Lecture_5.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Lecture4.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6yif5nscdji",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFO_uTC6c4Fj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7ec75ef1-5411-4ada-8870-d142289fe94c"
      },
      "source": [
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0-rc4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxzhIHNIdSPP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from mlxtend.plotting import plot_confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLYIbX2bdogb",
        "colab_type": "text"
      },
      "source": [
        "### 导入数据集"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D4SOxCHQdjMi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.datasets import cifar10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQTrInPbd1z1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-ZbrXezeAeb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse','ship','truck']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KjFG4hUyeJzM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = x_train / 255.0\n",
        "x_test = x_test / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rONK87j4fFBw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4687c787-7eb2-45ea-cad2-e62c098c8efa"
      },
      "source": [
        "x_train.shape, x_test.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((50000, 32, 32, 3), (10000, 32, 32, 3))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0TXY1iefLKW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6a9db28e-f226-4e11-d3ce-53288033af4a"
      },
      "source": [
        "y_train.shape, y_test.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((50000, 1), (10000, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_05ruQoOfRmN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "291d7f4c-78e8-49df-de1e-1591c193d769"
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(x_train[0])\n",
        "plt.colorbar()\n",
        "print(y_train[0])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[6]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LDflirHPgFOb",
        "colab_type": "text"
      },
      "source": [
        "### 建立CNN模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PpHjKXEKfyeM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu', input_shape=(32, 32, 3)))\n",
        "\n",
        "model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2, 2), strides=2, padding='valid'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(10, activation='sigmoid'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-PipxTL6h1Ab",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "08c69cc2-a741-4c7a-caff-84b94d068131"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 8192)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               1048704   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 1,060,138\n",
            "Trainable params: 1,060,138\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "alY-N380h38c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GBa7-iZBifTN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "3947af8e-0d99-4c6d-9121-c579e38f5129"
      },
      "source": [
        "history = model.fit(x_train, y_train, epochs=15, batch_size=64, validation_data=(x_test, y_test), verbose=2)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "782/782 - 4s - loss: 1.4544 - sparse_categorical_accuracy: 0.4769 - val_loss: 1.1412 - val_sparse_categorical_accuracy: 0.5955\n",
            "Epoch 2/15\n",
            "782/782 - 4s - loss: 1.1138 - sparse_categorical_accuracy: 0.6070 - val_loss: 1.0261 - val_sparse_categorical_accuracy: 0.6431\n",
            "Epoch 3/15\n",
            "782/782 - 4s - loss: 0.9920 - sparse_categorical_accuracy: 0.6522 - val_loss: 1.0144 - val_sparse_categorical_accuracy: 0.6496\n",
            "Epoch 4/15\n",
            "782/782 - 4s - loss: 0.9136 - sparse_categorical_accuracy: 0.6801 - val_loss: 0.9212 - val_sparse_categorical_accuracy: 0.6752\n",
            "Epoch 5/15\n",
            "782/782 - 4s - loss: 0.8481 - sparse_categorical_accuracy: 0.7024 - val_loss: 0.9118 - val_sparse_categorical_accuracy: 0.6817\n",
            "Epoch 6/15\n",
            "782/782 - 4s - loss: 0.7901 - sparse_categorical_accuracy: 0.7223 - val_loss: 0.8896 - val_sparse_categorical_accuracy: 0.6938\n",
            "Epoch 7/15\n",
            "782/782 - 4s - loss: 0.7406 - sparse_categorical_accuracy: 0.7390 - val_loss: 0.8712 - val_sparse_categorical_accuracy: 0.6932\n",
            "Epoch 8/15\n",
            "782/782 - 4s - loss: 0.6869 - sparse_categorical_accuracy: 0.7580 - val_loss: 0.8715 - val_sparse_categorical_accuracy: 0.6952\n",
            "Epoch 9/15\n",
            "782/782 - 4s - loss: 0.6499 - sparse_categorical_accuracy: 0.7701 - val_loss: 0.8834 - val_sparse_categorical_accuracy: 0.7008\n",
            "Epoch 10/15\n",
            "782/782 - 4s - loss: 0.6124 - sparse_categorical_accuracy: 0.7845 - val_loss: 0.8533 - val_sparse_categorical_accuracy: 0.7102\n",
            "Epoch 11/15\n",
            "782/782 - 4s - loss: 0.5694 - sparse_categorical_accuracy: 0.7990 - val_loss: 0.8805 - val_sparse_categorical_accuracy: 0.7052\n",
            "Epoch 12/15\n",
            "782/782 - 4s - loss: 0.5341 - sparse_categorical_accuracy: 0.8105 - val_loss: 0.9104 - val_sparse_categorical_accuracy: 0.6979\n",
            "Epoch 13/15\n",
            "782/782 - 4s - loss: 0.5023 - sparse_categorical_accuracy: 0.8224 - val_loss: 0.9351 - val_sparse_categorical_accuracy: 0.7010\n",
            "Epoch 14/15\n",
            "782/782 - 4s - loss: 0.4692 - sparse_categorical_accuracy: 0.8340 - val_loss: 0.9347 - val_sparse_categorical_accuracy: 0.7014\n",
            "Epoch 15/15\n",
            "782/782 - 4s - loss: 0.4449 - sparse_categorical_accuracy: 0.8404 - val_loss: 0.9247 - val_sparse_categorical_accuracy: 0.7105\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJ-83Pwpi6rw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "04801722-afcf-4d88-dc09-930281d9287c"
      },
      "source": [
        "plt.plot(history.history['sparse_categorical_accuracy'])\n",
        "plt.plot(history.history['val_sparse_categorical_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc = 'upper left')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc = 'upper left')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69gO4BmdkHrt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "90f6983f-66ad-40fe-99b5-c137e2538543"
      },
      "source": [
        "y_pred = model.predict_classes(x_test)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-16-bae1a118294d>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0MVCYy1ZkHuw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "b7b3884d-064f-48f1-eeea-df4123957b6e"
      },
      "source": [
        "y_test"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3],\n",
              "       [8],\n",
              "       [8],\n",
              "       ...,\n",
              "       [5],\n",
              "       [1],\n",
              "       [7]], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BU3vohlSkHz_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvSB_fskkHyX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mat = confusion_matrix(y_test, y_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qo3z-Lh2mao6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "outputId": "98e68af7-4bca-43e8-e850-b6e00f7b1239"
      },
      "source": [
        "!pip install mlxtend==0.17"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting mlxtend==0.17\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/04/c362f34f666f0ddc7cf593805e64d64fa670ed96fd9302e68549dd48287d/mlxtend-0.17.0-py2.py3-none-any.whl (1.3MB)\n",
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            "\u001b[?25hRequirement already satisfied: joblib>=0.13.2 in /usr/local/lib/python3.6/dist-packages (from mlxtend==0.17) (0.14.1)\n",
            "Requirement already satisfied: numpy>=1.16.2 in /usr/local/lib/python3.6/dist-packages (from mlxtend==0.17) (1.18.4)\n",
            "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from mlxtend==0.17) (3.2.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from mlxtend==0.17) (46.1.3)\n",
            "Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.6/dist-packages (from mlxtend==0.17) (1.0.3)\n",
            "Requirement already satisfied: scipy>=1.2.1 in /usr/local/lib/python3.6/dist-packages (from mlxtend==0.17) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.20.3 in /usr/local/lib/python3.6/dist-packages (from mlxtend==0.17) (0.22.2.post1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->mlxtend==0.17) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->mlxtend==0.17) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->mlxtend==0.17) (2.8.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->mlxtend==0.17) (1.2.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.2->mlxtend==0.17) (2018.9)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib>=3.0.0->mlxtend==0.17) (1.12.0)\n",
            "Installing collected packages: mlxtend\n",
            "  Found existing installation: mlxtend 0.14.0\n",
            "    Uninstalling mlxtend-0.14.0:\n",
            "      Successfully uninstalled mlxtend-0.14.0\n",
            "Successfully installed mlxtend-0.17.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "mlxtend"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YETDuapdmAe3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "outputId": "90ac7fd6-801a-4afb-f331-3291b9184063"
      },
      "source": [
        "plot_confusion_matrix(mat, figsize=(12, 12), class_names=class_names, show_normed=True)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 864x864 with 1 Axes>,\n",
              " <matplotlib.axes._subplots.AxesSubplot at 0x7f7e600ce828>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5NWTF0nufI3tGdcDPqyE9ozqYzpHbEjZT17USQS19CGrpw7TJEwBDO4hsH2y1dqDL4hyoCGSqqnpNURR3IOBfrGMD9DT+/BSw9a75twrxDEVRyhVa9u9sAF5WFKUEgKIoDRRFKfsv1jObp4Prs2bnSSo+VoLRvbwIGrOWtqPXMrqXF/Zli75IZMCMTbQYvoYWw9eweudJVu88ecf8yQP82Jhy+xW1N/JvsulAOj1bFrlbRzcWL/yKqK7dyM7OZvLE94lL2EH81kQmT3y/2Le7TZ82hbbBoew9eJi2waFMnzYFgFq167A+Op6dSfsYOXosrw1+CYCSJUsSFBzCd6tWFtmWXixZ9BWRUd24lJ3NlEnvE7NpO7GbdzBl0vtFCm+AGR9NITAohKT9vxMYFMKMj6aY5hUUFPDuuNEEh4abplV2cqKqczUSd2yzyv7cryULvyKyy+22ELtlB3EJ924LM6ZNoW1QKCm/HqZtUCgzCrWFnzbGs2PPPkaMHsvrhdpC2+AQvtdxW5D+AAu/XkAXYw4+mDiezVsT2bJtJx9MHF9sDj76cDJBwSEcOHSEoOAQPvpwMgAbflnPsWPHOHDoCB9/8hmvDzG8JdHJyQln52rs2K7f/rDo6wVEGfvCpAnj2bQ1kc3bdjJpwj1yMHUyQSEh7D90hKCQED6aasiBg6Mj06bP4vU3h92x/K128K2O2wGYrz84Ojgy9aOZvPZG8XnQc39YuXQhHSO6culSNjM+nMja6ATWxWxlxocTTYV3YXNnTqNV22C27jlIq7bBzJ1p+KNs4RefUt+tIdEJu1m1diPj3x5FXl4elSo7UbWqM7sT/3mEOK0sW/wVnaO6cik7m2mTJ7Ahbhsb47czbfKEYs+Rs6d/SJu2Ieza+xtt2oYwu9AL+gJatGbT9iQ2bU/irVHjAEM7CAwK4cfvvrHK/ui1OP8FsFMU5TdgMoZbW/7JVcBPUZRfMdyyMr7wTOPwi59jeIB0A/Bv3jzwBXAISDZu9zOsPPxk7zaPs273X4Q1dSVufxqZV/LIuppH3P40wr1c/3bd7i3qsGrrcdPvEc1rcvLcZX47dedIlOt2n6R3G/2+lGTlimV0jogiNnoDwaFhODo64uDgQHBoGDEbfymy/E9r19C339MA9O33NOvWGF5bHtCiJQ4ODgA09wsgNfX2HykRUV34ZsUyK+zN/Vm1chmdIqKIjdlIUEgYDo6O2Ds4EBQSRkz0hiLLr/9pLX36GnLQp+/T/LxujWne/E8+JrJrd5ycqtyxTufILqxaudyyO/L/9M2KZXSKjCKumLYQW0xb+HndGp4ytoWn+j3NT8ZX2PsXagu+fgGkFWoLnSO78M1K/bYF6Q+wcvkyIiK7ELNxAyGFchASGkb0hqI5WLd2DX37G0aR6tt/AGuNOVi3djV9+/ZHURT8/APIzsoi3TgOdmRUF1YsX2q9nfqPVq4w5iC6mBzcsx0Yc9BvgKkdVKlSBR/f5pQoUaLIOhFRXVm5XL/tAMzXH5z+Ng/67g8/rFpB+04RbI6Lpk1QKA4OjtjbO9AmKJRNsUW/7N+4fi29nuwHQK8n+7HhZ8P5QVEUrl65jKqqXL16BXsHB+zsDCVP+85R/PDtCuvt1H/03crldOwcRXzsRtoGh5rOkW2DQ4mLKf4c2buv4YVkvfv2v+MceS8dI6L49hvrnCN1WZyrqnpdVdWOqqo2VFW1q6qqQaqqblJVtbaqqhmqqp4wPtR593pDVVVtpKpqiKqq543Tnrk1jKKqquNUVX1cVdVWqqo+q6rqu8bpQaqq7jH+nKGqam3jzzdVVR2jqmpj43aDVVXNtlYeStjZUKdqOf46fwUXx7Kczrh9J0/qhau4ON77In6rhlU5l53DH2cMLx4qW9qOoV0b88GqvUWWPXgqC+96lc2/A2aQl5fHieN/Uqt2bdLS0qhevYZpnqtrddLS0oqsc/7cWZyrVQOgqrMz58+dLbLMoq8XEN6ug+l3D89GJCXtscAe/P/l5eVx8vhxataqTXpaKtWrVzfNc3V1JT0ttcg65+7KwTljDtLSUlm39keef+GlIut4efuwY9vdXzjpR15eHidO/EmtWuZtC4u/XkBY+zvbQrKO24L0hzyOm3KQWkwOiu8P1Yw5cL6jP6RRvUah9avfXt/bx5ftW/XZH+7IQWpq0X1I/fc5+Dueno1ITtLvGzQt1R/upvf+8NfJ49SoWZszaWm4uN4+P1RzceVMMTnIOHeOqs6GHFSp6kyG8SVlzwx8maNHfsfHow5hrX0ZP+kjbGwMZWITL2926vSbVcO54dY5Mg2XQu3AxbU66cW1g/NncTbmoGpVZ86fv90O9uxKJKiFN727R/D7bwdN0xt6NGKvldrBA/USokdNpfKlyLqad1/r9mpd1/QGUYCxvZrx8bqDXM0ter/UzZsqN/ILKFfajivFzNfShYwMKla0v+/1FUUp8kreLZviWfT1AjbGbTFNs7W1pWTJkly+fJny5cvf9+dZwoULGVS0N08OxowYyrvvTzIdcAtzcqrCmTNFD2J6YY62wN1tYXM8ixcuYEPsXW2hhE7bgvQHMjIysDdzDopTpUoV0tP12R8uWCkHtra2lNBpOwDL9Ifi6Lk/XLyQQYWKFe97/cI52BQXjWejpnyzegMnjv/JU9074RfQivIVKlDZqQpnz6T/w9a0cfGC+dpBk6bNSD70B+XKlSN6w3qe7tOTXXt/A273hyuXL1POwu1Al1fO74eqqg/dMCm5eQWUNg7Un3bxKtUr375S7lqpLGkXi38m1tZGoYtfLb7dfvuWFt/6lZnQz5dDc3syuLMHb3VvwosdGprml7SzJfdGgYX25P6VLlOG67m5ALi4uHD69CnTvNTU07i4uBRZx6lKVdMrms+kp1O50O0bvx7Yz5CXB7Hi2x+oVOnO0TmvX79O6dJ3P3esvTKly5BrzEE1F1dOn759+0FqairVXIre3lTlrhzcuoUlJTmJ5wf0pUnDx1nz43e89cYQ060eubm5lCldxtK7c9/M0Rac7moLr748iOWrfsDx7raQp8+2IP0BypQpQ+71WzlwLSYHxfeHW7erpBdqBy4uLpw+VWj907fXz83NpXQZffaH0oVz4OpadB9c/30O/kmeTtsBmL8//B299ofCOXB2cbnjFr30tFSci8lB5Sq3C+2zZ9Kp5GR4Lcw3yxbRMbILiqJQp+7j1KhVm2NHDwNwPTeX0jo9P5QuXYbr12+dI11IK9QO0lJPU624duBUlTPGHJw5k07lyoZ2UL5CBcqVM5ST4e07kn/jBhcyMkzr5V2/TikrtIOHpjh/GGVdzcPWxoZSJWyJ2ZdKaFMX7MuWxL5sSUKbuhCzr+hXlwAhTVw4nJZN2sVrpmnt3lmPx+Bv8Rj8LXN/OsS07/fz2S+GvwYdy5XiwuVcXb7V0sHBgYKCAnJzcwkNb09cTDSZmZlkZmYSFxNNaHj7Iut0iohkqXEUk6VLFtE50jBizam//qJv757MX7CQ+vUb3LHOhQsXqFSpcrH3G2rNvnAOwtoRHxtNVmYmWZmZxMdGExrWrsg6HTpFsNz4lPrypYvo2NnwpP2+Q8fY/9sf7P/tD6K69mDazI/pHNkFgD+OHaGhh2eRbelF4bYQUkxbCCmmLXTsHMkyY1tYtmQRnSJut4V+T/Zk/pcLqXdXW7io47Yg/eHOHIS1a09soRzExkQT1q5oDjpHRppG71i6eCERxhx0johi6dLFqKrKrp2JVKhY0XTrx9GjR/DwLHL3pC7ckYPwYnJwz3ZgzMGShaZ28Hf03A7AvP3h7+g5D/b2t3PQNiScLfExZGVlkpWVyZb4GNqGhBdZJ7xDBKtWLAFg1YoltOtoOD+4Vq/B1s3xgOH2nz+OHaVWbcNgEX/+cRS3hh5W2qv/pvA5Mji0HZviYkznyE1xMQSHFn+OXLnUMJrNyqWLTefIs2fPoBrfYpu8Zxc3b940Xby5eOECjlZqB1Kc61zs/lRaulch80oeU77dx5bJkWyZHMnkVXvJvGK45WXuS61oVvf2Va+ererccUvLPwls5MyG5NP/vKBGQsLC2bFtK46OjowYPZagVv4EtfJn5JhxODo6AjD4pRdM9wkPfWsk8bExeHm6sSkulqFvjQRg8gfvc/HiBYa+PoSWft4EtvQzfUbC5njad9TvUIIhoeEkbt+Kg6Mjw0eOJSQwgJDAAEaMGoeDMQevvTKIlGRDDt4cNpJNcTH4NHFnc3wsbw4b+Y+fkbB5E+066DcHAMFh4ezYfrstBLf2J7j1nW1hyMt3tYW4GJo1cmNTfCxvGtvClEmGtjDsjSG09vembavbbWHL5nja6zgP0h8gNCyc7cYcjBozjjYt/WjT0o/RY9825eDlFwea7hMeNnwUcbExNPZoQHxcLMNGjAKgQ8dO1KlTh0YN6zP4pUHMnDPX9BlbNsXT8QHJwcgx4whs6UdgSz9GFcrBKy8NNLWDYcNHERcTQxOPBsTHxjJsuCEHZ86coX7dGsyZNYMPJ0+kft0aXLpkeFZpi87bAZivP5w9cwa3x2vy8ewZTJ08EbfHa5ryoPf+EBgcxu7EbTg4OPL6W6PpHNqKzqGteGP4GBwcDDl467WX2JeSBMCQN94iYVMsrX092bo5jsFvvAXA62+NJmlXIqGtfHiya0fG/G8CjpUMz6NtT9hMaDv9Di8aFBLGzh3bcHB0ZOiIMYQHtSA8qAXDRo41nSPfGDyIvcZz5GtDR7A5PgY/r4Zs2RTLa0NHALD2x+9o4+dFUAtvxgx/k/lfLTHd8rI1YRPh7a2TA+XWXwjCPGwda6ulQ4t7Oen98apTiSERHgyck2C2bd5t2VshvLN0D8fSL5lle2eXDDDLdm7Zm5LM3Nkz+fyr4scrNYenevfgvQmTilxBvF/m/hZiX0oy8z6exWdfFj92rzl0ahfEspU/YG8cwcMcbMw8fv7elGTmzZnJ/AWWawt9jW3h7ivq98vcrxB4EPuDud+jkJKSzMezZvLl15bLQXhIW7757kfTiDbmYM7TbUpKMh/PnsmXFmwHfZ7owfgJk6jfwDztAOCmmWuOB7E/ZF+7YZbt3HJgXwqffzKb2Z9+ZdbtFtajcyhfLv0We3vz9IdSxlt2zWXf3mQ+mzuLeZ9b7hz5zFO9ePu9iTxupnYA4FS+RJKqqr53T7d99913zfYhAsZPmfmuXd0gs23vTFYO9mVLceCvTLMe2G8pYWdDCRuFzQfPmG2bw7t7mW1bAM7VqpGVlUmjxk2KfZDx/ysvL4/8/HyCgkPMts2bZv5/5VytGtlZWXhaKAcZ58/j4lqdJk3N+//u3zxs9V9Yoy0UFOTTNsh8bcHMKXgg+4ONmZNQzZiDxhbKwfnz53FxdcXLq5nZt20uls6BJdoB3PV2QDN4EPvD9Rs3zbYtgKrOhvNDQ8/GFsnBhYzzOLu40qiJ+c4PdrbmjdPZuRpZWVl4NrJsO2hjxnMDwNRJ76e/++678++eLlfOzczcV84fROa+cv4g0uP9+1ow95XzB5GkwPxXzh9Ucro1/5XzB5G5r5w/iMx95fxBda8r53LPuRBCCCGEEDohxbkQQgghhBA6IcW5EEIIIYQQOiHFuRBCCCGEEDohxbkQQgghhBA6IcW5EEIIIYQQOiHFuRBCCCGEEDohxbkQQgghhBA6IcW5EEIIIYQQOiHFuRBCCCGEEDohxbkQQgghhBA6IcW5EEIIIYQQOiHFuRBCCCGEEDohxbkQQgghhBA6IcW5EEIIIYQQOiHFuRBCCCGEEDohxbkQQgghhBA6IcW5EEIIIYQQOiHFuRBCCCGEEDphp3UAD5umdSqzZckArcPQlFPUDK1D0NzFdUO1DkHohKIoWocgdEJVVa1D0JytjfSHsqWl9DqTlat1CLomV86FEEIIIYTQCSnOhRBCCCGE0AkpzoUQQgghhNAJKc6FEEIIIYTQCSnOhRBCCCGE0AkpzoUQQgghhNAJKc6FEEIIIYTQCSnOhRBCCCGE0AkpzoUQQgghhNAJKc6FEEIIIYTQCSnOhRBCCCGE0AkpzoUQQgghhNAJKc6FEEIIIYTQCSnOhRBCCCGE0AkpzoUQQgghhNAJKc6FEEIIIYTQCSnOhb89Di8AACAASURBVBBCCCGE0AkpzoUQQgghhNAJKc6FEEIIIYTQCSnOhRBCCCGE0AkpzoUQQgghhNAJKc6FEEIIIYTQCSnOH3AvD3qeOjWc8fNuYpq2f99eggNb0tLPm8CWfuzZvUvDCC2jfnUHEuf1N/07+/0QhnTzpkldJzbP7EPivP5sndMXXzfnO9bzaVCVyz+/SbfW9TWK3Drc69ehebMm+Ps2o1VAc63D0URWVhZP9e6FV6OGNGvswc7EHVqHZFW5ubm0buGHn3dTvJt68v57/9M6JM1s3PALTTzd8HSvx9QPJ2sdjibmzJqBr1cjfJs1ZkD/p8jNzdU6JKt7FI+Lubm5hLYJoLW/Ny18mjDp/XcBeOHZ/jRv6kEL36YMeXEgN27c0DZQCxj75su0alybyODb/69nfTieLqH+dAtrwfNPRnHuTDoA2VmZDHnuSbqE+vNEp7Yc+f2gVmEDUpw/8Pr2H8APa36+Y9rbY0YyeuzbbN+VzNh33uXtMaM0is5yjp7OJOCVxQS8spiWQ5Zw7Xo+a7YdZeLAQCYu2UHAK4t5f9F2Jj4faFrHxkZhwvOBxCSd0C5wK1ofHcfOPSlsS9ytdSiaGD70DcLbt2fvr7+xM2kvbu4NtQ7JqkqVKsUv0XHsSt7Hzj172bjhF3YmJmodltUVFBTwxmuDWb12PSn7D7FqxXJ+O3RI67CsKi01lU/mziFhx272pBzgZkEBq75ZoXVYmnjUjoulSpVi9foYtu5MZktiErHRG9i9K5Fevfuwa+9Btu/eS05uDou++lLrUM2ua+++zF/64x3Tnn/5DVbH7uSHmB0EhXVg3oxJAMyfPY2Gnk1YHbuTybPmM+mdEVqEbCLF+QOudZtAHBwc75imKAqXL10C4FJ2NtWqVdMiNKsJ9qrJ8fQs/jp3GVWFCmVLAVCxbCnSL14xLfdKl2b8uPUo57OuaRWqsJLs7Gy2bt3CM88+D0DJkiWxt7fXOCrrUhSFcuXKAXDjxg3yb9xAURSNo7K+3bt28fjj9ahTty4lS5akV+8nWbd2tdZhWV1+QT45OTnk5+dz7do1qlVz0TokYQV3Hwdu3MhHQaFdh04oioKiKPj4Nict9bTGkZpf84DW2Ds43DGtXPkKpp9zcq6B8Zh47Ojv+LduC0Dd+m6knvqLjPNnrRfsXaQ4fwhNnjaDcaNH4v54LcaOHsG773+gdUgW1SvInW82/Q7A8E/j+WBgIEeXDGLSC4G8syABAJdK5YhqWY/56/ZqGarVKIpCZKf2tPT35csv5msdjtWdOH6cypWdeHHgcwQ09+blFwdy9epVrcOyuoKCAvx9vKjpUoWQsHD8/P21Dsnq0tJSqV69hul3V9fqpKamahiR9bm4uvL6G8Nwr1eLx2u5UKFiRcLC22kdltU9qsfFgoIC2vj70KBWNYJCQ/H1u30cuHHjBiuXLSW0XXsNI7SumZPfJdjHjbXfr+S14eMAcPdoTPTPawDYn7KHtNN/cTY9TbMYpTh/CH05/1MmT/2I3/84yeQPP2LwSy9oHZLFlLCzoXPA43y/5QgAgyKaMuKzTdTvN58Rn23ik6GGA87Ul4IY92UCqqpltNYTE5/Ajl1J/Lj2Z+Z/Mo+tCVu0Dsmq8gvy2ZuSzMAXXyJxdzJly5Zl2iN4r7GtrS07k/Zy7MRp9uzexcFff9U6JKGBzMxM1q1bw8HDf3LsRCrXrl5l+bIlWodldY/qcdHW1paEnUkcPHqS5D27OXTw9nHgrdeH0LJ1G1q2aqNhhNb1xqh3iU86TGT33ixd8BkALwwZyuXsbLqFtWDJgk9p2KgpNja2msUoxflDaNmSRUR17Q5Atx69SNrz8D0Qekv75nXYe+ws54y3qvQN9+THrUcB+G7LEXwbGB4I9W7gzKLRnfl94UC6tWnAzFfDiGxRT7O4Lc3V1RWAKlWqENml60P5UPDfcXWtjmv16vgZrxB1696TvXtTNI5KO/b29rQNCmbjxl+0DsXqXFxcOX36lOn31NTTpv7xqIiPi6F27do4OTlRokQJorp2Y+eO7VqHZXWP+nGxor09bQKDiI3eAMCUiePJyDjPxCnTNI5MGxHderPxZ8MtbuXKV+CDmZ/yQ8wOpsz+nIsXMqhRq7ZmsUlx/hByrubC1i2bAdgcH8fj9R7ekUmeKHRLC0D6hSu0aVIdgCCvmhxLywKg4YAvcDf++yHhCG/MiWHtjmOaxGxpV69e5fLly6afY2Oi8fBspHFU1uXs7Ez16jU4cvgwAPFxsTRs+Gg9EHr+/HmysgztPycnh9iYaNzc3DWOyvp8mzfn2LGjnDh+nLy8PFatXEHniCitw7KqGjVqsnvnTq5du4aqqmyKj3vkHpB+VI+LGefPk13oOBAfF0P9Bm4s+upLYmM28sXCpdjYPDql4Ik/b5/34zaso269BgBcys4iLy8PgFXLvsY3oNUd96dbm51mn3wXRVG6AkdUVbXYY/SKotQG1qmqWqRHKoryBTBdVdVDiqKcAHxVVc2wVCzm8mz/p0hI2MyFjAzcHq/JmHH/Y868zxj51pvk5+dTunRpZs/9VOswLeKxUnaEeNdiyKxo07TBM6OZ+nIwdrYK1/MKGDJzo4YRauPc2bM82cvwzUl+fj5PPNmHdu07aByV9X00YzbPDujHjbw8atepy2dfLNA6JKs6k57OC88NoKCggJvqTXr0fIJOnSO0Dsvq7OzsmDHrYyI7t6egoIABzzyHh6en1mFZVXM/f7p270Erfx9s7exo6tWM5wYO0josq3pUj4tnzqTzygvPUXCzgJs3b9Kte086dIqgcvlS1KhZi3ZBrQGI7NKVEWPe1jha8xr28jPs2pFA1sULBPk0YMiwsWyJ28DxP45iY2ODi2tN3p0yC4A/jh5m9BsvoqBQz82dCR/N0zR2RdXJTbiKonyNoXD+1oKfUZt7FOd3LXeC+yzOvX181S3bH62vyu7mFDVD6xA0d3HdUK1DEDrxKI6QIop386Y+zrdaku4A1/Nvah2C5s5kPXrj7BenoUu5JFVVfe+ebtHvMhRF+VFRlCRFUQ4qijLIOO1Kofk9FUX5WlGUlkAUMFVRlL2KojyuKIqXoiiJiqLsVxTlB0VRHIzrbFIUZYaiKHsURflNUZTmiqJ8ryjKUUVRJhTa9lBFUX41/nujUFh2iqIsNa77raIojxXabpEEKYrST1GUXca4PlMURbsnBIQQQgghxEPN0jcaPaeqqg/gC7ymKEql4hZSVXU7sAYYrqqql6qqfwCLgJGqqjYBDgCFX2+XZ/xL41NgNTAYaAQ8oyhKJUVRfIBnAX8gAHhBUZRmxnXdgHmqqjYELgGv3Ct4RVEaAr2BVqqqegEFQN/7SYQQQgghhBD/xNLF+WuKouwDEoEawL96MlFRlIqAvaqqm42TFgKBhRZZY/zvAeCgqqrpqqpeB/40fk5r4AdVVa+qqnoF+B64NU7QKVVVtxl/XmJc9l5CAR9gt6Ioe42/1y0m3kHGK/l7Ms6f/ze7KIQQQgghRBEWK84VRQkCwoAWqqo2BVKA0kDhm+5K3+fmrxv/e7PQz7d+/6eHXO++6e/vbgJUgIXGq/leqqq6qar6bpENqup8VVV9VVX1rezk9A8f/9/k5OTQISyYgoICli5eiJenG16ebixdvLDY5S9evEhUp3Z4eboR1akdmZmZAKxcvpQAXy/8fZoSGtSaA/v3AZCXl0f70CDy8/PNGrc5lS5px8apT2Bjo9A3zIMDC57jwILn6BvmUezyi8dEkDivP4nz+vP7woEkzusPgJ2tDZ+/1YHdnz5NyufP8FZvP8AwVnr0tN7Y2uj3ZsicnBzahQZRUFDAkkULaezRgMYeDViy6N7tIKJjOxp7NCCi4+12cPj33wlq0xL7cqWZOf328Fl5eXmEh7TVdTsAyQMYchAe0taUg0YN69OoYf2/zUHnDuE0alifzh3CTTlQVZWhb7yGp3s9mjdrQkpyMmAY5SWqs74flJMcGHLQPszYFxYvpIlHA5p4NGDJ35wbIjq2o0kxfSE4sCUO5Yv2hXah+u4LIMcEMOSgcztDnbB8ySJ8Grvj09id5UsWFbt85sWLdItoj09jd7pFtCfLmIOf166hlV8z2vj7ENzKnx3btwKGEV96RnWy2v7cj9ycHPp3Nzz0/eM3S2nfqintWzXlx2+WFrv8L2u/JyLIFw/X8vy6L7nI/LTTp/CpV5UFnxgeGM3Ly6Nft3ZWaweWvHJeEchUVfWaoijuGG4vATirKEpDRVFsgG6Flr8MlAdQVTUbyFQU5dbV7v7AZv69BKCroiiPKYpS1vg5CcZ5NRVFaWH8+Slg699sJxboqShKFQBFURwVRan1H+L4f1u88CuiunYjOzubyRPfJy5hB/FbE5k88X3TQaWw6dOm0DY4lL0HD9M2OJTp06YAUKt2HdZHx7MzaR8jR4/ltcEvAYbXmgcFh/DdqpXW3K3/ZED7RqzedpSKZUsxtl8LAl9fRpvXljK2Xwvsy5Uqsnz/D9YR8MpiAl5ZzI/bjrJ6m2Hc8x6BDShVwpbmLy2i5ZAlDOzUhJpVK3Aj/ybxKX/Rq62btXftX1v49QK6GNvBBxPHs3lrIlu27eSDieOLbQcffTiZoOAQDhw6QlBwCB8ZX8Dj4OjItBmzeP3NYXcsf6sdfPuNftsBSB4AFn61gC5du5Odnc3ECe+xZdtOErbvYuKE94rNwbQPJxMUEsqvvx0lKCTU9DKmDb+s549jR/n1t6N8/Ml8XhvyMgBOTk44O1dj+7ZtRbalF5IDWPT1AqK6GPrCpAnj2bQ1kc3bdjJpwj36wtTJBIWEsP/QEYJCQvhoaqG+MP1v+oKOzw0gxwSAJQu/IrJLNy5lZzPlg/eJ2byd2C07mPLB+6bCu7AZH00hMCiEpAO/ExgUwoyPDHVCYHAIW3cmk7AziTmffs7rr7wIQGUnJ6o6VyNxh377w3crFhHeKYrLl7KZO30SK9fF881Pm5g7fRLZWUVzUN/dgzlfLMM3oFWx25vy3ijahISbfi9ZsiQtWgexfs13FtuHwixZnP+C4eHL34DJGG5tARgFrAO2A+mFll8BDFcUJUVRlMeBARgeEN0PeAHj/+0Hq6qaDHwN7AJ2Al+oqnrrDSSHgcHGuByAT/5mO4eAccBGYxzRQLV/G4c5rFyxjM4RUcRGbyA4NAxHR0ccHBwIDg0jppgXivy0dg19+z0NQN9+T7NujWGA/YAWLXFwcACguV8AqamnTetERHXhmxXLrLA39+fJkIas3fEH4T61iU0+SeblXLKuXCc2+STtfOv87bo9At34Jt4wDrqqwmOlS2Bro1CmpB15+QVcvmYY13TtjmP0DtHvuL8rly8jIrILMRs3EFKoHYSEhhG9oWg7WLd2DX37DwCgb/8BrDW2gypVquDr25wSJUoUWScyqisrdNwOQPIAsGL5UiKjuhC9cQOhoeGmHISGhrOx2Byspp8xB/36D2Dtmh8N09es5ql+T6MoCv4BAWRnZ5GebjgkR3bpysrlxV9x0gPJgeHcEBHZhZjoYvrCPc8Nxr7Qb4Dp3FClShV87tEXIqK6snK5fvsCyDEBYNXKZXSKiCI2ZiNBIWE4ODpi7+BAUEgYMcYXDhW2ft1a+vQ11Al9+j7Nz2sNdwqXK1fONLrUtWtX7xhpqnNkF1atWG6Fvbk/677/htD2EWzbFEPLwGDsHRypaO9Ay8BgtsZHF1n+8fru1DGOcX63mPVrqV6jNvUa3FkThHaIZO331vkjzWLFuaqq11VV7aiqakNVVbuqqhqkquomVVW/VVX1cVVVA1RVHaKq6jPG5bepquqhqmozVVX/UFV1r3GZJsb1M43LBamqusf48yZVVSMKfWbhedNVVW1k/DfTOO2Eqqruqqr2M8bVQ1XVa8WsW/vWMIqqqq403tLSRFVVH1VVE7GSvLw8Thz/k1q1a5OWlkb16jVM81xdq5OWllZknfPnzuJczfD3Q1VnZ86fO1tkmUVfLyC83e2vbD08G5GUtMcCe/D/V8LOhtrOFfnr7CVcKpfj9PnLpnmpGVdwqVzunuu2auTK2cyr/GF8EdH3CUe4lnuD48tf4siSQcz8dg+Zlw3DOR08kYGP8W2iepOXl8dxUztILaYdpBZZ59y5s1QztgNnZ2fOFdMO7ubZqBHJe3abL3AzkzzcfUxIpXqNQjmofo8cnL0rB2cNOSg2h6mG9b19fNm2NaHItvRAcnBXX0gtJgepZuoLno1ITtJnXwA5JoAhByePH6dmrdqkp6VSvXp10zxXV1fS75GDwnVC4RysW/0jfl6e9O4exZxPPzdN9/L2Md3mojd5eXmc/us4rjVqcfZMOs4ut3NQtZorZ8+k/83ad7p69QpfzJvBK8NGF5lX392DX/cmmSXmf/LovBbqAXQhI4OKFe3ve31FUYqMsbxlUzyLvl7A+ImTTdNsbW0pWbKk6e1pelK5Qhmyr17/5wWL8USwO6sKvT20uZszBTdV6j71GQ2f/pzXe/hS27kiYBh/+EZ+AeXKFL1qorWMjAzszdwOimNra0sJnbYDkDyAIQcV7S2fgypVqpCeXvSPfz2QHBjODY96XwA5JoCxTjBjf4jo0pVdew+yZOV3fDD+9iB5TlWqcEan/SHr4gXKV6holm3NnfYBA14YTNmyRS/83WoHV69Yvh1Ica5jpcuU4Xqu4cqui4sLp0+fMs1LTT2Ni4tLkXWcqlTljPFr2TPp6VR2qmKa9+uB/Qx5eRArvv2BSpXuHNXy+vXrlC59v8/nWk5OXj6lSxiGlk/LuEJ1p/Kmea6Vy5GWcaXY9WxtFLq0qs+3mw+bpj0R3JCNe46TX3CT89k57DiUhk+Dqqb5JUvYkZtXYKE9uX9lypQh9/qtduBaTDtwLbJOlSpVTV/Pp6en41SoHfydPJ22A5A8gDEHuYVycKpQDk7fIwdV78pBlSq31787h66G9XNzcyldpozF9uP/Q3JgODeY+oJrMTlwffj7AsgxAe7sD9VcXDl9+vYtq6mpqVS7Rw4K1wnF5aBV60BOHD/OhQzDuxhzc3MpU1qf/aFU6dJcv264iFfVuRpn0m7n4Gx6KlWd//3dyPtTdjNtwtuE+nmw6It5zJ8zjaULbr9lPe/6dUqWsnw7kOJcxxwcHCgoKCA3N5fQ8PbExUSTmZlJZmYmcTHRhIa3L7JOp4hIlhqf0F66ZBGdI6MAOPXXX/Tt3ZP5CxZSv/6d91lduHCBSpUqF3uvndayrlzH1taGUiVsiU46QZhPbezLlcK+XCnCfGoTnXSi2PVCvGtx5NRFUgsV76fPXyLIqyYAj5Wyw8+9GodPXQTAsXxpLlzKIb9Af29uK9wOwtq1J7ZQO4iNiSasXdF20Dky0jSiz9LFC4kwtoO/o+d2AJIHuDMH4e3aExOz0ZSDmJiNhBeXg4go0wgeSxYvJCKyi2F6ZBTLlixCVVV2JiZSoUJF09f9R48cwdPzb1+krBnJwV19IbyYvnDPc4OxLyxZaDo3/B099wWQYwKAfeE6Iawd8bHRZGVmkpWZSXxsNKFh7Yqs06FzBMuXGuqE5UsX0TEiEoA//zjGrbfG70tJJu/6dRyNF/L+OHqEhh6eVtqr/6aivQM3Cwq4nptLq6Awtm2OIzsrk+ysTLZtjqNVUNi/3taSH6OJ3XWI2F2HeHrgKwx69S36PmcYQCPz4gUcHCtZpR1Ica5zIWHh7Ni2FUdHR0aMHktQK3+CWvkzcsw4HB0dARj80gskG+8ZH/rWSOJjY/DydGNTXCxD3xoJwOQP3ufixQsMfX0ILf28CWzpZ/qMhM3xtO+o32GSYpJO0LKRK5mXc5m0NJGtc/qydU5fPli6w3TP+Lw32uFd//ZV8F5t3fim0C0tAJ+u2Uu50iVImj+ArXP6sXjjr/x63HBVoG3TGvyy60/r7dR/FBoWznZjOxg1ZhxtWvrRpqUfo8e+bWoHL7840PTswLDho4iLjaGxRwPi42IZNmIUAGfOnKFenRrMmTWDKZMmUq9ODS5dugQYbnnq0Em/7QAkDwBhYe1MORg95m1at2hO6xbNGTP2nds5GDSQpD2GHLw1YhRxMdE0alif+NgY3jLmoEPHTtSpUxdP93oMfukFZs2ZZ/qMzZvj6dCxs/V37l+SHNzZF0aOGUdgSz8CW/oxqlBfeOWlgaZzw7Dho4iLiaGJRwPiY2MZNvx2X6hf19AXPpw8kfp1C/UFnZ8bQI4JACGh4SRu34qDoyPDR40lpE0AIW0CGDF6HA7GHLz28iBSjDl4c9hINsXF4NPYnc3xsbw5zFAnrPnxe1r6NqWNvw/D33yVLxcvM93ykrBlE+103BZatg0ladcO7B0cefmNkTzRqS1PdGrLK2+Owt7BkINxwwabhk2MXr+GIJ8G7E3axUv9ezCwT5d//Ixd27fQNsw6Q6wqt/5KEubh7eOrbtm+y2zb25uSzNzZM/n8q+LHKzWHp3r34L0Jk4pcUb9fTlEzzLKdW7zqVeHVbj48P3W9Wbdb2Iq3oxi3IIFjqUWHXLofF9cNNct2bklJSebjWTP58mvLtYMne/Xg/YmTqN/APO3AEh7EPPybe1r/i5TkZObMmsGChYvNut3CwoIDWfX9atMIT3rzoObg5k3znW9TUpL5ePZMvrTguaHPEz0YP8G8xwQzd4cH8phwPd+839DuS0lm3sez+OzL4sd2N4dO4UEs++YH7M3UH85k5ZplO7cc3L+XhZ9/zIdzvjDrdgt79fk+DB0znjqP/6v3af4rDV3KJRnfeH8HuXKuc17NvGnT1vCCBUvIy8sjIrKL2QpzS9h77Byb953CxkIvCSphZ8OaHcfMVphbQrNm3gQGWbYdREZ10XVhDpIHgGbe3rQNCrZYDs6fP89rbwzVbWEOkgMw9gVLnxt03hdAjgkATZt50ybQcjnIOH+ewa++abbC3BI8m3jh3zLQou0gtEOkWQvzvyNXzs3M3FfOH0TmvnL+IDL3lXPx4DL3lXPx4DLnlfMHlXQH8185fxCZ+8r5g0qunAshhBBCCKFzUpwLIYQQQgihE1KcCyGEEEIIoRNSnAshhBBCCKETUpwLIYQQQgihE1KcCyGEEEIIoRNSnAshhBBCCKETUpwLIYQQQgihE1KcCyGEEEIIoRNSnAshhBBCCKETUpwLIYQQQgihE1KcCyGEEEIIoRNSnAshhBBCCKETUpwLIYQQQgihE1KcCyGEEEIIoRNSnAshhBBCCKETUpwLIYQQQgihE1KcCyGEEEIIoRNSnAshhBBCCKETdloH8LApuKly9XqB1mFo6uK6oVqHoLmOc7drHYIuLHumudYhaC4n79E+HgBUKCOnGoCbqtYRaK+UnVwTtLFRtA5Bc64OZbQOQdeklwghhBBCCKETUpwLIYQQQgihE1KcCyGEEEIIoRNSnAshhBBCCKETUpwLIYQQQgihE1KcCyGEEEIIoRNSnAshhBBCCKETUpwLIYQQQgihE1KcCyGEEEIIoRNSnAshhBBCCKETUpwLIYQQQgihE1KcCyGEEEIIoRNSnAshhBBCCKETUpwLIYQQQgihE1KcCyGEEEIIoRNSnAshhBBCCKETUpwLIYQQQgihE1KcCyGEEEIIoRNSnAshhBBCCKETUpwLIYQQQgihE1KcCyGEEEIIoRNSnAshhBBCCKETUpw/4LKzsni+f29a+zaiTfPG7NmVyHvjRtHatxHBLb15tm9PsrOytA7TqgoKCgho7k33rpFah2JxK57zYUE/L77o25TP+jS9Y94T3i5seqMVFUvbAeBVvQLrXvbni75N+aJvU572r6FFyBZz7Ohhwts0N/1zq1mZzz+ZzUeT38fHo45peuzG9VqHalajXn8RP49adAz0NU37ec33dAj0ob5zWQ7sTTJNP/3XSTxrORIZ4k9kiD9vD39Vi5CtwqdRfdoGNCO4lS/hbQMA+PCD8TRxq01wK1+CW/kSs+Hhagt3K+78MGXC/whu6U1oa196d+3EmfQ0rcO0mNzcXEIDA2jt700L3yZMmvAuAK8Meo6mHvVoE+BDmwAfDuzbq2mclnT61Cki2ofi16wR/t6N+eTj2XfMnzNzOhXL2HIhI0OjCC3v9KlTdG4fSvNmjfDzbsw8Yw7GjR6BT1MPWjT34qknupOlo1rJTusAxP/PuFFDCQlrz5eLV5KXl0fOtWu0DQ5l7LsTsLOz4/13RjN7+hTeHj9J61CtZu6cWbi7N+TS5Utah2IVb377K9m5+XdMcypXEt9a9py5lHvH9AOplxi95jdrhmc19eq7EZ2wGzD8gebjUYeOnbuwctkiXnj5VV56dajGEVpG9yf70+/5lxg+5AXTtAbuHsxbsJxxxRTfNWvVZW3cTmuGqJnvf4qmUqXKd0x7cfBrDH7t4WwLdyvu/ODm7sHIce8B8MWnHzN9ykQ+nDlX40gto1SpUqz+OYZy5cpx48YNOoYFEtauAwDjJ06hS7ceGkdoeXZ2dkyYPBWvZt5cvnyZti2bExwahntDD06fOkVc7EZq1KipdZgWZWdnx8RCOQhs2ZyQ0DCCQ8N49/0PsLOz452xo5g+dTLjJ07WOlxArpw/0C5lZ5O4bStPPf0sACVLlqSivT1BoeHY2Rn+7vJp7k96WqqWYVrV6dOn+WX9zzzz3PNah6KpIW3r8FnCCa3D0MzWzXHUql2X6jVraR2Kxfm1aI29veMd0+o1cKduvQYaRST04F7nh/IVKpiWuXb1KiiKViFanKIolCtXDoAbN25w40Y+ykO8v8VxrlYNr2beAJQvXx43d3fSjDXB6BFDGT9xykOfk3vlIDSsnalWau7nT2rqaS3DvIMU5w+wv04ep1Llyrz+ykDCWjdn6JAXuXr16h3LLF/yNSHh7TWK0PpGDHuTCZOmYGPzaDRtVYWp3T35rE9TIhpVBaBVXUfOX8njj4xrRZb3qFaeEaghhQAAIABJREFUL/p6MaWrB7Udy1g7XKtZ/f0quvZ4wvT7V59/SlgrH4YOGURWVqaGkWnv9F8niAwNoE/XduxO3KZ1OBajKApPdO1EWKA/i776wjR9wfxPaNvCm9dfeYGszIe3Lfzd+WHS+Lfx9qjLd6uWM2Ls/zSO1LIKCgpoE+BDg9rVCAoJxbe5PwAT3nubVn7NGDNiKNevX9c4Sus4efIE+/fuxbe5Pz+tXY2LiyuNmzT95xUfIoVzUNjiRV8R3r6DRlEV9WhUMA+p/PwCDuxL4ZnnXyRm624eK1uWj2d8aJo/c+ok7Ozs6PHEUxpGaT0//7QOpypOeHv7aB2K1bz6zQEGLdvHyB8P0bVpNZq4VqCvX3W+2vFXkWWPnLvKkwv2MHDpXr7fm86EyIYaRGx5eXl5bFy/joiuhq+sn35uENtTfmNjwm6qVHVm/LiRGkeoHaeqzmxJPsza2ETGvjeZN19+hssP6e1fazfEE5uwi+XfrWXB55+wY1sCzwx8kV37fid+2x6qOjvzv7EjtA7TYv7u/DD6nfdJPvQnPXr1YcH8eRpHalm2trYkJCZx8MhJkpN2c+jgr7zz3kR2pRwkLiGRzMxMZk3/8J839IC7cuUK/fv0YtLU6djZ2fHRh5MZ8857WodlVbdyMHnqdCoU+gZp6pQPsLO1o/eTfTWM7k5SnD/AXFxdqeZaHW9fPwAiunRnv/HBlhVLFxG94Wfmfr7oof/K6pbE7dv4ad1a3OvX4el+fdgcH8dzA/prHZZFZVzNAyAr5wZb/7hAU9cK/B979x0WxdU9cPw7oGAhCihKscYCYkNAsdBBkY6xxBqNMV2TqImm/d6YGF9jXhNTNMWoib1FY69UEXs3XY0aBYxSVTo4vz8WV5opZtld8Xyex+eB2Z3ZM8c79x5m78zYNTBn4UgXVo11w8bCnPnDXbCuV5vcwhLyim4BcPBCJrVMFe3FojVJXPQOOnd1waaJ5psEmyZNMTU1xcTEhBGjx3Li6GEDR2g45ubmWFk3AqBTV1datHqYC+fOGDiq6mFn7wCAjU0TQsIiOXb0ME3KtIWRo5/geA1uC382Ptz2yJBhbN30nSHC07uGlpZ4efsSs3sntnZ2KIqCubk5I0aN5uiRmtsOQDOlZ9SwQQx5dDgRUY9w/rdzXLx4Hs8e3ejs+DDJyZfx7uXOH1euGDrUalNUVMTIMjm4bfnSb9ixbSsLvllmVLWSFOf3sSZNbXFwaMbZM78AkJgQS3vHDsRG72Tex7NZvGo99erVM3CU+vPOjJmcPX+Jn8+cZ8mylfj4+bNo8VJDh1Vt6tQyoW5tU+3P7i0s+eWPmwyYf5ihi44ydNFRrt0s4KkVJ8jILcK6Xm3tuk5NLVBQKl1IWhNs+HYNUQMf1f7+x5VU7c/bt2zEsUNHQ4RlFNLTrlFSUgLA7xfOc/G3szRv2drAUeleTk4ON2/c0P4cHxtNhw4dy7WFbZs34lSD28LdxoffyvwxtmPbZtq2czRUiNUu7do17d3K8vLyiIuNpp2jI1dSNe1AVVW2bt5EB+ea2w5UVWX8M+NwdOzA+BcnAtCxU2fO/X6F07/8xulffsPBoRl79mu+TaqJVFXl+Qo5ANi9awcffTib1d9uMLpaqeadNvsTiqK0AraoqtqpwvIFwIeqqv74F+uPAdxVVR1fXTH+UzPen8Nz40ZTVFRIy1at+WjeAvr79aawsIBHo4IBcHP3qLFX4z/IrOrVZnrp1BRTE4WYn69x6OLdbwXl064REV3sKLmlUlh8i3e2/6KvUPUmNyeHPfExzJpzp72/+9br/Hj6JIqi0KxFy3Kv1QQvPT2ag/v2kJmRTh+Xtrz4yptYWlnx9uuTyUhPY9yIgXTo1IVvVm/i8IEkPnp/OrVr1UIxMeGd9z/B0sr6rz/kPnPt6h+MGTEYgJLiYh4ZPBT/vkE89+QYfjh9EhSFFi1aMvvjmj2lo6rxYfKEpzl79ldMTExo1rwF79ew46GsK1dSee6psZSUlHDr1i0GDBxE/+AwIoIDSUtLQ1VVOnfpyoef1Nx2cGBfEqtWLKNjp854emguivzP2+/Sr3+IgSPTn7I56FMmB1Mmv0RhQQGRYZrr8rr38OCjTz83ZKhaiqqqho5Bb+5WnP/J+01VVS0p8/sY/qI479rNTd2VcOBfRnp/a1D3gfqbr0rB8/YZOgSjsGJMd0OHYHB5hSV//aYaTvoEjVsPznB7V+a15At7ExPjmT5hKJIBjQZ1TY+qqupecfmDeJTUUhRluaIoPymK8q2iKPUURYlXFMUdQFGUm4qifKAoykmgl6IojyuK8quiKIeAPoYNXQghhBBC1GQPYnHuCHymqmoH4DrwXIXX6wMHVVXtCpwD3kZTlHsCzvoMVAghhBBCPFgexOL8kqqqt2/uuwxN0V1WCbCu9GcPIF5V1WuqqhYCq6vaoKIoTymKckRRlCMZ6TX3EbhCCCGEEKJ6PYjFecVZfxV/zy87z/xvbVBV56uq6q6qqrt1hUdFCyGEEEII8Xc9iMV5C0VRepX+PBzY+yfvPQj4KIrSSFGU2sDgao+ugry8PKJCAigpKWH1iiX06uZMr27OrF6xpMr3Z2ZkMCQymF7dnBkSGVzuCXhJiQkEeLrj7dGVqJAAQPPAlqhgf4qLjfeWenl5efQL8KWkpIRlSxbT2bk9nZ3bs2zJ4irfn5GRQVhwPzo7tycsuB+ZpTlQVZXJE1+gU4d29HDtyvHjxwC4du0aEWHBetufe2FmasJHgzphokBQBxuWjXZl2WhXgjrYVPn+MT2bs3acOwtGdGXBiK54tLICwLaBOTvH99Qun+TfRrvOB490xMLcVC/7c6/y8vIYGBpISUkJa1YupY+bM33cnFmzsupbZmZmZjB0QDB93JwZOiBY+3TQzz/5gL5e3enr1R3/Xt1o3qgumZkZFBYW8khIgFEfD/l5eQyL6kdJSQnrVy8joGdnAnp2Zv3qZVW+f9um9fT3dqOdbX1OnziqXb43IYbIvr0J8elOZN/e7E+M17722KBQso34Sap5eXlEBmv6xVXLl+Dh4oyHizOrlt+9XxwUGYyHizODqugX/fq449WjK5HBd/rFiP7G3y8+6GMDaPIQGuRHSUkJK5ctwa2LE25dnFi57O55GBAWhFsXJwaEBVV6Suyxo4dp3MCcjd9pvkBPu3aNQZHGfWeTvLw8QvpqcrBi2WK6dXKkWydHViy7+xgZGdqPbp0ciQy9M0auWbmc3t1d6OXelb6+npw+dRLQtIXgQF+jbgt5eXkEl+Zg+bLFuHRyxKWTI8v/IgcuFXKweuVyenV3oad7VwIr5KC/HnPwIBbnvwDPK4ryE2AF3PW+OaqqpgLTgP1AEvCTPgIsa+WybwgNj+J6djYfvDeDbTF72R6bxAfvzajy0dOfznkfLx8/9h//ES8fPz4tfSJcdlYWr06ewOKV69lz8CRfLV4JgJmZGZ4+fmxcv0av+/VPLP5mEZFRA8jOzua/M94hYe8B9iQd5L8z3tEeUGV98P57+Pr5c/rHX/H18+eD998DYOeO7Zw9e5bTP/7K3M+/5MXxmssNbGxssLW1Y/8+432UeUjHJiSeTae+WS1G92zBs6tO8cyqk4zu2eKuBfW3x1IYt/wk45af5OCFO3lKycrXLv8w9px2+a6frhLVxa7a9+XfWL3sG4LDI7l+PZs5s95lS/RetsYkMWfWu9rCu6x5c/6Hp7c/SUd/xNPbn3lz/gfAsy9MZnfiYXYnHubV/0ynZx9vrKystcfDpvVr9b1rf9valYsJConkxvVsPp39X9ZtT2D9jj18Ovu/VRbU7Z2c+WzRSrr3Kj+Dz8q6EfOXfsu2hMP875OveHn8E9rXogYPY/nX86t9X+7ViqV3+sXZs2awI3YvO+OSmD2r6n7xkznv4+3jx8ETP+Lt48cnZfrFqZMmsHTVehIPnWTBkjv9opePHxvWGW+/KGODxrIlXxMeMYDr2dnMmjmd6Ph9xCTsZ9bM6VXmYc4Hs/D29efoqZ/x9vVnzgeztK+VlJQw7c3X8Avoq13W2MaGprZ2HNhvvOPDssVfEx6pGSPfmzGdmD37iU08wHszplc5Rs6ZPQsf3wCOf/8LPr4BzJmtyUHLVq3ZuiuO/UdOMuW1N3jx+WcATVvw8fNn/doqZ/YahaVlcjBrxnRi9+wnLvEAs/4iBycq5KBVq9Zs2xXHgdIcvFAhB+v0lIMHqjhXVfWCqqpOqqqOVFW1g6qqA1VVzVVV1VdV1SOl77GosM7Xqqq2V1W1h6qqT+n7Hufr16wkKCSc+Nhd+PgFYGVtjaWVFT5+AcTF7Kz0/p3bNjNkuOapmEOGj2LH1k2a7axdRWh4FM2atwA0T827LTg0gnVrVulhb+7N6pUrCAuPJHrXTvwDArG2tsbKygr/gEB279xR6f1bNm9ixKjRAIwYNZrNmzaWLt/IiBGjUBSFHh49yc7KIrX0YRThEZGsWrlcfzv1DwU62ZD0WwbdW1ly5PcsbhQUc7OghCO/Z9Gj9Kz4v5X0WwYBjsY9LWv92lUEhYSTELMbL98ArKyssbS0wss3gPjoXZXev3P7ZgYPGwnA4GEj2bFtU6X3bFy3hqiBQ7S/B4VE8N3aldW3E//SpnWrCewfRmJcNH18/LG0sqahpRV9fPzZE7u70vvbtnfi4bbtKy3v2NmFprb2ALRzciY/P5+CggIAAoJC2fyd8f6Bsm7NSvqHhhMXU7lfjI2u3C/u2LqZR0v7xUeHj2L7Fk07WPdn/WKYcfeLMjZorF29gpCwCGKid+HrH6jNg69/ING7K+dh+9bNDBvxGADDRjzGti13+oT5n88lPOqRcjkACA2PZO1q4+0T1qxaQUh4BLG7d+JXZoz0CwgkZlflMXLblk0MH6nJwfCRj7F1s2aM9OjVGysrzXji3qMnKcmXteuEhkeyZvUKPezNvVmzagWh4RHEVJGD6CpysLVCDrZUkYPuFXIQpsccPFDF+f2msLCQixfO06JlK1JTUrBv1kz7mp2DA6kpKZXWuXbtKk1tNWc/mzS15dq1qwD8du4MWVlZDAgNpJ+3R7lpAE7OnThx7Eg17829KSws5Pz532jZqhUpKck0a9Zc+5qDQzNSUpIrrXP16h/Y2WlyYGtry9WrfwCQkpJCs+Zl1m92Z31XN3f27f2zGU6GU8tEwb5hHa5cL8CmvhnXbhRoX7t2Q7OsKgNc7Fg4woUpfduWO7tu27AOXw3vykeDOtHZvoF2+c2CEmqbmtCgjnHek7qwsJDfL56neYtWXElNxr5MW7BzaMaV1MptIe1q+eMh7erVcq/n5eYSH7OLkIgB2mVOzh05cfwoxqiwsJBLF8/TrEVL/riSgp39nT7B1t6BP65U7hP+jh1bNtCxswvm5uYANLS0orCwgMyMdJ3ErUvl+sXUFBwc7uTA3t6B1NR/0C+e1fSLUSGBBHp7sHrFnX6xg5H3iw/62ACleTh/Ow/JNCuTBwcHB1LvMj7Ylo4PTcuND8ls2byBJ558ptI6Lq5u7E8yzvGhsLCQCxd+o2XLVpoxrtIYWUVbqJCDa6U5KGvpN4sIDOqv/d25YyeOHTXOtlA2B6kpKTiUyYG9Q7Oqj4e/mYO+BsqBcY7CAoCM9DQaNmx4z+srioJSeqv/4uJiTp04xtpNO8nPzyMs0Bu37h60adseU1NTzMzMuHnjBhYPPaSr8HUiLS0Ny4aW97y+oigoyl8/7qBJkyZVDurGoGHd2tws+Gfz3DaeusKSg5dQVRjbuwXPebfm/d1nSc8p5NGFR7ieX0z7JvV5N7wDY5YeJ7f0QTmZeUU0qm/G9Xzjm1uYkZ5Gg397PFRoC7t2bMXdoxdWZZ6SaczHQ2ZGGg3+xfFQlV9//pH3p7/JN2s2l1veqLENV6+kYmXdSKef92/ppF9UyveL327W9IshAd64d/egTTtNv1jbSNuBjA0a6elpNLTUzfjw+pRJTJs+ExOTyucsbWyacOUe//CtbulpaTT8l2MkFfrFPQlxLF28iJ0xe7TLTE1NMattxo0bN3jIyNqCLnJQcWzYkxDHEgPmQM6cG7E6deqSX/o1s529PSmX73y9kpqcjJ29faV1bGya8McVzVSNP66k0thGc8Ggvb0DvgF9qV+/Po0aNaZnb09+OH1Ku15hQQHmdepU5+7ck7p165JfkA9o9uHy5Uva15KTL2Nv71BpnSZNmmqnq6Smpmq/orS3t+fypTLrX76zfn5+PnXq1q22/fg3CopLMCt9qt61nEJsHjLXvmbzkDnXcgorrZOZW8QtVXMroq3f/0GHpprZWkUlqrbw/vVqDinZ+TS3vLPfZqYmFBbfqsa9uXd16talIF9zPNjaOZBSpi2kJl/G1q5yW2jcpPzx0Mim/AW0m9avIWrgo5XWKzDS46FOnboUlB4PTW3tSU250ydcSUnWTlP5u1JTLvPc40OZPXcBLVs9XO61goJ8zI3wmNDkoLRftLMnuczXzikpydjZ/Y1+sXFpv+hQvl/s1ceTH743/n5RxgaNunXqkp+vOR7s7B24XCYPycnJ2N1lfLhSOj5cKTM+HD92lCdGj6BLhzZs2rCOl18ar53ukZ+fT906xncswO1+8fYYaV/FGFlFW7hLDgC+P32KCc8+xcq132HdqPwf5gWFBdQxwrZQNgd29vYkl8lBSvLlqo+HCjloXCEH40tz0MhAOZDi3IhZWllxq6SE/Px8fP37ER8bTVZmJlmZmcTHRuPr36/SOv2Cw1lT+tXsmhVLCQoJByAoNJxD+/dRXFxMbm4ux44eop2jEwAZGelYN2pE7dq19bdzf5OVlRUlpTkI7BdETPRuMjMzyczMJCZ6N4H9giqtExoezvKlmiu0ly9dTFh4hGZ5WATLly9FVVUOHTxAg4YNtdNfzpz5FeeOnfS3Y//AzYISTBQFM1OFwxey6N7CEgtzUyzMTenewpLDF7IqrWNd787/pWebRpxPzwWgYd1a3H5ytF0Dcxws65CSnX9nvfq1uXI9H2NkaXmnLfgE9GVPXDRZWZlkZWWyJy4anzIXcd3Wr38Ya1dq7mKyduUygoLDta9dz87mQFKi9hi5zZiPh4alOSjIz8fLL5C98TFkZ2WSnZXJ3vgYvPwC//a2rmdn8eSIgbzy5ju49ehV7jVVVUm7+gfNmrfU9S78a5Zl+gS/gH4klOkXE2Kj8Quo3C8GhYRrp6ysXrGU/qGa//P+oeEcOlCmXzxSpl9MN952IGODRtm2EBDYj7iY3do8xMXsJiCwch76h4SxsvSuPiuXLyG4tC2c/PEsp346x6mfzhERNZDZH80lNDwSgHNnf6WDc0f97dg/UHaM9O8bRGyZMTI2ejf+fSuPkcGh4awovZvNimVLCAnTjJGXfv+dkUMHMX/hYtq2K3+dSkZ6Oo0aNTbKtlA2BwFV5CCgihyEVMhBaJkcjBg6iK8WLqZdhRyk6zEHUpwbOR+/QA7tT8LK2pqJU16nv19v+vv1ZtLUN7Cy1nwVP2n805w4ppkjO2HSKyTExdCrmzN74mOZMHEKAO0dO+AX2A+/3q4E+/dmxGNj6eCsKUaT9sQTEGS8t4oKCOzLvqS9WFtb8+rrb+LVuwdevXvw2hv/h3VpDp59ehxHS+eCTX7lVWJjouns3J642BgmT3kVgP7BIbRu3ZpOHdrx/DNP8dGn87SfsSc+juBg483B4YtZdLZvwI2CYpYcvMSXw7ry5bCuLD54iRulU15eCWyLYxPNGfJnvFqxaKQLC0e40K1ZQ+YmnAegq0NDFo7sxoIRXXk7zIkPY85p13dsYsGPqTcoqXjnfyPi4x/IoQNJWFlZ89IrrxPq35tQ/95MnPKGdmrKyy88w8nSOePPT3yFPfHR9HFzJjEhhucnvqLd1vatG/H2C6Re/frlPmNfYgIB/Yz31pqePgEcObgPSytrnp/0KgOCvBgQ5MX4ya9hWZqD1yY+q71t4q5tG+nj0pbjRw4ybsRAxjyqGYSWLvyCi+fPMfeDmYT7exDu70F66Tzk708ew8WtB7VqGefMR1//QA6W9ouTprxOP9/e9PPtzeQy/eLEMv3iCxM1/aKHizMJ8bG8UKFf9O3lSn+/Cv1iYjyBRtwvytig4R/QlwP79mJlbc0rU9/A37sn/t49mfLqm9o8vPDcUxwvnTs/cfJU4mOjceviREJcDBMnT/3Lz0hMiKdff+PNg19gX/bv04yRU157Az9PD/w8PZj6+pvaMXL8s09q50tPenkqcbHRdOvkSHxcDBNf1uRg1szpZGSkM/ml8Xh6uOLTp4f2M/YkxBFkxDnwr5ADX08PfP8kBxNLc+BSRQ4yM9KZ9NJ4+lTIQaIec6CoqhGPxPehrt3c1F0JB3S2vVMnjjP/s4+ZO/8bnW2zorEjBvPG2zNoU8UdHe5Fg7q6HdCPHz/G3I8/YuE3Vd+3Vhf6+vuwZt0G7VXa/1bwvH062c5t7WzqM9jVnv/uPKPT7ZY13qc1+37L4NilbJ1tc8WY7jrbFsDpk8eZ/9knfPrl1zrdblnjRg3htbfe1dnxkFf4j55p9pe+P3Wcr7+cywfzFup0u2VNf+NlAoJC6e3tp5Pt6bpPOHXiOF/M+5jPvvpGp9sta8yIwfzftBm0aaebdgBwS4fD7f04NgCY19LtOcGTx4/x2dyP+XJh1fez1oWQfr6sWP0dljoaH0xM/vo6qH/ixPFjfPbpR8xfVH1j5IhHB/L2uzMrnVG/V7rNgCYH8z79iK+qOQfT3p1Z6Yz6v9GgrulRVVXdKy6XM+dGrotLN3p7aR7AUx0KCwvpHxah085X17p1c8Xbt/pycO3aNSa8OFFnhXl1OHMth+OXstFxn17O+fRcnRbm1aFz12708fKp1uMhKNS4j4dOXbrRs493teUANLdW1FVhXh26uHTD07t6+8Xg0AidFua6JmODRtdurnhVY1tIu3aN5ydM1FlhXh1curni5VO9bSEsIlJnhXl1cOnminc15yA0IlKnhfmfkTPnOqbrM+f3I12fJbsf6frM+f1K12fO70e6PnN+P5I+QUOXZ87vV7o+c34/0vWZ8/uRZEBDzpwLIYQQQghh5KQ4F0IIIYQQwkhIcS6EEEIIIYSRkOJcCCGEEEIIIyHFuRBCCCGEEEZCinMhhBBCCCGMhBTnQgghhBBCGAkpzoUQQgghhDASUpwLIYQQQghhJKQ4F0IIIYQQwkhIcS6EEEIIIYSRkOJcCCGEEEIIIyHFuRBCCCGEEEZCinMhhBBCCCGMhBTnQgghhBBCGAkpzoUQQgghhDASUpwLIYQQQghhJKQ4F0IIIYQQwkhIcS6EEEIIIYSRqGXoAGoaUxOFemamhg7DoEpuqYYOweBWj+1h6BCMwpCFhwwdgsGtfLy7oUMwuPSbhYYOwSg0sjAzdAjCCJgoho7A8KRO+HNy5lwIIYQQQggjIcW5EEIIIYQQRkKKcyGEEEIIIYyEFOdCCCGEEEIYCSnOhRBCCCGEMBJSnAshhBBCCGEkpDgXQgghhBDCSEhxLoQQQgghhJGQ4lwIIYQQQggjIcW5EEIIIYQQRkKKcyGEEEIIIYyEFOdCCCGEEEIYCSnOhRBCCCGEMBJSnAshhBBCCGEkpDgXQgghhBDCSEhxLoQQQgghhJGQ4lwIIYQQQggjIcW5EEIIIYQQRkKKcyGEEEIIIYyEFOdCCCGEEEIYCSnOhRBCCCGEMBJSnAshhBBCCGEkpDi/j12+dInQoAC6d+tED9fOfDb3EwC+W7eWHq6daVivFseOHjFwlNXv2aeeoHVzW3q4dim3/IvP5uLaxZnu3Trz5utTDRSd/mRnZfHEqEfxdO+EV/fOHDl0gP/NfAcXp1YEeLoT4OlO9K7thg5T51aNdePrUS4sGNGVL4d3LffaEFd7Eib2oWGdWgBYmJvybrgTi0a68MWwLrRuVM8QIVebs2d+oa9Xd+0/xxaN+erzT/jgvem4ObfWLo+pge3gjYnP0qdzK8L9umuXffz+O0QGeDAgsBdPDI3g6pVU7WuH9u1hQGAvwnzdGfVIkCFCrnZundrh07Mbfn3c6evTE4D3//sOXRxb4dfHHb8+7kTvrHlt4bb8/HwCvHvi6eFKL/cuzHx3GgCqqjJ92pu4d+2Ah2snvvzsU4PGWZ0uX7pESL8A3F060b3bnTohIyODiJB+uHR0JCKkH5mZmQaOtPpcvnyJsP4BeLh2pqdbFz6fp8nB46OG4enhhqeHG52d2uDp4WbgSO9QVFU1dAw1iqubu5qQdEgvn3UlNZUrV1Jx6ebKjRs38O7dnZVr1qMoCiYmJrw4/lnenfk+rm7ueonnNkXR68exN3EPFhYWPPXEGA4dOwXAnvg4/jdrJt9u2Iy5uTnXrl7FpkkTvcWUU1Cit8+6bcIzY+nZy5MRo8dSWFhIXm4u8z//hPr1LXjuhUl6jwdgyMLqPxZWjXXj6RUnyc4vLrfcxsKMKX3b0sK6Lk8t17z+jFcr8opKWHzgEi2s6vKS/8NMWvdDtca38vHuf/2malBSUoKbc2u27E5k9Yol1K9fn2cmGKYdXM8rqvbPOHxgL/XqWfDqi0+yOe4wADdvXMfioQYALF3wGefO/My0WZ9wPTuL4REBzF++AftmzUlPu0qjxtXfPzSyMKv2zyjLrVM7diXsp1Gjxtpl7//3HepbWPC8gfqE2qb6Oyeoqio5OTlYWFhQVFREcKA3M/83h19//pnEPfF8Nn8RJiYmeh8fapnqb5CsWCd49erOqrXrWbZ0MVZW1kx+ZSof/G8WWVmZTJ/xnt7iKrmlv9qzYg58+/Rg+ep1OHVw1r7njVdfpkGDhkx9/f/0FheAZb1aR1VVrVSkyZnz+5itnR0u3VwBeOihh3B0ciIlJRlHpw60a+9o4Oj0x9PLGysr63LLFnz1BZNenoK5uTmAXjteQ7ienc2BpL0Mf+xxAMyBSH88AAAgAElEQVTMzGhoaWngqAxrvG9rvki8QNnzD62s63L8UjYAv2fmYdvAHKt6tQ0UYfXamxBLy1YP06xFS0OHohfde3piaWVVbtntwhwgLy9Xe+Zgy3drCAyJwL5ZcwC9FOZC/xRFwcLCAoCioiKKiopRFIVFC75gymtvYmKiKYFq8vhQZZ2QnMzWzZsYMfIxAEaMfIwtmzYaMsxqVTEH7R2dSE1J1r6uqiob1n3LoCFDDRViJVKc1xAXL17g1IkTuHf3MHQoRuHsmTPsS9qLn1cv+gf6cfTIYUOHVK1+v3ieRo0b8+Jz4wj07M6k8U+Tk5MDwKKvPsevtysvPf8kWTX0q8vZj3Rk/vCuhHduCkCfh61Ju1nIubTccu87l5aDV9tGADg1taBpgzrY6Plspr5sXL+WqIFDtL9//dUXBPZxY9L4p8jKqpntoCofvTcNPzdHNq9fzQuvvAnAhd/Ocj0ri8cG9mdgkCcb1q4wcJTVQ1EUhkSFEOjtwZKvF2iXL5r/OT69XHnxuZrbJ9xWUlKCV0832reyw9c/APfuHpw//xvr163Bz9ODQVGhnDt7xtBh6sXFC6V1Qg8Prl39A1s7OwCa2tpy7eofBo5OPy5evMDpkydwK1Mr7UtKxKZJU9q0bWfAyMqT4rwGuHnzJqOGDea9/31IgwYN/nqFB0BxcTGZmRnE7tnHuzNnMXrEUGryFK7i4hJOnzzOmCeeJnrvYerVr8/cOe8z5omnOXjiZ2L2HqFpU1umvTnF0KHq3PjVp3lyxUmmfPcjUV3t6OLQgJE9mrFo3++V3rv8cDIPmZuyYERXBnaz4+zVm+jx21W9KSwsZNf2LYRFDQTgsbFPse/4T+xKPEyTpra882bNvwbjtpdenUbc0V8If+RRli/6EoCS4mJ+OH2CL5auY8GKDXz+0SzOn6t5BdrmnXHEJB5i5brNLPrqc/YnJTJm3NMcOvkzcUlHaGpry1tv1Lw+oSxTU1MSDxzlh18vcuzoYX784XsKCwqoY16HuL0HGf34OMY/O87QYVa7mzdvMnLYYN6bXblOUBQFRd/zUQ3g5s2bPDZsCP99v3wO1q1ZzcAhjxowssqkOL/PFRUVMXLYIIY8OpyIqEcMHY7RcHBwICJyAIqi4N69ByYmJqSlpRk6rGpj7+CAnUMzXN17ABAW+QinTp7ApklTTE1NMTExYcToJzh+tOZ9g5CWUwhAVl4RiWfTcWnWALuG5iwc6cKqsW7YPGTOVyNcsK5Xm9zCEt7bdZZxy08yY8cZGtatTUp2voH3QPfionfQuasLNk003ySUbwdjOVED28FfCRvwKLu2ab66t7VzwNMngHr16mPVqDHuHn345cfTBo5Q9+zsHQCwsWlCSFgkx44epkmZtjCyhvYJVWloaYmXty8xu3di79CM8MgBAIRFRPHD9zXv/76soqIiRg4dxJChw4ksrRNsmjTlSqrmAukrqak0tqm5U3tAk4PHhg9m8NBhREQN0C4vLi5m86bveKTMt4zGQIrz+5iqqjz/zDgcHTsw/sWJhg7HqIRFRLInIR6AM2d+pbCwkMaNG//5SvexJk1tcXBoxtkzvwCQmBBLe8cO/FHm7hTbt2zEqUNHQ4VYLerUMqFubVPtz91bWvLzlZtEfXmYoYuOMnTRUa7dKODJ5SfIyC3CwtyUWiaaM0RhnZpyKvk6uYX6v3i3um34dg1RA++cCarYDhxrWDu4mwu/ndX+HLtzCw+3bQ+Af/9Qjh3eT3FxMXm5uZw6fpiH29Ws63RycnK4eeOG9uf42Gg6dOhYri1s21zz+oSy0q5dIzsrC4C8vDziYqNp5+hISFgEiaXjQ1JiAm1L20VNpKoqzz89DkenDkwoUyeEhIWzfNkSAJYvW0JoeIShQqx2qqoy/tknae/YgfEvlK+V4mOjadfeEYdmzQwUXdVqGTqA+4GiKL5Aoaqq+wwdS1kH9iWxasUyOnbqTB8PzcUO/3n7XQoLCnhl0oukpV1j8CPhdO7SlQ2bdxg42urz+KjhJCYmkJ6WhmObFrz+5luMGj2W5556gh6uXTAzM+PLBV/X+K/tZrw/h+fGjaaoqJCWrVrz0bwFvDl1It+fPomiKDRv0ZL/ffSZocPUKav6tXk3vAMApiYK0T9f49DFrLu+v6V1PV4LaoeqwoX0XGbtrnlTGXJzctgTH8OsOfO0y95963V+LG0HzVq0LPdaTTH52TEc2p9IVkY6vm7tGT/5DfbE7uT8uTOYmJhg79CCabM+BqBNOyc8ffsSFeCBYmLCoOFjaO9Us4rUa1f/YMyIwYBmGs8jg4fi3zeI554cww+nT4Ki0KJFS2Z/XLP6hLKuXEnluafGUlJSwq1btxgwcBD9g8Po1cuTJ8eO4rO5H2NhUZ+P531p6FCrzf59SawsrRN699DUCW+98y6TXp7K6BFDWfrNIpq3aMni5asMHGn1ObA/idUrluHcqbP2don/eXs6/fqHsO7bNQwabDwXgt4mt1L8GxRFmQbcVFV19l+9V5+3UjRWNbwG/lsMcStFY6SPWykaO0PdStGY6ONWivcDfd9K0Rjp81aKxkqft1I0Vvq8laIxk1spVkFRlMcURTmlKMpJRVGWKooSrijKQUVRjiuKEq0oSlNFUVoBzwATFUU5oSiKl2GjFkIIIYQQNdUDO61FUZSOwJtAb1VV0xRFsQZUoKeqqqqiKOOAKaqqTlYU5Qv+5My5oihPAU8BNG/eQk97IIQQQgghapoHtjgH/IG1qqqmAaiqmqEoSmdgtaIodoAZcP7vbEhV1fnAfNBMa6mmeIUQQgghRA33QE9rqcKnwFxVVTsDTwN1DBwPeXl5BPf1o6SkhOXLFuPSyRGXTo4sX7a4yvdnZGQQGdoPl06ORIb2I7P0AROrVy6nV3cXerp3JdDXk9OnTgKa+yH3D/SluLi4yu0Zg7y8PPoHluZg6WJcOjri0tGR5UvvnoOIkH64dHQkIqR8Dnq6u+Dh1pWACjkICjD+HESFBFBSUsLqFUvo1c2ZXt2cWb1iSZXvz8zIYEhkML26OTMkMlj7oJF5H39AgKc7AZ7u+PR0wd6qDpkZGRQWFhIV7G/UOQAwMzXh48GdMFEgyNmG5WNcWT7GlSBnmyrfP6Znc7590p0FI7qyYERXPFppniBp28CcXRN6apdPCmijXeeDgR2xMDfVy/7ci7y8PAaGBlJSUsKalUvp4+ZMHzdn1qxcWuX7MzMzGDogmD5uzgwdEKx9ANHnn3xAX6/u9PXqjn+vbjRvVJfMTE1beCQkwKjbQn5eHqMeCaKkpIQNa5YT1KcrQX26smHN8irfv2PzesJ83XF2eIjvTx6r9HrK5Uu4tW3Kos81F4wWFhYyckA/o85BXl4ekcGaPmHV8iV4uDjj4eLMquV37xMGRQbj4eLMoDJ9AmjuWOLXxx2vHl2JDA4ANDmI6G/8fUJeXh6hQZrxYeWyJbh1ccKtixMrl909DwPCgnDr4sSAsKBKD2E6dvQwjRuYs/G7dYDmji+DIkOqfT/+DRkjNTkI6afJwYplS3Dt7IRrZydW/Ek7iAoLwrWzE1Fl2sHWzZvo3aMbnh5u+PbxYP++vYCmHQyM0F87eJCL81hgsKIojQBKp7U0BG4/03V0mffeAB7Sb3gaSxd/TXjkALKzs5k1Yzqxe/YTl3iAWTOmaw+osubMnoWPbwAnvv8FH98A5syeBUCrVq3ZtiuOA0dOMuW1N3jh+WcAzWPeffz8Wbd2tV73659YuvhrIqI0OXhvxnRiE/cTt/cA790lBx/OnoWPXwAnfvgFH78APizNQctWrdm+O46DR08ytUIOfI08ByuXfUNoeBTXs7P54L0ZbIvZy/bYJD54b0aVT/j7dM77ePn4sf/4j3j5+PHpnPcBeP7FycTsPULM3iO88da79OrjjZW1NWZmZnj6+LFx/Rp979o/EtKpCXvOplPfrBZjerbgmZWneHrlScb0bHHXgnrtsRTGLT/JuOUnOXjhTq6Ss/K1yz+MOaddvuunq0R1tav2fblXq5d9Q3B4JNevZzNn1rtsid7L1pgk5sx6t8onf86b8z88vf1JOvojnt7+zJvzPwCefWEyuxMPszvxMK/+Zzo9+3hjZXWnLWxav1bfu/a3rVu1hL4hEdy4ns28D2eyeksca7bGM+/DmWRXkYN2Ts58umAF7j37VLm9WW+/ipd/X+3vZmZm9PL0ZfumddW2D//WiqV3+oTZs2awI3YvO+OSmD2r6j7hkznv4+3jx8ETP+Lt48cnpX1CdlYWUydNYOmq9SQeOsmCJSsBTQ68fPzYsM64+4RlS74mPGIA17OzmTVzOtHx+4hJ2M+smdOrzMOcD2bh7evP0VM/4+3rz5wPZmlfKykpYdqbr+EXcKctNLaxoamtHQf2J+llf+6FjJGwrLRWup6dzaz/TicmYR+xe/Yz6793bwc+vv4cO/0zPmXagY+fP0kHj7H34FHmfvEVLzz3NKD/dvDAFueqqv4AzAASFEU5CXwITAPWKopyFCj7xJrNwABDXBC6ZtUKQsMjiNm9E7+AQKytrbGyssIvIJDoXZVvj7h1yyaGj3wMgOEjH2PLZs1DNzx69cbKSnPWsHuPnqQkX9auExYeyZrVxvv46tWrVhAa9g9ysHkTI0pzMGLkY2zZpMlBzwo5SC6bg4hI1qwy3hysX7OSoJBw4mN34eMXgJW1NZZWVvj4BRAXs7PS+3du28yQ4aMAGDJ8FDu2bqr0nu++Xc2AQXfuhR0cGsG6NcZ9O62+TjYkncugRytLjlzM4kZBMTcLSjhyMUt7VvzfSjqXQYCj8d4Tf/3aVQSFhJMQsxsv3wCsrKyxtLTCyzeA+Ohdld6/c/tmBg8bCcDgYSPZsa1yW9i4bg1RZR7CERQSwXdrV1bfTvxLW9avISAojKT4aHp7+2FpZU1DSyt6e/uxN253pfe3aedE67vcyzp6+2aaNW9F2/Ydyi0P6B/O5vXGW4ysW7OS/qHhxMVU7hNioyv3CTu2bubR0j7h0eGj2L5F0w7WrV1FaHgUzUqvl7Ip8zCa4DDj7xPWrl5BSFgEMdG78PUP1ObB1z+Q6N2V87B962aGjdCMD8NGPMa2LXeOh/mfzyU86pFyOQAIDY9k7WrjPR5kjCzfDvzKtAO/u7SDbVvKt4OtmzXtwMLCQnvb5dzcnHK3YA4Nj2TtKv20gwe2OAdQVXWxqqqdVFXtqqrqGFVVN6qq+rCqqm6qqr6iqqpv6ft+VVW1i6qqLqqqJuorvsLCQi5c+I2WLVuRmpKCQ7Pm2tfsHZqRmpJSaZ1rV//A1k5z1q+prS3Xrv5R6T1Lv1lE36D+2t+dO3bi2NEj1bAH/15hYSEXzv9Gy1atSElJoVmZHDg4NCPlHnOw5JtF9O1XPgdHjTgHFy+cp0VpO7Av87AEOweHqtvBtas0tdXkoElTW65du1ru9dzcXOKidxEacedJaU7OnThxzDhzAFDLRMGuYR2uXC+gsYUZV28UaF+7dlOzrCoDutqxaKQLU/u2LXd23a5hHRaM6MrHgzvRxeHOo5xvFpRgZmpCgzrGd0lOYWEhv188T/MWrbiSmox9mePBzqEZV1KTK62TdrV8W0i7Wr4t5OXmEh+zi5BybaEjJ44fraa9+HcKCwu5/Pt5HJq35I8rqdja3zkemto5lHvIzl/JybnJgs/m8Nzk1yq91s7Jme9PGG8OtH1CagoODndyYG/vQGrq3+8Tfjt7hqysLKJCAgn09mD1ijvTozoYeZ9QWFjIxfO3+8ZkmpXpGx0cHEhNqXw8XK0wPlwtHR9SUpLZsnkDTzz5TKV1XFzd2J+0t5r24t+RMfJ2Ds6X1krJ5R4oZP8P2wHA5o0b6O7SkSGPRDD3i6+0y7u5ummnuVQ34xt9hFZ6WhoNG1re8/qKolR68M6ehDiWLF7Ezpg92mWmpqaY1Tbjxo0bPPSQQWbv3FW15CA+jiXfLGJXbIUcmBlnDjLS02jYsOE9r68oCgrlc7Br+xa69+yFlbW1dtntHNy8cQMLI8sBQMO6tblZ8M/mPG48dYUlBy+hqvBE7xY8792aWbvPkp5TyJAFR7ieX0z7JvWZEdGB0UuOa58WmplXROP6ZlzPN645lhnpaTT4t22hwvGwa8dW3D16YWV1f7SFrIx0Hmpw7zkoa97s/zL6yeepX9+i0mumpqbUNjMj5+YN6lsYVw500ieUtoPi4mJOnTjGt5t3kp+fR0iAN+7dPWjTrr02B8bYDgDS09NoaKmb8eH1KZOYNn0mJiaVz1na2DThypXKRa4xkDGyNAc6agcA4ZFRhEdGkbR3DzPeeYuNWzXfSNo0aVLlH77V4YE+c27s6tStS0F+PgB29vYkX76kfS0l+TJ29vaV1rFp0pQrqZozR1dSU2lc5uu570+fYvyzT7Fy7Xc0atSo3HoFhQXUqWPw618rKZsDe3t7LpfJQXLyZezvMQervq0iBwVGmoM6dckv0JwltrO3J+Xyna8aU5OTq24HNk20ZxD/uJJKY5vyF0xuXL+m3JSW2woLCjA3whwAFBRrzmgDpN0spMlD5trXbCzMSbtZWGmdzNwibqmae6Ru+f4PnGw1RVhRiaotvH+9mkNyVj7Nrepq1zMzNaGg+FY17s290RwPmrZga+dASpnjITX5MrZ2DpXWadykfFtoVKEtbFq/hqiBldtCgZG2BfM6dSgoPR6a2tpxJeXO8fBHarL27PDfcer4YWa/+38E9HBmyYLPmP/pbJYv+kL7emFBAWbmxpeDOnXqanNgZ2dfbvpBSkoydnZ/o09orGkH9g4O+Ab0pX79+jRq1JhefTz54ftT2vWMuU+oW6cu+dox0oHLZfrG5ORk7OwrHw9NKowPt6ewHD92lCdGj6BLhzZs2rCOl18az9bSaaH5+fnUrVO30raMgYyRULdu+XaQXKYdpPzDdlBWH09vLpw/T3qaZpazPtuBFOdGzMrKipKSEvLz8wnoG0Rs9G4yMzPJzMwkNno3AX2DKq0TEhquvTp5xbIlhIZFAHDp998ZMXQQXy1cTLt25edepqen06hRY2rXrl39O/UP3VMOwsJZXpqD5cuWEBpeJgePDmL+ovsrB5ZWVtwqzYGvfz/iY6PJyswkKzOT+NhofP37VVqnX3A4a0q/nl6zYilBIeHa165nZ7N/byJBIRHl1snISMe6USOjzAFoppuYmiiYmSocupBF95aWWJibYmFuSveWlhy6kFVpHev6d/bFq00jzqfnAtCwbi1MSk+U2DU0p5lVHVKy8sutd+V6PsbG0vLO8eAT0Jc9cdFkZWWSlZXJnrhofMpcyHZbv/5hrF25DIC1K5cRFFy+LRxISizXPsC420JDS83xUJCfTx/fQJISYsnOyiQ7K5OkhFj6+Ab+7W0t27CbmEM/EnPoRx4b9xxPTXiZEWM10xoyM9KxsjbOHFiW6Rf9AvqRUKZPSIiNxi+gcp8QFBKunbKyesVS+odq/s/7h4Zz6MA+iouLyc3N5diRQ7RzdAIgI9142wGUz0NAYD/iYnZr8xAXs5uAwMp56B8SxsrSO9qsXL6E4NI8nPzxLKd+Osepn84RETWQ2R/NJTQ8EoBzZ3+lg3NH/e3YPyBjZPkxMiCwH7Fl2kHsXdpBcGj5dhASpmkHv507i6pq7oh94vgxCgsKsC79I+XcGf21AynOjZx/YF/279uLtbU1U157A19PD3w9PZj6+ptYl05JGP/sk9o54xNfnkpcbDQunRyJj4th4stTAZg1czqZGelMemk8fTxc8enTQ/sZiQlxBPU33ltF+Qf2ZX9SmRz08cC3T/kcPP/MnRxMenkqcTHRuHR0JD42hkmlOXjvv9PJyEhn0ovj6d3DFe/eFXIQbLw58PEL5ND+JKysrZk45XX6+/Wmv19vJk19Qzs1ZdL4pzlxTDNHdsKkV0iIi6FXN2f2xMcyYeIU7ba2bdmIj38g9evXL/cZSXviCQgy3hwAHL6YRWeHBtwoKGbJwUt8ObwrXw7vyuIDl7hROuXllcC2ODbVnCF/1qsVX49yYdFIF7o1b8jcBM2jC7o6NGTRqG4sGNGVd0Kd+DDmnHZ9x6YW/Jh6gxIjfWKBj38ghw4kYWVlzUuvvE6of29C/Xszccob2qkpL7/wDCdL54w/P/EV9sRH08fNmcSEGJ6f+Ip2W9u3bsTbL5B6FdrCvsQEAvoF62+n/qHePgEcPbQfSytrnn1pKkNCfBgS4sNzE1/FsjQHb05+XnvbxN3bN+Hr1p4TRw/xzKiBjBsW+ZefcWjfHnwC+//l+wzF1z+Qg6V9wqQpr9PPtzf9fHszuUyfMLFMn/DCRE2f4OHiTEJ8LC+U9gntHTvgF9gP316u9PfrzYjHxtLBuRMASYnxBBp5n+Af0JcD+/ZiZW3NK1PfwN+7J/7ePZny6pvaPLzw3FMcL507P3HyVOJjo3Hr4kRCXAwTJ0/9y89ITIinn4yRRj1G+pVtB6++gZ9XT/y8ejLltTvtYMKzT3H86J12EBcbjWtnJ02tVNoONm1YTy/3rnh6uPHKxAksWrpCO+UlcU88/fSUA+X2XwhCN1zd3NWEpEM6296J48eY9+lHfLWo6nt16sKIRwcy7d2Zlf5SvlcVpq/9ayeOH2PeJx/x1dfVl4Phjw7kbR3mIKegRCfbue3UiePM/+xj5s7/RqfbLWvsiMG88fYM2tzlrhb3YshC3R0LAO2a1GeIqz0zdpzR6XbLmuDbmqRzGRy7lK2T7a18vLtOtnPb6ZPHmf/ZJ3z65dc63W5Z40YN4bW33tVZW7ieV6ST7dz2w6kTLP5qLu9/ukCn2y1rwhPDmPT6O7Ru005n22x0l4uW78WpE8f5Yt7HfPbVNzrbZkVjRgzm/6bNoI2O+kWA2qa6PSd48vgxPpv7MV8urPqe3roQ0s+XFau/w9JKN3eEqmWq20HyfhwjS27ptvY8UdoO5ldjOwju68vKNbprBwCW9WodVVXVveJyOXNu5Fy6ueLt40tJiW6LvdsKCwsJjYjU2QFXHVy6ueJVzTkICzfuHHRx6UZvr+rNQf+wCJ0W5tXhzNUcjl/K1k5JqQ7n03J1VphXh85du9HHy6da20JQqHG3hY5dXPDo7V2tOQjoH67TwlzXurh0w9O7evuE4NAInRbm1aFrN1e8qjEPadeu8fyEiTotyHRNxsjSHNSgdiBnznVM12fO70e6PnN+P9L1mfP7la7PnN+PdH3m/H6k6zPn9ytdnjm/X+n6zPn9SNdnzu9Huj5zfr+SM+dCCCGEEEIYOSnOhRBCCCGEMBJSnAshhBBCCGEkpDgXQgghhBDCSEhxLoQQQgghhJGQ4lwIIYQQQggjIcW5EEIIIYQQRkKKcyGEEEIIIYyEFOdCCCGEEEIYCSnOhRBCCCGEMBJSnAshhBBCCGEkpDgXQgghhBDCSEhxLoQQQgghhJGQ4lwIIYQQQggjIcW5EEIIIYQQRkKKcyGEEEIIIYyEFOdCCCGEEEIYCSnOhRBCCCGEMBJSnAshhBBCCGEkahk6gJrI1EQxdAgGpTzYuw9IG7ht1djuhg7B4Do8t8bQIRjc7wuHGzoEo6CqqqFDMLj8oluGDsHgateS0susloyRf0bOnAshhBBCCGEkpDgXQgghhBDCSEhxLoQQQgghhJGQ4lwIIYQQQggjIcW5EEIIIYQQRkKKcyGEEEIIIYyEFOdCCCGEEEIYCSnOhRBCCCGEMBJSnAshhBBCCGEkpDgXQgghhBDCSEhxLoQQQgghhJGQ4lwIIYQQQggjIcW5EEIIIYQQRkKKcyGEEEIIIYyEFOdCCCGEEEIYCSnOhRBCCCGEMBJSnAshhBBCCGEkpDgXQgghhBDCSEhxLoQQQgghhJGQ4lwIIYQQQggjIcW5EEIIIYQQRkKKcyGEEEIIIYxELUMHIHRn3qcf8/WiBaCqjBk7jvEvvGTokAzCqV1rHrJ4CBNTU2rVqkXSgcOGDkkvXDu2xcLCQrvf0XsOMnP6W+zYugnFxAQbmyZ8+sVCbO3sDR1qtTh75heeeXyk9vffL57nldf+Q2ZGBju3bUYxMaGxjQ0ffbagRuWgrV0Dvp7gqf29ZRMLZn57CjurevR3daCo+Bbn/7jB8/P3k51bBMDEiI6M8mlLyS2VqUsOE3s61VDh69zlS5d4ZtwYrl79A0VRGDP2SZ4d/wIz332bxYsW0NjGBoD/vP0u/fqHGDja6pOfn09IX18KCgspKS4mIuoRXv+/aVy4cJ4nHhtORkYGLt1c+XLhYszMzAwdbrXJzspi0oSn+eWnH1AUhTnzvsK9R08WfDmPb776HBNTUwL7BfOf6e8ZOtRq8+xTY9m+bSs2Nk04fPw0AO9M+z+2bt6ESenY8OWCr7Gzrzn94l/Jysriuaef5McfvkdRFL74aiEePXsZOiwtRVVVQ8dQo7i6uat79+u/GPzhh+8ZPXIYe5IOYmZmRmRYMJ/M/Zw2bdvqPRZF0ftHluPUrjV79x+mcePGBoshp6BE75/p2rEtuxMO0KjMft+4fp2HGjQAYP7nn/Lrzz8x++PP9BZTUcktvX1WWSUlJbh2aM3W6EQaWlppc7Dgi7mc+eUnZs2Zp7dYOjy3Rm+fZaIo/DT3EQLf2kFbuwbs+eEKJbdUpg3tBsC0VcdxdGjIwuc98f/Pduys6rLhtUDcJm/iVjWOBb8vHF5t267oSmoqV66k4tLNlRs3buDTuzsr1qznu3VrqV/fghcmTtZbLBXpc7xVVZWcnBwsLCwoKiqif4A3782ew7xPPiI8cgADBz/KxAnP0alzF5546hm9xZVfpN8+YcIzY+nZy5MRo8dSWFhIXm4up0+d4OPZ77Fs7UbMzc25du0qNjZN9BaTRR39nhfdm7gHCwsLnhw7WlucX79+nQal/eJncz/h559+5JN5X+gtJhMD1wlPjh1Db09PHh87jsLCQnJzcyzLG3wAACAASURBVLG0tNR7HPXMTI6qqupecblMa6khfvn5J7r36EG9evWoVasWXt7ebNyw3tBhCQO7XZQC5Obkohj6Lyc9SUyIpWXrh2nWomW5HOTl1uwc+HSy5fzVG1xKyyHudColtzTF4JGzadhb1wMgxK0Z6w5coLD4Fhev5fDbHzdwa9PIkGHrlK2dHS7dXAF46KGHcHRyIiUl2cBR6Z+iKFhYWABQVFREUVExCgp7EuKIHDAQgGEjR7F1y0ZDhlmtrmdncyBpL8MfexwAMzMzGlpasnjhl0yY+Arm5uYAei3MDcHTyxsrK+tyyxqUHRtyc2p0v1hRdnY2e/fuYczjTwCadmGIwvzPSHFeQzg7d2Lf3r2kp6eTm5vLzh3bSb58ydBhGYSiKISHBNHbw52FC+YbOhy9URSFwVHBBHj1YMmir7TLZ7z9f3R1as26NSuZ+sY0wwWoRxvXrSVq4BDt7+9N/w9uHduwfu1KXnn9LQNGVr0G9mzJun0XKi0f6dOG6JMpANhZ1SM5PVf7WkpGLnalhXtNc/HiBU6dOIF7dw8AvvpiHr27u/D800+QmZlp4OiqX0lJCZ4ebrRraYdfQACtH25Dw4aW1KqlOXNr79CM1JQUA0dZfX6/eJ5GjRvz4nPjCPTszqTxT5OTk8Nv585wYP9egv37EBUSwPGjRwwdqkFM+88bOLZpweqVK3jzrXcMHY7eXDh/nsaNbXh63Fh6dnfl2afHkZOTY+iwypHivIZw6tCBSS9PISI0iKjwYLp06YqJqamhwzKI6LhE9h86yobN25j/+WfsTdxj6JD0YsuueGL3HmbV+i0s+upz9u1NBOCNt6Zz8ufzDBwyjIXz9TelxVAKCwvZtX0L4VEDtcte/b93OPrDOR4ZPIxF8z83YHTVp7apCcFuzdhw8PdyyydHdqK45BZrks4bKDLDuHnzJqOGDWbm/z6kQYMGPPHkM5z48Qx7Dx6jqa0db776sqFDrHampqbsPXiUH85c5OiRw/z668+GDkmviotLOH3yOGOeeJrovYepV78+c+e8T3FxMVmZmWyL2ct/pr/HU2OG63XKkbGY9s4Mfjn3O48OG86Xn881dDh6U1xSzInjxxj39DMcOHyM+vXrM/t947rmQIrzGmT040+QdOAIu2ISsLSyol279oYOySAcHBwAaNKkCeGRURw5fMjAEemHnb1mv21smhASHsXxo+WvfRj06DC2bPzOEKHpVezuHXTu6oJNk6aVXhsweCjbNtfMHPR1sefkhQyuXc/XLhvu/TBB3Rx48rMk7bLUzFwcGt05U25vXY/UjFxqkqKiIkYNG8SQR4cTEfUIAE2aNsXU1BQTExNGjx3H0SMPxoXiAJaWlnh5+3L44AGys7MoLi4GICX5co2+CNDewQE7h2a4uvcAICzyEU6dPIG9fTNCwqNQFAVXt+6YmJiQnp5m4GgN59GhI9j43YMzDdbBoRkOzZrRo4fmG7UBjwzixInjBo6qPCnOa5CrV68CcOn339m04TuGDNXfRVjGIicnhxs3bmh/jonejXPHTgaOqvrl5ORws8x+x8fsxsm5I+fOntG+Z/vWTbRt72ioEPVmw7o1RA18VPv7b+fu5GDnts20bVczczCwV6tyU1oCutjxQpgzwz6IJ6/wzgXK249eZmDPVpjVMqGlTX3a2D7E0XPpBoi4eqiqyvhnxuHo2IHxL07ULr+SeueONFs2bqCDc0dDhKc3adeukZWVBUBeXh7xsdG0d3TCy9uXjd+tA2DlsqWEhEYYMsxq1aSpLQ4OzTh75hdAcy1Ke8cO9A+NICkxHoBzZ3+lqKiQRo0MdwMBQzh75k6/uGXzRto7OhkwGv2ytbWlWbPm/PqLpl3ExcbQoUMHA0dV3gN1K0VFUaYBN1VVnW3oWKrDiKGDyEj/f/buO76m+3/g+OskRBAjISRilyBWQmzZiUhkULNGtbS6tWjp0InWVrqrqL3VVmQhiBGzU6u0lUESEUTkSpzfH/e6kibBz/cuyfv5eHg0Ofece9/n3fP5nHfO/ZzPyaBc+fLMnvu5xd3gYAqXLl5kUH/tlbK8vDwGDHqCHsE9zRyV8aVdushTg/sB2q9yHx8wiICgYJ4aMoCzf5zBykqhbr0GzJxrullKzOFGdjb7YqOZXmA2lo8/mMjZP89gpVjhUq8+0+aUvq9vK1Wwxq+VM2MWHNIvmzG8Izblrdj4VgAAR/5MZ+zCw/yWlMUPh/7m0PRw8vJVXv/+iFFnajG1hAP7WbViGS1btaZ7J+2Noe99OJl1a1Zx+tRJFEWhfoMGfPqZ6WamMIfU1BReeHYE+bfzUW/fpvfj/egZGkbzFm6MeHIwkz98jzZt3Rn21Ahzh2pUU6bP4cVnhnPrloYGDRvx6RffUalyZca89Cw+nd2xKW/DvK8WlOobIp8aNph9e+PISE/HtXE93nn3A3b+uIM/zvyOlZUV9es3YO7npXO4X0lmzZnH08OHckujoWGjxnzz3UJzh1RImZpK0RDFuaIo5VRVzSvpdXNNpWhJSnEf98DMMZWiJTLXVIqWxJRTKVoqU06laMnK0vm2JKaeStESmXoqRUtk7qkULUWZnUpRUZR3FEU5oyhKPNBMt+wxRVF+VBQlUVGUfYqiNNctd1QUZb2iKEd0/7rpln+gKMpSRVH2A0vNtzdCCCGEEKI0K9V/vimK0h4YBLij3ddjQCLwLfC8qqp/KIrSCfgS8AfmAnNUVY1XFKU+sBO4MxDJDeiuqmpOMZ8zChgFUK9+fePulBBCCCGEKLVKdXEOeAE/qKp6A0BRlM2ALdAVWFtgjFkF3X8DAbcCy6sqimKn+3lzcYU5gKqq36It+GnX3lO+txRCCCGEEA+l1A9rKYYVcEVVVfcC/1oUeK1zgeUuqqpe171mlhnqc3JyCA70JT8/n2VLF9PGzZU2bq4sW7q42PUvX75MWEgP2ri5EhbSQ/+gjd9/+w0/767YV7Hl09l3h9xrNBp6BPjop9ayRDk5OfQI0OVgyWJau7nS2s2VZUvunYPWxeTA16sr1e2K5iDI3/JzENHTn/z8fFYtX0JH9xZ0dG/BquVLil0/8/Jl+kX0pKN7C/pF9OSKLgf79+2hsUsNfLu2x7dre2ZOnQxocxAe7GfROQBtHh4PDSQ/P581K5bSrZ0b3dq5sWZF8aPNMjMvM7B3CN3auTGwdwhXrmjz8OW8WQR270Bg9w74dfGgrkNFMjMvo9Fo6BMSYNF5sC1vzbaJQVgpCk94NSZxVgSJsyJ4wqtxsesvfKU7+z4OZd/HoZz6tDf7Pg4FwLeVE3GTQ9g/tRdxk0Pwdrs79eTGtwKoVsnGJPvzMHJycggN8iM/P58Vyxbj0aoZHq2asWJZyX1CZK8eeLRqRmSvu33Cti2b6NrBne6d2uHTrSMH98cD2plOHo8IMdn+PIycnBxCe9zJwRLatW5Ou9bNWbGs5D6hd1gw7Vo3p3dYsL5P2LZlM107etC9U3t8u3Xi4IG7OegbEWqy/XlYOTk59A4NID8/n9UrltDFw40uHm6sXlFyHgZEhtDFw40BkSH6PHwxdxYB3T0J6O6JT2d36tjbknlZ2yf0DvG36D6hYJ2wfOli2rq50tbNleX3qBPCQ3rQ1s2V8P+cI/29u+JQxZa5UieYtU4o7cX5XqC3oigVFUWpAoQDN4BziqL0B1C02urW3wW8cmdjRVHcTR3wfy35fiERkX3Iysrik8kfERefwJ79h/hk8kfFPuFu1oyp+Pr7c+qXM/j6+zNrhnZifXsHB2bOnsurY8YVWt/GxgZfP3/WrV1tkv15GIu/X0hkb20OPp7yEXviE9i7/xAfTykhB9On4uvnz+lfzuDr58+s6QVyMOceOVhjuTlYsXQRvSJ6czUri5lTJ7MzZj+7Yg8wc+pk/cmloHmzp+Pl48/hE7/i5ePPvNnT9a917tKduAOJxB1I5PU3JwLaHHj7+rNxvWXfvLhq2feEhkdy9WoWs6dNZmt0PNti9jN72mR94V3Q53Nm0N3Hn/3HfqG7jz+fz5kBwIujxxEVf4So+CO89d4kunTTPt7axsaG7j5+bN6w1tS79sCG+j7GliP/ULVSeSY83pqA937E/90fmfB462IL6hGfxeP19na83t7O5iP/sOWI9snBl6/lMmhmHN3e3MYLXx/g6xe66bdZHX+OZ4Is9zkJyxYvIlzXL06dMonovQeJ2ZfA1CmTiu0T5sycho9vAMd/+h0f3wDmzJwGgI9fAPsPHyf+0DG++Po7XnlxFAA1HR1xcnIm4cD+Iu9lKe7k4GpWFtM+nkT0ngPE7D3ItI8nFdsnzJk1DR9ff46d/g0fX3/mzLqTA3/2HzpG/KFEPv96PqNffA7Q5qC2kzMJBy03BwArl31Pr3Bt3zhr6hS2R8ezI2Y/s6ZOKTYPn82ZjpePHweP/4KXjx+fzdH2jS+9Oo7o+KNExx/lnfcna/sEh7t9wqYNlts3/rdOiI1PIO4edcJsXZ1wUlcnzC5QJ8yYPZfRJZwj10udYLI6oVQX56qqHgNWAyeBHcCdaVSGACMVRTkJ/AxE6paPBjwVRTmlKMovwPMmDrmI1atWEBYeSdTunfgHBOLg4IC9vT3+AYHs3vVjkfW3bdnMkKHDARgydDhbN28CtA/kae/ZgfLlyxfZJiyiN6tXrjDujvwPVq/U5WBXMTnYWTQHW7dsZsgwXQ6GDWdLgRx4lpCD8IjerFpluTlYv3olIb0iiI3ehY9fAPYODlS3t8fHL4CYqJ1F1t+xbQsDhwwDYOCQYWzfuvm+nxESFsG6NSsNHrshbVi7iuDQcOKid+PtF4C9vQPVq9vj7RdAbNSuIuvv3L6FAU8MBWDAE0P5cVvRPGxcv4be/Qbof+/ZK4INay03D/27NmJ74gUC2tQh9nQqV7I1ZN3QEHs6lcC2936gTO9ODVinmwv91N+ZpF7RjtT79UIWFW2ssSmnPSVsT7xA3y4Njbkb/5M1q1YQGh5BzO6d+BXoE/wCAokupl/cvnUzg4c+CcDgoU+ybYu2T7Czs9NPoXcjO7vQdHq9wiNZs9py+4S1q1cQGhZBdNQu/PwD9X2Cn38gUbuL9gnbt27hiSHaHDwx5Em2bdG2hUI5uFE0B2tXWW5bANiwZqW2T4gp2jfGRhfNw87tWxgwWNs3Dhg8rNg+4Yd1q+nT7+6zEkJ6RbB+zSrj7cT/aE2BOuG/7cFQdUK41AkmrRNKdXEOoKrqFFVVXVVV7a6q6mBVVWeqqnpOVdWeqqq2VVXVTVXVj3TrpquqOlBV1Ta65c/rln9gjrnRNRoN5879RYOGDUlOSqJuvXr611zq1iU5KanINpcuXcTZ2RnQTrR/6dLF+35Oy5atOJZomdM/FspBchJ16xbIgUtdkpMNlINWrThmoU8M1Gg0nD9/jvoNGpKSnEydAjmo41KXlOTkItukpV3EyUmbg9q1nUhLu5uDo4cT8O3SjoGPh/Hbrz/rl7dwa8WJxKNG3JP/jUaj4Z/z56jXoCGpKUnUcbmbB+c6dUlNKXospF+6RG1dHmrVdiJd96CuO27cuEFc1C5CI/rolzV3a8mJY4lG2ov/TXlrKxrWsuOf9Gyc7SuSdPnuaLvkyzdwtq9Y4rZdm9ciLesmf128VuS1iI71OXn+Mpo87TR3WTc0VChvhb2d5Q1t0baHv2jQoCHJycnF9AnFtIdLF3HS9Qm1nZxIK9AnbNn0A55t3ej/eDhffP2dfrlHO0/9MBdLo9FoOH/uHA0aNCQlOQmXunX1r9VxcSGlhH6xYA4uFcrBRjq4t2TA4xF8/vV8/XKPdu31w1wskUaj4e9CfePdPDi7uJTQNxbuE9LSivYJsVG76FWoT2jFiWOW2TcWPEemFFMnpJRQJ5R0LJTErWUrEqVOMFmdUOqL80dZRno61as9/IOEFEV5oAcrWFtbU97GRv9kTUuSLjngckY61QyUgzZtPTj2y1niDh7jmede4skn+unXu5OD6xaYA9DmoWq1ag+9fXHHwu4ft+HZqQv29g76ZdbW1thYaB5qVKlA1g3NQ23bt0tD1h88X2R5c5dqfDjIg9cKPMAIIO3qTZztKz3UZxlTRvr/3h4KPowhPLIPR0/+woo1G5j80fv65Y61apGSUrS4swQZ6elU+x8eMvffthAe2ZsjJ35m+er1THlEcgB3+sb/sU+gcJ+wa8dWOnTugr3Do9EnGKI9POg50sZCz5GlsU6Q4tyC2VasyM3cm4D2asiFf//Vv5Z04QJ1XFyKbFOrVm1SdI+pTklJwdGx1gN9liY3F1tbWwNEbVgVC+agjgsXLhTIQdIF6tQp/Tmwta1Iri4HznXqkFwgB8lJF3CuU3Qog6NjbVJTtTlITU2hZk1tDqpUrYqdnXYCoqDgEPJu3SIjPV2/nSY3lwoWmAPQtofcm7kAODm7kJx0Nw8pyRdwci56LNSsVYuLujxcTE2hhqNjodc3rV9D7wJfX9+Ra6F5yNHkY1veGoCUzBxcHCrrX6vjUImUzGInlMLaSiG8Qz02JPxdaHkdh0osG+PD818f4Pyl64Vesy1vTY7G8m4A0x4Hd/qEOsX0CcW0h1q1SdX1Cakl9Anduntz/txf+vZw8+ZNKtqW/E2EOVWsWJGbN+/0CS4kXbigfy05KQnnEvrFB8vBuUciB6DtG2/mavsEbd94Nw8pSUkl9I2F+4Sa/+0TNqwpNKTlDkvtG20rFjg/FFMnOJdQJ9zvWChOroWeI0tjnSDFuQWzt7cnPz+fmzdvEhgUTHTUbjIzM8nMzCQ6ajeBQcFFtgkNC2e5bsaC5csW0ys84r6fk5GRQY0aNYsdY2VuhXLQo5gc9Ciag17h4fq71JcvXUzYI56D6gVy4BfQg7iYKK5kZnIlM5O4mCj8AnoU2aZnaBirl2tnMFm9fCkhvcIBuHgxVf+UwmNHD3P79m0catQA4HJGBg4WmgOA6tXtyb+tzYNvQBB7YqK4ciWTK1cy2RMThW9AUJFteoSEsWblMgDWrFxGcGi4/rWrWVkk7N9HzwLLAC5fzsChRg2LzEPWDQ1WVgoVylsRfSoZ/9bOVKtkQ7VKNvi3dib6VPFXOX1bOfFH8lWSL9/QL6tWqTxrXvfjw1XHOXQmrcg2tapV5J80s0xSdU8F+wT/oGBiCvQJMVG78S+mXwzpFa6fxWTFsiWEhmn7hLNn/9S3hxPHj6HJzdW3hz//OEOLli1NtFf/P9Xt7bmty0FAYA9ionfr+4SY6N0EBBbtE0J6hbFSN7vTyuVLCA3THvd/3SMHZ/84Qws3y8wBFM6Dr3/RvtHXv2geeoSE62d3WrNiaZE+4WD8PoJDC58zLLlP+G+d8N/2IHXCo1knSHFu4QICgziwPx4HBwcmvD0R764d8e7akTffeRcH3dduLz7/DMd0Y4XHvfEmMVFRtHFzJTY6mnFvvAlAamoqTRvX47O5c5g+dQpNG9fj6tWrAOzdE0twiOVOmVUwB2++PRGvrh3x6tqRtwrk4IXnniGxYA6io2jt5kpsTDTjxt/NQZNG2hxM+2QKTRoVyEFcLD1DLTcHvv6BHDq4H3sHB8aOf5sg3y4E+XZh3IR39F+/vvbSKP24yNFjx7MnNoqO7i3YGxfN6LHjAdiycT1eHd3x7dKOt98Yw7eLlum/zovfF0dQsGVPH+fjF8jhhP3Y2zvw2htvE+rXlVC/rowZ/45+aMq4V57n5HHtmPGXx7zBvtgourVzY19cNC+PeUP/Xju2bsLbP5BKlSsX+owD+/YQ2MNy8xB7OoXOrrW4kq1hxsbTxE4KIXZSCNN/OMWVbO2Ql3nPdMa90d2v5ft2aci6g+cLvc+zPZrRqHYVxj/eWj/VYs2q2kc+uDdy4Oif6eTftszHNvgFBnHwgLZPGP/WO/h174Rf905MeHuivk94+YVn9f3i2NcnEBsThUerZsTFRjPm9QkAbP5hA53bt6F7p3a8/torLFq6Ut8e9u2JI7hnL/Ps4APwCwgi4UA89g4OvPHmO/h5dcbPqzPj35qo7xNeeWEUx3U5GDNOm4N2rZtrczBOl4ONG+ji2ZbundrzxphXWLh0xd0c7I2jhwWfG0DXJ+j6xjHj36anX1d6+nVlbIG+cezLz+nvI3ll7BvsiY2mi4cbe+NieGXMeP17bd+6CR//QCr/p0/YvzeOgGDLzUNAYBAHC9QJPl074vOfOuGlAnXCWF2d0FZXJ4zV1QkXU1NxbVyPz3V1gut/6oSeFnwslLY6QbnzF7MwjHbtPdX4g4a7YeD48WN8Pu9TFiwqfs5WQ3hiQF8+mvwJTV0NM3XaAwzd+n85fvwYn8/9lAXfGy8Hg/r3ZdIUw+UgOzffIO9zx8kTx/jmi7l8Ob/4OVsN4anB/Xn3wyk81tRwU+jdyr9tsPcCOHXiOPO/nMdn3y4y6PsWNHLoAN7+YDKPNTFMHlq8aNgp2No2dODFkOY899UBg75vQVOHebL92AX2/pxqkPf7Z8Fgg7zPHSeOH+PLzz7l24XG6xNCAn1ZsfYH7O3tDfaehjzfnjh+jC8/n8u3C4zXJ4QE+bJyzQ9UN2AObt4yfJ/w7Zdz+fzb7w36vgWNGNKfdz6cYrA+wc7WsM9/PKGrE757hOoEK6kTAKhkY5Woqqrnf5eXeIQoinINuNOT3EmjqvtZVVW1qsGiEyXy8GiHt492Yn1ra2uDv79GoyEsItKgB5uheXi0w9vXuDkIt/ActHVvRzcv4+YgJCzCoIW5MbRx96Crl49R89CzV4TBTsLGcPL8Zfb9chErReG2kS6u/HLhisEKc2Nw92iHlxH7xfS0NF4a/ZpBC3NDc/doh5e3kXPwyhiDFubGoO0TjNs39gyz7D7B3QR1gqWfI0tbnSBXzg3M0FfOH0WGvnL+KDL0lfNHlaGvnD+KDH3l/FFk6Cvnjyo53xr+yvmjyNBXzh9Fhr5y