Model_Training.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "#Importing dependencies\n",
        "\n",
        "import cv2\n",
        "import os\n",
        "import random\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "Yrc-zZeDgeOu"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#tensorflow dependencies- Functional API\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Layer, Conv2D, MaxPooling2D, Dense, Flatten, Input\n"
      ],
      "metadata": {
        "id": "TCwy2yKAgzL6"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating folder structures\n",
        "\n",
        "POS_PATH= '/content/drive/MyDrive/face-detection/data/positive'\n",
        "NEG_PATH= '/content/drive/MyDrive/face-detection/data/negative'\n",
        "ANC_PATH= '/content/drive/MyDrive/face-detection/data/anchor'\n"
      ],
      "metadata": {
        "id": "a7tOCpf7g9ds"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "anchor= tf.data.Dataset.list_files(ANC_PATH+ '/*.jpg').take(3000)  #\\* indidcating to grab everything in the jpg format\n",
        "positive= tf.data.Dataset.list_files(POS_PATH+ '/*.jpg').take(3000)\n",
        "negative= tf.data.Dataset.list_files(NEG_PATH+ '/*.jpg').take(3000)"
      ],
      "metadata": {
        "id": "13eGaoxVembg"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocessing(file_path):\n",
        "    \n",
        "    byte_img= tf.io.read_file(file_path)\n",
        "    img= tf.io.decode_jpeg(byte_img)\n",
        "    img= tf.image.resize(img, (100,100))\n",
        "    img= img/255.0\n",
        "    return img"
      ],
      "metadata": {
        "id": "HfF7cP5xenO-"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "positives= tf.data.Dataset.zip((anchor,positive, tf.data.Dataset.from_tensor_slices(tf.ones(len(anchor))))) #creating the input tuple\n",
        "negatives= tf.data.Dataset.zip((anchor,negative, tf.data.Dataset.from_tensor_slices(tf.zeros(len(anchor)))))\n",
        "data= positives.concatenate(negatives)"
      ],
      "metadata": {
        "id": "FN_bVHtgevky"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process(test_img, val_img, label):\n",
        "    return (preprocessing(test_img), preprocessing(val_img), label)"
      ],
      "metadata": {
        "id": "kk71FR2Oezhi"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#building dataloader pipeline\n",
        "\n",
        "data= data.map(process)\n",
        "data= data.cache()\n",
        "data= data.shuffle(buffer_size=10000)"
      ],
      "metadata": {
        "id": "wm9abBdne3uz"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating train partition\n",
        "\n",
        "train_data= data.take(round(len(data) *.7))\n",
        "train_data= train_data.batch(16)\n",
        "train_data= train_data.prefetch(8)"
      ],
      "metadata": {
        "id": "OZ2sM0UVe9qa"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating testing partition\n",
        "\n",
        "test_data= data.skip(round(len(data) *.7))\n",
        "test_data= test_data.take(round(len(data) * .3))\n",
        "test_data= test_data.batch(16)\n",
        "test_data= test_data.prefetch(8)"
      ],
      "metadata": {
        "id": "AJW5bwX8fEfi"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def make_embedding():\n",
        "    \n",
        "    inp= Input(shape= (100,100,3), name='input_image')\n",
        "    \n",
        "    #first block\n",
        "    c1= Conv2D(64, (10,10), activation= 'relu')(inp)\n",
        "    m1= MaxPooling2D(64, (2,2), padding='same')(c1)\n",
        "    \n",
        "    #second block\n",
        "    c2= Conv2D(128, (7,7), activation= 'relu')(m1)\n",
        "    m2= MaxPooling2D(64, (2,2), padding='same')(c2)\n",
        "    \n",
        "    #third block\n",
        "    c3= Conv2D(128, (4,4), activation= 'relu')(m2)\n",
        "    m3= MaxPooling2D(64, (2,2), padding='same')(c3)\n",
        "    \n",
        "    #final embedding block\n",
        "    c4= Conv2D(256, (4,4), activation='relu')(m3)\n",
        "    f1= Flatten()(c4)\n",
        "    d1= Dense(4096, activation= 'sigmoid')(f1)\n",
        "    \n",
        "    \n",
        "    return Model(inputs=[inp], outputs=[d1] , name='embedding')"
      ],
      "metadata": {
        "id": "0YHTYkr2fHZk"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embedding= make_embedding()\n",
        "embedding.summary()"
      ],
      "metadata": {
        "id": "Q7BiiHuLfIa5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d5f1f3c-023e-4c39-c402-92e322f9da83"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"embedding\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_image (InputLayer)    [(None, 100, 100, 3)]     0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 91, 91, 64)        19264     \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 46, 46, 64)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 40, 40, 128)       401536    \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 20, 20, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 17, 17, 128)       262272    \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 9, 9, 128)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 6, 6, 256)         524544    \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 9216)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4096)              37752832  \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 38,960,448\n",
            "Trainable params: 38,960,448\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#siamese L1 distance class\n",
        "\n",
        "class L1Dist(Layer):\n",
        "    def __init__(self, **kwargs):\n",
        "        super().__init__()\n",
        "        \n",
        "    def call(self, input_embedding, validation_embedding):\n",
        "        return tf.math.abs(input_embedding - validation_embedding)"
      ],
      "metadata": {
        "id": "Vi2_B4nTfRPC"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def make_siamese_model():\n",
        "    \n",
        "    #anchor image input in the network\n",
        "    input_image= Input(name='input_img', shape=(100,100,3))\n",
        "    \n",
        "    #validation image in the network\n",
        "    validation_image= Input(name='validation_img', shape= (100,100,3))\n",
        "    \n",
        "    #combining siamese distance components\n",
        "    siamese_layer= L1Dist()\n",
        "    siamese_layer._name= 'distance'\n",
        "    distances= siamese_layer(embedding(input_image), embedding(validation_image))\n",
        "    \n",
        "    #classification layer\n",
        "    classifier= Dense(1, activation='sigmoid')(distances)\n",
        "    \n",
        "    return Model(inputs= [input_image, validation_image], outputs= classifier, name='SiameseNetwork')"
      ],
      "metadata": {
        "id": "ysAItAvwfWYC"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "siamese_model= make_siamese_model()\n",
        "siamese_model.summary()"
      ],
      "metadata": {
        "id": "7Q9JQP66fZj7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6c715b4-ef93-46b7-d70f-4dcef95c0484"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"SiameseNetwork\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_img (InputLayer)         [(None, 100, 100, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " validation_img (InputLayer)    [(None, 100, 100, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " embedding (Functional)         (None, 4096)         38960448    ['input_img[0][0]',              \n",
            "                                                                  'validation_img[0][0]']         \n",
            "                                                                                                  \n",
            " distance (L1Dist)              (None, 4096)         0           ['embedding[0][0]',              \n",
            "                                                                  'embedding[1][0]']              \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 1)            4097        ['distance[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 38,964,545\n",
            "Trainable params: 38,964,545\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "binary_cross_loss = tf.losses.BinaryCrossentropy()\n",
        "opt = tf.keras.optimizers.Adam(1e-4)"
      ],
      "metadata": {
        "id": "UP7c9S3pfb7K"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@tf.function\n",
        "def train_step(batch):\n",
        "    \n",
        "    # Record all of our operations \n",
        "    with tf.GradientTape() as tape:     \n",
        "        # Get anchor and positive/negative image\n",
        "        X = batch[:2]\n",
        "        # Get label\n",
        "        y = batch[2]\n",
        "        \n",
        "        # Forward pass\n",
        "        yhat = siamese_model(X, training=True)\n",
        "        # Calculate loss\n",
        "        loss = binary_cross_loss(y, yhat)\n",
        "    print(loss)\n",
        "        \n",
        "    # Calculate gradients\n",
        "    grad = tape.gradient(loss, siamese_model.trainable_variables)\n",
        "    \n",
        "    # Calculate updated weights and apply to siamese model\n",
        "    opt.apply_gradients(zip(grad, siamese_model.trainable_variables))\n",
        "        \n",
        "    # Return loss\n",
        "    return loss"
      ],
      "metadata": {
        "id": "AGpxcrw9huP4"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import metric calculations\n",
        "from tensorflow.keras.metrics import Precision, Recall\n",
        "\n",
        "def train(data, EPOCHS):\n",
        "    # Loop through epochs\n",
        "    for epoch in range(1, EPOCHS+1):\n",
        "        print('\\n Epoch {}/{}'.format(epoch, EPOCHS))\n",
        "        progbar = tf.keras.utils.Progbar(len(data))\n",
        "        \n",
        "        # Creating a metric object \n",
        "        r = Recall()\n",
        "        p = Precision()\n",
        "        \n",
        "        # Loop through each batch\n",
        "        for idx, batch in enumerate(data):\n",
        "            # Run train step here\n",
        "            loss = train_step(batch)\n",
        "            yhat = siamese_model.predict(batch[:2])\n",
        "            r.update_state(batch[2], yhat)\n",
        "            p.update_state(batch[2], yhat) \n",
        "            progbar.update(idx+1)\n",
        "        print(loss.numpy(), r.result().numpy(), p.result().numpy())\n",
        "       "
      ],
      "metadata": {
        "id": "bHin6kXyiGid"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS=20\n",
        "train(train_data, EPOCHS)"
      ],
      "metadata": {
        "id": "VF7JJhgDiPtB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a782fcde-d4a6-41f3-a90f-e4cc9998a2c9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "263/263 [==============================] - 81s 309ms/step\n",
            "0.0002864219 0.9858156 0.9980852\n",
            "\n",
            " Epoch 3/20\n",
            "1/1 [==============================] - 0s 146ms/step\n",
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            "0.01733492 0.98892635 0.9995134\n",
            "\n",
            " Epoch 4/20\n",
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            "0.056854807 0.99047166 0.99903893\n",
            "\n",
            " Epoch 5/20\n",
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            "263/263 [==============================] - 80s 306ms/step\n",
            "0.015766611 0.9929841 1.0\n",
            "\n",
            " Epoch 6/20\n",
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            "263/263 [==============================] - 79s 301ms/step\n",
            "0.021224359 0.9636885 0.9965415\n",
            "\n",
            " Epoch 7/20\n",
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            "0.01346375 0.9971014 1.0\n",
            "\n",
            " Epoch 8/20\n",
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            "263/263 [==============================] - 77s 291ms/step\n",
            "0.0038752249 0.9980714 1.0\n",
            "\n",
            " Epoch 9/20\n",
            "1/1 [==============================] - 0s 270ms/step\n",
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            "263/263 [==============================] - 78s 293ms/step\n",
            "0.023056371 0.99670744 1.0\n",
            "\n",
            " Epoch 10/20\n",
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            "0.10472368 0.99810517 0.9995256\n",
            "\n",
            " Epoch 11/20\n",
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            "263/263 [==============================] - 77s 293ms/step\n",
            "6.6162843e-06 0.9956814 0.998076\n",
            "\n",
            " Epoch 12/20\n",
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            "1.4901163e-08 0.9961557 1.0\n",
            "\n",
            " Epoch 13/20\n",
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            "0.9214238 0.98408103 0.99706745\n",
            "\n",
            " Epoch 14/20\n",
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            "0.30206537 0.9937769 0.99759734\n",
            "\n",
            " Epoch 15/20\n",
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            "0.005112073 0.99473435 0.9985584\n",
            "\n",
            " Epoch 16/20\n",
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            "263/263 [==============================] - 78s 295ms/step\n",
            "0.001579928 0.99659204 1.0\n",
            "\n",
            " Epoch 17/20\n",
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            "0.00022607177 1.0 1.0\n",
            "\n",
            " Epoch 18/20\n",
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            "0.00021737655 0.9985598 1.0\n",
            "\n",
            " Epoch 19/20\n",
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            "263/263 [==============================] - 81s 308ms/step\n",
            "0.005594204 0.99667144 1.0\n",
            "\n",
            " Epoch 20/20\n",
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            "263/263 [==============================] - 81s 306ms/step\n",
            "-0.0 0.9995272 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get a batch of test data\n",
        "test_input, test_val, y_true = test_data.as_numpy_iterator().next()"
      ],
      "metadata": {
        "id": "7F1Z8UqHicdk"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_hat = siamese_model.predict([test_input, test_val])"
      ],
      "metadata": {
        "id": "Mfju3QzYiq-K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0a23cc5-884b-4d44-9841-11307ff3c789"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 21ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Post processing the results \n",
        "[1 if prediction > 0.5 else 0 for prediction in y_hat ]"
      ],
      "metadata": {
        "id": "lvhilBAHitnw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "667cde59-9372-4c78-dbd0-f917f0309a2c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_true"
      ],
      "metadata": {
        "id": "U7dCSKhqiw0C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "105f13e6-7cbd-4d00-997c-23704796810a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 0., 1., 0., 0., 0., 1., 1., 0., 0., 1., 0., 0., 0., 0., 1.],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a metric object \n",
        "m = Recall()\n",
        "\n",
        "# Calculating the recall value \n",
        "m.update_state(y_true, y_hat)\n",
        "\n",
        "# Return Recall Result\n",
        "m.result().numpy()"
      ],
      "metadata": {
        "id": "FuI77T0mizJ4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7679c97a-8a7a-41c9-ae8c-c0bf8437023d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a metric object \n",
        "m = Precision()\n",
        "\n",
        "# Calculating the recall value \n",
        "m.update_state(y_true, y_hat)\n",
        "\n",
        "# Return Recall Result\n",
        "m.result().numpy()"
      ],
      "metadata": {
        "id": "tL65vGuQi2GA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa22ce8b-95b9-4178-f4c8-087c91f3dd7f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r = Recall()\n",
        "p = Precision()\n",
        "\n",
        "for test_input, test_val, y_true in test_data.as_numpy_iterator():\n",
        "    yhat = siamese_model.predict([test_input, test_val])\n",
        "    r.update_state(y_true, yhat)\n",
        "    p.update_state(y_true,yhat) \n",
        "\n",
        "print(r.result().numpy(), p.result().numpy())"
      ],
      "metadata": {
        "id": "kKhXzAfxi8I6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82b3471b-cb73-48df-e1a1-ceca7ef62879"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 22ms/step\n",
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            "1.0 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set plot size \n",
        "plt.figure(figsize=(10,8))\n",
        "\n",
        "# Set first subplot\n",
        "plt.subplot(1,2,1)\n",
        "plt.imshow(test_input[0])\n",
        "\n",
        "# Set second subplot\n",
        "plt.subplot(1,2,2)\n",
        "plt.imshow(test_val[0])\n",
        "\n",
        "# Renders cleanly\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "qXmRWZDvi_Ks",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "b77f038f-29d9-4b19-b5fd-f5e403f1cd0a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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LJDGanXlYvu69e/Bxb20EV/Zy9bK03kGtqbwXD17Tpe7sIWxdYnNa72jrCgajQTw0ElskMkrkGauHRujSW4Af6kDpwNrEy92p44oFVJqM3ZTjOiVOqTXxLMP0CdgBlNGRGVC3jMsEByRkY+4MaHwbOo8UWJP4nUSvEDprLJ2+ozGw6lJy2JoEDPpTec7eY/+lMXVeTD+lFLDGXLHpJFva0xgycLS1ccfSVtBaJCaPVW6p3ECB66RA5wgaghOQS3TJigVx/6DLjUf9TvbVh+3v7SayvfInxUAx878A8C9+wgMwr4KJnCewL0zf7H6hCsSIyFq3fBMWlC8BWXEdHDXbxA8LBbcTV/kn6VYfUN6CAsR30d7NtaTgYthD5bj7OFkr1ucNIXY+Wx+1TiJvf6aBfEwMI8TY3qJ1PaEKHSTy5rIBw71R3KjWpMiz5ZLvfqqfrGugFrc2LFsg7KrsMXBBYcDppt3jO/IyRPArR6zWhq6yTCKKangnbDiTAGWzpLVOVRDU5bov3WlHpI54Pr97AFZbiisvuEeP5W4jvIN7r3q7HD2yeP4tADraMpDvKSX8Z1R+qgxjXfuO1Ym0FKMDRVRQbDz1erEfys8Gb5qNkCVieoWhM+X5PMw0jA3yM/k3Rtk1Mm6M8fgBoKFn6SpFK0XhbI05nS9pTc77aEtNPvcDBNo8jnnI8WIDb0nBUSmq37Nnw+0UB7ncuwAsZnRiWbHsJtvY21+KLgNSkkU9tYEhsoOAtnZQYQkJUBlqc7Z1xqpB2ibXQCFfBKTB+y+vLE7XYnOe0jxLA2liwe4xueTD5wgj3pENxTxKw9ze8I1haU7jkL1MwQ8FoGz8cmpwkl1p7GT3ZbzPzQELb8EeBkjzI+nIfWGTjEcMzYk+7bwhg6iPlX/wIPKh7KwrhvAWJgdUJPgEDSv4qc4CwxzbvHJzhRKjbZX3cFuAD3t7odhFJi5620orbeMOD1IWhgjhI8s67P3x9sNeE23qKnzznY6mbbIMzaXhnu1n4zlrTThWw9oda8LAbZzqiYmct2QT0JrXUkoFcQQkE0i9eh0VVaUDuRNFxsG2LLfc4ZD1O3xj/4oGsPoTpLt9mCDWqQqsFGgpQZiyFm8TJvoe4MGtQdc+BqpCQAtPydPdBBQbrcdClO3J/K3t0rkFcdZXUrUcbaAbqym+k889pSSIEYVbb7dBk7QRjmMxJVFgQeRamXs3KebOl1YIqKUMihqJP0QxsO4YE34Wz4t8NuWax623hlaKeEShsXosXtO85BL8pET38YkltJBtsQM2cFaSn6S7JEuBxRHe6pL8h5tw/h8zJ6fEZrfb9nGYnNe50cXp0mz3L98ul9jc8vkP9WbVIj9TRZkm2R076TzXHdFCJ1ka661pzJMur4FdZkcsGKGWMf5K+tTQVml3eKsB2PMeD1lRSgWD0daO1uX3sjSNC9rEqhJ897aPExFqIRBVhAddCNG6xNDVoYEjvYh0tzVZvJvtjlUOcsNM+Ujlt+M7DrQWuEjjnjhAvztAugSFe9xTY9hGemLpRykVhRgStM4KslR3yq4DH+uttEh2Y+qjfzJENvBLunN8xuZSkqvbur1vaZXlHzoG6o8u3jBOnp8eAErjWV0pUiK01wEMBBngJd0ylz9kt2frh5MLMwkAX7M35aCThUi2Y0owrWx1ZxT0Ll6AYaBK7MQLTw9cMLIKh670EOHUXeCZhZQ5e1SFTsrN5+ifTdQMh7rto0+0SvLQgxm5ZHog0hwQRGipwIK6z2UCpy0b+mBrBmwKqm4flXaZkteg+M4A97GPRPC1S1fuFifRZftxmiQ6T13JW5A0VQVRCqYKaSAjyHnQpzMbwAvQb+kn8rVu45KaBzYPVUac8suCPn0fTuI3UbI9+FGa4SkSJBhZ0wQ47e29xgChIGJZZTNXbHQyj/fw3uV2bYMpu85ZA9AC65KHLgHEPUH/Sy4EwlQqWm9YI+8GJJjYlp0kNqY14YtKBVwAXnRzShUQXXR81taAUrC0hsKMrmPOrMkvEl6SqZdl2QjoxcOhKTPM0NP5QlaJfjYA1Rmi/IhgmxHyBgMxDSInj4N8yPuGQGmTn5R4X4sBqN6N7wBQc9CXTJVkgATdiXSZ01LHaDoGKkXDHwroUFHK5M8zd/SFsHb1COmY1SLb/l3GQ7bY1yrguDVZ6hMApV4rBVJiCBfdlVsDQNVJd8yuaCtjWRrmq+QSbE2CrOWzBG0fDkdM0yQpSSaZr+VQAKpeNxGhrZJKhAh+jY1GGSgobWxzCkgdZ914BCL3WMJPkEjMKmzcOyVKV2SNinp0RyLCmz1As+18lo2+MvYiW6vsxiU1rlVXBoAtzhuu6xFqib1xdNPfUawkHs3zNXvgFOjBACD2S5Z7nwKifhYAtVXcn/r1gEBvn/L/b9GsEmLvS6tOBygH0MbOLCSBsnklx/Mx4Pr1KENuAaDKLNuFQTQCoT31c9uFsWE7elKvZ2ZI8OQTkbYBLwOCDgr0YnYR53a4BWBr9V0mH5EAEib1TvVQ9GZ5I9Hd3uOu+adauVH8N0B5aKMB56foZs+T15m/s/9vYkYSpsqjE4/z8Ovp3thzMZkD5CrNbrJ1Sn2uaH9C2TYv6gp+YXu3KlNi0u3HFrcRGn8r0L6UMvBKmuT7bn8DE4kTKL6yGDrEtx/kb7+P+ZafkMCv/mSl9GQZPBvRx0Fa8Ni+nUpgXG9xeLSRYMzBHuOTt55aBod3w/lN7jYvrC0ldRbDtXUG0GBxmNwZ69qwLCtaa5g1H1FpjFKbAJUqgEXaJ95oy4PVmgCeAIjW4A6JnWroRbIJiRzruF5nLPOCeZlxvVwUaIgnysAUALRjx6QAsE7ShuUw6edIsCrAS3KxlaPsBGYFLwKqTB6neUq2fOxm+uYnDcgWraYx8/+NT11HZvmq96TUF+Gtiqq3w77dpXnDSjd/PKFJn9Lpn1D2+G5bPraM97MAKAcanAcptGWm53aJYbc/SVDIn2MgrV2Td7NbdAxdElMhWEiDeEndjZbIQ5vbO4M0oVvTNACym10nsk9trc/7YMjeLDcFaCqlSO8JT5v01bc3Ew3B8bFRRgOlEYGQtoPFtuYPKDwDp0Rbu8j5S22Y0GhU+CVNMgMk+d2t5UmpFkmR7b9tFSs9T9FYuoJmEGefpACwrg2lp23HSWFll+sWILmXKAUDEpEH9VrQqlh4pMnu4r4AWl1BtI1FXtoz4KvU7SFAjAcJxn/kdZlw8/cZMNz0wdvs/QXAHeuygArhQMeUzdrewbDcRPZM7xjjxGzsfah8AQmWu8XjWtC1X2KdixcRKL0CpYN7AWkAq2yvTstOHxFAv6TCLMlaGRisZxEpRZfMOhZa9Im0xK1eVOdXVd71MKHU6sthHwROLNvvjeeKz0cpljOs9Y7WJWFgLKmGvLP5SgBI81AJx9i4RStEpplBYwI7rwyo3KoGpjWQ22jiwlYBnfIbabA9EaGzhHXDIsVIArjXZdXlMFmGqy28n0vr6BBPLlaVp7Qi9jnK/H7/7j2u1wuWecH5fAlDDYzD4YCXL16gVEmKa9mxTcasy4q2LqGn0hyFAUSCeH9qxbKs+P777/H4+Ih5nnE5X2AGKgFoTTa8AMB0OGhi6ECqpVZZniTLNUc4HQ44Hg84nU74i+++w+l0ksTJqwavvxTw1RqjX2eQpoUoRZbwzKPdewOz5mrSnHBsqxA2JkJFREJekfRVQ1dkI5d64Yuc3tCpoy2SN8bCbBjk3jb5sZUB0fNEhOlwwOFw8KVMIMnNHdeByNg9pMQAb7xPNpe0b4zQJ7ZpyYGh3f5JToTb8vN5oLyo4HciJEvN7nCBH8oqqsrW1wanMsb7M2G3BGe4sMvuZCAFq6sMGXduWdtzjeZSzA0a1CUcKKYJlgdyUJrpqUQUbLfFW9/24mgyjZ4svBma9NKR5okeDjAzTZK1JoT13ZQZzGzjcnKcQCFCTUtzrbEnsDS63NSzAR0Z3JFaiADQaosgRrUKaylArf5srnsA9Ab9TICCAhhjM0ZuB+Y6A8iPgDU4f7tWb8+WBCpb7yAmTJsBozReDrjS9bF16bPSecNNfq13BpUUsF9sWSf6TxT88FHT7hdaBhe/fPL5UEBifCEMPRPaDqIyL2lMD9kyOLCRI/Br/ttAjJJeJYmD9L6Zh7afaTSkTLmoyrPtxMlYTHcO19hETjJiBslLxZWkgS2j0VAtDGBBGSczago0bpbWwPonjeidAQVrbPKXu9cLquit4Xy54nK+YL7OeHw8ozcNJu8dx+MRrXVMdZKM4lPVY7omEAFtlYzfAscCMNt7zFNOJMuI8zzj7dt3uL+/xzzPuD5ewGDxHlFBaw3rIgB8mg4ef2k7bYUXzHiWn7uXL3F39xIvX674+qtfodaDZEhfGmq1ZTSK8AK2GChbAh49kj6m+TPgQDeGNnQgqWEUnBbX8xjnp0NPGs2g/Ca8Ivn3wtM2KO9dMJNXaET2fBTymH5yRfyEUHLnzE8XWp8dQBHg2+ljVo73iHv0tjNOQHcnRp0ff6mOkCu+/L4QEtu6AsU+VTfFGVNuvfgioCtay7K67TDR+Lddsx/7286NCukJZ0pnaDLrIeJgbks4dZOU37RqBDXbqyo2xP5LQ2jNsbXxpsLKAhz9WBoLIk8tsvYYuO0KeKTvujuFJX6KKXrRzc3es7UlKfvDDW/braWvrXUs6gmTnDAEkcec2m+kCeDu1FPhAPVm3fJGMeK5ePKnGUBKLZCZy5cPthPZFOIGTJuOynA/L9Vul1TzdesLWZ8QhkrRQGkM3/IAFAGIIVuSAcCpVs7LLl9O2YL+bvOw6+FCKREvcwquth1gLAHNUL6jEkpTtm+GvHH6ZeUCG2OkdtgDllxXPQzCqGD1xtgPaY6iogDADSESr7jHOSK9wxVliF9bRh54g7eNl0JDZSEnKM1lV7ou3wIp5mDlzoxeZBmtd6Fnbw0PDxcsy4J5XnG5zOit43q+Yl0WrGvDfJ2TnJAltPv7h1Dm1Y7kmjRG0hKPRuxiSfNj1fNSW29YmxyD9Ob1G5wfz2h6pBSYJXXFMPdIYkZJYufY4kcBNzSZCI2Aa5lBIKxLR8HvcTgcXKdM04TWGk6no6TIOJRBVjqeUSYiHSdyX2N4oEZVbOa/yRJO42pja8BMg9t1U05LsZRQ3Zb1mIy3GK1rawGgQHrSwrh3m9N/eYe90avEpdxZ5+ksSl3VCksFf2deTwZS/v1U+fwAypXOVkBjI/g1VwkGuOFemw3keOJtHL81yYQBCzmHKBgjiDgqH7c4Um6TwYqkIjtBbGcepTVi66+h5914lRsKOViSerIngYI2YLeAAhQWPxLFdwq6slYxRdq3RB1L1JmoNX5K94eNkeCUkZFFoXTN7TLPM+Z5EQA1ydltcnRFOmNJ29Pz21kCwW35UiwVC06X7LW9N9+ebGAplFbDMsshwXKelSzR+XETaIBm/7adxh1wbZh3S20tK7mNZfnElV32LoZFZZaXz0GbwZx/R9lbhgTgmysy4HW+yubAIC3GXCa+88Xfoxs2TOak/FSmIAY7h1kS4Si/SbvYrVCLHR8PIsZNH3/pJeYOO7+b4h+3y+s80PMMWbYt6fKaKM8TQQGUHcWxJ3sCxEPnys0p1dqmzi2yQtvRPQC68n+vVeZCKRJb4xZpDyYtJHmLUvUiesz4zMq2K2+mgd4O+qapvqTXgd669MmWjDW2Rz7rmznfL7vbSgEadVADVjWG5nnG3//97/H+/SPevXmH7//wA1pnVKqe8Z8tj59WaLvoRGnLe0upOBwPKLXg7uULvHgh57RVVfSnwwnHwxG965mHbcXlcsbj4yNaazif5RxEgGW5CyE95PzMg8yv1sGwTQdCQ/HyjjqRG2O5rgAe8MP3PwIATscjTscTjscDlmXGy5cvcDwd8fLlUc4zBGM6mDfNlsc40qhwE6DsQBsBkJKccI2gMWWkgNmAmI111TyFbQW4dPeAEcWGHjDgG++Ysa6LHsqufEEFNE0uzbz/BnigG5+A9IxopJsVrPwQZ6Rhl0yOJjbdGq2Io8s+VD4rgMqAOCthVzgD/g0X3QCPbvvuE9uvD4h0uCB/7RAr7vsjJf6gZ0dAl9XrXnsyaMqlDJcAACAASURBVLOAxi3ydXepRp5z5gr/ZYI2wMhNVxVsDQraPXrazkHjKwJgjq65gt/68uI7UyTr2lQ5FN9dacZRpo9NDhsDs/RkIk6AncfkXpjNrpGxCW4dm4cKBBTL4UMJrGvAaCkJ15hsyKTwzzewZfw8TMro4Yi6b9u8LSNky7R62lx46psBxOkFWXrIFxWkZ0/qp7w1171lyi+sbI3wzCsmmG2ux+fkUUn12Fy7oVTopbjgiBVb57vfYcymQzh+u5ElVr3t0PPnb6xtdgUErz2p9zRP7MINl+ywjXvpFZTl7go9KWhoc5ltLjf9LXRZWlMAteDyeMHl8YzL+YLr5YreGVOZ9GDhTDc7aoTiWCIdr1pVVrSCZZow1VWWq0tVr9QKQkHvDfOyYF1XzNcF83UWY22VgHUCI9SvCZMSstO8OL2HgURhqPtMMiOIJe2D+i3VIwbM11lBIKNUSY9wOCggV4P6dkjCy+MpLGysKL3dPmf96tN7nOeUb4LqvOSl2uOJW876UPl0meIzgbZ17zGjPUFPXP9w+fweKFZ4lD06rG5jTYfPquTFyi4S5N1ZD428Vdtk9QAmGTAoSkOnzOB0sr0xA3FXhG6M3f2HEKAGwA1DyMGXMlkK0ZAgbbAliNXSMvcmpbqiTiYMB2X6RXtOQUjR9AjE8ARxDD/aFg3h/TChaksFsiOOfTJuhRiVLfpObn4tEZCccs1oTpm1rWit43K+4vHxEaVUnE59yH3C6qEyD9GiwZVmvcip9RJUeXd3J4GUHp9A6OsiS24oOt4m7AWMLasI28vliuv1ou0X6hwOE46Ho/KHCKJDnXA8HKRvTYQa2QG7xQS+bM2dDpMKLQgtE394DIr9Y+NlBpjSoZzkhoOBUncbA+HaZkazJTULPskCD2n6U5oZKuQiIBQOmiN2I5SYANJQbt4+YONNFRYkDg+hVorhcMcNyPwiCglE6Gq9B6CSfpciwfqsAdytSQyNZdcHMzqqZo1mWfpZV5Ra5bQBYKzbxgsWnqvNUBk6yndRqnWaQK1hpQUWA1XN82r7YsCetRvcUbot9xigEqXpiVy3StCseokqxig9TEHLkkylgp74xJbFSi04HA4AEZZ1wdoaeivoK4ELsK6S9bs3mcu9dcxLBz9csawr3r6VOKNlFQC1rg0PDw+Y5wXr0lD1/YUBWMC8UrB18U6XEqsDpMK1KB0LSVqaZVnFYzQBpRPOywVnvqA38UC1tmK+XmXHHXdwW0WXIDy4hSHjRQ3cCOhm/DWNwVzFwdvIvWL1cFDAps8iPD+9LbheOtZ1Ftl1nDBNFdNRZNhf/tVf4NWrOxymCcfT0b38mU8YuiHB0kpYShlEuhvSdDqFOEJI0sHGnmdFeQpNVhIad1nSXEUPNDWOJ6oolXw1oZaCqVZM1dIvkOrT23kHBgplHjNZZjNEWdMfQBy43BlUVxB1UNckZOlA+Pyk10+jvnuq/AwACkgh1npRO6waP46By94GQPLg0KgovApywBLegwRBVbgH2rfaOd6dxGH+bG0wi2Cjv9Ry122llL/JCisRILWe86e4aVBq9nZvlyI/AUNwgDiIMZacMqborFVkuUBGg2FQxub9sXo83smrThTIwFV/m6W4LmKZ1VpRiwRMFk1iae7uZV3lvmUGYAn+SCb/8aAW1QG1FvQeCQCpNcjst5GJFjKHu9/jIXQ7MINxPBywnlYQyQQupQAHcbETyOOQCkuMVxnAiC5DVk1Up5N68GjpTpXwPCbgjAScdJxJx9QAjY0BA6F8DGDzaFFtpr0o+WACZF7cC7b38bM55Yjbfgw8xSXzRmalboLMAN4XBp1SsaVRIKBNFuwmg4QPPO5Ivx/AtQbkQxU92/cZPDEH5yRx5mA1EZo0maQBIAHJjNh5gTCYWLJAu+EIDOxiHgQ/WN2+VyVOgHqSjf9vyOT8nA0MAyuVigdrr81itwi9k85BAaNNczj1Lrvy1qXher3iD7//Ax4fz1iWFcsiRznZkU6FCmox1cZA5zS31NDqDYwKKibvhUa+w5ZI4to0jQGpwdS0DZKy4IJ1bVgXSV0g3BAQxMbKEy9wFwNe5Taj6HEnq3qCimQjxwRJV2fH/rDygUj47l44cQbUiwCTOlUcT0e8+uoVjocjwIRauxrOpjbCf944+KtUg5g63q4zdHwlV0PEL2dDiQxLhdepdQFRXWPOfKlfx8F3lxdJICpAbWAfZ0cH6zdGwz7EYX