FaII1t5ZsFzC7aIo+EzTg6tcUU0YiaJkc9Opre3DtqdECNMeNjoh/EUj82u0nejB+/jEMxKsKQ8EGQk7AJ+MqjzYAtheLGQsln8iBICrFQ4lhUlVtFyhuSY/XG/9/4PbdaPHu97lgwfBWV2EmEVdk1vdnJgaU8VXugQwqUX5zYf5FfMp7Prb/vz1HezOjcwk1pU36xl6Ru7U/eV3mzdbQvdhj38ebzbt55udlnUW+GJP6S29e6QNG6AJH18prJnTOclQDqvhOIV0hJsBPblxJvLjYEfkSB0hpjNCEZXp5HnIE2ag5nmYNJYmoKlVb4oEjZMZsl/JBJqowGQGPPMCe6fkW2DW17ibPHLA6mXFx0PP7yNVUz49G9CSlGHj9dgS5YLbx6cqGu5aa0VhGjuNFaCyEIdL2L7pa8D1mNK2Dw8L0f9PyjSU/RFVoFnj7zfPlaCLO5JIwBozOVlVZVUVDXNVoViIH7R9CHnm5wKC1xAP2gaH2HoVCZwmQCGD06tBZiUqQkVT6pSM6RldXLDn7cn3MRIvC0ztw/VTy6pzk5yiymiqZWVE6UQcbJ9+psYVejGwNV5Xjrnbd5+fwli4M5isjhgWMxddSzOVSW4WrFyemc1aolpkLXD3SDJ2XI20LlDMY6nIZrveJwpjhcOMzE0RfLxfma+rpnWtdcrVp0KsyOaj55/EpMnbQjRI/frAWNypBTprKWUhBfHaXeGBjehH7VHo1VamxyKcswRHltkQJoGDxUjtVlS1VJTkjOgVKiSBHHVUxdGea1tAhnswpiJBZIWWNVwWkolePf/dHPKEPknXcf0Q+B1G45evcdXr264OwgcHI44eH779G211yfn9NOF+TtlnfePuPq8oLZvMY0c15cZK6Wl1hnmM0mkCOzWcMf/sEfM5/U/M7f/Q2+/5cfMT88oa6fo8uG4BOt16wHMeLSxo2oQ6YEj2876rkjDRpfKlbF0s4stbI4Y/HKEFPAIMaKcq8aSsrEEAneS2un0mgrst3gBdUSMzJDjgFyoRqN1abzBa5pGHaKMmWpqLC+Qq1BtYo8BWYQK0g6kZUWBbLJ2NwTE0xSQ5UNOmpKMagcmU1r7hwf4ZyI/terJe12y/XynP/4x3/I8RcvGNqBd946YdsHjpqZhNVZB8MabQy52L0FedKamDJDkCwpHyDExMmdYx48fJvJdIbWBrTCD5mXrxJKTTBG07WDtHFKHlsrCaskoyKmIionJZ8bCqwtbNZrBn+NUU48nkbjMq16UFNQGqU0lXEYrZm4gTwE4tCJI26SPJysbyD+suMkaEbivuQC2SyLwVIKvu8wRWG1pUuRnSP2TQq7JiMFjdhEJHxAbBKyKDNLUaRsCFGsJGKf8D7irce7AYMlZ4XLkVJJYGAMgTSMhUqUlvous4g3Vvy3g/d2f/5KPg9vIir/WQfVN1ovNwXCm5P3m6/dFw23+yrj49t8mR1Sob5aFO3PT855Zz9zg8J89chjibGvOG4KNflr2QNNt89yx30p+yJsZB6NVv75qy/5n9m+1gWK0pqmPqQQufPgHpPJDPXsKc++fIUyhUo7PHHsdypiEGIQKaGNfJ1aC9G2cgofC+suU1WGoRjaAfwiU9cKV8ScbDqXyTsOjK6MjPKrNLLUE03V064TFxcXpDjn/GLNtpcWyd2jhm994w4FxbNnSzISDqZIWKvoeyHpKkTanIunlEzdCOdDVHkFjbwHjAKl+Q9/1TOZPOfsWHF0UPj554nn5wVrzWgMJCF/TaWZNhalDP0Q0E5DFkXF4bRmPlVoM4XYY4ymz5nzqwFbLYh9y9Qpmqmi7Yr0m40M8kM0KAwKxRDE04QRntRFko0Xc8WDO4q37xqOD4WQXFUGawvWFSaNSBlV0VRVwZqCNiNoWGDTbjm9e8LpyRHXy2uO75xwduoYlq+wfs3BvYa27blqLxn8wPHRAmU9y+WKunJoNCUmjs9OOL9a8vzCE7MjXw2k2FFi4r0zx6obaLvI+/ePuNoOhFxonCHESNg5fOYsahJnSCnThbwfz3Z5F7t1yu62FqMvjXZaUAElhEi5oTUxF5xiTBYtIqmOSQproJnU1LVl6ALTWc18YlG5MKTEfFLhTMOrq4FMRCkJlqyKYnPdElKiZMVmuST1az75yXPQSeTxB8ds1p5qMqOZn6GNIU2g7T2qWVCrhlfLNZfLLU1T8fCtBzx7+pwYEpVr8F3L9OCQz372OYvDYybHb/Ht3zrh2Q/+I9knjK4IQ0tlLXUzoR0CPmZU9sShpTo4IPYDQ9yg6wkp7dCyRImRkgrFyueqxxCykD0pRFSByjomzRRrDCknSckeETzhbghK2dgptauom4lMdn4kZiqxRCeJJ44xMLcH2KrG49mwpVUdxULSiqgCwUa2ekuTKioaHJYqFk4P7jOfTtHGklNkPm/48Q9/SCw9BcV/+qN/xzcfvU317l2aymLNqGgrioKmFE1GUNKcIjkb4Z5E2G63dENHHzLffvSA+cEBShsxJkyJ1arn1XnA1UeghDCaU9ytaqQoHr14YvDkEKjqZrRhkEyttl3i/RZlG4x1sn8K6ELSFXoM5DPOie/PpNAFkWmTCjlVYO2+pVso4k9VRGmZtSKCFKgZShQUOvgepw2VdvTkfQtDaSNF5ohOpVLwKTPEzBAKIUNMoiKkKPqRRDuERIlQPOQ+k7aJYDxFawIJNVEUiyw4Ok/0kRiDSMtL2qMZN5PNm2jJnptxu2q5hajID7v/fKX0UKLo2Yfp3UAWN4e79XDXGrqNhew6i7sCcJe5k3fdm6/OlWp3XsJ94fb+bpFii7pR5YxPv3mLu/c8nvuuCNkFFt4++1Ky7AvIjLSEfVn0y29f6wJFF7BFiJmXr1/w3/xv/wW/+7u/y3////03PHn2ZFxBQdsGsuTKoZVwJwSmEuTEOiG6xpQpRtNHGNaekBQ+GLZd4cEdzcFUYXQgRalEnS2YIoGCuhTaBE1jWDSFvniePf2c16/mDB7q2jGpMrNZw7EzTJqa08OKx0+WtF1EO2kfGaWoXZGkYqPxoYwrJFHrhCD8FLGeRjgeZLyHf/vHgfncEAZN1ypizlQu4UaEx+rxEsmFyURRWU2KGeMy2jYsNxGjFNolJi5xPNdkHCkEhuQJWrNqA+teS6vByaAkzrGZEAp5dGQU0CqjdGE6VZweKc5OCmcncHKYmU3E/dWYAipjK6hqhTEanUHpQtMYtCqYQeH9wIvnr9Da8PzLlxwcHtP3z9kuJ5wcV7iZwfdrQt+hTcGWzHa7JnvF1GlSDGhnSTnx6uVr2hBxrmLbRwxgKJwdOK62nk2XuNtYhuB5ftlS15XAwSMLXmtFXQnfovWBPOZBlcLeJVKg6fFGHVczKSMZQUqP0QDy/SmdsU6K55jFyCr4nSrqdn9YYZWhmRYOjybEkBmCpzIycV+vWgYf0UUxlEgyU8IgxY2pKp4/f8nJJNMcHfPs1TmPHj3g808vqFaZDz/8Nt///o+4f6K4d3/OeejxQbFeZVrfcr1NLJcbYsq8eP6S2azBuinz2QHeWV5dXqGWLcnOWNev+c53vsvRo6fcO9L85K9+zMl8wtYnmokj5sS2j1hboXJke33F9OCYttuwOLkHiGolBk+MXpKHtRrvAyfo50hsrI3DTSY09QRtNJkIOotkP4tsVhewrqapZrha4g3CEAm7VoNWRBI5eEKKzJopjZtT2yl1TmivKdHjq4iqCsFAVJ5QoFcBaz01luPqgNOHd8WbJQVySuiq4cNvv89f/fAjatfw9oN3+NbbD/ngvbepqwpnBHDP0YvEFSlKszLEBGHkkficGEKkHzwFzcOHD6hcRS6ZmAs5RV6+7lltDLPFrq3l99eNkFw1Wjnhm8VeCLIjx0RpWK8jfb8k5Z7KnaCUGycVWcQVpI1ktMZWooCsa0WwFVo5MoL+7BYUZFGdjYZMUmgWxNW2ZFKWQrNQ6NqW+SgZT1HGYrFIEA5bAVKRNrEYOo6xGglJbg+JlGDbBTadpxsyNhZKAiKUIZO7QjKJpIAA2WV8Goj9QBo80Q/EKH4st8kivxDaN963b7RG/rPbm8XJTStkv8cRD93xU9S+GPkqr/X2L/R4+HSrJ1NuHU6N5/imkkd8cPb7LG+2Ym5zRPZ8lHKrVYO6pUJS+0Job5c/2uiX8UTyCJ3por7KF/6lt691gdLUhdXyGmMy66uOP/oP/z3/5L/+Z/xf/y//Z/5v/+3/nc8//wJTrNyURuFjJ7K5oke5r7gK5lLGnl8BnVHFULJi2xYUhm0bWbeRt+4YbD320pTGacl6QEEI0uqYVYVKQalgeelJacXBwRxnLUeLOb3fkksFyfON945YzApPny958SqgjaYyiZwUdaXFvt2I3XZMGYompsLgR3mYAooml4RWmYjmaiPyVmtl9WytwiDcFj1e+TlDP7ScHkyYNBM27cDBNHD/2PD8qqfbFGJlWfeeeWU4mEphkiwsZuB78F6qbXQW1UOWCTgWaZ2pXGgqWMw1904UZ6eKw7lEEMxmhWmjcBU3/V4tq7Cq0mJdngvGjnmiarQfVzJJpZTZrK6ZLWpM1ZA8/OzZmn69YeE6is+cTq3wkuaWl68TKM3Z6QlXlxe0Q8a6irpyBBEkUdeKyy7hu0hjIdnCs9ftaGolBlGSBioumj4kWaHm2yuk8aYe/xHJqUEjBMCYsvjTjH3tnDPTxjFfTNh2A10XoGhKliwRBVTVWBwh6I07mJN9Gs34oDEQSVxcDfQBYixYK6Zi3id6HzloGoyBvt9ymeBsOqXvIgfTmuN5TYiZ6+vXnB5PeeuDe3z+8eciLR86+hAZSsPry4GU8uhGnOn7gF9G+q6gS2YIkV/99q/w2eNztHKsg6ZVB3z0sx9zcDBjmjuqQbPuJRQu+SDcB60wuSIOLbHL6NRDsZSc8L4nxkxVV5J26yTwUyHES10K2jgaV2GtHeWqYsqILmQk3NKgqdyEejIFU+gGT58yKWcokgYjaqAIWlPVM6p6ijMVJQaMN1Rdje0tqsmkOjGYQKcyQRVBVLTh7uI+R4fHKArby3O8T1xcXrJZnbP2AY/mO9/4gN/84H3mk4kM7Fas1tMgRYg2DcEPxFKkSMmZISRCyuI5lBSzacPZnVOUktWwVlKIvXzZkaMoh7yPxBDGuaSMY9aIaKpIDKIWUkYKvqrWfPlyRe9XUDJWVyg1IrkqUbQB5dBaAvWcrZhMNHVt8E1DNZmidE0qlRimwYiSpPEeZ0xnL9JyK5lS9D7Yb+h7ju0Rvo1sVhtBGLUezeCktZdiFG5KyaQCibFYiQVyxIfIetOz3nQMQYvXTC4QIA2BqCzS6x49SWISk8hhVPyFSEyeosKttsfYLxmhiZ1NBOyKjZvtzVbP7WHhK4VM2XFVbp5/Y0Y7Hmf/8ltji/rFlozetXLGtv9NHfOLxZNSCqP0SF6+UfCU8fhqd65qN7nI4ffS56/ub1++3izK9tEAo8xof4xdnfy3ZKF8rQuUD99RPH2c2QwK4zSXl1/yJ3/8P/KP/+sDfu93f4fl1YrVeovWlq7dYrPdk69SlsRgxWggNKIrWRWR9Jqbnl5Mmst1RqkMrjCvoXFQBYXTsqre9KBcEZRCCaE2pczMegxrrKqY1paqmRBzwpnC0G85PZxydFjTbZ+zHQLaGEqCnDRKByqnqSpIQaSCMWecHivn8dx37GnKKGUrabxY5DqxWnxdrJPWEChcZfApMreOt+7OSOPN+Y2Hc37+tKP1FusyF0OiGjSnB466KnTDwGySqawSMzY0wWdqo1CVIiSF1QUzVRxMFXdOFPfuKGbTROUUs0ZR15mqUiPpU0h6IYq0WusEZRfkBcZp4WEg3jG2MkymNbWtCTHx+MmSgzrzzoMpa2MgTnE1vHq9ZhgCGx9EYWIts9qxcRX9eou1cPfuQ168fM2y86w3kFPicFrRo/F9IsVCVRliKHgf2EGXgbQnHO/iA9gTyXarfUWlNUYLR0kKLj3ewJqupD2fonIV18uOkooUJuPEI4WAcKmk0Cl0mxWLBsLmmmwrep/wQ5L+e4z7vJ3KKkmUzgmfBqwylFIIuaKZn1DZC14+e47VhuNDBfGSV69fMTmosQYu1j3rLjP0CeMC3bZDpYKtHDFE7pzMOHv7kKvra0JyHB6d8OTxY5SeMvQdr19eM1mc0X9pMLFj6z1ZO2bTOd2QUT4IXwRFKAoXIylEQntFjhU+RPphwBpDM5mKZNfokWyd0VnQRqcV1o4FyQgjGy2RAeL/IWnDtqooRpKt/eiJpJUE/6FEeeVMRdU01JPpmGKe8UNPGjwmakw2VErjlKM3Ha609KrFW2mjnC5OJNdIS36TKgXrDC8vLvn89QbfddyZLzg6PKB2Dapk1Ei0T8WAsqSYyGhyERfpVAzed4SYGXxgiIVHd09omnpvUJZioGs9r14lnJUCwQ9hP5mbAuiCUg720uwBo4Scr5TCOsX66ophuEYM2mr0SDzVOpC1E2Wh1ljnsMYwnRZsXdNM58wPjlBuScZhxtZ3GYuIkmWRUcaMsZxEQiuFv5DAc0rU9ZR+G9isWimuxrlS0OKbKXvXWilFwlOHIRBNpO8967aj7XviUGGj+L9QyqjmiaS2By2csRIzoUTC4BmGgPeBmCJZJW53I4rMrOPPX2njjOdye/sqkvGL9YIsZHZtmT2PZSxqds+X1+7Gla+0ihRj2w7e5L3dIDv78yg3raDd73fPKG/gI+Nz1e74NxqhXTjhzuNkn1l1+4VvfAB5j6zc+MJ8Re78S2xf6wLlwTEc1Ybn54V2kDbJcvWKP/vTP+KD93+Fs9M7nF9eCeRrFalXwo3Q4xeWpQCRdE/NTumyU41oxPmzACkWzq+FJ3B8oDmaCkH0+FCjsnibNHWhrg2ozHYrRcykUmg83fY1r162HB8/4OBgRjM7IM9rlssNi4Nj/s5vBy4u1zx/GVivPdUkEwYkOAxpf2glF5R3EJORQLyc0cqMXi0SMLgH8oqUWAmFs5I/M6+EdDbEwrQ29NsBsudwbsX3RUfef2h4cu65us64qqBTZrVNaJ2ptBi1TepETNB2YOoMWeGjJmaERDkXxOTwUPxTaqcxOtM0hcWB5vBownrVilJKy93YNGL7LaRahdaFykobSG7CzGq1BhRuXmE0GBKzg1OePD/HaYtJCWsCH7634LMnUFJPdo66qWm7Fd0w7BOxvvji6Zjiq6jriqqpxOMmFrpBsktSgtb70atBFAVay8CeU6QgGUF1VTGZTllerzFaUAVGiDqOvfM8DkwpBEYfR7bdQNM0KKQNmHMayWcS6BZCpqks773/Ni++fMysMlS1JnpPYw0Hsxlbn7hebdFRMZvNsVpg8vW2JaPwvjCnUJVEbQovXz7j8GCCm9QsnMaaxOToLpuPXnL++DFlsmDba0rMTCcLLlZLUkncf/SAkhI5dcwWNTlHZtMZ59drnr94jjIVpbE0qXD16jFdpei7QqMzOVlM5WimDq4Ck6bh7N4DhsFzfr0iRU+JmfXVOX23YKICJWYm0wNm0wOqxsqgmIWboRHStXYaZfOohhBmprHjc5VCW4d1Dq13bQ9p92pV4ZxMpDFGcBW1sdTTOVUzoaDoQ08/tKRcsFYm3sZUODRVtLhk6LIhmgEzcbxzcJ9J00CKVE1DyR2bzYbPXm+I03to9Yy3Hj7k9OSA+bwhDxs2l1sm0yOKnaKzIQaPEK4HQgjEYSApRZ8hFuHYPHpwT6z5h54cE34YuLxsWV6DrRy5FELv9xELOYNWCa0qjC74kEhDwLlaLPjJDEOiXV/j/QqlqtErRY+oQSSruTi3KimqjYHFpFA3DtKMxcExqlqRqca2sxhnSncnjzOc3Hclj0XDWGAwTnymWFZXLe2mI6Y8BhyOrqW3JruclaAvo2Q5xkDK0PmBtu/ofSAHRZ3G3B1VhJsUI6WXGBRQlFTGAiUQB09KgSKjqrSlxqLgto8R8LeaZPeZTrdeJ8hD2S8iRwB5jAIY/aPYiQveLEx2z1e8uU9gvz8511stm7IzZLvBZLRSN2pqdmg8N+6/u+chkmPYSZVvipvbFdXe1+UrCM/up6/SbX7Z7WtdoKScsSZzelBYeEPnNf3gub58zfeXLe++8w0Oj4/44Y9+RDADKrfEdhAoTCt2dr9lB3mpG+4AjO2H8QtQCkJRLDtHFzJDgLbTDCkzcZnKaBZTOJgXzs9lMppNFc4UsdFPidBfc/GqJ4dTZotDjo7ucHIqyaAfvvc2H743cH3d8tOfv+DickuwjoyQUe0E6kr4IrUXrkJRGu/FwTLt6dFlPPcbGM8ohVWFxknA2NG0oHymHzJ3Dg0+ySR673BCDIWwHfjuWzVXB4Gfv+zoBmHUTx2YqlDXogryQRAjpW7M7VDQTGA2S8wmQox1Ti7cxYHi4FhAPmOhqiGNiayykit7bkZOiRQh2ZvvJ4RAzpnVes120+FqCYTbtAMxeI4PFzw4MDy46zh/tSb2geODhsvnHUezTG3BKEVlDXoceELK1FWNMoZNO+CstGK8jzhn6UPa8xScMXtYV1NonCSODDGymE755re/zZ9+78/2xa0VI1t83nloSrkot7XA5+MSkTRyT/Zkt5w5Pb3D4ANGK1bX11TWEnLi/uE9Li5e0swWTOczuqsW3RRMbMWbpvfcuXOHbd9ijRGvmhC4e1jTxsyjwwmHixkvX17S+sK8Ljz5+CM+/MYhi7M7/NmfPmdiDXcWlutlx+G8JmWZ/F6/XBNSTxsVVjsmk7E91W+pbGY6PSTFllIt2HQbFosJOvccAINR44pfpNsvn7/AVRWxb0cUUNNurgjDGU0zImyVpaklUdenjpwjFLBIsnRjxO1ULn25h0XlIwWLNdKWyApS8Ax+gKyojMNWlpA8GsOkmTCZzHCuoRhNTgNxaCnej606J+1SJT1/XaDJFVXSqFxzeHTCvcWBTDwFtG1QtcLiabdXfPLyGQeTKRawRtREk8l0zGcqlNyjiiLlKG7IOZJshWcg5kLbtgzBY63i9OiQsG0ZhsDQ94TQ8+RJpu0PmB3bUV4cRiR1nP2QzzeQ8WEgp4SdaGl5kHn55Yq+WxLjFmPnuylUJi5dKKP7bEEQK2M101mmmdYYDhkOzzD1FvToIosSJDgnbrMtpa2yK0okJgAKpihqVeO3Ad95kk6UXSYau8lVyJm7sS2N/89FwgeHkOh8pBsCqYMqBgYTGPDYHHBZLP7zkEGDyoqkIsEHQvSkkVAt6sidWu5mwn1DkVdupuGvFgk3qMlNcfHmS2+Itre3ERTZASy3jnn7xePf/yaZc7lVUJVyY0H/C+c+fh3lVien3LRhdu2mXevn1qM321u3oaGv1GwKQY3/dnjJL25f6wKl0gpswTvRvpf/P3l/FmvLlt71gr/RRsRsVreb02SebJw2Jl1QcAVVFiqkgsJXYCRE92LJDwgQlpD8gHhAQgIkLCQkxAMyLzwCEryVCt2XsoTgAV3kMthcg697p52ZJ/Ocs7u111qzi4jRfPXwjTnX2vsc2+QVL6kb0t6rmbHmjBkzYoxv/L9/IxlnHXe3twzrwOc/95SnKfNrv/zLFGtZnC3JCIf9qIgJNA5BVTWO1bYJYnFeJ6Pa+pjOGmrNVCzbUZgnQ74KVAfnK0MwQriDxeCgWrwpJ6ms8hYqzkM/zOy2z7i5vWEaZy4fnRGHFevFmhAKj9YTZwvLsxdbru82PHtxx07LWGpRCbIPEAxAIR77qXKU8zWWvoogAPDOUGfDEGC0aqz2+Bw+fg25Zq5WgTknXt8JnojB8fGriSeP4Kt94NvPKrXquTEYDUbNavbko2BNy9OxhtgJi4X+6ztNJbaNDDssDBTNQpvGnbYyGozsHFirEj9jDdaLpouWNinwoIAEpvnAnGZc8Oyfj1xcLqhlYs6O61vHb354x3uXWmwsg/D06oznrzasusBuGonWELvAnJoUMWX6rmecdOVKG6BrrSqpdC1fRAoeQzTQWzW9Wvc9827PL/7cz9HrW1QzKwtJamPNC6UUnDOE6BmnuUHpnsPhoPlLWC00jeWLX/wic5q5ub2l7yKWxHvvvstue4fzARsG+sWS56/u+NwXvszrX/gl+m6JCwP73YGXH3+Ct579fqZfDuQ0crsTlr1hdzfzhS99QU3XNgeKK9zuZ87efcTXfvFbpP0tH3zv++y3wljUW6hU4eu/+S0ssD5b4TFMhz3bux1zmjhbrzmMCWMLedqRD3vS9iVP3jtn3hiuesvKB77xYsNiueLFq2s14BIt4mpVFGSaduQ8IxIQbJO5KtdJciviagJj8aHDdRHTCkdjLd76ZjyWMdYDghhLqYY8JyiV4CI+OAW4SiEEz9AvWQwL8I45zcxpZFKilbr3RiVs22NSeDU447AWvPN84dGXWIUFpupxGOcYx9e8uLmj+iV3t5/wla98ib6LrM/P6TuPi56aKvN00FwTKUylkqphnjMFTzGWMaukWiqcrwait+zvNmzubtjubslF+NaHS6ReqUz3oPb7SIHmv2ObyRpAmked+G0AC7E37D+ZGac7as1E27XkZX2/zgnS0BNvDC56Yg+rpSf2Duscw5yo7hXC3Pw7Gn+secoKog7LBUXBTGntOkUqnRjWccBno/Efrmp7yN4rso6thVylFfS1+c9ZSs2kXFQ+nSppLuRaSLUySSaEhIiqgsRZPRViyMyklEglaaIyWVUntKLoXsJy4mLc4xBHLuCnbeePRcpDBOXt7WHbRf/ItnDFe+WfeUBrNXJPQL0vYt5GUO6f842Q5bdYqkfq3BvdmfaDlqytu8CDfcyDNhK8yRP+LOnQw5Pxf3D7ri5QLJVuqCQRDjuIwZBy4uZui40L/vN/+o88ffqU//v/7ffzH/9//5kpH0+wNK6AtnVo7Z1jJapqi9YbtI30Q9VgsKK+E9jK601htxXyE4N1htc7yFVYL3RFLGK1Kq86sQZvWHSGAzDPE69ffZ3dJrBYrjk7O2O5XLAaIn2s/MDveQ/rvsA3P/qQZ89veP0qkcaRMQlVLM6D84Iz7uQ2W8txba8+JSWrdLoLpRGkHE8uYByhc44vPoa7qeBc4bzz3GxnUjoQo2E/gbyyfP6p4/Ir8PK1MCXNXogB0lyZNRwaF6HvDbGD2AnLJSwWnuC16DBGPRZqhZyMFixWYVqD2j87V+mipVarEnDTZLuNRCvSEBRRu37aZxM7zzvvPeX2doM1QgiGb3+y4d3PP+bzF8LXvn7H41UgH6YWKNZW3kPPXAqmFY+dN0hVQz1rHN579WrwHt8GGQtEYzgLnsFbSsmYZrtvbCXPheAdkvVmTUW5QjEG9tOkhWMzgrLWEqPHuUialcw49B3b7RbnPR9//LHanDfSmfWem+3I5z/3Ja5vXnPIcPP6Dhkr3/i1rxG6nvWjp/zRP/4/8//9//y/ubt+zqKznHULjDEsw8CwWEIdeX2z5Td//ds8ebzk1z5+xgff8wGXppLGG+62ie0u8eKj15SsyEnJKu1cr3u+9IUv4KKh855vfuNbLFfnfPjRM+aUMKBOveeV17e32LLjN371OWcrz0VckPFtcsksh4Gz83OePXulUlgav2dqKAn+PgSuZCitiClFPyPv8DHoQqIRl6316unhwBFxTpoSCqgZA0QbqE6J5SQw4hhCR9d1+Ni11Xghz5pO7I5JudagAlmDq7r8tE59OxaLJVdP3sUPA1KKGr2Vyu3Naz66GfHvfD/v7hZ871feZ708Iw4DrjO6SMqFKnq9V9GxZ8qZWlUmnMUwjhNzgSkLnz/vKWnPYRy53t6wH3fMyfHqZo31AxXhMCdSztCmmGBqMzvTNvA0HTDW4aIuDoaVp5TCnDYISjIWrF7wtjbOhqIpzruWGu1YrQMh6vUZFytKHcDovSmgjtJNxWNN459IbQhKJaM+KDp8CcE6bFUjtyyZfFzitxbPcTvKacFoEWq9IlFNmYlxIKr0UiKtIVfRFVuqiLUaiSCqtpzyxFwnZiaqLZwYo9JcPB60Q+DIwzjKjN+ckx76oHzK/v7B4ycy6el535zgP0UmfVhEPNivciwUjkXNg8LpwWvqVx5AJo3324hz7e3eoyenQJ1WkB2LsNOOx87C8VVN6zjcv+4bhdvpZH1n1cp3dYHSDbpikgJDJ9gC+wl6nzlsX5PnA++/9z7DouMHfuB7ef7ilmmcyDk1OF2fJ8SgttlZzZ2ocgqwdM62itpixKrRkG3qirmymwqvN5XF0FF9x/OXhZuYqNXgnaGLWkipV4EaClGbCZsVOg9pfMHHm9fY6Hn8qGe9uEDqHX3neXwGV6sL5g/g5uYlL15mbu7UTdEFQYqhFEVOLOBCa09ZyE7j7tcLbVsZqSxDz3kfuN3t+PwTCCNsd/DkUeFsEEYc0QuXj2AcC4dtZXnm+NzTnpQSm7uMMZmLM02EnhJghWEFfadFk/WiEe8C/cKc0jhFwEeVHp/utAZDxwDO1dMKJc0K7FpbH6RlVmWhVzDWsFyqIdUnn7yk7xz4Jd/45IAzgcWY+ORZJlcIi44Xr/YMy467cSI4zXGpSQhOkZChc0yzUKxRojJNTonFNE7J0loufGTVB3BCQV1l52wwuRDwzFW9AZKpIIYiYHPBGEWSalEEa+g9Z6uOcVbDO9PeV6lwthzwznNzs8EY6IeBrut4/ux5M9Kqaq9/DoshUnPmbjOR3I5Prm/5vt/3P/EL/+k/UiXhLWx3W7ousHu558tfeMzi7BzMxIvnByqG27vC5dph3ILv/T1Pyb/yG2x3IzkXUi4sFwOX0dM3fw+M5+XNHedXl5S58qUP3udms0GM59XNhr7zmHnGuUj0M12M7K3DlMQ4TlQM3sC3Pvy2LgaMqmhsC6EEaRbnkOrMnDLeNsv0Ioi1WB8xoUdaC8dYJcMa13xoilUzQytNXgvO9pQsWvSWRqANAd/1+KjciZQmxjS1gE6HcxHjrKYS6zCs5FdniE4Txlfn5yzPVrgwME8bsI582LHb3PCrX/sNXqRvQzEE+zn1IbFWvT1SVlSg6n1QKhQXmQ97TFxS5w3zODayrKVKplsZXu5fMc0z+7IHC+MUOUw9yyvNK8qjFkjGSUNmBBFdF6dUyPNMFztsK7yqWHbbW6b5VlFLG5ucGJzLYNWgzVuHdwHvAudrx3KIiNGEbRsGUvG64DCKBEqTSB/RlCJQRSXG1hg0Yyi1cUAJvdOcmFIm+wxeWpupoZfHxktVBVY9PkZbJDRTOTFBAxwrFKOvm+tRW1cRVAFWQ+UgI/u6Z8+B0RzINp1QH44cjmOb51hcfEZR8jv9fIQaTnP1W60ZHSHrG22dYx0gVO7VPtJQD1WeHj3ejgHH0hZRp9fjYZGiCC1y9NJ6UOu0BdiRQ3J03zcNKcTcBw3evzVpH5tyhKrUUyvsYQFl4OQ8a96syP67tu/YOeU//If/wJ/5M3+G999/H2MM/+bf/Js3HhcR/t7f+3u89957DMPAD/3QD/Hrv/7rb+xzfX3Nj/7oj3J2dsbFxQV/9a/+Vbbb7Xd6KJwtqzLh0YkwWEc0jughzxPbzQ0//9/+C//b//azLLzh8fkFy2Hg/GxNjA0KboWIpgi7U9uwVoUja1VVgPVWmfAeYqcDYqlCiIFx8txthbst3B16dlOgSseL28rN1vD6tnKYDIcJDmNhf1DiqzGGEAuL6OgDmDSxeX3DJx9/i29++DV+6+u/zu3NNfO4xZQbnlwN/L7fe8nv/72BH/jKwBfe9Ty5MqwWhkUP5wtYd8L5ovLBY8uTc3h6Dk8vhcul8PQCpCYuz2A9eK7WkS8/dVwuwdbCV94PvP8IzhfCuq+8/8Sy6A3TTnj1YuRwUIQCq4nJ777r+NIXHWdnQuzAeVXiGGwr7kS9KACMmpTFTgjeKpEUUbt43wpCq2oCFwUXUNTCWhDbEKiovD0rhOCZppnxMCNF/Ry+8eEdz673OC+IFK73hfV6yXZfyLayTwlnmzV9zTipXAyei2VHLeqFMgSLwxCMIRpDRFgBX/aB37c64/2+5+nZBavhjH5xhukj3aM1k7PMUhALMVgW3nK27BDR1s5xINJWjmE5BK7OBha9wzm9efu+xzmrRFevfhVSK3lOvH51y5wyu8OBkgt95zlb9UjJeGM5Xy1ZdJFf+8VfwQ+B7/8Dvx8fex4/ecI7jy9Zrtesztd8/OwVdX/H3c2eMU9cnS+QUvn87/leJAaszeSS2U0T1WT6ReDxO2e8//l3ePzkEXd3W169eMWLZ9d8/Mk1GIOVyu3NhhfPXyLW8dG3n/HO40fE4BCrq8/DPnN9vWNY9EgtjNPcVE21pRE32vCxleACse+xqCy15nryp3Cux4eoAYItmNI4zYYxVmWUWYqSQ62j85GhX9D1AzZ4NU8yyp+KIRIaATZl5XRotpJRvyEjIMp9KlkdamsdcSIEZ+i6nvPHT3AG7l6+RKTiuwg+EKwnjQe+9ks/S1d2nHVaTKVp1gnXOlLWFpRYr6ZjtWruTAsETXkmVShiccFQfeHmcMNcZvrYc7ZYM45LqlniY1TUZU6A5t2UWpuUV8fJlNT/xHkPzfhuHDPTYcOcjgnGzQbWWJwriPVauDR+jwuO8zOLjw7vLN47xAZy9pzKiFoA5cFUaa1btAg5zo4i0hAUjSQYDzOb3YYxjUhDUE2bEa1z9xPzceo1Grtx9LSyTsnQKg5QB+Yiwpwz05y17TNV0piZp8R4GNkf9uynHWPZkRipZn7QG3lAyOBNROSNYuItpOTTyMmx2Llv69wXMQLm2A4zp+Kk9YdORYNtSzfTVnp6blpp9qCWkiOLVh60idoxmjbXWcC1suje10UdR4/Fjj6nuf+c2j+Vqd83uh5COvevY9q9bB/sw2d8/7tv3zGCstvt+AN/4A/wV/7KX+Ev/IW/8KnH/9E/+kf85E/+JP/iX/wLvvzlL/N3/+7f5U/+yT/JL/3SL9H3PQA/+qM/yscff8y//bf/lpQSf/kv/2V+7Md+jH/9r//1d3QsQqXMhiEIqzV8fVeYEPqQ6QfPqxvhsN1R58LHn3zSbLZHEIXiUyr0/aDVuAjOOuVFGF3pHltApSicpasw2uSYqBS1OUcNgkp01NuR86o+INs9rAa4XBvOlobDZEAcIpnlQiitDaMfWaGLqpBJOTNPQvIKK9+VPdEXQgj0faBzmdV5pIpmdly/LhQRnChsF4LlbGHZHbSIurws5FkvmNttpfcTZwuPtzOrodI5uL5Vrs3773g+etmgbGvp14ZpUpXUYUz4CL7Xi3tzm1mfGa6uqkpaQR1gvbaBrFUSbC2mmTpVNcUr2v6yzhCitor0lgFEqFnRrbhs5lLWEIPj/ccLXr3eMWeYkkL2hUxwnv12otbKeqWtga9/e6Lzaj51fcgMw4AvgrGWnPeIqBx68J6cEtFYqrWYqs6ZC6CzEEW46iLvRU/XB/YlkGxgMyV8tIhzfHJ9y94YXLdApKjENU3s91NLfjZ0MfLo0WO+8Y1v4LwmuH784o4qVkl6qXB3t2HoF2w3O6Z5wnvHxcUVh/GAMRqLUIrgbGW1GoDK+mzF9nZDLZnt7WvsmFkPkcdPL/ncF7/C829/k1gqg4cxJfrVANHCYWLwCz56tkde3RIHi7VqyrcYzjhbXbHbveZuM/LhN58T3EustbjQsdmNHMaR995/h/X5Gb/wi7/KOM5gwaVCshPYzM3Na7ouIPMB5zxlThyyYMjMBZwPMCW895qR1aY3aHwmr7lDhapux1UHxxAiPg4Y6yiAxRKselwUKdSS1aSsCs5HYgj4riPnjM0ZVyziDDF2dENPCKEFX05M04Gaiy5KjEe9PLRNK3nCSaEa7dS7rmNYX7C6vMIYhxMNoqt4fFxydvWUH/jil5i3I7//e7+Hx+fndBYOuy2Lhdqu53T03FC31JK2mNBTUybVxrcQ9d15erniqj/T+8xGoo+Y4rm5DYTQa47XfqI0+/ijQs7Y44QNeZp08RAa6uSEzd3MOG20aA9r3Re1SzAOqun1fGDxzun4slQJfWlQc8kwj0WDQY+TWiPp21PboIAUhKyv0XxS9EHLy+s75l1iX0ZSrBhvEHtsyd9nvZxoHWKRZg7XvLIp1ep4XYyqK51y91LOiNVFULHK0UluZld37N2e2YxkN6Psi5NehxPf4vjPvNnK+O0UPZ+yu5e3f284Gr+fipaHfJIHr3osAY6OrafnaArPY3fo+M882OeIfDw8iFPr5bjf6Zja67Z+lt6PtPvRPoRPTufi9JrHz/yt8/Zw+04lxvB/oED54R/+YX74h3/4Mx8TEf7JP/kn/J2/83f4s3/2zwLwL//lv+Sdd97h3/ybf8OP/MiP8Mu//Mv81E/9FP/5P/9n/vAf/sMA/NN/+k/503/6T/OP//E/5v333//U807TxDRNp5/v7u4AyMUyJ8tyKEhSmOlybXl09YgX1xu2biblCamBu80t1lj6zlFLJvjItozM00zX9xwOB6jQdRqspwF7eqJLrvjgsSbTRVWAlJwJwVDqrPBndOymkTkJi9WKNCcqkYplN48qhSy6Qup7IUYdWKdZECukXAkBatH+qVR1b6ylsLsTfFDHWO8yziWCS8RocMEQXCVgqAWWg1fTtjlxvrIMCzg7V9MwMcL5jXB7nYkObraFaISzwbLfOe7uZpZD4PzCcvNa2B8y/aBeEdFUbFRehbGG0FWCh5IN/WDpB80PCsHQ92pdXzOnnKIY0RZH1q++10LFeyXN1iJUp20eK2pipC6zR55fxabMo/OOV3cJ6zy5aKpv33tKEWIMzLPw7U92OFNYxI79VDHVMo8VccLr7cwiRKI4rKvkKbN0kbkkbcOUwtoY3u0HQi0snec8RLresRNhcobDPGvqcnBMU2G9WvGld9/lGx9+i5vXN1hjSEkjDAQdSLa7HfvDnhAc3tKM3oRcCzmrkdU7j664vb1ls9k1oqew2x2Yppn1akHOM2erFbc3G25e74lXgtTMze2OVGGWHUPJbF4PLM86PvjKF7HMTLfXmHRHHyIEw822cnE2YGOk6yuHvbC53rG6uuTFyy3GwssX10ypUHIhdo6rsxWvN1tevLpGxPA93/M5LtYrDlNunBoB61gPA94Fbq6vefz4MWk6QJ7ZHGb65Zrd6zsM6nGjreym2qoNIjZNWmq1/SJHI7+irZrYRbp+Seg7xGgYna7aHEaUz1SmTJkmLW6dIXSdFkE1A8oxiL5jGBb4blBy7jwyTSO5FJzxWB8aKdZgq4OSkVyVy+JQtYePxNU5fYx4Z2HokVZUxdhz8c4X+H/+8R/i//o//WFWw0IlzD6w32+YR3WOnabppBqkFoXfjWMuiZQLc1Y5cc6Jd88vWYelGphZjxW421Ve33h83+lYOc6Nw6OyePUBcRhnKJKZpgPWWLz3iAUbDbcf3TDNrxAqznW6mm5cNuMdEPFthe28JwToe8BogrdUYZyFaWqulSjadeTGQTmhBurbpFyUWlILEhSci0y7yqEkRkmUanDV3E+WbWUumBNXp4py1nI1iDhS1UiAnET/VoCqxNrSSMimCskkcpmYzIGd2XOIe+Y6Ukn3nFg4tXLkARrRDuaNFsrbxchn53Dx1mMq/z62bB4AEQ9ephUIp9bQZzRIzP03VrRRVJsx3/0j7fhOT/SWFPgBmPPgSDkSk98OOlR05MHfPEDEjs9//7zmTTLtd1ij/A/loPzWb/0Wn3zyCT/0Qz90+t35+Tk/+IM/yE//9E/zIz/yI/z0T/80FxcXp+IE4Id+6Iew1vIzP/Mz/Pk//+c/9bz/8B/+Q/7+3//7n/r9YbSErmC8cH0tXF44loNlu0uUuXKxsuyTZT/OuGpIVZUnMahXgmXgbrsHKThnySVr9d1Y7847JUI2PwtnrXJPUlbjLKdpyUYqzlsMjpIS292IFOXHpGQIoWOaJ3LWXmAqBofQhYbRdXAYW2ukkbJKsYx7oXa13YhVFScixE7IRkhJ3WZzU4DMk2tqJCiz5fKRZbkQDiM4o5bz7zwxTKNDsgaI3ex1ldQPwm4vPHtZGVYOZwv7sbAvFecKzsByJUQxFBFCgDgIzpZ2cSoR1jtDcJauM9ApSgTS2i6GklVeG4JQqBhc852pxM4Sg2OaKvMszKPFBb2xnLMMg2c3z1xerNjudhg/KPGtIbFSVdq28LCMASuGnGdMhVUX2SfBA0sfcBVMrfQxIFII1eCksjCGS+P5oF9wmA8suwW9s+AtpRQSsJXMWApOCovzBVMtrM4WGEQVCKKEYO+tOmlKMwVDBx3fzPRKk9we/QWmabz3WmkjwOEwIlI5HA7MqSDbvZISY2ScYN4dqALrZeBmP1HrzDTdUed3ePHsFeuLd3j54hrGwrK37G4nfOdZDj37SRhiJSdhebHk+fM7Lh+dYYxje/2aro+IW7Dd7vnGJ6958u4jznFcDJEnV2f84i9/UzN12mDWhcjjR2ccxpFpKrx+9pwuWM6XgTxucWXikBIxBBBhmg/AkTDYoOtSkFrwDRXJKGKBUUJqHxcMwxLXeUqZ9Nw5R3NdhFopaYSccf1A9BHnPBV1HT0q8kIMOKdW7nNOjNNMyio5PtnqI0hJ5FSQkrAlI2VCjCYVmxDpV6uG8nmQgg2dqvZaUX11dcXlk8fIYTytNGuNvL6+Y7VckMtMkKCZJVbA90gW5jkzzrO6AU8zpibWi6gqmq5TIuuUuL6Z2U2Ri3NPrjBP84nzYYTmMaRttpRmch7pXIcJSizGWu5e3zKnO7COe3t70fveL8jWUo0qqpz3yjWzrfXWLP23u0xRKFTHS6kIRfl3womLUhsnxqC2BEcEpbOR4CN5qAy+J8VKtbkZaLYB/8FKX21NhDlXQBFkTTjO5JQxxVGtEnWzFKhKGq4UkpmZZWTkwN7tGf2B1AiyR1HEERnghOm9hYjw2cXJw+2heZp58PM9eVTQTJAjivEQUTmSUzlJj9vhnIqBE6pEqwMbCiPHooEH9UtDYxRYeyiRPv4dipcIzS1XC7tj5s5b7+whmPJpDs6D/8yDSuYzC6zfZfsfWqB88sknALzzzjtv/P6dd945PfbJJ5/w9OnTNw/Ce66urk77vL397b/9t/mbf/Nvnn6+u7vjgw8+YB6F9QIOo6HrCoulMB0gHWY6b+mCIZdCcoY8z6TaVmbWUtKBGDyL3jKnTLCC9XolWKfx3trmcfR9R5oSwS+0KK9KptXE0kTJRbkAzuGtI5dMmi2LxYIsls32jnjlqRmqndntBG8VoSltdtoddGI3KB8j50ISRSykWI0lr3pjOu8QK0x7MEYnb9rKRLIgtpBaRk7XXWDKmpu7b5FrZT0Ih0mouZHjJo1Av7wKmAnGXJluiqYKR0gzYI+TbSV2Wg3YoCvmECvdIE0yCNZpS6MU9TUxrtJ5j7X3UetHcr4PhpoN1lW8E7ru+H4MzlW810Kn2+kAu90XvC30ZiREz+v9AR8C46TmZs4YOmcYgiFYyzQlLvrQ/BK0FjyPHQEIzuBFGKSSshBNoENYOMe5CyywWBdZxwXWGjaS2aTCy3Igd5axZupuZlELKcO3Dh+2FUdtK+hAztqG6rvYwFKDc4EpJeXbOM88T8QYsRb2+11b8TY5tyiy4JzhcJjUmh3oY+BLX/keDtsbhnrg3ScrvvnJDYdDIkslz5lXLzY8uhrAVtYXV2ye73DBEWNhGJymUpO4erom3mqa7M2La8qUmMaDOtEWYZwSc3vdFy+uuVwvyRh+/r/+KglNAk4pE0LHo0ePefbsmXpU5KyOpbXg+wXBW2pJeAPRO+UOCbzz7jvsdztev77RobZkTGkTrK1tYD9K/z0x9oQh4rzDzgVxELzXe0CEWhPUQnCBznd4r22gnIsashlDCAMudIhzpFKYpomcMla0baGW+QWq5nyZomgY1oF4MAFjO3y3ZLFc4vsFNnaYknHeI0XlvSZpijIO5Xt4g6SJGAz7A0xzIacKoSI2qJmfsczznkOamHNuBE/DqvcMztKFSOyWWOM4zFueXVtEFsSu4zDmpnSrjb/U3LKN1bbjrOiK7RdYbzEUcoL95pqUNli6xsMzgMW5jA2DFixGC+cQ1VFaJDHPcJgS8zxzd3toxGIaAiwglXTknhhFwmrRBR3VaEK3RgeqL401LPqe5CrJZ5J3TGnUQDqjCzjsvVtYLsKUKkowVh+UOWdMvS8CkmTGmolYlJeTyXUiycxsZmY7Uk2i2oTYZs7WwnfeqovuSa58Njpyeqx982Zz6Pio/u7UWWks14dussd2iSIT9r4AaBXLm/WAORW+wJu5N6dKQbgvTk5l0Glhad7SDD3MCTLK333wmvcF2akB1RYQIqdnPu35ZvFSHzz637d9V6h4uk5lgG9vi8ExzZp8uwiWnC2TrTgvdFIopXK5jAyD49vPJoo0YmtQWVotmUUfGXrD4ZDYJIUbjfWEGBn3I9E7vBUWZwumqXAYM9ZaiiRKFqz1zCVjnRCtp+u8EttSZbvf0MeIw7AbtacuzpBrZj9pGu6cBEylFCXMgRoQpQSlOJirymCtog/jqKiLD0ZtyJ3RwLFq8EG5Nc5FDmni7q5yeelZ9Jfc3D7TgaQafU0JUEUD0qxlTh3OC7kkBL3Il4MhOFXm9EMboDod/JQwJ4QoBKfuqkLF+0KMkLOiA/1giJ1OuD5U8qx3Zq0GHyu1ZjqnlK2cFJkJAWI05FTwwRIiGKk8XhuiVRjzei+42CkpkUIfLFTouw7vKpTCKjqChWwct2Nl4T1WCkOMOBHWdkGsFRsMphp6Edah56xlu0TruYznbOvEKJk5CMYb7saR691IxRL3sO47ZNwqBN5ag40nhnOexXJByYmSM2frjt0obDcHagtPMwjLvmcaJzCWfhHUvjvlxldRDwvnLV0fGLrA6+cf0UXPs82OJDBNlfV5z4vXml8zzQe+8fUNy8EzpQmsZ3u3ZdUHTIHbu0RKExeXS7p+wcVy4NfTzIcfP2e5iMQY2Rwyh1ELDWvVE+j2bs+jywseXZ5ztzlwuxsxzpBS4tvf/jbWWuZ5Zhg6Ukq8//n3MGWLNZHqevqu4J327UTg+bPnb9zTtZYTE8VgdJVfC8ZUvAl0sSPGHowgPmgSsYvadq0TdZ6w1eBtxFudcFNS2a3kRPCB0PUYp5lOc5oZD3tMrrhG+rSNzKx+LIaKqk6MqIRZgsN0A8P6gth1WN+BGJyPWsTUcpwFNMCwCCb0WAf4gGRhSHumXCgI3gYEQ3GRnBNp0s8wJ7Xln6bE+5eOPgZi7AjdCkTIZc+zG48fIrjA4bBlzrMqd44uFtXjndr5H6YZqS3o07oWpDozzxoQ6P2lTohtkjaukXOtxRlLaJLrEGorCGAaJ6Z5ZnO7p5SEC14LzVo0GLMWjFcYpObaWjpQTW1RBMrnUI4LujDMCVyli14Vbg291mtQr4yj4m3OmuVTqzom56QO4cpxMRQp5LagVIFmIkshk8lMZJuUk2KaF5Y54oFHqOL+2jwCGdqUPLqPH4uHhgD+NnPYQ/nx8UkfYAsnr6Q35u8HyMmxePm09dnRjv6etvqwgjkWTCK1OdTeE1yPj52YX40LYx6+7YcWsG1ce9iuuW8b3R/7KdfuU7WI+Yzj/523/6EFyrvvvgvAs2fPeO+9906/f/bsGX/wD/7B0z7Pn785KOWcub6+Pv39f+9mQ0ZS4dGFocxgnCHWSgiC8zAnSz84tvvKdnDczQJZDZt80HAxIwXnAn3nORySOn4WIc+p+XHA3Aa47W4meMdyGbHGM40TIXhNM26ZNM5DNIE9MyKZbQtTK8ZibWHVqc9JLpXgDcsYuNuqtM2binFKTnMY5lTJRfBOx700K4t9mvTCyEWIwbWbs7LwluViaESxxN2m8pvfeMawfEnJOuDn3AqgPFGN9nMxhlevNwSvBYd3amzV9xA6SMmALfRLzdHR6t+oEqcNwM4XgtcwNwz4oIGFmguiUQMY22zD7ckIyojRCl3nLFXpiCqmfG3yu+ZLYD0431NxjK92vHOxZMqVzVZh+3kWtvuRJxcLap457yPg2OwmFqgZlBO4sh0xVd4ZLuit2vDfbF+xth0L3+MdeB+wdsDHlSYIu4KdJl4+3/F6ys2vxbG8fMw8T6Txjq7rMUbPQckF7zxCaV4eivLc3W5UDWYc2EJ0VqPu04GzPhBj4G43nghnOatTsLMOY4VpTMxTZhc0uwbgsE989ctPeLmZWbikqgoPWHWtlTrhgqcPC+Z5JPjAtBtJaWJ3e03sOl693nJ5dcH44jXTnMlVXYdFhC56vLUE51gOA08uFry62zNOKgG2FVarFaVUpnHCOi1SYgzkNBFB5a1+r4X/pEhD3R/bmeU00GtisQXncS5QclJXWKfePza40xLVosZbSuQUctIgQlMFccppyoiqc6Y9DoP3keg7qrGkxntxtjZ0yeN9oJRMkdx8WJoPjbNU2yZHH7DdwGK9IvgjGaMiziFzUs+Ro5rBGmSewYHFIUrcwnqHqQXfbAwSgRlHLoqUpSxMWU3HjMC7l2d0XcR5jzGVmguv7mZudpHlVa9qlXFSaa9pJmwm6/1lles0T3s1s+s8WL23X377jjnfISKnIFXE4GzFWqg1tPHN4HzAWMvQZQRPmpI63E4zd9d31JIxnZJfq2gb+vg5nWZbKUjN2OqpuTRjMqAK8zwrL8aD7xRRcmjREpwWSrW5vCKVkjNpztDGq5QKJetgYsW2SV/HyiKKZlYq1TSJrhMUThXwWT2bfpc55zTFHudt8+kWyJvP8dZjb8Ax9wrHk+3Iwyc5TvhHLsrDVs9b2xsT/5EjYxrC0XpAjUDAEU25L06OxcTx2I5FSjN+OyI6HP/m/kAevv9ja5DjczZ+2RuIzO96ht/c/ocWKF/+8pd59913+Xf/7t+dCpK7uzt+5md+hr/+1/86AH/kj/wRbm5u+Lmf+zn+0B/6QwD8+3//76m18oM/+IPf0euNB8Oq12C0zajBW503lKhOpNPkuLgKRH9AMHzrpXC7d8ylqOOk9xwOezoHzhpi0IA15wJFmlGSVT+TnCvGCLlk5uQJNlBNoUgl2KDmQxbmnHjvaeSr3+swAi9eWb71XNsW+13l7nbPowuVx+32he/73IK5wuaQkKJckz4Kq14n8ylDygbvIRfttZrsFMovhug5QakpWw5TVoOwUpmqkF5bDoeqhCwxhI722lrEFStMpdIHnVSDMQxL3UfscbV7tLg2LAancuI2j+j4orJh7a83jorXooqqWTcYXXF0Q8EY9ULwQS90KUqKrRVqNhirxVdwKNpi1ONA3ILndyPOw6LrCKaym2d8GEjTRMmZ88WAyYnz6FlYw2EsdBWWzdMkWs/j2nG1OOdq+Zhlt8bYxBk96+GCXGdyGXEuMtvA1sLdXHmxG3lxGNlOleAd0Tri6hysJsOanAld4Xy9VKiZSQtN43FFe/O2+a0s+sCr21nfq4BYoe87LlZLZJ4wnSdLYUrHyHn97E1TSoWgk3oMHimJ9TJwe7fnfH3Oy+sDHiGWSoxqT14rzIcNNsAi6Cp4fb7SEMO0Yz8VbvYHaoXz1YrdfkdOlfViwWazw6DXk/eWL3zwlG988xM2u5HUnHG7GHn//ccsFgO/8N9+FRHousj5+TlBMqvFwN7P1Dox7RLWxmbOxj3UfRy8nMpffYzYEJBJDfaMM9gQ1AlUSgugE+V6GG0ppXGipoy1AesDxnmdKGeVB7vQtSBBiw2dyshjpPQLpCSCsU15ZrE4nFGzPisVcUZT0EWTsBfLMxarM6xXPo2xYFBUwBmvk2HVoboK2FKoFhCnrdpUtCXkPAnItTCVTJon5irkqoulIoZFMFxdnWNDJJVC2u/Ic+LjF4lUzgh9R05FCbJSsKatiI2089vOzzQyxA4bIs5ZrLfcXN8y5Q1gqUYjAdxxBRwiFYczStjXkEB1ifZWyHMljQcO2ztunr/ASMaYDjGtFVOrrtxbZIjUogo+oFCUzNsKFKmFu+2WmiFeBTCKbhl0bHZO22+K76rRm9RKzhmpmgE2z8oXstXgxGKlEbAfBtehXijFZIrLECsmHNtw7Zzpldmm4OP/DysDaZjDZ6EED1tAp4bP6QF5E+t4c3tQu9wXJMf2zIPGiXlzR/sW2tPyvk8/H+m8xhistCL7+GxyfI12D7Zi5KToOUEm7Zq4f4dvFWn39/FDi/0TcfjhyfkOtu+4QNlut/zGb/zG6eff+q3f4ud//ue5urriC1/4An/jb/wN/sE/+Ad83/d930lm/P777/Pn/tyfA+CrX/0qf+pP/Sn+2l/7a/yzf/bPSCnx4z/+4/zIj/zIZyp4fqdtnoWwVhmwxaoHh1RiPro8Fmw50LvMozMoVVftN20SXS96choBCN4ydJb9mHXV4jWxMyclzh4TNdXQrTAsB5SclYhDYH/QVfJXP+f44f+Ho1tUrDOc9Yaf+o+W//Lr4Flysx/ZHyrBCXsRrveF85UwHwwvJ5DqOV9VNaZSFwjGGbrenCA4UBQlJYs3Qgx6A+ZieX49gy0UAbJ6ewCn9GYXLSVbcnaMKWOcKBKw9uxuBe8rsRO63jKOVouUoBXxsBCGhW2umu18iCb7umMPsjZJGopquBOZuLb0Xy0ea9EcDeehoIS+WoXqHFYczioSFoJjEdbkWvn45Z7dduTROjJ0jptDBhuQqRCsYdkFeiq2Klozz5XeOpwYBhsI4lmHBU+693h69kW67oyh68jplnhxybq/Yppu2eY7ZnPLmDfM0bKt11S7hzmz7B2PLjsu1x0v7oS7ccd02FFyoYueR5cXvLrdE5wwT6nxYdSg6p13Pod3mc3dHRedUIMBMYxzweeZdCd0Qe3fpzk1/olTSN6oaSAlsxg8uVT2Y+IYnvb4quOj2x37eeTcJJzJSgzNlRgcxg7M44arZaAP8Or1Nd064mPP7c2B/UGQOqkSpsCwWLA+v2R7t6Fzjth3DNHzybef46wlWINdDuQKKSU+/ug585QQ0WImu8rNq5esLnrmCc7P1jDq8U9VqJKJMZByeWMVdnROUL8ND0b9c7oY6WKPsUb9UKqoxb1VmD2nmWneawZS7HB93/x6UkvGbbLZJjX1UaXKznkkz6TpQEkzps4aTokl+AAYSpnIJWGcI/qBYbHg/NFTlqulTt4Itu+1rWEbqb5qposSSSsuGEQykj0pzdTWvsqoT8dYKzMaJDnOhVxMa6Mk3r8Y6FYrMpY6TZR8YDyMfPu5xYee2AW2m0Se59NEeuI0WEs1MKUJKQUXWoyHFKo4dnfXpPkWa4OieujUbE3Bu4iY5tLrPT4EYhCGDjyGLBVKYtxu2N6+BtTDpqLyYSP36cXSyPW1uTnbZpZnjLZ8xArJzHhnWC0G/FkHozCO6gar3iyKX5Sq4X+lVuZSMUXJ6ZrVJYoCVXe/gjetSIFWnCRt75gZ8QW8InTmyDT9bbbThHsqEN567MG+Sla9RxlOjz/Y6bNaJcLbaMhnHMcDkuvRWPQElLTP71R4nGoZuUdTjuiHyIPjNm8ck3n4N/dvilM76u3C436X9vWz4B7zO5zdz96+4wLlZ3/2Z/njf/yPn34+klf/0l/6S/zzf/7P+Vt/62+x2+34sR/7MW5ubvijf/SP8lM/9VMnDxSAf/Wv/hU//uM/zp/4E38Cay1/8S/+RX7yJ3/yOz0UFktDF9RMyfuMd4C31KCrlsXC6Ao+OswkXC4ql+uOr39S+Oh6YrsVnFHL8r6LDH1gzplUClLVGqcLsTnP6mQB2m/fjwaK5l2MMmHEMMTIH/r+wqN14TefV1KufPV7LT/wPYGf/oWCyMzVxYLd3R5rC+8/Mfz+70u8f2X4jZXjt76tTri7SWHNvtMWTC6WwwTWVDAQvDDPMCdD10O0lppqU79A7NWLJM8GZ1AjKFHZny9gbCVEw7hTxwMN3EqcXzotRowy+PtBb+w0C11nGXq1rm/xHnh37w4Ygt5aVexpBdBF7X06awlRf3l02D2avSkBt7RKXVOFnYns5x3GW7JUDuOOWkVzZUKgi4FcLdvtyNAJeUqsFpFoHdNhZOUDRQqdD4reGk+P48wveNS9x3n3PqvuKTaGNoZdsu4HHI5oLasusnWJIe4RdoQ4Mz/bs+wDsxGohcMkbPZ7Dd1Byc5jKchmi5TEB59/zDhN7G92rLqO7X6DIMxzxVZhiIHcguhq1mylzmqLspZCiD1TPiAijOPEYtFjnSPlymanrZsqhiFa3nu6Vo+UcUfvHOfRQt6zvDjn9vaA7xyhv+I3v3bHbl9ZdHC2jFhb2Y0jm92WUvQ+isEz7hLGV771ofJcLtY9i2HJbrfFO4uUytl64NnNhnEWai1Y27WWEFgr9F0kmMLgK6FWynhgHQzzBPNcCCFSSwIp9zd0K3KlVrWvx+CsYRkWLBYrfNe1bor+TTBq3lVKIY0jFIhRM4pcCKrgSKXJoI1OzrZ5ebhA1y1woSOlUfetWhhoiK0ueoSqWTze47sBHzqGxYrV2Yp+WLbDtk25UjDSXHGRkwrChYDKmz2SiyoFQ0RE0SSkkg4jcz2A5JZ8DXMu1Jx598kTQujJYqglIWnkcLfn+naN73uwnvGgEQEiFTGOXCseOtDl0QAA82VJREFUo/wPA/N0wFohhKBBqVbY3B2Yplty2uPdCjH+lNHinYDtOCYJW2vogmPRC4EZySDzSE2jKo1mAeNUsVZUel5qa5k0OElqplSVhpcKpajiCHRMyjKy7FdQZ2yxXJ2f86K+hpxUxm6OPiiGXCpTydgWOFZSIc2ZnAqhhtbeMVS0QDtO1tUUkkkko9wTXAVXj8HjJ3XNaVo+oXsPlSvHiVtOVcHRZE3n/1Zs22OLQ//mM6uU0ysdv9YTEgHSVDTHPVr5Ye6LlDcKpgfPfEI/OHJU9ECOrZuT6kfau32rjfPw2U7+Jg/rpgeLik+3uMz935yeTe6L5u9g+44LlD/2x/7YZ8iOHhycMfzET/wEP/ETP/Hb7nN1dfUdm7J91rYYqiIEAl00WK8DYAjKg+g7Jd0VBwXLCsP6rHLWWxZR+NarwpRBqk6CwUVWiw7rMtOsAXklF71UqpLGQvC6KssZZy25OOqoMlq/dFRr6dcXmJcHru+2fPTMsN0USEK2KqEM0fHuk8qf/WPw5ArGSfjBR4bPvW/4X/9LZT86ppvC40fQecBoy8M2+MxZOMw6UKeskz5V+SUpC7koyXSe9MZLVfDOYZIgYhkGcEFQcm4juwZhMVQ1YnPakgkRjHFYKkOv59ipglCxQZMbz0SJtIKmzFbRLCARhWet0+TilDOm2hawpsGCc2tjWGtPBDdxyguQ7LG+o4sDteq+H7x7BrNKqwdnmNKMs5YhdEzjSDAWk2ais5x1FjcL0S+48Esu4rus4+cZ4iUYr0ZcSXB4KIEsCRsrLmbEGMYJ7sbEthx48rjnbp84W0Vux8Jmf8Aa4Xafaa7+VGDc75FU2N9s6RaeYIV8uMOkyutXr1h1YKRgUuEiOHKGdQjY4Mlodkw1Ht9F7DhytLkepxmfKxcX5+x2u5ZHVFkuel69OjBHiwfWC09V0SoffOEJr//r1+l7LS7ee+cJMm4Z95lFb3HB8fpmC0l5KxbL4ZDAOsbpQLTCqovYIqT9hLeO27s7jLOkfWlSz0Q/KBJR60QpheAsh8OOq8dnxFDpXSXbAs4rz8kabWPk++JEkWR9TmmtAaHQh8hisWJYrAmxQ1uCis4dk5/zPDLnWW3Y+yUxdlRrKNPEnA9UqXRhoAsd1jm8iwTvlc8qWUPipHl3FDXxUnWusieCs8RuwWJ9iQ2hJREPJyMyrLrZUquq6Ew+pl9CmXHDClMnxNoH6btZM39chNCDmynjoU3swpSEcc54Z3j6+BLvPSZXTK3UPLHZFQ7zwPnjnlor02FU9U5rYVhTwKgbcS2ZNE1Y4wmdFuXew4tPNqS8Q71kdEFm23RmvaWYeMrQMS21XPLIfLhlDgNlTlgUiZmLAXNMVpdmFqcxFoJRy/n2uRqpSEWl26LKDmsFGwqmIappzphYTtcGHG3qdasowT7nCYNTUnFOamRY35QKl1MRUSmSSKaQTKU6wfiqCIqvJ57GfSDewym2/WyOtvcPJiKhvc9jKfP2Jm98+dRjjYPC23/f/KBOBdHbHvu/zfbm/Nz+3hzxC3NCV94uR0w7lmNFdMogenB8D7f7TKEj+nL8O45CqDfPgHnj5f67tu8KFc9vtwXvlCQqyuROSTDOqI2vK4SgHg9Zjt4nAmXmnQvH1bln+c3Cr3+zMs4tuRbVfvdRTcSKgdQGKleBqq0U30hwzjokGGpKKoPdZn7xa4bv+9zI4AqLzpEn+NqHmmqbk2OyCVuFL74vfPH9yMvXjhfXE+slfN8XDL/0Nc9hqoxZ2O0M6yeWeWxtGoF51utnmnWQkapKIItFbGXKimLkZJizoVRDmCtnZ2qHbxpx0gJdp73zYSmEaHFBkRXb8ksAEMMwwNl5ZX1mkQq7DUgzxcrZ6XGUjGtW9Rih733zk4BSpOGsyocwoG2fosVf7NqkVMG6AsbRdxHvB6ZsCeEdjMA760hvC1kKKc+svGHAYSUSc6WmRGcdvfEsrWMonqULDH7gMrzD2fB5kCXeeQwZSWq9bUyk5IwJsw5UYpDJQaqcXRnK3vH81Y7ceXalcjMWdc6NPfN2yzxr5k2oRYMko0NKJd3eMRiIywUhzFycrxj3NwAsVwMuZ4a+p+wPzFRycOxFFQubuzvOz8+5u9vooFWFVGdev77GWsvZesBKobdgZCJ2a7IRZhzTDP1y4ONvv25SzBlvIwlheXFBvtswVRiMx4ja+49Tolv0pKnQe0/fecq4wxXIaeTyyYrXtyPr8zXOwMubDblkFkNH8EFVMm2wst7hrBBMJXgB58iTSn/zPJFTagF52sqZ5/leYtmmlZJnTK0MXc+wXBC6HpzBHNOum29EyYl53EMWXBcIMWCcI+eJad6TUiK6QAiB4CM+BuWyGHX7nebEPGthVYp61tRakKIZLt44nLV03YJhdYYJgRg9wSmRl5yhzpRZg+6Eon3+1oYSY6EqB6lWQ8qZ0nK/AHKCZEB8h/eTcnumiWkcSSnxeOVZr8+wxlEpeDJVKq/uOgoLukVkmjLTOCk3B8G3iUzVzRr0mOaZIUZC56Epc+5ub8lpAzjEdKdi2DrRRZ5E5eKgvCURy2F3w83Lb+LLlaY2owrERikCq5LiIuoaqzlAbWKv6icDlYK2+ZrhOmHhsdEwThP0nlIh7/ZKkMaeCihoZmQi5FpxOWOknMj/imQ4qO7UhqZxMqpUEoW5JpLJFKu8RUQRL2OkFcZHMudvtxB/2OI5IilvaH8a0nPfYjl+PVrV8+BPjyjHsQj57bUujfty0jt/uhD5LMLuEXk5Ij5HYrJpLRvMEaW5L8VOT/8ZqMlDL5c3TenuizvzoNB5w+judwA3Pmv7ri5QqNpuUN8CQ5ks/QB5rC3fpF3c6D6mgqmqGLhYWX7Pu5btXeZbNxbjonpUzBucsXRBraedh2kqJ7WBEuI0gr2UQhc84p1aZJfKr3zD8b/8r1u+/wPH0Bt+7pfgV7+png6gJFsjnnm0BPuI9dk533z+a5hDpeTC7WvDNClZdxyNSqajpdRCxTKlqiTYZDBOKEWLlRiMSvSozEmYJ2GuejOL1cIg9oahU16KqiUKIcJyCSEIPqjxm7HaRrJOuSchKOJhDYirWKf7+ZZqXEV9R0xj4MeohYmUQoxBV05GORSHndpve2+QahFroIi6xgYDRie64DuqDBzGzO7wEV18yueeLnn9ase6i6QMvTdYPNFH0mFHMJZgHBeu4ypGgvGs3ZLIwNniimHosC7ABK4YxBSqNXgx2JoRp6m5JugCeLub+Pgbr9lMB16myuJMBxxrLCFaYt/h7w7tfAUCjrPoSHnC1wlnK2fDEj/0lOuRR4vI81vHYCrnFWwJeByz8XgqN1NC4qLJemn8HmG5XtD1Pdev704Jv6YUeie4nOl6yzvvr3lxfeD5t3dcvtdTSmF/mFlfDhymkUfrc168vmO7O7CKlq7rePTkKS9fXHMRA9X1jLngRUmhdipcLBc8ffIuNy+fc7i7ZVidsZsTz6/vqNZydrbUUMFamaYZaLC2sQxeGJxgaya6noyhThMyzzgR9cIwcoqXPw5qgqoWSqk4HF2/aLLg+xVsozhRc2E67JkOe7wJRN9hQ1CUIk3kaYZScb3Dhw7fdbjQY50ll8I8jmz3W2rJeOuU02LAVKBmaq4kKs6vdDUQAjYG+sVAHJYnhMVgqHNWDk/KiPfaOs5ZCxRU4VOyEnbnWfOOjDVMJVFD0FOQE/M0kUoizTMlZd579Igu9po3VQu1zuQqfHLbEfqO0EU2m0lNJkWvzaMxYG0qojROmFoI0Wu8gFF+22F3S0o3KvFtzrQYwdqC8fFE0HYm0bkMksjTK26uP4Z0oOs7Yr9mP1ly0uRnDS9W5MMi2l6pOhbXUk/RD1US1OlEOhrWA1PNvHyxIxwOLK4WdMsO71R+rEhs27nSChsoAEeuS72ffK2xeC2tcDikOQir91Qm1USRhJWEM2rQpqqlI/Jyj1gc82ngONk/QB8eFCn33zYI4a3tVI+8ofw5TuL8NujCfZUgJ6kxJyPHI6H1YXGiryGnNtd9kdEKkQf7PRRGK5Xl3lTuuM/bxQgPHj8+Vo+9QcyniqS3Sb3fyfZdXaAYB1j1HLBAjJUYLLup4GyDx4wgLZHWiTlJ5oIt9MHw/R9Ezs+FD5/tuTso0VZSIfjY4rZU/ivW4W0gF/VBUXgxYw30Q0+aR4yFOcPP/5rw69+orJeezW5mTEIRg2+JwpjMtz+GX/mNV7z/3o5FpwTe3/gQbvee3T4rNFmFV9fqODvNWqzkCoe9KFPdGGQAqYZci+YEeXtCjayx9D2sV4bVorDsDX2nXAPnKgPQD9AvNOel6zklJIcg9J0WJccbKueK84auNxhTidFiTaVWORUlCgtWyqyFyN1WyYJdpxLleTZMs8XZzDB0zNlT0ogxik6RtYcuMpDNFctlxtyoAVQ5TPRVcCJ0Unm6ihz2jjLP9E5XhoHA0vcY0CRa64m+ww8WHw84a5ES0JGz4KvXVV8oiDHk0TEeLNt5Ys57sjE8H4XRGNZdoMwZamE7wrzZaeSBd3TOMQTHqjeMB0e0YENHWKzw5cDSCIdXN5w7w7kL+FHoyoB1HTeSqNYQguPFNDJNavqX5hlMU/BMI2eLnqFzdLYg06ycFWs5X55xc71jt68cpgOL1bt84xuv+OALEdd5pCpUH5xO1M5AHyL7bz3nS+GKi+6KeVfYm4kpTBzynloLYypsnj2j95bzx095vp853I100fPue0/YjgXnR17fbk7tGusMi8XAmS90KFd0MoVZoOaMq0JE2KOtvVJSW0Dc/6NB+i4EfB+xXp1WTa3YE41WiZHTvCdn8L3HhggItSSVJ+eCN5boAjF0uDCo/0nOTCmz227YjzuscfT9imDUCdEe+/a1kusBXztFIYoWb503OKuGbMZANQahYPJMTpngQ2vn1HbvKCqYciblgkgCyYhbYH1PLoY6jWQxzDkz50JOBXLi6aNzvHNIzqqCyRM3e3i1CQyXPcZaDruR0kL3aJJ8DfprZmXjAVCfF2NVJTemzDTdUeqoqwrTbPENze6g0yLA6HnQ2iCxjCM1zWxun5PSOb1Ybm6glEnPbVVbeZFG1D+2ZkSlzuoiqxJhRYR08jq7OmN1teb1Zk8at5jR4DpH9PppHEUKHFf9p3+t7aMcWpxXw0tfA64ElRtjG7qkfyvH82QFsU0hdrKrtafnPRbE3L8KD4sT0/ggOs3YB22TN2fht2qRB7+/b/08RF4evu4brylNncORi/M7zPZyLBbePI6HoM+xIpLGeXoABvGw0KitantImP2s137Y6vntjuw7rE++uwsU24L9GsG79Y2VEHfqCx4/azFYW+k68LaCtbhYuTqrPH1k+MJjx3/7TeGja2FCyGVuAUmokZIpWGcUjzW1QXFKiCtZjazU8VLRijEZ/Kw+Dd4VKKhhk7VYqTy7hf/lP878we+dMdbzyY3hkxeVYgKLpSi3JFU2d4bVu4Z5VmKZiNrHi4GUVcFgvXI3UobgHMGrIVLfG1arymqlpmvLQclvGk2v57AfLLFT6W/fg7FQLfS9oYs6iYPRZOeipMUYhZwMadIBIfaiqxwPWJ14am3BgAK7ESQ7UleZSqHiSfYRvXfKJM+CjSv6AHncMs4wjtcYs8Eag+cdahUkG64ue8bdxOAc0QfmdMAUhzGejGGSwl2eGQ0sxSF+xHrLYF6D6eglK2kxR0w1OHGYArlmCBVcoZotyd4iubB5lbg7CHbw3G4KIQbuxgShZ7Pdak6TcayC53zwUCoVx8IFGAvz9jn4ypOuYxV7Fr7HHGZ1zpSeSUaGGEnekOqBdbfiLt2yT4ounK9XhOiZdlueXPYqA37xGusNwXktIAtMo+HlzUgV2OwT1Tq87/jkm885Oz/nZrNDbOQwJT7//hPiNLGWMx4/+hxXZ7+Hw3aPMTe8ePmr3I4v2c4bDkBxsF4tGLd3DMVw2TuS89y+eE02hmFY8arcnNou3mv74Hy9xOQtczEYKVArOQvWVZxoQexostHm3lql5TDVqgZsvsP7SEGQMhOc19fBUaVQaqZkVSxY6/V+rYVcJnUsdepV40LEhQ7j1N13POzZ73dMhz3zPBFCh8QeikFqhpqRomNAzplQ1LdozoXBRKJryefzSG1wjpVCdR4TfTOh0zRi33moEyUX5qTEeUEw3jNXyFIp4lRWPs/qZzJn5pRYdHC+WqjZmRSoB4TCs9ueQ+5ZLxdMB+Gw34E8LFC0JeKsJ5XKNI9YY+m6gDGW2Fk+eXZHmm+odcL5nmNulAVcrOAGijHKz7IBcecM0fD43LPfRQqJSmV/mLi+3muGURWKCEktkPWY7ZF7ItQ8IzVhrFUHbRFa3B0Z4em7jxnnkc1uj3VtfW9orWJdLFlnNdwVNe/rY6SW1qadwFRLlEisAZs8khTREzFI41RUI9hoNS7BJ40D4GjtfmwzwqmlwkkNfSqP7wuV408oV4O3JmF5+xdv7SFvOsHCA6dXuG99Glq7TBfYbz7tm2jGpwqh9rrHcu4kga73hdCRs0NDkI7F1tt1yNvIyf37ORZ1b7eJjk9wxBot38n2XV2geKeKENuIm8dixDll3zvfOA+2UrMqXhbRYyhYCznrYNh74fu/FBmWjp/7lYlnN8J+LBgqGUGqVTUMhaHzKm+b86l9lPN8ghbVkKuSUmV3KPR91PwfaylG+6ZTVYjuw5fCsxuhi3ojFgHHjJXcuBqVUg3WVJyDcVQuQojgneZhCC3XpoUl9l2lHzRccLGE1dIQI0QvRN/M0xy4oJdQ12vAnwoqLMErihLDsdjTS/sYay5VwOnPOdFM1FRdEqLBR2mmcJboOrq+4nxi2lTI2ooq4ljGzxPMjjF/RK0FX2aygRh0wJ/nSMoOSIg9Zr04KHDYZ5Z9z36XMcbjnGGiclMSKVe2VW2zV1RWxTDKSPET53WgRiHKWv1JnCdbqFkzgWrKTHVmI1telx0fjRPPpsLcBV19ZE/eJcR0jGNqihUYvCc4gxGDyxBT5nJQ8mJxgaVzrMXTHyK9Dfi6YmnOcV64MdcY49jPeyKF2cx0FiwVHwKLRc92u6UC06Fyd3fN0gaMqVAzq/WCVCrblJkOI8PQcbcdud0e+OTlNXOG/WbPcLnk0eUVo3Ok51semRXn3VMW9st4+0UuLvZstgXvPQ5wLhDE0FkwU2G5OOfs8orr3/gNdbjVWG/udsrz8ME21MriTcHXRJ11UnfREJ1BspxUIno/NZ+XhjLogqPxIKzDRYM41AlU1NxOuw4GRAmjBsDrZ1lUQqMyZKNkXe8DvuuxoVNp9u7A3fa1qn6qtBwdi6lK0D1GRuQyk+aRDIiNVKsGZJ13ipCUiqkZjD8KRKhV1BclOMo062TgHEIgz5U8JZ2sa4U4aDRBLWTTUVqacklKwJ9y4fNPF6xCxJOROmJF+48fXQdwPbHvuduMpHGkmKKDf7UaESB6b5aaqGnGRY+PGhronXDz/CVp3lClEnAPoH5tX2NiY58Ygo9Y17EaRs7WHilLKoXYLZgm01yRldRcjudfVGaMmBOvJzU1pDECOSHSUo2B2+0WXOXicq1Or1bTxrvoCF1kqlW9W1pERHCKWgZr1SHV0qIEjLbyndDZoFy5rDlAx5IiBIftHQwOEy3WZk6yYHNEMtr3R06FXnWtOHgLEmnwwxGUeMO87YgKNmRCpJ44GUfOim28kntQQ5EPK63gfLD3w+n9WBqc1D0P2jw8fOyoTGq5P8LRkO6+/WMevB39xYO/O+76Nr+lcVfkqJhq5OSHR/iwu6Mtw+8MQ/muLlBqq8zt0Tq7OUMa03qJor4bIUAWg/eisKJrUlprEacQ4jglFqHwve8VFt7y4bUli6ccCp01eBfZHRLWG5ax5+52YpeU3GeMO13U6noYqLWQizpoUltGSLMrV/6YrgByNqQxMyw6dTYU6GNEyJiist6coOsdxhQEowt9Y4hOC44YLGIqfoDlurBaWvrOEjtt0zjXQvyiIUaDsZo+ax10vVrLl3S0mBed/ARqtdr3NLXxefSi7AfDsBDGgzBNhmmuJ85KyiBZCN4ypUzneobeUXMFmZFi8K6Q5q8hYgjOUWyg1j15KlR7hjPq5dKfPcXmLXITwMCcdIXpSqWjIrMauB0kcy2F2zJjDByoRGMYS+IGy2tmbg4jT2XgcXac+Qs62xNNjzc62ZsMc4LbcsOuv2W0M7e5sE2GHHQI34wFcDirLTVrfXPFFIbguVgOxFkQOvoaWPQ68Sz8kqUsOXePEZN0BWfWZLOho6NWx2Ayd/MBpgRZWAQ1/7u7vqFIZdX3vHt+gX15R2cdqUDoDdUKUy6kw6SGg6FjtxkxGPZTbiqJyu7mlvXZii72PFmccTYPrMuC+fk3efXiY/ZpZMwbxrRnEpgx1OYzEsyCOkc++dYrovVghIzKxKdxbEoRXT2FKuQxs5OCyRPeQjQLnBeKS0gxVFMxqRCkJbEoBPIAQlY+lbO2TXAFdySviudoM1UxYDzGOLWiz4VMaje/GqtZ7wkugjGMaWa737DbbBCBvlsQomv2AZrcLFX/fs6JqWZityYMa0LzTOlj1FVvmTQWxhS9r4ND0ozkGUKvPBDvkHmiOktO6rUizfW2GosLgXQYqUXt7efpwJyVQ1bSzDtXj8FUbG3HZWCXIh/dOKJq/pkP2+bEWxVJxWpQpdUJK03a/ul9h3FekRbj2O9uSHmDMR4xoXmUGfAF3y2RZj2PNXiv6NSyL1jv6YYFzt/HesxjRjBUC05KC8isrVmiYX7S+EaVihFDlbnNfzrNznlmtV5Sq0qI56o+OatFp7YC88w6euxqYEqJIQbWy1797oMWWOfLjmwyZ90KOwbMbJFstDAyhSIFsbrAMl7A66JKvFIB3pykzRtf37o6W5uIN7oxnzIle2s7TvRHGS7cIyQPebOaPH98zvsC5B57kTeeUx74shwDBO/hD3MqJIxpXq+nIMKjCue+xXQqiE7PdapT4Ii+vMFPeVDLPGgBtad/q8X0nTZ4vssLFJFTigDQVjFFmeu6A60Q0T6xs1CtkMRQJkFIWOPwDpUV58pFbwjvWEInTNnx4taz32ss+BQ0Zfd8vaau1GBJyXu19XtbjLkoNGixmubb4F6RCrbilDxDtdIKgML+MLFYqATXGofUTBy0CNkfhBAqQ2/x1lBqwgFdFJYLx3qpmTldVE7JsFBUxlnBeYvzLX04qnLHOy1abGhKCiuYoOftWO1r+7lVxc3cyhoaQx58zAwL5flMk+CCUTtyOcP4Ld6190uHdZdcXl2C/RY3Ny81HNCMpCJQI7H/gFom5v0zUnAMfiDaPbY+wy0/ICy+jAVCtnQm0MVILUJnAmf9BRsmnm+fIU0SaS1UY9mWhBjYp8RBHLsysp0c74QFl33gLEQ0/M2RfWX0E2PecJMO3IwTtzPk0JFLolA13C9lxpIRUQl7cI4oECss64K89bx/9g4eQ8iC94GLp59j8Gvq7YyYTCqJIp7L977K9ccfYsdn9JIZ3MS13RKNsFA8W11gK8g0E16MfE98gjBz4yvJGl6NBjx4q6GbBk2tRSrjbsJaS1xEygx3dyPrecWy+xxX3WNW88Bu2rC5fcE03TDWHckYjIsEBLwa1YjpcWbJuYkMIXJdNqTecrjbqJNnG96CsSysZ7CGMSU8joCBOmGTRUmxhZVVd1LjtQV7KKLOo0ef8zYUH+W41lSc0aGqltxcW63uj5wA71JmjCkt780g3uLD0BRjmXHcs9vfkaYDMSzwXpU9xmiytpQCOSOlKO/HBsLijH6xxsWO2AVi8DRrXm1lmqCHfCQkAqSM5IKLnpImKoGUK+lo4CYwJ20jSU5IVRdnwTSb+8IyeB6drwFB6qy8FWe5Hi2bQ+Ts3YFUhd1upFRt74jo4kNQAr0xlXkcMcDQBXw0eJ84jJVxf0fOe6zRto+S/4VgK9hOjc4aKdU3VVYXR8RmfN/TdR19DLzaHJizItaINoSrCEayRgM0RIKWXGyqXi21ZozJzZ3UMI87zp5eYXSgZH/YETqPs4q2KcdLr28rhfPlwNmiI88zi+WCcRKSLbjYswhL9kXjAOaayZIQU8jMiM2IyRirC0TrDcXSPFsMPJxUH8ynx8WZzjufhSQIR6+Uz5Ya86m/Ob7GCXmRdhmdygXTPtf7xonh3ou2sWk+Y+I/Vk5v/bplIb793jj96qhf0u9O7SJ52NrhVNgcz8WpfHrr/DxU/JwO5u1j+l227+oCRclpync4ve9WeioUbDFGi4di9QNzqKbbOU3oNRWsr9SkOQUuwFms9L3DhcqvfKPyrVyYsnIqSqlsN9fELrJcesY5q3omqclVEfV3MK1aOhZQpcGApgrVNh8HaY6stirhtxxzapTkixSitxqY54Rgha4HweKsFiTDUFmvYLXUVo13lhgNIWphJkZbXd6DdXqenFXb6CPgd1wb5CwtzbU5wXqFraXl9Rgr+FjBqIy5FPDBgik4p0Tg1fCIStZxWCb24xZfKuvlglrPkfqM4CPB99i6I6dEyS9wTliuekL01HlPyp8npzu62NHFDlPgovQ8Wp8zMeGMJ8iazz35KtULw82v8snuJdtxw1wnsgjFWIRMdsKmVuaxQLjFy5IgCzwd3hoO8wExmX15wUZuuJM7XsuB13lmFq8puKhKoDbWvLSEX6Tiug4njnEzsrZP2d5YrvorgvcM3Yq6X5L6FWfvdLgQcDVSivDo81cshvf46Js/zXbzTBVGBbxYXMnEzjFLobOOq9Txf/GfZ2k7tvKSbFZ8K424hcXVwraAqjMSqRYEyzgf+OCL72NwHOrIbj9S94Z9nJgHz9BfUkzlwAt8tYTiMdaSjCOaHlsn5lSwdsFi8thauDEGWwzzZk9vhM5ADRGRSgyeUCpP4gJXPZ0PQFZyrodpmulMJdhC5yILhKUId6ZyXWZy4y1UaeRStBB0GMSqGZ4pQouVU85AlRaloMOq1RuJYzicaZNPHSfG7YZ02GNELQqs+i5iTKUWdYiWrEZiYh0+9gyrNXGxIMTIsu9wNuD7AZsmvScMmqczz9RScM5pyxdtN2GsFiIiDXkTDA7jOmS8I6fMnCvTnCgYVenNifcvVF6t5mcZDfPxfPQKkkQW64FchelwaAOfafzYeoLySymkaa/y/6hmcT46Xj47kNIGKRO4lU7ICqEQPBg3cJwxjdUU6eAqMcxgDL7r6Ieljne1kGe1n68IplbE3FsZGANYSylHFxOB5mHyUMkS7ExwI7WO7PZbPWYLeZ7pfEAQuj4yTiNnQ8f5oseZymLdgxXG11vKbFj1Z2y3e6YRzNRCIimUOlNbYVJdAashrIp8H+eNY8tC7pGIB9s9afSITMgbv9dPoU30n8qh+WxkpdmXaWFk3njg0wjNg4ffyPR5QxV0fEP3f6BvTcBUVVRxatx86onveSX3b+g4Pyiv6m2r/ofS6gdFy4Nez/2h/Z8MQanSOouittGChlQ9TFNwjbDm1LUaY5SL0fUGvGG3LaQquGBh1K/Rwjxpts7nrsBby90Bru+ErdEUz5wyZ0tH8JZxUhM363RQMh5qUY+AYwaNE00Dxor2vp3K8jrvic7jPTin+RWWjDNaVDgDnTcsh8oQlZRq0BVz3xn6QVguhGFoUmFvCMHQRS1ISltldp0qJRHIzRrZ+SbZbJeYLtpFOTxGW2Ilm3uPA9EixXghz5q4HIKwWgZwMI6ZKi8ZFo6SEilbNWczt1Sxqkppq0cjmRDB2kDhDCk39F2mi44kPW54l3yoSO1J04bOXfKVy6+yCIFt3tDFFdGfs+4eIU74gfM/wPedVV7uXrCZn3E3vWJXZmLsWC/O2e+2uCI8ihdcxgu8CWppX16yO9yQZUdeJA5m5FU68O3DSA0dZZwpNdN1PXNO2g4ox5WDwea20LSWaSxEP1FlZpcmzTGJBkmOlCo1q0OvryMURz3cMix6nHmKq9/ApNcM1pFJ2jaZMgtxPK5nfH//JS7MOVkOJDvw7cMWWTp6hNv9rNJva5hqIcaO/SHhvPDo6RnXLzecXXZcXa3hReX6xddZzLCsS81rso5kDNUU7X1XJYE6EzGmElkwuHMG12PsyP7wW6wv4dXdHR+Xj8glE6y6FD/1j/ijX/x/4bNXi/i6pZoNznp2ZmScbhAz08dzOn9FoeNlvuM/Pv9ZXpbbxl9QjpY1aj9urNqta5tULdml6oocyXgfdWK1Du8MUiziRf1IxDJPBw4pczhsqKUSrcdbgzoOFjVALIU0J0rJRB/wwRD7BbFfYn3EO+VABWuwomOCGPUZwrlTeqtxljJnrPUoK0Mh+FyVuJ7nrLbq1jcLdmFOM3OtzEWYUqaWzLtPH2Od1wKGjMnCITm++YmiBDYEDq8n8jwh5CY7Na0QUgVPLhM1T8QQ8cGBFIK1XL+8Jqc7zQhqLTuR2pyEDWI6Rbham825SAyVrgfjPMFHYrfAGNjuC+OUqYiKeY2iJFUqYo3aGWi/GGqhGgFRm3srWqwAfOGD96FOGJt4ef2Ki/WaLlqGfsAaldxLTZwtOtarBSUllSBbw+EwIjVRq+UwjmxvZkgRk0zjgVa1ttewAIxVyXFFkFKOpRUP5bTHsMdPZ8mYU4DkscUBb7Yy3t6Etyb9h4/JEbngzfm7Fen3v37zOKoc+0Jtrjsee3v8viHUfJ3eOobjo/foz0Pk59NI0amUfFBsyGm/h49/+kR8ZvH037l9VxcoR2mZVnbqb6LisHvY1xgHoqssQdUvR0+FzlsOVrkW1mgIWzWFrnP0BcYJ1gOIFa6q8N5jx0cvhOs7tL2DxS0DIomcle5lvGOuhVnqKbF36AIlq9w2dra1Bgznq4j3mnsRjGG1UB+G3dYiVWW5na903tIFYRhMy6cRhk6t8H0QumgIEf0XtI3TBWW/55YIvFxYfFB32VKrJob6+z6hNEKWtSojLo3MZqMmE4doSZMO5CVVjLUtH0jddZ0VXFepZYYc6AdDL5oGoJ5UhiGuCC6wmQJZNrg64NwA5gxb77Ak0hzBDHg7U+2eXJ5Tq8e5nvXye2DecjlckKsQ7JL5sCfZibnsqQY6HIvuXd5ffY4khug7FvGM0e/I86T14Tyxzxsmo66Ss4xMacthP3Og8LxmthKY50QpFfXHNTjndBCoWghbBI/DW8uYM53vmUrCU9jlCeMist/jasbi2ewc3npc9jhx7G8iMXhSOhBMpcdzYQLLaJtDcOCyXPCVxfexNmfMdstsZ14eKnaICDObbWZfDMkUfPDUbNgnlWUjjg+//pKb6zs++OJTvvw9X+AXP/xlKJkPp1/lk9uvUZKAZKwRqAlbHZ3tCMYheHoxDCxIpXCdPuZFfsVY9gzbR3z+7Ev86stnGNSe3WL48uMv88VHX4W8JKeJlLbM82s8hu38irx8lzLvWcTP4cMVc0kYec4X1y+4uflVcvPQiJ1lzq5ZAniqqMGhDxbvHSUnqAVvDX0IBK+qFWe9OoQWpyhXzUwpsT+MzOOo6pLolHjdrPK1SVvJZQYsznlc9HRdT/Ta0olBFxIYg6RRM4BKS6qeKmRNyy21UopgPJBbJIENFDyljCq9rYW6e808zkzjnjllchVSLszzTPCGR+dLRCpTztQ8Qsm8uAu8uInERUfNld12Ty6ZSlXEtRkjOqcLoekwUmvG+QEbHKHrqCawvXlNylv0rlSFi0URV9f1iARFh7B4ozykxVDovJDF4oN6RuVSudskUqlgjaYE19LaPUrorLVQq5CzFvq0qAgjM6BCBIDf+9Xfi+Rbwjde8PP/7Ws8f10Z+o6+C7jQIXKHt8KTJ9oGGsdK7AZe39yw2RzIFZarnjzmZtJpmcdZXz/rZ1tNJYteNyZVbEpkkxFTtCVmNUfKHo0AG8kXe/QGoZF8j8QLHgToccKij8VGPRYxx4n8jXbNfWvn2OY5/f6Nosg0FMVwXwacOi+cWkCtlXY8nFNBcqxjji2bh89x3PeIeLyFpNyrgt5Ckk742IPfPCxmPtUCOwI95q2/+9237+oCxan2CusF7yDNtIK8AWf2+B2n3qKeu6pZO0ZTMnNRtKWLuiIqovyONEOphs4KvRdWXWEIlvNlZS6OzbZSjK6qtruixRCWgKUruhoxGKLXTJ9aEiEYsD21Zha9epz00RKMcL7KBCvso0K9XacXlLOVxRKWCxgGQ+wgBvCu4jx0nSYNu2YpP/QNqkXwFKwx9J0WJSfeuLtnk/twz652AaRU3KmQs4pUm0LsjUanGH0tHww5CfM8njwXskxqPpUiXdBMIHJPLRFcj3eZ84t3SJNg5AzsGRaP0DFOe4QNxiVCmagSCDYQ7RYjhs6dI95TTUKmiTzvGPPMXA/sx5G5ZioJGzzr5TnROlzKJGb24wgGtTV3noJhTpld3jPPI9knRjLXeVJSrKknLlHoevCVMiZqPioxRM3EvEWq+tOYYDmME9buWFjDOBaKRLxoMq61HcUOirpIRLKG5bnuFmcsF3nNWVww5RlTe4I7YxmuWIbHmCJUCbzaz6z7iHdLvrkfuauV7ZSIUVt7zqnEVEh0MbDZ7EEML59fk8aCdY5SZjZpQxpHdRT2nXIkcsJhWUjH0q1YssDYI7Fywro9i5rAaY7K7u45zgjVKb/E24BnTZkGvFvg8M1HCKTMdLYQGSEMkAvV7PC2EBj5oH/Er9vA5rjEiBCSUZ2ztZB01RqtIejsj6mZ4CzBWqx4jLFYp4aBWKPtwzqTxok8HZA5KyJjoiYOO6u5NEiz/y7NtKz5xYReE35rpXNGeTklIVKpITLPI74fEKDUTBWjxQpQ06ycFuOZ80yxjlJ10C/GUMYDaU7kUsmlMKXMNI7kLJz3jhgs6XDAppGUR0qufONZz3YaeOednlRhv9srz6vqZ+CMDnTqPFwUXTGW0AV8VLn1YYJp/5pSNhjrQKK2cD04X3Ehoh60eu/bqDEMq2GP1Jla1DVbjCFVuNuP1JyxvdMMoXJ0Y80tl8u2IkGVPUizwa8ZY+qJgNnFyHB2QSrCF774Lr/wv/8WffRcXqzpbCY44emjM5wtQOHsLLDbHbCmErzFZB1vo3NEHxkPmoZseyGPCZx6uUwlISVTJw2F9AI4RXg0iFKwvjVq6jEFWVs/Vaq68xotq8Qc2xvt/9YCObpwm4eTvGkTdOU04WshY07fP8Q/4DQkn9qVx8c+y8r+WPgcn+2+nDGtljrudzzW++c+li2/LT3EmJPr7NvE1/tdzBvfv22B/1DJ9J1s39UFim5CaEqWmoXEcVJXBc+pD4ecsjuMUUJoKkrwq0UZ6CFYuuo4pIx3KrXd7gVvdTUWvCEOgqswVWHdwf6Q6EyhDzAmLYeccfR9RGoiek8fYLV0qO7AslyqQ+p+P3J54Xl6EcnjjtU6E01lXDrGCVynBYAIDEtYrTQgse84+ZhYq4WCQcmhoQ002jKyjTBcGRaOEAxjgzeNtaSkA4R6uACm4p1tF6H2WK3T82ONPncyVjN/JkPJ2u+vogO4JrRqTABpIhMp2WDKQGJLrVtCvCR0Z4T8iqkKkl+Q7XuY/l3q4cCc9tjqdV6iAxPIdcQ5T+dXpGIQDlR2pHJgTpntuOOQ94xprzeHddzsJ5yLrIaOXGa2OVGLcNjtSLVinGW9WLCfDlST2MvEmBJ7qdzsR/WrqFV9E6QQXIfUidJ8LKStEJ1xhOLx1TEVvb6WtWJJiPV6zeWZaj3VFIxUsnEEKxgpeK+o3mCv6OM5UgypJKwbcGbAyoAUSzaVXSos/UAOlv040hnPdtpgncLYIQYOU26tRe2xxxiprvDkyRX/5T/97yxHwwKwqWKMo1hIVKaamBG8TWSZmUgUEZZmjaEiJeLtSImGV4c7TNZMHucKfuiZc9ZAu2nP62/9Fs4twAVsF7HGI1Rst1RlWK5Y46mmEmPhkfXk/YIr27OTCWOMWpE7Q6gWSkFKwbdixyEkqTjb2jg1k4vgXcQYj3WeIjoSzCkxp0kDP00lNImqDw7rtEVralF3ZdPMBp3F+qCeGyXh7YJOK36aFlkVhKVSa6KKBxw4yIcR6z0UXU9XCinl5iOk3KCSE6VmcrPALwVKLpqknitXZ0tFX3OizHvqfGBKnm88i4iJ+L5jntUJF6q2XRFNDxYHUXO6pnGPNRCjBaP7vX65IeWtqoLsEqE0JMljvcHaRSPu670cnMPbyqJLlNQIuc3huGLYblUq3eObdUNGmieL5vg1FKk+WGVLQpjVEM/o/XR7ewfVcHG+4Hu/9z2++a3nPH99S/fhJ7z//mOuLs+wktjvdlgL8zSS58TQOZx1GDS9WETvjbFMFK+xAhIKfafGm2kCPwTmkpWwm5S3ZK0qDaVollA1ipA61wwxG5rimidLbvQCg9oLKEm0tvpDTh5aUuVTM/8bZUhzfH2TUnJU3aCIXfO/OtnAiUALcXwTufiM6fEYsNheVx4CMcfp8Vg8nBRFD17n9MS/bfnyqe3TIYLmwb//E7V4annw4RStEZ21Kkt0OvHWqq0LU03ra+ukHqKhzhVvDdkq4S7nQnQOLM2V1ehk7/SiBZ2UooN+cCwiRJu5OINxNmwOQiqOUiJ9F1l1gT5aLs88iyETI2Ayi8EixZNTxJDpuwNSCl2weCdMK2E/gljIxVGKNIkwBFcJQVcL1ir3xVlNKratyHK2FS7+/qLKTQZ6XLIYsQSvrRz1o1ATJKx+FUViGyEMRPS8hKDtpJx0oFZ7iA7nAlYWxDjqqnUWXVEa8H6m5pESRdUc4wBUat7qJMce6jWLwbATw5yg667wi8+Rxv+K5QrEMO+Vw1JyoYpyh5KAcQFnOpDKfhpJZeZQ92ozfqvmXk6Xl0wpcX55Tjd4Xn37BSY6CEKWymxhnzPVemaUlClVmjto3+Lcy4nwqP/Uh2FpPeaQqASmmujFEwhUrKq2LNoKLBXBY8ms1hfKjSLQu0umSahlxlVHML1ixDUgpjKikvbBeqY8cxEXfHjzbd61lWwDGyvs5pl51haJb/bl+92OED0vXr1ksJG+W7I6X1IPW6itheUtsXO4KuSk5mPZeTbs8cFR40JX7GnHDRvwOpnPVR1vV2eXvHz5gloS2/Ga6/HrePHgHa5TJ1jnF/jQYVp2kgsRW/ekbLD+CWvn+cr6+3h+8/M4YxhNYvLCaoI6F2yZiKEn2NDcixOIupSmnEAq1vYQHQZNKM9FOSWlqMJETQZDa9cEQvDEEKl5IjuruEFtnqEt3taK0AV1JzXNJAvrGqfEIEnzpIw15JKaaVihGHW9zSLM2ZJKUXdbo2nQecrUIuTSsqVSpuYEZebJk88TY8TMVdsEtmNXIs/vBvrFgPeO29s9aR5VGWMtuSoxuDE+yHMip4kuBGxsrS+pvPz4OSVvEcnK8bEWK1rg9NFi3QLjHM7oIOJ9JDhD5/bkdFAiM+qXQ3Js7jQD6ORgK+q/rXLW2nxEUEJuTXofVG2zVOtOLYnXrzekWXj06Izv+8rn+JVf+5D/+urr/NZHHxO6wOX5+5iSG0oENWeCNWAr/WoA65lS5eZ2rxyuqK3xPRO+c+AqaZrozgIuWOqYm8mkQ5qNQm48l1IKGG2bGQzibCs4tP1TW8aQtsHQcyVasJkjUvJZm/BmQXH85u1kvTfQjrZna7kd/baOj+suDUE5LcYbGvOAl/TggTeQjXo0VztKjz91zA9bQ2/u8bvxTcyDwu54HN8hgPLdXaDo6l+/SS2pVydMnTxAT5Jt/Vkl3LVWhlUVzxFlKAUSuhI1RomhxigZ1Vk1Caq5wX7WYG0meMti8ARfuFzC5Uo5MClBFyvvXnT0oXB5bjg76zTq3EwY44i+pyQ1McIGcl4wHjaUWlmuDWEv7A+V0CLfY2cJwSkHxVW8LhjwQZU/ina083GCG1tqKErGU6M1hR+t06wM9UmqOO+VIOsNy8W7pHKj0t9