Train_ANN_with_calling_simple_nn_Module.ipynb

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        "id": "Q-DfthG1_8qS"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Import the dataset\n",
        "\"\"\"\n",
        "df = pd.read_csv('/content/diabetes.csv')\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
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              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
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          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Check NaN value appeared or not\n",
        "\"\"\"\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zUsAl2oAAPtn",
        "outputId": "4bed111b-ca7e-4977-a7cf-64097fd37a37"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "Outcome                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Split the dataset into train and test\n",
        "\"\"\"\n",
        "import torch\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, -1].values\n",
        "\n",
        "\"\"\"\n",
        "Should be convert this X and y into Float, Otherwise it will show ERROR\n",
        "\"\"\"\n",
        "X = torch.tensor(X, dtype = torch.float32)\n",
        "y = torch.tensor(y, dtype = torch.float32)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\\\n",
        "                                                    test_size = 0.20,\\\n",
        "                                                    random_state = 42,\\\n",
        "                                                    shuffle = True)\n",
        "\n",
        "print('X_train shape is # {}'.format(X_train.shape),'\\n')\n",
        "print('X_test  shape is # {}'.format(X_test.shape),'\\n')\n",
        "print('y_train shape is # {}'.format(y_train.shape),'\\n')\n",
        "print('y_test  shape is # {}'.format(y_test.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g4Y2BIllAVwh",
        "outputId": "1dd78b43-ec3e-4972-c856-cdf97ed52e23"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape is # torch.Size([614, 8]) \n",
            "\n",
            "X_test  shape is # torch.Size([154, 8]) \n",
            "\n",
            "y_train shape is # torch.Size([614]) \n",
            "\n",
            "y_test  shape is # torch.Size([154])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[0:2]"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mHJ55eooAmOL",
        "outputId": "981ade1a-1f2e-4f6e-dd2d-9eed5f6d893c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[  2.0000,  84.0000,   0.0000,   0.0000,   0.0000,   0.0000,   0.3040,\n",
              "          21.0000],\n",
              "        [  9.0000, 112.0000,  82.0000,  24.0000,   0.0000,  28.2000,   1.2820,\n",
              "          50.0000]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Import the DataLoader function so that we can initialize the batch_size\n",
        "\"\"\"\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "data_loader = DataLoader(dataset = list(zip(X_train, y_train)),\\\n",
        "                         batch_size = 16,\\\n",
        "                         shuffle = True)\n",
        "data, labels = next(iter(data_loader))\n",
        "\n",
        "print(\"The shape of singular batch size of training # {}\".format(data.size()))\n",
        "print(\"The shape of singular batch size of testing  # {}\".format(labels.size()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2uTnyHaXA9tc",
        "outputId": "9d59a77a-4fe9-4b5b-de6e-17fc43fd3eb8"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The shape of singular batch size of training # torch.Size([16, 8])\n",
            "The shape of singular batch size of testing  # torch.Size([16])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Creating the ANN architecture"
      ],
      "metadata": {
        "id": "gJ8_x614Bvxd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Import the necessity liabries.\n",
        "\"\"\"\n",
        "import torch.nn as nn\n",
        "\n",
        "\"\"\"\n",
        "Creating a class that will inherit the parent class named `nn.Module`.\n",
        "\"\"\"\n",
        "class DiabeticsClassifier(nn.Module):\n",
        "  \"\"\"\n",
        "  Create a constructors that might take some of the parameters. Here, I did not pass anything.\n",
        "  \"\"\"\n",
        "  def __init__(self):\n",
        "    \"\"\"\n",
        "    Call the super() functions that has some properties\n",
        "    \"\"\"\n",
        "    super().__init__()\n",
        "    \"\"\"\n",
        "    Create the first hidden layer with its activation functions\n",
        "    \"\"\"\n",
        "    self.hidden_1 = nn.Linear(in_features = 8,\\\n",
        "                              out_features = 64,\\\n",
        "                              bias = True)\n",
        "    self.activation_1 = nn.ReLU()\n",
        "\n",
        "    \"\"\"\n",
        "    Create the second hidden hidden layer with its activation function\n",
        "    \"\"\"\n",
        "    self.hidden_2 = nn.Linear(in_features = 64,\\\n",
        "                              out_features = 32,\\\n",
        "                              bias = True)\n",
        "    self.activation_2 = nn.ReLU()\n",
        "\n",
        "    \"\"\"\n",
        "    Create the output layer with its activation function\n",
        "    \"\"\"\n",
        "    self.output = nn.Linear(in_features = 32,\\\n",
        "                            out_features = 1,\\\n",
        "                            bias = True)\n",
        "    self.activation_output = nn.Sigmoid()\n",
        "\n",
        "  def forward(self, x):\n",
        "    \"\"\"\n",
        "    Formula of forward propagation `activation(W^T + B)`; `x` means training dataset\n",
        "    \"\"\"\n",
        "    x = self.activation_1(self.hidden_1(x))\n",