2X5cOiUuBLDFmQlVEXY/jr4+AJ+LkSaab/8+QMWKUC2hGBCexgbhdbHIh17PK4zBUDkQVeEgY0Es/eM3iQwlfvysaYcbS3Y2B2lVVqbb7i3jh9j53JdMOySUm6e9t2hjiMIgekrgGVXtGkaKMYamrhQIQxCJ58LL/DJkHvHessk/96uWKeF7S24nq5KkCS7cMrrXrsgWkAVg/UIpa6bnkFgKJBydavWiseHh51S3H3hHetdT3QU9z3zJL5tzXJ+/L4+IC2NlznKxaNSTAKtt7lGgHLIvSda8WlXpy3AGCaJhwOR9QqZzYJcLOkh6ogFchK7EFkIs9z1emcxzvzG8c4msXvHMoc53FZVdkj6WM+8t92mZp5/GD2QgAyGu5lHs2H3B7bkWcB/xnQudGggvWLKqyJThM0Hr5U71Fv4lnqLeaLRZUMwcnFFEkZ+SR5fHxUzZBJKMpNRs6Ap6fkicXvkWUf8m3yNPwgM6uDjNSksT3ehGT+ObjK5DAgmQyJdDao5YFblgXX64ypMUqRefbw/hH37++xLisuD2esayzdLcuC+XJFW9cUYyZ8GKFkWe7abwEhtZLErVLwqORElkDyajvhKLJ6Z2eFAeSelt9KVcPBVlaG2ZPnltKid09K66cFaELhDjE+i52zVzTNRG9Dtzp3LDNjLUWM1F7Re8ePP/yIx8czTscj7u5eotSCFy8kcWcthEnjoiy7ujiVuhtvivYSfRixw9HAeRgDQeZY9cm85af5JRnkRiZn2RYKO7FR/g9jRR8orCk4GGM+MTiDKD4YwS42f31Mhn1+AOUT3ybqCDKyUgEATQ+iAqAMg2PWW74/6w0HIiogtrTwgRuUQFhURV1Kw3EYSZDEyep7ixW3g+wAaYcoWU7Z8lhRELUd2tx+AzY9BwR6XcoEJQSFB6bKayXom5PwtfZRA0AS7MiWX0auTZOAmGVZcH68YG0r3r97j4eHR/TesM6L74SL3XBNhUWLbbvq5ZLdL9UGSibdUjCXBYUK5ot4se5e3aE3lvdrFt9lWXE+P2JdG67XGdfrjHVdcdagTtuZJwGVIjTXtWHRpZV1tSWWWG47TJKb53Q64dXdnWY8ruiQ3XpyZIQcXtq5iytaAdbNeJMBEnPp35pZ28BxE07YjEshPaS4BE9vwRKzZQnuwxLccI9yih2uLHJEaO9JCD2Tuv0twlPiycSKtl2SeZnQu6bxak+w7i+yyLJbZNzOClgEdENvK5ZlxjzP6LoFnhCKuHr2alnCkGzWBD/aggncN4rXlAGlMAHzqDCDeXVvlh0jY4Ape65LFY+rB65TcQCXtL3Kk+BFaFvM+BQjg52PHIkbgFJg0Lod0B7zy7mvk7QVjMv5ivePjzgcjmDdhPHjD6/xh+//gPk64/7NW93RZjxqpxfEoeCAAlVT/u4vx+Y7aNCznKHXtN21TrHRRWlbS9EjXizhAsG8SL03OYanrSAwDrVqvwAJ/wiASYDPxa5Lp603xwVuGIOABvTSXbcdJlmek3EJYGsrLder1DcdJhyOR4CAd2/fgcF4+eIFvv76KxwOB/z6u2/x1atXOBwmvDydJASkN/imBK3XjyoDQUSyLKv13hI4gm400H+GSQgYd6RrnkLLT4fYYMRdM/crf9wEbGd+emI2jiUMPddh2ifbuCGpLwCy1QP0TTUUOOETRdfPkMaAhs9+pMjnbENSKDcB5Rz33BZlhA3gG3+yFb779uGbPeg13Hr7+idLCLSP1JtbkCZluOu7v8gAVE4CZwJsWVbMswCW66xKo3c0P1E9zgWzLb+dmwAxsiBxs3rIBSSg3lWGJjXV980L5kU8XgcAFVXiTFbxPK3r6j+WQsGpkg5h7Qo6uVtQqpz8zgl0lN5RSsWyrugMzMsKKpIgcVom3akWO51KV/c9xXtuzPdPHchxQKU+fSSD9TA2zErYPM7bQPEPv8pb683N4J3h+c7My+avDcsx9+tT3/1LLbaAl5CwWkBm8SKNFYWMMAUDGvlEwUfSqEHVT2CZPDb5nVb8VfruTwa3GyN1oIC/a79FxsKm0GQXsMVKigKX1AOLLNHXK4gIl+tVvNrXGdfrVXe2JSlLBqZiklAajbGxlFuR6G4hpeYpQYCE4W1bXuY8Sfy5kWa39MpaoefvzCvnOcTSocq9gznCKeRX9EUAGUv8mIL71QLZWbzo69pwd7liqhN6ayhsMZacnAvKE2kHeLSPoz8lKJz7aMvGg/dpIMZYn5HvdkPX01ggJNFoDO68wb/0KcX57v1iwSo/pfw8iTS1bC3nNBoAxsGw+xns53bZ81sLe1t/4F/AtvVnq/+pH3+nvSdbAHZfeh/lKezySS0Pz8+SbcIoxb0vdpYR7TAjOUNYvyVQUiZUZ/Z18aCfMpyd7aZc796rHj8S1G1b5kmF26rLZDPOj2dZz1brtbWGZV4kwPM6Y10WByU2SbKXzOZhLdmly+DWsJqXz3qaAFZfJ5QSXqtSq3vBJMg0YiDAMgmmOqklBZ/Ytt3WBIwfIdONRtKkZV3FU9UazpcLChW8fffOgzJf3d1hmipe3t3hdDqh1uZbkM27IO0PwRsCSehSUlZqUPQ53SZ8S4RKFWz5liiCXC02ynLxcBkFwDb+ZAwkFR/qsKTiYxbZeu1QUsvWb1Ng8IzZdnBTTBSJAL8oCEUk1mvvGkCiXgFIwI6AJol3srha5y0JOAIYfnagjGMFoIkQQRKvwyKxLJ7SvY1qbLBZ0r78UDwg2s/86uF90pHxQ2E94zPfgizjueDbrOzMq9PDWDMPhs4xc/z4ZgSr17wBemjyOs+4zFesa8MPP77G2/t76b+mHHh8fMT5fEbXJXnWnFEC/KzfycLwv03Bjgpc4nLEgy7B4uKFO0yTylrd8Ysx8W5vTTLGa6zkui46VpJOYaoGLhPItCBxb4967DjNQbpV/mRaigG0LudgasqJzAc27hLiYHNVTicQANU0zcIVvcnmnfl8xfF4UM+aLOV+/auvcHf3EtNhwsuXL1BrxakcMU26xNcZRJJxvDrQkgbKdC9g6mirpYoBDoeDesuNHqznpJIfPWXGqq3sZP1uwNjA2Q2QSyNrMjvGXKlJIqh4Q/NB79t3CTTZq1n54BNSdX5+ALXn8eGMCjbFLLXts1vwNFoIARK2LzOFugVKUefTYMpqpnyd0/XBqkwPSEPl+BdIksvt94Ts9swW6gDLRgxt3hLdsore0awfN1KRB2FpCtAOgeytS5CmHptgB0TOKuQu5zPu393rVloFhXrOExiyQ9Lo0kf3+UB/siNT9FiGbgfyjtaq0EGtsIkj3b8CKzuuQPoSk0MNITl4k0uAGLJdY0Cj1dMXcI/xZa3Mcid1PUtLwJe873Q64vLqgukw4TuW9/QuSlCOUZi8nSL00m63jcC3a1mQ3hqyJEuDzEnxxnEy0H7blvSbHEE7xbwelBmKg45ja9RYSa0Umcjueo8lLQCJf0vKbfQlFJ/jRHq2qo6nyi/uXQGQejb06zCXREXKbA0QZaCIETIljB19s/Jw5xgwzm1SyW94jgDY+YRESOEGqS/p+aE+G3OXbbFcA4SctHaAESlGzIuyM+wWzwdmXK8z3t2/x7zMeP36Nd68fYvWOq7zMoQUEAjFDqi1o0CQDWZGTrocxjQ7GHSZrgq7UBUjBhJ4bct5Pkwqi2wDgC+56rl55nku5o3RMRPcS76xJOZSBqEbhkqUhI6VAXNSoOHg0ZJkOrqQ1GystG16zJB5/Ze+YNU8fJfHM0qpsBi9Wgt+89e/wbfffYvT6aibdYBDD13KLJ75UlNCTW0kQYBYc9o2lFJRJ5GHFq5ihnToVdU9G726LeEhTZ7cDMjVKbDRiEMJ8LQF2hjlXZoU9mfSCB8snxdAJbAB3HqNMij6tJLFQaYEduqxLfu37xjfSz/h/S7XHJ1vn83WRTHgZFA3Zl3yNo312xq1W4TKWN7b7OlJ7y9lrDMgGLkwkVPjdcfcKkHfsgzHfn6RnRW3zItn6JYT3+EWGlh3q7RY0vK+28QzAeL9yyyaOu2PdwGaDN8JU4iwzGJt96Y74ZLgi23XEWtAKKBKKUs27CioZIHnlBI2LCFwHUh3OZfrqjlf3t2/x7KsqNOEFy9OqLXixcsTTqeTWGCTJAwtRd7pQsUHOfP/B7yhFshvpCzGCaGECWZ1KXMMc8GUSL6ithfFu4e+8j4IzhbczXdQ48Lv+/zL85+n8OajCOhsnDgwT2BDdTNMvRtIEKVrlpTIKIYq0S2rbOUD4PEogOw86gWJD9RAKgZ6xatgfOgGDwFxuvkwETbAOgB4eKBCWRUODO+GA1l/AxAsy4Lz+azJdmfxYnQLzNb+aPvFUyEvkYBq8jaEBLF3jYaBVJE3/Vi7tJ+9AwUaiybAyhOhNkm5CwCrhiVYjiczGCSWKClqN8LzCKVBM9C0E7xsQMPZQOVSa6saMenMy60XLrOa9TclRO3cRW7o3O2dcble8f79e8zzEQBrkk/C8XgAwC4brJVbb5DxNxEpuFMZZNnpfWQSP2SZRTT+DMxt8mik4qcAGxtrO6GI8lxw+RVtcqy2MXW2c22v/GxLeAP0eQJIDd6fzIcDTxpati/0KgHmlmWDmYnpsocgkOotkNE5qZ9pQ/AAXuYsdyYwZam3FaqgmjxPLELFjmAsGqw49klct75GTxEaqVDIK+uZmcnybNQQGEZPiDJvmidpnhdc5wXLsuDHH17j8XwWALXqbiM7jdy7q4GbXYIpW1sVWCxoa1Ohko49oOiLtza1M1y2hNQdIHnpepc8Kbwukv+JSECReaWGc6oQ1hoIdJiGJUNZToH8MOTsKBSJxTMvgAY6WpwU7HaSa+vaQADevr0HABxPRw/W/O67X+Obb75BrQUHqIBlQVHijaoq4E2NQrnABJsoN3k3e6I9Zo5AVstvQpJnp1CNA6Y5CYA0y5iTwaLjWQp7C0zoi1KPnU0xIPLZ2kWdhusMtZh1mcq8eF8WfGIY47jXQ2UUKWg1r0Gz3WHmjS0h69yA6REs7GmaRRgIDZUXh6XY7RFNzHqUkSj9WicUdCwssXuynCee3FYlqW3FBFTWWC3JSG7b1KFthLczn2sJbV+WJ8oXlusK4kWXpupZf+rNsrnT1oaHhzPe/PgG13nGw+Mj5ussJwesfZAPzIy+Slt8eRwRIA89dJcAzxZuwI4RMUImjwi2tC7j2fsK4iKH9p5OWNcVy1nCEnoTr0pZZRdvLRXrOgMsIMrEeWf48Tx5Q08AqaQnPNN42t6vM1syu2tbS4UEb6/oTa7VIsmESy0okyEx/dUZ3HQ0uqi6znINADrJuyQzuJzk8PbNO7x/eI/j8YD7+zvNy8U4vjjqWaQ2dimtjcsBW8LvQtdaPXGxrEYEUI3lYF3ap9hEUYukuDEAnx3og+zgkFRP3KHvsP+g53UC1FbDlW60jPpneA1cH30CgvpZY6B+StmzeD9WMvBxEJXQl4An8usfdzyNQ+iYeQBdGbkmEIWMxeHnTcXz2k57zfBOGGKKP8eu+J3ZU7XrSVMmYt29YsHh6yJnNy3L4p6pvEwZS29JCZhpasqeLfich7a5+18BDOc+DATJ3U3KOXmVWm9hyajyyTEB3lZ9QXgBMLR9OyZCe/uLneTePBuYDt9F2Ft3gSnHO0hqhnVdwVx1eaDai71G2lY+vmSY7A7uzbukO2bi8agzINmHi7eEgXHJbyui9urSduzUaHf7ks8X6X2SEmrQ+OtWvOfxpNtvY2x3jF0muAc06hrv8jFXPgGPMmZsxtazmeefyMGbJ3nbxnTHgOE45Eqa1Jkq9t682WNWeSOJJDX9gu5g80nCwChvrObEvikHDakiFmdacKqJz9xnOI9GDj9SgrEmmew6OBLXKakE/JktsezH5+3NUDxRDAVlHtI5xQaitY2dwKUDXIZHfRw21bonz2ga+4PQ2oqmhlWtkgJhnhcs86JHwNTNuZibFyZgiAxch35EGY06xOctb92QJ8Z7++7dovWZUTe0nf1TyhS/X+vH4dPPFgOVxM+etyndeyuEKSaX3vN0sOzm70FqZCAVCDsfxuluXoQis7okcVkcQFmSW3IYbop25qVCA04Mc74YgGAHQpZUstRQ65zoFvQLIaCyLnS2C1fpLwFY1477+0dc5yuu1yvO54t4o66LWCwsNlHq/SCIJLMCi/XHRQ+5lYBGuUcakXeyCx8rZRKwzeI2QIA9pxZayfdbUjr9z5cdEhgKBIK2LLjo7kEbkt6bZ6SfUmZ0OaA0tqr33tGobeAUxdBCvFfoEODZO968foPL+YwXL1/iN7/5S5xOpIHC8p5axSPGZDXY0sYIfWSbdHdPjmBxGZsOaaOMgx7vQASqYYHH2LPW12GHtzqP6qHT5nWzuTTVik7AMjN6W318AOlLoVH4GL8bl3rQ7B6o+AUXGSfN/6XLk113kpHkA0FnjZmbKsAdfVV+IT00WHMNURWXlB2lYcksLTcQkFRpAgLFQEQCGLKTjDE8ZR4aKrIdHRK7IztpCVU3dcC9IPYy+NyRPyzIRpWx8WEgcChm8TYxycy1Q7vnZcV1XrAuK969eYvzwxmP50dcLhcPETA5Fssu8Hq5iAcrp2Xw/xkaoA/Y0qctEZlA6dy9PgNHCk80SS5jmWcFeB3cV5874kUkLGA0KpC4IJEd0l1NzdKa8EVvsEzyuQwxRDLSKsvSNT+Z3NJTUFw3PQLSFBDSA13dR/cxCtrICVpV2xp6zwyy3oR+DXIkTFtX/O63v8P9/T1OpyO+/fYbHI9HfPP1VzhMB02hEWEJVhcXQte2iue1+WkO6LFT1cdUN/wUIrR2FJ1DAcR2iBf0TrpvFC2mZzEsXY7fhgNAeEveFd7deIfrl4+Un8UD5ZOdx45m4GPCfAuGMimH6wmo2J0ZL9ma9bYmYAxA34v/GEGeHu9S4oBbYaxY0zWwsh1G5vFdW+TtOM++dhd4STsDEgPtKKgbIMlILlV5aVs7Hh/PeHw843K54Hw+69l1JszlPqLYbpttB1JBZ20ECJ0mRHI0i1kylkXQguBW6jiW0Q/TDaTPW2B9koLJcRJCx2MgAD3Ik9DWJc57cnSmSe4KqQtZA6E1OeSqVmbrDdRCn7gBrADF18kZkuhTA/Hv7+/x9ddf49tvvpHjYbx96rKuJQ6hVQW1Ndos3iKAlRkdomwlAF/jZ5Qfp6Q0XJ1ueFdq8jUMobMLCxmzqRY0/bv3FuNOAhkpjU0iauJCg9xfDniywrasqx6TfFCw8T7pIa7cxp2J0CUcO65DZH0HsZ7PhBFADe+FGQZDY9IYBKbJ+Ep26Va9ndFXPUzYd8UacAaYIyZO5lgwvrCIMAv7rruQKaDYqWmcYWdhGoha5gXv7t/j3Zu3WJYZl8vFl4vV1yPvdbmo/dI6i3mWHUYl4qgR4PO66pw2I9VbpWOofbSD2dd1VvAD2AHJMqwM7iRA02RLiqMlgu4ok8PJ5UDnPV1j77YrnAATp2tQPkgwkWySUrS5i35rPkQmHPW5tGwGr0n7rgDQUm2AgeU6Yy2E6/UK+vEH3N29Ahi4u3uJ0+GIr14JUCoWpkIaAsB9OKrHcpF5HKC9exDfHW1dsRL5vU7PxOJZMyRt7eApT4Wk9Uz9joUybxvZ8/LitqItQNsvnz8P1A4oyt/lz1smGO+9Bap75UP3ZOVi9w7u4tsnYJwQS6RbN/5Y99hmx9DYssfu0ouBP4o7d/BGAlMJhDGjkzDxuqxgDRZvenL55XKVLOGa4Vd23iXLyd8TSfA4vy/RKLu0bcJkvGMekhAem/EcMGUacxWkJdGbkWlPIWxJgYHTOWaEZewPy9n6INvHmxLOvXq2HGnAJ42OeYwojbsJC0BoTpDsyvf391iWBa9e3eHVq1cAZBt3hoqO7RzjpeVmJyIrAB7EhFCc4Gf1JRGb+CFATii7kX9vSgai+vcAdpNSJXsHdzCKeEjcFXo7b3/ZZfT8ZjrH3DYP4JiB+0OUSDpU6sLuqOjN7PPCnBpdAbMtJxsAsO9d4XTWpcE4OxPm3TIQMvY29AhnXrIHxl45H1u7FGBer1e8e3eP+SpnY1ruOF96dNnBw7s9GB3bVyXFPHzlsEg9hCZ79KbsBdL+e8oCXdtijh1ihh/J6K4vy7GBXePInBWcWHD+p6SMXawSu+HCqXogT5ukG1h393mc2qhyMs/sAQx7gRhsoweHGZo3SnVuB5Z5wfv3D1jWFcfDEcejZDK/03QHFkjaDShp/ZHSZId/vE/RPe/HBwrlD7xzfWAOupE7BkL34562Cm/Txo+UzwugKAkaEy7G2IkZ7GCDnFPHxflAgKdBir1j7zqlSbBdQrTAzv2lxTRLyKy27llaGbJUw+CYb8nStypC+CJdTDQya4HIF+Z7qsPkAKVEqjbhfQs+GIUEPD2+f1TPyHvcv3sva93L6l4Oc6Hbbwv2I1hwqAELi7wm05zB/QkcmaKWX7HtmCiU/kAUB0tI42zfkmcGBsEPmpZnLN+S5XChREdSpdHdi8ZFFHs3BcgdS1slINq8deBh/B1AuFdSRTRD8pvocgCXKnQiAjrh4f0j/vZv/w61VvzN3/wjTAfZKswsQaBVc8sI+ApSSjOkb4U0+JPUYjZeKtpOdNkoUHPw7Miz3ZVpLFN7SggfNhPi0WcTOvleR7AqMCMzMgNNY9EIkEOTBUx9ipHzSyrmgRp4xAGN8lfvWJokv/jmtAAAIABJREFUjG0OUiLCzks6jDfAjM55hntE/OgVAIQuGx80EJc70Lh70HNbF9/FRjpHWXekWi66UoqkHyFdEpSexS/jH50j5qnIuN5yP8UpHWGwCIBhrLqE9+bNO/zrf/23cnbcdUZfV53fyjsK4ghARxhxdrWkDSU5HoeVpuHhg/fTspMDLAH8KtMA8iV6dPNEETox5PQFy3KeFDuZEae7JbWr69rU0AqPFvRvuLcsgMQw9APVxy9Dd6RnKVYDChjcrMtxCLAlugX3tNlI3jTMVTv2q4k8NeNawIXkx2rrGZfr36OWgof3Zzw8XPDixQv89V/9Bi9fnEClg4r03zZMECQPVNdAeOMbPxWBO+JYFw3GsOaUuGaAedeJQdm3vfFE+j2BMXz8XPYNEi8GI6vp2+F6svzZBZGbchysjtTBn1puls02322u4GnSbQTNWNH4/fYWA4gAQB9RKgx3kduzjBtQ7TcTxnd7gKgmu1tX2Wkn24YveHh4DOUHyz4+AshSCODiQt3ARmTeHhl3B8Y6eBrNBtr8zn/p/zteR18qMCCdBKYnbFQaD3QicYUXnYyi22lY/hsAsoEoB1Pbpm5Ab/rhbHpD4syWZQYR4XKRXFoA0GrR8SRdxok6wwulL441ktSnUXnAFHf0KNUXbbtx6VMIsP1CaZRHMaU6FqH4ScFxUrL+/i+rPOmhTgQKDxWwN9lvZ4Eqt2EdIc1L9wAqj5EBslxJtIs3g2rtiHZ153G/gTa/kwdmqCmxeXboWLPlHaRJfbt7vB8eH3G9XGTZWeP3avV9pXBeSg2ypUUzYqItowfKfulT2vzwuIYHaqRV6hIs3jI8TyktBSQPW8Sq233jRhsl9IYo2OWBm0RZN/Jd6Jh9BdmfuXXy5qGI/lN6xsCT/Y5xZECMIgXqBRLGsKwLQIS7xzMeHs5gBuZ50aOuOoruSDUamBFx68TIOjHRQpvDLMu/xn5OEl2tekqKOP/zzdXhHVLXKE79sS3dE6b6lPLZ80CFAOpOPPlq/A2oCDfluBmY2+2HfPP3eM1c3n1gsPHZp5GatbX3UYiKZyCY1qaww4xtO535Ob2bxtdu5mC4HjGsW/utSs/e2E8KPz9edJluxcM7yVd0vcy4XmZX4IbyAzxxeETUhomAeqN5CntMNOZto+y3CzddmiJT+tj8NkBU0kSUZ4sD6pgBdnaYAKgaHqg0XmCAfFsxpb6qjzOPISMyejs7kE/ikVc4JqTFBJhFq7wgCkQCdt+9e4ff/e53OJ1O+PWvf43T6aj9E9ruJ5VLAE1pH563UDldDzXNgsuAsH12AUeR70o8FGTRoC5NWM9iA0tqjWk6wM4y3I4tM/TAavhAiyBltSr39rn8covMDaGr7U5qSLvHekoBYVJAx0QSMRbdal/cu16KbMLgNKaW98tnWtcFJtITC9SoYtKkjnpvoWgXt5BzflyWGR4oHrPJrLF+tnzD5MBgAF1ZHum8DrsjvLrQ7fTX6xW//e1v8fqH13j7+g0WjUUkBVDmDRYcwYNugCvN0HYmC/IsMUPTNMegp8liYEO5Gxnce8gCUqyucftPGCeDJ8hGyWQu69innGcyU8c5LTJ/X7ckeJx6aPMJ0Xl7O+c2ChLqKrdCdI26LR87ZbFWCZvBMuaXQhrezi437+8fwB04vTiht46XL0/41a/u8O2v7wAAfW26fEwgbpqtnb3fEYcFE2chW2FJQ0ddHS3DoEcGEm0/JwJJn2KcHD92dm/ZdifMjb36FHJL5WfZhbddFpOPPLCOL/U4eDDFuR8btWfthovdnnnaKpT7xgHfA2UAAmg4+IvvY+ceHI/lCeIxNKbM/Juik16EVtkAqMjDYUsHIWzA7IyxLivW1vDu7Tu8efNW1rLfyWG7ot3Vypiq0yQLBE7M5xyv/bGcTn1Dm5ikfiFT1XoN349BUCBJw/gSgFImzxheSpp4Ox4UczlbvhL7iWUxjrcz8mka8ENzW8q7tAHSxl3buD3jJLHWrGnxDgMw6yo75N69u0dnxt3dHe7u7jBNE0rpKEVBTg+rOdPPwHlqyAjK07typnYbk11PCXSJgiE5ZQgqcGIudVWYtVSgAg3N01p4+iPnmQC6DkqjcfgkKfQLKswNsqmh+DzoLLlv7IDUtqE7KbCJg4QtR5oFk5MHO8fB1gaiYiyJAFYDw/W2fhBRqV7NzmhWHyIeCLAlKPJlQQFbElhOvTjgAPIYmwZLik1ldtffzTZerB1tbjg/nvH3v/09fvv//Rarp/YwZSbb8BnmeVDw4qAmyU8DMQiQaW3zz5SuaVtjjrCDvZwYF8rjBojFg2pnWer8941BWrv21YwxlxksoNABir/a6jYwFoBNatpdgFJZEMxDBu1sXBTo2iWTrRZd6SDbZIDL8J1NByrNbM67xGRWPu54eP+Aix4Fs8wLTqcj/tHf/CVefXXUpMbd8+gRy4aangBb0CBKyLGcD0vbMyC7TBUZbFbQmLJXJLkHp5UBZTNIoOPNTZOnWsysgidwbidjd3A25fMv4d1sJcnoPCkOV7isVn0GG5/QM9wqPiu5hq2D0I4QcQvArjsauvUqDaA1mwHDdwaB7b6dBvNtsCml76wUku2rhWyZDe59ulzk4M15lsSWflJ5rvBJ8tF43xP3xDjFVBxc4gMStkk/dBMwyJ9fp7S1JTkHUKlRObjcQVMJIeuAVfspx0BIxnKyM5lYDuh0b4ymau4lKRrtjDU1FMnGR0qZqCbqIpbALNF1bX4GX2tNTlgntVSNmEZa72NmJXbL0d7tz+38ptsKdrHMCOLTs08An7H3iLGw/g9K78stxh828lnB+bE6G/wrJa44QCGE4YLgs2EYNnrFq0JY8kPrfHKmZe90OsFtfyz8PbZ680aHZLkpRtBtsYPDl2XVXaQJeFt7SPy2oQHTxgnEta30BNjfm9sRFxIvh751sOcVZRWUOuJkoZAAQR3ejHOAETd0gM1JSrT5nWscf1sfduf00EARsGwPpKpzeMPg9UmMZHIy5q7oXibhQ88oTnpMjIFBSKzvPF/BYNm9fbn48TYm/Yxvc49DkybC6/Dviqc0wIO0UeK4lB0Vub/XjGSK5mzfEJRJamgcradlYC6fFUAJxm+RlReQra/6rR1g60F+LICmQLbddpahLzadMqjwPE5b64+Ge2XQTG3ZoZgaBFgIZZos1zCae75VoZUq2a81r49zAUiYmuWwyvgn/SIyYGUu8VjPZxUishSijJg8MmBIjhldUrNc50XdB70zLusV61Wsvu//8D3m64z5Kiecc5cMumUSEGEBfjeCkQDbKSfW2DiLbVLBhL7fEYpa5Eq4+z2lP0kWXSgdugVZ6o2THhBciPSMO8ukXkahNgj/UNjFM+Bt2wJwLah2NIR6Dltn9xL00jzreLcjHLq65nvKDO5Bw0E5T6+ggMmnngpgyzzPDFzOZ5Qi8VCHacI0Vc+Q3np361GOnZFjdmSnTgd02zHMGk7KwgRXXGIPMmaGCEa9ljBufLCLKWljKeJdEa9EVzbPPkTj68zr0hI/5Nj94V8YjCqmWnOGa/HciEcKTtfCoUyMO4xNGZHNOSuT8BhoyZgiayYS/gMxSpUYHVoJdkB29ihQETFfpipb/PUAXUuNYmXwsHd22Ueah8nBwdCmNN8ZEjLw/hHnxzPa0iQrdmdJAMmstIpNH8QGQ4vKWFteTzjCAEsyLKIEU1MiLuuuul3uY7gniABz/o/AiQBQzsEGQxsIf7oQhex77Y+0mXzjScZ2WS37fty9bdZsEMlWLOD94fysyxxy2ln29+7glWHZworJYiTdiLyZwJpCqFU+SbMYra94//4eVAiHIzAdC46HA775+lc4nY4osA0PmjG9iA7oKWs/9IgwXW+WHF9FTtjwRQ8yXvNIv7TEZ/1lm0SJsEKdOKKrAIXBrYM6Cx8CsilJx8r5EGn8nGk+Lrs++y68dFBS9BkANh4oDlijwjvKGHcD7FlU/srNdzmGf3ybUM7c86NooaTAy02d/hdH38bvRjASFZu2t48JRLlK0jbmwabNt13Wodd5xfV8xeV6RVu7pyUQAKjMRdG8PYz9AVIOZaCNtTF/pyAm8yOp58y8ZtvvzJuU40NEaMY4W96p/G7fpEPRLge9ig6FV3SHD5GDKZiAMYEtqEMEFyGdM2Y/6b1k/BVTEIAG/RIqRZ0WUNtWWWL1IzzcGAjMMXzO1M7mUr6c/6a4kr3R9o7xgfiehxvSvNljCNr7mJYXaaTHl1VGomZApPpUMmAn/qadx+0kAvfQIvOfPknDI5uhMP42eqcbtDLO9wGw2KvhMPNtsWe3vOU9Hl/jn/SP3nucaNAjgaXVE/GMm7o5ZIDUuJf6IS96bdcOpNLBo2tSfWfO+BN0+5mccoYaKepSzSR/RCqCsTPWjs0EHHri1Q7fxhJ5utHSF+RNOWp45zG20xeyjkjiQPuoHnvXMtaW0IbWHdPNJrOZOxZNSny5XPH4eEY7dby6aziat4rSiPvA67glFJ53M7K+a+zzSLv95c7EgSYaN0OBzXOjuaf30+YuvsUNT5WfYReeWcuWL0XVUwJFufESDFt8Kyo9IZy3zz35Wf9n2Pb6EF4ii3TppQhYYmLxZQIDeBJPijxpdbkAyGNBsdWWShH+KQEuxnZt/5JhzqssTRF2aw3LIkci/OEP3+PHH1/jOs94fJR4Jz+A9pZSymwloONmi768OimIoV3acM8NEPfZVt9Q4AZwCiaLuSrkSwlTrQ6YqsZ21BJnVlHJY3gD9W4Jl/CFCQJlLhcUFrcEs6oVXJFmJveAUy7g0tGL7sDxH0SCOJuBADzoXgUFAx5zUaom9usNb16/xvnxAW35DodpknOp3MI24Z+s+lRnZ4glVRJ9KClq/6+n9vIAWBFN9oD9TMZsiKaRdMVu/ZK8YZJltFZCPk5HeDx5Ub+QQmq9x0aONDHVi9nYAmot/YeddQfP1wUY8KIAyaa0AkkNiMWx9g2iYTcGCHIeXAfQ1w3tyTZckCfBBfMQR2JrdpziAYdYomi43qeGLnfN7dTw8P4B796+w+V8wXy96hb37FWjNG8y2MHtOzeFn/gcMt2+C9A40Nsvxg7GYmPjOp/8txnM/hZ/qR9s6OElbIDNlsN8+LZgWFcQEknDl59ARZqsZL8NTMFOMkhU4Kin9a4nPiXjz3VVV08P6WqK1F43oFq8oy1WTjyOTnjkcr7g9Y9vcToeUVFwfXnF6XDAyxdynmAfvPW6ZlTULUWIVCAIOWNtzaw+sMLALENzPxnwbJ560mGwF/qzVz4rgArMq4XJd6zc3Ks980Mqp3BRDtZ+Ak6f5JFKw+PryH7QKnkwJYrFsHSgkyunCFQORRqdM3f3RrnrbNWNJxIgzp68OE20YKYczCh9y6CAMS/iKr/OM373+9/j73/7u2GJyXE2RTB2WjlPY6BCPgmBAcnf0JKQd5wYkPK4BrLDhOHHVpRSNHC6oEwVVYHDYZo031Qa0WyVJ3BmfzvYcLES/9tHlzOm5IZB0raS7gCBxkK12DJruUK6JtSMuDbZqSj5skYhlwPQLbuxLf/VesBUCrh1vP7xR2/qy7s72Zk1FT8QNXZeJdHCGZhpYtCcXRTswcG+s6XbLqw+8k4iKNn5ZzYlkhhTQgUI4lGomAAEoMHwcbKqKeXyRwm1P+9SqaCTLPESADvoh1nOaexdAFQ+IJoAX7IIAyu8RuTjoz/5DDzKM9p4Qu4zwCWBsjIWVedTkyRiMFkX3l018phzbcECnIwq967aDQaikqLvorCv16ukK3h/jzevX2sYwVWOurHjiDbF5q4Z0k9t3vH7eeDODYFCHkSrE43t3qSgbdeiACk4UI2NKjpejsyC9gII5LIfrAsBT9Kmnj4DWWZZSwLyhb4w31XGCqEbkt7aGJQ2diHHjbZGew1FMCBIhIrQZ7KsZ/2WhL+9rX50EQ9LZcDl8YxlXnA4HkBMOL8846uvXoGoyEaKDpWD1sXgQYA0yeogRJW2xnf2uiRD1K37KeBmu0q1DevZ+DluHDCfWn7+PFA+czMtafN7769PqHpLEK9WkUx+uU9AX8zz63k1IyZXqn9YxrH7k0cqvSm/EWYd5D8pfdbfLjQYHpQ5z4vkVrnOco6RbTPXt9laNJCYRwEFq5fCdsCEF2kPykbjxkltXQ9QYj+29CZnBcrOo8PxIACq2iG74oGy5HYu/pLnzGOohp4xckR6lusBDPKoEob1FCOFXs6c5sJSBZ6s4ZOfmSQP9dgxBfOeJpbygdJ+sCVnDQ8XACzzjPPjI+pUcTwdUKudMWWAJZ4P+502/GNtTWAyGxEm6BMJdicRJzrRzjsyLVP3RhSpwo1M0W+F4xdS0rj4+PSNx8+Avt2n4LaUtHxi2BfYCoX8MgwD6AxOhqujbL199u5czw0/wT3ifl9S5jclhl9foXXpaQfLvOhhtDOWeZYNLCY0dnnhj+GPPWJR+t8+0ebbdJeRnwA5/Yh0R2rIMvvtgHejm2xYnHwqE+xaPt7E9EHUoCBnkDeIem+6djsYbvCTPWU8kuviRP/8rMn64BExCNi/z2kPQsgB0F23vTPQGspSMF9nFBKD+HqadTeqbdixdoR/aSvRB2cBbcfyVmvmP7OO34KkLdD6GPD6Y0DU5w0i5whituJbVHmY1gmEpDPmlHJ76HJLyKeW8GzyWK6LaIN8aSkKAIiw4xhUpIm1jdPxwFlExtsMKnpeUssKKGt0+04/cMtZs+XHdre8fv0Gf/f//B0uF0mO2VpPFQOoVWLdmVWQiS0swIE0GNZ6nRQtbqdrKE5pHDHHmVQqiczTRIXc23Q8HnA4TKi14vjiKFmzUwzGLkz2oG1VTCqgjT8CXwbtMj70D9pWWQ7MuxYEo3Ubi27TmlApXOO+7FW0z/p36+STUwLPDezJ8LekTM0Fvi5yqCwRebD829dvcL1cMR0mfPPrb3A6nXA4HDRHVLCc0QIwKzl9Z/Qzy13zUAEQ9zxY4mDNWNgIWAN9Bh7Fe8U2URWc2hzRI2ttng7eKNaM6aRKSHY69h0A/IsuzGjrKkH/lr2/S/qCtWlm/96x9oa16YG0Xc8nqAXT8aje1wi0l7jAkH2+A4+TwC8c58z5BDVPBEBVsr73HsG4sozY4NOEAGqyAaeA/d1Uqh8cLswQMlHQAIfSM/7Q9nXNQr0sC97fv8fDwyPevn6DN6/foC0r1mVxjGDPhAd/lOEhozkBDhrvgdX1hJKj8GW7yFZKWZXC62VIlSK/S+z6Tco5ljD95c7RFsjdGeiaf6sXW9YskT4izWXxxlGcMwgTVeahypIt+uCIz6uysbI+hxfY9UcC+oIDTf8IuJG8dfLe1i3PG6d8eJFjzOSfj30TubaWjh/7G0zThMeHC66XFUQku8AtY4V606d8Xp+n1ClyQLmm+QidkCmwI0N0LH1MdsDRh8CQiEQL19h/9lPA1M9zmHACOMYcfGtSjR6ewE8fRJLb70YgdXv/6DVKdQwzDiMATsAo3L65zSN4wk0Vm/YbYypip3TZ5pkBO9sOf7lc8fbdPS7ns+foEQtTgFStBAu24i6xKuI5MQaWnUP2tl1WcYFzS9sQVOSK0w7xrOp1mg4TjscD6lRxOh3VyxKuYj9DKggPE9p2OhWzKGcTNgXqJk8Tx7GRdye+J6hlyYFRfUeHvTuDCCRhlkBXCEJNVMgkh4aW/HJ7vY0FK4BtnuWXiMClyJLHMuNwOOJ4OnpDLFbMeUrr8fHZGyiX8dpv4ztO4CrRxMZtqMKNCN4VHsPyyg0uUialxOV7t30BxTxOkWMmAWY2wN8j9MCAvxpdw6Hjm8Hk9CGxJmLpTgshO2HTWMVYZz+TyZTOXY5/IjEgchyOvU3+jjCF20KuvMNQkBio6/mCy+WC6+UqSWQtx5J3LqG5zbsp884fUTxo+oZvY+7Y71KwA6Bid9ogD9w44Zu5Z8ttYMgKGZGAE3Nv8xhXxHb4t3c8GTVs0CkPrEPPcf4nIAOVhy6vgIGwDj6TYmH920FsYjjuXQ9lNiDLDqKkvqBH1x12F76i1AWlVBwPJ8kNpefEyLwI8LYFwDJmmjzZ9XDogdyH/Ix/pg/jgT0QxGx9S/T5iZ4nKz/bEp53mnKSMFY5TA6yssViCvenroGOxMlIyZg2LuT0/BY0HNIsuz0TcKBQevaO7Xr+R/C031trxTRNLnBB5An61rXh/t09Hs+PeH//HgBQ6wSYpQnAt3JSLKUEENW3Z+ieSWIAQpUks1cXQMoUI7OnG5Dluurnux1PR5RScDoJOKhVvFERtCzKvbF42MDsCofVsmezhNQbAs8m2x2kZI/UOOcIxQ7gte+y0GHb2stuCYY/LoCP/jW8gyBbgZmAiqJpBhDLeiowwEDlCFQX8pLzGLrGSlVTMkITS4ppFrGdzQiI0NtO87HuLYfxhpfDumdmzT/U4wmy+TD+EN3WC+2rLVfFmDwBxr+gYnMz09VLxqkEyFllJYxAiOdCZkECE/641WdgBhhGPQWi28h5O8RaAHFxfrR2ZOWZR5Fhc9A7B5tM9inLMPGudM1O39HWjmVecP/2Hm9ev8HD/QPWZQF32Tqu7hU9Hsqeb3AjAzkNQG5dGG5iC+xz1WAgp+dsOa6WkuYI/JolEpUNEAKeak7ci2xUCLGdKlrRqh7yzkDTNB9Nk5tqBrgYQ2ZwKVC/riSfFNQl3+VBciq4xRecwvGtfEpG6DbTeRpo3/UpjbFohMHzB8Cz6jMwJnMeKGNzQNusxtq6rDifzzcGoOkg8/rbbr1h1cj7Ym+6BU/DkvSmPR8q+8YgP/n9Tyk/ewyUOx2Agdms+FZ2RiwJ5IE1cJWQ5NPXYhLl9d/cFvP0+FJeqkesNmVmsnpV2blFOXqzUu2DEt9fn/3/uXubntmSZT3oicyqd/c59xyMkIwxFpL/AmMmSPwAZp4hIyF5jMQAi1/gERLTKzEAiQFIIMEUITFgwgALiYGnICxdzrV9unf3/nprrcxgEPFERGbV++59rlu9b+/VXbvqrVorPyMjnoiMjDALzps3TzAhZ98PtZx2x+3A99//gD9+/z3O84TAtssSsGkQYJPIzwg/SFYGHIAYG0/rGcfT7pkxGW5i8TnIMrK9IoLrxYDf5XLBm+/eoPeON989BZi6PrnDuAqEczkHZHIbxIDTPDen0wBQyYw39hKfSUsGdKQYRBI42Z8zywoztSfpVIsXElspKMzLGWoyWsHUDHnB+ZrK8BHNrUeCVBCtH3O6EOzThzcBt+qEuFmbGj7Yko32XzI1J/3f03hYHtXEZ5Sdgxd0YXLfTgginvUnmGKkNVsbL1q1vq2LAKqm0NkFEWD0Ua1OZi1YSTLCZfjSNcyxOl1rmRNaMnOrmUmCPfJR961WQaR/CZ7nADlA0cYnSw+tDiD4BVeCwoDTVMU8Pdfd84Hv//g9/tkf/plt3d0OwEEEVweVDtVUCFaflVVgpmVBgo72Vtb54EMET4whR2s4kEK5+329nA6+dFqgGEdIU8iTXytiXU5VHGNGzLjRbM0fguSdfk1/dqpv8RHw+q7ZfLRKyA+wb+5xhrDMDwAILV4S/+SuffA+5wkiUKlBlsucGLmZHxdCqtnv3lSL4l2TuAuO48S7dx9sPC90b0Eo2kM1AAcPM6SxhPy7OJYU2lyBFFKh3xTI13ag7sf3C+995frqAArAyyvDr9fY8EvWqLrP/krBd3W/NoayvJcbE7d4mwrTCgLgjYhKq5WLvxO8iWicVDiOw44FHwdut1vEWAkstHVAHvWbfXXGmybf+HL7nAK/btfVqxWG1YRHpPPF3fkUxlKCnAVaTvRcXw+uZCll4O9oY9dVpZRXAcgOwnIwc+6S6co6gDEeMXIq4HYBrTx4tEW69M1odIwTx3GY/5i6v8tGI/noZ2g62pbV3YMpsmMJ076iCqkHdej9yJdRjbbeNeBbu6TQk9xRD3ZYv06Z0TbBC8db74TbOtcF2m515bO8c4FvO3+rjKnOD/G0f07wgm3GV/Kdc+D0mE/ncWKcZ/hbZscFj8hpv15i1bGWgLuVThbGdcgwKjXBeCq3yb88ksodv2pel0ToGedUAuh0JUgkyuPJPZscA1Vdi7Si0g61bbZZhlvyJLAb5xYufDc4pfMLe3l0/3KzxLyH83odcMltyJWa19F+ND1VZ1DRzDMnMEucA2eRlrxBkt3fV2CuES/XWL9WrOzmMb/5kt2q/fk/BUj94gBqtw4lWk6BcdcBKa/YX/6yuvbLiJfVWVm5vSHorUO5R44kdiuvBqIz1D2nHfls2mzbSAC02P4vRJZgaWoSG0/PUUM9jltoaIcnBn779i3+8Ie/xHE78PHTM27HYZrc4HbjjNguutC+sdZoQ8j0Cp5QFlN+5O49x6G5f5M53ZnpurVmJ+lE8NQ7Lr2j94aLMyPRCfX+tellankx4rHnJ0qfkhkMrM5j4Kz4Lwss2DDHG8CeL0AAa3/1z5AaXjW1b2MK2zw6KOI4W1qd3NptXXCRbkDYdyroEB+Pe7sVgnGeePv9D3j37h1+/zd+j37t0N7RTlmsT9YlB9fFtr5bWffvfaDL5yIc1YGTmJNzPryBJa3O8Yj+D3c0b5V+/Jk4Iv6K8vPrvASKhqkjxk/RYFsx9mrS0bu6A20ehHD8hLBsz+nryWkSZEtO70X7nnN6HDl3CFbFchpVMnwFhRjz4ukYwceCXvxvV+P9O3cX9vagRK8PGvfnGgSfPn7CD//ie3x49x6fPnzA+XyYIA42uG8DrlueCeq5znG35hceLlTJ8mKSZrP25XZc+BI6ysoo2UBv6Qd1cetgk+R1BGRSwFeVwwpubzm48nGcqjhO8SC5dqBGIeEnN+fE4S4KXS4YvS9r/JHuOD1hMbcDzaKdYlMD6K3rl2MnMW7kk5m/cEcyidkTLIrPk3pi4fTdS76i7hw+5oCeJkfHsAgvvZcQAAAgAElEQVT7vTdc3b2DzuSArwt/hcI/ExXyzhRd/txdKjjy43tAMMu437kyuKYciv1fUeH762GB8utlvyW7pH4qWtJyzxcMBpmUqGIvwYJoZiyKZRKlEFAoWXVisoJqAWCdIeo3xljLHiO3EW9+4u7du/f44x//iOM4kE7gm+ndaToJrvxRP0a/1DWkjTMAxXDiLFVMIDIdC9RS6zT3GzCTeb6o4QkHYGowVfY7xi3SRmwEHvXXDnojC0jkDEr5HAAgOYKVVcBVBY05v4iyyVtmjKsiHWs1mDKzJpIJmOCiEPIUGwudsV3GLOac5jfwLHh682S+EU3C4bcCqICOL2hVO91XoXX/fWkPR8C11WXYkALQHGlXX69WBetS3l+dKf21voSUxnHwvwtft5PDHfCghdwWoTUCANFrkOI6J6kkLuJCc6Tz36ovpbAQWIqZiXG3pRT9uJueBG5sczY1t/Xp43IeJz6+f4+PHz7gvB0YY9i2nSZwYamzdrbWYX/len9gCZBlge4AypReIQASMT/SfinGQk9h4u2+9ARQ3dPKMCm4IINrNqlJ3JOnqNq67wKo+6RFQFxVDFCO1ETRijH8dKoCAlO4VRXTf58LHXg/JwB3CYjxL/OVIJg/FC2Gc7XM73QHcutRgqHCCZw/0Q3C1jpPMhKU6WIBCww0Z5kw+7E3KbwTPoaksfJlNGLlJLXTQtnHGSnW+trnXZ7cX+UgW8EM61bh58Ma/PUAUBRSL7S1MqAUQHi42Hj/Z0GUpjBSj5Sr009MjemanixaT6JVazT3fmnlCJkcFCVlzVMbK3vPks6o0SadgFiwxrc/vMXHj5/w008/mZncw+graycBFIkn5V/bKgBWq2hZoJrB+HgDFwlDli7bc4xR5CCqNdPgeIKli+tBYX6b0cbzONBG83ES14zz9OAKYrj3TssQ++YeAVJ7Ue7Yx2FZWxUopNl80YIDeOldOTHWvFsRASOF7QYzTnm7uqQgcqaV7kcZnkBgvmHneeB2e8bUi43l9XqnscdrKmaJOB7zjayrWqdeAl3sy1yAnkbHkt5ZdF2oCi1jGqMm1HQf1/lrvRR+ZB0JeqrwoDyYHgcHmltBQBFOvi7rai3Y3UbVBUlVPLIhPCTCdpE/FsSVyGwNIYXdr+UOhsVd1epR6ax1iyz96eNH/PN//i/w6aOdvNMxYV6iBgCjfVpdBpKXUvhVoPTIAhW585hmq9zXGUoGtEABTVpY5OnoTCXPAJKig6FL+AxiC49bgbRMhbBmfwQGEj3fJCC5MdLdauXt9gEDAAyZzh88kr/STWOGQqJl7SrML3O4JXCUwJT3SytntRBGvEUYAiUwkuwR54HlSM6biTGz8mU9cF5R+JJ1FJFmIwDJxJwtghOHJekO4KzznjXxqqiQMm8NdhB3LkD/XpETIuXCix9dX6IA/uIAam9sgOdYFBWRFuEQfyOohwAEQLxn0tf7AfASFqYQyEctbs84h22rqR+ZL0QZ++LVElVY0gLwNJ8ByDucKbV2x5hoeif/e35+xh/+8g/44x+/j7QtAfbUts+uflqv8MdgrIwfBS0AKsDBRlBldKAozEbiFEtvgoszKEawtjZYOpZLa5EOQHwbzifVAu2NjGysaq74tgWZPhMUFqEJJtE4KNBgQE1yYwrO7oePj3ghO/nXZW4OrcDQuWhXSXPuWL7TkMDz4+Uoxvapgz9aaix8gkBw4tQyH/7ZYnfZsEuzOf/48QMu5xUXj6u1AKSpnjXdol43bL/X++jIvikWd5cCKg52ZrqzmvyWZRtOsyAXkljRo0jQmgGyx1kGfs2XxVhCJuvdX3467TxHWDgaN7MczIjC/GEodFHBFWKgBZKmkcrsCy8yepp5YrW8eGrZyhbwsDiYPbeSdgVZwdumR91Pf0xxpNEA/PTjO/zT//ef4ni+4Xg+LbUPEohoQX9UOqmQppWZv62nruuLB4la6y7I8/BKAEHhiegFSuSzko7lDQMNE63BeZrlraQxpz8Quqkc5PcXMbA4I3mzAbJYZgHIDPiNOXH2lgAKxq/OkwAqQRTHfYjFnlOdOKc9M1EczyvgLGlnjM0XnoPKX1uuYe8dpSxlncJ3QgS4NAt+bPTt0a90pKJMoIWye6BGY4zSPqc4LeZaYLiPMYbP6yVoehv18o5oIzTparcccUwq/VSn9ej1xp7+VKv5L+8DhYJ8gXXhAmVSX77uIcD9t4p0fn61rDqAC7B6dMn2/nL7HrXxNVFCtD/nwDntaDBz3dXgniuXLStV73liwuxHfXjUsoD2eWthLBKMmwtxZVhROkEaGXltNghiNfqsW5XL+ikAd23r497ZIl6tJo8fyD8SXz6myxevl+4JiwMBRYJoLaB6L2vOaVZGMR+Cu0CUMdaytZmgnRxFc+w+d1VwTcH2uDufKaeuPTbt2wJPgNPuwsG0/FuGvdDrF42CCzdO54pkEXO/VCbb37WMR1Xo3tqtjrtrRViqCkwEULJgmeZmgPmA3wZT2IQf6g4B0eIKCOzjBqQeAazgR5L8qNBwxfg7uIrv/cadvz3mnAky2Nwlz+8m4WgYaH5fjxycZsWaTYFmPCusUgqYm0QSg04xFyGiG9nbtxFO6QAtmgQMLy/n7GGykBxJc4aX4O/xcXlSy4AgCDppcrU8pfJXx3YHNvtMlDnw50g2Wn4KS9OLPb2/vmT3ql6/cC4835f3WBMA3zQWQ7kZErqbhPABEJqHyWgb3DFmDHMQAI89VO6zCFBqMB22p3tinpbPSnl0nrduS5LmZGFuKSBNmabm5MJEMl4Anj4AGNNioqjnV5tT8e7dO/zww484jgPPz89o0t1j0aiVwdpS+yrWEy4SpJBVMM7ICkR2AQDJmCmVUSezySjTArgmLRGB2yJ2JzMUtwbN025gqhJziKTD+Cht80jqlTY4cqFFlYVVhEqakJcBL5a3la40xinN7LGgSTvAsvVxt0KTOy2XILfxwHECclGKPdRaw6WWLcB5O/D2j29xebri2i8eoVf8KLbg0joul+5R38UVTs0AfXTCjCkVt7ZJBHmsHJSaO4UuE+DGmBbfNAogWrZy67quo6SbeZf8+dd/GS4yq6mHeTUrglhAwSkTyhxojVaqgYsTk/iciZQghUgaSRpXVFLnNjrBsd2r0JGEztOtsUWD5j5QMwJVMH6TTG4tFwkIRFwgVjVhvkJTFTosHdEcA+8+3XAeJz59+IhLuwJNMPqAYpYTuE4zRbhZX8m1nN4IN9SBBgAIFrDUPHJ4a2bhgd8nmrHo/LEyWels3eBbbgQuzssaxF016qk7U7132BvLn/NjMwML0WBzGvJn4zsiM8Ba63aDgtt/6gnLEVt6qoqzDf9bTE7MiQNmMR8Kt2ibDKFCOnV6lR4/EEoKtfe5W9XLqlXKKITlOmSyJmhmP/i8clCDd5LHJF+YU9DawJgnxrT34zyhAM45MDyeg0p3J3FvUFBKaS5x4g68wWqLAhgh5X2O/cUF95p1/ktB1GcBlIj8WwD+awB/y1v956r6X4jIvwbgvwXwdwH83wD+nqp+/3phBooEHq3ViTEmrMVNAMqpCD7Mn5OasSBaa/AKlrZxSCfw/FGIzhRxIoxyxuaLgK20ZtGAGN+F4CS3OEqGFjOBAwFErO0zzJxjKD5+fMYPP7zFeZ44zgGRZqkXmqPjqcj4G6UP7HIxX5KAH8n6hWAikq3ESZ9UhZOpUtiSCQIEbVFqjq2hJXOGJmKMhTZjuyO1LA16CJD3wCxbdLul1v3KNZb3BTR0B6YEUbVaakuChXY06W7dwHvQggJK7r53VGaxuVp8DwDjHDiOD7geV9x+/6/gzZvTTzZ2f0Zw6R5XKIC70ZBHsQK3R7LKIiQXUKneHv9iJgOM980aG+OEadCs4DFqe+pMDBCUbFpf7fpZ+ZciAD//m34qaLFMlUVnFgUHPnc7Z3n6k++sY+kD7nlekSThDwKkO4FV1ACZQdd2MGED07rVoRrrXfhDNGxgjBMf3r/3ZMEHmlgKDohi+tZei5yKWvhU9kaRNJm9KN2zidssT65Mb4qj8VMrV0qZ5EFC2cI+KmPkZYyqHLU6E4KlZRwPrRyIQr4CrEcXfV45piv/ZKBMnR4nak50uDVKgNE8btwc6NPAkwEuIHynTMJAi/UnKNJD4ti65TgVxT8pacEnTVryMY95ZuOQREw1HSKLYpoj4SBZ6UA/MGbHOQbgit3U6YnoC2yVdSbqPCRjzZ0Gbm2vl+Z7GfLPYaM/xQr1JRaoE8B/oqr/WER+D+D/EJH/GcB/COB/UdV/JCL/EMA/BPCfvt6y/YsUAlIXqv9WbM7bw6sQrX5AijIA5ACC5fc4oaZIqeDlhFO3sCwsgsd41Tq4wfDKKgph7G0MSwuQoe1d4xhj4vn5huM4cbvdwhoVfdNqAi3EKSt4WoeaPdXluXuZbhTViqZnoNBaTifNJi2SZqa5XBYL3NaAZNLI+CDTAUps48F8ZXahUHsb41B6v7Jk0pAkb1pGpXwuw0FzPfFeTW2z0xiBCo8NA7ugK22owM/b3unPVObknkFIrIPzOPD86RnzerXne7fIz80cUZm3KysyRlmblNY9bz8/F+uQ1Eb4eqgMtvaVQQOXhVF+ExFId7pT3ZfJ17p+Pv4lLoy10DxBlQeItDFKv7rQ18D7iiW6QAvd6qjf2UgnpUglzQdsMQCEz5UIoRqB1QPBxCLLfJsVMo+bT7eU356f8enjM47bkb+T6YlzPdKZqm9bl/ZHU30EvM4IlRK8yBQFO2XHE75UHh080Sp1N4imtLV6L2hdo6N43ObfI1pljUzZxO/i65Uslqr3jzuX4Dgz0K4yn2bzbT0xZaipYjagTXuHXjCnoquiTT/NJyYrhgJDk1Yof4y/ka9UQ0Bp2V1Dq+yQOzpZ+B5p7k6w1PvtmdN9eZvPJzMuZIqv8jhx2aMC70b1y69HLjqvgaXPAanPAihV/QsAf+GffxKRfwLg7wD49wH8u37bfwXgf8XnGBBy8Nkwe2vu/F2cagUWR0U8SeLGsHNiZSmPqViqBjPLoElhHuIhupPYxLV9K5fxN6YjKPpz7v1RFzwMrKYoSZMbiTgXCk9ERCqE48RPP73D8/MzPnz4iHEO307hKpe0jALBBLL3eVXApZE0gm3dYIEAwnQsyKBzEWgOYnGe4nt32KdFRMySQkC1zLEg5szMxwbKtNzDvH02N3Qmz9QELLPG5GIf4lh0aEPiDBXxnVljiiDyWDuVIQJ0+ixW0TKyNRq4cuu0dyu70KxwQJVpHAKP2FHpZvMyCIxdwMVzfq/DVDvVNBVPb94AAC6XbtF9RdCh5shbomFzfklrHOPJ+EJqrqe1b4Ich1gDyHURybQBB/UlJhVFoJiQneeA9JahLlCG8itePyf/Erj1uAkwbXvshFkKxpw4h21xjTEw5oyj8153eSHGKOU0gYZ4/KAUYcxdB8Cdz12wlwGmJXf3LUygggKIBbQw1nW7KwbqpwknkyePgfN24P1P7/H+/Qd8+vgR52mnliN/USiTvpa5pASoEjFcDwhq/JTv5XJJZQ60unqyWVl3JWwqWvAlFUb1B9Co6HHe3GEcDKDpa7OxnCwzWEOZ+cV6VJXaECahp+SXD9YBT+723tDkYjsPp8+Fu1BMneiNNJMHRc6LxY06puIciqETl+PEmGp+s9PTynikePpf2oZBg7Y1wr1PdlKIOM15e6kEMEZW3r6BEMW61EUTmToBzKm4PR8mRz31WffYgUxdRl68YIMU+ystMSef7mvgAR2Xtbc/E02WVYZ96fUn+UCJyN8F8G8D+N8B/C1nTgDw/8FM5I+e+QcA/gEA/O53v3t0h9+3olgKg1gQwvGretmKT4MUCnNafyvfLwubbUVariRr4ae17pcvXT5JfFoAfplUOg8zVAFP+Hhv6kO1B8u/S93eN93asIxEPFYsSiATWRkuBW0Ke4lxioW30V6aVMMGd6+l1FYvP24Ts/T6rifrVIrc3Z205VtoBBpeVTqlrnO0gvx1vMpIgFT5iM7WGcsyX+CvIWfGsKTR/dLtdKhnNafVslWG8HC0suw7hvHaA0u7HwBj9oGDV6wqdxHw/xoAqHr9y/Kv3/+rf4PfYpWKfOm9MOHiKOOt29xTzciyo/Kox4Zbc+xreaFY1nqLkLtfEi+SQCpg/ryqbe+qhqvBcR44jpsdeAiLWuVP+1qu628ZmaLQkMeUrTtsmQ3KUEZWTanDZB9CQYoyc9XWqYhnuSR3pdQFtWAdq7xrteiTPYbsWh+wj8RWrnxCPdZcaU8DXUIkyrPxayWgqfmyjWaKkaJhuEJqKViiIpAH37WVgHY3qZWG3rsr+NOCILgHcGT5iwoDT9xVpa/eW8ctXQ9Ky0PB3XvzGEg9uvb7ksdXefHlYOqLAZSI/A7Afw/gP1bVHzfGqiLysNWq+ucA/hwA/ua//jc1J2tFgxoLYKnTc0nZHjh9joJsdWM8Ag+Pz+0lInozedpdREbOtjgfoYaUVSprHTonppxgrKa6AEN4+ypajDBwpufh7Xl8/7iduN0OPD/f8O7de3x4/8G1WG+rW4ZE4T4qdBgHwjoFXx