Train_and_save.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kn8y7TEiBNZs",
        "outputId": "5c9f37cf-2f1e-418b-ddbd-53511ca22e2c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.7.22)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.6)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.0.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4)\n"
          ]
        }
      ],
      "source": [
        "!pip install kaggle\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "djGAWE3HBNXd"
      },
      "outputs": [],
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zzn8_9jHBGL8",
        "outputId": "e91de2d8-28ea-47c7-e5ae-ddb6d094cecd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading 140k-real-and-fake-faces.zip to /content\n",
            "100% 3.74G/3.75G [00:55<00:00, 65.4MB/s]\n",
            "100% 3.75G/3.75G [00:55<00:00, 72.4MB/s]\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d xhlulu/140k-real-and-fake-faces"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "stkLIkVEB8I0",
        "outputId": "1bcc06dc-6f7c-4b8b-e3bb-fd4832e8440d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ]
        }
      ],
      "source": [
        "!unzip 140k-real-and-fake-faces.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "ZMC795t-_EqN"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
        "from keras.optimizers import Adam\n",
        "from keras.preprocessing.image import ImageDataGenerator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "omXwfmPe_hyN",
        "outputId": "cf0aaf2c-c199-4acc-e521-6224c911e9f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 100000 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "path = '/content/real_vs_fake/real-vs-fake'\n",
        "size = (256, 256)\n",
        "\n",
        "\n",
        "datagen = ImageDataGenerator(rescale=1./255.,\n",
        "                              horizontal_flip=True)\n",
        "\n",
        "train = datagen.flow_from_directory(path + '/train',\n",
        "                                                 class_mode='binary',\n",
        "                                                 target_size=size,\n",
        "                                                 batch_size=64\n",
        "                                                )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NpkMMnXx_knl",
        "outputId": "35404266-50b4-438d-e504-fd67c37ec692"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 20000 images belonging to 2 classes.\n",
            "Found 20000 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "val = test_datagen.flow_from_directory(path + '/valid',\n",
        "                                  target_size=size,\n",
        "                                  batch_size=64,\n",
        "                                  class_mode='binary')\n",
        "test = test_datagen.flow_from_directory(path + '/test',\n",
        "                                    target_size=size,\n",
        "                                    batch_size=64,\n",
        "                                    class_mode='binary',\n",
        "                                    shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VvjSvt4PAq21",
        "outputId": "db55951f-9bd0-4e0c-84b1-b007dbcf311a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " densenet121 (Functional)    (None, 8, 8, 1024)        7037504   \n",
            "                                                                 \n",
            " global_average_pooling2d (  (None, 1024)              0         \n",
            " GlobalAveragePooling2D)                                         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               524800    \n",
            "                                                                 \n",
            " batch_normalization (Batch  (None, 512)               2048      \n",
            " Normalization)                                                  \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 513       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7564865 (28.86 MB)\n",
            "Trainable params: 7480193 (28.53 MB)\n",
            "Non-trainable params: 84672 (330.75 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "from keras.layers import Conv2D, BatchNormalization, Activation, MaxPooling2D, GlobalAveragePooling2D, Dense, Flatten, Dropout\n",
        "from keras.applications import DenseNet121\n",
        "from keras.models import Sequential\n",
        "\n",
        "\n",
        "densenet = DenseNet121(weights=None,\n",
        "                           include_top=False,\n",
        "                           input_shape=(256, 256, 3)\n",
        "                          )\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(densenet)\n",
        "model.add(GlobalAveragePooling2D())\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "                loss='binary_crossentropy',\n",
        "                metrics=['accuracy']\n",
        "                )\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3CzzlS2_Aw4U",
        "outputId": "e2a998e3-5b58-4cff-e2f3-ad7c70409119"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "1563/1563 [==============================] - 454s 290ms/step - loss: 0.0938 - accuracy: 0.9646 - val_loss: 0.3414 - val_accuracy: 0.8694 - lr: 0.0010\n",
            "Epoch 2/20\n",
            "1563/1563 [==============================] - 453s 290ms/step - loss: 0.0763 - accuracy: 0.9706 - val_loss: 0.1967 - val_accuracy: 0.9299 - lr: 0.0010\n",
            "Epoch 3/20\n",
            "1563/1563 [==============================] - 454s 290ms/step - loss: 0.0644 - accuracy: 0.9760 - val_loss: 0.5918 - val_accuracy: 0.7991 - lr: 0.0010\n",
            "Epoch 4/20\n",
            "1563/1563 [==============================] - 452s 289ms/step - loss: 0.0536 - accuracy: 0.9798 - val_loss: 0.1268 - val_accuracy: 0.9542 - lr: 0.0010\n",
            "Epoch 5/20\n",
            "1563/1563 [==============================] - 453s 290ms/step - loss: 0.0463 - accuracy: 0.9828 - val_loss: 0.1270 - val_accuracy: 0.9544 - lr: 0.0010\n",
            "Epoch 6/20\n",
            "1563/1563 [==============================] - 454s 290ms/step - loss: 0.0402 - accuracy: 0.9851 - val_loss: 0.0781 - val_accuracy: 0.9724 - lr: 0.0010\n",
            "Epoch 7/20\n",
            "1563/1563 [==============================] - 451s 289ms/step - loss: 0.0335 - accuracy: 0.9878 - val_loss: 0.3879 - val_accuracy: 0.8595 - lr: 0.0010\n",
            "Epoch 8/20\n",
            "1563/1563 [==============================] - 451s 288ms/step - loss: 0.0298 - accuracy: 0.9889 - val_loss: 0.1094 - val_accuracy: 0.9617 - lr: 0.0010\n",
            "Epoch 9/20\n",
            "1563/1563 [==============================] - 452s 289ms/step - loss: 0.0262 - accuracy: 0.9904 - val_loss: 0.1019 - val_accuracy: 0.9657 - lr: 0.0010\n"
          ]
        }
      ],
      "source": [
        "from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n",
        "\n",
        "early_stopping = EarlyStopping(monitor='val_loss',\n",
        "                             patience=3,\n",
        "                             restore_best_weights=True)\n",
        "\n",
        "checkpoint = ModelCheckpoint(filepath='model.h5',\n",
        "    save_weights_only=True,\n",
        "    monitor='val_loss',\n",
        "    mode='min',\n",
        "    save_best_only=True)\n",
        "\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n",
        "                              factor=0.2,\n",
        "                              patience=3)\n",
        "\n",
        "\n",
        "history = model.fit(train,\n",
        "                    validation_data=val,\n",
        "                    callbacks=[checkpoint, early_stopping, reduce_lr],\n",
        "                    epochs=20\n",
        "                   )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "uwgf-atEGIZa"
      },
      "outputs": [],
      "source": [
        "model_json = model.to_json()\n",
        "with open(\"model.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "\n",
        "model.save_weights(\"model_weights.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "wHHd5-TeAy1U"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "history_df = pd.DataFrame(history.history)\n",
        "history_df.to_csv('training_history.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "UeQS66GiA2qk",
        "outputId": "c1efea17-17c4-4371-ebe5-9058d7012d8f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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FCvD333/z7bffMmDAgEx9PSIi6VH4EhHxMAULFuSnn37i+eef5+WXXyZ//vw8+uij3HnnnbRp08bs8gCoV68eixYtYujQobzyyiuEh4czduxYdu7ced3ZGP38/Pjxxx8ZNGgQ48aNIyAggM6dOzNgwABq1659U/X4+PiwYMECBg8ezOzZs7FYLNx77728++673HbbbTd0rO7duzNnzhyKFSvGHXfc4fZYr169OH78ONOmTWPJkiVUq1aN2bNn880337Bq1aobrvu1114jICCAqVOnsnLlSiIiIli6dCkdOnRw2++ll14iNjaWOXPmMHfuXOrWrcvPP//MsGHD3Pbz8/Pj008/Zfjw4TzzzDMkJSUxc+bMdMNXyns2cuRI5s6dy8yZMylTpgzvvPMOzz///A2/lutZvHhxuosylylThho1arBq1SqGDRvGp59+SlRUFJUrV2bmzJn06tXLte+jjz7K9OnT+fDDDzl//jxFixala9eujB492jVccdCgQSxYsIClS5cSHx9P6dKlee2113jhhRcy/TWJiFzJ4sxJX5WKiIhX69Spk6b5FhGRXEvXfImISJa4dOmS2/09e/awcOFCWrZsaU5BIiIiJlPPl4iIZIlixYrRq1cvypUrR2RkJFOmTCE+Pp5NmzalWbtKREQkN9A1XyIikiXatm3Ll19+yfHjx7HZbDRu3Jg33nhDwUtERHIt9XyJiIiIiIhkA13zJSIiIiIikg0UvkRERERERLKBrvm6SQ6Hg6NHj5I3b14sFovZ5YiIiIiIiEmcTifR0dEUL17cta5gehS+btLRo0cJDw83uwwREREREckhDh06RMmSJa/6uMLXTcqbNy9gvMEhISEmVyMiIiIiImaJiooiPDzclRGuRuHrJqUMNQwJCVH4EhERERGR616OpAk3REREREREsoHXhK/JkydTpkwZAgICiIiIYN26ddfc//z58/Tv359ixYphs9moVKkSCxcuzKZqRUREREQkt/GKYYdz585lyJAhTJ06lYiICCZMmECbNm3YvXs3RYoUSbN/QkICd911F0WKFOHbb7+lRIkSREZGki9fvuwvXkREREREcgWL0+l0ml3ErYqIiKBBgwZMmjQJMKaBDw8PZ+DAgQwbNizN/lOnTuWdd95h165d+Pn53dQ5o6KiCA0N5cKFC7rmS0RERCQHcDqdJCUlYbfbzS5FvIzVasXX1/eq13RlNBt4fM9XQkICGzZsYPjw4a5tPj4+tG7dmjVr1qT7nAULFtC4cWP69+/PDz/8QOHChXnkkUd48cUXsVqt6T4nPj6e+Ph41/2oqKjMfSEiIiIictMSEhI4duwYFy9eNLsU8VKBgYEUK1YMf3//mz6Gx4ev06dPY7fbCQsLc9seFhbGrl270n3Ovn37+OWXX+jevTsLFy5k79699OvXj8TEREaNGpXuc8aNG8eYMWMyvX4RERERuTUOh4P9+/djtVopXrw4/v7+1511TiSjnE4nCQkJnDp1iv3791OxYsVrLqR8LR4fvm6Gw+GgSJEiTJ8+HavVSr169Thy5AjvvPPOVcPX8OHDGTJkiOt+ylz+IiIiImKuhIQE12UngYGBZpcjXihPnjz4+fkRGRlJQkICAQEBN3Ucjw9fhQoVwmq1cuLECbftJ06coGjRouk+p1ixYvj5+bkNMaxatSrHjx8nISEh3a5Em82GzWbL3OJFBJISwJEI/kFmVyIiIh7uZnsjRDIiMz5fHv8J9ff3p169eqxYscK1zeFwsGLFCho3bpzuc5o2bcrevXtxOByubf/+++8tj+EUkRvkcMCn98B71eDCYbOrEREREclSHh++AIYMGcKMGTP49NNP2blzJ3379iU2NpbevXsD0KNHD7cJOfr27cvZs2d59tln+ffff/n5559544036N+/v1kvQSR32j4PDv0Fcefhr6lmVyMiIiKSpbwifHXt2pXx48czcuRI6tSpw+bNm1m8eLFrEo6DBw9y7Ngx1/7h4eEsWbKE9evXU6tWLQYNGsSzzz6b7rT0IpJF7Emw8o3L9zd8BvEx5tUjIiLiJcqUKcOECRMyvP+qVauwWCycP38+y2oSg1es82UGrfMlcos2fg4LBkBgQQgIhbP7oN07EPGU2ZWJiIiHiYuLY//+/ZQtW/amJ0Iww/VmZBw1ahSjR4++4eOeOnWKoKCgDE8+kpCQwNmzZwkLC8vSWSJXrVpFq1atOHfuHPny5cuy82SVa33Ocs06XyLigZLi4de3jPbtz4FvACwcCn9NgQZ9QBdMi4hILpB6ZNbcuXMZOXIku3fvdm0LDg52tZ1OJ3a7HV/f6//5Xrhw4Ruqw9/f/6oT1Unm0l84IpL9NnwKFw5BcFEjbNV5BALyGb1f/y42uzoREfECTqeTiwlJptwyOrCsaNGirltoaCgWi8V1f9euXeTNm5dFixZRr149bDYbf/zxB//99x/33XcfYWFhBAcH06BBA5YvX+523CuHHVosFj766CM6d+5MYGAgFStWZMGCBa7Hrxx2OGvWLPLly8eSJUuoWrUqwcHBtG3b1i0sJiUlMWjQIPLly0fBggV58cUX6dmzJ506dbrp/2bnzp2jR48e5M+fn8DAQNq1a8eePXtcj0dGRtKxY0fy589PUFAQ1atXZ+HCha7ndu/encKFC5MnTx4qVqzIzJkzb7qWrKKeLxHJXgkX4ffxRrv5UPDLY7Tr9YLVE2Dth1ClvVnViYiIl7iUaKfayCWmnHvH2DYE+mfOn9nDhg1j/PjxlCtXjvz583Po0CHat2/P66+/js1m47PPPqNjx47s3r2bUqVKXfU4Y8aM4e233+add95h4sSJdO/encjISAoUKJDu/hcvXmT8+PF8/vnn+Pj48OijjzJ06FC++OILAN566y2++OILZs6cSdWqVfnf//7H/PnzadWq1U2/1l69erFnzx4WLFhASEgIL774Iu3bt2fHjh34+fnRv39/EhIS+O233wgKCmLHjh2u3sFXXnmFHTt2sGjRIgoVKsTevXu5dOnSTdeSVRS+RCR7rZ8BMScgtBTU7Xl5e8Mn4c+JcOB3OLYVitUyr0YREZEcYuzYsdx1112u+wUKFKB27dqu+6+++irff/89CxYsYMCAAVc9Tq9evejWrRsAb7zxBh988AHr1q2jbdu26e6fmJjI1KlTKV++PAADBgxg7NixrscnTpzI8OHD6dy5MwCTJk1y9ULdjJTQtXr1apo0aQLAF198QXh4OPPnz+fBBx/k4MGDdOnShZo1awJQrlw51/MPHjzIbbfdRv369QGj9y8nUvgSkewTFwV/TDDaLV8E31Tr6oWWhOqd4J/vYO0U6DzFjApFRMRL5PGzsmNsG9POnVlSwkSKmJgYRo8ezc8//8yxY8dISkri0qVLHDx48JrHqVXr8peaQUFBhISEcPLkyavuHxgY6ApeAMWKFXPtf+HCBU6cOEHDhg1dj1utVurVq+e2ju6N2LlzJ76+vkRERLi2FSxYkMqVK7Nz504ABg0aRN++fVm6dCmtW7emS5curtfVt29funTpwsaNG7n77rvp1KmTK8TlJLrmS0Syz9opcOksFKwItR5O+3ij5LX2/vkWok9kb20iIuJVLBYLgf6+ptwyc8bAoKAgt/tDhw7l+++/54033uD3339n8+bN1KxZk4SEhGsex8/PL837c62glN7+Zk+S3qdPH/bt28djjz3Gtm3bqF+/PhMnTgSgXbt2REZG8txzz3H06FHuvPNOhg4damq96VH4EpHscfEsrJlktFsNB2s6He8l60HJhmBPgL8/zt76REREPMDq1avp1asXnTt3pmbNmhQtWpQDBw5kaw2hoaGEhYWxfv161za73c7GjRtv+phVq1YlKSmJv/76y7XtzJkz7N69m2rVqrm2hYeH88wzzzBv3jyef/55ZsyY4XqscOHC9OzZk9mzZzNhwgSmT59+0/VkFQ07FJHs8ecHEB8FYTWgWuer79e4H3yzDtZ/DLcPAT/PWa9FREQkq1WsWJF58+bRsWNHLBYLr7zyyk0P9bsVAwcOZNy4cVSoUIEqVaowceJEzp07l6Fev23btpE3b17XfYvFQu3atbnvvvt48sknmTZtGnnz5mXYsGGUKFGC++67D4DBgwfTrl07KlWqxLlz51i5ciVVq1YFYOTIkdSrV4/q1asTHx/PTz/95HosJ1H4EpGsF30C/ppmtFuNuPY6XlU6Qmi4MRX9tm+g7mPZU6OIiIgHeO+993j88cdp0qQJhQoV4sUXXyQqKirb63jxxRc5fvw4PXr0wGq18tRTT9GmTRus1utf79a8eXO3+1arlaSkJGbOnMmzzz7LPffcQ0JCAs2bN2fhwoWuIZB2u53+/ftz+PBhQkJCaNu2Le+//z5grFU2fPhwDhw4QJ48eWjWrBlfffVV5r/wW2Rxmj1400NldBVrEQEWDTMWUC5RD/qsgOt9K7b6A1j2ChSpDn1XX39/ERHJ1eLi4ti/fz9ly5YlIEAjJszgcDioWrUqDz30EK+++qrZ5WSJa33OMpoNdM2XiGStC4cvX791x8sZC1J1e4BfEJzcDvtWZWl5IiIicuMiIyOZMWMG//77L9u2baNv377s37+fRx55xOzScjSFLxHJWr+9Y0ygUboplMvgwot58sFtjxrttZpyXkREJKfx8fFh1qxZNGjQgKZNm7Jt2zaWL1+eI6+zykl0zZeIZJ2z+2DTbKN9xys3Nnww4mlYNx32LIHTe6BQxaypUURERG5YeHg4q1evNrsMj6OeLxHJOqveAkcSVGgNpRvf2HMLlofK7Yy2er9ERETECyh8iUjWOLkLts412q1G3NwxGvU1fm750lgnTERERMSDKXyJSNZY+TrghCr3QIm6N3eMMs0grCYkXoSNn2ZqeSIiIiLZTeFLRDLf0c2wcwFgufleLzCuEWvcz2j/NR3siZlRnYiIiIgpFL5EJPOtfN34WfMBCKt2a8eq0QWCikD0Udjxw63XJiIiImIShS