3_Create_ANN_with_weight_initialize.ipynb

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      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv(\"/content/Churn_Modelling.csv\")\n",
        "dataset.head()"
      ],
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              "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
              "0          1    15634602  Hargrave          619    France  Female   42   \n",
              "1          2    15647311      Hill          608     Spain  Female   41   \n",
              "2          3    15619304      Onio          502    France  Female   42   \n",
              "3          4    15701354      Boni          699    France  Female   39   \n",
              "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
              "\n",
              "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
              "0       2       0.00              1          1               1   \n",
              "1       1   83807.86              1          0               1   \n",
              "2       8  159660.80              3          1               0   \n",
              "3       1       0.00              2          0               0   \n",
              "4       2  125510.82              1          1               1   \n",
              "\n",
              "   EstimatedSalary  Exited  \n",
              "0        101348.88       1  \n",
              "1        112542.58       0  \n",
              "2        113931.57       1  \n",
              "3         93826.63       0  \n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPR4giuZ_4NU",
        "outputId": "da878a6b-f910-4aa4-f8da-92c255e91bb9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYrglc96AHxy",
        "outputId": "c150e7fd-0a6a-4973-e51e-89846d8283b4"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 14 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   RowNumber        10000 non-null  int64  \n",
            " 1   CustomerId       10000 non-null  int64  \n",
            " 2   Surname          10000 non-null  object \n",
            " 3   CreditScore      10000 non-null  int64  \n",
            " 4   Geography        10000 non-null  object \n",
            " 5   Gender           10000 non-null  object \n",
            " 6   Age              10000 non-null  int64  \n",
            " 7   Tenure           10000 non-null  int64  \n",
            " 8   Balance          10000 non-null  float64\n",
            " 9   NumOfProducts    10000 non-null  int64  \n",
            " 10  HasCrCard        10000 non-null  int64  \n",
            " 11  IsActiveMember   10000 non-null  int64  \n",
            " 12  EstimatedSalary  10000 non-null  float64\n",
            " 13  Exited           10000 non-null  int64  \n",
            "dtypes: float64(2), int64(9), object(3)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset.iloc[:, 3:13]\n",
        "y = dataset.iloc[:, 13]"
      ],
      "metadata": {
        "id": "x4bTeeGRBTE-"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Geography'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D9EMIH8oA23a",
        "outputId": "1c92609a-a9eb-44c6-dce1-80c8be89f589"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['France', 'Spain', 'Germany'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Gender'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aoh1Fo6XA-MR",
        "outputId": "84c90220-18be-433c-e95c-b441f62b2224"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Female', 'Male'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create dummy variables\n",
        "geography = pd.get_dummies(X[\"Geography\"], drop_first=True)\n",
        "gender = pd.get_dummies(X[\"Gender\"], drop_first=True)"
      ],
      "metadata": {
        "id": "6xHJuV03BJpd"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Concat dataframe\n",
        "X = pd.concat([X, geography, gender], axis = 1)"
      ],
      "metadata": {
        "id": "ixH4fY6ZB4vM"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# drop unnecessary variables\n",
        "X = X.drop([\"Geography\", \"Gender\"], axis=1)"
      ],
      "metadata": {
        "id": "EkrEPA8MCHOY"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# splitting dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "metadata": {
        "id": "5EnraACTDNzV"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# feature scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "vqWvBCcdDyTg"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Let's create ANN"
      ],
      "metadata": {
        "id": "hKiXdcr1Gd3B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LeakyReLU, PReLU, ELU\n",
        "from keras.layers import Dropout"
      ],
      "metadata": {
        "id": "RetLohKMGUFH"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()"
      ],
      "metadata": {
        "id": "plDw706hH9kR"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# adding input layers and hidden layers\n",
        "model.add(Dense(units=6, kernel_initializer=\"he_uniform\", activation=\"relu\", input_dim=11))"
      ],
      "metadata": {
        "id": "GYTvuMbpImPX"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# adding second hidden layers\n",
        "model.add(Dense(units=6, kernel_initializer=\"he_uniform\", activation=\"relu\"))"
      ],
      "metadata": {
        "id": "veVactgaJL9u"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# adding output layers\n",
        "model.add(Dense(units=1, kernel_initializer=\"glorot_uniform\", activation=\"sigmoid\"))"
      ],
      "metadata": {
        "id": "8bJxf6k4JlvG"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qfkHckqKRX9",
        "outputId": "0b244d51-678d-4780-d79e-e7bde46e45a5"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 6)                 72        \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 6)                 42        \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 7         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 121\n",
            "Trainable params: 121\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compiling ANN\n",
        "model.compile(optimizer='adam', loss=\"binary_crossentropy\", metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "Adf9jrq8KI6a"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Nfit the model\n",
        "model_history = model.fit(X_train, y_train, validation_split=0.33, batch_size=10, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1HqTL_TtLN08",
        "outputId": "0bf7814c-9030-4e21-a915-089ca8a5abe1"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "536/536 [==============================] - 3s 3ms/step - loss: 0.5190 - accuracy: 0.7841 - val_loss: 0.4693 - val_accuracy: 0.8016\n",
            "Epoch 2/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4520 - accuracy: 0.8043 - val_loss: 0.4458 - val_accuracy: 0.8039\n",
            "Epoch 3/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4332 - accuracy: 0.8110 - val_loss: 0.4369 - val_accuracy: 0.8073\n",
            "Epoch 4/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4238 - accuracy: 0.8121 - val_loss: 0.4337 - val_accuracy: 0.8099\n",
            "Epoch 5/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4182 - accuracy: 0.8182 - val_loss: 0.4302 - val_accuracy: 0.8114\n",