4u0DgaEcZgIQxg8Z8SeBLNboEyHSylf4CEc6ppghk2YZkfPCA6QVmEdVgLSilaRzqKFyAXYpzMOdMfBAlJ9nGiHFFdFhG39lgfgw2S5BSZLYuWpbIFA6MlWhpzPHO7qzmdRBqWTb7GM27VUZ0RJD3sDZzDOS22zvMzzvNEk4bL9YI3T29wfQJ0KPQYaDIhbxq6n8HmFh3ZWp4OkmAWsYU0YSeA3OIX/pYBmgUDI8axSfPTQkXznut2FDj+0+YlT+x8/evn4F//xt/5N5XZ6gNgePgAUQ1ncLM4agj+znyZrUO6WZggEmu0Wo3q+gjrcVhOy0k0JC+09hDCGA1HvCqWI5xbZlFAxL+DeJsggIpZ0FzQnae/304cN0sp9cP3P+DHtz/i+dPN+o4EeCGzgXu5Cw1Awt/JO+AAqYHxmTLpbw3Oy6Aa3MEWVYjz9h59cLoWt1D5853bdcXqlCx/VSKjHPZDdelOdJE8zb8wq40s93FsqwIaVrAuaHIJeQSng045x9hPKpYNAMDUhqEWPuHaG8ZU3M4T7XZgquIU29KbCPLEKeSJ91tyxiuSZyWIqZ2wgSK/ty1dSpzqd1noVylnBDIsMPQ5Jp7efBf8aIyJ2+1Ea4Lr9RLAcmXfUvB5gig2q/I2kJ5QsUWGCcpI/eXwwQaW9nJeu74IQInIFcZ8/htV/R/86z+IyN9W1b8Qkb8N4C+/pKz9es2sZkTXigaSQgJAfBZIOfW2ajGQdd93jZ5aFgjvaRIqgZYXgIiVswhXZ2ZRD1LAU7SwEAIFi/ExcQ47BnzcDnz8+AnvP3xAxMkQKdggt/AiiFFRnULw7mMLRK4zcrbAhkJGZNsuLC0YFKT8Z1tL9EWgEyoZevY9pi3bJsUpNRhUrlABMNVOkBHLUvAvI6laQHTw6mX61jmrgr3QBgxozX203L8CaoHrKL4ixlMTQFsIKAKf9IXRFGgEz0ggqA5YG529fQLY9ug7AFVjKqSzp6cnN/UP2IE7hZ4DswmuuvoaETwtDVAkgxExGvY268xxiLFKlT7aYPPc/OSUjzAFVR1Kjx1TJNFXv34+/pWAieApDvX4q+n9CbLw6WmS0lvoQGs8bMY8OAivc+r1NT+tFXTj02RC1U+2InS1oPFZeBN5QEioZRnYpM2pGKf5co1hPk7nGDiPE7fnG9799A4/vv1xfXbj35+TPQE8/A9jvQHDC3Bac9U1rkuUIfIFFzLC6VrgaVsCLKXSKzwV6e0gkLJRqJxl6SQSWJXkKbUvPu4LwKp8kvICpc8erVPn8Fhvvt4qkFY7cQgVUxT9FF/3JL0NarkIfa13wLf2lG7c5MhYiCt6lgy1/rrIUUk3jyEw31V1ZX3jxjrJhc3qrwIcxwlpEgGhAQRI772hd3XwVGJpVQ6p2aacj5evCqpWRUWXe/5lrva5G8Rq+C8B/BNV/c/LT/8TgL/vn/8+gP/xSyrMJf7KPUtn5+LDdN9ApA8BEqSkE6QzPbxcLcFUvOqtZXy5+BD5eBJISfmwbPIool1ZoDc6QJUtwMV51B+iGV1CS/QfqyUAuT2m5b9HXU6mk8CryjuCpzXRZnPm4m2JewlS1jmN7T6CJkE5wl1M8QVE8Pv8e20ThG2ldpAaae1b9eF63M5Sfn0ZB4OiWhc16pTaFyGMquVzbFZNlXbp6J8zKmH09lZPVGooBbzmnDg8YStfp+c2i6ce0HX6xJA+yk2S8LjO2n4xlQWB61K2r83Y5vGuZV/XZ77W9fPyL3EH2HoiLAG9erBJdb8hpilCtVgVARYWoUrsQIBwraRQeINCy2EM8i5fycGM7PmIbo2kQ1YWCknph62rFrR5aX4KVIFxng7i52qlZV+QXUHQSN0OzB6wSyKIUAQEQPfxngpvivb62hSNtCxcR6niIOqqPDu4ZPB7HvXX+/a1jY5jYsp9WtuHBH6Fh4rzqgoGK+9IHuOBjDkuYsDPdggQ7x1WXuffHuj40jaLXbxKW5LUYoxizrbvqxxWKmtiuxFPb97g+vQmFOt9FyITAxd6c+g7Rm7nsex1jeyXlH8pE3IdvXTtvz1q52vXa9uBwJdZoP4dAP8BgP9LRP5P/+4/A/CPAPx3IvIfAfh/APy9zzYGNhGfayzfbc/UtkfmRX2bBIBrBgk4GHyMGoIPFDU5LWyrDFzu93soAZ259cV7H4yfIJaggYImvtA8+mphkmmZlbLufOlMYA7XLsUTGU/YFh+A6cdOAdM0FADm8GN8mqCRhL6MZeB/b022KRuGsPQYcLI7uniKFm8TtWjWW4HHGiypWFYImsTGhqdroLrG1fPaJ/1NY+hpWUltMsCLz2kCiDJf4qxzEkCyfUqkXWVMPDswoTpQt0mT0Qi6d0rVIp6TNAITadaUi1yJKcxCqhLH3ntvuDQmID2zD0Ej9uE8T3z88AHH5YLvvvsOTRqub57Qr78FxJLYTuW8WeXJ8HwENFtGYQeSdgRXq8RjXzCC7zhHMB2awyGAjOlk6PGjXPgmiEpV4itePxv/EhFc+xUiAwPDQgLIgFrQJ7MAjIlxnJhzAK2jNdvK5Cm21jQOm5ifj83XLHNF/0HOWNoOJIBTM6JzenRyF0C6AfIpCFgwPTn3BRJbiS2EXoskvJwtizzttN4sJt7H+QHPn264fbpZ3rtzeB45P1AhgDZxpYNJZisYkbK23HrdjFZ6a5BOfsPMB55nM0RuAoXCVM2y11uMUE4WCijR4ElG96YoWS5LcWzFGFQC2pbETx+rutuFxyxE8EwKcCQAZe68YATlHiokbGn0w2dXNIOJe7vCH1YSVPMRC/vmGRg6MK/dthDHxDnt+KmqH2rx8bUDD6n0EASrFtApla+q0yfT41gfrterWcbHwLv376G322IkqBlBQNoSZleAn8ZreHpz4upx7OzgVAtrLsdtm9SFv+7bdcvdDwDWDqCqH9R+fQ48AV92Cu9/ww5Y8/r3PlvDz3Clf4xdOxDiD4GQse3JfrZ8VDqGr5C7qwre9ftVCHEr53FlrJOAakaWakENWZACX73yR8mPWWQ8Eu3Y7lwI7+6nRcu71wLz9+WhrawoPxhKsbzI2oYHzVvRR7AZZ8rbdAqQ+adqH/2x+jvLydHTpRoyKI6k+MN1oQK56B6Bpb0v+zzF9nNpnzRB5q1+TC9k3kNMYzvHQB8jGSCK4hFtTSUg14GGRsg1wnau8CpqdsCbNL9ql77eFjMJ+5//fe3r5+ZfFRTatEksN8ORaflWaFjH1wWKfJ5AXJwnFMgPYBFK8CK82ri3/lu7yjnS5dsUKmkV3fuImPQmYsluVTGGRR0PH7ClurQALQ39zPWQ3yB5SALJ7IUBeSx8SaSOVd3WJgHn89U6T7Bax4ZjkLsCmj/r1pbyirZWBaXyXfJSZzorf1pbwL4zddWu05Pmms9NF0+/NTfLEwx0NzGQa6BxnZuFO7IzBTwE2CvfieSJ9RWQrP15KG0UmbrskdVpAS6KHMz9esRpP3/9nJbxr5JM+CWnrf1zvV88ZkSBCyvKRZ3EdQLq9li1PMyZLCj5U4rZaGbwhIW15cRzkZTFyEVSEFM0ndsex+3A+/cfcLsdOG4HxjmDdlSRvipFwNKysxxi1nzjFlAV7ny8AkALkqZu9jWt9eKigYmBRTJHFLOeQ4AJ8+ESLwPi5VF77D2YCAdHivAIy1JZPCHUiweh8Ni99gQMZYGy+NjZLCGd++aISCuMljLIRgWm3cqlhfUnGJa/UwueINOyElbWvIoxtg+K2IZWH4C17xb/JlL2BNOVLAOK8zhwe34GBLg+39CHn7qZ07VCLfGhksFTkE7Pdae0JpU6xI8WC+k62pvztK63Mrb8hzS+SJVv6xoz/f/UhSv9yNJPTIhwzAqu3C5zKzWPYC/0SP7UEP7sFWw5HQV3Io1SaRQkcKNjO3nR3TrInYA5mai3cBTnU3bizkDT+3fv8cP3P+DD+w+YY1jgQ9WS/47UlDxvBQ7GSMzXyK1vHg7ErDZrvrKA97G4VmJqEaZgB4G6fEdeY4BEy3cbiZJ3r2gzUBDDmWR6v5KZofC51VfXLft1G5BrPtadBFhawj6Qj4v5O4UFCgBqBgixbWWIRf+fU6GnoM+JPjjXijYVw6PKN/EdDm6fwbd6QbBWmF8J7TIdRIfc0xnbubvsXYAn+Z7/aeWMyPcXylaK8pgI8urgrwuzfR037L5O1Yp+55JQ7nv0+aXrF0/lspvVvgg8Se7f2hKdyzN1wdCCQ40kiHMp094ZI+ez1wKMgAU88bvK9OrAV0QUL0Cn4jgOfPz4EcdhUcfn1EoTkQvN+bLXlWPI1pcRDCZ7pyn77/yW+/SXYiq/lHHuZdz5Hhm5dWBs/bz1nf0AACAASURBVG9NPMVIi7ge4XsRbc36IQmAyUjs64a0BrU89sv52vPDoaQmEPY97whe5EiCTMKMfqtPVYxrpLrI0SXl2ZaLMWOhAKvju42/wNIv7OCJDAg+dmHR8fp4lJmMSGGB6G63G6QJbrcDFxfcc6qleGkC1dySsSZZG8JvxcdaKXVt8FPIKYDNJ2EHT4u5375YmNk3ip3A7bCdZ1WhEWuO4CnEA+OokTjJSZB/MyOA00P4bvpVQRT98+IOR/fU7JGl23wUWq5bvBKHPIwekwZsG+Y8Dowx8OHjBz9592wxvwig1NPatgJaQlOyf6gAk0/YWmtluzdPWCfYQvDxGFBYu0AQhirkCj8qv8e4PT5kWZSVlCV38gIAHNgykbogT3HVi2uPbiEC90mKeHKbUC/v6mvPG5MKTi5lAGpuHs3Kvqhv8YpC5WJpX3CiRdw99W092+obasouo5wzirl4vCmmhgn4U+SqJZeekYpNxxm/NcmDOUFPXkrFprzf3HMydRSrWnnHQzRbyvu89H5ta69+rqDqT/GT+ioWKODljr2EBl1WgcLkpfLSQrFpJhvqtAoQW0C7wAh8sEjiUhySEOZwn5CeedU82TokiKi8XBiNOSJhsJYgha/RRTU/x81kXGtWS3axWJ1oQi7O1g6ezMrUMpTBwkicWWtqGgmskHKYlgvmZluGe2v3S30UdilBEUWNMa+yJZH/LKDJvl04YxYUwGlb1ChlL+2WKM89VhBbegSzZAJK/i5b98SfI75OVqFsn2vovCnmmVYFmRjnibN3XOfVfFl6C9Bqlg0k7ZaWP7w23hRbPeX5+vSdwlPvYdtr8Xr31TdzmU8IgVNuO/mv24jr8lFQeNVWrsCD97o5YB/7vdgE+QWOBZjVADkV+FrEdIFl0hDbRob7Y7oCJn7IYLgz/BjDIuM/P+N2u0WAxW1Q1k5Gnx5cQeuPfpX7T1JgKHnNzt/L3yLFKv1CDVS6m6SsCJ5mCM3XdIKyaMVO+0gBnBa0ZkrvpsjWclgHY1qp57CjdTH8C3lxukuZlfdzSFtr6B4YeHbFnMDUgTnMd27C+LayHE6Z+LiQF2xQBtEeD+Ab6Io3Of/fOPGdQUGZ+9UtUMH26q7E0j3U1SLrDy9e924M+f2X+EB9yfWLA6iwTLzQuRdB1JzQMaCyTtBuiguQRUYgleCL1BD7neCGz4cGx+2Uql1XRuT3jfOEjmmO0rgAkkkhxaPo0oowp4fdd9B1e77hw4cPbs4smm3Ie43kitkEXYUfiV7cL6AyMmeincBJklE8ucWii5/4kIanSzfH0Hi+/qtQHYAvPm7XEQwEgPHFAcCcO7ntqtwO2sEdl05U5gskI58nkmXMIjGnbEkTb5QZYIlMycpSFqwaYAngJqEBXlrDUPrE5KzNhcocljQWyu2yvmj9jENGv4WwTvjWZCQaLqkLaB1L/1stAtIiDs/Z8Ek+4TwHLpcLLpcLnp7e4Hq1z/CxCOEr3Lr2cSkaVfhCeVdFYSfHsAkfH3Zq26sg1kU4M1abk4BdL58X+ZVe4omZzRqojhLFnaFjbWoKRc5jOMZOX9Oi0JZwnAtojhFO+71s/SxaM5w2GNWeVtk5oXMAc1r+cXgsKKdNHQ3Dq5oeqqX3htYFc0yct5tbEzp665hTcZ4W0JVbeGMMt0Ct12LR8kaGDlZAT5wAfaDh21JeGFusvQbS2AqAypNx2MVABNuli5AG6KJgVTBwfvNI6NY2y7QAP0EpkgcqbNzX9VR9tyIdVgNELoArnh5fIJIhSxmHoBXJubWtUQJe36rXaeCmMYtf6Z8KRK38JwBDG3oHmp850k+AzhO0cE5HaOHaLwa41OO3Ge90fmHM0UfaAmSMCQvtApejIiWtD2yeOUFkvt7WMQaO4zA61OlAk1ZRCrdUcoPoy7XTX8UNOwhbaOzBb/x72Wr9wuurWaCAe/D0+r1Vc1sB1Doo9beHJYHDHwIW28ATRWtUfDdjubWhgExD9MwuPt1ao+3ufigJJbU72wvemr+BpLUHFJJS7kuz6TKuZGQBovLvPNaawKoxjUepJ8fIyk4zfGpSi64QhJzfpuWMSDDnYOl3/TI0e6+71BFanaTAzvlchy7C6qg/AI2AlxKlbXqTsA5Z/1vmIscB27M1LUyAFMnRqL2I5yuXL8AeCqgY6D7lDO1PyIgjbQLnK+1pjxkC78iRCiDEdmzjuTyt3AqtArJIy2/8WgQXsDCaYh+1vwuoWksAakLreEbpn/R6hJmg57VRgOa2iFlLNO/z3xX0A+FXDXPaAQWdlkaEwM+UPmZKuGGO6gdUu2KdlH3+H/258YsXerd9VVcjypgn3UrhZZW307ePj2VOPOcq1WpUrTuFK8Ta32J07QC3tpKn1hg9nO2OtVsAVPI6h0aN/rAFqGnavnON+jfBr0xx7z7tXW2S6VAOFD9OEaQ7gsQ63jhzGWG4bLTL0pVlrxc5HHNQ24kow+jKfFvrNuwq58n1KQUeWD7LdY8FWN09F3tkefpT8AivXxhAKSIzdfzjgvYBQKEQsd+Y1Xm9N9CjJHHYoinySNZK7rBKmTRuR9iCZDlS2mrtMrfHZH3m02R7wuLR0wPMaBLXmJb7bowRDprmX1L4YRBhfrlMdiHaVeDms+FnU8YomYwTtIOw7kdcGXeE2rBpK1l/8+AlIvBURxTg9kWMnQt0/kafmam6tTsXDIWRxQnN490aE0UfojiXUgYsx2bXSrOqSmDFMlMARxMLFUFfO5Tn7k8eCahRaaGtsG0FqIhD0VB4CIDChLXM7aqVJ5WmSLYxOo4DP7x9i6ePT/jd73+H3/72NxBpuFxyvBltl++qiHdqetNPAaK0FeswLcwrYBP9b0r/E1IV8/83hqUUilPdA9PXiG25zbBI028RwjXjeRDDcu7W4qKdq2pEfQ8/SFVM52a95wGNGok8aBBORwq3mhBElTUFBL2ahab54+ZKAADX6wVQHjBpHqvnsPhPniFhzommxdrNtelWEjRB6xVApnCvvJVXKgGyvBaQwv8SNxQemXytMRiuyxlCGWd0GYRT8hCNs6HwbeLU8gOtXpwrVLou65aBMqmQ1vxxrXeoeh/D54G5HpTksvB47o6QHnJ17fAmgYXH4/STd85x3PrVm+B6aUtKKNCCDqdfTWtbyEQy4OBxOU4h28R2JhYAEtozHfgBVaOhMRvGOTD6ae1tjDMoUe4Y445ehFPw2rWUM5ex41jt4Omv4vvE6ytYoOjkS9RqQIXzoykxURky9++T2ee9uTgtEjCFBTWMOzlb57nUw1vWBZ7+QAG/NO+LSNU6cR5mvrw+XdHkSmMHSIDmO2EnW05/xfaPRkHJHPjsggQRCV5rQ+/Ac4xXch1qYuK/c+FnlnNnGBUQBFEhGVvEVWkllUvGwYrgc95mhXqepgpwvM1uumWsGkjGSVm2eQvANQ16xELIk2c5bHQ2N9M2v5UALzRdr0+yny1ocV94VcPC1i4AGYWbZQHuc9BAx9IgoACTznxVPUGwCYAggQUMCo7bgR/f/ojL5YJ+6Xh6ekLvgIglYh06jBl6eQnAbGTpZ9MgYYavykXejyT06CvXJAPgoQ5SMHNTPj4bp/fXdSn88AR8zpwmHDjRUd9+djprqXiYRdDWFn3n1PHOcH8QtfwYtmZGggBuybawkiBoecYRqgKeCtYth1MdWGWbGGm8ieB6uXq7TBJbYNCBc5yYw2JbmaBtDEMHiXUxAT+Zq+AaTmvGIgwl+UpquuQ/zIqw8e3K//x3aFGUxVO/tOaAtswb+Z7LjkzybKsTIE8t/IBtTAZfeGyVB0igDMTcGG91m0/wS3GXBnJhbgmOWEPG37AwyrD4hgWqSEZfgCanbOqYM1iQZXcHSyIGzO0xc4wf007wmTyLozJBh0H8pEWQN+uyRbxKT7YtB0qnBQCeo+EcJ/qweqozvpGwA2BD+qmM7lfFCpK0RmKvYUWcgy99Siv+Luse1PXC9RV8oHLh2VU2dxYUUIUGYm4EqxBe7n8BngpWfMIjofsD92L4vqBFC9r6VG/MPfo82KvOlIZv3Wmg9Kyx8IkHjdKlL/cP5p9bc5LReNvWJJ1Vu9MQmMu2X22OC4+4P6BlcaZGjrEJBu+/5Cm58M0IKwYATDcn1w7pPsBr9+s6jcmo9HVvlkb5Vev4aaFM5TOl7pjXAjJZ79IguNAs9bB58VRZ/AWIBVjleBbGtFgqy3yGUKVg9pQQAp4MtPKM5CR8yKLo7aVRGSXlRggxdhrveXrSGf0rc/ZrvGwNv8xnAARQWme+CDzUIfRPkhSqVThHPc74Ja2RCV5eas6jbxVYibK0O+c2+JZaqJXbcyYOJnuAg7qyDIJacy1IvgXvrryxNsKlQFHetsG6+9vKSRC1oMbSrexe8uQsrt4gpa11HW4NkOXJRSbIUpQiwHLpe/LLlXfUcQmOVYFnGaddRAgcQKmEUs9dBUynHc8E1Cbd5x7xVfIlt475n1rbYOx/aX/yynxP8l1BDpVejkskF176WcpYcRjIX5Z5xiYX61ZrzHnKYp61Wnaeorlf7lj+ywIoSTMjBYppROZMNj1FRd3GYBRZBgYD6uLjbdUykNoVyHDEcLMBmOnpuzhw3A92OnZNivXbTfbem8UusZRUjFxN83K+InFoKWWqxZA558TtduB2u+GcIzQu9SOdKIs2joImx/QFaX5Ki6B/AAgVJX1Ao+YF1za7hTDoHZdeYsEUpih8NhhkEfLF6sPtHYH7f7lWx1xfFDxa7iWwCoClGovLSN3ayuEP2e5jQ0foWP/s7DJzK3gKy2URPEELKtBwgqZFtIBg0dgLoRm+gbmgTKOd0Djmq6KhMU7Q8mTj2kWiDo6nAiW55cQ4q4YeuefN7wDApTVcerftV9e8JXIVAscYYTmNMA/O5Hp3GpIU1gEKuXbUrIY6p0XEb0DTqqG5xUXcn0GHW39n1KUBqL6dazKKc0EuiwIiFiHamPC0PHQyMTBwYqKDOfHEPbwV9L0B1ENR+LhxfYg722rzjAviz5GOndoX56RyFTYmHrY+6ZteJkQOTttiVoK3b3/A+/cf8NHjPxn9t4iFlkLMx6CVXJ5AgBEOkDQJuoxtePGTXQBEp4fw8MMvIdCRwEU8aKTTrMh0XsXP6pbVCqSSj1Ugs4Akrod495AgUfk6uGQ35l/kViBQpsAt42aJTfcGew+/KK/H5kmXwm06G7onG54eg4wSFMmSol8a7y7zPOTBOS2ThwxLAjwFmIeGgzrlsPXUKg+w1xSdVibJuQjnhODJNh/JkiVlM2VPM/7DgzDqqaoY/gaNfCkPpJC1a50niMtg3XgYG+ey00/GRGg20QwkqulLyqsacb4ERH1VJ/KFxAsIivg3XNP1Gdk+k4Gw4217wOsIGULBRTnhKFsemnXWtoICVWQBAuzIY18WRNtMYJvPwWAMJOctd2Jm6Xs1Z/v2EupDrwipYAZ8GSPpBHoB/HA3bsDWPoKAYLRZu+TPMY9zTksbwL5jB1AzmGhs/fjkZGA+E9w5CUvzfC6SlipNVQOfxuO+kuqwKRbtxeRjgsUymFGgFKIKTZPFs4w4QRnqQgqtTfPbfUCClkIY1Z4kk16fyblSNRjb2DdaD5xh1y3WO9kgOVc1cTRpP8bMab9ubxqv5Px+a/CJK7F2jAtVyjrW/E/KX5rUn8OdlBvrGvxKl1qpNC1KQ6kRSJ64TWcqWUGcq+CtDwSlTcXt+YbnT5/cAsXfyefYluQHAfqjMeWdoxVdrP0r7sYvWDyjLMFyIi9bVJyMYzEmH+EYL0XRX3NpL8p6klLcyqiLiMrX3enXIg+qXCiMytwmNh7rtLBvMXk3NyBVLNXiMeSQ+fgItObUNESo860qO5eGkZnsByUoo5NpVIMHee6du7emy0jd+VDAU0MlH8cGalhrzqS9FCVUzjavyajX17Ki4hBHbWZan77EqfwrAajKAcrecpWTdcH7F8YIslO7x33wHMnfWV4wHil/OxcRIlbNMAYRan6OiPPU5oB4sDq2aE61o8Mivl/LpTzT4qXqMS88w7kfDZ7L3vE6MrXdZYUvQmlhgDlCQawkxN66JwYFLrREdQugyWzlIogYUAQDpOFwJyhMxUAA0sLEUJlqcUe4MOggP+eMMTcFOoMSsvUQ5r0S1+aMGdmxY1rJfF3PnFtqXG3hbuvayYuOta6VzHKyJbsZY1dnRMEousbwx8x4QMPBw1CGRCBwqgufsmvG97H+CYLAfjbLo+YVV8HJUBjNrZbNGd15nsGkn56eAORx7kpNzGXHQRIBpHe3VJkVDVjbn/2wAbL5U8sHR0dXWkxSxOJbuwL8alnXY2CME2fEtpkBLiWEvVttBZ7zy05AcR0h/FIo0Mo2AoWVzzMVxumO0mxX+tgopOXaWmEK0DZl1egaOD1SPYOAfnp+xrt37/DTT+9wOw40Tyq8wbqsQcoWibeF75Z7D8mb7QH/l7SfrSXUnL6mFAJRiXyo9LMyEHO/3cYTZzQ7GXt2vkaG53MiSIu67SAkX2F+PO5+oEzJKsrtc5d0KM921Wn0elTNYoaMzp4KKDywpQMy/iZSyOMevPCNcrIBnm/TwRMUKoJr75ieo7ENxTEnxnG4srtJIBGsIZtfXtNVMaSakDwDLpqM75/jhJzNcgxqyqzP+yBV4KPL96Q7IHnXy5fzvg0E/oosUCuIwsJ2C/s1Vb7cXoPYleuVzs6gOoIliUnbaovQAgyQaSBgGIAanix42sJusG05qHjCXKJqjajZw48GR5nulHkchwkq0ugiq0l8RagnzIi+ROu5n+x94vfCrOq+TXdpgisBVCOAMhBlC/megOtpvtDWvIzICK81ETMiptVgdvrSf20SFpHTUwmQiRiAkhDkNEQ/XS/QRuZGerC6aCqnxrZoUyJYTrzxN/FtW1VPyImUT0Ac6S1DvYCXcw5PymkWNlU7nRVMLmgpBQu8T0zXwjlXyS02pvFoELR2WqyeINNcJ6o2npFewmnOABTQe8f1+oRw7oUG4CSNh/+dz3fvjOWSUcd5T1JbXircTtRg0DH8XFex9fjtXKR7nlwac+SaPg+j51ifJbEyHZxhQAUALBEvACkWKwLTELwJLigYSQOxdko8HqtDI10PCU3d4T2QeKyFotAM/97jH3369Ak//fQTfvzxJxy3A713e3RkW5M3WHHcKjFQbtHGW2ueBkogOsBwLwU++ek1GzWOnqUdyYS6FkF7GxfhibrCMakAUgnyNSaMHQfELoe4kzIP0dBlQYTjZMukUYGzqp0WKKOy7u5W/RUc8qYsoAHRvx5aTvoSkVeN4aMR8467jBV8NtoWgVg18+PBDpV0EcjF5lE6cFFAjgO388RcJA7AE8+5sO/XcpUX6ym8ArCF4o3tnziPEyItDk/YeGZ097uTcqhF7xzp0Xi83s5YUw/knap+cUyorwagHm1j8AqhoJV4Pv/8F/SXNWAFcAW1hsTEMmuxvB80mXCnChya67mggdzmyLQYoU4tAqsSrMSquL/2CSYGSz8iBFMI5tBWJhGsWVYiW4gKWohtG0Vn+DP4LoGROgB1Cw2F8kyGbf5SVpKJAvs7F7L9OqfGKRHGb4oxCzrwc3WS81V1EFn+qZMo93Tj41L9lPjZ5k8jWGrkuOPvqPfv2zWP6sk2rPS8MSYqEU4ac0zMPlaa5ZMxh/f0WttIqq73Be1+jiOxPN5P+nZaIV18WSm/psuVlOgzTyPm9hyQc75+W8rI1ZqKj/LevZT98fJ7KQbxtMZaXK3wuaoe8TnymliVrgSF8rcSz3ZJ8pPF7MLXC1CafIctrIsxthPqGiVXZSvrb16GYuFjIlh4VwVg0Xa/F8EXs81CABbPp8CvLIX3ys5i1u5mywlukTNdpzN4edT7WGYmreW/BJDN/yDPj9N/Ys7kCoQrR2sCukStpZf5fFD/n+J0fd/oorDVnyt4+mzZDwb8Ff61u6zU9v+p/fjFc+FlTqC5NdZFZ0HgIhbHJuNSeCmFQgNNknI/0/8ANQ+EhGoyC6MZ20YBuj/LbStA3QRvHTOCHGreeW2caDDtkuAFQATNtKzmR9kKNL+oOaYTc3XO2/r00gQXTQkwh8oGRe8N12aWqGu3VxPg0kveO0dTi+ZEJlO02/SdMRA058DtdGuMj8sYE7fjjDmeoQ1ZGycUgyyiMLb4XXl0m2OnwDlwjolLbwDz7BGkqaLz3tjgtzaSLYciRIZV5p4pDVgW70sgZHRxnua3NlVxTNuEO8f0PHcIBjA1hZOygEIDodmE4KAFzQSpyoz7CNa4ZeMzYo6Xt2eopwIya5RrbyGouJbKdk2Qe7al9XTWPM8EA1mf/aNAhmjYkBkBMkSQQbEnZN6vsV/zJYDHPsqtfYuPNMzizLP99AmG5/yawy209XLwBaOlGVuCblkSARM8Y3sOcDKntj5nWAEZluA8T4torih8JAHWII+L9Z4HX4y3mZXoPD3dlL+s2h5AidZUWsHiQIOkdp9WmIAq3iaPxg2xdjbxLbV0K2DZ1YJk9OgKS6RLKaVJlIrWU14kzhJfkxXfCTpYt1u26m8tHf3tiH0CKHWFLr+TUPAIh3ZrSuW1EcWdIFTzlJzAkrOvOQ79N9HgX8ycIVrCGUAwlfzOtuinAgMSsczM96rjzdOTOYqfFmneQDacJ9noGumNlRqLsePOvab0d3UxlqATJhaumQ5sp+HzIVCEvIi1VIOLFmtkaR/LTpuuXcQjvT9acy9fXy2Vy8sWqFXrf1wGQEFYv3wF+G81bB8KEq2EwAlarTFbJbQS1PaqWjoFW/3OlGyhEG2TUdZ6zTeCC+nBJMqGtZ0B1qVJptAAdAi6nyShhtFbBnkLzBmLedfmyLAEkfwTfvqBWu6clqRyAkP9pGFsV2pEzSbDMLBlwrZFcuIcXWMCCkbKhQID9p2IGMFqWn4YcNPO8ayOiPfUwMVUquWYvkA4ZCQUThMGFtX7MSIUg99XgFgWUkA+W0JGSyZAzll/J8PWrVdTI90H80nRnyatVsvApuUpaF4WRsUUDAv9l39fu+rWU2Kvb9H6RAClkW0gsgpwLceU2oepE03pncdxNWlLIZOC1uHNa9pzPN8CdDMNkc1B0uucc9kSt6PonKMSmDjyqPn6iIwG2FwZTMi0JMelVbWMhd6pxQgpKoFOQjj7haBEytKgglFZIoFgLOBsxVJ+KFYxzoWnZeMSeBWe2giiFgA14xny0IjmjVzXBFUAlnbvgEpNq1x/5ofo3i4PdrmX6zWmxu/vYiCqybTtPHVYpsBsNsdNbQufPlEiM5mF82CrQIEA16z+dTlOWV26nvPkDb5LVP5A9j3mQpV2ypoq/OtulyZAfZZQ6YI88UsVv188Erk5qL7WOIlBp7YCkEH4LXbeNe6Pe8lQ6kKJmnNB+o/Y54kI1D7zHGm2gUzFEHRHax0hKEQgnmCpWp2yV9kjmsPV66TVSd3/5Y5+vPEhFMkwaroQ1wTZ5C7u3yTuV4SiUbFQrQNSxjPaHwgLQOaNO52ZHsMsUBaeIX2ejvME/S1y3J05z9xIkKIlUEOkQM9gdwiCD0YooBtU+HoE8QeTLt2JbwoNiUTSzP2ypjsD8Po4DqoMeoigBUX62QWI2BjobiYWNeuPAaJoXrmnwbPqoLnlAC4Y+0XQLxf0fjFGOIaP8SWOyIszPg1QXgajAJzFWgYytpKD0AF9KApElIVRqSLmmwSW2zLf0qURqJLR5eN75BgauFb3i+zBkMcYEQKA9yd4Il+k/9OGFl7iCctXCSbI7AikNukFN+fYems251xn6jnwzH8z10ykfUJZw17e4h4Q3C66ZE0KWrIGpoD1JgXKlxwHB3fVIpXbcrXnFQAVJ/IybgGVJN+rMA+LsLD1VQExX9e21O93SfKRFM6yhFVJFtJi3pSjRD2y5JKrgEKRB3JoheL6DgbV3Ek82U5Yw80q5Qqp5MncNulbRj+siUsXnNMsQ819fTmN8KoqR63X3uboOcdT8r5zDMhp/nqxe7SBqMWitdVXaWe/quuDFnAW31W+hVW5pXHjS69fFECZRjNW7QRYOpiI1QUB/G9NR1V7iPfZOwlzuhCnBlgeL8IUodkAFH4mvAmgbCtPubKxCF8HUL1dPN2CT0Ie3yidyefqFtIcM+oEnIAXIRudjLFjX2py5CVGki8GUXMkvjiIujiDs60ubmFmwTvqD8uECMwR1ufOteObM9fjHPh0Oww0eUJkc662wuOoqo+NmZDTyba6yZOJ1dAKqY06Y2uCgjWWLaW4vziU7kB9MlFvjG8BUT5uKQpzDoUPiOUMYz9VLHYUrYdhhYnYUmv9InmCs186IJegU2pjdasvhUzOmaoJi6vHUREA8xw5Zq1FeQRdtGAFU+IaU5QowtFKMB2G3VtSLyHXi270E0LS/6uJXb+ZyyRZWkhxT2cBpuFby717Kg87edTQDbCUJxVlnO+EeymYF2nyEfoPYsq5GuMEoOHQTVqwLbOG1nusM8CdfM8D5znCOmBz2guvSV4s8Bh5bQ2NQgIU1VDsdtGrPpZxTQW33y2GHdyKln48j0GU83Q6g7MmqVYitrcALa43tOA34dIdAEVDaRcRXLxi1h1zXvlRkU/k103E09yIn9pV/862x46ZFm0rl4dOpvtcVjAgWY/4GWTT5wtIcF7syrXVo+hutexq7gFdgGs3mXbOiasrw8MPiSxkFvI51/w9QMFCmzneCVLO8wx+FImat7JetG7pUnhMwi7HmBngkasOlT6TK73QvvU5scfr1y++hbePx+dNZQ+68Ugj4/cv1UmpWwmANWis9aXeWHgViZW6wp9K8pVm65eZXGjsLzRalk6+1Nn7R8v6DYC4nKx7oZhqGbm/x7aHVpBAC5q3TRJAdk1Hbp6qEBFcencLkq8uwcrMZQNQKMytanyFgbLNr1qgyphvUgg0SaP0YwXmNsdVB5UdlgAAIABJREFU0O1uvneX00X5EzSxV1JfNKG9adlCULNe6KVgs2r94f1LY/YyV95XwH3WyU/ulg/ZSl5Xzz7Wlbl9S+gJIBXccYO7Ya5uAJs1WlfqCoAbRJB2dwMqwP2U6t3YhhIQloZcU1trt4c2waO+LX/a6cIk9twqqtcKYiS+e/mq627lrMlpCZ7kxbUfliA+F4yvlFkAm5SBlPivCHep9Ze1B13aJl5wrAhBUUq2btYx4XMB2JJ/NVf+ya8JJMTbHdhsn3MHT5GeaolrVFap1L4QJNIfyCxRNpZ1tfuYFOCb2E0fsZal79bA0tbabhRXGV0fWftXmFzpu9YPun23DM7rV1LDOldV8n7u+koAKhHy7ncEpAaXkX0qGQPVVyTL5UyvRyGtNr8c7QgQfgOLMJqKeU5HoeJbdBkDo74fpyUO7tfuR3TJE9SPxvoSmWyHt19tG0unmvOpW7p43DyIRe3hJNo63dkdWwwIDe3SzEn86dpxvdiWzqXzKHGawnksN7cbJXPY+fjULalzDD+m7dqJW26u1w7VjjdXB0NNAtFblGxnVl7fcRw4z1uAL0UVMmsfF+aIzRfCx8X63AOkXTOVvM2zah7hLyl8FB6LBwPJ/5IBc21zu3VMs7ox4aZN0brs7XsN8gyAXYgsogkMjbLoXxHM1MdAmkCnWSd5YjF8Xc6BUyQOJvRLOvYiKPURWxIOhTeyMFzhG58zr7ImFocLjUfGm/dTF0Et3kEVD+3xBUzs13bJ9C0RtfGtOdTSr8XmQMQyF3AbT5BhNyCIEBj2ckdi5pSEBBhvKDkn71vkINo+0+fSEpo3t0ZrtCeEsEfLn8OtsJJg4NOnZ7x//x6fPn7KnIkA4li7K0/i/BQg683vai5EhhkBWA/HwsEEARHMSi4Arr3jcmlxyMV4VeVz6dd5CVBVrNY+HsEzUObJ3c0jBAo8/pyzXrpRkLfaPFYrmPe5Jd8SjwmeEcbTGp700TJsgmcpsJh83ccBuPRuh0TcDUI8cj0tXGk98c8+j5uQw6IUIsfXxkZ9XuBZCxQTA2MqjiY4YLKsS4N0WhCNPhnpotLhvntBcRdyFVgVAS0xAsEwLIjtM4YSWsotvUJ83v01C+h64VqUba5d5nAtc7Ur0i9dXyWZcALLxwDKfttBJDH04wGySUqfmrofm5MpywO6zkYSZGiPmeJkwaRqAfTGFHS09M0h3COICytN9iH6TRA1ZyS+3bHv4vRYxmW3FtQl3IXO4s2AEwU0I/fydBpTFMhuBQp7iVmuHQDOMXAep58mEld60sLUpNurN1wvF/f/aL63nu2/dMFxOISJLidj57zXvoVsp/DZRDytVkwmagvATeFToY0OjeyZJ2/e1XuCgJiGGU7hBqRmMAfRVR9frEREYDFpnFUNoUi3FG51MJO8tpxzUbMmMOjnLJxpzgkZxUStRcUQ2o4+c73Eayp9BV0wvrH/7esp5ynXgO14f3vgiUu0bpsDpM20FEb3nQ8wvUn852vHXAdKzC3SVrAA+2KleCR9JlWFoinSPMZRM6DS1KNRC1aCcFrUCdWGpsAU8+86zhO35xuO2+m06wqDIOiXFy1CL10v/lYUwuBnkgCGyqCIp3QRKn/q/pE8HEMQVCzVxbolIKhiPSY4xQ9dZPBgFPp28CTZviYMsOkwRDjnftotelOUrFZnT5bvKr/tdP2An5TzTBV2CAB+mKbKSsaYE6gMau4xr6vszP5BEECR9GKreuIyFMBMBRuCtsg9+npuWO3BMk8/MG8RD1h4a1YLlJb7Uu7dyT4N6Vq7Wmgx6Yj3L8/v7VPytAqw2cjPAydeXwFAvTDqy7e52AO9fu5pTUbyGus2t6YCbLaHKBzM0pLMrW7XkYATKcP8Gvp6fNeKTYJXouuIRM4s3E5I3tmV/NbJfAieJBlJv7i1qefpOwa+XKLpboRyP2Z2n3oetwkN0HTp5sfRWsOlXSCSzrJNLKdRmNcrvSsgl46GCxR+qk3JiIq1UTmf6VhORh3l+XjRAhUAKrRacwgQoR8B8qSUj/nLMn79QQBPt9DQVOmuYpr7xgTC/0LU/YvKEleEtsg2WHumBc0swihy1XkJ6pPP7o9h0e+Zm41bq01rZvN75hSNCevs/SBo8TXcsWClx/iUKjFko95v6nJBOspYsq81hABQhEjyd0hZi8ngdCG3HeOwEik/mjwN7mJFzEJXSr5ThWmC3+RpbiWcEwOeu3JMfPr40SxQnz6Zr6b3T8dIoBEsWqNNXKNpKa4v64BWhrDQkmzv1iehW6n/wgMxAoQvWiulBbgNgJbAdIEzvk4TXKXAJWAj/6p9ok8WSt+mh4wx5aGjI4/LQwQt8itk/1p13GcICBELGKwSiiJ3SsiDoICOHPPWuq9/YA4eQODaFqaUc/6RJwVN1BhknLTwIa14Glpf1qWk46BPCTS2+h9pgLrl8NdCl3SIX+nk0bV/K963KitXOcP1ufK/3VjDz5mDtOKOdV2+dH21QJqF56bWVmAByU19LhZQ4lxitcyQaKqM2EeAJk8sqJ0WIRRhPueB8zxLfQgNRKclPh7WVMw50C4d1/7kDNJjXETwSE/KOibGeeK43fD8/Bz7+KGy6tqn3BJ4cLk2YUydpuCG6/ViwMYTzYog3isjMp7qIEnKArGK3eHbBTOjxTqXvD6ZA3PvHW+ubwzEXC7o3LpzjZvbS7UHs18wL+Z4fTCdjTQbXKpJas7mdmwauFwuxbJkZU21NnVpuLbL4gBKjVHgYQWaB72cviFb6S4E0b5MNZkIxJxtrWI/7aLQ4Q6H4AIuTEBJZxmVnpqksHRvA3xo0YAqcJoIJieLWq3Py3mcEeSQ9DvHxJAR8XSW3tBqVQFOAXFwcKzsdxH8PCD2YOc8bgkLWLmn+jh8C5dA0NCdhiqnMSB7ePwzbpmLzzfnLuMj5Rm+GKPC9FMM5fiRua+CItMkMcl0/pZ8jY6xtDorMp2PgeUWoTHGOfDup3d4+/0PeP707A7oiNhS0gSXTrRkrdzBU+3n6oeV/Dll1QrICVpMs2KWh57Wdb+nbS8CKq6lFMicKw/DQuDkYKHH6UMNYJgK28sASqQozFAMT6sjjuBiHYMHKnwb3tPl8MCSsQ2CDYItRZ/WBmiC8tbKdq1boHonr7XYZFrKE+Q2KaAZxd15lQav9QTnYpHLezfXARlOj15EwsA6n2bBAhJEUWbGe/Bs2w6eUy2pcfMUaa54VxC1WKDugBXp2OfXiUa2+tjL1eKFKJ8ADmrxtCj/yRa/hHV9HQBVjt/nFdDv/jv+ELRQBmgDGfn+Sve3nxag5UxhRbGcTJ4wafVhJ8T63FoBCWlJkUENkAIr7hR27NUJ9DUQTKci+fA5kByfx9je0anej1f4h/EXCgDAcuv1Fu8tUsbQt8DB7iROFdCiRO1twmM4xfrgPEqAw0gJIckIJEgn/afCUb4MThSpDho5ULsl5uHo3APvHHObM1BL9SmTuy2SvBoEk8+pLMeeWT4d9dNfrjbvET1NMOChMjK6mr9C03tGV2qy3yp4qr+5I2riwH2tUuDzs2w/KRhP8nUK/nVekYfaxyCGccdUcRnh5XxILPPH97MiG9scXdJFRbZAnY+gnArIoC/PA9tMARMW8tN9Fc+yXCo/rLQVsGdTIrYtlZ0enXTu+7fSLkE5y5IXXms1WfBioSC/jPVano7pkSxXHrzu6hM8kkd1HGitSnRYAVb5zuM0xW9NEFq6aPAYbjXa/6lwJW983Lwcw3UuBNWPln5eCSirdKhVRFWFR9zZLLbLZrlIvLCGrg/Ko3caSUhnW7vua3rUmG29xEKWhR517eGL1y8LoBQwlRplcXH60vmWVMpIqzaR9qqIeFmgWqbAkf/cB0BhFIKkKloZFGpOlZvfSxYpeLq+wfV6xdPT1R20PZaPN8SCG04DFBfN9dmAc5x4fn7G7XYDVNGRx9/ZtMegKRfeHZFQeHteu+YRxzuBXjC9wSUXZZJVUXiLExDETPnDHfymx7259o7f/uY7iDRcLlZP6w3X6zXN4GTWPmwditYb7Ai3lUMmrZ77pZGBe4RbkZw1Jtt0QzNEzLERgCUBVjK26UyKW3iZRsTmlNYkpyMfPAHSXF2eGXXb0qnI6jcAeOk8wi7mD4biHL6MK1xIWQ5AINNrDFWcdAz3p4YqdHg9IuapjOm+u0wdY8FKj/NE04Hb7Ybn2zNUFE/nEyamzQlJjzQlVfhrCJoASvyXDJNjayNjL18fw61/c2hwVGr7ti3NAyDtjjH+mi+jDcUJy31ohwvgUfgFUN/IUXH6bJDWIdJBW4lOxe0wq84YlsLISDMnQrFubSgmzumCNSJvV3v9QItkxPDxdwUF8Ej7AKbzm2ZO0YoMhKsT0AHoUHz88Iy3P/xkVvbhViBx6QqCcKs5wZP1j++ApwVBAoIqfRN3r2ugAowmLd0PyLOkKC+MeYZ8T3q2+tJHs0RIj/+sPHVZY6xFMYXZECT4STiEC3m1uwRMRpJXxzkSCYpVa3vUxl0uphC6xR4ESoAn5m4YY+BUhUxJAAkAndaRAUznl95maz+V84m0ClFMVMCmzpdsnV9U8V1vGCLQy4ToxDGAcVrMPzqOQw3EyTQWP5a1LUF3/JvjRVgSBxXQoGr0cRwDnz7dcH1SXK9X5I4M1r4vVEJEreXe/C3i5sHGQzA936sNnbleSPiUqvuaFhiIPHD0+vXLW6DcarZoZEjNl18l4t/2ViUZB5nMYrarqEoAHgf1p2Ph6DJcBURRaJYyyQAulwuenp5wvRrxx14/haP7pVyWSK2IxXacptVRy1CtPlZ4AKDIpqyQ1NGqDuDbPc19nlpaoIwAKeTq+LAkJ6CwZDpUmBPnOZBkaHGlvnvz3ZJLr/WGy8VjaDAZHvujPL1lkXAZ90TnhDqYosld/XMwVaHFSBzwGJBoaOhkSG7Otvt9G6EZozPBkJpNbEvFaiQXJy2S0fjsR2JkRaBBB+/LtqsYA1P/OeeMjC4dENQjfwawG8MVAtIiHFhZtGfzZ3JhJeQSzHE2LJgnFOc4Lb7Q2XAOTyZ86YB0hBAGBVbOZ9JmWVv+XulM+W/EQmP0bRQrZV6qlmi3ZcDmb+qaTupD7YDF1IitCUpX1XJq2J25eViDvpAmjzjWspKlclvMf1FggtuvxQJKoaUTHnzMgdL0AzUJlClMQEHvgODUaSBpwnz7BnDcDnz88BHQjIkUCwW1jcXKAn4mgCI/8ad9zbDfBUuxRJBT3/tPJT+zlmjQbXLICjizPBTglCchU/oEGMxmh4IRHJKTw7F0AW08QlzZsp9bqC6ppOZ6K4duWod42hAhU5sKTLeEnMP42myQYC4OIwWR8immxtGGjc10EMUxlqSlImNDiYUlmb8AGN1AnOjEpSXIIgsyVkArepElWt+rGCZoyrFwLAZV4DwHbscJWaJ/212kLPLBRFUoACfXDg0sObPOvwNP1HWmYCq20rUoPvjeZ66v5wMFotr8O39Mxq3BFTgFL1xFc4nPVKPjlvp0mg0Jwjh40QafDG693W43/23i6XpF1XisbWWJu4DhMfo51uS6IMOtS1kTLCb9rT1O7e/+N78B2Ptd+t/qOIHMiifxktB6MyHQ/ATf9dLDypWpYVpYn1YjrLMwnRjT/ZBc+6XzfYCpEBouKAgqAqS4Q6V2qyPmqZquTb2gDxYZsao79tKJxxcOB0QJCJCWIS1dqAvdHkvLkwrjWD0CUPVd3RrhzsVeoEIwhRHN3Xan2b+kv02BKDRL0DuGndqxAw1tOdlJ/4kQWhXglvxedes5/SYkGY946IIQmCnEdwpl9x+R56/+ooCwP8xKEwdCXKg5J6bA7H3130uFrghyDlthhIp7gBo0mCgXsXDh8ytJKabT8PSoVrKM+7LBWWYFx9Fa3iKVLvJVubM42FiBFjJwbYCl0jN+IZugLb+Hcsgy2W+UkCyS/IEHSyrvyxr5JAJ87iQr5RYbMxsRUyp4iqvMR4CsvZRUtkWa+U+5b2pY9lT8sI6gXbqBKa156bwpzYNvltlrzU4XqlqIBHiA3+SvZhioTTOrIgCVOO3XhvH37soxBNBhil0EMV6MGNnFl3wkNYnIx25/Whe+hEJ7OhVz5yVlTSxf670P5+LaETCM4GkFbFG28NnH/anXV/KBetDwYOKUc2ZSndOOjwK5KMlEQgtGIYqSpFBK5Ol7h/JSNQpjANzYMMN5mg6aYwx8/PgRf/Znf4bvfvMbJ+JkDuHBjNRMx7DYUqdH7rYAdSiTzMVT2sLynHCWfi6AjW2WXLsh4slEdLmXwStTELc8LeKF93JC5Hq9ol8sbc3lYkeyr+77BEicDFnOmxE8zgn1eEeMoTTGwDnMepJRYgWM+TU8waoxCnUm+ORH+SX2vyNkAfskCepUFVMSkMzwYy+05ltSdhLQQRQodDTAGpMh80UNUAqtPaIvKXQR4SrKfTInMLvV56mnxrDDCfA2SI0SXoBL+Lo5WDrPE601HMcBVaBfnPkpDECRkfp6mCNztyWAmm41TG4VYTyan1RRBF1UcFWFWNKtfhED+nVdZO4Jcs/bDbfbgXEccerShJdZaa/XHs7CzCUX8ZFQQXqCd0FuuYWmr3AfmbSypB7GY/AUD+Cmq225jpItIebKaMLcFVysiQWKMytsWoWtjnRCj/diUbkHU8mr6nfWv/VD8N2WgepULE6W5bD1LRZ3cDYg5VgrlBqrUIpyJ0ilKk/rBUP1yjUBCndH6vF9tXJTweCWuPeptVCKFgRaBiB4tLjlqQnapS2nthW2g3F6fZenJygU0k/IcZqT//A1CgsbQwUQVLwdNEF68BsmIx8OggSrz6hYNmbb0lXgogMXNKAD38kV5+jQ24nTiAVo6sqZRCy0DOZZ4bavGJdxYaHlv2GcJ3CakXdRJceeyeVbS0sdabfyWPh3pGmghLyRdL5n+CCtSCm2YBD1MuzP565fNpULipDFhqP2G6WCCo37C7a4e6xed5rEK6NhqPrB78EvvdWRCNgkcl1Uy2OJI0ILYHypeGYBTIDEIq3t+kIJJA8/3pUSUEvyzjvmFliQDIv+TmVrsBUmWf6hJaqOw+JTRoscgc2sVjeN8QovKDJQPiMOQLJDC9NOiwrW9llryqekp/iMXJicnvhcwVOUtG4rxPYj+XJZ3FPcF8nXrNGxmMDQ6tOXIAnbv/vFZwhUw7oX28LsYPYpi9N4NpSWFxeTrHTzKq2x7G0wvtVLcwwfKmgboCgIKXnOojRiGdTYTFUN/nTPbYJ4ECYU8h/ScVDs0vT4l4rpetOjDYysfQVKcsdHcgjuW1tkaflw/06yreT7EtVR2cz2cA0i1um6ftdPd4X5+0byr1xyfyPXO8oYe4MUfGd9BciFUpL3oyE1PBELm0NwYp1egKs4PWS9utBf7an6mPE900KJB3E1ZaAe6qG16XNy+IEk9n8J4haCj3EI7Aqj/YXu7xfBK/zrhUv3D9vgFKzx2vXLW6A0UWcMFgGn5qzQ5Exzm237zBA+ipKTqyBq6K7ts8yViS0+TvUlKRiNUFwIITVIxn9qrcENBgkiBPH7HB66YI51i4gqlM7SPAHQN2GaTQ+R5OMkLU8NZLvTVF3zR/W2M7h1+yvjYNhvvfsRWxFcrhcPxNnQLzRZa6B2LW1UXxDWLtMqxrB4WrfjDEE/fOuOW3gi08AENHylzLnTfOPO0+JlNRGAwUGfWizyy6Uv4SN0WuRw09osDxiBL+fGHKGdOfucnsPac7rlMRRLRRxFhuTWXQLJx8AYblI3N2Abew/hYv5hmCkYFWZtcMapyhj4mu0nkyXQg+J2HPjw6RMmFG9++xtIk9girZp2/AnbOGoU4mGJciJ+wDViFfm0B0BzGgLnTKcJhvCRKgvuG7rEB0KguF66DcrsmJ4cNfOraR61d/pMAarLlkcIIp+j0ExcawwwBW5Rr3MVQpThNvwZm3PSaGIs8XtazyjMMg3Q18jh5Kuj+Jd8DjjdbdvtSlrtL+5BUrgiO0/hRoLA+DHDEIgzaWZVQOQiRfbJ52EFQiZXyD98KOJ8ETeZIiExZO2rz+OEuDUjZcv0SZSW/FBUMN1NYvjczTlxnOoHgOy3UxtO9LRYq/pJZrM2zeY8kY01JgHRmSefXTE1r7m0lHaBWXAUEN/eF1jWikmjgFpZohNdgKdu2SumH3Y5h2LiRBuWPF5Hqp8ZZHUhSKfptMABNl/dI/T3S8fF83q2nkFnAzwh1XLya4YRUVlmKGitoaPyZNWcn4BvEuS8/JZkqhab7zPXV0jlwl3KCm5QRn5juc43Yiut4CAe344M5yGMAiVl+XcF5n0EILwW64WyzXkxYfGcE82dCONeAI1WgfIiWEiGZjUEkXiahNKNTZaxb9WBV3zBEzhlqgFTWoxwzYyd7WMfabUhIOCvl95xvWY0cfo9cdtu0mkVAjt5lMRrcpWnkRBJKZndPU6sKB29ne85sU/CBAp7UYw2jERE0FShvk1nvMyDhzp4aiKYTYFTMcNnh/PmwFYnBk8/ioMVhTEF9W29mcAjtDIkAzUMbAxYQSBiEywxz3z53MHjoKjxvy7NlEpuI0izaNABmpDrRMn2V8Fznna608D8QO89wRPv1aLdBl0lPVFA8FvFtmRiHVgbmKy7CqVcc04W0dpv7eJ8KOBxc3DpmGe3lDsp/hM8lXWZ2nRaDlD40FpVKpjh17eAJwdGvN/rSAqqAk2CFuJUnXArjFtyDdBGfOBFEnxorDn7Xu7e61qrIGoHT0G/yYqXl611xFjxTNxUHumgVcLXosdtqprv2n4JUIR1xMBtakbgXrizsF8VLNrJymVdAUURMv7VZssDJmDE7/+fu3fnsS3Z1oS+ERFzrpVZj3PuuwXN60/g4LQwoYWFBUIYSO0iIQRqDwMMLMACtdQG3pWw+Af9A2hhYiC11Kjb6Mu999Spqr0z15oRMTDGM+Zae1cdOJy6p2ZV7sxcOR8xI0aM8Y03edalgCP2LgMCrgoGFwVXEsdoSi4wFYQxUDlV+5/ApDXzD/BZM9oylybPYE5Gl2DNzlQlyEoab1Uq1I9WMQGUMnGMIWsBFiDGac0QR5Zx+YtU1pnckVI4Vdt/FadLLPeCZs7l9cvUEusVvIzB2RpvY6Bn4Mn2s8Mwxyk/dPzOAZSZFmPT2z+JqOVEfdn85X910JOZONJdFlcP1hPOE5M3g11Lef3zufo3IwBKk24E4vE/iTHOOdGPA6N31/pjvIk4OL9FBnaLcwyuFTl4ouVcM44+MSjE3CXASRQtX5Z38/eDW1PcHYnsAlIGPeHVsbsDTeufB3fjQX82IGUG+xQBBSgAalPBUInN9PBGmckZ2LDgWX1H62tnlkuFKcqkQ0OfNjYEU9LFgGhQ5Mw6hFLMrX3PwsBomhDWeGMzz4SQ0YFdZ7qYmS0MZPYxQL2j9e7B5IsC4dahoBynMTK+IVra6vZMjC+BN6T5hMEFFRrZZRLn/HwOZksoyUkJCAZsVsi05xjpb4SV99n5+V7pb84lUjq88YostO1z+ddoSf6RoqrJVRykCyiPAEm/va01DGhrEbY9eRZQ8M/OMVAAcI6JimsDwJmAy3xFWhVpf8AorgYuBWVOqaNWCGUCrN+hipcosOQZuj4dadsubHDhsQoCyGcjQBMCkHnqihN44hcULMDi1Zg0pEp5CU+JQQIfcj8zAYNhBUPvxyHZzyyxomAomLUHyBMKppR40fdGKRLvK/14UGsDwdL0LSDCwDtLrF56vE0DsRTUbKVIZXqIt6EWQlUAaN+H0w4W7+CnD52U07HK9nSXJOuBM62n5V1J9MnxBI0lKB0bQtY3Ejs+e1M/fqcAyoQFgzQbI5nnEKCnUA1BXqNKKQAHLwBi5xnY0ntYTY4H9GDgyYRdAhDPYhmssJlfrG9B2q5k2zZYCr2BPdDqQiEIyu+94+PbmwaRux7lzwyhYxW4KVmG0iskJG7NNKtWta1FNS2Ovy3xFy7dAyRZAB+R1HQS02o0uEwTJzFggGYTTq9JZFY1hpREMIDVj4E+JIj1GOK6yy68YEJhbnVgQbbW0UyUILVKUMqiVRizszljHl75PfdhGlOC+SXt2LLvzB0s7zChYG/InJg1j22jl4Jam4IPy3ZDFIIyOiREyxf/R9OcSYMjTTu0l2VgqiszapCRA8nJU+5JYfW63+84RgeRMOBSi1cnBwysJro2Pgz9rsIqlBNygU0Ow+FVlAMw6xiV3s3yZpWon/DK3/uDwehDg8V5AjCtnX3fghmkgc6GxplY+jEqvVuzdM+MWpi6PovhyQ25eK/xBhRt8auWsCwJ2euiCb1Ssz9rMDlIg4ehzg5gqw2vl1fMMXBpDYUj8BwIEOK6A62KVv7+6cQKvYGZnhKCYjb3vQQ6V9v35mUggjlEi2okgwkNBtwIpL3kfDZk8lwOkGsMMV8mc6yMjlnaxVqYBm6yQsMZmBlD+95NhlvLTAhL2InyJ1XR+mQcU5uXtwqUijEivOE4Bo4h0KSoK0vi6tWjoFNWK6MVKamylaLhGtLGiQqjkLR3mUMSTIx9G4edZY2TFLeoUMam3oYJoLIqk0NrYxFh0+mcBHR7Z621B0RIhwmcoIGQQ643mPzSNmALzeQ10yOkJpwH2j2drBT0cXpn42lZIXTgBFKUavKOPE71b6QF6scjVhePD5/Ez4BpMhlT+g/PmPgnHsyfOeX8uzGQMDc+EolTrf7NeuB5WxQ75RNa+hKjdQKCDzOStD1KfzjPlJ8PerinPSf3aLJxmMR1Fs1WliAyQ1gjDZghvnEFV8OrZJuGqXFALkSSMIaCShh4gli0WNqwlDy9y1xBtdYn8xeDDpCMvMHMvRVuLhurxZJkuqO0sX1WFo4NB0PPBkswil3n3ZiCxDHoZ8bcKcfABONnsIMqE94P5HRSCtb9wXCl4uFYpMeTv8sYbETO9xwC2dUYAAAgAElEQVSM/bg9/nt1qIAPBAokQgrJYASMdR/LJQq4EPMe/+Z5jn13GoSPJVFvGmLwnSBDBeG+Riu/NCWvavxUyQvow3q+ms9ioM6uvIc3MCuU3pv9fVQPIQZx8aoLYwpYECs2gydQdYNOreA/nY5pea+nY7a3sfc6dREwRZisIKNNR1KO7CGyAmbtiVlSx7h8ohi5j4G7tgcjBlCl+PL77VC3nWRqe29R0rAMzXo0RdKgCpNYjExDc4+ExkeJIl18TARoSYCgoQAYIWtMba/K5SosPMQrUYUMYb/DiUM9OZ6ekK2rvgBYP0zlCT5zy4THP3GcJeL6nRNv/zwFxfG7d+HBtOcQXGdmYf5hAjAw3FISrEZeMLuQ4uCFYE4Ph/m8zXKwWJ8Qa2t1dcZUTROAVNrVmJk5MEaOF3DjufrkyYMBx5DmwUc/tC8eYNzpCY55OJhjMcVawa6VmHKU4yzcOpzmwE2lVr2XzGJDbvGzeCfZF8rQ5kzgRpbpcHcRS2AhGxNxx6HMYRfr05gnC5Rq1guo4VWYE0l6PxWgdZZigdA+XBp3NjRYsvcujGIyZikYQy1NFsCv1jKP5pjTg8iHgr4+GEefft+hlk0H8YroJhBNgplhAdNWA0ifIPNllcbVjSnzN8BMGDTduG6VEgB4DJO4Pm315klYP1J4rRXXlxe8vr5g33fUWrTP2YxLOXZOYH21A4daKOnVp/vD6aWk/RijEGuczC1rOjw97sDf78PAglp6BeDm2KK0kAzMPtC1vAFpnAf8FANStqaUPrPHpVXQDz0FndR6BfYeacZnIgg56q05j2UJHiZrjGYgwWiDhd69Qr9eY3xAKuTDgX2OpXzmtlsUMcA7ESyHVyI1lzUwihThlRp0TZIj+sRgGdtgsRJttWAo2JgM1MJopWCrmswyWJv9CiBIU5nWlZd3sPVhA3MIvpljJs3Nde8DN60uz6q5FiootYIZuB0H+pjoc+I+hO+gNOEnQ3oomjI65ml0Oi+FCJd9Q6mErRK2TZahFQE8eyu4NrHH0VDrNpM8h60cC2MyKehUTkAm+zTuR5ePYFV5JMi9UdH6T+y0Ii2qAuuYbF/AOZEjTpMM3i/QJC5bWIyUUzA55q+/SDP2/5S5qgIQgXtuTXfyNRmTQjqMDkjuKG5ghrZ9WNnsZ46fJgYKxmuECSzYPQlRa34qL98QOGIFP/nw3xQEPGhH7taagYvs2sQQRZDOFMtk8R2yiMZQCNAyLBanonypFE3hnwGijgPMUqHb/OshvGLgjxpctjDpZqcgyBysuljHznNfCEVr0pjfWVx/xV0vy/qwBVTLHAx1zd3vB44xpIHqmG6xmTBmKmQ1NQOPFUBNFg0y4o2iyKO7whbBzKBJ6GUK09dYHWmVoVYXMOgACk3wLOAqAMqzIGeUTrAZc7M0BChNBvqYDqAc0DC07g4EFGhw/By5jAXglglgqVVogejRAzEyRwcRWGO6JqSvXbggGcxWgTisSu7yXVcVgAKo6xUvLy/Yt00AZeEoSJp2WSiOcSf/ncICETREDiLzGO0cgghGqQ4va1kMrP+MDt83kL10TlYhY9aaXzH7RNcaPm1fG0sFFtaoCwMYbskIcEJ2QVKYkqPK9/wYknUqyRmWSTq9vU7hACjgkowBwXtdyCHGB7CDMi/KZ3OSQNSnAFRWknI/UFfSksvZ4IsUwCyeJVhU8PcJBUvCs8asGJMlgJ8Ircr+KBYAzdOTNoyHmz5kCoONNRfGle8cp9i7kNXYEn43ARxj4O12U/mkim6tqHXDZMb3H9/wfr9jTMZhIQ/UwNbEWT/z2MwZTcItAanWgteXK1qr2Bph20QGVJU5r5cddJVkIRoMmtpSrFR9l+G8w7J7BUORtGhCKFr20pWE1zZIXNScc/EWW7xpKOkBooLQE3gizUx1OWWzrHRsMiiFnoQ4MIqM7FID9x63pDqBYid/l5jbR0TkNJ5rfy276/PHT5SFByFiNp3jU0NNjDwh3eUMm+jHy/x5y7n8BF0mgOt2qCdghJaFz08le5hM/8IpdQwcS+Km+5MQw9NlO7nnFMDZS1iLjnVu7D42bycmB0vZJSfs5epEeMbohm7qORldi2HOKS45AZxBtFakcWj805xTAddU5mNxH7y8LqcBmNwvsKKkNp54OxNWU8sB2NpYJW5n2olhO3M0ZjLZg1fn1OKD2qIDhTGIUaaY39OCriPm9B4OvlarprVwmemz5V302ofA24R4zLRsgtw0QXvwWeM30HYWWGcIJopGZk5nGlQGB5yIxc5N9JcxxY9R4X4PDwOJc06vDZdjE2OvRXao2ePMDQTV4VSUKYiKZ6xccZ1z+T/ndsa45PmI786vdK8nFdyvJMCUALMOUSkSsG0KbgZItNJpfraP+KQEGnCac3qiQ7ZK+diRvhcGNG6xaA82s7QTVfkMWmMNE3VKZHkhKxSrf09CMeY29hPSTLqSbIoBLDmCNPYp38HCCyIxxZQmYqBr+MG9D7VAyc8CoAArfOlhEM5Do0YeT8vGYxxdwPEcQB8yilbcIOiWmwYDKjPRBpx1md0g8ybL3PQZMkscIVyWrkSFokiZUp/wdKcBXwGTX2bBDfnuZ5rMtDWLJUKyP/la+guSWU19MHAZCOOctnJJjoZGuWy8H2NB/50X0vRASxgz0Yl6wmzFrGeFvIrWCkqb1YjcNigQ7jMXlvGvNRE8sSPY+olZXtLcJQi7Ipupos+ccD+JhwjGNzm2lvR4jA3gX8yqxZ3fN8yLAdhM21stUOZgMQudCbDQqpRIjIFqxduiRTEzMIzWCGlO0gbuCoB6H7jfxVd/OzqOMeQti+hAGTyZ9nT0gd5lPu+HXJsLy9m8i5BRTcnmgOEgqg9Zg1LtWcUz5WztCQDPijGCKU3W8gnd3IfB5OaU5r1HV1fkZBwau3Wo5arWiqbAZPDUwnLkMQlW2sAHnYQT2AIrDUhZtfMwmUslIXIXqGh0tp7KnmSjgCEuA1s7t1QpHbCNx5nycKG4uKkdwLEqDI+CcGiqstXIMUZndCj7a7olDtyiTpZNA7OmJ+FndRABYw6Mo7ul0/c4Q4Vt1cSOhla3JU276B6V9ZyyqCYwVAi4WE/Tly19i7CBuYeF13h5ORV8k7QiMwOULo3yFSooJtCPjjnUVd52cX+PDmaGFqLXUiGP7jqjrUybJfHi3ruCicP5g7m4MwXaz7OyWFdUecm8rRBh2xtqIVy2DYOhraegjb6rB1/v6tqReKD87gzArB0Oh/SzGDfAAiZrVasjPHdAQgXsS+IR+1Ar+7TvwE0B1NEHbvcjlCLdq3PM0zaxhQ9pbhXlRUEcmDzk/baq89Dw/f4ulqrLxVtvtWLuNllnh6eEAGlgDIgybue5zNGxCuDT8JQS8WkF7GVbgtc5C3xcWTKeN8BqBT17PiYkzIVW9IYMnxisvDLArGnYHg/rMsaAn6FIyCIabvPNZs8zPpcp8vnxk7Ryec5T6clfKL7TJ04BZFHOGhBEKwlArDpbKjdveNR/IfimMWR8tgh89q3O4Nf/lDEznFHmE9kvPD/DFjIhfjdxcSJSSu9jF7Bfv1qhkAjkE29jwlaU7ChAqRrhGMqNPUjRgsjNbScgpqurs6sFymjUAQjJrnXGnt6gwEBZWKAWtMe6dQxI6P3mySrgwDXd3zbcGdzas4ZcKPVcSISduAIIFVp3zJgy+2BiXCoUF43JAYxqfN67iVz4PFpME8gxk4IDLKx0n6fGBLJjuhN4gv355Ob2z4wmKWjMtqE/O5SLvD2dof7MwJMdQifTLSerdc80agGz9uXaAqAgKRKm7Z7Awp4e2YFKpfhYaYtT4VTEFjErlf+bBILxG3NRQwHYHMkCxdISSWJDzHqd32Odk/zz2RI6OcIehirRokyvfNV+LgnsBwHKO1Ih0CAwF9QacUNDMxTHLGIJKiF/Myn6ds0lQWC2ptP+s/dUAOEErvdZFblInBmTxerOwNGlJ6iBLaujBlKr3DAOEULb6wLq86XmsljBxxgYo+seZdQaYK9VyxIu6hXQGCGQK/Umj6bRMRs/SvQjE5TmjpZ5cBo7iTWXcwkAZnr1z9Ke0Tde7hNpM7EZwnCCx8NoxZgPPT8tDc7Pc96dLsjP/dzxOwZQLFoXeGl4GX8FHPkawRar+qrmUUovl9CpZRs4olTmYALWnuAZZkkoeHVlwAP/RMuaC9/T0xFVQRQEWAClOncLFWFMqkmYEDfBbn7peOd898efswCz9xdTdpEGv5puX7RXlP1s2qI1/61aFiILwTX2yQLkY6wCmsSSdGgJhq4aFZP0ugMVbPsF27aLpjXumDSxXTdcasX9OHD/dUefQ2L0ED5mgsQvtG0HIPFFnuJvG31OqTtChG3f0WpFLWlKkvYyLEhTKEliI2pFYeGmVRo4qam9oDaSVOk+wFPddBTbp8+pjEKeb/FiE2qhtKadY60N5ECNM2ijYFgESTd3VhFrvmg+apVi1voveh8l2lhnjcmZc4oFwKxHTuoBvC0L1t6S/D8D1iWB61PNI5iAtp3AHtumCDeB3M8g9N/TY1lXA8nmXp4cVlUNUrSect5bDlb/jEMp4BBe4YIgWKNXE/BkwsuEUMqiFFAeoM5jF3UvE2uHAYvj0/ex9Z6T8d5vmGOCasHLFy84jgP9Qw9LBbPQLDNAAtwGD7UEQMFQ8Yw025fWr9FAnsQw2f4OPurvj4hvBAiU9rrR4hwyjuOQ2bAipm2KDaVpEHprEVdji0NJY7MiyBq2pjFO1jOuqOUQusZWaR++jlNjrIZ+dSYMFiv27S5Wt0PLuUivN63zlAGE8SvdM7U27HuD9bfsx4FSCq6XK2qtboGSa+By4d4njsE4+sfE85Pr1r5mkrxk9KdWfpVtpRRsbUMpBX0yBhWxZtYNFRUdA5hDQOow+ck+N07NulkY8FirDJYk47BJQU2qIseTyDZ54UNN8j/DO29w/KQHZ4BF9vg1nO4jX9K6xkMnzqjqyfG7d+H51K6fywQnoQGCpbixauaDp5bwz6qPbooS7jxHo4jXZ806IZsge6oyL6tjUUoFV7gQCXalRJisHyZwPAOlWFPbohWnQ4iYcAMDVM66DtKi5cmh9G21wBBBwZNuFPteqvcuoryJStSM0inW9y1aYTzmwTLRmM3aJDFPXX3wAqDEhTdJAdTlBW2/gI8OLh3MwH59weXlivr+jl9/9x0GS1A4FXb1hQCgFGz7BoBx3AFFy5o9BNGMNStu33e01oDZYZlvpm2YMLMPjRG0amWa1GSu2iGI0UCYJC48aOCtAQSGMXKxbhVjcLrR2KoSa2xXXlDW9WIOuhagEXFQQUhGUyxzg+wqA7Qan9KVVWqX55UiTUmLFj/kOTGLUReDi3F7XXTjCSbkMnhCuHVlZJEdZZpAgKegT4khk3Y8DFLXfFn36c/pWMBTorukwZubV5QBBZoIa8wSN5WFjyHuNMcitAMsAyowEngyS9TkoQrQwBgszbyHtvooDbU1D0wWKC7/jTlx3KXQrwCoV5T3d7y9fcTsZuWaYC7OO3PMFCvoI7ADqrBiS2sleR0Wts5wOiaqsJg+m0PWvRJWL6NRBVhDsj7BEqRv8zKaJN83zR7bqKCS1DRyxYBW0AmWYGwQvE0IAShVQKfxAZ4sssgytTK/dBBF6Awcg3E7RPk8bscSQ8kM9NEx5tAaTmY0EGtlqQUvl4ZaKz7OA8eto5SGl+uOfdtgMICZPba0jynxVXNIVt8crkAKXxiKdHyxFFiqG83VLPneWsXL9SXcz6VKUdMG1DJR+A4eQ93F7PxvUfZjuwA611aR3TigBbrX0lCKgEZP0EoKwyIrnWMZmIf8nA0vy5PhHTAmHDI8VSCNx7u8XZ78ePwELjwKFRiyhd0ldRor+0vYebGFKJ2lO8I/XG+j8McuTyfyk7lZ0G9Gyv79HDXycIdA4wnG0ZOrXJO03wnL4M2o/DAaBXJEuQL52bxObhUzJvRDAi0AnDHI5bVcaMQgI+uwKhCbU9upEGHO4dXXPcuF4mY5OF/iNLQauhG+Pms148r4mFnrHMABsy8v4DQl4ysSVKmxGbUUtFrd3G6tFB7mI0+K8nevfaWxBSJAQ/O3+WOwFlyE13Az2bg8Kw3a3Gw2JyugPmW3QEF0rahbQ6sWQ5a5WFy+rtrnjvjriRxjf3EerwlKez8DAOZA+nkd6xo+2ZvBaJZTFpdqmlTnASbY0hyuMHUZhf5r+9T4zWksCST72DN4116N2YW9bG/jIeoJsAxgY7fBD/JcAEYCRrfIoNst30CrUuHTLOiyZ55TqT3BY8EQ9GlZZZ7ly+KKN8vf0BjJpmAW61Bcr5gc8aW2Zmzf9V0nLEyBXRGyZ0hZF4uJ6qpwWqxsKB8ynQ3MFqfUlJdraYZ9x+v1KpZ2IuytYWsNX335BTYHUAKU78fdi3HeAMxJHpRujmKQKq0cGZHQdyFdKI/CMt4/CHUMVAZKlRIQM9GZrW92qz3ijZWDGBBe90simvTN4LLxEvtb3PFzXOyZYPdFlTHTOgazjWQbyY85fscAiqJn0JNBhnEnNLKuTXs3wIPI5Zz4VyaVfEFtkSlPku0bQhBREliAua9SnRLXDGRmyTiDXeuDsY2m2iZNLe8/wwtpJfn1WeRzcDZIPgo/Az+kSFtEU1VXVnG3kmtr0GDPGnV7cqAe7BWcAcdnFgfEyxclQRkMrlCR5r6lYt82vOwXFCp4225gYtxub/jw8XuMMVEL4XrZRfMaYX42Lam1hgJCIwKmpF6zpTfPLrV0VJOak9Tl1pWpypiMASXSUBOxUEixDU8A1SLxWe83cB9qdQmqsvmxt51q9p4a2yXb25orB4N3S6NPMmvgpdF9Fq5Be0Ja02s/RRX6oGsDsfJ3We/Xlxe86JcAOrES+C7i2E/+dkG4idCEviXOcoaxyjR/Ur2HU39HFSwW3yJlPyQ8vpS60PXP4WDY2qoyoJZkSXiwBtzrG9tyzzkBDc6HziWBgEKYYpYM65QCFRH2JUpCUAYskb8sFi0B60Xr9ZjvVmKqBBaI9diddwDgyRYSsxjAwMZeKqE16ZHHiBIogDp3h9XwiSQCb4NExfenWCfhBFwKoV6UJ7nyEUxnqT9HaTwUVGWuGWYGTUaZjDYYtTI2nqizoPSOPqRelCUHVQ95gPIG3d88xcoiBZA8fineSUoWdEtAmYzOjNsYeLvd0cfA2+3AMcSad7u9gxnJO1CwVfEQXK8X7NuGrW344vqy8OjWGl5exPpDRap011JwuVykThwGJiQO6vsPH3E/Dnz4+IZff/8BvU9UqjiOEXyMLPSgaCyruFP76Oijh9xkuCJYBuN9vons3Ta0bdc5lxpvk6xERGRpgkOWhMCNfUBaXd77iio98DRLOasrjoJf2vra/ZKMd770AN6N/oLO42/6d+tgonJfyRB2Rrb2fu740QCKiCqA/w3AP2fmv0tE/waAPwfwRwD+MYD/iJnvP3yjeJnlwzMcREyUCz0/j0/3MKby7Dc4w3poeZDQqCHTDBKesf71sxPSZWhjWN2MLsQTwQSGenqcA4nz5/Ecdq3Omwcja1Tkfw8r1HNkfQZPa/BxjMOYn1uF9H6UmEOtFWUMF+5jDNxuNx0TxELFxQGUzUNx7UuAjcqV5PLIaxvuBK/DpZtO2mU8zpfEBxGKChbJrgMwhhckTTPydF0Wq0t3WAVwcpOSmfpXR3UBUK1VgBMBp73gkD+ew2ahE1axQnbyuW+1hVvTz8nrxk9eKT6g0+/5cFL1+VkVD7dAJNoJrR3PCe4nOn4r/CtN0/nNbD2WjWhnGqY/xXB6KQpTKjOIOLkh3LNgn6kM8J/ZQPzZUokkrIKOIz4pkj5mtkRlJVTfyxInFvpKFqjgXcHkLFzCRyVaBkrRhuWkdaDMpegCM57zMJZ1GnSvhAVqqBIIMMZU/kBAndbH0hfMIGEoGhx2f2eD6Wcp45Kfw55Yc4yBo3fNPu4YXVIsa2sK3CRLsJaKl31XELXjq9cvPPTCgPj1KvFO275j33YFMg1EBUwdE/IMIsbt3sA88X67odDA0SqmZQoqKdW2iVIzh7ghrQSHrox1h7AkGmLWrF9pYsxlCK+v4VJza6Yh+SdHXq5sgcuKRrZgizKS+P2MMI11MdhaRcT9E60YCojn0HKpK4jplrIdLZnnx7Gv38QC9Z8C+D8AfK2//7cA/jtm/nMi+p8A/CcA/scfcyN7CRPEjgtPwMHiMSImg5P3L1w7WVgZv1qcZrbp+fnEZKE1LFssj8UBVbYEwRnS1P5NsAKZMCKYGouQW7gwJDIuU0Sg5efgaZW7eUz2LOvfVOz7s1RjnWcnKGV4Y6i2Ms2iYFNmXFvu17YddUq9lckTVCqobrKp58Dt/SPmZFy2DVut2GrBZW+YQ3q2zTmBSt6Hy12Pc2Lc3zGJwJp+TzpXBUBrDdQaWi0YR5eNP3pY+PR1LMgzAqHte1RXZ5IMnzIZhUJ4FX1HY+IPHIHim1MWU1qO5M56WD8TJDZGqLrDfh9b03NLmkUxYCxmdCJg2xoulwv2fXcBdr6DWaLyeNY7n03qrPskXDiyB9WaSac+i6eRmhWOe3Yt/eTHb4V/RdFHO1ToWrB2mpMQGPkrrvRsazZFROKLTIOOR3AEqFezTAXtiAJVEgvkFGuSbyOZYrK3RQH57vsPeHt7w3F0vL+9ofeOt48f8Pb20S0V/k7KPAuqACqNXQGsd1tRa1beh2njJP5Za8W+Nc1iZR+tqQoz8UzWgGmvocYRhD2h/GpKqRTqHTwHaIrVm7eGvUpvxoomgMmKa+ocFyKgkicsSVsqK08se8oKAX94v+HD+w19zmR1uuPDm1ibWtuwbRvKK6HVX4KgtyaJS226b677jq2J671BSjZ4T9NasTexPDUCCg8UZlRUVKvHUAoGVczrBddWUHiCNdlnrzvux8D9EIuYWemEzwt4ZSrYiFC3TYqv3m4y36ReIvuuz7K5Aclcko7VSgOhqBUUSTk+KRy2tiKvKmqrrvwnULAoEOeuB3K9BNGFEh9/lYrksQEFK2tl/dRF4ZkT8f/N8aMAFBH9bQD/LoD/BsB/RrIr/m0A/4Ge8j8D+K/wIwEUYEgvgZfnz0WtUasDKgADv2QQlTS1pHHF8yCY4YmAyWOydP3V/x0a0FKzAko0UwjNgiddG4XWCuldi8dJzE52qMj5K8PMz7P3cWIAFkYDsAeH2/cAniXuQfne5JktxvB8jmB+8hVE1dKwN9JrVPiXglI3AIQ+B97fPqLUipf9AioFY+wY44rjOPDtdxP9OCDsdyU74oHj9i5DVA5fimTSlELYt01r6hSMfoCHMEFAU5odYIvVyjIPJbHAXAuy0ZkZlQpmifpRJsBqqSCW0guElU5CDqhQYNdxHHSer1k1cWsSDFgAaATBG9yfqYcWXBNyurD/7EMibNsm5v5WfVwr3cf5bqWgxD5c43tkKCH0izMmcwWUGYJ1wVHKCKcFmD5RCH7Xx2+Lf4kQmkv1ZQPN1nWgCJKPuVOBuVRmX+UByMB7CRdeKEusAEqqSBaVn3laJaHBlFD5XSl/IYewolhtt45f/eob/OpX3+D97Q3ffPMN+tGlmbhlb5HGDk5zm5DXtDKwYFwJrBm8WsDRwh8s9oVAIjRLQasF+yaxP00iARRsqMtM3fYSX2gup4lDlbeo35brznUwGL1XjH5IADhfMLYGLoRNQcEcA6QuPrC5BQUIohSwlqIUEFEwWFqw9DHx7Yd3/PW336H3ju8/fsS9d02wGait4g9++Qd4uV7x+vqCX3z1lbiB9HkiKwRgSCKKCPxGAGFiL5vUb6oVe62iDAMCBgnYmFEh4KU0AteCRhNjNmy1YCuEPgZe9gP3Y+L7jzfw/IA+Jm7HwDGm1AJU4FLLBiqE++2G23GIBZJYMngVcPj3WvVnVSGrhF1YwgLPqZY+NQIkEERuKFF6LRDgWFPyQGwyl3V+PUWGsf1uoRNYAFTUIbP9Ks834ARPmAIDubyNHWuYy6nEy5Pjx1qg/nsA/wWAr/T3PwLwDTN3/f2fAfiXn11IRH8PwN8DgC++/ALGGcw98ZsdJrV+JHq0R6xK0Gl86/1t8TMgC1fK44Q/PM/tPKH1O1LWOBtK58rJtIxr1dr0vCcWkeV90vfz9AREDaCBoO/0TrTMQWi0aoZXYjeLoMVngEiqFjOnSsG62bhgKsPkqRq2mWX13maPy+9j93no7+drw6G5pIV+MPOnOCPfcNM2oIIDs6pQqs7D5+vzhKa55AiVdkGVwTHWtV3PUhrjPL8xB6s7JB3GpAAfP5lV84e2Bp1+OJ/PYX3KZ4YrZ7Wk+DSdiM6Uj78hx2+Hf331JXx1U0BuMGojsHQ9Ym4eOR4v5xlrY79wGQeyVZ2wyh35PZ6Qz3Y3LhlPCn7Se8ftdsP9OLTZ+XSw7OPh/JRs1bXfxUWeN7G8d/G/G4AqiV7NSi5FLxWskWWFiaVDtkCN/TEJc0qNI+vjN6YqHqbYsCiuYKD3gaLA/+gdsxTUOVGYgcKoLLCwqNWb1JVpYJRJMo7fb3cvhHnvXZViWftSCva9ojWJBd22hs1aZJkVZ4aNzXhX7JnMw+wTdos5sO6lbFcsCvJa0Vg1AFtjgAe2VjyLsHQBOAbaQrG2cjcVpbDW6LIxhLvOyj0Uq/fg9JB/CsCzHEnRki8Bca01lyWUvj51rPOVZs1lWdzBjABO67xc+lSkPjt+iIf9IIAior8L4C+Y+R8T0d/54Uc+DOAfAPgHAPBHf/LHbBpXSMsoLP/JsXKaEEoERFFuax208QvS88O1ZgwuMwUjJtl42YV3QrTQwDlPAdZxwxiWMRwNssXUImdCLNvWHlBtQJos0OLvRipWp0MszRE4Xm0j2NwkSsyuSYNBAZLwIACWEYkEdIAjcw3fcBIUb9Xhpf4UK3Os1r9Kk+6JK77hTKMAACAASURBVF4vO3qruN1uuKkWVjSF1gLhfa04MuXEMiTxS0Ur1C4yytVx0rRjuYbBktlaIGUjyKxABNZUb+aKXWOHJmuhu0k4epfmxWeJt9CNWenCDmoWVe+LR6TVfTk17dRzDKgqWnPNTQGbWDPWSvQWaOptbaa6QvZdLXHFmZStoa1zWlq4hhcEligNXg0dNjwXx2URgOzWS91hFPuE9Dk/9fHb5F9//Gd/wqzmxjmw9Ik0hSS0Y5cWNhBHIw5okLZ7IcfqxcoA+KVmTVU6Y9O5FOyehQKrVUUDys16JMxXAZZUVcTHjx/wq7/+a+VLBa0pT5G7CxBxUarAqTSvgWYNxIksoYKdJ5QSACoCpKvUoyrys8QGTW09QtiqAClwg7T6rihlAwFqcRLA9PH9LpaV3kFUJTaJhljwxsD7cYAI6MeBVgiXy4b7/Y5WCy5V3Hq1VEwW5axPMbK0CgzqoCJJJlLP6Ya//Otf4Xa/4dY73vvha1fbhi9fX/H1V19J7NLlgq2qa/K4i3RTazlPq94PCX9QnjdYEi+kMIwWtxwHaIqbrOk8qqND4pqGbW6J49pqxetlw2gsQeRdrOj324GjD2BK+EtpDa1tKakFQJ247hf0XvF+t8QtaOJCwegTd77DPUJEmF46YhoLEzIuZZUpKp98b5CEHXzx+opdaweaciYlE0rwyBMgyPd+jBU1IC/JEGYIMKuxt12aLLULE198ZmV69vxnx4+xQP1bAP49Ivp3AFwhMQT/A4BfElFTLe5vA/jnP+JeCJQKGKsNTftxwGc33cOrGrrlJ1c7YE3gJKFYA03y3YBFpJ3aYd3Ord2KWUBwcr1l2OMAhKxYpdaYshiYp2vD5xstcxQmSy2kqa9zVgiey608kyu4coC1AHzTZOWGVhiUldEV3VykAyCSti7G7B3lKADamjCV2Qu6PsgsVbVkl4GIawNVpsAp9IEjKJsZExo2L0um5qpmmNZcaMamLRW1MGoZXozP787PJtPUM3IQZJqcblvR1tzqwE+Lxvr4WIQOn5+VMbXTqQnmJICV8djk2VPPwCjoOZ2zjCj+7ppKMsPDhGOy8Hm8Fedn5+OnB1D4rfOvrHikSuSuRiHWzb5c20dSiHnhWWI8STST7+OgLMYQxVDTdjW2pCMpRBj+wHSCjZQYx3Hg/f1dgpTrJoJdn2mxQsypawAKpG6TxAhxokPhBxLQreJULE0pnKCUqvwjtZVi4SziVjKXGitgk8BrghSobFbRuzOIpNL30MbgwqIZA0MDuCF1kVSxaUUVrB0gknIAVWMh5T+N3RqMwgLOjj7w9nbDr7//gPf3dwxIDhxRkdCCUnC5XPH111+jFon7LCSxnOO4L3M+tdI7AdJIWNdSn+pVzYm19ILut0mkoSdpv6mVzEiikmT4VZIwgUIFt9awN6kD1UpBL1OKi2pWsI1sksayVaDgruTi1KsWueH73loDhWIlg6JEr7ZPQuQGyCla92/fN3ephcu7LB4Kp3e3YlGK0Q3NYXG9Kb2LcgOv0bZugRUgfQpE/dDxgwCKmf8+gL+vD/k7AP5zZv4Pieh/AfDvQzJZ/mMA/+sPPg0mxteBmYB4drYxG38ZFyy0nJeVMKRT48zTA+iMMMPaJKZT9kEt5xEk0A7q7tGFZ4j1CpRSetOibPuGly9eMMeIgOrUR8+FET2+F2UCMYatPi1KY/BYCxWgNiti6AgQiqQRmm/ZLDQWd2P+4Y4hm16BjJmg5Z4zwJUDYlg+qASCWoaNliFoRQLLgWDKcm9NedZ7OKgipCzD1X1Eeo/q1pcABWbcCbBLHhdVZ0FjAR21FrRpFd0lsLQVcTsyEIx5wSSri3ehLULEutjT58Sk4UzaznMLEwUbwul29i5OI+nLW2OMIXERKSvlYTslgGPKgA3kGczx/cTRhiOD7ccT0wT8zQBOAP5/4F+cwGv63N9aafkhVjKdb7wlAyChsfgc6d9UNzxdkQeF5XNT3twVn8YmRbjlPq1V/OEf/BKjdxz3jrcPb54ha+tcS9Nb2+gLmAmT1RrpfM5cyOQAyimL1XqVhht8R/jIhLnPVEHABFh74Cn/mvpsGfsGUMUYhIIJkNZwogkqO7DJk4R3SnbZ7d5RivCz9/sdrTRctxFZxGpRr293EIBb77j3A/ej4/3oOFisa61WtG3DL7/6Cpd9x+vLCy5NazkxtNzK9IWdyounlopgMA7uoB6lQADCtg9JmCnae0/HVYjQasX3b+9oRSx4rcnsVnV/egFV1tYwym8rEbZS8MX1gst+kVhSTTjpWnrkKITZO45CGH2XIpxkSTXkSrosi3oBVO6w8rvCJCUtkHiu7xk4H/dSM0GaThOTGTTn6Wq7hzN0hAcLy5knFKA8z4CdeWDgCuvn4pt+bPjB/5c6UP8lgD8nov8awP8O4B/+8CUmkmAyTY8nTAFY3A/GC8JalO/5+BS7q1zLy+8GEhLEECKhSHn3HkfO/iz6HwhrAxyMTK3azcxSN6PVBSheLju++upL9H6Av5s4+qHdYcMVJMxTNLk8OnOjEQC22htsJnND7pQACcl5qUIvp38IEczpmUMkLQTc2sZYen1JcGwSCHNKELlaYDywnRk8FPn3jjmkejlNySTZakErm6/DWdwb+BMN1Vh9BlBpIaHB5tro08DMsrZsViUWF59ZqHTjb0cFM6N2Cb2tBDGZ1yrNiBcKYN/o4BOOT6DOyNmE7eQhWVSAjz/iQuLNVbLg2SECh71Mgq2bAShp6tmeMAWCwd74zeIcyHfeosHpemrjCq/xxMY8WfaI7UkTAGyNDuWGf4Ng1NPjN+ZfJviNJh18JkAlzabVumK05psvXLkBdoLps/+LDLUlMQIK3FKgbgASX8UYK1urmJBURQJgEJaAir/1t/4Ef/CLX+DXv/4W/9c//Wd4f3/3kgalSKC4tXqZWol7srWqKgBJcLF1jSBAizYm2qCk4JgCMFnAD8kbFkxQIQdQzAPMUudt6BtSaQA1EAq2tqM1whiEWiTrjs29XStaacqnDuWFAx/fxSJkvH6rDdftqtluajli+Lzdjjtu/VCpJcelVWyXDS/XK/70T/8UX37xispSyZwnYx6SBWilySPBQFyQh4KmzsM7Phxd5EZtkiyDQg6giIV31VJw2XdVQDdc902aCF+aJ9vUKvPrLitmNCKtbH5BqQ2lNWz7DoBwux84epcyCGPg6BUY00tJBPBXCmMGd1EESauTu2JdSOsaqwwwpc95DzsgNFe/7YFMF8PkPQwfkNOzlwEhyD3MmwN4K5fMPeXyULo5Kd+fOn7TuM3fCEAx8z8C8I/0538C4N/8jZ726Tt/5plYXnplEz90l8TZHv6YPjj9fRVCSSvUekPnQXCc9snxWH0PQffrefR05RGxKBk0cJyQAwEzOCRKwdBPXpHyV7ZeFbPAQd+5aC856d3mWq0DOxuSgSfVHpJ7Cxyhk0C2YqXxp8myPWNB3XJNuJ3OAMqY4TnAME6IsfpcZWsdpUD1QmAW4DZZNmiZ8nbTF/28UQlngiRQlCPgR5qlvAB2D9OImPAIgtJ1JrifnnJWLDhdg4VA420+ldJ7/uxzjCUz3N9eivBv+/ht8S/BMUlPflh/PAJI/oEZDA0HtucfYdHpIemGDlZ4hWE/dLRawbsEPldtCTS1PMjZ4ru8hwOk1W3pgyMb1Ok99R/p1qTNxd3OyxgDymusmjakfhsBNMWaxBTCPSZD4nHkV3ULYWrhWtlX3gJO3YyEiaMMVC3k6ADKgA8EZFocE6swMoDcWsXWGkiLpJIuRLiMFBRM+9Jmwmx1pLRPoD3PesVN6Zxg4MSU16EvUIdmIapFfZYpAGoKH5mqwAoZaZ26KkCqVrGggQhjDkyWBJ/WpAdsaxXtKC5mZI4FjDFNjKHUxQx3YedlONFmUtGQY5XozARNKWBOgfPk/HvhLRTXwCxT5+OpOPj8fjjHJ/+Y4ydo5RJHCONwpy3BYSW0XDkogAq5/rfobgYKzscCwpIbwsy7Cxq23xgwFwYA8FQkCwFEpO4+M3uXUlxgZZcHs2pzW4sq1VNqR9nzXECrWdTGIlqIMmzSCuOQ2khNayNZixRH+KWgsNa7sA0IdWGhpIBj7aGn17TWUKqk9rL6j2utoi32jvtxgOfEmFaWgTFn1zmdbtkzs7RsCA1+t7VExCid3XE+RhKbkxqQ3bomzNvWSZmD1QkjBFhKwEQEAXsl+GLxWRpfsW9VAjPnJpk12o2+lSHlDNSqaCx+ssRAhIal3xeMkjd6brcD79lIxfm8vqnSM0ecQMQSkNNXq1XqZenvoJjvouvKHD38wiIZe8wEnmh1Oj9GJfZsB6sy/5hkcugExKVkwdHvaNiAy657cwXZP4uDWSqGD/b2J4C+o835CaQ/XI/EY2xNOPpk+rV6Y1sTUwCk7xjcggnWJsIquK120mQDJ+z7AgzQNNisFacJ2DZxq79cdxAkvmiYFdktn+qZJ/LOu0TA9Li4eE2nt/SOErqo+4hEUB5WioSHfBFQi1lqJ4gkI3DbqrrFJ4ADAo0qwIT7YOk1Ah2jNyUxy7vV1gqr6Zwytk4TYxzON4sBL5Jwhq++/gW+/PpL3O93/NVf/xVu9zua1q/at4qtEloh8JgYXcoujH5g9onBUyu7T3y83XHvUmjzdnRx6VlCC8h5IpHwbSoFjSTI/PXlitfrVeZG9+QxDtzf7nLlRwGYthcLkbvqxhQ5wRCgXLWq/Mt1UyDIqI3QNgIVKRNRKqMUyyzcQaXguL3jfr9hzonb/R1jAmN23LXOmxXtNBoVvivg2ZJLiKSADaGiUEWtG1rbXI5ZpfppllPRKGExtgC8eLjXoOMJaNcF0k1ouD0rIxGvqPHNpl4YyDPwlvZl3qefO35CALUCpvzzQ6Q9gMyKGRRBlC5Yrez/o/77yWlISJkRQjCeE0AgzjIEpZlIsD5HEcC8xivBP5OAZfXF8gRgPaCQUtn1OQoihHlOU0o9pV9atRQPynRkb6mzRA+EYIAjWizIeR7MrW490TbINx4Y6FW7uM8JdGHKczC6Wp3I6gIZg3INxN4nCNU+9RRWojRH0RjXCwymeC1Dti5UzHWXBHteQblvrEG2tgGk78fYUl2STWMzMCTjh4k8iBOQxsL2crKavDzT6NUp0dfH3tV+NqZTfO7sugA+MT+2ruY6NaAT9GlCW5gaKZ04LfoYjdD1nUTS+URl4Z+/u1UtgV6C1gXTlktQkbBI1J/ToYBnqSGTGW/a76cLEWqCKUnByB2MGXLSMzOI8kMnngje+keET7huXKVMikVIIqgw0fIBmjm7bRVjNAUvBdY4XHix8SmreWYtZB6FUbxI5t+KP02gzSlFc8HA7CCWn4tmhVTNnq2FpQBitmr78whjAt6dGHPpWuBxWQ5UNFZVeVQhce0RCLVEYd1SC2phXF5e8Mtf/gE+vr3hm2+/Bd0PUCE0teRInKa4GGGu9SH3FMApYOB+DLwdkg33fnRNMmmAgiR3b05THAnggoKC0i7YX75Q8Ctre4yB+yG1saSu1ARpK5paCviLin3TGldFOiBYjGerhG0T2TW5gmlC+vIJ2LsfO3o/UGvD68sLSi14J4BYm8p3AdCdtSXMMs8mA8wKrfOZSl5AAZVka9dUqzCDbvNkcHI5IximP44kuzAL8oXi4vfAE5z+/sijngGpzx0/AYBaXzRhjTQNhjwTaNINk89b9uoirJaLTqAqPd/AU9IEI1g9gSa92LtXW1yRCTebcOUssu6kCNqGpWnoWmm2Vun9RsqYJMZBrRz6c5LToZWe34LSvUuuzxLgySpxF6vfUoqn+9tDosK0VjQuHLTGwozbrBqjMEGDMYtV+HZcEyBQVQFfL87jkYGbJnGOUfP5SoCH8mTANBs40LS/cd4cBlL8HutaANBiezVM2zYXU5h4q2qBnNMFRwgzChC/rExC5YADouUF7Ccbm0lKfnxnTveBgjkbR3HLYQ0aoQBo5yMsZInElTHFcsuCRjB82iNp8LFvTg/5mYInm3NOoGSMgaFNY/08UuXAQb+WA1FAPDEViLBbegHoRnLutk6jbSQD7A4UDNg7cS5jDtifwg8AEdMkbnpgYtsavv7FV7i+XPH+dsf7+13idrrGA90HjrtGI1kF/rT0K20kZUB/HkOsc2OhI9JhF38H6+knAcXAKMCkiVJYLdHKvzaNzTqkJ9xkAUbnPG22eFLKPT4VVHGR8yklBSmEARXc73d8//0HvN/e0Q9Z45frFX/0R3+Iy7Zh37bgzbrfzGInsU0cpReYNNLL0vQL3JxrvsVBUs6CJu7H1Iw0wv3WxQ2nNMjzBp5dF4CDH4DAE3i/S40qs+KXQthKRduET4ixgQWgNhn4xhVlEK7XDZMlLmzbRDkfW8EcDX0Q5tzQK2HeGfdhQwhrYyaBM0AJ0EQ+V9GSi9VDEjTsUkwVeqf4LHCcxmUFc8xfFgWmMJvC4WTK8XP+/mOPnxxA2SGyQzeUT2BJf6X1Uqhgsp+0xHzWtLNgZgMVJ03chIBkNAVS9S/AhbSBn6KCVv6Us9nUlUcEoqoBkmrdUZ85z4Zt2yVLYpIl3gAY3l8psaVgeEZcxhATsRbVisQaZVVXIw22Vumh5O460iBFyziECePQCKoxHiXIMozoJ46D0Cepa0/u0bUIH89E27YDGF4BXioyW0qz1RQR7c3AhMyhgry0liVRee5hZ64wWauVSAJcJnceQTVsiHuAgD4aNq1L0y2GgLSI3GQAHay987zIn1KgnMo+Trdc5vVLACprTNnaRN7gMkAffK0DaGWAJfXFpA6UCW2vIpEwJzujVrqPGfOfSAHoVPBeQZriE9Wrhd6Kg+JpRQlPe/nnCaLUeklihWSWQo3HcXjmqTBrU2a0nhfDFRgGe4V2HmKxAJCYfoQiuMWRAfW1Qs2ysLMUp4h1UJUF+8zZJk1jfTChFkqWWMKvL1f86Z/9CeaY+Oab7/Ddt9+DWQQOM/Dd92/o403AjXpBZglrFHF2zutUFKHpydKEl1kBlLaliSKKtoG1Tg/grI+IUXoHEWHbKtomAd/7fkXbNhwf3nB/l0SVwaQFH01z0hmiqQqQ8Xupzee8nSnKtCh/AhE+fnz39b3dbhhj4uuvvsK/9q/+bVQi8HEoT9B3mXL/PiaOwXg/JsZk3AfjmITOBRNNlzO5FM3iQgBIYqS6hkh8Wz9IkDgVXDbxYGwbcLnY8go3kZqAsi/72w0EaaC8b9Iu5rVWXC4XUBV6YAJKY2wglFmAKnOH8ortsgkoE1QE4g2VxOJUy0QfFRMTb/c7pvKLySmxymnM6ExlYLXimSY/JfxljAOFK6hG9rrRsVmjIodTe/Xp00Im6tPVEmc0HopmBYEwKRVdNTpdwJPJOSDztE8dv3MAJds2qS70+Pfnv8TVdhijMUYRIGx9cT/vU8en5ukM2FTDC6F+Ov086by6cYoSUqsVszYR/N70UWMYMHOG+ZNDRXZC6SYtbXx5PKvhxsZDC9VYfBWcKMmfHa5S2aSTSdNWi2xEjW3ynkWFHopQulUqgYls/RKt/pEQ8icPLpEEoOwpjzNlgsvKO4RwX9x56Wum80wfjY31SFt6u9B8/RsnIWCfBe2s9sQnx2f+/ABXHGxlhSKN47MP+fzfn27Bp5/FgjFwLpH28zjS+3H+yTTwzMizu5PS9ct0n+LS1sfA5v/TK3RSQEFPn5vUsef0oKBv33fMObWumFwstesAc/ubsiUs44cWmPydPS5y8gK64gVJRxeC2ILILU6vSs681lAq6lZcs0E9QQW2Vw3WLf0OZLaSQmKxlNVCI3Svj96l+CWrjapoKAYtXd9iRXQMAipEnk9Y1reMXwZoot9coXH9VJDKDHRmMA3UKhnMboVWJQZT4sowKdpzTSvfQGhTgK5Z2mCgRlv0EAEYjGN0mFipWueJpim99lmRTD8Uny9MnQe3nib5Y7LEqMGV2aiELtMc+8YC9Z/x2jCs/CZHgLk1RMeU0tPZlP7+w/jppwBQU4WyjG4mwvFzTGAlYOALpptRr0rXGQNn3+B4WIS4H015qFlPw5QncRwZctilGjKnsSBynznNHSIWLDBjlCn+db2pWC3krnut+MWXX2G+vgp4mhKw+fH9HUfveD8O9FtXIRSxMVODzmsrXmSyVtnsSy84teRM7yOlVi4qoFrAJEicJzyt0whTAlCnMChzU6a2BqUQiAu4VhAxeFogPUBlomo2iQSgssdImLbNxjATAvWgZAOm+jfJVFGLh4EXhClWAqZPcJqnMtsAR2L5E+tca9KywILSxyT0o4O15lTVOK+wVkG0aAuedRpLm9Ifb1Yr08TjXckvS7EBAOzlGezdfHwDPwAQ1fRg9WSEfY85MKbUMG419UGsem/lxFZNPoOsmWfPrEkl0b8OSGLr9FcVZvYLFXYGK9ZEgIa4B35uCIpZEgjmlFpCYw5Z0yL91cbsUsiRByZP1MJom7gsUCzAeUrNaY7+eSEWyK3XQiYBd5giQcXLiRj4MqsJpmdqpVGnn1Pat99XqLG1hv1yxZyMv/i/f41ff/cBhSou2wuIqpav0MplJSqNn93s2YMioyV3ZwmtWzcAtTrDYo8AqOtMQIUF/Ep4AwGgekHbX0AgvL0zgI7bjXF0BVNsjWeL1lhjgDukojmjlK4WjQlMRi0kSSSF8HrZcNmbuK60plPvHUfvKJDYIgJwvN/x7a++xdYKrq2iEWn1cyn1MbkocJJK5sPAEZGAQNMsqAJUJSvv3pXPymcFBC30gqLXbNuGr7/+Apdtw+vrhtcvdozR8c2vfo3b7YZjMPiQtbcMvp0Frw0C3jujHIxLbbhedwlZuGyoW8X3Hz7im3/xV7jfD8xjgPuQRCVI7z1AXKgDDLSCXhiv+4bjcpHx37RvIZC+pgJv6eNXS8V13/ByfcHluuN6aWqNYkgUGWPOIm5FpRthV9qKRj0oTrvsTjtkDSX3lV3kiVrQp60/U+JOpsQkF6KDus8fv1MA5VoH4FobEJs56c9wdJiFUOLHFox8UuiSpcIREXwWHSyo2uNmSvsyARNm3TWQ/fQygIAgE25aIpVngAd7I9nOmt3w8gJCBBn2IY0xQdIMUtUvF/TmVzdBbAXsMpq38chYLL6CwVpgrpCCFROQE2ItghXCY/CUGAe2+CcIMDRLlsUsSS2lqm4Dy/YrYtKfDJAIiDI1G4glJRhs2WyPc7rEFSUQlf/m2WxiVtLxJ0CtWp4AOtMsU9ubFOdl/vZK0SKhaEG4ADyJ/vKi+1AVSLgGmWue5G2NtLE5IS9hAqq2LheYazDPkwXRWi0wmuSMgRz0pWJ1iFpCQoPZJW6j0bUwek37x/fcGQjq+5BuHskojIB2UypWgPvzOIbO/ZwDk60tB4DBmDxUOVQmXYBaFdwUaAad7gkwIisoU1dYiZVETjwssf1kvSKt6Wb0SDgLgABWyl4WIqfacLm+iDUcwIe3G1ptqOWKWoFsTTc3ixXtJcTfYPfWkbECKGNrIhiLhwYADC6GyQjebgkDA6SWTG36XTbUdgVPxu0m1caPLvWkZK4St+Uarw0CeKJQk/gzGho8XaR5byl4ue54vW7+GRHh48eB0ScmprtTx9Hx8cNHXLaG6xcvKFuVeDbNyjQgZ24tqSUo6y08VGehbaDSwMcAuuX4VgBakLM0EKRrQiHGtm94/eJLvFwv+OLLK7766gW32w3ffv+OcesYc+A4ot2XkAxjmwBP4BjAMcRl17Yd29Zw+eKC7bLjPiaOMfB+v4GGxLcC0QGiQcZQCjCr8Jd9q7jsG0qfeL8PSESWuiVh9qSpgFl4xdYaLvuu/QKrls2Q9RVxLC1nUIqXu7G0lKouykgkoChyDCz0Z0YH408MON+00gufUu1ke9kbPFZEPx+/ewsU5ayh9TAGbun3ef/rXosJO9ne1kwkf9rpAbafzFBsH5t2ZHFPyRQfSELBiTLQ0aUOJjJjgQsaAWdJoOq58IVkfydM69StGX1p9OZOM5NtmD7Jf86v6SZpIsleMYsEqevNrrV4GU+nTyAzjRk+X7SoltYFify91UphPd9YdO2iDYSp6Po+MHZ/UXvb5d0DFNhXbh+j2EMnMsdVRRmAs4lfCcHekk0zibU2Kw7NNE6pJeHAJ883oMki6d4ZNJnQ8GsMiZhJ2gSZg5SwgAHwpqoF4uqcBpCIMFkrkQOYmjEp4Cb1pEouAhlhonenbxmC3C+5ZGF4lTz7cAkod7xlG5T9Zk8DzH/fjwQOAEpp11gVGkAUBuMDOheR1p/cNvYPnWDOqcm4Q6wnPHQJ4E1QjHUczBw1kZZbEszNJe9iit3ETHvZBJAAJ9bvEceUmCtCaAk4XN2UyW3pVmVzDQXXEZehKihqkeuDcLsNMBP6oRmIrKVX8gwxSdmNPLGYEHAiYKzo8+1nKaArsaTXfRNew5Kdez865pjoHRhHx4fvP2DsO768bNia2Eukfpbw0lJZMua0vECpFU1LrbDv/QamilKAUprKlwazwhXtLViLlhsozT8bfeL2fsdxdBA1tHaBx3rpficwam2oTaxNVHaANgAbmCsmS/IMc8UYwP1+4Ha7S2sbU6QUSHqoA0LmZJ7ssUZJ1TNZJW28qoJxbdUDBgzsW32MQhbeZ1sHrmxCgSnxsv0UI67kbM/PZJ72nW2wBXpRXGnK349lXD9pHSg5TAMGYrOpy8iDiG0SLVAzCVozoU7Bo9bAEgi3TxabLkXV+hTCVWOQkisu/w0wNxpjjANHv2PyQCsNVftHWZVrMgKBNMDNizHBmP3Q8SpDmwPcD8x+AHO4u6UA4Xqp2qW85P5SVrMHTmtG2LCMMgKaaopVGZUBKCIIqEoMCGmkdk8nZv1uWkHwY/IGwlbPZk6xfE1rT6HlAuQOfCLiYKZuCbF5xGo5ssByVkAzWVQsZkapFaUFgIIClyi9xaAiiza61QAAIABJREFUVdwt7XlMzeLR+jkAJI0ZFaMDGD3AtALv2LS5XQfBzZkqONyWkNJ43Y9PCUgpiWctygCvh8QRwKX4I7rec/aB43YDNymEKBZGrdKrdA44pNM5Vo3P6gdBwR5DXK3TsvsqSF2+VirDXH0WqwCymJWwujpoOAvr3/NDplOAjbmrxpjovcsW3ZrMWbE0cWD0cHNaaYAxZrJS251lqYinFtAlY3ursoR1n6xWQwPZBniDb8lTApUbCVtmLkDoXZSJ3jtG7yAU78lmrg+rQE7EatUNmjJeaaUEJg9YoDQt86eFOjUGR+rZVaEh5eNzwmN6oDWCbneIy12DwMEFpVWlUw0UJgEY4z7gVh0esD59YEkEYeOvLC6vjQjXWrDvDV9/cUWrFf31BWMCb+/vmEfH+9sN97d3/OW/uOP19Yqvv3jBvu1yr1pRUVDbELvLhII1wtY2NNFCRDGBlF8YDIAKeinqmtcq7yRxVtLCRUDH3jZUaiAuOG4d/X7HZEahC67XHWMAY0poxuwC+Nq2YdsvWnZh1+DthjF2UKkYY0flHb0TPnx4w4fvP2AnRiMAtQH1JcIZNEi+EoFJ2t60KvFxVb0hc0y3yhaI7NlqxXXf0WrDpTXspYgbs3fwJGBrwKggJgGexrPOllkKugoFJap9nfU55Ks52l6Z8UIu8w3mBBqttuhH8a+fHEAZ833QjE5nZS9HBk/pjM8/JwAnAlBR+jkNKP/+MFoDVSKITYe3oeQRubsSgc7dZZg+CyF3er/0PVtjXPhmAluuCSucX+M/53sG7Ri/XuZKh2oWtTwnYSmkOInM2hKZSgaCSAfjgaFA+p4frmNJG2JxFBh41nWkNAfmvsIyP+SMXYS/WN3sUbk+SPwbM7lSx0oVTn72ntaQ2uabl5NO19Jy/fm+FuBqpn+bp3X94YBmWqdxtxqsw152mILXOMXiNAADSY9WYko08rg7sjWFFGRwJvafy/HwOgFiKFGrz6XNvE/Fid/Y4j55xDOOmD/z8ILFlPX8Ol2SxPGgdEALuLFM5FzjyZgFnX7OvCreKu1u9ocgqwf6q+9Zs/hONr4S+5fMQkbiGhtTf/evBqmpRIi2MlaBXASxTH1z1c8CyuMdREGQRruSvdaadSQgzNHRivzOc+I4Jo6ju+VRGU7iz6uLiJTRiiIlCqAHXsMaMMeeJ9JYTZ2XWgVQEUn7FGnyLO7/Uo3fFXXpAzwPAeJlA9GupXc2EFUAYnWaswhgkxJuGF3qPA1JyAMXK+wArYOXLU0hS1wuZUIDNGucNHwk9Rp12lHKs4h5ivTtkJvGuISWUuWOBxrPdL387bPsZ8UdZx75I/DTTw2glKucXnJNMVTRQfCikw/MPf3+KfdgBj+2uTP88CcmgJHhzzq+MNcX3TByc+vzZ/WYWFLBSQXzPCRAcLcgxYF+dFQCrpfNrTjHsGJrwWSqMppWq27msphQy/K1Aq2qPuVSNGGYJMhXlSJx46V3DT+zzFvw0XWubEP5mQxvxmsVk+f06YbtAgsm9zIReZVI/3EmFCbkmA8dF6zGSQMQpRny6olbYghzYaipvYDRhGHMubRa8IacCkzGGFrXRcs04OSO8+/GRdT1gETHlGbOBatMmAPC092wzLm+j1ccDhffGB332w3ME5fLLuBQgt2w3JaTwLXZSQLd3aJpvqUw4BDrnFroCBI3VpxranFVGsDUNiCxcvg5HQQp7TB4Yg6GFxaCBamyCjMSl0Wt7naRNVXhWKrGJJYoyGnrXUIYO92cAfYD+7M1BLybPcOrpTu+moxBoWEDAM2h/e0A5hvGkLT9MTpabWibVN4+bhX3mizsZmW16v+K2EmFuQA2djBkSovsE61LV4w/qSvNlDADTQWQrLtdfucdBGkiXMougKAUTJKSBPehczHhJQGI3gE+wPMNfQ4AHeDu811rwVYJ163i9dJw3Ru+vFRsrWKigrmg8sAXWwUfBX0M9CFJBPKQqspIwYSEd4yp1jp1F06NC5pjgrtYUvqQUjCTK4CLZvdpQ2Ovvl5wvUjfu21reP3yC2xbCwsngN5J++wVKS8wGbf3A/0YqFsFtgpUAm0NZS/gwrgdE2UA9zlRPt7x62/uePs4cHsbuLxsuFwa9lYjllJ5oljrtHsFSN2gkeUsFj2DqApKS8FeJcFlI0Ij6TV60b6rNAb6+7v3ASRukjChgsNKE0lQlKt7Kr9XoGrUqWzJ/oE1Y54gMFmtevL9lY0RvseygvuZ43cMoPhRaH4K76QXcI2byOsJAatAWG75SRCFxIxO8IjyD/GdiJaUbE4Cx8ybfi9zMYqsc0sMgdB5AnOASsFlu6CWghsz5iH7cG8baq0YY6Deir6nSL5iTIbMDVccFAmrMe1A/dSIOkmmwQiRp8BgsGtLdh/7l6FB6CJ1zxBTr0/F9AhgbyILNfMSHheaNNtE7uU5QXlNl0dlxxOSYEnLCdPOSLQz5dRzmkthYgxtvMsA0QSgpv6h4IkFQE0FU9kV5Z9ZjTCnSROKCObvJBQAIqCc/euE5CByjeej9NYrAAsQRr4fxhi43+/S9mgOcU8+JX9TICLuya2nfttgKGZhnGN6U2lzvZg2aYcVGiVr78PCSH9e8EmOogqz56ibDqiB5UWLPUpj7qiynIF2rVVpSRhFtvg9gO4FYGMx8jzjcxb7COS1FuEz/Qb63Xib7nGpPD7R+6H91Nj7pNVWteis7XwJ8SWVa5mEjealLzCH3oAonmugypQ/Mh3ByiVUoXixHF0huVlXAFcQVdTtBUQNDJICmgzcNRnHQhwIE4U3EN8xmDDwpitQfDxVrSN7q7huFde94mWvGttUAWpAP3BtBUcl8ADutieU+wp4IvlihjRCzqEbZv1m9CGA6hgVfZCCwGtYp1Qxb5vQz/V6xevLFdu24fr6qkVzN5S6g0E4utxvjIpjVszBmHQHmtAiGgRAtYqyERgdx3gHxsC8v4P5wPffddzeBo7bBC6ErWqD4mxxYvacmZAvlGSQci6lhQBQolBvasGrkJCSi2YM9znQu2bm9wFuBJoFsCKnWnjU5ZC5rmH5nLZdgv9SLjJroK9obl8vKgMombQIKw/Gw++fOn4iC9QaDPaU4YdPAeGP+AwwSsezIMtgTPqN04OJpGSAiS6lGr8PpXPP2h8iJsYgj4kmB3tqTbI2JR67pKn4ZQpjsV57yh19vCbMjWCcsJOQyrBQQLuVV7AxZ0YGJ0ZwzLVNEZ/uGZOYpuw0r9mNYKCTA60FfEjnU77uE0eiAhECauEgj//SeCdRvwEqGscx3II0ugAo6bkkm3rWqL9iLj7LXhTQZIVV4z1Ne0ayQOU5MObiVp1kGYg5FSpJcBR+EkxoJOioAFyApaT5WtE/ANJ/awxM7dmHBJDO8xhA1aWVKwcRm/UJuxEhMZzn67T+9jOET7qXTICwtaQoEivk59j+VUXKp5tXta3IZhbrhJcfcAalruDzXCYn8xM+J7GcKc5DhcNylqNxsxABkdgy0VrDy+sVl33XTClRtKyoYVC70pqTk9oE9KTizDTGLkrcXEGlurVM65exqo2DGlBeANpA9UuU8iUkA/gCQF16TMAEqEnskSfNYIL4OxC/g2mCxq6FOkmLeUrsZmtiIZFMr3AOkjLZfav4+mux/vz1r7/Hh/d3LQA5tOiu7GgicanVWVEnUIpll8kYpU6u9ZncUeoG0vcCVZQqRSZLlUzAUgva/oK2v0ibre0LlNZAdQfVi21hKchcCmhIeRlqh7TGKZBG8AVAqxJHSwcKGggDNMUaX+mKWnbUcpdgdY2/Cndb+iIJrjfr0tAmzK4oWBywsn5SxYuZvNxJSxnRpkh4izJy0nRayEkZTsJPWMwzjpPlIn5A1jxc+yNA1E8DoJTJZO3X4zb0MF+4ZY+ElhZoMbuZzjgsMxdbXAAxo2QMTZiUuLQspV+0SC6QjcbJcmDaFwVQ8dYrACIyXSwgEngufuVidUY2MY+2IRt3FMm/MG2MPZ5FxjJ5qiZHIDR1oYiojTL+AYqMh08SjRla8BIMT/23oZJaD6AWHCICRlgKyVVMFe22YHoYlGCwM6XiNWuCzS6Tz5pVMcbyF1/N/DyFHGB4zAER4bLt2LZN11agpel8Y0y8v9/Qj477/Y77cUgdkqs02QSTN9scQ16/T6BPxhjSu+roXTunW1mEioIKFrYcdOaQJDJPCizX0Pz68Vm6MK2aFSm0yAARzm7J0jUoVealsJS4YGYcRwfoXWJIrIG1WtP8WmRrBNIzWQSLM8AAgQ7Q5Ca6H+aiSzweOfYq4hZ/VsdQi2+poCqW48LA/WDpDabzL5lFmjnLgNX8MqVGhK0IKmlbsjYfd8rXGxSyQih2BG8DsKy7FatkNmt0uNRPtl4A0yujzyn9LV9fLviTP/5D1NpwuW7qWpsY89Bn1gBL2lm4uNXLWtWQZ12BkXr9jYSpDCptmrYPwMoYUAOXDVSuqPsfguoLtuufYn/5M4xZ8OENYn0ZBHS5pm4VhQtqFbwAdGD8JTC/B24X9PEGHhXz+BY8B4CCy77jZa+4bOK22mrBVhjNwxwY9WXDv/6v/Evog/F//pN/ir/4y7/Ccb/jdrvjdr/LbjJL1ialECYIW58a3qM9+1AxqYFQwe0rEF7l/bZfgmhDu1xQtw2lkNauI7y8vOLy8iru1JdX1NZA5QqqV0wmlMGSaDIKSheXcOWOuU0QBga66Hx7Q7kWVNyx0wcQd8zxLXh8xL7dsdcvwGVirxsutaGRFFUoJqV10aoio/+Hu3fpsS3J8rx+y2zvc9z93hvPykq6VJSaAYIBUkt8AITEB2DGFMSgZ4ghfI2etpAQQyQ+AJOWmPaAKa0CCVpVjSorMzsyHtev+zl7my0G62G2j/uNiEKpiKrYmR7u95z9sG22bK3/eq+1clqNsy31SpXOUj0wHqiSFb/ofYcOaxHuTgunZeFuqQ501cBmLSzh0ZFBp+l5mrV7nbGDDDlmwuG4X2/A18ACU5kQBpbI3qE/8vjZg8hn0fnSnZFvd/PZx7Dm7fUfe964j/g1t3EFL86XGN/tIzXwhd1Tx+fDuuPGyDluqkQxSEmtaaDtSUop4LEEmgJ1noEpLiY/GlrhEYK/nIXbz2T6Ls/5yJyaRWY+18R/8c8j2yU0CsDcFnP2w/doBRr31MkXrkPIz5l2cUEIoCiCt20723WjV9OsIWoncciEUuVggYpYqDG+eFcHODAsTK/M2Zh2TZNyalQoerjqOPtHCp8R/3DdxUdmvbhpQzSL2Vv/yuGeJNXYbad9IN8Dfn5oi/HxXfpLOQboFEaWpR+T9Ulu1/lGSZRi8R4vlUJencBkL6+MSbkFyn6TtFjP1iu/h9exG6UVOrUWTqc1s9rGBr4FX/O7xIDDIn/kxZERGA175xQGiexWCQEqab2WUqnLiVLPrKcH1vM7pBVki5T9KCJVEM9SKxXKArABj6jsUE5E8UqQSWBK/kRsn4FOe6ciESd1pqvFr4Km5Xffm1vp3PJSxLLx6shQLCrenN3AbKeaBYo7pN5T6kPWuFomACVFKMsDdXlA6oLUe3uxco+W+yFfZJ4HoO5Ib0BDdbdwyLIgtVCo7nDcoF9Q2SiyUsVAbJWIfcJVRTmwD18eIpxkL32AHCJbd3CywdGG1SqyNyP+retw48oN3zlsgVeIXm6/e5Xf3Vzjxpm89AXu4MW/P3b8bABqaLiDJUhu1CH9A2X+0L00qzzHBB1ne1iv7Ir0pYYAxYVo6yOrqbWpmrbvPYKIZldHAMAw4w+N3gK0bXNWwS1QpqU4KjKB7wJ8a427Dx8sPieKWDphZjHINFGT7xtB5gmmZAruKxOB+gskmQeiV7KfFjj4mSwJcw+7Yxpo3Kmw1Jhji8MJs6wx6IijkUNdI6LE6PSsAFYBoBrG/CN+wn0Dvm6K9kZXSze+XK5cr1e+++67BFFtb1b+oAhLWy3leFnpvXO5btbramts287eGs/XK9erVYPvPlnpwkua9d+Tmy6EVFhpJIXXDHoSahN9LWedJ2Zzpq2kT59yKR5Hg3JaV07nE8tS2fYNRa2KsUScSdBZDHUIKXu1YFwBDDUtnqXUIVx1rE30Cps21+Q2CoVBbvbIP/xDIC1GGSeX1Y0DANhaz3BjhqsWZNxS+9V6jIHKMhAcmXgob2kpzGDxCegLlLqgqhTvM9IZffDs2X6fYkRqJS3U9ysZzLzUagUrnz9Y7M62Df5SXE2ahOUcvhCW6loiS1eTyA0oGchTV6KWqng5pZQBHaWLWohDqVYrSe5QeWPivzUuu4KeoJ5tPGp1lKR0pDTQK12/RtsHerPyBtoaIhbbVcRS/i3zzKxRp8XcZ2stFE8EgIJ6luD9eeXtm3ukVh4f37PtO2/fvOHdu08SVAhwWhe4iz6A1lNw6wvX/UzXFV0+R8vnSH2gnP7EANW6UpYVKVCjHMt6R5d7lMqlnRGtqJzQ/WQ0FoWcu9C8gGfTxq4dYafoZvGI9UxdF4o+UbSDVlr/ln1roIWH+wfW2nm4E+5XA7qrWAX3plFTok+KQUQoOZ9xXlOdn3uzHU7rypuHeyui6XWz4qdIWOqDpiIDL2gklAoYXNd5VtCdhodo7LXEUDookrTgipe10QxZDhK131E+4YeBGPwMAOr7tVoHT1mAEBcEN/d4xWrxGmK81epsUoebLmqXBIeJonitqdfD8ZokcX1WuvWMgwlMRJCtCdU6gukcQC21WGBeKd4+oOa9HJ+jCJfLlfP5ZBl6HsdzINCpKKaNKQL2ykHIpwDzWKFhmRwWq/hvNPckmlTl9TiRmXvTgrVDRBoY8ry+EVSsJOFLi+QJY9wBnKJWTMmWFAM2GxHbZulRyDIyhxhZKnEva7xpguzx8QPv3z+ybRvvHx/Z9z3B8LIs1KWy9k71Brytd3fx7Vz3xrZbhs3lunG5bmZZ8L5gtUw05gJrBFbPTi+cCQxt7ADcnZ6bz9UAs0NtGP+agOQwc0IAI4F1XT37rhhY7B2pldLrMatpYL2I+JwE/qAX1ZYCPABiWDVC4EmZ2gelhdXO/SUBpteOkTww2hVFM9icaw66Rc61fQHNG+sO6459kXqJu9SPh/PECMJ2vqB+vqq5+aRW6IrUTtQsk6ytpKNqPUMA6cRjBDLwd2s7z89PtL2x7zvRRinjeJhfMkShukXJiya6C0+clqM6tIEku3qpXrE9AJTA7nzC4mKqpeGXMyr3dIRra1x3pZZ7Sn2LcUEDOyI7yAZcUF3pvaLd2u1oa6zY8wpqWaa7AajT6gCqVC+qac1vVSJAHO5OC28e7mhdeXx8z+OHD5QivHUAFXX5ZBEWqXS1AqCtC1tbKeUO5QSnz2D9FVLfUNZfm4WsepmCIpTqa13OdDkBlb2voJWule6iOx2pKln5fNdGcwAFi/Gaek9dVooWpG/QhdYL183ik+7v7jkvysO5c167ta8BRDtoQdXcg7HPCRA18Y6w4pk0MyVsPa083N9zWhbOp5UlAOpSKYKDPqvaP5IThrz2vOdRM82VB6O9oRwkFcoAUdOujQtT/pjdYG4VNl8z4Y8f4Gg/KYBKQQDHcX0MVR1A1e3n07WvvOPrVijIgoGvgLAhFGf90afSN3dgWluAsJpNOpgOJnBwj8AIkgvrlGssisXgqPfFis+pZPXnsEKl60/mnyMznmO+DlOT/O7oJkpGOAnbnFoZZ7ycY/+tuCFJBv4FazSMGNCai3hMQxn397ioG2IYVh77PiZTlQz23rY2wNDlakDCyw+ICHVZxk/1DLzojRR1b7plH+1tBGVLxfsR8nJMPoup+QQjmTbfDGiTHnz8Fpum0xzHfR1GTYXkDJROllCxmIEIWi2l5O8I4oyMwZnO5/U8fKYMFykTeA5LCxxX5eYex8kJ5vpDrOcf8jGAUxTeTUVKUvGdzuawp2Bi+Ac+NOKfQFN5kmAq3zOhhzWS6Y/pbyky1fMJ3lTytKDepS5WINLBVdt30IjDkrH8EzDM4bkkCiBWSkCt2BeCiAHO7sQcsTAI4zeeHVxI0JhwQUIpVP/O3z6Bfkf7DrrR25XeLmi7GnDQ6G1nSq0J82UkpODNgiNJJXmqvdP5tPLm7Ru2befDZWNvZuHuzWKqBPKdS/AswuprwKezwLJAXZBlpawnB0pWCDcshMbTF5QF1WJAMLL+vFJ72FVmsZpZ24gp2GkJ9r6V1QDh5fLMd999g+5PLFWQsrIsm1kOgUhWm+XEkDk3vG+SGzOzqKVwOp84Oe91HJPJCUuEURRTVNWFgk7PTK6YPDagweDAQ569DhSO+2PQ6Pj8mNrD4cqPHz9rDNTtcOfNOF7NmMoQthzgpYYA/h4LlF3iWs+kJaf1aTw023pIXu+bG9tEYWnR3uiuqdXqRcmaJlMJYFN984z4pzLq6NRCWVeaC+++N9DOWqrVj1pWQOi90TYLvlzKQq3LyGQoVlcmu3Pfvr+EQJYU8mlGQiEsVyJEj72OJphLtB+38YWLqS3+7zInlU5TOpB9BNV7Gz7MPB+8T6IMguMlq6TiDNE1bKklrR+tdXrrXLedxw9P7PvO1998w3fv36PdepahcL47cz6fWZaFu/s7y8QT4bpfDTT1naaNy/XK+8cPtN64XDa21iiqWcVdtXgyg71ggJG5/pYIniFnwmZJTSkWZKxJ692bJQ/EaYG/U1PONEHH4RpTEep6ohThdFotbqIWb85p86MorYPSDBiHy2XgOWMZsaYTy8qAYPDssknsB9dJoTuBJZ8bYTKD/8IOVasXtrfdrMStsbUdxVufmEnYCmAXi/0RTfXKgAhy06VluOGytyF4IVKr1F2jWJt4fJBbgwxry7DapjlXDj+CufeyUA+RZGBNxlH11kXC/d0dp+XE+/eP/P53X/H8fGGpK+tqLW5bC+UxCj460HGwlyUuvRhlAbPgIll/TifLnXZ3MwuUqH8R9GWdaG2MJbJRhXVZ3JpQUG1At4QIBO3PtP5otZ8uX9Ovf6Bfv0b2bxF9Zl0bp1q4P628e3jg7rRwXk8eCL5wOp1YI/rdf6JB76efvuUvpPL44Zn/56/+X56fnni+PHO5XFgW6w+31IWySAr5bY/K42fO+kCXM70+oPWesr5lufsMypmtK1uYU5yxdk4oZ5oK101oKkhZiMzFInuC17BGnarQxQpuanNLoLhVqcBpqewbfPXV3/LX//ovefdG+PNfW72pM0+c5Nnai+1hnTS5FxXutRSLFtegAwebqmgbioQAp/OZzz//gtO6IM06bdSlcL6zUj5SN+pe6SLsUlERWijJQcbOUYoGLZeJu4yNNFTzsa8ganVGyyUjVqF7ucEQOpElGfe4jSV8/fhZXHgvoI7LkFeHGypyqLWTkk7cKyHyS6vTa/czPn8UG/b3iEdIAKXDFzuLs9TLD4Bpkk4xsANqlonYgxghHSaT5iYUM+dKoe2gYr2DIqbp8MwJ0KSyGt+/mOsjKh/VskM7lCn4lfxM4511ENn0itNzpr8cWPScgZG9GEORqeP8iGAea6OQgG4sbWRlmjtk3zYPGN/Ytj03km1sM8MviwHPqMHTe8tA8fj3vltTziiaaZlubsI+UMkt5UxzTpQZINN7x2QPADXw4lF3yk2bQG2asGBYGfgalqfh4p1dawO8QhSkvd1/+W8NUOi0EIDZN+ZhfV9RVmL8OS8/tA//gR4GbINP9LRCHahCjufbR2GNiH+/vOewoOjhYuMLr3HOmWJeCo6x0eLX2IeRyDGsUbbTANsnAkt9tndsDa0WtxnKUOx6cVAX9G+ONLf8TsHZXjDblUg8Vsee2wBvs2d06vgwgaLffz5KEbwo9yhwq8FLd1Q3tF/RdnEL1AV0Q3SnoJYh6ErH6gA1XVFe7FS1DeDr83xaF968eciq6S2CydvuY1yT79USLqPg/xVxi1KrC71Y6YJ1WaGs9GYWdRysIIK4y07Ball1D9koI6Yn3jqVWnfRahcHPRFi4e5Vb2N1vTzx/v23nJczSz1zWguLFqoaFOszkGXwuODjY4cfF0fjP2Kt1U6nE6d1oV2t1YuIzX2tlZoGC6Ex1vr23rMlauaWr6CJaRxjf6Rj6fZI9qyH+ypDBn7f8bNYoF4rJ5NMxP8xvTpBUaMzxETSCYSMKUwGvsPNj0Hk6mPIG5LxAkQQ5MzshqiLe0XTzREc6WP21UrjgozebHXxMv7ZvNZdclK5O59Y1wHIWutcNnNF7VS0eed3GWb4nAcdaesQoEiRsJrM7HciomGhsp/IQMMZkQGkOZ1+ekAS3hCbs+/6eMR4o0p3n9ZxuNAspqlPgfuabgSr6WQgyQKj8TF36mLm37dv3rK41S424vl8zhghK04nh/iy5laEMX/imjVIWtWmTWyDPExkvJngVbqLpTUvdVigYrsn7RchqoVH+YKo+CuHuw/qkjKCNnXSqvKaEGIeu3FY7kSuA4zKxKzmLJiSdKH5zhE7EG+LFiL+ODLRQLwKtCI1HBq/nEOBXiz4P8oPzV9qkHbEZXfS7RzxiKJmtQHSmmoFFvec53hWCi1/RNTUETyT1GOprOQ/w30Yup3/0EeBjJrK0KBLiDX0LBl/h+JKRynWx9L6RQ6WIUU9eSd4o4wxeO8y6VgJFS+6KQ4GKxEjWKhjOyfzaP6uyg7bB8yQ+i1Sv6b0StXKkpE61dz/DkD7/h39+ge0P9H336H7vwX9jlo2hJ2lWsp9/FiYo6tq2tjbBjTfxwEIbIGrwKkW7pbK2/sz+3ZP36/87ne/ZV1X/uTLL3h4uGedCqmaFc6y8xo2R3sVmrutolht8wQmG4l46QlzSdpcKlXBaqS7R6IereCmVLr7XsIlC+cF7k/C0/tv+f3v/y+2y3e063d88e7Mpw8Ld7VzLmDthgutFa77kJCj6L4p90Ws+bIWocjm+7+NUiq6I5j7t7cNrbAUkHWxOLNq1sa9KM2TCkSjCUarAAAgAElEQVRHjJK5d4OQ3fIvMR6n3olRzvwyEMHYO0oUN42kiaGo3KCqBIk/7vjpAZS8GPLLY8o+0xAvyhRH45Oofq9slgniaZgQC+5xI5lKrnmHHq0GnPuNADmFSYhweGowR01GefN6KURws3VxoVa8oW5rDW2aFpEK1HpC1Yo9nk8WRP7Nd+95vlzsGq8CXJB0dwUxde00bQmQjA8VrwQ8AFK+62wZsUkxDacb0YqDCA3g4+dPhVunNRhBzjb/8R9Si8LN/TbojlCmPmmabpGoRZOAKj5rpuXve+Natilg3ubf5tF+Pzy8yXcSgXVdWFYj887Y3K3t1gi2Wb2nKIQYWrpMTVZTc4+17r7hGRbF4lh68fTluXr8rMk037yhrSvirR5wS0PUZx/uigFgS5Jk98D/GWzGOpvVbTXaCPfnRM4Z5+dYKN+XKHbn5oJwIYqyt2b311HFeZQ7OoL5yLYcpWl/OUcAp5n1Bh0mT+igbarDhgNbBxKU4FVGPNqVzUF8LYO24+7TDs7SGdoafXcANcUY5tr2kD+xR0cMk9WdnPe/P0cmOu949fGaDbxb221FS7iDFST7CdjzXXmx23SrSs+khHlwsUiheg87gwh+/QRYVDv0Dd0fQRpSv0H2N1aoUk9WDiAAFEpnd4vZ17Trb9H2RN9+i7Y/IPqeIheqNJbaWauyTiBKir1LBwNQWlhWD49AM+Mxqmi3tfL24Y7eGt99eOKr337N6XTi/v7EcloQhNOyIliYB67ItoCy1azH9jy1NjCtG/gBduenWnryyqClRRsLBpxOZSrFIB5PK7YvtZUEUHcr3J3g2+dv+Kv/+19xefqGh9MHvvzkzNuHwv2inEtjLbBKYRdhu5IwI22svo5FKmsp9DISmCKj3DLYd9DGvm/erF1Yi7CWymmtLIuXiPBaW1H6Yqz9UbBG0lACoiBdl10BiuZjsg0QnQJyg2bVwHG3DHH4O7CsnzULb14Q+2BiSf7xQQDp7RQdQOh4b1+M/EKHRnd7pBDyf8+FtA4ZdtOoAyXP2UlpDXPwNet3mv+Zxhl8xv+hYdWY7hsDHwIdjgJJOarBx4lJk+tkXbgZxbBkyYsLHYgN8HRwUebZvgLxa7pVWDfC7PtjfMp53wnkSRGrtisBHMd9xv2x3mNzhhlmKs502aY3gCPmNsz2BaV73JfmfRW1mlHFC5zGWsc7Rz0vN00vqXkeY6Aiq0jVYdK8dh+bGh1aWEy93nyfgcf5Tj4HOubmqFXp+G8yLh3BpsItlTEnZei0T1+NPeR4/S/tOLDdaXnCtZ7uAg23/LGsQcxvuMQPWlgyqtiw8mI9bplJ1ElLWos10XHd2N4miez5gy/EHgCm9G4PqPaSBt1pcXC3QW9Z0X4a2yS/vLK3pPFaGLyii0xWqFAmXCALHr7QQC/QPyC9UnSn9GLxU90UU22btTPav0Pbe7Q9I/oMXChs1NItFqhEWZaxZhHbepjn2BvzvoJ0Ad55tvRl2xxEd7bdelMWhLaeQMTbGrknYJI1Y5+kDZrq4HbxeegSLU3IVOelFCuzIFlgPN/BsLNZWoSGSqeIov1K33f6fgG9Imyc1sKbuxP3d7AsSq0em+otv0rSdACVifdNJJoZnDLWFYnTQi4OS5mJAyVCNlKuDZaa+yCpKRXEUF11fDwW6/C3TkpF8q7jTrzhpfydj58eQN1w1sOYg4FHmq2UrDkTrz2Y9zT5zhQGbrKzE+jgnzljShzg9tlouhn+WiuQtk/mSH+mqwBm8emIumupiwXduZZimV5Rh5UxDudM0ak60oVVlWtvGUy+b1dzK+2Wdisoq7tkpA5Q1j3deQZ4EQNTSnFAMcXGJLMOi0OiDdcsY37cvC72vLT2oPn9iyw/nQgzyz10Cu7qbErEWMQmHHOrZKNoUYuZEtNyFKhdRqq1jmd1p5HQ2s3K5+Al599cgq23zM7b9z03Xq0LUqCtsO6d0hWVRmkWi+BVHNj3HemxvUcpCBunsHqvsLt1sYDJaW4ifdxmxOiyd9zqNlrKzLFnBy57APCDq+hM49mvbgDVXMdpjRSydgzdNN9i0j/vH3FxuU8Ej39Rd336eJMOfslw6XgEvSZAYlgUa11YrAS2WTSbIKVbZ/swrTgPCutgtBuKciRAZFkMS2u4R4E5FjCy6rQOC1TucYltGLXYhjVZvU6bopkNFTwBoO82rlIrd/dnunYuT8/sz5cU1FIKokKJZJNQJsSEW/HWHVbuZfRPLCoJpiJTrAbVi6XvAzQiY03Y9WqWDf5Au+6oFpZrRXrh2txl1Tv75WLWjv09ff8G9ErpXyE8UsvGWnZK6axLoXr7Fvupxi9TwEvy0QjoD0t+0U5R5bwUfv3l53zx2afUWnj/+IgCj999x/W68cmbdyzlxFKUtVZKljToqDRYuicnKZVOp3nT3epCzaxze19pvaYFU5zXnJaKFDit4lvX7tW69bjr7eqg0axu29OF9/vG9vQ7Vnmknq786ZcP/MnnZ5aycVcfKdKsAGffKbWxrNY6ytbAgXVrxnllhDosVVhrodXCtQqlO1j0+VSLdzGrVV2slGfrWZ2+1mJtzAxBJxmjJpeR18p6hFyNLaNHSzshc0m5CyOhA//ucD+sEr94DaIfYz3/eetAyfHfqdnMnHsS1rca8O2cpjVkEiz2zPg9IVwmUDCdG4LiMOYDZjtaoLLpLkNwjdG8FHbJbAgBRD4/GGoW8px6WiWgSXXUx/FKCfXZ7TYCzQfIDHhziIGawGV+FFrFSJKZxn2zVunPzLPccjUxcx3rfVyZ21X0+xTxIrtKmrNmzUMVSmwaD0xcFrMEufDZW/O2DaSw6tEEFrccaWRJVqBTi4bShESPMNTjMCLAdR4xWeS0elr0eNkAjvZWRXFztPib/oDao2rBtDosYkegOjGCAKQJvkZV+GBWE0EOBlNet+zmeghEELo95gigXuFtceX3v9s/0EN12u2uN8ydBeKz5EEx13F+eLN1uFjhCENjW9/CU9tig4PNlqexhnLzVyikDAbo14WClfwApo4HVrB1XRa2TKgQUMu4ip94XNxeLL0pOGCCKHNb+34J/hIKGdMcSlTsNjcxvdN1B57p3azRxWs7la5I69A6uj3R943entD2HcKG6BOiF0pp1NrwpL50ewUg9Fj1oZDHjOoxeNl+zAL1cH9HU7i/O3smtrJtG60r5/XMvjdYClVAqpjlOehFgwxMYGfr3ADM1aurUzLmtRarnbTWwmkp/rcH03vzYpvvHfqGlI0iO4VG2z6w9Wfa/oFa7LP7u8onb892TZd09ap65p0nOEkTz5jWQfATxWWxZo8Z68VoQMtEmaoZ/C4i3qpsKHoSBDoxorRgIUMpSGrOs6bzbo55zzFH6744bfp7PCEUo+87fp4g8vivejzuxNRzuOKIUF0dA8LfOQT5uDZM58OMPZ0/AST/KjVH1fjEro/g4vH52FAi4jEjkzCuxfoxuaneOUHGCUQ9D9PerRhdVGE1uTgsBwML2WIvi1lgele23TfI9M4Jombw5vPZu3i6sZVZKLlxj6wgBCTM2X0BwmawN+bDBMXQYsdzZwP+YVQ36x4ShgP1prCYGev0XAjZoRZwqVMLDDz9uzXP3rH1mNPN226BmtrTRmPB1iLsXanX3Z7tWkha3IKeMMGQyyxTgdOIF0lrn5+rNl/q5Q369LLzmwsDCI5kCvW5IP89isDh8f2FZYpVKdUsYXXqTRb76gVIkkIRzTVOl7QDpBJ+jRulIawwuV7xWdFcXrOy/V2ctn//D5t3zaQG9YwpmdwcIkJ1C0fxOJcsfhmA2cFsWL7VI3Q1eIDY7GqA7VBGfBQa2ojTRugtQ4N/Zd41ZNAtWJvWuA+6C4vHsi6su5X+6K6IsHWnjVPWSpL4PYHBAGji7+pFidxVXLIESi0jcaFkRXIrxmjtlS4IVnm96NV0ta3Tm1oSyNV4dr9erf5Tv0D7gNKQ8kwtG0U61sQYS7KoZu9r+8ZGp7eK6jKsuQFponqEz1IVWD2Lv7s18OHuxK+++Jxtbzx+uHC9XHh8/55CZV3PfPZJ5eH+jOJhAlIoxQpdVi/6qSKmpBRH2D45FRPSUgqr98dbF2VdbN/W2ilFaX2n9Q3pO7p/S98+IHKlyRNNN54vv0G3byg88sWnlaXc8fZeWcoFwXrZGRf1PopgwMzpoHr8Xi1Gl6akWhb5Wr1G1TpCJEQrop2Hs5WEsJpb1gdPZJTtCOdl0Ljx1ULJWuav5Z/adfZ/q4WTCvX4ddi3cORfkfGujLAdCcgf++mmafxrx09eSDOZUAAoF7OhrSTaV0mHDxNSfXlHGEAn/vZ7BYNx19r09ZDj0z16N5DTWgSYxfO82FxU1hXLnABorWTsSPHsBMuuifooxlyN4TZqKZyXxUy1amh/9FxzjU7tXdfVgsyjwrTL0yHwfIxHAKV53o5SVWndNcwo6nYAUPbuwQTTRz0DpxnVChYAGZa6KCIYgEBBo3dfCt241pl7bp4BpCIhIDJJMi1fhlkd4jxlE3PHhcYEgvZGU2v5glrg677tXN1117ZmaxvvVoR1sdYue1OWZUNaZyvNYq50BpDiAssGGzFSUsS7p9ua1lrTWmjLY4ColAD4E5h3goxUiZhvAyUR/D1ZBmJ9GcDI+mZ5MoLXgaqlZqZi15FgcXuEILNl8RYHRZHuANKLiNpyhTt7ZLIENdl6drSXgY2n4pK/nGOyErfoEhAKhSsXYbVZF9tPXr8oAEbgJ1VFm/90/x1IFwc6uXdI2rsFTzkynfZq+J1zfw2GF59K0l7QJA4c7Che7PB0WundisruzQpqdhlcZKmVqEmVI/L9mJZgmMBmyaKV4uOoIaBFrHitOBosFlRtJUcE5Rnt5v7p2zNta+zXjf1ytQzi5plWfUP7BUGtwHexjK/ie3ZJJVbZtyvSC62tqJ4s1qt1RKFSDhZFwev6VaF1ZW/mznt3f8ef/fpPeXq+8FdPf8P16QP7defx8cLpdOZ0esfd/SeYYr1QqAYQ5YrKSqlXm/O6UIo6cDDebhmfQinK+WRW8qUqy2K0J7Uh0tn0iuoT0q/o/hX9+h54RvU9bX/mm9//n3z47m/50y/v+fN//Bn3d2fuzztreUJkp7KDdppaWQkalEUs2WkRajfUWMVccypiRZ7F6kqhkQlodrXqtaPe3N9xrpVTLZxXS5DqvdH6xSywUsnEg3ALU1HceABTqZ3jkfG5zvu/b9/OmcS3xpRZviRr1pvwh48cP28z4UlrGqL8lZ8f5MMvMeqrD/voWcMNMSv/Lx6ROMdTx/WlkNDpCQMw5n9Se2da0HFzuy4bMoY1CHk57hsMNPHZw1zGt/raPXLe7LccL3r1GGORw6fzk4LJJ/KP8Y1XHNr0uCq/DHNuJhAEgYeQUB+tjN/zO8V8K94cuPWMQ+qTlj+Px54rA6EGWLqlgwlMxtqAcNBuxgveXC/TBwPJz6fc0t9sxp5p7WMrOp3w0ePWqnd73ViVOEsPY7pVZOa34sU3v7Rj3nA3IEWYZs5+K3OG0ci2HNmKfteg71B0BA63IniL5ucjpGEem41HcixHfpQoPj5T26+3YzKMPO51cyt/V++b2M1KkbFVEjSUcG26TqZ7HccWMOzwrNvacGqlBnqL7K6N3q9uDRpZtmhjbPQbeg1Lx0dkwsEtHWviZ5lhqKDFQEIXA5GndaW1zrpaXSlbAAPb1+uVp+dnSrHMaxFYdAcaIg3Ykt+It3UQtdpWxflfkegc2oiiCDbZO9Dp/Zl9e2LfL+zbI21/RHkG/QD9wlo79yfh7iScVlgXWIqaZS6k1mSNPix0zkB4BeYZmePcCuuCA2uz1p0Xq+pu8WVz+MPrcvIFu/y+4yNif/Cp115FXn52c92Rhr//+EkBlBEA6WM37BTIbwIQvlChpxQxa0CYmGfR/BpIzG9npClDQxNfRGMykn0SbwXDrcuieWqwFKHuFsx4XtfckJn27WPtYmnrZhDuGXDZe7d0aCUDcrv7sIuYRSH68kXw9G3QtiHn0Wogsn2icvhcZDGvksiGsXR5q80y0vF9Nmx+s0h2VCIXM2mKEDWAUicLd9cMDhG3rnnNIo1+TUxtMIYFrjcLFIwO8JEVl/Sg4z1tPYXsJ1huQRS03kBh3zauV2vRct0arXllbsFbQeye2WroLHoH1mrrbUGTE2XpiEcLV60qXDezXBYigH2cb3MgA1wqab0yl0HzORkZoZZo62uegnKyNAaS9M/n7MKeNAu3GhfTWo/vjWitpYvXERfLBJJSnBbbYe6HEDKNOYLJkz6nc34ph9vrhiBBBu2UjlXF7lmp3PbOsHpYQoW5/mNTWCyMdQAwvUPzWRH7YnwCyPYZQuvNS0scAcHMw4LPpbLQJ6tmdeuit0Oa47G0Ge+5blcHKVZzqC4WDBn03+g8X5+RUjjLSqFSXTgqwt7NQrzUkvsawd1VsYdkNNUWzAohI/nGAJ+l8u+9c9k72954vr63HpbbxnW7OpgLANiBhqXVL1ZDSQX1CKyCsIjVJVprYfF4IlHbv0sxN3gB54NWq0+Sgarx9bWydKWUlVI75+XE0xdfcH++43Ld+fC0oXR+//u/5auv/sD57i3v3n3Jst7xrj7wcFpRmo9rBRbQhZIASUDOUM7G76y0PSqV3qrRQ9+AzofHb/j226/Ytgvffv23PD+9Zy1XzvWJpSr/7r+z8u7+z7g/KZ++UWrZWctOLQ28RhI6EnMA6x/YAax2mYrY+d2SYUpX69bgxHY6VaQaYHq4Wzktlfu7lTfnyrKY63GpJkQ7Jcug7Ko0NVnZVOlS6MaMjvLdAZNq7AU7x9VLPyXg2KR8Or+NKupmyZ2KKGddwvEccYv6Dx0/iwUqoMDt3AR7iOTv+YdpYsK6YO6FVwK9XlGJc8PmvQJUTLFL83hmi4LOz7X4Iu09C5UdYeyEpF0gFtdEO0ZwuXCzFco/s2BBD9Kcxnw7WwYIj5azCCh9ETwe03GITFVemPbic8U1Z4kLfY5fIajk0qOQ56Db8G/rBCBgwABf9XALIVi13mn8MbuHoUaRtHA92nse0lX979asWGbzQnWtjzRdiHpKHoeW93RgKfM453WedFe/Ube+KdlfL5WswJSQlrn4QiSAoeScDD1COD59Aqk3XGWmn1uavbWQioxbxLNifufnzY0s5nuH9eqgocuL5Tms3y/pGLq4HzEvs/KU89Up3UGpuv1I1YEMY2+BgfcQDLk15UA/yfR8zVtvqWCG6VnGQJNOkgxVHexNO9l5acZ1OZDqzRvtTgpkuLOibVRXTaC49JIKiDjO6GqusG6amO3R2cI78TfVcGMP0BivHDV7VJsDx529Xdn2jb1ttH5N3pK7JyxtWv3lg3+5ci5T8HMCfbsmEkJ8MV1p8rpwohlQvXgD5KiRJBTePDwAUOTC5drYW+fDh0e2/T0Pb3bqesepdx7aM8gV096fQDdgQVgIC5PF91jsltFH0EhFy2J8UzbQxnb9lqenr9iuz1ye/i2X5/dQN5blwnIqfPr2M3795RsKV1Z5QqRTsbIOlqk8wlaGVV1csZotT1FOx5SAqH9nwflWrLjWwrv7M+fTymktnBYrU2HebO9xKBFCz1F5VCwEJMYy2OXxd5DvLfKZ/57+KTKyAqO/bGTnadLKhEHUrSo/cPzkAErmjfESt/iXzkQ8uHD0/pbBQ8Zdpr9l2kj+AI6nDFeRHv7OKz7K9EOs+QacUuZDEI5Lh0QJ4ARWpVqV1ECNWdkC9j26u0cdGc3xSFobdLKiyUjf9xRc09wcADmzFIA6gZCMT1KgTxvDUXy0HQgrSlYiH1aGg+AdS3b4yZM0gMMQO7PwQMxKpjXmtRDV26PcQ8pq19rVmXhVHabWEARjJe2Z4vFo6hmWhyUyTb5DauHqTDSBq+Dg7wjl4z16Nx0yqjS0prQ6ne0gv/ucNC8hEJV9DwwEzYJxAeYmUho3TBxnFqI5gDwbCssAaWONjmAqPw/SkAHaOsZguu+5A1Kfj1Qi7E5zIPov9ogJ0wHkgQnImkAqWgZQ0FAUhsVPg5ZnGp8eY/swaO6GmcvEq1yHCYB2e6c5aBYJwBBjHgQXhRitjIxaQkIptLAKxb2duIsWtATdeFC11yo0vm1xnhaAvORcxP6mxNwkd8ODoBwgWoHRD88X9r3x4bLxdLGkkG2/emZew8NJD4quKcYAXj0dqMWCx1dPuz8thfNpsb/XataoiFWblSkkvSaovV9XsVYpffDbKvD2/p51WRB5z/Pzhc3XvhaoNK7P37FvFzp/zTfffYPUO8r6KaWsvLl/w/3dA9ZH84RQUFZ0FtMKW29c92bFhdszvTcul0eenr4Fbby93/j0YeH+tPDu/sxpFT57t3C3WukEK7JSmCvChmzF+Zio5PuH681op7iCpVZfSlyuoRZbVq1J81KEpViJhtgr2nZaCYt8wbISjfc1TPZ1JhaTWuitTB4CKCSjk/ZhgwyFM2Q3KYsDKh6y2MVA8Lynfuj42QBUvPkY46TV9cCfag1zJYKbJYONXQ+LtfFjFqI35r/4Nhn8K2MLje3mwgQz/u8ik9AqNQUdGnEpATSm4nNOgF1h2xu79Ay0VbXaKzYor1/kAGMEdc9vZmNYakW8llSda1oRjN0BmLUmthnwXWJJMcHMpq7jvj1IJivT/6ovT4CNKdDOmYnGGFILH3OqOt7NeLa7OKoBIphceGW48AL89d5pwcN9M3dVcxWoMq/SsE5VpJhpvkx1sxTbIPtuxeYSQKWQEAYnHdoxE8111QzYtWbsytaUso9CdEmnTrfRxida7HQ4al/Exp0Asb+WcLOpxUDhsizZviWCdIP5ZAF41RSCAQoPQnamd+9e2Cig5j7JmmlKBhsP/juEiCVEjNT8X9zh+0e95dHsNgtLajYEDsUkQPS0L4jmpl09ZGHUenqpw+lBS05QHc+VmTJnnhoWc3Ww59YhKVlzqgWgcwCFf9+lW3LCslBbS3dzbokylBM0Yg0bosrutcWEFaplAdtMmSuoeIJN1HwK2rE8+AWVQqex98Z163z7/onL9cLT5crT8yVbTnWfk6WGFW0AqOTZ2qwGUYV1OQ3gtAp3p8rDeWVdKnenldO6eE0onyeXPdGfRn2yY+9r8z3RFNx9/9m7tyAW8/P0+Mh126lV2Rt03Xl6/ANd4fdf/Y6tFZb1jvPD56zriV99+Sv49HOWusB6T5FK67C3fC1UlW8fH/n6u+9obefp+QP7bkVCa2mczyt/8Wd/wmefvuHdw8KXn5xZCpzrlUV2A8Yeq7Zvnqkd/EBB+rA8RUC4BS4VmnYKjS7NLUmWGFCdJk5FOC+FpQrnCqcCVSx0hS70JjTpdCpdV1Ng1V14SIIoJioOpX1Iex+rcCzeOu2Z4FU9cQTMHpeMO07lZLr2lc++7/gZXHg6fn3E2qPzed/79+vH0d31/VfN7qSPuT0ON5EhMBJZQ2bPBYS74Xfz5QlibpmqaYmaWn1AgY+/6OzqmpjvTE+3EpfXbCk3AzyMOsCOvWPW61OYawO9tkaHux/+IQkIjk87uh7zt0+mIFbMUhhWKu0cW/wIcrt8EnjIGOOLQP+c/7GsOV23i8jImZvPd9ia95tfOb+frGR57StLkH+rvpikaRjDeliGgjF62onT5kQQQ74w1kinR9zQmn5k37wgqfkDzesGqP2FHHrzezpCQTrEfokc5yp5zHSjmVHM6/SCN84EEIrNfKeXSQUfjUF75T1SWTKyGRmwJZSZmmVVyvye8zjjXdxYpt1ao7RmbZi0mPuGbm6w3v15Xhypd8ViKgtba+x7Z9t29n23MiStWSyeD15kvEROix63a2xhEclCmdHkOGu3+Y+9pySfCGtZvpOOZUren1ZHzeWxmLfCulRCIpTiBT9RpEPp3dWnHe0Xa+OyP9H2E/SFhY5KYfc4pAOf6E8ULqg01mo1nZYFA4bnwpv7wpv7ysNd5e5cWAQqUHQEVSRP5OW+j4K+phwMmXik3THhY44ZcymStbVsPULxfIUH5t+Ti/ewhpN1TG7HcDzGnjgwu+nQ6a/X7yN83xOOx08OoEYHGrBN4KJ8XhzfDU2wwL6oeNsnnyUQgWQpMFKT86ijwwJNQlhkMqdrWkx6bx40OWoujXHFudB24Xq9Ur2eSCkWcBiuRkXRYmMN/7qqVTn1dAVEhK1tbNerMSwffykjfkaqNdosbcQeBescDSTL+NTfL8z0Wa/p5lqZ5mH4/yGA31gpAilB6zTxcgFlbLBZEOfqOmMbMR2hdZtlo1brXRVxNlbN3R9sdSAoS2VZV3+GxxdpRzyQ1Rhbsc+8qWoG5He1Ku5+z+LmoFqjVo1rzP7s1Gj7FNge2ytBnCQQisbD4VNH7H7imX57a8mwEQatTSBq+P19bYLTTN8H0wh8cqBzZ/h1WQ4WqDl5QBW09QxRG0HmE+37apdghjJq8qQrzrfAAGMyfhhAlzLPke2lV029/0APRdHWzPo0HSJ4GQkrJbGsS5aUQGL9OtpD4NjvaK2NMppZuzSandepsAEiNcGzWYknxc8/H/xkgGzwpVCsB2hmiYxiq1GKoVazNi3Lwno+I1J498k7YwOtsV0vZm3yPTjHKqp2PBGefdvZVdkuV54fHxGsAW0AsFoDsMXLFaRYM/Dm7py9NZ6vF3O1955lZEK8hhKKz6ySJZQQxEqLFOG8Vs7upjvXaj/LYpanZeHufOLu7kS0Y7LxLdRS3UMwrOvpdnK+1Vqj7zuK1ZUCy3D74tO35obcYe/4NTbGXa1QaOtwbYpyhe33PH79NUUKz7L4Gi+IGKhclwUpwid3K5+/Wyj1zP395yzrwmkV7k5Wb+nTt2fuTov1/RNr6kvboG90tX0Zci2SjDR4mMd+FrEYr14Ke7vS2ma199hRurfX0QFoRFlqzaKi61qoizEe1UZXe1fELE2bejBOhtgAACAASURBVOiDu0OtJEJ1+ZnlVhNMDTku+UwTL1PylJhLMMoWMcc4TTXXZjCY3CnknFNyUvSrGsg4fnoA9ZEBzbgy3kNRmlp2y5xZFN+FxjO/u9xsrvAXpaYtw72h0Vw2m8z2zDYamlVM9NA0TDhYHFNrVhMGCX95CMwYkD9LLRYqMp0Qq6O0bTuAt38xblvndwlNiEDyk5NQhpPNvgytYdaEx3zMkyscvwuQF+8sRNyPd3+PiQ6Q+mIdfY7sRsYYw986zZ0tgQPNwAz+BMBUNbeq1GVxBmvPPqR3hwWql5xLWjyj0wgQJMlMSwSbh49/svwdstiMeAaA0pigICd7r0jSyHgmCWDk9bxKySKuXVtSE+BxFEMjiwdE0H3S73SNRFZK0ERozR6PV+qsRcdFPu55pUJpiDXztTBsJIyLA/+ENseofD3fn1lQh7LRR+HFX8rhAvQwmb6/RQLMVusfVyopB2bFREcsUiofajwjpL9dYuqF+L5LK0GCp5uhhWY/Wb+SR4gDtYjXwRTBWZHytzisZ7iHVZXz+c7apWw7H3pjT34zihyL763u9NWaNZXdegenheLvX4q58IJmjLUUumefdYr3gdOxkyV4Byn4s5K7xuQ4b3RarkWyZcvqP4vHOq1uJVq9e8C6rjk2+22KnHYHuD39C0MJyrpgXtFQLfOwYDWQVGFrGFBA3EVl2WCUwmXb+fbDk9Wna++5bg1R4eqxqGU5UasVLC3nE0st3N1/wrtPzpxOJ7748jPu78+cT5W7s1UqP1UvsbBf2K9X6I2uO50GzVyjPVrweLeHlCk2OtOHogYWVqgzS0WkGSSAr51TqtXVW6qweOC4zZERlUdQhN3NAGVUZ3eZ6IID1cmzIpAu1EkWB8dEIrvcvRXJy3NzTTgg9soRT8T+zm0tLz977fjJC2kOhn4UDmNiOKJOmc6ZXioEMIzMIHChIxPD+thETIIghSK2afqkYSYwI4Y2TPSzqff4jrPWFxDB7mJFvyKwvLAsqzM0JfpcxdjVmZMRydEcn8QRTCMZ5xhvWoAiNikqS4f2OgMsne9xeNLNxKkD1jKIFBcA8V3MlQ/EGneapjuDhvGjef4cZKrxfjNhTz3bQghF5lvz7KF43zhHxJoE55TFcx3sNC+KmHFQt+015AjSNV/O76T2bFRpXvNEBHNVxPr589RpP1J4x4YetJIDjcnkKAxjfmJexzrGvV5z3YTlycc85HeuW071QGk3AMwFcAosX4nJ2lHSFVIO++KXcMRej8bREX8dQeNGh5FRGeCHsT8dYAy3D4PnTX87pCXbpUxKDtPUj2y1acVdyEe7okM1+ADJiFubwnroX4Y8jC4KYoqilf7AqvarpqKYY8qx2TiqFChmwcIrflenuSrezqUItXLga2aRsbIgu/9YjOPuzbeDY4SnYSJRmUComOtJ4jwpaZmtUam/Tt0D4nPPIru/v3MrlFl+2t54/mCB7KZsC+KFRmclt6sX/lSvUt/cEmI1SbDq61GUWayUBAvy5o7eNfvsAZQAUHVF6kIthbu7M7VWHt6ceXizsK6V+7VzKo1VlMVjz6JEQ69eDLMLe7f1NSzjvHrmd4ScivhZt0apjr+BYf0ZqMswoc21Vd4vyVMPGdaLAe6e1vzBN0LRHTAu+GxsoCEYZ64Se8w8ABPfDmDOPEbGZ8ed/YKeX+ehL4+fPgZKRso2TK8zWyg0kGo6DXKR7BaD+4eg9G2VwOIWno0jzhRTNXpoDv5sT9GNxRoaeLgKyRTYYQHyZ0/vKAa/nUgNele3ZkR1XSmF0/nOsiieL8aoLGrAAblt4AhIHTPmRNzVG5WSmpkxDR+G7xjF6k6JVrSWZCxZ78NpdK7IOs9egNIAa+AB1C48dD4552OAXxWrNtyRYUJlBhBj3opX0TaAdntjc2mOFQFtao2Xe2f3Vi0z6g6hHnWrNO9o82jNhb2Rs9d8CvdegImJ9LzsAS6IIhbK2/rI6AclwrAkEsIuau14fTAXhvE75cMsLLwFDDJctlIm61NYnMoUcH5EeLlePRilj30GR5nSS3ErxpEOEvxNGl5O5kQ7NSxiv0AABeZ+7s0y00JQWnX7lqAkQgBiCc36aWtljcj3Y6IFHGg9grVFraCi7dfBMe3/017NAOehhEaz1rltUQBn66hQXl1jUcuErcuS8UfX65WCcD6fPYbIyjmOOj0yVUiJ7GDMlSmwVOu+UNwaFN+XOikFxQKar5u1b3nedp63RtPGZd9MOYoaWkTAuzhvbYdyIAIJcq1Mgc39uiwsS+G0LpxOldNaOS0GRJbVXOAPD/f86k9/xel8slIOXblervxBv0YuV9ha0v4qVq9v0SUL9V6u1s1CvdyCWcYxACWFcFOd6kJdC3Ku1OUOi5synjHsegFmLf7MgF3ldDrbWtTC6dSp9WpWHyzN564uLLXQS6eL0HvhOQCUNRcEtxAHyPHUO6wHnnoGd3PwZNl7WvxaXB4Eo/c2L7JUal0Rsfp7ve2+DzyhYgGphaYSU0JYMSPbMXh0FqadWIiEBnHYkUbXYQEcca5B84Tmk2WHkgdOsRGJ3eIaZWRefs/x81QiH/jHDx1CeADQ+Y8fcZMBYF6DT0dN6SO3S02Rg1XK/n2ASEOwvjbG1OBfol11kBdCWXxhZyvaYL5hldLbmxxcAof3lOPvEMKvvfAQs3FOfPI6Sp+/D2YVWvT34nUZ7zre4cYKlbI//pj+ju0wBa3PgffHYHy7WazW/B63/x7jmCD9a7TxA8fBHHwAfcf3jXXT/Pu1eb4Z8ATC5uPgor2xTs7/mD9Pa8fNu07qyEfH8UPTkt//4kDT8Xjh4oSknggDiLU97unpgvxzXv8Bnkx2jG9dbcylE5kXJNWVaUABcCcNHN+DrzHFoAOnt3Drzft1AJIj8MrHysS6JYBUuNDMTVaKuc9qDbBt54bC1Io77Lqyd2XpFnC9SbPaQDmm4xpIAKfkfZMCMFmI4tyZH+Vv4u/i/SQX8FgfCZ+e+NxLgDSzFIV1egTdT8kcOd/BtK2AMW5dtMKd3uOuWsFViGLSZFmSWivnU2WpC8taWBdT1qpXRC/Og8VnI5S7HPMkFbMpcNKrjp+kuUEoFhcVHHWWu0cgZZZ1D/zoEZsWxBXgKPh30M28Pz7CuD5yxCrrNCbNz1/nrbMMkJvf/3+On6+Q5lCISHaR2v4QDknYALcLOKWXalLxcdqMgczMLCZ9oJBwL9Wlsp4WehO2awST4+hYs8CXiPdvqp79pJL9rsAAUS8+vh51RDS2zhiXGsMQYF1Xq0A+uZKuVwvcm2NWYtIUq9qqQJkCQgdD8Lm+oY6Yx7BqRUyemMmP4uY/mS5ORioxn77BRLOOhk5ArecLjneVYl3Jm/aMVdqjAnfHClwC+27jWiItX7jJjLG4iOt1Y99310odZPYAFDNI6am9Boix6s+2mYsUqtczKYJb6nrOX9BIur+0Z2VwNNkhXXoG40aD1XBXREwIsY4JHl1fdoETgu8A6NDJUmiWy1qWdEMYb7c1ey07Kqy10UIh90pI4xCaZvK0cXRcYA1hOTNUUwBCXLpVESvqGD0iX2Ro/RIOwWQfFpvZ3dInUo2ePRZyb42lmXCsHu+HksFyOkgRcMETYQc+rU2wLgTOrZYZtPnazXrV4Gz4epobzYLMZcT7Fucf6pbaGXwnzeA9Fit39/fUJQstmcWiWNDv8KqMhJCspi5KkcpS4LQI9yfrEVnqyM7Kfe3WyipYunsB68NY2RrsTRAt0/yDTtbrcCdVqSkrioOcKl7T211VvWPxVVi161m3VLV+fx+en7i2zSzTrbFvG9t2oTULwkZ241EOpsP61bUgsrA3d09iQeb9eU9rT+vmV+mq1G0zvh+JRcUC3ouYpc66UozkkLu7xfe8la0RESrW5UIUK0EA7DtWRbzt7PtG753turHtm8fuWmkBtYIC1kC476kAzKEZ9maVWsx1WYrTfRuNh20xhct14+tvvjEgqVZBri6V02lxEFrNTYsb5Zz3Z0HqVOgGQJ4ViqNKPEDywAwTQNLxE3X88qPYt7n/xh9ZF0oO33z0+Nl64c0gyiWIoXJxN8WsFRwwYhhpJ9DMtHEJa85LBDqAGD6xNqXiBGBBk5UdQPYcqWEhc4XFpM61qeysADAChdFwM7KhNATmDAM1hfGyWBD53hp48+DWdg8yd6OuHCGkTiOKzwNExfvmXM9z7teqTimoiJUIKOM+qI46T36xyGBYwShzAeS4JvlHCugBxtRBXGaydANgIaj7shjYEQeqyWw1weXlcp0oY1oHSCbQM2tmuFTi7Nh4kdEYQDm3pg6tJrl2Ul8Q0ViLJGWJdGkfT7MmxwcrVJ4/0Wxs2hngTEJxWLk0U7Aji1J8yYLGB4B6hRbCQjGIYQI8Xu8n0XJcFxQ705P9L7L6RLyicSdreP3SDovX0KSrEaTtSonH4/Xmbot5L+iIgUqAj2Or+GtiUpF0UnMdDpSX9525o+HlsRcNF8ugqXDpZeX/crQCyBBGtRbO5xOlFqtK3hpl2UEmUaaRrew0rEOpKmIuqaUIp8VptUyhEGFt8ZpSHUVrZKdGQ11rYNvFCzYWA6uNCB53Ho6kZUt83nI/+PtZnJoL7qx2HfNp/2u983y9ULu5L7d9p+/NK57vqLr4d74UrbOE4POV2myvtGYZwuXqCSTqgfVKVnFHFT0vINVq1YmBbrMwWYHP01r975JxV8EvChErJw7OxZoqd+itpZK5t91di+a+M+tXp0vEDsUPqcQFIjbLWjXs7XwS0RHq4PO77Tvv2weKWKHSWoSzuyBLqVn7zsjwJhR9DnaOl5NBY7iV7UCo0++M5Zus+rf23fknlM64Xqfzxl/zznr9+Ml74R3+pcfvYrJyY2S59+GqgYlJzIeSSPVjL34MMEuM8Mo503TqtK4DahzeaFjKIqiw5M1DDvYwqcuIkQkcYgI0CNViEBBYTysRrN3drBMWlqzTMmuQk3AfgnOySpSZcMZbHSbxcAxD6CEDLgBvvvm0EbDXVzCtETz1t3tmjvXwar2bFak3CwBvceewgti7FhFakwMjVJR927NMQVZNn0futGNA1ya6xuYHZ/LFu7IbyOnqVpROAtsbB6nTwwGyvqCHeZ5ujTC3GzXoQwnGhTOZnrRpsSmM/GyCiZVkSHZeDvFoPvcPh2HbwdL09eHfTDeUsb7x9JdUEuD45vV/gUe6XSXeewCo+G4Opk+Xn5IW7Aw+n/6H3zOEl2jJzKJZ8ZhOnf95HCNRmDW6G8ygfFBtCJL4d6zhCAq2jgtVO9v1yuX5icvlmiAgQHve1NnCKKwyZRH2blmphNLmcT4JeDTPm7PbQskrWPp7Bj7P8iCEYCodL2lQ1cBREWFvyr4rW+lcLhvaemboUYTr5UJtlW1vtH2n7Y3L5WrAxGOHLO5woShUKVmwNjrAZJ0pNffbsgB0tmb7snUDOaUI182sRFGfSosrSKo0EXYRao31FA5uxHh/VxBFvQyuWOZgxsHN6fvz/DmfkOTnzlF00PoM+Mcznc7zWg7XFHfdBkjOMIvW8Lqj5qkoxa3dOlhqENcsylO+f+SYZG3SQOBrcJBJehx/+Pi+h43jRwEoEfkM+B+A/8hH918Dfwn8z8A/Bv418F+o6h9+8F7BfQgB5cf0YlHp2AxDgpbRo2mYTONiR/65AHbvIVQ0f6e2qNOiO7FFZeaxMWcNJdBOWMecRSQnFfdTL96MdnYl+WndTPzmt654eyPSc+3VwKtX4lZVaqkWD7A3rpfNxuXjtoa3o+VJUltSyABOyzK1nBFbhCSsYM4eQHjYIUxEKaZJxDXg1jX/PgKhZVpLQRAVGo39utP2zmXbuGxX9r3x+PTEtu1eS8UBU6kUr3nS3J03KsiaxQq8NxUls3kC3JrMH/WZKL6RpFidJqyyNoIF4Huz3LLtqGwmeHYP8u591GnyF5/pI4EIg4mHFWdSblLKxFXu5CK0vO4TGPYtJVq9hOCz6yvVGDViCQYeRNK7Ca1o39TxtUQ97cbvoWT1edTPj20Ug50R3YSMDy2Y/Ii5CGtxObj8/v7AqD8m/0pX8KTwWHPtwTuyOrwXoERciQhNn+7RBy4UIfds79aUui6VRZYUxAOcaq5J9tezlxzSwgV0j0bYPZZVD8DnKAwx2hJJBCS1cDotlALfffstf/j6D+zbzrZvSbshSGfrp7VxibGaxUO7ZbCJx/sUUbfQhss/AvI9oWPvtL2nG7OIIF3RvTkoHLw95YZbUKLuW/ApRWiqbLuFDFyulaUUtG0s/ZG1FoROFaW2DZVGqYV9j3E0Lo/PFqgsFnBdauGhCMtqYKFagiK12V5o1dxvUoTzWZDSQXZkN2C4bVe2Zr39jEebM61zsrFpp5aw/O0sy8JpXZyvWX+8BOcih3WOyvPqSmvEI2nvEwD1v728RPVm1j1qtwUY6uH6jH55Q65UseIzVSCKtFhoiHlyzqfV22cZHbRmFrBdYeteGqcK0Tc5rPHZnguYDSXhn8iYrqmbQsr54yXjtoYEB5CaT/jI8WNCEH6snf2fAf+rqv6HwD8B/hXw3wP/QlX/feBf+L+/9wg068ODAA96c95shfILXwRSH1n566rYjzp8E76YK/3I3y+PISdNrwrNdL5yaE0DpRsDGkg3GFFkj9RSM712FM4cQGi2hX10bJM2eRDqKc6PTOjHHdOYA0TI8esxKeP9I9W7xc9u1qh9b2kuDwtVVB5urdl5+zhv33djZqkVjZmY12IuNpr/jqwctxLUYjVhIrYgPh9Zli/n2Pfi4YW/jzpf+0YZGz7J93YfcLtnPjK5DLgfYzvEMdz+Dib44i4fH7zc/nVzzpwl+tGb/HzHH4V/oQd7UYKe/DuYNBMokQMOnWbl+C/7JNZMX+F3t+v0Q/OrB9oa1s2P/bxOLJnFijfmvm159Mq7zABtIsj8Cevua/T3mpVk0ufy8xeugxfDuHlftTjLKNrbmr3Ptje3NDnPmXjRHlXQty1DBq7Xjeu2sW1+zm4gKNzY8cSBbZzPeLmEKDtiZUy6N0f2TOAW4QxTWZauCV5GYsDNys3zNWV+31qdBkEc5+8Q9nNYs8PC5PXqJw/lW3K9Y+2jgn3EYOpsXVTNuNQR8/mx4+U++eiZtzShAaCO1ic5XvR3esbt8YMWKBH5FPhPgP/KB3kFriLynwP/qZ/2PwH/G/Df/ainugaXf+fHA02q6kjNT5P4rDMd91AKoMOtX58UM28O4jpmc8XCxskc5jaFU3My9oKIdT4/UmzdyqG9T4FyFu+jKGutSK1mc1Db0MWD0wF2Ip5EWKpZV8JvHG4DU2/j2QZSimLmcp+I3k3jjeyQAUgVa+po91F4IUxvM+fS/XdIuwkQo4dTbX5GrFNU6d6cSV33nWswp+uGIJzWM7VUWl3pboHKpsL+PIszKx4AXi14NM+xDV292GcrhVZ2tFsQe++2k0JoLD4Xxfsadu0sW2PvnW3fbXN3MzFHpfLm9RfmqvHRbTDcD9krzDXncJGlRihWEdqumQMcB5EOzfq4L2YWGuMpCOKJAYf4o4l2w7KJC9PugdCCUDwGLzfI9GyZAb9zT2OYIzSzO3MKK0DPQNWfF0j9UflXSEW7jws3c/2imm0sbC9b7aTSI93e4/AmBjWDlxR0oRiJpeh3ZVgOpotu3VjDEpjvnevdIzPQv1NA3JqQgK9Y/SDBGmxLc7rzfXV3f8cnn37C9XJhv1iAtarXM1esXyJksdoYkz24DOGaIAr7fAx4Ah3YRuhZvttN3FPcUx+gL/hhzB2MkAeKJ/VoZ+/NFaaKqrAv1uZk74X3lytSoS6VDXPRbdedbWtcni787rdfcXm+JG+vS+Hh4Y51XXj3cMcnn7yxgHXnu833fC3C3d3CicJy3dBarRly33ner9AaT5dnU5YXywBsrswt1e7XPXautWZzjHohzMHvRj9FiFhNk2Xmdmz+O3o1RhyUYBZrdZk318ArN3wgad46opuxwKuWg7CcFk6L1aw6n1avRD5oPeJdrx0uu9HOWioLa85rKPtDKdUUO+E50tvwnZsjEhriWvtxd7BGAtG0H/wPnbK8f4TxCfhxLrx/D/gd8D+KyD8B/nfgvwV+rap/4+f8Bvj1j3ukHS/01YkRBDIt7taag3ztnLjJBB5m4f0j7SkfA1DqdVxmq3gyLyYQFQUbkzkNgrbdXFBx8CSDYTZVtMNSI9hW6W7etDpI1kOpuFSqRei1JPPoeniJBDWqlhHVxa2irralNSJdLGbCned9npM5g2sAqAEAI3Uvs/cwv3wSpgvTqO4d7rCwPAWI2tyadLleefzwhEihN1iWlVZ6BuKaq7JkkH9Y34rXD6lTA2KJtjpe0dxa7ICWbploOsBTABDDm9WLqCpSdvbWEYGtWU1zugnJYCIx9wXb0AMAmaAsrqJZ9okLKyKgvec0jrHI5NoL44Z8VAmIoxMCsiC9ZyHYBFGvXB7rGOBWRKhq8QoKR+7htCPTOOTw40wq6C/+lwX0fvbjj8u/Bqo9CCRx4VnF6r9ZQPmoZTb+N9bddB/7X/fUvGEx9r05uV7suUMpypZC89pMayfiz3JtJnlZnj/TsSdqAK03omu36SoWTP723RueauXbr79N8FR87aWHMC4QDdYDMN7wK+nqCkI8w8FPVkcn6VMDRMXvfBdXBCYREPLCps2tQD0EI+iuHuC8AAXthXMttA6n60Ypav3+xJT36/OV62Xj8f0HfvOb3/L4+ARSwbNg39zfsa6Vzz97R2927cPdiWVdnAeaxWlZVkpZqctCL5Vt2/n26RF9Mlfr5aoU2TmdTqyrtaVaF8vvXevgUwGgOupZbhBFTbsrkraqA0BZ25aeNeDCfWcAysG6qrV8cnoL5jQs8DLxvjAwuAtbBa/wx7pUHu7vvHSFF5vV4W61oqiwN7V6Xwj1pEMuBejH6eGGdJwsDuxJbnikSyMi5Sulbvef4xbOiw7PObpqvvf4MQBqAf5j4L9R1X8pIv+MG3O3qqrIrSMuxiL/FPinAA9vH8Y1x+vH+ZiLRdEppuKobR3hl+R1oYnY3/rq/QNoTOM7AIZq+adkVDMvF9Hu6RrQfO95ziUYiAG6OL/75n45rpn5Hcc73S7BW2qbMXaC2Q0f9ccysdKKk+//4xF3jkllKh2l4/1DW4gdML3FABjjPWIuojAerg1R5hYRxV2awxI5/8zuugzGd05simjsxKHlT6gXXyHX6DSzYczC5Rv7ZkMl6JZRDysWYZ77pEPhsC46ra3meS8XYbYKpav05j1lGl8CYL25nzKdM8z78eULt8yBaDSBwOHcic/8fYp5ujn+ePzrzZvhTrk9j5vtrxP5h6ByMBNxVDnfObf5zMO9Y98EL7xNoIltiI42REOTn2ns8JjjoQNMd7d6Dyu1W1PqwuItp+LWQd/zmGO8NsyRtSg+9K7W0mVYFJSI/TsO6Si0R7slIKvqC2l4cavaGHcUYx4AQMUyna/bTqVyXXdUC9e9sO6Wkbj2TsUrmNfKejrx5u076nLCcsFM2bq/W611T13YvXzHtrvlsSweA1Uo1dL4O5VzX5ACp3VhXazPXpSo2Zspk10Xzmt1a6Bn84lnFEs/yPdoLlFF6CWl5WH+ktdOweTzVM9weoYY2cEi5jm+C8NFrr9OxN9tjvK7ES4Q/N6U6CjnMFjVkYJe4ycvP/uY8jB25CSbxomklv/RJ3xsDMfjxwCofwP8G1X9l/7v/wVjQH8rIv9IVf9GRP4R8NvXLlbVfw78c4Avf/XFCPt6RXsChiWBaJQ7CdtuVWiHqdI3i4hnmM1g4IhM42+LQ49NP4x9YP3o1tUC33aPx8kBQHLEg9WKmejc3iERQimplXfM8iQAu2kPS6kZCHpgQJNgC2BkIELRvtO0u8ZY8n2l2CZaaz3ESSEjCHXgJ8/0g6xifTQ6aCL70AwOaxp2e8FrzaQUncCDbbLmaqWFXro1qt2UL0Cs751PdWbfZE2UylKs39viKb3rUj3Lo2Q9rlHF3OYCIobaBZbv1hGvQG5cQa0KsBpQU0p2VC9d3J13s/auFUKB6qXsxFpEhCaVzEcqSPd4ElKDTvr29Z6BS866V8fPANwyOshHnNwBtOnk3JhwjsVejNY1SbYqGcchXkk8zg8wFpaE1ECjRtos+KYx/5gAzJ/o+KPxr8+//EKv1yttb4dsMHSUXxkB1EAoWD6P1bsKtN1q7mRgfoKCkWU76f7JohRPV4/73mjeCc78E3NJg/njBu3Ccb8LnsiQSmMlaEe9XEDxViK9taS3dAfJiDMEhmWsGCDpHbZtNwEvXj/Jebe6K07pmVVLgKre6K2z7xu7xweFEE73s5grf1iGj/IkAGWARxH48HzlujW2tSLaOC1WM6vTObeVsi5uQaksp5WHesef/8VberPmt7tVMaB6TSvtG5fLha3YTG575eHhDXcP9zZXxQDU2pTlvLLtjQ9PH9h2c4U+P13oXXl6vnC5btydT6x14bRG2AV0razLTtdK6Z3SJwuRgBZ1YT6B5kAn7nZrnkWoPk+RM2DW78mK7rTUIvhcwdra9LR4oZMikbKl03W3Wl7eCFhKpdRqsmDb2fbGde88bw1FWFtnJSSnjOFPa5ZEIbEPxn8Gu5xkEIcyoNP1obxOpWq4xSDBQwOQf//xgwBKVX8jIn8tIv+Bqv4l8J8B/4f//JfA/8fd2yRJkuRqYh9UzTwis6unm0JuhrwGb8A7cE/hmvfgRbjhjjfhCShCriiUmdec7qrMCDdTBRfAB0DNPaqyRx4rX6WleLqHu5mamioU+PCjwP/s7//bb94tG33ScT8E7poRXEGFFvny7ML8rSDP9Vl84RuICkW9HHSB1NvwlOIiTavDswcAvGBwsNGiASoxejC0j57JZVvRXuNBAswFwOJ7BU/1dweV/vbEAiW/MrbXw6x4ydxzrJrHtwAAIABJREFUYvL/RaWIblflJ2SAZNxOEcGLlSUtTgkiJMDfxQqFy619QabaRGcK++/XBNiwJcj2VB6fxfpfYpuCSHIU+IykR44N21/o1LXjZ4c1KfFa51mWWIX6yA/f8jwtfS9cSaOP0XGnwWA1ARgqX0tT5L+941+TfylwsUCtIAYISgp6CkbfaGGVEPZNCr3/1vB94/CSpy6hBBBk3B8bq+sVMf9sI8AI101RUK7dqhnMSSNLgkq4BQoKi7mUAPl+xRUPgr0hYKpKC+k31zTSklWeP4BpPJfd73SAdzbgGKZEn+fAOTpas3p2TTTKJXVp2PoLoJYCYYR709bLcZ84TlPQxlQ0t0ZZ4LhZoWz+LbwCAuy7FTDOdZiKzdm7BZR39WByRSsB5ZDmpWHcE6GCJjWIPRUp0l9aonKctZBehZ4VRMV4Fs7Iz9F8sMfkDaHEaekPEEHkk7uOiQXq1Fd2srAWdjjPXuid/ZQiq7VcV2/wzD+4HN+24L41D9T/BOB/EZEbgP8DwP8AA43/q4j8jwD+TwD//bc0VLXk6r+s79WUe43HWTVtP0mTQETEa/Zgaf/aFu9zBXFpWUpmF0QoZfE6mYlksF3mLWnZJxcwdBJB4koMtUDlJlYvqoIDwKa9qeLgToypl+epYvIC+QJVxdkVmqD1FjED5LM13gkObMh01vYTnV2Bl4hEwd/zhDGCuqPOd7rMqWbxgxUpvd1MK9iYxqBveLndIqWB1Vjj9nDbocjdcr0xZxQDGmMUbKSZmwVmMm6wGmOmZXltOjcrW7iFMwK3tBBQN2keLFsBEt0MA4BisvhqI9gRT/i8Aluz1qjPhr2T8az9B0JzcM29N6uHte1bJGBFKBDrbD2yDsm5rkqKI+1gpETHzmv4HM1rGjLX1ywCisxTyrj8G8FV/2r8i0ctscTt+jocbE6myYbVFBMHML4ke+tWW6zE9NiYGVhnyFwTbvkv7myUz54gMwUVEEV2fQ3Tkm82ntPddADr2AmVVV7D55vO2yKWzvhlJDl2+uHvRgtu6cVEF8HZm7nD1UOqVHB60FJXQWMqlyJ0TVlkzKNEipbpqR8CckmS1rNYMC3jys3m9FJwvR8DeLsDZ2vYd3uuYwBbv2PfNry+CF73zaxm2waRhhfnQ6qK43jHHBOtbRC5oYng9dOLWcdvNxtzNXdgc/nRG4Au+OnzK3Qqvn59x7gPHDKMbNRqAr69H17XcwK64Rxmad+nBZtvveQBdD49p9OXWwhn1LvLxK/QjKG1862PvNU5MtknmN9KUAKsAy0F72CVg7DuCLzeov19jBNTFcd5euzrxDndhQeThcEFL7ch/qp4CvVzMVIgPmUfvVhJvCbptLS2urpdxl9v9sHxTQBKVf93AP/tk5/+u2+5/npUEHW5DwBgwlxU9l2M0WIpWXJAQJlY1yZwIoTD1ZUgZcCvFpHHPglbr9IsX9QAnImMMZ5bcpwgUiszwhpz4n4e5orp4pqKZEFjt26YyyCD6x/Q+jqKoFBON16eJwoHHa5NCoFYjs9irJHseAWuAlhOoYXtOugLYW/AwoDTXF5TzZ210YriYGjvmwOojlt35uUJ2ZhfR4RuSa/0bp1Ba76YYx6VKDQ9jjHvXgx2qGd9t7QKE0kT5qJQL6xpzGoSmHJ8CgPiXM1tQLRBPQeQcFwLYfA7zskKzrmAiwKgGq68Jg0vNwtWbX1zoUOhhmhnIQ32mWsIBUA5eLrSUHzyoFpRQBt3/GTA+AMFVmvBvwEE9a/Nv8y9RLrw+ZqKCSsovaj605UofzUHwATdLAdD+JL8iBZVd8nzn0s0aRNt+tL0XbYJGNhRB1CtlyB0v8gDkqRltYHmZQjE6ZrgK605M2kHAK2hADKPGV07YrmXJhClO+BASlySJYCyH+3dwEYWyp6FzsrTSYkjvJBYZeE5bhIBQwpbr8c58Aa1zN9vFnN0DmBrG/ZN0dqG2+abA7i77HXH6+sNYwx8+WLuxdY2ABb68fL6auVZPJB8qmKjog1gE4V04E+fXtHbhq11/PLLV6gKTlhC4TkNQJ2HKWWAYh8WTjBUsavFYdEt3ESMT0nKH3pSzjHCongte8c55c5gVZirlGEJAZY9tsx5RYynMxXmMKT8VfKWZu7bc1quv+M8cT8GToZuSEug4tfGrJUYvHR189fn2ObKaeJvQWzksk03VDZSaSCAq6Edem3wyfFdSrl8S8fStPmN15ZTFyR7aa+2mxasSlBJTB/2jS+tk26/UBuU+PtRiEyvE6RF8GUW89VS5EhqaeEZ8YgLwUWQLp2uQrG4D0uD1Fqv15kQlWgioWsC2hyZy9h6yysudbNza1ZSwBMOWrqGLbYaM2B1Yy6slvFFYUlxJu9rNgQAFwBB9+qyQ3kILStTfJt1jhGgZT5S1BmmjDSoC4xc3QsSgGaxZsY81FFax2j9U+I7AshGoL2MNLXAAtY0Y2ye3yyH5gF0xW+6nPfh8qgazw92VNBLBSp+q4JFqoChdl3Bc6UVBGqi6kOQm2up0GgdeaIPgif2rZ7OO0sqANFyYRPWd/9EQC25bquyGWuj/HilB6uv5qlZumVKtHQCZv2BCPrUXLMLOCKNt3zNGSBsqXv+wTzVZc7Tslal1djcmiUZ7q4EM6jZXHgzcs9BgdltB1zsiNSZueR6x7bfXHGki9LAU6trvwApFlruvWHrG8bmXgbPhqlTMTiGHvN0jAHLQJ5xrmgGDmq90BGgaaTSrQlD0zurMafcKZdrW0GrY3iFCtgnXT+IDOEYW58Gc/8Nzfx+bmkzHn1xzwaCD2J7MtUFagWLZa8T+QStBiGk/KOsU1s4qxSvgu43jt8dQAUbWHFHAJhnHa+oWVbnJoCLgFyu+8BNWO5vxGOTZyn+RxanfYaiBKWtFIi1fbLLZI4+jU4whxd03N1HboVhDSQwGzBJlM+3jlkyVgHCx959MTeaJC8biyqImKqAb3eXMI2lcKgMmPemqZ89a9JCQ51lgWmMCYEhmYJd2T24tMEsNMaAzLJ02zaLOWjdA+IbbnvZ/RMuAw3ZQacF56QspVjvwmB+mipjHvPJu3BXCIOEM4iQwjPijmKmzTqTAoeMEL67JmujRR4gIDR19pSgrJK+lrmv5npa4vZtC9oF6UBkoXkJpreulyvI5mkVHNn9yeQyT0y4qi/3yllnmx/CrD/scbX05fe5/mgViPi0YNz8zhNTuqCE5nqzUk69ZB/3nEZqG2gsz5InDKFyUCzE7FbkvXEFQ0XNkgvOp6Xl4KMQDPAeFSiGxc37O8fMotFYxwHINXX3YHsBoPsGbYJDJ85TsSn5g2Dz0IVKO1tvuN12iAj22wE0t8TTdeprugrwyu6o0lDZqkra6+sN+75brTYv5dR8nZ4DeLtbgsymAgzFvm1oEC/7lGECrTXcZMfWd7y82PjMMXEOhYii9wmdDaNNsgfbSaxi9QF7w8u+49OrWaOgd8xxABBLozIV98OByJgQUWxdcNs3nOfp4Q3W/85SNAKMcTpd1FQXKevm8PxsxCjTU+voukPaav9ZgXfOP62gTbxsDYBhRXYWxa53U3aPMS3x6Dnw9e2OL++nlTBuN4hK7MrT0HbVC0eTMa+yyfqr60+kO4FbQpPHBvhzQp/+suSaToPeWPC5Qoe/dXy3YsIAkNCwoBkewQnwKOyu8n5tpTT+QR6dsA4giQgplB/B02Pr1+4+/4ai3O9QhZFvZTaht1qf4rEKAljcIVXwkZHCr6dQezoaCNBBrTFA60Iy6/NerXaVcANUKC6Bo49HmOGlMDdhsOV2sUA1Y/hiYGGrO82EwbwuILx9G1LN7q8ctdDCdYms56FYi54tpKLo42p/4pgGPUFy/hetHYm/ZW37t3CHCHPeMJZrPnATKQ3V5uJ+8Qy/zS7smRDM6EPlol7z42Gn5ZAL+dDSmcDUT1gXM3LwSQca40stWSqAcWWEO86M5sua93YWu7UEGXtfDWWwxIkl/5XIj1Su9LXZgn5moaEMRE7rtdZrkfzF9LOJoYK5aRSStWUwMUTMstLWErE5vqZojV6srXJJzPoR2UqRv+UkpibZ+mZutiYGjgTQcUKH5WCy2COxhJfHCVEDb12AORrmqKWxxBUoK+dzzLvtXlOPMWrF2k8FVLLahIGfjt41Yt2ALNPDNAaAuDWsVlRQjz9iLiijw1nGJQBUzE+Z04tBIdd4zl/Od052tVBmyxx4pz8mbUYqXaxEMfmckvecImhBUynnovRbdSs4oS3woViXljV1JQw/fFkFd76u52us60fH7wugFMCQgn4jlRqqULOFJo4U4YuOjCfNbTUGg8K7PnQk/wtBmG3AgYwAYTFs0iIP1NlaBIOKc0gSTlpeDPHXRS0gumXOcGMGA1IYkPchtv5KoPCaiVw9mO/tfseXX75aAOi+QXo3062jmSrYuDgg5Wm1/E26zyXlYh7Zn4KS0ids1hSOpMVlrIAgmCgNN2XOpGgm28Zqwzam29axu2uuewK21jOBpjREIVajB6ZYwMLka3Z5pioYw9JRcPeHznUbdJRLUKafU/KB8nJKVUvG2Z3pE8StNF40dmZL1pwLli2sBqA1WqkwpYJtBFroJ62mxkQKY7j0Z7G+ao2hS+2vupCNJ4qPpyXMQ63U7proVGJACdTHtB3TaUHLhv4f5UjAa7u3dBrdiZhGa3RkQc82h8PXqFHAnJ75WWGgwtV4aZwnuIVAINyq7hs+kl94J2JpStC9Os9SAq34jIhZBBgTKMEfpGl8BhQYKURUgXkOSydwnE7HPudhpK27iclZgAnBOYH3Y3imbuNxUmI6u2Sd7AARvcFDNPHp5QXbNnCq4u1+PAAuWtrCguvPQLdkg2Ryxwbrg6cU2bpnpvbQsAYAY1pAtZy4w8ATVNF7w7/785+w3XIDC1gdw5eB8SwPvudaZW1NAGjqVh3118RxP3G/W6C1MDic70xBI7kDOWqxUob4Dk/yGvIXKjq2XrOwNEFVMTIG784wjrwWqkkX/ju9LAIxU5RYIs2XfUNvEnmupiqOMXFT4NNf/kvMtuPL+4n/+Pc3TAU2j+Fk7j2LfUI8RxK58WH+oDkCSQME7/7toHI5zZUKtRjEqfkM0X7h0caba+sfH7+/BWrCBGD9Lph2QcQwU6fyGsCIJFeIa1D62BYo3MtXKcL8fQZzCHYvEkHKx3Egd8/4tIhnthaaMdOdFMQEZtUwq0wxdBcDRDJUCq5z2nUd3dKwwGIF5ph4e7/jl1++YM6J258+YfMCppaRNgWqMXB7bPMESrk7iYNm+4ShCfSwWMKMfpMhG2BTSzLHa8NSU8Ct1uflc7rWtDV0tQdkHxcA5do3TcDiU8Yg6wgA9DQKJtRtlKu/n6DpPM81kFItUPIYZwqdEHo092oUISaPFEyITgsE7R0KxYm8t4Igx4fMd1nRJ8+FSYqpW9jt0XLMxcF2KAD8oczDNa7Bxnt1WbNfKAC7Bkmay8JodetpPZFuc2iMdsb1Czgsc5uH73KccMHy3LrwRz0MEORqB5oBImbQbgmepqlMRQvutmbLGp3SmMo7mAPLaggahNYXULFLhaTwfZBkddguUAABHrg+KVYaRZ/H6TRHTaIS9I7JdQDnBYLhBc3P+wkoIqeVLsI4xTC7OGGB2e93K1P1ssE2jiA33dCtQqBjfLahu1Ad+opjTLzdT5Dn0FqWVhQl1y8U6Wkvm6A3YPfxNABlgG7jxpMpUC+8rXNiQnFAAM+w/v72btduDZ9++oQOxd6df3MAGrymplvFnRdMAk1aRgAcp8cFnRPv9wP3+2kB9h6C0bcWiiTBGePBMj7Ocw46gNEyzwQ+V35YeUC4ahWgq3OGXPTzSKxAKcqLAGmAKZRoDqBuWcpl3zoggqECaR0//Vf/Hp9++iv+w9/+jvv/9X/j/W5Fkn1bSjzbYgjgMzUr+AwUhaCsg8TumQFdqSAq6dtejfkjr4sINAw43f82fvp9AVTp8vp9+ZILsV7jc1wWh6saSnFzuYkYc0+QUFwsFdUW1LmcpfrQ0Yd74AG6XXsYcDDnirEuYkGLCpyenp+Moc+J4dslzsMSyb3fzYesqujTSrw0XzCEkNUy0fidd0QBjxf4dnGmDx8Qw758oTTPl2t1HRnGLpEB0MpHYo18KUJwWrWti1vyAoppHayWFi7AND3n+GQwdwWQ2eZKo+t4JcDxezvgbNFvPmu6YMJS9qyhcpflvk7bYRnkwBOFLW2tfz+dYVoVL/f7EN0YwYQ6AEGAOUUyliXmBkkbpMmPb/AjHBea5yuAbf4iuQjtm0qDvzYRJFMCLCk/sBe6TG2hLUX9/zGUQSCeGyoF7QS0hRKad1MX4hLtP/K+OLNYVz+QQPr8JyNvCWFuyYK5xgogLEOyXMu7luWSsWi5w7mJLHFqAYDcqh7WOa5n4Rh+IL98DDlH7Eva+zSMAOTVmVqgFNQlSbBfBJPMd4fruKbVkXVX6/ByDT4ONemwfqW4kgitenCQXfm/FB5mQAvlbhLzU+XScRyQ93fcj/MhJc9VEbsGoXxAMh8eUt/1+n11fl9aV82H+42ae8D32oXnAOf5MvxougWebAJpIanDUEAVhWy4yrIhuiLIEGy8HM3DkDrdaWkZ8AY9QK21FsKZt0gXIgIATLcgkVhs10RDkx1QxX0M/Kd//OIds50dkdxsmml3nAPH+x1vb+/2w223rNeaJuQ5WxS7Hadtpe5luNBlyY9k69nrdxGd1mHXR4abAeRInz+szmcIBv+9WPVNc1ANy5703Em3NbrtDFQFw4Nnhd+2QgDq/XZqCHRYrEj0tdftu2ruEIXi9JwoFjPEZW6CBGpbrQlmM8+PvRh3AAdhALC1hu65ZZq/7/vmcRVO35R7zpwjf00wH5IsF24ZPFCvBgjYIjfOheZSRqeg1drUMr0agZiNGjSHuMy9kysKF4U0mwHuikw3AVcp4+WqbewHOlJrAVAFvv2mQGwLb1B3SQgYJKhTo+6YBWOP4GdllKM9S1xjwchmeJjIlcgLPNTAqxGoomRKR9Kxkpu1yOdBKxLfRQRt26JSAUNyWxfstw3H0a2igBeUTV5s43LBXjFGdHkZQNHgzUvsZwAlC6RH24Ax0bxyA0R8GzpCyIUbkiAp/rbX5hadbd88cLzhtm24+aadDvFamQJ0v97X4b5veNmNB6knWepR7cBddKUsDxVAvqIawqCrP+vCHefE/Zy4n1ZZYsBz0LG2XOuQLtj2jtvefcee85lY34rzPMyC1+wZjN0LMzYE+GHoAHMhEXxFdotQQD0zkwCNlugCyYT4emi0cZ4DCksOOqeFq5yntfl+KL7eB8Y88bev/w9O/Ee83Qe+fj0wVXB7YQjBdaHJSkv6yE2ST0m5CoEMKwBOvsZNHBNLKqTSVsYF/zb/+n5B5HJBwNef3Ux53Zlkl+Y3IfvLIC7KGlCQZFolUqgVPEpmU8x7dRipaaSGGVz0Sd/t6kUgxfPY6h7niXEe1uY08zR91KoT9/cD4xwYx+m7LrzkxpyYYeqUIoSTWU6YSZ6lqbMHWAYnY4l43iOhLjA1zMfIMVCCWazfYwWUml8YgPIg8QwSzTIY3cu0COA7lWwa2zLUT8B20aZn1ap8bmfEpuUiCzsPBSAKqIk5RoCgFBQpPFfLWQsnZyG2tZ+XvxRigoXgPL4t11bV+umheNQ3ffYoVVRjnuOxYkXZamJMCRlQDD59PDEWleGU28UAlfH7EY+a4AkJjLkOg41UhIoCOCl4gZWHSDk9ALdydvycVWgoGIMUcGaho3U9ku5RwC8TLJoARwlPgBoQbL1BHFixxlxVFNjhp+QZa0XrV1hHsAo9c001hVlfZkaspOPaV4dwlYhXsdCgzQRvxQLOl7TgNwKJJHK09nBXZfJEBoI/rhf1dSV9tZpHj0sMkqoVbB5ugaqZrowsbGCoMLWelrgr2FCdXiLMA0ekuGmL7OS8c2kmja4AguBJOX58ZskYt2Rn1mvGkNrziO+uA2Sqpy+wnYn/uH/B18M2FhzD3I9x24/ovxxhhMC3fR8AnWDDSca8GcKlmGcXTELPxm8d3yWNQRXGlfnGQigzRQ0FQs1bruO68CgSut2nfOsL3MCLt3ABQrbF0xI/ZtFFLQ2nIKI5OEFL9nvV/JJp5AIBoBPzHJj3E1Mnzvs7xhxlkan9Pif0nB6oar7zflgcwtgsLmKqLuMaQcBQtCmeabKM0cXCRO2pusieH5VRxsX+y4zxpoZTt9g31s9qzRPLSaRcoLsO8Lgo5eK1o4XwBzSexRaGTqurR8bXu6VGwHRmKAQDWnb8JMWY6TxBoMyYqRAuhCVBlsJ+OV1KdRVcBIjkeDNwnekMAlD6LZOWyu9+P2b27Q402U5MbJijnJFLaTh+11xLwWAzniC0d79OmM7WuDJCMROOgVaCXunq17SjP+yRz0i6sIM8ColOWBw7BGnGXtYjd9vVNYWSERYWu8E54wmX9cu1qfHPBbOWoGGVQtoOSKiowkFRPNvMe/njdLFNIFE+iUIIYucXIGbgyq5VmdAxI9apkz9OYIpiTs9PJa5IFGQ158D7+xve7geO427egRg8p1vvSyPoj3c7uGmEVunjNOXylIEx7Nrb1rFtOwSK7iBi2zer0SlAu7nSt+1htQmAJhvIdipcZRUK0kBsWlGzhp8OPJiG5ni/4+39wO224fbaPT+VWf+YqqDVtQeCmdWCmURUgFG45CdUR8inMAYUIBU8UpLvBQ6Bb8iZw+tnWlqeMSfa+4H25c0rJezYesfX9wO/fD1wjIG/f73jy30YcJId0jZP2kleyzVWgUz+f10zVxnG98AVhRdVt/kawpHjpereiOcqwNPju+WBioOwT8pQFe22bhXNHED2f7WV1HY5BBFwDCT3l4Q0FJ5BTO4CYyKyaLd+9gajvhEQVczrJEYRRj4jUoMyc7laWvv7gTEGvn75gvO4RyZsALGrAKqWuVcst4octhNlHxsIoKZWYpO02nT7Lvm0LiZNamgozwkUIvXvjfXzCRgEr+DOCO7A0hj3NFcDHnhd8hSJWKBkgA76+B3A2C62kQsY4jIpWYVIwxzDAaYHM3bKnwEVLz4ND8B0Oss+FzDpgaMCOvy4ZZpSzECSxUfbL5b5nFpuAVDU3JwhGJCkBjoD8KZiJPE5x86uadI9ir0mEm25myaUDgLgsKcu2mdFgNLEbP6SWahjvfmsh8iXbE2ivy7f47lSeOrU1OQvLP2HOMzPknzDwVM4kxw8RUWBAD4uRDkeqsvoiBLZuPC3mkMxjlmZgTRZjwRPsSPVIZRlybfA8qYNULpsHUwZYaOC9olpOZBKB0nffbNSJq1bPbYIji7CKXKkiQG0KZ7dWgSJ4XzruthmhSFYgBPvO+bAl69f8fX9HW/3d4sPLVDS1jNddwmiaKnmvc45oefAkGlx+91m4u7rets+ezJMYHc+s3crmdJ7w82LzG/7DWNQZPl63zbsuxfMnUeUfuK6RrO+qQKnW54sqaQVSBbPUXU/D/z85We8zhf86adXALYDuXMzQbekn7bO3bFWEmjm2FVQlPQiAt8dPkP2ZbD2o+eBhgbx/osaTQ0d5nacBpzux8Ax3LI0TVl+fZnY9h1f3+74T1/ecZwn/vbzV/z89o6+7dhvn7D1Dec4YxdryhaNyiJVduWUF5h4AUKpxOXzJJ/PcJrH63gm78fwnuugrMd3zQPFSVY4A5HrLooPryp/1f1Ll99DYbuSRp4vuLb4jf1+cqTZdv0u++J30xoil2g6dufQahMBUSnWYhcP6R4p1OxzIbj6/Jru0ABoHwz08vUzS0IszMd8U+xQhZwhggk06K4LQFWEt3CoNMH1pXfZb81FFp9TW8rAcz5wYb3ivQ+C04d75TBWi43PqaL0vYKPfO68kcYcPR9aB6APxCiXZxLQ4sW7xBQDAdiuE/sgci8dWYAOCarAcfYwiU2X3ypi4zjEO36g4zpuD0sjJzlxDsexjvEzOitjep1YEs9lMFXX9/x+VS6D7ShX7GolLWT4wDs0+lWE9NJj8nG7b5KCYqGp+HANvte1kScHQWBaihVgioWroGRf4rk1xoR8aaq62wuhVDGoGw1QtHgOU2rKsyrCAjWmeu3CiTHMRDsYT9bKyhl270iXoubuGm49Z8FgXhuB5ZVvFV6TI7lM1eXQ8sqxDYu7j+FqHHikmUeCvN6QzLZhKHA/J9pUQDq2qXi7nziOE0cFjAQxl1v+/3UYyeb6C9d2rJ8LpiB/E3lYW9fjdwZQRgFPESAQ+4Gr2245J0w6XBSZwbSat4OwCjdIN8xKdgqkuw6IwNjcDlz6V76h1YnXrH5vift3D4RmiYAgOAikdfRtB6RB+g4ZaQ0BJMzr4QIUCzIcQzG6bRFufl5dLgqJwpkiuY02NDPJVg2UWY86pATIk+MWi0YQXE1oVzRvFEuWM+OIBfKB3DzrOIrVhowR0EgIE7Pj65xCnXwpLDksB9HEC25NWF08W9Q6ZuSQipkMbSfvQVfHLItHWkPvpSSDF8BkJwhqVtDgAcSC2E3DHDOKfJ9qpRrMuEhXcKGzAixR6CyquxfAyTGn08UyKzuz5AYGPiqtRi4VWtAYKGFd+1NYNEUL66wqLYPVeunbv72vsdV6yWb9Ax2kbQpvpxuuAStfMmKL/qY5fxVQioOZalPnEdbfaTXlrBSG1zljcdcnPJSJCo2F5O4uy0QOtxgp5Z23499dhK3wHtMTN3DLvisl4Z6fSVOUNlH8GLlO0ppJIWY8kbUpESOUn9mHcQ6cx+k1NBPlhXI0ho+/VTgIPUR5lnq4gFl0jjkjRtTS1wD9/bC8RK1h3ixL+XlOdPHivGouNNUOVZuLdipEPMs43v2GZuFpzYPIBWARujknDn+Gr+8H3o8T7/cTv3x9x3EOvN8PK3UyBo7zRO8C1VvEbG29+Rb9abFG4jFShZsHwLzo4ieoAAAgAElEQVTASn6nboGKeXHXPKsuhBWQingwfuOrlnmjQdxl2RRo6OgTuB8n/vHzV6eHX6AwYHh4kfa388SpcJd0z9qvweOk9EGDBq7ronp8ruEzV88Jv+seTzuGFdTWmKv1uK6p3zq+rwVKM/jrm2MnlAIQWBf9clIwdDw9gxaL0DOWiWh1q0Nt0y6N/l7jt64ACpLusaxblgDOKo53AzCtQ1q3JG5xhnE5Er/AkoBNRVSxfjpE0b7djjmimtdNSmtUBqDXZ/jIRxz2Jq2E5mkZtMH9bzEttNoAzih9wTNlAYEAd84ly03w4YOdI8evJpl77bv/rnYfTGDw/Nqxy1jReRGCIMC20wPSN/4ACon12TVnOHTtyHKfy0vd8gQyvQqgigCyB3S3kHiF+hyLfJgKEu35Z5lLCYlp54Zl7aEJjWb4ZXXRBO0LQaM/NpKGDLz/iADKVmTOt4NJBbQmtFSthk8EhVxl2wN4Wm5l78FrCGrz+2vf5nWO/NaF0134UL28tmdXpDVLg9BDGaDgLfTx8ZxXRas81wdn1zGYtNSENYm/yBKmQUUz2DoAwBMUe/kcKC1IPodqDq3jHOZO7FaDU9XOtxgtYO8GTJtMnM13K6rdzJQLi+WxuK/ctWt98Sgxt3xNBxX3c+B+Drwfh5cQY3yRxjMDubOP2b2n+r2BiIlKJdPeFQz65nclJEEQG2ZylIssc4IRfz66BK1NcSBlPF+UMVrAuA98vZ+YY+I4D3fbNqjv2D40vSzGG3PX4wPVXAwdD3RxpXFgob2igtu9xDYXkJ6y2Y8D07/l+K4AanlgF9zLw3CSqQ19CBlKG8HKPxiEAGyPIChvWybnwoh+7Rq2y5AiKKJm1ByrWZZGEEv5r5jDdizMQssXkobCYgKOY2LbeghgxsNIa5EHii5kUaBHQmg3rMsiGyNA/TxPz6Bbns8FpfXHqD9+jfWqIfC5uyGuddAhnla4Ztal4hGWlpg6j81hX9zUTFADOEBy68iUKmA0gU9zbWd6z4TPzxvVnSVkGgTCmQ9mdRIUun2CYwyIWdtzWE28YIpIE34E/ie5hvYMbyMCIgEvQNqxbZuXvWHSuRR8z2IDml9P0JsATdwiQUpxx04r84FyTeCwHOd1LZR35e+PGt4f+1CguFeUwshI0aw6bT0918Bl4TzwEHn4i2uG5iK9nJUuBg1FKEF3ag5ct5WjrAprBSHij+npC7hvXQXznMAEbtuOl5cXjPuJc56gSw2eGqXyC3VQZy44swRMb7MFtZetDK5UwkF4l4bb7YbXMXHMO+Qwz4BZzTJqUQDf6CALf2sMhIYrkTC1tPu9PSOIBchLM5GuAM3yTNg7z2lgSyamAyh1vhrKuFqMk+oM6yxg1RC42eUctjHoy/2O+3Hg/T7w5eubW51GABXKHrM+Tk/gbHVTm8sNQAM4CWhN5ojKwhOzDmeCKFqfF0NGIVFek3FDCUhs9F2Rp8Ghd6B1m5Mx3PJvVlkuBVIa+b+178rzpMWJEygrwf9TxyrDpsc/CeBGDfK15zeQX/mtHr8zgLqY3FbJkx+p1YlndJ35HQX1cgHbi3efsPkEnRJIaJYSSF6yam/Msus9D6a5Aqjsq73n/cz87Gb+QbeJhgEtmYvinIpj2CKvE5eo34j2OAdOTPSthyWKuzsAYDZ3/zmxRv6iwmgS3/gycPceBV64YZ7NGefKh2ZyjJtCY+tzDIJtzZd015k5WlwrcNBysR4BAmkZiWDzQI3RGV+zsTbr1VjogVZEhe34GU3RtFncg5admGoMdMJBA8TLV4D6crD2K70VUkphVWdtAmdJkkfAdLqbdglRV3V3bW05rVMcy75tuO03yzPlW7OHGkOubuRl/XCjw3ArX6FRVbX4DH9GSxPh5XPAdtZ8WLlu5uN6YckXgQPAx3X6Rz+U+dr83SWXWQUpUMmLYGMsqun29b9ZOCgOuXwQCxo2y2NLkLBUcXA6cXBsOIb3kQDNAZ6rwoPymXyV1kTnV+bC3sJKPIbxiteXF+iYeMM75ulzDoEFqMtCXxPGmw493fq8e26xBE7qqVjsmTdPHWDP3FvH55dPEOm4T7rauJst2xBxV7s/g7kMgd3XiSgsAXFTbODGZMlqCGL1N5s0yz4uisgyDmAeExiKE6dt7OA8i+dn6rYl/7hb5QNailUV729vOM4TY5y43+8Yqng/7jjOE/dj4JevdysW7DTCzTexK/y0e7b2gq1v4E46+O/k3+Y6pfxw4OQaeVZZQDCuKEeGzAc1SgxWKHsTEb8VG17EdlQOCLRvNuLbBLYbdAzMNjH8OuZVHNJQuSt51WRoxFDnYeRBNId8bM1+5sWi0kkPgMLH0YFazV+3BjL+82jtu+aB+s+7RIIAPj5Rnmh433ZUyxP/jrae3HIFX/65/g5d2rxar6oiWI2qXAJXEURhdnUV8D7TNd4GLTteVleS+P0uxr6406Nl4QMzZ/1f4ZadZ0/g65YWlmU8P5jHCqaKFeXhNOFzpEXLFMIC4qSOZf2/dONy8wRO+Q2fRJdv86guOdQ5RwFKKOCJD/XEElvHj8Aoc8LUcg7eszpdH4ypRN+LpauewI6Wvx8trqS/X1mB/5lr79/84ZOW85rgE0ARqpxSBy+0oF4VRgJdXlze8n39vq7faOo63tfhv5y7sseVaVH2PsiWJFj05sW/u5e7Uqoaz4nCFIQEehZSsJ54JT0qJuI7T/e5WRJLX8wj7xij8tAGSlLL1rB72pS9dy8hkrtmb/uGvW9e3sW+38QzljfB3je/1gqbL/Ph95ggj8sfTUl2IOFehqGl/iZ5uJ+fOirlRa7S6tKntyWG/zrRv3JUY0PlnfZdPNTS3iqD7O90x/n8ScbtajlvEYyLWAg0l/SnVGaxXPPgnVr6k8Ap/r6sNY3O/Pbxz8CH372UCxOwVYHOT1dGQITPQrRSfWN2QlwTZsAilCtRLk1fa+gVomU8Tl2MeZ73W8SZAPP6WDg3nBm2mQwjCUPdJ4wQiAoreDjCP83AVC4S9k0X2gM8Nf55AoDlLdEGbe57l2kBjNRoYojV6wDBipcqsgxLaZyWOQCRwbxaM1DO13xAt/hJzJOIuEtI4LVQoZgYLBeu6YbKcdZcNA+LWJeFwPVkwZ2KMRtYilymjWZvFtcgc2K0CZkrMCI+DqCs1Hg8IZ+M0ICaPK/tFtf7ja+yaURST42iomHNpLSNYON0IQIMHDdLwL7f8HJ7QfMCnOJaZMTuD/U4M88SzrFUjWdSEFgr0IDW5kKj2W93wc3cyViVBEHmwYp1UoTBt8YQ/JEOhVry2zEw50ieIrAcZIvllmBhWP0tASAtrND2O2K8CMRUeuS7YzJCdAS/yDlNIZtlMbJdOACqtJ0WdEQf6jvzRUVoO0GiwGOIBV06Xl9e0aRhngPv7SsmJJJwAkVOFl5Ml9M5BoAJ7eaq681SO5glCpbFXRStGx+5bTv+/Kef8Poy8H5MfPnyjnNOyBgYvp7C8SW2/DexOMsugpe+49Y7Pt1u+Mvnz9h6x59eX/F6u1lagqjBaS48QzyWhuC2bbhtVtvttlsw93bbsd02TJ24H3fMOUIGqCq29hoxTCerIrQN2kwmgNawvmNrGyADY3ZLQjmNrqCCcSpOX5utWQA811cTRjcImloVCpBnl3Vnm4noeUg+zmL1hGRUfh+WrEiWABOCQUvceU7F6bzsNPLAUH8HcLIaBEirxt6aJqhtdc2EbC3y0S+2EJOU9Ut4wRM+w3AQoRANzXq1mjMxD2V6kUB57W8c36eUS3bb/nbBlcK4oGRQoFGAUe/zs4UMnq3Jw3Ov1hYFC2mWVgIxP2whLb0uquUyERZ3lNdUy1B9xtV9aYS5CB12zz9zL0tObEYLqO+4GWNgNDNJC6xAp3J3gzYHjxSaNJXn42jeKCGcMzKmEviYjoKTu+bq54c2LQmeiBNUI+5GnOnxXLYY/xcaSCzgwILtQzJUysE2lADRmDDrtWaQPxZGk4InGZAE06E1ar02QUPOLxChE+soqQYon07nk4Js0fhSI6N2yCzt3IG3bZttOJDKBFsBl+qg1f38TDKqOZYsBqJojjf1ouE5JYSgLVpyPLks2nZcXxfbjwaiFMV9OYOHAE7fvu6qQqae0EY9P/SV7oIngBAKhc6uYCT7gQBCV0FSeSGCsamS2yWYcgxtFK5wdy5c0Jcmih7TYDmRBMDbtmVwdtXA5NKFoHkJN4oIMD0ImZaMxVoPePLIjk8vr9h3xev+BXvrEEi4phQzckORQxLM9ibYtw23vuHT7QV//vQZt23Dv/vTZ3x+uaF3S/goksrvnBPzOAFVvL7c8HK7obeOl9uLxSDuHf22WX6qt1+iWPnpxcm7hw6cYwDHiaFmxZrSMGq+qtZ9bBq2XdDmxHEg3FnqcbGKzDXHwRRXlCG2K9uWvQQAXrQckaQtpXLV06jwsK7zsJAGVLYUKXSmJmCakvFQdExHqoiiZQSrB2IcctMWwJ13cb48lpqJvhVAlXT23Eoll3PCi8PnvijqfoNvwU/fA0CtkTh1EMKc6OfZwyEkaHGGWDvB7+tOtcsRsDZO9XaTOy0TQfuhFKqJEy/N+mkk5mcAKiFaNhWAscTYBrBQ6+QC+K7XQ6G1llS5Z90VZdtdm1m4pqktuePhAmCXW2mClshsjRQG5TxUEBTtamDNIgtAxKAEgTPPu44rn5OA7mEowuZMBo0ybxpyJMDLnLH6mYYhAXClgfSbx5wXfhRApfaBzxOdXKlQ82LKMz6Zn66XNhB00tpmGmg3ywRzeU118/1yk3LPUrKGo76eVtZTnSOOp5R4aI4vmec1qBQodGi/96oU/ECHNAFGceNyzXuKABXNVA9uJaCVbuo18kmWcavcph4aA0l+mBPycO6VZ1EJgq7yacE8ZabqRHMjAxAKrGzAp0+v2PcdX375xRXOjJlpZTHbM3EVJxBkPTbqaHDwyfIxXCQBpJqgA9i2jn3fIGPgmBNtDE/Z4S41ampqQKSxqInArTbuBoeiqaLphMwZ8Zb2jPAcTmK1LkX8lW10L63y6eUVYx+43+94e/ddgp5GwmLdJhomtibA5juPu8fGiq3l4xzY3y0G6utX4F2nxYhyAJOxPeEq1zCDlXq0zLdUGVj4Y/KIgNe4UiIBmgEn5iKUOJ+xuLXFvLEsnyOytLDWyJrioEWVtiHEfFZKfwJ3nh5krZQ9IY9jQWnwuxhH4oWKGn/l+F0BVHZpFqbsw11RNs9v+YIjDgIDwD1UrdkOB9e+lQlO4gQ7gpZY+Fac2S3gKfuRQW5AJrKkBCkAoWRI56KfOm1bf5Fu6sJd1TQLuB88zvBgaLMnOJeJ9ZOLJ4gNrpmgudCamQWbuz/UgwhHBqJbJlsJE6WibOt1ocffAkSBmgARXzL9lYW71YZ1k3w807qkLmlca/U1HRYzzo/4WPruDdEEdNe5MsxJwUQaElAXMk3Oiwh7ELXlkBoPYCA6JAXkay4wgUSpC9MEea8r2ymLv3YYGiqaMQ6Hsmp/McDSJtra6BvQt80tT6apqe94qozNbsd5mbFGlhD4MFnMzP1TABOHYPICCjgghOmYJSFgsNBkOATFcwL9Eif9hz8Err1zbAuQlIl5mstjnorZFNJTGA8nIoW621yS/gsYqmIxFZvprpQEwjyMPfnYV03DFYngYwLb5aTGi8xSqwlUXOiHRGwGBkjDjQW/pePTpxeoAj///A936VhmaqM5c4VdATtBk0LC5XdydxZaWFWtS67kqPHDrXe03vBy2/H55QX388T9HMEf0KSMnf2nnnsIMEXA3EX+EkXHRFOFTEFTu0dn1v+tA1DcesPeBK17XFQH9q1Z1nHZ8PrpBkDwyy8/Q4eVNjmCv0y0aYHeL1vHvjWI3GJLtHZzGxzHiS9f3nCeJ/7fBvx9nth6N8E/feC0BfIkfq6+GC7U4DaaTIvGOQtJcDqbWgwJAF3NjL1054gBX1W3bpnFaUzBOZu7TxuY19yy3nuPxGky3IfBPY2eXDRaEDqVQaGNNncWCjJEoSSfpjWNfc3HXtcGOTOHQylT6nkBDHjNFez9+vG7W6DSVkR08C1HueahPVwsIHb+M/SoIRjXBmyQC5gpAGFtIJbo8mM1h8ZvupD3QztkmmufP3gGF3ABaGLoCgGVy1Oku1laNRLWqQKxjT+u0WsTD21w9Fe4UJ8uBbXUb3JVl+fJltRBSaCUOrwPE1DvW2Nv6qml58GMc9EEhlh+8yvj0tKRp0e2tQbOr/26UqBe38uQSP1lmUsz95vgrjNRZ2MFcnGOyuNjLFLmUYGofa/k8fRZrkvYb8l7fPPy/oMcUj48X9vPVnvdjlA42OOUre2UBW9CRfO6h3tem/GGH9r3lftk3h46srIfu84VNMbQWOzK2vb18oUTF/piHCBdUKkw+j/SZmlfSh/WQOgL79EyzmXBr227Vd6TlXJrYCiAkLKSH+e19qW55YSftXwGDKw2fxePudLezCMAYNw29CbYNk9T0B4m2YEw3wsHLnNcjO/PDyFAWHlWlVSaoxBMNQGInZUu18ejyvWUtsnkYiVU/vswsk86vvC2J/yoym9lzFTl6Y89/dc4vs8uvJTkEevhP9ST/J3bXC2PCJCatjVlE0arSUD0CyEJVy8ZFy01hc+kQM2g31iMSgLLQHMZcMuR/0YEPxUibiEANb3S79iV43lRGiuFmwYbtaQubKiSeFhMiO+JmAkIhG1IxN00D+wTMa1L4HEGUsfV+koXkXfFns1zWlV0XoWsqlociLelUAvIFIF6vAPnwn5bxfV1WVfrZDwrtRHN/ErnNG30nGZ1Y7A238ekZTDN1Hwxm7mN2bpJ4QrACLYsNsBaiEfQ0u+KE4GwBLK9oEnSq/cqE8uBBI19v+Hz58+e/6kHjeWRNJCHm34qICRdlGehpgwFJv2BTcDt2w1YdkvxTinEOKeaMp/MlULxBzoq3WSMZmrGcvlHC68U2laUbeKTlsBsT+EWbGluYbdJtI0qyARvmrNOuqKCFfMBxDwzRKCmZvFFlPXsGG8yFVMsUk5aWi7s5pk9ets33F5vkKNB3++2pV5KX9hDBxWArQUdtplF1NKRHOfAvZ1Q7dh7A7qnEGhGp+d5t00zx90zSVuNzOYWjmm38rQnVsSY6QnGmDhw4K13fPn6FUfvaOPA2DdsrePltqM1wRgWLN7EXHciYvwdlgKlNUHTCW2KIRYcPT3n03EcULVQeKtb17F1we3F4py4KYDxRAoYYBNg3Do+3TaLozreMY53COB9QboOhe5Dd3MFf9RU4iQGYkVWoGiUJb5twuacpWSUfMqjHcY0Pjs8xc45FfcxcQ61wHHKN2aoV2B6QlAmPg0gKvDEo56R/TgsAJ2FiFtDlx556zRWDsIzEqzzET2Vj2vAeT5/lg7LRhTXekwpz2bWrfyV4/cHUFomXhGLcX1gvbxgoGaOZRCqyTqEPw/RhYEHkwuwADxDu1eBWaE2rUbquyxEmAm3WjkcgM26k8Xuz7mjTDMXIHch8LXOqV4+EcxpPpH/b6uzWmOEpm0n5EE3HwSIhJGA+k45VZS5yEVJIRGJETU1zzUGC64p54NGL/1zCWlM7WMmAIgZrYuCGX0BCJzxzBRCY2QMhsuEJWHlNWYnmE+AJzNTW9t1mze14xRSy4yU52PvKL/4NIrsU9BCUR5qf0RSyyZc2bYNry+vVhKo8VrSMme+IDZeGqNZwWneP17ccer5hTI4HQmK4pkKeEpNJMYpoP4FQP5IB2Mzgt1UkEqIGWNkR4y9BkXEeoq4xYtSKC7oRPAYaqC5DuILp23O3gzKKvnuCoCKvmVZg7iHlS4qrnZNmrGdqaZ89q1h2zcoLAkvd0LHGhbvZxmHcPNMQHSiq5VMObvxpjGtNErTia4dU9WydM+Jc5wYk/fJeE9yQMZAWcJM+37OiVMVx3Hg/f0No3fsGMDYsG8bIB74PaflgWoN4sHxQyxJaEfDOTo6POHxaUrzcbzbzmp/ahGkMtq22FAQ8YuqGKV8iDqvfb1tOMfELz/f8PXLZnQyXehL5rRqDWmdCl504dOSK54KNcF28HsHcTornyQ/Zc4ngc6GqWJ1+sa0nXf+PuCFsYMHmV9ujuk559QjXTRSTzDgTblDz+fbdrQ777mY0ao8qPgppMwTJkM6rju8q+WSWKGqNRHWEnijyLFfOb6DBaoI1ytYWABRFVqVuSTKzCbFTLEoXPyD+1I7erQs5KA/DPSlvcVidrk22kBF0uXCbxAqcYrUxynAo9w/rVTsTvZXw2Bkg6kEBuLEreqZJLllH25xMEapattOM4cL29MCth77rs58qbH6wEc/6nwu8r4caa0DjTHxxPkp3SMpLtgHXMBMdlBn3fqdPwQAc6sia+Plrqsq/tamn9wmevuwxou1gWPAhyR4Yu2u3pmVuQwU14UzKCasrAzzIYdlKASX3ss6F3YqAye6MewY0JVpExxyjToujFN/RABVLZH18Z7RMAFTxi8l/9LSQIKTFA0c4Qpc4z7eUGxCCRlaeBWJX0tsjIjHa2qZRo35I50V/J7P1wSt90iIKyJ4fX3FX//6V7y/v2McA+e51g+FP7+R5TpC6v1R9dJUQzHEAQoUTSYEI3IoLeQH8um0ugnBJhgb6hb+3m1HXreNGK23ZbekIi3Tq07AdT4dX1r9ucYEsUy1wNQHPsd058VACsq9JABlpKqYDBQUvNw2fHq5GVj0jOvNH1Cg7u0A4IkfYu5iDdbPhVfmwl5X76JAwhU9dQXULE/kiYjnKvzW6abRai10M/J26nPMDS8XB+JVdlzl+gdHtvdk09jlPL5XGf/k1qCcf/Bu/cbxXUu51EDpqzZvst3hQaOwWAENFzLrBikyWNpb8fcyzJo3CK2sDC5zHemYgIrFPFPQLZJ8BVZm7nbjvfRcWE6owutrGvJAvRT+K1cWr6uRAhb+t30f1hVJU7Yh+uoWda1FzLQ9PRhKXRNqU6w0Abz0ifepCaUhPOa/jDu7o8vajA9jTs9A7vWHvEQChEb25SERunosvpxHKd/TymT9d6YkuXU2X76zblaK4vWe/sGZ/fKbZwm3ulTTqoePM7LFRy2uBzBX2il9SJ5Wn1dCkCYNJPPlfL3sN2zbjtu+ezkNjpG3PgaoNdEepP5ZFe4eTcCqwWyL0rAAOOvbxIQOF5jibsPIjZbgSzGd+c4QcJUOElj9OIeqeg6omsagKE5IOl0EWmX0qmU3aAKnqxIGCjemAOE/AgVVz8BvfKLSlLU1k5+oKUaxXbwKzpH75CLzds074n3q3XfAtWYgRAR/+ctf8fLyil9++YK3r+94e39HjgQTZhqAZPypqwlOp5aZepyKA8Zvj206aBo4TxuTKc3dTbmL0ejTciM1+tF9AqyijqcxeLnhZeu47Tv2224xRvsG2ewZB2m5dWBzntJsg4SZRSaAjlNPt4qRtwG3ncntHKiKYN+6ZyXP6gPTNz9VMLVtBkbPMfD+rjhPxZ//9AKdP+E4Dvz88xfMOdGbgUkoME54qSlFa7PwECyBSXTLc00HTRphxWezFI3Vij9t78CYiuO0qI0BK9FifHkauPRzpCm2tqF7iSnj+TPuA8eWUDD9e7IlrOuHHoXLonvgsgtdXxfpcqlGm5HfsazN2nLtwzMX4EfHd3DhXR/ZhTQHPL5NKPvNbDgVgfwqGJPfC0VrviBUO68AFRfQEhpUUOXT2zPu4ddBtFw+JZPin1oIY0Hs6+WPAxOPRDTNMUihHWZ6Z+rcXFcBUYAWB4kRH/Nwa+VTL9/TXZe/6/NJrEN6/b7g4JoXp2pRlVrSKlDekc8b55eLCbiLzhGMb8kSXDUY16SeDb+3EPdZwMT17DqxhWYjMJVac2vL+IRl029ic2UisG5Tv67/6EuZmYf+lGeI7eUr3F2eu4Lqeh8K+GoN/VGOEEQhsz3C6dmi50Q9KHTXz/wmrVX5N2D5zHi/6EiZiJz4q6uvCpkALg6uVsoveefk2bJcLfMign3fAPmEMQZa7/XUkJoBHYsV7uG5NXmTbfzjXj1rTJsiad5vwbZIb7g8p/OPzKPGeK4WCgsTyj6uB40bUNQamOJfjA9z5YfjLOK7FZsrekxu6YmXW15jsVINqtMzpVtw/r5vUJ3ontwu3UpsL2VY9D4XpJ/nQODZXPKPRZkCaBBQZYB/5nzirro6VlTIDKOlBarOadxQsa6P6PuVDi4y+KODTXobH5391PL0DQzpn1H8ft9M5IpEmC6lw2znjCLORf4tZfGaYE1msZi/q/3QD1qNFouJkAiNFJadHBxwTiaMeKRM1UowRdr73wvPZB+aRELHKWI7P9xtxtwi27Z5UGeOUegN7CvcDC8SxWnrmGR8QAwy0sXC2C2g+0Jm3hNRT3lAxsMyX4w/EmZT9idWa7/JSmyWGM8elJXJjVnZZ1uMjKXyZ1SFTAMKTA3AOc/+p78emmbmDPa3JKLnOZzXBGIAjQGc+9Y8dsOfOdm8zUmAKLPHLICtMvLKeCmMUqZV83fiJdWIovHaVYjGmHG/t479dsPNrVArwKv8sogMYR+uQtrBJZKudRkUBYWjPX7WB1NP1ArMaN9SHDhAD0G1IuGq+T0DCn/kg4C7sGT7nr8VQM6xiSvEmD4haWs+hzEngLZSwJdtG/sw0c30E5q7vCKPmTJdp88pHLdIj3VIsI36FPFm50Qskypaa6G9j2lFbfu+edCvQHrH/bgl2OfiEFhqAgAkWqP/XG/nZNyT4JwCGcBxnpizWS21RmbUjW/qLIYx8m7E9vdgxfFAErTI38V5UZRd6e7FwMAxFFvvuG3dM2QzY3ZD33yjT886nAny1Hm5B1JP5kpyK7+PFZpAOjfZIPrfu3kVbtuGl22DqOJ4ueH0PFfjHDar2hYFBcg0LLGUkxgXcJFAq/ACP9ksq8avxgDOCagKJhpULH2BZY2YaakAACAASURBVBafOPw1IFBpUHpJLvJToBDfDBFrwoEUcYCMkR4gkC5q+Awi9YxyISBlnZHzI39ZQmkkvVQP1q0g/rqifwO8XY7fv5jwzEGNbwMFP7umgKjL9s6H2KVogot4PV/qOJWfUwggiHE5qcJ3rQDq8hwgs0QsWpoD2b/JWKO4LwFUx7Z1M6vq8hRBXNFnakdayM5WlWX6dc0ln4nAznfXNIFuVjgz8oEAyEwt9L27W0/ghUWzTAWjj0h+Ep0zU7sId/IRoMJ21eSUcj3Hve3rHg1WU+rV/TQjHxFCAM0xcR4nDBC0gnCSuAwUZrFcYFhcQeSfMgHETOEKOkOeA6kKkJY5UxRwJs7QElSROSsSJJsb1Vwk+7bhdruhbx0+2agHNVAKhXi4nI0cNwf1Kxiv1ygJK4SPAF5CgTuebGwwCqtxc/1VASHDYpD8D3lcsSqw8ALOQguNI8gLNfWFrROU3XGVkhwkIAt70w3RpKGJ78rUaVZkv7ICCgFyJ19Aq7yDvWvMvKi6q0wCPPF9zFKKqjdsXkB332/GG5qHPNAy0QQNXoJkuFsHAFTDAg4IhjaMqWiiOE/b6TZbhh5IUxe8BUAJnM+lazOeuaz5dFVr8E3LW2TDQovQVAMIrQHSWXPSdihLa2h7lh8xnlZGUjQFuSpUxcZQqazuHi9lgM0RkI27IPjqttm6hwK3fffaf83y2IlCdQPhoT1+0oN3I+gw1iPW38i3UN+B4KfnYH4uy6iu0jCnFUG3gvcTxxzm1mPQPtb1HwYIB1LkS3TgwgEUZAYApDWb/WXB37rEaozUVZY/Ox4xxmqNKi07MK0S7duO75AH6rdcXPXB67sgzcCP5r/rXZ626Qh42WrNXsl6+W8y/gICKKQEGnEocRQAsLzqk4lEmY6BYVaoy4Tr5b6IRbAiz8X1SblYni8Xn2liigQpKYAT/MxI3qZWl0sScJqWkQs2GLevnyydp+DORPHdkQEE47YZOCsEMXMdu0g5gEzmSLN/ZH8OQGUCiVvFwx+v63PWo6z71HrKmGa0CJ8quHXGRWkBe3Wqqhbs7xVsARn0uvXNA3bLjtPyX4Wuy0ows9DyHbXiJTbwo/Un+aGeXs8XwfrbtQnhs3xwjx/kCBopkxzrSLmT7aOrSTcf0WCC2PXHXCuuqjycV8FDvfDaFan/kzcqlbH1prb+Zu48nhMyCbhtzl9eXvD50yccx4H7/Z78Cbl2AW770Pif/DNjhlZFxcZkxh+WZ6nGUiWPiwGMd+s3U83onJhez1JL39KVZ6BnDLP4QJtZjCYwx4A25/M+XpNroYzuVEBm5QECncP4A5WQwtMXPk+SEE/RoARsEsrkVIv5mnQnVhlBfl+aglAyXecUwQ/VGVOOiX0e3r8xFOf0nc+km4JkF0v+hVckcRK8U4YUC+qcmdh1MWQkpws6LUfIuqegaD2qNSqtcaWlC81/6/E7ZyKvPnTbDVJTwC8aXHng1I5TcFwZ9APTKQJ6aZMgjNnFS8B1Re72WzalIpmIuzwLiVp0monW1p0TcjKI6ZnGg9FG46bpvb6+4HbbcX+/4x2ImkyRJqH2iQu+SHxD5wi30KKZXCQht41G6QkGj5dn4ioMgakaW4wj3YLkjpfeo1pwaNMKRXO/5dQBkenbiw0stG5J5TIhn7gyTavGXJ5lqmUVV/W8Ja41DQ/+HueMQHBqNmOMCBgP4bJUjWCAdQov1ZKiyvwskJlWwFh/4iCqHIM5woJ5rGkh0tWcwIilZVpveH19xebWp33fvQ1NugGZvYQlw3FTlN3Jh12ZRgA515BJf4jepeWSDK61lf2m9ZWMMJu58jFJLP/DHKvA47zA3NKzWUbyOTCH1T9DzA1ppljSQ9DZQjMaN8BvNRwVnrsi1lQXpkeBW3WMlwWgqojO31c4xY544kgp9TJd8RC0EG5Gc+7K9VptfbNiumYFs9df/4u/YNs6/v73v+Nf/sO/hKsdMOvOmKctvabujhQomu32wsSpzmM847a6+80eaQQf3XxXah/TxtwtIArmMuLgqM3FaDihGF1wjg1Aw9w6JppZnXSgDYtvVihkAO/3N5ytYd86tumJL3VAmmUstx3GCTksy7