diff --git "a/Code_of_my_model.ipynb" "b/Code_of_my_model.ipynb"
deleted file mode 100644--- "a/Code_of_my_model.ipynb"
+++ /dev/null
@@ -1,337 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "CnOuuUHQXQ13"
-      },
-      "outputs": [],
-      "source": [
-        "import tensorflow as tf\n",
-        "from tensorflow import keras\n",
-        "from tensorflow.keras import layers\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from tensorflow.keras.utils import to_categorical"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "(train_images, train_labels), (test_images, test_labels) = keras.datasets.fashion_mnist.load_data()\n",
-        "\n",
-        "train_images = train_images.reshape(-1, 28, 28, 1).astype(\"float32\") / 255.0\n",
-        "test_images = test_images.reshape(-1, 28, 28, 1).astype(\"float32\") / 255.0\n",
-        "\n",
-        "train_labels = to_categorical(train_labels, 10)\n",
-        "test_labels = to_categorical(test_labels, 10)"
-      ],
-      "metadata": {
-        "id": "MWwHW6KMYBwl"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(len(train_images),\n",
-        "len(test_images),\n",
-        "len(train_labels),\n",
-        "len(test_labels))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "E74SVCUAuEcb",
-        "outputId": "48f41ba8-89ed-426a-c68e-5c3178f8539e"
-      },
-      "execution_count": 43,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "60000 10000 60000 10000\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model = keras.Sequential(\n",
-        "    [\n",
-        "        keras.Input(shape=(28, 28, 1)),\n",
-        "        layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
-        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
-        "        layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
-        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
-        "        layers.Flatten(),\n",
-        "        layers.Dropout(0.5),\n",
-        "        layers.Dense(128, activation=\"relu\"),\n",
-        "        layers.Dense(10, activation=\"softmax\"),\n",
-        "    ]\n",
-        ")"
-      ],
-      "metadata": {
-        "id": "T9Yre6tDZRiy"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
-      ],
-      "metadata": {
-        "id": "9P6Wz-O-Z3Gh"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels), batch_size=32)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "z-pxzXbbZ4qS",
-        "outputId": "940874b7-05f0-45d2-f790-8f990fbb8bad"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Epoch 1/10\n",
-            "1875/1875 [==============================] - 75s 39ms/step - loss: 0.5035 - accuracy: 0.8153 - val_loss: 0.3674 - val_accuracy: 0.8669\n",
-            "Epoch 2/10\n",
-            "1875/1875 [==============================] - 71s 38ms/step - loss: 0.3554 - accuracy: 0.8697 - val_loss: 0.3113 - val_accuracy: 0.8875\n",
-            "Epoch 3/10\n",
-            "1875/1875 [==============================] - 72s 39ms/step - loss: 0.3117 - accuracy: 0.8832 - val_loss: 0.2909 - val_accuracy: 0.8906\n",
-            "Epoch 4/10\n",
-            "1875/1875 [==============================] - 74s 39ms/step - loss: 0.2817 - accuracy: 0.8941 - val_loss: 0.2907 - val_accuracy: 0.8917\n",
-            "Epoch 5/10\n",
-            "1875/1875 [==============================] - 74s 39ms/step - loss: 0.2637 - accuracy: 0.9003 - val_loss: 0.2581 - val_accuracy: 0.9058\n",
-            "Epoch 6/10\n",
-            "1875/1875 [==============================] - 75s 40ms/step - loss: 0.2494 - accuracy: 0.9060 - val_loss: 0.2616 - val_accuracy: 0.9029\n",
-            "Epoch 7/10\n",
-            "1875/1875 [==============================] - 75s 40ms/step - loss: 0.2371 - accuracy: 0.9120 - val_loss: 0.2466 - val_accuracy: 0.9099\n",
-            "Epoch 8/10\n",
-            "1875/1875 [==============================] - 74s 39ms/step - loss: 0.2264 - accuracy: 0.9134 - val_loss: 0.2435 - val_accuracy: 0.9121\n",
-            "Epoch 9/10\n",
-            "1875/1875 [==============================] - 76s 40ms/step - loss: 0.2156 - accuracy: 0.9181 - val_loss: 0.2387 - val_accuracy: 0.9119\n",
-            "Epoch 10/10\n",
-            "1875/1875 [==============================] - 75s 40ms/step - loss: 0.2102 - accuracy: 0.9199 - val_loss: 0.2376 - val_accuracy: 0.9115\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<keras.callbacks.History at 0x7fd230242950>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 21
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "keras.utils.plot_model(model, \"model.png\", show_shapes=True)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 976
-        },
-        "id": "way9gmt-Z5_K",
-        "outputId": "e9ad450c-f94d-426d-a34f-de50652c1fb7"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "image/png": 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MTEyzh4IlJSXp9AV7e/s2x+50xrinelvv5z5u3DgCQCUlJR1Qq7bTaDRkbW1t7GoYBIz4iNauoq2PaLWzs6OUlBTKysqimpoaneWRkZE0ceJEKisr05ZpNBrq0aMHAaDk5ORm+0xJSdF5prepCQgIoM2bN1NxcTGVlZVRQkICSSQSeumll7Tr7N27lwBQTEwMlZaW0vnz58nT05N8fX2prq7OpOMREV26dIlGjRpFAGjIkCHNlsfHx1NAQIDOd1ZjYyPl5eXRyZMn6eWXX+ZHtHZX1dXVzX5FMMPoyLY1leNmaWmpfeKeTCbTlq9duxZ79+5FYmIiVCqVzjYbNmyAmZkZQkJCTP5pfA+ysrJCSEgI7OzsoFKpMH36dAQGBuLo0aPIzc0FAPz1r39F7969sXDhQlhbW8PX1xfz5s1DWloazp49a9Lx/vOf/+Cjjz7Cu+++C19f3xbX+eCDDzBkyBC8/PLLqK+vB3DvmTNNT9zr27evXjGNjROGHnbs2IHCwkJjV6Nb6si2NeXjdvnyZSxbtgwrVqyAXC5vttzf3x9hYWG4efMmFixYYIQatl1ycjLMzc11ypqGX6qqqgAAubm5cHJygiAI2nVcXFwAANevXzfpeEOGDMGBAwcwc+ZMnR8AD1q+fDnS0tIQHx+v1/5NUZdOGFu2bIFSqYRCocChQ4cwfvx4qNVq9OnTB3v27AFw7xeaXC5Hr169MGfOHDg5OUEul8Pf31/7iyI0NBRSqRSOjo7afb/33ntQKpUQBAFFRUUICwvD/PnzkZOTA0EQ4OXlpXd9//Wvf8HHxwfW1taQy+UYPHgwjh07BgB4++23tWOaGo0G58+fBwDMnj0bCoUC1tbWOHz4MBoaGhAZGQlXV1dYWlriiSeeQEJCAgDgk08+gUKhgEqlQmFhIebPnw9nZ2dkZWW1q50fhoiwfv16DBgwADKZDLa2tpgyZQouXrwIoO1t29HH7ejRo1Cr1Vi1alWHtY0YGzZsABFh0qRJra4THR0Nb29vbN++HcePH291vUcdCzGfFwAP7WPtdfPmTVhaWsLDwwMA4Onp2SyZN11P8PT07HLxWmJra4uAgADEx8eDOv/WfYZljHEwQ17DiIiIIAB04sQJunPnDhUWFtLo0aNJqVRSbW0tEd0bP1YqlXThwgWqqamhzMxMGjFiBKlUKrpx4wYREc2cOZMcHBx04sXGxhIAunXrFhERTZ06lTQaTbN6ib2GsW/fPlq+fDndvn2biouLaeTIkTrjl1OnTiVzc3O6efOmznavvfYaHT58mIiIFixYQDKZjPbv308lJSW0ZMkSMjMzox9//FGnPT744APauHEjvfLKK/Tf//73kXVrAj2vYURGRpJUKqVdu3ZRaWkppaen09ChQ8ne3p4KCgqIqO1t25HHLTk5mVQqFUVFRYl+r03aeg3D2dm5Wbmnpyf5+Pi0uI1Go6GrV68SEdH3339PZmZm5O7uThUVFUTU/BqGmGMh5vPyqD7WVpWVlaRSqSg0NFRblpqaShKJhDZs2EBlZWX0888/04ABA2jcuHHtitXZ8Z5++ukWr2E0CQ8PJwB0/vx5nfIPPviAr2EYg7+/P9RqNXr27Ing4GBUVlbixo0b2uUWFhbaX14+Pj7YsmULysvLsXPnzk6r47Rp0/Dxxx/D1tYWdnZ2mDRpEoqLi3Hr1i0AwLvvvouGhgadOpWVleHHH3/Eyy+/jJqaGmzZsgWBgYGYOnUqbGxssHTpUkgkkmbvY+3atZg7dy4OHDiA/v37d8j7qa6uxvr16/HKK6/g9ddfh7W1NQYPHozPPvsMRUVF2Lp1a7tjdNRxmzBhAsrKyrBs2bJ217GtKisrcfXqVWg0mkeu6+fnhw8//BDXrl3DRx991Gy5vseitc+LPn1MX6tXr4aTk5PODMeAgAAsXrwYoaGhUKvVGDRoEMrLy7F9+/Z2xTJGvIdpulaRkZHRoXE6WrdJGPeTSqUAgLq6ulbXGT58OBQKhfZ03RgkEgkAaKfdPf/88/D29sbf/vY37anr3r17ERwcDHNzc2RlZaGqqgqDBg3S7sPS0hKOjo5GeR+ZmZmoqKjA8OHDdcpHjBgBqVSq90VEMUzhuBlKYWEhiAgKhULU+tHR0ejXrx82b96MU6dO6Sxrz7G4//PSUX3s4MGDSExMxLFjx3Qu7EdERGDr1q04ceIEKioqcOXKFfj7+8PPz097oborxHuUpmP866+/dliMztAtE4ZYMplM++u+M3z99dd47rnn0LNnT8hkMixatEhnuSAImDNnDq5cuYITJ04AAD7//HO89dZbAO79IgWApUuX6szjvn79uvaiXmcqLS0FcG92yoNsbGxQXl7eIXE7+7h1lJqaGgB46AXT+8nlcuzcuROCIODNN99EdXW1dpmhjkVH9LG9e/di7dq1SE1Nhbu7u7b8l19+QUxMDN555x08//zzUCqV8PDwwLZt25Cfn4/Y2NguEU8MS0tLAL8f867qsU0YdXV1KC0tRZ8+fTo0zsmTJxEXF4cbN24gMDAQjo6OOHv2LO7cuYOYmJhm68+aNQtyuRzbt29HVlYW1Go13NzcAAA9e/YEcO9++ESk8zp9+nSHvo+W2NjYAECLX0Yd1badddw6Q9OXyIN/7HoYPz8/zJs3D9nZ2Vi5cqW23FDHwtB9bOPGjdi9eze++eYb9O7dW2dZdnY2GhoampWr1WrY2dkhMzPT5OOJVVtbC+D3Y95VGe15GMaWmpoKIsLIkSMB3Bsrf9gQVlv9+9//hlKpREZGBurq6vCXv/xFOxvj/ql9TWxtbTFjxgzs3bsXKpUKf/7zn7XLXFxcIJfLkZaWZvB6tsWgQYNgZWWFn376Saf87NmzqK2txbBhwwAYtm0767h1hl69ekEQBL3/X7Fy5UokJyfj/PnzcHV1BSD+WDyKofoYEeGjjz5CSUkJkpKSYGHR/KumKYn98ssvOuXl5eW4ffu2drqrKcbTV9MxdnBw6LAYneGxOcNobGxESUkJ6uvrkZ6ejrCwMLi6umLWrFkAAC8vL9y+fRtJSUmoq6vDrVu3ms3LtrOzQ35+Pq5du4by8vKHflHV1dXh119/RWpqKpRKpfaDffz4cdTU1CA7O7vVceV3330Xd+/eRXJyMiZOnKgtl8vlmD17Nvbs2YMtW7agrKwMDQ0NyMvLa/Yh6AxyuRzz58/HwYMHsXv3bpSVlSEjIwPvvvsunJycEBISAqB9bdtRxy0lJcXo02oVCgU8PT2Rl5en13ZNQ1P3/+dA7LEQs+9H9bHg4GA4ODg89HYkFy5cwCeffIJt27ZBIpHoDG8JgoB169bBw8MDY8aMwbZt23Dy5ElUV1cjNzdXW9emoVhTjKevpmM8ePBgg+3TKIwxNUvfaWFnzpyhgQMHkpmZGQEgR0dHWrVqFW3evJkUCgUBoL59+1JOTg5t3bqV1Go1ASA3Nze6dOkShYSEkEQiIWdnZ7KwsCC1Wk1TpkyhnJwcbYzi4mIaM2YMyeVy8vDwoPfff58WLlxIAMjLy4tu3LhB586dIzc3N7K0tKRnnnmGPv30U9JoNATgoa+DBw8SEdHixYvJzs6ObGxsKCgoiDZt2kQASKPRaKeJNnnyyScpPDy8WVvcvXuXFi9eTK6urmRhYUE9e/akqVOnUmZmJsXExJClpSUBIBcXF9q1a5fexwZ6TqttbGyk2NhY6tu3L0kkErK1taXAwEDKyspqV9sWFBR02HErKCigI0eOkEqloujoaL3byJDTakNDQ0kikVBVVZW27ODBg9p+ZW9vT3Pnzm1xnwsXLtSZVvuoYyH28/KwPkZEFBgYSAAoMjKy1febkZHx0M9EbGwsEREVFRVRWFgYeXl5kUwmIysrKxo1ahR99dVX2n2ZYjwiotOnT9OoUaPIyclJG8fR0ZH8/f3p22+/1Vl3woQJ5OzsTI2NjTrlXW1abZdIGO3VdB+fruTll1+mK1eudHpcfRNGRzLV42bIhJGdnU0WFhZtSu7G0tDQQKNHj6YdO3ZwPBGKiopILpfTunXrmi3ragnjsRmS0ufCojHcP7yVnp4OuVyu/Xfq48zUj5s+qqurcezYMWRnZ2svgnp5eSEqKgpRUVHN7qpqihoaGpCUlITy8nIEBwdzPBGWL18OX19fhIaGArh3vSU/Px+nTp3C5cuXDRKjszw2CcPULV68GNnZ2bh06RJmz56tMwOGdQ+3b9/W3nzwzTff1JaHh4cjKCgIwcHBJn+DwdTUVBw4cAApKSmi/z/yOMdbv3490tLScOTIEe3/rg4dOqS9+eDXX3/d7hidyhinNZ15ShUeHk5SqZQAkLu7O+3bt69T4uorIiKCzMzMyMXFRXsbEGOAiQxJmfJx66j+e+zYMVq8eLHB98uMIykpiVavXk319fUG3a8xh6QEos6/G1ZQUBAAYN++fZ0dmj2CIAhISEjA9OnTjV0Vk8X9lxmTMfsfD0kxxhgThRMGY4wxUThhMMYYE4UTBmOMMVGMdi+pvLw8JCYmGis8ewhj3MiwK2m6zQP3X2YMeXl5Rrv5ptFmSe3fv7+zwzLGWLcwbdo0o8ySMkrCYMyUNU0p5jMIxnTxNQzGGGOicMJgjDEmCicMxhhjonDCYIwxJgonDMYYY6JwwmCMMSYKJwzGGGOicMJgjDEmCicMxhhjonDCYIwxJgonDMYYY6JwwmCMMSYKJwzGGGOicMJgjDEmCicMxhhjonDCYIwxJgonDMYYY6JwwmCMMSYKJwzGGGOicMJgjDEmCicMxhhjonDCYIwxJgonDMYYY6JwwmCMMSYKJwzGGGOicMJgjDEmCicMxhhjonDCYIwxJgonDMYYY6JwwmCMMSYKJwzGGGOicMJgjDEmCicMxhhjolgYuwKMGdPJkydx+vRpnbKLFy8CAGJiYnTK/fz88Oyzz3Za3RgzNQIRkbErwZixnDhxAn/4wx8gkUhgZtbyCXdjYyPq6upw/PhxvPDCC51cQ8ZMBycM9lhrbGyEo6Mjbt269dD17O3tUVBQAHNz806qGWOmh69hsMeamZkZZs6cCalU2uo6UqkUr7/+OicL9tjjhMEee6+++ipqa2tbXV5bW4tXX321E2vEmGniISnGALi7u+P69estLnNxccH169chCEIn14ox08JnGIwBeOONNyCRSJqVSyQSzJo1i5MFY+AzDMYA3JtKO2DAgBaX/fzzzxg4cGAn14gx08NnGIwB6N+/PwYOHNjsTMLHx4eTBWO/4YTB2G/++Mc/6syEkkgk+NOf/mTEGjFmWnhIirHf5Obmws3NDU0fCUEQcOXKFbi7uxu3YoyZCD7DYOw3Li4uePrpp2FmZgYzMzM8/fTTnCwYuw8nDMbu88Ybb0AQBJiZmeGNN94wdnUYMyk8JMXYfYqKiuDo6AgAyM/PR69evYxcI8ZMh0kljKCgIOzfv9/Y1WCMMZMwbdo07Nu3z9jV0DK525uPHDkSH374obGr8dg4ffo04uPjkZCQYOyqmIyTJ09CEASMHj1aWzZjxgyEhYXBz8/PiDVjj5O4uDhjV6EZk0sYffr0wfTp041djcdKfHw8t/l9xo8fDwBQqVTashkzZsDPz4/biXUaUzqzaGJyCYMxY7s/UTDGfsezpBhjjInCCYMxxpgonDAYY4yJwgmDMcaYKJwwDCgqKgo+Pj5Qq9WQyWTw8vLCokWLUFFR0eo2b7/9NlQqFQRBQFpaml7x6urqsHr1anh5eUEqlcLGxgaDBg3CtWvX2vlO9HfkyBFYW1vjH//4R6fH7oqOHz+O8PBwHDhwAJ6enhAEAYIgtPjv8rFjx0KlUsHc3BwDBw7EuXPnjFDjhxPb97/88kuMGDECKpUKbm5umD17NgoKCkw+XpPGxkbExcXB399fp/zw4cOIiYlBQ0NDm/fdJZAJmTZtGk2bNs3Y1WizgIAA2rx5MxUXF1NZWRklJCSQRCKhl1566aHb7dmzhwDQ+fPn9YoXGBhI/fr1ozNnzlBdXR3l5+fTpDjS5z4AACAASURBVEmTKCMjQ/Q+EhISyBDdIDk5mdRqNR0+fLjd+zJFACghIcEg+4qMjKSJEydSWVmZtkyj0VCPHj0IACUnJzfbJiUlhSZPnmyQ+B1BTN/fu3cvAaCYmBgqLS2l8+fPk6enJ/n6+lJdXZ1JxyMiunTpEo0aNYoA0JAhQ5otj4+Pp4CAACopKdF73y0xxe9DThgGNGHCBKqvr9cpmz59OgGgGzdutLpdWxLGnj17SBAESk9Pb3N9iQyXMIytqqqK/Pz8Omz/hkoYa9asIW9vb6qurtYp12g09MUXX5CZmRk5OztTaWmpznJTTxhi+v6YMWOod+/e1NjYqF1n06ZNBIBOnTpl0vHS0tLolVdeod27d5Ovr2+LCYOIKDQ0lPz8/NqUkB5kit+HPCRlQMnJyTrPUwAAe3t7AEBVVVWr27Xl8Z+ffvophg4disGDB+u9bXe0Y8cOFBYWGrsaD3X58mUsW7YMK1asgFwub7bc398fYWFhuHnzJhYsWGCEGradmL6fm5sLJycnnf7u4uICAK0+T91U4g0ZMgQHDhzAzJkzIZPJWl1v+fLlSEtLQ3x8vF777yq6RcLYtWsXhg8fDrlcDqVSCXd3d6xcuRIAQERYv349BgwYAJlMBltbW0yZMgUXL14EAGzZsgVKpRIKhQKHDh3C+PHjoVar0adPH+zZswcAMGDAAO0dTIcNG6btkIsWLYK1tTXkcjn+/ve/t1i3mzdvwtLSEh4eHtr6xMbGol+/fpDJZLC2tsbChQv1er+1tbU4c+YMfH1929JcBnfq1Cm4urpCEARs2rQJgLh23bBhA+RyOXr16oU5c+bAyckJcrkc/v7+OHv2LAAgNDQUUqlUe0NAAHjvvfegVCohCAKKiooQFhaG+fPnIycnB4IgwMvLCwBw9OhRqNVqrFq1qpNbpGUbNmwAEWHSpEmtrhMdHQ1vb29s374dx48fb3U9Q/RrAGhoaEBkZCRcXV1haWmJJ554wmC3iXmw73t6ejZL6k3XEzw9PbtcvJbY2toiICAA8fHx2ueqdCtGPb95QFtOweLi4ggArVmzhoqLi+n27dv017/+lWbOnElE98aLpVIp7dq1i0pLSyk9PZ2GDh1K9vb2VFBQQEREERERBIBOnDhBd+7cocLCQho9ejQplUqqra2l+vp6cnd3J1dX12anwR9++CHFxcW1WLfKykpSqVQUGhqqLYuIiCBBEOh//ud/qKSkhKqqqmjz5s16DUldvXqVAJCvry8999xz5OjoSDKZjPr370+bNm3SOQV/FEMNSeXm5hIA2rhxo7bsUe1KRBQSEkJKpZIuXLhANTU1lJmZSSNGjCCVSqUdWpg5cyY5ODjoxIuNjSUAdOvWLSIimjp1Kmk0Gp11kpOTSaVSUVRUVLvfHwwwJOXp6Uk+Pj4tLtNoNHT16lUiIvr+++/JzMyM3N3dqaKigoiaD0kZol8TES1YsIBkMhnt37+fSkpKaMmSJWRmZkY//vhju95rS30/NTWVJBIJbdiwgcrKyujnn3+mAQMG0Lhx49oVq7PjPf30060OSRERhYeHt+ma5INMcUiqSyeM2tpasrGxoTFjxuiU19fXU3x8PFVVVZGVlRUFBwfrLP/hhx8IgPaLpOmDdf+4ctOX+OXLl4no98SUmJioXaeyspJcXV3pzp07LdYvIiKCvL29tRc3q6qqSKFQ0Isvvqiznr7XMDIyMggAvfjii/Tdd99RcXExlZaW0kcffUQAaPfu3aL2Q9Q5CeNh7RoSEkLW1tY6+/rxxx8JAK1YsYKI2p4wDKm9CaOiooIEQaCJEye2uPz+hEFENH/+fAJAc+fOJSLdhGGofl1dXU0KhUJnP1VVVSSTyegvf/lLm99rU+z7+36TpUuXEgDtq0+fPpSbm9uuWJ0d71EJ429/+xsBoM8//7xdcUwxYXTpIan09HSUlpZi3LhxOuXm5ub44IMPkJmZiYqKCgwfPlxn+YgRIyCVSrXDHi2RSqUA7k1dBe5Nf7W2ttYZm9y9ezemTJkCtVrdbPuDBw8iMTERx44d096b6PLly6iqqsILL7zQtjf8m6Yx1IEDB8Lf3x92dnawtrbGihUrYG1tja1bt7Zr/x3pwXZtyfDhw6FQKLTDK91BYWEhiAgKhULU+tHR0ejXrx82b96MU6dO6SwzVL/OyspCVVUVBg0apF1uaWkJR0fHdrV9S30fACIiIrB161acOHECFRUVuHLlCvz9/eHn54fc3NwuE+9Rmo7xr7/+2mExjKVLJ4yysjIAgI2NTYvLS0tLAQBWVlbNltnY2KC8vFx0LCsrK7zzzjv4/vvv8cMPPwC4d+E5NDS02bp79+7F2rVrkZqaqvOIz7y8PABAz549RcdtiZOTE4B7D/u5n1QqhZubG3Jyctq1f1Mgk8lw69YtY1fDYGpqagDgoRdM7yeXy7Fz504IgoA333wT1dXV2mWG6teVlZUAgKVLl2r/ByIIAq5fv/7QSRoP01rf/+WXXxATE4N33nkHzz//PJRKJTw8PLBt2zbk5+cjNja2S8QTw9LSEsDvx7w76dIJo3fv3gCaf3E2aUokLX2ASktL0adPH73ihYaGQiKRIC4uDidPnoSLiws0Go3OOhs3bsTu3bvxzTffaOvXpGlmzN27d/WK+yArKyv07dsXFy5caLasvr4e1tbW7dq/sdXV1bXp+Jiypi8Rff7Y5efnh3nz5iE7O1s7iQMwXL9u+uESFxcHujc8rX2dPn1adD2bPKzvZ2dno6GhoVm5Wq2GnZ0dMjMzTT6eWLW1tQB+P+bdSZdOGO7u7rCzs8M///nPFpcPGjQIVlZW+Omnn3TKz549i9raWgwbNkyveE3P6ti/fz+WLVuGsLAw7TIiwuLFi5GRkYGkpKQWf/0NGjQIZmZm+Pbbb/WK25IZM2bg/PnzuHLlirasqqoK169f7/JTbVNTU0FEGDlyJADAwsLioUNYXUGvXr0gCALu3Lmj13YrV65E//79cf78eW2Zofq1i4sL5HK53ncYeJCYvt+UxH755Red8vLycty+fVs73dUU4+mr6Rg7ODh0WAxj6dIJQyaTYcmSJTh58iRCQ0Nx8+ZNNDY2ory8HBcuXIBcLsf8+fNx8OBB7N69G2VlZcjIyMC7774LJycnhISE6B1z/vz5qK+vR0lJCZ5//nlt+YULF/DJJ59g27ZtkEgkOqf4giBg3bp16NmzJ6ZOnYr9+/djx44dKCsrQ3p6epuuOcybNw9ubm6YNWsWbty4geLiYixevBjV1dX46KOP9N6fMTU2NqKkpAT19fVIT09HWFgYXF1dMWvWLACAl5cXbt++jaSkJNTV1eHWrVvN5tHb2dkhPz8f165dQ3l5Oerq6pCSkmIy02oVCgU8PT21w5JiNQ1N3f+fA0P1a7lcjtmzZ2PPnj3YsmULysrK0NDQgLy8PO0XbXBwMBwcHB56OxIxfd/DwwNjxozBtm3bcPLkSVRXVyM3N1db17feestk4+mr6Rh39R9uLTLa5fYWtHVWwKZNm2jw4MEkl8tJLpfTk08+SZs3byYiosbGRoqNjaW+ffuSRCIhW1tbCgwMpKysLCK6N2tEoVAQAOrbty/l5OTQ1q1bSa1WEwByc3OjS5cu6cQbM2YMbd++XaesaeZSa6/Y2FgiIiovL6e3336bevToQVZWVvTMM89QZGSkdgbHf/7zH9HvOzc3l1599VWytbUlmUxGTz31FKWkpOjVdoaYJbVx40ZydHQkAKRQKGjSpEmi2zUkJIQkEgk5OzuThYUFqdVqmjJlCuXk5Gj3X1xcTGPGjCG5XE4eHh70/vvv08KFCwkAeXl50Y0bN+jcuXPk5uZGlpaW9Mwzz1BBQQEdOXKEVCoVRUdHt+v9ERlmWm1oaChJJBKqqqrSlh08eJA0Gg0BIHt7e+2sqActXLhQZ1qtofr13bt3afHixeTq6koWFhbUs2dPmjp1KmVmZhLRvdvPAKDIyMhW35fYvl9UVERhYWHk5eVFMpmMrKysaNSoUfTVV19p92WK8YiITp8+TaNGjSInJydtHEdHR/L396dvv/1WZ90JEyaQs7OzXtPbW2KKs6S6RcJgbWfsW4OEhISQnZ2d0eKLZYiEkZ2dTRYWFrRr1y4D1arjNTQ00OjRo2nHjh0cT4SioiKSy+W0bt26du/LFL8Pu/SQFOseuv0dPn/j5eWFqKgoREVFPfQOxqaioaEBSUlJKC8vR3BwMMcTYfny5fD19W1x9mR3wAnDhFy8eLHZeGxLr874MLGOER4ejqCgIAQHB+t9Abyzpaam4sCBA0hJSRH9/5HHOd769euRlpaGI0eOQCKRGKCGpsfC2BVgv+vfv3/3vP9MK5YsWYKdO3eitrYWHh4eiI2NxbRp04xdrQ63atUq/POf/8SaNWuwdu1aY1enVS+88EK7/2T6uMQ7dOgQ7t69i9TU1GY3RexOOGEwo1m9ejVWr15t7GoYxdixYzF27FhjV4MZyOTJkzF58mRjV6PD8ZAUY4wxUThhMMYYE4UTBmOMMVE4YTDGGBPF5C565+XlITEx0djVeGw03WSO2/zR2nJDPsbaKi8vz+RuwCmQCc3jDAoKwv79+41dDcYYMwnTpk3Dvn37jF0NLZM7wzC1BuruEhMTMWPGjMfq/x9tIQgCEhISMH36dGNXhT0mgoKCjF2FZvgaBmOMMVE4YTDGGBOFEwZjjDFROGEwxhgThRMGY4wxUThhMMYYE6VbJ4ysrCy8//77GDhwIFQqFSwsLGBtbQ1vb29MmDDBKH/EioqKgo+PD9RqNWQyGby8vLBo0SLtA3UOHDgAT0/PZs/AkEql6NWrF5577jnExsaipKSk0+vO2ub48eMIDw9vdmzfeOONZuuOHTsWKpUK5ubmGDhwoEGfNW0oj+rDTerq6rB69Wp4eXlBKpXCxsYGgwYNwrVr10w6XkxMDPr37w9LS0solUr0798fy5YtQ1lZmeg6HT58GDExMd3v4WDGfeCfLkM+knD79u0kkUjo2WefpaNHj1JJSQnV1NRQTk4O7d27l/z9/emvf/2rQWLpIyAggDZv3kzFxcVUVlZGCQkJJJFI6KWXXtJZT6PRkLW1NRHde35zSUkJ/b//9/9o1qxZJAgCOTk50Y8//tju+hj7Ea1dBdr4iNbIyEiaOHEilZWVacs0Gg316NGDAFBycnKzbVJSUnSe321qxPbhwMBA6tevH505c4bq6uooPz+fJk2aRBkZGSYdb8KECbRu3ToqLCyk8vJySkxMJIlEQi+++KJedYqPj6eAgAAqKSnRK34TU3xEq0l9UxiqgU6fPk3m5ub0/PPPU11dXYvrHD16lDZu3NjuWPqaMGEC1dfX65RNnz6dANCNGze0ZfcnjAft27ePzMzMqFevXlRaWtqu+hgzYVRVVZGfn1+X2HdbEsaaNWvI29ubqqurdco1Gg198cUXZGZmRs7Ozs2OoaknDDF9eM+ePSQIAqWnp3e5eIGBgc2OWVBQEAGg/Px80XUiIgoNDSU/P79Wv4cexhQTRrcckoqOjkZDQwPWrFkDC4uW/8w+btw4zJ07t5NrBiQnJzd7Ipe9vT0AoKqqStQ+pk2bhlmzZqGwsBCfffaZwevYWXbs2IHCwsIut28xLl++jGXLlmHFihWQy+XNlvv7+yMsLAw3b97EggULjFDDthPThz/99FMMHToUgwcP7nLxDh482OyYOTs7A4B2yEns53j58uVIS0tDfHx8u+tlCrpdwqitrcWJEyfQo0cPPPXUU6K2ISKsX78eAwYMgEwmg62tLaZMmYKLFy8CALZs2QKlUgmFQoFDhw5h/PjxUKvV6NOnD/bs2QMAGDBgAARBgJmZGYYNG6btNIsWLYK1tTXkcjn+/ve/txj/5s2bsLS0hIeHh+j3OWvWLABASkqK6G0M5VHtFRoaCqlUCkdHR+027733HpRKJQRBQFFREcLCwjB//nzk5ORAEAR4eXlhw4YNkMvl6NWrF+bMmQMnJyfI5XL4+/vj7Nmz7do3ABw9ehRqtRqrVq3q8DbasGEDiAiTJk1qdZ3o6Gh4e3tj+/btOH78eKvrGaJ/AkBDQwMiIyPh6uoKS0tLPPHEE0hISDDI+72/D9fW1uLMmTPw9fU1yL5NIV52djZsbGzg5uYmqk5NbG1tERAQgPj4+O5x+x2jnt88wBCnYJcuXSIANHLkSNHbREZGklQqpV27dlFpaSmlp6fT0KFDyd7engoKCoiIKCIiggDQiRMn6M6dO1RYWEijR48mpVJJtbW1VF9fT+7u7uTq6trsVPXDDz+kuLi4FmNXVlaSSqWi0NBQnfKHDUkREZWVlREAcnFxEf0+W9KWISkx7TVz5kxycHDQ2S42NpYA0K1bt4iIaOrUqaTRaHTWCQkJIaVSSRcuXKCamhrKzMykESNGkEql0p7qt3XfycnJpFKpKCoqSq/3S6T/kJSnpyf5+Pi0uEyj0dDVq1eJiOj7778nMzMzcnd3p4qKCiJqPiRliP5JRLRgwQKSyWS0f/9+KikpoSVLlpCZmVm7r4U92IevXr1KAMjX15eee+45cnR0JJlMRv3796dNmzZRY2Njl4hXW1tLeXl5tHHjRpLJZLRr1y7RdbpfeHg4AaDz58/rFd8Uh6S6XcL46aefCAD94Q9/ELV+VVUVWVlZUXBwsE75Dz/8QAC0Xy5NH8j7xzY3b95MAOjy5ctERBQXF0cAKDExUbtOZWUlubq60p07d1qMHxERQd7e3joXRYkenTCIiARBIBsbG1HvszX6Jgyx7dWehPHg+/7xxx8JAK1YsaJd+24PfRJGRUUFCYJAEydObHH5/QmDiGj+/PkEgObOnUtEugnDUP2zurqaFAqFzn6qqqpIJpPRX/7yF1HvqzUP9uGMjAwCQC+++CJ99913VFxcTKWlpfTRRx8RANq9e3eXiOfg4EAAqEePHvS///u/2sQrpk73+9vf/kYA6PPPP9crvikmjG43JGVlZQVA/PWAzMxMVFRUYPjw4TrlI0aMgFQq1Q6FtEQqlQK4N50PAN5++21YW1vrjFfu3r0bU6ZMgVqtbrb9wYMHkZiYiGPHjkGlUomqb5PKykoQUYv77Ujtaa+2Gj58OBQKhXYIxtQVFhaCiKBQKEStHx0djX79+mHz5s04deqUzjJD9c+srCxUVVVh0KBB2uWWlpZwdHRsV7u21IdlMhkAYODAgfD394ednR2sra2xYsUKWFtbY+vWrV0iXm5uLgoLC/Hll1/i//7v//Dkk0+2eF3sUZ/jpn7w66+/tqkepqTbJQx3d3fI5XJcunRJ1PqlpaUAfk8097OxsUF5ebno2FZWVnjnnXfw/fff44cffgBw72JcaGhos3X37t2LtWvXIjU1Fe7u7qJjNGl6f/3799d72/YwZHvpQyaT4datWx2yb0OrqakB8PsX2aPI5XLs3LkTgiDgzTffRHV1tXaZodq7srISALB06VKd//dcv35d9I+rB7XWh52cnAAARUVFOutLpVK4ubkhJyenS8STSCTo2bMnxo4di7179yIzMxOrV68WVaf7WVpaAvi9X3Rl3S5hyGQyjBs3DkVFRfjuu+9aXe/27dt4++23YWNjAwAtfvBKS0v1fuJVaGgoJBIJ4uLicPLkSbi4uECj0eiss3HjRuzevRvffPMNevfurdf+mxw9ehQAMH78+DZt31aGbi8x6urqOmzfHaHpC0KfP235+flh3rx5yM7OxsqVK7Xlhmrvnj17AgDi4uJA94aita+2/IH1YX3YysoKffv2xYULF5ptV19fD2tra5OP9yAvLy+Ym5sjMzNTVJ3uV1tbC+D3ftGVdbuEAdybyiaTyTBv3jydX2v3+/nnn2FhYYFBgwbBysoKP/30k87ys2fPora2FsOGDdMrdp8+fTB9+nTs378fy5YtQ1hYmHYZEWHx4sXIyMhAUlJSi78axSgoKEBcXBz69OmDN998s037aCux7WVhYaEdqmuv1NRUEBFGjhxp8H13hF69ekEQBNy5c0ev7VauXIn+/fvj/Pnz2jJD9U8XFxfI5XKkpaXpVacHie3DM2bMwPnz53HlyhVtWVVVFa5fv67X1NfOjldcXIzXXnutWXl2djYaGhrg4uKi9+e4qR84ODiIrofJMtK1kxYZ8iLP/v37SaFQ0LBhw+jrr7+m0tJSqq2tpStXrtDWrVvJy8tLe5Hx448/JolEQrt27aI7d+5Qeno6Pfnkk+Tk5KSdudLSRcVt27YRAPrvf/+rE/vcuXMEgAYPHqxT/vPPPxOAVl+xsbHadTUaDanVaiovL6eGhgZqbGykwsJC2rt3L3l6epKjoyP99NNP7W6ntsySEtNeK1euJAD01VdfUW1tLRUWFtLcuXN1Lkz/+c9/JktLS7p69SqVlZVRbW0thYSEkEqlotu3b1NdXR395z//IR8fH3J1daWampp27fvIkSOkUqkoOjpa73aCnrOkNBoN+fr6trrs/ove92v60+n9s6QM1T/fffddkkqltHnzZrpz5w7V19dTbm6u9s9oM2bMoF69etG///3vVt+X2D58+/Ztcnd3p9GjR9P169epqKiI5s6dS2ZmZtrZQqYYr7q6mnr06KGdbVZbW0vnzp2jkSNHklKppIyMDL0+x0REy5cvJwCUlpbWatyWmOJF726bMIiIbty4QQsWLKDBgweTlZUVmZubk42NDT355JP01ltv0XfffUdE9269ERsbS3379iWJREK2trYUGBhIWVlZRHRvtolCoSAA1LdvX8rJyaGtW7eSWq0mAOTm5kaXLl3SiT1mzBjavn27TlnTbI6HdbTDhw/TE088QQqFgqRSKZmZmREA7Yyop556iqKioqi4uNggbdSWhPGo9iIiKi4upjFjxpBcLicPDw96//33aeHChQSAvLy86MaNG3Tu3Dlyc3MjS0tLeuaZZ6igoIBCQkJIIpGQs7MzWVhYkFqtpilTplBOTk67992ZCSM0NJQkEglVVVVpyw4ePEgajYYAkL29vfYHy4MWLlyokzAM1T/v3r1LixcvJldXV7KwsKCePXvS1KlTKTMzk4ju/cMZAEVGRrb6vsT04Sa5ubn06quvkq2tLclkMnrqqacoJSVFu9wU4xERTZo0iTw8PMjKyopkMhlpNBoKDg7W3mJEnzoR3ftXuLOzs97TezlhPIIpNlB3Z2r3kgoJCSE7OztjV6MZfRNGdnY2WVhYPHTuvqlpaGig0aNH044dOziegRQVFZFcLqd169bpva0pfh92y2sYrGvrDnf49PLyQlRUFKKioprdVdUUNTQ0ICkpCeXl5QgODuZ4BrJ8+XL4+vq2OFOyK+KEwVgHCQ8PR1BQEIKDg/W+AN7ZUlNTceDAAaSkpIj+/wjHe7j169cjLS0NR44cgUQi6ZSYHY0TBjMZS5Yswc6dO3Hnzh14eHhg//79xq5Su61atQqhoaFYs2aNsavyUC+88AK++OILnXt0cby2O3ToEO7evYvU1FTY2tp2SszO0PKtXBkzgtWrVzf7Y1R3MHbsWIwdO9bY1WCdaPLkyZg8ebKxq2FwfIbBGGNMFE4YjDHGROGEwRhjTBROGIwxxkQxuYveZ86cQVBQkLGr8djIy8sDAG5zEeLi4rBv3z5jV4M9Js6cOaO9f5qpEIhM57mB69evb9OdMxkzpIyMDAAwyPOhGWuPprsYmwqTShiMmYLp06cDABITE41cE8ZMC1/DYIwxJgonDMYYY6JwwmCMMSYKJwzGGGOicMJgjDEmCicMxhhjonDCYIwxJgonDMYYY6JwwmCMMSYKJwzGGGOicMJgjDEmCicMxhhjonDCYIwxJgonDMYYY6JwwmCMMSYKJwzGGGOicMJgjDEmCicMxhhjonDCYIwxJgonDMYYY6JwwmCMMSYKJwzGGGOicMJgjDEmCicMxhhjonDCYIwxJgonDMYYY6JwwmCMMSYKJwzGGGOicMJgjDEmCicMxhhjonDCYIwxJgonDMYYY6JwwmCMMSaKQERk7EowZiyff/451q9fj4aGBm1ZUVERAMDe3l5bZm5ujnnz5uGPf/xjp9eRMVPBCYM91i5duoR+/fqJWjcrKwve3t4dXCPGTBcPSbHHmre3N4YMGQJBEFpdRxAEDBkyhJMFe+xxwmCPvT/+8Y8wNzdvdbmFhQX+9Kc/dWKNGDNNPCTFHnv5+flwcXFBY2Nji8sFQUBubi6cnZ07uWaMmRY+w2CPvd69e8Pf3x9mZs0/DmZmZhg1ahQnC8bACYMxAMAbb7zRYrkgCDwzirHf8JAUYwBKSkrg4OCAuro6nXILCwsUFBSgR48eRqoZY6aDzzAYA2Bra4sXX3xR5+K3ubk5xo0bx8mCsd9wwmDsN6+//rrOhW8iwuuvv27EGjFmWnhIirHfVFVVoUePHqipqQEAyOVyFBUVQalUGrlmjJkGPsNg7DcKhQKBgYGQSCSQSCQIDAzkZMHYfThhMHaf1157DXV1dairq8Nrr71m7OowZlIsHizIy8vD999/b4y6MGZ0DQ0NUCgUICKUlZUhMTHR2FVizCj8/f3Rp08f3UJ6QEJCAgHgF7/4xS9+PcavhISEB9MDNTvDaMLXwllHCwoKAgDs27fPyDXR9e2330IQBDz77LPGrgoSExMxY8YM/jyyTtXazThbTRiMPa5Gjx5t7CowZpI4YTD2gJbuKcUY41lSjDHGROKEwRhjTBROGIwxxkThhMEYY0wUThgGtm7dOvTq1QuCIOCzzz7Tlh85cgTW1tb4xz/+YfCYUVFR8PHxgVqthkwmg5eXFxYtWoSKiopWt3n77behUqkgCALS0tL0ildXV4fVq1fDy8sLUqkUNjY2GDRoEK5du9bOd6K/jmzX7uD48eMIDw/HgQMH4OnpCUEQIAhCi8//GDt2LFQqFczNzTFw4ECcO3fOCDV+OLF93VB9tLPjxcTEoH//xkBY4QAAIABJREFU/rC0tIRSqUT//v2xbNkylJWVia7T4cOHERMTg4aGBr1ii9LaH/dY22VnZxMA+vTTT7VlycnJpFar6fDhwwaPFxAQQJs3b6bi4mIqKyujhIQEkkgk9NJLLz10uz179hAAOn/+vF7xAgMDqV+/fnTmzBmqq6uj/Px8mjRpEmVkZOi1n2nTptG0adP02uZBHdmupqA9n8fIyEiaOHEilZWVacs0Gg316NGDAFBycnKzbVJSUmjy5Mltrm9HE9vXDdVHOzvehAkTaN26dVRYWEjl5eWUmJhIEomEXnzxRb3qFB8fTwEBAVRSUqJX/CZo5Y97nDA6QEsJoyNNmDCB6uvrdcqmT59OAOjGjRutbteWhLFnzx4SBIHS09PbXN8mhkgYxlZVVUV+fn4dtv+2fh7XrFlD3t7eVF1drVOu0Wjoiy++IDMzM3J2dqbS0lKd5aaeMMT0dUP20c6OFxgY2OyYBQUFEQDKz88XXSciotDQUPLz86O6ujq969FawuAhqS6IiLBv3z5s3boVAJCcnKzz4B8AsLe3B3Dvlt2tae3fnA/z6aefYujQoRg8eLDe23ZHO3bsQGFhobGroePy5ctYtmwZVqxYAblc3my5v78/wsLCcPPmTSxYsMAINWw7MX3dkH20s+MdPHiw2TFrep5805CT2M/78uXLkZaWhvj4+HbXq0m7E0Z8fDyUSiXMzMwwbNgwODg4QCKRQKlUYujQoRg9ejRcXFwgl8thY2ODRYsW6Wz/r3/9Cz4+PrC2toZcLsfgwYNx7NgxAMDf//53WFlZQRAE2NraIikpCT/99BPc3Nxgbm4u+m6iGzZsgFwuR69evTBnzhw4OTlBLpfD398fZ8+e1a5HRFi/fj0GDBgAmUwGW1tbTJkyBRcvXtRrnQedOnUKrq6uEAQBmzZtAgBs2bIFSqUSCoUChw4dwvjx46FWq9GnTx/s2bNHu21DQwNWr16Nfv36wdLSEvb29vDw8MDq1asxffr0VmPevHkTlpaW8PDw0NY7NjYW/fr1g0wmg7W1NRYuXCiq/ZrU1tbizJkz8PX11Wu7jtLWdhXTH0JDQyGVSuHo6KiN995770GpVEIQBBQVFSEsLAzz589HTk4OBEGAl5cXAODo0aNQq9VYtWpVJ7cItO+PiDBp0qRW14mOjoa3tze2b9+O48ePt7reo/q7Pv04MjISrq6usLS0xBNPPIGEhASDvN/7+3pn9NHOjpednQ0bGxu4ubmJqlMTW1tbBAQEID4+3nC3lnnwlKMtp8Aff/wxAaCzZ89SZWUlFRUV0UsvvUQA6Ouvv6Zbt25RZWUlhYaGEgBKS0vTbrtv3z5avnw53b59m4qLi2nkyJHUo0cP7fILFy6QQqGgP/3pT9qy8PBw2r59u151DAkJIaVSSRcuXKCamhrKzMykESNGkEql0p7GRUZGklQqpV27dlFpaSmlp6fT0KFDyd7engoKCkSv09KQVG5uLgGgjRs3assiIiIIAJ04cYLu3LlDhYWFNHr0aFIqlVRbW0tERKtWrSJzc3M6dOgQVVVV0b///W9ycHCg5557rtX3WllZSSqVikJDQ3Vi/X/27j0sqmrvA/h3gLkwAzOAIBByR0XUsrykmGl5jl08mTeUMt/MPGFmiHg73jiKSBompGLlJSspEcUXPSbZqx4z0yzLC+JRiVREUy5yH5ABfu8fnpkYGXAPDMygv8/zzPPo3muvtfZi7fnNXnvtvUUiEX3wwQdUVFREarWaEhMTjRqSunz5MgGgXr160ZAhQ8jNzY2kUikFBgbSunXrqK6uTuBf4y5TDEk1t12F9IcJEyaQq6urXnlxcXEEgPLz84mIaMyYMeTv76+XZu/evWRvb0/R0dEt2jei5h2Pfn5+FBQUZHCdv78/Xb58mYiIjh07RlZWVuTj40Pl5eVE1HBISkh/F9Les2fPJqlUSjt37qSioiJasGABWVlZ0c8//2xsk+i5t6+buo+aq7zq6mrKzc2ltWvXklQqpa1btwquU33z589v1nVKtOY1DG3AKCsr0y37/PPPCYDeRZ+ffvqJAFBycnKjecXGxhIAysvL0y375JNPCAAlJSXRV199RZGRkUbVj+juF4RKpdJb9vPPPxMAWrp0KanVarKzs6PQ0FC9NNo6R0dHC0pDZHzAqD9mqf0S/+2334iIqG/fvtSvXz+98t566y2ysrKiO3fuGNzXhQsXUpcuXXQXO9VqNcnlcr0LZ0TGX8PIyMggAPTXv/6VfvjhByosLKTi4mL6xz/+ofv7GKO1A0ZT7Xq//kDU/IBhSsYej+Xl5SQSieill14yuL5+wCAimjVrFgGg6dOnE5F+wBDa3+/X3pWVlSSXy/XyUavVJJVKadq0aYL3zZB7+7qp+6i5ynN1dSUA1KFDB/rwww91gVdIner79NNPCQB98cUXRpXfWMBotWsYEokEAFBTU6NbJhaLAdydgtYYbZr6U8LeeustjB07FlOnTkVKSgref/99k9SxT58+kMvluHDhAjIzM1FeXo4+ffropenbty8kEglOnDghKE1LadtN20ZVVVUNTidra2shFosbjGMCd8dAU1JSsH//ftjb2wO4O6atVqsxdOjQFtVNKpUCALp3747g4GA4OTlBpVJh6dKlUKlUumsqlujedjWkfn9or/Ly8kBEkMvlgtLHxMSga9euSExMxNGjR/XWtaS/12/vixcvQq1Wo0ePHrr1tra2cHNza1FbG+rrrdlH27K8a9euIS8vD1999RU+//xzPP744wavlRmqU33afnDr1q1m1eNeZr/o/fXXX2PIkCFwcXGBVCptcI1Da/ny5SgvLzf5BUapVIr8/HwUFxcDAOzs7BqkcXBwQFlZmaA0pvbiiy/il19+we7du1FZWYmTJ08iLS0Nf/vb3xoEjOTkZKxYsQKHDx+Gj4+Pbnlubi4AwMXFpUV1cXd3BwAUFBToLZdIJPD29kZ2dnaL8rcE2v7QXmnfR679IrsfmUyGLVu2QCQSYfLkyaisrNStM1V/r6ioAAAsWrRIdx+ISCTC1atXm5yU0ZTG+npr9dG2Lk8sFsPFxQXDhg1DcnIyMjMzERsbK6hO9dna2gL4s1+0lFkDRk5ODkaNGgU3NzecOHECJSUlWLlyZYN0Go0GM2bMwOrVq3H8+HHExMSYpHyNRoPi4mJ06tQJDg4OAGDwIDAmjaktWbIEzz77LCZNmgSlUonRo0dj3Lhx2Lhxo166tWvXIikpCYcOHcIjjzyit0476+LOnTstqoudnR06d+6M8+fPN1hXU1MDlUrVovzNrX5/aK+0XxDG3LQ1YMAAREZGIisrC8uWLdMtN1V/1/5QiY+PB90dBtd9jh8/LrieWk319dboo21d3r0CAgJgbW2NzMxMQXWqr7q6GsCf/aKlzBowMjIyoNFoMG3aNPj5+UEmkxmc6vnuu+/i73//O2bOnInIyEgsW7asWR3tXocPHwYRoX///ujRowfs7Oxw8uRJvTQnTpxAdXU1evfuLSiNqWVmZiI7Oxv5+fnQaDTIycnB+vXr4ejoCODuLJZ58+YhIyMDaWlpBn8N9ujRA1ZWVvjuu+9aXJ/x48fj1KlT+P3333XL1Go1rl692