        "    x = self.activation_2(self.hidden_2(x))\n",
        "    x = self.activation_output(self.output(x))\n",
        "\n",
        "    return x\n",
        "\n",
        "\"\"\"\n",
        "Create an object of this class\n",
        "\"\"\"\n",
        "\n",
        "model = DiabeticsClassifier()\n",
        "\n",
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4LkEBjgrBsWO",
        "outputId": "a3d55015-cbf1-4aa5-f00d-1fbb17f974ee"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DiabeticsClassifier(\n",
            "  (hidden_1): Linear(in_features=8, out_features=64, bias=True)\n",
            "  (activation_1): ReLU()\n",
            "  (hidden_2): Linear(in_features=64, out_features=32, bias=True)\n",
            "  (activation_2): ReLU()\n",
            "  (output): Linear(in_features=32, out_features=1, bias=True)\n",
            "  (activation_output): Sigmoid()\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PLGBNldSFUW0",
        "outputId": "533dab18-c54e-4bd5-a37e-132bc122f202"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<bound method Module.parameters of DiabeticsClassifier(\n",
            "  (hidden_1): Linear(in_features=8, out_features=64, bias=True)\n",
            "  (activation_1): ReLU()\n",
            "  (hidden_2): Linear(in_features=64, out_features=32, bias=True)\n",
            "  (activation_2): ReLU()\n",
            "  (output): Linear(in_features=32, out_features=1, bias=True)\n",
            "  (activation_output): Sigmoid()\n",
            ")>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.parameters())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8GZ6uP2FQ6B",
        "outputId": "ab779549-1108-4f24-d773-b2393a1671a3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<generator object Module.parameters at 0x7be243a73d10>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchsummary import summary"
      ],
      "metadata": {
        "id": "EzpkgPKKHrqM"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Create the model summary. This will take two parameters(1. Model 2. shape of training/input data)\n",
        "\"\"\"\n",
        "summary(model, X_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qBHHscDxHv0z",
        "outputId": "58c84ba0-ac37-4b6f-a670-10ecf52806ae"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Linear-1              [-1, 614, 64]             576\n",
            "              ReLU-2              [-1, 614, 64]               0\n",
            "            Linear-3              [-1, 614, 32]           2,080\n",
            "              ReLU-4              [-1, 614, 32]               0\n",
            "            Linear-5               [-1, 614, 1]              33\n",
            "           Sigmoid-6               [-1, 614, 1]               0\n",
            "================================================================\n",
            "Total params: 2,689\n",
            "Trainable params: 2,689\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.02\n",
            "Forward/backward pass size (MB): 0.91\n",
            "Params size (MB): 0.01\n",
            "Estimated Total Size (MB): 0.94\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training the model"
      ],
      "metadata": {
        "id": "cH590rPYIy0k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Import the liabries\n",
        "\"\"\"\n",
        "\n",
        "import torch.optim as optim\n",
        "\n",
        "\"\"\"\n",
        "Define the loss function - I am solving `Binary Class Classification`\n",
        "\"\"\"\n",
        "\n",
        "loss_function = nn.BCELoss()\n",
        "\"\"\"\n",
        "Define the optimizer - In this case; Adam() optimizer\n",
        "\"\"\"\n",
        "optimizer = optim.Adam(model.parameters(), lr = 0.01)"
      ],
      "metadata": {
        "id": "MqjpH6AkI04p"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"Initialize the number of EPOCH you want to\"\"\"\n",
        "EPOCHS = 100\n",
        "LOSS = []\n",
        "model.train()\n",
        "for iteration, epoch in enumerate(range(EPOCHS)):\n",
        "  \"\"\"Call the data_loader so that we can pass the X_train data to the model with batch_size\"\"\"\n",
        "  for X_batch, y_batch in data_loader:\n",
        "    \"\"\"Pass the X_batch to the model\"\"\"\n",
        "    prediction = model(X_batch)\n",
        "    \"\"\"Calculate the loss function\"\"\"\n",
        "    loss = loss_function(prediction, y_batch.reshape(-1, 1))\n",
        "    \"\"\"Append this loss function\"\"\"\n",
        "    LOSS.append(loss)\n",
        "    \"\"\"Initialize the Gradient\"\"\"\n",
        "    optimizer.zero_grad()\n",
        "    \"\"\"After calculating the loss, we should be done the Backward propagation\"\"\"\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "  \"\"\"Display the loss function\"\"\"\n",
        "  print(\"For Epoch # {} :: The loss function of this model is ## {}\".format(epoch, loss))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4_p9-uTKI1EG",
        "outputId": "b2551517-e525-4f46-964d-617a48675de2"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For Epoch # 0 :: The loss function of this model is ## 0.687446653842926\n",
            "For Epoch # 1 :: The loss function of this model is ## 0.9370222091674805\n",
            "For Epoch # 2 :: The loss function of this model is ## 0.3934587240219116\n",
            "For Epoch # 3 :: The loss function of this model is ## 0.6038882732391357\n",
            "For Epoch # 4 :: The loss function of this model is ## 0.992443859577179\n",
            "For Epoch # 5 :: The loss function of this model is ## 0.6026235818862915\n",
            "For Epoch # 6 :: The loss function of this model is ## 0.6145830750465393\n",
            "For Epoch # 7 :: The loss function of this model is ## 0.6781362891197205\n",
