ConvolutionalNetworksModifiedDropoutv3_10Epochs.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ConvolutionalNetworksModifiedDropoutv3_10Epochs.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/preritt/CS231n-/blob/master/ConvolutionalNetworksModifiedDropoutv3_10Epochs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "sYiV4RkdLECs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f9ea8eca-497e-423d-a55c-28af0a6b1295"
      },
      "cell_type": "code",
      "source": [
        "%cd .."
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xoaDYXkvLITX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3ce60489-1cb9-4451-c293-983cb9fcd1db"
      },
      "cell_type": "code",
      "source": [
        "ls"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34massignment2\u001b[0m/  assignment2ModifiedV2.zip  \u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "14rvTP-1f197",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1e8dd39c-980a-4448-9384-a799972fd36a"
      },
      "cell_type": "code",
      "source": [
        "!rm assignment2ModifiedV2.zip"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'assignment2ModifiedV2.zip': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zyDcCBTeK-fk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!rm -rf assignment2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QQagLPf1f_uS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e2b5c7dc-b395-4cc6-a110-787ecc39cb92"
      },
      "cell_type": "code",
      "source": [
        "pwd"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "u'/content'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "metadata": {
        "id": "epO1jU1WgEDp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fa0377a3-b1d0-45f8-d6ea-deb9bc550ddb"
      },
      "cell_type": "code",
      "source": [
        "ls"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "assignment2assignment2ModifiedDropoutv3.zip  \u001b[0m\u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WvpGkXRnFDHc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "with zipfile.ZipFile(\"assignment2assignment2ModifiedDropoutv3.zip\",\"r\") as zip_ref:\n",
        "    zip_ref.extractall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_DUyji4bGIEW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9bd7205d-bc91-48ca-b354-43d055c45286"
      },
      "cell_type": "code",
      "source": [
        "%cd assignment2"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/assignment2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "kzD3TNRRHybd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c3aaf915-5146-4178-9c19-a8e543c2e1b4"
      },
      "cell_type": "code",
      "source": [
        "%cd cs231n"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/assignment2/cs231n\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "oyEIyCZVGMri",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "54d9758d-2344-4715-b108-a17f63d73c6e"
      },
      "cell_type": "code",
      "source": [
        "pwd"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "u'/content/assignment2/cs231n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "metadata": {
        "id": "O9cf0V5PGOPQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "1b64fdb3-fa5a-4a7c-d704-c9eb6dcbeb91"
      },
      "cell_type": "code",
      "source": [
        "!python setup.py build_ext --inplace"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Compiling im2col_cython.pyx because it changed.\n",
            "[1/1] Cythonizing im2col_cython.pyx\n",
            "/usr/local/lib/python2.7/dist-packages/Cython/Compiler/Main.py:367: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /content/assignment2/cs231n/im2col_cython.pyx\n",
            "  tree = Parsing.p_module(s, pxd, full_module_name)\n",
            "running build_ext\n",
            "building 'im2col_cython' extension\n",
            "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -fno-strict-aliasing -Wdate-time -D_FORTIFY_SOURCE=2 -g -fdebug-prefix-map=/build/python2.7-3hk45v/python2.7-2.7.15~rc1=. -fstack-protector-strong -Wformat -Werror=format-security -fPIC -I/usr/local/lib/python2.7/dist-packages/numpy/core/include -I/usr/include/python2.7 -c im2col_cython.c -o build/temp.linux-x86_64-2.7/im2col_cython.o\n",
            "In file included from \u001b[01m\u001b[K/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1816:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h:18\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kim2col_cython.c:612\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K#warning \"Using deprecated NumPy API, disable it by \" \"#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [\u001b[01;35m\u001b[K-Wcpp\u001b[m\u001b[K]\n",
            " #\u001b[01;35m\u001b[Kwarning\u001b[m\u001b[K \"Using deprecated NumPy API, disable it by \" \\\n",
            "  \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
            "x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -Wdate-time -D_FORTIFY_SOURCE=2 -g -fdebug-prefix-map=/build/python2.7-3hk45v/python2.7-2.7.15~rc1=. -fstack-protector-strong -Wformat -Werror=format-security -Wl,-Bsymbolic-functions -Wl,-z,relro -Wdate-time -D_FORTIFY_SOURCE=2 -g -fdebug-prefix-map=/build/python2.7-3hk45v/python2.7-2.7.15~rc1=. -fstack-protector-strong -Wformat -Werror=format-security build/temp.linux-x86_64-2.7/im2col_cython.o -o /content/assignment2/cs231n/im2col_cython.so\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3qPo7KaUH7tS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b754650d-4490-4faa-8e91-f664dcfce5cf"
      },
      "cell_type": "code",
      "source": [
        "%cd .."
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/assignment2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Ag0449_siA8T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2d4cb9aa-a240-4727-9f5b-8b342cf1893a"
      },
      "cell_type": "code",
      "source": [
        "pwd"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "u'/content/assignment2'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "metadata": {
        "id": "3W_zUjy4ICTM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "03e062e8-5238-4d46-d26e-6ce56ee6acb5"
      },
      "cell_type": "code",
      "source": [
        "# As usual, a bit of setup\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from cs231n.classifiers.cnn import *\n",
        "from cs231n.data_utils import get_CIFAR10_data\n",
        "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
        "from cs231n.layers import *\n",
        "from cs231n.fast_layers import *\n",
        "from cs231n.solver import Solver\n",
        "\n",
        "%matplotlib inline\n",
        "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
        "plt.rcParams['image.interpolation'] = 'nearest'\n",
        "plt.rcParams['image.cmap'] = 'gray'\n",
        "\n",
        "# for auto-reloading external modules\n",
        "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
        "%load_ext autoreload\n",
        "%autoreload 2\n",
        "\n",
        "def rel_error(x, y):\n",
        "  \"\"\" returns relative error \"\"\"\n",
        "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The autoreload extension is already loaded. To reload it, use:\n",
            "  %reload_ext autoreload\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "cvPZDQC8Jagz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "1067b13b-fd17-4e73-98bc-5880903ecbeb"
      },
      "cell_type": "code",
      "source": [
        "# Load the (preprocessed) CIFAR10 data.\n",
        "\n",
        "data = get_CIFAR10_data()\n",
        "for k, v in data.iteritems():\n",
        "  print '%s: ' % k, v.shape"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_val:  (1000, 3, 32, 32)\n",
            "X_train:  (49000, 3, 32, 32)\n",
            "X_test:  (1000, 3, 32, 32)\n",
            "y_val:  (1000,)\n",
            "y_train:  (49000,)\n",
            "y_test:  (1000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "S9MX7dQqM8yD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "74488cc6-f945-483b-eb07-3f2ecbf9be4b"
      },
      "cell_type": "code",
      "source": [
        "x_shape = (2, 3, 4, 4)\n",
        "w_shape = (3, 3, 4, 4)\n",
        "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
        "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
        "b = np.linspace(-0.1, 0.2, num=3)\n",
        "\n",
        "conv_param = {'stride': 2, 'pad': 1}\n",
        "out, _ = conv_forward_naive(x, w, b, conv_param)\n",
        "correct_out = np.array([[[[[-0.08759809, -0.10987781],\n",
        "                           [-0.18387192, -0.2109216 ]],\n",
        "                          [[ 0.21027089,  0.21661097],\n",
        "                           [ 0.22847626,  0.23004637]],\n",
        "                          [[ 0.50813986,  0.54309974],\n",
        "                           [ 0.64082444,  0.67101435]]],\n",
        "                         [[[-0.98053589, -1.03143541],\n",
        "                           [-1.19128892, -1.24695841]],\n",
        "                          [[ 0.69108355,  0.66880383],\n",
        "                           [ 0.59480972,  0.56776003]],\n",
        "                          [[ 2.36270298,  2.36904306],\n",
        "                           [ 2.38090835,  2.38247847]]]]])\n",
        "\n",
        "# Compare your output to ours; difference should be around 1e-8\n",
        "print 'Testing conv_forward_naive'\n",
        "print 'difference: ', rel_error(out, correct_out)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing conv_forward_naive\n",
            "difference:  2.2121476575931688e-08\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3yzl2o1aJn_x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "871f0f23-9466-4c77-c72d-e4f0d7e0fa62"
      },
      "cell_type": "code",
      "source": [
        "from scipy.misc import imread, imresize\n",
        "\n",
        "kitten, puppy = imread('kitten.jpg'), imread('puppy.jpg')\n",
        "# kitten is wide, and puppy is already square\n",
        "d = kitten.shape[1] - kitten.shape[0]\n",
        "kitten_cropped = kitten[:, d/2:-d/2, :]\n",
        "\n",
        "img_size = 200   # Make this smaller if it runs too slow\n",
        "x = np.zeros((2, 3, img_size, img_size))\n",
        "x[0, :, :, :] = imresize(puppy, (img_size, img_size)).transpose((2, 0, 1))\n",
        "x[1, :, :, :] = imresize(kitten_cropped, (img_size, img_size)).transpose((2, 0, 1))\n",
        "\n",
        "# Set up a convolutional weights holding 2 filters, each 3x3\n",
        "w = np.zeros((2, 3, 3, 3))\n",
        "\n",
        "# The first filter converts the image to grayscale.\n",
        "# Set up the red, green, and blue channels of the filter.\n",
        "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
        "w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n",
        "w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n",
        "\n",
        "# Second filter detects horizontal edges in the blue channel.\n",
        "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n",
        "\n",
        "# Vector of biases. We don't need any bias for the grayscale\n",
        "# filter, but for the edge detection filter we want to add 128\n",
        "# to each output so that nothing is negative.\n",
        "b = np.array([0, 128])\n",
        "\n",
        "# Compute the result of convolving each input in x with each filter in w,\n",
        "# offsetting by b, and storing the results in out.\n",
        "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n",
        "\n",
        "def imshow_noax(img, normalize=True):\n",
        "    \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n",
        "    if normalize:\n",
        "        img_max, img_min = np.max(img), np.min(img)\n",
        "        img = 255.0 * (img - img_min) / (img_max - img_min)\n",
        "    plt.imshow(img.astype('uint8'))\n",
        "    plt.gca().axis('off')\n",
        "\n",
        "# Show the original images and the results of the conv operation\n",
        "plt.subplot(2, 3, 1)\n",
        "imshow_noax(puppy, normalize=False)\n",
        "plt.title('Original image')\n",
        "plt.subplot(2, 3, 2)\n",
        "imshow_noax(out[0, 0])\n",
        "plt.title('Grayscale')\n",
        "plt.subplot(2, 3, 3)\n",
        "imshow_noax(out[0, 1])\n",
        "plt.title('Edges')\n",
        "plt.subplot(2, 3, 4)\n",
        "imshow_noax(kitten_cropped, normalize=False)\n",
        "plt.subplot(2, 3, 5)\n",
        "imshow_noax(out[1, 0])\n",
        "plt.subplot(2, 3, 6)\n",
        "imshow_noax(out[1, 1])\n",
        "plt.show()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!\n",
            "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
            "Use ``imageio.imread`` instead.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: `imresize` is deprecated!\n",
            "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
            "Use ``skimage.transform.resize`` instead.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:11: DeprecationWarning: `imresize` is deprecated!\n",
            "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
            "Use ``skimage.transform.resize`` instead.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl0AAAG/CAYAAABi5mI/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzsvXuwZEle3/fJPKeq7rOf0z2Pnemd\n4XWADRYWZMk4JCwFkoUDhMMOQFgC8dAa2RJCDrDBRmwEFsIQAkm2hS1AEnoRrB3IIR4KZEcIa4XA\nSAQPyRKCAwss+97Zne6e6e57b1WdzPQfmb9Tv8rKU1W3Z6ZvL1u/jttVdR558p3f3/f3y98xIQR2\nspOd7GQnO9nJTnby+oq96AzsZCc72clOdrKTnXwsyA507WQnO9nJTnayk508AtmBrp3sZCc72clO\ndrKTRyA70LWTnexkJzvZyU528ghkB7p2spOd7GQnO9nJTh6B7EDXTnayk53sZCc72ckjkPqiM/DR\nJE3TGODrgLcCIyJo/afAt7Rt++GBe34S+G/btv3FNel+B/Dbbdt+70Pm61uBZ9u2fWt2/GuBJ9u2\nfdvDpLuTnbweksbRnwX+JDAmjqVfA97Wtu0vXGTecmma5vcDf7Nt20+46Lzs5GNbmqYJwG8AXXbq\nT7Rt+3PZtb8X+MG2bZ9/RNnbyZayA13nk28H/iDwH7dt+96maep07B1N0/yutm1P8xvatv3cTYm2\nbfvfv/ZZhbZtv+f1SHcnO3mV8u3AHwA+r23bDzRNUxEVmX/SNM0nDSkwO9nJTvj9bdu+96IzsZOH\nlx3o2lKaprkG/NfAZ0inb9u2A76paZrPBb4c+P6mad4F/ADwx4E/BPwU8GVt2/500zTfnNL4beBv\nA9/Ytu3zTdP8HeCdbdv+xXT/dxBZgOeAH2rb9htSHt4KfAOx3T4AfHnbtr+9Js/fSmLAmqZ5B/B/\nAf8J8AnAtwJXgS8DPPD5bdv+VtM0DfC3gOtEBuJtbdu+PaX3lcB3Ah8C/irwt9u2NYm5eFsq8x7w\nI8DXt23rzlvPO/mdLWocfXrbth8ASP3k+5qm+aG2be+lvvozwH9GHAe/Afxd4HlgAvy1tm3/StM0\n3wXst237tSntq8D7gFvAlwJ/BjDAK8BXtW37y03TfBbw/cAxcQx9Zer3nw18D3BIHA9f17btP8ny\nPgG+C/g8IkP3/W3b/o+vQzXtZCfnlqZpvgX4U8BHgB9Tx68B/4A47/9L4GXgvW3bfmvTNJ8K/HXg\naWBKHCc/3zTNEfD3gU8mjrmfBP5027bzR1ik35Gy8+naXv594N1t2/5a4dyPA/+h+v1s27ZN27bv\nlgNN07wJ+Ebg04HfB3zJmmd9DvDZwGcBf7ZpmmebprlJXBT+UNu2nwi8kwh0ziOfk579VcBfIg68\nTwb+HfDV6ZrvBv5R27afko79raZpRmng/m9Epu8twB9W6X5ZKs/vBj4+/f1X58zbTj42RMbRr+cn\n2ra9p35+FvCmtm3/X+BbgN9KffVzge9omuY54O3AFyfGGeALiErOFPg24Hene74L+Px0zf9OdAf4\nJOAfEscURCD2Xen67wRKpv5vBD4V+DTgTcAXNU3zBQ9RBzvZyWsqCTx9PfC70t+b1elvBj7ctu0t\nYt/+z9M9lqgg/700Hv5L4EfTePoK4G5aBz6JaNJ80yMqzu9o2TFd28s1YMjs8SHiYiLyjwrXfA7w\nDtHum6b5AeAvDKT3Q0n7f3/TNB8Cnmvb9mebprnUtu0sXfPPiezaeeTH27btmqb5N8ABUfsB+DdE\ndgAiE2bS958mMldPEwfcr7Vt+29T/v86kU0A+CPAD7Rt+3I69zeJvm878+ZOcrmKGkdN01wB/kX6\neQT8L+n7T7Rt69P3rwMqgLZtf7Npmg8CL7Rt+1NN09wlArH/G/hPgf8DOAMC8Cebpnl727Y/nJ71\nScATbdv+45Tu97AAV5+R7oE4tj6ukPc/Anxn27ZTYNo0zd8jsnGl8b6Tnbwe8o6mabRP14fbtv19\nxPXln7Vt+yGApml+kKgcQ1S0vwOgbdtfaJrmX6bjnwzcJFpmaNv2Z5qm+TDwHwAvAp/dNM1/lNLd\nKdGvkexA1/byEeCZgXNPEjupyO3CNVez4+9b86yX1XcHVMnv5S80TfOFxAXomOh8fB4RJsEBtG17\nXz8jff/DwLc0TXODaGYxREZ0Xf6vAP9N0zRfk37XDAPUnXxsy4dR46ht27vEyV/A+kE6pfvav0dk\nt24R++rTLFj6twN/rGmanwJ+P/DVbdvOk8n/m4H/oWma/w/408Qx04+t5B4gC9gfB76uaZpj4lgQ\nxUPLFeCvNk0jJsUJ8HOF63ayk9dLhny6rrG8btxR34fm7ivE8fYr0asEgEvA9bZtfzhZN74N+OQE\n4r4+KRw7eRWyA13by88C15qm+fS2bf91du4LgL+24f5XiJq8yNPnfP4fBb4Q+Jy2bT/SNM1/QVwo\nXjNpmmYE/DDwJW3b/kTyYZHNAevy/37gx3aO+zvZQn4WuNk0zVvatv2lLe/5QaIP4fe2bRuaptGA\n/+1EP5V/DPxMAnGktL+4aZox0Sz4vUSz+rWmaWzbtj719zcAc+BvAL+nbdt/1TTNJ1JWaN4PfHfb\ntjtmayePm9wBLqvfN9T30tz9G8T+/Eoyqa9I27bfR/S1fAPwfwJ/gjhOdvIqZOfTtaUk09m3A3+/\naZoXAJqmqVO4h4roK7JOfg74A03TPJHAzFecMws3gXclwHWd6EN1tOGe88ph+vv59PvPAbP0nF8A\n3tw0zSckXwAdnuJHgS9vmuYAoGmaP9U0zXnLt5OPAUl+W99GHEefANG3pGmaLyX26XcWbrsJ/EIC\nXF9B7KNHKb2WuIB8J9G0SNM0n9Y0zQ83TTNO5vifJ5oOfx14L9EkCNFJ//uJC9QD4FeTP8vXpHTy\n8fWjwFubpqmapjFN03xL0zSf9+prZSc7edXys8DvbZrmRrKKfJk693PAFwM0TfMZRN9biBu63ts0\nzRelc080TfP2pmkOm6Z5W9M0Xw3Qtu37gN9iYX7fyauQHeg6h7Rt+93ESfrHm6b5VaID+jXgDypf\nq6F7f464A+uXgP+H6Hx/nk78duB60zTvTN+/BXiuaZq/fO6CDOfxLtHB/peapvkl4mL2I0SflVeI\n5pp/SmQW/rm69UeI5fnFVC9fSPSx2clOVqRt278E/E/AP0j95TeJLNQXtW37g4Vb3gb8w2QmPAK+\nD/gbTdN8fDr/dqKJ/0fT739LXCR+uWmaXybu1P1zbdsG4uLz55um+XXgjxE3fPxr4CeI7NbPEvvy\nvwD+WZaP/5W4UP0y8KvApxD9Hneyk0cl72ia5lezv69t2/ZfEdncXyQqyLpffjvQpLXjG4jjJKTx\n8KXA16Zx+FPAT7Zt+4C4c/HLm6Zp07lZOraTVykmhB14fVTSNI1JHZ2maT4f+Itt275lw22PjWT5\nfxPw023bXr3gbO3kY1yapvkSImBbtyN4Jzv5mJVs7v5h4tz9P19wtj4mZcd0PSJJjukfaZrmjU2M\na/UlRK36o0KS2eV9TdP8nnToj/JRlP+d/M6UZNL+Jha7Hneyk50oaeKbSX4smfFvEjec7ObuC5Id\n6HpEkqJs/3likLlfI5olv/Ui83QeSTu9/gzwd5um+TViXLKvu9hc7eRjWVKMrF8lhkLZmfl2spOy\n/B1i7LpfJwYd/sv5a4N28uhkZ17cyU52spOd7GQnO3kEsmO6drKTnexkJzvZyU4egVxonK5PaK4F\nTMCaCmv2qOwBwU4xxmLrPYytsWZEVVVYG/GhMQbnHNZ4vI9/8/kc5xzexuJYW1PXNaPRhPF4TDAe\ngiUEgzGGECCEGBPROYcx8Xg1qjHG9M8JIUCIrw+UZxljGGH76wBCCHjv+8+u65jP51jv8GEOeKy1\n1NUEYyqoLFVV4W1Mw6awpDYQy+kDlpiWc67/dM4xn3lscPhwRsccb2OeqqqisiMARqMRVdjD1BXB\nAKnuxqFKZQvEuKfE/CSRMmn2U+rdVwbvYzn0eWNMX/Z4s6QXCDhCqj/rYnl0nTnr+zRCCDENXy0d\n894zquJ1VVX195byaEI8p/M0J/UTFzCmIgRDCHOivmFxXWq/bspXfeVb+eb/7m2loJiPTF544YUA\nUNexL1dV1bfLeDzuyyrHrbX9mAghLH1Kn5X08j+pw76vs+jL+TP0dXk7yjkt0kbSDtPplK6LY07u\nt9ZS1zXW2qU/Oaf7o35m13V9Gbuu6//kWhkPun7quu7nEZ12nu+SSJ5E8jliKA1dp1qkLLqO9G+d\ndv5cuV7KkI9Z+d2PW1XX0ha6j+TPlXvk/Fvf+la+6Zu+6ULHxNd8zdeEruv6/qzbej6f98fzckj9\n5GNG7pU5VY+VUhpahtp0Ux/I+5wxJs7TWZ+U8kl+pI3yZ+rnWWuX+rv8yTjXaUua0o/m8/lSmdeV\n/zxWsaFrdf3L2NF1o+tnSPK+rY+Vxo2+rpRWPsdJ/krj/u7du5ycnPCOd7zjocfEhYKuemSAgLUG\ng8MwxxqLqSy2MlhTYSoD+EV8aGMwNuDdAnRZa2NHwiVw5TBm1HesurZgLN08pRPACuCp676RjVks\nYPG6gElkYGUs1sZOEowhsACAIV5ACDFctrPQmUBwHh8cxsQlPlhLZS3GVtiqBhNjRhhBXQGCD+AD\ncxzeG5w3/afzhnmIoMv5Dm88nsAI3WErCNWiTNYwd46qqnAhPiSCrlivRkWtsKnDedVp5908lT92\nFb2QwzLostZCf2+I/9Jvbyze6EEtge6hbxQCxiwWgjgxWcG9CSwvBquI92rBSX/BGJz3MQ+eBLY8\nIRggfvfeM5t2VFWNT5PvRYsAy3zA56LBpfzOJ84cHOVASE9OfTupRVokX7jXibW2XxTz/OZgcDwe\n03UddV0XJ1mdpxwU6sUyL5teZPX8IGOiNFnD6sIpvzeVuVQH6+4pAdy8zUppaECq05Hvy2Ni+V79\nnBxglPKgr7lo2dvbA1bBbglg6eu2ARCy6ObjJQezeVpD15TOl87pPJfyrZX4bUUDB63A6PyXylHK\nV0nWgdFtlJfS9bnCN1SX+b05UBsqWynPpbyXnidjSoOyp5566tzzQS4XCrr2JhWYSMRYYwjeR8Bl\nDLYyeOMiCDOGuJ4HjEmLr4kLqfeGrnME67FVBXgqW2HpMMGDC1CPicDNg6mQBX4hETDV1mIUKIuL\n/HLDGmPwCgBaUyXAET+NtRG0GIOpR7jZHO87KsASCFTU1AQ7AmsjXDKGHgdZMARccHjjccZBBT50\n+OBw5owQHF1whMpHegcLwabP9KRgiBk1jMd7afGpYz0QYl1QYY3qAkFqQ4G49N11IQI357C2Wjpv\nEmjybsGgxWeoQZB1/nh8FXQt7l9oozak64IV5IUxq6DESxthcB4CluB9D7jAErzBWJsAWFyQz85O\nqS2PBeg6Pj4GlrVyWLBC8r0kGox0XdcDOD1x6M98silpfDofcl6zJDqPcu1oNOqPaZCk8ymfOeiR\nMup8aZYmX6jy5+h08zJXVdXnUS9w22jVeT2vu0/KGsdKmSUr9TVh6PJn63LldZKDs6H8wmJhyhlH\n+S59R7fZfD4vlvFRiuRX2k6zekNjQffvknKR9/O87vPrdbolGWIlh87nxySfOfMiioN+9ragSSwz\nco+kMzTPrQP6uZTSyOs0z5MeM0PgaBPYApaUgqHxlecrZ3JLLHH+7JxNfq3kQkHXwV4VGS0bF12D\nISTQYWuHD2BNnASqqlpCs6Y2/QRibUdVeXwVj0XGxkdq1cywJlbmeGQTgRWWBmQ/qRMX86qOJjjv\nPdZlWmUCfKJJLBrMRVbFOOoqMBlbZmdzbBWwyATgsCZgTcAET2UtwRroEnMGeBYTSq6Zd10XQSGe\nikDnu8TspIESTGTNwoiqqrF1DTayPnVdYZwhhMgU9oCnAF4qvRCmydeaCEIFcGmGS2jIlc5pDNbK\npBHBrrVipjT4sDAT9O2KDNxFMlY9q7QA9YMnpaUXFe8lbUNIt3Qzj/eJKQkdAcds5l61BvNayGQy\nWQFGIjlgyEXqRwCXnhhlQRFzhownGDZlwfKkqxkjMU/qNDZNUDn4K52T55RYllxTFVatBIBywJn/\n5WUrPSf/ro+tA+jbTtIaVJUWgIfpj0OLph43JQa067olwKU/HxcREDvUh7QMsSf5ApszTSVQkPdz\n/VmSdX1b0tPrT/671E/XgbbS+N2WPcqB0jZs9pAMuZ/IuVzycabroSRyPFcW8rKU7iuN6VKb5Nfk\n7b4NU7hJLhR0HR5NsJUTl6PEXnQEC5UFj8NYQ3C9WxIgE5I0gMH7CucAY3sQFXwEc8ZEIARx4Ta9\nycuqzh0ZLVsL4xIXfe8DuIVZRKS2Jpka9QSW/CBwGD+nGnnG3jJLzJwxgcoGKjwGx8hYQugw3jAT\ns0FEDWCh9g6PxxqPI/mwGU9lLN5ZnIHKVpjaYKiwxmKTD5y1dWK1bKIEI+NFVaXMCNMV0F2gHyB6\nwjEGW1cYn01CPiyxZM6n+jHlARvEJCI1bAzGKf+t9M+EgiZkZMEAsCsTg/QFZ0VjX2j2+HQ+LHwa\nQvoeQdc8gjS3WYN8FDKZTJYWlNxkIMfySUImA+99D6jyCUnGhk63NBmWnq/P6QW8xDrkgLjEIkj+\nS+zCUJ70OSmP3K+13tx/JmcPtOQauBzbJBq0rsu3fsaQlBa6ofrMpXQu1+q1D1S+COcAS38+LqAr\nV44F/MNyW8u1ueRAUyQfW7mCU1rQ80VYjpWYV/18kRLzpPuHAPCSn1Oefs765c/OgYxck7OpOQGR\n+86W5hqdRg6GhsbPpvzK8RJDmdejlnXtuI5Zy4F1ns9NIPBh5UJB13g8ph4F6lHAhy4aqkK03y+A\nmCPU46XCh6CtVVUCXgZnieYjk+x08WpglU6v7DiltaCbg19eaIINdOmakVobjK3wxhGwESgGyygk\n50frGVlwLjD1pxgTcA6sqakqQzeag+nwdQQtxlSMhYIxEBIrZ/FYk0Acni50jOqA8Q5GHd4FrPFU\nxkS/LOOojAfrsbXB2Wla1EaYEMGXNZHNsz27JeBx4STvvV/QTMHiQ6p3swCdxkgjLLSYfkHrB2rc\n0BB8wISAt6vmolGok59XZPiMNbF8SiKQDXTeYXxkDT0OKpdMqhEwh2Bw3YItE2DYpUksAu4qQWrJ\niyH4aJIOYdUUdBEyHo+XNo5o0UzXEOiS7/miWdK+Yb2mODR5Srp6sitdq1mUyWSSWOll0CxlKpU5\n10jlmTlDpDcFSJr6cwjc5VICmlLX+cKp2SnJWy76mcvz1/IipeukVI96EZT7ciBRWly039bQ71Le\nNfjalrV7vUXyJCyjnrdzs2PpPvkOq32/xCTpe7ZhPtcdk/6S5ytnuKRNtVUnBwCbnq2ZstwkL9fr\ne0r+gTkLV3p2Xt8yNktzwToAo/vjJrCkx8Em5kqXdUh5KAG7vN5KdfNq5bEBXZgagu2ZjqVJyJDY\nqMUAWfa1ise65ChtjMGaujctueBWK5eFY23sMKEHcsYsJqeqtoXG8QRjSW7agMOG6LcVQsC5mL+q\nqnFd/E2QXVRSPk9VhQQhCkh6njS5KrJ6toKqNuDA+YpRGOFDZPIMkeUyiR2srMNUJtWRbB6wBDzG\nKnMk4IOPZt3+X9zpGdkh6XgDk4ZfdMz+fFiA4kWdLUx7qYKR3aSC3YJc17erDHzwLkQwzSIvERum\nHYleymIUGBdzKBgqMD62AzXezfHe4r3D+4B3JN+vi9fqZUeT9ouC5Ymm5DuRAwM5VprUSguRpDuk\njep2XscclPyOqqrqTZ65WV5rp8Ys/MFKWqjke8iJVteP/A0BQinLkJkqZx/yeoVVv5acYRhiO4by\nkadVWux0u+VmQnl2nk7+bG0G0nWrF3/9edGSsx9522vWS67XMgRoc0fz0jV5W24LunLJn5Wnrft2\nnh/9V5JSX9BpigiY0+kN+Rbq7+uYq7wcQ8Anz698DjFR656t7xkCePr+85hLS6BwE+g+r1wo6Kqq\nirqGqvJUdY13UNuFs3A/CCq/pHGGEOJC2ktcjCfIZB7BhndpkkpU8XLld0sdPE5Cyzb0+Dy3MoGG\nEEFU3MUYd+VVYZFHofNrE538nRPAAHacnmciA2eMWQJdmqJdlHkB5HxncM7igwXGWBvNbtYkgIjF\n1BGUWGuTf9vCbBqCX6aG/QLQgscYj60i6PLeLdg/DdS8hxAd53vto69bPZEsytHzjiH67sUH2sju\nBQGvqwupnjhi28fFLhifzKghATzT75aU9gSIxJnrTZNiXoyg0fTA73FZYCaTCVVVMR6P+2Mlp189\noZQmoZKmDBQnZ/le8scoAbZ1WvCQr5Nm3vJwFrlj/9CkqvuD/tNjTouetNf5w8lzhsx5+rjuj/n1\nuSY/5HtS0sx1H9+0SOhy5wBQylECZHnedJ/wfhE+IE/7oiVnR4dEg8ltFtr8Gh1CY6jf5OND0lm3\nGOv7S8pO6VnrnNJL/U6zY5K+bmP9vNJmjaFnbSvrAGF+/mGfkysmm8yNpbV0mzyvE82yPqxcKOiK\nk62YDEzcqdgv1FDXyTyQcul9UJpwiVaVBq4SkxJ68CA+XH3HNJo+TuCrb8Oy1iC/o4nL44JJuyhj\nXC2RUZ3MdSOD9yOcC4jVzNl5amyfAI1lZFap1xBWFznvPdbXOGdY6W8JrJoKMC4BnsjoxZhkFvE7\nW+psYdEFFo7SAvJCAq7094YQerYuhtSKZlWbdip6qrgLVT+i/y/CKmtsz6r5EHDBx42WAYJTbI4h\nAj9mCajSt12MuTUnhBqf+ox3i0mmj+UUHMZYfPALdo2OzsVdpc510bS9RqN71KLZHz1p5eY8few8\nMqQZa2VjnQZaul+k5OsEq4u8ZijOK8sgfGHCzCfhofAbGtyV8po74cvmAQ26JO85S6VB0FBdlET7\nKw0xEOukBODke4np0p96gdZg9nEaD1BmanJleB3gKgGAnBnRx87TDtua5h8mP3maQ4y2/l5SjHSZ\ncnP7pvyWQOKm69aVdei6IaUIVhXGoXtypWFonOf1omUbf8qHlQsFXQKu4p8wMk4OYPqOLOaCxb3W\nFPwlggAaG4EXhmAjEIDlDmmrkj9EqcHdSqeuvMWRmJLEVC13CKioMGEGWLwzPegKJB8jBzYFY+1S\nvpcWOxZ+KgvHcLBeGLlojgVwPt1vqug7HwKkeFzWRuAkJlNd3ig63w7rHNZU0XxHlRYzOS/1J/dI\nx1zU6zwsdgEuaVFeMyrp+ioyYBWLMBzWSVuIn5vHhAQWfcDYuA/SJSBtrQMTmU+LW2onyasxIQI4\n5xJ758HM4+5JM09oe7PPz6OQnM3Sx/WxkjZcmljWMTubjq2TfEzoY6WFLwcmOegqmSWH2I2SRi+h\nJvL0hkymAjK1Nlx6jj6nJ+KhSVnuqet6Jfjouvt0+iGEFUfuUp50PeebK4YWpbyPlFitx0kBgdU2\nk+85cB5ayNfVud6IUXqeyBAQEtlmcd6GqZFrNHN53nEpkgNTXQYBXgKuh0CRBrn6fOlZ2+QtH2+l\n5w09p9Qem56n08tBtc57PpeuA7avVi4UdIU6xDAPNvr82MpBSEwWJEd1k8IoLPxDjImBLxcNF/8z\nZtLzTT6ExLYkE6D3WCx1Ct/gC7vsKpadJ+PnaEkzjBrvYokOIVAjZqqs40q4iVqZd9y4Pxav9Yzt\nclyjEILYxeLzejwgPkuigaVJnf3+2qqqmM1mODsD0oaEkcSlWvixLQaa1EM8X4cAYT9p81PqSpzT\nq0LHEzYx+V2FwMgUmEFrl3y6+kXX1NQpGCyyMGWLp7MeHyKw0ybnMWOcc/3i5r2j8tXSc0MIOB+B\nr3NxC2xn4maH+dxhKh83OVhD4CxuOHgMZIiKz/tXzrKUJr584s1ZHn1+6H55dj6BayChWZOhRXBF\necm07SEAlC8AeTpDmrAGS5ppyxfxoYWtBCYFLGpTbKmc2tSl85y3Q152ub8U4ysvV85i5WB1CFxr\nls57329w0HlZZ9561LKOqciPyxxYknzH3RCYkPMlIDbkA1jqg1pKgD0vh7CpeZrr5oA87ZIMteG6\neF15/vL0dH/elvkqgR35zNPU7bNOqSwd0+nmc0WpL0lbr+tnr6VcKOjqJViMDT07tY3kGuw66any\nh6i7fPJeN0GvDAi7ukiI2XPpWrtKj1oWIQ7WDXKAoCZhYwL1qManurRG4ikZfJgtlSmEQDQvRqYn\nEMFZrCeL97pcpQ0FkX3zLrKEwVvifs8IDpcXeJ3v+LtL6VXGQmLLfNZGzomDvDw/EaEBoMIYkN2r\nesAuNPgRIRici8C+sjFerrGxfPN59BML3mIfA7IrD4qag6O8/+Ws4pC5saTN5ufPI3K9MEz6/pKD\neSn9HKjlWmkOeiSNocViCMToMZhP8Hp8DTEc+UJYckLXz9LxrvT54hyhzuvrx+Nx/1s7igurJ8/Q\nz9Hzk8wdsrNTp10pH1ddNjGjis/d48J4rQPyef6GFuhNfb6knJR2NQ7Vh9xbCqWgRY9PrURuyxSV\nvq9jREt51yAtxn5crrMSwMzHcIk5GlqLh8q27h59fh3oyndfDgHxHODl+dJ9vpSPIUX4YeSCQVeK\nIwVEv6rNfgSliWvTPf0Acue3z8okqxc/3ceHzAtA2ilohLYDotlxReM1y/cLQNTb43VZ8ue4FBKj\nqmXhTYEx+0juEi1/jNaiQ5ANCXF3nzEVAZfMkRHoRPBSHlgx0GlamBwJWC12HYL4CpV3Jc5lUOF6\n82PeQs7J8xd/xoBh4RcUw4+tAsMQQnq3YgRmXRfjtNlgMNi0qzQCQOdi+IyLltLg3hSaYQhwbZrs\n1oGNobzo30MMWSnEQZ5X/X0b08xi7C1vBBlixUrncyZNa7dL4zGbX/J+X3JCl+vlfn1828Wl5GtV\nyktuEiz5guUKiK4bvZFB/NOm02m/CGtn4dxsexFS6h9DoGidYpGD403POQ8YykGbfkYJYOsyDOUx\nl3Ukw9DxHJDr64cYQc2EbhLNcudsaX7dkAzVjxbdx/V9OdgqPTd3bSjNd3lbD80Dr4VcsCO9aPVx\n8bPG9gu8XmhikFOzMtmu095XvIFvAAAgAElEQVTzRaEHT1vU29BgWVT66mJUmtx9vFEujMfcKkgL\nLMeKsTYyP/qavHz6+rqqVzpEX39InUqdWOpam+H6OxaR6utFJ+0nYMXQ9Z0/BYcN3iRwFNDZ0ANC\nXpMk9RNCjF1mAoTOLdrFLu9Sra0BFg60fVRqIrAMda5t0fehECDUCTh6y9wG5iYwdwZrDPMUsyyE\nQDAVVX3xoEtLrk2uA1z5ffq7HiNDzMB58rTt5CNpa4fyh3VMLQHEdXNACRDmdVUyFeVsQum8Xmi0\n71UOIkv+UnpM5EBKX6OBkgaGpYVFlz9fYPTroOR+zXRpdkveg7kyF12wDIH7TUB2HdjZBG62SbN0\n/TYEgKQ11G833b/NGNQKW6nPrSMs9Nsmhspb6ovrTKOb/BOHRF9TchHIJWercjYXtlNOS4rStiB8\nkzwGjvQRCESH9VXqLy7u6xtmaJdQn4YcKPTTpQ7PMKLVkx8FE2jJ/s+GgZFfPzSQtNlAa7Jyb9xp\nuHxPlQCUMdG0GDAYs9hVGPoQEhbkPYk+UnLa/6oydbw2tYOujxDiTk6Mpaqr+Jyw2Lq/bCdXE2fC\nzjOfmItxhQnRLX8e/MozpF9oh+k4mSwir4cQlkwuPeOQdl+GAMZajDVYVyVfLk/XhbhRoaoI4eEA\nwWspJaBVAtyl7zoNkXzi3GbSKF0/1De31b4fZrJaFyagVC+lCTovS16eEgjNGbFNk28pOGsJaJXS\nyvMvQGjoPYAlEKcBUg5wczZIp5+zB/LqKNn5+1pp9q+FrFOw9TWwDGzz69cpHdsAu1JfWJeXdfe9\nFvU7BJ5KLM0QuBgiLIZEp52z2kP5KykL552L9O/S3LCtgrDJ727ddbn7x8PIxYOu1FZVVVFbQ2eT\nhmjkGqiJATx1Z3Bh0dk2OeD24RwMi3QLRV90YA+9Uz2IfVBekDz3bjFoTAJFbnVg18ZkwAOW3i+d\njnUkZ26lQYzqxbvGvICBAPm7EkN6dY7JgWAfrX2RTvBG4U6piIV/TOx0lpAxeRrYLGlLgp6MlGXV\nGVGYAQtUVb0UT2k0qjA+vpTahtjulVkGr94Hgu0gWOrESnkHlVGbCoig01Iv+Ug4F18KHoIsigFr\nR9Qu4LoKWwWYGapQg50yGj8GTl0sGAu9IObamw5rcB45j4a/bvJdt2icV0EqmR83OQWv08SH8l5i\nPkosQH69nmOG8lJiY7TPztC9Og/S3nkdLCt8w6yBLIIacOaLk7z0W8Z0CeDLPbmD/UWLZlhK42Ko\nL+TKy1C/GXJw35SfbQBhnu7Q721A3LZgTdfV0Hgt5bvUZ/NxN5S/fNzpPOi3TuRM3xCYHXqWpKfP\n52kP1dO6l3WXNqbofLwWQPnCQdcSe2KWz+lGWdX2V9PT9bHc8KWnFwZJsCyitYuPmSxuC7OYia+w\nXqSBvm85D7ozDmk7lgg+DTFMRgiB4BcT3nLZC4OyOE6XNe9YD8ODZUjywTEkfVnsshmyH/Ssdnbv\nfQwDMZCffrLQ14t2FfyijOk9knVd9/52IQSqyuD7gLkV47HtA0BSxa0Do7HBdR5bjx4L/5XchD6k\n0Q1puLkMLQi5A+qm+0sLVok12rSAlABM6XU5uZ/aOi15W+C5buLUeS8BjdxsV3LgLaVdar+8PUp1\nq8dPngetCInk76AsgclS3jTQk93AwnTVdb3yZoSLEF3WvN437cDTc3Due1d6Rn68tMN2XT/KF/s8\n30P9dh34XwcgtgEBOejUUspDSYHIAVzpnjyvQwrjEGAeUkSGyjSkvK17Pqy2qU5Hr5UylnT6+bGH\nkYtdZUxFjKFk8CZQpajixlREx/qkRaRrQEGJIsDS7wdUFWNWB4kwQ7o9JGpU0icBu9KANu16y7Xg\naqAjbgIqQDLrLU+0hiFNuQS6SoM/fupJuDT5rhtAm46VypNrGXrCXNGebIjvjez91/zagdh39uzx\nelJdmVhDfNl5CC71rfgqJmdCZM7SS9bta0Abvx6Sa14PAzb09TkrU+oLJS1XT4L6nhwclBaMdWBs\nU1/KRU94ks6mV5mU8rNOk94kJSC7Dshp4LUJLJcA2NCzYb3zcg5wS4sorC5C+vmPA9Old5Zt6xO4\nDmDlss6MXaqbvM2HlGlJe0hKCkx+PE8vz0MpzfPIEHAcGqPnAX/nBYqblLd1pEEOwta1+TqAq6/Z\nZp56GLnYOF0mverEACbghLgwBkKCRYEUUDTbgRCW/YX6+/pr6LU154ReVRNQSk4zM4t6jb4/epDH\nRqgIYQHo5HHRsXvxEuDckXaJ8TFmBe2PJUidaPUh9N8lPVlwfUGxM4VddxJzqqQti/Psth2pxGgM\nSWmyM8ZgMiC6WChjPDWTSE/J0erClgOB0F8d38EInhi1XrdbVRm6uQdMZEfrGNoCHJ2PAMyYqghc\nL0pKDt4i6yaKoWuGHEfzBanEvOh+u66/bFIuckBeelZ+LO8rOcBY9xLr0mK3yWyyjYIk1+ZhI0r3\nasCpGbv82pJyVapzDZZL53PwkIMm7fyfzwFSFqnXbcb6o5IhFmqb+7ZRfEt1L5ID+tLYzNthHWDO\n8ze0uG8CEEPntwEopTTyezeBvG1BUUk2gZnz9L1SXW9STM8DEHU668D5eeRima5wRG+ZsyEufD6x\nWmpiIUUfF7HGQJj37/BbsFZZhRhwcw8S/2spQmf8kPAI1hiM+DcZTwge78PiubpRVEMuGsH3C8S6\n3RGlCdOkopv+WtM7ukNZu18u5jDTpUXSkUk3/i5s51dBQnVk5NVnrA683AyTf18GkT6yXVL+9PJu\nnZ4xJiJkeQl3n96yj1lc2CQ+mHq2AVuBQRhKjwtQVZaaQNxMajD28TCl6E0TJdH9Jz++7vr8um0m\n96FFP5dNWuEQoCrltdRnZMLT9wy9P+5h5TxKSH6flm3qK5d1oQq2XVBL+SoxJnK/BoEa9GlAVirf\nRYi0/3mAiL6mtIiva7d1zymFR1jnI6TTKY2LUr5Lz13Xl7ZhwUpAaujabcf9UFqbrj0PoBtKL5/7\n87Ty+WgIyK1rAzlWavNXIxfr0wUYU4GNZh4HmNClhXNReXU1Aq8WmwDG7qdKcYm18qB2zvWTTnpd\nj8+Ylj6aewgYY3HOx7haLC/YJgtmGi9InIzx/YughSnzXkBeypNhOeBnClCq82K8jSHLgFBZnAGr\nmLwlDcSuOlA7t3jHXN8Zax1YNV7ru3m/w8kIag+21/Zsv/19ERNJ0vShS4ye0uD9avyT3Pmw98GQ\nTxM3DGBMjOQPGJuYqcpinAAf34PouEMyEEgR9W0gLML0E8LyK2XkubH+4vsMjAmE+I4DXA3WWKpZ\nwFqojMXRrQzIi5Rt8zLEiKy7f2ii2aSdD02Gm5iEIW106H7J/zoGQudhaKItyZDDbH5Mn9vk1D90\nLC/fujxt+4y8PoZ2cK1z/Nf35Uy6OM+HsBxu4iKlfwtJBm50Px8CTfK7pPgOXVdapBeKXazfPB6a\nvhZWY8JtA0j0/Vr5GgJMpfvyZ+jv2+7GG8rrpjkmryvdz4YUs4dRds5THzlL+jBgMt8t/mrXiQsF\nXZWdYI2NL7oW85HEc5JGNUbQGZgFeIrkTvS78p4EgBQIAMQ+6X0MJdA7ydGfjARYWtQXWwt1p3Xp\n5cy6cYQhMvRMmEm75vD97j9DARkHiSIqDWcg1D0LF3wMJBrE3BgWZs/YSRa7ElWiQBZUMU2keqKq\nMyfUWL/RTGerVIbeVBnS9/hn0rsc470x/8aUd4Cu09L1NSE7Z4whmAVzKMBWLlxyRjXLYQFKu2Lk\n2njOUFU2viuzvye+EN3Y+IqoxwF01XXdMw4ieuLQdTVU10OiGQ2po6FNFiUgsAT+FXDbZkEpTcIi\npfg7+QKon6kBwxDQyvMlwCIvVx4yYQiIlraTa7BSApalxW+on+pzQ35Y+liens6XzHVS7lyG8qX9\nprYB1I9K5vP5Uv/fxNqU8p7fU2rnku+diByfz+dLv4F+V946tuS8gGNog0AJ7JTSyDei5N9L4zA/\nJ8+Tui/5c67Lx5BClP8eSlPXUw5IS2nIjttSeUvz1BAQLOXxtZQL3r24j7XVUuVE0LLst2BCNCHq\nwAhSYQtg4FfcciLTBdZMex+tfvDpBk3eREbYk6UF4XS1Y8oux+AJ6n2NwgQJSAgr8dVB3tHYm0TT\nXkivQlR479MrdbKApJgYPoEMsbO8aISQYGKAFV84YxLzZnrcpzU40H4GCwAW68RiUgEXgE3y0D+l\nN3fqtxtpVixiJkMMlhHBnPhl9SZiFbaDIEyeWaqPnoXLNJl8UMVPAeeGyqbJpDYxCyw2bVy0lHes\nLjTSbYDWOhpcMxvb5gXOH9R0G1Zsk5apwdY2z8/BNiwvPpLOOnZoCNCU6iw3+5d8qvT3IaCQ+6cN\nga9S+fLjeTiIEvjKlZTSM3KQf5Gyrt9vAw63vV4DLj2HlAB1CezqZ5TaPgchuUKRA5tt0tF51Pfk\nSs4QUNtWaRqqs21lXX0NXauVK92HZS7MAdryOrYdu6hlHWjXz/ioZrpMNSYYg61HfWBLKy+87gFD\n3L1orE0vsU4VgwOtYRoPPu+k0nAGSDGf+gZIZrUQo5L7sDrBLcDRcuNaU+NDB0Tfr3iTx/vlBqvM\nwnTXd7K+YaUzgUthE3zi04J3BB96gBavi2XAz5cGFkRnce99jF9VVTjvMGaxC7LPu/ELs2Jf1hCT\nTb91TDCpS+cdo/QKoR7sUva/Ac3wqc6u2mqxQ3MEKRirwRK8AK8FIIyp6FcnrU6+22qZ1sYYZNa4\nVP9AbTAuMouPgwhzsmQqNsMM1xB4yhmjnI0ZMqXJOWOWzTlDJrDSBLRuItd52eTYXpJ1E6IeE3rc\nDcXz0emsY5fy6/PJN1/g8ryWJNfccyYmv7ZU/6V7hsbEEPOl09Z962HjwL0eUvJz3FRfQ7812NCS\npzcEkkvmpRz0yOfQc5eUf/W8IWBdKlMO4EplGmKq83TW5Tu/dxMwKaWfl7dUt/kcJ+uejGF9fb6x\nZoj5LpV7G9BUyt+25d1GLvY1QHWiZU38HkLAkjkzGwPBY2wFuqMJC4RLvljVcmgJudbaxLLIQp4Q\nsVGvIMIk8KE7qTBoNUvggRBjPxFfFO1xy9oJC38i7zrVgEJ/KebJL3dgHwLeRJBj04aCEAKkEBrB\nB7oeuy0mSDfvFoPWRWbQuW4F+YPH2poQNHXdocUapeUSF97KTgjJuVb6nEkR7he1UqCpl3aLdqs+\nGYz79kD+TALJwaB2WUDa5dg/L+jwIKsT1mKx1X5tBoyJptQEYkMAYz0FUvJCJGe0StouDDvclyby\nXHK2K2c1JA+5P0++OOdAR55fWiBzZmUImJUWgXzS1eUrAYlSPoEVELmujnQehvpWziKsY7pKsg0T\noZ+Zly+/b90iLvU0tFNSRJubHwfAJSLgcEhKPnrrri2VrQRS8muHTFs505Iv0kN1mStRm4DkOgBZ\nGnf5M4bYmhzwDEkJtOpxqM3t+T1Dv3MpxXPMn1VSckps9DZ1W5KhevioB11iC5dJVUCUSGzA6PSc\ndyjnvBqIqx18efLXE1BiU6gTcEjXE8Au7PV9WiGyMXKsrqr0nkGTdt6ZyNSYBTMmwVN9SEyd7oBo\nXx3SM1L5U0kiEBjFPISAD0R2zTsMY6y1jEYj5vM53bxjZEcJTFSJLYq7MUOMt7F4nqkwdgy+U/lZ\nFmstlY0d3znPeHyAMYb57EE2QRh0nyx26KAnAEdlE4tJYte8tJ36Cx0LgOwR0+ImEf81vQDCcp8w\nJjJdbuk+iKCu7EPxqCX35doEuIYm2VzysVEyZ+kFI1+c87wsjZHsmpIWW0qvJPlkXVoIhxgIrRnD\nshliaNEeWszyOUSDP/G7W8cWSh2tk9Lit83CsK2pV9eVlF+3heS31JaPE+ASGfLFk/5ayvMQaB8a\nI3k/2DZg8hCIkXTy70NMl36jQA6QdNt579McvbyBSL+s3BhDXdd9fy3lbdt6WNcvtR9haU4YkvzZ\nudP9OuA6xLqf1w1CZMi8P8Q8vhq5UNA1NhMhmvqFWIs0vlU8ik8kiLF7i+t8DDuATZ3UWqxRaNgI\noAnKt8utdL4gvkNp96GtLMGJs/bC5wpTRYATAsZEINEv+vWIerzH2dkZ9R7MpqdUBoLzVLUhuIWZ\nrgeczFMmRjhv2ds/YDQ+4OxsSmUtVy5f4+joEi+++CKYOYcHe9y9/UHqGubdA1wFtTCBzhF8R1eJ\nmXYxaGtrcZ3pGbYoo76DW2sJLvlXBc9kVBN8YDqbgT1c1FVssKX4YH3n9K5vN5nwvPfUdowGoDFP\nLraKCv8Q7B7gscHHegsBH/zSQFxMXAszinNpp6pizUIIeOZgYsDbaF72VH6cAG1N57oU++zxAF3r\ntPRlAFlmsESGWA9dhyUNOf+ePztG/V/W/kuarJyX0CSTyYTT09M+4nlVVXRdt5Q3nT9ZREejEaPR\nqFc05O/o6AjvPffu3QOg6zrOzs7ouo7ZbLY0ceb5zZ2g9YaMoXoWUCflE4fq80iuYOqy5zIELPW4\nGrq2ZK7MGS8RXfb8+k2L7aOSXBGB1UV9HUNznh2YJXC2DkAMPXPdhhE5VnqPbgm4SB+ezWbMZjPu\n3LnTB4ydz+d936/rmr29vb6fHRwcMBqNGI/HS2OnruslwkOXVfIzZCrX18gxebXUeTdgDF07NM9t\nYuny+j6PrBvP+Ty4jT/sOrn4954kWYe4DfSALJEs2OSzhExeS9csa0DaQTaf6JYazC+zI96F5J+U\nMQ4I4ALhimw9YR5m1NWYanKIdTUHhyMCY/COeTjDmApbxQWiqsVp37E/uYa1FU/cfIrD40vMO8+t\nF15gNptx9cp1jKl43/vexxs/7k0cTDwvv3yHl+8+zYsvfoj69D7Ow3x2EsGbmeGNwSYwaKyaQJ0F\nqStd74Hk02SprGU0qvtB7pxjVO/h0oaCWG8phSWra6pPuzCVRvYuxJAeFpyaiEKKgRYZRpaAkmwr\nsAYIvk8r+vMLsJYHWzDRJFsRFu+rTDtWfbaRIIRAFdL7JUMAX8UND4GtWbXXU9ZNVkOAKL9308S0\nBLI3jYnCsSFWKD8vYAlgf38f5xx7e3t475lOp0v3at8ray3j8Ziqqrh16xbj8RjvPVeuXGF/f5/j\n42O6ruPll1/GGMPe3h537tzh3r173Llzh9PTU87OzpjNZktlENEmtKHy6QleL1Bd1y2Z6baRkuY8\nBJw1yCsBrFw0mCqBgxxsb8rzun7zOMimfp37++jxUuqjJZA1VJfyfWgjRulcrnx23cLNQv+WPp/H\nRRSAP5vNmE6nPHjwICrfLACAtTEM0N5eJCJOTk76vjYej5lOpxhjODo6YjKZ9GNL++2VxnxJEZF8\ny7lcucnrMFeUc9HsVGne0rIN6zQ0Bw6B8hKIzMeovu/VAi64aEf6rOKGtBbvoR6PFlRqWveXGhf6\nXY+6wWF5AC7OLUwq1sZ38kV/J91YkUELwYNapKypo9+Vtdgq3j/ZP+LKZIzHYOsxVy7X3Lh+lXe/\n+12cPjjhcP8Yaw1uekYIgePjY5xz3Lhxg25e8cbnP443vvGNHF46xgc4HjusrRmPx5yenvGpt57i\n4OCAB9MZJycn/Mqv/Qo3nniSeTflgx+4zf37d7l3/y5n7hWCdUBN52aE0BFw1PUIqJY2I8Q6T/WC\nx1aR9eqcj8yRnWDx+ACBCbaySwuHONvHgRPrQyLzE0Jv4oyTzlJLIzsREXZKzIvGQfBxY0EgmhuN\nZbFhIbWLkYEu7exxwYGN7lkel3zKFn4Z8S8VOiRgSI1zHW6+HPvromTdmJDfGjTpa/J0ZELLFxJ9\nXo+1IfNbvgjlSo2+X9K01rK/v89oNKLrOo6OjnrANJ1Oeemll9jb21ualEej2P8uX75MXdccHx/z\nmZ/5mb15ZH9/H2MM4/GYs7MznHNMJhNCCHzwgx/kxRdf5Nq1a3Rdx3ve8x5OTk44Ozvj7OxsySFX\nFkENOofqPF9UdJ3n72nL27Bk8toEqnUe8vlLJGdRcmCl21e3f17G0oKqrxM25aKlBJiGGK8SGF5X\n/hxM6HFRysdQKAeR3KdJ1/98Pmc6nTKdTvvrBVBJv5a86T51dnbWM1rz+bwfN8JsTSYT9vb2CCEw\nm83Y399fMrdPp9P+fZonJyf9OzVHoxH7+/tUVbVkgszXSy0yBnL2UcZDyZS7jn0aUnxK955XASg9\ndyhvmxiyZTzy6sbEYwW68uP9ALAVwRsMVc9o9Yu8WYSSWCZeNms1cmw+nzMej5nNun6iXQCLhZmy\nN1MYz+HRfjSXjGpu3LjBeLTP889/HLfv3mU02aOuxzz77DM89/Qb+MD73sP1q1c4Oztjv46mlvl8\nzuXLl3niiSc4m85pmobRZAxV7NB+9oDRaIT34C4dRY3bjrg8P+PePcvxZ72Zeee5ffs2n/SC4d69\ne7x87y63b9/mIx95kTt3XmY2O2M2P8Van3zl0i69bPH1IVCPJ2AMXfKL8hK3qjeB7uHTuzGXQoyR\ngFsK7koQTa2vYSCyV30bhIiajTq/uM4hLl3WBLwZYQ1Lvn6Q4pCZxYt6EwG6YCiCp7YxAIlskOjf\ncSmhLkw0G9v0jFLcsYuS0gJaWijkfP47H0Ow3P/XATY5XnpmaRKWMSMmw8lkwvHxMXt7e1y7do35\nfM5kMqGqKm7cuMFsNuODH/xgz2wZY3pWbH9/nytXruC955lnnuHZZ59d8qHKwYQ80znH4WE0gZ+d\nnXFwcMDp6SkPHjzglVde4eTkhDt37tB1XW/ezEFnXo/CcGlfQam7oajteb3r+loXO6uUTgko63RK\n4LDkyJ23+5AjuQbMsri+Fpr9ay0l9kmk5MgNw0xHfm8upfrfxMjk9wkYEgVAlACJ/i9g6v79+4QQ\nVny1YOGfeHR0xM2bN6nreql9NCCUfvvgwYOe9aqqivv37zOfz/sXm08mE5544gn29vYYj8fUdd1/\n5qLT1r6S0p91vx9ijEp1NwT+9fmhc/r8uvrXx0pK5FAehhTN84K/XB6bVWbdxGVYxLKRRV+YKu89\nlU27reyq7R/Wv8pGJsBI8dZ9cDXBBibtchuN6h7NT+dnjEYVIYx5/vnnOTo64pkbb+CJm0/yKZ/y\nKZycnDDZP+T44JDRC89z59Zz7I1rRqOasfFMJhPOzs64fPkys9mMyWTE4dEeVDaGojCBqdnHGYOp\nLLaODt/ew5G17NVHdBzTecczT99gfuYhWKad46WXXuID7/8Q73r3b3Pn7oc5O3vAix9+P/P5lPk8\nalhaSxENS/wDcpPTwpQiA2cx2QtbpU1DQS3CcTBCBFRCZdOzhrHuk09XateAxQZhFDzGOIIxfaiL\nOGg8o9EkgUHApJhdviN4jw8eW49S+nEHZOxH0iFiGxvAWg+2w5oxhseH6RoCVvnkli8U20wict0m\nEXAxtMjlE5hM2FevXuX69etcu3aN69ev94tAVVUcHx8zmUx49tln+34jfiZd13F8fNxr3pcuXWIy\nmRQBjJa6rnn66ad7BWo+n/Pkk0/2Jpk7d+5w+/Zt3vnOd3Lv3r3e/ChgLWfs9KKj/W70xKvHiWa9\ndD3pMaTrf4i50pKD3Bxs63Tze/JjkhcNckvgWa5fB8wuQvI+rY9r2Wbx1t/zvjw0loaekbdRzlBJ\nf5zNZrzyyiu9aVru89734Ef8smS+1a8kG41GTCaTnpm6evXqErjOx7u038LVwveKhowPUT5eeeUV\nzs7OmEwmHBwcoBnnXATkLZESDAdYXmeGzes0V0ZysLVtmw6lvy6tFaIluy9/5ke1T5cLQr8H4itz\nPIS4qzAgOwENtbX9cmhD5Ej6V9ZUter8i7R1UFBxgteTjbMWE6I7kQmAN4QqvhYG77DGUFnDWUrY\ne8eoqjk8POTITbh54yYHBwfcunWLJ598kqtXLjEej5nsHeDDNarxhNrEDnn98iGz2Sza7GdTRqMR\nl69eJYTA3sEB1tbRlNaBMWOMgUlwCSQks0YKkeGrGmMrxsYwMnFA7dXRGX/fwcHkMjeuHfLs80/w\n4Rdf4r3vfR9Xrlzlwx9+iTt3P8h8Hp3zI7tXY0zFbDbHmEBVSZ3pKMYyUUcmavF+Q5OiLhgqU4GL\nzJOropnXkUx9Pr5eKXi1cJoYFT5YEzcomvTuyxC5LgyY1D7e+OjbpaLl2wpcMjvaABaPCWkSNLEf\nGUzaTEE0+Sqzg0mm6tpa/GzO2I6YWwlPcbFSMhPJZ34sH/xSxhwAPAwdPsQs5ZOTaMuTyYRr165x\n6dIlXnjhBY6Pjzk8POwdebWDsLAR8sqZEquiTXMlzVgvNHUdx2WufcuiJ2zCm9/8Zu7evctHPvKR\n/u9d73rXku+XTiMHOnn9rNPCh86XgG8JhOnr9E5D/Xs0Gg2y90PgOGfI8oUrhKAUz1e/wLxWso65\n2HT9Ngs4rAbHzWWI9RUriAAd8TcUE7iYBMUUOBqNlvypNJMrda6fI99FeRmqgxxMHh0dFceRKNjT\n6ZS7d+8ynU45OTnh5OSkdwcQRUjGvIBAYeLyNPP+BsNBTEvH5PptZRulUcq6TnSdDCkk+trfET5d\n/QAwYiBaHUglfwlB8UMVVKrs5UpdHXjOOUZVhVMhFcYpYr4JcOnwiGvXrvHszatcu3aNp59+mitX\nrjAejxlPJgRbYatoK3d+MalNJuN+0FQsJrJF0EaN8AFMbw6MhRaTXkhhHwzBdXg3j+mm3Z3WBKpx\nxaSqqQxc3T/i5uXLvPvd76Wae3yYcv/+fU5PTxmND5i6Dus6qloGZmSehtoon6z7gQeL3ZwZW9Qb\nf02Blk1NLiyUWVRAAprpOUXfmGqRhDBdOMSjq2/nEP3MDDEUBhDBW3DpXDw/GtlzDfrXS9Zpg5tE\n+rz2PdpG1qUtLI6kLeYFa22vpd+6dYurV6/25vIrV64s7TrUTruSpjaL5H9yPB/b+cSeA6T8Gsmv\n5OHatWscHR1x5coVLp08rDMAACAASURBVF++zP7+Prdv3+b+/fvMZrPijrN1wEk/UwNTfd02Gnj+\nrHW7yvTz9H0lQDEE3vX4LY1tyUNpkX+cZNMCqT9zGTITlQDr0P263QVcif+VAC5rLYeHh725XZsF\nc2f/PF+lttpULhFhkAUo5CyStG8IoTd5TqdTzs7OeOmllzg4OOh9MjX4GgK66/KzzdyVl33o/m0B\ntC7ntkrnkDK7LcDbVi48Tpd03liw6HwNy5VqCxVb6rClXSQx3eUJ3nuPxJAKIVApNkcvCCEE9idj\njg4O2J/s8dxzz/HEtevcevIqly5d4uDggMkkmrlI8VDmXoKvivkNCJ7KRlDU9cFc4+IfSZwE8owB\nYwjORZZGQiyEgAnxFUPzzlNXFd3slHE9wrsOvKeqAoQ5tR2BqZhcOcJ1gcuHB1y/fIVPfOF5fusD\n7+bf/fKv8u73f4DT0zPGdoStOrpuRufnSTNJ+DeTEvW6/B7MNBhD7pwt1PNyPJkFKFpshEiQO6af\nvltrcX7xTriFtpF8WFR+g49vF8AkrQWw1Qhr84W5o0pp1HXAO/Bh+lgsMKJIDDEV67R7uX/o2Doz\n1pDkE5Ex0Ry9v7/P/v4+165d44UXXuDatWv9RC27qHKwlT9P57VUtpztGQJWckyHoNDXywIjjsf7\n+/scHR1x48YNnHN84AMf4O7duzx48IDZbNabXjTTNyQ5EBoChjlA29Y0kov0/5zxyxch/Wz9DO0K\noMG5ri8x9QpA+GiWHJTn53S7SJ3kUmKXYDkEhJjwJGRJVVVRGR+Pe9Od9oca6lc5qMgVFPksjem8\nD+jdw5r9lXJLP5I8npyccP/+/X4n8P7+PgcHBxwcHHB0dNSHmhhieEskSF7328w3JZCVfx86Vrom\nB14lIFbyX8zdAl4ruXCmK31DzIHLAQ2EBSmh38X3TZNWz74kcLD0GeLrdrwPBBuwVcVoFHd3XD4+\nBu946skneeGFF7h66TLXr15jNK7ijpH9/UjFVxW+m2NIi35I4QoSAJFArobomyVt6xPYqoi7I8XR\n23uPSyAI0qAPcSEZmRpmJ1SzE5i5GNYzjAiVx3cd1XgPjMdUB1gbqGrDeHyJo6M9nrl5hU/7+I/j\nN9/7Ad77vg/xW7/929x++Q6vvHIX49JCVRmpqIG20vWqPoNqrwFtZUWLU4kI6Or98rxXACymqSnu\nPotBTzYLyl5Mpd53CkimyULCaHiPtam8/vF44bXIOmbl1aS17phIibkRrXk8HrO3t8eVK1d6n60n\nn3ySw8ND9vb2igGPJU0NEEoa/RBzsfImg0IfEzNiyVldxpFm6aqq4uDggLe85S08++yzvPjii7z4\n4ovcvn2bF198cWm346vV4teNh6G5q6RA5vfmuyOHFsN8sdaLbg6qc1Pv4zQmclk3728DZPMxVqrT\nofvk+vl8ziuvvNKbFcU0e3R01JvVxZyYO6HrdErs5VCZeuKgcD5XNHSec0UE6PMmrPXBQQyGfXJy\nwoMHD5hOp8xms6WdxnF+XbUKDPXnIeawpMiXjg+1ZWls6jlkSMGT37oOtR+nrqM8T68F6/X4gC6p\nlLBaiSXQVQIGm54zVGHS6Zy1kADS/v4+Tz/9NM8/9yyXL1/myvElrly6HCnjuqKqR8zmjqpKVdhN\ncc5Q1dERvAvLphn5Ltr4cgYC+PgeR/H9Eotj7jAZujPc7BTrpnEHpwl0wWMTxT2qaqjrBNpGeO+o\nxyNMtUc1e8D46mWwNVevXqeua37j3e/CGHFAntK5Gc6tBorbDLqkbm0C0Dlbs7zlfgh0SWgIo545\nUu/m7Ae2WdXAi+DE+KXJLZ5PgVLTi7Yj8Ho8QNc2dPjD+j6sW8hL9x0cHAD0W8yPj49585vfzKVL\nl3qmV0CYTMSyqEs5xFlYvusy5kxMvvAJeyXHNPuit6Z3Xbe0DT8vq4w9/Xoj8Yd69tlneeqppzg7\nO+PBgwfcvXuXn//5n+f27ducnJz0JhcxIWkGfROYLc03+XkNIEuL6mIjS9kpeWiRyZ+pF8Qc7Iro\n9nmcQJdmluRzG3CkZQgMDI2P/JjUlfg+TadT5vN530eBPmivxMKS+8QUr9lY7eNY2kmrFYdla9Dq\na7vyskn7iSIixyR96cvS/yRqvYAuCTwsrN3JyQnvec97+rwKYywMmQRfLYGUvP51n99m40IuJWW0\nVH59Tb4uyHNKG2akfuX6ksLzasfEhe9ejI0g8QYqVJDzNYCrPBkEp15polZzH1bNNQafVnbHrOsY\nj8e84YmbjCvL5aNDbly7zK1nn+Pa1SfYPzzAp+dFADPCjkeE4AiuowqBMDog+C6+u3A2ozYhRsh3\nAeMcdVXhZ54ai+0CwcRwBnVdM++quIfAB+pgsM5RhwBdF8GojybHylb4s3vgPZ2bYwMEG5iHUw7s\nGDd3uJHFUsfgGq6jNkDoqEyAscWEwNVLIw7GEy696Ra3bt3gPR98ife9eJcHD06Z3r/PmT/h5ME9\nptNT3PwUEwLer3Z23TbySkkTHAtToh4MpgeoPbPh40vLvfd4Y/BhOaZUPsksL1Kr4DX0k5FaYMKI\nGFRV95kUGsQYrPWEAC75xz3ussncoycWPdlsq6FprVt2Ih4fH3NwcMDx8THPPPMM+/v7TCaTHgDJ\nIpJr1saYHqjki7uwUnqy0xNabgqRhU0vRHKvLCAlR2i9sSBfuGABYPf39/st829605t46aWXuHv3\nLicnJ7z88stMp9EfUoKuPizDNaT1579L2vY2oHmbfA0BEGnHTezeRYkAeg1YYJgxkXNDfX+bBVSz\nviJd13Hv3r1+t/fVq1d7B3kZE6XdrEPPzEOSlPKZl0cUEX2N9rnS4wNW+8u6dhZwKABrf39/CWTO\n5/OVKPR6jPfzu1ll8XL2e12ZN7F9Q6Lvexhmqqi8Z+dejVw46FonpQ7aV6J6cXTPuFQKner7BhrP\nBsAHKuOog+XAe97y6Z/O4eEBR5cPGY/HHBzsgQ2M6hE2YcPKhBRDKjAyFabzhMSouOlZXPQ9GCze\nOVzXURl5pZDDBA827v4LHmww0RGemO8wnxMEZDhPSNHVzTxpSnhwHh8cxhvqkcXNZ7iuY+Sm4CeE\nzi9phCGE/uXco/E47hb0Afb30g60m9y7f8qdO3f44EsfIri4w8VTYYzHZJHdpX0Krba2TZcXHdEi\npa2hNwEaBekEPy89r1rz/LBo/sI1WlN8HBeXdZLneZsFRQPWdSJpiaY8Ho+5evUqt27d4tKlS+zt\n7fV+jKK9l1gTvUjlLI0ArdxpXZyO8zLkedNmP62169AOsiBpMKa1ez0mdHnl+Tdu3ODg4IArV65w\ncnLChz70IW7fvr30iqHz7Ajdtt5XFYvh9i35Ym2Tj3ULbc62PG4ytJhuC8Ae9lnSd2azGXfv3u3B\nluzM3d/f702JpR24uk/qMVLyy1oHykvlkus1WybHS/N03m9KfVnAt1wvvlwyzoQlkuMlcJWXa1N5\n1pVxXXmG+urQ8dK8lLOmQ99fK7lw0JWjSl3EdYu796uTU8RhIbdQLk02/WQ8D5gqsD8ecfP6DT75\nkz+JT7z5Bg6Ojzi4dIw3lsnBPmMbOJ3O+lhWVV1jqehmU0Z7Y0I3jyDKOQwB382pLXjXYRjhnYvO\n3ni8m1NRYU3AOR8jvIeOejLBp/uNhenpKZPJfqKio2mum8/p3BxCSC7k8Z2SbjbDVGO866iMwQSP\nwcWI8sYkisfGIByywKWt+qPRiEPr6PYslgoTau7eNVR2wt7kkHv37uGcobYxur1Q5CVNJq/rTRO3\n1nb0bk7nuqU+MUT/h7A88CIzs7wxI6a96kysndXjovz4afbrGK11ACrXKrdhu/QEJA7A+/v7fNqn\nfRo3b97kypUrffws0ea1749M0HJsuW6XQZIGT7njrab19ZZ6OZf7a+VgSjOksi1eO/6u2/Gs2T3Z\nCCD5eOmll3qtX140P1SP+vnbSj5mdLtJ2wz5b21iv84D+PLfj4tSss1cso6dyGVo0db9R66TwKXT\n6ZTT01Pu378f583Dwz7grwZcpeeXjmuzZWl34TrAUQLmOaOrze95mfWz8j6jx5Ge78VcKumKD5tW\nnja5PQwB4nWM1nmkNO/ptGQ8l8ZO6dmvV99/LHy6hjrHOs3L2ro/H+P9VDGwKKXKWg1UuLe3x9H+\niDc8dYOPf+5pnrzxBJevXaKux3gCo3GF8YHOz6hrS4xjZcAEcB3jyYj5bEaYzxgZQ02c/I2fgw/4\nrsOE0MfEwphY2cHH2FTeEbCMbEyntinfXdSoRjYubm52gsETujOCi2EOGFlCFyf+4DusA5yP0ebd\nDNy0j+uVUBtYi61qjLXI2Kjrmj0CbmKojWU2s1y6dMTUwdnJKYcHl6gwzKang4tVPtmdb7CUfL8W\n4Ntohy8WDEoeEmFo0Fpr07uEltMO8r1fWM6R5ddZtAasj5W+l0T7AOrP/F69mIuIf4YEN33uuef6\nkCii2coCkptR9OKh42/ljJS0i14UcuZRAzQZ3zrAsZhjhDHTQSBhmTXLAZnWboEVU5WUTcwq8r7I\no6Ojfmej954HDx5s1Q5SvnXtt26RzSWfD88D8nJAnufncWW4Xo3kdSl9Pg8UW1IWpS8L6JrNZhwf\nH/fmddnYo3d55nWomahtwI7+XpoLhoDLiu+vSmMd+5QHNi31o3yMCOunx6TUackhvZTvoWOla/J5\nalN6D9unh8bm0LmHlccCdOnvQS2SpU6jz4norbEyCS8tMCwvZqKxTyY1Vy4d88yTN3ni6lXs/pi6\nHhOCwaZXwszn82gKtIZRPYlMk7W4eUddW6anc2bOMx5XkW0iQHBUNpYleMfIVrjZPHZIAwSP6+aM\n7BjjPcY6jB2BiwN8XFt85wi+o5tPGVUQXAehw5i9FFkqsmexnIFuHgM8uvkMqhlztwig6LynHo8J\ntop+TxCZNWOoTGBiPYzg+rVLPHBjfHrGZDLh5Tsf5uW7dzg9m/f1WtIqFyzFOTpoqKB/BZB0hDSQ\nWQVdS3SwCuBKCP0dkpw1VUp21SQhITseV9AFqyanbSVnIoWNygEQLDRw0WSffvpprl+/zjPPPNPH\n2xITgp54xb8qB18CngSYlF5XIyYKY0zPRIkIWNN51w74wjBpkJU7tmuWS0yB0+m0L7/URwgLR9q8\nzsfjcc+I7+/vE0Lg/v37XL58mdu3b/Pyyy/zkY98pI9qL/fqBVazfCUzkn5u3n663fOFd4jRyeOg\n5c/LmZFc9HnJ86b3DD4qydeJoXWhBFZLpiNhauSenN3S19y7d6/fpHF4eMjh4WHPammTtJ4b9biD\nRTDS3LdQs8uaLdYKiS6rzqt+LY+MuZLoMSL1oV0DxE9L1914PF4BWWdnZ0t1NJlM+vdIimLjnFuK\nzyfM0qa2Wrde6Hos3T80hvQ5zW7J87Zh2TZd97ByoaDLhmT9MovdTDqmkvc+0RJRo/Am/gWg9hF2\ngEzki0l7ZC11bVODB6bTOb5zGB99qy7t7fP8czd5/tYtnrh2ncvHlxjVY4ype2f84LvkvxUj5I+p\nsN7TzWachdgg47piUlnmbk43j2Yxb8D5FJ9qfhrf82ckJMUhVV1HHy8P3dmUMAmEkcF1AeMdo7om\nhAmzkwdxR0k1YnYa359lqQj1nBDGWFvjuhNs5QnzGjy46ZSJrSF4TB1fDuw7B6bCe4ufx3zWlYlR\n3I2hGk3Yp8KezRj5gLkCx0eXuXIYd+Lcu/4EH/rw+3l/itxtqxBjghnP6VSCs6aFwfkYLh7QDFbo\nQVE83ouJrwmyir0QXz0fAojjbEj0tVGTlln0k6oWFi76ci0BdxSzlQ7LxBUHtISVuHBL+6DkwCtf\ndIa0a5nY9UIgn/miMx6Peeqpp3j66af7wKGl96zlz5dxmr+bTSZ7nUftZyX35eBKg5R88tM7sTT7\nlTsQ6/zKjkdZAPQCI0yZ5E3S0EEljTEcHx/3bJ+Ylk5OTnqH+p5VpbzISRm28QXToKrkr5UrOrrc\num30s0usiqS1Dtxv2rRxkZKzOZuu1SLKgj4XQljajaj9DiXeVim4qe7fJXChQf7yfLhwc8g3m5TG\nuuQ7Pz/E+pdAjR7/Q/0qP5/3bX1dXdd93uWcVtJKZcjBj3xuC2o29dm8rob6xjbM29A1W5MKA3Kh\nK40AqLjWxmCbwQsQiybE88gijtPCuTYExzxUHEz2eeLKZcajiiuXLvPMzUtcu3KZo4N9jIkgojKp\nY6m6lg51enrKwcFB3GruHKGbMXeGuoohIkJw2NTRnO8IwTAejei6OVVdM5t3VBicn2O8j4jTRTar\nIrJcFsDFHYu9xj6dUcliYi22sjgnfl2yZdAT3AwT5nTzU0bG4KyhrsZYU3F2esbY///cvWuMZdl1\n3/fb533f9e7q6p7pmdEMORwORZmkZIKxIkBUZCSWYEtWEMSCnSAwEgdI/MUJEMBAAiMBgjyQT0GE\nKEjgBIkQA1ZgxbYiCQop0pTEx4gcPuZBTg9npt9VXVX3fc9773w4d53a9/St6h5yqG5mNxpVde95\nn733+q//+q+1FbnJcdAUeVVg1fVctDY4nkfU9ihLTVxqdnyHiIjA87jruUwWC6KoXVVdLpLq/A5n\nhWFLvSz/oB5YdFwGcXPCOa81vVK71cd4yL7rBsUDnxmHuhq+cnCcJ0vXZQMHMcBNmv9RW9OzNsY8\nkPrdbrfZ3t6m3+/XgMs26E1GwPZuRe8o/9cVUJRsxXUZi3JNcMZUNwGKvV2e5w/0E7k2mfxtZk/K\nTgArqft2iMkGgMKEi8GUTE35PIoibt++XS8gbDOH54WZfpi2bkw86uS/Duw9bJzYY/ZJGhPwICCx\nfzb7q3xn34N8Z4Ns2VeKwgpzlGVZndFqg24bUDSfq/QbYVftPiV90nYO7Gta15rAV96LzV7L2GoC\n/ua55Fg2YJSxYLd1rJmMcbuESxRFtcaxeV77Ga+7v4f1q4vA0nmslX3PzX0u+vu8tm67D2I8PFbQ\ntW5wSNYeSlUL4ahllh1LgOYIaj/zsrXWNepeHtkqH1AthJ0XKfFixmBnk92tPr1Ol8Dz68FmlCJL\nFstjnw1IpRSugtKUnB4fsbOzU2cTFllKqQxhWK1XZThLp63Cdz6UmrRI8PygWty67WNcSGZxZYSU\nh8rSs5fpOORxUoUFl8ZHcdaBy9LguR5mKTgvC12BRl3iO5oinuKYAqWq4qimWNaIKQ2B71EWGUZr\njONAUVKiKdIM3/FwPJ8oCCjiFO1p0sjgexrf9+n3NpbF/3JAU5Y5WkALZxopqVJvDzi4eKK3vSnp\n544FPI1SS1JTnW1jvV85bvNczd/PQIeDchXGKGSJJaWenAV+4UEvbp0xP8+7to8h2zUnI3sCbbfb\n7O7urhR0lJpy60KR9mQthknOk6YpQRA8ABjlXdoA3B67Yuhs8GQbDdnXNiwXTeo2myVhDwFc0t+a\nxkvuzQ6fSihGa10DriRJ6hpFsq3NVjSZgmZrGtLzwngP89Iv8vTt322DK8+9uU2TUbmoX/15t4uM\n6MP6/kXfrbM/9jqhou0TwG0zRbKPHUo2xlR63GWIzV4sXgDdmQzjQTBkM2DrmlyXfQxbx9gc5+fp\ncGVfO9Qo17aOIVPqrAafzAtimySRIM/zmv21dV1NFnpdu4h1Ou/dP4pz/n7b+3Fkfpj2mBe8XlL7\nyqlWDzQGpZ2a6YLqp2vdozEGA5Tlmee/Tn9wNhlrfM8Qeg79lsulzQ6bLZd+u0XkuXgOFGWGzjW+\nXoa4XLcGDyXV4AsDH4xmeHrCoNvBdSArEtI8Q5URynPRVMv/6LIyZmVeDYooCMjKEs8JKPMUU2qU\nKStNfp6RpWkl1E0LfD+srsHI4DM4VHWyvOVA1lpX9b+Ui+cpijLFVRpTpBTpgsAtcdEVaxh4aMcF\n1614MTciCKrwaWGqtQo9tfRqtSHwfPzIoNOMwM3ZHXTAeBDHTKdTHEdWenTPqG3rhZ1nBO33Iu1R\nxL8VEF/9zMByGZ+zPmGfu+n5NM9dGmfZ50Arw8Ov4s+v2SE2G6Q0gYa0iwxQkwlogjjHqTJYZUmf\n5rIhQA2EmueXyTrPc+I4roGJVOWWbCc5VpNpsxkz23mSsVyW5Yrmal2YVL6TY5zHgohBCsOwNpTn\nGYLms5ZnJN8Jm7Gzs1Nfpw0q5d6aTIZca1Nf0nxv695ns383AdLDWvN8FwGwD5Kh+yDbRQ7cw1iU\ndczIOsAlLKsAkKaWTt6fNHusSD/UWpMkCVEUrYxdAW6wXmvX7DvNY9v3ajsK0taFtZv3ajs7Yi/t\nEKH8tJku+xjN525rMOWctl5s3bN/FLtg7/eoTvu638/b/rx9/7ycjMcbXpTOYHu1unzgBTtLdsvI\nPlCF6qxsqNWHbdBLytVxXHqtgP3tAU/tbXOws8nWoE/Y6qM8F+U4FEajlyUdjDGkSXlGwfoRs9ms\nplOjKCJezHDUUoirnOrvIFwJS8jgdb0qe9ELAowGlgNX+f7ZICkKKAqKJF5mPPpkeb7CIARhiCmW\nNHK57BymWuR5CR/QOifyHHSRQlpS+CXRVpfcUTiBh1IeGFPdqzYEfgh6wZLKq5+tNlXoqRNqcl0S\nORqlzqp4S9ahUg8OoHWGofm9tPM0J/a2SlVFZOU52K05QT1sQK568nIPy2teewV//m1dwU9YDRXA\nqk6juS+cCaubzoiwWDs7O+zu7rK9vV2XhOj3+ysaFBGuy4Qsz7rJDoVhWOthxBNOkqQGLDIuJAwh\nTJgwT3KPvjUmxPDleV4L2+3wpdyLsFjn9SFbuzYajer15CRUZFcOl23XgScBlZ1OVb9vOBzWGW0i\nP1jHIMk7ugjMNEHQecZJPrOB23lGrTkWznN2zhj0s0w0Oc6TIqSXtm6cNw3uRczieQZWQM98Pq8B\nvOM4ddi9KYhvnsMW1ovmS9gguaY4jldCk00gJL/bqzrYWim5RhlTzWfQvHd7f3kGdlaxJAfYejNx\nItI0XelHck5htiTkbp9TPhf23NZuPkwbeFF/v4gxPu9YzWNctO06EN48VtPB+2Edk8erHjZmJZOu\n6iDLcI81wAoL6avlf12roiu2ovqXAh4YB8dI/BqubLa4stNmpx/QilxwFUU+xykdHM8HHBzRoihQ\njkdalBRZiZvO8X0XyoLFZEwQeBjfxykV+aIkDANwQC9mRP0BeVHiegbX1WTaEDounvLQRbVOYly4\nOLrEVy6+J8yLB1mJm2dVBXlfococpapOl+U5rt+m0AUGjet6GCcgLxKMcQncgFyDKTSl62AcF9KC\nqBuAyQlDH8IIHWzgoHF0iaPLarkfbXDLAkyJgyZQZbUEkdH4SuPpnMh32Oy2iDd7ZCTM4wV5nKH0\nMhUajTYKoxyU1jjKQ2Mol+8GwCsf9NgL1+DpCkCLvs+Y1TCjUpXo3xjD2StXYIVc64nJVN3HHkDO\nmgnaX2Y2al0xk8rRpHiYx1+2Dngw7CFtHZNnt+bkZnvF9jF836/Blixb4rpurZWyjZQ8YzEWeZ7X\nnrDoSCSkApCm6YrjIaEL+zpsZsC+Vvu+ZHKTxYPt9wyrdb7s6xNnxzY6cj+iyxGjIWEgea4SEmpm\ndDXBvZ2xKIY2CIJzM8jWGY8mkyKf2e/J3n/d8c7rG+c5PRd93nSA1oH6x9WabAo8eJ02I7QOgNjb\nyv7yuc1YSp8Q56QZVrf3XZeIYTsQNthet6JCszUBioD9dePdzlpcp6dadzz7/NLs1RVkzIkj0mTH\n5JjrMhIFmNmOS5N9s+/rUVrzHHJN52173nbnnW/dOLK3b46VHxZsSXvMoCtb/qyYl2pSlcWpz0R/\nFQBrPDj7QarKCBtdpf+7KKDE9xTbm30uX9pj0AkJA6+qDK8LXN8Dperqwtoo8lKjl8fxg5BSa5Tr\nkGUVKIkCnyLLKfOEUFXZjHg+DiVlWVBmOaXyUcoljbMqFOdVl25KTWFKvDBAUenB0CX4lbi+KA3a\nZCzihMhRhFFUFVa1Bo3rulS1KKpsSM/zyLKCrChxHJ+kBF8Zyjyl091BGwc/aEPYIfcilAcu1f4K\nh7J0oDCUJLg6w+QxkFMWGsoCigxXZ6hsjslT8jhmNplWYVRHVcsTPWJHNIozNsn+/Zwmg0Frjeuc\nfVYfj6V+TCYYWBaXrZaOEvbU7jam8cvZ9azP6HkczZ7k5P4FsNgVr20DYLd1k6wtwA+CgF6vV2co\n2jWGajC8BElyfjEq9nHEUNkaDwFhcrya7bX0YfYkJ3/bgMmusSXHzbJqnmieX+7XNna24V33HGxv\nfV1BS/sZ2MCt+ZztexDGqwlwL2o/CJix7+s8Rus8gNb8/VHO9SQALmCl39vtPJC67pnYz0BCaPJe\nBUAJKLf7hc18CQiyzy+6Rjir3i59y95W+rKdFdlkF+3QpXy2LhpghyttFrrZ95sMn4AoGaPNc0qz\nIyxyvfYYXMcK2cy8zBc2a76u/SAg5oeZo9c5Pxcxyh/0+aU93pIRqqI4DSVKgXIMVe0mcJYLELuO\nS2kqk7qCrC2hj2LZ0RyHwHUJvJCW79Bue3S7EaosoHRRpYMy5bJ4aYbGIcsL9DJUqYuSvCwIgxYh\n1aLXjuOSOQkUObP5hDLLUeQE7ZA8jTG5j+uHuGjKIqM76DFLcqJWlyKJodSUWuMqQ5ql+G61/FCa\nVXVPTKlot1vMpmO6oct0OqPMXLSuOr8uKu1XkaR4YUhWZDjKxXU9cimQqiDXDiU+AaCKnCTPafkh\nZZagtMYNQkqjKZWqmERctKvw3B5aeZTM0HmMrxzKssBV0AkUlA50fYYhbA1a3B+FLLKUwqTLrMqz\nWmEPh1KP3mxRMmtUV0o5GKOtgaQojamWN1LLJZaW6jNp8ruzRHHKWdLFThXCfhJAF6waVJk4mx7w\noxpQ13VrVsd1XaIootPprEzIMlna6d+wGla0jyOTqtTCEnZL2K48z+vtxJjJddshEzlHE2TJZC1M\nWRzH9b0IMyWhBOOimAAAIABJREFUHjmPPA/bODXDY6I1s0OjzWZruCQDTZ6TGN0gCOqaTfP5vM5i\ntD3nH0VfagKtR3Z41gAx+zjye3O7J8URWcd4SB9ptocxXPJZkiQkSVK/N9/32djYWAH2TXbLBhAy\nNm0RuowXG3TJNVYOcrbC8DZBl83mrQuJ2kyqra0Up8a+Lvu+bUfFdubs8Ww7GDYgkWPZ2by2Y2b3\nRTssKddlJ9Vc9F7k80dlti5qTXC17twPO++66zvvs/fbHivocpktb3hJ4WNAndUFcpSDMg6uu7rG\nmzFmxb4bKgMaBS0GvQ6Xd65w7eAqm70ergN6foxPQeQqTJ6B45IXORpVhcWWpTb9MCRQIUZXtK1X\nliySEtdodJ6TLhYUSYxTTDg5jYk8UK5Lq72N52uMypjPwfG7laA4ywn9gHg+pd9vg0nRixRQRKoq\nxeB3Wig3oL+xzfDuDdqhTx7HeK0WGoXbahEqp2JushzXd9FlFXZBVcZwPEuI/JDSiaqSDnEC2SFt\nHeN4GvIBZRKi3CqMpByvenzKwWhICkOpHYKgQzpbiuWXXlnowVbb57mnd/BczdHpmOQkhrKos0uN\nsTujVMJfUszLMGBpQSC1fF/SKmNZLX5tD8i6HzirRsYYQ6mcuiZYqTUVv+VQ1YQ1cpZ6e6VUff5C\nWwvBLpeN+qCo4w+irWNrmoP9vHCLtDrzVSk2NzfZ3d1lf3+/rjAvQEaMgBRAtJsNQICatZLvhNlZ\nLBY1AJMyFKKbEj2WHEcYJKVUnfWUpml9HxKqDIIA3/cZj8dABYCiKCLLsrpektxnk8kRbYk8Fyni\n6LouWZbRbrfryTiKopWEHPv5SiabPCtb4L+9vU0YhnWF8lu3bnF4eFg/m3VetRxDtlnHUjYZjWaz\nQV2T+VpnXB7Wr8XYNj9bd8zH1dbpaNYBwqYxP89ouq7L9vY2WZYxn89JkqTWMgEry/rIu2wCFpvR\naYbjbF2U9H+gDhUKIIHVWmuiG7Q/E4Ajf+d5Tp7nJEmykmhi6yylZVm24pDIee0Qar/fJwxDgHos\nzufzmtkSh0vG1GKxWHFAbGbQ8zySJGE2myErOWxtbT0AjuW+zgvrrfvsYY6M/f267Zr7N+3MurZu\n/P7Ygy5HiSm2/2UrSN1xHIwTrDwAx1HVYtXLJg+0NJpSR2idY7QmcELaUYBWCSaZY3SG42jKPCVf\nrk2oXA+jXMwyJKkxYByKoqyKsIb9Knswh1YUkeqcbB5T5BMKk+C5ITpX9DZ90tjQ7QwwFBijAYf5\nZEqaLQgDhygMyGczirLEKI9W4LNIUlxfQ5nS6QzIFhPyvMBvV8DPLKoyFp7vk6UpXhDhtyL80mBM\njtYFUbtDsojJCs309ISuyolnJ5jZEeboFu2nS9S2gWBA1O6iAhflVEBTYeh0WhTagzzHVQEqn5PE\nU4oiRec5utD4TslTVy4zKwNmScywXJBn5UrxTHggCHz2jhrhRYQcY3WSX+fhNRmLCqDbA2h5QKtP\nCOtR/ZQ1GJfbW8tC2ezLo4aGfpStOXHY9yF/NwEX8ECYwDZKtqhXjE4QBPWyNs0QoYQQbOAmBiGO\n49oYCYASLzfLsrpKuzGGTqeDMeaBgokyodv31zQs9hIreX62GoJcqxgDuSfZXkCkLRiezWYopVgs\nFkwmk+UC7zsMBgO01jVAtCfiIAjq52FntBlj6oSAMAzZ3t4GII5jjo+PH8h8Ow+0rOtrTW3ORW0d\nUFpnWJoGYx3IajbZzg6FPe7WvA77Gdti8fOMov0slFK1Y9Dr9WogY4MT+SlA2872k8QNOa+EJaXv\n2SFLcWTCMFxxcGzgZvflJrtsj3vRXk4mk5VnIMCvKQmwf5f7EQdIHBepQSdzgYwl+93bc6XthMEZ\ne2Y7GjK3iD6yKfy3jynv5v0ApvfTmn2iOU7WnbcJtuy+80E4IY9X0+WkVT0tR1XaHGOW5ZisGlsu\nqGUYEqitul4Kph3lVtXJcfALj3hyylAptvttep0A3C5unpDnc1xTEuFTZjHKDXDDNq1OhyTXSxF4\nge8GlMqDEuLUUEzexdMOOovxnZI0m+DmM+bjCb4qaAUxnpuzGO3gRxFMMrxOCyd0mOgCsgxPa0ya\ns4jBVyV5mizP74IuUHqExkX5LfxuQNspcKIOZZGiTYLvOVAagsCDwoBrwAtRXkS5mND22mTlAk8X\nnAwPWaQTSKecZEPag4/S8gvy+AZR+wq9HZ/WVgTa4DsZyglwcHBNSOlA4i8IcoXyI5xCUxqDGxi6\nkUNaKA4ubXLnVpdsep+FX2UAFkVeifuNAWcJElC4xuBCVQRWWVmpEonU8qNWwLPapxWOUuimR49B\nmbNFhx0q5rNchqCVrv6udV5odGkjsnLZ3ZZaQa2gdKufj7mdsXvOyoRsA63ziqQ2JxT5ezabrbAy\njuOQZdlKJpacRzxh29jYhR3FexagJMZFSkfIPcg1SghDvGlYFSLLOG9O+KKRksKU9ve2MN/Wptjs\ngICosiwZj8dkWVZnGAoYlCVMHMeh1WpZc4678g7EWNnp9bI8kFzXfD7n+vXrK0JiaWK8fxCN1Dow\n1Xy/67a323nA6SID8qSwXNLWMYM2syPg/aKQYxOc+b5Pu92uNUtxHD9gYGUfYZUEpMj3oumSPmmH\nxoXtkvcl7JIcX7azzyPnt8G7DS5lX2FyZckem/20HTXbMbEzI2VfrXXN9imlaLfbDAaDuu7WYrGo\nmUB73pGx1RTy2+Auz3Pm8zmtVqsGhReBK/tdPUprgrYm07nO4TiP2XrU6/mxZ7qUU9TrBzqWF2Z3\nHpReCuVXm+stlw7CVOJwSpReELghTjlndPdduibH39zGo6jqUpUZiyyl2+6B0pg8ZnQ8p8Cl0OCo\nGI1LmoHyQnqDDaLeJiZLQeWU8ZSWzlBhSBRFlOkM13U5PT2lt+EQhSFaL9BFQKYLlAoYz2fsDDqc\njoYMNrcpC9AolOdXi+b6Lot5hu+1iPoh+D6T8SlhsiAMPZQGyqV+xQvRoYcThJRUy99o10OZFF3m\nKDTJImZ09zYxYxaFRzCbcUUdw92U1uA91GiP8OnnoH+ZpD0gdM7qVBntEqoWQdsnKHMyrzJ4Ck1e\nFATKY4cWH3rxZVLlcvfeDRaLxXJClFpJwvI16ibJT7sTW7+esVL6gQlEwn+257Vy7HqSXP5eahxV\nsaGZztcM9IrNlAvQWqNNgTFPToq8fc0XTRbNfZoTj7QkSTg5OakZK5nwxbMWg2Bn/9mZijJpSwq9\n7GuXgZAQo0zkQJ0VKYYEqAFVURS15yyGRcpUSBjQFiSLJstmYuS52BO67fELwDw6OmI+n2OMod/v\nAzAcDonjmMViwdbWFu12e4UxkGYXy5TrscGV53kcHBywtbXFcDh8oEK3rZG7qDX7dNP4rwtbrhsT\n685jH9tmTs9rNlvyJLTznp19H+vYRRuICXCTkHhRFPUqI2EY1uCiCbZsQbw4BHYfFCZWtpfwpHxn\nl4kQJ8TWUNrXajskAnRsUb/Wug6zy32KU2Tfux12t38WRVGH24WFEk3m9vY2Ozs7NXu7WCw4PDzk\n8PCQyWRS9xt7jrBBrrB99udS8qU5h8k9NUFcc25vvmP7Hpv3dx7oWsf4nnfM5n7N67goMeBR22MG\nXWUNuqSzuZ6LUoKzlnTIOq8e0KYE4+Dg4hhF4Hl0ooiub+i4OXp6n4IcHQUEvsKhqk2V5zllHmOU\nS4GLF7ZwFQTRJto4tFo+nh/h+i1yVRLnc3qeAzojnw3RStHpdJhmcxaLBa0oIFuccJplbHoBKA8d\n9Gi1elShL6cCXKWh3e2DE7OYLzAKup0Os2lBuxsRz+YE3S69nV0Ov/8ul/Z3SLOCVhTg4qBLjfE8\niAJMacD3UGGIV6S0Om2mixFbO7vcevsN8tJwZ2bY3Mk5/cYf4CVHGCfhYx96jsU7V3nmL/4SeusF\nim6E40WwLAKLCam4I4XjFuSOj85isqIk9F0GgWKzE4LjoosMU1ZiYz+QBY8rZknZ8UPAGP+Bd6is\nQVLqarkkx7Eo8eU/Ux0AI8VrUaxUTBXPETG2ZbWUEhB4auV41fbWRFcqTJGjTYbhjD17nO0iwGUb\nlnUThxgZ20uWsISwXmrZf9cBMzmnGAr77yiKVoyCTEB2CEWMiVJVOE+M2MbGRr0OIjxYU0yKqgIr\nzJYI8aMoqg2lgDwbcInhkeMLQArDkH6/z7179xgOh/VcM51OyfOcra0tdnd32dzc5Omnn+bSpUsr\n2XJ2tpbouWyGS8JN7XabVqv1gKi+2ZqTf/Mz+dt+lzbItY/5qCHxZuLAReBczt8UVT/Odt41NMfG\nec+iGbYVMLRYLIjjuGZi5Dh2TTpbeyVgTULo8j7srEfpX3ZiiTCv5z13GxSsA0+yffOakiSpwZcN\nvM5rct0C6CaTCZ1OhyiKuHTpEs8++yxbW1toretVF7a3t+sQv4xzYyotpNzreWFWCefb0gX5fl2/\ns1eMeL+t+Tzttk472fz+PCa5+dk6xvX9tscLulS1hp9STS9FhNCCSu19lgPDaHzfRRcGxxgCx2O7\nM2DQ7TFodeiFLULlkiUzfBOijYfr+2R5gi4dPEeE+5UeQylFnES0+5uEfgcv6uGHLQI3xlUOyfge\nSZySTqYErYikrIyEKTLuH91jb6sPvqadzDFmzMb+HqPphF5/g7zIKIoEzw8xboDrGYLQ4HqK0+MT\n+luXKLKUdD4HB1r9PvvPfwiTL4h8lzSJCdsdijjDa7XRRVWqoixK/DDA0S3aroMuclqeor99meHd\nCTpz+MMvfJ63vvcqkYF+BN/73vf41//aL3PvjT+mvXuD7nOfxu1vUroR2nFJFglKzwk8l7LI8RQU\njkMaJ6TDE7q+4oW9Pq3PfIKvbylu3LjBZHLKIp7jOBrQSJ+uwPOS9qd4cLCpM0Gl62q0PvPeHFto\nX/UMXEvIpzgbOHqZxVgJ65fbrejFzoqg1s2IbtCgVInj5ij1+PUr68Ij68IH9nfSZHIX5kq0G3t7\ne4RhWIdGxOhIdpV4rnCWBi+hvTAM658ikpdtBFyIkF6u5/j4GK01vV6vDge2Wi263S5pWi15JSya\naEwkXCe6sCiKarbNcRy63S5RFNV/y087bCKGx3Vder0enU6H4XBYf3/v3j3ee+89vva1r3F4eIgx\nhlarxfb2Ns899xyf/vSn+Zmf+Rk+9alPrWR8xnG8kgAgz0syF42pst8+8pGPEIYhJycnTKfTBxIT\n1r2vixhM2xm1930UoyTjap0x+iBCJH+ezX72djuPEVlndO1CpQI6lFIkScJisah1jnafAlaKiAqo\n73a7K/ZKgIcUBRZHQYC49E0B8DJW2u12HZ60QXUzXGaH1uUehZXNsqzOIJbrlHPZoUy5PhtgzGYz\ngIpAmE75whe+wOnpKQcHBxwcHHDp0iUGgwGdTofxeFwL7IXNlvlD7l/GXqfTodvtopTi/v37K+eU\nd7AOXNnz27px0tz2YePgzJY8uHqJ3ZqLn593fGEKf6yZrgowLpU35ky/Aqve/tqJAxfH9fAch0D5\nhI5Hx+ux0dqi02oT+C6+U1HDIQWuBwpNmlaLUadaU+BS6oS0hHa3R28rIC7moAK0icnSjMl0RJwt\naLsuTmtAZ1MzGd5hOhnj6BSdLxiNRhitCDsGfe+Q7naL6dF9iqiHcjWeLogCH+U6OJ0u6Sym3e6g\nTF4twdKJmA9P2Oi3WKQxJDE66FA6EY7rEXZ6pLMFfrtXhc5cH8dxwRjyvMD4AZ4b4HdTsjxj++AZ\nRskNDm+e8gdffYO50yJLu4T+lC/du8fvfvM3+Vc/8yJ/7+/8Tfz9j5AVObozwG918XwfE+csplPa\nkUepM8o0xsOwmA5ZJCfkrk9e+ty4+RbT2ZR5PEI5Gih4oEspBWgc7AK4Ipr3ai1fJfCqVicA6uK4\n0kPOhGDSzs7juksPVpU42mCcpT7QgEGvZEpWzVkK+dUyeSMHVVT/H3NrDnZbt7RuQoJVY6qUqkWy\nMgHLSgo2OyTHtgGXHUqEs3CB/V0cxysGQkKO4/GYOI7r7CcBJIPBoAZNxpiVkg221y6GUK7dNhBi\nVISxk/3WhVCEfZOJtNVqkaYpg8GAIAg4PT3l6OgIgH6/z8nJCaPRiFu3bvH973+fu3fv8sILL9Qg\nTzLTmjXEJIQoGV2yTZIk5wKude/6UZqMmXU1lS469nn95DwWzt7uSWG5YD3j0PzdbjKGmiEjcRIc\nx6mX6bFLO8hP2V9AhLxnYWzOVuY4e84SthNQIvpBGY9wFvIT9tJ+RxKat2t+SV+X8KVcozgcnU6H\ndrtdJ4usW3zdnk+k/wjQs8fpcDjkzTff5Pbt23z84x/nxRdf5Pnnn+e5555jf3+fy5cvc3R0xGg0\nWmG37HCsOGLyHGTMp2lah2kfxs7az/U8J+E8trDZV5rNnkvtviLfrTtH8zNxUn+Y9pjDix5V+FAv\nK4ebeiHjMwOr0LXqyAEks0To94Ct3hZXog7t0CP0FB4aJzcUega+xjUO+aIqwOq5EWUOizJfDrwA\n7XdQTgRptS5fnJT4oYujDIMrz7CtwNU5TnyFZHwf1faYJa+hpwu++9Y74Hc4vH+Lgyv7FCZAeT3G\n05LdvafIAa/XISsDOr0+4zun9NoR5As0ht7mDpOj23Q3tphnELTa3L/+BruX93B6HUy7R648gsEO\nJjcU5BXIyBIcz8H3PYzfAq2Jdvbw2i2e7W/z1AsJX7r92+z/9F/l5371b/Bv/Ft/lf/0v/nH3P7n\n/zPvvPNV/pcvvs3J6X/Pv/233uajn/hl3INPkuCxlc+YZjFZlkJZgaJS+RRGEfQ2Gd5/g8hkvLh9\nQPqRn+J3v/T7jJiiXIcohdI/C9FVHVdVbNKyq7nuGU1emrzCPnVYElR5NtnXHorx0EavMJ5anel5\ntK4Am2MMRpl6O2MMDg96SNWXqopSGoXrFhhnwSPawB95a4YT1+kNHrZPr9d7oOq6DRzssJmcw2a/\nbPFulmU12yWerOwvoEMAhzGG4+PjOrwg1zQYDIDKQ5drFdG+gDFbHyYGSozMdDqtwzYiem968nIv\ncCYSlvN1Oh1eeukl3nvvPV566SVeeuklfv7nf57f+I3f4Hvf+x7j8Zh33nmHPM/55V/+ZZ577jk2\nNzdrIyHgtOkMRlHEfF7JDHZ3d3nqqacYDof1s5BtH6Wty8ZrGpOmoVmXjm9vb2u+zmvrvHpbFP64\nm30ND7sPWK/PkXsUdjWKogcqz9vaKzjT8tkgTDRddvhVQIzN+AA1+AJq4CXMroB5+VvYLrvJsewE\nDnFGhHGRkJ6wa3JN60JqYRg+UH7j/v373Llzh9FoxGw2q52U4+NjHMdhPp8zHo959tlna8dLwFpT\nRG+HYqfTaf1chsMhZVnSbrfr8XhRwgOsyg+ac+APE96zNX5yzvfr/PxYM10/TPNViNbQdttEKqLf\nHhD5Hq7rVxovNFqHmDIjyWKgYtEW6YI0KVnkMaboEHUGhIFLniWY9gbKDXHCLqXfptXt4kUhvutU\noCtQOGWKO4/IBgNuHd+i1/U4OjnEc0KGJ0fMZlNOhxMOnv0Y9+8XdLp9Nnd6BGFApxXhBA5FnuB1\nujh5SpwkBO0ucV7Q7vbIs4T+Vod4kaA0ZLOY/vYOJpnhuCGOV1XSryCKj9EavUhwlUGZkrDTJux1\nYSPn7/4Hf4uj3/jH3Bgu+C//168wOrmFakX0d/a5f+uUr756lxee/wrd6BLXcDD7z3BMH0dXxsVR\nBp2nlGlCMR8xOTmipXqk09vE4S0+dvWjhH/pL/LPvvIK95MZZTCpw3ZVkzCxQTn25G5AWUVP1XL5\nJ5ab10v8LPVBaJTT9HgtOnrJkika4kmtK0bQviJj0Fpij1Vs23E1jtFVcd4f4yaTmYAZWeJHgI0d\nvpBwhM1k2TWy7Iw920h1Op2VhbG11vT7/drbnk6ntcfueR6j0YgkSdjY2KgBnFybXRZCQnTr2ATf\n90mSpGbS5P5sBsgGQ7ZBFKME8PLLL7NYLLhy5Qq9Xg/HcTg4OGAymeA4DqPRiKOjI771rW/VQv/B\nYFCzHbZeTjx6SSSRa33mmWdI05Q333yzTu232zrwZOtE7In9YeGzi0Is5zEFFxmZZhj7gxANf9Dt\nUUOr0kSfJO9P7lG0UAJYpK832WS7rlozq9cuirpOY2izzOL4KKVqB0aOL2BeQpw20LMLBsu5ZNws\nFos6XC8lKWR/0azZJR1arVatSxTpQRzHHB0d0e12+cQnPsHLL79MnuccHh6SJEkVxTGVJjMMQzY3\nN1FKMR6PHxh/rVarHpeSCbqxsUGWZdy7d4/ZbMbly5drACrv57z2fgH/D+ogNEGqHKc5Tj4Ilgt+\njEGXo1022gM2owF7vS22+5vAMsyUpRR5SpoluAo8ZciLlCxTFLkhirqUKPI0ptSKve2nyPDIlYvX\n7tPduYQbRvi9Ll6R4CoF2jAdT1iMJ8RHY9556z3uH97ndHiXWRLjOm3m0xOMMezupww2dmj1Ajwv\nIvJL2p2AyfCYbqdNmmUoN2CRplXsO+iSL2LSZEbUCtDKQZkOKgoJTQ7TMeVsxng4pGz12Tt4Co2H\nwsfgwmJO6SjwFUa5lZA8Cnn+2V3+x//iP+Sfff0Of/DN2/zhV36XD29ucns8Z2NzlySZ8Mq33uGZ\nwZ+yGxl2NjzS/sdwUih0TlHklHFMNp9x9+brlAvNycmQjSDmbvYOz7c8/sLV5xlP/wKf+863OC2H\nInsHwHWrDMFqEsyXBVnhYtBVgTJtwFEOjgtKLScmK0zoYJVRWK69aYcVtKmWD6rCnmfNaF0VjK3B\noYPSVNmf6snJXvxBWxRFDAYDut0u7Xa7noyBFS/d1qLYwmEBRjaLJPV8pLaRGCB7ck3TlMlkwnA4\nXDEGi8UCz/Po9/uUZcnm5mYNmoRhsDO/xGCJ4ZBrWle3SzKw5PhwNoHaoUDxwK9du8b29ja3bt3i\nlVde4XOf+xxvvvkmxlTC4H6/jzGG73znOzVgbbVahGFYG1gxYhKSmU6ntSZ0NBqxt7fHRz7yEYwx\nfOMb33ggnPJ+QoTNZgMieNBg2d/ZbLG93bqwo93kfTTDX4+zrWOw1rWLQKqdTGCH3taF4exjGWMe\nAFg2qBe20/f9OoQm/VjkMk3NkPRdSSCRcJwwu/Z1y/ltgCNOgM2OSThc7kWuR56DzS7LcYIgqPWe\n9moNOzs7FEXBeDxecaZk3ArjbYNM+/gCYMXx2tzcZDabcXR0RJIkK8/Efi5Ntt5mpR6F3bKdIrut\nA2OPcuxmf2oK7n/Q9lhBV816cHFxNLvVISVVdV4PRbAUVQd+WAnkI8Vini47d45xcooioywNnhty\ncnIf1/fY2Nqh1d/heLKgO9im1d2gv3cZJ2yRA6YsMNmCJMuZj47Jxscc3Xib+N6QeRFyfwKnE4c4\n0bTbkC7mtAOfQZIzHQ3ZunwFR5ecHB+Spjkb25fIi4SwFZGhibo9NAWkOYqCKFKQLsAoclMSGIMy\nijxJSNKE7b09MhwgR5UZyrgorcFdapeomAKUS+ZCS+cMgoxf/+k9fvapgJ35X+JffO7r9Fs+/8rP\nfZbf/kf/J5snHd743k0GoeZ532P/pU0ynTEbT/Acl8DzUcqw0Ym4fuv7XOpt0I4MHW+LbHaM397i\npz/yYSba8PvfuI3rrzIqZ82e+MGYsg7nqSXQOjP8Z9k4VX8ocZyzsCGAY2UgVnVRl8Lh5d8iijeq\nETZxwRi9BHrVUkIA2qTwBICudezEw8aEPamIeLfdbtf6qCAISNO0ZooEZNnFTYWxEuEvnK3T2O12\n60kyjuNaY1WWJdPplNdff53j42NGoxF37tzh3r17dfaTsE2iP5GCpLPZjCiK6Ha7dWjEGFMzAM2w\nikzs8pmwF2JoJDRph4hkPxtEdLtdXnzxRT784Q/zq7/6q/zmb/4mX/jCF0iShKtXr3JycsJ3v/td\nHMfh9PSU5557jk996lMrjInrurUW5s6dOzXoK4qC2WzG5uYmn/nMZxiPx9y4caPW6diTux1CXOdl\nN/tCk6FaF1q0+4odElsXtmxeh31su07ZkxBe/EHYNjtEKH1aAJEABXtRahvYyJjIsozxeFyDGDgr\naSIMlvRhu7iugBPJ3hsOh7UjY4vhhTGVzL5Wq1WzUVJHT65Jrt2+Dhs4xnFcs8xQjd2tra363iXk\nJ03u6cqVK+zv79fygu9+97u8/vrrbG1t1WVQlFJ8+ctfrpmuS5cusb+/z2w2q5fKEsfLZsvLsuTG\njRv4vs/m5ia+73Pjxg1arRYbGxt1SZgfpNkO9kVzpcgq1jV7WaV1gN1mHNcd+wdtjzl7sRLR8z5v\nxnEctDL4kUu73cLzHObpAqUhVzlxfMLw9BAXhdaQoymKBGMUaTJja3OHqL+B64WM5inuYIfSa2GC\nNrMkReU5usxIRwV6OiZPUlQRM7p7g+vfeZXpJON0PGWuNzicD5mOXTaKgoAARylK7aIICd027ajH\nyfGQMGgzHR0RdXvMkzndrR2U0Tgqg2JBlswokgKdQUmI228DhlIbHD+is92BdovA5JAllFkG5QJK\njdvtUaY5Oq+KlLqui0dWsWFaoTzF0wd9/vO//df53zYu8f/8vuad66/hBl2c3jO0t6/Q6vW49fYb\nXN59DhVt0gs84iRjGiccnw5hNiVJFnx/cshP7G9RGEXvoIVWmk5k+ORHX+Tm6T3euvvNJahqhjEe\nNnkL8AKlnOX/sz7yfjt8vf2a/ezxpZRCORrHLVHO4wddP8jAFgMp3qvoRdI0rT1p0R2JIbYLowJ1\nyEPOL8eROlviicdxvLIszmg04ubNm5yenjIej5lOp3UqfpZlhGFYGwdhBFqt1grLJts0a3DZYnV7\nIpdt7dUQpC6YNAlh2N6vDcgkHPIrv/IrRFHEl770Jd599120rrIuNzY22NzcpCiKuoq9gJE0TTk9\nPa3vVdZHszPiAAAgAElEQVTwi6KoZkUcx+HFF18E4L333quNtVyPDWYuAjZNFsBmux5F/Gs3eQ7N\n0KZcl91sBuNxt/MYrIdtawN2AV32sjw2y2uzwdLfpd8JAJOlqOS/ZPQKcJPwniwtJSG8TqfD6ekp\ni8WiBj6SCCL/ta7KUdhjQPq5LEwvwG4ymdT9Sc4t708Y6VarRa/XWymIKs6JAD25f9FPBkHA7u4u\nvV6vDgdeu3aNXq/HZDIhyzJu3rxJv9/nYx/7WP287PCsgFnJlrbreYmkQDKUbRmDvLvz3m2zD6xj\nx9YdY92YuCgkL9+vC+83t/tB22NnupRyMNqg3ErU/MA6MksDjudjjMIpXXwd0G13aLkhSmcoHZOM\nY0zQBzRJmhEEfWbzEWVZMB/NiUKfVtShuznA6/fxHEXuRLi9HYKdZwl7W0ReRqENeVHgmZzpyT28\nYsL45JRicsr0vddQd99k8vaMt0YxE5WRpWNyJ6JtLjOZ3+EnrvTxW23iNOH5j71MmuU8vXdA2OlT\nGAe336WPRukEijFkMegMN85x27vQCsFzmBWAUnhBgIMiixNMkpJPbi8n/pgkWbC5uYm57xC2+gS9\nTXAVuCn4fYo8x1MlKk8hTWj7u/ydv/3rmPmc/+wf/Lc8c+mjfOhDHybttnj2k59hp6+4c3ib7laO\n4waEfpcsiWnlCfdPE+LpiPtH7+Cll7h2ucPpiYPHEG+rYNC+xMvXXmQcHzEcn5Llcwqdo1wP1/Ew\n+sEaWEYvY+dUYUjHUZSmGsCFNRk6StdATj7TajUksORL62Nr8TqVWjG8AEo1l8xRlKnX0KM93ib3\naU8i67w6e3KQkB2chRLtUJ1SZ5mAQv1LSQhb6BuGIZ1OpwZDovdSSjEcDutMrtFoxOHhIbdv3+bW\nrVucnp7WbJAYC3uNt1arxd7eXp1O3u/3a72YMJuilZIaRIPBoK6BJNdnV5sX/ZgwErZuRrIPbeBn\nP9eiKLhy5Qq/9mu/xuXLl/mt3/otkiRhb2+Pzc1NXnjhBba3t1fWmxNGIM9zJpNJDTRFtyahk263\ny9bWFleuXGEymXBycrKi21oXBlnX7D5uh7SkP9iG4SLmq6lbucjA2dv8uDabwZW/BVABK8yWABpx\nFOy6UqIzFOAQhmHNIkufkiKjwuYKsJUxJJpEKZMiYAdWQa+EC8XBEAAlJSrKsiSO4/oehLWT80td\nOhGs24tNG1MtyyUMmp1paJfLAHjppZd49913uXnzJq+88gqtVotr166hVLXywmg0wnVddnd3a5av\nmZAg9ydlMaDqixsbG7WeVArH2s7Tee9S9l/39zqm67wxYreHMWTrxudFx3vU9tiZruphP3xbXZSA\nB9rgug6hE7HZ3aAbhIROQF6WBH4F3PIMFvGcxSxmsZixNdii3Qrx/JBs6WHn+CinomsH7QAoiOcL\n7o+OwFH0Q4cinpGMjrl5/TXuvvsWl3e3eeNwyj/9/rd45wSSFLpli/2NLRyGeMZlNHPYmDs88+Er\ny8V5W4SdHqVyq2svYpQpoUwppjPGJ0dEnZDI72DShCIrq9Dg5i6u52GKgtPTMRjDu+++S+RUdHXg\nV4P5dJoSp5rdvQNa8wLHd9jd38IxPoGjSOYTdLogclwc/xDmJ/z6v/lZ/qXP/BR/9vXX0Se3efpg\nk/2DK0SXdnn+Qz9NfPh9Tk5OmJ+cEM8TRienjMdD7ty5Q1FkHN0f0vEKNqIOSTrHPTrEO9ij1+ux\nt3ON8WiG7xmMTii1pqBkHYls0+zNrJJ1g+f99q33054Ur/79NPuZiLEXoCGTtJ2laC/w2+l0Vupv\n2eJRmbzhLFNJmCI5xtHREUdHRywWC65fv85bb73FaDSq2a2trS2A2kCJLkuOLWyBNDF0cj6o3olU\nr5f+IVmOUuhxNBoxn885Pj6uQzciYJYMTqkRJinr4ulLWLPf7/MLv/ALXLlyhVdffZXRaMSzzz7L\n/v4+e3t75HnOeDxmMpnUYdrRaMRwOKwFx6KBCcOQNE2ZTqf0ej36/T5bW1s1w2EzXhe1R83QWnE8\nzFkm6rrt5DmeF3Zct8+T1B7lepQ6q6XUBFwCmCWUZ4dgBYwADwABKYtiJ5IIaAfqvm2M4eTkhNls\nRq/Xq1lRCcFJ4dHJZMJ8Pl+bHSkhRHtcikZSaoC1Wq36vuTcMp6bFfEFRMLZupFSksV2ZGxn7dq1\na2xtbbGxscEbb7zBZDKp54zt7W3yPGc4HNb9Rxg10YSJwyTg0w5rCyiVMWzrwKRdBL6azJZ8fl6f\nP6+PPEr7UfX/JwJ0VTd3kVGtSkWEnk+kQvpRl44TERkH3yiyZEEURKRxJfyrMqaqztaJeuS6JE4T\nul5Ap9ulNIbcCfDdgG7kk03vk2SaWZrjRR267RA9PyVSKafjU77/7Vf4K7/0l/nCq2/wP/zutygV\n5ErhRD2SdMZo8jaLzOXD115ia+/jjOIpmRvQardxHBfPcynStCpqWqbkRZVK7CqHzsYeUcuvWBbt\nobWDH0YkiwVZWnA6HhMnBfePTzk+PmE2u89kPMf3WoCLwoUQotszAs/lJ567Sq4UuwONHyjKxax6\nyUYxm96muzFAxwtefmGPp3Z+mnL8HFEU4HV7EPZI/QFOe0p2f8pkdh+dZwynp7x78waTeE7LdzEq\nYrowpHmC50EYQpzNubK3z8kiZXo65/74NllZYFSBqa7y/LdrCTGbGhOlFAq31l6dZUR+sO1JAV3v\nF2TKZCnhQAEewjZprWv9kXjJzVCVXSlafhdAk6ZpHTKAysCMx2Nu3bpFp9NhMBjw7W9/u67fo5Sq\nK8fnec7ly5dpt9sr3rnNOkn4UMJyUqtLwizCsEkYIs9zjo+P63XhRqMRp6ennJ6e1uUx5HqlQOXu\n7i4bGxv1PQgb5zhVSryEVV944QX29/e5efMmGxsb9Pv9GiBmWcZkMiGOY5Ik4fj4mOPj4xW9lqTR\niwGRpVXG43F9nc2Fle3W1HbZYKrZ1jFlj9J/349G60kZE3D+va0LO9mhaaAOz8kzlX4ifc5OyLCN\nuhxHQHy32yUIApIkqeuwSR9TqioKnKZpXbfu5OSEyWTCaDSi2+1ycHBQJ6JI4okUEhU2Vtg16f+2\noN2Ys2xLO5Ox3W6zvb29whbPZjMWi0UNsm3gKP8lZC4OiDgjosN87rnnGAwG9TJAUBVSlbIS0udt\n3aSMUWENbWdHxPqe59VAUnSctm7zPJ3WOg3XRX3iPJB2Uf9Z9/kHPQYeO+iqvTPKlc/t35VxcB0P\nX/ls9zbZ7WyyG/bxnQKlY7TOyQvF6SSuBkh3k1LN6S6rQweA64aMZ1O6fove5ibK6VCUJafHRxg3\noCwMnZ0D2q2Q+7ffw0+HDG9f58bb7/HXf/3X+fv/1X/N7//JdRK3hXEjfvJnPstrs312Xv4F7v7x\nPyV897e4qkLCzS0+9bFP8tGXnybPC8oyxQ18dF5QZBmzYka73aU32KYoYZFkmFyRxDHKKYi6A7Tb\nYjIZ8ydf/TNORnNG85wkh/7GJq9+c8rW5g7D4YRsSdMuzIRuq42vNF989R2uHGzzyZd+gmuXBzy9\n26fIMwoV0OoegOuysb3FYjSj1+kz622TlzmtfgdCn5ZXUu5coxfnjIanpPGCJC8ZzmYcnpzS8hX7\nOwcsshlvvP0aeXzKT+0d0PINi0RzrbND8OxH+M7bKXfHBdMyrsCXs8abXr5mOxTQDJmdDZwz5qba\n7vwB0/Tim4NGvm8atUdlF36UzR4T52WnNTUfnU6Hfr9Pp9Ops/vkuYk3KRXfJYxiL8Zrh/jESxVv\nX0Ib4u1++9vfRinF/v4+v/d7v8dXvvIVRqMRnU6H3d1dwjDk0qVLvPnmmzXzs7e3x8/+7M9y9epV\nOp1O7WWL5kwmeQFM4pmLWFjCJOPxmLt37/LlL3+5NkSe5zGbzZjNZvW+YpDstRI7nQ4/93M/x+XL\nl+n3+7W+rNvt1oYJqnpiUk5CwqNyHEkAEObr9u3bdaglDEMmkwnXr19nOp3y0Y9+FKi0ZQcHB7Ra\nLW7fvs33vve9lZpOD2vn6ZnqMHsjVHmRhsUGE4+SFfZBhFI+iHaR0Vv3XbMOltSVA+qw8K1bt1aq\nxkdRVPc7W6ck9eWMMXXhW6VUremS0OF0OmVjY4OrV68SxzG3b9+uw37dbpfvfve7vP322zz11FN1\npXe7Zp2UcpAq87JMlYTpbFZMKbXiVIh2qrkYvWioBLwJIytrLXY6HQ4ODpjNZrWDJXW1ZF4Iw5Cn\nn366fgbybESXaYypS1MsFos6rDkYDOrvoMquBDg+Pq6zq0UakCTJSpLDeeL6h/WDZn9tzpePcpx1\n7YMGYE+ApktVLMYa8XX9ILXBU+Apt1qORqWk2QjtFjimErK6QY/t3WfodDrcvXcLxy9599ZNXNch\nWBaF6w16uH7lKcwnx3h+i+7GDtNFgRcEJEnMfD7DMRmj+7dIRof8lb/2S/x7f/8f8OXvvkfuhkTK\nI271+darr8BTP8/x5B5/7z/5d/jv/v1/zjhdkBb3efrqT+FkCUErwkWxmM7I84woDDHRBvNC0wkj\n/JZH5BYUeYryqg4c5wWLeMbhyYSou82NN29zPM35/s1D8Dx67Q5vv/bt2mB1Oh2KMsTzKq9jsNHm\n9XeOeOPt2/zLn3yeT75wwNXdTfqdCO2HaF3g+4bWoINyfKIkofRdcsfFDTcoALcLOwcHjO6+QzKb\n0m1vk6QV29bvutw7us0zB10GG12ms1NO777H1vaHcFVI2+QMXLi2u0emY5g7pE5BpucrFcSrF/yI\nE7px6jpcCrdaj9GcZZ48Kd74B9GaWq51k0hT6CkTrjBU8l8m8W63W2uKxuMxeZ6zsbEBUIMKmTQl\nBCjASFgrWRhaa81TTz3F5z73Ob75zW9ycnJCGIYYYzg9Pa3DaZ/97Gf5nd/5HYA61CeGTHQyshhu\nU4hsC94lRLJYLDg6OuLOnTvcvHmzZgCEQbt//34NQsSDFxAnodE//MM/5KWXXuJDH/oQe3t7dYkI\nONP1OI5DkiS11y7sWBRFbG5u1gJ6Yb/G43Fdj+zq1as1E3J6esq1a9dqpkTCmN1ul9lstgIOf5i+\nsu7v/z+NB7utA6D2PcvvwhjZ1ebtY0hYudvt1uFCu5wKnAE32V5YGPncdhBOT0/rNTzjOK6Xvomi\nqM4C/MVf/EWuX7/OvXv3aiAiujA402dJYoa9pqJIBGT8S5hQ5AFS9FQAmugZkyRhNpsxn885OTlh\nOByuLA/W6/V4+eWX63IuwgALGLRF9sIGS7kLedaiQxOGVwoi29miUjNM1muU+xIm3ga5tixC3mvz\n/a7rC/a2F/Ube960tz9vnn1YH/xB2mOv01XdhAPGwVFUixoLzaiXoSSlQHmURdXhTBnj+iWmzMiy\nOclsjuMb+k9dRZUn7A1cXr03Yr7I6Ic+Di3CqEVZKMw0Jp9ltDc2UJ5iOhmyyBWRbhMYh+JkDOkh\nJpvh9S7xv//f/4SvvnaDLNUEbonfCXn2qT75rGB2+Hnuff+f8D99c4srL32Yj794hacPdoiTOduX\nL6PnilhPwC9JFLQ7+2jTwlWaUmlcIPRyjC4ol/Hut28d4vd3uX6c883vvMOXvv46O/v7LPKUdD7l\n7Te/RXfrKu8cJvzFT/8sn/jEJxjPcu4d3aeTLOi3fL7xtT/l3nDI9Vv/Au9v/mv0L12l5bTpEpGX\nxySEaK+N3/bxjCFLM7LZnKi1gSpBOTm6hGhjl+k713HcOZs7Vylf+yb3kyk7ewXd4YIsN/zEVY84\nvQHjY9p7lzmdDHEDh71Oh2Jzi8BzuHlyRKB8cjQogzEax4VcrxdGyu9nHbxithxFxYgqs7L2otWb\ngNXQjKP8ZXkI6mM5jruWAXtSm+2MnDdh2JlQ9lpu4nl6nsd8Pmc6na6EMUTnkmUZ/X4fpVTt8cp5\nRU8ihUXffPNNvvjFL9brF/b7/XoCjeOY1157jWeeeYZ+v8/+/j5PPfVUraMRnYtMvLYI1/bigfp4\n4p3fvn2b73znO1y/fr0GWAIGpaTFYDDg2WefpdVq1V60gLbXXnuNu3fvorWu2UE7HCiTvmRsSk0h\nMXpSjV8yMEWYLB7+cDgkz3PCMKwNq4BIEdcPBgOUUkyn0xUx93ntYbqU5k+7v6zTvsAZa/IksFiP\n0prhpXWtaVgFgDSfj4TkpOac6BQlXAZnIXA7s1H2FxAv80eSJLRaLXZ2dupsXimbIM/Z8zyefvrp\n2im6d+9eHWqDs4Wi4UzzJ98JSyvXI0J7O0kkCALm83n9fCR7+L333mM4HNaZh1LIVzSOg8GAVqvF\n/v5+nfgi7LFci10OwgZ68gxEYymgUVg/yUoWVlCcO1nMXp6thDebyQXnhQXPe/+rUqWztm58nXfc\nh4UiP6j22EHXozSlFLosMUrjuw6R55HnKZ7SpHHVwXzdYsPPGUQ+33rrJidHdxn0dsgXU5RDPThc\nHzrtKrTouQbKAg8HozPieYEpUmajEzpdD88P+T/+0W+TllWHakcerU6bIp5xeDxknpS4joebFPQ6\nOUE25/mrH+fS9ibdVkBezCm1xnEDuq2QopjRDRdAJcKcDMcURYaiGkBHxyfg+XzxT7/Kq2/d5ktf\n+hO8qEW/2+OVr7+K6/r81Kc/y+vXb/F3/+P/iDjR/OnXvsbHfubn+PjzL5PGU8jnDMenhOWY9955\njX/4D/8vtv7dv0H3Jz+MM7yLH8a0N/bA9cDzwMvpOBHzRcZ8fELQ6pHHMxxjiJMM12tzf3JMicvm\n1h5vvTPhzuERT289y93DI7LJgkj77FyeYIq0op3TFpQLdssNjKtAGw7n40rLkCdoZbDHwrq6QY/S\nJ6RdZDyq7348jIu0ptd10f1JeEmAgYiCBXgJWxPHMePxeOUcwsJIuQM7vCvHFg94sVjUIvo/+qM/\n4t69e3UIT/Rk0+mUyWRSF0Jst9v0+30Gg0E98Rpjap1Wu92ur0c82/l8Xl+XUqq+7uPj41qwv1gs\nCMOwFvN2u102NzfZ2dnh4OCAIAi4dOkSvu8znU45OTmpdSl37tzh85//PBsbG2xsbNTPxzaQ8hzs\n1Ha7xIUwixLeOT4+rjM35/M5cRzjeV5twOT5SIV7ecbCCogurNnWAe2L+sajGqmLtGJPYns/4R15\nJrY2tMmWCAAG6n4tbKg4bBJqlFp39r4C+CULcG9vD2Oq5a/G4zHGmJXMRVmzUHRTAo4ExEj/kkxC\nyYK0AbuwTNJXbAAv5WCkj06nU46PjxkOh2it677e7Xbr5alsNuzo6IidnR02Njbqfivn832/1rzZ\n+jag/kycImEGhXUXKQOclbIQZ88GtHZyh32/sLraxKO8/6aD2mRDz2u2fvFHHVZ/4kCX43jLm67q\nNUEVhVIGWoFH5DoEQJmm5EVCKfoV12PPn5FPjohI+MkXX+Q7b73H5qC3NCpVPa8oilgsZrhhxMnJ\nCYVW+EEbzw9xlQdxzOXdAe/eeZs7d+ecjKEwHv1uRB6PcOZTer5Dms8ogc1oE+f4PgcbA/7yp36B\n567u4SmNKVKORods9C/Tbe8ymoxo9xckx1W20+loBJ6L6wXsbF7l1s17xPj8yavf42uvv8NXX/km\nYbvFdDTm85//f9na2iPqtHnr1pjW9jP88Z+9ztbOHt+/fUT3/yPvzWIkza47v9+3f7FHZOSeWXt1\n9d7VzbVJipQoqallxKEkYzQjAxYMezwD+2EAG/CDYQxgwH7ywwAG5lEQBvY8SDMmxyNRGlLj0YhD\nUiLZzW42e6uu7qrKyqxcIzP29Vvu9UPk+fJmVFaxSUqqFnwLiaqMivjiW+6953/+53/O2bzL7//f\nv8/FtQXW63luvvV9Cu4QKw0Yxjn+4MtfY75e5Kn5AoEd09rsU1u+BsesEWlMsRDQ6/dBpXihA5bN\n5WtPMerHpGmOrcZbpLaP51o0Gj2Gj2nqi1eo+BN6Y5tGY4+ltQlYGte1KYY5PJ3iYxNEmiTVNIc9\ntGsx0SkKDWoqRv1RE1yf9fcHXBR/20DXWSBLDMFs+B3ItCVAptWSVHDLsqjX61kh0itXrrC7u5uF\nH82mv6ZgVxgf2aBFGHt0dMTXv/513n777czLlZCCFILUWmc6rhdeeIHPfvazLC8v47ouR0dHWJaV\nVc8WwCECdbNYoWhvWq0Wr7/+Ojdu3GB3dzfzipMkIZ/P47pu1iLo9u3bbGxsZIVbJcwovRCFgbhz\n5w6/93u/x3vvvcev/MqvYFkWCwsLVCqVUwVjJYQo7EgQBFy+fJlSqZRlLso9ODo6AqBWq+G6Lq1W\ni2azmRky8fpXV1ezrEYJVQozYIIFc5hGY1a78pMAqNlM4b8t40c5WsJwSejKDC9LWEyAjQBq4FQf\nRjjdbFq0fLNJDq7rZt0KBoMBGxsb2fEk7CjZuEmSsLu7m4nRi8Ui1Wo1Y6vkXES/KEJ7aS4v5yFh\nO2kk3Wq1TpW5kFGr1VhfX6dcLmcMkzBckvkokoGDg4MsY7hUKjE3N8fe3l52TDPLUfYCWQ8iERgO\nh1nIXMpViC5URPOj0Shzhkx9nXzGrFcm4G42m/0s8P2wkPPsnJmdKw/73F/n+NAI6c2qsSYVbNs2\njuUSuj6BY+MkMXE0Jh0PUekElUxIopT1i0uQ9LGSMSvzi+zGIX6YI0lTLEuTpA6jcUocp9i2S5RE\nJOPhFOQBYeDQ7xxS8l1e+d5f0B4OefdOizQFPwzQFuQKBdwgT/PwiFzg4lk2S3n40i//Kj/3sRco\nljxII+bqK2xubVGul6Yb92hMpRCS6j7jvqY/TPGcEpM4oZArMxgN+dZ3vsPhUNNXIXc2NrFQHO4f\nTBkpx+ftt97ghY98jJu33qFYW+KNN9+ZalJQvP4f/wBrMmKv8xZb3QZ62KLvgOuWmK/UuXl7ny//\n4ddoPnWZL3z+GcqFPINeB0sVyRf8aa14leA60zIPcexSKJTA9rj4+FMoNyC/fYgXVsDK0eyM2N5v\nU59fZBAryqlLo71DfdTBt3MoxwLfxUk91NjFyZUZqJRkMk15nig1BV0Pia3LvJj1OuR3Uwf0sKHU\n6Ur20zl3/zz8MI9Zwyqbr/wtG7xsWJKhWKlUMqF9mqY0Go1sozNrb0koUY4h4QPxUsfjMbu7uzSb\nTQ4PD7NNXMo5zPZL/PSnP821a9c4f/58VhVbmv8KUBKtiIR1RLQr4UKYhlgajQZ7e3s0Go1T7VJE\nGFwqlej3+xweHgInOhkxGibzJxqyUqlEkiTcuHGDF198kXK5zHg8zrxxk/mS0IrJiC0sLLCyssL8\n/HzGmIi4vlqtZgZQwC6cVCmXMA6c9PMzywGY8/useTA7V88yFrOe/uwws8Tk9w9DEsmDxoMMrPma\nAFKzXhRwip0062SZJUiEgTH1ffL52ZILAiRMgbr0FxWAZIaNJdy2v79PEARZhwcJq0lihrCmjuPc\nV10+l8vdByJl/ZodJkQTaYr8gYxdE4G+/FvYcLMKvhmSl/srTBactImSZyJaRcdxODo6ytauAF/R\nh0lSjHkck6E3HQrZvwSAnjXHHwaQzPd/UCD147CpP+145EJ6+fvkQm0krS3zPBRYWuMo0JMJaRKh\nozFaxWgStIZKuYYdFCjUXQ4Huxx1xrQHIzwnoZbz8bxpQcajowaVSp3xcEAQ5CgW86SpYv/eHarF\nkK07W+R8zV++8gMOOgGe5TABuv0eqeeg4hgvUTBWPL62xH/3X/4D7GRIpehQX13ACiz6kwQLh0p5\nDaVibKdLvx/R7/epVhbI5xKG/S7RuMVwPOHuvU1KcyscJV3eeeMGN999Bz/IkcQxcTTBcVPy+ZCb\n776DO1Z0e/t4uQKF3BxJPMFORlTCgP2tLXwiJoMmCrA9n3S0Q6lYpNuLyM9d4pU37vDCk4/hFHzc\nXAHt2nje1HD6lkuvO8B3p8YzTWJqa4uccxRP9jS7d97n9s13GU+G3Nrc48mnzxMnivZgzNWSS+fw\nAJWv43sukQLHcykX89hKc8WrEvo2e8M+d48atEZ9LMd6oLbKDAkojHliW1jTvj+n5tGDFpfrumhS\nY659eI0LPDwtepYCF2peXjPT5CVrSoy9sF8CXGSDF4MfhmHG7ohBGI1GKKWyWljb29vASYNq0/u2\nrKnYdn19nY9//ONZrS4JlcRxnLUnEq9WwmuS0i/AT2udVfDe2dlhY2ODTqeThTrhpLabpKxL6EcA\nkgBGMR5mbS75nkajcSpEKeENSdeX300GSpiUCxcu0Gg02Nzc5N69e3S7XQ4ODrLwpjCNkpklRkW0\nOnBimKTuUb/fP+V8yvhRoMMMoZ3Fgj3oePLsPuzjrOs4y1EzDbeAK3EKBFDIsxRgIvdLXjf1XOLA\nwEnhYfmRTFYpH2ICLgFjvV6PZrPJaDRiYWEhCzkKeJPvsCwrO76wypLNKPotk3kTp0VY7VnQItcv\n60vulbDJ4iTJuYqGSwCq9FOVNSDnYt5vAVGS+WzbdtaHtN/vZ8+jVpv2RDbvi1ks1fwOOAG88h5z\nnzHP4YPMk9l5Yb7+QZyXv67xaMOL+rhwIyBAK3ViLOViawfLdcBWJKmipCwKqU3B0VhERKqJhc84\n8qnPr5ELcpQrK0SdmzSb94hGK8TKp5/sUKs9zubWPk4as7CwRCHwcTywUot40CYZHuGNWgx7CQe7\nB4xjmyvrVyjYO3TGJQ4HCaOwSm80oVIK+PzVBZ597klWVxZRgyZXHnuc6nINN3Cx/ZCD5ojc3BKj\n0QRFD20NSSYWK8uPESsbZfdJGFEoag72thgmY/xcnTtb7/PG229Rq9U42DkiVjFB6DMcdhhHIyxL\no9PjOzX2OGxOjd3E1hTCHDpNGE7GJKkir2O0lRK4ZdJEc/PWXZL1p/n+d7dZW0u4tGSj1ARL50iU\nJsXFCxz8YMi0gEeKdhwUMYtL8zz1jEOn/RK33r9H842X2d7q8t7tXT7y+AXKYczWnQPWLhfp2z6T\naD6iRwUAACAASURBVEzeKxGlPo5nof0S6dEBV3KKOdsmb9tsdUIOBh0SPQ3nJCRox8Y7nhpKqftC\niKcWzvQFNNPq81pr7BmGVCmF4qQS/tRwTrNlZ1m0Dxvb9UG0OvIeMQJiOCSEJyUkhP4XUCYMlVn0\n0Mzekw19MBgQRRHtdpter4fneaysrJDL5TKPeTKZUCgUWFpa4vHHH2dlZYVisUi9XqdWq2UbsjBh\nAn4kRCOhUSBjIwQwKaXY3Nyk1WoBZIZTagEJgyHDFLyLATVZQNO4SlgzSZIsvCLNuAVsAacAk2zW\ntm2zuLjIxYsXuXTpUhY6Ojg4oNVqsb6+ThAERFGUFcmUOWn2+5Mh6fVa61MVx2ef/wf17s358aC5\nY2qfPuzAa/ZaZkGm+buAYwEpsg9IRXjR2EmnAymjIPdBWFJhZCTbzyw8KmBf1oUAfnmuaZpmgEvW\nh9SrEg2XAAszFCq6LsuyMqDl+z7z8/MZIBMnwmRH4SRhQ/5PEkHMeyCsmAm6zP1PAJ0ZQpT6YXKf\nZMzOSakDJsye3L+lpaUMVArgMtfhrJMh5yBOlXynyYiZz/1h42FAa/b8zTVuvv5BvufHHY8UdKVY\nKH1a7IZyQNuk2sJNp4so0C45P4eHjUpHpPEY1/UZDmLyhepxrZWQeDwtoxBNWozH3rQNjlPh6KhF\naCcszM8fa1D6TJIJahKTRiMChgz6HQa9Iaur6wzGKUmimJ+bw801ORwkTPAIC2UWFha4ulzl8uWL\n2FbKufU1ut12ZgzS8Zh6tQZWQhoP0TrFzxXojTvsHdzDt116vTbt9h5HB/d48/W3UMUK47TCm2+8\nwaDbIZ9PUTrBd0+yqCwLUpWCsk6MrDr2RlLFIB3gOg5aH4t07ZC8leKnQybDDumgxNf/7Ds8tbZG\nozcmt99iYXWdVFlYboDvuCRpjO0GJInCjhVeLpjiHstlYb7GM888wV9ePMfRwQYH+3fZunfAxaV5\nvEJKUPPAOvEkleUSJTGe4+IdZ66lJDhJTC4I8awOnjNdXOlxjTZLn7Tv+Wl9jhPg9fB+ih9GsGUy\nK3CSaDBrHGWDEs8WpsClXC5neg4BPKItkc1XWJVisZhlFcrGKseS9jb2cWatHA9OjLxUsH7sscd4\n7LHH0FpnYMsMeYp+RUIbklkpIFEMw8HBAfv7+5knvbm5mWVdCVNhZlfKjxglky0zEwPkfpqM1XA4\n5OjoKNOgCLAU73w2nV++B6ZFIi9dukSv18v0WxIGnZubw3Ec+v0+CwsLpzZ2YcqCIMjOV0JCoiEz\nQeLs/HxYKNE0FCZQOQuAnXVPZsOMH7a1IWP2Ws1rFHAvYANO9gIT9AhzEoZhtjZkHkjWqrBM5vMX\n/WCz2cy0goVCITsXYWPb7XYm2JdnK98tDJaANXEOpKaeyAJkXQi7JhpFKeNiDnEkZu/PWSDFbEUk\n83FWQyVhfAFAs06gsF/Zfn/MHkoDe9mTpF+l3EMBl7J+zdISZlaosFqzoHp2HjxsPGjtzIKv2UjJ\nWcDrQcf8ScYjBV0qtVFqxkvTDmgLsHG1RWj75LVDJQjJqwhHJyTpmEmSTlNpjwWNnW6LahhSyhdQ\naki7fZuCv0wchTTat3nq6nmUTqaC7mafvO/huRaWFTHst4niIZVqnWKpyvxqDaVgNBiyVBty0O4z\nSizmFpc5d/4iI21TrlbQccS93V3Ora9gW4pBvwOpYtQ7pFar0WrvoCly4cITLC2sEqVddK9FaMeU\ncy6rTz/DeDDh5Zt32W9Nwzeu5xCPBmgVE6tpWCTwXMbR5Pi+nBbCWpaF7zrHi/A4+8bxSDV4SYKX\nxgy1z2ZTkbvV4Auf/zy99jsk5Bn0Y/pxn1yxTJArEfo+bljAGY2wlIXj+IBNOo4JQ4v6fJknnnua\nu1vvMhw32Nlt0xtB2dPsHza4c+uHXL7+y9w9alAoLJErFum3m+QDH993SVRIAU0uTSj5Pu3JmAnH\nl6WngWWtZVKbk/usxXV/mFAAmzpezEpr1KmPHm9AWGhsNB+O4o/mML1P8zUgY2DEA5byBaJXStM0\nC+dVKpVMMCwtT8QrT9OU/f191tbWMnBgsjumpmNxcTHLPhSDIll6smleunQpS20XUCTaLTl2sVjM\nvF/JapTvluxIAYedToc7d+6wvb3N1tbWqRR12cxl45d7dRbAkrR8YRVMj38ymVAsFtnb2+PatWsZ\nIBTjZ4aSpLUPnISZkiShXq/z3HPPZef21a9+ldu3b1Ov17Ftm1u3bmFZFlevXqXb7WZ6MTGuYoyE\n6RqPx1n9PRP8nLXpf9B5a4rATRDyoPea9/LDMmbvh4xZhktAsYRwRY8lDJcUuwXY3t5GqWlzcymQ\nKkBGeoLKceX1KIrY2dnJwu7CDkklehPcitMjgEsAn/xba50lV8y2+DGBibSUMiviy/yV9Scygdnn\nZtpVUxslbJeAoDiO6fV6maj+8ccfB8jAnVkg2AwDCrtnhiAFXEqpiu3t7axNkGVZWXkKqR9myiHk\n+HLdZkKDKa0wx48LvB70mbPW21/XGni0oEudTlOXjRTLwcYGpfHRLORylFwLazAmGQ+xtIXvB6Bt\nfD88poAdGu1DSislrl59jv34Lo2NPjt3e5Tr03i2migsS2NZDtEkZjKagIqwLZva3DKV+kWC4hx2\nUABscuUE51yBUqeP5fnkSjXGkeLi+SUsZdPY3WN5dZ04HRGPB9hYBIGHDdy9dYOVc8v4fhl0Qjwe\nkageHoql5TpvvX2E7TtUFte48e++RT8KGUUACb5t41oKZUFqpfhe7rhwXYptFI1TYmDSFFtP7125\nVKHVbFP3Y6KxZrOdMimu8OLn/nPWL1zk5R/eoBLtMD8/R7Hio9SYfL6I4/l4ns9wPIDJkCQe4+VC\nNA6266CTMZVqiec+9hHeePM1Bp1DmocNxjFYfoFOq0nrYJs4jihWiijbAc/Hdh3SJGISj8mFOfKO\nQ03FtNsurgIbC8u20YnG0qCtk0wZuU7U6VIG0/9PH+gBnQ7f3N/PUWuYrrEP1vfzUYyzDJ/8bm5u\nEkqQ12WDNNO5i8Ui6+vrWSp5s9lkYWEhCzeIVykbtwCXarVKrVbLwB1MQ2Eishc9VrFYzASz3W6X\nXC6XhULEEEqboHK5nHm9kqElZRmEEROwKOJ7AY1mSAY4FYYTAyT/NsGJMHlwImhfXV3l+vXrp+7t\naDTKCqaa3r8Y3iRJMkMp96lUKnH58mXu3bvHq6++mtUzCsMwY0SGw2EGSOU85R4LGJj2aQ3P1J/M\nPv+z/m/2uuX95n2RefWjxocJcMGJ1sg8L/NZy7OQcLUACQmjicGWeQMngMysY9fv97O1IOBCwlrm\nXAVOhYlFYwUnYToRkZuJGQJc5LylFc5oNMK2bcrlMvV6HQkzD4fDrByEOANyTrIe5V6cxco8jL0R\nnVscx+zt7XH37l0mkwnnzp3LGGizlImct1lPz6xhJ8eUhBjXdbOG8EEQZBmXZna0CQjNeWky+OYw\nWU3z97Ou94OOs5yYv+75/2hBl86jtMLC6OhtxWA5aEsTuC4V36PoTGDUZdJvY6UJrhVi2RrH9o4p\n4TG1WoVROiJWIfniZVrdm3QHTZq9NnNLcwwGPezQJV8ISZKYlASlYjQKS+eYK69TWb2ME5SJlI1W\nFvkgoFRfoTSYhlmcsEi9WGMyPEJjYXkBkYrwXfCTlHanSxTmKVdr5Crz+Lll5mpF4qjNvY3bRKMh\nE1zq3QFrl5+iNUj5T6/9GYW5VQKrQqWp2Nx7nyeuXGR3dBcVJziuBSgsrfHcAEV6nA6fQx+HzrRK\nwbIBB8fzWV5dJxp0SPIl/tE/+ae89s4m585fYdTdo3Bxjuef+zjFuQKFWo3e5hGDTgcvCIldFw+N\n5ym64wnRuEtQroO2sRILpS0WV8/z8y/9KvFgRKf/TfaPmpxbXiGXLzPu99navM3KuUu0B0MKx4DY\nTRWOOw0Z+25A3vGYL+S525mmUCfHGiuURtnCRh3T5ErhnNFrcTZsopSaVk89HrNlJqb/ltDl/e/7\nsAwzNHTWRiA6CzMzyQzRSUaWsDymaFjCioPBgPn5+QzQSLh6tlhnpVKhXC5noAfI0s4F7DmOQ7lc\nPuWtm0VXJUVfNmI5f6UUOzs72XGkNYgUWhUmr1arZSEcM8w6u+Ga6f1yH80wyOLiIv1+n+XlZV56\n6SXq9Tp37tzJwvdyfgIG4aRApbCAZuV/AbaO41Cr1Xj22Wf58z//c+7du0en02FpaSnruddsNjl3\n7typcI6pOxQjLSyHqV16kOH8USEXWRMf1Cg9yFh/WMasnkt+TA2X6K8k/DybdSgAzcxENWtzmSyp\ngAoRu4smrFQqnWLXBLgJYDbZUck6FBBvfo9kLop+0QzzS1srKbsgLLXMZenfKHrI2VD0LMCa1RDK\n+UVRxNtvv83du3dxXZcXXniB69evZyBSQJLcZwFDcmyzmKnU3pI5J0Vb5TzFmRHmVda3sIbm+ZrJ\nAuYzM99jzokH/S7vf5gTc9ZcfxibPHu8n2Q8YiG9B3oqbkYfp/E6Cm1NDbznBQSug+eMiIcDAt/G\n1TmUdpkkQ7Sa0qSjdptGo0F5uYiXr+D2bZQVsNPYJtYWtj0/9VRTxdHRtHcWOiEMfRwcCvkKzfaI\noXdEdcGnUl9Ca4tiuYIT+uQIKZTyxHhYgYdlFTnab5PLl3Fp0+50yEUtCvkSoySlOxzh54vYbpXD\n5j5xtIdOu+Rcn0uPP81Bo09nmLDXHnLu6jNceDrHn/zpdyjWFrhQsLl56wYlG5RKCfwAfVyvzNaA\nc3qD0FpjO9OJIhkzK2vn2Otb/PuX3+WHb9zmG9/+57y/96c886mPs7O9QX8t5NKV52nsbVIpFsCG\nfrfD/NwcOomZjAd4fsBw1MMOAlwnxLKmRfsWVko8c/1jtHb3mEz26bT2GU3mcTTsb+8xLNziwtUn\ncCOIVYoLxHGE47ok4+niyYc5cn5A4PlYI422piL4aWbh9FrTNDneWG1RzWNCpNlYvG3bWZYjGAtG\nO/e/dqqa/YfPwMD9oBJO6gOZtL6AL3NOCDAAMuZFGJput5sVUpSwixmCEWMkbMFoNO1nKv3rpHG0\nMFMms2ayUFI/CE6E/lI+Ik3T7Dw8z2Nubi47D601a2tr2WeFIer3+9lGL6Jcc5jGQO6fgC4Bl88/\n/zxf/OIXyefzfPOb3+Tu3bt85jOfodfrZRoz27Yz4yfXY2Z6CYgUBkPu14ULF1hfX+fo6Ih2u02n\n08nu98HBAfV6PTNMcCIRMIXQEoqaLWJ71r9/1Jwxf5dxFsv1sFDKhwV8Pah8xizgEmBs1suSNWOC\nUHnGwvSIlk/eIwAeyMJdEr6X75H5IaFzE9SJlss8hpn1KCBOAFupVKJUKmUlUkSILmvfLJ4qx5MQ\n+GwmsbkPyLnIdZnXJlqzy5cv88QTT3Dt2rWsbdWdO3cy1srsySrhSJOtkzUiwFb2EbOUijnHB4NB\n5qjJPZcsS7k2uR65DvP/5HmfNUxgbs6RszKCHzTHHgTEZufeTzMeLeiyQ0BNs88sje1aWCSgXULP\nJnQUoTfGUQrHDfDcHMl4iE6GOJ6N60y99/r8Eq6liHbHjMMRdj1k/eJVCq+/ixOMGHR7qLxLlFrE\nUZ8g8MhpC2Xn8HIl8uU65VIVu1AjXypNO7dX5kmUxlJg52p0Oj0c12XS79PtHkA6pVOLYUoQDYlS\nRb/TY3H5AuXaHO1+h6P9V+kc7uGS4uXLePPLbGwc4QQFdKLpdPsUwjy5YpVSMeDylQu8/MYIv3SB\nZmMTrV0cpRlPuliWRlspDgGlUj4T7mttYx8bBtcGFQ3pNLZZKp/jH/293yKyUnY33uFjT17nm9/8\nNl/6lZc4t36Zw71dAh1BWKJUKlAoRzhaYePQ92wCLyCNNFFviPJjnLCKbVvkc3Du/CLnrlym8FqF\n7Y2btNtt/IIFlmKdbfq7N0j1ItrN4SYpyaSHF1ZInRSlU8AmDEqU/QZxBLGfI9UT3CQhTY71J5ax\nsCx1n/5KWf59Bslsmi5xw9ML5NiwGEIv09RY1tmL+W9ymGDBZDNEXGoWOxTQJR63bOrCcgkwkc1T\ndFQmSzPrJctGGIZhpg8LwzBjuIT9kfdJKERajcj9FqZBtCtSK0wKoUpVeTjRXomWZjAYUK1WmZ+f\nZ25uLhO9i6ESEGQOKTI5G36Qc5HK9N/+9rczrUkQBBnAEUNm2zal4z3AZD1MRtVs0CvGrlKpsLKy\nwu3btzk4OKDT6TA3N5d99+HhIYuLi5nxE6MmwMtkakyH6kGg60He9o/LWP0ojdiHLdQIJ8wOcOo5\nwGmAY75mhv+UUqdYIgmzFwqFDJTJ58zsOfm8MFoyr80wtNY6K9ciWcByrmYZCtFv5fP5rKl8r9dj\nMBhgWdapJt1S1Nf3/VPN6oX5EmBpzhdhuGfZYJnLSilyuRwvvfQS6+vrhGHIzs4Ot27dotFonBL/\ny/WaoE1C+yYoMsO7kvUoRWO11iwuLgJkoFPCuiZYlB85V5PNNJ/NWWN2vTxszOobzwrdmzqys8K3\nP+l4tMVR8bEtPQ37WMdZEdrCtVLydkrJhjBJsI4ncxpHOJbFIBoxGTu4vmZ1ZRHLsohiRbU2R7PZ\nZeHceeLxcR0Rx6U/6hMnLq1kTKEQkCYJXi7HMErRHijbwfZ9XM8ijkd0GwNKSYQfhDTaA4qlCuMo\not0dMokSLBKqpTKlfI5R9wCVxIRhgZwTkA8CBr0uk+EQy9ZoZWN5Ac32iMpqCTXsEY1GKHfMCy+8\nQKvV5qtf+wueffaTbBwO8W7sUiy5FCtlut0uhUKJyTgiTfVxJ3ufJOoz7u/iWQ6WZWPZGlRyUjxP\nR6juFr3uEYuri1QvLeEWK3zxs5/lwvoK7998jx8c3WG+EvDY1XWajk3oOizMVXByAVpNM9eCoMTB\n3h4rK0tEXoTnTDNOE0vz+NNP8Qutv0syHrG59R7FS3VKtRzlMM87r7/Okx//AkfdFk7eJnV9fNvG\n96ftnGxLUy7kqeVrhG6PiQVKx9huSCoEhjG3Xfs0izH95aTWy3SxkSUaAMd9O0/L7bOFdca6SRMb\nrR596rxstGfpHGQTFTAj3qBZZmFubu5UEUVphVMsFjOdFEwbRMv3CMtSr9cz4yObpoRq2u32qeKh\nwkxJtWkpOxGG004Psimb+g4p9SCgpV6vAyfPVbzgxcVFkiTh/PnzPP3007zyyiskScL8/Hx27XJs\nEeH3er0s4xFOmCQzDHvr1i22trYyAzA/P8+FCxdQSrG7u5s15f3EJz5xqv+jsGgClPr9PpZ1uo2R\n53m89NJLFAoFvvGNb7Czs5MBV9/32d3dzYyxGW6SMIpSilqtlhXbnK2VNMtSmQZUhoSATMMrwzRI\nPyrMaDILH0QD9jc95BmbDKwAWTHMcn8F6JgAX1rWmOEvk4mS95ilI+S+FAqFU2vA/B7zvksPRnnG\nkogC0xC9tN2SorrynTLH5N4LuyzvlXIUcu5yjrIuRMMm55lFQwwdobA/ouc6OjrKmrePx+Ps+4QB\nl1Cp3HuZS5J9K98tUgUBq6VSiYsXL2bPpdfrZSVUOp1OBs60PsloNoGWXI98PgiCU+v/x2FnZ7WO\nJgiHExbNnP8PmvsPA34fZDxS0OU6uVPo1rIsHGzy1oii7uMM+6BGOKGL49pYFgx6fUaTEbZfp1Kd\npzfooiwLZXucXy+wtbPN6tUcg16Xg4MdJkoRuha+6xG4Ho6TI6yU8XIVwjBHdW4eOygQaZd40Mdy\nxkRKM7IVqR/iKod2Y4B2XBzLxrETimGBJB7j5lw6/QM8Ig4PB9RqFe68/ya261MqVYjUhFKpxjhW\nuDmLg4MOV69coDOYkNgBX/nKV5ifX+Azn3uOr3z1T1l97HmqVZu+naLdOrHyGEcJo4mFZTnkigsc\nNjZQ6QDPtgg9G63UNISKdXx+Dmmc0Bl0Wags09i+Q662wouf+iJxv0nBXUdrxYuf+Tm+/idf5qkn\nL1D0fdwkZdjpEMc+o0mEbfmMhkNyvsfO1l2qKxqrVALnWCeUzzGObRLt0+lHdPp9xqMjHl+9DCpl\n0jmgVqoTJTFuoY6rFVol2A7Y2IS24sLCMm9u7dNLFLEqkOh0mlVoGAetNdo6LbwEsOxjpkMbIRJ9\n0tfM9IZlIYtOzDx25vklijR59EyXGE05N5Ppks1SNlF5/2w2kmzCknkkGYwClKRIorAEZkhRGtqa\n2iL5fq11JrY3a/6I0RLmxhTRW9a0N5wcWxg8x3HodrvZ90lY6OjoCMeZVuUeDAaZgRLAqdS0t5yA\nTzkXuX5hnkzWyzQUIli3bZuFhQXiOKZQKFAoFFhZWcmMmslcSEkAuSe2bWcMmRxLdCrCCnqex3A4\npN1uZ2HYg4ODDCSYIWPzuyTUJL0tZ8H3WYBKhmlYzWGuCfndnFuzYGw2RPWoh3m/zNcE+AhAMA2p\nGVoyQ0emqN62T1pdFQqFU4BLtJKiyZL7IHXdzNClzAEx5nIMk/mCk2dtOi2mDlGuyczoM50nCZ8G\nQZCF20UHaZaUECAkYVYZAujk/iRJkmVjiiRBEkBkvYmYv16vnwrRzurqTKmDFHw1r8FkxU12S/Ys\nU39nOhPmuc46AWdpumZD7GeBsA+SCTl7DrPz56cZj9i1n7ZosW0Lx5mCqoAUN+njJh0Ca4Jr2XiO\nBWmK79rTMJvWJCl4fojjuaRoSuUq2oZcLqTb2ObihRWq9SKd/hHjaMxoMgbbx7FzTCbQaPXo9CNi\nbWN7IZFSuK4PysK3HCbDEcNel3Gvja0SovEAlU4oV6YexNrqIl4A5WLA/v4OaTJhNOwAI0I/ZjJs\n0W3u02y1GKcpYbFIuVbOWoO0Wi0ee+wxDg4O2L59h1/++V/gT/7w3zBfrXD9mWe48erLqEGbC4sV\nkv4BenRA/+A98laCryN8B5I0JtUpru3hWC5prFCJxtI2kVUkqlzk/PXPg5Pn+9/4D3zsuWd47ZXv\n8tYP32B9/Twf/diLFGtL7Ow2qc8ts7/XpjdK0MqaFmG1pskE0XDApNdm0uug4wgPG98NWF5fYxJH\ndPsDEhzmF9YYJgnnLpzj3XffoNtsYKeQjsRYp6BSSGIsUhbLeS6sreFYAagiKi2Rqlz2o3QeTQGs\nEE0AVpj9aF247yfRYfaTkiMlB+rkR6chOg1ROpf9xIlPnPio1EGpD0coRRa3LPBZz0qMhvwb7i8N\nIFlCEjabTCZZ+QfJwhLWy9Q+CViSsIUpOJbQmmRdiWESYCR6L7PFiIQFzQra4mWb2ijZVOv1eqYP\nq9fr7O/vMz8/n7XukbCkFG81Ga7ZrEDxlM3stnK5zIULF1heXkZrzerqKoeHh5kWy6xpJlX6p5nD\nJ9oQx3Ho9Xr0+/1TDZPNtida6ywLTECnFJuVMQtqbHtaD00MnLxH/p41IHLN8nPWMJ3aDzLMe3bW\ndz6KIeBG5qEZUpS5ZJ4znIjcTWbHDEkK2BeALkJ5k0UWMbh8pwAgAVbC2kqGoZRTEbBj3nf5jLxm\nNpE3Q+XCrElfRyBrhdXv93EcJxPgm10WpFG8XNss+yZDzl/W4/b2Njs7O1lfyEqlkjFWhUKBhYWF\nrDG2gElpbC/3TK7PbFY/e89E4iDnJ6Fc0ZwK82gywPJes+XSgxyBWQfVXBezIFHGw5yL+xx/w5H/\nIBqxh41Hq+myTtOJjmMTpCmeTih4QBqjbQ9Lg3ucRj5Fx5ogzDMajajWyhw221y+XKDR3MP3PJrN\nQ7qdIxYWa9zcvDNttlkMSVNNp99naX4Jx/NwfB/HDXC8435o0YRceEw1e9NbE40njJMYtKZaLYPr\nwcQmlw9o3GtzeLjPQr1Gqztid2+fYimkWPDwAw+d2ChbUygWccKQOE3odIbklD01Lkc9nnnmGbbv\n7vIv/8/f57/+nX/MV7/+Z/zgez/giSsXKJVK7G7f4Ykry9y+fYtCqPCVRaEQMoqGxCrF8wKUdjOP\nznGmRRd/43f+G7YaFve2N3ji6tNcKpV4843XefLxx+m1Grz8yqs89dSTBF5CqbzAG2/epD5fp1Sd\nJx53yOdK6Dhh0O+xMFdnMokY6y7DcYrrFiCGuXqNp555msHhTfq9IXfjEc2dPX7z/EV836HXaZEL\ny8SRheWnBI6LpTWpSkClOLbN4kIdd7uNm7pYGmwrQqNPaZpUGt1nWLQRSuR4gSTaaPdz/MfJRPgW\nWoyIZU8/ozW2dZzVaEU/NW38VzVmGSb5e9ZLN/UOpgcmpSSA+8COtLsxf8RISD0d2TBFz2GyMlIO\nQoyctBOSMgnSrkdCH+LBmmEgrXW28Uo4bTKZMDc3Rz6fZ3l5md3dXd58802uXr3KjRs3sk1YBMBa\n61PtV4TxMJkNOKlMrrXm2rVrGSAT4NTpdLKwar/fZ25uLgOpIpyW0IrJWMg9FrAl96pcLlOpVLh5\n8yatVgvHcbhz5w6Li4sUCgUmk8mpgpPCTIiBlLpdplhZjNZZYZVZIzI7Hga4TMMyq/0xGY0PyzDn\noRkimmVF5DWZe6J9MkNZcm22bWcZdDKvZJglJ2RNmOygPBsp3msybDLvgiDIMgXl3KXOlrBD8lnP\n8yiVStnxZW1LGNtxHAaDAaPRKAv/y7yRFl31ep1qtQpwqpaX6ahFUUSr1crm59zcHIuLi1lyzPnz\n57NSFp7nUa1WabVaBEFAPp8/1epHQu/CwsnaMp02s67f/Px8xgbL68Kwmd0qTNBvJvaYGafmOGtt\nzDK6MmZ1XGft+7Ohe/OzDxLyf9DxiLMXbVKmGWquY+GmER596qGmnCpyDvi+RXqc1TYaJ6SWi/YK\n0zTy+gJeoUL3xtsctZt0ejEffWyV2+++xZWLT/D9d+/h2+9gJXpaad12sL2AIRbFsARBAWyLwFZ4\nagK+JkqG9HsR+UJ5WsRxYYk512UwnpBg4TkuK5fXGbQ2iXoNHKU46g9Raczc3AKu4xNFPhMdglBC\nYAAAIABJREFUk2gLdMRo1KK9f0ChOE8pLBEnbUrLLqWSwztvvsdv/9ZvcP2jz/J//O6XGbaHPH5x\nlRtvfY+B7/Mb/+3/xCuvvsaXPv6z/MHv/XOiQhmtfLALuCrG9wM64za2ExGGNVaufRIKF/jTf/N1\n/qu//9u8fvgG+3c3+e//2b+gsfk6r732F/z23/9N+u1dXv3Ge+TKc3z0+tN8650fEFYD7P4IFVlo\nO8HzLdzQY9Ds0eq1KFVrqCTFKlhoJ0BNYpbmV6nV14iiBkkyZOewye3bb7K6fIUk7tFqblFZXICx\nIkra5AMfjU2iQyw3pe77+DpPSylcPQTsYzBkeCdigMzFYxmCYaHndZAJ7uUntU7q9BxXXz0GXmBZ\nxwwCmomqkj7i5SDD3CgFUIiA3Ww3AtOmyqbOYW5ujl6vx8bGBpPJhEuXLpGmaVbGYHV1NWNgxPOU\nwqaVSoVqtZoBJDkPcxOsVqv3hQAWFhawbTsriCghCcdxsnITUtdL2IMkSTg4OMgMnfRklLCofP/7\n779PEASZ3sn3fS5fvpwd//3338e2bZrNZrbhHxwcZGybZVlcu3aNhYUFDg4OWFhYyKrIP//882xs\nbLC5uZldd7fb5fHHHyeXy2Vgx8wAE7Apmh9hIwR0+b7PhQsX6Pf77O3tkSQJzWaTJEl48cUXOTg4\nIJ/PMz8/nx3HBKS2bZ8qgjmr04IHgy4ToM/OJ/nbBFizbJrpAH+Yhhl+M8/ZsqxT/UbFGJtz1wxJ\nyf0VhkXArhhRadY8G56UeSuZfDKEeZHEFHEyBHxUq9Ws4blZ8FR+h5OEEwEWkszh+/4pB0P0YUEQ\nsL+/n51/s9lkZ2eHixcvAlNWbGNjIwMnURQxHA5xHIcrV65k7N78/DyXLl0il8tlDpKwW0dHR9k5\naj3tx7q4uJi1PbJtO3MMzKxJ0xmTeZbP5zNnYzAYsLu7m2Uvyv3RWp/qg2nqrMxsRjNZ4CwW9kFA\n6WGhxoe9fpZD89MCLnjUbYCOs8VcDQGKUgDLriJQClcrPMcjxcKxHbROGY5HxGnKJJqQWHkuzy0S\nxRGTVNE4POLy1cfwPBvbSfF9l+WVRZIkYq68TBCWsZ2AMCiSL9QoFcssLy9SLReYTEYk8VSf4Tk+\n1bJPoVRGcxx/DwuUC1XcsEClWsOJRvSTCJ1MBcyDfvcEldsRnjftGr+4vMhwMiQsFOh0mgSew9bO\nWzz7wvN8/Wv/ii/+5j+gWLb4X/7n/5HdxoDPfeE/46A15P/5ypcpLjzD3/ni32Vz4x6d2xvUfvbn\n+NJv/RO+8m//Lb/2G7/Fnc17fPyjH+PLX/4yUev7FAs1RsOEHCmt3Vs8e3GZ//jvvszKUpGnLi7z\nv//T/4HPfvaT1GtVvvOd73Bv4x2evnaZ3u1NHrt0jpW1NaI4RaUJQTDNBBsMRzhpQqomWPaUEnf9\nqbelrAmd0RGxNaY/GfHRp5/i/Te/C7bNd157i1/7pYuQDmnstqjXCrhOjng0ZqRjcrka2nHA07hu\nQqrAdkPsNEYfZyWe9lIebgQyR0XHGcMlf7AUWCd6rvvodi0aqRjb9u479qMaYuxlsxfQM2uAzY1e\nPDa5d5LmbX6mVCpl1bdNKj8IgqzyvNanixgCpzIQRbskfxcKhazZ7WxVddkwpVaYnJccdzweZzq0\nwWDA4uIig8GAvb09Op0OFy9eZGtri06nw8LCAhcvXmR3dzdjOsQLF91XsVjk4sWLvPzyy5m2JEkS\ntra2CMOQvb297LreeecdnnnmmUxHlaYpR0dHNJtN6vV6VilerkWYD7m/opkTL1/Cn9LTUbRgh4eH\n7O3tsb+/T61WQ6mTIrECNAVMm8zcbBkAGQ8CXeaQczQZkwd95ixP37btU3PqUQ4BS3Cyhk2mVwCY\nWTfKNMxi0KVEidzXNE0z7ZKADvmsRFUkvCWfMxmtWXbQ1A5Wq9WMOTZDenBiuAWAyVqaZWilBJCs\n0X6/z8bGBkAWYk/TlPPnz7O2tsZgMMjqYZnnKf0QJTmjUChkjKw4D+beItmJEj409VrCsMl8NYcA\nLxPUizMi19jpdDKto5SukDIs8hmz3tespvEsYPUwNtZ8VuZaMhnLBx3L/L8HscI/yXjEJSN8HJ1Q\n8KFEynrZZS6NGXQH5HNTDyHVFskkIk5j4lRhuw5W4qJxsV2X5lGD9bU1HNui1ToknziAot1p4vs+\ntVodSwX0exOK5ZAwX6VQqFAslhgOxpCMqBY8/FxIPB4z0iPKlTrxeEx9cRHL8cALqNWXKJVKdDtt\n1KDLsNtCpxFpMkX3tVqNw8NDSsUKWk9TyA+ODmk2D/HDHINhwvLiGp12j41bG1QLNX7w8hu8+fYG\nSxcf5xe/9Am+/8Yd/vCP/5T2KOLFTz5Fv9fkz7/2r3nppZf43vf+E4NRzFrF4c1vfw3t5Xj1L7uU\ncxa51cuMBl2Iegwae5Rtn4P9HT7+/CfodLa5997bXLryHD989WU+/qlP89abP+TTn3yea5fPUyiU\nGI8jBpOI5dVVSoWQZuuQRMFkMsK3UvKePa0cr4576Y3HaMdjPB6ys7ON7bq89sbbLFbmyAcL3Nu+\ny+7+Dku1OgXfJul38HIutnXcu862iMcJnhuQKEWUKCzXwXIDbOVn2awytE7v3/ytk4Uo3qpOp7W8\nlDp+vwVaq1MLzbJmFiLiMY2xePRCetkcBGyJ9mE0GmVsF5wOb5jaBRHJC3sizZwdx8k20VwuR7fb\nPVU93fSohf0y75NsfsViMWOrpBK9aEzMkg7AqUrd4qVKWEK0YK7rMhgMALLaSb1eD8dxuHr1Knfv\n3mU4HLK2tgacNMU+f/48t27dOlXzSDb9Xq/H6uoqGxsbWThV2AgJj8prolO5d+8eq6urrKysZBX4\nJa3dDFOZNaDkmsT4y7nFcZzpvebm5lheXqbdbrOzs5OVjRAWTp6lCRRMw2IacnM8KHQi/2fOp9m5\nZX7+rGOaxvfDMEyGywyvCxievQ4BxnJfRSRvzm2ZM47jZCFy8/PiIMBJuFWOZ4bm4XQoUJwX0eWZ\nWYVmjTb5nAA6WcNmSQlxYCQcLaL/Wq2W6adqtVqW7Sv7g+yH0opLGK/Nzc3sGiQj2Qx1CtsmgM8E\nXGaCj5yzOBpyj8z1Ltcpek55XcComYgja8qstWdqsEx9pnzXjzNm32+e44PWijkexIT9pOORgi4b\nhW9p8jpipQgL7oBkNKaYn4pr+8Mhvh+QCwKOWi1SBSiNZTsQp3TbTYa9Nvm0j+V0meRzUMpTKJSI\nwhJ3N+6RJhZJmuK6HrlCiVQpxpMIpaA6V8VhjNYxk8mIUi5PwQ8p5HN4XoCtNakF59bPo22HUbeJ\nPenS7x4yGXZIxiPQU29pOBxmRig5rjUlHsZauUqvPWTv3l0WF5eplKrY2kf7Zf7eb/42dw7usbW7\ny4WL53jq6SfoDMaMWvd4+eYPqM2VwdY46YAXn3+Kbt3h5rvvMBo0CUuKojWmqzVhmKfXbjLutQhc\nWF1c5PDwkFq9guNP+zPaKqF9uM8//of/kKOjHba3dwmDQ4Iwz6c/+zN8/7vfYrFWYn65jqUsJhOb\nOB7Tn/RI4+mmVQpLWI6Dsm1KuSJL88sMlte48/4BR0dddoZ9XnjuCd6/dYPzP/M5HEKUgvGgS+hp\n4vEE7IggKDCKYrBsXDfEsj0sFWE59kkI8HhY2GhOL55T3vsxk+UcM1WWUa9LO/fT0FpP9VwYBma6\neT96pksWvgCuWS2DsCJwIro1h4Cp2XIOIoZtNpuZMNysCyWevjS2hhPjYwqPBeRJaEDKVYi4Xgzh\nWR6qFJlUSmWp6MKciZgWYGlpKTt3aT4dRRGDwYDDw0M8z8uy+0SDJQ28BVQJ0BQwZBog0ZIJWCoW\ni1y+fBmATqeTeef1ep3BYJAxImIYxWCYGjJTw5LP56lWq+zu7p5iMyQJQNgzOZ6AAWEYJKQ1q606\ny0A8KJQ4q38ygdtZbIEJXmZBxqMeplMh99kMH89eq/k5YXHN0GMcxxnTYtZamy2ACietpuS9ZohZ\n1qTJfgrbKuvWfK8JpE1tkNmyyzxupVLJvtucO+IwyLM5OjoiSZJMewhkYUUZs6yS3AcpNSNrMwzD\nrFixef2S5SxlIuSYwKnMYTgdtpZ7Y2ZTm+Us5PkJMDNlFeazfdgamB0PYqvMYYaaH/a+2ePOakZ/\nkvFoS0agCVDk7IQwHpDTA9JcEct1iZME1w3IByHtXvf4wTokkh6fRuzv3CMZttnafpejTswzP/MF\nPnLl09y8t0O706DTHjHoR/i+jeU6tDpN/DAAGwI/R6/Xx7XGlPM2SRJTDHOgNYNeh3J1nmKhgFed\nzzI29OgIa9im0+yS81wax+2BVArWcVZJpVyjVqtM6/4kEVevXiWKIh5/7Aquk+M//OX/y+OPXWPz\nzj2GSUhnNCCfc/jItav8r//bP+OlX/11vvXdV3jq8nWia8/yynu7tHouK3NrfOurf0KutIJKCxRD\nm/ZBn2GqsdIBuWKJc+tr1HM2a0WX2Kty+fIzvPbmd6iUfMZRzOrKEoN+j9/93d/lU5/6CMVCyMJc\nje3DJju7e3hBSBKNp0xX6tDrDSgGFumkTS4sEUcJhwf7ePkqkYL2QZd+e4irHcq5Ao6VoiOb9959\nm3oxYDyeCkyDRGNbCamjSJVm0u9Tmy8xHEwYjSwmUUzih7i2jbbOaoFzDLKMNaYMTRfOVPfncCw4\nd4y6K1j3leWy7CmwE6Ortca2PKxHncx7PEyhqBjgfD5/asOUDVqGbMbNZjObr1Kq4Nlnn82y7aQm\nEJwAIdF0SVjRNPpwYiQklVwy9ERsK0yXqe2QDU3Ch8IoybnKxgtkRlCE+FInTM5PSliUy2WazSaD\nwYDxeEyxWGRjYwOtdQa2arUajUYDpaZ1r0yQZ9YtWl1dzc57b2+PyWTC9evXs/IVo9EoEw9L+EXu\nsYRthZkQMb2EamTu1mo1hsNhZtwkjGqG7sQYS5PwNE3vy2iTcRbAOstYzLJjJgB+UHjEBAWzQuNH\nPQTwCEiXbEMxgLPMiLB0syE/AW1mXz8zpGgaegHrAtzk/shcgBPNkdSdK5VKmR5P3i8gxUwqkfMR\ngbzJkkpBYmGTpTyEea65XC5rFJ8kJ62pZL2bGjUBcWYxU/P5CssmWcfCzJXL5VOh/kajkUkT5Bjm\nvTHnreM4WeFTrXXGoidJkmU9iphe1vZZNeFk/zE1e7IOzflthtLPCr3PrhHZQ81x1jqaDUXKOf2t\nDi/6VkzNbrFEkznLIklz2LmTdgye7zNJU1wnYByn4PsMey1UNMF1iuQcRVcPCOsVivRJu23evX0P\nnV+kd9gkVUMGUR/XD5mME3K5kFy+hOt6KB0RJzFeYJEmipXlVZLYIrV9coUSbq6Amy+jE4vRqIGV\n9En6RzR3t9CkTIZD8oUKQRDQPDwk0Zql5XXanQFxmpArWQwbMb12j1hpmq1Ner0BubDK7n6XG3f2\n+OzP/gqLtQXefvsG3//eO/ydX/t13rjxPr/w0i/z/e/+kEp9josL8Oqr36ZaKPHRj13nYH+HhYVL\nKG3T6Y04ODhg9cI13rr1Pk7gcdDcx9clSqU8G+//kKcvP8ZgEjGIY5JEcePG23zi05/kh6+/yfXr\n16k9e4XUCygWHfZ3eliFCu1ul8X6IsNOhJU6BH4Oy/Hxcj4HrS5Fx8VWLm6YoOjRam9iO4pEl1ic\ndzk4PKI/Srj51us899HPs9mdcHXOJUk0tuMSRyPSaIC2Q3ZGI3ToEugUW3vTkKARXlRKQarxHe+4\nx+TxwtQnYtls8dkpSh+L5F3neMFoLGYWn7aZhiGnzc8tC1I7eeQFVODEozQ3BWFZJpPJqfCYeJFm\nyx3Z4CScuLm5yZNPPollTWvuiLZFNs4gCDJNE5D1Iczn81n4T/RcxWIx0zl1Op2s36JkTkmvxXw+\nn4UDZvvPiSA4iiLu3LmTvR+g1WqxsrKSpdP3+/2MXUuShMPDw1Oi6PF4zIULF9ja2spKTIjYeH19\nnWazmYVOl5eXM93YbHNtAYevvfYa6+vrPP/88xnDJyyEiJtNgyWGUqqIj0bT9dhqtTKwJfey1+vR\nbDbZ29tjdXU1exYSSpb6RUqprJCsjFkP+2GgaJbRktdmmYIHGSRTP/Wg7/ibHqJ3MsskABmDJeco\nhltC4wI29/f3T4XKcrlcBog7nU62j0i4WYCHzCURyUs4TP6W75qbm8s0SwK2ZH3IOo3jOGN0Z1lg\nAVJSOLfT6WTs8Wg0ykq9SO23RqNBuVzOWunIecAJayZstgBN+azMBQGtsr7kdSltIq2xfN+nVCoR\nhiGTySQr+ips+Owck3Uj11Wr1bL1Lu1+xAGbTCaEYcj8/HzmcMhal/ZjJstpVvc/C1zJeZz1b3PM\nOqtnzXETxJvv/atgfh8p6KrQJhe38Rnj2Hlc18NxpxtzNBlnrT2U0hQKedr9NjqJUUlCdzKkZldx\nwyLFconRaId+v0urdcTy6nk8z6NWm6NSqREPJxRLU4Phu1OPOU1TKsUcOh7i+yFRFFObXyOfK1Kq\nVKeU7mCA542JJ0OahzuoyeC4qm4FrW2GgwmFfIjrBgx7R7TGY5aXl0lVTHLcKPTgqInr+AxGI4Jc\ngYvr52k2W3zucy9Rrs7z5ls3ePvmLX7hC7+ESjWL8wsk/R5bd27x/ns3CIsFSoGPjsa89f57PPHU\ndeZq8xwc7rOzu8H58+dRyYQrF89jeQ6HOx7Veo1qELLx/gbReMKv/fqXOOq02d7e5cVPfZqNzbv8\n+m9+iTRN+Ve//6/5/M9/hsD1efbJ69x45x3qC3VQNjDtf6kSDdg0Gg2049JoNGg2mty7/TaHe5vY\naoBrjbEtl+F4gp/PoZIBnW6TVuMuYe0CR/sDquUi5eo8/fExQzJRHDSOSIGEKXtlqfuFjq7joVSC\nZTEFTNaDhPVTMHUyNNPenie/ftiHbNpiNGb1FKY3OhvukE1UQnbdbvcUeyThQ2knInWlJMxolokA\nshR28b5938/0F9I829zcxRDlcrkMdIhORACKlLCQ65mbm8vCMoVCgUajkV1ftVrNNmi5dtu2T9W7\nOjg4OJWB2Wq1Mq1asVjMwjaWZWXFWUXsm8/nGY/HmaZHwO27776b1fOq1WoZ4yXMFpy0dJHwI8Cd\nO3fY3t6m0WhkzJ8AHTHe/X6fXq+XGRmp9i3hYKlQDicAaJaB+v/bEMMrzJKADAmxzTIRJhADToE0\ncQDMGnXyTOW9ZhhT5qkJYEydoiSICLMjxxCQJp81Q9FmWG2WQe12u6dqjoljYDJbArbMtSQZyXJN\n5pD5I+tBnAhZl6LTkqr5ZthWQJCEzUulEsPhkFardYoNN50h0coBGdiUc5I9QJwyKWdjJkbMsvim\n1mt2zDoPDwJQ5r2YDVGeBbxmv2sWgP0045GCrprVJaf7+CrFtyxs3yc2qvlmyFxp4miMTiaoNGI4\n6hJWFqbeuRUTTXo0Dg+59uKzxMkkaw9Src5RLJQZxn0KuRw2x56e0tj2FDkXgoAwzIO2GY0jarUi\njh2QphMcRzNoH9DvNbBVzGgwwHE8hsMxnuPjuj5JosjnikSTFpVKmcm4QxAEDIaTac2rozZ+GHL5\n2lO8d/MWiXJ44aOf4s13blKdq/O9V3/Ib/8Xv8PW1j0GvT7RcMC//+M/5tmnnuPnfvHn+Rf/8v+i\n1zki7wdcu3KR7e1d7t69y8c/+TFilfLeeze5dPUao+GAqBdTqtQJCjV83+IXf/mXiMYT/uiP/ojn\nP/ZRvFyeWNsEYYG//Ivv8fO/8Hls22Zz4y6bt94i8GyuP/MsR4eHVCslVldXONjdm3qOKVg4JImi\n3+8RBB6dwx0mwxYqGuG4Gte2scM63XaHQjmk3ekSx336e7cpLa5MRcmpIghzdMcTGp2YYaLRzrSM\nxBQfTdtWZ1PetlGa6f+jAAV6KoiHWc98CrpkEwML2z4BMVo/eFMyN5BHOcxzEA/VBFtmgUDZuASY\nmJlWUqRRdFYwNQbSS9FkemY3TAEfYiSEUZDvFSZK+sCZNdXE4ElISJpIy8Yqx3Ech1KplFXLl/Cp\n1AGan59nMBicEulL9qToWwTgieZFsgVNUb8YEmmFJGFU0brVarXM6MCUbRPh+3g8Zm5ujpWVlUzQ\nbLJFpqBajFWv18v0bWabGPlMt9tlYWGB0WiUPWszVCl1mGQunKW/+iDjQaHIB3n25nkIs/aTfO9f\nxzCBibAxUjBT5qjMU9OoyrwWraDMP/m/2X6FcBLCk3sxm30obJmALDkXYePMyuny3EUnZoJ281yF\nmRKwI86PzG2z6KhUoJewooBHATAm2BNgJgk04kCZobxer5e939S8zWYotlotCoVCxnrJd8KJtEGc\nGblmraclLUzgKiBLmDZpUyYOkZy/7CnmeBg4+qBz9Sy290HHPEtH9lcxHq2VSXqoaIyjwPc8onhE\nPBmRxhMsneI5FiqJ2NvdAh3T7TSJ4jE2Ch0PicYjPAv27m1TKuZoHB7guvbxRNLYlku3O00TtjQ4\nWLi2QxLFYKXY9lQE6DrTtj3zKwsklmYSR2gd0xsc0jm6R7+5Q3t/i2jYP67Lo+j1O/i+g+tCnIzw\nwzy5QoGj/4+8N3uyLLvO+3577zPeOecau9BoNhoAARAkJU6WbJkhWw75D/C7/yeHHxzhJ/PBDlkR\nsK2wQgqFKJriDBAkwWY30F3dXVNWZt7MvOO5Z9zbDzfXyZ23b1UXiIaqYO+KjKy8wzn7nLOHb33r\nW2tNLnDKcH45Z5XnfPtXvsNgtM8qrzk8ukdZ1vzv//Jfsr+/z2q15L/55/81H334t+z0uzx/+oyi\nqAiDhJ98/JDvfe//ZDy+YLlc8eDB21SN5cGDu1hb8+TJM7JlzuHBbcaLFR98+CHz2eyqhlyODbtM\nsorPnp2xf3SX7//VX/PsZExvtIN1iv39Q97/mw/4L//JP+LhRx/ykw/f5+hwl2U2YZUtqOuC07Mn\nBIkm7qQsFjlV1ayLW0cRT5485vatu9jaEYQxqAgTdQmHd7n11e9wvlI0YYfFPGPQ7VBYS61DKmWw\nUYfcaeZaMSlKGqUxVhOs8dTnfix6XeoJ3f74zdcpSPMttW3vv6lNFnhfuyUiV2kSSl4UBVmW3ch0\n7V+vME6ipZBFTlgyaf7GIk2pdaJPATKyEC6XSy4vL5lMJm2EnjBxIvKXRUpcDXJcyfS+t7fH/v7+\nDV3L06dPWSwWaH1dnqdpmjYBqoBHYb9EAC/ATs4raSxgLTC+vLxs9VkC9mRDOD09ZbFYkKZp62YS\nZs65daTlcrlsAVhVVW0WcXGlCriVGnh+SRg5VqfTYXd390YovoxPHxDIe375lpct+C96b1Pz4jfZ\nRATI/CI0MSoEYAmA8UGkz3zJpi/jTlx/cq+F1RSgJM9CagH6xouvrfSTd0p0sWiTBHDJM5Q5JscR\nN6AvRBc3mrj7kyRpdWG9Xq8tJzUYDNjf3+fo6KhN5OsDLmPWiX5lXIlOSphtMbKExJDcWb6eS5hq\nYV/9MmByn8VFnmVZ60oVhkxAoq/TknuyXC65uLhoS2LJeWT98o04AV1ybh/8vQgk+e1V3vPB1OY8\neRmb9mW118p0VVXFIE4Jw6uoHdesGQ7bEAVr6/ViPMa6msViHUZeVw1GKQwNi+kFO6Mei9kM2yxZ\nLGaMx2PKep2ZPYrWlnyVF6z0ik6nIJsv1r57Gnb6PQY7oytLu0dZ56RRjNZwNn7OdHoMixmdsCEw\nDh3GrPKSbDXF1TVxYrBNRRQbBru3mc3HlE1Dg+Ldr38LAsPJ2Zi6Usznl4RRh0U2592vvcNiNeP9\nv/oBaadHP+nxP3/ve4Rxn7t33+HO3Qf0dnd5+PgzvvbNX+bi4oIPP35IJ4xJOwHf/s63+Pjjx5go\npt/b5enpc37zt3+Hf/N//x/cO7rPxficIOmyv3eHuJvR6STEoyHv/NJ7/Lt/+294+8Fb/OVf/hW3\nDo/4oz/8ff75f/tP+ejDv+aP//g/8N1vf4deb8D5+ClJN2W5nBNHJednY86nE8JuymRyQZyEnDyZ\ncPvOA87PH2NtjQ77VGZIkCTcfvvblOd/R2k1QRSjg5jCgasclWs4vVzwaFoyL2ucTtZVBxqov9AM\n0Gzm7ZKF0dnrTPMyTZRWn3PTfFHer9fZhMr3czgB7WK/Wq1avYjot3wLUKxY0WSIRkMsfNFoiRhd\nFm9himTDkQgsuGYMqqpiPB6v87RdsQ4C2HzL1Weg5D1r14lVh8Mh1to2Q7VY58IcZFlGlmU0zTpD\nfBAELYsHa6ZoZ2endZ8WRdGKlyU/1nA4bDPjn52dtTo4seCFAYP1grpcLlumTfQuo9GI6XTK2dkZ\n3W63PYZ/n6VAsCzg4joaDoft/+VZhmHIwcFBOwZ9EbIcb7lcMpvN2hQaf9/2MhfLphUv7U0GYAJU\nfBeeAEfZoH2Rte/CEsbVb/J5AXLy42vE2jXlijESoCXfFcAhKUX80lcAg8EAuK53KFnvBQxJEIow\nvuLiFgOqLEsWiwWdTqfVRcm1TafTGyyeyAX8aEox3KS/oi0UtlxAkkQkyjWIIbANoIgOS+6FGCcy\n9mXuyz2Q8/tMMNysxyhgSyIv5VhyP4TN9IHYFwEjn53aZnz417aNKdt27S8790/bXivo6jaaxuYU\ngaGYrYjjIXlZEMRdamWolMEFGmsNdVFiVIC1Gh0k5NWMVd7BzA0HhzusZjF2ltE90oS1wsUhdb3i\nzu27PP70J5jIEUQ16JwwqLGFpWDKQjccq5LaWOJ8SBWOyVdTAhqCSlMqqGtHMV+ShhXZbIFJHKu8\nQbk+nf6AxTJncfKEQf+Ag5232RkdcXL6hIdPn/Cdb/8az46PWeWOvd2Qu7cekOcZjz/7lKOdATs7\ne3z4wad8/Rvv8eC9b3I+WTJ+fslf/umf88vf+WU+fvwZO3v7ZEmPpmo4uPeAv/nzH5CIg8HgAAAg\nAElEQVQkCYO79/jxp0/QTckf/P6/RxnNRXbGspkwnTxlJy44OT7mwVe+xkc/ecinT55x59YRibFk\nywu+/q1/yLPPPuQP//2/49e+/R5hdURPZ5yNZwwHPU6enjEc7PH46UOaxhFEhtPLM1bTMbtRiA4r\nsjwjCDTKJNTa0qsLFF3KwVdZuZTj5RnvUrBy+0RliTXwfDLn05Mxn1XvYF1J3DhQjiYUq3PDWtfX\n2oj1YqhBBTSSuZwrULJlgllYuyi5dllqLMrLSA+O2jqqN4QNs9ZyeXl5A/T4YnRZCAS4CINT1zXT\n6bSNZhL3wuXlJYeHh6078PDwkIuLi3ZxFZGx5O2Rzwlgs9a2KSX8DOACZMSCFo2IuCkFOIrWSn6A\nGwu/JE18+PAhsC57kmUZw+Gw3Syk9pzk3ZLzjUYjdnd3OT8/bxfmLMv40Y9+1LoRhaU6Pz/n6OgI\nrTV37tzBGNNqU4BWrJymKavVin6/z+HhIYvFgtlsdsOlJaA2CALKsmyBsJ/aQNyR8ixk05tMJq2b\npmkanjx5coMJ2BSzy3O/dptz472XiYq3aVhe1nyG4U1xMQowlfEqrKr0TdhBv3KAD7jkHklOKBmT\nwpJKOhW593I+AcZAG1ThA2kxHnzGyXfJiQBfAlbkeYtRsr+/37rb/PxcWusWiPkVDXzxvrB0YjD1\nej2A1kjrdDqtMF/chhLIIj9RFLVJev28XP44E9Zc7oVERi+Xy7Zuqlx3WZZtsAPQ3icBfn5KizRN\n20Lz5+fnFEXBcDhsNabCJAuA8wX10l7kJtycEy/Se72KTmsTlP3Cg65GVYTGUlQVBqirKVGc4GzN\nMs/pD/qs5pOryW/I8+vkdifjMwa9gzaiYne4j7Ih01VGHeecXMzIGo3V5ooKVRTl2hqYzaYExqFq\nQ1MUHB3doS5gWV6Cqxh0I1Z1ga1rlIW8zNHacXa+1jctpjmjnUNGu7t89vgR+/s7jJ8vULqkbE55\n9OicqjIcHN7h8ZNjJrM5g+EBnX6P5fSSh598hFM1J8+fsNi/xfl4wu7BEc+eHPPWV95jOS35znd/\njUfPnhCmff78+z9kMV1w9/Ydnj59ymwxw2L48M//lEXVUGbn1E1OkoY413B+PiXTGX/8J/+RUBvK\n2lKVll4vYNQzPP7kI77xta9QZkuG3R5nJ6f8x8tT/unv/mcU2YTYwfxyTNpJ+OjH79Mf7rEsHEnQ\nY9DtkV2c8vHDn/D1b7zLk0efMVnN6PV6rBpHpQMapal1TLDzgHnd40eTiH/0Nvzk+QUXao8PJ5qz\n4jYEFZgNn/krjmkfgLT5Xez2DMLbvrvZvoxQ4C+jiejbX4DFgoRrfYpY4z7DIharuC3ENSg0/mw2\nY7FYtK4PAVN++RHRtMhiLp8V61wWZnERyEIrLhsBf0qplo0TC7rb7bJYLFp9Cqw3pouLi9Z6HwwG\nrXZFrnc8HrNYLNr8WgJ6gBuRjtJ8F4/cS+fWmbmPj49bxkRr3UaelWXZlkUaj8fs7e21m3y/30cp\nxXw+bzcXAbZAy3qcnp627hZ/k/ZdevL8JGlt0zRXkoDMqy379x+HL4twfFVX5aal/7qbr4vbdB1q\nrVvWUoCDP5d9ABsEQZuAVzLR+0yNz2IBN7RJ8lyEDfPF3n60nq+LEjC2maZC3JICsv1nJqlURDwv\nmeSlb1KzVHSMElwi/RHmS+asnNta2+ayE+AnQE9YK/mugFifTfTBZ6fTaYGVvw75DKPMN+kr0AI9\nuU9ijIiGW84lrJsYUfId34Dwx6Y8m5cxYNvmwRfNtZexZD9Le62ga5LN6ZgGEoiVwShDGBgWqyWD\nwZB8lXF5MaYzGBIECVVVos06H06aBMxnF7j+Do1yXC4yvvmN7/AHf/jHvPOeog56zPMaiwLXsMoW\nBNrQ63SpqwKDo8gdSbfH+ek5wx3NIp+QhGaduqDJsU1FohVlnlPlUxb5lMZZysJx7/5XuBgfk0Y1\njz79EWl4wPn4EY1dECVD4uiA09mKJO0QBYbZ5GKdrDRNMbrh3r27mChhcrlg7yBk0B/wK7/+m/ze\n7/0Lvvq1b/KDv/whOoiJuiPuP3iXqsyxdUUQGII05Pn5GUk34fJsQhhZFstsnTxWWUwA1jaML86I\njWYxn1JWjkdByV73H5GaguNPPyRfjPnuN97j0fkzuoNdfvLh33H3aI98ekZtK87HBXXjOD0/ZrD/\nVWZZhqsLBp0OZSfl4Scf0Ot0ODzaA2VQjaEs5jilWTURmC55eMhPphXxqWKp7nCy1FwUGhX2qdx6\n41ReCLW2/qRY/6Dd56x/ads2ixuTZcscuZkE1V0xXm9GyROx7kQbJK4ByaYubkG4dpPI5qCUajdu\nsej39vbaGoe+i01cGMaYNmO3H9ouC68wB5sCWT/sW8Chn5FdmLLZbNbqVUTYLwzeZDJpF1uxcCUP\nkHOuzenz/Pnz1i0pWhLJVZRlWZuSwQdX8PloP7lm59YCXxGsHx0dteBR2IXxeNxqdQTEScSmHNfX\nhwnwnc/nLUMnbKK/uMtvyZkm7IP03d/gv0hr8irtVd0iL9pM3gTQJX2XMbq5Wcp4ks+Ka02+I6/L\nfBFQLOBZxqC4vXxtowAXn5WV5yUGgO/iBG5kWfcjg/3+SCCFRK6KG1IY7OVy2YKq0WjUMkoC8EVP\nCGsgJMDMZ+cE3Mg9ETf/putNWGd/3RQQpbVuRe5y72R9kibGl6RO8SMRxajzc5L5z1Q0a2LQ+S5O\nvyrApoxiczy/DED5rmJ4cSDVZvsiN+bft71W0FVai6sLdONolGHY7ZEvF1TNepFfzCYY1lmid3fX\nldMFCVu3Huh1UxKnXR49OiVOPyXPS3YHu+Qq4fnlc9IgQTuLqyGNQ5RtMDgclqrIOJ6M+eSzh+wf\n3mJn1OH20SHzvKSfxihbM56OKRZTinzJ7t4e55Mpo/6QbLVgfPacQT9gdvkMM4xYlSs6vR6LWcFo\nWBBjqbIFaaDoRAFpr48xKUPXEMQJf/Qn3+c7v/IP0PNzVvmC/+l//B8Yjg4IAzjY6/NXf/MhD979\nZb7x3jf4sz/5AwKzpo0fPv6E8fSculE4DIHxMh67K6tJWRyWpqnJypIgCJkUFX/2lz/k3TuH3L9z\nQC+0PP7sA3YGPeazU+7dGfL9H/wpb791l8n0