u6n2tbvDwBgY2PT5BCWJdI+ZaCkpMSo7ZYtW4bAwECcOnVKt8xU/V07S9LYJwrcS0hfB0zXR9u6vMLCQoMzP7OyslBbWwtPT0/BddLS9gNXV1fB9WiKWQOGl5cXgLuPL6iqqkJWVlaDcdHExER4eHhg9OjRAIDY2FgEBQVhwoQJerfLC1FXV4eioiLU1NTg7NmziIiIgJeXFyZNmgSZTIZZs2Zh165dSEpKQmlpKTIyMvD222/D3d0dYWFhgtKY2vTp0+Hl5dXoYz7Onz+P999/Hxs3boRYLNY75ReJRFi1ahVcXFwwZswY7Ny5E5s3b0ZpaSnOnj3brPHVyMhIeHt7Y9KkScjJyUFhYSHmzZuHyspK/OMf/2jp7rappvoDcPeX3e3bt5GWlgaNRoP8/HxcvXpVLw8nJyfcuHEDV65cQVlZGTQaDdLT0802rVYul8PPz083DCmUdmiq/jCnqfq7TCbDG2+8gW3btmH9+vUoLS1FbW0tcnNz8ccffwAAQkND4erq2uTjSIT0dUBYH7XE8hQKBb799lscOnQIpaWl0Gg0OHXqFF5//XUoFApERkYKrpOWth+Y7MfcvVfBjZ2VkZCQQHK5nACQj48Pff/997RixQpSqVQEgFxdXenLL7+k5ORk3ZV/R0dH2rZtGxERzZs3j5ycnMjBwYFCQkJo3bp1BID8/f2pV69eJBKJyMnJiY4dO0ZERDNnziQrKysCQCqVik6ePCmonmFhYSQWi8nDw4NsbGxIqVTSyJEjKTs7W5emrq6O4uLiqHPnziQWi8nR0ZFGjRpFFy9eFJzmgw8+0O2nQqGg0aNH09q1a8nNzY0AkFwupxEjRlBiYqKu3Tp37kzZ2dm0YcMGUiqVBIC8vb3p0qVLdOjQId2jHLQfsVhM3bp1o9TUVN0sjcY+cXFxRERUVlZGU6ZMoQ4dOpCdnR099dRTFBUVRQCoU6dOdObMGcF/82vXrtErr7xCjo6OJJVKqV+/fpSeni54e62WzpJqSbsK6Q+FhYX0zDPPkEwmI19fX3r33Xdpzpw5BIACAgIoJyeHfv31V/L29iZbW1t66qmn6ObNm7Rv3z6yt7enmJiYZu+bVnNmLYaHh5NYLCa1Wq1btmvXLvL39ycA5OzsrJsVda85c+boTau9X38X2t537tyhefPmkZeXF9nY2JCLiwuNGTOGMjMziejuHc4AKCoqqtH9EtrXie7fRy2xPCKiESNGkK+vL9nZ2ZFUKiV/f38KDQ3VzTY1pk5Ed+8K9/DwMHp6Lx72R4OEhYWRk5OTuathtMTERIqIiNBbdufOHZo5cyZJpVK9L4X2xpyPBmkv/aE5x2NWVhbZ2Ng0OXff0tTW1tKgQYNo8+bNXJ6JFBQUkEwmo1WrVhm9bWMBw+yzpNpSqzy9sRXdvHkT4eHhePPNN/WWSyQSeHl5QaPRtLsxdkvS3vqDUAEBAYiOjkZ0dHSTTyy2FLW1tUhLS0NZWRlCQ0O5PBNZsmQJevXqhfDwcJPl2a4DxoULFxqM4Rn6tOUfyZRsbW0hFouxefNm3Lp1CxqNBjdu3MCmTZsQFRWF0NBQKJXKFpfzoLfjw2j+/PkICQlBaGio0RfA29rhw4eRmpqK9PR0wfePcHlNW716NU6fPo19+/bp7m0ziXtPOR7EIan58+eTRCLRXWfZsWOHuask2JEjR+gvf/kLKZVKsra2JpVKRcHBwZSYmNisp1BaEnMNSbWn/tDS43H//v00b948E9aIWbq0tDSKjY1t8ERbY6CRISnRf1fqpKSkYPz48aZ7WBVjjQgJCQEA7Nixw8w1sVx8PDJzEIlE2L59e4MHnLbrISnGGGNthwMGY4wxQThgMMYYE4QDBmOMMUFsGluhvSDJWGv58ccfAXBfa4r20Q7cRswS8BkGY4wxQRo9w+Cpjqy18bTa+9NOq+U2Ym3J0FPDAT7DYIwxJhAHDMYYY4JwwGCMMSYIBwzGGGOCcMBgjDEmSJsFjNTUVPj5+ekelb148eIm069evRoikQhWVlYIDAzEkSNHWqUeIpEIYrEYHh4emDBhAv7zn/+YpBytVatW6d6z/PHHH+uW79u3DyqVCv/6179MWh4AREdHIygoCEqlElKpFAEBAZg7d67u3QiG2kAkEkEikaBjx44YMmQI4uLiUFRUZPK6sbZ14MABzJ8/v8HffOLEiQ3SDhs2DPb29rC2tkb37t2bfJ2oudXV1SE+Ph7BwcEN1q1cuRKBgYGwtbWFQqFAYGAgFi9ebPQrnc1RnpC87nd879mzBytXrmyd973c+/ja1n68ufY1kW5ublRdXW0wTU1NDXl7exMAGjp0aKvVQ6VSERFReXk57dmzh7y8vMjOzo4uXLhg0rKysrIIAH300Ue6ZXv37iWlUkl79uwxaVlERIMHD6bExEQqLCyk0tJS2r59O4nFYnr++ef10tVvg7q6OioqKqJ///vfNGnSJBKJROTu7k4///yzyeunZc437rUXLTkeo6Ki6KWXXqLS0lLdMn9/f90rf/fu3dtgm/T0dL1XtFqiS5cu0cCBAwkAPfbYYw3WDx8+nFatWkV5eXlUVlZGKSkpJBaL6a9//avFlyckLyHHd0JCAg0ePJiKioqatc+wlFe0+vv7U+/evQkApaSkGEyzfft2Cg4ObrOAofW///u/BIDeeecdk5ZlKGC0puHDhzd4Fv64ceMIAOXk5OiWGWoDrR07dpCVlRV17NiRiouLW6We5gwYarWaBgwYYPF5N/d4fO+996hLly5UWVmpt9zf35++/PJLsrKyIg8PjwZ/W0sPGKdPn6bRo0dTUlIS9erVy+AX+KhRoxrsd0hICAGgGzduWHR5QvISenyHh4fTgAEDmvXenMYChlmuYUybNg0A8NFHHxlcv3r1asyaNastqwQA6NevHwDg3LlzbV52SxARduzYgQ0bNgAA9u7dC2tra700zs7OAAC1Wi0oz7Fjx2LSpEnIy8vTG0p7UGzevBl5eXntLm8hfvvtNyxevBhLly6FTCZrsD44OBgRERG4fv06Zs+ebYYaNt9jjz2G1NRUTJgwAVKp1GCaXbt2NdhvDw8PADD6lbVtXZ6QvIQe30uWLMHp06eRkJBgVB2aYpaA8eyzz6Jbt27497//jYsXL+qt++GHH6BWqzFs2DCD237//fcICgqCSqWCTCZDz549sX//fgDAZ599Bjs7O4hEIjg6OiItLQ0nT56Et7c3rK2t8eqrrzZZr5qaGgDQdQwiwurVq9GtWzdIpVI4Ojpi5MiRuHDhgm4bIWnudfToUXh5eUEkEmHdunUAgPXr10OhUEAul2P37t144YUXoFQq0alTJ2zbtk23bW1tLWJjY9G1a1fY2trC2dkZvr6+iI2NbfCyk/quX78OW1tb+Pr6NtkG9U2aNAkAkJ6eLnib1na/9g4PD4dEIoGbm5tum3feeQcKhQIikQgFBQWIiIjArFmzkJ2dDZFIhICAAKxZswYymQwdO3bE1KlT4e7uDplMhuDgYJw4caJFeQPAN998A6VSieXLl7d6G61ZswZEhBEjRjSaJiYmBl26dMGmTZtw4MCBRtPdr72N6bdRUVHw8vKCra0tHn30UWzfvt10O30fWVlZcHBwgLe3d7srT0heho5vR0dHDB48GAkJCaZ7Ade9pxxtMSR1+fJl+vDDDwkARURE6K0fNWoUbdmyhcrKygwOSe3YsYOWLFlCt2/fpsLCQurfvz916NBBt/78+fMkl8vp9ddf1y2bP38+bdq0qUE97h2O2bp1KwGgOXPmENHdMWCJREJbt26l4uJiOnv2LD3xxBPk7OxMN2/eFJzG0JDUtWvXCACtXbtWt2zhwoUEgA4ePEglJSWUl5dHgwYNIoVCobves3z5crK2tqbdu3eTWq2mX375hVxdXWnIkCGNtnlFRQXZ29tTeHj4fdugvtLSUgJAnp6ejaZpieYMSQlp7wkTJpCrq6vednFxcQSA8vPziYhozJgx5O/vr5cmLCyMFAoFnT9/nqqqqigzM5P69u1L9vb2ulP95ua9d+9esre3p+joaKP2tznHo5+fHwUFBRlcpz3+iIiOHTtGVlZW5OPjQ+Xl5UTUcEhKSHsL6bezZ88mqVRKO3fupKKiIlqwYAFZWVm16BrZk08+aXCISKu6uppyc3Np7dq1JJVKaevWrc0uq63LMyavxo5vorvffQDo1KlTRpUPS7qGcfnyZSouLiaFQkGOjo6kVquJiCg7O5s6depEd+7caTRg3Cs2NpYAUF5enm7ZJ598QgAoKSmJvvrqK4qMjDRYj/oXvXfu3Emurq7UsWNHys3NJbVaTXZ2dhQaGqq33U8//UQAKDo6WlAaIuMDRv0xzMTERAJAv/32GxER9e3bl/r166dX3ltvvUVWVlZ0584dg220cOFC6tKli97Fz3vboDEikYgcHByaTNNcxgYMoe3dkoBxb3v8/PPPBICWLl3aoryby9jjsby8nEQiEb300ksG19cPGEREs2bNIgA0ffp0ItIPGELb+379trKykuRyuV4+arWapFIpTZs2TfC+3et+X+Curq4EgDp06EAffvhho5NsLLE8Y/Jq7PgmIvr0008JAH3xxRdGld9YwDDbfRgqlQqvvvoqioqKkJycDACIj4/HtGnTIJFIBOcjFosBQG8K2VtvvYWxY8di6tSpSElJwfvvv29w25KSEohEIqhUKsyYMQMvvvgifvrpJ3h4eCAzMxPl5eXo06eP3jZ9+/aFRCLBiRMnBKVpKW1baDQaAEBVVVWD08va2lqIxeIG45rA3THRlJQU7N+/H/b29kaVXVFRASKCUqlsZu1Nqy3a+159+vSBXC5vcojRkuTl5YGIIJfLBaWPiYlB165dkZiYiKNHj+qta0l71++3Fy9ehFqtRo8ePXTrbW1t4ebm1qrteu3aNeTl5eGrr77C559/jscff7xVry2Zsjyhed3v+Nb2g1u3bjWrHvcy64172ovfH3/8MYqLi7Fjxw5MnTq1yW2+/vprDBkyBC4uLpBKpZg7d67BdMuXL0d5eXmTfzCVSgUiQk1NDXJzc/Hpp5/qxgmLi4sBAHZ2dg22c3BwQFlZmaA0pvbiiy/il19+we7du1FZWYmTJ08iLS0Nf/vb3xoEjOTkZKxYsQKHDx+Gj4+P0WVdunQJABAYGGiKqreYOdobuHtNKz8/v1XyNrWqqioAaPQC7b1kMhm2bNkCkUiEyZMno7KyUrfOVO1dUVEBAFi0aJHefT9Xr14VPAmjOcRiMVxcXDBs2DAkJycjMzMTsbGx7aI8IXkJOb5tbW0B/NkvWsqsAaNXr17o378/fvrpJ4SFhSEkJASOjo6Nps/JycGoUaPg5uaGEydOoKSkBCtXrmyQTqPRYMaMGVi9ejWOHz+OmJgYo+vm4OAAAAYPiuLiYnTq1ElQGlNbsmQJnn32WUyaNAlKpRKjR4/GuHHjsHHjRr10a9euRVJSEg4dOoRHHnmkWWV98803AIAXXnihxfU2BXO0t0ajabW8W4P2C8KYm7YGDBiAyMhIZGVlYdmyZbrlpmpvFxcXAHdHEOjuMLjuc/z4ccH1bImAgABYW1sjMzOz3ZVnKC+hx3d1dTWAP/tFS5n90SDas4ydO3di5syZTabNyMiARqPBtGnT4OfnB5lMZvC57e+++y7+/ve/Y+bMmYiMjMSyZcuM7pg9evSAnZ0dTp48qbf8xIkTqK6uRu/evQWlMbXMzExkZ2cjPz8fGo0GOTk5WL9+vS7QEhHmzZuHjIwMpKWlGfx1KMTNmzcRHx+PTp06YfLkyabchWYT2t42Nja6IbyWOnz4MIgI/fv3N3nerUH7VIGSkhKjtlu2bBkCAwNx6tQp3TJT9W9PT0/IZDKcPn3aqDo1R2FhocHZkFlZWaitrYWnp6fFlickL2OPb20/cHV1FVyPppg9YIwbNw7Ozs4YNWoU/Pz8mkzr5eUF4O7jDqqqqpCVldVgHDUxMREeHh4YPXo0ACA2NhZBQUGYMGGCUbfqy2QyzJo1C7t27UJSUhJKS0uRkZGBt99+G+7u7ggLCxOUxtSmT58OLy+vRud3nz9/Hu+//z42btwIsVjc4PEfq1at0ktPRCgvL0ddXR2ICPn5+di+fTsGDhwIa2trpKWlWcw1DKHtHRAQgNu3byMtLQ0ajQb5+fm4evWqXl5OTk64ceMGrly5grKyMl0QqKurQ1FREWpqanD27FlERETAy8tLN8W4uXmnp6e3ybRauVwOPz8/3atdhdIOTdUf1jRV/5bJZHjjjTewbds2rF+/HqWlpaitrUVubi7++OMPAEBoaChcXV1b/DgShUKBb7/9FocOHUJpaSk0Gg1OnTqF119/HQqFApGRkRZbnpC8jD2+tf2gZ8+eLdpPnXuvgrfWLKldu3bpHgvi7Oysm5VBRDR37lw6duyY7v+LFi0iNzc3AkBWVlYUFBRE33//PRERzZs3j5ycnMjBwYFCQkJo3bp1BID8/f2pV69eJBKJyMnJSZffzJkzycrKigCQSqWiNWvWUJcuXQgAASB3d3cKCQkxWOe6ujqKi4ujzp07k1gsJkdHRxo1ahRdvHhRcJoPPvhAN+NBoVDQ6NGjae3atbr9k8vlNGLECEpMTCS5XE4AqHPnzpSdnU0bNmwgpVJJAMjb25suXbpEhw4d0j3aQfsRi8XUrVs3Sk1NpYyMDL11937i4uJoz5499Oijj5JcLieJRKJrH+2MqH79+lF0dDQVFhaavB/U15xptUL+JoWFhfTMM8+QTCYjX19fevfdd2nOnDkEgAICAignJ4d+/fVX8vb2JltbW3rqqafo5s2bFBYWRmKxmDw8PMjGxoaUSiWNHDmSsrOzW5z3vn37yN7enmJiYoza3+Ycj+Hh4SQWi3WzD4maPv7qmzNnjt602vu1t9B+e+fOHZo3bx55eXmRjY0Nubi40JgxYygzM5OI7k6nB0BRUVFN7tvx48dp4MCB5O7uruvTbm5uFBwcTN999x0REY0YMYJ8fX3Jzs6OpFIp+fv7U2hoKGVkZOjysdTy7peXkOO7vuHDh5OHhwfV1dU1We69YCnTalnLJCYmNrh35c6dOzRz5kySSqV6XxKWztKeJRUWFkZOTk7mroae5hyPWVlZZGNj0+L7DtpSbW0tDRo0iDZv3szlmUhBQQHJZDJatWqV0ds2FjDMPiTFhLt58ybCw8Px5ptv6i2XSCTw8vKCRqOx6PH19qBVnvDZxgICAhAdHY3o6GijH01hDrW1tUhLS0NZWRlCQ0O5PBNZsmQJevXqhfDwcJPlyQGjHbG1tYVYLMbmzZtx69YtaDQpEuxxAAAgAElEQVQa3LhxA5s2bUJUVBRCQ0Mt5noDM6/58+cjJCQEoaGhRl8Ab2uHDx9Gamoq0tPTBd8/wuU1bfXq1Th9+jT27dunu1fNJO495eAhKct25MgR+stf/kJKpZKsra1JpVJRcHAwJSYmNuuplOZkSUNS8+fPJ4lEQgDIx8eHduzYYe4qEVHLj8f9+/fTvHnzTFgjZunS0tIoNja2wRNtjYFGhqRsTBd6WFsYNGgQ/u///s/c1XjgxMbGtupNXeYybNiwRh/kyR5ML7/8Ml5++eVWyZuHpBhjjAnCAYMxxpggHDAYY4wJwgGDMcaYIBwwGGOMCdLoLClDD/VjrDVwX7s/biNmCUT/nXOrk5ubi2PHjpmrPoyZXXx8PADc9+nJjD3IgoODGzzCvkHAYOxhN27cOABASkqKmWvCmGXhaxiMMcYE4YDBGGNMEA4YjDHGBOGAwRhjTBAOGIwxxgThgMEYY0wQDhiMMcYE4YDBGGNMEA4YjDHGBOGAwRhjTBAOGIwxxgThgMEYY0wQDhiMMcYE4YDBGGNMEA4YjDHGBOGAwRhjTBAOGIwxxgThgMEYY0wQDhiMMcYE4YDBGGNMEA4YjDHGBOGAwRhjTBAOGIwxxgThgMEYY0wQDhiMMcYE4YDBGGNMEA4YjDHGBOGAwRhjTBAOGIwxxgThgMEYY0wQDhiMMcYE4YDBGGNMEBtzV4AxcyooKEBpaanesoqKCgDA77//rrdcqVTC2dm5zerGmKURERGZuxKMmcuWLVswefJkQWk//fRTvPHGG61cI8YsFwcM9lArKSmBi4sLNBpNk+nEYjHy8/OhUqnaqGaMWR6+hsEeaiqVCi+++CJsbBofnbWxscHw4cM5WLCHHgcM9tB77bXXUFtb2+j6uro6vPbaa21YI8YsEw9JsYdeVVUVnJ2ddRe77yWXy1FQUABbW9s2rhljloXPMNhDTyaTYfTo0RCLxQ3WicVijB07loMFY+CAwRgA4NVXXzV44Vuj0eDVV181Q40Yszw8JMUYgJqaGri6uuL27dt6yx0cHJCfn9/kRXHGHhZ8hsEY7s6EeuWVV/SGpcRiMV577TUOFoz9FwcMxv7rlVde0RuW0mg0eOWVV8xYI8YsCw9JMfZfRARPT09cv34dAODu7o7r169DJBKZuWaMWQY+w2Dsv0QiESZOnAiJRAKJRILXX3+dgwVj9fAZBmP1nD17Fo899pju3z179jRzjRizHBZ1NW/16tU4fvy4uavBHnJ2dnYAgOjoaDPXhD3sBgwYgMjISHNXQ8eihqSOHz+OH3/80dzVeKjk5uZi586d5q6GRfH29oaPj4/esp07dyI3N9c8FWIPpR9//NHifkBb1BkGAPTv3x87duwwdzUeGikpKRg/fjy3eT3a92D4+fnplolEIsycORPjxo0zV7XYQyYkJMTcVWjA4gIGY+ZWP1Awxv5kUUNSjDHGLBcHDMYYY4JwwGCMMSYIBwzGGGOCcMAwoejoaAQFBUGpVEIqlSIgIABz585FeXl5o9tMmTIF9vb2EIlEOH36tOCyhgwZApFIZPCjvY+gLe3btw8qlQr/+te/2rxsS3fgwAHMnz8fqamp8PPz0/2dJk6c2CDtsGHDYG9vD2tra3Tv3h2//vqrGWosTF1dHeLj4xEcHNxg3cqVKxEYGAhbW1soFAoEBgZi8eLFKC0ttfjyhOR1v2N9z549WLlyZZNvcmyXyIKMHTuWxo4da+5qNNvgwYMpMTGRCgsLqbS0lLZv305isZief/75Jrfbtm0bAaBTp04ZVRYAg5/nnntOcD7bt28nU3SDvXv3klKppD179rQ4L0sEgLZv3270dlFRUfTSSy9RaWmpbpm/vz916NCBANDevXsbbJOenk4vv/xyi+rb2i5dukQDBw4kAPTYY481WD98+HBatWoV5eXlUVlZGaWkpJBYLKa//vWvFl+ekLyEHOsJCQk0ePBgKioqatY+W+L3IQcMExo+fDjV1NToLRs3bhwBoJycnEa3a07AeO655/S+hLTCwsLo4MGDgvMxVcAwN7VaTQMGDGi1/JsTMN577z3q0qULVVZW6i339/enL7/8kqysrMjDw4OKi4v11lt6wDh9+jSNHj2akpKSqFevXga/wEeNGtVgv0NCQggA3bhxw6LLE5KX0GM9PDycBgwYQBqNxqg6EFnm9yEPSZnQ3r17YW1trbfM2dkZAKBWqxvdrjkPuPvmm29gb2+vt+zatWs4d+4cnn32WaPza+82b96MvLw8c1dD57fffsPixYuxdOlSyGSyBuuDg4MRERGB69evY/bs2WaoYfM99thjSE1NxYQJEyCVSg2m2bVrV4P99vDwAIAmh2gtoTwheQk91pcsWYLTp08jISHBqDpYqgciYGzduhV9+vSBTCaDQqGAj48Pli1bBuDuI6tXr16Nbt26QSqVwtHRESNHjsSFCxcAAOvXr4dCoYBcLsfu3bvxwgsvQKlUolOnTti2bRsAoFu3bhCJRLCyskLv3r11HWLu3LlQqVSQyWT47LPPDNbt+vXrsLW1ha+vr64+cXFx6Nq1K6RSKVQqFebMmWOSdlixYgVmzJhhkryMcfToUXh5eUEkEmHdunUAhLXrmjVrIJPJ0LFjR0ydOhXu7u6QyWQIDg7GiRMnAADh4eGQSCRwc3PTlffOO+9AoVBAJBKhoKAAERERmDVrFrKzsyESiRAQEADgblBVKpVYvnx5G7fI3X0jIowYMaLRNDExMejSpQs2bdqEAwcONJrOFH0YAGpraxEVFQUvLy/Y2tri0Ucfxfbt20230/eRlZUFBwcHeHt7t7vyhOR177EOAI6Ojhg8eDASEhJAD8JzXs16fnOP5pyCxcfHEwB67733qLCwkG7fvk2ffPIJTZgwgYjujiFLJBLaunUrFRcX09mzZ+mJJ54gZ2dnunnzJhERLVy4kADQwYMHqaSkhPLy8mjQoEGkUCiourqaampqyMfHh7y8vBqchs6cOZPi4+MN1q2iooLs7e0pPDxct2zhwoUkEonogw8+oKKiIlKr1ZSYmGj0kNS9cnNzKSgoiGpra43azlRDUteuXSMAtHbtWt2y+7Ur0d0hNIVCQefPn6eqqirKzMykvn37kr29ve7UfsKECeTq6qpXXlxcHAGg/Px8IiIaM2YM+fv766XZu3cv2dvbU3R0dIv3D0YOSfn5+VFQUJDBdf7+/nT58mUiIjp27BhZWVmRj48PlZeXE1HDISlT9GEiotmzZ5NUKqWdO3dSUVERLViwgKysrOjnn39uTpMQEdGTTz5pcIhIq7q6mnJzc2nt2rUklUpp69atzS6rrcszJi9Dx7rW/Pnzm3V8W+KQVLsOGNXV1eTg4EDPPPOM3vKamhpKSEggtVpNdnZ2FBoaqrf+p59+IgC6LxLtwVZ/3FL7Jf7bb78R0Z+BKSUlRZemoqKCvLy8qKSkxGD9Fi5cSF26dNFda1Cr1SSXyxtciGvONYx7TZ8+nT766COjt2uLgNFUu4aFhZFKpdLL6+effyYAtHTpUiJqfsAwJWMCRnl5OYlEInrppZcMrq8fMIiIZs2aRQBo+vTpRKQfMEzVhysrK0kul+vlo1arSSqV0rRp0wTtlyH3+wJ3dXUlANShQwf68MMPdcGrPZRnTF73Huv1ffrppwSAvvjiC6PKt8SA0a6HpM6ePYvi4mI899xzesutra0xY8YMZGZmory8HH369NFb37dvX0gkEt2whyESiQQAdK/snDJlClQqld5YZFJSEkaOHAmlUtlg+127diElJQX79+/XXWv47bffoFarMXTo0ObtcCNu3LiBPXv2YNKkSSbNtzXc266G9OnTB3K5XDfk0t7k5eWBiCCXywWlj4mJQdeuXZGYmIijR4/qrTNVH7548SLUajV69OihW29raws3N7dWbedr164hLy8PX331FT7//HM8/vjjrXqtyZTlCc3L0LFen7Yf3Lp1q1n1sCTtOmBo50U7ODgYXF9cXAwABu9LcHBwQFlZmeCy7Ozs8NZbb+HYsWP46aefAAAfffQRwsPDG6RNTk7GihUrcPjwYb3HZGsfj+3i4iK4XCFWrlyJv//97wYvrrZXUqkU+fn55q5Gs1RVVQFAoxdo7yWTybBlyxaIRCJMnjwZlZWVunWm6sMVFRUAgEWLFunds3P16tUmJ2S0lFgshouLC4YNG4bk5GRkZmYiNja2XZQnJK/GjvX6bG1tAfzZL9qzdh0wHnnkEQBAQUGBwfXaQGLooCouLkanTp2MKi88PBxisRjx8fE4cuQIPD094e/vr5dm7dq1SEpKwqFDh3T109J+od+5c8eocpty8+ZNfPXVV5g2bZrJ8jQ3jUbTrL+PpdB+QRhz05b2RTlZWVm6CRuA6fqw9kdKfHw86O5QtO7TVu9cCAgIgLW1NTIzM9tdeYbyaupYr6+6uhrAn/2iPWvXAcPHxwdOTk749ttvDa7v0aMH7OzscPLkSb3lJ06cQHV1NXr37m1UeZ06dcK4ceOwc+dOLF68GBEREbp1RIR58+YhIyMDaWlpBn8R9ujRA1ZWVvjuu++MKrcpK1euxGuvvQYnJyeT5Wluhw8fBhGhf//+AAAbG5smh7AsTceOHSESiVBSUmLUdsuWLUNgYCBOnTqlW2aqPuzp6QmZTGbU0wSaq7CwEK+++mqD5VlZWaitrYWnp6fFlickLyHHen3afuDq6iq4HpaqXQcMqVSKBQsW4MiRIwgPD8f169dRV1eHsrIynD9/HjKZDLNmzcKuXbuQlJSE0tJSZGRk4O2334a7uzvCwsKMLnPWrFmoqalBUVGR3v0O58+fx/vvv4+NGzdCLBY3eFzHqlWr4OLigjFjxmDnzp3YvHkzSktLcfbsWWzYsKFZ+3/r1i18+umnmDlzZrO2txR1dXUoKipCTU0Nzp49i4iICHh5eemuyQQEBOD27dtIS0uDRqNBfn4+rl69qpeHk5MTbty4gStXrqCsrAwajQbp6elmmVYrl8vh5+dn9Bv6tENT9ef3m6oPy2QyvPHGG9i2bRvWr1+P0tJS1NbWIjc3F3/88QcAIDQ0FK6uri1+HIlCocC3336LQ4cOobS0FBqNBqdOncLrr78OhUKhe+WoJZYnJC8hx3p92n7wQLwf3nzX2xtq7qyAdevWUc+ePUkmk5FMJqPHH3+cEhMTiYiorq6O4uLiqHPnziQWi8nR0ZFGjRpFFy9eJKK7M0nkcjkBoM6dO1N2djZt2LCBlEolASBvb2+6dOmSXnnPPPMMbdq0SW9ZRkZGo4/qAEBxcXFERFRWVkZTpkyhDh06kJ2dHT311FMUFRVFAKhTp0505swZwfsdGRlJr732mtHtVZ8pZkmtXbuW3NzcCADJ5XIaMWKE4HYNCwsjsVhMHh4eZGNjQ0qlkkaOHEnZ2dm6/AsLC+mZZ54hmUxGvr6+9O6779KcOXMIAAUEBFBOTg79+uuv5O3tTba2tvTUU0/RzZs3ad++fWRvb08xMTEt2j8i46fVhoeHk1gsJrVarVu2a9cu8vf3JwDk7OysmxV1rzlz5uhNqzVVH75z5w7NmzePvLy8yMbGhlxcXGjMmDGUmZlJRHfvcAZAUVFRTe7b8ePHaeDAgeTu7q7r325ubhQcHEzfffcdERGNGDGCfH19yc7OjqRSKfn7+1NoaChlZGTo8rHU8u6Xl9BjXWv48OHk4eFBdXV1TZZ7L0ucJfVABAzWfOZ+NEhYWBg5OTmZrXyhjA0YWVlZZGNj0+L7DtpSbW0tDRo0iDZv3szlmUhBQQHJZDJatWqV0dta4vdhux6SYg+GB+6Jnrg7jBYdHY3o6GijH01hDrW1tUhLS0NZWRlCQ0O5PBNZsmQJevXqZXA2ZXvEAcOCXLhwodFHltf/tGWHZ803f/58hISEIDQ01OgL4G3t8OHDSE1NRXp6uuD7R7i8pq1evRqnT5/Gvn37IBaL26TM1mZj7gqwPwUGBj4Yz5sRaMGCBdiyZQuqq6vh6+uLuLg4jB071tzVMqnly5fj22+/xXvvvYcVK1aYuzqNGjp0qMlvKH2Yy9u9ezfu3LmDw4cPN3hIYXvGAYOZTWxsbKvexGUphg0bhmHDhpm7GqwNvfzyy3j55ZfNXQ2T4yEpxhhjgnDAYIwxJggHDMYYY4JwwGCMMSYIBwzGGGOCiMiC5nGGhIRg586d5q4GY4xZhLFjx2LHjh3mroaOxU2r7d+/f7t/mF57cvz4cSQkJLTpu53bo/HjxyMiIgIDBgwwd1XYQyI+Pt7cVWjA4gKG9hHirO0kJCRwm9/H+PHjMWDAAG4n1mYs6cxCi69hMMYYE4QDBmOMMUE4YDDGGBOEAwZjjDFBOGAwxhgT5IEOGBcvXsS7776L7t27w97eHjY2NlCpVOjSpQuGDx+O48ePt3mdoqOjERQUBKVSCalUioCAAMydO1f3kp3U1FT4+fk1eAeGRCJBx44dMWTIEMTFxaGoqKjN686a58CBA5g/f36Dv+3EiRMbpB02bBjs7e1hbW2N7t27t/h9162prq4O8fHxCA4OblEaSyxPo9EgNjYWAQEBkEgkcHBwQI8ePXDlyhWD6auqqhAYGIhFixYBAPbs2YOVK1c+eC8HM/Mb//SY8pWEmzZtIrFYTE8//TR98803VFRURFVVVZSdnU3JyckUHBxMn3zyiUnKMsbgwYMpMTGRCgsLqbS0lLZv305isZief/55vXT+/v6kUqmI6O47nYuKiujf//43TZo0iUQiEbm7u9PPP//c4vqY+xWt7QWMfEWrVlRUFL300ktUWlqqW+bv708dOnQgALR3794G26Snp+u909sSXbp0iQYOHEgA6LHHHmt2Gkstb9SoUdS1a1f68ccfSaPR0I0bN2jEiBF67wivLzIykgDQwoULdcsSEhJo8ODBVFRU1Kw6WOIrWi3uPgxT+PHHHxEWFobBgwdj//79sLH5czf9/Pzg5+cHBwcHZGVltXnd7OzsEBYWpnupyrhx45CamoqUlBRcu3YNnp6eDbYRiURwcHDAkCFDMGTIEAwfPhzjx4/H8OHDcenSJahUqrbeDZOorKzE0KFDcezYsXaVt1ArVqxAcnIyzpw5A5lMprduzZo1mDhxIsLCwpCZmdmu/oZnzpxBdHQ03n77bVRUVBh86ZeQNJZaXnJyMtLS0nDmzBn07NkTAODu7o7du3cbTH/s2DGcO3euwfIZM2bg999/x4svvogjR47ofQ+1W+aNV/pMFVGHDx9OAOjEiRMmqFXrmzZtGgGgCxcu6JbVP8MwZPLkyQSAVqxY0aKyzXmGsXbtWvL3928XecPIM4ysrCyysbGhbdu2NVjn7+9Ply9f1v0qnTJlit769nCGofXkk0/e99e8kDSWVN7TTz9NvXv3FpRWrVZTcHAwnT9/vsEZBhHR7du3ydbWluLi4oyuhyWeYTxw1zCqq6tx8OBBdOjQAf369RO0DRFh9erV6NatG6RSKRwdHTFy5EhcuHABALB+/XooFArI5XLs3r0bL7zwApRKJTp16oRt27YBALp16waRSAQrKyv07t0barUaADB37lyoVCrIZDJ89tlnBsu/fv06bG1t4evrK3g/J02aBABIT08XvI2p3K+9wsPDIZFI4ObmptvmnXfegUKhgEgkQkFBASIiIjBr1ixkZ2dDJBIhICAAa9asgUwmQ8eOHTF16lS4u7tDJpMhODgYJ06caFHeAPDNN99AqVRi+fLlrd5Ga9asARFhxIgRjaaJiYlBly5dsGnTJhw4cKDRdKbonwBQW1uLqKgoeHl5wdbWFo8++ig/EuYe1dXV+PHHH9GrVy9B6RcuXIh33nkHLi4uBtc7Ojpi8ODBSEhIeDBev2zWcHUPU0TUS5cuEQDq37+/4G2ioqJIIpHQ1q1bqbi4mM6ePUtPPPEEOTs7082bN4mIaOHChQSADh48SCUlJZSXl0eDBg0ihUJB1dXVVFNTQz4+PuTl5UU1NTV6+c+cOZPi4+MNll1RUUH29vYUHh6ut/x+ZxilpaUEgDw9PQXvpyHNOcMQ0l4TJkwgV1dXve3i4uIIAOXn5xMR0ZgxYxqcBYSFhZFCoaDz589TVVUVZWZmUt++fcne3p5ycnJalPfevXvJ3t6eoqOjjdpfIuPPMPz8/CgoKMjgOu0ZBhHRsWPHyMrKinx8fKi8vJyIGp5hmKJ/EhHNnj2bpFIp7dy5k4qKimjBggVkZWXVomthD9oZxuXLlwkA9erVi4YMGUJubm4klUopMDCQ1q1bR3V1dbq0R48epREjRhARUX5+vsEzDCKi+fPnEwA6deqUUXXhM4w2UFpaCuDutQIhKisrsXr1aowePRqvvfYaVCoVevbsiY8//hgFBQXYsGGDXvrg4GAolUq4uLggNDQUFRUVyMnJgbW1NWbMmIGcnBzs2rVLl16tViM1NRWTJ082WH5sbCzc3d0RExNj1H7a29tDJBKhrKzMqO1aytj2ag4bGxvdr+mgoCCsX78eZWVl2LJlS4vyHT58OEpLS7F48eIW17EpFRUVuHz5Mvz9/e+bdsCAAZg5cyauXLmCf/zjHw3Wm6p/VlVVYf369Rg1ahTGjBkDBwcHLFq0CGKxuMXt+iDRzlZ0cXHB8uXLkZmZiVu3bmHkyJGYPn06vvrqKwB3/y4RERFYv379ffPs3LkzACAjI6P1Kt5GHriAoQ0U2iGh+8nMzER5eTn69Omjt7xv376QSCS6oRBDJBIJgLtT8ABgypQpUKlUSEhI0KVJSkrCyJEjoVQqG2y/a9cupKSkYP/+/bC3txdUXy3thT1D+bamlrRXc/Xp0wdyuVw3BGPp8vLyQESQy+WC0sfExKBr165ITEzE0aNH9daZqn9evHgRarUaPXr00K23tbWFm5tbu2nXtiCVSgEA3bt3R3BwMJycnKBSqbB06VKoVCpdgF6wYAHeeusteHh43DdPbT+4detW61W8jTxwAcPHxwcymQyXLl0SlL64uBiA4TMSBwcHo37B29nZ4a233sKxY8fw008/AQA++ugjhIeHN0ibnJyMFStW4PDhw/Dx8RFchpZ2/wIDA43etiVM2V7GkEqlyM/Pb5W8Ta2qqgrAn18+9yOTybBlyxaIRCJMnjwZlZWVunWmau+KigoAwKJFi/Tu77l69argH1cPA3d3dwBAQUGB3nKJRAJvb29kZ2fj6NGjyMjIwJQpUwTlaWtrC+DPftGePXABQyqV4rnnnkNBQQF++OGHRtPdvn0bU6ZMgYODAwAYPPCKi4vRqVMno8oPDw+HWCxGfHw8jhw5Ak9PzwZDE2vXrkVSUhIOHTqERx55xKj8tb755hsAwAsvvNCs7ZvL1O0lhEajabW8W4P2C8KYm7YGDBiAyMhIZGVlYdmyZbrlpmpv7UXZ+Ph4EJHexxw3sFoqOzs7dO7cGefPn2+wrqamBiqVCps3b8bBgwdhZWWlC7za9l2+fDlEIhFOnjyp2666uhrAn/2iPXvgAgYALFmyBFKpFJGRkXq/1uo7d+4cbGxs0KNHD9jZ2en9gQHgxIkTqK6uRu/evY0qW/s+j507d2Lx4sWIiIjQrSMizJs3DxkZGUhLSxN8neVeN2/eRHx8PDp16tTotZHWIrS9bGxsdEN1LXX48GEQEfr372/yvFtDx44dIRKJUFJSYtR2y5YtQ2BgIE6dOqVbZqr+6enpCZlMhtOnTxtVp4fR+PHjcerUKfz++++6ZWq1GlevXkXPnj2xZcuWBkFXe/a7cOFCEJHeEKK2H7i6urbtjrSCBzJg9OrVC19++SXOnTuHQYMGYd++fSgpKYFGo8Hly5exceNGvPnmmxCLxZDJZJg1axZ27dqFpKQklJaWIiMjA2+//Tbc3d0RFhZmdPmzZs1CTU0NioqK8Oyzz+qWnz9/Hu+//z42btwIsVjc4PEfq1at0suHiFBeXo66ujpdp9y+fTsGDhwIa2trpKWltfk1DKHtFRAQgNu3byMtLQ0ajQb5+fm4evWqXl5OTk64ceMGrly5grKyMl0QqKurQ1FREWpqanD27FlERETAy8tLN5W4uXmnp6e3ybRauVwOPz8/5ObmGrWddmhKe1Ondpkp+qdMJsMbb7yBbdu2Yf369SgtLUVtbS1yc3Pxxx9/AABCQ0Ph6uraZo8jsdTyIiMj4e3tjUmTJiEnJweFhYWYN28eKisrDU5MuB9tP9DeBNiumWFmVqNMPY0sJyeHZs+eTT179iQ7OzuytrYmBwcHevzxx+nNN9+kH374gYjuPnojLi6OOnfuTGKxmBwdHWnUqFF08eJFIiJKTEwkuVxOAKhz586UnZ1NGzZsIKVSSQDI29ubLl26pFf2M888Q5s2bdJblpGRQQAa/cTFxdGePXvo0UcfJblcThKJhKysrAgAiUQicnBwoH79+lF0dDQVFhaapI2aM632fu1FRFRYWEjPPPMMyWQy8vX1pXfffZfmzJlDACggIIBycnLo119/JW9vb7K1taWnnnqKbt68SWFhYSQWi8nDw4NsbGxIqVTSyJEjKTs7u8V579u3j+zt7SkmJsbodoKR02rDw8NJLBaTWq3WLdu1axf5+/sTAHJ2dqbp06cb3HbOnDl602pN1T/v3LlD8+bNIy8vL7KxsSEXFxcaM2YMZWZmEtHdx2EAoKioqCb37fjx4zRw4EByd3fX9V03NzcKDg6m7777TnAaSy2PiOjatWv0yiuvkKOjI0mlUurXrx+lp6c3mr6pabXDhw8nDw8PvSm5QljitNoHOmCw+7O0Z0mFhYWRk5OTuavRgLEBQ3un99atW1uxVqZVW1tLgwYNos2bN3N5JlJQUEAymYxWrVpl9LaW+H34QA5JsfbtQXjCZ0BAAKKjoxEdHa2b22/JamtrkZaWhrKyMoSGhnJ5JrJkyRL06tXL4EzJ9ogDBmOtZP78+QgJCUFoaKjRF8Db2uHDh5GamgkJQXsAACAASURBVIr09HTB949weU1bvXo1Tp8+jX379kEsFrdJma2NAwazGAsWLMCWLVtQUlICX19f7Ny509xVarHly5cjPDwc7733nrmr0qShQ4fiyy+/1HtGF5fXfLt378adO3dw+PBhODo6tkmZbeEBeN4ue1DExsYiNjbW3NUwuWHDhmHYsGHmrgZrQy+//DJefvllc1fD5PgMgzHGmCAcMBhjjAnCAYMxxpggHDAYY4wJYnEXvXNzc5GSkmLuajw0tA+e4za/P35IH2tLubm5FvfATRGR5bw3MCQk5IGYSskYY6YwduxY7Nixw9zV0LGogMGYJRg3bhwAPuti7F58DYMxxpggHDAYY4wJwgGDMcaYIBwwGGOMCcIBgzHGmCAcMBhjjAnCAYMxxpggHDAYY4wJwgGDMcaYIBwwGGOMCcIBgzHGmCAcMBhjjAnCAYMxxpggHDAYY4wJwgGDMcaYIBwwGGOMCcIBgzHGmCAcMBhjjAnCAYMxxpggHDAYY4wJwgGDMcaYIBwwGGOMCcIBgzHGmCAcMBhjjAnCAYMxxpggHDAYY4wJwgGDMcaYIBwwGGOMCcIBgzHGmCAcMBhjjAnCAYMxxpggHDAYY4wJwgGDMcaYIDbmrgBj5nTkyBEcP35cb9mFCxcAACtXrtRbPmDAADz99NNtVjfGLI2IiMjclWDMXA4ePIi//OUvEIvFsLIyfMJdV1cHjUaDAwcOYOjQoW1cQ8YsBwcM9lCrq6uDm5sb8vPzm0zn7OyMmzdvwtrauo1qxpjl4WsY7KFmZWWFCRMmQCKRNJpGIpHgtdde42DBHnocMNhD75VXXkF1dXWj66urq/HKK6+0YY0Ys0w8JMUYAB8fH1y9etXgOk9PT1y9ehUikaiNa8WYZeEzDMYATJw4EWKxuMFysViMSZMmcbBgDHyGwRiAu1Npu3XrZnDduXPn0L179zauEWOWh88wGAMQGBiI7t27NziTCAoK4mDB2H9xwGDsv/7nf/5HbyaUWCzG66+/bsYaMWZZeEiKsf+6du0avL29oT0kRCIRfv/9d/j4+Ji3YoxZCD7DYOy/PD098eSTT8LKygpWVlZ48sknOVgwVg8HDMbqmThxIkQiEaysrDBx4kRzV4cxi8JDUozVU1BQADc3NwDAjRs30LFjRzPXiDELQvfYvn07AeAPf/jDH/48xJ/t27ffGx6o0cebb9++vbFVjJlEfHw8AGDmzJlmrom+I0eOQCQSYdCgQeauCo4fP46EhAQ+HlmbGj9+vMHljQaMcePGtVplGAOAHTt2ALC8vvbCCy8AAOzt7c1ck7sSEhIsro3Yg83ogMHYw8pSAgVjloZnSTHGGBOEAwZjjDFBOGAwxhgThAMGY4wxQThgmNiqVavQsWNHiEQifPzxx7rl+/btg0qlwr/+9S+TlxkdHY2goCAolUpIpVIEBARg7ty5KC8vb3SbKVOmwN7eHiKRCKdPnxZc1pAhQyASiQx+7OzsTLE7RmnNdn0QHDhwAPPnz0dqair8/Px0fytDd7EPGzYM9vb2sLa2Rvfu3fHrr7+aocbC1NXVIT4+HsHBwS1KY4nlaTQaxMbGIiAgABKJBA4ODujRoweuXLliMH1VVRUCAwOxaNEiAMCePXuwcuVK1NbWtqgehnDAMLHZs2fj2LFjDZa35g31hw4dwvTp03HlyhUUFBQgNjYWCQkJCAkJaXSbTZs2YePGjSatx1NPPWXS/ITgBxU07p///CfWrFmDBQsWYMyYMfj999/h7++PDh06ICkpCV9//bVe+m+//RY7duzASy+9hMzMTDzxxBNmqnnTsrKy8PTTTyMyMhJqtbrZaSy1vPHjx+OLL77Al19+CbVajf/85z/w9/dv9AfgwoULcfHiRd3/R4wYAZlMhqFDh6K4uLhFdbkXT6ttI8OHD0dJSUmr5G1nZ4ewsDDdo7nHjRuH1NRUpKSk4Nq1a/D09DRZWTKZDKWlpQ2mnk6dOtUs9wq0ZrsKUVlZiaFDhxr8kWBOK1asQHJyMs6cOQOZTKa3bs2aNZg4cSLCwsKQmZkJlUplploa78yZM4iOjsbbb7+NiooKgz8YhKSx1PKSk5ORlpaGM2fOoGfPngAAd3d37N6922D6Y8eO4dy5cw2Wz5gxA7///jtefPFFHDlyBDY2pvmq5zOMdoiIsGPHDmzYsAEAsHfvXr33OACAs7MzADT5a6c5rx395ptvGgSLa9eu4dy5c3j22WeNzq+927x5M/Ly8sxdDT2//fYbFi9ejKVLlzYIFgAQHByMiIgIXL9+HbNnzzZDDZvvscceQ2pqKiZMmACpVNrsNJZa3kcffYQnnnhCFyyaUllZiTlz5iAhIcHg+iVLluD06dONrm+OFgeMhIQEKBQKWFlZoXfv3nB1dYVYLIZCocATTzyBQYMGwdPTEzKZDA4ODpg7d67e9t9//z2CgoKgUqkgk8nQs2dP7N+/HwDw2Wefwc7ODiKRCI6OjkhLS8PJkyfh7e0Na2trvPrqq4LquGbNGshkMnTs2BFTp06Fu7s7ZDIZgoODceLECV06IsLq1avRrVs3SKVSODo6YuTIkbhw4YJRae519OhReHl5QSQSYd26dQCA9evXQ6FQQC6XY/fu3XjhhRegVCrRqVMnbNu2TbdtbW0tYmNj0bVrV9ja2sLZ2Rm+vr6IjY1t8hf99evXYWtrC19fX1294+Li0LVrV0ilUqhUKsyZM0dQ+93PihUrMGPGDJPkZYzmtquQ/hAeHg6JRKJ7ECEAvPPOO1AoFBCJRCgoKEBERARmzZqF7OxsiEQiBAQEALgbVJVKJZYvX97GLQLd/hERRowY0WiamJgYdOnSBZs2bcKBAwcaTXe//m5MP46KioKXlxdsbW3x6KOP8uNO7lFdXY0ff/wRvXr1EpR+4cKFeOedd+Di4mJwvaOjIwYPHoyEhATTDd029vBBY/zzn/8kAHTixAmqqKiggoICev755wkAff3115Sfn08VFRUUHh5OAOj06dO6bXfs2EFLliyh27dvU2FhIfXv3586dOigW3/+/HmSy+X0+uuv65bNnz+fNm3aZFQdw8LCSKFQ0Pnz56mqqooyMzOpb9++ZG9vTzk5OUREFBUVRRKJhLZu3UrFxcV09uxZeuKJJ8jZ2Zlu3rwpOE1WVhYBoI8++khX/rVr1wgArV27Vrds4cKFBIAOHjxIJSUllJeXR4MGDSKFQkHV1dVERLR8+XKytram3bt3k1qtpl9++YVcXV1pyJAhje5rRUUF2dvbU3h4uF5ZIpGIPvjgAyoqKiK1Wk2JiYkEgE6dOvX/7N17WFVV3gfw7wHOlcsBBIEBQTmgiJJkakr5amPjvOWIApqYNllTYWaEqBFeSLmlYeiooK9pvJOWdx90VKrRxorxkk76SphKeEE0uclVbgf4vX/YOeOJA+wDBw7g7/M8PI+uvfbaa6+9Nj/22mvvbVBbPiw/P598fX2psbHR4HWnTZtG06ZNa/e2idrfrkL6w6xZs8jJyUlne0lJSQSAioqKiIgoJCSEVCqVTp7Dhw+TtbU1xcbGdmjfiNp3Pnp6epKvr6/eZSqViq5fv05ERCdPniQzMzPq378/VVVVERFRRkYGTZkyRZtfSH8X0t6LFi0iqVRK+/bto9LSUlqyZAmZmZnR2bNnDW0SrSeffJKGDRvW4TzdZXvXr18nAOTv70/jx48nZ2dnkkql5OPjQxs3bqSmpiZt3szMTAoMDCQioqKiIgJAS5cubVZmdHR0u85xtPDyQaMOSfn6+kKhUKBPnz6YOXMmAMDd3R0ODg5QKBSYPXs2AOj8NT5t2jS8//77sLOzg729PQIDA1FSUoKioiIAwODBg7F27Vr87W9/w2effYadO3eirq4Of/nLXwyun4WFhfYvJV9fX6SmpqKyshJpaWmoqalBcnIygoODMXv2bCiVSvj5+WHz5s0oLi7Gli1bBOVpj4CAANjY2MDR0RGhoaG4f/8+8vLyAADp6el44oknEBgYCLlcjuHDh2PKlCn49ttvUV9fr7e8xMREuLi4ID4+HsCDS9e1a9fi2WefRWRkJGxtbSGXy2Fvb9+u+j5s1apVePvtt2Fm1v1GN1trV6D1/tARkyZNQkVFBZYvX97RXTDY/fv3cf36dahUqjbzjhkzBgsWLMCNGzfw3nvvNVtuaH9vqb1ra2uRmpqKoKAghISEwNbWFsuWLYNYLO5wW/cmmpvajo6OSEhIQHZ2NgoKCjB16lTMnz8fn3/+OYAHxyUiIgKpqaltlunt7Q0AyMrKMkodO+0sl0gkAICGhgZtmlgsBvBg2lhLNHkenhL2xhtvYNq0aZg7dy727NmDDz/80Ch1HDFiBBQKBS5fvozs7GxUVVVhxIgROnlGjhwJiUSCM2fOCMrTUZp207RRbW1ts8vJxsZGiMXiZvctAODAgQPYs2cPvvzyS+29hp9//hnV1dWYMGFCh+v3sDt37uDQoUOYM2eOUcvtDL9tV30e7g89VWFhIYgICoVCUP74+HgMGjQIKSkpyMzM1FnWkf7+cHtfuXIF1dXVGDp0qHa5XC6Hs7Nzj25rY9Pc/xgyZAgCAgJgb28PpVKJlStXQqlUagP0kiVL8MYbb8DV1bXNMjX9oKCgwCh1NPmfhUeOHMH48ePh6OgIqVTa7B6HRkJCAqqqqox+g1EqlaKoqEg7/UzfswS2traorKwUlMfYnn/+efz73//GwYMHUVNTg3PnziE9PR1/+tOfmgWMXbt2YdWqVThx4oTOp0Xz8/MBoMWxzvZavXo1Xn/9db03VnsqTX/oqWprawFA8M1XmUyGtLQ0iEQivPrqq6ipqdEuM1Z/v3//PgBg2bJlOs/t3Lx5s8NTUHsTFxcXAA8+4vUwiUQCDw8P5ObmIjMzE1lZWXjttdcElSmXywH8p190lEkDRl5eHoKCguDs7IwzZ86gvLwcq1evbpZPrVbjnXfeQXJyMk6dOqUdaukotVqNsrIyuLm5wdbWFgD0ngSG5DG2FStW4Pe//z3mzJkDGxsbBAcH44UXXmj2DMWGDRuwY8cOfP311/jd736ns0zzC72urs5o9bp79y4+//xzzJs3z2hlmtrD/aGn0vyCMOShrTFjxiAyMhI5OTmIi4vTphurv2v+UFm7di2ISOfn1KlTguvZ21lZWcHb2xuXLl1qtqyhoQFKpRLbtm3D8ePHYWZmpg28mvZNSEiASCTCuXPntOtphq01/aKjTBowsrKyoFarMW/ePHh6ekImk+md6vn222/j9ddfx4IFCxAZGYm4uDijdLQTJ06AiDB69GgMHToUVlZWOo0NAGfOnEF9fT2eeOIJQXmMLTs7G7m5uSgqKoJarUZeXh5SU1NhZ2cH4MEslqioKGRlZSE9PV3vX4NDhw6FmZkZvvnmG6PVa/Xq1Zg9e7ZR7oN0Fw/3B+DBPY7WhrC6I81bBgx9NiUuLg4+Pj44f/68Ns1Y/V0zS9KQNwo8qmbMmIHz58/j2rVr2rTq6mrcvHkTfn5+SEtLaxZ0NVfES5cuBRHpDCFq+oGTk5NR6mfSgOHu7g7gwesLamtrkZOT02xcNCUlBa6urggODgbw4Iaur68vZs2ahYqKCoO219TUhNLSUjQ0NODixYuIiIiAu7s75syZA5lMhoULF+LAgQPYsWMHKioqkJWVhTfffBMuLi4ICwsTlMfY5s+fD3d39xaf8rx06RI+/PBDfPzxxxCLxc1e17FmzRo4OjoiJCQE+/btw7Zt21BRUYGLFy+2+yZ9QUEBPvnkk273pTxDtdYfAMDLywv37t1Deno61Go1ioqKcPPmTZ0y7O3tcefOHdy4cQOVlZVQq9XIyMgw2bRahUIBT09P7TCkUJqhqYeHOY3V32UyGV555RXs3LkTqampqKioQGNjI/Lz8/HLL78AAEJDQ+Hk5NRlryPprtuLjIyEh4cH5syZg7y8PJSUlCAqKgo1NTV6Jya0RdMPhDzXIchvp00ZOo1v3bp1pFAoCAD179+fvvvuO1q1ahUplUoCQE5OTvTZZ5/Rrl27yMnJiQCQnZ0d7dy5k4iIoqKiyN7enmxtbWn69Om0ceNGAkAqlYr8/f1JJBKRvb09nTx5koiIFixYQGZmZgSAlEolnTt3TlA9w8LCSCwWk6urK1lYWJCNjQ1NnTqVcnNztXmampooKSmJvL29SSwWk52dHQUFBdGVK1cE5/noo4+0+2lpaUnBwcG0YcMGcnZ2JgCkUCgoMDCQUlJStO3m7e1Nubm5tGXLFrKxsSEA5OHhQVevXqWvv/6a+vTpo/OtXbFYTIMHD6b9+/dTVlZWq9/lTUpKIiKiyspKeu2116hPnz5kZWVFTz/9NMXExBAAcnNzo//7v/8TfMwjIyNp9uzZgvO3pKPTajvSrkL6Q0lJCT3zzDMkk8lowIAB9Pbbb9PixYsJAHl5eVFeXh798MMP5OHhQXK5nJ5++mm6e/cuHT16lKytrSk+Pr7DbdSeabXh4eEkFoupurpam3bgwAFSqVQEgBwcHGj+/Pl61128eLHOtNq2+rvQ9q6rq6OoqChyd3cnCwsLcnR0pJCQEMrOziYioqCgIAJAMTExre7bqVOn6KmnniIXFxdtH3d2dqaAgAD65ptvBOfprtsjejBVfObMmWRnZ0dSqZRGjRpFGRkZLeZvbVrtpEmTyNXVVWdKrhBoYVqtUZ7D6AnCwsLI3t7e1NUwWEpKCkVEROik1dXV0YIFC0gqler8UuhpjPEcRnv1lP7QnvMxJyeHLCwsaPv27Z1UK+NrbGyksWPH0rZt23h7RlJcXEwymYzWrFlj8LotBQyTz5LqSp3x9sbOdPfuXYSHhzd75kQikcDd3R1qtbrHjbF3Jz2tPwjl5eWF2NhYxMbGtvrG4u6isbER6enpqKysRGhoKG/PSFasWAF/f3+Eh4cbrcweHTAuX77c4qu2H/7pyoNkTHK5HGKxGNu2bUNBQQHUajXu3LmDrVu3IiYmBqGhobCxsenwdnp7Oz6KoqOjMX36dISGhpr05YxCnDhxAvv370dGRobg50d4e61LTk7GhQsXcPToUe2zbUbx20uO3jgkFR0dTRKJRHufZe/evaaukmDffvstPfvss2RjY0Pm5uakVCopICCAUlJSSK1Wm7p6HWKqIame1B86ej5++eWXFBUVZcQase4uPT2dEhMTqaGhod1loIUhKdGvC7X27NmDGTNm8HcGWKfTfK9j