8RyVwH/4I9S8FihZbDb/14vjZo0Mdor/0QtC68iIiIeCiFLxHJXL8kr2pf5xFjxsLMUP9xsNrgyAY4vD5zjikiIuIlWrZsyeDBg133y5Qpw4QJE675HIvFwvz582/53Jl1nNxC4UtEMs++X+HA72D1hxYvZt5xgwtDrQeN9prJmXdcERERE3Xs2JG2bdum+9jvv/+OxWJh69atN3zc9evX89RTT91qeW5Gjx5NnTp10mw/duwY7dq1y9RzXWnWrFnky5cvS8+RXRS+RCRzOJ2Xe73q9YJ84Zl7/EbJE2/sXADnD2busUVEREzwxBNPsGzZMg4fPpzmsZkzZ1K/fn1q1ap1w8ctXLgwgYGBmVHidRUtWhSbzZYt5/IGCl8ikjn+XWIMCfTNA82ez/zjh1WHci3B6YB10zP/+CIi4l2cTkiINeeWweuT77nnHgoXLsysWbPctsfExPDNN9/wxBNPcObMGbp160aJEiUIDAykZs2afPnll9c87pXDDvfs2UPz5s0JCAigWrVqLFu2LM1zXnzxRSpVqkRgYCDlypXjlVdeITHRmGV41qxZjBkzhi1btmCxWLBYLK6arxx2uG3bNu644w7y5MlDwYIFeeqpp4iJiXE93qtXLzp16sT48eMpVqwYBQsWpH///q5z3YyDBw9y3333ERwcTEhICA899BAnTpxwPb5lyxZatWpF3rx5CQkJoV69evz9998AREZG0rFjR/Lnz09QUBDVq1dn4cKFN13L9fhm2ZFFJPdwOGDla0a74ZOQt2jWnKdRP9i3CjZ8Bi2GgS04a84jIiKeL/EivFHcnHO/dBT8g667m6+vLz169GDWrFmMGDECi8UCwDfffIPdbqdbt27ExMRQr149XnzxRUJCQvj555957LHHKF++PA0bNrzuORwOB/fffz9hYWH89ddfXLhwwe36sBR58+Zl1qxZFC9enG3btvHkk0+SN29e/u///o+uXbvyzz//sHjxYpYvXw5AaGhommPExsbSpk0bGjduzPr16zl58iR9+vRhwIABbgFz5cqVFCtWjJUrV7J37166du1KnTp1ePLJJ6/7etJ7fSnB69dffyUpKYn+/fvTtWtXVq1aBUD37t257bbbmDJlClarlc2bN+Pn5wdA//79SUhI4LfffiMoKIgdO3YQHJx1f18ofInIrdv5AxzfBv554fbnsu48Fe6CghXhzB7Y/AVEPJ115xIREckGjz/+OO+88w6//vorLVu2BIwhh126dCE0NJTQ0FCGDh3q2n/gwIEsWbKEr7/+OkPha/ny5ezatYslS5ZQvLgRRt94440012m9/PLLrnaZMmUYOnQoX331Ff/3f/9Hnjx5CA4OxtfXl6JFr/4F65w5c4iLi+Ozzz4jKMgIn5MmTaJjx4689dZbhIWFAZA/f34mTZqE1WqlSpUqdOjQgRUrVtxU+FqxYgXbtm1j//79hIcblzx89tlnVK9enfXr19OgQQMOHjzICy+8QJUqVQCoWLGi6/kHDx6kS5cu1KxZE4By5crdcA03QuFLRG6Nww4r3zDajftDYIGsO5ePDzR6Bn5+HtZOgQZPGttERESu5Bdo9ECZde4MqlKlCk2aNOGTTz6hZcuW7N27l99//52xY8cCYLfbeeONN/j66685cuQICQkJxMfHZ/iarp07dxIeHu4KXgCNGzdOs9/cuXP54IMP+O+//4iJiSEpKYmQkJAMv46Uc9WuXdsVvACaNm2Kw+Fg9+7drvBVvXp1rFara59ixYqxbdu2GzpX6nOGh4e7ghdAtWrVyJcvHzt37qRBgwYMGTKEPn368Pnnn9O6dWsefPBBypc3ZmQeNGgQffv2ZenSpbRu3ZouXbrc1HV2GaW/WkTk1mz9Gk7/CwH5oHG/rD9f7W7Guc7th38XZ/35RETEM1ksxtA/M27Jwwcz6oknnuC7774jOjqamTNnUr58eVq0aAHAO++8w//+9z9efPFFVq5cyebNm2nTpg0JCQmZ9latWbOG7t270759e3766Sc2bdrEiBEjMvUcqaUM+UthsVhwOBxZci4wZmrcvn07HTp04JdffqFatWp8//33APTp04d9+/bx2GOPsW3bNurXr8/EiROzrBaFLxG5efZEWDXOaN8+GALSjv/OdP5BxmyKYCy6LCIi4uEeeughfHx8mDNnDp999hmPP/646/qv1atXc9999/Hoo49Su3ZtypUrx7///pvhY1etWpVDhw5x7Ngx17a1a9e67fPnn39SunRpRowYQf369alYsSKRkZFu+/j7+2O32697ri1bthAbG+vatnr1anx8fKhcuXKGa74RKa/v0KFDrm07duzg/PnzVKtWzbWtUqVKPPfccyxdupT777+fmTNnuh4LDw/nmWeeYd68eTz//PPMmDEjS2oFhS8RuRWbPofzkRBUBBpm7noi19TwKbBYjTXFjt34+iciIiI5SXBwMF27dmX48OEcO3aMXr16uR6rWLEiy5Yt488//2Tnzp08/fTTbjP5XU/r1q2pVKkSPXv2ZMuWLfz++++MGDHCbZ+KFSty8OBBvvrqK/777z8++OADV89QijJlyrB//342b97M6dOniY+PT3Ou7t27ExAQQM+ePfnnn39YuXIlAwcO5LHHHnMNObxZdrudzZs3u9127txJ69atqVmzJt27d2fjxo2sW7eOHj160KJFC+rXr8+lS5cYMGAAq1atIjIyktWrV7N+/XqqVq0KwODBg1myZAn79+9n48aNrFy50vVYVlD4EpGbkxgHv75jtJs9n6FZnTJNaAmo3slor52SfecVERHJIk888QTnzp2jTZs2btdnvfzyy9StW5c2bdrQsmVLihYtSqdOnTJ8XB8fH77//nsuXbpEw4YN6dOnD6+//rrbPvfeey/PPfccAwYMoE6dOvz555+88sorbvt06dKFtm3b0qpVKwoXLpzudPeBgYEsWbKEs2fP0qBBAx544AHuvPNOJk2adGNvRjpiYmK47bbb3G4dO3bEYrHwww8/kD9/fpo3b07r1q0pV64cc+fOBcBqtXLmzBl69OhBpUqVeOihh2jXrh1jxowBjFDXv39/qlatStu2balUqRIffph1I2ssTmcGFyIQN1FRUYSGhnLhwoUbvhhRxCus+RCWDIeQEjBwI/gFZO/5D2+Aj+4Aqz8M/gfy3to3aiIi4rni4uLYv38/ZcuWJSAgm/89klzjWp+zjGYD9XyJyI2Lj4Hf3zXaLf4v+4MXQMl6EB4B9gT4++PsP7+IiIjIDVL4EpEbt24aXDwN+ctCne7m1dGor/Fz/cfGMEgRERGRHMxrwtfkyZMpU6YMAQEBREREsG7duqvuO2vWLCwWi9tNXdQiGXTpPKz+n9FuORysftfcPUtV6Qih4UYQ3Pa1eXWIiIiIZIBXhK+5c+cyZMgQRo0axcaNG6lduzZt2rTh5MmTV31OSEgIx44dc92unE5TRK5izWSIuwCFq0DNB8ytxep7eZbFtVNAl7CKiIhIDuYV4eu9997jySefpHfv3lSrVo2pU6cSGBjIJ598ctXnWCwWihYt6rpdb/rL+Ph4oqKi3G4iuU7s6ctra7V6CXys194/O9TtAX5BcHIH7FtldjUiImIizSMnWSkzPl8eH74SEhLYsGEDrVu3dm3z8fGhdevWrFmz5qrPi4mJoXTp0oSHh3Pfffexffv2a55n3LhxhIaGum7h4eGZ9hpEPMYf70NCDBStBVXvNbsaQ558cNujRluLLouI5Ep+fsYQ+IsXL5pciXizlM9XyuftZvhmVjFmOX36NHa7PU3PVVhYGLt27Ur3OZUrV+aTTz6hVq1aXLhwgfHjx9OkSRO2b99OyZIl033O8OHDGTJkiOt+VFSUApjkLlHHYP1HRvuOV8BiMbee1CKehnXTYc9SOL0HClU0uyIREclGVquVfPnyuS45CQwMxJKT/p0Sj+Z0Orl48SInT54kX758WK03P/LH48PXzWjcuDGNGzd23W/SpAlVq1Zl2rRpvPrqq+k+x2azYbPZsqtEkZzn9/GQFGdM717xLrOrcVewPFRuB7sXGtd+3fOe2RWJiEg2K1q0KMA1r/kXuRX58uVzfc5ulseHr0KFCmG1Wjlx4oTb9hMnTmT4zfHz8+O2225j7969WVGiiOc7FwkbPjXaOa3XK0Wjfkb42vIl3PEyBBYwuyIREclGFouFYsWKUaRIERITE80uR7yMn5/fLfV4pfD48OXv70+9evVYsWIFnTp1AsDhcLBixQoGDBiQoWPY7Xa2bdtG+/bts7BSEQ/269vgSISyLaBsM7OrSV+Z2yGsJpzYBhs/hdufM7siERExgdVqzZQ/kkWygsdPuAEwZMgQZsyYwaeffsrOnTvp27cvsbGx9O7dG4AePXowfPhw1/5jx45l6dKl7Nu3j40bN/Loo48SGRlJnz59zHoJIjnX6b2wZY7RvuMVc2u5FosFGvcz2n9NB7u+9RQREZGcxeN7vgC6du3KqVOnGDlyJMePH6dOnTosXrzYNQnHwYMH8fG5nDPPnTvHk08+yfHjx8mfPz/16tXjzz//pFq1ama9BJGca9Ub4HRApbYQ3sDsaq6tRhdYNgqij8KOH8xfh0xEREQkFYtTCyLclKioKEJDQ7lw4QIhISFmlyOSNY7/A1ObGu2nf4ditcytJyNWvWUExhL1oM+KnHl9moiIiHiVjGYDrxh2KCJZZOUbxs9qnTwjeAHUfxysNjiyAQ6tM7saEREREReFLxFJ3+ENsPtnsPhAqxFmV5NxwYWh1kNGW4sui4iISA6i8CUi6Vv5mvGz1sNQuJK5tdyoRn2NnzsXwPmD5tYiIiIikkzhS0TSOrAa/vsFfHyhxf+ZXc2NC6sO5VoaE4X8Nc3sakREREQAhS8RuZLTCb8k93rV7QEFyppbz81q1N/4ufFziI82txYRERERFL5E5Er/rYCDfxqTVjQbanY1N69CayhYEeIvwOY5ZlcjIiIiovAlIqmk7vVq0AdCS5hbz63w8YFGzxjttVPAYTe3HhEREcn1FL5E5LJdP8PRTeAXBLc/Z3Y1t652NwjIB+f2w79LzK5GREREcjmFLxExOOyw8nWj3egZY8p2T+cfBPV6GW1NOy8iIiImU/gSEcP27+HkDrCFQpOBZleTeRo+BRYrHPgdjm01uxoRERHJxRS+RATsSbDyDaPdZCDkyW9uPZkptARU72S0104xtRQRERHJ3RS+RAS2fAln/4PAgpcnqfAmKdPO//MtRJ8wtxYRERHJtRS+RHK7pHj49S2jfftzYMtrbj1ZoWQ9CI8AewKs/8jsakRERCSXUvgSye02fgYXDkFwUWN6eW/VqK/x8++PITHO3FpEREQkV1L4EsnNEi7Cb+8Y7eZDwS+PufVkpSodIbQUXDwD2742uxoRERHJhRS+RHKz9TMg5gTkKwV1e5pdTday+kLEU0Z7zYfGgtIiIiIi2UjhSyS3iouCPyYY7RbDwNff1HKyxW2PGQtIn9oJ+1aZXY2IiIjkMgpfIrnV2ilw6SwUrAi1uppdTfbIkw9ue9Roa9FlERERyWYKXyK50cWzsGaS0W413BiSl1tEPA1YYM9SOPWv2dWIiIhILqLwJZIb/fkBxEdBWA2o1tnsarJXwfJQuZ3R/muqubWIiIhIrqLwJZLbxJyEv6YZ7VYjwCcX/hpo1M/4ueVLoxdQREREJBvkwr+6RHK539+DxItQot7lHqDcpsztULSm8T5smGV2NSIiIpJLKHyJ5CYXDhuLDAPc8TJYLObWYxaL5XLv17oZYE80tx4RERHJFRS+RHKT394BewKUvh3KtTK7GnPV6AJBRSD6KOz4wexqREREJBdQ+BLJLc7ug02zjXZu7vVK4WuDhk8a7TWTteiyiIiIZDmFL5HcYtVb4EiCCq2hdGOzq8kZ6vUGqw2OboRD68yuRkRERLycwpdIbnByF2yda7RbjTC3lpwkuDDUeshor51sbi0iIiLi9RS+RHKDVW8ATqhyD5Soa3Y1OUvKxBs7f4TzB82tRURERLyawpeItzu2JXlCCYt6vdITVg3KtQSn4/L6ZyIiIiJZQOFLxNv98prxs+YDRtCQtBr1N35u/Azio82tRURERLyWwpeINzv4F+xZChYrtBxudjU5V4XWULAixEfB5jlmVyMiIiJeSuFLxJv98qrxs84jULC8ubXkZD4+0OgZo712Cjjs5tYjIiIiXslrwtfkyZMpU6YMAQEBREREsG5dxqaN/uqrr7BYLHTq1ClrCxTJbvt+hQO/g9UfWrxodjU5X+1uEJAPzu2HfxebXY2IiIh4Ia8IX3PnzmXIkCGMGjWKjRs3Urt2bdq0acPJkyev+bwDBw4wdOhQmjVrlk2VimQTp/Nyr1e9XpAv3NRyPIJ/kPFegdH7JSIiIpLJvCJ8vffeezz55JP07t2batWqMXXqVAIDA/nkk0+u+hy73U737t0ZM2YM5cqVu+454uPjiYqKcruJ5Fh7lsLh9eCbB5oNNbsaz9HwKfDxNXoMj201uxoRERHxMh4fvhISEtiwYQOtW7d2bfPx8aF169asWbPmqs8bO3YsRYoU4YknnsjQecaNG0doaKjrFh6ungTJoRyOy71eEU9B3jBz6/EkoSWgWiejvfZDU0sRERER7+Px4ev06dPY7XbCwtz/wAwLC+P48ePpPuePP/7g448/ZsaMGRk+z/Dhw7lw4YLrdujQoVuqWyTL7PwBjm8D/7zQdLDZ1XielEWXt30L0SfMrUVERES8iseHrxsVHR3NY489xowZMyhUqFCGn2ez2QgJCXG7ieQ4DjusfMNoN+4PgQXMrccTlawH4RHgSIT1H5ldjYiIiHgRX7MLuFWFChXCarVy4oT7N9QnTpygaNGiafb/77//OHDgAB07dnRtczgcAPj6+rJ7927Kl9eU3OKhtn4Np/81Zu1r3M/sajxXo35w6C/4+2NoNgT88phdkYiIiHgBj+/58vf3p169eqxYscK1zeFwsGLFCho3bpxm/ypVqrBt2zY2b97sut177720atWKzZs361ou8Vz2RFg1zmjfPhgCQk0tx6NVuQdCS8HFM7DtG7OrERHJGnFRsPMnsCeZXYlIruHxPV8AQ4YMoWfPntSvX5+GDRsyYcIEYmNj6d27NwA9evSgRIkSjBs3joCAAGrUqOH2/Hz58gGk2S7iUTZ9DucjIaiIMWuf3DyrrzFZydKXYc2HcNtjYLGYXZWISOZxOmFud9j/m7HMRsf/mV2RSK7gFeGra9eunDp1ipEjR3L8+HHq1KnD4sWLXZNwHDx4EB8fj+/kE7m6xDj49R2j3ex5Y80quTV1e8CqN+HUTti3EsrfYXZFIiKZZ8NMI3gBbJgFZVtAjftNLUkkN7A4nU6n2UV4oqioKEJDQ7lw4YIm3xDzrfkQlgyHkJIwaCP42syuyDss/D9YNw0q3g3dNfxQRLzE+UPwYWNIiIaiteD4VrCFwDO/Q/4yZlcn4pEymg3UHSTi6eJj4Pd3jXaLFxS8MlOjZwCLsWj1qX/NrkZE5NY5nfDjICN4lWwIfZYbM7zGR8G3jxvXD4tIllH4EvF066bBxdOQvyzU6W52Nd6lQDmo3N5o/zXF3FpERDLDptnw3y9gtcF9k40v7Lp8ZEzSdGQD/PKq2RWKeDWFLxFPduk8rE6+SLrlcLD6mVqOV2rU1/i5+Uu4eNbcWkREbkXUUVgywmi3egkKVzLa+UoZQQyMf1P2LjenPpFcQOFLxJOtmQxxF6BwFaj5gNnVeKcyt0PRmpB0ybgoXUTEEzmd8ONgiL8AxetC4wHuj1ftCA2eNNrznobo49leokhuoPAl4qliT8PaD412q5fAx2puPd7KYoFG/Y32uhm6HkJEPNPWubBnCVj9odOHxpIaV7r7NQirYQxln/cUOBzZX6eIl1P4EvFUqydAQgwUqw1V7zW7Gu9W435j/bToo7DjB7OrERG5MdHHYdGLRrvFi1Ckavr7+QXAAzPBLxD2/wqr38++GkVyCYUvEU8UdczohQG44xUtAJzVfG3QMHk4zprJxvAdERFP4HTCz89D3Hnjy7qmz157/8KVoP14o/3L63DwrywvUSQ3UfgS8US/j4ekOGN64Aqtza4md6jX25gd7OhGOLTO7GpERDLmn+9g10/g4wf3fZixiZnqPAI1HwKnHb57Ai6dy/o6RXIJhS8RT3MuEjZ8arTV65V9ggtDrYeM9trJ5tYiIpIRMadg4QtGu/lQKFojY8+zWOCe94zlNi4cgh8GqMdfJJMofIl4ml/fBkcilG0BZZuZXU3u0qif8XPnj0YIFhHJyRYOhUtnjUk0bh9yY8+15YUHPjF6zHb9BOs/ypoaRXIZhS8RT3J6L2yZY7TveMXcWnKjsGpQriU4HbBuutnViIhc3Y4fYMd8sFiTF1P2v/FjFL8N7hprtJeMgOPbMrVEkdxI4UvEk6x6w/jDv1JbCG9gdjW5U8q08xs/g/hoc2sREUlP7Bljkg2A25+D4nVu/liN+hr/5tjj4ZvekBCbKSWK5FYKXyKe4vg/xoXTAK1GmFtLblahNRSsCPFRsOkLs6sREUlr8YsQewoKV4UW/3drx7JYjIk68haDM3tg4S0eTySXU/gS8RQr3zB+Vu8MxWqZW0tu5uMDjZ4x2n9NBYfd3HpERFLb9TNs+wYsPtBpsrFUxq0KKghdPjKOuXk2bP361o8pkkspfIl4gsMbYPfPxj98LV8yuxqp3Q0C8sG5/fDvYrOrERExXDwLPz1ntJsMhBL1Mu/YZW6H5sm9Xj89B2f+y7xji+QiCl8inmDla8bPWg8bC2CKufyDoH5vo73mQ3NrERFJseQliDlhDI3Oii/qWvwflL4dEmLg296QFJ/55xDxcgpf3mDDp/DfL2ZXIVnlwGrjv6+P762P3ZfM0+BJ479J5B9wbIvZ1YhIbvfvEtjyJWAxZjf0C8j8c/hY4f7pkKeA8Xtv+ZjMP4eIl1P48nQnthvreHzeGRa/BIlxZlckmcnphF+Se73q9oACZc2tRy4LLQHVOhnttVNMLUVEcrm4C/DjYKPdqB+Uisi6c4WWgE7Jv/PWTobdGnotciMUvjxd/rLGH+Vg/BKccQec2GFuTZJ5/lsBB/8Eqw2aDTW7GrlSyqLL276F6OPm1iIiudeSERB9FAqUgztezvrzVW57+fff/L4QdTTrzyniJRS+PJ1/IHR4F7rNhcBCcHI7TG9pfBPvcJhdndyK1L1eDfoY3zZKzlKyHoRHgCMR1n9sdjUikhvtXQGbPjfa904y/i7IDq1HQ7HacOksfPekZn4VySBTw9ehQ4c4fPiw6/66desYPHgw06dPN7EqD1W5LfRbAxXvNhZCXDwMvuiib+M92a6f4egm8AsyFsmUnCnl29+/P4bES+bWIiK5S3w0/Pis0W74FJRpmn3n9rXBAzPBP9i49vW38dl3bhEPZmr4euSRR1i5ciUAx48f56677mLdunWMGDGCsWPHmlmaZwouAo98De3Hg2+AMUnDh41h509mVyY3ymGHla8b7UbPQHBhc+uRq6tyD4SWgotntPaNiGSvZSPhwiHIVxruHJX95y9YHu5532j/+qYxQZSIXJOp4euff/6hYcOGAHz99dfUqFGDP//8ky+++IJZs2aZWZrnslig4ZPw9G9QtJYxHGBud1gwCBJiza5OMmr793ByB9hCjbVaJOey+kLEU0Z77RRjuKiISFbb9yv8/YnRvnci2ILNqaPWQ1CnOzgd8F0fiD1jTh0iHsLU8JWYmIjNZqy8vnz5cu69914AqlSpwrFjx8wszfMVrgx9VkDTZwELbPwUpjaDIxvMrkyux54EK98w2k0GQp785tYj11e3hzH05tRO2LfS7GpExNvFx8CC5C/m6vWGci3Mrafd28baYtFH4Yd++hJK5BpMDV/Vq1dn6tSp/P777yxbtoy2bdsCcPToUQoWLGhmad7B1x/uGgs9F0BICTj7H3x8N/z2ji6Mzcm2fGn8twosaAw5lJwvIBRue9Roa9FlEclqK8bC+UgIKWn8O282WzA88IkxM++/i+GvqWZXJJJjmRq+3nrrLaZNm0bLli3p1q0btWvXBmDBggWu4YiSCco2h76rofr94EgyZtCb1QHORZpdmVwpKR5+fcto3/4c2PKaW49kXMTTgAX2LoNT/5pdjYh4q8g/Yd00o33v/yAgxNx6UhSrBW2Sr1Ve+ooxYZSIpGFxOs3tG7bb7URFRZE//+WhVQcOHCAwMJAiRYqYWNm1RUVFERoayoULFwgJySG/+K7H6YStc+HnoZAQDbYQY3KOWg8Z14qJ+dbNMBbNzlsMBm0CvzxmVyQ34stHYPfPUP/xyxehi4hkloSLMLUpnN1n9LbfN9nsitw5nTD3Udj1k7Hm2NO/6UtEyTUymg1M7fm6dOkS8fHxruAVGRnJhAkT2L17d44OXh7LYoHaD0PfPyC8EcRHwfdPwXdPwKXzZlcnCReNIaEAzYcqeHmiRn2Nn5u/hItnza1FRLzPyteN4JW3GNz9utnVpGWxGJN/hJQ06vxpiK7/ErmCqeHrvvvu47PPPgPg/PnzRERE8O6779KpUyemTJliZmneLX8Z6PUztHoZLFb45zuY0hQO/GF2Zbnb+hkQcwLylYLbephdjdyMMrdD0ZqQdAk2zDK7GhHxJofWwZrknq6O/4M8+Uwt56oCC8ADHxt/X2z72riOWURcTA1fGzdupFmzZgB8++23hIWFERkZyWeffcYHH3xgZmnez+oLLV6AJ5YaQwOiDsOse2DZKEhKMLu63CcuCv6YYLRbDDMmSxHPY7FAo/5Ge90MsCeaW4+IeIfEOPihP+CEWg9DpTZmV3RtpRpBq5eM9s/Pw+k95tYjkoOYGr4uXrxI3rzGWOClS5dy//334+PjQ6NGjYiM1GQQ2aJkfXj6d7jtMcAJqyfAx601YUB2WzvFWJOtYEWo1dXsauRW1LgfgooYUy7v+MHsakTEG6waB6f/heAwaDvO7Goy5vbnoGwLSLwI3/Q2AqSImBu+KlSowPz58zl06BBLlizh7rvvBuDkyZOeM4mFN7AFw32ToOtsY02pY1tgWnNY/5HGameHi2dhzSSj3Wq40SspnsvXZix0DsYQIf0/JCK34sgG+DN5NNA97xvD+jyBjxXunw6BheDENlj2itkVieQIpoavkSNHMnToUMqUKUPDhg1p3LgxYPSC3XbbbTd0rMmTJ1OmTBkCAgKIiIhg3bp1V9133rx51K9fn3z58hEUFESdOnX4/PPPb+m1eIWqHaHvGijXyrhm5efn4cuHIeaU2ZV5tz8/MCY/CasB1TqbXY1khvqPG+vdHN0Ih/4yuxoR8VRJ8TC/PzgdUOMBqNLB7IpuTN6i0Dl5Wvx102HnT+bWI5IDmBq+HnjgAQ4ePMjff//NkiVLXNvvvPNO3n8/49M0z507lyFDhjBq1Cg2btxI7dq1adOmDSdPnkx3/wIFCjBixAjWrFnD1q1b6d27N71793arIdcKKQaPzoM24y4vljilMfy71OzKvFPMSfgr+R+mViPAx9T/JSWzBBUylnAAWKtFl0XkJv32DpzaCUGFod3bZldzcyq2hiaDjPYP/eH8IXPrETGZ6et8pTh8+DAAJUuWvOHnRkRE0KBBAyZNMoZuORwOwsPDGThwIMOGDcvQMerWrUuHDh149dVXM7S/R67zdaNObIfv+sDJHcb9Bn3grlfBP9DcurzJomHw1xQoUQ/6rNB6a97kxA7jiwuLDwzaDPlLm12RiHiSY1tgeitw2uHBT6F6J7MrunlJCTCzrTGEMryRMeOyhtiLl/GIdb4cDgdjx44lNDSU0qVLU7p0afLly8err76Kw+HI0DESEhLYsGEDrVu3dm3z8fGhdevWrFmz5rrPdzqdrFixgt27d9O8efOr7hcfH09UVJTbzeuFVYcnV0Kjfsb99R/B9JbGPwhy6y4chr8/Ntp3vKzg5W3CqhlDeJ0OY7iNiEhGJSUkDze0Q7X7PDt4gTGDb5ePwRYCh9YaE4iI5FKmhq8RI0YwadIk3nzzTTZt2sSmTZt44403mDhxIq+8krELM0+fPo3dbicsLMxte1hYGMePH7/q8y5cuEBwcDD+/v506NCBiRMnctddd111/3HjxhEaGuq6hYeHZ+xFejq/AGNmpUfnQXBROL0bZtxpTIuewYAsV/HbO2BPgNK3G3+ki/dJ+eJi42cQH21uLSLiOf5435ikIk8BaP+u2dVkjgJloeMEo/37u7BvlZnViJjG1PD16aef8tFHH9G3b19q1apFrVq16NevHzNmzGDWrFlZeu68efOyefNm1q9fz+uvv86QIUNYtWrVVfcfPnw4Fy5ccN0OHcplY5Yr3Al9/4Qq94AjEZaPgs/uNXpv5Mad3QebZhtt9Xp5rwqtjeUD4qNg0xdmVyMinuDEduPLOYD270BwYXPryUw1ukDdnoAT5j2lCb0kVzI1fJ09e5YqVaqk2V6lShXOnj2boWMUKlQIq9XKiRMn3LafOHGCokWLXvV5Pj4+VKhQgTp16vD888/zwAMPMG7c1bvBbTYbISEhbrdcJ6igMR39vRPBLxAO/A5TmsA/35ldmedZ9RY4kow/zks3NrsaySo+PtCor9H+awo47ObWIyI5mz0J5vczvuSs3MEIK96m7ZtQuArEnID5z2gUjeQ6poav2rVruybJSG3SpEnUqlUrQ8fw9/enXr16rFixwrXN4XCwYsUK19T1GeFwOIiPj8/w/rmWxQJ1e8Azf0DxuhB3Ab59HOY9DXG54Dq4zHByF2yda7RbjTC3Fsl6tR+GgHxw7oAxe6iIyNX8+T84ttn4nXHPe945KsI/EB6YCb4BsHf55XUuRXIJU6eaefvtt+nQoQPLly93BaU1a9Zw6NAhFi5cmOHjDBkyhJ49e1K/fn0aNmzIhAkTiI2NpXfv3gD06NGDEiVKuHq2xo0bR/369Slfvjzx8fEsXLiQzz//nClTpmT+i/RWBcvDE0vh17eMsdtbv4KDa+D+GVAqwuzqcrZVbwBOYwhnibpmVyNZzT8I6vc2ruFY86HnrdMjItnj5C5Y9abRbvumsUaWtwqrZrzGnwbDijFQuimUrGd2VSLZwtSerxYtWvDvv//SuXNnzp8/z/nz57n//vvZvn37DS163LVrV8aPH8/IkSOpU6cOmzdvZvHixa5JOA4ePMixY8dc+8fGxtKvXz+qV69O06ZN+e6775g9ezZ9+vTJ9Nfo1ax+xvVKvRZCvlJwPtKYSvaX18GeaHZ1OdOxLbDjB8CiXq/cpMGT4OMLkX9otlARScueBD/0MyZhqni30WPu7er1gmqdjCH43/Y2RtKI5AI5Zp2v1LZs2ULdunWx23Pu9RG5Yp2vGxF3ARb+n9EDBlCiPtw/3eghk8u+eBD2LIWaD0KXj8yuRrLTt0/AP99C7W7QearZ1YhITrL6f7BspDEVe7+1EFrC7Iqyx6XzMK0ZnD8I1TsbwxG9cail5Aoesc6XeJGAULh/GjzwidE+8jdMbWZMsZ3z8r05Dv5lBC+LFVoON7sayW6Nk6ed3/YtRF99GQwRyWVO7zFGjAC0eT33BC+APPmMwOXjC9u/N/5mEPFyCl+SuWp0MaakL9MMEmNhwUD4+jG4mLHZK73aL68aP+s8oh7B3KhEPQhvZMxitl69niKCMQPqD/3BHg/l74DbHjO7ouxXsj7cOdJoL3oRTu40tx6RLKbwJZkvtCT0+AFajwEfP9j5ozEl/X+/mF2Zefb9akzNb/WHFi+aXY2YJWXa+b8/gcRL5tYiIub7axoc+gv8g6HjB7l3yF3jgVD+Tki6BN/01u9H8WqmzHZ4//33X/Px8+fPZ08hknV8rHD7YCjXEuY9Caf/hc87Q6P+xjdcfgFmV5h9nM7LvV71ekO+cHPrEfNUuQdCS8GFg7D1a6jX0+yKRMQsZ/6DFWON9l1jc/e/DT4+0HkaTG0Kp3bC4uHQcYLZVYlkCVN6vkJDQ695K126ND169DCjNMlsxevAU79Cg+SZJNdOhhl3wIkdppaVrfYshcPrwTcPNHve7GrETFZfiHjKaK+doushRXIrh8MYlp90yRimX6+32RWZL7iwEcCwwIaZxjVgIl4oR8526Ak02+FN+HeJMbY99hRYbXDXGGj4tPGNl7dyOGB6czi+DZo+a3y7Kblb3AV4rxokxMBj3xvXeYhI7rJuBiwcCn6BxnXSBcqaXVHOsWKssX6oLRSe+Q3ylzG7IpEM0WyHkvNUamP8I1OxjXFx8eJh8EUX7575becPRvDyzwtNB5tdjeQEAaFw26NGe82H5tYiItnv3AFYNspotx6t4HWllsMhPALiLxhLdGjdUPEyCl+SvYKLwCNzocO7xjC8/36BDxvDzp/MrizzOeyw8g2j3bg/BBYwtx7JOSKeBiywdxmc2m12NSKSXZxOWDDImA24VBNjAXZxZ/Uz1sFMWbYm5ZppES+h8CXZz2IxrgF7+lcoWgsunYW53Y3x7/ExZleXebZ+bUw0EpDv8hpPIgAFykHl9kb7Ly24LJJrbJgF+38F3wC4b5J3D7u/FflKwb2TjPbq/8He5ebWI5KJ9H+9mKdwZeizInk4nsVYXHFaMzi8wezKbp09EVaNM9q3Dza+wRNJLSWQb/5S6+CJ5AbnD8HSV4z2Ha9ovcfrqXbv5cm65j3t3ZcoSK6i8CXm8vU3Jt7o+SOElICz++Dju+DXd4xhe55q0+dwPhKCikDDp8yuRnKi0k2haE1jtrMNs8yuRkSyktMJPz4LCdFQsuHlNf/k2u5+HcJqwMXTMO8pYxIrEQ+n8CU5Q9lm0Hc1VL8fnHZY+RrMbG9cmOxpEuOM8AjG1PL+QebWIzmTxWKsewfGzGe6qFzEe23+Av5bYcz0e99kYy1MuT6/AHjgE2NWyP2/wur3za5I5JYpfEnOkSe/8Uu28zRjdsBDa2HK7bDlK89aD+nvTyD6KISUhPpau0Wuocb9EBxmfF62zze7GhHJClFHYfFLRrvVS1C4krn1eJrClaF98heav7wOB/8ytx6RW6TwJTmLxQK1H4a+f0B4I2OIxvdPw7ePw6VzZld3ffExxvokAC1eAF+bufVIzuZrc1+A3JO+ZBCR63M64afnjGnTi9eFxgPMrsgz1ekONR8yRsZ894Rn/D0gchUKX5Iz5S8DvX6GVi+DxQrb5xm9YPt/N7uya1s3zRibnr+s8Y+FyPXUf9wYinR0ExzSN7oiXmXr1/DvYrD6Q6cPweprdkWeyWKBe94zZoq9cMiYHVlfVomHUviSnMvqa/QePbHU+IUbdRg+7QjLRkJSgtnVpXXpvDElLhiLRFr9TC1HPERQIajd1WivmWxuLSKSeaJPwKL/M9ot/g+KVDW3Hk9ny2tcmuDjBzt/hL8/NrsikZui8CU5X8n68PTvcNtjgNMIOB/dmfMWp10zGeIuQOEqUPMBs6sRTxKRPPPZrp/gXKS5tYjIrXM64echEHfeWM+y6WCzK/IOxW+Du8Ya7cUvwfF/zK1H5CYofIlnsAUbC1J2nW1MzHF8K0xrAes/yhlDD2JPw9oPjXarEZrJSm5MWDUo1wqcDlg33exqRORWbZ9nfJni45s83FAjITJNo75QqS3Y4+Hb3pAQa3ZFIjdE4Us8S9WO0HeN8Ydq0iX4+Xn48mGIOWVuXasnQEIMFKtt1ChyoxonTzu/8TOIjza3FhG5ebGnYeELRrvZUGM9P8k8Fgvc9yHkLQan/708tFPEQyh8iecJKQaPzoM244yJCv5dDFMaw79Lzakn6pixThPAHa8Y/zCI3Kjyd0LBihAfBZu+MLsaEblZC4fCxTPG4sDNnje7Gu8UVBC6fAQWH9g0G7Z+Y3ZFIhmm8CWeyccHGveDp1ZCkWoQewrmPGj0hCVczN5afh8PSXEQHgEVWmfvucV7+PgYw2kA/poCDru59YjIjduxALZ/b8zSe99k8PU3uyLvVeZ2aJ7c6/XTYDjzn6nliGSUwpd4trDq8ORKaNTPuL/+I5jeEo5tyZ7zn4uEDZ8abfV6ya2q3Q0C8sG5A7B7kdnViMiNuHjWmGQD4PbBULyOmdXkDs1fgNJNjWH/3/aGpHizKxK5LoUv8Xx+AdB2nDEUMbgonN4NM+6EPyaAw5G15/71bXAkQtkWULZZ1p5LvJ9/INTvbbTXTjG3FhG5MYteNEZhFK4CLV40u5rcweoL988wJuI6tgWWjzG7IpHrUvgS71HhTuj7J1S5xwhEy0fBZ/fChcNZc77Te2HLHKN958isOYfkPg2fMmZIi/wj+3pwReTW7F4E2742rkG670PwtZldUe4RWgI6JX9ZtXYy7F5sbj0i16HwJd4lqKAxHf29E8EvCA78DlOawD/fZf65Vr1hTA1eqZ2xFplIZggpDtU7G+01H5pbi4hc36Vz8ONgo914AJSsZ2o5uVLldpfXS5zfF6KOmluPyDUofIn3sVigbg945ncoUc9Y+Pjbx2He0xAXlTnnOP7P5UDX6qXMOaZIipSJN/75DqKPm1uLiFzbkhEQcxwKVtC/B2a6a4yxoPWls/Ddk5q0SHIshS/xXgXLw+NLjNmQLD6w9SuYejscXHvrx175hvGzemcoVuvWjyeSWol6EN7IGD67/iOzqxGRq9mzDDZ/ASSvPeWXx+yKci9fGzw4C/yDjWHbv403uyKRdCl8iXez+sEdI6D3IshXCs5Hwsx28MvrYE+8uWMe3gC7fzYCXUt9yylZJKX36+9PIPGSubWISFpxF+DHZ412o75QKsLcesT40vWe9432r2/CgdXm1iOSDoUvyR1KNYJnVhtTeTsd8Nvb8Embm1sXZOVrxs9aD0PhSplbp0iKKvdAaCljsdatX5tdjYhcaenLEHUE8pc1lhqRnKHWQ1Cnu/Fv/Xd9jCUARHIQhS/JPQJCoPNUeGAmBITCkQ0wtRls/Ayczowd48Bq+O8XYza6Fv+XtfVK7mb1hYinjfbaKRn/jIpI1vvvF+PfDoD7JhnLREjO0e5tKFgRoo/C/H76/Sk5isKX5D417jempC/TDBJjYcFAmPvo9b8dczrhl+Rer7o9oEDZrK9Vcre6jxnXL5zaafyxJyLmi4+GBYOMdoMnoczt5tYjadmC4YFPwGqDfxfBX9PMrkjEReFLcqfQktBjAdw1Fnz8YNdP8GHja/+B+98KOPin8cu8+QvZV6vkXgGhcNujRluLLovkDMtGwYVDxnXErUebXY1cTbFa0OZ1o73sFTi62dRyRFJ4TfiaPHkyZcqUISAggIiICNatW3fVfWfMmEGzZs3Inz8/+fPnp3Xr1tfcX7yUjw80fRaeXAGFKhlTBX/eGRYPh8Q4931T93o16GOsxSSSHSKeBiywdxmc2m12NSK52/7f4O+Pjfa9E40eFsm5GvQxrp+1JxhLzsRHm12RiHeEr7lz5zJkyBBGjRrFxo0bqV27Nm3atOHkyZPp7r9q1Sq6devGypUrWbNmDeHh4dx9990cOXIkmyuXHKFYbXjqV+OXNMDaD2HGHXBi++V9dv0MRzcZCzff/pw5dUruVKAcVG5vtP+aam4tIrlZQvIwdYB6vaBcSzOrkYywWIyQHFISzv4HPz+v67/EdBan0/M/hRERETRo0IBJkyYB4HA4CA8PZ+DAgQwbNuy6z7fb7eTPn59JkybRo0ePDJ0zKiqK0NBQLly4QEhIyC3VLznIv0vgh/4Qe8oYXth6NDR8CqY1g5M7oNnzcOdIs6uU3ObAHzCrA/jmgSE7ILCA2RWJ5D6LXjS+AAkpCf3WGJM4iWc4uBZmtgenHTpNgTqPmF2ReKGMZgOP7/lKSEhgw4YNtG7d2rXNx8eH1q1bs2bNmgwd4+LFiyQmJlKgwNX/oImPjycqKsrtJl6oUhtjMo6KbcAeD0uGw5QmRvCyhUKTgWZXKLlR6aZQtBYkXYINM82uRiT3iVxzedKGe/+n4OVpSjWCVsON9s/Pw+k95tYjuZrHh6/Tp09jt9sJCwtz2x4WFsbx48czdIwXX3yR4sWLuwW4K40bN47Q0FDXLTw8/JbqlhwsuAg8Mhc6vGv0NJxOvs6myUDIk9/c2iR3sligUT+jvW7GzS8QLiI3LuGiMSICJ9R5FCpc/W8FycFuHwJlm0PiRfimd9pru0WyiceHr1v15ptv8tVXX/H9998TEBBw1f2GDx/OhQsXXLdDhw5lY5WS7SwW4xqwp381eh1KNoBGz5hdleRmNbpAcBhEH4Pt882uRiT3WPm6cb1Q3mKXZ88Tz+Njhc7TIbAQnNhmzIAoYgKPD1+FChXCarVy4sQJt+0nTpygaNGi13zu+PHjefPNN1m6dCm1atW65r42m42QkBC3m+QChStD74XQZznY8ppdjeRmvv7GmkIAayfronGR7HBovTEJE0DH/0GefKaWI7copBh0Tp64aN102PmTufVIruTx4cvf35969eqxYsUK1zaHw8GKFSto3LjxVZ/39ttv8+qrr7J48WLq16+fHaWKiNya+r2NiWCOboJDf5ldjYh3S4yDH/qB0wG1HjauCRbPV/Guy9dv/9Afzmskk2Qvjw9fAEOGDGHGjBl8+umn7Ny5k759+xIbG0vv3r0B6NGjB8OHD3ft/9Zbb/HKK6/wySefUKZMGY4fP87x48eJiYkx6yWIiFxfUCGo3dVor5lsbi0i3u7XN+H0v8Zw37bjzK5GMtMdI6FEPYg7D9/1AXuS2RVJLuIV4atr166MHz+ekSNHUqdOHTZv3szixYtdk3AcPHiQY8eOufafMmUKCQkJPPDAAxQrVsx1Gz9+vFkvQUQkY1Im3tj1E5w7YGopIl7ryEZY/YHR7vCelnfwNr7+0OVjsIXAobVG0BbJJl6xzpcZtM6XiJjms06wbyU0HqAJAEQyW1I8TG9pLDFSows88InZFUlW+ec7+PZxwAI9foByLcyuSDxYrlnnS0Qk12nc3/i58TOIjza3FhFv89t4I3gFFoJ275hdjWSlGl2gbk/ACfOehJhTZlckuYDCl4iIpyl/JxSqBPFRsGm22dWIeI9jW+GP94x2h/EQVNDceiTrtX0TCleBmBMwvy84HGZXJF5O4UtExNP4+EBE8rpzf00Fh93cekS8gT3RmN3QkQRV74Xqnc2uSLKDfyA8MBN8A2DvMmMpD5EspPAlIuKJaneDgHzGpBu7F5ldjYjn++N9OL4N8hSADu+aXY1kp7BqRg8YwPLRcHiDqeWId1P4EhHxRP6BxrpfcHkRWBG5OSe2w69vG+12b0NwEXPrkexXrxdU62T0fH7bG+IumF2ReCmFLxERT9XwKfDxhcjVcHSz2dWIeCZ7EszvB45EqNweaj5gdkViBosFOv4P8pWC85Hw47OgCcElCyh8iYh4qpDil69LWTvF3FpEPNWfH8CxzRAQCve8b/wRLrlTnnzQ5RPjS63t3xszyopkMoUvERFP1qiv8fOf7yD6uLm1iHiaU7th1Tij3fZNyFvU3HrEfOEN4I5XjPaiF+HkTnPrEa+j8CUi4slK1IPwRsaQqfUfmV2NiOdw2OGH/mBPgAp3GZPYiAA0GQTl74CkS/BNb0i8ZHZF4kUUvkREPF3jfsbPvz/RHwkiGbX2Qzi8HmwhxrU+Gm4oKXx8oPM0CCoCp3bC4uFmVyReROFLRMTTVe4AoaXg4hnYOtfsakRyvtN74ZfXjPbdr0FoCXPrkZwnuAjcPx2wwIaZxjVgIplA4UtExNNZfSHiaaO9dopm6BK5FocDFgyApDgo1wrq9jC7IsmpyreCZkOM9oJnjXUVRW6RwpeIiDeo+xj4B8OpXfDfL2ZXI5JzrZsOB9cY/7/c+4GGG8q1tRwO4REQfwG+fQLsiWZXJB5O4UtExBsEhMJtjxptLboskr6z+2DFGKN91xhjTSeRa7H6QZePjN+xR/6+PFxV5CYpfImIeIuIpwEL7F1uTKEtIpc5HPDDQEi8CGWaQb3Hza5IPEW+UnDvJKO9egLsXWFqOeLZFL5ERLxFgXJQpYPR1qLLIu7+/hgi/wC/QLh3ojGjnUhGVbsXGvQx2t8/DdEnzK1HPJZ+84iIeJOURZe3fAUXz5pbi0hOcS4Slo0y2neOggJlza1HPNPdr0NYDYg9Bd8/ZfSmitwghS8REW9SuikUrWUsDrphptnViJjP6YQfB0FiLJRqDA2fMrsi8VR+AfDAJ0bv6b5VsPp9sysSD6TwJSLiTSwWaNzfaK+bAUkJ5tYjYraNnxp/KPsGwH2TNdxQbk3hytD+HaP9y+tw8C9z6xGPo99AIiLepvr9EBwG0cdgxw9mVyNinguHYcnLRvuOl6FgeXPrEe9QpzvUfBCcdvjuCbh0zuyKxIMofImIeBtff2jwpNFeO1mLLkvu5HTCj89CQjSUbACN+pldkXgLiwU6vAf5y8KFQ7BgoH7PSoYpfImIeKP6vY1hVkc3wcG1Zlcjkv02zzGWXbDa4L4PwcdqdkXiTQJCjOu/fPxg54/GbJoiGeBrdgEiIpIFggpBrYdg42dG71d4Q/fH0/2WNp1tmbmfGedMd7+MHiu9w6ezn18A+Adl7PmSPaKOwZLhRrvVcChcydx6xDuVqGss1r3kJVj8EoQ3gqI1zK5KcjiL06l+0psRFRVFaGgoFy5cICQkxOxyRETSOrkTPmxkdhW5gAWKVIPSTaB0YyjVBEKKmV1U7uV0wpfd4N9FULwuPLEMrPquWbKI0wlzusKeJVCoEjy1Sl/G5FIZzQYKXzdJ4UtEPMK3j8M/35ldRe6Tv4wRwkon3wqUM64Tkay39WuY96QxHOzp3yCsmtkVibeLPQNTmxqTHN32qDGrpuQ6Cl9ZTOFLRDyC0wlx59MfLpcmDKQTDtINDDlkv5xSW+wpOLgGItdA5Go48Q84r1h8NaiI0StWuqmx1lRYdV2DlBWiT8CHEcbsc61ehhYvmF2R5BYH/oBPOxr/79//EdR60OyKJJspfGUxhS8REUlXXBQcWgcH/4TIP+HIBrBfsd6aLQTCIy4HsuK3ga/NnHq9hdMJXz9mTH5QtBY8+QtY/cyuSnKTlePg1zfBPy88/auWNshlFL6ymMKXiIhkSGIcHN1oBLGDa4xFWROi3fex2qBkfaNXrHRjI5jZ8ppTr6f6Zx582xt8fI3rborWNLsiyW3sSfDZvUYPeLE6xvWGvv5mVyXZROEriyl8iYjITbEnGUMTD665HMhiT7nvY/Exem9KN0kOZE2MGSwlfbGnYXJDuHgGWrwIrV4yuyLJrS4cMa7/unQOGg+ANq+bXZFkE4WvLKbwJSIimcLphDN7LwexyD/hfGTa/QpVuhzESjWGfKU0iUeKb3rD9nlQpLrR66XeBjHT7kXw5cNG+5GvoVIbc+uRbKHwlcUUvkREJMtcOOLeM3ZyR9p9QkomT22fHMgKVQYfn+yv1Ww7f4S5j4LFCk+uMK6fEzHbomHw1xTIUwAa9TPWA/TLA755jJ8pt9T3fQPAL9DY1zdP7vz/2YMpfGUxhS8REck2F8/CwbXJk3isgWObwZHkvk+eApevGSvVBIrV8v4JJy6ehckREHsSbh8CrUeZXZGIISkePmoNx7fe/DGstqsHtfTC2tUe8wu89n0FvUyR68LX5MmTeeeddzh+/Di1a9dm4sSJNGzYMN19t2/fzsiRI9mwYQORkZG8//77DB48+IbOp/AlIiKmSYiFw+uNIHbwTzi0HpIuue/jFwThDZLXG2sMJeqDf6A59WaVeU/B1rlGr9/Tvxl/SIrkFNEnYN0045rOxDhIvAhJcZB46fIt6VLyY8ntK2dGzS5W23WC2s2GwHTue2nQy2g28Iol3+fOncuQIUOYOnUqERERTJgwgTZt2rB7926KFCmSZv+LFy9Srlw5HnzwQZ577jkTKhYREbkF/kFQrqVxA7AnwrEtxixrkWuMoYpx52HfKuMGxqLDxeskXzPWBEpFQJ78ZlSfOXYvNoKXxQc6fajgJTlP3jC4c+SNPcdhvyKYXRnUUtrphbj07sdd/bHUQc8eb9ziLmTue5CeqwU9V1C7wRAYGg6FKmR93ZnEK3q+IiIiaNCgAZMmTQLA4XAQHh7OwIEDGTZs2DWfW6ZMGQYPHqyeLxER8R4OB5zaZYSxlAWgo49esZMFilQzwljKUMWQYqaUe8MunYcPG0H0MWgyEO5+zeyKRDxPStBLSu6VS693Lk1wS2/f5PvXeswen3Wvo25PuPeDrDt+BuWanq+EhAQ2bNjA8OHDXdt8fHxo3bo1a9asybTzxMfHEx9/+YMTFRWVaccWERHJVD4+EFbNuDV80phR8XykEcJSAtmZvXByu3FbP8N4Xv4yycMUk28FyuXMGRWXjDCCV8EK0GqE2dWIeCYfK9iCjVtWc9ivMeTy4rUDYLr7pnosNDzr689EHh++Tp8+jd1uJywszG17WFgYu3btyrTzjBs3jjFjxmTa8URERLKNxWIEq/xloE43Y1vMycszKkb+aaw9du6Acdsyx9gnqIjRK1a6qTGZR1h14w82M+1ZDptnAxa4b7Ix7EhEcjYfqzFc2j/I7EpM5/HhK7sMHz6cIUOGuO5HRUURHm5+0nY6nXT44A/yBfpRMNhGwSB/CgX7UyDIRsFgo10wuR1s88WSE7/BFBGR7BdcBKrdZ9wA4qLg0LrkGRX/hCMbjFkEd/xg3ABsIRAecTmQFb8NfG3ZV3NcFPw4yGhHPAOlGmXfuUVEMoHHh69ChQphtVo5ceKE2/YTJ05QtGjRTDuPzWbDZsvGf2AyKCouiR3HMjYE0t/Xh4JB/hRMFcgKJQe2gq6fl9sBfiZ/uykiItknIAQqtjZuYAzxObrx8lpjB/+C+CjYu8y4gXHhfMn6l9caC28ItrxZV+OyVyDqiNGDd+crWXceEZEs4vHhy9/fn3r16rFixQo6deoEGBNurFixggEDBphbXDYI8PNh9hMRnImN53RMAmdi4jkTk8CZ2ATOxCa3Y+KJTbCTkOTg2IU4jl2Iy9Cxg22+FAz2p0CQEdYKBbsHN9fPYH8KBPrja/XOqUNFRHIlv4DL134B2JOMoYmpF3+OPZU8w+Jq+B1j5sGitZJnVEwOZEGFMqeefatgwyyjfe8kDV8SEY/k8eELYMiQIfTs2ZP69evTsGFDJkyYQGxsLL179wagR48elChRgnHjxgHGJB07duxwtY8cOcLmzZsJDg6mQgXPmaoSwOZr5faK1/+H7VKCnTOx8ZyNTeBMTAKnY+KNgJYc1k7HJnDWFdYSSLA7iIlPIiY+icgzFzNUS/5UQx/dw5mNQsm9awWSh0WG5vHTEEgREU9i9TWmqi9eBxr1NSbxOLP3chCL/NOY1OPYZuO29kPjeYUqXQ5ipZtAvlI3fu74GFgw0Gg36ANlm2XOaxIRyWZeMdU8wKRJk1yLLNepU4cPPviAiIgIAFq2bEmZMmWYNWsWAAcOHKBs2bJpjtGiRQtWrVqVofN581TzTqeT6PgkV6/ZmeTAltI+7epdM8Lc2dgEHDf4KfL1sRg9asG25GvUUg+FTNu7FuhvVVgTEcnpLhxx7xk7uSPtPiElk6e2Tw5khSpff9HVn4caMzKGloJ+a7JndjYRkRuQ0WzgNeEru3lz+LpRdoeT8xcT3INZjBHMTse6D4U8HRNPdFzSDZ8jwM8n1dBHo3etQLA/hVL1rhmTjRi9a/6+GgIpImK6i2fh4NrkSTzWGD1ijiv+DchTIDmIJa81VqwWWP0uP77/d/j0HqP92Hwo3yq7qhcRyTCFryym8HXz4pPsnItNTGfoYzxnU65XizGuYTsdE098kuOGz5E3wDfVZCKpJhRJmVwk1WQj+QL9sfqoV01EJMslxMLh9UYQO/gnHFpvrN+Tml8QhDcwglh4Q/hpsDH9fQ5ZSFVEJD0KX1lM4St7OJ1OLibYjV60VMMdTydfm5Yy9PF0qt62pBscA+ljgfyB6c8CWSB5W/5AP/IG+JE3wJeQAD+CA3wV2EREbpU9EY5tSZ60Y40xVDHufNr9QkoYww0DQrO9RBGRjFD4ymIKXzmTw+EkKi6R0zEJyZOLxF8x9DHe7bFzFxNv+lxB/lZXIDNufq6fIQG+BNvSbk8JbynP0QyRIiKpOBxwapcRxg6uMQLZxTPwyFwNNxSRHE3hK4spfHmHJLuDsxcTXLM8uqbnT/55Orl94WIiUXFJRMcl3tQwyKvJ42dNE95CAvzSCW6XQ92V23R9m4h4LacTnA7w0bqTIpKzZTQbeMVU8yI3y9fqQ5G8ARTJG5Dh5yQkOYiOSyQ6Lin5djmYpd4WHWdM1R/l2n758UuJdgAuJdq5lGjnZHT8Tb8Gm69PqmBmBLL0wlvIFT1wqdtaUFtEciSLBSz6/SQi3kPhS+QG+fv6JE/aYbvpYyTaHcQkB7G04SyRmPiUx9y3pw53sQlGgItPchAfE8/pmJsPcP5WH1cgCw7wJa/tyqGSaYdPXtkbF+Dno+UARERERK5B4UvEBH5WH/IH+ZM/yP+mj2F3OIm5IrylhLbLvXFXD2/RcUnEJCThdEKC3WHMMhmbcNP1+PpYjOB2RXgLCUj/2je36+OSr5EL8vfFRxOZiIiIiJdS+BLxUFYfC6GBfoQG+l1/56twOJzEJCS5euFSQllUOkEtdXi7MvA5nJDkcHL+YiLnLyYCl6577qsJ8rcSZLscyIJtvgTZfMmb/PPK7SltY7uVYJsfQTargpyIiIjkOApfIrmYj4+FkAA/QgJuPsClLAdwvevfLge7JGLi0z6eskRAbIKd2IRbuw4uRZC/leCAK8JbqrDmdj8l5F25PcCXQD+rgpyIiIjcMoUvEbklFouFoOTgUjQ04xOXpOZ0OolPchAbb0xSEhNv9MbFJhgBLTbeTkx8IjFxScQkt2Pj7UTHJxnPibv8vNj4tEEObi3IWSwQ5O9LkM3qHt780+mhu9Z2m4KciIhIbqbwJSKms1gsBPhZCfCz3tJEJnA5yMWkE8rcgl180uXwFp8c6uISk4Pe5X3tDidOJ677JzIpyKXufTN65Ywhk8G26/fWpWwP9LdqkhMREREPovAlIl4ldZArlElBLjounfDm6pW79vbYeLtrdsorgxxRt/ZafVKC3DWHUFoJ9DeWE8jjZyXAzyf5/fEhwNeKLbmdJ/k9S/2YeuhEREQyl8KXiMhVpA5yhfPeepCLS3S4BbXUvXJXG0J5td46hxMcTohO3pYV/K0+2JLDmltw87W6thv3fcjjf7ltuyLEGY+5h700z1XYExGRXEDhS0QkG1gsFvL4G0EkM4LcpUR7qlBmJzo+8fK1cfF2V1hLCXFxSQ7iEu2pbsn3ky634xMdJNgdrvMk2I370XFZE+6ulDrspYS1lOB3ZdgLuDLEXfEc98fSe64Vq8KeiIhkM4UvEREPY7FYCPT3JdDflyJ5M/fYdofzckBzC2wO4pPD2qWE9ILb5f0vJbg/Nz7RkbyvnUuJOSfs+Vkt1wxutpReu3TCni1Vj12AnxV/Xx9syTejbQTGlEBp8zWe42/1UQ+fiEgupvAlIiIuVp/Ls1dmB7vDSXyqEHcpnbDn6qVLdLgej08nHKY8P3XYu/xcY/+EpMthL9HuJNGelG1hL4W/1SdDYe1yO9U+6baNoZ6p97ddZ39fq0+2vmYRETEofImIiGmsPim9eNlzvivDniugJdmJS0gb9q7s4Us7bNP4mZDkID7JQXxSqnainQS70XY6L9eQ0sMXc+tL2d00q4/lOuEuOQCm1/bzwWY1Al9G9/dPZ39/q49m6xSRXEfhS0REco3sDntgXKOXaHcaQSzRTnxyD1yasOYW3BzEX3d/BwlJdtf+RtC7PJwzPtF9/5T178AIoZccRk+hmfyv6KW7MsylBLeUoZ6uIZzJPwNSDel07ZO67Xf1fdT7JyJmUPgSERHJQhaLBX9fC/6+PgRn03DO9NgdzuQgljrQGb146YW11Pu4ta/c39VOb//LoTFlW2oJyduiyd6hnwC+PpbLAc33cs+cLb0QlzrouWbuvLl9bL667k8kN1P4EhERyQWsPpdn3DSL0+l0DcVMMzzzKgEuLrn3LyX4xSX/jL/isbjEy89ztd1+uk/wkuRwkpRgJzYh+3v//K0+7kEvI4HO170H8Gr7pBwzIJ1eQg31FDGfwpeIiIhkC4vFkhwSzAmAKb1/Vw9tGQl2l4d6xqUJeFfsk2pSGEc61/1l1Rp913JliLP5WvH1seBn9cHXasHXx4KvT6q21Qc/qwWrjw9+PhZ8U9rWdPbzsWC1WvC7Yrtv8vN8fS4fyzdD+yXXlbqm5Me1VIR4KoUvERERyRXM7P1LtF/u6YtLunw935VBLb194tPbJ3UovEqPYMrP1FKO4+ksFlzhzT2kJQe5VEEyJcilBElfn1RBL1W4uzLwXS1I+ll9kvdLHUDTCaJu4TXV83x8ko99eZ/UwVPB0rspfImIiIhkMT+rD37W7L/uL2Wo55VDMeMSL1+Pl2R3kuRwJP90kmh3YHc4SbI7SXQY7US7kyS7MXFLyv6Jdif25J9J19gvpZ1y3ESHsY89+Vxu+ybXkGR3kOhwYk++pX1dKT2I2fp2ZguLhcu9fVf0NrqFwyuCYOr7bgEznYCXOlxefr57UL0cZlMfL3VNVwTK9OpIFUxT75Obh78qfImIiIh4Kfehnn5ml3NTHI7kQJYqyKUOeJcDont4S3KkCnJ2I8SlCY1X7Od2zFRh1HVcV0B0JO/nfixXAE1Vqz1VoE0dQK8VLI11CL0wWSbzsXBFwEunfWXo9Ekd6C7v16xiYR6JKGX2S8owhS8RERERybF8fCz4+1jwx/uWB0gJlkYgMwJfSphMEySvCHXuAe9ywHQLeFcJl+4B9XJodA+KxnHT9nam6h11C6Tu50h5Tc60+RKHM3m200x4DwsEZePaIZlA4UtERERExAQpwRIgD+bNRJqVHA734av2DAbKlFCXtsfQ4XacCkXymv0Sb4jCl4iIiIiIZAkfHws2H+8MljfD+/pvRUREREREciCFLxERERERkWyg8CUiIiIiIpINFL5ERERERESygcKXiIiIiIhINvCa8DV58mTKlClDQEAAERERrFu37pr7f/PNN1SpUoWAgABq1qzJwoULs6lSERERERHJjbwifM2dO5chQ4YwatQoNm7cSO3atWnTpg0nT55Md/8///yTbt268cQTT7Bp0yY6depEp06d+Oeff7K5chERERERyS0sTmd66057loiICBo0aMCkSZMAcDgchIeHM3DgQIYNG5Zm/65duxIbG8tPP/3k2taoUSPq1KnD1KlTM3TOqKgoQkNDuXDhAiEhIZnzQkRERERExONkNBt4fM9XQkICGzZsoHXr1q5tPj4+tG7dmjVr1qT7nDVr1rjtD9CmTZur7g8QHx9PVFSU201ERERERCSjfM0u4FadPn0au91OWFiY2/awsDB27dqV7nOOHz+e7v7Hjx+/6nnGjRvHmDFj0mxXCBMRERERyd1SMsH1BhV6fPjKLsOHD2fIkCGu+0eOHKFatWqEh4ebWJWIiIiIiOQU0dHRhIaGXvVxjw9fhQoVwmq1cuLECbftJ06coGjRouk+p2jRoje0P4DNZsNms7nuBwcHc+jQIfLmzYvFYrmFV3DroqKiCA8P59ChQ7r+LAvo/c1aen+zlt7frKX3N2vp/c1aen+zlt7frJeT3mOn00l0dDTFixe/5n4eH778/f2pV68eK1asoFOnToAx4caKFSsYMGBAus9p3LgxK1asYPDgwa5ty5Yto3Hjxhk+r4+PDyVLlryV0jNdSEiI6R88b6b3N2vp/c1aen+zlt7frKX3N2vp/c1aen+zXk55j6/V45XC48MXwJAhQ+jZsyf169enYcOGTJgwgdjYWHr37g1Ajx49KFGiBOPGjQPg2WefpUWLFrz77rt06NCBr776ir///pvp06eb+TJERERERMSLeUX46tq1K6dOnWLkyJEcP36cOnXqsHjxYtekGgcPHsTH5/LEjk2aNGHOnDm8/PLLvPTSS1SsWJH58+dTo0YNs16CiIiIiIh4Oa8IXwADBgy46jDDVatWpdn24IMP8uCDD2ZxVdnDZrMxatQot2vSJPPo/c1aen+zlt7frKX3N2vp/c1aen+zlt7frOeJ77FXLLIsIiIiIiKS03n8IssiIiIiIiKeQOFLREREREQkGyh8iYiIiIiIZAOFLxERERERkWyg8OUFJk+eTJkyZQgICCAiIoJ169aZXZJX+O233+jYsSPFixfHYrEwf/58s0vyKuPGjaNBgwbkzZuXIkWK0KlTJ3bv3m12WV5jypQp1KpVy7XwZOPGjVm0aJHZZXmtN998E4vFwuDBg80uxSuMHj0ai8XidqtSpYrZZXmVI0eO8Oijj1KwYEHy5MlDzZo1+fvvv80uyyuUKVMmzefXYrHQv39/s0vzCna7nVdeeYWyZcuSJ08eypcvz6uvvoqnzCGo8OXh5s6dy5AhQxg1ahQbN26kdu3atGnThpMnT5pdmseLjY2ldu3aTJ482exSvNKvv/5K//79Wbt2LcuWLSMxMZG7776b2NhYs0vzCiVLluTNN99kw4YN/P3339xxxx3cd999bN++3ezSvM769euZNm0atWrVMrsUr1K9enWOHTvmuv3xxx9ml+Q1zp07R9OmTfHz82PRokXs2LGDd999l/z585tdmldYv36922d32bJlAF6zxJHZ3nrrLaZMmcKkSZPYuXMnb731Fm+//TYTJ040u7QM0VTzHi4iIoIGDRowadIkABwOB+Hh4QwcOJBhw4aZXJ33sFgsfP/993Tq1MnsUrzWqVOnKFKkCL/++ivNmzc3uxyvVKBAAd555x2eeOIJs0vxGjExMdStW5cPP/yQ1157jTp16jBhwgSzy/J4o0ePZv78+WzevNnsUrzSsGHDWL16Nb///rvZpeQKgwcP5qeffmLPnj1YLBazy/F499xzD2FhYXz88ceubV26dCFPnjzMnj3bxMoyRj1fHiwhIYENGzbQunVr1zYfHx9at27NmjVrTKxM5MZduHABMAKCZC673c5XX31FbGwsjRs3Nrscr9K/f386dOjg9ntYMseePXsoXrw45cqVo3v37hw8eNDskrzGggULqF+/Pg8++CBFihThtttuY8aMGWaX5ZUSEhKYPXs2jz/+uIJXJmnSpAkrVqzg33//BWDLli388ccftGvXzuTKMsbX7ALk5p0+fRq73U5YWJjb9rCwMHbt2mVSVSI3zuFwMHjwYJo2bUqNGjXMLsdrbNu2jcaNGxMXF0dwcDDff/891apVM7ssr/HVV1+xceNG1q9fb3YpXiciIoJZs2ZRuXJljh07xpgxY2jWrBn//PMPefPmNbs8j7dv3z6mTJnCkCFDeOmll1i/fj2DBg3C39+fnj17ml2eV5k/fz7nz5+nV69eZpfiNYYNG0ZUVBRVqlTBarVit9t5/fXX6d69u9mlZYjCl4iYrn///vzzzz+6piOTVa5cmc2bN3PhwgW+/fZbevbsya+//qoAlgkOHTrEs88+y7JlywgICDC7HK+T+hvsWrVqERERQenSpfn66681bDYTOBwO6tevzxtvvAHAbbfdxj///MPUqVMVvjLZxx9/TLt27ShevLjZpXiNr7/+mi+++II5c+ZQvXp1Nm/ezODBgylevLhHfH4VvjxYoUKFsFqtnDhxwm37iRMnKFq0qElVidyYAQMG8NNPP/Hbb79RsmRJs8vxKv7+/lSoUAGAevXqsX79ev73v/8xbdo0kyvzfBs2bODkyZPUrVvXtc1ut/Pbb78xadIk4uPjsVqtJlboXfLly0elSpXYu3ev2aV4hWLFiqX5EqZq1ap89913JlXknSIjI1m+fDnz5s0zuxSv8sILLzBs2DAefvhhAGrWrElkZCTjxo3ziPCla748mL+/P/Xq1WPFihWubQ6HgxUrVui6DsnxnE4nAwYM4Pvvv+eXX36hbNmyZpfk9RwOB/Hx8WaX4RXuvPNOtm3bxubNm123+vXr0717dzZv3qzglcliYmL477//KFasmNmleIWmTZumWdrj33//pXTp0iZV5J1mzpxJkSJF6NChg9mleJWLFy/i4+MeYaxWKw6Hw6SKbox6vjzckCFD6NmzJ/Xr16dhw4ZMmDCB2NhYevfubXZpHi8mJsbtW9b9+/ezefNmChQoQKlSpUyszDv079+fOXPm8MMPP5A3b16OHz8OQGhoKHny5DG5Os83fPhw2rVrR6lSpYiOjmbOnDmsWrWKJUuWmF2aV8ibN2+a6xODgoIoWLCgrlvMBEOHDqVjx46ULl2ao0ePMmrUKKxWK926dTO7NK/w3HPP0aRJE9544w0eeugh1q1bx/Tp05k+fbrZpXkNh8PBzJkz6dmzJ76++nM7M3Xs2JHXX3+dUqVKUb16dTZt2sR7773H448/bnZpGeMUjzdx4kRnqVKlnP7+/s6GDRs6165da3ZJXmHlypVOIM2tZ8+eZpfmFdJ7bwHnzJkzzS7NKzz++OPO0qVLO/39/Z2FCxd23nnnnc6lS5eaXZZXa9GihfPZZ581uwyv0LVrV2exYsWc/v7+zhIlSji7du3q3Lt3r9lleZUff/zRWaNGDafNZnNWqVLFOX36dLNL8ipLlixxAs7du3ebXYrXiYqKcj777LPOUqVKOQMCApzlypVzjhgxwhkfH292aRmidb5ERERERESyga75EhERERERyQYKXyIiIiIiItlA4UtERERERCQbKHyJiIiIiIhkA4UvERERERGRbKDwJSIiIiIikg0UvkRERERERLKBwpeIiEg2s1gszJ8/3+wyREQkmyl8iYhIrtKrVy8sFkuaW9u2bc0uTUREvJyv2QWIiIhkt7Zt2zJz5ky3bTabzaRqREQkt1DPl4iI5Do2m42iRYu63fLnzw8YQwKnTJlCu3btyJMnD+XKlePbb791e/62bdu44447yJMnDwULFuSpp54iJibGbZ9PPvmE6tWrY7PZKFasGAMGDHB7/PTp03Tu3JnAwEAqVqzIggULsvZFi4iI6RS+RERErvDKK6/QpUsXtmzZQvfu3Xn44YfZuXMnALGxsbRp04b8+fOzfv16vvnmG5YvX+4WrqZMmUL//v156qmn2LZtGwsWLKBChQpu5xgzZgwPPfQQW7dupX379nTv3p2zZ89m6+sUEZHsZXE6nU6zixAREckuvXr1Yvbs2QQEBLhtf+mll3jppZewWCw888wzTJkyxfVYo0aNqFu3Lh9++CEzZszgxRdf5NChQwQFBQGwcOFCOnbsyNGjRwkLC6NEiRL07t2b1157Ld0aLBYLL7/8Mq+++ipgBLrg4GAWLVqka89ERLyYrvkSEZFcp1WrVm7hCqBAgQKuduPGjd0ea9y4MZs3bwZg586d1K5d2xW8AJo2bYrD4WD37t1YLBaOHj3KnXfeec0aatWq5WoHBQUREhLCyZMnb/YliYiIB1D4EhGRXCcoKCjNMMDMkidPngzt5+fn53bfYrHgcDiyoiQREckhdM2XiIjIFdauXZvmftWqVQGoWrUqW7ZsITY21vX46tWr8fHxoXLlyuTNm5cyZcqwYsWKbK1ZRERyPvV8iYhIrhMfH8/x48fdtvn6+lKoUCEAvvnmG+rXr8/tt9/OF198wbp16/j4448B6N69O6NGjaJnz56MHj2aU6dOMXDgQB577DHCwsIAGD16NM888wxFihShXbt2REdHs3r1agYOHJi9L1RERHIUhS8REcl1Fi9eTLFixdy2Va5cmV27dgHGTIRfffUV/fr1o1ixYnz55ZdUq1YNgMDAQJYsWcKzzz5LgwYNCAwMpEuXLrz33nuuY/Xs2ZO4uDjef/99hg4dSqFChXjggQey7wWKiEiOpNkORUREUrFYLHz//fd06tTJ7FJERMTL6JovERERERGRbKDwJSIiIiIikg10zZeIiEgqGo0vIiJZRT1fIiIiIiIi2UDhS0REREREJBsofImIiIiIiGQDhS8REREREZFsoPAlIiIiIiKSDRS+REREREREsoHCl4iIiIiISDZQ+BIREREREckGCl8iIiIiIiLZQOFLREREREQkGyh8iYiIiIiIZAOFLxERERERkWyg8CUiIiIiIpINFL5ERERERESygcKXiEgO0qtXL8qUKXNTzx09ejQWiyVzC8phDhw4gMViYdasWdl+bovFwujRo133Z82ahcVi4cCBA9d9bpkyZejVq1em1nMrnxURETGHwpeISAZYLJYM3VatWmV2qbneoEGDsFgs7N2796r7jBgxAovFwtatW7Oxsht39OhRRo8ezebNm80uJV07d+7EYrEQEBDA+fPnzS5HRCTHU/gSEcmAzz//3O121113pbu9atWqt3SeGTNmsHv37pt67ssvv8ylS5du6fzeoHv37gDMmTPnqvt8+eWX1KxZk1q1at30eR577DEuXbpE6dKlb/oY13P06FHGjBmTbvi6lc9KZpk9ezZFixYF4NtvvzW1FhERT+BrdgEiIp7g0Ucfdbu/du1ali1blmb7lS5evEhgYGCGz+Pn53dT9QH4+vri66tf6xEREVSoUIEvv/ySkSNHpnl8zZo17N+/nzfffPOWzmO1WrFarbd0jFtxK5+VzOB0OpkzZw6PPPII+/fv54svvqBPnz6m1nQ1sbGxBAUFmV2GiIh6vkREMkvLli2pUaMGGzZsoHnz5gQGBvLSSy8B8MMPP9ChQweKFy+OzWajfPnyvPrqq9jtdrdjXHkdT8o1TuPHj2f69OmUL18em81GgwYNWL9+vdtz07vmy2KxMGDAAObPn0+NGjWw2WxUr16dxYsXp6l/1apV1K9fn4CAAMqXL8+0adMyfB3Z77//zoMPPkipUqWw2WyEh4fz3HPPpemJ69WrF8HBwRw5coROnToRHBxM4cKFGTp0aJr34vz58/Tq1YvQ0FDy5ctHz549Mzy0rXv37uzatYuNGzemeWzOnDlYLBa6detGQkICI0eOpF69eoSGhhIUFESzZs1YuXLldc+R3jVfTqeT1157jZIlSxIYGEirVq3Yvn17mueePXuWoUOHUrNmTYKDgwkJCaFdu3Zs2bLFtc+qVato0KABAL1793YNbU253i29a75iY2N5/vnnCQ8Px2azUblyZcaPH4/T6XTb70Y+F1ezevVqDhw4wMMPP8zDDz/Mb7/9xuHDh9Ps53A4+N///kfNmjUJCAigcOHCtG3blr///tttv9mzZ9OwYUMCAwPJnz8/zZs3Z+nSpW41p77mLsWV19Ol/Hf59ddf6devH0WKFKFkyZIAREZG0q9fPypXrkyePHkoWLAgDz74YLrX7Z0/f57nnnuOMmXKYLPZKFmyJD169OD06dPExMQQFBTEs88+m+Z5hw8fxmq1Mm7cuAy+kyKSm+grUhGRTHTmzBnatWvHww8/zKOPPkpYWBhg/EEYHBzMkCFDCA4O5pdffmHkyJFERUXxzjvvXPe4c+bMITo6mqeffhqLxcLbb7/N/fffz759+67bA/LHH38wb948+vXrR968efnggw/o0qULBw8epGDBggBs2rSJtm3bUqxYMcaMGYPdbmfs2LEULlw4Q6/7m2++4eLFi/Tt25eCBQuybt06Jk6cyOHDh/nmm2/c9rXb7bRp04aIiAjGjx/P8uXLeffddylfvjx9+/YFjBBz33338ccff/DMM89QtWpVvv/+e3r27Jmherp3786YMWOYM2cOdevWdTv3119/TbNmzShVqhSnT5/mo48+olu3bjz55JNER0fz8ccf06ZNG9atW0edOnUydL4UI0eO5LXXXqN9+/a0b9+ejRs3cvfdd5OQkOC23759+5g/fz4PPvggZcuW5cSJE0ybNo0WLVqwY8cOihcvTtWqVRk7diwjR47kqaeeolmzZgA0adIk3XM7nU7uvfdeVq5cyRNPPEGdOnVYsmQJL7zwAkeOHOH999932z8jn4tr+eKLLyhfvjwNGjSgRo0aBAYG8uWXX/LCCy+47ffEE08wa9Ys2rVrR58+fUhKSuL3339n7dq11K9fH4AxY8YwevRomjRpwtixY/H39+evv/7il19+4e67787w+59av379KFy4MCNHjiQ2NhaA9evX8+eff/Lwww9TsmRJDhw4wJQpU2jZsiU7duxw9VLHxMTQrFkzdu7cyeOPP07dunU5ffo0CxYs4PDhw9SpU4fOnTszd+5c3nvvPbce0C+//BKn0+ka/ioi4sYpIiI3rH///s4rf4W2aNHCCTinTp2aZv+LFy+m2fb00087AwMDnXFxca5tPXv2dJYuXdp1f//+/U7AWbBgQefZs2dd23/44Qcn4Pzxxx9d20aNGpWmJsDp7+/v3Lt3r2vbli1bnIBz4sSJrm0dO3Z0BgYGOo8cOeLatmfPHqevr2+aY6Ynvdc3btw4p8VicUZGRrq9PsA5duxYt31vu+02Z7169Vz358+f7wScb7/9tmtbUlKSs1mzZk7AOXPmzOvW1KBBA2fJkiWddrvdtW3x4sVOwDlt2jTXMePj492ed+7cOWdYWJjz8ccfd9sOOEeNGuW6P3PmTCfg3L9/v9PpdDpPnjzp9Pf3d3bo0MHpcDhc+7300ktOwNmzZ0/Xtri4OLe6nE7jv7XNZnN7b9avX3/V13vlZyXlPXvttdfc9nvggQecFovF7TOQ0c/F1SQkJDgLFizoHDFihGvbI4884qxdu7bbfr/88osTcA4aNCjNMVLeoz179jh9fHycnTt3TvOepH4fr3z/U5QuXdrtvU3573L77bc7k5KS3PZN73O6Zs0aJ+D87LPPXNtGjhzpBJzz5s27at1LlixxAs5Fixa5PV6rVi1nixYt0jxPRMTpdDo17FBEJBPZbDZ69+6dZnuePHlc7ejoaE6fPk2zZs24ePEiu3btuu5xu3btSv78+V33U3pB9u3bd93ntm7dmvLly7vu16pVi5CQENdz7XY7y5cvp1OnThQvXty1X4UKFWjXrt11jw/ury82NpbTp0/TpEkTnE4nmzZtSrP/M88843a/WbNmbq9l4cKF+Pr6unrCwLjGauDAgRmqB4zr9A4fPsxvv/3m2jZnzhz8/f158MEHXcf09/cHjOFxZ8+eJSkpifr166c7ZPFali9fTkJCAgMHDnQbqjl48OA0+9psNnx8jH+C7XY7Z86cITg4mMqVK9/weVMsXLgQq9XKoEGD3LY///zzOJ1OFi1a5Lb9ep+La1m0aBFnzpyhW7durm3dunVjy5YtbsMsv/vuOywWC6NGjUpzjJT3aP78+TgcDkaOHOl6T67c52Y8+eSTaa7JS/05TUxM5MyZM1SoUIF8+fK5ve/fffcdtWvXpnPnzletu3Xr1hQvXpwvvvjC9dg///zD1q1br3stqIjkXgpfIiKZqESJEq4/5lPbvn07nTt3JjQ0lJCQEAoXLuz6A+3ChQvXPW6pUqXc7qcEsXPnzt3wc1Oen/LckydPcunSJSpUqJBmv/S2pefgwYP06tWLAgUKuK7jatGiBZD29aVc93O1esC4NqdYsWIEBwe77Ve5cuUM1QPw8MMPY7VaXbMexsXF8f3339OuXTu3IPvpp59Sq1YtAgICKFiwIIULF+bnn3/O0H+X1CIjIwGoWLGi2/bChQu7nQ+MoPf+++9TsWJFbDYbhQoVonDhwmzduvWGz5v6/MWLFydv3rxu21Nm4EypL8X1PhfXMnv2bMqWLYvNZmPv3r3s3buX8uXLExgY6BZG/vvvP4oXL06BAgWueqz//vsPHx8fqlWrdt3z3oiyZcum2Xbp0iVGjhzpuiYu5X0/f/682/v+33//UaNGjWse38fHh+7duzN//nwuXrwIGEMxAwICXOFeRORKCl8iIpko9TfrKc6fP0+LFi3YsmULY8eO5ccff2TZsmW89dZbgPGH+PVcbVY95xUTKWT2czPCbrdz11138fPPP/Piiy8yf/58li1b5poY4srXl10zBBYpUoS77rqL7777jsTERH788Ueio6PdrsWZPXs2vXr1onz58nz88ccsXryYZcuWcccdd2Tov8vNeuONNxgyZAjNmzdn9uzZLFmyhGXLllG9evUsPW9qN/u5iIqK4scff2T//v1UrFjRdatWrRoXL15kzpw5mfbZyogrJ2pJkd7/iwP/v737Do+q2vo4/p1JDyn0auhILxogNBEBDcVcQVQElKLCRQEFRAHpqGBFlOrrBUQRwQKIoiBEehcEQYr0IBCqJCSQOvP+ccgkQwIkkOSk/D7PM8/sOXPKmiEks2bvvfaAAbz99ts89dRTfPPNN/z666+sXLmSIkWK3NH73r17d6KioliyZImj+uOjjz6Kv79/hs8lIvmDCm6IiGSxNWvWcPHiRRYtWkTz5s0d248dO2ZiVMmKFy+Op6dnmosS32qh4iR79uzh77//Zu7cuXTv3t2xfeXKlXccU7ly5QgNDSUqKsqp9yuj61p169aN5cuX88svvzB//nz8/PwICQlxPP/dd99RsWJFFi1a5DTELa1hcumJGeDQoUNUrFjRsf38+fOpepO+++47HnroIWbNmuW0/fLlyxQtWtTxOCPD7sqVK8eqVau4cuWKU+9X0rDWzFqPbNGiRcTExDBjxgynWMH49xk5ciQbN26kWbNmVKpUiRUrVnDp0qWb9n5VqlQJm83Gvn37blngpFChQqmqXcbFxXHmzJl0x/7dd9/Ro0cPPvzwQ8e2mJiYVOetVKkSe/fuve35atWqxX333cdXX33FPffcQ1hYGFOmTEl3PCKS/6jnS0QkiyX1MKTsDYiLi2P69OlmheTExcWF1q1bs2TJEk6fPu3Yfvjw4VTzhG52PDi/Prvdzscff3zHMbVr146EhARmzJjh2JaYmJjhD7YdOnTA29ub6dOn88svv/D444/j6el5y9i3bt3K5s2bMxxz69atcXNzY8qUKU7nmzx5cqp9XVxcUvUOffvtt5w6dcppW9LaVOkpsd+uXTsSExOZOnWq0/aPPvoIi8WS7vl7tzNv3jwqVqxI3759eeKJJ5xuQ4YMwcfHxzH0sFOnTtjtdsaNG5fqPEmvv0OHDlitVsaPH5+q9ynle1SpUiWn+XsA//d//3fTnq+0pPW+T5kyJdU5OnXqxO7du1m8ePFN407y7LPP8uuvvzJ58mSKFCmSae+ziORN6vkSEcliTZo0oVChQvTo0YOXX34Zi8XCl19+ma1Ds25n7Nix/PrrrzRt2pQXX3zR8SG+Vq1a7Nq165bHVqtWjUqVKjFkyBBOnTqFn58f33//fbrmDt1MSEgITZs2ZdiwYRw/fpwaNWqwaNGiDM+H8vHxoUOHDo55XzeW/3700UdZtGgRHTt2pH379hw7doyZM2dSo0YNoqKiMnStpPXKJk6cyKOPPkq7du34448/+OWXX1L1ED366KOMHz+eXr160aRJE/bs2cNXX33l1GMGRsJRsGBBZs6cia+vLwUKFCAoKCjN+UwhISE89NBDjBgxguPHj1O3bl1+/fVXfvjhBwYOHOhUXONOnT59mtWrV6cq6pHEw8OD4OBgvv32Wz755BMeeughnn32WT755BMOHTpEmzZtsNlsrF+/noceeoj+/ftTuXJlRowYwZtvvskDDzzA448/joeHB9u3b6d06dKO9bJeeOEF+vbtS6dOnXj44YfZvXs3K1asSPXe3sqjjz7Kl19+ib+/PzVq1GDz5s2sWrUqVWn91157je+++44nn3yS5557jsDAQC5dusTSpUuZOXMmdevWdezbtWtXXn/9dRYvXsyLL75o+uLXIpKzqedLRCSLFSlShJ9++olSpUoxcuRIPvjgAx5++GHee+89s0NzCAwM5JdffqFQoUKMGjWKWbNmMX78eFq1auXUU5QWNzc3fvzxR+rVq8fEiRMZN24cVapU4YsvvrjjeKxWK0uXLqVbt27MmzePESNGUKZMGebOnZvhcyUlXKVKlaJly5ZOz/Xs2ZMJEyawe/duXn75ZVasWMG8efMc609l1FtvvcW4ceP4448/eO211zhy5Ai//vqrowcryRtvvMGrr77KihUreOWVV9i5cyfLli0jICDAaT83Nzfmzp2Li4sLffv2pUuXLqxduzbNaye9ZwMHDuSnn35i4MCB7Nu3j/fff59Jkybd0eu50YIFC7DZbE5DN28UEhLCxYsXHb2mc+bM4f333+fYsWO89tprTJgwgWvXrjmtVzZ+/Hhmz57NtWvXGDFiBKNHj+bEiRO0atXKsU/v3r0ZOnQo69at49VXX+XYsWOsXLky1Xt7Kx9//DHdu3fnq6++4tVXX+XMmTOsWrUqVWEXHx8f1q9fz4svvsjPP//Myy+/zPTp06latapjweYkJUqUcKxF9uyzz6Y7FhHJnyz2nPTVq4iI5CgdOnTgr7/+4tChQ2aHIpJjdezYkT179qRrjqSI5G/q+RIREcAow53SoUOH+Pnnn2nRooU5AYnkAmfOnGHZsmXq9RKRdFHPl4iIAMawvJ49e1KxYkVOnDjBjBkziI2N5Y8//ki1dpVIfnfs2DE2btzI//73P7Zv386RI0coWbKk2WGJSA6nghsiIgJAmzZt+PrrrwkPD8fDw4PGjRszYcIEJV4iaVi7di29evWibNmyzJ07V4mXiKSLer5ERERERESygeZ8iYiIiIiIZAMlXyIiIiIiItlAc77ukM1m4/Tp0/j6+mKxWMwOR0RERERETGK327ly5QqlS5fGar15/5aSrzt0+vTpVIthioiIiIhI/nXy5MlUi7GnpOTrDvn6+gLGG+zn52dyNCIiIiIiYpbIyEgCAgIcOcLNKPm6Q0lDDf38/JR8iYiIiIjIbacjqeCGiIiIiIhINlDyJSIiIiIikg2UfImIiIiIiGQDJV8iIiIiIiLZQMmXiIiIiIhINlDyJSIiIiIikg2UfImIiIiIiGQDJV8iIiIiIiLZQIssi4iIiIgIdrud+EQ78Yk2EhLtxCXabtuOT7QRd5O2cbt9OyPnv/HYLg3LMv6xWma/demm5EtEREREJJMl2uwk2IxEIeF6ApHUjk+0EZeQ9PzN2/EJduJtNuITricctuvbEm1O7QSbcVxa7fgEI5lJuMmxKZOZBJvd7Lctw+ITbWaHkCFKvkREREQky9jt9uuJyA331z/sJ28zHickpnicaDwff8PjpOPiE22pzn2r8yYlRIkpHqd17oQ0rpV0budtthTxpji3zY499+UxaXJzseDmYr1+S267ulhwT9F2c7HifpP2jcfe7pwZOYePZ+5KZ3JXtCIiIiLiJD7RxrX4RGLiEo37eOPxtbhEYuKN27WkW1wisQk2rjn2TbxF0nKLZObGhCMxKWlJK0HJI1nIXbJYyECCkiIhsVpwc72+bxptNxcrbinb6UyWbtZOGZer1YLFYjH7rctTlHyJiIiIZDK73RjqFRNncyQ+SUlQUpKUMlGKibthH0fy5Hy8I5GKsznaibk0ubFawNVqfMh3sRof9F2s1uv3Ftwc263G/fVkIOXjpONcrVZcXCyOY5PO5ZbGuV1dUl/L2Ob8OOnarlbLDee2pjhHcjyp4r1+XFJi42JVEiNKvkRERCQfsdnsxCbY0ugNMhKaVIlOit6klI9TJ0Mp9rl+y+5hZ1YLeLm54OXugoerce/lZtw83KyO57zcXPC8fvNwteLumiJZsFpwdbHekMRkIOG4/tgtzWTI4khCXCwWrEpGJB9S8iUiIiKmstuTE6Ib71MmNMm9QYlcS+oxSpUk3XrYXUx89k/Od7Fa8HZzwdPdBc+kJChFApSUFHk6tludtiXvY3V+nLSPqwue7sZQMQ0RE8nZlHyJiIgIkGKoXLyN2JskQrEJad/fmDjFxicSk5BIbLwtzfuka8Qk2IhLMKdambuL1Uh0nBKflMmQ1SnR8bxJkuTpfsOxbkYylLTNzUXLqoqIQcmXiIhIDpO01k6GEhvHPmknO+lJpmITbKZXaLNacPTueLpa8XAkNVbHkDmPVD0/VkcCdLNhd45EKkWipDk4IpLdlHyJiIjcRtKwuGtxiVyNT+RaXAJX4xK5en2oW/p7iWyOYXBJSdTN9je7hoLFgjGczc2KRxr3HteTGQ9X5/ub7e/pZiRSqfd33qZeIhHJy5R8iYhInmC324mJt3H1emJ0LT7xeoKUYCRNcYnX7xOuJ1DJz1+98fkUxydtMzMZuuPEJilJcvQgWVNsM+6dz5P8nJuLSkyLiGQ2JV8iIpJtbDY7V+OdE6KUCU5ywnRD71JaPU5xiVyNT5FYZVN1OXcXY/ibt/uNlePSTmySHt9pL5GHq4ooiIjkFUq+RETESUKiLVXPkFOiFJ+QImG6IVGKd+49urE3KTabCit4ulnxdnfFy81IkpISJW93V+PeLWmb6w3Pu+DlduM21+S2mwuuGhYnIiJ3SMmXiEguZrPZiY5L4EpMAlGxKe/jiUqx7Vp8iuF0N/Qc3bgtLjHrEyTL9fWIkpMaV0fy40iK3FxSb3MkSC4U8EhxTIrjvdxctH6QiIjkSEq+RERMkDT8LilJuhKbYNzHJBAVG++cTF1vR8bEExWb4JRURcUmZFmMSWsTed0k+Umz58gtRe/SjT1Hjl4oVzzdNJRORETyHyVfIiIZYLfbuRqX6OhdujFJSk6ijETpiuO55MTpSkwCUXEJmTo/yc3Fgq+nGz4ervh6ujrdF7h+cx6C53rThClpuxZsFRERyVxKvkQkX7Db7VyLTyQqJoHI6wlTUpJ0xak3Kd6RTF1JsV9Sz1N0bOZWvXO1WowkydMVHw83fD1d8fUwHvum2JYymfLxdMXveqLlc32bp5tL5gUlIiIiWcL05GvatGm8//77hIeHU7duXaZMmULDhg3T3Dc+Pp6JEycyd+5cTp06RdWqVXn33Xdp06aNY5/y5ctz4sSJVMe+9NJLTJs2DYAWLVqwdu1ap+f/+9//MnPmzEx8ZSKSGZLKh19JGoqXoqfpSsrepNiUyZJzj1TSfpmZNLkkJU0exs3P082RCCUlTr4eSUmT8VxyUpXcQ6VKdiIiIvmHqcnXwoULGTx4MDNnziQoKIjJkycTHBzMwYMHKV68eKr9R44cybx58/jss8+oVq0aK1asoGPHjmzatIn77rsPgO3bt5OYmOg4Zu/evTz88MM8+eSTTufq3bs348ePdzz29