            "Epoch 6/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4133 - accuracy: 0.8188 - val_loss: 0.4266 - val_accuracy: 0.8076\n",
            "Epoch 7/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4085 - accuracy: 0.8186 - val_loss: 0.4235 - val_accuracy: 0.8065\n",
            "Epoch 8/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4044 - accuracy: 0.8190 - val_loss: 0.4198 - val_accuracy: 0.8073\n",
            "Epoch 9/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3999 - accuracy: 0.8197 - val_loss: 0.4171 - val_accuracy: 0.8061\n",
            "Epoch 10/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3957 - accuracy: 0.8190 - val_loss: 0.4115 - val_accuracy: 0.8080\n",
            "Epoch 11/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3911 - accuracy: 0.8194 - val_loss: 0.4070 - val_accuracy: 0.8099\n",
            "Epoch 12/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3867 - accuracy: 0.8210 - val_loss: 0.4024 - val_accuracy: 0.8114\n",
            "Epoch 13/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3819 - accuracy: 0.8233 - val_loss: 0.3997 - val_accuracy: 0.8111\n",
            "Epoch 14/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3768 - accuracy: 0.8229 - val_loss: 0.3954 - val_accuracy: 0.8137\n",
            "Epoch 15/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3735 - accuracy: 0.8252 - val_loss: 0.3892 - val_accuracy: 0.8198\n",
            "Epoch 16/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3688 - accuracy: 0.8321 - val_loss: 0.3858 - val_accuracy: 0.8311\n",
            "Epoch 17/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3647 - accuracy: 0.8459 - val_loss: 0.3831 - val_accuracy: 0.8315\n",
            "Epoch 18/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3610 - accuracy: 0.8483 - val_loss: 0.3800 - val_accuracy: 0.8372\n",
            "Epoch 19/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3574 - accuracy: 0.8494 - val_loss: 0.3793 - val_accuracy: 0.8421\n",
            "Epoch 20/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3558 - accuracy: 0.8507 - val_loss: 0.3752 - val_accuracy: 0.8410\n",
            "Epoch 21/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3528 - accuracy: 0.8507 - val_loss: 0.3744 - val_accuracy: 0.8432\n",
            "Epoch 22/100\n",
            "536/536 [==============================] - 4s 7ms/step - loss: 0.3507 - accuracy: 0.8535 - val_loss: 0.3733 - val_accuracy: 0.8448\n",
            "Epoch 23/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3488 - accuracy: 0.8550 - val_loss: 0.3714 - val_accuracy: 0.8459\n",
            "Epoch 24/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3467 - accuracy: 0.8576 - val_loss: 0.3710 - val_accuracy: 0.8470\n",
            "Epoch 25/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3457 - accuracy: 0.8569 - val_loss: 0.3695 - val_accuracy: 0.8485\n",
            "Epoch 26/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3444 - accuracy: 0.8561 - val_loss: 0.3685 - val_accuracy: 0.8478\n",
            "Epoch 27/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3423 - accuracy: 0.8600 - val_loss: 0.3689 - val_accuracy: 0.8478\n",
            "Epoch 28/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3417 - accuracy: 0.8597 - val_loss: 0.3672 - val_accuracy: 0.8497\n",
            "Epoch 29/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3400 - accuracy: 0.8587 - val_loss: 0.3670 - val_accuracy: 0.8501\n",
            "Epoch 30/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3384 - accuracy: 0.8608 - val_loss: 0.3677 - val_accuracy: 0.8497\n",
            "Epoch 31/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3378 - accuracy: 0.8604 - val_loss: 0.3657 - val_accuracy: 0.8501\n",
            "Epoch 32/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3376 - accuracy: 0.8600 - val_loss: 0.3662 - val_accuracy: 0.8489\n",
            "Epoch 33/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3367 - accuracy: 0.8619 - val_loss: 0.3666 - val_accuracy: 0.8519\n",
            "Epoch 34/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3359 - accuracy: 0.8604 - val_loss: 0.3666 - val_accuracy: 0.8512\n",
            "Epoch 35/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3355 - accuracy: 0.8614 - val_loss: 0.3675 - val_accuracy: 0.8523\n",
            "Epoch 36/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3354 - accuracy: 0.8615 - val_loss: 0.3659 - val_accuracy: 0.8501\n",
            "Epoch 37/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3347 - accuracy: 0.8636 - val_loss: 0.3666 - val_accuracy: 0.8535\n",
            "Epoch 38/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3352 - accuracy: 0.8638 - val_loss: 0.3650 - val_accuracy: 0.8542\n",
            "Epoch 39/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3339 - accuracy: 0.8643 - val_loss: 0.3646 - val_accuracy: 0.8542\n",
            "Epoch 40/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3337 - accuracy: 0.8615 - val_loss: 0.3662 - val_accuracy: 0.8561\n",
            "Epoch 41/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3333 - accuracy: 0.8628 - val_loss: 0.3639 - val_accuracy: 0.8550\n",
            "Epoch 42/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3335 - accuracy: 0.8642 - val_loss: 0.3639 - val_accuracy: 0.8546\n",
            "Epoch 43/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3332 - accuracy: 0.8632 - val_loss: 0.3644 - val_accuracy: 0.8561\n",
            "Epoch 44/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3318 - accuracy: 0.8647 - val_loss: 0.3692 - val_accuracy: 0.8516\n",
            "Epoch 45/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3327 - accuracy: 0.8645 - val_loss: 0.3635 - val_accuracy: 0.8576\n",
            "Epoch 46/100\n",
            "536/536 [==============================] - 3s 5ms/step - loss: 0.3325 - accuracy: 0.8643 - val_loss: 0.3635 - val_accuracy: 0.8561\n",
            "Epoch 47/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3324 - accuracy: 0.8634 - val_loss: 0.3655 - val_accuracy: 0.8546\n",
            "Epoch 48/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3319 - accuracy: 0.8649 - val_loss: 0.3639 - val_accuracy: 0.8535\n",
            "Epoch 49/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3317 - accuracy: 0.8638 - val_loss: 0.3632 - val_accuracy: 0.8565\n",
            "Epoch 50/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3312 - accuracy: 0.8634 - val_loss: 0.3631 - val_accuracy: 0.8561\n",
            "Epoch 51/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3308 - accuracy: 0.8630 - val_loss: 0.3646 - val_accuracy: 0.8554\n",
            "Epoch 52/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3307 - accuracy: 0.8651 - val_loss: 0.3645 - val_accuracy: 0.8546\n",
            "Epoch 53/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3312 - accuracy: 0.8643 - val_loss: 0.3627 - val_accuracy: 0.8603\n",
            "Epoch 54/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3299 - accuracy: 0.8640 - val_loss: 0.3626 - val_accuracy: 0.8569\n",
            "Epoch 55/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3306 - accuracy: 0.8655 - val_loss: 0.3632 - val_accuracy: 0.8569\n",
            "Epoch 56/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3302 - accuracy: 0.8658 - val_loss: 0.3619 - val_accuracy: 0.8610\n",