7969Jq5J98XnIzMFkUiE3bt3N3vBaY8ekmKMMdZ1OGAwxhgThAMGY4wxQThgMMYYE6TFD73u2bOnK+vBHkGa1xZwX2uZ5p1p3EasO2gxYMyYMaMr68EeYdzX2sZtxLqDFgMGT+NjnY2n1baNp9UyU9D31nCA72EwxhgTiAMGY4wxQThgMMYYE4QDBmOMMUE4YDDGGBOEAwZjjDFBuixg7N+/H56entpvKyxfvrzV/MnJyRCJRDAzM4OPjw++/fbbTqmHSCSCWCyGq6srZs2ahZ9++sko29FYs2YN+vbtC5FIhM2bN2vTjx49CqVSib///e9G3R4AxMbGwtfXFzY2NpBKpfDy8sK7776r/ZiOvjYQiUSQSCTo27cvxo8fj6SkJJSWlhq9bqz7OXbsGKKjo5v1i5deeqlZ3okTJ8La2hrm5uYYMmRIl30T2xDx8fF6v+cydOhQk5YFAGq1GomJifDy8oJEIoGtrS2GDh2KGzdu6M1fW1sLHx8fLFu2DABw6NAhrF692nQf//rt+847+3sYmu8KOzs7U319vd48DQ0N5OHhQQBowoQJnVYPpVJJRERVVVV06NAhcnd3JysrK7p8+bJRt5WTk0MAaNOmTdq0w4cPk42NDR06dMio2yIiGjduHKWkpFBJSQlVVFTQ7t27SSwW03//93/r5Hu4DZqamqi0tJT++c9/0pw5c0gkEpGLiwudPXvW6PXTMOUnWnuKzj4fY2JiaPLkyVRRUaFNU6lU2u/IHz58uNk6GRkZOt/97m7i4uL0ft9+yJAhJi2L6MG3vQcNGkSnT58mtVpNd+7cocDAQMrKytKbPzIystn3utetW0fjxo2j0tLSdtVBCHSnT7Q+8cQTuHv3LtLT0/Uu379/P1xdXbusPpaWlpg8eTL++te/oqqqChs2bOj0bU6aNAnl5eWYPHmy0cu2srJCWFgY7O3tYW1tjRdeeAFBQUH44osvcOvWLb3riEQi2NraYvz48UhLS8OePXtQUFCgrWdvU1NTg4CAgB5XtjGtWrUKu3btwp49e2Btba2zbP369TAzM0NYWFiPPP7bt28HEen8/PjjjyYta9euXUhPT8fevXvx5JNPwsLCAi4uLjh48KDeK5aTJ0/q3c4777yDYcOG4fnnn0dDQ0O79qm9TBIw5s2bBwDYtGmT3uXJyclYuHBhV1YJADBq1CgAaHfHMhUiwt69e7FlyxYAwOHDh2Fubq6Tx8HBAQBQXV0tqMxp06Zhzpw5KCws1BlK6y22bduGwsLCHle2sfz8889Yvnw5Vq5cCZlM1mx5QEAAIiIicPv2bSxatMgENex9Nm3ahOHDh8PPz6/NvDU1NVi8eDHWrVund/mKFStw4cKFFpd3FpMEjN///vcYPHgw/vnPf+LKlSs6y/71r3+huroaEydO1Lvud999B19fXyiVSshkMvj5+eHLL78EAPzv//4vrKysIBKJYGdnh/T0dJw7dw4eHh4wNzfHiy++2Gq9NNFaKpUCePCLODk5GYMHD4ZUKoWdnR2mTp2Ky5cva9cRkue3MjMz4e7uDpFIhI0bNwIAUlNTYWlpCYVCgYMHD+K5556DjY0N3NzcsHPnTu26jY2NSExMxKBBgyCXy+Hg4IABAwYgMTGx2dexHnb79m3I5XIMGDCg1TZ42Jw5cwAAGRkZgtfpbG21d3h4OCQSCZydnbXrvPXWW7C0tIRIJEJxcTEiIiKwcOFC5ObmQiQSwcvLC+vXr4dMJkPfvn0xd+5cuLi4QCaTISAgAGfOnOlQ2QDwxRdfwMbGBgkJCV3YWi1bv349iAiBgYEt5omPj8fAgQOxdetWHDt2rMV8bR0TQ/p2TEwM3N3dIZfL8dhjj2H37t3G22kTqq+vx+nTp+Hv7y8o/9KlS/HWW2/B0dFR73I7OzuMGzcO69at69rXxvx2jKor7mFcv36d/vrXvxIAioiI0FkeFBREaWlpVFlZqfcext69e2nFihV07949KikpodGjR1OfPn20yy9dukQKhYJefvllbVp0dDRt3bq1WT004/ca27dvJwC0ePFiInowviuRSGj79u1UVlZGFy9epOHDh5ODgwPdvXtXcB599zBu3bpFAGjDhg3atKVLlxIAOn78OJWXl1NhYSGNHTuWLC0ttfd7EhISyNzcnA4ePEjV1dX073//m5ycnGj8+PEttvn9+/fJ2tqawsPD22yDh1VUVBAA6tevX4t5OqI99zCEtPesWbPIyclJZ72kpCQCQEVFRUREFBISQiqVSidPWFgYWVpa0qVLl6i2tpays7Np5MiRZG1tTXl5eR0q+/Dhw2RtbU2xsbEG7W9nnY+enp7k6+urd5nmHCUiOnnyJJmZmVH//v2pqqqKiJrfwxByTIT07UWLFpFUKqV9+/ZRaWkpLVmyhMzMzAy+jxYXF0dubm5ka2tLYrGY+vfvT1OmTKHvv//e0GYyWlnXr18nAOTv70/jx48nZ2dnkkql5OPjQxs3bqSmpiZt3szMTAoMDCQioqKiomb3MDSio6MJAJ0/f97g/WoLWriHYbKAUVZWRpaWlmRnZ0fV1dVERJSbm0tubm5UV1fXYsD4rcTERAJAhYWF2rT/+Z//IQC0Y8cO+vzzzykyMlJvPR6+6b1v3z5ycnKivn37Un5+PlVXV5OVlRWFhobqrPf9998TAIqNjRWUh8jwgFFTU6NNS0lJIQD0888/ExHRyJEjadSoUTrbe+ONN8jMzIzq6ur0ttHSpUtp4MCBOjc2f9sGLRGJRGRra9tqnvYyNGAIbe+OBIzftsfZs2cJAK1cubJDZbdXZ5yPVVVVJBKJaPLkyXqXPxwwiIgWLlxIAGj+/PlEpBswhB6Ttvp2TU0NKRQKnXKqq6tJKpXSvHnzDNq/vLw8+uGHH6iyspLq6uro1KlT9Pjjj5NcLqcff/zRJGVlZWURAPrDH/5A//rXv6ikpITKysrovffe0/6u0uzziBEjKD8/n4haDxiffPIJAaBPP/3UoH0SoqWAYbLnMJRKJV588UWUlpZi165dAIC1a9di3rx5kEgkgssRi8UAoDPN7I033sC0adMwd+5c7NmzBx9++KHedcvLyyESiaBUKvHOO+/g+eefx/fffw9XV1dkZ2ejqqoKI0aM0Fln5MiRkEgkOHPmjKA8HaVpC7VaDeDBNDv6zSVoY2MjxGJxs/sWAHDgwAHs2bMHX375ZbMbm225f/8+iAg2NjbtrL1xdUV7/9aIESOgUChaHWLsaQoLC0FEUCgUgvLHx8dj0KBBSElJQWZmps6yjhyTh/v2lStXUF1drXPzVy6Xw9nZ2eC279evHx5//HFYWVlBIpFg9OjRSEtLQ01NDVJSUkxSlmaYe8iQIQgICIC9vT2USiVWrlwJpVKpvf+4ZMkSvPHGG4Im/WiOX0FBgUH71BEmfXBPc/N78+bNKCsrw969ezF37txW1zly5AjGjx8PR0dHSKVSvPvuu3rzJSQkoKqqqtWbj0qlEkSEhoYG5Ofn45NPPoGHhwcAoKysDMCDGUe/ZWtri8rKSkF5jO3555/Hv//9bxw8eBA1NTU4d+4c0tPT8ac//alZwNi1axdWrVqFEydOoH///gZv6+rVqwAAHx8fY1S9w0zR3sCDk72oqKhTyjaF2tpaAP/5JdYWmUyGtLQ0iEQivPrqq6ipqdEuM9YxuX//PgBg2bJlOs873Lx5U/BEjdb4+fnB3Nxc26e7jtjYlgAAIABJREFUuiwXFxcAQHFxsU66RCKBh4cHcnNzkZmZiaysLLz22muCypTL5QD+czy7gkkDhr+/P0aPHo3vv/8eYWFhmD59Ouzs7FrMn5eXh6CgIDg7O+PMmTMoLy/H6tWrm+VTq9V45513kJycjFOnTiE+Pt7gutna2gKA3g5fVlYGNzc3QXmMbcWKFfj973+POXPmwMbGBsHBwXjhhRfw8ccf6+TbsGEDduzYga+//hq/+93v2rWtL774AgDw3HPPdbjexmCK9lar1Z1WtqloftEY8vDXmDFjEBkZiZycHMTFxWnTjXVMNDd3165d22wKq+argx3R1NSEpqYmwUHS2GVZWVnB29sbly5darasoaEBSqUS27Ztw/Hjx2FmZqYNmJp2SUhIgEgkwrlz57Tr1dfXA/jP8ewKJn81iOYqY9++fViwYEGrebOysqBWqzFv3jx4enpCJpPp/dDH22+/jddffx0LFixAZGQk4uLiDO50Q4cOhZWVlc4BAoAzZ86gvr4eTzzxhKA8xpadnY3c3FwUFRVBrVYjLy8Pqamp2kBLRIiKikJWVhbS09P1/uUnxN27d7F27Vq4ubnh1VdfNeYutJvQ9rawsNAO4XXUiRMnQEQYPXq00cs2Fc2bBwx9viIuLg4+Pj44f/68Ns1Y50C/fv0gk8lw4cIFg+qkzx//+MdmaWfPngURYcyYMSYra8aMGTh//jyuXbumTauursbNmzfh5+eHtLS0ZsFSc2W7dOlSEJHO0J/m+Dk5ORlUj44wecB44YUX4ODggKCgIHh6eraa193dHcCDVxnU1tYiJyen2RhpSkoKXF1dERwcDABITEyEr68vZs2ahYqKCsH1kslkWLhwIQ4cOIAdO3agoqICWVlZePPNN+Hi4oKwsDBBeYxt/vz5cHd3177m47cuXbqEDz/8EB9//DHEYnGzVxqsWbNGJz8RoaqqCk1NTdoOunv3bjz11FMwNzdHenp6t7mHIbS9vby8cO/ePaSnp0OtVqOoqAg3b97UKcve3h537tzBjRs3UFlZqQ0CTU1NKC0tRUNDAy5evIiIiAi4u7trpxi3t+yMjIxuM61WoVDA09NT+011oTRDUw8PfRrrHJDJZHjllVewc+dOpKamoqKiAo2NjcjPz8cvv/wCAAgNDYWTk1ObryO5ffs2du3ahbKyMqjVapw6dQqvvfYa3N3d8eabb5qsrMjISHh4eGDOnDnIy8tDSUkJoqKiUFNTg/fee09QOz1Mc/yEPNdhNL+9C95Zs6QOHDigfS2Ig4ODdsYFEdG7775LJ0+e1P5/2bJl5OzsTADIzMyMfH196bvvviMioqioKLK3tydbW1uaPn06bdy4kQCQSqUif39/EolEZG9vry1vwYIFZGZmRgBIqVTS+vXraeDAgdpH/F1cXGj69Ol669zU1ERJSUnk7e1NYrGY7OzsKCgoiK5cuSI4z0cffUROTk4EgCwtLSk4OJg2bNig3T+FQkGBgYGUkpJCCoWCAJC3tzfl5ubSli1byMbGhgCQh4cHXb16lb7++mvtaxs0P2KxmAYPHkz79+/XzsZo6ScpKYkOHTpEjz32GCkUCpJIJNr20cyIGjVqFMXGxlJJSYnR+8HD2jOtVsgxKSkpoWeeeYZkMhkNGDCA3n77bVq8eDEBIC8vL+3MFw8PD5LL5fT000/T3bt3KSwsjMRiMbm6upKFhQXZ2NjQ1KlTKTc3t8NlHz16lKytrSk+Pt6g/e2s8zE8PJzEYrF2hiJR6+fowxYvXqwzrbatYyK0b9fV1VFUVBS5u7uThYUFOTo6UkhICGVnZxPRgyn3ACgmJqbVfVu4cCGpVCqytLQkCwsLcnNzo9dff53u3LmjzWOKsogezI6cOXMm2dnZkVQqpVGjRlFGRkaL+VubJTVp0iRydXXVmZJrLOgu02pZx6SkpDR7dqWuro4WLFhAUqlU5xdAd9fd3iUVFhZG9vb2pq6Gjs46H3NycsjCwoK2b99u9LI7S2NjI40dO5a2bdvWa8sSqri4mGQyGa1Zs6ZTym8pYJh8SIoJd/fuXYSHh+Mvf/mLTrpEIoG7uzvUanWPH183NZO9BbSLeXl5ITY2FrGxsS0Ob3YnjY2NSE9PR2VlJUJDQ3tlWYZYsWIF/P39ER4e3mXbBLrBPQwmnFwuh1gsxrZt21BQUAC1Wo07d+5g69atiImJQWhoaLe538C6v+joaEyfPh2hoaHd/gWDJ06cwP79+5GRkSH4+ZGeVpZQycnJuHDhAo4ePap9Dq3L/PaSg4ekurdvv/2Wnn32WbKxsSFzc3NSKpUUEBBAKSkppFarTV09g3SnIano6GiSSCQEgPr370979+41dZWIqGvOxy+//JKioqI6dRvMONLT0ykxMZEaGho6dTtoYUjKomvDE+uosWPH4h//+Iepq9HrJCYmIjEx0dTVMImJEye2+LJP1r1MmTIFU6ZMMdn2eUiKMcaYIBwwGGOMCcIBgzHGmCAcMBhjjAnS4k3v6dOnd2U92CPo9OnTALivtUbz+gduI9YdiH6dQqV16tQpJCcnm6o+jJlcVlYWgC5+Rw9j3UxkZGSzFyw2CxiMPeo030bfs2ePiWvCWPfC9zAYY4wJwgGDMcaYIBwwGGOMCcIBgzHGmCAcMBhjjAnCAYMxxpggHDAYY4wJwgGDMcaYIBwwGGOMCcIBgzHGmCAcMBhjjAnCAYMxxpggHDAYY4wJwgGDMcaYIBwwGGOMCcIBgzHGmCAcMBhjjAnCAYMxxpggHDAYY4wJwgGDMcaYIBwwGGOMCcIBgzHGmCAcMBhjjAnCAYMxxpggHDAYY4wJwgGDMcaYIBwwGGOMCcIBgzHGmCAcMBhjjAnCAYMxxpggHDAYY4wJwgGDMcaYIBwwGGOMCSIiIjJ1JRgzlU8//RTJyclobGzUphUXFwMAHBwctGnm5uaIjIzEn//85y6vI2PdBQcM9ki7evUqBg0aJCjvlStXMHDgwE6uEWPdFw9JsUfawIEDMWzYMIhEohbziEQiDBs2jIMFe+RxwGCPvD//+c8wNzdvcbmFhQVefvnlLqwRY90TD0mxR96dO3fQr18/NDU16V0uEolw69YtuLq6dnHNGOte+AqDPfJ+97vfISAgAGZmzU8HMzMzPPXUUxwsGAMHDMYAAC+99JLedJFIxDOjGPsVD0kxBqC0tBROTk5Qq9U66RYWFrh79y769Oljopox1n3wFQZjAOzs7PCHP/xB5+a3ubk5/vjHP3KwYOxXHDAY+9Xs2bN1bnwTEWbPnm3CGjHWvfCQFGO/qq6uRp8+fVBbWwsAkMlkKC4uhqWlpYlrxlj3wFcYjP1KoVAgKCgIYrEYYrEYQUFBHCwYewgHDMYe8uKLL0KtVkOtVuPFF180dXUY61YsTF2BjtqzZ4+pq8B6kcbGRigUChARKioquH8xo3rhhRdMXYUO6fH3MFp7BxBjjHUnPfzXbe8Yktq9ezeIiH+M+LN7924AMHk9TPFz4sQJfPPNN4Lycv/jHyE/mvOpp+vxQ1KMGdvYsWNNXQXGuiUOGIz9hr53SjHGesmQFGOMsc7HAYMxxpggHDAYY4wJwgGDMcaYII9kwKirq8M777wDZ2dnKBQKPPvss+jbty9EIhE2b95s6uq12+rVq+Hj4wO5XA5LS0v4+Phg+fLlqKioMFmdjh49CqVSib///e8mq0N3dezYMURHR2P//v3w9PSESCSCSCTS+22OiRMnwtraGubm5hgyZAh++OEHE9S4dfHx8dp9ePhn6NChJi0LANRqNRITE+Hl5QWJRAJbW1sMHToUN27c0Ju/trYWPj4+WLZsGQDg0KFDWL16NRobG9u1/d7ikQwYH330Eb744gtcvnwZ69atw9y5c3Hy5ElTV6vDvvvuO7z++uvIy8tDQUEB4uLisHr1akybNs1kdSLq2Q8qdZb3338f69evx5IlSxASEoJr165BpVKhT58+2LFjB44cOaKT/6uvvsLevXsxefJkZGdnY/jw4Saqec80Y8YMfPrpp/jss89QXV2Nn376CSqVClVVVXrzL126FFeuXNH+PzAwEDKZDBMmTEBZWVlXVbvbeSQDRnp6OkaMGAFbW1u88cYb7f6FWlNTg4CAgDbTuopEIsFbb70FR0dHWFlZYfr06Zg6dSr+8Y9/4JdffjFJnSZNmoTy8nJMnjzZJNs35fFoyapVq7Br1y7s2bMH1tbWOsvWr18PMzMzhIWFoby83EQ1bL/t27c3e2jtxx9/NGlZu3btQnp6Ovbu3Ysnn3wSFhYWcHFxwcGDB/VesZw8eVLvdt555x0MGzYMzz//PBoaGtq1Tz3dIxkw8vPzIRaLO1zOtm3bUFhY2GZaVzlw4ABkMplOmuZb1C39JdXbmfJ46PPzzz9j+fLlWLlyZbNjBQABAQGIiIjA7du3sWjRIhPUsPfZtGkThg8fDj8/vzbz1tTUYPHixVi3bp3e5StWrMCFCxdaXN7bPVIB4x//+Ae8vLzwyy+/4G9/+xtEIhGsrKxazP/dd9/B19cXSqUSMpkMfn5++PLLLwEAERERWLhwIXJzcyESieDl5aU3DXjwQruYmBi4u7tDLpfjscce074qIDU1FZaWllAoFDh48CCee+452NjYwM3NDTt37uzwPufk5MDW1hYeHh4dLstQmZmZcHd3h0gkwsaNGwEI29/169dDJpOhb9++mDt3LlxcXCCTyRAQEIAzZ84AAMLDwyGRSODs7Kzd3ltvvQVLS0uIRCIUFxe3eDy++OIL2NjYICEhoYtb5MG+ERECAwNbzBMfH4+BAwdi69atOHbsWIv5iAjJyckYPHgwpFIp7OzsMHXqVFy+fBmA8L7VWv/s6err63H69Gn4+/sLyr906VLtVbo+dnZ2GDduHNatW/doDrdSDweAdu/ebdA6Tk5O9PLLL+uk5eTkEADatGmTNm3v3r20YsUKunfvHpWUlNDo0aOpT58+2uUhISGkUql0ytGXtmjRIpJKpbRv3z4qLS2lJUuWkJmZGZ09e5aIiJYuXUoA6Pjx41ReXk6FhYU0duxYsrS0pPr6eoP2jYiovr6e8vPzacOGDSSVSmn79u0Gl7F7924yRve4desWAaANGzZo04Tsb1hYGFlaWtKlS5eotraWsrOzaeTIkWRtbU15eXlERDRr1ixycnLS2V5SUhIBoKKiIiLSfzwOHz5M1tbWFBsb2+H9M7T/eXp6kq+vr95lKpWKrl+/TkREJ0+eJDMzM+rfvz9VVVUREVFGRgZNmTJFmz8mJoYkEglt376dysrK6OLFizR8+HBycHCgu3fvEpGwtm6rfwoVFxdHbm5uZGtrS2KxmPr3709Tpkyh77//3qByjFnW9evXCQD5+/vT+PHjydnZmaRSKfn4+NDGjRupqalJmzczM5MCAwOJiKioqIgA0NKlS5uVGR0dTQDo/PnzguthrPPJ1B6pKwxDTZs2De+//z7s7Oxgb2+PwMBAlJSUoKioSHAZtbW1SE1NRVBQEEJCQmBra4tly5ZBLBYjLS1NJ29AQABsbGzg6OiI0NBQ3L9/H3l5eQbXu1+/fnBzc8OKFSvw4YcfYsaMGQaX0RXa2l8LCwvtX8++vr5ITU1FZWVls3Yz1KRJk1BRUYHly5d3dBcMcv/+fVy/fh0qlarNvGPGjMGCBQtw48YNvPfee82W19TUIDk5GcHBwZg9ezaUSiX8/PywefNmFBcXY8uWLTr5W2prQ/pnW15++WUcOnQIt27dQlVVFXbu3Im8vDyMGzcO2dnZJilLMxTr6OiIhIQEZGdno6CgAFOnTsX8+fPx+eefA3jQnhEREUhNTW2zTG9vbwBAVlaWQfvUG3DAMIDmvochU+uuXLmC6upqnZtrcrkczs7O2qEDfSQSCYAH0wENdevWLRQWFuLzzz/H3/72Nzz++OPdahxfHyH7O2LECCgUilbbrTsrLCwEEUGhUAjKHx8fj0GDBiElJQWZmZk6y7Kzs1FVVYURI0bopI8cORISiUQ7dKfPw23d3v6pT79+/fD444/DysoKEokEo0ePRlpaGmpqapCSkmKSsqRSKQBgyJAhCAgIgL29PZRKJVauXAmlUqkNrEuWLMEbb7yhvefXGs3xKygoMGifegMOGK04cuQIxo8fD0dHR0ilUrz77rsGl3H//n0AwLJly3Tmk9+8eRPV1dXGrjKAB4HN0dEREydOxK5du5CdnY3ExMRO2VZXk0qlBl3hdSeab4Vrfom1RSaTIS0tDSKRCK+++ipqamq0yzRTO/Xdg7O1tUVlZaWgbXR2//Tz84O5uTmuXr1qkrJcXFwAAMXFxTrpEokEHh4eyM3NRWZmJrKysvDaa68JKlMulwP4z/F8lHDAaEFeXh6CgoLg7OyMM2fOoLy8HKtXrza4HM3Ns7Vr1zabInjq1CljV7sZLy8vmJubGzwk0B2p1WqUlZXBzc3N1FVpF80vGkOuUMeMGYPIyEjk5OQgLi5Om25rawsAegODIW3U2f2zqakJTU1NgoOkscuysrKCt7c3Ll261GxZQ0MDlEoltm3bhuPHj8PMzEwbMDXtkpCQAJFIhHPnzmnXq6+vB/Cf4/ko4YDRgqysLKjVasybNw+enp6QyWTt+rpfv379IJPJcOHChU6o5X+UlJTo/QZ1Tk4OGhsb0a9fv07dflc4ceIEiAijR48G8OAeR3uG7ExF8zYBQ5+viIuLg4+PD86fP69NGzp0KKysrHR+kQHAmTNnUF9fjyeeeEJQ2cbsn3/84x+bpZ09exZEhDFjxpisrBkzZuD8+fO4du2aNq26uho3b96En58f0tLSmgVLzVXs0qVLQUQ6Q3+a4+fk5GRQPXoDDhgtcHd3B/Dg9Q21tbXIyclpNi5sb2+PO3fu4MaNG6isrIRarW6WZm5ujldeeQU7d+5EamoqKioq0NjYiPz8fKM+TGdpaYmvvvoKX3/9NSoqKqBWq3H+/Hm8/PLLsLS0RGRkpNG21VWamppQWlqKhoYGXLx4EREREXB3d8ecOXMAPLh6unfvHtLT06FWq1FUVISbN2/qlKHvGGVkZJhkWq1CoYCnpyfy8/MNWk8zNGVubq6TtnDhQhw4cAA7duxARUUFsrKy8Oabb8LFxQVhYWGCy26rf4aGhsLJyanN15Hcvn0bu3btQllZGdRqNU6dOoXXXnsN7u7uePPNN01WVmRkJDw8PDBnzhzk5eWhpKQEUVFRqKmp0TuhoC2a4yfkuY5ep8vnZRkZDJjWeOPGDXr88ccJAFlYWNDw4cNp37599NFHH5GTkxMBIEtLSwoODiYioqioKLK3tydbW1uaPn06bdy4kQCQSqWivLw8+uGHH8jDw4Pkcjk9/fTTdPfuXb1pdXV1FBUVRe7u7mRhYUGOjo4UEhJC2dnZlJKSQgqFggCQt7c35ebm0pYtW8jGxoYAkIeHB129elXQ/gUGBtKAAQPIysqKpFIpqVQqCg0NpaysLIPb1RjTADds2EDOzs4EgBQKBQUGBgre37CwMBKLxeTq6koWFhZkY2NDU6dOpdzcXG35JSUl9Mwzz5BMJqMBAwbQ22+/TYsXLyYA5OXl1eIxOnr0KFlbW1N8fHyH9o/I8Gm14eHhJBaLqbq6Wpt24MABUqlUBIAcHBxo/vz5etddvHixzrTapqYmSkpKIm9vbxKLxWRnZ0dBQUF05coVIiLBbd1a/yQiCgoKIgAUExPT6r4tXLiQVCoVWVpakoWFBbm5udHrr79Od+7c0eYxRVlED6Z3z5w5k+zs7EgqldKoUaMoIyOjxfytTaudNGkSubq66kzJbUtvmVbb4/fA0BOWCWPqDh4WFkb29vYm275Qhva/nJwcsrCwaNezMabS2NhIY8eOpW3btvXasoQqLi4mmUxGa9asMWg9U59PxsJDUqzb6o1vBvXy8kJsbCxiY2N7xOtaGhsbkZ6ejsrKSoSGhvbKsgyxYsUK+Pv7Izw8vMu22Z1wwOjmLl++rPc1z7/96cqThnVMdHQ0pk+fjtDQ0G7/gsETJ05g//79yMjIEPz8SE8rS6jk5GRcuHABR48eNcq76HoiC1NXgLXOx8fnkXtnzZIlS5CWlob6+noMGDAASUlJJn1Fe2dISEjAV199hQ8++ACrVq0ydXVaNGHCBEyYMKFXlyXEwYMHUVdXhxMnTuhMPnjUcMBg3U5iYmKvedCwNRMnTsTEiRNNXQ0mwJQpUzBlyhRTV8PkeEiKMcaYIBwwGGOMCcIBgzHGmCAcMBhjjAkioh4+BUckEmH06NE99oV03VV+fj5Onz7d62YnGdu+ffu4/7E2ac6nHv7rlq8wGGOMCdMrrjB2796NF154wdRV6VX27NmDGTNm9Pi/iDob9z8mRG85n/gKgzHGmCAcMBhjjAnCAYMxxpggHDAYY4wJwgGDMcaYIBwwHrJ//354enq2+hrx/v37Y82aNdrvM2/evNnU1Wa93LFjxxAdHd2sf7700kvN8k6cOBHW1tYwNzfHkCFD2vx8qSk1NTVh7dq1CAgI0LtcrVYjMTERXl5ekEgksLW1xdChQ3Hjxg1tnszMTDz11FNQKBRwcXFBVFQU6urqdMppLc+hQ4ewevXqXvntlU5hum83GQc64Yt7KpWKlEql9v8NDQ1UXV1NBQUFNHjwYCJ68OU0ALRp0yajbru76C1fCOtsndH/HhYTE0OTJ0+miooKbZpKpaI+ffoQADp8+HCzdTIyMnQ+5dodXb16lZ566ikCQMOGDdObJygoiAYNGkSnT58mtVpNd+7cocDAQO0nh3/88UeSy+W0fPlyqqqqopMnT5KDgwO98sor2jKE5Fm3bh2NGzeOSktLO21/e8v51OP3oCsCxsM0J2J7AkZ1dTWNGTOmzbTuwJQdvDPbxNhld2bA+OCDD2jgwIFUU1Ojk65Sqeizzz4jMzMzcnV1pbKyMp3l3T1gXLhwgYKDg2nHjh3k7++vN2Ds3LmTRCIRXbx4scVyZsyYQQMGDND5tnZSUhKJRCL66aefBOchevCt9TFjxpBarTbGLjbTWwIGD0kZKD09vd3rbtu2DYWFhW2mPeo6s016Snv//PPPWL58OVauXAmZTNZseUBAACIiInD79m0sWrTIBDVsv2HDhmH//v2YNWsWpFKp3jybNm3C8OHD4efnp3d5Q0MDjhw5gnHjxkEkEmnTn3vuORARDh48KCiPxooVK3DhwgWsW7fOSHvZO3HAMKLvvvsOvr6+UCqVkMlk8PPzw5dffgkAiIiIwMKFC5GbmwuRSAQvLy+9acCD7xXHxMTA3d0dcrkcjz32GHbv3g0ASE1NhaWlJRQKBQ4ePIjnnnsONjY2cHNzw86dO0227wBAREhOTsbgwYMhlUphZ2eHqVOn4vLlywCA8PBwSCQSODs7a9d56623YGlpCZFIhOLiYr1tsn79eshkMvTt2xdz586Fi4sLZDIZAgICcObMmQ6VDQBffPEFbGxskJCQ0IWt1br169eDiBAYGNhinvj4eAwcOBBbt27FsWPHWszX1nER2qda65fGVF9fj9OnT8Pf37/FPNeuXUNVVRXc3d110lUqFQDg4sWLgvJo2NnZYdy4cVi3bl2Pfxq7U5ny8sYY0EVDUsePH6ekpCTt//UNSe3du5dWrFhB9+7do5KSEho9ejT16dNHuzwkJIRUKpVOufrSFi1aRFKplPbt20elpaW0ZMkSMjMzo7NnzxIR0dKlSwkAHT9+nMrLy6mwsJDGjh1LlpaWVF9fb5Q2aM8ldExMDEkkEtq+fTuVlZXRxYsXafjw4eTg4EB3794lIqJZs2aRk5OTznpJSUkEgIqKiohIf5uEhYWRpaUlXbp0iWprayk7O5tGjhxJ1tbWlJeX16GyDx8+TNbW1hQbG2vQ/hJ13pCUp6cn+fr66l2mUqno+vXrRER08uRJMjMzo/79+1NVVRURNR+SEnJchPSptvplezz55JPNhqSuX79OAMjf35/Gjx9Pzs7OJJVKycfHhzZu3EhNTU30zTffEACdc1JDLpfThAkTBOV5WHR0NAGg8+fPt3t/WsJDUr1ceXm5zuwoId8PnjZtGt5//33Y2dnB3t4egYGBKCkpQVFRkeDt1tbWIjU1FUFBQQgJCYGtrS2WLVsGsViMtLQ0nbwBAQGwsbGBo6MjQkNDcf/+feTl5Rm8r8ZQU1OD5ORkBAcHY/bs2VAqlfDz88PmzZtRXFyMLVu2dHgbFhYW2r+SfX19kZqaisrKymbtYqhJkyahoqICy5cv73AdjeH+/fu4fv269i/h1owZMwYLFizAjRs38N577zVbbuhxaalPGdIvO6qqqgoA4OjoiISEBGRnZ6OgoABTp07F/Pnz8fnnn2tnOen7vrZYLEZNTY2gPA/z9vYGAGRlZRl1f3oTDhgtUCqVoAeTAkBE+Oc//2lwGWKxGAAMmrJ35coVVFdXY+jQodo0uVwOZ2dn7RCCPhKJBMCDqYimkJ2djaqqKowYMUInfeTIkZBIJNqhI2MaMWIEFApFq+3SExUWFoKIoFAoBOWPj4/HoEGDkJKSgszMTJ1lHTkuD/ep9vbL9tDc1xgyZAgCAgJgb28PpVKJlStXQqlUYsuWLdr7Og0NDc3Wr6+vh1wuF5TnYZr2LigoMOr+9CYcMAQaP358mzcXjxw5gvHjx8PR0RFSqRTvvvuuwdu5f/8+AGDZsmU6Vzg3b95EdXV1u+reFcrKygAAVlZWzZbZ2tqisrKyU7YrlUoNuoLrCWprawGgxRvCvyWTyZCWlgaRSIRXX31V5y9nYx2XruyXLi4uAIDi4mKddIlEAg8PD+Tm5mrvVVVUVOjkqa6uRm1tLVxcXATleZgmgGjanzXHAcNI8vLyEBQUBGdnZ5w5cwbl5eVYvXq1weU4OjoCANauXatzhUNEOHXqlLGrbTS2trYAoPcXUFlZWad8YEitVnda2aak+cVlyJXpmDFeq5eBAAAgAElEQVRjEBkZiZycHMTFxWnTjXVcurJfWllZwdvbG5cuXWq2rKGhAUqlEgMGDIC1tTVu3ryps/znn38GADz22GOC8jysvr4eAJpdebD/4IBhJFlZWVCr1Zg3bx48PT0hk8l0pvIJ1a9fP8hkMly4cKETatl5hg4dCisrK5w7d04n/cyZM6ivr8cTTzwB4MF9CGMNm504cQJEhNGjRxu9bFPSvEWgvLzcoPXi4uLg4+OD8+fPa9OEHpe2dHW/nDFjBs6fP49r165p06qrq3Hz5k34+fnBwsICzz//PL799ls0NTVp82RkZEAkEiEwMFBQnodp2tvJyamT967n4oBhJJqpe8eOHUNtbS1ycnKajQ/b29vjzp07uHHjBiorK6FWq5ulmZub45VXXsHOnTuRmpqKiooKNDY2Ij8/H7/88ospdk0QmUyGhQsX4sCBA9ixYwcqKiqQlZWFN998Ey4uLggLCwMAeHl54d69e0hPT4darUZRUVGzvwD1tRPw4FUSpaWlaGhowMWLFxEREQF3d3fMmTOnQ2VnZGR0q2m1CoUCnp6eyM/PN2g9zdDUwzd5hR4XIWW31S9DQ0Ph5ORklNeRREZGwsPDA3PmzEFeXh5KSkoQFRWFmpoa7c395cuXo6CgAO+//z7u37+PU6dOISkpCXPmzMGgQYME59HQtHdLz34w9Px5XjDitMZ//etfNHDgQAJAAMjZ2bnZ1Dsioo8++oicnJwIAFlaWlJwcDAREUVFRZG9vT3Z2trS9OnTaePGjQSAVCoV5eXl0Q8//EAeHh4kl8vp6aefprt37+pNq6uro6ioKHJ3dycLCwtydHSkkJAQys7OppSUFFIoFASAvL29KTc3l7Zs2UI2NjYEgDw8POjq1asdbov2TANsamqipKQk8vb2JrFYTHZ2dhQUFERXrlzR5ikpKaFnnnmGZDIZDRgwgN5++21avHgxASAvL68W2yksLIzEYjG5urqShYUF2djY0NSpUyk3N7fDZR89epSsra0pPj7e4HYyZv97WHh4OInFYqqurtamHThwgFQqFQEgBwcHmj9/vt51Fy9erDOttq3jIrRPtdYviR68ygMAxcTEtLpvp06doqeeeopcXFx0zrWAgAD65ptvtPlu3bpFM2fOJDs7O5JKpTRq1CjKyMjQKeubb76hUaNGkVQqJRcXF1q8eDHV1tYanIeIaNKkSeTq6qrzVLix9JZptT1+DzrrhH3UdbcOHhYWRvb29qauRjOd1f9ycnLIwsKCtm/fbvSyO0tjYyONHTuWtm3bZuqqGKy4uJhkMhmtWbOmU8rvbudTe/GQFOsxHqU3inp5eSE2NhaxsbHa5xK6s8bGRqSnp6OyshKhoaGmro7BVqxYAX9/f4SHh5u6Kt0aBwzGuqno6GhMnz4doaGhBt8A72onTpzA/v37kZGRIfj5ke4iOTkZFy5cwNGjR7XPTjH9OGCwbm/JkiVIS0tDeXk5BgwYgH379pm6Sl0mISEB4eHh+OCDD0xdlVZNmDABn332mc67vHqCgwcPoq6uDidOnICdnZ2pq9PtWZi6Aoy1JTExEYmJiaauhslMnDgREydONHU1eqUpU6ZgypQppq5Gj8FXGIwxxgThgMEYY0wQDhiMMcYE4YDBGGNMEA4YjDHGBBER9ezvEbbnBX+MMWYKPfzXbc+fVtsZ3xRmj7a1a9cCABYsWGDimjDWvfT4KwzGjO2FF14AAOzZs8fENWGse+F7GIwxxgThgMEYY0wQDhiMMcYE4YDBGGNMEA4YjDHGBOGAwRhjTBAOGIwxxgThgMEYY0wQDhiMMcYE4YDBGGNMEA4YjDHGBOGAwRhjTBAOGIwxxgThgMEYY0wQDhiMMcYE4YDBGGNMEA4YjDHGBOGAwRhjTBAOGIwxxgThgMEYY0wQDhiMMcYE4YDBGGNMEA4YjDHGBOGAwRhjTBAOGIwxxgThgMEYY0wQDhiMMcYE4YDBGGNMEA4YjDHGBOGAwRhjTBAOGIwxxgThgMEYY0wQC1NXgDFTKi4uRkVFhU7a/fv3AQDXrl3TSbexsYGDg0OX1Y2x7kZERGTqSjBmKmlpaXj11VcF5f3kk0/wyiuvdHKNGOu+OGCwR1p5eTkcHR2hVqtbzScWi1FUVASlUtlFNWOs++F7GOyRplQq8fzzz8PCouXRWQsLC0yaNImDBXvkccBgj7zZs2ejsbGxxeVNTU2YPXt2F9aIse6Jh6TYI6+2thYODg7am92/pVAoUFxcDLlc3sU1Y6x74SsM9siTyWQIDg6GWCxutkwsFmPatGkcLBgDBwzGAAAvvvii3hvfarUaL774oglqxFj3w0NSjAFoaGiAk5MT7t27p5Nua2uLoqKiVm+KM/ao4CsMxvBgJtTMmTN1hqXEYjFmz57NwYKxX3HAYOxXM2fO1BmWUqvVmDlzpglrxFj3wkNSjP2KiNCvXz/cvn0bAODi4oLbt29DJBKZuGaMdQ98hcHYr0QiEV566SVIJBJIJBK8/PLLHCwYewhfYTD2kIsXL2LYsGHaf/v5+Zm4Rox1H73ybl5ycjJOnTpl6mqwHsrKygoAEBsba+KasJ5qzJgxiIyMNHU1jK5XDkmdOnUKp0+fNnU1HlmnT5/u0e3v4eGB/v37d+o28vPzsW/fvk7dBjON06dP99o/WHvlFQYAjB49Gnv37jV1NR5J06dPB4Ae2/6a72B4enp22jb27NmDGTNm9Ng2Yi3T9P/eqNcGDMbaqzMDBWM9Wa8ckmKMMWZ8HDAYY4wJwgGDMcaYIBwwGGOMCcIBowWvvfYarK2tIRKJcOHCBVNXp0usXr0aPj4+kMvlsLS0hI+PD5YvX46Kioour8vRo0ehVCrx97//vcu33RMcO3YM0dHR2L9/Pzw9PSESibRPqv/WxIkTYW1tDXNzcwwZMgQ//PCDCWosTFNTE9auXYuAgAC9y9VqNRITE+Hl5QWJRAJbW1sMHToUN27c0ObJzMzEU089BYVCARcXF0RFRaGurk6nnNbyHDp0CKtXr271K4yPKg4YLdi6dSs+/vhjU1ejS3333Xd4/fXXkZeXh4KCAsTFxWH16tWYNm1al9eFX0DQsvfffx/r16/HkiVLEBISgmvXrkGlUqFPnz7YsWMHjhw5opP/q6++wt69ezF58mRkZ2dj+PDhJqp563JycvBf//VfiIyMRHV1td48M2bMwKefforPPvsM1dXV+Omnn6BSqVBVVQUAyM7OxsSJEzFhwgQUFRXhwIED+OSTT/Dmm29qy2grT2BgIGQyGSZMmICysrLO3/GehHqhadOm0bRp0zpczs6dOwkAnT9/3gi16jrV1dU0ZswYg9cLCgqimpoanbTp06cTALpz547gcozV/qbU3jYUavfu3dSe0++DDz6ggQMHNjtOKpWKPvvsMzIzMyNXV1cqKyvTWZ6RkUFTpkzpUJ0704ULFyg4OJh27NhB/v7+NGzYsGZ5du7cSSKRiC5evNhiOTNmzKABAwZQU1OTNi0pKYlEIhH99NNPgvMQEYWHh9OYMWNIrVYbtC+9of+3hK8wWtFTXzy3bds2FBYWGrzegQMHIJPJdNJcXV0BQPsX3KOivW3YmX7++WcsX74cK1eubHacACAgIAARERG4ffs2Fi1aZIIatt+wYcOwf/9+zJo1C1KpVG+eTZs2Yfjw4S2+36uhoQFHjhzBuHHjdM7d5557DkSEgwcPCsqjsWLFCly4cAHr1q0z0l72fBwwfkVESEpKwqBBgyCVSqFUKrF48WLt8g8//BAKhQLW1tYoLCzEwoUL4erqiitXroCIkJycjMGDB0MqlcLOzg5Tp07F5cuXAQDr16+HTCZD3759MXfuXLi4uEAmkyEgIABnzpzRqUNr5YSHh0MikcDZ2Vm7zltvvQVLS0uIRCIUFxcjIiICCxcuRG5uLkQiEby8vDrULjk5ObC1tYWHh0eHyjFEZmYm3N3dIRKJsHHjRgBAamoqLC0toVAocPDgQTz33HOwsbGBm5sbdu7cCUBYO3ekDb/44gvY2NggISGhy9riYevXrwcRITAwsMU88fHxGDhwILZu3Ypjx461mK+tviakvQGgsbERMTExcHd3h1wux2OPPYbdu3cbb6d/VV9fj9OnT8Pf37/FPNeuXUNVVRXc3d110lUqFYAHL5MUkkfDzs4O48aNw7p163iIVMN0Fzedpz2XhEuXLiWRSEQfffQRlZaWUnV1NaWkpOgMSS1dupQA0DvvvEMbNmyg4OBg+umnnygmJoYkEglt376dysrK6OLFizR8+HBycHCgu3fvEhFRWFgYWVpa0qVLl6i2tpays7Np5MiRZG1tTXl5eUREgsqZNWsWOTk56dQ9KSmJAFBRUREREYWEhJBKpWp3+9XX11N+fj5t2LCBpFIpbd++3aD1jXFJfuvWLQJAGzZs0KZp2v/48eNUXl5OhYWFNHbsWLK0tKT6+noiEtbO7W3Dw4cPk7W1NcXGxnZo34jaNyTl6elJvr6+epepVCq6fv06ERGdPHmSzMzMqH///lRVVUVEzYekhPQ1Ie29aNEikkqltG/fPiotLaUlS5aQmZkZnT171tAm0XryySebDUldv36dAJC/vz+NHz+enJ2dSSqVko+PD23cuJGamprom2++IQCUlJTUrEy5XE4TJkwQlOdh0dHRBg9L85BUL1dTU4O1a9fi2WefRWRkJGxtbSGXy2Fvb683/6pVqzB//nzs378fHh4eSE5ORnBwMGbPng2lUgk/Pz9s3rwZxcXF2LJli3Y9CwsL7V90vr6+SE1NRWVlJdLS0lBTU/P/7N15VBRX2j/wbwG9QjeLsimLbGpcGaNGWo1xnDGv8YgiLmQ0M5jJb4gTwxCVIW5EUTQGRzwanYzG4bxRo6g4mhhxMpoBj2+Ik5xAUIwbKmIIAgZsoEGWfn5/GHpoAS2goViezzn9z61bt5663ObpqrpVJbqdjubp6QkPDw+sXbsW7733HubPn99p2xZDp9NBq9XC2dkZYWFhqKysxJ07d0zLn9TP7TF9+nTo9XqsWbOmvbvQapWVlbh165bpl/CTBAUF4a233sLt27fx9ttvN1ne2rHWUn9XV1dj165dCAkJQWhoKBwcHLB69WrIZLJ29/XjGk6JOjs7Iz4+Hjk5Obh37x5mzZqFJUuW4OOPPzbNcrK2tm6yvkwmQ1VVlag6jQUEBAAALl68aNH96a44YeDRuWGDwYApU6a0et2cnBxUVFRg9OjRZuVjxoyBXC43O+X0uNGjR0OtVuPKlSvtasfS8vPzUVRUhI8//hj/+7//i1/84hdd7nx+A7lcDgBmr1Z9XON+7q6KiopARFCr1aLqb9iwAYMGDcLOnTtx/vx5s2XtGWuN+/vq1aswGAwYNmyYablKpYKbm5vF+7rhusbQoUOh0+ng5OQEe3t7rFu3Dvb29ti9e7fpuk5dXV2T9WtqaqBSqUTVaayhv+/du2fR/emuOGHg0aOmgUe/XlqrYdpdwzsUGnNwcEB5efkT11coFCguLm53O5Ykk8ng7OyMqVOn4tChQ8jJycHGjRs7bfsdoaGfu6vq6moAaPGC8OOUSiWSkpIgCAJeffVVs1/OlhprlZWVAIDVq1eb7gMRBAF5eXktTottK3d3dwBASUmJWblcLoe3tzdyc3NN16Uev2/IYDCguroa7u7uouo01pBAGvq/t+OEAZh+dTx+c48YDg4OANDsl6ysrAweHh4trltbW2uq0552OpK/vz+sra2Rk5MjyfYtoXE/d1cN/7haczNZw0t8rl+/jvXr15vKLTXWGn5gJSYmgojMPpZ+H4SdnR0CAgJw+fLlJsvq6upgb28PHx8faDQa5OXlmS2/ceMGAGDEiBGi6jRWU1MDAE2OPHorThgAhg0bBisrK6Snp7dpXTs7O3zzzTdm5RcuXEBNTQ2effbZFtdNS0sDEWHcuHGi27GxsXni6Ze2un//Pn7zm980Kb9+/Trq6+vh6elp8W12lsb9DHRcH3YkFxcXCIKABw8etGq99evXY/DgwcjMzDSVtWfMNubp6QmlUtlpT0KYP38+MjMzTe8rAR4dGeTl5WH48OGwsbHBSy+9hHPnzsFoNJrqpKamQhAEBAcHi6rTWEN/u7q6dvDedQ+cMPDol1JoaCiOHj2KvXv3Qq/XIzs7W9SFZqVSiWXLluHYsWPYv38/9Ho9Ll68iMWLF8Pd3R0RERGmukajEaWlpairq0N2djaioqLg5eWF8PBw0e34+/vjp59+wvHjx1FbW4vi4uImv5acnJxQUFCA27dvo7y8XNQ/R1tbW3z++ef44osvoNfrUVtbi8zMTPzud7+Dra1tt3rd5JP6GWh7H6ampko2rVatVsPX19d0+lSshlNTjS/ytmbMPq3tRYsW4eDBg9i1axf0ej3q6+tx9+5d/PjjjwCAsLAwuLq6WuRxJEuXLoW3tzfCw8Nx584d3L9/HzExMaiqqjJd3F+zZg3u3buHd955B5WVlcjIyEBCQgLCw8MxaNAg0XUaNPQ3v9v9ZxLO0OowbZnWVl5eTq+99hr16dOH7OzsaMKECRQbG0sAyMPDgxYsWEAqlYoAkKenp9lUU6PRSAkJCRQQEEAymYwcHR0pJCSErl69aqoTERFBMpmM+vfvTzY2NqTVamnWrFmUm5vbqnbu379PkydPJqVSST4+PvTmm29SdHQ0ASB/f3+6c+cOffvtt+Tt7U0qlYomTJhgmib5NMHBweTj40N2dnakUCjIz8+PwsLC6OLFi63qy/ZOK9yxYwe5ubkRAFKr1RQcHEw7d+4ktVpNACggIIByc3Np9+7dpNVqCQB5e3vTtWvXRPVzW/vw1KlTpNFoaMOGDW3etwZtmVYbGRlJMpmMDAaDqezYsWPk5+dHAKhv3760ZMmSZteNjo42m1b7tLEmtr8fPnxIMTEx5OXlRTY2NuTs7EyhoaGUk5NDRI+eHgCAYmNjn7hvGRkZNH78eHJ3dycABIDc3NxIp9NRenq6qV5+fj69/PLL5OjoSAqFgsaOHUupqalmbaWnp9PYsWNJoVCQu7s7RUdHU3V1davrEBFNnz6d+vfvb3ZX+NP05Gm1nDA6SUREBDk5OUkdRqeQsv+7Sz+3JWFcv36dbGxsWn1fjJTq6+tp4sSJtHfvXqlDabWSkhJSKpW0ZcuWVq3XFf//WAqfkupE/PTLztFT+9nf3x9xcXGIi4vrFo9qqa+vx/Hjx1FeXo6wsDCpw2m1tWvXIjAwEJGRkVKH0mVwwujhrly5YjblsaVPd/xC90YrVqzA3LlzERYW1uoL4J0tLS0NKSkpSE1NFX3/SFexdetWZGVl4dSpU5DJZFKH02VwwugEK1euRFJSEh48eAAfHx8cPXq007Y9ePDgJlMem/scOnSo02LqKFL2c2eKj49HZGQkNm3aJHUoTzRlyhQcOHDA7Lld3cGJEyfw8OFDpKWlwdHRUepwuhSBqOc9VWvu3LkAgCNHjkgcSe/E/f90hw8fxvz58/mhdj1QTx7/fITBGGNMFE4YjDHGROGEwRhjTBROGIwxxkThhMEYY0wUG6kD6ChHjx7ttu/k7im4/5+O+6hnmjNnjtQhdIgemzDGjRuHt956S+oweqXExEQA4P5/goyMDGzbtq1D3n/NpNUw/nuiHpswPDw8MG/ePKnD6JUa5p9z/z/Ztm3buI96oJ54/0UDvobBGGNMFE4YjDHGROGEwRhjTBROGIwxxkThhMEYY0wUThhPkJKSAl9f3ybvjpDL5XBxccELL7yAhIQElJaWSh0q62XOnDmDFStWNBmjr7zySpO6U6dOhUajgbW1NYYOHWqR92t3FKPRiMTEROh0umaX19bWYuPGjfD394dcLoeDgwOGDRuG27dvm+qcP38e48ePh1qthru7O2JiYvDw4UOzdp5U55NPPsHmzZt77Iu42kWaF/11LEu/ItHPz4/s7e2J6NG7kEtLS+nf//43hYeHkyAI5O7uTl9//bXFttfd9eRXVFpKW17R2iA2NpZmzJhBer3eVObn50d9+vQhAHTy5Mkm66Smppq907srunbtGo0fP54A0MiRI5utExISQoMGDaKvvvqKamtrqaCggIKDg03vnb906RKpVCpas2YNVVRU0Jdffkl9+/alRYsWmdoQU2fbtm00adIkKi0tbfV+9OTxzwlDhMYJ43FHjhwhKysrcnFxobKyMottszMYDAYKCgqyeLtSfmE6ap8s3XZbE8amTZto4MCBVFVVZVbu5+dHBw4cICsrK+rfv3+TsdjVE0ZWVhbNnj2b9u/fT4GBgc0mjIMHD5IgCJSdnd1iO/PnzycfHx8yGo2msoSEBBIEgb7//nvRdYiIIiMjKSgoiGpra1u1Lz05YfApqXaaM2cOwsPDUVRUhA8++EDqcFpl7969KCoqkjoMi+rIfZK6v27cuIE1a9Zg3bp1UCqVTZbrdDpERUXhhx9+wPLlyyWIsO1GjhyJlJQULFiwAAqFotk6f/3rXzFq1CgMHz682eV1dXX47LPPMGnSJLNHrkybNg1EhBMnToiq02Dt2rXIysrCtm3bLLSX3R8nDAsIDw8HAKSmpuK9996DWq2GRqNBUVERli1bhv79++Pq1asgImzduhXPPPMMFAoFHB0dMWvWLFy5cgUAsH37diiVSri4uOD111+Hu7s7lEoldDodLly4YNre09qJjIyEXC43ezXmG2+8AVtbWwiCgJKSEkRFRWHZsmXIzc2FIAjw9/fvvA5rRkftk5g+bU9/nT59GlqtFvHx8R3eR9u3bwcRITg4uMU6GzZswMCBA/Hhhx/izJkzLdZ7Wn/v2rULtra2UKvVOHHiBKZNmwatVgsPDw8cPHjQ1E59fT1iY2Ph5eUFlUqFESNGdMjjTmpqavDVV18hMDCwxTo3b95ERUUFvLy8zMr9/PwAANnZ2aLqNHB0dMSkSZOwbds2fjNiA+kObjpOZ56SIiLS6/UEgDw9PYmIaNWqVQSA/vSnP9GOHTto9uzZ9P3331NsbCzJ5XLat28flZWVUXZ2No0aNYr69u1LhYWFREQUERFBtra2dPnyZaqurqacnBwaM2YMaTQaunPnDhGRqHYWLFhArq6uZnEmJCQQACouLiYiotDQUPLz87NYPzVoS/935D6J6dO2tn3y5EnSaDQUFxfXqv1tyykpX19fGjJkSLPL/Pz86NatW0RE9OWXX5KVlRUNGDCAKioqiKjpKSkx/d0wjs+ePUsPHjygoqIimjhxItna2lJNTQ0RES1fvpwUCgUdPXqUSktLaeXKlWRlZdWua3rPPfdck1NSt27dIgAUGBhIL7zwArm5uZFCoaDBgwfT+++/T0ajkdLT0wkAJSQkNGlTpVLRlClTRNVpbMWKFQSAMjMzRcfPp6TYE2k0GgiCgPLycrPyd999F0uWLEFKSgq8vb2xdetWzJ49GwsXLoS9vT2GDx+ODz74ACUlJdi9e7dpPRsbG9MvvyFDhmDXrl0oLy9HUlISqqqqRLfTXXTGPj2pT9tj+vTp0Ov1WLNmTbtjfJLKykrcunXL9Ev4SYKCgvDWW2/h9u3bePvtt5ssb21/63Q6aLVaODs7IywsDJWVlbhz5w6qq6uxa9cuhISEIDQ0FA4ODli9ejVkMlm7+/VxFRUVAABnZ2fEx8cjJycH9+7dw6xZs7BkyRJ8/PHHpllO1tbWTdaXyWSoqqoSVaexgIAAAMDFixctuj/dFScMC6isrAQRQavVtlgnJycHFRUVGD16tFn5mDFjIJfLzU45PW706NFQq9W4cuVKu9rpqqTYp8Z92h0UFRWBiKBWq0XV37BhAwYNGoSdO3fi/PnzZsva099yuRzAo+mtV69ehcFgwLBhw0zLVSoV3NzcLN6vDdc1hg4dCp1OBycnJ9jb22PdunWwt7fH7t27Tdd16urqmqxfU1MDlUolqk5jDf197949i+5Pd8UJwwKuXbsGABg8eHCLdcrKygAAdnZ2TZY5ODg0OTp5nEKhQHFxcbvb6Yqk2qeGPu0OqqurAaDFC8KPUyqVSEpKgiAIePXVV81+OVuqvysrKwEAq1evNrtPKS8vDwaDQVQbYrm7uwMASkpKzMrlcjm8vb2Rm5trugal1+vN6hgMBlRXV8Pd3V1UncYaEkhD//d2nDAs4PTp0wAezbRoiYODAwA0+2UsKyuDh4dHi+vW1taa6rSnna5Kin1q3KfdQcM/rtbcTBYUFISlS5fi+vXrWL9+vancUv3t7OwM4NH7H+jRFH3TJyMjQ3ScYtjZ2SEgIACXL19usqyurg729vbw8fGBRqNBXl6e2fIbN24AAEaMGCGqTmM1NTUA0OTIo7fihNFOhYWFSExMhIeHB1599dUW6w0bNgx2dnb45ptvzMovXLiAmpoaPPvssy2um5aWBiLCuHHjRLdjY2OD2traduxZ55Finxr3qaXb7gguLi4QBAEPHjxo1Xrr16/H4MGDkZmZaSprz1hszNPTE0qlEllZWa2Kqa3mz5+PzMxM3Lx501RmMBiQl5eH4cOHw8bGBi+99BLOnTsHo9FoqpOamgpBEBAcHCyqTmMN/e3q6trBe9c9cMIQiYhQUVEBo9EIIkJxcTGSk5Mxfvx4WFtb4/jx40+8hqFUKrFs2TIcO3YM+/fvh16vx8WLF7F48WK4u7sjIiLCVNdoNKK0tBR1dXXIzs5GVFQUvLy8EB4eLrodf39//PTTTzh+/Dhqa2tRXFzc5FeVk5MTCgoKcPv2bZSXl0v2D7Mz9ulJfdqetlNTUztlWq1arYavry/u3r3bqvUaTk01vsjbmrH4tLYXLVqEgwcPYteuXdDr9aivr8