vbOolcpIna7nSuxCfwbHcel6Dj+vRrHpeh4Ll81Hl++Fu/U83Rj8YjETMyarBbSHp53fZtfigQqKXFy3s8VXw83zVkSERGRDLPY7dmxKkragoKCaNCgAVOnTgXAZrMREBDAgAEDGDZsWKr9S5cuzYgRI+jXr59jW6dOnfDy8mLevHlpXmPgwIH89NNPHDp0yPFBqUWLFtSrV4/JkyffceyRkZH4+/sTERGBn5/fHZ9HJLex2+1ExSZw+Wo8l6LjuHQ1zimp+vdqfJpJVsJdJlAWC8k9TE7D8lLeu6UYlmcM2UtKovyuJ05ebi5KmkRERCRTpTc3MK3nKy4ujh07djB8+HDHNqvVSuvWrdm8eXOax8TGxuLp6em0zcvLiw0bNtz0GvPmzWPw4MGpPmx99dVXzJs3j5IlSxISEsKoUaNu2fsVGxtLbGys43FkZORtX6NITpdUPOLmidP17dHxTo/jE+8skfJ2d6GQtzuFC7hTqIA7hbzdKOTtTkFvN6OHKY2kKimh8nZX0iQiIiK5m2nJ14ULF0hMTKREiRJO20uUKMGBAwfSPCY4OJhJkybRvHlzKlWqRGhoKIsWLXIaZpjSkiVLuHz5Mj179nTa3rVrV8qVK0fp0qX5888/GTp0KAcPHmTRokU3jXfixImMGzcuYy9SJJtdi0tM3RMVHcelpKQqxXOXr8Zz6WoccXe46K2nm5XC3kYSVbiAuyOpKujt5vQ45XYVhRAREZH8zPSCGxnx8ccf07t3b6pVq4bFYqFSpUr06tWL2bNnp7n/rFmzaNu2LaVLl3ba3qdPH0e7du3alCpVilatWnHkyBEqVaqU5rmGDx/O4MGDHY8jIyMJCAjIhFclkraY+MTk3qbo+BsSpxQJVYpeqtg7TKTcXVMmUm5OiVMhb7dUCVYhb3e83JVIiYiIiGSEaclX0aJFcXFx4ezZs07bz549S8mSJdM8plixYixZsoSYmBguXrxI6dKlGTZsGBUrVky174kTJ1i1atUte7OSBAUFAXD48OGbJl8eHh54eHjc9lwiaYlNSDQSKEfilJRIOQ/nSxridyk6jmvxaffo3o67i5VC1xOo5CF+bo7kqlBSkuV9fXsBd82DEpGc4+olOL4ejq2Do2sh6ix0/QbKNTY7MhGRu2Za8uXu7k5gYCChoaF06NABMApuhIaG0r9//1se6+npSZkyZYiPj+f777/nqaeeSrXPnDlzKF68OO3bt79tLLt27QKgVKlSGX4dkv/EJdgcCVRSr1Ty8L7kYX6XHc/HER13Z4mUq9XilCg5J07JvVQp51EV0NwoEclNYqMgbDMcW2skW+F7gBvmlS4dAC9uBFd9CSoiuZupww4HDx5Mjx49qF+/Pg0bNmTy5MlER0fTq1cvALp3706ZMmWYOHEiAFu3buXUqVPUq1ePU6dOMXbsWGw2G6+//rrTeW02G3PmzKFHjx64ujq/xCNHjjB//nzatWtHkSJF+PPPPxk0aBDNmzenTp062fPCJUdJSLRx6WocF67EcTE6lotRcVyIiuVCVBwXo2K5EBXrqOp3OdpYkPdOuFgtjgITNyZUTkP6CiQP9fP1cFUiJSJ5S0Is/PO7kWwdWwf/bAfbDb9Xi1WHCs2hXBP4+TW4eAg2fQLNXzMnZhGRTGJq8tW5c2fOnz/P6NGjCQ8Pp169eixfvtxRhCMsLAyr1erYPyYmhpEjR3L06FF8fHxo164dX375JQULFnQ676pVqwgLC+O5555LdU13d3dWrVrlSPQCAgLo1KkTI0eOzNLXKtkrOjbBSKKiY7lwJZaL0XHJ99cTqotRcVy8PtwvowsuWC1Q8Pp8KOeiEjfMmUpKsrzd8fV0xWpVIiUi+YwtEc7sTk62TmyGhGvO+xQsCxUehIotoPwD4JuiGFdiPCx6AdZ9ALWegMIVsjV8EZHMZOo6X7mZ1vnKXok2O5evxjl6o847kqdYR4/V+evPXYzK+HwpiwUKe7tT1MeDIj7uFPHxoKiP8bioj5E8FfFJTrL8PN2USImIpMVuh/MHjUTr2Fpj/lZMhPM+BYobPVsVmkPFB6FQ+Vuf74vHjHNVecSY/6URASKSw+T4db5EYuITnYb3XYyKcyRVF6JinYYAXoqOI6Nr9Hq4Wo3kydeDogWM5MlIrpITq6RthbzdcVEyJSJyZy6HGfO1knq3opyLaeHhD+WbJSdbxaqlP4GyWKD9hzCjCRz6Ffb/CDX+k/mvQUQkGyj5kkxjs9mJuBZv9ELdYv5U0hDAOylCUcjbjSI+HhQp4O5IqpISqiIpeqqK+Hio8ISISFaJOp+caB1bC/8ed37e1RPKNjKGElZ4EErVBZe7+MhRtAo0fQXWvQ/Lh0GlluDhc1cvQUTEDEq+5JZiExKN4X23nD9lJFaXouNIyGD3lLuLNUWPVIr7Ah4U9XWnSAHjcTEfDwoVcMfNxXr7k4qISOaKiYATm673bq2Dc385P29xgXvqXx9K+CAENMz8yoQPvAp7vjUSvTUTIfjtzD2/iEg2UPKVz9jtdiJjEpILTlzvjbpww/ypC9d7rK7EZLyyn5+n6/UeqKT5U8m9U8Wu90ol9Vypmp+ISA4Ufw1Obk1Otk7/AfYbRiuUqG0MIazwoLEGl4dv1sbk5gXtPoCvnoAtM6BuFyhZK2uvKSLmsdshLhpir0BsJMREGveO9vXtpe+Dqm3NjjbdlHzlAfGJNi5FpzG8LyrOkUQlDQG8GBVHXKItQ+d3tVqMJCpFL1TKhCrltsIF3PFwdcmiVyoiIlkiMQFO70xea+vkNkiMdd6ncKXryVZzKN8cChTJ/jirPAzV/wP7l8KywdBrOVg1IkIkx0mMN5KjmIjrCdOVFMlT0vYrqRMpx37Xn7en4zNr/eeUfEn2uRgVS+BbqzJ8nI+Hq2NuVMr5U0VS9FglzZ9SZT8RkTzGZoNz+5KTrRObIO6K8z6+pa6Xf7+ecPnfY06sN2rzDhz5zeiZ2zUP7u9udkQieYfdDnFRKZKgK0YilGaClEYvVFL7xuUk7obFBTz9jN51D/8U7ev35Zpk3rWygZKvXK6gtztJeVHhAs5V/Ipcnzd14/ypoj4eeLqpd0pEJN+w2+HS0RRFMtbD1QvO+3gVMtbYqtDcWG+rSOWcWdLdvwy0GA6/joCVo6Fqe3N64URymsR4514jp56mmyRLNz6X3t6m9HIrYCRIKROmWyVSnn7GdkfbzxhynBN/F90hrfN1h3LSOl+XouMo6KXeKRERSSHyTHI1wmPrIOKk8/NuBYxvjJPKv5eonXuG8CUmwP89CGf3wn3PwGPTzI5I5M4l9Tal6kFKK3m6xZC9hJjMi8nR2+SXImHyu0ki5XdDUpXi/m6qnOYyWucrHylcwN3sEERExGxXL8HxDcnJ1oW/nZ+3uhlVCCtcH0ZYJhBcc+nfDxdXaD8JZj8Cf8yD+541StuL5CSxV4yfz8jTN+lpStEmE/tC3ArcpEfpVonUDb1Qeay3KSdR8iUiIpIbxUXDic3Xk621cOZPnD/AWaB0veTy72Ubg7u3ScFmgbJBxnyvnV/AT4Pgv+vAxc3sqEQMdjssfAaOrkn/MSl7m27Vo5TW0Lyktrtvvuptyo30ryMiIpIbJMTBqd+Ty7//sx1s8c77FK2aXP69fFNjHlde1nocHFhmFA/ZMgOavmx2RCKG32cZiZerl1GNzzOtROqGXij1NuULSr5ERERyIlsihP+ZnGyFbYb4q877+JeFis2ThxL6ljQnVrN4F4aHx8MP/WDNO1Dr8ZxTlVHyr0vH4NfRRrv1WGjU19RwJGdR8iUiIpIT2O3GPK1j64xvzI9vgJjLzvt4F00ukFGhORSqoG/K63Y15tWEbYZfhsLTX5kdkeRnNhsseQnio43qoQ37mB2R5DBKvkRERMxy+WSK8u/r4MoZ5+c9/KBc0+Rkq3gNJVs3slqN4hufPgAHfoK/V8C9wWZHJfnV1hkQtgncfeCxqbmngqhkGyVfIiIi2SX6QnKydXQt/HvM+XkXD6NqX9JaW6XqafJ8epSoAY1egk2fwM9DjB6HvFRcRHKHC4cgdLzRfuQtKFTe1HAkZ9JvdBERkawSEwknNiUnXGf3Oj9vcYEy9yfP2QoIAjdPc2LN7R4cCnsXweUwWP8BtBptdkSSnyQmwOK+xlpblVpBYE+zI5IcSsmXiIhIZomPgZNbkxc3PrUT7InO+5SolVz+vVwTo9qZ3D0PH2j7LizsBhs/gTqdoVhVs6OS/GLTx0Y1Ug9/+M8UDQ+Wm1LyJSIiuZPdbty48Z40tt3uPsU5M3SMHa6cTV5rK2wrJMY6x1moQory7w+AT7GsekekWnu4tw38vRyWvQo9ftSHYMl6Z/+C1RONdtt3wb+MufFIjqbkS0QkL9v2GfzxpVGBK73JxE33ueF5yGCyYndcIuPHpLEtp/IpmVwgo0JzKFjW7IjyD4sF2r5nzKc7vh7+/AbqdjY7KsnLEuKM4Ya2eKjaDuo+bXZEksMp+RIRyauizsOKEal7YiQDLNd7Tm68T/GcewEo29gokFHhQShaRb0tZipUDh58zSh88OsIuPeRvL/YtJhn/QfGenxeheHRyfq/L7el5EtEJK/6fZaReJWqC63GpE4aMnIPKbal8xxOx9wkgbldYpPue9JxvQzEqA9QuVvjAbB7gbFuWuib8OgksyOSvOj0H7DuA6Pd/kPwLWFuPJIrmJ58TZs2jffff5/w8HDq1q3LlClTaNiwYZr7xsfHM3HiRObOncupU6eoWrUq7777Lm3atHHsM3bsWMaNG+d0XNWqVTlw4IDjcUxMDK+++ioLFiwgNjaW4OBgpk+fTokS+k8jInlE/DVjyCFA01egcitz4xHJTq7uxofhuSHw+2yo1w3uCTQ7KslL4mOM4Yb2RKjZEWo9bnZEkkuYuvLbwoULGTx4MGPGjGHnzp3UrVuX4OBgzp07l+b+I0eO5NNPP2XKlCns27ePvn370rFjR/744w+n/WrWrMmZM2cctw0bNjg9P2jQIH788Ue+/fZb1q5dy+nTp3n8cf2nEZE85M9v4OoF8A+A6o+ZHY1I9qvQ3Kh4iB2WDQJb4m0PEUm3NRPg/AEoUBzafWh2NJKLmJp8TZo0id69e9OrVy9q1KjBzJkz8fb2Zvbs2Wnu/+WXX/LGG2/Qrl07KlasyIsvvki7du348EPnH3pXV1dKlizpuBUtWtTxXEREBLNmzWLSpEm0bNmSwMBA5syZw6ZNm9iyZUuWvl4RkWxht8PmaUY7qK8W6ZX865G3wNMfzuyG7f8zOxrJK8K2wqYpRjvkYyhQxNx4JFcxLfmKi4tjx44dtG7dOjkYq5XWrVuzefPmNI+JjY3F09N58UkvL69UPVuHDh2idOnSVKxYkW7duhEWFuZ4bseOHcTHxztdt1q1apQtW/am1026dmRkpNNNRCRHOhwKFw6Cuy/c/6zZ0YiYx6d48mLLv70FV8LNjUdyv7irsORFsNugbleo1s7siCSXMS35unDhAomJianmWZUoUYLw8LR/OQYHBzNp0iQOHTqEzWZj5cqVLFq0iDNnzjj2CQoK4vPPP2f58uXMmDGDY8eO8cADD3DlyhUAwsPDcXd3p2DBgum+LsDEiRPx9/d33AICAu7wlYuIZLHN17+Rvb+78a2/SH4W2AtK3w+xkbDiDbOjkdwudBxcOgK+paHNRLOjkVzI1GGHGfXxxx9TpUoVqlWrhru7O/3796dXr15Yrckvo23btjz55JPUqVOH4OBgfv75Zy5fvsw333xzV9cePnw4ERERjtvJkyfv9uWIiGS+8L1wdA1YrBD0X7OjETGf1QUe/cj4P7H3ezjym9kRSW51bB1snWm0H5sCXgVNDUdyJ9OSr6JFi+Li4sLZs2edtp89e5aSJUumeUyxYsVYsmQJ0dHRnDhxggMHDuDj40PFihVvep2CBQty7733cvjwYQBKlixJXFwcly9fTvd1ATw8PPDz83O6iYjkOFumG/c1HjPWOxIRKF0PGvQ22suGGJXqRDIi9gr80M9oB/aCyq1vvb/ITZiWfLm7uxMYGEhoaKhjm81mIzQ0lMaNG9/yWE9PT8qUKUNCQgLff/89jz1280peUVFRHDlyhFKlSgEQGBiIm5ub03UPHjxIWFjYba8rIpKjXQk3qhwCNO5vbiwiOU3LEeBTwhgytvFjs6OR3ObXkXA5DAqWhUfeNDsaycVMHXY4ePBgPvvsM+bOncv+/ft58cUXiY6OplevXgB0796d4cOHO/bfunUrixYt4ujRo6xfv542bdpgs9l4/fXXHfsMGTKEtWvXcvz4cTZt2kTHjh1xcXGhS5cuAPj7+/P8888zePBgVq9ezY4dO+jVqxeNGzemUaNG2fsGiIhkpm2fgS0eAoLgnvpmRyOSs3j6Q/AEo73+Q7h4xNx4JPc4vAp2fG60H5sOHr6mhiO5m6n1hzt37sz58+cZPXo04eHh1KtXj+XLlzuKcISFhTnN54qJiWHkyJEcPXoUHx8f2rVrx5dffulUPOOff/6hS5cuXLx4kWLFitGsWTO2bNlCsWLFHPt89NFHWK1WOnXq5LTIsohIrhV3FX6fZbQb9zM3FpGcqlYn+ONLY17kz6/BM9+DxWJ2VJKTXbsMPwww2kEvQoUHTA1Hcj+L3W63mx1EbhQZGYm/vz8RERGa/yUi5ts+C5YNhoLl4OU/jCIDIpLahcMwozEkxsGTn0PNjmZHJDnZ4r6w+2soXAn6bgB3b7MjkhwqvblBrqp2KCIiabDZkgttNHpJiZfIrRStDM0GGe3lwyFG63bKTRxYZiReFit0nKnESzKFki8Rkdzu0Aq4eBg8/OG+bmZHI5LzNRsMhSrAlTOwRms1SRqiL8KPrxjtJi9DQENz45E8Q8mXiEhut3macR/YQxPBRdLDzRPaf2C0t86EM3+aG4/kPD+/CtHnoVh1eEiLc0vmUfIlIpKbnd4Fx9eD1RWC+podjUjuUbk11OgAdpsxX9JmMzsiySn2fg9/LQaLC3ScAa4eZkckeYiSLxGR3Cyp16tmR/AvY24sIrlNm4ng7gP/bIedc82ORnKCK2dh2atGu/lrUPo+c+ORPEfJl4hIbhVxCv5aZLRVXl4k4/xKw0MjjPaqsRB13tRwxGR2O/w0EK79CyXrQPMhZkckeZCSLxGR3Grb/4EtAco107ezIneqYR8oWRtiLsPK0WZHI2ba/TUc/BmsbkZ1Qxc3syOSPEjJl4hIbhQbBTvmGG31eoncORdXaP8RYIHd8+H4RrMjEjNEnIJfhhnth96AEjXNjUfyLCVfIiK50a6vICYCCleEe9uYHY1I7hbQwKgWCkbxjYQ4c+OR7GW3w9L+EBsBZeobpeVFsoiSLxGR3MaWeMOiyvpVLnLXWo0B76Jw/gBsmWZ2NJKddsyBI7+Bq+f14YauZkckeZj+YouI5DYHf4Z/j4NXIajX1exoRPIG78LwyJtGe+17cDnM3Hgke/x7HFaMNNqtxkDRKqaGI3mfki8Rkdwmqbx8/efAvYC5sYjkJXW7QLmmEH8VfhlqdjSS1Ww2WNIP4qONf3etlSjZQMmXiEhu8s8OCNtsVONq0NvsaETyFosF2n9oLFp+8Gc48LPZEUlW2vYpnNgAbgXgsWkawi3ZQj9lIiK5yeapxn3tJ8CvlLmxiORFxatD4/5G+5ehEBdtbjySNS4cMtZ2A2O4aeEKpoYj+YeSLxGR3OJyGOz7wWirvLxI1nnwdfAvCxFhsO59s6ORzGZLhCUvQkIMVHzIGMItkk2UfImI5BZbPwV7IlR40FgUVkSyhnsBaPuu0d40Bc4dMDceyVybPoF/toOHHzw21RhuKpJNlHyJiOQGMZGw8wujnTQkSkSyTrV2ULUd2BJg2avGWlCS+53dB6snGO0274D/PebGI/mOki+RW4k8A3ND4LNWxodfEbP88SXERkLRe6Fya7OjEckf2r4Lbt5GUYbdC8yORu5WYjws/i8kxhmL02upDjGBki+Rmzm3H/7XGo6tg1O/Jy9qK5LdEhNgy0yj3bifKnKJZJeCZY35XwC/joSrl8yNR+7O+g8h/E9jjcSQjzXcUEyhv+AiaTm2DmYFQ+Q/4F3E2LZpKkRfMDcuyZ/2LzUm/nsXgTqdzY5GJH9p1A+KVYOrFyB0vNnRyJ06vSu5eEq7D8C3pKnhSP5levI1bdo0ypcvj6enJ0FBQWzbtu2m+8bHxzN+/HgqVaqEp6cndevWZfny5U77TJw4kQYNGuDr60vx4sXp0KEDBw8edNqnRYsWWCwWp1vfvlpYT67bvRC+fBxiI6BsY+i3HUrVg7grsH6S2dFJfmO3J5eXb/ACuHmZG49IfuPqDu2v/+7f8Tn887up4cgdSIiFxX2N+Xs1HoNancyOSPIxU5OvhQsXMnjwYMaMGcPOnTupW7cuwcHBnDt3Ls39R44cyaeffsqUKVPYt28fffv2pWPHjvzxxx+OfdauXUu/fv3YsmULK1euJD4+nkceeYToaOd1Onr37s2ZM2cct/feey9LX6vkAna78a3Y4j5gi4eaHeHZJVCgCLQabeyz/X8Q8Y+pYUo+c3IbnNoBLh5G8iUi2a98U6jbFbDDTwONocCSe6yZCOf3Q4FiRiKt4YZiIovdbl75nqCgIBo0aMDUqca3ujabjYCAAAYMGMCwYcNS7V+6dGlGjBhBv37J69t06tQJLy8v5s2bl+Y1zp8/T/HixVm7di3NmzcHjJ6vevXqMXny5DuOPTIyEn9/fyIiIvDz87vj80gOkZgAywbDzrnG4yYDoPX45Lk1drtReOP4erjvWaM0rUh2WPisMexQP3ci5oq+AFMCIeayUSWv0YtmRyTpcXI7zH4E7Dbo/BVUf9TsiCSPSm9uYFrPV1xcHDt27KB16+SqXVarldatW7N58+Y0j4mNjcXT09Npm5eXFxs2bLjpdSIiIgAoXLiw0/avvvqKokWLUqtWLYYPH87Vq1dvGW9sbCyRkZFON8kjYq/A152NxMtiNcaCP/KWc1EDiwVajTHau76C83+bE6vkL5eOwYGfjLYWVRYxV4Gi0Hqs0f7tbaMaruRscVdhSV8j8arTWYmX5AimJV8XLlwgMTGREiVKOG0vUaIE4eHhaR4THBzMpEmTOHToEDabjZUrV7Jo0SLOnEn7F6DNZmPgwIE0bdqUWrVqObZ37dqVefPmsXr1aoYPH86XX37JM888c8t4J06ciL+/v+MWEBCQwVcsOVLkGZjTFg6vAlcv41uxhr3T3jegAVRtb/wSX/1W9sYp+dPWmcbPW6VWULy62dGIyP094J4GxhzgFcPNjkZu57c34eJh8C2VvGi2iMlML7iRER9//DFVqlShWrVquLu7079/f3r16oX1JmWX+/Xrx969e1mwwHltjj59+hAcHEzt2rXp1q0bX3zxBYsXL+bIkSM3vfbw4cOJiIhw3E6ePJmpr01MkFRKPnyPMQ685zJjUc1baTUKsMC+H+DUzmwJU/Kpa5dh55dGu4kWVRbJEazW63OGrPDXYjgcanZEcjPHN8CWGUb7P1ON8vIiOYBpyVfRokVxcXHh7NmzTtvPnj1LyZJpl/8sVqwYS5YsITo6mhMnTnDgwAF8fHyoWLFiqn379+/PTz/9xOrVq7nnnluvXh4UFATA4cOHb7qPh4cHfn5+TjfJxVKWki9SGZ5fCfcE3v644tWh7tNGWyWHJSvtnAvx0VC8BlR8yOxoRCRJqToQdL1C8s9DID7G3HgktdgoWPISYIf7u0MVLUwvOYdpyZe7uzuBgYGEhiZ/a2Sz2QgNDaVx48a3PNbT05MyZcqQkJDA999/z2OPPeZ4zm63079/fxYvXsxvv/1GhQoVbhvLrl27AChVqtSdvRjJXW4sJf/8Sih8+58ThxbDweoGR1fD0bVZF6fkX4nxsPVTo924nypzieQ0D71hDGW7dBQ2fGR2NHKjlaPg8gnwLwuPvG12NCJOTB12OHjwYD777DPmzp3L/v37efHFF4mOjqZXr14AdO/eneHDk8dUb926lUWLFnH06FHWr19PmzZtsNlsvP766459+vXrx7x585g/fz6+vr6Eh4cTHh7OtWvXADhy5AhvvvkmO3bs4Pjx4yxdupTu3bvTvHlz6tSpk71vgGSvm5WS9y5820OdFCoH9Z8z2qHjjPOKZKa/lkDkKShQHGo/aXY0InIjD19oM9Fob5gEF28+bUGy2eFQ+H220e4wDTw1UklyFlOTr86dO/PBBx8wevRo6tWrx65du1i+fLmjCEdYWJhTMY2YmBhGjhxJjRo16NixI2XKlGHDhg0ULFjQsc+MGTOIiIigRYsWlCpVynFbuHAhYPS4rVq1ikceeYRq1arx6quv0qlTJ3788cdsfe2SzRLj4ceX4bfrhTKaDIBOs8HN89bH3UzzIeBWwFh/6cCyzItTxG6HzVOMdsM+4OphbjwikrYaHYxiOIlxsOxVfRGXE1y7DEsHGO2GfaBCc1PDEUmLqet85WZa5ysXib0C3/Y0KhparND2vZtXNMyI394yetKKVYMXN4HV5e7PKXJ8I3zeDlw9YdA+Y5FvEcmZLh6B6Y0hMRaemA21OpkdUf625CVjOZjCFaHvBnAvYHZEko/k+HW+RLJFRkrJZ1STAUb1pPMH4M+FmXNOkc3XF1Ku20WJl0hOV6QSPPCq0V7+BsRoDVDTHPjZSLywQIcZSrwkx1LyJXnXnZSSzwhPf2g2yGivnggJsZl3bsmfLhyGg78Y7UYvmRuLiKRPs4FQuBJEhcNqFXcwxdVL8OMrRrtJfyjbyNx4RG5ByZfkTUfX3lkp+Yxq2MeoeBURBr/PyfzzS/6ydQZgh3vbQLF7zY5GRNLD1QPaf2i0t/0fnN5lajj50s9DIPocFK0KD400OxqRW8pw8lW+fHnGjx9PWFhYVsQjcvd2L4R5ne68lHxGuHnBg0ON9rr3jfllInfi6iX44yuj3bifubGISMZUesiY72W3wbLBYEs0O6L846/FsPd7sLhAx5l3XkhLJJtkOPkaOHAgixYtomLFijz88MMsWLCA2FgNt5IcILNKyWfUfc8YQ06uXoAtM7L2WpJ3/T4bEq5BydpQ/gGzoxGRjAqeAB5+RhXcHZ+bHU3+EHUOfhpstB94Fcrcb248IulwR8nXrl272LZtG9WrV2fAgAGUKlWK/v37s3PnzqyIUeT2UpWSf/nuSslnhIsbtBxhtDdNMXowRDIiIdYYrgTQuL8WVRbJjXxLQsvrQ95CxxmJgWQdux1+GgTXLhlfWjV/zeyIRNLljud83X///XzyySecPn2aMWPG8L///Y8GDRpQr149Zs+ejSrYS7aJvQJfPw07vzBKybf7AB55E6zZOKWxRkfjl39spLHgpkhG7F0EUWeN+YM1Hzc7GhG5Uw1egFJ1ISYCfh1ldjR5258L4cBPYHWDDjPB1d3siETS5Y4/ncbHx/PNN9/wn//8h1dffZX69evzv//9j06dOvHGG2/QrVu3zIxTJG1ZWUo+I6xWaDXWaG/9P4g4lf0xSO5ktyeXl2/YRx8gRHIzqws8+hFggT8XwLH1ZkeUN0Wcgp9fN9othkHJWubGI5IBrhk9YOfOncyZM4evv/4aq9VK9+7d+eijj6hWrZpjn44dO9KgQYNMDVQklXP7Yd4TRkXDAsWgy8KsqWiYXpVbQbmmcGIjrH0X/vOJebFI7nFsLZzdC27eENjT7GhE5G6VCYT6z8Hvs2DZq8Ziv/pSJfPY7bB0gFFUq0wgNB1odkQiGZLhnq8GDRpw6NAhZsyYwalTp/jggw+cEi+AChUq8PTTT2dakCKpOJWSr5J1peQzwmKBVmOM9h/zjDWbRG5n8zTjvl63rC8OIyLZo9Vo40vBCwdh8xSzo8lbds6FI6Hg4mEspuyS4X4EEVNlOPk6evQoy5cv58knn8TNzS3NfQoUKMCcOVrzSLJIqlLyv2ZdKfmMKhsE97YFeyKsfsvsaCSnO38QDv0KWKDRi2ZHIyKZxasgPHJ9weW178O/x82MJu/49wSsuF7gqtVoKFbV3HhE7kCGk69z586xdevWVNu3bt3K77//nilBiaTJrFLyGdVqFGAx1h7RYptyK0m9XtXaQ5FK5sYiIpmrzlPGshEJ1+CXocbfMLlzNhv80A/ioowvXvWFleRSGU6++vXrx8mTJ1NtP3XqFP36aWFQySJmlpLPqBI1jT+6AKHjzY1Fcq7oC7B7gdHWosoieY/FAu0nGdX4/l4OB5aZHVHutv0zOL7emB/bYbpR3EQkF8pw8rVv3z7uvz/1Inb33Xcf+/bty5SgRJzkhFLyGdViOFhdjXHpqnYladk+CxJjofT9xre4IpL3FLsXmr5stH8ZCrFR5saTW108Aiuvz6l+eDwUrmhuPCJ3IcOfXj08PDh79myq7WfOnMHVVZMeJZPllFLyGVW4QnLlutBxGm4izuJjjG9xwej10qLKInnXA0OgYFmjQNTad82OJvexJcLivsbwzQoPQv3nzY5I5K5kOPl65JFHGD58OBEREY5tly9f5o033uDhhx/O1OAknzu3H/7XGsL3GFWjei6Dau3Mjir9mr9mDI/4Zzsc/MXsaCQn2fMNRJ8Hv3ugxmNmRyMiWcnd2xixAbBlOpzVKKEM2TwV/tkG7r7w2LScPepFJB0y/BP8wQcfcPLkScqVK8dDDz3EQw89RIUKFQgPD+fDDz/MihglP8qJpeQzyrckBPU12r+9aXx7J2K3JxfaCPovuKRdNVZE8pB7g6Hao2BLgGWDjeIRcnvn9ifP9W4zEQoGmBuPSCbIcPJVpkwZ/vzzT9577z1q1KhBYGAgH3/8MXv27CEgQP8pJBPk5FLyGdX0FfAsCOf2wZ5vzY5GcoIjoXD+ALj7QGAPs6MRkezS9l1wKwBhm2H3fLOjyfkS443hholxUOURuO8ZsyMSyRR3NEmrQIEC9OnTJ7NjkfzObof1HyR/y1WzI3SYmTMrGqaXV0FoNhBWjYXVb0PNx8HV3eSgxFRJvV73dwdPf3NjEZHs438PtBgGK0fBr6Ogaruct1RKTrLhIzizy/gCM+QTzY2VPOOOK2Ts27ePsLAw4uLinLb/5z//ueugJB9KjDeGYuz8wnjc5GVoPS5vjO1u+F/YMhMuh8GOzyFIX1zkW2f/giO/GVU7g/5rdjQikt0avQi7vzZGQ6waA/+ZYnZEOdOZP5OLk7T7APxKmRuPSCbKcPJ19OhROnbsyJ49e7BYLNivV3GzXP9GIjFR81okg2KvwLc9jYqGFiu0fS93VDRML3dvePB1I7lc9z7c1w3cC5gdlZhh83TjvnoIFCpvaigiYgIXN3j0I5gdbHzZWO8ZKBtkdlQ5S0KsMdzQlmD8rqz9hNkRiWSqDHcrvPLKK1SoUIFz587h7e3NX3/9xbp166hfvz5r1qzJcADTpk2jfPnyeHp6EhQUxLZt2266b3x8POPHj6dSpUp4enpSt25dli9fnuFzxsTE0K9fP4oUKYKPjw+dOnVKs3y+ZIPcWko+o+7vDoUqQPQ52DLD7GjEDFfOGlUOARoPMDcWETFP2UbJ85eWDYbEBHPjyWnWvgvn/gLvItD+Iw03lDwnw8nX5s2bGT9+PEWLFsVqtWK1WmnWrBkTJ07k5ZdfztC5Fi5cyODBgxkzZgw7d+6kbt26BAcHc+7cuTT3HzlyJJ9++ilTpkxh37599O3bl44dO/LHH39