            "Epoch 57/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3298 - accuracy: 0.8656 - val_loss: 0.3632 - val_accuracy: 0.8569\n",
            "Epoch 58/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3296 - accuracy: 0.8632 - val_loss: 0.3636 - val_accuracy: 0.8591\n",
            "Epoch 59/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3302 - accuracy: 0.8636 - val_loss: 0.3619 - val_accuracy: 0.8595\n",
            "Epoch 60/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3296 - accuracy: 0.8645 - val_loss: 0.3628 - val_accuracy: 0.8607\n",
            "Epoch 61/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3298 - accuracy: 0.8636 - val_loss: 0.3625 - val_accuracy: 0.8588\n",
            "Epoch 62/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3292 - accuracy: 0.8638 - val_loss: 0.3624 - val_accuracy: 0.8595\n",
            "Epoch 63/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3291 - accuracy: 0.8671 - val_loss: 0.3619 - val_accuracy: 0.8588\n",
            "Epoch 64/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3285 - accuracy: 0.8655 - val_loss: 0.3628 - val_accuracy: 0.8595\n",
            "Epoch 65/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3289 - accuracy: 0.8642 - val_loss: 0.3625 - val_accuracy: 0.8588\n",
            "Epoch 66/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3282 - accuracy: 0.8660 - val_loss: 0.3645 - val_accuracy: 0.8580\n",
            "Epoch 67/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3280 - accuracy: 0.8645 - val_loss: 0.3615 - val_accuracy: 0.8595\n",
            "Epoch 68/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3282 - accuracy: 0.8668 - val_loss: 0.3624 - val_accuracy: 0.8607\n",
            "Epoch 69/100\n",
            "536/536 [==============================] - 2s 5ms/step - loss: 0.3277 - accuracy: 0.8666 - val_loss: 0.3657 - val_accuracy: 0.8580\n",
            "Epoch 70/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3277 - accuracy: 0.8655 - val_loss: 0.3623 - val_accuracy: 0.8576\n",
            "Epoch 71/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3281 - accuracy: 0.8643 - val_loss: 0.3621 - val_accuracy: 0.8580\n",
            "Epoch 72/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3277 - accuracy: 0.8656 - val_loss: 0.3624 - val_accuracy: 0.8595\n",
            "Epoch 73/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3274 - accuracy: 0.8670 - val_loss: 0.3632 - val_accuracy: 0.8595\n",
            "Epoch 74/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3272 - accuracy: 0.8656 - val_loss: 0.3637 - val_accuracy: 0.8595\n",
            "Epoch 75/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3273 - accuracy: 0.8653 - val_loss: 0.3634 - val_accuracy: 0.8584\n",
            "Epoch 76/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3272 - accuracy: 0.8655 - val_loss: 0.3616 - val_accuracy: 0.8599\n",
            "Epoch 77/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3269 - accuracy: 0.8653 - val_loss: 0.3649 - val_accuracy: 0.8599\n",
            "Epoch 78/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3268 - accuracy: 0.8675 - val_loss: 0.3632 - val_accuracy: 0.8584\n",
            "Epoch 79/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3263 - accuracy: 0.8640 - val_loss: 0.3626 - val_accuracy: 0.8591\n",
            "Epoch 80/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3258 - accuracy: 0.8681 - val_loss: 0.3652 - val_accuracy: 0.8569\n",
            "Epoch 81/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3271 - accuracy: 0.8649 - val_loss: 0.3639 - val_accuracy: 0.8599\n",
            "Epoch 82/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3271 - accuracy: 0.8651 - val_loss: 0.3635 - val_accuracy: 0.8580\n",
            "Epoch 83/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3256 - accuracy: 0.8660 - val_loss: 0.3659 - val_accuracy: 0.8546\n",
            "Epoch 84/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3269 - accuracy: 0.8658 - val_loss: 0.3626 - val_accuracy: 0.8584\n",
            "Epoch 85/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3265 - accuracy: 0.8668 - val_loss: 0.3637 - val_accuracy: 0.8573\n",
            "Epoch 86/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3261 - accuracy: 0.8658 - val_loss: 0.3654 - val_accuracy: 0.8591\n",
            "Epoch 87/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3268 - accuracy: 0.8653 - val_loss: 0.3651 - val_accuracy: 0.8588\n",
            "Epoch 88/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3265 - accuracy: 0.8668 - val_loss: 0.3620 - val_accuracy: 0.8607\n",
            "Epoch 89/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3260 - accuracy: 0.8668 - val_loss: 0.3621 - val_accuracy: 0.8595\n",
            "Epoch 90/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.3258 - accuracy: 0.8664 - val_loss: 0.3626 - val_accuracy: 0.8565\n",
            "Epoch 91/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3263 - accuracy: 0.8643 - val_loss: 0.3621 - val_accuracy: 0.8580\n",
            "Epoch 92/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3260 - accuracy: 0.8651 - val_loss: 0.3639 - val_accuracy: 0.8580\n",
            "Epoch 93/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.3251 - accuracy: 0.8655 - val_loss: 0.3627 - val_accuracy: 0.8591\n",
            "Epoch 94/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3258 - accuracy: 0.8653 - val_loss: 0.3614 - val_accuracy: 0.8584\n",
            "Epoch 95/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3257 - accuracy: 0.8666 - val_loss: 0.3636 - val_accuracy: 0.8557\n",
            "Epoch 96/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3257 - accuracy: 0.8662 - val_loss: 0.3650 - val_accuracy: 0.8557\n",
            "Epoch 97/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3260 - accuracy: 0.8662 - val_loss: 0.3631 - val_accuracy: 0.8588\n",
            "Epoch 98/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.3254 - accuracy: 0.8668 - val_loss: 0.3638 - val_accuracy: 0.8591\n",
            "Epoch 99/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3250 - accuracy: 0.8662 - val_loss: 0.3646 - val_accuracy: 0.8550\n",
            "Epoch 100/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3252 - accuracy: 0.8658 - val_loss: 0.3629 - val_accuracy: 0.8576\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predicting test data\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred = (y_pred>0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUMvBeK-LX5U",
        "outputId": "e93d8a9b-6920-4402-df0c-3a719cec7d2e"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 1ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CqflvG2lNoWo",
        "outputId": "7c4d3ebf-b50e-4383-bd06-222c40493880"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1523   72]\n",
            " [ 210  195]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy\n",
        "from sklearn.metrics import accuracy_score\n",
        "score = accuracy_score(y_pred, y_test)\n",
        "print(score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K2ElS6WDN0Ey",
        "outputId": "fbde695d-0976-4615-da24-6fd9dc7c9eb5"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.859\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# summarize history for loss\n",