nx0OHxpBxzEZhlS4fFYOnu69HHd07M0wK54XxZIOitA2rv3fmk8blpY9jsDmsh6Vj8ATCb5yxnuEkCJ1rxLWicCU1jB54KDs+r93ZMfD2GzxUs4B4NEHsfc0LP0yzWcMteWfukbwTfkihPZc9gMWWr69+vlWKpJ6OLlhkD+hj0ffVOcQyVss8xAOdo5VRFMfqN4/e3QEm6uRCkb0c10ZUrViZ8zX1Q2n1unntoDh8l+HsavPZRk+VU1WSAIDOS8pt9ADXWZ/3kBHfPGbIGk9aOrH1MdH59Ft5nIeNoIfUBE6iBlq781z+apUldO3ITvbp53RemunYVylTwcwp6EjTlu7kHEppoghNeFaBGczeYg79Zvy/gRxVxHlMR1OGRy3jwXlX75ZmynEG0kEDq2Vyw3/KERqrGfKUC8bQF/cH6VK1Z7IfGeIO7VeO/OtNUQpxG+duvIJuK8Z6dxv5X7ZDffyvj+cMeukKSanWqQPdXeRHXWQBeudBOuoSInzntEtdrrO+i5D/QFPFE3Dfvwi9Dk5+Yjw34006dELcgmwXKc6Y149m32w2fPr3i69evTrd00mn0tQovrjfxMVz+abBLAEWZQQpUhgU0wAtiR/BEecYilC/v8WQ6PR8TPF0LY2UUbfq9mljyTrWQhxkgKQEsXf/C8faFbtM8PW2MoDmPipxIFyWY0ySCxfrEtVZ5YfwdBELGa00ybs4eVoKYUglAAVSkvdykkukMLP7N8j5xN0PSTcg0AhypEp7ToWRbBrKaLCC92kiDVhaazc/xTWFAD+EepS3+ngaSAsAewNM/d3yfNAblGepDE3ma+ZJ8wra72rZWJy5mFivHo7UlF2CKZ8sqG+c7pVSNksg92kBhhiSMULv5Na+QQqgIdBvACoVZaCUZhCVm3zcAnyz24L3jPM9IIgcAfWuR9j+KYzIBHJIVj0HiXImbuwrNeuAGda6JGmNXKx3Hc9uj9Z5lHNS31Q63MNlD+rbbRktjMoMmgs05BKuoSxM0WokigLbOoc2VmdfNGhU5SsICZYU5z0ErH5zxl9QHPo+WhT01/um0N0ruJ8YqaZnnpAlvG6mp5KKWKC6dqxsxDo9I12JIpAleXm8W9xS774xDSKW3VZtIlFplIxlLmPmd7uaIdaaMZ6HYqbRcLLEVIKTWym38Xv+MmwUW4VuB3I90+E455NyYzEgLLpPjAkAW4NZF2Ji7iNbOzE4uSheRLrwi7uukVSslWJC4LH0M8ICLEIULx5IBvfXuLq6kI95zalqoyJ/P0+pIWuxOh0Dx+dMn7H3Dl5+/uAVu+AYTCjYJvpOgzJ5vaEObtvPufpzGI7rtTIOPp3UWNrYQ7DvQZ/daavbEG32m9oAQsdp1vflmGdaPRHPwE/sOo1ZeoxYwAR3DYKA223fXjNcIaO0nXvB4WCBinXK4JXg/5sTUs6wKAjwDbF2sDJ9AzEo2NfpuoQ8GmsaYFupRj5JhnAhOxVx9tNGR56GEQdCKz2zkYwJjKIYCx/A0BpOZpyxw3CaSqUqLdQgBx23enH9ZFn3fiYiJDsG27VZdwSssJL9LWhOxItLBb8m/HTxX9vJMablii6dpDNTHFrJc863H7w+grtA0aP7RdKfuOFadmfFVGJi5MvrlFlKQvd+EaeWpJVAg8J3MJghAaVVhF9UXTEWxVaKl/pMERSbFbhDuX8M5E/ht+46tb04sFsx4nmfs6Oi9Y9/SUhW7RHr39jOIPEHogu+X8AuRFoSZhj0Cg0diEhFsupYU8GWSADTxJsg4Cbi23iGtuwDxyuVNIGLomJaX64IQIPOVwBc7BZADqHMMnGP6DLorU4v26QCio0WQ4vRgTyv5MiNPFMc/57TEbATjQyz0KsCYrHBG/1c3MRtVWDDwftvRtw0vLy+43W42BpG+IIXmCqBKTY/QPH9Fk/JYENVpQanO1CTyfemypq4BmBGXx5wqaqG46uVEH+P/tLx+kEMLbQOgiS4AlIOpVj4vABQE3i6jSZdOey720ALoFM18ZRdsIDomIqVgORbAFpErBUDxnC7Nd+RO6GwJnDwuRfyCyeAeWNZxO69HnvHPnz5BX17xt3/5F7BqhNWUy51jNlhunVdbvyZYLXfdmIr7ybqdDer5ppow8aXzki7Y/TmHKk5mV4+1pb5ZCdg8ENxCbHwk1HJOUT7Q2tO7p7kkwJ2wpJ6qmGLusOH828rseLkdNVAn4kWHGyO9OOcatf/GPJFKDZe0SaYmaq5ANQBlm3H8RbmhiqkDc5pCHZbqZHlJrEv7DWHH8vx4OhgWgQcQdU7gPA1Ineor3AYKbsFAyfGzgKhYH3z+spmGCvK2bSHrqgEg6F41jApXeWD0I5fl8DF4evYKvnSRk//s8R3yQCHTzZeHjB8BVE7x1K13Pe2DwePnbF1DcFXwdEFKWDlVfkemRI0l3SyP/at3ZT/C5XRtOSbR2pEmwJyWxNAXW+tW2TvNukVYSTYUtjBBMPEkZntOlhC4Eiav/jUXhAh3ZxmgnEwipYQWCCBlDM93oVELmb4FlhoQXOtuLe6d/UK2CfgCdwuUj+eccKuUvXMLNhnImAm+MwWCeo6ra76nDDhPM3+Z1MvnmOEnYPM6fA90LJbsr7WGrW/Ytg2t9UcF4zr+uAaOY/m7Wjwj5kvW/ig7SKHmNAFdn30Bey5UGMOQJGcUttJ7AsYf7ohhk6Rz4DIeD5f8WnM+tt6eECiUlbsO7UODMZ9Y+c4iKLQ28KQP1y+4hIoFjYKQiocl+lyVhH3f8en1E45+YJwjY+0uHVckfRJMGn+wAU5XG++N5NOXQX10Jhf+JxI77cIwfDmPvZP49Kgex/khJyTuER0SxgeuV3GpUf3Krta5snfCLnG0ztWV85lVEYA63wnu1xEvShY7owngCKT5WIw9C/C3yMTLK7XCRfkKa5LzoBoxw/uNOdBmC8srKq2W6QyZXUDVtxwfhfQs2MD/C/G10JE8zP2z4/fNAwX3jKtaJWxkXqios0bCLykLOHBcBAopoR7PQcnSNooFStKUydf0jMrrgFcQkURoZm5Dz4xXMfMtsf4KQGy+1V2QZ7Rm7rNcrqH1OzF3Eby+vkD1hvfjDiu/MrG1LCzpDdl1jXaXBCK952InEcb4l2DzcJ16P0jk+ex1rZi2CshiRGbgIMqz08UIETTftTNk4BBqYTZvrZjp6fqwec/5Z/8H3YNKzV29DhcDIjUXvi/UMbid2LUZmZinA7Fh1pTjHDg8aP+cA8MzeZPMlmznmu+ksNz9lG8L3SBBSQRRto6+bfj0p894ud0iqH5hOChCg8BEjdHZ2GTCwXB9t8pcnWEyGYOu/cmx1mW8RczM3rvFqDE9hj2DxcJweztpvJCLreOPt6H9YY+gbdekMnxQwnKYagzAcSmiMsTkKPQaOe8ka2Ma/fmLv2sKFTacmadJO7m9PeQCUoFIhQmp2MUmCQ2aUyh02JkWuNz9fUA7wYQngnRLwk8//YT/5r/+93h/v+Nv//I3fPnyNXhsgKGyTAQIC9K0AfDiuXRdTZi3mfGaVfhbC6R/Ph8tSuYK7JGkshH4q5qLz88hX6AxtnoaauiBJz8JRNRLEXhyi9Y7tt7dqm3zIgTHSFVDMCHMOTdYq/AE1NTLqMLhQAqqmPMIOWWuc/GNQA4CFgtUjpHJBQd7Cge16q46yj+BTlb1YPwTA0JcHktzF17KO1vnPj6+kxzCqgoJRhWIfp/nibe3N5znwKfPn13BNeNAg89FVAArwCoOKaAHoOJRFb+PLU/ZRiAoB4MLgJb63cfH758Hih2uSJCfM/1xLItH1Cm58i7A6TqAcccP+HgMapWIHx6y/NwKo6uaOu9JZJv3SpDIeJgF79LkL2KMScTroAFjDgNqauBF4vx1XBxP5e/VgpBUHEGTHINMbtp8reaYFat4jCstUHXM0bhAM4Ecd+Xx/gGwJO+tqtDWwvqcFqgrgFoteAmgvDClm54rczWANMLNRkZIkMq+qmowvBqczim112PQN5y2jC8VqbDQbDL+mKmiqTGB6rbvsWiz9bhLneHLrzkHSmmpAP361YUEzadZW66A7JFZEbxScESsT6Wxy7g8Mr0f44gRs8W6UEVY5j7gus9YzPodmba9k83Fib5+Htq/gBL/CrREQp/R7trR1PSxkJgpjKbwMnuTCe2JKRZQjdbQvYP7tuHz58/oveMff//HYqRYnl1zLKngAlRIxOOavDO+WzhwI0GUXy1e0oWgT+JfAlsWUc+NGdmvapmSy/cIy1Vxw/MetLaU8WQMnCggw9eL0sKb9yirF6m8zxyBiKegDSllFXmgxPM6eVxoIOYTZrhoHuiO2s5MQOpDG8HjbMv6TsAkq/IfA5jj0WYtPeVvcQ/unB4QkQSPiYYowKJTNtcfLaqVssOLw3kusuC5V8Wf61rCBFf5+vz43dMYtNqp4LMSVpwkZrqDMjCZV8cgLW3p9ZuCIJMr6PIPgccUXMgzAEACsQuaYJuaAYVaCZs++7jS+yaM/UERspxoRiosAwbAgjVfbjsUGgGFWwA4d4fpACTHNxY2F4kPZYCoIE7NUdMEuBXAXo85sg4YP8zmOVrKqlVFBIJadmWFtoto5fjrOkcR8Bpzhgj2NouIjSPzPLEuYBQeDW4rYRVgvFOD99UB2PAActWMTwHSmF6fc2lbM56uSp2IkYrYjPgvaGjbN9xeXqN+FHcKirKuzgxQyRiHEB7QZJaFNBmaQIskeRF7Nb2vlj34YVqRdFoEjc9Laz2ZrrrGGrxYYr6y3dyq/SMdjQHIKcH9sPkmHw4rOuekSaxVnAiFpLnwuVq2SPlm650BXCkopQm6bIAqTpoM6u5kAQTc4Zq0S+BiJGu8AQG2nM4coASX9eVomyYG7scdclrM0HGYBWbeLPGjQvHy+gIRYN839N7Mxe7xh7TDeMs+cm49EgHTbQ5opDZo3JWcAqOMkX8v2Z4tNV35tS8QEUFvG7bejQfQeLeJW5RYpYHrztvxZJ6m9Dg/a7Yrz+ZZgG4bcrT5uHYpQIUWYAayZ7242JVYWERVUNN9ZbUDWxQT5jPLhRb9z0KmFYSR1k61LONjAscwfnpOYAxmH7cYqCkIF0O1zIjTECg1GtD2jobpGcw9nMIfLLKSh4vPxjeVfgXaKkOCt8b8KuCFnUlFsf4KeErQxafPV2zouQwa12T9+7eO75LG4FnPBOnGCQYFt3Qo2fGT5acXTQ1AasopALne1LfqKozhaFJXWDSmGsNqzTSs2l0KlxCkQlELD6xVDyK97PYj4RAEKBZBvvh4L5ps7x2vry9+f6dltYRq4oxtzOEBfp4OoVlwKK0wZmXx4fExYt/En7/+nQCKywPRvzEG60eUcUGYXXl0BXT6eLmFSgEzNUuQsy9siflIZklwCNDVcDInVNmFlykLzF1nfc0F36QDapY8hXpZBZvvc4wAUNT/asB4TMJFTQsrY5xzAXyK0K56b8suE4i5xz59/uyV4ZvLv6Rb216N0OrECVjzJjF6wXQZeB7MH7EdXn3+7DLuoMlnEiTYSwDlIEwsH40qMMYZAlZzmV7YOd1OD3z9D30IzLU+p2D4c+c6NSUmjOgET6yX1prniVMozIXNDSBcj3aTBAmsWjDjO41533rD1ncrt6OHz21dj8Xq5PO+nqFBE8zDw3U/SxxMo/VKCKatwDYAT0RtQEqn7R4VCF4/vVqg982AiqUZGabAOe0FfYIi0Xo2YCDyVADTxmibFLAcH455Ph/nIS1F1/NckWqC1q3wLXM36jQlsjP7eOdYuN25eSB3U7TN3FQBkEWsHMzmSnNza1krUzYnJsYabxmzUfopyEDwYNZlM4rn7IIKmpYMisrBSeBEkpqLKSnvqHAXsgrOOQ1ATSsgfEwvJuy4XDugrglkYfNcALRMogn61sHiyWMSHLnHpbkJhUCKvM5lMRw4ppfDeDF5UcpUuKyoVqJKEELWVUIvahs0E6RcW64F2f1vc6/fH0BpWcSVZ8gHFxQ9JRhITGBBi+UKEtVDuifKjOsXSJDw0WHgoQgh3v36MM+uffZH0PX1uhR4yQySeFosbCCDDMsjSsrptE4kMn9wuQgi87f/4fcjoa4PQE01BHOqqg9DoGXBhrVu5syZy0O9UCXdfuuiUGRqATL7BFAoJmgHLWoTTy0mkj4iweB0pGQJ/rjFlYGrdVZIecsA4EJE6zNfxxYFBDXxLONZKPjqtqvt0JX77Kja+0f9iKkrnVqnU56c7RaoMguXR0qac2a+oP3S2o92kBG7LFoOuijypcVtYVdTMbgul2VOtIyfhtzzZLd2Nt8rL6pJVK9zzmXN8AADRLLy4kWkXK69zCbXiGUCUIwhkdqAOxBVzQq+bZsl4IXFn3506MPL1zvc/eS8Vy7nP5MDUgAF6hzoo1B4SuNUIvwzc0HRYiitxTlkuLmOq/L1a8eCABFWGqcGcRmnDjTgvCvEoK7tkF/l2CUt1mcP2Kbuqit8NKz3le683aoUreN/YVFy/YJnSzlXg19/NE5F13w4Yo6Xli/naPacZyooJ3Q559nxLeAJ+F55oLgAax9VEy2LJzJz37XFrZhGzwy9XKRGXDbrxhjg6FPzFhdaDb5/mZ3q09aRLrXVQgRYTab4NSfLG05gldQYZtzCfasGwn7yLzJJiLnwulc7F/Ut48Nz8JQ2BF64srUs0liIpQNu5tToRzxXBU+B1rOvjxOo2XPnpWnN4LjNKIzKIHKbAD+vPYpputviTppxTGDrvvApQGgMO4firJnIAbBWG+cXAE4dtoXXA9CHz8tYdgwlM4K6dZEBv6E9F8BF8BUL32PYFNhca922DZ8+f8K2+xbebcu5v4ClJK/iJmTbAKjVpf27TI1fF4LU22BG33peGuON3ht827g0RNisqqcFK/NLIcNMhgHaOb/MYv4jQSnBQPMi0xK8hL+J2osbGlQAGUpPBVrzLOO8xMF4rEUUvtUAcS3cptoFdaMwA85pO3NnrsRc70iSEs+m36ZG4fCIrCmgRqGZTigAhCO3CtYovHxNAMAcrJlovGqOgdv+gp/+/Ge0X77g/n4HRmYUT0KUJHY1d9ZU21GL6UCiM67JeR1BBQAUyy7TokA8Y3vhu3Mydktzpy2QFkJa4phd3WUPi67T5cd1nACK/LlUU+B9C/+1IQ0nLBiyomrWKYWiwfJ5aVOojgSM4tby4/SM5UAm70vq1BiHvD9BEZlBBUbnmJ62YOI8+dmsUmYts/QF6R1y2ka+IIIOjXBXMiMG4McNAYjSo6SYY2DAxj1iB53PyALCnsKjhbcFT+bZaTmwRP6SxZ3n/8fdu/NKti1rQl+MMTNzrap99tnnXm4/1DhImJgIIeEBBh4OBkJCCIHaROIXNAYOEhImUksYeC3RQsLAwcHGAAwkPF7dtOD2vfc89q7HWplzjMCI+CJizMyqXac5vXffmqWszJU5H+MRI+Ib8Zwjgg2CBxbFwpcCJx4/SxoDCl09fh8zvRJdqEMWSG0XVUScQt2/o3bkzkEshcDxODpn5yKozK7ehADFvngIvlEnrIIMTcEox8vEQZoJswRQgsgHIqUtSvBfd0PZJ76zYjqJn0U+jY/Vax5gpxDihZnykzM/HK5hdmPWr+LOkgKWmEq9PW0WIOGgZOy7gWdJTVz6OuUjQ3BpzasDcIfYmg30VEv8qWr+I2ECZN+x0kvu6DSY1DIshSwX0nBG3zxas/ceJVpY6w6oIdv3oL4eqak9EkwB7cc5KIMk5T66XonwhBMJ4JRn0GTJG2WaimCAQo2IUsaVgfh6DjoSlJyTgMLzBCU9Wtok15x4yg+G4hfuktqcwhBjDDl15C1A0DLvleYgXpn8iJwmMj4LwqH5gePDPU8OoCKRu6meYFoMj6Bzm6aZeg2kkN6v12uhkfIwcoRKL/4bXbqkIcw54ZXIdSgUyjlOMykZAWbY9DJefOhi34h1YiugM0mxuH+UZNJiFBZLvvSZpRvrg/fnU+zhzABPMFd4NkFIRKsjxElO6jKFwY8iKvkwZ/ybSXHnKGkpaL7VHM8OA9Kcb1UzK5dc82UtPAY85FgJlBTaZoJEyEIHqy9SBTey3Df+rqDpUQsIKLUETTn4XwMB8vgcL+bxs2igxBdkRYt3uKScK6TWQIqrANFKFcdDsUqMcuf4xN2EZlg/b0nBa4S1BLNXKi7Pi47dP7Po+bnbi6RgFdFF09YJjOXu4yDN8rTajilXdJjZon82Xq0hSgA0P49gazHFsUfild1DONa+ru8K5G600HLuuPzsKEqKaGOd+3AELP/PUfPJGLOpmcK5o8jkmvZ+JCjuGCPzM9IhPjKWA4e5MJDAaD0DU7zPgymOsROvEya4nM+4XC7Ytg0nap5Kmo46p2wnfSykOJZpcEcb8VwbvN7vJmWH9uAQF/ZGphxpf1enEaGDs5ZnInzOmM3cGBO1oYjdpEC+iAH9ZTqMTucCWEIcCny+nFnP4fPhe3hn/qIa5u+4q/psSvm88Dd4IVyByIyIrrohYc3MOERWP2ophXiVvVj7JqVXd99r/l2BSAB/8nFVy/+kCvEcZ6fTGU9PT9jHwO26W7WAaGc+KUxExAzOI4eqWx2sLdyMUeiyakKkigIin5Qi5YYwCaSv90ZQdeCzAbbgaSlE8p1R13cAKoEMr5+uGmdwiwGjY64BDnhphaS2OH7257UGS3yppJVktrrcUpyvpUYMbJcv6d03nMN9nqYyutmvc7mhvLfLqoiiDrDOFDJW89DqldJBkjLLW6aK1hBZyJnQOC0leHBU4LQy9Xq6AHEfAGGFmVptPeX8BaTpJ3/73PFFAEpE/kMA/7634X8BdGXwrgAAIABJREFU8O8C+OsA/g6APwbwPwL4t1X1+vk70QxWdkW1AwVM1faLE1SrWVBBQp3l7nwGL/Tvp5Z7PdopOOOD5eTI0Er+ZtfUAr3qxPvJ+V7vfvh5BsiBpqYJsk7cPVRMAWXhIh0CRAmCcEgHgV8r7VWglLEZnnCyiTHmaFthlACWBJeZpLImcrMj7ec5PpkiPxeAzmFZisu1xiz9PkoaaUW1azc0QULGnYxyTPO/UGeYAar87rk2NP4x07iz/+hffitFtVvy6pA4NRkc54VP4JzTz+np6QnffPON+YScjGlMTdMLBywCCOpO1+9FRpxqfS3PDSi1Lhysc8J7GXOWlS9Rf6IS2j6lhtBHNcwhYxhzYkkXFkKVFfzF5T/z8QflXwVC2apV7hTMZNncLOOlctoU7tAAn8eCGYKAAhBITG0+lWvPBZkBYAPo1AqElob0wLktWha2cc5Zw03Lc9YP5NWKIqhBALUmdDQcb7TDtdykoZ0aLpcL3rx5g30feK8fnPccx7W8azr/CgD4PUt+xuANqmq5qgSx8gw72b02BwBg/iJpzjemFRT2exJocUosiI7AqUFaTyDFdelTSuf0mFQ1TBemwqlRGSv4u4fexfBFZ4vWT83sSB8w06A3y203uZEs4mYpFyXu4yTBJ9kelr7KZMRaAJS9Ju8nGb4VICr6SV47IWrBRft+wz72hScYDfrI6kRrG86nE7bTZu4mBOR6YF+fOApKWP6KIfQBYdJl8vUj2Ir7kc4LkKu1SD93PNZd1QeJ/A0A/wGAf15V/zkAHcC/CeA/AfCfqeo/C+A3AP69H33aFx1HJAgE/I7dLbAOAco3/z9YtgvqKnRT+OhyGhdKbPDIqD7x+OPXsYdauOnn2nU8R5KZ4DHhLah6OaEI3NL+u9OWsyu4O8I6xN8ZMYEH5xD0PnplVnDbAdWFrYuWiBF46zsdwRELgfeiAMjSGYhnEIgFfmd7CBh4zgEcVh+H1dchAUQvzuI1yeLdgMS4P6aB1P6Vcz5JLp/44UDHywwq/16B9HE3KIf3vNeDLFmfIeef8vjHx78qS9b7l9Q5Xc/jZjHoRvVwXq7QRyvtQS9T+FYgHRreo/hIoMvPn+zlA5WA1nXzoCnHz+k71DNpZWnbso6OI1Gec3xmfXZSNRxMyfJjXcN6vOdhHS98ZRbt86MHx5jk+/HzwoYPQwRZ145U9Mwak7hf/4HHcT9uwT/J/wy3FL5XXhMLz43nxFD6xlCrqdh/oVgOfp9jeRidB5/qHxwVXcbs+FrH4dHdP3/UDWrwacn+/aMcX2rC2wA8i8gNwBsA/w+AfxnAv+W//5cA/iMA//nnbqJqyH/NNq3xHurgu4miFuhxQdZPMYBP6IcQWX3rGgsmxgmbseOPu8VH9ezWdoPeewj4I4hbzDL+nanSj21FMNUVgLgqdtguhHkzggv4/XrrAageEQMBEJ8dOx9J/5V1HLkifBw1F4o3Edz2KWx3aPcskRplTlNRWDKBCZmYpkmN43jkRGyL5jUc8921UpAJ1QxTjXH1g2aONXax+En4jp5zIVBI1M9KbQ7vntqyLOvTtx4Zed8+v7Ead6xvJ777V4s6rOr3HHHbzYlrEaqZxOYgWaVqzivg0YVSfJLYD2emOkp8s7qQOZDKyuxl+V+haQbyzZ9omhIjG7vC5mLIPzJj+gMffxD+BQAiEw0TNG0GJalrGUSpHEZj+DtNqmgYaoXBY+6QgrwKLQGiKHXVMrKQam7AbO13WU0hS6X70Kwm3z2aiwGskXwB7HMOV6BRgjriTZJHAIA/t582vHn7FmMM3HYz4VldS0uINWcmu+WtmrTw1eJYqQhqvWAoIpUAOCZSqTZT08gY2EfDZB3P0QCd6DBzlc2RpX6ZsOzq+xy4jd38ui6C3tSczvvq02PBT86X6fNZAddDOvJryhAuCI3mXE0ANxm+0IC2mRZbdzcPhgacJa38Wnevmgyu0XQsH9OSB09V7OHbhCgYbCk2mEoms90DrB3Yjarc0f5283QaM2kn5Aec84pEIk2IR2tKi+9FpmvacmBSLNUt3moBOAyurc1oqwc2uFaMfLFevGjpv0QN5sePAihV/Qci8p8C+HsAPgL472Aq79+q6u6n/d8A/sbjvsjfBPA3AeD5zXM09kdBT1nAfqf73fv6oHJdPe/LGPhxl3/ULChyp5ACfG1jMI4qhJb223tkG+fTgtmtbV66NBVeW/lec1B2dGD3l/6wBYiaZ9mue0a5PBdJaNn+0keJtX7QPiF8AGLvYigFOMwjWUnY5/lsZyCVE0WTgrnQxk1gpAHM7p9Rv68AEdCSrVmDaZc5DzpIWqFZb/GBkCzufD6fLeKub+tORzV2ljne4qMg2UEX0pmQU0oLj6Po32o60S6ZiTX7QLq5Xyv+c/Lj3BCUzQG1rcTxy4yWCdI7U81Pf/wh+dfl6Qkh3ODgKNaGgkVqWSeS4ClIzXPcsCwOy13EslACAa5pBCAAgZMDKinFVC3aKc3Qpe13G9NPbT4rN6hrp5o36nfBG+JiSRDlPMYA+rSSRZeOOabXfLR+j1HWsAOoGaaTUlTZCXYqwGoJNdZE8kOREUKytU1Zo9tCMwHeDPwPF/hjCmQo1LPLszzLVMXWFaftBEDQYzxzHBcT1JFfHBhRWeo+3bpMCTfyKXWoYcp3iFjuqchT6dd4f8dMMx1fkWQYcFCl2B1MTVQ/IRJg8iW770T0TuhmkO4GR3pbNk7KNuYXc06Io7slvxNsDvwx8f1Ktp8HOLle1k1C8umD/Ckb4c/5RT06fhRAicivAPzrAP4ZAL8F8F8B+Nd+9M7ZiL8N4G8DwK/++Fc01NtvB2IDcmhW1VphBKECWZ6Rn/l3IYDPtC3vy1258hoXaIvKpaLXqvJ1ShZYTqUCMFh3KDPQaghtTnRpUdlt8h4pxPM7hBCM77Tam4vgK6Av7lE0UWnFdQ1GJeBy3fHgb4IMCEimWrRQFAxASPRiES8DtQxDvomU+/I7nwJqWVyAeOuPm4u8bf0+8VO8hypca8sIysoFYsw3tTG2G5MmOJ9OOF/OXp7FIu9ar353Uu7NEF6nnVjA62YhoXf+nlzuOE8L8oGu/xXwlBR9ZByPRk+PXx8krcijAb27zU9+/CH517e//FZpValjWM52+rMghaaWCVy4y+B8FXo93iGgqQKgy6GnNLBEnIWCan08XdujwKoJANlW4V05RgeBlwLFx2D5LZ9wAHUHEoBIOF9bbbqGN2/fovWO6+sr3r9/H4W+yZfIXxUmdINjaMZdLNzenaSDlpHjIypQT2AKtchFggULJJE7X8ip8GTK6fulqni9XtG7abcFCBO9iFgNuWb8twQWRz+qyj/5v82DabGLr45rhGj9YKWErLZQ+lDMb6mpAm77zPxOBUBV/kZT5Qi55FPWJFIWjNgkuSZeLCmxjdj0qG0JOhVkHrCFbop2TMsYC6tAjGGg2oF1ink98JWUw0iqKbRZvjqCnyNmKN9VDevvo30CvsyE968C+D9U9c8AQET+awD/EoDvRGTzXdw/DeAf/NiNOMDWAQKWVWhxUdo5gjlNFf5I43PcFUE10uKnsDkykHXAQsU9U/XIa5adjRMqQRbNTKaKtt1Tkw6qoYJQ6Xwc5sDUxkTZB6wCUHyXJQr3ZuR51FwxX4tBEeE2q6WKH3DGCgnGwh8EMBsDgCGe5A4KYMB9VQOErkKwQAqhYLf/ZwU5FaX4fKBxd1c6HLvU1UwVKIl/Cu7Ww/Ss5SoCeDQi/RU4/sejtov6lEcm5DoXFIABXGQGw+DOyxIGusP48xPevHlGbx3nyxm9MfOy9yn6RypNWlt3c3Us7Ghhxs72N0hoQYJ5CHJONeltMbHxVDHhTqd9M/Xxe7Y5K6bbWwo6Cm2q68MxPjYmDybipz3+YPwLQETRheaJaziQjYGnMXewKgAkw94tE3ZzoURGVzVZhV/RJAs4iPLrQVrWoBE+Xr18C9xMY+DGBD99eypNVL57pH5Vgj/ENVqIMoFTAin2J4AijKa3ZgEvv/ruO0xVvHv3zqLybldIE4y9BWDIdZhmPXLArCIa3Ca1+T4mDbZEGwRba3BXf+v7tPxHpvRo2Jpx5KnMCG+/QxRDG/o04DeHmZaYk661htNpi6LF7P+2dTM/phDITbmWEVQroEtzKAGQVY1wM5yaq8j4lPO3WiHm6VF0+04AVcBVsdojZo8AyvPrxRALulhRYIpCAi3D/85/yjxXMC+q6NIsWhSAaa1KfiwHT/DAHwzB2Hfvs5UmEnDM0k0gditLItr88V5EVQ4enQ+3EPKykBVHHPF7HD/qRA5Tff+LIvJGjJr/FQD/K4D/HsC/4ef8OwD+m9/rybn261ePBd/h/dHvsaP60f6nwIz3cg1BVj0SfNT9HcriuH+oLpNVnvf5Zt19lnjJ+nugwcpMkxlWtnOH1GvfarcKeL1vnN5/VRv4YCb1UW8plD/RpBAgwsZ9qiUJzuwld+c8pCfF43YdulVbtEagrG2vwMfyPVlSvdbXcOfVZPoFi5RzfNeaHJq6W6pD9dk91KMBKZ/XYZfD+6Mr9fB3+fzzgyfgHwv/KuDycFTKDzpdfrPvSBt69xvuB7IApKWSySfGN/cnK09Ybq7Hy+++uD9KIz8naOI07jkIslpD3zpOp81fJ5xOJ9feZkLMOhbJnn1kNQEAzWbRTX8V1hib3fxOD9fz7hq8IbTYWgJU3CfW8ibNSOUR34WjuS68P7BdaU/ybX3w8ueWTffxN+6Xl+CaWYJu4pXO4qpVU1XHIwebckbgmqScQqPBZXBzsPXwN1A2xOXOhSDANUA6dcxdWnF/dQK29VjaeSTE+PyAXv8A/OlLfKD+BxH5uwD+JwA7gP8ZptL+bwH8HRH5j/27/+LH74UHIaz+m7+IMbnjZx+5c0oBUrY8BE/+vWXrRezAElylA10snKJ1ohq1pivgkTs8X5QMKw4gRsFsOwXmF6IfSJYJ4V2y38m73Jnb79Slezi4OYkaAx3RTh07BEDHBm09bsjxEbfjNy6YT0DRomjDHRmKFtpjX7lYBNUEmL+h9NV+Y7FkgURNQHO4dP8e7p+5qAK0MItsMj4+LYRV9K90JuVEMEdydZv/Qy2pZVTuAd7KDoz2Wrfd53ba8Pz87DULn3C+nK2frO0ntYechRrOXuiB64P07R1zRVFiuACZACN3qN01gWXXsd4Xw8JXE3jRRIB6SnvuwsLEWm8h9j5/U7IIahmcTDQq+cyf8fhD8i+7oSe0BQLN5PKw9aDaoGgWlBCh4HamnSKLAK2DaGYE96XyiW9KTWPJ33WYH4Dz7HxnMN9aFhgPHgNzHg5+eODJVfvOazPbea7FqZ7cFlZkGSKu+XJ6LOY7lkIhbb15+xZ/tXfsY8df/MWv8f333wNTrF1TLXs0qnYb/j3Xh5N/U1fwke87D3V+c5uKIQLdOnrbABWMfUDmhPRmSfTV3gesCywpyRQC0GZh/U0x9om97ZAh0DnDlLf17poX8UziGnNKmWXzPUJOqPPF3WuVZlkpxfW2uwaq+C45D9sna7ZS26S47YrbreRwegCQqJGeXJmaGyaaWq0vLdIeTPWapj7+Q2+glYWbS3husrHfcL29Yt+Hz3+HaasreGpofYu8eBaAZQ3sJblwzG/sMur6KBtHPDg0KV01Nx3BS1HSP3xuI/CZ33h8URSeqv4tAH/r8PX/DuBf+JLry53AzLV2pGmjivbKvBNUZYeIXOvgxW9NygCnfZdii8g+rjkM0nFHIIcp4r0NP62giG2gWS/QORKoRT8DW/CbYsvX9EtpLC3gACqSF3okgyigvSEq9Gq2U6qwFIFQhQ8KdV+RBUBVgc9RC5NefONjs2yDCwjQdTbDYVoV8Hwq3uVsHuDAOT9r3qGADWc9JVlkmI2kXFCHtv5AIFbmdAFO8emxSYPjVEGqOYxbgky+w0FuGdCCne8X5qPFKvW93IZtiIb4F/UxC0ArJsI0zXJOmF9oBgCo85NDV1G2BMALJh39S2H9c4MnHn84/gUHNs2dYsmHFKthQfzb5jmIismYJ/lVy1rRwvR90ilEmBSTn4GVT0LoVuA8zPN0QcRAHBD0kpqMNB9Fs1DpqGiylfwSoc0wJ3pdzvPxTrBEzVIjuDeTzvlyxuXpCWMOvP/wEe/ev7NVSbrGPf2sGfuzjQ3uf+RmH46rKLBjsKYzVLtvahXDweicCoGVeslcWpxITX7T1Hyd3GeHa0lEoKfuvNoKTUNbjC1g+aS42TZ/WQ2namqwQts13WwXZVYSQJm7g5i5zrOMj6EYA9h3xfU2fMwMwAOVB6a1ZHpC6GQhEr5L3V8i8AIzNo5d3XNBPc8XkSYAmqjn2DH2G8bOHFA2kDPole4MHdt2CrcHu525rrRGQHXkyoIjRXAjWwjvjm7Cu62Kel3Pf3R8qSnv56mFByDY/FGgSIIdW1ALXs/vywUqyERmIYCPakS7kthgEa6+0KWZ6QXAEs1C5hbMB4AqHQ3J8AxdA85YnDqrINbhbU/MdNc2KSBCnHhlTujufRp7JKOcYzqadmbojlMqyFBzsM2++yWIqJoif2WlcE5JjvxhlmJxLD1QzhWvqn448DpXc1nUFZzZOBMUuFBSlLYqGCbM8SpPiUkVl1Lrvf3+juce6IEWMRg0tnSRs0naTWdPlmsBgDlK2QW2mxF1B9pdxo8XSc5RdSzPMRPUrzTG6+5mToMr/mEyUvY/otdDeMidUCShpPBNgSr1kWRYhf6+qkOoOfA/UdbaMimwWnbTBoiRZ2W5WZZ/tNgQ2WVFcJTIJXHvZA36K7RUQeuihfRXtIm1OOfBH4S+ILxdXTvJpyfXBZ8BmnqcxmLdZQJYObQl2gn6DAsulwvefvML3K5XzDGxG6MMfiKuKZmSWtYEeilsCX5SsK4mn9DKaD23fBbyXg6E93syVUDmSxK/oQBh1kMTzNmARr9X1663ZmBN09RHVaI5hMMdqzVet7EmuATcERzTNVD2+z6yTbkSZWl/cGT1SUpRmfuhkD+6vDpcEzXs9y6CDgDdfMusDwAtJylQyC3F6ZwyjeM7oUwe3XIuda5666QZv6+QBjmhpHck3/Tr6eNkyR+YOEbjXoUDBp/8fY+fGEDdC10eFLDGJDy8kWioIQiPKuWw0EZGZuQ7CxbWMHMvqMkWEB4Z+PJim72jbxukTei+Wx4TMpkDsNt1ok+qlw08MUMtmnoBTAQzMQdBzX4BUcdpGR4SshN1s9ARzN1ypoz9irnvTg5WEmXOjqnd2JuPlxJIYWG1cZg6mMIgQ4lZOV2A0lugfAE6DHNnbbdKwZIlFYpWLhaxwlIHc9wCLVp4MmyMcsee2kBP7xtjFMw1tJoaWrw0AWu0m+bIqrgJ6HbYvRQ2vYwbQ6wBZ5yqOJ2BrW/YTmeoKm77DkGpvQeFUCjwrkXYpiDUpS1AFoc200AZMmoTSvMq0OXXPTLJTwiDMUomeZvDGATfWEr0XMhy5kxa8SzLNNeEkCzjx8zJXxuIChYc/UrwZMmuWwZ/iAK7mbn61jyvjofOg2YE97EZWkCFP4tx6q1FAYZ1ra0ACuSXEEgXNNcmNs8yP8aEOh0K7UFzROH0WEalLBQfWsWicWErvNsJlry7Qn6KFH5o5pgMQUSUZVBGx9tffIvT6YIPH97jdt2h+poFxMv6bmJRdQKgtx4LWT2qQ1CqF7g5jw7/CrgzNnDipgI+Z+JRgr322R2wvezULoomijYUfbNRmDPi1AB4SS0I0Jub4jxLxrZBm32376bXoWyb0wDRVPVi6BP7mHi5DXMa95ZOVez7CMfw4ZsYOpZbRgBjCOrozEActVKarxZkgnXDvgKoDS4X9h143T3f4MlAbG8QnEwWYreaeTqNLqJ8UUNTwVSvrinGDRFRqsPLuljJK8wB9aCmJL8UDpGuIdYigt83Fmcu8IgRjUOnr7nq/1WkIsmdtPN7MK2fUQOVh9YtwTo8BW95x0vnghDj+EzHv0Qlt+zaKg5eHxFMrAhdKScsk/+gSev5d7cPPlhFLLdO9G2wsamCN6HSQaF+B37Ur4mr/MOxv3djwMZxJ1O/PvTv0d98bmiK5BOjXO/PtmkujMeHRlcftymBdvVne9yC+xlaqSx34gHWhCYyFGBatYB+T/3UE/L7+FxobG2Azf2i3TzeR+/Nz/UZXHKPjwIjD0vyqF63jsiDa78kPuUv4eHjEfNXsctiTgB3Eeta1sN7uQXXZACoOn+VD4IrvT7MGrZErYneU0Bd8ECZW4034Zxq/VmX06mlDKBNSQYJcBBBFGCqE+dmwRbt/947TucTtuspim5XC0DwKxF/zpp2Ie+VvOXRETykOO/FRm8ZxRwSKovqhrA+W+GapSlAM2AgU0J4C0rEttKFIjd61B5F9nOtmqec4/odQZSCDuL5Xtemlv6RkUohPin8K8Rv/pxjS4QGMXAk3dK4OH1ESlV1AKZaxcMdH6pJbOokGtsncy6yU9bx5nuVB+v3lStq8Kyctk/TyO+74/vJARRtxyudpynMzrF3BdIRsnfPzJsqYkPZvEcugSTwFGq5WyuLjEwiCBu+g0zp4mzJ/89FOvcJ9NRyqfJ52asjyKKDNO+b7SaDNStxm1ZHrAPosNWhw3OB7GZnbk3QN0QhYZZIAJi1VpyyBVvrqGSrqp4N2UNkHdnTia9qoDL9wjqG2V/m7RALyxZ3PBXEmC6cmAsYxmQ4X0H0sUPItsYL6hY8rQMIZmfh+Tav3gclmCiH2FWkucpAgy5AxlJZfQrNJlmm5XI2p3HWkKsEHMDNuXMIEU3VfKWZY2SdFGFxFNAqmQgPQGpAy3yRiWvVppKhoAj2APue5M4HVjwkmwpwmkjSZ6E+kne0QbornPoVHMIx8LkjjfcJqHDtUBPiAr8A50haqL7uOH48JdYHAumr0PG3ivcUMFX8a722UXjvRCHr5k4ta7oCFhSgCtavlFiHHvxRNKYPx8UDd6JskSCTIgEYt92FdjMfmzI2W+9m5nme+O6773C9XvHu3Tt8+PCh+JOy2b5+PMt5a81yPWFi35lyxJ3uvQkNgqED+zAevEsH/djaDWijbnrDuhapbZi2qzVAbzvGtLxFWzerA/2nmgjmUPd5Sm3imCyxmHxuDltPZoYbmFPxettxuw0MBfbdnMSznAzsvABcrNxghGPFfOE8pwQu+ZcZ5JhgqfeGzdMwUFMNAHNa8XaTOYCOAexWcFCHQDHsGnRoA2RTTAFOA2j7RBvTUm6oArA6gt5ra5OIgbDmFTwicIbZ5zl4lScD3LAucCrWF7XC9l0CLBt15ttaytWU46EP6heAqX8iNFAAVt4LgIMyVctetux6DruQxxuPhNTUDFSgFSChAJ0l/5PfogBzF8oak54RLBoEmm2qanbE4j7uYAI8qS32DkBUHEAVx/c5TQM1ho+ElW9hIWEhYoBpP6YLeosWY1p+3zHBHB4jN5CfW0dZAajMVHlSMHP7E+rVddxo82Y+FgKJO+LPp8Q5AZ40gUEdw2WeQ5AdBEvi3+WIr8K8lyZDtijOO9wgzK6SwOZ0OuG0WTg21BguQQeZJXNxeUfSV08rHSRgI/57REt15NQQFFhyiAJsHd1UV4fQLi+JAXSm2ri5GVCIm2coVPMVY9CaAdpYV+uaLPDs6zmc1AKcw8ay9Z5rQgrz5frifCsykEbyfkEiJU9B5ilUz2RUxvYz9EHQxfliqZHFZMFTuW5ZckYrhtMAUJnP59MDY74sLTdzzcZjzoHdE2Zum6D5ho63YykaXC745ptvcLvdsO87Xl9fAQBjiPt3lr2JF3znWlVV0DEgNtreJssALwmgCCaG4LqrlyVp4c/VfGNN1c70rOWqgrkz8WODnjb0rtDZwiw53cG8t4Zto/vGiLVPp2mWs6Hpbqriet3xet2dR1tZqTHsd1W1nE+a7izqvEUBSxI6k4eSJujILtSGFytL9wCYLg4GAYwxLC/TTACJMYExYRF1u/Gz1tHaBjSaVgXbNBOnDM08hoaqfRxmaAFZpFmB2LBpY/CB80LYTTQJfmUnmh8UCEdxyvS6Ez/ywHoc/w6Q/hhULMfPCKA4y/cDw91sGiEOK/eAIlcgtYY58ru6816fv96aC/LuHofWHx0jsx3qhMfw/ONxr1YXIBwlDUAVtbg6cPKkZ+oOoALb6XVpjLcAtS6pIQBqkjdvOJo2bOheNqGMkYOPtcOSu1Jq7MSIPTAp+1C3cYdPVftWn5PfBeUHUFq0UliJv0DjvHYd5kVQrH3KnThl2BRknS176B0N0ORIIWNMckvNXUvzbxUQbJ7AQVZoRStOk/KMKlOPYypg9mW2JR7jbWb7rZ05XqFNq50n50EFpznXNlX3DIe44Hgs5husoO7rOCRoJgF8rjkTwu575IkHgy4kKWMZ08pHAqAfrgtCcR5T553r5ACwct/BRmoAv7VHh88kcK61mGxruZBu6jhw8+T+TgLnE+4YVYUSrQoiHbVXIs0jWM2sF75JrXlS0eKv94BHlwGx85hsl6Pmf+9F47E5tzXHbfdXcyA1h0UytunO0K1BZJovlPPENhVbm5itBZBoItBeOJuavyqGQMQ1T5pRd9d9YOrEbZ/u94TIEL4PJtB0E15sioISEutF3zX4QygOi/9kd17FlAWRBBzpZza5kaIFwnlJ5CObCt2HbQg7gC6Qoej0eVINnzgN2klxv9APV5RI9qXI6vy/yB8KCV7/Cc3SJ48H4OhTQOpzx08OoBLMpKACqryuWpsy84w00bQp+wX1alA1fQRYYwyfJvXxTiZGfkEhk6CDYdrRWBAAiTv3huOgmuoTAEZkePVdvNCOzn4VICekbyO4TRQXQqIxQus0fJFhTs/4Cpz75junBkyFdM8dJda+qFVUwBR3Ax3cCXW0tscYRcp/jkn1aiXwcb3/1Im9hKw27xXztwRjHAZrAAAgAElEQVRoEEbwwKuCy93SiNFVzXIFrqbOhM1VeCQNRbmcFG0GJvzGDZZ3Sj0cuBUASMd78ZnlAg7agcZONn2cLHvvtln+JxEJhr8cWnoYIMrb6jst+klxd1jrVZjmgU671pPoH8emClSuJWqYCNJI162EV89sJNso/EfHc9jucInaW3Zxvj41TSxUs5vb6NdowmuAuOlLFdBpwlUsF5CZNRtEemSotyCW7uB2JjiNe7oGURHFca2eYnOtRUeYUjWCy1eBEYCpICdGfkaYZR73PkTrpoRJhxYacp1wA6yNKfeCX7becTqfAQBj7m7aggVA+Boc0wr0ytYXTWvvHU+XZ4xt4HT6IdZUmKLVNqbGxorJqWyqArg5i50RldwgTTEEmLcdr1Bc9ITtcrI0D2PiNieaNJy8MPt+2zH34aBjd62NBaA1EVxvaco7bxtE4ICEGihPWeAAyvIqeSSkWwT2MfD6evWyKvSJQnzedzNhcnmnnExZSHqwuYPTpDr4pGnV5q1vDeezux5sm2meys2ldTRRL6Q8MXeTP/a7u5gAwG1Axw3ogM4GdKDfFJchaLPh5tq1jBCk+d9pJZg37R3NOZCBN/pcoeR0LNwu+PKa4+xTECq/V03lxiMQxXO+BDwBPzGAohbh7nvyYnyyT7wBGFKRiqsVLEn5La7xV6jHQwDL3ZA/0qLcHXe7wuyIxrsLcNEs2eCPDeIpfDSQPRzoUOUYgIZEnMCxOrxnvxDf5Yuq7mwyzXVNDQhmQU+OS2Wwpf2F4dV+iY8L+5Ly1DoZ2quEVQ8GNoX5AoLLh7qQOObL3/Fdtn15WtBaieTjTwKsJOK/HrrPPtDfgwkDq/andKk+4b5PiZfKbmz9xj4mXaZj+aO1pHd0Ym8kNnc+PwrV4rT5Oeah8YzHv2cfvowB/eU6ar+oH5flc/g++WILPiHH6w93FqfI0GyjaJ/iMVUUxL2WaQww4SuJ0y7VsbeuiBVIPdrH16fI3Q9cI4Uf0TFuSjADAhtb2vdyIDTm7hfT6LTjEje1YKXrpOlYD1x/yeup7c68ce7vquolUayJw3mLioEoUaYVsO3gVPP7MQd0wXSGzYLHTaZrH9Xlz4QMzkkCqMFoVqeHfUzcdt+4wuLXNABUpjWIfnm3Kbuqq0NKQ75kAS3WTkb2ivvOSjHD2+9Tc7MYaz0a4IM/FbbFE7D+l0yNbRNLTHFTHbzljq7qpwoCETQbNU/5f6Wd34PNPOCsd8eXmO3q8TOY8FIoVb8Sc66c8TfPVM+jYtldURaSxO5lGQy911AAWfkcy1RQmb6qxMsJee8AABWYIDQ3LOVBjQ+dGHMrNDN0ODRRgEia7hqADs+zAQQBC0MstC6P/Dexfj+9/7b7S00Uw1MVMMd0mH/ZtjnTEvOpGWNiZ1K2yqCI+LhbEdtxmXqZ2Y0Xfg9FqvwjqWAYx+1ewYe1zl8K8nQiLxMTkyPBYJM0juYzzfvRl8JPpbcSIClgYtfjANZUA+YkWpZf7x3ni1VpjyEqwO/On670o2px1P1LWpNFyAUYcdpVrThIowPiWaCzqnnJYUb+VzSLpjjJ9cOxDzOJ8vmVxWVhXEuGSJ8ZFw5UU/h1Cokmfk2HiJgmwddkpNWZVn0ACmDjKFhCQzMHAfB8NKIWqEIfGMA2MgBywDyvqSogc2IyA3fjujgC9uQBbCdEzHcl7ltFiAvfqZgj8y7FeoT3oUjfyDfma8oKp3tagd7Ml4maWN/Fi5t0qNmmdhxivlGmyWqoAQn0rJ9jRLFZS3hJy4P481dRSKsEwSjXMETQpmnMKzDdp+LD69W0SAQVIrgNlwxTodMBko/x1gTDtUxDbQ2MTTBhAKoPjSwQvSUXDI7ta2LoDvPLGni50mJhGmZqodztFSMUJnVB2YdZfObSFG/PE51QbWF96A04bR3Pl5NZT0AtYvKsofngJsAUcUvDHny9YZoGETa/uAEYgj4Fz9sFpzlxHVeMOaDuE6ciuNkyySACzh5l25yYJZ/j2lcJOszv1HkWWU/KFI6DTg2ZFDRSN+cPjuXRP3L8DCY8biw4UNnpdORO3xxGEXXnArFm1ITaEUnWbLWLmroMvgJF0yAp2B7sjKKNIb1SGBszncWmny/SAZPXic7MV5TIBHSEFBfQBqIAqCQxK7UllUWmKVPLOFn5RhdsquhNsQUky0mwqCxAmmDbzAG2uROxYHd/KzDR1WE3osEgzSnSd3XzQQoFP8wHys1lhc7pV0RJH+AXEtekRiUhVAJb/0+yrTFnPFvz3FUfVc6vi8yFxhzU/AmkZwSaOkizul4nsOgnVCOpHCS1EPZod2b13Dap8RMXIohd6XpI+S6BGqdCVDLnjY+DSprVtIxrgKiI1ikg1S70nSeFcxWlvssUQDz/GBBGT2jUbjeTtepcNcFfzeHBB+74ynFXNQBF3+2cj3SUJWBuPrbq5nkIc4aVddYIaDMBoPnnFM1My40DQU2C7NRePRIGwe/Iv3gTVF6p4fSsqDnaZnxn5uyO1iyHHjeRfieQdgmgek/ePnSaczMduH2TR37ANcIoNUbU1TV9t2S8/2TANoQWJSae+sMc2Rt2VXx8vcECQjpO3vat8Alu2MV5y3R3fhFFm+qb/xb5i7pPjT2Lmv8jc3HfJjVn8terlW0xHzUWPvbVNevcFl5GeehzLHTA5jSqa7kcYTRR9NZw2hqezlbkXOfwbPVwsK4mr5h/y+lSp0Vt0xG9oRl4hm8ubwCaAaOnfsIuE2cZlh1KBLM316wpdnhgkxQAVWjSBFj5hfMsi/hd+gmXYyvmIt/zNZHUkgCrYIKHUusRFjgcP5MT+SMEqIfPufLl+LVPbnALrGDpx447cIt1APPHMuzL5MnyOsj35GhAMFQjYio0FWEmU7flUmsVQEWXIVkitgJ7GlWRGVqCw4nJRSVJiAG+qAWDhPaLi5FjwLQIofHibtgZOl1eKvNqTdBhkSPUgMVIazxx0cpkrTiveN7UEq9NCgG/t9hOyC8C/djyKWwEwrfHcFkBUeVjDiEB49ofa36KgEocvTeczuY4vp02W7jRXgnGdbyumq9XStVybvVmKNor1WWxW//qeJQ5DgeQuBT3huq4S4xe+FBQAFODAOos8yzSnUVaSYy5Xat1Wd719ms5cjZIJ1JIzNejqmsu6lVa+MURDSdnynm7R6Fxy+N73F/KraRMRhUyhX54cjSHKFBckPqpWjg3Nwl+e2G9u6h5l/daI0gLLflCiXHzRJNj7B4JlpF2cUu+L+h8lQESn+vN+TejEqeDD6fzYWuhicT6XVZHMHjbTIi4xhhi1oXd+s16egJ1DKdA1RL5rJppDq65L2wZBGrOS9R9iAgGsOKxRNBJf9Syba3htHX0JjhvHZvX7GN2iQBa8cpnaMw0J9jubhpTak6pka60D0CaPbt1jCAee2Yrc5laqLTWsH8o94z7fpKNZfuCly/gaOW7CfAP9wfun/kjx88CoIzO17wmdaHleqt+BvFVMKg7E4kfdDzkfev3K+tbmZsoFo2P0Xt9toCxxQclZLSLiEN9V0cyaBBI79a5OW0hDwtTbQJ0ATYxO/IYu2MwLcxIU7PBrOdU+XoukabJELat+26P6vOB5mYXAXe8bsBy53fumKSLF96EOa/7mISdfkxnQrnQtm5Or1MVYx/WTcpyQXFabkDn/OQ8mvkQ2V4KGo62AKy5xAVOCorpmQr17G5kzsZ4/IObgysVcM/bIGFrDy2AP5JjA524XJ7x7XffYts2PD0/oZ82YJj5kubmAPrOBLj7doKsLXa61PCfoF8Bd+RkcFC1jNQBPsXHQkKWMFw9ZBycFquwRu7xTYWf2kmeMSfXlj/HaZsaNQHzYDlgnsN8PkHzq8/ag7X5NRwkRRKLaYwSTEyd2OeOLgqZzi98PqdrqsKZlRoAB74hpgURum/DzlqYDlILcAWAQyKpQgY+x1KiTD37NTCjzIh65uyqibfgCAXVxgHU1c3/4s7cvaFtDf3U0VtH2zYDP3K7Cx2fHsBgmp5SAF2BfR/48P499tsN15dXNy1qEbpF5BbaOu6dqcgw4IMA/QLHG2wD7yuCPid6sxQEzU3q3TUlPmgAgK0pttGCxzYB2px43amRhEVJFwdu10svAIUFSwJMgxthz41Xx5pj5+u7QbzuX2b4puVCOCcCPJ03vHm+oHdzGD91+mySMc6IGCdNRqJOGP/SBhNO3VI17DdzRemnLKp9EjN/ThiA3DHxTT+jo+HWBC+W7cQ3wa6l83lpDrYUiKChLgxxQsrfwpODZsifgOCXEYlcZTiJ4guULAk9vsyR/GdIpFkHgtL1/jDhef+b+o/rAjqCpB9tBZLDPHpKvVe2UQphH8VwkRsILujf8UxGryRupPBm4jdxgcdFk5Jw2RRyV1CeXXcQaz/pL1AcDMmMCHByrxG/xY/Nyjak/KcZTgq1IfyGMFNbxKyyqdWw/lNRtGgRXSDJNM1YRSKxw/HzBOImy8OCCI/FBK7KEbhbOwl/S+8dq+bk0SxmYMUiW87nM7bTycr+BIjPOz067jWkISprk8pv65lSv3dwA6BEEKIAHd5MEzzp3W2R0jnvHdqj2t5DExUJFr6AJ319B8d8GSdqanT9p+bbEnREIfpgjcdAl6Ulh/c4tazb0qwH3xRWgZw/8J2CR6pAL4KcWqJAdQ+Gw/mBeDx8DbBJ30UbmeS5qekhg9M5sd9uuF1vAZ44vg6bilb2bkRAEAKfF5ujBzLE10PuTe0PWzuaGhoHI2S+VZPRRCCzQRswh5sIgfDDairhYM7FR58z6plpsudZtdB94OBFsZAemNR2V77FYe8O5nt3DVRvZqJsPWk3B8PZQ9FEFTmRE4zggdEmf7z57xZ3GmnYPJpRG9BdA9eAUCpItDk1oLkpL89d5q3+VTh31TYV5UkMY5DRA+INhvfg+ALm9vM4kX9C1uTuuq04A0lIIUiBQlh5s9DaHO55/3cRQlzU1O6I3p3HXYL9LvDCV/E3cwKJpzU49Q1zDNxeXg1ZjwnMgSYNT6eTO1wKmjwZIdFRXMSSV0KxexjwmBnK2k5mx6azJnccvXtUBb+HpEYE5n+zEG5B6CFyj4xWBO0kgPbMCAyFds8gqx5VoqzNpIDnF4nyBd6GSEmg7nPhOymrEZWMQ+D+Upxjn7N09k5ZU2Y5dk9pJtMkHnE2RZAG1/Zo3mcBUQe6qMcYAx8/fMB22gC8QWtPoKOCFEGQ3FnCFIFgUIjP4TPXWrQzBFK8s12UwImHUjhlP6U2O9bKfLCGTDOJYObst9OhaiZklJXRkh7mHMFnajvjha/okPVF/8MUjsMFrnsiKjPBT9MYifvBMexD0mQblC180KqBpfdP+IeWxToLTdQ1sWw8CqFIswAIC/4YUZ9Osx5IzJ/m5GauIIcSGYHqfpAeer5j9wK09b5sU2ml5zV6eb1Bx8TLxxf8+td/gdv1hpePHyDuZ9bJb7WuxjpySZt1uSrKHDm6yKe7uSiAAfx55i8kAHaZseYIniJpMQTbbU/+yxp9MW4TDOZorGWoJeUC55m8SRDpQggWgfyNALapO4Q7f9i25qkUBCf/fN4suu583vB8PhUnecJ79r8W2IWnm0GkH1AA2mC02xtUXdOuE7t/FjV5t/lAbnDt2rnhGcC1AZem2FXxfn/Fy9hNe+19OG8bni4X09qO4TKEfnJHG9Fxe1AM524ZSiuQf51IMZUQThx6f1Le+4sUMT8HgBJqQ4BluYfwSIfKMGEAwYwa7lFmRemPAJMemIJfVRg+lt8Jio5AzBa/FdGUxhfb3NG7RTegbRBV3K5X3D6+YOw7xu2Geb3htG3YTiece8dlO+FyOgOqBrTGwGgzwmt1dxu5MzkA2PoJ22mzMXKmYAu4rwBKJ3SQSbvjaehFdbGjh3p7GQ9n3b5QrRDm7hjPxmeMGUngZJ8eLaLQLhZ945EzIZC5yyjFQem4tEZgSuSsUgDC/EXc/QAQnWCqUiOTpBVvBX/FAW05gPQdcKWj5YzquJ8X7/uODx8/Yrt2m8fz2bIQs59knL69Ju0SRC0FrkWWz+FbBCCnlwn81k3DLGufGsHcnQPM5xEiRjN7fsx13Uk6gIuxUMWa46kAU2gyvNKfXCelP18XhGJCt+gWo3ihE2vGbwp2TQHNXEjiiRV5Duo9nZ4FUB1QdX7IdQFA0q6cGkY4IRDvNK5pT7Qrmala0GDeBIpxk9jAhKlYsNCbOX9LfN8cDDURtN4i+ISBJ6qIjV+a8LxdVaPlv71+fMH15RXv37/Hr//sz3G9WmRc8+d1z6E1kCW3qL2afh8BfN2tm6GHR9GeMNt7FDiXFkkyV45Q1k5yldg8n1wbbT5GDfvYsd+shAuT7VY4wL4JkHnXSqH0KEXTGpgqOcx1TSyvk1TQ1HDZTAY8XTq2zXJTXc4nl61Ja+OwmasvK76rbnLz1jTnxU2xO68+q/H96RUzNgJ8IxKcTxtmb3gVA1A3ncBHBYbRn8AB1GnD09PZ0kXcbpYnC0nKMYuhGePfvlbC4VxiGaQQyO/txXXiN/gccPqcZqocP30UXvQuCZSaSO4MUrdry/0ebrlw/5LnHYBW3CcGMf6w3z5zU0t255onP7e1jm3b0DwVPlQx9t1r1u1GKK2jdYWegFPfonSBkAPO3HUQRK4pfiTS4gvSPyYWIIrAKv3JnUwBgseRE2ri7oHncTxW7Zx4NltXZXfOlkZeEeEOp2gKm060BUCzErr5hwW4djABwMO4EyyompmwZoEO5h7O7wU0xVhK+Z8/OB0u4633Z/iCmtN8vAC4L9jEEaAzZQbHblbhFKt8Hee1hZyX1RzC89UBsPOQwmTyBmYiKcL5eISmjvfIsbCuUgMVECyuy5xkx5tK3Dq6+rUesWmjYFr5UbB7fikH056fxbNz3gpropaw3HsVKnlNHARhx+9CHpRdevwuwEJn7Jus/JDgiu0oIJkgHVhLZjAR6/TyT+r8DgqIlwu5vrzi9eUVt9era+p9PbkpKrRiC1EZCmNAiTWNyExqVxDrm3zu8J3WhaTK7EbrINdhQGrvLdu254vyhzaxqNyhBHll3Zd2NP4xdclBx6bQktB4vjequ8WhNzPN9dZw3jrOJ3cYP/nv3bRptilLHpy0W+hDyoMPfYUD5dgIi2k9h/Nyzrdpxlgnx7V0UHQH0JfWoacztDeo++eO2w0vHz4khxGEYgIoUfVyL+8/uUFz2aCoa/TBaZ/8hZ3/cQb2EwOoLG6aDBngAuQkhnJW4aH/qzBn5mnpx505owLsvlFzqoyDlokI9a5PcApCZ4tF3SwAtr5FSRJVC/G9XC54+/YbF/6maXj/ww94/8MP2HrHm8sFl/MFp6dnnD3d/4m7wqnYX15tEU5Lg2/t7xhtBoEImZ/vdnrr5qQo4mn4s0BnVcEzSkRY4kWrWUWCqTKppioLxkrucn3wWmMNK8pOG/++mSPxPrwwsUeXWCoRt/mHtsLnbFhx0zQ9dJxC6CZ1KIgvR/gODPfHGlNCK8f5bb1D25qygSDHJz/6xJ27hSdnXpm6ZEK8UXvj72MO9N5xfXPF5XZx8Jbh2eIgjs730Ik59nJjxcrhFUxAAaRJjyHktS0BroOIC3kzDxfNzGomGppQWnPaJegLmUmhWpiRg9DQUIVANG1ojGltCMcVKUDXEf3Lf6gHiMyo3Jrzp2C2cH+JumevZYDCnJg6jIapgaKwpDBHBbMpb31VQ2F5lQ4IyAUu4jryAVNeUTDmXA0KlybORwFLK+0bwMEi7s1Bu7dFHdjAr3PtKAvXCgiMptW0u9m95m7mGZ0DU4dVV/DfPv7wDi8fPtpzrzvaVHQwGaOZQMHRYLfDHB/yOnhHTlb8ByBTEpDf28DWjRZTVOgyN/BnhNk/gKRZJeagaa7OlATPVlU0d39gIMsmDdoM2NBR3TKc27W9m2msNxaKN6d2Oq+fTubG8c3zBafThvOp4/ls2dB781IzdbNDADUnxj7jqybN+LA4TxdPWT4VE8O40tZwej5DrjteXl4wJnDDxOvcoVBcx25t3TZcumnihriMnBNDd0wILk/P0N5xE+C1w8x6v/kN/uIf/inOT0/49le/wnY6YXvacLqcMefEbd9LseE7CIW7w8Ei5frUAbov5K4u6YmrTIHFAkAZ92PHz6CBsv8pxPm39yDOCEjufxLT2rxnxz63U7CvOZg8X8vTy+9H0RnbAV8YRaiZOdFMeb2bBkrnxNwtiuV2u+Hl40dczme0yxO23nHZNlxOJ4h6/pBpk2vRD4j6dwR4Qt1yIMtkkmkiQbwnuvR+SlnEd8Mj5ZOEViOJLM1BFIICyQzDkTBTzD7u04WhFrUxFG0Cw6P+pgq6AlMmOhSqvahTyXgI4jLXEdMhjGlM1FgQx1+C8VRzXQXInP9MW5/3znkFypefPGLnRs2MeuFN92sTJk9sDW2ygn2h1ALOQjQeJieWgL+yuC+bn/2w+xwA3zL35PXGRKlaF03j0bp2VqFM8KTLOTSj0nMC5S4u+r9qtRNAakstjsZUKtS1fql9CuFexy/GroyV5FtwniCI8vSYOAlBXm+wzGmQeLlHWQMuNmz9C/mAHXNauZqFUAL4S9BYaK4WPjojhxM3MKzjOfYdY1iJlOHO4q8fX/D64aMJ+zEjiq0mKXVmnYukaiWWsZOclwPOfLDa8rqyBMJNgL8Kyo3IszgFflFEoWpsrnuY5jxbuPMQUURi6FgzAeaodeJGSty6LhFVvW3mHL71jvP5hPNpw+XU8fR0ctYxjF+OAavhvNJbury0Yu1IcF3HSX0uGgRtM4RH89rUieEaKGoYe0naPBXoquiew+vcNsj5hCsAyETTgd9dr3j/7gfMOfH2F99aLjFk0eWjK80ymWUqjnMcvSb/VI3vuMl7hBlyzvXRz3fHz5PGAJWIc6Fj+uRx5RJEOAFX7UjiBTrqyd0TIl9QIfgQPtxZI1GoiAGnqL2GdJRsYtqm07Zh2zacL+eox/TxwwcDULcbdE68uVzw9q/8Cbo0XPpmGiIIhBl/ndigXuXM0TqA2N1O3+mqg7nIr+IjUQHodE0DtVFA+shIfU2OiYY9nQvaFk5zASDh/M1xAIDIcF00C9UpmmcKC1cOWNsBtO7mT84JqOoPaGgzNjVuNWN10LiZZFGnlB4DIc+KRrFxR6VrslGlwCtulEdQUHf0tY9TAR0T795/wD4mTieriUczJOeI5E0fudROeDI596OjStwySxgT5byW5uS6yf9SG4gETpGRWW13O0PDWOeMY49gRuxvc/Nya93TSxA0OZWwDJCkxiXvf2TSX9ehEa5W0AWBMn+SpK8ZfIhS3tZfrCpdptPo5qgwFQ33xclbkAcEny8Ro/DGrDex9pS1a1jonjeCaVLgczqcwroEfgG1TvvuPNkTaM6sp4lZotjEMmq/fPyI19cXu+fOWp/DNDKQqJkHTRN9Aj5Em44gKLnI8p8nZUw+4t3LNCetjE8xV9d7JQ+1P8TvGdrWOswVaHmiFZ1igMFTfwgsWSdB0taYLdxe3X2azDm8m5+TNJzPJ/e36jhvVs/u6WT+bFtTiA6TYXOYXFSjG8vhp6F8EW8wccX6Wjd6gV2Y48tTVui0ICfivlCf+XWWg9/o/wJ3THcFw7Y1PJ87Lm1D/+V3+NU331hdRAC43fA6J15eXkyj9eYJvXfc9t19cL2e4CQ3ouwgrlafG6M56eL0S9/UA3CiyXeZPa6JB8DtcPzEtfBwCP1Fdoj9EjGziogxKUeCPM0iSIzgwv9E0u8k1HVY0eiy2KjeDHHpg12ZmyRw2bo5AT4/P+PydMGb52d896vvIAD+/M/+DL/77e+gY2C8vkIA/Mmv/gh//O23mGNg/+j5TMYE9hVAGYHWNARm9hljeCSET7oA0j0HEgW/AsyLZLlVFOoLUYoANljkJDHTRAekFiung/E1mbyN28DcccdMgrop+4kmiQR6opphvdxRNKDPdfFWhDBngh86tgoUA24GUXsmd4wigu75ZLJcoHrUovXPtIYGeoxeKmBysKplHLir5vyE9snOmToxoPjhh3d49+49np6fAElHUgqYLB/Uovjs0Ol0laV/CJj0EwBq2YFV/hwMz+aolejVkIvS0Np0E2gFT5zXmDYHdtygZHDC9GR/gEI84zJ/p3r93tfg+PdXcMQkSQJ2yXeggCcBVMwMEqBTuFaS9xC9Bu1R0EEtozwQSX4o3i2CtWCD4J2Fx5GNIcUDNSGq3CRKtDP7BZ9/llOxiFkp5mQiCvVITPP7M/PP2Aduu5mrN/p6ug/ChOLl40f88P33aAp0F+ZNga21omkCBmZs4sgiEpgC4TAukmBI6ALBRuaYVEqkS4PNVwWd/J3rDvEunhtKNCskhPwp6VO4FjkHVeMh24aTu4E0WL97F5w2Jrc04LZ1weXUPWWKaZi23vH8fLHyUb3j0j0lgXhEc1M07Man3Gxl69qAqMXu+IcpS3tZmmgFUa6siIEQyBS0raPNiXmbuI0dQTq1diHM70m8xJMCGACu1xt2uWE7n3A5b5C+4Z/69lv0pyd8uF7x5+9+wPV6w8f9I17GwPPbN/j2l9/i8vyEjx9fPFrUChxP16obXRrPoqaJGnKCXWmA6igbcp+PYmVKf8MQLP/kASiKkxq4a9/73wc54Tgn+1EWxpFvqE/cwkvKwbD4FWkezuEZLkRaA7o7iTPz9Gk7QUSwX28AvHyD96H1DQIzx8HNdBIantpFAaMRqQXK5/v/ZbLDn0mY/RZBqNkj3lmW65ZjWdg+F0VT8AX04m1bR20dy0dC1HwFQlON8h7PLLvb+gCp9+GfmkAB67weWxCDFQipAMMixNKY/GAQjkRDATddSLo/h/ERJqrLPC8MD0+FEtX0a+Rb3Nzbp1jnk20JvyQu+DtiTpowYMR30lalLznM+/q88Ochnej62+eOr1ABlS8xMwAAACAASURBVNrNZbUiPtuL/xcwpIfUKMu1+Zedbkyv4iFGLhVdbI5/peu4hBuZwi9JA5IcuPLWMOE5/wMjovwZ+Tx7o4+jiPtDEZw5vY9pZvux33B7fcXuwTU+gJG6REMrck8wdZXWz/y7gsoAo5Wmy5jwDANCoMVsuRfKd9V8RN8kESaUsAFVPy+rj7Q4v7uvWGt2zVY0R83P7M0AlIhXTvTrzh5pfT7ZNb23SFbMlDVhYYl5VKJLUItXySPGwq+Z9Tunjzt2I2L8w+mp94bZO3TPpJsPfR2LSBcg+stqGzIVMtxkOyaaKk7SoK1jk4kG85N7/fjipbJG1N8bbRTfVTk+8o4dkqbvzHI5ROu5OIaEfPr4iU14vlsogr8K0ONBAWICgLsiiYnXooaUkcKhXltBFxCm6uUZdg/1+9mzTucLNijOpwvePD1j6x1vv/kGl6cLPr5/j7//f/19jLHj0jY89Q2nrePN25PtLsbAy2+/t93V1EI8DD9eV7VC06lTEapGgQGFtnWcTqbdaJuYWrK03upQlXpSzgylrK4kiOQYWTrOVM3i2iZpGfGRDN0nSzlWrnnwCcyF5CYyLREUsLFFjHMBQ9ETiUegCXRahfScVKqX3NYunmGZ4+X3nozwKVxXlA7rM4oD04HdFnnuOAS2WyJLWIScsKVkSvZ5XHe8/+E9Wjc1+3ayLO7MkdR6j1Bsap16t9phlRxVEbl06PdgPl0txiuFJ0O2yRQk30PDqBDZ0JoxIHWGN9xE3EQi2qX5cxZtxRxQDM/5NPxZnM9pu7pJWqhpsQuNfEUgSr3fE9NpM19By97vyLg9ByIoY8KFUfO8bHUTY08Ah9I1fIKGrW3ofTP67L7TX3KLWdJJAgcLrTfXAVDj6O0vKZ6t6bvTANcB14ta/rIxb7mu9pm8RCS0Tebe4OboMaG70czHF0vh8vLxA374/nvonObWIGkWxlRLROmRyOGCAAunV0TQXoBAFHFdQVTT0B8ceL8iNMKaF1UQ28qGpXUHQeHPk9rdmp28bmRIA0w50ptgc5+ly+mErZvP0uW0RfqB3ppnCBC/txQAZRqobWtxn0iVAKB7KnUpI8FcgjooR2LWM/IRcFCoQOT2c42yEiS6z5NMSgYoBL1veHp6xvk08TLe4+P4CFGrgjHHwPQAHhHTronAUxwQmDd0sbx/88MLZhPsr1dg+wHYTvjl8zP0Iji9vKCj4fq64+/9b/8ndp34K3/tr+JP/vpfM6vKxw8GxtX8Y5cZEEA9Nc5QtXp/qjGX1L6nts1pKnBDGU+Sz2eOn8UHatl/rTYkIH85YEs7lwJ9EWzILKbxbZy7PLg8o+zMnPlQrNfw8dPphMv5gr51XDyi7sO7d/jd736Hcdvxq1/8As9v3uDSN7x9ekYXweu7d7i+vLozXPddSDo957ZHg4/N2jwtEykm3DbfpUS1SiggdFZOezpVM1wIMd6SwjceLQkcF+yuRSQcx0+sbdPNrLGAA0AV1+ai8TChGyN+NyHhJ+Jv07uSbleF4pG7QcB9ksrvVdMTXVA2ZB7akwxWPIoKAUrWvlPAKEfHz5n78Nw1TG9hAmL33XbfToAXMG2dO9sWGqj7AtjxhBCw6UDOfiKuWRqpCPpVRbkuI6nC6bM1L1wNQGXxswPganJ4vpeSuiBAVAUR98dXGYUXDNj7XZC6oAZjTKgyY74J96rpu9/haiqSNOIxI8tzWxL4IvxakuYLMqgHwYq3lQl16+/eoOQX0rz+ZM4dQZT4c0UsD9zYh/lNbkRe8CLJA9fXV9xeX/Hh/Xv88NvvoTrxi7dvcL5cLA7RQZTOCYv5F2gvfBnrhszIu6xpcPXm2Oearz5hy8D72hVAck54h6K0C54d/BXwqDiWoKm8z9bKxg1Sa9g2AwxPl7P5zvZupjlpOJ8IjIJl+70l8jo1TxPTw0fK39VqLFYexDkK9kVgocexquKnaBXr5ptj6VpQFSM4cXk428S1C4ZO9CnOH1zLWknPX/TDioSocLqBWpSmCLZnxZtvvoFsG173gdd94Hq94Xe/+S1ebld8+913OG1nqE5c+2b9GhPm3HE4BKYMIfCHueJgikfWO4UE/pXQROlxDf0I+/rJ0xiwRSrpvAwi4pTfRpSy7J1C6h/7dIBO4KkL6RyoZ2HszCLbN2wn+751d9K7XHB5ekITwevrK15fXvDywRBy6x3PpzPeXJ7QIZivV2N+Qy3iAJkQzVyqKKhLWx0MMnGkzBHjVHf8xqwrP0jCjxd7ZjeMkzM52Mp8guDpx8I4t96gnusqTV1i4clQ1LxG1DtkvTeBljFO7VDt9QoklRIh2sXFnckGCYgrg0ScVxgBCiavAINCJ3geCQILTaiCeaJJqfbZkwI6AgkgwRwv8yZQaRhNMCT9pmxw1HxbYBnppwKzWxbv0HxFQlYJZsaxIFhpwk4gdsacz+Xv6HfVo2losc3kkKkOFsnuvV50lgRkce+ivp+r5qkeixb0qzl0fQWfcfCh8cnW/zKXvjZJH34pgXywKC1r3a8zH0dk7iBw2pxvLPTvQG7hcgLUOfW/WmvA1tNx3RrgvkENUQzS+fSEAAMuiBq2fo4Nw9h3fPz4Ae+//8E1Tx+x324YtxvOWwfQDbCPARkTMlNDmUHHNC/qcaTLDMjyjYSoXnkIYVHBiDE+HEApAxKsNOSBFd+1eXQNVGvo0guoKgDLf28iYXJrroE6eZJLfrd1iZIr4uC1N2SG8W7fNyjg1QIOAjJHQFNWOKkYm6Jvkw+w5qUB9sw/VDCUrICWANKvj7f7GEl33sHEu9TSFQ2PwNPtSPOEzoPs0uYkNtuAiFq09m3H9Yf3QDdz5S+enoGpeNpOGF7R4ze//o1pB3tD304YeoMyMIvjQgBILZNTAV8hczX5vVbTXhDEotL45PEzpDFIhhtRDzUNezmy3llJOH9AT9WvIMAD7+8C1nYVdbFI4hLAiUHQT5ajiUi794anyxPePr/FVMVvf/0XeP/+Pcbthk0EbTvh7dMzvn1+i3G94frDO+gYOLeGp+1kXfP8QeGbLi4IYz1YD7fWzDmZ4Ii72JozBgAKT6Ps4wJk/12RE8AwCpEqys4khTKHbHh22a4dG3rsxsQRYO5csizIVAIMBzEOBIGy863zBYln8tcAhTG9vsAIWvxn7o6i/RTkM0EaNS8BfEDglO3gYquxjFNhZQr8ysgU4n1m7iNRQIY3l0kCZWKnMy3UBEMFKTKhMgBtUEOhFsbtkS19M3OL9DQfBAQKIAlMTT+YuPfhoFauiiBlcIQoxDMYT/rS6d0t8h4FlBFE7XPPAIeSRDTGNrRlgmIv+mqO7K6z5hDURfgU8ESnfAY+JE3SN87uFiOdqpbEZb4WJMq5pIYW/tzFmcPX1/B7LYCBp/iDt94xvS1zd+nrmioVAVr3nbsE3xzD1qeF0W+2gRg7bnPi/ffv8Gd/+g8tXcH1ijkGTqcNT5ezAQudwM00s4wKVmiY5hBpLDVLiyC7l70kwjQ64/iHHPHBps8otccg6GkImWAvTVOaa4bO22auE8iceluz4rdNrFwXI+V6t4jV7lqr3hMsnXr3lAbpuNycnxq7FEhca71rXLu+Ecv8W2X2XL6RVggazHQFjKkYt+Hdbtlfv8tp2yBeSssqSijalNhopWxogLtLTDFe33tP4dPdDNw4hx492CSqFVCLaOl74CAKGA0YAoyXKz6+3qCt4fmP/gi/+O4bNBW8OZ0x9oGX9x/xp//vn+J8ueBXf/LHOD9fsE+FXm8Jloo8NO1vvqwPQEZdu1sHUhba+vVk0YdN/aeOn8WEd3dQun/JibGQ1s+MJAntA6/gDj8Wm9zfj6OOBmnODJsxvijb4vbi2+0GjGkRSl73zvCFz+DMnf6hg4eePPgu2lGa+UXjgtQmid5dskDMREGhVeDfdw7DoDJ37UdoiD7VvEBF98cKd9cxqOaRCr7uYVgS/jKGj555HOb7Yf+iIyhH871+Hy9uAQXwmhcGOKcvWVXblWkrTrSKKVqKj36qBUiS5ddsBwWBnyDhJfto05FCPq+vM/Pg6a5lSMfaT5/39R/3oQYczxRx5VuNk/w9mZQGCONPJapv0ZRUYc+b2fxGFNrSwsMzS9via7GerCY9Pq9FEAQBGTdg+z7CZ8nMWax7574w/uL6JMRp0YaMQqb2jYCdDa4rO7UVsvRr7XKO/h2vKONDds/P3Mx235gSBBHQsLZd1LqjmU1afO5Fs0QQ1Jtg430MY3Aq413K6EshkmpjEe8/O3ZkXanIJN8vmhctPr+FnMD1L2leo8x6yAUceC/Z0jlH/rA7cQckzQKRvYN9bX7NVM/b6aBXFUY/nuKit45TN5/S2/VmqWLEXWOYL0dR+u70o9Rl5uhqfC5UciCYCjC/hJX95ABqbVRKWi3/+5nLDqvJmlwLoACFE4T7kzBDsGS+mhoqHv4ydQfPEOM2ogk6m6NmFxgKXF9e8eGHd7hsJ3zz9IxTb5CpuH54AcaOTtV7MW0szNFfnT4GRbtAtN89ARtVjFMt+WTzHByxKyljOL2MQCwCWVG1LR7qLiQIVRWZ5K7c15xHfZEx+rrOW6k5lQwtI8oUSD3yA7CSjNAYiM3JhE5gH5adOAFd9oUaJ5vHVAOTUJIVqTuKI1S0kSJAWzhb2top6lw2lyChgLg0e+XOUXRL0vFrNhH3K1ITIj6esw2PvlHAQ5CHE8QYptLaTidATgX0u1Dw9+75yAQIsBU71MYC00kXqmpmV1UTbjAu1npDC4AspW+SsgzJACu5WV4tsaTVvYcTeQDuEIKS8/MVHRZVm2uJQLX5+LfWsElHl46mDc1VRbEDj+uAAWZJLsKlNXNzDNAkHgFsdCFumm/c7S80CiwiUG3DRDEdVn3ktQA8RUXy5r51SFNgDOyqUTR67raB/N1vv8f1esU3b9/iF998A4Gatmma30oTgYRWxsBTByJlAfPBTT6zAKNYg07XdCa3PGk9IuAIM5ZNZ/acdwnBCmSNVZqZWhOcTp5vyRNWtlZBUMPm63vrAmkNm7TI2xS/NcUm0z47b28wbVvQipIP54pQX2hzeptbi4SmNO8F0J4eaAT6dlWeZ/dg5YN9sHyORqBQZx4nMS1aNiLlT5jj/J4eW+DjaECf6Q7GGNhvN0hT7NMSCk/diEDiGsSca1h9CFwpA9zn3QoR68TH332Pdx8+4gbgF89v8fz8Fj/sV7z78BGQht42XM5PuN0mmlzNV4/3B/ssdken/6ms25l1VW0IiUG8FE2kRcix/dzxMxQTxhEn2RGEjkC8y8LgxEoCKKmX8TuRZFStLdfMACVU+jZyrXI3Je+xSJTSyH3fcX19xUmsUOPJEyeO2w0yZ0QxgDsm3E+CKQlD6WyTLbkojEk2NHd+iwKTvqjnpwAotx1Sf+PQkYGmgJuBLphvqJh8gIhWk9qnmKu65QiUG1oKzAJGon3rdHMsaJa1yyzaa45DRNfhsaGp4n0f0PmS2E6x5mR9tC54M6cHw8yJHri3ZciyqDuZ+mILHwjxvFuai9rGZAK9Y04DOToFc7haeXhETRP0aeUYagFZPl+YfFMJ4DhrueulH0KOuTsyl7XUCNBIjRTYpaePj+wrQVvDxJzp01O1Uz+W5uAv3bEIgPwssHknwDXNNLXTAmjyLiXI1KR7ILlP5OFCgqhWwHHQcazlbFrwxuLraGuMAjHigMF0BiMakRtWNCuoOyFQ2aGNRc3N1PPu/Qe8vLxY9N35AoFiv14tF94Ynhy44exO1GCGcWDJwRSpB6jhKEMt3kZuIqcqRBvqaszRR3k//OV8K86P3BDqwIgJK+nAjdQc+TwvvksOsASutRKgi6LLdG3T9DxPmqDYE1jZ0iBs8OYJInSFVgFb+0Um+ck17yGaoK7YCQbTmOmOPqnBHo2gTI50G7coBA4Cp9TZoAAIY4vZRvVAgTEnukfpZs26dS6E88xNgmrwVhXXRGmkOgMA3F5e8eHDR8j5jKdf/hK6bfj4buB2veF02SHSsPWTRzYzCVdaTYL/0ddFLKBD1X34PMVnyi4Oz7r2vuT46X2gCtOwL7zLCqSj8coYrIDrhGAPQc3f7s6PZKPqGVmTzBTA4LVCDw2B+FhOR9iQyGcYOwgjOFNVY2oAEh0Tu46IjDAka7Xp2DI6yxOcx8uFZjBXJ9omPtFE/DMd7k3lWhlBMlO752Fw/Z3PatEuawWz3wIWdj9VQ6sSDNkXRXV8jZ1RqNftv9QYGQhLHyZ7iLtYAGCYMkPrbQFMVU+YmUw18JpISRDJKLOc/2AaVXtVRoN4iEzq6HjOe+e19EEohJXI0EN9UxXOpUm1OIEbBYFOYL9eMXpDGztk29F6x+X5Cd1DlMc+Yqco2hKEOV1RY8aQbfZhTnhIso1tPtO0i3Mysz2nTiFoaEJtYhGgpA7lOOfucfqcTt/lKr9z07W9V1T7NYEodb+8GebXEDpT0YZiNhcwvp514dPubD33WCeePSnozKg/faoggv12jbXYTrY4TXNMB9qyVUkUBJEWPoL2m4KliEgPHz6+4PV69fvUWpc2l/vYY/6ldUCsuJ8qcL3d8P79e6/5ecb21DD3He/dfHdyTSWBZmFHCSrhG5VFXq3r7/gy/GkLVrHytIRU5Ie+LpvlUEoQhdA0hcmtm/boVDOD+zw0F/KixbNPS1s5f+LtltxcGYgrcxQ9P1CX8zOuKfF7IdZ6GUMP6KE2fyp9epSYNAGBwHMZtpKfCpieToPZybkx40GZopwjwCMC1asUuItLCguQBPl8Uj5dCbgGEOOR/JNmvQ6xXIoKd0AX8xm97Zi34clMzSgcbHkKIvKOGwIkCG39ApUJ1SuGO+U77Iq1ZjRfAf6PHz95JnKa0CqICsdfAg371oWAIfvbbbfisU4UAEpyTAE9qykEp6oXTXRB7xMsvQONJkEJ5zyoJQrfh/qugsDC7i0QT5g4oBf1nB0Nc7/ietuxiZhJz0FJACiXJQmcOK3qwreD+poJ2thbMOnpRXgNPBHZAXWWs/Ds6pvBHYGIa7Ca3R8AxKNLTKVpteluuxfojehHXwRNwiSZmhUXnlXzJU70XPCRVbmoRBnmDGD3HdBQK9BrwsXDmguTqawHQuU98j2A2/Dw2JIawFXXZDgsEN17c0ZV0hpMdx4vJk2lSXjMiFYUFU+CZyp+tkUA93ewMZlkhg4WsU9cd4vUtPoLHafzGW+/eYun52fc9h3X69W1XB2sgjHG9Ajx6btZDQGeIyShueO6KYTgdbGcZWgZ0UWLsYa4Vy1HRFoN8wW0kkMawtgyUjutHbQjX8uhQNBnmOhBcKAQmBbakut65E9ZfwJY7qTrFQq14uSt0CHMnBG+KD6O83XgerM0GafLxTLA/3/tfUuMZkl21nci7v3/zKyq7urxNHZ3j4exjQUaIYGRhYxACBkWBizMAvEQSBaCHRIGgZBhx4IFEuKxQEjIBnmBeGiwhMUCCRkvWI3AzALjAWENtmdG8+zu6npk/v+9N+KwOOc7ETerqrurpzurMvueVnVm/o97b0ScOOc7z9DuQOOg1roie7uTWthOA1D3kNVasMwLllLw1r17ePDgocm4NITlRE8Zgf0wWENhScXDgsDhcMAyTTjZ7/GJu6/jpdu3MR+PmJcZWir2OXnaATqjy5ElgGbCxuODHlUNKKkhL6yIw6vBEnobEkzzEM48jRwq59xOCmBRkrUIsM+M2TxmSaQBqG59aVAwFGnyrBlUPOBeIXYczeow8L7YwuWX0FuMcByw2TBUUQitV56djhHXzOfAwasaRSFJvQm0tVUYRwNQDQAqNLnhUyzs19SKxnwCHrlhvNX5dcjZKtUlB4jq8yPXf196XDQvkXntjG+zz+cAwQirDlzmAi2KcpgwX0wouxlZBYNkiFokpRZAq32+1orFZaPlSQEiFgK3zvmCqpNxl+sEVk4qWmPsS1rnqXTlHiguhiobeOGSV6pZZUSrZtnWZk7Afwe9H7SeJZhLqytj/q1qVWlJIdWtF62dV6jL2XClHypa233YD0SpxHqzgBtLevdqW4j2aiN+CtI+uyJd/xTIurhphfR5RTLn6ibojIQulwBhlabEcs/uKvwCF6p7JJ+a9S9KC1a7de0kXee5akCsD8nR7azoWx8EpOtA8+VbP0byZEtvtT7OR3xECmW9tK79qDlDba2Vhl8I2m7E9h3OSW3eKC1A9uq9nBNKbTyn7uWp9HoCkKV61qfi0qZZWY59Aj5fJughP9ub1StU2vdWAk9k9ZNgrpQKKNs6qAkiB9QpaVwnDKObQvS86eMASkRQq/e48eaECscKIh66EA9Re1NTqvuuqpXuHyZwCy9SrXCFipm5iz1f2to66MoKSdZvZ1msV06VDkAtFm6bpxnTNDuAokfDvFx2VmN2by3C2mc42QB1ieav4k0mkyTUpCue9NF04Imz100iBWi/7y5tcgOIlBD+daHnqRlq4lqEkYQkrdEwQSKrJFulHtey9X1i24g+9NixQ6e/OHcNBNG7sRYfgY7XgjSG2C4a8tB5YiXnhZfinLXXKduT95SKf64nmf/YctDWhncv8gFE1/Z4dFckAZAaMuqWdQ2cVtKIe8LXToWg13+Kp6sAng5i+4JFCPwPaPKFU2ddy41x6MULrRayKjrLEG+6YYpQBevnfjpdrQeqKqbjbBarb7peIZJJmiXB8JZgqYKU6auRGCiBlvA8NCpm9PqFFlWFFBdk1XJtDJ2PkWMgeb3JtHqzr1Kw3+1x584djHnA8XDAIgknSbDLdlhwY9CuDZMLwcB+l+ekQ+I2jrUCS95igRspp4w0uF+8p7i+bY44DTwYne7otaAAqOgUSCOGADP23+IhCz5LuP/jvs2F3TdvAxh+bCMF2BhQwjNDT0/tBAabD7JrPQ8dNvnb5ifBQxSerA21brkKjeOpzFIP9BnlufRikmc4LoglXpP3Uq7uIYCVkguFh+VP7IbBFGBxZdaV9jcXOAENe+QicghqZoO9HVhqXGvF8XD0ZFDfK4rIqlG1k9bbbtcQvhTqLQE5NSAZU8df2vdXPNmB3Fjb+L3G8R3k8abMAdUFQA+gnt4n6rpRqRUPHj4E0EB0DiWckPMCEeB4PLoyZUUdLB9TpOMz70af2ZQRcd2IwvtmZQuN6gBW6VH0TveVWqHzAKTEw30R/C/+WshOrXh0foGLw8HvbCoq5xEpDRjGAcOwByRhnhYc64SyzMjDiJMTa12gy4yqFYeLC/duC16+exdaC7AsqLV4Q2HzfmiVtUxwXg0AhdrYlNNAIARFf1B25IiR11MKUMR+SplyL0m0J0jJij+YPM7PoVaE5u6uDWAVJlzpFtXAhNWRL1sySCWActlAwAMgpQqG10S4r9HtR29dUmnoS3Q5J/AQIFqUiCCS21e9qIYUeoVyv++kLp7Arn7uqs11ohBxXuKcwLvGe9PQZCcs9KG8COkFPvTXoM2j5V7qlrJiOj27V3FURRVgFsFxWVD8XrdPz3Dr5BRjHq0XlybPJTXQVBU4TgsuDkeUWjBPRyxlsWhB8JfxkHk4TYampLYezle9/HovuloApXDLh0dDYAWg2OckpbboyQFUgrTk5BAAtHYTRHqGowLrknng1okrmHmaMc8zRAQnJ4rsLuoxj6vv8BwerRXjOODs9BSo1atNgN1uhzSOnbuXlosDQm3ovqnTbkLsDTTPkqwsjra5mIeQonTYLxIghHeAIIR6ADOfauY7ScckEcNOzQsXoAKzx5ebd6J5FhzVEAQFgJJYixqNYh3oRoJks9wCXnTeqHbdbk0DQK0bQfJ9s5gZ3vVrFkJujblpYNCqmaoqpNYGKGO+tAlMzrjSqrFrDDl5rxHnReasCTx8EbYSyO5Jmehppd92wvroYtPKxOf5Aofj0fOLvOw7MFpB8dyUBqA0wqlmcbKf2eBAqinr5lEi2GsAqoGnTlmzUrNzJ6WUMObc8aYHXdqCN2B+Q0hrxcXFIQwlEXGlZcq7lDZ2rkfx8feHNDPHLeeM5Md0RMUw0EJTYlyzLAvmZUEpBYfDEaVYqLp4eN8OHzeFzHMYQ240cwlsjCnOH1DLY5qXJYwEQDCMFUO2h1DvobMsM+bpiKrFgciIMql9tyqmaUbOR0CAW7duoZaC46OHlkPF8QmASmigDdiDMmjtAuBh5DQMW88tCbyVYm4dQCVB7irlsrQCD7NFBWmgRwZR7Sa1uJUkQITUmjQgaAkDPowWf1bmADpiFspDf9DYZ/4lM7gVPHMVgtVe6Y06yt4M5mxJeIX6NAaCx9HznZjXZfPe5BKNy1Zl1+uQVmQCafyTvBagAa2WOsLEa4JOzlPTb3SE+Hw2FeB8ycIqoyysyFPLfYIiQ3Cy22O/20XyOEGnMpRXFctccDhMWMqC80fnmKajr7PNkXWE91xSF6gpNY+juDeuP87s3eiKc6AUy+KWd6kdgKKS8ZLKSoQtrWJIl7agoQBoeBXwxO9o7ieu+jowZBDaoDRBkaTkTNFyiDhv4RVxobff7yFasRyPOM6PosuruDCKcYrnqwDEAS7MemWihP7tQ0qQQ3d6s1oJOIdS7dgEAKuKDsofn01Rup0lqoHo5QvABn7eN44/uwKobqlmzU1wuBAoxQCrTbOjeSbQUka44FszYfMuAmrnIqlAC5BQXXZJJG4KHegULp1V1Dx2gGBwD5pPhFcQAUBmAzVvdmJjSD7XHTTiXNIC8dcboGvzRo+e6SNBVgdLKlDU8FQRxFjisQR/Qbxk2o/oYHghp4ycKjQB7HFi6it1vMN5SM7f8WiRo8W9AXj+1Gr9Gv9xnR7jS/9sJOXXy15HANXbMNjkgm72ANac65uDnwB4SKfziFY3RKDVQ3hUfibvWosAgaAGeLGQn0JKlLL4T7Tf3JIvi+UrlVqxzDUME8v7cAUK4z+yAD33nH+TEY7CqytEwIs3HZEy1gAAHpxJREFUaBBJXItnJNayoIj35vFTEnIaMKSMaalYMAMqmJeCfJwxpITTszPUsmA5HiwBXtXz9xDe5D4dwkCjQsUUZaWhF7KDHKqmxZE6g64xmDi4SARLaN29U7Zz5QzAslBGMARWSEDXB6rtx+S3pZdcI7eTBkiSBPViIE2wCsZqodIw2mIhmATvylrMA0JZRt3Wo7OVkevyJw6X98hJCg+bjTP5OXtcR3rBVBuAbYcBI9SRPUcv75pnraKieJsZrbXll/Efr6HdMNAAZ3OWcHwui0Fd7u0+vJ1DKQXHMmOqFen0FLfPTnBydsv2jhsNQeppEVWhReM8QGEbIn8Y81Sx1ZFXP5cSALkVP6/Dmk+jKw/hHQ/zuns0B4fO/czsgM4yCws/2REqvagx5Bu+SARK4PvqaduhLPjTNmOtg/Vr0UyDoZX2U0DljNsv34HcuYUH997Bw/v3UZcF9fQUKQ/e+8lATnFhIfBqgh7c9HhOTXFrx2wWNhrc2sgQyagV5vHKFcNuRB41mLslfLt1oZ4o6D1oklu6BFWhc8UZHS1XgWCVzwGYlREz5vO3lE65hqLuXiutL9DTHBA2dIJdC7URBCnoHWNhawqhG9YOZ0wzdHRloMW7iTc3bcoZg2ZbbRd8y+JnMfn9xL7sSatcCQKH0tYmrHsD1Dkl5MGP0XDvXcGMUs0bNvrJ6zovUSEX/b5EoKJxWGjOGZaGLIAu1keI/Yjdm1aleYHYB6gdst08gMyzgQKlFuOLxV3xYdV25WHof1Uw7Ma9oPDcAb7o+61qz9u23smZPIoOnrz815YSmkdOHPC3xFnmBXlOEwzocnoUcK+lid3mCdQwKPsO8wyF1FIjFL3Mi6+zwNNu/d4eSi0lwFBA9urXEfWQD0x5wPGIMswrDu7pyapY5gm1ztZZfJmRU3IvQAIWxYQZWoHDxYRlKrj78m28/MorqGXB8fwc0+HC5qQu5jVxmZElYRBP6qbn1g0SFcEkVOTNO8LUDQs2SnsP1YwQbzEwJMHoIMjaDsC6oZ9aL62qxb/TjhbtTU2GuMY8YMiDh9Qnky3aCqHofcspI+chrpB8f0STzsxIQAR1kaQiesqxLUJO7p0k4BYfVwPZlEPDaOs07uzEjABQICDkVpUA8iyIoae6MPTlVd5W3UbZFArCvIZizDLPE5a5oJYFGdb3bgAwqGCAGZOMvjBaYGrHwXystx/ZLsZ/CkHxfzVn6JCxTBUPpgMOy4xPfuJlvPr6axh2e6ScMM/TyuNryeSKsijKVFGLAoX6UMAGHtNcXCYqUnZp7/uZfaDoVX+q8uroyqvwVJsl3AuWQOV8nd/xF2gBJKjHUJ9yA2i7gnZvKEEAD+DtbhYbu7uzb9gGugTDMCDBwn2Rt9O7XbtbqvAaAG8UG4BfIDBH9znhZu7Da2yv0JXSA93BiJfJBbCjb2EPktU8dQ/iDyVeFtw9Wih8qyiyAuvUnW/H4dNzY/MCjyW/G4BS8IgIup4r0E6Gj3AEQPAbB/DyoV2Bh5Nfq4Ub7UnAmYwESLdY17yz5kVwTbV7Ty9PWPtTHI3ymrFuaMqQHqsGS1vpLG3whl964I9uNfq90cbdco0kvIEOixF8T++cJ3v3AGrFi5fmJO4ZfExBeHnOurmS9v5NI1cHaN7J9ftx5E7Xg6d5NhErQ/kV4fKqnphvIIetTcgzDONYONen97JnnSzTGZAhXynjEvcFOhHZcRg9q50Cjj1FA0c1EsXX8qnGsa7ZK3zzMCAPA3QpZlyuPLltTsHhOPio0rNj+04YiiFH9fKnwjrkGBieSakdyivu6bBcKd63lc9E64Ls1Ywwgyn59wjd+PMyhUzkFgzZ00asVHzxBXF5y+d3yZCkkyHN+8TwWc4pAFTi+CglKJOUdyRvEJRiBRSkm1nh8681VoAN1T44LI/93talmx/HZP1z9JdvO8v0QoUZgEst1u7l5ARpGM0YqGvd2gmo2GzS/devDXNs2aadTUcjd80/+H7SD64UQIkIxt0+lrKBGrhLchVsas6k9oMXQnNPO0Ou+krQLciV6RQxy/1dQiSvFSdT8Cp8vlIqpmmyRN/TPU52Iy725yYYPMH3MB1jIjN84zF05IvUFGUgDjTtI/E80g3PrNWMAvWke79HSq27dgfUmvJuLGMMU83D5lMRG7XDkPFIYAM9XSlok9c5wA2tsOqlDOa2tt9rIvM9CeW2e5VqvXXpXldFWHl9+CcStzvB0l+HeybV5Of5KbJ7okrtXNUsvV/1i2reTZ4Qz55U6Dxs9lnzjFbTdKHQVl12PUct5RTJlVlq5FzpJbczec8qohZcHI+WAzXNKKXArNAMSjT16rfiZdPVLXUCMvugV4Hxfhxn9PggEGohqJya9ZvEudm/a7ko/hI5NsAj4tku0/tJwrxOZHzWxDY6AEHvKHPxeg8U4hti/DGYB7315oqNbGvmMqMHpkTr9FaItKMsWkeLCh76GiC3aksukT6h395P/j+Bh73ckzHm7B5XPhN51Y9ySba/d+MOTDEoZcHhMOP84oAsgpdfvouXbt/GxcOHeOfNN6PJphl1roxhZ8UNOUPFwsLGkxxH77dpkq2BzxpJzVqrVRSnCmgO+c5mmdx7dkTLgJy89Yy0nlC23+wnE6RVFftxdBlhM1eV3sCKJNnzUiU2SA/YeNZdZFOqwkJEta2FALvdiHHMTeaSr3zOxmF00CR+2LDpmlX+WLe2nLHAFJR5bBlT/FzDSv5CdMKH6KWmzfY85uUpQAWyZAyS43xA+5c9cd/avlgOG8EmkbGEd4q/29l1QAEw14LDUjAtM5ZaUVSxOznB3Vc+gSqCR56/PC/GPwUJBcW8bAJIzkgABs2tY7+jjiFbgYV2rSWkbznRAacXLomcTMDS3j5pmN12AUTujrmkG5oWMeW1dhBIh+bZW4lgQTpekvaDDMqcGVeuIfRAo0Atm3+2Z035DON+b2G0nFA9cXSeZqhINGobUo5MPQJExld5v/5xeiHZNo5XU+SM6mdMER3TK1RplcXm7K8ZEMoqccRAjtK8cy0YllTgFXuF4ZkAL+L3dT5LviFpOScxF7wqU3EuAah+HcS+m9yyzSmZ21a9uWaACg97dYmKEcCTBrIJ5qSat82uww1SoIUCtq0zHylyfHgdePJi7RLPwXU0AVoVzYPg4ITpVAKB5BRnNUWFliQHaC5KQpZqKIF5mb36rmDxpOGccuQx8KG1A1AWBqgO1NwbBU/+VeWpO9EBGIBXN9Kr4Ms0uHJ3AQ3AmtjVGh2EmxjifuxQFW0VZyTm+dwo0sAlK+DEn5GCkKzHDNfC3l+3BrDruZe72/8KtJ5ltKRjLiV4wfqE5eZdgoWF4Ao/qkETohSc4idI/I402KxFnvVH8uRj22OdwQFToJpsb1p/nYLpaJXK8zzjeJiwGwe8dOcOTnYj3k4JD+7dM8PGe1GhVtS6AFWRdiOG3ehy3PedMKewA5CrtdC2f11eRGpIhP4ReiX5fjX5Sc+NeJjdQ32+hmw0KWhVb4nd+8SraLWFU8Mzrs0zklPCaIlYAaDANQVRsu8otfSTk5Md9vudr6+sxppSxslu79XiugIGgHdrL6WbL6KJNfOqy9WVR9PbpxA8upsOiL3uoFJcJy8mNyy8mJG9+36W5OsLSKGrtBlf/ZAqFSTllRsCFcBSCyZVTKWlZIz7PW7feQlLrXh4fAfzXLBUtWag1noZ1JCSLGSXkZE88b2qhZCRLMxqRihWzhsa2CHQ3of4ulIAlfOAl1+5G/kufZUB0IQTvPzdEnCXJrg7jwHgmNi1KPOPWBoJ/wTQAJltOIbwqEh8o6i4FVVsQlNy2WMgSgriWYdhwK3bd7DsJuSlYCnFEslZ8dCNpXfF8749eIp+Ly7Egn/prvUNbLk4KeagXYPM7pZKd2wEKW6pl2HNGnRp/Na9v9LdLtCFIKh7O0CZ5+e4kpaYjzU3mieMuU0tpAFW1LmrOtaHyikuyDnuXO++GSsQvZjgv1sZrU1ugXb814Viw+Pkc6o9OF/PjYJVhuzmw7UQCDprhvmKBMacC69mgWqsc60Vx+kYPXoIllqLACoNREhbxO+fuDaN95Tgl39rW28b1orh/Dri+RyI/mCxTxEi32ajS7x/csL4DQNQNCggMXYaGBZSyWA3ZA0h40YhWwj4943NBJKsoGYpHlZZya5+fchT9t1SXS5JsmaXbkVH2DvkavPaFCxgg9WV9xDMOfSKwJSQvMFvKbWtd7QANkNAxMrkaxXkxdpXLKXi/OICyzLgZBwwpow8jHjppZc8j8qSkOuyWKEK1vwVJWIrwdIe1rd8YIMAUSLNA6UB8wG/ttCYjoG3ar6AD8Lkcf7OsydbyFLhhqtaI06tCuZOAa1wIqVWCcjQGu8LAKrtsGWuyW4cMfg5mWsNhljb0BMxDI7UjSlt9+j1jJDfehQjjCy4fpSOW6TJbonWDAhvOVS7AphWCBMpdnxuINJZYsW6taVMqYIwwIva+XoVijyMGGB7Z6kFc6mYS8G8FIsUQKDCfmUVwzjiZG+5q9OxoGgDlWoIF2xay/zf2JGcV+KnF80DdbLf4wd+4PsxzzOmeQ7Xr6q1BZimyRZoMtfoNM2otUSoTMPKsAlJaTDvRS1YljmUIGeA+TLWdj5DtWLx+wWDJLGkdLcM82Adf3X0Cgy3FmpVzA6W9icneP1Tb6BMC+59/Rt48Nbb0HHA2TiASaNVq7urczAkhQ9hARcvFkta07MMi78PmjAXNcGTJOYrSODWrSKplfCKb+nHFRpcadOF2sAa4ul8yCCAcKWMDpCgq7DjxgIQlQuhZJpgafu9KYqcBIrsFkKzinqAaEKJvzUBEE0mqwIu5DWZf8r65hR7TnVrT1ndaEnklphroVFarvRkmQAzy4ZtEHqpRv5bloIJs7ch8DPskGJdIzyj5u4WVFQenaKtCWESwZAHlHnB/XfuGyCXHBafeiI756YWxTwXV9TF1zEh5xLAW8BQNVyPNMAWOIqfdeWJZJ2n9/s9WFkG0FL1o0wDJylX3Q6bLTWAXc+cN8kLZWGdAey7AyDaZgzDgGEYEXwvYkI9ZwDmSRb3WB8Xa0FRvXeNtVQ5R+n6jPGOgK2tMAQsBjiWpWKeFwzDiJfPbmG33+P09AS3bp254VlcyQ8Yhz1EgFJnVC2Oxx4HwRbqrjhcTDgeJyzLguXi6E1YBXAAVap5u8ZhxG4cnUcVsmRM04xvfesc4zhgFOtctt+d4NOf/gxqLXjrzW/j0cOHWCZBWRZo9RMJ5tk8bSn7+JOdZaarAGfsT3tmhqmbZ8X2mgCaAzxVyjhLAvNjtwBRy6lN6icIeBL6fsxefGM92kQ8LC/NA9UxhXkMKxUxdQ5BlwMoerU8LMo9E65oaMj+XtoSvEFYau/FQvQQuceuQqMI2WeHk9R53uAh/+QAyEKAliBuc1mlK2aWFHlcBGrzPOM4HTHWjDEP2GU7E3bMGRkpwv0JkYWJvsJZyUUEoxAUmPeoiBm/x6ngfD6iiGB3dmpnzg4ZF9MR01Jwfjji/DBhqoIKD9VmO2JmHHe4dXqKWhbcvzfjoJMZ5K5flvmIMi9+JuCAlp7iOs8R32obvgtdbQgvJZyenSJPA1LuO9maBURLRJQKsmJZUlgqFeIVJYxlt7BfTcWrC9zDRBAB9gfxIxPYsoATJOIbt1kYVAG0bmq1vB6Ct5QyTk5OUfOC+zl7fg0VE0MdtDLjf+u5eMJfDQ37q53FYMqveaACfIG+NeYWNIvzMaL2a536nvBUT0BdnbWzHk8rJe6PVWhPdskVvb6YWz5AVbFwHgGKXh5780jGa/VxJpdLP3kb/utHRxjXQhOXhu4f5uvSvcap6EMbvdXcT1cThA4AtbswgaGPq1Y77b6UimEQIPlxO5HTpAFuVVuo1PihhjCgpXqJmyjN0DyJj0+YeaH6/mEMEaobBgS4nAjOg0SzvffKf7vO1ELaNoGsXE0pR/NKhpxTykjZjkdJ2bxESIJUDagIFTkluJsmNBR4GLqE/Go8CxZ8iJWyD+OIcbfD/uSkAaiqGIYRu50B4lKnBqCEhoKtY9VmpBYHZ+Jhqea47IpGbDK8Z589Q1bFXCw/RdWq1JZlwbi350Kt2O/3mI7HaPVBQEhDgt7zlvtj920e5W7viP3N1Iu2F5uRttqfDgSAtjMu7RDvK8UcJuYxWT8lcQBlAAAIz03naabHisAJ/rPxid0rSUVNdE83AeXSDfRKsdXJZTnXpCi1Tbcu/FDvce6+A/9GFJ90b0b0guMTz510K7p5xFtn9+BXIFIF6PWPZZJOffBpLonCaKEDz0PNgmEcIe5hXcqCpZgTZCkVRVv3KFaLZ7FwbC0J4zhgWXJMSq0VZcjWgiElpMHzCIW8rSgs1CIvvgddKYCqteD8/CFKLZHfIiLIg2AvO4zjsLKQa6lY5mV9kUAl5jlKKbcNE5vHwhr8bPK4uwLhvkNcxoQbUsK8FBznxT1Vs21yKHgG+PF4RBLFPg842+0gGNzyHIAkmOYZtSSk0br4Xt6e3Z4HvQMtHEbPGqtcJKwHeijscxXLPCMNGeM4msXCcliIWwESlgBfBwhEeN8UzdEqtWl4I3xPd4AFQORfrDdih1BipO3d6h6Slnzr9+nAC+EMujW0Dcawhc8elTWvR4GolkfA7szMB6C7uXhp91Kq5zrVFfJSaNfrSFmc4fuozR1f4zoyoRYZ0OwgYsWrGjwuKaFvBGsniluSavHckcUVTimWy5L8c0mscSrL52up3XQ3L9JaFRCUR9s88BiYfq75z4xSwSxzVPTsBk+GTxQyVm5eQ8C0xxBJqFVRZq43biw1r0KyytyUzFsxjP6+w+WUkOIAW1eLtWIplt92PM6YJz+sVyjP7Hr7/QleuXsX4ziGR0vRiiKm44zDwbzlp2encVjssswATNZarpyXbQOoWjxbxDyXJjP9aBq0NiTH44zZ+05ZGDh3+9J73LnXh+0cTk9PAAWmQ8bB5+j+/Yc4PDrHS7dvIdWKccj45Kuv4rXXXsPbb72FL//6b2A6Hm3fLnavTN7b73AmwKIVuszejb/aQbAhq2B7ssKAWDKropSCZRZoEmuVAAuz1QwAlkA+DoPJWRA4NTnPA7hZCZaSePsHt9iSOBhoIcVmYHl1q8S7MKPe9FF/dJI7gkziKD9HmUFvPvcq5bgAkQBNDzRBJDedxrzU2uQj5SZTFrQ6QIZ5udiHj3qRKSnmyHBPN0weCdsDqAcwtcNdPVDVPiitnfr2IiuxM+wU5oVafC9ITtifneGV11/DeHoCjCPuvfMAUyl4dHGB41RQMKDIAEGy00RSxn7MON2PEACvftctoC5ICRhSB4rECoAWj0Icj0dMnpj+8PyRHejeydMvffHpsuBqAZRWnF88CmazGPqAJNaJ2Q4nNNcpW//TnZbzAHbzTR7ekJTDWs65HXNBd2OzNrqEVipEPpSIIVwRvHP/Ib799j0s84xH5w8xzUdnAGO+aZqgdYGcnOL2ySmYf5AH6wM1e9v5MWfs2SCMAGalUGg1WHKeGe8NPBBXi49BgDhqRqtiXmaMqXnW4A3ZBJbIJ352Fuo6o6ICIfCSK0yz0jgVzZJo5bRt2shTQhQBpsC2+6xpraStWMBCo6qUfLr6LKLOheFTqukmSLTDa1QC5q4vFupUjUZr1XsiVQdQTBhnTgSBd40wYoNz4nNCIRnD9tvTI1mlG+MKmPn6eTWeSuViu4WbPQRdG3ha7NBs8Xway8ez5ynFQpPRk+kx+5lrSfDJnAgiYv/Vm7JS6AqvrQLIEgBhyCcYXKBmpLY+xLfRPdvKwIt4ImtXXHTTyAwa5hulOAYqsRfQylXAzvgWpjBAU6Mx5sXFBQ4XRwuzjXtPIRgx5AG3bt3G93zP6zg9Ow2vgCqiUvRwOOJwcTTl7XbG7N3C+9zSIkuAKvJ11YJSFwNkxdMk7ANQJZ85oIpGab7PwNw7hr0rshjgG3K2sLwbLQ8fPEKZJyzThNPdgLPTE7z+xmv4La9+El/9ygm++Y1vYCkLqqdGmJfHwp273R6aB0xlwQx4QUNBLX3jhS6E57JRkxveskCToGTx0JX6d/3A4DyAjSyb51+b8q8KldKaSIofRZIS+qpun9juifhe16/O5ZXXddinuv5tcRAxu3xLb4Qy7cP2NA1P8CAsJSBum63lFOsqtH5ZFodBjeZxhvfhkiR2ZJgAy7w4UKP+TWDeMP007Z92Y1baDuuJQAfW4C0L1HOfoKgJQE7Yn53itTdex9mdl/DNt9/G1996G3OpOL+YMRc1UOtFL7vRwslnpzvcuXWKcUh46WyHk92AYUjYjZbgfnJ6ht1uh2lecH5uR708uP8Aj84f4eLigG+/+SameQJB8HvRlR8mjMYOjRzxcmP0oRq6Q2nFJ0jXJ0XcZd7lHSiMCRXRk6blhCBW0/Vnuxe/zzfjw5e0gGL9mlx6T7r333v+35Me10G6flHaL/HUl+/7QZ/jQ3j+x+k706rv6tl4DKQ+/v673v3y2j6VOpHwYbhatP3yxPVefeaxP943MUz+2PUvJ/i/2+WfsCX84h/omW4Svd98L730iz5JXFC2ER25uyUUlb/fqc1Ymv61p5k2/Y0Z0r287u+9V9rPZrBLJ8t1pcyBJsdD1r4bCedUPiJZ9EEohPxH+9UnfXalX55FBuhTxMcHHcsHG//TRMcTPxl6ugcyPT9degzHEDT6Ay94Q1UaPCz2yKnae8qu7X3PPkLh9x6nfCgK4H2SiHwLwCMA376ym3609ElsY3nR6KaMA7gZY/mtqvrq836ID4M2+fVC0zaWF49uyjieKsOuFEABgIj8d1X94Su96UdE21hePLop4wBu1lhuCt2kNdnG8mLSTRnLTRnHu1F6749stNFGG2200UYbbdTTBqA22mijjTbaaKONnpGeB4D658/hnh8VbWN58eimjAO4WWO5KXST1mQby4tJN2UsN2UcT6Urz4HaaKONNtpoo402uu60hfA22mijjTbaaKONnpE2ALXRRhtttNFGG230jHSlAEpEfkxE/o+I/JqI/PRV3vs7IRH5XhH5JRH5VRH5XyLyU/76J0TkP4vI//WfrzzvZ32/JCJZRL4gIv/R//4+Efm8r82/FZHd837G90MicldEPici/1tEvigiv+86rouI/HXnrV8RkX8tIifXdU1uKl1X+QXcPBm2ya8Xjz6OMuzKAJSIZAD/FMAfBfBZAH9ORD57Vff/DmkB8DdU9bMAfgTAX/Fn/2kAv6iqPwjgF/3v60I/BaA/5efvA/hHqvrbALwN4C89l6d6dvonAP6Tqv4OAL8LNqZrtS4i8gaAvwrgh1X1dwLIAP4sru+a3Di65vILuHkybJNfLxB9XGXYVXqgfi+AX1PVL6nqBODfAPiJK7z/ByZV/Zqq/g///QGMyd+APf/P+cd+DsCffD5P+GwkIp8C8McB/Iz/LQB+FMDn/CPXYiwi8jKAPwjgZwFAVSdVvYfruS4DgFMRGQCcAfgaruGa3GC6tvILuFkybJNfLyx97GTYVQKoNwB8ufv7K/7atSIR+QyAHwLweQDfrapf87e+DuC7n9NjPSv9YwB/C3ZyLwB8F4B7qrr439dlbb4PwLcA/Et35/+MiNzCNVsXVf0qgH8A4DdhQucdAL+M67kmN5VuhPwCboQM2+TXC0YfVxm2JZE/A4nIbQD/HsBfU9X7/Xtq/SBe+J4QIvLjAL6pqr/8vJ/lQ6ABwO8B8M9U9Ydg55St3N3XYV08x+EnYAL1dQC3APzYc32ojW4kXXcZtsmvF5M+rjLsKgHUVwF8b/f3p/y1a0EiMsIEz79S1Z/3l78hIq/5+68B+Obzer5noN8P4E+IyK/DwhA/CovD33XXK3B91uYrAL6iqp/3vz8HE0jXbV3+CID/p6rfUtUZwM/D1uk6rslNpWstv4AbI8M2+fVi0sdShl0lgPpvAH7Qs/J3sASzX7jC+39g8hj7zwL4oqr+w+6tXwDwk/77TwL4D1f9bM9Kqvq3VfVTqvoZ2Br8F1X98wB+CcCf8o9dl7F8HcCXReS3+0t/GMCv4vqty28C+BEROXNe4ziu3ZrcYLq28gu4OTJsk18vLH0sZdiVdiIXkT8Gi19nAP9CVf/eld38OyAR+QMA/iuA/4kWd/87sByCfwfg0wB+A8CfVtW3nstDfgASkT8E4G+q6o+LyPfDLLpPAPgCgL+gqsfn+Xzvh0Tkd8OSSXcAvgTgL8IMg2u1LiLydwH8GVi11BcA/GVYvsC1W5ObStdVfgE3U4Zt8uvFoo+jDNuOctloo4022mijjTZ6RtqSyDfaaKONNtpoo42ekTYAtdFGG2200UYbbfSMtAGojTbaaKONNtpoo2ekDUBttNFGG2200UYbPSNtAGqjjTbaaKONNtroGWkDUBtttNFGG2200UbPSBuA2mijjTbaaKONNnpG+v/nO7YbKylwHgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save weights\n",
        "siamese_model.save('siamesemodelv2.h5')"
      ],
      "metadata": {
        "id": "DrnaMubujD_x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6171454b-7cd0-4a6d-e803-41d53ef0e0ea"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        }
      ]
    }
  ]
}