nb-1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Work I .ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8blKFtOq2dZd"
      },
      "source": [
        "**INITIALIZATION:**\r\n",
        "- I use these three lines of code on top of my each notebooks because it will help to prevent the problems while reloading the **Project**. The third line of code helps to make visualization within the notebook. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwG6EdgC1A23"
      },
      "source": [
        "#@ INITIALIZATION:\r\n",
        "%reload_ext autoreload\r\n",
        "%autoreload 2\r\n",
        "%matplotlib inline         "
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGwXv2QO3cBv"
      },
      "source": [
        "**DOWNLOADING THE DEPENDENCIES:**\r\n",
        "- I have downloaded all the libraries and dependencies required for the **Project** in one particular cell. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s0t1fOqr3V7x"
      },
      "source": [
        "#@ DOWNLOADING THE DEPENDENCIES: UNCOMMENT BELOW:\r\n",
        "# !pip install kora -q                                    # Installing the Dependencies for RDKIT.\r\n",
        "import kora.install.rdkit                                 # Getting RDKIT. \r\n",
        "\r\n",
        "import os\r\n",
        "import pickle\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import sklearn\r\n",
        "from IPython.display import display\r\n",
        "\r\n",
        "from rdkit import Chem\r\n",
        "from rdkit.Chem import Draw, Descriptors\r\n",
        "from rdkit.Chem.Draw import IPythonConsole\r\n",
        "\r\n",
        "from sklearn.model_selection import train_test_split \r\n",
        "from sklearn.decomposition import PCA   \r\n",
        "from keras.models import Model, Sequential\r\n",
        "from keras.layers import Input, LSTM, Dense, Concatenate\r\n",
        "from keras import regularizers\r\n",
        "from keras.callbacks import History, ReduceLROnPlateau\r\n",
        "from keras.optimizers import RMSprop, Adam"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mD2lpJ2tJ7FC"
      },
      "source": [
        "**GETTING THE DATA:**\r\n",
        "- I have downloaded the dataset from [**REYMOND RESEARCH GROUP**](https://gdb.unibe.ch/downloads/). I will use a file with **SMILES** containing 8 atoms from the GDB11 dataset. And I will do random splitting of the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "OYAtw1oG4hh2",
        "outputId": "6f46f12e-cc94-47c1-c5ab-da32a45c68c6"
      },
      "source": [
        "#@ GETTING THE DATA:\r\n",
        "PATH = \"/content/drive/MyDrive/WORK/gdb11_size08.smi\"                           # Path to the Data. \r\n",
        "data = pd.read_csv(PATH, delimiter=\"\\t\", names=[\"smiles\", \"No\", \"Int\"])         # Reading the Data. \r\n",
        "display(data.head())                                                            # Inspecting the Data\r\n",
        "smiles_train, smiles_test = train_test_split(data[\"smiles\"], random_state=42)   # Splitting the Data. \r\n",
        "display(smiles_train.shape, smiles_test.shape)                                  # Inspecting the shape of Train and Test Data. "
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>smiles</th>\n",
              "      <th>No</th>\n",
              "      <th>Int</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CC(C)CC(C)(C)C</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CC(N)CC(C)(C)C</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CC(O)CC(C)(C)C</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CC(F)CC(C)(C)C</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CC(C)(C)CC(F)F</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           smiles  No  Int\n",
              "0  CC(C)CC(C)(C)C   1    1\n",
              "1  CC(N)CC(C)(C)C   2    1\n",
              "2  CC(O)CC(C)(C)C   3    1\n",
              "3  CC(F)CC(C)(C)C   4    1\n",
              "4  CC(C)(C)CC(F)F   5    1"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(50029,)"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(16677,)"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YGjIl9RNOAS7"
      },
      "source": [
        "### **PREPARING THE DATA:**\r\n",
        "- I will vectorize the data into one hot arrays."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nZr9zNluNpPs",
        "outputId": "09b11c2a-d994-484a-ceaa-cd2952ee2d82"
      },
      "source": [
        "#@ PREPARING THE DATA:\r\n",
        "charset = set(\"\".join(list(data.smiles)) + \"!E\")\r\n",
        "char_to_int = dict((c, i) for i, c in enumerate(charset))            # Converting the Characters to Index. \r\n",
        "int_to_char = dict((i, c) for i, c in enumerate(charset))            # Converting the Index to Characters. \r\n",
        "emmbed = max([len(smile) for smile in data.smiles]) + 5              # Maximum length of the SMILES strings. \r\n",
        "print(str(charset))                                                  # Inspecting the Characterset. \r\n",
        "print(len(charset), emmbed)                                          # Inspecting the length of Data. "
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'o', 'H', 'n', 'O', '[', '=', '3', '!', '#', '(', '4', 'c', 'F', 'E', 'N', ']', '2', 'C', '+', '-', '1', ')'}\n",
            "22 28\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ROu92ZkYR2Cr"
      },
      "source": [
        "- Now the character set and dictionaries are used to set the necessary bits into arrays. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "63HsNn6ERRB5",
        "outputId": "9d6a18be-25a9-4b95-bf18-d2c875f5c0c5"
      },
      "source": [
        "#@ PREPARING THE DATA:\r\n",
        "def vectorize(smiles):                                                          # Vectorization.\r\n",
        "  one_hot = np.zeros((smiles.shape[0], emmbed, len(charset)), dtype=np.int8)    # Initialization of an empty numpy array.\r\n",
        "  for i, smile in enumerate(smiles):\r\n",
        "    one_hot[i, 0, char_to_int[\"!\"]] = 1                                         # Encoding the starting character. \r\n",
        "    for j, c in enumerate(smile):\r\n",
        "      one_hot[i, j+1, char_to_int[c]] = 1                                       # Encoding the rest of the characters. \r\n",
        "    one_hot[i, len(smile)+1:, char_to_int[\"E\"]] = 1                             # Encoding the ending character. \r\n",
        "  return one_hot[:, 0:-1, :], one_hot[:, 1:, :]\r\n",
        "X_train, y_train = vectorize(smiles_train.values)                               # Encoding the Training set. \r\n",
        "X_test, y_test = vectorize(smiles_test.values)                                  # Encoding the Test set. \r\n",
        "print(smiles_train.iloc[0])                                                     # Inspecting the Data.\r\n",
        "plt.matshow(X_train[0].T)                                                       # Data Visualization of the arrays. \r\n",
        "display(X_train.shape, y_train.shape, X_test.shape)                             # Inspecting the shape of Data. \r\n",
        "print(\"\".join([int_to_char[idx] for idx in np.argmax(X_train[0,:,:], axis=1)])) # Converting the Index into Characters. "
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "N#CC#CC1COC1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(50029, 27, 22)"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(50029, 27, 22)"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(16677, 27, 22)"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "!N#CC#CC1COC1EEEEEEEEEEEEEE\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 353.455x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j2F4jvJiMcji"
      },
      "source": [
        "### **LONG SHORT TERM MEMORY:**\r\n",
        "- **LSTM** is a special kind of **Neural Netoworks** that are designed to keep an internal states for longer iterations through a **Recurrent Neural Networks**. They have input, output and forget gates. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ha5eVhuxWB7z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "526dd0c4-44c3-4e65-dfd2-b80da1a48351"
      },
      "source": [
        "#@ LONG SHORT TERM MEMORY:\r\n",
        "input_shape = X_train.shape[1:]                                           # Shape of input.\r\n",
        "output_dim = y_train.shape[-1]                                            # Shape of output. \r\n",
        "latent_dim = 64\r\n",
        "lstm_dim = 64\r\n",
        "#@ ENCODER:\r\n",
        "unroll = False\r\n",
        "encoder_inputs = Input(shape=input_shape)                                 # Inputs of the Encoder. \r\n",
        "encoder = LSTM(lstm_dim, return_state=True, unroll=unroll)                # Encoder using LSTM. \r\n",
        "encoder_outputs, state_h, state_c = encoder(encoder_inputs)               \r\n",
        "states = Concatenate(axis=-1)([state_h, state_c])                         # Concatenation of two states. \r\n",
        "neck = Dense(latent_dim, activation=\"relu\")                               # Implementation of RELU.\r\n",
        "neck_outputs = neck(states)\r\n",
        "#@ DECODER:\r\n",
        "decode_h = Dense(lstm_dim, activation=\"relu\")                             # Implementation of Decoder. \r\n",
        "decode_c = Dense(lstm_dim, activation=\"relu\")                             # Implementation of Decoder. \r\n",
        "state_h_decoded = decode_h(neck_outputs)                                  # Decoder.\r\n",
        "state_c_decoded = decode_c(neck_outputs)                                  # Decoder.\r\n",
        "encoder_states = [state_h_decoded, state_c_decoded]\r\n",
        "decoder_inputs =  Input(shape=input_shape)\r\n",
        "decoder_lstm = LSTM(lstm_dim, return_sequences=True, unroll=unroll)          # Implementation of LSTM.\r\n",
        "decoder_outputs = decoder_lstm(decoder_inputs, initial_state=encoder_states)\r\n",
        "decoder_dense = Dense(output_dim, activation=\"softmax\")                      # Output Layer.\r\n",
        "decoder_outputs = decoder_dense(decoder_outputs)                             # Implemenatation of Decoders. \r\n",
        "#@ DEFINING THE MODEL:\r\n",
        "model = Model([encoder_inputs, decoder_inputs], decoder_outputs)             # Instantiating the Model. \r\n",
        "display(model.summary())                                                     # Inspecting the summary of the Model. "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 27, 22)]     0                                            \n",
            "__________________________________________________________________________________________________\n",
            "lstm (LSTM)                     [(None, 64), (None,  22272       input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 128)          0           lstm[0][1]                       \n",
            "                                                                 lstm[0][2]                       \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 64)           8256        concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "input_2 (InputLayer)            [(None, 27, 22)]     0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 64)           4160        dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 64)           4160        dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "lstm_1 (LSTM)                   (None, 27, 64)       22272       input_2[0][0]                    \n",
            "                                                                 dense_1[0][0]                    \n",
            "                                                                 dense_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 27, 22)       1430        lstm_1[0][0]                     \n",
            "==================================================================================================\n",
            "Total params: 62,550\n",
            "Trainable params: 62,550\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qoTRlhEHlOul"
      },
      "source": [
        "**PREPARING THE MODEL:**\r\n",
        "- I will create the **Keras** callbacks to record the history and reduce the learning rate once the **Training** stops progressing. The model is compiled with optimizer and loss function. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X85TbiDBTvvi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ce16e9b-14ad-4573-a5c1-23c5a0c1e26a"
      },
      "source": [
        "history = History()                                                    # Instantiating the History Module. \r\n",
        "rlr = ReduceLROnPlateau(monitor=\"val_loss\", factor=0.5, patience=10,\r\n",
        "                        min_lr=0.000001, verbose=1, min_delta=1e-5)    # Instantiating the Module for Learning Rate. \r\n",
        "optimizer = Adam(lr=0.005)                                             # Initializing the Adam Optimizer. \r\n",
        "model.compile(optimizer=optimizer, loss=\"categorical_crossentropy\")    # Compiling the Model.\r\n",
        "model.fit([X_train, X_train], y_train, epochs=200,                     # Using the Training Data. \r\n",
        "          batch_size=256, shuffle=True, callbacks=[history, rlr],      # Implementation of Callbacks. \r\n",
        "          validation_data=([X_test, X_test], y_test))                  # Training the Model.        "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "196/196 [==============================] - 11s 13ms/step - loss: 1.1965 - val_loss: 0.6557\n",
            "Epoch 2/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.6289 - val_loss: 0.5704\n",
            "Epoch 3/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.5855 - val_loss: 0.5430\n",
            "Epoch 4/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.5378 - val_loss: 0.5002\n",
            "Epoch 5/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.4928 - val_loss: 0.4639\n",
            "Epoch 6/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.4634 - val_loss: 0.4453\n",
            "Epoch 7/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.4398 - val_loss: 0.4219\n",
            "Epoch 8/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.4224 - val_loss: 0.4012\n",
            "Epoch 9/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.3830 - val_loss: 0.3519\n",
            "Epoch 10/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.3611 - val_loss: 0.3455\n",
            "Epoch 11/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.3292 - val_loss: 0.3187\n",
            "Epoch 12/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.3015 - val_loss: 0.2849\n",
            "Epoch 13/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.2741 - val_loss: 0.2509\n",
            "Epoch 14/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.2305 - val_loss: 0.1890\n",
            "Epoch 15/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.1905 - val_loss: 0.1627\n",
            "Epoch 16/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.1649 - val_loss: 0.1298\n",
            "Epoch 17/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.1312 - val_loss: 0.1137\n",
            "Epoch 18/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.1138 - val_loss: 0.0892\n",
            "Epoch 19/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.1008 - val_loss: 0.0913\n",
            "Epoch 20/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0767 - val_loss: 0.0602\n",
            "Epoch 21/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0588 - val_loss: 0.0516\n",
            "Epoch 22/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0536 - val_loss: 0.0796\n",
            "Epoch 23/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0491 - val_loss: 0.0364\n",
            "Epoch 24/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.1283 - val_loss: 0.0558\n",
            "Epoch 25/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0463 - val_loss: 0.0366\n",
            "Epoch 26/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0335 - val_loss: 0.0302\n",
            "Epoch 27/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0312 - val_loss: 0.1239\n",
            "Epoch 28/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0606 - val_loss: 0.0295\n",
            "Epoch 29/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0279 - val_loss: 0.0199\n",
            "Epoch 30/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0199 - val_loss: 0.0206\n",
            "Epoch 31/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0201 - val_loss: 0.0160\n",
            "Epoch 32/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0751 - val_loss: 0.0682\n",
            "Epoch 33/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0567 - val_loss: 0.0307\n",
            "Epoch 34/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0283 - val_loss: 0.0220\n",
            "Epoch 35/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0222 - val_loss: 0.0232\n",
            "Epoch 36/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0354 - val_loss: 0.0173\n",
            "Epoch 37/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0251 - val_loss: 0.0191\n",
            "Epoch 38/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0156 - val_loss: 0.0160\n",
            "Epoch 39/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0171 - val_loss: 0.0112\n",
            "Epoch 40/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0097 - val_loss: 0.0092\n",
            "Epoch 41/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0541 - val_loss: 0.0196\n",
            "Epoch 42/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0161 - val_loss: 0.0112\n",
            "Epoch 43/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0094 - val_loss: 0.0091\n",
            "Epoch 44/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0076 - val_loss: 0.0072\n",
            "Epoch 45/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0848 - val_loss: 0.0306\n",
            "Epoch 46/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0226 - val_loss: 0.0132\n",
            "Epoch 47/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0119 - val_loss: 0.0105\n",
            "Epoch 48/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0085 - val_loss: 0.0085\n",
            "Epoch 49/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0356 - val_loss: 0.0083\n",
            "Epoch 50/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0072 - val_loss: 0.0068\n",
            "Epoch 51/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0062 - val_loss: 0.0058\n",
            "Epoch 52/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0046 - val_loss: 0.0055\n",
            "Epoch 53/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0867 - val_loss: 0.0093\n",
            "Epoch 54/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0076 - val_loss: 0.0060\n",
            "Epoch 55/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0052 - val_loss: 0.0053\n",
            "Epoch 56/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0040 - val_loss: 0.0053\n",
            "Epoch 57/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0049 - val_loss: 0.0050\n",
            "Epoch 58/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0034 - val_loss: 0.0051\n",
            "Epoch 59/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0033 - val_loss: 0.0036\n",
            "Epoch 60/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0684 - val_loss: 0.0129\n",
            "Epoch 61/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0094 - val_loss: 0.0058\n",
            "Epoch 62/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0047 - val_loss: 0.0057\n",
            "Epoch 63/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0038 - val_loss: 0.0038\n",
            "Epoch 64/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0138 - val_loss: 0.0123\n",
            "Epoch 65/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0079 - val_loss: 0.0045\n",
            "Epoch 66/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0034 - val_loss: 0.0034\n",
            "Epoch 67/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0028 - val_loss: 0.0028\n",
            "Epoch 68/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0021 - val_loss: 0.0027\n",
            "Epoch 69/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0268 - val_loss: 0.0086\n",
            "Epoch 70/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0059 - val_loss: 0.0045\n",
            "Epoch 71/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0032 - val_loss: 0.0028\n",
            "Epoch 72/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0021 - val_loss: 0.0029\n",
            "Epoch 73/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0018 - val_loss: 0.0029\n",
            "Epoch 74/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0117 - val_loss: 0.0125\n",
            "Epoch 75/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0078 - val_loss: 0.0041\n",
            "Epoch 76/200\n",
            "196/196 [==============================] - 2s 9ms/step - loss: 0.0030 - val_loss: 0.0030\n",
            "Epoch 77/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0021 - val_loss: 0.0023\n",
            "Epoch 78/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0019 - val_loss: 0.0050\n",
            "Epoch 79/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0021 - val_loss: 0.0029\n",
            "Epoch 80/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0045 - val_loss: 0.2898\n",
            "Epoch 81/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0788 - val_loss: 0.0066\n",
            "Epoch 82/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0048 - val_loss: 0.0040\n",
            "Epoch 83/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0029 - val_loss: 0.0031\n",
            "Epoch 84/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0021 - val_loss: 0.0023\n",
            "Epoch 85/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0016 - val_loss: 0.0019\n",
            "Epoch 86/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0018 - val_loss: 0.0019\n",
            "Epoch 87/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0012 - val_loss: 0.0018\n",
            "Epoch 88/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0677 - val_loss: 0.0137\n",
            "Epoch 89/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0095 - val_loss: 0.0066\n",
            "Epoch 90/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0044 - val_loss: 0.0038\n",
            "Epoch 91/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0028 - val_loss: 0.0032\n",
            "Epoch 92/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0025 - val_loss: 0.0042\n",
            "Epoch 93/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0649 - val_loss: 0.0062\n",
            "Epoch 94/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0045 - val_loss: 0.0034\n",
            "Epoch 95/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0025 - val_loss: 0.0025\n",
            "Epoch 96/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0018 - val_loss: 0.0020\n",
            "Epoch 97/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0014 - val_loss: 0.0019\n",
            "\n",
            "Epoch 00097: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455.\n",
            "Epoch 98/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0011 - val_loss: 0.0014\n",
            "Epoch 99/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 9.6849e-04 - val_loss: 0.0014\n",
            "Epoch 100/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 8.5016e-04 - val_loss: 0.0012\n",
            "Epoch 101/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 7.7275e-04 - val_loss: 0.0012\n",
            "Epoch 102/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 7.0822e-04 - val_loss: 0.0011\n",
            "Epoch 103/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 6.3535e-04 - val_loss: 0.0011\n",
            "Epoch 104/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 6.0621e-04 - val_loss: 0.0011\n",
            "Epoch 105/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 5.8603e-04 - val_loss: 0.0010\n",
            "Epoch 106/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0084 - val_loss: 0.0023\n",
            "Epoch 107/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0013 - val_loss: 0.0014\n",
            "Epoch 108/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 7.9013e-04 - val_loss: 0.0011\n",
            "Epoch 109/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 6.1693e-04 - val_loss: 9.7799e-04\n",
            "Epoch 110/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 5.3108e-04 - val_loss: 9.3338e-04\n",
            "Epoch 111/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 4.5933e-04 - val_loss: 8.2767e-04\n",
            "Epoch 112/200\n",
            "196/196 [==============================] - 2s 9ms/step - loss: 3.9623e-04 - val_loss: 7.3429e-04\n",
            "Epoch 113/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 3.5743e-04 - val_loss: 7.4971e-04\n",
            "Epoch 114/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0066 - val_loss: 0.0038\n",
            "Epoch 115/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0019 - val_loss: 0.0014\n",
            "Epoch 116/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 8.5985e-04 - val_loss: 0.0011\n",
            "Epoch 117/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 6.2731e-04 - val_loss: 9.5251e-04\n",
            "Epoch 118/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 5.0746e-04 - val_loss: 8.5768e-04\n",
            "Epoch 119/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 4.4875e-04 - val_loss: 7.9325e-04\n",
            "Epoch 120/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 3.8989e-04 - val_loss: 6.9886e-04\n",
            "Epoch 121/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 3.4497e-04 - val_loss: 6.6371e-04\n",
            "Epoch 122/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 3.1211e-04 - val_loss: 6.4487e-04\n",
            "Epoch 123/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 2.9432e-04 - val_loss: 5.9875e-04\n",
            "Epoch 124/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 2.6468e-04 - val_loss: 6.1067e-04\n",
            "Epoch 125/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 2.9679e-04 - val_loss: 0.0074\n",
            "Epoch 126/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0114 - val_loss: 9.2262e-04\n",
            "Epoch 127/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 4.6678e-04 - val_loss: 6.7617e-04\n",
            "Epoch 128/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 3.3461e-04 - val_loss: 5.9281e-04\n",
            "Epoch 129/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 2.7006e-04 - val_loss: 5.5856e-04\n",
            "Epoch 130/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 2.3695e-04 - val_loss: 5.2907e-04\n",
            "Epoch 131/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 2.0721e-04 - val_loss: 4.8216e-04\n",
            "Epoch 132/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.9270e-04 - val_loss: 4.9698e-04\n",
            "Epoch 133/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.7446e-04 - val_loss: 4.5430e-04\n",
            "Epoch 134/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.6529e-04 - val_loss: 4.2464e-04\n",
            "Epoch 135/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.4594e-04 - val_loss: 4.0248e-04\n",
            "Epoch 136/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.3607e-04 - val_loss: 4.0961e-04\n",
            "Epoch 137/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.3028e-04 - val_loss: 4.1184e-04\n",
            "Epoch 138/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.9777e-04 - val_loss: 0.7245\n",
            "Epoch 139/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.1183 - val_loss: 0.0021\n",
            "Epoch 140/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 0.0013 - val_loss: 0.0014\n",
            "Epoch 141/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 7.2932e-04 - val_loss: 0.0010\n",
            "Epoch 142/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 5.4888e-04 - val_loss: 8.8346e-04\n",
            "Epoch 143/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 4.5784e-04 - val_loss: 7.9274e-04\n",
            "Epoch 144/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 3.7747e-04 - val_loss: 7.1519e-04\n",
            "Epoch 145/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 3.3453e-04 - val_loss: 6.3778e-04\n",
            "\n",
            "Epoch 00145: ReduceLROnPlateau reducing learning rate to 0.0012499999720603228.\n",
            "Epoch 146/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 2.9087e-04 - val_loss: 6.2051e-04\n",
            "Epoch 147/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 2.6653e-04 - val_loss: 6.0017e-04\n",
            "Epoch 148/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 2.5322e-04 - val_loss: 5.8040e-04\n",
            "Epoch 149/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 2.4263e-04 - val_loss: 5.5995e-04\n",
            "Epoch 150/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 2.2396e-04 - val_loss: 5.3960e-04\n",
            "Epoch 151/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 2.1191e-04 - val_loss: 5.3534e-04\n",
            "Epoch 152/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 2.0222e-04 - val_loss: 5.0761e-04\n",
            "Epoch 153/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.9304e-04 - val_loss: 4.9726e-04\n",
            "Epoch 154/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.7936e-04 - val_loss: 4.8096e-04\n",
            "Epoch 155/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.6987e-04 - val_loss: 4.6508e-04\n",
            "\n",
            "Epoch 00155: ReduceLROnPlateau reducing learning rate to 0.0006249999860301614.\n",
            "Epoch 156/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.5932e-04 - val_loss: 4.4785e-04\n",
            "Epoch 157/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.5473e-04 - val_loss: 4.5640e-04\n",
            "Epoch 158/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.5004e-04 - val_loss: 4.5100e-04\n",
            "Epoch 159/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.4506e-04 - val_loss: 4.3971e-04\n",
            "Epoch 160/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.3926e-04 - val_loss: 4.3819e-04\n",
            "Epoch 161/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.3589e-04 - val_loss: 4.3235e-04\n",
            "Epoch 162/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.3062e-04 - val_loss: 4.1939e-04\n",
            "Epoch 163/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.2495e-04 - val_loss: 4.2740e-04\n",
            "Epoch 164/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.2367e-04 - val_loss: 3.9974e-04\n",
            "Epoch 165/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.1741e-04 - val_loss: 4.0614e-04\n",
            "\n",
            "Epoch 00165: ReduceLROnPlateau reducing learning rate to 0.0003124999930150807.\n",
            "Epoch 166/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.1010e-04 - val_loss: 3.8974e-04\n",
            "Epoch 167/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.0717e-04 - val_loss: 3.8861e-04\n",
            "Epoch 168/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.0669e-04 - val_loss: 3.9262e-04\n",
            "Epoch 169/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.0047e-04 - val_loss: 3.8681e-04\n",
            "Epoch 170/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 1.0139e-04 - val_loss: 3.8043e-04\n",
            "Epoch 171/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 9.7886e-05 - val_loss: 3.8023e-04\n",
            "Epoch 172/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 9.5877e-05 - val_loss: 3.8421e-04\n",
            "Epoch 173/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 9.3514e-05 - val_loss: 3.7616e-04\n",
            "Epoch 174/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 8.9901e-05 - val_loss: 3.6434e-04\n",
            "Epoch 175/200\n",
            "196/196 [==============================] - 2s 9ms/step - loss: 8.8156e-05 - val_loss: 3.6897e-04\n",
            "Epoch 176/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 8.3776e-05 - val_loss: 3.7185e-04\n",
            "Epoch 177/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 8.1982e-05 - val_loss: 3.6178e-04\n",
            "Epoch 178/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 8.0611e-05 - val_loss: 3.4459e-04\n",
            "Epoch 179/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 7.5321e-05 - val_loss: 3.4979e-04\n",
            "Epoch 180/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 7.2651e-05 - val_loss: 3.4452e-04\n",
            "Epoch 181/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 6.9194e-05 - val_loss: 3.4962e-04\n",
            "Epoch 182/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 6.9231e-05 - val_loss: 3.3604e-04\n",
            "Epoch 183/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 7.6086e-05 - val_loss: 3.4679e-04\n",
            "Epoch 184/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 9.8544e-04 - val_loss: 3.6773e-04\n",
            "Epoch 185/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 7.9966e-05 - val_loss: 3.3544e-04\n",
            "Epoch 186/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 7.1223e-05 - val_loss: 3.3133e-04\n",
            "Epoch 187/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 6.6959e-05 - val_loss: 3.2439e-04\n",
            "Epoch 188/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 6.2992e-05 - val_loss: 3.2378e-04\n",
            "Epoch 189/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 6.1621e-05 - val_loss: 3.1516e-04\n",
            "Epoch 190/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 5.9324e-05 - val_loss: 3.1651e-04\n",
            "Epoch 191/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 5.8107e-05 - val_loss: 3.1703e-04\n",
            "Epoch 192/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 5.5595e-05 - val_loss: 3.1201e-04\n",
            "Epoch 193/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 5.4272e-05 - val_loss: 3.0882e-04\n",
            "Epoch 194/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 5.3255e-05 - val_loss: 3.0772e-04\n",
            "Epoch 195/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 5.1628e-05 - val_loss: 3.1159e-04\n",
            "Epoch 196/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 5.0680e-05 - val_loss: 3.1246e-04\n",
            "Epoch 197/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 5.0224e-05 - val_loss: 3.0238e-04\n",
            "Epoch 198/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 4.7677e-05 - val_loss: 3.0697e-04\n",
            "Epoch 199/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 4.6352e-05 - val_loss: 3.0208e-04\n",
            "Epoch 200/200\n",
            "196/196 [==============================] - 2s 8ms/step - loss: 4.5811e-05 - val_loss: 3.0312e-04\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fc8867f1ac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tLBDvokVwaC3"
      },
      "source": [
        "**SAVING THE MODEL:**\r\n",
        "- I will save the best model recorded in the **History** object. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCEQDWtvo1JC"
      },
      "source": [
        "#@ SAVING THE MODEL:\r\n",
        "import pickle\r\n",
        "filename = \"Firstmodel.pickle\"                        # Name of the file. \r\n",
        "pickle.dump(history.history, open(filename, \"wb\"))    # Saving the Model. "
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gG4aL9wRyycJ"
      },
      "source": [
        "**PLOTTING THE TRAINING PROCESS:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "jZzI1iNqxcXS",
        "outputId": "7c96fcab-65f2-4ef7-d0b3-80a3ed66ec32"
      },
      "source": [
        "#@ PLOTTING THE TRAINING PROCESS:\r\n",
        "plt.plot(history.history[\"loss\"], label=\"Loss\")                # Plotting the Loss.\r\n",
        "plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\") # Plotting the Validation Loss. \r\n",
        "plt.yscale(\"log\")\r\n",
        "plt.legend()\r\n",
        "plt.show()                                                     # Plotting the Training Process. "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YVUqfKCbelxlZBVy1UUmd6BhRHX5S8OODRDo5OtvoCOr2+8+dTIefY6STnLTV4pUTUk/aFiJ5KfhTMsMHqJoNn34OTrkKX3kDdaKDZnsXAIuNPXRFbHx2L64vRMwswQlnVukEHRXpVMv2EUY6CYef/sMZHWG3zJE8IJKRzhRw+LY79MPpgz9bzy+e3DnmoRUMaZGOfkgWCnkp+FMyw09QPh1MH+V1MwiJCKWuEvXFnsMPOr0QKMXxlUAsvQ4/7rgUSRWlhNxu2juzd9TMRmJStneAsMdGmF0nxDsX156MdCbR4UeiuS36Gs7hP7+nnc0HMh/Axyx64VVBkpeCXwhMa5qV/LOsaGaxoQS/WPZhFJVBoJSg05e2120k7hAS/XvPOt0Hc3bP4UEcfqpjz2WV6UginYSIHvWJ0JQ6/Lau3PbqTWT4gz2c4o5M/l1p0GWZBYoW/Amioq6/kahY/i6VyR/YiV/Y+IvKMIvKKBER9hzpF6xw3CFEv2OtoTOjbUHccemJZmaqCdHNcPieiDmuTLr9oRhJLh9LRjojE0rXlWPrv5Pi8Dt6MjuOZsMeJn6KOa4u2UxFRzoFiRb8iSLUoH4vmw6zVgPQtXUtAIFQOcGSckKE2d7a30AtEnMJiTAxXxkAdaKdPW3pDvbGv2/jou8/nnG7hEAPXHiVKvKRWA6Cn+jWmcMni7idKMscmSB896FXedeP143oPWmkCH5nTzintwy10tZxJY4rteCnoiOdgiQvBX/KTtqmUuoJfv3SZK+dFUL12CkuraS4tJKQCLOtJUXwbYcSIvSWzgagVnRmCP4zO4+w+0hfhkMOD+HwQwEr7ZyhiI6gjDM+Soe/p62P3Udyi2KykiL4Xb0ji3SyPZwmbfI5n9ELrwqSvBT8KT1pm6CoEkpqoekUKKmBiplcEnwegFBZJWZRGWUikib44ZhDSISJljYjETSanexNEXwpJVsOqIncRDO21PcC9GXppVMWtLK+lo2k4Ofi8EeZ4ccdd2y9fTwBikqLnr4cHb47+MMpcUxn+CmkCH53JDqJA9GMJ3kp+AWBEPCJZ+CMa9TXx19Opa02Za+vrQV/iCIi7Gzp/xSjMvwwMliBKKmhOdCT5vAPdEaSk7ypNfpSyn6Hn7LwynUltispL/Ynrz8cI6nSGW2v/Zg9dKviSNwZurVDQvDx0RuO4ObQBsIe4uE02k8qBU1KjPPPtz07xImaqYQW/ImkpBosJbac+AFA7V9rBFWVDkBLW1syngnHbEKEEYFSCDXQZKVHOpv395cNdqTU6KcKVaqLT+T35UXK4ee0mCqeu4gPNmkrpeTx11oHFfSY16o48QlhIOd9+zF++dSuQe8rHZu4NHGED+HGOZjDqmBniF46seRchBb8JCkTtZ29uX2K0uQ/WvCPFhUzYP4b1J8D/YIfdPrY7Yl6LNKHJVxVthmqo470SdstB1IFv9/hp4pz6gKrqO0yUxziy+1fIkRf2mtdkTgf++VGDg0Qy5GVZWY/d/3ONj5wy3qefz2zdQRAzPsUke0Th5SSfR3hjLmLVBwnjo0JhoWFw9724QVpsLGmvqYz/BRSIh2pJ20LhrwU/IKYtM3G6s/C9JVQNg2CqhKnQvQkc3wnrL5fM1gKdYupj+4kGgknNzXZcrALv6X+yVIdfngQwY87LivEqywJ/4NFYk9ax8zN+7t4aMshnt3dnjbE8Vh41eHFTh2DrBSODVHdk3D9Q803uLYSfNew8Aknp6jKzsHh6ww/hRSRNxgmYtNMGfJS8Ati0jYbzafDlQ+Dvxiq5wMwX+xNlmbaYfW7VVQOM07FcmMsEzuTbnfz/i5WNlcC6Rl+QsiL/Sa9KTX6MdulRCgHXyO60oSx25v0HVjTP5qFVwOdcf+agOyinXhfNqHOZUtG14njYCANHz7snFo/20N09hzteoKCRroglDwYuMmHu2Zqk5eCf0xQuxDMAKcGX2fbISX0bkRFNlZxKcxcBcAK41We2XmE/R1hdrf1ccrsKgxB2grdhHBWlfjThDJmu5Si4o4a0ZkmjD1R9f7egYI/kgx/kEVaiWsM7Nw53PtSXxtKxF3HJp6MdOycxppw+Nmqj5KfOLTg9yNdMNX8k6kFv2DQgj9ZmD6oX8pJ/j1s2N2GlBLp7XHrL66AUB2ycjZvKt3FNx/Yygd/tp4Sv8VlJ0ynvMiX5vATgldd4qc3ZicnS2OOS4lQgl8rOrM6/IF1+8lIJwfxG6w9cuLrwRz+UP19EuI72Hsh4fBNpOnHh5Ob4DuDt0fWnTSz4Dpgqo17TNzk359maqMFfzKZdgJz7e283tbHriN9yIgSfKtITeiKmas4SbzK7KIIkdZd/ODyE5ldU0JlsT89w/dW0FaHAkiZXlceSjh8sgv+YJGO48phXd1gG6AkO3cO4tKH6siZi8OXXoYvDB8WuWb4g68vSNwz7sicSjyPCaQLpqru0g6/cNCCP5k0Ho/f7maGaOGxrS0Ir3umCKgJXWacihE+zF/4JA9X3sA5C2sBqCj2pQl+qsOH/pgmaruEkhl+eqTTFRkk0klx9sM55/4oZGCG72a9dvJ9Qzl8Z+iHBYB0bRxpIKzcBN91VRkoDJbhp8RgWtgU0kmLdPTfS2GgBX8yaTwegHPLDrD2tcMYMW/VbSCkfp99FggTw19CoO8gtGwGoKLYT0c4ZdI2Ifgh9RE8IZapDr/eSBf8QR1+ingOJ6TxQWKS4Rz+UG2Vc+nfLx1bOXzTh084w7aBSKyyVdfP5vD7Xb2uxfdwUwXf0ZFOgaAFfzKpWwKGxSfEnfzrzo9Q0bNNHfdq9KmeC595ET72kPp6x2OAcvjtvZmTtkmH7+XfMcelJGXSNnU3rJ5Ehj+Ewx9K/KTs776ZkeEP5/CHqASKJQV/8AxfOv2RTkDYw843pJYUZp0odoZ+IByTSKnmmdCRTiGRl4JfsHX4A7ECMH0FtbHXWST2sKLjARwM8BX3n1PeBJXNUDUHdq6FnlYWuzsyFl6dKrZw6YufwMJOc/il3qRtNZ1pTrg7Gelkn7RNXHcwElUvPlMQd2S6qNq5ZfhDC/7QDt/BBNPCL9xhyzJTV/RmE/R46kNOV+oopIM0+gVfRzqFQV4KfsHW4Wfjvb9HfG4ruwMLCIkwYVGk+vAMZPbZsOsJ+OXFfGTrlRTHDqdNcJ5hbqL+8DpmipZkOWRqpFNEFDfa36htuElbGHrxVcLxlQV93vsyHxTZKm0S/X1g6Dr8qO0OuthHujY2Bph+/DksvErtLDq8w9fCBoB0cVMEX0c6hUFeCv4xRVEloqSaxlPfDkDMKM5+3pyz1ZaIh1/FkA7vtx5O5vjhuEM9qo3BTHEoJdJxKBERpKGiHrf7UPJygwl+zHYJeKt5h+qYGfdy79Jgok9PZhSUrQ4/Npz4DtIXKA1XZfh4Gf5wgp/64Mgm6PE8iHR+9Nh2bn1mz6TcOyuug+MJviF0pFMoaMHPE/xLLgKgsrI6+wmzz4ZAOZx/HS31Z/Fe8yG6upVjD8cdGg3VIqFZtCSFMuHw7fJmAHrbDiQv1z1ElU55kfpBHyoqSQh3KJjZmG0ohx9PE/zBI50h75/I8E0fPpxhN2CJe4IfCliDTNpOvsO/+/n9/OWlA8OfeLSQDo5QRsHC0ZFOgaAFP1+oXwblMxHB0uyvF1fBv+6AM66hdelHqBVdiC13A8pR1xvK4TeLQ8lcPha3KSGCWzkHACvcmuzL0x0dLNJxqChWgj9Uhp8Q7tJAItJJEfIhMvzYMGWfqcIycFFYEtfGkf1VOrlGOiUBc8jWCgPHdzSx3TzbYlG6OMIEdKRTSGjBzxeEgMt+AOd8afBzvIUwzFnDEVlK8HW11WE45lCHcvgzxaGkM3ajvRhCqglf1Grbba3duK5MCn1v1E5rYxyN9zv8oapfEsKYzeEn7p+tSic2jMMfrNVzKtJ1sDE8hz98L53EnEGJ3yLmuBmLq/LB4dtOnm2x6Lo4wot0dJVOwaAFP5+YswbmnjPsaRUlfp535xE6rHbQikfDVKL68DSLlmSUIr26flE1G4mgRnSyvaXXa7+geu+4ckD+nhLp5OTws0Y6g1faxFNq3rM581RhGUzIhZfhG5YPS9qEh6mdT7jTxMNpoLCmZfhj2YlrDMRdd9LunRXpEPcE39KCXzBowZ+CVBb7eUHOpbRnJ72dbfgih9ULwQqvSkfFNoZXlWOFqqC4ijqji22tPckJ24ayIJAe60RthzJP8IcSoFhGlU5mpJPa16f/fZkPhrTX7dwiHRsTYfkxc8nwHZeV4hXeF73dG2v6+fni8POqPbN0iUsV6SiHryOdQkAL/hSkJGCxcOW5GEiu/8lvCUZa1AszTyMg4tgd+wEQMeX6jUApIlTPXH8H21r6Bb+xXAl+InqRUg5w+EOVZUrK6OGMjrsBmebwEzm5lJnXSF3VOvykbfZIR7iqDt8wfVjYOVXpXGqu4x3dv8YgMytPFbPJEt143kU6Dja6l06hoQV/inLRm7yqnvYX6W7dqw42nwZA+JBasWvEE60aSmHmaZzgvMTeQ4eTrZEbytMdftyRSEl/lc4wkc7F5jO8Ycd/M0scTBMrK9bF16xbKCaSUamTmuEP1jztf303cp7xj8EXX0nHi3T8GDKHSVvXJUgME5caOjMeNNF8cPium1+rfKVLLCn4jhb8AkEL/lSlqIJ45TxONF4jFFebozNTCb5o34ntuCm9eUrhuHcQkBEWdT1Ba7eq3x/o8BOCU+K3sAwxdIZvu5TTC0A9HWnnLrVf5v3Ww6wwXs2oxU+LT7K1KrbjXGY+xZnGpkEjHeEtvDJMP6YcftI27kiKRBSABtGWIeox26XYr+KLyRJd25H51cdHOsRkqsPXkU4hcFQFXwjxFiHET4QQvxdCvPFo3rsQ8TWfygpzO42iTbUaaDwBV5hMlwfYdaQP01aCTCAEM1YRLmrgEuMpXtirSjgbyouA/nr5hBAGfAZBnzlkpBNzXEqF2omrVnSkiVWRqx40lfRkOPyEUyzxm9kXdkXVmMtF7/CRjuVT+bJtD9nW2HElQdRDrkG0ZTzI4o5LKKDEbfIinTwry3RdYl6Gb+mFVwVDzoIvhPiZEKJFCPHSgOMXCCG2CiG2CSE+P9Q1pJR/klJeCVwNvHt0Q9YkmXMOlXTxZuMZuqxqsPxEa5ZytvEiWw50YSUjnTIwDGILL+Ns4wWe27oD6Hf4PZ4LTwq+ZRD0GUOvtHUkZZ7DrxMdSWccd1xKpSf4ojujtDIhqGVFvuzO3PtUUkHPoJGOIfurdAB82EOKZdxxKUI5/HrRntXhD1bBc7SwXZm1ZHTSkC5xKXCk0GWZBcRIHP4vgAtSDwghTOAm4EJgCXC5EGKJEOI4IcS9A37Vpbz13733acbCoouIWyGajRZ6/DUA+E68nGXGLg5vfxYr4fD9qt1y2cmX4xcOzS1/B/oz/GSk4znfgGUSsMwhI53Uxmx1oiP5aSASdyhDOf8q0Z3RnC2R4ZcX+bI+UIy42hOg0ugbVPCFdHCFhTD7V4IOlePbjiQovHkL0ZYRncQdlyKfiWmIcYl0pJR886+v8MrBrpzPT7R/yJsVrdIh5oKDoSOdAiJnwZdSrgXaBhw+BdgmpdwhpYwBtwGXSSk3SSkvHvCrRSi+AfxFSvlstvsIIa4SQmwUQmxsbW0d7fd1bOAvJrroLQD0eYJvHf8ubEwad/0Jn91LHEt15QTEtBNo8U3nEuMpTENQ4/XPT90wBVIc/jCTtgmHXyv6M/xI3KVcqOMVDOHwg77kTl2pGDHvvaJ30IVXQjpIYSbb9w4r+G6/w28Q7RnfV8xx8VsGAcsYlxw9HHf44aPbefDlQ8OfzIBunvmS40uXmCNwhaknbQuIsWb404HXU77e6x0bjP8HnA+8QwhxdbYTpJQ3SylXSilX1tbWjnF4hU/o1A8BUFE/Ux0oqWFzaBUru/5G0O5K774pBPuaLuI0YzPN/u5kbt2TukMWfZx372mczcYcMvx+h594WETiTlLwszr8ZKRjZa2fT1QWlQ8T6UihNjEHht3X1k7J8OvJPmnrN5Xgj4fDHqr9c/bxTX7ztgxch7gLLgaWGH67S83U4KhO2kopvy+lXCGlvFpK+aPBzhu0neEAACAASURBVDtm+uGPB00r4dSrqT/t8uSh/c1voYYOlvU+Q1ikd98sOeldmEJyme9pTENQ5DOTm6FE4w7zxT780XZOcl7M2eHXpzj8qO0kj6scPnPSto52FrI7a6STmHcoo4e+SHaHb0gHaVgpDn/oSh07rUpnKIefvdfOSOkX/Nyuld6vP0+EVTrEXIEjTHxC99IpFMYq+PuAGSlfN3nHxsQx1Q9/rAgBF34DZq1OHmo6+TJ6ZYBaeZjogHbLc5asYLOcxTXxn8EPT2eB/3BalU6zUDHEbGf3sGWZCYdfKzqT4p0a6VSJ7ozSypjj8lnrTq7a+wXijkzrVQ9gOSr/N3FxYz1kw5AOUljgte/1CXvYh1OR5/Drskzaxh3l8P2WMS4OO/lpJ8drpf4d5I/gu0QdgcTEb+gNUAqFsQr+BmC+EGK2EMIPvAe4e+zD0oyFpc11rPefAkDETBd8yzT47exv8qfqj8GRbVwh7kur0pltHARgRnxXDittlbBX0oUdVQ46HO93+JWim74s/fZrRQfl8RYCxDIatPkSE82AGW3Pem9T2kjDTO656hsmw0+UZUphUibCOJHujDH5vEhnPAR3qE3as2Gn9evPg0jHi5hirkQKHekUEiMpy/wdsA5YKITYK4T4qJTSBj4FPABsAW6XUr481kHpSGdsCCGIL7wUgKhRkvH6dR94A5d88n9g8SW80XmMWFiJbNR2mCWU4Je5HQRjRwa9R9yOUSKiyNJGdaBPtXeIxB3KvPp8VYef6fArvE8ADaItI4rxpwi+Fc1S5eK6GLhepKMyfAtnmIeTQ5GIYZepD6NGz4EBr0sV6fiMcanDT4hjrvFQfLwc/raHoWv/6N+fQHoGwJu0tYTUkU6BMJIqncullI1SSp+UsklKeYt3/H4p5QIp5Vwp5fXjMSgd6YydE855J70yQNQKZbzmMw1MQ8CKKwjJXpZ1PgoogZolDuJaakHW9NjOQa9vJMS4ZgEAYW9zlUhcrcCVCIpEjHgkPZaJpazQnS4OZ7hgn9Mv+L5Ylge+J0akRjrDOHwZV9FTok10rD09dex3+Ob4OPyRTtqOV5XObe+D9TeP/v0JpPfAckB6Gb6OdAqDvGytoB3+2KmrruTRE7+Lffq1g5/UvJpD1jTO6bkPUJO2s8VBYrPOA2CmvXvQt4qoikVE7UIA3K6DqvlaNEJIRLBDCeefHsvEnf6Mf7o4nBFh+N2+/j/bWf79Xa/R24BJ28gQk7bCE3y3ai4ATke64EftlLLMcYhUYiPN8MejSsd1wA5DNPu8x4ivBcQcwBN8XYdfGOSl4GuHPz5c9Jb3seq0Mwc/wTB4pemdLHM2c+S1ZxDhI5SJPtwZpxK2Kphp70zukDUQK5bu8EvtI3T0xXHCSqSdCuWmzUh6LJTq8KdxJKMWP+j0Jrs0Bu0skY7jjcewkhl+kPjQDdQ8wReewze702MPNWkrvEnbKVqlY0e938Oje38qnsOPOCANE0u4GZPrmqlJXgq+5ugx8/yr6ZFB2h/+HkXduwAwauYSr1nMArGXdTv6BXv3kf7FUKa3IpZq5Zpr6WBPWx8yrPr0yKrZAFiRdIcv4xEC3qrX6eJwhgsOyjAdPrUou8juzuinn3D4GBZUqLUHM4yWoeMTT/CNkmp6rCrKIvvSrhuzXa589eNc0n3HuGT40ZFO2o6L4EfU7/HI6N6fSjLDB7wMX0/aFgZ5Kfg60jl6zG6axsNFb2LWwQdoOLwOAKtmHiWzVrBU7OKVl9SC6JjtctH3n+D/Ht2uzol77ru4BjtYxRnmS3RuW4fwBF9UKzctw+mLs41o/7/ptCwZfpEbpsdXhSMsyujJzI69uAHTBxXNSMPHXLF/SIcvPDE0A8X0lsxgmnuQrnB/9VCp005T70ssiL08KRl+PDXSGe2uVwmHHx8Hh+/9HdtSgGFi4RLTkU5BkJeCryOdo0v4pKuISotVe27GkQKrejbmGf+PmFnEea99FVyH7a099ERtth5Uzt6XqJEPliGXvpUTxTbOeuw9VLU9B4BZM1+93nskzU1bnuC7ho9p4khGlU6R7CNmlhDzlVFOL53hAZGS5/CFYYJpIarnMt84MKTgG7aaFxC+YuzyWcw0DrG3Qx1zXcki1OR0dfzguGwzONJIZzwdfizay5+fH+NSGO/fy8EAw9KRTgGRl4KvObqce9pK3uF+nefcebzIfLD8UNrAxkX/wnHOFuQN05l267ksEnvYd6QLwu34E/l6sBzfJd/hg75vAjC97WkArOpZAITcTjpS5gH8cSX48cp5TBdHMlxwsQwTt0pwAhWUix5auqLpg3VTMnyAmvnMNfYPPWmbyLV9xZg1c2ikjf2H1SeRmOOyTOwCoCp+YFwEP1mWmaN4j8vCK8/ht3V0cs1tzw8695ITXqTjYiAMQ7dHLiC04GuoKw3y5jVn8dbYV/mI8bXk8frVH+Lf4lfy6sx3YUQ6uMv/FX7b8X7kd5ZSHvMagwXKALCrFhIRAZq6lMMXxTXEfWVU0MO+jv6YISH4dt0yAiKO7GlJG0sxYWyrBFFUQTm9HOxMyaT/8Qt47UF1fa9Ch5oFzOAQsVhs0O/PcLyHhi9IScN8DCHpOqB2BYs5LksN5fCDbh9Bp3uwy+RMwuHn+vCIj8fCK8/hG97vuVYIZcWbtHUxkMLC1JFOwZCXgq8z/KPPlWfNYVZ1McUBX/LYwoYyHi25gO8ZV/DF2u/zd/cENrmzEfFeFvWup48iMNQmGU3VpWyjGb/riXtRBW5RFVWim73t/YIfsJWgGg3LAZCde5OvSSkpIYxthTBLqqgQvRzsShH8R26AJ/9X/dlMOPwFWDiU9KX28EvHSHH4oUYVNcVb1VxE3FYOP26qtQe1dm4dLociudJ2NK0VRluH76gHnul40c5Y5iK8DF9FOirD15FOYZCXgq8z/KNP0Gfy0ytO5htvX548ZhiCNyyp59GtrTxx0OK/iv6Vq+OfRQqDafE99KWs4p1RVczztqqaieIDXxFmSTWVdKc5/IAXBQVnqPtED/fX+sdshxIiuL4S/KFqykUvhxKC79jQ2wodStiNhOBXKwGv6Ns16PfWL/hFydJMo125+nhvG81GC/vrzgKgQR4a8yYkCbGNO/197odiXFbaJiamPcEf03qCZKQjQJiYOtIpGPJS8DWTw7y6EKvn16Qde+OSBsJxh/a+OG9a2kAvRXSUKpHtM/pX8c6sKuYldxYAvUI9CMzaeSwx9rCvrX8xVcLhC8/hi45dyddi4W4MIXF9IYziSipTBb+31YsalIAaRiLSmad+iwy+SCwhgviKobiaPlFMUe8edf+DagO3g41vAKBJtI55VWmqaOdSqZNehz+2Kh3TjXr3HcP3kIh0pHL4egOUwkELvmZIVs2pptTb/u/8xfWYhmBX0TIAwikOf/W8Gl5hFgA9Qj0IxOyzqBZdyJbNyfOKnG71QCiuotOsoqxnR/K1eJ96GLj+EBRVEqKPlk7vYdFzMG1cIuHwg+W0G1XUDin4/Q4fIWgPNlEZUZUs5kFVdtresIqoFaJJtI65NDM2QsFPXWk76ijGc/g+14t0xvLQclMcflLwtcMvBLTga4bEbxmcu0gthFo2vZymyiJeFF7/nBSH31AeZMbCldjSoM8oVQdnezFJ2/rkecVON32mer29ZA6NsV3JCMX2Vum6/hCUNgAg2j0h7x4o+P1zDW3Fs5kW3Za5SMvDTEzaej2CoqGZTHMP0NYbI7jnMV5xZ0BxLb1FTcwQrWNur5AqjgO7gWY/X1JGDyH6xlyl4/Mc/ph68ngO38FAaMEvKPJS8PWkbX5xzXnzuf6ty6gq8dNcXcKTEZWDR8z0xmyXnz6fzbKZTqtaHaiYyRH/NOb3PZ88p9jtJuy9L1Ixjznsp7VbuVIn7FXI+EMwYxUAM3u893and7g0LCv5586G01jMLg4fzD5xa7kRYvjAUP/d/fXzmSFaeeXVVyg+sIG17nL8liAamk6TaB1bSSOjcPiO5Cbf9/lG4Gdjz/BxsLDHmOH3V+lgWN4WhzrSKQTyUvD1pG1+Mac2xPtObQaguaqYdR1l7BMNtAWmpZ132txqvlX5Fe6d8bnksUNVJ7NCvkRfRDnPkNtN2FSlnGbdIkpFmAN7VazjRBK1/aVQu5A+XyXLnU1KNLsPAQLpbchupDh8c/75ALRvejDr+C0nQlQEkl9XrnwnPuEw49FrMdyYEnzTxKqaRZNoZUfr2BqQpcYpuUY6jaKNZqN17CttgSCxscVSKZGOdviFRV4KviZ/WVAfojvicHH8v/lb7RVprwkhuOmfLuaL7zw7eaxv+hmUiz6ObNsAQEj2ELGU4Iea1FxA1+tqC4WEwxf+UhCCIzUnc6rxipq47T4AJTXIajVJm+rwGxedymFZhrnz71nH7HPTBb9k1gqeM49jRucGHDPIBnchPlNQ2jiXEhHlwP69Wa+TK+kOP7dIp1hEqKR77M3TUM3kxlSWOSDSMbTgFwxa8DUj4u0rmpheUUS77cey/BmvlwV9FPnN5NfW/POISRPnxTsBCMleopbK8KtnHQeAc2gLANJruWwE1evR6atoEodp27cNeg5BaQNOhWrKZpr9964tK+IZsZz61qeSuzWlYrkRYimCD7Ch8b0AdNSdQhQ/fssgOONE9eLrz4zwbyWdVLHNxbHbjksJEcrpHvPCK4CgiI7N4XtlmRKhI50CQwu+ZkQU+y2uu3QpoLZLHI5Fc2fzKCup2X4X2DHVEM2nHL6/vJ5OQgTa1arXgYLvm6MmfeXOx5XDL23E9gQ/WaWD+mSxvWwVIbsdDr6YMQafG80QfGvhm7jTOYvnG9+lzjENmL6CGH6qD2/I/S8kC9G0SdvcBL+YKCHZix0ffMXw0BcZGOmMIcNPWXhleA7fceWY1ydoJp+8FHw9aZvfvGFJPZ+/cBFvO2n6sOcGfSY7ZryNkNOJvelO/NjEfd7cjBAc8M9kRvfz8NcvULX9jwCYReqBUDl7OUdkKaX7n1BVOqF64uVqwlj4gmn36Zrm9f3f/nDGGLIJ/vIZlfxz/Gru6VOxUsAywArweug45oefz7jGSIjZLoZQf84l0nHtKD6hRNafbZevXEhx+EXExhjp9DdPE6aFgbeQLMunJ83UIi8FX0/a5j9Xnz2Xk2dV5XTunFWXsE9W4z7wHwDE/WXJ19pD85nh7IGNP6O4cxtb3SYsv9p4vTToZy0n0XT4CbXwqrSRrrkX8bnY1YTL56Xdo2H6TF52m4lvfSjj/n4ZIW6kPyCWTCvDNESy37/P+7TSXncKC+Ru2g+PvsVC3HEJBdQnkFyctoj1b+sYjHeM7qbjOWmbjHQMhGFheF/rWGfqk5eCrykszlrYwH9xFa8b09kra2grW5J8bc/ST/Lx2GfYf9XL/O3SDbwp9k38PjUHIIRgS9lqipxuNZFYWk9cBPiDexZ+y0y7x/z6Uh5zj8fctx6i6Q3Q/G40Q/CL/RYXLmvgkNeN02+pHwVj9lkYQnJ48yOj/n5jtktZkaoiysXhi5SN24uybeuYC6kOX0THVoefEukkJm0B3U+nANCCr5lwgj6TwOI3cVnvl1gd/T7tFcuSry1dtIgH3FPYsD+arARJiC+APescItIrwSxtTIqOb8D8wfLp5ax1l2NIG3auTXvNL6PEjfRIB+Czb1iQjF4S96xeuIqw9CN3rM04P1ditktZMCH4wzt8M97feqLEGaXgO/3Zf5AYMWfsdfjJSMf7Wm9kPvXRgq85Krz5uEZ6omrzklRBX9xYRihgsX5nWzKG8KeI+ZJZjTzuqmoeShuSouMzRdr1K0v82NNOJiyKYFt6ju+XUewBDh9gbm2It5/UhBDqoQTQVFPJOrmMhv1/y1rxkwsxx6WsSEU6uTh8I97v8Ect+KlVOsTHuNK2v0rHMC31EEVHOoWAFnzNUeHsBbWUeOWa/hSxNg3BSc2VbNzVnpxoDKQ8EE6YUc6fnNVKsCtnJ0VnoMMHOH3hNNbaS3G33Nu/2TkQGMThA3zl0qX8+iOnJjN30xBsLD2XslgLvP70qL7XeNzmCx3XcYaxKTeHb/c7/JCTZeP2XLCjSG9vgrGXZXoOXxoYponQkU7BoAVfc1QI+kzOW1wPpDt8gFNmVbL1UDet3dGM1+fUhFjrW81Xl9zNtffs4aePq1W52QT/7AW1/M45B6P3EGz+c/J4gCiO1+9+IKGAldEh1Fj8ZsLST+z520fxnUJJvI3j+57mzdY/cirLNFMy/FKZZeP2XLAjUFQJjF9ZpovASJu01YI/1dGCrzlqvPm4RiBT8BPVPmtfa8143TAEy2eUc+uzR7jruX3c+6LqqTMw0gE4vqmc5/wrOOyfDs/8SB2UkiKi2GZmpDMYZyyexUPuSfDyHyE68jYLZY6q/Jlv7MspWrHs/v0CqkT36LJyO4obUFVtYy/L7O+lI4KlmHYffuI60ikA8lLwdR1+YXLuojo+c/58zppfm3b8+BkV1JYGeG6PKkn0D3DvxzdVYLuSJY395Zw+K/O/rmUanDG/jl85F8DeDfDI16HN69NjZo90srFyViV/Ns7HH+uAH5wEr/0t5/cClNttAMxlX06RjuWoSKfPX0MF3aMTazuC4wn+2Msy+ydtjbrFGNJhtjigHX4BkJeCr+vwCxO/ZfCZ8xdQHUoX36DP5P5Pn8mHTp/FRcsbM1bwXrS8kXMW1vLrj57CsuneKt1BVvmevaCWn/aeQW/T2fDYfyvBhkEjnWz4TANz3jlc6fs60l8Cf/vySL5NKl3l8KvpwIi0D3u+5WX4vUWNVIlR9tOxIzhmkKj0ERTj0zzNMAxEvSqhXSj2asEvAPJS8DXHHrWlAa67dCk3vfekjNeWTivn5x8+hepQgI+tnoMQUFHsy3IVOGtBLX0EuXXB93jxbY+wacnneEiewu7yk0c0njUL6/hbdzOH578LWjbDgM3Wh6LSaev/c++uYc/3eRu0RIqnjb6Bmh3DMfxE8KmyzHFw+MKwoHo+rrBYaOzRkU4BYA1/ikaTP7zlxOmcOqeKxvLsjr2xvIgF9SH++vJBftreR1f4FBx5MleUNI/oPmsWqtjpcXspbwNV23/cO3J6b5Vsx8XAwKU6snPY8/1uHxECxIPVVIke2kbTItmOYAs/EQJjn7T1JmmFaYLlJ1I+hwVHtMMvBLTD10w5BhP7BGfNr+Ufu9s51BUlHHeI2W5Ojd4G3mNhfSl37q+GYDnseDSn97mupI522opnESVAfXTXsO/xO31ERBFusJIK0Us0NooGanYU2wgQkX6Kx1qWmYx0VBlttHIhC8XrWvALAC34moLjrAXKnS9v6p8D8hmZVT3DsWZRLRv2dGLPXA07Hks2FRuKmONSK9rpC9RxwDeDhtieYd/jd/qIGEW4Rapaye4dPvfPwI4QF34i+AmZ9jhFOkoeYtWLmGm04ka6h3qXZgqgBV9TcJw+t5p/edNCfvLBlRR7i71MY+T/1dcsqCPuSF4tWQGde+DvX4OO7NsoJog5LnWig0iwjoP+Zpqc4QU/ICNERRAzpLaGjHTmPl+QxI4SFz4i+Ckx4uNSpWN6Dt+uWQRAsOO10V9TkxdowdcUHJZp8Mlz5lFfFmTZtHLv2Mgd/spZlYQCFn+IngqzzoTHvw2/vBjswSOXWNymlk6iwVoOFs2l3m0ddsI34PYRNYoor1Htpntado1soFKCHSGGv1/wR7tVIvRHOqYSfLd2MQDFHVtHf01NXqAFX1PQLJuuBD/bQq3h8JkGZ8yr5i/bo8gr7oH33g7tu+DZXw76Hru7FZ9wiBXVsrPMqwzann3rxQQBN0LMKKJi/mlEpI+y14c+PwMnDkjiwkdY+ikaa1lmItLxNpkRlc20yxCVh58d/TU1eYEWfE1Bk8jxRxPpgCrP3N8Z4bWWHpj/Bph5Oqz9FoSz5+xO10EA4sX1tJUupI2yjGZuAwnKMDGzGKuolPXWScw9/PeRNW5zVEuKGCrSGftK2/46fACfZfGEu0xtITmatg+avEELvqagOc4T/KBvtIKvJoAf3doCQsD516nNWL5/Ijx1Y1qTNgDZpVo/OCX1BHw+npDHq124hhDwoIwQ8xaGbSo9kwr7MOx/LvdBepufRL1IJzBOK20NQzl8n2mw1l1OcbQVDr08+utqJp2jJvhCiMVCiB8JIe4UQvzT0bqv5thmbm2IG997IhcfN21U70+UZz66VfX5YeapcOUjMO0kePBL8KPVsOsJ9VpfG6JbOXy3pI6gz+QR+zjoOwIHBt82MSjDxA21y9fB+jXYmPDKPbkP0muNHJUWYeknIKPEHHf0e9B6Gb7lZfg+U/C447WoHiae0uQ3OQm+EOJnQogWIcRLA45fIITYKoTYJoT4/FDXkFJukVJeDbwLOGP0Q9ZoRsbFy6dRPsjK3FxYs7CWDbvakv38mXYCfOAuuPw2iPfBLy6C7yyBb86mYd11AMhQA40VQdYmhPKF2wa9fhERbM/hV9fW84S7DPn8rZCy9eGQeA4/gp8ofvzSi3hGWzc/IMP3WwYHqeZI8ZysewZrpg65OvxfABekHhBCmMBNwIXAEuByIcQSIcRxQoh7B/yq895zKXAfcP+4fQcazQRz/pJ64o7ktvUDSiwXXgifeAbWfAGmnwRrvkCsuIFt7jSsQBGLGso4Qjl757wb1v8YttybeXEnToA4casEgOkVRfwg/hZEzyF45se5DdBz+BHXIkwAy/UintHGOomyTM/hByyT2tIAm4Inw+6noKd1dNfVTDo5Cb6Uci3QNuDwKcA2KeUOKWUMuA24TEq5SUp58YBfLd517pZSXgi8b7B7CSGuEkJsFEJsbG3V/7E0k8/K5krOXlDL/z70WrJnfxJ/Maz5PLz7N7Dm82y46K+8MfZN/KbBwoZSAO6Z9mkVAd11Jay7CRy7//2ei7dNFek0VRbzD7mQtunnwhPfg76BP3ZZSAi+9GEbAUwZx8AdfXuFAWWZAAvrS/m9e66as3j6ptFdVzPpjCXDnw6krkLZ6x3LihBijRDi+0KIHzOEw5dS3iylXCmlXFlbWzvYaRrNUUMIwZcvWUI47nDD/VuG3KAk5qg+8n7LIBSwmFlVzMstMbj8d9B8BjzwRbj7/6W8QQm+41MOv6lSRTvr53wS4r1w72eHr4zx1gWEpZXcrL2YyOgrdZIOv7/V1vz6EI8eqUAufSus/0luDyJN3nHUJm2llI9KKT8tpfy4lHJIi6D74Wvyjbm1IT5xzjz++Nw+fvP07kHPiw3YiH1hQymvHOyG0gZ43x2w+rPwwq2w7SHvDZ7gW8rhN5QHMQRsdmbAuf8Om/80ZN0/kHT4YWlx0FSbzCwWe8YQ6SiHb6Y4/AX1pYTjDoeO/xTEeuCJ74zu2ppJZSyCvw+YkfJ1k3dszOh++Jp85DPnzee8RXX85z2bWbf9SNZzYgM2Yl/cUMqO1h61EYoQcPbnoXq+cu6xXiWegONTgu8zDRrLi9jbHobTr4E558B9n4NX7ht8YN6kba9jsdm/FIBTjS2j38jcTQh+v8NfUB8C4CW7CU66QpWkJqqTNFOGsQj+BmC+EGK2EMIPvAe4e3yGpdHkH4Yh+O57TqC5uphP3vosr7f1ZZyTFHzP4S9qLMOVsK3F2yrRF4RLvw8de+CRG5Ce4LvepC2oWGfH4V4wDHjXr6DxeLj9Cti9LvvAPIff51rEfJX0lC9Ugj/aDH/ApC3A/Ho1H/FqSze86Qaomg1/vBrCHaO7h2ZSyLUs83fAOmChEGKvEOKjUkob+BTwALAFuF1KOS6rMnSko8lXyoI+fnrFydiOy0d/uYGOvvS+OgMjnUXexO2WA139JzWfDis+DE//EHf9LQBIz+GD6uGzaV8n3ZE4BMvg/XdBWaP6VDBgoReQ5vADPoOuhlNYYbxGPBbNPDcXkoLfLw9lQR+N5UFeO9QDgRC87SfQtR/u/5fR3UMzKeRapXO5lLJRSumTUjZJKW/xjt8vpVwgpZwrpbx+vAalIx1NPjO7poQfvX8Fuw738aGfb6A32l91k3D4Ac8dN1eXEApYbNg1YJLzDf8JoQaMV+7mGXcR3aFZyZfOnF+L48r+2KioAi74BrRugad/mDkgz+H3OhYByyTcuIpiEcV36IXRfYPJSCd97cL8+lJePeS1SG5aqaqTNt0Om+4c3X00R528bK2gHb4m3zl9Xg03vvdENu3r5JrbnsPxVrUmHL7PUs3aTEPwxiX1/OWlg+kbmgfL4Z+epOvTr/Lu2JeR/tLkSyfNrKTYb/L4a4f7z194ISy4EP72FfjrF9KjlITDd00ClkF0+mkAhA4MEgENh3RxEfgsM+3wwvoQ21p6+qt/Vl8LTafAvdcO2zZakx/kpeBrh6+ZCrxxaQNfvngJD21p4T/+/BK9UTtj0hbUtozdEVv140mluArbpzZl96Wc77cMTptTzeOvpaxDEQLe/lM4+aPK5f/PArj700rsveZp3XGLgGVgltaxwV3AjNd+A9FRbFoiHVwpsAZsGrOiuYqo7fLsHq9xnGnB225WVT13XalFfwqQl4Kv0UwVrjh9Fh9dPZtbn9nDWd98hLWvtmII0rZUPH1uNTWhAH98LrOIzfY+GQzs13/m/Bp2Heljz5GUieFACC76Nnx8LZz4PlWu+bvLYc/TAPQ4BkGfcvnXx99PMNoKa/9n5N+UdHExMraFPH1eNaYhWPtqyoOoajZc9B01hu8dB799p6oocsfQj18zYeSl4OtIRzOV+I+Ll3DXJ06nqbKIjbvbkxO2CSzT4NLjp/HIK60ZK3UT+8T6BrRvPmdRHQD3vLg/84aNx8PF34VLf6Camb1yLzSvptc2CFgGAZ/B83IeO6Zfqlb2PvebEbU1lq6Dg5Gxh0BZ0MeKmZWsfW3ACvjj3w3XvABn/TMceBFuey/ceDJs/DnEIznfVzPx5KXg60hHM9U4nLuO6AAAHsxJREFUaWYlv//4abxzRRPHTc/8f/v+VTOxXZdbntiZdtx2sjv85uoSzphXza3P7EnOD2Te9IPw6efgc1vhw/cRtSUBy0zGSc/MuxZmroI/fxL++HGI9uT0vUjXURl+lo3fz1pQw0v7ujjcM6ACqLJZLRT77Mvwzl+o6qJ7P6Nc/+PfVmWo2SqMpiD3vXiAP2X5tDYVyEvB12imIkGfybfeeTx3XH16xmtzakNctHwav163K62U0/b65A+MTwDef2oz+zrCPPLKEFskVs1Wq3iBiO0Q8BkEfGqytdssgw/+GdZ8ETbdATevAa9f/1C4nsPPti1kYoP4xwe6/ASmBUvfqlpIf/BuaDgOHv6qEv4bpsGj/z3lhf9X63bx8yd3DntePqIFX6M5SnzynLn0xhx+8viO5LG45/B9Rqa4nr+knvqyAD9/KjdxicZdFel4kVI07oJhwpp/U+LbfUDFLfv+AU//36Dtl13HQSIyYiaAZdPKqS0NcN+Lwzw4hIA5Z6s20lc/CRd/DxZdBI9+HW5cCff9MxwYZdnoJBOJO0RGu4p5kslLwdcZvqYQWdRQxmUnTOMna3cmV972RzqZP4o+0+Bjq+fw5LYjPLzl0JDX7o3ahOMOVSUBLEMgxID2yLPPVBU1+5+Fn5wLf/08/OHKrJOrQzl8wxC8Y0UTf3+lhYOdOebzDctg5YdV1PPu30DNAnj+t/Djs+CXl6q+QlNo68Rw3CE8lk3iJ5G8FHyd4WsKlX+/aAlBn8GX/rgJKSXxZKSTfZP1K06fxdzaEr567+b0Ov4BHOgMAzCtIogQgoBlZG6AsugieNtP1SKu874CW++Dv/xbxvaLrpMQ/Ozy8J6TZ+BKuGPjKMowF1+imshduwXO/084/Cr85u3wf2fAX78IP38z3Poe+PvX1DaP8UjePQy04Gs0mpyoLQ3wxTcv5pmdbdyxcW/S4WeLT0DV5F936VJ2H+njpylR0ED2dSi3Pb1CtVcOWGb2B8Tyd8Kqq+HMa+G0T8GGn8BdH4N4OHmKdB0kBv5BHkLN1SWcPrea2za8PviE8nAUVcDqz8A1L8JlP1TtHNb/WK0a7tgDj39HzTlcXw/XN6pPAw/9pyr/3Pgz2D/4lpETTSTuEolNTcG3hj9Fo9GMJ+9aOYO7nt3H9fdv4b/esgwY3OGDarVw4bIGbnxkG289qSkp6qns70g4fPVaQ1mQnYeH2SLxjV+Dklp46Ctqc/KTPgiHXiaw+1G6MLAGeQgBfGBVM//022e5f9MBLjl+dPsFA2D51ZqCE94Lrg2Jdg59bfDag6pfT+9hOPgiPPm9/rbMpl81cTP9UDFDdRUVg/8djieR2NR1+FrwNZqjjGEIbnjbMi7838f5xl9eAcioeR/Ily5azCNbW7j+vs388H0rMl7f1x7GNAR1pQEATp1TxR0b9xKz3Yx1AUmEUC674Tj40yfU5iwlddjFjdzePodZQ4zpjUsbmFtbwo1/38ZFxzViZJl0HhFC9Is9QHEVHP+e9HM698HeDVA9T0VR9/9z/2tNp0DtArWyuPugOmfaidB4AgRK1fGuvVC7SP0aw8MhHHewXUnccbOWruYzeSn4QohLgEvmzZs32UPRaCaEeXWlfOb8BXzrga0AQ7ppUFsffnLNPL79t1d54rXDrJ5fk/b6/o4wDWXBZO5+2pxqfrVuN5v2dbCiuWqYwZwHn35WueryJnYd6ua733ucHw4hZqYh+NS58/js71/gwc2HuGBZQw7f9Rgpn65+gar+2f0UlM+AHY+omGfb39WWk6F6ePUBNTGcjYpmWHEFBMqgfZeKkOoWw/J3Q/XcIYcQd1xWyM0gIBx/oxb88UBKeQ9wz8qVK6+c7LFoNBPF1WfP5bFXW1m/s23ISCfBlWfN4c5n9/KVu1/iL9eclebc93WE06KeU+dUA7Bu+5HhBR/AX6J+AXHbqxwaxrVfsnwa3394G9984BXOXVQ3+CeJicAKwNxz1J9r5sEpA6RCSujcCwc3qXkBXxGUNsKB51V3z4e/6l2nSLWe3nIPrP0WrPoETF8BXfvUJwE7oj4xLH0b+IpwH/kGvw98j6i06N11Kiw68+h9z+NAXgq+RnMsYBqC77/nRH61bhcL6kuHPT/oM/nKJUv4yC828j8PbuWLb16cfG1/Z5gVMyuTX1eV+FnUUMq6HUf41LnzRzSuROWQbxgBt0yDL1+8hA//YgM/f3InHz97aHd8VBFCZfsVM9KPTzsBVnxINXozferTgBBqruCxb8K6G7Nf76HrQJgEop383l7DKcYWZv75Ctj/EdXqYvZZqgNqnqMFX6OZRBrKg/zrBYtyPv/cRfV8YFUzN6/dwYyqYj6wqhnHlRzsjCQnbBOsmlPN79bvIWo7BAa0Oh6K4SqHUjnn/7d353FVVvkDxz+H9YKsLqhsguK+ggJOqMDo5C7uivbLpU2nxbImdSqXMVMnZ3JcRtPU1DExyzEtK5MKl7JAYgvXlBQXXErFAWI7vz/uhRDZRLj3uXDerxcvLufe+9wv516+PM/3Oc857dzo196Nf0WfJqKbB82cdVV+HZMq/Y/AyR2GLofgp/QzkDp7wbUT+iMJK1v9ko5IMlpHMmtbFq3ERT6x34TloX8WrwGMlQ6cPKBDBDRoDHlZ+mGlTu7g1gHc2oGdq/7ah5vnIfsX/Unnpp2MdsJZJXxFMTPzhnbg4s1s5n2Ugruzjk4ezuQVyHsSfljbJrz7TRpfHr/KwM7Nq7z9/IKKrw0obe6QjvR7K4Y39h1nRaR/1X8RLXL7/aiJBiG/3x75NgC3MjKBg/wkPTgesQ9/d3v9lcs/H4Gc2/oS0pHlxauGgQBKDF21tNF/LyixUlrTTvrpMW6ehyZtoYGb/iroPn/Rl6tqkCYTvjppqyjls7K0YGWkP+PWfcsz7/3AnEH6I4TSwzV7t26Cu7OO974/f18Jv3gRlyomfO9G9kwLbcWK6NOMD/LioVaNK3+SmcouMf4+O69Av/ff4iH9V5HfMvUJ39oeLKz05xKuHtevWJb1i/6+Rn76clLmZYjfYhhZ1BqupMBvt/XnU3Jqfr1gTSZ8ddJWUSrWwNaKjZMCGfHvb5i3R7+UdOk9fEsLwbhAb946cIoLv2Th1dC+rE3do7ikcx8jUP4c1ordP1zkic1xvDGyMxHdPKr8XHNScvx9uVc+25Y6H1N0LqHNw2U/vseUGoqucuY1pkhRlGJuTjrenRKIg61+v83d5d76+dhATywE/Oe7n6u83eIZPKtQwy+is7Zkx1M9ad/ciRlRCbzy3+QKp4IwVyV/p+xc85tATSV8RTFjrZs6snlqEC/+qQ2OOut77m/ubMfgLu68eySNC79klbGFexXP4FnFkk7J19r+ZE+eCm3Jtu/OM/Lf35BW2dW+ZuauhG+G/9BUwlcUMxfg7cqzfcsfejlnYDsshGDhx6lV2l5Fc/RXxtrSgjkD27NhUg8u3cpmyMrDlU+lbEayVcJXFEXL3F3seLavH/tTM/gspfLkW9ULryrSt31TPnmuN22aOvD0e/G8urtulHhKlnHMcQI1lfAVpR54vFdLOnk4MWdXMlczK57HvujCqwe9ctbDxY4dT/2BJ/u05D9HzzNk5WF+vGTea1yoko6iKJpnY2XB8nHdyMot4MX3Eyuc1rh4UZYHnRANfYnnr4Pas/WxIG5n5zF89RHWHzxLYXWnVTaxoiQvhEr4NUateKUoNc/PzZEFwzpy6PT14knbypJXUP0afnl6t27CZ8/3IbytG4v2HeeJLXH8+r/cyp+oMTl5BQgBDrZWd43JNxeaTPhqxStFqR3jg7yZEOzN2pif+DjpUpmPyS+s3iidyjRsYMPb/9ed+UM7cPD0NQavOMSxn3+t0deobdm5BeisLLG3KWeBGY3T5IVXFcnLyyM9PZ2cnCqup6logk6nw9PTE2vre4cOKsY1f2hHTl7J5C87k/Bzc6BdM6e77s/Lv/9x+FUlhGByiC/+3q48sz2ecW9/y6wB7Xi8ty/CSPPJPIic/ALsbCyxs1YJ3yjS09NxdHTEx8fHLD4gCkgpuXHjBunp6fj6+po6nHrPxsqCNRMDGLrqMI+88x3/ntidIN/fp1DOq6U9/JK6ernw8bO9mfVBEov2HSfp4i2WjelyX5O8mUJ2biF21pborC1VDd8YcnJyaNSokUr2ZkQIQaNGjdRRmYa4OenY9nhPnHTWTFh/9K6x8vkFhVhZiFr/G3O2s2bNIwG8PKAtexMvMXbttxxIzdD0Cd2cvAJ01hbY2ViSnaeutDUKlezNj3rPtMfPzYHdz4Tg7+3CjKgf+CzlCqCv4Vd1pswHJYTgz2F+rJrgz9XM33h8Sxx//MfXbP4mjf/9lm+UGO6HPuEbSjrqpK2iKObESWfNxsmBdPJwZvq2Y6z+6gy5+YVVmgu/Jg3p4s7Bl8NZGemPawMb5u35kZ6Lo1n0SWqVp4Qwhuy8AuwMCd8cSzpmV8PXAgcHB+7cuWPqMBSlRjjqrHnviWBe/iCJNz8/iYOtVa3W78tjbWnB0K7uDO3qTvz5X9l0JI2NR9LYcPgc/Ts2Y2ovX3q0cDXp0WJ2XgEOtlbobFTCVxTFTNnbWLEy0h93FzvWHTyLm6OtSeMJ8HYlwNuVOQPbseXbn9n+/Xk+TblCZw9npvbyYXBnd+OuoWuQnVtAYwdbdFaWZjkO36wT/oK9P5J66XaNbrODuxPzhna87+clJCQwbdo0srKyaNWqFRs3bsTV1ZUVK1awdu1arKys6NChA1FRUcTExDBjxgxAX8M8ePAgjo6Vr2mqKLVJCMGcge2wthRcvqWNE+zuLnbMHtiO5/r6sSv+IpuOnOOFHYks+/wUM/q2ZmSAR41eIFaZ3/IL9TV8Gws1LLM+e/TRR1m5ciWhoaHMnTuXBQsWsHz5cpYsWcK5c+ewtbXl5k39CjbLli1j9erVhISEcOfOHXQ6M1kHVKnzhBD8pX/V19g1FnsbKx7p2YIJQd7EnLrGWwdO8fKHSayJ+YlXBrWnX4emRokjO7cAO2sLVcOvCiFEAyAGmC+l/PhBt1edPfHacOvWLW7evEloaCgAkyZNYsyYMQB06dKFiRMnMnz4cIYPHw5ASEgIM2fOZOLEiYwcORJPT0+Txa4o5sTCQhDezo2wtk34IjWDZftP8viWOKaE+DAttBVNnWp356n0SVsppVmNQKvSsZAQYqMQ4qoQIqVU+wAhxEkhxBkhxOwqbGoW8H51AjVXn3zyCU8//TTx8fEEBgaSn5/P7Nmzeeedd8jOziYkJIQTJ06YOkxFMStCCB7u2Iy9z/Zi8kM+bDqSRvAb0USsOsyqL09z8komUtb8eP6cvAJ0NpbobCyRUl/iMSdV3cN/F1gFbClqEEJYAquBPwHpQKwQYg9gCSwu9fypQFcgFahz9QtnZ2dcXV05dOgQvXv3ZuvWrYSGhlJYWMiFCxcIDw+nV69eREVFcefOHW7cuEHnzp3p3LkzsbGxnDhxgnbttHcYrShaZ2tlyfxhHZkY7M3+1Az2p2awbP8plu0/hXdDe/q1b8rgLs3w93LF4gFn/ywslPoavpV+Dx9+H5dvLqqU8KWUB4UQPqWag4AzUsqzAEKIKCBCSrkYGFJ6G0KIMKAB0AHIFkLsk1Le8+9RCPEk8CSAt7d3lX8RY8rKyrqrDDNz5kw2b95cfNK2ZcuWbNq0iYKCAh555BFu3bqFlJLnnnsOFxcXXnvtNb766issLCzo2LEjAwcONOFvoyjmr3VTR1o3deTpcD8ybudw4HgGX6Rm8J+jP7PxyDk8XOwY0qU5Azo1o6unS7WSf06+vmZfNJcO6Es8LjX6m9SuB6nhewAXSvycDgSX92Ap5SsAQojJwPWykr3hceuAdQA9evTQ5DXWhYVlH8YdPXr0nrbDhw/f07Zy5coaj0lRFL2mTjomBrdgYnALMnPy+CI1g72Jl9hw+BxvHzyLh4sdU3v5Mi7Qq3gB+KooGoZpZ22JnY3lXW3mwuijdKSU71b2GCHEUGCon59f7QekKEqd5aizZmSAJyMDPLmVlUf0iQyiYi+w8ONUlh84xYRgb6Y85Esz58orzTmGen3R5GlgfougPEjCvwh4lfjZ09D2wKSUe4G9PXr0eKImtqcoiuJs/3vyT7hwk/WHzrL+4Fk2HDpHiF9jwto2YaS/J872ZU/hXbQ3b2sYlgmY3Vj8B0n4sUBrIYQv+kQ/HphQI1EpiqLUom5eLqyeEMCFX7LY8m0aX564yoK9qSz97AQj/D2Y9JDPPesEFCX3u0s65jVKp6rDMrcD3wJthRDpQojHpJT5wDPA58Bx4H0p5Y81EZRa4lBRFGPwamjPK4M7EP1iGPue683wbh7sir/IgOWHmLkjgSslrjguKt/Y2VjSzEmHEPBRQo0UNYymSglfShkppWwupbSWUnpKKTcY2vdJKdtIKVtJKRfVVFBqiUNFUYytg7sTS0Z14bu/9mV6WCs+Tr5M+LKveeuLU5y9doc3PzuJlYXAu6E9Xg3teSbcj53H0nk/7kLlG9cITU6toE7aKopiKi72Nswa0I4JQd4s+fQE/4o+zb+iT2MhYEWkPy0aNQBgRt/WxKX9yqwPk0j/NZvn/uhn1Hl9qkOT0Wl5Dz88PJzPP//8rrbly5czffr0cp8TFhZGXFwcAIMGDSqeU6ek+fPns2zZsgpfe/fu3aSmphb/PHfuXA4cOHA/4Zfp66+/ZsiQey6dUJR6zauhPasnBvDli6G8PKAtax7pzpAu7sX3W1lasHFyICP9PVkRfZrBKw7z1cmrtXKFb03RZMLXssjISKKiou5qi4qKIjIyskrP37dvHy4u1btUo3TC/9vf/ka/fv2qtS1FUaqmZRMH/hzmR/+Oze65z87Gkn+M7craRwLIystnyqZY+i8/yPbvz2tyBI95l3Q+nQ1Xkmv2xZt1hoFLyr179OjRvPrqq+Tm5mJjY0NaWhqXLl2id+/eTJ8+ndjYWLKzsxk9ejQLFiy45/k+Pj7ExcXRuHFjFi1axObNm3Fzc8PLy4vu3bsDsH79etatW0dubi5+fn5s3bqVhIQE9uzZQ0xMDK+//joffvghCxcuZMiQIYwePZro6Gheeukl8vPzCQwMZM2aNdja2uLj48OkSZPYu3cveXl57Ny5s8rTOGzfvp033ngDKSWDBw9m6dKlFBQU8NhjjxEXF4cQgqlTp/LCCy+UOQ20otQXAzo1J7ydGx8nXmbD4XPM2ZXM4n3HGdS5ORHdPAj2bfjAUzvUBE3u4Wu5pNOwYUOCgoL49NNPAf3e/dixYxFCsGjRIuLi4khKSiImJoakpKRyt3Ps2DGioqJISEhg3759xMbGFt83cuRIYmNjSUxMpH379mzYsIGHHnqIYcOG8eabb5KQkECrVq2KH5+Tk8PkyZPZsWMHycnJ5Ofns2bNmuL7GzduTHx8PNOnT6+0bFTk0qVLzJo1iy+//JKEhARiY2PZvXs3CQkJXLx4kZSUFJKTk5kyZQoAS5Ys4YcffiApKYm1a9feV58qSl1ga2XJqO6efPJcL3Y82ZN+7ZuyN/ESkeuPErL0SxbvO05y+i2Tlnw0uYdfZRXsidemorJOREQEUVFRbNiwAYD333+fdevWkZ+fz+XLl0lNTaVLly5lbuPQoUOMGDECe3t7AIYNG1Z8X0pKCq+++io3b97kzp079O/fv8J4Tp48ia+vL23atAH00zOvXr2a559/HtD/AwHo3r07u3btqtLvGBsbS1hYGE2aNAFg4sSJHDx4kNdee42zZ8/y7LPPMnjwYB5++GGg7GmgFaU+EkIQ3LIRwS0bkZ1bwBfHM9j9w8XiqR08Xe0Y2KkZAzo1x9+revP6VJcm9/C1LiIigujoaOLj48nKyqJ79+6cO3eOZcuWER0dTVJSEoMHDyYnp3qrBk2ePJlVq1aRnJzMvHnzqr2dIra2+uXqLC0tyc/Pf6Btubq6kpiYSFhYGGvXruXxxx8Hyp4GWlHqOzsbS4Z1dWfj5EC+f6Uffx/VhdZuDrz7TRqj1nzDH5ZEM++jFL796QYFhbW/56/JhK/1C68cHBwIDw9n6tSpxSdrb9++TYMGDXB2diYjI6O45FOePn36sHv3brKzs8nMzGTv3r3F92VmZtK8eXPy8vLYtm1bcbujoyOZmZn3bKtt27akpaVx5swZgOLpmR9EUFAQMTExXL9+nYKCArZv305oaCjXr1+nsLCQUaNG8frrrxMfH3/XNNBLly7l1q1bapF3RSmlYQMbxgZ6sWlKEMde+xPLx3Wjm5cLUbEXiFx/lKBFB5izK4lvzlynsJaSvyZLOuYwl05kZCQjRowoPjnZtWtX/P39adeuHV5eXoSEhFT4/ICAAMaNG0fXrl1xc3MjMDCw+L6FCxcSHBxMkyZNCA4OLk7y48eP54knnmDFihV88MEHxY/X6XRs2rSJMWPGFJ+0nTZt2n39PtHR0XdN+bxz506WLFlCeHh48UnbiIgIEhMTmTJlSvGMoYsXLy53GmhFUcrmpLNmuL8Hw/09yMrN5+uT1/g05Qp7Ei6x/fsLeLjYsSKyG91bNKzR1xVaHjPao0cPWTR+vcjx48dp3769iSJSHoR67xSlYjl5BexPzeDDY+ksG9OVJo621dqOEOKYlLJH6XZN7uEriqLURzprfc1/WFf3yh9cDZqs4SuKoig1T5MJv7KTtlouQyllU++ZopieJhN+RRde6XQ6bty4oRKIGZFScuPGDXS6Ord+vaKYFbOr4Xt6epKens61a9dMHYpyH3Q63V2jgBRFMT6zS/jW1tb4+vqaOgxFURSzo8mSjqIoilLzNJnwtX6lraIoijnSZMLX8myZiqIo5krTV9oKIa4BP1fz6Y2B6zUYTk3Ralyg3dhUXPdHq3GBdmOra3G1kFI2Kd2o6YT/IIQQcWVdWmxqWo0LtBubiuv+aDUu0G5s9SUuTZZ0FEVRlJqnEr6iKEo9UZcT/jpTB1AOrcYF2o1NxXV/tBoXaDe2ehFXna3hK4qiKHery3v4iqIoSgkq4SuKotQTdTLhCyEGCCFOCiHOCCFmmzAOLyHEV0KIVCHEj0KIGYb2+UKIi0KIBMPXIBPEliaESDa8fpyhraEQ4gshxGnDd1cjx9S2RJ8kCCFuCyGeN1V/CSE2CiGuCiFSSrSV2UdCb4XhM5ckhAgwclxvCiFOGF77v0IIF0O7jxAiu0TfrTVyXOW+d0KIOYb+OimE6G/kuHaUiClNCJFgaDdmf5WXH2rvMyalrFNfgCXwE9ASsAESgQ4miqU5EGC47QicAjoA84GXTNxPaUDjUm1/B2Ybbs8Glpr4fbwCtDBVfwF9gAAgpbI+AgYBnwIC6Al8Z+S4HgasDLeXlojLp+TjTNBfZb53hr+DRMAW8DX8zVoaK65S9/8DmGuC/iovP9TaZ6wu7uEHAWeklGellLlAFBBhikCklJellPGG25nAccDDFLFUUQSw2XB7MzDchLH0BX6SUlb3SusHJqU8CPxSqrm8PooAtki9o4CLEKK5seKSUu6XUuYbfjwKGH0u6nL6qzwRQJSU8jcp5TngDPq/XaPGJYQQwFhge228dkUqyA+19hmriwnfA7hQ4ud0NJBkhRA+gD/wnaHpGcNh2UZjl04MJLBfCHFMCPGkoa2plPKy4fYVoKkJ4ioynrv/CE3dX0XK6yMtfe6mot8TLOIrhPhBCBEjhOhtgnjKeu+00l+9gQwp5ekSbUbvr1L5odY+Y3Ux4WuOEMIB+BB4Xkp5G1gDtAK6AZfRH1IaWy8pZQAwEHhaCNGn5J1SfwxpkjG7QggbYBiw09Ckhf66hyn7qDxCiFeAfGCboeky4C2l9AdmAu8JIZyMGJIm37sSIrl7x8Lo/VVGfihW05+xupjwLwJeJX72NLSZhBDCGv2buU1KuQtASpkhpSyQUhYC66mlQ9mKSCkvGr5fBf5riCGj6BDR8P2qseMyGAjESykzDDGavL9KKK+PTP65E0JMBoYAEw2JAkPJ5Ibh9jH0tfI2xoqpgvdOC/1lBYwEdhS1Gbu/ysoP1OJnrC4m/FigtRDC17CnOB7YY4pADPXBDcBxKeU/S7SXrLuNAFJKP7eW42oghHAsuo3+hF8K+n6aZHjYJOAjY8ZVwl17Xabur1LK66M9wKOGkRQ9gVslDstrnRBiAPAyMExKmVWivYkQwtJwuyXQGjhrxLjKe+/2AOOFELZCCF9DXN8bKy6DfsAJKWV6UYMx+6u8/EBtfsaMcTba2F/oz2afQv/f+RUTxtEL/eFYEpBg+BoEbAWSDe17gOZGjqsl+hESicCPRX0ENAKigdPAAaChCfqsAXADcC7RZpL+Qv9P5zKQh75e+lh5fYR+5MRqw2cuGehh5LjOoK/vFn3O1hoeO8rwHicA8cBQI8dV7nsHvGLor5PAQGPGZWh/F5hW6rHG7K/y8kOtfcbU1AqKoij1RF0s6SiKoihlUAlfURSlnlAJX1EUpZ5QCV9RFKWeUAlfURSlnlAJX1EUpZ5QCV9RFKWe+H8etOPMlAxexAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pupYDxbqz9Zb"
      },
      "source": [
        "- A quick test directly on the **Model** shows that there is a good reconstruction accuracy on the test set. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x14moAjOzlrf"
      },
      "source": [
        "#@ 100 TESTED SMILES:\r\n",
        "for i in range(100):\r\n",
        "  value = model.predict([X_test[i:i+1], X_test[i:i+1]])\r\n",
        "  idxs = np.argmax(value, axis=2)                                        # Flattened along the axis.\r\n",
        "  pred = \"\".join([int_to_char[x] for x in idxs[0]])[:-1]\r\n",
        "  idxs2 = np.argmax(X_test[i:i+1], axis=2)                               # Flattened along the axis. \r\n",
        "  true = \"\".join([int_to_char[h] for h in idxs2[0]])[1:]\r\n",
        "  if true != pred:\r\n",
        "    print(true, pred)                                                    # Inspecting the result. "
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vdrxfZG82YUo"
      },
      "source": [
        "**PREPARING THE MODEL**\r\n",
        "- The parts of the trained autoencoder will be used to build the various encoder and decoder models. This **MODEL** will take a vectorized smile and encode it to the latent space. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mda29OTr19Fm"
      },
      "source": [
        "#@ PREPARING THE MODEL:\r\n",
        "smiles_to_latent_model = Model(encoder_inputs, neck_outputs)              # Building the Model.\r\n",
        "smiles_to_latent_model.save(\"SecondModel.h5\")                             # Saving the Model."