fdu3fx448/AgDCwsLg6upqkceRLF26FN7e3ggPD8edO3dw//59xMTEoKqqynRxf82aNbh37x7eeecdVFZWIiMjAwkJCQgPD8egQYNE12nQ0N8t3fvR63mDKzUAACAASURBVEg1PasjWWpa2yeffEIjRowgtVpNcrmcrKysCAAJgkAODg40duxYiouLo/v375vW2bx5M6lUKtM023379pmWGY1GSkhIoICAAJLJZOTo6EghISF09epVU52IiAiSyWTUv39/srGxIa1WS7NmzaLc3NxWtXP//n2aPHkyKZVK8vHxoTfffJOio6MJAPn7+9OdO3fo22+/JW9vb1KpVDRhwgTTdMr2akv/d+Q+ienTtrZ96tQp0mg0tGHDhlbtb1um1UZGRpJMJiODwWAqO3bsGPn5+REA6tu3Ly1ZsqTZdaOjo82m1T6tv3fu3ElqtZoAUEBAAOXm5tLu3btJq9USAPL29qZr167Rw4cPKSYmhry8vMjGxoacnZ0pNDSUcnJyiOjRozwAUGxs7BP3LSMjg8aPH0/u7u4EgACQm5sb6XQ6Sk9PN9XLz8+nl19+mRwdHUmhUNDYsWMpNTXVrK309HQaO3YsKRQKcnd3p+joaKqurm51HSKi6dOnU//+/c3uCn+anjytlhNGFxMREUFOTk5Sh9EuXa3/u2KftiVhXL9+nWxsbMx+hHR19fX1NHHiRNq7d6/UobRaSUkJKZVK2rJlS6vW62rj35L4lFQXxE/JtLye0Kf+/v6Ii4tDXFyc6b6Erqy+vh7Hjx9HeXk5wsLCpA6n1dauXYvAwEBERkZKHUqXwQmDsW5kxYoVmDt3LsLCwlp9AbyzpaWlISUlBampqaLvH+kqtm7diqysLJw6dQoymUzqcLoMThhdyMqVK5GUlIQHDx7Ax8cHR48elTqkbq8n9ml8fDwiIyOxadMmqUN5oilTpuDAgQNmz+jqDk6cOIGHDx8iLS0Njo6OUofTpQhEPe+pWnPnzgUAHDlyROJIeifu/6c7fPgw5s+fzw+164F68vjnIwzGGGOicMJgjDEmCicMxhhjonDCYIwxJoqN1AF0lLt37+Lw4cNSh9ErNTxOgfu/ZQ0P5+M+6nnu3r3bbR5q2Vo9dpZUT5g+yRjrnubMmdMjZ0n1yITBWHvMmzcPAP/6Z+xxfA2DMcaYKJwwGGOMicIJgzHGmCicMBhjjInCCYMxxpgonDAYY4yJwgmDMcaYKJwwGGOMicIJgzHGmCicMBhjjInCCYMxxpgonDAYY4yJwgmDMcaYKJwwGGOMicIJgzHGmCicMBhjjInCCYMxxpgonDAYY4yJwgmDMcaYKJwwGGOMicIJgzHGmCicMBhjjInCCYMxxpgonDAYY4yJwgmDMcaYKJwwGGOMicIJgzHGmCicMBhjjInCCYMxxpgonDAYY4yJwgmDMcaYKJwwGGOMicIJgzHGmCg2UgfAmJTOnTuHjIwMs7IrV64AADZv3mxWHhQUhOeff77TYmOsqxGIiKQOgjGpnD17Fr/61a8gk8lgZdX8AbfRaERtbS3OnDmDKVOmdHKEjHUdnDBYr2Y0GuHm5obi4uIn1uvbty8KCwthbW3dSZEx1vXwNQzWq1lZWWHBggWQy+Ut1pHL5Vi4cCEnC9brccJgvd7LL7+MmpqaFpfX1NTg5Zdf7sSIGOua+JQUYwAGDBiAvLy8Zpd5enoiLy8PgiB0clSMdS18hMEYgFdeeQUymaxJuUwmQ3h4OCcLxsBHGIwBeDSV9plnnml22aVLlzB06NBOjoixroePMBgDMHjwYAwdOrTJkcSQIUM4WTD2M04YjP3st7/9rdlMKJlMht/97ncSRsRY18KnpBj7WX5+Pry9vdHwlRAEATdv3sSAAQOkDYyxLoKPMBj7maenJ5577jlYWVnBysoKzz33HCcLxhrhhMFYI6+88goEQYCVlRVeeeUVqcNhrEvhU1KMNVJSUgI3NzcAQEFBAVxcXCSOiLGuo9cljMOHD2P+/PlSh8EY6+aSk5Mxb948qcPoVL328ebJyclSh9CtzZ8/H1FRUQgKCpI6FIs7d+4cBEHAxIkT29VOYmIiAOCtt96yRFisC+mtPzp7bcLobb8MLG3+/PkICgrqkf04bdo0AIBGo2lXO0eOHAHAY60n4oTBGAPQ/kTBWE/Fs6QYY4yJwgmDMcaYKJwwGGOMicIJgzHGmCicMNrgtddeg0ajgSAIyMrKkjqcNquursbgwYOxevVqSbZ/6tQp2Nvb49NPP5Vk+13dmTNnsGLFCqSkpMDX1xeCIEAQhGbvQJ86dSo0Gg2sra0xdOhQfPvttxJELI7RaERiYiJ0Ol2zy2tra7Fx40b4+/tDLpfDwcEBw4YNw+3bt011zp8/j/Hjx0OtVsPd3R0xMTF4+PChWTtPqvPJJ59g8+bNqK+v77D97Ik4YbTBhx9+iD179kgdRrutWrUKV69elWz7veye0VZ55513sH37dqxcuRKhoaG4efMm/Pz80KdPH+zfvx+fffaZWf3PP/8cR44cwYwZM5CTk4NRo0ZJFPmTXb9+Hc8//zyWLl0Kg8HQbJ358+fjo48+woEDB2AwGPD999/Dz88PFRUVAICcnBxMnToVU6ZMQXFxMY4dO4a///3vWLx4samNp9UJDg6GUqnElClTUFZW1vE73lNQL5OcnEyW2O2DBw8SAMrMzLRAVJ3v//7v/2jq1KkEgFatWtXq9QFQcnJyB0TWeQwGAwUFBXVY+3PmzKE5c+a0er1NmzbRwIEDqaqqyqzcz8+PDhw4QFZWVtS/f38qKyszW56amkozZ85sV8wdKSsri2bPnk379++nwMBAGjlyZJM6Bw8eJEEQKDs7u8V25s+fTz4+PmQ0Gk1lCQkJJAgCff/996LrEBFFRkZSUFAQ1dbWtmpfesL4bws+wmij7vzKzqqqKkRHR2Pbtm1ShyKpvXv3oqioSOowzNy4cQNr1qzBunXroFQqmyzX6XSIiorCDz/8gOXLl0sQYduNHDkSKSkpWLBgARQKRbN1/vrXv2LUqFEYPnx4s8vr6urw2WefYdKkSWbfwWnTpoGIcOLECVF1GqxduxZZWVm9/rsgFicMEYgICQkJGDRoEBQKBezt7REdHW1Wp76+HrGxsfDy8oJKpcKIESNMjx/ZtWsXbG1toVarceLECUybNg1arRYeHh44ePCgqY309HSMHTsWarUaWq0Ww4cPh16vf2r7rbVq1Sq88cYbcHZ2bmOPtN/58+fh5eUFQRDw/vvvAxDXT9u3b4dSqYSLiwtef/11uLu7Q6lUQqfT4cKFCwCAyMhIyOVy00MEAeCNN96Ara0tBEFASUkJoqKisGzZMuTm5kIQBPj7+wMATp8+Da1Wi/j4+E7uEZj2j4gQHBzcYp0NGzZg4MCB+PDDD3HmzJkW6xERtm7dimeeeQYKhQKOjo6YNWsWrly5AkD8uLTk2HuSmpoafPXVVwgMDGyxzs2bN1FRUQEvLy+zcj8/PwBAdna2qDoNHB0dMWnSJGzbto1PkYoh5eGNFNpySmrVqlUkCAL95S9/odLSUjIYDLRz506zU1LLly8nhUJBR48epdLSUlq5ciVZWVnR119/bWoDAJ09e5YePHhARUVFNHHiRLK1taWamhqqqKggrVZLmzdvpqqqKiosLKTZs2dTcXGxqPbFOn/+PAUHBxMRUXFxsaSnpPLz8wkA7dixw1T2tH4iIoqIiCBbW1u6fPkyVVdXU05ODo0ZM4Y0Gg3duXOHiIgWLFhArq6uZttLSEggAKY+DQ0NJT8/P7M6J0+eJI1GQ3Fxce3aN6K2nZLy9fWlIUOGNLvMz8+Pbt26RUREX375JVlZWdGAAQOooqKCiJqekoqNjSW5XE779u2jsrIyys7OplGjRlHfvn2psLCQiMT1t6XGXmPPPfdck1NSt27dIgAUGBhIL7zwArm5uZFCoaDBgwfT+++/T0ajkdLT0wkAJSQkNGlTpVLRlClTRNVpbMWKFa0+vWyJ8d8d8RHGU1RVVSExMRG/+tWvsHTpUjg4OEClUsHJyclUp7q6Grt27UJISAhCQ0Ph4OCA1atXQyaTISkpyaw9nU4HrVYLZ2dnhIWFobKyEnfu3MHt27eh1+sxdOhQKJVKuLq6IiUlBX379m1V+0/bl6ioKOzatcti/dNRWuqnBjY2NqZfzkOGDMGuXbtQXl7eqv5ozvTp06HX67FmzZr27kKrVVZW4tatW6Zfwk8SFBSEt956C7dv38bbb7/dZHlVVRW2bt2K2bNnY+HChbC3t8fw4cPxwQcfoKSkBLt37zar31J/W2rsidFwUdvZ2Rnx8fHIycnBvXv3MGvWLCxZsgQff/yxaZZT41fpNpDJZKiqqhJVp7GAgAAAwMWLFy26Pz0RJ4ynuHHjBgwGA6ZMmdJinatXr8JgMGDYsGGmMpVKBTc3N9Phf3PkcjmAR9MIfX194eLigoULF2Lt2rVmUwjb2v7jVq5ciT/84Q/o37+/6HW6gsb91JLRo0dDrVa3qj+6mqKiIhAR1Gq1qPobNmzAoEGDsHPnTpw/f95sWU5ODioqKjB69Giz8jFjxkAul5tO3zWncX9bauyJ0XBdY+jQodDpdHBycoK9vT3WrVsHe3t77N6923Rdp66ursn6NTU1UKlUouo01tDf9+7ds+j+9EScMJ7i7t27APDE8/2VlZUAgNWrV5vmyguCgLy8vBanDj5OpVLhiy++wIQJExAfHw9fX1+EhYWhqqrKIu2fP38eFy9exGuvvSaqfnekUChQXFwsdRhtVl1dDQAtXhB+nFKpRFJSEgRBwKuvvmr2y7lhqqidnV2T9RwcHFBeXi5qG5YYe2K5u7sDePQSq8bkcjm8vb2Rm5trui7VcG2vgcFgQHV1Ndzd3UXVaawhgTT0P2sZJ4ynaPi18vhNQY01JJPExEQQkdknIyND9LaGDh2KTz/9FAUFBYiJiUFycjK2bNlikfb37t2Ls2fPwsrKyvSlb2g3Pj4egiDgm2++ER1rV1NbW4uysjJ4eHhIHUqbNfzjas3NZEFBQVi6dCmuX7+O9evXm8odHBwAoNnE0Jp+stTYFsPOzg4BAQG4fPlyk2V1dXWwt7eHj48PNBoN8vLyzJbfuHEDADBixAhRdRqrqakBgCZHHqwpThhPMWzYMFhZWSE9Pb3FOp6enlAqle2667ugoMD0RXF2dsamTZswatQoXL582SLtJyUlNfnCN/waX7VqFYioyemL7iQtLQ1EhHHjxgF4dI3jSaewuiIXFxcIgoAHDx60ar3169dj8ODByMzMNJUNGzYMdnZ2TX4EXLhwATU1NXj22WdFtW2Jsdca8+fPR2ZmJm7evGkqMxgMyMvLw/Dhw2FjY4OXXnoJ586dg9FoNNVJTU2FIAgIDg4WVaexhv52dXXt4L3r/jhhPIWzszNCQ0Nx9OhR7N27F3q9HtnZ2WYXDZVKJRYtWoSDBw9i165d0Ov1qK+vx927d/Hjjz+K2k5BQQFef/11XLlyBTU1NcjMzEReXh7GjRtnkfZ7GqPRiNLSUtTV1SE7OxtRUVHw8vJCeHg4AMDf3x8//fQTjh8/jtraWhQXFzf5xenk5ISCggLcvn0b5eXlqK2tRWpqqmTTatVqNXx9fU2nQcVqODXV+CKvUqnEsmXLcOzYMezfvx96vR4XL17E4sWL4e7ujoiICNFtP23shYWFwdXV1SKPI1m6dCm8vb0RHh6OO3fu4P79+4iJiUFVVZXp4v6aNWtw7949vPPOO6isrERGRgYSEhIQHh6OQYMGia7ToKG/W7r3gzXSybOyJNeWabXl5eX02muvUZ8+fcjOzo4mTJhAsbGxBIA8PDzou+++o4cPH1JMTAx5eXmRjY0NOTs7U2hoKOXk5NDOnTtJrVYTAAoICKDc3FzavXs3abVaAkDe3t70r3/9i3Q6HTk6OpK1tTX169ePVq1aRXV1dURET2y/raScVrtjxw5yc3MjAKRWqyk4OFhUP127do0iIiJIJpNR//79ycbGhrRaLc2aNYtyc3NN7d+/f58mT55MSqWSfHx86M0336To6GgCQP7+/nTnzh369ttvydvbm1QqFU2YMIEKCwvp1KlTpNFoaMOGDW3etwZtmVYbGRlJMpmMDAaDqezYsWPk5+dHAKhv3760ZMmSZteNjo42m1ZrNBopISGBAgICSCaTkaOjI4WEhNDVq1eJiET399PGXkhICAGg2NjYJ+5bRkYGjR8/ntzd3QkAASA3NzfS6XSUnp5uqpefn08vv/wyOTo6kkKhoLFjx1JqaqpZW+np6TR27FhSKBTk7u5O0dHRVF1d3eo6RETTp0+n/v37m90V/jTtHf/dFScM1iZSfmEiIiLIyclJkm23RlsSxvXr18nGxob27dvXQVFZXn19PU2cOJH27t0rdSitVlJSQkqlkrZs2dKq9XprwuBTUqxb6qlPGfX390dcXBzi4uJM9yV0ZfX19Th+/DjKy8sRFhYmdTittnbtWgQGBiIyMlLqULoFThjd3JUrV8ymO7b06Y5f5t5qxYoVmDt3LsLCwlp9AbyzpaWlISUlBampqaLvH+kqtm7diqysLJw6dQoymUzqcLoFThjd3ODBg5vMfmruc+jQIalDtYiVK1ciKSkJDx48gI+PD44ePSp1SB0iPj4ekZGR2LRpk9ShPNGUKVNw4MABs+d2dQcnTpzAw4cPkZaWBkdHR6nD6TZspA6AsdbYuHEjNm7cKHUYnWLq1KmYOnWq1GH0SDNnzsTMmTOlDqPb4SMMxhhjonDCYIwxJgonDMYYY6JwwmCMMSZKr73offjwYalD6PYs/fC5nqbhkRM81lhPIRD1rvcSHj58GPPnz5c6DMZYN5ecnIx58+ZJHUan6rVHGL0sT1qcIAi98gvTGnPnzgUAHDlyROJImKUJgiB1CJLgaxiMMcZE4YTBGGNMFE4YjDHGROGEwRhjTBROGIwxxkThhMEYY0wUThjtkJKSAl9f3ybvnpDL5XBxccELL7yAhIQElJaWSh0q6wHOnDmDFStWNBl3r7zySpO6U6dOhUajgbW1NYYOHWqR9213FKPRiMTEROh0uibL4uLiMGTIEGi1WigUCvj7++PPf/5zk5dLffzxxxgzZgw0Gg28vb2xaNEiFBYWAgA++eQTbN68uce+dKtTSfOiP+l0xCta/fz8yN7enogevUe5tLSU/v3vf1N4eDgJgkDu7u709ddfW3SbUkMvfUVla7TlFa0tiY2NpRkzZpBerzeV+fn5UZ8+fQgAnTx5ssk6qampZu/47oquXbtG48ePJwA0cuTIJssnTZpEO3fupPv375Ner6fk5GSSyWT0P//zP6Y6hw4dIgC0efNmKisro8zMTPL19aXAwECqra0lIqJt27bRpEmTqLS01CJx99bxz0cYFiYIAhwcHPDCCy8gKSkJhw8fxr179zB9+vQu//a07qCqqqrZX6Jdve32ePfdd3Ho0CEcPnwYGo3GbNn27dthZWWFiIiIbje+vvvuO7z99ttYvHgxAgMDm61jZ2eHiIgIODk5QaPRYN68eQgJCcHp06eRn58PAPjb3/6Gfv36ITo6Gvb29ggMDMTSpUuRlZWFCxcuAAD+9Kc/YeTIkXjppZdQV1fXafvY03DC6GBz5sxBeHg4ioqK8MEHH0gdTre3d+9eFBUVdbu22+rGjRtYs2YN1q1bB6VS2WS5TqdDVFQUfvjhByxfvlyCCNtu5MiRSElJwYIFC6BQKJqtc/LkSVhbW5uV9e3bFwBgMBgAAPn5+XB3dze7+9rT0xMAkJeXZypbu3YtsrKysG3bNovuR2/CCaMThIeHAwBSU1MBAPX19YiNjYWXlxdUKhVGjBiB5ORkAMCuXbtga2sLtVqNEydOYNq0adBqtfDw8MDBgwdNbaanp2Ps2LFQq9XQarUYPnw49Hr9U9uXChFh69ateOaZZ6BQKODo6IhZs2bhypUrAIDIyEjI5XKzV32+8cYbsLW1hSAIKCkpQVRUFJYtW4bc3FwIggB/f39s374dSqUSLi4ueP311+Hu7g6lUgmdTmf6ddnWtgHg9OnT0Gq1iI+P78Te+q/t27eDiBAcHNxinQ0bNmDgwIH48MMPcebMmRbrPe1vIHbsST2+fvjhB6hUKvj4+AAAfH19myT6husXvr6+pjJHR0dMmjQJ27Zt40cDtZW0Z8Q6X0dfw2iOXq8nAOTp6UlERMuXLyeFQkFHjx6l0tJSWrlyJVlZWZmuc6xatYoA0NmzZ+nBgwdUVFREEydOJFtbW6qpqaGKigrSarW0efNmqqqqosLCQpo9ezYVFxeLat8S0MpzuLGxsSSXy2nfvn1UVlZG2dnZNGrUKOrbty8VFhYSEdGCBQvI1dXVbL2EhAQCYNq30NBQ8vPzM6sTERFBtra2dPnyZaqurqacnBwaM2YMaTQaunPnTrvaPnnyJGk0GoqLixO9rw0scQ3D19eXhgwZ0uwyPz8/unXrFhERffnll2RlZUUDBgygiooKImp6DUPM3+BpY4+oY8bXc8891+w1jMdVVlaSRqOhyMhIU1laWhrJZDLavn076fV6unTpEj3zzDP04osvNll/xYoVBIAyMzPbHCtR772GwQnDAp6WMIiIBEEgBwcHqqqqIrVaTWFhYaZlBoOBFAoF/fGPfySi/35pq6qqTHV27txJAOjGjRt06dKlFi90imnfElrzhTEYDGRnZ2cWExHRf/7zHwJg+mfcnoTxeP9//fXXBIDWrVvXrrbbo70Jo6KiggRBoBkzZjS7vHHCICJatmwZAaAlS5YQkXnCEPs3eNrY66jxJTZhrFq1igYOHGh28Z+IaPXq1QTA9PHw8KD8/Pwm6//9738nAPTRRx+1OVai3psw+JRUJ6isrAQRQavV4urVqzAYDBg2bJhpuUqlgpubm+nUQHPkcjkAoLa2Fr6+vnBxccHChQuxdu1a3L5921Svre13pJycHFRUVGD06NFm5WPGjIFcLjedOrKk0aNHQ61WS7bPllBUVAQiglqtFlV/w4YNGDRoEHbu3Inz58+bLWvP36Dx2JNyfB07dgyHDx/GP//5T7OL/6tWrcLu3btx9uxZVFRU4ObNm9DpdAgKCjJdGG/Q0Jf37t3r0Fh7Kk4YneDatWsAgMGDB6OyshIAsHr1arN7N/Ly8kwX8Z5GpVLhiy++wIQJExAfHw9fX1+EhYWhqqrKIu1bWllZGYBHM14e5+DggPLy8g7ZrkKhQHFxcYe03Rmqq6sBoMULwo9TKpVISkqCIAh49dVXUVVVZVpmqb+BVOPr0KFDePfdd5GWloYBAwaYyn/88Uds3rwZf/jDH/DLX/4Stra28PHxwZ49e1BQUICEhASzdlQqFYD/9i1rHU4YneD06dMAgGnTpsHZ2RkAkJiYCHp0StD0ac0b7IYOHYpPP/0UBQUFiImJQXJyMrZs2WKx9i3JwcEBAJr9p1RWVgYPDw+Lb7O2trbD2u4sDf/cWnPDWVBQEJYuXYrr169j/fr1pnJL/Q2kGF87duzA/v378cUXX6Bfv35my65fv476+vom5VqtFk5OTsjJyTErr6mpAfDfvmWtwwmjgxUWFiIxMREeHh549dVX4enpCaVSiaysrDa3WVBQgMuXLwN49AXetGkTRo0ahcuXL1ukfUsbNmwY7Ozs8M0335iVX7hwATU1NXj22WcBADY2NqitrbXINtPS0kBEGDdunMXb7iwuLi4QBKHV91esX78egwcPRmZmpqlM7N/gaTpzfBERYmJicPHiRRw/frzZo6OGRPfjjz+alZeXl+Onn34yTa9t0NCXrq6uHRR1z8YJw0KICBUVFTAajSAiFBcXIzk5GePHj4e1tTWOHz8OrVYLpVKJRYsW4eDBg9i1axf0ej3q6+tx9+7dJoO+JQUFBXj99ddx5coV1NTUIDMzE3l5eRg3bpxF2rc0pVKJZcuW4dixY9i/fz/0ej0uXryIxYsXw93dHREREQAAf39//PTTTzh+/Dhqa2tRXFxsNo8eAJycnFBQUIDbt2+jvLzclASMRiNKS0tRV1eH7OxsREVFwcvLyzSlua1tp6amSjatVq1Ww9fX1/RucLEaTk01vn9B7N9ATNtPG19hYWFwdXVt9+NILl++jPfeew979uyBTCZr8gieLVu2wMfHB5MnT8aePXtw7tw5VFVVIT8/37Q/v//9783abOjL4cOHtyu2Xquzr7JLzZKzpD755BMaMWIEqdVqksvlZGVlRQBMM6LGjh1LcXFxdP/+fbP1Hj58SDExMeTl5UU2Njbk7OxMoaGhlJOTQzt37iS1Wk0AKCAggHJzc2n37t2k1WoJAHl7e9O//vUv0ul05OjoSNbW1tSvXz9atWoV1dXVPbV9S0ErZ4kYjUZKSEiggIAAkslk5OjoSCEhIXT16lVTnfv379PkyZNJqVSSj48PvfnmmxQdHU0AyN/fn+7cuUPffvsteXt7k0qlogkTJlBhYSFFRESQTCaj/v37k42NDWm1Wpo1axbl5ua2u+1Tp06RRqOhDRs2tLqPLDGtNjIykmQyGRkMBlPZsWPHyM/PjwBQ3759TbOiHhcdHW02rfZpfwMxY+/atWtPHV8hISEEgGJjY5+4bxkZGTR+/Hhyd3c3zW5yc3MjnU5H6enpdPHiRbOZT49/EhISiIiopKSEoqKiyN/fnxQKBdnZ2dH48ePpH//4R5NtTp8+nfr3709Go7F1f4jHtHb89xScMFibdKUvTEREBDk5OUkdRhOWSBjXr18nGxsb2rdvn4Wi6nj19fU0ceJE2rt3r9ShmCkpKSGlUklbtmxpd1tdafx3Jj4lxXqEnvokUn9/f8TFxSEuLq7JE1q7ovr6ehw/fhzl5eUICwuTOhwza9euRWBgICIjI6UOpdvihMFYF7dixQrMnTsXYWFhXf4Bg2lpaUhJSUFqaqro+0c6w9atW5GVlYVTp05BJpNJHU63xQmDdWsrV65EUlISHjx4AB8fHxw9elTqkDpEfHw8IiMjsWnTJqlDeaIpU6bgwIEDZs/tktqJEyfw8OFDpKWlwdHRUepwujUb5QZ5HQAAHXZJREFUqQNgrD02btyIjRs3Sh1Gp5g6dSqmTp0qdRjdzsyZMzFz5kypw+gR+AiDMcaYKJwwGGOMicIJgzHGmCicMBhjjInSay96z507V+oQur3ExEQcOXJE6jC6rK+++goAjzXWcwhEvetdhRkZGdi6davUYbAu7OLFiwD4eUPsyZYuXYqgoCCpw+hUvS5hMPY08+bNAwAcPnxY4kgY61r4GgZjjDFROGEwxhgThRMGY4wxUThhMMYYE4UTBmOMMVE4YTDGGBOFEwZjjDFROGEwxhgThRMGY4wxUThhMMYYE4UTBmOMMVE4YTDGGBOFEwZjjDFROGEwxhgThRMGY4wxUThhMMYYE4UTBmOMMVE4YTDGGBOFEwZjjDFROGEwxhgThRMGY4wxUThhMMYYE4UTBmOMMVE4YTDGGBOFEwZjjDFROGEwxhgThRMGY4wxUThhMMYYE4UTBmOMMVE4YTDGGBOFEwZjjDFROGEwxhgThRMGY4wxUQQiIqmDYEwqH330EbZu3Yr6+npTWUlJCQCgb9++pjJra2ssXboUv/3tbzs9Rsa6Ck4YrFe7du0aBg0aJKru1atXMXDgwA6OiLGui09JsV5t4MCBGDlyJARBaLGOIAgYOXIkJwvW63HCYL3eb3/7W1hbW7e43MbGBr/73e86MSLGuiY+JcV6vYKCAnh6esJoNDa7XBAE5Ofno3///p0cGWNdCx9hsF6vX79+0Ol0sLJq+nWwsrLC+PHjOVkwBk4YjAEAXnnllWbLBUHgmVGM/YxPSTEGoLS0FK6urqitrTUrt7GxQWFhIfr06SNRZIx1HXyEwRgAR0dH/PrXvza7+G1tbY0XX3yRkwVjP+OEwdjPFi5caHbhm4iwcOFCCSNirGvhU1KM/cxgMKBPnz6orq4GACiVSpSUlMDW1lbiyBjrGvgIg7GfqdVqhISEQCaTQSaTISQkhJMFY41wwmCskd/85jeora1FbW0tfvOb30gdDmNdio3UAUjt7t27+PLLL6UOg3UR9fX1UKvVICLo9XocPnxY6pBYF6HT6eDh4SF1GJLq9dcwDh8+jPnz50sdBmOsi0tOTsa8efOkDkNSvf4Io0Evz5vtJghCj/lCpaenQxAEPP/88xZtd+7cuQCAI0eOWLRd1vGe9HDK3oQTBmOPmThxotQhMNYlccJg7DHNPVOKMcazpBhjjInECYMxxpgonDAYY4yJwgmDMcaYKJwwLOC1116DRqOBIAjIysqSOhzRNmzYAEEQmnyGDRsmSTynTp2Cvb09Pv30U0m239WdOXMGK1asQEpKCnx9fU1/r+be5TF16lRoNBpYW1tj6NCh+PbbbyWIWByj0YjExETodLomy+Li4jBkyBBotVooFAr4+/vjz3/+MyoqKszqffzxxxgzZgw0Gg28vb2xaNEiFBYWAgA++eQTbN68GfX19Z2yPz0ZJwwL+PDDD7Fnzx6pw+j2+F6Ylr3zzjvYvn07Vq5cidDQUNy8eRN+fn7o06cP9u/fj88++8ys/ueff44jR45gxowZyMnJwahRoySK/MmuX7+O559/HkuXLoXBYGiy/IsvvsCSJUtw+/ZtlJSUYOPGjdi2bZvpnhbg0Q11CxYswNy5c3H37l2cOHEC586dw7Rp01BXV4fg4GAolUpMmTIFZWVlnbl7PQ4njF5u