            "For Epoch # 8 :: The loss function of this model is ## 0.40171611309051514\n",
            "For Epoch # 9 :: The loss function of this model is ## 0.6775991320610046\n",
            "For Epoch # 10 :: The loss function of this model is ## 0.528370201587677\n",
            "For Epoch # 11 :: The loss function of this model is ## 0.8693141937255859\n",
            "For Epoch # 12 :: The loss function of this model is ## 0.3977154493331909\n",
            "For Epoch # 13 :: The loss function of this model is ## 0.3425613343715668\n",
            "For Epoch # 14 :: The loss function of this model is ## 0.4184350073337555\n",
            "For Epoch # 15 :: The loss function of this model is ## 0.416021466255188\n",
            "For Epoch # 16 :: The loss function of this model is ## 0.4360102713108063\n",
            "For Epoch # 17 :: The loss function of this model is ## 0.6634065508842468\n",
            "For Epoch # 18 :: The loss function of this model is ## 0.8099863529205322\n",
            "For Epoch # 19 :: The loss function of this model is ## 0.4905428886413574\n",
            "For Epoch # 20 :: The loss function of this model is ## 0.4672985374927521\n",
            "For Epoch # 21 :: The loss function of this model is ## 0.29071953892707825\n",
            "For Epoch # 22 :: The loss function of this model is ## 0.8066837191581726\n",
            "For Epoch # 23 :: The loss function of this model is ## 0.17439568042755127\n",
            "For Epoch # 24 :: The loss function of this model is ## 0.4125474989414215\n",
            "For Epoch # 25 :: The loss function of this model is ## 0.518200695514679\n",
            "For Epoch # 26 :: The loss function of this model is ## 0.8561646938323975\n",
            "For Epoch # 27 :: The loss function of this model is ## 0.36037537455558777\n",
            "For Epoch # 28 :: The loss function of this model is ## 0.4733641445636749\n",
            "For Epoch # 29 :: The loss function of this model is ## 0.6186003088951111\n",
            "For Epoch # 30 :: The loss function of this model is ## 0.33387458324432373\n",
            "For Epoch # 31 :: The loss function of this model is ## 0.5287619829177856\n",
            "For Epoch # 32 :: The loss function of this model is ## 0.7214396595954895\n",
            "For Epoch # 33 :: The loss function of this model is ## 0.6407549381256104\n",
            "For Epoch # 34 :: The loss function of this model is ## 0.6096802353858948\n",
            "For Epoch # 35 :: The loss function of this model is ## 1.0143494606018066\n",
            "For Epoch # 36 :: The loss function of this model is ## 0.8714232444763184\n",
            "For Epoch # 37 :: The loss function of this model is ## 0.6489116549491882\n",
            "For Epoch # 38 :: The loss function of this model is ## 0.6645459532737732\n",
            "For Epoch # 39 :: The loss function of this model is ## 0.9154350757598877\n",
            "For Epoch # 40 :: The loss function of this model is ## 0.3314657509326935\n",
            "For Epoch # 41 :: The loss function of this model is ## 0.5095146298408508\n",
            "For Epoch # 42 :: The loss function of this model is ## 0.5845025181770325\n",
            "For Epoch # 43 :: The loss function of this model is ## 0.23343008756637573\n",
            "For Epoch # 44 :: The loss function of this model is ## 0.6331292986869812\n",
            "For Epoch # 45 :: The loss function of this model is ## 0.6442390084266663\n",
            "For Epoch # 46 :: The loss function of this model is ## 0.3903341591358185\n",
            "For Epoch # 47 :: The loss function of this model is ## 0.7975584864616394\n",
            "For Epoch # 48 :: The loss function of this model is ## 0.7166334986686707\n",
            "For Epoch # 49 :: The loss function of this model is ## 0.41228851675987244\n",
            "For Epoch # 50 :: The loss function of this model is ## 0.25086450576782227\n",
            "For Epoch # 51 :: The loss function of this model is ## 0.5105929374694824\n",
            "For Epoch # 52 :: The loss function of this model is ## 0.518815279006958\n",
            "For Epoch # 53 :: The loss function of this model is ## 0.4736713469028473\n",
            "For Epoch # 54 :: The loss function of this model is ## 0.5850301384925842\n",
            "For Epoch # 55 :: The loss function of this model is ## 0.504547655582428\n",
            "For Epoch # 56 :: The loss function of this model is ## 0.6420188546180725\n",
            "For Epoch # 57 :: The loss function of this model is ## 0.48032817244529724\n",
            "For Epoch # 58 :: The loss function of this model is ## 0.3495785892009735\n",
            "For Epoch # 59 :: The loss function of this model is ## 0.5391223430633545\n",
            "For Epoch # 60 :: The loss function of this model is ## 0.6322569251060486\n",
            "For Epoch # 61 :: The loss function of this model is ## 0.6447650194168091\n",
            "For Epoch # 62 :: The loss function of this model is ## 0.3998950719833374\n",
            "For Epoch # 63 :: The loss function of this model is ## 0.30708909034729004\n",
            "For Epoch # 64 :: The loss function of this model is ## 0.30962762236595154\n",
            "For Epoch # 65 :: The loss function of this model is ## 0.6263981461524963\n",
            "For Epoch # 66 :: The loss function of this model is ## 0.45030179619789124\n",
            "For Epoch # 67 :: The loss function of this model is ## 0.6372808814048767\n",
            "For Epoch # 68 :: The loss function of this model is ## 0.39575257897377014\n",
            "For Epoch # 69 :: The loss function of this model is ## 0.27929237484931946\n",
            "For Epoch # 70 :: The loss function of this model is ## 0.6480767130851746\n",
            "For Epoch # 71 :: The loss function of this model is ## 0.3543485403060913\n",