        "plt.plot(model_history.history['loss'])\n",
        "plt.plot(model_history.history['val_loss'])\n",
        "plt.title(\"model loss\")\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "HGQfVWBEO7Nh",
        "outputId": "82f63e3f-329a-48a3-e571-d3e0c926d817"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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TJk3i+uuvd5UNCQmhbdu2rp8nT57M559/zldffXXOST7vvvtuhgwZAsCLL77I9OnTWbNmDX369CnTtYmIiFQUtSDJeXXq1KnEzzk5OYwfP54WLVoQHByMv78/W7ZsOW8LUps2bVyf/fz8CAwMdL1OREREpCpRC1Il8PG0sXlS71KV3ZuWR05BEXWDfanh51ku575Ypz6NNn78eBYtWsQ///lPmjRpgo+PD7fffjuFhYXnPI6nZ8nrsVgsepmviIhUSQpIlcBisZSqmwvAz26j2OnE7mkt9T7lxcvLy/WalHNZuXIld999N7feeitgtijt3bu3gmsnIiJSedTFVsVYj79ew+mGCc6joqJYvXo1e/fuJS0t7aytO02bNmX+/Pls2LCB3377jb/+9a9qCRIRkUuKAlIVc2LqI3e8AGb8+PHYbDZatmxJrVq1zjqmaOrUqdSoUYOuXbty880307t3bzp06FDJtRUREak4ehdbGVXUu9iSMo5xOKeAWgF2agf5lGeVqx29i01ERMqb3sVWTVlcXWxuroiIiMhlTAGpirEe/x1xKiGJiIi4jQJSFXNikLZ6PkVERNxHAamKOTFIWw1IIiIi7qOAVMW48zF/ERERMSkgVTEapC0iIuJ+CkhVzMkuNiUkERERd1FAqmJODtJ2c0VEREQuYwpIVYxakERERNxPAamKsWiQtoiIiNspIFUxVjcO0u7Rowdjx44tt+PdfffdDBgwoNyOJyIiUlkUkKqYky+rNTRZpIiIiJsoIFUxJ1qQoHJbke6++26WLVvGv//9bywWCxaLhb179/LHH39w44034u/vT3h4OHfddRdpaWmu/T799FNat26Nj48PNWvWJCYmhtzcXJ577jnmzJnDl19+6Tre0qVLK++CRERELoKHuytwWTAMKMorVVGLYWA5XtZZYMNmu8gM6+kLfwpdZ/Pvf/+b7du306pVKyZNmmTu6ulJ586duffee/nXv/7FsWPHeOKJJ7jjjjtYvHgxSUlJDBkyhJdffplbb72V7OxsfvrpJwzDYPz48WzZsoWsrCzeffddAEJCQi7uWkRERCqJAlJlKMqDF+uUqqgFaF2e537qEHj5nbdYUFAQXl5e+Pr6EhERAcALL7xA+/btefHFF13lZs+eTWRkJNu3bycnJ4fi4mJuu+02GjRoAEDr1idr7+PjQ0FBget4IiIi1YUCkpzVb7/9xpIlS/D39z9t265du7jhhhvo1asXrVu3pnfv3txwww3cfvvt1KhRww21FRERKT8KSJXB09dsySmlbcnZFDqcNK7lh6/XRf4WefqWedecnBxuvvlmXnrppdO21a5dG5vNxqJFi1i1ahU//PADr732Gk8//TSrV6+mYcOGF1NrERERt1JAqgwWS6m6uVy8nBjFDpyefnCxAekCeHl54XA4XD936NCBzz77jKioKDw8zlwPi8VCt27d6NatGxMnTqRBgwZ8/vnnxMbGnnY8ERGR6kJPsVVB7ppNOyoqitWrV7N3717S0tJ46KGHSE9PZ8iQIaxdu5Zdu3bx/fffM2LECBwOB6tXr+bFF1/kl19+ITExkfnz53P48GFatGjhOt7vv//Otm3bSEtLo6ioqFKvR0REpKwUkKog1/vYKnm2yPHjx2Oz2WjZsiW1atWisLCQlStX4nA4uOGGG2jdujVjx44lODgYq9VKYGAgy5cvp2/fvlxxxRX8/e9/59VXX+XGG28EYNSoUTRr1oxOnTpRq1YtVq5cWanXIyIiUlYWQ7MRlklWVhZBQUFkZmYSGBhYYlt+fj579uyhYcOGeHt7X/Cxdx/OIaegmMgavtTw8yqvKlc7F3sfRURETnWu7+8/c3sL0owZM4iKisLb25vo6GjWrFlz1rLvvfeea9LBE8upX5yGYTBx4kRq166Nj48PMTEx7Nixo0SZ9PR0hg4dSmBgIMHBwYwcOZKcnJwKub6ysOp9bCIiIm7l1oA0b948YmNjefbZZ1m/fj1t27ald+/epKamnnWfwMBAkpKSXMu+fftKbH/55ZeZPn06M2fOZPXq1fj5+dG7d2/y8/NdZYYOHcqmTZtYtGgRCxYsYPny5YwePbrCrvNCWa3uex+biIiIuDkgTZ06lVGjRjFixAhatmzJzJkz8fX1Zfbs2Wfdx2KxEBER4VrCw8Nd2wzDYNq0afz973/nlltuoU2bNrz//vscOnSIL774AoAtW7awcOFC3n77baKjo+nevTuvvfYac+fO5dCh0j+KX5H+/D42ERERqXxuC0iFhYWsW7eOmJiYk5WxWomJiSEhIeGs++Xk5NCgQQMiIyO55ZZb2LRpk2vbnj17SE5OLnHMoKAgoqOjXcdMSEggODiYTp06ucrExMRgtVpZvXr1Wc9bUFBAVlZWiaWiqItNRETEvdwWkNLS0nA4HCVagADCw8NJTk4+4z7NmjVj9uzZfPnll3z44Yc4nU66du3KgQMHAFz7neuYycnJhIWFldju4eFBSEjIWc8LEBcXR1BQkGuJjIw87zWWtQXI4nrMv0y7XzLUgiYiIu7i9kHaF6JLly4MGzaMdu3acd111zF//nxq1arFW2+9VeHnnjBhApmZma5l//79Zy1rs9kAs5WsLNSCZMrLM1/a6+np6eaaiIjI5cZtM2mHhoZis9lISUkpsT4lJaXULzf19PSkffv27Ny5E8C1X0pKCrVr1y5xzHbt2rnKnDoIvLi4mPT09HOe1263Y7fbS1UvDw8PfH19OXz4MJ6enlitF5ZDHUWFGMWFFBUY5OdXqwxbLgzDIC8vj9TUVIKDg12BU0REpLK4LSB5eXnRsWNH4uPjGTBgAABOp5P4+HgefvjhUh3D4XCwceNG+vbtC0DDhg2JiIggPj7eFYiysrJYvXo1DzzwAGC2QmVkZLBu3To6duwIwOLFi3E6nURHR5fLtVksFmrXrs2ePXtOe8quNHIKisnIKyLb00b+0ct3HqTg4OBSh2UREZHy5NZ3scXGxjJ8+HA6depE586dmTZtGrm5uYwYMQKAYcOGUbduXeLi4gCYNGkSV199NU2aNCEjI4NXXnmFffv2ce+99wJmMBk7diwvvPACTZs2pWHDhjzzzDPUqVPHFcJatGhBnz59GDVqFDNnzqSoqIiHH36YwYMHU6dOnXK7Ni8vL5o2bVqmbrbvNyXz8pKtXBUVwpSBzcqtTtWJp6enWo5ERMRt3BqQBg0axOHDh5k4cSLJycm0a9eOhQsXugZZJyYmluieOnr0KKNGjSI5OZkaNWrQsWNHVq1aRcuWLV1lHn/8cXJzcxk9ejQZGRl0796dhQsXlphQ8qOPPuLhhx+mV69eWK1WBg4cyPTp08v9+qxWa5lmgPbwtHMw20HdHIdmkBYREXEDvWqkjEo7VXlZLN6awj3v/UKbekF89XD3cj22iIjI5azavGpETuftaXYt5RU63FwTERGRy5MCUhXk62X2fB5TQBIREXELBaQqyOd4C1J+kQKSiIiIOyggVUEnAtIxBSQRERG3UECqgry9zN+WY0UOvW5DRETEDRSQqqATLUiGAQXFTjfXRkRE5PKjgFQFnXiKDTQOSURExB0UkKogT5sVT5v5wlqNQxIREal8CkhV1IlWJD3qLyIiUvkUkKooPckmIiLiPgpIVZSPl+ZCEhERcRcFpCrK1YJUqKfYREREKpsCUhXlrS42ERERt1FAqqI0BklERMR9FJCqKNcYJD3FJiIiUukUkKootSCJiIi4jwJSFaUxSCIiIu6jgFRF+Zx4Ya262ERERCqdAlIVpS42ERER91FAqqJ89KoRERERt1FAqqJ8vDwAtSCJiIi4gwJSFeXjeXwMkgKSiIhIpVNAqqI0D5KIiIj7KCBVUXrMX0RExH0UkKooPcUmIiLiPgpIVdSJLjY9xSYiIlL5FJCqqBMtSPlqQRIREal0CkhVlMYgiYiIuI8CUhWlLjYRERH3UUCqok52sTndXBMREZHLjwJSFXUiIBU6nBQ7FJJEREQqkwJSFXWiiw0gv1gBSUREpDK5PSDNmDGDqKgovL29iY6OZs2aNaXab+7cuVgsFgYMGFBivcViOePyyiuvuMpERUWdtn3KlCnleVkXze5x8rdG45BEREQql1sD0rx584iNjeXZZ59l/fr1tG3blt69e5OamnrO/fbu3cv48eO55pprTtuWlJRUYpk9ezYWi4WBAweWKDdp0qQS5caMGVOu13axLBaLHvUXERFxE7cGpKlTpzJq1ChGjBhBy5YtmTlzJr6+vsyePfus+zgcDoYOHcrzzz9Po0aNTtseERFRYvnyyy/p2bPnaWUDAgJKlPPz8yv367tYrifZFJBEREQqldsCUmFhIevWrSMmJuZkZaxWYmJiSEhIOOt+kyZNIiwsjJEjR573HCkpKXzzzTdnLDtlyhRq1qxJ+/bteeWVVyguLj7nsQoKCsjKyiqxVLQTLUh56mITERGpVB7uOnFaWhoOh4Pw8PAS68PDw9m6desZ91mxYgXvvPMOGzZsKNU55syZQ0BAALfddluJ9Y888ggdOnQgJCSEVatWMWHCBJKSkpg6depZjxUXF8fzzz9fqvOWF29PM79qDJKIiEjlcltAulDZ2dncddddzJo1i9DQ0FLtM3v2bIYOHYq3t3eJ9bGxsa7Pbdq0wcvLi/vuu4+4uDjsdvsZjzVhwoQS+2VlZREZGVmGKym9E11sGoMkIiJSudwWkEJDQ7HZbKSkpJRYn5KSQkRExGnld+3axd69e7n55ptd65xO8/F3Dw8Ptm3bRuPGjV3bfvrpJ7Zt28a8efPOW5fo6GiKi4vZu3cvzZo1O2MZu91+1vBUUXz0uhERERG3cNsYJC8vLzp27Eh8fLxrndPpJD4+ni5dupxWvnnz5mzcuJENGza4lv79+9OzZ082bNhwWmvOO++8Q8eOHWnbtu1567JhwwasVithYWEXf2HlyMfLzK/qYhMREalcbu1ii42NZfjw4XTq1InOnTszbdo0cnNzGTFiBADDhg2jbt26xMXF4e3tTatWrUrsHxwcDHDa+qysLD755BNeffXV086ZkJDA6tWr6dmzJwEBASQkJDBu3DjuvPNOatSoUTEXWkY+J8YgqQVJRESkUrk1IA0aNIjDhw8zceJEkpOTadeuHQsXLnQN3E5MTMRqvfBGrrlz52IYBkOGDDltm91uZ+7cuTz33HMUFBTQsGFDxo0bV2J8UVWheZBERETcw2IYhuHuSlRHWVlZBAUFkZmZSWBgYIWcY8L83/nfmv08dv0VjOnVtELOISIicjkp7fe32181ImfnrUHaIiIibqGAVNU4nZCTCo4iPcUmIiLiJtVmHqTLxrTWkHUA7luOj6cvoDFIIiIilU0tSFWN3/FJMLMOnXwXmx7zFxERqVQKSFVNUD3z18wDGoMkIiLiJgpIVU1gHfPXrIN/GoPkdGOFRERELj8KSFVNYF3z1z91seWri01ERKRSKSBVNa4utoN6ik1ERMRNFJCqGlcXm8YgiYiIuIsCUlXz5y62E+9iUxebiIhIpVJAqmoCagMWcBTi78gA1IIkIiJS2RSQqhoPL/APA8AvPxlQC5KIiEhlU0Cqio53s/mcCEhFDvROYRERkcqjgFQVBZkByTsv2bWqoFhzIYmIiFQWBaSq6HgLklfOIdcqdbOJiIhUHgWkquh4QLLmJOFlO/4kmwZqi4iIVBoFpKroeBcbmQfx9lRAEhERqWwKSFWRay6kA67XjaiLTUREpPIoIFVFroCUhK+HBYB8tSCJiIhUGgWkqiggAixWcBYR4ZEDqItNRESkMikgVUU2T/APB6CuLR1QF5uIiEhlUkCqqo53s9WxHA9IakESERGpNApIVdXxJ9kiOAJoDJKIiEhlUkCqqo63INUy0gB1sYmIiFQmBaSq6nhACnUcD0hFetWIiIhIZVFAqqqOd7GFOA4DGoMkIiJSmRSQqqrjLUhBRamAxiCJiIhUJgWkqup4QAooOowVJ3mFxW6ukIiIyOVDAamqCogAiw2b4SCUTI4VagySiIhIZVFAqqqsNjMkAbUtR9TFJiIiUokUkKqy491stS3pGqQtIiJSiRSQqrKgE7NpH9E8SCIiIpXI7QFpxowZREVF4e3tTXR0NGvWrCnVfnPnzsVisTBgwIAS6++++24sFkuJpU+fPiXKpKenM3ToUAIDAwkODmbkyJHk5OSU1yWVn+MtSBGWdPLUgiQiIlJp3BqQ5s2bR2xsLM8++yzr16+nbdu29O7dm9TU1HPut3fvXsaPH88111xzxu19+vQhKSnJtfzvf/8rsX3o0KFs2rSJRYsWsWDBApYvX87o0aPL7brKTeDJFqTkzGNuroyIiMjlw60BaerUqYwaNYoRI0bQsmVLZs6cia+vL7Nnzz7rPg6Hg6FDh/L888/TqFGjM5ax2+1ERES4lho1ari2bdmyhYULF/L2228THR1N9+7dee2115g7dy6HDh0q92u8KEEnxiAdISWrQN1sIiIilcRtAamwsJB169YRExNzsjJWKzExMSQkJJx1v0mTJhEWFsbIkSPPWmbp0qWEhYXRrFkzHnjgAY4cOeLalpCQQHBwMJ06dXKti4mJwWq1snr16rMes6CggKysrBJLhTvRgmRNByAxPa/izykiIiLuC0hpaWk4HA7Cw8NLrA8PDyc5OfmM+6xYsYJ33nmHWbNmnfW4ffr04f333yc+Pp6XXnqJZcuWceONN+JwmK0vycnJhIWFldjHw8ODkJCQs54XIC4ujqCgINcSGRlZ2kstu+MBKYyjWHGy90huxZ9TRERE8HB3BUorOzubu+66i1mzZhEaGnrWcoMHD3Z9bt26NW3atKFx48YsXbqUXr16lfn8EyZMIDY21vVzVlZWxYck/zCwemBzFhPGUfYpIImIiFQKtwWk0NBQbDYbKSkpJdanpKQQERFxWvldu3axd+9ebr75Ztc6p9OcXdrDw4Nt27bRuHHj0/Zr1KgRoaGh7Ny5k169ehEREXHaIPDi4mLS09PPeN4T7HY7drv9gq7xolltEFAbMvdT25LOviPqYhMREakMbuti8/LyomPHjsTHx7vWOZ1O4uPj6dKly2nlmzdvzsaNG9mwYYNr6d+/Pz179mTDhg1nbc05cOAAR44coXbt2gB06dKFjIwM1q1b5yqzePFinE4n0dHR5XyV5SDw5EBtBSQREZHK4dYuttjYWIYPH06nTp3o3Lkz06ZNIzc3lxEjRgAwbNgw6tatS1xcHN7e3rRq1arE/sHBwQCu9Tk5OTz//PMMHDiQiIgIdu3axeOPP06TJk3o3bs3AC1atKBPnz6MGjWKmTNnUlRUxMMPP8zgwYOpU6dO5V18aQXVhf1mQPpdXWwiIiKVwq0BadCgQRw+fJiJEyeSnJxMu3btWLhwoWvgdmJiIlZr6Ru5bDYbv//+O3PmzCEjI4M6depwww03MHny5BLdYx999BEPP/wwvXr1wmq1MnDgQKZPn17u11cuAs3QVtuSzqGMYxQWO/HycPv8niIiIpc0i2EYhrsrUR1lZWURFBREZmYmgYGBFXein2fCwif43ojmvoJHWfzYdTSq5V9x5xMREbmElfb7W00RVd3xySIbexwG0DgkERGRSqCAVNXVuwqARo49hJKpR/1FREQqgQJSVRcQAbXbYsWgh20De9WCJCIiUuEUkKqDK/oA0NP6q1qQREREKoECUnXQ1Jyi4FrrRg4eyXRzZURERC59CkjVQZ32OHxDCbAcIzzjVxxOPXgoIiJSkRSQqgOrFWvTGwC4ll85lHHMzRUSERG5tCkgVROWK8xutr9Yf9Wj/iIiIhVMAam6aPwXirHR2JpE+v7N7q6NiIjIJU0BqbrwDmR/QDsAfPbGn7usiIiIXBQFpGokrU5PAOoeXu7mmoiIiFzaFJCqEWcTc6B202O/Q0G2m2sjIiJy6VJAqkbCG7ZitzMCT4oxdi12d3VEREQuWQpI1UjdGj4sNdoDkL/pWzfXRkRE5NKlgFSNeNqsbPS9GgDbrh/B6XRzjURERC5NCkjVzNFancgxvPHKT4PEBHdXR0RE5JKkgFTNRIYG873zKvOHz++DnFT3VkhEROQSpIBUzTSo6cvzRXeR4hkJmfth7lAoynd3tURERC4pCkjVTIOafmThz0S/Z8A7CA6sga/GgKEX2IqIiJQXBaRqJqqmLwCrMoIx/u99sNhg48fw06turpmIiMilQwGpmokM8cVigez8Yo5GdIW+r5gbFk+GzV+5t3IiIiKXCAWkasbb00ZEoDcA+47kwlUjIfp+c+Pn90PaDjfWTkRE5NKggFQNRdX0A2Br8vHXjdzwD4i6Bopy4ePhUHTMjbUTERGp/hSQqqHoRiEArNiRZq6wecDAt8GvFqRugu+ecGPtREREqj8FpGrouitqAfDTjsMUO47Pph0QAbfNAiywfg78/rH7KigiIlLNKSBVQ23qBRPs60lWfjG/Hcg4uaFxT7jucfPz12M1HklERKSMFJCqIZvVQvcmoQAs23a45MbrntB4JBERkYukgFRNnehmW7b9lIBktZUcj7RwghtqJyIiUr0pIFVTJwLS7wczOZJTUHJjQATc9h/z87p3NT+SiIjIBVJAqqbCAr1pUTsQw4AVO9NOL9D4L9DtUfPzV2Mg80DlVlBERKQaU0CqxlzdbKeOQzqh59+hTgfIz4DPRoHTUXmVExERqcYUkKqxEwFp+Y7DOJ1neFmthxfc/g54BUDiKlj+z0quoYiISPVUpoA0Z84cvvnmG9fPjz/+OMHBwXTt2pV9+/Zd0LFmzJhBVFQU3t7eREdHs2bNmlLtN3fuXCwWCwMGDHCtKyoq4oknnqB169b4+flRp04dhg0bxqFDh0rsGxUVhcViKbFMmTLlgupdFXRsUAM/LxtpOYVsTso6c6GQRtDv+Itsl02BfQmVV0EREZFqqkwB6cUXX8THxweAhIQEZsyYwcsvv0xoaCjjxo0r9XHmzZtHbGwszz77LOvXr6dt27b07t2b1NTUc+63d+9exo8fzzXXXFNifV5eHuvXr+eZZ55h/fr1zJ8/n23bttG/f//TjjFp0iSSkpJcy5gxY0pd76rCy8NK1xOP+5/6NNuftR0EbQaD4YTPRkJ2SiXVUEREpHoqU0Dav38/TZo0AeCLL75g4MCBjB49mri4OH766adSH2fq1KmMGjWKESNG0LJlS2bOnImvry+zZ88+6z4Oh4OhQ4fy/PPP06hRoxLbgoKCWLRoEXfccQfNmjXj6quv5vXXX2fdunUkJiaWKBsQEEBERIRr8fPzu4A7UHVce75xSCf0+yfUbAJZB2HuEM2PJCIicg5lCkj+/v4cOXIEgB9++IHrr78eAG9vb44dK90Xb2FhIevWrSMmJuZkZaxWYmJiSEg4ezfQpEmTCAsLY+TIkaU6T2ZmJhaLheDg4BLrp0yZQs2aNWnfvj2vvPIKxcXFpTpeVXNdUzMgrUs8SlZ+0dkL2gPgrx+DTw04uA4+vx+czkqqpYiISPXiUZadrr/+eu69917at2/P9u3b6du3LwCbNm0iKiqqVMdIS0vD4XAQHh5eYn14eDhbt2494z4rVqzgnXfeYcOGDaU6R35+Pk888QRDhgwhMDDQtf6RRx6hQ4cOhISEsGrVKiZMmEBSUhJTp04967EKCgooKDg531BW1lnG/FSy+jV9aRTqx+60XFbtTKNPq9pnL1yzMQz6EN4fAJu/gCVNoNczlVVVERGRaqNMLUgzZsygS5cuHD58mM8++4yaNWsCsG7dOoYMGVKuFTwhOzubu+66i1mzZhEaGnre8kVFRdxxxx0YhsGbb75ZYltsbCw9evSgTZs23H///bz66qu89tprJQLQqeLi4ggKCnItkZGRF31N5cXVzbb9DPMhnSqqO9z8b/PzT/+E3+ZWYM1ERESqJ4thGGd4PrziFRYW4uvry6efflriSbThw4eTkZHBl19+WaL8hg0baN++PTabzbXOebyLyGq1sm3bNho3bgycDEe7d+9m8eLFrgB3Nps2baJVq1Zs3bqVZs2anbHMmVqQIiMjyczMLNE65Q5LtqUy4t21hAXYWfHEX/DyKEXu/fE5WPEvsHnB8K+h/tUVXk8RERF3y8rKIigo6Lzf32VqQVq4cCErVqxw/TxjxgzatWvHX//6V44ePVqqY3h5edGxY0fi4+Nd65xOJ/Hx8XTp0uW08s2bN2fjxo1s2LDBtfTv35+ePXuyYcMGV4vOiXC0Y8cOfvzxx/OGIzDDl9VqJSws7Kxl7HY7gYGBJZaqomvjmtQKsJOaXcBXvx06/w4Af5kILfqDoxA+HQnHSvf7JiIicjkoU0D629/+5hqDs3HjRh577DH69u3Lnj17iI2NLfVxYmNjmTVrFnPmzGHLli088MAD5ObmMmLECACGDRvGhAnmy1a9vb1p1apViSU4OJiAgABatWqFl5cXRUVF3H777fzyyy989NFHOBwOkpOTSU5OprCwEDCnJZg2bRq//fYbu3fv5qOPPmLcuHHceeed1KhRoyy3w+3sHjbu6dYQgP8s33XmSSNPZbXCgDfNeZKyDsCCceCexkQREZEqp0yDtPfs2UPLli0B+Oyzz7jpppt48cUXWb9+vWvAdmkMGjSIw4cPM3HiRJKTk2nXrh0LFy50DdxOTEzEai19hjt48CBffWW+mLVdu3Ylti1ZsoQePXpgt9uZO3cuzz33HAUFBTRs2JBx48ZdULCriv4aXZ8ZS3ayPSWHJdtS6dUi/Pw72f1h4Nvwzg2w6XNocj20H1rxlRUREaniyjQGKSQkhBUrVtCyZUu6d+/OsGHDGD16NHv37qVly5bk5eVVRF2rlNL2YVamuG+38Nby3VwVVYNP7u9a+h1/ehXiJ4GnH9z/k/m0m4iIyCWoQscgde/endjYWCZPnsyaNWvo168fANu3b6devXplq7FctBHdGuJps7B271HW7Usv/Y7dxkKD7lCUC5/dC45zzKckIiJyGShTQHr99dfx8PDg008/5c0336Ru3boAfPfdd/Tp06dcKyilFxHkza3tzd+Lmct2l35Hqw1uewu8g+HQeljyYsVUUEREpJpw22P+1V1V7GID2JmaQ8zUZQD8GHsdTcL8S7/zpi/gk+Hm51vfgraDy7+CIiIiblShXWxgvhPts88+44UXXuCFF17g888/x+FwlPVwUk6ahPlzfUtzgPZ/lu+6sJ2vHABXP2h+/uIB2PxV+VZORESkmihTQNq5cyctWrRg2LBhzJ8/n/nz53PnnXdy5ZVXsmvXBX4pS7m7/zpzkPXnvx4kOTP/wna+4R/QbigYTvj0Htj5YwXUUEREpGorU0B65JFHaNy4Mfv372f9+vWsX7+exMREGjZsyCOPPFLedZQL1LFBDTpHhVDkMHhj6c4L29lqhf6vQcsB4CyCuXfC3pUVUk8REZGqqkwBadmyZbz88suEhIS41tWsWZMpU6awbNmycquclN3YmKYA/Hd1InvSci9sZ6sNbpsFTXtD8TH47yA49GsF1FJERKRqKlNAstvtZGdnn7Y+JycHLy+vi66UXLyuTULp0awWxU6Df36/7cIP4OEFd8yBqGugMBu+flQzbYuIyGWjTAHppptuYvTo0axevRrDMDAMg59//pn777+f/v37l3cdpYyevLE5Fgt8szGJXxPL8K41Tx/4vzng6QtJv8GuxeVfSRERkSqoTAFp+vTpNG7cmC5duuDt7Y23tzddu3alSZMmTJs2rZyrKGXVPCKQ2zuYE3fGfbuVMs3o4FcTOprvxuOnqeVYOxERkarrouZB2rlzJ1u2bAGgRYsWNGnSpNwqVtVV1XmQTpWUeYweryyloNjJrGGdXFMAXJCsQzCtjTlo+57vof7V5V9RERGRSlDa7+9Sv6z2fC9zXbJkievz1Klqaagqagf5cE/3hry5dBcvLdxKz2a18LBdYMNhYB1oNwTWv2+2Ig39uGIqKyIiUkWUOiD9+mvpnmKyWCxlroxUjAd6NGbumkR2pubwyboDDOlc/8IP0m0s/Poh7Pgekn6H2m3KvZ4iIiJVhV41UkbVpYvthHdW7GHygs3UCrCz7G898PUqdTY+6dN74I/P4Mrb4P/eLf9KioiIVLAKf9WIVC93Xl2fyBAfDmcX8O7KvWU7SPfj3aybv4AjmjFdREQuXQpIlwm7h43xNzQDYObSXaTnFl74QSJawRV9zNeQrJxWvhUUERGpQhSQLiM3t6lDy9qBZBcUM2PJBb6C5IQTrUgb/gcZieVXORERkSpEAekyYrVaeOLG5gB8kLCPA0fzLvwg9aOh4bXmI/8/PFPONRQREakaFJAuM9c2DaVr45oUOpxMXbS9bAfp/SJYrOZYpD3Ly7V+IiIiVYEC0mXGYrHwRB+zFenzXw+yJSnrwg8S0Ro6jTQ/f/cEOIrLsYYiIiLup4B0GWobGUy/NrUxDHh54dayHaTnU+ATAqmb4Zd3yreCIiIibqaAdJkaf0MzPKwWlmw7TMKuIxd+AN8Q6HV8DNKSf0BuWvlWUERExI0UkC5TDUP9XDNqP/PlHxQUOy78IB2Gm91t+ZkQP6mcaygiIuI+CkiXscduuIJQfy92puYwY3EZHvu32uDGV8zP69+HQ6V7HY2IiEhVp4B0GQv29WLSLa0AeGPpLjYfKsOA7QZdoPUdgGEO2Naba0RE5BKggHSZ69u6Nn2ujKDYafDEZ79T7HBe+EGufx48fWH/atj0eflXUkREpJIpIAmTbrmSQG8PNh7M5O0Vey78AIF1oNtY8/OPz0JRfrnWT0REpLIpIAlhgd48c1NLAP61aDu7D+dc+EG6PgwBdczXj/z8RjnXUEREpHIpIAkAt3esxzVNQykodvLkZxtxOi9wLJGXH8Q8a37+aSrkpJZ/JUVERCqJApIA5gzbL97aGl8vG2v2pvP5rwcv/CCt74A67aEw25wbSUREpJpSQBKXyBBfHunVFIC477aSnV90YQewWs33tIH52H/KpnKuoYiISOVQQJISRnSLomGoH2k5BbxWlrmRGnSFlreA4YTvn9Jj/yIiUi0pIEkJdg8bE282B2zPXrGHnallGLAd8zzYvGD3Ulj7dvlWUEREpBK4PSDNmDGDqKgovL29iY6OZs2aNaXab+7cuVgsFgYMGFBivWEYTJw4kdq1a+Pj40NMTAw7duwoUSY9PZ2hQ4cSGBhIcHAwI0eOJCenDEHgEtWzWRi9modR7DR4/utNGBfaChTSEK593Pz87XhY/0H5V1JERKQCuTUgzZs3j9jYWJ599lnWr19P27Zt6d27N6mp534Cau/evYwfP55rrrnmtG0vv/wy06dPZ+bMmaxevRo/Pz969+5Nfv7JuXmGDh3Kpk2bWLRoEQsWLGD58uWMHj263K+vOnvmppZ42az8tCONRZtTLvwA146H6AfMz1+Ngd8/Lt8KioiIVCCLccHNA+UnOjqaq666itdffx0Ap9NJZGQkY8aM4cknnzzjPg6Hg2uvvZZ77rmHn376iYyMDL744gvAbD2qU6cOjz32GOPHjwcgMzOT8PBw3nvvPQYPHsyWLVto2bIla9eupVOnTgAsXLiQvn37cuDAAerUqVOqumdlZREUFERmZiaBgYEXeSeqppcXbuWNpbuIDPFh0bjr8Pa0XdgBDAO+eQx+eQcsVrh9Nlx5a8VUVkREpBRK+/3tthakwsJC1q1bR0xMzMnKWK3ExMSQkJBw1v0mTZpEWFgYI0eOPG3bnj17SE5OLnHMoKAgoqOjXcdMSEggODjYFY4AYmJisFqtrF69+qznLSgoICsrq8RyqXuoZxMiAr3Zn36Mt5btvvADWCzQ95/Q/k5z0PZn98LWb8u/oiIiIuXMbQEpLS0Nh8NBeHh4ifXh4eEkJyefcZ8VK1bwzjvvMGvWrDNuP7HfuY6ZnJxMWFhYie0eHh6EhISc9bwAcXFxBAUFuZbIyMhzX+AlwM/uwVP9WgAwY+lO9qTlXvhBrFa4ebo5R5KzGOaPhuyz32cREZGqwO2DtEsrOzubu+66i1mzZhEaGlrp558wYQKZmZmuZf/+/ZVeB3e4uU1trmkaSmGxk6c/33jhA7YBrDYY8CbU7WhOIvnD38u/oiIiIuXIbQEpNDQUm81GSkrJAcApKSlEREScVn7Xrl3s3buXm2++GQ8PDzw8PHj//ff56quv8PDwYNeuXa79znXMiIiI0waBFxcXk56efsbznmC32wkMDCyxXA4sFgsvDGiF3cPKql1HyjbDNoDNA/q9ao5F2vgJ7F5WvhUVEREpR24LSF5eXnTs2JH4+HjXOqfTSXx8PF26dDmtfPPmzdm4cSMbNmxwLf3796dnz55s2LCByMhIGjZsSERERIljZmVlsXr1atcxu3TpQkZGBuvWrXOVWbx4MU6nk+jo6Aq84uqrQU0/Ho0xZ9h+4ZstpOcWlu1AddrDVfean78dD8VlPI6IiEgFc2sXW2xsLLNmzWLOnDls2bKFBx54gNzcXEaMGAHAsGHDmDBhAgDe3t60atWqxBIcHExAQACtWrXCy8sLi8XC2LFjeeGFF/jqq6/YuHEjw4YNo06dOq75klq0aEGfPn0YNWoUa9asYeXKlTz88MMMHjy41E+wXY5GXdOIZuEBpOcW8uK3W8p+oJ5Pg18YpG2HhNfLr4IiIiLlyK0BadCgQfzzn/9k4sSJtGvXjg0bNrBw4ULXIOvExESSkpIu6JiPP/44Y8aMYfTo0Vx11VXk5OSwcOFCvL29XWU++ugjmjdvTq9evejbty/du3fnP//5T7le26XG02blxdtaA/DpugMk7DpStgP5BMMNL5ifl70MGYnlU0EREZFy5NZ5kKqzy2EepDN5+vONfLQ6kUahfnw39hrsHhc4NxKY8yO9dxPsWwHNb4LBH5V/RUVERM6gys+DJNXT432aUyvAzu60XN5dubdsB7FYoN8/weoBWxfAjh/LtY4iIiIXSwFJLkiQjydP9mkOwOuLd3I4u6BsBwprAZ2Pv95l1fRyqp2IiEj5UECSC3Zr+7q0qRdETkExUxdtK/uBrn7AfOx/zzI4fBHHERERKWcKSHLBrFYLz9zUEoB5a/ez+VAZX7sSXB+a9TU/r9EgeRERqToUkKRMrooKoV+b2jgNmLxgc9lm2AboPMr8dcP/ID+z/CooIiJyERSQpMye7NMcLw8rCbuPsGhzyvl3OJOG10Gt5lCUa4YkERGRKkABScosMsSXe7s3BODFb7dQWOy88INYLCdbkdb8B5xlOIaIiEg5U0CSi/JgzyaE+tvZeySPOav2lu0gbQaDPRDSd8HuxeVaPxERkbJQQJKL4m/34G+9rwBgevwOUrPzL/wgdn9oN9T8vFqDtUVExP0UkOSi3d4xkrb1gsguKGbKt1vLdpATL7Hd8QOk7y6/yomIiJSBApJcNJvVwqRbWmGxwPxfD7JmT/qFHyS0CTSJAQxY+06511FERORCKCBJuWgbGczgq+oDMPHLPyh2lGGwdef7zF/XzYH0PeVYOxERkQujgCTl5vHezQj29WRrcjbvJ+y78AM0iYF6naEwGz4eBkXHyr+SIiIipaCAJOWmhp8Xj/c239P2r0XbL3zAttUK//ce+NaE5N/h27+VfyVFRERKQQFJytWgqyJpczEDtoPqwu2zzXe0/foBrH+//CspIiJyHgpIUq5sVguT/zRg+5e9ZRiw3agH9Hza/PzNeEj6rVzrKCIicj4KSFLu2kYGc0fHSMCcYbtM72nrHgtX9AFHAcy7C44dLedaioiInJ0CklSI2BuuwMfTxvrEDL7flHzhB7Ba4daZENwAMvbBfwdBQXb5V1REROQMFJCkQoQHenPvNeZ72l5euI2isjz271MDBv8XvINh/2r4cKBCkoiIVAoFJKkwo69tRE0/L3an5TJ37f6yHSSiFQz7EryDjoek2xWSRESkwikgSYUJ8Pbk0ZimAPz7x+3kFBSX7UB12v0pJP2skCQiIhVOAUkq1JDO9Ymq6UtaTiGzll/EO9bqtIe7vjgZkj76P00kKSIiFUYBSSqUp83K433MySNn/bT7wieP/LO6HcyQZA+CxAT44kFwlmFsk4iIyHkoIEmFu7FVBO0ig8krdDDtxx0Xd7C6HWDwR2D1gE3zYemL5VNJERGRP1FAkgpnsVh4qm8LAOat3c/etNyLO2DDa+Dmf5ufl78CG/53kTUUEREpSQFJKkXnhiH0aFYLh9NgevxFtiIBtL8Tuo8zP381BvauvPhjioiIHKeAJJXmseubAfD5hoPsSCmHp9D+MhFa9AdnEcwbCrsWQ3HBxR9XREQuewpIUmla1wui95XhGAYXPxYJjs+2/Zb5hNuxo/DBrfBSlDmh5KrXIf0inpoTEZHLmgKSVKrY65thscA3G5P442DmxR/Qyxf++jG0Gwp+taAoD3b+CD88DTOuhv1rLv4cIiJy2VFAkkrVLCKA/m3rAPCvRdvL56D+YTDgDRi/A+5fCTe8ALXbmS+6/WqMut1EROSCKSBJpXu0V1NsVgvxW1NZn3i0/A5ssZivJuk6Bu763GxROrwVVvyr/M4hIiKXBQUkqXSNavkzsENdAKb+UE6tSKfyDYEbXzI/L/8npG6pmPOIiMglye0BacaMGURFReHt7U10dDRr1px9zMj8+fPp1KkTwcHB+Pn50a5dOz744IMSZSwWyxmXV155xVUmKirqtO1TpkypsGuU0z3SqymeNgsrdqaxbPvhijnJlbfBFTeaT7l9NQacjoo5j4iIXHLcGpDmzZtHbGwszz77LOvXr6dt27b07t2b1NTUM5YPCQnh6aefJiEhgd9//50RI0YwYsQIvv/+e1eZpKSkEsvs2bOxWCwMHDiwxLEmTZpUotyYMWMq9FqlpHo1fBka3QCA2HkbOJRRAe9Vs1ig36vgFQAH1sLad8r/HCIickmyGIZhuOvk0dHRXHXVVbz++usAOJ1OIiMjGTNmDE8++WSpjtGhQwf69evH5MmTz7h9wIABZGdnEx8f71oXFRXF2LFjGTt2bJnrnpWVRVBQEJmZmQQGBpb5OJezY4UObp+5ik2HsmhTL4iP7+uCt6et/E+0ZhZ8Ox68/OHBnyE4svzPISIi1UJpv7/d1oJUWFjIunXriImJOVkZq5WYmBgSEhLOu79hGMTHx7Nt2zauvfbaM5ZJSUnhm2++YeTIkadtmzJlCjVr1qR9+/a88sorFBcXn/N8BQUFZGVllVjk4vh42Zh5Z0dq+Hry+4FMnv78Dyokr3caCZFXQ2EOvHUNfPEQbP9eT7eJiMhZuS0gpaWl4XA4CA8PL7E+PDyc5OTks+6XmZmJv78/Xl5e9OvXj9dee43rr7/+jGXnzJlDQEAAt912W4n1jzzyCHPnzmXJkiXcd999vPjiizz++OPnrG9cXBxBQUGuJTJSrRDlITLEl9f/2gGrBT5bf4APft5X/iexWuGWGRAUaU4oueFD+O8d8EoT+GwU7PkJ3NeQKiIiVZDbutgOHTpE3bp1WbVqFV26dHGtf/zxx1m2bBmrV68+435Op5Pdu3eTk5NDfHw8kydP5osvvqBHjx6nlW3evDnXX389r7322jnrMnv2bO677z5ycnKw2+1nLFNQUEBBwckWh6ysLCIjI9XFVk5mLd/NP77dgofVwn9HXU3nhiHlfxJHMSSugs1fwdYFkJ10clvoFdDpHmg7GHxqlP+5RUSkSqjyXWyhoaHYbDZSUlJKrE9JSSEiIuKs+1mtVpo0aUK7du147LHHuP3224mLizut3E8//cS2bdu49957z1uX6OhoiouL2bt371nL2O12AgMDSyxSfu69piE3t61DsdPg4f+uJ6fg3F2eZWLzgIbXQr9/wrjNMHIRdBwBnn6Qth0WPgmvtoDvn4aiChg0LiIi1YbbApKXlxcdO3YsMXja6XQSHx9fokXpfJxOZ4mWnRPeeecdOnbsSNu2bc97jA0bNmC1WgkLCyv1eaV8WSwWXhrYmqiavqRmF/CfZbsq9oRWK0R2hpunwWNbzafdwq6E4mOQ8Dr8pyckb6zYOoiISJXl1sf8Y2NjmTVrFnPmzGHLli088MAD5ObmMmLECACGDRvGhAkTXOXj4uJYtGgRu3fvZsuWLbz66qt88MEH3HnnnSWOm5WVxSeffHLG1qOEhASmTZvGb7/9xu7du/noo48YN24cd955JzVqqGvFnXy9PHjyxuYAzPppDylZ+ZVzYu9AuOpeeGCl+V43vzA4vAVm/QUSZoDTWTn1EBGRKsPDnScfNGgQhw8fZuLEiSQnJ9OuXTsWLlzoGridmJiI1Xoyw+Xm5vLggw9y4MABfHx8aN68OR9++CGDBg0qcdy5c+diGAZDhgw57Zx2u525c+fy3HPPUVBQQMOGDRk3bhyxsbEVe7FSKr2vjKBTgxr8su8oU3/Yzku3t6m8k1sscEVveDABvnwYtn8H3z8F2xdCi/4Q0ghqNoGgemCtgOkIRESkynDrPEjVmeZBqjjr9h1l4JursFjgu0evoXmEG+6vYcC6d2HhU2a325/Z7NDwGrh+EoRfWfl1ExGRMqvyg7RFzqZjgxr0bR2BYUDct1vdUwmLxXyq7f4V0O1RaNYPQpuB1RMcBbDzR5jZHb55DPLS3VNHERGpMGpBKiO1IFWsvWm5XP+vZRQ5DD4cGU33pqHurpLJ6TCfeFsaB5u/NNd5B0PPp6Hj3eDh5c7aiYjIeagFSaq1qFA/7rzafFfbi99uwemsIjneaoOwFnDH+zD8a/PJt/wM+O5vMLUFLJoIR87wBF5hLqTtNOdiktJb/z4kvKGJPEWk0qkFqYzUglTxjuYWcu0rS8jOL+bl29twR6cqOHu5oxjWvwfL/1ly4smG10HdDnB4G6RuhqN7zfXhrWHAG1C7EgefV1cHfoG3e5mfhy8wx32JiFwktSBJtVfDz4sxf2kCmK1IaTlV8N1pNg9zioCxf8Cgj6DJ9YAF9iyDFf+Cbd+eDEdWD0jZCLN6wpI4KC48//Gdzsuz1ckw4IdnTv688t/uq4uIXJbUglRGakGqHEUOJ7e8vpLNSVnc1KY2r/+1g7urdH4ZibDhv5CTArVamF1yYS3ML/1vYmHLV2a58NbQ92Wo0RDsAeDlZ64/shN2LzVD1t4V5kt1//J3uPpBc/D4xXIUQ2KCOX2Bpy9c/QD4VsCrXS7G1m9g7l/BwxschWA44f6VENHK3TUTkWqutN/fCkhlpIBUef44mMktM1bicBr8566O3HDl2V9FU+UZBmyaD9+Mh2OnPP1msYKHDxTlnnnfFjebL931Djr3OYryIX0X5Gea5zOc5nIsHbb/YM7vdOzoyfI+NaDXROgw/PT5nQqP1+VEeKsMjiJ4owsc2QHXPAbpu2HT59BmENz2n8qrh4hckhSQKpgCUuV6aeFW3ly6i/BAOz+Mu44gH093V+ni5ByG7yfAjkVQkGUGmBNsXhAZbY5janQdHNpgTljpLDInq/y/OeYYJkexGSIObYCUP8yn69K2w9F9wHn+WvvUgKa9Ifl3c4wUQO220DvOrMue5eZy8BfAAl3HwLV/Ay/f819bcQH8/rHZalav04Xfm7Vvm9Mn+NaER341A9J/eoDFBo9ugOD6F35MEZHjFJAqmAJS5covctD33z+xOy2XIZ0jibvtEhrkbBhQlAf5WVCYY87U7elTssyBdfDJcMjcb3Y7RbSG5D9On8TyBO8g8A01W4QsVnOxeUKDbtC8H0RebY6fchTDL+/A4n9AQea56xlcH258BZr1OXuZHYvgu8fNUGP1gJv/De3vPHv5UxVkw/T2kHvYPFf0aHP9nP5ml2P0A3DjlHMfwzDg4Hqo2cgMguJeTiesmw3+EdDiJnfXpvLtjDf/g9N2MHQfd2H7Hsswu981c3+5UkCqYApIlW/NnnTueCsBgP/eG03XJlVkbqTKkpcOn98HO344uc7LHyLamC1KtZpB6BXm4lfrwsYr5RyG+Odhw0fmvg2vNZeoayBlE3z3BGQdMMs2vwk6joAaDSAoEjy9zYHoC5+Cbd+YZTx8Toa3656EHk+Wrj6LX4Dlr0BIY3hotRnqAHYthg9uNcdMjdt09jFTBdnw1SNmN6ZPCNzwArT7a/mM3ZKyWf5PWDzZ/PzXj83X+bib0wm5qRBwAd31jiL48Tnzz9jVD0JY8/Pvs+F/8NXD4Dz+oMVtb0Ob/zv/foe3wddjIXGV2XLqH27WNaA2tLoNWt9e+npXJsOAfSvN91nWusLdtTkrBaQKpoDkHs988Qcf/LyP+iG+fPfoNfjZ3fo6wcrndJpjiApzoXY7891w1nJ8GLW40AwlpwaKghxY9hL8/MbJf+xPCKhtjmkqzjf/Mb/6AbjucfPJs59eNcu0G2q2JtnO0jVamAeHfoUPB5rB6o4PoGX/k9sNA966BpI3mpNyXvf46cdI/sNsZTuys+T6qGvgpmkQ2uSCbgW5aebLijP2md2eNi/wsIM9EDoMMwPi5WbvSkjaYD656WE/f/kdP8JHt+Pq8vUOhvuWu/feOR3w8TDYugBa/x/0mQJ+5/nPVnEBfHqPuQ8AFvPP5zXjzzxlh2HAiqkQP8n8uWYT88+lhzfcsxDqtD/7eX6aav69cRadvT6lDVqVaV8C/Pgs7F9tth73eBK6jTNbqs8lJxW2f2/+R6zFTRDVvcKrqoBUwRSQ3CM7v4g+037iYMYxBl8VyZSBl1BXW3WQstls4Tm81Rzr9OcB5VHXQN9XzLFHJ/zyrjmeyHBAg+7QoMvxSR8N89eMRDP0HNlxchxWZDTc8/3pIW3jp/DZSHNs0tg/To6HMgz49QP49m9mSAusCwPfhv1rYOkUM3DZvMxXxrS63WxpO1eLUlE+rH7T/KIqyDpzGS9/6P0Pc2B7RbZOOR1Vo3vF6TBnj1/+T8CAVgPNL+lzhfP0PebYsfwMs5s1das5pq1Oe/P3tzQB63wK88xxd17+x7umvc+/z4/PmVNwnOBbE2582bymM/1eFuXDx3eZLbc2u9myunPRye1Ne0Pjv0DNxuYYwaB6Zpfa2rfN7V0fgV7Pwtwh5jEC68LopeAfVvI8e1eYrUZHdpw87o0vmaEqOwmyk82nOzd8aNbj7gUQ2fkCbtY55KWbXe112kPjXhf2ZzplsxkEt39n/mz1OPmfqHqd4daZ5r05wekww9DOH2Hbd3BgLSXGTHYYZr7nsgK7xxWQKpgCkvv8vPsIQ2b9jGHAm0M7cGPr2u6u0uXJMCDvyPFB4ZgTY57pH9btP8And5/96bwT/GpB3U7Q50Xzi+ZUjmJ4rb0ZqoIbmF8chsOcBiAj0SzT5Hq49S3wq2n+nL7HDGi74k8exz/C/JJrdJ05xYKHt/ll7eFttmLFT4LM48eLaGM+PWc4zNY1RwHs+Qn2/2xub3oD3DwdAs/xZ/BEELRYzj3A/FiGOTA+dbP5BZKyCY7uMb+0bp7uvikOslPMYLr3J/Nni9UMs93HQcxzZ96nMBfeucF8eKBuJxjxrdlS8NY1Zmtjp5Fw09QLq4dhmPckMcH8fTq0wQzqhuNkGb8wM6CEtzS7doNPmVz2RMgG6PGU+bqg1E3mz1f0Mb+YQ684+ee4MM8MNruXmt3GQ/4HjXuaoWDFVPjjs5IPWJg3CPML3wJ94swWVTCfKp3VywxA9bvAsK/MfTd/YYapA2tPXkPfl6HlgNP/PjkdMO9Oc341v1owavHFP7Sw40f48kFzWhIwH9boPg5a9D93OHcUm93yq14zr9digw53mfd9zzLzPywFWWa3eI8nzaC5/2dzAthT