JjeWi+TI2JKw8o1ZtVRmrIYNSDVI6xD5F5BrvV2AmhF2DO19jTSFPEMMGypJUKlJuEYn0/bLBnYJxkWV/TowZqR/hjKESqaIOlQY10TJS8cYw10KqE3MZdUXacoesaBzBfhrBeKx1WO8oOLbbmVA91jiV5JLJtrKZMnOFvncciobaYQzBFbxN9J1jt59P/d1jHoa3irJ0psMRCNViSsFIBusQJ5o9gmGqFkfHcHZGNyzVHdSsmRJgC65aTA0gTqFxqUDCkPFe0Y4kcLO749244EURtqIJ1iZVSq4EK1gRyjhRxBJiRGplsYiY3USdHBElnkYKqQjFO/CG2YA40ORXYTPu6TngbMBiObeOznTsa2WUhHeB29s7BCFWtJ0TNP6g1gnmHWkyiHQYG3FOVSE2eIJRf6HpsKPUypPVB6xuf5loDJXEaGb6ZKnzRGctQ1zQx54k9woQVQbPmOAIkikURU3GmZzm5m2SMcbQx4Hlck0fPaHz9P1AjIE8wbjfYUVTln1zQq1F5ffr9ZLg1QIAo/yjlBNiVWFESYoS5YxaRisnI6s3oRa/+w1H+WbNmSSVipCrUGom5cxcKutF5N133iM4qPMBEfVwenFXudtbrj6nfib77V45NuaoA2krZeeptTBPe6gJ7zticNggbG623Lx8pgiKOAS9dzHaRg6xAxv0PkSLb+Md3s3YOpLGA8UIIqoyzEnYb7bNNVZToUs9qqC0QHGoFYG6yWosSW1Sa/Xr0JaY7zqSzOQMVxcX/OE/9Pv4pV/9kN0+8cmrVywXgXcuF/SdZ7O9BTtzcXlOmmdCdJpD5Bwpd+rBYgrWKWo5TjNTTWSb///k/deTJFmW3gn+ziWqambOgmUkqcqqrmqCBhYrgOzs7Mz87fuw5AUyMjLonQZ60A10F0kSGcTDmRHVy84+nGvmHlkFSNdjCkwkMyLczdS43u9+5yPEMZJKxcWBMXpWU8R7Z+xKzri2MKfZnoM3VkGw4lPpRQtHO7D0+P2A6zZlfQxdO5EGn85EPnHf2Kn6NEp+xBHtUcjyZEU/rfVH0KH6KWQ4Oo9OmoYjMDgN/uzf7cnjeXq/f6AjeWon7o/nv8Gk/Bh8PB0nnY7d/qs3/6OXnzRAwTnr1nFmedXjeEcePxciridUOnq1FLUoKSu5WMhYqQZqald2Bw+lmaMlBu10J9SglCKEGBFXcOpZRe1zbWcisrawWi08vxj4+nXk5dWKy7PJAIUvjMMFigcRWrsg5R0pH/hwd8+hFlBlwASsPthjCBHGlRKHQvAdNHn78JnrxtgicbajNOuyofnWv03u+Hl1PUlQhDhYMmMqNteVLrALrlCKgaAsHhFhnNZsVhfUBLv9d2ZXk0oVcP6SEK9I9T20CQlfgnxHa5mViDmamhJqg7EgLHiZUG8hURo8In9NXf4JcXvwgVpvacUj8hw//JqmjaQFKUdL+EBVe5wld+WP2lgjBE9uCS+RUJtFXDdFmmN0jlAa8y4RXER8I61hXzOHWvCrid1yINdCCIFcK6UmluRIySh527kIg0QrfmwwACicq2PEWeGdFrRBlIGiAi0xhQ3Pz14yTZdIcTQZaG5CpUKoaHdV1NTQ3Gg6g1rgWk4ZglJT4rl/xnXxjBJYuZkimVrB0xAJ4Bdev77i++u9nciqsGx3nGtkoDKEAS9KaI7RRXIcEK/ExRFWKw7LgVRnUs1czzeMcWS49DALqWoXtyqlFKazNVodQYRRBtvpCaBCroGmYt+9tiUXRZISQuOgHhFFXaLKhCp86S44kxEks/d7puIZS2VYbRjGCT+O5GK5JUUtJwVxjG6FCwMo5FRYltl6nVTBebzAsFozTCvCEInDwDSuGIfI3CwWQDu1Xjt75spCjOeshsH0GkctQrUuJJkGSsloLrjkaTUT4gg9IVVbpXYGrqYFYjCHS61UFZI6DrmScrLP2Zz57M9es16f49IWcBQg5cq7u0BjYFqPpFSYtzu0ZdQL/liE1yw0RDSR0gFBmMaRMARQuPt4Qzp8pJUtfZZlmxR6mGOcqC3YeEmEIM56ikKi5i3zvLOTT7Uwu4f7zLLfoVIN2KXSwUdDHYg6GkJVpdbFyi+RnttSbXdu3C+r1RrvBnyYuL3f40R5/dk5P/v5XzJNIw8317y/vePXP3/O2YWjtpmzzRkfP9zRWoIwMD9krq7W1LowxhXb/WIjbqcclhnnAillltTZIxHG5vDeG6McA6g3QFUyrWUz0zSHdCAXY0BrxtG1TuI6CDNXkqLgxCzxcJzjnHQo9PPE8fKprVcfscyR/eDTHJEjUJDTdZ4c68mx5Vh02J5CiicjluP6yONI6Xh/T+/ndEv5Yz/7dMR01L88gqSnLBFHIuhPuvykAYrzjZ4FRKPRxCGqvcfBvPchHq/drXhqSpAlw25v/9FDyFJphGJiQ3WN1uQ0JrL5bKDVgvdW5tekWOOvo7sPLFelamVOmcP8kejXrMcLptU5Lqi1nLbGnA6oNvbLPde3Oz4+ZDo7T6GHJo3G/JjwFYaABawZvulaGrEMFewD5J1YkJXYHDQGOObBKIr4dhr/xAjiKw2xwrKKjTvqQnCfk9r3iMucnQme/8Rh6xG/wcmE+png3Ine9v4ZgQ2pvkfrO0rbUrThyx0BxQ8j0ta4YCeGMDynaaS0DfNhS/D/hKgzrUC5ow2vcVIQeQe6RtwXuKlQDgupLjQ190Ap2UBoCEAlzTtyc0BEtTF4K4tLyXbTIXics/TguImUkDhoYXFQPLSyMBdrmo2DI+0SqSg5b/vOw/YiQSyYbMIRfWBogVFBqXgXGHoo9nHX7Jvj6uxzri4+Q1ukqVj/j4CnUp0H8bQm4BPEQMsGRqSapkCZqdqsAbk2XG28eL5huyQ2NTLnhbN1xHvP/bawWQXG0bF/mJHgKVVYx5ENDqewF4tlxzm8RLzz+DCy7BPjdEVcGeDe5z017qwjiUp2BdfsJFxFKcvBZvPB94I46QLMtVHlmmia0SaWoBoy1XnmAl5nBKXWAwPKv9r8FVfjK/Z6T/N7mkSC88Tg8YMJW7U87jqrOEIcGVZr62URC2VLx7JAb1ZZ5yPjuCYOk8XbSzS3jo8glkQqwTMQ8Qg1ZZokVkO0pu5SewKsOTmORXcmS2n2XmqlOYf0/qrSateKLLRaaOKhLOScyHgqgVwqy5L7aK7wxefPESotJ1yPxU8N3t5FwjAQh5Hbh9nao8WAUMMxuAZVQCqpNtJyQFDiKuC80Gjc3e1oeqDUBcT6d6w5yjP4hgtrHg2n5pYKXhj8zHywgDvXPGk+cDjsuf6w5bDsUayclLLgtKDO24awfx9qadSaTDzrBDR3nUroeUKOeT4QHbxfFkSUs7MLfvGLL7m8uiQMG0qqbO8ycbUmyExLldVU+cs//5LSGtc3O/azVRysJuHs/CXffPcDpQmbOHH7sCME4cPHe1pt/dzoKKWwWg0WH+AdojCOExomS6DVQkmNnIq1vQerC6BZeu+x0doJ+AbaK01OK7M+/bv98akMpIeNymO+yCkg7TSKsWWfI8bRR3DwB7klHMGEPgEUPxoZiTuBiOOmHqSPkZ4EtP3ocjzuk598qio5PT9zjp7Ym/7c7Cn9aQjlJw1QvJgWoCo9qfUogHWnYr3W5899TGgx0J1hsNRGSxNUtAMS46DEYTu/TpvV0gih04FHPUf/xIhFV1qEvLmaWXLlh+uKczbqeB12jM70CXNa2O5nFlXu9o2Pe6h4hkGpteI7e+KxY0+jCWOHgR5KxenD4LyFzh0RsXOuf64tDyF4mzMq2sde0mPubeTT2lH9761NtTVchaiXNH3HerXqIK8yzwfTAozn1B74Iy7h9A5tbxnGf0EM19T0G0Yn6DKgEVy8IrhGii9x069o+3+iuoj3A3H1a3z5AfLviPHXtDix2/09gZHz1YD4Z7TeFaLOwVDJw4F5v8U1pVKoJGqDKtZdtKRCRRhdxKt1c4w+0JoVlznxIJWHupBcIbVKc404OObFnAbOy+ncItCzD+QkgHPqzJkDTF34uXIjNmQ/fkItSfZ884KLsxdM05X10ZSCYoCqtURLNtP2fabdNJi+KAacX5tzolZaM4Gy+AHSwkVYsc0Hfukv2KWZW1eZ/JoxDpx5z+73B+62e9aXE69ev+Tt799y6yuXVDZhwrdALUorlhYaV2uQim+eksEHjzu7IOUHfrj5HlZKvBpNGzEnYzLEwgcFywaJkyfPCV893lWjxF0gukBpivbvV2s2XnJuwjOgAVQCjkBgtBPnkJFVYeNXtsOOFlrW+g7WiS2iq2liPY3GeJWC1mJaAlWCekIYWK3OGMeVNQN3sG66s2LgtVruhPcGLJd5T/CeaTXhvAexYlDxnlLNGVeLuVGOxYCCp3YRLa1QWmVpR81Uo+QEpfSRWiClSquNvNReWiicr9Ysu3v8sse1gvORRUZut47N2YgEx/5hS6m2kLuj60m72LI1Up2pZTH79RhPbjxz/RxMtxLs1C/9vDUMAcIFQugiYbFSTRy+3VHzFq2ZIpXdwx3OD3y43pOzCWJ9KzYyEkWk9vfYxuP2VTDtiqg5jOSka7DHf31zw8VmZY6pMfD82SV/9vUv+Nv/8/esz1/g3MB09pw373f8i199zuHhPcHDagWleaa9YzUq0zRxdTXi4zkfPn5A/Jrbhy3aCruDcH9IfUyjRG8nzZSSOXiAzWpisx5PbrjDAof9wfrChkZwmRCsVdh7b4t8yzgEH8zJRG//tdOAbYqP1Ijwo1EKx989ZSPkdLtPwMzjWOBxETTqr4+Cj8D9CB2e3snpBo8/6H894ZYTsNE/uP6Pw9d48rjlqC15Cqr6Y5Xj88eYfFH+pMtPHKDYG1GKLUxNMbDRG4VByNk+Kc45Wp/VuyLglFKV2uiCLrMUp9zdMLG//n3wV6pSa+61312V3lFBbUeb19FiZn0YDzN8995EfEu+4dmVbXLmRZmTkprjUMAP5kwYfAdJ3rJHTKemPdvE7MQiUKqlmrquq/FeT4JY1WpfLgfS6VNpjmPLsPeOYWjEsDKq1Y0Ef05pB2JdQxNS/kBqf88QHdNwgbYRZCDGd0i5Q+nBePLCWAVXLeI639H4imH8EpH/QNWIVkeVARfOGVShWNS96g0iBedu8DGxpAOJheHsCokvWFJgWe5QH3D6FaqF3B5wrqEeklRqXaAkHJWWvS2UpSKtGs2KdLbEEZxnHDytmkbA2lALc8ssJaOhdyZVY1lSLmRNOOeprRBjRNUApGk3IkEcwQXOyshKAk5h7QZic2zCJRfPfsbZ6gXRjyfAW0Upng5yLBQtt4KUxSaQ3s4Y6pTmhCae6pRGoLhAxtJxVTagiVW2ALMIDEXZ7CaGMOD8hlsy+crDixX3tzuG9cjw9Uu+/eaBr7Uie0tyceJxqrRtQdzAMIyMgydcDJydX3D3+/esrlbc1I/cpwfWK3N87PdgkZ0evJBJ3A3XsCmEvCKmaK2yOuFlBS6irhGqIwZv830RgotW70Cwn/luQfUJXQVW4Zz1ejImoFrgmYWIeYKLTKsN0/oMESGVBE4RH9CcKK5xHtes1hvGIfZYC7PJa8vklkhpT07JBK/ObLClFC4uL1mtRsT33JVqzqdWKuKF1gqt2EijloqLI04VFwdKyuRm4+PWah9zZDQnUink5jkcDpSSmVMmzQdeP98QWmZ/98BQZhgCMpxxvXMcUuPyYk1JjeVhZwJnsPE1DVWr8hBp1DQjWonDihAcwTuWUpl39+S6M7ZIQt9F19507EAGBGNhnQg+BEQTk98RXaU5pbTKfn8HYcX93UzTekTwPRStIPVxh24LYLPOqj7WsRGYgRPXF8v50JiicnY22ee7FX7161+QquPv/uEbnEyANWG/ebtlftgxhg1s9iy7W85Wzxlj4XINL1+/4M37Hb/6xZfsFmX/93/P569f8PvvbkBN0yMi5to52HitdcFoK6VvCoe+iVUOewvQC87atqe1NzbFdfekt1GlaKNkE0qf1mV+zDr0izwZiOino5IjthB9HMMcAcxjHP5x0NM3yU8BzhPQ8aM7fTz4Ew7nePmxwPX4s6calT+I+//kT3nynDuT8iMQ9KfKZH/SAEXUtCe5KKg7UUm1tm5xg5w6O0LtYMa+MA0DJ8c313uhFLUdZXTmEDqKTHvxYK3HWO1OL/cIa3FyeiOPoxc6o7Mr8MO92TkfcrVeELXmYqM8GqsRwLQTdoxGGBwxShfo9hC20I6aMtQLSOvtnqazQdRGXn3X4r2gVKIHHx0lOVyoDMOK6H5BZs8QPgNdofxnokTSfDABndsjQMpvSdmBi3jxOKem7Ri/5jB/sBOe+5oozynz3yPO0ybp5YXPUB1p5YG0v8P7RpgukPAZ7fBfrBcjP+Cj4uKGefkO8b8iuglxD7hgwjcfPE0b28M9owsG3GJkd9gRCDQ3kGrBM4AU1Ke+MRMbW6hF6DqBYRyptfCgM1kyqS4kaczVrMdebBfkxBi2Vo+x6f0E4RwBxyoGzsOIWwAcK7ciEHi2vuJZfMXoLSVVF0/1oN7GayemRBUVR3MRH3ricK2klk3kl0yIaMWC3dCoI8ErKhmCJ2dhENM+7Evm8vIl4aFBEvZ+Ye/3XAh8f3NPWK/5+c9fU0uiPhv5cCicJ8UfMEbBK8GNaGuMboMkpb0t7G53PH/2c5YZXjyL3Ptr/svNv+ftw3fsFNQ7KJXVEBiuRm4uHsjnptVxzePbSGiRkCfGMjGUNUOZ8C0iOTKyQmW0AB6qpbR6wbw4jWWwhXacVqgcCyONmQkxEnwwQBUHilZq15lFfAeowjiMTMOAD0ZLGrPYCyjTzLIcSD1vRAQKgvcDz1+8ZBxGtEJaMqEnQ2tLxmJV69wBMYGsOKoTKjaqXJZEyY2cFnLDUktLoVYllYWcFlKZSTlTi/LiYs3h4Ya8v2ftBLm8gDjx5v0MElmfr9jtMvO8643Nx6RUherQYOPlJR0QYJjMsRJGz/3NQk0P1Ly1z3MPU4OGd4U4rlG1+H/njZ3y3jGGzNm6MLgN4oU5V4oKhyWz2x2ozdjGqg1rJ25IdRxHBjSl5WpjHUBrA+bOWCvHxggXRubFQtaC1WPx7Pk5v/zl5/zu27e8f7+jtUbYBN69P1CWBzZrz+sXL/HMiIeLdeRsnHm2dnxgx1evX/FP317zq198RaqO2/sdS0osuTuwxFK1XS5WERI8oo1WC9FNjNNEKcKDLOSitNbY3i3UFticTXivWFmknS/+AJSc1n7t/z8ijx8t8jyObB4vT1gWNZ0jPAEn+vT2Pxq7nK4jj4CCJxkmHej0xe8PHosd43GE9DRZ93jdT55eP9SPL38sQfePgrX/xuUnDVBKBWMYu1hUbBdRqvYwHceSjX4NzXYbpViEvCE787DTLN7dS6Mq5NwtyyekahfpJ4TH1/jIohhrc1TTi7P79zg0VmQobLPgd46xTwEQ04cM4VHk6kIvNlTBB4ixEbuzxnnpkc92QjIRrXtEuH08YADJ9bGTCWe9F4bgbFDiB4Q1wktEbljat4zuFQGrrB9X54TynFq/QXSPtkAIV7iwh5bAbWi6opaJQZ5R9BonH1H/mrhaIe0btDgTKPKe1WpFG19S0hpX3lHrNXWuaFuj3hZYl27xeoHWd3D4Jxi/NPeL3AITuSVUlaVlm2XnjFJtkU4FSsU7x5wTTcWei2WH2S5IYRSx9llt1FA5uMyuzoCSWzM30GJ0fW7H+am9tt67XieveGDyHmqhhYbzI1oiX33xFzy/+ILo1pT9Qlvsw1lro6K4JpZ35vrM2TvwDVezgWUfLSulOmPnpCDtYCNIySDVRo4OnHjGQXAt2KIsBzwjmgaq92TJPLCjuMb65RXr3Q7Ec//xwUol/ZokyhKFi0OzzA6tZAoSzsiLZ/BrBh+RAncftkzTM8byjFfhJR/rlg/ujvv5pn/ebNRDEPxKyHFBBBOaymK7fHVMsmFTL3HygljXjPkMlyMUs6xKiTbWdDNBzNY5h0yVQPCRXAu1VlpWKwUVwYWBIQ74EKnFmnKlWqCW9zAOkWGccCGgzlJi4zgSQqSVRKuziVZLohQFyTT1XGwmLs4u8M6cXq1mZIxoKfheCFpzQtRmuk2EVq2JGleoWkiHAwVjW0sDqZWskPGkbn+eDwspmZjzYozkhxvSYcdwdkZ1kX2BN9eZYb0B59ju9izJ9ByW+9F6Y7WAWiBaTbNlmEwDPgZwjrube5oeyOWAMvZzmCVCe6+I36DN0/pmy3mHc5Gz1YGz84iwgTggqZCbslTYbg+0VixHqZcA2oghWx+QOJpAyYuF1rlGU+s98j2rR8mgkc3Zinc/fI/IhovzicPhwP39Pa9fPePrn33BbvuOh4cdu33iYjBR7Xff39Jy4vmFEGIiuIrUzCoUXlxEGBLPrwZWq4m7h8Rf/PnPCf4bbu/2pKyUbCFv0xjs8xIcThrUzBgKP//ijPPdRCqV1XpAc2G/3bK9T5gRIRIGO8+Mw2jOwmo29db7QWxNfzqSOYKEP76mPfYAPa45T4ZDjwDov8FEqPbxisBRBXe8z6djp1Oyij69rf3jlFvTN/CfxP6fJkQG8h7tw3oCTkdh7yfH1z+VP/mJA5RclarKEIQYlCAmfpJm2RRzgUPqL7jC4IXWjPlotSNM6LoCjN0oBnoE7NVxPUWv2oJ/zAYxoepRp/BpAJw1y0IIpmsYJ2WIShy1NwubkDUEK+SzdmFHCJbgmntaq+tAJERTwqva9ZBqo59go6yqjRBdt7jZF5/+4RFnOhXtnTHOjTSU3H5P447KA4vfM/ovqNVT9QbHGSKB4Dc0RqL8GifXVPkGx5rcHlD/H1BZU5uyYked/x2NPWE6s/4et0PUUzknOGGu3+IFQjBRYks7pAyIX6gMaPgV45Ch/ZaaF3z4jFYatVWCFLRZdPdCRarZSVWS6U5qI4ahA5mMt9gtxCmTH2kF6+AQoXohO6U5s4DutLIv+RhA3Isn7fUGTqwWKN55JgJnfsBVxTdPaJYKu6TAav0SVzysRrJPsGRazWittHL6CuO8IOqRenQxKKIFFQPazjWaa9Qq1svkGrlWXDFXENLTj5vgTeJIZGM7fZ9Ywp7ohH1yTK7y5cs1H3eN5oTddsvZ1Rkf04HqolktkyMi5JI4lBucbFmPL5Hp0l5HhTonsl+4OnvNv/nl/8L+zcxN+ncsZCR4XHC0tuBdxNNn8c5TaKjLZntm5uDvuZUPnMXnnK1ecy4vWNcrhrRiaBOxTeTpA4OuGNRzkMReM01NP1RLoeREKcWaur0QomWtSMsmwsS+z1OcWA0TQ4wmWRYMoIQRvAWmLa1YsSSCc0NPB/WcXz5jtVmhWH5GwJgAC9cbyHmhLAs+WBJrFXPtmFus0VrtWSl0i601lJdcSM2TSyXnhZQKuVSeryIbD2VOxGEgTOfIsObD+8rHh8blqzW1wvZ+S23JNkJqbiDXjB0EIZdMbQvjasU0RXxwpFT58MMbVLfWX+R811RZGNkQHYQz7AgCzVuCsDjON4UweIQJXLCxbEkc5sD2cICWULWsJkGhB0NqMz2K4ij1gEpC1dE0wTE3Rc2yfNzYXT67JEaP1sr2/p4YPZebDV9+8Zz/8Hff09QxZ2E3K1ebyP3tnrwsHJ4rZ5uRs/VAXA1cv/+BVZxIdeFyHQlTpFThbPL87ItXbFZ37LZ7aNYAjsA8J6sNUGVZhPvbB/jiOWfTxHq14uxsQ3CNH95UHh6sFmNcOaZVZLVaMU2DtZTXQmuVzGNOyVMG9qmo9agpeWIU/2R9O5UHPhkBnRJdn16PP85MGHCoNl3o4OSUlXL8wZFJ+QSlcHp8x3blP3b8dmRIVI/7fbvxicI5orFH1uW/K4BSK6DmfIti7IM4aEFICxwOagxLv0Rvi/5J3HMMqcE9jkfUgEupFl7mfc9PESto0v6mKcf5r42MnLO5dgwW8BYHIY7K1DtyhmiZJb4zIt41YjStSQgN51p32fdSP2e+ej22YjbACX5oOOsUpLXWc0/s8ftOX9dqrBFqAkaHx/s1lQNL2eHdBuQd2iwjpGqm1T37nCwBtN3SdI+TET9sELaIfkktM0t9g3OWyaEyo1JoDaKck2morojjc3x6S0n3pMXj218R4wUp/w1khw8viX6hiIC/QLUhXvFs0QalzoT2HpGEyD3OfY6KWsJpLgQVBu+IQ7TYey3kMjP5QGtCqVjnDdCyECQgNKqzbIOsjaSJ7ApzqT05EnKznJuLq0t2uwOHw4zl4pjtMqrjfFix9tals9LASkeCj/zw4QPr+C0/+/xnDD7QgrMdd5opu4NpFWozx5dWpGAR/2LsWHOgvRW6qc3ri/M0IoqlrlYs/dK1ZhqVjjq9OiY3USTj14JqY18zyWWmFKjVc3v9gKxG8mHhs3/9kn36lrOwYkkH1s3jquB8QNpCYSEVx4Vfc7/don4k+YXDLnFIe4o3q2at1l21CoMJfzXgQ7SgQ/uGIjRaX7zVKYmFxIGk9zzoNe9kxcafcTm94lxfseIZMRYGXbMmcC97buqBz2uipELOifmwMyeGrPEuIL6fSKXT22LeliGOrMfJQCaWXutDxIWRSiGrWhtvVVxYmRVDC9EHNpvJEoi6QFmdMSRerFemldQBSaOk1vNHbGQRpBqzJ4FWZ9PBObM8V1VSWqzBeFlYUiGnxPNXaxMVD5Fh85xhc0nzE9++3VKzZ9qMHPaZZb/Hae0dMEcXhfZNcaFka2sefGAYrKNouywcHm6obWfnKo5j6AYSGMeAY0XrwmMvQvQRL8p6yohY6q5XwffxYsnKvF36+FzRVnEUE78ez0na+2iq6cJUFK0Z2xoAWDQ89HNmXHP38Rq3drx6+cKSemvm17/6il/90wf+5m9+SxgGbnd76yEqgbxLeNdIy4LTDXkVKOkj4+oSZOL11Zq7MvL27Vs+f/WM1ThBK4yhO0vUWYN0KdbnVpU8F97uM+vvb/jrf/mvuToMfHj/nrhyPHt+hmoGrfb6jJH1NJ5GVscWuKejFuisAvbCP05mTlREj8V3j2zH8Rj9akcmxu7hUUdyJM+O9/F4v080K59wMK2zHH9krPR07PMUEP0IVZx6gRS0tUfGpD25fz0e4ykgO97HP//ykwcoxoQAGN3Zmglhl6QsSU2og6XOtgaOx1HIKdisWTaKtv6iY508Uo/Ytr/2/bV2voujxFJljwxMCMJwLO6LnP4+RPuduWesJTgEtWTYYOFyoLYLURO8umBAyXdRrnfH3Yl9GI4R1uJMdNuKdRN5D2E4Hq8L11zAh9EW6TIjTLR2wIknhr8mxJ+zlP8VHx0r+ZKkv6UVyJLxtaHhQOG/dB/dC9D3OJ2oLbNy9gnOas4UlT0avqLVz8mMtLTHu9/jZGFyUDXiZA3DGegtbfkd6jweSM0YrBBGNIxQHU2iOSWaWkkhjlJTLywLDOKNmm8VUcfgxayLqkgIDN4zElAXqG5hqXs0FJJUHpIFQ1mB2+NOZllSB382S1csUn3AceZGVj4gueGTZ/SBiAMfePvumovVJWeXVyZ4dgGRCVFIDtqyQK5IEyqKdxknSu35Fd67HoQFQu1Uu6Ak1OZDSFPQDLUa2O4nWZzgvCMvjjCMbMvOdutL4W45oDSWOUGBj9+/p5ZK3QjJZZ7FSNAVU2skSTYOaTMp33PQHane26h9WvPd3Uc+lmtmdyDJwjR4VmthPa1ZRSV4IUszEC8ZPVoOvY2o7BwoJEks3OHljgOBB75n8pec+xd8Hr7ivJ2z5hnwluv2kf1yScuVZZktj0atoduL2OuZkhVy1tqFhGoFll2rZeNOZ84UZ2murSTTRDhP8IFKRRps1iPnq5UJwdXGqA1zX0n0lFRNjA20kq1zphRK3zE16NH6jloqpfb3vFiUfc2JRiBXZUkZ0caztYmu/eqCsL6AYeKQhe/eLbiwwbnA/cOeZT5QqPh+LhJ1ve0ZcjO2R1SJ6wjBxoUPN3fkfE9JNuozDZ1pZ4JrDNOKirFMZpt1BuRcZTV1hqynS0vwSIHtPnOYD31BVRMC03U6zoPTjlMaWjPae3holvqreAMz/QS+3215eLhj3u1YD1d478lL4vbjR158PvDznz3nP/7dN/2c76z7rDW8LkiFdDAAOobA1UUkiHB2Ds09oIcDf/XnX3J7I3z3/RvONiuWZU8tCxcXV3y8uWWzFnIOlNTYpsJ2VvY5cvXqc74cLnl7c8c2ZZyLDOOAo7AaVgw+GBNXC3PKLDlReoDeoxakL1hHZ8uRRZFH5v3Ir57ySzjiBD0Bjj966ayFkx5q0NpJH3LEA38QVPsEPJ1AlJ5i3E73f7r2CcjI413253XU3ggW33HUFD19DEeupRcD/PHn8V+5/KQBSsqwpNaZEenhWAZOcj7a3B4Ra6l2QnPO0K4Fk0HJ2jtvHj8c2oTC8U04OsfsNk46IPFySqZ1HougH4RxtDGOj+3EqPieHue6MyeEDk56wJr0+zgCnRCNmZEuhH38INkuh3DMYDG9Sspd8AXE40y4Kg1HygtownlvOpz6gOe5LX6uoBIRucRzTZN/wLs1bnhF0rfkes0qrmjMFF0I4TNiHHERyuE7JK4Rzs0NFb8GvUPTPSK/YhjWOL+jlneoHPDrv8DxQM43OPcMdEN1A5p/i1djdsLwAtEFlXtk+AqnwVTxHbFH76kxUmslymA6n0GpsZEOM8shgQsWWqUB1UBzgclHllrJTrnTPXc1k5pxaA3b8Q8hkGtjtzt8shs55p6cO8+qKLE5fBsYwoCyAhkI0qhSefP+e75eBYZhAyjBBWScAMvsKG6h5AUpRs/jBDcqlGJ2Y2+ZNbUtZqsWC3wrzpENx1j/jBakFlxqBFdtdJUqMXhSBVcqcXBI9MzbPdZWkmEc0TjQ1CLw8zqw3QuDE4Y64OZAqQ3vPKk8oOSemyHcL9d8LB+YZSEBONuB1wp5zlyGFVOL4DIVR9V2cl23zvaKE6rYCcvhLFtEM80VFlmobLnUDVftirN2gatv2Lc7HuqWIQlLSlSUGAJ+GMEFSp4pBeZcSMn0Dk4dwVvmjRXBRXwY8WG0tOGsaDH3jTRzYAwCPoxcXF4wTBONRt1bUJ44bzvRYrkeUCxNNVXTnzTL+nAOcm0mNo/Rxjyt4BRLi10WUnNkVXKFJRXOo+dyslFi6SGOKHy4m/n4MbM6Gyi1sX3YU0s6qgowqqIgPX+j1UwuB6I3XUVwQqFxe30LuqeUBSHYAqmKiAnww3hpgIEubnUe5wPrSdmcDcQhWDeRWP9Nyjsedtlea63WAdgasFAA0WZJzqJoFWqvJbBwttoXQUXtDAvAbrtl+7DlbL0il0zJhYuLc4YINOXPfvkzri7/gR/ebfHNrMWarBftoRkIdGHhMB/4N//650yriJPMPGfWfoMGx28/fsuvfvkVb97ekNsFad5T24HN2uIAcnY8bCu7w0LRzNsPd3z35j1f/vxr/qf/8d+y3+749ne/Ybfdsj5bM60GG7PVxlIyh5xYcu5stjlY2mln+3j5r5IIjSfs/j/nVnICFMf5ycmK3OHBkbWxq3T25TRaOuGbT8ZLgq2bTZ+yOY/gReQIRB6Rz/FqTo4P5ZG1kY5gTFLxp1EoP2mAUhLM3voyajGhqWIn6pzpr5otbpZbAE0sIbVWGw314klz96CnXZ69Cf1F9gYsrIMFfBSC5frYfNEJPgghNobYGEfrzXHBrheDASFFzQUThBDFLKWiTCPEwZFTo1U7lnMGQV2XldTWq8A9+MnGOcd2zdgfT1OxCIbaY5ldtUUCZ6F2KLXaCduFBdUDrV7T5DdIEcb4GepuUHdP1YXYPNIyrf7QWZuKakbGf4X6B4Z8S2sDTv4cD+T8FtUR2i1++B5xQs4fkbCmyYCGV7R0i8hzpH5PK/dQHc6vqHVvb4h6mjzD1wOURPMTTRdqaxzywtmwxhchupFUKvf7B/bLQnO9WA7r9xjcwMBIaIBrpLLQAjy4xE3eMddOz6tSuzXbe2GpFsCkenxfA60U1gSu/BmTrpBswWbrcEZoowkzg4mjM5X37z7w2ZcDAVuM7VsWTc8UwS8FnZWqnqyNcCi9q0TBeapYZb2gZGkWIEUjBNt9+WY6AVUBv9BUSTnjyGafTwfOpwktC6U4Mo44CtrATwPT5QXufsv1x4+srybqhdBUcPdK0AktCe9X1FKJEthzYKEy1y1V90j0J7bRiSctlcv1hlf+c16UDffcch/uyX18aNSusy+kZlSwUEWaLawq4EGjZ+8ai894gRUXhCrM7LmrB56VEWmOIYwM48QwDuDUnEk1s8wH8nyw8r9gwWYG7h0SBuK0wg/RQtNyoqSDZZmoVVlEgTGOXJxfEp2jLomWEuNqsMJHGtlbfo2dhX1nSuz8YycLRyqZ3Cq6VFLz9vxqIRUbJZQWyDWzLIWSF55dbojeMacEMhCLMU/f/7DlMCtffjFSq7Lf7kCtfA9n9mLpYllwxsy0Qhis96ipsjws3Hx4T9UDtWVz2YHdlmCN6/4cwaziitmSvQ+cn1vJnovNgvyiUldrEsph3lHKAn2BbpoRtcftWrX+GmzDqO0RiDS1kYDvYyG6CFQb/Nkvf81+d4f3CW2WabJeOdarNZfrS77++hXffn+N1t6TpY5aHUtJzKmR0sLFpec3376Hqrx+eUZZdvzV/+Uv+H/+v/4P/urXX/PDhz3TCC/DBT+8M5bxbDOyXo3sD8K763vu5oyKcnP3wH/8T/+Zz7/4gj//xdfMhy0y3zC2B549uySXA56Z4oTdLgOWuWOZi8Y6WBdUeZzmPJn7fDJmeYJjjmqUpwmyjzfqV/sRMDiBFT0e4fTjT7