            "For Epoch # 72 :: The loss function of this model is ## 0.5107157230377197\n",
            "For Epoch # 73 :: The loss function of this model is ## 0.9685423970222473\n",
            "For Epoch # 74 :: The loss function of this model is ## 0.6567190885543823\n",
            "For Epoch # 75 :: The loss function of this model is ## 0.6914710402488708\n",
            "For Epoch # 76 :: The loss function of this model is ## 0.6215217709541321\n",
            "For Epoch # 77 :: The loss function of this model is ## 0.7623984813690186\n",
            "For Epoch # 78 :: The loss function of this model is ## 0.3395511209964752\n",
            "For Epoch # 79 :: The loss function of this model is ## 0.29227951169013977\n",
            "For Epoch # 80 :: The loss function of this model is ## 0.2625497281551361\n",
            "For Epoch # 81 :: The loss function of this model is ## 0.5468212962150574\n",
            "For Epoch # 82 :: The loss function of this model is ## 0.4776216447353363\n",
            "For Epoch # 83 :: The loss function of this model is ## 0.42183542251586914\n",
            "For Epoch # 84 :: The loss function of this model is ## 0.35242095589637756\n",
            "For Epoch # 85 :: The loss function of this model is ## 0.6163643598556519\n",
            "For Epoch # 86 :: The loss function of this model is ## 0.2967489957809448\n",
            "For Epoch # 87 :: The loss function of this model is ## 0.45832595229148865\n",
            "For Epoch # 88 :: The loss function of this model is ## 0.3933999836444855\n",
            "For Epoch # 89 :: The loss function of this model is ## 0.31288179755210876\n",
            "For Epoch # 90 :: The loss function of this model is ## 0.45341941714286804\n",
            "For Epoch # 91 :: The loss function of this model is ## 1.0126259326934814\n",
            "For Epoch # 92 :: The loss function of this model is ## 0.5465232729911804\n",
            "For Epoch # 93 :: The loss function of this model is ## 0.5656790137290955\n",
            "For Epoch # 94 :: The loss function of this model is ## 0.6311011910438538\n",
            "For Epoch # 95 :: The loss function of this model is ## 1.183800458908081\n",
            "For Epoch # 96 :: The loss function of this model is ## 0.5310785174369812\n",
            "For Epoch # 97 :: The loss function of this model is ## 0.4138281047344208\n",
            "For Epoch # 98 :: The loss function of this model is ## 0.2834794223308563\n",
            "For Epoch # 99 :: The loss function of this model is ## 0.33642593026161194\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "with torch.no_grad():\n",
        "  plt.plot(np.array(LOSS))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "SysPaMp0OI_o",
        "outputId": "d9c77f40-f69e-444b-f2fc-da14649e5e3c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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FCyQkJAAAnnvuOQwaNAgfffQRxowZg6VLl2Lv3r34+uuvHZwV+6hUKlcngYiIiAzIKlFZsGAB8vPzMXjwYISFhWlfP//8s3aZjIwMZGVlaf/u168flixZgq+//hqxsbH47bffsHz5crMNcF3BjXEKERGR4sgqUbFmyJXk5GSj9+6//37cf//9cnblAoxUiIiIlIZz/dzCmh8iIiLlYaByC+MUIiIi5WGgcgtLVIiIiJSHgcotboxUiIiIFIeByi0MU4iIiJSHgcotHEeFiIhIeRioEBERkWIxUCEiIiLFYqByC2t+iIiIlIeByi0MVIiIiJSHgQoREREpFgMVIiIiUiwGKreoOJIKERGR4jBQuYVtVIiIiJSHgQoREREpFgMVIiIiUiwGKgqh0QhXJ4GIiEhxGKjc4somKuuOZKPLW+uw/ki2C1NBRESkPAxUbnHlpIRPfb8PN8sq8eT3+1yWBiIiIiVioEJERESKxUCFiIiIFIuByi0cRoWIiEh5GKhUY6RCRESkOAxUbmGcQkREpDwMVIiIiEixGKgQERGRYjFQucWV46gQERGRNAYqtzBMISIiUh4GKkRERKRYDFSIiIhIsRio3HIyt9DVSSAiIiIDDFSIiIhIsRioEBERkWIxUCEiIiLFYqBCREREisVAhYiIiBSLgQoREREpluxAZevWrRg7dizCw8OhUqmwfPlys8snJydDpVIZvbKzs21NMxERETUQsgOVoqIixMbGYv78+bLWS09PR1ZWlvYVHBwsd9dERETUwHjIXWH06NEYPXq07B0FBwcjMDBQ9npERETUcNVaG5W4uDiEhYVh+PDh2LFjh9llS0tLUVBQoPciIiKihsfpgUpYWBgWLlyI33//Hb///jsiIiIwePBg7N+/3+Q6CQkJCAgI0L4iIiKcnUwiIiJSINlVP3JFRUUhKipK+3e/fv1w+vRpfPLJJ/j+++8l15k1axZefPFF7d8FBQUMVoiIiBogpwcqUnr37o3t27eb/Nzb2xve3t61mCIiIiJSIpeMo5KamoqwsDBX7JqIiIjqENklKoWFhTh16pT277NnzyI1NRVNmzZFq1atMGvWLFy8eBHfffcdAODTTz9F27Zt0blzZ5SUlOCbb77Bpk2bsH79esflgoiIiOol2YHK3r17MWTIEO3f1W1JJk+ejMTERGRlZSEjI0P7eVlZGV566SVcvHgRfn5+iImJwYYNG/S2QURERCRFJYQQrk6EJQUFBQgICEB+fj7UarXDtjv1u71IOpqj/fvcnDEO27YcbWaucnkaiIiIHM0R9+8GPdfP8E4hrk4CERERmdGgAxUBxRcmERERNWgNOlAhIiIiZWOgQkRERIrVoAMV5TcjJiIiatgadKBCREREytagAxUWqBARESlbgw5UiIiISNkadKAS0zLA1UkgIiIiMxp0oNI5PAAtAn1dnQwiIiIyoUEHKgDQq00TVyeBiIiITGjwgQoREREpV4MPVFQqlauTQERERCY0+EBl6oB22v+fyr3hwpQQERGRoQYfqAQ19tL+//TlIhemhIiIiAw1+EBFhZqqHzdWAxERESlKgw9U3FTS/5eyOT0XK1IvOjdBREREpOXh6gS4mm5jWksFKo8t/gsA0L1VE0Q09XNmsoiIiAgsUYEtlT1Xi8ocng4iIiIy1uADFd12KSqbwhYiIiJylgYfqOjFJlbGKUI0vHmXb5ZV4I/9F5B3k6VJRERUexio6MQcLE8x7fXlaXjxl4N49FY7HSIiotrQ4AMVoROpPLr4L2Tnl7gwNcr1Z+olAEBqZp5rE0JERACA/RnXG0RPVAYqBrU4HyeluyYhREREMtz75U48tzQVB+v5A2SDD1S8PfW/ggqN5fYnDa+FChERKdW5q/V7VPUGH6j4eTX4oWSIiIgUq8EHKgAQFuBT84cVxSUNsNMPkeJprCgNJaK6h4EK2NuHqK57fukBDP4wGcVlla5OChE5GAMV6A+jb90zGZ/ciJRkeeolZFy7iY3Hc1ydFCJyMAYqVqovg7zVl3wQUf32054MrDuS7epkkAIwULFBXb3Xl5RXYsQnWzHrj0OuTgqRU9TV3ybpO3elCLP+OIynvt/n6qSQAjBQgeVZkw3V1WvhuiPZOJlbiJ/2ZLo6KUREJl0tKnV1EkhBGKjAukClPjyp1Yc8EJnDU7x+4LWKdDFQgf6syWzDQUTkWrwKky4GKgaWp17Csz8dMHq/pILdHomUjg8aRPUPAxUYV/38efCS3t+pmXmIfnOd9m9eC213PLsA+cXlrk4GESkYr7Gki4EKLA/49n9rjtdKOuq7feevY9Sn23D7nE2uTgrVU7zBEdU/sgOVrVu3YuzYsQgPD4dKpcLy5cstrpOcnIzu3bvD29sbkZGRSExMtCGpzqOy0JpWGNSYsnjZNptuDcZ1o7TCxSkhco6KSo2rk1Av8Borz6EL+a5OglPJDlSKiooQGxuL+fPnW7X82bNnMWbMGAwZMgSpqal4/vnnMWXKFKxbt87yygrBKUTImTQagaOXClDJE61OS7uYj9teX4NPN5xwdVJkS7uYj79/sxuHFXLDq++/hEqNQEm549o9/nf7WYdtS4lkTx08evRojB492urlFy5ciLZt2+Kjjz4CAHTq1Anbt2/HJ598gpEjR8rdvVPInetHCT+irPxiuKlUCFH7WF6YFO2DdelYuOU0HunbGu+O7+Lq5NRphqWftemdlUehEcCnG07i+WG3uSwdtrh/YQqKyyvx18KdSH/P+uu7s9T3ApW7Pt+O07mFOPDmcDTyln0bbnCc3kYlJSUFw4YN03tv5MiRSElJMblOaWkpCgoK9F5OZSlSseFHc7Ww1GmzuZaUVyI+YRP6vL8R5SxqrvMWbjkNAPh+13kXp4TsUodvrsW3nu5LK/SvJ0II7M+4jrybZa5IVr11LKsAZZUa7M+47uqk1AlOD1Sys7MREhKi915ISAgKCgpQXFwsuU5CQgICAgK0r4iICKem0VKcohGGbVTML7/n7DX0