/qgq6cr5A405VxSlu6IoT+t+rqkoSiNDByiEEEIIIURZd9/iXFGU94EJwFu6RTbAMmMGJYQQQgghRFn0IFfO+wARQDaAqqrJQBVjBiWEEEIIIURZ9CDFuUbVDpRTARRFqXyf9YUQQgghhBAP4UGK8zWKonwDVFcU5VkgCphv3LCEEEIIIYQoe+57y7CqqjMVRQkCrgKuwHuqqu42emRCCCGEEEKUMQ86n89poCLaoS2njReOEEIIIYQQZdeDzNbyDHAYeBzoByQoijLC2IEJIYQQQghR1jzIlfM3AA9VVTMAFEWpARwAFhozMCGEEEIIIcqaB7khNAO4VuD3a7plQgghhBBCCAMq8cq5oihjdT/+CRxSFGUT2jHnkcApE8QmhBBCCCFEmXKvYS13HjR0Vvfvjk3GC0cIIYQQQoiyq8TiXFXVD00ZiBBCCCGEEGXdfW8IVRTFERgPtARs7yxXVdXfiHEJIYQQQghR5jzIDaHLgd+ARsCHwHngiBFjEkIIIYQQokx6kOK8hqqqC4BbqqruUVV1BCBXzYUQQgghhDCwB5nn/JbuvymKovQCkgEH44UkhBBCCCFE2fQgxflkRVGqAeOAz4CqwBijRiWEEEIIIUQZdN/iXFXVrbofswA/44YjhBBCCCFE2XWvhxB9hvahQ8VSVXW0USIqBW6rJaatTLBCMXcIZicZ0DqTet3cIZjd2W8GmTsEswv78oC5Q7AI21/qau4QzE6RzlHOD0D+7bJdJ93Pva6cHzVZFEIIIYQQQoh7PoRosSkDEUIIIYQQoqx7kKkUhRBCCCGEECYgxbkQQgghhBAWQopzIYQQQgghLMR9i3NFUVwVRYlWFOUn3e9tFEWZaPzQhBBCCCGEKFse5Mr5fOAtdE8KVVX1FCBzgwkhhBBCCGFgD1KcV1JV9fB/luUZIxghhBBCCCHKsgcpztMVRXkM3QOJFEXpB6QYNSohhBBCCCHKoHs9hOiOl4BvgeaKoiQB54ChRo1KCCGEEEKIMui+xbmqqn8BgYqiVAasVFW9ZvywhBBCCCGEKHvuW5wrivLef34HQFXVj4wUkxBCCCGEEGXSgwxryS7wsy0QBvxqnHCEEEIIIYQoux5kWMusgr8rijIT2Gm0iIQQQgghhCijHuYJoZWAuoYORAghhBBCiLLuQcacn0Y3jSJgDTgCMt5cCCGEEEIIA3uQMedhBX7OAy6qqioPIRJCCCGEEMLA7lmcK4piDexUVbW5ieIRQgghhBCizLrnmHNVVfOB3xVFqW+ieIQQQgghhCizHmRYiz3ws6IohykwraKqqhFGi0oIIYQQQogy6EGK83eNHoUQQgghhBDigYrzUFVVJxRcoCjKNGCPcUISQgghhBCibHqQec6DilkWYuhAxMN5YdRIGtVzomO7NoWWf/3l57Rr40YHj9ZMfHtCCVuXPmd+/53OHTz0/5xqVuPzeZ+aOyyTyc/Px6+bJ0/0iwRg755Y/Lp3oHtHd14a9TR5eaVroqVLKUm89mQkw3t14amwrqxb8g0AC+Z+zIgIL0b29uH1EX1Jv5gCgKqqzJv8JoN7eDIiwoszP580Z/hG4dGyCV6d3PHt2p4A704AZF6+TN+InnRwb0HfiJ5cycw0c5SGt3qkJ98/6cGCoe58O7gtAE93qc/6UR1YMNSdBUPd6dzIHoAWTnb6ZQuHeeDVpIY5QzeKks4NAPM+nU0VW2vS09PNEJlpebZqik9nD/y7edLDpzMAP506QYh/d/2yY0ePmDlK0/nis7l4erTG071VmTo3PmrtocTiXFGUF3RznDdTFOVUgX/ngFOmC1Hcy5Bhw/lh8/ZCy/bGxbJty2YOHjnOkeOnefW1cWaKzvRcmzUj4chxEo4cZ3/CUSpWqkREZB9zh2Uy33w5j6bNWgBw+/ZtXn5uBPMXLSf+8Anq1m/AquVLzByhYVlbW/PihI9YvO0gX67aycblCzj/528MGvkyCzfvY8HGPXTx7cHiL2cCcGhviqGTrwAAIABJREFUFBf+/ovlO48w7qPZzPnwdTPvgXFs3BZF3IFEovceAmDu7Ol4+/hz5MSvePv4M3f2dDNHaByvrjnNyGUnGLXi7h9daxOTGbnsBCOXnSDhnPaPkr/SbzBquXbZGxt+4vXAx7BWzBW1cRR3bgC48O+/xETtol69sjPPw4Ztu4nZf5RdexIA+Ojdt3n9zYnE7D/K+LffZ9J7b5k5QtP4+eefWLTwO/buP0TC0RPs2L6Ns3/+ae6wTOJRaw/3unK+AggHNuv+e+dfe1VVh5ogNvEAunt5Y2/vUGjZd/O/Zuzr46lQoQIAjrVqmSM0s4uNiaZx48eo36CBuUMxieSkC+zeuYOhw0cAcDkjAxsbG5o0dQXA1y+QrZt/MGeIBlejlhOuLbVXSSvZVaHBY01Jv5hCZbuq+nVu5txA0RVe+6N3EBw5EEVRaOnegetXs8i4lGqO0E1qx7YtDBwyDICBQ4axfetmM0dkXrl5t8nXPVrPxtoKVb33+o+i4s4NAG+OH8ukj6ehKKXsr5H/B0VRuHbtKgBXr2ZR28nZzBGZxu+//UqHjh2pVKkS5cqVw8vbm00bN5g7LJN41NpDicW5qqpZqqqeV1X1CVVV/y7w77IpAxT/f3/+8QcH9sfj59WFnoF+JJahr+wKWrd2Ff0HDDJ3GCbzzoRxvD/pE6ystM26Rs2a5OXlcfzYUQC2bFpP0oV/zRmiUaVc+Ic/fj1Ni7btAfhuzmT6+7Zm99Z1jBitvTKWdjEFR2cX/TaOTnVI0w15KS0URaFf7xD8vTqyeOF8ANLSLuKkK0Bq13YiLe2iOUM0mll9WzF/iDvhrWvrl/Vxd2bRMA8m9GiKXQVr/fIWTnYsftKDRU+2Y1b0WX2xXppt3bKJOnVcaN2mrblDMR1FYWDvUIK8O7Fk0XcATJo2k4/efQuPFo35cOKbvPPBZDMHaRpubq04EB9PRkYGN27cYOePO0r1OeF+LLk9PMgNoeIRk5eXR2bmZWL2HiDx6BGGDxnE6d/+tLi/DI1Jo9GwfesWPpz0iblDMYmdO7ZR09ERd4/2xO/T3qutKArzFy3j3TdfJ1eTi59/ENbW1vd5p0fTjezrvD/6KV5+a4r+qvkzYybyzJiJLP9mDj8s+46nR79p5ihNY9uuOJzruJCWdol+ET1p6lr4GXKKopTKvuCl1adIv66hesXyzO7Xin8u57DxZAqLE/5BVWFktwa85NOYabv+AODX1OsMX3KcBg4VebunK4fOXUZTiiv0GzduMGv6VDZu/dHcoZjUlp2x+vYwIDKEpq7N2LJxAx99MoOwyMfZtGEtY15+jnWbS39emrdowdjXxxPRK5jKlSvTpk1brErpOeF+LL09PMgNoeIR4+LiQkRkHxRFwbNDR6ysrCzqRgdT2PXjDtq6t6N27dr3X7kUOJxwgB+3b8WjZRNGPTWE+L2xPP/Mk3To1IWtu+LYHXeQLt28eKyJq7lDNbi8W7d4f/RTBIb3w7tHeJHXA8P7s2f3FgAcazuTlpKkfy0tNRnH2qXrK23nOtpvBhwdaxEa3ptjiUdwdKxNaqr2G4LU1BRq1ix9Q93Sr2sAuJJzi31/ZtDCqQqZN25xWwUV2Ho6lRZOdkW2+/tyDjmafBrVrGziiE3r3F9nOX/+HF07eNDStTFJSRfw6uzJxdTSPayrUHsIi+R44hHWrFxKrwjtvUgRffpxPLHsfLs8/OmR7E84yq7oPVS3t6dp09J3TngQlt4epDgvhcIiItm7Jw6AP/44g0ajoWbNmuYNysTWrllF/4FlZ0jLux9O4fTv5zn+8598+/1yunv78fV3S0hLuwRAbm4u8+bMYPjIUWaO1LBUVWX6xNHUf8yVAU+/qF9+4fxZ/c/7o7dTv1FTALr692TnptWoqsrPJ45QuUpVatRyMnncxpKdnc21a9f0P8dF76aFW0t6hoaxevlSAFYvX0pIr6J/xDzKbMtZUbG8tf7nDg2q81dGNjUql9ev49WkBufSbwDgXLWC/gbQ2lUqUN+hIqlZN00etym1bNWac/+m8vOZv/j5zF+4uNRlX8JRajuVnuP/v7Kzs7lesD3ERNG8RUucnJw5EL8XgH17Ymn8WBNzhmlSly5pzwn//vMPmzf+wIBBg80ckXlYenso08NaFEX5ALiuqupMc8fysJ4eNph9+/aQkZ5Os8fq8/bE9xk2fAQvjhpJx3ZtsLGx4ZvvFpXKr7FLkp2dTUz0buZ98bW5QzG7zz+dxa4ft3P79m2efmYU3j5+5g7JoE4fO8SuTWto7OrGyN4+ADw7ZiLb1y3jn/N/YqVYUbtOPcZ+qG3inX2COLR3N0N6eFLBtiITPv7MnOEbXNqliwwf3A+AvLx8+g4YREBQMB7tPBk5/AmWLV1EvXr1WbB4pZkjNSz7yuWZEuEGgLUCUb+lcfj8Fd7p6UrTWpVRVUi9epOZUdqZKVq7VGVIh7rk3VZRVZgdfZasm6VrmtHizg3Dnx5p7rBMKu3SRZ4e0h+A/Lw8+vQfhH9QMJXt7Jg4YSx5eXlUqGDLzLlfmTlS0xkyqB+XMzIoV748s+d+TvXq1c0dkkk8au1BUUvjbeoPyBjFebv2nureA4cN9XaPJKsy9IdASXI0+eYOwSL8lHTV3CGYXSuXqvdfqZTr822CuUOwCNtf6mruEMzuhvSN2FUo09dFAbhdhmvPgqrYWieqqur53+VlbliLoijvKIpyRlGUeKCZbpm7oigJunncf1AUxV63vINu2QlFUWYoivKTWYMXQgghhBClWpkqzhVFaQ8MAtyBUKCD7qUlwARVVdsAp4H3dcsXAc+pquoOlPjnvqIooxRFOaooytH0tDSjxS+EEEIIIUq3MlWcA17AD6qq3lBV9SraByxVBqqrqrpHt85iwFtRlOpAFVVVD+qWryjpTVVV/VZVVU9VVT1rOjoaM34hhBBCCFGKlbXi/JGTk5NDz0A/8vPzWb50Me4tm+HeshnLly4udv3Lly8TEdoD95bNiAjtQWam9nHVq1cup7OnO53atyXAtzunT2kfb63RaAgO8CUvz3JvhsrJySE40Jf8/HyWLV1MGzdX2ri5suweOQgL6UEbN1fCQu7m4PfffsPPuyv2VWz5dPbd2ww0Gg09AnwsPgfhPf3Jz89n1fIldHBvQQf3FqxavqTY9TMvX6ZvRE86uLegb0RPruhyEL9vD41cauDbtT2+XdszY6r24RsajYawYD+LzgFA7s0cXh0aTn5+Pj/+sJIhwR0YEtyBH38o/gbHr6a/z7CQToyI8GLiy8O4djULgF9PJTKyt4/2X6Q3+3ZvBeCWRsPooWEWnQc5FsCmnBXzBrTGSoGebrVY8XR7Vjzdnp5uxU8R+XSX+qwf1YEFQ91ZMNSdzo3sAXCqWoHdo7vol48LeEy/zey+rQo9tMjSGOrc8Pvvv+Hv040aVSsyd84s/fqPwrkBtHnoHRJAfn4+q5cvobO7G53d3Vh9j/bQPzKEzu5u9I8M0bcHgP379uDfzRPvjm3pHRIAaPMQ2dPfovMg58jS1x7KWnG+F+itKEpFRVGqAOFANpCpKIqXbp1hwB5VVa8A1xRF6aRbbpZ5+ZYuXkRE7z5kZWUxdcokYvYdJDY+galTJukPpoJmz5yGj18AJ37+HR+/AGbPnAZAg4aN2LE7lkOJJ5nw1juMful5AGxsbPD182f92tUm3a//jyXfLyQiUpuDTyZ/RFx8Anv2H+KTyR8Vm4NZM6bi6+/PqV/O4Ovvz6wZUwGwd3Bg5uy5vDpmXKH17+RgnQXnYMXSRYRF9OZqVhYzpk5mV8x+dsceYMbUyYVOLnfMnT0dbx9/jpz4FW8ff+bOnq5/rXOX7sQdSCTuQCJvvDkR0ObA29efH9avMdk+PYzt65fj1SOM7GtXWfzFDL5avYuv1+xm8RczuJZ1pcj6nl19WbRlPws376New8dY8e0cABo1bcE366JZsHEP0+evYdb748jLy6O8jQ3tOnsTu+MHU+/aA5NjAXq1rM3ePzKoXKEcT3Wuz3MrTzJqxQme6ly/xIJ6bWIyI5edYOSyEyScu5unpCs39ctnRd+dgnPXr5fo09Zy58A31LnBwd6BGbM+ZfRrxfeLlnxuAFi59HtCw7XtYea0KeyIiefH2P3MnDal2Pbw2ZzpePn4kXDiF7x8/PhsjrY9ZF25wptjX2HJqg3sPXyS+Uu0f/Db2Njg5ePHRgtuD3KOLH3toUwV56qqHgNWAyeBHcCdJw8MB2YoinIK7Xj0j3TLRwLzFUU5gXb4S5ZpI4bVq1bQKyyC6N078QsIxMHBAXt7e/wCAonaVfTJVtu2bGbI0CcBGDL0SbZu3gRA5y5dsbfXXi3q0LEzSUkX9NuERUSyZlWJo3bMbvWqFYSFRxK1eyf+BXLgHxDI7hJzMByAIUOH63NQq1Yt2nt2oHz58kW2CYvozeqVlpuDdatXEtIrgpjoXfj4BWDv4EB1e3t8/AKIjtpZZP0d27YwcMgwAAYOGcb2rZvv+xmhYRGsX2PZU+xFbVlHN/8QjsTH4NnVl6rV7alSrTqeXX05vC+6yPoduvtRrpx2ZgS3tp6k6R7EY1uxkn65RpNbaKrR7oGhRG1ZZ4K9eThyLEBQC0fiz2bQsUF1jv6TybWbeVzPzefoP5l0amhvkM+IP5tBQHPLHaZoqHOD4z37Rcs+NwCsX7OSnr3CiS2mPcQU0x5+3LaFgYN17WHwMHbo2sOGtasIDe9N3Xr1Ae1Di+4ICYtgw5pVJtibhyPnyNLXHspUcQ6gquoUVVVdVVXtrqrqYFVVZ6qqekJV1c6qqrZRVbW3qqp3/sz6WbfMHUgBjpoyVo1Gw/lzf9GgYUOSk5OpW7ee/jUXl7okJycX2Sbt0kWcnLVXe2o7OZF26WKRdZZ8v5CgHj31v7u1bEViokl37YFpNBrO3clBUhJ16xXIQd26JCclFdnm0qWLOOty4OTkxKVicvBfLVu24piFPiVOo9Hw9/lz1G/QkJTkZFwKHAd1XOqSUtxxkHYRJyfdcVDbibS0uzk4ejgBny7tGPh4GL/9+rN+eQu3Vhy30OMAtENOki/8jXPd+qRdTMHRuY7+NUenOqRdTLnn9tvXr6Cjd4D+919OHuWpsK48HeHF2A9m6ov1Rk1b8Nvp48bZif+RHAtQzkrBuZotqVdzcbSrwKVrGv1rl65pcLSrUOx2fdydWTTMgwk9mha6uu5czZbvhrozb0Br2hSY9vJ6bj421lZUtbW8ae+MdW74L0s+N0Dh9pCakkwdl7r61+rUcSE1pbj2cInauvZQq7aT/kFtZ//8g6wrV+gTGkiQdyfWrFiq36a5WytOHLPMPMg5snS2B8vrdSxLL0VR3kKbp7+Bp0z54Rnp6VSr9vAPCFAUpcjDh/bGxbLk+4XsitmrX2ZtbY2NjQ3Xrl2jSpUqD/15xpCRnk51A+egONbW1pS31BxkpFPVQDlo29aD47+cxc7Ojt07dzDsiX4cOfErYNk5AMjKzMCuysPNGb7061lYl7MmKLy/fplbW0++33qAv8/+zidvvkRH70AqVLDV5qF8eW5cv0YlO8vKgxwLUK1iea7n/v/GfW48mcLihH9QVRjZrQEv+TRm2q4/yMjW0H/+Ea7ezMO1VmU+jnTjycXH9HNxZ964RU07G65a2AOKjHFuKI4lnxsALmekU61atYfevmAe8vPyOHniGOu27OTmzRx6BXjTvkMnHmvqqm8P169dw87C8iDnyNLZHsrclfP/D1VVV6uq6q6qaitVVXupqmrSeRJtK1Yk96b2kdJ16tThwoV/9a8lJV2gTp06RbZxrFWb1BTtFcTUlBRqFvhq7qfTp3j5hVGsWvcDNWrUKLRdbm4utra2xtiN/4ltxYrczNXlwMWFC/8WyMGFC9RxcSmyTa1atUnR5SAlJaXQ15P3orHQHFS0rUiuLgfOdeqQVOA4SE66gHNxx4FjbVJ1QzhSU1OoWVObgypVq2JnZwdAUHAIebdukZGert/OUnMAUMG2IprcXAAcazuTVuCqWFpqMo61ix8fvGPDCg7G7mLijG+K7YAbPNaMipUqc+7Mr/plGo0GmwqWlwc5FiA3T3tFGyDtei61qtjoX6tVxYa067lFtsm8cYvbKqjA1tOptHDS7vetfFVfeJ+5lE3SlZvUs6+o386mnBW5ebeNuDcPx9Dnhnux1HMDgK1tRXJ1fYKTcx2SCwzXTE5Owsm5uPZQi4u69nAxNYWaNbVDl5xdXPALCKJy5crUqFGTzt268/NPp/TbaXJzqWCBeZBzZOlsD1KcWzB7e3vy8/O5efMmAUHBxETtJjMzk8zMTGKidhMQFFxkm9CwcJYv096lvnzZEnqFRwDw7z//MGRgP75duJimTV0LbZORkUGNGjWLHWNlbgVzEBgUTHSBHERH7SawxBxo79BevmyxPgf3Ysk5qF4gB/4BPYiLieJKZiZXMjOJi4nCP6BHkW16hoaxern2a9nVy5cS0iscgIsXU7nzVOBjRw9z+/ZtHHR/qF3OyMDBQnMAUKVadW7fzic39yYduvtzZH8s17KucC3rCkf2x9Khu3+RbQ7ti2bVgs/4+Kvl2FaspF+ecuFv/V33qUn/8s9ff+BUVzvWNCvzMtXsHShngXmQY0E73MTaSsHGWuHw31fo0MAeuwrW2FWwpkMDew7/XfTG4BqV7+6HV5ManEu/AUC1iuWw0v295lytAnXtbUnOuqlf16FyeVIL/G4pDHluuBdL7hehcHvwK6Y9+BXTHoJDw1mtG7KyesVSeuraQ89e4RxKOEBeXh43btzg2NHDNG3WHLjTHmpYZB7kHFk624MMa7Fw/oFBHNwfj19AIOPfegffbtrJYya8PREHBwcAXnr+WUY++xzt2nsy9vUJDB8yiKXfL6Re/QYsXq69iWXqx5O4fDmDsa++DEC5cuXYe+AwAPv2xBIcEmqGvXswAYFBHNgfj39AIBPenoh3144AvPnOu/ocvPj8Mzzz7PO0a+/JuDfeZNjggSxZpM3B0hXau6tTU1Px6tqBa1evYmVlxRefzyXxxM9UrVqVvRaeAz//QA4d3I+PXwDjxr9NkG8XAF6f8A72uhy8+tIonho5Co92nrw6djwjhz/BsqWLqFevPgsWa2/u27JxPYu++5Zy5ayxta3I/EXL9FeT4/fFERQcYp4dfEAduvlxOjEBz66+PPni6zzXPxCA4S++TtXq2hsBp098lYiBT9G8tQdzJ03gliaXcSP6AtqhLOM+nMXpxARWzJ+LdbnyWFlZ8dr7M6hury1Mjx+Kp4tP0ZO6pZBjAY78nUlrl6ok/pPF4oR/+XaIOwDfJ/zDNd2V8PFBTdh0KpXfL17nea9GNK1VGVWF1Ks3mRn1JwDudasxokt98m6rqCrMijqr375ZbTt+SblGvoU+ZdxQ54aLqal4d+uo7xe//HwuR47/RNWqVS3+3ADgU6A9jB3/NsG+XQEYV6A9jHn5OYaPGIV7u/a8MuYNnn1qMCuWfE/d+vWZ/732Bj/XZi3wD+yBX5d2KFZWDHlyBC3cWgGwf18cgcGWmwc5R5a+9qDcuXIiDKNde0/1TtFrCCeOH+OLeZ8yf1Hxc7YawuCBfflw8idFrqg/LKsHGLv1/3H8+DE+n/cpC4yYgycG9OWjyZ/Q1NUwOcjRlPhA2Ydy8sQxvv5iLl/NL37OVkMYPrg/7344hSYGOg4Afkq6arD3Ajjz80nWLv6Kd6Z/bdD3LejdV55k1Nj3qNeoiUHer5XLw42TL8mjeCz0+TbBIO9zh2utyvRv58KUH88Y9H0LGu3bmPizGRz713CTdG1/qavB3utRPDcA+vH8hnLqxHG++WIuX8z/3qDvW9DTQ/oz8YMpPGagPNhVMOx10UfxHHnbwLXno9oeqthaJ6qq6vnf5TKsxcK5e7TDy0f7cAFj0Gg0hIVHGvRgMzQPj3Z4GzsHEZEG63SMoa17O7p7GTcHoWERBi3MjcG1ZVs8OnkZLQ+3NBq6B4QarDA3BjkWtOPDj/+bpR+SYgx/ZWQbtDA3NDk3aLVx96Cbt3HzENIrwmCFuTHIObL0tQe5cm5ghr5y/igy9JXzR5Ghr5w/qgx95fxRZOgr548iQ185f1QZ8sr5o8rQV84fRYa+cv4oMvSV80eVXDkXQgghhBDCwklxLoQQQgghhIWQ4lwIIYQQQggLIcW5EEIIIYQQFkKKcyGEEEIIISyEFOdCCCGEEEJYCCnOhRBCCCGEsBBSnAshhBBCCGEhpDgXQgghhBDCQkhxLoQQQgghhIWQ4lwIIYQQQggLIcW5EEIIIYQQFkKKcyGEEEIIISyEFOdCCCGEEEJYCCnOhRBCCCGEsBBSnAshhBBCCGEhpDgXQgghhBDCQkhxLoQQQgghhIWQ4lwIIYQQQggLIcW5EEIIIYQQFqKcuQMojawUxdwhCDOzspJjAKBJbTtzh2B2t1XV3CGY3eYXupg7BIvgOPA7c4dgdhlrnjV3CGYnPYLUSfcjV86FEEIIIYSwEFKcCyGEEEIIYSGkOBdCCCGEEMJCSHEuhBBCCCGEhZDiXAghhBBCCAshxbkQQgghhBAWQopzIYQQQgghLIQU50IIIYQQQlgIKc6FEEIIIYSwEFKcCyGEEEIIYSGkOBdCCCGEEMJCSHEuhBBCCCGEhZDiXAghhBBCCAshxbkQQgghhBAWQopzIYQQQgghLIQU50IIIYQQQlgIKc6FEEIIIYSwEFKcCyGEEEIIYSGkOBdCCCGEEMJCSHEuhBBCCCGEhZDiXAghhBBCCAtRztwBCMM58/vvPDl0kP738+f+YuJ7H/Ly6NfMGJVpldUc3Lx5k7AevuTmasjLzyOi9+O8NfEDQoN8uH7tOgDpaZdo59mBZas3mDla4zj7x++8MGKo/vd//j7H62+9R5fuPrw59mVyb96kXLlyTJk5D4/2HcwYqXG1b9UUOzs7rKytKVeuHLv3JADw3ddfsHD+V1hbWxMYHML7k6aaOVLjuXnzJr2CfMnVaMjP07WHdz/g2aeHceJYIuXKl6d9+w7M+fwrypcvb+5wDaZpnWosfSNA/3uj2lWZtPIo1SpXYERQc9Ku5gDw/rIj7Ez8l0HeTXitTxv9+q0b1KDLuA2cOpdh8thN4bO5c1i8aAEoCi1bteab+QuxtbU1d1hG98KoEezYvg1Hx1ocOX4agA3r1/LxpA/5/bdf2bP/EO3ae5o5StN5FOoERVVVc8dQqrRr76nGHzxi7jDIz8+nSaO67NmXQP0GDcwdjlmYMwe5ebdN+nmqqpKdnY2dnR23bt0iJNCbT2bMoUPHzvp1nhzcn9BeEQwaMsxkcd3Q5JvsswrKz8/H060RW3bvY/xrL/DMC6PxD+pJ9K4dfDVvNuu27jZZLDbWisk+C7TF+a49B6lRo6Z+WfzeOObMnMqKtZuoUKECaWmXcHSsZbKYypcz7Ze0RdpDgDefzJxD5uXLBAWHAPDMU0Pp2s2LkaOeN1lczk8sMNlnWVkpnF0wBJ/xGxkW0IzsnFt8uulUieu3bGDPmreCafn8KqPGlbHmWaO+f0mSk5II9PMi8eTPVKxYkWGDB9KjZwjDnnzK5LGYuuqK37cXOzs7nh0xXF+c//brr1hZWTH65ef5eOoMkxfnpu0VS2buWqlyBatEVVWLJF+GtZRSsTHRNG78WJktzKFs5UBRFOzs7AC4desWebfyUJS73d/Vq1fZtyeW0PBIc4VoUvF7YmjQsDF16zdAURSuX7sGwLWrV6nt5Gzm6Ezv+wXfMHrMG1SoUAHApIW5Ofy3Pdy6lYeCQo+eoSiKgqIotPfsQHLSBTNHajx+bepwLvUq/6Rdf6D1B3g1Ye2+s0aOyrzy8vPIyckhLy+PGzdu4Oxcx9whmUR3L2/s7R0KLWveogWuzZqZKSLLYal1ghTnpdS6tavoP2DQ/VcsxcpaDvLz8/Hu3J5mDZ3x9Q/As0Mn/Wvbt2zC29efqlWrmjFC09m8YS2RfQcA8MHHM5n83lt0aPkYk957k7fem2Tm6IxLURQG9A4l0LsTSxZ9B8DZP/8g4UA8Pf26ERkSwPHEo2aO0vjy8/Px6tQe1wbO+AYE4Nnxbnu4desWq1csJ6BHsBkjNK7+3ZuwpkCx/Xyvlhz+tC9fv+xD9co2Rdbv1/0x1uz705QhmlQdFxdefW0czZs04LEGdaharRqBQT3MHZYwM0utE6Q4L4U0Gg3bt26hT9/+5g7FbMpiDqytrdmbkMhPZ/7mWOIRfvn5J/1r69euom9/y+uAjEGj0bBrx1bCevcFYMnCb3n/4xkc+fksH0yZweujnzNzhMa1ZWcs0fsOs3L9FhbO/4qD+/eRn5fHlcxMdsTE8/6kqTz71GBK+5BGa2tr9h1K5Oc//ubY0cLt4fVXX6Zrdy+6dvMyY4TGU76cFb06NmDD/r8AmL/jF9yeX0WnMetJzbzB1Ke7FFq/Q1NHbuTm8cs/meYI1yQyMzPZunUzP//+F3+eT+JGdjYrVywzd1jCjCy5TpDivBTa9eMO2rq3o3bt2uYOxWzKcg6qVa9Od29fonfvBCAjPZ1jiUfo0TPUzJGZRmzUj7Ru645jLe3/+3UrlxEa3huAsN59OXGsdF81dq7jAmiHroSGRXIs8QjOderSK6I3iqLQzrMDimJFRka6mSM1jWrVq+NVoD1Mm/IR6elpTJk208yRGU9wu3qc+CudS1naG0AvZeVw+7aKqsLC3b/i2dSx0Pr9vZqU6qvmALExUTRs2BBHR0fKly9PRO8+HDp4wNxhCTOy5DpBivNSaO2aVfQfWDaukpakrOUgPS2NrCtXAMjJySEuJko/nnDzxvUE9+xVJmYlANi0bg2RfQfqf6/t7MzB/XsB2L83lkaNm5grNKPLzs7Wj6/Pzs4mLiaKFi1aEhIWQfzeOADO/nGGW7c0hW4YLW3+2x5iY6KfL9B+AAAgAElEQVRo6tqMJYsWEB21i+8WL8fKqvSe/gZ4NWHN3rvFtpN9Rf3PkZ0aFbpCrijQt1vjUj/evF69+hw5dIgbN26gqipxsTE0a97C3GEJM7LkOqFMTaWoKMpo4AXgmKqqQ8wdjzFkZ2cTE72beV98be5QzKYs5uBiagovjhpBfn4+t2/fpnfffgSHhAGwYd1qXh073swRmsaN7Gz2xkUzdc4X+mXTP/2K998aR15eHhVsbZn26ZdmjNC40i5d5Kkh2q9o8/PyeLz/IPyDgtFoNLz64rN4d3KnvI0Nn329oNANw6VNamoKLz47gvzb2vbQ5/F+9AwNo2aVCtSr34Aevt0BCI/szfi33zVztIZVqUI5/Nu68PJXe/XLpgzvTJtGNVBVlb8vXeeVAq91b+nMhfTrnL94zRzhmkyHjp3o/XhfunVqj3W5crR192DEM6PMHZZJPDVsMPv2xpGRno5r43q88+4H2Ds48PqY0aSnpdG3dxht2rizaduP5g7VZCy9TihTUykqivIbEKiq6oUCy8qpqppnqM+wlKkUhXmZeipFS2WuqRQtiamnUrREpp5K0VKZcipFS2WuqRQtSdmpukomvaJWmZ9KUVGUr4HGwA5FUbIURVmqKMp+YKmiKA0VRYlRFOWUoijRiqLU123zmKIoCYqinFYUZbKiKA82J5UQQgghhBAPocwU56qqPg8kA37AHMAN7VX0J4DPgMWqqrYBlgPzdJvNBeaqqtoaKL0T4gohhBBCCItQZorzYmxWVTVH93MXYIXu56VA9wLL1+p+XkEJFEUZpSjKUUVRjqanpxklWCGEEEIIUfqV5eI821BvpKrqt6qqeqqq6lmzpuP9NxBCCCGEEKIYZbk4L+gAcGc+nSHAPt3PCUBf3c9mmW8nJyeH4EBf8vPzWbZ0MW3cXGnj5sqypYuLXf/y5cuEhfSgjZsrYSE9yMzUTpn1+2+/4efdFfsqtnw6++78vhqNhh4BPuTlGeyeWIOTHGhzEBbsR35+PiuXLcGzTXM82zRn5bIlxa6fefkyfcKC8WzTnD5hwVzJLPxwkWOJR3CsWoFNP6wHtFPP9Yu0/HnQc3Jy6NsrkPz8fNauXEr39m50b+/G2pVLi10/M/MyT/QJoXt7N57oE8KVK9o8XM3K4qlBfQjq7ol/F3dWL9ceSxnpaQzpF2ay/XkYOTk5RIYEkJ+fz6rlS+jk7kYndzdWLS/5WOgXGUIndzf6RYYUOhb279uDXzdPvDq2JTIkANC2h4ie/hbfHnr1uNse2rduTvvW928P7VsXbg/bt2ymW0cPvDq1x69bJw4eiAd07SHCstuDrY01uyaHYWWlMMSvKae/HMjpLwcyxK9psesvfT2AhDmPkzDncX779gkS5jyuf61VAwfipkaSOK8fR+b2o0J5awC2fRha7NNELYmcHwrnYPnSxbR1c6WtmyvL75GD8JAetHVzJfw/OfD37opDFVvmPsI5KA3HgRTnWq8ATyuKcgoYBryqW/4aMFa3vAmQZerAlny/kIjIPmRlZfHJ5I+Ii09gz/5DfDL5I/3BVNCsGVPx9ffn1C9n8PX3Z9aMqQDYOzgwc/ZcXh0zrtD6NjY2+Pr5s27tapPsz8OQHMDyJYsIi+jD1awspn8yid1xB4jac5Dpn0wqUngDfDprGj6+/hw99Rs+vv58Omua/rX8/Hw+nPgWfgFB+mU1HR2p7eRMwsH9Jtmfh7V62feEhEdy9WoWc6ZNZktUPFuj9zNn2mR94V3QF3Nm0M3bn/jEX+jm7c8Xc2YAsPi7r2narAW744+ydstuPpo4AY1GQ42ajtSu7cyRBMt9OMmKpd/TK7w3V7OymDltCj/GxLMzdj8zp00p9liYN2c63j5+HDrxC94+fsybMx2ArCtXmDD2FZau2sC+wyf5bslKQNsevHz82Lh+jUn36/9j2eJFhEdq28O0jycRtecA0XsPMu3j4tvDnFnT8Pb1J/H0b3j7+jNH1x68/fyJP3SMfYcS+ezr+bz6ovbpsY9Cexge0IxNCeepVsmGdwa2x3v8Rrze+IF3BrYvtqAeNjOazmM20HnMBjYePMemg+cBsLZSWDjGj1e+3kf70esInriFW/na2aZWxP3BqJCWJtyr/z85PxTNQWx8AnH3yMFsXQ5O6nIwu0AOZsyey+gScrD+EcrBo34clKniXFXVhqqqpquq+oGqqjMLLP9bVVV/VVXbqKoaoKrqP7qXkoDOuhtFjwEmf7Tg6lUrCAuPJGr3TvwDAnFwcMDe3h7/gEB27yo6J+m2LZsZMnQ4AEOGDmfr5k0A1KpVi/aeHShfvnyRbcIierN6ZYlD6s1OcgBrV68gNCyCmKhd+PoHYu/gQHV7e3z9A/VPPixox7YtDBryJACDhjzJ9q2b9a99+9XnhPd+HEfHWoW26RUeybrVK427I/+jH9auIjg0nD3Ru/HyDcDe3oHq1e3x8g0gLmpXkfV37dhC/yeGAtD/iaHs3K7Ng6IoZF+/hqqqZGdfp7q9PeXKaR/7ENwrnB/WWm4e1q9ZSc9e4cRG78LHL0B/LPj4BRATVfRY+HHbFgYOHgbAwMHD2KE7FtavXUWv8N7UrVcfoNDxEBIWwfo1q0ywNw/nTnuILqY9RBXXHrZu4Qlde3hiyJNs36LNgZ2dnX6+9xs3sgvN/d4rPJK1qyz3OBjk04Qth88T5FGX6JNJZF7P5Uq2huiTSfRoV++e2/bt1lj/RNBAj7r8dP4yp89fBuDytVxu39ZO9rft8N8M8HrMuDvyP5LzA6wpkAO/AjnwM2AOwi08B6XtOChTxflDaA+c0F05fxEYd5/1DUqj0XDu3F80aNiQ5KQk6ta72+G61K1LclJSkW0uXbqIs7MzAE5OTly6dPG+n9OyZSuOJVrm3Oz/x959R0dRdnEc/w6hhBeEJCQhBRSlJrQkhA7phZAGIgIiYgFFRaVItwOCglLEXujVRhUhHUInBRRQioCmQRKSUEwhYd4/dlkCCRbc7A5wP+d4hNmZzZ0fzzN7Mzs7KxnoMjh98iT33teEjIx0nBs1Mjzm5OxMRkblGTjoM2hYLoOMjHQ2bVjLk8NHVNjGzaMDu3YkVtFe/HclJSX8fvokje9tQlZmOk6Nro0FR+dGZGVWzCHn7FkaOuhysG/oQM7ZswA8PvxZjh39lQ4uTQjo3oG3Zrxn+MbIdm4d2KPRM6YlJSWcPqUbC5mZGTg7lxsLTs5kZmZU2CY7+/oMsrN1Gfx2/Bj5+fn06R1AgFdnVq+4dmmQi2sbUpNNfi7iHyk/HzIz0mlUbj44OzuT+S/mA8DGdWvp5NaaAQ9G8MEnnxuWu3l0MFzmojU1qlejScN6/H72Ik42dUjLuXaX3/Rc3bKb6e7qwJn8Qk5kngeguVN9VGD96yHsfO9BxvRtb1g3/1IJtWpYYHNPrSrbl/9CXh+uzyCzkgwyb5LBzebDzbi2bkPSbZDBnTIOpDn/C6qqbldVtb3+jLqXqqrH/34r48nNycGqvtUtb68oyj/6FkALCwtq1KzJhQva+4Y4yQByc3Oob2WcDCaPH8PrU2dU+tXldnb2ZGVVbO604lxuDvXq17/l7cvnEB8bReu27Ug6coot2/byyvhRXDiva1Zs7ew5k5VplJqN7VxuDvWNlEFpaSkHU5NZ/vU6Vn+/ifffncGJY0eBa/PhohbnQ47x5gNAWGQf9qYeYtnqb3n7rdcNy+3s7cmq5JcdLbC9x5KCSyW3tO3DPZvx9fZrL2XVq1Wjm0tDnng/Fv9J64jo3ASfdk6Gx7MLCnG0+d9/rrkqyOuDfj6YKIOaGs7gThsH0pxrmGXt2hQVFwG6M6Rpf/xheCw9LQ0nZ+cK29jbNyQzU9dYZGZmVrh04WZKiouxtLQ0QtXGJRlAbcvaFBXpM3ByJj3t2i33M9LTcXKqPIMsfQZZ5TJITU5i2NDBtHdpyvq13zJu1Eg2bdC9nVdUVISlZe2q3p1bZlm7NsVFxQA4ODqTkXZtLGSmp+HgWDEHW/trjfaZrEwa2OnuprRm+WJCwvqgKAr3P9CMxvfdz/FjvwJQXFSEZW1t5mBpWZviYl0Gjo5OpKeXGwsZ6Tg6OlXYxs7u+gyu3lHKydkZH/9A6tSpQ4MGtnTt3oNDPx80bFdSXEwtLc6H2tfmg6OTM2nl5kN6ejqO/2I+lNe9hxenTp4kNycH0M2H2hqdD4UlpVjW1H1oM+PcJRrZ1jU85tygLhnnKr8ZmUU1hciuTfgm8TfDsvTcSyQeyiL3QjGFJWX8mPw77g/YGh6vVdOCwmJtftOvvD7oj4v6DBwrycDxJhn83XyoTLGGM7jTxoE05xpmbW1NWVkZRUVFBAQGExMdRV5eHnl5ecRERxEQGFxhm95h4Sxfpvt08vJliwkNj/jbn5Obm0uDBraVXmNlbpIBWJXLwC8giLiYKPLz8sjPyyMuJgq/gKAK2/TqHWa4e8eq5UsICQ0HIPXwcQ4cOcGBIyeI6NOPWXMXEBoeCcCJ40dxcdXuh7+srK7l4O0fyLa4aPLz88jPz2NbXDTe5T7gelVgrzC+XrkMgK9XLiMoRJeDc6PGJG6LAyD77BlOHD/KfU3uB+C3E8do6eJqor36d8qPBV//IBJiow1jISE2Gl//imMhuHe44ZKV1SuW0ks/FnqFhrN3905KS0v5888/Sd6/l+YtWwFwLjcXmwYNND8f/CuZD/6VzYfQMFbq58PK5UsICdNl8NuJ46iq7vrqAynJlBQXY9OgAQAnjml3PuRfKsGimkKtGhZEpaQR4OaMVZ2aWNWpSYCbM1EplX9nnl97Z46m5ZOee615j0r5g9b32VC7pgUW1RR6tnbkyB/XPkDnYPU/Tp/V3tlSkNcHqJhBbLkMYu/SDO6EcSDNucb5BwSyc0ciNjY2TJj8Cl7dOuHVrRMTp7yKjY0NAM+NGEZyku760LHjJhIbHU071xbExcQwdtxEALKysmj+QGM+mDeHd2dOp/kDjTmvfxt/W0IcwSHavW2YZAC+/oHs3pmItY0NL0+Ygr9XF/y9ujBu4itY6zN48bmnSdFfJzxq7ATiY6PxbNeKhLgYRo2d8Lc/Y3tCPEG9tJsBgJdfAPt278Da2oaXxk0m1K8boX7dGDV+CtbWuhxefnEEB1KSABg5ehzb46Pp0cGVxIQYnh89DoCXxk0mae8u/Lt5MDCyF5Nfn45NA93Zwp3b4/EPCjHPDv4DPn4B7Nm1A2sbG8aMn0yQTzeCfLoxdsIUw1gYPfIZUpN1Gbw4ehwJcTF0dnMlIT6WF0ePB6BFSxd8A4Lw6epBL99uDH7sSVxc2wCwY3s8AcHaHQt+5ebDuIlT8OvZBb+eXRg/qdx8ePZpUvTHhNH6+dChrW4+jNbPh/Vrv6ObZ3t6du7AuNEv8OXSFYa3t7dviydIw8eE6NQ0urk4kHexmBlrUkic3ZfE2X15e3UyeRd176589LwXHk2vnQXv37Mpa7afuO558i+VMH/9QRJn92XPnH6k/pbLj0m6M48eTW3Ze/QsZfoPiGqRvD7oMthVLgPvbp3wviGD58tlMEafQXt9BmP0GZzJyqLFA41ZoM+gxQ0Z9NJ4BnfSOFCunjUQxuHRwVNN3GW8DwykpCSzYP5cvlxY+f17jWHQw/14a9oMmrdoUWU/47+4HTMoLr1ilOe56kBKMh8vmMcnX1Z+z1ZjCA3yYfnq77Gytjbac/5ZYty3w386kMLnH81n/qcLjfq85fXr7c+XK77Byso4OdS0+PtrGf+Ng6kpfPLhPD76fJFRn7e8xwf359U3ptO0uXHmQ43qxj0PdCAlmY8WzOPTKpwPvQN9WLHGuPPBcdCXRnsutwca8EJEO56aG2e057zR7Ke6snHfaeIPGu/a+9w1w432XHB7vj4Yu+tK1WfwxW2UgXGPirfnOACoU6takqqqnjculzPnGufu7oGXt+7G+lWhpKSEsIhIzTbmIBkAtHf3oIdX1WWQk53Ncy+MNmojUhXatnenW0/vKsshNyeb4c+/aLTGvCq0c3Ov0rFQUlJCSGiE0RrzqtDe3YOeVTwfntf4fEj9LZeEnzKoVs3Ybc41h37PM2pjXhXk9QHcTJBBuMYzuNPGgZw5NzJjnzkXtydjnzm/XRn7zPntyNhnzm9Hxj5zfrsy5pnz25Wxz5zfjqTrMv6Z89uVnDkXQgghhBBC46Q5F0IIIYQQQiOkORdCCCGEEEIjpDkXQgghhBBCI6Q5F0IIIYQQQiOkORdCCCGEEEIjpDkXQgghhBBCI6Q5F0IIIYQQQiOkORdCCCGEEEIjpDkXQgghhBBCI6Q5F0IIIYQQQiOkORdCCCGEEEIjpDkXQgghhBBCI6Q5F0IIIYQQQiOkORdCCCGEEEIjpDkXQgghhBBCI6Q5F0IIIYQQQiOkORdCCCGEEEIjpDkXQgghhBBCI6qbu4A70RVVNXcJZlXdQn7nU8xdgEZY1pCxUHbl7j4eANzlh0SDvG+eNncJZmfd50Nzl2B2ud89Z+4SzE6Oi39NXjmFEEIIIYTQCGnOhRBCCCGE0AhpzoUQQgghhNAIac6FEEIIIYTQCGnOhRBCCCGE0AhpzoUQQgghhNAIac6FEEIIIYTQCGnOhRBCCCGE0AhpzoUQQgghhNAIac6FEEIIIYTQCGnOhRBCCCGE0AhpzoUQQgghhNAIac6FEEIIIYTQCGnOhRBCCCGE0AhpzoUQQgghhNAIac6FEEIIIYTQCGnOhRBCCCGE0AhpzoUQQgghhNAIac6FEEIIIYTQCGnOhRBCCCGE0AhpzoUQQgghhNAIac6FEEIIIYTQCGnOb3PPPv0U9zd2oJNHO8OygwdS8fXqRrdOHnh168T+fXvNWKFpFRUV0aNrJzp5tMejfWumvvm6uUsyiaKiIvy9utCjswddPdsxY9obAKiqytQ3XsGzvQudPdrw6UcfmLVOUygrK8O3myeDHooEdBlMf+NVOrm50tWjLZ/d4Rl4tmmOdxd3/Lp7EuTdBYCfD6YS4tfDsCx5/z4zV1n1ysrK8O7qycB+EQCcPnWSAO+udGjbkicfG0RJSYmZKzSdP/74g+AAX9zbueLRvjUL5s8zd0lVprmzFbvnDzD8d2bNcEZG6F4fnw1rS+rHj5D04SCmP9EVgIE+La5b/9L652h3v605d6HKHP31V7p0dDf852BbnwXz55q7rCqX9scfhAb709G9DZ082vLRgvkATH3zNbp2dKN7Zw8iw4LJzMgwc6XXKKqqmruGO4pHB091207TNcOJ27dRt25dnn7qcfYmHwQgMjSY518cRVBwCFt+/IG5781mc1SsyWqqbmG+3/lUVeXSpUvUrVuXy5cv4+fdg9nvz6Nzly4mraOopMykP+/G/Q4J8GLGrDkc/eUXtm+L56PPvqJatWpknz2Lnb29yeoqM8Px5aMP5pCanMyFC+dZ+c06VixdROK2eBZ8aqYMrpg2A882zdmSsIsGDa41GA9H9uaZ51/EP6gX0Vs28+G89/j+h2iT1VTDDMeED+fPITU5iQsXzrPq2/U8MWQgYRF96dd/AGNefI42bdvx5PARJq2pdk0Lk/68qzIzM8nKzMTdw4MLFy7QrXMH1nyzFhdXV5PXYt3nQ5P9rGrVFE4sfhzvMd/QxKEeEwZ0oO8bGykpvYJd/dpkFxRet37r+2xY80pvWg9fVqV15X73XJU+/z9RVlZGs/sbkbB9N/fed5/pf74Jj4tZmZlkZWXi5q4b/17dOrJyzXc4OTeiXr16AHz84Qf8+sth5n7wscnqAqhX2yJJVVXPG5fLmfPbXI+eXlhb21y3TFEULpw/D8D5ggIcHR3NUZpZKIpC3bp1Abh8+TKlly+jKIqZq6p6N+735culKIrCV198wvhJr1Ctmm6qm7IpNYeM9DSiftzMo0OfNCxb+MWnvDzx7smgMoqicOGC/phwvoCGDnf2MSE9PY2oH39gyOO6caCqKtsT4ojs2w+AgYOHsGnDOnOWaFKOjo64e3gAcM8999CqlQsZGelmrqrq+bZvxMnMAn7PvsDTvdsw++tkSkqvAFRozAEe9m7B19uOmbpMs4iLjeGBB5qapTE3NQdHR9zcr43/lq1akZGRbmjMAf7885KmeoXq5i5AGN/M2XPoGxbClInjuaJeITou0dwlmVRZWRndOnXgxInjPPPs83Tq3NncJZlEWVkZPt07cfK34zz19LN4duzMyZO/8d23a9i0fh0NbG15Z/ZcmjZrbu5Sq8yU8WN5fdoMLl64aFh26uRvrP32azZtWIutrR1vz5pzR2eAojCgT28URWHIE8N57IlhTH1nNgP7hvHmKxO5cuUKG6MSzF1llZo8fgxvTJ/JxQsXADiXm0v9+lZUr657yXNybqSpt7BN6fSpU6SmptCx051/XOzv1Zw1+ma7mbMV3Vs78eZjXSgqKWXSVztJOnb2uvUf6tmM/tN+MEepJvfN16vo//BAc5dhcqdPn+JgaiqeHXXj/63XX2Hl8qXUq1+fTT/GmLm6a+TM+R3oy88+Yeas9/jlxGlmvvsez48Ybu6STMrCwoI9SakcP5XG/n17OfTzz+YuySQsLCzYvjuJQ0dPk5y0j8OHfqakuBjLWpbEJe5h6BPDGPnsMHOXWWW2bN6ErZ0dbu4drlteUlxMLUtLYrbvYcjjT/HSs3f2fNiwJY7o7XtZ8e0GFn7+Mbt2bGfRF5/x1oxZpBz5jbdmzGL0yGfMXWaV2bJ5I3Z29hXGgYCLFy8y6OF+zHpv7nVnDe9ENapXI7RTE75LPA5AdQsFm3tq4TX2GyYv3MmyCcHXrd+xRUP+LC7l8Olz5ijXpEpKSvhh4wb69utv7lJM6uLFiwwZ1J+Zs943jP/X3pzGkeOneXjgI3z6iekuufo70pzfgVYsW0JEnwcB6NuvP0n7754PhJZnZWWFt48vW7f+aO5STKq+lRU9vXyIidqCk3MjwiP7AhAW0YdDP/9k5uqqzt7dO/nxh424uzbj6ccHk5gQx4inHsPRqRFhEX0ACI3ow6FDd24GAI5OzgDY2dnTOyySlKR9rFm5lNAI3TiI6PsQKUl37gdC9+zayeZNG2jv0pRhQwezPSGOSeNGU1CQT2lpKaC7/MnRycnMlZrW5cuXGfRwPwYMGkyfvg+au5wqF9zhPlJPZHM2X3f5SnrORdbu/A2A/UfPckVVsa1naVi/v1cz1iTcHZe0bP1xM+3dPGjYsKG5SzGZy5cv8+igh3h4wCOG/qi8hwc8wvq135mhssrdMc25oihNFEW5O06R/g0HRycSt+netk6Ii72z38K/QXZ2Nvn5+QAUFhYSEx1Fy5atzFxV1cvJzqag3H7HxUbTvGVLeodFsD0hHoAd2xNo1qyFGausWq++OZ2fjp4i5fBxPlu0nB7evnzy5RJ6h0eQuC0egB3bt93R8+HSpUuGSzkuXbpEfGw0rVxa4+DgyM7EbQBsT4jjgabNzFlmlXrtrbc5dOw0B46c4IvFy+np7ctnC5fSw8uHdd9/C8Cq5UvpHRZh5kpNR1VVRgx/ipatXHhp9Bhzl2MSD3tfu6QFYMPuk3i30/3i2sypPjWrVyPnfBEAigL9eja7a643/3rNKvoPuHsuaVFVledHDKNlSxdGvjTasPz48Wv/3ps2rqdFi5bmKK9Scs05oChKdVVVS81dx614YsgjbN+eQG5ODi2b3svkV17ng48+ZcLLoyktLcXS0pL5H35i7jJNJiszk+FPDqWsrIwr6hX6PfQwvUPDzF1WlcvKyuS5p5/U7feVK/Tt9xC9QsLo2rUHw58cwkcL5lG3bh3mffipuUs1uZfGjOeZpx7jkwXzqFO3LnPv4Ayyz57hicG6t6rLSkvp238gfoHB1Klbl1cmjKG0tJRatSyZPc+0dyTQgjemzmDY0Ed4+63XaNve7boPDd/pdu7YwYrlS2nTpi2dO7gB8Oa0t+kV0tvMlVWN/9Wqjp9bY0YuiDcsWxx1hE9f8mP/hwMpuXyFYXOuXV/co40TadkXOXXmvBmqNa1Lly4RGxN1V/UFu3fuYNWKZbRu05bunXUfDH3tzWksXfQVx44dpVq1ajS+917mztfOcfGOuZWioihNgM1AItANSAcigZbAJ8D/gBPAk6qq5imKEg+kAj2AlcDvwOtAGVCgqqqXoigWwEzAB6gFfKiq6l++spv6VopaZM5bKWqFqW+lqFXmuJWi1pj6VopaZI5bKWqRuW6lqCWmvJWiVmnhVormJsdFnbvlVorN0TXQrYF8oB+wBJigqmo74Cd0DfhVNVVV9VRV9T3gNSBYVdX2wNX3O59C16h3BDoCwxVFud9E+yKEEEIIIe4yd1pzflJV1VT9n5OApoCVqqpX7xu2GPAqt/7qcn/eASxSFGU4cPX0RhDwmKIoqcAeoAG6XwCuoyjK04qi7FcUZX9Odrbx9kYIIYQQQtxV7rRrzovL/bkMsPqb9S9d/YOqqiMURekMhAJJiqJ0ABTgBVVVt/zVk6iq+hnwGegua7mVwoUQQgghhLjTzpzfqADIUxSlp/7vQ4BKv31DUZSmqqruUVX1NSAbaAxsAZ5VFKWGfp0WiqLUMUHdBoWFhfQK8KWsrIzlSxfj1rolbq1bsnzp4krXP3fuHBG9g3Br3ZKI3kHk5eUBsHrlcrp4utG5Q3v8fXrw08EDgO5+p8H+PoZbjGlRYWEhgX7elJWVsWzJYtq4NKeNS3OWLbl5BqG9Amnj0pzQXoGGDFRVZcyoF2ndqhkd3duRkpwM6O7wEhHay2T7cysKCwsJDdaNg5XLltChXSs6tGvFymVLKl0/79w5+oYF06FdK/qGBZOvz+Cq5KR92NarZbh7RU52Ng9Fav/DYYWFhYQH+1FWVsaq5Uvo2N6Fju1dWLX85jn0C+9Fx/Yu9AvvZcghcVsC9zs1wKdrB3y6dmDWjGmAbj6EBflqfj70CfGnrKyM1bjdgSUAACAASURBVMuX0MXNlS5urqz+iwz6R4bQxc2V/pEh142FHdsT8OvuiVen9vQJ8Qd0GUT28tN8BmHl5oNnu1Z4/oP54PkX88HuNpsPclwEy5oWbJ3Rh2rVFAb7teSnzwbz02eDGexX+V03lo4PYvf8AeyeP4BfvhzC7vkDABjo08KwfPf8AVxa/xzt7rcFYNO0CKzq1DLZPt2KwsJCggN8dGNh6WLaubagnWsLlv1FnxAWEkQ71xaEhVzrE3795Rd8vbphfY8lc9+fbVi/pKSEIH9vzR8TQgL1vdKyxbi1aYlbm5YsX3bzDCJDg3Br05LI0Ot7pa4d3eji2Z6AG3qlXgGm65Xu9OYcYCgwS1GUg4Ab8NZN1pulKMpP+tsx7gQOAF8Ah4Fk/fJPMfG7DUsXLySiT18KCgqYOX0qsdt3EZe4m5nTpxoGU3nvz34Hb19/Ug/9irevP+/PfgeA+5rcz+aoOPYkHWDCpCm8+PwIAGrWrImPrx/ffr26wnNpxeKFXxHZ50EKCgqYPu1Ntu3Yw/ade5k+7c1KM5j97kx8/Pz5+cgxfPz8mf3uTAC2/LiZE8eP8fORYyz4+DNeHPksAHZ2drpbze3YYdL9+jeWLVlIeERfzhcU8M6MqUTH7yQmYRfvzJhaodEAmPPeO3j5+JF08Be8fPyY8947hsfKysp445VJ+PoHGpbZ2tnR0MGR3bu0mwHAiiULCYvow/mCAmbNmMbWuB1Exe9k1oxpleYw7/138fLxY9+BI3j5+DHv/XcNj3Xp1oP4XUnE70pi3KRXAN188PLx4/tv15hsn/6tlUsX0Ttcl8Hsd6azOTaRH+N2MPud6ZVm8MGcd+np7cvu1MP09Pblgzm6DAry85k45gWWrPqObXsP8PmSlYAug57evqzVcAbLlywkTD8f3p0xlaj4nUQn7OLdm8yHue+9g7ePH/sP/oK3jx9zb5gPb96G80GOizA00IV1u36j/v9qMuWRjniN+Yaeo79hyiMdK22oh7y7lS4vrqbLi6tZu/M31u08AcCq+KOG5U+9F8WpM+c5eDIHgBWxv/J0aBuT7te/tWTRV0RE6vqEGdPeIj5xNwk79jBj2luVjoX3Zs3Ex8+Pg4eP4uPnx3uzdGPB2saG2e/P46XRY69b/2qf8I2G+4SlixcSrs/gnelTid22i7jtu3nnJr3SnNnv4O3jT+rPv+Lt488cfa/UpMn9/LA1jt37DzD+hl7J24S90h3TnKuqekpV1Tbl/j5bVdU3VFVNVVW1i6qq7VRV7aOqap7+cR9VVfeXW/9BVVXbqqraRlXVl1SdK6qqTi633FdV1QJT7tfqVSsIDYsgJmoLvv4B2NjYYG1tja9/ANGVfLnOpg3rGfzoYwAMfvQxNq5fB0CXrt2wtrYGoGOnLqSnpxm2CYuIZM2qFSbYm1uzauVywiMiidq6BX//QEMG/v6BbN1SMYONG9bx6JChADw6ZCgb1q/VLV+/jkcefQxFUejcpQsFBflkZmYCEB7Zh9Url5tup/6lr1evoHdYBDHRW/HxC8DaxgYra2t8/AKIjqp41dXmTRsYNFg3DgYNfowfNq43PPbZxwsI7/Mgdnb2120TGh7J16tXVu2O/EffrFlJSFgEsdFb8fb1N+Tg7etPzE1yGDB4CAADBg+5Loeb6R0ewbcazuHbNSvpFRpOXEzFDGKjK2bw46YNDHhEn8EjQ9isz+C7r1fRO7wPjRrfC3DdeAgJi+C7NatMsDe35up8iK1kPtxsHAzUz4eB/2I+fKPhcSDHRd0Z7w27TxLocS8xKWnkXSwm/1IxMSlpBHW49y+37dej6XX3Qb/qYe8W193vfNOeUzzsre3vRli9agVh4ZFER23Br1yf4OcfQNRN+wTdWBj86FBDn2Bvb08Hz47UqFGjwjZhEX1YvVK7fcKaVSsIDf8XvdLG9Tyi75UeefQxNm7QZdD5hl4po3yvFB7JmtWmyeCOac7vRCUlJZw6+Rv3NWlCRkYGjRo1Njzm7NyIjIyMCttknz2Dg6MjAA0dHMg+e6bCOksWfUVg0LW3K11btyEpaX+F9bTg+gzSadS4XAaNGpGRkV5hm7NnzuCoz8DBwYGzZ3QZZGSkV8wwXbe9RwdPdiRur8pduWUlJSWcPnmSe+9rQmZGOo0aNTI85uzsTGZlGdwwDs6evZbBxg1reWr4iArbuHl0YNeOxCrai//uuhwyM3Au92/p5NyIzMybzAcHfQ4Nr58P+/fuxruLBwP6hvHL4UOG5S6ubUjR8Hw4fUqXQVZmBk7O18aCk5MzWZVlkH2WhvoM7Bs6kJ19FoATx49RkJ9P394BBHp1Zs2KpYZtWrm2ITVZwxnox0FGRjrO5eaDk7Nz5ceEv5gPmzas5cnbbD7IcRFqVK9GE4f6/H72Ak4N6pCWc8HwWHruRZwa3PwK1O6tHTmTX8iJjIrn2h7q2ey6pj3/UjG1alhgc482L20pKSnh5NWxkF7JWEivfD5cNxYq6RNu1Lp1G5I1+s3CJSUlnDr1G/fd14TMjEpeG26xV1q66CsCg6/vlZJN9Npwp30g9I6Sm5ND/fp/95nWm1MUBUVRrlu2LT6OJYu+YmvsNsMyCwsLatasyYULF7jnnntu+edVhZycHOpbGTeDytjb21fa3GlBbq7xMpg8fgxvTJ1BtWoVfy+3s7MnK0ubGYAuh3pGmg/t3dxJOXyCunXrErVlM0MGPcS+A0cA3XyoodH5cC43h/r169/y9uUzKCst5UBqMt9s2EJRUSGh/l506NiZps1bGDK4eOECdTWWgbHnw+u34XyQ4yLY1rOk4GLx369YiRvPjl/VsUVD/iwu5fDpc9ctz84vxNGmDucu3NrPq0q5OTlYGblPqIyWj4tV0islxLFk8VdsibmhV6phmgzkzLmGWdauTXGR7uuFnZycSEv7w/BYenoaTk5OFbaxs29Ilv4tyazMTGzLvVX7808HGfns06z65nsaNGhw3XbFxcVYWlpWxW78J7Vr16bIkIEzaX+UyyAtDScn5wrb2DdsaHhbNjMzEzt7+2vb35ihs277oqIiLGvXrrL9+C9qW17LwNHJmbS0a2+zpaen41hZBjeMg6tv2ackJ/HU0MG0c2nK+rXf8vKokWzSv51XVFREbUttZgC6HIqL9Tk4OpFe7t8yIz0NR8ebzIcsfQ5Z1+bDPfXqUbduXQACg0MovXyZ3Jwcw3YlGp0Plpa1KS7WNQgOjk7XveWakZGOQ2UZ2NlzRp/BmaxMbG3tAHB0dsbXP5A6derQoIEtXbr34NDPBw3blRQXU0uDGZSfD05OzqSXmw8Z6emVHxNuMh9Sk5MYNnQw7fXzYdwN88FSo/NBjotQWFyGpf5LnTJyL9HI9lqz5NygLhm5lyrdzqKaQmTXB/imkua8v1cz1iRUXF6rpgWFGv1iOcvatSnSHxednCsZC86Vz4frxsINl3TdjGaPi+V6JUenSl4bbrFXWvl1Jb1SiWkykOZcw6ytrSkrK6OoqAj/wGBio6PIy8sjLy+P2Ogo/AODK2zTOyyc5fo7FixftoTQcN33Kf3x++8MHvAQn321mObNW1y3TW5uLg0a2FZ6nZm5lc8gMCiY6Oithgyio7cSGFQxg9CwCMOn1JctXUxYeKRueXgEK5YtQVVV9uzeTb169Q1v7R07epTWrbX5oR+r8uMgIIi4mCjy8/LIz8sjLiYK/4CgCtv06h3GSv3dO1YuX0JIaDgABw4f5+CRExw8coKIPv2YPXcBofp8Thw/iotra9Pt2L9UPge/gCDiY6MNOcTHRuN3kxxWL9ddrrF6+VJDDmfOZHH125GT9+/lypUr2OgPwudyc7HR6Hwon4Gvf8UMfP0rZhDcO5zV+ktWVq9YSi99Br1Cw9mzeyelpaX8+eefJO/fS/OWrYCrGTTQfAZ+lcyHm42Dq3f0WVVuPqQePs6BIyc4oJ8Ps26T+SDHRd3lJhbVFGrVsCAq+XcC3BtjVacWVnVqEeDemKjk3yvdzs+tMUfT8ki/oXlXFOjXs1mlZ9QdrP/H6TPnq2Q//qvyYyEgMJiYcn1CTHQUATftE3RjYfmyxYY+4a/cLn3CP+6VQsNZoe+VVixbQmhYuV5p4EN8/qV5eyVpzjXOLyCQXTsSsbGxYfykKfh074xP985MmPwKNjY2ADw/YrjhOqgxL08gLiYat9YtiY+NYczLEwCY+fZUzp3LZcxLI+nWyQOvbp0MP2N7QhzBIdq9bVhAQBA79RlMmvwqPbp2pEfXjkye8pohg2efHkbSfl0GL4+fSGx0FG1cmhMXE83L4ycC0CukN/ff/wCtWzXj+RHDmffBR4afkZAQR6+QUNPv3D/k5x/I7p2JWNvYMG7CFPy8uuDn1YXxE1/BWp/Bi889TYr+OuHRYycQHxtNh3atSIiLYfTYCX/7M7YnxBPUS7vjAMDXP4A9u3ZgbWPD2AmTCfTuSqB3V16eOMWQw0vPX8vhpTHjiY+NpmN7FxLiYnhpzHgANnz/LT06uuHdxYNJ40bz+aJlhrc1E7fFE9grxDw7+A94+13LYMz4yQT7dCPYpxtjJ1zLYPTIZ0hNTgLghdHjSIiLoYubK9viY3lhtC6DFi1d8AsIwrerB718uzH4sSdxcdU1Yju2xxMQrN2x4FtuPrw8YQr+Xl3w9+rCuJvMh1H6+eCpnw+j7oD5IMdFiE75g26ujuRdLGbG6v0kzulP4pz+vL1qH3n6S14+esEXj2Z2hm36ezWr9IOgPdo4kZZ9kVM3NOEezezY+8sZTX/dvH9AoGEsTJj8Cl7dOuHVrRMTp7xqGAvPjRhm6BPGjptIbHQ07VxbEBcTw9hxurGQlZVF8wca88G8Obw7czrNH2jM+fO6PLZpvE/wCwhk185yvVKPzvj0uL5XGvnstV5p9MsTiIuNxq1NS+LjYhit75XemTGVvHO5jBk1ku6dPfDufkOvZKJjgnL17JEwDo8Onuq2nXuN9nypKcl8OH8uny+s/P69xvDIgH68OW1Ghd8Sb1V1C+P+zpeSnMwH8+bw1eKlf7/yLQrw9eLr79YZPqX9XxUZ+S3QAynJfLRgHp9+Wfk9W42hd5APK1Z/j5WRMgAoM/Lx5UBqMp8smMfHX1RdDkMH9efVt6bTzEjzwdgv6gdTU/j0w3l8+Pkioz5veU8M7s8rb0ynqZEyqGHkY8KBlGQ+XjCPT6pwPoQG+bDcyPOhdk2Lv1/pH7odj4sA1n0+NNpzuTW15YVIN556P9poz3mj2U/3YOOeU8QfSPv7lf+h3O+eM9pzAaSkJLNg/ly+rMI+YdDD/Xhr2gyat9DmcTE1JZkPP5jL519VXQaDB/TjDSP2SgD1alskqarqeeNyOXOucW7uHvT01n25QFUoKSkhLDzSqIPN2Nw9PPD28a2yDLKzs3lx1BijvgAZW3t3D3p6Vd04yMnO5vkXRhu1EakK7d086FGFOZSUlNA7PMJojXlVaOfmTvcqziAkNMJojXlVaO9eteMgJzub5zQ+H+S4CKknckj4KZ1q1f7+A4236tDpc0ZtzKuCu7sHXlXdJ0REGq0xrwpuJsggNMJ0vZKcOTcyY585vx0Z+8z57cjYZ85vV8Y+c3470vLb4aZi7DPntytjnjm/XRnzzPntythnzm9HclzUkTPnQgghhBBCaJw050IIIYQQQmiENOdCCCGEEEJohDTnQgghhBBCaIQ050IIIYQQQmiENOdCCCGEEEJohDTnQgghhBBCaIQ050IIIYQQQmiENOdCCCGEEEJohDTnQgghhBBCaIQ050IIIYQQQmiENOdCCCGEEEJohDTnQgghhBBCaIQ050IIIYQQQmiENOdCCCGEEEJohDTnQgghhBBCaIQ050IIIYQQQmiENOdCCCGEEEJohDTnQgghhBBCaER1cxdwp7miqhSWlJm7DLOqbqGauwSzs6whv/cCqDIUBKAo5q5AaEXe2ufNXYLZWXtNMncJZpcWNdXcJWiadBBCCCGEEEJohDTnQgghhBBCaIQ050IIIYQQQmiENOdCCCGEEEJohDTnQgghhBBCaIQ050IIIYQQQmiENOdCCCGEEEJohDTnQgghhBBCaIQ050IIIYQQQmiENOdCCCGEEEJohDTnQgghhBBCaIQ050IIIYQQQmiENOdCCCGEEEJohDTnQgghhBBCaIQ050IIIYQQQmiENOdCCCGEEEJohDTnQgghhBBCaIQ050IIIYQQQmiENOdCCCGEEEJohDTnQgghhBBCaIQ050IIIYQQQmiENOdCCCGEEEJohDTnt7kObZrj3cUd3+6eBHp3ue6xjz6Yg329muTm5pipOtMpKyvDu6snA/tFAHD61EkCvLvSoW1LnnxsECUlJWau0LTy8/N5ZEB/3Nq44N7WlT27d5m7pCo34uknua9RQzzd2xqWnTt3jrCQINq5tiAsJIi8vDwzVmh6H8ybg6dbGzzd2zJ0yCMUFRWZuySzaNX8fjq6t6Ozpzvdu3Q0dzkmV1RURI+unejk0R6P9q2Z+ubr5i7J5P744w+CA3xxb+eKR/vWLJg/z9wlVakXBnQnadko9i97icVvDqRWzeosfH0AB1aOYf+yl/hkcj+qW+hawHp1avHNu4+xZ/GLJC0bxZDQDmauvmqUlZXh282TQQ9FXrd80sujuK+hlZmqqpw053eA7zZFEbdjP1EJuw3L0tP+ID4mmkaN7zVjZabzyYfzadGyleHvb7w6iWdHjiLpp1+xsrJm2eKvzFid6Y0bM4rA4GBSfz7CnqRUWrZyMXdJVe7RIY+zdsPm65a9N2smPn5+HDx8FB8/P96bNdNM1ZleRno6H3/4Adt37WN/yk9cKSvj6zWrzF2W2WyOimXP/hR27N5n7lJMrlatWvwYFcve5APs2Z/K1i0/smf37r/f8A5SvXp1Zr77HikHD5OQuJtPP/mQI4cPm7usKuFkW4/n+nej+5ML8Hx0HhbVFPoHtGPV1lTaD3ofz0fnUbtWDZ6I0P2i+ky/rvxy6iydh84neOTnzHyhNzWqW5h5L4zv04/m07zl9a+FKcn7yc/X3kkbac7vUK9OepnXpr6NoijmLqXKpaenEfXjDwx5/EkAVFVle0IckX37ATBw8BA2bVhnzhJNqqCggMTEbTz+xFMA1KxZEysrbZ0VqAo9enphY21z3bJNG9Yz+NGhAAx+dCgb19894wCgtKyUwsJCSktL+fPPP3F0dDJ3ScIMFEWhbt26AFy+fJnSy5fviteG8hwdHXH38ADgnnvuoVUrFzIy0s1cVdWpblGN2rVqYGFRjdqWNcnMucCWXb8aHt9/+A+c7esDutfMuv+rBUCd2jXJO19IadkVs9RdVTLS04j6cTOPDn3SsKysrIw3pkzk9WnaO2kjzfltTlEUHu7TmwCvzixZ+AUAmzetx9HRmTZt25u5OtOYPH4Mb0yfSbVquuF8LjeX+vWtqF69OgBOzo3IzMgwZ4kmderkSWxt7Xhm2JN06ejBs88M49KlS+YuyyzOnj2Do6MjAA4ODpw9e8bMFZmOk7MzL40aS6tm99H0Pifq1a9PQGCQucsyC0VRCO8dTLfOnnz5xWfmLscsysrK6NzBjXud7PELCKRT587mLslsTp86RWpqCh073ZkZZOScZ+7K7Rz9fgIn10/i/MUiYvYeMzxe3aIag3q5E7X7KACffLuLVvfZ8dv6Sexf+hIvz92AqqrmKr9KTBk/ltenzTD0CQBffPIhvULDcHBwNGNllZPm/Da3YUscMdv3svLbDXz1+cfs2rGdebPfYcKUu+Oawi2bN2JnZ4+b+515jdytKC0rJTUlmWHPjGD3vmTq1KnD7He1d2bA1BRFuavOFubl5bFx43oO/fobx0+l8+elS6xcsczcZZlFdNx2du1NYu2GH/js449I3L7N3CWZnIWFBXuSUjl+Ko39+/Zy6OefzV2SWVy8eJFBD/dj1ntzqVevnrnLqRJW91gS1tMVl4dm8UDEDOrUrsHAYDfD4/PGRbIj9RQ7DpwCILBzCw4ey+SBiBl0HvoBc8ZEcI/+TPqdYMvmTdja2V3XJ2RmZrB+7bcMHzHSjJXdnDTntzlHJ2cA7Ozs6R0Wyc7Ebfx++hS+3T3p0KY5GelpBPTszJkzWWautGrs2bWTzZs20N6lKcOGDmZ7QhyTxo2moCCf0tJSQPd2lqPT3fN2vrNzI5wbNaKT/qxQ3wcfIjU1xcxVmYe9fUMyMzMByMzMxM7O3swVmU5cbDRNmjTBzs6OGjVqENGnL3t27TR3WWbh7Kw7Ttrb2xMe2Yf9+/aauSLzsbKywtvHl61bfzR3KSZ3+fJlBj3cjwGDBtOn74PmLqfK+Hk241TGOXLyL1FadoW18Yfo0vY+ACY/6Y+dVR3Gz99kWH9IaAfWJRwC4Lf0XE5l5tHyPjuz1F4V9u7eyY8/bMTdtRlPPz6YxIQ4enRsz8kTJ+jYrhXurs34888/6diu1d8/mYlIc34bu3TpEhcvXDD8OT42GncPTw7/lk7Sz8dI+vkYTs6NiN6+h4YNHcxcbdV47a23OXTsNAeOnOCLxcvp6e3LZwuX0sPLh3XffwvAquVL6R0WYeZKTcfBwYFGjRpz9Ffd9YVxsTG4uNz5HwitTO+wcJYvWwzA8mWLCQ2/e8ZB48b3sm/PHv78809UVSU+Lvau+GDwjS5dusSFcsfJmOgoXFu3MXNVppWdnU1+fj4AhYWFxERH0bKldhoRU1BVlRHDn6JlKxdeGj3G3OVUqT/OFNCp9b3UrlUDAF/PZvx66iyPh3sS2Lk5j7226rrLVv7IysfHsykA9tZ1aXGvLSczzpml9qrw6pvT+enoKVIOH+ezRcvp4e3LibRsDv+WRsrh46QcPs7//vc/9h38xdylGlQ3dwHmoijKKcBTVdWcG5ZHAK6qqmr+OoDss2d4fHB/AMpKS3mw/0D8AoPNXJU2vDF1BsOGPsLbb71G2/Zu130I5G7w3pz5PDH0US6XlNDk/gf49Is7/241Q4c8wvZt8eTm5ND8gca88uobjB03kSGPDGDJwq9ofO99LF2x2txlmkzHTp3p82A/unfugEX16rR3c+fJYU+buyyTO3vmDAP7686SlpaW8vDAQQQF9zJzVaaVlZnJ8CeHUlZWxhX1Cv0eepjeoWHmLsukdu7YwYrlS2nTpi2dO+gu8Xhz2tv0Cult5sqMb9/hP/g+7md2LRpJadkVDhzN5Mt1e8mNeZPfz+QT/9mzAKxLOMSMhbHMXBTLZ6/0Z9/Sl1AUmPLRj+QW/Gnmvbi7KXfaRf//1M2a8//KzaODWv6Whnejq/dOvZtZ1pAMAO7Sw4u4wV10qf9fups+8yBuztprkrlLMLu0qKnmLkETbOvWSFJV1fPG5XdFB6EoSh1FUTYpinJAUZSfFUUZoH/oBUVRkhVF+UlRlFb6dR9XFGWB/s+LFEX5RFGU/YqiHFUU5e461SCEEEIIIUzqrmjOgV5Ahqqq7VVVbQNc/SRMjqqqHsDHwMs32bYJ0AkIBT5RFMXyxhUURXla38Dvz82587+NUwghhBBCVI27pTn/CQhUFOUdRVF6qqpaoF/+nf7/Seia8MqsUVX1iqqqx4DfgAqfolFV9TNVVT1VVfVsYGtr7NqFEEIIIcRd4q5ozlVVPQp4oGvSpymK8pr+oWL9/8u4+Ydjb7xq1qRX0RYWFhIZ4k9ZWRmrli+hs5srnd1cWbV8SaXr5507x0ORIXR2c+WhyBDy83RfS7tjewJNG9ni290T3+6ezJ45DYCSkhIievkZbjuoRYWFhYQF+1JWVsbKZUvwbNcKz3atWLns5hn0DQvGs10r+oYFGzK4KjlpH3b1ahnu5pKTnc1Dkdr+UFBhYSFB/j6UlZWxbMli2rq2oK1rC5YtWVzp+ufOnSMsJIi2ri0ICwkiT5/Br7/8gk/PbljVtWTu+7MN65eUlBDo563pcQC6HIID9DksXUw71xa0c23BsqV/nUO7SnLw9eqG9T0Vcwjy13YOkoHMB9BlEOjnbcigjUtz2rg0/8sMQnsF0salOaG9Ag0ZqKrKmFEv0rpVMzq6tyMlORnQ3eElIlT7H5yVHMCyZnW2fjicatUUBod48NPqsfy0eiyDQzwqXb9dc0cSPnuW3YteIPHL5/F0aQRAWE8X9i550bC8Wzvd7Rdtreqw7v0nTLY/t6KwsJDwYD9Dr9SxvQsd27v8Za/UL7wXHdu70C+8V6V9QsP6lqwv1yc83Ce0yvfjqruiOVcUxQn4U1XVZcAsdI36P9VfUZRqiqI0BR4Afv27DYxpxdJFhIb34XxBAbPfmc6PsYlsidvB7HemVxhMAPPnvIuXty97Ug/j5e3L/DnvGh7r0rUHcTv2E7djPy9PfAXQfbV7T29f1n67xmT79G8tX7KQsIi+nC8o4N0ZU4mK30l0wi7enTG10gzmvvcO3j5+7D/4C94+fsx97x3DY2VlZbz5yiR8/QMNy2zt7Gjo4MjuXTtMsj+3YvGir4js05eCggLenv4WCYm72bZjD29Pf8vw4lLee+/OxMfXj58OH8XH14/39F9CZG1jw+w583hp9Njr1q9ZsyY+vn58s0bbdzNZsugrIiJ1OcyY9hbxibtJ2LGHGdNuksOsmfj4+XHw8FF8/Px4b1a5HN7/ixy+1m4OkoHMB4DFC78iss+DFBQUMH3am2zbsYftO/cyfdqblWYw+92Z+Pj58/ORY/j4+Ru+mGzLj5s5cfwYPx85xoKPP+PFkbo7edjZ2eHg4MjOHdo9LoLkADA0zJN1CYeoX8eSKU/64zXsI3oO+5ApT/pjdU+FK3GZ/nwI07+KocvjHzD1i2imPx8CQNz+E3R6bD5dHv+AEW9/y0eTdHc5ysm/RFbuebrq75WuRSuWLCQsQtcrzZoxja1xO4iK38msGdMq7RPmvf8uXj5+7DtwBC8fP+a9f61XKisr461XJ1fSj9GafAAAIABJREFUJziwx0R9wl3RnANtgb2KoqQCrwPT/sW2vwN7gc3ACFVVi6qgvpv6ds1KeoWGExezFW9ff6xtbLCytsbb15/Y6C0V1v9x0wYGPDIEgAGPDGHzxvV/+zNCwiL4ds0qo9duLF+vXkHvsAhio7fi4xdgyMDHL4CYqIoZbN60gYGDHwNg4ODH+KFcBp99vIDwPg9W+DKa0PBIvlm9smp35D9YvXIFYeGRRG/dgp9/ADY2NlhbW+PnH0DUlopfJrJxw3oGDxkKwOAhQ9mwfh2g+xIWT8+O1KhRo8I24RF9WLVqRdXuyH+0epU+h6hKcqjkS1U2bVjP4Ef1OTw6lI3lcuhwkxzCIvqweqV2c5AMZD4ArFq5nPCISKK2bsHfP9CQgb9/IFsrzWAdj+ozeHTIUDasX6tbvn4djzz6GIqi0LlLFwoK8g1f3BUe2YfVK5ebbqdugeQAA4Pd2LDtMIFdWhCz7xh5FwrJv1BEzL5jBHVpWWF9VVWpV0f3DaD161qSmXMegEuFJYZ16tSued3dtjZsO8yAct8yqjXfrFlJiL5PuLFXulmfMGCwvlcaPOS6PuHzTxYQHtkXW7vrv4gpJMx0fcJd0ZyrqrpFVdV2qqq6qaraUVXV/aqqNrl6G0X93330f16kqmr573ON1l9P3kJV1Y2mrLukpITTp05y731NyMzMwNm5keExJydnMjMzKmyTnX2Whg6OANg3dCA7+6zhsf17d+PTrQMDHwznlyOHDMtdXNuQmry/Cvfk1pWUlHD6pC6DjIx0nBuVy8DZmYyM9ArbnD17BgdHXQYNHRw4e/YMABkZ6WzasJYnh4+osI2bRwd27Uisor34b0pKSjh58jfua6LLoFGjxobHnJ0b3TQDR30GDuUy+Cut27Qhef8+4xVuZNflkJ5Oo8blcmjUiIx0I+XQug3JSdrMQTKQ+QC6DE6Vz+DGcVBZBmduyODMteNihQz148ijgyc7ErdX5a78J5ID1KhuQRMnG37PysfJth5pZwsMj6WfPY+Tbb0K24ybu5G3n+/Nse8nMGNkCK99cq15jfByJXXlaL6bPZQRb39rWJ58JJ3u7ZtU6b7cqvJ9QmZmBs7l/h2dnBtV3iudPYODvldq2NCBbP0xITMjnU3r1/FEJX2Cu0cHdu80TZ9wVzTnt6tzuTnUr1//lrdXFMVwX9127d1JOnSc+J1JDHvmOYYO6m9Yz8LCgho1axq+bVRLcnNzqG9ldcvbl89g8vgxvD51BtWqVRz2dnb2ZGVVnMBakJOTg1V942TwV66OgwsaHAcAuZKDZIDMB9BlYKzj4l+xt7evtLHRCskBbK3+R8HFwn+1zdMPdmH8/I007/sO4+dt4uNJ/QyPrd92GLdBc3h44lJeG37tso6zeRdxrKTR14Lc3BzqGemYMGX8WF6f+nalfYKtnT1ZWZm3/HP+DWnO/4Kqqo+rqvqNuX6+pWVtiot1n1l1dHQiPT3N8FhGRjqOjk4VtrGzs+eMfvCcycrE1lb3tsw99epRt25dAAKCQygtvUxu7rXbPpYUF1PLsuK1aeZW27I2RUW6K4mcnJxJTyuXQXo6Tk7OFbaxt29Ilv7tyKzMTMMlLKnJSQwbOpj2Lk1Zv/Zbxo0ayaYNure3i4qKsLSsXdW7c0tq165NUfG1DNLS/jA8lp6edtMMrr4lm1kug79TUlyMpQbHAYBl+RycnUn7o1wOaWk4Od/5OUgGMh9An0G542KFcVBZBg1vyMDe/tr2N2aoH0dFRUVY1tbmcREkB4DC4stY1tRdlpWRc55G9tdO6Dnb1yNDf8lKeYNDPFgbr3v3/NvYn/B0bVRhnR2pp7jfyYYG9f8HgGWt6hQVX66KXfjPalvWplh/THB0dCK93L9jRnpa5b2SfUNDo52VlYnt1T4hJYnhjz+Ku2szNqz9jvGjX+AHM/QJ0pxrmJW1NWVlZRQVFeHrH0RCbDT5eXnk5+WREBuNr39QhW2Ce4ezesVSAFavWEqv0HAAzpzJ4uq3wSbv38eVK1ewsWkAwLncXGwaNKj0uktzK5+BX0AQcTFRhgziYqLwC6iYQa/eYYZPaK9avoQQfQaph49z4MgJDhw5QUSffsyau4DQ8EgAThw/iotra9Pt2L9gXS6DgKBgYqKjyMvLIy8vj5joKAKCgitsExoeznL93TuWL11MWHjE3/6c3NxcGjSw1eQ4gBtyCKwkh8CKOfQOC2f5Mn0OyxYTepvnIBnIfIDrMwgMCiY6eqshg+jorQRWlkFYhOGOPsuWLiZMf+wLDY9gxbIlqKrKnt27qVevvuGyj2NHj9K6dRvT7di/JDlA/oUiLKop1KpZnajdRwno1ByreyyxuseSgE7Nidp9tMI2mTnn6el+PwA+HZpy/I9cAB5wbmBYx62FE7VqWpBb8CcAzRvbcui3v78czBxu7BPiy/VK8bHRN+0TVi/X90rLlxr6hORDx0g5fJyUw8cJ7/Mg7875gN5m6BOkOdc4H78A9uzagbWNDWPGTybIpxtBPt0YO2EK1jY2AIwe+QypyUkAvDh6HAlxMXR2cyUhPpYXR48HYOPa7/Dq7IZPtw5MGT+aTxcuM7yNs2N7PAHB2r2VoK9/ILt3JmJtY8PLE6bg79UFf68ujJv4iiGDF597mhT9dfOjxk4gPjYaz3atSIiLYdTYCX/7M7YnxBPUS7sZ+AcEsnNHIjY2Nkyc/Ao9u3WiZ7dOTJryKjb6DJ59ZhhJSboMxo6bSGxMNG1dWxAXG8PY8RMByMrKotn9jflg3hzemTGdZvc35vx53ZmVbfFx9Oqt3Qzg+hwmTH4Fr26d8OrWiYnlcnhuxDCSy+cQHU071xbExcQwdty1HJo/oMvh3ZnTaf5AuRwS4ggO0W4OkoHMB4CAgCBDBpMmv0qPrh3p0bUjk6e8di2Dp4eRtF+XwcvjJxIbHUUbl+bExUTzsj6DXiG9uf/+B2jdqhnPjxjOvA8+MvyMhIQ4eoWY7vZxt0JygOi9x+jW7j7yLhQyY2EsiV+OJPHLkby9MJa8C7pLXj6a+CAerXTvBDw/8ztmvhDKnsUv8taIYEa+o/vKl76+rUlaNordi15g7suRDHn12ocfvT2a8uPOX0y/c/+Qr/+1XmnshMkEencl0LsrL0+81iu99Py1PuGlMeOJj42mY3sXEuJieGnM+L/9GYnbEggMDqnS/bhKUVWT3rb7jufm0UGNSthttOc7mJrCJx/O46PPFxntOW/0+OD+vPrGdJo2b2GU56tuYdzf+Q6kJPPxgnl88mXl9601htAgH5av/h4ra2ujPJ9lDeNmkJKSzIJ5c/lyUeX3bDWGgf37MXX6DJq3MM44ADD24SUlJZkF8+fy5cKqy2HQw/14a5pxczCm2zGDf3BZ779yu86Hf3J98z+VkpzMB/Pm8NXipUZ7zhsF+Hrx9XfrsDbScbEq3I45WHtNMsrzXOXWwokXBvbgqbeq7pbIUR89Tf8JS8i/YJwb1qVFTTXK81x1IDWZTxbM4+Mvqq5PCAvyZdnq74zWJwDY1q2RpKqq543L5cy5xrVzc6eHl+7LNqpCSUkJIaERRmvMq0J7d48qzSAnO5vnXhht1AlnbO7uHnj5VO04CI+I1GxDepW7uwde3lWbQ5jGc5AMZD4AuHt44O3jW2UZZGdn8+KoMZpuzEFyAEg9mkFC0gmqVTPyb8F6tlZ1mL8q0WiNeVVo72aKPmGUyfoEOXNuZMY+c347MvaZ89uRsc+c367k8CLA+GfOb1fGPHMubl/GPnN+OzL2mfPblZw5F0IIIYQQQuOkORdCCCGEEEIjpDkXQgghhBBCI6Q5F0IIIYQQQiOkORdCCCGEEEIjpDkXQgghhBBCI6Q5F0IIIYQQQiOkORdCCCGEEEIjpDkXQgghhBBCI6Q5F0IIIYQQQiOkORdCCCGEEEIjpDkXQgghhBBCI6Q5F0IIIYQQQiOkORdCCCGEEEIjpDkXQgghhBBCI6Q5F0IIIYQQQiOkORdCCCGEEEIjpDkXQgghhBBCI6Q5F0IIIYQQQiP+3959h0dVfA0c/x5CFZQiHRQU6QHpTUoSeseCKKAiWLCA2BDFggoCgl0sWJEiYEXs9KaAStOfBVRUeg+CAiHhvH/MTVhDUODd7L3ZnM/z+Bg2d3dnJ3funjtzZsaCc2OMMcYYYwIip98FiDY5RMibK8bvYvjqwOEUv4vgu3y5s/c5kCrlyBG/i+C7mBzidxF8J2J1YJwjR9TvIvhu29wRfhfBdyUaD/S7CIFmPefGGGOMMcYEhAXnxhhjjDHGBIQF58YYY4wxxgSEBefGGGOMMcYEhAXnxhhjjDHGBIQF58YYY4wxxgSEBefGGGOMMcYEhAXnxhhjjDHGBIQF58YYY4wxxgSEBefGGGOMMcYEhAXnxhhjjDHGBIQF58YYY4wxxgSEBefGGGOMMcYEhAXnxhhjjDHGBIQF58YYY4wxxgSEBefGGGOMMcYEhAXnxhhjjDHGBIQF58YYY4wxxgSEBefGGGOMMcYEhAXnxhhjjDHGBIQF58YYY4wxxgSEBedZ2MYNG+jYtiX1a8fSoE4Nnnv2aQDuvXswdc+vRuP6teh56UUkJib6XNLMVy+2Ii0a1Sbhgnq0adEIgP99u5oOLZvRolFtel/ajX1//ulzKSNjw4YNtG0VT+2a1ahzfnWeffopv4sUETdc149zzipJgzo10x575OEHqXTuWTRpUIcmDerw2acf+1jCyEtMTKRnj+7Uiq1K7RrVWLb0S7+LFHHXX9OXs0sXp26tWL+L4pvsek0Itfann2hUv3bafyWLFuTZp5/0u1iZbuOGDXRq25IGtWNpWKcGz3txAsCLzz1LvfOr0bBODe675y4fS5k5BvSK55u3h/L1W/cwYWQf8uTOCcCwmzqz5v37WfnOvdx4eYu04x8bfAnfzXiA5dPuplaVsn4VGwBRVV8LEG3q1K2nC5Ysj8h7bd2yha1bt1Crdh327dtH8yb1eXP6u2zatJEWcQnkzJmT+4cOAeChEaMiUiaAA4dTIvZeqerFVuSzBV9y5plF0x5r26IxD4wYTZOmzZky8XX++G09Q+57MCLlOSNfroi8T0a2bNnC1i1bqF3HnRdNGtZl+tvvU7VatYiXJTnlSMTea/GihRQoUIDr+vVh+Yo1gAvO8xcowC233h6xcqQXk0N8e+9r+/ahSdOmXN33GpKSkvj7778pVKhQxMsh4l8dLF60kPz5C3BN3yv5ZtV3vpXDT0G6Jhw54n/MkZKSwnnnlGXBoqWcXa5cxN8/OYJ1kD5OaNGkPlOmv8v27dsYO3okb703kzx58rBj+3aKFS8esXKVaDwwU1+/dLGCzHntVmpfPIKDhw4zaXRfPl38P0SEFvUrcu39k1BVihUuwI49+2nbtBo3XNaCbjc/T4Ma5Rl75yU0v3JsppYR4OCqcd+oar30j1vPeRZWslQpatWuA8Dpp59O5SpV2Lx5Ey1btSFnTneHWL9BQzZt2uhnMX3zyy/raHxBMwBaxLfkow/e87lEkVGqVClq1zl6XlSpUpXNmzf5XKrM17RZcwoXLuJ3MQJj7969LF68kD5X9wMgd+7cvgTmfmvarDlFimTv8yK7XhOOZ97cOZx7bgVfAvNIO16c8Mr4F7j1jsHkyZMHIKKBeaTkjIkhX55cxMTkIF/e3GzZsZfrujflkfGfkNoxvWPPfgA6tajJlA9dx+ryb3+j4On5KFn0DN/KbsF5lPj9999Ys2oV9eo3/MfjE994jdZt2/lUqggSoUe3DrRu3pA3XnsZgMpVqvHJRx8AMPP9d7LlTcrvv/3GqlUrqd+g4X8fHKXGPz+ORvVqccN1/dizZ4/fxYmY39avp2jRYlx/TV8a1a/DDddfw19//eV3sYzP7JoAb781le6XXuZ3MSIuNE745ed1fLlkMQnNGtOhdTzffP2V38ULq8079vLkG3NY+8nDrJ81gj/3H2DO0h85p2wxLmlTl8WTB/P+szdQ4exiAJQuXoiNW49+P2zalkjp4v51ZlhwHgX279/PFZd3Z9SYxznjjKN3emNGP0LOmJz0uKyXj6WLjJmfzWP2ouVMeWcmr730PF8uWcSTz43n9ZdepHXzhuzft4/cuXL7XcyI2r9/P5dfejFjHnvyH+dFdnLNdf1Z88M6vli+gpIlS3HPXXf4XaSISU5JZtXKFVxzfX+WfrWC/PnzM/bRyKW3meCxawIkJSXx8YczufDi7n4XJaJS44SRXpyQnJzMnt27mbPwCx5+ZDR9el9GNKU5Fzo9H53ialC10wOc22Yo+fPl5rIO9cmTOyeHkg7TtNejvPbuF7z4QDDjIwvOs7jDhw/T+/JLuLRHT7p0uyjt8ckTX+fTjz/i5dcn+ZrvGSmlSpcBoFix4nTo1JWV33xFxUpVmD7jY2YtXMaFl/Sg3Dnn+lzKyDl8+DCXX3oxPS7vRbcLL/rvJ0Sp4iVKEBMTQ44cOejT95qo6x36N2XKlKVM2bI08HpIL7zoElatWulzqYxf7JrgfP7pJ5xfqw4lSpTwuygRc/jwYa5IFyeULlOGzt0uRESoW78BOXLkYNfOnT6XNHwSGlbht8272LlnP8nJR3h/7moanX8Om7bt4f05qwGYMXc1sRVd7LB5eyJlSxZOe36ZEoXYvN2/xTQsOM/CVJWb+l9D5cpVufmWW9Men/X5pzz5+Fimvf0+p512mo8ljIy//vqL/fv2pf08f+5sqlStzo4d2wE4cuQIT4wZyVX9rvOzmBGjqvS/th+Vq1Tllltv87s4vtq6ZUvazzM/eJ9q1av7WJrIKlmyJGXLnsXan34CXJ5t1apVfS6V8YNdE456a/pUuvfIPiktqsrNGcQJHTt3ZdGC+QD8vG4th5OSOLNo0eO8StazYetuGtQ4h3x53eIM8Q0q89P6bcycv4YW9SsC0KxuRX7+w8UJHy34lp6dGgDQoEZ5/tx/gK07/VvhLepWaxGRQkBPVX0uDK8VB9yhqp1O9DmRXK3lyyWLaduqBdVja5Ajh7vPuv/B4Qy+fRBJhw5R5MwzATcp9Mlnno9ImSDyq7X8tv5Xru7lhihTkpO5sPtl3Hrn3Yx/7hlee8l97g5dunHvsBERG0Xwc7WWJYsX0yq+GbEh58WDwx+hXfsOES9LJFdrufqKnixatIBdO3dSvEQJ7rn3ARYvXMCaNasREc4uV46nn32BkqVKRaxM4O9qLatXreLG/tdyOCmJ8uecy4svv0rhwoX/+4lh5ufo3ZW9L2fRgvns9M6L++5/kD59+/lWHj8E6Zrg52otf/31F1XOK8d3P/5CwYIFfStHJFdr+XLJYtplECfEJbTipuv78e2a1eTKnZvhIx+lRVxCxMqV2au1ANzbvwOXtKlDcsoRVv+4kRsemkK+PLl47ZGrOKtkEf46cIgBI6by7Vo3OfqJIZfSpklV/j54mOuHTWLF939kehmPt1pLNAbn5YEPVTU23eM5VTX5JF8rjgAH50Hlx1KKQeNncB4kkQzOg8rP4DwoskNqnTkxQVhK0W+RDM6DKhLBeVaQnZZSHAVUEJFVIvKViCwSkQ+A70WkvIikLXQrIneIyDDv5/NEZLaIrBaRFSJSIfRFRaS+iKxM/7gxxhhjjDHhktPvAmSCIUCsqtbyer4/8v693utVP57JwChVfU9E8uJuXM4CEJEmwDNAV1U9ZpxDRK4DrgM466yzw/hRjDHGGGNMdhKNPefpLVfV9f92gIicDpRR1fcAVPWgqv7t/boqMB7onFFg7h0/XlXrqWq9osWKhbPsxhhjjDEmG8kOwXnorhvJ/PMz5z2B528BDgK1w1moE3XgwAHat44nJSWFyZMmUCu2MrViKzN50oQMj9+9ezddO7ahVmxlunZsk7bpyrQ3J9O4fi0a1TufVnFN+XaNW0ooKSmJdq3iSE4+qXT8iDpw4ADd2rckJSWFaZPfoFGtajSqVY1pk9/I8Pg9u3fTvWt7GtWqRveu7UkM2XhmyaIFJFxQj+YNzqdb+5aAq4Ou7RICXwetE1qQkpLCpDcmEFu1IrFVKzLpjeOfBx3btSa2akU6tmuddh6oKrcNGkj1KudRv3ZNVq5YAcCOHTvo0jH4m1UdOHCAdq289jBxArWqV6ZW9cpMnnj8eujSoQ21qlemS4ej7eGnn34kocUFnHlGPp564rG045OSkmjbMvjtoU3LuLRzoUa1StSoVulfz4VO7dtQo1olOrUPqYMffySuWRMKFcjLk48f3aY6KSmJ1gktAl8H2b09WB04Bw4coG0rrz1MnEDNapWoWa0Sk/7lmtCpfRtqZtAe4ps3ofDpx7aHNi2D3x46eHHClEkTqB1bmdqxlZnyH3FC7XRxwkczZ9Ckfi2aNqxDiwsa8OWSxQDs3LGDi7q0j9jnORV58+Ti85dvIUcOoVfnhnw7436+nXE/vTpnvNlWzUplWDDhdpZOHcLiyYOpV/3obrHN6lZk6dQhfPP2UD5/+RYAcuWMYdYrg4iJiUzYHI3B+T7g9OP8bhtQXETOFJE8QCcAVd0HbBSRbgAikkdEUtcgTAQ6AiO9NJmImjjhNTp3vZC9e/cyesTDzF34JfMWLWX0iIcz3O3wibGjaRHXklXf/USLuJY8MXY0AOXLn8PHn89j6derGXz3UAbe1B9wW3q3iE/gnbemRfRznYw3J75Oh87d+HPvXsaOHsEncxfz6bwljB094h+Bd6pnnniUZi3iWbrqe5q1iOeZJx4FYG9iIkNuG8AbU99l4fLVvPTGm4Crg2Yt4nn/nekR/VwnY8Jrr9K120Xs3buXEcMfZOGSZSz6Yjkjhj+Y4Xkw9tFRxCW05Lsf1hGX0DJt85nPPv2EX35ex3c/rOPZ58cz8OYbAChWrBglS5biiyVLIvq5TtbECa/RpZtrD6NGPMzcRV8yb/FSRh2nPTw+djQt4luy6n8/0SK+JY977aFI4SKMeexJBg66/R/H586dm7iAt4cJr79KV68OHhnxEAsWL2XhkmU8MuKhDOvgsUdHERefwLffryUuPoHHvHOhcJEijH3iKW65NeM6eHt6gOvA2oPVgeeN11+li/cdOXL4Q8xfvJQFS5Yxcvhx2sOYUcQlJLDm+7XEJSTw2JiQ9vD4v7SHAF8TJoXECaNGPMychV8yd9Hxr4upccLKdHFCi/iWLFm+ksXLVjDuhZcZcKNbfriody4s/SK458JVXRszY85qChbIx9Dr2tP8irE06z2Gode1p9Dp+Y45fsSgbowY/wmNLhvFw89/yIhB3QAoWCAfT91zKd0HvUjdS0bQ685XADicnMK8ZT/RvU2diHyeqAvOVXUXsMSb+Dkm3e8OAw8By4FZwI8hv74CGCgia4AvgJIhz9uGC+THiUhE9zyePnUKHTt3Yc6sz4hv2YoiRYpQuHBh4lu2Yvbnnx5z/EcffkDP3lcC0LP3lXw4cwYADRs3SVtCrX6DRmwO2cq+U+euTJ82JQKf5tS8M/1N2nXszLw5n9MiviWFixShUOHCtIhvydzZnx1z/KcfzaRHzysA6NHzCj758AMA3n1rKh06d6OsNy+gWLHiac9p36kL706fGoFPc2qmvjmZzl26Muvzz2jZsnXaedCyZWs+/+zY8+DDmTPofcVVAPS+4ipmfvC+e/yDGfTsfSUiQsNGjdi7N5Et3lrgnbt2Y9qbkyP3oU7BtKlT6NjpJNrDzA/o5bWHXr2v5MMPXHsoVrw4devVJ1euY1fV6dSlK9OnBrc9THtzCp06d2X255+REFIHCS1bMSvDc+EDennnQq8rrmKmVwfFixen3nHqoHOXbkwNcB1Ye7A6SDVtqtceZmXQHo57TfDaQ++r0q4Jxf/1mtCNaW8Gtz1MnzqFDp27MDeD6+KcDOrg43RxwkdenFCgQIG0lZX+/uuvf6yy1DHgccJlHeoxc/4aWjepypylP7Lnz79J3HeAOUt/pM0F1Y45XhXOyO+SJwoWyMeWHXsB6NG+HjPmrGbDVndTs2PP/rTnzJy/hh4d6kfg00RhcA6gqj1VNVZV66dfBlFVn1bVCqraXFX7qOow7/F1qpqgqjVVta6q/qqq81Ofr6p/qGp1VV0Wqc+RlJTEb7/9Srly5dmyeTNlyp6V9rvSZcqyZfPmY56zY/u2tHWcS5QsyY7t2445ZuLrr9K67dHhymrVY1nxzdeZ8An+/5KSkvj9t/WcXa48W7dspnSZsmm/K126DFu3ZFAHO7ZToqSrg+IlSqZtRvTLz+vYm5jIhR1a0bp5Q6ZPmZj2nCrVYlm1Irh18Nv6XylXvjybN2+i7FlHz4MyZcuyefOmY56zfds2SnnnQcmSJdm+zZ0HmzdvomzIeVSmTFk2b3LPr1O3HksWL8rMj/L/8s962Hzs5zjF9pBeteqxfBPg9rA+9Fw4pg4yOBe2pzsXTqAOqsfGsiKgu6lae7A6SPWP9rApg3rYFKb2UD2WFd8EuD14cUI4roszZ7xHvfOr0f2izox74eW0x2vXqZeW5hI0uXLGUL5MUf7YspvSxQqxcdvR0YJN2xMpXazQMc+5c+zbPDKoG+s+eZiRt17I/c+4G5SK5YpT6IzT+OylW1gyeXDaxkQA//t5M3WrR2bRj6gMzqPFrp07KVjw2JPqRInIMesLL1wwjzcmvMqDw0elPRYTE0PuXLnZ5+2yGSS7d+38f20WEVoHKcnJrF61gklvzWDqex/x+KMj+WXdWsDVQa7cudN2Gg2SnTt3UrBQeM+DjBQvXpwtGdzsBEVmtIeMxMTEkDt3MNvDzp07KRShOsgV4DrI7u3B6sDZZe0hLNdFQuqgc9cL+Xr190yZ/i7DH3og7fFiAT4XihYuwN59f//3gSGu696MwY+9S8X29zF47Ds8/0AvAHLG5KBO1bO4cMDzdLlpHHdf247zznaj7EeOKIcPp1DgtDxh/wzpWXAeYHnz5ePQwYOy/xfCAAAgAElEQVQAlCpdmk0bN6T9bvOmjZQqXfqY5xQrXiJty/KtW7ZQNCR147tv13DzDdfx5lvvcaa3e2iqQ0mHyJv3RObHRlbevPk4dOgQACVLlf5HOs7mzZsoWSqDOihWnG1bXR1s27qFokXdCjqlypQhvmVr8ufPz5lnFqXRBU3533dr0p6XdOgQeQJYB/ny5eOgdx6ULl2GjRuOngebNm6kdOkyxzyneIkSaUPTW7ZsoVjx4kefH3Iebdq0kdJl3PMPHjxI3nzH5uYFRWh7KF269LGf4yTbw785dCiY7SFfvnwcPBRyLhxTBxmcC8XTnQsnWAdJQa6DbN4erA6cvKHtoUwG9VAm+ttDOK6LGdXBBU2b89v6X9m1cyfgzoV8eYN5Lhw4mETePC4dafOORMqWOLoLcpnihdi8I/GY5/Tq1JD356wC4J1ZK9MmhG7ansisL3/g74NJ7Er8i8UrfqZmpaPnUe5cOTmYdDgzPw5gwXmgFS5cmJSUFA4ePEjL1m2ZO3sWe/bsYc+ePcydPYuWrdse85wOHTszZZJbxWTKpDfo2KkLABv++INel13CS69MoGLFSv94zq5duzjzzKIZ5tr5rVBIHcS3bMP8ubNJ3LOHxD17mD93NvEt2xzznLYdOjPNS1mZNmUi7Tp2BqBdx84sW/oFycnJ/P3336z4ejkVK1cBYPeuXRQ588xA1kHoedC6TVtmz/487TyYPftzWrc59jzo2KlL2moFkyZOoFPnru7xzl2YMukNVJVlS5dyxhkF04Z4161dS/Xqsce8VlCcUnvo1JnJXnuYPOkNOnbu8p/vE+T2EFoHrdq0ZU5IHcyZPYtWGZ0LnTunrWYzeeIEOkVRHWTX9mB14PyjPbTOoD0c95rgtYdJE6LqmpCQwXUxIYM6aJ8uTujgxQm//PIzqbvGr1q5gqRDhyjideT9vG4tVatXj9CnOjmJ+w4QkyMHeXLnZNYXP9CqcRUKnZ6PQqfno1XjKsz64odjnrNlx16a1a0IQFyDSvz8xw7A5ZU3qVWBmJgc5Mubi/qx5flx/VYAihTMz67E/SQnZ/7O1xacB1xCq9Z8+cViihQpwuC7hxLXtCFxTRty1z33UqRIEQBuvuHatJzxW++4i3lzZ1MrtjLz583h1jvuAmD0yIfZs3sXtw26mQu8ZZJSLVowj7btOkT+w52gFgmtWPblEgoXKcJtg++hbVwT2sY14fa7hlLYq4Nbb76eVSu+AWDArXeyYN4cGtWqxsL5cxlw62AAKlWuSkKrNsQ3rkO7+Cb0urIvVau5L54li+bTqm1w66BVqzZ8scSdB3ffcx9NG9enaeP63DP0/rTz4IbrruGbr915cMfgIcydPYvYqhWZN2c2dwweAkC79h0455xzqV7lPG7qfy1PPfNc2nssWDCPdu07Rv7DnYSEVq35cklIe7igIXEX/LM93NT/aHu47Y67mDdnNrWqV2b+3Dnc5rWHbVu3UrnC2Tz79BOMGTWCyhXO5s8//wS89tA+uOdCy1at086FIffcS7MmDWjWpAF3D73v6Llw/TVpefO33zmEuXNmU6NaJebNncPt3rmwdetWzjvnLJ556glGjxzBeeeclVYHC+fPo12H4NaBtQerg1Sh7eGue+6leZMGNG/SgCEh7eHG/tekXRNuv3MIc2fPpma1SsybM4fb7zzaHiqe69rDo6NGUPHckPYQ8GtCfLo4Ib5pQ+L/JU64zYsTaqeLEz54710a1a1J04Z1uGPQAF6b+GZa2s+iBfNp2y6458LspT/QpHYF9vz5NyNf+pTFkwazeNJgHhn/KXv+dCkvz93fkzrVXM74TQ9PYdRtF7Js2hAeurkLNw93q7f9tH4bs774nq+m382iiXfy+ntf8P0vbpShRf2KfLr4fxH5PJJ6l2TCo07derpgyfKwvd6qlSsY98yTvPRqxmt6h0OvHhczbPjIY3rUT9WBwylheZ1Ua1at5MVxTzHupdfD+rqhru7VnXuHjaBCmOrgjHzh7WFZuWIFzzz1BK9OmPjfB5+iVvHNeevdGWmr+oRDckp4exhWrVzBuKef5KXXMq899OxxMQ+GsT3E5PjvnNaTsXLlCp596kleeT3z6uCy7hfz8IiRVKwUnjo4kbzek5FV20M4ZdU6OHIkvDHHypUrePbpJ3klE68Jl196MQ8ND197SA5zHaxauYLnnnmS8ZkYJ7RvFceUt94L27lQovHAsLxOqlpVyjKgVwL97su8Opg69hruffoDfv5je9he8+Cqcd+oar30j8cMGzYsbG9i4MXx44dd3e/asL1eyVKl2Ju4h9gaNcmRI/wDHUlJSSSnJBMXlxC21wz3hadEyVLs3ZtItdgamVYHKcnJNAtjHeTJFRO21wIoVaoUiYmJ1KiZOefBjh07KFOmLLVqhXevrSNhvvkvWaoUiZndHpKTiYsP37mQI8yBaSmvDmpkdh0khK8Owh2cZ9X2EE5ZtQ7C3R8YsfYQxmtCmL8iM/26uHPHDkqXKcP5YTwXxr7ySdheC2Drzj8pdEY+vl23KeznGLgVYXLmjGHBV2vD+rrJW7/aMmzYsPHpH7ee8zALd895VhTunvOsKNw951lVuHvOs6Jw95xnReEOzk3WFe6e86wo3B1YWVG4e86zquP1nFvOuTHGGGOMMQFhwbkxxhhjjDEBYcG5McYYY4wxAWHBuTHGGGOMMQFhwbkxxhhjjDEBYcG5McYYY4wxAWHBuTHGGGOMMQFhwbkxxhhjjDEBYcG5McYYY4wxAWHBuTHGGGOMMQFhwbkxxhhjjDEBYcG5McYYY4wxAWHBuTHGGGOMMQFhwbkxxhhjjDEBYcG5McYYY4wxAWHBuTHGGGOMMQFhwbkxxhhjjDEBYcG5McYYY4wxAWHBuTHGGGOMMQFhwbkxxhhjjDEBIarqdxmiiojsAH73uRhFgZ0+l8FvVgdWB6msHqwOwOoArA5SWT1YHUAw6qCcqhZL/6AF51FIRL5W1Xp+l8NPVgdWB6msHqwOwOoArA5SWT1YHUCw68DSWowxxhhjjAkIC86NMcYYY4wJCAvOo9N4vwsQAFYHVgeprB6sDsDqAKwOUlk9WB1AgOvAcs6NMcYYY4wJCOs5N8YYY4wxJiAsODfGGGOMMSYgLDg3JhsQEWvrBhERv8tgjDHm39kXtjHHES2BjIhUAR4QkQJ+lyXSRCQm5OfT/SyL30RE1JtkJCK1/C5PEERLGz8V2fmzG5MqfTsISkdWIAphIiv1ZBSRSiJSX0Ty+F2mIEjfSDUKZkt7F5qyQDHgVhE5zeciRYwXmLcSkTgRGQhcJSI5/S6XX0IC837AIyJS0Oci+UZEzgFXJ9ktSA35vDm9f8f8y+FRL/TvH5TALFJCYoGCIlLY7/L4JK3TSkT6AHf5V5SjstWJaBzvC6kL8BbQH/hMROr6XCxfpetV7CUi94nIxSJSwe+ynSoRyaGqR1R1NrAaqA7cnI0CdAHOAMYAA4GPVTU5u30BhxKRC4BLgIGqujc7BWYhgUhF4GMRGQrZK0BPvc6JSAPgBxEpoaop2ek8CJXuun8lMERELheRIj4XLSK8c6Eb8BkuDrgvu3x2ABEpB7wpIvW9h3IBv/hYpDTZ9ksqO/N6jW4E4oAPgTOB9X6WyW8hF+iBwLW4+rgXaOVnuf4/VPUIgIjcBvTABavNgcEikt/PskWCqiYDy4Ek4AugiojkS62X7CBdr2AeoBluJKWrF5ikZJfA1AtEugIjcefFpSIyLOR3UV8P3udsA3T2HpovIiW98yDbxQMh1/1rgetx58XLHK2fqCYilYGbcPFAH6C+93N2cQBYBNwjIjUBBQKR/pjtGqMBYC+wBLgBGAx0VdXdIpKQ3Yb9ReTckJ8LAhWBeOA0YDvwsojkzkq9zekCshJAB6CzqvYAngaKAzdmpc90Krxewd+BBOAToBPQzftdNREp6Wf5Mlu6XsGzgXyqOgoYB5wFdIHsE5iKSCHcDfdTwNXAlUBrEbkboiON7b94wdgruJ7SxrjOmeVeWzmS3QJ0Ecnh9RTXx3VglMLdyE/ytWCZRERKiEgfccoCo3AB6Q+q+j1uhPEqEenha0EzWep5rqrbce1hAfAg0BrXiVPPS/lt732HRly2aojZVchwbh4RyQ3sB84FugP9VPVXEWkOPIMLTrMFEcmHG94e5j30J653eQnuhqWtqqYAVwBZYgKdl8qSGpC1B8oApYGm3iFzgUSgJ3BTtAZlInIzMElExgCXquqbwNdAExGZCryJ+1KKWiHnwSDcF9BUEXkCmAxsAJqJyKWhx0a5FGAn8Ks3evIdLgjr542YZQd/AbNUdTGwU1XvBFYB80SkqBegR+U1IVXo5/PS/nYDPwMvAb2ANt5IwmARifernJnkPNzNRxFV3Qi85z3eTkSKqOpvuHrI51P5Mp3XaZE6qnw57jtyOjAPqIuLi9oBQ3D557n9KKcF59lASI75NI4G4E8AW4HeIvIA8Dxwl6r+4F9JI0dEyqrqAVzv4YUicq8XoMzGpUG86h3XG7gNV1eBF3LRaYHrJVyJy7nuKiIXeKkea4HFwBvRGJSJm9RzOS49qRxwh4gMVtVXcUH5GqCnqm7zr5SR4eWTdgDa4oLRWqq6D/cFvBuoLVG4io/XM5jaKVFaRPJ4n3sp8I6X3pSCu0n5BNeDXs3HImeKkDpIDTD2AnVFpG9I25+Ku2mZKCJ5o/GaECrkpnWAiIzyHt6PS+8c5n1fXoIL1Df5VMxMoapLgC3ACBG5W1XfwMUF3XD59hcBN+PaRVQK+fv3B4YC+1R1M+67YQxuftYUVb1YVeNU1Ze6yFYpDNmVN5R5Gy4ALwG8i2uMqXnnxYD+qroodCg8GnlfVmfilhYcrKprReRC4EMROQS8gBvyv0ncBKFyuJ7XX/0r9X8TkUrANm+SX2dgAnC990XzFS6P7jURmY8bumsfjcGpiNQD9uFSWHrhJoQOBEaLSE5VfQTXc5RdJAKP4dLXagJtvMfPwn0Rna6q+30qW6YJ+QJuBzwArBM36fEe3IjJChF5BXduXIE7V6Kus8pr/22B/iKyDTd83wWXylIK2AFcB9wJXEwU1kFGxM3DuRiXZw5Hvxv7eyOpZwC9VXWtPyUMn9QbtJDv9QPAB0AnERmkqk+KyEFc7nlB4DpVneONwkbd/ByvPkrjRo8v8jIHRFV3iMhMXHwwQkT6Aof8qgMLzqOcuEkOI4D5qjrNe+wv3HBWP1V9LfT4aA7MIe3z7RSRAUB9Eamhqs95Ae2HQJKqPiUik3EX652qusPPMv8b70KTG3dhHQagqjNFZB1wK/CWqn4nIj/gJr6UAkaqatRNABaRG3DB5524a1sr3BfsThHZDDTyhu53+lnOzJL+xjpk+P4VYIWqtvEevwZoD1zpDelHDREphrv5fB8ojJtj0Q/YhuuQmIIbsl6LW5mhPe7GtR4urS2qiEhj3CjpUFxKy3jgUaAOroe0NC7/vjjQAMgL/O1LYTNRaNsQkTNx6R0XAgW8IKw57mZ1D1AId93f7ld5w8UbMTrk/RyPmwy+UVU/FpEDwGUiMlBVnxY336wZkENE8qvqXz4WPTMJrtNiP7DZeywXbsQc4EVcqHDAh7KlseA8+v2KOwnriZsAsklVXxGRXLglhGoBe70h3qgW2hOgqgfFTQC9QkQOquqrItIJeE/c5Kh7cMP+QZdDVQ+JyB24FIUrgVtVtaGIfC0iM1S1q/f3Xen9F3W8tK0bcBNff/d6Bc8AKnl/1yNA32gNzOEfvcU3A+fgPv8Q3ATIPl4Pah1cys/l0fbl692MtMFNAM6JO9fneCOCOVT1UXFLp3VR1cnec+oDTwJXq+offpU9nEQkxsuZLowLRGaq6nve7+rjcmuXqOrd3mPNcCOGF0bbzRocE5j3xXVmlMHNvTiAG0nLDQxS1Ws5GrBlad7f/yNx+xrkAF4HZgAXi0gjVR0pIgpcLSK3q+pjIlIGd8O60LeCZyIRaQrU9DrkEnF1comqJnnpkF1wKY8HfSwmYMF51Em9EIlILBCDyzPtg+s9uws3O3uTqr4gIh9G48X4eELysevjcsg/wl2In/C+vF/2cg0nZJUe1pCbqqLALqAGcJuIPK6q9URkoYjMVdUE/0oZEaWBqV5gnktVt4jIR8AA4Gzgpqzw9zwVIlIaSFTVv0XkJlwP8XW40bF7VfVWETmC6xUrgkvT+tG/EmcOLwCbLG4Vnka44emuIrI8ZIRwFxC6Ss92oJuqZok5Jf9GRMoDqOpv4pZLHIgLxuJSj/GG7mcD+b3nxOB61NsGPXXvVIUE5o2B7qraHnhB3LKaS1V1m1dfN4hIgWhJ81LVPd41cBrwKe4GdK6I1MGldQ5R1VHeObDDe84jIlI4WuogJB7KgbtRrQacL241mn7AGyKyEFiGu6m/OgiBOVhwHlVCTsQuuDzLNbihmheBvt7/HxKRB1R1o7rZ2lFP3AZL16jqDV4vwu24Ye19uKHMW4DHvCHAcSLSXFUP+1jk/yQiTYCzVXWql6JzLTALd7NxEaAi8oSqNheRz8VNgI3mv/fvQDcReUdVf/Ie+wkXjE3ze4gys3g9XUOA70TkVdwqC5fjlgncCNztffk+raqHJUrzSFN5owNdcB0ThXCrMDzkjaT86P1uUOrx6pbazPK8wPxT4BJxE1uvAe5T1ZXilshdhvsOKIVL95oCaTf3K3wpdIR4Iyo1cCk936WmbKjqDO/3t+DqpncUBaU51U3+H4ebU3Y57vwHFxc8AIzxOjIe9p4To6opqrrHl0JngpA0v7Kq+oeITAIOARcAMaraXdycMwVeVNWf/SprehacRwFxG8qIqu4XkdrAHbjVGXrieg7B9Zz3B17DTfqI5kAtvW1ADRGZztGGWQjXuzYcF9iOBgaIyCRV3etbSU9cYWCkiFQHKuDyJysAlXEX43ZAKW+4ss3xXyZqLAGa4NI3luD+vrfg0jeiMjD3bAa+AWI5uuRnZ9yKDF3V7Yh6M5AiIi8SxctHikhx4D7chLbvvVGEErhrXixu74J7VXW+f6UMPy/4rAd8jPv7foFLyTsfWKmql4vIU7hFAcrjVuVa7lNxIyI0lcX7/xoReRQ3AbSOiCxRt2xkDG7U8TKNopXKvHbfDbgfNzl+LW7VqmWq+j8R+RY3kh66rGTUpLamS2UqDSwSkf6q+omIvA3kwa3nnhO3Mkuyn+XNiGh0z/+LeuLypofjgpMZuAkfp+OGr0fjVmS5EZdj9xCwSLPhH93rYRwHVFHVKt5jxXGTKGep6nuSxSbBiEhr3GSv1araS9wOkOfibjYW43rQb9MomNh0Irze0a643tG9uImva/wtVeYJGSnrC1yC+6JdjpscfL+XV9mHoxuNrfOvtJnPy7H9BBisqgvFzasZh8u/nwa84tVX1K1IJW6DpR+Bg7iJ4BVw6TszVXWBd0wuIK+q7ovGOsiIiPTCLR28HbemfUdcL/mDwLKgj5CeKnFzyV7H3XT86D02Gbf62I3Z4bro/TwY+AU30fkO3I3p597v5uE6NoaraqJf5T2ebLFsUjTzenl/xq1Q0A7YoKorcDuePaaqy3Bf2FuAHdnhggxHdwDzfs6tqptwQcufIvISpO0OlgO3xBxksVUKVHUWbhWGDiLSQ1UPeb0/5+FykHtnl8AcQFW3qOoLuJzrq6L5CwjSlsnrhRsduwf3JZQCvAEMEpHncTdql0R7YA4uxxaXxhInIrFe4PUOLqd6cbqe1GjzJ24CbAzumvYmbrJjWxFJAFDVw+rWeo/WOvgHb+RkAG4Flsq4XVE/wy0zOxY32hCtDuE2l2ohIveLyCzctaEQMF1EArFFfWYICcw74OZbfKVuAvgY4HER6eil/h7AxUiBC8zB0lqytNT8UXVL/12P6ykVcWt1bgWeF5G8uKD0+mgatvsvenTyZ1/cMOZu4C3c2raviFvvexIuDeBJ7zlZ7gtLVWeIyBXA0yJSFXdBPgeIipUnToWqJv33UVGjMm5YdpW4tZtvxKVwvIgLQpKD+uWTSabj0vceF7e+/yW4ycBRNwE2lHe9ay9uNZo5uOVzx+FSWdqLyMpoyiXOSOr3YUjPaQ1gYGoKj4jcAzyqqtd4I85RtcFQOhtwOyJfhbsReRu3S/TrwM+pN2nRxBsJL+al7fTBzcX5Wb1VmFR1iogk4TbnO4BbnWeLbwX+D5bWkkWFDGmnTfQTke64XsO3vTSNa3A96DNU9WM/y+sHr1fxbtwwb0/cjPR5uAkxn+PyMntHw6QwL7/wHdxa7bdqlK68YP7J+7v3AYaq6v+8x77CnefDVTXq1u7+L16vYGPcTco3qWkd2YU372gKLuVtJm6jqSy/mc6JErch23rcakULVfVR7/GauICsr5/liyRv1DhJ3AplE4ABqjrH73JlBhGpiLsh3YJboetV3OTvCar6dMhxBXGdFoFOYbXgPAvzhm3G4PKmDuOG8NriJgd+iLtbzuFNDskWOYaQNkEqB/AIsEZVJ3v5qFcDFdWt2lIaQN22vVFBRFoAv6vqb36XxUSGl2t8p/fPubjVWgbhNhiKmnPbnBxxK1S9DTTT6F6lKaOVq27BBeZJuJ7j+9XtY9ELt3zeRbi9PaL++9Cb8FoLeA54RL0VaqKViIzFLSN7l6o+LyLtcZOA56jqM/6W7uRYWksW5V18O+J2eduK6z17Hzcz+0zcxLgF6m3RHu0Xogxm56eIyB+4dY4XquoGL9f8IxE5W6Nkw5FQ2a2H0ICqJorIOFzAMRRIxk0CtsA8G1PVb0SkZjSmL2QgdeWqKrgFEdriNqM6A5gNDPdGE+KBHtkpzUvdZlQ/4iaGrs8GnXQvAKtxe33sVtVpIrIdeE5Edqrqmz6X74RZz3kWEpLKUhg3+et7VW3q3R0r8BLwiaq+Ha0B6H8RkYuBSrjd3/LgbloO4HqRzsMtH9Ul2vMvTfYjIUuq+l0W479sEIil8Vauehy3qdC13spVFwNn4YL38bje8l0+FtNEiIh0xs27uJOjK7VcqarrfS3YSbDVWrKIkMC8Ey6vvAtuu/Z+6jYOOILbcOVsgOwSmHspLKk/98ZtrlAOl39dHFiAW8HgRdyOeQMsMDfRSN3GKhaYGyD6R0tDeStX3YsbKb1MVQ8BU3HzjI4Auy0wzz5UdSZuCdmxuHln/bNSYA7Wc56liNt++GbgNVWdLSJNcTPzx+HW930KNxnwMx+LGTHp1jMtiRu2/EpVfxaRQbjlJUerW/O4MJCSHSfIGWNMdiAiHYGRuPzqqd6SuvmzSXqPScdbwUVVdYffZTlZFpwHmIicCySo6sveMN10IFZVK4Qc0wS38siXQF8vtzpGo2i3r4ykC8wH4SbDHsRtLNHXe3wA0Bu4U1UX+lZYY4wxEeFNAhyP66h62+/yGHMqLK0l2A4B34lIcW+YbgBuE53nUw9Q1S9wC+3XBFp6Dx+JdEEjLSQwbwrUwU0AuhHIIyIPesc8g1tO6TefimmMMSaCVPUT3C6gK/wuizGnynrOAyq199ub7Pkt8IGqDhGRsriA8wdVvSXk+DjcBgO1yAbLRHnDlRWAd4EfgCtwk2Lr4jZd2qKqdx7/FYwxxhhjgsd6zgPIS9lIEZEKuJnm7YA2IjLUW7O2L1BPRJ7zjs+pqvOBKqqaGK2BeejkT3U7o64DbgdK4ZbPOgwsww1pFhaRor4U1BhjjDHmFFnPeUB5SwENx6Vk/ILLK38Ct9vVKBE5Cyil3tbE3nOyxdJZInIVUBvYjlsysRzwMK5+ZgAC5FbVg74V0hhjjDHmFNgmRAEkIo2A+3GrjbTG9QQfwG1DP97rKR8ObAh9XjYJzPvjRg6ex61hOxPoDjwIPAYcVtWPcJNDjTHGGGOyFAvOg2kjbnJjLdxWxOfj1uku7/17t28li7AMVp4ph9uad573+w3AcFXtLiIjgO/8KKcxxhhjTDhYznkAqepGVf0KaAFMVtWfgQlAFeAbVV0Qmn8drby1yat5P3cQkdK4/PJeIYfNAw6JSF5VfVtVf/ehqMYYY4wxYWE958H2LXC9iOQCLgIGpu78mR1SWICyQG8RKQU0UtVK3prms0VkrKreATTG9abnx1JZjDHGGJPFWXAebB8DeYAuwAhVXeJzeSIidWKrqn4rIonAQGAIgKomikgX4H0RmQhUB66wrZmNMcYYEw1stZYswJsAmpxdVmNJ5W0wpEAJoAOwEPhUVbeLSCEgBcipqnt8LKYxxhhjTNhYz3nWkALZJpUFABEphkvlyYubBHsA6A0cEJEqQFHgdlVN9q+UxhhjjDHhZT3nJrBEpD4uQD8NuBs3QbYV0Ai4UVVX+1g8Y4wxxpiws+DcBIqIXA2cp6pDvX/Xwa3OkozLu/9TRAqo6n4/y2mMMcYYkxlsKUXjqwyWhJyPW6HmbgBVXYFbu7w1MNRb99wCc2OMMcZEJcs5N74JneAqIjcDsbjlIzsCH4rIEVUdjcs3Xwg8kW5DImOMMcaYqGJpLcZ3InIj0AOXvrIGtxvqcuBZXE96M6Ctqv7gVxmNMcYYYyLBes6Nr0TkDKAOcBnQHfgKt6lQceBK4G/gLlXd6FshjTHGGGMixHrOje9EJA9QBXhSVeO9PPTdwBPAaFU95GsBjTHGGGMixHrOje9U9ZCI/A3kFJEauJ7zz4HXLTA3xhhjTHZiPecmELze80G4dcxLA91V9Xt/S2WMMcYYE1kWnJvAEJFcQEngiKpu8rs8xhhjjDGRZsG5McYYY4wxAWGbEBljjDHGGBMQFpwbY4wxxhgTEBacG2OMMcYYExAWnBtjjDHGGBMQFpwbY4w5LhGJE5EPvZ+7iMiQfzm2kIjceArvMUxE7jjRx9Md87qIXHIS71VeRL472TIaY0ykWHBujDHZkIjEnOxzVPUDVR31L4cUAk46ODfGGHOUBefGGBNFvJ7hH0Vksoj8ICJviwoe2WgAAAMgSURBVMhp3u9+E5HRIrIC6C4ibUTkSxFZISJviUgB77h23musAC4Kee0+IvKs93MJEXlPRFZ7/zUBRgEVRGSViIzxjrtTRL4SkTUi8mDIaw0VkbUishiofAKf61rvdVaLyDupn8nTSkS+9l6vk3d8jIiMCXnv6/+/dWuMMZFgwbkxxkSfysBzqloV+JN/9mbvUtU6wGzgXqCV9++vgdtEJC/wEtAZqIvbGCwjTwMLVPV8oA7wP2AI8Iuq1lLVO0WkDVARaADUAuqKSHMRqQtc5j3WAah/Ap/pXVWt773fD0C/kN+V996jI/CC9xn6AXtVtb73+teKyDkn8D7GGOOrnH4XwBhjTNhtUNUl3s+TgIHAWO/f07z/NwKqAUtEBCA38CVQBVivqusARGQScF0G75EAXAmgqinAXhEpnO6YNt5/K71/F8AF66cD76nq3957fHACnylWRIbjUmcKAJ+F/G66qh4B1onIr95naAPUDMlHL+i999oTeC9jjPGNBefGGBN90m/9HPrvv7z/CzBLVS8PPVBEaoWxHAKMVNUX073HoFN4rdeBbqq6WkT6AHEhv8vo8wowQFVDg3hEpPwpvLcxxkSMpbUYY0z0OVtEGns/9wQWZ3DMUuACETkPQETyi0gl4EegvIhU8I67PIPnAswBbvCeGyMiBYF9uF7xVJ8BfUNy2cuISHFgIdBNRPKJyOm4FJr/cjqwRURyAb3S/a67iOTwynwu8JP33jd4xyMilUQk/wm8jzHG+MqCc2OMiT4/ATeJyA9AYeD59Aeo6g6gD/CmiKzBS2lR1YO4NJaPvAmh24/zHrcA8SLyLfANUE1Vd+HSZL4TkTGq+jkwBfjSO+5t4HRVXYFLr1kNfAJ8dQKf6T5gGbAEdwMR6g9gufda/b3P8DLwPbDCWzrxRWy02BiTBYhq+tFAY4wxWZWXtvGhqsb6XBRjjDGnwHrOjTHGGGOMCQjrOTfGGGOMMSYgrOfcGGOMMcaYgLDg3BhjjDHGmICw4NwYY4wxxpiAsODcGGOMMcaYgLDg3BhjjDHGmICw4NwYY4wxxpiA+D+rgTxoh/CccgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_C5t2IPAmS1-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}