/+NRp735d3rzF+bP/uHmf7Za9K+Q/4AoIFUwBST3ennhVt5YuosgH08Wjr2G2kE+599J3Cdlkzk3lKMQ8wsE8x8+v1rmP8gRrUs3HuSXd2HB2NPXW6zmXFHdxp3eqmEY5jQB6+dA4s9mK9P5BNQxpz5oM+j04zkdZldj/GQzMHkHm0/5+dY8HrSOt4ykbD7+Rf7rySkdrrgRrvsb1O148nh56WZX3uq3oDD7zPWxephPEXaPPfm+P6cT9q0wr60wz/yiPLEE1TNbVTzs5oB/m9e5v2iykszxYxmJ5peTf7jZdZqfCV8/ao7X8fSD/tPNsThf3G/u128qXDXy9Pv92b3wx6fm7+/oZRBU19z25y6322ZBmzvO/ftwYs6ubd+a3VsngvCf+dY0v3xPDeD2QOj9otl6ZbGYA/ffvdH8/e82Fq5/3gy9K6fBspdPdmnZg8xus9ptzX0SV5nXPvTj07t/juwyx+0d3mY+mJC+2zy+h7fZcnLlrSXLp+2AWX8xA0L9LmbrV94Rc5vV06xrzLPnbj0pyIHZfcxJZ8OuhJHfmwO5z6Yo37xvgXXA7n9yfWEeLHrmZEtXcAPz4YiiPPPnmk3M+9Rm0OnvmDx21Oxy3LXY/LnlLfCXZyC06ckyGfvhiwdOBus/8wowW5Ob3WgG08A65vp9q8wxhCda0ZrfZAalE9vLiQJSBVNAcq/CYie3z1zF7wcy6dq4Jh+OjMZq1UDcS96JJ9QKc0o+oRdUz1zOpygfDqwxW2r2rjDHGRUXmF9qxQXmF0Hn0dDl4fNPaZC61Rw0n7Th/Oe1epotHSdaG5rEQJeHzHqsmWVeD5hfSpHREH4lhLU0Q8riySfHvoS3MoNgYoLZGpJ18PznPsEvzAyitduYLWM1Gpj3YMvXJ1svziaspTm9xImBt8tehiX/MO/94P+ZTzYW5Zstdb/NNSdDtXqYrSRR3Uoea/E/YPnLZstCi5vNOba8/M0v+eICyEk2W62yk80v9j8/XenhbT6JWbej2epQp515j8D80s5INJdV009eU9MbzHFr/xsC2YfMrqsh/yvZOpK6BRZOML+gHafM2O8VAHd+CvWvPv89djrNc9gDwfss3wvbf4D/3oGrWymwHnS6G9oPg4Dw858DIPOAGbRyUszr6fLg8T/Hx5fMRPM/Jcl/mGOfTrSy1YgyQ1VYC7P17EQQib7fbA0szIM1b5lhPT/jZP26jzXDm6ePGQb/N8Sca83TFwa8CVcOOPv9WD3TDMs1Gpr38MSf77O1ThXlw0//NLtBnQ6zO7Z+dOnuSykpIFUwBST32304h37TV3CsyMFTfZsz+trG599JpDw5imDNf8wv4xNdcMUF5hiMmo2Pf4l3ML8QMvabg29/n1eyWwjM4HLdE9Cs31lawOabXRYnWhtOsAeZX041oswpIE4EhMyDx1sCLuCf93qdzcCRm2Z+8WYnm6GjZX9zfqw/B0bDMJ/O+vVD80vyit7mFA8ngh6UnKbhz5wO+PA2s9uqNHxCzFaG5n3NsT6lmbTU6YCE180w9ufAE9oM7v3x7OHFUWR22x3aAEm/mUHkusfN7uPytOG/5nik1v9nBpzzDWQ+kwPr4L2+pWsR9fQ92TL0ZwG1zclnm/Qqub4g22ytTXj9ZNebXxi0HQTr5pgtYEGRMPi/FfdeyZRN5n8gTnRRliMFpAqmgFQ1zF2TyJPzN+Jps/D5g91oVfc8s0yLuFv6HnP8ysZPzfEu1z0Ozfqef6xFzmFY+ATsWgINuppdH01vOPvAZMMwv/CLj5n/K8/cb37pJ280Jwg9sssMRC1uNoPZucZRnYmjCD76P9i95OS6wHpmd0urgVCv49n3Lco3x5vkpJrjlQpzzC9lm6c5Riwg4uRj7bWaly1AgNnK98UDcGi9OTfYqCUlBwxXd9u/N1vyHEUnx9HZvMyuzYhW5uuMwq8072Veujne6sT4Np9gs8v2XK8ZKso3H4BY+W/zz88J9bvCHe+Df60Kv8SKoIBUwRSQqgbDMLj/w3V8vymFRrX8WDCmO75el9mj/yLukp9ljmPx8jcHFdftWL7TTpQHRzFs+dLsVvzzGBkpveJC2PgxrH3H7CaLef70cUnViAJSBVNAqjqO5hZy479/Ijkrn0GdInnpdj36LyIiZ1ba7+8qFvVFLlwNPy/+NagdFgvM+2U/C34/5O4qiYhINaeAJJeELo1r8lAPc6bkCfM3sj/9DAMSRURESkkBSS4Zj8Y0pX39YLLzi3l07q8UO06dwE1ERKR0FJDkkuFpszJ9cHsC7B6sT8xgevwOd1dJRESqKQUkuaREhvjyj9taAzB98U4mzN/I0dxCN9dKRESqGwUkueT0b1uH0dea7/L635pE/vLqUuauScTp1AObIiJSOgpIckl6qm8L5o2+mmbhARzNK+LJ+Ru57c1VbE85y7uuRERE/kQBSS5Z0Y1qsuCR7vy9Xwv8vGxs2J/B4P/8zN6087xVXkRELnsKSHJJ87RZufeaRiwe34PWdYNIzy1k+LtrOJJTcP6dRUTksqWAJJeF8EBvZt99FZEhPuw7ksc9c34hr7DY3dUSEZEqSgFJLhu1Auy8N6IzNXw9+W1/BmP+q7mSRETkzBSQ5LLSuJY/bw/vhN3DSvzWVCZ+tQm9jlBERE6lgCSXnY4NQvj34PZYLPDf1YmM/+R38osc7q6WiIhUIW4PSDNmzCAqKgpvb2+io6NZs2bNWcvOnz+fTp06ERwcjJ+fH+3ateODDz4oUebuu+/GYrGUWPr06VOiTHp6OkOHDiUwMJDg4GBGjhxJTk5OhVyfVE19WkUQd2trbFYLn60/wOD//ExqVr67qyUiIlWEWwPSvHnziI2N5dlnn2X9+vW0bduW3r17k5qaesbyISEhPP300yQkJPD7778zYsQIRowYwffff1+iXJ8+fUhKSnIt//vf/0psHzp0KJs2bWLRokUsWLCA5cuXM3r06Aq7TqmaBneuz5wRnQny8WTD/gxufn0Fv+3PcHe1RESkCrAYbhyAER0dzVVXXcXrr78OgNPpJDIykjFjxvDkk0+W6hgdOnSgX79+TJ48GTBbkDIyMvjiiy/OWH7Lli20bNmStWvX0qlTJwAWLlxI3759OXDgAHXq1CnVebOysggKCiIzM5PAwMBS7SNV0960XO59/xd2pubg5WHlxVtbM7BDXSwWi7urJiIi5ay0399ua0EqLCxk3bp1xMTEnKyM1UpMTAwJCQnn3d8wDOLj49m2bRvXXnttiW1Lly4lLCyMZs2a8cADD3DkyBHXtoSEBIKDg13hCCAmJgar1crq1avL4cqkuokK9ePzB7vSq3kYhcVOxn/yGw/9dz3peoebiMhly20BKS0tDYfDQXh4eIn14eHhJCcnn3W/zMxM/P398fLyol+/frz22mtcf/31ru19+vTh/fffJz4+npdeeolly5Zx44034nCYg3CTk5MJCwsrcUwPDw9CQkLOed6CggKysrJKLHLpCPD25D/DOvHY9VfgYbXw7cZkbvjXcuK3pLi7aiIi4gYe7q7AhQoICGDDhg3k5OQQHx9PbGwsjRo1okePHgAMHjzYVbZ169a0adOGxo0bs3TpUnr16lXm88bFxfH8889fbPWlCrNZLYzp1ZSezcMYN28DO1JzGDnnFwZ1imTizS3xs1e7vy4iIlJGbmtBCg0NxWazkZJS8n/oKSkpREREnHU/q9VKkyZNaNeuHY899hi33347cXFxZy3fqFEjQkND2blzJwARERGnDQIvLi4mPT39nOedMGECmZmZrmX//v2luUyphlrVDeLrMd0ZdU1DLBaY98t+Br65iv3pee6umoiIVBK3BSQvLy86duxIfHy8a53T6SQ+Pp4uXbqU+jhOp5OCgrO/V+vAgQMcOXKE2rVrA9ClSxcyMjJYt26dq8zixYtxOp1ER0ef9Th2u53AwMASi1y6vD1tPN2vJf+992pC/e1sTc6m/+srWLUrzd1VExGRSuDWx/xjY2OZNWsWc+bMYcuWLTzwwAPk5uYyYsQIAIYNG8aECRNc5ePi4li0aBG7d+9my5YtvPrqq3zwwQfceeedAOTk5PC3v/2Nn3/+mb179xIfH88tt9xCkyZN6N27NwAtWrSgT58+jBo1ijVr1rBy5UoefvhhBg8eXOon2OTy0aVxTb4e04029YI4mlfEXe+s4b2VezT7tojIJc6tgyoGDRrE4cOHmThxIsnJybRr146FCxe6Bm4nJiZitZ7McLm5uTz44IMcOHAAHx8fmjdvzocffsigQYMAsNls/P7778yZM4eMjAzq1KnDDTfcwOTJk7Hb7a7jfPTRRzz88MP06tULq9XKwIEDmT59euVevFQbtYN8+Pi+LkyYv5HPfz3Ic19vZvWedEZ2b0jHBjU0HYCIyCXIrfMgVWeaB+nyYxgGb/+0h7jvtuA8/remUS0/BnWK5LYO9agVYD/3AURExO1K+/2tgFRGCkiXr40HMvng5718/VsSx46/w83DamFA+7o83LMJUaF+bq6hiIicjQJSBVNAkpyCYhb8doi5a/ez4fgrSqwWGNCuLg/9pQmNa/m7t4IiInIaBaQKpoAkf/Zr4lFeW7yTxVvNKSSsFritQz2e638l/po/SUSkylBAqmAKSHImGw9k8u/4Hfx4fAbuK8L9mTWsEw1qqttNRKQqqPLvYhO5FLWuF8Tbwzvx6f1dCAuwsz0lh/6vr2TFDs2fJCJSnSggiVSATlEhfD2mO+0ig8k8VsSw2at5+6fdmj9JRKSaUBdbGamLTUojv8jB37/4g0/XHQCgbrAPHRrUoEP9YDrUr0HLOoF42vT/FBGRyqIxSBVMAUlKyzAM3lu1l7jvtlJY7CyxrYavJ0/e2Jw7OkVqwkkRkUqggFTBFJDkQuUUFPPb/gzW7zvK+sSjrE/MIPNYEQDRDUN48bbWmhpARKSCKSBVMAUkuVjFDiezV+7hX4t2cKzIgZfNyoM9G/NAj8bYPWzurp6IyCVJT7GJVHEeNiujr23MD+OupUezWhQ6nEz7cQc3/vsnPfUmIuJmCkgibhYZ4su7d1/Fa0PaE+pvZ/fhXO58ZzUPfbSepMxj7q6eiMhlSV1sZaQuNqkIWflF/GvRduas2ovTAF8vGw/1bMJNbWpTP8RXA7lFRC6SxiBVMAUkqUibD2Ux8cs/+GXfUde6OkHedGkcStfGNWlYyw8/Lw98vWz4eNkI8PbQuCURkVJQQKpgCkhS0ZxOg89/PcjctYls2J9BkePsf1VtVgvXXVGL2zrUJaZFON6eCksiImeigFTBFJCkMuUVFrNu31FW7TrC6t1HOJxTQF6Bg7xCB8eKHCXKBnh7cFOb2vRvW5eromrgoYkoRURcFJAqmAKSVBVOp8HutFy++PUgn/96kIMZJwd21/D15C/Nw7nhynCubVoLHy+1LInI5U0BqYIpIElV5HQarN6Tzvz1B/hxSwpH84pc27w8rDQI8aVeDR8iQ3yJrOFLWKAdb08bPp7mWCZfLxuNa/mri05ELlkKSBVMAUmqumKHk1/2HeWHTSn8sDmZA0dLN2WA3cPKVVEhdGlck66Na1Kvhi87U3PYkZrN9pRsEtOP0S4ymHu6RRHs61XBVyEiUr4UkCqYApJUJ4ZhkJieR2J6HvvTj7H/aB770/M4mlfIsUIHx4qc5Bc5OJpXSMafWp3Oxc/LxrCuUdzbvSE1/e0VfAUiIuVDAamCKSDJpcgwDHYdzmHlziOs3JnGz7uPkJVfTL0aPjQLD6BpeAARgXbm/XKALUlZAPh42ritQ13q1vAhwNuTQG8PArw9qBvsS8NQP7w8NEhcRKoOBaQKpoAklwOH06DI4TxtTJJhGPy4JZXp8TvYeDDzrPt7WC1EhfpxRbg/kTV8ycovIi2nkCM5BRzJLcTTZqV2kPfxxRwbdcOV4QR6e1b0pYnIZUoBqYIpIImYQWnp9sP8tD2NrPwisvOLyDpWTFZ+EfuO5JFTUHzBxwz29eSB6xozvGtUiWBmGAa/H8jkpx2H8fa0Eepvp1aAuQT7euLtacPuYcXLZtWM4yJyVgpIFUwBSeTcDMMgKTOf7SnZ7EzN4VBGPsG+ntT096KmnxchfnaKHE4OZRwjKTOfpMx8Vu85wu7DuQBEBHrzSK+mtKkXxDcbk/jm9yQS0/POe16LBbw9bESG+NCidiDNIwJpUTuAJmH+1PSz4+2pACVyOVNAqmAKSCLlz+E0mL/+ANN+3FFiPqcTfDxt9GhWC0+blcPZBRzOKSAtp6DUA8vBnO6ghq8nNXy9qB/iS8s6gVxZJ4iWdQKpE+St8CRyiVNAqmAKSCIVp6DYwUc/J/L6kp3kFBTzl2Zh3NS2Nn9pHoavl8dp5Q3DoNDhpKDYfBovr8DBnrRcNidlsTU5my1JWew7knvO17WA2b3XLDzgeMtTAM1rBxIR6I2XhxUvDyueNgsOp8GmQ1n8tj+D3w9ksvFgJoHeHtx3XWP6XBmB1aqAJVKVKSBVMAUkkYrndBoUO41yeRLOMAxyCx0czTWnMkjPK2Rnag6bD2WxOSmLHSnZFDsv7p/DFrUDGRfTlOtbhmMYsCU5i2XHx2gdyS2gUag/TcL8aRruT+Na/jQM9cPPfnrgO9c1FBSfPmheREpPAamCKSCJXFoKih3sSMlhW3I2W5PNlqetydkczS08LTiFB9ppUy+YtvWCaF0vmHX7jjJ7xR7XoPQrwv1Jzy0iLafgvOcN9fciMsSX+sdnOa/pZz8+TstODT9PDh49xsaDma7WqvTcQuqH+NKmXhBt6wXTul4Qgd6eZBwzg9+Jua2aRwTSrn4w/hcQwEQuBwpIFUwBSeTy4XSaXXiFDieGE4J8T5+GICOvkP8s3817q/aSV2i+QNjXy0aXRjW59opa1K/py57DuexIzWHX8ZnJj17A2KmysFrgivAAOjaoQZMwf3w8bXgfX/zsNlrWDtQkn3LZUUCqYApIInImaTkFxG9JITLEl44NamD3OHt3WFZ+EYlHzFnN96XncSjjGEdyzXmi0nMLSc8tJNTfTuu6QbQ53lpVN9iHrclZ/H4gk98PZLDxQCaFDidBPubA82BfLzxtFjYezCzV62WuCPenS6OaXN2oJuFB3mw/3nK2NTmLXYdziazhQ7cmoXRtHEqHBsHnvB6R6kABqYIpIIlIVZeSlc/6fUdZn3iUQ5n5FBQ5yC9ycuz4a2VOTKlQWnYPK23rBRMWaKemnxc1/e2E+HnhNAyyjhWRlV9M1rEiihwGTcL8aV47gJa1AwkLsJ/x6UDDMEjOymdbsvmev2KnQVRNP6Jq+tGgpu8Fjc8SKS0FpAqmgCQi1V16biFr9hwhYdcRft6dztG8QppFBJhP8EUE0rCWHztTcli5K42VO4+UakzVmdTw9SQ80BsPmwUPq/k0YLHTYGdqDtn5Z59MNCzAbgamUF8aHA9O9Wr4UMPXixp+nvjbPVzBy+k0yC4wA1qhw4m/3QN/uwe+XjZN3SAlKCBVMAUkEbmcGIbBjtQctiRlkZZTSHpuAUdyCjmSW4iH1UKgtyeBPh6u18RsSzG76nYfzuFcDwfarBYahfpxRUQAXjYre4/ksjctt1TjszxtFoJ8PCkodpJTUMyZvs2sFvCzexDqbycswE5EkDfhgd6E+nth97DhYbPgaTNnYK8d5E2rukGlarkqcjj5csMh5q8/QG5BMU7DnMfLaRjUDvLm0ZgraBcZfMZ9d6ZmszkpmwYhvjQO8y/VQHrDMNidlkthsZPQ4y13Nk0pUSbVJiDNmDGDV155heTkZNq2bctrr71G586dz1h2/vz5vPjii+zcuZOioiKaNm3KY489xl133QVAUVERf//73/n222/ZvXs3QUFBxMTEMGXKFOrUqeM6TlRUFPv27Stx7Li4OJ588slS11sBSUTk/PKLzKcDM46ZTwMWOwwcTicAUaF+NAz1O+O4psy8IvYcyWXfkVz2puWx70gue47kkpyZz9G8QvKLnGc8n7enFU+blbxCB44yTNtgtUCTMH/XU4ot6wTSLCLQFWIKih18uu4Aby7ddd4xXre2r8vjfZpRO8gHgPWJR3ljyS5+3JJSolztIG+ahPm7WsgiQ3yJrOGL3dPKmj3p/LzbbOH7cwue1QIhfmboax4RwJV1g2hd16zvuQLX4ewCNidlkZ1fRC1/O2GB3oQF2C+r7sxqEZDmzZvHsGHDmDlzJtHR0UybNo1PPvmEbdu2ERYWdlr5pUuXcvToUZo3b46XlxcLFizgscce45tvvqF3795kZmZy++23M2rUKNq2bcvRo0d59NFHcTgc/PLLL67jREVFMXLkSEaNGuVaFxAQgJ+fX6nrroAkIuI+xwrNcVSZx4qwe1gJ9PEkwNvDFbYMwyC/yEl2gfl+wLScAlKy8knNKiA5K58jOQUUOcyXMRcdn2R0T1ouSZn5ZzxfVE1fmkcE8tuBDFeZUH8vRnRrSLPwAGxWC1arBQvwxYaDzF9/EDAD27AuUfx+IIOfd6cD5utwWtcN4lBG/gV1W9o9rPjZPTiaV3jG1rITx44I9CbU35wuItTfjr/dg91puWxJyuJw9pnP5+dlo0vjmtzSri4xLcLx8ToZWo8VOvh59xFW7kyj2GkQ4O3hajH08fJwzVdW7HBS5DTHox3ONme5T8spILfAQdMwf9rUC6JNZDAtawe6dS6vahGQoqOjueqqq3j99dcBcDqdREZGMmbMmFK35nTo0IF+/foxefLkM25fu3YtnTt3Zt++fdSvXx8wA9LYsWMZO3ZsmeuugCQiculJzc7n9/3mE4K/H8xkS1IWKVklQ0V4oJ37rm3MkM71SwSJP/v9QAaTF2xm7d6jrnWeNgu3tq/L6Gsb0yTMHzBbynYeNt9XmJiex/70Y+w/av6aU1BEu8hgujQK5epGIbSrbz5FWOxwkp5byOGcAg5l5LP5UBZ/HMpk08FMDp0l4J1gsUDDmn7U9PficHYBqdkFrmkpTvDzstH7ygia1w5gxc4j/Lz7CIXFZ26xKwsPq4UmYf5cER7AFeH+NA0PoGGoHzkFxa46HT6+PN2vRbnP5VXlA1JhYSG+vr58+umnDBgwwLV++PDhZGRk8OWXX55zf8MwWLx4Mf379+eLL77g+uuvP2O5H3/8kRtuuIGMjAzXjYiKiiI/P5+ioiLq16/PX//6V8aNG4eHx9l/EwoKCigoOPmXJCsri8jISAUkEZFL3JGcArYkmVMf1PD14qa2tUs13YFhGHz3RzLvrtxD67rB3HtNQ+oE+1R4XQ8cPcaR3ALSsgtJyy0g81gR9UN8Xa/QOfV1PTkFxew7kst3G5P5YsPBM3Yd1g324doralHTz4us/CKyjz+xmFtYjIfVenwAvgWb1UKAtyeh/nZC/b2oFWDH7mFjS1KWGToPZHIkt7DU1xP/2HU0ruV/0fflz0obkNzW6ZiWlobD4SA8PLzE+vDwcLZu3XrW/TIzM6lbty4FBQXYbDbeeOONs4aj/Px8nnjiCYYMGVLiJjzyyCN06NCBkJAQVq1axYQJE0hKSmLq1KlnPW9cXBzPP//8BV6liIhUdzX97XRvaqd709AL2s9isdC3dW36tq5dQTU7XU1/+wVP/ulv9+DKOkFcWSeIx264gvWJR/lywyEOZRwjumFNejSrRZMw/4t6GrBPqwjADI0HM46xNSmb7anZ7EzJYXtqNvuO5BHo7UmtADu1AsyxVbUC7AS4cWyU21qQDh06RN26dVm1ahVdunRxrX/88cdZtmwZq1evPuN+TqeT3bt3k5OTQ3x8PJMnT+aLL76gR48eJcoVFRUxcOBADhw4wNKlS8+ZEmfPns19991HTk4OdvuZ/2CpBUlERKT6q/ItSKGhodhsNlJSSo7mT0lJISIi4qz7Wa1WmjRpAkC7du3YsmULcXFxJQJSUVERd9xxB/v27WPx4sXnDTDR0dEUFxezd+9emjVrdsYydrv9rOFJRERELi0X/4rsMvLy8qJjx47Ex8e71jmdTuLj40u0KJ2P0+ks0bJzIhzt2LGDH3/8kZo1a573GBs2bMBqtZ7xyTkRERG5/Lh14oPY2FiGDx9Op06d6Ny5M9OmTSM3N5cRI0YAMGzYMOrWrUtcXBxgjgPq1KkTjRs3pqCggG+//ZYPPviAN998EzDD0e2338769etZsGABDoeD5ORkAEJCQvDy8iIhIYHVq1fTs2dPAgICSEhIYNy4cdx5553UqFHDPTdCREREqhS3BqRBgwZx+PBhJk6cSHJyMu3atWPhwoWugduJiYlYrScbuXJzc3nwwQc5cOAAPj4+NG/enA8//JBBgwYBcPDgQb766ivA7H77syVLltCjRw/sdjtz587lueeeo6CggIYNGzJu3DhiY2Mr56JFRESkynP7TNrVleZBEhERqX5K+/3ttjFIIiIiIlWVApKIiIjIKRSQRERERE6hgCQiIiJyCgUkERERkVMoIImIiIicQgFJRERE5BQKSCIiIiKnUEASEREROYVbXzVSnZ2YgDwrK8vNNREREZHSOvG9fb4XiSgglVF2djYAkZGRbq6JiIiIXKjs7GyCgoLOul3vYisjp9PJoUOHCAgIwGKxlNtxs7KyiIyMZP/+/XrHWwXTva48uteVR/e6cul+V57yuteGYZCdnU2dOnWwWs8+0kgtSGVktVqpV69ehR0/MDBQf9kqie515dG9rjy615VL97vylMe9PlfL0QkapC0iIiJyCgUkERERkVMoIFUxdrudZ599Frvd7u6qXPJ0ryuP7nXl0b2uXLrflaey77UGaYuIiIicQi1IIiIiIqdQQBIRERE5hQKSiIiIyCkUkEREREROoYBUxcyYMYOoqCi8vb2Jjo5mzZo17q5StRcXF8dVV11FQEAAYWFhDBgwgG3btpUok5+fz0MPPUTNmjXx9/dn4MCBpKSkuKnGl4YpU6ZgsVgYO3asa53uc/k6ePAgd955JzVr1sTHx4fWrVvzyy+/uLYbhsHEiROpXbs2Pj4+xMTEsGPHDjfWuHpyOBw888wzNGzYEB8fHxo3bszkyZNLvMtL97psli9fzs0330ydOnWwWCx88cUXJbaX5r6mp6czdOhQAgMDCQ4OZuTIkeTk5Fx03RSQqpB58+YRGxvLs88+y/r162nbti29e/cmNTXV3VWr1pYtW8ZDDz3Ezz//zKJFiygqKuKGG24gNzfXVWbcuHF8/fXXfPLJJyxbtoxDhw5x2223ubHW1dvatWt56623aNOmTYn1us/l5+jRo3Tr1g1PT0++++47Nm/ezKuvvkqNGjVcZV5++WWmT5/OzJkzWb16NX5+fvTu3Zv8/Hw31rz6eemll3jzzTd5/fXX2bJlCy+99BIvv/wyr732mquM7nXZ5Obm0rZtW2bMmHHG7aW5r0OHDmXTpk0sWrSIBQsWsHz5ckaPHn3xlTOkyujcubPx0EMPuX52OBxGnTp1jLi4ODfW6tKTmppqAMayZcsMwzCMjIwMw9PT0/jkk09cZbZs2WIARkJCgruqWW1lZ2cbTZs2NRYtWmRcd911xqOPPmoYhu5zeXviiSeM7t27n3W70+k0IiIijFdeecW1LiMjw7Db7cb//ve/yqjiJaNfv37GPffcU2LdbbfdZgwdOtQwDN3r8gIYn3/+uevn0tzXzZs3G4Cxdu1aV5nvvvvOsFgsxsGDBy+qPmpBqiIKCwtZt24dMTExrnVWq5WYmBgSEhLcWLNLT2ZmJgAhISEArFu3jqKiohL3vnnz5tSvX1/3vgweeugh+vXrV+J+gu5zefvqq6/o1KkT//d//0dYWBjt27dn1qxZru179uwhOTm5xP0OCgoiOjpa9/sCde3alfj4eLZv3w7Ab7/9xooVK7jxxhsB3euKUpr7mpCQQHBwMJ06dXKViYmJwWq1snr16os6v15WW0WkpaXhcDgIDw8vsT48PJytW7e6qVaXHqfTydixY+nWrRutWrUCIDk5GS8vL4KDg0uUDQ8PJzk52Q21rL7mzp3L+vXrWbt27WnbdJ/L1+7du3nzzTeJjY3lqaeeYu3atTzyyCN4eXkxfPhw1z09078put8X5sknnyQrK4vmzZtjs9lwOBz84x//YOjQoQC61xWkNPc1OTmZsLCwEts9PDwICQm56HuvgCSXlYceeog//viDFStWuLsql5z9+/fz6KOPsmjRIry9vd1dnUue0+mkU6dOvPjiiwC0b9+eP/74g5kzZzJ8+HA31+7S8vHHH/PRRx/x3//+lyuvvJINGzYwduxY6tSpo3t9CVMXWxURGhqKzWY77YmelJQUIiIi3FSrS8vDDz/MggULWLJkCfXq1XOtj4iIoLCwkIyMjBLlde8vzLp160hNTaVDhw54eHjg4eHBsmXLmD59Oh4eHoSHh+s+l6PatWvTsmXLEutatGhBYmIigOue6t+Ui/e3v/2NJ598ksGDB9O6dWvuuusuxo0bR1xcHKB7XVFKc18jIiJOe5CpuLiY9PT0i773CkhVhJeXFx07diQ+Pt61zul0Eh8fT5cuXdxYs+rPMAwefvhhPv/8cxYvXkzDhg1LbO/YsSOenp4l7v22bdtITEzUvb8AvXr1YuPGjWzYsMG1dOrUiaFDh7o+6z6Xn27dup02XcX27dtp0KABAA0bNiQiIqLE/c7KymL16tW63xcoLy8Pq7Xk16XNZsPpdAK61xWlNPe1S5cuZGRksG7dOleZxYsX43Q6iY6OvrgKXNQQbylXc+fONex2u/Hee+8ZmzdvNkaPHm0EBwcbycnJ7q5atfbAAw8YQUFBxtKlS42kpCTXkpeX5ypz//33G/Xr1zcWL15s/PLLL0aXLl2MLl26uLHWl4Y/P8VmGLrP5WnNmjWGh4eH8Y9//MPYsWOH8dFHHxm+vr7Ghx9+6CozZcoUIzg42Pjyyy+N33//3bjllluMhg0bGseOHXNjzauf4cOHG3Xr1jUWLFhg7Nmzx5g/f74RGhpqPP74464yutdlk52dbfz666/Gr7/+agDG1KlTjV9//dXYt2+fYRilu699+vQx2rdvb6xevdpYsWKF0bRpU2PIkCEXXTcFpCrmtddeM+rXr294eXkZnTt3Nn7++Wd3V6naA864vPvuu64yx44dMx588EGjRo0ahq+vr3HrrbcaSUlJ7qv0JeLUgKT7XL6+/vpro1WrVobdbjeaN29u/Oc//ymx3el0Gs8884wRHh5u2O12o1evXsa2bdvcVNvqKysry3j00UeN+vXrG97e3kajRo2Mp59+2igoKHCV0b0umyVLlpzx3+fhw4cbhlG6+3rkyBFjyJAhhr+/vxEYGGiMGDHCyM7Ovui6WQzjT1OBioiIiIjGIImIiIicSgFJRERE5BQKSCIiIiKnUEASEREROYUCkoiIiMgpFJBERERETqGAJCIiInIKBSQRkXKwdOlSLBbLae+aE5HqSQFJRERE5BQKSCIiIiKnUEASkUuC0+kkLi6Ohg0b4uPjQ9u2bfn000+Bk91f33zzDW3atMHb25urr76aP/74o8QxPvvsM6688krsdjtRUVG8+uqrJbYXFBTwxBNPEBkZid1up0mTJrzzzjslyqxbt45OnTrh6+tL165d2bZtW8VeuIhUCAUkEbkkxMXF8f777zNz5kw2bdrEuHHjuPPOO1m2bJmrzN/+9jdeffVV1q5dS61atbj55pspKioCzGBzxx13MHjwYDZu3Mhzzz3HM888w3vvvefaf9iwYfzvf/9j+vTpbNmyhbfeegt/f/8S9Xj66ad59dVX+eWXX/Dw8OCee+6plOsXkfKll9WKSLVXUFBASEgIP/74I126dHGtv/fee8nLy2P06NH07NmTuXPnMmjQIADS09OpV68e7733HnfccQdDhw7l8OHD/PDDD679H3/8cb755hs2bdrE9u3badasGYsWLSImJua0OixdupSePXvy448/0qtXLwC+/fZb+vXrx7Fjx/D29q7guyAi5UktSCJS7e3cuZO8vDyuv/56/P39Xcv777/Prl27XOX+HJ5CQkJo1qwZW7ZsAWDLli1069atxHG7devGjh07cDgcbNiwAZvNxnXXXXfOurRp08b1uXbt2gCkpqZe9DWKSOXycHcFREQuVk5ODgDffPMNdevWLbHNbreXCEll5ePjU6pynp6ers8WiwUwx0eJSPWiFiQRqfZatmyJ3W4nMTGRJk2alFgiIyNd5X7++WfX56NHj7J9+3ZatGgBQIsWLVi5cmWJ465cuZIrrrgCm81G69atcTqdJcY0icilSy1IIlLtBQQEMH78eMaNG4fT6aR79+5kZmaycuVKAgMDadCgAQCTJk2iZs2ahIeH8/TTTxMaGsqAAQMAeOyxx7jqqquYPHkygwYNIiEhgddff5033ngDgKioKIYPH84999zD9OnTadu2Lfv27SM1NZU77rjDXZcuIhVEAUlELgmTJ0+mVq1axMXFsXv3boKDg+nQoQNPPfWUq4trypQpPProo+zYsYN27drx9ddf4+XlBUCHDh34+OOPmThxIpMnT6Z27dpMmjSJu+++23WON998k6eeeooHH3yQI0eOUL9+fZ566il3XK6IVDA9xSYil7wTT5gdPXqU4OBgd1dHRKoBjUESEREROYUCkoiIiMgp1MUmIiIicgq1IImIiIicQgFJRERE5BQKSCIiIiKnUEASEREROYUCkoiIiMgpFJBERERETqGAJCIiInIKBSQRERGRUyggiYiIiJzi/wF0Xz2z1tQvAAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}