k6JyDBg3ixx9/5Ntvv2Xt2rWcPn2axx9/PKNvhdytG0vJ98plpeQzwsUNWo402hs/gauXzI1Hst/2/xkTx+9pCAENzI5GRMzUejx4FYKze2HrTLOjyTn++d2Y6wXw6GTwKWZqOCJZIcPJV2JiIr6+vgAULVqU06dPA1CuXDkOHjyYoXNNmjSJ3r1706tXL2rUqMHMmTPx9vZm9uzZae7/5Zdf8sYbb9CuXTsqVqzIiy++SLt27ZxK3N/unBEREcyaNYtJkybRsmVLAgMDmTNnDps2bWLLli0ZfTvkTqVVSr5MLisln1E1H4cStY0qjhsnmx2NZKe4q0byBcaiyiKSvxUoAg+PN9prJkLEKXPjyQnirxnDDe02qP0k1FANAcmbMpx81apVi927dwMQFBTEe++9x8aNGxk/fjwVK1ZM93ni4uLYsWMHrVu3Tg7GaqV169Zs3rw5zWNiY2Px9HSufOfl5cWGDRvSfc4dO3YQHx/vtE+1atUoW7bsTa+bdO3IyEinm9yhvFRKPiOsVmg1ymhv/RQiT5sbj2SfPxfAtUtQsKyx1o+ISL1nICAI4qJg+TCzozHfb2/BxUPgU9KY+y2SR2U4+Ro5ciS264sDjh8/nmPHjvHAAw/w888/88knn6T7PBcuXCAxMZESJUo4bS9RogTh4eFpHhMcHMykSZM4dOgQNpuNlStXsmjRIs6cOZPuc4aHh+Pu7k7BggXTfV2AiRMn4u/v77hpTbM7YLcbBScW9wFbvFFK/tkl+avUbpVHjIQzIQbW6o9LvmCzJRfaaPQSuNxxkVkRyUusVmg/CSwusH8pHFppdkTmObEpeRmO/3ySvz4XSL6T4eQrODjYMT+qcuXKHDhwgAsXLnDu3DlatmyZ6QGm9PHHH1OlShWqVauGu7s7/fv3p1evXlizoRz58OHDiYiIcNxOnjyZ5dfMUxLj4ceXk9fwavIydJqdu9fwuhMWC7QaY7R3fgEXj5gbj2S9wyuNb3M9/LRIqIg4K1nLKD8P8PMQY+hdfhMbBUteBOzG78h7g82OSCRLZShriY+Px9XVlb179zptL1y4sKPUfHoVLVoUFxeXVFUGz549S8mSJdM8plixYixZsoTo6GhOnDjBgQMH8PHxcQx3TM85S5YsSVxcHJcvX073dQE8PDzw8/Nzukk6xV6Br582kg2L1Viz45E388YaXneiXGOoEgz2RGPhZcnbNl1fxyewB3j4mhuLiOQ8LYaDXxn49zis//C2u+c5q8YYr90/AIInmh2NSJbL0KdfNzc3ypYtmylrebm7uxMYGEhoaKhjm81mIzQ0lMaNG9/yWE9PT8qUKUNCQgLff/89jz32WLrPGRgYiJubm9M+Bw8eJCws7LbXlTuQX0rJZ1TS3K+93xuLSUredGY3HF9vDCtqqEWVRSQNHj7Q5h2jvWEyXDhkajjZ6sjq5GJEj00FT32xLXlfhrseRowYwRtvvMGlS3dfKnvw4MF89tlnzJ07l/379/Piiy8SHR1Nr169AOjevTvDhw937L9161YWLVrE0aNHWb9+PW3atMFms/H666+n+5z+/v48//zzDB48mNWrV7Njxw569epF48aNadSo0V2/Jknh7L78U0o+o0rWhlrXF44MHW9uLJJ1kuZ61ewIBTVPVERuonqIMSfYFm+s/WW3mx1R1ouJgB/6G+0GL0DFFqaGI5JdMjzze+rUqRw+fJjSpUtTrlw5ChQo4PT8zp07032uzp07c/78eUaPHk14eDj16tVj+fLljoIZYWFhTvO5YmJiGDlyJEePHsXHx4d27drx5ZdfOhXPuN05AT766COsViudOnUiNjaW4OBgpk+fntG3Qm7l6FpY+KxR0bBIFej2bf6oaJgRD70B+5YYc4JObIJyTcyOSDJT5GnY+53RVnl5EbkVi8Wo8HdsnXHb8x3UedLsqLLWijeM5WYKVYDW48yORiTbWOz2jH29Mm7crf+DjBkz5q4Cyi0iIyPx9/cnIiJC879utHsh/NDP+AavbGN4er4qF93MT4Pg99kQ0AieW278AZa8YdVYY7HQsk3guV/MjkZEcoN17xuFqQoUh/7bwaug2RFljYPL4evOgAV6/WLMhRbJ5dKbG2Q4+RKDkq802O2w/oPkioY1O0KHmfmvomFGRJ6BT+6DhGvQZSFUbWN2RJIZYqPgoxrGsJqn50O19mZHJCK5QUIszGhqVEht0Bvaf2B2RJnv6iWY3giizkLj/hCswlOSN6Q3N8in5eYk06mU/J3xKwVB1wsxhI431oSS3G/310biVbgi3KuEWkTSydUD2l+veLj9f3Aq/VM5co2fXzMSr6L3QsuRZkcjku0ynHxZrVZcXFxuepN8KPYKzO+sUvJ3qtlA8PCHc38lzxGS3MuWmLxYaKOXwKrfiyKSARUfhNpPAXZjaLrt7itM5xj7fjD+zlms10fGeJkdkUi2y3DBjcWLFzs9jo+P548//mDu3Lm3nQ8meVDkGZj/pFHR0NULnpitioYZ5VUImr1i9HytfhtqdABXd7Ojkjt18Bf49xh4FoR6Xc2ORkRyo+C34e8VcGaXMS84LyzREnXeSCYBmg2CewLNjUfEJBlOvpLW1ErpiSeeoGbNmixcuJDnn38+UwKTXODsPvjqSaNaUYFi0HUhlNEv0zsS1Be2zDQWmvzjC6PsruROSb1e9Z8D9wK33ldEJC0+xY31IH8eYnwxV/0/4Fvi9sflVHY7/DQQrl6EErXgwaFmRyRimkwbF9aoUSOnhYsljzu6Fma3MRKvIlXg+ZVKvO6GewF48Pp6dWvfg7hoc+ORO3NqB4RtAqsbNOxjdjQikpvVfw5K3wexkfDrCLOjuTt7voUDP4HVFTrMMOa2ieRTmZJ8Xbt2jU8++YQyZcpkxukkp9u9EOZ1MtbwKtsYnv9Va3hlhvt7QMFyxkTkrZ+aHY3ciaRer1qdjGIqIiJ3yuoCj35kzI/a8y0cXWN2RHcm8ozRgwdGj1epOubGI2KyDCdfhQoVonDhwo5boUKF8PX1Zfbs2bz//vtZEaPkFHY7rH0fFvcx1vCq2RGeXaI1vDKLqzs8dP3bzY2T4dq/poYjGXT5JPy1xGhrUWURyQyl70sehr7sVaMUfW5it8PSAUb119L3GXO9RPK5DM/5+uijj7CkWAjWarVSrFgxgoKCKFSoUKYGJzlIYjwsG2xUNASjlHzrcapomNlqPwEbPzYqH278GFqPNTsiSa9tn4I9ESo01ze7IpJ5Wo40qgRePAwbP4EHXzM7ovT740s4vBJcPIzqhi5uZkckYjotsnyH8tUiy7FX4JsecCTUGP7Q9r28UXkppzr4C3z9tFE98pVd4FvS7IjkdmKvwKQaxtyMrt/AvcFmRyQiecme7+D758HVE17akjuG+l8Og+lNIO4KPDwemr5idkQiWSrLFlmeM2cO3377bart3377LXPnzs3o6SSnizwDc9oaiZerF3T+SolXVru3DdzTEBKuGcU3JOfb+aWReBWpApUfNjsaEclranWCCg9CQoyxSHFO/97cZoMf+hmJV0AQNO5vdkQiOUaGk6+JEydStGjRVNuLFy/OhAkTMiUoySHO7oP/tTbW8CpQDHot0xpe2cFiSR5uuHMuXDpqajhyG4kJsHWG0W78kobiikjms1ig/SRwcTeG8e1fanZEt/b7LDi2zvjStsMMLTYvkkKGPyWEhYVRoULq7u5y5coRFhaWKUFJDnB0LcwOTi4l/8IqlZLPTuWbQuXWYEuA1fpSI0c78JMxvMarMNTtYnY0IpJXFa0MTQca7V+GGcOdc6KLR2DlaKP98DgoUsnceERymAwnX8WLF+fPP/9MtX337t0UKVIkU4ISk+1ecL2UfGRyKflC5c2OKv9pdf2P157vIHyvubHIzSWVl2/wArh5mRuLiORtDww2/h5fOQ1r3jE7mtRsibDkJYi/CuUfgAaapiByowwnX126dOHll19m9erVJCYmkpiYyG+//cYrr7zC008/nRUxSnZxlJL/r0rJ5wSl6kLNxwE7/Pam2dFIWk5ug3+2GUOBkspBi4hkFTcvaPeB0d4yI+d9MbdlOpzcAu4+8Ng0DcMWSUOG/1e8+eabBAUF0apVK7y8vPDy8uKRRx6hZcuWmvOVmyXGw48vw+q3jMdNXoZOs8HN09y48ruWI8HiAn8vh7AtZkcjN9o81biv/RT4ljA3FhHJH6o8DNX/Yyxt8dMgo7hFTnDuAIRe/6Iw+G0oVM7ceERyqDsuNX/o0CF27dqFl5cXtWvXply5/PWfLE+Vmlcp+Zztx1dgx+fGENBevxgTr8V8/x6HT+4Duw1e3AwlapgdkYjkFxGnYFpDiIuCkE8gsIe58SQmwKyH4fROY75yt+/0t0rynSwrNZ+kSpUqPPnkkzz66KP5LvHKU1KWknfzhqfnK/HKaR4caqztErYZDq00OxpJsvVTI/Gq1FKJl4hkL/8y0GK40V41BqIvmhvPxo+MxMvTH/4zRYmXyC1kOPnq1KkT7777bqrt7733Hk8++WSmBCXZ5MZS8j1/gqptzY5KbuRXGhr2Mdqh43POEJP87Npl2PmF0W7cz9RQRCSfCuoLJWrBtX+TqwuaIXwPrLn+ubDt+8bfLBG5qQwnX+vWraNdu9RrPbVt25Z169ZlSlCSDVRKPndpNgg8/ODsHvhrkdnRyM4vjOE+xapDpVZmRyMi+ZGLq7H2F8CueXBic/bHkBAHi/saRbqqPQp1nsr+GERymQwnX1FRUbi7u6fa7ubmRmRkZKYEJVlMpeRzH+/CRhEUgN/eMgqkiDkS440hh2D0eml4jYiYpWwQ3N/daC8bnP1/G9a9B2f3GuscPvqRfh+KpEOGk6/atWuzcOHCVNsXLFhAjRoZn/cwbdo0ypcvj6enJ0FBQWzbtu2W+0+ePJmqVavi5eVFQEAAgwYNIiYmxvF8+fLlsVgsqW79+iUPDWrRokWq5/v27Zvh2HOdVKXkH1cp+dyk0YvG8NB/j8EfX5odTf617wejx7hAMaitodYiYrLW48C7CJzbZ5R6zy6ndsD66z1vj34EPsWz79oiuZhrRg8YNWoUjz/+OEeOHKFly5YAhIaGMn/+fL777rsMnWvhwoUMHjyYmTNnEhQUxOTJkwkODubgwYMUL576P/H8+fMZNmwYs2fPpkmTJvz999/07NkTi8XCpEnGL4Dt27eTmJjoOGbv3r08/PDDqeaj9e7dm/Hjxzsee3t7Zyj2XCcx3ihJm/Shvekr0Gqs1uDITTx8oPlr8Mvrxvj6Ok+Dex7/uc1p7Pbk8vINemspBhExn3dheHg8/NDPWHi55uNQMCBrrxkfA4tfNMrd1+oENTtk7fVE8pAMf/IOCQlhyZIlHD58mJdeeolXX32VU6dO8dtvv1G5cuUMnWvSpEn07t2bXr16UaNGDWbOnIm3tzezZ89Oc/9NmzbRtGlTunbtSvny5XnkkUfo0qWLU29ZsWLFKFmypOP2008/UalSJR588EGnc3l7ezvtl+vLxd9K7BWY39lIvCxWY4HGh8cr8cqNAnuCf1mICodt/2d2NPlP2GY4/YdRfbLB82ZHIyJiqNvVmEYQfxWWD8v6661+Cy4cBJ8SyYs+i0i63NGn7/bt27Nx40aio6M5evQoTz31FEOGDKFu3brpPkdcXBw7duygdevWycFYrbRu3ZrNm9OeNNqkSRN27NjhSLaOHj3Kzz//nGYBkKRrzJs3j+eeew7LDeOQv/rqK4oWLUqtWrUYPnw4V69evWW8sbGxREZGOt1yBZWSz1tcPeChN4z2ho+MqnuSfTZPM+7rPg0Fipobi4hIEqvVKL5hdYUDP8HB5Vl3rRObYdP1EQAhH2vqgkgG3XHXx7p16+jRowelS5fmww8/pGXLlmzZsiXdx1+4cIHExERKlCjhtL1EiRKEh4eneUzXrl0ZP348zZo1w83NjUqVKtGiRQveeOONNPdfsmQJly9fpmfPnqnOM2/ePFavXs3w4cP58ssveeaZZ24Z78SJE/H393fcAgKyuEs/M6iUfN5U5ymjyl7MZdj0idnR5B8Xj8CBZUa70UvmxiIicqMSNZJ/N/3yGsTd+kvlOxIXDUteBOxQr5s+U4jcgQwlX+Hh4bzzzjuOBZb9/PyIjY1lyZIlvPPOOzRo0CCr4gRgzZo1TJgwgenTp7Nz504WLVrEsmXLePPNN9Pcf9asWbRt25bSpZ3XnOjTpw/BwcHUrl2bbt268cUXX7B48WKOHDly02sPHz6ciIgIx+3kyZOZ+toynUrJ511WF2g1ymhvmQFXzpobT36xZQZghyqPQLGqZkcjIpLag0PB7x64HAbr3s/8868cYxR98isDbSZm/vlF8oF0J18hISFUrVqVP//8k8mTJ3P69GmmTJlyxxcuWrQoLi4unD3r/MHx7NmzlCxZMs1jRo0axbPPPssLL7xA7dq16dixIxMmTGDixInYblh49sSJE6xatYoXXnjhtrEEBQUBcPjw4Zvu4+HhgZ+fn9Mtx1Ip+byvaju4p4Exvj8r/sCKs6uXYNdXRrtxf3NjERG5GQ8faHt9weNNU+D8wcw799E1sP0zo/2fKeDpn3nnFslH0p18/fLLLzz//POMGzeO9u3b4+LiclcXdnd3JzAwkNDQUMc2m81GaGgojRs3TvOYq1evYr2hSERSHHa73Wn7nDlzKF68OO3bt79tLLt27QKgVKlSGXkJOY9KyecfFgu0Gm20d3wO/x43M5q8b8fnRqJbojZUaG52NCIiN1etPdzbxvgcsOxV47PB3YqJhB+uf/FU/zmorMXlRe5UupOvDRs2cOXKFQIDAwkKCmLq1KlcuHDhri4+ePBgPvvsM+bOncv+/ft58cUXiY6OplevXgB0796d4cOHO/YPCQlhxowZLFiwgGPHjrFy5UpGjRpFSEiIUzJos9mYM2cOPXr0wNXVuZr+kSNHePPNN9mxYwfHjx9n6dKldO/enebNm1OnTp27ej2mSoyHpQOMCkRglJLvNEulsPOyCs2hUkvjD+xqDf/IMglxyZUltaiyiOR0Fgu0fQ9cveD4evgz9dqsGbbiDYg4CQXLwcNpT/UQkfRJ9zpfjRo1olGjRkyePJmFCxcye/ZsBg8ejM1mY+XKlQQEBODr65uhi3fu3Jnz588zevRowsPDqVevHsuXL3cU4QgLC3Pq6Ro5ciQWi4WRI0dy6tQpihUrRkhICG+//bbTeVetWkVYWBjPPfdcqmu6u7uzatUqJk+eTHR0NAEBAXTq1ImRI0dmKPYcJfYKfNPDqGhosRq/dFXRMH9oNRqO/Gb8cW36MpSoaXZEec9fi+DKGfApaaxnIyKS0xUqBw++BqHjYcUIuDcYvArd2bn+/vX6GqEW6DDdGNooInfMYr9xvF4GHDx4kFmzZvHll19y+fJlHn74YZYuXZqZ8eVYkZGR+Pv7ExERYe78r8gzMP9Jo6Khmzc8MVvVh/Kbb3rAviXGPLAuX5sdTd5it8OnDxj/v1qNhgdeNTsiEZH0SYiDmc2M9bjqPwePfpTxc1y9BNMbG2tLNnpJRTZEbiG9ucFdrbJbtWpV3nvvPf755x++/lof+kxhi4eocyoln5+1HAkWFzj4M4RtNTuavOX4+uQvNgJ7mR2NiEj6ubpD+w+N9u9z4J8dGT/HL0ONxKtI5eR5xiJyV+4q+Uri4uJChw4d8k2vV45SsCx0+06l5POzolWgXlejHTo+cyZXiyFpIdF6XVW4RkRynwoPQJ2nATv8NBASE9J/7P4fYc83xnSGDjPBzSurohTJVzIl+RKTlaqjUvL5XYth4OIBJzYYc//k7p3/Gw6tACxaVFlEcq9H3jLKwof/Cdv/l75joi/AjwONdtNXICBr13EVyU+UfInkBf73JBdZWTUOblj3Tu7AlunGfdV2UKSSubGIiNwpn2LQaozR/u0tY674rdjt8NMguHoBiteAFsNvvb+IZIiSL5G8otlgcPc1vt3ct8TsaHK36Iuw+/o81sb9zI1FRORuBfYypibEXTHKxt/K3u9h/1KwukKHGeDqkT0xiuQTSr5E8ooCRaDJAKP921vG2m9yZ36fBQkxUKoelGtidjQiInfHajWqHVqsxvIZR35Le78r4cbCzADNX4PS9bItRJH8QsmXSF7S+CXwLgqXjsCur8yOJneKj0leVLnJAC2qLCJ5Q6m60LCP0V72qvG7LiW7HZa+DDGXjX21tIZIllDyJZKXePhC8yFGe827EH/N3Hhyo73fQfR58CsDNR4zOxoRkczz0AhjwfhLR2HjZOfndn1lFBlycYeOn4KLmykhiuR1Sr5E8pr6z4F/AFw5Dds+Mzua3MVuh83TjHbQf/XhQ0TyFk8/aDPBaK+fBBePGO3LJ+GXYUb7oTegeHVz4hPJB5R8ieQ1rh7J1ak2TIKYCHPjyU2O/Abn9oG7D9zfw+xoREQyX83HoeJDkBgLPw8xquMu7W8U47inATR52ewIRfI0JV8ieVHdp6FoVbj2L2yaYnY0uUdSr9d9z4JXQVNDERHJEhYLtP/QWBvyyG+w8Bk4ugZcvYzFlK0uZkcokqcp+RLJi6wu0HKk0d48HaLOmRtPbnB2n7FAtcVqDDkUEcmrilSCZoOM9sFlxn3rMVC0snkxieQTSr5E8qrqIVD6foiPhvUfmh1Nzrfleq9XtUehcAVzYxERyWrNBkHhika7XDNoqC+dRLKDki+RvMpiMb7JBNg+C/49YW48OVnUOfjzG6PduL+5sYiIZAc3T3jqS2jwAnT6n7EWmIhkOf1PE8nLKraACg+CLR7WvGN2NDnX9v9BYpwx2bxskNnRiIhkj5K1jPlffqXMjkQk31DyJZLXJfV+7f4azu03N5acKP6akXwBNO5nbiwiIiKSpyn5EsnrygQa87+ww29vmR1NzrN7AVy9CP5loVqI2dGIiIhIHqbkSyQ/aDnKqOJ34Cf453ezo8k5bDbYMt1oN+oLLq7mxiMiIiJ5mpIvkfygWFWo29VorxoLdrup4eQYh1fBhb/Bw89Y20tEREQkCyn5EskvWgwDF3c4vh6OrjY7mpxh81Tj/v7u4OlnbiwiIiKS5yn5EskvCgYYJYUBVo1T79eZP+HYWrC4aFFlERERyRamJ1/Tpk2jfPnyeHp6EhQUxLZt2265/+TJk6latSpeXl4EBAQwaNAgYmJiHM+PHTsWi8XidKtWrZrTOWJiYujXrx9FihTBx8eHTp06cfbs2Sx5fSI5ygOvgrsPnNkF+34wOxpzJc31qvEYFCxrbiwiIiKSL5iafC1cuJDBgwczZswYdu7cSd26dQkODubcuXNp7j9//nyGDRvGmDFj2L9/P7NmzWLhwoW88cYbTvvVrFmTM2fOOG4bNmxwen7QoEH8+OOPfPvtt6xdu5bTp0/z+OOPZ9nrFMkxChRNXkT4t7cgMcHceMwSeQb2fGe0taiyiIiIZBNTk69JkybRu3dvevXqRY0aNZg5cybe3t7Mnj07zf03bdpE06ZN6dq1K+XLl+eRRx6hS5cuqXrLXF1dKVmypONWtGhRx3MRERHMmjWLSZMm0bJlSwIDA5kzZw6bNm1iy5YtWfp6RXKExv3AqzBcPAS755sdjTm2f2YsPF22MdwTaHY0IiIikk+YlnzFxcWxY8cOWrdunRyM1Urr1q3ZvHlzmsc0adKEHTt2OJKto0eP8vPPP9OuXTun/Q4dOkTp0qWpWLEi3bp1IywszPHcjh07iI+Pd7putWrVKFu27E2vCxAbG0tkZKTTTSRX8vQzhh8CrHkH4mNuvX9eExcN22cZbS2qLCIiItnItOTrwoULJCYmUqJECaftJUqUIDw8PM1junbtyvjx42nWrBlubm5UqlSJFi1aOA07DAoK4vPPP2f58uXMmDGDY8eO8cADD3DlyhUAwsPDcXd3p2DBgum+LsDEiRPx9/d33AICAu7wlYvkAA1eAL8yEHkKtv/P7Giy1675EHMZClWAqu1uu7uIiIhIZjG94EZGrFmzhgkTJjB9+nR27tzJokWLWLZsGW+++aZjn7Zt2/Lkk09Sp04dgoOD+fnnn7l8+TLffPPNXV17+PDhREREOG4nT56825cjYh43T6P0PMD6DyEmn/TkOi2q/BJYXcyNR0RERPIVV7MuXLRoUVxcXFJVGTx79iwlS5ZM85hRo0bx7LPP8sILRrns2rVrEx0dTZ8+fRgxYgRWa+pcsmDBgtx7770cPnwYgJIlSxIXF8fly5eder9udV0ADw8PPDw8MvoyRXKuul1h4yfG3K/N0+Ch4WZHlPX+Xg6XjoKnP9TranY0IiIiks+Y1vPl7u5OYGAgoaGhjm02m43Q0FAaN26c5jFXr15NlWC5uBjfXNtvsmZRVFQUR44coVSpUgAEBgbi5ubmdN2DBw8SFhZ20+uK5EkurtBypNHePBWiL5gbT3ZIWlQ5sBd4+Jgbi4iIiOQ7pvV8AQwePJgePXpQv359GjZsyOTJk4mOjqZXr14AdO/enTJlyjBx4kQAQkJCmDRpEvfddx9BQUEcPnyYUaNGERIS4kjChgwZQkhICOXKleP06dOMGTMGFxcXunTpAoC/vz/PP/88gwcPpnDhwvj5+TFgwAAaN25Mo0aNzHkjRMxS4zEoVc9Y92v9h9BmotkRZZ1TO+HERrC6alFlERERMYWpyVfnzp05f/48o0ePJjw8nHr16rF8+XJHEY6wsDCnnq6RI0disVgYOXIkp06dolixYoSEhPD222879vnnn3/o0qULFy9epFixYjRr1owtW7ZQrFgxxz4fffQRVquVTp06ERsbS3BwMNOnT8++Fy6SU1gs0HoMfNnRKLzR6MW8u+Bw0lyvWp3Ar7S5sYiIiEi+ZLHfbLye3FJkZCT+/v5ERETg5+dndjgid85uh7khcHw91HsGOkwzO6LMF/EPfFwXbAnQZy2Urmd2RCIiIpKHpDc3yFXVDkUkC1gs0GqM0d49H84fNDeerLD1UyPxKv+AEi8RERExjZIvEYGABlDtUbDb4Lc3b79/bhJ7BXbMNdqN+5sbi4iIiORrSr5ExNByJGCB/T/CqR1mR5N5/vgKYiOgSGWo8ojZ0YiIiEg+puRLRAzFq0NdoyoooePNjSWz2BJvWFRZv/JERETEPPokIiLJWgwDqxscXWPccrsDP8HlE+BVODmxFBERETGJki8RSVaoHNR/zmivGmdUQszNNl+v3NjgeXD3NjcWERERyfeUfImIs+ZDwK0AnN5pzP/KrU5uh5NbwcUdGvQ2OxoRERERJV8icgOf4tD4JaP921vGvKncaPNU4772k+BbwtxYRERERFDyJSJpaTIAvArBhYOwe4HZ0WTcvydg/1Kj3eglc2MRERERuU7Jl4ik5ukPzQYb7TUTISHW3HgyauunxpplFR+CkrXMjkZEREQEUPIlIjfTsDf4loaIk/D7bLOjSb+YCNj5hdHWosoiIiKSgyj5EpG0uXlBi6FGe937EHvF3HjSa+cXEHcFilWDyq3MjkZERETEQcmXiNxcvWegcCW4ehE2Tzc7mttLTIAtM412435gsZgbj4iIiEgKSr5E5OZcXKHlSKO9aQpEXzQ3ntvZ/wNE/gPeRaH2U2ZHIyIiIuJEyZeI3FqNDlCyjjGUb8Mks6O5ObsdNl0vL9+wN7h5mhuPiIiIyA2UfInIrVmt0GqM0d72GUT8Y248NxO2xVgY2sUD6j9vdjQiIiIiqbiaHYCI5AKVW0G5ZnBiA6x5Bx6banZEqSUtqlz3afApZm4sIiL5VGJiIvHx8WaHIZLp3NzccHFxuevzKPkSkduzWKD1GJj1MOz6Cpq+AkWrmB1VsktH4cAyo61FlUVEsp3dbic8PJzLly+bHYpIlilYsCAlS5bEchcFvZR8iUj6BDSEqu3g4M/w21vw1FyzI0q2ZSZgh8oPQ/FqZkcjIpLvJCVexYsXx9vb+64+nIrkNHa7natXr3Lu3DkASpUqdcfnUvIlIunXchQc/AX2LYHTf0Dp+8yOCK79C3/MM9qN+5kbi4hIPpSYmOhIvIoUKWJ2OCJZwsvLC4Bz585RvHjxOx6CqIIbIpJ+JWpAnesl3EPHmxtLkh2fQ3w0lKgFFVuYHY2ISL6TNMfL29vb5EhEslbSz/jdzGs0PfmaNm0a5cuXx9PTk6CgILZt23bL/SdPnkzVqlXx8vIiICCAQYMGERMT43h+4sSJNGjQAF9fX4oXL06HDh04ePCg0zlatGiBxWJxuvXt2zdLXp9IntNiOFjd4MhvcGydubEkxMHWT422FlUWETGVhhpKXpcZP+OmJl8LFy5k8ODBjBkzhp07d1K3bl2Cg4Md4ylvNH/+fIYNG8aYMWPYv38/s2bNYuHChbzxxhuOfdauXUu/fv3YsmULK1euJD4+nkceeYTo6Ginc/Xu3ZszZ844bu+9916WvlaRPKNwBQjsabRXjTPW1zLLX4vhyhnwKQG1OpkXh4iIiEg6mJp8TZo0id69e9OrVy9q1KjBzJkz8fb2Zvbs2Wnuv2nTJpo2bUrXrl0pX748jzzyCF26dHHqLVu+fDk9e/akZs2a1K1bl88//5ywsDB27NjhdC5vb29KlizpuPn5+WXpaxXJU5q/Bm7ecOp3owCHGez25PLyDXuDq4c5cYiIiKRQvnx5Jk+enO7916xZg8ViUaXIfMK05CsuLo4dO3bQunXr5GCsVlq3bs3mzZvTPKZJkybs2LHDkWwdPXqUn3/+mXbt2t30OhEREQAULlzYaftXX31F0aJFqVWrFsOHD+fq1au3jDc2NpbIyEinm0i+5VsCGr1otEPHgy0x+2M4vgHC/wRXLy2qLCIiGXbjFJQbb2PHjr2j827fvp0+ffqke/8mTZpw5swZ/P397+h6d6JatWp4eHgQHh6ebdcUg2nVDi9cuEBiYiIlSpRw2l6iRAkOHDiQ5jFdu3blwoULNGvWDLvdTkJCAn379nUadpiSzWZj4MCBNG3alFq1ajmdp1y5cpQuXZo///yToUOHcvDgQRYtWnTTeCdOnMi4cePu4JWK5FFNXobts+D8AfjzG6jXJXuvv3macV+vK3gXvvW+IiIiNzhz5oyjvXDhQkaPHu1UJ8DHx8fRttvtJCYm4up6+4/OxYoVy1Ac7u7ulCxZMkPH3I0NGzZw7do1nnjiCebOncvQoUOz7dppiY+Px83NzdQYspPpBTcyYs2aNUyYMIHp06ezc+dOFi1axLJly3jzzTfT3L9fv37s3buXBQsWOG3v06cPwcHB1K5dm27duvHFF1+wePFijhw5ctNrDx8+nIiICMft5MmTmfraRHIdr4LQbJDRXj0BEmKz79oXDsHfvxhtLaosIpLj2O12rsYlZPvNnoF5yCmnn/j7+2OxWByPDxw4gK+vL7/88guBgYF4eHiwYcMGjhw5wmOPPUaJEiXw8fGhQYMGrFq1yum8Nw47tFgs/O9//6Njx454e3tTpUoVli5d6nj+xmGHn3/+OQULFmTFihVUr14dHx8f2rRp45QsJiQk8PLLL1OwYEGKFCnC0KFD6dGjBx06dLjt6541axZdu3bl2WefTXOqzz///EOXLl0oXLgwBQoUoH79+mzdutXx/I8//kiDBg3w9PSkaNGidOzY0em1LlmyxOl8BQsW5PPPPwfg+PHjWCwWFi5cyIMPPoinpydfffUVFy9epEuXLpQpUwZvb29q167N119/7XQem83Ge++9R