JHntq/T1d5gmmkszCfHFwf4/tO+TV/bBx0/OsToa97fBig7b+vHBRtQk6wb2Y19k5BnIk062M2iRxfR+x/rUJOxljU/p/96sms7EhKiTEUvkND540dOY5sjrHy3puYdeyBajHYKMg0JjbSMRGtWCqt10c2JcI0WmtmKnJ6vK43Zzax8ZT3HheagY0gpjVDbKzh7Xl4P9C02HgoTiiVlrvgMphnWrVR2geLb3a3ONnReiy5SMNzRnTPqXpPkz1oRtxzfJip+Xta+Qrvf0Hke6o4Gu8Q/zVBXlJ2f4eLq/4l/dwWJP8C0R1SfkfQhdJmK06LXxHCAXSBYhkXMjxH6z0tfIV3F7YDK2+orXHzcE+qhbMwUV3jfjmwXwpeHaOPjHEkNo9zjdJgxEKhVD0yeB7aNQ/tgb0WUiuWTdEjqZ0P7OfUA/6O+SlQS2HC8VzWnPkLYu1VB8Xj6oAXo8ajmjhUvGc/z2zvbrm4WHWbuoDa+6hxsM+WS9a101MqtVSkCiINvPZFW/GitFxwvtF6F4zF0Fl7Ll6o1dtJP67YtXviNJBSI/qB+3nm6vklu8PBBMCt8f7te1KtjCJsLleklnnLB1ZniucMf/Mc5yMSEku+twVQqzloxMYkY6sULQyDx6fGF8MFX9aveTn/nCu343b4J27jGx7CjoMcHveEPRocMQGwE9NveB/xYeCY6urEc+GvmNrEgxZu9YFNsxCx1bhinMyZ48RRaiFns+22WhjjwLDaEKcN4jwSBoZxzTCu8S4aC7DsyXkG0W5XjngHm80Fq/UxHK+PCl2EejBgU0wfZInRhZIKGkLfCzWqVvJsJYz2+QLVypxtEc0lk0omFcdhnkHhsyt7nHNpaMnkJaNZ+P6HGXETfhjZ7QtlWfjUvlpth+XA9CcHaEqI3gTEwfHu/R2H/R2t7pD+Wbf23IqXxLhag1sZIG+2I5JgI6OLTUHLQqYioTFEx9pvSBrYPbyx0Q1HZ84MWsEZE+0QmgTTw5XSRx5i40lKF6U3E4uj3D/cQ6uUvOfVyzNS3vHm+y1/8ed/Rl62rC8u+Z//p/8b//E/fsvt9R2t2QayVTW2OzUzMIjwzfdbypyp5ZKfffElv/3HN7x6vuJsJVB3fPHqgttdJuVzdlslBM+clR/eHXjYzeAcDStz/Pa7N/z7v/n/8fmXnyHpwO27b9kMjecr4ZAVJ4GDBDYNVms7dzSt1r9UzKGFWldXa49jkk/WMe1Cgk80K/Z1+QTY6KNT6hE8PD3S4wTgUWF7zHS1251qeAQEd9IAfXJnPLlfHvOwQZ9ksDyCjyM8UaXrMzugUVtTRY6uoD+VP/mJAxT6/LPUbr+F0/jm6cXmc5+ivJLFirr4lMaCXrPtpI9blNCD0I5NyMeenCOQca7hQhfDjjAOdAAi1v4bnoyDnP3Od82AxXw3lsXewKMg94hQjzZO5+lAaEAxpbjrbB1iz1vEI+K72O0ZY/glQd4z63co5khwfodrleAc6gPBvQQpFP1IUCzXwa8J/jleJjwKLSHyJU52EP4ekR3UQi17GtmyQdpvyE1x0zlh/Bc4+YG2/AbqA0H2uPEvUe6hLGi960zRPeq/huxRf29jsuHX5PyfaaXS2jcgjnH1L+19azAvyerOF4jDxKY1phZxWDLtNHqGKZJzpez34K15dtGFXVs41EzVRoyBmjOl2qJ5WHJvrLYQNcFONE6EkcBa1mgNOIkmtowD0Y2MGi0YTSNNPDFYeu1hNzOuBgsIdNYDNfhGTor6SEKQkvFRKXWguYy0RhC1D3MzKtwJ+AqlmHtLqCRfSNLM1lsN2BECCwvTemRpQm2mWxkdlO3CZvA8LBVXlZYOlsQ6eBKZ2maiT8zrxNnVwCaf0djgJqWkSNiaTbY1a3ydd3uaKOerEU2FaT0Rh8g6PmdYrvDuilXdcJafcz1+x238wM7vKVIQZ+MXk/cY4CB4Rh/te6kWLy7OMcqalaxo7Zo7dnzWLgjBEYdACB7xgYpSmuWNaBOCj4zDhtXqgjiubMfuHOIC4mzkktJMTqmPDGMvgjMwOKxG8J4mtvX0guWdtNzPDY2WzfWl1XJcpceeU811ltKCBkfOlRas36UpaEmU2si1cZiVJc1spsDLqw3qBtQ11I9obez2C++vC+N6jfrAbrsllwWVo+tFbFzSLEen1EZJC06EYYo2vm3wcPcRrVtK3tt5od/OXG6BadqgMtq4USCKMETrqFqFhZJ3tPmArFasx2fEacXt3nN/v7MxpJiQtVYrtHTSX6XqURZq7axrHwto30Uf99cqfXeI8PHjDevJ23e3NJa05/r9e9abDWGcWE8bzs83vH93a1qNzka0amJ4Lc1Y6KKQTaP0/PmXbB9+4Ozqkg/vfuDq4opDKoTVphdJOt6+v+P9hy0394U4rcjbnUX3A/vdgb/927/jH/7+HxmoBF24Ovc4Zs7P7LudW2Q9rZBg7dWlNFLNVC1mWnAOqrXGn5gH7LV4XHb0yc8/BTIWZcFxUfjkJk+zUFQNfD4NV3s8xiNYOIEJjuDoyUN4sh4ewfCRPzmusejxdqeouH48+1cP20AwEbTjcVzU/kQrz08boHwiRLIXpx1TXbuiX7TrquUpvdVR4THYkD4rk0cQMkRnY5zQ3TaBXk/eNy2Wl9ztycaYDD3tdRiP46POoHhjWETExGPuKOADI1shHwW8zgDVacaHMTE+CD6sif4Fpd1R6p09Vj+edvuKEuQ1wW+oMiD+jKArxiGYS8NlKx8MiVwsFVfdSG0RcWtQh+BRRqSdUcMA7Q5xzyl6j+oFjZ8Ryh6RAxomavM4XdD8Ay6sWU3/lqJrGP9navnfEJ2QcE7N/wmwPA/xjUEE9WtwA6y/Zp7/gaDXtHSHi/+WdPhfcf4ZcfUSbR9w8mesdEKLQ9JAqI7z8aKP2waQQPSOYZzAC9PocENh+/CRpjvQZgyGNoY+VliwiOgQHa0JvheIpZT76dMx4tnIgK9CcJYhMvqJ6Ed8jUxuYpqs7j3PCSnWCuvjwDJXpoFTbLZKwEumaiK6At7yMhbdUtjhQ7TW4CZ4ibRis3snECgIVmVvX9oKtaIxoD5AKZTaGL0Fi9WqTOcD89J4v90xPT9HasXHAQmRtCSqF+rNlvPzgTCekfWOuhG4DmzcM0ppvP7iOfXdQNvdMvotr9ae64ctpfVMklK5mM54Ls84D5fQHFKF0J5xldZs0pc8G99wM3zHh+Eti9+Br6TgcTIQvKf151y7TfxEBavjXC6gfWAvB2apXMqIE+v6kf59bzhytfj0YZgYpjV+XCPBo9W6mkQbNWcWzewPD6RibIQXbyONdvw+e7Rkqtqi4od4irMX8WjKtFyoIjT1NqqqpS/QkJaFUgvOBVuAWuvaEAvpS9VyllI2ncWrZ2s200hSIcYBjSOqysebhe1eefXlRKrKfn+gNgtCU7qYMljsirRGqYlaslUWDAFxQirZ2oZlodS5AzHp+2FnYG88g2ZBj16O8QeRwVW835HmHSXtCL7h5JLgPYdFuH/YovTm4mYAzrZ71g5vQlj6a1dxLRorodVAEgDHDBfbqZeU+bjfstvdscxfcHfzPavJA82KHtcT55driloekeujLWMtrOC1psZ+73iXC2EsjONv+ezqFbe37zm//JLtshBDYRgvmJ8FPtwn/umbb9nuChfPLrl72LHkXmWPUmrl4X7PEg9MHkYnDKGwlJnLsCY7KKmazqY4stoYL+dMzsmqD/qKf5ysyI/Qw9Oemqdx97ZUKTR6q7J7PEp/CUW63vB0H0cnmzFLx4s+AUaP/9ZHXcrxPn/0WPkR2Hn6yFW7q5VO2PS/HEmAdgSiR8Ajp+SVf/blpw1QjpMxOY5j7KfOyeNvj6+99lmc2kKu8ugmNzCjlnTqYQhiuSVBCbGPcjqfJR3AWIeFAYcY1ASyfbwzRHPqGAjpQWz+iF77qMcLPhit5vTxgVrImu3czQkBY3Q4N4JcEPwrxAviZtAJ7/8a2OE00eSOrFbo5YOnti0SBO8O5PQObY0w/YIQ13j5DWiwE4oMNA0g1pgqWqjle+AM/IiyxzNQ6m8QOUOlUMt/wLWFYfU/oOUDpH/AhWfgz2H/H8htR8kHgvOom3C1ov4c8a9p9R9ROQfdUNsB7z8S3Yi4X1P1Acc7wsrh9crGPM7jnedic0ldFNcGwjgwDitcC0Q34IfI4D0ikRZMTRpc49zB/bLjbtlCq4xTJC8J1FlPD46SjsC29o2M4IGNC1xo4LlMTBLR0m2Z0wWbaUObGy47NMM0ecJqQpzg8ITVyrJbUiNGs1hmJ124BGTTmagX3FDMldF3GojgvTXwoo3ai/ZQRyMQquKCLUrqBKcGeoZBaK2yTwlr8XbUAj5Ecqk4LazGyKvPrxge7llmYdnPyCTUg7PXbpxhszCVDU09y4cbnp2d0YC3D99xe/OW3Ap40/pcTis+Xz/ni/gLpnZO1YhII3SBssuRyDln9Quu9Hveu2+4Gz8SXQYZwQeKZoouLCRrkO1Jz63BubtiKp5FZpZQjPlxlnGD9u+xBlQt42QYJoZxhYRIFQtTP4o/S1mYlwPLvCeXTOhJm6KWkeOGAe+PuSUN1xq1Olo2t1XwZhNu4nr4mplsqdZ860Qszl6st6fhqHkxJ09aTH9SCiVXa6bWxudXmz4CEPwwoj5SaubNx0pTTxwHtvczy36P1opKRcTRqLjmeqaOUmsyTZofiMFen/uHHYfdDaoHtCzghr4ZU5xUYhScP6eohVeam96D80xDZYqVsiyUtFDyYM9Jlbvt0keGVnBIW6B1pw7Ws2SrlHTmybQtBjxrZ3I6iOlNpyUn7u6ueXZ1wcX5Gufg1YtnaKtsd1vG1Rr1a56/2Jzup7ZjvL51a5Wa8d5xWBo0x4cPlVWs1PKOv/4Xv2K/Wxh84MWXr7hdLvmbv/sb/ss/vqM0x+XzZzzs9jw8HE7fv2OMeHBCDLbwTiu4ehYIY2BXhaUo97MZCKpyylKxxOB8Cjw7SlLlmLZ53BX3P59akk/twicGg0/GL09vdzxfuQ5u2hOAc7yehZY+HuKTQ512+J/88aMV1sZEj5dHy/TjtR7/eXy+rjvsBDOg2trm+FMuP2mAchTsHDUbn7zJPNJRp39rf6H7wk/PMbFsEmsLHqK1GQ/xmPBqyOZIWR0PJmKWyhjNUny0E8d4ZE2OzcJ6Yl3kCVqy0Y09cK16Qr3OgXcDLkKq2T7LIn18U6jtAyoRHz4DuST6f0Eu39D4Duefs5Rv8ar4FmnsoA54UZyvVAaqvjACzj83gapMiFdyvUKbIJrw7dbi1P0rkNlGAvEcXELaliNwKXqHtGvQG5xsqLyizf8Hrr3D6QVh9S/x+oDU34N8jrZbdPg1fr2htRHNBS8Dte7Q+gP4/wHcBeiC0wHntkDErf4cZYeTieAB8Tgd8NWEj6swGo0/jn00IDSnlnvCGZENd+03FC2EBs+HM7QJ+6KIVIbVSEmZZbaFyYsjiOecyAtZcybRagMKtJyoSyZuIuNFhCrkxai4YRpYna/Iu0wl2g4370mlEL2l64LCJPg4kraVog43rpAo0Bo+i2lRnIlja+1ZNs1RWjMBYlVcUwaxMWVrIEEZCdztdpQ8s1oNzKWyLdYvE1DcMBDjwMPDjpphv515dbVh97BnfH2G+EYOC4erj3w2B4Z4hdbK5ivHu7//FuVAcNadk2tFBc79wJV8zuheAccCK9MgqHoCAyQI9YyhXbGur7lrb9jynl3ckeqBqpnUCqUVW8SrnijhiXMmBvaa2LsFxRxBlkLo7VtZE9a+G4jTChcHKsYiKcawaQi2OJYDLc1m6UPo6yZuGImrCec8tIKlU5uGqaWEBkG0GtUp3VasGcEjEsBByQdKVqrzaGsUoObSRxaJVMWswKVSSmVwcLYeWEqh+QHxJlQtJfD+QyXEMxqe3fZAXmaq1Tib+1C0l7QZK1jTjNAI0dpDVRz7hx0lbal66FUfYCJl61+axgB+Y1ZRHNp8FxUL55vCEJT9XsllobZNj+iH6+sd8/4e1NiR2gqqVsJ3ZLCbPD0/e1SKuakU01mpsTitO7riYI9nGDyb9RpH4uWLK6TBMs/sdjsk3HN1NuClITTTi4mnaO4fu0BtSs6Cl5HSIt+92fLixSW77UKab3n+6jU3Hxf+v//b/87LL17z53+2wscfePPhgdv7HYgwjRFVoaSEB8YgRFHWE3z2auDlqzXFKfe7A0s111ZTs9aX0rrVt51GOCezxdFN0xcnOQYA/1dIBecsobwdKYnj5cmaBkdQIv02rt+v9u/IkzXndOeP93taMjuYeRpn/1TQK3SX61P3j/SF9Anrcvr7o+r2EYydbvfHn+8fu/ykAYr30hesx+d8fNPkD9/P0zgHoUc5C9EpcVCG2MWt0TpvvD+CiE5XwSejIyfawYn2/wykhGgWYmfRFH1X0lGlPrInR5bn9D52nUkQxzD9DOcvcekHcrmmaAM/Ev1rEI+2PV6fg3pa+z9p3OP9Gu+enxb+Jnsq74jtc3BXOB/sRJK3tHrb3SIBYQt5xrlnqFvTSCgDQTxS75B2T2s7ahOQl/h2jbYd2u6NKSh3VD8g+oa8CG78S8Q/Q9yItn+PuAtUDxD/Na1tIf89xM9Rp+hwhteG878CiWj9BueeU/MP1OpxcQs6oK0g4oirS87OBvYPB4YwMISR4EecG2zMg7MSN7E5sHeBsBq40EvGsuLWXVNq5VkZeF4vCazI8zuKQFY7gccQOQsDA4F1iVz4C4bmewFaQ5lZDjsOdxOrl58Rp4HoKrJ4XI6UncfFgIsWFFfcxLLcsqRkVt8oSDadw5HD895sXiIzLThKGU67+uYs0r4WYxVoFpFv0hnBURk8iJ+gNlzyrHrp2/0h4RpsxgEVT5oXbq8f2G0dq83I+iLipsjUVny8vufsEgYHh7MbHuQ7fnn1cx52H/hQf8/d+D3DpjApTFPkflfZDJFnZ2vOpuf4eEkrVpp2PPk5EbRZkFogEHNgKANTes5luuMwveV6+IZb957SLMxuqaanQJxpgurAhg3XbcdWtxR5zaDWUq7OtDF5KRalPkyEYY3ElQGCZGPMQTx4c+9YwqH00a/D9dGNH9fW1XM8W7SCirPvTK3dkaXUChI9qY+BtFuQpS6keWGpBnhyq1TvqaWScjEhb4KcK8tiCbKfnQ1mC28NgoFiHwP7u8r9Q2N1OZFL1/zUbM4pOTIgfdHDsp9SNvDmo4X/LSlxf3dHk4WS7+159RXJYUnU42qFuMkGB2qauWEIeIHzden3WU/ZGrnBcqi8e/OenPaoKrXlU2CdumZ5Qn2RatgGQGSyltTWQ9rU28iz9QgFYFkWAObDgevryrOrgcPeEV3gmzff8OVXC19K5HIzEXwjzZlSGzV30OYx23i1vKSHNpNr5nwD33yz5fnFB14+O6O1yG9++z3/6q9/yc39gZRuOcwHbm+3gBAHD63iUMYgjCGwGuHyXPjss5HL84AfI/epkrr20ak5xSpK8Bbv35o+GjXkuEafViL06eJ9XLg+YTY4vY4/+tUfXFxnJVprn2zSjwuMjYf6vT4BIHY//cH9iK053edpotSBh3QnzylI5el15MTguNNK/Ihh/tQeHviJAxTX9RpP374fAxPlSE8J6noFShe6Rq8MURiHY1swJ2CC2IlG1D3GoB8ZDm/gyNiWZqmxg7EprrMuzj0CnCPIkQ5Y7PbHDwidUrSUW+359C5cMBBxPoCP+PASJ5supHU2juANtHuEc4L/jEYBvyenG3LaIlJheI9jQNoAHMC/QPUlrlwTfEF1smNSQRpN16gGqnqk/Gd8/Aw3/ALaNSJ3NO9QhNZZnVrfovwSt/43xPZ7kBuce0Xzr/HlPYRfQF2h5Rvc9G9g9/fQ3iA6oO0Hqib88IrSIqTfkcXh/GvQiC5v0Pod7GbQ/ytlrhwkMcYJNwyE9ZroByQEnI+UnHHV4wYhekWrjSLG9Us+05/xdr5hrjPfpsLbfCAWR5DGfr+gCiE6gipfjVeMOuCBq+HCQv5KMXpWHaKJeb9ld3vG1cuJ9cWKtgh1cQzDhIzOwrmAEBxVVtzfmEV0bErIxU4W3tQAuRQcEfFnNG+iJqlyajZupX/ZHbjOBAiepoXWlj5q9KRUGZkYx4G5NkYPawGVTMGZiDAnmg/k+8pms2GOCzmXngsygBbycMt7/Y/4FDmcvePd/He4Ycezs4GyDGxT4nw9cRbh7LOBvX/LrVywya8Zygbfqyasqfi4UCmqHt8CZ2lgVTes5yvW40vOxt/xgd+S63uyJttZ4shNKbmyrmtosHd75jYz4lEtqMC8ZNKyNaAbLglxBBFyKRzSjHeRYeVsp00it2JJvk1BTCQpQ0RiwERpUHFIr7BvuXZaXkg127y+OUrXPbQ6m3mlZnJN5GZANjXBOdNo1Go9P6mY9sRSeDMvLi9t89NM/O5DIIbI24975ixcTSuWpZCXhdZKX2xs42MnFANUpSzUkgg+Mgz2s2VO7Hb3eJmZS+qi0h6ap4HBwzBekmsALOwtCAQ/EgJsptz1JTaSaURyVu6XHe/fvDHHUP+9toxqfrSeU2jqTguz5ZaZ/uSoCVSxhbPv01iWzDRNLPPe0rfFOpbm2YoPS87kvPDy5WtePr/kd7/93pyAteKxcyLSKFp7em2jHJTcPOO7hb/+l2vudoX99e/4s1/8nHc3H7m9vmazOSf4Ay+endMa7PcLwQlOK+ebQAw2+n3xIvD65SXiYZsLqp7oPV61xwIoTnvvlhNKsXSl1pNWbQl5HI0cy/qeSFR/hESe0A4nYGHOpcfYfDuGuKfjG6NlPhkr9fXvcU08gou+8z6OoY5AyoYGJ0fOUSTLp3MEe0adCUOP0tgOdvtzrNpvr0+mGn/C5acNUDhanR6ftr0BRw6s014odFdNcBCDMgxHcKJMPbfErMA9vKs9jgrpOxZBEG+24OghDmqjnQ5QzLljboCjkPbIuKgaZWclgMeck9YTahXv7MTWaOT2DUEdzn+Jd3+GF4+2HbW9xcklTs6oesD7l8BErcFOUm6Ldyu831EaTOEvwDuUG6gfcS0h8R/Q9hzxE63uEWdfOvF7pFa03uFcRqtSZABGcBmRM4QCUqjV4dwBqKj7DOWW5v4SZKTVAy19hwwbZPq/g8748f9B3f47Wv4eiT/HSUDn98AZuIzWW0JJqCz4+BJ1l3h9Q8l7kC/RMOKdY4rRTnbDyBBXeCbcEHDBbLYuAq0gOiBhoLUGWqmauQoveMVzEjPbXFio4E1HIN6KBOMYOU+Rz+MLW2TFE2TF0hKIsl5P7OeE1oK6zJIemG8Dw7OXDOdr5Dxa8V4wF0VpBZHKajxje/OGlrakomQX8eOAlmAgBlDnaTp05sE0BSKOohV1EIaKV0XVoWKFedoE3GBOIC00VxiJlKpIm9m4gX1J+Oi4PswoQsaCwwYXaKmRlsT5xYq7t4lWIjUnwlDI4w98o/8fmmskuWd6vkI+WobO4ZDQpTGeB5ZYSZsf2LUd5/qK8/QlZ/UzxnpOrcFQVdXTSuUk4HD4qvi2IeYV0/KSy/ALLt3veCv/yIrYRySZmiqrumJsjpmZBz2wbgPiGloTaSmUNBPHkXGYcGGieaO9c1rITlmXYq6wkqgpU7NVBgTsJOyItoDWguL6vFxo1egQbXS3UMWLh2Xp4lx6JUaltkYqNtZR6WFrZaG0ao3FRUiLCSdzLQSx9mLBRio+BOI4oC7y3ZsFcQaadreJOZnew7JxnEkjnOsLXyN1/ckweoZVwHnH7n5PPtyjbaGVjDqL+xdV1EGMHgmXthGhA2+CjclCYxoTohWlGpNUK0tKfLhOfPz4A01nIHcRbO7grj5ZwEp/fAEl05AOrI8MmyDqzMkoYiOrVjjfrFhNnlIK85x5uN+a3icr9w9bnn8eef7snN/9ppoGRk2L4sRYNRvr9VXBOXJ1POyF9zeeh7Dw/Gri/Zu34Aa+/Owl333IvH51xmbjeLg/MAXYbw9MYyT6xjDa+EmcsE+JYTUShomotaeT19MG1pYKffKPx8sRJPz4N0dm4bF1+Me/1x/9/UeiVTUk8YfiU/n0Memx6PbIyhzv70mvjz7+8VSacuJxjs/tCaNypFlO66Q+WpmP8Oj02xNo+udfftIA5ccV0Paz4/th1MVRl+iDiV5jcKcI+hAbwwDTcHTp9DfENFidEns8ro1nTF8SYgcqoTcLd3DiLPXcmJTjrPEIWKWHtAVLixVxlGr2SKPaOjHWMrXcEsIrnAvU9pFS39HKzBA2qIcWKtF/ZQI1faBUS/0Mcs4QXvYU2QmppTNHK1QS8IzKBcoIfTfu2kiWG7wv0PYGAvzn1mWjCdcmfJutF6YJhC+oLPj2AVn9W0TvKPf/b3y4QPyayo7IjCuFwpY6/AoJZwgLKl9SdQfrl2g+IPV7NAwgb9FyhpcNtf4OcVvcMCLDX+Iwlif4FWH0DKszvAaCRnMg+IhRx0bvttywmJCeraJGUYc2UZZOB2vgMCdE6LHaC6U2Xp6vqb6xkg0jDqpncIHslMNc8cUTvRBiRfzMXndcXDxDJlhvNmgS2gIez6HMFMnUtma6fM39mz30SHCvFcThnScgNCmUvlPyPtDw5tAQExwWGZG84FrBILcBXPH9xNPEXGZeqKkw4BGvjNqoktmnA7UWYgvkBcIES54J7ZKbjwfOz84Zose1mSCeMhRwt7QqxA14NdFnlMp6iNznxPpiRR52pMmx44GdfuRu9S1nvOZcP+esfM7q8AxZBnyy71LTzgqqNQ/HBjEFpnLGM/8lXw3/ilmv7cRfrKBvlDUbHdnXAzf6wIVuGJzZ8+uyIDSmcU2Y1kgcsP4ky55ATTyZ8kxZFpYlU1JBWsE5RxVr1g2aCdpMO1GUFiyMzmHUZ63J/KwukHOhtmaC15YIIrSqZMV6nTDmrtZCqVCW2jNQCiUlci48P1txfr7Ge486TwgBH0futvDuQ2K12tioaH+g5mTCUNGeVdJHxdjuPi8zoo2zs8jmYiItcHd7R8n3VJ1pFERD38FWHJFpjCDnxlZ5b0nc3uNCYDMWBl+hNFrVbvmttO2ejx+37OfZhMzdQqxaQDNHYuc04BHXG+G1A5hCk4BT18/bjd66ymqMREcHOQaCp8nyZJZU2C+Zi2aPf7MZKdUyZKyJulheULPFsWizxHAVohfud5V//7ff8csvA6MfePX8BYUM45rnlx7nV/zm99/xMS3sD4Vp5YmuApXNKrLaBKoT7uaFASGMk9nQnVJqI5XMMS2k9VH+0/9sfTrWPBxXrycM+o+FGsgnIOTHa97puNo9WU+FscdjP8lB0dP/9ZOfPb0L6WOg9vQ2T0c9wKeDm8d7PBURdvDp9enxjy6hJ2OhP+HykwYoTaXrgIwKP/YYOAEcJg70anHz8diPo10ESwcL9ntx/e3pFJo4my0eX2DXf+b94zFDUKz0015856W7do5Wrw5q3JGCtMpxFdsFiVfLI3EBEU+rFWEF2mh1oblb1A8oe7yzHY/qgGgweyYV3ID457i0o7U9RR9Q3SOuUfV7XI348IKqkaYf8O4loivQDG5Na9egCc9rGltwn+GYaU4IsgZZAxdUtzNKWQZ8SzR/hrQb8uFvCOErHJUmI64uhOFLZPoldfkdhHOk3qNcIrLFi5Llkrp8i/df4/wZbflbY6zdBbV9RNzXPe/lA80l+/A7T5w2Ft/uPUKgpEIolmcyrAZqUwLOdjaaUQeLVh6WB27312gVprIhtQdT2bdqWgYtZq904CK8j7fctC1X/oJxGfBFcDHSasVHwblI04GlCVILtz/ccvEswCGyOr8iriPeOWieeT8TYiUML9nfvCHND7Y7TxamV30hd9usOmfaHK04NWt6cw5Vb5/vwVGTmkjWi0WcqzOBXrVTRBVw0aEaLYxtPOO6PKC1EXrxobau88CRkhUYahR2+5n1WnCDIwwDEgoqSisOyR7/WeRFPWf7/Y7olIvXG8K5p0UbEaqrHGQm85Gdm9m0O85XLxh2Vwy7S6Z0xng8iSp49TT1ICZMdrJmlJc8SKTUD2gWmmZGDZzrivfsedAti2Z8k14mVxhiZFxtCMPKdhqt4Ly35mwVaJW8HFjmLemwIy97QhOKV0v+dILZd9uptRzMJqwItRZcU7SpCXmrFUvWnI9XpWg/H2FOHtcqJRcOS2YphaUquWYTVNbK68sN0zAQOhvinQcJvHn3wP5QefHFyvQqcx/v0PMksLI/baD+KLhdTKM1Rlzw7G4O3N6+RdstrewwYaNlcIizpNhxfYa6lSUI9/+CN+fX5aaYY6moaWdKgt2ewyEz7w6kXPqIuQtCe/dVQ/t9Wcot3goW2mm/7nCdkbGRsjshmmHw1LSnlpma1ZJ4ubFiSPE87Ha8xvPx4zVXV+fknKyrqxpT0zCBaCvGSje1sVKpmSiO79/d8/zinCUFcIlnly+4m5VW9+y2NyiOWgOvPz9j3t0whsqz55H1egIRlqak0tjNCc3FgALaX//cU3HtfYGjHuRxQZaO3o6um/4VeAJEPtV0yHHc/+RiWTLt9HfkCTg5joy6qPHHpp/2BBA96iEf81CaPnb+/Fgr8kRmwrH00D355fFnJ/D0xIV0dK4eb/snEig/bYCyJGWUDjB6hoU47f029HRXs/0Oo3YL8GPSq/d2orcgN9uFtp6t4gTEP07NDGg0nO/ha4MxJcH3fhT3mA77KTg53g6OhFdrSlII6onhDGFEmUGOoxR7F50/B3eB44xary3e3FktfNMD2j5C8zRXcAzUUnCuIS4Q9GeI21HdR6TuEfcVhInGR5RbalFcEbxY50uM51QG1AW0VmJ8htaEth3OB5r7DMctsrqiLd/R3DkS/gqt3yBhhUx/gUoEF6n6j9TDDi+fIUxoeUvWAyIXDO0eqQuxXJsd0b/ETb+glbfAGvJHqnfo8Od4LpHSZ+QIMawYpghiSZ6oWrBchhab5Uj4RkqZVmwOrHgGGQnB5vlDHNH9nuoaXgOtJqDhxLGeAjJlbjlQgY/DDTEGni1nXJZz1u0M79cMfkJqM0rZNebDgVY+cPCFS42cXZ5boZh6nIsc5j3SIpcvr3j7uxvrKvFW895qpSo08Yg6EIeEQCYBig8rY0loZJQwDnYSRnFFqaXSxDJepAmhB24RAj401gMcUia4yNlqYHEVzcnaW5vQHh44v1yT5kwoFV82uLJhuEq4MaN4e55Lw0ch3myY0wMLlTAF/OgZo8czoG7VP/wjBbhv1zxwiyey8hdcLl+yyS8Y8jmhRibxSLMWZecFXB//iGM5CmExDdha1rj2gZ3bMWth1QZrh6bhfTTr/LhCXKDVguBxISJqroa0HEj7PW2ZIWe0U+USR5zHwIjz9qfYwmzzNhPTtlagQanJouqLhbVFZ4CvNXpZWrNdaE7UXKlpZimFVCtzKRxywgm8en5poESruXecpzbh2+/ueorwwO5uYcmdAeGRepdj/odATgtaFmKcwAXSXNnu9uT5HucXymHPcf6PVMDhfWOYLqEF2/l3l6ILZtc9W839585GQYsj5R3eT5RcWGZLfzbxbNeqoKCPLcU2TuplePQgO4NJfFJMp5bhNAyB++3BbN61EmLkYfdAWjLbeWE1L5z9cEnuSc+1VmouHZjb+TYe3Qld99GU/t1qiDquryu7z4UmjjnNPNzfUXJgtXrGerPl1asDN9c3DCFzcWYtz34IqARCaTinpNZYWiHVZOOvvhAbODPw8AmwOJEaj0Di6ZjjyPhDv/lxZHJ8WvIpYPj0348jl0/u8Ck4eRwpPK5lT69+ypk9Pswfj5T6n/K0TPDxMZwkDHJ8KHL6bp0+d/0YBhz/NJDykwYoD1twxbGalLE7alw4FvGZ3ffInIwDxLGzKD1+3iy9ZtNsDUtT5MjAdHSpgHQNSh/fGPMi+NB6HH3XnXRwo5imxHsQ1zpQsTfGRHFq9l53DnJ5QtSeEXXRdmG6gGYiF7hwRm47mhyIfkIk0toHgoCS0LbguMBFNdpWAAakVURXqIPGf4LyDDd8jsoOLbemPncrJEzksiWENciOipXJwR2EiVquUfc5Ih4nDTe8gvyO4L9E/L+h6D1x+LlF2cuKpX6Fq/cQfkA0goxEN6L6QKlb02nES6TeAxMtf0CW36Ljv0bkBa45NP3ORkP6D6h7ZWkWGtACcWX9OHioGdysFLHRTRal9NGHVs88H/h42HKdbkm1UGtj8oEsjX0uRB/IPbsA35g5UJw170Lh4Bvb6YGP3PKiveAqXXHpzxnKhK+WWdPqQnGOVG5pt4K2hAsvGVYT62kFqmz3C3H1OcNwS8031GphUN6paUsQcq3mJlLbeWoA8gGpo7nOvOBbs9m796j3SK2me9SAi9B8I6jHaeRM1mhQDiVyLjO5VYpsicGyNmKUXswGuRW882zcBetbx3Du8FdC85ZaXDeKPBTu9YbtnAlrx7De4KYVzgWkOfvs6qMFtLVCazuKZD4O73nP7xgYWbcXPONrnrufM9Q1QR3SAlEbQkFb6eV7nS2gMTIyqmerlZ0uXOiItsW+a3FAhgkXRkCprfbxWCeX89ybfhNaMtIqeMH5kWFages7XrVMDsVRciaIUnF4706xBKVlSj3qK8wGmkshZQN8pTQKCqWSciJlazuuZSEvM2UpPDvb8OLy3J6bYEyD8zxsC999/0AcLKztsMzUvOB6RD19LGKuEVuESz4Y4xaEqsK8FOb9wb4YIVBbfaI3sAVjHAL+qD85CjZFiH4gelhPBr4RYRhXjFlY5plaEnd3CyntaOqgmQ6kaTmNZmy60PrIw/bZZrmt5kDrrp1TsFS/Ti3ZzoFhME1PbeRc2M0L+/1Myo0f3r5nffGMlG1FbK1Rmz46gbJ9j7VrXNpp5w6H1Nimxtv3B549P+PlswNjnDgPA/tlT5CZITY268J6FcyC7TxLqzY6FWM0VzHim7BdGqV0ezGtl+SZi+XpWOQ4grH/ngg7nvzVmJen45kja/EUPPyRVb2rWh8dP8fruMexynEcdBLmfnq0I+B5CpSOl+N0Tjrocf0z706yiuNjePqcj6zMKWUM01YesdefNub5SQOUD3dKzXCFdUvEwVwTwwDTqIxdxBo6eDlqP47dOOGY5Fqx0rhukbRE2SNAadAZlaPw1Xksrt5DcNr//Xidp1Zk7xzi3OObJnY87yLebUAms+fp0D9EipNzxF1QNIPe4HXkyKs17mgVPNm+EDKgeAoO8a8Q2VDbjqYHpD1Q2x6vwdhU+Qj5OF4aqCxUNxIkoHmHikfrO5qs8E1oNeO5tJ1hviWOL6iH34O/Aiq5/BMSfo2QaPUObZlWvkVkoLlzqFsCM+oLEl8i8z1VJoQ9TfdIdKiPaPLgLlH/glpv8e4FzP8F4dx2XG02xb93eIflxoRAFcW7gi6ZmoW2L7CiLyhK0wPzfEPRLVkXKoW5LQyjcD6uSDWzZOsT8U6Ig4lQEaWGYuFbzqHOMYfEe/+R+UVhGRfO2TDuN0y7NbIXUp5tJLNsubteqDmxfvGMZy+ec/n6gk1d8/GHge3zLQ/vFoJfzB7oK6mUnhxcGKThio0lWhaaGyAs5NqYfKQdw92qsygPa6y0ssim5qpwEcSxCoPZlKfAWM/Yl3sOtVCdndjnZcH7yGG3x5+vmdzEYdu4YMVaB5hW6FcBdZZPkg+ZHz7+wE4rax2I8QzvBo5749QWs0dXA/ylLtaRkxu57NGaaE1w7gfOw/e8cN8zpgumcsUFr7moZ0wazFXQzOnifUTxuLpmVQYe3MxWH8i6QnIf5QwjPkYERy2JNB9Iy4FWFS8mnCzVmAYtFSeeGAfCNOKn4dSpdYrlRhHsOVMj6j00i44vlVMYGxLQmqglWSN6M6BXm/UnzTmzpJlUAzXbgoZWPru6YBzMPt/ExodVHG/e3XJzlzi7vCClxrLMtGolkFVsnBfVFjN1oK1Ry4yIEocRGuRlZjnco5KodUbJwNgXh4rIwDhG1F3SFBPn9/OdD4EhZEIwgOidIw5W2FdzZbvfc3O3o5auQdHSc+YbKl24h43CrB+I02jJ6Cq1k5i0/p3zNiIXK0ItpYKz71zKGecd4zRRNbDdL/zTb79n2pzz/LNfME2Whmz6KxtbNW3UZot6U6VieSyu3/31ds/zu0idHZsQLC24VZ4/G1ltzmltwelIiI5xNYETUi1kqcYA1AZObK2ox5LEviz3874ROE8QSL88jlMek1uNdegyAtEjeXJ6HR8HKY/jmOOxWrPB2Y8ZlsdR0dFR0xlVdY/vBfIjgCSPoEkfH8dxbAPHjTod8+gJHH0CdBonsHN8KIq51PR0vD+6lP9XLz9pgPKwB82KOAMiGzEgspqE9Vp6rklPcw3SE1376OeY7oq9ieq0p949zlBP71tH6K7H1x9L/rw/Ah9bEIGT/sTYFMX7gPcjrdkXyGaLGSQZeHATsLLvsNzT9K6Lu36GkwGRAMxEf4HnJbkdgDtE7MuJP0NkhbRIVesO8RoRMuIdoitSeyDKFV7u8HKN+EjTSyrZwqeaJ/o1rT1QxXchW0QkGmDhHCl7MhXvr3DtYMDBjYhujdovW0u0lAsT3OXvqRQaEadrNP0trUWCrElyi/OfoW0H+gbqjAwvQd7ieEDaFW38GtVrRBcC510nAanaJFtViSGiohbYtD/gSkCLw00R9Y4WhSwN3yJTGPjYKlVg15SyT0YVl4pznhisR8eFRhXtdfE2SvFScdrwUpG4sEwOdzaj64Roxm03tN9N6HVj2WacjhwOhbNtpjwULr98hg+OzcWGr//VX/Cfdrek3VuGIRgDRMVLBYm9E6V2nQ1EZ0LBcbPGiUPnTGRgbsL5q0sOd/fkQ8Y7h+v2s+IyDMoiFV0FxlVE90I14xIM5kpZlgRkzjZr2mHGtUjwBWkB//YcbZUwDLQvzwiu8HDzLd+/u0axhfh2v+X8ckPui1JT0yPU2hecOpPzQiuWrGlnMGMtb3jLfb1GnTKNay7qZzyrn3PJ52zA9JMNcF0cSmTdVqjsedAdc83E0ljFiRhHHIFSMznt2B+2pGU2PZnxGWaX7a4LP0zE1RlhGEAcrVqvlXUaeVyDWhLSnTqW1+HsudTuRNDu8NFGrpVSq+XwERBXKT3ALNfarcWNJVuez+cvXxA60+OGFSFOLBW++faWXGEYA9s5kZcZ1N4jGGxB6uc+lWzMVl3wTqyugUYtheVwj5fCvBz1JxXEGwspzvp3uvDcpOQ9/8MFpmGP6GwjRAy0hEHBNXJRHrYHW4C1GkOmZpnXdnRoNB6p5/AITo5LmTzqYcAhYkFWIZrLaJ7n06I4jiOpzJQm7A72Obi7veN//F/+jPXZmrv7PdLr6O1ujLXRrg9pGJtSOzDI6ikq7A47htXIy6tn5OvEbveRxsSrF8+5OBvJbSG1ghdhlMi2HNgeMq3azOgYWGZ33M28fQE/ppIfEcCn4xkeGYSOAuQTvchxJnL82aeOneO/n3b1fCrGPfIYR4OxO35Yjo+gb5J5AjR+dLzj23Ua4zQebcacWKBPJ1ePLM/Tx6vYSKf1j8PjO/XPv/ykAcoUjA047GA3ODYrZaM9Z8TxqBXpSbHypDunp1/bzKwDD3WWQGrU1nHG9vif80fdit3HEGFcCTFYIV2r9aRfMRuxUcfOKeARmTrgWFB60Z42HGu8/4zGO3wPPqq62C6jgUqiyR5lA35F8K+o+Q1NP+KqR3ym5QfLZHAOh1XAa33AiVkjrdreU9TRSMCBEC7QtkN0T5EEqvj4c3OEuD2WtZEJLDQO1PoW51+j7jkuDGjdQd3T2h2ONdJuGXShjREZF2NrfEDdZI/NO3J6gx+voD2Af40yI/lbpGwgrKwTiAN++BpXJ1q7poplUYTJWku1CbXYCSg4b7uZIZB3CxMRdZXiM/e7j4gr4CsPhx05FxRPa5lUM6VWYoycX5xR8g7vK4dcIBx3e+DEIzhciMQ4mhXdHxA3UuOONDW4KsTzA/63K9J3EZk9Ls3sayIftuzubtg8u2S8WDOuYLy8YM53FFnQtuDVIW5AteE0k6vie1OvNMzdsFP2ZSE48D0TY/fmI2EUtGZKPX6gTYhYU6MNkXYo6LaxaoFRHKEFlryQW8X7YKOmpiylsB8qQ1KuxsggK/T7gtaEz8Lw+Zr3b26ZS48ZV2WXE5NO5mqplUrr0e5KKY5SCvTjm8XYIdJoLVOdY9GGSGOWA/fulrfyGzZu5Nf+r3gpn5n9XbSLTwOTnBPqNVn2HNqeKBHvg2mkxJHSzP5wYNnvSCmD9iA7Z/H4HqFqwYknxBV+iCDZelTcYD0xJzumLerak3rBSv4QhzSl9rTZCtRsQKU1tWJINTdPyZXSGqUpSy/Auzw749XzK5w2mjjiuEHCxLIv/PD23nJcfOCwHMglUbWYvsE1vICpkaIFx+UFbQUXVogzoHVIC3l5IPhKy1aIqE5xWsAJwTfCeEVrEYs5aAiWv+IcbFapjy3EQrmc5cOUlLnfHnjYbvsIrqItAVZKSWeaLYbePof0MUs/yyJY4rIBPIFuDjhexAWmcaRWK4usTTksle3+YLq66NicrSg5U4oaA+keWYbjpXZNiIHLE+1gjqqmLLXy7jqj8pbSRtbrNYc0EKJjSQ8GvIolG8cxELwQQmCpVmfQtBnj1CMpOI1vni6+P7YDwyOA+BS4nJ7/U4fOCRTo6fZdH/vIxpzGN3DU9fR7Bj0KY83x93jcJ+yOPsGSP3rs9Mj1R/zxOKL6cX/P8WdPMJtdXx6PL8ef//c04rm6dJAdJTfu7jAXT+9m8mJuHSemR3HBZmnWCmwnvqNexDnMuls7EOmI5KSY7t8D780ifEyODdExjI4hTrhcyCwcRWemOzH6vani3ICwQQhm11XpfxdEA849o/tfQSLqHFUegGLZC2oJokd2TdxoQWQ8EGVF0vc4t6O2B1rziFzaBzoMxCjUtiP4l6iOaEk0cm/rteyL5kyQqW0EHanLAUKCEHE6EMIKdDCnTstITWhLNA7QdjSnuPgc/AYtv8Ozx0nCqaf6fV9sGy2eQfwSLW+hfI/ohA6fU91oolJ/jsu31PTB8lfqA85f2PP2JuKrecE7q7jXKPgpoiURJ2UpB/QgZFkIg+eg8Hb3kbnNJFFyy/jo0HSMiFbu7u44Pw8UaUZN+067Yrqi6FxnxDzOjdZvJGuGtrEeI+fI55nyuhB2K3jnCbWAHMjLyNzO2S+3rD6M0Crb7Q+UvCWTGAtUqXjBRmwMOFeQlnAEWnM4y8xHtLFkJdaCeGutPuTS9VPJTuLe3qNaEy4VtLq+e7ETVRM9Ff3FGKn9pK2tst/NnPmEL2qALE2U7/fMacfhyz3pfoGa8OJMnH600OZCa4VckzXbFmjFdtWhmO/EiVkUxRkF79WCrBCwjuaCukT1e2bZIvI5MYw2Osimo1jJGVOeuPeJrS6ce0uNdTGiVFou5OVAWix8Tpu9ri4MTDGY663P0MUHXBxwZFCrnTcw0kAbDo/ioWWawwoCc8GFfsLvQ/ucGjnb4lyrMUgNJWUDJUsp1pxeK6XCy6sLNqsR0kyMA3Fao+PA/fs7Pt5biV3KSjostF5o2AdQ2HJon/uqlVxnFMs0cV5oWijzjJYZfKI1E1qfau/xDKPDDVdktX+Ls64i5z1OG1NcbAyNt9uIoKWS9gfev3/P/uEjJ1BCn+UZd2OdYn1xNKaYzvp0BYK4k55BaXjxOGcMTqmwOntuoXURYozs9wecb1YX4hshOO4eHvjt736HAMF7Ujb3TIy2lKVSDAodyYgTJw6pNd7fLdzenHN7dc/rz55zdTYiwwt+98M1P7x/w+3DB+I4IE7JtZCzLQD2mWnWQ9XXDekjfOsEUh4hwh9ePtF4nPDBE6DQAYG9dvb3Y0T+EayIe1Lmp/rkOJxAylMA8hQ0CWLsVRcuH0v9zI7/eJyjI+fxKD0c7ngX8Ci8PYEU+nFP/+p/2C2cOya7yye3+edcftIAhWYuhiXD/c5xt4WHXeOLxUrgzMGjSBTTLsjRtdNTXnn6oVGaF7wKtVNsp0ZkMYfOUWA7DlYiKK6BerRNiGS8X07I0+i+gLgR8RPOX9Kq0NoCMlguiZ1eQAu13tHcbA3EMlA5IDzrJ74ZlRXin+FbpuYfkLrDmJhIrdFq64mgI+LXCGcgDeEClQ2EG1STFQkOG1JrtJqQVsit4MLniL+k1Huca2gIxtYmRw2WQeJxaLlGpCCSEL1E5QwYQdY0vcG5lxAvqfkdEiqt/YDmivrPqMsb/HQF7dbGRPUe8feI/xqtC1IOaFgoviGyRttNd4lWWzwySHQEF8iHjGtKLZYEGnwgD45aZvKh4X0hSqPmhHOFJI3cGmGAuBrY7veIKJvNmpITw+BIZYF4nLs6CwLsPUZOgoV/5QYFsghTGHG6Rmol6x5ZJzh3xNsVeU49/ltxtZL2D+yrOaT2ckP2HxmGkXywGP2qDaeKw9vO/cjceIC++20BgiKtQe4koIj1EDVPKza2LLn2HbXJBH1UxgYxV0Qqrln2h3aLbmsmRs15JrUD6mB0awgDtTXuP3zH+7fvaMsbXnmlObg6G7mYoTxAdpVWszELVaja0NKFcc4WpSBKUNP4NGx85aWPVKXRPKeYZxtN9u9nM0svOKIb2RC51x0Hma1EcpiQYbIFoCRaWqglU0pBtFnp23H82u28Vodgr6vrp+LWKtKj7LVkvNiMSbvItBZzbaAjTa1LB63kXCit9l1qoImSUjEbe1nIqbLkxlJMf/bVZ88Y+pLthwk3rigE3rx9YF6U9XngsE/WNn0UniKm7XEVY2KLsWZlj6MxTIM9x1ZY5h3OLZSyM42IHIv5bOM1jXYekXYcOfQYhBAIIbMaswGaZjbhlhLL7o77u2tubq5Z0tYYFj2GWXZA8snibAe3jZrtGPW05AlQEe3uLRc7SA0073n1xeespjXzvLD//jvwmc25Rw57HIUYPEMMBO8syTuIPUfnCGKjX9Nn9F19f0TaNSkVUGY2qyvOz8+Yq7BsF86mgdevP+f64YGl9sh4H6g4tDpqy6fnXZuxiEfjhNjy8USv0ZmKo0bgdHlkQz5FF3xy3U+Fqz/SmPz/2/v3YOuyq64b/4w551pr387tufc9nZCXEBIkEiBNLPQnqURMlSIpq6QiBqX0BTuYgIVcFLWkQij9w1IL8ael4Q9BSqpENCIag6DRkJCWAEmkE0xCdzr93J9z23uvteZlvH+Mtfc5p9O5NO9L0k32Nzn9nLPX2nvPvefea445xnd8v6qcDg307H+e+kyrB3/Kv2cebh3CrLyDnnqmrMd0KhvCKojR9e2rv9f3WvFR9OSRnqnc/XM6QOlTMTGn4skq9H1CD+0iXVdK3ZiMfVUprnL4xhyGT9dyVx8qAYpT8DJ8+dT4XCJD5qRQV4PqrFmpWFmIQtFsaT9nbPyiBXUO/Ayr9VaINhRJg+bBFGEMHIJ0oEtSjqhWeLdNRsllH09A/C5ZKqCjlMNh1JlCh0gNpQJZPa/g3HmcTHBum5KPUFlQck1oriDlNone1DzFEcIWOd3BhTnB3TYNEXpcMY2MXGpwAe8m1hKc5zhtwS9QtZq4M0s0Sj5Ec0vSJ8llgWiNq2rENTj3fGuN5ghlhNOEuikiBS+HUG6gTNDSkRc3EGdpea0uDBfeFqUQ05Kq8pTaUSLkNiHeWVt0LUhw+CbQ90e0y57URo70iBQKOfTMxsphhBt3DnGVowk1XdfRuMCsGnOkPeqSeU04q+A673HeEZxDAvT05OhxuRCKaV+Mgu1CtRHy+UR1FNFrA/vBFfDG89HgqZwyUuHWeM4N/0m2/Ijz/R6jOGZcTEm2wuPUDTw0bwZw4hBVQjJVTpyaIJL3+ElFu2iNyJkLEgu+CUg9IpYC0T53oWkYxUAXhXE9YSmZtmsJweOCN95LXZinY9oU2Q4TrqdjfuvgUWbB8cD4Mi/YvUC7FblDy61bLa6BNClQHF7Esk2lYO6yxu0IouZAjKXFGYiZJhrmSA48lilSD8EHW1CSZbziymytBCZlQtBjlqEnOsE3E6pmig/eSiIYF4ZSEO9xPuCrgMMIk3VV4asKFwQhWqlwUBwO3rpeyIksGbeSkk49mkCDJ5do5dw+2sYiZctopGLy9ynTdj1Ji5kDxkQXC6nA9qjh0u62qa+KQ+oGV9f0y8QTTx6DVHgXWLYtKS3tPDeQ9BmCAVG8ZnOyjsYz86GyFmhNtPNDnM+03YJVJ0VW22hVzlGPtkhltL7miRoBM3jPZJQZNYWcjRSaUia3cw4ObnI4PyRmLDBbO3/noZCy7tE4tfV2wy2nFz1Zp/h12Lnr0P7qmwlbkykFsxHYu3CZm3cOUTki5iXee3a3pnzlV76UZrxN0wS2t2c4TESua9tBlNNUoT1KHpoO1kGKCLk4rt9ccP02uBq6LBwvC81kl2XyZPX0XaSqbfwqQsLE/lJK65W7lHzSuSurOsmpZV1YC5itSi729qz7a064IKv151MW7yFzIqcDm1Nv8ypKGG44Ewusg5anCrnpenCqDMJyJwHMisS6CilXY1uVcFQxkbpVAmfIpqzb9of7r4OsIYu2HtIzxHM7QOkdTgMhwNgVUhZSUm7vW7VEaqVuYDyCcVZCtrZO0wdapRpPxHidWN3Mr7hFwzvqnFJVsjYT9IMWiXMVYKJYTvzQOtwgmowZ73YpWUzNsSSC37bnJpLzISK221GSaYa4qUlqA1BRyh2cGyFhYgFTKUAcXsM2aEfKS3ux1Oa5E6yrJxNBRjg/wekY1QWl1JRSIXlCSvvmB+TMNFH0DkXv4N0V200O3wAXttB0jNeeHI8JQcnpCOe2cdXILOZF8IzI9RWQESFdt91b2MWXlpQfJ6eL1P55KAuUnqKHhKDgvhTiTULzPIp7AicXkf4j9N0HqP2LwJ3HcRWAlGxnOjtvBM4i0C07KEJ0zkz31FvruC/0sWfJkttpQS8d2+Oag9SRYs90OkZxdF1kO1Ts6Q5VcNzWG6SBh+ScLbreBbyvCT6AZMrAgcmxkIOSvOJcoHgok5q4F5D9gram/yClsmBOisnWZ8du3OJWuMknpje4tXuLPfY4355nMt+i7qAuRvgTAtahWRDxqAuE1S60QMqCtoobnHtxFb42b57xeMbxwSEFT+0FnzN7OyPQTPaBg8U+FMWFQNTMSDJj79lqZswXCyb1Npf9Fb6+ejWNG5PSklxa5v6YtPcJ9pefILLAaYXzlX2+cZTcodriXWeiaVVN8WFYQDzOBbw4xBXrshmkygUgOLyvyZotE1KMt4CYqmmda5wone9IVaAaTQn1BOcV7eZDG93gLeNNut2poJot8+QDoQo4r4B1amlOODxZinFm1CTvlYIUczTORdEsw30SK5uKjI2/z8kMAjP0XU+XE13s6VNHKo5chHM7W0zGU2IyBdW6miJ4Dg6PuXFrwaiZkTPELpJyi5JwBcTJIKi2yhDkob044f0UQSk5E1NP196hqRIltoPSLIgUhEwdhGa0jeKtJKFCweGrgEhgZ7wkuEwcVIfJibhYcHy8b10FMkKzdb2ZpYSVHdeL07CkrZbg9S6/2GfX1tti116x17TS/pjOdtnd3aHtWvoYmS+WOF+xvbuHBM/hwZ2hucEzXyxwlePBe1/A/OiIw8Nj3LFnOV+aiCYKWUmlPGVRVBZdR6dTXHWO2c4eoxyoJ5nMhKPDpXmiFaHve9MeKivtFrUSy4oEzGmtkpON7mrltlXlVJZkMLc5m1HgVEwzUA1Oydavg4UzxNpPzYOsAoVP5cGsjuqpn9VjnlZjWc0bgzipeTueDXms3CfD/K5jspOjp94Tte/LqXIRKid8oGcA99lPOcFb3/pWvvqrv5qtrS0uXbrEN33TN/Hoo4+eOadtWx5++GHOnz/PbDbjda97HdeuXTtzzmOPPcZrX/taJpMJly5d4nu/93stOn2GyAMZvAowHQt7247dqaPxRpy9eVO4fTuwWHhibxcYKVYLd2JfKFnlpixhYroUw48Z/tkH0ongBwdkJ4HgpgS/C7pjvA4akD3EXUTcLjCyRVSNLyF+2NlpTSkdpdwGLXjZQ6Qh65xS5jhdILpA9AD0CTQ9jsYOtMbUZiN931JKjciMUk6cVV0YgRuhUuPdiBDGOBlThlS9cU56KLdxbmT1fSd4vwsaKHmOcIjoDbzewcshTm9BPsDJHZy/Q+aG8U5kjNKQdQJuj+C3cPk23svQQt0aT4ZzaPwEkh4nptuk/qNoewvtj8i6ZXoj4S7E1fjmfqrgcW6JuAWZA5AbiFidOgTz4jm+c0xMkRygVEqmo/aR0s9JcUn2cCwd1/UOt/rbZD0mxcj+/pJ20VFXJpJ2fLTAIUzrCeM85W65my32EBk4E4MVtWWcKkv5e8hOiARSEUo2Ea5OE1GNnBubBGPLL0k2BxyRRCYNX/zAJM24mPcYuYp+0rN//phr99/miRde58n7b3Lrwm2W4wX4DiQhFDT2iGJdMyGQHYBxchDBV8FEy7zHOeiXHQ6P9xNEPLOmZsSYbb9N3/agicY7yHlwSxYO4pwudPhJRXGFxm+zGx5kLBeZhLsY5UtM71zh4vX7ueTupnI1QsC5hoaGmjGNH1PXU0LVUI3G1PWIaZgxrbeYjKbMxlMmoxHTZsyomTIZbTNqJoyailGw1n2KklIclH5tx55TT8iBcRJK7llKxrkG5xszAyxKSnZBF2+tpw5FUo/GDk0JEPDB2pj7OKgOW4dI0UwqkVKMn6F54OvESMq98VBypqQyNKsIqo40SNqXVGhjocuZru9ouyV9tLKgKFw5v4PTQoytLSgIXdvzyatHHC8yVVPR9omYWtO6KWVIuxsvZnWdUlYBTCHUbghgMsvlAtWWlAcn4uE6KcP1q24qXHMO1OExcqpTqEOFiDAdd6R+yfz4kMXxAYvjA47mBxQKzWhK11lZWDVZS7uuSluZtVbpsHiL6RqcbMFliEQ0rhcuL0PHgmCk8CqQckZx5AKzrR3q0YTtnXNMpjNiznz8scepwojzFy7ygv/rS3n+C1/EPfc/j9F0aqVBgeCGjOeZXLktyn1SjpfKzdtzbty8xf7BEfN5pO1bun45pHUEJx7FuEk5J2KKrMTmVgHJ6Q4akRNblM8M+ZQ/TZV2WMjlVGB3iti6CihW9i7Oyfq+n/m5zo7JTj+V7eIkwDoTZJ2CLZGrnIrdcNo/6OmeV09e0PrvVSnpmeAZZVB++Zd/mYcffpiv/uqvJqXED/7gD/LqV7+aD33oQ0ynUwC++7u/m//wH/4DP/MzP8POzg5vfOMb+eZv/mb+x//4H4CpBL72ta/lypUr/M//+T958skn+XN/7s9RVRU/8iM/8owGnyk4LajaYuKdUI9MNC2XzPK4cO1JZRIcToWYYDJV6sZahM3TYnBeFKtPShmI4YIpIQLByxCwrHROQNzAL5FT+waZgRsP/hA9mW6Q3N61/vkUKRqhVIg0qGvA3WPy7+njpHhAkArnZggVAKU8TtEOwr04HwjVCHqxfn8xgzn70piOSkwZSHg/Jjgl4inlppVn3A7iGtBDM+yjsSDNRWA0fBJHOO2Gi35CdY6vdlB3iVC1lPQJXKkp1DgSvrpEya15W5RjYv8YlU5M0S59klICgSnqekp+HOcFTU/ggwUnqdxGXYPvbxNcTdZ9ktvCVXMk3Ub1Buh5+3KUhAtibZ99IZWIQ+lyz+GdY5PbJ7OMPUd5wXE5oogw73t842ljJNvbRtfZBXx3Z0atgbbr2KsucV/zALH09DI3OwM/dIB4+x0HrvaIy+QSyUXwUiPSYK3jUKqMTBU5dEgUohayUzwg3urjPjXs5Qu0HHMQ5lBXxCqQRJiPD7lxbkGVai53F5jdGjM6HBFcMwTXFTlaKUhqK2HEXCxtXpRKA74U2r61z5AUnBsxKTsc37nJdNxQFhEvAUIg5UTlKlQdCzJXFze5NLuf4Bw5JUpw1G6C10TDmIaOuhszvTVjtv1xrspVVCqkEnPEdTWqnr5S1CvBVQSpB78r6xqxlHbEFjYTG8P3Vg7z0URPhyyGw5FyJMeIdw0znbGvhyxpSUGIauq4fdfSdwtUy9qCntyTck817NrFW+o+54jTTCkRKYWsQikCJRGHbAXZWAslLgdRs2hWBIOeSRsLKSsxQh8VnNLHROos05dSHoIXYTpquHL+ApojJZt+S0498y7yO0/cJhfjgSyPWmJccrp0Uqz7GRQTSEuJkjsCUNe1dQ+VnnZxQJBEzh06EILtMSzgrscVuF3jbTmbBXHm/RR8onJzlkcHLNsD4/cNnW6+qtGstL3xYmzOhvKOxkFMD4RBF2S141+1FK/JvcrKyG+VSXMuIAj1qKGL0dy6pWL33Hnaq1cZjSccL1q6mDi3M6FP8MQTV9nZ22Nr9xx1M6ONhfraVZwPiBscp1NZj+t02SOTOVh03DnouD/vUVcVOhhJ1t4zHc8IOZKKBbWpGOekFCPQr8odAOYqX9Yx2Nn/wtkl+SkU2lP1kHX3yzr7dIKVi7EOnUPrpMyao/Ipy6LdZzh2kuU5y//Qp9FqWQ1mFVc+JekCnCrZyEqEb1UKOuHGrNyMdf1AZ1/2M8EzClB+4Rd+4czfP/ETP8GlS5d45JFH+Pqv/3oODg745//8n/NTP/VT/NE/+kcBeNvb3saXfdmX8Su/8iu84hWv4D//5//Mhz70If7Lf/kvXL58ma/8yq/kh3/4h/m+7/s+/vbf/tvUdf0pz9t1HV3Xrf8+PDQuxsqLJwMalbbLBApNELwT8ELbFZZzoV8Ko6oQvZVoVq3Iq1qcyqqgqMMbzEB8coQgRpQcpO4RK8T4oDjfUEpF1kBxYxCP+BpXJggRXKBQE/wIJ0uzeS9jkjrUj4hSIfQII7tQumgXT3cO5bxlU/zUSKuMgCWIEvMcdRN8mAHZCLmCuQwXQdU6ALwfE6QmSaLkFqHGh22MQFtAKyu5SGWPoUrxFZmaXDLe1QRfEbOibgtx95hqomQK+3jds1Rx6XBkXHycpGPI5izr0iFFJ5R8YMJ14UtMXK46R9VdJymE5m5UD1FdIHIf3l2EsrAOJ+kG47bCsj2m3hlZvT8qbR8pEs0EMdtiV2JGfCa7TFTzw8jF0S1baMwYULNn2c8RhOVxy+XZLs5PWEbHxfoifbXgE/3HB5F1u4CKqGUK/EDuC86IlNJZR6WOKHlMZkFyc1KAaT0lRGclgmCdWDL4xIh66m6bc/3dFLlJrmzxTNIPXCWlayKPnbvO1oPb7JRdmqPIbDmi+kRB7zhEHZWYtLeookXwxQ8dM8rIB5IWkiZzefWBURjTl5baV+xKRRToco8GaFOEUjiQOywmdzjfXLKdZIn0GapqQu0bSC2aa1w/ptnfYZtzXPNXic3QUYJl0RpfGd9EKxwJURP4im5hXUeiAwm1Bx+NoGxRIZrMv6pynoS17EaUum7YlV2up2OO4j5d6ol9T98tWSwWpD7ixOG95QhEi/noiFKFMTIaG6G89EMGxL73KXXkqJgAi71mGfLxKVtnDitOSFFiEXKCXCKxT0M21wTG+rgk5UIqQpcKSR17W2O2x2NEswnL1SNSShzME1evL3HeMhp9a47LaEQZ3JWHhd2Wr0gqLaX0hFDhPaQcwRdK6U1NeTkfWmEDq+XDBUfT7FC04YSBjGX5XKB2mRIPOG4PSalHBr8m8abUm+ZK15qsfdGCaGKlkbHK8qz6AwyrErplaZxdTE1kTrGOOF8PHkTCfQ88n+PFnPvuu5fbt/Y52D+imYyZTKf4qqGqHFcubuFd5sMf/h1e8pWv4L7nPYgmW9avXbvK0cGBZbj6hJp0pb3+4fOQs8kVSPAse2jGY3yo0ZAppSFJYZoTITYcLY+ovVXVh0SlaV2tumhYBSiDuPs6ZfCUooQ+9Y8hXDkTnJz44LCq5J+6z9kW4VVwMnRYceZk1g962ixQ9dTzDNmNpxB4T8vZswpCVvOpT3kZp59KBq2TYgRit3rc9XOeDGmt7fEM8IxKPE/FwcEBAOfOnQPgkUceIcbIq171qvU5L3rRi7j//vt597vfDcC73/1uXvrSl3L58uX1Oa95zWs4PDzkgx/84NM+z1vf+lZ2dnbWP/fddx8AqqYSGZPpDYhzVJUzrRKBxgnbU9g7p5w779jeEaZTGI2sE6epzSDLD9496+zI8B46UYIv2MbZDW+XR2QQMSuBUhqcnyB+ClJbJU/GVPV5QrWN+BFZM0kTMCFUV5BqisgULxMcCeesDOTDCKFCNdtY/AVKeAD83Xi3bc+vAe+nuGpEESgkcj4kl1uU3BIk4CWgKiQtpuKHZYyayhOcEbyEQEr7aLqNxzqCLG0rCA1JQPzUXifm1eMY0g9uhLixpaDLPriEyBGlXMeVOzi9RklX0RzBb+GaexBXIz5C+jiIJ6V9shwC19B4C9y9oA7iExQ9Jus2moXsXgrVl4NAEmG5vyTHRFeWaFmSuwWL+SFtaVnmSK89RSKkjlEwLo6XoYVUIcXe6svZdpjBC7V3NC6gZHwKXMyX2fM7K6NVvASKQiwdkWKEayeoJlQHZV4FktLFyFE84ja3OPaHZClI9LgIzmVEzJAuEGg0MO22mXY7SKkHBVjbnWjwMAJfB9zEky8I5cUN8ZU15U9N4ZtGdA/B/O6OI2ecAVHTa3GVQAioN/M4M+KryR2EEnCpoiqB6Whkn+qhtp5TYhwCk4mj0wUxdZbu9hXFR9oyZ6kdhIpaRozcmEnc4uLt+7n/xovYW+wSVCihoBVIqIbuigRlTq8HdGmfvj+mi3P6vM8832bOEZ3vibWH0Rh8TdFCisl4H1kpGWpfUTUTZpOLTFxNGw84bI/p+gXL9piu78A5QtXYwuNMhsy7gOCRygIDFTMZjLGlJBM8M1fx3vxlBmmAomYmmYfgoFAGtUxBYyLnZMeTlX66PtNH46LElIwzlc2T5u7LlxjVgVA1NNt7UNdEMrePIvuHHT5UtMuePi7I2pnZpSZkaMKmDGaGmsj9HCnRfIfU2bgV+m6OSDLzwEHMcCCHWPdLs0fRkXk+Dft1HzzeO8ahJcdD+v6YMvC0Cpm6CtT1hMUSll23WiJRNZ4Bw9+27pgGjBNhxexbdSHpcMwKW8bnwHnEB0CYzHbY3rtCXwJJHTEXtnf2AMeF8xc5Pl7y2ONXadtE2/fc/+CXcM/9D3L53nvZ3jvPaDIlpjy0i4txnDgZnzjLFjlxTEc1u9sVTS2MGmFna8b23h7NZIZzDX3XU7mKyldUPtiGLwEJRvWI4MOwzK6CAwumh3fG/ndirHMGcvIOniWvruKPFU9ldTsnAcnptX3VrbS+26lMiV1DVqW2VflmePaV0JpwUrI5mcVB5G4VXA5aMkNXI8hJ3LMqOQ0xvXHs5FPGc+bx191cnzt+1yTZUgpvfvObeeUrX8lLXvISAK5evUpd1+zu7p459/Lly1y9enV9zungZHV8dezp8AM/8AN8z/d8z/rvw8ND7rvvPpzzuOLMD6IosnIo9gIFQqVs78CFy7B3KTMeY260HosWVZECJQsaddixDxPK6sOkiMuIH9jOGnBuC+cnqDgjyuWE4GwhzL2pO9Yj1Duc1hQNlFQQWZKlJ8ajwetibqqrZHA9hd4WRB3ZYk5NJRdsF1oKRY9tt0zFpL4b/JK+vYNjTNaenB2KN9OsopT+CPHLQTOpR/wM50aUdICmiNMK9Hi4GFcoY5SeygUqD05GFL2BaqFEKBxSSW3ZkxIpZHJZ4vwIcROEbYQRRQ4HN98doiqu/x3r2Bl/KTkuzVlX7+DKAVo6evcJnFR4DjEuzxiqKziX0JzQ5Oi158gt2ZWxtRhXgeILue+oRIk66BNIIcaOVBLH/ZyWjkRLwjpN6qrmuFuCDiW7gbswqgKVWAp4qtvc5e4laSZVCQ3WyrtKq3n1VNRGmhPIydpQU9ty3LbM+56cDsArozBl3DW4kpFigY464wOJ8zTtiK2jbfotZTFKCEqmUCprkx9VE6owGloyzX8n78LoUgMvnRByIMwd6WMLuo8ukVsZtwhUyZGSIFRUKsS0ICDUOZA1EYpj7EbkkOmjcSmKg+KEemtsWaqSGFcB1BFcoZeOpY9EmdJMJ9SlQdoGSTXuuGaUZhzH6xyfu8GiOSBTIdnKEa0u6XJLzgklWpZIgMr0hVw1YlRvMap3CNoQU2LZd9Q+kTGuWFWPqV2DhIqZm3GQ9rk5v85uPSN1LeRMFRqqpkEoSJQ10RmshAJlkLxv0WweMeKGC7Iz8p+kDMVMGDUZqbOUCFjtP2uxIIRETCZyV8QRixinJWXa2NPFnpSVaTPm3ovnqULANyOq6Q5dVrrccvX6gj4ltrdqFvOe2LcUbWHdolkQsfZitAMt5LwABF85489oIsdiWUcXB30k20XbKy/UdYWrLpCKlbhcUSQoPtQ4EZowp+sOSSmvC0MW1NQUFQ6Pl6S4xBjb1rlnIUg+oTpoHrgmpg5rAUvBctzVmYqBYK3kzpl434cf/TBbexe4vQ93bt1gZ3uH8XTGdLbNr//arzGfH3NzfoQTuHzX87jveV9CPZmSijLd3mE0ng1lBzds8Mw3bLVSXrlyCXGB6098grQ4Ii+NAxOqESln+tTy5Cef4MbNm2aFUAcQU9N1zlGHmrqpialD8yowOV1mGYKIVZcSDMHGafG10wv3KTIsq/WorP46U75ZlXkswHi6ms7q/V8P68zzOHd2DCdHTHRu/RynjstAYnYnI1wrwp44HxtOBybrAMytKBKrNhS7/zPLn/y/CFAefvhhPvCBD/Cud73rd/sQnzOapqFpmk+5PQSQPJRjMJ6IYp0NZB1ajB3NyNOMlXqS8d4IXGVI7+rQM1+SUpzpIICl7+wLJIOkve02cAHVZiCpWvpfNQI9IQSwyxalKL6a4JyHEohxiZMIJaN6DDhKUghWuhK2EbeFSKaUQBb7UlfVhKRLc90tzkhqzHG+st78bGUY7zxKsja90iKup2qsI6LkSFEhqyLY7gDtceIoumc7C5cRJpS8j0oHw4IvVIPnxz4uNKbV4gSnDU4zoks03SQzBzcG9owEnAXPJ/D1jOIi4nesLq81vnRQlqTwAOgxzkXQHu+2KM0VSneIlwrnR2i5Brlhniveffs3+MrdL+Xy5BJpaQ6+VAHtrS0ylkxxipZEW3pTi6Wn04SMLDNgJnZ5nS5LSZnt7lLlEVt+x3Q8Mpyv7qXVyC25MZCgTVtlFbyacFNtrYtxSWojy0VkuexYtnP62JPkkHF9ROgn+Ky2yEllu2InhODwyTM+HjPeb2nrnm60pPiIcxVVNaJpGlxVQ+2t40o86hw9hUohVwE97wh373Hu/3cJ30H35IL8eEu8FWnniWap+ANPc1QTYoGcuTjaYZkypS9MQg114Kjr6XPh1tERW+E2sV7Su0SQhkBFiLYgxXBMdHNq2aNyE8Yywy/HMD+gSg1byx2uXvgIt0bXWOiCnDqWyWTZkWymh8ETasFXtXkFhQlVmFCFBp+9zWdKlDS08IeKkauGtu+G3fFFrh7sc2d+leXkIvQd1pLdEEJAU4toYl24dYHiA2gi5dZS9iWbySF50BsbdvYl4waSaYzDBbcokCj4QdzODBH73rK3RYQ+RWJcmHvx8JOzsrvVsDOdUDVjqr3zEMbk42MWfeSJq0dms1BVdHFhnkElDulyh7psmxOpBiG01jpoxFMH09XBK/PjfUQifdeiGtc7ehMZ84yaEfgtNKZBHM1a433wODJBDkhpgaqSsqllV95KI/O2cHC0JKclQjLexcAfWjn4Gsw1WFfiINh6fbJQrRbjEy4KVEBmf/8O12/cJpXE3rkduq7l+vWbHBzuc/WTT5BzYf/gkEXb8pe+85uoRxPm8wVVqJhsbXHuwkWms21yl0yXp+STUgbKeDxhe2ebW9eeQKqGvnj6nFkcHFo2o3I09Zit2Tb14HmVS6aqaiaTKZnCfHFMW0zYsCinFumnI42uX+2wnpz8vqJ/rGTuLZh5auDxVJ+dMzTV9eOttFmMF8LQ2qxn7rvWhlmVd9aPcXozzvrc4ekYdnwn5+tJdXB17lNl/09GP2zuV0HVECw/wwTK7y5AeeMb38jb3/52/tt/+2/ce++969uvXLlC3/fs7++fyaJcu3aNK1eurM9573vfe+bxVl0+q3M+V3gphEqoBt+SJiijSokdxCS0LezfUe7cKmzNLLtST6xteJW+yklNTCvIoAnASfgqg0ZAMKXCEGa4MAZpsFIPa5Jtzi25xEF3YUTBYa7FCfGRQDahNq2o6y1Uzb69SEFkG+f2jEujx4SmoaiH4skc4nxCZW6mElhLscgCHXRR+gIudDhaanceZUKRQnATpBrhUyJoNAfgNEEYkfUOhRbnJkZDdBlRh/fb5HxoIk3UOAWRihIOzHSw3Bl2nTsUTI4/hMY6jVyNqwPkKVUa4aWx9KCItUWnTyLliKhK4wueypwcEYpcJOkdXPtJcFPyoA9T3BFZd1Ayt/Uaj9xu+bJzX8rdzV2UWKwXoapMQC0ropHjvCSWZJkezUQ19dVRNWLeRvuyOsGpMmtm5KXgxyNUHaMQTOshw0XuIkvhUI5sR5sCrhqDG1mgUQJEpYvKYjln0S5o25aybM0hWTpuuhtMqwmzPCapDDtGITvLqDipqfvC5GDMfHJAGxSpBR8qqmpMXY3wdW2CZGFkZTfNw67eHFfRQROI3tqi760ZPdCw5SpKdrAs9J884vDRm6TfEbYWNXvbF/jIR/4PVRXIWBtt0cwyRVxsuRPmHOR9Kr+7JjM2MiGUTBc7+tGcxeR3KHHErNxDLTOmRenKgnDLc3f/YsquclD/Fgudk5IFj6H21KOKuplQ1x6patwQqEgdcLWDzi6BblgkclFqHJpNTyRIzd7kCqPj3+Gov87h/A7jJEOr/0DQGzLiJSdSKbgQcKVQiBZslN74C6Jmbhh7FGeZ0mKMj9T3pGQij0UiLickQSyB2JmhXkxlUIlN9KkQY0+Kpo+SsnE0Lu7tUI9GVNvnqGcXaLueLhaO5plbt49pRrYAxr6llA4palwXd7IoaTH365yWZG2pwwRxmJ+WeNr5PiJKigu7OOpwhZOC98JkusXW9i77d04WMNvUWVu+l2NKUWIfUTJV3VBXNb6asDjoOT6ck4q9d1rSsDid7PgtWLHs4JriMqRiCoLXk5yBDOUeHci7IJSYWM7ndLGj6+aoOvZv7zOfm8fX7s4eh/sHw7rvuXbtGltbu6QucryYW6BrfiaIWIbf1k6LAD7x+OM0N0b0SWmLwGhMaCY0ElCBvjSMR2n4bCh939r7m8zCYLGc08UWLYoXM5gsAw8Hyini7ElGZSWEcjow0HVEIqv/nyQ+TvFMLOg5FTDA57S4ny2hDNmY4aZVkCLizFNqHVyuGCcn2Z+iqzLU6nlPZXvW9zgbNJ28BjmjHHvmZT1DPKMARVX5ru/6Ln72Z3+WX/qlX+LBBx88c/yrvuqrqKqKd77znbzuda8D4NFHH+Wxxx7joYceAuChhx7iLW95C9evX+fSpUsAvOMd72B7e5sXv/jFz2jwOVlNUZwyquH8rjCbweGBcuNGoe2ETz7p6Dvl1k3h3gfhyhXY3jYBt5XsNmJGVUYyVVw5VTl1gyqi84gb4/wY8d7eOlEQs1NHAgWHyAir12UrT8hgla5WBsLZc/mqxumYpDUwpbiMc5NBmtzUKwtKSq11QYQacceW5Uk248FV+BpyHg3pyBnIFqCmV1BuokwhF0QOEBqKBPBjpOyZl06VEGxnifYoJu8uYWcwQOzwsmWp5tQh3EfRW5b6ZozKMTlmyIUSb6Hh2HYIWmMCcgEnDVqOgQrNieCmaDVB8j5RBMcW3kU07FLiIfS3IFzASYXqjFore03acywHfPDwQyynS66Ei5gGjKLBxq2Dpob6RIwdfU5WisKzjErbW4pMi6UhU07k7BmNdqhjhS8O34CGiI81Fzrom4/TytwyNkUp2RGzmL9JynR5Sdsu6Y4X9F1PSolIIiHcdgdsVbcZcxdSgl3A8dZNJIUgPZU6pouGxbzmYHoLHTtcBVQVWs+owxbBjQgr4qc4RL2J5xW7UHjJoN6ItmpeJEkjiMNPKrhrm0ozfqdBPnzA/EbHrNohlUQjsN8vkZIgdVAJ062KPh4Q8z5BR4gXggvUvsFnIURhng65U32Mq+W3OV8e4MLsLkbdNjnXhKPAvfEPwJbnw837KfWSeuTx45pmvMW42SaECglQBR08rgoiVuwXTKxNs/EJXOWtnFEy3jnGo/NsjXc5nN9if3ETxy51tWptzUhOoINpYZ8sS5Ba68QphZxNuVfEowVyMZ8mRHAKWZKJuCWgKuSUB3sHZ/L1KRNzIcZIXzJdUmLM5CGT12shJ2VcVVze26Yeb1HP9nDViOXxgj713Lzd0y0jvnbMjzr6uLSuIkvvUEoeyKUVjjyUWpfmleXNBqFQKKWQ4xFOIiX1tnuVkzbbuq4gnOPOfiZlZ0TvQVjSOUcTOmrf0qZCoVBVgaZpGE22UBmzf3DEcnFswclpcTbhhIuwXsBOJNl1CKSdGJ12LYUv3rIsOJAKxHSi2nZhwUJvZNrlssWjtG3Lx2/c4uj4mOl0jHOO6WTEbDLmzp1DFsdzlELXdRQt9H1cZwdWvIcYE0hPydC1LV3XouqZt0vqUcPR0SHtcklwjphtYyNAcFD5QMrmVVSEwV+p0KeOXAZdmJNVkjOFDJG1y+/w55pcuy7hrO41RDerRuMT3sjJfdeR95nyz/Aoq4zR6eca4iEnZmh7Ot5xcjaAOf0KVo+zzkCeykbJ6QE85TUbh1HOlnP05F7PFM8oQHn44Yf5qZ/6KX7u536Ora2tNWdkZ2eH8XjMzs4O3/7t3873fM/3cO7cOba3t/mu7/ouHnroIV7xilcA8OpXv5oXv/jFfOu3fit/9+/+Xa5evcrf+Bt/g4cffvhpyzifCX0q1larhXFwjEfC1qzQto4+O5ZtYdEL82Xg1p3Mk9eFB57nuPseuHSpsLUl+DAQxsS+cDIQZqWodfuElQpmAzhUCs6b9oPNYbQvoa9NXlYqVnVj01YLlrItgESUuU1iHqGuG1KfAaUHMJOsXIYPak8pS4SaEiPBV5izqSmTIuac2oRt201rMXLdwPxP6QYl3UYkUoUG2B4iaHNRzoCjtnhKe1Q9uAmmu2EfZnGBXA6BpbXIhmyZC92x4EvSekwix2jeJzjrAoqppXINUs0gW3dKlgpf1MzK3AVI4N0YKdfR1KA6Q30giCljmjvpIbCLl4LzPZ0/4rH4MYpfcq66wEgn+E5RyWRsZ9CnZN0rElFRloN0uBN7f0MIhLrh3MVLxMPC8qhnsrtHiVBlzAagSkzTRXZiT199nJ5MRUR0gcsmgV00E9uOrl0Sl5ESM0UGASxXiL5jP9xmT/eYxl00KUXsIp5IeKnw6gilYmu+xdbygPl2T/A1tauofcC7Md7VxkXwQxdFERMXy8XeoyQUBxFL+Zc8lKI8JDJaOcLlbeqdLRYhkP7PbbZkyvL2oZGfl8eMvLA1G5HVM9ubMu4L5fiANjbgdxA3IpWAOE/NCGmN+PyR8f/iN6oPsqfP577my9nqdqmZMZp77u9fgrvQ8DvnPoSbgZ+MGddjmnqGSIULafjsLzEmsR8yl9jFXYqVg0IAsX4W7wMhbLE3usATx1e5091k6sZUVYNiarqsUu5O0ADFD95apQXNpv2iBTdsYxVTkdXcU4k5WSeUQiL1iRQTbmijbXvLIHRJSUlNXTZZ0NPnnpgzOSpJE9vTbc7t7BAa8w1qozI/Pma5XPLJT+4b0bZkFvMjSloOhNdCcLr2+8m64n11xNwjai26DGTf4+MjtPRk6VHth0XI2rPFCVXVIGFv4JdYWkMI+OAIDraaFpGOUgo+OMbjEbOtHUI94nCe2D/YJ/aLges3dJmostZmGdL5KgwdZI4VgdSez3SMbIEcfG0wo0AfTMQwxsiybY3f4DyxjxwfHRDbJV3XsVi0FmymimtPXuWe+59HXwpt39MtOz7x+BPM53O6tsV7T0zZpBiGAMq6qxJNqBhVIKWn6zpc8LRdx2g0pqonLNsO6KwMJhZ6pJTRBpqQiFqIOdKmaJnH3lrOz5Z3Vl02QxlETi3yrN6vU7mndUCx8n87Xd45WzZad/8wZGfOPOsQCJ3u0JFTBzlVHDjVg63C4EZ9EiStxuPkZKpPAg57Dhlmek0MXj1+WWV/jLa3uo/yzEOUZxSg/PiP/zgAf+SP/JEzt7/tbW/j277t2wD4+3//7+Oc43Wvex1d1/Ga17yGf/yP//H6XO89b3/72/nO7/xOHnroIabTKW94wxv4O3/n7zzjwWdMRhscWYU6KLtbjmWnpkmQhE6VrAkfCzdvCoul49p1uP8BuO8+4dye7eCcXX5sUoZuHu8FHyCEai0GJW6G+PFAwuoppcKrlYgQR8bhZWSpZBVSKQTxIC2269ChJrvES0NRR9aIY2y7c+nsQ0Eha0cIHsmKSksa+vFFKrJ2OF+BBorrMJ+dwkoxFkxLQcoM786hWllpqiSc9jjnLJtCNXwot1Fph4yPGNdNFMWjaQl0JBGkdEYGFkVYGPdFtlH2KdkhOrEPYjnEy2ho/40UqREdW8CiN3G6BBootynOIdUuLi8oFLxmyGbY5d2Eov0w4zpwggR8zx25RRkVpmWLUQ5I9BQPKTiC1EgJRnjESL1aHF2OrOL53BcW+4eE7NBYCFmQsFK3rBB1jKrAOT1PV/a5I3fQktfO1UkLWVvatKTrO2K2OVYVKjXOSAkw55g77oBp2sKrN36SmJ5EdkKhhuIYLWacOz6PnN/HOaHxY2uNDR6PENSTFQtwFJJae7ErVt4ow8LrAMQkw32pEEzZWEZCrpXqgRnTMciosJOmLHqPO7zNXjOlJGUZE3cODpmNayaVo2gkagvFI9LS+ApXgnXI5Cvcu3wxS/cePhrex+Pyf7infj4vmn4F7k5g1O3xoHsZo50ZN3c/SVV7y8K4GpVIoZDEskHCSuzKApTg7bPZVA21b0zkrpiXEKUw9bsE9SzLkig9DAqpMfa4mAlOcaGhaqaEekSOR6QSkZLRbNeIlURBTplCAnVkVygxQQLNiZyX5k5crCTca0azDB07xtkwBdChqzBnYlFEHXef22N3tk1VjykIy8Wc+dEd7hwcc/XaHdxgeNd3C/PcKgkRJRcPFIITRAeyZ+nNADFYviIVew0pLkCUlLrhqmj8OXXgBZpmjDoLvt1ACBZvJFhxwqheQF4iOTFpArPpjMl4SsZzvDgyA8Y0OBaTEKOcDwtcWW31QRkyzwNRVrGxrMgRwzlD6Ixzft1mvFgsrPSZEod39oldR86Jtu2sIyrb9yqEGh8cVVUzm23xicef5Phozs1rV40bU4aMUilPKT6YvMS0qbhyoeLSxSmz2RZd1xOakQmyddGylMEznU0tGxMTjUJTxrRdy7JvEedJCq5kgi9oMD2UUvJgE3BSOllda04KNyf/rsoq67dvnY3i1P2HTBCr0tBQQlkFKkPJ6Kyb8OnnXN0+5EL05HlPH1/FnGdGuppPPS14Z3N5ulS14pasx73O/PApQcnTcXw/E55xieezYTQa8WM/9mP82I/92Kc954EHHuDnf/7nn8lTPy1ygVXDV1sU8XBuB7I49vaM8JoTVAJehZKF+bGybJW2g75z5Pthb09pRiuFvlPpMWdBShVmeLdLdgHv76GuGuCAko1MKm5Il7kKlSHTkkcEt03iEGOV2zhjGeG0ovL2BRKxNKI4i8IL1ZBJ88bR4IiShl2RWmDgXERTBMngvclx5w7N2dRufUA1gExRPCLNOuuT9QZZM8rIxusc3k9J3AYEpz0Mu03NLU5GWE2yRlwxue1ck2lxUih5bhoWUhHk3BCNG/9DVnVwAmg9pKY9lNoyPfkOSkeQ3tK+1TkkgmIaEAiQPgFctgsySoWjDgEJBQ0dndtn6Y+p6sBUtwmpZlkix3Fp8uOsjL08aagnr2rEqfQct8dMtaE9WtA2mfF2g3qFJKBCTh3TZptL8gCLkmj1iNhHfOhNnyO3xN7cc2PxeHWgmSDmUqRO6UeZO/kGe+UcW4stJGNeLM5Zl5NzVOoZdcLO4QXisafdA1yDE0+NUkkGtyrrZIp0JvinFVpF05eQMgh2WkYwD+VPkRWBz1FSQqZQX5mSNTMuifKJW1zodqmqmusHtym5cOfOAbsEtpttfD8i9xWuhlwLyXuCWAdd0DF7/X28sHLQvJsn68f5RLNPu3cbf77ikrvIdrVHc2HChdm9hCaYnojGwVXZ2edieK3BV9bSL9aN56WxfKCzNl/J2YigeHwSxiVwLHNilWDY8WpKhBgp3iEhDPwdT+ksW6HZto1SHJIKufSWyRDr6ssS6PtCiREVa79PKVEyxARZhJSVrM4chIsO52CSB8n+roLjrgt7hMkUqa1bZDk/pF/OuXFzzv7Rgma0Q99HUmrX2ROhoK5Ydx/W/i7aU9ICtODEgxRSbJHgSbEl+ELJS1vIhp27qOK9p2p2KDr476jHD9onLlQEyVRyQN/1OC+MJjNm23uMxtvMFz19Sog4YgLUzBNVV8GK2oZrxTMYul7WBF3UMqxSWVmjMAShmN2BC+AbQDg6OuT2/gExJtp2aR5HxTLkbRuNjJwyOw+eY2d3z1q7Y6ZvO+aLY5bzI3LfD2ujdaecpj6Ic0wnE7aaitplzu9N0NzTLhe0XQ8CBc+8TcSU6KNtAFPOtpkrheAr6gpSbglSWfAeGopTI8WnHifJysYpsuL6nMa6ODL8slaiHUo5zrlTJaFVEHLmnkMCQ55y+0nGxrI1elKqGc5dcUFk/cinCzVuvTlYx0Krhz6JLU8CmLWJ4SnxNtUhe2Ipo6e89HVA9EzwnPbiyUVM4nrls1EJoRbGjRAqI0k5r1RiacnjiLUrOmX/JjwpysiBF2VnTwiVTYB39oEWV/ChIVQzJIzwrsH7FifZ1FNTRHxCNFCygJsQXENJh8igsGheGgmRhOrAxyAjUuNlC+8SWZOZ7WnEuxGEBnSEZIcyIlQ1WuagiqeQObQ0fzKhJi2RlOZ24cmO0ExIeWIOrFITqj186NH+JqLHFLdFUsVpQrSjpIIvDSa2NKfIxB5XPaoNGhJBAiUfkPMdnOwStJBpTco/CkUEJ1s4Kjsn2KKiaYnTJVptIe4asIXSoNmjHCPSU3QOeYLjBpIcVHsWEEW1bJAbAwxpYnDe0VQecUomknTBUdVxrVxjxhTvGro0R7HAMGVLya9SqEagsz6CnHv8SIhpzuHtm4TmMtVsis+Kxh6HR2Li3OgKxWc+Hj9M61tCXhqJLi9JuSfr6pvvyWUoR0jC+YxUkELLUXWbSZzh+0BJIN6kzKNPMBg4unnNzu1dqos9eXuwmh9yp4UytPop6tV4CcWZmzXmxO29og4KFiAWDUOAkvCaBqK1koPHnd9inBLUheQLQYSbi9sEp9yat+xsK1fqFp0fUXKApIgLQ6A4YkSNczCWMbV7AXWpaEbv5tbscbrpE0xmF1mOt3CTI+pmyixcwFVCYUGMR0isKEQqpxStcVJRMej4OGsBVQbd0lXmUYt5xaSMpsyIhsN0QDeOVmHVRElpcJw19c0g3rROUodm4yOY03FFznaetRTbK5OU6WNPTqZDohjBMKVClwQVU2xO6ijZuCoxF7qo5ruTI7ko56cTds/tEMa7SKjpup7F8SGLvufq9QUxKQFH17dDeWdQ1rUXbRy5YhIE5k1kUv0r3gyuRyNYz6zpsqxKW6tlINQ11egcmgNg5FvLfiihqqh8hriPSmI0mTDb2mU6O4f3DcfznlKEtlVK6m0B0jK0oayeYaX/YTttK8tZcC/r46dKDquSjxOcb9bE1hQTRweH9G0HMnCHBhVeGDKEOTGZTFjMl9y+vU+houuX3LzxJPPDA7Mtwb7vQ1PWydMWJcdIs1WxszXlypWLFFpEenZ2JvQRll2iqWE0bqjrEYtlZwTxwV277+2aXvlAFSq7vonSxg7HkujMTFL6znhOJZ/a1LuTAGLIMKwihhP34rNdNjZNq0Dl5ICsxEWHctCnqsqe1JTWhF1WlZ+ToGcV4Mj6dDljrrgew6qcdCrYOT0ke6/LIDB3IvbGmYzM0C30e5lBebahCg5Hoq6Fe694HrgbZlNh2WfGjZEm24SlurEdrVNlUsGoEbql0M4LaSHoJKAuIn5IZwnWDulqxE3xzQgfbBeSsknFh3qMSB6SmAV111HdxskEqiW5XEdJCN4qwt6IVqK7qJhYtHcjSrcAnQDHwBIpEwoVRa/jyggftmj1ADTiXbVWuJVhkSoaCL6G0qESyKmAm+DDxJQQZUaO/4ecruGri1T+hRQNwL7thMoBSMbk9SuCjHGSIYzp+zkld5gfTkBcRSlHBBT0NiKmspnLHPE74LZR14JCzh6RMUkjrsxNfZV6IHFmRCeIVGie47QGjUh1F66+C9d+hEKmNC/AuTlOoA7mlSRSTA3UQ19MUCrlSE/ijpuDq8lOCOpoSxo8iE60AJxbCfQFQgmMfEXtHV1ccrR/yMRVjCcjAg2p70Hs83M+38WdcotOP07ve0RrklrwK2p6GytfjkIedooOHwSZ1fQTJcdMdSOQLN9uF2PtcQgRj+RAfVBI+0I+5xEyRaz2jZo2AcggPFcQbyWpIhnvzUrCFTVhcQfZRTwVTgJptVvLoL3giqMaN3S7W0yTZ3nzDl5g1tTst4nDridLpqoTadnixNNri08FvCDeW1uwQlUye+ke/q/667nefIjFzgGj7V0m40uMassgeOdwLhn/SCtUIkHqYWftcOpppDbVTxj4YINvjA8UdOjUypTcUWJiksdIucM8Lymotd1qQlGTDiDhUk/Ox+QUratEFVeMm5MHzRAFSB6pIA6+PapmhKlGc6MvhaQVUiIpZit2lELMiS4mulSIWS1Y1cKl3R0m0138aIyKZ7E85mi+z+Ey8eStJeI9OScLhnI78A+se2ctG++cBV0lUjQZcdwN2TIn9F2PuEJKpvJqC58RBpw4RnWDqy4ZKXsIFLQI4kxssg77wBF1HRhPJoym21TNmJwdqTi6vnA8t+4i49tlVuqxRuM3yKpUIafVQlftxG5gxQ2HnOLFRNqcryxRGiP90gJPK9EoXTJjzqLGA6mqgKBcffKTZHV84uo1jg+PWBwdkfpI7HtytvJOLp+6sMeUiMVKMVXdcPnSFfqdSBetfVzCiD4W+r4nl0RdVYgT4xb1EbQQwpjZxJodktrce29yE33yxGydSXlQVl0Jtp0u3qy6cwR4aofPSVbkNM7mO9bk2uG+zrlTgc/poEaGmVll1SxIKYWzxn3rQELPPtNJsuRkRJY0OxmzDEqyp4Ije47TkYg5szPwcZ4JntMBymwC56fCPZfgy17oeP7zCuOxtQ3fdUno5sLBkXB0VOh6m7RgBsRr0s+4dkzHmSYke9OzcmLiIAie4Kf4cBFfeZwv5OiARPATina2ydG55X+rniLbqHqcJEsR58bEzPzYLNxxiHiSHqKlAVeMrCoeLS1eI+IS6qaoWt28kK3kox1KQwhWay7qUHqCb4gaUHUkFnjZBdeY9kS6gZQ7OD+GcJniML4Kk3W927ml6YNkD2TrAKEiSI8WC06KbOHFxpY14LUyfogUgquQ0oHuo34K6qi0R6odujiHuED8jIKQS8LkpwWRGnUzVBfkMkY1Id3HIB/h6PH+CoRzOOcYT8b2RRwWYgRi7kklmhdNUVtItCM5T+4t5V+HQEbJ2VLeACkVYukJTuhiT5gF6lrQMifNa/A1rq6pa7GunC5RhRF31fezn25yxC2CVPgS8GVMEDH9hdUuU7K1b1aKrxtcE3CNp9eeZl4jC2flGAdZClkSjYi1Ccw91bWAuxgoW9aqLZpIxYTGMrabdcNVJxcrD2YZ1ByLETy9WhBSnBtaPYe0bCyk2JN7JeMt8tvx1KXi3KVdSl/opOf28ZyDVKh9IrmeHCtqdRaU5QyNkrK1qjbiqBmz193LeL7Fsb9B3HaMRltUdUC9IKUgLqJlIEgWTwgezQ2+yJDdDPi5J6opsTofjAPmgn0tRdGVsFopBK3wKszjEcvSUynWpqsmvKglE2NH7hekNLQSC+AGnkLOFCdIsffRR0fftsRkC3GBgdeg9LGQMZ5DNyjOxlSI0coPKSk5W9BTuYq7LpxjtrvHaLyFamF+fMSyPebwsOfwcI74EV2MpGgSBarJUq7FmfSBA5Mw9UOAkBFnViCaFVd5YjvHudUCupLFsm4mcZ7QTFHdouSChDD4jil18PZ+ywEqiboeUVcjQj0GHH1MdDGzbK3EYsanFnihg7qtwomaKgxhCm6dubCNm+X+hh26gMPck53U4ExJtus6+t50nVIug7/QwLUZWrmruiblxO2bN9DB2fvwzr45dhfrGMq5rPz+hoV3UNIWYVIHam8l375fcPvObbpuQdt39CkTwohS/HqcdV3T9T0xWhdXFQKj8WQQTQx0qTBfzsk5EaYzYmpoU8+iW9rnR6EMG6RVOefknTr926nSDadKMqv38ClBzNP9vYpLT+7HqunGEirDZ2NFFVjNzglf5nTp51QT8erBVg7UpbDKja0f6jQheDWe9SMMZ+qpMtMzwHM6QPmSe4XnX/Lcc3fh3nsKO3sV+EAuPecvZu7cKYiDUePoOshF8QhOhcop41Fha8sznTiqynIdZajdqg5eGz5RdIkrPSk3eFFMi6SjSD8s3EvbpTMj55qkV6llui4TKbtWikiJKkwAtY6YVChlgXcBLXOzLheP6sKkmsMOxStSlgQiaaiJV6FBXUB0THBbpHyDVJaIDwS5iMqCTEvFBBpH7vetfdCdxzEzHoYcoxqh1Lgyx6vaBVIU1QOK9igdPkwp+Q5CtsUuFcTfQ5He9EDKbaQcIEURachlHydTMmPUjyllTuUmqN7CDB0tSDFGREPRfWOpqJXR8CA5o9WXUOJvI2mJlinOOSbjESVnRKzVsY3ZdjeaSWWohRYl4G1v56wsENPQKg7Dl8nMFv2wE9U+0cSKxtdkX9HdOcT3gd0rl/ChwfzRCjl17FTnecC/iEfTrxG9aVA4cVQ+oKXQl1XJIFMcVN5TVZ5RVeGqQL5Y4+cO/5iYCZk4KIkoPb14Kufx2RNuFsq1jOwJTJQshVI60qCs6hFwkVQiXiqUEUWd8aGs2IcUx6rdFAX1gyJxFFKCLmVyrySFlo4+LPCXK/xRolkkDufKJ1NhjFLXS7TNlJShdFCPcMU6nkRGJF8hrqYqNeH2DqNPzIg7Edl1uJGJoeXSU9TbnrryhFxTlyklRuOIqUm6iwNKIsdEKhmVQHAm9Z6TqaoWEbqk+FJRJc+8P+KoP2Dsd/C5DItktrbSfkHpFyZrTyCEQQO1RFtes/GGSlHIPX2/HAJ/yw5KKUZ8zZA1klfS+FhrcZ96+uG48ZwKW5OKyxcu0DTbOFfR95G+XSCh4XC+JMaeyo9JfUvJC/su6knXiZDQUqPO1G61dMhQDjDion0KclzifKbkpe2CFVQsa+tDYDTeJdOQiuIL5oclgx2AKwQOcFJMvymMAeug6fpIn3q6ZKWrUjLoULKQsqrpnFpxFJHB4JRTJQNxQ1PA6lYLTsV52yyIR4HY9bRdb3o8WYfNmskAMJQmFMG5wGK5JBwekAvs37lD3y9p286CvazEp+zUnfPsTEZc2Kq4uFszm8HWdNc6QCWwu7trgWUpaw5MXVeEoKRYiJpoqkAzHlGPGuq6wfua+cJ4J2U8oWpqUi7cOTpk0fV4V1HXSkymUm6aNCflj5MunZOSzyo40IFovw4U9HTmZLjdLqBrpddVRuR027GWciqrImcjF10R0ofcyYoHc7oVeeCRWLOWrYmFQTlWWefPTociyKcGYuuMD6dLfZ8bntMByld8iXJ5uzDdgsaDZ4KvzjHbOuTK+QOuziIxCpfOw9ZUiT3cuqUc3DFL9+DsQtUnIWQBr3hv/eIFS4Xl3BP7O0g1HhZrj1+10JWAw4TNEktCdY7KXcKXxxCdW5q5FBArbwysB3LuCYzxWlPKElyPkEwojsY6DaQn6T74MbV3SDlCfEOOmSwZYWQfBgdeG2I+Gi5eS+Mm5DlFRvhqgoQpRSYm165zPBMSEc1LHMYJEFWcCil/EhfuQqRGOAQavN8DF5Fk3Q7qGDQNRlRh1zx9yj5oDb6xBUiMHBuLgh4ZF0YLqVwHv7vqVQQdDR1Mt9GqwdcXkOMPUfw5XHM/Od2g5AnCDlWYoS7T5WNi7shDJ01RE+cqg3BU0UxWQaqKftlTijIeN6ZdESNgXzLvA96HQWivJ/kxdRAYOfLygPk1obl4fgj8Ml5rYnLcs/s82uU+H4sfowxqn8UPF9Zs3SjeOUIohMrh64YQxriqQQKki4Jc97Cw3aU4u0+vCSeOgMN1SvXJwvzykjTyVkoUQVQIDlSsKymLmAIq1g1kuhMOwerCztnFXbSgOSF5EJ+KhdR1pG5B2x5yvLxDW44pfo73hdm5CdcOj3js1i32Ls64VBuZNfetSdxHR/QV0JnGgq+QyiEJXKzx1xx1lWEsyL0CPpNoyCQ0W8ZQyogmZzR01sFWCqI90S8ptGQ1MTuG7oxcErFtKbkQXUXsI1IcW27MPB9y2B1woTExOWEIMARK11JSSy6CdZRVZC0UqyNRcrLmFzJ9skUOkm1WsMU5RSWmQtFi+ifmDkjMK5KsLRIx2275wt4e29vbiLfnWiyXpNzh3IzrN5/EuCaFlFpy7gf+iQ5BZR4ClYIWZ2MuESuxil0rCMQY0dJRxIwC0aEDT23ZqqqaUF8gZVkvgkU5cTB2icm4w9c14gLqKitdZYixp+tNgbrrW6ylqQz/cmqx4yRTMXAUTHtjFZAIKmG9k3YCuIBzlZmqDi3IJetgr8CgEWTlkTKUK1QhpcR8Pmc0mVig6sxEtG07SjZtkqScdBiJLZuVF5o6cPfFbXJacN+9l2hGgX5h14bF/IDxdIoPjtFozMULuwMHxjSsRqOaKtTUjQlw1iPrvnROmc2mjHVCaGratmfeLql8Bc2EtoeYInqmriHD3J5aqcXyPCvLhSHlwWkF1tN5qrM4KV2fihXXmRQdKgFnUxe6DoJOztNTR4e7rDY2wsB7scBvxYUrpZiNxHrOnzoyzn5Wnml0wnM8QJnsjHEj6HDsL6GlYZT3UJkxmnouX1qyNRHO7cDWjrJcKFEchy3kUjhoHY9fd3Re2dmF6bRQ144iOriTFnxyLFUYyYQqXQRZ4KQf/G8SQkfOE0xRNZDlEO9GuEHsp88e5wUJ4HUL1Y6i3lodWYIEEE+QLVK6Rc5LxG1T1xNi6XFyALJAkkelwumMvixxrkc4xIcxqheI/QLxLcIhIUwpatojUm0j7KB6bGULGUFxZM5BuWOOvG7PFrkyJ6WEutsEfz9ZHEV7RHdNRE4XlNgS6sp2ONnj6Mn5LjTtW+tx2CW4e42cWuZkdbgyRpmyNKNllAjhHILgc7LnEEeJE/zyPOk4oOEG1egrkK7i+PgO4naZjMZATygNbVJKKuRiRm7mt2Jp++ILpfLkJMzGY7o+AdZWOZ2MEYHJZEyOCZ88o3pGqAOUQmgUN/VoKqQ4x+WG8WxCVdVo9Pi+R6LyvOmDHMwPWPo5NULKFbHK9LG1nYkvhLowHjdU0x2mox3GzYTgPOwJoysevd6YJ01lOjiiMriYeCtVRGhuJ/w5yLVpWHi3SkFX624O83XKiO9xMsIVh3i7QHgrqq0l/q0zQun7TOkifT+njXMoHVISghDqilk9Y3YrQu655Xp2tjyTRildoeQOp6bnEl1BfUGrgmsUN2mQLLgg6IGDDyd84+CimmqoeiRb11JQQbNpY1h3SEayp5SEeI+vK4ILhOCN81IsO6AorhQLGsdjLrtLxJxR6XC+ULsayJAF59U63nwwT5JiHBaHszEOhXkVEO+NHxNsYQpFEC/GFyIbmTdn8AwZR6x1N9gGJ+RCqAIiwl2XzhHGNZlM1y1ZzA+JqaNtA6k7YHs7oJJxWmiCmtgjDFkSUxv2zrFqF02pxrmKUDWoE5oqEFMibHkyhcaNOCkcWHlnZ3vKbDxjmXsrvXjFO2VUB0bVktkoMm76tVmk5kjfHYM4Ut8TuzmimXEVYVqTkwWKqzKPQdeLm/Me8SOzYwBbXMUNmRULVryvqZoRo3FgOhHqxtqqx5MJ21vb5IJxpU516iqYL4wTFvMFs9nSpAZQC7xLZjQemQL3wIkY4jFEhHEz4fIOJIV777sLH8Z0kaHk6xAf1j4zToq18iuIC4RqTNNUZp+gUIaMZE6JUVMzdp6kZukYXSFIYGe6TR97I4RmqH09lH1PZUH0JOuw6uA5ya4MPJGh22wVMKx+X/NEVkGJ6kl3osgQzchaX+UkQFkRmE9ItqdLQqvHXL/pnAQ5oqsxY5uJ4YCVlFcB0tnSj2NV1jnJAG3NZqfrW58Vop9L7/CzDAcHB+zu7vKm//tbaJr6KUdPEbT01C1DpH+qBGfH5Oy/nw6n62qff5z+WD7d7atxfbrzng6f7rWc3ho90zGefuzf7RhO3zZ84FXNO6ms9mG/yzn4NG/P6R7/px356Q+HFYpPjePUaJ52C/H0jyvrL/NKs0Cf/kwZLk6f49x+ru/8yZhP75xOUrarndl6RMMu6dM+q3Dqvk/BM/RMP+sH8hnO40TV8uQT8+negc/9M/Npz/w0B04IkCcnOJGTa8ZqURiOnSUQPrOxneCp352ne4wVf+FkcuTUMWBI85++5eyjnSww+pQjn2lcnwtOtDxkUID+nFYiGeZ4/Z08rcnxme4zfIPWpd7PePr6t5PP2FmcCRRO3/6UTAWf20f59y+e5poYU+If/f9/kv39fXZ2dj7j3Z+TAcpHP/pRXvCCF3yhh7HBBhtssMEGG/wu8Pjjj5/x8ns6PCdLPOfOnQPgscce+6wR2AbPHhweHnLffffx+OOPs729/YUezgafAzZz9tzEZt6ee/himTNV5ejoiLvvvvuznvucDFCcs5zxzs7O7+uJ/P2K7e3tzbw9x7CZs+cmNvP23MMXw5x9romFZ1gd3mCDDTbYYIMNNvi9xyZA2WCDDTbYYIMNnnV4TgYoTdPwt/7W36Jpmi/0UDZ4BtjM23MPmzl7bmIzb889bObsU/Gc7OLZYIMNNthggw1+f+M5mUHZYIMNNthggw1+f2MToGywwQYbbLDBBs86bAKUDTbYYIMNNtjgWYdNgLLBBhtssMEGGzzrsAlQNthggw022GCDZx2ekwHKj/3Yj/G85z2P0WjE137t1/Le9773Cz2kL1q89a1v5au/+qvZ2tri0qVLfNM3fROPPvromXPatuXhhx/m/PnzzGYzXve613Ht2rUz5zz22GO89rWvZTKZcOnSJb73e7+XlNLn86V80eJHf/RHERHe/OY3r2/bzNmzE0888QR/9s/+Wc6fP894POalL30p73vf+9bHVZW/+Tf/JnfddRfj8ZhXvepVfOQjHznzGLdv3+b1r38929vb7O7u8u3f/u0cHx9/vl/KFwVyzvzQD/0QDz74IOPxmBe84AX88A//8BlTwc2cfQbocww//dM/rXVd67/4F/9CP/jBD+pf/It/UXd3d/XatWtf6KF9UeI1r3mNvu1tb9MPfOAD+v73v1//+B//43r//ffr8fHx+pzv+I7v0Pvuu0/f+c536vve9z59xSteoV/3dV+3Pp5S0pe85CX6qle9Sn/t135Nf/7nf14vXLigP/ADP/CFeElfVHjve9+rz3ve8/QrvuIr9E1vetP69s2cPftw+/ZtfeCBB/Tbvu3b9D3veY9+9KMf1f/0n/6T/vZv//b6nB/90R/VnZ0d/bf/9t/qr//6r+uf+BN/Qh988EFdLpfrc/7YH/tj+gf+wB/QX/mVX9H//t//u37Jl3yJfsu3fMsX4iX9vsdb3vIWPX/+vL797W/Xj33sY/ozP/MzOpvN9B/8g3+wPmczZ58ez7kA5Wu+5mv04YcfXv+dc9a7775b3/rWt34BR7XBCtevX1dAf/mXf1lVVff397WqKv2Zn/mZ9Tn/+3//bwX03e9+t6qq/vzP/7w65/Tq1avrc378x39ct7e3teu6z+8L+CLC0dGRvvCFL9R3vOMd+of/8B9eByibOXt24vu+7/v0D/2hP/Rpj5dS9MqVK/r3/t7fW9+2v7+vTdPov/pX/0pVVT/0oQ8poL/6q7+6Puc//sf/qCKiTzzxxO/d4L9I8drXvlb/wl/4C2du++Zv/mZ9/etfr6qbOftseE6VePq+55FHHuFVr3rV+jbnHK961at497vf/QUc2QYrHBwcACeO04888ggxxjNz9qIXvYj7779/PWfvfve7eelLX8rly5fX57zmNa/h8PCQD37wg5/H0X9x4eGHH+a1r33tmbmBzZw9W/Hv/t2/4+Uvfzl/+k//aS5dusTLXvYy/tk/+2fr4x/72Me4evXqmXnb2dnha7/2a8/M2+7uLi9/+cvX57zqVa/COcd73vOez9+L+SLB133d1/HOd76TD3/4wwD8+q//Ou9617v4xm/8RmAzZ58Nzyk345s3b5JzPnNRBLh8+TK/9Vu/9QUa1QYrlFJ485vfzCtf+Upe8pKXAHD16lXqumZ3d/fMuZcvX+bq1avrc55uTlfHNvj/Hj/90z/N//pf/4tf/dVf/ZRjmzl7duKjH/0oP/7jP873fM/38IM/+IP86q/+Kn/lr/wV6rrmDW94w/p9f7p5OT1vly5dOnM8hMC5c+c28/Z7gO///u/n8PCQF73oRXjvyTnzlre8hde//vUAmzn7LHhOBSgbPLvx8MMP84EPfIB3vetdX+ihbPAZ8Pjjj/OmN72Jd7zjHYxGoy/0cDb4HFFK4eUvfzk/8iM/AsDLXvYyPvCBD/BP/sk/4Q1veMMXeHQbPB3+9b/+1/zkT/4kP/VTP8WXf/mX8/73v583v/nN3H333Zs5+xzwnCrxXLhwAe/9p3QTXLt2jStXrnyBRrUBwBvf+Ebe/va381//63/l3nvvXd9+5coV+r5nf3//zPmn5+zKlStPO6erYxv8f4tHHnmE69ev8wf/4B8khEAIgV/+5V/mH/7Df0gIgcuXL2/m7FmIu+66ixe/+MVnbvuyL/syHnvsMeDkff9M18crV65w/fr1M8dTSty+fXszb78H+N7v/V6+//u/nz/zZ/4ML33pS/nWb/1Wvvu7v5u3vvWtwGbOPhueUwFKXdd81Vd9Fe985zvXt5VSeOc738lDDz30BRzZFy9UlTe+8Y387M/+LL/4i7/Igw8+eOb4V33VV1FV1Zk5e/TRR3nsscfWc/bQQw/xm7/5m2e+hO94xzvY3t7+lAvyBv/v8Q3f8A385m/+Ju9///vXPy9/+ct5/etfv/59M2fPPrzyla/8lBb+D3/4wzzwwAMAPPjgg1y5cuXMvB0eHvKe97znzLzt7+/zyCOPrM/5xV/8RUopfO3Xfu3n4VV8cWGxWODc2WXWe08pBdjM2WfFF5ql+0zx0z/909o0jf7ET/yEfuhDH9K/9Jf+ku7u7p7pJtjg84fv/M7v1J2dHf2lX/olffLJJ9c/i8Vifc53fMd36P3336+/+Iu/qO973/v0oYce0oceemh9fNWy+upXv1rf//736y/8wi/oxYsXNy2rn0ec7uJR3czZsxHvfe97NYSgb3nLW/QjH/mI/uRP/qROJhP9l//yX67P+dEf/VHd3d3Vn/u5n9Pf+I3f0D/5J//k07asvuxlL9P3vOc9+q53vUtf+MIXflG0rH4h8IY3vEHvueeedZvxv/k3/0YvXLigf+2v/bX1OZs5+/R4zgUoqqr/6B/9I73//vu1rmv9mq/5Gv2VX/mVL/SQvmgBPO3P2972tvU5y+VS//Jf/su6t7enk8lE/9Sf+lP65JNPnnmcj3/84/qN3/iNOh6P9cKFC/pX/+pf1Rjj5/nVfPHiqQHKZs6enfj3//7f60te8hJtmkZf9KIX6T/9p//0zPFSiv7QD/2QXr58WZum0W/4hm/QRx999Mw5t27d0m/5lm/R2Wym29vb+uf//J/Xo6Ojz+fL+KLB4eGhvulNb9L7779fR6ORPv/5z9e//tf/+plW/M2cfXqI6ilJuw022GCDDTbYYINnAZ5THJQNNthggw022OCLA5sAZYMNNthggw02eNZhE6BssMEGG2ywwQbPOmwClA022GCDDTbY4FmHTYCywQYbbLDBBhs867AJUDbYYIMNNthgg2cdNgHKBhtssMEGG2zwrMMmQNlggw022GCDDZ512AQoG2ywwQYbbLDBsw6bAGWDDTbYYIMNNnjWYROgbLDBBhtssMEGzzr8P4XOmglM55hoAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1724,7 +1864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 105,
    "id": "af604ea6",
    "metadata": {},
    "outputs": [
@@ -1734,7 +1874,7 @@
      "text": [
       "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
       "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-      "100 44.6M  100 44.6M    0     0  24.2M      0  0:00:01  0:00:01 --:--:-- 24.3M\n"
+      "100 44.6M  100 44.6M    0     0  32.9M      0  0:00:01  0:00:01 --:--:-- 33.1M\n"
      ]
     }
    ],
@@ -1744,7 +1884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 106,
    "id": "572d824c",
    "metadata": {},
    "outputs": [
@@ -1778,56 +1918,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "train Loss: 0.6819 Acc: 0.5984\n",
-      "val Loss: 0.3048 Acc: 0.8758\n",
+      "train Loss: 0.6276 Acc: 0.6352\n",
+      "val Loss: 0.2228 Acc: 0.9412\n",
       "\n",
       "Epoch 2/10\n",
       "----------\n",
-      "train Loss: 0.4707 Acc: 0.7951\n",
-      "val Loss: 0.1704 Acc: 0.9673\n",
+      "train Loss: 0.5340 Acc: 0.7541\n",
+      "val Loss: 0.3350 Acc: 0.8562\n",
       "\n",
       "Epoch 3/10\n",
       "----------\n",
-      "train Loss: 0.3930 Acc: 0.7992\n",
-      "val Loss: 0.1557 Acc: 0.9542\n",
+      "train Loss: 0.3838 Acc: 0.8197\n",
+      "val Loss: 0.1851 Acc: 0.9477\n",
       "\n",
       "Epoch 4/10\n",
       "----------\n",
-      "train Loss: 0.7669 Acc: 0.6926\n",
-      "val Loss: 0.4019 Acc: 0.8366\n",
+      "train Loss: 0.4967 Acc: 0.7951\n",
+      "val Loss: 0.2312 Acc: 0.9477\n",
       "\n",
       "Epoch 5/10\n",
       "----------\n",
-      "train Loss: 0.4818 Acc: 0.7910\n",
-      "val Loss: 0.1617 Acc: 0.9477\n",
+      "train Loss: 0.7173 Acc: 0.7500\n",
+      "val Loss: 0.2374 Acc: 0.9477\n",
       "\n",
       "Epoch 6/10\n",
       "----------\n",
-      "train Loss: 0.3933 Acc: 0.8197\n",
-      "val Loss: 0.1570 Acc: 0.9477\n",
+      "train Loss: 0.5194 Acc: 0.7951\n",
+      "val Loss: 0.3891 Acc: 0.8627\n",
       "\n",
       "Epoch 7/10\n",
       "----------\n",
-      "train Loss: 0.3754 Acc: 0.8320\n",
-      "val Loss: 0.1770 Acc: 0.9346\n",
+      "train Loss: 0.4527 Acc: 0.8443\n",
+      "val Loss: 0.2329 Acc: 0.9346\n",
       "\n",
       "Epoch 8/10\n",
       "----------\n",
-      "train Loss: 0.3656 Acc: 0.8238\n",
-      "val Loss: 0.1637 Acc: 0.9477\n",
+      "train Loss: 0.2927 Acc: 0.8730\n",
+      "val Loss: 0.2199 Acc: 0.9412\n",
       "\n",
       "Epoch 9/10\n",
       "----------\n",
-      "train Loss: 0.4324 Acc: 0.8320\n",
-      "val Loss: 0.1714 Acc: 0.9477\n",
+      "train Loss: 0.3591 Acc: 0.8689\n",
+      "val Loss: 0.2230 Acc: 0.9281\n",
       "\n",
       "Epoch 10/10\n",
       "----------\n",
-      "train Loss: 0.3326 Acc: 0.8566\n",
-      "val Loss: 0.1610 Acc: 0.9477\n",
+      "train Loss: 0.3879 Acc: 0.8361\n",
+      "val Loss: 0.2645 Acc: 0.9150\n",
       "\n",
-      "Training complete in 9m 53s\n",
-      "Best val Acc: 0.967320\n"
+      "Training complete in 10m 4s\n",
+      "Best val Acc: 0.947712\n"
      ]
     }
    ],
@@ -2028,6 +2168,161 @@
     "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "790512eb",
+   "metadata": {},
+   "source": [
+    "- ##### Study the code and the results obtained.\n",
+    "\n",
+    "The results show that the ResNet18 pre-trained model, with only the last layer refined, quickly achieved high accuracy on the validation data (up to 94.77%). Concurrently, there is a clear trend of diminishing loss value on validation set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a52933fc",
+   "metadata": {},
+   "source": [
+    "- #### Adding \"eval_model\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "e37a903e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import shutil\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "def create_test_set(source_dir, test_dir, test_size=0.2, random_state=42):\n",
+    "    \"\"\"\n",
+    "    Splits a source dataset into a test set while retaining the original training/validation set structure.\n",
+    "    Args:\n",
+    "        source_dir (str): Directory containing the dataset (e.g., 'hymenoptera_data/train' or 'val').\n",
+    "        test_dir (str): Directory to save the new test set.\n",
+    "        test_size (float): Proportion of the dataset to allocate to the test set.\n",
+    "        random_state (int): Random seed for reproducibility.\n",
+    "    \"\"\"\n",
+    "    os.makedirs(test_dir, exist_ok=True)  # Create test directory if it doesn't exist\n",
+    "\n",
+    "    # Loop through each class\n",
+    "    for class_name in os.listdir(source_dir):\n",
+    "        class_dir = os.path.join(source_dir, class_name)\n",
+    "        if not os.path.isdir(class_dir):  # Skip non-directory files\n",
+    "            continue\n",
+    "\n",
+    "        # List images in the current class\n",
+    "        images = os.listdir(class_dir)\n",
+    "\n",
+    "        # Split the images into train/val and test\n",
+    "        _, test_images = train_test_split(\n",
+    "            images, test_size=test_size, random_state=random_state\n",
+    "        )\n",
+    "\n",
+    "        # Create class-specific folder in the test directory\n",
+    "        test_class_dir = os.path.join(test_dir, class_name)\n",
+    "        os.makedirs(test_class_dir, exist_ok=True)\n",
+    "\n",
+    "        # Move test images\n",
+    "        for img in test_images:\n",
+    "            src_path = os.path.join(class_dir, img)\n",
+    "            dst_path = os.path.join(test_class_dir, img)\n",
+    "            shutil.move(src_path, dst_path)\n",
+    "\n",
+    "# Paths to your data directories\n",
+    "train_dir = \"hymenoptera_data/train\"  # Use the train folder as a source\n",
+    "test_dir = \"hymenoptera_data/test\"    # New test folder to be created\n",
+    "\n",
+    "# Create the test set (20% of the train data)\n",
+    "create_test_set(train_dir, test_dir)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "c6e376b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Charger le jeu de test\n",
+    "test_data_transforms = data_transforms['val']  # Même transformation que 'val'\n",
+    "test_dataset = datasets.ImageFolder(\"hymenoptera_data/test\", test_data_transforms)\n",
+    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=4, shuffle=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "8d656c94",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 0.1252 Acc: 0.9200\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define the eval_model function\n",
+    "def eval_model(model, dataloader, criterion):\n",
+    "    \"\"\"\n",
+    "    Evaluate the trained model on a separate test set.\n",
+    "    Args:\n",
+    "        model: Trained model\n",
+    "        dataloader: DataLoader for the test set\n",
+    "        criterion: Loss function\n",
+    "    Returns:\n",
+    "        Test loss and accuracy\n",
+    "    \"\"\"\n",
+    "    model.eval()  # Set model to evaluation mode\n",
+    "    running_loss = 0.0\n",
+    "    running_corrects = 0\n",
+    "    dataset_size = len(dataloader.dataset)\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        for inputs, labels in dataloader:\n",
+    "            inputs = inputs.to(device)\n",
+    "            labels = labels.to(device)\n",
+    "\n",
+    "            # Forward pass\n",
+    "            outputs = model(inputs)\n",
+    "            _, preds = torch.max(outputs, 1)\n",
+    "            loss = criterion(outputs, labels)\n",
+    "\n",
+    "            # Accumulate loss and correct predictions\n",
+    "            running_loss += loss.item() * inputs.size(0)\n",
+    "            running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "    # Compute loss and accuracy\n",
+    "    test_loss = running_loss / dataset_size\n",
+    "    test_acc = running_corrects.double() / dataset_size\n",
+    "    print(\"Test Loss: {:.4f} Acc: {:.4f}\".format(test_loss, test_acc))\n",
+    "    return test_loss, test_acc\n",
+    "\n",
+    "\n",
+    "# Add test dataset and DataLoader\n",
+    "test_dir = os.path.join(data_dir, \"test\")  # Assume 'test' folder exists in the dataset\n",
+    "data_transforms_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",
+    "test_dataset = datasets.ImageFolder(test_dir, data_transforms_test)\n",
+    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=4, shuffle=False, num_workers=4)\n",
+    "\n",
+    "# Evaluate the model on the test set\n",
+    "test_loss, test_acc = eval_model(model, test_loader, criterion)\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "04a263f0",
diff --git a/hymenoptera_data/train/ants/formica.jpeg b/hymenoptera_data/test/ants/formica.jpeg
similarity index 100%
rename from hymenoptera_data/train/ants/formica.jpeg
rename to hymenoptera_data/test/ants/formica.jpeg