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ezzcGs73xYm"
      },
      "source": [
        "**PREPARING THE MODEL**\r\n",
        "- The next model which is needed is a **MODEL** that can decode the latent space into the states that need to be set at the decoder **LSTM** cells. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KKQWovLw3YFI"
      },
      "source": [
        "#@ PREPARING THE MODEL:\r\n",
        "latent_input = Input(shape=(latent_dim, ))                                            # Latent Input. \r\n",
        "state_h_decoded_2 = decode_h(latent_input)                                            # Reusing the Layers. \r\n",
        "state_c_decoded_2 = decode_c(latent_input)                                            # Reusing the Layers. \r\n",
        "latent_to_states_model = Model(latent_input, [state_h_decoded_2, state_c_decoded_2])  # Building the Model. \r\n",
        "latent_to_states_model.save(\"ThirdModel.h5\")                                          # Saving the Model. "
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FzOzZRe1D0MT"
      },
      "source": [
        "### **PREPARING THE DECODER MODEL:**\r\n",
        "- The **DECODER MODEL** needs to be changed. It was trained on batch mode but it should be used in stateful mode predicting one character at a time. So a batch shape and **LSTM** layer is set to be stateful.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "Spm0GnOE54j-",
        "outputId": "95ab0e3c-2e9d-4f17-8f0d-3af38c39edf0"
      },
      "source": [
        "#@ PREPARING THE DECODER MODEL:\r\n",
        "inf_decoder_inputs = Input(batch_shape=(1, 1, input_shape[1]))                    # Decoder Inputs. \r\n",
        "inf_decoder_lstm = LSTM(lstm_dim, return_sequences=True, unroll=unroll,\r\n",
        "                        stateful=True)                                            # Stateful Decoder LSTM.\r\n",
        "inf_decoder_outputs = inf_decoder_lstm(inf_decoder_inputs)                        # Decoder Outputs. \r\n",
        "inf_decoder_dense = Dense(output_dim, activation=\"softmax\")                       # Output of the Model.\r\n",
        "inf_decoder_outputs = inf_decoder_dense(inf_decoder_outputs)\r\n",
        "model_s = Model(inf_decoder_inputs, inf_decoder_outputs)                          # Stateful Decoder Model. \r\n",
        "#@ TRANSFERRING WEIGHTS:\r\n",
        "for i in range(1, 3):\r\n",
        "  model_s.layers[i].set_weights(model.layers[i+6].get_weights())                  # Transferring the Weights. \r\n",
        "model_s.save(\"StatefulModel.h5\")                                                  # Saving the Model.\r\n",
        "display(model_s.summary())                                                        # Inspecting the Summary of the Model."
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         [(1, 1, 22)]              0         \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (1, 1, 64)                22272     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (1, 1, 22)                1430      \n",
            "=================================================================\n",
            "Total params: 23,702\n",
            "Trainable params: 23,702\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0GbkgolGJSP5"
      },
      "source": [
        "- The **Smiles to Latent Model** is used to encode the smiles into the latent space. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3SdhWWq0Ipoe",
        "outputId": "1ef4fc84-7db1-41cf-d130-ea0e751d2df5"
      },
      "source": [
        "#@ LATENT SPACE:\r\n",
        "x_latent = smiles_to_latent_model.predict(X_test)                             # Encoding into Latent Space. \r\n",
        "molno = 5\r\n",
        "latent_mol = smiles_to_latent_model.predict(X_test[molno:molno+1])\r\n",
        "sort_l = np.argsort(np.sum(np.abs(x_latent - latent_mol), axis=1))            # Sorting the result.\r\n",
        "print(sort_l[:10])                                                            # Inspecting the sorted result.\r\n",
        "print(smiles_test.iloc[sort_l[0:8]])"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[    5  1243 15472 14589 15039  3991  5251   744  2449  7197]\n",
            "45051    COCc1cocn1\n",
            "45007    COCc1ccoc1\n",
            "45061    COCc1conn1\n",
            "44719    COCc1ccno1\n",
            "45069    COCc1ncon1\n",
            "44727    COCc1cnno1\n",
            "44751    COCc1ncno1\n",
            "45025    CCOc1cnoc1\n",
            "Name: smiles, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aXHSSnEqpE3x"
      },
      "source": [
        "**CHEMICAL PROERTIES:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "3X0Lbejum_ws",
        "outputId": "4911e407-6ba3-4fb0-f36a-19635ecdb3a1"
      },
      "source": [
        "#@ CHEMICAL PROPERTIES:\r\n",
        "logp = smiles_test.apply(Chem.MolFromSmiles).apply(Descriptors.MolLogP) \r\n",
        "pca = PCA(n_components=2)                                                   # Instantiating the PCA.\r\n",
        "pca_r = pca.fit_transform(x_latent)\r\n",
        "plt.figure()\r\n",
        "plt.scatter(pca_r[:, 0], pca_r[:, 1], marker=\".\", c=logp)                   # Plotting the Scatter Plot. \r\n",
        "plt.show()                                                                  # Plotting the result. \r\n",
        "print(pca.explained_variance_ratio_, np.sum(pca.explained_variance_ratio_)) # Inspecting the result. "
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gZ/a77M5+B0NLcFrLddRGFjyv9zGkPzliMs05pGv+h3Lp+5jmApKpjz2vbQe8tAmE/AQhZkQ4v/nJLAdT4518Y8l/P2P5zbnf4CkLVFWse0qrmZ06/5Bytx68h1t67wIUN/XcSVdsJnVhG8uPItBYXj8dgH+472ZGrDIQRckakqagJlpAiGq0ua88Sm4FNenSEFzYeCEXtpzClrFfcPOACWIOJRnhc7tew8JUH8NOHf+z9GU8nttIQzTD0poB2sN9+BM+97Ks8MOxpaRDFkKAr0pU2ATMQ/qDkxatLT2+3TeMzfeQSnLl1PeytO40TmL20641YjwZsWJ5GfZkr0fh48pxNo38O+d23HLI96OUYm/xEcadg8xMnkM61PrXb9YREo1dQjR2yTGrLyDgmQiE/DjiSoeDlT7qQw0kn6X/uy40haKd5oGeqSh0yvIA+8oPcUHTmWzKDPLNtSsZLBWJ1Qxghz0cX6eglRi1NxMxfeIhySXNr6UxXG03Z1uTdRddj/svu5p/XfsriuQICQ/wuLT591xXPBddC9ERr+G9c14JwJd2/YlhuxraVxMvUxeycbUwpyb2k8nfT77UiqFNY8RL4ElA1xDo+Mpg3IuSDNnoEytKdWFQ8Rz2FA3atE6QfRz04tiEsWW1j4+M3sfSusP7m5VS/GHwMX6w7zY0lvKKhs3UmyXE5NZ0T2dD9jYezfwQqVxWj97IP8z4MVE9/azuxbPhvqF7uHPwdzSGm3jfjA+QNmuet7YCXjoEQn6csHyLL2z/F8bdHKC4Zva1TItPf8byvvLoLW0laiRpjkxncd2beM99vYxWPBCK767vZ8n0jXx/w2bW91XwlQIFoZIg1pDmib0uWxJFdA1c6XLdjj+yYzzDl095Pe+fq/OdnS4CwVvmdXLt2htZNZgjHUrzobn30xUZpc4oszBVJJa+hZNrWwlpOlJJBiwNyzOQSqOi+aRCFlHdZ0AlGXd97h+fjo+GqwxWFmbyxsa9tKc/jNKXcsvwdYw5DnVmGUMTzIifxWsf+ioV3yEszuRHy05marIZlfsJ4GOKENNih/qbpXJ5qP/99JU3syXXwSnxEhtK7TwwNo8r23pZ1Hj4zaH3FR6pjmwAhWTE2k9rpJ3ekatxvL3EIhcRDp9FXewCDO1Q19WzYdAa5Jd9v8BVLgW3wM97fsb7Zvxtcr4HvLgJhPw48PPu27hn6A+E9RJPCOx9w3fzzq7Dh8kppbjhwD8xZO1FKsX5zW9ned1rKGMxY8ooQkAuH8ORLut6S5PuD4BwzJv4LAgLl2a9wIiM40od3Szx0PAWbLuFt7T9L3PjNfw6fzK7nF4sQ9CYSpDJJ5kTGSaqOzgqRCzciU2GnBOlKVLPgdIgUS1FSVmAwJU6ZS9EWPeIamApk6cmjfXRaYouZ0bNWwH47vJ/49d9X6fiF7i49Z08NtJNwbWwpUsZuHUozYfnvowrpyb4Vd+vaY40cVHLaw/5jjZlPkvGWkuLbnFK80o0FK+s3cb1mbdx4dQf4kmfnuIozdEUYf3JjZu7kqczaO3Eky4FT7Ah9zhlcwO+sx7wyJduolS6g77xb7C47fdo4shzvfw5lm+hCQ0USCQlr4Tjl8m5fdSEOo94wjog4M8JhPxvzL5iN3cNPoCvPEKaqq6oFCZN4eZnPCfvZhio7J7cQPnx0V9zSv0lzGi2sCfK1KTKmCJEKmKQt/yqeAqIE0GKAraqitfbmtbS5yX4du85CCEQuoXlDrOjUscv8vNxlA54aEKnIV4mV2zkhv7XsKxpLQCDdoktpR8S13MsjI0gpYWUJwMxqk0qpBJky1Fyso2R6Gwua53JbwYewxCKN7XNY1bDkxN/NaEm3j7980hl4XoHkdqHeGubyZZKKzsKHbTH6nCky/f23UjRK3LQGuPHB35OY1gyaPXxsqbXUB9KM1i6F4Bmo0RYq/rWPV/xnq5FFF2LKx76HkNWnohucuPZ76Y+7LNh5Gu4sszZjW/jT6MPk7UHWT32BzZrikuTEm3ifaiUheNnsNwDxEKzJ45VX0/PJhpmamwq81ML2DC+HlOEeFXL+fxk31vwlYchwlzR9T3iRv0R1xcQ8ASBkP+NcZWHAHylU/ZD1IVMVtSfzitanjkjX8xIoQkDlIMmDJrCUwHoSjawo1Dd49rQNN7V9Ua65s3hcysfYPfoKJnxMsUR+ODMNWwpN3N+7V5Oig9T55V4IuY6pGkcKP2Bh0uNuOgIAUqBKQSza2aQLReYkt6OmrCr60Pj1DlDLIr1Mj2cwRCS9tAoX+25CKk0YrrGy+p6qDNH6CvX8o29K/jK8nP44alvQ0PDVy6PZe7groE+dC3FisYOlicl/dkPIpXNo+MzWVnqwlU6zbFxVjROYczJYksLicSRkjXZNdSFcnjKZd/+rbSF8mgkaTay5GSYmHDQhUbMiNBRcx6/PbidYSuP5bs4vseXtn6PM+p/D6o6Ihq1tlD0OvBVdUWtowxcUUuYMTx0fDR0YRA2qouCxiprWDP8IXxpMa/+k0xLvfmI7r0QgvfN/CAFt0BEj7Bh7CZsv4hCIoXL7vwDLKh9LQ8P/5iD5e0srn0VJ9ccOoEdEPDnBEL+N2Z2Yjqn1C3mkZHVRPV6PjX3Y7RFn9kaBzC1CFdO+wIPZ24kYdRyfvPbAfjk3Cv5j20/YtwtcvX0Szm/eTl39W9kB1uwa3yUFaNcCrF6/xw+t+JuUBUcESac+DxNkQPYvsv/m9tOzrqD9lCU3ZVmfDRA0BLq4tWtr2RH4Rt46EjFxMIgRckL02gWMLXqyscas8Ks6BC7i81c1q5TY+YQAlqj48xNdXNb70psNc45TYv5Tc8n+fYenbJvIujjkcwmru26k1qjAsBBN4mr9Inr1snYo8xPzSFpJHCcMUBiijLeZAZEiS0FYU3g0kAyNJtU/FQMfBKx16Hr9dSaBya/S10oKt4OpCyiTUTkuDLP/NTprM0+gEJRH25jYfsNSJVnuPgrkt4Aram3oWvVENCNI/+MJwsAbB/9Ih2JyzC02BE/A0kzOfGzGV2YeMpGoJM0m1g1+kvWjd2OpxyGrD3Uhztpic464roDXpoEQv43RgjBh2ZdxftmvA1d6Ec8NG+NzuTyKf/ytGPtsUa+s/yTk5+VUvz7ptuwpVd1qzSXKewLs2nsZOpbvoGpAWgIIfjdy6rnjJQfZGsGpkVGOVftYH1+Cquz00jWNFMTqrpjNhc7WJzsJqp5bCm00VupZWOxg7PTe1BKUPTDrBzswpEmK4cP8MqOJ69JodiY38SOnZtYl92JkNso+0sxNEkiVHUMlX2DGr0a17483k2vU49AI2kk2TTWz8NDezAYI2FU0FBUN6TTq0KsPELCQ0PSEGpjYct36S38gqK7lw5ZITr+TyxXN3NF0wLuGp3O0uQAH+98lMfsespKR9dCtMfP4fTmq5mRXELRy9MYnotCx9QbaE+/59B7yLNfXHQ45qQuIOv00l18nBnJs5meOJvt+a9MutAEGjl3MBDygL9KIOTHCeNZ7gSklDoi0X+qyIR1g9ctWsJVS5YS0g8Nv3OlQyy0nHTkHEYr92AgGZdxFjT00xqpYWZiCq2RBkbsLA+MnEzR1pmf6KdJz7M630VnqEBBLuXH++I40gQE60Y6OKVxgIZwlpA2g0G3DiUcLAmPjW7l9S3TkEoQD9k8cTk3ZU7hPa0PEdY8orpN0Y1zZcsgJ4XuxlM38rnBixhy0yypL6AJSVor0V2ZykdnvYbC+LXsdNIkNIsO8RA7Ds4kK+MUpcaO3B0MVOqIGM2cXL+bq9rWTvq9z4lm+G3lTM5t+RgtsaUIIagPdfD73quJiDw7KjP5wMzXMT31KobtEX7e81OkklzR+WbK4jQ8NYAu4OT6Tz8ra/xp90ponN74Tk5vfOfksWV1l7A7/whCCCJ6kq740udUd8BLC3E8NqJdvny5WrNmzd+83b8lSilu3b6NVX19XDJ3LmdNmfqc6/nc5t9ya89aWiJprj/jHXTEail5WYruCA2RLvSn5Al/YHAb/7bpV2hC8IXFf8cZjU+35hxps2V8E1lnhNv7b0Hi46sIlm/i+BYxw0YTIJWgWJzH3PoG9pR24Kk8M6PDvDy5jZThcHNmKVvK01lSczKPDW3jYD5Be804YcMnptn0FTu45ZwP8pE132awUCYR8zijaR6fnvcmXnn/lzDMcXTtiWdPkdAs3tK6ijpVZot7KRcnb0HDQSrYVGzny70vZ2lDLxHN5aRoHzk3yUn11zI/EaNO209P9rNINQ6AVLDTbuDGodPxlIYQcE7NTq6s2UN4os2tlRa+Pnw2jvR4x7TXckn7edyx/1Xo6kC1DgR7Kq2c37iAG4ZMRuwMCkVDSCet53GVjSnCvKL1apbUXYQrS3jSJmrUPaf7/FSK7hg5t5+myExC2nPPFxPw4kMIsVYpdcj+yIGQP0/cum0r/3L/fVQ8j4hhcMvlV3ByU9NfPMfxPYatIq2x1GRO8k3ZXq5+7P+o+NUY7wtaT+KaefO4rfefEAhqQh1cMe1/JrdA86RLd3krCaOW5sjTXx6+8vjctn9mzBnBVTZPhD4qBXHdYtBKETdchKiK4bbBFrKVGMum9GJJE6VgSniMN9etZl+lgeuHzyasCVqiJfIueEqfsLIVEd8BOZNVe0x8fISANy+RzK2tp0ZfzM17b2JE81BKIBUYmuSixi3MDGcI49Ou5XGEyaOlLkZlnIFykmTIps4oMi86yIib4OH8EhBw5ZTLafM+hJKjE9cp+PnoMrYX2/EnFgJNiYzwhvqNzDElKTPBNT3nk3HKABhC52crvsgfe05FPJH8S8Ggm2RquMzKQhcpPc9+uxEfnYaQi69cNOBl9WcwNXk2fxz8Z5SSzEhdxOlNn0aIJ0dAUrmsGvpXBiuP0hhZxoqW/0I/ijDGgJcuzyTkgWvleWLtwAAVrxoFoSHYnsn8RSHfmN3Jp9f9hNFKNRTx5vPeSdwIPc1VIqhOOK4avRFvIt1szjnIQGUbnfHF+Mrnh/s+zYhzEKl8Tq35e1rDK5hf34wQgmFriBF7hJLnEtJVNQ3XRPW92Vq0kMLRNKTS8ST0j6eJGi4VP4SciEU/YDdw7ebXowlF2YiSSBZwlYfCmHSV6PicUbubX+2sxZXN1W9ASO47OM6AtZ3X13yTa6bl+VbPuWwoVfOi1Jgl7uxZSNRweHXLZv5v/AxKMkJUd5iXGGBR+iBKgSt1NhY7GXFjWNLF8XV+sv8XvG/qKwl7NyGE4rHCNHaXWiYmbqv9aQ3l6PdbOan5KyTji0n2f5LpoX2MeXH2Wq0oJamLnMVY5QEAPDRODudJGWnmR3u5Z3wOjjKIabVE6MFGEtV8FqqbuDezGn/ifuzJ/46wcwsnt/yKiDkDgJ7CXfSXH8JXFoOVlezP38bM9N8d/UP2HClX7iE7/nl0vZmGuq9j6C3HrS8Bx4ZjJuSiaoKsAQ4qpY5d8uwXKJfOmcNt27ehCYEmBKd3dj5j2d5yN9/Z91+0p3zaU7BlGO7p38Frpyxkfk07l3Uu4Zfda2iL1XDNvIvZkR+mr7QJHxeFJGE0ADBqHyRj905Y23Bn/83cvW0/53dM57qXX0bCSLN5tI6Ca1ITLjMjPYopPEzhsTffQTji05IoolGNb3/jnLXszTUglcETKv2a9C5e2bkXheDO4TmsktOpSIVT0dEjYOBT8kLcObqQkm4ihESpqpAnYhZp3SIZsojoHttKbYBAKchUUoAAW3Hr4FIi4epCJss3KXphWkMKoYFUkoIXJmr41MgiyhCkTJtbh4eo1ecRM1weH++iIqsWryYUpyX2szBxEF0zeWTwPeS8Gv6+bheGqLZx19gC3rv63+irmJyWmsWU6CjnxHPMCvsIkeMH+cWM+3EAKn6JN6cyzIjm6LZSfKpnGQtqBojrgidGOFLm6M99lY76L/OH/s8zUN6AgUZcA5B40uJIkLLEePk2NBEjFbvsaVb+c8X3x8iMvhuFhevtYWTsI7Q03nzU9QYcX46lRf4RYDtw9EmzXwSc1tHJbVe8hW2ZYU7r6KQt+czLuzfl1qHwMLSqS6M2UqQ2VF3lJ4Tg2gWv4doFT74bGyPvwpUWo3Y3y+reRM2/dRsAACAASURBVMmP8s3NN7OrsJuZqepiGF9qjJZiRCI51mTXMlw+j8dH9pF3QiDAlRrz4v2Yolo+OcXm4ZGZKAVoAgXstVtIhkv0OXFSoWokxSvTe4lMLLh5Xcs27tg7jeaoRb+WhrLkwqY9PJSbjaNMkmkLpcapWGHamscImT677Sa+0X8+56d2kNAtxrz4n30b1SiYCBO7JFENeaz+rI5IasMVpNCJ6i4lPzw5gdln1fHI3um0NBVAAw2FBJYnDuBqBo5yGXRTaNJCCEloInzylOQ+Ml6UHjmFuyZypr950a0YAkBOhkNW+yO4cWQmn+lcw7/3L8dSOiOjnZxbJ6kzsjTrOXShoWtJ1o78jN7Sanzl4mASEiESRgqt/L/0uLfTVv/tZ7SGlVIcGH49trsXhKBoPUhz7Vf5zOaf88DwdqbHm/nG8ndRG0oc9vxnQqrCUz75we5FLxKOiZALITqAVwOfBz56LOp8MTCnoYE5DQ2Tn+/t3cN/r32QlliSr5z1appi1X/CKfEufBkhY5kIAfNrZtMUieNKD12AUh76Uya9TC3KxW2f4O5NO/nyY4+zte4u5ISYZe1WTk5XODiuMVhJMq+zHyHg2/u+ScJegVRVQfR9DVM8uXoxZVqU3BANsTITaVrwlMDFoGCFq7lUpMaglaAzUt22bdwLs2WglVCHj+PrlNwoD4/MwNOrLg0hFAva+uiIjrE22wUIirbJsJfkx6XT0QRoSDRNoCkPRxoI4IL6bQzLNMNOipDmUpARRt04Ec1loJwiGnIJ6dX6PSkIaQqpIFeMoYXAUTpCKgSKplCRtpBLn0pS8R1AMeImEFRfmq7U0TTJaxs28Mq6Lfz33lcy6ibYUa5jSSKDQPK6ml5+PDYbTylsP0yfivGL0sdw2UxIczE1yeO5DmrDrdjS55SkxasTBiPlu/An4t0NEWVpw/vRCp/A8x2Kfj+DYx+no/Hw2+fl7GEsdzvgg4J85W7Wja/klISN5k/h8ZzDT/Y9yEfmHjr49WQBgY5+mGgaQ59CNHoBlUp1JWxt+tojfZwDTmCOlUX+P8AngWc0O4UQ7wbeDTBlypRj1OwLhzGrzAf/+Bss32NffoyPPXIHP73w71gzdJB7u3MM2wk8Vf2n3ZTfzjseHaYhHObtU29HFxVmpN/Mwoml7fvGxnjHjb8i01/EqLOJ1gieGHVn3RgP96aIIWhszGDo1eF+d7mHqcacJzskJCN2nLpQdWXouJNCKUFvPk0yZKOAmOmyO9dMc6rAYDHJuBXjH7e/gk/N/BOmkFzXfwrxuIuhPCqOgSd1eit1xEIOEcPH93VGiNIaMnBdMEJQdKsWtBBVq9/QFXWhCi9r3IaSsDYzlVqzzMnxQaSCAaeGXVYr3XYDYeGhG4qiDNFXSFLwIrSHswhPsX20jUwhRUvd+IQXSHB2zS5OS++n14/Q6y7C9nYhNY2o4XD94FksiB3E0Dxmx4bQBEQ0j7PrdnL3yKl8at9ZvLtzLftlBEvpTIvGmZk4i4dH7wQUm0srOaeukZy/o/r9uVEsaQCCOrGfbGk1jQh6mIpCoAmdjugcfj04hduz89BQ/H1Tho4nM+9Osmp0F5/e8GP+dVqcerOEIQwqygAsNAHL0t3EQw6+6jrk3J7cdfTkvgFozG74Ik2Jpwu9EILGuu/h+d1oIoWuH32UTcDx56iFXAjxGmBYKbVWCPGyZyqnlPoe8D2oRq0cbbsvNPKOjab5zGoZJBJ2GbMLbB45hyvv+gW27zBvpj85WSiEpOw7DFkVdhXjzE0W2Zf/BTPTbyFmtvKJu3/PQLaArsAvGFXzWVVNbenDaDEGGsQck1TERlC1rn83uBEmBNT2wzw2Oo22WIHWWJ6y0Jnf0M+B8XrSYXvCnSEoVCJkSzFmtQyTt6IctFJcu++Cyb4aumRaKsvUZI4NI63MSQzxp7GZOBO+7ymJPJ7QWdLQz1ApTlaL4KvqY6cUOJ5OWWpoUnHX/gX0FOpYNzCV109fx7Kmbnwp8HwN31MkohKEwJUao04Chcb+SiO2Y5App1A6uL6GpvlEdZtT0/vRhQJ8GvTV3Dx2CmN2jFqzxPxEP7amUReqIJ7wbSO4qG0Zp7e9gTW5R3m03EdtqMiAlWLY8lmdfZiwHqI+XAZM6swS476aHNU84QhqNMcBhxG/fvJYxbf4yYH7eHDsJKQQSOCW0U5eM0Pyh8E19JSGuLBlOV2JVq7f8wds6fGf3Rfx8tp9XNpxLnnrEWDv5POka9AU6X7aMyalTU/u65N53PeOffYQIa8+XwLTmHZ0D3TACcWh+4c9e84ELhVCHABuAs4XQhx+vPgSZmqyhsXtikjYRRNghsf5/M5v0lCfQSnBaC6BQENDo2RXh8RKCVLGkxNjT2TeKzsOE3sP42lQ2J6mVDAplU0GM7W4voHrGmw72IbyISIc9ufi6MKbrCuiuXRGc3QXa7GkiS4UUd2jJlpBEwpNq04UNqaKeL5OKmLREiqgciGssfDEe0NxUnoAQ1PomuSy9s2cX7eDWelRZqYzzEiPkDRtTF1iaBLXDhGqQEhzkQpsV8dyTYasFL/oXkZ3vh5XGnjS4L6+kyj6EbYW29h0sIWHd8zFk9XH1fGf6rPWmMgqgBCQLcWoN0rEdBulxMT3WBXpjug47dFxin6CjYXpDFXSGBLG/RgVaTLo1nJSwwe5uG0BH5z1FurD1QRWw1YSiYavoOyF8GV1xer68fRkG3HdxsAHFDsqTfhKw1diMhOlrySPZnaSs5+cEwhpEW7qvp9v7voVN/c+wAfWfo39xb00RdIYQqPoR7h7bBF1yXexpOk/Cest+EpgSZNmw6It8vR0vkLoCPFkZkdNPLeFSgEvPI5ayJVS/08p1aGUmgZcAdyvlLryqHv2IkMIwfkdM9CFhlLgKUVJFkinijTU5RnM1DA23sT/LvsnXt6ygjmpFj560tnMSdUT0mpYVP8pIhOZ8T57wcuJRQzMmUXkDAurVVGy4hRyCZgIEwSB7+v0lWuwfZ2Y8JmVGKIxlKc5PM4b2tcyZCUZ6K1neDBJe2gMzxeUvBBDpQRZKwIo0uEybaksNWaZUj6GrJjY2RjF7gRTw2NMTY5NXmNMOLjCJG7ajJQSaKjJ8EZfCUp2hO5MIwUrzEmpIRzPmOxr2Q9N7kYkkAhDccfwfDZlO3BCJiIi+dPuWYyXI1VlBkCh41OnFatiLUHZOmu7p7GpZxq/GVhE0QtRlGE2VaaAEHRGc1zaVKHNHOWqptUsjfUzLzzKmBdHCMmqwe+xZfgzrDt4OhfWbGd6Yi4RXU5chiIkfIp2jCs6340m2tmeb2KwkmBvoZ6YZtMeytIWyTLkx4hpFo7UUQqGrCS7i2lawp2kjAS1Zoq3Tr2AR0c3YsmqH91TDt/Z86/8Q9ciVjTMZXq8mX85+XKaIjWkw3O5cMrdzIikmB0aY0Gkl1nhPpTyUUph+UXKfoGuuv9GKDCQbC6bDFT2P89PdsCJQBBH/jfkjZ0Xc2vPo+iGM3lMCEU0ZhOnjKfpaMLkM4sue8pZFx9Sz7L2Ft5z0QZs2YcnNX65aykjlQTCBCUFmqhO9LWk8sxIjbA718TIeALfFMxODdESHueBnnnsGmgnHa0wu3UAQ5NENJfRchyFRtFV2BWT5S3ddDRnieoOdqfJw7mTkVIwq36IBbV9FNwwvtIZLCZprsmimR6PbJyLNREds2TeHtpiecaKCTb3dWCaHouaB6gPFZmeHmF/vh4NxcJUL6pesGOolZDhM6tpmB3jzSgEQkAiXSGlWzy2r4ur5v2RXV49S2r6SIcq7K80c2ZiH9v7mxmq1CCVwFWCLdkOltZ1s9dpwVUGpubj+oJbhyJcmD6AryCkgVCSztA4/X6KEft2LNciobu4/gFOCifI1dezN5/DEB7n1ewgrjv8eH+MT8/9FFet2kXGStIVHyFuODSZ4xhC4aPjKYEjDbaW29mbm0JYk7xr5kWc2zyPG7u/zB+Hf4TnRzBEDVJJNCRd4V7yuTdxZUOKrsbr2Fmy+NrOLzI9MZOLm09H+f1oEyOrbOmXrM/eQc5PkvFSIGBxzKdGczGEZElkN+tHf0Vrx8ef70c74DhzTIVcKfUg8OCxrPNEY6Cc40BplIW1HcSN8LM6tyaUpDQ2h97iKDM7hzF0Wd1lhyjxiEZjOEFD+M/D8Q7lYHkTrurH0CS6kJzWup87989HIYiEHJa39qAQNKaKgGKuMUCsxWF1dhqrS9NwfZ1iJkZjawHD9Ll3eB7z0gNoopod0FPVMLtsKcbe3hbaZufQhWJe/QDDM1KkNIuLpm3B0CSe1NhjNdKeUuxXzdxzoAnLMZFSAxQ7DrbTnaonM5xGGIoZHcNIBGNujJm1I0xNjdKk56kxywx7aUKGYv3uqRSJMj2dYY/VBBNino5b1CQsHszPoSbqUCZC2YsSMqs+4QWd/UwrjNKZztJTrGXTaDu/z8xn3K+6GBYmetlS6SCm29zSu4Tr957JjFiGL877DXsq9ey3G5gRHYKnpA5w3I3oaipvauwhNBF2KRUYDDJo9dKstzDsZbBUiD3lJqyQTquZI6JVE5f5tPKpeZ9mqOLTEaujK9GEI222jq9EIamPWER1G08Z1IWyLIt1o6FwZYU9mffwo+FlONJhb3E3IWEyRavBkxkEBuOex5pSBzHdrYq7As3fj2HIid4L6s2jjz0POPEJLPJnwbrRbt678qdoQiNphLn1vA/gSo+HhrfRGq3ltIa/nqXuW+dexgce/DVjw3E+vHQJl01byt39W/jWrrvJeVne8/j1fPe0dxPSDZRSbMvvQwEnp6ZPJs0as4aAJ0RFYEuddKKMUzG4ev6fiOgeQigOWA206WPMnJrB0HwuaN3O97rPpr0mT74xTHexHrTqrj295ToW1/WhC4lUAl9quLkwlXSIuLDpKdewNj8VYjA9PYSGZMRJoOOT0i36i3U80j0dzzbgCd80YLsGqZBHKFWhqz5LXayMROAog1EnTns4y6p8F0pVpxzHR+K8/aSHmZquLrffkOvg7tH5NMTKaBPWc1O8ggAcZaJJiTbhIBTArLphwoZkrjlM1o4z5iUmJ2Y3FKeQ0G0cx2DciaIQHKjUc33fuWxzqxOTq3LTeH/HA2S8JApBg5anRquQlxFqRQkBFL0w9x6Yyz0H7qaapixF0Y4wpWGYvVYzvXYDr2tcQ1x3eVltmdnJWUyNdtM3+mF2Fyu01nyGsCawJkIk2yOjvLz5Sh4dexjBxslnxZcOYsL76SiHg5UDLI7alJRA4LPbbUCf3PyiOkfQ7zVQa7hVC1/EWdTwZEKugBcvgZA/C248sIqK/0QebMVDgzu5bs8dFL3qLvDvn3Uxl089k5xd4f0P/obduRHeNncJH1p05mQdCxpaeOiN7wVguFRk3cAw68equcEVip35QX62ayVXzTuLb+2+mQeHqzlpzmlaykdmv4VfHLiR/aUb0ITAQJGxE+yzG2hJFpjaNEZI8zA0iVKQpEItZcJGdSgeVrCwoZ+sH+dAbxPU+ghASYgbDhqKc1t3s2r/dAYGG5COhtfuszXfwu5KMxOL+lk/PoVBK8WAU4NSgvpQgfW903B8E3QgJMERoEtcIcgVIyilUyhGqI+VecJxLoExOzKRORFQirDpMrN2eDKh1sL0QTYWOtEnFMvQJBNrlhACxgoJahMlhFCUrBDt4ao1KlDEDRupqjlgqjtmVN8xIc2v+uMn4uWHZSPexMtHR7Gz0kprtLpwJiPT5GSSUTvNgkSSwfI43965BIkOKAzhc2rdfipaaCKvi8BWgj2VFhYlenFlEYD9mauw3T2AZNvgmzg1vozdVg4Dn8WxMTqjTWwtlPDcGZyRqK6cba/9D6LZP07EvLssj4+gSYeEVn0Gp4fybJ1wGxnCRyhoTb2b2bUL2Z79EdsLWzjQ+2Eubv8ScfMwcY4BLxoCIX8WzE4286C2A0t61c2NhcTyHeyJyarfD2zk8qln8sV1D7F6qBdXSa7b8jjntE9nUUPr0+ralxvjtb/8MbNaexjXdHQRqS448Ty+uOYhvrb9IYRm0VrjYmiKewdXMTV8Mo9kfkt73EdR3ZxhR66ZhOZRGylRtMKIiU3ZXaUx7CYRKBonNkGouIK8F6VYiTA8UotR9IjXlvF9jV2FBhxbEI5I0g0lIgmXsjQoqTCrx6aRjj4ZPeMrjV6rOvHq+hrdbj2OfIpZHJEQlkzOdBZ10sKmuaHA4FiK5toCuiZpC2WRUtEczWMISdkzaYzlyVkxaiJlpBL0jteze6iZC6dvpd+rw/I1Egb4shre2DNUz6ahVqQQRA2XafFR8jJK3g2TMsssT3Vzb2Yurq9hIHGVwZTEMM3hPCNWnDE7zpK6rTw4MoeKrO4vWhsqM1ROcsueZbhS58Ozt7KsdgO6Vk8x/A7eOP32aoqC3vm8c8ojTImN0OPU8+D4XLJ+dbFRQncQhGmKv5nf9N3AFHkQDcmwm2RbpZW14yk+3LEFXdjEw8sImYsQSIoywh8Ls+lxGvjalEv47PxX8Fjfe8m6m8hUHqMpVJrIkROlKKMwESbqKQ2lBP1WL2W1gs2FbXjKwXb2szLzLV7edvjNpwNeHARC/ix4x8wzqfgOm7MHuXzack5tmMZXd1blKqyZnFJXTZI0blt4qmoZakDBsQ+p6669uzh99no6asfwlGDdyHQGiknK42FUxEM3HGpjZRxPxwh5tETqeWR4H/2lJM3RHIZWNSe7e5u4euGDtCVyWJ7Jb7sX0dUwwpCVYtBNc2/ffH7insWC1h5GRZyTa/rYMNSGVAKnEsKpmETjFmUzzCYrTsj0iEVsfE/DdXXMqI/tmfieoC5ZdWkUHBNTU2hC4kwsgkmnK4zldJQSNKQKjBdjeFKjMzrK5SetA2DDaDsHcrUUMybpRIUEZWrDFUzpA4KY4ZIO2/xo+1ksq+vB9gzu2T8fr95jyK9BCAjpirBw2DvYwnA+Ra4YpzZVYFyYVDyT+wZmk45Xv28BpB2bV9ds4MHMXLS4ouhEaA4ViUQ9ulJjKCCpWQglOSV1kEsaekloFW4ZbmKgnAI0Pr9lGbectRblDzHm3kxNuPqCev209cyMDTPgp4ibNq+o38y92ZOImdOYkb6MMesucqNfBwGr3Wbmhge4c3whHjqJSJlv9a/gk/Peyaz06QxafZxfM0qDXo0VP1UU0NCI6lF8uYaoLnDR2eUmWJy6BDO0gBr1A6a6oxRkhJmhITZVZrHMvJv8yHUsDEXZYDcjEfjK+fPH7xCONN99wIlJIOTPAkPT+fC8C5527AenvZ/fHlxDZ6yBSzuq2SX/cfGZPDJwgJLnsKSxnXTM5/eDKzml7iTqQtVUNDNq68kYOQxdYgBnNO/l5k3n4bsSo6tEazo/kRccUrrJ5xd+gPUjg3z80Y2UyyGaYgW2H+xgamKM5ngeU5fomk1nJMdNe0/BNDz0ik6xEgYBq3pm0NCW5/4987F0g//P3nvGWXaV557/tXY++VTuqq7q6qBuSa1WTiCEkEm2McEB44RnwJFrc8Fzr33H+OI71+PxzNjjhI1/wDgANsb3koTJIDAggXJohQ7q3F05nXx2XGvdD/tUdQtjjwTy4NDPp6pzqvY+++y93rXW8z7v81LKoG8hbc3MjlWOzE9gtCBWLknsUq73EBLCxEUI6CU+lTAisyVgkeqcl5UDCsRzM3ZsW8O3FRLNC6bO4WYx05WVfNIBrqgvcM/cbspuzFjQp2MKNPpFAjvb6hWqjeAcVc4uXJMrcOr5jiQxzqBRdcak1+TGvae469xeTpbG2D6xjhQQZzaNONiigMCwnFS4tLbI3toyK6LGWqeMLfUWby7RnA2HWY3L6MIydTmPI1J+eHqduf4Qd61eQqotlJEYobmwks2VipbyB1pxgRRweXGBnpEcbDxKXznMJfsJZMK1xTP8zfrzETJDCoMlwLMSfv/Ih3nl9iZ/t/Z+XlE5jSM2E5UbfGr+D7l++IewhTcwQhNExqFc+y0s6bLTvgz0/4w2fZridr6nNIKXPgpoKlbENrvDhtnJjaNv+gefaWMMj679FqfbHyWwJ7h18t0Une3f8hi5iO8MLgbybxOzpTHevO97n/baJbURHnjdL9JOYg62DvH2J94FwJ/Lv+VPb/g1yk6Rl+/cw9FDB+jqx7GEYCyYwe37RFGMvWkVLnIueD1RvO3Bz/CG3S8gjHye7E9xRGao0KVih1tGt0pLupGPaFu4YzHd1AX3fOhRSqAzC2OBDDQEioIXo4UAI3AcRakUorWg5EU4BU2vZ3NgaInFfpVtlRZHO+NcuHAzRmANgo9npRgjaMce1VKD8Won5+ANHGmOcTobY8/2NSypEcIMVtiaTOWqlMdPTXNf6iBsgwjOFy8Zch26heH6yinGvU7O5+94imRDIkX+/56doXoS6ZzXrztSk2qLJLPpG49U2/RSl7ITY0tFWfSp+V2+LC6hYkVgFAjwZEbRjfCCmB3+BqGy2FAVGmlAbVCk9ejSNNGoxZTXRApQBmLj0Fct6lbEiWQKEITa5XB/ktnS9SzHj9LPQvrKpSBjnl/7Gl9cXsOxDeuqhCubSAyJgfvaX+brG/fwo9vfxkb391EmYf/w27EG3vMl/0YKtc/TSFs8v7aXfv/9rDfz67ZweN7oW6iW3vhNV9qJ6rLQvwdlepztfAqDpp8t8vj673PzxO9+K0PhIr6DuBjInwNoo3lw4zDKKG4c3o8lLCwhWWx3+MzCvcQ639paQnK0c5brhy7Le3de/hscan+VVEfsr97OH6TvRiBIn6qQTTSxRV6I0k1cHmifYndxHEtAeaSLEAajI06v1fnIiWu5ceIkvdjna6t7mJ1dougmPNLYsRXQMIaw7ZKuBlBNECWNZSk8mXByeRghFUNDnVwBYvIg61iaWiVmOSnRET66YyGMYbM8JlWSNJUcGF0gNC4S6GUO7cSjHQdsK3Y2T007C3J9+8DtNTM5Zw2CyaDBo3MztPoFtJHIVOPaEVhgWZqRYo9O4uFZGRU7GpTd58dxUGTYW5e5rdIi0TaZlvhWRsUOGZEd/nz9+VxZ38X++lk+e+5yXrfjQaa8DUoyJjEWs8E6CJHnHdozQMw93V24rmbFlPnbtSu5dfgYjlGcioYpWjGXjK2S4XCot40Rt8uo0+WyYAGJYSMNOMTU1jNyqj3Ga6deiePcwh889ccoBE1V4s7Wfvb5S6zrMg/3dzJitRm2uyxltQEXHnPH0v200hvy76p+CZutuj+7+HXec+IjSCGZ9Ef53av/Hc3uHaTpQ5yIypwIbd709OJPADId8cmzrydWTSDF36r4FVsqmYv4l4WLgfxZwhjDU93HCVWf/ZVrcaTLHxz9a7629igAV9f28Z/3/zQ//YmPcf/cHMWRBmMTNhkZ2mh2FM7blkphcUX19q3f3/LiW3jHF7+OAW6wbuOr8X0kClJlobWk5niUA9AyD4haGxJs7l7cy92Le7lkaomhqQ6ZsWmmTr4a3jKAFcTrQR7w2i4mNCghWJcBVinB99PzFykEequ8XdDMChgEKws1RqYaLHXLeF4++Gt2iJAw5eZt1nqZy3KnzHpURBu22sYt9au4BY0lDNoIMi1QwqJoRazEVeY69fPnBLSWOG6Wq1Ok2fImf7w9yfW1swgMPe1xqLmN7aUmRScl05JI2RTsbGBRq7l3YYY7s31I4FT/CCNBj2sn2gx7PYoy7xtqo3nx0AleUDrDLxz5fjrKGxhg5VDGYimu8lC4C41EAr5QW5OSLTWLYZXtXmPLd6VgpWz3NjgbDxPIhMfWd7ESdnnV+DW4VoG+igffr6QqusxldXw3ZUOVaOpiPomLXNJ+preyNSH/8fF38MPbbyKwqnx8/tBWon0+XGEh6vCR5g9y19osGoMvD3Lb2An2V58ezTeio8SqQWYiwCBkBa3bFJ1xDgz/0rMeExfxncfFQP4s8cnFD3L32ucAwYQ/xVsv+U2+uvowqckD230bjzPXbnHPuXPEKqO/VKRsBfzgVbt4yfhNjPp17ls8x6985bNIKfi9F30v14xNAvBTL7ie11x9OULAULHAU63n8Zb7/jtnonXKxuP/fuhreFZKMQAEZKk98PrIR/nCRo3xsfaAbzaUZUI788DSCCUxnsEKEoaGu6RKMFNvUgpizq0McWZtiEIhxXYUUhiUkcSphWVptBa0+gFJJ6CU9hiu9FFGkGmLULtUBn0+AUp2wja/iSgITodD1OyIc50a/Z5DK3WoFEJSJJFyMQZKlkcjLiADheibQfW9Qdp5QwptNCoFy82npBP9URJsbGEITd7wuZMGJCanGywUmZZYUoOBwFPERhGIFD/KKIiYo41tbPdbWEITyIT5pMaLi2c5FQ7RzHzSgamXIOfEbaHYWdkYdBwS2JZGIZFolBE8tjHNJcESwmzdCoQQXFmc58riPEoL7hGX8bnFe2mmPX50+rW898xfYYzClymTbsi8ji8w39raRuEKzQ3Fk4TK5hMrV9HLPJ5qPsrtY2fw5D4cYW89e3fM/TpPtFI0pW84znkYoymFfwAmHHxfNpGOsESJdtalk85xpvMpCvY4M+XvQ4iLK/R/CbgYyJ8lHtj4KonOV1ML4Rk6WYvZ4iQne/MYY9gWjFAPClhiU/dsMWXN8qY9PwTkAfYX7/orqiOraCV405e63Psjb906/nDpvNHRJZVxXrGzzD3rR7n/pAPCJoksrI6L42ekPScXRou81N9Izq+/BVSKfXpniriFhNJUC6MEe0dXkdLgyAyL3Bxr9/QyfWnR7Xm5X7mT8+0YKEUZo5NNVtIKle1tpJuvRC0M2eBsD61Ns7O8znihSzdzcX3oKJ9uOEHd7vHY8hQjTpeFdhkJGFcRpTbaQN+UUFoiLUNlrIvOYKrYpL1WYcME1Goh4+U+jlBMBxusxBU2snJ+lcYwuBF8mAAAIABJREFUU1pnPS7jmvxz5SoNSaYkk/YG+yaWuHt1D/Us5FW7HkWQe5B//NyV/Pe16/GsjJtmTjJrdWmnHpkZeKmjuaS8QmokMS5LaYUR2cuDrcmln56lmO9VeWx9kgfSGRJtcfPIKXwrxSMhws29xGOf2Ci+MD/PVxYX+fHdN3JZ6RoOdx8mNB4fbV3FgWKDntZoBI4Q1K0ejtBcG5zhgd4Md7f20s9c8onL46lOhWvrC1xefQ2r0QYzxR5zvafYXhD0Oj6xdrh19Four+x++gOsTuKnD/GSIOFQ6pIYwZr2SE0KCO5aeCtSxFjCYa33abaXX8Zo8ZXIC8y4LuKfHy4G8meJmcJujnYOoozCt4oU7RK/ceBN/Lezn2Mj6vPAuYQf+8IH+bUX3cbHDh1mrFDkv77oxVv/H6mEkbElpMxXn3Jojs+cPcJtk7so2E9vyLsQLXPfxsNkZlDAEkpMZtFPHITOE5TCMhhHU612wdf0YhfXVsSZxZ7xdUpBTFu6SAuKdoyUBkvm1qub67WTrWG0JwjclDi2SbK8sAUg3CjQbwDbyJOQFyzytM471CMEJzsjnGnVqAURvqsH78PSSh1lGZrSZ8f2tXzCQXAq8tHmgtWeye17t1dbzFYb6HqTo09NIbpg1QxSGNqZT6jOt50zCKLUomZ1qNkxHe0TG5sMC61hJa2wqqoMFSKu9M5xLhzCszJGnA7tfoF+6mE7irPRMO9dqpFom4lyh3bs41kps+V1TkbjFEXGSlLGEQpfZhxe28axjWkmqpq1JKQehGwb73AkmeLTj13JD+16grrXoGL1GbG6WPjYItewJ9rwN6fuYnZ4g9IFt/tIv8Rt1WNkRlKyIjypEGhCbfFEuP1pi+tc7pox7Jb5sV0/AMCXlt7LXF/iyYzramu8Zvsvs7dy099/gEUdMFStlGtknxXlsRG7KASWcBAoQFMSDXrxFzmefI313ue5fPxdz3iMXMT//7gYyJ8lXr/jzdy5fAd91eW7xl6FJWwqjs3P7P4BXvSxd3Om08AApzsbHHzdWxFC0EljYpXhWTaZUVhy4PYtQNqK//i1T2JLUCJjJhjhv9z4cq4dneSeM4scfHKcbs/DtgYFNptBbGAjGxQihibb+ejWmiE/xLMUsbLItEWxFNGJXJQSrPRLRD2XtZUyRT/hyj3nwEAv8zBGEIcOSjPg1gENsidp9yr429q0WgUKXoJjq60gfmFgXw8L+G6Gb2IMAmMEZ45OoI1k5LoFrEGTC6XBsxRhNvCfza+I7xo7jHLyCkkhDWNTLVJjsdEsMt+oUKz2cWyYrLQB6MYu3dihnea7GJ0ZZodaSCcj1ZLlaAhjJGHiMO+WiQZ9PG+snWKpX0EIzc7xDaQ0NEKfSNmMFnqMF7u4Mg/4pwZeL2lmc+epy+nFHsLVCJkRJW3qXsZ4qTew/VXsHlvjqxs7KNkTvHj0MCUrYSpoUrQTdJZf66WVJdazEgUnT4LLwQ6nbvUpWRkd7eYW85y3wa26EW3Hp5t6jHhd9pWXaSarhFkTT5bYV9rNXH+apWiRy6ovoObU+IvjP0ysutwy9vMcqL8KAGENQ+0diPZvQnaSMSvmcqfPErNMll/Hqdb7yHSKgxpUlIY0orue62F0Ec8xLgbyZwnP8nnF5I887TVjDInOWIt6WzrjXpqQGc07Dt7Nuw7diyUl73zBa3jx9ku4bXQXRzsP08scnlydIBEhTjEmjR2ORYv82BffR8WUCduGWAUgDKmSCGszeQnFYoRJAqrjXTZpzLFij6KTIIBTS6NsdHMDLml0Xp8vDalvI4cyugsFHnp8lt37lgmshOVGjSR2EGgun1mgUojptT1OPTGDLKV0Yy9XncSGuG/TVAGul+EPDKuSTAISYyRLp4YRGjrrJeK+hxCGdt8jcNJB4DdblaBmoJCRQnNz/TRf7+5FoImVg7HBkYqq0yeVgrPrNfxiRjv2sYTGGI3rGnwnJUkttLA5ND+BY6dYbr6K9+wUZQTtrLA16dzb3IV2JFIZ2LTOFaCNZLlXxhUZU94GHzt+NVdNLbCWVTh+eoJ+3wMEJpPY5RjH0ZScpxd7CfLk6JjTZsjqgYGFqMrv7/8Ij7SmqTs9DmdjWEJxuDNJQxWYDJocKC6gxBhn4n082SmzM3iA6aDJvvovcJO6j/tbMZfVljDKkGpr4AopgYRPnr6dtkqQCF4x8XPsH3oDf33yjXTTNQDuXnknl1Rux7fyBl7Cvx3Hvx3deSdJ/6/Y7R/g0vLbSfUSO0t/zkL/btY7f0qqFrHJCKyATnQXZf/W52AEXcQ/BS4G8m8TraTHz97/bs721tg1UePoQh7QfnLftYRZyrsP30tmNJnS/PoDn2d3HZbiByk6KQU7Y9hPWLMFSWoPHAMFwoZWN8SonJc0gHFAaoNdSikHEVfOzCMEnOnWSJSdywWlQgroRS6NbhEzoC7UwGQp8BLEQPFiTUb0j5Q53HYYn22gkpxOGar0KRdyCqZYjhi5ZoVO5uLPVREZrOsKkXYwRpAUUmIvBSPQCHwro3OmTHth4BOAQUhNqRjiioQCEYGVshYWt2gVASSxRRwG/N6jL+dNB/6Oli5wPBqnaQQCg5SG7fUmZTfiqdUxCqWI2VqDfmbTyXxAEDgp016De87uRhkLC5X7rChrK4BrDWHoojKJ56UMDXWJUwvPzjn7KHGQUlHwFH0ZkFoOZ9eGecnUaQ6GO84XGmmQlqYR+zTiAE8rmhtlAjfme/c9SqkQc2l5CUsalBacjeuc7Y+wr7zC6aTONZWzJNrmoeUd9GKXq4fOUXVjulpgxCM82bqW+zdu4jcOvIAPLd+JxVmuKGV8aXlvXriE4Fw4wa9e9t2888hfcKQ3S90Nuby4wGMb72X/0BvoqZjVrIIBijIjb0OdI9UR/+3MbzAXHma2+DJeWXsdJ5Zfmt8vUWD3xJ3Mll/JqfX/RD/6HOhlTq3+FJeMf4zA3f9POp4u4lvDxUD+beLD5+5lvr+OwdDOuvznm1/Ki8YOMF2u0c/yVdKAqMCRFvevHSRWGZYEIRQVv89yK3haxSCAsTXSgEkZtP/IvUWynktfQ6vvUy+FbAvanOvVyLTkXKvGrvrGgIfePBC5O5V5Gs06yIoaGNVsWAWko9Dp+eIeyM8bjIR0DpXQfRttQZL4W+1IZGyRJYI0dcDWJJaLt3ze2tcIw8iOdeqjLc42R+j2C9w0eZJmv4DRAiFziiiNHTCSlbDCXx69mWtGznDXsb3s3LWKtDQCg2dlTFQ6jBR6HN8Y4eGTM8xuW93idoQwBG7Kjuo6C2EZz85ydUkmaDWLWI7CcjRpmk9YYeTi2RlRZrPaK6CMndMYRlC2Y6SEHcMNNqIyn1q/AsdLUeFgonUV0oHM2GgFnfU8+frzl36Wa0fPIkWuallSVSxp2F/LFS2xthjx2jhS8/6T13OoNYlGcjYc4opgafCMKL5/8hE+cOZGvrr8RyAFIwOp50pSHhhzwWrk8R8evodEJyBm2FNbBQzPq+ZqlFBXMfTxRcKQ3eGDJ3+Aq+s/wmzp+RzrHmcuPIIyKWd7B3l09SlqpjMQqSo64WepFn+U1EgglzcKBGFy6JsGcmMUrcb/Qhx9Ade7kdrQuxDC/3t/963AZHMYvYBwrkKIZ2cb/W8JF7VF3yY86SAvkGiNBiWmy/mKtGC7/PbNr8C2BJYFK3Gbjz2+PvAxkWRKsNSpUxAJs9V1pMiZUQy4boqIDbXpJhP71qiPtsE2YEGcujx2cgdxamFLjSMU3dBntVvinkO7eeDJ3ZCRR0kDKInpOUQNP2/KbkCtu8SBxCqneX/PaoxfTOiuBYTtXBoYZzbz7RppOVenXBjjxWB2SCMPMgsiGzSIsRBhaYTIdwcrc8M8sTxNOwlYC8vcee5yzi2MkEU2OhNEbRednf/+TnZG+dT81XQTn0OnJ3BJKdnxgLIQbCQ+c+tDGASThRaSPNBrI3Bliqs0lWLE9kqTohMTxjZGKoRtyC44j8CgNQNaShJndp58xlCUCdu9Dcp2SN1rs7ZuEykbtAANbvUCOuWCGfjqkXN4VoYjNZ7IyLQgNZJEW3hWRtmOGXL6GAPrcQm96Sa5Po0yuQpHYKg7Pa6qzjHtb7AUlsl0rrsf99qDExrKdpFIZ2jyjlOdxCfWRV68/U8ACOwqRZEw7TaQwqBNwsMb7+Or8z/NQu+LgMERKa7o8lB3hSNxXmakTIptTfObh/6Qv1poDGgcF4Og5L/gm46BKPw4UfQpjGkSR1+h133fPzpmjPnGZcs3h4q+TLL6MtKNnyJdezXG/H3PoovIcXFF/m3ih2Zu5oH14zzePMsto/t4ycSVT3t/slQhsB16WUKsMw4+EWKJK6nVu3Q6PuFMxoGRZXZUG7TSgCSzaJ+rkPUCCsN9/EqCkOBXY4LUIQzzlY7E4CjF8eOT6PEMlQlUaKPj3MTKpFa+CrcME5Mb+EFCby2g17OJHQvKMFRpMlNrkBnJYreMsSW9VoFOo8DJqI7GQmeAsUhrGVZbUowysopG2oYkOe89LoSh6MVUKjH9UkJ2tEwiJcYx5zWR5BSSVdSEjYEYHgOuAsts/ZrOFcGA6jo8yg6et+8YO4J1jrTG2IiLzIxusN4p0Q09inaUt4nDcKo3wupqld5yhU4whLWri+2C7WZoA1pKklhu0TUjlT5SGoZLfYIkodkvULX6XF5doGqHHOpPspaV6UYeQS3CcRRZaJP1LKjkq+SRUodmKuh1Ap7cmOSqkXNIYXisPcUjnWmmiw3moiFeNvokEpP3/iTg1bMO7zqcz4w97XOkO8FVpbP4IiMzkjGvzan+KAeb05zqjVFzejSTAFsqLDR7SvNsxEPowZ6v6MAbd/8yVXcWgNds/0XClRfx1WjkaZONMhGk97Ez2M9S3M6fFQTn0iH+n8Mv4eaRM/yi9Qdcba/zqd4sx5MRRp0eSvhUel+iq2/nb88+xr7qOD+w42q06aH0xgXt9xTG9L7pWDHG8Ddn/4x71r9MzR3izZf8GqPe+Df9WwDVezcQ5WsbNY9JDyLcG5/RuPy3houB/FlgI2kikdQGxlcAvuXyh9e/Ecgf1NO9BYQQzBbzIp/txRp68JB70iJwbLp9w8pyPac2RJdDjW15B3hj0V8qojouSIGwzi+BhQA7yJBZhlSCih8yU2hwYmMHrcTHDCt0ekE3GCPAQK3WoVCMkBLKo32iThGR2lhCEaA4cXwb9XqH8XKPYwsTlKp99uxepL82SSf2iJpFjBYQGHSQIXzN2HgL29K0uj6NpdrWKXeMrLO7ts5yr8RjB6uYIpCAiAXGz+Uw0tGYVF7A8whIJcLNEBYQSYYnWgyPtwl7LqtLFV46fAhbaPYWlvlKYy/n4hHKhZh+5uIITcHOKDoxjtRUL1/glILmahkx8GCBfOtppMHzU0aLXQInxcjz7wduxkxpnltrT7EvWMZGc0Vhnj/LbsXxM2xHISQ4xZSdoysYAYl2kBZYssXoUIO/7V/F3HqN461RHmnNAFC0Ehwy9hQXmfTahNrjwc4sUsxz03YbX2oKbsLRzhglQspuTJxZRJnN0e4Eykg2kiKNJMCzU4puChiM3WFHBVqJz5DXZ8wzXFk9T3uMelM0LcNN3jpfj0bIkATEaCRjbokZ68t8MdlOz+T5jjBzaGZF7lzex4jzOD83cZq93hJPaY8YC0zK8db7+ZVHzxCqFE/CsbXf5bbR+xAIdrg1hE6xrO00xPP5k8M/QaojXj7xU9ww/D0AnOmf4MHG1zEYmskGH5//ID+963wNxTdC2JdgkkeBGIxCyMlnNE7/LeJiIH+G+NC5T/Lx+c9igB+f+X6uq93IocYKVw5vY9jP5W/vPvFRPrt0DwCvnryNN+x6JROFMr938yv5pXs/ThhqihMGOafRCtgWI2Rewt7t+SQni5ilAMoGbRt6GwHBcITl5BWEqZB4lYSrqnPsqa+gMpsosrnmirM4pZQj7hgrC0M5VSEAI7Csp2u/N382kcVSawitJf3QY3Z2ickda6z1ihybH2fP+Cpz3SqnVzcbOucYGWtjW3n5eLUUEQ93yRKH7cMbHBhexJIGp5Dx0IRGCQkarFULszvBcTOUcjAXTFAYIJaIngcjMUEpZHysgbQMtptR8KKtz+0IzZTf5Fw8AsLg2HqrTD5KbWIhKDgJ05csE8z0WE/zVf+FO/mCm0IqabZLFKoRjpurbnyZMFnoMOF0zjsQGvBNguumTBabjBV6HG8N4w+aVwTEhJnNeDminXqkxubLjX2sdYtb31moHGSg+cjSDdwyfIyFZIiu8reu/WzPw3fy67i3s1lKbxhz2kyVWjRaRWLtoICCk51P3CIZC7qMFzq5DNXK+Nzil+hmBdrpOgvRAq+s3MCsdTf73Q1OZcWchsFmNtiPig9zU3Cax6JtLCUVPnH2wOC4gof7E9zTm+L5xXmOJ4XB2RwyM401oBFjDadDxfNMTvWcitvcNP0YUlZ5x7GfJ1S5z85nl/6UK2u34VkFxDdUmspvUnl6IezKr5IhMNlTWMWfQ9gXXRn/IVwM5M8A2mg+Ovdp9CDz/8GzH+eXv/zYQDYn+PT3vYGpYoXPLv4dZTck05I75v+ON+x6Jcpo3vbwHfS7QNdhjRgK4I30YJC70bHFsblp/FVJUtWoAoN+j4K1U1XsgkLUMvJtsOHUyjitpTJnkjrXPe801VKIEHDt+DwPaIFOLHonK6SOpN0IKFX72JYmSWx6Z0qYIYUKZb7SJg9YUewyVOlgW5qV1SpukuK5hkq5T6cb5C6HfrrlWigHHLnu25RqIbUgT7KlmcXjp3eg8wY6YBn0RIaU4NgajKJQinGKPRqrZdKui8gGQ7wvKQ7Fm2wNQoByGPiz5LrquWgIpQVKQT/xcCyNsDTr/Ur+2YADQws0TEDZimiHweBzCXyR4cQwt5R3y5Grhv2Xn8oNvxSA4Vxap2b3clWOtnmsMUm1GNPVATqymCp1ALFlu+vbGZsmCQYGFrlmwAPnah5b5hWbZ8NhMpE3gTYGFJKbq6cZ9zo80NlJUxXy4xjBkxtTNHoBM7V1XJkxU27QyApb/u/LUYlxr49DgkbSyTw+fO4DnOqOU3C6lOyYxdDm0uAyRp02ctBvNNOSk6HNiLYp2inXBwvcEV9HLysiMBSdhIIXM5eWWBZXcdXIGznT/gAldzfjhZ9Eio/hSY0rI35w6pGBbQGAhZTVf9TTfKawi+cP387da19kyB3hNVM/9o+OOyECnOp//f8YnRcBFwP5M4JAEFg+PdXPXzA2/SzGAK60+fy5Y/z43iuYKLS3/DnKTv7Vfuj0Q/SyGKJNThjAkHY8CkGP7mIFowTInHbQDhcugBEdByIbXVYYkys91jOPNasAAXh+urVKE8IQBCnrB4chlcjAYHoep+UoUhq0pSgs2sQOTO5YZ/HUKIZcb12t9WkslOg+WUMGGjMiEA5MTDSpRT2qdp++cmhFAWO1BtcMzSOAJ7OQh5ZmOGdpdhQ2+MKDVxGl+XZ9y3fEzqWPe2srNDdnKUBqWG6OIBAIoRkdajNU7pIqC4HCIFhdrXJ6YpiyHRNrB1trzs4P08scxKDAKPATbE9jjMibXSgJwlB0U3wrZa1R5qbhs3z97B66kb0ly5QyY8LqcabnsBjW2FHd4GwyQlf5hKnN4e42Cn62lZSMlIMvUzz7/I5CG5Gfy07oZw5h5JKFFtI2+F6C7yRImdNc7Syg7CaDrnOGEdHlefWT1K0+gUz4xMbVbO4g4szGIJlrDfGiXcd4amOMqhcy36rm1+wkxKnLdGljyxsGAUbHNCOf6foGo16PBVViXQfs8xcBONYf4m+WE+BVHAjmiE2JX7js7bzxEo9ff+L/JDM5b65teLTfYiR+G5b0iLNjuPGH+KP9Hgdb4wx7jdzPZnAvHWt8K4h//9Rb+eDZ/2OLWvGswuD5FPzg9Ov5wenXf2sD8SL+QXzbgVwIMQ28HxgnX5S8xxjzh9/ucf85QQjB2y57M+85+QEsYbHamAS5MBCYaPZUh2mlTTxpkZrc5HXYzZfbS2ELx82IPYXJ5FbeSaWC7nwll7yJPBhMXzvH6WMThMY+X6iSgbFBpYPXNOBozKAC8+jaOAfG88+y0iuhjIXKJOF2deEFYCwDsUM2leJ7KbarmN67TBLb+H5MFEr6dw1jRTkts5GMUX/hCgCtls9KVCGNbWTX4vZbjm9ZyV42ucChxgSRcvnCkf2EkYvjZVQrIe12ATdIiAONbRQzpQZupFiLSygjSYzEuBoLg1+JELWMVNskDRcni9ldW8IzmoWwxrjbIlQu82s1uj0fGaitgBxGLmUvhMHmfSMt0E9dAjsliSxeu/tBOkmAQmJErk3fRL3Y50RrhNVOhSfXt1ELQg6rCYpOjO2Ar9MtjxOtBccXx9g1uTrwOZdshAWGg5DASokTi+VGFWMkItMEdspVY/PMR3VsqXAHFbeO1FxSWKZiR5xJR2jrHnuCFSbsJm1T4ERzmF6cPz+ZtvIG1k7IqdYwW5RN6ua7LGUj5PmdVWpsIuXw4NoOXj51GEdoOsrnzvVLiTOHjtlshAEH+9MYPD50+mH++vT9BFaVt1/5QzTjP+epnkc7k1QCC89EWEBTaZAhV9RPUzYJ6wSDZKvLVOV8cJ4pXsZ/uuyvnouhdxHPEM/FijwD/oMx5mEhRBl4SAjxBWPMoefg2P9ssKe8k9++6j+Tac2lH/xtII+/lhS8cHInyijG/AnW4lUyrUjDWe6aO8W1xVn+dP5hiqWYfiJQ2srHogWOTEni89rYI0e3IywQMRhbgJchhAXF7LwT1mZpv6UxoaDx5Cj3ZkPEO1Iix2J0qoU1FQKbRh6Ger2LX0gxWtBfDUgzK9/aZ4LGSgmdVskim/pLG9CxMF+vwL6QRlhApZJ2VMizhQFoW5OkDtqEW259MshQ2EQND+XA7I41qtV+To1owfHlYbppwKdOXkHF75OkNmHPY6zSYXR2Ddvb5O4FnW7A2HrMz7zoS3h2RrLT4lOLB/h080CuzijGVPwefemidM6yOrbClfnK2bdTUuPQCgN21Ne4efYMy40qj61N5wU9rgFL4VoZN+w8ieuk7Bpa5cTGKCcXx7CEZmy0SdnNLW7rXpg37Ih92m2fMPLoRj6Oo1hcqxIlDm0/oFCI0ep8stkgSTOL7X6Tq6tzPBVO8LXlXSTKQmDYWVhjMa0BgtB2+dzqZbiuYm95haMLY+d5/SynlJBs0TmbEAJcMtLBME61RazsAc0jaMQ+VTdGGk2a2syFNepBhNr6f8EbZ1/Nbz32JbQx9LKE//ep+7lsuEuo81X0/T2PW8tP0VcS38lveGYEmZSM0ic2NrY0TJe/OU2S6oT3n/5tTvWeZMYLeO3YXqqVNyFl9VmOwIv4x/BtB3JjzCKwOPi5I4Q4DEwB/6oC+SYsISjYLp0017RWPIffeOJ9XFO/hP+497/woVN38o6HH2ZlPeVjX/pYrh7RDm7TQ1Xj89lGJXP3wEE3dyszCE+jYxuJwCQGIR2K9ZDqVJO5qJpTK4C16LJ9epm1xyeRXYnBwum4JLf0afV9VFFD34ARuK7CCwb0izQUx/Nmy6uNEq6r8cfyDkNRw6cxV2Ni7xrhdR0oKbLQQQidm2KL80nDOw/u52XXPUbgJzywOI0tFP2+T1oEUJhvqE4QEhg0S9bawnU0KsiYGm5yrlUHYwYl56ATyezICvaA021GBc5kw2QD+qCnoFrvkvUqpNLCsTKCQh50S06MJQ2dyKXq9GipAu89eTOOVGyTHWypyLSFdBS37DrKw8sz3Dc/S9GPGBtpYAHr7SILy0PoxGJqqIklDUli0Wu4zFQ2WBclotSm2SnQC/OS/V7PR1gG10mRMt8tAVwxukDYdPnAiVtRLkQVgbAEBTsi0s7Ws7CalTnYnsG3UibLR7h1x0nuPH4pibK5dGSBSbdBM5tivNRhpZtb1Lp2BhpuLZ/mwWiSDJu1fnWLqxcY7l/bScWK+J1L7sAbykiMw2fb+3i4M0OmLfYVNC8avYbflZ9jW6FJP7OpOD6h3qpCo6d9OrrIg80pXjZ8CFcqlJHYooBFh4LIEEi06SAJ/t54eXDjS5zqPUlmUs5GEfevf5jrkrsYGfvUtzcQL+JpeE45ciHELHANcN83ee9ngZ8FmJmZeS5P+0+G0+0GHz7xGNtLNX54z5WAQQrJ+77rdfza/Z8l0xkqOM1XVs9y3/ohGv2U+85oltsedsfifNYOEqVBSbjAJ0obCwYKDmusj1p5+kAwSkOgGap3KeqYdujTixz60y4LsoIfyS0lgDYGZ9mGisCup6gsRcV5YY+8wCjbkAdWN8gTkJu1TF41Jm4NtvNlQzhfRSWblYwZ2jcQ2rnJFhZ3PHB9zsOnYG8LSYw1CPhwbqWG76S4XsbKRpkocza/BiypcR2FI1NW+kWkrQkTh9ZakXohxCfjkWg7I80NrizN0YiLJLGNZaucGjGChWadUi3GFXnSMMpcMmXRDgMmyk2iyGa8GnKuU0drQaZs5l3BbbNHWOjWma42GC+3ubR2P3+3sI+28fBsRT/2iAc7pKVG3t+zWAwxGi4ZWeXIyjbqQQ+rYWikHlhi67p0JuikBaRlEJbitTsfYNzu8sdf/h4yndNiThLhj0ek2kKT9+IxBpajMghBpB0sFBU35DJvhbn2EGHf5yOHr2N6fIOilzBZadHLPMBgZYK/Xrma64bOEWnDpdVlLvWXOBGOsp4W6WUBt9ZPULRiHGlwjeGG4jk+Pb+fdupzuimoul/mpdtPkuk+o24HW0i0UZyL62gkO7yMjh7iqXCcZNVmd2GVJ7qTvGX3AUz0HgQ2Rf9FWHL0m46hM70TpDod5AVyP/QsfeJbHZIX8Q/gOQvkQogS8BHgrcaY9je+b4x5D/AegOuvv/6ZlXZ9B9FKIl64ynYqAAAgAElEQVT16ffSTWIqvuErrXehCLmsdDVv2ffv+fQrfopPLtzDnxw7STeRzK0X+fXDDwBsDlHOZ/sGPSgtYGAWhWCr9B4MSbMAWg7+Mw+Io7vXsRzDUqdKUWVkQtA37lZpe7g7IQ1yh0KnYcOqB+s5j14f7TFi1lnsVvF0SmZbZMZCD5QqmZI4g+YLGMgim9J4h1bHI0mc3Htls9inZ+O0Tb7iHoxIAwgHtA2mbyEKg+sVkBibI49No0r5JOUECU6g8OyU4WKfRqPI1EiD5W6ZBAvbMlTKId2DVeRNG0jb8OnVK/ny8j4aqyXikqRe7GNbimYYbEkON9MLckDLGAyLy0OYjsuSkiSJC2cKeZGRryjecpLrt5/dusdHFyY5+uQsxjZURjuUaoNk9uC+ZZlFvRBio3hscTvKWMRdm6lCkyu3neORxem8otTJsFqStCQwMmfgH29MkwQrW23xMLmaKG+jZ1gLC1S8mH7q8sTaNgSaihtSc/p87pGrObk0jtKSRqOMGk45tTzMC3YeJxYOmZbcMnSCbV6LE71RbikcpWrHFGSKJQxnohq/ffq7saRiJS6RGQuHjEQJHl6/llYaoExeMPbhM/fwPTMxRTtBYjDkFbmXBUuULc3NY7/FPet/ye3Dx/ji6j4e727n6uoix7ofx2EUiebGytv+QbXKo63H0YO+riNWn/1BF89/yTf9228FxkREzbej0kdxCj+BV/qfnrNj/0vCcxLIhRAOeRD/gDHmo8/FMb/TON1uoE1uNTQ9PE9mcs73ifajvPPwJ3jz5a/mxqFLeY+wWOsUSXpP94HQFYVog9Dg7Opi+QrXQLjmo3suxgIUgxX6gPyUoH3A5Pa2jq9gkJo7e2oMe7JLyY/IlEVrsYTZzH3lvlV5flQLTNum7/osVGxMSdNOA66oLnKuW2UjK6K0JE7yD+A4iiSzSKTArBewKzFabobFQdl4KrDbkjTQeQWmEU/TBGddB+NlW4YPwoAwAq8h8WohaWSRNT1cL+LkmTLbZ1d59IldaARuIaE63sUONO4VHeSm57mArvHozNVx93RoxoWtrymLJVoL5GDCS5VEJ5Ks4+YZm0RijINYd/PkMAITSx46s5MXXnoUowWNTomvPHkF9mgf21fUal0yZWHbGUpZCKDgJwwVQ9q98wlCbQTGMWQ9m13eBlkB7IIiURYLGznnLdAstGp0tYtwDCQDr516TNXv8yNTD1Kx8+Tsl9YuyYO9gHYa8PWVXSw3q3kuZXD3904sM1TsMhSENFMBXsRiXGM2WGd/eZkFVWfcmduSab5n/rYt2eND3Rk+MXeAm0ZO8/XVXfzF6X145YTNcs9ht4sQaqvNHuRWCB1V5Gh/Ox9e/TDaeHxfPeV3dn0cgMh4nEirrCsfsPn8/K/wqh1/iWudb4qyicAqsJZ1kFohhMOdvbcwKW7ipUY/zdrimeB45wEe3PgkE/4ebh37USxhE3f+mDS8A4iJO7+F5V6J7V7zrI77rwHPhWpFAH8GHDbG/N63/5H+eWBPdZjAdsi0Ju/ffl4r/PFTT/Lmy1/NmF/nL276X3nhJ9+JsAcViwxWrLbBzIQDsya1RY27QzHKy6WEWd9hy1EPgXBUXlovYWSihW9lJMpCCYllK6qlnAt2bU3kZ3lfSQG6OChgGShJYgHleoo2udpFG8HDq9vJMmtrNS+NIHATWmEuBxQCqGeodQ9RUIOVpMGyFVY3f0y8nkFPJphUovrOIBYIjK9gM89ncqWNVBqtJTE2rpeAhm43AC1ZWBnacj9M+i5ZYiE0iEDlhxzs17LQRhhBdraIPdvDOJo4skFCZ93H8gwmEYgIlBzoNqUZ7HwEXCATBGhqn7vO7cZ0Heye4Sef9yU+tHA9sbZZ65RIUhthK4YKfcp+RLkYDTotRRT8mF7kYUvNSNDBLhjKMg/g7cRHAPViD6M1Seyy2i+xFlYY3tHMKSoLSn7EpaUlKnaEKzXGwI6gwcNiFjX4Po62xrGLKYR5kZDrp9i+ZigIibVDqHLfk42syLH+OJcWF0kI+Gp/L5e6i2RGsJ6eL0hypOKvz9zIn554AZmRuH6CFBptcqsCLeCR9Sm2+Rkv31bjXO8g2+wNriws8L/Pz6IGtROfaVzOC8onAAgEpARMOg08kSFY5Y7TP82D3edx+9h1fM+259PNOty7fjc31F/Iw42vEaqYz63vo5/NY4s7ONFd4d/t/d5nPB7X4zk+cu7/IjMxZ3pPIITktrEfR6t5YNODRWLU0jM+5r8mPBcr8luA1wOPCyEeHbz2NmPMp5+DY3/HUHRcPvN9b+QzZ49SdgwfWnwPjp3w+MokiYn5vi/8Eb9z1WvZMzKKjmyEHyM0mDj3H7GVg25JvLEEg3rasa2iyqsuA0W8GGDcPPhXxzpUghRpZTiuJtOCRr+Q0yFjMUqBPWiQY6OQfQddMBgLVCWDkkY5BmfZIVt16NvOliGV5ydI73xgM0qysVzGKqsL5O0CHbrovOtXXpRkLNxtIVHXg4mEQjWfTKJVTdby88CNjcqy3NQLMLFEjMakHQ8Ru6SJg+fFyK5EB5CozWqh/MSJkkSJg4hBiBDL5EnP3sncP1vHErXkkg5plJLQdzDGkEUCmUKx0KejN1uRCYRtsFsSb6JLlJWQscQdDglbDrIk8IZDfuWGz/CuEy8kHhTYZFpScGOum5ij6OYmXUeaY9gy53f3TK3gGEXJjbAE2FZ+T481RmnHeaXmqN/mu7cdxhWKtajEB564iSyz2De+xCvHDuJIw3JU3JI/2ij2+Ot4UhEqgTaSLLOIi5Kd1Xk2khJWMSXOLMbsFgtJ/fz9IzfkslHscNaIjc3j0RT91GVzJrTQHCjP8bKZQ3zg3PN4ojWJ7eSFZZbM6zx72ifNFN0e/IT/EyR6lGvs38dCYwlNZvIZevNnKSxWzeUoXDyxuiVDLXOQx1ujPNU5y7hf5yNz76GdbvC80lFuLjTZyIrM+ddwvDNEZgR3rx16VoF8I1nMV/AGMhOzHJ3Mn+vSz5BFnwM00tqG7d32jI/5rwnPhWrlbp5WwvKvByNBkdfvuxaAXdX/jV998A4yswJozvTW+OGPvpdfuuqlpJFEGTdfECLQGjKt8RyLf7/3JXx+7WGOtPOCjJweEFuB0rIUqm9jFROqhQTHS7cohFbfzyV2Az69n7iUZN5VJsoc3AX7f7D35tG2HXd956eq9njmc+fh3TfqPc2TZdmyLHnGGIxpVhMgBNs0ECAYwtAryTKdGJIVIB2CuzGDCRgCmAAG2g628WxLsuRB07Okp+HpzeO99935zGdPVdV/1Ln3SihpRDoEFnKtddfS0jtn731q7/2rX31/3+/3N2IZWkSk8fYM6G9GkAqSVoSp2x1mRJr4lPzUbRg05F0fqyVGa7yasyrVa4GDTLbpKQKX7bZL+IDJI3Q1Qyk7ysi3C6gWUQisZx1clAuyJAQfbO7+LcuDUYJsoDO6bgkyKBh2Q+j5WAFJZqm3LNftvcjWXIenLi5gQ8v83jWupHVMGjjvGh8QFuPD5Gwb2dd02o7RUfIS0k7EuMzZc9MirX7M1mbMzHSLuJyi0JxuTyGFdbiwsOwb26LkpVTCdCc4zZfbrCVlyn5GoLTLonEF223q5VSpy1ZWouxn3FBfJpQFUsBY1OdQc5XNXpWKn7GS1ym04GhnPzXT567xs7yudokikPz2tX/Gf1h8DQ+u7N9ppl3EgtfveYZAGS4kYxzbnKXbLTE+PkQLSSAKml6POX8LTzj3x8KX/MK5b8YCJT9HeYZnOjPEkeTdt72Tn3zofjp5gRIFtzUucTltkDzHGvbL60/zz6/5J5xf/g2OJXWury6yllXp6Yial/Eri6/nmuoihczwxZDZ0S7MjLZRVTUgtT6nuxfp5B0OhJcZ99oILE2vz83Vc6wkNXJT4q6J6/5a7+Le0vXEo8YYmdZkWvJU+6vcUH8V1ekHMXoZ6R1EvER7i35d2fkix03js/zgtXfwc8f+gtS4F9poywe/8jWOHBznmfVVwGK1BeMQ5Cw13D27j398/Sv59s/8Lqf65/A8TTYyjbKAqRkafofZmTbDxN/1RTE8z3YVIEt9ttoRWSHRxsOOa7xyAeOuDfJgpYRJFNlCgXnunbUWL9T4ofPotlqQ43Bfm3nk6wo8kLnDXYTGYfgGvN4uM0amMFyLkZUCu83AAUAg1122tn1ei8XEI0ZOBuSSouI+ixV46TbmrCDzkKHGr6XYXPLdr7uPUpBRGMXYdIsHTl/LctpAKItfy8ktmBGsBDDvt1GTmvGxLkoaLq3VKYTh8laTpV4VAeyf20BKy8agxNpKnZNiFiEMs2Mt+gR40mAd8dNdl3FY8aV2g9DTTER9+onP5maNO685M7p2GBQ+tTBBCktu1QhLd8fQVjAUHoURPNLey8qwjkVyxdQ42OsgaxcpqYJQFtxcWuIh9o++aXnr/NOUVcJs0OJVtTN8uX2I/+eZO7l8SfPNLzvKRKWHv1tKRQrrWDyjm9IZRuRDF9TKnuC+8E/5mesy7lkeo1I8w+2zp3igdYgHtg7vFCq/YeZWfFVh3/Qn+dzZH0VJy0zUATo8276JJ1oeh+rLSAEFkqWsTtMbUFjFpilzpNxiMfN4dOvj7AuWuTZedru6UQnfF4pvnX8lV1WO8KaZm5/3bFtrGeoBkYr/q9h5qEr80KFf5+jW5/j08h9xvHuU072nyOcWKZkvEqo5Fho/gfp6IH9pj48/9Sz/8csPc2C8yS98yzdQi15ojP/WPTfy+aXjfPHKCWzPQ2xE7NlT52h6HsIC+r57kUdBUFh4+4O/wnTJ49LyON20Qrk5QBSQC+UKTFKgR5Ti/uUylYU+0jd0LlXxfEsa7fZt1G3PUQpDV+TTdYOq7xopyWqOTj1s8BzxiHEZcTyREviuDyPS0pjtsnl5JMoQABYTFai+j7Cj4KsM1nMZ/E5xU0Ex4rrvLDoW1MgvZVstbswIOgmhUBaZiJ3FC2ExxiKsKwwiBdHUwOH3RvPg5gHeNHeCQGoWKi0KFJ4YFVgFeJWCrO0ofSqDh49dg5Ugmil54HqFMmpOraRkcqaN7xk2hyX6gxBrBBk+YOkPIw7sWaefhxRWsdhvMB70MUIw1CGHxjaZj1tuvo3gqPFZ7tcQBShfu2sYSdVP9qep+wljwYCLgyYDL2BmbAvPc14v20NKuJTWya3CoyAzHk925lHKIA1E0lkLzAYtYuF2aK9pnOTe+nWsb4zxkYfvIPRy3vWGzzI0PrF0O6r71l2bvHKQgQ+tQjBIIp5dnqAcXcC2z/NfHt2PFK/iicsLvPP2+xj3+1grOdz8B9w+fjUAgX81JW8fPb20c81zseKJluBUd4qDlXUiaRkSkxbBznNwU/OVdDafpVP0uKO89Dw2UU+XqMVv4zsW3kbJe35RNNUpv3Ti33F5eJGG3+Td176Hut/gROdxHlj/FHPxft48/R0EKsaFLLfc5TblsfVf45roIoKQzKxyZOL/fhFv+9+/8fVADlzcavEvP/E5kqLg/NYWP//ZL/Lvv/UbX/A5X3r82h3/iMcuLfHez32J5pGY97z19XzLZ36bdFVglNjJEq0A5Tkd/eowp52koKC36by2RWUk0hGWRnlIknpsDcu0H6i5DEkI1DVtV5QaBWURgsgs1riCnvDsTkZmR4wVChxTQ+7EZ/AKskwRB7tBX/mjbDkqqIx3qUQ5eaHYPDmOGI4YE4Vk7PoV9JkKg2FEUhbYfPQibdsFjH6sEAZqGt9YCu05BeX2GMFIMMr05aiDkTaoskHo7Y9ZlGe4ktQotMRYwTMrc6DF7m8ERObmeaGxQWe9ysCU3Q7Afw61M9CQehgt8FXhlKxG7DSA3h4tHbPZjykJTbcXs9Grk835hFExgsosnSxECkiNx3S1x/FTC2jjFoyD+5dZqG2ymDQpjOLBrQN40mItlMOUZpDw5PosxgpCL8eT7vxf2TrAezW8qnGRB9YP8MhgwQmiNBglOd8fY2+w/jznyrmFdQaEDHohh+Yvoa3kVDZLWSRsZGWW8jGmSt2R0lMw0eyxuOKjjcQOBQ9vXuUaX3gFr77qOIVRvLV53BmiyV8gzV5NGNwAwKHy21ka/pK7r1ZyU3WJouhx38ZBzvfHmAtKjPtnOFxbxpeWiehG6qWXw8ZxAHo6JJYZUoC2ko56B0e3vsajW4/xgwd/jJsaLxvdU8vRrYdZTpbQVrOVbXLPyue5e/Iufu/8e8ltxpme0xZ+8+x3cyjscXO0xNlsgq4JmPWdy6IlpZ8+/YJ39qUyvh7Igc3BEDl6Y3JtWGy/gAb/vHHrwhz/+fu/E4DfevYrFB2LGkpMZTtIWPwopz7rDPbzRCHLhVM8tn0XcNsBKs4QHY9BHNDWMXYqpygbWAsw5RzZiZFj+TY9G2EttpIjPNcsQTpasgvyA4E4F6FiiR5Y12wZoIBorMAKQV5I50AIpAMPyhleVKCs+/+m76FSgRUWWU2JyglbZyawiWPSVCa6yFrB1pUKxpPITBA1hgRjCXY0f1YLirNV5FCiPeMwc0ZYtLS7dEsAD/yLoWOajCUEJdesoW9C7lk9Qqo9zqVOrl6sR6hyDpkgzyV4sLg1NsrqrQs427M/4sZjQSSuQKYLQaA1hIJybUgyDBDKIn3DYrdBIDXZZkjRioiqCWNBDyHgSrdKK3HND2phQmgs2shRf1W4st6gXuqzP1jjkY39hH5BLUrRRvKysUt8eeXgTsEwKQLqcsBGVkYjuWfjKj69fC1SGvygoCg8AlFgpODzV65hkPl8y8wxBLCcNRiPh0xcfYZh7rEQbrGVlPGEZtnW+PQTNzNTblOdSdnM/e3HECkNc6rNv7nqE3jSoI3k3o2rmK63mPAGSFyTDWMz+snn2Cr28bOPfIYnNpYpuJnDzVVuGF/k8nCRVzZWafopHzr3ck4OPSS3MF/Zx3dcdZbvnPt3IEp8fuVTbKRrPJveSCCfQYmUUvQtHF09RmHd/f2zy3/ETY2XcbzzLO879WvkZog3Ws0NBiUUK8kS0nUbJ7cZS8ML9Hu/h+3+HHeWcu4oXUE2/pQr7X9FUmSAYbb2zhf1vv99HF8P5MANs9NcPzPFU1dWAPjx17zqRX3vkbUL/Prx+xkmAqF9vL5ABwasIJYBkchJbUqW+KOmCWpkHetw1HptSNjIWU0rMGKjUNbo/qjr/aaHbGYuezUC71SAbyT6tq7L3oxAaSiXUkQAw6qiKCJkKjFilDXnAqMl0jckuU+hNQqNEIbAsyT9aPQXcEPzClfdvs6pE3toEaDHIRjrojcCiqUyRTukOjWk2kjorZfBM3jNDDtiEyBASIs3kZBdKSMS1yFIINh3YJmOCGklzg5W93yEFggrQAvEpgelXSfHXHiMl4Zc8DW1mTa95RpiMUJmDrnK6xpTFlhPoMYzfL9AeoZce+64LR+RSwSSIvHotWLSIsIA2dAjnE6cP3cu0ElAEmmKUMBMyqYMyQcChWFzuEvlayUxe8rtXb8TYSmE5PjiHkI/o1xOyY3Hhc0Gk+GAY62556DYAJbJUo/945v4FNgMnriyDxMYZqsdxsMBJzan6Bkn/X9w8yClOCeSBVp4TIQDBjqg5g2ZDDvkeCRFwPHOHLdcf4GrK1dYT8s8ulkCYZmK+mwVFb5p3zNEyu3GlDBc11hmi5ih8bDKPYuZVXzg7EU+cu79rA+2DZsr7Jva5PHePgSW9bzKq+ob6LyCsRkGuNAd59H2EO/0t9EzB7hz7J3c2ryJsXAMicJiSE3Kp1d/zE0ZgornitJ/fP6XmVBXWDG1HRszgeChjXO8/9QDHKnllLwQgeXuibeQDH8Oa51oS4mIhtdnavYjtIZfJlBTVMIbXvQ7//dtfD2QA56U/ME7voOzG5tMlMs04hfXOHYj7bvA08ih46G0RAwlRsIgFwQrPkyn1MKEYeq7zHM0hLBIZWiv1rAVC9sNICxYZfBWPbzJDC/Qu9BE1WA3FDIVqDMRDBTRnZtI35JsRBShROQGlQpIBJW5HkWgSFo+djLD+aVIpsa6gEU0e1xcHmOYRNw0f5nJahcp4bbbT3L/pat2hDlqPKNYjfAbjvUi1ci+9DmQyQ504vJjLBYbbZfu4Pz6JF4jRSiQJe0Wl7Zy3HcLMtYIsTs/UlhKfoZMBa1ulbCaIrZ2LXD9jiKPc4yEoJbhxwU+EJqCZLmETUecfulEXZXxjPHYZdlry3W2LlaRtRxawc457dg2RVPQy0K32xlZDAPEKmdvfZPSvoyl9THahKQDHyUsew9u4CtXg/DRWA8GOsSTTmyjjaRCTinKnaEWHkMZMD/TwliYCt3cHx5b48n1OXIjMVrypcWr6OQRscq5c+4sNW/IXdWTCGHRVvKE2cPKoML5zhjnO2O8YeYEb5w5TmBzHto6gPCglcU7ZE/3iFlyI2mJiPvbB9Fa8Vh3gUc6MYNsiGGXyaKfY5xzOW0Seg1ubM7w0NpFjLUEqiAn43ObrjvRI60P87XWSd597btGj4UiViV+4OCP8uHLf0TFq/L9B97F1vA+3lT7AsbCyWSao/0DeEJzZ/UMDe8o+/0GH1m5lflSmf/z5p9kIpyh130jef4YjHp3et41SBEyVnrDi3pf/z6Prwfy0ch0wa8//hCPryzzD6+5kexUwskLq3zXm1/GXbce/K9+57Uzh1koNblgt/Cv1viZz0o26lfY8RhmltjA5ESHwZJPR8ZOpJJJwg1JcbJOMq+gD0ykWGkxLR9hJPGGJJ90gU24uh1WWmxgyZdjCiuJBk7Sn7VD+ksVsCO1j3Ze3/1LNbKGQWbgXVIQGOJ4iByzO8Hp0MwqT53bSznMkNsQs3Ay8ue6DIQzfcKJIUYLepslwGH1Wd8jKLsts7VQZIqs7+EFBRlqN8Db3aKtEM4gTDeF8wzTEl+KkUTccZxLfo7AkqQhWEE6CBnVeN0i4VtkpaBCTpJ4zkFxtFBqBPWpLkXq0ZzvEFUTBOz8vrHJLltrFUzbH62t7qihNaQG8kzh+ZpBK8akEq/sdgrdQZkNUUHFmvHZFkm3gckUcwubDHoBB8MNMuvRkSGFUDv3zgwEm5s1KlMbO8+OtcJZ6o7WicwoIqmJg5zr6ld4+MJ+ik7EMNB4YylDfJ7ZmOE7Fx5FiW36o6GbxGTawyLYymKOt2e4qXmZx9sLdEyZ6ZkW96xfxduSJ5gMe6RW8Ym1G/lq6yCRysitT39k06uE4bWzz3Kst5eiUKz3Ss4giwKJZcLv8nsXr2EYnONNC4dRKGT0OFeyXRP9Anhs6yk+evl9vH767dT8cQBuadzGLY3bdn7/Myv/wsEpAg6FqyzpWyiLU9Q91yRlKuhwbeUKHfMKJsIZAMqVf4JSsxTFWeLSt6HUxF/5Xr9UxtcD+Wj82tEH+eSZE6Ra8/4Pf5nqiiTPNcdOLfHBf/t29s2OveA7X1x7hNnGGkViuHi2RGtisEvJizXmTIjJDf0DCclWCXIPmcLYUwJZMujXdeFCEwqBvRK5JFe5ot9gFuiEqGbh/McHCt0L0CHkoXCWsrFBPVWhmNM7nG7AmY8YRgC6wIQW2gp6irRsEXLkNohhvtrmaWN5dnGGWw9cxFrBeq9MejrGPzx0EvhCYUqS/oUmxkj8sKAx30FIS7cTcmvjIl85f5i88FFSE3QlYiBhkp2myqUgRQuLsQ6TLQYCPMgrFjmAwUaZqJahAk2oCqTRPHr2AINsOzsU5FWD33ce5nrOYSyZEugrAX6Usmd2C4GlH3UZiJBI5TTCIe00ZH21QX8Q4pdzStWEaLJP0oqxfWf7ioC4mpItlQimM7q92NnSKkHeDyEXeEpzojtN/HCASCTFQkFpdogt4B0HH6TiJxgreHTxAEfTvc7qQEEYFFTKfaTnnAOVcF2EMu0eFotjqox4PyQFFOtu92EySWEF/sSQlc0GJ7059uzdIvScOrhjYuaqbRa7DddgGtfAYiN3nGsp4fDsKu+9/Hqafp+ujrmSNrBA2gvo9Mtoz/32uyZOsmbqzDY6SCx7qls8tTxLozzE9wrO6iblcIgQQ3riKK+Z3sex9tZz1nu3kMYq5VjrXhaHJ3jX4fez1v0g6/0/oRK8nD3Nf4UQPuXgejrpwxibUPY83n31P+dC98851/69nU5cFa/Cj161a48rhCAufdv/31f97+X4eiAfjSu9Hpl2BRdvYMlz999KShZX2y8I5Gd6l/nNMx8mMzmmLBB1gU4DCCxKCvZGTbq9BHE64M2H7uZ39NcQWOSoIYS4uYeq5sRxwnAwgnJ21Poj2MJKhpeqyMQitXQimFEIthJ0DNlygAn7ozfJybrFyAvLiRYtQQvidYtBoDJJ1UvIjEfVS6j7CdIIFtcn2OxUCaym04+hYrG5v5NFIyxWuOJZbbqHGqk4a/WUI2Nr1IOUsxuTTFfbHB/sp71WJzSWfNpgjWSQlpEdzcKBFS6tNbGDAALHDLHKYgrB+ukm0jeIiZQLehxbCGDkGmktVDVmvMCEu+wgbSWykjM37SxnASrlnF4/YlAElFTGxQtT5KnLGovcI4pTZqc7+DNbnDkzhRkEeHFBPwkwkaTdc8wiFO48CtCWerOHPVpFrntUKkPm2n2WRESpllHyUvyRk+X1c4s8uTiP1YKs4xFUCyZM37UB1AKrXYBeTapUooy632dgQjAQi4y8CLc3CWwHcwTEA8PHnrmNiaDHVVNXeHI4z0rRQAoIvRwM7C87gVDDH9DOYySWXhKymDQ5xySTpT6HG2tc2Bzj7OY01ko8qblj6hzNsM9SfxyLRAONUsI/u+mzfGrjRiyKWW+DA6UNTg+mON2f4qn2k2y77piRGVhFZizEm1zo1znZyXhN49cZDn8FxZAkP4WvZpip/wgLjZ8EJMv9L3Ap8xms/AKxfgCJxCCYLb2Knz34qyixC339XRjd4RdJi/PU43LWvHoAACAASURBVG/E92b+ti9nZ7xkAvkDJ8/z2adO8YqDe3jbLde+4N9/6Nbb+ey50+RGU7+qTPH4EE9K6tWYW66ef8HnN7Pdotfi8gS9zMnVySUL5Qof/c7vZfH1HULPIwgV/+nM44CmiC2DKUHZCIrH6qjVgDIwnARtGAVr3LE04Dv1otAjvDkwWDWi73mWwbRELpYgyjEhWN8guq7gZxSoBErLEmEcu6M8NURJS0nl5EbxwNNHUG0n7kltSJ4JqDvRkJSaIvfQmyFowf7pVerNHstFfTfAA6fWJjk0vk5FJawNqgSTCUVaGmH1CuHYcBgruXBxGh057+7xao/mfJc8U1xaHEcqi6rk9BNX7BMKkAaZCfyZISJwVBTd99GjY9occv18EYgFtjZKNMccQ0R5ehTIIQhzGlXXX7RAMjbeZ7UIyIyiVBRkcpvsvk19AaTFb+Z0N8tUB5K5hXXe/O0PYa2glwX82cnbyPZ5eGa7N2fT0T+lIU89VrWT5S+ldUqdjPl6m5JIWB9UGC+t4CkIpKbuDdFW4JcKojglGTrIg1ygtGVuYZX/fe8j7Ana3Nu5mqf7exDCCce6K2VEAetxhfmxFq9tnuCa4ApnBpP81qW70UhuqCzyzvkH0ULyga27Acs1k0vsqW8ihaERpMTDnJ5x4bkezBGqFkrCfLjOrZXzDGzEa6MTjPk9zmXTbK+o20/D6dY0Qx2xNCzzXTOPcLn7FZQQxMKnJBNSfcFNqfBRwRs5unovhe3TFPdjZUpFghQlrh//qb9zQXy9+wcstf4tWMuV9nu5du4B1N+RBhkviUD+xKVlfuIPP06SF3zyiWfxleItNx553meOjE3w1e/9YVb6PfbWGqxudFlcbXPDVbPE4QvVYkcv9njymRmE1DSaPQbD0PmaFHBxY8BdH/gthr0CoQUzlQpqy5LXIAgLyq/oIjKBOV8C7brcRCuW/pGcyrM+yZTLqr0uDGcNuiQwFYPA0BzvkA9CelsxpiSwkcWEQGh3uuxYf1eYI80IOx6pTZ/HobCCThpTxOB3XLC3WAoMxDhlYKJGPG7B5bUJDs2ssthqQtlBJv3VmIdP3cjxiT7r1mWy0abF3zcgXwB1OUKjdrJL7TmqYBgXNPd0kBKCqGDPvnW6eUyaPueRNCAKQWm2D5GjztlzJVTLRyqDX8rpj7trO784yYE9awgsF6+MUasnI691QaWZkA8CDIJS2fmobM+Vigv88QTdG0Es1tkaSGEJo4TUeKjQILBE5ZT+hM/1t53BD9yOrSKgITN++ctv4fY9Z4gaKSeGu5maVg7736YgdtKY9qUKh+eXGav0CD2NwDIZdJEjvcBc3CG5ZolBL8JauHhqhn2T6/ge/MXWLQSZoZtGXOiPEQcZy8tNdOZWtnueuZ433/AYS6rKqXCWYeGhrcQXBT914AuEUmMs/PDB+/nl7E289sBJfGV2aKwLccHFxBKpjEgu89Qg4Eg5RZLw+GA/oyeMq+NFzmfTbrkbrXkWyUy1zSCfYjxY4XBpdWeeExtQESFTlf9tZ276enPneRwaj1jmiFFT70BN/RVv9f/80Rp8FGtdEmCtzzB7hkr04hhuf9PjJRHIjy+t7mTPw7zg2KVlbjo0xR+fe5ixoMz3HHwlgfIo+wEHGw5CmZusMzf5wtU2KQre9pEPcmpjA5dmenQ7JeZmNrh8ftLBHgq6RY5XuPh1pdvDek76PnnVpmsabEHe3qJ/3wRYnPmVNIgcwtYudOD3Jbrm8BhrBd3FhusUZARCG6zPKPiMfFUsEBhIhYMpJguKtRjlDDFIKoI8V/i+5uTSNMNAQZA7z+q2D8IirMIOQBcS8bzeYo6Psr+8zvlinDxXJN0A2TAuiCPwhhCvglgpEzWcB3Y2bshigY01XlwghYFS/rx5NUYwTJ2ZVuDnpN0IRsKkLAmISglSGrKW4+ELoyjakvJCm+GFCuZsmRPnY+K9XVpphalya+fYflgQjA1I84BcaaQwO8XXXhI5Wl6lYNxrk/UaCCO4fs8S/XbEhWEDpVzz6PmpNgvXnqKHRycLqAUZUhm62qNHwBcuXcPYoMvUWA9t3WLcW4thqgCsO2chMFYwSEKmx7oURuI/R/Xj2rdaelkIgWW1W6axf4vQc/f3zOVJhBAYC5uDElZUodjtVlJYyQMrR7hr3zkyfJQHM6Uu673SyMPTlVAiCvb57R04SAhLKAoORqfYH0meHsxjhEIKjSc1+0qv5In2k+4cRnD/xtXcWLvA4/39bnc2Ek8JYZgubVJXW2gkatQuKfDmuGHuw3iqsfNbD5TvoOJP0svXaJkp5sUikDFV/QF89cKa1HPH17ZOcK63xB0TNzAfu6YW6+kWH138PJEK+V/n30zsvTj22Ysd1ei1DLJjWJsAhqXh0wT5Bvsq3+RaMv4tjpdEIH/14X14SqKkj7GWN1x3kH94/wfYSgf4UvFs5wr//rZvf953rLX86lNf5p7F07xl4Wq+78jL+fOHnuG+C2c52d1wLIgUaqfB0z4Tb4gYELFl0ud4kMBzfbuFsu5vlBrLcoGXglGQVAWqFTKchXDdYgMoShbjWzDO4VBoxwnfPqrIhINSLNQe99El6F+VE5YzwrGRAElAdocm3wqxmcRrVXjyKwfRPpjpYpdVUtfQ8nb8UARgM6BS4ON6Pu4Z3yD2Uu5tH0ZriVKGaHLIoB8jegI0xCtukQEQHUVpPCU4H5FWIG94yEUfsW9AFnl0koh6nFBoyVq/suPBba1A9L2decw6IVEzcddaLqDruUxQQbESUlpUCCvw+oK5+QFdGdPaLDM20UMIS3+1RHksQxWWIvMZS4ac3Jwljy35yBxGCsPemU0m0x4L9Rap9ji+UiPUhlI5ccGsurljlnW8P8+pCxNsZmWKWBKXB0gJrU6FQRoR+TmhKfieV3+FR9cWeHprHp0qKBy1crw6oOJlrLariIplqH0i5Wwnz7THWenVACj5KdOlLgKLNoJaY0ghHI68p1xwaW0cAo1InQLYDwrGa73nUTn31ra4Mqhxz8YRXj9+EovgQ6dewckTCxyYX3EqZGmIlGtMUVhDIJ0XPkBhM45UX84TrWOjCg1c7De5sXY7773pdXzw/M/hiU0S43F2MEFiDW8cewWXsjXG1RagmC99C+fbf8Bs5a2UA8cCC1SJ7znwAX7z1J/zgfMPUVbX8yN77kXwfmZqP8zXWktspF1eM3Ud5ecE5XtWjvLLJ/8EbTX/+cKn+a3bf5rxoMZPH/slNrMOUkhOdS/wr2/4p3+tOPFXjanaj+GrGZL8NE9376ez9TsIJGvDr3H71M/8Dz3XX3e8JAL5wliDP//xd3D0/GWum5umVFX0n0wxWFJTcHTj4gu+88FnjvK+J76EEZZnt1b56Gee5soZJweuB4r2NQWNZwV+F3cz/6LOa96wl8+dP03mW8qXoYghq+G42CnkkWDYConqbnufnioDlqzusniBwASWZMbidwUqdX/eQDDYb9hGKLYpeDJy2WX5eIBKJTKzVJYF2ViAnS122rj5oSYtmVGAFVAorDK7bAM7KqBuy+6fSzuMcybmu+RrAdfPLHL00j5nJYtAa0k+ohjaikb0Fdbf3hm4A4TX9yjujdEhGCkoeRkHjyzy9PoMm/0S690y6WZENJnsesYol9nZbTBotNHQQ+XOV3E7mqIMfjvcKStgobNR5nWvfJZTl2dZemYcrERIi/AEcTNhMwl4+NRBrC9Rw5xwLAMrODS2Rq49ZspdPGlRImd8pkU/GWWQz7E2txYSrVhJaiBgOu4wHvdZSSp0dYlEhySZ45B/8tz1bJoyUoEXaWbiLhOVHrVSwup6jQsXp9l3aIXTmY8sLN80c4x7B4cJpNslXdVcZzzuk2qP1aRCLhT5CKbBB68NM2Md5g5vsN4r06i6ptjtwpmiSSy91ENKw4dWXsGnNm5ACouKgIWMj3/qDq56+Xl8P2NiqosSbta13XXhL3lVDldvZkyEPN4psTqsIrCcSR/kV0/fx1QwxAChLJgMemzkYwTeFKG+k0vDFjfUNlju/g4Ww8XO7/PqPZ8i9Laz6B5/dukxwPI9sw8yE3bpa48/Ov8Jfvfck4Dg987ewx/e+VN4UmGt5Svrx0iNcwEN8DjRucCtzSNs5R0MBmMNZ/ovfKf/e0eut1CyjBQBY5XvICk26Wx+FIPbVS4PvvQ/7Fz/veMlEcgB5ps15pvOOlNbw1ypwfKwDcA3zV//gs//7qNHMSNfjrTQLF/ouGCHy4TjyxJvsJtvF4XlnmOn8Iyg1BMkJYsuCUY9hFEDMFcP2Vqr4g9CiDSiG1MasUx2lJHgfFTMbjYvtxu6SEc5FLkAYfHHEtSWh9w2ZLJgex55LAgKCSM/FWuBQiC3PKy1WGUwYxqhwQsLlyFOdumpEunJGnnTcdZL5SG+NfTXY7rLNe7ZqlAf67ggvU1m6Y5MXYzEhobgjg7isTL0FOENHXQM/VnQkft91fE+FT/lZbOXSAuftXaZtUnHeLAjoyvRlYzta9FeqWKNoDTeJxn6mJaPLhnMyEDLYsmrEG0IrHUBaNkr0z5ziPlaGykcjCGwIA15LjGZGnnWCLTw8IYZzYkua2mFy20fW1tlvtEGLJezOoGnKQcpIDi5NsXhiVWebU2zlZaolFJikfKmPScBZyX7B6uvGnXbscSm4I7ps3xy+UZ3H5WlVYQ0RJ+tQcy5i1PkmcepZ/cAlrv3H2fBtmieg+VBhSM3XmKq5HQJHoaV1RpRY9ss3t3YuGuZOdLG9zX1yhCLZFhIgpGSU1uBEd6OdXK7iKkGKb5XUJoccPfESTajgJsbl12t3igG3EAgVwGBFIorQ/g/nvzXzAfzpF3oZorb5y8Rec7Yp7D5zk4FoOb1eGTzUwg7xBaSzz4xz7uuE4TKgenPdr7AF1bvQwrJLdV/RGcj5PapC+yPN5zXvtS0e3/IWycHLERbPNo+zOXhBvcunuMXj32BRiljvOJR2AJjLYerC5RUxJHKAc71L2GBuyde/rz32TynI5G1Fm26KFlB/H90KbLW8uzaT7Ax+BxShNw48/tUw5sJVZ3Ym2JQrCCFYjp+xX/zGP+zxksmkD93KCH50Gt+kP/07Fe50hnwlsmbX/iZngJHxXXFyIokiRKyzIdCIlNF0jDEm+4zaRN6ewTBBmgFRUnsULOwFoOl2IpBWUxpFJWvSUhshL+iyBpOz0OlgMkM3Y+dAyFQRCATXFFT4Xy/rUWvRNixDK9kkD33QOaRe6MGS2XCCSeuSLTE60O45gQ4yaSDJaKwYHqijRzR9kSQ4VcHpF2PhIChLDEcjiZEWoZIhkvjUC9ccXUokRd9mB8JjCy0e2UmX9tCaosdKraeHXNGWaNVqpWFHN+apl8EhKogLbydoqRQFk5E0FeomT4T+xzOXRSC/nqIlhIqBhIBuSsS20AzuDWBgcRIhfXBCzXlsQH79BUuPzODkKAzwUCEeFEBEditAD+3VMtD1Agz9wLNscvzDLohXqCJ/Ix4p7uTxfiCZ7amaWXlHYaoFgpPugUz04L99TUuDh2+a04EPLB0C2+78zE+tu6eMSFhaVBHYGnu3WL9/IRTuUrLk9kMj3zs7ZjMQyA4d2madhBhjCAoNLU4ZSzusjRwuwQlLeWbtvDLGbaQLK01yAJJ5OXUgt0djsDS7sREUcZEtU/sOwFXwx9wZHIJbST9kUF8Nw250JtivNzCVyBEmVZhsDbnMw/HdFoVCmPZCNuUZjfIjPNG13hUVI2J2jdwuvd5tM05FG8AloXDGzzdneXq6gpVZfn48sdIjXuwPrH8q3RXX0lbVrD7Rmx0K9gbdSirZawVXLpS47tPfYh1M6BZ79BLAsreJK+dm+Xh9cv8i699iJ+/+bv4Nzf8Ux7ePEYoQ25tXMPTrc8yKDp8dvVxLgwucVXlCD9++Md5ZuUH6GVPEahJbp39E8IRjfBS99M8tfk+IjXOK6Z/EWu22BzeiyVH25xzm7/ITbN/iBCKN+75fc51PoYvKxyofeuLjj1/U+MlGcgBTqxv8Bv3H8NozX956BS/9W3fxl0H97Ha7/EnTz3FnYf3s/pID+NbXnloluN3PYvIC0IE6WN1bKooIklnj6GoO043ArJx0ANH+7PYHa/nZMGdV4YuRd9OBNL9OYO6RNRzVKR3ceFJgxwoZJRjx3OCNQ/vok9/vytwiqHADiWZDjHj4AcWmgVSC6cH0pL8fBmpBUq4rL6oORw7XJdk05o83+7U41z3im5AslYiG7MIY1Ed0HWHa/g9QT4hEHXtTLQ6EplAcTiBZESTQ1AkHhtPj7vg1N/2MrdYR2ugrwN6p8fdXNVTgnr2vPsihaG/TzC83CTQlvpUh3KhEaUBa8Zl6EJvU94EolBYUUDDQq4pK801e5dR0hLOdhhuxmwsjqHPV5DX9NlJHSsZCljeasIWHJhfZW2zRrFaptfoUS4NqYj8eRJ1qQwlz9DOdwEuKQzaOghDAF0vIIhHSterNeoTFR6dOkx5OmG4VsKb1CPPE0vgGWaOrNLpRGghGeLDgQRxogqeZvzIFlI6Z4eh9in5CdNxj7LnvNovdRqsrU1w5co4KndGavFMn0ER0E4jGpETKC11qyjfUo3SnSAusBwqr3E6mcYXmm4rwg80H/qa21HE/k181+1PcmH9Rk6vrjNR6tDaijGjHqhnzsxSnhiwL1jnULiCFCBYZyyY43TXdT/abtCBNKgAjiV7uXPsG0l79+7MqZIFhbE8vHSIu/ae4NapC3T1Hp7qj9EzDVrtEkdX95ObFE/Cbc3L7Glu8cTq7Xx68TSZKVgZ9nnPE3/K7975I7x6wqlHP730HzjVuR9tC5QVwAQXB+d5aPX9yPwkloJUr3K587scGvtpUr3J0bWfxdiMYbHC0dX3cPvUe9jF1BSequ1cd6iaXNP83hcZbf7mx0s2kH/p4gXSvCBYA1EYfuwDf8773vk23v2lz7MxHOBJyWuuP4A4X3C2d4k0LhAeWG1RzZziisRGgpeVZ3iyt8pAOcGM0OD1YVTGByxp2YxYK2LkAeKGNWAKibQW0ZXYaOQXbkGUc2xgkTND59NdK0imNN6ic9eyQqHL4CUKrwXeVIqezhGhJRgI0k6E7PsgR6HUtwQtiw7ABEAh0LnH5vExqpN9ir5P71SV4V7rqIsW1NBRIKvnHSlmUNVk4xpRBRZDbNkgY4swGps5AF8MBVnFYP0cv/CdcKlmsAa8tsJKuYMM6HZI1OiTWIdy18IhWxM+FAp/1cda6GyMU33ZEhPxkMAYrpwdo6j+pZtppXM/9CylMNlhKEllKTcSNhZHjSIShWj7MFQI35DUNGa0om5sVelvlUFCuZbsyPktZgSVCNJuQKM2oBYk9PMAKSzTpQ7rWZlAFpxbnqCXR7uZcOFgrfapJjzuEwQW7kpAGcpBukM3rNcSNvoVd59KBotBqF1QXggnxPKV4fGNPZRlSmYVaxuN3XqFcn1YR99guV/jSq9CmvgIJRDKbEvJCJXGEwVWSDTOByZTPk9c3EuhFbfsOc9YucfTy02OXkrJTZO1ThW5g6cZojgDBJeycW42lyipnGGh+blHP4uoTDI30WJvtIUnnW1Yio9B8njnUc529rC/6iCQo6cPAG538cW1a+hVxri6+n08PvgLQGADaFa6rHZcm7u1tMq47vGGPVt88FwNgeXOidMslI7yobNr/IP9/xJPKs73HqYYebIEgl3GDrtFU4FCCWfgVZiEXXKuJdNtYn8/+5vv5nL7Nwi9PRwa+9f/zXjytz1ekoH8iVOLfOHpk+i4IJ0UxMuKQht+7XNfoV0MsYkh8wxfPHuO6kko5hJoip2elGagMD6MmYAzy5uoHHwryCuuWQLbchkzqvkpgRpKV0zUgnwlJNAGCoUJIdpQgMIOJcVUjgoKhG8Rz4FDAQgM+bwhuBhQ1ADpsGdfgHcqwjsd4uca2ZHkDUtnYffrMofaOfdStfa5wGC1JNuKaC3FrngYWgfbjGqMJrY0ToMYSclLzwTks0MXsD2L6PqYnkFVC2yhsRrkmKEYus4W+YQLrtuwi0ntX/pB0DrdpFoZEk0OqUUJSSMkvxSO5tHhlINWjFcqiKsJuorzOk/cPONZCNxCQSbY6pVZtpo886jX+2xdqSGkoWga2AgduwOBTQWyD6ZiERjSVgiZwIaGJPWIolHLPS3Y2KwgPcPG5TH6zR71sS4z5Q5SCMbDhMSEJCZkbHyIvC/EHkwRuSB+KEKHFtH3nMBqCNljDczhPvX5BGCECRuwzjbBZBITAUbR3ijTGO87Rika39fYAvrad52VnsNMEcpQnewRqJySl6EwLPcbSB8G3QDlazJP8ormeXxpGGjf0QNHT+uh5lV8+kTMqw+e4La95/CkITeKY0vz5MajEIL9R1ZZv9QkilIOXDNqXQg7XPLVfo1Ta7OEvYwVqqwNKsxFbYT0mCy1kSgmwwUmwyPct1ylMIZSuc5rDg8R/jkOzawgRcz54YWd58QTmrdf/yBfOnuY4+15Jus9OrpE1Z/gf9lzE8dbH+NwZQ1PGtbTe3nPY+f50SPfykLpFs72voqxBi18wOfq6rXcMfUuzm1tsjb4JNXgJhbq3++ebW+WvZVv5mL3LxBCcdPEPwNgvvYO5mvv2JnnzeRZLIax8Nqd3fbfhfGSC+RPn7vCP/7NP2P1SAYe6LIlHddEG4pn+qswEPijIOL7Aq018owHIqbYk5O3QlLrIwJLM49Yzp13+TbrBAE6BJW6lywv7QZ36wSLUHgEF0ANBUFsKSojLL2nsEhEGWQq8FY9iplsW1+ILpxm3P6l+oxRoH2BKFzxUSDwOiDzkSrUQvWC6z4DEA4sg6EP1pKXLM4zSbgdRYrrMCQNM5MtjFcnE4LuAYtRQCaRgUEkjvInOh62qlHGooICmQv8buH46bhrkl018iU3TqmKw+orMqFYL5FtVMgulZl4Q5epZofBZoztqh1qfFjNHCyTBYztaZMWil4rRg4Eal2iPYm2bkdgEFxcmkAaydp6g+sOXaRSGvLIyUNo/Zce90yiihyZC/JB6Bq9FZbNrTLTMx3yRJEID6+iscaxevxyhooNeeET+gWFlfg4QU0+9BGrAawHHDyyCNd1Od2aJlzcyWWpxkPWNsv0yxmlWoKQluvKSywv3kBhFLrvY+vOxnijXaOf+Fy7b4m47G7egmpRWc04MHuFP+6+0mXaWhLHKQcnNtgYxtw1eZa6n7A8rHHPytVUGgmdjZg79p+n4rks1RjLWlaj7GcURrJRDPnGmy4RiY7LogUcCVb4g9f9Nhe64/zHE3fzjmu+irzWcO/WtfQJEQiuLxsSXePDJw5z9PIhrFZQBPgm5eJgnIuDca6tTfHq6S4lVeHNM9/Hdy5EfPDcl+jmQ95+4NVsZk/x8cXHkMSEssybpt/IQxvHEaQILAfjNfZft85nOjcghUAi2Ve5g+858A18+MKTrA5Pg3szWEkLnt74eV4+9X8xX7qR+9e+wkMbHTwZ85aZb2dp8ABHWw8gRYO7x38IJcvuu0Lwsqmf4frxH0OJGE/GL4gdX1t7H6c6HwHgQPWbeMXUu1984PkbHi+5QH7s9BL6OdtWV8m0FE2NKRmCQbCDir1qfoHHT58nrQiKVoS3GCIjSTl3hcm5G2u02gOMtei2Zhg5eMWULNnU6CgFqFRtCxt3/vpz4CViJ50RhWOqRJsCtiKXzQ8E3tEK6fUDcu1hrXSQnTLIXGICEMbiD4VryaYgqwuikRZGGkvasPhdCBxzEiMteWnEDrHC8Y19XEs4IxAJGKm5+vASE40e7buHPHNpfqcGwMBHrjhYqGjmGAFiOSTa13O4f2QR1YzoSkCSe8ihRFiBwWJqIDxnEzs/s0bn2IRb/KzDxoMUGs0+dt8q59NpQk/TmO7ilzPyzGPz8SlAoA51iZoptgFUFOpihJ62u/x9z0LmREbr/TIrokRtruOUrCeb2FHPVOMDqYewI1ve0Qg8g/1/uXvveFuzss7zu9abdz453hwr3YoUQVIViAwwaCsMtAFR0NYeZz6igz2mblvbQRubke62W4HWASNSgoAiRSyqCgrqQuWqWzefe8895564c3jTWmv+WPuccwsowKaM67+zz37XG/bez3rW7/k9v58Q9JWP9Ayu0PhOjty3QYak3gvR2qHg5DSJiGSG04O1+yZJ96SYqmb60CYnTuxC5Q7Ktwu7EynETIppFlm/OIoXpbhBTjYtmAo7XNoYY6shanpunUEeUA4TfO/Kpiz4zKWreUlRkyoXEWkkOdVyD1cqrqquIo1htV9hPOhysLTOyfYUFW/A/vImjrDuRaGTc749xoXOCJGbcuPkEoGrSfEYaI8Zr8W+YBNXGA7XVvmZmz+BKxVSwL+YeIBz+S/ymfVHuGszIzdVaiMD5vUmixcmiZx4u+nKk4K9xZTX7noLzf4nWOr8MRcGS9xQm+amsTfhyYjp6PmM+rM00mX2lW4gdEr8x2M/y99c+GHmgzqhTFmMR/CExJUBRbfEtZUbuHftD7l3c4H9kVU+z4xDmjsYoxhkFxkLb+fz9c+QGkGqYv7H+T/nWOGjKGMTg3tX/i3/24E7nxIfgm/QiHSqfQfa2JrOmfaHedbEv/lHk5U/I4FcCPFy4J1YeaH3GGN+45mY9+9i3HJ0F9EHHbw4Jw8h9F3EZE5KRpC4yE0rDetKyY+99Nm8+YHz9KcESEFagaBubKao4MuPLvKOH3s1C5frfOQLj3Gy1SQe0ZjiDpywRSt0BqBcY4X8r3gdYWl02jU4W4FdCMtuKYPbc7iutkoDn9VO1eqCb1gd76QybMXfwkaFIA9BO5pk1DAYAxBkZWhcrSleFiQ16MyxvXho3yA9helb9ogRIAoKN8io9yKK0wNMQ9m0H3vdpd0dmr3iFRREAw+UrcjV3hgxneLUctzz0c7uwQUkge7mmAAAIABJREFUdjEC6s0KjqcwwmwjBGHJ/khaG2VS45Eql/ZqgLuu8NZctiZTriBPJFlvqMWxK0dsdTca7CI43Me0VUBoUuumpA3u+ICkG1ghruGPUJZTVGrndl1NqdpHK0Ha9iiO9fEcjSM011Uvc6YzQTsJkS7EuGgFi6sj5NqFQ4LiWI9BK+T06Rk2lkcRQpCO2us5cNMSWkvW21W0lqQDn0zAWrvMgfENXjSxwm2zT+I7is+v7+czq1fRulBBzEkmam3KTszF9XGUcPjymYMc2b/CqeY05WBApdjHIFjrlfj02tHtJt8gTFEZ1GIrceBIW2z1ybnQHiXRLonyeGR9jmfNWNy6IBNKMh5SFg2O0HhSbWP/gcwoeTn9fDA0zjAEMuXozGU6mcfz9i6QKsnaoMxI0KPsfYX3nL6XY+FpnkxG0EagkSx0F3jdvt8CYDraz3S0n34e876FvyFWKcdG/xULzXcQS4fNpMybpgOEd5i9tTfz2yd/kiPBGb5vtEliXD7WOMZqVuW1c08iZcBU8Xba+Y60hkAQOeH298J+la9YIL+FUXSn6GSXACi4E/9ogjg8A4Fc2N7U3wG+E7gEHBdCfMQY88S3O/czMZTWfPQrJ9jo9Hj1zVdzaNcE/98vfD9feuIis/MVrtk1ww/8xZ9wUdSZDMpMXl8hSj1+6Pob+bd3foq0BleSZLUrkDlDHW1Dv5fwvrseoNUdEApBVrOazGggA5lY95zSosIbQOugtDBIYgs5gM1IlUZmDtu8NmG50XkE4UjCnEyYG+vQrwY8uXiAwSTIXCCzIdQyrPDFk4rOviHO7drJhTCo62PWD1jjYXJB4aJDPG7PozMHR9ninMgEeRUeW5zHjcFbl3i+IRna2IWFFBlpGOzg3cG6i0mGZMuzEaaWkXdc25WqAHa6PbduuFiImbq1RfN8lX47ZO+RFbxAoZQgST079fC558rFhOBlw/vJBFnqb58fbesPYiux9vRwByTI86e2Touiwg0S8k2765HFlNSF2r4m/UGAlIb6Uo2s65PGDsXxAZ6jCGXO0qCGnynyxMHxNVJa9pFGWj0aJeivlRBRzqKpbJVUAHAczexYnRyHPHNYWh8l9a2hRhgqNtMSr97zyJCbDc8ZP88DzT3UK5IL56a46rplDlbWufbgMhUZ87yZ08xWmghhaOYRH1m7EZUJHl2btbouxj4rVzs4QrPWrNHsFqmVekhhmPOaQ0aOfYbdLKCbeWwmJVb6ZTp5yBsmvsSk3+Ge9kEcmbEv3OQvlm6mk4cY77h1hsLwHSPnmAlaSGEI5xMUHgUvZ6/XwCMDIWjkKZ1c084CSm6KIzQLncdY6jeZK1g6ZaYz3vLQL9FXTS53y/xeUuQn91S4KlpiOjhHLztLOfss768/QTvXZJ6zLTdwS2mRicqbOFYpUQ2uIXBGiVz4oT3fzZ8v/g1j/gg/eeD76aaH+eLaryNweN7UN+7G7OZtFnpnmAl3MRZMcNvsf+aBjXeC0dw4/n9+w2P/vsczkZHfCpwxxpwDEEL8GfDdwN9rIDfG8N6HHuTuCxd41eHDfO/Vtsnnt/7qbj7wxUfJlOIP732QO3/+TRzaNcGhXbaz7Dc+ehcbpwd4OmBNDLg430FHhrvvOQ9CICYFbmeY3jg2u0o1hGs2k/x37/n4NmdcSYPMBeFlC3ukVU15wQZsryeINjVeV9OdcxlMiadIiDsdB1yshvUwDiNsx+eT9+xn97VLJN2QFEFSMyANIhc24yvbv1VFI7sObpdt9osqK5xIIQeCsJCQZdaQ2B1cGeCEha/bNrvSXYkpaSpf8RFqiP/PaLxDPVxPM2j4SEcP5QLs7mSreckA8UaAUt62AbPTA2EEsivRJUW50mdmsokUMHKgRdzSNLMQL8lYWRih1S3AdrZkr8mvJbjzObrhkS2FMP5V2dSQYum2XLRjyGtDb9Kuj+/lSF+TIdBSIn3bTJV1PJS0+uDNThGT2QJi7GmO7F5FVqwy9ljQ32aYnFqYpdWo4pUTSlM93DS3zyGXbNmGugpEUaNGcmTD/sQKI12eVT5PLHwefnwPuZZIbfWGVWrrEoly0GYnb8iVIG35CGWougm+a6mrz95zltmosf2+ihuzsjZGL5UITyOMxiAxBtKeRxZ7yJLig1+8lclqm9v2Psb4ng7jYZe1uIwxMFlssxpXMEjS3KGX+vy/F2+j5GbW21QY/qoX2CCORCSa6XIbXyjmw51rOVpZ45HePGhrlVcLYhTWqPqP1p5LKysw4vVoZhHNrEA9+1XevMenGtzEQ90aA9VAaUlmJPOVBnujZYRNjdDA4/EIj/ZSFAH39w5QdmNGnR43jdyK9sd474X/QeSUeN3un2EinONVs7fxypkXk+qMwPGZDL+LfeWX2a/MN8io21mT3zjxVpRRaDQ/dfCX2FM8yAtnfvNpj/mHHM9EIJ8DFq/4+xLw7K9+kxDix4EfB9i9e/czcNqnjg88/Cj//b/chdMxPDZ6nomfL/KCvXu5+8QCcWa/CP04ZbnRZv/kDg42SDPUMJsVWGErJNYjc9M+HiPAyQR6q1gpIava15ORYWG0aTCOQQ4zU5ka3J5AOwaZQFawLfTaH7rifNX1ewNBXjZooaGgUAHItoPQkn4r5PRdBxiy4DCzGd66a2VrhXUN8sdjXC3Iu8Osfoi9y76kcM5HZgFEiuD5bZTy8bpWNkCFNhAHdUAYspIh9wXesMFIe4LBJGAc8hMVMtcg5mMK4zFKORALlOcjcgBDVjQo7Q2f59DyzQHjakt7VJJA5k/pZI1Tj3qrQicLyO4fRd6YEo4kxM0QlbpD0asE4doMVucOTtNDlbU1rjDgNjycxC4YbiqQKWTj1nCjtVFCavCmB9tJvLPo4a/59OcV0dyAbn+Y4RswmSQvQCAhkjtdi0IYrj28yN1fqpB2fOrGwUHjOxm5A0a7jM40hxCRoRsGtP0yaMmmLPDhhRvoS5+4H+Bqy9t3ZIxfMeTa4UPLN/ADu+6n4GR8ZvkIG5dH0B0fgeCO48/hh19wF+VoQCuJSJJJ9lU2MAI20hKHJpa4FFfYVW7Q7YU04iLTQYvjiwcwRqKlwYzGvPKqL7NvZIPLWZXJSpdilOJKTejm9HOPEb9LP/WpJwU2Tk6wnjo4Qc6uY8v0VIBhqztSDH8xWME1FAbo5AEPrO1iNOjzkpkqqXlw+OxgxB/QyErUsyL93GPS6/Cq0U9T7yka/Q/TTP8XLmyMEfoJowUrNXAmnuRotIIvNX0V8Mdrt1J0Mkb8AVIKmnmZ+Wg3c7W38vYn30puMprZBu+/+A5+6vB/opV2+ekH/zPLgw2OVHbz9uv/NYHzzeVxT7QfJjMZqbbF4fvrd7OnePCbHvcPNf7eip3GmHcB7wK45ZZb/nbg1Lcw7vz0EzhdS/tzm4Y77znBC/bu5fZr9vOnX3gI3VGobs7P/e5HefdbXstIuQDAv7r92dx9coH2+TbhpsGcEWzcalBCblPgtjFtYyheMoRNyAPbyWkcAcqKXA2NwLeH1wQhbUY+GDMMRh382Da0+E1DOjqEJjYMTmrIjMCJMuJdQ/eJcYV/NhhiyFsLgC1K5uM5su2iI0FlXxM3VGAgLaT0L1jVRiMMRhjSkiCsA8d6CMdgYklvTlBYspxxo6BQNyRlQ2+fTQkzz9DZa+dwEhBY+EKFGrkcwqUIMRWjc4lx2RbbUuGQNumI7aApM2O538O/N+oVfK0pjMc0uyHdTogRgrV2BXnQEBRzywEfH6BzYMOz3ZmZpFePML7EjWKc0JD1PQtfYc8vY5A5OAhk7jLYbe3EtAEvNYTVhPiRKnLTtwt013lKe/nWDuDsyjjzE02qkQZ2mpY8mRMEKT2sCmOOC9ohrMQkDUFUTrax5FIhoW2sHktYTljWNSregNEjDYwR6FxS8BLmyi1aacRSr8K7jt9G/4kaSVWgimabu62N4MzGFCOVDl+4dJCJYhfX12zEJXJH4AWKmkhItM+L5k4TOTkn1qeRwqCGiUOsPe5dO0hUTPnk2tVo5VIsxNu7jYKTUhAZqevh96QtCiPIE5eLp6e5/ugZznSmUEYSeRmeVMyELR7rzrInqpNpyf2NvYwVYw6V9/CDe1/N759/GFAoLehk4fYzFgZmgtYWURdjYhbrT3Bh48VEfspVu1cA+FTzWrr6ILdPXM9/OHGWxHj08pCe8in7GTdM/g4n27/PVxZ+mlCmdJUPGAaqC8CHl+7hclxHYzjfvcxn1x7k5TNfk2duj16e4AjJVDiHGSZ4vgiYi/Y87TH/GMYzEciXgF1X/D0/fO3vdRwdH+fEFae9ZtLqGb/lFS9AJ5o77nqYDM3FtSbv/cSXuXZ+iv/+/nuYHKnw+2/8F/zIz76PHNu0M/qAw8aNNpvcggxEBn4fgoaFPrwYwk3DYFLg9QVCG8sEUVjYwwjceAdy8LpsF+KUZ1/3WuC3FeXLlu0SNmD9do23ZINTNpFZIwlpNVu2FhbtDmF0bYOOG+0UovxqSt8Y0Ia8asCBQcmgCoZCoDH3V3D6LukoDOYsa6XyhEFqgZeaKxYth6xir1f7Q9aLtJxykdtsTF8OUa7dopohlmOEwWsJVMlepxMLwk1Br6rRrjWcLo/06Tsu64tjqI61chOuwUTK7jKu+FyFANXwMJMpvXpkYQwhyJRPrRXTK2eo3EdgC7dWxEygPFAFgxjCFgDSNZSCFGoJ+YE+RoJYKyAScFxldxgGnEKG42tWOxVi7UFumC51cdCkmYsywj4LIIoSslzi+IpoqrfdjASWNSOkAWNwgxzPVUReZgOasDLDM6U204UuE2EPYQy65OO/rEHriSrn0xG2pIMRcNqMEQ0qTI+0iGTKu75oTYfHqm0mZ1oYJLH2ONWd4vrqEvtG1ikHA5oDW5j2ixmn4inYgLWVERrtInt3r+O5OUiYLHZo5gUcx+C5loK4dT9p7hAGihcWTvHltd0kmcexyiIbWYWWKvBIt2g9W5VDnlT4d895E9085rHWNIeKlwmwksZFGbMr2KThFlmMRwBj9WCM4L61g4AgkBlVCvREjCc8Xrn7Z1nobZJjhbAMgtxIci347ZPv5MbaJUBTcRxS7aPxeOWs5YcHjo9EsJVjBfJrvQW2xu+d/By/e+oupBC87cbv4wf3/Gvur9/NwdLVPHfsH7fB8zMRyI8Dh4QQ+7AB/PXA93/jQ5758ZOveQGPPr7MuYUNbr52F699yQ0ASCm4+cA8f3nPY2S5BTEvr7f5wB3HITWsXGjyI7/+p2RKbxPQnBSiJcj2pziexh9LSJdD5MKOu7gxUIp82rUYmQu8nlUBdGLLt5ZmK7cbdpSpncCrjbE2bBKiLRaMAVJw13xkbHcD/iWfpKbxEkgmFc5AkFc0KIOMJSqwc6vUwfGGmWfbJWhYgkc2araLp1kZ3C8VyIyDUIJoFXRBkXsGJ7cZrRyA38QWeLeGBOMbCBSqpBGtK7D17Q2LvQ6vnJBpDyEEXne4EGoLG1VOCjr7oLK/i+fbhcedVNQ7lo9scrBcRoibIZgYL8zhkk9uJIPNIkbtFOcQhtkj62ghOH1mFm8T0gMpOnYRsSCZYLjwCEjBBJpMCgom5ui1l3myNc3iuRncyGqfHz1yiVa3yKVmFentFHK7gwDfKeI1DfX1KqtrNRQS4cK+I6v4Xo4Qhjh3EcowUepRTwoMMo+SmxBPtpAGYuMgMHhyi+dhzxA4FvZzpGHU79OZtEXU2nVNpk9JgrkBjU6R1JXU4yKRSkkcB0GB2fEGq/UqqXHQxsrYYkBpa41XkDGlWofYFwjHmmU40ppkHA7WuSercfLsPL6Tcd3BCzQGEblxKIUJY9MNGptFmvUy2je4xZTJsMs13hKfVUd48/57qZvS9qJrDJxfmGRto4YSgvD2EOVori5c4jnlc7hC8azyeTLt8Hh/ju+deAiN4O7mITyhGBiPR5rT7J1aRbkO52PDSyfbvOXIOyj644x4o0hcNDkGQ+TmQ4KXwhg9lD3weOO+X2Q6uprAsTzwV889n4cap3mivcBzxq7hhZM3fN34EauM3z11F7mxdYv/57G/5q7v+jluGHn67P0f0/i2A7kxJhdC/BRwJ5Z++PvGmMe/7Sv7W45SMeB9//ENX/d/Lzq2n+dfu4+7Hj7LzEiZ++49DemOWVm2ntCfFBTWhoHRh7BpCA+1kdLguTnF0T5tr0reifB6oALDyr4BugB50VA6LQibAtdgaYEOZBEgbVelOxSfMoCTA74humUTfWGEtAhxTeIkxnYeXlEFNcIglMSrS1TZ4G1I25FYlOiCXRTa58sUd3fIjEM/9wlu6JG0AkrnBF5Poj1DWjF4l1yyqS3Iw5C7gnhKkIxA5SwMpgSkgvJJW2RNJjRZCbyRGG93Rt72ySSEay5GG/KCtotWKnHKGcX9bVgvkMUFq9iIrR0II3AySbSucQ7ZlneJNYEWwhouCMCkEhSIXJK1S+iuwW8I9P7cdqI6Gp3ar+zEaJsgVGgNU2NN2qcmEMWc/FBGsCnZKmgMHSVxywl7i5v83IFPYASYOXibeCV1CiCgNwhZvTxqn0241UlgteNVLnjksYM7mL+AYqVP4Gfbrfy+q5gsd5ECZt0OSgvq/Yhnjy7w6ctHAUGWO4y4fUpeTKI8lIaaHwP2GTSSAp6/0+NgKhoRatSQJQWCQeazp9pEYIgqKfum1llNbJu5MTZbPX55F4vdKqUoZ7o2oFrMWajXKEcpAtiMS7z50Cd5dHOeVhpx/dgiL51/jHpc4g8e+Q7mxptIYegGAne+y5jX4+b5BVbiCp5WvPvWH2a8+FweWv23nO1OMhfUub12goVonD89+Vwurk7yx48+yhtuuJYbyx18qdjMizwRz+KimfYa3N/dy+W0xsnuFDm21vBdRx7j4dY8W1IC53rwX0//NNK5iTivkCqXzGhqfoojDIPMY61X41hlAwfFnuKt7C7e+BRFw8gJeNv1P/FN44cjJI6QNpADBTfAGMN6sownA0b88W86xz/keEYwcmPMx4CPPRNzPVNjkGe8+/H76aQJP3r1Lfzmm18JwL/5Tx/m7mRzu3fEYLnYaU2ifUO4ucUTN5bJ4Vn9EyGheqTFkvSgI4nWJYVlh/68wriGwoZBZgACJ4HBqMEJBVkwpGAPrHCUMNhCage6x8fIj2rcxNnmkxsJIrULivYtlzwJFMGyxKs7FuwVw6JebMjGDXkIrVbJ+rpJQaIF0arB67kgLV0yWgGpDG7PkFbtYhVP2Izd+NA+bOcsLIHQILUgWpbIuRSvI8kPY/nmvstgbigiogT+umVKmOmUdhxC2TB6bJ00dmnVi0TrgZUmEJDO5qT90O5cpMbLDUYP+dsNgSpupfgWQ9UFQeoZvIHBnY+RrqHVKlAtDpiZtF1PQoCHonckQyjBpD+g0RrDNVbyV/vgFizz4uZokWBI70uVw7Vjl7inaS3/+vhkSiLKOUoJHMfgSEPoJ/TXd7r8bAOTIajuqAsaA5ly7AI1JMVrIxgr9Hh4fX7LPQ5jHM43RilFCdKBopfiCIVjNIvNEU4uz3B012Vcqel0Q7Rr0AaSvotREi/KkU5OOw2sXrgImQg6OI6h245obhbxwhSnZBBCbrFYibyM6WKXgRgaUGuHM9kkf/TSdxNrl882D6JwqAYD5qoNzq1MIqVifrqO72mag4CHuvOMFWNODqZ5y11/wvfdsJfz/ZfTHrR4bu0Mj3bmuGPlFqgYKlGLU83L/N+P/AVzbo0bCms8PNiNRpIA5/MpXKEpeBkHShuc7E5bGz7LL8URihdPnsaXCoNhsX+cVl5CmSraOHTSIiPeOGfbOf3M5VTrh/iNm16Ap8+j9RqO862ZIhujMSZByghPOrzjWa/j1x7+KKHr8/abX8MHLv0eDzY+j8Hw6tk38Lzxlz3l+H62Qr31qxjdZKL2C4T+td/Sef8uxj/5zk5jDH/x5cc4vnCJVxw7youO7APgpz73Ee5ZOo8ymo+ef5LPv+YnaA5i7n7sDMPdLDiwb26M3TdP8bFTZ5iaK1IqCZYWGjYnuLcEtze4UrJYlBUqMmSp5Q5Hiy79vZk1Gt6qmm0tBD0wRbtQxGPgt8FJxHYw99uQlS0P3MY62wGZlEFHCjPM6LVvr9Xt26Chh1K1eQAikTiJzdz1CBbT0QK/yTYKsR2sDylSJJ7QxIdy6F9Rvd9CLK6gEiIgn1LkqYNf98ndITAsdg4QSKhmw9u2vOLEuHSTkGLfYF7cIF+MyGIfSoqt6Kdyh3TTR0YZspBBq2ht5YR4SsuGs6eHU8mQ0lAKMsIgRyn7uRtjC6DL/YrFwY2keW7ULgSAE0v86Q7JagmpBGflJKl2rPuNgMWh3KzRkA48iIbqk0aicoMykjQp4SgBjsE8pZgtaCUhBS8lV5L2ICDeHGfXWBMCw1KjQoqgrwJCPydJHYSAuVqTpYUpdNNl7Ool4tyj3Y04fvoAmXB46NRugr7hmplFXnn0AX7vkReTNWxhXgxSStMZ9X4RIQzlcEA7ixgMPJYWxjEI28qfGlLXwwTpEHLQTJdbXOiOWXxfGD7XOcDpwTibnSKPdOcIRUrBT2mqIn45Y6bSohxZYa/Qy9js2WtIlcQba/OXC49RDQdMl9s8Ec/zZGOSdGgUK4ThlPg466slJqM5PrO8m5tnlrYXPgvuWJXOspfgiZyaG3N1eZxd0THuWv8CnthRTxxx+3TzEgVX0M8lhpyuXmFXTXBqY5z71hb461Pv48Wj51Em41Hz89w88X2MB0/fpdlPH+f02utRusto8XvZM/pbPG9iP5982c8C0E4XeO/CZ9kyJPzE6ge2A7kxht8++SE+uvR5QunylvmHidPXcHDuEcQ/kGH0P/lA/tGHTvC2v76LQZbzycfP8L43v5Zr56d5cH2ZVNtf3mbco50m3H3yPE5rB1JxA4c/eueP8J6PfpHChqG90eE7X3SMF77hJbzxDz+IdkP8R0apXlcHCZ1egDIS2XFRvt22p4Fh4gtWesgMWX/CWGhmMCFwW1ZPXIeQlYYaLEMpUJkLwlXbgLOFM2vXZvaqyk4gxmbnYNDhUNwKrKzbNmQsIFM24PsZ2ZxL8XFNXhi2o++NMdM5arVondrP+qQTdpERCkQKeIZkFELrKUA2qZBND9my1mq+q4mrGhNZhNffdIb8wS1KGrYp5kIF2ZUkVUUhgFx5tpMyk1aGUA4hpkJGOJIMs/UY6sPC5/AH7FQSvGpmO06NoD9wKRasRvilepX+2RpkhmzGvt8gLVQzfGZCGOamGywuFzGXAh5r7ePPq89iX9bkodXdLI3WkFIT9wN6l4o4kzGFQkaWu6Sps/0BqMBgPAsDuV1NtOGgCEmvVSS5i8olu2hw48xF7li9iTj2UQNbVHOjjFJlwM2jizz6+AGWFubIA3BHM1Z1ibXVEupcSHDRx/MFMjM4saS1MkHh6oykFw6rv4Is9smSBL+Qo7VtnHIDxTVjl7ll+iL3XdpHS4UICZtpiNtXhH7GwdIGvpPbzs9BmV4esjqoUKhkXNA1Qi+j1SkR55kFRwHfzXfYPAZGoz7GGGLl4/karTUb/RIlP6HixVS82NJHjbXo6yQBgZPy2PIcxgikEFw/ZckIg8SjHKRIIZgvHOB5I7PcULuBqZItKL5h38t415kfQ5OjDcTapeZ0eOuNv8RvPvn7dPIGntQkyqEaDshy0CbDmA4YaHbey/+1fIr/dtOvUnC/VjMF4FLj11DaGsvUe3/JyeYnuZyXaPJibhtJqPc/we2VnK/097GZV6h6Y9vHXuyv8fHLx9EI+trn/Ws389bdn0PrLs438Rr9uxr/5AP5I4srDLItjWU4ubLBtfPT/K97j/KBM9Ysdn9llLVWl8OTY1sVSAwwOlLEGMO7/+qLKKVBw/s/+RB/dPxhdNlmh2knoPGpafKKJh5S2UQmdrJWQEUSYssr1x44iUbMZxy+eoVk4HPh5BRp1WB8QV8Iiqs7R/uxIOzZ4IwSqNDi035PMpjV6BC8BqTFIdTiYhcCT9kofMUonwPpC+IgRA8cNq8x4BhEYhCuD6tDIrwAVbGa6WiQmeW9b00Xj4AuGrQrcQY7bBlyidFW0lEOwGsJ8qKAvoteCsi9YdHU0zCdoVMXfd5KwwrfINZ8azEXGkzXJRgd7NjRjcWoltW5qc00mZlvghGs9wskygMDg9ylsxnZhSARCCPJKwqjsewQIJ1MCTY8tBaM7q8TuArfyUhMQN7xue+vbuK+RFJ9xWUKBQvkF7yMwWbI5ETHfj0ENNshaW71bXQqrSqkFkgjcZ/TIvNhc7WM4xryrmSsErP3wAb7+us81tjL1iKQDzy6wvClR68BLZEYvBzMFXRMUdQgrK0flvzEYrHCf/ncywlaktQ1qND+T2AYxK6Vr5VwqLZGwU+Rwj73LbkDbSRnlybZH23g789ZzauUvJSSv8lyt8pmUqKbBQjpkOaepSdmPntGNlhu1qj3Csx67e0CbdUfkGlJV9jFVkqIgpSbRy7gCMO03+Ke9CDnWuPEmYvyJZGfWlVKITixMcPptSn0wJ7r4MQaxkheePgAD7XWCfwSUxbqp+JPc/Po/8FK+5cJRE45SJj2++wNl3nlzNXcVz9JrgUrcdWyiUSP/cVNADLjsJSVyY1iebDKwfLerxs3pCwwbBjBmIzI6bNLJuTJvTzQgD1BiiPgusIGS/o7eM2uH98+1pPuduIl0HjSUAxv/wcL4vDPIJC/4vojfPCBx5FC4EjB8w5avuevPPulPH92L4utFr935/28/sT7qYQBb3jj87jjjq9QrUT89i98H0IIqoWQeru/PadWX0Vz1wJyF3dNkI/mGE8hUvvDcjLbWCOGQTwZswjL7a9+BC/IMVpSHunxwBOHAIPX16hA4KRD3HhfSqAzxrOMdVHA1H2MEBgpCJcFTgd0EbwrPikdGHTrZk7NAAAgAElEQVRRW/Gvof6IiA1e3UVoQdGF1m6NLg4PCEEM7Nf2Ssq0UJYuKbMhXznHOhmNAkJsU/qc1MIYxjE4WqCMQQwEbs9i7caD/AoXceMInnXVAucuj7PeqWyl6rgtgZ8CRUWiPHr9AAZWD8T3bUOJDjUzc7brE2EYL/S51BxuT3IwKZhcQiuwHaepaxcH1+5GlHGIi5ZWubI+guNr8qY/3NZbPXiBQAY7lE3H1fijQ1nZIXpUiDJ07GKMJuu5GIRl70ynUFW4AtxEoeoBfsthuTPB2/QrUa5A+EN9doaXnTtILXBLKcFMH5VKEmkla2WgMaUcORFbmKyc0mmVQBn0QhFHCwpS4+zucXBshUcGM+SexbfayopFDTLXUhoTsdWZj1aCgpOwIUPuOn8V1+xbwhkudmNhj0YSMUhd4oGLZ5SVGHAMzW7Ewck1AErOwFIRh2qIHgpf5CRDw+pxt4sr9LY9m2p59C6X2TW3zux0A4BaFHNyZQoB5Jm0KpIIzq/PMVGWPNg8QW4Uv3PmTzlUmiZJ/oI/XTrJhXhAQV7LD458lqKTEUjJysYbWO8dQYoiibYQhr0nwwPpy6j4X+ELbYf7ewcIHYfZaOpp48bukV/jbL5MnJ1mMOyEdYWhIAfbEBFIZqNreOX0Lz7l2NlojDftfznvW/gU437EW69+NXOVFz7tubZGqno00nPU/L0EzleL6n974598IL9pzxx3/O8/wJOX17l5zxxTVbusCyH4zt2HeOddX6Adp1ahUGuKM0U+8V7rrp3kOb/0V5+E/T4jTUn7Qs9mPy7IxAZMJ7Z+myoCtyNxOz5CCZxh0bC4MtzWG4irlk8dFDMcz9LIkJrRmTacsBBGcU2jPE3veoUpaoKxmOfMn0NoOAIc//jVdAeFbSxalRmq+u1k8doxGM8WL2Vfk5c10bJApgLj2diui+xEbQOmqjBoaPo2A0/AGyoiIix88JQovzWUQJdzjGe2beSEsfOryBA0LGyUl2wddmsOxzHMjrdY71S3VQlVZDArPnIpxOyxxgQYSAY+kGIyEF/1jTS5ILlUQqUCMZXCRrTTGTs0zZCxRBeHkJUeLjrDBW71xDjFhiauOBjXkBXA6xv6J8oUru5gBLR7oTU0FkPmh4E037kQ4elhT5AgF0NLYgGV0T7pmkNiXJKawTjDHYmvMcaglWN3TkJiqhmF/S2EY8UZiyKn048YW8uozHZZ2jXCoBHhVnPccIBaDxDDQrAUmtcf+BKlmS7nztRoqi3pVTjTHqebh6QDj269gBCWq6+UQPgw7mS4ji3Y2/YCQTMOmS9bbZSRKOb8x/dSzlx6xxK6acjpNUvmf9Ge00jHkGoXY6AZhxwbWeKLF/ex3BihNhGjjMAVBm0kncRKZM5MNrcXjdFin8AYJseazJYb7HIadPoRk9GttMOYs72hY5YRLDd+gROd8yzF+wCHxBiW/LfznPDT9AYfwpiUkuziiDKe3ClYuELh6ePUKj/D0XCWsVqdF4zf8rSwCoDvznHVzMdp9z/EI2s/hzJ2galnRXaFGwgKVIKruGr8bV/3+NfteTGv2/Pip53/q0cv3+BDF34EZVIEku/Z/R4q/ty3fPw3G//kAznA/olR9k98/W3NVLmE7zjEeY4Ukqlyaft/7/r8cT7y6JMkeU7kufzKT3wnv3zHJwFbrCz0JE7fkBuDv2lIx20wDjcMMgc3gawgSCo2gqnANu7E/YC47xMOt+6Lq2OI3HKqow2NcaA9qhChYa7SRAqNY+XBmThYp/NYZMP2UAURgy0EDofXEeTSwW1bTXC3IymtKZzMkBYNjWeJHdRl67BAYzKB14byIiRVtvFQY2zzDsbCLcGGIRkbQkWBQYxmdr6uZT1sXZoRwop6jQ7IGyHxpIVyjDScX55geqy+c35jtVxkLJ/S9bk1jBDoUYXsO6ycGGf68CYawdLpSVTPA0dh1vxtrvl255IBmQr8ROJWEsxoxmCtNGy+MoSrkPuSyoJBakNWGvqrrpRoJkXURIZKXfA1G5slClqh+y7JiEYKbUWxMrlzup5LNuLgFnJQEJZikp7/lPsxRrB3ZtNyq1fG7Wc4ng3hn53nN9bIedbNp3lkaRfNxRpGS5JuQDjVJal4eK2hTAKST/SPsHq2TGqGVhDC4Dqabm5drbd0b4yWKC3A1VQLPfZM1RHCUI+LDHoeA+WipMRrwqWlCTwvZ3K2S3XZYeDGrIsiGInJDccv7Way0COVDvWkgMDQHkRcXJ1ACHhwaQ+jhS6Hxtfo9kZZbVkcOY49SiVrWC2Nx9FdCTEZDVWkpwNeNfMge0u38e7FCxgjhqYUHn9y2bChjrIvWGNPUOdyNspir80LywfpxyHGxDy7sEDDXEddGo6VypzpnGNXsMbR6AL/7fx76JsQV5S5vnqIsWDkG8YNgFL03Vwz1qHe+0sW49PMhi0QZXaP/DQHqq/7psd/9TDGsND4dda6H6DgH+boxLvwnBHOd+4i0z2UyQDJ6fad3Dz+o3/r+Z9u/LMI5N9ovPbGazm7scnnz13k5Vcf5iWH92//b6nVJsktvp4pTSYNjuOgc2VpfMYgQ5B9Q3FVkY65BE3bAi6wgdx4oHyB2jJGBpSGez51HZMHN0lTj/XzI4yezPA6BqlhUIHKfZLuc3NS7bIF3Gsl6XTDYds/9jXX4GmF7FsDBTGk5nmtnWKcMLB5zLbKF5e0Db6e3tbdpmg794yEZNamnEFTopytwq8A1+LvpVWb2UbrgngEVCiQ0pBs02gtVux0h3sEASZ1KO7uEKfFIX4jWG1USbXAz3NoejipIFqRKB/ygqH0pKRzjWWJOJ4iT1ycngs5tLsl1EMlOvuHmuG+welLi+kLyMtD8ksxt7ZtWqBxSFsRMhzi7X2H4hMuRkricUE8gZXt3ZVgQo3oONDwMWuuhXE0kPpkLYkwBnmsh1OzmjDRSExypoRU0u7Uvlyjvytl9ugqE/s2WXYU6+0SaWH4hHJYXa1QjBLoSQKtKFZTC18MpYLr7QJhUSOFwc3Y7qDUGlLloiqa/r4MJxb4pZhFXWNL58SRhmIUU3MHNPMCQoIfZcSdgGF9H+FrOmlApiWhpyg4A24qXMDzcz67eITTF3djjCTPHQblmO951XFGix3+68MvoYPLWKHHS/c9yfnGOA8u7SYophhH0EiLfMdVp4mClM4g5MTlWW6ZusCCMrzxqs/x6OYszy6cJSnOsx4foi/bbGbt4f0JUuPy2eY15PXjDJRnFSQFlII+a1mBXeEmL6yexhOaPcEm7z77FUr8AK+a7LLW+QM+U9/Le86N4ArJvhL87KFHMCZhKRuhbwKEgNx0+I0nf4WXTb2GV8y+lMjZsXe7cnxi5YPcuXIHgRPy4/t/kxtGFJe6d1L1DzNfevn/VLxpJ19itftnaNOnmzzMYvO32T/27yl7MwgcIMMVPhV/9n9q/qcb/+wDuSMlv/hdt33N67nS/OCzbuDDDz+BNqByzR9/9iF+5AU38+5P37+DNRqNU0rYvBZkU2PMMCsFkIbewRwyQXTRJR2xwVAoKDwpaKxM2ECgbfONKQuCVk7pQo5YgNqD0PzJEqcfDZnY16S+WKF1chQ1jV0UDOiRnLSgQSj8iw5uxwY7FNu2bAbICzZIdA8aggmL9xol0IsB2gctBaLjEF2WCAXJiLFmzIBMrUm0lRlg2I5hO1y1D+WHfcR1GTHGMkNyyGuQl3PCywI/yuisFeGKjlCjBd1zNRypiC7anYPBipIFGzmjT6RMfd5COpe+xyWrSRxlRbHk3ph4ysFVgjQNLPRTxBpLJ+C27TPOiwr67jbWjQGdOKR6aJgxmmE6AVuEau3Z62Ldh8HQwQgB2nbkCmOlBZyewCkMMXQBEmNlfhNrBmLQmLpHRWcUKwlzB9aY1Jt85fxeMBCsuuR5SIsyoZ8zeoOFVFQOyUZIRWc897qTPHZ5hmYcsW96ldPnZsmVQZc0Ca79UIuakd1tgiBlpVlja3sjpCbwFO1GkW6vQGmqh5CGUrVDmnskyrWfJ6AzQaNT5PmTl5irWJXC22ZOcvrslnCdYK1T5Q+O38bu2gY3TVzkrstHaCchK90qh8bWeHnlBH3h8Z7l5zJdbBP5CYHUpF7KkfnLpFoSSMVL9j7KK/Y/BMCZepMvNiqE5QGhdOkri2kHMsdzDEIoRr0OhwrrLMU1Hrq8m1o4YNy1Ugj2ygyT4Tp/vfgk3737ezm9eQefWjtKZlyEyJn2L7GRv4bj9UdZzipUC+m2N6dA8aGlv+bh1mP86rVf6+Rzx8IXeM+5u5mMQsaiPn+++C5+7ujbqQVHv/Xg8nWGNvHObwCNMrYTcHfxedw09qOc797FrsJzOFj+rm/rPF89/tkH8q83fvkdH+Fzdz2J9gTevGMZARrO9+uUgi3lPjvyWs7gADaTqmnkIMO97GMwxFPa6plog7ykKazYrNhJIKtIlIRkzKB8QfW8g0ihN+PgliQyM8TjHuqLsG4k66esNowUhqBhSGsgjUFFevtTSuY0SV/jdqG8IOnckCISUKm3zWEXY9k2E0QYQ9AyZE2feJcm3JDbPPG8tHOT2re7DimgPy0I1wxSiaEDEYgYZFGhXNeSrodaK7iQzyhq5Tb9xyNcB/La8IeUQbACOvDoTYGTGrwWGE+gfJf61YLRJ2PSWYG77qJzW4uQngYtCGopKnZIs6EqoWDIt7cNTgDucki+BYMONWRTi/lgELQnBSPFPklcxChDeQmcUz55Abq7hhCNMUPphB24R0cGp+FgJu2JTC5QUuBnkJY1WRXA0OpGPHZxnjBIKUQpYRSTbUZWXni4cHkdsV1IdFxwAk1jsUyWu1w7c5l77r2K6niPfD6xBdX+TqKgtSTwM1xpKEUDBonHeKlPOUhIEpfYEZgNn3bdxwSa6liX6fE2K60quZLsLmxy8tHd5MZh4ew0r7npixyZusxkqcPEWJ1es0Qvt0ymTLlcbIwTRLGFqzD4MqGkYz62cB0/cODL1Pw+YKh5Ma7UlJ2YU60J/mzxVg77K3yhuZ+JsMsD/T1c2hxFODnGCBYuTDJIPQqFhMPzVgzLFZqbyouM+AOm/Tbr6xW6nZDzzjg3FC/axRjByfZeXjI/xQeXPs4YgvlCnXPdcf79kY8yG7YIHY9+5Rb+5uw81znLjAUDciPp5AEaxdnu+a/5/X986VHeeeJjXF9dQRlBN/GQkfM179sag+QrbLTejutMMVn7FRzn6SGbWvh8KsGtNOO78Z0JdtWsbrkQgmOj/5Jjo//yaY/9dsY/y0D+N6dO8UsfuRPPcfid1303N8/uFBVOnV3lc595EmFssbC4qsj3hwhX8MKr9pHlCkcK1LCDUpYlRqjtHpjBLihchtZVOemk2aEzyiHkku8U4vKyIC8aygsGJ7VvlTmkNQ/jWIlb5QHpzsKhXDthYdl6f6ZbiZMGlEBoMK6gO2NQniESOW7LISlJC+MOi3Vb2WRWkKhMI4sZyCvMGL6KmON1IR21HqPxhMFIjV8XlC4ZOodh0CvgSI0qPPVAE0vW4wr9mqXPhRc0ODa7dzJBMmqfhQoMoixwhkXDbBril/bZODOO0bYWIXJgw4eWh76+RSEa0G9Hw62JwIktjLR1c0KZoaGEQNdyZLz1v51b7EQeeTkjWpQ4icTIYUF6E+IxK79AZwf7N8JgplNMURM2BAMVoFo+QkjS6vD+h+e/uDpBsTpg1+wmSklqGaw8WEQOIC9aiWDZtbIDJren6LcDYt/lk/cf44bD5+i3inQrLhQ029vApzxgg2MMR0fXaScBjgsGgVdI6C6XkZltwtLaob1ZptcusP+qZYQ0XLpnljyyvO5cO9x37hAHJ1cI84TffcEfAvD+07fy548/19IsjeCJ+iyFYsy1tUvcd/Ion859DlZXOdEZJ9YBWergDS3fELC3XGehO0bZiWmoAl9qHEBKkCVNf7XIRte1Wu9GEscB56VidqzF1aUlRnybvXpS8abrJvmxD42yfqDISlxmPmxwOZng1ftuYy1/H+caARPuXr5j8iyeUOyOmrhSATnHiid548HXc9fqI1TdKkKu44pNEIIj5QNfEx8eaVziR/d8ionAKiSupmO8fPd/+Jr3AWjdY3H99RjTAzyUrjM/8Ydf970AQrj8/+y9Z7xlx1nm+6+qFXY+OXcOarXUyi3Zli0HyRk5EA0eYzBgA0MYLmHG/gGX7Ev63QEGxmDA4xmMjRO2zDUeYVvG2ChnqdXq3KfPOX1y2Hmlqrofap19ui3JxshwQdf1QeHstdZee4X3rXre532ey8behzZdpCj8q7kIPecCeao1//nPP41qWTQpP/Kev+Yvfuq7eWDxPDdMTNHtJj1xIQBlBb/x5tcgpeCmS3fz6S8f4Z4j02gFP33ri9g1NcCb//GDPQxTNlzmLk17JEOugUIkAhJISha/DUI6zFxkFpmA3946v83XVJcszWti1LoHGsJZ5SAN5XDh5i7X/EPDR2iDtBqtBMHGVptpOONROueWrCUB2Y0NTBXShRAqBhZ9RMvDDiUgLcm2FLkUOH0UNDZTWEE+UwYb5I1HChCCZAiikibyXKCzqWDoHli9NnP0xAyCFUnbK+PZPMgqiV9N0KnANi5+vKy0vSRXnOqQZd6WWqBwHa5Sg00hmS9glc/IjlWSRkhjukYWCvzEuOainNbjdQAFasFxzcOWIZ5ws3N/SeJ1JGbAOuqihSRnMgoDQVPgXdrm6svmOXL/HjqBB9UMxlKsEcTTRbTyLkoOW0VNi7SWvkqHM8cmiWOf0rzFazl6o99wiba0CHx8CLszptUv6PpuBmwtPHJ6F7Kqe4VU0/AhkY7WIgFpmZ0dhZakO7zBvm3n2aDkZu4IvCDbOiltsVaiM0gTxdWD05xr7YTNFYuBelqgk/gcrk33ftKb9t/LHfMHqXfK7r5Jy+smH+HE2jittIBFMN0cpjt1Cm3BWIW2EteDDO3M4dK3jB9lPu3nWDJJQcaMFeowBXedugzy/kghLKtrVdK2z/e84C7AQ4kyCMvVI29l//CHiXzNhi6z3ipT7xY5vnE3SuzmutFznI1HmEkmqIQFQu8U2qwj8CiF1/LDk6+hmc1wpHGcg9W93Dp5C0ooXjT8VNGrF4yM0G43etdge6H5jDK12qxBTzsxJUlPPe12XzmibIGZ5kcpepNsr3038ivpWN/g8ZwL5Jk2qOYWWS9dy3j1p94HgGcVn3zd93L55ds48tgsKMGhl+7mjz9/D7dec5DV9Tb/95/fgUgyCkpy5IE5ouWY4btCNnZpVEdQmhFolbM8mhJCx2tu7nPfpyKoPalRXYcly0SQliytg5riOc/5cmpoH4jwZ0KXBICsz+I3LdEI6LxotlmItFIy9oWY1Ss9x50mX7bXcwaDcUG0dKePbyLaGXS2l3rbeS1FQoZNnBGwAIyQeJHGCIWusPWsCi4IVhDbrUC2ebz+o4JsVKOldFRMndPZVX7NWwFiXwtvWiFT4ZKDhGx/G3mqhNWgM4nna8dCydObNPl/CmC+QCqgWQ8ojHWolCJaIiTSgnBtcyFkEYGGWPXut0wV4bxwrkRKkpUcVTMehqC++fty2mJiiZcr3L1wAFOxeMMxtmi2WsmFwHjG+ZhmAmkFIgYbWEphwuRym2WqeeMQkF3QKCZcj0Hab+js0BRDg1gNIZcBsTgJADGUoKYL6Mlo63ekOaW1bNxxy5b5xUEOTMxQ8FIi4+MJTbRW7DWm2Nyg2QJ+mnJz/zGWdo3z8PldREXH326aKvv95YvyUpz56AFDbdhxUbtdj9vOXUPBS5DCoK3CIqhnBQJlqAURM+0+dpbXaWcB5ztVnj9yhj4/4lTXR2K4sjKLJww2bHP1njrzj19NFPv4Qcb+Xef5nsmHqQaSqcrrGSq9mIHwACVvjI9/x8/xSw/8Lx6NTpJaH2N8tLVoqzjbGOS6kWWuH3oDw8FOHmju58rSk0yW9jJS/WF+7dG/4rH2EYSwHG2c5Pqhq3j52IueNkYkzNPQRcrKdfGl4pkLj57aRjE4TDd5CNAM1n7sGbfdHKlucPf8d5OZFlKEdLNZLh16Kk7/jRzPuUBeDHwG+oqs13O5wU33dyBD84lTj6NfJ9lx8zjXyV184AuPEKUZZ5bXCZHI/ClPU80jM+dJC4ZSS+E95NgG3UE3m5OJoHbGIxrVpGVxAVcauuOSkUcy4poiqQo6ew1eU6Eikc+6LV5bQXLBi2/cbFj3qK8XkLIFqGsiRo/A0sG8UzIv0MnEFSVllFE5GyGsa23vThV65yR8ix9oMnNBoRbQSl2gce6mhTJxvHBwsra64CAoDKiOw6dbL4h75tJy1iKXXbdeUHcQUtwH0XyZ7pUaX6WY0AXTQktSSmPitZBmpeQaYhKwubNBj6noG7y+DBsrTNOns0OSFj1sVyCkIO2z+LFBBAYmI+yxXP0PB3sII/Biiy5YtHSBGClo7LcUl1yPADj2jM7y4JlZkrUQbyh2NMEVj7i2eV8tnk6xndDprnQFlUJCdKyGqCrsfoH1LN0Ji3faVZ91yRCuWdZuzLAedLVPuD2iaCFeLzp2ZGAwqcRvCQr3F2jutwztWSUspSCg3SywvlF19z+Be6b38x+uvRPf0zSzkJNTw+iCwlpBILVrjy9p9lRXUNLwllfcwXXT27nn3H7uW9iHNfDE8hTDUy185RqwfvOxV4EnMMYJdGkhkWVDJiTF9QQdSHb3LfFItIexSh0pILEB050h+oMuB/uXqNDmy839HI/GKcrEQXsSBBnDhRYH98+wGpURAtq2wF/MHaY/Tblm9Ebeea1rpFmOl/itJ3+dlt9k2ISsdkMsedOPkIwXRrmi7zCPbsxxrPklMqv5WySXVA3Pqz3BF88f4VVTp8lQPN7eSbZJ33maMRSO8on65UwEK2gkM/E4L5hcZKzw1AYiIQTbRj5ElDyIkgME/td2Cepms1jrZiXGRqxF933NfZ7teM4FcoCPvPMt/PGn78JTitvSo5BtObx8buUIi1EDg+WxuWV06qJdkmZ8+dHTDO6vcmZ2HQ1MV9eZnziL3FFGzgZ0+kWvkAUgY8vAUcnqIXoBeLOYGI1Yon5BVoa0BEG6hacDyELmfBvbDqpR3RyCGc+3yc0LLK4VfiUZILtaYjGoBCrTBplJVOpSgRdtPbhhQxOspSRDvmvekQaRgqqk6K7KW+Y1ZN7W+RgonRXOGDqfEUoriIcdZl6cF1RmLPE27YqOeZIw/Rk69gg2rDPYsIJiriCpI0myL+dqNj2iRJH2gwpcQojWi8hqiupL0Q0f3Q4wxYzSpQ1E7lwcz5ToRFvNMYIcBtrWRZVce35WNsjYORgZz1I+b0j6FMazZENuNWCNw+C7ByK8VR9jJaSCyozD89sTYAKBXi1CAxfA+zfdnAUyNPjTIBPIKhYxbLAjGn9FEZASC0VWs2wc0ojMUhnroGMLBO5gCrSWFGoJQaVJmkja9SK6q0irlqAp6IsjwlLGJvZXrXZoNorYtQCZCOIo4E+/+HKU1NiBBDyBqDlsPWsq8J2OzRNrU7x28jE8Ydi3/TyfPHnYiakBf/jYyzjX6WcsbPHpM1cxY/uRyuD7romNfAFmjSAJfNLM44nV7UyIJqqqsDg2T2oVS1GV2awfYQ21YpJT/xSJ9nDUKsGTjVE2kiJSbk1MutrjxPIo9y/fz89e/RLuXZnmjsX/TSNpIITm9SOPUZYR7595Aadbk+yqDPFDl7yQ957+n8Rm6102GI42T3KqNctP7f4C20qrSGF5ce0YO6rf9ozx4aq+G6gGn+B45IgNBRmylqw/bSAHEEJRDK9/xuN95Sj7e/BllcC0CUXCQLD9a+/0LMdzMpCP9FX4ue98GdoYbtnYz/f93UeItOY79x3ic6uP5+geRNWYgiqTZQaTGW7vnMaLJKoIWIFoerDoY3em6MNtOjN9BOc9gnq+jJXQHRT4604wS5i8aNhnWa/5eC034w5WFMlYht+QeG0XO+VCgNwTY3wP2/bRBYEpQWktI6p6qAhKi1tJQ/sWYQ39Jx3+u3rYMHgfWKmwWNKyR0E482bAzbIUFFctnYJH4cseYdMwfPM8ansMseDMg1NEykdoCNZzep+mh9kKISguOb317qSlOiNojUqUJe+iBJMoTBnk6ta5WgG6Zqgeh7WdOLf2VCI8S1YQGAleLBB9CYUDDZDgmy7dEzVsfwq+7SUKNR5Dq+QgD+0MKISxmFUfHQvESoCX+4smIxoKGV3jHHiSft1rekLZXN1QkY1obGwZvMdzzkcWgoZl7YUJxkpsTUE7x+GNSwThkkLkjk9+E/TJImkSYCuCyqyg+OIN6qsVNApV1ATFmDRQ0BJ5BdpJIudy12RNH9kRrhtWCbojgoJ3gcxmPqS2ELlZvjACKimm4RK072UYK9CZxBYMk5U660mRmTMT/M7K69k1Oc+J9ijnvSpUtOPAC8UnTl3Phb6uopaRorBNhdfvVgNo4ZQS87FaDxgfdcwmJdyzIQQEvqbeDanitM6NlXxu/iBDXpuVdpklUXVSul5C0TNYNPtrK1w1MMfj9Sluvv13aCWW1GZMVAY5PHyGmuryQGMnDUr0l1t0RczRxilSk110bdxjarF02Vle6UkIV1XMQ8s/z/bKS5DiqY5AQgjesvOt/N6J/4ZEMhwOsbfy1KLoP3coWeCKoZ9lZvXHgZQo+lsa3VupFV/+DfuOrxzPyUD+2SMn+LmPfAZjLD928/N54i0/jbEWJSXvOVrjPU9+yb2QC2UimWE8hzGmRQgXcyabcFi2va/muiqHM2qJYG2vISsLpLbOWmzDQwrpcNvMBTykY1hsDpkKBu8VFFczlg57ZGWB11H4T5TIygJddJBAWgRbsxiZ5c0DeWDEoj3L4BMGqd3fhu+VdMct/lJG0q+QmeTct9QorKRkRZ+03wdj6Q7lbIwwo+ezdd0AACAASURBVFpoU9zVQQbOY3RibIOZmWGEFoSNXDRrkxGyefI251V3wAqBTKHvjgLpuCaqSoxSoHIMupErQPoQD1uMlNhVH6oGSnn69AXkOiSymoud5dRCtaNDZmXvu60B23Du9FZAUI3QM0W8RAESlgIwm2drGas26YaC9rLDhoQVzndx84AmZ5xId038Nj2RKausE0qzBlMxCOFRmPNQxj0XuqW2XhYBWT3sJa6s41NMM95w1Z0IY7lnYxfLSY1AGiq1DlnqOYu/YyVkoEkLEt3y8BKBF4NWlqxmibSkWPfw+lMElk47IFEKxhO8SDpmTltQOWdhT4TNYcA48bECvLaiFEFsLbP1Yc6FlZ5gGX6Ktn5+zheA5KF2n1vnxWHqPn6Y4h/38VqS5j6D52smtq2z3C0RqoyylxB6m23/gmYW4jcttVKXtvYRITREjWpllPVOF1/CYJiwrVyn5jUIlCHSHtuqazSiLmvxMJlRLLT6YKQPIWAprZLl7clSSIaCYQLpuxxjDXuruzjRPIsUiiv7D1DwjxOlxwFL1/hYazA2e9pADnB532X89pXvZi1ZZ2dpB578xobCTJ9jc7lrbUw3eeKbgfzrHb/2qTtIMgc1/NEdd/PWF15LMXA39EcPvohXTB0g1hlveuDDrvUgtwQbeNQ12Vjh6HKOTugefLvo0zkoKK9YqIMQ0mGvPW0k61avMsdIQyCz4Bm8JpSWLa1tkqzs9snKFpGK3CA5X8BbEKsBxboLMjaPMsKzBLtbiOMXGBxoZ+6sYlB5wbS0pGhslwRNCdr2Ok2FhWRMYte33GfAdRFmZYMuu7Z1rynxu47FssnskRkUF0EXBd1+KJ8Ff0MiI0m0WyCLubSub2nss9jAOo66hKRqnUyed0Ew9Sxkzg01a/qEFqfzLXHejSs+jZJHxab4M4ry/QHhqKA94WGljwKs51rnScQWXBVoEiPdZxlYDwoLDhoyIXh1iYpcgE9HU6wSRMNQXHbYfLYj3byRjtUSakorXi5DINAl8GLrEkNf5s654aApsyviDfsfoeK7Zf/Li8f40PRhwGlu+77GnC5QfUIhdyYEL15z3Z0n+ul2CpiCdUU3PMJlwdTRhDu3j/cSjggspuigCluCaIcmNKKnSxMUM5rzNeaXC+AJbCEvBF+gkuZYmxdgez2IL9c6Ee4emHNFiudCeFGdocoKb939GEc74xyNJhECYuMRJ5KyyZDC0k4DjJEcWZxEKc3eqSXKXoy2km01n/96/Q/yy0d+m5aOWEsDElNhvNCgroustCtM1wdRwuHJUmq+NHeI2ZLHDUNzHG9bPFmgqEJeOfE8Xjx6JSdbZ9lX2cVoYYgTzbPEJmGqOAj2Naw2/xvnWp+hrotcOvB2PPnMWisAA8EAA8Ezc8ITE/N3Cx+jnq7xstHXM/l1GDDXiq9gsf57QAEw1Iqv/Fq7PKvxnAzkpSAAnJqhkgIlL16y7quNAPC6qw/y2SMnyIxBbGRkRSc65Xa0yMjiWiMcJbD/IYudTDDXdNCpgvsqxBWZmzlAd7ON3YKILemwAQHpAGy0pXMK2nyxJKRTKXJegZBYk8+IVb6/7dk+O/bWXJHWZZrKEy5zRAOW2mlNY+fWzFAmUJvO174bua5IP+iahkwyu7tE4VyH0u4uyVrA4rlBsh04DNlN4EmVC950wYuMw7txjL+sKGjslUjjxLJcwdVJ4Aor3TJXWbyWU0dM+7YKuT1LJgNWgfYsIpGIf+iDkRRWfSrLksZuAQVL2vHIqhnNV2jkcpAnO4GuppiJnM7WktgVnyACDnZoW6fjEox10DMlgg1BYc1pqW+6IQH4yz7Z3g5mVNGuSEzXw27TCKQL1FZQ9BLkpKVlC3lbvSXZE+NNRYjQ3W97LiS1guK+NoG6WMRJYkgzn6QegJaET4ZIYwlesoHIzbMH9m/QPpbTWHKkY7Y1SOdcHyVt6ex00dZLce5Jm5fTbtYoHBFxrNYmXQ2RVQ/d8PGUQVcMRltU0T2DJpYYz7GErHF642bVw/gK66XcNH6Cg/2LzOlx7l24jImpNkfq/fyP2RvxpKYcpr3vr/kJkfZIjEeaeqyulwHnB3qgssie0jIPbezgdHOaU61TdHJHexC0dYgnBokzOLsxiMXx15UwCDSL3Q6L3Z08sDbFW/Ye5eap/5PL+/dS8YrU/BKjhS1d8P3VXdyxeBsfmP4oAC8f+zZetuPXMWh8WeafOj4z/4984OynGQz6+YXLf5Cx/Ds+OvMnPFa/l8ymHG08wC9c9h4K6qsnh81R8PdyYOLzdJKHKAVXEXj/sjj5cyqQn1haYbnZ5re/69W882O3001SfukNtxB4T9+19e5vfyXfdt3lzK83+NX33U7mb73sGIhf1EI+UnHWbBEQGMxLmuCB1Bp5eZPS7RWSPli/BEAhY1e4FMaiB+gFsNZOCNZymAMLnsWWNWYIgkWnI2Jymlx3HPzTuS2cwP1Dg6pmzN8i6Su36fuUz/qlAUkFiis2D/75ttYitaG4AjKFdigBid0IOS0Hqf5JQFqSpFM5GL65m3Jt6lY5FkZSc0lKGIsYSRDnPbyWpDsqiPotlWlLVoLuhMgpfQ6CCdatm42XDYUNyEJBVnPXVraFW84bwchkneTcAFnskVZATLkg6nUlyb4ofzotui9GnXIvkBlxujFoXNILQA+kjvnoAVaQVA3Kz7DS22z4vIgEJEcjSrUEDoGc8wke9MjqEjLQTR8igWkHZMJQ6otpR4Hrfh1LnZQt+X3dFWMWXea/d3UHLxg+Cxbumt7LgYElRCJp+UX6qi3ue+wKTP3iFdEW1XMTr8YVpUdSaqcDSouueSmuQefyZKv3Yd1ntNuFkYxAaSoqpj3UgilDd65MFimKkxGpkKS55KwMDWGYG1oDoVRktS5xINhbW+KGsWm0lZzZVqHynQsUwwLmTIFW6t6dTjlmfFudFwwfIDHnWEkavGL8+YzJQ/zElz9O4GumhppcUlni3rWdnG0PY5D82uMf52B/PyvJGmDxhOCtu9/Fh85+jAdE1NNg8ZS5yGsTAb6IuaRaoPJVVAz/bvEjZNb9ps8ufoxbRr8V9XU04azE67z31MdJTEY9bfEHx/+K37jSUQxnO6fJrJs0GGtoZuv/5EAOTmEx8L5xCodfbTxnAvlfP3SEX/3bO1BCsGtogE/95Fvx1FOLRxcOIQTX7JwECTddt5c7HjrpaHgCKBi08Ul3CDJjqc4AmdmKewqoGiQQNC1hYukWIKh7WGHJShKRWKzvZkSkUFgVJBMpJjcSQAjCpgviAucO47WdpZtM3MwV5YI+e7t4f19g8qY6/t4OqzeM01IeWQV0wVJYsajI0B1TBE2Lt+FoeEHdklbzRhjAJgrrS4oN8LuwMuxgiE0sHJPrq+M6J1VmGHzVApHv4wUZ+sEhxEaI9oX73lWIxvJzBTDWWc+NSIYfNhQ2LM3tgpZUPdhJpBavKYjm+vCmuqTdEgiBtQJPaMxQjN7UEsPVL1ySsojEwVHByYLjoFvILkjUFrAFiHdo4j5Bedp9plVuROdZgskL/DYnU3jE4h0rkA0IvM0PpGHft5wBC512yBMnJzHNAN8zKN9xzbO6T/mBItlgxnHGOdsaprVUotWqcGjXOfZMLNFfaWO6kO5JiIVC3lkjuNE52dRP9qGkxuQrqjA2pJ5i22tn6RyrsXh8nCw2qK6k/EgBXdEYI1GxoDNbYce285CbV/uVFI2itK2NAUKpWWlvzUqFcNx3g0LnWW2kWObkqqHcnyKAE60xhy8jWE06VKoFNtYctTNph3hIZtobXFa5ArFU4thawLU3lNkx3iYyKRnOWLuRFjH5DCaxgquKX2YtrCAsVOVOQjHILxz6OXT2ST569ggl5XPT5BADYYW/OTNDIzUc6ltgRynjwZU/YiV6nO3ll/KCsXddHOyBkqrSyJz2eVFVLuqkNNbwqbl/5Gx7gddMPJ8DtafOimOd9h40C3Sybu+zG4dfyd/O/xVSCIaCcQaDZ9Y3//96PGcC+fvveoAodwo6vbLO6ZU1Lhl7qvP1fKPJA7PnOTQ+iu8rXv/+v6CzkDgudh5oZOIccrwnCw4vH7SkOyXhk0U6K20YyVxR72iJzrDDUNEGUzWYliWrWqznWBwilsiOxfqSzphFNaQrquUzZ+NfQEsUrqGosOqUFcn/nNYE9pEKaQDRzACFIUXaCPAlhOtQOp8SNiznX+pjvbwNPk84jkrgjiQjS/mEj99xf/YTwdiXYe2gRMS5uYTNsXkLYd0iU8GpU1PYgoNY+rc1UfMByaRlfY8lXJCIzP0GAQRpRjysCFahuOYgouo5x9mO+8Frue7V8oJAGIlpFPFKFhM6UavMCtJaLuCVc9XVkqK4ZIn7BN65ANPvjBB6QlwND5IQsye/aC0FsSCrQv0K1zkpWwLVda38vnHJQAjczD4vIMtihok9sALlG5RyF7FYipGhQVuJXi8R1iKCQoZc8LFWYO6uEPUbzIKgtVfhyYyjZyaYPTFMWIlRQzFpv0bfkNGu+zA7SHjWR7YEl948jZKW8eENHjm622mfCyju0jQXLaorCJquXmM9g40UVkJjtsbZLykqQzGRJzFTsYMAjaBdLyBqXfrLHVZaFax1gmkHhwdBVZnrrPGD+17M9nKHX/ryFzm3uJ1gctEZSGw+c0Y5Rs3mYyQgMTDdWWa6tUJno0g038/RpWX69pUxSQuJILWvZlf5QdaScu8RnChsMGRa/M6RV9LKQt5z7E/44xd+F79yzbfxi1e9ESUEa0mHR9Zmeduu15GYB4jNCrGu88T6BzEkTLc+y1T5+eys3nLR+/xDe97JR2fei8XyndvecdFnH5z+PB+c/hyxSfnswgO8/3nvZKTQf9E2k8URXjZ6mM8t3kMgfd6+d4u2eNPIa9lZvoRmusEl1StR4ulX9v8WxnMmkB8YG+Hs6gaJdjzX0Qt0xwHOLq8zV6/zH//m02RakxrDgakhWhsxCuGYK5v63B65w4t7rMNVQTZkefnrDzFuS/zN6cc4M11HRBLbB2kVrJIUpy1G5TPcC4pMVsqeCBW+dHSMfEltphJMVyAbClM2WC2Q2uHjIjWIHHrtDgsoQ3FBsnS6j0BaiusumJUWLdGo6AXvzoQgXLeoyJKVLe3tLkuUV8FLZT6Lc9smNdAVjSgJ5EbOghiE0nnr9NaLLvBa42oFzUaJILRE4xYRS7Ka+x1YS7AuMEahEpdMtO8aWYSBvmOW1naJCZw0bhZa/K47Z78tsB2LLkCyP8uRBgcn0VLYVkAWGroTLsEES+oi4oUFxEqAHcmQKz5y3RVFZcWQjLhrXeiLUDMVzERCZ6VEseaap9KFIrJqCC9tUtgRYzNB464hqvs2nL2ohCjNeef5l6adgEIlxYyktLRHWnNV704NvKbFDhuKJ0K0CdnoKxK55gKIQZUyhBJ0L03oPw0rWZGqzHjw6F4iJRgaa7PWLdI5PYEUksreDfomG1gD3cxn4fQwXkNiS3Bp9QDXTk7xLTft5UPnvsRDy9MsmQ3UUJdux2dICUbDNuuzVdrG54kzIfd/39sRQtBK67z76I9y/b4UgSCRV/GOfc/nXQ9/mnVdxBiBKiWopkuEL9k3yFmxQWxSkBZZ0GTGcHp1jb9/83/iM/N3ocQ657tLrOtRtlUSOmnISiyYjQbYiIq0spDY+EDG+0/cy4vG9+BJyWK3wes//x5n/mIN/+um7+fQwCQPr/wJNm85tkBmt2bLm2OyuIv/dMm7nzYmPLpxyp0vIIXgXGfpKYFcCMFPXvI9/OCeNxLKAE9eHKx3lL52A9C/hfGsArkQ4neA1+H8U04Bb7PWbnwjTuzrHb986y3UiiHnN5q846br6S9taRD/99vv4n1fuJ84NMQl2+ORH11YRgUC27VsamtD3n5vRK47LhCRJWjAB9cfJkBx9fZxzsat3vZCuyQgE1cadRNiBweIi2Y1OW9YAwK8pmuOSQ7GvZmzd6SIUY7KSAheBH4EaQxJnyDps5gyZBnYDXf8tCwJ1nL2QWaxSlDfZzF9GVputdh3p6C8BFngqGbat6xfY/KZrytUBisK67nkFNZtz0cyD62YukdWzPA2lON154VSrMNzrXGJQlhBe0QSxBpiaO7xyIqQDbnA0N0GfU8YvM5mo49wnaodgRlgC4aJFUZZmvvorVziUYvfBGWcOqRBIFqGEinJWonNG+M1Jcmow6VT39mqhUMxnfMF4nOVHsmyPWUo74pyloclHI2Yj8qsTweoMOu5ubthUZ47pi0YssEtuz2URYYGk2xlmS2RLbevtRKBBWXpe94qK8tVFjaqYKFvskE7DQFLaarF1YUDnB+dQyuBkpZQZNiqJi5b3rz/Kt590yuZ6ZxjrdHi2ydewm3nfh/PS5ESKpWM2YcnSbV7LqSwrJXafPz0w/z1wh3MdlYYDPvZX1sGYemaLgf6bmVvcY07648TLRdIj/RRtCB3RNzzaIvqJQbpCZTSXDk+y7HVEnvHQn7v+Lsx5jgGjRQGGMLgUwk0XT3Exxeex3dMDuYPvqWgPC7tG+1do39YPEliMiLtVtSfOvcohwYmOdD/HZxp3k47W6Q/2M3OytdH33vV+PU8Xj+DQBBKnwPVZy44lr8KDv/vYTzbGflngXdZazMhxG8B7wL+y7M/ra9/lMOAX3ztzU/72fu+cD9xmqEBk7fsWyxGWrZP9LHmtVENgdWW1Bi0sjkPPA/wUmCsowmmiSbLbK9b0Ip8Bm5c8Bt40vSKf17LqeCJHK4QBkZHNujEIdF8kfIcmEDROpyCZ5GLPsIIklpuVJEzV6rTBpk6qoKRUJp1YlHWgxRLutNHJJLROzNWr3bwisgUtiWgll8E67B3K3CyAeOuKAv0im46tA7eKENadgHa7xoqi4ao5iCH0rxm7VowoUa1JKojsdolM5HiJH0zsNKQHO4Slw1oMGdK2DBPljK/Xn25AXSeKIzvpFuLpxXJmHEGw12V0zBz7EkAyqK3p+i8+5MMikcDSo8WWLmKrQRrcVREBWYxIA0dVTOsdciaF6/YuCBWN4bBZIKODhFdn11jyxSDlPnFQUcTjBXN8xUIM4JCmlvVgRQGO5qgU0dLsdLitYUL9giUcEqaYKkVukTtkEar7OogXUG0WEKNt1EFzXClQqewxEqjjO2AXgswjQB8S6nP4zsOHOJ3jv4EZ7vrWCs4vjTC2sYwQ7UW5UJEqMusS0F6Qae6pyS/9eTH8P0MIWAlqjActhkMuhweuI4jGw/xUPNBunGJ5Egf5F3P+kyReMASPzFMoRrxgsuPMTW4we4XHuV8EnCmkbGtnOErpw3T53dYiQeYLI7xgef9grsXQnCw/yT/8/i9XDYwzk9e/pLeee2rjvRuWlH5XNbvmDztbJ26FkS2zGXlV+LJpzeIeKZxy/h1TBSHmO0uc8PgQSr+v+9g/dXGswrk1tq/u+B/7wa+49mdzr/MGKmVmVuro1IotiXdikEg8K3gvbe+kT39A/z9sdPcf2YOIywfePxhZMv0NL6tsOgbmyAhPVfi7Jllfv8HX8cffPJLPNlZz2OAgxeyUDD4qKW5X1BYlrDkgr2RFs9YCjszSmNd5tMQsSAI1iWFI7mudo6R96AZKbDGklQFSQ2stfjdDJl4CBzOiwTrCYynHPPDCIf5ClCZxCYOnvG6EC4KmjucYbLXsfhtQZybRyAcq0ZlltQAnqWz2xAsSvyOR3na8cPrB3NVRgG6YlBt4TpYU7AB+Ku5HvtUhi0ZxyyRIAcSxGKI3vQSzRNLZ8IQNJz0QWvKYeXegk9hBqJBB/PggShobJw3EvnaBb/Nia7ndGdUbLeKxDjGjjQG21SU5pWTK7hrAK5oYwrGeX36Fqtg+cwgA2N14m5AGvnkMh8AdNOAQjGjUIqJEudnaSKBaPvIoS6VIHPGwtNFkj0K6wvalyaohkRUU1TRIqziFWM7eWx5nuXlmLqosR6I3rWQVpC1fRrTNbZd3uHHL3kNP3b3R9w6SIBUFlmXKAG1akglPM6Z7gYW5wK1b2iF1aRCf6mL9Cxp2mLbYD+LGyWaaYwcdF6eTpnMDW0F95/cxXdd8TAPrX+Ebhqjo4M02kVCs3V5e+sLK0hbAf1FNwMQylmNNJMyVCDOFKlWFOQ4Lxy+kjfv/NaLio8vndjHSyeeClVcM7Sd37z2jfzN7KM8b3g3b9hxFQBfWPhdOnoNgPtW/wcH+l5B2Xtq3eurjcv6dnFZ366va59/j+MbiZH/APDhZ/pQCPEO4B0AO3bseKbN/kXGH7/9W/n1j9/BY/OLrAbx1geZYN/AIP/xrz7FPWdnwMKthw4wer8mzjTNHW5mZbbHkKsGqskurVOCP/7DO1g7vY693u8t0S3QmhAoI6muCJy8igvwyRA0hwTNuREm0lWyckoy7qP7HQYtYksyZNEKwvoFnRwCmjsseNB3IqG4LGhuZ6v1PG//FkBSdcYRRjhOupWgWoLKnMPSjTAY3wlz+bnLTvmkIhm2YISDSpRh/IsZa1cGmNCZYVhtScsWGwqMv1kO652e0z5RFpk6D1OVgEi3oCosiHJGUYKYC0j6LaUFi+oaqHquWAz4HUE0qKnvEYSrEiuhPGdpXZohSxpb0oiOQHQFpiWh4jAqf0liPYm0UJlLaU+4DsCsqimsSGzkukM370X4cIn4co0OTC/pJdpj9diQKwJjSSc0QjortjBwdZdqJSJaC3s/XHYVURIglwOnjigt/rxHVrGYsiUb0nihQQqBJxJ29r+fm7ct88DqTv7q0efDpu9bLnfgZu2W37/ih3h44wnEZi3DCDhb7Nn8qaU1Hll+JyY9gPCd2FU9KmITcl9Q9+/2RoPb3vG9DHj9fO7Jk/zl9Jc4kbXQWuMpQ9r16fe7SKlJTYqn4ODQAjPtAczOLupsCYXkZ1/3Yj588gmOLa+wZ+cixTBBCY89wQZXFeZZqfTxp0+8kMVGP9YKfvjQDfz4/pd9Xe/oq7Zdxqu2XfYVf7VPu+03x1PH1wzkQojP0RPfvGj8vLX2tnybn8ep5PzlMx3HWvte4L0Ahw8f/le9QztHBvg/vvUm3vSXHyGnhQIwVqmQas0XT5zB5NW/2x49Sn9iCDqWoaMpUT9svDDZmp0YgS5LZo5vIENBYdUQDUq3fA+AAFRT0PXAy7bggM1Zb1aA1eNDeEagrQuCOoRkykVjXYWgYTDSwTNWuYYiGUPtjEGmkFQz4n6P0lyH2pkuVsHq1TXiQR/ZFZiaC6wu8wj04Raioei2C6RVdx7dKSg+alGJQBcFpiBAOa0MF8RxnX6hoxPG4wKRCsIlRafkuNyqLSCDrA/8dYHXNSR9gkJfl/4b1lk8PQiDGuFrvD6NuSKlcNpQfDKk/5QmrUBUE72Vj8xco40XSayDimlNWmgqbE3jz/rIWJBeEiHzzk4x51M6ZemOW9qTHuU5Q9BJ6QxJyudd921addeyxwzqQGne0plwvHWXjRx/XhehsKgIjgm8MKbvxnWktFgDaez1+N4kjo2kU4EuavwNhdeUiKbEW7Wku1LGpwIWu47s97Kxo1zRP0cgDS8aPcmT45MciSYx6z4i8smCzGmT+5qfefx3yRpl9g42WOyUsZmlaWu95zZOFWupz8JfT1L4lnVSIzkyP0mceoynLbdSywR6PSDTGT9y259ydrFJF0UyFaJF3tXb8DGe1ysopplkqVPhuqkZ/B2GPS/cxy9e9rP4yuMtN17L3505yWq3y8t2jbPQ/jNmW64RZyyIqbeHMbmQzJ8euZ+fve4leFIS64xf+dIXuH9+ju+57EredtW1/+T39qXjP8P/M/tfiHWLw0Pf93XPxv//NL5mILfWftUKgxDi+4FbgVustf9mU+hQqcTm6QkLQ6Ui1xVGefPvfpBqGNCMnXqbjgyJB4Gr2RG0Qa96qKEMkQnUiSLCQDLoIbO86BZp1q+QGA+8zSa2suvQVymkFYc5bw7VhbDhOkGNgKxqtjofgWTACTNhDaqeg8p5Z2WQWqrnUiozCV6928OB+59osfSiQZS2JJ4LXALAWoonBeUHJdpLWbtU0NqhnFNOTsxx0rWbSUdsUSKBTbkBi3B8dm0ZuksSD7sGIC0sXhdAkAzIfHarEHcW6VuxNCdDvMubrpAowY5kiKMhGwd84nx7Kxztze+PkWngmDoih46Uu2FmLUDVJdnOhJ4Mje8kAdaeByjH8W/tdbh931FHfbQXXFeHW0NxIWL4UUN7SjD/emeugQUtPJCCeMgw/ICg+OIOMvePTDNFPQoRvivWBuuSeMxCJkEK0gGD3xCurmFhV2OI29/+A7zhQ+/n+Jl1Hjl/Cc+vnGai3+l+V6pdbOREzuxQjNUSbRU6kcwtDbJ9YgVhYf9gk7JXZt9NV/EXdz6GsSm7D87w4bnrSYYC5k7v3lqdCcu5s4MMrhp843HL63bz04+8G7HdkA30oYykLKDVDTHWiXvVG2U+98RhfuQGn88/0qbrJQyUmggB56MZTrROclnfpQgheNWe/b1LuRGVEUicGqKgGnh0cpXRouf3mnL+5MH7+PiTR4i15rfv/jJXjI5xeOKf1iQzUriEt+37BNbafzWnnX+v49myVl4N/GfgJdbazjfmlP5lxli1wu+97rW8669vp9WK0a2Ufzh3Bp0agkBx87V7yDLDYw/M0h0wrB4SeQFQoH2w0wGF8067WmhXuETmBgWZxHhOizvYcDKwKhYYIOp3FECZOAw5WHUGBwII16Az7gJxr/PQugCEcMG1sCZIJCAEa5cW6D+RIDNLFkiqje7W6lPimnEiJ4ma1Bx+7mcZ5Qc8ZwCRwchDhtqplIUXFukOX8A6SW0vIAR1QzogsdLNxo2gx+oJ65awIyhMu4JxWs61SHxLVnU/oO8+iAtVTEVROm1JDmzBMXo9IKtJTHFrJi48Q23/Bl0lsQtBr6NVSIO/QaSUPwAAIABJREFUqwPK4msBSyW37tu8VpDfh/waCEBaTC1j/XkCf91QfUIhstzcAtChIRr1KDRSOruEY9jks3XrW8hcQdyULHI4YVNLqdktItRWdkv2ptDxtmb00HsGAFbONjj87t+nrS0gWdno5zduexNvPHwPl+6e4aGNHW6mUMox615RUqC1xFjJmeUB/vbWH6EvqPJnJ7/ALa8a5rWT/fzK0UXOnxvGDoHq2rzuYJHSEhU9Vnd6fP717+A/3Pm7jJQ1nTRwidjlTQI/Q9qAyfIgA+U+fu553831E9v49h2W/37iz7hv4z4MTv8kkE8vOrVv4AdYjx+lHh9lsvIa3nfLd/MzX/4MqdG8+8ZX8Y+nztGKY87VHSUY97qw0Go97fG+2vhmEP/a49li5H8IhMBn84t9t7X2R571Wf0LjQCJSd2L07YZhHkMkJbpcI050yAeySgsCnRJbgWLpkQXLTqHGYwP7s13//Iiw+CDUFqFqA+SIZHHGkHYtFRXJKnnioxh3fbijsosfgOysqM4IsFfd4XJZMB5UaaFPATmFLjWzhCsRSQWXSzRd6yL8aC5s0T/iYT2pMRrCuf9qSzBWJeLKndSUFyF0lzOTskFrrBQmc4YfKhFsJGyek0f+AFCW5o7t/KF3YrJvSFwAdVikZlFZRCXFZsmD/KLNZJLUmwisU0ffyjGdB3WbLHgW9KzBYqPhuhxSVbOjZ9rGSjb0xWxwxn+jE/qG1dIXfMQHY8gsqSDGlsA2RbYCqiuoHLSw/pOO6a5Q2NDtwpJKh6t7T6V+a7DyPO3QDXyez4e0bwBgtMlCle0sEBgda4Z4358ITGUuwHRbEZWgWjUkpUNYV0hE4E00I4sBGLrwlnBbQ8+n89EB4g7IQQuWAoj8ktqEcIyMtRgPSqSAO+9+zG+uHSUpXAVC9yxCMQVTG4YrX0DbYEXagZHIekW+Kkrb2Kq3IcvC67rU25RVxQSoyXGGnYMVfmzF70JbWO0TVHS53v3vImVY0vMRwvcPPpS9j2N5yWAL6vcOPnnF/3tf7/xbQD85u1f5CP3PwYCdg71U/Z9DDBaKvPSnbuf9njfHM9uPFvWyr8ptvxivcWP/dknOLda5003XsnP3Priiz5XUvbgld4sKoT10ZjlxqL72ySEQRnPZmQYrLTITGEzFyzUnCSaFKR9UFyA8nmD1zG0pjyaZddYJOhNrh0/2kpsbMCHeEQQrlin+62hs8M4tUCgdlzidTfxYkE0Bp3t9A7YLUD5nOvANAVojBVp7QkYOKIpLzseeGfSy3+ehRTSuSpJNSHI2f3GE249L3PYYbOuKgTxAISrrh4w9NAGs68eJq0J0gEQ2jirNO06MEXmIJe04L7LBgZTcE4z2YUsL2sRqcQsh/kM3GA92aMcWs8lPzkTEmaC8rLFrFnifkHmg81cMBSpRdSdbouaLiAzSGtudeN3JcwJOiOaZNIl6mBJ9iRurbB4iSSp5MnXExBbNnaHEGvQFrUhkR2JDTSkAh1YWmtlko9VyDyJKQj8MY0djSGRpK0QjhqKRmLX3Ew+HnYyvyKnpea/sFcMx+QriIUCN5cmuOvIAu0dCgF4vsFqiRqKaOmALFXEy0U+NPcolGPEgEB6EGvLdm+EOvnMNk8QnmfZXhkgUQXaaYy1lt+99i384qMfwJPr3DQ6hS8G+If5aeIsAwz3rUxz5+IfMdf6AEIoXjj+m0yWb+KXD/38P+8FzMdtjxylk7piVCc+znvfeB/adjgw9KtUguBr7P3N8c8Zz4nOzi/NnuUPHriL5ZUW64stjIEP3/kIr7zyEq7YMc7icoNSMeDGvTt49aH9fPLBo25HS89ijQtWb80soYiHBJLABQZnECGIRh1erUvQ2mkpLht0EawU6ACkuXgZKAAtDUkRuk50keJEkcvSfu7qLCBSgcgM1rd4nS3rN79jiWAr0Obna5ULbklNYD1QKqDwsoCNEy2iQl7Qk5tLCYvsQGO7ZCDKg5gQ1Hc7yzcVW7I8uFkBXsdceBlcgM6PVT5vSPpB4hHnTJvNqbm/YSmsQ3Ov40vPv0RSO27wO67YaBQUNgRxBWTJkG34zlLP4vjxEkTqVizCgsqgsG6RJzropZjuoRD/SU27UoOCcD6OxjFt/M6WSmRxTRGkBisEadVuZVMLxt90+3GiXlkB8AWi67kvjQSqC96yRLcDwmVJYd3t60mIhyz9aUz3oQKNgxBGW0FaGIHfcoJn9f2WcAO8SkKqAkTi2EEyBSEsSc0SpyH/uN4gHghRKqU42XL3wAqaawXiOLc4iyQ2s4g0wDZ97K42UsJbr7qcTz16nsdWF7BYVFGzZ7LM0YUm3ajOk/Mr3Ds7R61Q5FcufxvXjE6ijeGPHv571tfPYH0QEnyZMtf6SywaazX3L/0mr99901Per2YSM9dqsLtvgFB97ZBxaGKUu8/Ooo3h7S+7ndQ4LZRjqz/BcPmBZ9QI/+b4549/94F8udPmHbd/km6WuSAwKiguuOiXac3/9fuf4XP/cJQsM4hQcviqnXzsR9/Mb33mi9x/Ys4V6uY8OntdV5nsOnf4GI0UgkIiiTyNsNB3ImX1auUEnRzegfElJhAERuCNhbTqMV6Ux0zrqIDdIU0mVC/Arncj7s4WHO3QCryGJB3QGN8FNiEEWc7aUC2cOTIuIHRGobLguM8YGNpQ/Nfv/1be9t6PYtKsB8GQS82qDKzwWL0uw2pBWpD8v+y9d5xdV3X+/d37tNunj0a9N8tFcq8IMBgbjAs1mOYkQCoQehISfgR4UwghJEBooYcSTDHGYGxkcMEFy5bcJau3kTR95vbT9nr/2Gdm5BgwPTh4fT4guHNPuaesvfeznvU8xrNdlMoT0pxBR3aG3JrjUVviM3JGBRPY5b4WhW4ZaissX91pWjjInpACI5Z10wlBKyUsOJgcTJ5oKIQx3q5cRvsDvwam5ZCUIM7ZuoFKQIkh7PBICrbwmh81ViYgdun+8ijaVJk8qZPGuccc12a+WcYJgo6E0nabWBPPnpNoQaeCX7VQkzuh0JHCdM9CYyiFE1vxs+aShLgI5T16tis3m0m3JvIWikKsCJmy/QHKKBJfoRLbzZtbUcPraaNrAfWhMgbBaTtEgclgOWgQ4yQavyOccUMiFdx8ShJ79pipmrl2pECk8AsxXzr4Ta659J/5ygMPcvW2bZy9aBH7GOLhw9uh7pEAt+w6gMkZNh3Yxc0vfDXvu+02vvnIwyQmh/IMuYEGL191MkpdjwgcrHZz79G1TDa38LLjNsx41+6aHON5V3+BVAy9+SLfet4rqPjBT30n//VFF/PxW+9iqFGjtzyLw6Um5sov/zdLu+fyV0/fSM57wqef35p4wl/J4WZj5n+LAuNZMaTzj1/O/HKZTbdsI87a20w7ZctDBznnwSN89lUv4uPfvZOPfvdOTFPhDbp21llUpGX7EBsjBKHGbULnA23cpqE6ZYWqyCZ4xrUemLVeAQmhbJkRXsMaFpic4E8ZPKOo5/VMMk+FmRdYCeQPWqedZUu62D08iZ4ScnGMWZBC1cPEjnXoQShP2KW/Uhq/M+C6+x+x1ltYmqIoIahaaqE49nN/2GXq+Bhpa3BsF2VxnyBzFEnOFpRECUfO7zyGFy/oqlA5LLSXCK0VEc1VDsXtHk4rc/JRUJ+fmUmIS9Fpk4QehY4W1XqJY+de4ghht53l60jhVe290vs863mqFSYQqsstC6SxJEcc9NF/4xhuk1kpAUUmTAYSCIXDGnGE8gEzsyJyE6BuSHMqE+5SmCFD7Yw2akdhltWD3adXM1SXaxQOumUNpHNj9t6IBvHFmlWngtOwBcbxE6wOTxrYFYXT1uRHhabpoDiUQ7UUlYEGaRGeu3wVX7zn0AwN0mladyCpOUgxRmkr2mZSB8dJcUNNcgwJTAGxUcTNHLvaPt/fs5O/v/lmWknCQ2NHkLlNJJwV+RHsikUrxYHaFDfs3kWcFV5NpOgNEi5bdDqOeTebDv0bH7/3LKLU4Z6hm6nHEX+6/kwAPvfgVmqRFeQSaXDj/l1cvnLdT30nS4HPhuP6ef2PbmV473petuRWXKW47uEN3LF/hC2HxnG04h3P/PGd2E/Gzx9PqER+ZKLKVCtk1UCvVSoEVnf3MuCV2BdOIgpyhxQvOvF4/uaKZ1JvhI+ueCsrbfmVTVv57Lfv4oUXncw9//p63vilb3L7NTutYXDZgbaFA3QKSZxSOgxxR0BSFAqDCe05trMSY/W4p/c9/Z80ZyVimysiSjsUYdGzrfVHheYccDxNBZfJjNTutxXfef0r6SoV2Dw6yOu+dQ2On5KsjMAFr1uIj/gw7qJDxehKhcHayR2M6nzpzntxRVs8WuzMcJ7Oc9QPbfs82FldrO0dzy5J0qHwqobUV4+GcLLQEVQOgTIOhT1WI6S5OiGcD/4hH69pVxBJl7GrBoFWNYcSTW3EAclw9wlBxYJfN4SdlhojrhXP8tqg0tmElUG+lqqoFVPrS1TXVmzfU2BrC0aDzlhBcUmoLknpeSjbHpsrFeC2rWKiE1rIKFqQYDoNYW+S3WCypAphj84clRSkMLXKEI2CV1Wo/oi4GUBsoZTcsCauCElgqaLTEI4zKbR7QNeFuOZbfL7q0XXWMFvCe+jqKzA+WkaHagZGkyN5UlGsXtrJG056Bh984CZ2HTxMOpQj9mPavmXGmCBFGQelhdRoHpkYn6k5t0nwkmmqpcw8irnAoTuX5/iefrpyOabCWf/BYmslS4pzgbksKS3B5RoiYlpJwk3793LH/QcBWLO0l8BxaacJAswp/A9pg58QH952K+004baxhTxcvYI3rnga1z9yLxATpim7Ryces01smuyrbcLXJRaVnvoYydon4yfHEyaRf3vrdt5x1ffQGk5btpAP//6lKKVwtea9p1/An37yG4TtFHEVX9n6IO045t2/fxHvetslfOg/f8DoZJ1mEuPkXPYdsm2/H/rczczprdATFvA6AtqkBDWrq2Icy/0Oqjaho1SWfPQMPRBli6Vgk0FSsktr3QbjCE5LE5Zdq5uiFG3fmjN39+f5x8su4Gu33k+nl+NNl2+kXMrx4JEhPvb9zcRNSLtnueWJGHxA6pajLDFIYHFXFESxIU7NbDLWMFJKiFMLzSAQ54FIWwJLdu7iQumwg9ejqZLamWfWug6gnRRROmOlKNwpjVaCX0kIxgOc2LJrvJoC16oXmpzgtGzDiQaLFzcM+Qkhzk0fO1vxBBab1om9ZhJYCmRantV9F61Qziyn3EzDWgnoSEGsyI2lxEWHuAyV/enstnmDbKyR++8SOhHcCNrdHnFJcMbt4OW2bZdrXBFrw5ddQ7euaXcbK3LVykwtPEMaCHMrReb1dHLX6OFHDX4aSH3BOabWAZA0XRIJ6emv4bUNzZ1dxBW7gQDRlE/ZqXBu/0r+cvRrRA9VLNtHCU6fIe7OTEtTBdoKb31661boqVEKUmS0Qt7zafttUAYx8JSFS3jJmvWcN28xOddjbrnE3vqEHTwcIe/MwiP9FGi3Y1urULBj2zDtjN21f3yS55+yjs1HB3n+qnWcPX/xz/S+Li51s23yKLEYWsblrEXrmF/Zw2C1SmqEV5156sx3d42PMdSoMWbeTj0eRAFHW3dzRv9bf6ZjPRlPoET+H9+7I6u2w492HWRwosqCbuuWcNqaRbzl2Rt573duIUxtJ903799O5f2Gt77pYrr6Stz+wF4+eeNmGmOzXZoCfPCqWzgyWkPEejtOJwGVWnLD/DkdVOs1oji1ZrfZ8h7UjP0YCmvQrA0q1BAoUq1wh5TlVitQkaXBiYKpeos/+eTVkOmTvGDjSazO9/HK//oqtTAjIrdcSCNI7cuVjDso386SXcCd4phkN1uURMQ2vXQHtMeaRGU7S3dbUNoLjWWWPqlSofeeBL8mFPZBdKpHc4G9KjpJUDoh7tOke7RNTjGESxI6c02qOzrIYWGHYFThT9hjj21IrZ2dAUopUnfx6hZDRqws7jSFb5rVYZT93GsrjBYaC421thOsbEAIzE56j4ksWaaCbmALt0VFfZ6m0LRJKdzYJNjm2UIjCm9K0XE4ZmypxnQlqJpjWUKuIhi3eHuSt/BU0NUmPZwHx+LvSU6IBmxyO2jqXLxwHXcfPTyDeetQKM5t0GjkSAMXt5URCh0h19eeVlKAcRd/ygqskWmupwHcdmQ/1x/YzsTkLD1ViUKHikBrojjFG3VIyhoih0kJoZ2jsnSStcf5vP+Ul3Hhtz5F6lptz9eccDrr++bNsESev/Z4Ng8NEmuDRvHOs2dhjcGjUwzsyFHNR+i2QsQgmaPaWKPJ2848h6sO/ADDEFNRnQ7/8Wfl7zz5IpSC/fUJ/mzteSwqd/H1K6/g1oO7+d74FjZVf8TKVge37x/k7TdtouyH/Ok5e3EyLZgD9ZueTOQ/RzxhEvn87g4OjVdJjXVn6cg/WgntBRtP4qp7HmT74PDMjO+WTds48/y1vP0j3yZKUlJj9UK8ZPolg8GhKdR0EjRY5x9lcdFWCfa7TU5b3c/DDxwmLihMYPXCBRBPZQlJMIHgtvTMscWD1FdcfPJqrtu6w7IPssKVaVhnnekR5e/+63r+8w0vphnN6geI0ZRuKhL220KpSm2DThrYpCcuEAnBlGQu8/bfNICpZfDMFcv45o4HUCFIMSU+tY0+6NOx2yV1LLPCFFzaefAnEjp3pui8wmmm9NwWkZR9ppZ6xCV7krErFPsaTA2ViTpcnB4oZhNSez2E/BFF4mrL4mm4SJaknLZgXDuQdW9PmVpsO0MlgPIBQTKutRILmaiulJ7eKubWCv6wptmf0pirrTBXhkdjBN2G8iEATWt5TGo0/oimemKMO99q6jj/wwsgLaXohk+a2HvV7ga3ZUgrQt6L0G0ftzOiuKxGbcIjxEV8iDtmVaSMFj62dTPKWP6/G4NXiNADId7WPDQzDXcEXxyiai+hqpOgmPJ8WAfTmV187Cqk6jNYraK7DTgyw9fP1zXeds2JqxZxX/MoNT8hzrpNEYibLoeTJj1BgVsu/yPuHT3Cjbv28IdfvxqlFO+74EIuWrESk1qPVFegWHZ474M38a9nP5fuXIH1K+bjGk2l6qMUnHnGUjYd2gvAlWeezF/e91H2Ng4jKO4e387HTnvL476vZS/H+06//FGfBa7Lxw9+hwONUQR4YPIgzcO9tJOEMNHUoxwduTZaufTnT3rcYzwZs6H+N7rqTz31VLn77rt/rm3G6k3e/fUbGak2eP2F53D6illt4c1b9/E3f381DVLiwOpt56dSTu7rxzuui5vv3Q0ZwSH1QQe2IULXBCdrxnA8TbESMG9uB5EW7p8YtvrivmJuuczkg5O0elRm9JC52ZSz9nZXEDfj8GWDQuA6fOD3LuZpy5dxyyN7+fP/uoYkNVnTDOQmZotSooUPvOBC3r9jC9vGRkEJ/riQq1oNlFa3PUbcAY5SSCIERyE3Lrjtx85WjQ9ByxAFFicQJcSrQ6K1Ee6dRXRNk5uUGdNmFRlECbW1CmdDFf+7ZZyqptGvrZEzdqBoLjBW+VAEb0xTOqjwWrPHDzusm1HYKbPdlolQHE9xRzWpbwcgpRUitiAcTBlafZkdW06onpCAK/QcSshv9UkCjUqgMcdqwoiy8EoykFB6ROM1NFOrUsJusYqIqSJ/yMHtb5MfaBKN+QS35wkmrKBXbYHG9MZoRwhT394vRxGUFF7nBIVOe0ElVkz+qI8kcy5Ki6lNutmP1VMaJ7U/Mu87xAurpFMuDFm9Gye0BeRLzz+OLx66164qGgqVOhnZZla9cnqAePbClYyMT/BAYxAJhKTpI7FD8ZDQP6fE6vlzuHnHPpq9BqXB8RJMwyFwXNb09nHVy15CM445+WP/QWIMxjWovLFGEOPe7AoOQQ8kPGfxGv7t3EsBODw6xR0P7Wflgl5OXD6PfWMWw17S08WFN7+ZVDK9exTXb/yXX7jb8qzv/i1Jti+N4gx1Ftfv3UWUpvQVE/79ohJlv4s1nc/H0T+dHfO7GEqpe0Tk1P/5+RNmRt5TKvCBVzz3x/7tvf/+XVphjAkUTqTwY8WJS+fx5jdcxEv/5cukgX37vEgxt7fMhees5T+33INuJUhs2QJJDoZ0m8mxmNVzeq3ZsgBtISIm8cCvCo05yirMxdnq37ONQX4ToqKh3Wu7A4+LypzQ1U+zHRGmCYljkNRCMrYpdLpRRBGV4evfuIvfu2I97/7ODyAV0rJBdwRcMLCM6x/eSXWu3TTFvvzmJ0kzZzBOPL2WP6YIC5DOjSAOZj/IKHz1hQ702FmsmZOimy46trPP6TDeMSyKFExOEWk7MKU+xIXZv02vVJQCZ9yhuTArxiUQjBuSDqgtt+5AblVIKinRgMw8kXoKGn2ZrrdgWS3TzBwE05WA4yOI5Yw70z8epDPBW9JEtKE2lCdc7OL02/sZl8FoHzGCXtiyNLvhgHZL0WpXSEIP7aYkuwqQWt9V5YIKLbaMBtWG/ChE3fZ69CxwONIGRnxrqt2VkNQdTB6uG3lkdlDTCtwUXbEPj0z400x0SOG22/aSJgZH5YjLglSU/Q0npDT1FLvSSfLikj+kWLdwDkdUgyNSJ0xSto+McN3W7Zy8dD6u1sSSIrn0GE2w2YHbIlKG4fZsu/y83g6ev/FEqlGbd225gWrc5s+Ps3zPs3vWsXl8OwpY37XqcZP43WO7+ejO6+nLdfC24y6j058VGrp4/slcf+Q+AM4fWMeb1lxAx+05DtWq/PmpZ3DKwG/GrPj/WjxhEvlPC9fVM5grQCpC78Iu7tx1yDqkZDy5C89by7uuvJBmM0Qp+LJ/P11pwOp8Fz+6dReuhsZc4aEDQ7PJT6A60SQ3ldLuVOQmrOmBP65RVQspIFiTCUdbsStg19QYb33319iV1mn4CcmcDHUxhiRvOc5O23oy4kHHii7efeNNGN8men/CYXh1m+9v2YWKBL9gOztnZ7oQlZjFYjV2WwXE4BgHjMVoTUdKvMy6sKtxh8RVtLogqFn4p7nY4M5pogcsJq/H7WPh14XIEXSa4s1rYPI+rcmchUCMIuwAt6EwjtCen6BCjT+lcesQd1gxrdIBaM7JkrhS4EJ9ibKFzaxxJwnAbTh4Q4Lb1qAFNS+ER5iloBy75jCgj7g0ujWlNgQjitYCC32oROEvq2PQGKPIuxG0Z/XbVcqM2TRGIYUUJ4jI78qR+hBVC6QB+LG9dlrApKAC6/mJAre/iXckh7eijfIjJlGkiY9a2qRQCO3zEGnSeyq0e5KZeybaoLtiy0AUq9Ge1jxINDoRUpM1n4nVhY96Q1wRlKOJsi77uNuQO+KybXCY9RsWMNZsEacpUZzyD5+/ESWKd7ziqfzrfbcxJDX7fAKmnFBs50GEuBLheR5vPmnjY96lP7v9a2weOUBiDLcc2c3tl7yet697JbePPoCIcE7vCfb5Hhxl9+AYp65ZwJf33cdnHrmbRR0d/MMZF/LmLZ+lbWLc6iBhGvP+U66c2f9fH38ZF86zsMnJ3UtRSvHup/58zj9PxmPjCZvIW2HMX334Wh7cfZgNa+bTCmNG2m08z0FrxSXnn0DdxGilgZSc57J+xXwe2XmUv/jLL5Maw+olPewL2tz1w51oAQ8oDSa0u10oKlzPIUlSgqEEN4TisDC2RmhXwB+ePZc0bzsxjcsMtBJVFFvMKMEYSEHh+0LfHU0wsP+yPOIqkpKFFy4IFrLkjAWY7+2dGXSML+gQjFjKXfGwneUmRcH4Vmc7mBKCupAqiDtto0+7x2L/uWEhVwVSaCPEbQfGFH7DRTo0c+Z1cKAxQVQ2GBxcx8WdhPzDPqqqSXoS0tNqzPkgEGcskzUuaoGlWjhhQtThEpch6kmp7HCoL4OwT6weSS4l7NBEXVYWdjaEtJCijJ7u5bEKjpHCyTTMBUVtrEzgMLNiEMkKymKxdn/ExXjWjEIBwbAmKQp+HdS86WMpHN/M6lHZj2b/NQpd1+Tuz2V6J0LagKRg4alp5ok21k7ODu6CxA5JJ3h+hAD1yYAkdvBz1m9UacAz6I4E2e+iFysMgltV0GmPPT2p9aqatDchLQrmqIPvaGIxJF2pVbwURc4N0fgQOkiqac1LSbtifhjtZn33Ao6O1miPhjRbMQoYPVDj5pe/mude8zn21sZIxDCnXOIzl72QNT19DDaqdAV5it6j2+X31ofZMnqQOBtQpqI29TikM8hzXp9Nvl/d9QD/3903Up8I6d2VJ+w0jM21Bfrhapu362tnhtxEDIOt8UcdQynFKT3Lfsqb/WT8IvGETeRfuv4eNj98gDhJuWvHQd75ugs5Zc0C7t02yJL53Sya142I8AfPOo0btuzgzDWLuPTsdbzjPVfTbNkH75HxcVpFmF74KcCNrGFwXin++opn8pFPfJ9a2xYhRYEXCZJaTrVgZ8L1BYbikWNmjGIZFFEnlPckuE1Fx6EEvyk0+o7hxmoolgI+9LoX8cDg0ZmZmi2ECT37fRYt7WbP/lHc1nRhTzAdhkrbQ4WGmBRXK+JA0e7NdM9RtAbs4KJTaC4VUk9Dl9DaEOLsyTPUrPOs45dw7d49KATGXcr7HaTh0OoW1Fl1/EHsLD61hd3WPG9mhpzmXPpvG+fACzrwJux3uh6CsNM6/0R9ilRBfkhhHGj3WsaPV4V23+wlUFhKpuQEk1gNm+lIHSsgpmMrB4xk/2ZJNc4LOhQ0lrPu1oxtEpryoCMGBf5RIW5Z/RfjiWWLZMc1kx5OaJk2OtNlUSmQGqYbd0QJppQyqxWL1bAx9uWJQ5cktL6oUcvD9VKCfGYgHWlUS+EOa1QnuDWHeNRg+hI7IA35qFBjxl2kO6WxIsHUXFTeEFdmh5+o7rNuQReHdidUnRBBoac8CFIeHh62kEkBci0oaJclA13kXJdrL30FD48Nk7QNb/zUtfzeli/y9HXL+ZeaWG5TAAAgAElEQVSXXTzThzEd1bjFlXd8DHESVOrhaodTehfS4ee4detu/ulzN6Lyiu3HTRCLgQIcWdfK7OBmYbqRRsiGBUu5d2IvIsKrlj/a9f7J+PXEEzaRt8IYk80cBGiGMZVSnqecNqvjpZTiVRedwasuOmPms56eEp7rECep7dLzHeKCwmva2V5S0bR7FaED337gEd78qmfyjvdcbXHawOK7HQfBpEJY1oinWHYozyVXHM9VX9pMtU+hY0NcytxfBJxIWLWgj/2TR8iNT3OCAQXhUMgdu/azqL8LFUDX5hC/Zqgvdsn15dk7MYGUNVEgePWUYMqgpjSJTigWAzqCApddeBKnnL2c533qC8y8VA60MxPjtHOak26TKZ4QNVJuveMQXt4mqHwYo2q2W0inCnN3JyEwdGUN+hLcu3OWfinTmCscuqQC2uLO1ZVQOqQoDAsqUXg1TW2JtklbIBi3PqhOIjSWqlkVxQzHD8YUrX5mIa2sc9aJssIggk7tRgoQsS3+2mAlE7TgNA3tAUgPFjCTKR33g19z8ZQwtDEh9bPjRQq35aBaDtJyMp6/oDJlS6UgmR+hqw6SN2gvoXifUFvm4NXBOeyjlCLcV0T64tlrDiRtly7l0DjoQsMFN1NmNJpofoqrfU7wF7D7kXHCrONY3IzO6kHaJ1ywaCXfPvSwPdeWxs3F/NMZl3DZtv+eqROIAdXSM7i352gWLujg+RvW8Zwz1gIQOC4b+ufx+s9ew2SzhQj88JH9bN03yCnLFjzqfRpsjiMi5IIEzzXMy3fzmY0vIUkNf/Uf1xLFKXEoxIlB1R2kZG38xEwXYuxE5i9PfjoXLFjFztpROrwCA/lHu9Y/Gb+eeMK2Tr34mSczp7uMoxXL5/dw/mkrH38j4I9+fyNnnbGcBfO6+IsXb+TSM4+n67gu+k/qo93t0OjOhJ5EuHvPIfzOgFe8aSNT6z0m1jh4dWvSrAC/aXHZ55xzPK993kZecN6J9O1K6NydMGdzhDclRJ0OYbfDzqkpFi7uJdAOK44UKY4oiocUwRi8/p++yheu20xlZ0Ju3OCG0LEroTHaJE4NqbGwTVqy1DuFQgxMhW0eXFbnQ5vu5Hmf+AIZLA4IFC1pXRzsjDAGXznoVFPAIxiFpm/wxxX5o4q0YaVGZ5yOshZLM1yAHCRntTKcWxAlhD3GaoorwLe4c6tPMPkUyadEHdNQhMURjA9Bw1AcTOh+QNA6hUQIRnXmUmQZQMdC4U77mOIqj55BKrGsHZXapp5gEopHoW8L5EeFOK9Q2eMtQPedtsEGLThtO4t0IgimbNOX27LeFDgQrWshgcH0xLhpQt/3LP9eieA6BrMyJOk1SKgJB3P2oqdAoijcFVD8bI4XRuu49m1XUl4fkM9bkTLdAn1EOLB3kmcdn004LGY027+A4tlz17BgrBsmHVwDl8w7ieO65uG5jmU5ZaOpFAxaK/Kei+c6fOSPLufKZ532mGJkwfdntFNEhJz/WNGqpaV+yl6OvOtRChxeseJ0PO2QpoY0zaSfB0DVHStQPx2u0F8scNHS1Xz9uS/lwoVr0EqzujLvyST+G4wn7Iy8t7PIN/75D2m0I4o5/2emQxULAW9/y3OoNtr0dpRmlpg33rWDd338u0TGNh0J0JKEV3/u6/SWixR78oStBEVr5u+4ipzv8pQNVrP5zjt3kRhjtcxToXhUZhJZwxcu/cNz+e+tD7JjcJSKBDRaIYVRQRu45itb6c37tE08s3+dgFYKRytM2+CNYRt/jIV0asugPSBMpoJkrkA4ICl4KsVvuuT2huRuj2ktAV1RfOAtL2XnxCTv+/QPcJtWX11hDZPTrBlmNgS8bGgQ0CSEfeCNasvcmZ6MARjwO1tEz852sCeA8cLM35WxHZ7tRYba0xL74IUKNZrPQGXrtqTFNuW4dTuoiENmupD9RgNeyxpo6NTqqU8bdRhP4zaFwhFhch30n9XJ0S1V6+IjmsI2SAuCiCKp2LHKCZkRx5q55m2NFITiZo+eB2OmVluNdJWAdMXIkfwMuB+IwR3UmBx4NejYkRADt965k++u20kzqNJXCaimLtFeByWKuJWw4PgOnHnQMjGSamvsoRR55fN3X9xELQrR3S4SwXUP7+XZy/YQxcnM6gSBV244mb0Hq9x56CDr+vsJXOcxbjqtJOIvnnMOe0fH2NuzG6c75jPDN/D3816Cp2df/5zj8aVzX8tNQ9sYyHdweo99pnOBx8ueeyqfueYu0gKoyEEwkChwBUcr3veUi9g4/0nc+38znrCJHCx0Usr/fFzTrdsO8dp/vMouEfOa56xfxaHto2w8bzV/++pn8aNtB/jRPTuY3D7K2MllxFWM1hs8a8kqXnrOesaeMsVXvnY3pXLAhtOWcNrxizlu6QC7945weLxKY54/k7xMALo1e6637d7PtoPDxKnB1Q7eMbNMZWAkiJhTDGg0Q5K8Iqpo5naUqO6rQc0qMKaBZTUkgTC1loyZckz1DKHT9dEHHVpBittO0QLFvVAoatwp4fdOO4mt9x7i+ge2cyw3MQksg8atQ5oXNAazrAEGooN52istRJN2Gir3urTm2nxmFOTHFM654ewab0WIvqWAEwkYyI2lOKEwsSE95qkTKAg0s/pDKDiJwp+yA1VjPpi2WANoDeIJxhVrvhFbGMStW+cjRFCpYJQQVqBns8ModXty2q4OvJpkImIKZ0ww2koX6GnXIbL83FbkduYwHkwttVi8KIVXU8ix+h8CBkXYZwvAOrQyBRILjTkxk3EDzzPUTILBA1W22xhh79FxvvX8V3DFt7/M8EjLthE7UAtD0ljbVVGGzrWShC/dfz+e4xCmKQpFf7HIyR0Luequ7xEbw71HjnLe+z/O8ZV+PvsnL6IQeLzr/q9z7eA9iCjmrhzA1wmhSblzdBfXDm7h8oWnP+rdqHh5LlnwWE/NP7v8PPrXdvDOm2+k3RS0aExVsXagl49cfAmLKl0/1zv4ZPzq4wmdyH+ReP/nv28lbQFpGa6+fzuV/QkHDo7x9rddzNte+nQu/+AP0ZlcKSi00hR9j1MWzoeF87ng3P/p9g2DRyaI847Nidks//SVCzk6OMlYtckLnrGempvMmDybMKXkuIhKMVluyK0q8/H3XsHHr7uTr93zIEopxvZXCap2VhzlIeyyCnw4MmMRVzxkaMy3/HVvUujqzZEOwNRkldpCj65HQvK+h+O7vPGT18JnFOuXz6d4WGh1W3606KxbNBX8JuT3WgaEbCsgGva/iJl6n2gQpSjtnR2InBZEbQX5jPAWKfxahmkLxGXHQjFhMtOajgJC25BlHGgOWN0VZawYlvZAQmukobCFS7eZbdZpj53mhBjLNBlI84zX2+jU7kew7e5pZuc2jctPy83iKZKKvR+5TNHSaRu8fQHTXbhRp0uaSyntwLbrN13I2xmxEbKVkP0tUSfUlrjUVhpMIHjVAL/XjuSqIwEtGC2Ecwz35Y7yrR3bqR9NcRLH/s6K4vdPOpm4P+Eb9z1ErOzqMO+6nL5gAWPNJjvGxmhFMSONBn9/601ZdTy7L8De4Qm+tWUbZ544l+sOb80YMsLheIhZREVmVp4/a7xwzYk8f/UJ/GDPHlpJwtOXLqXwpEnEb038ziXyjlL+UYjA9HuQpoajR6esYJYxBOOG0v6QxpIcq+b28Kcbz/hJuwTg1PWL8dBkSiko4CknLuMlbzll5jtDk3Vu27aPwbEqEgutdozT69AxkGeCkGQk5vI3f9Iu8d1pTDOrCTrQHMhkcAVEaz7zjMv4r2/cze59eynvIxOV0oyOVWnMBxxFc75PVNGc7feztT6OaUc0e6G2dRdBCrkxQ22Rbf8Hu49mD+RGbEJVoqzaYGww2azf4tKzwlCCVYHUd5Yx6xsWv91WzK6DTXTKCMbTBNsCjBcjBYN7yEdXFc2BGNNjUA1tC66O7Yz0JgxuVdtZtLLCWjrrlA8mhfpCobTfeoYqIxyqhKiCojT46HsjLuh2Qs4YxPVsQROFToR2KaW8z5mBV1RLW9OJLDmLJ7TWWBYKUy6iFPnDFmZJOo5x/wEQRWNROsMciiOPJApxItBHPEw+pt2nwVfsqU/wgQduw0ktx91XDm845Wxec/ppiAivOG8DW48eYdO+PWyYO5c/OOUUXrFhA5d//gs8Mmxb3IdrTRZ1dnBgcgqdgtsA5ULOcwmcWRxcBEzq0FMsMRJNsqTUz8XzZ5/LnzW0Upy//Mdbvz0Z/7vxhC12/qLxjj+6kPkDHeAo8n05+qsOhYJPsRjw1I1r8H2XV73pQlxXM2dXyBsG1hF9eievesb7+Opnbv2J+y0UAr72qT/macctY9XcXl73oqfw4mfOLlNFhPpwnU/8weXM1Xm8TFE0SaE2GpGMGJJqjKqnxI6gRPCaKW4jJSyCLjuzmjBK0enmWFTu4ki9RuIqTMHBBA46zWzkFEzTQpKywy3BOLVuodVjaXhp5husDRQHzaMKk+JC2OmSBBZ6mFrmUno4oPywQ3GPQ+cWLxPpkpmRMMlDjIPcXcH5YQVVdRBtoQ4RIfUhKSiSgsbflSN4oICecEg6U8zCBIoG6U2sFydWcdIbs0VSyYq4oo+hgQvkjiqmVigaiyBxFU7T/u525ywhJslB3B+TnxCC8UzJ8Zjka4rT35yNuGgHJ1FCe3EC2bWSXNbZGSSonohCw1hRL8sZxWuYxzwXjdEcelMH+t48xT0eTsNgsuOJYeYaxmnK6QMLsturWNrXzUWrV5EMx3z8mjt5+X/+NzsmRtgzNmFXMJ0xpi/kUG6E+St9ehe4lAo+T1u3jItPXsvcfBd/vPKZKNGkscMyZzlf2/gGbr3g7/j82X9O0X2y/f3/UjxhtFZ+XVFvhBw8OMbiRT0UCrMPdxwnaKV46fn/xOS4Na9wXM037nwHfvDTraoe3HuU13/oaurtkNdedi4ve+Yp/P3ffI07b9lBixSdWnNik3Nxxus4o3XiroD62m6CKrQ7dabHbRNX4VBM94pOnMUFDoxOYkR403PO4x823YwJE3q2yQxGLiJEHZraAjVb9DpmCeJOpfRsi617kaOoL3IJK9n0c9p5J7MmS8qCqaSkc2OcCU3hYR/jCq0By+zxJyE/nKJjRVJSBJ0erVqM0trCRcdUEdWiNrorIT3iYyb8mU7LxtoIUzIzh9YTLs64CwJujVnrPLH/FWTuRDmjGFphLLQhNtn7IyAF+33dEopHjJ0NVxKcYY0yirCoaA5oRFvp2va8lNJOh8KQPYHmHKGxGEjAKUVWETPRdmUUapw2oBzQgtvTxr8/oLFYkAAwUNipaC7FrnAcIT8OxUfcmcEjdULGT9P4rosaScnvd0hzCpMTetaUOWv+It79lGcQOC6fvuVu/v2G24kS251s5go9fpkjzSqmN7IdsK6VQXCV5hnzV/Hhc57/mOcxTlP+ZfNt3Lh/N+cvXs5bzzhvhsXy646d1SN8dOcNlL0cr1/zHLp+BuXEJ+MnxxNea+XXFaViwNo18x7zuZfZUAW52aTtOBqtH38R83efu4GJusVGP3j1bWw8fim3bHqIyAWnkaAmagQK4jll/MEpS2trxuQqOXIr5nCkXifOK4vJipCUNGNDdc5cM5fff/4pbFg8j/dv+iGpCMrViDKPmqnmh2Ma/YEVeQLLtsCyUkpTxuLQgEoFt5EwcZxH4WAGf7hWYdFPID9saPXHJHkhcVOSkkKlimAcwm5F2Cc4saZ0xDoJmWZMACS+wZSOKcAqITixinLBLGzSvr0Lphxai4Q0UNaYPsv5uqopHBLqi8B4ChXKTNKPCxB22qTZrKeoWFtDaJXplGcdreLYwqNfh+TUJq1eofDdQkYZFUwCo6uTmWvTLkHightCY1E2KLqCCR1LiazaZ8AEhrRsUKG1x0smAryKIRh2aC+y4lTNJWRcbwUpxMVjZumpUNkXo7tyfOwNL+aGqx7g6uo2Ii1MLhOatSkOP/AQe0cm+NSlz8sUO6cJpUKUpnQPBKxmKTfVd2AyTj1kXZSNSbZNHmFxqYeCO4tff2fPDj774FZaScyh2hRrunu5bNVj6zy/6ohNwp9u/gS1pIWrNEPtKT5y+mt+7cf9XYxfSSJXSr0JeB/QJyKjv4p9/rbEX//LS/iHt3yZsB3zundchus5j7/RsSHCX//hJ0nFMjJ0tY5KUptUBidnvmYKAZ5bYKFX5Morz+I9V93I9MpbtxOmFmpu2Lub6/fsothQ1MuZNLqraPZDcciC6W7DYrR5o5G2EBvrZRlMpcRFRbtDUZ7t38AJLY2usVzAgZ5hj2g0pTyY4DWE0mGH+lpDfYXFhdF2buk2IcqSn93RLOzhRpBkM2hRoEopygWpa/hON7nQ0gj9BkyWHWTSQXmCbmtUaAXMjCNE3RCMgDa2IzMuWXs3FCQ5y3+fLkrqWFAxFrISK28gGvTOgKTqUJ+ryE3YmbuaEno3O9mqBCZXKtIK+IU2yvGn5eZxasoC7NmApEONKRskECS2g6aJpvEewR8Hp65o99vNwDYrGW3ITRq8aoQsSeg8B17zua/SE5T52N+9mBdv+gKkCt2wM/8tBw7z1E98kg2VAYoFn8l6iASC2+lw6twFvOOMp/Pp7Xfz7/fdSkiMaINSikPtEV7xw0+Tczy+/rQ/pi9n21iHGnUSY5uPEmMYas6KZf06o5GEtFNbNUrEcKDxfyo1/FbFL42RK6UWAhcAB3750/nti9XHL+Az172ZL/3grzjraWt/pm3e+coL6C4X8FyH5yxbxNHx1gzMIeXC7BcFxHcxriZe0EMzTnl422E+/8Xb0aFBxYJbT2l2guSzhh2tqBcsZcJqlBjCkmbZ8l4qsUK5UFvk4ehsRqyEjv0J+VGhfNCQH4epJQ5Ggylo0pxD7z2KNQfKvGfJRszBBGWwhhBitcZLj+hH2bEhgooNhcMJ/riZQZiPBelSLZmBsFCvOIw92Eu6pQRRdl7auiq5gwrRGl1zILbiWo3F2NVEaoh6oDXPSgYDs2xJR2XSwfaoWilKrdkagv0QVNOltM8WT8VTM7K8OiXrilTkR7Pi7lGfSkeN3KhQHBX6DjYzZtBsE86x1wAEYk3UZSjuUvTe5dL1sKb/NgUJECqcKZ/qSofSmT20X2eoXi6MqhrRwkn2tid52be+arskM264yuoUk+0WP9y1j5YknHL8AhYt7+ZFK07gL0/dCMCZlUU4D7kUH86T35HnxUtOIJSIRhIx0Qj56AO3z8zmL1u5ls4gR9Hz6AxyXL7y1z8bB+jwCpzeu5K84xNoj5csOffxN3oyfqH4VczI/xV4K/DNX8G+/k/EuiUDbHrfHwFwy3e2cisPzPzN5D081xbdLn71U/mvPftIJtrk6mqm9Xy80cJR4CCQCFGPN5ucZFbBLz+UUDxqEA0nvmQRb3zvlXz4utu5Y8d+Fi/s5ppdjyBtqx+isCyUoCpMLhKqfRHl3Q1ycxcStiKSLaP8x56bcHOaNJAZz1KAqKiIjE9lypD4CrdlcNrC5GqHsFvwW4ZSEmBGE0SsNIApalRDaM63PG4TO0TVgGMEdFFYaqA4irhoz0/cDF/Whs5dkB9KCbtgYo3GbUNatk5CKhXcekIwqZmzspe3XraRj33lNnYcGLEDXDJbNwAhP5IQd3o8SiYTW6SFzEKv5eDc0kFBIOxS1L0yXQ+lNBZrojIkldRqsUQZ/B85hJ1A4lDaN1sScGJF5SFF3GkLx0kgTAYhsWTNZjUXebCMLyA65aXrT+Hz8T1IY5rfKTPwTJOUzUcGERGGxpq8/uSz6S0W2fTQLlqx5Z/6XTEPj+/D14rapEcyGfDFoW3sOVznU5c8j/5iiVuueDUHapMsKneS9356jedXFUop3rvh5TwweYCiG7CyPPc3ctzfxfilErlS6lJgUETue7zOSqXUa4DXACxatOiXOexvTYgIN92xg117R3jq2atIopSt9x9g/QkLOS7D3c9+1kl0fXgTE2MtxFGcsnEV7/rb55GmBj/n8e23fYpRBaISnIbBdTVnnLGSG+56xBKVjVhzZHtAVGzNjMMuKA6ZmQR9/y170C8/n2eevIraRJNrv/0geW2oL8w6JZs2OYQVMGWfdsFFpYbLFs3l9hseRkaaUOrCSTS5SWj1ODixIZ/3GFqfYHJQWwLl/ZDmNZOrrCJj7byQxBESJ+V8vYY77jtEknHF/ZrQPLbLsEfhtAU3a72vLgSTV7i2lozTgrDXUv4KI1A4ageT3JjQsVtYvKKfXeEELZNQ2ZNS2SfMn19hYXc/b/jwNVY0bJEiN5pCYmUJwCb93FCbpODablEBXEXqCNVl1jvUrUFuVKESRXM+VqMmAFEO+aNCOJAy72bQiSJ1bcPU6HEgOTuDTgoyI+wlkJlyWwzLiRS18RA3LeAsb2IGA2tjh2WubLp1Fx3tPNVcYpkxCjpjDxVoak5Emq06FLB9ZJRzi0XWzZ9DznNJBqZwFrXYl1bJ+Q71egcIhGnKHYcOMtxoMKdUIu95rO4+Rq3sNxSO0qzvWvIbP+7vWjxuIldKbQIGfsyf3g78NRZWedwQkY8DHwfLWvk5zvG3Nr5944P823/eSDtM+PLVm9HVEBPFOJ0F3vGXl/Chz9zM4aFJzjxrOe94vuWhr1sz71Et1J/7fy/l2tseoqOU54zVCykWAjZ99wFuG7yP1FW4UcrqFXO4rzpq29ObCp2CMyyPwjIOTVX5wI0/5NO33E1+p+0CdYHiIIyd4ND/o4R2t6YxfScdTVry2XjKCm67/kEmTujCEaEYuNTjhLRLk/b41NsJJrDnG3UaWrUEHfmUDmriLoNuKEyXEJGyyTyCWu3h1BXu/fbk8kehNSBZ5yPUFtmZpkqFuZMBXVJi39EJ0gIUG4pmwzC1Omt2ykIJUIqZvH+c512+jht+8BDBYAKOZmi0xoHbdhLOzyRAlLJNU5FBPJuYSwdaOLGQG2kwfGYB4ytKI5p6b+bTqi10k9bBNI+5wdqyScTTVLYp6gsTSgcySCvWKDWrgz+1HCp7LNun1Z3BONPc9GwNEtYDcrf7qEgQT0ArfK1pthPC2BDEGjI9+2XzunnVM0/lh4f38o37dmLEtsMf198PwNOPW867nvcM3n/waqqOYLBJ87iefh4aHiUxBk87dOR+szTDJEnZ8vBBKqU8a5bN+Y0e+3c5HjeRi8iPVX1XSp0ALAWmZ+MLgC1KqdNF5Oiv9Cx/S+Oue/fSDm3G0YcncfaM4igwxYDPXXUnR4enEIF7HzrIJRecyLmnr3jMProqBV5+0WmP+uyH39+GExqcEDDC1D3D9IYJoiCeEzCwpJu53RUO7h5hfHCK1IHRxfCRW+/CBEI+24/CFh67tqU4KeRGrdZ6mrFcrjhhLWdccDxT37gpE0cSTNt2dOYnFK+88FQ++P07qDwSU9nepnDQqlolZY+xM7rQsUPukRzh6pC0MyVMU9u0WVT4C1wIFV7VzrQBS9HLColB4LHsuAG23XcYlUKuqRHAa0LhiKI5IJT3Cl7TuiG1V6Z0j2jO7p1P/0kuX9t7N8ZYQbFpTfPpEAdMkHHGXcUzXnYa93zlPvZtdEgKFhefzAlei2M6wyCqCGGXwq/Z644Ct5Z9wUBtuRD2Q27QQVDkh6E5N2PJxEJcNASTgvgOntboMckke+05iiMkSlEawmq95OHik1bxgwP7rLG4YGVzew3Vrhbv2fd1UpWSX+hyxfxzefHxJ9JdyM/8zuesX8tIeYhP7Pw+YZrQTGL6aopnr1jFRLvFG886l5z7m4FRwK5QX/vuq9i5bxgjwqteeDZXPPe0x9/wyfil4xeGVkTkAaB/+v8rpfYBp/5fY638tHjaWau54+49pEZwBidnjQ8aIZ2Bl828bYZxnZ9cV96y6xDf2byd4xcPcOlZ61i9eoD7t+yb+XvFdYgB7WjmFjv48HtegZexZ875548y1sgyJUJagHY35MYtnbA8JqSh1dd2DHSOao47YT7/8NpL6Crlmaq3Zib2SsiswRTGCGk9ReVh4Ft1i5eLZZvEZXcmISujyE8GVPPtGfMFYwSJFUor4s5jcqXKJGiVEEUJ9993EBNZyMeIwfUcVKrIDSmMoxg5L8HLx5hA6LwnhwBfunozuw+MznRn4ik65hRpTjVIXUE87Dl2gJq0HZ/XPbgLjiuS5KOZkxFHKO0VmOPSTlLcupDm7dw57BCcyHZKuqG9hUqgtNOboUK6421wNH7VobZUUzhi6NqRog30HFbIyWXGkia5USx9swDigZNA0qGsafakkF+tec0lp/HvN91OXlwmy23afspONYaaEEqVECcfUexLmFepPObZeeWyjXznnj1sGz9CMh6wl0ne+vSNnLF04WO+++uOkfE623YfJcrkea/67tYnE/lvKH7neeS/TDztnNV0duTZvW+Uu3F48PadpElKEHi88c+exf/7t++we/8I55+7htM3LP2x+9hzdIw/+9A3aMcJ375rG6kxXPknT+eH37mPsbE6Okp51Z9fysoNixkdrrJ67Tw8z8EY4d3/eA3J9jpqiYNylPXx1EJjoWWEOKE1a/6nf3wxUZKy6/AoE7U2L33uqXSV7Myuo5TnwjPX8L3NOwiTOJvZ2ixdVRH5ulj52pQZFow3ZW3jMHYIqGw11As+JrA0Q29CoTI98dkOU5tB454Yp6oIjrjkD6a0ehzivFCbr1COYX6hwshgFSXgH/ERx+W0BXO55OmrWbGoj79451WIsdzytKAIOxyW5PJEh5sM5Q1xxdIBRQlu0xZlmxVrMpEbg3aPPefiYYFAUGXD29eeyye/cifD+QzPyZqZ3Lq2VnoKon5Dq1fwJ1wwQnnHCKozz9GL+jCINfHIKONaKYbGaqAcnNiKcoW9xt6TlkJQeIX/v73zDq+izP745525LeWmJ6SRCiFAEjrSOwIqVVFRZNW1sLZde3ddy8LaMJQAACAASURBVK6rsv503XVd+6qgiIhlFREBRQQEpFchkEIS0nty27y/P+YSjAQSweUmOJ/n4XnIvTOTc4fLd2bOe873qJhcMGxIKtdtW4Ij1K0P1ga981MCHoGj0oLNohB7AktYIQTdbJ3ZXVyD1DQ0kyQswK/Fbf/XBNttWMwmnC4PZpNCaucIn8Txa+QXE3IpZdIvdayORJ+MBPpkJDBhWDov/2kxZUWVzL7zfKJjQ3nxr5e3uv++/NImK91Gp5vN+w9z4bAsXvroVvZsySG8UxBxSfoiVUzMsf/MO3bls25DNn6NGtZKjX4Dkuk0tBPbi4vIXlmEw+zGbRbUBKr07p3IY/9cyqr1P+B2u1m6dBtXXTiY8ydl4e9v5aHfTqBfciyPL/gCt6Jhcguc0SYWbN+B009gNyl4pIaQXhOpAAv+ebr/q4oJpVGCx4S53IQ9X0Fq+nQfZ6ieT9ZMGlIRuII13T1QeBc8JfiXeChP1zstJXC4vgbVxlFjdYRLsHNHETOH9aZ7QrQ+eR794cBt0x0md1urqO+iodSD1DSEoiDc+mKrVODohcRaoT9RqA69G7JsoIbqkmwsLaQ8zI3SqLsXCgn2QxJniKA+GkIS/AiNNpO7tRrNDCAoHRvJLHsS76glOD0e6mNVAvM1VCEwmRSCkgIoqWxAdUicQRK7ZsZVpaFZwCQFk0b05IKBPQgItyK26Z/VIyV2iwW3ppc7upwarhoLUihsPVjJBc1nQTRx17kjKKurZ2teESO6JJEY5hsfcKvFzD8fvoTXFq8jNMiPubOG+ySOXyPGHfkvhD3En1ufmf2z9+vXNQ5VUfCzmJFIJg3Qa9UtVhNZ57RsUFRSVcv7m3ZSY9f9xM2aQoTNj3uGjcDt0Rj0xXPHSu8sCqqqsGlHLg6HC3Othwa3i5dfWcXy5Tt48YWrEELwyCcrqI0SuuuShKO2sB6LQv6kEPxKXLgCFUwNKtYqmtINtgaojNcdHC0NIL0+L6oH7EWChiB9eDCA4lJwh2gE5CqMTO/M1op8NI/eaXoUTUpMdd6Wd/SnCkuJ5JvV+5l2TQ9mzxnMK4vWoqngDlawh9kodTfgCNeQnXQ/c2u9BgES0yEFj1vDUqVh7mSjxu7BFaDhEgKpepAmiX+lwqrqQ7jNAr8SSWChB00IapJVAjUT9gArY5O6MH+DPvm9aY6nzcq9d8xErFnNf7ZsoTFcob5fAJNTUllVmoPNLQiOsVGs1eMOcWPadcwywS0k7+fsZdGhvbx/zSzGd+7Kstx9gGBaZAb7qstIjQhjyfbdNEoPLqnxxsbvGdsphUFpx1d82W1WNI+kweHk8537KKmp5eXfHN+qfybokhjJ47dO9snv/jVjCLmPiQwOZNEDc1i3J4e0uEjSO0eddHuPpnH53xZQWl0HYQpmm5netnDm/nYUoOfiZwzP5JN1u5AS5ozXbRl6p8ez8us9KG49T+1yedh/4AgulweTSaHOqjXZ74LEpqo4PR5MEtwWhfo4K2gSq3eWrvCmTEZPzOAjJRsa6pstHAIkRYex21He9LLiAP9cFZvNzNiRmdzzm4msXv8D/9i+icOiHg8Ss1P3GjdX6AdSvY6HxdmVvL1pK8/t2IhINzPAvxOjEpNYXpbDkdL8po5Pk6oyc0gGc/ucw4a0g/zzra8JsfvxyK2Tuf3LZeRVVtHY6CJhnRtrhcbsm0bycOF3ADREK6gRFuxmKw8OP4fBiQnYzCYmPvu61+BKb5UXQvDw1HOxWM1MSU3nw5U7aXS4qFPdvMxOZKR+kQs5pKB110DTP8PRChcpJLUOJyZFYcW+bJ4bPoWi+hoWbt7Jy99tpMHtZuehEgICLTS6GvTzWSf5w2sfs/bPN7Q4RGXN/hzcmoYIcLPBbzuXrC7i4azpdA/Wy2C3FRVx+2ef4dI0/jJ+PIPPkhJgAx1DyNsBUSGBTBnUs03bVtc7KK+px3M0Px3px4uPz2m2zZheqezMLiI6zM7lY3UHRrtN996Qql76p6oKKcmRmM0qTre30cWrwIoHHrhkDI9+soJG6UGVAptDgXI3qvPodrqYVFbX85+rZ/KnVStwh7rJKy6hUfPgjhCYU6xMsffgs+/34kQfiGASCqO7pzKhbzdMqsKFF/TjvAlZfJebT7ifP48tWM7uuhIabJruXBgu8S8XDOiawOPLVuH2divu1Sp5fdogRpV1Y9b8dylEbztXFEFGeDSxAUFMHd+LqeN7NZ2XxVdext7iEsIsfuSNOEJ4pJ2u6THUb7Iw79s1BNtsvDJtOt0jj9VbVzc06p9UX6Ulyh7AUzMn0TshhmW7f6C8pl7vskWgWY52BHm1N0IjyR5C0cEq7KWC+kDvwrdHX4w1qwrdOkUihCAmIIjtRUU0uPXHE5NQuW/IKB78YBlup4apFuqEkykvvcnNAwfRNzGWiNBjBlQZcZ3YVVAM3SoQZsne6iJu2vAmX467G4C5H33EkVr9HF3/4YdsvemmVqdqrT+Ux7MrvyU6KJCHzhtLiJ/tpNsb+I5fnY1tRyckwEZqTDh+FhN+FjPjezWfVVpWXcftL3zMntxi1mw/yF/e/hKAmKhgrBYTHpuCCDBzweQ+PDPvMgAsJpXpgzMwW1RMFpXrJwxiQmYaTkXTC1U0icmmktYpAiyARyKcGqY6jZ5dY+gWHsH8Cy9m4RWX8c9bLyKgRwCOMNhQXsiqkkOkxoTpa3dmqLdpWMMtzap4/MxmRqYmkxHbiTnj+qPaVFAEqkv3d3FFK/QZntzMsc+k6lU7SeGhrLrhGiakdMFusTImMYXpJzCEsqgqmTHRxIUHM2h4Gl3T9U7Dq/v1Y+fNt/Dttdc1E3GAID8bj00fT3igP6lRYbx61YUMSI7niv+8x90ffs4TK1aTlhSFv8VMl5BwrCbdc1e4YUhkZ1ZMv45Fk2djkwr+pZKgCkG/xDjOTe/CI+ePY1hqYtPvmpmZgZ/JhJ/JhNoAuTnl3D1iBP4NKgKByw/yt5Tw4NMfM+PWl/ls9a6mfV+6Yjo3jBrEj2zIqXE1Nv29zuls+rteJtq8lUPTJG7PMYOvqoZGrluwhE15BXy+ez/3fvh5i+fUoH1g3JF3MIQQvHbLJXyxZR8BNgtjMpvXppdV1Tfdabk8GjlHKgC4ZEp/jpRUs23PYS4Ym8nMyc0HCzwyfTyXD+6DWVVIjQoHYExaKmuyc5BScsWQvoxNTubavy5E1juRUpKWEcul5zU/Tp+ucdQv8+Bx6UJR1tBAvJ8d7ajXmIAlW3cxe2BvukYdX9VwsKQch8ubNJeguAQmVUFRBH+bNon7/7scq0nl/6af17SPVTXx4qRprZ67ytoG7nnrMyrq6rljykgGdG1bid7kXt2Z3OuYz05pbR27ivRFToDsqgq+e+wmAHKrK3l7+2Zi/e3M6a2fmx5dY7h86gAWL91KcudwHr9lMkH24ytLJnTrSlxwEM9+8g2b9xfw2sqNhAT4sepP1zPi+ZdwNriwVOtpG6fLwwsLv2HScP2iFWizcv3IgZj3N/LCDysAuDFtbNOxHxw1igeWLwfgjmHDUH/k4rlmfw43z/8Il0fjjgnD+M2QfpTXNzS979Y0DpVXtOlcGfgGQ8g7IP5WM1PPaTkVkxoXTte4CPYfLsWjSa6epM9lNJtVbp87vmm77MNlbNqdR0ZqDLERQXz21S7sgVYmjuhBeVU9r3ywljiPjScnTyA6PIiM2E78++N1ODwepF3FpCqMHpHe4kT26T26s3D7DgAyOkVxx4ChXPHGe0cLURBCHK1cPI6JWWm8vmYTEmhwuZABkkkpXRkc2xkhBOemd215xzZw6TPzKaioBgnXvLCIVY9cT2igf+s7/oRgPxtWk76GIJFUuRrZVlREVnQ0CUEh3Dt09HH7XD1zCFfPHNLqsTOiO1FQUt10Mat3uCirree20cN48ouv0d249BLHyLDjvb2v7jKCqZ37IoAw67H3L8rIYGJaGlJK7Nbm3Z4PLFnm9W2Bpz//hpn9s0gMC6FPfAxb84vQpGTukIE8Me9T1m3Ipn+fRO65/TxMpp/pBGrwP8MQ8rMMVVF46c6L2Z1zhPAgf2Ijgo/bJvtwGVf+6W2kps/XDDPZqKpuQFUF2/YcZlduMdn5ZYDk+z35LHzqKgAyU2Kwmk00Ot2YVIWMpJZbsB8YNYpRycnUu1yMTknhyec/J/wAlCVKNLNgWkZ3unVqucY4NSqcT2+9kj1FpfSIjSIswK/VXG5LaJrkpSVr+Xb7Qcb078ql5/bRRRzwOt+yM6+YYd2Tmu1TVFJFWEgAth8NDzlUUsFtb35CRV0Dt50/nMl9u3N+n27M3+itG/QTbCooICu6JSeLn8+4zC4sWLMFTZOE+NuIDwtmTlQfJqZ3Zevew7y5eD3Bdj/uv3ZCi/uHW1se3hB4ghmbZuWYIAshULx/Xrl8BruKign192P7hlxWrd5Do8PN6rU/kLVsO1PO6336H9bgF8EQ8rMQk6qQmXJip7kNu3LRNKl34ElJWU0tUoLLDWs3H6TC0dhkgZpfVImUeqXG4J6J/OW681m3K4dhmcn0SWte2FxYU8OTa1bj1jTuGjocd5mDNav2cCC7GOo8hO8Cm83EZXMym+0npeTrHw5RWFXNgPg4LEJlWNdEhBDU1DSy4uvdhAT7M2JoWptF/dNvd/H20o00Ot0cLChn3xG94fjog4AqBJkJx4TX6XJz0/3vkJ1Tismk8M8/zyIlUc+X3/fuUn4o0udk/nHRF4xIT2Zyj+4s2r1L90ARgiGdW0/T7C4o5nBlNYNTEwiwnnhw8R/OH0aPzp0ora7jvD7pmL13vlH2QMb378b4/t2abV9RWUdlVQOJncObehJ+Dk/NnMTv3/mEBqeLBy8Yg807VEVVFDJjo5FS8mFjIe4QFxwReDwa9fWOFo+VXVnO5uIC+nWKIyk49GfHYnBqGEL+KyQzNaZJEBVFoAmvFzbQt2cCikVh5YYfABjcK7mZeI7olcKIXiktHvfKJe9zoKICpOS73HwiFtfowiLAGqCimBUS48KaBPIor367kedXrcPt0fA4NSIOC8YOSOP+6yZw/S1vUFpWi6IIdu0u4HfXHp+2+Cmrt2TzzPxVNDr1dIHm0Sgsq2l636QIbps8HLNJoaiihsjgAB5a+gXfBB/BFirxPwJ3PvkBi56/FiEEtY3OZl7rjS43/ePiWHDxxXxfUMDghATSwsPRpDzhCLWPN+/mj0uWowhBRKA/S34/p0kwf4oQggm90lr9nACfrd7JvKeXoiiCHumxzHvsYtST2EG0RK/OMay689oTvv/wjvmsCdiF41I36roAonKjmXRu1nHb7Sw7wkUfLWgqN10ydTZpYUZ355nAEPJfIT1Sonn29ums2XqQpRv2UlRZo3uLmBVmzxhISmw45w5JR5OSczITWz+gl9yqqqZqiJLGegIdXgMufws3XDac1PRoenSNOc535pPte5tytAhJAxpfrtvHRWN6UVZei9MryF99s5euadGs/Ho3/fsmM+283sfdodc3Orn3Hx83+X0oQhDob+WWGcO46bWPcLjcxIYFkdopnNF//DceTSM4wZ+DahVauKQhGNRGOFJZy8HCclJiw7lv6mhuev1D3B6Niwdl0SlYT11kRUeTFR3NmsJDzJj/Jg7NzUP9xzKne19+yvx1W2j0fsayunr2FBbTO+H4EYML31nHe++uJz4+jIcemUFoaMAJz/fmggIe/eenmL0Ly7v3FJB9qITEpAj2lZYSY7cT7t+2NYDimlr+sXodADeNGExkoP57692NrCrejkdqYAK/kR7eGnlti3f+X+YcwOF2oQFmRWFVfrYh5GcIQ8h/pfRN70zf9M7ExYQwb+FXKH6CpOgwkmPDUBTBoKykn33MSzMyWbhzJ0JAT3MYtbYCHI0uNI9Gv76JJCa17Ic9JCWRg2UVNLrc+vAfFygmQXxcKAH+VlwuD2aTSlJiOE8++xkOh5uNmw8RGGBl/KjmpYZOl4cfDxQPDLDyzF0z6B4fxRcPXcPBI+VkF5Yx76OvaXC6AMitrUT7UVe7MwjMDgV/b+39oK4JrP3TDTjcbgKsFiobGwi0WDEpClJKbl/xX+ocTlDh0Y1fMrNrJn4/cR3MjI9mb2EpjW43moS4UH3toqKugeziMtKiIykrquaNV7/G4XBTXdXAC88v574Hp57wfL+9bRsuG5i8lSxuTcMWYGHqW29xuLoaCbxx4YX0jT3+gvFTrnzrfQ6VVYCATbkFfDJX702wqhb8VSs17gYUBPEBESdM32RFxmA1mWhwuzEpChkRv8yagUHrGEL+K2fGiCxqC2r48stdDAiLQmrylLsLHho5mind0vFISd/oGD6O2MyeXYc5d1LWCUUc4LZxQ0kIC2ZfYQk524pxxLu4+fKRBNv9+ddzc/jv0q2EBPvjkbBpex4AjQ43Bw4WHyfkIXY/ZozuxaIVW/FoGhVWJ5e/9C73nj+Kyb27c8fLn1BZ10Cjy40i0K0FqhRcQUfNXfQRZYoJrpm3kMd/O4leKbHsyS/mg2938F3ZYfZpFditVhbMvJhnPlxN7V4nAZhpSHQj7IKWZO6OSSMI9rNxsLScK4b2JdIeQHZxOZc9vwAAs6ryxKSxCK9IejwaNTUNLRzpGF3Dw1na0wKaE0sjXHflSLIbKjlcXU2dS79IvbRxIy9MmdLqv93Bsgr9aUrqfz+KKhT+3m8uL+z/lADVyi3dTnysUZ2TeWbU+azKO8jYxFSGxBrdo2cKQ8jPclwuD2vW/oDZrDL4nC6sWf8Dq77ZS7/eSUwal8HefUUseGstDoeb4kK9YuOyWYNP6XcJIegTc+zub+r0fkyd3u8ke+ioisIl/bNwuTzsjssnPDKQuM56LXudx8X8qr0cLK9mTEIKZrOCSbWgScnYES3PUO2XkcDC9duo9QNUfRX33199R/dOkVTVNVDv0EXO32YhKNiGZpKUljfgVD08fO4Yntn7NbVOF9X1Du588RNev/tSrvv7Ihqcuie8EiqpDG3k4Q+/4ODqQvykRmOYQtAeleBylUvWvMpf75rWbLCCxaRy47jm5/WjTbuo8+bfrSYTuc46+vZN4rv1B7DazPz2JOsBhyoqWJubS3hIAOETIrm6b18uSE9nb0lJ00Qhq6qSEHx81VJLnNczjRX7sgEY3625x08Xewzz+vy2TceZmJzGxOS25fcNfjkMIT/Lufv+hezeUwhAr16d+X5nHg6Hm2/W7sfPZkZosulR2eFwk5dX3rRvXnElz3/wDSZV5Q8XDScypOWytrbi8WhUV9UTFOx/3IKc2+3h99e+yuG8cjSPxp0PTsU/yMZ99yzEIzWiIhS+NeXy5wcnEF5nIjUliuiolkVq7a4cnC4NAvTPJYC4kCA6R4Q0mYlZzSamDerJXReOwun2sC2vkMigQOJDg3jy1ZVNx2pwuDhQWNbUQCOknkPXFIUjK4tRazVUwNygIYSCS/NQUl7LY//4jLfmXQnAkZpanl29FoDfDx9MJ7t+HpMiQ7GZTTS43CiKICEyhFl/nklVVT3+/tYmz/mWuHrxYnKrqgBwejxckJ4OQLfISB4bN47Xvv+enlFR3DK4bRflp6ZNYk12DgBDU9q+LmLQPjCE/CzG6XSzZWteU85405YcMOmC1OhwsXd/EbMvHkSQXa/V1jTJtKn6Qp2UkuvmvUdJZR2KgAMFpbzz0BWnHEtVVT03/+4NSoqriYwK4vl/XUlQ0LHuxoMHijmcW06N5kK4NF7++xcERtnR3BoKYCvVqK/0EGS3MTSzZW/3o4zISuHjdTtRq904LBITgtIdFZgUhddvvYR3vtpCfGQws0frn9ViUumffKyU8vaLRjJv0VcIIbh31hiykmIwm1T8LGacHg9KmEJmpygKavOa0iiqRyAsAre308njOZan/82CRRwqrwTg+/wCll73GwCm9O1BUWUN3+7LYWLvbgztlgRAcHDrC5RFtbXgAVMNVNTXN6uYmd6jB5PTunHrcx8y9o3nsNdAclwY9949mWRviiu/oIJ/vbIKk0nhhmtGExUZxPDUpFZ/r0H7xBDysxizWSU6OpjiI9UIRdAlKYqconJMqhlNg1HDuhEYYOO1V65l/4EjxMaGEuatkvBokpJKvb7cI/W789Nh2dLtlBRX43J5KCmuZtnS7Vx08cCm9yMi7VQFgVrsQW3UOJJXgUsIVFXB49EQHgj+xsm+uDwGXRbPgv9upLy6gVmT+hH7kzvzoT2T+MfNM3jgxf9yuK4W4dao1xrZ8UMB52Ql8eCsFqcXNnHJ6N5MGdoTRQis3hLB9++bw5pdh0jqFEZmkr6Id//BJWzYqt/Fjh/RneiYYP797rf4Wc3cO/fYKNtDFZVN6Y6cimPnUVEEc8cNYu64QT/7fF7Vuy/z39sIbjApCm99sYk55/Zven/F9/vZui8fvxI3Hgn79xfz4EPv89Z/5gLwh3veobSsFiEg+1Ap/3mxbakTg/aJIeRnMUII/v63y3l30XdYLCqzLh5EXYOTHbsPk5baifhYvWHDZjOT0bN5c49JVZh0TjorNx9ASslFI4+vG/45BAZam1I4iiIICGjeJh4aFoiM9EPNqfcOFZLUl9bSb0gqW7bkUK/p4+reW7KRvUUlbNiTh8vtYfm6PXz09+ubmmaOEhMeRIFswGMTeDSocXoIDLDxwKufUVPv4MZpQ0mLP/ECrN9PrAfC7P5MPqf5wuojd05hw5ZDmEwq/bISEEJw+ZSB+iClH5VFTumRzud79wMw4TQsBn7M5ORufKBsoVG68Xg0Pl2/u5mQN5V4/qgAvqyugb2FJaREhlFaVoOUICUUHqn6RWIy8B2GkJ/lhIcHcsP1Y5p+DgiwMmZ4epv2feSqiWwZXqC34yefXinZuROy2L4tjw3fZTPwnBTOnZB53DbJCRHkb69AcWgIAVm9E3j0r5dw853z2b4rv0mU9h4qRpY68Kvz4LC4yDtcflyTUV5ZFVazituh+6wnJUcyb/FXbD9YhEfT2JJdwPKnrsesnrpfiKoqDOrXvDmqpdK8Jy6YwLRM/SIwOPGXmaUZFxmMWVVx4MZiNtG/W/PjjuyTyog+qXxdthtTlRtPiInCLgqXv/AuPWKjmDgug5Wr94LEaLU/CxDyJ3aWZ4L+/fvLjRs3nvHfa9C+qaiu55k3V5K7OZ9hfVK54rcjsdrMHMot5a6HFlFRWccVlwwm50g5q5Zs0+eLCpgytS+33DoR0HP7DocbqcCMJ9+kok73bv/L5RP5y3++pKJWL+kzqQrLn7yeoICO67Gdc6SC97/eRmx4EBeN7HXCAd+lpTXcuOBjdhw+AoC/xcyr116EUqehKoL0tBPbORi0L4QQm6SU/X/6unFHbtBuCA3y55Ebzz/u9aSECBa+Prfp5+/WH2Dtp7twOtwIBHV1uu9HRXktf7judY4UVtIjM54FT17Ktvwi4sKCSY0OJ398Jf/+ZB1CEZyTntChRRwgsVMot80c2ep2ERF2kiJD2VtUgsujoUlJhD2AmM72MxClwZnAEHKDDke/fslkZMazdXMugXYbV1ypD/ld/M56iouq0DTJ/r1FbFp7gLE/SuFcOWEAQ3omUd/opPpIHU/M+5SsjHgO7CmkqqqB2XOG0jk+nA/eXc/hvDIuuLA/qV3Pju7E+6eOxq1p5JZWMnfsOcSEGCJ+NnHaQi6EuBm4EfAA/5VS3nXaURkYnATVpPDXp2dRW9uIv7+1qSbdajOjKAKPdwC01Xr81zstPpLNW3P481Of0Ohws+zLnShuDVweNm7IZsLYnnz6wSYcDjcrlu3gjfdvJuQkficdhSA/G/Mu05921n21h+lDHkPTJLc/Op0R4zN8HJ3B6XJao96EEKOBqUAvKWVP4OlfJCoDg1YQQmC3+zVrLLrw0kFk9k7AP8DKiDE9GDKi5UXdffuPNI0106TEo2lICY2NLrZtzsHh0M2tBFB4+OybjPPU/YtpqHfiaHTx9AOL8cU6mcEvy+nekf8OeEJK6QCQUhaffkgGBqeGn7+FJ56b3ep2gwam8tpbazCbVVwuDyapOz926dqJ8aN78K9nlyGEINBuI7lLy8MzDAzaE6cr5GnAcCHE40AjcIeUckNLGwohrgOuA0hIMMx0DM48e3YX8MxTnyIUwcN3T6am3kH3bjHUVDVQXdNIv35JmEwqSSmRFBVWMXBIF6xWE8VHqrEH2fDzO/EwiI7EXX++kCfuXYTUJHc8Ov2UJjAZtC9aLT8UQiwHWlrxuR94HFgJ3AIMAN4FUmQrBzXKDw3ONFJKZkx+hppqvfwwIsLOO4tvOek+Ho/GfXe9w/ateagmhaf+dhnpPeLORLgGBi1yovLDVnPkUspxUsqMFv58COQDi6XOd4AGGE7yBu0OKaG+7th4stYsYgF27zrMzh35OJ1uGuqdvPbKV//LEA0MTpnTWuwElgCjAYQQaYAFKD3doAwMfmkURTDnquGYzSpms8rV145qdR+73U/3Z0evlAkN6/jVKwZnJ6fV2SmEsACvAr0BJ3qOfEVr+xmpFQNfUe41igoNa5sl78dLvued+d+SkBDOPQ9ObZMz4f8al9ONpkmsNnPrGxucVZwotWK06BsYdCBWf76dp+5+D03TuPq2CczwNkMZ/Do45Ry5gYFB++H5Rz7E5XTjcWu8Ou9zXN7B1Aa/bgwhNzDoQFhtx0ogVZOCcgKjLINfF8a3wMCgA3H/M7OIjg8lLNLO/f932XEj8wx+nRimWQYGHYhuWZ15bdmdvg7DoJ1hXM4NDAwMOjiGkBsYGBh0cAwhNzAwMOjgGEJuYGBg0MExhNzAwMCgg2MIuYGBgUEHxxByAwMDgw6OT7xWhBAlQM5JNomg47kodrSYO1q8YMR8pjBiPjOcSsyJUsrIn77oEyFvDSHExpaMYdozHS3mjhYvGDGfKYyYzwy/ZMxGasXAwMCgHe0D3AAABD5JREFUg2MIuYGBgUEHp70K+b99HcAp0NFi7mjxghHzmcKI+czwi8XcLnPkBgYGBgZtp73ekRsYGBgYtBFDyA0MDAw6OO1WyIUQNwsh9gghdgohnvR1PG1FCHG7EEIKISJ8HUtrCCGe8p7jbUKID4QQIb6O6UQIISYKIfYKIfYLIe7xdTytIYToLIRYKYTY5f0O/97XMbUVIYQqhNgshPjE17G0BSFEiBBikfe7vFsIMdjXMbWGEOJW7/dihxBigRDCdjrHa5dCLoQYDUwFekkpewJP+zikNiGE6AycC+T6OpY28gWQIaXMAvYB9/o4nhYRQqjAP4BJQA9glhCih2+jahU3cLuUsgcwCLixA8R8lN8Du30dxM/gWWCplDId6EU7j10IEQfcAvSXUmYAKnDp6RyzXQo58DvgCSmlA0BKWezjeNrKM8BdQIdYQZZSLpNSHp3euw6I92U8J2EgsF9KmS2ldALvoF/o2y1SykIp5ffev9egi0ucb6NqHSFEPHA+8LKvY2kLQohgYATwCoCU0imlrPRtVG3CBPgJIUyAP1BwOgdrr0KeBgwXQqwXQnwlhBjg64BaQwgxFTgspdzq61hOkauBz3wdxAmIA/J+9HM+HUAUjyKESAL6AOt9G0mb+D/0mxHN14G0kWSgBHjNmw56WQgR4OugToaU8jB6liEXKASqpJTLTueYPpvZKYRYDkS38Nb96HGFoT+SDgAWCiFSpI9rJVuJ+T70tEq74mQxSyk/9G5zP3oq4O0zGduvASFEIPA+8AcpZbWv4zkZQogLgGIp5SYhxChfx9NGTEBf4GYp5XohxLPAPcCDvg3rxAghQtGfKJOBSuA9IcRsKeVbp3pMnwm5lHLcid4TQvwOWOwV7u+EEBq6wUzJmYqvJU4UsxAiE/0fZasQAvQUxfdCiIFSyqIzGOJxnOw8AwghrgQuAMb6+kJ5Eg4DnX/0c7z3tXaNEMKMLuJvSykX+zqeNjAUmCKEOA+wAUFCiLeklLN9HNfJyAfypZRHn3YWoQt5e2YccFBKWQIghFgMDAFOWcjba2plCTAaQAiRBlhox85mUsrtUsooKWWSlDIJ/cvV19ci3hpCiInoj9FTpJT1vo7nJGwAugohkoUQFvSFoY98HNNJEfoV/RVgt5Tyb76Opy1IKe+VUsZ7v8OXAivauYjj/T+WJ4To5n1pLLDLhyG1hVxgkBDC3/s9GctpLtD67I68FV4FXhVC7ACcwG/a8d1iR+Z5wAp84X2SWCelnOvbkI5HSukWQtwEfI6+wv+qlHKnj8NqjaHAFcB2IcQW72v3SSk/9WFMZys3A297L/LZwFU+juekeFNAi4Dv0VOamznNdn2jRd/AwMCgg9NeUysGBgYGBm3EEHIDAwODDo4h5AYGBgYdHEPIDQwMDDo4hpAbGBgYdHAMITcwMDDo4BhCbmBgYNDB+X/0cj0txbjvNQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[0.1763033  0.14416036] 0.32046366\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "50aA3IJyqyWx",
        "outputId": "7a522d63-26d9-4bd9-f1f2-0db6d4e7d217"
      },
      "source": [
        "#@ CHEMICAL PROPERTIES:\r\n",
        "mol_wt = smiles_test.apply(Chem.MolFromSmiles).apply(Descriptors.MolMR)\r\n",
        "plt.figure()\r\n",
        "plt.scatter(pca_r[:, 0], pca_r[:, 1], marker=\".\", c=mol_wt)                  # Plotting the Scatter Plot. \r\n",
        "plt.show()                                                                   # Plotting the Scatter Plot. "