+XKlfHw8KBs2bK8/fbbALRs2ZL+/fs77X/+/Hnc3d0JDQ297XuSnUzr+SpatCguLi6cPXvWafvZs2dvmv2PGjWKZ599lhdeeAGA2rVrEx0dTZ8+fRgxYgRWa3Iu2b9/f3766SfWrVvHPffcc8tYgoKCADh8+DCVKlVKcx8PDw88PDSnRMRJwz6wZQZEhBkl34P+mz3X3TLduL+3LRStnD3XFBGRdLsWn0iN0Suy/br7xgfj7Z55H2+HDRvGBx98QMWKFSlUqBAnT56kXbt2vP3223h4ePDFF18QEhLCwYMHKVu27E3PM27cON577z3ef/99pkyZQrdu3Thx4kSqaTFJrl69ygcffMCXX36J1WrlmWeeYciQIXz11VcAvPvuu3z11VfMmTOH6tWr8/HHH7NkyRIeeuihW76eK1eu8O2337J161aqVatGREQE69ev54EHHgAgKiqKBx98kDJlyrB06VJKlizJzp07sdlsACxbtoyOHTsyYsQIvvjiC+Li4vj554zP/R42bBgffvgh9913H56ensTExBAYGMjQoUPx8/Nj2bJlPPvss1SqVImGDRsCRkfIZ599xkcffUSzZs04c+aMY7TcCy+8QP/+/fnwww8dn9fnzZtHmTJlaNmyZYbjy0qmJV/u7u4EBgYSGhrqyNJtNhuhoaGpMtckV69edUqwAMcCZ0nfdNjtdgYMGMDixYtZs2YNFSpUuG0su3btAu5utWqRfMndGx58HZYNhnXvQ71u4OFz++PuRvRF2HX927Amaf+uEBERyQzjx4/n4YcfdjwuXLgwdevWdTx+8803Wbx4MUuXLr3p51eAnj170qWLMTx/woQJfPLJJ2zbto02bdqkuX98fDwzZ850dAr079+f8eOT19ecMmUKw4cPd/Q6TZ06NV1J0IIFC6hSpQo1a9YE4Omnn2bWrFmO5Gv+/PmcP3+e7du3OxLDypWTv+R8++23efrpp52m4qR8P9Jr4MCBPP74407bhgwZ4mgPGDCAFStW8M0339CwYUOuXLnCxx9/zNSpU+nRowcAlSpVolmzZgA8/vjj9O/fnx9++IGnnjLWI/3888/p2bNnjlsCwbTkC2Dw4MH06NGD+vXr07BhQyZPnkx0dDS9evUCoHv37pQpU4aJEycCEBISwqRJk7jvvvsICgri8OHDjBo1ipCQEEcS1q9fP+bPn88PP/yAr6+vYyKhv78/Xl5eHDlyhPnz59OuXTuKFCnCn3/+yaBBg2jevDl16tQx540Qyc3u7w6bpsC/x4xesAdfy9rr7ZgNCdegVF0o1zRrryUiInfEy82FfeODTbluZqpfv77T46ioKMaOHcuyZcs4c+YMCQkJXLt2jbCwsFueJ+VnzAIFCuDn53fTpZXAqMqdcjRWqVKlHPtHRERw9uxZR48QGJ0RgYGBjh6qm5k9ezbPPPOM4/EzzzzDgw8+yJQpU/D19WXXrl3cd999N+2R27VrF717977lNdLjxvc1MTGRCRMm8M0333Dq1Cni4uKIjY11LGq8f/9+YmNjadWqVZrn8/T0dAyjfOqpp9i5cyd79+51Gt6ZU5iafHXu3Jnz588zevRowsPDqVevHsuXL3cU4QgLC3Pq6Ro5ciQWi4WRI0dy6tQpihUrRkhIiGO8J8CMGTMAYyHllObMmUPPnj1xd3dn1apVjkQvICCATp06MXLkyKx/wSJ5kYsbtBwJ3z8Pmz6BBs9nXQGMhFjY9pnRbtxfiyqLiORQFoslU4f/maVAgQJOj4cMGcLKlSv54IMPqFy5Ml5eXjzxxBPExcXd8jw3FpSwWCy3TJTS2j8j89nSsm/fPrZs2cK2bducimwkJiayYMECevfujZeX1y3Pcbvn04ozPj4+1X43vq/vv/8+H3/8MZMnT6Z27doUKFCAgQMHOt7X210XjKGH9erV459//mHOnDm0bNmScuXK3fa47Gb6/4r+/fvftJt2zZo1To9dXV0ZM2YMY8aMuen5bveDGRAQwNq1azMcp4jcQs3HYcNkOLsHNkyCR97Kmuvs+Q6izoJvaajZ8fb7i4iIZKKNGzfSs2dPx3C/qKgojh8/nq0x+Pv7U6JECbZv307z5s0BI4HauXMn9erVu+lxs2bNonnz5kybNs1p+5w5c5g1axa9e/emTp06/O9//+PSpUtp9n7VqVOH0NBQxyi1GxUrVsypMMihQ4duu5wTGO/rY4895uiVs9ls/P3339SoUQOAKlWq4OXlRWhoqKP2w41q165N/fr1+eyzz5g/fz5Tp0697XXNkKuqHYpIDmW1QqvRRnvbZxB5OvOvYbcnl5cP+q/R4yYiIpKNqlSpwqJFi9i1axe7d++ma9eutx3qlxUGDBjAxIkT+eGHHzh48CCvvPIK//77703nN8XHx/Pll1/SpUsXatWq5XR74YUX2Lp1K3/99RddunShZMmSdOjQgY0bN3L06FG+//57xxq8Y8aM4euvv2bMmDHs37+fPXv28O677zqu07JlS6ZOncoff/zB77//Tt++fdNVRr5KlSqsXLmSTZs2sX//fv773/86FeXz9PRk6NChvP7663zxxRccOXKELVu2MGvWLKfzvPDCC7zzzjvY7XanKow5iZIvEckcVR6Gso0hIQbWvnv7/TPq6Bo49xe4FYDAHpl/fhERkduYNGkShQoVokmTJoSEhBAcHMz999+f7XEMHTqULl260L17dxo3boyPjw/BwcF4enqmuf/SpUu5ePFimglJ9erVqV69OrNmzcLd3Z1ff/2V4sWL065dO2rXrs0777zjqK3QokULvv32W5YuXUq9evVo2bIl27Ztc5zrww8/JCAggAceeICuXbsyZMgQx7ytWxk5ciT3338/wcHBtGjRwpEApjRq1CheffVVRo8eTfXq1encuXOqeXNdunTB1dWVLl263PS9MJvFfrcDSPOpyMhI/P39iYiIwM/Pz+xwRHKGE5thThuwuEC/bZlbBn5eJzi8Chr+F9q9l3nnFRGRuxITE8OxY8eoUKFCjv3Am9fZbDaqV6/OU089ddP1b/OD48ePU6lSJbZv354lSfGtftbTmxuo50tEMk+5xlAlGOyJsPrt2++fXuf2G4kXFmj0YuadV0REJBc6ceIEn332GX///Td79uzhxRdf5NixY3Tt2tXs0EwRHx9PeHg4I0eOpFGjRqb0RqaXki8RyVytRgMW+GsRnN6VOedMWlS5+qNQ+PZr94mIiORlVquVzz//nAYNGtC0aVP27NnDqlWrqF69utmhmWLjxo2UKlWK7du3M3PmTLPDuSXTqx2KSB5TshbUfgL2fAu/vQnPfH9354s6D7sXGu3GWlRZREQkICCAjRs3mh1GjtGiRYu7LsWfXdTzJSKZ76E3wOpqDBU8vuHuzrX9f5AYC2UCISAoc+ITERERMYGSLxHJfIUrwv3XKxKuGmeUib8T8deM5Au0qLKIiIjkekq+RCRrPPg6uHrBP9vg7+V3do4/v4GrF8A/AKr/J3PjExEREclmSr5EJGv4ljQWQwYIHQ+2xIwd77Socl9w0RRVERERyd2UfIlI1mk2EDz94dw+2PNdxo49vAouHAR3X7j/2SwJT0RERCQ7KfkSkazjVQiavmK0V78NCXHpP3bzVOM+sIeRwImIiIjkckq+RCRrBfUFnxJw+QTsnJu+Y8L3wtE1YLEmD10UERHJgVq0aMHAgQMdj8uXL8/kyZNveYzFYmHJkiV3fe3MOo9kHyVfIpK13AtA89eM9tr3IC769sckzfWq8RgULJt1sYmISL4VEhJCmzZt0nxu/fr1WCwW/vzzzwyfd/v27fTp0+duw3MyduxY6tWrl2r7mTNnaNu2baZe62auXbtG4cKFKVq0KLGxsdlyzbxIyZeIZL37e0DBchB9DrbMuPW+V8KNBZpBiyqLiEiWef7551m5ciX//PNPqufmzJlD/fr1qVOnTobPW6xYMby9vTMjxNsqWbIkHh4e2XKt77//npo1a1KtWjXTe9vsdjsJCQmmxnCnlHyJSNZzdYeWI432xk/g6qWb77vtM7DFQ0AjuKd+9sQnIiKZy243Rjpk9y0D60o++uijFCtWjM8//9xpe1RUFN9++y3PP/88Fy9epEuXLpQpUwZvb29q167N119/fcvz3jjs8NChQzRv3hxPT09q1KjBypUrUx0zdOhQ7r33Xry9valYsSKjRo0iPj4egM8//5xx48axe/duLBYLFovFEfONww737NlDy5Yt8fLyokiRIvTp04eoqCjH8z179qRDhw588MEHlCpViiJFitCvXz/HtW5l1qxZPPPMMzzzzDPMmjUr1fN//fUXjz76KH5+fvj6+vLAAw9w5MgRx/OzZ8+mZs2aeHh4UKpUKfr3N75gPX78OBaLhV27djn2vXz5MhaLhTVr1gCwZs0aLBYLv/zyC4GBgXh4eLBhwwaOHDnCY489RokSJfDx8aFBgwasWrXKKa7Y2FiGDh1KQEAAHh4eVK5cmVmzZmG326lcuTIffPCB0/67du3CYrFw+PDh274nd0K1m0Uke9R6AjZMhnN/wcaP4eFxqfeJuwq/X/+F3rhftoYnIiKZKP4qTCid/dd947Qx3D0dXF1d6d69O59//jkjRozAYrEA8O2335KYmEiXLl2IiooiMDCQoUOH4ufnx7Jly3j22WepVKkSDRs2vO01bDYbjz/+OCVKlGDr1q1EREQ4zQ9L4uvry+eff07p0qXZs2cPvXv3xtfXl9dff53OnTuzd+9eli9f7kgs/P1TF6KKjo4mODiYxo0bs337ds6dO8cLL7xA//79nRLM1atXU6pUKVavXs3hw4fp3Lkz9erVo3fv3jd9HUeOHGHz5s0sWrQIu93OoEGDOHHiBOXKlQPg1KlTNG/enBYtWvDbb7/h5+fHxo0bHb1TM2bMYPDgwbzzzju0bduWiIgINm7ceNv370bDhg3jgw8+oGLFihQqVIiTJ0/Srl073n77bTw8PPjiiy8ICQnh4MGDlC1rTFvo3r07mzdv5pNPPqFu3bocO3aMCxcuYLFYeO6555gzZw5DhgxxXGPOnDk0b96cypUrZzi+9FDyJSLZw2qFVqPh686wdaZRiMOvlPM+u+fDtX+hUHmo1t6UMEVEJP947rnneP/991m7di0tWrQAjA/fnTp1wt/fH39/f6cP5gMGDGDFihV888036Uq+Vq1axYEDB1ixYgWlSxvJ6IQJE1LN0xo5cqSjXb58eYYMGcKCBQt4/fXX8fLywsfHB1dXV0qWLHnTa82fP5+YmBi++OILChQwEtCpU6cSEhLCu+++S4kSJQAoVKgQU6dOxcXFhWrVqtG+fXtCQ0NvmXzNnj2btm3bUqhQIQCCg4OZM2cOY8eOBWDatGn4+/uzYMEC3NzcALj33nsdx7/11lu8+uqrvPLKK45tDRo0uO37d6Px48fz8MMPOx4XLlyYunXrOh6/+eabLF68mKVLl9K/f3/+/vtvvvnmG1auXEnr1q0BqFixomP/nj17Mnr0aLZt20bDhg2Jj49n/vz5qXrDMpOSLxHJPvcGQ0AQnNwK696DRz9Kfs5mg83TjXajl8DqYk6MIiJy99y8jV4oM66bAdWqVaNJkybMnj2bFi1acPjwYdavX8/48eMBSExMZMKECXzzzTecOnWKuLg4YmNj0z2na//+/QQEBDgSL4DGjRun2m/hwoV88sknHDlyhKioKBISEvDz88vQa9m/fz9169Z1JF4ATZs2xWazcfDgQUfyVbNmTVxckv/GlipVij179tz0vImJicydO5ePP/7Yse2ZZ55hyJAhjB49GqvVyq5du3jggQcciVdK586d4/Tp07Rq1SpDryct9es7T0eIiopi7NixLFu2jDNnzpCQkMC1a9cICwsDjCGELi4uPPjgg2mer3Tp0rRv357Zs2fTsGFDfvzxR2JjY3nyySfvOtab0ZwvEck+Fgu0GmO0d34BF5PHgnNoBVw6Ah7+UK+bOfGJiEjmsFiM4X/Zfbs+dDAjnn/+eb7//nuuXLnCnDlzqFSpkuPD+vvvv8/HH3/M0KFDWb16Nbt27SI4OJi4uAysW3kbmzdvplu3brRr146ffvqJP/74gxEjRmTqNVK6MUGyWCzYbLab7r9ixQpOnTpF586dcXV1xdXVlaeffpoTJ04QGhoKgJeX102Pv9VzAFarkY7YU8zXu9kctJSJJcCQIUNYvHgxEyZMYP369ezatYvatWs73rvbXRvghRdeYMGCBVy7do05c+bQuXPnLC2YouRLRLJX+aZQ+WGwJcDqCcnbk8rL1+8JHj6mhCYiIvnPU089hdVqZf78+XzxxRc899xzjvlfGzdu5LHHHuOZZ56hbt26VKxYkb///jvd565evTonT57kzJkzjm1btmxx2mfTpk2UK1eOESNGUL9+fapUqcKJEyec9nF3dycxMfG219q9ezfR0clLumzcuBGr1UrVqlXTHfONZs2axdNPP82uXbucbk8//bSj8EadOnVYv359mkmTr68v5cuXdyRqNypWrBiA03uUsvjGrWzcuJGePXvSsWNHateuTcmSJTl+/Ljj+dq1a2Oz2Vi7du1Nz9GuXTsKFCjAjBkzWL58Oc8991y6rn2nTE++pk2bRvny5fH09CQoKIht27bdcv/JkydTtWpVvLy8CAgIYNCgQcTExGTonDExMfTr148iRYrg4+NDp06dOHv2bKa/NhG5iVajjPu938GZP+H0Lji+Hqyu0FCLKouISPbx8fGhc+fODB8+nDNnztCzZ0/Hc1WqVGHlypVs2rSJ/fv389///jdDnxlbt27NvffeS48ePdi9ezfr169nxIgRTvtUqVKFsLAwFixYwJEjR/jkk09YvHix0z7ly5fn2LFj7Nq1iwsXLqS5zla3bt3w9PSkR48e7N27l9WrVzNgwACeffZZx5DDjDp//jw//vgjPXr0oFatWk637t27s2TJEi5dukT//v2JjIzk6aef5vfff+fQoUN8+eWXHDx4EDDWKfvwww/55JNPOHToEDt37mTKlCmA0TvVqFEj3nnnHfbv38/atWud5sDdSpUqVVi0aBG7du1i9+7ddO3a1akXr3z58vTo0YPnnnuOJUuWcOzYMdasWcM333zj2MfFxYWePXsyfPhwqlSpkuaw0MxkavK1cOFCBg8ezJgxY9i5cyd169YlODiYc+fOpbn//PnzGTZsGGPGjGH//v3MmjWLhQsX8sYbb2TonIMGDeLHH3/k22+/Ze3atZw+fZrHH388y1+viFxXqi7U6mS0f3szuder5uPgX8a8uEREJF96/vnn+ffffwkODnaanzVy5Ejuv/9+goODadGiBSVLlqRDhw7pPq/VamXx4sVcu3aNhg0b8sILL/D222877fOf//yHQYMG0b9/f+rVq8emTZsYNWqU0z6dOnWiTZs2PPTQQxQrVizNcvfe3t6sWLGCS5cu0aBBA5544glatWrF1KlTM/ZmpJBUvCOt+VqtWrXCy8uLefPmUaRIEX777TeioqJ48MEHCQwM5LPPPnMMcezRoweTJ09m+vTp1KxZk0cffZRDhw45zjV79mwSEhIIDAxk4MCBvPXWW+mKb9KkSRQqVIgmTZoQEhJCcHAw999/v9M+M2bM4IknnuCll16iWrVq9O7d26l3EIx//7i4OHr16pXRtyjDLHZ7BhZEyGRBQUE0aNDA8UNhs9kICAhgwIABDBs2LNX+/fv3Z//+/U7dlq+++ipbt25lw4YN6TpnREQExYoVY/78+TzxxBMAHDhwgOrVq7N582YaNWqUZqyxsbFO3zJERkYSEBBAREREhidEigjGfK+pDcCeCBYr2G3QZw2Uvs/syEREJANiYmI4duwYFSpUwNPT0+xwRDJs/fr1tGrVipMnT96yl/BWP+uRkZH4+/vfNjcwrecrLi6OHTt2OMo+gvHtQOvWrdm8eXOaxzRp0oQdO3Y4hhEePXqUn3/+mXbt2qX7nDt27CA+Pt5pn2rVqlG2bNmbXhdg4sSJjpKj/v7+BAQE3PmLFxEoUgnuf9Zo221QrpkSLxEREck2sbGx/PPPP4wdO5Ynn3zyjodnZoRpydeFCxdITExM9SJLlChBeHh4msd07dqV8ePH06xZM9zc3KhUqRItWrRwDDtMzznDw8Nxd3enYMGC6b4uwPDhw4mIiHDcTp48mdGXLCI3enAouF7/5kiLKouIiEg2+vrrrylXrhyXL1/mvffey5Zrml5wIyPWrFnDhAkTmD59Ojt37mTRokUsW7aMN998M8uv7eHhgZ+fn9NNRO6SX2l4ej60/xCqtr39/iIiIiKZpGfPniQmJrJjxw7KlMmeOeemLbJctGhRXFxcUlWMOXv27E1X7x41ahTPPvssL7zwAmCUj4yOjqZPnz6MGDEiXecsWbIkcXFxXL582an361bXFZEsVPnuF10UERERyQ1M6/lyd3cnMDDQqXiGzWYjNDT0piUer1696liILUnSCt12uz1d5wwMDMTNzc1pn4MHDxIWFpblpSVFRERE8ioTa7iJZIvM+Bk3recLYPDgwfTo0YP69evTsGFDJk+eTHR0tKPMY/fu3SlTpgwTJ04EICQkhEmTJnHfffcRFBTE4cOHGTVqFCEhIY4k7Hbn9Pf35/nnn2fw4MEULlwYPz8/BgwYQOPGjW9a6VBERERE0pZUTvzq1at4eXmZHI1I1rl69SqQ/DN/J0xNvjp37sz58+cZPXo04eHh1KtXj+XLlzsKZoSFhTn1dI0cORKLxcLIkSM5deoUxYoVIyQkxGm9hNudE+Cjjz7CarXSqVMnYmNjCQ4OZvr06dn3wkVERETyCBcXFwoWLOhYU9Xb2xuLxWJyVCKZx263c/XqVc6dO0fBggUdnT53wtR1vnKz9NbyFxEREcnr7HY74eHhXL582exQRLJMwYIFKVmyZJpfLqQ3NzC150tEREREcj+LxUKpUqUoXrw48fHxZocjkunc3NzuqscriZIvEREREckULi4umfIBVSSvylXrfImIiIiIiORWSr5ERERERESygZIvERERERGRyudCCwAADJJJREFUbKA5X3coqUhkZGSkyZGIiIiIiIiZknKC2xWSV/J1h65cuQJAQECAyZGIiIiIiEhOcOXKFfz9/W/6vNb5ukM2m43Tp0/j6+tr+kKCkZGRBAQEcPLkSa05lgX0/mYtvb9ZS+9v1tL7m7X0/mYtvb9ZS+9v1stJ77HdbufKlSuULl0aq/XmM7vU83WHrFYr99xzj9lhOPHz8zP9By8v0/ubtfT+Zi29v1lL72/W0vubtfT+Zi29v1kvp7zHt+rxSqKCGyIiIiIiItlAyZeIiIiIiEg2UPKVB3h4eDBmzBg8PDzMDiVP0vubtfT+Zi29v1lL72/W0vubtfT+Zi29v1kvN77HKrghIiIiIiKSDdTzJSIiIiIikg2UfImIiIiIiGQDJV8iIiIiIiLZQMmXiIiIiIhINlDylQdMmzaN8uXL4+npSVBQENu2bTM7pDxh3bp1hISEULp0aSwWC0uWLDE7pDxl4sSJNGjQAF9fX4oXL06HDh04ePCg2WHlGTNmzKBOnTqOhScbN27ML7/8YnZYedY777yDxWJh4MCBZoeSJ4wdOxaLxeJ0q1atmtlh5SmnTp3imWeeoUiRInh5eVG7dm1+//13s8PKE8qXL5/q59disdCvXz+zQ8sTEhMTGTVqFBUqVMDLy4tKlSrx5ptvkltqCCr5yuUWLlzI4MGDGTNmDDt37qRu3boEBwdz7tw5s0PL9aKjo6lbty7Tpk0zO5Q8ae3atfTr148tW7awcuVK4uPjeeSRR4iOjjY7tDzhnnvu4Z133mHHjh38/vvvtGzZkscee4y//vrL7NDynO3bt/Ppp59Sp04ds0PJU2rWrMmZM2cctw0bNpgdUp7x77//0rRpU9zc3Pjll1/Yt28fH374IYUKFTI7tDxh+/btTj+7K1euBODJJ580ObK84d1332XGjBlMnTqV/fv38+677/Lee+8xZcoUs0NLF5Waz+WCgoJo0KABU6dOBcBmsxEQEMCAAQMYNmyYydHlHRaLhcWLF9OhQwezQ8mzzp8/T/HixVm7di3Nmzc3O5w8qXDhwrz//vs8//zzZoeSZ0RFRXH//fczffp03nrrLerVq8fkyZPNDivXGzt2LEuWLGHXrl1mh5InDRs2jI0bN7J+/XqzQ8kXBg4cyE8//cShQ4ewWCxmh5PrPfroo5QoUYJZs2Y5tnXq1AkvLy/mzZtnYmTpo56vXCwuLo4dO3bQunVrxzar1Urr1q3ZvHmziZGJZFxERARgJAiSuRITE1mwYAHR0dE0btzY7HDylH79+tG+fXun38OSOQ4dOkTp0qWpWLEi3bp1IywszOyQ8oylS5dSv359nnzySYoXL859993HZ599ZnZYeVJcXBzz5s3jueeeU+KVSZo0aUJoaCh///03ALt372bDhg20bdvW5MjSx9XsAOTOXbhwgcTEREqUKOG0vUSJEhw4cMCkqEQyzmazMXDgQJo2bUqtWrXMDifP2LNnD40bNyYmJgYfHx8WL15MjRo1zA4rz1iwYAE7d+5k+/btZoeS5wQFBfH5559TtWpVzpw5w7hx43jggQfYu3cvvr6+ZoeX6x09epQZM2YwePBg3njjDbZv387LL7+Mu7s7PXr0MDu8PGXJkiVcvnyZnj17mh1KnjFs2DAiIyOpVq0aLi4uJCYm8vbbb9OtWzezQ0sXJV8iYrp+/fqxd+9ezenIZFWrVmXXrl1ERETw3Xff0aNHD9auXasELBOcPHmSV155hZUrV+Lp6Wl2OHlOym+w69SpQ1BQEOXKleObb77RsNlMYLPZqF+/PhMmTADgvvvuY+/evcycOVPJVyabNWsWbdu2pXTp0maHkmd88803fPXVV8yfP5+aNWuya9cuBg4cSOnSpXPFz6+Sr1ysaNGiuLi4cPbsWaftZ8+epWTJkiZFJZIx/fv356effmLdunXcc889ZoeTp7i7u1O5cmUAAgMD2b59Ox9//DGffvqpyZHlfjt27ODcuXPcf//9jm2JiYmsW7eOqVOnEhsbi4uLi4kR5i0FCxbk3nvv5fDhw2aHkieUKlUq1Zcw1atX5/vvvzcporzpxIkTrFq1ikWLFpkdSp7y2muvMWzYMJ5++mkAateuzYkTJ5g4cWKuSL405ysXc3d3JzAwkNDQUMc2m81GaGio5nVIjme32+nfvz+LFy/mt99+o0KFCmaHlOfZbDZiY2PNDiNPaNWqFXv27GHXrl2OW/369enWrRu7du1S4pXJoqKiOHLkCKVKlTI7lDyhadOmqZb2+PvvvylXrpxJEeVNc+bMoXjx4rRv397sUPKUq1evYrU6pzAuLi7YbDaTIsoY9XzlcoMHD6ZHjx7Ur1+fhg0bMnnyZKKjo+nVq5fZoeV6UVFRTt+yHjt2jF27dlG4cGHKli1rYmR5Q79+/Zg/fz4//PADvr6+hIeHA+Dv74+Xl5fJ0eV+w4cPp23btpQtW5YrV64wf/581qxZw4oVK8wOLU/w9fVNNT+xQIECFClSRPMWM8GQIUMICQmhXLlynD59mjFjxuDi4kKXLl3MDi1PGDRoEE2aNGHChAk89dRTbNu2jf/7v//j//7v/8wOLc+w2WzMmTOHHj164Oqqj9uZKSQkhLfffpuyZctSs2ZN/vjjDyZNmsRzzz1ndmjpY5dcb8qUKfayZcva3d3d7Q0bNrRv2bLF7JDyhNWrV9uBVLcePXqYHVqekNZ7C9jnzJljdmh5wnPPPWcvV66c3d3d3V6sWDF7q1at7L/++qvZYeVpDz74oP2VV14xO4w8oXPnzvZSpUrZ3d3d7WXKlLF37tzZfvjwYbPDylN+/PFHe61ateweHh72atWq2f/v//7P7JDylBUrVtgB+8GDB80OJc+JjIy0v/LKK/ayZcvaPT097RUrVrSPGDHCHhsba3Zo6aJ1vkRERERERLKB5nyJiIiIiIhkAyVfIiIiIiIi2UDJl4iIiIiISDZQ8iUiIiIiIpINlHyJiIiIiIhkAyVfIiIiIiIi2UDJl4iIiIiISDZQ8iUiIiIiIpINlHyJiIhkM4vFwpIlS8wOQ0REspmSLxERyVd69uyJxWJJdWvTpo3ZoYmISB7nanYAIiIi2a1NmzbMmTPHaZuHh4dJ0YiISH6hni8REcl3PDw8KFmypNOtUKFCgDEkcMaMGbRt2xYvLy8qVqzId99953T8nj17aNmyJV5eXhQpUoQ+ffoQFRXltM/s2bOpWbMmHh4elCpViv79+zs9f+HCBTp27Ii3tzdVqlRh6dKlWfuiRUTEdEq+REREbjBq1Cg6derE7t276datG08//TT79+8HIDo6muDgYAoVKsT27dv59ttvWbVqlVNyNWPGDPr160efPn3Ys2cPS5cupXLlyk7XGDduHE899RR//vkn7dq1o1u3bly6dClbX6eIiGQvi91ut5sdhIiISHbp2bMn8+bNw9PT02n7G2+8wRtvvIHFYqFv377MmDHD8VyjRo24//77mT59Op999hlDhw7l5MmTFChQAICff/6ZkJAQTp8+TYkSJShTpgy9evXirbfeSjMGi8XCyJEjefPNNwEjofPx8eGXX37R3DMRkTxMc75ERCTfeeihh5ySK4DChQs72o0bN3Z6rnHjxuzatQuA/fv3U7duXUfiBdC0aVNsNhsHDx7EYrFw+vRpWrVqdcsY6tSp42gXKFAAPz8/zp07d6cvSUREcgElXyIiku8UKFAg1TDAzOLl5ZWu/dzc3JweWywWbDZbVoQkIiI5hOZ8iYiI3GDLli2pHlevXh2A6tWrs3v3bqKjox3Pb9y4EavVStWqVfH19aV8+fKEhoZma8wiIpLzqedLRETyndjYWMLDw522ubq6UrRoUQC+/fZb6tevT7Nmzfjqq6/Ytm0bs2bNAqBbt26MGTOGHj16MHbsWM6fP8+AAQN49tlnKVGiBABjx46lb9++FC9enLZt23LlyhU2btzIgAEDsveFiohIjqLkS0RE8p3ly5dTqlQpp21Vq1blwIEDgFGJcMGCBbz00kuUKlWKr7/+mho1agDg7e3NihUreOWVV2jQoAHe3t506tSJSZMmOc7Vo0cPYmJi+OijjxgyZAhFixbliSeeyL4XKCIiOZKqHYqIiKRgsVhYvHgxHTp0MDsUERHJYzTnS0REREREJBso+RIREREREckGmvMlIiKSgkbji4hIVlHPl4iIiIiISDZQ8iUiIiIiIpINlHyJiIiIiIhkAyVfIiIiIiIi2UDJl4iIiIiISDZQ8iUiIiIiIpINlHyJiIiIiIhkAyVfIiIiIiIi2eD/AZ3Wuyt41AkgAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "loaded_history = pd.read_csv('training_history.csv')\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n",
        "\n",
        "ax1.plot(loaded_history['loss'], label='Training Loss')\n",
        "ax1.plot(loaded_history['val_loss'], label='Validation Loss')\n",
        "ax1.set_xlabel('Epoch')\n",