3759ICKzz6VLlySJZfr06Xjw4AFmzJghyfarqqqa/ZUrtXfffReHDh3C4cOHodFozJZt374dVlZWiIiIwIMHDySKsG2+++47vP3221i8eDECAwObrWNnZ4eIiAg4OTlBo9Fg3rx5CAkJwenTp5Gfnw8A+Nvf/oZ+/fohOjoa9vb2CAwMxNKlS5GVlYULFy4AAP70pz9h5MiReOmll1BXV9dp+9jTcMKwEL4TtPvbu3cvioqKpA7DzI0bN7BmzRqsW7cOSqWyyXKdToeoqCj88MMPWL58uQQRtt3IkSORkpKCBQsWQKFQNFvn5MmTZi+1AoC+ffsCgOmIJD8/H+7u7mbfQU9PTwBAXl6eqWzt2rXIysrCtm3bLLofvQknjDYgIiQkJGDQoEFQKBSwt7dHdHS0WZ36+nrExsbCy8sLKpUKI0aMQHJyMgBg165dsLW1hVqtxokTJzBt2jRotVp4eHjg4MGDpjbS09MxduxYqNVqaLVaDB8+HHq9/qntd0fnz5+Hl5cXBEHA+++/D0BcP23fvh1KpRIuLi54/fXX4e7uDqVSCZ1OZ/p1GRkZCblcDjc3N9P23njjDdja2kIQBJSUlCAqKgrLli1Dbm4uBEGAv78/AOD06dPQarWIj4/v5B6Baf+ICMHBwS3W2bBhAwYOHIgPP/wQZ86cabEeEWHr1q145plnoFAo4OjoiFmzZuHKlSsAxI9LqcfeDz/8AJVKBR8fHwCAr69vk0TfcP3C19fXVObo6IhJkyZh27ZtfPqzraiXS05OptZ2w6pVq0gQBPrLX/5CpaWlZDAYaOfOnQSAMjMziYho+fLlpFAo6OjRo1RaWkorV64kKysr+vrrr01tAKCzZ8/SgwcPqKioiCZOnEi2trZUU1NDFRUVpNVqafPmzVRVVUWFhYU0e/ZsKi4uFtW+GOvXrycPDw9ycHAgmUxGAwYMoJkzZ9J//vOfVvUHEREASk5ObvV6jeXn5xMA2rFjh6nsaf1ERBQREUG2trZ0+fJlqq6uppycHBozZgxpNBq6c+cOEREtWLCAXF1dzbaXkJBAAEx9GhoaSn5+fmZ1Tp48SRqNhuLi4tq1b0REc+bMoTlz5rRqHV9fXxoyZEizy/z8/OjWrVtERPTll1+SlZUVDRgwgCoqKoiIKDU1lWbOnGmqHxsbS3K5nPbt20dlZWWUnZ1No0aNor59+1JhYSERietvS4y9xz333HM0cuTIp9arrKwkjUZDkZGRprK0tDSSyWS0fft20uv1dOnSJXrmmWfoxRdfbLL+ihUrzL6nYllifPcEnDBamTAMBgOp1Wr69a9/bVZ+8OBB00CsqqoitVpNYWFhZuspFAr64x//SET//WJWVVWZ6jQknRs3btClS5cIAJ08ebJJDGLaF+POnTv07bffUnl5OT18+JAyMjLoF7/4BalUKrp06ZLodog6PmG01E9EjxKGvb29WVtff/01AaB169YRUdsThiW1NmFUVFSQIAg0Y8aMZpc3ThhERMuWLSMAtGTJEiIyTxgGg4Hs7OzMxgwR0X/+8x8CYEqIT+tvS429x4lNGKtWraKBAweSXq83K1+9ejUBMH08PDwoPz+/yfp///vfCQB99NFHrYqPE8YjfEqqlW7cuAGDwYApU6a0WOfq1aswGAxm01NVKhXc3NxMh//NkcvlAIDa2lr4+vrCxcUFCxcuxNq1a3H79u12t/84T09P/OIXv4CdnR3kcjnGjRuHpKQkVFVVYefOnaLb6WyN+6klo0ePhlqtblV/dDVFRUUgIqjValH1N2zYgEGDBmHnzp04f/682bKcnBxUVFRg9OjRZuVjxoyBXC43nb5rTuP+ttTYa4tjx47h8OHD+Oc//2l28X/VqlXYvXs3zp49i4qKCty8eRM6nQ5BQUGmC+MNGvry3r17HRprT8UJo5Xu3r0LAHB2dm6xTmVlJQBg9erVZvc35OXlNTt1sDkqlQpffPEFJkyYgPj4ePj6+iIsLAxVVVUWab8lw4cPh7W1Na5du9audroChUKB4uJiqcNos4Z3i7d0QfhxSqUSSUlJEAQBr776KqqqqkzLGqaT2tnZNVnPwcEB5eXlorbRkWPvSQ4dOoR3330XaWlpGDBggKn8xx9/xObNm/GHP/wBv/zlL2FrawsfHx/s2bMHBQUFSEhIMGtHpVIB+G/fstbhhNFKDTNVHj582GKdhmSSmJjYZMpqRkaG6G0NHToUn376KQoKChATE4Pk5GRs2bLFYu03x2g0wmg0iv4n1VXV1tairKysW78hreGfW2tuOAsKCsLSpUtx/fp1rF+/3lTu4OAAAM0mhtb0U0eOvZbs2LED+/fvxxdffIF+/fqZLbt+/Trq6+ublGu1Wjg5OSEnJ8esvKamBsB/+5a1DieMVho2bBisrKyQnp7eYh1PT08olcp23fVdUFCAy5cvA3j0Jd20aRNGjRqFy5cvW6R9AHjxxReblH399dcgIgQFBbWrbamlpaWBiDBu3DgAgI2NzRNPYXVFLi4uEASh1fdXrF+/HoMHD0ZmZqapbNiwYbCzs8M333xjVvfChQuoqanBs88+K6ptS409MYgIMTExuHjxIo4fP97s0VFDovvxxx/NysvLy/HTTz+Zptc2aOhLV1fXDoq6Z+OE0UrOzs4IDQ3F0aNHsXfvXuj1emRnZ2P37t2mOkqlEosWLcLBgwexa9cu6PV61NfX4+7du00GdksKCgrw+uuv48qVK6ipqUFmZiby8vIwbtw4i7QPPJqeeOjQIZSVlaG2thYZGRl47bXX4OXlhcWLF7e6b6RkNBpRWlqKuro6ZGdnIyoqCl5eXggPDwcA+Pv746effsLx48dRW1uL4uJiszn6AODk5ISCggLcvn0b5eXlqK2tRWpqqmTTatVqNXx9fU2nQcVqODXV+P4FpVKJZcuW4dixY9i/fz/0ej0uXryIxYsXw93dHREREaLbftrYCwsLg6ura7sfR3L58mW899572LNnD2QyWZNH2GzZsgU+Pj6YPHky9uzZg3PnzqGqqgr5+fmm/fn9739v1mZDXw4fPrxdsfVanX+dvWtpy7Ta8vJyeu2116hPnz5kZ2dHEyZMoNjYWNPsjO+++44ePnxIMTEx5OXlRTY2NuTs7EyhoaGUk5NDO3fuJLVaTQAoICCAcnNzaffu3aTVagkAeXt707/+9S/S6XTk6OhI1tbW1K9fP1q1ahXV1dURET2xfbGWLVtGfn5+ZGtrSzY2NuTh4UH/7//9PyooKGhVfxC1fxbJjh07yM3NjQCQWq2m4OBgUf107do1ioiIIJlMRv379ycbGxvSarU0a9Ysys3NNbV///59mjx5MimVSvLx8aE333yToqOjCQD5+/ubZox5e3uTSqWiCRMmUGFhIZ06dYo0Gg1t2LChzfvWoC3TaiMjI0kmk5HBYDCVHTt2jPz8/AgA9e3b1zQr6nHR0dFm02qNRiMlJCRQQEAAyWQycnR0pJCQELp69SoRkej+ftrYCwkJIQAUGxv7xH3LyMig8ePHk7u7u2l2k5ubG+l0OkpPT6eLFy+azXx6/JOQkEBERCUlJRQVFUX+/v6kUCjIzs6Oxo8fT//4xz+abHP69OnUv39/MhqNrfo7tHd89xScMNqQMFhTUn6hIiIiyMnJSZJtt0ZbEsb169fJxsaG9u3b10FRWV59fT1NnDiR9u7dK3UoZkpKSkipVNKWLVtavS4njEf4lBTrEXrqk0j9/f0RFxeHuLi4Jk9o7Yrq6+tx/PhxlJeXIywsTOpwzKxduxaBgYGIjIyUOpRuixNGD3PlypVmH1n++KerfZlZy1asWIG5c+ciLCysyz9gMC0tDSkpKUhNTRV9/0hn2Lp1K7KysnDq1CnIZDKpw+m2OGH0MIMHD24y3bG5z6FDh6QO1SJWrlyJpKQkPHjwAD4+Pjh69KjUIXWI+Ph4REZGYtOmTVKH8kRTpkzBgQMHzJ7bJbUTJ07g4cOHSEtLg6Ojo9ThdGs2UgfAWHts3LgRGzdulDqMTjF16lRMnTpV6jC6nZkzZ2LmzJlSh9Ej8BEGY4wxUThhMMYYE4UTBmOMMVE4YTDGGBOFL3r/rPFL5VnbJCYm4siRI1KH0WV99dVXAHisse6LjzAYY4yJwkcYP+Nfxu0jCALeeustzJs3T+pQuqyGIwsea92PIAhSh9Al8BEGY4wxUThhMMYYE4UTBmOMMVE4YTDGGBOFEwZjjDFROGFYUEpKCnx9fZs8Slwul8PFxQUvvPACEhISUFpaKnWorAc4c+YMVqxY0WTcvfLKK03qTp06FRqNBtbW1hg6dGi7X5/akYxGIxITE6HT6Zpdfv78eYwfPx5qtRru7u6IiYnBw4cPAQCffPIJNm/e3GPfjyI1ThgWFBoaips3b8LPzw/29vYgIhiNRhQVFeHw4cPw8fFBTEwMhg4dim+++UbqcFk39s4772D79u1YuXKl2bjr06cP9u/fj88++8ys/ueff44jR45gxowZyMnJwf9v7+5jmrr6OIB/C325bW1tDW+diIPCdCpgmDKpOreQkCiJiLDYRP9g/lPNNsZUgm8w5EW3YJghkRgzxxK3uPoIgb1Yk70ElkVilgyGgajIBMIYb07sC60F+nv+8GkfG3AUKC3C+ST8c865v/O79x44l95zexMSEvyU+b9rb2/HG2+8gUOHDmFkZGRCfWtrK1JSUpCcnIzBwUHU1NTg888/d72DfufOneA4DsnJyRgeHvZ1+gsemzDmGI/Hg0KhwJtvvomqqipcvXoV/f39SE1Nnfcvw3kRWK3W516JzufYs/Hxxx/j66+/xtWrVyGTydzqKioqEBAQAJ1O98KNrz/++ANHjx7FwYMHsX79+knbFBcXIywsDKdOnYJUKkVSUhLy8vLwxRdf4M6dOwCADz74APHx8dixYwfGxsZ8uQsLHpswfCwzMxNZWVkYGBjAhQsX/J3OC+/SpUsYGBh44WLP1P3795Gfn49Tp06B47gJ9RqNBjk5Ofjrr79w5MgRP2Q4c/Hx8aiursbevXshEokm1I+NjeH777/Htm3b3B6k2759O4gIdXV1rrLCwkI0Nzfj3LlzPsl9sWAThh9kZWUBAAwGA4Cn70EuKChAREQExGIx4uLioNfrAQCVlZWQSqWQSCSoq6vD9u3bIZfLER4ejitXrrhiNjQ0IDExERKJBHK5HLGxsTAajVPG9xciQnl5OV599VWIRCIolUrs2rXLdZWYnZ0NoVDo9ua2d999F1KpFDweD0NDQ8jJycHhw4fR0dEBHo+H6OhoVFRUgOM4hISE4MCBA1CpVOA4DhqNBrdu3ZpVbAC4ceMG5HI5SktLfXi0/q+iogJEhJ07dz63TUlJCV555RV89tln+PHHH5/bbqpz4OnY89X4+vPPP2E2mxEREeFWrlarAQAtLS2uMqVSiW3btuHcuXMgIq/nsmjRIqfX68nbh0GtVtPSpUufW280GgkArVixgoiIjhw5QiKRiK5du0aPHj2i48ePU0BAAP32229ERHTixAkCQD/99BM9fvyYBgYGaOvWrSSVSslut5PZbCa5XE6ffPIJWa1W6uvro927d9Pg4KBH8b0BAOn1eo/bFxQUkFAopMuXL9Pw8DC1tLRQQkICBQUFUV9fHxER7d27l0JDQ922KysrIwCufcvIyCC1Wu3WRqfTkVQqpba2NrLZbNTa2kobN24kmUxG3d3ds4r93XffkUwmo6KiIo/31SkzM5MyMzOnvd2zoqKiaM2aNZPWqdVqevDgARER3bx5kwICAujll18ms9lMREQGg4HS0tJc7T05B1ONPaK5GV+vv/46xcfHu5U1NDQQACorK5vQXiwWU3JyslvZsWPHCAA1NTXNOA+n6Y7vhYr9h+EHMpkMPB4PJpMJNpsNlZWVSE9PR0ZGBhQKBU6ePAmBQICqqiq37TQaDeRyOYKDg6HVamGxWNDd3Y3Ozk4YjUasXbsWHMchNDQU1dXVCAoKmlZ8X7FarSgvL8fu3buxb98+LF26FLGxsbhw4QKGhoZw8eLFWffB5/NdV85r1qxBZWUlTCbTrPc5NTUVRqMR+fn5s85xuiwWCx48eOC6ov43SUlJ+PDDD9HZ2YmjR49OqJ/uOXje2PPl+HKuhAoMDJxQJxAIYLVa3cpiYmIAALdv3/ZqHosZmzD8wGKxgIggl8tx9+5djIyMYN26da56sViMsLAw10cDkxEKhQCA0dFRREVFISQkBPv27UNhYSE6Oztd7WYafy61trbCbDZjw4YNbuUbN26EUCh0fXTkTRs2bIBEIvHbPnvDwMAAiAgSicSj9iUlJVi1ahXOnz+PX3/91a1uNufg2bHny/HlvGcz2Y1su90OsVjsVuY8Tv39/V7NYzFjE4Yf3Lt3DwCwevVqWCwWAMDJkyfdnt3o6uqadFnhZMRiMX7++Wds2bIFpaWliIqKglarhdVq9Up8b3Mud1yyZMmEOoVCAZPJNCf9ikQiDA4OzklsX7DZbAAw6Q3hyXAch6qqKvB4POzfv9/tCtxb58CX48t5z8l5b85pZGQENpsNKpXKrdw5gTiPGzN7bMLwgxs3bgB4urojODgYwNOXDxGR209jY6PHMdeuXYtvv/0Wvb29yMvLg16vx9mzZ70W35sUCgUATPpHaXh4GOHh4V7vc3R0dM5i+4rzD+B0HkpLSkrCoUOH0N7ejuLiYle5t86BL8dXZGQkZDIZurq63Mrv378PAIiLi3Mrt9vtADDhPw9m5tiE4WN9fX349NNPER4ejv3792PFihXgOA7Nzc0zjtnb24u2tjYAT3+Bz5w5g4SEBLS1tXklvretW7cOS5YsmfDw4q1bt2C32/Haa68BeHofYnR01Ct91tfXg4iwadMmr8f2lZCQEPB4vGk/X1FcXIzVq1ejqanJVebpOZiKL8cXn8/Hjh078Msvv8DhcLjKDQYDeDzehJVjzuMUGho657ktFmzCmCNEBLPZDIfDASLC4OAg9Ho9Nm/ejMDAQNTW1kIul4PjOLzzzju4cuUKKisrYTQaMT4+jp6eHvz9998e9dXb24sDBw7gzp07sNvtaGpqQldXFzZt2uSV+N7GcRwOHz6MmpoafPnllzAajbh9+zYOHjwIlUoFnU4HAIiOjsY///yD2tpajI6OYnBwcMLV5bJly9Db24vOzk6YTCbXJOBwOPDo0SOMjY2hpaUFOTk5iIiIcC1pnmlsg8Hgt2W1EokEUVFR6OnpmdZ2zo+mnr1Z7Ok58CT2VONLq9UiNDTUK19Hkp+fj/7+fnz00UewWCxobGxEWVkZsrKysGrVKre2zuMUGxs7636Z//H5uqx5xpvLar/55huKi4sjiURCQqGQAgICCADxeDxSKBSUmJhIRUVF9PDhQ7ftnjx5Qnl5eRQREUF8Pp+Cg4MpIyODWltb6fz58ySRSAgAxcTEUEdHB128eJHkcjkBoJUrV9IPP/xAGo2GlEolBQYG0ksvvUQnTpygsbGxKeN7C6a57NDhcFBZWRnFxMSQQCAgpVJJ6enpdPfuXVebhw8f0ltvvUUcx1FkZCS9//77lJubSwAoOjqauru76ffff6eVK1eSWCymLVu2UF9fH+l0OhIIBLR8+XLi8/kkl8tp165d1NHRMevY169fJ5lMRiUlJdM+Rt5YVpudnU0CgYBGRkZcZTU1NaRWqwkABQUF0XvvvTfptrm5uW7Laqc6B56MvXv37k05vtLT0wkAFRQU/Ou+NTY20ubNm0mlUhEAAkBhYWGk0WiooaHB1a6hoYESExNJJBKRSqWi3NxcstlsE+KlpqbS8uXLyeFweH6An2O643uhYhPGHDyHsRjNp18onU5Hy5Yt83caE3hjwmhvbyc+n0+XL1/2UlZzb3x8nLZu3UqXLl3yWZ9DQ0PEcRydPXvWK/Hm0/j2J/aRFLMgLdRvK42OjkZRURGKiopgNpv9nc6UxsfHUVtbC5PJBK1W67N+CwsLsX79emRnZ/usz8WATRgM84I5duwY3n77bWi12nn/BYP19fWorq6GwWDw+PmR2SovL0dzczOuX78OgUDgkz4XCzZhMAvK8ePHUVVVhcePHyMyMhLXrl3zd0pzorS0FNnZ2Thz5oy/U/lXycnJ+Oqrr9y+t2su1dXV4cmTJ6ivr4dSqfRJn4sJ398JMIw3nT59GqdPn/Z3Gj6RkpKClJQUf6cxr6SlpSEtLc3faSxY7D8MhmEYxiNswmAYhmE8wiYMhmEYxiNswmAYhmE8wiYMhmEYxiNsldT/PPuOYGZm9uzZgz179vg7jXmPjTXmRcUjWtwvvO3p6cHNmzf9nQbDMPOcRqN5ob8e3xsW/YTBMAzDeIbdw2AYhmE8wiYMhmEYxiNswmAYhmE8wgfwH38nwTAMw8x//wUA2EKWpCmDrwAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 25
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model.summary()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "sr3wN_PCfJGJ",
-        "outputId": "eaae0246-1b35-4faf-bfc5-675000114d30"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Model: \"sequential_3\"\n",
-            "_________________________________________________________________\n",
-            " Layer (type)                Output Shape              Param #   \n",
-            "=================================================================\n",
-            " conv2d_6 (Conv2D)           (None, 26, 26, 32)        320       \n",
-            "                                                                 \n",
-            " max_pooling2d_6 (MaxPooling  (None, 13, 13, 32)       0         \n",
-            " 2D)                                                             \n",
-            "                                                                 \n",
-            " conv2d_7 (Conv2D)           (None, 11, 11, 64)        18496     \n",
-            "                                                                 \n",
-            " max_pooling2d_7 (MaxPooling  (None, 5, 5, 64)         0         \n",
-            " 2D)                                                             \n",
-            "                                                                 \n",
-            " flatten_3 (Flatten)         (None, 1600)              0         \n",
-            "                                                                 \n",
-            " dropout_3 (Dropout)         (None, 1600)              0         \n",
-            "                                                                 \n",
-            " dense_4 (Dense)             (None, 128)               204928    \n",
-            "                                                                 \n",
-            " dense_5 (Dense)             (None, 10)                1290      \n",
-            "                                                                 \n",
-            "=================================================================\n",
-            "Total params: 225,034\n",
-            "Trainable params: 225,034\n",
-            "Non-trainable params: 0\n",
-            "_________________________________________________________________\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "test_loss, test_acc = model.evaluate(test_images, test_labels)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "lD42yWpihbZR",
-        "outputId": "d19fbc7b-93f5-43de-d416-8bb8034d3c1c"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "313/313 [==============================] - 3s 10ms/step - loss: 0.2376 - accuracy: 0.9115\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "predicts = model.predict(test_images)\n",
-        "predicted_lbs = [np.argmax(prediction) for prediction in predicts]\n",
-        "\n",
-        "fig, axes = plt.subplots(5, 5, figsize=(24, 12))\n",
-        "axes = axes.ravel()\n",
-        "\n",
-        "for i in range(25):\n",
-        "    axes[i].imshow(test_images[i].reshape(28, 28), cmap='gray')\n",
-        "    if( 0 <= predicted_lbs[i] <= 4 or predicted_lbs[i] == 6):\n",
-        "      axes[i].set_title(\"Одежда\")\n",
-        "    elif(predicted_lbs[i] == 5 or predicted_lbs[i] == 7 or predicted_lbs[i] == 9):\n",
-        "      axes[i].set_title(\"Обувь\")\n",
-        "    else:\n",
-        "      axes[i].set_title(\"Сумка\")\n",
-        "    axes[i].axis('off')\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 669
-        },
-        "id": "D95WvS-9poHX",
-        "outputId": "71ec59a3-5118-4a30-98ae-f2d9cc03b9c1"
-      },
-      "execution_count": 36,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "313/313 [==============================] - 3s 10ms/step\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 2400x1200 with 25 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model.save(\"/content/drive/MyDrive/my_model2\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "_8TlDhYesoJU",
-        "outputId": "4ba162c6-fb08-42fa-9779-6718764de705"
-      },
-      "execution_count": 37,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
-          ]
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file