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a9A4A7pv6GWumrsdQw1w6+7/oDC16Tq+jqiT3/b850MvLOq5ife7XtAUXcFrzG5/TuX3+sfEV+QuEsBbkJe0r9v2s60toa1970PH3TP4R0zMBkHhsLj7OqtSB1uODmRtZk7kBieSeietRxcmMlo/ntMQgKd0mGWwc87mtX6dgFwGVjNVNa2CIshPFlQ1L1pUuJbtGo655I9zpvNYL6I2/nPHCj9hYnELIMFKqrC4toTeZ47Xxx7mw850k5U38e/ctVKXLHZVF1LxG8+SSa3FjfjnNwSqKAFPWmLa2AUuZqBVx5V4fdZVXpz9FJucBHummr9EWvpg2nhzHHtX3P+zK9jT3Z36Oh0vdLXHb6Nd527xvHHB+pJRsLT7EtDXGksRpND2LjkvPlkWJs1iUOOuIyfPxORi+In8eMV2HbflJuiMJ0sEDremnoyPYgYpBwYYKgj+O7WSkIrm4axV95QEeylyHYISyUyFbh+l6hGk3QtXrw5Vd7Kx18anFKzFmOu2Unf19QGquxRt7v83nNt+Awh404QIOq2KPcZu1DFUx6AimuKi7UQ/9+4N3MGV3AQJduHTredr0Mtgad09cQ0It0KwlEEiSSoGq1wJCx/UUKm6ANDUadVtACAPLq2MoU3SH44xUSyyNjRPTHOTMm0S5ci3R8MVPeV6klOws3sy6zNdJqzWmnQgO6kFDOh/I/J7VE9fhSod7J3/F+xd9i7AWf8qxR4LdhevZkf8BEa2bk9quJqgd/r6Hj4+vyJ8nKrbFJbf/kMlaGYnkuhdfxvHpzoOO96RNprYBQ02SDCzkvPaXcv3gaiyvjifh4dyD3Dm+nZtH72OsPoaULrPDFVJqnd/sPh4hJK5UmJXOo6lQdT3+NHkdk2Y/L+78T17T2sqvJ0dQgEs6evjm9m/xaL6KIWwuTD9ObyhDSq+yKGoyK/X/WBDrRlc0POkxYQviah1N8TBdjYeLszkjYdITGKfPbCWlTiMRSAQJrU5cq9CeeB9RbTH3Zb5H1rRI6jXqXpDjEifxvV1vw/bqnN2lMDdyJfMiQXTzD0gJghABY8UB58eVDtcP/AfD1Y3MMyaJCAcpgjRpNer0cmHnU0f9bCs9jC33vtlIxusDdIc62Dr5Lqr2blKhF5MMnUw6fB6aEjusa162Btg8/VU8aWK5OTZm/5tVbVcflkwfH/AV+fPC1Y//kR9vX0vdlngzJVV/vG0tXzn9FU85XkrJvaPvIG9uR+KxLP0e5scvJW/ZTFsNSz6k2piezVBtb5KMwp5qmqzr4kgVJAQVmw5RZRqduF7HxmJXaQ3nmg/xqvBNdLeEWVNLUqjvZF7IpJjo4pF8D4sjY+iKi+kFCevdVNxp8naMlkCaotVHu6HjyDJCQFC1CakOw2aKrkAeTXh/8W0U5keXcGz6VQD85JT/4Ks7fkrJrvCOea9mpLYB063gzCjXVHA7xze/jUr16xSL/4Om9ZCIvfeAc3Tr6P8yVH2cLm2aDi0PSFJqmWmW84ren+BJh4o9SFBtQ31CNMui2EkM13biuDYFW+GB7CMsMDZgmo8BDpOVXzJV+T2D2jdY0XUbijD+6uvuyMq+TWuJi+0Wcb0ylrObgDYPRTm0tzIfn734ivxvzKbcGNfuWovp2XhSAQQBVaMn1nTQY6rOODlzC+6MctuZv44FidfjeEmg8VnN1QkoGiFFUPdMJJKg4tAdlOzIOdQ8DRC8q3MTN5S76QlnEUJQly6OO8HmWoJrsvNwpEAgOT25i2OjE2wqLeSewtmEjDEkgrLjkp+4hqBSZLqSRro1ljVP4Yq9BbgEmnBwXfhj9jTmxXuZHVcYLl2PQDAneh6Lmj+177u1BlN87rj34nkmdXeYx8e/yHw9S1kGmXbTJI0OpKxTKnwE6eWwvQGm8x/nO8Pn0lea4i3zT2VeLMC2wgMANGtllJksUFfC8U2vxPbK3D/yeuruOIoIcXrn9biE+cP4D7HcOi9pu4w7J+6nZE9xz9Q9PKxIXt3k7YuBl9Sx3Clq9h4iM2GQe2uaH0o0TMJYTGvodMar96CKAAuTr2fX2ClIaSNEkLntd6CrR85H7/OPg6/I/8ZYroMAhAKKKomqQS7pWcI7lpx20GMCahKBBpgIdOLGfAC6wm0Ui4MAqELlAwtfwfFNPXyn77dk6wN0h7YRVh2umjPI2kIbl7T2sySWJW2nUWeUnYLG1waHWZ9bhKY0PlNxyTlxlsZ7eWPnOiYcQVUGAElQrRGVHlum2xkpJ3Bliv5KEy/v2YKLgiZ0TC/OTieO4ynsGMtxestbuWjO+xGoSEwG8z/j/vwuTNnM3OhiFoV1+rLvw5MW006QkgwhUUiqReaEl+C6YzR6ebsgXTLl1dw00ITpOazP9nNGxwCGqpM0NLJOhJhSRxUCQwnTFX0ZE5XV1N0JXFnHlRY/2/YFdlplQoGG5T5c207BacKZSXwypYpDCoMsjc1dDyE0AlrD9VWtP8hI5gqkrNGS/CRNsSue1bUXQrCq/WpMN4emRJgufgvXKwIuQloUq7+jKXoFazPfY7K2icWJS5ifuODQbjCff0h8Rf435sR0N+d1LeL3g5tpDoe57pw3Myf29OnbmhLinK7vsmX6ewTVNMvSDdfCx5e8nk88fi15q8I757+cl3QsZ6J8My9NfB0nZrPLaiHnRkkk6nypaxDTs/lNaQ4XdLyFTblf4EiTztA/8Yvhh/CkgpQuQkgkgs7gHJanLuV+671k3GaQkr0RKx6CXD2MKxuVEauugeFZRA0Tm9OouGWE8NAUD1vUWTN1B2ljET3RcxmYvIwfjEsqUkfK3TxWWMv58U3E1DoANipyxv2gCJ2KM0bcWAEijeON4SH5U64Dc1/tdo+So5JUBZpMIIxFtCXmERQWLZGL0dUUkv2uFM9TeGSyQCJdgZnMzZpbZmXT+dyXuQ8JtAXaOKfn27iyyET5JkxnjM745WhKFIDx6ffhyQIAk/lPkYi87pDcIgG18fala10IYTQ2cYWGrnaxcfp6tuR/hStNsuZ2koFemoPPbdikz9GPr8j/xgghuHrVq/jcykvQhfKsX82bAos5vePLT/psVriFH528v2uPlJId2Y/hSRNFwFxjikdqMYbdNrS2O9BRuLyjMefJMz7q9dOPAw/hoWB7krBq0RYs4Smz0GcUV7cxzaDZTMkLUnUNXKnSFc2zM9+KgiSiWZiKju3qTJkDCJHeVx9XCA/h3cL9EzcxWl1LpL6OijyFkh2kv5RCIjgjso2IAoqAmKhTlwYCQUBtYktpmHX5rdSqcdq1MhVXZ8QJYSgCVUhUxSFmmGh4zAs187rZn+GR3O30mYOcEHC4a/JbPJK7g3YtQJvuMEsz+dIxD/GFoeNwQgoBVeOY+CpeO+uNHBtfRtkpsig6CyEUDKWZWYm3P8VFPDJFzRLh12DZfZTqdxMPvYxY6GXkC5/d50ITKBTtUV+R+zwjviJ/njAU9ZDGSymfpdLfP0YTBstTl7Ay9SpUcWBDBtuzOCY+hxOSi3l4+jFCisuxsfFGqVlKJANLiGizqDnjpNUaApitZ5mw44TiFueldjBsn4VUdqLMVC9sMkpkrSZqUtAWamVhYA0JvYwjYah8HytmknIGyk14M5b3DeMncVnHQ4QUG/AwEFTEAnZVCnjlaym7BppIcGK4kT2aMEr896JbScY/j1v5L/5cTdGkVulQH+f+wWXsttoZdaJsyd9GUKkRUVSm3DiXxrJE1UZ5gY/N3sjrN7yNL5z5MubFliCEoCfUzHX9H+OhSYekqrEo9RFOaT6DmjPMxsznkdJjafoqRuTZJOUImpC0Jz/9V29SCqHQmvwIrewvEHZs8tXsKd+LEAoBJUZ3+KSnkeDj00A8H41oV65cKdet+/tutyWl5I8TD7G52MfZLSs4oemYv1rOZzfdzK+H1tEeTHLNKVfQFW5iqlZholJiUaoF/QkPhanKnWzPfBghFBa3fIVU6MwnybO9OrvL68iaOW4e+wWudFHQcRG06eMzzobGhmfROpbTUyFU9x7alXEMYTHpGrgobK+1kTZc4sEXccPwOD2RCcpuCFcqTNdC/EvXRlZ0/o7bh9+B7U7iEKQjfAZntX+YT216Nw9m041oGhpbpEsSYyTUKmdHt1JQzmJrdRoXFynBkQo1z2B5ZA8qHqN2kolagjf1XkbSmE2XOsCDmc/geMWZcwYP1eYwy8jN9D+CSSfOZbEJkmrDJbPDbOKH+WNxpM0rOi/j7NaX8e2dl5O3M/vOVdUOkQpeyLLQb6k4wwDUZDPjdhhH1tFEgBe3v5NlyZdSc2tYnkVCf/alcg9G1clQtEdJBxagK6HDlufz94MQYr2U8oAGub5F/hxx9+TDfKfvRkzP4s9T6/nC8e9nXnTW0x5jezYFO0/KSO9LYNmUH+aWkUdxpWS0luer2/7AazpP44o7f9mIAok3cdNFbySgNi5lPPgi0G6iORwhFWp5knxXOlzb/z4K9gTjpoY7YxF70sX0FHTdwmR/eN24Ocm1gwafmD3MFqsZVwrm6jmyrkqXkaNCmKHyA+wqno4nPOK6iRCQCJrcX2zjkeqnWRrag9QckAUGSlW+mLuDM1reScn8MZvKIaQUdEfy6KrHPGOcL26/CCkEp3RNoSkeLVoZQ3EoOgFcr7HpaqGBIvn01nspuQHeOf8VpNn/MJNAs1pEIPdFnsSUOreVm3l5rEBci/Lz0rGYXqPS429Hr+OU9Iso2nn2v9FIJA4PTz/G4vYhQsLElDp1t7rP5+5Jk2p9PY/mOvjGrm8jkZzevIoret+CEE9Yj7QZm34vldrdhAOn0tH8HRRx8IJeYa2ZsJ8o5HMI+Ir8OWJrsR/Ts2Z+EvRXRp5Wka/P7OLzm64hGiwzO9rExxb/Z6MQ1hPcKYKGrfytjQ9ScxqW5Z5invWTI5zW0YPjebz2lp/Rl8/iSsmHT1rBy+d00hqc36j5bY1SsMZBVhAiDnJvTDMowsOWCqV6gP5cC+2RArtraZq1Co+abVSkDgget9r47egyBLAsPoGDgiVdFOS+pWrCZU5wEiknAXtfS7k2/Q5uGLKZrz3AxekK6cA8pr1Gko3nSn4+dgqOKpjKxMg0xwjoDjXXoCeYxcJgxEkjJWzJtVJxAniqzdzIOH+cuIZXtp+HNH8FSDJumKhq7ztrngTTUwmrClOxn5OMHUMw82/MNSapeAGmnCY86TEnsoK+ysONcyKhWQgWJKepOwq35VZiSY3jYxZBdRpTegSFx7HejXx6sIA906rvvqn76OEHnNr9U8L6XACK1Zso1+5AyhqV+r0UytfRFHvr4dxeh8V09Y8M5L+IobayIH01huaHPB7tHDFFLhomyDpgREp50ZGSe7RyVssK7p58GIGCIgTHJRYedOyW3ARvWH0jnowBcbzeMTbk13Nq+nSWJrq4pHs5vxpcS2coyfuOuYBvP7YWQxnC8lxc6dEebijD3YVpduaz1JyGUvnGY3dRUTYwJ7qKi7o/TlhNsDCwh7Co0Wc2s6XejURQsXWimknQdfjuhlPwpEARkvMXbSQRqlCdUeIACaXMe3vuAuCRYi9/yKwCHNr1hoWu43BMeJwaBhG1kXWqCLA9hUfKPRyTmCKomhiKS8GLsFfR9leb0Q0PTbfomDOMrrqAwJQaU3Z0JjRSIIRHWLdRdIWYVqTZqKAoFe7JZokps2gxKjQpZWJKBVsqVGWANlWyujyLP9e7ebz8CV6S3MXKYBUx40haX57LnQMX08oAw7KXnBvmnGCWU5um0YXkQyMrybsNP/iDBZ1PtG+n06hSlx4PmUE8b3ImtBJAgpxmIPc1FjV/juHsu6maDzRixQHwnlC07OlxvQqTlZtRRYiWyEVPsvL/Wmw3x47Me/BknZq9m53Zq1jSdu1hy/V5fjmSFvmVwFbguStUcRSxLLmAL51wFX3lYZYlF9ASOHjCz+rhHdheY4tR4JEph4hqDeUshOCjSy/io0v3Pxs/uvJsao7N9lyGf122ClWFDz7wW9aOj2K7DUtdFR7N4SKOrNFXuo+SlSFbfxADE0VITFdjqNo006ChEUK4caIL22sk9rgSxnNJepsyPF7q5NhoIyEopVXQZmLQVyX6WF+ZzTFGmUWxKT/1W1UAACAASURBVIpOgHatQI0AEoWyF6LmGrTpRe7ILWXIaoRZ/ia7nHOS29FpZJ16spFI1Pi+oM5sSO5FpVGKFsCVCgHNQ+BgegYFJ0STUUcIyLtRMqUoZycKWGgEhIMhaszSNDZWO4iqZV6TepAJO4qiQFBpKPJuI8Puapyk3kxfvRUXlZObdxJU9taIf6LbRnB3rZW3BnZzbz2Ii+D4yDY2lBdieS6rorsIKKApUaaK/0ulfg8Si8a7lIJUmvnNxAOEpwe4qOvDxA7SOUhKyWPjb6Bm9wGCXO3PLEh/nkcnP0Sm9kci+nxO6vgBxl9UxXwmXK/E/pbbLpY7cUjH+7wwOSKKXAjRDbwc+CzwgWcY/g9DT6STnsj++il3j23ny1tW0xaM8fkVr6Il2FDWS1MdBFQFRzQs6SWJRShuK5brogiBI12C6v6ok7BucPWZF7Ims56bh2/lg+sqON6MvacppLQg3fF+zujZ0djsEw63jH6KJu0kXKkgpCStlWeUeOO4uquzp7pXqTT+0BemxmnSa2TtKPfl52MoDr3pKRSl4TKypEbGjLAyOcCEGWd7pZ3e4CSzw41uOa5UmLKjbK13MmSlAEGzUSSs2Wyo9SIR1BwNUAgqNqanA5LuYA4HDVuqNKkV5hpTTHlxym6Q6XqYvRHkQkjqrg7UkRICikNPNE+REMIDHYe4sIgrGiHVIKZMI4G8HSYdKCMllF2DX2eXY0odFY9UoIKmSAatKPOMIiB5XdMA388uxpGSFqNKHYU18go8bqTmKhTdIC+Ob2RxMI+UJiZdCBEgW7mFvZ2LhAiTjH2c60Z/ieNlEVaOP4x9ldfO/vRT3jtle4yKtY29Xbmnqnfyu8xOHGmhu2106OOk89dwTPojBxxruhWEUDGUA2vUB7RZNAXPJVe/u3GPJv/9Kef3Obo4Uhb5V4F/Bw5aVUgI8Xbg7QCzZ//jFdnPmRU+sPZGTM9hTynDR9f/hu+ffjnF+np6gn9E1R3cmbIkvxuY4LaBa2kKhKhSxPRcLp+3ig8vu2BG1hA/2v1/eSCnkauF8GTTvtBEoUnQCxw3a4KA6u7zW0/VdxGMzKPf7EQI0KRFSLGoew23SZNuYxg2MgrYglnxaY5vHyTrRDk2OsosN0dNBthqdtKp5VCE5LHqbObHMwzXkwzU0rho5Mthsk6WOaEsOSfMuBlnbmiKpFah4ERoNUqUvOC+pB9bqgwUW6naChLJ8alhorqFIiykhJhSQ1WhWZTRcUhFGxuUhrBIKVXyTpDdVhs5J4KhOMwLTsBMrMqDxblsLncjEBwfCZNzBUU3xLzQFD+aOI3ZwSy7ay2YM64jF4Wao5MKSK6ZXsSKwBRZIbERnNTksjR+KhPlHyCEZLp+O03aUm7PuQjghNQY0NjsrbnTTJeuR6FOQmnsHQg0VOMkMuatDJrRxr6B2B8h80T6yhv4xcBnWRHSCAoPVRjk3SCOtAFBXQnw1Z0ruVJ1OeYvDPoHpq5nzdTPEEJwYecHWZw4+0m/F0KwsOUbmM4gqhJHVw/+puhz9HDYilwIcREwKaVcL4Q452DjpJTXANdAI/zwcOc92ijadVThcnzrCBHdwrTzlMwT2TJ5OVXHwfEug71RJEiqjk3dsVF0B1WV/Gz3Oi6fdwqd4QR3jH2RcbOAJE1Ib7Q4k14jI1PHoSM9zcZCC8fH66SNKoKGXbenfDuIhpJzhc6piZ0MmC1UvQBB1eGMWf1sn2rhn+euJaqZOFKj6gUoeSFUz0UqUJM6ayoL2WvJ64pHyQ7THKwwVY9hewqD9TRDZhoNl7e3/Ylmvcx5sW38NnfCvszRBpKKHaDqKlRdjbPadpMOVpBSwdubdenpWJ7CdD2EYTSiUFRc5hsZFCFJaRUkKuNWE5bUKTlB4loN09PYUu6aqboIm6oFzk5NMebGCHtBjosPsctsY8rZ7wkUwOnNp7IqvZRbRjfyYPVeeiIZcnaYfK3KjvJdpPUUx4QnAIMRK4lLDhDYKI3qjAIsKZDYGE8oGGZ6Ne4bvZGhegxE451nR0XFkx7rcvcyWR9lRdOZdIRmc/fEddjSYn21h26jyMrUK9lW2AkM7T1tWFJj9Xgbb1yw/x5zPIs1U9fh4YKE1ePfOkCRQ0OZB5+hB6nP0cWRSFE7HbhECLEHuAE4Vwjx0yMg9++K2ZEUJ7V7RHQLRYCulfiPzd/l3sIcdMXlxa1b0RWJKgTKTHw1gn0FoEDuSyKyvRopvdyIFEEyO5VjViLLotQEy3rGCBouuiLZWJzFVC2KK2FXNYG6zykBunBp0yuk9ApB1QEEEcPitK5+wlpjjQKIqXU8qfBIqZeHs708MDWXgWITngTPA9sTaFpDYQVUG4lEFRIFyfzgFAmthiJAEx7nJraSVKvElDq6sBkpJxisJJEIQrpDc7CMrniNBxOCihPgoek5/Hj3yVyz42zcmSgbXTj7vLyKgCatMvPmIVhX6uWxfDcD1dSTHhiK8Bi2UwxZacoyTFTzWB7Zw2ta1tIVmMYQNq2BEm+YfTErU6dw5cI30BJMA4K8E0YicIFJO07V0QGPjGPvm+G2whJyToiaq7Gp1IHlaTiest8bLT0eyG4lb+5PHgqoAe6e/C2/Hv4R90z9jv/d+TFGqjtI6M0oqNhoDNntdCXewkVd/05EbcFyVfqLaWZHKixO7m9Q3TgXKorYb5vpT+Fa8fn75LAtcinlR4GPAsxY5FdJKf2+Vn+BEIJz2hZy1+QgjpSYEky7Tt5uIyBqvG72OsKRMO9Z+D98e+tDbMyOcuHsY7ht7DGyZoUPHHsezcFGyvyL2t/Lb4c+SlTW2FWYhSM1EoE6qViWqqvvDwNUJHeMLWZpYpRV6T10aHkybgJFSJYYo2yyOhpukeE2xgab6Zo3RUtzjrw1l7ReYUFkgqprUHc1YmqdbeV2ynYj/nmiGmNF0yCOoqOqEiS4ntIo0yo9HCmwpMJeVSZp1FEJCYvVuxfx/oX3sqY6D1Aailvu9dc3GkwU7DAPT80mW402koJQuKn/RM7t3EZLUOJIhb21UnbWW/EkuJ7ggf6FmLaOoTms6umn5AVRkXSEikgUcm6EpOwhwvqZhCHoaXmYnWY7406SW0au58Rojkz1l1yQ7GLQXcqIOY7pNb6FiscNfSfzuZNeS638e6bMcWqegQf838GXMVGLYToas0PTtBlFXt36KLNDOXZWW3mw0M38WCcBbQhVCC6ffS4PTf95Xz10R1r8dM+HuXT2J3ClQ86a4OzW1xPXm4nrzbxn4Y/45o7/QyAxwaJEnhOa+vGkO5PKb2K5Li/teB+/H/0CABUny2S9j9bgvL/BHe7zfOLHkf8NeVnHxdwztRr5hBLdLiq76m0MWs3kvCAhAz6/6sJ9v3/XkjMOkNMROpaYGuPM5l2cmt7NdXtOJlOP8qqufvrqrYyZSTwpKJsBarbBjnIbJ7cMUEdntp4hrNg8VOlhW70dxRLkh+N4noJhWAzXGpbsQM0l60SI6BbdRo7uRA7pSR6Z7kECdU9jvBYlZZjUTJ1oqIqOTUXq7Ck04UiF0WKcVbF+egPTVD2dcTuOg8I/L1jPMZFRXpbezB3TiwFYGhthVylFV7iI6Wr0V9NM1yINq1qArnmc3NrH6uFFnNwxwNZ8K7MieSpeABRBbyjDhqlu6raOEBJUyXA+ydmzdlD2Gn1GAQank1y/Zw6X9BTo6l2HrkiQkqBigZD0V+9EcSbpNGwsdw89WpxH1R5Syg4U4XFiZIBLUw/znS2CL5z8XrK1/0NV6mSsRlNq12uUJh6spRmspVlbmIOhQjIg0BSPK+ZewJmtx3Ln6CfZNP11LFtHIYLEo1FDfYrBzOXMUeO8bNbXUb0xxqYuJWCsQA+/BdObQhEu4LK5uJoN+Vvpz7Vx155FgODSY0aJzSSDutJhXfYmLuz60HNxO/u8gDiiilxKeQ9wz5GU+UJjpFSkP5/jhLYOosahNRmI63FagykmahNYbiPuWBUaZS+J8BRiepKk/szRmxO1DbjeMJrioUqPM1p2csvocTTpVY5Thlg71osUzFjPkohh0l9tpjOgUtcMcCUjVpIVsUGCOJz64j521FsZMlOU6gaNjT+VMTNBj5ZDoqAJyexQjkrCQLqC7aU2tpc7UYXLMakJQkIjETSplQ1sT0GiUJc6t04t46yWHViehisVtlY6yJpRHi928PrWRzgj0UfZ0xGqx+rpY9lWamdJfIQLmrcQdefxSH4WIFCER10JsLJ7mEWBUZaEJthc66LuBtBnwgRXtg7RpU2zpHmcneVW1kzPY6IeJ2I0XEqK5/LAwHyWto2QCcT48shLadOL/HPLg1Q9g6RSwRECc69rC0nd3ojrwrnJrU8oYyNJGf0M10aw3MVUrBGydoR0sILr7a0M2XADtQbDfHH5ZVTdKl2hFL3RNmyvTn/pT0g8YloNVdhUZAhdVFloTCCQWG6drZPvoE3NIGWNurmWpAgRVKJU3HzD9eLViCsV7htageU1rIOSM/aEiAOBofrNKv4R8C3yQ2Dt6DBv/t2vUIVCLBDg9n9+M0Kp83hhHSmjhWPixz2jjLfNeTff3/2/xN06L2q9mNPTZ3Jf5hG+tetWxms279vwTb5y4rsxFA0pJeuzQ0gJK5tn7YtM2VEs4UmJKsCbaY58bud2arbOt9a9hKpj4ElBIFmnM1kgGTQZqKcZMZtYEB6j7IUJqyZBYaPvjaUO5Mi4cRQBnmzEbcf0OgJJRKmD9JgbnmJeeIqNhS62FtsIaVbD8reCRHSbih3EljqKkLiyofeKThApBbvqbdQ9nV2VFmxPZZIYXx1+ER/qvpMyQfAkyxNDqJpkVWQPUdVkwewJlsZ7+NXoco5JTeIJlYQoc3p0CA2PM6K7WV/rZshphG4oSE5q3UNQ8zgxOcSkFUdTYV8MutBIhyr0tmQYqjfCIafsGBvKx9AZGicqzEZrDM/jJxOnYkmNs2LbSGtlKp5BRN3bxMPgMbObRx/7BaAgmEVMs+iITNNk1FCFR8KpcHJ8kGXJDnpTH0M6g7jFq3CyddTYf6AJFVs2HkAprcSL0pdxz/j9SHbvu1c8abJ3G0tSo25tpuIquFLDRaDhoglJWLMp20FAsCPbRXesgsRDVwKc0fKmw7nlfY4SfEV+CFz7+KP7UuMl8KehnTxU+yY1t4oQgos6Xs/ZrS8lb9Z415qb2FnI8KYFK3j30v3ukdnhOfzX0q8AYLsTVK11bMzvoe46SCS7yyPcOvpHXtl9AZ945FZuHnwcgAtnLeFzKy/my1tu5//1Pcjy5HxWpgfwEJS8IJuzHdxXXkDd1WY6D0kwBbGAOVNvROBKnawTx1BcdlWaWRgaB8DxFEpuEIGkN5yh6hpoikdQdVgcHMVQbALC3Ve35LjECJurnUQNCwHowqZiB3BRCOoOyWCNohkipFtoumRnrQ3T06maOs5MwhFA2Q1we34RxyXGAYGQkmatRFxtbJAiYHlykKIIUCOEh0KL3mgSLQRoSKLYuJ5ACMiUQ/Sk9jbMkIQVE9tRMZRGGKYnBUK4qELuKxsAkJEpusQ4e330a8tz9mVy3llYSnewyB8KaV6drlF0Jvn+yFI8VBo+c5fFkTHsmQ5JCIGH4JjEFAvDk3iyIcfN/Su4fYBHeeoyOvVlZJ3dqEjadYtWvYvfjYQZTy3lpelNeFIwv/mTWOXP4CFwpcNj5Tk4bMCaeXgjDISo8ZoFj/CbvhOoOQZvWHAWL+5+O/dP/YK+8mNc2/8RLu35FHHdr93y94yvyA+BRelm7uzvo+44eNIjEqpiVUxsaYGE9bk1nN36Uv7nsbtZNzmELT2+veUBzuyYe0Bj5bq9m8dHL+He3By2V1pQRQpHShzPYv30d9hd+gaWMPA4lrpr8OuBxzi9vZNrdz+AJ2Fdrpe1073YtYaLRtM9HFOZUZQNbBRy1RBGrNwI+5MSTbiYrspEPc4tk8s4Lj5MyQkwZUdIGjXajYYirUudsGISVFz+suumBGKGhRCSkGIRUB1ylg4z4Xct4Sot4SqNbEbBpBlDweXkxG5G63FM2XBJuZ4ga8f45s6FTJsRliRGqUuVpeFRIoqJRJCxowyaaeYGp6jKICVXRyBn3DeC9ZVZ/K5/GUUzRFQzWRwfY2u2g5FyE9uK7TimxskL+tFVl80THUxUkgQ8i+PjgxTtEDknjOWVsT1lxkUjKDgRgorFnHAGVXisiNRIKUMopCm4b+LY+O0gYGupjVelNxBV62yudTFiNyGUmWgfxQICRCNvYnP2K8x3h1HxMKVLRRbZkc+xPGWBtIgHTkTlRKR3K/dNz+fP2fmENIfbe1+BEn0JN+64ilbjbqT8CZY7v7FnIAIERBUpIR2q8NYlayi4QcJBECylr/wotjSZMgdZPf59Xj3rwMQhn78ffEV+CPzr8lXUbIdHJ8a4bOnxrGrr4PaZnA5dGCyMLQGgYJk4MzuaQkDJNg+Qla/eyveGT2RLpR0pIWxIDMWlJ5wlplVJqmWOS2ZZGhzm6m0X0Bpu46FsH42I8MaGGhLcnM7C+eOEQg3LdPOeDhxXQ2gewbBFPFhHFw5JrUJ3KI+Cw5qp+bhSYdxKMJ6JE1Qc5kcncaXKiJmkpATIWWEsV2VlagAXDQWXiNJwpfx+fClZJ0xnpEBkJlQxqVeYtqJIBHG1BohGoSqlzopEI/55e6mN2ZEcttQIKRa64rEh203WjCBR2FzsAAE3TZ7I8sQgjqeysdpNQqtTnmn/NuEkuSV/LGHFZne9hX4zzbxUhkfHuqi5Ol9/7FzKVgjHU1GEZEHbBPeN9BLSHTxLpSsyzYL4FBJBV6BAxTPYXO5iXbGHk+KjnBR2OKWzn401yUYrjhCCrXWVU0MOjjfJ7vrNhGeSbI9LjJLQatyQORkJeFKhNVBkYTTO8tSLqdZuIZP7LxQB22SE+bpkwmt0QjovvZn7igs4o/uzLE6cSl95ErfexjSNhCehh1GEQBExwuqDKAqEsVke6kcG305HaC6PZn+J6VVQpUdd6igEGa6O8d2+DyJm7hKExJUOz8Szr3fv80LEV+SHgKYoXHXqk6NIPrDo0zyYvYeWQDunpF8EwJXLzuD+iX4qjs2J6S7aIyYPZe9mcfxE4noSgKA+n13VfhzZuAQVC45vypGph1kUcVkWGEYVkni4xrvmr+ei+T9jW2GQwfLtuBJGSkkGB9uIR+sEgzaKIhFC0pkuMFqLN+LLm/KEdBsHlawTY66SoerqjFWTCEUSNmyCikOTUWHETBKYqXEyKeIsjYwwUE6zo9TO3FiWumdwb2Y+m/Jd+2LMU4EKTUajmJWheHSE9tcDz5aT1GyFs7q273PtzA1nWFOYz5zAFCfGhxACkqLMjYMrZs6mQMH7/+y9Z5xl11nm+19rx5NDncqpc27lLGwsI9tyItnYAw7gYYyZGTDpjkkzYLiEYYCLBy6/YYQxYMCAxzIYG8mWLcuSLLVaWZ1zV1dXDieHHde6H/bpasljM8gWF8/Qz6eqc3at2r+993rX2u/7vM9Dxg450ptAaUnGTNI3kTb7NEU4HQ4RxQYV2aLedWjGKQYHOrgyIGqa1L1LOjUKx4oYyzfJmh5KGry2eJQ+ESa5pyj82GQlqlCS8F25Q9giopIRBEJyMsihksx5Uo94Qawz0JzsDaN0UhwG6EQ2Xb/NWvuzfHz5Oh5pbKNidfiJyS/wWFxg3AhwjQhXxmxzl/mTmY9yXbHDrx16hOgFkgmNyOM/Pfsp3rPtdmKdRukk3ZSSEa8eey+GdBhLTXHP7AfpqR5jqZ2kzBGONZ9AJ3qWaARZo8Crh3/gaz7TWmv+8Nxf8sDylxlwyvzi3h9j2B38msdfwTcnrgTybxDD7jjfMf6OF322vTDIwe/6MZqBx/nus9x97tcQQmAtfIyf3f07ZMwshdTruHngOR5brwEG05lRTjcd2rFHN39xI39rSs2As8Dvnf4r3rvlVqZzNWwRsCuzwF+cHySOLvd0aQ1haCA9KJfbZCx/I68tSMSfdJ/ZHcYWUc9gLF9DC4mBJmv4TLg1vNjkwfkdrHtZck4PT1jkDY/Z7gBCiI1uydlWmbFMc6Pg6sUGtoiRQtNTEW+aOLrBI5/xyhxuJ9IMF4JB9usFXBlxTWGevzf30glTGFoRKoPT1UF2DKwiRNQPupe7QSMkWksMqWmLRM/8kjuRry08w8QQMRqB6C+EvY5JyoiwrBDbjjBQxEiUTtQVJ1NVmq1RJqx2v+0HTKFpBi7P1cYoyi7XO5K5tQrPHtnK1TcmBcnzjTKWfVngSxBTtrv06HG0O8TB5mY0krUww8dXrudtUyMI9Vk81cQSihG7wZuLX+BXT2eI9SWuSXK9Yq357MJTfGbueX77ug/S838dgcf28gcxZMLln0jv452bfo1etM5o+nqeqt3PsWYiw2sIk9cM/2tuGnjTV91pe3GHc+2nqAWah1cPotCs+VU+OvNJ/sOu972kOXAF//y4EshfBiiteHT1FLFWfMvgTkxpYAjJYrvFo+sPbuTQhZTMds+wO38NQgh+5doP8PnF5/HikNeNXsNrvvjLgODepf3sT81TsZPuzS/Wd3LeO8OT1SKujPi2XBIgX3nXWf6vR99Gp2OTzvh0ei7dwOaaiVmG3AYXwwrmC9rEPWWRswJ2FpY53RwmZfgEwqQXOGRMj93ZRQyhiQ3B9sIq55pDNIMU15Qusju/yJqf5sn1TX3tEBhy22S0z1xYROvEQ7OuUkg015WTN4rk+kAruuR0IwiUwaxXZnt6BYVga36dU/VhWl6iwXK+PoBrhWwrrFK0ujSiDAqZ5Oq12Gh4irQkZ/t0/MsdjFcPXmR0tMnz9Sny2V7SIRrYXFe5yJbsbk41ityQ+xI1laGnHQIMUkZIzvR53ivxpthiJiwTxjEfWbgahWSJAr81exe2ERO6Jp/7uxsY3bnKyJYaAAOyRS92eOfgQUatBgrBfYt7CGJzo3d6NSowVfhBGsE78Rpvp2x2saRmSHTZlFpgvreNtBVhGhFBZFDO9DClouU7fG7xy9w5ZAE2RfeyuMqpxqd4YvVDCAzy9gSvHf99DtUfYr53il4cc7q9wE1fRVwxVB4fOffv6UZNWpGB0pf1yCVX0iv/O+JKIH+J0Frz5dlZWoHPt23egmOa/NLhT/LA0lEAbhrYym9f9w5+8Av3cHDpItMDDfaMWShCNIoRd2JjLENI7hq7duP3H97+Gv776c8DBkeCD3Bu/QvUIod6lMaRmliXmLK6OCLqa3xH7N6x0JdZFSwFNpEjONYc5VhzlOFMC+sSAwRYDzO4RszW/DqznTLNKE276VBK9UhLgU6yJBhSU3YTdghCM+42kGjqKo0lYhwdUcokGi5Pr02yY2CZLalEWXA9zDDTG6AVuURaYAqNQtCILluWaQRpGRBrwf9YuJbjzVE6ff76JZyoDpGSIUMD7Y1UiEGSCgn6PG9DaI4uj1LIeVhmjEAz75e4uXyBLYV1QiU47w2yeWIdKWChe4iMBU93NuEYERkjSBYlocmZHjm7yy8uXd9niUgylk8rTBFj0PFNziwVUUoysrPO8ObaRqpHIxg31hmyWsnbgdZ86olb6JYM3MEecWAxXe7QDGtcU34FRzs2QnSTywtcnZ1jQZUwjMtXIOw3FuUcnxuLX8LryxMfWPwJRvPvxpEFzjY/tWHU3Axm6UbLWHIz1WAJjeLZ+kFuGXgl23K7X/QML/fO0osahNrHkbAl7aPjOrHYxLs3veXrmRZX8M+MK4H8JeK/PPpl/uzQcwBsLw/wybd/L/cvHiZQyUR7eOU4F9sNHlucxY8jji8PkjIyvGPvJm4qfyslu8KTF+b4ub+7Hykk/+U7X8fVE6MAvHPzt/KGsev7JsZZzrZezS8f+xNa0SqGyvIrzx7gbVNpdrsSKRTzYYlYSwwJoKlk27SbDpdodK3QQqNxjJgYwVpvhGeXp/CqLqRi9o0tkLc9GqHLci9HPUxRsDyk0Mx3ChSdDoE2GXKaPN+ZxJKKt0w9R870mO8VeWR9O0iYcusbmjAVq82R2gjPe5OsB2lGnBYITTN0QCSsmV3pRSpOh27soCJJt/fCIK5JWwEBBs9XJ1js5pjK1BjIegihWejmaUUOBprhVBOQmDLGNZPrr5BUwzTDdgtHxky561TjHOt+msdXdxApyVCqxdb8ekKdlDHtyCZv+cRaohCovmxAye7RCpPdfr2e2ejaXOnkWe9kGMx1MIRid3YRU8WJzEBfEMuPbNqzWdqzBYRQyLEZnqw+QBDX2Fb8WVbqP89DjU0ca4/w+KFdjOxef4GuzqU7qMkZIXmjS6gkv3fq2zjbGmTQPce7dzzLkJNDYqMI0Aj+9WOfwLLOMpRSG29NXwmtFYXeb6N1UpDOG4rvzD+AJSzgeeLobXzi4kEqTolXDt68YTl4Bd/cuBLIXyI+efwY3TDRmD6+tspat8u27DCnWotoYCJdpuymMPrv/6YwyLKXt0y8DUh29D/893+NmqijI8kP/809HPjRH9kYv+xkN37ekh3l+tIYj6pFji5rYiX5qyM3UtzTY09xgccubCfOSAwj0SfpxZc1ywEmUzUacYpRt8G1+VnWOjk+8eC3opVg5zUXqLgdpARbdhAIDjXHSRGwt7jEdL7OdKHO06vjPN+eoEWGTe46+b4I1qjboKQ7nDi5iT8+8Wru2HeIHcPLrAVZpGmQJqIaFTjfHWRndgU/tvG1yajTZNhqMOcXiZWgkuqwtbjKxWYZP070yN+//SFOd4e5f2Uny90CC+0SYkVRyXWwDIXup1fOtytcPXyRc80KjhX1i7Cafe4iaSPgcHuMlnCo9VyeqU3S6CVvBTO+ze7cIsNWA6UlkZA0ohQjdp0qyfWXKCbcKtusFW7MXeAv/Zt4rLs1yb0jEUJhElGxWpjEPN3bRM4I2JdaAAH/6vqH+fBjVb2K+AAAIABJREFUryXWktFNq2SdJku9p1j1nqMx8CYuev+WT64tEyPIbGsxYkTUgBiJIzWbrVVsqbijcIIYOFjdwsVOCRCs+xkeXRrnjVM1dhW/i3a4xHO1bcx0LmLKAQp2QMoMua50K1uzX2H6HZ8jEz3F9+U1T3sFxswqBh5Ke4Dg72d/nvvr27Gl5HTzS9xauYHdhTswxJVQ8c2MK3fnJeLqkREevjBDFMfkbIei6/J7N/4Af3T2QWq9HidOerzrrz7Bf7ru1XzywlEGUxn+71tfs/H3Xhygp6tImeyO/LFVDqw9y7WlPbjGiw1553vLHKg+S6RjhNBE6w4qMPjQ43cBCUu7mG8zNrLOYL7Jei/d54snHpy3VGaY7xa5vjCLJRWLsxVQAmKJ44YbueZW5CKlJmeFlK0uWiT5bq1hZ2mN+V4B11RI+WL14ePHNhN6iZb3fU/egLzjcaphdmNcrSFlRiz6Bap+mkq6SyNO82B9D8NuI2nqcWC7s8Z0sc5DM9v47pHDvLZwjlfnL1A0evz13NV0QwdDKDKxj7TAU/bG+N3Y4Ps3f5mjnQm82OLtlWfYllrHi00iDI60JhBC0woc8o5HrCTd0MYmxpaKRmhTi9JYImLIbpEzfBbCEmnhM2w1uD1zAVvG/NiuL7LYK7DQLbJvZJ59AzUmzIABa5ZDnUluy5+hrWz+cP1bsDDopgX/6nUPcm36Ik90N2OJmBgDRcTT6/cw2ykRczk3ffDiEO/afYAQgz3pRfKmByT3oKvT/d163zxEC56pTjGU3c73bf0xAJ5v3I9kDj82ObS2lf983Vu5Y+QrgjiAKAGaEdPnjdkVOipiMb5UYnVYDXMoFJtS86z757h/8SCnWgf47slf+DpmyxX8/4Urgfwl4kN3vYH/9uRBap7H+66/EcswKBppfmr3G7nzwx/hQq2OBi48UOfpH/13CCHw4i6hCrCkTagVhhQo+oYDbsR/PfVnLC2VabQtxqxhPnj7nVwzPko7eIodznmONEZJ2QFtP08SvulTzDRIjZ2JaKkUE7kG5+oDoGF7YQVfW+StxB+y7qc4F5VhyGfT1iXCvkyM1om5g9KCZuASxzDktJLvSHSvs1bIsxcneSqY4h17DlKyu8x1isTh5S5NrQTrQZZm6GJKNoK5YyQph6wdbOTqNUmbu2uEG1RGx4j4wO7P8briDIYAUKxEWcq5HrnYJ4wl+8rzNFWGs+0KiYSAz2vKx5hO15lO13iuMUnBTNyC5sMCT7WnQcCA3WHnABv/f6mdpex0UVpw3q+gkYz3lRALZo+C2UMhWO9k0enkb/JWjx+5+gEaymUpLBEqk6dbGXamy4w7NaQAV4bsSC1xpJPUQRbjMttZZV96notxpa/wCINmHTcTcK5XQaATtk3PZtxoMJLpcMkVCC6nR64pz3G4Os6xxhhKCExL8fByQNXvULAcbimN83h2mPPdVV47upebSz6nFq4nVk2GC/+Jci5p1RfGABR/F936VYjnyEiTQZ2mJQZwM9/P2aUZTNElayYmGZH2mWk//TLMnCv4p8SVQP4SkbYsfuq2F3PJtdZEOmSt092gDXaCgEgpvrDyCR5c+RRSSN696SfYk7+eV41s4cnqcYJYkHED1toSs7jG5iEfU57jpx89wTSCN930EPvyPruyi9yzcA01p0gcJIU+JxVC5DAxuY5MOj9Y6eQ2gsVsr8LSfAkNPKAi5lcrCKHJbPKYowA+6E7EcKqLQcx8p0wvslnRWWZbpX4A99mSWyNUJmvtHI4MuX92N1sGVnlmeRprsIu/nAU0peEGlqmJQwOjX4ylz4sWaNq+jWtEfa1xyZdntrK9vMJksZbolwvFpvRq/3rCw41NHOqMopBIAVHH5uF4O7eNXGBPcYlYSSQxF2qDfGl2DzcMnWdTfp0PnP1OptJVxjNNRlJNmqGDQGMZl30/R7MtutrG7TvuQNLMoxFEJIvac81xjtXGmJZ19pYXWI4zZAyfrOExYHZ4trsJw1Cc7o1wTe4il/a0sr9Ae5GBp2wOtjYz5axzdWaGpspgiQBDCIbNFm8d6nGmPcTfHr2O/eNzHI2uoh7kETQZlIeomF12lN7PI2sPITjO27c8zXP1JWphmmdq0yTTN+Ct9/0ah5ZygOBnbrqD9+7/Fs4uvpowWkIhWK7/IoXMt2PIpIdBuHcg3DtQ7f8O3b+k4Owlk/1ZvHiFX9//nRysHmem/Rd48Qp77AbbHBvlP4p0bn+5p9MVvEy4Esi/QXSiFh869UFW/UVe/4oRPvPwBEobvOvaa4jw+NLqp1DEKB3zybmPwPgAx1rHsYwIxxCYQtAVkLcD7L41W2WqhtvqokgYCUJorh+YI9xm0Kzn6CiHWpxCa58YseFMc8k4WQqFITWqz33r9IWr0jkfaeh+MIWLzQF6PZM1P480xYZ7kNe3PmuFLifrQ7hxxCu3nUJLMKUixGL/2AInrCF6GZ/dlWXS6RClBKaISJkxYdxnkGswpCIKYamdw5CKtWYGPzI5sjLGei9D0e3RlRYXWiX+/fjDDJpdzgZF4kvcPQ16ycFv2yxkczhGxLNLk3iehQ5NtJY8s7KJgVIdK6VphikmRBMLRcpIiqACUFqz3M3hxRbtyGYyXSdSAik0S2GeapQlb3ZxdchSUMKyFL89eyf2SZefvv7jGwJjKUIWejlmu2UG3TbzfhEpBQYxKXy2OUs8E0wTa4OFoMRKkEei2ZuZJ8QkJQK6scN9F/ex7OW5c+dROkaKC77BBb9BzuyRNnbwvsk38cczh/jE/FYCtY13TD3ONaWLoAXXFpfZVviPfHb+N/HsFpVBzepqgd995nHeu/9bWI9iHuvsJkayyaqx/QXayX7s83tnPsSZ9ml25d7Me6bfwNGFxNxbihSvGf8MavhmLq79MlP60xiEqNr7EAMfR1h7/snn1BW8dFwJ5N8gHlm9n/VgGY0mlOv85ve8hr2Z25gsFvBjD4EkijTrq3mcUo6D1UOEKtkJKjTr7TRKKwqud3lQDRfXKwRbLbrKxBQxY2aVH5k+zwP5XRxtj9NeN/FDm5m1MlsG15FSE6skqKsX8BW0BsNQCNlPxbwAEo02TQqWTy9MBK0u7egvnUjFbZO3feJk25+oGvYPmc7VWSONYwTssJcYTTW5v7Y3kb2VikgJqn6K7bkVtkys0gkdTtQHGXQUi1GJSBustHNEhsSxY9aDLB85fxvX5mc5xwCuDOmFNsoz8GYz6FBy5LEdZPetMVRs06hmqAVJjsiQipafImf5lPNtLnSK1IM0SZ5ZYMtkcWlHCatnrZclY/hsctcItUEoLCIMalEGl4SWmLd9mnaGwFbUgjR5ywMN80GRs50kv13z06hs8pa001mgZCS0zU32Gn+9dgsayYDVZG9mAYnE6r+tfHTmZg7VJ1BIcj2fwWxCR4yV5Oj6KHtLC5yo/jrP1m4iUIlOz2S6lqSdhGbQqREF/4GUDnnvFsFH528BNE5vBIDjwRYiFlBacMIf4vTJH+S60tvZkr2F852TnG2fIdIRx5sneGD+JIOy3b/jEevdzzOc/R7GzQ5Gv7CPkOjoxFcN5FrH6MbPgf9FsG9AFD+EEM7/dNzXAx3NgVoE62qEeGmy0f+ScCWQf4OwpI1AAjFCCCqpHJPFAgCO4fLWsX/HD//xI/ieySEsrr5ujshNgqKUmq7nEsSKXLqHbURIoNd26BoGi0Gez8/tZqFTYE9hgfFcjSOdCWIMhotN5tZK+JHNxUYRIRRd30atu2jfIEwFFMZbCKkJlUG20CUOZdIlqQWGUOwaWGbZyyOBtBUSxjGGVsQImmGKjBmwLb+OrwzqUZJC2YBOFo41lWW1nuNwY5x3TR8k7/WoWlk0mpOrw1giZrDSxZQaW3bZml/ntrFT3HPhRi50y+wtzXNBJTlvjeBCp8LJA1sYvnmZQqZD9dEh2uuJwYS2NCobkk0lhdqpgXWanRRSaAypmMitUUh7VOwOd+ZP8WRzikfqW5BopnNVlnp5mn0+uwbygcffnriRVMEjX+yyOVclZ3mca1bIOz18bTKcqdHwU/zu2TuZEAl3vOa4G40+L2yaLBkdzD6FcMhqsd1dZMarsCuziC1fLD224uc33piWW3kGMl20Tvjra50M5+QgwwMNJjI1TraT4HyuU2FbNkk/ZYwsinUcmXDOt6bX6Ph57v7WpMvYNkrYzBFok0T5MuTg+p/z0QuPsSs/hkYTKYGvNZ9ajdmV2sa35JPg7hoTfGH+/YjgGV6REkhhJkky+9avPgm8e8H7LNAD/8vozscQ2fd8zTnzj9V10f5D6NqPgjDAGIeBT14J5l8DVwL5N4hXDL6WU60jnO+cYm/+Wq4tvfhhF51pdPAMYRQCMY/NB2hzCNcN8H0LY9Yms6tBjEGoDYTWpPIeo+Uah6tjXGiVibXB4doEDZUiTgS2sWTMTUPnsKXicGecotOlW02jfQO0IO7arF0skhttEUQmSkksMyTwBEGc8La7OYutuTUCZTDfLSQ665bPeKqOTYRpaDqhxdnmIIE2qbgdhJEIuHY6DitRZqN1XmvBsdoIDx65CsPQbN0xjx+bpJ2gv8tPjJO1kvz+mTso5HwG3RbLFPoTO0nbZEpd1Cu6IKAZpxi+dRl1okLTtAjTCjcdEsYGhlQ46YC37n4CFUl2FZcYdjqc8irMR2X+tn4VQWywt7ycXC9ipjNVVr0soTKQaL5w+GpUbBC0HTa5VeZEnp35HpV04mO6FmRBS0JlsLOyTN7yWGtnqVYzlIvJDloS0QhTFK0etShD2ewg0CwERbKWzx3p4ywFBUJtYImkiClwecOEw92nkqDvxSbrXZeal6XluygNw1aTC+EAoWlz1eACvchkMS5h+smSN2gJhswqUiTXrhEV+dXr3sPmQiJX+7rRn6C68p18pjWI/4L111cBRxsX2VsY5GRrBUi498e64xyobWZbZp1B826m1CGOKIszYUhBBgSYlLv3sqJfy+eXn2JrZoy7Rm8i1h1QVV7gTgq681Xnitaa3zj2N3x6/kmG3AK/d8N7mUh/ldbTS8e3/xBI3oKI5yF8Huwb/9eT8l8grgTyl4A1v4EUkrJ92YPFlg7/dtvPAsmDuujNIpCMpiYBmCgVUDp5yG1TYpkGrVjT6aRAgxkJescKDN5RR/SFmRTgYRHGYiPVobRAKYFWGtuIyZsebxw+wlxQRluK2V6F6XyVs/VR6GuhoCSdVgrDjkEIwshCqaQAacmIZi3L2fPjjA1UGR2q8+TsNAW3x8R4DS0EsdI8U50kUCZoaPsOGT9m8dAwIhKUt60jyslOM9aSpU6Rt15/kNlWGc8wMWVM1Uuz6mUZSrXQWvDsygRdZZLW0caO1DEiio6f0PM0GCLClBqlBSGC5ohA6xhLaGw7RiGJY8H5RoUZMUBKhrx19Aijdpv97hr3NndyTA2SMi8XM0MMsmbATZUZHp3fwnKtgO43+ChlELRtbp88Q1c5lKwmAhh02jzXmKDsdijaXQwJo/kGd1ROMBuUqKs0Wki6sUknyrHq72Kzu0ojclmMSgAsBkVqvsuktc6kW6cTOTzS3EE9XmWoUEIjSJkBr6qc4guLu/ENA8eIeE3lOKf9IWISxk/O9sjLHhP2OlJrLoYDxLpIRno0Y5d3Vp7jxvLOjeey7ExiGjHfnlvkk60xAi2JtEHR6pE1hxD6LJbIEGq5cf86KsXh1hifMWb5seEeQ0aXpTikqxPH1bXm3fzkmdN4KsAWcGL9d7gx9ywSwXWpQWwdgTHO6fh2funAz+DFPv9my1t4w1hCDjjWuMjnlp5DoVn2Gvz+qXv59Wve9bUnnLkNwueAAHQMcvTrmbb/InAlkP8j8SfnP8fHLnwRNPzQtjfyioFrOVZd4arKKANuwlH72/k/5cD6g4DmlYOv501j38twPstvfM/reP8X76UVREzEFr7SBFpgVWXC6VDgeyZeZNMObARgGxHDbgPLiPFjARJqKosVxdxSPMGe/AIaQTeyeEX5HL6a5WFzOxeXBwk8CyE1ylJJobBPWXwhhC84szJKrAwa7TTC1LQjl3bbwZhX7BpZpuqnkiDe75FXGuYOjSJ9AEH19ADTt18gsCRRT3LX9FFsQzGar3OwuYV9w0uEseT5tVH8aJJuPUPoCiwjIlISKcEQMfvzC0ykGjzVmMYLzaRV/ZJYVj9VIfosmFgZgEJp2RfrknRjweerO3hFeYZxq8nV9iJ/cPI2XjV9uh/Hdb/YCWu9LGu9LNpKfD1FpDANxWRljeWowLDVvEyTVODKgF5sUwtSDDpdpIypigwZOyRFk3qQYk9ugec70ygMTnsjxFxWMQwxCLC4t3Yt+7OznOsN0Y6TbtGRbJsztQLv23SAUJvYrmLMbRIqg985dwc5w2fP4DKmEeHHBlPuOo6Mk7qH0NTiDNUoSyNw+cO5PfyC/RDtKM2QfYyUcYii3sc280musZsc8MoIIRhPNdmbu5qjrVOMuiGrfhZfmaz4iQGGRvBkd4gvtmrcmZtjWZhAiMCmx/RGp2eg4XjboBlvJ2t4BHqOV04dRMg8v/XUL9OKkl35H577BK8auoG06b4onSLgf9k1KvI/ndy56DQi+16EOfEPHv8vGVcC+T8CsVb8+cwXiPuV/w+fvZdffPhphAApBPe++T2MZ/I8tf5pJu0agTJ4ZPUzvGnse4mV4icP3EvQizE9wVInImPH7KsscKy3GY0mTisuLFYoDXcu7aUJlclcp8xwpg6GoBmm+8FM04kdTrRHea4xyRuGjpAxArJmwJtGD+N4EGcUWglOL43Qjq2+e01SEMwYIaEwCHtWv+U8SYvMrFX6T4PgQmOASqmDbSqylk8ntFFa0q27ifgXAiEVwoxpBS71lsPNwzN93rFk1qugdNLmbhkK146p+xmCng12iN+1WekUmRpZZU9liel0Ij415tQ4HQ31hb4S9ULXiF9wJzSmkQQytEb3JZ6ylkfVSHNvaycCzYTXwostnl8Z45rheQRgiogj6yMcXxvdaKU3hnpcnVpge2mFuptYpXWUgyMvtfsL6v2cejNKEyibHdlFoL/QaM2EU8MUCkMki8tXLphaJy3/GsGMN4gtIm4rnMHXJodbE+wrrdDRLjPdASJtbChLaiGZ75TYXVwia3p8T/kEVe3Q68seD5t1loIB5rs5jrdGGch0+PUj9zPidHnr5AF62mY1KOLI7YSaDQZQrARe1EGisCSMp1poPc1yr+94JBRCaM4EOSbUddjZH6DduZuSvZlS5ocQ/AmWAAhp6QxHujkMFK04z7fK/D+Y+96dn+A7xm/kb+YOMuKW+JEdb/iaxybXOIUo/OI/eMwVJLgSyP8RkAjShkMrSpprhDbpBAEIsA2D+2dP8+5de9mbnkcSJ4U0M7m0f3n0MO3IxwytjX1xGBvsH7vIkc44pU11DEvTaVoY4nL7eQLBcK6NlFCfzRE1beJCwBPdTUSugUJiiXjjeCk0I0M1PJIUynVbLqCU4MDSpv55x+TSAULDxaUhkl7BJIXRFhbbikuUs10sYkKZnP9Quo0XmZxfL9LuOpiVkHwUMHbtMkJoupHJ5soqOwvLgOZAfVtfLrffuKQh9EyC1RSprEenljja97Dwaimmp6rJAqAEs80SZ2sV9g0vJXonaLzIgI2mRgEKKtk21V6aOJYJvVEotBAE2kRozafX9iDQzLfKLLSKbC6ts6e8SE/YWEZMECVSBkJAdsBjSWap99KMZFq0Y4dQSSIlWPELyd3v/29PmdS8FHY6xux3uQY6cWjanlribG+Ii90S8908o+kmRadHJ7LRJNZ77cjhDZXDWFIRK+g4No04TTNOo4SBRBFjJNcsNtFIHl7awQf23c8X29vY7SzzpfYONIJeZNIOU7RDi0LKS5qZHI/VepqPztzKYKZFIeUnImcyJtUXKcvKDpb6FLtcTT1OYQmb75r+USJd5ucO/wadKLEtrGLx8ark0Pm/x5SbEcB1hZ/jrsEu53sFpuwlDrR2JPUYDKrxjo0g/hM73sWvHLt7I7WSNt3+9Rb8+K438+O73vyyzs8reBkCuRBiEvgoMEwy5e7WWv/Xb3TcbyYIIfiNa36I3z7xPzCFxKsOcdxbBDTKgW2FAXrRKpY0UTp5sR52kmCx2G6BqVCWQsQSy4zIpXu0I4stexZoxwkdLl/02ZRZ52R9KGlO0YlxQcYMadUz9I4XUUrgz2XobO2QsjykAQ+tb+f1Q8cAzcVeqa+zDY0ojR8ZVJtZXGJ8TEYybXKGx8zqYNLKL/sx0orIOj0802LeKyHQlO02qb7Wdsc3uXV4BjmmOVwfJd83sgAS6VURY0o43RqhG9mYhsIWEZFOCq8LKwVEKiRd7hEj8FqJP2hPmpzpVJhM1RFCs39gkUaY5tDSCOV0FxVJDDOGlmRqeI2C3aOpXWyp8JW5QRlphikiJTBlYgEnbMU2e4XTjSGKrsf1lQsoDEq2h5/usNTKb2io5x2PuV6R9SBLyemSNkNaoUMjTCOEQKL7VndJveK5+hQDzTYTuQYzzRJj+SapfIglInqBwdlWBY3gfGuASthhe36VtSBZlAwU7dghi8/nFndzfHUMTM1Eoc6tg+fIGT06ymWxk6fXp1VGWtIMHUwR8WB7R39RANeICXWIIdULdsGa3cPLmFLTDS2UhggDU2takUM3stldXMDo37ui0UMIxcHqF3l8/T42ZRzuGHofB1bu44FFk25s4zoRipAxq8GtmSMA7HahEWU42fWpx4BwuHPk1RvzZU9hCx+79T+//BPxCr4mXo4deQT8lNb6GSFEDnhaCPF5rfWxl2Hsbxrszk/x4Zt+ikgpdt79O/1PBUZk8MrxzSgdkbOm6ITzKB1hyHFm2k9zy/Qwf34xpmdqRlSTW/afRQjNeitLtZrFzvTNnJVg1Frn4PIWYkNgKI0IJdGApLqa2yhSaq2hbWJVYpQSzAclPr50HSW7S6hNKnYnYcDEkmdObSaKJDoSoAUtsuSyHgNum46MMB2PPdsWcK2QYadJLUqx1CtQ81PMNgeIlEHFbXHX+DHSRoAGht0WB2tbNpgmAr1BiTtUHSOdDsnbvRcYHsNwpcVyJ0e9kWH7+DJRZGAJhS8kC70Cw24bCRgS9pUXmGkOsNpJKJz4mtunz/DG8cOAoBG6fGbtqv71TwKSa4TMdwrkLJ9akMKTFvV2nmsqF3nF0BnWgiwzvSIIwXC+jW3G6FiztbSGbcSUzQ5VP83p5hBKCdbrafZPLCepIakIlCRUkl5koZCcbAxzqjHCVKlKR7k81dhMNUjTi03YeM+RBMogZ3oUrB5rQZaFXoH71/Yi0Jx8dhrVSXxOz2+22VZYRWvBcKpNrZei4zsIoSlnOigtWQ0LL+oPuPT8SRGj+ukWQyicflNZ2goIYokwFJC4RzlmUpuwZZ89IwR7yz/JPQt/hUYRqB7P1O7h4+cHaQTppFbjRUyWGuzOzG1o28dasNtpc8f4QZ7vVSiagv1jv/VV502kAj4z9wvM9Z5n3C7wxoEbsbLvRcj81z8Zr+B/wjccyLXWi8Bi/+eWEOI4MA78HxXIL8EQgrRl0wqSrsu8Y/PBwx/lutJ2Xj/+Rxyu/RlPrN3DanQcP/w3ANw+uYun6jvYll9CGknwKWS7uHMxkSMQRtKi/tkLVxGIhCESA0Zo8uihnaQyXiKPqnVSd8xEXJ2bY9YfQBgQYLES5NmZXSZveqx7aVrdVJ/l0t9293OvfmzgFQSDdh0zGzPfK0APWq7DVaV5OrHLSjtDEBvEocF6nNnQ7Q6UQTXI0AxsKm4XUygmU2s0fZdPn9xFtuzRiyxCZWwEC6Af4ASxhpVmjunBKkGcvC10aymi/CKWkUivdsIXN5JoBbdUzm/Y0BXokTN6EIiEhigUI4NNEIL5boFIG7Q9m7Ld4pbB8zxW34YUmqLRphs5ICTFdI84hOdObqbezrB/eJZXbjmFY8Q8sT6FLCvaoUXaTLRgtIbVdgbftzGtCLRgrNCg4CZMm1glDVCOivq58uTcN2XXqXsuJ1ojmDLGNpPds9+2UB0zETAD1FKK3i6bUsojRrKlWMW2FLEWDDtNnmhtpmD5GOhEhUVDN7aRQlEye8z3F6mS1dvIQgkgZcZIocgYfn8JENxz7kbePPUshlQ8u3Izr596DfAxTBGhlMCLoBkouOQkpUxuz5+Hfv3DFAqNoGy6ZGSX27LLgA2qBYbLV+Jo4z7meoeIdci8v8Lx2l+xPziAqHziG5qHV/BivKw5ciHEJuBa4OBX+e6HgB8CmJqaejn/7T8ZZpo1PnHmMBPZAm/bfhUChRAGf/rGt/DzD3+eSEeEhfN8aeU8j68dp+aFND1FW0u2pVobDjnfNniC5xqT+JjESmDIxHCtu5yjdToFUlO6Zg3fe0GzgwYtYwpuj6ZtoLf0oG2gAolYSvNUZxcje1b66QVBN7A4tT7MSKbJVK5KENc4qsehzzO+RGOUhkZK6AgbV0UbPPD1ILNRaI1DSbeRQsWSHi4HFjZzw8gMn1vZQyeyiZVguZVBKMHZboWO51IZaNIIXGIlCWPB1nwV24hp+i7tvoNPonmSGEB0fJvGaoamIfjYE7diNQW33HCChSBL3O1LDaSipBDbLlNxWxtB5Hyzwvp8Cfr89OU45KotswynWjy1MMnWwiq3lc5ysLmVSBm0QpfAMuiGJj1l0w4c/Mhkz/ZFupHEkSGjqSZLfoGqyoGAduTQiy3SZoAfmHS6LpmMTxgalJwulXTncjdQX/LA0xZ520MjuGvgCE3S3Lewl1gbGMSkrYC8G2BYSfotWV81xWyHy5ovYKAg1OhY4JsWQkpCFWHKmIbnMtOuoLTgusJFri1cYKFbIEbQCFMMOa1E/ExIMlaU1FC4zPpBSZ5vT5G2AzL5OR5c+WvyRkw3VswHAxB0uHmswzPL40SxwURrX0GsAAAgAElEQVRW8R0Dp/jr9Z081ZlmxGpywS+zNXcn+ejDSAyk80qQla86h7zgebT2uNRBpYghOv6NT84reBFetkAuhMgC9wA/rrVufuX3Wuu7gbsBbrjhBv2V33+zoRF4fPvf/yntwGcq02Gf8w5SRhVh3cW1wx/mvrd9P5+ef5z/99QZYh0ThYpfOvBlpNCYxq38wg2fxr6kYicgbQacbZawjJic5XHqxAStZkL5Qmlqzw2iLA3DUcIMUXDVzhlsS+EHJscujqJMEM1kd9tqpDBW8rSVjTRUUmDUktO1QW4bE2QdD0uHGPmYouyxXi+AqclX2igFXs/CsuNEewVNweyx4OVZbOW4vnyRz1f3cym4PLmwBSudGDBokjRDjAQpMHMx2cijEzoIM+nfbwZpnlzMsL2yjkQzVqyz0syTtn0mSnXWemk6ocPgSJNYC5SWeDmHR5Y3QdMhDpPHUgUSt9zl3pV9dLVN0e7y2PpWun4ihKX7e89WN7Wxc75lbIasDOgqmym7yt+cuI5QGThWSDbXxe9L4AoUjggpp3yOt0Y4UNtCzgzYuGEk7fKL5yqYZkxu0EtYSjJChYIBt001yKJJUhot30ULgeozhFaDHPvTC3yWvclYGKSNgKzRQ6UEu68+z7nT4zipgL1XnSNjeATKoBtmOL02SLWbRiNY6+bYO7LIWpAi9A2UlNhGyLdVTjDh1jBlzN7sPLalsGWMFND0XM42B3HMiK2FVWwrxhCaSGmGShYZO8A0kjTJ09XPkDd9WlGGWEtAYRmwa2ANL0rzS/u/D9M/yA2ZC9zf3MM5fxhXKH7y5Dwj5qtIyZD37/mPTH4NtspA9AnyMktdpagYIXvsGJw7Xp5JCmjto5u/BMHzkP5eZOadL9vY/zvhZQnkQgiLJIj/hdb6ky/HmP/cmGnWUDopdL1j00O4MnGU8fzPc//FP+J1U+/l5oFd/IH8DAWrx8mFoT63Ofn7h9Z2ckflJCYxfzp3C7UgjWNGnGkM0IlsosAlle/itd0k/aFA+gJ7zkabGltE2LsS8SvHjthm1zjnD2BPt4jaJl5gsuZlkp1WpJDmJdNeg4VOgU51CDMfY2dDIgl3bDrGqZUh1npZwsDE6xkMOm1SqYDFapm1Wpmj7gRSKjZtW9/o0xMoHCdk2S+Q5OjpFwovF9gG8i3mvTKWGfWZKoJQmRxfGSIjA4LQJJf3sGXI6eogeafH9tIaAO3AYa5TwMkFjDstTq1NXh47kJQyPt3A4sG1Xf3PNVm3h2Ep4r4MyLbxJUIl6cY2YWiQSq1j2vDcxSn82AAkfmgyYXisKAspFEW7x1iqztHGCFLHZO0QT1k4MiJQRnKv2w7tpRxmLiA/0Ei85oCUHbLQyVP3U2SsgLQdkXcCmmFCYTSI6WJzIhjGlAqhEtHhV1VOU43TnO4MExdg+oY5JCHDmeTtTclE7KEdTGwUNYXQvL70HI80diIzgjU/gyE0x9sjbM2sYgm4Pj/DGX8ULRJ2zOMrm/GViUCz2suwb2CJittmzc8y7wXkHYmUCqUFOREiiDHF5bcEiWJHboVNrs/x6vtRWrESjVC2e/1nzCLoBGx3TpLvRTx97kcZ2/unGPKyKcol2DLLK9LnE0s8nefB6L0U41u4SauX7D50rv0Ez1Y/xZC7ndsG34khTHT7v0Hv04APrd9EW/sR9tUvadz/E/BysFYE8EfAca31//ONn9I3B7YVBkiZFpFSaGGiEBttyPcuHuZ1UzDkFvnoLR/gx5/7GbKuT6vnJsFcCRY6Rf5GX0M3cljo5dFIvMgiZ/aIVxzCwGRovMbCqeEkryk1IhWjeiYiFIQjMc8vjLO9skLWCbhqxwwrSy4agZkLiWopwg11wEsKiMn5XVgt4TeyFKfqZKyAMDZ4dGEznXoarZPJnnE9Xr/1MB9+6E5Uv7tPdzTFiRqn2kMUSm26HRchFCPlOqAxian10sku1NRJLl8YnF+rEGGRd2JAE/sSV4bkXI96N00cmdAF4ShCZTKU6Ww03WRtH6cX0gsdpnI1ltIV2j0XhMZNJ7l514rwwySKDqS6pKwAJlfxPRfXCXBTHp3IASEwTM259iDXuxdIW35iJq0TLXIpFRrFtvwKO4urLHZznFofJlIGbS9FNuUjUBRMn5SMePb0DrSWhE2HsGth5wJAUEm1CLRFyo7ohDb1ehbHihgoNslKD8uIGHabCCm4pjJHEEl2pxaxUCx0xzf44gBZK9xoszeERiqNjhJjEFPEDDpthlMd0r2QepihFTpoJMt+gWcbk9xQvMDR3iTNKMUWd4W87CVOSyJp7mkHLqcaQ5xuDWIbEWNGnS+e28F0qYofmwwYbW4cvcCQGTGc2sXJ1nEs4WGICEesJc+mgEGzQSNOOPW2hB1OnXu/fDPLrQKxliw3PsDuzQGT2TewufDddKMGRxsPYFnvo8RHCFSPP1i9lm58DEOcZN6b5y0Tb/9Hz8eqf5G/m/sVIu1zsXsYKSS3D74b4gXoq4QiJKgl4Eog/3pwO/Au4LAQ4rn+Zz+ntb73ZRj7nw0Zy+a+N7+H+y6cYix9K4v+e8nLDu9/4J0sNMocP/ZH/P53vZ7N+WHQgvFyDYmi0U3T9lOcWp7CMHx2jFVfNG5rroC/lk2CfWuI8U3LrC6VyQ+0UYWAtW4RJTRYoGLJsblxlBIcSo3jpH2EEAgJwoghurS0CGwVkUoFdGOLaycWmLlYYbxYwzEjhNBcqBfp6MzGeVhmzJdWdibB/wWJrupcieJ0HduJsZ0OWkPZaOFjseJnaXVSyczWCgNN4NlJgw6C+mqyI3PMAKFNhAVWSmE6ATLQNDspTFcRRMaGNjmAbcaU3Aa1KMtte04yuzqIEtCVBjEmhlSU0x45O0QDndAi7USM5NZIGRF5erRFkooQAmwrZrWX4ZaxszR9l9VOnk2lVRrSIWXFXOgMsCVf5XQtCeIgaPguA+k2O/OruDIR5VqcHGB5tpJ0Ia6aVCrrNEOXkXQ7kbQVcPDUZhbXSwgBA6kWbTeNigSdTpo92QVKZofTnWF+//nXoJTBSLnG0GgDgLzRY9quohEonVj5rQUZBsttpFIULI+U/f+x997RlmdXfefnnF+++b77cqr3KnVVda4OanWrgxAwSCIIJANiCAIjgy1mGWOCPcAAtgdjvDAYw1gYRNAIIQYUUaK76SR1DtXdVdWV36uql9+97+bwi+fMH79brySaBRKDgVFrr1VrVdW64dxf2L999v6GmFO9SWatOvXwasWbDq5Noshgv7NF37IxSdtongjxMVMRLt9koz7C1GSDt84fJ4fPsxuLHN+eQQrNTftWUlVHfO4Yu5dFL+KZ5vMoLYiVgT0cMqeQWIEUBqaYpZDEbHZK+HHaqvrTl0r886nP0AhewbOm+Njae+nHdRbMdUpml0uDEc7UPabyPolIeKl57MtK5I0wVY+E1Oxi27+QXvnZ70cHD5JuaSfBvvtL/syvpPi7QK18nqv77K+oGPWyfM+h1OX+VOvP+enH/oytTrq9XKo3+P7738tP3HkvCSmUa7rSptkvUMl3kELT6ZX4jtlv5dMbL3GytYklEgY9CzVkVCaJRHgJN951FldHPHb5GpR9xQBBk8QGegg7HPgOlhNjmCkI7dD0FscvzaZDzERTdH2+bt8p7r94CNuIGJtq4VrRbuU76gzYtiLCyMKzQmZH6wgTZhaqrF4c22V5giCJDKSVokhcM2JgeCRaUsn2aXYzaCVxdUy/kUUokNk47ZkPdwUiExOGFnHXo5AfpNWhCZZKcK2QrX4OQyosQ7HVzeKaCZfqIym2vGgQWHBdYY1mkGUpqGAZaXXaamXQQmN4MZNeJ21bAFkjpGz4rIYjABhJzOO1fVyXXeMbF19muV/h6a0FVGKibY3QUGvnEcO6WIqEu2aWyZoBWSvYPWbXzq2y3cozWuhRmOzQjlNFSFOqXSz23vEajU6O6/as4mYj+kNeQKBMGlGGc/UxTEdTKXXphxYyGyFVzITb4Z3jzyDQRNrgs83rOdOaoKudVJM+02POaxBpg0frB8kbA/qRiUhASUgCk5dP7edb33IsJRhpwXaY5xcvvRk3G2EnMZaRsDC6w0uNRX7upndC/Dhh0uQ9hx/iD07fxXUTaxwup6JiSgsu9Z7gTVM/wbHW9/Hg0h6eNRa4aeoyRbNPLS5xrjVJ0eui6KIjsYvFt2SMl/fxlUnGUOz4p+jHLVTss5M4lM0uc26Dw9kV1uMxyo7ixtLNX9a9OJe5HtfIp0gf7eOIkKXOo+zN3wtjj0KyAeYiaZf3tRdfZXZ+iXG4OMt3Ld7Dz134CxJ1hcKd8LG1Rxi351gbXCJnDtg3sYE1HC4Nwha3jHwv79x7D2//wB8yKD1LOKVYWbJJlEQLwYXmGNs6i+klOHaYQuRIB2iGofCT9N9XfDjzBEwU2jiWYnGsyvork8imSd+w+Lh/MyqXcKYxQaIFBSfFdysFEZJ8uU/e8XFkjG3HJJgUSwPsQxucPT+DDAE03a0cufEulVKbSqaPr+xdTPXkSJuBb+OsS+LqsGncFVT27VAPchQrqSqgYYBMNGW7T5CYdJSN5SVDGJvBajcVldJKs9nyKLkDFstV+qHNd+15AteIiLXBI+cO88DStejE2N05jE41mJtr7Vb0j2/u5Qf3PcGcU0drzbPtRWbybWqqwO+fX0ALiX66SP6mOvOVGs+d3sejqgRSU5po41oRthFfkZQBIEkEnXaGe645w0S5SZBYnOmOUhvkd1+jNHQGLnccukDWDejEV6GTaVvDIRQGMo4ZLzU43x6jNsjT8DO8fu8SpkiHk1IrrG5CtVsYtpM0RwqbBInBseos/dimaXhYhsKQUDteIWzb/NSbP4olEyD1WDVF+nch0j5+2fHpao+i2+ejq5/kjZN3YLCOtNcJI4tWP0uixS666kjxjThGhnfv+xU+dvp9TI2tsxJWuBSM0unPcaGTcNTrpcfd1lx/2zm2lkfJ5wbcdO152kkRWww40/pTLneKPFY9goFiX7bGP537PKaQ3DN2HzeV93DbyOu+6P7SWqN0GynyiL+id24bGd61739wsvlpnq+9l5XeE2z0n2et1+KjKwFTXpH3HFog8xrNaK/Rn/3qeLz2NJ9Y/zRT7iTv3vt9ZM3Mq17zzYcPcf+5czy8fB4v51MZ7zHhLvDZi11cM89UgVTHJDbImiFZJ+QHn/svFEyX9eYYve0ZFvZuMjFfY61RJrYEYND3HQqZAbeNLfPM9l78xOS20YsIU3O6MUU+55Mkkq31Ap6rMUvpjTfqDthuSxQClRjoyxmSQ/2U9YjgYmuEsUyXyLdY83NM5HsYQ1JHqMy0qtTQ79sYMmF8qsnWZgWVCFrVPDkVYDkdfK66SSgtkFrT3C4ihhWZCCTNXh6Mq4g8zwqZH21hSE2QSAbd1HUoVgYqSZONaSgKxoCBtLh1+hKm1CRen8thhUOZTWyRsH98k88u3QT6qvRXs5YnntnCEIqdXpZtv8ivnX4TMlHU+6kL0YGpKlk3YLLY5cLSJG5gcHhik1ovi1JD8S2lMQYgXI0UqXZLN3YwlGJzc4RjL+9HSM273vog0uoz4bT43M4BLrZLHCluUg2zXO4WmRlLdcpzZkCkDcLEZKeTYcvPM11pMJ3rUOtnkZ20X59og3OdcV5fvpCae2jJya05aq0Ck3N1bDPGFIrPVfexE2SBdNeVswdk7ITJm7dwRcgtExfpamtXR+XxxgFcGXJv5Rxlu8+5/gSbYYmJXIsnqyZ78idTCKd2+JZrj/HBY3dgyYSFkRq3jL6VPblbARh357l+PEM47N1LYDJrstxJqPazVLw+Uki8YsAb73o5HdRqwf783ez4DxEmOzxfP0KiDRIMzvXGOdefYCTzFr5/8RuxjS8eiirVZ736bYTRSUxjiunxT2Ia4xxrvMIn1x9mMTfHd8y9BVt6WF+Ajol1wAeXP8KjWwvY0qDqd/jlW9/+d5AN/v8XX03kwLZf5XeW/oBQR2z6W/zflz7ED+/7gVe9zjYM/se3vo3T7WV+f/njFKxJfnj/P+HJtfdyeGSVjajMlZouSEwgRqFpxgPaQYDjxiwvTxInAmM0gGF/1smEEEGY2Lzn8MMAmFLz0cs3ks/6qUGxoSgUAjbXyoyW2xQLAzJWsLs2jUZLjUCRxBJpaLqRQ6OaIW6aWIUIo6C+EPrMjdnLNJMsc+YOjhtQsH16oUOnm0k1NDKCZ0/s5eC162gFG5cqtHcKCKFwbAXxEBs8pMYXyt2UwYPAEsnQ+Z3dYSpAf+AQDQeXo9kOtctjlCZTidsgkfjKohrl2KfS/v9Lm3NcXXHKUCznelxsj1BoJjSMVFu9FzlE/pB4pAwuVUc4MreJYyWUpjr4Z20cJyLH1WMmhCa2JCUZcL41ymKhTojB4w9dR+Cn1bWUio1OEc+JeHJzkXqQ5X8/8pmhcbSm0c1ycXWMhZkqCGh3XE5szHJloHzI3qAeZhAmjGdThisIusLj+d4C884O22GBl6tzCKExtGJfdodQGQTqqrk1gNTQD6w0+UuFQqZa4VpSD7M8Vd/L106+woyXOgndmF+h0cjiJxZSpfouhkwf4qvhCHcdOceeTB2tBaf7n2JjcC9T3l4A3nngTv7g4rndIfrh4jam2OFMq4JAMWZ77EQRK4MSZctn3L2WhdxN7PgPAVC2+3RjZ1eq+ODIPdQG7+OjF3+XuyZ/kdls2svWWtPt/xlRfA6IiZMN2t33Edj/lP90+rcJVMiJ9jkAvnvPNxNpj25i40mJRHGxOwZAqBJOtTb/2vv8Kzm+msiBVtROoVAaYp1QC3b+2tcfKizySzf+KABP1/6E79z3OIO4z/pO2i4QpDZnjSC/++8DN14m1gYXlyfQkUkmVlQyfRqmhWpZtGpFnhJllicmuf3acxREn0wY42eukoRsNwJbcXxpnnfc+ixuJiY+ep7jx/cSIAnGEvJ2Qq/tYpqpAFfsS2bHdmjoLL3QImNHKSPTrZMISdEacLw7TSPMUtsukZzNkZWa7MEm/aaNqMSc3xkDLYgSC2UpMiLi4IFV1o5N0cuYRBlBKnku0FoymW2Rs6OUWLSdQbgJyLQVkSbxNDnV+zluPXoJNATKYCfMo4F6kKHZ9WgMsmwkZYRM5Xjz2QGuG2BnQ2JtMLO4Rd53aFS9v0RfTxO+0lAfuEipEUc7XFwaZf+hTa5fuMzy9hjCVeQKA3xsAt+k3s4yWehgFkL8ID3uemLAZzdTLHikDG4pX8I1ItyhF+hNxUv89vJ9bDXyWGMh9V5qL2cZMZEQDLSz23OfLzfxzICiG1ByB6yEFepxnvVOkXypR644YDbXJOuEdJTLgWKVY8MEP5et41kR1aBAVgYcKm/xQOcwR73L9GKbX3v0zdSSIt50nNrBkbbjTGLqvTxvXjhNqG2iRLM6KNFLHCp2Dyk0Umq0VpzpPIshKvzKqU9wpnOJCJesGTJmd1kftJjN9hj1BvQSG0c2mTbATyzW/T38m8P/mqxpcLr5QXrxOu+YvcyH12x6ieDb5xeo+X+KIgINL1R/jdns3Ty/c4GffPEDXO+d5Ydmo6HbEUTKZW2wdVUyV0Us91Z5ZufTPLD5+yjt0lcG373np/n02hNkzRQq/L8u3v63uf2/IuKriRzYm1tgITvPUu8SAnj77Dd/Se9b6R3nieoHMUWXvBnx+vISF3qjdC/lWT+xB/ueNtros5itstav0K5niGNjt3/6uj3n2Tu5ye89+CbQ6fBoc7PCxuI2U/kGDcfDkgnxUBJWG5rxuQbb9Rx/tnQdd8wsE+YU7XmFRu+yODO5gF7LSwelRsJOksMwNA0/Qy9M2F/aRktBgI0rYo6Ut5hw2zy4cj3MxlhbFq2lMmF+2DrJRZjlEOmm8MjFmQ1uX1hCzy3zu4++ERWmEqi99TyF+Ra9yCFnxVx6fJaokkDfYNCz0QsBhqPQCpQS2EaMMZQsqPtZFCkiByDjRUxktlk5W+HGvRcxMgkrOyNsN4roPlTKLURFU8kOuF6ssdEr4jsWO+0cQmi8TMBKu5RqixgxlXIbxxmw1KpQKXQYs01iZRJGBv3AwbVCdro5ttpFZEEhjTDVSSgnaG3snvNtP7/LgA0Sg4utMUDQNWxm3A62FVP3Pca8PhknJFESOSTfKAWJNFgdlNkO8txWXGbQsWglFm899BKL7g7nB+OEmAgERSfgm6dexrZj8nZIrCQfWb2JgW9R0wPMEc2DncMc9DZ4w60n+NjazXy+to+3zb4EwNagwOOX93Lj+BqmULsMX2fYVx8kabsLNImWfOj8OS4FJ2lHESmHNksmG1EN07lAL3HYnwsxY0E4JExkjJBu7PPvX3w3vf4Cd0/8a966d46CPcb3HDDQKGI14CMXr1DyBY5RAuBnXvpjmkHI48E8h7J7eX3xImf7E1zo+NSiXyRWJRyZDo+/ceo+Xmp+kEinOypT2MQIPnTvD/JUdZkxN8eR0vSXd+N/BcVXEzlgCIOfPvzjbPhbFK0COTP7N78J6CdNEBAoC8eIGLX7mJ0aH3/kCFpAQfbJZdv0Oy6Y7CYtSCVnPTOkq1yyXkC7M6SlC0VR9nm4fhDHjnf1uFMJbsEAiTQVpp1wbGeO1k4WJDhuiGXHqBgG/ZTtaWVDklASDUxk5orhs8AwUhu1TuIxYbfImDEvb82ichokBHtCRC1tWQDoroXKh6jASIeViYFlKCJtDHHpAAIdSxjiobsNh+iIn5JoVITTjgjc1JFHyBQtUil0SYYu9tu1EiOVbqozrgR+z2F+pEmuBacv7GV0ps4Whd011VsFapUsE9lu2mLwAopewHixS9N36Uf2UMtEY4qYO0YvUXb6CODlxhS3FC7xyNZBtnfKQ32SzBVJmhRlk091z4VKHy4aIIGV2hgf1K/nzvHznBuM8uDZ68iaIfccOkNf2QTaImdE2E76kIq0RKq0B98MM7uollgZ7LUb3Dm6xCCyOasqGAYclJuc7M2iBGzXCjzbn2c7ypOzAt62+BJqYFA/McrTYgzHjvi6+57j/3nx9ZzenkbaCZcmBL917m5sGTGS6zORaxMmXzw8dI0Q0PQTmzONMQytWe+UuNwWjI2EyCv0BBjyJ9JHVyvyqNjjlKwcS72TaK0ZRBZPnZqiFyyggb84/ygPXdrL+77h7cOrwsA2crxh4j/wQu3XcYwid03+Ox7bOsfldojGRErFe1ffwO+t38GPLDzMHd7v0UiyCA5giAn+5cGfZcobpx/fwlr/LLFOGbjj7jyOYXHv5MEv6X79So6vJvJhhCrhvWc/z4nmCt86dwsV9zLrg2XuGnsrRwq3/JXv2Zu7jZI1SSPcYKBzHHv8Rl464aEdyZ77LqGMGKXh7PY0kRKMVLq4XoA/cJCZmNN6lINJjVtuOM/xUwuEkcnEntSppuYXKDk93OEAUQ81OEwSFkfqINMqVhga00rIZIMr8O60DRKYJJHB4vwWzVqOVi2LNBXZTHdX81wDrdjDkV06wVVDYQRISw1hksN9ui9RvoFlJtyy52LK1BMaJz/A73oINJnRLgXLx0wUW0E2TeLpPBeRS+ALEoo0FbV+jqwdECqLRidLq55LWx09m5n5NkKklnhaC7ZXyzCTDJeTtk7ONsfpDRzCwMItRCnhRwNKU3QGRMpgPNulaA2oOL1dyOA1xW1ON8aIImsXh6/ReE7IILDSYyg1qmOhlImZTymkqm0hBwZB1uECo3xi9SZKjs8P3/EQp1oTvLAxjzQ1E6UWcthP0Uh6EdhmKilwRVpADofOnhkjtMALY0JMMkbEiOjywEs3sL06AjkFUyFdHB5cvYbuRg6UIEEShHD+4jRrnRIgUKFB0HaQZcVktkXBiciYMeutLIPYxDVT7RpDwrSXkryeurhIdZDnSmWe1z7b1TKGqShUOsMBcyrjULAGnO9axIQcyt2KIQwevBjTD68SnCIFj64u8eGV3+BrJ99J0Uo9OWdz9zKbu3f3/H/gwgeuHnslmMoWOZR9hmm3jRBQMPrMOzUCuYcpbxyAO0ffRsGqsBOsc33pHnJm6cu8y79y46uJfBi/e+ERHtw8QahiPrPxIRbyLRIdcbF3hn958JcZd2de9Z4ndp7mXL9ErW9y7MwY8ck8aiivsnO2QnzQJ2NHZDyfu+fP0Qsc9mdqHOtNgxDEQ853xgu5/eaz+InJ2qDEQFuUvAHNZhYr38dzQlwZM5urM+21ONObJNAmGSsiDAyi+AtOowDDUEQIlAKlJKVKj0atQOKbdFRmyADdlW3i5c0p/MgaEns0hqFwSwN6zUIq3mUrCC1uPbyE48Zcjso8tzVPoiX26IDCeI/ZfAPPjVEKzlVH6WsLSyhEkmpbaXUlXaahehK7GGEI6EY2zohP53KR/rpLpdBmrNTkoWdvoNPPDH+WgdXQJKMxSHBzqanzhl+gdznP3HyVPZM7gKbZ9Vhtp/OKvBXgiIhHTh9mo1Vi/9QmB6Y3Od8eJ9FXH5KCtAd/cHyDy40KzWoehkPZuGkgzYTZsR1uGl/h5MYs66evYcTrcqi8TTN0+MSpW1KjCwG9gc2BmW3QkDEi9jg1TrUm2Vfc5rwapx27VKwetxeWUTr9/tVemUq+O8Rzl6gtjaaVcEMilUDN+2z38wgpdk+dBrxiwBv2nOGh568jiCw8I+RrJs9QUwUUEtPQjGQDHly9BidMiBKDQzPbjHhdVrdGiHo2ptJEQmBYCWvLk4SRiZQaT8UEswGm0JSsPpKEWtQGBM82Okxat7FUXwdVvHo5CU0l2+FY42lW+mf40Wv+G+vtP2Sr+ycUnFtYHPk3SGFxuDjJc7WL+CrGMRx+644foNnL0Oi/AiQIBBmjwNvn3nP10haC60v3/H+91b8i46uJfBhbfotoiA/Pmj6JTqswKQzq4farEvnl/mU+eOkPCXVEFEucnZgoSC5QIykAACAASURBVLnghglj+TFOtvrUEfzUbYvscIqi59OOXORgGkPGBInF0s4oe0bqKC1oRu5waCeQAnLZkBOn53n7Tc8wW2oCqRa0KRWRSoW4JvMtLu6kVY9SIDREgYEQCs9NMcntvoNRDEkiSS+yOH9+ilzOx7FCnNGYE7vIEE0+4zNTabDdL9D1r5CThubPKe+bLT9NEggYyQ3ohTa1KEuOgEFsUasVMfIRUmuSwCAKLZLYTOn+Yw1sX7EWjTDiNBhEJp4ZobSksNhAak3UNnn/i3fiBgrDikhiIxUj60Jpsk0zf6Xtk/5xsgEz441ds4uD41W2l1O966XaKGUVUK8WUVrywoUM0k2oZPpYZsKZyCAIHSwzxrFiqkER21OUx3vU11J9GbTGNBW377nAM2cPstocIe/4lI0O7bzDZr+0qxePhsB3MKVCA/N2lT2ZBhc7o1zYGOOW+YtINL3E5eRgmt5mlk9Vj3BhYxoVmkxO7bA4vbGbGIUWMBBkrYC8EbJdzlHWbWQgGZ+rMTraIU4kI4Uuvm/xfTd+jowV8XLXYCfKEyvBqa3JlLegBTaK7SBDOJC8tL6HRBnYMuKHrvk8x1ozPF+9Bo0kUTDoZpGkjOVe4pIgSLSBLWOk0nzwiR5xUkYqgZkPiROTyXyLu645w9nGCMdCwXTwcYLod1iorNCPzmObE8wV/xk/cviNSCF4bPs4BXeHXz/9QR5br/HuhQp7c9uMeXdx38L/hRRfoAj6jyAag88xiC5RyXwdjjnxD72c3XjNJPJjjZM8uXOM6woHuWf81dPt7128m0e2ThHrhChZwBJnkcIga+ZZzB561esbYSPNmhpWHpqnv5FLWY5OxNSBHO///h9gK6xjS4OiDb99/vdRQN70mfPqrAcldjYLrPXGOH1xhiMHV5Ey2a20tAa/awOaJ5f387bCMaRUNAOPfmIhUASDtJq6dnKDS/UK/bqHblmogsZ0Y8qlLt2BS72dTXvZBohYU62WqFbTbf75UwphAoUYHQvi2MEc0ylSRGpECO66iYjgzNZe5o+uodwvYM4AUSwBm27g4HdtXCekZPewnYSLO+MMHShSFmoomZ+psVUvs7oxhsgmGCjGSm00An9g0Ryk2OnAkUwdrlNIQnbOVAj7LsmGx9h4k9ogRQSpvkQVh0+wK6EFVlcwNtdESE1j7aojkLRjXulOpO0NNAU3ZDt0iRNj119TCDCdGNOOiUMT242Ynq/ySmeSlp9hT6nGd9/8eRSCfmLx1PYCUmgSnbZ8SrlUS0ZpONOdJOfEHBzdhlE4sTNFTznsKdQ5FU7xmfoR2lsF5jNN7rvtJD3fYT0pkCv06XU8tIbxxR32T68xGzW4o9LiSKbJRxoLLCfpLEdpsIoDOq7kfGeM60c2UCG8vDlJpMwhazedRWhTEfgmGTtILzINYWJyqjHFT173AO9aWcSPLISAw5MOljlCT6UiagaagjmgFbv4DZskkagk3bVMeR2umV3j8mCcU41ZzjdGYNXh5589hyHezNcfepG33fwMfnQZAEsafP3sDMd7HyZQIY9sKPqJxa9eeCOeYfFH9/zgP7okvt7+Q5Ybv4RGcan5X7lt5i8wjX8cBhmviUR+trPMfz6TYlI/X3sOU5rcOXr0i16zLz/BZ974k1SDDjNemXZUYyfcZk/mAPZfIZhf8y9RMpsooHukSm87izEZkH39Dn25xXc+9u+J12Oi2YSKW2DKzuPKgK1+ns1BkawVUA3KaC1JYsnJ03OMzTRo+y4lHRFHBo31PMXJHklW8MD2NVgioXkpz1ylQdcyUqswwDHj1BrMEFBOb85YmaxXU8yvGKImEBrTUiSkNGatBTowsHxgLEAUFLpjEAwkrZ0cGAqrkSZxgUB3DS4cn6W4WCdXCVKno+0cOqPwuyb9TiqqlMkM2D9WJW8H9Nou1W5xl5U5VugQRAblkQ47KtVHSbSk1c8wWugRfdFcTqM1tC0HMR/AwGZysU650GK20CKITM4xRqIsztfHODhSRaA5c36GylR7d7icG+kz6HhIoXCy0W7XVyEIMTCdhCQSJLFEmAo9TOZefoApNIVKH0NqXBGh8xGvnz6LbV41zmhFWax8gAhMxnJd5sevwleLlk+UGJjDtZSsAc9f2oMfm1SyfdqBh23EfNcbPodtJiSJYNRvkbk3oL2TQ1oxA8fiqc8fIQpsPlHqcfvNZ/Ajk+X6OAV3wPnW6JAUJPjMynU8VtsHw+pZ66vHUpoKhGDTTw04TKGGDrOCJ7f283XzJ7nrpjVeXCnR9h2e2LbxWi7XzpUouOvsz6XHdwbBUlTZPaeGTJgoNNkzVmNStXh681YKxoB2N5XGTTB4+OwNvP3oK0wXvmf32DTDFlcqAscMCRKL5Mp14r5aSfEfOqq9T6F0KtGhseiGr1Dy7vgHXlUar4lEvtxbSd11gECFnO0sc21hlufqnyJrFrh15JsxpUXGdNhjpkSQEWeCEefVW6dIRfzbY/8nhnkiVawD5vdtEyuDunQRZjr26TMgmDARymB90GW1O8fGdpHxsXSY041dKpNtamsFpKlRWtOPDcLYoLpyZfgEZgKFUmo8oLTgm25+EVMmCKnphw7LzTEG0VBfYlglywHonEIaCtOMSJRJkmgModk/scnZ1jw6kRh9wdAwHhomYjokQXD52QUMDRTUX3k8941Xybk+QWixGRa5aWaNly7vAQS2HTEy0mO5P0rckjQHHsTgej5zY3U8L6RWyzFa6VCvpYNMgSZWknrXQ5JCB/ttD7TB5naFbGFAfrSPl29QHm9jmBqDBMdIcERIIiS90OGFjVkGGxmILKZGtnbXa3sx9934IgUzRpmKhzevQQwBdv0olR8wLI3vS7L5iFgJBpFFeaRL0fGxZcyRwiarrRKD2OYTqzeQ9QbM55sYUtFs5ZCRJOmYaAv6sY0tYwaJxVaQYT7bROv0N651ymgtWGmWGSg7tXIzxO4GxzA0Fgmb1dQ79cLL88QS4sBGILh77jStps255gQbgyLICtKJdpEmAI6R6qMjUoSSUqnAWfolaQuoGzsUcn2qrSsDQ839tWvZP3GJiUMrfPiVm9GhoB9YnFkZ56fu8lgfbAMQK0gswR1HTnP88h4WKlVuWLw81DOHqcIOM16H56wiQWgiBcyMZLlt9hEs4+qA8qbyjYyslamHDe4c3+ZNxVcw8Mllfoyy89cjx060XuFyf5Wj5ZuYdNNhaD/e4lzz/Zgiw8Hyu7Dkl4Y++1Kj7L2BbngcpX00mnO9C7hBj2uL9yGF8Td/wP/EeE0k8htLRzDkx3C1RKN5XeV6fm/px+gnbQxhsuUv8y2zP/5F79Fa85G1T3KscZzbR47yDZNfwwNbT3OscZxqdJFJMwXzeTIECQuLPv3NCpFuXjHtuYrauAK5Eyla5Ipbi2EnLBzYJowlW80ifT+TtlZKATQdsiN9HC/C0jEhFhWnh2nG2MMK+7rRNZYbKbMtk/NRWuB3HaayHcwxn2YyHGzqCInmYH6LeyfOszy6SqNR4HOP33hlgeiGBVMBcsPZpd4bbUk4EWH0JCQa5Wry421Obs7QDVxyjs/XXHOK+WyT5c0JeqFDqdTbTSqmpTAzCRGaxJCs9wssnZ1m//wWeTegkulR7aW47zgxiIfbdMNKEGr3QNFre+TKfaSr6HdssmUfgFhJiu6Avu8O05PGKEUkVZPGepHKXNo3b1/K85hX5NqJdY5OXmbabbERFAhCcxc+KYTGFzZR06YXONhehJ1JuKawxazbJEhMHls9iNKSWBn8zql72FOoUo/yKAsyKiTqZNmKRqm3C9hOjG3G3LDvMiv91E/05NY054fnSxqa1sBhf77OIGtQDXOMyw6WjHnywkFOXF5Ij6EZ4Xoh8cBGK/hcax9TIy2mRtt0Nz06sYuOJDipnnjO9rlz9Dx/vnbd7rVsmYqfue7T3L9xmGfr6eceKG1zrjEBInWr0kIwW66nqo6KoVtVevz9KGHRex2r/ad2MfTbQYG7567jF+56I7969lc505/EEIqiNaBob3Hz6B2M3fsUx07uwbY0b9n/Jv77517hrdcdYt9oKm7mGS6/eP3P87G1D/DEzmd4pDvHndlzeMF/Jk6+nXO9JdpRixtKR/GMq5IZj9ee5n3L7yfRCR9e/TN++YZfYMQu8ejq9zFIakgkjeAEb5j+719eovgbYq74z3HMSfrhBR6pH6NW/RMEgsu947x15kf/Tr/ry43XRCKfdEf5Lzf+NK+0z7MvN0/BNAhUPyUr6JDL/ZOves9nNx/mw6ufRKO53F/lwxdf4In1FFs8mp0gNxUyabcxhkSL8cwGyvhfeC54FmHEtHoephljmam9mS3iFB/tWzhOur3POAFaCDoDd7ePCRqvGBIoSWfg7fa45/ZX6SbOLu09SiRbO2WkD+5YgGFqpNZk8gP8WBAnmav2XgIMkWAaGksq9mQbSCUQQg8t4DQYGiFUKqHLVV0TLQWD6QSk5rq9K3Rjm8s7qVN8L3RYb5dZzNV584Hj3H/hCBFXtNHT42haCQhNHBlEkYfrC8JsuiufL9eZzrU4c2EKP3fl6QdJYqRrI12/ECnuPmsH9AxJGHhIoWmHLpGQQy/L9L253IBOCPqCx/blqd11qImIk8Y0N0+s0B64hDsZTB0TFtNvkUIhpaYfuGgtCfo2q3EZv2vxtv0vEasvZo56dshGv0QiDEpen7wRMrK/z7mNKaLQJgpt+lKx2ilwoLxDKGwmSh02BkViBNLQ5I0Qx41wZMwLvXleL5a40bvMmc1pbCMiVCbZyR5OPsQsRrTOFdk3VWWm1EQKzVSpxWdPHMbLRBTyA0bo8h0Lz+HIhNX+COtROoTda1bRGr5x9gT3Tpyjp2yaKst0tsXnjP0UVMjBiU0yTorPlmg8JyCMU//YspNhJnsb9Y05GmGfzaCAKRIM9RHev/QZuvEkDFtkg9gmbykKzgh7Rw8xfmed5eVFfuORkyQJ/P7Tx/jz97yLsVxaLXfiJk/X/yJF4MiIE8EcZpiws/FhHq49CsBnNj/Gzx75pdRIQmueqx8jUOlaTWGx1LtIwTyAn9SABEVCI/i7swwexG1sw8MQFhO5b6MXN6lufS+JTrezF7rP/J191982XhOJHGDcrTDuDtEdOqFkT9AKtwHBkcKrNYw/vvYQaugUH6qY41shWqcu6Tu9LCvtImOVDuaw+lQ65uGlM/SjCrIc4xW69EMHEYFnRkx5A2RD0klS78dbcitkigNeaO/ZpXBfCWko4ijVDdEIwiDV/O4nDk/XF6hEPtutIsfPLmJl4t3kLoTGMDX1aoGc00t7oqRJNWeFHC1fIkoEW4M893/+FpTUYCi01CTTAaLqoicDjA0XFCS5CM+MGCgLoxCx1KlQsAe7EELQDBKLy1tjPPXyNbhOiLPYIxLp4LAfmmSdgJmpFmc2JwCBVQoIEoP1XgFLJuysFenXc9AAUQoQGQV9SX68S6+RQWtBqdJhKttGoFlplMh6UWpirTXjbodu4KIUQGpkvTBSZ/vsNOGVk6M1yNR16ZHzh5CRwVihTbOZI9OLcSf8lKauIA5MgiBtVQWxTS3M86crRzFlQs4Z0PY9Dk5sU8z4CKDa9VgsNXcr1eXqOHGc7ixsNyRSqS44QlNwA26bvcjLtWnixMBsGsgRdvHvW2GOtutw3+uOU/XzKTPVTG9Rpxgwc/s6lexVPLwUiowXks2GSA2HCluEicXLjVmqcR45hKJKV+8ierJmQC92MNFMeW1+fO8DPLGzn0+9cj03Tq8yW2ihmocITntYGTAtQeR1edtD72V//gjaXqETKd4ycZK8FRCE1hDhdbW3Eyp4pPoQZVmnYnRpeD0SfZi09lc8tvYczwWfRAjJN019A5YIsEWUtgwFRNrgROuzfGt+iXmry1ODGarBNk/VzvC7y5/AM1Rq+qJjNJqF7B5MmWPEvYFmcBqAudybv+ieUl/gSKS1JlA9HJn5K5UWd69urfnE6n/kXOdJTGnzHXt+kSnvGjwjT94cpR3VkMJgIfflSfL+zwihr05D/t7i1ltv1c8999zf+/d+YQRJn7/Y/BQrvT73jH8dh/8Svffdz/w87WR9t3e5Vp9Eii4d5RImFq4Zcd3oOgdyVSBl7pHAgy/ewMXtceyFDskQJy7QWGFMP3GpuF3+69E/TisaoflI/SaOtefY2CnR9jMwkNA0sUYCIt9GA/lCn9J4GylhZ71It5EDoXDLfeiYGOMhwk4/bzbf4NTyDCo0cIctCD+SWCREoZ0KWGmw123wFGrG37Uwoy+hYTI+VcdyQ3Z6RUJlonVa53iZGDcT0mlniEKTnOtzzdQWJx47kNrVobFyISPX1RhEFpZUzBUaGELz8uoMvcDBJGZyor3bz98+OUY8xMELobCCBLoS5/Wt3RaNJWMmvA5LW2P0Gh4ZP4GZlLJ+wNtmYBrshFkw0iGlapqIZYcgtPFnYqSVIEzF6xYvknFCpNQMEpNu6LBSL2N5yVBvRGCJCHzBgrdDkJgs67GruwsRk5EBJS+42pPWmryVVodKwfGNSbZ3yiAgX+6SdG1Gym2OTGylolqhi6/SB4XfNMkV0vVIqXB0QISJaWqkgBGzQ9ny0cBzO7N0Eo9Jt82om0oFh8rgRGNiSPASLLrbPL+5iGEoCnl/uG7NrNuARLEnU2cu36Sj0qF0QQz4muxZBonNtz/6z4gxMI2Yb7ymy8MPjCO0SSZnUJ+rgdDsG2mQd0ISrTjgbbIn0yD0DZ6+eAAxpsgmBe6e28+T9aeoyAbfVj4GIoWu/sKz38LSyjRGlOGurz2Nr9KhoYFm1m2hdZzqyw/RPvdmIm50ltFa8DNn3sCLveuIZI3RfAeNYMzJcntlgdXBCUbtIt+z8L9RtkfY6D2CITxG3Tv5+NIp6kGPJztPstRf4/riIv/pxu/jw6s/y8bgHDlzhO9d/BXyVmoc/Xj1WT5w6aOU7AL/6uC70brBBy/+BJFO76O5zPW8c+GX01slbvFy834cI8sNpa/HEH8/NbEQ4nmt9a1/+f9fMxX5X45TrR1+7sUzxErxW2fex3973Xdx1/g+tv0Of3rxBRa9m3im2UYKxdHyPNfknmEQp1upR9YPECqDzV6BTuxyW/kiBhphwtfceJxjm3s40ZlkyJBAaUGvnoGS4rriGhKNM6TeHxBVMqMxD2xfS2f9qnri5ESDRBvESmB5Ee2+S6OeQ3TstE6XiqDlkK30uHZqnYI1oD7I4pkxs5NVtuplZKCRRUXWSei1XBJ95XRrdCZBxFdhhEMkGoZvENoSDIt8rs9GcwQhNLOTTULfJOxZlPM9YikoWAGvvDKPUlcaMYJoYLFxqcL+hU1GhjC8RAmSRKQDZ0uzsjYKGorF7qvOS9YKqe8zGWzlMBW4OR/dszmX5AmzICMIHQmJCRLO+ePcNLXKXqfBarfERrVMdLoAw9aRDASJJxEIXCfcRbKYQvHK1nRaPQ+gUuywkK9xtLzKhNXEFTFhYvLey/dwxT9TaUnODYfV7bANJkMMkl3USC92kNn03A5Ch9GxLhtL40yWOuwMMhTcAEOmpKGV9VF40eTu+46zJ1fHlApfWSz5YxgkQ4/MdBh8y8gqT9YWaUZZfGVjioS1ZoHtCxOoWCKzEc0xl0in/qRWEJMZtm1uzS/zR6uvY9TqUTZ6mEKB1izVx/ies+9iymmRsX0CbKYn6pzoKabuusSEBmUtIHtdPOnjWRGa9CGzElTIOwHVcxUu/vE+UBLHNnjHzx5iEL/ATLaZ9tuBUEtu23eBeAzeMn4vx3ov7p5vBWhSNI0klcM1RIWnGyH3xyMEocmDOwsEykeKDJVMh5I3QIocS73HiXXEymCHD1z6DX704H9gNvf1APzYY5/i08tniHSCMCKmpuBMZ5WPrP4R2/5FFAmduM4zOx/lTZPvphV1+M3z7yfSMTthk988/wf86MHvTNcGCGRqbDGMjFnkjtF/8rfKPf8z4jWbyJ+sXsCPI8JYojW8+3Mf4jfv/HZ+4cU/YyfoYUrJG8ZvpxtFbHRfpuSFWEaalMYzHda6JSa9Nra4mWM1wWJ2i5de2Uetnac5KoYCUBpDKibtDivGKKJtsGyN7m7D/djksYuHOWVXqA+V/9IiSuNvuRT2duhFDgkmnhejR3oMMiFoxTv2vcjBwjaXe2Weu7SXl7cXiXIazFS3Gp1glK/K1mYKAZ3GFXMGgR4LsUjwWw7kEkhA+QZqIqLWTUWS8p6P6wRMjaUD3GYrR7edRaBxCz5+4uFHFtICGaW/KXY0d+87xwvVOUwzxjVjLF9xIFflVGeCKLBTqifQauXYv3eDyxemQcOhAyss1SroWGLUU/q837DpS42RaMbtFlPX1Dh+eX73ASREKvhkSM10tsX6pVGuPEDRAukLkvSQ0tzMMV7uUCr06GoblN6llnf6Lv3IY12UWZioIkXas72rfJ7P7RxECs1CtkYjzpC3ukRDg+Z3jDwPGGxGeTZbBR6ODnzR2rQSyFDz0lMHEE5C9roVpKU4uzSFUgaMaI5dXOSao6kJSFaEKK13zR6Gn4QUYMmEVpyhH5skWtLcKqPi9AGi+hZGlFbmIOj2Xbo9B8eK+d363SjBsM0Dlk5oBg6f3rqeUJl0Y4dYGoxke0ip2ZOpkzMCtvwy59pVEu3gC4MizeF6NK6RaveMHGziZCOCjkNMwJ+f+22iMZdL/gi355axZUoquxyOICQ81T5GwbTxh1Vu0RgakaDZb29zyG1zRr2H39h4aFfSoGD7VP1c2n7qF0DCDWWXSKfzHEtE7ASn+IPl/8h37/lJDGnw8MoSg2QIyVJGStgywJBXsekSiT0cogZJeOXuQ6PpRF3K9jT3TfwgT9Y+RNGa4Gsn/8WXm2b+3uI1mci3By+h+DhvnNui2s/y4tYssVb8+slHqAd9wkQRKcWjm+eJEpjOx9zqSMyhT+MgNtmT26EfjHO808FXE7x8ah7VslIGXWVobzYUbWrh4rUkiW+wXp/m/+i+nf0zG6x0