        "ax1.set_ylabel('Loss')\n",
        "ax1.legend()\n",
        "ax1.set_title('Training and Validation Loss')\n",
        "ax1.grid(False)\n",
        "\n",
        "ax2.plot(loaded_history['accuracy'], label='Training Accuracy')\n",
        "ax2.plot(loaded_history['val_accuracy'], label='Validation Accuracy')\n",
        "ax2.set_xlabel('Epoch')\n",
        "ax2.set_ylabel('Accuracy')\n",
        "ax2.legend()\n",
        "ax2.set_title('Training and Validation Accuracy')\n",
        "ax2.grid(False)\n",
        "plt.subplots_adjust(hspace=0.3)\n",
        "plt.savefig('training_metrics_plot.png')\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z1oVvYiWG_yZ",
        "outputId": "0883c926-1840-461b-a7f0-0fb0dc6317d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 46s 148ms/step - loss: 0.0745 - accuracy: 0.9740\n",
            "Final Test Acccuracy = 97.395\n"
          ]
        }
      ],
      "source": [
        "accu = model.evaluate(test)[1]\n",
        "print('Final Test Acccuracy = {:.3f}'.format(accu*100))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "t38cepGNH2yE"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mcudXsqIHUG5",
        "outputId": "ca51e8ee-08be-4ed8-efdc-56e01cc2e1ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 48s 146ms/step\n"
          ]
        }
      ],
      "source": [
        "y_pred = model.predict(test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "W0WRWDNFcv0q",
        "outputId": "3e0c0eac-0a80-4ea1-b464-bbb313bf4751"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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pA5eUGgW9UaNGaciQIcrJyVHPnj3doS43N1dr167V3Llz9eKLL56XieLiElYnSBOG/06XRYYrv+C03l+7Q0/PWK7y8h8/gxfVKFQvjLxTEQ3qynHCqQUrtih9zmqPc4x58R+qrHTp7Rcf/PGByR/v06Ppi8651v19EvX+up3nLNwA4DuFhT8o8+UpynU4FBYWrp6/7aXhj45QQECAKisrdfjwIS17f4lOnTyp8PBwXXlVe73x5gK1atXa11OHhVi4EOc1NX5g8qJFizR16lTl5OSoouLHBRS1atVSXFyc0tLSdM899/yqifDAZMC6eGAyYF2+fGBy69GrjQdV0+eTrVltrvG/nt///vf6/e9/r7KyMp048eMH7xs2bKiAgACvTw4AAAC/3q/O4QEBAWrcuLE35wIAAFBttG6N+bDgCgAA8OtZebWst/AVaAAAABZFRQ8AAJgSBT1jBD0AAGBKfn4kPSO0bgEAACyKih4AADAlWrfGCHoAAMCUWHVrjNYtAACARVHRAwAApkRBzxhBDwAAmBKtW2O0bgEAACyKih4AADAlKnrGCHoAAMCUyHnGaN0CAABYFBU9AABgSrRujRH0AACAKZHzjNG6BQAAsCgqegAAwJRo3Roj6AEAAFMi5xmjdQsAAGBRVPQAAIAp0bo1RtADAACmRM4zRusWAADAoqjoAQAAU6J1a4ygBwAATImcZ4zWLQAAgEVR0QMAAKZE69YYQQ8AAJgSOc8YrVsAAACLoqIHAABMidatMYIeAAAwJXKeMVq3AAAAFkVFDwAAmBKtW2MEPQAAYEoEPWO0bgEAACyKih4AADAlCnrGCHoAAMCUaN0ao3ULAABgUVT0AACAKVHQM0bQAwAApkTr1hitWwAAAIuiogcAAEyJgp4xgh4AADAlP5KeIVq3AAAAFkVFDwAAmBIFPWMEPQAAYEqsujVG6xYAAKCGvv32W/3pT39SgwYNFBwcrPbt22v79u3u4y6XS+PHj1fjxo0VHByspKQkff755x7nyM/PV//+/RUaGqrw8HANGjRIhYWFHmM+++wzdevWTUFBQWratKkyMjJqNE+CHgAAMCU/m/e2mjh58qSuu+46BQQEaNWqVdq7d69eeukl1atXzz0mIyNDmZmZmj17trZs2aKQkBAlJyeruLjYPaZ///7as2ePsrKytGLFCm3cuFFDhgxxH3c6nerVq5eaN2+unJwcTZ48Wc8884zmzJlT7bnaXC6Xq2a3d34Edxzm6ykAOE9Obpvu6ykAOE+CfPghsFtmb/Xauf45tEu1x44dO1abNm3Shx9+WOVxl8ul6OhojRw5UqNGjZIkFRQUKDIyUvPmzVO/fv20b98+xcbGatu2bYqPj5ckrV69Wrfccou++eYbRUdHa9asWXriiSfkcDgUGBjovvbSpUu1f//+as2Vih4AALjklZSUyOl0emwlJSVVjl22bJni4+N19913KyIiQh07dtTcuXPdxw8fPiyHw6GkpCT3vrCwMCUkJCg7O1uSlJ2drfDwcHfIk6SkpCT5+flpy5Yt7jHdu3d3hzxJSk5O1oEDB3Ty5Mlq3RdBDwAAmJLN5r0tPT1dYWFhHlt6enqV1z106JBmzZql1q1b64MPPtBDDz2kRx55RPPnz5ckORwOSVJkZKTH6yIjI93HHA6HIiIiPI77+/urfv36HmOqOsdPr2GEVbcAAMCUbPLeqttx48YpLS3NY5/dbq9ybGVlpeLj4/X8889Lkjp27Kjdu3dr9uzZGjBggNfm5A1U9AAAwCXPbrcrNDTUY/u5oNe4cWPFxsZ67GvXrp2OHDkiSYqKipIk5ebmeozJzc11H4uKilJeXp7H8fLycuXn53uMqeocP72GEYIeAAAwJV+tur3uuut04MABj30HDx5U8+bNJUkxMTGKiorS2rVr3cedTqe2bNmixMRESVJiYqJOnTqlnJwc95h169apsrJSCQkJ7jEbN25UWVmZe0xWVpauuOIKjxW+v/ge1ezWAAAALg42m81rW02MGDFCmzdv1vPPP68vvvhCCxcu1Jw5c5SSkuKeV2pqqiZOnKhly5Zp165duu+++xQdHa0+ffpI+rECeNNNN2nw4MHaunWrNm3apGHDhqlfv36Kjo6WJP3xj39UYGCgBg0apD179mjRokWaNm3aOS3mX8Jn9AAAAGqgc+fOWrJkicaNG6cJEyYoJiZGL7/8svr37+8eM2bMGBUVFWnIkCE6deqUrr/+eq1evVpBQUHuMQsWLNCwYcPUs2dP+fn5qW/fvsrMzHQfDwsL05o1a5SSkqK4uDg1bNhQ48eP93jWnhGeowfgvOM5eoB1+fI5en1e3W48qJqWPhhvPMiEqOgBAABT8uO7bg3xGT0AAACLoqIHAABMiYKeMYIeAAAwpZqulr0U0boFAACwKCp6AADAlCjoGSPoAQAAU2LVrTFatwAAABZFRQ8AAJgS9TxjBD0AAGBKrLo1RusWAADAoqjoAQAAU/KjoGeIoAcAAEyJ1q0xWrcAAAAWRUUPAACYEgU9YwQ9AABgSrRujdG6BQAAsCgqegAAwJRYdWuMoAcAAEyJ1q0xWrcAAAAWRUUPAACYEvU8YwQ9AABgSn60bg3RugUAALAoKnoAAMCUKOgZI+gBAABTYtWtMVq3AAAAFkVFDwAAmBIFPWMEPQAAYEqsujVG6xYAAMCiqOgBAABToqBnjKAHAABMiVW3xmjdAgAAWNRFU9HLzc709RQAnCf1uo319RQAnCdnsif57NpUq4xdNEEPAACgJmjdGiMMAwAAWBQVPQAAYEp+FPQMEfQAAIApEfSM0boFAACwKCp6AADAlFiMYYygBwAATInWrTFatwAAABZFRQ8AAJgSnVtjBD0AAGBKfiQ9Q7RuAQAALIqKHgAAMCWqVcYIegAAwJTo3BojDAMAAFgUFT0AAGBKLMYwRtADAACmRM4zRusWAADAoqjoAQAAU+Ir0IwR9AAAgCnxGT1jtG4BAAAsiooeAAAwJQp6xgh6AADAlPiMnjFatwAAABZFRQ8AAJiSTZT0jBD0AACAKdG6NUbrFgAAwKKo6AEAAFOiomeMoAcAAEzJxvNVDNG6BQAAsCgqegAAwJRo3Roj6AEAAFOic2uM1i0AAIBFUdEDAACm5EdJzxBBDwAAmBKf0TNG6xYAAOBXmjRpkmw2m1JTU937iouLlZKSogYNGqhOnTrq27evcnNzPV535MgR9e7dW7Vr11ZERIRGjx6t8vJyjzHr169Xp06dZLfb1apVK82bN6/G8yPoAQAAU7LZvLf9Gtu2bdNf//pXXX311R77R4wYoeXLl+vdd9/Vhg0bdOzYMd15553u4xUVFerdu7dKS0v18ccfa/78+Zo3b57Gjx/vHnP48GH17t1bPXr00I4dO5SamqoHH3xQH3zwQY3mSNADAACm5Ceb17aaKiwsVP/+/TV37lzVq1fPvb+goECvvfaapkyZohtvvFFxcXF644039PHHH2vz5s2SpDVr1mjv3r1666231KFDB91888169tlnNWPGDJWWlkqSZs+erZiYGL300ktq166dhg0bprvuuktTp06t4XsEAABwiSspKZHT6fTYSkpKfnZ8SkqKevfuraSkJI/9OTk5Kisr89jftm1bNWvWTNnZ2ZKk7OxstW/fXpGRke4xycnJcjqd2rNnj3vMf587OTnZfY7qIugBAABT8mbrNj09XWFhYR5benp6ldd955139Mknn1R53OFwKDAwUOHh4R77IyMj5XA43GN+GvLOHj977JfGOJ1OnTlzptrvEatuAQCAKXlz1e24ceOUlpbmsc9ut58z7ujRo3r00UeVlZWloKAg703gPKGiBwAALnl2u12hoaEeW1VBLycnR3l5eerUqZP8/f3l7++vDRs2KDMzU/7+/oqMjFRpaalOnTrl8brc3FxFRUVJkqKios5ZhXv2Z6MxoaGhCg4OrvZ9EfQAAIAp+dlsXtuqq2fPntq1a5d27Njh3uLj49W/f3/3rwMCArR27Vr3aw4cOKAjR44oMTFRkpSYmKhdu3YpLy/PPSYrK0uhoaGKjY11j/npOc6OOXuO6qJ1CwAATMkXX4xRt25dXXXVVR77QkJC1KBBA/f+QYMGKS0tTfXr11doaKiGDx+uxMREde3aVZLUq1cvxcbG6t5771VGRoYcDoeefPJJpaSkuKuIQ4cO1fTp0zVmzBgNHDhQ69at0+LFi7Vy5coazZegBwAA4EVTp06Vn5+f+vbtq5KSEiUnJ2vmzJnu47Vq1dKKFSv00EMPKTExUSEhIRowYIAmTJjgHhMTE6OVK1dqxIgRmjZtmpo0aaJXX31VycnJNZqLzeVyubx2Z/8DZ3Glr6cA4DyJ7PG4r6cA4Dw5kz3JZ9d+besRr51rUJdmXjvXxYSKHgAAMCVftG7NhsUYAAAAFkVFDwAAmBLVKmMEPQAAYEo2ereGCMMAAAAWRUUPAACYEvU8YwQ9AABgSjX5RotLFa1bAAAAi6KiBwAATIl6njGCHgAAMCU6t8Zo3QIAAFgUFT0AAGBKPEfPGEEPAACYEm1JY7xHAAAAFkVFDwAAmBKtW2MEPQAAYErEPGO0bgEAACyKih4AADAlWrfGCHoAAMCUaEsa4z0CAACwKCp6AADAlGjdGiPoAQAAUyLmGaN1CwAAYFFU9AAAgCnRuTVG0AMAAKbkR/PWEK1bAAAAi6KiBwAATInWrTGCHgAAMCUbrVtDtG4BAAAsiooeAAAwJVq3xgh6AADAlFh1a4zWLQAAgEVR0QMAAKZE69YYQQ8AAJgSQc8YrVsAAACLoqIHAABMiefoGSPoAQAAU/Ij5xmidQsAAGBRVPQAAIAp0bo1RkUPAADAoqjoAQAAU+LxKsYIegAAwJRo3RqjdQsAAGBRVPQAAIAp8XgVYwQ9AABgSrRujRH04HXzXpurGZlT1K//vRo55nEVFJzSnJnTtTl7k3Id3ym8Xn3d0KOnhqY8ojp167pft2f3Lk2fNkX79+2RTTZdeVV7DR8xSm2uaOvDuwEuPXVqB+rpIb30u+5XqlH9Otp58JhGTV2unH3fSJLmPHm37u0d5/GaNZsP6PYRb0iSunVsoTUzh1R57usHTlfOvm/UrWMLDe93veJjmyg0JEhfHD2hlxds1DtrdpzXewMuNQQ9eNWe3bu05O+L1LrNFe59x/PydPx4nh5NG6MWLVvqu2PHNGniMzp+PE8vvDRNknT6dJEefXiwuv3mRj32xHhVlJdrzqzpGv7QYK38YJ38AwJ8dUvAJWfWuL6KbRGlgRMW67sTTv0huaNWZj6oTn+comPHnZKkD7IP6M8T33W/pqSswv3rzbu+1uW9J3qcc/yQXuoR39IdFrte3Uy7v/xOU95ar9z8Qt1yXTu9Ov4eFRQVa9Wm/RfgLmEFrLo1RtCD15w+XaTx40br8acn6PW5s937W7Vuo4wpme6fmzRtpoeGp2r842NUXl4uf39/fXX4sAoKCvTnlOGKimosSRo8NEV/uOt2fffdMTVt1vyC3w9wKQqy+6vPDVfp7sfe1KYdhyVJz732L91yfVsNvqOr/jJnjSSptLRcufmFVZ6jrLzC45h/LT/d2i1Ws/7+sXvf5PnrPV4zY/Em9ezSWrffcBVBD9VGzjPGqlt4Tcbzz+q67r9RQtdrDccWFv6gkDp15O//4981ml8eo7DwcC1b8g+VlZWquLhY7y/5u2JatFTj6MvO99QB/D//Wn7y96+l4tJyj/3FJeW69prL3T9369RCX698UjvfGalpo/uofmjtnz3nrd1i1SCstv62YvsvXjusTpBOOk//T/MH4MnrQe/o0aMaOHDgL44pKSmR0+n02EpKSrw9FVxAa1at1P59e5XySJrh2FMnT+q1ObN0R9973PtCQkI0+9X5WrVyua7v0lG/SYxT9qaPNG3GX91hEMD5V3i6VJt3fa1xD/RU44Z15ednU7/kDkq4qpmiGvz4mdqszQf04ITFuuWRuXpy5ip16xij96c+IL+fWQI54LZ4ZW05qG//v+1blb492yuuXRO9aRAGgZ/ys9m8tlmV14Nefn6+5s+f/4tj0tPTFRYW5rFNmTzJ21PBBeJwfKeXMtL1bPpk2e32XxxbWFio1GFDFdOilYYMTXHvLy4u1sRnntI1HTrq9b+9o1fnL1DLVq2VOmyoiouLz/ctAPiJgX9ZJJtNOrT8CRVsmKiUe67T4qydqnS5JEnv/uszrfxon/Z8mavlG/fqzlHzFR/bVN07tTjnXJc1CtVvE9po/vKfD3DdO7XQX5+4Ww9Pek/7Duedt/uC9di8uFlVjUsly5Yt+8Xjhw4dMjzHuHHjlJbmWfkpcfFhe7Pav3eP8vO/1739+rr3VVRU6NOc7Xr3nYXatG2natWqpaKiIj3y8GDVDqmtyVNf8Vhg8cE/V+i7Y9/q9b+9LT+/H//+MXHSZN14fVdt/Pda9bq59wW/L+BSdfjbfPV6eI5qBwUoNCRIju9/0N+e/YMOf5tf5fivjuXr+MlCtWzSQOu3f+lx7N5b4/V9wWmt+HBvla+9vmOM/jF5gMZMW6GFqz7x+r0Al7oaB70+ffrIZrPJ9f9/s6uKzaAEarfbz6n8OIsrazoVXCQ6JyTq7b+/77FvwtNP6PLLY3TfAw+qVq1aKiws1CMPPaiAwEBNmTbznH//xcXFsvnZPP7bsdn8ZLPZ3FUEABfW6eIynS4uU3jdYCUltNETM1ZVOe6yRqFqEFZbjhM/nHPsvt5xWrj6E5VXnPtnfLeOLfTeiwP05MxVev39rV6fPy4BVi7FeUmNW7eNGzfWe++9p8rKyiq3Tz7hb2SXmpCQELVq3cZjCw4OVlh4uFq1bqPCwkINHzpIZ86c0VPPTFRhUaFOnDiuEyeOq6Lix0cyJCReqx+cTr3w/AQdPvSlvvzic00Y/7hq+ddSfOcuPr5D4NKSlNBav+3aRs0b19ONnVtp9fTBOvj1cb25YrtCggP1/LCb1eXKpmoWVU83xLfU4oz79OU33ytry0GP89wQ31IxlzXQG8u2nXON7p1aaMlL92vmux9r6b93K7J+HUXWr6N6ocEX6jZhATYv/mNVNa7oxcXFKScnR7fffnuVx42qfbj0HNi3V7t3fSZJuuPWZI9j7//zX4q+7DJdHtNCUzJnau7smRp43x/kZ/NTm7btlDlzjho2ivDFtIFLVlidIE0YepMuiwhTvvO03l+/W0/P/kDlFZXyr6zUVS0bq//NcQqvG6TvTvygf205qAlzslT6k2fpSdL9t3VW9mdf6eDXx8+5xp9uiVNIcKDGDOihMQN6uPdv/OSQklPmnPd7BC4VNlcNU9mHH36ooqIi3XTTTVUeLyoq0vbt2/Wb3/ymRhOhdQtYV2SPx309BQDnyZls3y2m3HqowGvn6tIizGvnupjUuKLXrVu3XzweEhJS45AHAABQU9ZtuHoPD0wGAACwKJ5ECwAAzImSniGCHgAAMCUrr5b1Flq3AAAAFkVFDwAAmJKFv6LWawh6AADAlMh5xmjdAgAAWBQVPQAAYE6U9AwR9AAAgCmx6tYYrVsAAACLoqIHAABMiVW3xqjoAQAAU7J5cauJ9PR0de7cWXXr1lVERIT69OmjAwcOeIwpLi5WSkqKGjRooDp16qhv377Kzc31GHPkyBH17t1btWvXVkREhEaPHq3y8nKPMevXr1enTp1kt9vVqlUrzZs3r0ZzJegBAADUwIYNG5SSkqLNmzcrKytLZWVl6tWrl4qKitxjRowYoeXLl+vdd9/Vhg0bdOzYMd15553u4xUVFerdu7dKS0v18ccfa/78+Zo3b57Gjx/vHnP48GH17t1bPXr00I4dO5SamqoHH3xQH3zwQbXnanO5XC7v3Pb/xllc6espADhPIns87uspADhPzmRP8tm1dx79wWvnahsRqJKSEo99drtddrvd8LXHjx9XRESENmzYoO7du6ugoECNGjXSwoULddddd0mS9u/fr3bt2ik7O1tdu3bVqlWrdOutt+rYsWOKjIyUJM2ePVuPPfaYjh8/rsDAQD322GNauXKldu/e7b5Wv379dOrUKa1evbpa90VFDwAAmJLNi/+kp6crLCzMY0tPT6/WPAoKCiRJ9evXlyTl5OSorKxMSUlJ7jFt27ZVs2bNlJ2dLUnKzs5W+/bt3SFPkpKTk+V0OrVnzx73mJ+e4+yYs+eoDhZjAACAS964ceOUlpbmsa861bzKykqlpqbquuuu01VXXSVJcjgcCgwMVHh4uMfYyMhIORwO95ifhryzx88e+6UxTqdTZ86cUXBwsOH8CHoAAMCUvLnqtrpt2v+WkpKi3bt366OPPvLeZLyI1i0AADAlX626PWvYsGFasWKF/v3vf6tJkybu/VFRUSotLdWpU6c8xufm5ioqKso95r9X4Z792WhMaGhotap5EkEPAACgRlwul4YNG6YlS5Zo3bp1iomJ8TgeFxengIAArV271r3vwIEDOnLkiBITEyVJiYmJ2rVrl/Ly8txjsrKyFBoaqtjYWPeYn57j7Jiz56gOWrcAAMCcfPTA5JSUFC1cuFDvv/++6tat6/5MXVhYmIKDgxUWFqZBgwYpLS1N9evXV2hoqIYPH67ExER17dpVktSrVy/Fxsbq3nvvVUZGhhwOh5588kmlpKS4W8hDhw7V9OnTNWbMGA0cOFDr1q3T4sWLtXLlymrPlcerADjveLwKYF2+fLzKnm+LjAdV05WXhVR7rO1nPhz4xhtv6P7775f04wOTR44cqbffflslJSVKTk7WzJkz3W1ZSfr666/10EMPaf369QoJCdGAAQM0adIk+fv/pw63fv16jRgxQnv37lWTJk301FNPua9RrbkS9ACcbwQ9wLouxaBnJrRuAQCAKfFdt8YIegAAwJTIecZYdQsAAGBRVPQAAIA5UdIzRNADAACmZCPpGaJ1CwAAYFFU9AAAgCmx6tYYQQ8AAJgSOc8YrVsAAACLoqIHAADMiZKeIYIeAAAwJVbdGqN1CwAAYFFU9AAAgCmx6tYYQQ8AAJgSOc8YrVsAAACLoqIHAADMiZKeIYIeAAAwJVbdGqN1CwAAYFFU9AAAgCmx6tYYQQ8AAJgSOc8YrVsAAACLoqIHAADMiZKeIYIeAAAwJVbdGqN1CwAAYFFU9AAAgCmx6tYYQQ8AAJgSOc8YrVsAAACLoqIHAABMidatMYIeAAAwKZKeEVq3AAAAFkVFDwAAmBKtW2MEPQAAYErkPGO0bgEAACyKih4AADAlWrfGCHoAAMCU+K5bY7RuAQAALIqKHgAAMCcKeoYIegAAwJTIecZo3QIAAFgUFT0AAGBKrLo1RtADAACmxKpbY7RuAQAALIqKHgAAMCcKeoYIegAAwJTIecZo3QIAAFgUFT0AAGBKrLo1RtADAACmxKpbY7RuAQAALIqKHgAAMCVat8ao6AEAAFgUQQ8AAMCiaN0CAABTonVrjKAHAABMiVW3xmjdAgAAWBQVPQAAYEq0bo0R9AAAgCmR84zRugUAALAoKnoAAMCcKOkZIugBAABTYtWtMVq3AAAAFkVFDwAAmBKrbo0R9AAAgCmR84zRugUAALAoKnoAAMCcKOkZIugBAABTYtWtMVq3AAAAFkVFDwAAmBKrbo3ZXC6Xy9eTwKWlpKRE6enpGjdunOx2u6+nA8CL+P0NXFwIerjgnE6nwsLCVFBQoNDQUF9PB4AX8fsbuLjwGT0AAACLIugBAABYFEEPAADAogh6uODsdruefvppPqgNWBC/v4GLC4sxAAAALIqKHgAAgEUR9AAAACyKoAcAAGBRBD0AAACLIugBAABYFEEPF9SMGTN0+eWXKygoSAkJCdq6dauvpwTACzZu3KjbbrtN0dHRstlsWrp0qa+nBEAEPVxAixYtUlpamp5++ml98sknuuaaa5ScnKy8vDxfTw3A/6ioqEjXXHONZsyY4eupAPgJnqOHCyYhIUGdO3fW9OnTJUmVlZVq2rSphg8frrFjx/p4dgC8xWazacmSJerTp4+vpwJc8qjo4YIoLS1VTk6OkpKS3Pv8/PyUlJSk7OxsH84MAADrIujhgjhx4oQqKioUGRnpsT8yMlIOh8NHswIAwNoIegAAABZF0MMF0bBhQ9WqVUu5ubke+3NzcxUVFeWjWQEAYG0EPVwQgYGBiouL09q1a937KisrtXbtWiUmJvpwZgAAWJe/ryeAS0daWpoGDBig+Ph4denSRS+//LKKior0wAMP+HpqAP5HhYWF+uKLL9w/Hz58WDt27FD9+vXVrFkzH84MuLTxeBVcUNOnT9fkyZPlcDjUoUMHZWZmKiEhwdfTAvA/Wr9+vXr06HHO/gEDBmjevHkXfkIAJBH0AAAALIvP6AEAAFgUQQ8AAMCiCHoAAAAWRdADAACwKIIeAACARRH0AAAALIqgBwAAYFEEPQAAAIsi6AEAAFgUQQ8AAMCiCHoAAAAW9X/tS6YlmSylEgAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn import metrics\n",
        "plt.figure(figsize = (8,5))\n",
        "sns.heatmap(metrics.confusion_matrix(test.classes, y_pred.round()), annot = True,fmt=\"d\",cmap = \"Blues\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "amQqKfaLdozZ",
        "outputId": "f18a948f-253f-4a92-9bd7-1f6f278aa913"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ROC-AUC Score: 0.9739500000000001\n",
            "AP Score: 0.9693894878427315\n"
          ]
        }
      ],
      "source": [
        "print(\"ROC-AUC Score:\", metrics.roc_auc_score(test.classes, y_pred.round()))\n",
        "print(\"AP Score:\", metrics.average_precision_score(test.classes, y_pred.round()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZKN6tGMH08Z",
        "outputId": "404c6f4b-3ffd-48cc-aead-f68880345bf8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.99      0.97     10000\n",
            "           1       0.99      0.96      0.97     10000\n",
            "\n",
            "    accuracy                           0.97     20000\n",
            "   macro avg       0.97      0.97      0.97     20000\n",
            "weighted avg       0.97      0.97      0.97     20000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report = classification_report(test.classes, y_pred.round())\n",
        "print('\\nClassification Report')\n",
        "print(report)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3z8xXuNEmic0"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "V100",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}