eG8DnnTS8M/XdS4axQ4sPiSq6+r7k3htSz5xGfd+uRODP0yu1f26smSMlEeRvX5mzZqF/Px87Ssz07lDvltuTCvPN9vOAAA2HHP+TK5yq61IWYrLGGg6kisDlfp4c91wtOoalneTQwqQ6zg9UAkNDUVOjv4NOycnB2q1Gr6+vpLreHt7Q61W672cSaqKRrfVudwW6HLnDpKrrj411tV0G/p25zmM/GQrcgpK7N7Wy78edECKHGPuuuO46/NtuFlWd3tl1ZNTTDEMr2UajcCUb//C+6uPOXfHBgeSvYAaNqcHKvHx8di4caPee0lJSYiPj3f2rq125kqR0Xu6vwvDn4ilJydVLZZzWCoNUjIhBL7deQ57zl5zdVJkmf3nEaTn3MBH69Pt3taqw1kOSJFjzN98GmkXC/D7/ouy1pv+4348sDDFaW2y6oqGcC/dc+4aNhzLxddbzzh1P7qnUmpmHnq/vxHLD8g7L+uTr7eextq0bFcnw2VkByqFhYVITU1FamoqgKrux6mpqcjIyABQVW0zadIk7fLTpk3DmTNn8Oqrr+L48eP48ssv8csvv+CFF15wTA6cpFLnqmN4/Z3y7V69vy/lFdfqU6ijr4fHswuMRuOtDVtPXsHsP4/gga9MN6xWMsOGh/WF3LFAVh3Owp5z13A8+4bd+07PvoGjl2xvPM8nb+dyReP9f/ywD5dvlOL5n1Nrfd9KcCDjOt5ffRzTfnBOm8e6QHa/qL1792LIkCHav1988UUAwOTJk5GYmIisrCxt0AIAbdu2xapVq/DCCy9g3rx5aNmyJb755hvFdE02pboB7VdbTuNgZp7eZzd12hVkXruJAR9sRmMfDxx+qypPtVnI4Yhdjfp0GwCgqZ8Xbu8Q5IAtWuf8VeOSLHI9W+/19rbRKK/UYOSnWwEAR94eyW6bTvTL3kxsOJqDeQ92g6+Xu8nlarN0WJfuuVTWwHs2Xr5R6uokuJzsK8HgwYPNPrVIjTo7ePBgHDhgPNGfklVnMcHC8PnbT10BANwoqSlRsSdQSc++gajQxgZpEViyJwMdQ9Xo0bqJ054aj2bl12qgUncrrZRp95mr+CjpBN4d18XoHKoN9p6WuiVUecXlNgUqLE+xzqu/HQIAfJtyDtMGtbdrW7/8lYlG3h4YExPmiKTVmj1nr+Gvc9fw9KD2cHPj1UjJ+MhigmGXZDnseQqRGq9g68kr+NeyNADAuTljbN421W8PfbMblRqBh7/Zjb2vD7O8gglyzvwyB1Z/OeRW4dJeP3WPpd48lh66svNL8OrvVUHPmBjHXZv02gg66YutrnIOVfvgvh4tnbMTcghFdk9WAmvbBdZGHH4qt1Dv73pTDe/EOjIhBC7lFTeoNgvVQ/BfKbSvqNja70yjEej9/ga79lWf1MdzzVKWCkpqAh1H5l+Y+L8znJXoTCGlpLwSmdduOjk1JIWBign2lKg4OnoxdwFw5I/Y2dfZY1lOHmFYxxebTqHfnE34YtMpp+2jPtyXKjUC/91+FkcuyZ/j5UZJhd4TuSO/D1tvekoby0TpwYu935fupc5Zx18p3+Fdn2/HgA82Y995jiZb2xiomCCsLNF2dKGAK3+Szt735vTLTt5DjY+STuj9S8byb5Zjye7zeHflUYz5bLvd27P7plfHmwkY5n7dkWzEvZOE5PRcl6THESwdE93P7Xq4qwOqS7bvW7DTpsDekRraUAAMVEyoFMKqAb2k2qPU5vVW6mnjSmEptp+8IvtJRO515mZZBf538BJulNg2amUdvy851ZrDWVh24ILTtn/6ciFi31mPN1YcMfrM1vvN3V/swMU86WkxaouS7pVPfb8P+cXleHTxX65OimmOLAVx3KZqterHlgDbEYG9rb7deQ7d3k2yOVh65deDuG/BTtnDELgSAxUT3lt1FH3e32h5QQn2DML2H4mBlAwvvpYuxkPmJuPv/93t9MHEZv1xGM/8dADTlyi7R1dJeWWd+lFWVGrw9I/78cLPB53WNfG3fc4Jgt7+0zjwsYWSAo6GzPKVrGYJZx0zZ58Lde1cm/3nEeQXl2t7bsn1674L2Hf+OvbeqsLafvIKpnz7F7LyXfuQYQ4DFRP+sHZ0TgcXC2w8nqvXQA0wH/FLfXKjtKqr9KZj8oqc5T5ZrEitGiRu6wnbqnSsieeuFZVh9oo0pF20vag15u31GPHJVpvXN8VZVRW6R8HwXJCSd7MM56xsEFjNXNLtqcKxZ5JMR9ww6tg9p15xaPsgvV4/tm9XCIFJi/bgHz+aHizN1q0LIbA2LdtlDWztvf5UV9X9/b+7seFYLmb+ftgBqXIOBipOYO/9q9xBXT5t+QEWlJRj/Pwd2okVq1XUYp1o0tEcPLf0AIpKK/DGijR8m3Ied31ue1FrWYVGcpoEe9XGk5g1F+m4d5Jkz25r7iLnqidMR+zWpZMS1sEoSamN8XWDHns2e/7qTWw9cRmrD2c7tCs9UDUi87Qf9mHAB5u17+08dQWbjjt/MlpncMTcZc7CQMVOutf7sgoNKjVCciKvl389iMQdZx2+f3MXB1ueRL7ZdhapmXl4b1XVpGM3yyowUOeH6EimxpuZ+t1erEi9hAXJp3FcRk+htWlZePanqgDHVltPXMYby9NQYkfJgCM56+ZnzVg/daHB3sHMPPSpw12kKzXCrvPVXpaqFi1VY+t+vFFmCa6usgqNXjdhvfPextNw3/nregG8rSUQpq6jMwyqvCs1Ag99sxuPJ+7FtaKq8bCW7snAJzY26K+N3k7Hs2uvJ6Y9GKjYqFLiIt7zvSTc9fl2o1vApuO5+G3fBbz1v6NWbVulUqG4rBLj5u/AmM+24WqR/iBwzuqCKQSMbtC//JWJDJlFm/nF5Ujccdbu9hVyI/xpP+zHnwcvYeGW0zbvc9KiPfh+13mjEqXaZG5CTEcxW6ICIO1iPtq9thp//2a3k1IgsV+djFt77MfN34GcgprzzJXdk23Z8z1f7kDn2etcNkz6Mjsn+tM9jX7cfd7m7Uz8zy4M+TAZG485rjTiqe/3Wl4I5h8G1qZlIe6dJGw7abl6W/eekF9cVWU784/DmLfxpOyhGd768whu/7/NyC8uN3le2RvHqKDCE4nWfUeuxkDFRr/vr2qMqPvEUVBSIXlCFtrwxPTL3kwczMzDkUsF+GqL6ZvmtaIyfJl8SvLCbu48zi0owYEM4/EADKN4uVU+Go1A7Nvr8db/jmLyoj2y1jV0IDMPpy9brrIxTLO9A54BcGnvFd2brbO6fFp6uJyxZD+AqikiTJUuOeJp7NCFPIz6dCu2nrisd77+bWGK0xr8OsPFvGKb7hyHLlS1vXLkDbo26V7/bDlXC0rKcaOkXDs2yU97quaJc0Sw7ojq6mk/7Ed+cTke+a+8a5nh70t3ihVDn244geEfb9EGNwCQuPMcLuYVY+meDJPrOcJlnWulkqsuGajYyNxohod1Gn7aOuunuUaJuifUsz8dwAdr0yWffM39Tnu/vxH3fLkThy7k6WzXujNVqmHrr3szkXujBBuP1xT/HrXwFGGpKNZwRF4AOHelSO/GWVhagSEfJuPNFWkWUl137D1XE0CaOiRSJXqymPnyhQAKS2u+Y1OTwk34epfRe9tOXsHyW0/pJeWV+HH3eVy4blwidymvGPnF5Zi8aA+OZ9/AJImgdu468/NsmUp7bVu6JwP952zCwQs1vwu51WYKvkdYTe53X1ahQcxb69H1rfVG29Cr+bF18D8re0s6qhTO1u18uuEkTuYWInHHOVnr1fVxh+RgoGIjc78d3VIAWy62Klj/dFIdFJ2UuKlbY8/Za7LXkWrY+spvh3Dfgp0mSzMc0eZj95mrGPxhMsbq7H/ZgYs4d/UmvkvRLXaunV/wnwcvOWW7O09f0f5f6jz4dMMJdJ69FunZN2zeh6VeP/ZcBKsD8083nMS/lqUZ9bjKLShBvzmbEPv2er2nSHOn/O/7LqD/nE21WqcuhMDNMsulof+31vg3LrdKRclPs9aSm4drRcbzmlVvQm9kWpvTY+WaDvrudXdny++nUlO7QyioVAYjCys4XGagYiMBgW+2ncHm4+YbkNlS7fOfbWfwwdp0yc/ybpZZfTr97+AlPLfU+jFO7L1YZl4zXV0yb+NJo/fk/pZX3AoMTuYWorC0AteLyiQTbeoicSyrQEbAZLyR8koN3rJynJDiskqUVtgWnFmakO3TDSdRUq7B+6uP2bR9OfsHpI+TNTeB7aeq6vVvlul/D6mZeSZ2bHpbL/16EBfzivHCzwfN7tORl9pXfjuE6DfX2dQ1XrekslqlRpgsaXHkTeLt/x2R9YBk7jdhcWRanf9XyryASG170/FclDtozCOjEpVavBHbMzGt3nZkbEZuo+zs/BK97Ss5WGagYqMDGVU9YywNqmbLCftlsunGoBeuy2s7sSL1kqyGeron62YHDv294ahxHbzcpw7dxbvMXodu7yahqMz4Imtqs6PnbbO6cWh12nQHilu6JwOJO89ZXLekvBKd3lyL3v82PWBgVn4xHk+0PGKpsy4ebjK+fKkkfLLBOPA0R3ecF7kDIubrzCdUZin4c+AXVt1GxpbG2YapKK/UYNDczbh3wU7p5WUmu7BUuj3cxbxiLN5xDvM3n7b6ht/pzbUmP7OULmuH0N9+8gp2n7mq915puXT6DNsr2XpIDVczXfXjGHafejJ/F4b76zx7HRaYuXcYev7nVJSYOAZKw0DFRva20r98oxTZ+bb1W5dbZ1tWqbEpvfNk3ozMkfoN6gZxN8sqLF5YpW6u568atxUy93vfe/46vk85h8mL9qBYIsipduRSAf67/Sw6vrEWe89VVY9lWThe205exvmrRTh/tapNRn5xOc5cLsQvezNRUl6J3Bsl2v/P/P0wNpkojdPvmWn6WJvLp6UgU86TspTPJErIzEm7lI9tJy+jvFJjctum8vrP360fgdMZcZ2lwEpqnwXF+gP1dfjXGly4XozUzDz8vu8C3vrziF7pitx0j563FaPnbTMabLFUp3Rkye4M3PX5NuTeMH/eGl5Oqs/VH3adtxiY6/6GTTXLyS8ux9//uxsTvt6l/Y2XlFdi4FzpYQ8M21/ZWhJiS9uWsgrbrpWAMqpOpKohddWFIQekeLg6AXWVmx0lexqNQK9/V439cPSdkbLWNffbO3IpH53DA4zeH/v5dlwrKsMzd0QafaZ7EXbWKZydX4ITOebb0ES/uQ4RTX2x7dU7TC4j9Z1LNSq1VIpVPb/ND7vOY8qAtsi7WY4mjbz0ljmYmYeDt6ooXv3tEDa9PNjsNlMz87Q9A5JeGKh9/46Ptmi3Ue141g2zQaqlqh9rPLb4L5ybM8bk55ZOX0c31Ksec2LGkEh0axUouYypvG7V6Rpqz/QUziKV7uWpptsvvfRrVfVVv/bNdLYh70BXV7N+mXwKA29rjopKDTzc9Z87Z9+qpvx4/QnMuS/G6m0PmrtZr8u3KWUVGhzI1G34LZ0H3aCtUiPg6Q6csdCbT3dTtjYct2Wt0fO24vTlImx+eTDaBjWStz+DNipy0y11ZjuqCqnadynnTH6m5BCGgYqN3K2NVCQW0+1FYUv0buqEOpCRh/VHjKtYqhutLU81buBXG4MK9U2wbs4kc21cAOmb1C97be/CWlhagZd+OYg/bjV8/P6J3iZ2XL1/09vSbZNg6V664VgOfD3drUqjueL05PTLWCFxTA2dyi1E62Z+8HS3rgDV8Jxw5KXyx93n0b11oKx1dNu4aDRVDVz9vGrv0mUp/7b+hnJk/vYzr93EB+vS9UoRd525hkXbzyJhzTGMiA7F9lNXjNaTO62BNUEKALz620G9gMzUuWrb11OzUnml+Q0IIbBoxznERQSgR+umsvere/yqO0KsTcvGY/3bWJ9cGOd/3ZFsWevXRgy+9K9M5+/ECVj142SG597atGycyLG9t8al/GJcMjHGx+vL0yQbrVaTCgSW6PTTF0K/+NbpIYzED/OBr1JML27lD9naGHJNWpY2SAFgcqwElfZf0xs+pNM1Vbdrr5SMazeRbuU5YOkYPLc01eznyw9cxLCPt+CJb/UHdnIz8yUZXuCrAwUhhGRPDVNMfV+m3jfMa05BqVHD2zNXihD95jq9diu6PlyXjuk/7ndoEbfhV1VcVomVhy7h9eWH7RpvR7eaxpqb6kPf7ML/Dl7SO9cA4J2VR1FeKbDqcJZeLypnMyw1MlVzK1UlsibNfNs+a4OMotIKjPlsO95deRT3LdC/dhjud9vJK+j97w0mh7jXHctGIwQ+2SBvRFnDJMudVV7qd2HummfN9bCsQoPpP+7Xjk/j4a68EklrsETFRpaqMkyZ9oPpybGs8c7/jjp0MDLdItjM6zf1Btk6mJmHozKnEtcdA8QW5rpLW1sMam31gLXHsLptjNRmS8or4ePprve9jZ+/w6rtmqLXNdPOEq/Ft9oY2DpxJFA1+NQ/R3XEW38ewbcp57Hw791t3tb1m+V4fbn0mDdSeTX1XaacuYpRXUKN3i8oqcCqw1lQ+3riH4PbI6Kpn81prWZ4Pt23YKd2jKDUzDwUmBnMyxzdklVrjrOlEkdXu5RXjLybZQj0069GlcpakkTjel0/7rZuoLO569JNjtdkuN+p31UF648n7tWrFq1ezjCYl3ste+2Pmkn9DK8VQlT1+HJzU2H7SeNSL1PeW3UMPVs3kfzMmkvDr/sysepwFlYdzsLE3q3g7ma6bKI2StdtxRIVF5NbB3nZAaOumiI1EqilYldD1SP2WkNubG9PuyB7VF90pHY/f/Mph/7AU07r94w4e6WqYe7WE5fxSdIJu0oKrE1ncXmlXvH/guTTyC0owbe3xqox1XXekKl40RGBtqX6/5/2ZOhNFmcPw2zo3hjTLto+rovuJHlZCp4Qzlr5xeWIeyfJ7u2kXcyXrMKSIjW6djV7fpW2tItZech0KdGEr3dh+CdbUFGpwWqd0qTfda65hy/mY9wX2416R+09b/vDX55ByaOnuZJUm/fifAxUnEyJjf8cwVTRuyFzN9YVZhocSqnNGZx1qcw0UknNzMP/zFyg5Jr4n116F4yXb40fMmnRHszbeBI/77W+jvneL3cgO78mKJi8uKY7tLnTUqp7/Kc6VYqOnIlamPi/JQu2nMIbJkpmdCUdzcGPu8/LLobX46SfsG6g8tWWM5jybc3xqajU1Go1jqOdyLmBTIk5wqpjZXPXxUs29oY03pnpj1759aDZxeydukIqf6cvF+HUZf1SXN3qsw3HcnDwQr7kiM+2qNQIzF2n/1BhrspXyRioOJnFcR9kb08Z/d4/XG/dU3Xv96Ub0p6/WmT1U1M1a8YwARzfKE2lqgq4TG32Fyc3UOs/Z5P2/7N0ipct2Z+Rp1cyolv9I2ccFQAod9J5Z+v9IO1iAb7fZXkSvKnf7cW/lqVpB4orraiULFkSQpgc+MzRPS+qGf6WNxzL1Y68e++CnYh9e73J9mhylVdqaq1o/2phKUZ8slVbomVtd/tqcoIEw4Bgw9EcbRdoc9v5Vack47/bzxp9z3K/KnNBmd5+917AEiurtey1VWoiRXP5UnCRCtuoONk6iV44umwdvdTVrLlJAKYnCHzpF/MjjNrD0TeW49k30OnNtfCQeBrZJqO+2Vq1cUOR+w3JLcyyJQ+Ld5yVvY61NhzLwfWiMvR5fyP6RTZD4mP6Pbye/H4fko7mYNesoQgN8NH7zFmFoqUSwd/Zy0XoGKrWNphdfTgLUwa0s2s/14rK0GX2OgyOao6vHulp83ZGfboVvds2tbic4fhA+m2uLO+noNj6Nj+Gx2bKd3sRFxEIta+nrBJYw7F6hBDYb6ZaydBBg5GIlx+4iKYGQx4AVUFRbSk1CLz3nb+miLFebMESFRcbbjAPSkNhT72rJdbMzyJXaYVGchRcZ1Bimza5ReG21PHP3yx/BFg5VqdloaxSg+T0y3pP0KdyC7WNO6XaWDmrsNzUTUO3msrcoITW2nbyCkorNBYfmiw5nn3DYE4taa/ojBlkS8C6wc6ZpFMz82Q3HjdsN6UR9v0O565Ld/nv2HD/9y1I0c5SXdcwUKF6p66OFVDtm1p46jLX8E+K3MBD7rwvte3t/x1BpUbgvgU7MezjLdr3PdxUEEJgl06DRqmSD2fSnU34oyR5XWSVRiOk2yE5KviTW4VpiuH5LTcwl1rcUhdsZ5NueyNveaVg1Q9RA7I5PRdqH0/trNvWknvhruWJYGVbdyQHYz7bhuMGM1C7u6kw+88jeiUHfx68BF9PdyTc29WhjRGlvtL61va+wuBEqC5hcUQ+n/xur8NKCCoqDQMVeetLLe6MamE5FP6sIAtLVIgakMcW/4VTufIHHJRbArPykLzJMGuD4YXbMEgBgN/3X5Ss3vh5bybWH81Bm5mrnJU8ANI3l6uFpXhssfRghEpnPG9PFXsDlT8PXsJ6C2OxyGHYw0pulVVtNVT+SmeCzCOXzHeNr6vtUaSwRIWogbluZddye+i2U6hLpGYkrraoFhtC6urx3gaHbm/+5lMO3Z45G4/lYrnO6M/7z1/H4Khgo/E9rHUprxjhgb549qcDjkoigKrpNHQ5ourHGRLWmJ90UJfcNHHANyJSjDkyLnZU41K+Y0eGlZqN29oRWe1hOLaGMz3z0wFs1OkF9Ojiv7Ai9SIuXLftu6yttP9nm7ygVM5Al86268xVvL/6GN5YYXmcIV3KDVNYokJEZBVbb66mGHbjBSB7bKG66Es7eneVm5pQyMVc3R5F14MOGjBOSViiQkQNgj2TgZLjWDshpxQlP/WT8zBQIaIGwZoxQEjZDl/Ix/Ql+12djHpJwU1UWPVDRER1Q8a1m8iQGK6e7Kfk75UlKkREDcSRS/LGzyFSAgYqREQNxHNLU12dBCLZGKgQETUQp3ILXZ0EItlsClTmz5+PNm3awMfHB3369MGePaZHTUxMTIRKpdJ7+fj4mFyeiIiIqJrsQOXnn3/Giy++iNmzZ2P//v2IjY3FyJEjkZtrPCZANbVajaysLO3r/Hm2viciIiLLZAcqH3/8MaZOnYrHHnsM0dHRWLhwIfz8/LBo0SKT66hUKoSGhmpfISEhdiWaiIiIGgZZgUpZWRn27duHYcOG1WzAzQ3Dhg1DSkqKyfUKCwvRunVrREREYNy4cThy5IjZ/ZSWlqKgoEDvRURERA2PrEDlypUrqKysNCoRCQkJQXZ2tuQ6UVFRWLRoEVasWIEffvgBGo0G/fr1w4ULpudGSEhIQEBAgPYVEREhJ5m17smB7fDt473h7sAp4ImIiKgWev3Ex8dj0qRJiIuLw6BBg/DHH3+gefPm+Oqrr0yuM2vWLOTn52tfmZmZTk1jiNrb6mVbN/Mzek+lAgbd1hwn3huNfu2bOTJpsoQHsJEyERHVL7IClaCgILi7uyMnJ0fv/ZycHISGhlq1DU9PT3Tr1g2nTpmeatzb2xtqtVrv5UwdghtbtVx8u2bw9jD9lbm7qeBl5nNnGhzVHFteHeKSfRMRETmLrLuql5cXevTogY0bN2rf02g02LhxI+Lj463aRmVlJQ4fPoywsDB5KXUilU6NzRt3RWN4dAj2vDZUclk3lXH1zj3dWmj/bzhfwicTYhHkb1xi8+Zd0Q6tKvLxcIenO4fFISKi+kX2XD8vvvgiJk+ejJ49e6J379749NNPUVRUhMceewwAMGnSJLRo0QIJCQkAgHfeeQd9+/ZFZGQk8vLyMHfuXJw/fx5TpkxxbE7soNIJPgZ0CMITt7c1sRyMgouFf++OjqE1JT4anUhl8WO9MPi25hjTNRyVGoFf9mZi9p9VDYl7t22Kf46KwvurjzskD4OjmjtkO0REREoiO1CZMGECLl++jDfffBPZ2dmIi4vD2rVrtQ1sMzIy4OZW82R//fp1TJ06FdnZ2WjSpAl69OiBnTt3Ijo62nG5sJNu7GGpVMLDIFCRKi2pNiQqGADg5VG1zuR+bbSBihCAxsJslbdHBmH7qStG77dr3ghnLhcBAN4aG43wQF8M7eTYLt/x7Zoh5cxV2esF+XvhSmGZQ9NCRETOp9EIuCmwU4hNdQUzZszA+fPnUVpait27d6NPnz7az5KTk5GYmKj9+5NPPtEum52djVWrVqFbt252J9yRdA9L26BG2v+P7mLc7sbwIPr76Md6GivnyhYQuFenysjQ7ZFBkg13z80Zgz5taxrshgb4YETnUIf3OProgVib1vNwY/UTWTY+LtzVSSAiA+UajauTIIl3FTM+fiAOix/rhc7hVVU7E3pFGLVR0a32AYzbqJji4+mOYLV0L50/Z/THN5N7wtSmdJMg1WbGFo/316/u8nC3bbv3dDcdfBFVU+JTG1FDV15p5Q2sljFQgX4bFV2+Xu4YEhWM35/uh9XPDsDdseGwdH21VKLy6qgoTI5vjdtCTPc0imkZCB9Pd6OgZ1TnqhIe3SSYClSeGtTOfEINvDIySu9vL3c37f7keG5oB5f1fKK6w91BATYROU5FJUtUFMvSJdPH0x3R4eqqCRU93c0ua6lE5R+DI/H2uC5Wpat7q0C9v4Nvjfeie41v6u+lt8zOmXdg5TO3Y9boTlbto5qvl36+PNzd8P69XSWXjW0ZYHI7Xu5uGBHt+CkSWjU1rgajuktJccqADkGuTgKRIrBERcHkXDQn9DI/Sq61VT/WuK97S3zwtxjt39XtUFQ6oVX3Vk301gkP9EWXFqYDCWt5uqtMPvWumHE7erZuIvmZSgWTVVa62um0BTI078E4o/cSTARNUjzNVFu9N966INGZnhpoXNoVHebcsYLItP9M6unqJBApQgXbqNQPY7qG4aE+rUx+PupWA9wWgb42bV+3ga2bmwoP9KwJjOQGQd8+3tumNACAp5sb1L4eGBEdgjs6Bht9/stTNePmtGteE3SorIxU+keafoodF2fczsXa7/P4u6Pw7B0dJD9bMqUP/t63tVXbcSZviVK5J3WCl15tpIPA+qSiFp/cLJXGKal0pz5QqYCWTWy7/pFr1ebvUg7Z3ZPro2mD2mPDsVyMibE8CJ1KpUKnUNPtSyb3a4PWzfzQrZW8m81/JvVEM38vdAm3XBrSxM/Tqm0Ous22sVVaNfXTNnb8+tbTZpuZq/SW0W0MGRbgo+0uDVT1aKp2W4g/TuQUGu1D7s2hjZkSGF0+nu6S2+7Zugn6mQmO5Hj/nq4or9Rou5rL1TbI+Mbp4a7C/Ie641hWAfq1b4aHvtltbzKNvDoqCp8knXB58e78h7pjTVqW0/fzSN/WePdWCZrh+atLZbHyl+QI8vfmvGd1VBnbqChXzzZNkfrmcHwx0bpu02pf04GCu5sKQzuFoGkjL5PLSHFTVVXjWNMQ9clB7TE4qjnm6lQLWcPatiMP9Gwpa7sBvp5Y/ewAbHppEACgrKLmRvjjlL749z1dsGJ6f6u2FWOm/Yu1mkmMbWPvrVm3Ouq+Hi3QRObx1TUu1rjESAUVxsSE4WWDRs2OFB2mxorpt5tdppOZKihPdxV+f7ofVj5zOzbeOtZyvXFXNMbEhNXKk1tsRKBVy8kNmr9+pIf8xDiQuWOkBI29PRzWG5Hs9+wdkVYvq9QSFQYqtwT6eZns/WNoTNcw3B0brn1aq23+3h5IfKw37u9p/azSLZv44utJPfHFQ90wNtb8GBbWfg+6osPVaNfcHwBQWlGpfb95Y2883Kc1GnkbF94FSpQMVVdvvTXW9gEB/9bDfKA1srP8xr5dddr9qKAy2fsrMtgf/SP1J6a8p1sL3B0bjttC/PFwn1Zwc1Nh5TO3m67OctI1XqDqOJkzxMwIx24qFXq0boIuLQL