K5xYn8WcCtAuiPEAXbNxB4pR0aEbX4U0CaGZLHbIjYYYQcShwha2kbCY22EpmkGtepi2InfrDqahMGxNqK+eXiH1bhtTJDGWSFL2JSbxIB1qYSow0ySogUFosW96m2h4mYxNt+i2s2glGDQyKR9KaJLMkAxjJlhSsXe0yt7RKifaU8y4LYpFnzgR1FYzrO+MXD0RGtZ3ykzM7TBaabHtZwk9iajJq8NWNNqEyIFWM4cz18cb6TOI0hsyY4WMeD2iRLDRLpApd2mNWURSIHsGSSE1hPa2BI3mCPtntlPWrBlyqLLF8eoskoSjI5cpGz7HLs0zZrXYV6qC0Bz0tokzBusqzx3lZdaCErEWPPDcUfpdj3/77ffjmTGHvG2SnOD3XnoDqpQOTosZn6hrQixT3ZqBYOnCJIVcd9fuDyloDjIkKt0ddSKHpUYFA5hyWruiaeuDAgNlIdC7WGshvhgeGmoD1wnIGDEjdpdvmn2JjBFxf+0wx3dmeKK6l2OteSJl4MiIZPhAVQhum5jGtB8nbw9YzOxgCM2M1+Jip0iibWIlObsyztx4g4rXZd5LvWi1AG1qtKHJj/Y445bY2CliTyZ8qnkDM3aDM4NZ1sMyhjCY8yaZ8kY52TpOgsKQo1zrbeFynjmrjhA5LvYu7T5gbZHwL/Y9yyPbczyws4g0FfUgiyvHOFqc5dn6/VjD43Cu8wz/6oUf4Y1jP8Rd03t44PJ5Eq2wTY1rCo6W9/OO2Xfy0HaHU+3HmHYP8rrKt6XXtjPCPWO382j1KaQweNdiWm0fHfkmjo580+4x9sOX0Vrh2jcOH5r/OOI1l8hr/it87PKPc8kvYRuSyWyHAyPbnN6ZYKm3RhAb6VBGgxaCWCmWN8cwI1gcr2KKmHunzuMnFi9WD+CrNvy/7L15vCTXVef5vfdGRGbkvr19qXq1r9osyZItL6PFGAwYA81i88EMH2BowLht4OMPDO3unhmDGGjo7hmaNozbmM1us9rY8i7bkq19q5KqVNurelVvXzJf7hnrvfNHZL2SwMKGFlvPnH+kepkZmRkRee655/wWIA4UZlilsa2gEiEwpHSIv5DDeIqwAEbCuf44l+olnExAcbZFL0oliSsTIwoh33Trk2gLNihxuVPBkLjAZ6wQJQ2uFe4wGQWGlBuQP94AZYYDI4FjxwxaDspNtLwH3dROdZ52I8ZHWsQGvNDGcvRVJjomTCCaEsPh6gqRVMQdi/bTFXQgsfIQuexU9kMeOdlqj7HRbSZybTa9LFkV0AwzVK0+j6zN0Q1SRBGkMj7hwEHHEivS+O0sK40sq0tVcnuapFyfKG1hetcWMaOSRSiKYeOJMexCQHlXmzi00Bjq/QzPLU4TxEO9mLJO5HDLIcX8gLQdYNZy2C8g8QgBE8UWjTDLnLvJDbUlbKXZU93g95+6nUOjqxworXKsvMJrxs8TC8knG9dxXW6JSafJp8NbMEZwcXuUA9U1JIb6IAc9G8vTVA7UcayYsWKfE6J47QZccegeS6HSEbFvgYZqrcW53ghCCJa9IrVMn/F0h0aYJxhI1rw8nkmBSOYFuUHEaLrLk3YOoQw6BisVMlprI4OYuO9wpj6NHlj8y2P38z0TT7LQqdH0MvSjBDcfRWIITU3YqM2ox2Z3F1OZNfZltxACXpWq86PHP878oMjPL9yOShu0kVxYGWP3vi0sCY3WAbZuS3OkdIGbDizwgXO3k0/5iWZMWONyWGPKneXW1DR5K8sP73kLKWnx2bX76Md97hl/IzJ8mKXtdyHIomSeN07eyWfWFlDCR2K4rbDK7YUVngxHEUKghOR48UbeMnM77bDFfPfBq3ciQm7zv5+4j/9w6/dw6/g057Y/xaHKJymkFHdM/CCfv3yRn3sgQolX83/f9W040h3eD4If3/cDfP+uN5OWG9mjygAAIABJREFUKVLqr7NKN7b/Hc3e7wGCQua7GK/8ysuRkl6W+P9cIt8cPEv4gkJGCUPGCknbEVLFiFDtoDJeWZvj/nOLZDMDGmGG1fl9FMtJNaWUYX+mwoY3QBuDrvqEPSuBFcaGPRMrSAH19QLLAwehDEaSVMVG4PiGI7s3kv554HChXsVRMbv2bzJvRvE9i9FMh9n8NvONKpOFDrbSxFpwsTHGdcVlJjJtWr7L+XQFxw2umvPsROxbdDfTibWMq6/2bXDTAVIlJsSWNEQmaTGYSBCHEsuK+eZ9z7GvvMlyr8Tnv3gLcTvRrk73Dd0ZM+xcJD1vUfQZG99mLt9ACkNgbM6encHLw4PNvUQ9m2jbIT/TQVgxZELiGAYLBcxVGUYtSGmDnQmgCmGoSRlNK0qDSpAdUzesIwQ427B/fJVYS+r9DE+uzhJF9rUvb66dBIuI/ZUtzB114lDRbGapVLvECBphjpF8lz3ZOvZVzXAjyWc8Hlrdz80jC6SsmNgIWn4aQs0nlo8xmW4ydniDwVqO3zzzer7z4BNIY/jw8isozTaJjCAWChHHPNWYQtgGYoNDjJMOiRS4JY84lKQtn0HbZa1SJDQWcQxdL83GWpHKSJtyuk/0gp+pQfJ8cwIvW6ffzKCFAMsgMxFKaErK40RrDBMp5pujPLK+l9snLjDoWwjrGiwUIcilfPqBgzZwqVNHCMF8Z4S5Qp2bC2vc5DZxpOFots5UrkNXJ9T9tB2i+j/CgdH/wO6xx7h5LGDBq9GIXLJ2gDGJWmLiMytxpMu7D7ydM+1Hudj6b1R4hFsz04wUfxYpM+B8Kyl7jiC6RC79GpQs8oFbfoLnlt/O9ZlNSpbH6X6VtAIlUhTsDHfUDvDQ4oe4b2mZPQWJNXSpWh/kiI3mSq/Od+yt8cWlTxAbj0jDM5u/yru+dD3+sOXyzvs/wdM/+I4X5Yeineelotn9XczQYarV+0PGyvf+k6nKX5ZELoR4I/AfSfAP/48x5t6X47h/HzGeuYmiFZIKIzxjY8kUnbhIIeuBtvEDiTRgScmPH76Dx5Y+SHmkg5QJ6WajlSeMFeVcn7P909x70//M5d4Wf7n4BPNxBznQHD26hFACL0oqLiSYtE7KagRCaPLZBFWghCHrBKTCmJFCFyk086tjdAcpLqiYW/Zc5jt2naAZuGxFBZa2KyyujfCf1t5ArtTFzgZkUkkvXEgSPW9ASU04GFa1WmBFmshJ+vbFQh9jDAXpMzPdpOWlOXlliji0wAiq2Ta1bAdPW4xlWpjBC+RsDZD3oT+sWCIBfcVas8hqs8Te8iZThTa18TaPP3EAtbtHtOFiZcIhKSp5mRz2r/ULNNqtdPIDi32bICWJ2japvgRLM3psEzn8Ort3bxAaRU+ncNKacdFjyZR3FqprHxRGcokLDhYIETG/PMqVqICbixJHI6Cp05RMn1gL/MhipVcEDGe3x3CckPlwHIMgndZIH5bDCtIGd6ZHYAz3bx/g8Ogat8xe5nNnj5B1PapWj5Nrs0mLoKTJ2n32nPZZ8/NEe0DYoGxNxe3T0rC6VKFY7LF0chITSdKTPUzRYKSgmuqy1i8Q9ByafRcjJBcbI8M+ukE5MXbJxyCobxeQG6mkiyYUH+28iqfHd9EMcmRywc65TquQfuAQ6he070gQJhd7NXale0OmZoKGCoayBZBAGl+76yJrvW6i36Ih7ioiY1PLt5nKdRO0ChBrw+n2PD934t2MOxu8Kf8YbRETG4ul7jyvmPkQAK5zFNc5yiD2uG/p43ixz821H6HZ+TUuhhZP9Yr86qE2npjkWOXtfOenfoM1LUDleb5xlKliQvdv9UZIK4s7Jw6hRJdrepsCS2Z4IUrva/OWXzosa4owWkj+X43/k0ni8DIkciGEAn4TuAdYAh4XQnzcGPPyIfL/OyI2ms+tPUHD7/CGiZuppfbzptnf4cbeE6TsWUr2MX7oKx8gjrcYcUrsrlawpM3b9tzCvc/9Bdl8/0X0Z8dKpEC7gxTpYg9Eh48ufplm0CczIriudIXxTAchDIvtMmfXZgADpQiV0oiBouAMqJbaBMM+qUQzM1Kn4Hqsd0v0h6y6KFacWp7glsolpjJtpmgzLlqcvzBJZqpHJCVhmCblDFDDKsjpanTHpjLT4PrjS5xYmKW+VUCdSdE/EmLShs1WnlIYUtrdxFKGsjtgLt3iYreGyoYUSz3aOsO2J9gOM+T2NWk9XwUBYTUa7irYKe9kNh4mBMG5+igVt0+zkaGc79JsJlA7HagXIWT8jkOUMRBrCqUeqUpCajIa4khhAoXoDTHYAXSWC1T3NjEGgsgiEtcs43bNbrF5KZPMJyRk0wMiYyEkiRH1cPYpJHSLkl6/zFwmQYkoEdOKXD78zK1EtkhIRkgQhi8uH8StxGydrdBazFPZv03VC/Gq0JM2BokShv21DYyBM80xCuU+x4tLTLptnl2fGRKuINKKzaUidleCY+B4n+mxBuPZDs+uzLHeKNJdyWOGcxFvNUtqdEBHuhRtj/UzI3jNpA0gSyGiYyECgQ2IlCE1E3ND4Qqfee4VO8xcYwwmkpxZn4JChI4TIhLAjdVFHlzdx7UtnOFAfo27x88QIVkMqlwI8hRExJ/X9xIEikJ6wO2FC+RUyGc314Apjrir/Nkjt3FudRKNpDDVZOpAdyfJDTybxnaBQa7P7GibyIA1bBGtdZ9iqdNiOl8cfl6fh698M/vkJR7s7uGP2gVuzKSoqDY3FBfwogUwX+bfPLXBBhZCJcwzYwSNfoH3HL+bMWeSY+VJKqksUORY5Z2c2X4/rjXGK0Z/kf/ztU3e8+BnUELw6697Mdb8r0Y/arEyOMNIajdFZ4yZkQ+z0fy3YDQjpfd+oynoHyRejor8VuCCMeYigBDiI8CbgX/QRG6M4ZMrX+ap5mnuqL2CO8deCcB/ufCXfGL5YUIT86dLD/JHt/+vlFP7Kaf2A/B/PPU5LtQDQpNjgwDbWUIIw1euLBF1LPIqizXdQTkxShpGCl0qWtDoZui3U/zrJ/+UUCqkBEuEXOmVebI+SznV54ZyQvrptZ2kt5fWyBj6Vwows80Ok9Eo+jiEgZXoU7/gewVG8anFo7x6dJ6ecfBQTO7boBmmMXGSzLZ7Lq4dkvINzcerGAQbl6qcOdblpqkrTAUdTqXmSJ9JEY5EpKoe2lLDFkTSI7WFxq14kNKkZERsBM93JoiNwN7bR7gW/V4Kt+Yh5rP4luCqA5ol4x2TXYB+M8Vbjz6ElIbVTonfeeT1xIFDfyVHarRHFCi8bRcQSEtTrvSIHIMSGq+bwvdtXjjHMwBuTD+0Ecbw+NIM10+v7lSIxgjsdIiLod3N0B+45PIeGknTS1FzLGKj2OjlqPeTrfOFjRo3ji8xnumw0srTS9mk3ZBs2sf0DeOZLntHN6mfKzP/mTl0pNg8PcIrv/cZxka3aYQuF/ojzLoNHBlzsjHJQqeKRvBYY467xs9y28Q8Dy0nyVIOBBuvkFhNwdT+bXKjg0R4y0A6N6CQhsFyjp1VB0AbXBMSaoUVs5OgRctKoKNXnxdIXjN6kef7k7iuT39gE2uVVKOFEJVJDCV6nRS2rbl+9ArTmSalVJ+mn8EYqGa6fPfMUzgqMWlGCn5ve47TrUl8rVAYvr/yKK6VVPWvVBf5bOsopzoTPL88s2Ow3VwpoQ+sgIbAt3jysYM7eu/3vPY0vazDIDac9Kbp6Aznlt/Fnvx1TGeOMKsWmFLnCFFcDGpshTnuyJ5BcFUQCB6sT/NgQyCtpCDQw4O/bvQ6SmKSX/jq5yk4Kf6v176ZvcUq+0rfz97i9xGaEEc6fMf+Sd6873ByHv+GirobNfjg/I8TmxhjNN+3+14m3INM1T7wkq/5x4yXI5FPAYsv+PcS8Mq/+iQhxI8BPwYwOzv7Mrzti+Mzaw9z76lPE2jBF1YvU7IL3FQ5zCNbz+PpRFu8H3usew1ms9fEsPpRSGyuDg4BDDqWxNsW6U2LkBxbK1ncI03K1W7SDpGGnBWy0irvULvHa9sMYputOIdGsu1nmG+PUE33cKyIpWYlIaYMJEYL/uo9NIhsLBVgp0Imak0GvkPfd8jlPbo6xWOtuSShGbBUzNHcKie2prFVTMEZsDe3QXAly2lTTRJ0XxF3bObTI8iaQZY6mAj0lksryiKaipnpTbJZnyhULG2WYTQkGlis9qpMZNsYk9i6tbw0ohiTUQGpFnRChdMGkxJIJyQWcvhjNRRdj+sqizhWhBQwlmtxYG6JhUGNUFuJrKqvGEJeAEinAlCGlBMRlwLy6QGebdE4UyVopUhVfdyaT0IDTww5nl8fZa7SQErDcqvIgZE6Jy9PoY0g64SICMbzTXxtcblXQUaGrUFuZ0gcasXjK7PMFLdpXy6SnR22umSMcQOmqw2EEGyv5NHDKtkYOHlpF2+YaZAlwJIx53pjbEdtVvpFNJLxXJusE3B2MM6ucp3MSYEfOkTC4juPPcZUqcGX+/tYGpShD2FeMjbdZIwmG5U8S89Mon2FUx6Q0xFF10Mq2H/TFU4/MkfgOVhuhJKauGshMYyONOljYcuIX7rtz3jPp7+fgRbgakQ22mm5uZaP106z2SzxpW6e2dEm1XCAkpqc7RMhqXs5rgSV5G9WwG21BXxt8UR9BiWvDYstkSDntRQ4VpQYk5AM889sjVBxPabDw0BvB6Hz+JV9HBldJjQyaTkJ6Oo1TrbWON3+EofdGS62DuNaHhtBAYPgkldjn7uBLQztKMV9jeuwrQgdgkahBOzPj/HTh+7kDR//IH4cIXrwjgc+xn3f9sN0ow6/dvbfsemvszuzl3ce+AUc+fXlcS91nyTS4Q4Z6Nnm55lwD37jCekfOP7Bhp3GmN8GfhsSZufLffxPLj9DMBT39yLJZ1af4abKYV49cpSPLX2VCXednO3xB5d/lZ/e/15ydqIj/JNHX82XVucpZebZX1tDG8Hjy7PUu5lrW1QNXjMN1S4jqQ4Fx6efdljfLhJpRUr67CnWafgZFruJM40hoUO//th5AB46t58VcnQzLt5mipXna0wf3wBp2GhlcezkR5JSGic/oDAUwWp6LmkrvCrpjTbgG4tmkGEqu42dMtxde56i5WFqsKtc574vJuuokYb+ahYtJXIkwAsd4pxOetNS8MTT+3DsiNAxiEqMjCRBI00dlxMXZumaFFoInJyPkAKRibEyAW6tSRRL9Eqa2DY7s0WDQGvBVj/H3lJiOizF0FxCKFBDFZdMTKk7oKcdRsabialDOqkaLSfGEwpiqBxuEIeJAh/CEPctOudKmEhw9NjzZJTH/YsHGS92kCJpLU1Wm8m8A4MlNMvb+R08MoGA1NWJcNIfX+qUsD2HnPGS9osAW2lWekUyMsSaCeDkte2BnPQZRBZPrMwRpCRCwqpXJhYCgSbnBEMbPcnF3gh+mKgWvu26r/KmA0/zeHc37X6GnBOgjcCPLTb6ebJ2wGi+Q+tYne1OFr+nSIsYaQ3p+BgK5R4DL2RkzxZzxS3y7YhGlMUZ9YlQ1NJ9Pta9LoEDRhKlIq6mXiEMRXfAkcoaZ1cnsYs+ZSPJOf7w3krgqid700QoitaAjIqS4aYMublyhY6fp+ZuExvJol9GYihZA+64/VlOnN6LEYYtx2Fzo8p4bZx33vxNfPXZPyQGbBWxq7q5cx5Do3YKEwREJuD+xhanO0dJyZC8k1T+D3QOMWAPr67exPtOXyRla7L2gO+deZIDbp2Z2gfQ3q8waL2fb508xJ8u3ogBWkGSgL+08Vnq/iYGw/JgkSe3H+b26uteMo9EuodAUXVmdvrrlkgxltrzjSejf4R4ORL5MjDzgn9PD//2Dxq7MpM8+oKNwZ5s8pF+bO+byKouTzXvQwOb/hpf2PhLbH0Dv3HqfsYzBT5y53fzny7+HLFJxJmOj61y/4UaZmB2kvnAdzCepFD0kAKyTsC+kXXOrE9ydHoFW2lqbp+lTikhAwHXl5d2dKtvmLtMe3M/Ek20X9LcytA4O0Ou1GO01kEOYXxxJFm/WCP0bMpTLVRGJ8qDZmdWio4FkVQU3T6+sanY/Z0Kf27XGiYdoycDxHIa3x/2Pxop4smAqVoLaTQr82PEQhJoC6vYx0i5U3mCYaNb2hkeRrEkW/aQSpNyQnq+g7BAzngIY4iDaxVOv5vi0+1j5F2PyUyTh9f20CWDjhMTYyEMaTvEjEqqLZ+N+RE2xQi5Yp+9R5cTNuYLrqtQBr9jkUrHdM4X0V7STnr0xFG+8+4HeF31PCfP78Ec7zJb2cZ2I5Q05C2PyVSTnPB4rjGJNgLhJNK1SmryGQ9HxQitaVJG+xI5hGFW3B6unQikXbaLpO+pY3cU1rhHquhz4cQMXsHBcQOmUw3aoctAlMilvOHpG/YS4quOl4ZDtRUCYXFf49gLXKAMa70CXuTQ8lyiWLKxWk5e58TUWxnyI73kCJHELDnkjGF6osuhkTUO71/FILjsV3i0m2iQDyyLmT0bnDu9CzkQKBkRkuyAtJKsRkUmx+oEQtEOUuScAIXhSG6JyKghZyAx2Nhp8gyhrx9bO8yk06eW67Kl0xzPLLEWFUnnQm699SzGwEqnwNnV/fz2nW/BtbZ4620P8MX5g7jZAaVqG4xgO8izGRTI2X4ycByerk0/WXS1gaLK0NE+lrB548x7uNhtELGY3I8oloIStxcvcXnrh6k5fSDm7Xsf46ntOVYGNd57y90AODJpa179MvbfYFZxYfv9nG/+FgLJ9SP38i2TP8Op1heYzR7nuvIbv34S+keMlyORPw7sF0LMkSTw7wPe+jIc928VP77/jTyzfYULnXVuru7lu2ZvBxL403XlfZxsKwKdIBWWek3ef+ajWFZEvbvG931piZunYtRVwoww2OMDQpVGhYK4GGMk9HouWieVphCaqbxGWctknACESKCMKmSzl8WWGksmCVhrSR+HYs4nbxJs7JoQ+H6KSrE/FM1Kqtm1CzUGzTQYyfr5GpOH18jkQxqDDI4V4wV2wpYcGA5V13i6O0snSpO1/IT5FuYovXYDNNQfSlTpAEzXImW8pOUhYfbQKp3tDFEAXZVosKM0thsSei+8LQREgrQKmMvXh0a9Vx8xKAyW6xHHiolsk7WtMv0wxR+cvR0MuMNzEwQWlhOTz3oopRG2JkgJzGYyrOo0M7S7Lpc2qggBlXIHOx0TRgrlKbp9hzhQO0QhgC9sHWYs3aG/kKe1v8X2uTKlXS1ypR7XFZawpKGW6pJSEU9uzvI9c4+z2i/yTHeWrOXzypEFupGLGlnE2hIseRXWM2lcO96RGx4vdtgwOSZmtmgGGbY9l5VLJbJ+zDt+6E8ZsROk0X0bx/ji/DEunK0wPlfHdX16kcXxQxdYb5U5E49QiToJqewFcXUYqpEst0oQD3cLgaJfFkgR0b5cYnSuweE3XGTjfIUTvSnOnh3j2488xaHcCoOBMxxkJvkqnfGx7ZAoG4HUVHIDhBBkVEDF7nKlU2ZXeZtGkKUduEh0ouKJThY8oBWmcVVATiUGIkEsOd8e46bpx3lo/SB3TJ7HEUmRYkjWrovtCpuDPHZ+m5zjIIzLElm2ijahSfOBhTu4d/8DGOFxrjeGry0sETGS6lBzumwFaTxfserVWGppXj3e5N8eex81t0jRamBJRawDLKG5vbAABiQxxiTXy5YWH7nnO8ilbyJrJwn7dSP3cK5zmou9Cxwv3shN5b/W9QUg1h7nm7+FIcIAp+rv4+5dX+ZQ8a8L6v1TjP/uRG6MiYQQPwV8hgR++F+NMX9dF/bvOXJ2mj949U9/zceOF1/B0cKNPNt6gpwa43dON7CcaKeK9VSfZ67McN3MErGRnGuNksn7NLGIYGgcDBv9DM2+SzWXGD10TEAuI/C0jSagE7hkUj5q4BJqxReWDnAotcVAWDSs7M5sylaaveOb/MjYg9x7+k3EKqa/kcPKBHjdVNJoJPmRby6WEAdidhWaND2XWr6HEiG5bMxFr8rx3BKPN2fJSY+un+bp+gxIGC+0iac8dE5DKBDrDsazd86JECCdmEYj6d2nCwNsW2Pl+kDiluO1UwSew4GJdWZHGmSVx0i6x0Prc0RaYXxBdhGOveoSwcCm1cwzXWpwdmOc2CRStIFvEQ9xg1EgEBnQoUTaeghHTMSqjBFcWK9hSU1cT9G4NIpd8VBWDDYgQec0spUkGmesj3RgM85hHeix2CpDyyU45VC4sUesBZY0WNKwJ7fFzYUFIi358+UbMSQww2ebk4RD9uvx6hIr9SxRKMD1h+c/YeeOpDu0wgwxCssyuEc7TD4XUFXJIgHwmuoFPnr/6zBGcunZaaTUjB5bI8DiuiMLbIgSD3T3U1J9AqUItIUtYxo6gxSJ+qH2r10fgEw65MbsIqdmQA1bLGMH6/Q6AIaH2ns4NZjk5IP7mD2yRmWkQxhZXPDKuEcaZFPJZ9NA1erywzNfBSDQFk/0dtEJZwmNomANiIXFalghNmJHjXNlUKCgPKpOF4uY189eQFuKX7v9R/CiOp9e+/f4gcKSCaN3LN2h3s8Cij+78ij/YtetXOztJzRJS7AfOrzvwqt43/4v8LrSGS55Nc70xlno11gaVNjl1nm4vSfZNQlN2+/yr554H1X3EAVHIjFEwLdVTzHhtFjwqvzRyiv5mbkv4oiIXPp/YjR364sUDVMqzU/tf8/XzR9CKIRQmKE0rSWzGGOYbzdwlcVUrvh1jvCPGy9Lj9wYcx9w38txrJcrBlHA784/TCf0+MG9t/FDc+8E4Ce++lF64VlyNjsO6kYLLjdqrPcLFMpJm0IOk4yOdxgUSEtzd/U5NoMsf7b8CpSCyXwbW8Ws9ErEJjG4HSn0aA7S5J2IqVqLvrbY7maHjumCpp8h0pJfev5NNC+UkeELECT5MPGNBJxMwMShLYyBxU6RjBWy7bvsKfTI2hGNbpmn2gnlWhtJs+sSGQtiQ6uXRhcTfLlRBj0VELUdtjbzVGpd/MBmY6MECHSo0IEFToA2CjBIC9yST+qEpFNMIaqaUCiU0dxeXeD08iQXGyM4o13igeSJB44mpCLgllefx7cgiG1OL8wkJZs0EEHrfPKelhuSKniYOMEyWmWPialGcj2qks3Hx4nWXcJqgDOIUSMBztiAgcrgZHzcid7OtRa5iPRXS2hg17dcoVrsczmoUY27lO0+g9BipNDlQxdv42rjxhjDtpchM0x2Z9oTDHyHwYkyZqTD6IFtIgSbbZd7Zs/w8Pbe5L0EqFLE8nFrRxo2iBXNOIOl4qF/aSJFvN4tMK6SdgICGjpP1gkYVR3akYsShutHV2j5aYJYsUqJcGgg4bo+2axHEFlshWlMLMjZAWkRMp1usDdfxyA43xzFjxw6q3nG0m1arTTpXERKXZMwlgaOFlZwRFK5Sgxjdoeby1fQCC6sjhIVFEpoZF8QZxIVyD3pDQqWzxPNXQRYjGZ6LIdlfvfSLzPj3sWmf5wn1yJGci0soXm2niiIaiM5u73Gz7f+LbYdIcigddKyWQ+y/Oy5u/npw1/kuL3MqNPh/sbhHVNxSKCJb5t7lLSKgEt8ZWuLx+tjuE6EFIJPbV9HxanxifUSF4MKH2n8Ir9w9C42gzqdaJuCXf2GcoQ2msiEODKFFDY3jf4Gz239byjhcuPor/Kehz/Fxy+dxmD41zffxQ8cfLE9ZBCuI7q/jDBtVP5nEfaRb+h9/z7inz2z0xjDJ1cf5eT2PHeO3cRttQRa9LNP/AkPbc4Ta82nl0/xuTf8K5r+gM9dvkgY2bQDSb7gsa9YY5fcxce35hm1a0xkeyz06yhpGCu0WN1OEg8k//nQ0quRynC8uoprBZxrjVLJxkPyxBCNYiAOBKdboxwsr1G0B0w7dR5r7KIXpvEjiyBQiDgZ/l1tqYJAxgJZ65NJhZQnEulaY2As28GRGkdFOy2g5nqO9XOjIKB6aItcxqfRSaq61sDdOUdCgLAMTnFAv+HQ9qpIJa7pfVx7JgkccfhpDDgLNkWhuMQ4c1PrPHR+P61+YgqgVMT4bJNzl2eJ9LXFqLleoLS7yVKzwMh4g37dZSBsTM/Z2W1EAwspHJROdgzFqXbSH5fJDyxdHdBbd4mFwJuIsSNFKhdguwliIfIVdjpOwC9PFJC+RKiY0WJ3x1BjK8pz9skZlipZglGLQZToySdNIYGtro4CDX5kE2ymMKGkvVyitVzE1EL0ZMDldo3dmToLvQTa2fds2v0sv3bmm3jz9DOsDQpcCEaYPbjChbMzSTeq5nOwsMYd0xcZhA5LYRGDZHWQpZz2ubxRppD1mCy0Ucaw3ikgpaY42sHRcSItYIU80ZvFcZIlsh85PLMxhUDwzPoM377nWaZyTa7UmrzmllMYAc8PRmi1s9SKnaHS47BLLwQxkkvtGica01w8O02m7GFPeFzqj7C4WMDRmtsnLpPPeBRFl6rdxZKGyXSTP1lNklhWeIzKFvX+n/Pw6hFavks9ctGaYREAGMOn/uwC/YNZXnXgAhOu5sHl/TuwWk9bCWlNGEadDmkRUbE9bi5W2O0e4UurXyU1rPIBDuXXudyfwMLGkQF52eO4+xBHdgnedf67eLQxz3uffYgxt402Id819gr2lt6ObU29ZN5YHVzig5feixf3uaH0et4y/VNU3du5c/bzAFzpbvAXF/+CYNgJ+40TX9lJ5MYYPrL4IR7Y/DyuiHlH7TQz4dtQo48i/pEMo//ZJ/LPrT3Jb577GJ4OeGDzWf7jTT/JwcIMJ7eXCHTyQ234PTqhx5dW5wmjIXY7thi0i/zJt72T31/4LKfidTDwqtFX857at/PuZ95PoBXj5SYbzSLGCDJpH5TgeGmJSbeFkoaJTJs/P3UjTjYknUr6a2kZccPECudao/zl8jGETtibYkgj7g+Si22MQOZDqKcwJJT5wJfo2CKXGewQka56WmbtkEhLLBUTx7A+mODjAAAgAElEQVT6/PgOhK9xtkr++i3AcKS4TNdLc7E7siOUlUn5pJ0IP2+BFnhLLspNpFrT6ZBsyiMyEm0SeCVaIC45HHrVZSaPr4MEL7IRViI8CoKRWuKQlC761xx9pObi4hjy3CjpjiZzVwPPshJjYwlD3AogUJZG+CIxdFhLY/YEOzZ5wUBhyhFmNMQYhR9IzEDhpGOirsNgLY2cGKCISQ+Gdm1xonJ3lfSiteD0ygzC7XMqnCaVM2QHPpFWOCrCjywQMWkVouwE1TKzZ51OM8t2IwdOsqN5emua10xd4DXV8zx9cZb66VHIa05a05xsTicLtz90Mpr2cKyQTDZkxZS4b+EY3zZ3Ejd2qageE9Y2S1GF8UqbbT/LQqdKZy1LbzODdDQ6lGgLWrmA6w9exostLBJGrCU1GTukF6TxYptzzRFyIiCYjPn06iGML9geZNFGslYvMjXaJEHvxCwHFf7oSpUnNnYTGQUFSJ2vMlZaYaayxXKvTDewsZxkh1KwPKzhjsMgOJDboGvS3JhbStpfRvAvdj3N78/fimtHtP0UCI02CUlq7/Qa8/Uqo+kuQhiOV1d4rj4BAu4aPzMc3ktG0kf5xX1zXFe6gVTmLgB+dP+d/MnC92OICbVk1SsQxIbfue0nObv+TsbUc6z7eQp2n0O5NS4OJgl1iK8HgOGpxl9iB3/I7vFHUPJrU+4/tfpBBnEio3yi+WXOtD+LEDFT6QNsh0d5tHGS47tjzq+O0B24jGeuHWfNW+GhrQeTRd1Y/HFrN+8eOQemB/9/Iv+7xfPtK3hD2yeBYL67wsHCDG+cOsafX3kagD25KkavcLhYedFrR9ykD/b7C58lNpqO7/DBC4/z3xYeJeGDC7CgkOsRGkUpk5BMCra349KCARFJ1lfLlCtdSqUes/ktnm9OkLUDYiPohQ6zmW2agUtoCbomMcYVAsgZMqcFUQrQmuB4CDY0BhnyuQGWbegPbAJH0olcjIH5Xo0bilde9F2MEVSdDgeKm5xZnaBxpUihPMBNB/RRKKXx42R7iwR3qk+wnGHXng2irRSdx8rIfISyNK1RQxRbmLQiGI8SUS1ItLfdEPqJENfVQV22OmDs+nWCDCAh7FpYsSG+lKF3Ko8zl7gYBbmEt62MITXiYRbcZMEA+ot5yMS4bkhjqYAnU8mWwI8R6eRcx55icDGXXGlpwLeYnNzGnxT0lnJgYPGPZ5n6ziWkNCycGUenBGO5HmpY3blOhDEx7UEabQT9MKkMdxXrvOaWs8PJneDk2WnWnEThsZrukRYBpdSA8VyPM9NRAvELk5aJGboTmUhgLE2mGO7MXzrS4b+efBU/cv1XSKkECZOPfR4K9ib0dgRWKgIE2rcwGIwwBF2HK/UyKhVTcHwyw5lOrK/SaqHpp1kypSExS9IK0jvEHI0giiRSGrZ7WZaaida9USBijVGSOBeDkGz6eSIjkWnNye0pbq5eoRFlGRNJyyQnfd5efZSmTnMxSkwYlDAU1IA3zJ5NFt7I4nR9jEHocKiyxujuLtl6B43BEnCwssG+4gZpK8KRMdtxch3Pebey1K/zNlnk7qEtZ9EZ41UjP8F/OfdRtrwsJ7anUSi6AVTdb+EdTx/FtTzePvcIN5aucEtlkbWwhmdAoSmpPpiQMJpHOTd8zbzhyPQQlGrQRBg0AsOyd46l/jahySAlHBjr4vqH+eXbr7FALWnvXAOBwREgUq9DyPJLZKm///hnn8jvHLuRT60+hhQSieDmygEAfuH4N3P7yB4a/jpRdC+fW/4IjirwnpvezX9+7mkqKZffu+t7EUKQtzJsB92kQktm9y+CwEllEHEibpWyIy50qlxfXkUb6HguzUGSwWQMR0trGAyltDfc1IItNZVMn7Lbp9jIE6Qd2sMfXaXSxT7kEbUcesZKNEFCgfEcFtoT0BG4dszo8YQGDtDppXgknKOwq0H7cmL8UNrbQNmSertA8EiF6sE2u/evJY4rWnCpXuaFWgPK0lhK05kvUO4HtGIL3bTACQkrcDXhP7W6m93jW4mfZWATx1YC4/Mkm1sFimkPN+cTuXDVSNzORTR7abJHumRV0hpCQrvtErlJSysKBM1aCkOIsyWw+ormahGvKfAqQ9C8EdB0EBMDBFCtNDGDgFYvQyAt6NhcPj/BvhtXqN60ydKVCispxZHUAAwcP76AF9vY6eFCf7VDJhJNkWvM2kSbBmGGbSvD6EyTtY0sRhvuGj/DgeIGBljNtSE1vBDxVVJTAm+TPQUdi34ImbFEZ9toQRAnbkmL7TKPrM5RcDz8lEKYJH2oVETS/kjmGcKAnfcYYEFg0w1STOTadHwHf+hnCoLFbpWp4jatII0Umlq+y3qzgACyts9cfpO88vjM5nFUKt7hIghpkKFm92SdPoKUSgg9GljqF9n0jyAwHC2u8oNjj1BTiUFIzvh0ovQOkmUlKO64+kgrIOo5rGzVqMoBI1M9JkeaGNhBwqRUhCUSZMxA59CUeax+jsjE/PrZP2Z/foIz7S/zn5+/zHzHp+wcoenHJAh9m//loQ+DiPG1zcHMKlIkHrQGmHKmCehSEWfY5zRB5LGtvS+ZN7518kf5w8tbbHpXEPhDu8RkaBabq2YSguPVWd579Pte9NqR1CjfPvnd3Lf2MUpWlrfu/mFk9vUv+V5XI9Yd/PAMKXs/Spa+7vP/NvHPPpEfL83x27e8iwvdFY6X9jCSSqbLQgjunDjEifqXObXdRBuNiTV3zWzxL4+9C4BY+zy68Uv8wPTzPNuq8bFBjrzTZTrXZKFbJTYSKTQDP4VQidBQze0z0Gk+ffkgWTtiYWUsWZuFYa64hRQmqW6GN4YAXDuRn40iyfpmmbKIKI5sEyMTJbnjBhMLagLmV2v47QxXe5tWJiblBImS3NU2Sd6n2c/SURJ2e1ipkHK5RcYK2VgroA04bog/cMjkfLROxKNaHZeectChJK8CVnplBkbgKwdLRcSxGia4a3ytEMGKV8CRMQ88e4QoljuGzdrA/JlxCucV7l1biNI1JJBBJEa8KkAO77KC7eOEmvV+hkErBSp5cjBicJ8zYIHKhiQwleQxKQ1jxQ452+OezBk+cOkuQqV2KmejBStrJfbvX0VXDKN2GyE0ario7DuwxJn6CI6jEyExX6FsTcoK8UM7wUjbAV5sJ2fcQGwEW8M5AMBir8zB0gYCqGW6w/traCytBWFkIfpquLsQBK00Vi5ApWJ6nTSxUTy5NsNXV/YTaYUUmnTKp5Tz2DgxQuyAzugE86VBGINdCnZWHmMEi80SUWShNUh5daJikBsW/jOj5Ca6TNyyyki2TxBJ5gobTLkdzm2P7OiWJ2qEmpFMj5vGEsx+pCXdKMW42+Fiq4qvLQZGIjBknJCHe3u5O/c8ltB8rnWQM8EkxzKL5KTPqN1hObKHA2RJ10sTxhb7xtd34JvGwIaXA5loCoWxxFUhR4uHebaZox+uIoQkJQWfWv0tHl5f51J3HIOiHUp+7OBrudRp8sml5wh0jKMElpDUg9zOPRJqxRfXQt665128pnqZKF4i537HS7ZVAErOCD+5/9c5uf0An1j5Fa6yk/qxQz3MkpIOc9lZfnLfD37N198z/i3cM/4tL3n8vxphvMbFtTdgjA9I9ox/Csfa/Q2//uvFP/tEDjCbHXsR7f6FkbFGUMIhNh4CSUaN7Dx2avtDLHQ+TWx8DuY2eP+t/4b3L/w+QmgOFDcJYpfL9XJi4qU1o7kekZas9/KERlLvWISOIZMdwKKDt5xFV9pYSmMJvaMsF2lBGEpirVhdraKUZm76CoGx6W5lKVS7KDuR/awIj1WRGd5YAmEbgk6S2IxJntNvZNEo9FBrJYwlx/asUXA9LozGLJdrbCxW2ViusvvgCuWRLpatSUnNxloGa8Vms5AZ+myCjw0HehT7IZ2ywoptoiEUzvI17ShDEAxNM8wL9ioGYhfUdS3sFYGfh+1GHj+wUXaEm/WGFWuyba2Ve9QbWXT8AjXFYQgDjAZMTWzha8ni8hixEWTHuwRa0fNt7lu7nuBqeyjJ5EkVXQ5Y6RTIZj0yrs/VibMxkHM9HCfm0mYVbQTGUwhPIqRmevcmxZSPEYlZyEKnguUZ6t0cq3GWSqaPJTVTbpNQJ7Tys52xxNptKJVQcfsst168pZYigT3GCKQ0KB3zlaV9iWQvCaojii1WFysoJMaO4erXEiDsmFhLlLlGifc9mzhO7ictNbYdk1IR6ycmEnVLJ1lUHCvGVjEbfpkrnRrzjRrK1ug4Gao7tmZfeYvl+RqXnpvGcUMO336RTCYkr3pgMsn7NBUPrh7i2JErXPJqxEhekztLwbnAtNMcnmLDwDis+GUubexiq5NUme2By0iul2zqhE3eyTHQieSDUvDY+iyHszfxfOtRIiMRBrKizYfvH2V18zC7Z1eZ2bvO6qDEht9gX36UtLLw4ghjYG+2SDPMs+6NEurHmO+WeWp7jPTKv+f+uk/GSvMTe+9m/BvIbkdLdxDoHieaX+LJ7Q02gzQp6fADu97CGyde//UP8FfCGMOnVz/Ik9tfYCw9y9t2/TwZq0C7/0m07mAIAEmz96eMFn/mb338l4r/IRL53xR7C2+mFSyw1n+M2dxdTGVfu/NYL1ojNglmONSatPKwpcSLDbGWWHgsbhbJuz0OzmwSGUV9kCHUSSJynJgosogQOFsOi0sT2KmI4kiXSEo8qTAIglDy+IX9tLdzxFpSKnfo1TPIYkR7M0uhMmTvGUGIRDkRcZBcmkK1g3YcmlfyyFyMP0gx6KYx6XjHNFli+MziEdx0wIH0ZrL19iXagfWlKm4lofv72iIKbOLRCNUdln9GggA/JdnIOoSd4bDGgFNXSCTt7RT54UATTAJMvkr5A+oqi7Nnk2Y7hzdUbox9mzuqz/PY2n6m5rZIqcRAuSB9ttazZPID+q1EOMtdFvR3xxw6vEI2lWiY7B1f55GNObymS38rS6XapjDZYq68wqXTk4AhV+0hCxGZ7HBIiqEVuJxtjlJNdynYHvPNGg0vi1AGqQ2xZ6MDSboYkrJjUlbiIKSM4fyVsWRWIWCm0KSSTwyCTw1muOxXaIcZlvpl9k5sUV8pcHRsmWfO7EZJgV3oo9suUagoV9oUCx02ehnKVo+j5VXOXJxhzc2ClcBap8vbiIzkSmMCYcQOHRyhKeQHRDK5bySCbODTid2d840RxLHk2NgyJ0gWkfZynurBBkJd2xWlhkJvcaxQMqaS7eFrm0tbVZrPTKC1JAwU80/OcOvrT7Nvap1e2+bD529DKkNlosepxSlmJ7dwiChXemgEZ/1JAmPhioApu0ErzjDfclG5AO0Lvro0xz17mxwsQFEt8nCrCUMZXIXm6Mg6X9n+GF5cgaGU8OXVKt1GlsnROndc/zy2pdldaHC+E3BT9Xt569wtfOTiQ7TXbZ49nVTmn7hUpT99M76OGct0KKZ8ENCPB/zxwjt46+QbKed/FClzXzM3/NaZB/jNM18mYzn89qvewZ1jhq9sPc7u7DR31G75u6QbFnqneLzxWULjs9Q/z/3rH+Fbp34Mx5oBYYEJECKFY+36Ox3/peJ/+EQuheLmkXf/tb9rE3Og+D2cbX4Wg6YdOPzCo1f4rgNv4d4TTyboDQyOHVDIe5xaniSf9hD2NWaeEIZqsUvoKTydBwMXnpgFBb1dEZnqIGGJuiGpqocVORhtsdopQacEwiC0YaVZoJjx+H/Ze+9wy66zzPO31o4nn3NzqqpbSVWlLLlkWbYlS3LA2NjGxrgJDTZ0D7HpmQYzhvHA88DAMN0wY5ghtN0eGnpIDmCwBQ6SbKxgZVUplSqnW/feuunce+KOa635Y517q2xJNsaGZ9Az66+qe/bZZ8dvfev93u99u7FP2/GoTln3G8dVlAsRwXAXECzMDRF1Q/vjObDJzhC2iBUpn+cXpiksDDI/x5CFkn43IMo84rbP973qYQIv4/4je5k7bXm/ppojPAbWdGDTQtC+QXYlZaO5cHiC8WKPluPTEx7GETaNTkF6ilZcvOz7dkn9909dh+/mzFy9YTNUbesMjtHUqxG1aoxWsOZXUMoh9HK6yyXmHpvC2wA9o8iKdjWyulSnUEwplGL8sT7aMRT6KeKYDzcmdlIzUPVivmPsCAbBobUZ4ty3PJnB4WpjCIci3GqGVnKrgHhqYxhdMlvrhHr5UkFbeopj65PkRjJa6iGl7YB99KkrSGLr1uCUNFffcAYMHDk/ycqFcYSA+sgKDz90LUZLPKHJqxpd0pwRI8w01gmKGSJ1SEJAGEr1BCfUdo42IL5QwesHyDs0ehDrfS8nVw6+yJFXttHPV9FacPrsMNsn1gnrKVs9MUZb6QBh6OYhIOjEl8xTQLLeL3Hv/D5GCx2+b+Yx7pg+yhcX95PmDkElpyxissxlvtvA9bWFX7TPUqfKc51tjIy02DG8xtG2S1gEheSx5YDrq19BqZxpv875xEKVGoEjIXBzfJHTziy+7wmFQTBc72wVpqXQFNweD6we4r/b8b3858eeRHbcAWauGKvPcefOCR5YP8JwoWMbq7CwUEn0aXU+RJzcy/TYXS94/x9Y/hyPND/BaKHOxX6VXz70GT79+p9kZ3nbC7b9ZkZmki1JAIMm1TZRLIdvZLT6c3Siv6Uc3kGt+D3f0u987XjZB/IXG7/+1H/lL8+fpOFHlMU1LHVrrMYVBC0+OxcMOOGQGagV+yxsWC55nPrUgx4ICYHGdWzG44Q5SSMn61tqn/HA6Uu6dR+6LmJJEKxLhAJKmnCqD6kki13yRkY7KtKObcleSo3vaoyTErgZoZsPeN2Gar1Ptz3Yrphbj81MUqpHWy9udqq0pQ8jFagNn/OPTJGMwluvP0xhIOq0Oj/CVrt720MLcFoenmPI6jlIgZNYPZCwmODVUkZ2rVNXkiNnLuPnBgavmpLlEi9QiNhqistEkGlBcTbiqbUpym5C0WQ8uzBNrhzEakBt1waOYyjXItKmz8lD08QrRWQmcVINGxI2E1EDeeYwv1En9S0GsRoWGT7hIQ9EmIJ1Yn/96FGKrlW7vHFojuOLUwgPtLIdpdQUmQuOEfRzj7qO6GW+rQ1s1lgNdFKfWhBvZbfjpQ5G2K5cT2oKYy36zSJ918WJDOlGwOJylbCQWV0eIzHG0O8Ut6iZ0kicnsA4AlPSXFhvMDrVotx2aC4MgYFgosOlvgWD23QRRjHZaNLsFdErAaZbolrqURuPSeIQcUVEMBJhFkIWHp9k5lWLuGFOM/WYaVioTwjDSq9MP/PRgcBUUnTXRxqB2BWhkazGZSLlMRJ2cYTBETl1r8/xlXFumJjn9x57I2+44hnyoktf+ShHcn5ljIeeOsCuG8+hcsG1Ixd45fC5AUFVkWvJF49cyVJcpVyK2DtzESkt/NTLfJR2UEhGh1sUerCwPMR1++Rg0Wfo5lWuq83ye0e/YoumrsGkmrfd+ii1cp/UPcJrxieYj2MkhgxBXUa8sngOyEjSQy94/59t3c/fr3yU3dWY2coy987twxUTLxkvTHoI1f1thBxDVj+I+DqFyt3l65ktXsmJ7mHKbp07xv+VvZVCMFL9CUaqP/GS3/1WxssykH/p4tP8L8/+FZ50+M0bfohrG7Nbnx3ZWORjZ0+TG4+lyKFSjehkwwSO4I6pPWg3xRESZay0kdYBxgg2dXdavRLVRwOCm9dxJxWbwvbSVeSOZZ0YbIePMEAu8DdsEBcI6Eny+RCn51iHlxjM9uwSlS+0M/hIoYfAkBmbNWol6HdDPD+nWI5JfYMjNb25Onlk8KatYJMpWFldMciOs6rGFHO2xMMHw3G+RvMj9lBlhS4pS+0TIKs528fXWEgr5COKKHcHruiXhnQ0gZfR7YakSYCQGrcjES0Hb3dEWEmQEroq4MLG0KBxCEgFO0tNnmlO2k7WuqbfKYAr0CVwIkFhRROP2QspfU1Qjuk3S2wGOuMZvOs7mE808N+yRlOFlLZfcsFxpGY1L+K4yqL0DriuolrsgxF0Ep8lSujcsc1Rg+IcwMJGlU5aYmqmiREC1zNoZVkSm4W82T3LPPvILMV5y/BprY6yujNHZp51hAoNpWJM6lTQyopQac9YUaxlH+oZQ9Nd1hdHB5OvQUcOTim3KyIlEErjjyXMDq1yrj/KhfUaIOhsVLjrsZvRUiKERiuJN56gG5ozX9hh4fZb19k2vLG1sqgFEWnukCeS/IoUYzJMCm7RYvECGHHafGzpJnZUVrmwOM66GmKk0mGtW6QbF/jc0Rt49fXH7ESPYWpHkwvnxpCJ5LbJk7x29BS+Y2sIuXF47MIuTjbHybXLerdMOUyZHV/lfKdOpjclCQTX1G+mPPYxHm9u46HmTupeTDcr8baRN/E7jx5BlvoUqhCLgFIhY6hqV0bKKELRYXvxh7lv+WluamjeUf8SjnYQwiX0XwiRXOgfQ2A1jhwBr506z3t3vv9FY4nRPdT6e8H0MXhovY4z9NEX3RbAEQ4/tPOXSHWCJ/x/Nhehl10gz7Xi/U98nEwLQPGTj/wxf3jjT/Pk/AIHt03TJ91aPmsEufb40KvfgRSCO6Z287nFx3l87QSZcnj/1W9gZ2GYH/jix7decNpWuEkfLeFNReRIQjelsH2N5eU6STMEV6DGUlupT8DtgR7oUmFA9h1wNePXrhCtF4iWPfRESrGU4kiDyQVnLw7RG3RnDpc76I5PP/PZufciYClkzbUivQCSOCQ55bN/7xzeK5ucaO/ESQTptgxGNKZl4YkHTu5laLxFJwsoTLTpngkt5O2DKShMJb90ngiSquFiXsIog5Cw3C8zXOgzNrTBUrOOUFB/ygW3itltg5nBweQCXDFg7lwaUlh8XaSC6bFVYnXp8RPSOg3plg9CY3Ym4OQ0nimQ1AS9Azlza3U8kQ/cgawC5ey1i+idHuFExKwRPLi8i9eMnQYDnz19FSp3kOc8RKAxwznj9bblbxtw+z7r949RP9hEJ3LQAGQ7W40QrGYFxo3c0h7R2mqAaGy7ba4FXlvQnbXXUCYgYg+BwOlJRJizb+oiemyF5YU6672QhZXRrRWTF8G1tQWe8CpsUMab7WIChTYClUiylZDs1pzi7AZrG2VWzjY2e7Hso6SlveZGohIX30nA11BQiL094tS79KxrCEzG1ZU5HkmtUqIQYAIwXQe/mrC7vsKn12/k4Ox5Dp2a3aLjNnslZjLr1hSnLplycB2rD9Pv2URnx/Aa9UIXYwSrnQpPnNrFaNnn/OJO8lzbxjQFBZ1RkTFz7QZCCkqujzGGH9l9O3905h5i5ZEZl34WEN8/zH9ZOo4vJb0bHcKJmFJJU/N8Sl6VWLWRwmGqsJ+3TL2FI+ttPnHuDOc6P8yvXOnjSodK8V0viBEzxb0capotVtBQkLK/9hIZuV6HraJzhlGnX3y7rxkL/TafPPc408UG37vjJlzpfOMvfQvj5RfIjR4EcXuTOpniLX/y3zCAbyR/+YM/wE0j0zy8sojv5Lx5+xSHu3/MwaHX0kxH+f1Tn6RWznCFw5n4eZrdvcjcQQ2se7yewJ3tEU70mSqtY4RtxEhGYHxklbKb0D1c5bRfRQiDIxSeMQRXt+CBKqbtkBeg9Lo1mmeGSCOrtS36Pmq0Q2Gmj3ANSyu1rXNo9spUz0rKu2znmtzkZg/s00QGQkkWFocJxyL0jpi4ckkuQBYzdM+n77gk/RpCQM8FEypM5tjgKsxluOkg49WCVEj8ywwFciNRkUtpCQpnXdyeAGko+obeZv2moihE0N0IKdUihK/QsUO85uNGEhNLspLHcNjjVGsEIRRaS0zHutr4e7s4lYwc0KOa9HiFsC3Yt3sZ18/pJxb3Hit1MMbgF/OBxK8hkz4fOvR6VO6gjYPTljitwXUq28akzYzaDXLSYcHi6VG8ckpeUbihunQJYpfTT00zPN6iPNZjoVujmkcMFyLy1OPM4jDpkEZ71hoOydZKCKBe7NPbKLJwbpgcQXOlYpmV9upisIJrr7juOBe/UrMWbsLKuArXkCYBYJg/N8b2HcvEA07lFuXZ01udvW4twaKykv4OhVd2ULng7EqDkUqPOHVZ3ShTqURb9xesu9Pe2RUMkhSfVGlOtcZYWGkghbGQkxFEiY8X5hTrEUcWxpmut8lil5PHJxHjMWEhZW69SqUR84d3v5Ek83ClQGwY3FFBXjR4Qc6SLnN6bhSJx7u2Xc9tE7vZX5tkolDjl6/+XX7z+f/Cg6sLqNUQVgtobRAa/NMBWdnjfVffyBWNEQ43d7CtPMfeynZuGn4Xv/bsx/jKynG0gUfW5rhn7Tt5z+wrXzRG9LIVQNg2ACNw5dfRZnGmwb8RsqfAaETpx19628FoZxH/+oGP0M1jAulxob/Oz1/19W3lvtXxsgvkoeMxFJRoJlZUSeTOlvFBiuavn3kOd22CG7Ih3naLx8OdT5OZlKXFRYwJBnJVkCjNk6sLTDllPEeihQJjGLl2BTe02chCVCfoQVayGatB0MlD9r1qju7v3kD7qox0IsNvpHDOh47N5t3UIJquDeLYF8WkAn/DRQ4c6jc5uGD/XdrRIV8OEQNzJWMgiTzcFvhtgS5o2tMe7a4HBbP1sg/IHDZDrF0yPwas52E2CNp9B6q5zfg0NkDkAuEPmCrYFnESQfxMA6EhrkEAeD1wUgguCtwIxKgmdgNkU7OcDdnfySXFfk79QkJ6S8TFi1X27IXbpk6ylpSoejErXo2ljTrt0EGtBYhAIauKkdl1GpNti8ELQbUQE7oWjsq0iyMGKn8a4syz4mUDYN1RmsBTpGmAc7JAVuniVix00V8PyTcpnF0fJ4hQUiJdQ951cZY9MgQX2z4zJkeWDb08pNcJMYntxjUjOXQ88DTaN4jWAIPzNHtGljj84BUDmqihuKNNsd6lvVhBG0Huw+nlUXaOrDB83TKtqEC0WCLv+/hBxqFVqEkAACAASURBVPDIBmurdbLMpZkWMI0MGQ0m3mLO5L5liF3SXKKAXrdA0g4RobKYvGuIlMe51caW1rzt7h0g0EagtCDXDo7UpEpysdtAG0EwGZEuFjCOZqjU4ezKFI3ta1b7R8GFx8bJuh5mVnGgcZ4z/6tk5a6M4zftIHuzvaa5Blk0jBw2OD+4gvTsXSkkCa0LZYJahduv3w/AXK/Jv77/D1lP+xhdxLQ8wkF115GSHY0aP3KToKm+zK8+LUm0JpCGX9n3IMfdER5ePUyWFzGAgyLTl5KPrx11f4JDqztQxkKXFzpjvGf7GjvKLwzoQgicxh/ZQC7rCPcbG0zM99dt3woQ64zH1858w+98q+NlF8gBPnHbT/P7x76IJx0+88gcHaKtz+4+cpKldg9tDFFpkclZi6mmKuf++RNU4ynOd9aJtGRepDwZHQNtg+1kdQMvzNHYDC/XgoVnJhm//iJ+0RbYfGlx8zyE6W3reFMJnTggW6tsakoB0BUu5aEevZbljIs+ZJ2A8GoByjBcb9HcsFl9GKR0NwK8k0Wa0RjBng7JhSJJGuC3BcIIdMFc6uORAmJsQM+gnsW00xBnzbO0RYC+g4kuGSI7WMuwNHPI+5Le83XcVCL6mnxbTNHPCGRGb7UwSAntsj4rGqJtCi0gWHERWmDmAsKdCSY09JMAtGHHzmWGhrrEkcfxk1PkgcOzp7dzYGqBTrNM0NA0xroY17D26C4YeFK6O7uUZ9dw3Uu+jblx6GWW/WCAbuJRDjJ6mc/p5hBSavLcYbTY4btvOIQrNOfnx/ji/dfTPDTC8P41+lFA3Au29mkM+K6h07NBWqYCd4uuLqnolLnlIfLUJSgnDA93SDouqatwEhflAqFBjWSggFzyzPEdgwYrAEHWCajP9ijUE+K+x8pinSPnttnJpGCLyyq2k3uaWHlOL0gRJUVqHLyOROf2WLcduEhYyKGUojWcPztMzUlZUgWIHShlSKFxXY1RAiWNNaIGZFtiEgenJ0kmNCeWRhkpddh0eEIIpKMxoSGLPZbaDYqlHFc6aBTe/SHhYR9joH5M0L/Yo/eALZ07h3rINymMtBNFuL2LLGnbHT24FFJq0o7gDx9/gvff9hqOdY/yxycfZi3poLVAnSphYklje5tssczOqTq//L69HGr/Mk+tT5JqCw3lWvPMxil2Bv+epe73kg149krBcFB9yfhwReU1GPMwz2/Y5q6S63Mxar9oIAcQwrFZ+T9w7CyPUvYKRLmtFcwWx/7B3/3HjpdlIB8NK3zgqregjOENjWV+5FN/RZznvPuqq7nn8Alr2AqcmmswsWOZXCtyJfj4IxskZQMFn01vR9ezuGmeSaYbGySOQzuzNC6Rwfg1FwkKKUpJ6mHEsN/j6adnca/v4U9aXmvRTens7aOWfMyaR1KDjhPiD0XIMMIIcBopWdOl+fgQXkUR9Xz0GJbKGBdwhaAYGJxTAcmpgPYuqJyBuAFGGmRnQFsZ1CKdJR/RE1SWNONvWSWqSrLLW/TLOcFMh/hiBWFgaHcTxzEUnJz+fAmv51h+83wIFU2vLmkmJcaHOgzKqhgB8VRONqopnHYvwQoCMiOZ2LXMhePjlEsRYayJFoqEkz0mp5rMzY2xtFJndbFui8XSsH//HOvP1qy5wqBZSS8HyF2QaBcpMoSC1oUysikob+szPb1G0UmtdvvGBHUdcSEugYRbZk4NeOIwM7nK8OgGK60G62eHuOnaoxw6v5Mks+ciBEgnQwgPPGcLFjUYXKlpq5A8cTFGEndCVo21RjNGIIdTqo+FtK/SqM1abAbeWEK6GmKwEEg41sdYmRr6bYstOxsCb4+m5sf0xOWKlZZv7kqFKNgVhHAMxteIxCHp+HQ7Bfwgp1SO0SVNb92HTOC2JHQK3HHDk6z0avjFnGvGzvP55atZODWBv+RscdfLWcIdr3sG4xi+fPYKTLAFHV82CUG/J6moQRH8mI/IB+5HGw7d8hQl9yzkBplqQrVBPlYiCw3FiT5ixi7w5KCpTTia6WsX2VNZ4+ef/vdkOiPVObPVCqcXRiBx2D67zM69CziuwZc+iypCm4xdpRUeWduJwF6fG6tz5DoeKFuKrXv2C4//NXdO7sd/EWxaCMHPXvUufvrhP8cRgulineuHZr65IPN1Ruh4vP/Ad/Lzj/8VuVHcs3iMv794jNsn/uk8P1+WgfxzZ4/x3993F8oY/ofrX8Oz/+5n0MbgSMl/Ltb5g/sfQQjI6gW+8Nw+Cn5CtxtA5GIa6QDasOlYWEiRg/b6880Rrt12nqJrHXsSx6FUstZvSmvObzQ4mo4jfYdyX1slPmlwhCE44hEvSrK3b1CoZohuwEa7SFBO8QtWZMmM5ORGkAca3RZbkwkaZB8KTYMc6KNXTxqShsBojdpMPjZcEOC2JTKxy/nw1Ruousacc6C4WSkTKGMIqwmy2CJKfGRmMz0hQCfO5V36mFSgNnXZXYOqGEwMejwjKwAI0jGN17LMD+1AEgrWe0XC6S7ecwHLi5MAhON93Os6W+dlBlolWsH5x6ZwFl0Y20zdNE4lQ2swSrLWLdG+a5zyAmAglpqJH+7i7k7Q2uAuO6w9NwH7bPae5B5KgyNtcTgX1t0pVy7tToU0uyQFoDH0dGgxbE8BkjwTOJFErknmGcM07EURxpCk/tbbk0nJzO3nOdEaR6USUgeBpLVSZfTaNXTPg0DRiXzydYj7AXE7wEkkXimnl7l0uyGBl1CqRPTaBQrFBBdNslFAdBVT+1cozHRZXy7jzkQsnBgbsKkMbpihChrfEwRSoY2LSB0eOruPdlZAAOdao6xs1K3Q12VjstxitNLFGHjTriN85sR1uH6OuRggWh5UBlx6LVk91cArZphhKA5uIRLM+DDm5ouo44rmbZO06yP4uWSqEKBkhESSxgYtJE5gzaTNgsfZv9uJX0mZfNMCbkExUe6wXJiiZQSlSrRlpiEQuM52HOEzVdC8b/YwgbyRK/y7mA5TCv6d7K2McaKzwuaDq4xGaT0Qv3vhuGV0F597w8+wGLU5UJ/Al9/eUDjX39hq8kpUztHW0v8fyL/Z8UsP30OibEr124ce5N9cdZCCa6tMP3HrK3nj/t0kec53fe6/oXFII4utyZGMkXIPpGY9KiCcTYMJcF1Nc7HBA+0ysd2a0UaLcmghFSHAdbR9SIuK7ukS9e4GtZEu/VZI+mwVDkT4g2aNQjUmUQ5aWL4sCISC4imXXt0hHhFIN8MtKgSGqpshnrrEXxUanAjyAyliNgVhCLSAriQwDokKKAxFlGa7IGBsrMVCp8LlhS4w+Bc8xGJALw9wMnC3xUQjBrkARhuMZ0jLBpSDSKG5WLaFybJB1xVCCUwuCRoxybUuedtHhYBju1SFFOQLBcRgIojmS/RnBpTCghpUXQew0KqL3BEhtyv0coj2NP0hw/xCHfFUFZRAugblWF0SJ5EsHxqlsbtNriWEhiDMyNYd8rrm/uf3UTqQUiv1Oby8jekbl5hmiYVjI+zatkBfOJxcmMQgqDR69IVziUvuGGRqs04vFjjLkryq0NLgdCUmsxMa0lDvZBzpTaPcwbX1wKQGiUEhCUZj0lQyWmuTLhTInytQ0oKkBqlwOXNqEh3acxIJ4OdE6wHxus3QG0M9Rso9pGMoNmJOLYxsSTgYI0gjHyM0pdEO2lek3TLGGFppkc3OyYVeA5lLCEC7IHMbZK665uzW81vyMqJWAVnOOFBbo7Nd0lIBeccnbhYxyiHpSFauy5latc+rKghEQaF/ZZQ8cWgfHYccUqGpmSr/16v/A9/z2T9lNYowQCF0GS710H83RLcq6ZfLzH2hznV3HEeEIFplKKasLU4wOdUicD1CJ+QVQ3dyY+Mgq/ERRsIrKXtTxOn3oHVES13Fb78y46MnHuAzc88gMPzU/tdtvfMvNcYKVcYKLw3BRHnG7zz1AIv9Dj9x9as40PiHQyR3TuzjD459mQDbhPb6yX+6IA4v00BevOwGOlLgXGb9BLB71GJh79xzJZ89exxlNLFR7J5ZpjSw+RqK+1zslwfcYoHOJaYFaeAgKgojNBsLFRqVPo6ryZWkl/ibCS+FkS47p1cQQG0ypnNTlV7Zwb0sjgpHkzYDgqmMkpNw3fAC3ls1T8zNstCt2SA+2D6qSLxrEoJnLK7bmzAIJWEmHWiOW33vsZk2wcA4WCkrKnXmqWlU5mCkxt3WB9fgSUVhAzpnBg1ExmBSQX6iBF5McmUfeq61ijMSkWnGhjqovx4hrkA0jF0pOIZXTJzi1ulT5FryyecPstSrEpYTzIC3p8sK2bZZvgkMTiOjPJySNz1kPUbFLrKgyCYE+bkAljXUUtyzIe6yi/INbm6x2zyA9QM2y/K6Bq9fZGW9jCxpRrZtMLptg0P37qPTDEmLHp9+8FWA4frXnNhqmpq8Yo3PLRxgKOxxw1UnWU+LDA91OHRhO1LYlZSIHLxySnDcRxiBTGH4jEL1XEQmQLrEUwpT0DhzJdQ1X82vRxhwDNrR9BMPX9rW+f4zddC2pB60DL0iiFjiJBI9lGOswgFGGejbYBsWEsvtH4ywkNK+DPZwYoHyIM8dvFqGOx6RroV4saFw3hqYxMMDc4myIRkxhE1BXDWc3hilUutx6MIOji5Oga8wGtpzo+x5/VFW4hItUWK+eUlEzPiClVs1tedc3DDHv33dWtUpicwEIjfoRs5znXmemL/IWpRuyesmicdoschFR9AfEyAFuZEcOTxLNpLT60bgCeZTH/3otfz+99/JvtoVFJwiUKbsTV26Dv4N/MEzD/Ghpz4CwM9c+2qeePsvoLSm5F2qf3yj8ecnDvG/P30f44UKH77tXcyUbcL0ga98ls+fP0aiFV+8cIqH3v1TlP+B+91ZGeHTd/4UT6/Pc019iunSP63E7csqkB9vrrLc7/E7t30XP3v/39LPM3791W/Cd158efVbr30L37v3Gha7bX7u/r+jVEi2FAYLhYze01VK5YSsDGkzxEiDaeRbzT6qqjm73qDsJZTijFT5CMeQdT32X30OsJmOIwzVa9dZXBqnpiSOYwN/HPuUz0MiSvSiOk9UC9x63fPcsu8Ef33o8uKKzapqsy3mp8vMVFrkh0fJxmMSIXAGmL82AkcqTCbo/c0IZtEjm9CoGWmhIi1R50sUSPGVQy8tfDWEMjgvH4NTzTCuQp0oQwp+I2F0usMFd5TSqibsQDsNUUM5t15/yna4SsV7rnqMj554DaOFDku9KipxiA9k+OcsNS+aUjgrHsXRhPpEl24WoFNJOldCSIOTS+SqwD/tbx2bcWBgrWn5+FKABhXAclbDPTXJzmsW8FzLJho7uEhzcQKxZPXOYcAB32y5F5qrty8C0M882gSY1DDRiVm7ULfNSDs6mJ5DviOHVY96EJMZSb9ttWiENhQWJZUDLdoMUZwT9LcbewFzQW2iixPkGKyCZpY7BP6lgu2lOzvAdY3ZwuVFLmxjVkFBLlhfrDEy0bbbSYiNi0wHVEeFDZy5oN/zCVWGKOTs3DeHOVRlNbcBJFwdMDDm7e/5noMq5jz/6CzHj07Rqzn2YY1tJhKPFXj01B76a1YSQgYZ3nrAa3fOctZZY7HU4btvvYFbt43wS4f+hCzyWL8wjMwEaijDlDS5hPc//HdMFCss9OzxjxQjfvO29/KfnnicL2eLbL4kae6i86+Gfbp9l0I2MwjiLz4+9NQDWwYyv/P0g/y7a16NcMRLbv+1Y7Hf5leeuIdE5TTjPr/wyGf5k9d/PwDPNC+SDPada81y1PsHB3KAqWKdqeK3V672pcbLJpB/4uiz/NKD9+AIwa7aEF9457/BlfLrfkcIwcGxGZ7U83zH7B6O9ZcoF21GHvV9vK6Hs+Bb1/dRg3H1pYYMsUnhlUS5zyum59iZrHHfyQNg4MT5CU4zzr6ZRRqVHp2TNbKNCsuph0wHRG7PoBzIeg5IwXqnzOmFMUxZUarEJLGFC4SAopdRqfa4yWlSL0acaWQsbFTJ+wWMVshEoDoeFxMoHnPwLvgILXAXBIUCxCODc84EuhPS921Q86QNIsLY4GQCjZ6ymL0INFlBYXamxIHh6PIkYgacExJX5wSZJtnwrNP9IEilysURmtFyRHu1zFqzgvE10U7L5kFBUeREsU+vUyDwU9Llgi2+KYGuK8JyH566DAZS1ogi63kI35DFLuGaGKwkIFotbM1H2ghi4TE00kWW+2yctYGsmKfkrqSXBhT8dCuol/0UGRk6aYh6cohQDeQZWjWar8xAGLwRxVXbznLs7HZUS+P0rCVB4dVrtC+WGRpvsbFcpXAEku05fS9AJRK/YKmbyWKI6+QY5VC8coP+8/blFtMxJh9o5wgQOQRrAqNddEnja0mWGRIjOfroTopjXbKGIk08dKjx1ge0HSEwRUvNTCIft+nynXfcz989+ZrLnnWgrJCZa7nZCIbKRS6GPZx8YJRwWSBdTfqIfmHrFujEpdDTnHNXuWFyigPpOO04ocYIq6emiXJLXXWwcNymoL8ymvyuHrVxB6lhlzNLcOsIv//B9/DLf3U3n3rueYq+y40HazTKRe56Yol+luNmgrLj81v33s9T84u8Yd8efu1tb0R+TadkPSiwHFn2Sc0Pv6qTUmvDXzz0FCeWVvmeV17N1TMvbPqJ8mwLbDRAJ0u2PvuhK27gNw/dhxSCHZU628v/PEH5HzOEMeYbb/VtHgcPHjSPP/74t3Wf3/GJP+JYcxWw0Mqn3vkD7BsafcF2S1GLQ+vnOFCbxss93van/w9rYc86uitDwc8IKzHtTgHpA6nAOxJSUC75pGSj2gPPDIqKEFZTyqM99pRXOFC9yN1nrubMxVE2DQek1OweXeL8U1OIXJCOKEQut1qyhdbkobSZmdDs2bnAcr9CdLLGZkqa1hVIw+hQBzd2mZxo0vRcWt0CG90i3gUPp+XQm1UgofY8VE9haYmOobVLEo1auVCU5Xxv6axog3ZA5gNs2DM4V3QRBU2SuOgBLr3FSY8EnC6iqzmqqimdc5iobXD77YcxDvz9hSto1HqMFPrc8+i1KG2bfCgoe90wSBRqq9BokB2B23cxLnhhyt4bLtB8YoTemQpa2w7Tba9cYGpinf5GyNHP7cHr2nMwgAoM5ZtXmNm1xkYashJVCGSG7+oteQNvHV6x6yxPr07iFzTlMBkIZglOt4Yt1vzZYZxkQOf0DM0bNzNojZuDEtLqrTQFrq/xx/v0z5fx/ZzQyUhbPt26izQGHWikpxGZwD/tgScom5RrvusY0tXMRzU6ScDSoVGUa3nXwTqYSoZJfQLpkLcV2jPoQUIaznSgkaNyh26zgIwlbi7RRYUu5BbPV4ZCrHjnwUcIuvCpT99GmrqYhmL79VWGjteZX23xo995E9QcfvXBL2FcCMqSTpSi88GzYayUwKbCJhpkI0NvQjwKpPa4cniMjdWElX4fRwhuGp3i0aNniGZtYBeJYPLzNl9MqwIjBWHg8ls//Q5eddUOcqVxpKCZ9DncvMDeyihPnV1irRexHkX84cNPkOaKgufyG2//Dr7zyiu+6n0+0lziFx76HAbDb7zqzVw9fClYf/jeR/jIlx4lznIKvsddP/deJupfrVFujOF/evSzfPLUMwSOyx/f+a94xeglBsvh1QVWoh63Ts4SfgPM/Z9jCCGeMMYc/Nq/v2wy8gNDo5xprZMOipzjxa+Wrjy90WQx2uB/fPrPyHWOMooD+Qzrom+z654ELej3XfrdAHc4QQsBgSbdF+McK/Duq69hqFHkrkef4+z8BjIXaOnj3BxxwRtCdyX1YoRQctNjGK0Fp05MIQu2rVtocQkyANR4Dn0Xk0lwDBeiCqwHVnyrkUEtR6YS1bfMhtIRn2ORQ3Gmx0bXGlC4LQfls7Xfzi4oLYLTN+R1Q++axL6cGy5O19v67QFJGiTkJXASu8xI5wvo6QTbN7/JHBgMz4BnyIc04TkX2ZKstEb4+J+8nt7uHFUyDPe7TO05SaUY0eoW7aQWS4TIccIcV2pSI8lz1/58LvBaFioZvnIdIQwjB1eo7Gqz2irTykq0+wWGVQenmlPftU7v2cagr8WQjChQAc+fncSvWxMPZRxSJfAdBcLQfazKXc/egtjfwa1mbBMbCGGYb9eQwtBdLJPuEogcyucMyXTK1tILQS4snguQNUD3XZL1EkzkJIEmwcEMa8QK6IIG5aCVC9qQWMIOmbQTYynM2Vlu8mRzG37H0NjXxC1n5Bd9oicaaE/jjXlEQpNVc7IRZSdSX1B3M3Kp8BqafcF+Do7P8I7dB/jo849w9Pg5oge6JOs+X7xwkHe85zDf/77Pc//qHkoyJ+iO8pEP/G8IIVjr9rnjQx/F5AYpBFeF47zrDfv5wJc+b+UKjLBQm7ZX4JbpMQ7lF23mPXhycqM53Wpy//f/OH/x7NN0dMLGl1fZ8bhD+pwgmpE0i5AXNDLH1kwExGnOX9zzJK+6ageuI7kYtXnb3R9GDdx5/uR1P8zbGgf40JceROtLiWaUZS94768cGufTb33vi8aEx85cIM7s8TpCcGal+YJALoTgN25+C794w50UXA/va1gu149M8S9hfEuBXAjxm8DbgBQ4BfyIMWbj23Fg3+z49dveSDUIWei2+cnrb6YeXuLkfuixB/nw4cdwijFBPUUPwthZfQ4nH0IrM8BSB+Eql7YxIXEQQFhKSPYZ/uzBQzAkuak4xfm8Pdi75XCPjHbpqYC1i1VMzsDkxmzFSsAugR0bzI0xuD2DKRmyWsZmdlrOcvqOR17MoGizWBnmGCWJ2wWyUUMmQ7LupeJaXlM4zcEDqEF70LwK1FiGKoqt5aapK+h6aM9a1xlpyCtmcNr22Ny+VefTWm6ZGMNlXaaRQI/GkHnEE4a8qCjObf62/Z1WVEQpie64kFjKn9cRuK6kdH1/QLiIWVuqkGcuXmvQ8ZoY+rnL0IBX7jVSshQcrZjdsWK7Zw0M719neaNKsC7JGopoWjNSjAjv8ml9/yYPWpArB2WkNWmYVJiuIW0WMcUeJ9ZGt+63yQV5N7ATugvdbYawlBKUbTer5+RsrFU3b7dt+AEqXkzPdbZgBIFBJAJdugzvHjTY2P8YNjplKqUEgaF5vk51d4/J3au42rB83ww0LWnNzxVX3DDBQ+acTRg8Q9L3MDWB7ynetesG3r//3RxdWmGt2+fHrj7Iv/3zp6xdXw7iuQp/PncD2eBAXKF468zz3Hf+QX7tvhOcaa1jAltE1ga6ccq791zHF88d53PPnKdQi5jZYVe4nfUicvo05tw4jnDQuaR6xCXb4bC9UucHP/ExTqpVcqOhbpgsQLAO5S7ktxTo78y4c2Q7jx45T6Y0geeyd9ul1fJ9F0+R6JxY2aD7N+ee4ZrGFD900/Xc9exRLra77BkdfkE2/o3GO19xFYfOLiCEIPBcrpp5cfMZgKofflP7/v/a+FYz8ruBXzTG5EKI/wj8IvCBb/2wvvlR8nx+9bWvf9HPPnz4MWKV4yTgattOL9FMDq8y3JnhVDcj9wxaQ640WoOKLCfbIIh6IWNjG/RjQb4YktY231LbciybDuudYXZdM89xxrcgDGopJnEQQm7h0DgGVU+RHUnlqMBrG5o3W0eY4WoXryPpHy8iKmCiwe3Z1Ue6Go1A+VA9KvA3yuiiIZ5UOB2JSA3Fsw6qbGlzTizIXQGDjlNjsNj84DiMNGTDFubZCmieQZc0aigHDaGfEfg5WSowSOLcQ3sCmiGOEuhQk1cMWc3qYahBd2nZjzl/YYS4WRigUJa66G2LbUPoYOVekAp1NkDmAAYTGNpJyGLTUC/FtHohG90irrTmD8DAd9KQbc9IB5aMntDsmVnmmX17Eb0+FAeTXM8hd+w1z2OPUp6wZ+w8y3mReae29XyIzd0Le0/DbR1y306AGtg7tkqr2GVhboRKI2Lb+CrN9TLyy0XmdnlkDZu2yhTEVMTQUI/VuSFbwJQ2y0cIHOFQr3ZRWnB+YQSnqCjO9Fnpl3EfKJCeKoBjRbvGwxJtegQLHhhQni309tMyY3sT3jzxSt78O3/E2fYGYKjOtqi+rcvNlTNsMy0Wz+zmXjlDNlihSmFwhOT9n/p7VkXBTj4BmAS8GL5r2x4efPIkz/4fj+K9s8HMjlVc117HxkgXRcp12y/Q6Ya07hlDzfkUez7nrmgRm8yymyTgCro7DcW2YNvMEJ//4I8M7pvggadP8xf3HmL/9jF+7O23bF3/PdVLksoFx+PKhoVHlrs9Yq3AEbzpwF4K3jcHbbz1hv3MDNU4u7rOrftmqRb+ZQfrrze+pUBujPnCZf99GHj3t3Y4/zRjrFRirt1CpS5ps4xHguq4dKqG//q9b2csnOTYxuPce3qV9W6RPzt0mDgVmMsK1BPlLuLqDhdKI5w/7fK773s7v/fgvZw6v8HqglWla61WaFyzQX62BKOK2kjPeng2HLrP1i1GXgGMxK8YjA/OGY+pazqImRSDYP34qA2WAxjGYKDtojMHkRu8nsZrW0xd9qFw1sO4NsNXjm2/3kQEnFSSxw7C1QgNpufgtkFmkIwpjLcZxAwMGC/eTEyeeYReznSjxfJ6hSQKaQy3SY+XMJGLltabUkaS3M+JpnNEIgjnBPEUbKRlkovFAYJtXd+DoZhYefiaLROIvO0jPU21HiF8TXsE0C4r7QorrepWl6rWgpVeibFyD20EK2tVa1K9CfMjyCKPtOchH6vh7bLGx/pIEXb1UQi6hRBCeGJ+N9938338zdp1JLmHWPCRCwEOkFUNhUqMhe/tzqUwuCj2ja6wY2gd5EDUatjQPOAy9khIay/kjqX/la/q4PqG0R3rxF0P8XQR96JL3oCffWdCz+3xcKdEK/QY39UEwCSSdLFgC845FKo+P/He1/HTf/mZS7UMT5ON5WS4TKUeF85HNogPVh+d81UO7j/BtfUL+I6i5hXDIAAAIABJREFUPnKY7mrIoe4YierzqtF5+q0C3VYIm0w4A17bUJ8XfHzuMfpJRvzWMuFI9FV0RwaPiCMN1UJMa92GjTxWJEmO2PQQzQAFE6UKt9++nR/7t6/7quLja6/dxWuvfaFWyY3D2/iPB9/Op+ee5ebRHbxz+7UA/M9/ezcrXauZ9H/e9xDvuOYA45UXd/t5qXHdjkmu2zH5TX3nX+L4dmLkPwp87KU+FEL8GPBjANu3b/82/uw3Hn/8lnfzS/ffzTMLS8TnMrLcUsjOrxQZD6f55Nyvcq73FBThhvE7+cRXIDwlWb/BFg8nhlp4g+xk1+4leksRv/7QFznXbuHH7tbLFvUCIu1jroipO2pLac8pKOS2hLgXWou1XJGWNWq/pDvuYubHcM4p1FSGrGvk/CbcwQAb1UjfoH2J8Qzigtz6cLOXRkgoDvfpbxTR0qDHM8xQbjPuyMMgMIEh3xvjpKAzW2Az0uD0rG5KXjOovo8MFGO1DlIaAk/R7gni8yXcJY+sxBaUsHl8bjnFS63+s+8nJGlIIiWhNGiFbTcv5Zhlj25ew60l5D0Pvepx6x3PUKn1EUDUDbj/r64n2ZNgyhmyqFGxi19OWYkqrMUljIGip2gQsaFDhDSMlTuExQwpIHUF4pkKUhnY36NR6XNBV8m1tnBSX/DJ4zdTHu8hei7Z4qAnXRuK8wJ3xtBteLiBwhEaV2oaoTWYcKUix7WrMGlQExq3JXjzm57g7vMHcJoe6ckSYjzCG84oVhNYLCG74Hc1/+nTPnnxZjxyKgfWcRq2kctVEtWx05724cO/92YeOH12C5EzaJLpfOu6n1zVfGD+U1jJMgvJmVDTjXycxqUMvBtvcNft/wFPTHDfwnP83395hPr6RcZ2z7OUVFneqFtuvzH0Y7tyc0/76O0ZvZ5DpZjjOJIf3fXdPLD2IOf787Sea6D6Lp7rEOSSiYcNqibojEtK8zaDeNt3Xc3PvOc2vpnx5pkrefPMlV/1t39+Gsa/3PENA7kQ4h7gxcR6P2iM+ZvBNh/Emo/96UvtxxjzEeAjYFkr/6ij/UeOnfUGHzx4Oz/40Y8jL6NYjVXLKJNzqvvoVjvtc+170clrKa4KJu92MdMxU+9sbX0nbgU0l4Zo9tu4qWMZH8ogpYGKotcu4ZRzYqkv74i3anwCVAjhsgPLLllVoz1b7BOxg3vWAU9jAo0cSayAUjVHVHLcZY+0psgqNpsOlh2iBmR1m72HG5rylW1k3KcZlbbgBT/IySSgJG6QIwNlmRc6I29azRhy0NPpQNsaRms9ygNO/XC9S7fv079QQRQETmysfvkmMyPQFCsp3TzEl4rdUyu0OgUObF/kzPEpVi4MkTiS9cCHGY2/Ikl7ZYSyzUF4ekvvu1BOEIB/KgAZ2IzxFW2yXOC4xmq1J5L1YyOgoVxOmdi7ROdElSVZZeLKJRrthKlrmixPeNQqKVHqcer5hj03x6AxKBcCnRN3ihTChKFKl+VmFeN6dMYFOnfIYodQxtxy5VlcaeG2NHetgbKAXhqwdLGGt83h7mMHyCIP83wFtCBeDOH6NrW4Qb9vcd9oWJAXLF6eGZfmkWFKqzB5yyLVHR1W3gNnTk9ijOA7PvwZrt05jyhOo2OB/hqvam1gZnqek0uziMBYH9CpmCebO3j9+DEcbY2/F3t1uongfX/9F8wvNykv9fnxt9zD/8vee8ZJepTn3v+qekLnMGkn7M5sTtIGIWlRlhAomGCLILI5YMDggI+N/b7OvMb2wRhjTvCxzTHGNhkMBoOEkQTICOW8knZXu6vNk1PPdO4nVb0fqmdWOmADxkm27g/7+21Pdz9PV3fddYfrvq7h9RWQmj+78xoOLY+iTZcrUhgGRhd5/7lfouh06JjL2DDyKRzpcMPaK7lr4SBLYw3Oe9VWPvc3D/OVrz+OALIdhTcrCYz9rJ/56sP81Csvw1GSSEd8/NRnOdo4xtUDV3Dd4Hcvf343+90Xv4i3ffZvqXY6/OzlF/3A0fh/JvuejtwY86J/7O9CiDcDLwVeaP4tsIzfp/VmM6tkWcJAbzbNntIAr/uDz7DvJQUSWQMki7Np2kVNxhfIAJj1aS27ZEshSaCYv2eQoOGRXwK67j/ohVYfaF8ijHWgYS2NMgYvHdOuewRNd3UzCgNOC4yyAslyBQ6owYTSDr9Mp+jZs8jSqRLOMR8ZC+LtATpjaJ4b0wxinKnUStGYoCxQuQRbRtSrZQdjBM6yQi264DqwqQV+F04obG3bOM/MAJQ8O1EKBq8tCKXACFu+EbEhLtlsxW0aktksfsuhvT7h6OwgaRNxplYmzkVs2D3OwYVhVmA8ccHgqQ5h4KN9wzcO7mT36Dgb1yxQbact94o4W1YST2TxdjdAG0vLOp22sDgEQcNDh4pZUWDmaAkwpLyQRjmh12t3dUIVUhiS7qHqpGJ6jgpas0P0D1V5/atvtd9X7PDhx65ApzxkEsNCmuvOe4Sh7BKLcY5W2+ebd5yH50UEoUvUk+BWBM3tBppWrzXXXUajYVuY4xP/5V28ceovOTo7j9sQtFGs4Pm8ZYEjIpbyDkdOrUPPp7vNA4gcwZPTA2TyHQInTyGV5fodm/nY0YeJtOaa4QO8Y9Nd3CTO43PNvasTqwbBp89cSFEHgMcL1+3mBX/6N8iqZt0X51BG86Wbnscrf+9+RvcucvW2AxyYHCPY5PCi0ij7z0zzxvMeoqg6KGHIyUdIovtx/EsRQnB5/7mr+ymXTaGkROsEKQTplEvQRYikPBfVPZ1vnrqVOxfuJTIRnxv/IuuzY2zLb/6+9uw5Q2u49xfeYftL/0pKO89W+2FRK9cD/y9wpTGm9c9zS/8yNlDI8Yc3vpj3fPJWGvUOmewSj04tsbyQYv7z5/GON2qCEG671adQM8ydnyBDMCmYmxuiPNfCPFTEGLnKDSK6RZVUxdAaMcgIpBPjnHZxZzxi49Hotw1B2XXQbgNUy77WbRg6vbbxaFak4AxnJxfbLjRdRGy9kJr00CORdUhTzjNLHNoO1FQX0iRSodzEOpWOwFl07cBNKDGn04gtLUwg8WY824A1EC8qdI9Ny5fP5CluD5BSE8Qujfkcq0ByY5Ba4C25GAxGugSA3zRgEhIj6aQlx8eH0G2HM8YgPL2KR3YzMVIlaGG6mYrk4OQISQpmj/Zg/ARiuZpXq7qEO4sIX8NlS4iVQ8dYx1WJMqsyeSAIE0Xiu9QNtNseWdWhmG2y1MhhEHjEtHoUTtWwe/sJPNeOzgtl2Do8w/6FUaQyGCdhqLRMr9ek12vydwefZ7lG2lbVo/yQYXm3tlphAMKgfYO01JA8NCW45GPvY2EDiPUgQsg+Yaiv17gzErcpiMqSxXrBikCz+hFs49TVbNoyyQcu/G3KmSyfOn0br97Vy878ZrarP+Xe9kb6xmpcMnOC+5YtG2Ah3aElU8ROjk9f8v/woi/+OSZUpKdC2wuJBTGKA7esY3RvHSU2c8G6Ed79gku5YHQErQ1zC0+ioxPYBNsgxHdvEL7xhn0cOjrFkROzvOjS7fzo9Xv47T+7lShO+NWfeBEPVY7SjDvMBvNEpstHhGApXPqB9+5zTvx72w9bI//f2ELd17uLfZ8x5l9GXfSfwXwjSQLNrsueYmTTPAg4cWCYU4c38qnHyky2awTliHQgQAk7iKHBtBULMke+bKMwrxCgK+6qaryIYOAecNDkdzep9bmkNi7SPJODiRyq1EENByzPFfCWxap4hYwhdyamvt5BtQQytqiROGMj37ieRWViCnsXwdW0Kmmi+aJFMXgGbxmSrD1UvDChcn8fsQOOFkip0Ck78fd0M4kkCRWMp1Ga1YjeqTlQVwT9MU3pc/LBNeh1MYmWkIsRdYsYUIlZ5VBZNWlLRgabbaAFut2lFRUCuQzO2g7S0Th+QmfSojNWJIxcL2a2mYO1EZ1UiJzycJYlUhvodCc4Q4E7rUjKAUnNRQcKkY3pLPkoEhIsDcHI+nlSToQ2gsUwwyIZij1t2qfzmFChRZZkWw3WhdSWUpafxLVrtBRkoC3J7fdIJDw4spGrdx7ECOjvX4YTViIOAVFaUko8Ks3QUgj4hk45wa05GNcOviwaAc5Z5SXjA/0R524Z5+iBMcyig0kE+XSbMKsJm2kwBt+JKI9VmHqsj48lj/Lg0hMMbHoMJTW3LNyHW1xDhO1x7B6cpKfUYjHKok2RuXaJt268hsFUmXTKIRSGsOSs4kedlEBu0Ny1tIl66nw+9aYbCdoRURjjeg79ve+hsnicOD5OJvsmPO/877qXclmf//Xe1zzjsU+9700A/PHRr3DTE/chgLWZPlIyjUFT8orsKe36Qbbsc/Z92g+LWvn+cqR/JZtdbvCuD3+J8fkqr758N79wwzMbLlJIhNSMbp1dTUc37Z7kkNvLE9XAOpY+cHMZHAyx1laNBYlIDI2xhLX7Ztg9PEWn4jP1yBoqh3po9Qmys9D/Y5MwGGJiH+kY3FJI0nHx17dIRELWtOioFOlTCmLwq5qlcyEqJEQ9IDwrfGwQEAmStkNmfY3IE4DC6w1xayFR4KGyCT27F2idzBOezqKlgkjSc9IiVEgEtVEIehy0Z2yZSEJUSpCrk6Ur0aT9x0gwrnXs7ZJD3x0OYY+hsU1D2g4VpQ7ahqdJaVQqIYost7Uz3AJcm6w8jdAJYzHr+nQGcgmdUOAtCPrOxCydI3BzERu3ztLSDovLeXTDJc5B2BvjLYHTdFaj73ZKEmU0alsdxwiCJwvEWQ2DIRJwA03Q8DDlFkGyMthhqV4z+ZDWbA6k1TDVTkJ9u+RYp5/eVouHJ8c41erHrQukAddoThwZ4clDY9QGBE4uwRttEE6nkVVBWJI0dYIM7eSq1mDSgiTufmZhrOJ7AqjuTFRJICd8lnWBq3cV+erdbXb1TrK9b5bl/gxSSw48ug5nY4dkyqcyX+QzDx1gcHSOvljg+JBWHQ62L6HkH7A9FyOJjUJJwbZslmsHFhjNjGOM4cNX3Mi7ki/RKErG3r6b0TPwQPko95y3HhYFvjzF//mDL3LzH30LpSS/9pG38fxrdtE/8NUfah/eNvMwncQKthw/tcDgnbvodCLe9s5rSav/uBDAf0v7DzHZedfMSf7o4F3MTzdYnuugI/jcnY/xor1b2bV+kEq4iC9TXLx9lBft2UonuBu3Sz8bGmWVRVbTN0MtCUhJB2GEHXLoOrqUG7J7eBIlIdvXYePVZ1j0UgSdLLlJ8Ec6tBLHSpt1TRUjoroi9AVNJKInxBvKsDco0x68B+UnHJwcZqGWtw25lftwDbTB8RNWcd4YHC8mjhzKo8soz1De3MYfKDA+0yGuA4lcpYxNL0C71yCShM6Q6EbBIJZcG0U7Bvk00j6t9KpvRxnCiztEFhxMf3+N4fwyJx/bghNo/AsrIA0aQWUxS0sJRNvizeO2QKUidMdFxJA/LVCxoDHiYEqanv0RTiIZ+hbgKJznJYQdn6WpIpiuYEEgMaWQJDTIBYdkJCTpsYeq0QYz69pIt5is3rNOGepPlHDrgvzWqvWetuRO3On+1I0gnkkztHGest9iLi5ysDPMxGIfXiohNdZiw84F5u8dpnMsh6uhPA6LzxcoN6G/v0ql2WMVgbrRuUDYoR3HkOQ0MoI1QxXaGQhClyRQJHWXGIckm3C83svUfIjJCoYzNb744D6EMLgqZrC/wolqH/jg5ew1psb7WZwrse+KQyBgZ/GlTDXLdHichnZo6yxDfi8+B7h/OstXn7qLwU6Mavfyp5e8ij3rhkiM5i+fuoWlkwdWlgXampv/6FsksSaJNf/7lz/L86/5zog51nWCeJK0uwEpvjdp1Lb8Wh5dOkZiNPprRcaX7Izge37/Jm759M/hOP+yQsT/Ge1Z78gXOg3eeefnaScxQoHaoEgf9RAIYp3wsVN/xoOV+4h0woHxMbYO7OK3z30/X5v+Cx5bOs5kp8SanhrjM70YLVZH6IPENnE8JBExRggKPc1VBfZQO7gyoTlgMKKF62rKrkcztp5RG8vx0VhMkco2aWvLLWKApbDF7JqTDBfaCAHP23Car+/fiRAa0628m7jb/Jx1YK0l8pGJQR5KMbCphuNHmBi8QyXe/8ZX85qvfYZQnmXX0xKaaw3RaPexhiJb7OAXAsvc2HBIqj7Npmdx6TGohiQuW5kugaCmUyAh63TYPjCLkoalsWUanmOREhKkMfjZiHbdx+/EmBwoNyETxdRCQfGAu6oan1nQVHd0GPjxWYaHl1iayDH16ACNxRSnp9fYw8sYW55VIOuKcFsHudOiZM7G+QI5EFo91hVkkDGYpoNbUTRbRaqninjbGvjFgNZshk7Nt6ufjZGR4JKtR0l79jDPqg4zAwW82LCrOMFUu0TYbaoKQEY2oxGnM7QqWSjZsliyOrGLnaZMQKZisqUOO0YnqEYZnjwzQlTzcdouUTnCuLbU0vQCnI7LwYm1xPqsYzuV9LJlZI6s3+HQ9CYIwWhJGDrUaxkqUYr3HHuQA6/5db6w/yBffuJJLtkwSn//fXz+qQb7J9bZcpieJj0+z+2HT3DLu9/Ch458k5vOPE5ssrgqZrhY57WbLuJL6l6SWFPY3mH9a8bZX/kye8ovQ3RT1lZ0nCdmXokxMZ7qZ/fQl3HkP8zhDfBbu36cT526nflGjTujWRrY6DyKE173pc+xbaCf37j0qn8X3CX/UexZ78jn2k1WI1YBOmMRG1fv2cT6kRQfPXgvsbENrY1rJrn3eJE7xjfzk9t/h08e/gafrX4VP9SUTkF5R4XFdpY6Geimy46ROAr8tXVipejELvdNjhHGDik3InQljjRsu+oERmiGMoKFdpbZZoH60QLCMaQOKhLHId5mMI49KLJe++zkNlCsJwxtmqdHb+CRAxH+McPWc8YZGZ3nxPQAEws9BFNphAP6QIlKLYtxBCpb5KYTh5EIkhTM7zMUjkCcETS2no1WySXEVRcZQjSfRnoJO8bO8MQTm6xg8iqJl1jlFMFYgqj0rM/jp7bQXmvYsHMW0fFom7M/Hd3V/wwcyJ3WpKZdwpEYPbrCVWDNdRK29c0SGjgZl6kNevAjLWrzOXvNlXv1DCgIHIFT8RC9IVoLO2nYLbMEgYspGmRN4Zc6xEKSOpgms71G7lwbAS6dKjH/xIDNtrr9UxMoMqmItBOhugpMJbdDdSaPN9Bhf20UHQtK61qEx3IYbadphRak5iwfjwoMSRqcOiSepaPRrsR0DH2bFtnVO8dTp9dS7XisLVZoOin2Zi/h5rlD3c9ocFIx7saAmWoeKRK0URijGO6psKk8x8JSDu0nqMiKRxgjuO/JTavrdPtTx3n/N+6gHcUcr45zXvpJTi0PkzztUNC+QcaCiUqVr08eIdR2QCuIXTbkalw/dilb/2Q7H/ngJ9j1oeNIX3Pn3J8T6jb7+l4LwEzt4yS6DhjCZJ6l1u305274R/dkxklxzuJW3vu+L5OsTD8rSW0DnJ6f4vHKLEpK3nv59w9FfM7+cXtWOfLJRo1q0GF7T/8qneXWYj+DmSyn6ksYINPX5tVv28av7v4R2klrdVjHGAhiByM0fz1xNx8/czuvX38JN73wQ3zu1Af4Yv8JMIbqeAoZx5imAwlEKiY10gQFUaK4f3KUILYDNq3Iwt8cN8R2+C33eM4LmGxK0huayG9nqCU5dGzIHonQe9uUCiGdVj9JagIBTE/2cfNbf5JSJssD4zMceOxm1q2fYvclx3BczcBwnbtv95iQPmGPpr3GI3YdjK85JWp88uAjdFGHhEXQjmRNNkvN1NDmbLnE9yI6kzkwlkfmiaMb8bMhut5NlzV2aKb7q1BtKD0pEVpBxaBCw1E9TH/YpJNy8Moh0aKHczhD3kodkZnROKFAZATROTHBrg7+wRRCatx0yJHxYdscFJDPtSkV2ohMAo2V0Lprti1AbBRxPYWKNO6kgrxGtCRqKELUHOJSjD+jGOmvUVU+uXOXEV1fVt6wTHU+Z7MbAWiBqzRp06HV9slkAowWnJnuwylGq30T6RgqY4JcqYE8bilpS487Z9exYahvDWA6heqyBYrEEBciNhYXOT1bZnyxhDaSajOD7EgWOI7wBCatkSrBS8X2er0hSVWwLT3AL190BSfC/8nppSongj52bT9BWMsQBB5r181zaGqE2SULs3xqcXGV/wanTSt2kW6C6GaMQkBWu5QzaXYMD9B7OEUtsiLkSmoEOxhKr2PoxesYviLkpvHfJaFDbAKOVh/mD++3b/6u3WtwhI82HcDgqe9PJecTn76HILDZYCbl8IZ3XcH7n7oHooggSTi2tPgdr2mGIX938Ch53+PaHVu+g7L2OfuH7VnjyL987El++Y5bEEJw8fA6PnrdKxBC4EjJ+59/He964OMEJibWis+e3s9yCO8//+W8feO7+Pz4p5mod3hifIBCPuZUexYh4ENH/o41qSJH6ls5UwsITYQsJhQKdeKOonUyj1sKLY0rWIxvw7PoAwFGC0xH0azmmMyXWTtQASGYbeYtitDXNEQanQKEJE485L0u/gafX3zl9XzhwKP0KJ8PXncthXSKg5Oz/PltDxC3YvL7KkhlyzRGxBRGWrhP9pOaVxgBC7sMMhIY15AaqpGtaFIPFZAdG3q2s22cRUXYazkwVE2SHgroTJ8dqtCJJPRTFHsN7RmD29JkHtLMP986c9VcEaEDqQVO065DErh0ZrN0TtsJQtkbI+oOoi2IMxInNEQ9Nhtopx2aO6DngKTZSKNNYmvbXXhgqdDG92OcuiBeuTXZfQLYcosUqGVlr1G3eHblGtSCgzvjEI20GVy7zMi6ClPB2bRfSc3ei47y4KkNIA2e0ewujzMlszzy2Gb6e6tUgzRHTw/jjLWQfrLqzKMlHzW4jL4vh6ulZftLGcIClDZm2XtOjrvn6xCejYBFSpNxIpodf1URxxiBFhDFGtl2kElkSeC7gjtSGQb6qmyQI1w1toGvPlDni8cvxhiJEgk7B6Y5t2+aRAuylQCBRmjBJ+5+BG+4QjYfE57pwZdF8rkAqaqEocMVazbwsosu45LNY6Rch+Fslko8hUGQT3VwRN/qfcfNIZptjXIUQsDfPK54tDEOwKlajk9f+wpqwUMMZF9BMX3J97VfR4bLPHV8lji2Yh9X79zMJ2cOMlGvkhjDO/buW33usYVFZhsN3n/rHZxZqiKA+06N81svfi5i/37tWePI/8fDd6+yo90zeYaJepV1BUv0fkHfJn7x3Ov5wIHbiLGO4u+m9kPk8/sXvwRf9nP37FEePPUwbd1kVTDIwAce+BbL6Rk7fIJhhazOSSXIvGG0WKKiFgl0jEEQK4GOhVU0DxQEFgY2vtBH5AkWazmW21kcFZNOhxacuaLkosEowVwr5C3f+usukZbgVYsXsnVogLd89As0OpY+9eThEbaPTEFincGTukz9spDy3R5uR5KqKEy3NKDy4Iy2SR7JgzIYLfA3KtQyuNMejp9Q3LyMkw2QjiaJpY3aYhALhrqG4mSEX7WNwbU3GaaulMS+ICwa8DXuoiAY0qwpVmmFHkZZrDNbG6vU1fLRLKau0NIQr0lwZUzQcqziurERPppV+lllDGHFIzyUI7vooIWmPWKICwa/ZEWv221Lp2AcrIPXWJx1zTImam0Imh71SoaewRqDbo3FwBJ49/otTi/YqU6EIDSKB2qj9JUbJGMxrVaB2VNrEAiCiQyMtJCeJpxN2z7IN/tQWgAa7RmUK8i0NZnZGc7veT77N95P40jBwkFz1sE2ltP0+U2mddmKIztdwewuNrMoOtQaGegLMNrylwwVl3no5HG+deYgdy2uWT3DEqOodtIoXIyIyWfbpCNNUnWpmhBO5Om9aJY9+yTv3/Mr/MT9f0gqpzHEvG3nRWwvjuBJu34vW3s+h2onyfpNtHb42S2vWN1bx6da3HLzCyj3T1OtZpkJC5gRC8lc6LTo93+d2259CG0Mb3hZi1LhH1bsWbGf/9lrAZiaXuJNr7+UkYEyX7nxjdw3fZQn27dzQt/CueGN3HF0lt+89ZtIIGzFq1DN2w4fe86R/wD2rHHk6/JFxmtVYmNr4MWn0dQCvGLdRfz1yUd5sjqzWnv+6qnD/OjmDfzWgU8S6oRin2F+IU/KSezmiyWnphsU1pvVSGwlmdNaEAnDxHLA3g3DHKifIux4xOFZQQRaK4hwQyrb4fjswOo7xImi1Urxuhds5Mu3n+7yKttpxSiOu40y+9z/72+/xkff+nra4Vm+5Volx22TO0kRMVMt0dG21twa0hQmFakahB5oX9B+qkhjvEA8JlCxnfB5ydgOPp5+2HJjC42bSVBKYIxc7SUgJAngVqHTpyj21Qhw6DTSeHVBe9BQP6d7WGxI2DZfIfx8HqftUn+BxvOtkhAK25jtjakPJDilBr3DNo1vuwVio4hy4Dah/IiitiUhcRVB6NA6lV1dd4HVeySB4j0KXTakFxzag4bOoEb5GhVJK4AcSozQICwf+4GDo+Qm2/T3VUnJiP611RUY+yq6BCCbbuOJBNdJ8MohPZsr1GdyrC0sMT7Vi9G2ZGVKIUmvYWznLPVRSfxonupUsfvdZLjvyJdwy70Uz5+nvZShOZ9GLfgc3r8FFQuKjiFOGzhHU04JqnETIsFr9t7J7Y0dLAYZIqPYWF7EV5r1G2ZZiufJ+QkLElsSM4LKk2t44Fgv11ySIek/yMRjiubT4J1JIphu1yi6eT51ya/xZO0Mlc7t3DP3c9w7L3jh4K+yIX8lcUuyNF1kUWXZ3Nvh72Y/w3DmJ8m7ec7bOEIYpJg8PQbANRev59alpwB427kX8F//299w/PQ8APc/doqPf+BN33O/5rI+v/HLL3vGY75yuKP2V8x1ZjDAyeZTPPjAHjor6kICJAJXSi4YHfme13jOztqzxpF/6AUv5jfu/DpzrSa/tO9yCt5ZGNQJiPXaAAAgAElEQVRdMyf5qTu/QCeMMXEa6cYIKTmnOMDNkw/QiuMugZVAKYPbVpTTDU5N9yNih9ZEntxgh7zvszZbJkkMDxytQSwIEs34XIyXTah3nNVoSSiN0xORdByUG+PmQuicFaj1lcNHXvBjXD64jWtGTvJzf/UV4kRbh/UMDLfhYHuG/XcfZ8eGAgdPLWGMwF3XJNQOrTBFELsQCVjwiDOSyqihdATiHqvQjRB2orBoiD0QRvKpmUe6zhoSBEstn3wmIJVv02inzjY0saISMgHZElz2X/fz0Ad2o06maY+J1elRnQiWH+/FXwbpGmRskE11loXHgA4V8dqE1JrOqvbp4OZ5Jo4PUBuVZGbAZBJ0f4IwCc6cQkYSnTZWqciFqGw/TyBcMgsJ/oYamWWXeBbUFXUwsHymSEl1qJ/OE3dckrzBOJJ6M0O9liUzA8ujVWRvxMRSAYqsZlyNVpogcMnVDSODFQaLy+wqT/Hk3EbioRgLmenahXWCXATSIZ3t0JA5q3hkBIVMA3Qv0oVSj6a1LEmUprHR4C5Dak7iteGGHTv4qwOH0dpn49oZmpkMF2TG0UZw38J6PGUDE89LmEgOcWGpREpNstDMUTtZIug4zDcltz/q8rIrz2X23Kc48NB6jJGkt9YIUSwFCW+778/5q0t+kvPKo/zFU19Ek5Akgl97+HM8sXQnnZmVCViXI02HdYOH+Mz4p/jJje9kTTHHF37xjdx95DRbh/rYu36Yk1XLzLih2MMVH/rvxIkt8504s/BDjczPB7PoLq3lQjDP1r5eTlWWCZMEL+Xwhj27GcjleMOFe/5J7/+f1Z41jrwvneXD1373bvmv3H8zzTBCt+w0oYpczhsc4g8uu4433fthWrGHAfJexBq/hzds3cKC+T98YaZAPRGISNGYy7Lkx8yqGjuKAxA1bH8shnY7oil80vkOrXoKQmnLGsqgshFCapbbWdLpgFbdMvJtLJbZVhihGYa0vYgwEyMbFsutRzvQncwDCIYMX7j5AV7+rp3sv+eb6Bj8noCc47EnOZ+vLR0ljoxtUmLAgegf4g8S1mnJZgIpnjnGDxT769RnPIyRKwAKZGQozIb07lsCYShvrtF5JIXTEMSF7ntKgexotALjGrx5idKC5KkU9MSw5BB3ASpRrJBu1yFKq51IMaFeBqciyT8iUTubqO0hZkTRrPpsH5lk/9R6+4LEcoyXbphGuFZYuKH91c/Ss2GJvlSL+kwW0zFoZ2UpbVMzHIiZHXAxsUfsKVInJE5ToH1DVNKIIEU9H3Ps+ABSC2rfSph5vR0cQtvGeM4L0EIy28lRTAU4O+psiCIqywUuuuAgD8pBK4smwGv3EDvtVYBOnAOtDL7SfOXYKbS2Nz7gN0mrkLzqkBjBuvQSHeOBMBgMh+sPkpiEobQkmM5SWU6Bo2F7k3nV4i8Ox2zyYXTLFLvXDvBElGG2UyPUMU/VZ7hl6nEu6B1DCodYJ8wHOQ4ur7FoHWF/PwBSajQJ1eisBsxIT5FXX7ybKKlxYOH3iXSNreV3AD1cdsEm7t9/CgRccO7o93TiC+37Obr0P0mpNZzb9x48dVZB/qKey3lo6V4Azivt4xXnXEMxlWKiWuNnLn4+z1v77FDk+fdmzxpH/o+ZK9WqOg1AYgzDmQIPVI4T6oQVBPLVAxfy3j2vohEF3D5VR13yFWYXxmh3dnPLnEWtBEnM4/MzWDka+7pqPUAsZDGlCC8ToyOXRAmijhWtlUrjZgI61TQkyqaNtWnefNsXmJhuEBARrUvI5FvgaLy6RJ3ySVKGaChBGkFxZ4HffOh2tAu4sLSUwxF1bjlyAn9SkwkM9fVWvQdhkCE4TQuNM8qg05okYx2ZakLukIfZHRMOJDhKk/FDlNAECxlUNiJpePa5KmG7M8fQj1YZumAOHUvq4/aUcOoGXUgwoc06FvYpVB2SIYMzZWGAct5HzLqkFiMqzwMMzFUK9GYaSGVYnCqBR7dODXHZYBKNGgotuqSQ4DkRDc+jr79KPfERGryu7oOQNgPoQpExBtqNFCfnc9SKilQLZAA6Y1YrKMmWDrLLee63YpyWb4d2AnArCuMLxLJLNGzQuYQzNzgkoUB2BE4AJhcjfCAShMYlDGLW9rSRVzXpF9Pc+flzCa5sU6uWCBqK+TigfMQ2mZd3WO6csNegJhVJ6+zEVbXtU1TtLhrSsDU9yzfndjBQqBMnkkgZlATlaHr7q5w+MYhrEmRLEhcSMJKjE4MwmWL8KcGlLyxTCZvEOiHQMe878BWEEHzwee/m4PJHUCJcvbbf00HV87gyZtv6SVLK5+Ujr/yOvfTQ3C+y2H4EQ8xc626uGfsGv/PzL+XbDx7DGLjiQjvM/dTsAk/NLrJv41oeb97CnfO3kHeGeOP6t/Do7M+QmA4Ch2Q+4MLBP1l9/9ePvpULey4FDFtyOxBC8N5rn6uF/7D2rHXkrSjiZ2+5iUdmprlwdIhWLmIuCPGkQgrBa7fvIVKtVQhTSrrsLq/nicUZXnvbp0mMZnPuahZPBEyXj5+NXCUYJ0YEHh7KaoA2gUQh5iRxn8AEDnhn69kSQ7SQQq/MYgNh6HLCTBI102jHIBxBo5JBRJA/5uAagSMs8uTKkbWsv3wE89hhhLDOSihDEiqMMfQciu0EpoCg1N3U0kE74FcFRmk6eVuSELFtDAZFQeFR2+hK1gTIfVBZzqM7Holw2NCbx3efYuu2cTSCeugz28wz8+VhmvNpkh0Bo5dWOH5kxPKzRB4mrSFxMFXQhQRZB6GgXy4TNLOoprLanNLQu6WNLhiymYCTB4dIntYf64yFPH0+0E+FxLGgiYfjJqScmMZeRUFZJkFjBOG8A3lDdT5LVEuhPUBBeyQhl2pTzraYm+ohCeVZfL7gOwQSnmGJxWcnQkEiSCS4S4J0zWCKdCNsgWlLZJ89JuphGq8/pPNwgWBN9/t51JBegOawvegK5FX7AjluUMMC41ghj5XDxkJV4fj4Go77/eRnE/ZdfAzXV0SJYW6mDAgiHLLHE5LNBlIQL/poR5BkDN98pMLe7SNMRzXaNGgRIoAHF+GdWz7Fp0/+DBf0T/Pw/BADhTx/ct1r2VZcQyVcJOvkSP1f4/LHagvMNA/gSPvbjnSNWDfxVJEXPN/KrH1z9j4+evxLLC3HVA6MsHHdNDt3PYkEatEsnz/9YTb5ds0NMa14/BnXEEKwNb/jH/5OnrN/kj1rHflf7H+YuyfOECYJ95yc4A+veTEXjazlgZkJNpd62ViyUls/sekKbps+wEV9m7hh3fN45x1fohnbSOXY+CImkGc7nMIiS1QMbl/C+877Ef7gm99mIek87e8CN1EYExDSdRpVD113MOWV+qpBuhq3EKDPpBGRwKSEjS7PBkkIIyjXsnz4V17P44tWW3CFapdIEJ/uYdPaMlNbA8pHNT2HEyJfs7zdJd0riWuKME5whERl4i46wg70NNYa3Lo9KGqDiqhRso6pDxwC6j1NrhjYxhLjSAxey3DwKzsIln06uzVDVy0TNPyz6bijcRtydYReJwq1pc62jdNkThlqCz2oe4u0ByXeWIuRoUWOLq5hqZInPS5pbtQYB9JTAgoKuexiNkSIhiQ+41HbDp6K2dpnm2rGQG05w/RykVrHR9c85AT4SwLV/ZjtYQ2JoG3SeI5mdHSOU08OQUNhcpb90RuXyLYhWYGMunT5XyB/SFHbatA+dicoSDJgltRKOxtCcA4LkmGrIyqEoZ54VEYMJjZkpiA7a9E+uYkupXG3TydiK6+XPaQo90X0bAuZ6JQZSS1jjODrx88BLUifVJQelJx6cAvlPXWWimkmlvMrPyUYC9kej3BmIqAWBTa5aQl0IeHR+YXu79cll0vIuQ7rc/040uP1G/6Ya4aPsxwo3nn/F3ndXR/mBWu288ELXo0Uz6y5VcM2r779L7msfx1XDBzDkZKB9F5cWeD2I8d5782346c1+d2HiEmQWUHx/DOsL48/rd2iWY479BQvYKnzCAbNltJP/xN3+HP2g9iz1pE3o4hE66f9P6SUSnPt+i2rjwkhePuWq3j7lqtWH1uTzuFJZUsuClwjCVvSRptAelahxyKEk3DL/OP81gtfyM9+/mYAjKMRdcdGclMZlDRICUOjmh/ddQEfeeBhop4OQhm8XGg3obad+G19/RxamidJraBXrNXikHufOs26oRIydEjlmyilCY2H6vU41qpgdri0BxMG74ypj3motqDdgVzWoZROceMle7j43DFe/rFPEmW7u0rZ4SChsQLLTxMIThU7tOuSxzqHGe0qby2fLhAuphBGkJoWLH9uLQjD2IvmSa1rU+t4VA72o8OzKgfpvoCpqMiWrbNcdN5+Dv/tJs7cN4LaGHJycYDKHcOrIaizLMifNMi8JnQEzl1ZC8c0hnhXTLOeZjBdX1VV0hry+TZHZwYxoXWsTtted2UK1WkJiAUyMVTbOfKRRrYFejKFcDW5w+A3LQa8NQBGgmjYNfCWICpICByKhRZOKqbaTuFVPVQTCp+D9h7wToN6zOdEYYTUxjo6UZg9TXoRVCoWwbPaANfgV0BucEiWY1RsYYcqNJwpeJx5fAsPym3sW1fksYVFGl3NbX/BQhTjukv1gX4uun4z4+I4GIM31ERUHH73xpfxyk9+hpV0QxiDcc9O7rpSsS61hhs37uYlw3sBcKTHUHoHHzjwGZbDNgbD3fPHeLRyhvN71z9jP403l9HG8LWpXRyuDbG1kOV/XfJeokTzC5//KkGc4MQh6wNN/Vv9+FctIxxDQ7tkTMjKxMFLh9/A7tIF1MIjeKpE2vmPL7P278Hk937Kv0/7iT3PYyiXRwnB1t4+XrL5+1PY/uXzruLqtZvYkC/z61dexSt2n8uGuIft7TVkD3qYUgSOVZJ5cP40mZziF667FFM2RBkD8dmoWWibmv/IwIX80uVX8Lord+JkAM/Q6XjotsRfAr9imD41z8ZSD56nKA5lMD4kPsS+4Sc++0X+4t6H8dMBrpuglCFbatN060RaExtDWJKcucEC4AU2Yq12OpzYNMcfHf57brz1I8ixFjIVgjakZEDYn6AdW0tHGzypUAqyyiU+kmd4bJIHp0a55dgOpvpSliN8xbSARBI8UkRJQykd4G+sI/MRIhWT2VxFpS03zLHmAIFSrL18GrZ1UPuaNE/n7KSosRhykQBK4I8L3KEOQa8hzhhmr9csbRWEiUOoFfppt1CvpOykYve+9P8VdhiLrCR/0jDwILQPFCmehsJJQWpSoaJuicMzeDIg7NN0BhNkYA/X9iCU1tboXVulNNBgdN0iueEG57/qALnramS/IfAPKOrbc4i1EYHn0mqkqd8zwMKhfuS0S3PYgGswjsEoCPoFUTriuudv5avvfjPrRJZ02rMMiCoh8kMeX6xzfd9Ouyba0B4ytomsbPBx1eVbGNku8Nc2UE3JtZv2snNsDaVKlyPcaLLjIc6SRCFIKxdPOnz44jfy5k1XfEczMu14qyVGgyGtvO/YF5sLfRS9FGnlMdMe5oKBG5DCJdHasoAC7V5NZX8vccNZ1VM91hyglYyyvXApb97wB+wp70MISdHf8ZwT/1e0Z21E3p/N8u03vY1GFJJzve8bDpX3fP7HJT/KcrvNQC6H3GFfd9v+o/zGyVtpxMHq6LN4QPHuL3+Z3mKW8s4UTRkR1s56GinAcxxeuNnKuf/9xDGS0KDqDjqdYBYtSbdAEM5Kfvra8/n48QMc0fPkAo96O7S1YwGfePgxBrenaGIJsgRQKjRYDkuAIpYxshSQ9NopRwwEQzGRUASlGNONuh0/pPdbmuDSkKUNBrO5g6M0MpG4QvOXV7yOU9VF3vvQnZxc6mOumUcbyXSjiL9Fkzkinoa9NshUd7rUgH9K4TcCwh6N4yaro+BgMfknOn1kbqiAArc3gBNn1904BqcNnR6oxDm4sBtVpi2UzWBYjlKkWjlyXsByO0VzLk8mG9BMUhgHjOpi4J0u/7ljBahVJBDaElkZF1RoUT2D12Q5PlulI1yQHirQ6DUxnZTAfczyzqTzAXKFrVIaUtvqJJ4gu77N6X3r8CoCb2sTmYlBQOvJAu01WHCNVpRFk5GfmyBe9NClhImZUYwW3Db+FA8unCD3wjOcmxH4lRwna1ZaryU6DK/JM/a3HuPntQnKiplLDf6SIL3W49dPfpmm7JDpD1EDgvuc+7lnbhPiqiXyHQmJIJVJeNXOq5npPc2jyyfYlh8m7cjvgAa2woif334NJ6sLHP9GDbng8tETD/L+d74U92kshCnl8pVr3s43po4ynClw8cAGANKey1svO58/+/aDmJShNp0mNy9JKi6qN8Ioh1eu+yku6Hmu7v1vac9aRw42esl735tW8+n24OkJ3vzJvyFONA6CF1+wjcOtBX5k41Z+53XXcu+JU9zZOMb8EwFy0TqwxWqTFyZbeeXL9jJdafDR+x6mkPa5ZMMol46NsmtokCPz80xMNkmNdxUWjGuj15V7Be4YH+fAwiyR1lCwDigSZ1n95sZhzTafVtwm6wTs3TxJo28b9876uKkAIaGzt42qOGhlCNPSwuWUeUZulR5KUV+jcFUHqboshVKTcWxt+cbNF3DPzlm+Pt7AdKHvxgjCPCTnRqglSXre1pnLl1lOj9pdPajHsojY1qibWw2pSyrgGQaCBqcfXktts1rlOSkMNlnwNIUzmihjewNuU7C8k1XvbwQ28u8iTJjxmPdzzGPJqoJqjh2jp3FGImp39dE4UiIsGpY3gmornDqk5w1Op7vkwsLsOiVorIcn6y0S1WVfNJD4AnxDNGhYKgfQEjQX07ip+GxT1MByO8Xh5UHk82uoJYjaCk9AcDRvSamexnoY5OG+8Y0opTnHmSTlhrQDj5gYPzPJ2rKl+83nAkRtGIMgMYajzQU+/t4f5zXf+AzTrRZRURIVoS0C0nGIktpi8YWhoyO+OHE/bkqiPduHKffmufCcXn7v0LeJTcLB2jjX3/4BNmSH+atL30pGefzmF27jbx86hBGGscEi7oJPEMXcd/A0N919kFdcufsZe6PopXnl+u/Eb7/76stZu67Iex+5lc5YTHpS0fryANvOK/G+n3o5w7neH2gPPmf//PasduT/FPtvt37LpooCYmO46YHDdNYkHF+q8N9f9GJ+/cdexL73HMHEXfQIVpAi43lcMLgWBuFlO7d/x/ueXq4il9VqOQFg3+BGprwqC0stXnv5HpYKEXrSeoGOn9CT9gmIWNGQKaXyfO7Sd/CNuc+zv/p14kTxeDVBZgIQkHJC1uQ6hAWHmWoe0elirk95hLvsc3RHkr5OsD4lmGoHFtamFWnl4gqHt3/pZoSR7BsYptlMIbzIyqeFgnQuwOShUfJpbLH114WZQQSGteMaEXf5Q5RBbmuiXA1aMBWWOHN/D2vWTSDTdrQ7iRX9jyfICFISWiOGxQshyqqz0I1u2aWbthD2JYiTOYsdbztIITg230vKTehskxAbwpREaEjylqc9EQk6EFBXFEY9JlIdOj1WOCOJAGkwiSUHxgABOLOeLZEVYtonCpiKi5OKCebTOJmQ+Q0Fi3/HlhNEXdN6shdd8XHaXZ1T7KHRFB4giBPFUzNrSDqK0n4PEQj0WB51rSWHKmVaCAnCJJSLLZac43x1uUwlila1Ol0hecvu84nSNb4y8Sgrw0kp6fK88kaWwiYnGrPUmprjy/B7y3dgSl2EiLHDYacaC9w08Rj7Mpu56ZHDdmmN4MxslaxeEfc0hPEzlaO+l716y25etXkXfz91jPaVEVcPbybjfmeJ5jn7t7H/dI68lE6ddSSw6kRjo5mo1aw6fHfKME6D24HN6/p4249d9I++7yVjoyhfshKuCQlXbtvEm6543upzZpp17hg/yXitimgZ2pWQVE5QGMlSjTtU/SaXf/LPMI5BO3swgCMMqWKAIzQDuQZSQNqN8WXMb279Gb74zTu5ac08ybxvU2plOLjYolRo0VrMorUkXW6xOzXAPYcaBCpENQzfXD4BaYlZ9HDqguz2ZZyuLJwThFQqXdQEAkclBGMxXqXb2HMM/lCnG30bnGJMbVvE9P4+evYsYTTMnuihr0u8KDSklw3e1hrL9TStahojQXYkMvEp75illAmozuaoRFlMaGs7OgFzOE/HsV9YUNR2dYUALSluXMTdYmifp5ALDsIYQt+DxWfC6oSXoB2DSRn8CR8RWoigWHCJy9aBh90fRNJ2YH199bNHsUNQy4ADomSIyoLUtEFLCIsGXRCrv6V25JI76iKCLp/7mSLNxRS4hqXFPBkdkekN8L2Eyc48n5y6hcj0AhJXSX7pwst4x959GGN406aLOVg9zV3zT7KrNMpr11/KjWMX8/KvfIwT83bq8nQ7YndPkRm9AAhiLXGUrZmn3LPybgYLXeztzTI/32DDUC8vu+Sc72e7PMOkELxwZMv3fuJz9q9u/+kc+ft/7Dre+LHPM7Fcpeh4tMtWed53HF66eRue4/BLL7mcD371TmRZ8LpLz+MTdz/K9R/8S37++kt5y5UXfNf3zXke3/7pt/OeW77OzHSdG87dyRsvO2/178YYKo02n7z+Rv7LH3+OhVoL42r0OXUaqkWjqiAU0FFQSlbqBMTa0Km59PVGXa1Pi+rIe2nWlcucGlpEjwOqi4RJBFpL6rP5VVWcuF3gvrm6hU6mNKqhSNJd2J0QRDmNk01W692OnyA64LgJTjZm/ZpFOiOKahFkXRKvj0gZgdSmS5sq6AxpOlqxNN0PbQchE9R5TWRbEB1J4e1t4Pc06TiCYDZrlS8w5HqaDJfsAZUZWSaqp6gvZcFA2g8Yfv4sQhkak1nmxsvE/tN6IbFgfu7/b++8w6yqzsX9rl1OnZkznRmmwADD0DsCCgqCIJagsWtsJLEk0SQmJhpjcuONxtxrco2Jv9yYRHM1GrsQK4oVLEiT3oY2DNP7zKm7rN8f+zADMgqKMgzu93l4Huacvfb+zj77fGutr4bAFmipcWJeC8WyIZRAtjjZvKpmYQedGO59tn9B1znsDBPRoB1gLlHLNazBTkiJ0eZxzFYSx2EpJYFhbWRmdFDXkEZLxA8B2SlP53lwYthjMY2N7w/ATu7SRCiO9OxbRUunEbUtMGyLiflO3KIQgpLUbPJ8IRa8tZvf7VnLy0WV3H72KWxpanWOSQikBRWvWAzdkU80lKD2jDgnlQzmzIJRaIrKDaefyH2vvoehSoaOzOFf8y5xkq30r9zP/rhHSCkPfdQXzIQJE+SKFSuO+nW7oy0eZ0dLE4MyskjxdG0VE6bTIWjGXQ/Q1OEUgNIUhQ/v+C7eQ/wQ1lRX860FC2iPx7l52jS+OX48Ny14ide3bSceNxGKIBEwsEK205VA4jRytgVKh4JMNzvjtwEwocROY/j4VqpjddhILi/6Oj9dtgTTtLGjnq5YeClRdQvadeR+W2lfnWNusHUDJVkzxvI5Ow8hJSkD21HTDMc8E1VIVWLk5LbRUhuioTwTNWQQzzdQFZtUr2OvtxHE2zzEdwewgxLd56c54mQgDiypJhhwuhHFWzzE2z0YDT4SOSZhTSdR42QI5Q+uIzM10pkIVbcng7qKTGxVUjy0Bn+qE3hvW4I963KJmV6EApqqYvhi2JaSXKFL9KwYIhnUbMcEdk0ANeYkL9kZJiigtCno1U4uvZViYRUY+NokssLvZIV6neYfAknWoAZqWjJQOhw7i+WF7Pxm1DQLTbEYHKpj+bYSEl4nR0DaArNJQ2nyIhVB32ALaX07KF9b4MQ+AlpOhNDgVjyqRntE0FDttLcLpMQYWuxhfOYAfjz0XDyKxoPvreDeN98jYTq9XNNLOxAim72tYScLNSzp9+8oigWapjDl5DJuv/uCg55Hw7K4b9MbvFmzjRl5pfxw+KyjVut7Y30dv/tgKSGvj9umTSfLf+jKiS6fjBBipZTyoNXkV35qTvN6GdPn4DApT9Kj79N1YF9BfoHy8Q7y3XDLq6/SFHXG3LN0KTNLBvDKxi2YhtNX0shwQhwBp/5rcqUd9Gqkq0Gq463Oqq0zegRqa6IM3zmUaybNZlROMXctW4ptS0dxif1sRQpgCrQWBTMVpC3Rw05gdrTYRo8m25YhUOOgSQtPg0qbLwVPqwGqxIgpxESA5sY0jMog0lIQUY2UQidG3pAaurRQhUQ265iNAeyEQaIkgu5PTgQpsc4VvjcjQd3rToao3GEjR3eg50fQvAZxpaumsBBQVFRHU7ufmKJhKeKAyBgiKiiQ2gBqMEZjHwGBZBycBUZURyT7jqrtKlocLB1sU+mM6bdTbMiWJFIS2LpEVyysmAY+C2kIhMfZCUkgWhekoDWMucExM0WKLBqygwQiCYKBODvasslKDbMnnI7H74QGyrgHK1mYs8pOIyfQ0PlcSCDW5oetAR679Os8smE9T9VsxOtPkJ7Twd4wbKmNsb6mg79Mu5S4aWEnQ/8kEsOyGNnfpix1AEu270ZNmJ1fu2na1FS3sKm6jn5ZGQQ8XZ2ZFlVt5NEdy4laBlWRFgaH+nB20YGOzi+DhGVxyXNP0haPoykKVe3tPH7eRV/6db+KfCFx5EKIHwkhpBD7Vas/Tvj9ZWdSmBkiOzXA7y49E11VDz1of6Tk2r8+h2gFPQJm0ExOnwduw/VWhdB6ncGNIe46cQ4HLJgsSKRZvFq5je8++SazHv4Hr763HX27F7VJRWgWSfcbim6h2JDSqpGxUyV1l4K/VkKOgQiaWPtNREKBtKIOtDQDf4OCGfHgMwMobR7MFj+xmhQsVWCroIZMUPaV+xVYUkHaYIUdhWEXJiC5OFb8NrG4jm07iT2xFi/SUrA1SXshxFpSiO4NEtmbSrzNjxUTBNQEqXoMj2aR4o0jbKhuDGG06Vhxhfa1GeiVOqFdArVVIKs8KEpyshMkI18E0laRpoptKwhDoEWdRB4knhMAACAASURBVCK1yoNo0RGNHhKGdBo/WCpGQieeIYgX2RSPqups/yaQhOMezI2piGQv18Bup6ZPJOrBthUkgpaGgHPnJdChokRF525K2mA2eiCUwPLa2F4bT3aCvAEJrnnsSbbu3MmCsy5iSFEdApvaPZl01AR5b1MjJz/xF9at3Uu66QEp0QImqbkG43L68dAZ5/HzadMJ5gcQfT3oXhWPV2NNdpRvPPQUs//wIHXtHZ3fc12sHdN2/B+mbVEXbedo0JGIEzWM5HVtdrQ0H5XrfhU54hW5EKIImA1UHLk4xx4ji/JY9NP5n2nMb+fM6TStnNVvMC+9v7lzde1tVIkF9kUMJJWqASnbVWLSZF24hsoX2lEDCmbQBhM8HgO12Fl9yeoA8RYVPSIQKBATUBRnxIBUtjaGiUdtPBUeFE2AIZGqTXxiDOmVTtnwHEnCBm8MUnKi5JY1IIZC0Mpljnomv3npHfArXfIJnIqNMbVzcpESLEvBiirE632OM23fXJL8nLtW9yU3sxUsQV19OjJHOvpNSdqobbASCoktPioHZDEyZQ/SFhimQrtPQ/EZWHGFujf7dib2AAhLdpYJUCMC2+cocyFAVQVmMtRIiTnt1/a1YbO8ApFwPo/tlY7iT+5eRJvzM6hLZDKxbDPLdpTh0Q3S09qIebNRI8mEJE/XVyYsG2ttClE7gJplYdX7UJp157RxMFMh4I3TnqPQN6eB1O39qRvQQMI2aZYgi6FeNnLLuj+Tlx6mJuxDxtVO+31zW4L3Nu7C59E4ZXoWrdmNTMw6gesGzQVgemk2S42NMEZg1qmUcQaPrtqCmUgQkwZ/ef9DfjZrOqqicFbRSB7a9h4RyyCg6pxd/OWvxgEyfH6mFfdn2d49WFLyrbHjj8p1v4ocsY1cCPE08J/AQmCClLLhEEOOKRv5l82r67bxw3++0KngpGoTK3Vix68qG8uzL6wnhuV050n+iGWuSkRLKnspUQojKOlOKJodUTF3B9DDAjPTxMxxNOi3Ro7h1lFz+dOr7/Hett30Kw2xsHk9pmWh+LpSuaUJ+pogtkcSLGuntH8rccsgYgSofbkPlqWQCMG+kEAAW5FYHghoCfSiKHFTJdbooyy3hrRQjIryXDpEiFhGxLHy1HoQ7ToiRlLRJiNCNOmYlJIJQFKzSa0waRus4fMk8HsShHUVm2RJWUBYArVZRd/kw/YKfIqJ3ebUfIln2YQHWPTPSOf2CTO556O32dRUDwI8FTpqIhkuicTySmxf8v56bOw0y5lU2lWE0eVLEFISyuqguKCBSMxDRWM6wVVeSCi0jTIxUyWBrSq+GhUpBP46G8WGurEKYl97NyFJ5JggFXR/gtScMDnCR4MWQwJmVKN9bypCSFTV5qYZJbxZ/yYfrhmIsxdwzEzpG1RESQR9WBSpSAKqh2dOuYksbyrP7FnAgqoXnBj/vRmktA5h494wHaqJHQCvpjK5sIgH552LEIKoabAn0kxRIAP/Uexeb9k2q2qqSPF4GZqdc9Sue7zypdjIhRDzgL1SyjWHyqwUQlwDXANQXFx8JJc9ZpBS8saKbWzdXc/MiaXEsVlevocJgwoZ1c+xu586bCC5wSB14TBIOKV/Cf9z+dmY0saraLzz5k4a2sOgSxQTNFXh5NKBLNyxlX3KVCRX8NIGaShYqRZaXMXM3qegBW9WVHLbGMGcUYNpikZ5esV6pKpi9zWdFHmZDLyo11ATAiUB4a2pDB9WxlsN62g3AM1GiSt4WhzbspFpEerQaQlajoNWFVib08Bj48uP0bAmh7oUQTymo0qV0/oO573NlUQTJtgSNS6cfqVJFMNZDUsbhJBkrLEJ1kFsXJyYoWE2+yDTQvHYTjQMAhQwsy3MMoMhWh71hbtI7FKQW/1oYYVRezKZuDONX//v4+w51wNeJ7beVvbZDZ17aKZIAtXCSfNPCIxCBRFKMLisCp/PoLEhlYqduWhek2GDK1FVSUowhmZIdocKiAcFIttEV8AcahHup5D6lo5iJadfzSmitc9cJqWTIWt4obk9QIsAr0fDFzCJtTjFyKQU2MDDi+qJNQ+BPnGwFYSQZLV68flU4nmt2ErSDyCgvL2GLG8q/YP98CgeKjamUbs5B2Q7mqKg9FGwsYlbFu9X7qEuHKZPSgp+TWdw2uE1Tv4iURWFiX0Lj/p1v2ocUpELIRbT1Qdmf24DfoZjVjkkUsoHgAfAWZF/BhmPWZ5fsp57/vkmsYTJP19fScIvnYxRr8o93ziDPz70FlW1rUwZW8IVl5+FBEb3zz8ghfqJ71/KghUbyQj6mTywiBSfh+dXbuLl1VuxFYFiwtBwMRtje7BNBbPJhwxaxIoSB9jRK8Mt/HH5Gzz+5oc0tXuTVhEFvdqLURhHa9GwVfDsdr5ygUAxJZOyRvHKhm3Yq71oqkRP02hPmFgZoKXrpL7cQstcx9ln2ypa3xhqs4BtPozCOEaH7tiksflwSQVZNZJEikJzqoUQSY2qJqP1U03UvnHHrGGB10ojdHIKtXtbSAmYhDb4CAcNYpMiTpDHfiGBdsCiuryVc8aO48VdG0gkVIQNNYl23qhqoH6iiulJrv5VsP0SM7mqN9McB2i4nwUeG6EI/LU6+XkN+H0JhAKZWe00V6ciFZL16yWqKvF7E6jtKv4OSTRbgzTTKRcQkKh2VyCj5ZHIoIUwBVaKBaaC8CedkcJxnioC2tp8TgOUpBdXExrhdouYcOrikFTafYaE+OFZk1hmbuKVuo+wpI2CQlma03hhfOZYvmlfwe1vL8O2bECiaQrDsrPY2FSPadvoikrI99kyn48Uw7L4sKKSkM/HiPw+R/XaX2UOqcillLO6e10IMRIoAfatxguBVUKIE6SUNV+olMcoH6zbTSzhmDwStoVsSdqhoyYPPbeMmvo2JLB6YyXnNEWYNn7QQefITAkwf/qBO6XFa8sRFqgWYEP15ghaLBVVSNRshYLMLPILAmzrqKcm2oZEErdN/lD+HmUj6mn+sBhpK46pxhSoNV5EVEWVNtILMu4ouPOnjWJ6nzJ+uSQNDBtbg9YsA3SBJ6Jw7dRJLGx/GV+9QaRYxZsRI5AVxe4jiMV0LAXItaBcgVYdq8MkHpGIGIgCi45iG6VdwU5NrigV0JJmd59HY3BpLiu31DvRLB0+4qmgN6jIrR7igxPJiD0JtsCOa6hCYYw+CM/gVB7fuRrawZI22BLF6FoNIyXCsJGagmLCFX1H8nzjNpr1sONnEJJIXqKzHR3Jy+CzaY/5iUY9+P1xFAl1m7rMAbJNQ1oKekYc4bFoGuEldzkIE9S4wJMXwReME24NYHl0tMxoZ/NoAXg9Bt5GQXS3ipEBwis4u/8Q3m3ZRcJOlkC2INigQsTkdymPY2sWHg0uKDqZc4omku7paid4YvZkLhmv8OfXPiCesIhFEmSvNjljZinNsRg3TTkJ31E0o0gpufKxZ9hUW4ctJTdOm8I3J3efd+HyxfK5TStSynVA515NCLGLw7SRHy+cOrGUpWt2YNlOyvO+1ZkwIT3Fn1x5O9pF+5Rol5U7K3lh1WZGFuVx7sThjCjow/Lyys6x6YoXQ7dRFEGhHuKxKy7tLHg08Zl7aYxHALClwJNqkJrTQVttqpNTlANKnRN+p9gC39gOxqQX87vJF5IRDNDaEUXui+DbL+PasiVGxKJ5aohIgUQKQbTVj1BAD8U7O/4gwJMukXsl/vqkU1BIwkW2szJOt/c7KU6InyYxYjYrt9UQT/aytqQNPgWhgrbDgxEEK9vCSmjYhkKwTkcC/2/P+2xurcceAf5dCroNaT4/ckULkb4KZghQbey+EhkW2FHBa8u24QMYSle8vSrZHU5hDAlsGaO92U9r3AdCsHpbP0KpYUSVjrk3AEJiBm0CA9qd1bUEO6aSSBHUjxQY6ZASCtOnsBlFleRmxzGr86nWJP5AAjOh4vclCHgNrJ0BPHugDYHUBd7+KteePZH73nqPNMWL3GFhtVjU0g57A/hmNaAg8GkKef70g56db06fyJLXNrG5ug5vi02V1shdl89gzOijb76sbe9gXXUN8WT6/8MrPnIV+VHiKx9HfiTMOqGMjNQA5ZX1vL16O6u2VWJZEq9H46dXzOT21hcor2hg1pQyJo3q3+05ttc2cu3fniNmmLywahOWbXPDmSfyxrKt1LWH0RPwg0umMWRQHnUt7Yzol4euqdi25ObHXqK90oBCJ2IjJzWMpkqyBzbT0hHAVAUer8Lfrvo6RsRmS0sDzYkw3xw7kYygk5gRSvFz+qQhvLZ8K1Zyd7Ev+CQSTRAt0pFqshuSFBgxDV+647QTzo4e2aihSTADTlOOaKFT1tU5SHSeUCiSnI443rQETWtyae9ro8YEQrOQ/WKEFSgYk07VrghGvuOMVFSTKfFBzJkxmMH9srn07X85pW4ViA60MLwGtp5FU6HATgmjeJP+BAlp5RJ/tSCaZSAV8FYrxPvu8ytITF1hS30fbhh0Cn9e877TgRrHvt0W9hOo1gj3cyboYHaUItFKVVU23lAMMyywogpmjuNM9Ye6qigqQtDc2AZeUDXpNOdWLWJbg4hqH4qAFEvDDAhOHTmQ65c/RzzdRBWC9FaP4wtBQqsGK31oqQr5w7r6Xu6PEIKROX2o+qgO03Qc06GQ/8ge7M9Jut+PR9WImxa6qlCWc9xFIx+zfGGKXErZ/4s6V29i/NAixg8tYu6Jw/jDk+9Q19zBtfOmkJ8T4m93XHbI8VuqGzqz7KKGyapde7lg8iieveMq1pVXk5ORQnGe8yMuzAp1jlu9ey9LNu9ES2hobSonDilm2giFHR27efZdBWlaqCYoDSqTCor56YuLWLS1HNMyefbdTXzvpMmcN3EEQZ+HX149h9GT8rl9wwuYMYm+y48e0XjmvXVYXhOy6TRb6FoCe6OOtHzYIYnaqGG1K3QMcmpqi4B0SlUbCkK3UYWFFdHQPRYlBfUEvQkiHR4s1dmtWAGJmpkARWIL2Gu1YBeIrkYYCqzpqOBS32hG5uftF3ovEYqNKiV13irkoARKsxfbcmzkahRSdylJm7czk/hqNZSIQiLXBI9EybRJmLCyZS8tAQMPJom47lSMbFdQDYX0LZA1TmVAX5sP3ijCtgW24mf42O30LTiBF3ZtJWFZtLcFSMuMoAqBqirkVuVQVduOnWOiVutYQT8R00aWSAKtggsmDOPMycNIyfJ22dmlxJOtIZqd+21FTbSlKQhVoVxvYc63u3+GrrtmBk2tYZbHqphwwgD6FnWv9L9sfLrGo5dfwP9buoysYICbTjmpR+T4KtJrG0sca6QFfdx+9Wz+eNPXGTHw8AvqTxxQgKoq+D1OoaMzxzp1nT26xvihRZ1KfH8aG9pZ/PgqPDsiYElUWyHLE+Ty/l/ntmHfJ27sW1MLNFtBUxTer9hDPG4gmiThxjj3vrSUq/73KcBZ1d2+9UUSqgVBG6M0gmlaGJaN7RH7co3AAv+bOpFwGjLmQTZ48cS9GEGJrUinCfS+jgMoBDQbO6FiKwpxU2f7rj7s2tSH3dF0pp1YjEdT0YRwsluTGtqWEjOSLD1oATEFJSXB+9HVBHUPPxg5GUWxUFQLfwT6eH1E4gbGljTUCj/enV4C1Qp5K3Q04djI/fU2KYqO4lcQloK3xoPeqCEAT1Tj9d07sRQw2j3obQqiScVWFXyaTk5qCrP9I1nzUjJ0USpYlkpbUwZ3nTabS4ePQgGi7T4iHxYy1TONpvUlhHO9ZMbSUTcEiSgewrbt2PwVQSQdFr+1mR/c/SxaRHBaUSleRcWraMw6aQiDhvbhjMlDCTWAtMBM2Dzx2krerdzd7TOUEvRSc7JN5agEz8Q28603nzns5++LZkhuDvd9/Sx+OedUUo+yo/WrjGta6WFy0lJYcNMVfLBtN4Pzcxha8OkhYpZlc+PVf6exoYMUAXqbRe7sfvzozJMBpx7MRWNHsmDdRqSk00Y5KaeAdz7aQjyEUzLWtNhcVYdhWmiq4jSZ3hcGo4PPq5FIWGgJgZlUskJKbEV0KiSk5IwThvBa3XYaohH28zYCUBzMZXOkZb+8J5VoFvg0D2eOGcwvps/mte3lPPnoB1T2byYRslHaNOKahqh0xBEJQWhEPXW+ap58aw2PPvkROSLIsGG5nFg6gHcD61m+2XR6ryLQVJWLS8bw7Ssm8v7qnfz5kXcIpfq544az+enzr1LZ3Eo0YRBq8KLUebn2nCncVfm2I3WqjerzENK83Dh+CtNy++HTNc679SFMNGyPM6NpquR7J56HV9M4p2AIrzy7gVjMIKrA/7Gzs0yOmiUQGYpTSmG/qrHClIRjCTRVYcnqHdw392vURNp5atUG/rZsBVHD5KO2Gkr8OrGEgRQQzpJc+/JC1n3rhm6bqLxTtRNT2mhtgnUfVHLp0kf4xdWzGdLPiRxZU1vDTa+9hGnb3H3qbKYUHh8hwC4OriI/BugTSmHehMMrK9rRHqO5KZwMOYNsw8cTN156wDFzBg5kw44a8kOpXH2CU0Y3J+ZxuugYYOtOvHppfja6ppKwLGRU7QqXsxRum3cqv3x7MdGggVAV0veqeLfF0ZpNwsXezvDAxkSEf55zAf/xzhvEbYP10SqMuEXaLp385lRGDS/i3/Xr8IfaUFUJtodT+pQxt2AEmqJw5ZhxXDRkJB+t20N6ZoCb1y5iW6Qa0uNkB8PkpHRgKxrj00u55x9vYSY/956drXz7u5OY3TGIy3c/Qm1yJ6AIhbKSPuTmpDFv9mjmze5qlPDkdy5la009GYEA2/c2kBMKMrS4D9ZGuGf1O4QCfh489XyGZXZNpu2RmFOT3gRvgyCzTwp3Xj2XMcX5LF67jaY2Z1fkFCRLmnGSetaSkoEZ6expaEVNQEJ1Ahs9EcespKkKg4qyEUKQH0xjbVUNUcPxU6iqyjd/NIM7HnuZmG7TUQQikeCiux7hutmTGTWwL9mZKZ1yjsrKY31TLSnrFBRDsDVcz/fvfY5F/3MdANe+tJDasJO2/+0XF7Dumu4nhP1ZsWUPf174HrkZKdxy6UxCQd+nHu/Sc7imlV5GWshPv5IcvD4dn19n2qkHtthqaA9zwyPPs2lPHUs27eSO514HoG9WCK+uoXdAIK5w4Qkj+cd1TqU8j6pyXt4ElKYgSlOA7/Q7ldljB9ORaWJ7wFJtrL4Kg3NzUXQPqTviqGELIQWjC/Mpy8rmX+deyLPnXcajp13MkG0ZBCsUNn9Uw4bnKxjXL4BQbWKmSsy2SPN40PaL/fP5dCZPHMCQgXlcP3E8gVQTxSNpNnzEbJ2AJpmcXXpAxb59UUD9UrJYfNH3OK2slFSvh+mlJcwb1X3bMY+mMqIwj4LMNE4eOYChxc5q9ZvDJrLpsh/xwfnfOUCJA6QGfPz86tlkpgUYmJPJA9efx8SSQubf/xQ/f2wRv3thCaUDcvF7dQZmZeLV1U5T1OTiQhZddzWPX3kxHlVFNcBjKYwbUMCMCaXccvVpTB7Zv/NaF4wegV/TCGga/hqbmo0N3HjhdOL9VUDgqxJUrK/jl3ct5KJrH2DRmxs6x/7fzIv4/qipaFbXfW2PxDv/HzYSnf+PW1Zn39l92LbsnCQB2sIxvv/HBXxUXsUbq8r5j4cWdXtPXY4N3BV5L0MIwe//ehVLXt9IIOjlxFMO7FbU0B7pXGkZls3OeqdQ0WVzxlPd1MaabVXMmzaCS04bd8C438w6jSsbxqKrCoMyndZdpxUP4p29u5BIrho7nlkXlnDx6w9j1ilIw2ZEXj7zxx94nol9irCaLWzLURTN7VGyLS+mtS/8UvDvPR9xackkBnWTabizvZ74vphqBHFLQxMWqqpw5zfn8ut/Lsajqdz97TM6x3g0jT9dePYh711LPMytby+gqS3Gj0+cxcS+/Q45BmDulKHMndI1OTS2h9m8t76zy86uxmbeve97AFS0tPDY6jXkp6VxxTinm/2oojzmnzyBf32whkF9srj30rNJDxy8up0zpJSCUBp//fvbbCqv5PHNHxJK87Hyr9/h9J//lWjYQIs6yjZhWDzw6BLmzHB2cqkeL98dOYXguSp/Wfg+ANef2+Vs/MXUGdz21mJAcvOUaaj7TaTvb9rNTQ/8G8O0+eG5U7ns1PE0J0s3A5iWTUWdW/DqWMZV5L0Qv9/D7LPGdPteaV4WZXnZbK1pwJaSa2acAICuqdx6eVdu166KBlatq2B4WV9yC0I8t34TIZ+Xc4YPpTEc4U/vfEAfmcK9086gbyiNkdl5/HHDOySkicwBTSicMWpgtwknc6cO5d9vrQdgSEku1wydxNXLHmKf7VwIgY08aBzA6YUj+Md2RxHFrThp3jiTsiYyPG0wI8YJZo77/B1qLlnwIDvKE2ALrtj6DO9+/zoyg5+9PnZawIdXU0kYFkhJuCnGpvIahg7Kozg9nVtmnHLQmO/OnMJ3Z0455LlH5PehYXcr8WQoaCRm0NIU5oazp3LvM++AsJycACEOMK3s46ozTuDsk4YjhCAzreuzXTBsBHMHDcaWkjTvgU7IXz36amdi270LlvL1k0ZRlJPOqAH5rNtZg21L5s89gV88soilG3YyaUg/7rh89mevBOrypeEq8uMMVVH4v2svZMPeWrJTAhRkhg46ZldFA9f8+J9OrWshiJ4cpDkeQxWCFZVVbKispby+ESkly3ZXsuj6qwAYk1WAV9WIWSaaojA6s2+3MvzgG9M5cUwJkZjB1LED+M9/vIp3YxqRE9rBJzm7cDSD07pP3x6YmsNLs25gc2sNw9LzyfQED2nL7Q5bSu5dvZS39u5gbr8y5g8fz57qSLKEIdjYbKitZdqAkq4xtqS2rpWM9CA+X9cEtSdSz+1rH6HV6OD6QWcyO3888waV8cT7a0FCSgTWbali6KDuKll8dqZNKeW5F1dj2zZpqT7y+6RzSWEWs8aVsnZDJY8+tYxQmp9bvnd6t+OzQsFuX9+/ccr+7K+QhRAI4dTd/9MPvs6WijrSU/wsL6/k1dVbiSVM3lxTzsJBBZw/9ehUUXQ5NK4iPw7RVIXRxZ8cArlybQW2bZMwLCwd6sLhzkiLt3fspLUt6jSoBnY3tSClRAjBtLyB3DflPJbWbGd631Im5hwY+VDd3s5/v7UUS9r8+OSpxGIGr60vZ8veeqxWgfe1NHxejQt/OumAcVJK3qzcSVVHGyf0KcSjqkzNHYQQgtZYjBe2bSHT7+f0gaWHrdSf3b6ev25cTtQ02NbayO7YXvSMOEZYAylQhcLI/K57lDBMbrzlX+zY1YCqKdz/X5cyoJ+Tnn/nhsfZGa5GAv+1+WkmZw9lzpQhvPL6BqddmyIYP6LokDJtaqlmb6SFKTkDCOqfHJp37dWnUFaaR1NzmJmnDEXXHUWbE0ph5olDmHnigea0prYIze0RSvKzDqvxyce566q5/PhvLxCNG9x60an4PI5aUBWFYf3zkFKy9b0GDOE8E5Zt0xFLdHuunY3NrKmsZmxRX/plHpyJ6vLl4CryryDDyroKd2m2cHbryUXZ5OIidKmwaNM2AE4e1P8A5Xlq31JO7du9eePqJ59lR1MzJFfyRm2i00Gpe1V0FPrlZzKo8MCMv7+sW869q97DtJ3YdZ/UOGNgGf81fQ7znvgndR0dCKHw0chqbp12sNni47y+p5w7lr1O1HQyUm3bZm+4DV9eBMVrIRI6N00+FV1VqG5tJzc1yH++8xLLSnbjSah4d2ncdP+zPPfbaxBCEDZj+5dxIW4ZjB5ayP13XMT6LVWMH1FMSVEWtrRRRPfxA8/vWcMvP3oBBUG2L8iCU7+DT+2+DooQghnThnT73sd5+d2N/Pqh11AVwYiB+fzx5vMOsH8fDiNL8ll05ydkGwE3PfsSb27dQSxk4wlDnj+VcyYfHGW1qaaOSx98AqdRHjz5rYspzXWzO48GriL/CjK0NJ///uX5vL9iOwu3bkXrCGN5wKMoXD9xIgP7ZHHW8DJsKZk64PAcggB7Wlo7oyEaohE8TgtQAh6d711yMsP65DBiQD6aeqCi+ff2TZ1KFyBmmby4fQtXDBtDfThMzLIAi5e3b2VUqA8vb9jKiQP7ccnEUQet0MNGgu+8tZC45dh8FQSpHi83jZzJLesqSGSb5PkD9PPkM/X3D2BJm9xCnWp/PXaBJJZrEYoK9tptlLc2UpqezQ/KzuHWNQ9hSIt5hZPJ8TnmqqED8xg6MI/tHWv59cYbMG2DufnzmZx9Bh/nsR3LiVnOZ2yMw+bWGsZkHryKf+S1FTy8eCXFuRn897fPOsDO/XHWb63iV395BVsBA1i/vZrtexoo6ZvFjj0N5GalkhE6PB9AfWsHD7z0AQDXnjGF7KR5piOeYNGmbVi28736srws/PHV3a7839yyg7hhYuPkM7xTvstV5EcJV5F/RRkzoogxI4rIWpbJb194G0UKSjIzGZCbiSIE0wb2/8znvGj0SJ5atwEBlGVksbOtgahhYkvJpMHFDOqT1e24qX37s6O1iahpdr6mCEH/jAxSPF4SloWuqpSmZ3Hbv18jZpi8u303qT4vZ486cOUat8wDQutCXi8PzzmfYRn5LDz5Vna2NrC1ooXfvryESLINWWW8A5HSNSZabBEvVgnqjk15fGYpL55yBwnbJKB6aTMiBFQvmqIipeThzf9LXDHQdYtXah5iXOZMPMqBppORGQVsaa0hZjvyFQQcs0NrS4SKXQ0MGJRLTVsHf37+fWKGSUtHjN89/TZ3zp/7iff7uUUfIa197e4ElmXj9+nM/+kjVNe1goR7f3EBI8q692Xsz7X3PdMZmbJ6exVP//wKAPy6RorHQ2ssjioE/TIzPtF8M7IgD4+uETNMNFVhuFvG9qjhKvKvOBdOGkV7TZjFSzYzKS0PacvPnV1w+8zpnD1sCLZtM7ZvPk8sW8vaihrmjR/2iUoc4KcTp9E/lM6mxjo21NURbtKwIgAAEatJREFUNUxuO3E6GT4/Cy66jCc2rCXTH0BEYcW6SsCpS7Olpv4gRZ7pC3BZ2Rj+uXk1lrSJax18Y+nfuXXkXM4uGM2ND75McyRKzDBRVbBUkM0eRMhI9qAQqP2CpNV6uPyvT3HPeXMZW9SXjXvreWblejabW2nMqCCoe/nDuO/wmwXv8+6OgcAApk1dT15umO7U3I9HnEZI97Gzo5HLB04mx5dKxa4GbvzWgwDousqNd85zmmnj2KFbI7FPvd8lhdkEpELEtBEqfOfCqeytaqGmvo1ozJmkHl34Ib/5yTmH/O521zZ3ToC7artCDVVF4eErL+CexUtJ8Xr42ZxPNm1NG9Sf/zr3dJaU72LG4AFMLjm038Dli8FV5Mc5hmnx1kfb8Wgq00YN4J2PtvP68q2cMKyYs6YOZ9P2Wh59+kNiCZOa2jYyQ0GuOGfSoU/cDUIIxvbtciBeMmUMlxw64g5VUbh0yGgSpsWaPdXkpAbpn+3UmImGE7zxwmaqa9uYOroEj6qiqU71xzNHlnV7vhNz+vP42rVoWWFQIGbBA1uXMEQroCUaJZJwlFxQ9ZAa8qG3SdqW6xh+k5+dMYv/XLyENiNOWyTOjU+8wOPfvJj5Dz5N1DARik2gvx+7Xwd3v/U8695oJyAlHfkKG18cxM7tXi4J/o07/+M8ykq7olg8isZ3h844QM7FL68lGokjJXi8Go07mjmhrJh3N+zE79G5Yd4nF52qrG5m5frdZKUHGZQW4KKzJzDrpCFs312PlUzs8egqBXmH53CcPX4wb6/bgQBmjB54wHtD+uTwt8vOPbzzDC1l9tDPHyLq8vlwFflxzg33Pcf6nU6fj/GDCli1aQ+xhMlbq8vx+zwIU3auAuMJk4qqps6xFY0t3PvKUjRV5cdnTCM37eC45c+CZds0h6NkBP0HOeQMy+LSvzzO7oZmLCm567w5pHq9XP/gc1i2REmRLN9Uwa+uPA1fmoeyvBwK0tO6vc6SPbuJGTb7irkKBH396RRlprMvf96na3x93HBuPXM6hmmxdmc1OaEUCrLT+MWitzrPFTUMyusaO+WVtoLR6kUVUXY90Y7eYqEDnrCJ2uEhbtvEYx3c/bsXeeh/vwlAXWsHf34xmaRz5hRyQ859LCzOwuPViccMFCEoLMzkf86fSEtHlKDP01lzvjtuuvMZqmpbACc5aNZJzs5kYL8cfnLtbJ54YQWDB/Rh/oWHMZMCd145lw82O0W5Jg85fL+Iy7GBq8iPY+KGyYotlexrsP3hpgqUpCk4ljDZtKuGq8+cRCjZBEPakvPmjAWckMCrHniK+jbHVLCtpoHnfnD555alJRzl4vv/RW1LB3npKfzre5cekN24raaBXQ3NROIGwoLfv7iE9IAPSzo2YFsHy5CE/D5OGlLyKVeCmf0H8PTm9ZgNAdSUOIpUKW+IoU1SePTbF/HoBx9RlBniypOcrFRdUxlf2tVX8pY5p3D3orcRQvCLM09ldFE+Hk3FL3UMyySzwGJYSjFrWyOdZhQ9LlAVMJNOQcvsSne/9o9PU1HvKN01O6p49udXAjBr7ijqaltZ9eEOTpk1nAmTnZVwesqh64nXN7ZjqRDNgXgijG3LTtv16acMY+a0Mm5Y9hhTHv8tvvWpDAjl8Ov5pzOwwHE+7qlt5o9PLkFTFb5/0Sn0yUrlxGH9D3ldl2MTV5Efx3g0lb5ZadQ0taEIQWlhDhVVTWiqU5Fv5oTBpAS8PPb7q9i2q46CPulkpjvRCpYtqWvrYJ/fsKKx5YhkWbByIzUt7RiWTU1LBwtXbuTKaV3p/bmhFOKGgRJ3GkXX1LVhZdqoioJl207HtxaTtRsqGT+kkL+tWEljNML88eMpCh2Y9DS9XwkPn30eNz/9Mg3b2rE80NHHYHVVFdP69+dX53TbvbCTb0waw3ljh6MIgVd3fiILb7iCpdt2UZKTyahCx2Ty80kLWPHRLgBmTR9G35w0/v5/S/D5dG7+YZeTsqK+pTPqY3d9131UFME35p/MN+af/Jnv5wVnj+d3tR92FkD769rlXDvmhM73X6/exMrG3ajLUjBN2Npaz033/5uFd80H4PrfPkV9szNJl1c28ORdV31mGVyOHVxFfhwjhODvP7mQR15diVdXufL0iYSjCdaWVzGkXy5FfRw7tM+rM7Ks4ICxmqpw1pghLN6wHaTkwslHlsWX6vOiKgqG5bSsS/UdmGWYnRLEo2skImbnKjccjnNSaTHLt+5BNhooFjz+0kpWy3qW1u7BsCxe3LyFpddeg+dj6eJF/hAd5RF0S6BFJbYwCXl83Pzsy7RH4/xw5kmU5eXwSfg9B8Z4Z6UEmDd22AGv/eqWr7F89S40TWH86H4IIbj4/ElO+d39wiLPmDCExR+Vg4BZo78Y+/HJp5XxpwWriZgGBjYLtm46QJFrQgEpnJruSRojUTbX1TMwM5P6ZmeSlkB1fdsXIpNLz+Eq8uOcnPQUbrqwK9Igxe/ltBO6dxJ+nN9ceDoX7K5CVxRGFh1Z+vm88cNYtWsvS7bsYlpZf742bthBx5T2yWJjWy1JawrjBxRw//xzue5Xj7M2urczKWd9bR1mq4UehQ49xq7GZgZ/LF65sqEVr6YSsWyEFAxNzeHuV95mbWUNpm2zak8V79587RHVC1FVhckTBhzwWneheb+6bA5nneB83hMGfzGRHMVp6WiK4jR1VjWm9D3wvDPyh3Bq3zIWl5Yjyr1Iv0JznslF//cEw/NyOWvqcBZ/uBUp4bwZbqp9b0fIj5WzPBpMmDBBrlix4qhf1+XYpikc4TcvvMW2ygamDynhutOm4PNo7Kxs5KbfPktTa5irzp3MRqWZl97f3NnH4uIpo7n9nFMBx7YfS9b0vuDXj9DSEcGSkl9feTq3v/o6TRGnqp+mKLx787WE/L23xvaOliYe27CGwrQQ3xg+5oDSwPtT39LBdc/+m3U1tQAEdJ2HLzsfEbFRVYVhJV9MjRiXLx8hxEop5UEdrd0VucsxQ2YwwH9fdHBWZElhFs/9sSuFfOmWnbz+4TbipoUQgo6YU3e7oSPMZX95gr3NbYwpzueRn1zM+l019M0KMTA/ix0dLdz/9gcoQjClpLhXK3GAAemZ/PykGYc8Lic9hZKsDDbX1WPYNraU5KQEye+behSkdDkauIrcpdcxeVA/xvUvYPmOSlL9Xr4zazIAD7+7iqqWdmwp2VRVz9Ly3Zw9pquO+LenTuTkQf0JJxI0dES4dcEiJvQrYG1HLU2xKDeOn0xJeiYPly9jd7iRS0omMCT9+Fit/mLODEzbZndzC985aRL5aa4SP544YkUuhLgB+C6OW+VFKeVPjlgqF5dPQVMV/vqtr9MWjZPi83TGePt0HVUILJx+nz794Me7LC+HD3bu4ScLXiFmmCxctwkjzcb02LyzZycXjx/ME7tWErNMnt+znsVzbiDT231Z2N5Ems/HveeeCcAbW7cz7rd/wpaSu782h9OHDe5h6VyOlCNq9SaEmAHMA0ZLKYcD93whUrm4HAIhBKGA74DEoitPGsf4/gWkeD3MGVHKzKGDuh27sbquM/vRtiW2IZFA1DRZVltBLFlwSwAV4eOvM85PFiwinDCIGiY/XbiInvCTuXyxHOmK/HrgbillHEBKWXfkIrm4fD6CXg9/n3/eIY+bPriEP731fmfjaQLg0xWGZ+dy3qDB3L3uVQSCNN1H2Sc0wHBxOZY4UkU+GJgmhLgTiAE/llIu7+5AIcQ1wDUAxcXF3R3i4vKlsmFXDXc8thhFCO4990xajRgjC/JoNmO0xKJMLeznVFkM5bA33MrJeYPwqRo1Te2Egj783u7rh/c27jn3dG569mWklNw9b87n6sDkcmxxyPBDIcRioDuPz23AncCbwI3AROAJYIA8xEnd8EOXo42Ukhk/+V9aw05Fwdz0FBbd9cnNFMCpDfO9+59jdfleVEXhL98/nxH9jw/np0vv5JPCDw9pI5dSzpJSjujm30KgEnhWOnwI2IBbSd7lmENK6IjGO/9uC396iViAdTurWbOjmrhhEYkb3P/8u1+miC4un5sjcnYCC4AZAEKIwYAHaDhSoVxcvmgURXDdWVPwaCq6pvK9r514yDFpAR92skaKpipkpfb+6BWX45MjyuwUQniAB4ExQALHRv7Goca5phWXnqKhNYwQkJV2eEr5qXfW8o/XltO/TwZ3XjX3sCoTftkkLAvblt2GV7oc33ySacVN0Xdx6UW8snErNy94BVtKfjxzKldPHt/TIrkcRT63jdzFxeXY4T9efoOEZWHaNve8vtQJn3T5yuMqcheXXoRP6zKnqIqC6oYOuuAqcheXXsV9559FUXqInJQg951/5kEt81y+mrjeEheXXsSogjwW3zC/p8VwOcZwp3MXFxeXXo6ryF1cXFx6Oa4id3FxcenluIrcxcXFpZfjKnIXFxeXXo6ryF1cXFx6Oa4id3Fxcenl9EitFSFEPbD7Uw7JpvdVUextMvc2ecGV+Wjhynx0+Dwy95NS5nz8xR5R5IdCCLGiu8IwxzK9TebeJi+4Mh8tXJmPDl+kzK5pxcXFxaWX4ypyFxcXl17OsarIH+hpAT4HvU3m3iYvuDIfLVyZjw5fmMzHpI3cxcXFxeXwOVZX5C4uLi4uh4mryF1cXFx6OcesIhdC3CCE2CyE2CCE+K+eludwEUL8SAghhRDZPS3LoRBC/HfyHq8VQjwnhEjvaZk+CSHE6UKILUKIciHELT0tz6EQQhQJId4UQmxMPsPf72mZDhchhCqEWC2EeKGnZTkchBDpQoink8/yJiHElJ6W6VAIIX6YfC7WCyH+JYTwHcn5jklFLoSYAcwDRksphwP39LBIh4UQogiYDVT0tCyHyWvACCnlKGArcGsPy9MtQggVuB+YCwwDLhFCDOtZqQ6JCfxISjkMmAx8txfIvI/vA5t6WojPwB+AV6SUQ4DRHOOyCyEKgBuBCVLKEYAKXHwk5zwmFTlwPXC3lDIOIKWs62F5Dpf/AX4C9AoPspTyVSmlmfzzA6CwJ+X5FE4AyqWUO6SUCeBxnIn+mEVKWS2lXJX8fzuOcinoWakOjRCiEDgT+FtPy3I4CCFCwMnA3wGklAkpZUvPSnVYaIBfCKEBAaDqSE52rCrywcA0IcQyIcTbQoiJPS3QoRBCzAP2SinX9LQsn5P5wMs9LcQnUADs2e/vSnqBUtyHEKI/MBZY1rOSHBb34ixG7J4W5DApAeqBh5LmoL8JIYI9LdSnIaXci2NlqACqgVYp5atHcs4e69kphFgM5HXz1m04cmXibEknAk8KIQbIHo6VPITMP8MxqxxTfJrMUsqFyWNuwzEFPHo0ZfsqIIRIAZ4BfiClbOtpeT4NIcRZQJ2UcqUQYnpPy3OYaMA44AYp5TIhxB+AW4Dbe1asT0YIkYGzoywBWoCnhBDfkFL+8/Oes8cUuZRy1ie9J4S4Hng2qbg/FELYOAVm6o+WfN3xSTILIUbifClrhBDgmChWCSFOkFLWHEURD+LT7jOAEOIq4CxgZk9PlJ/CXqBov78Lk68d0wghdBwl/qiU8tmelucwOAn4mhDiDMAHpAkh/iml/EYPy/VpVAKVUsp9u52ncRT5scwsYKeUsh5ACPEscCLwuRX5sWpaWQDMABBCDAY8HMOVzaSU66SUuVLK/lLK/jgP17ieVuKHQghxOs42+mtSykhPy/MpLAdKhRAlQggPjmPo3z0s06cinBn978AmKeXve1qew0FKeauUsjD5DF8MvHGMK3GSv7E9Qoiy5EszgY09KNLhUAFMFkIEks/JTI7QQdtjK/JD8CDwoBBiPZAArjyGV4u9mT8BXuC15E7iAynldT0r0sFIKU0hxPeARTge/gellBt6WKxDcRJwObBOCPFR8rWfSSlf6kGZjlduAB5NTvI7gKt7WJ5PJWkCehpYhWPSXM0Rpuu7KfouLi4uvZxj1bTi4uLi4nKYuIrcxcXFpZfjKnIXFxeXXo6ryF1cXFx6Oa4id3FxcenluIrcxcXFpZfjKnIXFxeXXs7/B/jJ5VT7eqqZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ttQHaJi8sA8Y"
      },
      "source": [
        "- By inspecting the result obtained above it can be concluded that there seems to have some distribution of predicted molecular properties. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xjNlsTdtsT4B"
      },
      "source": [
        "**MODELING PROPERTIES FOR LATENT SPACE:**\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1YA9YXLPr1xi",
        "outputId": "b4fe90ca-fd58-476d-a43e-d7792673091d"
      },
      "source": [
        "#@ LATENT SPACE MODEL:\r\n",
        "x_train_latent = smiles_to_latent_model.predict(X_train)                        # Encoding the Smiles to Latent Space.\r\n",
        "logp_train = smiles_train.apply(Chem.MolFromSmiles).apply(Descriptors.MolLogP)\r\n",
        "logp_model = Sequential()                                                       # Initailizing the Sequential Model. \r\n",
        "logp_model.add(Dense(128, input_shape=(latent_dim, ), activation=\"relu\"))       # Adding the first Dense Layer.\r\n",
        "logp_model.add(Dense(128, activation=\"relu\"))                                   # Adding the second Dense Layer. \r\n",
        "logp_model.add(Dense(1))                                                        # Output Dense Layer.\r\n",
        "#@ COMPILING AND TRAINING THE MODEL:\r\n",
        "logp_model.compile(optimizer=\"adam\", loss=\"mse\")                                # Compiling the Model. \r\n",
        "rlr = ReduceLROnPlateau(monitor=\"loss\", factor=0.5, patience=10,\r\n",
        "                        min_lr=0.000001, verbose=1, min_delta=1e-5)             # Initializing the Callbacks.\r\n",
        "logp_model.fit(x_train_latent, logp_train, batch_size=128, epochs=400, \r\n",
        "               callbacks=[rlr])                                                 # Training the Model. "
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.3518\n",
            "Epoch 2/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.2067\n",
            "Epoch 3/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.1751\n",
            "Epoch 4/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.1585\n",
            "Epoch 5/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.1451\n",
            "Epoch 6/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.1387\n",
            "Epoch 7/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.1334\n",
            "Epoch 8/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.1284\n",
            "Epoch 9/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.1236\n",
            "Epoch 10/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.1216\n",
            "Epoch 11/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.1179\n",
            "Epoch 12/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.1158\n",
            "Epoch 13/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.1108\n",
            "Epoch 14/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.1066\n",
            "Epoch 15/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.1030\n",
            "Epoch 16/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.1007\n",
            "Epoch 17/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0998\n",
            "Epoch 18/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0965\n",
            "Epoch 19/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0966\n",
            "Epoch 20/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0916\n",
            "Epoch 21/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0924\n",
            "Epoch 22/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0915\n",
            "Epoch 23/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0886\n",
            "Epoch 24/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0869\n",
            "Epoch 25/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0834\n",
            "Epoch 26/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0855\n",
            "Epoch 27/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0859\n",
            "Epoch 28/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0826\n",
            "Epoch 29/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0811\n",
            "Epoch 30/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0815\n",
            "Epoch 31/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0773\n",
            "Epoch 32/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0765\n",
            "Epoch 33/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0781\n",
            "Epoch 34/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0781\n",
            "Epoch 35/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0752\n",
            "Epoch 36/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0734\n",
            "Epoch 37/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0759\n",
            "Epoch 38/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0731\n",
            "Epoch 39/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0724\n",
            "Epoch 40/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0692\n",
            "Epoch 41/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0716\n",
            "Epoch 42/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0714\n",
            "Epoch 43/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0690\n",
            "Epoch 44/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0690\n",
            "Epoch 45/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0681\n",
            "Epoch 46/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0678\n",
            "Epoch 47/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0674\n",
            "Epoch 48/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0660\n",
            "Epoch 49/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0667\n",
            "Epoch 50/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0678\n",
            "Epoch 51/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0648\n",
            "Epoch 52/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0635\n",
            "Epoch 53/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0658\n",
            "Epoch 54/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0640\n",
            "Epoch 55/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0649\n",
            "Epoch 56/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0641\n",
            "Epoch 57/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0629\n",
            "Epoch 58/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0632\n",
            "Epoch 59/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0606\n",
            "Epoch 60/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0596\n",
            "Epoch 61/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0601\n",
            "Epoch 62/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0618\n",
            "Epoch 63/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0639\n",
            "Epoch 64/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0576\n",
            "Epoch 65/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0594\n",
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            "\n",
            "Epoch 00168: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "Epoch 169/400\n",
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            "Epoch 273/400\n",
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            "Epoch 274/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0311\n",
            "Epoch 275/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0310\n",
            "Epoch 276/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0310\n",
            "Epoch 277/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0314\n",
            "Epoch 278/400\n",
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            "Epoch 279/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0306\n",
            "Epoch 280/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0312\n",
            "\n",
            "Epoch 00280: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "Epoch 281/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0283\n",
            "Epoch 282/400\n",
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            "Epoch 359/400\n",
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            "Epoch 373/400\n",
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            "Epoch 374/400\n",
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            "Epoch 375/400\n",
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            "Epoch 377/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0272\n",
            "\n",
            "Epoch 00377: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
            "Epoch 378/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0254\n",
            "Epoch 379/400\n",
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            "Epoch 380/400\n",
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            "Epoch 381/400\n",
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            "Epoch 382/400\n",
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            "Epoch 383/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0250\n",
            "Epoch 384/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0252\n",
            "Epoch 385/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0250\n",
            "Epoch 386/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0251\n",
            "Epoch 387/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0256\n",
            "Epoch 388/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0256\n",
            "Epoch 389/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0251\n",
            "\n",
            "Epoch 00389: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
            "Epoch 390/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0246\n",
            "Epoch 391/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0244\n",
            "Epoch 392/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0241\n",
            "Epoch 393/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0244\n",
            "Epoch 394/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0240\n",
            "Epoch 395/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0243\n",
            "Epoch 396/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0242\n",
            "Epoch 397/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0241\n",
            "Epoch 398/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0242\n",
            "Epoch 399/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0242\n",
            "Epoch 400/400\n",
            "391/391 [==============================] - 1s 2ms/step - loss: 0.0240\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fc7a4e3e898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wv1UncP3w5dH"
      },
      "source": [
        "**PLOTTING THE TRAINING PROCESS:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "zw-n-qHDuubZ",
        "outputId": "65faa765-1448-412e-ce5b-3050d2af74a3"
      },
      "source": [
        "#@ PLOTTING THE TRAINING PROCESS:\r\n",
        "logp_pred_train = logp_model.predict(x_train_latent)\r\n",
        "logp_pred_test = logp_model.predict(x_latent)\r\n",
        "plt.scatter(logp, logp_pred_test, label=\"Test\")                           # Plotting the Scatter Plot. \r\n",
        "plt.scatter(logp_train, logp_pred_train, label=\"Train\")                   # Plotting the Scatter Plot.\r\n",
        "plt.legend()\r\n",
        "plt.show()                                                                # Data Visualization."