0klhd9eft4YZuBg3Ade3511DtqM8P9nbMjOhv3KV/nkwf0h7j4sIx596uZscp0iX367a1676jtJEYX0lJOoT46wV/HYL9XZcYVP0mdXUI9jfq5VifyblPlLNEpf7wcHfDZxO74REHtnewpv2JcEBL3btiwvH5xG64s6vprseWejZZIjW6r1Ts8+tT8RgfF64tiQFqunc/ajCui7371/3uFjzcA3fHhlsMaHQZPiGaamj85MB2CG5cMz7O8XdH4ZMJcfhsYjesf2EQ/n1PVaPgLi0CsP2flieR/NjGgfeeHWrcTsfS+fP23Z3x7NAOJoMwU8X5ix/thcf6t0HSC4Ow7B+mS850v5fBUcHY/PJgs+mxhuF0F6+M7Ih5D3bDg71bWT1Wi9zA3Nb2huN0BrmzZ8oLaweVtMa/7uyEoR2DZc0gL6W6JPSebi2053g1Vz+jf3i//m9IAPjH4PbYVk8mcbW1PaSUCkvDpbsIAxUXu7NrKFo19cPtZrpINm9cdREZYcO4JtUj7Y40WHf+Q91NVrP4e8sLVHRHygWA2WOj0TG0MT6ZYP4m2yGkMT59sJu2JAZwbK8pXbqbdXNT4bOJ3fDh/bFWt+Np1dQPvds0xdCOwfDycDN5czN811zQp7sN3XY9YQE1F557u7fUGy158aO9LKb1tTs74sXhtxm9X/3dLn2yr+R6D/SMgI+nu8mpHUwV54cH+mL22M5oJfNJv21QI6x9fkCtzFNlrqRHbvmIbqBgbgoNQx/dH4ukFwbibMKdaG1Hd3tH/UYae3tg6sB2+O+jvbBzpvQkrObofqcrpvfHifdG45MJcQjy966VVj/v32NdT8A4gypAjRBQqVSIaOrn0Ju8OVIPDrodEuzhqPZAvds2ha+dD6nOwkDFxeY/1B3JLw82e0Pb+NIgrHr2drM9ZUz5dVo8Pn4g1qioU6VSIcCgrc1TA9shtmWAZK+barrVJltfGYIP74/Fwwa9oFo3a4S1zw/EPd1qSiwMf0qmflpynhaXT++v95RqzsRe0j213hwbjeaNvfHGXdF63VS3vqL/tOXmpsLPT/XFf28FCqYuDiqVyu6LX9ugRvjioW7agEK3JES3h5Upj/RtI/l+9Wb6tmsm+bmlggXdLJsrhTDX9dxQx1A1Eh/rjUWPWu4ivGRqH3z5cHebunIvmtzLZJsuOaPkPtqvjV7Q+/oY68cr8nB3Q4eQxlCpVBZLcToE+2sfUAxJBZKGYy5ZRScJhudzeEBV6deozqH4TKLt3vDoEL1rikql0mtf91CfmtJmR5QESzHX+9IcZz0MmfNQb/20tmnmZ/b3Nn1Ie6u3balzhbX5/eWpeESZ6SjiSgxUXEylUlm8UKp9PNHZit5AUoL8vXFv95aSgZDhhXDWnZ2wYsbtZoOmiTo/uFbN/PC3Hi3hYWEiR8D4xtajTVPJ5az5UTXx80Ta2yMRFxGIeQ+abwCdcG9XrHzmdtxvooFw++b+2PNaVduJHjpjw7Rq5qdthDZjSKRRHswdsn8MaY8He0XY1T38rphwbUBx963AsWNoY6u+H1MXQGuvz6ZKCRr71FwQDSfn1LX6uQFWlfzoat/ccjuGfu2DcGfXMDzar43e+1NMzHauq0kjL9zfMwKP9muj192++thanc5gf71jYBjsW8tSUBgZ7I8BOg8mUTojWUvd+KcOkDcSNWC+JGn9i4Pw38k9MW9iHO6WaNP21d/NNyh+TOcYTbmVto4SN0Gpkj9nc3TgZE0eDI/3/5653ez3P+X2dmgs0a6vWtcWAXhvfBdMH9IeMS0Dze5buLzyzX7sntyAvXZnJ+TfLLc4iJ0uRxQzfnh/LMaa6ApuTYmKr6c7/HV+xKM6h2LtkWz0bWcc/Hi5u1kcAK86AGnayAs7Zt4Bv1uB2gvDb8O93VtKTg7ZwsQ4ESoAfl4emHOfvB5Z5jxzRyS6hKvRu21T5N0sN/rcy8MNZRU1DSdMBipWXqB9vWoCz4/uj8XHSSfg7emGz3SCwpZNfHFvtxbw9/EwahPk4+mOsED9eawslTzI6SUSGqC/bWsC5Wpv3d0ZADDl270oKC7HCzbcKHX3b2t7EVNdon9/Oh4Lks/g1VEd8dnGk9r3g9XeSM+5YbTPMTFhiA5Tm/xdzv1bDNYdycGGYznWp01V1WDf3Izslh6udD/v2iIAx98dBQ83FUZ8ulVvKIMhUcH4OOmE1WlzBLnNMNo3b4TTOmk21D+yGT5OMr8Nw9O7sY+n3oOPv7cHHurTCl9vPVOzvJmvuEkjL+2YUG+uSDO/83qAgUoDFuTvra3KcDbd39zY2DCjEpanBrXDV1vO4HWdXhwJ93bFrD8O48uHu+tvy2DduffHYGinYIyINm7DI/c2oltto1KpTI7f0jFUjY/uj0VogA8e1hnzxBm9Mj3d3bTtk3QDlf6RzfDR/XFo5u+Fm6WViH1nPZr4ecLTxAzWugHbtleH4L/bzyJx5zmj5XTvvff1aIn7JBodq1QqfDwhzmSaDe/fUyw88cv53gZ0CMIrI6PQKazqCd3clA6mfDNZv6rp7thw/HnwktFyr4yMwtx16TVvCIG4iEC8NTYarZs1QqWNjQ+DTTRe7dG6Kb6Z3PTWrmq2/dbdnTHhq114enB7XCksxeb0ywCqqo4BYP2RbMnt3d8zAp3C1JKBiqnqJ0tB4+dWDuPw73u6IPNasd55l/TCIMS8tQ5FZVU9A2trtl4fTzeUlFfty1xwOSm+NQJ9PfHZplMAqkqyvn28N1YeuoT3Vh2TXMfwe7w9MgjbT13RX0Yi6tCN9dLeHokrhaXaQMXWSTvfuCsa7648qveeK6q6HI2BCsniiMGxpH44s0Z3wjN3dNArKZnYuxXu7d4C3h7mG3g19vE02QXPWfXjACRv4LayNpm6iy1+tLe2XUCAnxsOvDEcKlXNRc5NVfX0OKxTCF67syPCdYKwiKZ+mD02Gv7eHvhi8ylHZaMmnTK/djklKiqVCtN1qmxGdQnFxw/E6nUhl+ujB2Lx1KB2GPPZdgBV1RTju7XAtEHtse3kZew6cw1Azfdf3Sttc3quTft7tF8bzFlz3OwyujFQ++b++OtfQ6FSqVBcVglPN5XFxvW/P90PQFW3cSmmvnJLR6J6eAPdRt9SHtZpp1LNsORHd9yOl4bfho+cVLqS9MIgDPhgMwDT5+bf+7bCO7fmYesfGYTN6Zfx/LAO8PF0R5tmNQ8se14bivzicgz/ZCsA/XP3mTsi8eLw29B21mrte3ERgZIBrWGAE+TvjQd7RcDNTQW1j36V4jN3RCLtYr42QNVdc0CH5vgu5TyAqjGwjAIV6exq/TYtHoF+to8LVRsYqFCtsKbKyF+iTtZSkGJJbTxMbHppEO74aAsA5w/H3kxnEkrDdiKGg9BteHEQVh/OwuR+bfTal1RTqVR4acRtOHe1CH5e7nZ3S9clVV3mLCqVCvd2ty9o9HR3Q+fwAOyceQdSM/MwqnOoNuBrrtOt2vAmNyAyCH3bNUV0WAAW7Tir99nbd3c2OXqxNd+1bnUeUHNj8/Vyx4sjTI8D0rtNU8y6s6N2dOzIYH8MjmqO5Fs3Oe32TKxvbdDYNqgR5j/UXfbglrpfoe75bNiQc+mTfbE2LRtdWwTgpV8P6n0mt21QRFM/zBzdEXPWHMec+2p6Cz05sB1m/3kEozqH4r3xNe/3adcMfXQanetev4LVPghW15wTut9WYx8PowBkVJdQvfFJqgcMlLokmqoyfunW8f5662ks3HJGb/ygYZ2C8d3jvREV2lhywFBLD2s9TbQXVBIGKiRLhxDbBm9q2cQX4+LCHX5DtKgWIpV2zf0RHabG0awCDLrNeF4kR1L7eGLlM7fD28PNYvFwu+b+mGFi3qNqKpUKXzykX7XmiEKoRt4eeGdcZ7y5wrZpBgzV1miw4YG+eiVPQNVQ/P+TqBYCqtrHLH2yqptpdaDSo3UTfPt4b/h7e9g8zQKgP3CiJbo30l+m6Xd7ValU+HRCHOLesdCQQru88XuBfp7Iu1luFIBaM+2IoQd6RiBx5zn0bdcU7Zv74//u64rgxj4YHNUcXVqokXaxAEBV77TqBuU/7D6PAxl5+PqRHkjceQ7/utXmqZGXu7YayZJpg9rjsf5t9B5+JsW3Rt92zSz2pusfGYTbQvwle5zpjlQuVeLs5e6mNzv9kFuNuS3NQRUXEYhtJ6/AS6cN1pMD22PqgHZ6wZBKpcLAW8MsWFuC/PTg9liQfFpWrzVXYqBCVkmZdQdulFQgRO1jeWEJKpXKYg8dZwiwcl4ke/3vmdtRUl4pOQKvozliduza0ERGcbLu5XVSfGvsO38dTfy8sP3UFYyNDbdpDCFH6d225onTmhuBu0qlLR38fGI3PPPTAXxgQ+NqOfOuDOjQHLEtAxBtondgoJ8X5j0YBw83N0xfsh+AvDYqv02Lx5fJp01O+CnHzNEdEd++Gfq1rwpCJugMHTCpbxu8+vsho3V+n9YPN8sr4e/toXcurHp2AP44cBGbj+fi8MV8o/UMB7Y0LKFVqVRWdcn18nDDuucH6n1nH/wtBteLytA2qBEe7dcGSUdzMEFixGUvDzcE+Xsj4d6u8PV01zY+D/TzwqaXBpl8cPvogVgsTD6Dh/rob9Nc13bdz9o088Pb47pILv/KiCg81q+NXsmQkjFQIauEBfgirG7cHwFU9Sw6mJmH4WZ6LjiSu5uqVoKU2hDXKhAX84rNdsG2RvXTsDWDoml06vD/OaojGnl7oKi0AltPXMagWhgQzlrWPK/qtmWuCrJCbKrCfPaODthx6qpVc295ebhhxQzz8zhVj480fUnV36arfozfiwxujI8fiLOYDmv4eLobDUBZ7a7YMCzYchrx7fXH+nFzU0lWDbcJaoQXh9+Gh3q3wnurjmLlIf3JLh/q7bjRww1v+Loz2791d2fMHhutXaa6BAoAxt+aymFib+NxX9qZ6ZYf3NgHb9owlcizQzsgPbsACx7uATc3lWRw7eamqjNBCsBAheqpv/VoKWuIfKrx7/Fd0Lqpn93tPpo39saBN4bDT+ZIx9VP9I28PTC6q/yqBWeypmTdsD2Wre2s+rRrhoOzR0Dt49jLdItAX1zMK9ZWQRiyZa4vR/Hz8sCmlwbJTkNogA++eKg7Vh4ymOW9FrOim+aerZtgw7FcNPJylwywnMlwXBeVSoXEx3rh0cV/AYDZ8VmUqu6lmKgeUlIPwkA/L7w6qqNDtmXPLNNKZM1xam7lsPo9WjfBvvPX9Z6+Ddk6oJw5vz/dD+uPZuM+E4Fok0a1U11qiiMDJVcFXf93XwwW7TiLv/VwzOSb9hocFYwz79+JM1cKrRpcUWk4Mi0RuZxu9ZBUz4W6YOHfu2Pgbc3xrzHWFdf/OKUPVj5zO56XmAfGmUIDfDApvo1RVeW3j/dGTMsAvakk6hrDmZprs0RFVzN/b7wysqPePF2u5uamQmRwY5eWmNmKJSpU5zhzbBRyDV8vd2z/5xC4u6kcNsmaM5gLokZ1CcOoLtZXVfl4uqNLiwBEhTbG+Ws3tT03XGXQbc2tnqRTqX6Y0gfLDlzUjlEjd6JMUiYGKkSkCC2bKPem8vKI27A5/TL+Zme7HSme7m6YPbazw7fbEIWofTBtUHsM6xSM/OJyi4PSUd3AQIXqjBHRIVh/NAdTB8qfgI3IHjPu6GBxTBpSjshgZc4CTLZhoEJ1xvyHu+NUbqHkLKx1HauziIikMVChOsPT3Q2dJEaGJCKi+qtuNq8nIiKiBoGBCpECKKkbIxGRkrDqh8iFlv2jHzKu3URMy0BXJ4WISJEYqBC5ULdWTdCtVRNXJ4OISLFY9UNERESKxUCFiIiIFIuBChERESkWAxUiIiJSLAYqREREpFgMVIiIiEixbApU5s+fjzZt2sDHxwd9+vTBnj17zC7/66+/omPHjvDx8UHXrl2xevVqmxJLREREDYvsQOXnn3/Giy++iNmzZ2P//v2IjY3FyJEjkZubK7n8zp07MXHiRDzxxBM4cOAAxo8fj/HjxyMtLc3uxBMREVH9phIyp23t06cPevXqhS+++AIAoNFoEBERgWeeeQYzZ840Wn7ChAkoKirCypUrte/17dsXcXFxWLhwoVX7LCgoQEBAAPLz86FWc1I6IiKiusAR929ZJSplZWXYt28fhg0bVrMBNzcMGzYMKSkpkuukpKToLQ8AI0eONLk8AJSWlqKgoEDvRURERA2PrEDlypUrqKysREhIiN77ISEhyM7OllwnOztb1vIAkJCQgICAAO0rIiJCTjKJiIionlBkr59Zs2YhPz9f+8rMzHR1koiIiMgFZE1KGBQUBHd3d+Tk5Oi9n5OTg9DQUMl1QkNDZS0PAN7e3vD29paTNCIiIqqHZAUqXl5e6NGjBzZu3Ijx48cDqGpMu3HjRsyYMUNynfj4eGzcuBHPP/+89r2kpCTEx8dbvd/q9r5sq0JERFR3VN+3Zfbb0SdkWrp0qfD29haJiYni6NGj4sknnxSBgYEiOztbCCHEI488ImbOnKldfseOHcLDw0N8+OGH4tixY2L27NnC09NTHD582Op9ZmZmCgB88cUXX3zxxVcdfGVmZsoNN7RklagAVd2NL1++jDfffBPZ2dmIi4vD2rVrtQ1mMzIy4OZW0/SlX79+WLJkCV5//XW89tpr6NChA5YvX44uXbpYvc/w8HBkZmaicePGUKlUcpNsUkFBASIiIpCZmVmvuz0zn/UL81l/NIQ8AsxnfSMnn0II3LhxA+Hh4TbvT/Y4KvVJQxmfhfmsX5jP+qMh5BFgPuub2s6nInv9EBEREQEMVIiIiEjBGnSg4u3tjdmzZ9f7rtDMZ/3CfNYfDSGPAPNZ39R2Pht0GxUiIiJStgZdokJERETKxkCFiIiIFIuBChERESkWAxUiIiJSrAYdqMyfPx9t2rSBj48P+vTpgz179rg6SVZ76623oFKp9F4dO3bUfl5SUoLp06ejWbNm8Pf3x3333Wc0OWRGRgbGjBkDPz8/BAcH45VXXkFFRUVtZ0XP1q1bMXbsWISHh0OlUmH58uV6nwsh8OabbyIsLAy+vr4YNmwYTp48qbfMtWvX8PDDD0OtViMwMBBPPPEECgsL9ZY5dOgQBgwYAB8fH0REROCDDz5wdtb0WMrno48+anR8R40apbeM0vOZkJCAXr16oXHjxggODsb48eORnp6ut4yjztPk5GR0794d3t7eiIyMRGJiorOzp2VNPgcPHmx0PKdNm6a3jNLzuWDBAsTExECtVkOtViM+Ph5r1qzRfl4fjiVgOZ/14VgamjNnDlQqld6cfIo6njYPvl/HLV26VHh5eYlFixaJI0eOiKlTp4rAwECRk5Pj6qRZZfbs2aJz584iKytL+7p8+bL282nTpomIiAixceNGsXfvXtG3b1/Rr18/7ecVFRWiS5cuYtiwYeLAgQNi9erVIigoSMyaNcsV2dFavXq1+Ne//iX++OMPAUAsW7ZM7/M5c+aIgIAAsXz5cnHw4EFx9913i7Zt24ri4mLtMqNGjRKxsbFi165dYtu2bSIyMlJMnDhR+3l+fr4ICQkRDz/8sEhLSxM//fST8PX1FV999VVtZdNiPidPnixGjRqld3yvXbumt4zS8zly5EixePFikZaWJlJTU8Wdd94pWrVqJQoLC7XLOOI8PXPmjPDz8xMvvviiOHr0qPj888+Fu7u7WLt2rWLyOWjQIDF16lS945mfn1+n8vnnn3+KVatWiRMnToj09HTx2muvCU9PT5GWliaEqB/H0pp81odjqWvPnj2iTZs2IiYmRjz33HPa95V0PBtsoNK7d28xffp07d+VlZUiPDxcJCQkuDBV1ps9e7aIjY2V/CwvL094enqKX3/9VfvesWPHBACRkpIihKi6Ubq5uWknkxRCiAULFgi1Wi1KS0udmnZrGd7ANRqNCA0NFXPnztW+l5eXJ7y9vcVPP/0khBDi6NGjAoD466+/tMusWbNGqFQqcfHiRSGEEF9++aVo0qSJXj7/+c9/iqioKCfnSJqpQGXcuHEm16mL+czNzRUAxJYtW4QQjjtPX331VdG5c2e9fU2YMEGMHDnS2VmSZJhPIapubro3AUN1MZ9CCNGkSRPxzTff1NtjWa06n0LUr2N548YN0aFDB5GUlKSXL6UdzwZZ9VNWVoZ9+/Zh2LBh2vfc3NwwbNgwpKSkuDBl8pw8eRLh4eFo164dHn74YWRkZAAA9u3bh/Lycr38dezYEa1atdLmLyUlBV27dtVOJgkAI0eOREFBAY4cOVK7GbHS2bNnkZ2drZevgIAA9OnTRy9fgYGB6Nmzp3aZYcOGwc3NDbt379YuM3DgQHh5eWmXGTlyJNLT03H9+vVayo1lycnJCA4ORlRUFJ5++mlcvXpV+1ldzGd+fj4AoGnTpgAcd56mpKTobaN6GVf9lg3zWe3HH39EUFAQunTpglmzZuHmzZvaz+paPisrK7F06VIUFRUhPj6+3h5Lw3xWqy/Hcvr06RgzZoxRWpR2PGXPnlwfXLlyBZWVlXpfMACEhITg+PHjLkqVPH369EFiYiKioqKQlZWFt99+GwMGDEBaWhqys7Ph5eWFwMBAvXVCQkKQnZ0NAMjOzpbMf/VnSlSdLql06+YrODhY73MPDw80bdpUb5m2bdsabaP6syZNmjgl/XKMGjUK9957L9q2bYvTp0/jtddew+jRo5GSkgJ3d/c6l0+NRoPnn38e/fv3186c7qjz1NQyBQUFKC4uhq+vrzOyJEkqnwDw0EMPoXXr1ggPD8ehQ4fwz3/+E+np6fjjjz/M5qH6M3PL1GY+Dx8+jPj4eJSUlMDf3x/Lli1DdHQ0UlNT69WxNJVPoP4cy6VLl2L//v3466+/jD5T2m+zQQYq9cHo0aO1/4+JiUGfPn3QunVr/PLLL7V6YSbnePDBB7X/79q1K2JiYtC+fXskJydj6NChLkyZbaZPn460tDRs377d1UlxKlP5fPLJJ7X/79q1K8LCwjB06FCcPn0a7du3r+1k2iwqKgqpqanIz8/Hb7/9hsmTJ2PLli2uTpbDmcpndHR0vTiWmZmZeO6555CUlAQfHx9XJ8eiBln1ExQUBHd3d6MWzDk5OQgNDXVRquwTGBiI2267DadOnUJoaCjKysqQl5ent4xu/kJDQyXzX/2ZElWny9xxCw0NRW5urt7nFRUVuHbtWp3Oe7t27RAUFIRTp04BqFv5nDFjBlauXInNmzejZcuW2vcddZ6aWkatVtdq0G4qn1L69OkDAHrHsy7k08vLC5GRkejRowcSEhIQGxuLefPm1btjaSqfUurisdy3bx9yc3PRvXt3eHh4wMPDA1u2bMFnn30GDw8PhISEKOp4NshAxcvLCz169MDGjRu172k0GmzcuFGvHrIuKSwsxOnTpxEWFoYePXrA09NTL3/p6enIyMjQ5i8+Ph6HDx/Wu9klJSVBrVZriziVpm3btggNDdXLV0FBAXbv3q2Xr7y8POzbt0+7zKZNm6DRaLQXlPj4eGzduhXl5eXaZZKSkhAVFaWIah8pFy5cwNWrVxEWFgagbuRTCIEZM2Zg2bJl2LRpk1E1lKPO0/j4eL1tVC9TW79lS/mUkpqaCgB6x1Pp+ZSi0WhQWlpab46lKdX5lFIXj+XQoUNx+PBhpKamal89e/bEww8/rP2/oo6n/HbC9cPSpUuFt7e3SExMFEePHhVPPvmkCAwM1GvBrGQvvfSSSE5OFmfPnhU7duwQw4YNE0FBQSI3N1cIUdW1rFWrVmLTpk1i7969Ij4+XsTHx2vXr+5aNmLECJGamirWrl0rmjdv7vLuyTdu3BAHDhwQBw4cEADExx9/LA4cOCDOnz8vhKjqnhwYGChWrFghDh06JMaNGyfZPblbt25i9+7dYvv27aJDhw563Xbz8vJESEiIeOSRR0RaWppYunSp8PPzq9XuyebyeePGDfHyyy+LlJQUcfbsWbFhwwbRvXt30aFDB1FSUlJn8vn000+LgIAAkZycrNeV8+bNm9plHHGeVneBfOWVV8SxY8fE/Pnza7Wrp6V8njp1Srzzzjti79694uzZs2LFihWiXbt2YuDAgXUqnzNnzhRbtmwRZ8+eFYcOHRIzZ84UKpVKrF+/XghRP46lpXzWl2MpxbA3k5KOZ4MNVIQQ4vPPPxetWrUSXl5eonfv3mLXrl2uTpLVJkyYIMLCwoSXl5do0aKFmDBhgjh16pT28+LiYvGPf/xDNGnSRPj5+Yl77rlHZGVl6W3j3LlzYvTo0cLX11cEBQWJl156SZSXl9d2VvRs3rxZADB6TZ48WQhR1UX5jTfeECEhIcLb21sMHTpUpKen623j6tWrYuLEicLf31+o1Wrx2GOPiRs3bugtc/DgQXH77bcLb29v0aJFCzFnzpzayqIQwnw+b968KUaMGCGaN28uPD09RevWrcXUqVONgmil51MqfwDE4sWLtcs46jzdvHmziIuLE15eXqJdu3Z6+3A2S/nMyMgQAwcOFE2bNhXe3t4iMjJSvPLKK3pjbwih/Hw+/vjjonXr1sLLy0s0b95cDB06VBukCFE/jqUQ5vNZX46lFMNARUnHUyWEEPLKYIiIiIhqR4Nso0JERER1AwMVIiIiUiwGKkRERKRYDFSIiIhIsRioEBERkWIxUCEiIiLFYqBCREREisVAhYiIiBSLgQoREREpFgMVIiIiUiwGKkRERKRYDFSIiIhIsf4fSZb/OaNkkBIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted = model(X_test)"
      ],
      "metadata": {
        "id": "gWoXBmUXPUkp"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = torch.where(predicted.flatten() > 0.5, 1, 0)"
      ],
      "metadata": {
        "id": "kHzYq8vRPYwV"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "print(\"The accuracy score is  = \", accuracy_score(y_pred, y_test))\n",
        "print(\"The precision score is = \", precision_score(y_pred, y_test))\n",
        "print(\"The recall score is    = \", recall_score(y_pred, y_test))\n",
        "print(\"The f1 score of ths is = \", f1_score(y_pred, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pacy_bybPd8V",
        "outputId": "5517a596-94a4-40df-da55-7938e5d08cfd"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score is  =  0.7532467532467533\n",
            "The precision score is =  0.4727272727272727\n",
            "The recall score is    =  0.7428571428571429\n",
            "The f1 score of ths is =  0.5777777777777778\n"
          ]
        }
      ]
    }
  ]
}