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FN9URSfdyhcK"
      },
      "source": [
        "**LATENT SPACE TO SMILES:**\r\n",
        "- To compute the latent space I will compute the states using **Latent to States Model** and set the initial states of the decoder **LSTM** network. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgdT9rfpyPPu",
        "outputId": "c4f0115c-8fe1-4a51-8908-866ee09e07ac"
      },
      "source": [
        "#@ LATENT SPACE TO SMILES:\r\n",
        "def latent_to_smiles(latent):\r\n",
        "  states = latent_to_states_model.predict(latent)                           # Decoding the States.\r\n",
        "  model_s.layers[1].reset_states(states=[states[0], states[1]])             # Resetting the States of LSTM.\r\n",
        "  startidx = char_to_int[\"!\"]\r\n",
        "  samplevec = np.zeros((1, 1, 22))                                          # Initializing the arrays of zeros.\r\n",
        "  samplevec[0, 0, startidx] = 1                                             # Encoding.\r\n",
        "  smiles = \"\"\r\n",
        "  for i in range(28):\r\n",
        "    o = model_s.predict(samplevec)\r\n",
        "    sampleidx = np.argmax(o)\r\n",
        "    samplechar = int_to_char[sampleidx]                                     # Converting the Integers to Characters. \r\n",
        "    if samplechar != \"E\":\r\n",
        "      smiles = smiles + int_to_char[sampleidx]\r\n",
        "      samplevec = np.zeros((1, 1, 22))\r\n",
        "      samplevec[0, 0, sampleidx] = 1\r\n",
        "    else:\r\n",
        "      break\r\n",
        "  return smiles\r\n",
        "#@ LATENT SPACE TO SMILES:\r\n",
        "smiles = latent_to_smiles(x_latent[0:1])                                    # Invoking the Function.\r\n",
        "print(smiles, smiles_test.iloc[0])                                          # Inspecting the Result."
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NCCC1OC=NO1 NCCC1OC=NO1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHsKyX6i16N3"
      },
      "source": [
        "- I will inspect the fraction of the **SMILES** which are malformed when sampling the test set. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QcqifriS1RY8",
        "outputId": "f62ff414-7202-42c9-e8f2-de2e422b38bc"
      },
      "source": [
        "#@ INSPECTING THE SMILES:\r\n",
        "wrong = 0\r\n",
        "for i in range(1000):\r\n",
        "  smiles = latent_to_smiles(x_latent[i:i+1])                                      # Converting the Latent to Smiles. \r\n",
        "  mol = Chem.MolFromSmiles(smiles)\r\n",
        "  if mol:\r\n",
        "    pass\r\n",
        "  else:\r\n",
        "    print(smiles)\r\n",
        "    wrong += 1\r\n",
        "print(\"%0.1F percent smiles are wrong formatted\" % (wrong / float(1000)*100))     # Inspecting the result. "
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0 percent smiles are wrong formatted\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "atYN4Dbr5ksi"
      },
      "source": [
        "**INTERPOLATION TEST IN LATENT SPACE:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7RGuFl7S3IVG",
        "outputId": "6bad1b3e-f8fe-429f-f12c-3ea8a94c5855"
      },
      "source": [
        "#@ INTERPOLATION TEST IN LATENT SPACE:\r\n",
        "i = 0\r\n",
        "j = 2\r\n",
        "latent1 = x_latent[j:j+1]\r\n",
        "latent0 = x_latent[i:i+1]\r\n",
        "mols1 = []\r\n",
        "ratios = np.linspace(0, 1, 25)\r\n",
        "for r in ratios:\r\n",
        "  rlatent = (1.0 - r) * latent0 + r*latent1\r\n",
        "  smiles = latent_to_smiles(rlatent)                         # Converting the Latent Space to Smiles. \r\n",
        "  mol = Chem.MolFromSmiles(smiles)\r\n",
        "  if mol:\r\n",
        "    mols1.append(mol)\r\n",
        "  else:\r\n",
        "    print(smiles)\r\n",
        "Draw.MolsToGridImage(mols1, molsPerRow=5)                    # Data Visualization. "
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RDKit ERROR: [08:23:52] Explicit valence for atom # 7 F, 2, is greater than permitted\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "OCC1C=NNCF1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "RDKit ERROR: [08:23:53] Explicit valence for atom # 7 F, 2, is greater than permitted\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "OCC1C=NNCF1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "RDKit ERROR: [08:23:54] Explicit valence for atom # 7 O, 3, is greater than permitted\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "OCC1C=NNC=O1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x1000 at 0x7FC7E647B550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ElkeGbsP5tVx"
      },
      "source": [
        "**SAMPLE AROUND THE LATENT VECTOR:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "id": "OBctLU7F471U",
        "outputId": "a4e523fd-75d5-47d5-a082-342f5f06e3aa"
      },
      "source": [
        "#@ SAMPLE AROUND THE LATENT VECTOR:\r\n",
        "latent = x_latent[0:1]\r\n",
        "scale = 0.40\r\n",
        "mols = []\r\n",
        "for i in range(20):\r\n",
        "  latent_r = latent + scale*(np.random.randn(latent.shape[1]))\r\n",
        "  smiles = latent_to_smiles(latent_r)                             # Converting Latent Space to Smiles. \r\n",
        "  mol = Chem.MolFromSmiles(smiles)\r\n",
        "  if mol:\r\n",
        "    mols.append(mol)                                              # Appending to the empty list. \r\n",
        "  else:\r\n",
        "    print(smiles)\r\n",
        "Draw.MolsToGridImage(mols, molsPerRow=5)                          # Data Visualization. "
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RDKit ERROR: [08:32:03] SMILES Parse Error: ring closure 1 duplicates bond between atom 5 and atom 7 for input: 'NCCCOC1(O)O1'\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "NCCCOC1(O)O1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "RDKit ERROR: [08:32:05] SMILES Parse Error: duplicated ring closure 1 bonds atom 4 to itself for input: 'ONCCC11CN=O'\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ONCCC11CN=O\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "RDKit ERROR: [08:32:05] Explicit valence for atom # 5 O, 3, is greater than permitted\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "NCCC1CO=C1O\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "RDKit ERROR: [08:32:06] Explicit valence for atom # 7 O, 3, is greater than permitted\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "NCCC1ONC=O1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "RDKit ERROR: [08:32:08] Explicit valence for atom # 4 O, 3, is greater than permitted\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "NCCC1O(CO)O1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "RDKit ERROR: [08:32:10] Explicit valence for atom # 4 O, 3, is greater than permitted\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "NCCC1O(O)CO1\n",
            "NCCC1OCO1N\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "RDKit ERROR: [08:32:10] Explicit valence for atom # 6 O, 3, is greater than permitted\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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hO++8IyLPP/+8G0tKS0s7cODA2rVr3VbBmTNq3Dg1a5aaNEnNmqWUUlaruvVWDx9xT0lRkZHK3z8/sjdtqsxmdelWSrHk5uaaTCat84qMjMzIyHBasZedO3fuk08+mTJlyocffnjhwgUXXLH4Ll682LFjRxEZPHhwoR78Oo4/8cRH3bp99O67Tq3NQ1y4oBYuVC++qEJDla9vftsTUZUqqVGjdrz11lspKSlWq/U6Z/jss898fHwMBoN7Z+gtWbJEu+Pk2Jhl16OHElE//ZT/4fr1+f9JURZJSUl3XtotskqVKiaTaeXKle3btxcRPz8/k8mUlZXlrtomTJggItHR0Q4859GjR/38/Dp27HX69PX+T6Eo9FfFVKi/SklRjRrlDBmy2RnXch6vDe533HGHiNhnR0yfPl1EjG4aCLJarRaLpVKlSp07dxaR8PDwlJQUVxfx44+qQQMlourWVfPmqUcfVUqpVatURIRnBvczZ5TFotq0ye+AfH1VeLhKTFTFzpmF/fe//9VGrTp06LB7926HFnuFrVu3mkymkJAQbZhfRNq0abN9+3bnXbGkxowZIyLNmzc/e/Zs8b9q/vz5InLrrbde/weAByqqgc+frxo0UDk56sSJy8eXL1fdu19+oyiiAgNVz55q0iSVnKxyckpw3ZkzZxoMBoPBMG3aNMd9NyUzcuRIEZk0aZKTzh8To0TUc8/lf5iXp0JClIj6+28nXdCb2Wy2xMRE7YeXiNSsWTMmJib90vBgZmamyWTSZjW0adNmw4YNrq9ww4YNNWvWFJF33303NzfXUaf94IMPtHEER51Q1+ivnN1fvfhi/t9LXl6e9pPaPjtDF7w2uGv7by9ZsuTYsWN79+79+uuvReShhx5yfSV//PHH7bffrnXEHTt21Na3MRgMERERW7dudUUFR4+qyMj8/9N33qlSU5XVqoxGdc89qm9ftWePBwb3EydUUFB+yQ0bqsmT1aFDDjjttm3btFsxVapUmTt3rgPOWEBmZmZ8fLx9nExEevbs+f7777du3Vp7fDYhIcGxVyydjz/+WLttar8fVUxWq/WWW24RkcWLFzupNie5zg/C0FAVH3/FD8JVq/LfKIaGKpNJJSaqkry7KSwuLk77/z59+vQyfAeldPLkyQoVKvj4+Ozbt89Jl1ixQomotm0vHxk8WImoL75w0gW9k9WqZs+e07ZtW63rqFev3rvvvnvNO3Vr165t0aKFfeg9OzvbNRVu3LgxIiLCYDBozzKKSEhISGRkZGJiYtkTfLdu3UTkW56NUErRXzmzv1qz5u/Gje9pW6DDuv/++0Xk888/d9IVncERwb1QKyu0YombvPbaawMGDLj33nvr1q1bq1at9u3bjxkz5n//+58ra8jMzIyJidG6ufr16//www9KqZMnT5pMpooVK4qIj49PRETErl27nFWBzabi41WNGkpEBQUps1k55/ZTMV2zP7rmKIJSqm9fFR6uEhKU44Z1lFLq3Llzw4cP1/omk8nkkPtxu3btMplMNS9t8FalShWj0bh5c/7dt/Pnz4+8tMKZ0WjMKdEYiKOtW7dOuwnw5ZdfluLL33jjDREZMmSIwwtzqqIe4pg/X735purX74q2l51d1h9+hbz//vvaf/bS/Z2XxXvvvSci9913n/MuYV+Zwb4YSFycElFOeLTMO1mtKiFBtWypevR4XERuvvnm2NjYixcvXudLLl68aDKZtEnJ7dq1K7ialjOsXLmyb9++9nnS48ePf+WVV7QREE2DBg2ef/75devWle78+/fvNxgMQUFB58+fd2zlOkV/5bxLZGdnBwcHGwyGo5c6LO1uzyO6WnfBCcG90Iol7mCz2b744ovq1auLSMWKFbU/aCOgq121nl1ycnLLli21gGg0Ggste3Ls2DGTyVShQgUR8ff3j4yMTEtLc3AFe/ao8PD8Uev+/T3h1nVRwf3qUQSlSnaPr0RsNltsbKy/v7+I9OrV62hplx/Ly8tLSkoaMGCA4dJas9oq8tccJ7NYLNpbuB49ehx29CpXxXTq1CltyPzZZ58t3RmOHDkSEBDg5+d38OBBh5bmXNcZwXr7bTVlivrqK+feanr33XdFxNfX96uvvnLiZa6i3e358ccfnXqVu+9WImr27PwP//pLiaj69Z16TW+Qna1mzFBNmuR30t277545c2bxR9BXr16tPcZXoUIFs9nsjDnBycnJ2tr82j3D6OjoIwVWzktNTY2JiWnWrJk9wd98883R0dHJycklusrUqVNFZOTIkY4uX6/or5x6Fe1d6Hfffad9mJqaqt3jcupFHctBwb3g28NCK5a4XFpamn1XiHvuuWfv3r0ZGRmxsbHasoDaFPPff//deQWcOXMmOjpaGw5p3br1dZ5G3bdvn9Fo9PPzE5GAgACj0eiQSJebq47FfqMqVlQiqnZtz1nr6JoDCdccRXCBlStXasvZ3nTTTcVZnaagQ4cOmc3mm2++WWtRFSpUiIiIuOF7wt9//11b7Kh27dqOXUmtOKxW6z333CMiXbt2Lcvt9REjRojIxIkTHVibs13/B+Hp06pnT6e3PW2LE19f329c9f9x9erVIlK3bl1n3+SZMkWJqKio/A9tNlW3rhJx4w1XT5edrSwW1bBhfmRv0kRZLKW5tZiRkREdHa0NHHTt2tWBD9IkJSV17drVfgvRZDKdPn26qBenpqaaTKb69evbE3yrVq1iYmJ27NhRnGtpD8o7O67pCP2VUy80ZcoUEYm61GHZbLa6deuKyLZt25x6XQdywoh7oRVLRo1S8+aV/onCksjNzY2NjdUmkYeEhFgsloKfPX/+vNlsrlatmj2+O+MOY2JiorYrkL+/fzGf/d++ffvIkSO1oH/33ZNeeUUV3UPe2B9/qM6dVdc2560+fioiomTLrzhZUSPurhlFuNqhQ4fCwsK02aLFWcLWarUmJSVFRERo77VEpEWLFmazufi7O504cUJ7u69dsfgrupRRenr66NGj5dLk/oMHD5b60suWLdPGJ9w756dECjW87dvVmDFqzBgVF6feflsppaKjXdH2YmJitJ+FrpnLq/2Lv/rqq86+0LZtKjZWFfypN3KkElHue8LNc124oGJj85cJEFFt2qj4+LJOYFy8eHHDhg2128tms7ksz45rT8faH8qqVatWTEzMmTNnivO1Vqs1OTk5OjraPkZmT/B79uwp6qu2b9+u9UseskS9J6C/cqp169aJSLNmzexHHnzwQRH5+OOPnX1pR3FCcF+w4PKKJd275/dPnTurhQsdcK2i/fnnn/buJiIi4ngRq02fOnUqJiamcuXKcmmKeTFHBW7oyJEj2uRpEenWrduWLVtK9OV//vnnAw88XKXKWRFVrZqaPFkVsadQkTIy1IQJys9PiajGjdX+5U5cOKV0rtkf3XWXeu45F40iXE1bKVIbsnrooYeKWrcxPT09NjbWvodFQEBAREREUlJSKeJvXl6e/YqTnniixP/MJZSSkmI0GoODg/39/bU3h/ZvoUmTJuHh4Uaj0Ww2JyQkpKSkFHOhTO2G5rx585xaufNs3apOn1abN6sPP3T1pV977TXtXb2zxxfPnDkTFBRkMBic+AhN0SwWJaIiIlx/ZY/2ySeqZs3LPxIdOKJ19uxZo9Fo/+mzc+fOkp7BarUmJiZqg9/aXUGz2Vy6lXPz8vKSk5ONRmOVKlW0s/n4+ISFhcXGxh65ao/KiRMnisijWmbAtdBfOVZeXp42gPv3pfnDM2bMEJHhw4c7+9KO4pyHU+0rluzYoSyWy8MLd96pli51wBWvdPHixYLPgBYnTxw/ftw+xVyL79cZErghm80WHx+vzaQPDg4uy5jHunVqwID8v60aNZTZrIrZc65cqVq0UCLKx0cZjcozH/Ip1FK0/ujDD9X99yvlqlGEa5o3b562G1HLli0LvePSgq/2MLGINGjQICYm5tixY2W84vz582+pWzejZUvVvLn6668ynu1qFy5cmDlzZmhoqFa2wWC46667nnrqqYcffvjOO++sXbu2XIuvr2+jRo369OkzduxYs9k8Z86cjRs3Xj3epj291K9fP4eX7TKZmerxx1WZ/xlL49VXX9XeOCUmJjrj/Dk5OcnJyQMGDBCRsLAwZ1zihvbvV2+8oVy/5q3rleiB+9hYJaK6dSvTsrbXsXDhwgYNGohIUFBQ8X8M5eTkxMfHayvVSPGeji2mrKysxMTEyMhI7Ta41sNoCd6+qbn2kKuL143QHforxxo0aJCIzNLmhii1e/duEalRo4ZeVjp2yXKQ2oQ+bdqjiAoLUytX3virimfZMjV06EatR3j22WdL9Fj6/v37o6OjtUU2tCnmh0q+6ODu3bvtj+/cd999DlnGaM0a1bt3/t9WrVrKbFbXuYuYnq6MRmUw5C/KVton+93Djf1RIdu3b2/Tpo2IVK5cec6cOWfPnrVYLO3atbOPGIWHhyckJDjwCbCs3btVu3b5m2Q47l7k9u3bTSaT/YHsatWqGY3Gq+//ZGVl7dmzJykpyWKxmEymAQMGNGnSxD4FqJCQkJDQ0NCIiAiTyWSxWObNm1exYkWDwVCKgT1PkJ2toqKUG9ftffnll7U+Z8GCBQ45odVqTU1NtVgsERER2ltQEalVq1bVqlVXrFjhkEvgmkr0wP3Fi2rZMufWk56ebh9679u37/V/HmVnZ8fHx9ufLm3cuHFsbKwztna6ePFiYmJiRESEfSnJwMDAAQMGvP7661pDPXLkyGdsmloE+iuH01aneP311+1HtGUbNm7c6IKrl50L13G/cEGZzfmbc4io8HBVtv0jTp9Wjz+uDAbl46MGDXrTvtdSSf39999Go1Hb1SIoKCg6OrqYy4zk5uaazWZt2L5OnTrx2iO5jpOUpG6//fJa5td8eikxMf9+RoUKKiZGuWpJX8dwe39UyPnz57UnLw0Gg/bPKiL16tV77bXX9u/f75RLam9ctH9jo7Es/37Z2dkJCQnh4eGFlrgp0c3unJycnTt3Lly48KOPPnrhhRcGDx7cpk0b+92Gq+loUmBBcXFq8GBlMqn5891Ww4svvqjFl59//rnUJ0lNTf3ggw/uv/9+bQ8RjcFgaN26dVRUlLaPT2BgoH1gyWWKGnL2Ph71wH2BquZrT95XqVLFYrFcPaMvKyvLYrFoj2OJSKtWreLj4x24oVJRTp06NWPGjD59+mg/cLUxkaioKK3aUi8o6d3orxzu3LlzhabFPvbYYyLyzjvvOPvSDuHyDZjOnVNms6pSRYls/UdUREQpVx6wB1Z/f2UyOSCwbt26VdtdQkQqVapkMpns+9Vd06ZNm+zzECIiIk445xlQm03Nm6fats2Pds2bq48+uvwT8Y478o/36qX0OPTpCf3R1SwWS48ePapXrx4WFpaQkOCKRzDj4/OXAOrcuTSrdqalqZdfju/fX2uNlStXHj9+/J9//unAAk+fPp2SkpKQkGA2m41GY3h4eMOGDdnmsCxsNttTTz0lIhUrVlxakgmEhw4dSkhIMBqN2vOIdvXq1YuIiLBYLAcOHNBeqT1NoX3WaDS6IJbZFTXk7H087YF7u2PHjg0bNkz717/nnnvsreLChQuxsbH2FWDatm0bHx/vpO3lr+Pw4cP/+c9/7rzzzpUrVx48eFAb0x00aJCLy0AxeXd/ZbPZoqKiDAbDM88847KLloWbdk49dky98EKPxge0Hb/GjFHFX8T88GE1bNjlSTeO3Xt08+bNERERWtOpXr16TEzM1XvCZ2Rk2PedbtKkiQt2kbTZVEKCatFC1aqlEhIu/0QcOlT16qViY5VO5mXphtVqvc7aZ06xYYNq3FiJqJo1VTFbVF6e+ukn1b+/8vFRIhcrVuwZFjZ9+vRzTn7a1c6VHatXstls48eP1270LV++/DqvPHr0qPbDr3HjxgV/+NWpU0f74XedXSBmzJihzU/o16/f9QcjHOXECfcPObvMdYK7ux64LyghIaFGjRoiUq1atbi4uNjYWG3lOxHp0KFDQkKCyxa2KuSFF14IDAxMufQYxMmTJytVqmQwGFLKw4MR+uSt/dXu3bvDw8O1wf69njMB4LrcFNyVUkodO6ZMJhUYmD9wbjSq62/qom0DWr26ElFVqzoxsK5du9Y+bb1mzZpms9n+pM7y5cu1GYF+fn7R0dFFLULiDLm5asuWcvQTsdw5eVL165e/e3VMzPUa95EjymxWt9yS//41MFBFRKikJBfWCsfQRnpEJDg4uNDkznPnziUlJZlMptDQUPv0J+2OSnh4uNlsTklJKWbqWr16tfY4crNmzZy6VnFuroqNVZUqqTfecP+Qs2tcZ6Us5dYH7u0OHTp03333ablEa0Ldu3dftGiRe6vSpl4U3IP5hRde0G5fu7EqXJ/X9VeFFxB31/vYknJncNfs26eMxvxFDAMClNGorlowSimldu26/LzmgAHKSVOOC0pOTu7Zs6fW+G666ab//Oc/jz/+uNYi27dvv379eqdXcC2ecBMWzpKXp/71r/wHje+/X139tjA5WUVEKH///P8JzZops9mjlupHSVmt1lGjRolIlSpVli9fbv/hV3DtzuDgYPsPv9Kte7B///5OnTppNxKTnPMeLyVFdeyY3zBHjPCUIWcXc+PKfdf32WefTZs27b777rv+WKnLHD16VFv+b/PmzdqRI0eOVKxY0cfH5y8nrLIFR/Ga/mrTpk2dO3fWCr7OAuKeyf3BXbNtmxoxQrvnrypVUv/3f6rgFJX4+PyB+Xr11Pffu7Sw+fPnd+jQQUTsT6++9dZbbpwk4Dk3YeEsP/+sqldXvXurnBw1dqwKD1e9eql33lHNm+fHIn9/NXy4WrLENfuawdny8vIeeugh7Qee/YdfhQoV+vTp88Ybb/z6668O6XDOnz8/ZMgQrSsrznZjxXfxojKZlK9v/g4Sixbld1PKM4acXcxzVsrycNHR0SIycuRI+xFtFvUjjzzixqpwQ/rvry7aZzs3btzY7XefSsFTgrsmNVVFRCiDQVWrpr799vJTmHffrSpUUJGR6uRJN1Rls9kSEhImT578yiuv7N7t5l2NyvNPxHIkLU0dO6Z++kk99phSSq1alb9wZP36ymRyxf0muFZeXt7EiRPHjh3brVu31157benSpc7YSNJqtb7yyisGg+H5Dh1s48YpRzx4vXx5/jtKPz8VHe2hO0i4jKetlOXJDh8+XKFCBV9f3+2XVqjYv39/QECAr6+vo3ZFhJPoub+6YrZziRYQ9xyeFdw1v/2mvvuu8LoEZBWUOxMnKm2NUatVNWqkfvqprHujA0rNmz3bWr++ElE9e5ZlntXp05d3kGjfXrlp8qBn8cyVsjyWNmF69OjR9iNPPPGEiDz++OPuKwqexVH9lTp+/JWnntLuD3Ts2HFD2ZYjdy+DUko80oIFsnmzrFolX38t48bJ99+7uyDAxSZPlkaNZPRosdmkeXPZvdvdBcFb/PGHDB4s+/dLw4by44/SqVPJvlwp29fftJo8csdu36AgmTRJnntOiti8CyjS/v37mzVrZrVat23bpo2DpqWltWjRQtvcTdsTByhrfyUic+bIU0+trF//3l27XnzxxVdffdW+F5ge+dz4Je4TECC9esmiRe6uw5MsWCATJoiIpKbK8OHurgZOFRoqK1eKiKxZU5quCihKhw6ydq106SIHDkjPnjJvXgm+du9euecen1GPvFrns549ZdMmmTCB1I7SuPnmmx9++GGr1frOO+9oR5o0afLggw/m5ub+Mn26e2uDBylLf5WWJnffLSNGyIkTPWvX3p+aOnHiRF2ndvHw4C4iUVHy6afuLgJwi/79xd9f+veX118Xs9nd1cC71K8vK1fKqFFy4YIMGyYTJ8oN777m5cnbb0ubNrJ4sdSsOSqq4ooV0ry5S6qFl/q///s/Pz+/L774Yt++fdqRmP/7v5O33/5kbKwcOuTe2uBBStdfxcVJ+/aSlCQhIWKxyP/+V+PKpeV1ytODe0iItG/v7iI8zNy5cs89Mm6cu+uAs/n4iMUiCxfK4sXSpIm7q4HXCQyU+HiJjRWDQSZNkpEj5eLFIl/855/SrZu89JJcvCgREbJ1q0RGurBWeKdbb711xIgRubm57777bv6RFi1qNG4s2dlyaRgeEClVf/Xcc3LhgkREyI4dYjRKgQXmdc1z57jjmhYskBUr5J13JDVVJk5k6j+AMlu4UB58UM6elQ4d5Kef5Oabr/hsZqZMmiTvvit5eXLLLTJ9uvTr56ZC4YW2bNny/WOPPZ+XV2XBAqlXTzsk7dpJQICkpeUfAexu2F9NnSr/+Y/k5nprf+W5I+5M5gYAV+jfX1avliZN5I8/pFs3OXv28qdWrpQOHWTqVLHZxGiUv/7yvp+CcK/WrVvHNGxYZeNGee89+yEZOFCysiQuzq2lwSPdsL+aNEmsVi/ur3ikCADKvTZt5PffJSJCevSQypXFaJS9eyUvT0JCZOdOaddOZs6UO+5wd5XwUq+9Jj/8IJ98Ii+9JLVqiYjExEhionz0kUyYIDVrurs+eJii+qvq1ctDf+W5U2UWLJBnnpEWLeTCBalblzkhAOBkubni5yfz58tPP8lnn0lyskyZInffLU8/zcIxcK6BA2XBAnn1VXnzzfwj/fvLokXy73/LpElurQyeqrz2V547VUZEhg2TRYuEVaEAwBX8/cVgkE2bpGdPEZGwMNm+nUXa4Qr/+peIyEcfSXp6/pGYGBGRuDg5c8ZdRcGjldf+yqODOwDA1QyGy0ut+fAzAi5xxx3St6+cOycffph/pGtX6dNHzp6Vjz92a2XwbOWvvyoX3yQAoLjY+QtuMXGiiEhcnJw/n3/ktddERN5///IRoJDy11957hx3AIAb2Gwyfrzs3y9Wq0yfzh4CcJ2ePWXVKjGbxWTKP9Kjh6xeLW+/nb/MHFBI+euvCO4AAMADLFkifftKzZqyd69UqiQikpQk8+fLiy9Kw4buLg7wCAR3AADgGbp3lzVr5L335Pnn3V0K4ImY4w4AADzDK69IlSrCkCJQBEbcAQCAZ1BKzp2TqlVFRGw2GTcuf2+d6dOlRQt3Fwe4HyPuAADAMxgM+aldRBYsEKtVkpJk8uTLj6sC5RvBHQAAeJ6Ce+ts3uzuagCPQHAHAACep/ztrQPcEP8TAACA5yl/e+sAN8TDqQAAwPOUv711gBsiuAMAAAA6wFQZAAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADB3RFsNjEapW9f6d1bduxwdzXwFrQrAABQgJ+7C/AKCxaI1SpJSZKcLE8/LV26SNWqUq3a5V/2DwMC3F0r9KOY7apqVQkMdHet0BObzfbvf/973759VatWrVatmva7nf1gIO0KgLvRXxVCcHeETZukZ08RkbAw+esvWbKkyFdWrHiw+8i+Bz4vGLpCQi7/uVo1ufVW2bVLxo2TvXvl7FkZN06+/95l3wk8SUnalVStar3ppjBfX3tHFhISUrBTs/9er149Hx9utZVf2dnZt9122/nz50+ePHn9V350111PbdlyxVvEatWu0WE1a5bfYeXlyfTp0qKFa74RAF6P/upqBHdHMBhEqfw/BwTI5Mly9qycOZP/e3r65T9nZmZk+mzffr2TPf209OsndevKt9/Kvfe6oHp4qhu2K/uHmZmSmWnz91934MANz7p8+fJu3boFcPOnvPrhhx/27t3brFmzd9999+zZs2fOnCn4e3p6uv3DallZcvSoHD16vdM9/bT07Xv51pDJJD/+6KLvBIC3o7+6GsHdEUJDZe5cGT1a1qyRrl3lX/8q8pUZGfXPqS0Fkrz2u/ZL+3PbtiIiQ4fKN98Q3Mu3krQrOXvWevbsr2fO2Hu0guxH0tLSevfuvWLFip7aWD7Kn08//VREXnjhhVGjRt3gpRkZV/RT9t8LDka0bXvFraHRo53/HQAoL+ivrkZwL7ODB6VqVfH3l/79xWqV6dOv9+Lg4MrB0qreDU65YIEEBEivXrJokQMLha6UsF1JcHCF+vXvvNFZn3vuubi4uOXLlxPcy6e0tLQVK1ZUrFhx5MiRN351cLAEB0v9+jd42eTJl28NMQsLgIPQX12TLov2LNOmSY8eUqOGLFwoixdLkyaOOnFUlHz6qaNOBr1xTrvq06ePiCxbtswhZ4PuzJw502azjRw5slq1ag47aWiorFwpIrJmjXTq5LDTAijf6K+uiRH3ssnLk/h4EZGBAx1+7qVL5dQpcWBzhW44rV317NnT19d33bp1GRkZwcHBjj05PFxeXt6XX34pImPHjnXkefv3l8RE6dhRTp6UhQsdeWYA5RX9VVEYcS+b+fPl8GG57Ta584aTFEpgwACZMEHefltSU2X8eAeeGDrhuHaVlZX1888/WywW7cOqVauGhobm5OSsXr26zFVCZ+bPn3/48OHbbrvtTof2V+LjIxaLBATIwYNy6JAjzwygvKK/KgrBvWxmzhQRiYpyxrn79BERWb7cGeeGZ3Ncuzp37tzAgQNfeOGF7Oxs7Yg2W2Y5Dav8mTlzpohEOae/osMC4ED0V0UhuJfBwYOyeLEEBsrDDzvj9Fq7WrrUGeeGB3Nou6pdu3arVq0uXry4fv167Ujv3r2Fae7lz8GDBxcvXhwYGPiwc/orOiwAjkJ/dR0E9zL49FOxWmXYMKlZ0xmnDwuTwEDZtEnS051xengqR7eru+66Swok9e7duwcGBm7cuDGdhlWefPrpp1arddiwYTWd01/RYQFwFPqr6yC4l5bNJrNmiYg47rGJPXvk3nvlvvvyPwwKkq5dxWrNfwAa5YIT2pU2xG6fGxMUFNSlSxer1bpq1SpHXQIezmazzZo1Sxz+mFdBdFgAHIH+6vp0ENxtNlteXp67q7jKokWyb5/ceqs4bj3sGjVk8WJZskQyMvKP9O4totdZWJ6u/LSrXr16+fr6rl27NuNSwyoU5eFAntmuFi1atG/fviZNmjh3/X46LLjV6dOno6KiPv7448jIyL///tvd5egA/ZUe+yvPDe5paWkzZswYMWJE7dq133jjjbffftvdFV1Je3zQaBSDwVGnrFZNOnaUnBz59df8I9osLGYjO1C5bFfVOnbsmJOT8+ulhqVNnlmxQn8jDR7Lw9uVtvug0Wg0OK5dXQMdlsvYbGI0St++0ru37Njh3loOHz78888/f/755ydPnnRXDUqpL7/8smXLljNmzHj55Ze//vrrli1bvvDCC6dOnXJXSZ6M/kpEz/2V8iR79+797LPPHnnkkXr1rthcNCAgQESmTp3q7gIvOXJE+fsrPz91+LBjT/zSS0pEvfJK/ofZ2So4WBkM6sgRx16nfKFdvfTSSyLy8ssvax9mZ2eHhS0MDr5w9Khjr1O+6KVdHT161N/f38/P77Cj21Vh3t1hWa1q7FgVHq569VLbt7u5mJ9+Uo89ppRSq1apwYPdVYXVarVYLFWqVAkKCjIYDJUqVTKZTOfOnXNxGTt37gwPD9f+9/3jH/9YunSp0Wj09fUVEXeV5IHorwrTbX/l/uB+5Ij6/vsVTzzxRJMr94asW7fuQw89NHPmzD179syePVv7T2g2m91dr1JKzYmL+/Mf/8iOjHT4mRcuVCLqjjsuH+nXT4mob75x+KW8HO2qoCVL1vTsGT1y5Er7Ea1dffutwy/l5fTYrqZMmSIiQ4cOdcXFvLjDujor//ST6tpVrVrlhmImTlTx8UopZbWqxo3dUIBS69at63Rp48nw8HBtnVkRqV+//owZM3Jzc11Qw8WLF2NiYgIDA0WkevXqFovFZrNpn9qyZUtERIS9JIvF4pqSPAr91Q3os79yT3A/f14lJSmTSYWGKoNB9eyZf5umUqVK4eHhZrM5JSXF/t9P88UXX/j4+IjIW2+95Zaa7Ww2W9OmTUVk0aJFDj95RoYKDFS+vio9Pf/I1KlKRI0d6/BLeSHaVVGubldmsxJRRqPDL+WF9N6umjVrJiILFy50xfW8uMO6Oiv37KlElMGghg513Rj8kiVq1iw1aZKaNetyMf/4h0pIUFc2QudJT0+Pjo7W0l6DBg2+//577fiaNWu6d++u/e9o0aJFQkKCzZklLViwoHHjxiJiMBgiIyNPnjx59WvWrFkTFhbmspI8Af1VCeizv3JdcL9wQS1cqF58UYWGKl9fJZL/q1IlNWrUjrfeeislJcVqtV7nDJ999pmPj4/BYPjoo49cVvbVlixZIiINGzbMy8tzxvl79FAi6qef8j9MSVGNGuUMGbLZGdfyArSrYirUrtavVyKqaVNnXMobeE27Wrp0qVPbVWEpKTmNGm0eMsQV13Kxgln51luVUur8eWU2qypVlIjy8VGRkerQIScWcOSIeuih/FY4a5Z69FGllFq1SrVvn98677xTJSc7sQCllFKJiYk33XSTiPj7+0dHR58/f/7qF2jZS0S6dOmyYsUKh9dw6NChyMhI7RLt27f/9ddfr/Nim802e/ZsbVhERMLCwn799Q+Hl+Re9FelpM/+qpTBff589c9/KqXUX3+pYcOuON6ggcrJUSdOXD6+fLnq3l35+19uTIGBqmdPNWmSSk5WOTkluO7MmTMNBoPBYJg2bVrpKi+7kSNHisikSZOcdP6YGCWiXnwx/+8lLy8vJCRERPbu3eukK3oO2pWz29Vzz+V/mJenQkKUiPr7bydd0IPQrpzXrgrx5v5qwYLLWTki4vLxEyeUyaQCApSICgpSJpM6c8bBl7ZaVXy8qlFDiaiKFVVMjLp4URmN6p57VN++ascOZbGoevXy22t4uPrDKcF0165dd999txZ/e/TokZqaWtQrc3JyLBaLfS51eHj4n3/+6ZAacnNzY2NjK1euLCLBwcFms7mYE2C0kurWrevj49Oy5YbwcOWgihyP/or+6vocH9xDQ1V8/BUNa9UqJaJ8fVVoqDKZVGKiOnu29BXHxcVpt8amT59e+rOU1smTJytUqODj47Nv3z4nXWLNmr8bN76nbdu29iP333+/iHz++edOuqLnoF05r12tWKFEVIFmpQYPViLqiy+cdEEPQrtyXru6mtf2V1br5ax89RDvjh0qIkIZDEpE1aihzGaVmemY627apLp2zQ9lAwaoohLGhQvKbFZVq+YP/0dEFPnKksvMzCw4lTw2Nvb6w7eXKrpgNpurVKkiIj4+PhEREWWMRykpKZ07d9beDAwYMKAUrfr8+fPvvjunUiUlovz81BNPqIMHy1KRU9Bf0V9dX+mD+y23qH79VFhY4Yb15puqX78rGlZ2dlkbUyHvv/++1hF8+eWXDjtp8bz33nsict999znvEtnZ2cHBwQaD4eilJT8++OADEXnkkUecd1EPQbty3iXsD9DbV5KJi1MiygmPwnoc2pUrL+r9/dXp06p6dRUerjZsKPypdetU797auPj9nQ9YLKpMN/wvXFAmU/7Uh/r182fYX9/Jk8pkUoGBSkQFBKjo6MvPtZTWsmXLWrZsaZ9KfuLEiYKf3blz5/Dhw48fP17Ul584ccJkMmmhPyAgIDo6Or3kJWmz6rV52E2aNPnll19K8Y0UKOnyX5J2j6TMf0mORH/lyovqsb9y/Ij722+rKVPUV19dcdzh3n33XRHx9fX96quvnHiZq7Ru3VpEfvzxR6depW/fviLy3XffaR+mpqaKSL169Zx6UU9Au3LqVe6+W4mo2bPzP/zrr/w84PVoV668qPf3V8uWqWrV8ge2H3nkGgPb8+evGPmJNkTeqZNavLg0Fzk9d1n+7Bc/PzVhgrpwoQRfvGuXeuCB/OH/6tWz4+KysrJKUcPhw4ftU8nbtWu3Zs2agp/NzMycOHFihQoVROTJJ5+8/qn+/vtvo9GoJe/q1aubzebMYt+RSEhIqF27tn1W/YUS/VVcryQVGWn/S3LkPZIyor9y5UX12F85JbifPq169nRuw1JKaVsG+Pr6fuOqpXxWr16traOUU6KJYyWnLYcUFRWlfWiz2erWrSsi27Ztc+p13Y525dQLTZmiRNSlZqVsNlW3rhJx/5rUzka7cs0VNeWivzp1SplMqkIFJaL8/ZXRqK7cE8FqVQkJqnHjy9POrx6dL8ru3eqee1Tn2y7k+Qaozp3V77+Xssjff1d9+iiRxb163XTTTRaLpfgP/NkXaBeRoKCgmJiY7Ozsgi9Yvnz5bbfdZh+Gv86I+5UV/W5fNbJhw4Y3LKnQAu1btmwpZv3Ft3699pekRFTDhqqsN0kcgf7KNVfU6LG/cszOqTt2yKOPyqOPSlqaiEhIiLRv75ATX8+ECRNiYmKsVmtkZOTs2bOdfj2RmTNnishjjz3m7+/v1AtpXduySxt6GQwGbY/6ZXrc4qsMaFeOdf/9Ehsrzz2X/6HBIL16iehz57iyKCftStt98NFHH3V2uyqkXPRX1auL2Sw7d4rRKDabzJghTZvKyy/L+fPa5318JCJCtm8Xi0Vq1ZIlS6RzZxkxQvbsud5Zs7Nl8mRp00YWLZK9x4N3fblW1q+XS7O6S6xzZ1m6VC1cOCUz8+DBg1FRUZ06dVq4cOENv27jxo133nlnVFTUuXPnBgwYsHXr1okTJ2rb9IjI0aNHR40a1adPn23btjVv3jwpKenLL7+sVatW8SrqvHTp0qSkpI4dOx44cCAqKqpdu3Zz5sy5+pXacH7btm2XLFmiLdC+YsWKVq1alegvoDhuv12WLpVffpF27eTAAYmKkjvukHnz5KabJDdXTp6U4cNlwQKZMEFEJDVVhg93eAk3QH/lVLrsrxwS/7duVadPq82b1YcfOuR8JfDaa6+JiL+/v7Nvr5w5c0bbHG7Xrl1OvZBSKi8vr1q1aiLy96UlP2bMmCEiw4cPd/alPQrtytksFiVyxQoZ5UE5aVfaozJuaVflq7/680/Vv3/+mG39+he+SCi0zMnp08pkUhUrFjU6n2/FCnXbbfnrwkdGqmPHHFagzWZLSEiwb8HTrVu35CJWjSxqgXaN1WqNj4+vUaOGFDEM76iSirNAu8NpN0maNFGPPVb4MdCihsBdg/7K2XTXXzlsHffMTPX4447sa4rv1VdfFZGAgIDExERnnD8nJyc5OXnAgAEiEhYW5oxLXG3QoEEiMktbNlip3bt3i0iNGjWK8yy/N6FdOdX+/eqNN1RKilsu7k60K6cqj/3VmjUqLEyJfNjtmxYtrrEb0r59avRo5eOjRNTHH1/xqSNHLk+2bt5cLVnilAKzs7MtFos2WVxEBgwYsHPnzoIvSExMbNCggRSxQPumTZu6dOli/1qHLJ93dUmrV68u/gLtzpCZqU6fLvwYaFFPi7qyKvor59Fdf+WY4J6draKiHLj2VIm9/PLLWttasGCBQ05otVpTU1MtFktERETVqlW1fqRWrVpVq1Z1xnYSV4uNjfX393/99dftR2655RYR2bhxowuu7iFoV3AG2pULlMP+Stlsmd8vaNncqg2+d+umVq8u/JLNm9XYsZdX1756ffbSDmEX1/nz581ms7YOur+/v9FoPHz48PUXaD9z5kzBYfj44ixuUxLp6ekvv/xyxYoVRSQ4OFhEKleuHBsb66IteK6l0GOg7h1xp79yAX31V44J7nFxavBgZTKp+fMdcr7SePHFF0UkMDDw559/LvVJUlNTP/jgg/vvv19bk19jMBhat24dFRV1xx13aJewD4Q7z7lz5wo9O//YY4+JyDvvvOPsS3sO2pWzXXNHD69Hu3KBcthfaXJylMWS/9i39lhqoY1+Cv6n69Qp/2UDB7p0K7SDBw8+8cQTWhavWLGiNqu4Vq1as2bNsl15p8C+T6qfn190dPS5c+ecV9Ljjz/+7rvvjhw58qC7F1cv9Bioe4M7/ZUL6Ku/cthUGbez2WxPPfWU1g0tXbq0+F946NChhIQEo9HYsGFDKaBevXoREREWi+XAgQPaK/Py8kwmk/ZZo9FYzA3bHMJms0VFRRkMhmeeecZlF4Xy9nZ1zR094ALe3a7or7TdkKpUucZuSAX/0w0Zovr1U65d/u6y7du3R0RENGjQoHHjxlcv0L579+5+/fpp7ScsLGzz5s3uqdIdtOCulIqOvkZw375djRmjxoxROhmfdQD6K4/iPcFdKWWz2caPHy8iQUFBy5cvv84rjx49qjUm7QkYuzp16miNKS0traivnTFjhvZ8fb9+/UqxkUQp7N69W1sSKzAwUF8b83oHb21XJ05ce0cPuIa3tiv6K7ujR9WTT+ZvRx8UpF55ReXledx/utOnTxcaR8/JyTGbzdoC7SEhIcXcJ7X8cOPTom5Ef+U5vCq4q0vvnEQkODi40GSpc+fOJSUlmUym0NBQg8Fgb0yVK1cODw83m80pKSmF7hIWZfXq1drzNM2aNXPq2p+5ubmxsbGVKlXSOlCLxVLMCuFYXteuVGysqlRJvfGGi3b0wDV5Xbuiv7oG+0Y//fsr5cJtdErt3LlzDRo0KNEC7eWNG58WdSP6Kw/hbcFdKWW1WkeNGiUiVapUWb58ub0xaXu2aYKDg+2NqXRjCfv37+/UqZOIVK9ePSkpyeHfhVJq06ZNnS+t4BsREUEH6l5e065SUlTHjvkza0eMcN2OHrgmr2lX9FfXt26d0kKIK7fRKbVFixYVtWQk3P60qBvRX3kCLwzuSqm8vLyHHnpILj2irqlQoUKfPn3eeOONX3/91SHTp86fPz9kyBAR8fX1NZvNZT+h3cWLF00mk/bkUOPGjRctWuTAk6PU9N+ulMmkfH2ViGrcWC1aVHgqJ9xC/+2K/qoE+E+nd57wtKgb0V+5nXcGd6VUXl7exIkTx44d261bt9dee23p0qWZmZkOv4rVan3llVcMBsPzHTrYxo1Tjtiqd/ny5c2aNZNLT/EXWkwX7qXndqWaN1ciys9PRUcrmpVH0XO7or8Cyhf6K/fy2uDuSvNmz7bWr69EVM+e6soH80vm+PFXnnpKe//asWPHDRs2OK5G6I+j2tXp08pozN/epX17tX69A2uE/tBfAdAL+qurEdwdZNMmdfPNSkQ1bKhK1yASElStWivaty/jVtLwKmVsVzab9cuvWzTN0xa1ePtt5cIltuDB6K8A6AX91ZUI7o5z6JDq0kWJqEqV1A8/lOAL9+xRffvmPy3Yt+/JoldKQnlU6naVlqbuvluJxIdZevZUO3Y4rULoEf0VAL2gvyqA4O5QWVlq1CglogwGFROjbri0kH1ZPhEVEqIslht/CcqhUrSrt95SQUFKRNWsqb780iVVQm/orwDoBf3VJQR3J4iNVT4++YvtZWQU+bI//lC3357/RjAiQulqNSK4Ae0KzkC7AqAX9FcEd2f55RdVtaoSUR06qH37Cn/24kUVE5O/n94ttygdrkYE97hhuzKZlJ8f7QolQ38FQC/KfX9lUEoJnCE1VQYPlrQ0adBAtmyRqlXzj69cKUaj7NwpPj7yxBPy7rtSqZJbC4Wu0K7gDLQrAHpRvvsrP3cX4L3atJHff5eICOnRQypXFqNR9u6VvDypXl127pR27WTmTLnjDndXCb0pql2FhNCuUHr0VwD0onz3V4y4O1lurvj5yfz58tNP8tlnkpwsU6bI3XfL00+LH++aUFq0KzgD7QqAXpTX/sqbvzeP4O8vIrJpk/TsKSISFibbt8svv7i3KOge7QrOQLsCoBfltb/ycXcB5YPBIPY7Gz78ncNBaFdwBtoVAL0of/1Vufgm3S80VFauFBFZs0Y6dXJ3NfAWtCs4A+0KgF6Uv/6KOe4uYbPJ+PGyf79YrTJ9ujRp4u6C4BVoV3AG2hUAvSh//RXBHQAAANABpsoAAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDAAAAOkBwBwAAAHSA4A4AAADoAMEdAAAA0AGCOwAAAKADBHcAAABABwjuAAAAgA4Q3AEAAAAdILgDQLmTmZnp7hIAACVGcAeAciQ1NfXDDz+sUaPGwIED58yZk5OT4+6KAADFZVBKubsGAICLGI3GmTNnGgz5nX/16tWHDh06cuTIXr16+fr6urs6AMD1ENwBoLzIzc2tV6/eqVOnkpKStmzZMmfOnDVr1mifqlGjxrBhwyIjI8PCwgwGg3vrBABcE8EdAMqLBQsWDBw4sF27dn/++ad2ZOvWrbNnz549e/auXbu0I40aNXrggQeefPjhRu3aua9SAMA1ENwBoLx45JFH/vvf//7nP/955ZVXCn1KG4D/+uuv9+zZIyLpHTpUO3NGHnhAxoyRli3dUSwAoDCCOwCUC1lZWXXq1Dl37tyuXbuaNm16zdfYbLZff/114dy5b373nRw5kn+0Y0cZOVIeeEAaNXJduQCAq7hhVZkFC2TCBBGR1FQZPvzywZtuktxcOXny8kEAJWOzidEofftK796yY4e7q4FnmT9//rlz57p06VJUahcRHx+f7t27v/n++3LwoCQnS3S01KolmzaJySS33CKtW8vUqZcDPQDAtTxoOci6deXbbwsfTEuTEyfcUQ2gRwsWiNUqSUkyebKYTO6uBp5l9uzZIjJy5MhivdrHR7p3l7g4OXBAfvpJHnxQgoNl61Z5+WVp2FDuuktmzeKNIgC4mJ9brjp3rqSmyoULUrfu5YNDh8o338i9917xykcflVWrpEIFadJEWreWJk0u/2rcWFj5ALjCpk3Ss6eISFiYjB4tf/whlSvLrbe6uyy43/nz5xcuXOjj4xMREVGyrwwMlEGDZNAgycqSpCSZM0fmzpVly6RiRalePf+NYnKymEzy449OKR0AcIl7gvuwYfLOO5KaKhMnXj4YECC9esmiRVe8skoVCQmR9HTZulW2br3iU5Ury+89XmgRfFCaNpVbb5WmTaVZM6lf//IrbDYZN0727pW8PJk+XVq0cOK3BHgCg0HsT634+Mgzz8ivv8q998qzz0p4uFsrg5vNmzcvMzOzV69eDRo0KOUpKlSQgQNl4ED58EOZN08aNpTVq694owgAcDL3BPeCduwQs1kOHJC2beXf/5YhQ6RmzcufnT9fRCQ9XdLSCv/au1carPxGMo5dcbqgIGnaNP9X586MBqF8CQ2VuXNl9GhZs0Y6dJCqVeX332XBAlmwQEJD5fnnZcQI8fd3d5Vwg5LNk7m+qlVlzBgRkTVrrnijCABwMvcHd5tN3ntP/vtfWbpUQkKkfXs5dKjwa0JCJDRUQkOvOHj2jKq0a77s2SO7d8uuXbJ7t+zeLcePy+bNsnmzBAaKycRoEMoXpcTHR/r3F6tVpk+XJk1kyhT54gv58EPZsEEeeUQmTJCoKHnmGalRw921wnVOnz69dOlSPz+/IUOGOPK8Bd8odurkyDMDAK7FI5aDzMqSp5+W//xHatcu87nOnctP8KdOyYkT0qiRjB4tNps0by67dzugVsBj/fWXtG8vTZrIjh1SaO/6rCz5738lNlZSU0VEgoNl1Kis51+p0KyhWyqFi1kslnHjxvXv3/+XX35x2EmtVsnJkeeek/37L79RBAA4k/tvbmo9/2uvOSK1i0iVKtKpk4wYIePHS2iorFwpIowGoVx4/31RSgYMKJzaRaRCBXn8cfnrL0lOlgED5OJF+eSTsEE1+vaV+fPFA968w7kcOU/GbskSadBAGjSQhQtl8WJSOwC4gPtH3D/4QJYtk5YtpXt3GTDAoae22WT8eEaDUC4cPy6NGklOjuzceeNlZLZt2xX/a/sPHs/MFBHp0EGee04efFACAlxQKFztyJEjDRs29Pf3P3r0aNWqVR123jFjJD5eJk+Wf/3LYecEAFyX+4O7K3z/vXzxhXz7rVSp4u5SAOeIiZHJk2XIEPnhh2J+xdmzMmuWvPOOHDwoIlKnjowbJ08/fcXT4fAC77///gsvvDBs2LDvv//eYSfNypK6deXsWdmxQ5o3d9hpAQDX5f6pMq4wbZr88ov897/urgPXwE66DpCdLRaLiMjzzxf/i6pWlWeflT17JCFB7rhDjh2TSZPkpptk1CjZssVZlcL1vvvuOxF54IEHHHnSX36Rs2elc2dSOwC4UvkI7lFRIiLTp7u7DpTANXfSxbV99ZUcOyahodKjR0m/NCBAIiJk3TpZulQGDJDcXPnqK2nfXmbNuuK90zXfX8HzpaWlrV+/vnLlyvfdd58jz/vddyIijn0zAAC4EfcvB+kKQ4ZInTqyebOsWydduri7mstyc3NPnz79+eefN72kcuXK7i7KDYq/ky6u7cMPRUReeKEs5+jTR/r0kZ07JTZWTp2SmjXz3zvxT6Brs2fPVkoNHjw4KCjIYSfNyJCffxaDQUq6CSsAoGzKR3APCJAxY2TqVLFYPCq4v/jii8uWLfvrr7/sR0JCQpoU0KpVqzZt2lSrVs19NbpC8XfSxTX873+yebM0aOCQFNW8uUybJiKyYEHh907XfH8FD+eUeTI//igZGdKjhzRq5MjTAgBupHwEdxExGuXtt2X2bHn3XQkJcXc1IiKzZ8+Oi4vz9/d/+OGHL168uHv37t27d6enp2/YsGHDhg0FX3nrrc1r1drRtKk0a3Z5W9jq1d1VuOtERRXeSRfX8P77IiLPPOPwLVELvXe65vsreLLt27dv3rw5JCTk7rvvduR5Z88WEXHs4pIAgGIoN8G9SRO56y5JSpKvv5ZnnnF3NbJ9+3aj0SgisbGxTz75pP14enp6WlpaWlrali1btm7dmpaWtmvXrgoVQn77TX777YozVK+en+Dtaf7ECRk/XvbulbNnZdw4GTNGVqy4nLQcuJ6EU+3YIWazHDggbdteYyfdL7+UVq2kc2f31edptmyRxYslKEieeMIZp+e9k6598803IjJ8+PAAB670mZ4uixeLr68MG+awcwIAiqfcBHcRiYqSpCT55BN5+mkxGNxYyPnz54cOHXr+/PmHH364YGoXkZCQkNDQ0NDQ0IgC0x6OHTu3c2f+hrD2X6dPy/r1sn59/muCg+Xbb3U5KXnAgPz1+9u0ke+/l23b5L335ODB/L2z4uIuvzIlRZ54Qnx9ZcYMiYx0T7WeZsK0aR26dx/YvXvVGjUceNodO+S336Rq1Wu8d0JRVq9e/cYbb/Tv3//QoUOTJ0+uUKGCuyvKnyfj4H2X5s6VnBy5+26pU8eRpwUAFEP5WMddk5cnjRrJ4cOyerWEhbmrCqVURETE3Llz27Vrt3bt2lI/MXb8uOzadTnH+/jIgw/K5s2yapV8/XX+iPszz0iLFvmTkvUy4i4iWVny9NPyn/8U3kw3L09ee02mThURMRrlo48cPjdEZ06cONGoUaOsrKytW7e2bNnSgWd+/HH5/HP5979l0iQHntVrLV269I033lixYoWIBAcHZ2RktGrVatasWbfffrsbq0pJSbn99tvr1q178OBB36s30y2t48OG1f7hB/nsM3nsMUedEwBQTOVjOUiNn588+qiI5K947SZTpkyZO3duSEjIDz/8UJZ1HmrXlrAwGT1aXn9dvv02f5H6qyclL1qks2Uwc3LkuefktdcKp3YR8fMTs1ksFgkIkBkzJDxcjh93XWGnT5/2tHe5n3zySWZm5sCBAx2b2o8fl2++ER8fGTXKgWf1TkuWLOnWrVt4ePiKFSuqVKliMpl++eWX9u3bb926tVu3bi+//HJ2dra7atOG20eMGOHA1H7s2LEGiYm9O3a0DRniqHMCAIqvPAV3ERk7Vnx85KefMk+fdsv1ly5d+u9//9vHx+err7669Yb70pdKVJR8+uk1ju/YIY8+Ko8+Kps2OeOyDjN9uhw9KtOny4IF136B0SjLl0u9erJqlXTuLFc+x+t4R48ejYuLa9eu3aBBgzp27Lhs2TLnXq/YsrOzP/nkExF5viSbLhXHxx9LVpYMHizOaaHeQCk1f/78Ll269O3bd+3atTVr1oyJidm3b5/ZbP7HP/7x22+/mUwmpdTUqVM7d+68ceNG11e4cePGWbNmiUijRo3y8vIcddqEhIS8vLyqN9/s4xmP+ANAuaPKmZVPPtmqZs333nvP9Zfev39/rVq1RCQmJsYZ558/X739tlJKRUerYcPU/Pnqn/9USqm//lLDhqmtW9Xp02rzZvXhh864uKsdOqS6dFEiqkIF9dVXjj9/bm7uTz/9NGjQID+//OdAtCX2/f39P/roI8dfr+Q+//xzEWnXrp1jT5uVperUUSJq1SrHnthLWK1q9uw5bdu21VpFvXr13n333QsXLlz9yrVr17Zo0UJE/Pz8TCZTdna2ayrcuHFjRESEwWCwP5AaEhISGRmZmJiYm5tbxpN369ZNRL799luHlAoAKKlyF9x//PFHEWnRooXNZnPldbOysrQJr3379s3Ly3Plpe0yM9Xjj6tjx9xyccfLzFSjRysRZTCoV19VVqtj/kF37NgRExNz8803a6HH19c3PDw8ISEhOzs7JibGYDCIyCOPPHLx4kWHXK7U2rdvLyJffvmlY087c6YSUaGhjj2rN7BaVUKCatlS9ejxuIjcfPPNsbGx128GFy9eNJlMPj4+2lusTZs2ObXClStX9u3bV2u3lSpVGj9+/CuvvNKqVSv7ME2DBg2ef/75devWle78+/fvNxgMQUFB58+fd2zlAIBiKnfBPS8vT8tkK1ascOV1n3jiCRFp1KjRyZMnXXldu+xsFRWl9u51y8WdyGJR/v4qLCy+f//+6enppT5PZmZmQkJCeHi44dKKQy1atDCbzUePHi34stmzZwcHB4tIp06d9u3bV9bqb+Tw4cPTpk27+nhSUpKI1K9f3+HjuO3aKRH13/869qz6lp2tZsxQTZooESWiunffPXPmzOL/za9evbpp06YiUqFCBbPZ7Iz37cnJyXfddZf91lB0dPSRI0fsn01NTY2JiWnWrJk9wd98883R0dHJycklusrUqVNFZOTIkY4uHwBQXOUuuCul/v3vf4vIgw8+6LIrfvnll9qP7ZSUFJddtJC4ODV4sDKZ1Pz57irBWVasuFi/fgMtam/btq2kX56SkhIdHV390o5WFStWjIiISEpKKuqezB9//NG4cWMtN69du7bM5V9DXl5eUlJSRESEv7+/iKxZs6bQC+69914RefPNNx173UWLlIhq0EDl5Dj2xHqVna0sFtWwYX5kb9JEWSyqFPNNMjIyoqOjtfeEXbt23b59u6MqTEpK6tq1q9Z0tadjtaeoryk1NdVkMtWvX9+e4Fu1ahUTE7Njx47iXKtjx44i8uOPPzqqeABASZXH4H7gwAFfX9+AgIBjLpk1smnTpooVK4rI559/7oLLlU8HDhzo3LmzNtz4ww8/FOdLTp8+bbFYOnToYA8xoaGhFoulONMATp48qQ1wBgYGzpw5s8zlX7Z9+/aXXnqpbt26Wkn+/v5Dhw4tNMVix44dPj4+QUFBJ06ccOCllVL9+ikRZTY79qy6dOGCio1VDRrkR/Y2bVR8vCrjWPnixYsbNmyovTk0m81Wq7XUp7LZbImJifblJmvVqhUTE3PmzJnifK3Vak1OTo6Ojq5TYCF2LcHv2bOnqK/avn279t4gMzOz1GUDAMqoPAZ3pdTAgQNF5G3tWU5nOnXqlDY6++STTzr7WuVcZmbmqFGjRMRgMJhMpqJSkdVq1Qaz7Y/u1a1bNzo6+s8//yzR5XJzc00mk3YGo9GYU7Yx6mJO1NFERUWJyLhx48pyxaulpiqDQQUFKTdN5vIgn3yiatbMj+ydO6t585Sjnog5e/astmWyiHTr1m3nzp0lPYPVak1MTNQGv0Wkdu3aZrM5IyOjFMXk5eUlJycbjcYqVapoZ/Px8QkLC4uNjS0400YzceJEEXn00UdLcSEAgKOU0+C+YMECEbn11lud+oiq1Wq95557RKRLly5ZWVnOuxDsYmNjtVWrBwwYUGgAcv/+/Waz+ZZbbrFnFO2p07Jk7q+//lq7ndK9e/drhuwbKtFEnZSUFKPRqI3HP/XUU6dOnSp15VczGvNElLe+wSy0yJL9oDYv6MSJyweVUrGxSkR166YSEx0W2QtauHBhgwYNRCQoKKj4Q+85OTnx8fHaSjVSvKdjiykrKysxMTEyMrJSpUrayX19fbUEb7+roz3k+r///a/slwMAlFo5De5Wq7VRo0YisnTpUudd5dVXX9WGxA4cOOC8q6CQRYsWhYSE2Ke8Z2VlFRrMbtasmbbqtkMut2HDBu1x55tuumn9+vXF/Cptoo62MswNJ+ocPnx4ypQpzZs3t4cq7Q+VKlV65plndu3aVfbv4vjx4/XqNejd+/9KPgSsD0UF99BQFR9fOLhfvKiWLXNuPenp6fah9759+16/NWZnZ8fHx9ufLm3cuHFsbKwzxgIuXryYmJhY8H5UYGDggAEDXn/9dW1CTtkXlAQAlEU5De5//fWXdud3xIgRTrpEYmKiwWDw9fVdvHixky6BouzcuVMbIAwODrZPAwgODh49evSqVascfpvl+PHjvXr1EpEKFSrMmjXrOq8saqLO5s2br35xoUdURaR+/fomk2nXrl3JyckDBgzQ3or4+PgMGDAgKSmpLN/CpEmTRGTQoEFlOYknmz9f3XKL6tdPhYVdEdzffFP161c4uLuwqvn16tXT5o5bLJarW2ZWVpbFYrnpppvsM9Hj4+NdkJ5PnTo1Y8aMPn362N8l+vj4PPzww86+LgDg+spjcN++fXuVKlV69Ojh7+/v7+//999/O/wSO3furFatmoi89dZbDj85iuP8+fNDhw69++67tcHs2NhYx04sKSQ7O3vs2LH2Ke9FRavx48drr/Hz8xs0aNCPP/54zVdqa8lrDzJKgbXkC714x44d0dHR2lwdEenQoYPFYinFs4NZWVna9Jvly5eX9Gv1oqgR97ffVlOmqK++ck9wV0odO3Zs2LBh2r/gPffcY787d+HChdjYWPsKMG3bto2Pj3f9FhCHDx+Oi4tr2bJlYGBgeHi4i68OACik3AX38+fPt27dWkSGDBkyZMgQbSvTkJCQsLAwo9FoNpsTEhJSUlLKssPIxYsXtUfHBg8e7OJtnlCQzWbLzMzcunWry65osVi0ofSePXseP3786hcsWbKkefPmRU3UufoR1ZYtW5rN5usvf3Ts2DGz2azNmdaG8GNiYkq0XYCTNmH1KNcJ7qdPq5493RbcNQkJCTVq1BCRatWqxcXFxcbG2lcW6tChQ0JCgnt7kpMnT2rT3924oC0AQJXD4D5ixAht9vPZs2dPnz49efJk+zyEggwGQ8OGDXv37j127NipU6fOnTv3zz//LObSDdraJs2bNz979qyzvx14mtWrV2uR69Zbb/3rr78KfdZms13nqdPKlStrza9y5cqRkZHXWUv+ato06LZt29qnJkdGRm7ZsqU4X+ukTVg9SqHgvn27GjNG3XWXeu45pZSKjnZzcFdKHTp06L777tP+7bR/xO7duy9atMjNZV3yz3/+U0SGDx/u7kIAoFwzKKWujq3e6r333vvnP/9ZqVKldevW2XcCf/zxx9PT0x9//PFDhw6lXbJt27aLFy9efYaQkJAml7Rq1ap169bNmjWzz6IWkbi4uOeee65SpUq//fabNrSP8ubQoUNDhw5dv359pUqVvvjii+HDhxf1yvT09Dlz5kybNu3PP//UjoSGhhqNxoceesi+vkdJrV69eurUqT///LNSysfHp0+fPtHR0fYJ8VdbsmRJ375969evv3fvXvvMe6+3bZvUrSsHD8rKlfL00+6upoDPP/88Ozv7559/njBhgvbghIc4evRokyZNsrKyNm/e3KZNG3eXAwDllbvfObjOmjVr/P39DQbDnDlz7Ac//fRTEalYsWKhwdG8vLy0tLTFixdPmzbthRdeGDRoUKtWrewjYYXUqVOne/fuY8aMMRqN2iUSEhJc/v3Bg2RlZT366KNSxKLyJXpEtXR27twZHR0dFBSknb99+/ZFTX930iasni8zUz3+uHLJJmxe4umnnxaRhx56yN2FAED5VV6C+5EjR7THvF5++WX7wVLsaXr69OmUlJT4+HiTyRQREREaGlpoZLRp06Yvvviic74J6IzFYtEmYt17773p6enq0lry2lKkUuCp0zLu31SU48ePm81m+5okderUiYmJKbjfqvM2YfVw2dkqKkrt3evuOnTlwIEDAQEBvr6+O3bscHctAFBOlYvgnpOT06NHDxHp3bu3fV0OR+1parPZDhw4sHz58pkzZ5pMpqNHj7LUMeySkpK0hw5vvvnmrl272uerNG/e3Gw2Hz582AU1ZGdnJyQk3HHHHdqltenvqampymmbsHq+uDg1eLAymdT8+e4uRVe0pZPYPxUA3KVczHF/5plnPvroo4YNG27YsEFbRsZms913332LFi3q0qXLypUri5oDA5TdgQMHhgwZkp6enpaWVqFChYEDBxqNxrvuuquoSefOs3Tp0vfff3/hwoU2m81gMISHhycnJ2dnZ2/ZsuW2225zcTHQo7S0tBYtWhgMhh07dmgDHwAAV/L+4L5r7txWI0b4+vuvXLmyS5cu2sFXX311ypQptWvX3rBhg30iAeAkGRkZp0+fXrZs2ZAhQwo+yuwWu3fv/vDDDz/77LOMjIyQkJA777zz559/dm9J0JHRo0d/+eWX48aN++STT9xdCwCUO94e3DdvljvvPN669f+MxsgnntCOzZ8/f/DgwT4+PgsXLuzbt697CwTc4tSpUxaLZdCgQTfddJO2WRhQHDt27GjdurWfn9/+PXtqX9o9AADgGl4d3M+ckdtvl9275ZFH5KuvtGO7du26/fbbz549+/bbb0+YMMG9BQKA7nzx7LNDV66s2rOnxMW5uxYAKF+8N7grJcOGybx50r69/PqrBAWJSFZGxu1du6ampg4fPjwhIcH1k4wBQPe2bpW2bSUgQNLSpF49d1cDAOWIj7sLcJrXX5d58yQkRH74QS6tZl3hyScTQkI6tWnz2WefkdoBoDRatZLBgyUrS2Jj3V0KAJQvXjrivmSJ3HOPKCULFkj//vkH4+LkueekcmXr+vW+LVu6tT4A0LNNmyQ0VIKCZO9eqVXL3dUAQHnhjSPu+/bJgw+K1SoTJ15O7WvXyksvicEgn31GageAMunYUfr3l4wM+eADd5cCAOWI1424Z2VJ9+6yYYMMGCA//SQ+PiIix45Jp05y+LC89JJMneruEgFA/9atk65dpUoV2bdPWJgIAFzC60bcn3pKNmyQW26RWbPyU3turowYIYcPS+/e8uab7q4PALxCly5y111y7px89JG7SwGA8sK7RtzXrZM775SKFWXtWmnXLv/gs8/KBx9IvXqyYQMLIACAw6xYIb17S/Xq8vffUrmyu6sBAO/nXcFdRH74QXJyZOTI/A9nz5YHHxR/f1m+XMLC3FoZAHidf/xDkpPlrbfkxRfdXQoAeD+vC+4F/fWX3HmnZGTItGkyfry7qwEAr7N4sfTrJ3XqSFqafeFdAICT+Lm7AKe5cEGGDZOMDBk1itQOAE5x993Sp4906iS5ue4uBQC8n7eMuNtsMm6c7N0reXkyfbq0aCEiMmOGzJwpK1cyDgQAAAC985ZVZRYsEKtVkpJk8mQxmfIPGo2ydi2pHQCcy2YTo1H69pXevWXHDlmwQCZMEBFJTZXhw91dHAB4D28J7ps2Sc+eIiJhYbJ58+Xjft47FwgAPMQ1h04AAI7mLbnWYBD7nB8fb3k3AgC6UHDoZPRoEZG5cyU1VS5ckLp13VsaAHgTb8m4oaGycqWIyJo10qmTu6sBgPLk6qGTYcNk0SKZPt2NRQGA9/GW4N6/v/j7S//+8vrrYja7uxoAKE8YOgEAl/CWqTI+PmKxuLsIACiX+veXxETp31+sVpk+XbZudXdBAOCdvGU5SAAAAMCrectUGQAAAMCrEdwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHSC4AwAAADpAcAcAAAB0gOAOAAAA6ADBHQAAANABgjsAAACgAwR3AAAAQAcI7gAAAIAOENwBAAAAHfh/VjG1Hsvs2mIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x600 at 0x7FC7A289BEF0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4rBjEq7I7Hqi"
      },
      "source": [
        "- Here is the **LSTM** based autoencoders of **SMILES** of molecules and trained with **Teacher Enforcing**. "
      ]
    }
  ]
}