gsqENEEHk46YFwVGG+Iw4vIyY39vh8fPxozPF+z2\n+8RJwtlkyv7OiEVV0VE1tbY4FVDrhL/LeuRFQ2UtUawoqgmG9WKptaaxVwNa+6Bp/ePcTw+IWuvR\nvdxl+Ca4T/wmC7ksmEmStJafL4yH6+LXcHMxE3ZqsVi0KSM2EzPKxiJMobwm7gFxd0mEn587zBeP\ni5tPNivZkHz3gOQIqqrqRqJJX8ws1vmzZ89aAJPneavJkpB4SYMhOhnn1oL3y8vLG9coi/824NU0\nTRtYcHx83PZDXKWDwYDlcsnp6Sm3bt1qtWKyaYl7SEAuXJdeUUoxm80YDAZtWgjRvsgzlc/PZrMb\ngPllwGfTtfiijcL/zDZ3inx389j+Z9+0oBPfpS2gQZ6vuHx9ILx53b7OSNguiTAVpkhysInb0k/I\nKgBA2DFJiSCuY/+++tF3Pnsmx8/zvI1QFWPHNyhFHiBjQsaRuBVl3Od53hofomfz3Y4yH+V++XNc\n+irGlH+PZc0QMCb3WYChBMfIPZd1YLlcUhRFe5+EnRfGTBg6CVgRpk2uaTNhrS+e/yK3+jYw9tOC\noxcxvT8r87ytvV73otXQKGrXUACmHwEVnSTlYnzGYraO0Ip6/VZbIg/AGEVjrxbx2pAVOc8eP+HO\n0S2WiwX9vSGDtMvJ2SUoR5rEwNrCWGthVnQTg2pywjgiy064d/hV8sU5rsqpl4qmLimrJdo23Lp1\nyMnpBUrHKGP47LPPONjf4cln74OtMEHJ/uAWuBDXZNRNQZWt6A9HDA52sBZQhiUJSRjygx9+n7ff\nfY/T8YSqXlJZw3/1u/+Y9z/8lL99/4d8+vFDvvvtX6ewIZPzMXWekZVzmuY9ZospqyJH6QBcQ1M3\naB3Q+uacxukGnMMB3TSmri2kPR6dXfCddx5gyxWEDUEUM5udEscBnz78gN29IU+ffUrQ6aHCiL39\nezTBgFVdomrL8SdPGKSa09Pn7O6NmFyso8OUjtY1weiiOwmlSimdoSBAm4hyVbGymkIFJHHKoskY\nXBmUBtX+q5z93MRRSt+YFK/Stk2+9hjO08vYNwt0yWLnLzyyOEr6BAEXsthu21yjKGIymXB8fEyv\n10PrdaLCPM85PT0FaF1hQLsZ+Nax1nrtNr6aMwJM/MgpWRjTNG3DzyXPlOhSZKMQRkLcDCLcl41O\n3AySr0vrdd086dN4PG7doAJ4RP8mEWD+fZR7sXlv/CCAxWLB+fk59+/fb/u2XC7pdrvked5uEsI2\n+rUq5dgCnmRjFRH0YDBoN0cBVf7zkoABn5nx+7nZfID9s2wEX/TdTTbidTcBBLIx+/3zAYLcF99t\n+CKXlO/Kld/yLPySTnBtZPgubN99J6yQXx5KGCk5lp/CRVhoGRvyXOUaxL3oB734oEIihvv9flsJ\nQZgk/xr9MkDyXfntBxfJmJXxKffad/n5gQq+tk6u3Y+6lb5LlnuZp+Lq3EyrIUaJGHbyLOT9TYNk\n29+brsCXsWJ+8+fl5n2S97/s9lpBV8Y+YfmMYVJTlTlL25AYha0KitWSuiwJ4w7ONZSFI037zOaT\ntaCvCrAupCkNVZYz6lgmZUEPy6CXUmYzbt/a4wfv/9V6EJiEbJkTJ+tEm51eQGD6aLq41TnvPLhF\nvXyCDkK0s0xmK7ppB6cbpvMl0/mSu7fuMb2cUOYaE9T87ft/wYP7dxmfWqZTxWinz/nlGTpal7OJ\nY0O322G2WEIQUdWWh59+QKc/YDmr+Nbvfosfvf8BDz9d8lu/8zt88skjGh2wMzqguJ+QFw7bzMh0\nxsniGU/PjvnsX8+YzTMCFaLcVWSeUWAbLybPEliFwhChoKzpGk1uoFCQs6LWERezCe/svUU36VAV\nK4rFnOOiIo4CstmMRGniTpfO7i9xcppR5M+JTMXk4jlJGsBqxaCTUpSWeZ4RJNCJh9QmYuVCGt3F\nEVFZxeoKEBrrUGVD1EDJesBX1uGuQJfVNxkbh8PU63xbcL3hOHXt1pFJV7mrCci1NEypz+fiqb3k\nqs5cTar6ZiHU19VkMfatbRGgSrScLNSijfJ/i5hbdFbL5ZJbt2612qadnR0+/vjj1sWyWCxal53k\nsBIRfJIkN5IySvSeWPsCisTlKXmmBCjJ85HIMNk8JbJLakEKG7darej1eq0oXgTtQFusN01TLi8v\n18XnZzNOTk5ubB7Stmk8ZAGVTU7+Pjk5YTQatcyDAN26rjk5Obmh4xmNRuzt7fGVr3yF6XTKxcUF\nSqmWTZTNRtxPAtAELApgFuZA2iZA2OZm3ARDvm7pRUzZy1yUL3rPB3dvAvCSzXwwGLRMi4AByWHl\np1mB62hfP3pR63VVh9lsBlyzXv499vVDkpbFZ3cERPnlngScyJhRSrWAQwCWP5+lpqKAETGyfCNI\nzidltATcNE3DaDRqQZBEukppLjEmrLWtESOvC2slx/f7DtcARPRa4nIU2YEPMH3hvTC/4n6X94Wh\nFvZbyv/IGrOZomYzYlTapj5x27zeNt9l/mx7T95/FUDmz8kvo71W0FVHfWzVY1KWdJqabH6KTXrt\nRhrGMbppIFTk+YK6DtF6Xdh4tpgShw1VtUJpB05x/+5XUDU4pSnqikBXGLWu3ZYXGXGUtpZ0anax\n9SVBOOfwVszl5GM63XvUlaNpamytWKxmBLGm2x3QTbo8e/4cZRU6SSgry4N33iObz9Bxl24v4vjk\nKcoE9NIORVWyf+uI5yenNC7k8M4e87NLjo6GZKuC27dGPPrsIUat01G8/6O/5uJiwu27b/F3H/wY\nZ2Lm06tEr7OautZkS8syO2W0s3utLTEauyUFglOglQZn1wncreP+smKpwJQVdmnpG41qFKvFkqLM\nsAocjnR0xOz8OeloDxpLYBu+/s13+eQnKybPDdbWlKslrm7oj4aUVUmv1yXtD7FNTFE0JJ2ELfWY\nWyvGGAN2y4QQFsq56x+9Tpm6BlusdVrqyvkov4FtInx/M3sT9Clf1PyoQ+dcC8DEjSJCbLhZk0wW\naD9nlgiGZRGVTd4XvvoCWQEbEtEli7ZsJH6TDUKsetns9vf321QPSqmWPRP3oQA26YucRylFr9e7\nkdBUrglotWCysEv+H2EBZKOBly+m/gYkDJ0A3cVi0QIm3yXVNE2bBkJcqVImCWivUYCon6NIwLD/\nHLa1Tdblp1ngX+aafNn5flGarw0SV5SMVbmOTfnBJkOyqevxI+mAFlDJvBCwIaBZ3vMrKwi48F2f\nAp4EUMkYkooGfpLUbcDDB+Lymlyrr42S9UHmlcxjcff5aWe2tU3gvgnAxS0u646wzbIe+DnJrF1H\nZ+7t7bWRz+L69++/3DOfTRcG7EX98/v4orbNYPBf85lhv0+bbNm24/08WK/X617s7VFRM19ZbFMR\n5xMaeyXcY73RxmlCQ70GW4EiJgZV4VyDUg1ltSJbzZlO5+zfXpeoqRtFUeWkwQBrMxazKf3eiDDQ\ndNMEnCUJLSaxzOcXPH1iicIus2rJoNsjMAlO1YwOblGuZmBiKqtI0nXiustlxt7uEZeXpwQmosYx\nW16SdvuEYchiWRAnXUpr6Q1HhFGH8fiUXm/IYnFBFFa8/fYDPvroMy4vZ+ztHrC7s89iPuWjH/8d\nu4Mh08WUMA1ZFiuOzy45OTkHIka7fcqyoGquI2NaS+DqvmoHGo0GQq0InSNWisPGkvcSjo+fYu4c\n4oKIYHyJbXKsy4k6a5fJoizZO7zN/uEdAgW2WJCtptx76x7Ls9s8Xx5jy1nLqKRpj/LKsqqrgNHu\nbZ7OclC9zz1zoXLXm+S2hV8WT0/TJUJ6pbBXqR5oC1lfM1ufL239xdbMm9bWyXfXbnCf9fKvw7dA\nZWORciD+RiQARhIQ+kkaRZ8hP+IuEaCTZRn9fr/Vo8h3/XxfssCJ7kxqCAqjIDnHfDG5zz4sl8u2\noLT0S4CVWNyDwaCNOJRFezKZALQFtOGm/sIHLZuvy2a8qbcRF2Gapjfq6IlORhg6yQkm5YN2dnaY\nz+ctUyf33a9NJxuYRDRuti8CWfK+72qGl4uHv+g9+HydxVftz3/qJmBHXNT+2JMN2wcr/kbvs4HC\nOknJKT/C0Bhzg5VSah2pOBwOW62iGCFyfl+j5xs0AnQk2ar0wQ96kb+lCXDyIyflWftuNola9M+9\n6Vb1Izu3GZ0vAhX+GiPnlPnqB40ICN5MMSHAsmmaNghAvivHEQPOd6u+bKz689f/7Dbj5EVs1+Z1\nbms/zfs/K/h6vUxX0Ef1QmyUcHk8J7JTkjrDaUVRVhgTEsQRzdWi3DTr6L1lNltv2q5GOcvu7ogk\n6vP08UeUFTx+9pSDu3eZzSfU+YrhoEcSryNPbFMTBJonJ39FFGpCHZEEe1hSbJUzXxUoZ7lz6xYn\n43NuHezSTVMuL8/ppl1q6xgMRjx/fsKgFzG5vCAMIlygmM+XBGFDkva5nM0ZuuZqsmcoazl99glx\nbAjQPHr4EU0Jz5485Td++13+6E/+H+7d/Qp5luFsST6f0N894LPHn3IyzdjZu4WbTJhNrzJriyWH\nRWHXQEspAndFMStFojVx4+hqQ+igosQ4zU53l9///iO++Y07nK0y9kY9otBQFDXDQYJziiTuEZj1\notHphqAyBv0Rd996l/Pnn1K6JaG1OL3eVKI4wV0xB2s9ywGXK01VXw3Yq3Eqk6VpGhSfDwN2nlBW\n2vZJqdFaNDLr/zu7fUN7WZOF0Kk3owyQbCKi/fAXDYnek81XFrht4dRSwLqqKubzOfP5/EZeIL9m\nnHxf3C7SD1k05dhiwUtSRWF2pElB282QfgFiwnjJZueL4YWp0lq3iVrPzs7Y29u7UR7IOddq2/zc\nVX7yxG36D99al0XfB11BEPD8+XN2dnZaMbBEq0nme3ldQKiwcwcHB+099jcDcRXJtQnL8SKh+qZr\n0X99G0D6IlD1ouaDuBe9L8/uTWCHZYOW1CfyzKSJO8+fF5sbsM8M+e5vObY/ZsW4kLG6WCxadlVc\ngwJ0hJXeZNb8RK6+NkpAh4w9Of9mQlI/MlKiaMVYEgCzCZL8a/DB0CZAeZEhunksCTQQXaYYPRJw\nIuNYwKKvlRQWTNg5XzC/qZ3bdB9u69PL+vsiAPaq7VW8IF/mPHitoEuHPazpQdQj2slYnl3QVCtM\nFLJcZuweHLJYLqnrJdZCXTdocxX2jaUocrq9mMZWPHz4hFVZkKQD5ssZyWKX2XJKEqU416yj+LKa\nKApRytAdHBLoAVGQ0rgZjVlhUHTTDrZaJzC8e/sOVVWzzGvitE8QBVxcjKmyM7rdkOfHz3BNTTrc\npazWLocwSphnc+7de4tsMr6iX9fsU9dYVkXFg7e+wvPTCX/6x3/Gr/3qb/Lk0cfcu3XIb/3mr/K/\n/N7/ynC4g6rhk48/ZrqYkzc1QT6jyJeEOIwJUCicFIc2BgWESmMc6Ct9VGqhi2agDanWmF5MrzaM\n6hHlHcOHxxcMRwkXdc3bdw+4t3/Andt32BsOGA6HKB1RNTWT/JKRaigsmHDI3u33WFxq7OwptbOo\nK7o8jtZRK5EJucwyUL2rzU7RvCSKUCbeevJJ0WpFy3R57lN/6m2KQf0t5KVWnTd/2pD/v98Q/tKb\nn1BQAEhRFK07JEmSNsJQ7plz1yL0oihaF0ee51xeXrZWvAjRN6OPhI3q9/s3xMpifQvoECZI2FVh\nc8bjMXAdKu+LY/2yPEqpNveXL/KVNBXL5ZJHjx61zBnA2dkZcF2G5dmzZ205Ickv5G9kQAsoNl2J\nArrkumRDUGqdQ2x3d5fT01PG4zG7u7tt+ZWvfvWrrZ5IFncpH6T1OsL06OioLbYtm4+wKr4mR+7D\nFzU5z8sA1ObY3mQAXiS495mPF21mbxLTJQEXkmeurtdFn2VOCHiRMQ3X989nVuI4bktoyXMRZlXy\nrslaJG7s1WrVsp2SxFcMEGGl5ZyylsjY8pkucUkKayZGgpxPtFlyTKAF/cIai7hcmFd/Dk4mkxZE\nyn2Q5+jrFDeBl4wP6Zv0T44zn89b40IMNrhZ7NtnwbXWjEYj7t+/z2q1Yjwety7YNsIebrjf/de3\njbttxtSmwbDpDfANUP/3y8b2f6ox/1pBl9MNloTKRaj0Fnn3Pnb6AboMyG1NZRvKPMM1NcppmqLE\naYeqKpRLqcsV2aImjTX3bnV5Pq6pqhyajKdPPqa7M8QZS2Q1qAoTxCRpCij6ox3A0lQ1qkno9/fo\npIooVCjXoK1mtWjQsaOoQ5K4y8n5GbFOSTsZj598RJxGNC7kb37ykHfeOoQ0oqlqRt0+y+kYG3dZ\nznLu7B/y6NOPCRNDt/MWf/HD93n3m+/y3X/4q/z5H/4lv/I7/wCH5YOfvE8YOmxZ8Ggy5+JiStPA\nQMfYxZxe0KzrE+JQ2mBx2AZ66DVMsZpAaYwJCW1FYGEQJqQYekmHOI3oKk1qQn7jq+9Q/u2EiyTl\nG7ePeHB3wPD+Pr80OGJZFeTZChsUaOfomJilctT1kmJ2yagXQtFjseqhbI3SjthEBCTYsMHokKAw\n5PV6EaiKBm5YnwKo1u3GYoDocvyFQbevKSWU/PVC0k5GE2DdWnx/fbwrCKfUWgfmHDfKAF29TuNu\nRDW+7ibMimiXRNPk16GTTcW564LY0owxDIfD1uKU98V9IZa1gAYRFft5p0ajUWvFig7Pd+0ArS5M\nkq/6i7lEL8qi7yd+DcOwTdYYhiEff/xx24fZbNb2RUCkhMlLZKRYzL7gVoCWn75BXpc+C6sgm3C/\n329BYqfTIY5jptMpR0dH3L59m9Fo1LoThWWQTVWuVXQskmNMnoMI8P3z++kP4OVs1SYj5mvWfJDp\nf8f/3IvaNpfMmwSyNps8H4l0FeDeNA29Xu9GTi6fzZT7JHPEN1L8iglwnXHeTx6q1Dplg7i+BZj5\nrkC5b75bctMdbK1twYp8xh8TPvCRoBhJiyKBAv74lb4L2yfn9pktXy/mjxsfjN3wMHiMsejNhL0S\nAwe4YaD5AQZyfD9thbryfEjQgfTDv5bNPrxqe5PH6xe11wq6pFmlUXEfPXqL+WpJU2b0VUOzOGeZ\nW+LIYmtHtlqQRIZseU5JhygO0azdVGEYc+twwKo0XF6ccvftbzCbLwh0QBKsiyVrY4iigChKqKpi\n7WrUisP90dUiukTDWnQfpnTSiEaH9DpD6rokjkPKfEF2eUxgHMfPnmNtRK+zw2pVEQYrOp2Qy4sx\ne/sjFudjeknMfDGhcjCdZVTPP8AFhovxkpPjU95+54imypnPJgTa0AkCiuWS/SSiDjQ6TrDasMjm\nmCgkJAKlqS2EYcyqyEnCGIUhUAGBMQQmJLAOVTXEOqQXJSRBxG7UY5QkBMbRVAH/+Fv/Of/q/ANs\nlNCEEZPpkoezExpdE8QGZQzDEWgC5vN1yo46X9KUc7LVHB0khLZGBQ4ThAQmwDmFcZpQgbrKE+Zc\njVLxCy3vL6ttLia/6G1bpu3NgsiiSRFrWCxrWXClhuJkMmF/f7/VYshGJYuohHKLFS9uNPi8u0n0\nLrLxSWJRYbrk8yIeFxeMWOA+EFFKcXx8TJ7nHBwccH5+3maxlmuW419eXt7Q0gi48t1gfjSnnzFe\nQJiwAQI0hUURMNbr9ZjP5y2zIWV7/D5LUV9J+SAMgLiZZCPyrX+f1dhkY37eLrxf9Dkh90tYYEmK\nK65uHxz4Y0EYLj/flIxzX+sln/MNDl8zJm5BoAV5m6BYnqOADjmvX8pGmq+Rkn4KWyRgyRfs+25T\nYZz99Asi/IdrxkrcgNI3uVbfwJW/N4XtfiTmtnvkl/vxdV++5lPuo6wTvubNn8xLsTcAACAASURB\nVAdy3m1GxGZ7lTH8Ki5332j8+7okf5b2ekGXWzM0jVKE6R61ctjbXbLT9+nVxzR1RVHVYBRNbVEG\nrC2pm4K8diTRmvIMgwRsvRbY25r5dJ1ZfpwtsHmBTdb1+uI0RVODLdEKkihgZ2dIU1dQO7Rp0EbR\n6SZEYUpRFUTJEGUilvM5KnBkxawd0P3ekOmkJowHXEymhNGQy8kJBwe7NE1DJw4YDTo8PX5OUeUc\n3XmLP/mjP+Sf/Bf/jH/xv/1fmEDx1Xdusx/f4snZKYFVPP3xI5QLmS8qBkHIKl9R4eiZgCSIUa5H\nECU4FDoI6Yc1zhqSKAYLQRARByGBNriiglVJv7tLL+nQ744IHOzt9KkC0N0Q/eQnEKYc3nuLnX6H\nyCXULiOOpTBrRURNpx9xeT5ltTynKlfUTU7gIpyyaBTOKRocqY6oTURoA0JCKqfQGBrrblg2P5fh\n9Au+ufhNFicR80pmdwFNvotQwI+EaUuGad/CzbKM/f39NrWDLI4+KPBTVPgaK6WuS3H4rk8/5N3f\n9BaLRctOCQgaDoftwioZtP3Eqbdu3eL4+LgFRMKIKaU4Pz9vAZ3PLmxqYqQJWBXdlZxTNimgjdDs\n9XotIyUuE3GtShoJPxDA37wlZcFqtWqZxG0shtxrf9PfdO/8vNr/1+aEAC/JpyallGQj94HApsTA\nHweb43sT3MhzFGAkQEpe88GJD9T8KEJfY+UzRWIY+Yl14VpY3zRNGzTQ6/VupMTYZE3FCJOxJwDH\nB10+CN10L8p88I0C+dt3m/rM4GYNSp/dkrngs49ybGkvA37Stj27LxrH2z7jH2eb5ETaF4Ev/7Wf\ndT69VtBVOQPO4VxDqRQu2EEP+xhbs5hYynJMoxoyFxCGGuc0y2KGMyGhNQQ6JOxqAm2ZL2cUrqHM\nHZ00ocgmZLNTkqBBEZCkKUY1aAXOVqRJgjGwWkzXzrlQExuNcgalA5zVOKXpdntczOak3Q6L2YRO\np0eWj6jI10kjC0u3McwKjZoVVEXJcA8eP3tGLx1yenZCGBp2d/pkixlvv/Mu//bf/mu+/vW3WCyW\n1FaxuJxy/uQ5Xz26z65OWGaOw2QHpUOqKKIxIcsip5t0sWrt8omSlEWWY1LDYrGkl+xCA2EQkEYp\nYdwhaECXNcO4Q2xCTNhhOBwQGk2SaAZ7Q97q3GI5zRl1hxykITqK0ZWjqFYslxNc4yjqFQR7dBPF\n4nIFtiTUISp0OKfQxoAO0CahNimF1TRE1M3aHajsdUg1eIPWvXgA+5Ntu9DyplbBt5T872xO9hed\n503ZmGQREnBhzDrrtHOO6XTaWuOySYjA2F/4fVAgYMgY01qoct/EPSBNLFURuksOI7hZUkQAzqab\nQBitoijodruMx+O2MK6AjV5vrfMTUXyWZYxGI87Pz1ksFjfqukk4urhHBWSJu0fyf4kr1mfHhPXz\nXR5+pJsI5eVHgJqAsLqu6ff7DIfDlrWQzVE2KwEBfsCDD8x8AOZrWfzN/GUbgNzfzXH7MoZsm6Zl\nc7N5kaXvj8E3qcm1+sB6bRAWbWUB39UGN1MH+GkVZGxI5KtokmRO+eDaB2lyPD/CcRMsyH3265OK\nBksidOH6/goT7K9fEqUr1yRzWZhaWdukBJdfAcK/Xz6jKk2MEP8zvuEmzQef8rrkK+t2u+24lvVJ\nWGFhhqX/m+uUgOJt49Bfr/1xvU2T9bJxsu21V/nufyqWC153yggVYJxFUaOtBhdgVQCD+6yaknqp\nifSYSidEUYxjSVNWuKCho2OMCajLgsItQRek0Q5REJAkIa5aEeoSbIHWA3ANYZhgjCIvS5rKUeYN\nUWgY9nvrmmtNhdERZV2TVQWd7oiL6Tn9wQ7LbEEUdliu5hS2S9Tp0yzP2Ll9wHgy48efPMc1z/nG\n1+7xH/7oL3jw4JBfeu+7PPnsA6xdsZivqJuA86nFmYbGnhGEMZ8+PMMehCQmJZ+sGAVD0k6ICzpY\nF2JNSo4hCEpwiqh7hA4MJoyBEh0YekkDFnQQEIchg96QolaM+j2CymLKhp3hiP04ZZB22Bl06STr\nfES/fu87/Gj6Q1RVEWYZeWU5jHuUoUJVNdM8Q6mYbBZQVSu0Wk8chcaE5VonpTVNo9AqYhEYShWw\nrB3WRjhriLWh3hJZKO3GZNgy9n2rVNqmLkYptRZ9bY6xjTw4vwhtc5OGdXZ30W7JdYjIdptmQzYH\nSegp4EAW/82acBLm7YuAfVeBLOoCbIRJk0i+Xq/XJkYcjUZtXcWTkxMALi8v2d/fb7VZsgFI1F9R\nFARBwHQ65fHjx22/Op1OW4ZFNi0/9F7YPQFfAjRFAyebpkRqClMn7Nve3l6bc0vA4a/+6q+yWCza\nNAES8SaAMsuyNjeXbCS+DkbGo4xbYTTk/sMXW+5fBHxeBry2AS2/vSkGxqs2H9QISJZs/+PxuH1G\nEsHos3syBvx8WuIiE92U5J3a1AHCdY4wOZ6cxwde8PnIUz/BsZzLn79RFLXBJJJqQcB8HMc3xpr0\nU/oi80yYZhkHcm7fMPHvxSZI3NSlCbj03ZLC+j579ozz83Nu377NwcFBq9WUPglzLWuGzAefffOB\nqq8F80GutM0x6oNh/71XGcvbjvWi9qI+fJluyNcKuoJ2YdE0OBwVWhty1ceM3gEVkaz6RK6gQePi\nBFOHqKa7LrUTOGptqasc5WB8fMlv/NZv8uT5MZfL5xQmJ+xEpFiMqdF1hqGmqzVV3ZBEMWEYYOua\n1XyBjmF2Pifu9un0hmR5xq39+6yKirwo6aQ9MCv2dlNOT59z6+AWdZ1zdvyMt+/c53R8yocfPWG4\n1+PRpeXx9/6Ub/5Sj+dPP+beV3+ZH35WML6csd+5w7iI+YMffMR+N+IvPnvIbxzuchtDr9NhkQeY\n4A4qDGkIqIyhdob5KsfEfZQJCZIOg0FMtiowGLL5glFvQBLFxGHEbhpj0HTjkGp6wf3hgH48pKsC\nhqM9TGjoacN/f/8Wf3Oxx2oKxa2HdKuIWp2zsg1Rp0tiA5aLmia4pKwttrbEYUTVVNhGYUyIcQlB\n2ifXPYpKk5WOyiUUlcOpCmMUuq5wWpKbXmlrLK24XX4armtwiRWmriIysdfiVK3ahF3r5q6E8M6x\nzv8lC/V1JFtLdTe+ePjqGG8gIPM3Xq1163YTd4iwS/L+Jm0vLY7jG64X0ZD4Fi1cuxrh2iUpi6Ov\nQ5HkpcKuyYYiQExSUoj2TFxBAI8fP2YwGLQW/Y9//OP2/51Oh2fPnrXHj+OY0WjEYDBo3RnSF7nO\nPM/bYsSyaYp7Fa4LDmutWz2OpAJIkqStbSfALYoifvu3f7vd0ISVkpqXsvj7ehdfdCzPwme4xN2z\nrVQNXG86/vPzN33f7SPPxv+/f6xtLvxNF+PmxuJ/f3PMvQltU38lGiLJMSelZpS61hpKE0ZUwLhc\nr1/KyddOwecZRh+k+CkPfNeYb7z4DJncW2GtZOyuS9G5G8BP9I8yh3wDQuau/L0Jyn2w6d8juX5p\nPrD0m4xTP4LTn/ciGzg/P2/7uHlNcu3+er6pI/ONEumzrEl+/7e1VzUWNr+/ecxtx/n/DdPlN3kA\nja3QKkCHCUF3h8rlmHJJ0VREYYTpWnTl0FWEo4SmJNYjrFpydBBzeX5GXebYuqQbR1cpDRRKraPW\n1gOgwcQhjpoyLzFJwmK1YjDqry2oosa5dX6UbDXHqoDBYMBiPqPXHbKanxKnEfPllIuLU+4/eIun\nTx9zeBiyXHV5+GjBL3/rbRbNlGlm0ckOnx3P+eM/f8S73/w6x+cT7kR7ZBVYs4MKTonSPsPeLqpS\nWGdIursQrDVRpTKYuEN1fk6ajkBrok6foqxJAkMSBQRO0UlSAm1Io5hu1CUKYnpRxM7+PqrMGYyG\n7MUpnUEfHYekjSPpG97rfZP49oAPzo5pwoiL1ZxlUVCSU9aWplHQKCrb0DiHMgGhDteuRXGX2LVo\nHq4KxzY1zmka2wAKpQyNtWtwoxXOQd1uOpqrmMOXjhHfclP61Sagv2H5m5xENUp70yCXDy5k4fJd\ngT5NL58XvYWv/5DfIvoWIa7/PX/j9d0CQBsWbq1t2SQRy/tuRQFYUjqo3++3of1ZlrFYLDg9PW31\nOPv7+3zyySecnZ21LsWiKJjP5+2iDuuweblW0dX4fRfWQBgIoAVx4hIRHY0EFQjjZa1lOBzS7XbX\n6V6uQJ1fzPrk5KTVp0movwAtn1mUjdbvF9wcszfGrwe0fpYmm+PLNqttbZsbEl7s0nydbbOPPhiS\nHFICaCX/m8+e+IBS2BcB3sANECXn8bWMPnMprKb/3MW17Wu4fEZMXORioEg/5NiiV5S+iYtO5u+m\nTs13HW4CfL//PgDzf8u92HzGMveFefOPf/v27XWNXXVdUcJPx7ENkPrPavP8LwJpm8/af/5++2nG\n+iY79iJ2ePN8Py8g9saArlaQaDS1tSgdQbpP7gIiPScINI2uoJrRlJeQjVGVxWmNaQyB6aMC19Kj\nZV4QxglGi3AyQCmDQiqcL3BKEZqA6eXkKkNxgVOGwWCH6WJGVUO/30WHHUIiet0+1WrFcjVld39E\n7XLSokNRltzeTcgrTRxFhF/r89HHH0IYc3b+nOGwz4efPmLahPzZ337Anf0j/uZP/5o6iHl4vsBF\nCd//7DP+4a98hZ3OCGMVcfcIpzSr2hIHEdaEDGLFXucQdAAmJNhJAc2ynLGTDKFu2BkM2dvdRVUB\nURTTCUI6OHb7KaFp6AYxSRLRHw5gkQERDwYP0HT4cB4w361xJqVJEvJiSeEy6nqJUd11KR4d0DiF\nUhqlDUatJ1JiQsIoJXMRlWsoy4CqBmtlUim0Ctb5utwadCkng1pJjoiXoh9Z2LTWWPf5DNTbmh/R\n1C44zn0OdJmt3359zXdd+MBRwIL/mtwT0XeI9SgARdIg+C4UfxGXc4hw3We05PgiBJ7NZjeAC9Ba\n3vL53d3dtgajbCyTyaRNbCkRaFJ4W5KqjsdjrLVtKomyLOn1ei2T4WvD5D4IOyAJWwVUyrWKa1DS\nQ/ju0yAI2vxbwoKJTkus+IuLixsuQd+lIs3f4PwfYQkEoG1uMnJMHwC9iv7Eb5uA61XAlwA1+b68\n5v9+E9u2jXHT1edv+r5uUUCEABoB0Jssos/0wk12TVx6PsiW8e0f0++X/O27mKUJoPLBoEQ7+vPU\nT9UiY39TAC//98fSJqvqg7fN65VrFfeknM9fH6R+qgScyLyU4wrj7QNSOa706YsAj3/vX7V90fc3\nXZf+517mTtzmztz8++/T3hjQBVfWulsnx6wdOEJId1FxSlYU2KYkSVKieIA1luVFRqIcTVPgSku/\nt4OKOxze6VEfP6d2FmevXTFaX1WGD9d1/+qyom7qdRHspiIMDGWx4umTBTpIiZIORVGwPzpAB4Y8\nW1I3OUdHt5nNZvR7Qzppb109ftoHVbO73+fkfIxyhk+fLdg/uMX4MiOv1jlfJsU5pV2ilIWgorEl\ntY5ZAXHaI6VLxyhiFYEJSKMQFyQ0JqQXDgiUJk57WBVg4gSlNJ1OQhLFnD47Zre7S8d0iIJ1Usok\nCOjHIaMkpLRLunGXNE2Ijb6KNgywVhPs7GOrJdXkOTrcIzAxoQpRUYfcNdgGmkbocwjjiChISaKQ\nQAdYZ8jqkrJZA631wicRQBVKrXNoCcuklMKpq8GruAGCZGC3zMDV6zcWQn3TQlp/4Hos+YzCpntl\n28bys06kL7v5fdwmiPVTI8B1GRJhoTZzSklNQVlI/UUduLHA+O4IX7e0WCxa8CU6KefWAn9hpIRd\nkv74wENcKAK6RKwvUYnyHXEV+YlcgTbqy88DJGygCHz9UipSbNcXzAtYjKKITqfTfs+/XulLr9dr\nyyjJdcRxfKP8kP98/BQRvq5GPuOzky9z971sLGyOZf/1zU1u83jy3Ze5DX0Q8qYBsM1rAVpgDdxg\nWOBajyXAXD5rrW1zq/lCdb9kjzQZi/464s8roE2XIiBONFm+UB2uU78459q8YP465bNaMo4EPMpx\nBNz4SXg3n5kw2v78FmAnbnqZ/75uU358PSdwYzwbY+j3+y2DLYmK5V5vAzj+Wu7rKjef5c9rDX5V\nFtj/zLb/f1n9e73JUbctHleJM7Vbu6KUDigIcZ0EZS1lVdLUCZiSsBtTTSyBcwRpRI4jaBT9/oCG\nC+q6orKWUIm/3lKWFY6GNOmjQo12UBf1upBoY7F1RRQl5FcRWEeH93A6oKoKqnqFNhXLVU5R1Veg\nIsKEmv5eh9lsRlXUJDqgHya8+1bKZLqgzitCBQcHfabPzqFWaEJoDMqVoC1KG/YODogvYJAmdOIO\nVht0mFCqABUmhKMOFxcXDNMOQdLBKk3a6TGpc+Ioov9WikYRqIDdQQ+nDJ0opBMb8nzOzsEAXTr6\ngy5lvqKbdsFWVKoi6MGP3v8jdnbOqYIjhgdvY6IdlquKvHbowPPJmwCt1kNH6/VmFBBSOwON0NQB\nznEVnepQbaZ5/1cLs1r3otpSvHtzvKwnwatNgO1j7M1tr7LRCQDYzIkjLjGpASgbRVmWrS5KWBcB\nZb51628Wch5xp8jiPRgMWsZIdEpi9Trn2ogsWbz94/X7fay1ZFnGeDy+UdJo8x7I4i2uGz+flljU\nAsDyPG9dhz47oLVuC2KnaXrDvSgATRLI+tnt5XqDIOCTTz5p84NJ3UVx40of/fMJUyYbubh7BXRu\nc+tt+/+rNv+7r/r9bWPsTQNYm+1F1+WDYRlrfrFpay3n5+c3PpskCcPh8MZnRSvlA3mfMZONV4CW\njAGZXz7gl+ctLm7RP0lAiHzWOdcaFmLMyLn844sRJU1AyybIlO/7bm4fKMq8kc/5wMsHaFLix68+\nIceV70lKi93d3ZYB9I0R6b8AXTn2prbOH7cvAmzbmKsXjY3NefAiI+dF8+WLDKAvA3i95ujFK7+x\nCKUB5a6yjiuFdY51NoceOIcyCqc7lEFDoBbUdQW9r2BMn7C5pMRyMS/IygviTkrcdKgri3Mr6sZh\nMTgTkKZdQrVOVWHrkjA06KCioaKxNaGu6YYB/SigKizWZmgNe70dLifnBGGfMO2QdPqcn42xdcG0\nrInSIcuLZzTlkv1uwqQuWcwXpLGjn8LiZMzIRtiiwTpL4BoaZTmaaU5Cy5PFgu/WEdHOPrGN6aQ9\niromSHtUYUDS75BYR9BJCft9GrNOb3FUxzjlqA87nB0f87XdfeKDIePnJ6SdIaG1FLUmGAyIwojz\nHz9mN0lhrwcXBZPLiqP3MvJHc3bv3+IySzg/OaVKl9gkwmpLXUZXLqceadSlaSxLW1E0lm4YEIUd\ntDakoSFvoHENBWvgpbRGuatwbFTrQZRM834LuLYMxePYeIwYcAXOtujAlDcprgT7fn3H1jr2hKLy\nWqXBvhm6YYCtYESatTcTjPruImFXfP2QFMgVV4u874MrH7D4wlafdZTF3o8E88PhxS0hBakFFGVZ\ndiNhpPwt2ap9Tco2/Z30zWcrZIMTECVlXWQDETYhDMOrouxpG3El1yFuEnG/+mknZFOTTRFguVy2\nrJyfXkD0LHAdLSsbnWxS8rw2F+0XLfo/jXvxi97fBGbbmCyfVXnTANg2oLr5ni/8Ft3UZpJRGUO+\nXsm/3767Xo4nQEWetS9YlzkkGii5d75LedOdJwaOGExyfj/Sz2+bfdx0G8qxBSj6n5Hj+yyZHMdP\nQ+HnB3POtRpO6YtvPIlrU3Lw+cJ7HxjKtQv49IN0NkHRtufqN38t+qK2bT69ylzy17qfd3uj3Itw\n07XkDxL/ISmlUMkhhg7aBJjAYHMI6inW0pYOUVoTBBpHTBAqGqfQgcFaWOQ5RkGnm9BUK/K8pBv1\nKcuMJO4SmHVUjK6WhGFAGBmWiwuSSBEE64zdi2zFwU6PamXYN7BcXBD0YmzkKFYFw25CUXRpbIFS\nAdPMUStFbWuSQFMBFkWhauIkZac3oFc6wsDQC/sUecXuYEATRLg0odGGTiehv7OLSlJKpUjSLtli\nRRgZ6CWsplPAUuY5/U6XJIxIAsPucIBdFJidGNNPCUzM8viUbqRITQPnj+jup3xy/JB4dBed7OK0\npqpCHBHa1CRJiiIEFHGUrhOoOkuoNAqH1gZbW1zjoAFzBZpwr84u1d7Cv6m7+jLG1i9i27YRbrPg\nJKfX2qV7bW3LgijWqrhBfKsXuFE3Ea5dFcANl4Nv+QrTJZua/O1b8pK/RzYoETCLzsXfSDazaPs1\n8uSYcnxfwNvtdm+AqTzP6Xa7bX+FTRBr3Bc6iybMz6YvdfYkD1SSJDcYELnf/qYq/fE3Qx/A+hvH\nm+bOfpOb3LOXbb6bIEaek89AyZjddL/L532g4YNk/1nJ+PT1lr5BIM/ddy0LgJfjyvklD50wz3Bd\n8F4+678nc1PSUPjX6zefvZbPbNNuyXc3c8ZJRKjMDfmur+GUzwuTJ+uN/6z8/duPdpa2yT7599Vv\n2xiwn6Zt++6rMFw/r/3ijQNd22hG27qerlgOrahdShAqVJ0TuhJbzNBuTtM4QGGtI8BgjEZpTeMU\nzq5F1E1jidM+TVOxymuMDrEoGqvY2d0jW5ZEfUPSSbB2wXJZ4ZYNURRQ144ya9BhSrVckpUlqqkJ\nXIZ2NWlY4YwhjnvUZcXBqEegQwJdYnB0gpDZPCfREUUTUNYVtlMRViXf+1ff47f+2X9HgmJoIqok\nRDkNYUSpNb3RDsuiQicRdWAwgSEMDen+DsZaMlVx53AfPa8IUXQGA1xVEgZrPU+4WhHujkiPDlh9\n8JCuCaApyOcLOlXK/e98m6fjBWXUwemAwgU4QowOwYRUdu2Tr12ItZqobnBYnFkL62vboF2CUg0a\ng3Ia7dY5xGrtF7J+cft5Whu/qKDrZYzXZpPs6T7Y8lkvWQw3Fzk/stGvHSgMmc+uiT5JRMECvmQz\n2LYQS3HiqqoYDAYYY1qtVJqmZFn2uQ1NQNhwOGxZK5/BErAln/VZJfmMUusakHCz7qKI/vM8J45j\n0jRts5sLOxaGIcPhsM05tqmx2fy92ba5NH6W5jMZX9ZYfpm+601qm+4o/7XNtnmvN6shCPPjg6zN\n6EUBYX6VAZ8l9PuyWfAZuGG4+G59AVz+3PANIOnTNlZU3hN3voAfX8fos1ybek3fpbZtjZUUE+IO\nlfNJkICkrpCi3z7TLBHCwir64HUboNpkWV/0HP3fL2OxNoHSF7kJN8/hf/ZFe9CXtTe9VtDV3iBA\niVtJXechakWA6w9fMx/OkTQVqikwzYo6nxJUc+q6Aacxeo3OQx0TxwlFVREaTVHWlGUN2uFURBDE\nNLbAuhoThCgTUtaA0WRFTlFX9LsBRinyImO1WCdcpHKoMKPX6WNDtRacN5aGhqYJsBhWq4ZFkTPq\npVxeTLh/64CDETy6GJMGMdNpQ1BZOlHAMgI9szyxl2QJ/NrRW6wuuqAUudLofpcLZxmNBqh5RtFY\n+v0epVPocB2CHzSOrlHsDndo6gmVU/SS+P8l701iLdvSO6/fanZzmttE8+L1Tb5MO1NVxhgLMLgm\nlqFUA0tgSxYWEwQzJCQQRghqkAhhRpRKjEoqwQAQpZIRA2chWYhCJRdIFpOUbKdxWXZmvnptvIgX\nzW1Ot7u1FoN9vn2+s+PcG/Fehh3xihUK3XvP2c3aa6+1vv/3/zoenj9m4vrN5rXX3uSDTz7lvZun\nhHYDKSO4kjvvfYN7D/+U7PQtJut3cCffYNNFsIZ1syHZmtzfwRjHdHaCtb1PV55FnDE4k+iiIZIT\nYkYyFpt7ss4SuwQmQNwaFpMSqnZ/4zRiUgaM3XnXy7K4iu051HbC6UlBpU1YV21AL6o9qxC8Cohp\nXxJt4pBri1+JFjhyzngcNAsAu3xbOtRe2CsBOdZajo+P9yLKBKAJcBMToXNuKJItoOv8/HwQSDFG\nfvCDH/DX//pf59VXX91zMpYSPmIO1H4j1tqhMLa1lqOjIy4vLzHGDA7AYkYpy5LFYjGYGpfL5WBy\nWa1W3LlzZ89hWQQTsCdwtIlWCzYNfoG9868y8x16v1cBjKeZKw99d5XwO/T3y9KeVYjK92O2UZo2\nq+tricKhzXMawEg7VE8Tdmkn5Hzxx5L7SZSiBkQyF/Q6nc/nA4MkPmbiByb3ms/nTCaTIY2JZsxE\nwTnECor5fDwGGkhqsCfASxQqUZDkmpKwWTLjS2WMq0Cqfh+aQdP78SEgddUcGMuO8XfP0q66xyGA\n9bzkxEvHdI3tz+MmD17bHGtbnCvI84JYFpQuEUJDin1Y7aSYkKLFeYf1GcYbikmiCxHvc2JoCW2F\nd9vNKPTMjTFSKw0e3vucybTAWii8x6RA9A5fZGATk/mE1NWE7oQYamLsC4EWWUl28zZYw51XO+7f\nP2O5Dkx9x+3X3+TiuOHR2ZKqbVgtK6jh8vSI/+PDP+BfmL5JlqZgIJ9MOO9a3nzvbR4uL7l5fJOz\nxQJizyaVWUYWOupHZ9y6c4uzR48w52vyVwtSXTMtC+bTKd5bzto1r7zyCu0XD5ifzLhYPsZlkD7+\nIf80/ojs2yccnR9x996f4ua3iMUxxk3JyglFfkxZTulacK7EuZxIRyRRh442GapoOd9UnFeBdcxp\nk8OEDmsiKWltZevDwy7B5PB+t2snpu1CJEF8Ppr9sAH8xFf6y21P87PRWp5sjsIKaa1fGCAdaaWF\ngDiSjzctfR1xmtV+VnLMWNiNkzLKRitg7vLyctCEpS6kMWbIut00Dffu3ePhw4e8+eabQwSmPIdE\nTI4ddccRnfKZCEH5XY7x3g8Z5jUj8PDhw0Gj10WwU0qDY79OTqvHa+xMLVF0WjCP3/HT2vid/P+l\nXcU6POs5Egwi81P7VMl81CkSNGiWFA7aAVzmkfZl0u9WmzT1tQTsaLOlDorR5jvxK5S8dnptxRiH\n8lSPHz8enlPO12yVZok02JL7j5MkW2v30ljAbv3o/1VVDdGfAiq1b6c8cLqx+wAAIABJREFU+/g9\n6TGRPWi8v10HoK4DZYfe/ZeRG88C1p6HHHqhoMua7eZnDSElkqJD5WdKiaQdpO3W5yJuiDaRiiNi\nuk1an7NKFYUv8anDWcOmq2i7CJ2hDR1NG2iaDpdlTIs+d09ZzAbHWOsSm82KLmwovefy8hLrLdlk\nympxiQ0dDkPb1JjlklfuvIU1BSafUxawWS9o8XjXErqK84sF1loenz9iVdW8++5P0cQKx5S7f/Yh\nf+Vbr/Hwsac4TrTVlC+Wkf/5n/w//OzpG/yb/+o3mNYzeODIQp+B/Z2/+gaxLqiWC2zmSVmGC5F1\n1ZLnnmT7RRy7lklbUceO2zdvc/dHP+Rn3vsG3a0Zsam5f3HG23de44QMigRl5P/6xz/gX/xrv8Cr\ni49pomURC3x5E+gzN7tiToiJsszI8wKTICRHGxKrNlEFw6ZzLNKUjpoUWxyRZBLR2L4wtrVIvcVk\nenBrYE8Q2W0m+TgI7J4HTal3vN8tuCdBSI+dVS6uxJ4jvQi2Vp2btlRa7QLmJZBjelMZb0ZaM9S/\n641Aa9Ayr7WfiC5OrX+KhqwzaYsCpPsjjuQCgARcSF+EwdIAT2/mEvEopYBms9nQXykVVFXVkKph\nuVzy/e9/n+PjY77zne8MvjJyzaOjI4wxXF5e7t1bmCwRrpLGQkCbsF1HR0dDOaWmaQZh6ZzjwYMH\nvPHGG9y5c2cwCwnA06VbNKCUMRawJaaZMbtw1bsfN63NHxIKzyKErmtflll7WdqzClZZDyntzObw\n5LrRTJFmZGaz2TBvxGSuWUwdoarBxvhYWU/aL1DupU2eujSQmOQFhIkSU1UVr7zyyhANfO/ePZqm\nGeamMG2iEMgY6YAC6YuMpR4PcYCXIBcBVXL/1WrF2dkZjx49GtaYMWbwtdQKhFZ25Ll10wD40LvU\noPXQ/L7uM32tQ2bEp13nL6q9lEzXs7SAJSRHDAaSxfsJPpWwjYR0BgwOVxpCnbCuIMsM5TTRdYEQ\nOjabQF33fikpJcrZFGMMJyc3qKpVT5/ScH55ibeW0AWKWcF0OqUspzRti7E1Pres2hqTedrWMinm\nROuZThJ1U3Hr+JTXb5eEriN2Cz798EPee+eY1eY+xzdfoW1f5Q8/+Bh3cot16fhv/+9/wL/9z/8b\n8M+9B2ct5d0lpgWX5sSJ58xayiKjChBDy/x4RnY0p6HD5Y5sNiW1DUWeU9cb7rz2KmfLS8rSMiky\n3v6pn6K9exdXN9jjU5gXFDduULXw6u03ed0VTGqo7JzSFoTYO7g7sytr4owlYMBajLfEZGijoW0D\nMfZjP05BOp7Y2oFz+L/9bmwSkLmxW4S768h1g/6sP2HoQVL3E+D+dWpfxuyox2u8oelM13qthRCG\nqEJd+kc7CmvtWWfmFmd3ceIHhgguuY4APO28fuPGjSH1xKNHj4bzJcv4crkkpcQnn3zC7/3e7/HO\nO+/w5ptvArvCw5IUdbVaDUBQANdsNtsTptpMKJGTIhAmk8lgzpG8TwKobt++jbV2iNjSphs9J/U4\ny1jrYIZDTQt5ueeh38dmlEPMwLOCLw20ruvXy9IOje+zNu27NQTopF30njb9je+hff+E2ZV3qVkt\nHZ0nrJisM+1/KH2Ra8i9dbFqAVu6VqMoQ2Jer6qKs7MzXn/9dd566y2MMdy9e3dvLsk+rde6ToEh\n61qAqIAsXTZLByJotlYULz2Wuu6l3GMc1SvPrpsGYodMovL5IXPfeN4fMjVe1Q6Zq8fnj02Yz6u9\ndKDrWRdXsiXRRKz1xNjQ+Rllagj1iqqryWykLKbECFnmMTFQt4EUEjEBpk/SaKwZWIHNZoO1kNKa\n0LU4Z8EEvC8os4yYOkIHdQx0sSLLCjAdKVSQOUJIzE9OqDcNTfDk2RGp7cjnEy4fPya0HevzBa+f\neOrqkuSgatacLT1VA+3jirUJ3Ls55Tf/zn/Bf/affBfLCad33qRdRyZnEftmznRSMD8+prm8pMz7\nEiqha6G0HJ2ecOPkFo/u3sUaS+48+aSg2SzIQyRsNrj5jIvHZ9y+cYMOaDJoj2Y0toDJq7DcYLuW\npm7YpIjLp8znR1hjiG0H9GByHQxdMixqwyZktHiSMSRjtqV+ehAVAXvFgoL9umEphie+l6bpfY1B\nhkUy8tU4tHjh+UZE/mW0sWPsdU0Ak864LZFO4tMlYEJHMcpn8rdOC6FNdwJeYoyDVisCSbNF0rR/\nhwZv0G/0dV1zeXm5p+3LMy+Xy6EfH3/8Md/73vf49V//de7cucN8Ph9MGmL20zUhZ7PZkDpANnvR\nuo0xTKfTvc1Vs1TCfM3nc05PTwGG4wWoaXAqYE7XV2yaZo9FHLexwJDfx2BKa/jPYk4Zt7Hvzpg9\nPcQKvGxtbKY6tLavYjy0f6BW7kR50OkdNBMm60AYWflsnP5Ds16a2dJsr4AcYO8eer7LOpG6pLI2\nNEDTYPns7Iw8z/nGN77B8fExFxcXQyUHubcoOtonS6eW0NGG0sTcrteqPI8w1CGEgVWTqGANwnST\n+aYjffV8fhaTnozPmDW8DqSN58HTcMUhEDb+/HmtkRebpyvsymLodh2FLq2J4FxBTAb8FMoTunZN\nGzcU1mBtb6Ly2QRjM5wx+Dz19QqNoQtZP6k7Yb76zXQyLVnXfaQhNpH5ghQ76rbPgp5Zi896Tdha\ny3p12Zsa3BSbIqELOBvxdGQuse5qms2CanNBvYr4BO0qMZ3fxOcRO7vDDy8vSUVOFj23O0e82PD3\nbz8k+1/+Lv/Vv/bvcp5nzN/+Dhy/AeYcm2dkRUYxycmMp1oumOYZnXXkRQbrhkDgZDbDFQVtV3F6\n65TLT+9z+tod4qMHlEcTyAx+NqF+Zc5f+5W/wXp2yeaiIfkp5TQxd4HCldTR0TSRzNne7y1FjEm4\n3NFsEm1rqQKsQgvYrV9Wb0qMpCfe4/jn3sa//UxvalpA7Tbg+MTild97f7FtpOuW7cKYJ49Rfx/a\nMF5EG2uCuj2Lk7MIEg18xFdKwIBEGslm3nXdYH7TTvVSpFqXHZHUCWOApGs6Xlxc7CUhlSZAr65r\nlssl5+fnwz2lSQJSiWyUxJMxRv7oj/6IP/mTP+G3fuu3ePfdd7lz584Auo6Ojjg6OmIymRBj5O7d\nu0MRa2E1JI2ECNH5fD4InhD6gtvL5XJg3H7hF34BgMViMQBKAZjS9DwVIVRVFW3bslqtnphXh4SS\nXgvaHCPvU8479Pu4HVoTch1tNhsL+zEge1bW7C+zHRrHQ+yENGFpDvlE6mLNcp7MQxl/YX/EPAwM\njI5OgKsTpo6ZZm1ChH4tTafTJ9KLhBDYbDaDj5ZWsMYVIgT8f/jhh9y/f5+3336bX/zFX+Sjjz7i\ns88+G9aO9GH8DsfO9Nq/TTN3ukmqGWGBZex05KSAK/k+pTT4po0d8vV7PMQoHZp30r+nnXPVnD00\nR75Me15r4aVjunQbFtYB12eLTKa+BmCIFm8deEPoWrpQk0JfBiibObouUDcd2J7dcnZGbnpBk5c9\nim+7DTFE6qbF0OEbi3W9VpRnDlIk4qi7jtA0VCYxLXOaqmZqwFmo6w3eWVy74u69e7TNmrarcT5n\ndpqxWCzAOhabJXk24fJiyazecHoy5WIRmRhP5gw/PFvxver7rP/+I777H/2XTE4nXJ5/wuR4wvTG\nnJQZQm5pYiQHjsspZlawrJY4DPNbx/jc4YCinNBUG05ffZWHdz/j5HjK7OYRi8dnHNkb3KvOmL5z\nSr1Y4Sc3mJzc4vzTz7A+I3UBl08xZOSZI5hEoiN2kYxA5x1FnrPeQEgN29KKAEQDbGszxvbJGojS\n9KYi3xxaWPsb6oG5YrfmRKPYLAmCTEld/MvNw69b0+yS/K0ZF/Frko1Xa+6wc3bV0UtyrE4TofMf\nCVjT5jlp4qtSVdWgKRvTO88vFouBsZLrpZSYTCZD0WxpIhh/93d/l1/7tV9jOp0Ofi/yvAL+5vP5\nHhB0zg1Zv7W5RYCggNXpdDp8LiYhnchV5qrO+i3/tc+MLnXyVRiqQ9r2VULly7JVGmh9HdqXGadD\nLIcGE9KE0YFdTi5hmLTpUAekwC53nfbJ0kWxZR7J8TLOkmRXQGBKaWBD67pmvV4PmeB1Cgr9bPI8\neu2uVivu3r3Lt771Ld566y3atuXzzz8fclXK/BbgqJkubdbTLLA8p/weQhjMnsKUaxOrNlnKM8t5\nWimRz/TzyDONwfPTlIpD7VkUkfH9vmx7HsDrpQZd1zVnEiEGjElY2WxlwsdI17b0ZYwbqkWHcR7v\nMozvF2BbW1K0GDybdT9B5/M5VbVm6qeEriGElkhfW6veGKyD3G+FTGjwRLyNZN5RXZyxWi3xNpJS\nzWRScPtmyXR+m03d0DWOT+8+4s67N6EtuLxYcfzaDbp0ylFcsPrigkWMtHVHyCw//3jC3dvwvy0+\n5Kf+3v/Ef/jT/wrF27fI2sDZ/cewWXFy6ybNuuL4pidsKqwpOT46hqzjxus3uPzoHsfzkqZaUswK\nusslt27f4NH5A44mJbMbJ3A6h+yMKtQ9S9As6ILh6PQGi0UfCGCzjBR7h9HMOWKKRBNxBrz1FD6j\nKCwZHV1nB9CVSERZlOz7Zl25AA4IlrHWL1fXzVq7x6rpczUT8c9Ke1bWSzsSa5Alfhnav0OaNpcI\nsyXmhfFmrPshTvuy2VZVNWi31vbh8OI4D330Ytu2e2bB6XQ6gKvJZMJisXjClPD973+fyWTCb/zG\nb/D222/3JbxgYL0kf5cWAiJ8pPaj7jPshLDOOaaFkTy39EMAmZ5XcryMoWTrl7/l55ixOWT2G7en\nWQP0ta/6bvzM0r4u4OtQuwp0aSAqv8sYa/ZR5oI2/+mcWjIPZI1ok534YulgEbn/mLGU+ad9qKqq\nGsp2iSL0yiuvDEBPK53S97EZE3pm+YMPPuDtt9/m3Xffpeu6wble90ubKXW/5Bh5tvFclPM0ey1r\nVDNm47QbEmSgx/KqdzOes3qt6Xf9NEbs0Pt/FjZM+8Jdpyz9JIBN2osFXVtmQpowV4l9ARtit2fT\nBnDRgbFEIi0O4z2z/IguVtDlGLMhhg6XOspJQVbOaIIlWQ/G4bKWaCC0AV9YUorUVSDPpiQC2AwT\nI8QassikyDEpUdcV0TqcdVhvqGJHFy2zwnA8u0WeObqmxTlD6Y4hBk585KN7P2QaGxaPl3jjeeXW\nLR58sSI5w2Of89atOYVZsDIOX04hq3lvVtCVBf8w/Iijv/e3+Pf+nf8Yvvkt2ssN5WZBmd+hyI9p\n7CPyN25RPXxEOTvlMl5wHOG4jTzOFtw8vsnlgwfkJxP82Yrbb7xOFVtsk2BVc5ZXRBPIz7+gipc0\n6xUXiyV1sETnCOsl03yOs46UQg9WTYmziaL0NKmliBYfPDGFHvwkuy350ydFTS6qjaJnnWw8AJJS\nj7t2C84MZsIQd6V7jN0WAdKbbtoWk4pgtyWGWjqsLLyY+ghFe2Cxp4wXvRzgSb+tZxHE15mddCi8\nXE/YKB3dpE0gAiS04+24TqJ8rn3Dxrl3xBFZsz4iQLquG9JCwI6JEiYN+jxA2mFfnqNtW/7gD/6A\nmzdv8qu/+qscHR0xnU57Fnl7j+Pj48G8IffXDtNFUQyO8cCQ92gymewBVLm/Tg4JO0Er81H3T5tY\nxsJX+qfH8dB71u9R+nzIVKT3xadp4dcxAbpPTzPTvIxNm53Gn2sBfgiEiJKhwZhWLMZgVq4hAP2Q\nb5SeG/pa2mlecsOdnp5irR3Wio5WFNZUzI96Dus19cEHH5BlGW+//Tbvv/8+IQQ+++yzYR3qgvcC\ntDQA06yaADudJV+CZeSZdD1WbUaF/bxbOlhKBxzo8+R7DarGc/AQA3aI4X3WNXAdeJPvDrGmPyng\nghedHHU8Pmlnmtr7OIH32TAJAWof6SMVIz51ZHVFqD4n1ReYrsIlB8H0pqZoCG2NcznWbSMzbIn3\nOUXevwjxHamqdb9xek/hPW2zxjmDcxaLIcuPiAQMEW8TKfaM2KqKZD5Qdx0mNVSLBXP/gMXFJZ9/\nfs7t2zNOXim5kRWQDNW6osgdroAbxYyLi8C7b79GNJ6qjaSqo+42XKaaZVzyv//o/+Tif73g3//X\nf5M73/l51g8jpBZzOsf4KSnBF/Ul7+SnTGclTCZsisTN6YxQLTm+dQNmGcuuwU4nTMojWK75rLhH\nk845u/eAN2Yl4XLDumsgc7RdQ9usSC6nMH2IfZY5UtfhXP9OsiznpMiZbxLV4zVnraQZiKSYwLje\nAf+aeXCV1gHXgA5BZ1cshOG8A/Pp66zZ63ZICEuTzVVHzx0aJ50eYmweKYpib+PU/jGa+RGTgxZm\nwBPmB11fbrlcDlnfxewnz5Jl2R7DJhFTeZ7Ttm3PPNc1v//7v8+dO3coy5Lbt28PAEeEjIAmSSUh\n0VcydqK1iwlRNPU+oMYO7Jdk/taCeuy7I4JLhIpktRdGQJ7vkJZ/qF3H4DzreeN2nb/g17WNBech\nQazdGsRsrE1uYzAl1xofMwbWGniN38shIKI/s9YORdaLohjWwP3794dEwTrLu8xb7z2bzWYvOtIY\nw2az4ZNPPqEsS1599VXeffddHj58uAfQJMmqPN94n5XnGpdIGtc2FfAowEzWnKwfvUeMU0aM5/91\n5sRDQOiqv69ip65SVsb3veq8cV++9qBr3Iw5xHxBH6GYkKSlKSUwiUQgxUiKLbHd4Gnx1vRmxJTA\nGaIxRBKxbXGhj6Lz1oDLMMYRY6BtAiRPpCNhiSmQukDTdpAihgxbFJjY369pW4xJRGfwNuuz2fdV\nu4mxwVoopqdsLu5hveOd9273E7fISQY+++RTUkjceeU9TD7Hrjxhk5gfTwjGc3a+4tHFJd/85ns8\nfOh4/Oghdy8+5R/96SW/+M6/xF85PmZ+4xUoMiDiSo/Jc159/y1wjmpVUxhPujGBxQb32i0e/egT\npq+dUHnL7dkxPLjLavGAz179nMuzB9y6U/DRBz/G2QkXm5bp/IjSlpg2EDE03YbJZIbznqzIMcbR\nhgZnLLmNHOWG4zJnnSBVFZ1M0pS2zKV6x8OE3k1k2dD0AuXAXBjmStr6+mkN3ew0FDlea3P2AAIb\nrvuSBm89zew03jRk7CSSThxZdX4r2OWSEpOaCAAd9aSFzZhtkU1ZO9FKf7SZYczcifAoy5JvfvOb\nQG++FzPinTt3SKlPSilO7wICN5sNxhgeP36Mc302+9/5nd/hD//wD/nud787CERxnq/rmtPT00Gp\nyvN8yN4tc242mw25kWKMbDabIfFjSr3fjQjBPM85OTkZxlbGVfu26WeWBJa9MlftmWO1AB6/X63l\nawbnEKDS46u/1+9r/A70da8y43ydmu7/daYh2Jn15P2KcqIdvDULNPZJ0mMpY6cZY2njNBRjtjOl\nxOXl5bC2ZD5JMMh0Oh0UDGBYnzH2iVEFzMtcruuahw8fsl6vefPNN/nmN7/J+++/z2effTakQpHk\nx7BLgSFmTQFHwOCTmef5Nj1SOfRZFClg6LeAr9VqNYBAeV59P9lbpI39Tp/WrgJOT1NEtMJ5HUi/\nTuY8bV59mfZik6Me+OygeEl28PURYW1jB9tqjMYGXJbI04RkLFlwGNfQhYYQ45A5F5fIvMfnEZND\njBBCxHlDU3e4HLLcsV5vwKSt9mFJMWwdIS3OQDk9AiLeGpyNEPrJ7yyklJHnntDW+JsTUmgIzYYQ\nKi4v+qRyr965jfc5XbAkDKXZ8P4bpzw8v+D8bM23f/o7PD4t+eGf/TE3T2/wsz/9Pnc//4x1teJv\n/4O/xd989R1+zv7LJJswN+eY8w4yR35UEquW4mhCNitob0zg/orl2SNu3DwBkzE5nsLlgscXH/Mj\n/wUru6RMl6w+vcDFwGrVQnHM44uKmzdvggt0KZLPJrRtwLoMl/WJIWf+CBtasi5RB3Au4UxfGmgo\n3ZQkInF4haTeungt+3WVljF8fmDuf92ExfNoWnDqzUU7zGrmSZel0T5PPYuZDX4jwJ5PiHwu7Jdc\nT1giHVY/Nrlp84Vmz2SjDiEMSUqNMQPDdHJywoMHD4BdTcnLy0vm8znW2iGy7JNPPuH+/fvcvn17\nyAMmQkYzBMJmbTYbYuxzc8m9BFxJf0IIQxRijHHPV00DQc2eHBLamtnYCxg5sMk/K8CW9z6+1lhw\nyd9j14yvY7uK2XiWvzVo1cBBvxuZd+OxFGZHj7cGz5odk+PlXH3c2IFclBMxT2qTmzBbUsNUHPxh\n54Omry3gTtjczz//HOcc7777LiEE7t+/vzePNRiSZ9MstaxnCVQRhUev+6qqBkA4zlF2iE0b+5Ad\nOubLAJtDZsKrzruKIdMKjW6HrvPPLNP1LE0GyaVISGDo80YZbzBd2Ttvm0iipYl9EtTST3tmxBhC\naKk3CRw4mzOdTag2/QTPyhJamPk5MfUakTfgrdv+XVPmOZi+TE0iYXOHcX3EYUqJ2HZUm4QxBT4v\n8HmiKDbE+oJlteG9994ndoFJUYCfsWzgqDR8/Omn3H7lNR7cv49PNY6Wd958lVdv3uHDDz8ktA2x\na6lut/ztv/Pf8D/8zf8ec1RSVxVuvcGTcCdHmLygffAIjqZMuxP4+Iw0zbDFFOqW5uISuzrjR6vP\n+H/fuOT2ek1x+YDl8gFpfkSIM8rJMdOZZb1ek/kCUiDZyOx4Sp5NMDigX2STMsd1LfPM45YrnNsm\n3wS6pu1BF4YvO1cPTXwBFSklntQtIRzQTL7ukYpPM4VqwQr7zqbaDDI2i2nhEOMulYQWQtpcJsyW\n+HIIUBMgosGWdraVPo4jpQTEiNlC+4QJ2JM+lWU5mEakz48fPx5AlPee733ve/zKr/wKr7/++l4h\nYJ1LqyzLobi2CDrxkxG2bbVaDVGbOidRWZaDYJbn0CVPtK+KjG1ZloOpUrR87dPzVdqYkRqbT64S\nChokPKt5/XkImOfRxsyftKcBLzlHny/zcOx/OE5oOn6nMsbjmorja0kfdSTuOOBE+iksqVZkZC4K\nUJZj5DoyBzULrfNmVVU1KA4/+7M/y8/8zM8wn8+5d+/ekPdOnydO+2LulP6cnJxwfHyMtX2S4sVi\n8YRZXQCp7Ms6uESeUwPb8bvSoOdpc+0qU+QhFve6v8fX0u/2qu+fdr0v214w0+WI2wcMJEKKGLre\n0rNlQ4wxkLalgESOGqhah3dgg6MnmzytuPl0HaYzOEq8N3g/7ScZgRAiwUQmXY3NbH9/IrNZQWgN\nnpyIJaUtIk8dMUDCUOR9Vvl1ddGHqlsLvuzluk2YGPAiGFKgMx1d29E1DaWfMrn1FiZ0TCYlxlma\nGHAeyuNT3rQdTbXh5/7qT1G1a3yM3Diastg8YHrsyMIJb5Rvcffe58R3HvFv/de/xt/97v/I8Ue3\nKN55jXS5gosELlDkOZf/5GOmq4puvWGyuGTVfELIHe205R9/9I9ob7VkD2v88YzkKm7dOebHn31B\n+coxJjQYk3M8P8J6x3JdUTiLjR4C2wzEObmF9fqcLIM8S7x74lg3FakJLKPBOE/sUh+Q4HYa92Ci\nYjfZLYYUItiAMVsmTEwx0W2Pt9t5YQjKITCwdZBN+/m9ADLjiSHi7NZJnJ0fWH+MbMgq18ULbBqg\n6Pa0jelQxJH8HGv34vOkI/dEU9UOtVroyIaqHWOlvwIkxqHz8i6kf2PhpwWO9MsYM4Ap8eeSzPJi\nblwsFkNhYAEyP/7xj/md3/kdfumXfol3332XmzdvDu9YMmY/fPhwL0wfeq39/PycBw8esFgsmE6n\nvPLKK8NYiC+ZCBURUhp8yrOKWUaE43w+H/KSialXA2DNNOp3poXA2PwxZlz0uF6l8eu5ocH5IdDy\nspsZr2I2nkVww46JlXku60LemQB0DbZkjAXsaGd03Q/9HjVrBjuT3Zh91sxPSmnIsaVTsoyVI81M\naYCn2bAQAh9++CHf+ta3+Pmf/3kePHjAD37wg0GRkPGaTCbDmpYqDrdu3RpY4sViMURZCvMr610A\nqrDWYxZL5p2MgfRR10+V8daK4qF2Fcv7VebooXscYs6e5byv0l4o6JLhioZdwZgnBJ9EwG2PjXFr\nutqaF00kJkMMFmI3mPqsAe9yIomQGCLgwNDFwOKyI8sjvig5Pr5J1dQ0zc6kQuyFQtt0tNWGIs+o\n60hKgUk5I896c0e16Tfx3EOWe7zNMES60NGE3scszyYUhd863XdctDU+OgqfMXGO5XrJdDYhtzCZ\n5Lh1YD49omo2PHp0yenJnPWmYbk4ZzK3ZFlL9n7Gd/+7/4B3br/Lf/pr/zku5DhbQFFC23B8/5yu\nuaQ8gS/iA3746AN+GP8pF9UFt96/waK74Pj4iIBnsVqxaTPeeOfbrOIRKeudOq13ZMWEk6ykrdf9\nuCRL2/Zjb53naHqCiRusgzfuzOis5ZN7Nb6CVWepDMTo6GK95zcgr1r+kvffs2i7Nw/PRjvLZqap\n+mdpsuhfVp8uaddpXmONUWuyY0d6ESLyU1rXdWw2m8HpXITEOE2EzvsDO+d67WSvM+BfZW6BnclQ\ngGZd10NNQ9HuT05O9oSbBlG6QPZsNuPjjz/mt3/7t/m5n/s5fvmXf5npdDps7AKuxKdHEl9+8cUX\nnJ+fD0JOp3mwtq/tKEyGZumkfzKOmkmS96VzhUnx7DFb8JPMg2ed51/1Xi9LO8RujUHp+HdpmpUd\nmw41GNA+XdqMNu7HVX0R9lizmnpN6mtrxl4DKgHRuj86J9cYsOtEvRIIJoxsSok///M/p65rvv3t\nb/Pmm29SFAWLxWJgegW8yT0lF5dWRC4uLgbTpIyJyEhdaF6zwGNAqPcieVaJKtbj/mXaIbPgISbs\naQDq0DWummPPCuyf1l5sRvo+6L+foEYc5Pdp2gSYvo4M4kxvDGS2I3YdpBYbO5xJOBJRnKkxtF2g\nS2Bjh3Xg/XaTShFrfO/T1aXeiddZ8ryv+dbGQF724evebSd1XUFcwTgbAAAgAElEQVTaOimHNUUX\nhqgTl+VYEwgp4TBELCSPNzkpBmyesa4arHUkG2mTI25q7pxOaOqaYpKTuhbjIXcdseg4u1xjTOK1\n105ZLCuit4TSY2PB/HjGeTrni/XnrFYX/OY//C55KihTTpksr71ym/T5GbMbBfcWH2GOEuu05Ma7\nr2NniXVzRuo60mXLWTyn7RpinZiaFj/LMWabCiAZbJTCwb25p3/WXpB1MZGalsI5nImUObxz54jM\nJLLzmvuLQBu25pR49ULRGj7sCq6iQNdVGu74PH0PfbzWLrU2Jn/DyxHZpTf660xGWjPUG/tVLJn+\nqcGP9k0Cnijjo2vUAQNrI2BHmCPYRR3qNBWyseskp8Cej5N+n1rzFZOH3Es08yzrq0lkWTb019q+\nhErTNPzxH/8xH3zwweDDAn36CWvtYPITYXDz5s1B8FhrOT8/Z7PZDL5es9lsAKI6pcZ8Pt8bQ/mp\nza1y36OjI27dusWDBw/46KOPBrD4tGjCsfA45C9zCHzI+F4Htq5TYMbr6EW38TqG61Ns6BZj3DMD\ny7hUVbX3fFcBYXFCl/PF9CxN7ytieoQdkBtH/B5iK3WTOab9LsXHSjO32nSpTdaa0Tw7O+Px48f8\n4Ac/YDKZ8P7773N6espkMhny2C2XyyExq/wuNU/l+TSDJ2lg5DsJ1JF+Sr/HbJ7eC6Tvuk6qlgPX\n7fX6Mz3/9Ry4ChyNWePxOfpv/ftVLNtXbS8UdEUSxvYvrA1hy3RsQRdpYDrcYFvsCx0noAj0v7Q1\nvrvE1F8QYwWSMBVH9AZnLJnJiTEQ0nZDdI7clhhrwVmsBef3KX6ZYG2MhAjFdNZHL6ZEItJ0LV0M\nbOp+A82cJ/cZKSYsEWc8fgvgQwwYV9J2HSbBZD5ls1qyCh0xdvg6YUNDRkusa1LXMiun3Lx5yhcP\nH3E8zzEhQcyYZwWXiwWv33l1CJP37WPqTUM+nWJtx+Puxzx+y3Hj+IjuRs20yJi0GalekpdTwroh\nz6c8vH9JNj0m2pzOOEzyTCNkLiMvS3yWExI0XcAZy/HJaV9vkq1ztQvQBgoM3kTaWJMXnldem3J3\n8SmpqzGhxQPNofdv9n8COBwhRPbDLA6H/Uob0/y6yQLUbI3WNIcF+hIyXdeBLv39+LmF3ZKwbnjS\nhHXI/0I7gsMOTGg/Jg1YtTlNTBaykQpbNY5kHPu+yLsQJ2BdK04fp82c+vm1k7vWnMV8Ktq4aN3a\nfClsgfQf2BNgmpmVa+poTc16yTNoJkNfZz6fc3l5+ZXNIV+GrRqzklcdc0i4ybt6Xlr9X1S7rm9j\nYanNgQKA9DuXYzRLDPvr6pDpXs6TY+W6wkTJfYWZ1cADDpf30aY/zSLrZxuz2XI/7Tep3QdS6tOg\n/Nmf/dnQL61M6ZqK0EcWa7YKGMyC2ryq2Tedg0vm63hstNlV1pHkwBtHgF63Tp7l3Y8/exoQe9p9\nr5M/X7a92DxdMnmChPw/Xfb1oAdoItZ0hKYiNGvytMYksM7g8JjYF7aOOJq2xTpDlmfbiQw+ZkQi\nbCc3W9Oj9x5nDU23jfbIS24WvTN527a01YZkHWVe9JNl63yYG4exELuAz3KMScSm7oHZZsPJjRsY\nb8i9p9kmgFyuK6yLTLpADA2Zj1wuznqThvN0oWE2zUkpx9+YYS+WtKy5cZxReFgvltiyZOomzI9n\nTI8mBDZEW/EeE7KUY8oJsYvk7pRH8T7eeChKKtOwCmvm4YQuRI5v3cb5HJ/L5rTN1ZRvfdzaaqv9\n5xRFTgxgCkeyHakzWGeZ2JImBbyzvP3OeyyaT6nas63/zJOmkDT6CfuASJr4wlynzcp8OjRfxpuA\n3riGxZi+fiaYsXYIOzOFbJCywekx1Vq03igF3GjwpcdKR+9pfwzZNLXpQzMLAojkeBEUYkqUY4SB\nkOfR70gEhRSx1gBLnkvmiRwnfZWxErAlY6CFszafaFOjHp+xGVVv5romnYBZPSZ5nnPz5s0h79LT\nQNRY4I/n9nUCQAOH6+bOmCUYs2gvU9MKk/77ENOh9wr5KWYxGRt9nvbvGkcmjtfNOCWCnC9rY8zK\niRIhJrlxcMq4HTL9y/oQQKYjGAUojRUoeYe6r5qJ0/fQioKshfH1dASn7CnCNMl3OjO/rrt6FYvo\nvR9Kg8lzPQu7dIiNuqrpfU3aIWvC09rTFOAv014w09WB6fksmwwmWuSZDAa79e9qsgQxYlLAmt7x\nvTUtLjY4OvJkyN0RxHOcsdhoCDEQUwQbKacznO03+N4XLNK5DO8M3ltSjNgu0djQo76UcMYSMXRN\nTRqERcRlE2Lq0fp8fryl/F2fv6tutr4xEeugzB1dDMxPpnjXUW8uqVZi1+5ompqizHG+YOI99eoL\nQl3RTCecTjw+y2mrFl8U2CxhfElM2fb8lqP5rN/cHaQUwK6YFAVtC7bMScYRk6GmonUVPk7I/JR1\n43DZFGIiOUfmMuzWMErq+msRcK4gJYO3Hjc92mlUXV9HrF2GrUBP1F2FjaEPhjA5Uzx3btziclOz\nCREj2o4CN/KrVcI12W1kqNltdM5vBasEUkCfo00WzNY0bYL4Bpjdz230pDVuD1elwYG+v5/xL59W\nP6bPdTu0OQmgkXP15/JzDIq0z5U2o8C+8L5q45PNMqX0BICDnU/Y2FFZNkIJixcgKGY9uc563fsS\nTiaT4XNt7pPrieDrum5gt+R46ackoJR+yZiI07DenEV46GeR340xexFnIni08NX3kLldluWQZV8f\nd0ggaRPfGCDp48fCaXytQ+9Nf3bd7y8T0zU2y42bfjf6ncHOnK7B7iEhfCiVxKF+6HtIv/Q6kvmg\n+y1rTqcb0f59GqhrQKP9oXTgiyg78iwajAoAGgPTMciSYzSIEoAl/ZH765QwsB8UNWZix8z2ODhH\nA+Esy1iv18Oz6QAF/W71cxx6Hv3z0Dt72j566Pvxd89rPXwtUkaY1P9PbAcgJZILRJOwncG4HGOm\nONeQmo5AxGYZhTW0RCyeEERTT8TUbn3I/Jb63VKl7DZL7cOhBYYOKxat+PLyEpoGSNv6VJ4sd1ib\ntpm1+0z3vS9U2PqjOJzLMFiSs7RYgvHY8ghTHLFuE6lrmJ/c4uHZOUfHJzy+WHDrtI/akmSPKSWS\nsRR5SdMFksnI8ik28zR1ICWD8TNCioQU6ZiwWG+YHDluvvomXXQU5ZRgPGU5A5/j7Xbj6Fom0xKs\npd3aAIPxOG9pu5bMGdq2Z/OMMYSm5qLuqLoNZ8vEg2XLuk3YrMC0DSlFrH3SaXKPug89EDJG/O+2\nE2A8J3AIR9a/n8Ohw2MNftx2wObl8On6su2qjUhrmaKBysYpc1ibBcapH8T8KE2+z/N80LA1cJB7\nikO8RAfKfcXBV9ZVjH3+PM1AjAGETkCqc4illIaM3V3XDazWWPDo0icCzKRfcg/R/ruuI8syTk9P\nhxpz1tqtP+O+Vq/HUgtUEVQiFMUkI2H8q9WKxWKxC9ZhB8ye9o6/jGnjKvPgIeHxvEwmf9nt0LNc\nxXjr8RsrI5oR0uyMBi+HrqXByvgeml3WSoysGclyf8jkPy4rpMGRsLuXl5eD7BGzpTDA0g8NIA/t\nfzKfNSjSDJCsUdkTdE4uCSwZA1TNaAlw0xUvgD0TZVEUTKdTZrPZoIhIvw8xmOP3oNfRISVUj+0h\neaCbBnDXMWvPo73Y6MW9gbUYTQuzk7UO+np5UUrLgHOWzDlczPDJE6vUn+88s7LoTRWhzxPVdCq7\nsDE0TSTPHMn2TJB1Wc9eKcdt2Zwz1/uFNU2DtwaLwRhLaDvapqGlxsSEzzKck4iOnnnKfI7zlmq9\nwqQ+Qaho1DFClhVb0ASPF0s8GZNyyjLknJQzjDF89PkXnJ6esqxaJrMjYkxMJlNSMmw2Faenp0Rj\nCMnRhMRsOqdqAilYNlVHDGkbRJCxrms6Ojo8XbBkxQwTPJPZEcVkSpsizntiaMnchC4E2qainMyw\nRUGeeWLXa1dZluFTwNl8WEhtTNTR88X5go8fXLCJnrrtqfUu9n5aIShN3Dy5SWoNcvf3odmj66m5\naxeKZlbG8042KuOezNz9MrRnWfDjzVQzW7DLVyWgRR8Du812HKKuzWdyPS3YNNAQJkEExDgVhN6g\ntZauzT3a10QAkZw/zlFUluVe/UPpk2jPu3W2Y8HEfCnvWQcIaDPn0dHRXl6wsblVzh2bnwa2Nu18\nWESoSSJYKeEyjuR82hwYswmH5oWe44cYSs3MXHW8bi8jIHsWZkK+l7+12Uq+k7QFg1xI+2ZHzVZp\nZWTM6Mj80qy0Zo5kfHWiXX1NYYrH7+Gq96u/1+9Tmwc1STBump3Sa1PW37AfqnOdc32KJPX3IeA+\n9ssSoKj72jTNADwlablWYMbXHO9Fh57lebdD93qe7aVhuoTB2vtMCiLLuMaEt9sCym3vhE5dEbs1\nuWnIDBhrIAVi6NNHOAP4jBQNXYgYG8mLCd4VWAdNiLgu4qyFmIYNViZvW636iRgj3aCpdINGUpZl\nD7RMIsv81hE3UpQZISTW6yV5ZknRYIwjhLZnuIwhBiiKCV2zxpoMbEG0BSE5zld9tFY+OWJVdYNf\niM8LIo6QWlw2ZbFqwOcURUYksaxq6rqhw1BXffLJdVUznzu8yQltZFJMeqEYAqazZMZiSUwyT5ZZ\n+iz8HWU+xTrbm1pTh40Ja7YRnFVFCFt/hRCxzpHlBSfljMs6Me2gWlS4GPDWbHOf7cYXlE/X3nu3\n4295XizUIRp6t2hfPgHzrE2bosQMoTdS2AkRHSquz9eASP5rfyg5XvzFtEYtzrACJqbT6bBxCyMs\nwkXA2XjT1hu9PIeco4WbNPH90teXzVKEmzbDyLOLUBEnewFk2odL912bYHUfDpmpNMMlzJkwdZPJ\nZHCm1+1ZgddfVHsZgdW4HWK1DoEv/dn4WG22ElCigY+eH/q6Aog06NJzTSsTck8NoHQ5oHGaEV3s\nXY4fX1v3QWSSnKOVIc1YaWVp7L+mn1uz3dJvuYcw0uOyQdLGaR5knuu9aNxPzRKLgqNT0ujxu4ql\nHbdDc+NQe9r34zH6iwReLzXosjKpUuzNi6lPMWFSIuvj3CB10FV41lvbFHRNX6U9GnqGzPXFrfuJ\nEgix7nODdQlrElVVDQV2tTahSyWIcJFJJXlNrLW91mShbfuCujdunDCfT1ldLraTOJE5R+gkuqQX\nInlWUFUNMVa9E3s2IeHAOOqwZjab9uHrba8RRGPout2EDqFPW9FsEk3b0IYW4wJdaGiTIy8nvVkx\n9ik5iqzEZZ5NEygzT5lnROOZZJ6YIpM8p2rWODclukjX1LhoWIUF3hiicxS5p2uqnuXb+rBhHCYr\nwCaqtO6DFrKMySThMeQYurrbW+D9QBya1M8KuvRx8v/ZnZNf9vY0el0fM6bEtdlQvtORjJoBGDvb\n6mvrjXa8EWlfE81U6YhH2dQlwan4cOnn0oJLfoqgGvucib9XSn1tRnk+nRRS1qYINAFk8uxSUkU7\nQQNDrq9x7iABdePIxM1mMwhy+a6u62Gc9fvQQk1cAvT702viKobjunnwk7TrhNlfxP2+atNKxLP0\nayzE9TzXCoauHagBl57vGmBr4CNt/L2cJ2xmjLugEWlyPx3Yoe+r/9bmv3ESUj23x88sP8fslfRJ\n/z1e27KONFDU544jL7XLzfgzeR65zyG/Uc0S6+cY70v6OQ/Ng6+yx4+B23Us6vOQIS8WdEUwxveR\ncDYBHSntKpWHXfpUorEk6wixxW1L/BShYhJXuLSkTR157OhC6vNkuQJrPdZ6op/it+i66zpSH9+I\nMZFEh7GBFBtwRxhj8VmP8J23hADGur6WYpZvz7eUZU6eOS7PzyiyjLoBY/u0Ci7L2FQNyfYO+UWR\n0dQrutiSuZLQ9abHetOQkiFlR/0ktB6XObrYkZe3qbsOnMGanKatCcEy9Z7laknmc7wvqWpIWLzJ\n6NqO3GR44wldxWTu++jJKlC3gbzM8XkOobfPxxixPuG9xWQlkYzcT2Eb5TWZTHbMwHaqLBYriA31\nekXdLUjRkBczfHK9T1xT4ZqOtKxYLyuWVds71yd6ALj1FzN2m+F+29L2nxmA064odpvAbIMqnDEQ\nI8mpyL3tf2efjMgK/SX6TcQYTOpTlQz3Tb25OrSeEA8ltvjLbWMAIp/JmtDfy3f63EPCXmu7AhL0\n/0P+H3K9sUYrJjm9icq54gTc1yn1wzHiKyNASI7VrJv0T0dYSX816HDOPVFM2JhdvUYNjrTfmQhL\nEQLiVyN9lr5J06YXua74lmlBJqAypcRyuXyC1ZP3IEqbFM/WJuBD71u/h0PH6Hc/Fpjj5zj093UM\nwSG25WVpT+vPIcbiUHSiZmklTYgG0RrQaOVlnEVdj/eYJdPgRY+nnKfnq2aG9LXHTLVWRmDfJ1BH\neY/NpppM0H0bR0LqtaIZr3GKGWGa9f0EEI79ofV6kWfR8+9ZAnfG7RAbNd4brzru0LWe1p7nOnhp\nmC7d5CUNqN2YgQRzBmwIFGkD7QLTrclTAGPAO2zmyV1BJMfaEms9yfeTs+m2xW/zDGssdb0hdgFn\nDVXT4rLLYXLKxOtCxPmM0veJGJ11FFNPVzesVxVlOd1OTkNMvePs/GjG48cPmU4KrIHNZtP7qNGb\n4GxIA3hLGFI0ZH6Csds8ZNFitybJlAzWejJvqOsN7fackGCSl72Ayx0hRbIip2nbPkFlKomhzx5/\n88Yd1nW1pYsDp6c3OT46JZLwrqRuOkzqyF3G0fEJJttpZUXZlz1JsTebNm3L4uKMerMmUGN9BjHD\nxhxnHa44woYGn3cY05JSL9zY+uJ9peZsz3QGqUawDySk6YWsARmy2W0/SLxcguRp7RAQGzcNMgTI\nQD8W4p80zlWkNVbZVHWpE+0oPwZ9kjZCriFsmmSaljUkQEUYIGHXhGEQh1r5W/oj52l/Lp2YUjZT\nuaa8e7mXaN1yL4lclPOPj4/3/HHm8znHx8dPpKEwxnB0dHSlmUnGUZJKwn4ySC34hTkUoPasbcw2\njufEswoEOedFmzS/SruK7buKlZD3pKNadQCIgG7NfI19v4zZBZTI8VKfUAM5MV/rcdXgQ9bG+H2J\nsiDgRtacrCl532M2We4r1yjLEmF8NWgZzzH9fGO/SWCPMR5XmBBfSOnnbDbbY7Xkp85bJ1YgDXT1\nHJb3Iv0ZK1j6ecdKify8Tum8rn1ZFvfQ/b9qewkc6XtH6Jj6wtWSoV6/gGQSKSZS7Eipg67Bdgt8\nqilsX38xJUvKLM5lZOUcZ0ti8niXU9ULvLM42w9WXdc0RKzJsLkj8xkptHRhM9icB9+OST9EXdeR\nbXNztXVD0wXA0IWEzzyZ8ZSTI8rSU1X9dazxhNDSdZHMWUJI5HlBiC1102z9vMzWSdHibH+8tYbE\nLtdSjHFbfw6SiRjrKcopyYDLciKG9bpiNpvRdDWFcThrAEuel2RZQZEMIURm0yOars/sP52UQE45\nm9O0gURv/vEmI8vcNmJsCwS7bf2t1YK6WmKJBHo/NVK/WTRtzdKWNG3AFSXlNLBuWmLTR07GlIYq\nTzHGwbqoJ/HAQpktODIQU1+ZwFrH9hcs+7UFjTGDRVI2LNgt3l4Ab89X621Hae+cWV9kG28i+ver\n+qfBk2x2+nid0VqO0Xm6DmnRGuxobVb3SzZ5MavpTVXyVuV5Tp7nQxZ48RPRAEsEmL7G3vpPaY9h\nkmcWICX31AlPtRlTGA3RvgUAyT4jiSTFDyvP8wGoAnslj8bmI4lOlEgqLfS0ABXhpgHoWKu/SoA8\n7efT2m6OX80MHAIzLxPLdZWQHDN/Y9OTZhnF104rJjr1yBgQyTqR//KdzveljxWwrR3o5bxD15Y5\nMn6OQyZUPbfGz3torlwFrMfgR5+nx0w+E4Apa0CuK3vKuC9a4ajrei/zv8x/PUYCzETOjR39n0e7\nTlmV75/HdZ61vXCmq58wccs+7NiQPUEcxDQCNnRkNuJdwm1pD5tPyMoj0rZ8jXUZXbJgoDWJfKsJ\nyMaalyVN7E1/ZZaTYqSlpfS7elKidVTtdrIZqNpmm/vLkZdFb6rqWspJQb3p6dbNZkPT1n25km3E\noEFtxsaxWC1xLtvmonJ0baTIsy070INAY7Z1uOx2k+4MzhZEaox3RCxO/KiaGqwD6zDO08VEkZVg\nLMlA5ku6aJiWU7KsoCzKPqFlXuDLHGM906MpXexNuF1XE0IDlDvNvAus1itC02LzApsiufMY58Fk\n1AEwltWmYVV31N0un5Exia5ztKEjxuud4uOhOZ1s79mVEnbAZE8XPP131/vDPK+F/TzbWOAdYjTG\nG/nY50i0Ts2w6M1WCjeLUJDPtV+WmCu0b5UWIgLMYKflSn9l/WjmWAMn+S/Hj8172rSihdhYIZPn\nGucCkvN0H6V/GmwKoybrXptItADQZkS5pggVzRDocRRgJZFaAsxk7DU7eWgOjNv4mQ8dd0g4H2rX\nmVyeZo55WZtey2PGRxKUyniLAqBBwKFx076Q2lFd/9QMqPwtfdAMlf5ez8ux+XHsZzm+rlxbgxy9\nluRYvYZkLA6Bbg3A9uTulvmWcZLn0UEuOuJZZKwGunr8dJSmZgJlrYzn3HV/P219PK09bX6P18Dz\nYrngZQFdMW6dqvv/4weTSRVD2FY2TGTW4bKMMpuReYvNT3BkWO8JsYXUge2j7rpm66gHGOcIXUdZ\nTPsXby1d3WGdJbYNbZPIMovBYbB4t71/gDwr+0nkAt54Uujw1mwZrHw7eXdJ8OpKHIL7FBHGOBaL\nFdm2WHYMAWPAuQxZ785b8rygaRc9G2Egz0u6LpCiBSvHR0LYX5RaOyuyCdZ6mq7dmie3198Kk+l0\nRhcCdB2l6dNmlHmGL6c4z1YgtzvhYCw2m/SLzWZ9YfGs6POMmQwbDet1X+tyud6wqCOXm4Y2tsSu\nI8SvPmGTYQikCLAFXk9qaOM5M2xabCP8YtoyXfubc0rpUML8l6aNAdb4O/3+BdjofDraD0s2dF2u\nRzZsnZhRTHMCnGRj1QyYZtM0WJMNdOyQLqkTxqZhneRRmzvk/UmftClz7N+mhcaYUdBCCXYlfGC/\nbqKwc3KuADHJMabvr0050nctVIwxg5InbNhms9ljBX/SDVwLhqcBrbFg1WM8XkOHTPcvsun3eUgQ\nXvW3PJvOfSVzUiJVNauqzcqwm8fjVCo6AEX3TwDJ2LFdrw3dxv5Z42fRz61/akVLKxhXzfcxoJLv\nxv5f0v8xUJOxG78H/azajCtrY3wfneJFj73sO3p96ee8qunxOARM9TiPzxlf/5AZU+8th87/qu3F\nmhfxJBLWG2I0pOTh0ICnFpvAmYiLCU/E2T4i0DuPMxafZWR4uthiaQkx9sApZuB6QR3bDgPMZlO6\ntHVYJFHT4XKHTYaQAnW1GSavDxHjPDY5vC+JyWFdz2plRUm1WWFTxNr+hWw2FZNpiTGOrrrEO0fo\nWjCwqVp86h2Bi6IAk4gxkLlECi3WeBwOgiRgFUHT0jS9fT4vHNZ4vO8FRFGUtKsV3mVYk5H5aQ80\nfIbPEnVTQwxM/ITM9kxRSoloYHpyQkdB9Bk2L4jJ9uAwQJblGJeD2SaOdYHj41Oaut1qjpFka0yy\neJ9jksHGDRcXvanl8TqyDtC1LZ5AxJOS6Vmr7QYQZYHoeRx3kXfS7Db7vJGkrSR82jd5hRBgAAtu\n6/tlcHaXk6fPdt+PwXhBSS3Nl6FdB7DGv48Bt/arkN/Hx2rQogHX2FlWs1JScHospOVeummzpPwt\ngEY7ymumbaypyyY8FkrSd80UyPEaQI03ShGeoo2nlPYErxyvSxPpjVj758h46WeQMdLCT5sR67pm\nuVyy2Wz2BNL4veoxGI/pWKBosCltbOLU7ImeB6Ks6XcxvtbzAIbPq42B4nj9jgWlfi7tryXvR+aj\nzD09B2EHPHXtTmAvHYisGdj5J2lwLv3RzBXs+0BpwKOfY/y3NM3sjq97iI25CozLOhjvH+P7acd+\n/b12SxBWS/4WZlGUPO3npZnyQ0zc+PnHfdbjoD8b9298zrPO4+uA/PNaCy/cp+u6hT1Mppi2CQEM\nfYEaQzIWrNjSLaGt6cKGlCLGAcZiTcLnRX98Sr0DPdtF0fb1AJs6MJ0cARAIhLbBxkjqatq6wmYl\nzhiapgIsWebB9Q69jaoZlXk7LMadQzIYE+m6HpTFGLFZDqEDawgp9pGSKWKNpQsdhZ/QhIa2awkh\nQbIYu8vsHWPXg0jb10eUiZtS73TvnOvBpjF9clbb0LaByVFOMonGGI7KCV0bqZdr/NxjU8CzTRBq\ndokX8jwnbU2jnfG0KZFN5rhiu5mwLaxcN9uC2IYlOY83DWXbstm0EBMRCGnfwVve7yEB8yy+VePF\nZIzpAfzI70iEi9449LnymRayL7LpPo3XxSGBOAYXAra0hg/saZZjtkpAgAYxRVEM81jYGimMq32z\ngL1s2DHGwRwBOwAmfhs6lYM4+sq4Z1lGVVVP+Ipo8Ke1eGHzdBJVzawdYibKshzYJslkb21fZmgM\n5OSa2lQzjjhLKQ1MmBYEArim0ylFUQwO18vlcs8vbczSyX3Hm/2zzs1DAlLPl/Gx+nv5OfZVetFt\n/OxjoAWH+y9/6zQgmo09NO9l7HS5Hu2Ir/cVqYqgGTJtitfjKGtFzyUBflrR0e9Jm+41S62Boo68\nHM8jURKuYrhEuZX1LetJFCw5xpje71iDQ/lc1qqMh/a1lP4dUgqkvJdmh/UerJWE8buWNrby6GfU\nbSwTDq2z8XmH5v4hUPtV2gs3L8LVDzEMjv4MSzSOSO8UHVPY5rHykCLWJKw1FFlBxONdxjI0eOeI\nXaDpWkxKJDpIjvl8TjJ9AefGtuAhNBUp9Nc1xmG3tWmKPOtrLG5NW9PplGqTyDM/5NLqLXZbYWcs\nTdvhfF9+JCZD6BKZLyBZYgCXZ5As1vUsQ5ZlNOL0m4SNgMA1IOYAACAASURBVBgTUuomJUOMCe/8\nVjBEvO8XXduEnv3qOopUMpke9xtGCvjJhK6L1E3keH5EjBCamug9MTR9Gg1rsbYP54/G9Ilkfc9S\nWWuJIeG29zKpwGWpB3bNGpdnHB+XHF0smNeRug1UQaIfr7bZX6ddPMvckU0k8mR0Voy9X9dOYO/8\nwmSDjTES08sV1XVorDRjcRUAG2+8ms6XTU2uMzbZaaZsbA6AXe4t+Ux8k2Dfp0yc0HX0oGaYtIYt\nJkzp35gN0J8LYBQBqH3IZBy0eW/sIC9CTIpOd13H0dHRMAZidpI+auZCA8cY416Ba9g3Wcr9BYAC\nQ2mkxWIxjOX4XV+lVY/B9SHwdIgt0X152h6rr3XoeV5kOyQkr3ueMfDSoE07ict3Mj7alC7HaqCl\nUxtIkzQgun8aTIzZ4UMKnjyTXoe633KONP1uDjGbYwZJzjl0vt4D5RlFYRv2VcWGa7P++Hllzzg0\n9rLG9BqSz0X5uQpEj1lN/bkev+vaVQBuvM7k+/GavEoR/v/Ye5clyXLsXO8DsG/uHhEZeanqC29N\nHorGYzIZZZpodEZ8Bk010pATvQDN+AR6GplJZnwFTUgekbLDbnY3i93VVV15i4v7vgDQAHttX47c\nHpldXR2ZbYbfLDMi3PcFGxvA+vGvhYVvg49OunIWnjdSABMDcd6AOtia3ns2oWaqIpUfMHEkhnFe\n0RYhNsQwpJWC08i2mZfU+kBVJSXKNS3GpESkPnqsi9ShwocKTEUwHdurHaZO2eb9MGIri3OGcUg7\no3tj2G424NOgvt/v08oW5jwwY3KJEVMkWtNtuXlzy7Nnz+bzLdFVGOe43/dcXl6l9A3WYUn7NKas\n755Nt1sImLUpxqzaNIurb7fdAeD9ns1mR7SBfgxcXj45JoP1ls+ffw8fwdQNm6ahDz3ROMYpEgg0\nzjLMK7/iNHF3f0humLbDVE1y57rk4kybWyd1pes21K6iw9NsO/jxL4jxNb/sx7Qy0q4TLP3u19pG\njBGdRPXYLk5XuSbiFN65ftqjEdysChKTZppfz/BpzOoFa4OEdgnpgVX3mVzpkAFWztXuAblmHtui\nB1ZNsmR2romQBNrKknqZOMgzyP6KYuz0Mdq46XQQJy5jjsZIGwgdJ5MPjPKZdo3I8XqQl882m82i\niOi4Lm0chCTJjF7avdRfHs+lXZw6ifJXX3118n7fZwDWjskNkEAb4A/BmsL2+4I1wyi/5243ne5D\nqx6anOX9Q9rs2vvVCpnsxqDbU4xH17Um73qBhpRBX0/KcxJasTIR0S7svK/n5CrvU5qgyd+6X8lC\nEp3OQSZOuo50GeTZxeWqJ1e6vWrFTsqQx78J9HPleJ8C9tDxD2GNVJ3rk78NPjrpEuhGkVe2wRBi\nJMyZrYiGEA1DP2LiyOj31NZjTEyz2GnPNAXGwdLUW6ZhIvqACZG6qnBNzWGM+BBwNfhZhTFhwhqD\nqzdULi11x1XE6MH0DOPAcHdPsBMXFxdsmoZxODCNgeDT3mpNUxHC7LIBKlvhg6fpWg6He7puwzSn\nm5B/PhrGELBVQ7QRH8e05Y61s6LVLC5EY+ZVYjF14HH0bDabtCqzaZfM+wc/0rYXKUB+23LobzAT\nDIeR3dNrvAXqho1NKSds3WJcQ3QVTTXPkpzDh/mdDD0h+DmBa4dxULUpPs0El9QxbzFVYNu1PH9y\nwdu7npt9YPIHpnhMCPlQR1lXcB5uO8vgsnrc6YwyBA/mu12N8rtGPujq30VNksFM3A160MoTemoS\np2exeuDX0IRLYlz0ve/v7xf1R1wL4l7TZEyurYnfMAzvBO9rBU6TQ20oxIDpGXqu5GkVND9mu90u\nRma73S6r2LSBlnqD48pHgTyXGFltlHO1omkadrsdT58+pWmaJZ3Fb0p2NFH4rlQoMTSaWH9Kii+c\n9tVcZclVX32O/FtzL+r3B0fFVepX6kCvZtflkPap4xd1+5J7CHHRG6fnLs283+X5r/TfOcHU9QLr\napy2rXoM0WEG8rv3aes44CQmUY6TulxrI3ps0JOSvM/L8+ZuWzlHK3vn3m3+3Pq7DxnTz43/54he\nft5vg49KuqYohlIZWhPeqTiLS7YzBIgD1gQG46nDgB322OE1e39DY1p8u6XeXFFvLMYNjHYghguq\nNs1uqzYNei72TH2P8TVhnH3bscZaefEOW1WYemI67Ikx4jBUtqa92FE7Q7/vCdPEMEz0wz1121A3\nDcPdHeBmTuVwrp4b1YZ6UzHM6lTbpvxcxk8pwN3MhiwGXOVmo5My2vd9inXp6iYJPzYdj02zqjSI\neLquYRgONM0WQsRPE7a55HKzZdi/YfID1la4ZouZyVPtDNGnzcQbS8oAbwy2Tvm+phCo4lEB8GEE\nEzBc0LUNwY/EaSKGQBhqzGTZNi2VdZiYVoJaemKc3Urzq12Mf1Szs3hs9MsMLh5VBj3z0+1EBk0M\nEOOyhRTGEEI6P+IxNmBCel5QPC3E5bOPCT2YabdHbgxz94F8L/FKMrDFGNlsNktSRx1kr4OEQwhp\nyyml6Ihao4PsJRFj3/fLPdq2PYnhks19RQUzxrzjgtEqnLgM9X6FUgZRluQZ9cozcTFqwiCfbzab\nJTfXdrtlHEc2m82JUrbdbpfEj03TsNls0v6mahm7dhFqtUK7G3L1S+8XqQ34ZrNhs9ks6l++ykvX\njx7/tMIp9aAJ6ENESbtp9Gf55Fa7O4U46FxiHxNrysY5pTBXSOR9S/2Jq1vvCpB/p9UbTXS0Yqtd\niPo4qVvZJkorunplX+5iXKtnKbvcT55Z9zX9ftcUNF1HUsZzaV6AJc1D27Z0XbeoeZpcahKnJxh6\nIUmukOu+IJ+LsqzLkROpNUXzocnKGolaOyY/Ll+s87vEJ6N0HSv0+Nnx4Q0z60qxN8z+92nCjD3V\n3CB8hHH0THFgnCz1pqWuHbuLlrpNG1CPw5gym8/npJn23DHMnG6hOy6192EkWItrGoKbgx9dYPQD\nxln6w0gMgcqlQfvm5gaDU7NtQ9N0KSv97M4EnzK5W4ezNSH4xVj0/YGutngfsNZhbSCEiHMVYJLq\nYytsVeNnt6W4+6ZpYre74Obmjqo5dtS+77m+vqbbPOXufuCuP3C52dE0xxxMqXyGKUQwLi1aGGZX\njAMTj6vdhATEcWByDmvSaqDoAyHAOAWsi2mj73jA+8Ock+x0n7m1WZkx9oMafd4x80FLDziLgbFJ\nWfx9ULe00chnuHAal6GfXQcI59eTNl3X9eLy0m4AqSdNuOT6WjGT9y/qWNu2J4ZM7i0ERAJz5Toy\nyIrbJSfZ+n3q3FgaecC7du3kg6q4LrWBEAMombVlL0ed9PGcccxdNfK7rkOpH/lMuy+lzjUxzrFm\neB4i3WvnfQjWZvafAtE6B61unXOzPlSnOq2BPsa54166+po67xScrjiE02BuXW/alSjl0dterRl5\n/U7za+bqj37G973z/Pp5XKRcyzlH13Xc398zDMMymRLbJGO//inX1S7YnAhr9VpypeV1rPv4ued5\nH6H6bcb1tcnud30PwSdDuo6NyDLLFYgkEo2dHYuWYCwEg6GeA809MU7JDde0tN0l2901VbOl6VJm\ndQiMhwOjD9iqxoTIuMzCHXWdAn+n/UhlLWPfc3G5ZZoGDIHKpuB14rzNiB+w0TJME66qwM0zpTiv\nrLRuYf277ZbDODJFsFXNNHnGKVDPJApXMR4mjKsYRw8kZSj4CWcd1kRigP19Ik4pYaXDzOpZxDAF\nGPwEJrkqozGLOtB1qVPd3Nzw5Pkl2yctmEDf7zGVw5kGTFqBNgVP223BJReR9x4/zR0sHle0yEAS\nppEYfEovkZZU4qpIYxquueJ7/ciXr15zGA7c9u8OLKsN2KS4vA8dSJbTzHGrKH2fFEjP/HsgxnDC\n7L8Lufh3gfcNqOeMrpwjxkD20JQB9FxOHB1bodMjaOUrT/4pxxtjTlYpaaOkSZ0MZnKODr7Xg7OU\nRxs7baS0ERDk5E0H9FprlxWEmpz1fc/V1dXyXDKrFyVN6kAbEymr9C/9TFJufY6cJ0T38vKSm5sb\n+r4/UZfW3r2GrvOH3vuaKrR2nDbqvw+TEA1tFHNFcK2ONEmR42WxhybIeUoRTYzhSC60qpX/LmXQ\nKxjzPiDtTSu4WoXKFat8ZaJu5+dULbmuVodypUi7DaWMMl7Is+ftTse5iR2QvqzLqN+VLo/uu7rO\n+r4/UQ/z963LvdYX3keS9Ocfop7lavN3iY9KuvIHTp9JcCzEKJ3IpASZtsb4KZGweTVdytrkca7G\nx4m7/S1jNNTVgLtLFdd2FU29wdiGoZ/wPuLqdJ/kmhtnt57lsL9nu+2obMC4wDgGQvBUlUmuw2Fg\nCj6pW1XF1PfLBtmHvsfPrsHWdUkFI+WGqpu0WfYUPLaucK4i+kA/DKkMY1z2WoTTzNuiTnjvU+b5\nuc7CTAIjhhChqmqisTx5+oyxH46Dwqy8vX71iovLJ9S2woURM/ZEZwkmYlxD227Y9yO2Nuw2aU9J\nYsRwjIeRe8cYsSHtORknz973bLcXbOoKZwfevL7BjxPfe/6McfTc9m9PZoXn4P3RmB1xukInhEC1\n0rlyYyLnLh1ojuWKTg9gn1Zclzbia+6BXH3JVZc11ehwOCwuME0CdFoFPdjLvWVQFrefzkmk26fE\nx+RuBT2zFVVMyqBdPtp4ybNIu9cIISzq0ZqbWV9LJ381Ji15l0169cTh7u7uxD2q1ScdawYsQfai\n0OUTCB2DptVAUT36vqeqKi4uLk5csBrnFJpcfdIuxXOz9LxP5NfODfbvA9aM6Fr9rKl3cm5d13Rd\ndxLnJfUp5EhnrxcFNH/fug/oyYNWa6UN6tipZdKauYZzd2bep/N3uaawyfF539DH6XN1W5bJxGaz\nWQiolCt3ZcqkJK8PaXOafOYrdfUx51T199mJD7ElD+EcqVrrI9/1xPyjK136BaUPdAC9xWBhDp7H\nJGUnkLYGmqaJmoi1JNXLBowJDMOeYX/A2tTw+0MKqG+6Sy4urxO5GFMSz/RvWoJ6m6ahqi3T1IMJ\nWAxdmwbkfjwwhTERJgtvX7+hcpaqSSkn+sMtYJfM9VUFzlVYK9m9hznGy+FJrrQweajS/SrvqeoU\nnL7pWqytcI65M3icSwH52IoQwRo7p3hI7tfDYWC7vcDZmmpzdD21TTt3CM/tm7fsfIDRU0eIdU3V\nNLimwdQNnWs5+J63d29oqpQXzGEIc8OTjYRjjDRNIo/GVtiqBePw3lDXG66vX3B77/nF128IU4o7\nu7+/f2ewhHdnQ1Lu43Hhnca/1jny2ezZNqf+/bad93eFhxQuTX5ldix/69gLqQtxn/V9vzyrBPaK\nAiN5ucTQyECZ79GoZ986V5f8rvcYFGIRY1z+lpl+ThhyF56oVXIvMX5aHZNnFYO4NtvXJFUChOWa\nIYSFkGqjfHFxsTyz3FsMsb6m3EvKJM8hhlUHVhtj2G63q/s/fhvS81AMl247v+mE4kOUso+FcwqX\nVlrWvpPP9LnaxaVXyer2H2M82d9ToNueXE+3Hz1h0uWStiD31243ua5MhHIykuOc6rmm9GhSqify\nMcZltaJG3/dLKIEus5AtqSvtJtXPLMjV6FypkklY3/cnbv1czcuRt4GH7MJDbXhN8Vq7xndNvD4q\n6fJeskR7ljxURj9wJMS0EXM0BjPnWKpwbNoJZ8ANUHlLZQMHaowFZwOTSasBnXHE6gndxSVN0+Cj\n582bV/Tjfg7adVgmTAwpQ3rlOAwDTeWASNs69vcjxliih7bumMaem7dvcday212kPdj6lOwtrSBs\n6ftpSZqoJdVN1y5yatd1TH5iNAbTVFjjcTGpXxe7a1yV9k6M0WMrN5ObBmMsfoo0Nhmb0Ri8Geg2\njmkauL6+YpzmnFquJtqaSGC36dJsP0aCH/n6qy+58CPX5jO2dZuMnHVs6obgJoZDn1TEuqa2LT6m\nFWrRQDSGtm4IpNWVgVmRax0hGnZPn/DHm459mLjf3/DNTcBWKQibMGCNZwxzqgJzVHAmP0vOiny7\nGCDtgL2suwhkHYMzG/ratJH6Er2PSclvl46b/htN1Hz/o0ErQLm7RH8uf8O7uaI0AcgVGZHwJbDe\n2rQcXALjNZkQF7lcU5JBVlW1BOzLsRIcLnFc+/1+yb4uyVOnaUp7ks6qrShkMqtedg6Ip/Edojps\nt1uAxRDqpfpa+dN7xQm50gHu8r0E3Esw/TRN3N/fL7FeXdctrllBvsRdyJMYjFztk++FbP7VX/0V\nX3zxBT/+8Y8Xg/NtFKe1Scj7yIY+Txt6MYAPGbNPAbrdazVkzdDmipNWauQzmbDIP9mDUxZVAIs6\nKvfX14HjpET6khwj5ZK2K5McIVy63Qu0S1LKqaEnPPlYoBW3NeUPTvOA5RMKnRJimtJKfB2OsNvt\nloUowOIe15OGc+OTlE+/H5mYdV3Hzc3NstBGJoB65bBuw2tjvL5XrpJ9yKQjb0trZOzc/b8NPrrS\npQNicyyVt/KMhynQxEQqCDVxDiaPAcbJYG1NU2/nAT/S798wHqpF1QombXkT40hdtfjp6Kdecjs5\nx+3tLeOQAtnrxhFCCkC8uLhYyrff7xmmAM7impoQ4e3tPZ9/7xk+BIJJMVfRJjfgME74EBknD8YS\nSUlTQ2sYfeT+/sCTK48xx4E8dYiUhd4YQ7Rx6cSbzY77+3ucO64y2+yuOBySUbq6uub29p7BH3j6\n4jPevHnDOIw0bcfNq5cQI69evaLZbNlePIHq6GoBwzQGTB2om3qpF6wlEAkeQgVV3TIFqD1pj8MQ\n6Kqa//QnP8L3A7fTL/mPr14xhCmRvhgwpnpnYHkIa7PcDznnHYP0wWd/HJx7vty9sAZtkHR8FBzV\nqUZc3bOSpNUxfZ18mb2Ov9DnaDWpqioOh8OSFkHIzt3d3UI8BFpl1K4JuZd8DqfbB+XQLhnpw03T\nLKqblK1pmkUxEyJojOHi4mLZkFoC/0XRHceR3W53Yujy+pH+Ka5POUavMpN31jQN19fXPH/+fInt\nur29XX2uD0FuJD4Eucr4++Be/E0VD92u1hYtaPUGkuv48vJyaXeyQbbeiF27mrW6Jv1Dp1iQcsV4\n3IZIJh7a5mkipM/Rz5i7IUVlet/YmfehPDZLPtNkTfqHqH2iemnXqHYt6vi3fAWsvk/u2tSpIbbb\n7RISICQuj2tbe7f5798Wa31H18n7jv1N8dFJVz6b/1C5wTmHjZaAxTYboq1orAT6WojJzdcfPCbs\nAcvERF2nmXZwNTHKBtEBa+e8XISkpCjWv9tdAYbIRIyGqk5BuW3X8fXXXzOMKQt8jJG22fD27W1S\nElzD4Ic5MJ4UWxXSv8pVxGBwtiaGiabbYKiwzhCNA1dhqwpLSgERsNi6AuPmgPkJK8H0ATabHbU1\n+MlT1+28OnIkYqmalosrxzS1mGrD0xfbZWZvGbAxsNk0YALD3RuoN3POpY4AWFvRdVvGaUqENNo5\n3UVSiiIWH41KcBkx0UCIVCHwvadP+cFnA7/65iX9JHFiFmM/LCFpOOHeKV+b+0Dq9PtIuvI+oZWQ\nXNXKoUmGVoDgSLpk8JeBUJSffAatZ5tyTyFrWv3JZ7QHtT0WsKhKWoGS58pnmFoByO8v19fKjBgN\nKXfufpWfYvj071VVLYHzkj9LjKJWDWVrIh3/o10rAh0XJvWq258oKkK8rq+vubm5+Vaka202r9Wd\nh/D7QLI0PpRwrT2/vCutaGmynxNyibPT8XbSp+Q95m0WTre10mUUgqEJV64uagK3trhCE65cqRSs\njQf6GbWyl0+wdDnzewiRFBVQnjMv05riJmUQIqXfjR7LRAXUqSn0u9XP8z7S85uqXO+71ndBsnJ8\n9L0Xc4TAyQCaPkt7FVplEMJ4YJp62tkdmeRguyxPd66aX2LE4THW0bRbtrsnGOfoJ3GhTDRttcxC\nIRB8hOC5u7uhqY/qjg/JcIxToK1bDn0PxlE3Hc7NM+26YRg9ddPRjxMRw2EYqZq5YfVTWoFJUoTa\nuiKGiHWOtMOkA1OnlZoYqrrhMPT4ELGuBmuI0c6pM0wiIj7S1C1N5RjiHmsq6qqlbZIS2B8Gnj59\nyq9vXhPrhsl7Lp+/SDE2Q83bmxumYNjtdmy3G2LtmHxyL1bdhoilP6QUGaK2pZQWlqZtidGBrTDW\nEaPFx5HJT4TgCdHjw0RXG7Zdw/4wpH0DzLuysfyUd7/MTnOp06QM8nrgjTG+Q6bStU7bWzrOvHPc\np4K1wVQjL6ueNetZsQRx673UTupUDbRd1y2pDPQAmeecMibl29IrIPWMW2arcNz7UA/mQsrF+On4\nFSm/HpjzAVyeXw/KOQnTddQ0zYmhEVIk44qURwyekMObmxsOh8MSB6ZTc2gDrAPn5Tkk9YAuq65z\nbXS7rltcpuegjZvGudn5Q8ecMyCfalzjQ9Ak66Fj1tRbrQZrJVfUTpmQ6MVDwDvtXa4hE5G1Y+X4\nYRhO2ufRe2HeuQ+8G+Oon0G+z9uFnpzJ3/o5teq01o+kbnQYgRBQnQBZX1vKovuBnrit9U/dD4Al\nnkv6p94LMi9njjWi/b6+ce46a8c/dO9vi4++enFNwvuQc6w/UIWBykRqAsPhnmgNMaRkmIdpj2Rv\nr6qKpt3hmi0Hn5StTdPOjdBQuYa0yi0SwpxPZd7ap2kqptGn4PkYiTFgqpp+HBl8+j25/DzO1uwP\nBwKR7XZLjIFh6HFVM+/FCBGHDyldRQwBY+sUEE/Ex4gJKfgeDAaHczJY1DhXEzxzstRkSAyWae4M\nlbOMZg72NdBudwz9yGEc8BjazSW2qnF1cmNeXb8g9BvGmPatHCd4/eaGunFsLq7TwoXJ0+y2KU2H\ns8hCAdek1aLj6HF1lTLhVy1TCLS2xbg90U8M44HRD7x59RITA9tdx91hTxinJXeXDo7+rmSoh9rU\n72L28l3i25Qvn23KoK2TQIqrJCdbWomSawlhEYIhM3WZlWolSJPkPDBXu/30IgwhLDIYa0Kj48rg\n3a1LdKyaPjYnoKKUicHTgbo6wF/Hg0mwvRgZ7/2J0iVlSP37aEQlU7eUV9Q0KZcYBzHofd+nrcBm\n5UveTU4ctWHNSXP+/nMDcc6QrOFT7hOaJOQEHNbj29a+1+4x7XLX7Vs+y1N+5PFQ2tBrRUygVwbL\nREbOl7aR5woTaGKXq2tSPjlOrq9/yne5+pSrdbkiLMfpbYukXd/d3S39Ic9lJ8+mXa8CrVzl/V2r\nwMaYJTGxTAA/BA+1+bwO87KtnS94H6H/bfBRSdeabJr0HUOYc3MBGGdT0k4iNZ4u3tMwYQxgXVJO\nYsQPx4G7qez8Ih2jBx/BEbnYtYx+Yj8OTFPgyfU1+31PXVsM0+wvbxgmsLEiWItpIt5GjG2YxpFh\nXgXWtRv8BE3t8Id7Npst39y/omt32KrB9/t5FWMNtmYIAZxhYN6gepo7igXjKkzwEEOKMTMW7xq8\nhWAnfOghGHwwbK+fcH8/ACk9RRUdlYVN1zIOKcnp4Ce6JgXQ19ZxuLmjfnJ5Mns5HA7snjznipr9\n7RucM7jOEmnovefq4hqqOm2/1DW0XZfiHKJnYxus6XCuZvTzTK+q6ObBprm4Yr/fs710PJ0MT7/6\nmn/69y+5G0hbLdlwfL/RJ65lwMd5xjSvXY1EonFZp4jEEHBOEmymFa6TPWaZN5KfK74bM+jJM1ef\nrj76mJA+kc9ic9VHx5fAKbmRWbSOJZJ+IYOpJlx6Y2hRyLS7QgxOjHHJb6QHMZ2KQc+E5Xp93y9x\nG0JihKiIwYrxuAxdP4s8q1actOGD1IckRkvPumWPRbkGsMSzAbx9+5bnz58vaocEtj9//nxJ66DJ\nExwDfcVA6E2/5d7amInrSs7V6Qru7u741a9+deJSekjJyI3qmqHIFRN9zPsMyRp5+RRckQ/F9+Rl\n1u1cTwQE4h7TKpUQhnxXAb0AKlfIpG60u1ImErpd6tip3BUOLPfRpCVHvjJTHyfX1YtadNuRvqAn\nDHk70mXV+crkOGm3EksmSrW+Rq6WynuRHSxkDMnTTMi4M44jb9++XcILrq6u3nnv+rwcD6lgDx23\n1qbWiNj7lLLfBB+VdOW+3jXXTzBACLPbMSzExBmQ8SWEgI/pTN14IM1CthfPqZuOqmnpxylt+9Ft\n6RrHOPQ4AyamWCRrksvOGEPTtVh76hoY+hFjk3HxUxrsx3EEY9jv0yDtY9qY+jD0iRBYi7MO308Y\nq1alOTDWYkxai+fmxptUsFQfztRUs7qQ0mJYgoEx+GVLowpDW7dgDXXb4eOc/A9DXTU0ruH2/o5n\nz54xTZ4nl9fc3d0RY6S/36ftHoY9GI+RbYuMSaTpqqWt27RXIywrMg+HA1XTYcxsKDnNsCzvwBjD\nkydP+MH3/4CLf/01fdgzHg5YVzGFmUyody4hfVGWKhqTAsfytjMvY4zWnKxN/BDk0vHvC/QAsRaA\nKz/zmV0emGqMOdmeRFwqkhRU90npozIA53K/GC1RBsTwi7ok5ZTvxABqsgIsrgttNHMDJ8fJikBN\nwuSnLr8mmfozaZc3NzcnJEc+l5gVMTBCmrQqokmwkC1RFcWQakMrEGK52+34/PPP+eabb/jyyy9P\n3p/U+zk81G71eR9iHNaUlE9h8qGRt2cp7zlDK+9HEx443TUgP0fH28n7k/aiFUwpQ+5W15D+mSvP\n55QXuX/uvpR7rj2Lvpe08TUIUcrVUakHrVbrnGS5eiV7lUo5c4IoBDVXBsVNKH1QkzM9KZFy7Pf7\nkwmgVrPlHHluXX/6sxzn2sr7sHa976JvfNxAemMXwxpRFcoc8zM/s7UWE+f99KLHxBFrwIRAiCHl\nsHcVRLPsuWfml7i7eoJpLojGcLMf5kbsCNMBS4MfA5vNDoAwjIxjWvFXtxXYeZNtZ+nn2bmrK5q6\nWjqD7DLfuIrRT/hoaNuOYRxJWfIbrK2wruYwTFjjf+cungAAIABJREFUGH2KzwqkbO4hDhDB1jWM\nyT0aiGk1YOUwgwHrMM7ijKNpOpxN2fRDAM9EDOBsxabb4qdIjAYfAlVT0262/OrX37C9uWe32/H2\n7Q1dl9JHBD9y3ydXSlVXWFdhnANjaTZbhmmk3mypbLqXqCMxRnrZX8/WSbkzBjt3fq1iNE3D97//\nh3z+/D+4O/wKqkCY0v6N52bfWok619C1dP0h0ITkVOn69HHOnSfIDVEee5IrQDrRp9SjHvyFgGjj\nkaej0O9XBlUpq579y7X14CgkRQ/A2ohptUvnNMrVHB2PpVeaaZWsruuFHGqSpMmN5OiL8bjMX+pB\nG00pgz5f72mpVUFpX9oFquvs6dOnfPbZZyeZ0HV7PBfP9V1Cqx+fap/IyYIgJ4oPGWQdRH9OwRPC\nBUeCpTeL1/eVa0hb0PfXpEFPDPQxuj/riY1uK2vK5bk4LkHuctTX0rtLyLV1+EAIKXBeCJa+vyY/\n8sz5ysS8X8o9tVKoy6Tfj8RCatKsn+Vcm3yfnVj7PP8sJ/JrJO59xO43wcfd8DqAbK8DkIK0YfJ+\n3sx5lmnjbDhiwPgRMw7EOdeTM+Cqlq7bYtxpAKtzjtF7wjARpiElgqzTLNxPE77vsdZhQlpdOI09\nlTVUTQV12o/Q2RqwRONwVYVxETuThRDDEgvjJ88U5tQJPuJjOqduO4yr6fuRiKMfRoytGEPEzted\nPFxstjib0knYqsFjmHykqlswe1zdEDFpxSKWaCw+RPphZLvd0F5smaLB1lvu92/ZYRl8WkAQXcXV\n8xfcvnxFV9dgIQRPP/bE4HHzKs5+GOmJdF3N9mJHxFC3G4Yx0jTzzH9KOb42mw3bbdrPEmUgRxVQ\nKq4ZSDOlH7y45pe//CV7n7Y9CrPrz6vFE1V1nJUtg5o9LuNejLtNZDgIiZqdlUtnEgNrTmfza7P6\nbzsT+l1gzc0p5dWz3Zxw6UFBXCS5YiQDmVaB4UhQdBZ3Wakk95HZv85qL+fm5ZNBU96hVsxijItL\nUydgzSHqmSCPlxKDpo2HrguJPdOGRZ9f1zWbzSapuXMwu5RPCJrkAZMNgKVO5b7alSNKXa6qSb1o\nYypl6rqO6+vr5d75gH6OVGtjmLfntWucU7HkOnnusbVjPybWyvTQc+vnODehy1Uo2SlAE3FpP3k9\nyERFvxOtBufEPi+7vi+c5n7Tx+o2qz9fU8Lyv/UYkb/X/Dt5Vq12yQRC92k9HuTX0ZBr6PAErYLl\n70e7cNu2fYew5mXVyN/z2vieH7t2rYdUre96IvJxVy8e+ZZqgBPBzGpXTOvWrHQ0PIaQyJYzVMZi\n5q2ChikQp3FJaudDwIfUMQ6H1zR1xaG/oycQJp8UnDrthdbv93gfaKqaZtNxc3dHZVowhq7bzjEp\nF/NMOqY9GTNDEozF2pSdfd/vqduGaaioXMMUYfJpUO7Hge12l9xrw4itKkyoQFYGVjWVNURjk+Lk\nKiIz4SGtxnNNjatronF41CpPD66yNPUG75PRqLsN4xTYPrlk8j2vX37J1fOnYCxVHRj3PbZusXFW\nLC42uJiSxgYDVdVQ1e0yu6nrmnEc2e/3dLsUmyIrEVPHOS7LF8NpraWNgT/64XO++OUlb/d37McB\n647NTxr1FOfkoDY9qx7vNHkI+JPBU4iXVkvhtMNqQpMPpPmA8rHw0Exe/l6b7eYDhFZnRNUSAyyr\nkbRbQrvGxGUu9xEFSBO63GUpg6YYIzgqkaLwyPHnAu3lbx2or59dK2FripeeGcugL5MiIRfSJmOM\nbDYb3rx5szxjPshLPIout04boes5xW5WJ3vH6Rm4ntHLdyEErq6uaNt2SbPxEJFYaydr7fncd2sk\nSrcb3Z8+RbUrN9g51vqLVvC0m0vHaMm4Ju1Cq6RCGKQdrLkYZdKgY5tEKdblyNuCnlwCJyTlN8G5\ncSufaOqFMvr+8iyaeImrX8oj40G+AlJDK7VSb7lSmCt3UpcC+f5wOCyxj9Lfz/UB/Sxr+NC2/F2q\nWQ/h465etIEoRpN5dky1EC5jkkvRhWlRujAT1kFrIjF45BXWdUvXSR6RFJsUfGpAXdvgfQ9mIniI\nxtJ122Vrmu12S9M29MPAdD8P2CQ33jTcUVnDtuuIPm0bYhsYRxijxbUN/bDHWcPYDzR1xRQHauNw\n7YYxpk65dOhouNjusM7gbGSKI092F8nguZohDmBrYIJosSZluMdaMJ66S6sGI55pOFDbFBt2P4y0\n3SXeGqqrFk/KIF+5FovBDxPNxTP83Wv2Nzc0Vc223hA3O8ZxZPQpO/em2RBijasqNl2LqS2uTkHK\nwUSqtqLZdARvmGLAWXcywHg/zsZOb+aafr54es1/+sM/5uZ25Fcv3zJNI9EabOUQqjQSIEZMiDgz\nx9dFkqJFUkFN5WhIRAKn3Ib0wLxx8Uy4U261lMssrVCtqIME38/qRgj8Qf8/sI314zT8B6AHZj3Q\naDVubSaXxz5oIyMDux7MnXMnrkVNoiQrez5jFgVMBlxRw+7u7pbjJDbscDgspEfHx0i+H02qpmk6\nGWBlxitGT8fY6Kzhsq2RKGea3IlSJYH/ekWUfqbLy0sOh8PimhRFS5MkWQSg432OOek4IVsyoxel\nQN7RmmFpmobnz5/zF3/xF/zsZz/j1atXSyycVlhyIp63kZw45UY1V/r0d2uEXZPoTwFripF+tjVj\nnBNI3f6NMcvkXI6TNqCJt1Z9NOGXdyv9R/pZTgh1/JOOT8rfU07S5X3p7/P3lBMz3V41mdTPKGUS\nGGNOFrXIc9ze3i5KrIwfsnuELqf8Lv1WlHLpk8CSJDl/Rv1P6lirhbKbRZ6dXiNXt/QYqY85N5nR\nz5H3mXzslZ+6X39bfFzSNaWNi9MDQvQ+xWyZY2i1MSYF0lsD0nijZewD1liqytI0G0zVMU0B78Ga\nmmlKqstmc8n9/S0xynq4ORbDBw77lHF40+14/eotddvgXEVdt1R1TV21hENSte7u7ueNsQM2NgTv\nITqqquZwGMBCDI4QLJVrsKbGuaM7LEbZ52puQPH4Equ2wQ9jykUm0m6wONdiTYWzLVXVQYQYLX4y\nWDPHitkacOkYl4zCGFKszOXl5TwoHBWJq6sr3rz6Nff391w8uaYfBjabDSGEJdYnxGNm+zCOVHXa\nj64fDnPOJ09Tb+jmhLCod2U4NlIdQwHJyDy5vqTrOip7S7A2KVo+LKqnI6lUfvJEa9PWT8hg5NL2\nPyEys6j0XIsbMv3tjMPUs+E0kuoDxsHj/VER0wPZ7eX/w+v4v/zuGvsHIu/QOrYiH1z0DFoGYjEK\n2q2mYzmE+OhBVAZlvZJQK5VCarSyBUkd0wkNpazaLaENXR5EK/c9N/OWMufnCBZ1VGX51rEyckyu\nbsiSePle9oUUA6Q399Ybg+t2LeqIToEh5EuQB95rZVy7pzabzYmB1EYyn82vGRb9uT5XtyHdltYU\nsLVzQgj89Kc/5WPjr//6r/ne9773DjlcQ4xxScchREry1ElbXjPgcq5WauGULOTESSYImtTKe9B5\nueCYWFh/Ju0wb1tS97of6Ptq9U6ea7PZLM8Nx9hbUahk4qHd5PKZXEPuJXnHdP/PSZNcR55fFHJd\nb9pFmBNK+UxwnLSn7cF+/vOf8+bNG/b7/VKWD1Ggzo0Ruq3rMTafhOTXyn9v23Yhkt8W5pwkV1BQ\nUFBQUFBQ8N1hnfIXFBQUFBQUFBR8pyikq6CgoKCgoKDgEVBIV0FBQUFBQUHBI6CQroKCgoKCgoKC\nR0AhXQUFBQUFBQUFj4BCugoKCgoKCgoKHgGFdBUUFBQUFBQUPAIK6SooKCgoKCgoeAQU0lVQUFBQ\nUFBQ8AgopKugoKCgoKCg4BFQSFdBQUFBQUFBwSOgkK6CgoKCgoKCgkdAIV0FBQUFBQUFBY+AQroK\nCgoKCgoKCh4BhXQVFBQUFBQUFDwCCukqKCgoKCgoKHgEFNJVUFBQUFBQUPAIKKSroKCgoKCgoOAR\nUEhXQUFBQUFBQcEjoJCugoKCgoKCgoJHQCFdBQUFBQUFBQWPgEK6CgoKCgoKCgoeAYV0FRQUFBQU\nFBQ8AgrpKigoKCgoKCh4BBTSVVBQUFBQUFDwCCikq6CgoKCgoKDgEVBIV0FBQUFBQUHBI6CQroKC\ngoKCgoKCR0AhXQUFBQUFBQUFj4BCugoKCgoKCgoKHgGFdBUUFBQUFBQUPAIK6SooKCgoKCgoeAQU\n0lVQUFBQUFBQ8AgopKugoKCgoKCg4BFQSFdBQUFBQUFBwSOgkK6CgoKCgoKCgkdAIV0FBQUFBQUF\nBY+AQroKCgoKCgoKCh4BhXQVFBQUFBQUFDwCCukqKCgoKCgoKHgEFNJVUFBQUFBQUPAIKKSroKCg\noKCgoOARUEhXQUFBQUFBQcEjoJCugoKCgoKCgoJHQCFdBQUFBQUFBQWPgEK6CgoKCgoKCgoeAYV0\nFRQUFBQUFBQ8AgrpKigoKCgoKCh4BBTSVVBQUFBQUFDwCCikq6CgoKCgoKDgEVBIV0FBQUFBQUHB\nI6CQroKCgoKCgoKCR0AhXQUFBQUFBQUFj4BCugoKCgoKCgoKHgGFdBUUFBQUFBQUPAIK6SooKCgo\nKCgoeAQU0lVQUFBQUFBQ8AgopKugoKCgoKCg4BFQSFdBQUFBQUFBwSOgkK6CgoKCgoKCgkdAIV0F\nBQUFBQUFBY+AQroKCgoKCgoKCh4BhXQVFBQUFBQUFDwCCukqKCgoKCgoKHgEFNJVUFBQUFBQUPAI\nKKSroKCgoKCgoOARUEhXQUFBQUFBQcEjoJCugoKCgoKCgoJHQCFdBQUFBQUFBQWPgEK6CgoKCgoK\nCgoeAYV0FRQUFBQUFBQ8AgrpKigoKCgoKCh4BFQf8+b/+//2v8YYPQAxRgBMnH83kRhYvjMxLMfE\nGNO/MKWffsJ7zzRN+Ajeew59zzh69kPPNE3zdxOHw4FhCkQMUzAMHsDiTZWuHy3GRqxzeB/BOv78\nz/+Cf//iS6qqYgqR7faCzW6LszXWWpqmo2laDI66rjHGUDmDcy49k7N47zEGnHGAxeCoqob7/Vt+\n8MMXfPXVVzx/suP7z6+43HVcb2rCNOIsRFdhjQPXYIzBGIOzAYgQPYfDPd9884r/8//6vzkceq6u\nXzB1z2iahm5zSdNuqbodPjqMMWAN1lqsiUx+rndjlvdi7ZGLy+f6Z4xxKYccL39bE8+eo9/zNB0g\nTBAiTB5CwI091TRigsdOPYRA4/e4MOHihPU9NkwAuBiwYaQOIzaOBCZi9BD9/B7TTxMihLkcIeLx\nhBAYp9S4QggEY4kx1cX/8Q//cayIj4C/+Zu/iVKuHNLu9e/yLuR37/3y0/v0rMMwLD+994zjyH6/\nZ5omxnGk7/v0/NO0nJ/f01pLVVV477m4uODi4oKbmxuqqqKua6qqoqoq2ralaRrqusY5R1VVOJfa\nXVVVS1sBaJqGcRxP7lPXNeM4cnV1xTRNdF3H9fU119fXdF2X2v58PWvtyfWk3YYQeP36Nf/yL//C\nP/7jP3I4HLi6umKz2dA0DU3TsN1uqeuaEMLJtTR0n8g/k/au+4L8Lt+f9Iv52rrf5O90mqbUHkNY\nftfv1fvTsVJ/L9fM+5k+R/5J25Kf8pm0F/lbfv793//9R+0Tf/d3fxfff9QReV3k9X3u+Px7eWf5\neJejaRpCCOz3e0IINE1DVVUndb2Gc3157Tj9+TiOJ+1K+kSO/P3e3NxwOByo65rLy0u6rqOu65M2\nCKkd5vWk+4Yur0CP8e+r77VzzmHtfejv9Hs5935y5Nd531i7hr/927/91n3io5Ku9PAWOBKqhXQB\n0ajGGA0xHhtR+lcRjQfc/LkhhjTI1FV6tBDqI1EJhsEOOCLBGCwGQwACBE+qxYC1NU1dMxGompqf\n/OQn/Of//N/zs59/QVtXOGeYhh7TGgjVMmAZE3DBzc9llvuGYNhuLvBhTM+CI0ZHIC7GyZhIWzu6\nrqNtGqoqfR/8iAmRaOP8E6w5Nu6qqpmmhuefveBHf/wn/Mcvf8l+f4exGyob8aHDTz3WN5jKQlSD\nhzHIr9pAaOjOLY1TBpRVA0NYJV0CeXfOOYIxYALRB6IxRGOJxmIMROPARCYikUiIgTpGiEBM74uZ\nYJsY8PiZiIeFfAEL6RJCHYnEyPxeAmBnA/P+AeAxUM3tNu/0eoDWg4QeWOU7/c7EkMo/wTAMJ9ce\nhmE5Z5omVT+nRME5xzAMvH79ms8//5wQAvf398u1hBzkxF2Iki6bGApNHI0xbDYbttstb9684fLy\nksvLS7bb7WLcciIh5ZI2WNc119fX/OVf/iXTNPHjH/+Yw+GwlMdauxDRtm3fIVK6njRh0p/l/UT6\nxzLROkPkNPnS9S/nynVyYqTvoc+R36VO5G/9uSZR+pra0E/TtHyn29JDpOGx8CHGWSM3xmvPkNf/\n2j2898s7EWhiL39P04S1lqurK7z33N/fn33/58oj5Vib8Djnlvej26ces9cIW1VVyzvU7VgECkj9\nJp/MOefeKWNO5tcIVn7s+6CvsXbOuTFQoMn1hxIueLf+9cQtH0N/F/jopCtV5PHh1h7TGAMhABmb\nNgaig/lXY6K6ZkKMERvBRgiAMxZvI/Pwkz43YAnyCWEaGUJMCtVoaKqK169f84d/8AP+7ac/p21b\nnDFJpbEx/QzxpPDRpH8YMLaiamp8P+FmRa2um8W4SaNpakdbJ8UgPU9WGyZgonQSqZuKruuIWC4v\nL6m+/hpzGAl+xAcHwROiqEAR8HPB0p+6E62RpFzRyj9/5x/vzjzyd5KeuUplMpGIxcwKma43Kx1h\n/swTManQJ7OtGBMpO5KuAAEiHhtsIuLREqPHuvSsSWGxhOBnpevTMDBSV2vGX+pQD/z5DFMMc05w\nRK3S38t1cuOwNtgI2anreiFV33zzDS9evDghdtqwy33zd6+fAViISlVVTNO0qGZ1XbPb7RaFSsq+\nNsjK/VPfScQN4NmzZ3zxxRccDoeTck7TtBicvH6l3jWB0tDPoAfrNWKp32t+rq4XuZfUbT7R0eXL\nxzfdTnR95wRVq1f6OaVsYog1WfsUJiL58669j4fKmdf1uXusKTTnDK9+93K89I/dbsd+v3+nT60p\nX/o+58hTmlwnuyFq9Pv6q25Dch+ZtMg1RE1dqwetnK2VOy//uXJ8SPt5aNw9V//55+8rxzmsTah0\nX/xd4KOSrmPluFlxCRCOje7E6FhLjIYQjoPI8XyttBxnGM5anDPECNaCnVIlVw7wgX7y6Xggkiy9\nI9C0G8bRY3D4KSkAr379NTev3/D5i894+fIlT58+xc+urohPhh4rTAaDU4NvjTGOGA0YZreLxURL\n8BFjIhcXW55dP+HqckfjLC6MDPKcgIkxqYDGgw+EcGwcVd0yDAM/+tGfcnNzy0/3P8NPe4x3mNDD\nZDDegxmI2EQ4jSFwqsjlA0muZGmjL0Zc/l6MTVSDAZqIyTuTn2l2GIPDmIkYLAGLxRJMWPhrwFDF\npHaZVNOpjWjCFQIhQoxhJl1ACHifVC8TDIuauvQjyxSTmzl4T4iGED6+gYFjveez0BzaKMOxT+Su\nAD3r1v/0LFoIjxheXRb93rXa6b3n1atXdF3HMAzLZ6IQaFKsn0u3G00A5Rwpn7gBxRVojFkdCLVi\no9WBuq558eIFV1dX3N3dvaP46VltXpf6+R8yavrv/Kcckz+/JmY5kcrVLF2O/JlzaJUq/1ueNSdT\nWtHKFbNPRekSrCkda/1Dtwc5TpPjc9dbI7XJg3FstzKZ0CRKK0jSlsRF55xbyLhcQ5D33YcmKfqc\nfLx+SOnRxFr6hIQYyKRfEzN5Dn3vvDzn2oWU49wEQc5fe2fnJjb5tfT5a31ijYCvqY15/awdq/vi\nuff2bfCJKF3ycsyiFulGCEAIqhHIYCIPr2f06RMhJLX3WGMW1aTxPhliY2hrxxiTiBZDBNJLOxwO\nifEag7VuJj4B70eury5OZjExHGcL0hiPL+tUQXDOUVlH0zSJcBlPUzksgcvdlm3X0rgKN6tctasI\nk59dD6mD+WnC2IBlNmoY6roBDBcXF3zve9/jX//1vzE5iE3NcLin21YM/Z7KVriq4jAbyMoYjDm6\nM/KYGz1Yraknq0rXosCdkrdz6o11DuMcEYOJDhddIq3BYZjZsrFYX2GYAJ9ccCESfUxEHDEsZvY6\nBtU29GBqiNHjiemdzwpZ+j3gPwHSpdWXoNq8IB9UtDswJx9yvCZdMmOu63rpX0K49Pd6divvTc+C\nhah579ntdkt5ZDauVSKtkGkCL31FjIH0G/ldEy7pR3JvbfzkurquxE375MkTPv/8c7788kvGcWQc\nx+U5Aeq6BlieX8/wRVHI+4SukzVlK+8b+t2t/b7mztT1l38v9ZwbL3Eny/c5kdJjbU661mK/8vi+\nj401EqD/zg31+8qe96VzRFaTG+0SX3MByzFVVS1tTepyjdg/pMrk7xqOxl/CUnRZ8janCbY+X8aM\ncRxPxgzdhnM3c/7ZQzj3/RpBzPvVubrJSfTaPXNyuGa78mvlf+fu/Nzd/13g4ypdOIzVs7qkOQkp\nAi0LH2cOIUinmis7yGCV3ErYI+nSDbOeDUITDaPxVNYR/cQE2Dglt1YEa2oMFhPnWXAMWAyRwM9+\n+hP+6I9/xC9+8Qu6i0uqqqYyQAhYl1xmMZ7O8qvKYTHUlSV6j58GalvjjOfJkyucnbi42HK56aid\nxRAW1WUxVgBhwkSPw+JqR+WSguZ9pOu2XF9f86d/+iP+6R//gZdvXmNGh2sa+ps37K5bwtAz7A90\n2yvAYEJkP/Y0TbMYzncbtHTGU9ULTl0my++sy+PyDpbBS3zC1oO1RAI2Glw0xGAxNimBrq4xAawd\ncVQ4FwmTTwsLbCBYg/EkvVIWZQQzK1+IeHp0raiBJIi7OppExOLHn9XrASCPHzl3vJCmfGDRREW7\nJPRgohUmieXQRliQGyj9t/eeJ0+e8OrVK9q2Pev6WJuB5m2jqqrFlShB76IU5LPO/Fn1ZED6/Waz\nWdSur7/+enn3wzBgrV2CkoV8yffOuaUcuXsqv7+u97Vnf8iwyjlrxErfN3cJa6KhDcM5IqFJmDam\n2qDmcWG/K/fKt8GaodSf5eQhP1f/PKeQnDPKgnNEWUPeQdu2Sx2uGfRz5ZN763aUB7ZrUi/Qbmm5\nV+7yl4mGc46+7+n7/uTZZByQ9y5uSE3itNJ9rp7P1dm5z9/XP95Htt+HNUKm33l+/7wPf5cqF3xk\n0pUGRgfm2DCSVOLeGTiMPVbeIsOqWBwTAhiDDQEbJiyGyUxpZaMBy1FpsTbgZBY3RIwPc6B+JJhk\nwI1J7rEQQwq+9hNTiFxcbPjZT3/C//hX/xP/8P/+M8+fP2cYBpqmopkbrZQn+CTcNRsHBGrnSJ4v\nT900NLZh21Xsdi2Xlxdc7rZYE4neE+PRly91wNIhoa1qbFVjXQtAfxjpuo5nz55xfX3F65dfEUbH\nr39xh202TN7RXkRsVUOYqG3LoR+4un661LN29+QGQ8+8H/y30hHWBjGJ4bLRYSsPISl81gbMlAh5\ndMBgcdFijKiGFa5O7xkfcFQY21BNIx4Z9CLEo5Ll/WxwPEwEvDJAYX4mH8130qF+W4jxh3eNRd4n\nNJGSAVMHRGujKuRKz7hlZaGoRzLwDsOwDMznDJC083EcefPmDVVV8cMf/pBXr169Uy4pDxxdMOIu\nzFdPyWrFzWZD13VcXl4uzyrXaNv2JD5LBsa2bZeVlPL51dUVf/Znf8br16/54osvAHjz5g1N07DZ\nbLi+vk7K81yWcRy5vLxcrqlVQE22HlK28n9reB8BFaOtSaCOR9MTGO2OFEMp1xRoJUvXnV7tqu8n\nbeRTUbo0mXyIED1U3odIQt7HcpXZGPMOsZHP8+vKcVdXV+z3e+7v7+m67r3l0oRJE15Z9CFlklW8\na9c4p6TJNa21y6pFsYfi4dDtSO4vK3zv7++Xicj7yO9DOFfuc3+vXXPtHmvHnAsXWPuZE9YceUjE\nb4NPI6bLqCBCa955icYY8GDs6Uwtzq5BGVtCCBibVkQKOTuRYDFqZYajMha/GIUU+yWkQb/CpDyF\nOalZwBnLr7/5iqqqOBwObLeXIMfYd4O8LWlVpakM1hksjto6qtbStQ2Xuw1d21JXFnxgzFi4ftEO\ncGqQl+ez1mKsp20a+r7HGWDyhGmk7baMw4FqHHC2SosJpgl8WDrVSXkfmMmfU7qWf2fedT7TcJjk\nPjRpcYOxYANYY8EmpSvO7kWHA/z8rBFLCrKP1FgmMA47x/qF4InRYE1FNCMGB8yxdwZ8mA1Lunoa\nYGJyTfpPQOnKB/RzA5o2ugvRz2ZlgjVlSNQjTXjEZah/f0ha1+3ycDjw6tUrXrx4wcuXL5f2IAO9\nLqf8Lm5H6ZPWpsUgFxcX7Ha75Tvt5tIqnW6XMvOWPq/rseu6pZ/0ff9ODFvTNEAybjHGpU/AachA\n/n7W2v/aZw8hNxa6X2uXbj4zz0mXfo/6vWhDmteZLoN+P3Bchfqp4pxxfl99f+j5D5Hlc4oaHMma\nKKiSFkXITU6Uz5VNfy/udGkXa/GC74bqvDtuy3Hyt5D0fEGJEHc9kROSpushr8sPJV75efrea8R6\n7bi8HLoN62d/X5nWSPbau12L7fq2+OgxXfNvi9olKSP0oAIkFmROK1fUIGmfTg3G2mUCp6Qrxojx\nKTbIc7yPF/UjJPIQTcBGS5jD7GVFlLWWX/3yS/7n//Jf+K//9M8Mw4Fuc8kwDLSuysoY50440VQ1\ndVUTbMBVhuurS54+3bG7aLBWYjLGk05tgfDOzPrYISV3mAnpeYKf+Oz5U379i59zuL9lGuD1q2/Y\nPam5uPqMyjn2d/dMIfL5558ngjarHe+bGZybxZ/EvKy853wwTH/PSpcxRBchzMv+mTAYiMnNWndd\nys3lLc4b8BPTNGBDwDiDnUasCdggeYaSqpjCxiSHAAAgAElEQVTuk1KKpHun1CSzbpkI+9ysQkjf\nhE/AxuSdem3mmhMu/ffaoKDbjhybu4ZzJUxIiRyj1RN9Da3y3N7e8uTJk0UBE+QDov4p9xI1R0jX\nZrN5ZwXVGrnRz5gbFiFL1tql7/Z9v8z6QwhcX18vMS4xRrbb7aJsSZ3maQPye671g4dmxjr26txM\nP49X03WRG85cCdT1I4RMu47OEY48Nkn//NhYaz8PEaMca0Qz/zt3z+ZGW3+ex/oJ9KRgGIYl/cnd\n3d1Jm5Lz1oitVhi1eGDMu7nu8nFAP1e+Elb6rrSNcRyXdiPHGJMU37ZNHhRJLXOOhOSTAP13Xme6\n3nNStUYO836T5yLT70vHzsl9zr2//G85X9fBGqn8rvBRSRdOD2R2yZNFVCv2IAVNGwMYrG5gEpNj\nQ8rFRFo9Z6ObyZunrWtiVTEYQ90ELJHBgLfzgIbDG2itXr01v0yTYsxCjFjrieM9u+2zJUDyP372\nE/7yv/tT/uX/+28cbm9oum2KAwseg6VyhqqyjH7AOkc/TmzaC6bhwNPrHZvO8OSixrlA11RYn+hd\nBYQQqc28GKCu8HF2GxmSO61NsS6uSiv2EkFLazGDd9T2gvv+DWHs2WxrGrvHhtfEOBLdFc417GOk\nrjpijNzvB9p2Dq6WtA8mprgrwNq00CB1XomXq5YZUaqjCmsmNWvWbzcAflGkAGxMZLHGY23Ehglj\nArhZhXKGdgqElOwDqHBEnKlh3M9xdDUhAtWEdYbagpksZpyoqnlgiWDw+JjWR4YIwadyTjEsgfjh\nEzAwuXHPY3jWZmb5AKcH0VzFFMgAJYRDLx8X8qVdUA8pAxJ3BfDNN99wcXGxDOgyGOr4MU0aRAEI\nIbDdbpfEqzIREMix2jDockiKCe1e1IZGCMx+v8cYw3a7Xc6V+C5xdUodSj2K+pdDE8CcCGrDqEME\nNDnSzyW/C7SCnasZucGQcmoip4+Rd5w/iybGunz57x8buSF/6Lh8grf2PZyPkVu7vq7ztXgqfc/c\nTTyOKezDe8/hcDghTbqPyrm6zkVtFNKtc9HlxFCXXxM6PWnQ5ZLPmqYhxsjhcFj60W63e6et5ZOo\ntcnCuUlfftyHfvaQK2+NgD9EjnJSt0aqHvoOvhuVCz4V9yK6Q6iZI3ODNykxKHCSKiBGqSCXSJoE\nCpqWw+E+uQPtcbXHFALWTovM21RhWYF1d3e3NMYKT5gTpwYs1hz54f3N27QE/dDz1Ze/4uuvv+bF\n8+9zc7unrR1+nAjmaMxCCGx2WwiB2qUUFt3FlqvLHZfbDZtNR2UCkZSx3lqLt5YpDIx+TIHJzlJR\nEQxE7+m6TXK7udmojCNVXUOMuMoyTSM/+7d/o+1qnn/+WXreuuJ+f0trLK++uafbXeHaC+pmQwyB\nrqmxJuWySu/ALm7M04HoNB2BBPo75/AhkPjYHBNnju4Rk4KnTgZGGwPWRFxMymJdJUJnIsQKYjBU\nTERfYayfr2sx3gE1NljCBM5ACHO6jmCPqt2ca81ay3TiWslWas3EevoE3Isa+eCqP9NKRj4wyDH6\nuzzWRxtfTVBkpaBzbkko+pARk/Olzqdp4rPPPuPLL78ETmPN5Ji18kp6iN1utwSw6+eQ+8g1tRHx\n3p8EvYvx0mqT9563b98SQuDi4oKu62iaZiFc0zSx2WxOiFfuqtQ/11QO/Uxr9STvbU1Rke/lb62y\naeg4rLUFErqvCuHWq0bPuUrzewg+FaVLcI78658PnZeTJX2u1KW0L/lej1n6fWh3rXbjwnHiolfL\nSn8S4iXlyZME64mKjsHUao4+Vp+TX2etHnT/Msaw3+8Zx3FZdalXA69NRM7VY07Uc6Vp7by1d2nM\nMVmvPkc/X07AH3q/H6qu5eXS99Xv+LfFJxBIn3B88KO06vTsJkTEbSQSSrSyIg2CtfgxGZZhHGhc\ng2kitTtuQ9JuOl6+fMkvvvwSay0vXjzj9eu3UNXLUvmbmxviErSa4oCscSmB6rydz9s3r3hy/Yze\nB4KBP/jh9/mHf/xnNl2Hc5a6riAGKpJKZSSzMZHdpubJ1SWXlzsuth11bXBYJp+y33szgk2GZIgD\n3kis2pxQdTYwVVUl7WiaIEaGfs/+9o6+7xn6O3Ybx9OnTyBOHPZ7+vu3PPvhH/GrL7/Aux3RQPA9\nr9+8XDrhcUuX07wyeZyKdFg3u1JltlRVKdmo+IIT8UqKoyhxmESwKhOWpLXOxHlbn4gz8eiidI6u\nrqAKhGAxLmI8BA8NHdGPTCM4GnozQog4Ny2rG733YA2j99RGpO/0mQ9T2h1oTmyrjfvHhCZDawNU\nPgPNDY7eVkcrVZJwVK/qFcPd9z13d3cLKdrv98u9ZGm5kLZ80DUmpVi5uLigaRru7+/593//d/78\nz/+cly9fcn9/v6xIlJm7ToJqTFrp9eLFCy4uLnjy5MmJa03P8J1zJ+la5LtxHNlsNsvncp9pmtjv\n97x69YqXL1M7f/HiBSEE+r7n9vYWgC+++IKqqnj+/PnSviWezNoUeKyJTU5a9XcnMZYrBkerUrqP\n5SpBPuDr1ZXyUxNpeS86Hu1wOBBCWOKKdCZyeU6d7VzIs/z+qahd5wzs+whhruzq95Cv1LPWLosX\npiltFycpFeq6XsZIic/SJGxthw445rI7HA5cXl7SNA03NzcAS3+Qe58rl06nAu+6HuWd5eEhuUIm\n5ZS+KM/zk5/8hMPhsKjV0i+lzcj4LotMZCuxfOw5R3zXVKR3J/Lv4iFFTF9rjYDpcujvZDzMSdXa\nBMoYc/Lvu8QnEtOloRgoerZ/2ulinFeehYg1FdOQVq8ZY9LKPpv2UAyTp+979vs97jZJp3/6J3/C\n3d0dv/jyy8UAhMkzDANXF5fc7g94Hxj9rK5YRypCoGmSsrPbduzf3NK2NT/9yb/y4vlTXr1+y5Or\nmhAmapdii6JPKyitSyko2qZmu2npmorKWipjwExUpqJ2acB0RJquXdxe7aZDCGfXdThbJ1UsRsaQ\n3JlD3xPDxDgc+PVXX7K72PKDH36ft3e3NNPE1lpuXr/m7eu3NJeOehwY+3tM09DHPmWzn42gzKr0\njOzUmBxnV/lMLMzvKu9cxhjmuHksERfAmphUxJgImTUxuRxNXFJ0VG7OJWVSclwTUyJdE0aCcVRz\nZvuqbiFGvHEYk+IQwgiGeOLeCYCdYlrU4NM7wRiiNch2Uh8T5yRsPUBotUirJmIgtFGQmW3uwhAi\nJZ9L4Prt7e2JcYYUy3g4HJaVjTpuBVhm8bI/YoyRly9f8vnnn/PTn/4UOHVJaLUlkfVqyckl7U4I\noTZI8jlwshpMEy5NNIV0vXnzhl/84hc8e/aMFy9ecDgcuL+/X7Yz2u/3dF1H3/fLc26328VI5UQ4\ndzHJpE4P3vo95gqEJtBrMXv6fK3u6brTn8u7l7YgdSCrPMW1KD/zuBddbxLPJp/lStvHwjkj/CHq\nFryremh3mZBeISdd1y1xiff39/z/3L1ZkCRZdp73XV/CY19zr6ysrqWrep3uwdaDWbD0DAlqAJDA\nQIAk0AgapRe9ASbxQcZXmt4kMxklo+lJpFESSYELCFIAAWGMmOlZ0DM9Pei9q6qrsrIyq7IyKzMj\nY1/d/erB49y86R1Z3TMDWhd4zdIyFvcID7/L+e9//nNOHMdm8yCRg/a6Ng9wyOYCTtjJTCZDEARM\np1OzwU0DSFtjZfTHqT6ynwtAs8GxjA9xtdtzTeaR7/tMJhMGg4FZB6IoMvNXIpfFBSn3S+6VXb0g\n/fvta7Yf230xr08/LsB5FNOV7oc0uyX3+AcBUmcByR+2PVbuRbtprXFtylTq+HDiYgTQSuMpFzfw\n8LwMWkc4M7ejGkHkRig1NezQZJTs6sM4YnlxkXw+T7fbZX9/n0IuYDgeE/gZQhUm7k2lUK7CiyHW\nCs+B2PU5bh6QzxUJpxNix+falcscHR2z9/AQzw2YTgb4HjhuFh0q/KxHNJ1SCDwKOZ9SMZcYRE+B\nPtnJTqMQPAcvyKC8ZPeSLxYT8fhsZ+qrDMNRfwbQYBxOcOKIWzevMxoM6PfaXLt6KUkfMSrSG4zY\nOzikebCPxoXpkEm/SXPfwavEZIM8rlLkCvmkFp3jgVIJgHElKuwE9dtuRzHy9k5LzdisWGvDbrlx\nDDrG0Yl83VOJvs4hxicBWoGTJIpVOLhqFrIchhA7xCpEKxc1q0Gp8NGxItIa8MBRiTvRCYk9F0dN\nZ+WFZlGJThIVqWdRRWEco1WSnz9SoLSD1qezsX/SzXY7SJvHoMCHDZC9wNjsmAhjbRZJCmA7jkOh\nUCCfzzMejzk8PDRGIAiCU0lJ5Vrs56PRiEKhwGg0otvtUq/XWVxc5PDw8JRBlzFjA3o7J5fNxsnY\nsl0ugGElbMZGWAjAfN/R0RH7+/sMBgOWl5epVqtGW9Ptdjk4ODBGcDQa0el0zDUIQJH6jGkmSo6R\n9+a5FdNA0zaoZ7li7P6UjVDaqNhjQ/7S1/eoz5Rrktdsls7u58eB6YLThnqeEUyzWvPOl/th3z97\nwyLRq9I/vu9TqVSYTCaMRiMmkwm9Xi8pA2fdK5uJncd2yXHZbJZisUin0zFjLs0cTyaTD80zu6/n\ngW+bFZP3ADNubUAurd/vs7+/T7PZNEErwpxKsIls2OR7JSWLuCJl82aDwfR4s+/vo8DKD8IonQXi\n5Lc/ivVK613lnLSL1/6eeeDwR2mfONP1qB+g4aT+ntbIoVprXE4WnNiizMFFx6FZXJJjPILghJLP\nZDJ4MxfDUCkqlQqQ5O9xXZfxNCIMXaZhshMZjseAwnPVqSzDC40aOzv3yWbz3Lj+Hi986tM8fPgQ\nHYXk8nmiaYgKYDwcsrqyiKdy+BmXbCZD4PlATBSF5HOBGbi5XA6tk11WqVwmNAM8yTk0Go2YjCbG\nmDpOkmjUz3h0222CjMfy4hJPPPEEpVKJW5u3yWcz5LIZdBThug6u0gSeQzjqo3JjhuEUP+NSckpJ\n1u4wIghyeMqbARcnKVs0m0BhFOFZxtHzPJROhPVhPKOeYcZWadRMu+UwS3dBjBfpE6ZLKVydADB3\nlpDWVaAcBy920SrCc3yIHXCTCE8njtCOuC81OlToWfZ+pT0cX+Mp0ET4+iTbuZ3DyXFm2gFnxkqq\nvxih5I/SZJE+a+cGp1mUtLbLBDbMFh8brCiljAYvzZjASTQTJOxRo9Gg3+8zmUwSTaSV+T5dMFdc\nkTaT8uDBAzY2NoyWShZw+T25XI56vU6pVCKbzZoIQ5m7NoMkxqVQKJjvE0AUxzGj0ehUglilkrl6\nfHxMGIbU63WeeOIJAB4+fGjcKzs7O0aAr7VmMBiYx1EUUSwWTY4iYQMFYNkAcV5urLSRT2ul7FB9\nu9kAL+0CSRsxm+VKMyTz3OW2W0v6XCllCp7PG2+PWzuL9YIPMytnsSrymv1bZUzb2ilhOsvlMtPp\nlGazyXA4POWiswMYbPZTzhe3pOd5BEFAPp+n1+uZ75DvlH4YjUaGYbLZsPQmLP1b0xGuMubEHShr\nX6/Xo91uc+/ePcbjsQlckeux57wwY9ls1rhLhY0ej8enmDm72WPxUf2Vvm8ft6U3NPM2oPbckCb3\nLD030qD9LLbuo9jVj9M+WabLfTToUtZvm+1LZtqumSh1dnO82WJu8vloH60jdBih3FkEVxTPsrmf\n0O/JYh7Sah7hOA7nz62hlOLO3W2Iwc/46JlhzroujucymUzRs0Wxddzk6pOXaB13CcMJr377Gywt\nrdLqdBjrmPpCA3RIxgFfxSzW6yw3auRyPo6KgJhM4H9oAMigjqIIdzZp8/k84XiC77iM9IgwnFIp\nlQlHQ0b9Hp6jaFTKHB0d8eu/+isEmRwP9vcol5Lkjw/2D8j5LovLS4T49AddxlGX3mSmjYlHTLrH\n5IolKo0l4miC6xVQjo+XyRBOhrNFI4nEIdaIfVGJhCt5rBSz3KRJJiydsEmeya8V4eiYjIpRJCJ6\nlwiHGI94BrZiPGdGhTsK8NDEqFihtcL3XeIZsGb235m5iHGTXGeEU9zpCTCQxS2KIrRSRArCSJPR\nCbsYRU7ij/yE21mL1KOabcDTbi0bINnuCNmQ2PonuxCuLCzVapVCoWBccaJJEb0HnLBmol9pNBrG\nLXP37l0uX75sFvh8Pm/cnefOnaNer1Or1YyLUNyGaQbHBl8Cnu20FnZ5I3ELtVotlEqSSX72s58l\njmM2NzfJZrO0220GgwHr6+sUCgWOjo4SBjwM6XQ6ZLNZ8vk8pVKJer1OuVwGErdmEATGDWMDMJjv\nHrYXbJtZst+bd5z9eN64mOfWkr6z+1kYHHGZCTAOw9DkLZNcTAIORNf0OOgc7TbvN/+g7Sw2xnYj\ny/0S8bsY3uXlZSBhdXu9nkmmC5wC/elNUBiG9Ho9s+nP5XL0+30z3kU7KSysAC577Uq7r+cBcGGS\nlVImTYXNIEdRZFzrKysrnD9//pRWbTAYsL+/Ty6XM+Og3+8DmKSq4pUpFotmTIku1Bb5y32wge+j\nxr3d5rne020eUJLvO+vcNGMNpwNHzgJX8vijiKKP0z5xIf2Hb751A6x5JYlJtT7J5WUSmUotxhnr\nQTzLteUrHJW4I4linFCBr41WKrn5MaGfYRonBqPVavHcM88SxzHvvvsuOIrFWpXecIBWkM9miWbf\n57kODx/s4nkBceyQ9T2effop7t+/T7vbQ08nZLJZqqU8tVKJhUaFbODiuRqlRGOjCCeJ2yWaJhT0\nNJqaHcZ0OsX3POLphNFwiNaaXKmM0iHjyZBht40OI3Ye7JJxXFYWFhn1B9y+e48bN27gKYfhcIjv\n+Dz/zLP4mQy3N+9SDHymrQ5eFNMfDWlkYepmiYYtJoMOxVKVOJpSayyhxyGZbN7sAAEcL6kjiYqN\nED0BwjP2KektlIrwVIxH8pv9OInSzDjMElycADJvxno5iUcyCR5Q/qmBoFRiTFx9Ahq0jnAcF5wI\nH4ijpLSQTNxQKXAivDhGRRHONHE360mIH2SIp4rYSdyfn3RLT+qPO8FtkGK/Zu9C7f6b5zaShTKT\nyRjjbYOQtbU17t69m4zJmSbEdoPIQpXP543rAaDZbJ4KlY/jJD2EsFx2qR97xy0AS3bsaRed1FIU\nhli+UzRbo9HIgCfP87hx4wZHR0dG6+X7PoVCwbhWRbsGGE2MGCkbnApjMC9n0jzQmwZQ89wWacBs\nu3DtcWGfc0pLaRk3+7Ht3koDPhto2C5H+74/LqyXbQT/oq4pzYJ91OcKSyTC+oWFBQaDAc1mk2w2\neyoJr/0dshmYTCamMHwQBLTbbQOK4HShafua0p9pAxKZM/Y8l7kjbLH0dxRFRggfBAGVSoVqtWpY\nzuFwSBRF5PN54jjm+PjYXEOhUDDuxSAIKBQK5jpFLpDL5QyIt397+jd9nDVNfl+6T9LP06yW/X4a\nuNlegfRcO4spndf+UoOu+ajXoudToMssGLaRZ5YAwGK+RHSv0bizOoixSlxYcRzjyOLkhTjOTCg7\nGhGhWVla5PbNGywtLXHt2jX29vYYTyf4YeJWGE8niRbJdXBcxXA44dq1Z9i6c4/+cMx777/Liy98\nmu+/8eeEocYLHUqFJYr5HLlsloznJi4+WSgtv3IsItboxM88mUzIzNwdsqj7vks/TFgviBn0k4XA\nISabzTIYDHjzjbcoFAocNg/JB9lkBztONAkXn9jg8PCQ1atP8vCoyXjisVAKaPaG6ElI7PnofJ5x\nv4OqLTCNJvhBbtYRHq6yCgwrhSStdWdBEPEMGM8SeSQFx0n6x1EK4qQIudIxLklhcaXBc5L0HCBM\nV5IWA+BkWMhkTFJEKCBJpJqMBeIZJ6qTlCKuO0XHlkZIz5LIhjO9zOw+h4+HbTk1J2zjKK/Zz9Mt\nvejYi4vtgrJfs5kaGXOykGqtDQATTculS5doNpt0Oh3zGbLIyq5ekqS2Wi2TquHChQscHR2Z3yAs\nkhS0tl1iNngQgGdrV9KLuRgW22jZoAng8PCQzc1N4jg2gEvcOLVajUajwd7eHmtra3S7XeM6skXU\n4nYRYybuUDGK6fqNYhDSu+Y0eLKZAft9GQP2WLC1Y7bhSLtQpG/t+yT3QvR5tpslzfJofaL/e9za\nD2P0bPbjLBZDjpnHVtruYQH6Aj6EOYrj2Ljh7HsPmPEkAvVCoXDK/SeRgTKW5c/ue/n+9HO5dhlH\nsmESMGfr81zXpVarUS6XKRQKZiNhSwuGwyHj8djoJmXMR1E0k79ow8hJmhU7ZYuAPgF5ZwGZeczt\no/p23rqXXhvlvs9jyh7FnNlrjnxG+lp+GDfovPZYga5HMV1gx5ZZPm2t8bwMsY6IvdmCHc2Yrygm\n9maoO4rx3BjXHZtzPd9hMqPbC8W8mUz1ep04jjnYe0CpVOTyyhXeffdd9GyBj2a6owiHer3KzevX\nyeaKVMtF4jDie6+/hsKhXKvhOA4rK0tUqiUajRrjyYDpdEomk+yWJ5MI1/EMlV0olXCdk0K8+Xw+\nYY9m1HAuEzCZJCG+ehZxKa7H41ab67vvE8cxh81jbt66zbUnr7Bz7x5f+NxnGQ6HvPjii/zDf/gP\nufTkFbIZlyvnF3GUx1G7Sy2Xwcvm8Ap5Or3EYO7dv0MUaRbWM6aP8tnMSRi9SphG1+i3kuhErfSs\nLxOxv0uiyXN04pZ0tMZVSSWCBKzppPYiSZ3NZMEBV1lsqJt8vwdoLUxIAg68qYf2pkTKQXshhCFx\nFKF0TOhKiL7GDR3iSIPnoCYRkeMQj0Y48ccvIfIfs9kCX/k/b3eXfmwbeTidWkJeF9BlN1ko06xS\nGIanjLNotobDoUnvsL+/b96Xa7dp/lqtRrfbRSlFrVbj6OjI6EPq9brRcQmzJSDANnwyJ+X7tT6p\nS2gvsOPx2AALqbIwHo/pdDrs7e0xGAw4OjpiPE4idTudDrVajdXVVRqNBu+88w6lUomlpSXTB6Ld\nkR3+cDg0bqJ0f8lx6T6x++ksBtMGZ7bxkP82+EoDMftz7DGQZiDFtSSurvT1pJPNyvmPw5yAsxmP\nNHiad4/TADf9WY8CM+lj7P+ie1paWjJBGP1+3zCocj0ypu06o8L2jkajU6lY7IhDexzZ+kEZ8/Z8\nl2Ns1tPeqIstyWazJlgGEh3zcDhkOBzS6/WMS17mwWAwoFwun7q3ArjsSGlh+kQ6IFIDu4SR/NkB\nKPBh1l3eS4Ph9NywZRByjhxnM8DpNo8Bs5m1dHSjvUn9OEzYR7VPHHSlhadGvaVUkn7dvKdnWc0T\ng5v8T1gtT3toXGIVgxOjpf6hExPHHnEcJSBAhXi+a3Rhns7hT8fGyGTc2eTwkqKeEjl099YmL37q\nBUajETeuf0CxVKHZbOJmEkF6YaHMxsYT3N3eRqkJnpfhpz7zGb797W9z5coVFusFyoUA4mmSgd1R\nSd3HWOPomDhMgGAm5zMc9fAdFxWFhGGSHLXf7xsRL66DGoZ4StEbDdDEPNi+y73tHe7cucP2vR2K\n1QqxjqkWs4z7Xa5dvki9WuEoCnnnrTf5Kz//c4xGI9566y3OP3GZWq2I3x+wUC/T7Q8IvBFjp0cc\nTrm3uU0mmycKFcp1KJcqxPkC4SikUqkQhhFxmKTF0GGE54XoOMRTDhknyUTv6SmeDkGHuDpE6RhP\nJYPP05qMSvRcjo5wHJWwWr6baP4SRX5SANtRMIuiJHKTdCEz8bzrx0SOAq1QsYvyfKLxCJXP44xd\ndBQRuBmcsE+sFPFoAk7MeDQhEziM4wmO/8kL6ee5mNJt3qSftxu2d/ZwsmjYAAc4BbRs5kN23nKO\n7FybzSb5fJ5Lly6xv79Pu902nyeL0mQyYXFx0ezsB4MBGxsJw1osFqnX68bA2AyVXKdESNkgQRZf\nydclQS1i1MRFEscxh4eHHBwccHx8zK1btwiCgFarRalUMjqcjY0NCoUCd+7cwfM8FhcX6Xa7BEFg\nQFyxWDyVX0wpRbvdPhVh5jhJcWMxOgIURSN3Vp+exVjaLIe0s1yZaVeUbXhsw2VHscrvkO9S6iRA\nSMaQnST3cWjzxnyaRZzHRKTvl5x31mfPC0J4lItLdHFBEFCv1+l0OiYYwwbHdrSwMLhBEJiUDXKv\n7chIuQ4BxHKdEjFpzwnRcdlssA0kfN+nXC6bKP5+v89oNDKgazQa0e8neR6XlpaMUD6Xy1Eqleh2\nuwZIxXF8Kmu93URbaY8lm722Qe+j+kGe25vIea+nj5f37E1LmmW235/Xp2n2bJ6L8kdpnzjoeuTO\nJMWAKeS5Y024OEnrEM9uMFZosVIQYo5TCBg70R9pPNwZyxHPQJdyT+8c6vU6W7c3eeaZZ8jlcsRx\nTLlcJp4lnHNdl4ODfZ68coV337tOPl/k4CCJkJKF2HWTbPVwkh1c/mdmRaq1owjHE2LHMfqA8UyX\n4nkeg0HitplMpxTyWUqFIt2jJq1WC8dzebC/x7PPPsvdnW3C8Zj19fXEcMyiZfL5IkfHTYbjfrKL\nL5WIFezu77G8vMw0mjIaDIhiByfW7O/dx82WYThh2G3h+z5HgwHnL13CdzXdXhtIwpxzGZ8gH5D3\nfcLpNIkkDCfAiSvYnbFgKIVDPNNuiQ4sKYLNzK2I7OpVArRAoxx39jgZG44DejaGIuXhaIXrJnm3\nYj3FywTJ+VFsxpKvkig8P1IoFeJHSc3NpK//Iw72j9nmMV3z2llGKO2eTL8mi7Gtu5CF0U56GMex\n2bUK6BLwJO83m00KhQKLi4scHx+bUHNIXFi9Xo9z587RarU4PDxkY2ODp556Ct/3WVxcNAyVGKVS\nqcRwODQMgrhsZGct+by63a4BJpPJhEKhQKfTMUxTs9nkgw8+YHNzk16vx+rqKtPplJWVFarVKqVS\nicXFRSaTCd1u18zz7e1tYwyz2Sznz58niiLjlhS9imTOPzg4MP21uLho9GACJsXICaixQa3dv2kG\nywYIdjqCNBhLgznbOEh/pZlMWa9sl/uM31sAACAASURBVJIwh9OZ1lHyGo7H4x9g5P7HbWcZ2XnP\nzzKg9j1P9wOczrgOp0kBe16k22g0YjQaGTZJWF1JOiplgOT+x3FMp9MxCYUFxOdyuQ/ZQ+k7e/Mj\nrKXNaAVBYNgn2VTZwFl+S6/XY3d3lwcPHhhWTubbwsICFy9eNBt8YXe73a7RTFarVSqVCqVSyVyb\n5LeTAAABf77vU6/XTWCL3Ce7P+ZtDOE0+5XeUM4DUWexUGk2cB7bnx4Hj9rUpomiH6Y9NkL6ueDr\n1I9TKOXhmIz18t9NGLHE0wUkxxga2KZkmSWqC0+iefxoBuC86QmNGfundvmj0Yh6vc69e/dYXmyw\nvLzM7du36Q/HqDhJwzCNQu7cvkWxkCPjuWxt3uKnf/pzZDIZ47+fRiG+7xrjFAQBx8fHZnKEYQhK\nUSgUGI9GeK6LipN6cd3WMbVymcloDCpm7+5dxsMR/c4xb731FgdHhzQWF2g0Gjz/9DO8/fbbVKtV\nrj39NO1uh6mG7Qf36Q8GvPXee1TrNZbXVtk9OGRpaYkP7tzm+PiYvb09vEyW8XhKqdTAyyZ5aQ72\nD6nUqrhewOJCns0b+1QXlsgXCxTLVSIXpgpGvTHj0QjfcShmMsRRwmw5OjRspYMio8IkXQQKV83K\nAblJYEHiZ1QoR+E4syHqqFmurZkLy5lp+1yNiiHULtqNiKxQ7TAMcZRHrBVEETghrqsJYwj0BMdz\nCbRCA5NYM3kMKl7bAOksozLvtfTucd4CZDMg6dfhxJUnC5jtqrALQmutTai57LrX19c5Ojqi0+kY\nF0I2m2U4HBqdVBiGNBoNgiAwACl9Hb7vMxgMPgRCJLJOgIu4LuT4brdLt9s1LpI7d+6YhK0bGxuU\nSiW2t7e5du0ajuMYQX2z2aTb7fLw4UPjehFjub29Ta/XY3t727BdQRCQy+UoFotorU1UYz6fp9Pp\n0Gg0KJVKBEFgBPp28kpJcZHuTxt8yeu2sUznBJP37d19mrGxv0euwdbc2IyX7V6UjaDNQv5lamnX\nVJrdSAMx+/30fLLTSEibN7fEdgizW61W6XQ6BoxJvwsg0lrT6/VOpUYRXV2a+U3n7RKdVDpxr1yz\nbBoAY2eKxSK7u7tsbW2xu7tLr9c7pd9cWVlhfX2dUqlEpVIxds/zPGq1mpEbVCoVE3lpR77K9dm/\nRQBYHMem8LftGhwMBqb8nt0nNqiy+yTNQj6KkUq3s1jls95Pu/vt1z/qsz6qfeLJUdM/7BQIi+cI\n15QzE8JLnUDQeo5BmreTmUWnOW6ywLg6SpKgxtbE1JpoPEiiCC3KvdPp4Hkew+GQO3fucOXSZTZ3\n7tLv9ykVakyjmGkcsXZug/39fRqNBqurywwGIyIdE4XxLNNxSJDJ4MwMk+956DhOxOaOY55Pp1Py\nQcKS9Xs9skFAp9MxhuWZCxcZ9Xp887132Nzc5D//jV8nDEOuXnkSNY04Ojril3/5l/n9f/fv6A2G\nRAreu349KTWkY3A8Xv/+G2ysn+fOzjau0mxu32UyivEyY7JBnk6/w2KhiONAPO4TDh3a4wm7WwHN\nToeFhTrDbpvBoEehXEomW39MtVROIi6jKUpHswLgoIiNnstRMZ5WOAK4VJIMV7kkURMuKBeUVCgw\noMvOyD4DByoZAxHgaB+UOyt+rtC+hsjHmaViVU6IG0a4UYzGwfMivMg7pZn4JNt8nSMfemw321DY\nuzr7M9ILhbBg9kIlebROpdeYs4OURdUOFXddlwsXLnDnzh2zy5XvcRzH1DsUwGKDCVvvIou1XK8A\nA4lUlGuSRb7T6TAej+l2uywvL3N4eMgHH3xAo9Egm81y8eJFrl69ymg0Ynl5maWlJV577TXu37/P\naDTi5s2bJiS+WCyyvb3N0tIS3W6Xfr9v5pxk6xam4fj4GNd1zbnD4ZByuWyy+juOYwCqUopSqWRc\nlWmhbhpo28DKBp/y305PYTMG9loqIMI2HgK4bPZN+sDO0yVsgF3J4HFpaVB0lmGcB0Tt5/K+DSpt\nYJNu6fuQ/ix7jknwR7lcNoEltjtQEq/aYnQ4XRhd/ttAy84ubwNIOU+qKciGRjZGlUqFwWDA66+/\nzuHhIVprSqUS1WoVgKWlJS5evEi1WjWygl6vZ8qBybWJl6fZbNJsNk+tH+7M6+M4J4XjxfUq+jVx\nnUq6DfnctAvS/n2PWv/SDNi8NfMsptNmyB7FfEk/256vs9bhj9seC/fimTdW3k+Kt5j3tY5m0Yyz\nRWKm9XJMCSGXIMgBMfYUmsYJIpckmjqKEwCmkscw6yT3JExWMglnMhmiMCQbTMnn82zvbPHpT32K\nt99+l2I+S4zDeJqwOs898zSHzWP6/T61Wo1oNrmDICCcgpr53yeTiREbm6zCKCbDEXk/YNDv0mm1\nWVlZYjwYcv3mTQqFAsuNBt/45it885Vv8NwzT/E//N3/nsFgwOLyKkEQMOqPuPTUVfaPj3jrvXfo\nD8eMpxPu7+8T5LLkCiXeffttPvvZz7K5tUWz2aQ/GKEUTGJwpzGxNyIbZLjX3CUMY9RoxEPHZXVt\nnTs3/px8ocTe1nuEYUyrO2Q6DSlXalw+fwE3HhMFWYqZgIyjcFWEp0I8rfFJBO2BDhOwJUwXCtdJ\nXMeoWSJc5eCoE5eidpzZMJj1qoPR57naBeUkAHxmgDJ+hijMoJWfJFQNI1SoCFFo5eJ4UyJ0UvfS\nqkv4SbZHbUQe9dxmptLHpdkM2yjJYicuR9tY23oI+WzReggYcl3XFI5ut9tcvnyZO3fumGgmaQK0\nxHjY2jEBMnJdEnqe1qcJOOj1eoZls1ND3L59m+985ztks1leeukllFKsra0ZnVUul+Phw4fs7Oxw\n9+5dOp0OrVbLsDqHh4eUy2XDUEhSV7k/wqBGUWSMhuM4RpjveR7NZtNEo8l7lUrFgHoBrPPYmHl/\nacCVdkGmx4ptuGy3lIyDtNZPGDABYmKIbED2OLSPMnT2tc9r6ffSc0PuTRqI2efOm5v2MfaxMiaF\nNZJ7LLKStFGHD2uH0i5lGYc2uwUneQjlN8jYPXfuHJVKhd3dXd58800mkwn1ep1iscjS0hLFYtFU\naRCWrt/v8/DhQ1PuS1yDpVKJw8NDADP+xZ2ZyWQoFotmEyYbIzu5qowprbVxXcs1y/s2g2WPUXt9\nSt9r+/9Za2Z6fszTotnzMd03aQb0PxnQ9egd/ew4ffIcxJ0I/uyR5mSy6DhJGRBjd6ROznJcHMfF\n8RJg5bsnwsIoimakyiwiyvOJp1MKhZIZmNE0ZGlxhZ3tbZ595inubG2TzRcpVaoo16NQKjKNYqrV\napL3pFw0A0wpRafToVKpEE2mTDWUC0lpiDCMqTYq3Lp1iyuXL3J8PCYfZOg0j9nZ2eEnXnyRnZ0d\n/ujf/r8MJyO+8htfIRdk6bfb+K5LVrl0jlt8cPcub958l3a7zd7xMUdHR1y4cIFskGFtZYXmwyP+\n+hf/Cr//7/+Ev/d3f4f9/X2+9s2vk83lmYRTrt+6Sz6TRStN6/iYTDagUkh2T53eEZVilawbM2kf\n4jo+uTAkAJx+m8PDXTJqkXDgMnVdCkFA0Vd4jiZ2k6hGV4FWY7wgwHMSoOk4szqUTiLYTwB30tVK\nOWiVuJtjAEcGgpMESMTaVC6AWZ4iY8QdYg0q8oi8CDWJyWhISi4mwDeXDZhGIcPwkw+Rn+deTO/O\n54lA5ZizkqueReHbol07G34YhkZTZZ8j4EXOlVxcsoOVoA9hhiRzd7VaNTUOxfUhO19xV4qBscFN\nv9+nWq2arPDizvR9n1arxXQ6pV6v89prr7G9vc3CwgKf+9znaLfbJkJyMBgwGAzY3Nxkc3PTRDNK\nzqVsNkuz2aRUKpnUFpVKxQidK5WKqUkpRYtF/yTXIlFeUihZ2I5sNstoNDKPJU+S3HcBYCIvEJeR\nreWSsSCP04yXPW7sfrZBkw0eZQzJvZaEnYB5Lsb0cWC6zmIu0u/Ne26fk2ZS0vNIzrVrfNp6R1sn\ndxb4ElZEROdBEFAul5NKJfqkMoHMJbs/ZA6mdXxpUGbXyJVoXcdJgjmUSpKiykbixo0bvPfee0wm\nE65cuWJYZ0lILAXgJfFxv983WkWxd0opdnZ2DIvlui7lctncDxknnU7H2Dl5TcaYbKLsig4yhtP3\n12a+bFbbZt7tvk0zjXZfzHs9fYzc+zTrddbG5kdtj4178ePs7M/62UZgj2eAl3bA0WrGhs1CqRUG\nuGmdaLz6/T6uSlwBuVwuqUPXTQZPkM2f2pWM+snCKn7ovJ/n4OFR4r6YhLS7PQozGnVxaSkBXKWi\ncUvASRSRik8K+sZxEn7sKo/79++bxb7dPKZYyPHu2++gdczX/vRPqVarOI7i81/4LE898wzvvvkG\n5UKB9ZVV7m7tsLO3y9s3b5Kr5Nnb2zMDZ3/3AV96+WX63R55PyDje/xXf+MXOdjd5fjoiB9/5nne\nvXGdYafLF3/2s/SHY966fp1CLke3P8SdGcLRaEQuk2c4PGCxschwOiAMY7qdIa7rUqqXabWbqDhm\nuVolcGImkcZXMXgu2cABNL5PUsoH0LFCeUnhci27eBLGUquT/jNjxvR8AqK1A0qfZgc0swXTUUnE\np5q5Nf0MYXgiaLVLW/yoAsm/iJZmNB51nLS0YZynTUkfNw+A2QyYCGnTi77kqrKpfckxJOxWo9Gg\nXC6b4zzP4+LFiwZsSEJUYdZkMR2NRqcWUdFwNZtNowOTAtXf+ta3uHjxIvfu3aNYLFIqlfjiF7/I\n4uIi9+7dI4oitre38TyPra0tWq2WSR8hmqZsNstTTz3F9vY2L730kgE8Fy5c4ODggEuXLpmcXlEU\ncfPmTXZ3d030o1yjpMI4ODgwxlR2+QLCWq0WDx484Pz58wRBgOM4JhWFhO+LW8jWEdnGyX6eXjfn\n9WG6j20WR4y6DXDt74APlwz6pFraaKaN7qOOTwMt+36d9blpRkyAta0pEqAgm5N0v8gxEplYq9Xo\n9/sMBgNzj4V5FiF7GmjLhsYW408mE4bDoWGIZJzVajXiODaff3x8zBtvvEE+n+f8+fOUy2XzBxhW\na29vj8lkYuawsFriGhR35Wc+8xl6vR4HBwf0+3263a5hrwWoCUstaWDkumW8T6dTo/kUexvHsYk4\n1lqbTYnc33T/pBlFeTxvLtgu93njZB67ldaNCeCzWa8ftT1WQvp5wCv9WgKa0jsUa4HBtVJLnJ5E\nnpbHs9271sRxkkJCDMB4PEb5AcVSFnRyw1tHTXzfxXOTyJ5MNsnYSzglW/RpNltcfPIqOPusnj9P\nNldAO8lgzeZyeJ5vwtC73S6NRoPJMPHnHx8lO+xKqUwcJ1Vsut0uDx/skc0EOBpcR1EuVrm4kZRs\nuHbtGr7v4mooFYvc297hu9/9LqVShRhNvVbjYfOIQbvPUr3B/r1dfvNv/U3+4A/+gHq1wqVLl9DT\nEQ+bTaJylU7rmBde+DSryyssLC3yL/7NvyFXLFHNF6mvLDEaT3nYbiWFjFttjjotPOWwu7s7y7uU\no1prUK0W2Tu4j6+gWixR8mL0JIOXcVlu1PAVZIkIMh6BE+OKl9BNajv6jot2XLRycNxZvUTHQQtg\ndl1iQIbEycRzDXNlT7woinBcF2aLoPY8CD1inETQ7zhE4XS2QPh44090OgAfjl60J/lZc2KeC2je\nQmO7muYZpjSrJe/JIuj7PqVS6RRAkghciba1C/kuLS0xHA4NuJBcVrKbt1NXiA5EgJ0s2MIOyIIt\nYErmU6PRYHFx0UQ/yuK9u7t7Sj8FmAgxAUEXL15kd3eXWq1m6q8KiAIMC/Dw4UO63a4RFYtREjGx\nCJsnk4lhHTKZDI1GgzAMefjwIQCFQsGklpCcSWJobaMrhtdOE2G/bvex7QpO96EYDHtOpJlU+Uwx\n8AIUJALzcXAvpsdyejPyUcBLzvkotkK+Z97cERBl3zfZUNiuM+k3G3gBBtzamw275JXNcKYBYTpF\nhMwxOVfA/eHhoWFh+/2+iaAtlUp4nke1WjU1VOWapDapsP6y0RLXej6fNxnqJVK4VquZudlut2k2\nm0wmExYWFk4BR8CAOdlYiaDejpgVQCn3UJh2+x6mWah5Lc162eN+HlsqfW6/ZrPE9vv2xuVHZb0e\na6Zr/mvuHMbrdMFNNWfiaJ24oZLjHJSOQClc5RCrmOk02ZGUikWGo7EJHXcch4XlJVpHTTLZnBEa\nep5H97jJNAqpNRaTQV1v4Lo+xWKRw+MWxVKF6TTCdfWpjL/ifpDFTtiuaBoTzZKFOiRRJceHByw2\nFpIEdjNj1HNgdXmFvQf3GfUH3Lp1iwjNYavNuXPneO211/jZn/1ZnFjz+uuv8/zTzzAdT7h4YQPP\n8zi/cQ7XdTm3vsKN92+QK+ZwMi5vfe/7PNh7yBd+/uf53p+/wdraGoVKmf5gRGcS8XC/iY6hedzF\n98D1XfBcnJxPc9CmOWgTRZpaqUg0HpFTMcVMhmKtxqDfJZ/xyAc+ehqD5yQuXgeUA66aGQelUI6T\n6PNwT15LOhZ1EqZ6eiK4iZ5LEJmOT/rfFmsyMzyRZcTsv0+62WBrHuP7qLlx1mfNa48yVGJcZOGz\nDYUsRrVazWSpl12sJD4VHaRSinq9boyR4zgmqa49P2UeKJXUSZTyI1J/zhbZKqU4Pj5mfX2dRqOR\nBJzMDAPA1tYWYRjy/vvvk8/nTTb5/f19nnzySZrNJu12m6tXr1KpVFhZWSGfz7O6ukqr1TIyg8XF\nRabTqdnZS7ty5QpxHLO7uwtgmCx7Jyx/BwcHxmWYzWYplUomslEMiITnC7so2iqb1bL/2+Ng3niw\nwZdtqKV/bYMk646wMRLxK8Y/HR33uLaPO/7nMRvzWtpuzGMPpbmua0CJuMhs5jzNeMl4hhM3ofyl\nk4bK+WlAJ1ng7RqNEvFr10cMgsAkMgaMOzyXy5HJZBgMBgAG8Aijd+vWLRzHYWlpydSVHAwGFAoF\nqtWqqV0qm6BqtUqtVjMRxkJeAEa7JePLZrdkTZH5bR8n99tmFOe50B81Js465lHjJc2M2uPgRwVb\n0j5x0DUPUUr70C5fi7D+0U1rhaM+vKP3/BmLMBNfi4Bea00+mzOGxvODBGVrjasU/cGAYqVMKV9I\nhLIaCvkC4WSShI4rl3AaJbmH/Az9/pBiqZJMpIxv/NbHx8fkcjmazSYLtTrlYomHe/tJXpzBAM/N\n0Om2ULHm1s0bNA8Puf7eu/zEj/8YP/3ST5rcQUf9Lt/+2iscHyQ76J3d+3T6PXbuP4A45je+8hu8\n9b3vs7KywlOXrqCJufHue3zl138FrTXffvXbHB8fMxz2OXfxIgcHB/yHb32NtdUNBrv3efO9d1g7\nv863v/NdtOuQyxc5PB4wnoY4QKGY7NDGkxEx0D1uEhRyyY5smiQ6JZxQzfqoIKDrKhoZyJKlP+6T\nCwLwFa5TwFFeki/N2qUoEuCFLEDKoCycmb5La02sQM0YruSJwpmJ7mNHJyAtOr2ARdFJQkvtym71\nRD/xOLS0juAsNvis19ItbWhO3es5rIEdkQQnBtw+VzYLxWLRlAQS3YXtjikWi8YNIgurPHacpC6o\n53lGX2W7XiTp5GCQVHHo9Xo8ePDA5PuROouXL19mMpnwwQcfsL+/j1KKg4MDHMeh2WwSxzEXLlzg\n8PCQxcVFcrkcvV6PbrfLyy+/zGg04vj4mE6nY4CPJFYV4Dkej1ldXWU4HLK1tYXW2tSqE6ZC7pXs\n4AWMCogV9zxgdF6SF0tci7Z7z2aq5rkV57Gh0mTNmdfH6XVV+tdOVSAs2+MwJ84ysrYhTG9C0tc9\nD2ylP9cGo/ZcmacvSjOLMuYB4wK0j5OSU+nvt1lOOKmRKY/l+wS8SVSkuOcqlQq+77O7u0u1WqXf\n79NqtdBa89RTTxmdYRRFJgder9czAR9SU1GuZzKZsL6+TrlcNu5Dm+VynCTXlyRJLZVKNBoNcrmc\nyZ8ngMsGUgIo5Xvt327PD/v+yH0XQCoMnfTjvHVxnsvY/jtr3bM/N93v6e/8S810OcrDUc6HJoz8\nV86HaUCYN9FSCSW1lVzNYkXi6KTTko9SOJ6PN2M5tNY4nkdmOiv2q2OmOsLLBqCgPx6hPJeVlRWi\nach4UiZyXWqNBQqVKrHjUanVaLVaLCwscHR0QDabpTvoGwGxh6JaKhsmK8j6ZPMB02iCG00JRz2+\n8bWvc3j0kHq1xvLaImMVc/PBfZ5/8UVeeesNPnjtdd69c4tivc6fv3uDznGXbrvHX/vZl3n66Uvc\nufUuv/pbX+Hf/+EfUlkqETge0+GEzRu3mUYxzeMem9t7FBZqHLx5i2kUcnB4SOiVqayco6tjNt9+\nh71OG1CM949wgwClNMpx6A9G5PN5JhMIw8SIOlNF3stR8hOtStbNkC+VcR3FKA457LYJp32WSnlc\nzyVfypENXLROUkZ4bpIcNZ4BJ+WA9jSaGKWkfxMFfKLGU8Q6Yb2UcoidGQWtk4LmyfsanFnurxm4\ndnwPL8xCGKFihedOiRzw3TFZ75NnutKsBpy9wMhzu6UXH5vZmNfSOi+lTuouyp8svHKMvQON46Rs\nVr/fP8VWFYtF40qUxVoYXnHJyIIrxkTEt1IQWDQmR0dHNJtN9vb22N7eJpPJGPfmhQsX6PV6vP32\n27z66qusrKxw69Yt9vf3GQ6HLC0t8eSTT+L7vhHEHx8fGxfJjRs3jCjfdV12dnYMc9Dr9cwOu1qt\n0u122draYjgrPG8bARtcSZMgAmEfBJzJ5m48Hpu0ErbI2I7atDel9nPbgMzrfzhdlsk2OrbxtzOl\nCziW3/y4gK6zWnpOpF1D6ZZ2IdnH2vNl3uelQZvd12kDLQBMXO8CyAWIp6sLpK9R/mR8ibZOvkfY\nqsXFRcMsK6VO1QbN5XIsLy+buRTHMQcHB+zt7bG7u8t4PKZSqTCdTqlUEoJAAlOuXr1qcks6jmNq\nr8Zxki6i1+tRLpepVquGRZNAE3En2hsGGc922hf5HfZYk7VH1op0suY0oDoLcKePscHbvHPO+tw0\nwz7v83+Y9okzXfN28yfvnT7W/m8//tBk0vOPlwgdu4m/WRYje8fi6BitZx0fRQS+z3SWxbpRq9Oe\n5SfKF0pkgzxexjdZs3u9HjqOicIQlGNSRsSTKZlMQDSd1T+bDbBcLkcpCPjeq6+itebm9Rs899xz\nVCoVPMcl8Hw+eP8Gn3rxBZo7u7z6L36X8sICO/cPyCjF888+zeJiiUGvzeWLFznc3adRrjEejlhe\nP8e5c+f4/p+/CY7ixu1NvGxA57jFaJD8/p/5mZ/h/Q9uMRiPuPdgj+EkwnVPcscUcnnWVlY5bh2Z\nXb6jwPcclKNRjsb1VKKtchyGkyGtVpNiPovvu0SZPMNxSJjzGAxiXB3ilRXK8/Fnxlk5nKQJUYmL\nNeRsGjkBabMOlzFDEtyoVQK2lFbEThLNqtSs/JJzYsRO7V7+AibUj9psF8MPw3SJEZExnT42/X+e\ngZJF0QZk4vay55wkgpRIPkkYWiqVjAtDjI8wPLlczojuBaCNx2MTDeU4jsl2LSke3nvvPe7fv8+b\nb77JxYsXTc1EiZis1+v83u/9Hu+8886sckNScuj555/n4sWLZLNZ7t27Z77v/PnzXL58Gd/3+eY3\nv8nu7i57e3unEkpWq1VWV1fpdrtcv36d69evo7U2LIPk5JJ8X7bw1w6VlygyWZtE+C+uGgGqkvFb\nKXUqfYPtAreNWJrpshkRu0/lWDE6dnJO+zPswBIBBQLEHof2UcbONpz2BuGsz0obc3tcP+q75t0P\nW3Mkc0rc61KvVMCQnWYhCBKPigAV6R+5RgEjIjSXcTQajbh//z7379836RuazaYBdKKhfPDggWGl\nhsMh77//PqPRiEajwfr6utFF3rt3j8FgQK1WY2Njw1RrODo6MkBMNiGFQoHl5WXy+TzD4ZDbt2+b\njPpBEFCtVo1eS+6JXYlBNiFwAvir1ao5VnSR9tpjb0Lm9XeajZrX32mQNe/89Do7jy39KLfmx2mf\neMqItE5BHgOG6UoDrY9CuEp/+OYrpVBppgtO0fryfzoaE5nyNUkJIdd1GU+nKBy8IEOkY8q1OgsL\nC+SKJfzMSTj4YDAgmoYsLjWIpyGj2eJZKZXpdVrkcjmG8YDxeMz58xc4PHxIsVjk4c4uP/mTP8Xu\nvV1+4eW/yu7uLlcuXKaxtMTa6jkai0v02gN+94/+mDEZIgI8BZcvLPHf/NZXuPfBTarVCv3+mIOd\nAz597QUuP/MUW/e2+Se/+7vsHuwTxZqM57NaqTDsDem0m/zCL/wCzW6b7fv3aPfHBua8/PLLKJUI\nLP/0a9+g00q0Np4LnuOQySQA1fGSxWXUGxFnMmScgGy+wN7hAZVCnjhwWS74+F4Oz3XI5wJyrkPW\nT6IVXU8xmYzJFn0ch6QU0Kx4tqsUaDEcoGaFtbVyUErjaodYgaeSQlH2xHDc5BxHQ+TM0kqoRD8W\nO86MVZvtslzPMGqfZLMB11mASo6zX0+Pd9s4zDPG8hnzFhGl1KnNB5wwx/buT/Jf2QWUHccxLJLs\nkkWzJXqPXC5n0hRIRJYYfYlS7HQ6ZDIZHjx4wOrqKjdu3KDRaKC1Zn19nVqtxuLiItVq1WSgl40N\nwLVr1/jCF75At9s17haJnFxeXqbb7fK9732PN954g36/b9JbACapqiQi7nQ6OE4Skr+2tmbcmwK4\npNluVXG1iiER8byU3xkOhwa8ibBY2HDR66TZLbs/0yBDjrHznc1jZdJusbOazT580u0sViK96X7U\nefZxaVZr3rHpZrNP6fNtLWjaMAsQksheAV5pNmVef8xzc8JJElS7QsPR0RHFYpFcLkejkVRNabVa\n7O3tMR6Pk2j4dpulpSVTFULGSblc5vz58zz11FMsLy+zu7tr2OVer2dc8Wtra6yvr5PJZNjZ2TFJ\nkRcWFkyNRqWUSawax7GpRCH3TdzuLwAAIABJREFUq1wun9o8xHGSasJ2r9oslz1uH7X5tPs1/Tg9\nTtLj6az19qwx8KO2Txx0zfvBjwJd8wzPWYYj3byMP/c428dsdnzpCRtGTJmigmS33mq3WVpfR/kZ\ncvk8YRgbwXGlVGb/4QOGvb7xqeeDLOF0bAydH2SI0dQqFY5ax2QLRcrVCv/L//Q/o6OQ6WTM9p0t\nfvu3f5v7e3uAw1e/+lU6nQ5vvPM+P/+Fz/Pmm2/y117+PF/+uc+zt3mbwHXoNI9pdUfcvf+A1Scu\n8k//5b/im999leawz2gM5XKWIPDo94f83Od+hkq9xj/4B/+AF3/8x1hfX+e845Er5Ll5+zbf/e53\n6XZ76Bh8YOPcOQaDAa1Oi6tXnuThwR4rK0so1+HBg/sEQZn2aES726I/cMl5HqOxQmcKZjKNB0O6\nkwn5Up5wNMb1PFQQkfFdQKOZomKXpLwTCXsmgBgFxGiSjPNowEmAl1aJk1nqcsbMjBGaUGv0bII7\nWhPHIUQhWDvdOI6J9ONhYM5aBNKG035N2jzm6ixGCzhlMM76DKVOXI6yyAuzI8BMyoGIxkQYLXEV\nDAYDs9AbV36KWZbrER2XlB156623CMOQarXKzs4Ozz33HNPp1JTseeONN0zG+eFwyOrqKi+99BKt\nVsuAukKhYCL07ty5w5/92Z+xs7Mzq0ma1FIEeOKJJ3jyySd57bXX6HQ6PHz4kNXVVVOh4u7duwZ0\niUAeElC6vLyM4zjGwApLIPdJjKbkPpL7KQBUxqGAKFtHZzMpZ4HxeUZJQEH6fNuNZTd5buuYHoc2\n7zp+0Gv7uADyUfdlHvuRBmI22ywuRmG17KCdeek4hM2yAYe43OAELEudUklf5PtJANfq6iqrq6vk\n83nu3k0qpgj4WVxcNAxxq9XC8zyWlpZYX183ASX7+/tcv36d0WjEeDw2CYQXFxfJ5/OMRiPj8pf8\ne/l8Ht/3zXwxXp1ZJLIwpiIZgJNSRRIoYAejyPohmxa5r7Y7P90PaXev3d9nMZlpUJdu6Xtuz50f\npT0W7sV5CDN57fTz9OMz/+szDJP+8CCXToaTCWQ/lzYeTwiCgKNejyCbp1ZfoFhOyia4mQDHiYhj\nTfu4Rb1eT2rOLa+glCLwXMJwYnQbwgyIIVMqcUX0R0MeNo8oFnK8/+47/Mznv0Aml6XeaJDJJPlO\nlOtyfmWRjKMZ97r82LNPc2/rDpcvXmRzc5MXPv0T/Ns//CPq51b43ltv8PaN99k/7hPkXJaXyywv\nLhFPIn7q0z9OLhPwyiuv8KUvfYnrH9zk/v4etfoCkY7JZTLkikUajQZbm3f5m7/xG2xubvLWW2/y\nhc9/loODA65cuYRSitdf/z5PPfsk/X6fw1abQj5PpVLBCUN8pUz28Ol0SqszYaVeJQxn0T5xTODM\nDAKJzkornUSXQhLBqBONFsSgXBQxGoVWST4uVIyrHCKtUTomqZs920lqjdYxMCusreOE+QJOykg9\nHoYFTjOu6TF/FvOQ3gXCfMp8Hpia1+YxBPJ5NoMmqRK01qaumkTjCfgQVkfKBomw3t7syEbHZmuE\nEfA8j1u3bpnPqtVqVKtVer0eg0ESuSuJTZeWltja2uLpp5828zgMQzY2Nrhz5w7NZpPj42Pu3r1r\nohyr1Sq5XA7Xdbl69Spra2vs7OxQq9XIZDK0Wi1WVlbo9/u0220KhQLFYpHj42OuXbtGu93m8PDQ\n6NjENXd0dES5XKbdbpt0F8Joic5Na30qu7fcBzt9g90H8/on7XKxnwsjZu/mbUMv328DMLuvHyfQ\nJe2j2C1576yx/VHnPer1NIMl99oGA3KcuN8FZIi+Txge+/ek01Gkf6t8n9iPhYUFlFJ0u106nQ5H\nR0dsbGyYQtSAiSzMZDIcHR0xHo+5du0ao9HIAKZiscjCwgLlcpkwDLl58yZbW1scHx+TyWSSiPnh\nkEajwbPPPku73Tbsr+d51Ot1MpkMnU6H4XDI3t4euVzObJrG47GZF8Vi8VQwgdSkFDBmAy55X4BY\nmrlNaxLT7SwW7KyxM4/5kpaeg3/pmS4bcNk7YKOBUKmkmI8YlPZzSYD6oWOV9+Hz1ExwDYmx9lyi\nWWbyjOcbhO1mAsbTKbWFxST56eIioQP5XKLP6LW7xHGE6yalQM6dO0cmlyWXC3AmSSTWdDJhMisP\nMRgMaDQajKcRa6vrRFHE3Xs7vHfjBv/lr/8a165c4Td/8zfZ2b1PsVLh26/9Ga997/s8eLhPZjhi\n853X+W//9q9xuLvFcn2F73z7dX7sJz/Lb/93f4/F9TX2hx3W1tZwcwFrKzU2zm2wsrxMp9Xm/No5\nvvDSS/yPf//v45XyfP5zP803v/kKf+e3/jZ//NU/4eaND4g0VAdDnn76GX7s+Rf4vX/9L/jSl77E\nF7/480zDMV96+ef4P/7xP+a555/hxRef5/Xvv021nufTz3+Kt99+mwftLoGfoVossL27j66UicoF\nGueWGSkPnQ0YRpHRv5VKJaLxiBgPx/XwAg8FSQ1F1KwUkErAltazRLcnIntijeskQnytNbFOSguJ\nexEUynUIowh0hMYyNgoirYgeA/uSHudiOO2FJu2Sf9ScSL+WBmfzjkvv5mzXhoAuEQiHYcjCwoKJ\nphI3nYAs0WbZqR8kvYNoWUTXNJ1OkwhgxzEpG771rW/xxhtv8OlPf5onnniCL33pS8at1+12ee+9\n99ja2qJerxPHMb/2a78GwMHBAVevXmV7e5t/9I/+kal1J5FVTzzxhDE6EtHo+z5f/epXTVh8FEX8\n0i/9Eu+8845JuJrL5ajX60DCWD399NNorQ3jMBqNWF1dpVgscu/ePc6dO2c0N5PJxEgQJHu/Hbko\nTXKJScklwOT0mtd/0l9pxktes8GUuD3FuIiYWQy/MJgCyB6H5Khw9lov7SxQOu9z0kb245wn76fd\nunCaLRYQJRormQvibpf7GscnxbFFg2WzxwLAZWNji+olbdFgMKDValEqldjY2DB64lwuR6vVYjAY\noHUSbFIul9nb2yOfz/OZz3yGfD5Pu91mf3+fra0tHj58aOqJijt9fX2dZ555hmw2a5ICt9ttXNdl\nY2PDRA8LibC+vs7x8bHRaK6srJgUMA8ePKBYLKJUIvjP5XIm0lEy90sSYslxJ/dYNKEiureZ37Rr\nd56bNj1XpB/PWgPttXaeJ+A/CaYr/fij3v8o4KXmvPdxwJo0m9Z3HI84Dk1HFYtFCsUik+mUQqWa\nULXHbYLZgHF9D89zyRdyHB4dsX5+LZkk43GyKzhuoZSaiWgj8sUio8mYKJwyGg+IYnh4sM/f/lu/\nRafbolqt8rVvvMKnXniR//Of/VOef/4F6PZYXi2jopCjowN8leXaU8/xv/5v/zuLy+do9TvkSwUO\nDg+JJ1MuPXGRcbePF8c8/eQVAs/nz771DQaDHp/77E/y2mvf4Xd+53f4J//3/8Uv/vIv8c9/918m\nESqlEr7n8nu//2/5L37ll9nd3SWMp6ytrfH1r3+dL3/5yyytLPL1r3+d556/xvLyMq9++zuUiyXK\n5TK79+4Tk9Q6HKPpjse8d3uLUj5HvLpAo1EjdlyWGnXCaQzEKC9JC8EsjxpxSKzcGcmlZ4K9JAFu\nPCt6rtTMXaUVjsIqBmVF3MluUmu0jtDRaSPzuBiYeeyvHdH4UXPiLwJ0zXvfjoQTwyJGvFQqnUpk\nCich8uI2E52TiGTtxVMyX0skn7Bak8nkVGj7Cy+8YNwbnU6HBw8e8P777xOGYbLBcF329vYoFAos\nLi4yGo341re+heQFgmQR39jY4OLFi7RaLa5evUqpVOKDDz4AOJX48erVq+zs7NDpdFiaVZhQSrGw\nsMC1a9eSuqpRZJguSXycyWQoFAqsra0xmUwYjUasra2hdZJMUgyIpHUQBq/f71Ov12k0GhSLxSQR\n8ywYIc1Qyb23DX8aQMzbmdvudDsC1f6f/ntc2l8U63aWq3IeE5JmPeR5Op2BzE/pT7ucjz1/5Vy7\nEoB8lg2qxS0+Ho+N8N7zPEqlEpPJhHv37tFqtahUKqyurhpNlc0yKaVOuf4KhQIXLlzAcRy2trY4\nODjg8PCQVqtl2CtINlarq6uUy2WazaY5RlLA1Gq1Wcqh4alkwL1ej2w2a+ZAFEWmIH25XDYVJSRy\nFzjFjts54mxwJMfJfZH7Ny+a9+O2szwG8l56jT3LHf/DtMcCdNkDVhZtz0tK+vwgBsZmuj4uQJN2\nijr2MsRhCLEyFOdkHFIqVqhU62gnKbswDqc4sxJCx0dH1Ov1mYYjxAsy1BZqhHGMDkN830WHJ/lW\nlHINoycTbOv2bX7tV75M+7jJ5s0bLC8vguNy8+Z1vv3qn6GJ+e73vsNnnnqK/qTPn7/9Hs899zxP\nPvkMtz64Qy8aUS+UWF29AOGEjOfzX/+dv8P/9+//iJd+4SeoV8rcunWLUqVMxnd46ukneeFTz1Ot\nVrl3/z5LC3Xu3tmkXi2z2Khx6dIlJqMpv/7Xf5FITzh3foWDgwOarRbZfJ5ytQKOS7PbZWNjA8fP\n8Ku/+qu8+t3XyOVyLK1v0O12UcplfzRlu9mkGmTwuz1iZ8oTrmIBhdZHVAp5sq5PNsiD4zIaDfGC\nLEqJuF6SpSZwK44dHCcm1k5SEF3PJqhSs2LoGrROUkXILj6MiKcT4jBkOh0TTpNFLYwSXU34GFBd\n9nywX0sHndjHpl9Lf1768Q86J+T7hRERnYq48KTUhx215LquqbUoO3MxHGKoBDDEcWyAih3p1+v1\nuH//Ps8995ypo9hut2m329y4cYPNzU1T5WF9fT0JYIkikxn7lVdeYX9/H62T7N9S3PeFF17g8PCQ\nS5cusb6+zt7eHsvLy2aui1h4OBwasfLx8TFaJ9GLssBLOZThcEi5XGZxcZF6vW60MBK2v7a2xt7e\n3ql0GaIh6/V65p7KfclkMqZUEpw2OHC6NmBabJ1mY+y+lNdt4GUzL6Ivk9/8uIGueYbyBzG2jzKY\nH8UWp1+356Jdjkc0fALc5TvlXkr/Os5J8Xfpd8dxDMBOR/GJG07K0In4vNFokJ/JOSTVycHBAa1W\nywjmhSE6d+4cpVKJg4MD9vf3uXfvHtvb20YfKb9jbW2NQqFgGCaZP0EQmMhiGziJi3xhYcEUmG+3\n2yaNxMrKiqlDnMlkTEoJYbdEI6nUiRSl1+udyp0omwo7stpen9J9m5ZXfJxmbwY/7pj4YdpjIaQH\nTrkVjdDWnR+x8FEMloCuD1Hw6sPnmGbTxlGyAE6jiOFMNLjQWErOdx3y2TxhFJPxkoU5mwlYXlpl\n98G9xO0YTpnoKZValQ9u3+L84gphGNI5bpHNZo0uJJPJMLJo4t//f/4ln3npJ/kbX/7PWKw3eOWV\nV9i8u0WQz/Hq997iyYvnmEwmHHSbPDho8cSlywx0hn/1h3/A0dEBSxuL3Nna4is/95tcrTSSyTwY\n8Usv/zx7e3v81F99mT/56h/z5V/8RQ6bB7zwwvM4Ombv/j02b28S+D5372xy5dLFWTFin2uXkiKp\nf/wf/pharUZ9cZEHe3uUKmX+6T//Z6xvPEG702N75wFa77Kd3+Lg6IjxNCRfLpMpFjhoNumGLk6s\n2d1/SK1Swdsd4QZZxlFMfnGZ41GLtUYjyZ/lgZql2VAxRLMkpq52iF2SgtizMYMSgxLjaAcdO2gr\nzYQmMoAriiJ0HBOGE6KZcZlOp4wmY4bjkWFDPsk2b9ye9f5Hga5HAa6PA8DSLkZZ9AaDQVIrdCaC\nFzAlWkhZGAuFAt1u1wAzCVRpt9uUy2WzKEveoVzuJEFxGIYcHR0xGPz/1L13lFzneeb5u7dyruqu\nrurcjW6gEUmCEWIQgyhKtqWRLdkryxp5VjNO2pHH5+zas7NpbM3ae/ac8dlje9ee8RyNNCuPNUe2\nxgqWKSqQFklBFAEGgIiNBtA5Vc7h3qp7a/+ofj9ctBpU8prwx4PD7uruCvd+4X2f53mft8nRo0eV\naL5er3PmzBlarRbZbJZAoN8lolqtKvPTpaUlNE3j+vXr6j09/PDDTE5OKjQtlUopY8jV1VVFu0mF\npCBUcjCOjIyozX5qagrbtrl69Sq1Wo1EIkEoFMLr9bK6ukomk1FWAeL11Wq1FMKVSCQwTZNSqaQO\namerF4/Ho4JAZ+DlvAcShDuRrt1Z+V730fnYXlSiM/AS6vd2HnuhU38fryNnl5xXotUzDEMlI7uD\nArm+gKLfJaERHaNQiU7fN2fg4XK5qNVq6muv16vo+lKpRDabpV6vA/1uB076vlQqqWBrZWVFtdES\nBEsqbEXjZRiGWpcS8Dkrlp2BmlT3WpZFpVIB+m204vE4sVhMPS7P5zRrFaTM2UFBPrfTOBZuGMbu\nRn53+3j9MIH4bjTTeX+dIvrdj/2o47YwYZHNQpCuG/qEm6sG5Gvn3+31mPYmP3uz96C+3rm43WZf\nCJnP5wkFI6SG00D/UAlHIzSbzT43TV/4NzI8xnZmk9hAHI/uwex2VNYt0Ktto3phSQZRqVVZXV3l\n0bc/zHAqjc/t4dVXX6XZrGN3urxy6jTvfOR+vvvKK+zbN0OhUGBq3wGarQ6NZpvHn3iCU6e/i8uj\n89//y/+B5174Dj6XzuTkJC6Ph0KhQCAQ4C/+4i9IDAzQsUzK5SIev49cJkuxXGJra4NoLMH+uQPU\nanW2t7cBOHfmLOFwmGAkTMe2mJ+fxx8IkMnkiMfjXLt2jXA0Qr3Z7FfT1HU03Y0/GMQwTSqNen/j\n1jw0qlUCHo1CpYKnDCGvl0apQkJzkUrEqVQqxHQ3PbdOw+7hC4bw+dy4NB12DpmeroPo6m91L8Wu\nrde3nXBCwzdpuXo3ysDl31s99pq3e1X57hV43ervf9DHbvVe4IanjqA7pVKJcDiskB/JSmXtSkAg\ntJs0wZXDolqtqt+VjVjQAmkiXCgUmJiYULqUVqvF2toa2WyWcrlMIpFQm6a8H+gfNoFAQAnfT5w4\noQwgpfdhu90ml8spbcnMzAwXLlxQGitxv6/VasrctVKpoOs6a2tryiU/EolgGAYXLlxQbt8yj6Qt\nihhYyv6Wz+dvyvpFayaHkAR/wWBQUavOogMRZH+/cSs6ZPf8d+q55H3KwSpC8Ld6OKlt55x3+lrd\nCuVzjh/kd/Y6vPeiaeXnfr9fNZPudrvKhHT3ehWEqNlsKt2W8zNJQmIYhgqUJKARvZYgZIFAgPBO\noVMgEKBUKlEsFlWS43a71VoVbeXa2hr5fJ61tTW1psbGxtR7iEQiKlgUBFYSpmAwqIoB3O5+U3hB\n0KSnaKPRUImEePUFg0GF/EpBjWgaBUWTOe3ci9vttvL0cqJscm1kOC1rnMj5m91bZwB1yxjiFsnL\n7v32RxlvLb3o0m/KGHRNx9qJWH0e9/cET7uhv71QAABN3/tA0rmBdqHfvJhcmo7W60fSBi661Qbt\nQp7s2hrXzp/l6htneOSRt/Pokz+BLxymXG8RCnjoNtuYnS54PVg6jI9MMb9whQPHDtAq5HA3W2zW\nC3gDfly+EG6Xh2A8SaNRIxaJ0GxU+fbz3yCfzbGxssr+6X00Gi1eOPltHnvsMZ59/gWO3XGEttEi\n7HcznIyxtt3A1W3y/ne/i5deeonMuouxkVE+8pGP8MrLpzicHGZgeppss8loPEYzt02xVuK+E/eR\nyeTIlxvY7iD1NqxsLbO4uEhP18jPL+E7e5HkSJ9GrNWbaG4XWrGMyxelViljWx3sbgeNHl63Rqfb\nY3g4QrVWp9e10TxBMts5/H4fbl9/kTYadej08HlDRML96pp8vcB8oUmo0sawdYZjYR44ehC7XqSH\nRWwgQdBr09M7aNh0TQtdB6MDPp8fe2cj8Xv7FVkdtx+wsbsdlVG6tB6a2cHdNvD0LNrNFh27hW00\n6RgtzJa5s/hNGq0ONdP4/2GW/3BDAhYnvSiZnVM74syyv9+aeLNyaPkd5//VmnBA+r1eXyR+9epV\nlpaWVE/Co0eP8tGPflSJfmXzE6rO5/MRDAYpl8tEo1EVrGSzWcbGxhQlGYvF6PV6yu19YWGBL3/5\ny/j9foaHh7ly5Qrf/e53mZubo1AoEI/HFSIwNDSkDqChoSGKxSIHDhzg3e9+NyMjI2QyGfL5vMqw\npfWO+Ap1Oh0WFxeVVuyll15ic3MTn893k6dWpVK5qfy/1+up3nKyIQulGAqFyOVy2LZNuVxWGb3c\nQ3HvFpG1IBKGYZDL5eh0+trJoaEhotEoyWRSaW7kvkiPS0Hp5JB0tgCSoEroQrnGgmyJuF+QuFar\nRbPZVNWatwP6CzdLUZwBmMwfp72A/L5zyJzeTcHudYDu9dhe9JUESkJ7Ly8vo2kasViM0dHRm+6F\nIK5iMtpsNhW6JfuVaZrfI5h30opCXQuiOzw8TK/XI5/Pq3uqaRrZbJZer6fQMkGD6/U6Pp+PRx99\nlHQ6rfYVSTwELfL5fIyOjgIojZWgaVL8Ifor2+43hBfTX6neLZfLqrVWJpOhUqkQiUQYGRn5ngRC\nnlc878QyotfrF6hIkCqouvj8ieDfqYVzFoU46fTd99ZZLb17bjmD5L3mz4+Lqr7lmi4ZctPlA5um\nidt1o4RUsuLdUewPQ6FoaAohcV42nZ2DrNff0HS739cvk82zvrGJ1+OnZVtsFXJ9WwVzgGQyRamS\nIRwMYdCjbtSxrC6+cIKJiTHq5Sqdtomuuam3mgyFQ0QiEerNnZ5rbjfNZoNet4PWg//06U/x6//0\nlzh06BDnzp1D13VK5TKTk5NUq1Vq9Tp33XUXzWaTA7NzHNw3y8bqGo+//VH80SjJ4TTbG5vcdcdx\nnv7il3EFAmxubtLr9Df76elpXn/9dYqVKu5AmFbHYDNT4Mz5CzQaDZKpIVpdk4ZpkKuU6VqguTQi\nvn4/r249j0sHu9sh6PfSbhsEAn4ev+9+Op0Oy2trJOJxrl1fZmAg0c/oDINmq0U4GsHqdInF4zR3\nFm3A66KnuzAsi1q7ycRwimvXl5idHGFwIE7XMCm1sniDEXz+ILjcdA0Try+IbVs7dYwOuF9E8zv/\ndGzs7o5OxTLR7P4m1LE7N1EnRsfE7HQwux1M861HugRKdwZAsrE4aSVJQPaa67uDrL2sIt4M6fqe\nZMVBb5TLZQCmpqbUASONogFFKdq2TSKRIJVKqWCsVqupQ0asGiSQkM1M13U2NzdZWVnhwoULPPXU\nU3g8Hq5fv660Ij6fj3a7rQxSS6USg4ODTE9PU6lUmJqaYnp6Go/HQz6fV2X1Um4vtKNlWdRqNXq9\nHtVqVdlP5HI5NT8ksGs0GuqQ9Xg8CtFy7k0ul4vZ2VlmZmYUjaPrOiMjI3S7XfL5/E0tVOr1ugp4\nAKW/kUCwUqn01+aO+aQgXzIPpMpRUAK5jk70R4IuGbuLR4Ril+DLMAxldSDf3w7DGSw5EwE5PHcf\nmvI3t3quvb7+Qd6D0zJFglbpmLC+vo5pmqTTaR544AGGh4eZmppicnJSVddubW1x9epVNjc3lTWD\nMCGNRuOm9S3ocTgcZmhoCK/XSzabVcGLzFtBzJrNpprPUiHv8/lUsnHgwAEmJiZIJpOKvpeEQox7\n5TkETYvFYvh8PnK5nKqGlM8u81SQNAmanAH9xsaGsksZGBggm82qJvWCDGYyGWUgnEgkCAQCNz0P\n9BEtCbAlON0dPDv3EOfc362FleH83olcOtGw3ff/H3zQ5Vws0kbAGXxZO95Woh2RG/3DUivq8d4N\nFEwQL5fLhUvTMdv9jNWt6bh74A8ECMbj3HHfAxw7eBjCftpWh5Fkil7b5Ev/4ZOcPneaeCrJmcsX\neOTJd/De97wP02gxGB+ksLJNIByhUCmTHh4mHA5jaz0GBxO02028bjdet84XvvwFPvup/8hwLE69\n2eCLX/4Sly9fZv/+GV588UXq9ToPPnRCwbibm5vsm9zH1OQ+Tn33u0xMQTZf5PrKKucvXQTL5vD+\nOarFEpOjY3znhW8zPJrGHwxQrje4/8GH+fIzX6PWanPq/HkM26bjdnN9Y4teD3QdbBvcbl1l9wBe\nXaPbsfC5Nfy6xtS+KfbP7uMd73gHf/iH/zcH5w4xOj5GNJbgtdfPYAPT+6aYmZmhWq/x2iuvKhQj\nHApRbzTotgySsQhtzcW1rS2OTQxjaTrlUpWhWISIz0+z3QCXjhsfbaN/eOPS0V1uQMOyujvzZQcF\ntSw0u4tmdenZNlhdeiIAtzp0Om3Mdr9Mudk2abYM6m2DWrtN3XjrqRTn3HXSccBNGZz8bLe/z14B\n116bzV7rxvm9tNiRTcu2+21IpqenOXr0KCMjIwrxGRoaolwuc/LkSU6fPk25XMYwDGZnZ3n/+9/P\n+Pg4yWSSxcVF9drSm9GZ7QMUCgXm5+d5/fXXVSXWmTNnyGazDA0NsbW1RbVaZXJykng8rrJcaeuz\nsbFBLBYjk8mQyWQolUp0On2aPxwOs7m5yebmpkK5/H4/kUiE7e1tWq0WuVxOBUOC1jnvha7327Ls\nduF3u93Mzc1x+PBhwuEwCwsL3HPPPWiaRiaTYXNzk3a7zeHDh4nFYkp/IwUGUr0lOp1Wq4XL5VL9\n9SRAkt9z+v3J/52VXM775tRsOUX0zkowZ+AlwZZ8fTsMZyDlDKwk+HLO893Bl3O8GcL1g7y+E1WR\nQGN5eVkFDkL/SdWt3+8nlUoRjUZxu90kEgmlKywUCiq4cfYTlTUh616CImlmvX//fiKRyPe8pojR\no9EoLpeLgYEBUqmUQmwnJiYYHh5WSFQgEFA6LbFuMAyDgYEBRdWLLlF+LhYW8tmdCK4TXRVvukKh\nQCqVYnR0VPVnlYIWy7IoFosAypxVAj85650IupirStIhgAzcYAOc62A33bgb0XLOB+fPd8+PHzfI\n2j3ecqRLFos42MINkaFTjOjMaORvvx+y9WaPSdDVNTt0ej2w+5VuRtdEd7nwuD0cvPNOkol++aut\na4Sxcdk92o0Gnp5Fo9DpA+/BAAAgAElEQVTgwsUF1vN5Lp9f4D9/+s/5L//1LwmFQszOzPDN57/N\n7ME50unBvsiya1Fv1IiGwmidDm7dzeWzZxhLxHnPe97DV59/gfn5eQ4dOkTLaJPN55mcnFBVVlev\nXmVwcJDUxAR/863nmJ6e5rmXX+LAoUN87i//Ep/P1/dC8XnxdXusra9w5MgRFpdXOHDwIKdefY2/\n/urXqBgd7J1G40a73Rc8Toa5PH8Vj65x5113EAgEyG1nOPTIIV46+R20HS1UJBRgOJXm8OHDXL5w\nkT/75Cf59V/5FT71nz7D9voGPZ+bO48dZnJykqe/+nXWllaYmBhlaqxf1bK8vIzZahJNDOJ369Sb\nNTbLVapuFy67iwebwWAIj9XFdLtxRcK07ArBYIiO2SESCdE0WvhDIdDsvu+WpmGZ/SzL3jlcep0d\nx3mzTbfdb0nRabcwzRZto0mj1aLZMim3WjQNk2rbpNK60dLlrRzOjcO5UcDNLWBuBYXL7zn/fvfz\nv1nQ5aRrnMgKwOHDh5Vw3Ik8+/1+YrEYxWKRkydP0u12OX36NKVSiV/6pV9SlNv6+jqxWEyJjwEl\nyrftflPea9eucfnyZeWzJR4/0KdnRGMirU6EcnjttdfQdZ1arUalUuGVV14hHA6rhC2Xy7G6uqp0\nlqFQiHPnzpHL5ZQFhcvlUmXw8rfFYlFpZwTt2NjYuAmNGB0d5e6778br9fLGG28wMTGBYRhcvXqV\nVqvVr+ZNpeh2u1y4cEHp3SR7hz6tMjAwgMfjoVar4fV6lUYnHA6r6ySVjyJqlnsjDcNlXuwWycv3\nzgpFJ7ol/+Twdfqa3Q5Dodq7CrCcn9kZaP6g1OH3G5KEiCZLkChZR5FIhGAwqFo4eb1eVSlYr9d5\n7rnnePHFFwmFQuzfv5/p6WkOHz7M1atXb7J3kH/OgEs+b7vdZnt7WyVCuVxO6Z7k+1arpdZlr9dj\nbGxMFadEIhFCoZDSVzUaDaX96na7NBoNhd6mUikVNDUaDUU9C8IkQZvozpzdKFwul6rcNAyD8fFx\nZSZcKBQYHR1Vzvb1el/ve+TIEdxuN4VCQX0uqeqUQgGn19/u4NpZoOC8/3uhUrcKzPeSYLzZfPhx\nxlsedMlw6lMksnUuIjGN2/13b/acez22++fdbpeeZeN1ucHlomOafS5edxEKR/CEArhcXgIeFx3D\nxKrXKOYLXFtZQg/EmDwQJ5yq8vq5U7RLNf70P36ST/xv/5pWq0lq6EbJrsvrw+dzY7baBP0Bejo0\na1Xy2W3uPHqU3MY6ug4ul4bbrfPGG5eZmppkcHCQuQOH+OY3v4mmaf1J+cYbHDxyhOeee479+/fz\n9DPPkMmVefjh+2m323z5a9/iIz/9brYyGYI7VSlud9+09Sd+4ifYypdYWd/gwsIVZsfH+u7FhoeZ\nsZF+M+HBJFeuXOahE2+jWqngd2n8Nz//Ib74xS8yMTZONBzE73Xz8EMPcfToUS5euMzPvv/9VMpV\n/uvTf83Q0BAvPPu3BFw7Hk5uj6KSHnrgBC6Xi8WNDbJbm7iwaLgh4A8TTw3hD0cwO11arTZDw2nw\nutHdXty63kckjRZel4euaeLy9PC4vbSMNi5tR5RqWei2jd2zoGvRk6pFy8bqdLE6N0TzHav/z7As\nzK5Np/vjVaX8XYy9bCGcGaTTQ070Sd/v73+YoEsOZWd2KBualKhL1ZIESqVSiYWFBc6dO4fb7WZi\nYoJarUahUOBrX/sahmHwp3/6p0SjUdUfTqq9JBCQtV8ulzl79qzqkVgsFhUKvrW1RbvdZnp6mjvu\nuIN6va4Ss2vXrhEMBpXWrFwuc+nSJcbHx4lGo+RyOUWFSjPiRCKh9DGijSqXywrB8/v9tFot7r//\nfoW6DQwMEI/HOXfuHMlkkitXrhCLxRgYGFAu2o888gher5f5+XlmZ2dZW1tTaNjS0pKiOaPRqNJb\nieZGfIwSiQQej0eJp1utljqERDfjRNvgRrGDBFxwAx110orOwhFnACaaMgm2bhd68VZIw60QLWdi\n8mZ/v/tne6EbuwXXgEIG5f+it5NAwePxKEqxWq1y6dIlXn75ZQAmJiZ4/PHHeeihh5ienmZ+fp5a\nraaQG5mbu6vo8vk8pVJJeV3J+xVRuvQqFdpe6H3DMNRalebWjUa/PZ30ZOx2u8pp3jRNVlZW+sVi\nO2i0vIbMF0GEI5EI4oEnrb6cWktJ1paXlymXy6TT6b4h+I4mTPaCbDZLPp9XSJ/oD0WMH4/H8Xg8\nSj8plhPOAhUJhJ307240VB5zVgLvRj6dwfxej+2m63+U8dbSiztu4xo7k7oHuqbjdkuUf+NiOrUK\nTtM5GbeCBvdakP1n7mF1+ptd1+7TCFa3i9/jZTiVpmtDIB7D0sAT8OK1dMxGla994W9YWlnktZUl\nGD5AOJ4gmLK5f2gU02zxZ5/7K77y9Fe58vKrzM3O0DINIpEYhUIBX7BfpdVo1ghoNmdfPc3xw4do\nl/OYZpNiLovdMbly6SJPPPY4zz77LE899RT5Yomh9DBf/spX+JVf+RU+9Zn/zPnz55lfWGFmZoaN\ntTX2T46ysbLKxkaGP/i3v8tv/Y//mgP70kzNzRIKhJmemeVn3vM+Xj51isWVVfbN7Oftx+8ls73K\n8XuPc+XyPL/28Y/x8nde4t577+UnH36QXs/G7/FS39qkkNnmp971FPffex/FQl+UHIvGmZ6e5sK5\ni/zRn/wJM+MTjCcHqVbK3HfsKAMDA7hcLlZWVrjvnntoGn0ofunqAnXLomdbGB0bK6ixnS2SGRjA\nLJcZHUwwGBllPZfH7XHh2aGhQpEI5XwGGw3cHjxuH7HBJJrVxer0F6SgFFqvr/myzBZ2p5+ltZoN\n2maLer1Jpd6gYXSotg2qLYNqu03TfOs9iZwZ7+757LQFkCGHqdCQtxLI7xV47X693WJkGVKdJRVT\ngkxrmkalUuHTn/600qhYlsXs7Cz1ep39+/fTbrc5d+4cn/3sZ/nwhz/M1NSUEutKguWUFJw8eZI7\n77xT6V8KhQL1ep1wOMzw8DDlcplHH32UQCDA+fPnOXv2LO9+97vZ3Nwkm82yvb3N4cOHWV9f5wMf\n+AAXLlwgn89z7NgxXn31VcbGxojH48zOzjI9PU02m+XUqVPK4kKqG0OhEM1mk+PHj9Ptdjl8+LAy\ne5SNf3Jykscff5xUKqXQKGkGfP78eV577TXV4NftdjM7O3uTg71c14sXLyphsARXsrnn8/kdr7sb\n4nuhISVwbTab6nCOx+M3CeslyJKD3Fmq7xTPixhbkA1pcuxs6P1WjVvRhM5DUw5rJ+Il13GvoGv3\n4br7d5z2AM7XcmrKJDgIh8Nq/QkCGwgEVFPoWCzGE088gaZptFot1Wj9xIkTDAwMqPvpdKIHVNB9\n/vx58vk8Pp+P4eFhXC4XxWKRRqOhgvKJiQni8TjRaFTpAbe2tlRgFQqFVPNrodYHBwcVAiWC+sHB\nQY4cOaK0VtK/VJrGy+eWhCEe758BLpdLFdhIQ27RhD344IMMDg5SLBZVP0gpWmk0GqqDgwScUhQj\nFb1ra2u0230ttLQBk+pMudeCOjvngNxf5/2UeeK8z3tJMG74aX6vPvAfPNL1ZsGSy+V1ZNw6Xk+f\ne0bXoaejcSPrBw1d72fLGo4MyNESSBpo93p9fydd72u59B0URXO78brc6Lob304DZpfLDdiYzRaZ\n9U1WVlaptQwmDs7x3JUtAo0Wq2tbjAzEcfU6TB+YY2VhgUgsSj5bwO/xUtnx5+qaHdDBo2tcu3oV\n2+owNpwmOTfNX/3l56mVSlTLBvffd4AXX/gWvV6PXK7A8Og4Z944D7qLZCrNyFCK7XyOiZFB1lfX\nGE4k+We/+N/yR3/4Bzx03938we//Po++/b5+9lxrkh4apl6tkdncolmtcfexO9nKbBMMBJib3sdI\ncoiJx4dxdS1ioRBHDx6kUChw6uWX+/0c77yTIw/cq0z3ZmZm+gsEnS984Qu8+urrPPq2E1y/vkTb\nqPPBD36wj2Zdu47P5+PuO+/g2tUrjIyMkN3a5L67j/PMCy8SDgTRgz3i8RjD6RQba0uER1MYtsb1\n9U0OTk8SS0RuWuz+UBDd5aKn6fiDITqtJka3g4sdR/MdukWqzgzDoGMYGKZJp2NgmF2MTpd2p0Or\na9LqdDC6O0J6660X0t+KVnQOJ63oPDCcGd/ucatERBIapyeQlGQ7jYql0kgOB6E8rl27RqlUUhTF\n6uoqk5OTSq8ifj7nz59X2iXRnjg1GJqmsb29zd13360CG2n3E4vFOHToEGtra0pMnEwmWVpaUtfD\nNE0GBwdJJpPous4dd9yhNv3Z2VnOnDmjTFb37dvH1NQU1WqVYrGoNnIRCh84cIB4PA7A/v37lVA5\nl8up9/yhD31IVTiKdUan0+G1115T/mEHDhwAUH0hBwcHWV5eBlB0qWVZSuMlVYpioSFu3hJEyT2R\n+yMImcwHab4t3QBkTjjpRqlOFFTLqdVx0opOtOt2Hs414hR5C8ri1EPe6u+d/5ex++CW9eFsVO2k\nHXVdx+fzMTg4SDQaVRWEQrmfPn2aYDCoWmZJ31Cn/YGT3QEU9ZfNZgGU4L5SqShzVFmX0gYol8tR\nKBT6hVilkipecbvdLC8vE41GabfbDA0NYVkW29vbiqKPx+MMDg6ytbXF8vLyTRpCoRJFlyZaSEGA\nBZGWn0m1oVQTLy0tUS6X+/KSHV2W3+8nmUySSCTUPZJ7VqlUaDabCuEzDINDhw4pOxfZW8SU2Xmf\ndwfczmpjCbqcwdru4Hz313vNtR9n3DZB1+7Jr2ma0ivIz6X81Ul/CNQONy+UvSoWdH0ng+RG9Ovz\n+bA7/UmFZeMO+QhEIrhcOj1dw+XRCeguXnj5ZV57+RVKhslKrsjXTr7IB37hozz0znezUK7z9De+\nRnlznePjBxgOx3F7/XiDAXS3i3q5Qs+y0FwuehqYhkk07Cc8NYk9EGHx4jmeeuIx6vbz/NQ7D1Cp\n1clsbxONJZifn2d1Y5vNbJ6RiSkuXVlkIBbnvvvvp1Kvsb6yyu/8X/+K61ev8W/+p/+Vz37usxyb\nm2M7X+TjH/84I0Mp3jhzlmI2h9lq8d6f/EkWl5cIB6d49InHaVTKimNvtxqcOHGCq1evEo1GmZqd\n4f4HHySXyxGPDTCUTJOIx2nWa2xtbTE6OsrHPvYxPveXn8fuafzUe99DNOInGAj0S4sbNWZmZrh4\n8SKzM/1DbnAgRia7yXve9SQ+n4/JyUn+9tnneP2Vs6QTEfCFCAwO4dZsLm5uMtWK4/X0EZ5YNEyj\n3YewbbuH1TFBd9GzNVqdDlq3H2Dpmlv1COu2DdqtFkbHpN1sUDd71Jttaq02daNLrdWm2m5TabRo\ntm4PI8g3S0ac2Zf8TFpu7K7WgZszvL0Cr92UiqwJ0ftIkOX3+2+qNgRYXV3l7NmzVKtVlpaWWFhY\nYGRkhJ/7uZ9jeXlZ9UWUAE20VfL8Qq1JINHtdhUSVSgUGBsbI5fLKZPFUqlEMpmkUCioKi5ptXP0\n6FHV3LdSqfCud70L0zQZHh7m1KlTJJNJIpEIb3vb20gmk6oRdTKZ5NChQ8oOIp1OK+8xJ5UguqxA\nIKAQN6ElBelYXV3l2LFj+Hw+1tbWGBsbIxQKKY2LiITls/t8PjKZDIZhEA6HmZiYIBqNEgwGOXny\nJPV6XTXeDgQCKhgSLVg8HlfFKUIpOaUZUvwgVE+r1VLXXPrmSXC1W9MlSNjtYI66FxLlHE50S4bc\nN5nTzjZWP+iQAFuGUIDOqjo5f4QyHh8fV9ddeiBevXqVra0tdF2n2WwyNjaGx+NhbW1NoaNOyks+\nsyCT4+PjKlCwLIutrS2l/wMUWlyv9/0Va7Uafr+f5eVl1elBbEh0Xd/xi+xbPcRiMYXuxmIxDMPg\nypUrau03Gg263S7Dw8NEIhGlKQyHw4pSrNVqWJalqnQBNUeLxSLFYpFms6moSNu2GRoaYv/+/czM\nzKj3Lj5ewWBQFXEIxSmSBNFIRqNRDh06pKwshAp3Bq6CPjqvpSQnuwNquebfb/ywc2ivcVsGXfK1\nTGi5aE4qBW7oWkTrJX9/q//btrg+77Tj6fZpAiybeqVKPB7vb2SGgT8YwKeBbnbYXlzlk//+3zE8\nOcW55VVeO3seTffxv/zkz3By/goPPvUUX37+WzTsHp1mh8n0BLjd9IJeKo0mdLok02my+Ryp1AAr\ny4t0a3Vym+vklhawahWKhRwPHL+TfLHEA8ePk83meeChh/js57/E1ZUNfutf/iuC0Rhf+tKX+J9/\n6zdZ3lrn0pXL/MIvfhir0ebIscOsLi7xvve9j4XlReKROOdPv4b/vvtIhCKsZPJ8/GP/Hc8+9w0e\nfvhBao06Q6kElUKe+YUr/PwHf4GvfPVpZg4eptioU2g2mZrrC+JnezaNag3YyaLdHpKpIXS3i83t\nLd7x5JMUyhWGhoa4euUcz3z9q30YuVbH6Bo02g3QNeYXLpMYSvbNKWsljJqLv3n9NZ568l185EO/\nwOunX6aU2+Y7r77BUCyIx63hsyzGx8YwW00CgQ4Bjwez1VaC6tjAAC2zhQudzNYmsXCMaqOC3+cj\nn8/TNPu2EEa3v0HUDY16o0290aJudqk2m7RMi5ZhcBs4RnxfGhC+V6PizMB3r5Hdz7H7MSdq5vS2\nEapWNCJyKMhrFQoFnn32Wa5cucL6+jrz8/N0Oh2OHTtGMBjk2LFjFItFNjY26Ha7zM3NKSEs3EiK\nut0ugUBAmTFKI2tplHvHHXdgmiaxWIxqtcqhQ4eU8H12dlYFIY899hgbGxt9ZPXuuxkeHqZerys/\nsFAoRCqVUu15pDXR448/ztraGvF4nIGBAbUJl0olhoeHFYIn10iE7hMTEwA3VW0JrdpsNpmdnVVN\nqsXH6OzZs+TzecLhMJOTk1QqFaXPkffjdrvJZDIkk0nVmLhYLLKwsIDb7Vb2AeJ+LpSU6NsETZTr\nK9e10+moilTpKOAMunajXE7LjLd6fL+Ay3loOi0XZC3ADy6Udq4FGYJmCRAgiY2zT6A0Qvf7/cqf\nTRqdRyIRTpw4Qa/Xu6mDg9iYyPrai86U5s8iTN/Y2EDTNKampkin0xiGQaVSYWNjg2azqbyx2u22\nCo5EIH/06NG+vKXRYHt7G8uymJ6eVu+70+mQy+UUYid0trSlEnRKgilpuu0M7p0GqkKBi5+YYRhK\nyzg8PEw6ncbn87G9va0SC6nezefzqgJTejOeOnWKRqPR74yy857lPYi+VYJip5WF3FfnfZTk483Y\ngd16L2dh0Y8zbougy/m9c4hI1rnZS7m0c9Penenc6uByfq+jYe0EbT53P2gTPt3n82LTw+XWod3j\n8oXzVCoVJn0+Ko0mpmXj0tycff4F5u66h9NXr/fblRw5Rv7VlxmPBPvZmcuFLxigU20qI8N2u00g\nECCfy9BoNAiHw1y+cpkPvP+nuXBthUvzV3j59P/L1IGDZDIZnnjiCc5cukyhUODeE28jGo2xcP0a\nI9PjjNRGCUUjlGt1ao0micFBBlJDdD09qvF+EFkqFHFrOg8/9JDSmIBNOj2EpmkkBpLcc+/9/D//\n7k/41V/7GNevX+foHXeCphEIR9jO9/l+3e4Ht9FYgkK+D3e3Wi1arRbbmRwj4xPMz8+zdP0auXye\n0dFRErE4pmn24XFdw+711CI9tP8Ame0c9959N+FwmEuXLqHRFzK7vR584SAnHriXUKXKpQvnSaeS\nBAM+9J1NKRQIorl0yoXiTiuf/uGdy2UIBEKUy/3G4hrsMh/szyGza2HuZPy9nkavd7N321s1bkUv\n7kZwdz/m1HU5fy4UxW7/LxlOcb4gUqLjkeeTdiBC2VSrVT772c/yjW98g/HxcRYWFpSB4fb2NvV6\nnT/6oz/i+PHjfOxjH+Pzn/88H/7whxXqAKiMV1qV6LpOo9FgfX2dyclJisUiP/uzP8ulS5dYXV3l\nueeeU4aNTzzxBJubmxw8eJCZmZmd+57j3nvvBSAQCLC1tUUgEOCee+4hm82ysbHB7OwsxWKRTCZD\nIpFgYmKCXC5HOBwmHo8TiUTodDoUi0VSqRSbm5vccccdiiqR9y6oot/vV4UMEhiKAayu68rvSxCo\ndDqtgmMJLtvtNkePHlU6GGmbsm/fPvL5vEIdjhw5ohC6tbU1yuWyQky8Xq+iWBqNhjoU5AASh295\nL0Ittlot5clULpdv0nSJ7uvHFQ3/XYw3Q7qcB6acFYLOOalrJ2rlnIcynBS904ZFEhl5HaG05Gyy\nrH7j6j76bjM/P082m1W9C71eL3Nzc7znPe+h1Wrx8ssvs7q6qqhsoeyd55ncT6H62+22ohT9fj+j\no6NsbGxw9uxZGo2GopglqItGo6rvYqfTIRwOc+DAAYWIR6NRxsbGaDQayrLB5/Opool9+/YpzzBJ\nusQCQ5AtaeAu53Kv11NzyemhJfR4r9dT7bQikb5B9uXLlxUtWa1WKZfL1Go1ReM79Ykej4f777+f\n8fFxGo3GTeL8YDCoAkZnQYPP51P6TFmDhmEoylGqp8UuxTmfZO44q37l/vy447YIunZn8/K905fL\nWRYtN1s8bpyHj5Ni2X1guXo+PGh0uzYut5uubeF3u7DqNUbjUXxeF412Ba0bIhwIYnQ6xEN+Xnjh\nBXQdxsZHWf3CX0HPgF6X02Yd16XT/PGf/ilffOZpPv8Xf8m14TgnfuqdlBot4rhotQ1qAQ94NAYC\nQQoba/jtLv6eSSoaxB8Pc9fBI2xubRDwhLj/nrcxNj7Lu//Re/nqN77JfQ89wsOPPcn09DTBaBy6\nNuVICMPo8uADj7F8/Rpel07LtEkOJnj11Gmi4QjHHjpGPp/H3lmIvmiYlewW6XSasakZsvkcZ85e\n4MD0DCsrK/zqL/0y21tbbO4gE6bVZf/cAeh0iEfj+P19D5VEcpCm0WYolWJra6uvh0mm8brcjI+M\n8up3X8HviROJpKi3TbL5Mh/48D9lY2NDVYEtLCwwOjSsFtqrr77SF2p2TH7xo79GLpPl3Otn+Ox/\n+ToP37WP+594F41alXa73r/HmpvKTt++ja1NZmZm6Lm9tG0gGGJxawtvMES1WsUXCGC4dAqVJma3\nS8kyybdqNCybmmFSb1o0LYu2pWPw1h8wMm6VjOye25JpS+AkOh8nrShB2V4BG9wsNpUgQigvWXti\nogiwtramNr0DBw4oAb2maRSLRX7v936PQqHAiRMnePrpp1lZWVHIkLzHbrerBL1SfdXr9ZQBaCqV\nUnort9tNMpnkyJEjrK6ukk6nOXDggLJ+sKx+U13DMJQ5q9AopVKJZrNJOp0mEolQrVYZGxsjGAwq\nFEDcudfX15UPkqZpjI6OKi8hZ1NvgFQqBfQ9lGzbplqtquxZ7B0GBwc5e/YsCwsLdLtdgsEgY2Nj\njI+PMz09TaPRYGhoiF6vX7UpKFq9XufixYsEg0Gmp6cJBAJcvnxZVcAdOnSIkZER9fper1dVoEnw\n5mw+LhYazv2x0+ko/yVpreKsYrwdEC4ZPwyd41wLuxN2+X53dePuv5dDG1CJvvPwdaJdkux0Oh2l\noZJ5IABBMpmkWq2yuLhIOp3m4MGDCvWU+6dp2k0NruX9O4s3hN7b3t5me3ubTCaDbdsK/QSIRqMK\nQQIUdalpmmIHxDhVKhzlngvVKaa+yWRSrU8JOEVmIJ9PAlQJ7MXiQdZlKpVS70GuiwSzlmVRKpW4\ndOmSosClQjEejyt7DLk2L774okoqotEoDzzwANVqVXVOkASk0+koOlLTNMrl8k37oxS3yP2FHX23\no9eoUyPmDMi/H9X9g4zbLuhy/n+vx2SyS6YpZaYCfwrsvucB49KxrC5du0vP6mu8LLtLwO8n4Pdj\nWR18gQDutgleL/6Al0qlRrXWoIuLmmHQMEx03YPHdvGpz/0Zpmmgazrv/0dPQc/mX/yLX+c3f/1X\nqWezbGS2MK0uM2MTFAo5MpUi3p7F6soSjVIRt64zc/gwtUoVf3SAuqvJT7zzSa4vLhMdHeW9H/x5\njJ5NLJ7EtLpki3nMjkkiEsS2LNyaRTwSJhwIUna5OX/+POPj432tQDxOtlhkcXGRaDTK62+8wYc/\n/I8pFotcu3aNgwcP0jG7lMv95sOXrlxhZnaWuYMHSSSH+oif3096OL6zoEzcPi9LS0tEYzGuX7+O\nrcHKykq/tNfvZ2Njgw/+/Ico16qMj48TG+yX1xfLJUzTVMjeyOgYPl+AQKB/iP3E+96LrmsU8nk6\n7RZ3+/0cOHqUV185xerqKl986RxDiQRjw2kuvvE6ZqvN3NwBNM3DSlXHqOkkQhGe/cazHDx4kHq9\ny7XXXt+pzllkeHgYcFMu1+m5LCqtLi3LxrQ1TLtH1+rdNuHWm+m5diO5e60Tp8ed0HnfL+jabSIo\nzyMHlVMXIYJy8dhxwu22bbO2tka9XsflcvH8889TLBZVSxQ57BuNhsrwK5UKtm2roEAEukJlSPl9\ns9lkdHSUSCSCruskEgm12VuWpShA+WzBYJBWq6VaBgn9MDQ0RKvVolQqkc/nVZeH1dVVBgcHicfj\nShclKJiYRUovRTlo5bCqVqsKKRBkV6g8l8vF3NwczWaTI0eOMD09fdOmL6jT1NSUQjj8fj933XUX\nnU5HiY99Ph9jY2MACg0QNCSfzysrjEgkQj6fB2BkZIRr166pA7dcLquAUPydpNhE3ruTov6HMG5V\nmSiH4+4iEWdC7/zd3X8vQ/REgApMhVKWa+Scf1IFKG15oG/4+8ILL9Dr9RgdHSWXy3Hu3Dnl7SW0\npWVZqhpRghrDMCiVSkowL8G93+9nYmJCzYFIJKL0hfIckUhEzdFer6c8vOR+S3ImrbrEwiUWi6nk\nSxzupRpR0Ld6va5QJXmOeDx+0/1w6seEmpNCgEKhoKoXBwYGlBWL0IuAQoxlra2vryv9GMCzzz6r\nHPkHBwcV5Qgo41teUVMAACAASURBVFRnJagUqhiGoRzxg8Gg0pg6jWDlmjkBn7+rcVsEXfL1Xv/f\n/ZhQHEKbCA9cr9cVbLmXn1f/BliqT7JlWZjtJlrbwLZ7hL0eNF3DaLUJe4GuBT0du9ejWCzTaLUo\nVWvYuguP7sYyu7h8YfRul0g4QNWo0utZ/OI//nmMdgOPW8frc+PRPBitNj6vFz0cYWttlWAgTKVQ\n4K677kLTXXT1JkOTU2xfu443EiM1OUkHGBobpVCponl1ei0wuwY9rcf1hSscO3aMZqVCJBigVq1Q\nKRf71MRAok9t5PMMj4xw6fJlHjx2jEOHD3NlYQGvx8PBgwe5du0azWaTO++6s8+T2zbdro2m9TMU\no2NidjtUaw1s22Z4bLTvkJ1Ksbq6SqVeIxgMMjt3ABcawZ1y4+eefZ63P/YoXr8Pq2dzfWkRo2My\nOjqK23vDdbsHmB2LQjGD7tXROj3SoyO4NB1d05iZneWxd76Dcq1Jo1ajXq0SD4f4wC98hFaroeiQ\nxwN+iuUK3/rqV3nkXe/h7NmzLC4uUiqWmF/eJhTyUuvYCnnotNp4fD62twtYmkbN6OLSbg9qEd68\nynD3z5x0l5OikCxXxOtOF+29khgZcvBKZiuUiQhaJdM3DEO5zpdKJXUIyc/kvWUyGdxuN4888og6\nzA3DULoPQaTEANSyLObm5tSmPjEx0af1JyeVYWI4HKbVaqlm0fK6mUyG0dFRRVu2221KpRLxeFy1\nDRLtSaFQUM7cUmWZTCYpl8sUi0Wmp6eV87YcuIZhKENJ+TsREwtyIBVl4osXi8X6UoJ8niNHjqgm\n1hJkhsNhdWBJVi40K/T3sXQ6TTqd5sSJE4pqEjRF2rzIPXO73bTbbRqNBqZpcu3aNfbt28fa2hpb\nW1vk83mlG5ODWbyO5KAXqkVkG7fD+FHRhd0BlgQPtyou2eu1hAIUak5oQachpxRIiJYJUOug3W6z\ntbUF9MXl8/PzXL58mStXrvDEE0+oNSP6I5lfghwJeildCWzbVpWGzqpi+ScIp6B18rySBAiFJ4he\nLBYDUHNIaHL5rE60XCoZJSGS4Ej2dDFilcBMEhDZN+R6CIUaj8cZGxtjenpaoXiChMnaFomDbdvM\nzs6q91UoFLh+/boylzUMg+3tbYXWy1qBG4morve7rGiaRigUIhgMEolEGBsbY25ujkAgoHSYcq0k\n1ti9//4447YMunZnH87HhL+WzU4mgfjqSIPQvaq8NJcG9PDpHjweF7Vqkcz164Q8HnwjI+g+D96w\nH53+69gaeL1+rC5sZwpcW15jcGSYwtY2IY+/j474PFTqJTy6zWMPP8yx2Wna9QpaDzwuL0GfH6NW\no9ezqJYraJpGYjBJMBwhlhrpH0I2dHUP++64k4pl4Y4n0IEWPUKJBM16jVq9QnJokK7ZIbPQplku\nEggEWFpc6le3hPv9uJYWV/p+SrF+tZfm9pArlnBrOrYNQ6lhquUK6dQIY2NjvPr6a8ovyB/2kx4d\nwbL7uphao65Qi+XlZUKhEJ1ul3AsytjkhKI0SpW+wFnTNO5724OkRseo1WoMDo/w3Asv8uSTT/LM\n17/Rz76CIY4fP45tdhRMLK1aqo0+fZiIxlhaXSUUClJrtImFI3hCYdqAprlwRRIMJ4dpNpvg1hk/\nHOKOe+6l2+3yq7/5W7QbTSrlIpcvX+YbX/s6r79ymla3zeLqOh6vF83twhMM07N7eDwm7Y6Npf34\n/it/V2P3/N+daTkDLqdORTZbOTQkMJFs+FaogHMICqRpmjoAnAGdM+FpNBrk83n1OvJzQaB6vR6z\ns7N8/OMfV3SD6ClkY240GmQyGRVgSUm7WDCk02kVbMkh4mxy7Dx44IY+w+v1kk6n8Xg8FAoFEomE\nCvIGBwdpNBrKFkL0UIJmFQoFRdOKMaMEYJJJN5tNRe9IEDM5Oakq/uTAEKROWr3U63UymYxytxfD\nS6FgAIVYCOImh5wgHHJvnK2AnHNEHn/7299OuVxmY2ODTCZDsVjkjTfe4NVXX1VierEdEJ8kQXJu\np/HDBlxOYb3z2si5IQHEXoje7uo0YVJk3kqLJmcFvaBUYh8igZczsen1eqq68NKlS+qeOisrJTkS\ndE5Qn0QioR6Tee98HekHKkUVQtM7pQhCsYoezUnxOddSKBRSj/t8PiXKHxkZUWtLpASlUkklaH6/\nH8MwVDCmaX1PMkGw5f3FYjGFosu6kQIW6b8oVJ/T6Fc+U6/XU559c3NzN3VUcK47CWZFyys6Skms\npDhFKNGHH36YQ4cOMTw8rPzvxEBWgva/q8DrLQ26ZOyFbu3+mQzn4SGbq3wvZdV7BVzyt/QsdMDl\n9uBxaYSDIRqlEuVikcF0kqDPT8cy8bn6GZLf61eRd6FQIBgOUujZdPUeZtNA97kIRCLsH0nx+//H\n/4m9UwJXazTROjYts0U0HGZhYZ5oJIxtQblcZmJqirWNLQaHkmhuH5FYDI8GVs+mbXbx+Lx0DQPD\naEG3i9YDv9vFC985yV2z+9jYWGdqeoZqpcRar0ej0cRT7sOk6XQat7+/uJ566ikuXbrE0MCgKiuf\nn5/n3nvv5dKlS0xNTak2J/VWk0wmRygcJpVKqYw9Go0SjOzw/RsbKisRyNY1Nt73YfJ4OHznPeRy\nOdLDw3zy059idHSU8xcvEA6HOX78OB6Ph0a1RrttEo9HWb6+qPh3v9eP2ekvDGl8GgiY+DxeIuEg\nhVyermXR0zR63Q5evw+Pz0smnyPg9uD1eFhdW+ujJl2buYMH8Xm8XHjjLKFQiEg4TKXVolSpEUnE\nsWwLXXfBbUMu3txc2llZ6PzZbprQuRk4/0bTNKX3cWpUnMOpXZAqJaH9xBZE+sLJWjty5AjHjh3j\n4sWLXLx4kf379ytasdPpKJH6U089xW/+5m8Si8Wo1+tUq1XVVkf8faSwRJIoOQij0aii82TTBhRN\nWK1WVbXUyZMnabfbrK6u0mq1GBgYUL+naRqTk5OKdhE0yjRN6vUbSYU4XzcaDbLZbD85SiSUYN/j\n8VCtVpVJpVBB0vpEDhBB8FqtFhsbG6r3nayXpaUlVUUpppW2bSt0S+67XBPRV8m1cwasghoCKvl0\n6pgk8B0bG1Ol+e9973tZXV3lhRde4Ny5c6yvr6vPJUP209sJ6XIiUM69fa/36Ayy4MYcl+viFHg7\n14Mz4JJ1JQiTVOvJ/Xa+hmmabG5uKo3rsWPH6Ha7lEol9XuFQoGFhQXW19eZnZ3lkUcewePxqMo7\nCeikilTQI6HBxELBidI5kSCZg85OEUNDQ4TDYfUepdeneGAJRSlUmhSSyXtwu91MTU0pXzEnkqfr\nOgMDA8ohv1KpKLRMkFZN00ilUkreIO85HA4TiUSU+evGxoYKGEXYHw6Hb9KMud1u1fRbEPN6va6Q\nYhHACxPmlENIIOq0jZBAU9oinT59mmeeeQa32834+LjqU+mcU7sLlX7U4frEJz7xYz/JjzpWFxc+\noesauq6h6aBpfZ2V+ufaeVzvP65p3PRP30GuvF4PltVFd2n4/T5cbp1O10TTwONxAz08HjcWGm5d\nx+52adSrmM0GcX8Av8uFZbRot1vEB2OYLheaW8cfCEHXZnp8kr/99rdZXFni4NFDjE9MYHUM4pEE\nI6kkE8NpXn/9DBF/AGzoWdButPG6fGS2s5itKvUdbjw5OEihVCYUiWDbPRpmh3h6mGqzRSAYoqe7\nGRxM0m4buHQXmmVTyedxWxa59Q0CLg/f+vrX+cpXvkK72WB5eRU0jUajyb3338fMzCwDyUFOv/oK\nrVaLzc1NVhaXGBkZYWH+CmOjfZrw7Nmz1KtVYok4Z8+e7TcHLpcJh0M8+81n+faLL/YP7E6HYqFA\nrVFXPe+GBpOUdxZus9mkZ9sMJZN93Ui1xeBAkm89/zwf+Sf/hFarzfjYBKFQGNPscOqllzHNDiOp\nITqmyb6padYWl8lubbN8fZFOy6TVaoMNWq9HvVal2+1gtlqUS2Usu+9v1N3RB1h2F7Pdxu0L0DI7\noOsEgiHaZpdYPEEkluCuu+/m3vse4Mwb5wlGEjTabQaH0gTCIQrlEj1srF7fgPd3fud3/s1btiCA\n9fX1TzgDKec/Z6UW7E3JO7NaZ9m5M7iS53J63AnlVSwWVRWTBApCmYlHj9vtVn3l1tbWSKVSqvpP\nHKpPnDjBb/zGbygxbqVSUVlwq9VStKJoT6T9jqDWopGSgEUOP9lMi8XiTX5Sy8vLrK+vq0qrwcFB\nEomEEtzrus6lS5cUfbexsUE8HiccDnP48GEAstkstVqNgYEBKpWKQqeazSavvfaaan0iVWGiOQMU\nJSS0kqAKciD4fD7W19dJp9Osra0pzZnQSKIvEdRCAqK1tTU6nQ5ut1vdAyf9l8/nKRaL6uCUwLfR\naNwkXHbaRoTDYcbHxxkfH8ftdlOr1VQDZAninFV7v/3bv/2WrokXX3zxEz/M7+9eD3Bz+b8ziJP1\ncKvDNBgMqopSl8vVR9fhJkRZaLN8Pk8ul0PXdebm5gAU/SVrK51Oc/z4cXWgOwECp9WBE6WS33HS\n0E4PKk3TFAorNKPM83w+T6FQYHt7m7W1NUzTVB0ZZmdnVRGABEzlcplyuay6M4jOq1QqUSwWqdVq\ndDod1YB7Y2NDeYCJCD0QCCi7CtGayXsXnaOcJYDSTYp9hnPNSPWhBGnNZpNKpUK9Xr+JHnbSoM7i\nIqFZnfsp3PBck8IACdjk82ta35ZDkDcnymdZFk8++eSPvCZuC3rxVoeHjgOtkq97jt/b+U8mv2wU\nmqapjuUSsXe7XXSvF92Cbq+LS9PJbmewSiXmpqepteoEvB6yW5tMHklhNpp0XT4ioShH7zvOP//n\nv8qXvvLXvPLii7hcLo4dPoQ/lGR1cYmXnj9LJZvF5XaztblJyO0l4Q+yXcwTHoxSWFtSmhXbttk3\nOUW700Vzu2g1W5idDq2dAyQciVGvVAl4+kiAp+diZeE6x48d5fmnn2ZoYJDZ2QM0m20KhRIf/NCH\naTRbZHJZPve5z6HpblKpFOOjYxiGwcTwKOPpEWq1Gu94xzv48z//c8I7XHYul6Nz4RyJRKy/aXct\nslvbvO3++wiFQvR0ja3NTaanp1m4fo1wIIjb52VjfZ3mjki52WySz+U4PHewjyTUKqwYLYaSA/yH\nP/ljzJ2GptNTU+RzefSezWunT7E0fwlN09i/fz/hQJDBaISH7r+PUqlEuVJhe2UFfWKcYqmiWtA0\nWh1GR/tOyrl8Cbenv1Bcbjdb23k8bjcej4tcoULA66NQrPaF+1MHGJ9x8b//2/2sXL/Gn/z7P+ba\n8hL+YIBoLEYkHuubbUbjf4+zf+/x/dbEXgju7q93D9F8Ceol6JFzw5fvC4UC5XJZNab1er00Go3+\nfHBQmLOzs7zjHe8gm81y9uxZ5XHn9/uJx+N89KMfJZ1OK+2LBHdSZSQIl1BmEswFAgGlpxKE2ePx\nKLpBslOpKMzlcmxvbyshb6fTIZVKKR3YlStXVLubeDyOpmlEo1FCoRCBQEC1WXG+jqDFEvhpmqba\nrEjxjlwX27ZV4CWZuaAJ4rRtWZbyHTp16pQq9pGAUoIlQTLkOkvlVzAYVAJksdiQwM+JijUajZs0\nS1KNJfSoBA2xWIyhoSHi8bjqTbm0tESxWFReR9I30GmG+1aNvdCsvVCvN/v93WtHPpfTe0kOaLn+\nvV6ParWqKk9FsC2Biri027ZNLBYjlUpx5coVvvvd7zIyMsLIyAipVIp2u83i4iKxWIzDhw8zPj5O\nIBBQNivOHqSyVgGFxkkQAzf0U7utDJz+bLquqzZOTnTv4MGDyiRYdNASiImVQjwe5/HHHycYDLK5\nuUkmk1E0YbvdVomRs0WUCO9FYyiIrMxFoesEwZa9SBKmQqHAxsaGCsZ8Pl//XAiHVSBoGIbqvWrb\nthLAC/0o6KB8v9tLTeaFM3GUPa/T6TA9PU08Hmd4eJilpSWWlpZIJBIcPHiQaDR6Ewrp9AX9UcZb\nvqLeTL+lcetDxfl3TmsJQIlMJYqVDbunabjdLixNp+fWiUYiZLPbmG2D7NY23W2Lux86Qb1cwucN\noNkWPSx0r4ef/bkPcOjgHJ/788/w0snvUFhdYaV0gV/4mQ/QblRxBfyqiiWS8GMYbdpGi4i/L24s\nlYsMxBOYptmvWvF50XQXiyurJJJD/UjbNNC1flVmo9HE5/FimB0CPg9bmxs8+vAjXF2Yp1qtMjE5\njaZpPPPMM1RrdayezdS+WbazmT4s7feDbRPdER7Ho1H+2Uc/yuDgIL/8y7+sfJf27d9HIBBgc3NT\nbc7rG1uKX49Hwnh0jTuP3aF6ZlXqNcLhMLlslkajQTKZZH5+nrGRUZ7/zilOPPQglUqFe++9l6ee\neoqzZ8+ytbHZbwdz9g0SiQTZbJb3ve99bG9ssri5hWVZrK+ukc3nmJyc7OttLHsH3g6zuLhIJBih\nWOhnIfFYjHK5iE4Ps20QCfSrJ+v1OvF4nLxh7lS9tfG4vGiaRWpomPTgAGsb7+PTf/YZ6s06+UKV\nUrXM4GCStbW1v4cZ/4ONW66JPdbB7t+Dm9u/yMEi2bFzTcCNrF2CENlopaXIxMSE0jbEYjFVBv/E\nE08wOjrK7//+7ytbBF3X+d3f/V2V6YtlgWzWTiTI2c4mHo+rn+VyOVKplPodoTUE3alUKjQaDRqN\nhnruxcVFdTDmcjk2NzcxTVMV1wwODqp+bSLGz2QyfP3rXyeRSHD06FFFx05PT+Pz+dRzSyAkuhF5\nXjm4JMDxer1kMhkVwInwXjRrhmFwxx13EIlEFB1jmiYvvvgilmWp9yFeWXK4rK6uEo/HFbXi1N04\nNTaCBIhmR66ViOflNQRNE93bT//0T3P9+nVOnjxJs9mk0Wio6nChPd/K4aQWnY/9KMNJcznpeqFn\nnUhyr9dT1Z1CJ8t9dtJagjTF43ESiQRra2t85jOf4cSJExw4cEBd0yNHjii6TgIm5+uJNswp3JbX\ncWqbBAmT9S4oqNCIEkgIRScWCjI/r169SrVaxTRNUqkUsVhMoUoDAwN0Oh3OnDmj5q/L5SKdTqvA\nz9leSAJA5+/KtZTrLY3gdV3vF1S5bzR3r9frtNttIpGISoZCoZCiYwFCoZC65rIOJLAEVE9YCZol\n8ZG1IfuSIGDy/mQ/9Pv9DA0NEY1GGRgYYGhoiPn5eS5duoTb7ebQoUPKTkOQzh9naG8lb3/ym1/t\n/cBI15sEXbrbpSB4pybGqQdpt9uYuk4s4P//yHvvKLnO88zzdyvnXNUZjQYaaDQiCQaAYpJImaRk\nyyNLIx/NWpZs7Zn1mTmrczb42BqvPZ7VeO21xqPxjK21Ja81HlnBkilZpKhAUmICwYjQQAPonLur\nuit05Zz2j9vvh4sWyLFlz0A+e3l4ELqqUV11v+973ud9nufF3GxRKhXIp5Ocf/557B04dfIE/nCI\nmZV5jh65g1Kpgs0XwN8Twz+8B61aw1prYak2WJ66xp/86R/x8V/7NQb3DmNyOEmmsjRabQKhIJVK\nGZPWRWvWadbqmG1WHDYriY04/T16PlWj3cJqs5PK5fAGA1gsNsJBL822hsPppN3usry4hN1soppJ\nk89k+PY3/5o9g0OY7G7W47q2auzwOEeOH8PhcJDezrGVSurgaadlkc1mKZZKTE5O8o53vINQJMz5\n8+exWq2cO3eO0/feg8/n4+WXX+aee96hbMXG0SOlYoVUPq9CHCOxKMvLyxwcP4TTZufs2bNkM9sk\nEgm8wTCnT5/G7fVw9uxZtlIpDhw4wPS1a7RaLU7ffYrbjh3nz/7iz3E79Rwin8tNNpvlwrkLjI+P\nY7VaOXXPaebn52nUWzssRYhILEqj0SAajbK8vEg4HKR/oI+rVydJpvVoApPJrNjNcrlKuVLZmUum\nJzvbbSbyxQJ/8aUv8qWvfIX2Tusakxm7w0U2m7+lavrXXnutuxtg3axdsptW3/0YqeqESTFe8nfG\noOFuV8+K2traUgOYBQDIbLdGo8Hw8DDhcFiB9kajweTkJF/5yleYm5sjFArxuc99TulKisWimr8o\nM/80TVOtLrHa+3w+tYnKbEUxeABK+FwsFrl48SK5XE7FIyQSCebm5ujp6VHieDlIZAyPy+UilUqp\nQ6jZbLKxsYHFYuHkyZNq8y8UCthsNnK5HK1Wi8OHD+Pz+bDb7Xi9XtWGEfu7gBcBtrJ+KpUKm5ub\nSsQv44Wy2SxbW1u0Wi0lMhYXV61WI5lMsr29rZxawgpIeyWTyaBpmnpfxTgkwntxnu3+Xw4bYfFs\nNpsa/9LpdFhcXOSzn/0sr7zyijpIBWRks9lbuiY+9alP/YMdUsZ24+5IAAFdxkgIadGK008+O2Hf\njc7AcrlMoVBgc3NTZXJ5PB4VzDs2NqbYXRGgy9oU9lPYLflc5bXIIHRAgWhpnxkT46WNVy6XmZ2d\nVWN8Wq2WSqGXcVY+n0/lvcl7ommaAvlDQ0MKzAhL3Gq1KJVKxONx5d6U9yAajaqEeGHPJEdLnLh7\n9uxRbU8B9hLAKp9BpVLh8uXL2O129u7dq15jPB5X0gYpMEQ+IfexgCwp7ASEiWRCHlculxVr5XK5\nlLFFGONcLscrr7yCpmns27dPZfsJsP2N3/iNH3tN3HKmS66bHSz/tcfKJYvFeBMYg8ykMk3uoHu3\n2YLFpKdvR4IhWhU9mVmz6om5dquNprkJ3Z1U524Lq6ZhcdpJTF5jcXqa5Zl5Ah4ndquZlUQCjyeA\nvd2lWq7QNWtYbWZM3RYb8TiBnhjNlh5ZkErrLILb5WVjfY1Dx49RqFSVMNbh0l1a1WqFdrfDyy+/\nQp/fx8U338Tv9+PxeFjc2OQTn/gE1UadRrNJOBxWP2MgpLNpS9PTVKtVent7uXNwkP3792Oz2fir\nr3+NYDBIoVDg3Y8+wrnXX8fr9RINhYlGIzSbLVVJS+RAvVFlZO9etjY3aXc6WGxW+vr68Hu8yoKc\nTqcxaRof+MAHeO6558hkMjzy2KN4vV4uXLjABz/4QQb6+slltsnlckQiEUqlEleuXGH8wEGOHz/O\nQF8/b755jkgkwg9/+EN9VEpum9On3sHK2irx9Q0VQxAIepidmcLptDF6YB+xUJCrlyYIhiOMj48z\nOzuL1aIL8BPxOIFAgJ5YRIVyPvDAA3Q1+PLXv0K93sXmQB36t/LazWrdDEztvoxORmO7EK633I2t\nQSPtLzoSqZa9Xi+Dg4Nks1mcTqea+yZjRYwUu4DztbU1enp68Pv9PProo8TjcQV0RMwuDAygxLLS\nYpB4CWndRSIRJYQX8NhsNsnn8+TzeRYXFwG4du2aEtS/733vUxujJFSLNV023qWlJdbX11WlLHEM\n8l55PB7q9TorKyt0u119vaZSKp27Wq0SCASUdEHYQgl5tVqtKnZD9FYyAy+bzapB21KtyyFYKBSU\ndkv0aMIeyAH53e9+V+lsPB4PlUqFcDistFjSvkmlUipiQGb/SWZUNBpVB7JojERLFovF+MQnPsFD\nDz2kAHQ2m/1veKf/7a+/63nwVu1Iad/BdSea8blybhjF2HJ/tVotNWbKbDarvCy5lySuIRQKMTY2\nxsMPP8zExATLy8uMjIxw+PBhxZzJazCC4UKhgKZpeL1epcGUokZahsK4CaMjjxOg4Pf7laFEsrCE\n7bHZbJw4cUKxVcZcMGnrS8tTigspwoxTDqRdPTg4qByS0iacnJwEUKyZzWZjZGQEi0Wfhbu6usrM\nzAw9PT309vYq4LSysqJAkZhTTp48SaFQIB6PMz8/fwOLJQYHMchIikG9XiedTisg1e12FRiWe0Dk\nAy6XS00NSCaTqgiU1+31ernzzjtZXl5mYmKCyclJRkdH1RD7v8/1E6Hpequv0e0YDh1u+L3xV7P1\nuoNRfpVqU1whzWYTn9VBt9Ol2mnhD8XotjsUMlksJggPDuB1O/G6XUytrRALxbCbNeqFEh5/AIfb\nRXEjzvef/x6VYpb/5df/JdvNJtm1NXoHBsgXq7jdbrSOvoHXazU6zS79o6O0G03ajSazy3OYunqQ\naygaoVSrEU9s4Q9H8ASC1Ept7B0blVKNerGIpd3mwVN38Qe/+3u4XS58bhcer58Dh3sotTU8oRg2\ns0bXbieTj5NMJHHa7DTLVZ747tPccccdlPJXmZ65xi/+wke4cuUKI8N7WVhYwOaw47TZePChdymH\nzNraOpFIhMnJSUJ+vdLp6enD7/GDw8HogQNMTk6idWE7nWF1WdcDPPTOhzGZ9OyiV868TLvR5MTR\nY1ydnOTUqVMcGR9nfmGWixfeJBQKMTAwwF0nbmdpaYn4ZoL7Hrhfz7CZnSESi9Ab62F5YZGn/uZb\nnLrrFOvLK9RrVfYOD5PNpLFYzAwNDmMx2xgfH8dis7KxscbBw8d1tqJcI1essf/gELVqg749AyST\nSeKZLFisWMwae4b38b5AgFQqxSuvvEK5VsPc/YcNwftxrpsdIEY2a7eQ/mbMF1zXrMj3kK/J5iTt\nCKkIhSWRTdTpdKoRG6L1kkpQ0uOlhZZMJhkfHycUCinXqYhmpYKUSh4gFArR6XTI5XLqUBEXl7gJ\nBZjI65YWhei/Lly4QK1WUyNzJHNHRoeIVkQq4XZbH/h87NgxarUa09PT+P1+Njc3VdXr8Xjw+XyM\njIyoQcHClG9vb6uZh+JUlLaPgEhjTEQ0GuX2228nlUop8Ak6sA+FQpTLZZ2d3onckCiIgYEBSqUS\n+/fvx+l0srKyoruBe3oUgHM4HMRiMdWSWV1dVREYojuSz11cm7Vaje3tbfUYidUxzqjz+XwcOXKE\nD37wg3zjG9/g6tWr/7A39495GUHU7jPj7xrkanQdyvOEqZEWrLE1L2eHgGm4zroK4xSLxQgGgyp3\nrdPp8Prrr9+QG5XL5W6INgEUkDDmiBnbnMbCS/7eWDRJbEu7rY/JSafTZLNZEokEiURCrUcR1wsj\nJz9fs9lkfTDorQAAIABJREFUcXFRgUcB9ZIobzKZ1OQGcRyLLlAKE8nbEuZP1qgMgU8mk8pMYLPZ\n8Pv9KuhUmGiJkfD7/UoOsLW1pdqawgDKZy3gKJPJsLq6isPhoLe3V0W+SHEmwKqvr0+xitVqlUQi\noQZsi0NfWvICsDVNH4w9MDBAp9MhHo+TzWZv6Cr8uNctbS++8sPvq1bKzdwlJv7r4amapoHpR98E\nyc8x5ptYrHbo7FQ73TYhn1fP0Go3cFlN+Dxeuu0WNfQ3v1LWh2FPXblGyO2h02xw9Ohh1uNr7Bsb\npWnT0XSzq6GZ9EOuXK3T5fo8R5vNRiG7jcvlIr2ZoJTNE+uJMjszh9Vu4+htt1Oq1giFw5hcftZX\nlvF6PFi7HSyaiX/5P/1zHnn3O8lms3zw/e/XrciajYHhPTTbLVx+N+vr60SCQWxmC2Y0WpUa7aqu\nC5m6epXeWA9LS0u8455TbG5t6SMg6PLkk0/wTz7wc+RyOSqVCg8++E7y+TyRSIRMJsPa2hrzM/OY\nTCaGDhxQA3GbzTYn77wDTdMNC4V8kTZ6Jbm8vIzP52N7e5vh4WG2tjapVCp85Utfor+/l1OnTrF3\n717WVzdYXl7Garfxrne9i9nZWYaGhui2O2xtbXHl8hXe9cCDvPzSGeLxuF553H0XY2NjdDSYmZlR\nw3sDoSDxRIL3vvdnWE/EdUo/vkmsr5d8oUStUVfW+Q4m7FYzFgsUijmSyS3OnDnDl//qr6i3odPt\n3tJWyptvvtmFG6NRjOzV7kPnZsALfjQaYvfXRZBr1DkIgyMbuTiKjAJxEbZKXpZsXDabjVgsdkO7\nTcCWsGnGzUx0SyL2FkF6JpNRIEoqWqlURQP2J3/yJ7jdbkqlksrW6e3tJRKJKIlBPp8HUJuqALr1\n9XXV+kin02owtjxvYWEBj8dDMpnE5XIxOjpKNBpVKeOpVIpKpaIEudLi6HQ6jI2NqfDFarVKOp2m\nXC5Tq9XQNA2/308wGKTT6bC6usqFCxdUxS7veaPRUKJ9GSmUz+d54YUXVOTKxMQExWKREydO0NPT\now4LYZ3Frh8Khejr67shOV3ykkQULQBZgKkwHE888QRf+tKX5Hk/0e3Fv80ZZixYjNEQxj8bxdVG\n3ZeI0uWeN97TJpM+LicYDCqzSbFY5MqVKwCKeRUwIRosAU27Bd+iGzTGUkigqNzLRteiAOh4PM7m\n5ia5XO4Ggbvf779hVJS0L8UJKw5iaevJbEe55PUZg0vlnpHWq3SYjKybuBQF8AtAlVZpt6vnbgWD\nQUZGRvD5fGrvkUJHSBMBc6JPLBaLZDIZYrEYHo+H9fV1MpkMXq+XPXv20O12VVvVbrcTDAZVkSnz\nJqUNKq9TDA2iH5UWsDwnkUiQy+XYv38/breb3//93/+x18QtBV2vPvd09620WpqmQaf7twJdmvlH\n2yvADVRso9Gg0dTpVLNmolatEg74adXL1CsVBmIxTHSwmDUK1Rb1ln7jW7oapcQWQbcunM1VC1Sb\nDaxOBz0De8jmc9idHlpt/X2s1hs7YmU7zbq+gbmcdjY2Njgwspel+QUyqTT79+9neXWFh979CKvr\nGwwNDbGeL+N1OmjV6qwuLTI/M8NAT4xzb77O6Og+9uzZg8/nY+jEHZQqZRxOJ4VqEYfDQT6XpZov\nkklu4bE5mJmcxLtj262UyvT0RHnq29/GYrWyurpKKBLmHe+4h/MTF+nr62PPnj1MTl7BZDKxvLxM\nX1+frq+y6BqB7k7IYzAYJplOMT8/T//g0PUYgFpDuboy2ynW1tZIp9NsxhN4vR6Gh4a4fHmC119/\nnXvvvZd6Vc+NCUcjHDt2jAMHDrCxsYHT7sRqtfK//6//G5/5d3/A+Nghpqen0TSN6dkZdTBoFr0y\nlTDK2+84SbFUIZvNMrR3GK8vQKVeo39ggHpTH/FQKJcY7OvfodELtNoNGo06Gxsb/N6nP02ra2Zt\nY/2WHjDnzp3rwlsL6XdXWjdrQcJ10GX8O6OOBbhBYCqHgIh8m80moVBIPV/e906nQzqdVroW2XSl\nPSnuOqNGQr4ur0EOEXHwiTA3FovRarUYHBykWCzi9/sVuyAOqq2tLQqFgmJtBKAMDg6qNowwOALo\n8vm87nhNpVR6vBygly9f1nWLpZIaAyRmDBHTl0olWq2WYjPkwHG73USjUaxWK7lcjs3NTQYHB1XY\noyTHC1hNpVKkUindKbsjxE+lUmxtbXHgwAGVjeZ2u3nggQfU5q9pGhcuXODSpUvcd999HD58mK2t\nLYrF4g1uRWHkBHRJG6dYLBKJRHC73bq0YQcciPYmHA7j9/sV69Vut5mamuJ3f/d3pcr/iQBdu7sZ\ncKNGy/jn3ddbga7d4EuYX/mzgA3JnjJGrxgZKePvjTEs0mYXJ6wwxLKWjcy1JKcL86Rp+sxBCdMV\nBkqAgaypUqnE9I6cROIcvF7vDQJ5YYtFRyWscLFYVLMdxQ0rrmMZii4gqtPp4NyZPCIjf2QSgxhU\nhNEWgCbzUY1tdym8ZI0VCgVAHzlUqVTULEnRcApjLvIDaRmurKxw9OhRBgcHSSQSpFIphoeHVYtW\nojqEoQJuKAql2JRiTvYacWBKpIvo2NbX1ymXy/T19fGFL3zhHyfoeu35ZxTTdTNdisb1av1mFb78\n2mo31PcwHk67c43MVgvVio70bRYruWyWvUODtFsN7GYTGl2cNjttk4Vas0anWcNttWIrVllaWCQQ\ni1E1mbG73RTKFbSGfiMFIxFsdif1epNGWz/g9UNEFwMvzs7gdDrxer1cvHiR/fv302p2OHbiOPGt\nJG6PB7fbTbZa49rFS4RDQdrVOpFgiMuXLnLy5G1Ah2pd15SUW1YqtTKlaoVgOEizUSMSDLCdSlPK\nZUkntrh4aYJoNMptx08wNDTEd7/zHTUdPtoT5ZVXX2Xv3r3YXU7VeikUigwMDChXTk9PD1ubKX2g\nsdmM1apnDbk8bk7eeRfZfA63y0vHrNFu6RuZy+3gpZde4rkf/JBPfvKTHNw/yl999csszM1y6dIl\nfvljH8XpsnP+3EXuvPNuXQ9QrzE/P8/HPvpLPP/881gtFj784Q/zoQ/8PAGfG7PZzNTUFMePH1dC\nycOHDxMI+pQVPxCKMrJ/H5FolAsXJsjkc9x7771sZbbRzCasVjvhWJRaoYDb4yEY8mO3W3nuheep\nNuq02m3+7M//C+vxjVt6wJw/f14xXTcrMN4OdMlzjFX6W60tY9tSqlBhvwQQyIgt+R4CoIQpkgNC\n3FFyIFUqFaLRKJqmqTwdmW0m2TjCXsmhIk49CfGUNgJAPp9XoabNZpPl5WVV5coBIpWqTKUQwXCz\nqQ8i3tgJ9RUmRwDV2traDaLySqWiDrd8Pn+D3d3tdit2cGtrC7vdrhL0RUNiMpmIRqM0m02SyaQ6\nIObn52m329x9990qSyuXy7G0tITD4WB7e5tYLKbaKXJYDw4OksvlGB4e5oUXXuDFF1/k8OHDxGIx\nstmsaqd4dvYQOZikGJJRRNlsFq/Xq+bxeb1edci3222V/C8HTDqd5oknnmBlZYVXX331loMuY4yD\n8frbnl/G8+GtQNfuxwrbLODHGEYrbUi4bkyRVpyxHS9uPGOwqTBpu+cDSvtPQJUEgMo9Ka9NYhea\nzaZyRgIqwsTn8xEIBAiHw2pddDodFQEiRITZbGbPnj1omqaKATGAyJqWGafb29sqe8tmsxGPx9W/\nK+1LYcBEY9hsNtnc3FQREgJU5X2X1xCPx5WDUfSY7XZbTaEQwNfb24vD4VBhrLOzs+RyOY4cOcLh\nw4ex2+1EIpEbPsd6vc7q6qpiCsUc0m7r01CksMvn8+r9lABX4zxVgEKhwNzcHL29vXzmM5/5Rwq6\nnvtB1+g2hN3V+nVdytuBrna7dcOikt8bc7sAOlobk2a5TmGaLcSiYVx2B9ViAYtZ0xN8MdE1damW\n8jjpsjY5RbFSxh+LYg6EcDpcNGttmrU8DruLbhdyhZLuTsoXdnrVukV3bm6OvmiEiYkJjp44zje+\n+S3e+9734vJ78bh9dOjiD+nC9kKhgNfholGvUy2U6LbabKyvE4no+han20mxWKTeMFOqlLA67FRr\nZUb2DJHPbbM4O4vNYiXgcfOFL3+F1FaS8cOHGOwf4P7771d08MuvnKVQLHLXXXfhCwb0rK10mnPn\nzjM+Pk44HGbfvn1sZ7M0m/qGXGu1abf1ga2a2cTk1WscGj9CtpBneO8+4pt6hV6v64AzGolw5sWX\n9MA7q4W9e4ZIpVKceek5ut0u/X2DXLs2TSAQ4M6778JisXD+4gT/xyf/FU8//SwDff0U8wWmrl5m\ndHSUmZkZrly5Qr2hU9NmzYTH4+H06dNcunSJfLnC0NAeLDYrw3v38sKZl9FMJt717ocxW2x0Nb2S\nyW9n6emJkt7WoynS2xlWN9bx+gL82id/g3a3c0sPmAsXLtzgXrwZiwU3JtfvBlXGw8V4Gatyo7he\nntdoNHRdoqYLegUIwfWqv1KpsLCwwMWLFzl06BCRSASPx6MeI04lmX0mVT6gXEOigTJa8g8cOKAG\nAMtrNZvNbG9vqxlw9XpdVdYilpWNuVAoKGeY6DWkxeN2u1WI6MrKikrM37tXn/m2ubmpxp0Eg0Hu\nvvtulauXTqcVoyrCWzk8pEKXYFgJUpRJDcaEfdEGTU5OYrFYWF9fV6Gm58+fVwertEm73S7Dw8Pq\nubfddpsyHmxsbDA1NUW32+XVV19VLlCbzaZarSaTHj4reWG9vb0EAgH1/skBFwwG1QzNTCYDXM98\nmpyc5NlnnyWVSt1y0CW/fysma/e1G1QZZSbGy1jc7P6asX0mbbzda9JsNjM0NITNZiOZTJJIJJSL\nUJ4vBSygmBX5nqJPEpAiDKkI2EVnKC0wcSeKCF9aYslk8gadmoTzCmPl9XoZGRlRZhYBkevr6yoy\nQearykxDGQCtaRrRaBRAsccCsoLBoHJOCtCUIstkMuH3+29goDOZDMViUcW9FItFDh48qAC/5OdJ\nPp3L5VJRRqlUSrHWVqtVtX03NjZUoGsmk8FutyudXSgUUpl9wtAPDAzg9XoVCy7soNz30moVrbMU\nialUCotFn2/6iU984h+ne/FmFbyRJpYbyLgojItEDhuhYnczXXJzSM9cELNuK9dw2Ox0Orqbym6z\nYDJbabZbeFxuMpk0bjOcee6HDPVG6dnXj9nloWPSKwef20m+U2EzsYbd5ibgD9CsVYnuBMVl89vE\n8znmZqfplofottpMXbnKL/7SxwiEIxSrFTCbyBeKNCtlaq0mxWyassmC0+5AM5vYTG7ROzhALp/G\nZ7disduobdfwOfxobRNDQ3089/wPsbVqxNdXWV1coica4c3ZOXxeF6HgQRIbcTqtNn/913/Nww8/\njMfjIRwOc+r0aRxuF9l0hmatjtPpJBaLsXfvXqKxGI1Gg4sXL3LkyDGmpqawOHQn1Oz8DPVmm6Wl\nZWYXF7Ba7Wym0hQrZQ4fPkog5Mfj92G12skXSvqN6/bwl1/8EivLS9x91+1MTl7ihedepAsEAiH2\nje5nbWODe++9j689/g1Sm1ucOnUPW4lNHn7kpzh79iyaxcwvfPQXuTxxgXa7zYVz5ykUCrz+us7Y\nNTfiRMMBzk9cZGFujuH9I/T397O1sU65WsHn81EoVTg2foiJiQma3Q4zMzPYnQ5qzQZzS8t4/b7/\nvgvgH+DavUZk3RhBm9E6bdysjVV3d8fgIdWq6JYkc0p+f+3aNRYXFzl8+LBiR4T9AR2AbG5uKpGt\nsADCVFWrVb3lvuOaAhgZ0T+rcrmsdGLSkshkMgrstVotEomEcjhJ0ry0KSWk0mQyKRZoe3v7hn1F\nHH0mk4lUKsW9997L4OCgSqB3u91sbGworUc2m2VgYACz2czm5ibxeBxN0+jr61PanJ1YBWUUEOAV\nDoeJRCL4/X40TR+yWy6XCQaDxGIxvf2+uUl/fz/5fF4l0LtcLu644w42NjYYHR1VwnuPx6MyiQQA\nDg4OMjMzo0b5CFuxb98+7HY74XCYTCZDKpXCarXS29uL3+9XGUmbm5vEYjEVfikRHm63m4WFBfUz\n3crr7YDWW33NqGk0ZnLJn3ezXcbHGMGXcWC0AC8j6yaH9dzcnBKHS5imsfiRDDp5bbvF7aVSSTlW\ni8WiMnaIOFxAl81mUwnug4ODKndta2uL9fV1NjY2VHK8ccpAqVTijTfeUAJzp9N5Q0RFPB4nFAop\nF7IwPCI5OHToEFarVQEleV8ApfGUQqTZbCqXbaFQUOtZQIyAO3E9CmAEXb8WDofZ2Nggk8lgMpkU\nAywuWznbRa82Pj7O4uIiW1tbhMNh1XoXMCjFZKvVUuJ7KVAkjiKRSCitm7DEMmFAWHcByH8X48ZN\n79lbyXS98cJzb5vTdbO/M7ZXjADM+Fi4Xq3JValUMGkamsmEy+XWb2CrhXqliqZ1aVTKeD0u2u0W\nQbsNU6fNX3/li7zzwfsJBv1sV8pgseJxB8lnC9DUaFOl2Whhs9hZX48TDkcplcvUmg1SmTQDQ/36\na6i1QdMI9sRwh6OUqxWylYoe4pnJKDeMVqvwF//lL/nh8y8yu7KC1WSi3ekwPjrKQ+98gE/8y3+h\ni53LZRqNOjMzM3r8QzHHyPAefviDH9BuNOm0m/QPjTA3N8ddd93F1NWr+jy4eh2Hw4XJYubOu+6i\nUC7x0vMv0G63GT96hJ6eXr1qSqV0DYHXg6aZWVlZoX9wgHA4zNlXXiEYiVIuVXnw4YdJpVL09g+w\nlUwzODjIuasTDPYP0azXiYZjDA8OcW1igqe+9Q1+8Owz3HnHMbbiCX7ho7/M1PS0TqFXqviDIaan\npymXqzz6yHtYXV6mWq1y8OA+Hn74YX0MS3KLY0eO8vnPfx6XQ+/ZO+16UvD7f+5nefwb3+AjH/kI\n3/nO93B63BQKBUxm/aBJpTK4vR4atTrVeo09+0bAZKbUrBGJRvm9T/87avUmjUbjllb1Fy9eVO1F\nePsoFeOmbgRYcu1mvgR8wfX0dAFFxqoUrlvKu92u2ribzSZPPfUUhw4dIhwOK/u4tPNEpyWtR0k/\nl8gJY3CnfE0cS3a7XSWtFwoFBaZWVlZYWFjgpZdeUptgIBDgjjvu4J577mF4eJhYLKaErxsbG/j9\nfqWbkrR5l8tFsVjk6tWrRCIR1e6MRqOEQiHMZjPBYBC73c6lS5cA6OnpUQekBIZK27VSqdDX14fb\n7VY6qp6eHhX3IK1aieMIBALqs5EYh6WlJSYmJqhUKvT09OB0Okkmk+RyOaWfkXT4U6dOMTc3h8fj\nYWRkhEAgwKVLl1R785lnnqFcLqt2bk9PDydPnlSH6crKimIuxsbGlJBYwIG0HYUF9Xg8fPnLX2ZL\nN97c0jXxb//tv33LQ+qtQJcRZMllbLnLZyE5ZsJI7tZ4SZadtMWN+Y8OhwO/38/GxgaJRAKr1XpD\nq1tiVaRlK0DLGPYLKJ1Su90mn8+rSBYZhyVxFMFgEJ/Px549e1T0iDGDymw2k8vlmJmZYXZ2lrW1\nNbWGjSxzs9nE7XYrQ4U4DoWx7evrQ9M01a40Zs8JKBRWutlsUiwWlWNXfo5ut6sAj9GVaTabVXq/\nZOrJAHBh1WW9pNNp5ubmSKVSuN1uDhw4gNfrJRKJKAe02+1WbdRkMkkymVRtQAGY0WhUFT/lcplM\nJqP0i/JzS86diO+F3cpms+TzeTqdjgqI3d7e/nvldN3S2Ysby0v/5u8CuuDmgsndAkljqB2gRL5W\niwW6XSxmC1aLFU2Deq1CIZ+Htt7DtttsdPI5Lrz5Og/cfRelXBab1UKn1cZmMtOsNoEuzXYTuh2s\nFgvlcgnaHexWG+1OC03rks/nGBzsZ2F+HrvFSrFYItbXi9PjxWy1Ua1UsVmtRAIhrly6zJVLl7l8\n+TKf/dzn2crmaJvM1DsdWkB2O8ubFy5y4tgJtjZTdDtNbHY7d7zzncRXV9iIJ/AHdTFs/8AgaGby\nhSzj44f43neewuHQD4l6o4HDYWdgcAiT2cyrr73G6P79esXgcBIVJ1SrRTqdplavc+GCnkzsD3jJ\n53McPXacfKHI/tF9JBIJiqUysZ5enHYn9UaDcr3K6toaS0tL0IHERpyZa9coFwv09/WxMDOF1Wah\ni5nBwUEymQw9vb2srW8QCIQoFotMT83wzgcfZHpqlpdfPUtia4v9o/v59pPf5qGH3sW7H343y0uL\nevsnlaFYLLC0vMD73vczfPd73+PBBx+gXCxitVixWcy0O13CoSCNeg2LxYrL7abRauH2eOnp72Ny\n8iqlcplGs8Wv//qv/5//zW/8t7k2Nzf/DbyFYWTXASNgy9hCl78XPYpxwzNW6MaxIXIIOZ1OBeKk\n8pYKTwCQRDlIoSD/nrQxJPxUgIeIYU0mE9vb23i9XsWmSIK3bOrSdhEr+7Vr13jqqaf42te+xsWL\nF1leXiaRSJBMJrl8+TITExNKf+jz+TCZ9MRriaMYHBxUkQkmk96OHh4eVvqaer2u9CciVJYhxSKa\nl1+NFnrRqonjTzKOAJVjJm2XUCjE9vY2q6urKljTarUqMFmr1VhYWCCdThMOh9m/f796PXKYCFgV\nPdsrr7zClStXqNVqxONxBgYGGB8fVwaFWq1GJpNR+qBcLkc4HFYaIwG10q4UhxagHJbpdJqpqSk6\nnc4tXxNvN3vxrUCX8V7f/b8RYEkXxDhL0dimkyLB2JI3RmzI8HUBAtJON7qCZR0YhfMC9iS3S8Tb\n0jqTNqLEJuzfv5+jR49y8OBBIpEI+XyeyclJXn/9dc6ePcvCwoKa7NDf308kEmFra0utVXHZii5R\n9hNhukQELxpPYbhE53jlyhUVLizaKImCECZONJsmk0k5FgXwBAKBHflJXQEfkQ4MDw8TDAYVoBWW\namhoiNHRUWKxmAK4ZrOZ9fV15ufn9bNielppuwS0AWq4veSf1et1XC4XkZ0ZwfI6jeOzhHGs1+sU\ni0XVFZD9UpzH9Xqd+++//8deE7ec6dqdOyS/apoG2o+2Toy/Kqar+6OCYXlD5XDodrs0qzXC4QgO\nlwur2Qx09cT3SolcNo3P7YJOl/WLr1IplrB1od5qcnBsjI3lNYKBAJrNSUVr0/W6odnAarJitzlZ\nX12nXm/S09dLNp/HHwxQKBeo1mtYulbMVjuHjx2j1GyjYaZWq5PfzvLEN77J33zjG9htNlaLefKN\nJlhthAcGuevU3ayurnL+7FkcJjMjAwMEAz7+4j/9e+xOB2fOnOHAgQOMjo5iNpvJZDLMzcySz+fR\nGiX6+/uZnZ1leHiYcrnKq6++yqPveQ9PfPsprDYbh44cJhoKq7lWFrud3t5eKjuLZy2+gdWuj4Y4\nduLIjk7mKsFwlFarg9myEwgY6dErh1YH30AEt9NDo9HCbXOQWFtndXGZz/3RZxjo72N07wCzM9MU\nq02OHT/Offfdx+/9/qfJZHNspVP84R/8J1KpFH19AyzNL1BtVThw4ADxeJxEfJ1GtYbTYec9jz7G\nv/7N3+AjH/kIT37rCYIhF/V6nccee4ypqSnMZutORaZXQlqnS7lcxeH3k0qnGRjei8vvZXpxgR/8\n8HnSuSy1WoNWq3XLma7/WiEi1+4ICeMaMFb1u7+H0jjugDXRhUjCtmz+pVJJVYALCwtcunRJ5WDJ\nKCdhqmTDE2ZHdCaSvC7MjTAMpVKJgwcPEovFMJlMFAoFstkszz33HE8//TSLi4sq4bvb7bJnzx7u\nueceAJ555hmy2axyhwWDQb7whS8wMjLC+vo6c3NzPPDAA3oBsRM4ahxGDDrT19fXxzPPPKPEtaFQ\nCI/HQ39/P8vLyyqwdXh4WLU9pbXS19enkqsBYrGYGhAsphl5jyVXS9gNCZ28dOkS09PTaoakMEyR\nSESxJ4VCgVAoxB133IHJZMLtdrO5uUkwGOTy5cuqTdvtdjlx4gQXLlxQ7kSJWpGsIWEDLRYLoVBI\njTSRlonT6cTv9/PGG29w6dIllpeXBTjc0jXxO7/zO295SBkBEtwchN1s7Rifayzkd3dPjIyYRAz4\n/X6Gh4cZHh5WGigBN6J5ku8tLVujy1RAi3E6gjxHmDKJJBkYGODIkSPK9JFIJJicnOTKlSvMzc3h\n9/s5fvw44XBYgey+vj6Gh4fx+XycP3+epaUl1daTwkjAXTAYpFKpqDa8OIFBH/cj4vtwOKy0meJM\nNEoEhAUWHZZo1GTkkOTwiYFDgF+lUmF2dlYxZOIkTCQS2Gw2Dh06xO23304sFmN6eloVVhJqLOe7\nFJXyGcq93Wg0uHTpknKhttttRkZGlJ5UiiRh66QQAdRoo7GxMfbt20elUuHatWs0m01+8zd/8x+n\nkP6NMz982wPGvEtyJq/V2FaRWAjj865/nxsPHZvJRFez4PG40LodXDYzFbOZThe8mgVLs4rV3OJ/\n/B/ex1A4zP1jhzg4tJfvf/8HnLz3AdyRMBa/E4/PTbvdwBoYpVwpsp3ewmW30KrXWJpbJBCK0Gib\n6BkaZnpmjojbjjcYINLXQz6do6+nn9//4z/m9cuXuLy0TLPZxoyZtmZj/9Hb+H/+3z/nwx/75yRm\nFrD6PZjcJiyFIj46JEtZTt92lL/8L3/ByPAerl2+RLUsN1gLl8utVzgmff7k+NghapUS//o3f4uD\nYwdYXl5k/MhhBgb6cHk9WC16nEUkElEVg91uZ21tjd7+AQKBAPl8nqWVVaLRKFMzsxw4eIiBPUMM\n7dlL38AgwXCEaq2mh0d2LEzNzBCKhAnGQnQ1fe6dw2ZlO5UmmdAPjP/w7z/N49/8GwB++mf/Cdls\nlkgkhtNho1GrM9jbQ6fZotNu8J+/8J/pAgPRCPfcdScmk4lnn3man3r0EUKhABuJOIORHi5dvYLb\n76WraTitNiKhKIFAkGylRL3bxRMNUa9BIODjtttO8NzzP+BLX/oiFpuVSq1BCwvlSu2WHjATExNv\nuSbAe2cAAAAgAElEQVR2Hwhwne0ytt+Nz9sNvHbrHmXzEzZYmCNxGVksFpaWlvjzP/9zCoUC9913\nHwCZTEY5nyQfS9or1WpVbbLlcpl4PK40GIFAQOm73G63Gi9ULBY5f/48f/zHf0wikVAbn6ZpnDx5\nkl/+5V9mYmKCp59+Wol7xQLfaDT48Ic/zEc/+lHuuOMO4vG40mgJ45bL5dTmH4lEVJbS5uYmpVKJ\ngYEBRkZGFMhMJpOqhZTJZCiXy6qIk1+TyaTKHQqHwxw9ehSfz6eCGo2ZP6KRi0ajqnIWjcnW1hal\nUokzZ85w/vx5EokE73jHOyiXy2pvk0NVRMXnz59XAKq3t5dQKES9Xufuu+9mampKRW5sb28rDY98\nRpL4X6vVlNvO7/fj8/k4d+4czz77LK1WS7Ej1Wr1JxZ0GVmrvyvgutnfGc8YuG5UkeiPVqvFvn37\nuPPOO7HZbMzPz6sgT/lsJLoBUHEhohMSQCX/lnEuodwfwuoIY5VMJpmfn2djY4NCocCLL77I2toa\nhw4d4oEHHsBqtfLkk0/qc3+9XmKxGAMDA7z//e/HYrFw5swZLly4wKFDhwCUQ09GDEmRJK9vZmZG\nsdN+v19FRIhr0GLRB97L0HUZoWVk9UQXODAwoFh30WTW63Xlkpa8PZlqIlljwvJKbtjo6Kiulc5m\nlWZs3759WCwWGo2GMufAdWepCOTT6bSaiappmiqOZA1ks1nK5bJisY330r59+xgcHKRSqRCPx0mn\n02iaxm//9m///wN0wY29enmcfMg/WtmbbmC/zCaNTrurazg0sJpaVDUTmKy08iWiXif/8y99mP6o\nn5jbwbH+PixmM06rl4nLk5x+8D6mU+uMHzlCPlsg0H8Ah8PB1NQkLpuZq1euMDoyymYiSSDSQ2xw\niJmZGY6MjPDmpUvcee9pFucWmZ2a4+vf/Q6bhSKJSg0NsGOhjZmXz11kbmWDf/bPPkYs0sfJw4eo\n02IrEWcjsUg+l+GDjz6Ew2bln37g53jH3XexMDdPpVJhz569zM8vcOHCBfr7IpQLRVaWlunti2E3\nWQiF9RlwtUaVmZkptvM5PG4fPp+PRqOhUoW//e1vMzAwBCaN4eFhNIuZQ+OHefnllwkGw3omV7OB\ny+0lGAnj9ng5dOgw5UqFRqVFu9uhUinhCwdxez0UCjmK+QIuu4NMOs3q8hJup4PnX3qR1197g3qr\nzcDAAOvrcVrNOq1Wk7DXR7FY4Gd/+lGe/t7TREMhaqUiWhcOHjiAz+dlaWmJR9/zCJOTk9hMZpLp\nNIN7hnY0GLoW4uix42yk02A2YXbYKJRb+P1eLlx8cyec0ka1XqPZ7pIrVahUm7f0gLl06dJN1wT8\nqIZRvn6zVrvxazeLn9itGROrtxwIwsrUajV+9Vd/ldXVVY4cOcL4+LjamFIp3QEqw2IBFVlQKpXo\ndDqsrKyoFpc4iqTtIXMAc7kcL774IhMTE1y6dOmGVui73vUufuVXfoVnn32Wxx9/nFAoxHvf+17y\n+TzJZJL19XVSqRRDQ0N4vV4+/vGP8/73v5+zZ8+qw2Jzc1PNQWy32+RyOTVmRdK0ZQivsBmi7bBa\nrayvryt3pKZpBINB5TBcX19X2hpJx5Zk7+PHj98AYguFgsr1kqo7nU4zPT1NLpej3W6zubnJ5OQk\n8XhcifSFtbfb7bRaLcbGxpR4W1xifX199PX1kc/nOXjwIJqmEY/HFfgSbZAc5saRMqAzda+++ioX\nLlxQ+USiz/tJ1nQZr92OReP5cDPnolw3Y4LlsdJelwLf4/Fw4MABQqEQCwsLLC8vq/FY4r6TtrMU\nNdK+FGAmQF3YJml1dzodxsfH6evrIxQKYbfb2d7e5sUXX1S6wXK5zPPPP8/Jkyf5mZ/5GarVKmfO\nnOGJJ55QYaLCdH3oQx/i7rvvplarcebMGWUOEU2h0QAjrcxyuczCwoKaDCFTDyKRyA17icSeyPsm\n+sNkMqlY6EAgwKlTp+jt7VVuaJlCkcvlyGazKgBYXousFRljlU6nuXbtGul0mgcffFAZBwDlFu52\n9bmxq6urNBqNG0aQCbuWzWb16KMdQCb/jjBdchkd3idOnFAj0VZWVpSu02Qy8Vu/9Vv/ON2LJjQ0\nNOju3Pg7v8p/RsS5ezEAyooqVl65rrdZrmeyyKKx2R206w3anSYNSxu72YHNZkHzO6DbZI/PTzdd\nZmz0MFenrnLs2DEikQhHxvZSTiUIWWxYa23WFzfxu3xkG3Wq6U3i+RzNapmpK/qgzmqlQCa5ztjY\nGKvzi5hqDT79f/0er1++jMPpIl2pkWt0aFuBlgnMJv7i81/msQ//LOmFDR45/V4apg7v+fnHuO3E\n/Swmkjz+ncfZ2lhlaF+Yr335SxSLRd7/cx/gh888y+y1Wd7zMz7CwSAPPfQQbpedubk5Tp8+rbcc\nsxlMZguayUQkFiMQCpHKpFWo49raGqFQhLGxMcbGDxOK6qF2X/rLL+NwOLjt5Aq33347b547h98f\nBLOJc2++jtenD0JdXVlmcXGR0f3j3H3qFMMjh0nnM8zOTuMLBthIrNPX24vTacft9fCnf/Qf+dDP\nf5if//mf56++9nVm5xaoVstogMPmoFyu4nN7efbpp7nt2BHodtlcb2G32ijl83TbDYYG+lhbWsbc\nhXCfLnKs5It4QxEGBvfgDQdJ16s4AwFazSY+pxuH28Rrr73KlcuT2CxmzCbotNq0W22s2q0fAwT8\nSLUt1+4qzAi4drdIjKL5t6r25XlyIIjORDY/q9XKc889RzabVUGG29v6YHERYMuhImNOACXols1d\nnFuS0C6ZVm+++SaVSoVkMsm1a9fIZDKqdSGv+5d+6Zc4f/48X//614lGo4yMjHD//ferOIk33niD\nubk5qtUqly5d4pvf/Ca33XYbGxsbFItFxsbGFKiSBO5wOEw8Hmd7e1uJ6D0eD4ODg5RKJebn51X1\nKwethKbKHDnRt/X396u2SKFQYHFxUXc3+3yKqZLAVJfLpZL4JW9IgN78/Dzr6+v09vby4IMP8uST\nT5JMJm/Q1UlUxszMDMPDw2rvk4NUspXm5+fVUHJhaWw2mwKhPp9PTZeQg+vs2bNMTEyo91+Ayk/C\nZWSgdl9v13bfrd8yPudvU8DAdU2XPN5ut5PJZJicnCSVSuHxeCiVSurroo2TVhygWs7SBhamR4Cv\nMftL0zT1ea6trbG8vKyKBWGOT548yWOPPUar1WJycpJarcbRo0cp7MwWDgaDBAIBZmZmVMbb8PAw\n586dUwWDuApLpZJarwI+ent7VfK6punTFcQxLKyxSAaEWZMQVAlcFUbv2rVr6r6y2WwMDg6q90L0\nnQJOW60WxWKR7e1tle/V09NDNBqlUqnw1a9+lVgsphy4AlhlGL3b7SaVSqnQYAHAm5ubKqMul8sp\ntkqE/haLRYFKCVX2er0MDw+TTqdZWVnRZzMbQPzf57q1A6+7OjAyAi70PO6d//XLWIFIdSYCSCMY\n213psGuMkGpH2jQarRY2m4VcIkkkFKVOA7/bjtdsxkSH5555jnsefIBKucWrr52hpTXxBUIU8nWs\nmoOBnjDtcoFKqUhyfYVCpUoHE26vnqly4MAB0uk0kxMXOHzwBNv5bdC6mG0WssUKjfbOj6p71Wi1\nOhw6cpz02ipefxBbq0O3XSXicjKbjtPx+lnbSLM2s8LtA24K5RL33n8fz373uwwPDOOyuagUS7S6\nHUqVCrlsmiNHjpDNbHPfAw+yvrpCra7ncb3y+msUCgX2H9jH+nqcqakpIpEIh44cJpPJcOzEbZw7\nd47XXntNMQhuj4fllRXFiGQLeU6fPq2n1ttsVKtVjh85TKFU59kfPE04EsHl9dBo1clkUowfOoTb\n7aZTbxAJh/j4xz/On/zp53n44Yf54Ac/yMWJyzzxxBNsJOJUGjWCTo/e/nBYmZ2Z5sTRY3q7x2bB\najNTq1SJhMJUSmUKuSyNTpu9g0MMxPr1DSIUwhsKUy/oVnxNM9M/3k/Y7+O733+Gwb6wPii13cBq\n0R1GdstPxvx34z1tPAR2HxzyNWkRyJqAGwHbbqG98ffGGAlZWzKeR6j5kZER3G43yWSSvr4+0um0\nEr9LVIJY2x0OB4VCgeXlZaVnEcaov79ftQJsNhsLCwtqqG29XicSibC6uqpen7irHn/8cRwOhwJI\nvb29ChzYbDaKxSKBQEDZvb/whS/w2GOPkUgkWNm5Z6UyD4VCANxzzz1KaFutVllcXFRup83NTVZW\nVvB4PIyPj6s2ZbVaZX19nXg8rhyTksadz+cZGhri8OHDKifLuLGvrq4qsX61WlVDdaPRKIODgxw+\nfJg33niDixcvsrGxwYc+9CHm5uaYnp5ma2tLtaAk+XxtbY2+vj4VBivxAzLHTvLSenp6sFqtqrUr\nGh5pE8nnePbsWaVpkXtI/q1bff1tuzE3A1pyoBvz7HZ3RG4msJevy0geEbfLeCeJHRGThoAnaVtV\nq1XVzpXCBG4MVa1Wq+q1iEMyl8upyQXLOw7u3t5eANUyO3XqFJqmMT09Ta1WIxQKMT4+DlwfJi8x\nJZlMBqfTyaFDh0ilUuRyOXp7exUQFEZJ3L3i6BNdorBQEjAsTJXoH6VFKUO2/X6/yu2TRHjZAywW\ni3JECti3WCyqZSgmBZn+UCqV2Nraor+/n9OnT+Pz+VheXmZqagqn08nevXt1g9yOlk4cmZL8L3o0\n+b6iM5PJGhIBIXpWMQNJSHM8Hmd9fV1JJW5m4vtxrlvaXrxw5sXu7gPmhsXAjenA8iHtXiRwIzC7\nXpmYdmlYOljtdtxtDZO1S81Sw5auUsjkcQ0FCNgsfOu3foc9riY2Ty9nrq7gDHnwxBz07RnkpTOv\nMbpnFLfNhLNbJtzXz/TMHJWOmSvzS3j8ATbTGU6ePInDolHaznLHydtIluv8h0//AeHBAcy+AHff\nfZrPff6LZGpV8mYLtDX8ngD3PfAoU6+8gD8YIrZnL0PlDj+99wi9//FfMVep8Fsf+xfUi0W8nQ3m\nr84wfmAfa3OLpJfXWF5aYm5+nnqrycM/9W7+8A//kMHBQXqiMcqVIh6Pi2goTLutC4vbnRazs7ME\nwvpB1ml3SWxtkkgkWF9fB5NGJBRlZmYGl8vFffc/iNvtZmlpQdHF165dY35+nmDIz9GjR2m1Wrz/\n5z5MJrtNvdXEsmMjlkGs2e00ywuLugPr7FlOnz5NFxMvvPwyjUaDg2PjDA4M4LDZ2dqIUykWaJeT\nmDWdQj40NsZ2Ko3FpDvt6HQIhUJ6eKDJStdkxhsK4fL5MVtt5IoFnYmzW/H4fXzvmWeYvjpJLBah\nUipQLBax26zUqnXaXajWm6xm8re0lXL58uUb2otwYyvQKBYVpgluZAPkcTeLith9GUWtEo8g7UGf\nz8fFixc5e/YswWBQaYkkbd7v96tsHAEhcihtbm6SzWbVIXTixAmCwaB6fc1mkyeffFJt6IcOHeKN\nN95QGV9ms5m9e/cq3ZTMt/N4PHzsYx/jvvvu46mnnlJg4amnnqJcLrNnzx76+vr47Gc/i9PpZGtr\ni6WlJZUKLi5Bee8CgYD6eUulEplMRj22UqlQLBbVvEUBKY1GQzkhJQU/l8uxvLys2pJyKIRCIZWT\nJC0XcXqJbmx6epqLFy+Sy+XUkOKNjQ2q1SqAyuaqVCqKuTJGFkgrWHQ0chh2u13FsInGTFgWGXx9\n/vx5rly5okCBtMlEi9Rut1lfv7WjsW7WXjR2MH5ElmJw54Eu/N5dlL8dY2xcY0YGGFAD4MUdX6lU\nGBoaIhwOq2Rzi0Ufgi1ZV7J+hUmSMyydTt+Qh+V06uHXFos+/NntdtPpdFheXlY/hwwll5E0MtBZ\n2vf79+9nbm6ON954g4GBAZVPVSzq00aECRKAEo/HlfhdwIkAE3kvRHjvdrvp7e3VQ8B3RPaSfSlu\nTqN8QQZGN5tN0um0YmLh+j3tdDrVODFp7WuaplyTohETljkSieB0OqnVaqyurqqRXgLWpKUoOjMp\nHGSovFFnKcBL0zTFjg0NDdHX10ehUGB2dvZHAtYFg3zqU5/6x9leNK6kTrer52hpGuadqt24UKQi\nFzoQricCGw8XEdbqz22rhaNnp1jRLGZa9QbtRh3Nb8JlsxLo7WU1n8IfcFNOZ/CMh3F1c4y7aszH\nk1zccnIpvo0vFObS7EXcJgvH9h8kvbLB9PIGdk+Ia/Pr2Dw5UvkCPfvH6NaqHNu/j3gqjzvmw2Q1\n4XM4mJtf4PvxLXwWK0duO8LTExMMDo8QicT4zne/Ra8/gqPZpuLr8ML6PCd/+mH+1a98mOlnn+eT\nn/wU89tbPP5nL2F32lhaXuVTv/nb/OcvfhHaHcbGDjISi/Gtb/4NjzzyCJlMRndm7GT3FAoFXC4H\nyWSSeqPG4OAgC/OLjIzuZ3FxEZPNQiQWJRyNEI1GSSUzDA4OYjabmZq6SqvVYnR0lMkrl0gkEkRC\nYU7efpxut8viwiwAf/jv/m+8fn0RZXeqojvvvJO+vj6Sa2s88fhfc+LYcR588EG+//Sz1Nsd2jv3\nwNLiIon1Dei0qFYquKw2ArbmzoFh1V2NsR4lBHY7dTfOdi6H3+XD6rNRbDUx0aVTr9Pp6lRwLBhi\nI7lJMrmF226hUsjjdbtwWCyYNRMmn4liuYTb3vzvePe//XUzQbwALNnkdlfmxrbi7gLGGO8AKGbM\nyAAYU7JFpyIp8KKlcLlcOigHNVR6cXFRAZF8Ps/S0hKapqkKtl6vc9ddd6lRNBLw2NfXx8rKCtvb\n20xPT6u8KnHs+Xw+ZmZmCAQCKoE+lUrR7Xb53Oc+x1e/+lUOHTrE2NiYEtdvbm5y4MABXnnlFfbv\n36/aaisrK4yOjipwIjZ9SZkWds/v9yv9ltjJA4EA0WhUOZucTif5fF7Z9EUzEo/HCQQCKn18aWlJ\nsRWyP0keUSQSwW63q8NjeHiYcDis2oyi4xLXnLgbpSoXhqper6vDUKz/Mi9P9GRy/wgQlxiNpaUl\nVlZWbrgnBFSIVkxid36Sr92MsLH9Y2Qo4MbWohG43QyISWdEmCwBXnL/aJru9pPoA8mdEwbF+DwR\nzHe7XcWaGef7AarNLZ+5ZGIJMyThoJlMRsV+CIB49NFHOXDggAJV9913H8vLy5RKJcV0RqNRXC4X\nyWSS7e1tVQQYQZYwXcIyi75KmLNSqaSyq8RQIinyMpZLzCbCIMt+Iv97PB51f9dqNfVa5HwHfW+R\nHDkptiqVih5FBCq7TNaAAC8Z+F4qlVRQq5hFut2uAnJGWZLE3Mg+s7a2pl6T3F+798+/z3XL+ylv\nV23s/gGNB4hU5bIoxCEE10WBFsv1waJ6WKGeH+JzuanWWnTabTBZaDbbuOwOpq9O65tO20RFa9AT\nduGwOpi+ts42bebX8jSW5/E6vVicIeZmr+JwuCjEc5hsdqwODxvzy8wtr/HPf/EXWJq8woH9+/ns\n5/6QwwcP8NKrVxkZ6aerWQiP9rO4to7dZKJUKeNr1UFrYw34sLtczMzPkC5s88dPfYPp6deh2WRr\nbY7Hv/41aLaod8BqsjA1O8NAJIZGh3w+xyOPPILH5SC3nWV9dY2+gX4GBgbw+T1sxhOk02kOHTpE\npVRkc3OTvv5eMqkkNpuNrUyKQqHA8MgIm8kkyUSSqWvXKBQK/NMPfYi1tTVW15bxe3102x1MJo1z\n586RSm5y8OBBfcFanDSqFYqVMkeOHCEajRKPx2k36qRSGX72PT9NNpvlhTfepNXuYNLAYjbTaum6\nmGajjhmwaybcLgcusxkzGlaTGZ/PQ6yvV09N9gWp7oxzWl9fx+3y4guFcIaC1OpNWmgEd2zOZrOZ\nuekprN0uDq+Hnp4eMukkwUCYclEfGeOw2TG2tG/19XYUtvEgNT5+t44LrreIjBoWIzsG1wdfS8Ei\nz5cN3263Uy6XlZNIWoMbGxuAzo4I4yWHi7TuOp0Om5ubLCwscPToUUwmE4FAgOXlZcXcnDhxglqt\nxvDwMEtLS2rwrRwYMgokk8kQj8f5zGc+w9ramtpcv/3tb6tNtd1uk0gkmJmZUW0Ot9tNLBZTw3Ml\nekF0aZ1OR7EFkiGWzWYxm834fD71HkiIZSKRoNlsqjDIXC6Hw+FQw6UvX76MzDM0yiEAxsbGVBtD\nqvH+/n6l38nn8+rnEJBknCIgn7EIhMPhMD09PYoBqFarVKtVxYANDg4q4b5ksblcLra3t1X6vRw+\noh+T2ZrGrMNbfe0+D+R+v9ljjEYMY8vd+Dh5T+V7wfV1IfcEXAdwRhejfJZyGNdqNbVW+vv7lXZI\nQJqxKyOsqkx+kNBSCQxdWVlhfn6ezc1NlXV1+PBhxaZ5PB6l8ZLWpRQ2ExMTapZoJBJhcXFRMUei\nPRP9WSQSoV6vq1FWcCMDJz+jMEQSA5HJZMjn8ypUFHSHpqx3oztT7iNhuPP5PIuLi0oDKfEagMrE\n8+9MdLHb7SpWI5/Pk8vlVJSNuD4F0ApYbrfbqkUKEI1GFQCWHC4BemJSkecODg7i9/splUp6/mSx\nqEaSGYkd2Uv/PtetBV0mja4mzsLrC6jd7dBud3Sh/Q3twhtHmogbw1jBgX4T6EMtOzcwZrVaB4vV\nTqlWQ+tqdBpQ63ZpN1vYbA7+P+reNMiuOz3v+53l7vt+e1+wNQiSIMFNJIcaUaOhNCONvKQkxYqd\nSiUp61MSpyr54KpUMpETf0ilVElUpSROVFZZshQlZZddkkajoSwPSY0HFEECBLgA3QAavfftu+/r\nOScfzn3/ON1sjEaasUEdsqsb996+fe45/+V5n/d5nzcZimNEY/QHBoOgyXDYJ6QZPJvP8L/+2TtM\n/AZO3SLmb5E/O6AH3FnfIJ+ZdUtcIxEsG8rVI1rdDl98/XWigRBBy6FTqfJf/ie/yPmnn2GvVuWF\n17/MzY8+5Td+5//lD65eZWZ5llgkRCgYoTcZMqrXsKtN9su3yGDT88E//if/N37dz0jTMUydycTi\n9gM3Wn3hynN8+Yuv8a++/a/55OaH/Gf/xX+OYbrmfPFYjN3dHWKxCInEEsO+64a/s/WAzc0tms0m\nl5+/QjQZU+0sIpEIV557hkjQrYYplUuYBqQSCYaDAfNzs5RKB1x++knG4wv4fAZzc3P0O33OnDmj\nyoB1XWc8HHBvYx3TNHnzm39ILpdj9ew52v0+vd6A0cRdnHTHxg/4DA2/oWFiYVsTsrNFioUZwokY\n/lCQze0dzp6PEEkm+ZM3/5iLF5/goFVn9fLTjAdjUoUcZjREo9NmZA25fvMa2USU5JlV+s4Ax7II\nzxTdPL8FQZ+fwXiEYT5+0HVyvMtjwLFI++Qi4A0ujlXsThcM2WjgOIvmTX9I1Cx/yzAMJeaWuSYu\n0KKvqNVqx9qeiPmhfA6xoRAB+blz51RaSyr8FhYWaLVazM3N8Xu/93v0+3329vaIRCIKaMg5dbtd\n1tfXFWMlIl1ZPB3H7f9YKpWo1WrMzc1x9+5dDMPg8uXLKp1pWRa1Wo2FhQVleKhprhBaXK3n5uYU\nWBObi9nZWZaWlhTQlJ6LUrnW6/WYnZ1VaRPZyCSKNk1TeY91Oh1qtRqbm5uqvF3WLtFwydolm5ls\n/IZhkMlkjrEA4ge1v79PoVDAtm1SqZRKeQqLIj0o/X6/MpOVtJjX3dw7lh7n8Sh24STDJYd3DnnZ\nYfnuTb17D/msJ32/ZN7I4W2VNRqNVCZlNBpRKpUU4JJzABTIkMISKTIRE9JYLMZgMKBcLqt7IF0d\nNjc3FQgRRkjSgSKAf+edd9RcFtsTsfyQqkfx05P3lwpJSZ9LkDE3N6c8xASo7e3tKU2ZgMhOp6NA\nuZctFaCmaW4j7Xq9roxVxbZB9GTiMC/BhXhnSccI0dSJTMF7z73jQtKTArKkn6WAXHkNoFhiGQeR\nSIRcLqdS/qLHk88jQdMPq7DkMTNdGpomG8TxvleO4xwjHrzpEhHKedOMuq4r3x6ZBIZhqgFiWRam\n7sOeOGh+H8PBkHgkiN0bE0okMKwJd/aPGFg2h7U+/lQMK5RlYlqUe7usZDLc2K6imaAbGvfvbtB3\nOq6TrgYmNr1WnYXZBM9dfhKf5nDzgw/odbpE/H7mV1aYmZ9hOB7wEz/5Bk1d5/Lzz1D8o3+FYVkE\nTYNuv8fA6mNgEdB8aI6OGQjTG7YZ2WDqoI9sNAN0Q8diQqlcppBM8803v8XP/c2/RqlU4o2f+HG+\n+c1v8sUvfpHdg12KxTyVyhGb91pY9oRUPKaa6xYKBbc6LZ1ic2eXs2fPki3ksSyLj667bUZ67Q5n\nVpdZX1/n4OCAeDxOs1lX1LquuxG8aZqUjg64d3+DTqdDJBxTEXosFqNarfKjX3wNx3F4+/ontKf+\nYs70ywB0AxzLwefTMDUN03ArwXw+H7YFY8thfnGRpZVl/H4/b3z1q26PuUGXYDhEIBhGN310B65Y\n+fbHnxL0+4hEI3RGA6KhJNVqHZ/fZNgfgGYT8JvHxt7jPLxASxYGOU7bZLxgSxYR4FhE5t0AvAvW\nyajdq4kQgfr29raKhjVNU1H9eDxWabWTzXYlbSJO7ZJivnDhArqus7m5qfQZMzMzJBKuJlC0GrVa\nzTXrnQpfvbpN7/rgTYNJMCYbhUT5P/dzP8dwOCQYDPLRRx8xNzdHMBikUChQqVT44IMPVLonFAox\nMzOj+tJ5PccWFxcBaLfbyrVejEpLpRLD4ZB0Ok0ul1PeYOl0Gl3XVe/DwWAAoNgk2UwKhQKTyYSt\nrS1l0+A9vJo70adImlZYLOklJ81+BcDJ5xJAPR6P2d/fV68XxsCrUxI7D0mxfl6O0wDWSYB0Ms0u\nKTOvZvhkECKPyeu8j538O5qmKfAsa1uxWFR9Pg8PD7EsSxV9CBgQOwcBtgK6RLQOUCqVcByH2Vk3\niJf0WK1Wo9lsqpSibdtKMC7ARDRNjuNQr9ePZY5kPHrTlwLuFhYWCIVCilkTh3pp/SUA6v79+yn9\nt3IAACAASURBVCqVLc7yAvJkjAkzJcGRVEcCCgTJmuPVdAkYE1AqpqTxeFxZTchnhOProNzjwWBA\nv99Xz0lqXsa0BI3ebIDf7ycejyt9o3SLkGskGEPGkFzTv9LVi46uYU+ZLksUXppMLg3DU8Ivi2y/\n31d0uK7rypBOUgCapqnB5zWdCwQC2BMHB5OhZWP4fAzaAzqDIeEAONU6c/l5/off+z2+8soXsSpD\nfuOtb1EHgmGNL168zFMTgxulI3qGxTjsIx/NEQ2E0IcTDsv7ZAt5XvmbX6U4O8tMNoY/EmJ3a5tU\nPsvs6gKBdIz8/BzEA2iGQUgL8lM/8ioffPghN797lWdffY0Hn95jZmGejhPF6bSpjNpg+N0WRNaE\niQaaNXF9pww/nf6AWu0umjXhn/zWb/GjP/IKk06LYb/Hn779FhcurnF0dEjA5+fslWdcindjnX6v\nw/b9e4xGbluH7d0dwokYhqPT77RptVw3cns0dstsjywW5mZ47tnLvPnmm2QyGWqNBtFolNXVVZrN\npivWXJrn/v37zGUW1QITiyXY3t4mkoyyfneDg/0SjZGJofsY2RNMdDTNQXNsdAdMAwzHwTQ0VhaX\nWFpYIhaPE0okGEwsxrZDIBJBM3QSU6F+JBDGDIbQQyHGtkVls0TlqMyk2WY86uGEAmg+0G2HYjbj\npq6SCUZjV0cwjtgEPgfNfeF4xH0ShJ2WGpTqRW8rk0elXk5uSrIBecGbCHslFfHmm28yPz9PsVjk\nG9/4BqPRCL/fz/LyMqurq9y8eVMtjLOzs8p6odPpkEwmef7553n66acVqAqFQjz55JO88MILjMdj\nFhcXVVXhz/zMz6hN7d1332VpaYlSqaRYbCm1926OAhgEaDqOw61bt/jwww+pVqu8+OKLbGxssLKy\norQbUtovgEfsG7rdrmIIJLUZj8cpl8tqbREHd13XKRaLKl0nm7hE8bLoh8NhZc4oUbbf72d7e1u5\n5Uta0OufJfdEGEPRdJ0/f575+XnOnj2r2quEQiHS6bRqvi3aHLmuYlIrrJqcqwA/6WUndhLClHwe\nQNdJvZU31egNxr36Xy/QOsl2CXAS5laCcnmdl1kVVlE0WNK/MJFIkE6nCQaDig02TZNUKqX6jIqG\nTzR5AmpkXieTSYrFoureUJ324RXgMzMzowyHt7a2aDQaXLhwQVUfSlAgBRfeuSzXSeZHq9Vie3tb\nNcsWf7yrV68qECfXodvtKqZL5vrs7Kyyz5C/JSygZJ329/dxHLcJeDqddveIqd7NaxIr+7h8F4F7\nJpNR8+bg4EAJ671MvhcMS5peWF5v6rHb7TIcDgkEAirQkuDT20XDMAx6vR7b29uKsBFcIWBVxozg\njJPp6r/o8VhB10TzH5s0gEopAgyd8bHnNU0jEAiC4zaQHlsWhqG7InzToD+daJLH1zQNe2Lh2Baa\no9M3Jkz6XSL+MGg6E8vPuNuj02kybrQYdTq09CjvrG+R14Ks+qJUGNA34Nr7NzAAC2h2IFDv8dzq\nRYImLBVinD/30wwtIJghV1zABsqNHaK5NF/+8S9TrtfRtAjFs5eZmAEiU4+SL/3tn+fNf/M2Zn/A\n+vqH1BotuoMy588/gR0N0e72GQ1HjCY2GAbYNgHDx2A0QDMgGNKpHDUoJBN88O5VDjbWWcwUePaV\nl5UOxxcI8MKrL/POn75NKpVibmmZQj5PZfeA5qCPo7n5dl0zaTc72Jblto0I+UnEYmiOzUG5QiKR\n4Bt/+PukMilmFmeYWZxhNByjAyEzSHE2T6nZJJcpMh72Cfv9OL0+H733Z4wdnUy+wMLCOcxAnOq9\nPSYMMQ2TkAkhn8mg0yduaPh1jbDh4+zsPMuLS6wsnyMQiZJdWiFZnGPi2FQbTXrtFoYeYOncRSYx\nP4N2C7tZZ9Bp4zTKFCM+9GiBkOnHmkyYDEeMmTAY9NF1N4Ka2Jaiszv9xzQR/gKHNzqHh9U0cpzG\naJ1crLwRvDBJAliE1pcIPZ1OY5omOzs7CnBIxF4ulxUr0mg0cByHfD5PPp8nk8kQiURIpVKKFZLN\nKBQKKZfndDqtNhFJTbz++utUKhV2d3cVe12r1SgUCkSj0WPNuOWQDdSbDkilUjSbTR48eKB8eiQi\nl43hzp07ijWSkvZarabctR3HUR5koVAIx3E+kzLd2dlRrIE07pYUivRzTCQS9Ho9NM1tfVIqlSiX\nywQCAbLZrNvrdMqECSiDh3o7cCvnMpmM6meXyWSYmZkhmUwSCoWwbZvd3V1CoRCpVEoZ1Q4GA1Vp\nJvfTe++FwRSgKfdB0s+P+zjps3XyOJluEnAlY1o+o5flOI39godmqDKvpDpPxoJ4Q0nloPSyrFQq\n9Ho9BYA1TVMpd9ESyfsLyyYbfqfTUcBpZmZGMT4ydyORCMFgkIODAyzLcg2rtYd9HXu9ngIQcohu\nSoCU3+9nfX2d4XBIsVgkk8kAqOpGQAEYQBWNCPiWJuwiipeKQ2HQgsEgmYzbUk6qJYWdkz3Z7/cr\nxsi2XYd/n8+n/OSkHY98dgG6XpcCORzHUVIGYd3j8biqOJZektFoVIFTsY1IJBJKEnB0dKT8A2Us\nnWxw7h1bP4zjsVcvegljTdOOPaZpBnD8YluWhaHpWPZDGlzTdYbjMaZH8CYUY8gfUIvKxERtLIZm\nYju22wKm3SJgmsp/qG4PebB1n5DhMPFr/I2f/VnKmzvs75VwqhW6ls2w3GB+bpFzqwsYdo/1Bw94\n+vJzDIhQnJ9je3ePC5ee5KObH5KeKbBXrWBrEItF6DsOzXYL2wZ/zM/f/aVf4p9ns3zwm/+Y5YUF\nHmztEI8lqUfb1BodDAx03USzHEAHnx9Dc9yFdjzh3OICX3jxRRr7BwQMg+JMjt29PdLpNIPBgDNr\n5+kMujx9+RkqR2XS+Ti9bpe5xQWMWpVAIMD58+fZ2dsBXWM0co0TNcfmxo0b7gYSiTAej1ldXSWd\nyeBgs7K86gqJK3VC/rAq6+31eoyHfR7cv4czcUuNG50+BwcHbNx7QKc/poWOge06sukm48EQE5hM\nHDTNgaDB3OISEzRufHSLVDZHYn6BVqeNPt1gh5Mx6WTcBdz9Pj5N57BWwxmPlGDZmoxoN5puFDix\nQNem5+hGRn79YTucifP5EA3L4Q1GHvU8HE/JC/Pr1T+cbE4th7eU3utrJPqKTqejHJ4lYrVtm+Xl\nZc6dO6dE34CqBkwkEszPzyubhFwup3r6SZRZr9dxHEdpB4WVEUYhnU7zta99jU6nw6/+6q+q5rWy\ngFarVXWe3g1VeskFAgGVrvT5fAAPg7BpVC7pjpWVFVVCL59BFl2fz6eqoCSVJJVSlUqFYDCoNDBS\n8Sju9AK8vIJnn8/H3t6esoyQcxFPM68+xguWJS0Uj8eZnZ0FUP5FkrLxioplo5BoXwCXVGDK35VN\nU9KQksIRsCZSjsd9nEyzew+ZA95N2Rt0yPg4+RhwDITJ4a1SlPcXBlhsE+LxuLpW/X6fVqtFrVaj\nVqup63rS6yyVSqkxFYvF1FgXVlFE3aPRSJnuihZL2CHpkbu5ualSyAIOvIGIfCYp1BBQJIUvohUL\nBoOqylUqL5PJ5Gec3aWKV85f01yjY689CqDGlvhrAcoyRUCgl2GUrge1Wu2YY79o3Hq93rGWSnLv\n5B41Go1jaVtJIwrYEkNXCTRkbovdhfhvSfpd7vfJceLVyJ4GAP+ix2PWdOk4yghVQ3Omm4hsJpqD\nppkuENN1NEDXZXMJYtk2tg1MAZjjOFj2cXO7/mjqjeIzMW0bGxtb07EcC10DTINwLEpre5fxeIzf\n9HHjaJvCUo5wJMyF1WUK2RzPXnyao1KZZKpAs9ni8nMvYsQt8vksWzvrvPzMC5SOyhSK81S6bZL5\nPAe1MrNnLzActcmsrnDh4nm+/c1v8sLLrzA/N0u722U0GbJy+Ql+aXkexx/lV/+vf8R8psh3vnOV\n4vwcmt910MfUsQfuQh6IJ3h57QlG3S6V+3f58utfQB9bZBfmCYeDLK8tEc8sk82l2dnf48b1m0SS\ncVKpFGfOXHB1WAEYazpPPnWZUqnE9vY2YwsyuTyBQIBcJs39u/dYOXOOUCjE3t4efn+QS088Ra1R\nVe1V+v0hM4VZBu0+zWab4tIsuuaAPWF5cQFn4jaErbVcBmE8HmNqENBAs907b48n+IF03J3ghUKB\nS5efZmgaRBNxXrvyHLmZWZbPrNHsD+n2e/zBH/8xD7Y3iURDU0Fpm7XVVZ4+ew4zGKTZbLrlziE/\nkXiMUd9dfNCn2qVAwBVuDnpqc/xBaeN/G4c3jSKHdxHwpla8/jPwsOrG6zUjn9ULWk7+LUD5dYlF\ny2AwUHqhM2fOMDs7q/qvSQ/G5eVlVUknqU7bdl2rdV0nk8kwHo/JZrMqkjZNkzt37nD27FlVbWRZ\nFrOzs3zpS1/i93//95UG7PDwkHQ6fSzlKucdj8fJZDKqwuqpp55iMBioxTsYDHL27Fl03W2uvbe3\nRzQadfuFTsGUbHy5XE6lG0ejkTIY1TRNWVaIJiscDhOJRJT/VqvVwu/3KxAnxrGi7/H5fLRaLarV\nKpqm0Ww2VbrUe48FrAlYnJmZUb5Luu42/T1//jyzs7Pq93Z2dvjkk0/Ude33+8zMzChBv1tc5ALL\nYDCo0ljynKRUvEDrZJr6cRzfC3TBZ1kIL2iFh1V5AmpPpulPssIn309eL88ZhusYv76+zu7urmoM\nDyin/52dHXWtc7kcmUxGBUPC/ArbIprC4XCovO285qStVksJ023b5vbt20wmE1UpGQwGlY5LDrFP\nkHFbKpVIJBLqcwqzJsJ1y7KUwbAE64BKaUvKTYBVOp1W80AqDOv1ugpmgsHgsYIQGWdeGxMBk61W\n61gqT8CRgDDveifXUACpVDBLm55MJsPs7CzxeJyFhQXm5+eV27zcQ2m7JYGF+KGdLAr4YRqieo/H\nao76navvKyNIXdeRpIc8ZuifzdtrDpjmwwaamqbhD5gqspQb5DqM+45FjD7DrYBANzB8AdrdDtp4\njN3tMSmXGTVbfOuf/wvyKwv82I++Ru3oAF0HX9BPOp8nkclw1OowtCw0f4hmZZ+FpUX8AZNup4k2\nsUlF3EmgYdIbuch+ff8uCzNFRvU2rUqN4XhCvdPj4uXL5JaWaPSGGD6TSaXDr/zq/8bVd9/j+qef\nkC/O0hz1iWfTDK0J0XiM4WRMe/uQdCjMf/wf/h3uXL/BYnGGv/Xv/zzvX79GpVYmlU3zzPM/4urc\npn4x/f4QX8BPt9vFHwwyGLkTvN/rqFLZxaUlbGfCxsYGvV6PaDhEdBpNJaei6X6/y9gaqSgnEomx\nv3vAZDCmXC6D6RAI+Egnkwz7riVAr9fjqFKldFjm/vbU46nfp11rYGqQCkfpdTv4DJOnn3+OUCzK\n3/zFv0WmkKfZHRAKhdF0ncPDCg+2d6k3G/zrt/6ERrtBt99he3uLfDRBIZXix176Ec4sLzE3UyQQ\nDjAauekBQ9OwRjaj8WDKRAywxw8jfduyGA0G/L1f+T8fawnjRx995MDDKNwLMLzj+2TaXTYmb6rQ\nq9s5WbUlrxOQI6BNNEXD4ZC7d+9y8+ZN5er+4osvEovFlB3CwsKCYr8k0hQfKymHHwwGquefpBAA\nFZkLO2DbNqVSiaeeeopisag+09bWFt/85jf5xje+wYMHD5RgWFJ94sQ+HA6Zm5vjueeeU7qn1dVV\nyuUyOzs7mKbJK6+8oj6z95rI55d/1+t1pT8LTMG5VKR5W48IsJHPLc74fr+fZrPJ3t6eAjqyqQFK\nLN9sNjk4OODjjz9WrJNofkSfmkwmOXv2LG+88Qarq6vE43H19/v9PtVqlXq9zr1797h+/Tqbm5uq\nTUsoFOLChQusra2xtrZGNptVwmRZF2UDk81Trq+3pP8H6TP3wzh++Zd/2W3e4dmrvBou7+PeVLqX\npfI+Ztv2Z8CWvKf3Ozys4vQWbIilia67BqACzr2pfpmD3vSZzBVNc5suz8zMUKvVaLfb6nxkbIsX\nWzqdVoUpvV6PWq3Gm2++SalUUmn6mZkZBeAA5bgeCASUI7ywW7FYjEQioYTs+Xxe3WvZQwWceeeH\n91qOx2NVTCIgS4I5QIEzL+sua4SXcfTq0eQ6iYBfAgCxnZBzbLVax4pXxGrCm26X85LAp9FoqPkq\nKVJd19WcFSuZk4eXrTw5Tv7hP/yHf0XNUackl5y9xNzCfelMb5A27caouRWNo4k13ZSmpbrjEWgG\nhlyk8QRdM7HR0XQNHMfVfek6o9EE3TAYjIYYpknA76PV77G9s8e46TakbdbqlHf2SaeTENBJFjP4\n/UHGlkOikGei664AfxCj0WgRSyaIx1L0Wk2GvT7WaExxfh6r36c3GBGNxZlYbg46FgkRc3Tub27C\naMCDe/cwI1EyuQIDfchf++tfJZNOEIu6JpTWxCI8GDFsNjAtB9uasJpN8x/97b/N7du3+cmffIMf\nf/11yuUyz77wIm+99Ra2Y2KaEArFphNaQ9c0ep0umWyG3Wkz3ZmZGZr1hov0HYdqtYrh9zG3sEB/\n2jdOc8AajVXqMBwOs7l1NC1NjqsqkwfVLXrDAT5HJx53S9ix3fvUbDa5c+cOtWqDuw+26A0c4mEf\n0VCYyXhEt+O6AT/z3BV+6me/RjqXJxiL0x2O0IMB+hOLWq3MoOdWoe18tMPR0RFje0y1XsXWYNgf\nEJ4J0ZpGXMW865ruCwTdDXVigW+CT/dPxZkm+HT06WJgaxq26ft3Pwn+nMMrcj8JwORnb9GId6E4\nDbTJ4RWjCgCTjUOAjKQUYrEYAPV6nXg8TqFQoFgsKuCSyWTUhiGUvaZpyufGcRyuXr3Kiy++qP6m\nl5mTVif37t1TOhCxQ8hms/zUT/0UyWSSd955h8PDQ3q9Hj6fj2azSSaTIR6PE4lEKBQKhMNhLl26\nxNNPP+12HJgyCK1Wi2azqYTlAqrq9brqkyf6FKnOEvZcHMbFtVq0TgKoZMORMn4RJouoWoxbvZt+\nq9ViZ2eHcrnMwcGBAqey6chcu3z5MpcuXWJtbe1YgYXYWEjV3d27d9X7SHpLPo+AM6lEk3SQ6Jq8\nDZrheNrNq/973MdJRuq0571j3Jsul+e8gOs0EHeS0fCm3wWY6rqubFISiQTj8Zi9vT00zW2GHolE\nlDZJgIMUPMjmr+s6y8vLRCIRSqXSMfd0AfatVovhcMjy8jLxeFx5ql25ckV1A/nwww/Z2dlhdXX1\nmBgeUF0UJMUuBSsSVPX7fcWmyWMyfrzXR1gg8Tx0HEd1cvCm9+Px+LFUrPiHSfNpx3FUlaSk6yVF\nL2Ne2oR5C4O82jUBqaFQiIsXL5LNZonH48qMdjweq3HutfVot9sqoJQ5IIyvMP5e7aB8ZrkOp+n/\n/rLHYwVdNjq6prsbHmDIpJl+uaDM1Xk5mgaO5rJhmobtaOimj/5ojGm6VPHEmk4aY2oBMP1dNA1N\n15hMwDD9jB2wdcN9ztBJ5wukXniOyvYOOxsbPP3cswzGY9774DqvvvFjJGaKbHy6QTgaQzMgnS8Q\njMYYjy129/aIpHLcuvo+g06b115+gd//w98nu7zIpVdewoj4KJpZrMmIxnDIxvptFucXWMhn+Y1f\n+zVe/dIbFJdW+GT9Lld+5FnisQs8c/lJ/vrXfob333+fT29+gjnRGPUHzOUKlEolZs7MEfP7+Gtf\n+UlimQz7tTLDyRBsOLv2BPOFGT65dY12u00qlcGyLM6cvUAhk2J7d4dgwGQ4Gk6ZPJN2p8md27dZ\nu3iReqWK7jPRdfDbNsO+a/4YDQYIh0PUqlVyGbe5abfdod8dsLe/T75YIJsvYPpcrVmz3qDdbjIc\nT6jWGywuLPPk00mCf3aNRrNJ1Hbvz2gyZoJDvljgqz/3N0jkCmimD80X4MGDbQbo05LeILpp861v\nfYtPPvmE4XjAcDxAc2AmXyCp+THQlPZhc3uLmZkC8VQSzTTw+wKMWm0C4Qi6P8BoMMSZWNjT1ijW\nYIShf75Al3fDgOPslvz7ZJR+MoqX9/G+xnt4FxDve0sbm263q5rPxmIuiF+b9tGUsvfxeKzSJT6f\njwcPHqh04NHREaurq9y6dYvZ2VkVidr2QzNjiWAXFhZUyX2326XVaqm2Pl/5yle4ePEiH3/8MVev\nXlUbiRgv5vN5leqcn59XGwKgKqlKpRJHR0cqypeIv9PpqLSJbJIicBZzVAExco1rtZpasKW1jn/a\ng1Sqp2KxGOl0WvmWTSYTqtUqvV6P4XBIMpkknU4rfYncD8MwiMVirK2t8dJLL5HP59Vzko6UqsNK\npcKtW7dYX19XKRNhOcLh8LHNr1arEQgEFKgWJkJSbcJyCQiX8fe4j9MA1p+XoRGg5N0kvRsnPDpl\n5N1k5Z7LtRKGx+/3KyuIaNQ1XJa56gVlXqYmGAyysLDAwsIClUqF7373u4oZFVDnBcsC4uv1OtFo\nVHmtLSwsuDKTQIDNzU1llhqJRI7dV6n8i8ViFAoFLl68SDQaVWOh3+8TiUSUMajjuEUjn376qQo2\nvCyPt7hE0zQ1voT9kr6PAsIAxXjLOiFjT6QLAr6GwyGHh4dUq1W63S65XE4Vvsh7bWxsUKlUiMVi\nai2R6yXzZ2ZmRhW0SEcWr6GwBJYyJuR8xCbGa1EDx7WCjwL7f9HjMTNdOo6mT1ksF3hpnv/0Kehi\n+jjYWNNFwgHGlo1p+rA1T885YJqVnOrF5NBwpnqu6ZPYuqsVcxw3DReJRglFIhQzBer1Kj/x01/h\n9vodYukk3fGYgKExGowIj0YEQmHSmQw+vx/HtmhV6/z6P/o1Lp35Ff7OL/4HHLbrtNttYqkE/WaP\nSCREs15HM3y0ux0+/ugTvvYzX6XZHVDefkA6m6VZqZHIZGm022RmCrz2pS9x/omnuHntA4KGj6Dp\n4/zlJ4ml41x54Xlq7SZjx2ZsTbh27RoLs3OYuoOpO1j2kPFkyPvvv0cgEKJaq/Pqq68Rj0fZ3LzH\n7OwsYwNqdbcn3BNPrIEOmWzK9WVy3Ij33LlzrmCx1cQ0DaKxGBq2MpAzTT8XL17Esmyq1Tqa7gpE\nM7ksn376KbZts3r2/FQjNMI3HeSlnS18oSAYOj/2kz/JpWeeJpnN4pgmaAaddo9sOsfY58ceuxHH\nH/3RHynRcafXZjweYvh0Br0B/kSEcDisHMKjsQjd3oBgfEI4EMTw+QiGQ9j2RC0KFqADwYDBGLfS\n9fN0nAaqTjtOSxt6n/MyBCcZDHm99/fkezqdJp/PUy6XVaSeTqe5e/eu0kcBSg8kQEg0XNVqld/+\n7d/mueee44033lCLuvQvlA0+FovRbDbZ398nHo8rBurs2bMcHBwoH6QzZ84obZOI+EVzVSwWld5L\nTBbv3bunytEHgwHValV5ZoVCIQqFAi+88AKWZdFsNpUuR8r9JR0onlci9vWawUoVmkTRolmJx+Mq\nJSRRt+M4qkWSbBiWZXH9+nVVNSq6l5//+Z/nmWeeUakeYUEkSh+NRmxsbPCnf/qn3L9/X1XPSUpU\nQGAs5nrlyeNi4yGbswAL7ziTFMwPQzT8b+s4LajwHicDlu9ns/S+52k/C5CVilCxIRiNRqoasFar\nqSbToreS1jneikXDMFhcXFSsqoAPqfCVOeVlYWUcC5uWTqeZm5tjZ2eHO3fuuMVE09/RNNeFXtJu\n4h/X7/cVqE+n0yp9KWa+otUSw1RJjXt7cUp6Hh5ajgSDQRqNxjHdnNdBXtM0VT0ojK4cEkgMBgNV\n8SxtsmRuVSoV2u02xWKR+fl5VTAjAYXjOCQSCWZnZwkEAooJlnOWtUZkDF49phTxSMWuV27gLUD6\nYUmxHnP1oo6Dy3RpmpsCE9G8AwqEgcuK6I6O6dc95bfmtG+fNzEJ1nS+6HgYAcAyDBzH/btoYGBj\nWY7LsPl9hJNJltfOsXlng4Wn19ht1Uil0uytPyB7fpH8whwB3U+1Wuegtk8/GSczk2fSaZKeyfL3\n/qv/mlQyy//0P/7P3ProBv/Lr/4K/c09ugMHw0oQS6Qw/X4KuQz97oDf/e3f4bUvvMJgOObJ1UUO\nKjXu3L5HMBJh7eJTmH6T5IU1zq49QbVWIxKP0e33WMgvcP/eBv1uh+Ggh6k7XFxZ5dOPbxKLRvno\noz3+v//nd/GHwtz66BNa3R7n1tb4znvvceXKFc6eWaZSLeOMBwxHbmmuX9fYPzxQJdD9fp+ZQoFr\nf3aVVqPJ0tKi6x6cS1M5KiuGQSLpWCKB5jPdNi5d198nmkgRi8UIBN0Ujz8QoFQ6chcrR2N+Zo5X\nv/gaX/jyj3NYqbJXLhNLZQkFwwyHFpFwDNP08+Gnt9nb22F9Y4NGp0G5XsaeuBNYt2GhMEMqHOLs\n2bM8ce4sftPA53cLMIbDMQ49goEAumGi6RqmbqLpJj7bwbFdKlwzdCajx+9JBA+rr05LDX4vYb0A\np5Opk9MOr/brtHJsQNkqFItFlUqTqLVerytrCGk/IxvF3NzcsRRAKBTi13/915mfn+f1118/lt6J\nRqM4jqN8cw4PDxVb6fP52NzcPLbRhMNhnn/+ebXoi6BdtBtS0ew4DvPz8+zt7ZFMJhkOh3zwwQc4\njsOnn35Kv99XfmKLi4tK6ySAQyJ9ryZLGAgBaKLVEbAWi8WUv5njOMqaQhgRMc0Udkw2C+kXJ2nB\nlZUVnnnmGRzHUaXswiLKhlqv17l16xZ7e3s0m03lDi5gQ9JGZ86cURVkAhplk/cCcK8Bq9dU8vMA\nuv48gHVaStB7CON18ne+XyDm/d1wOKyAqXRM0HVdpbIlxSceUfIllinvvfceOzs7pFIp/v7f//uE\nw2F2dnbUnibMjKT3u90u2WyWyWRCt9tVTLYY+YbDYVKplEplCzMlFg5S3RgOhwHUHAkEAsoyodFo\nUCqVyOVyJJNJ8vk8juMoTaIAFOkrKQ4A3upnuTZetuhkACjXReaWgJxGowFwzAdMNGvyn41TYQAA\nIABJREFU/NHREefOnVO+ZvJeUpQggLjdbqs5IelzpemegkApMpCKY7mGci9PsqQyB35YKffHKqT/\nk/c+cmSTMLXPCoR1jeMbj/PwQngrSRznswyFcQqF7OgOuqPjEmM6YGM4FgFDxzfqw2jMweYm+xv3\nGIVMLj31JLXdPXKJFL/1h7/Hf/pLf5d0OIGBRqc/4F75kIBPZ34mDdaEkD9Et9Hl6OCQYaVMu7zD\npN/Hn82TLhQwIiHyxQLdZoutO+tsb2xQr5dYWJij1Wly/qUf46UrL/H++9dxJhozM3NUaw18kQjZ\nQp5QzG0U+mcffsT5c2eIhoL02m2++87bXL78FLoB4/GI2+ufEgiE+G/+u/+WM2tr3Nt8wN2tXXzB\nALFIiK/++OvM5HKsLS9z5+4dxuMxxZk5LNzB2e52VK+tSMhtsjq2JrRaLQqFAns72yoSDgaDYJi0\ne300dOLJlPIYEjNAzXF7a9XrdY4O97l37x5+HPqjMfPz82DozC8uce7SUwQiMSaWQ3c44c7tDR5s\n3qNUPqJcqXBv+z625hof1itVJuMxznjEmZVVMrEIT5w5z8vPP0c6mcAf8LkspOlWyGrOQ6DhYD+M\nwOypOHQ4wrbG/Hu/9Pc+F0J6Eac+6gse7V7/qGqv04Ca9znv78jz3W6Xg4MDZe64sLCgTD739vYI\nhUK89NJLWJal0mpSsi0LdK/X491331WAYH9/n06nw+uvv66YG3Gi//jjj9nf3wdcpk0E8dVqVbEz\nUskl7T7C4TC3b99WRqXNZpNKpaICg263S7VaZX19nd/93d/F7/dzeHjI3t4e4XCYXC7HE088wVNP\nPeU2ei+X1WIuGhBvys1xHNVgWtr3SLWsACPZFAC1uYjGxKtpGY/HbGxssL+/r5iubDbL+fPnyefz\nyrG/1WpRr9fZ2tpSXQJu3LihNhcRSksroGw2y/z8PC+99BLLy8vKrV7SKXLPvZH8SYG9+CX9wi/8\nwmOdE//gH/wD5y+zT3k1XfLv7/c4yQ7LewgTJcJr0VoJOBHPtEqlothPb2cGSasNBgP+4A/+AMdx\nePXVV/nKV77C7OwsN2/epNFoqPdzHIdWq0UikVCVwQKyNU1TKThvw+pOp+PaJU0tU2ReSmpNABNA\ntVpVjA+gqnyF2ZbWRKKzkrSlFMIIuyuFHSeLeAAFwCRNL+NO0tmSBhXQCrC1taUqgVOplGKXvca+\nUsyTz+fV59zY2FDmrjK2pdBF1vxwOKzmrujPhEn2sqPecSDro2g4f/mXf/kvPSeMr3/963/Z3/2B\nj839o6+rTUR3Oa3jm8uJipKprsswTvbN0j77pelojoarAps+Zug4DmhTDkx3HBzbAtsiHDAJBQIM\nuz2saQuQwWBALBrHHwzywY0bLMzNYegGqUSSbqdLo95Bm0zwmQ6RWITBxMbSfIRicezJhDNLc/zm\nb/8m9VYXXzhAdmaGbq/PYDCkWalgmgYBn8H7199jNBpw5bkX6fX6dLttUpk0b33nHRbPLNMZ9KjW\nKthYDEd9ZooztJt1JuMxm5v3eOry09x7cB9H05jYDr6gyyj5AwHev/ERmt/HzmGZkT0mHAoz7PWY\nLRSYDAaEggEMTScYCjIc9kmmkhQLBUxDp91q0u32XA8tv492uw1AdWrkuLi46G4kgwHBcBjLsen0\n+himSbfXw7LBZ/io1qvUqlU6nQ62YxONRLi9sU6ukCcYCnHx4hPk8nlsRyMUitAfTrh95y7/7F/+\nS1r1GqPJmEq9wmAy5qB8SL3ZYNTrYY0mBANBstkc+UyKVCxBMh4jGPATDAbcwgpDRzcMdG2ax9c1\ncMCRDghCIzs2mg4Xr7z03/87Gv6nHkdHR1+H4218vh/QdTIYOS0deZIxOPl+3kME1rKACtMiXjuS\nGtM0TVH9oony6jkkLVcoFMjn8+RyOb797W9z9+5dzpw5QzQaxefzqZSLAHnDMOh0OkrE3+123fFj\nP2zALbopOUcpkZcKSml1Ig7zsvFsbW2pgEBSMl7hczKZVIJ+QDEDwWDwmFBeNjmpFvNedyl5B9Qm\nJywhPGSIxQNMUiLSSPvcuXNq4zNNk1qtxvXr1/nwww85PDxUxqxyDt4ec9FoVDEW4sYtG7CItE9+\neQGJ/CxB7cWLFx/rnHj77be/fpqu0QuGThvLp22g3yuIOe04yTALWygaJ0lrCSMiDaGluERSc1Jo\nEYvFVApamGSAg4MDtra2aLfbag4JcJegoVKp0Gq1OHfuHLFYTAFtYcfkfATkt9ttJaTXNE1pqIT1\nFGNUYeni8bjSU0rFo7B0wt56W2bJmiBgxKu/ApTWSwp8BLDJXBAGTcyJ4SErKZ9FBO8SwIm/mVR/\nSkHN3t6e6pkqVZHyVa/XqdfrNBoN6vX6sXaBXgd6bwHJaePCu7a+/vrrf+k58bkAXSrN4Tiga675\npeOgC9CCh991DdsB281DurotfVoJqWvqy9HAMTT3S3e/NNsFX7Y+Fe9rAA6apjMZ9+mPhxhhk2g0\nRX80ptfpUzo6otZo8vqXvoRpBugMh9Q6HRbOn8dyRqQzafZ3D2jXurQqTZLxGOFggEguRQ2NtZdf\nQzMjrDx1GSOZwDI0ItEo2XiU8XDAM09dotGs8eqP/ih/+M/+Ba1GlbW1M1QqB8zm0+w9uEcyFGAm\nk6K0t00qEkbXRtzbuE23UyeRThCOhbAci2A4iOnz8+7Vd0kmo8zMFrl56yYf3rpDMh1nMLKoNTsE\nk3G2Skfs1Kq0u10qzTb9yYSd3V1a7TZH5SO2tx+QzWUIBEwGoz6pZJx2q0EmnaTVbhGJx3EMk73D\nQwxfAEfXMX0+Iv4Yg8GIVqdLvdFkMLEwQmHimSxmKEx3aBPL5Ekl42i+IBNMOhPYPaqTm19ma/+Q\nT++ss729xWTUJ5iNsbO/Q6fXoV2vkg5HWcwXSARDzGZTnF9Z5vLFi1w+f4EL584STyXBMAhGoowd\nB8ty0HUDTdexNR1TLbRuEYc2LagwDQPQufDMc58L0CWLAXy2WvE00HVy0/SmKE9bQLzvfRKkycIn\nEaAs1CIgj8fjdLtd0uk08XhcLebiUq3rOqVSSWkqhP6XaFpSZC+++KKKbGUja7fb5HI5RqMR+Xye\na9euMRqNmJmZwbbdNjVHR0dKzCxVtbL4imu+pBOkaqpWq6lNo1Qqsbm5qQCLaE1arRalUkkxUmIK\nKWXl0hZJmAoBeLJhyLXs9/vHAIG3f5tsqsLYSTpK+ovCw4a8ksLq9/vcunWL3d1dxSS2220qlYoC\ntaJNSSaTLCwssLi4yIULFzhz5ox6bwHRJ9mb0wCN999ra2uPHXTJ+XiP01KEp82Nk8efx3g9KmAR\n8OAVzAPqMe+cE4d4ue+O4yjgLnNb7BuGwyEff/wxt2/fVkGEvEbTNOWfpWmaagDtbXjt9fEaDoeq\nHZGIzOVcJTCSMT8cDlUxiTe1Lp9XdIaiAROQB5DNZhVj5GUURbsojKC3AlEAk4A0r9ZKrp/Ypghg\narfbTCYPm4LLZ8/lcly4cAHbdpu1i1BfmEQvWN3a2qJUKh1znpcUujc9+qh54A1CwF07/8qDLqEb\nvX4fpmm6bITmqWL0fD/+2BR8Tb/L18nHdcdxXy9aLzSwJ2iOg4aN5thMLAvT1ugPBnRabUIhN4Vh\nOw4TywbDbQGRKxQxdA3TMOh1etRrVWzHJpvNUG3Up6J8N8c9X5xlNBmTzWfZ390hHAjQODri6PAQ\nQ3MYDnoc7h/wzOVn+ddvv0WlVmVmdo7lpRUsy2Z9fYNoNEY8keDbb32bUDjCwsI83W6PUCTE9tYO\niXicRr1BMOBn7fwab/7xH3H7zm3GE5tgOMD97QNi8RiWbXF0VGV1ZYlrH1wjHY0Tnm6K9UaDne0t\narUqc3PzTMauT08mk+Gtt94ik8kQCASIxeIMB0ParQ5rFy9yeFiiOxUM22MH02cSCoexbPd6Ntsu\nY+BuVNNmvaZbOFFvNJldWGBpZZXBaMyDBw+4du0a3/nOd9ANjXKt4jItPj+6BqauY00mzBSKzM4U\nufLss1xcu8jywgLZQl5t8MFgEDQeRlCI0aPj/u9ZeDXN1RMCnH/6yucGdHnBk0Rm8FmR7/f698nX\nn3ydd8HxLjInBaTymFD+cm4CWMSIVDRB0o5DmuR6TRLT6TRra2uqIlKYKMdx2NjYUMadnY7rIbez\ns8NoNFLiX/Gh8vl8RCIRHjx4oFqhiHbKbcSuKyF7KBRiY2ODw8NDWq2WMlUUMb4YK0pDb9mEpP2K\n6LhkfQoEApRKJQVkxFDVcRylr5F0jtxP2aDEiFU2NnEEF13bYDAgn88Ti8WUiPh3fud32NzcpNVq\nKZ8oSbdKY+JcLsdTTz3FlStXuHTpEhcuXGBubk6V1IunkqyzcLo+5SQgO3/+/OcCdMHpjJb38dOO\nR732e7Fej3rOy2h602hi1eIVlXtTVmJb4GVrhcEREXc0GmV5eVkBNm+Bi/QAjUQirK+v0+l0jjUy\nb7Vaag8VFlXsLODh+JOuDuI/J4GI2JCIxQOg/r70Oxb9k+M4HB0dKY2ZrLfdblfpyuR6iWhe1i5h\nWmXtkcpDAZNS+OIdg96AIRgMqnSjnJuXHSuVSspWotPpKAYbUAyZVIyeZLbkMz/q/nvHzw8Cuj4X\nFtyygAsqf1g2e7wZ78mo/LTn4LP5ePV3NH2aVBL2zMF2YDIVUqObjO0h4XCITDFPbzggl0lRLVeI\nJxMclo949onnabbaDPpdOt0BsWiUbD5PJBJlMBhw7YObbkVHIIgR8BMKx5mMhxhjjfKDXc7NL9Ht\ntbFDAZ57/Qvsb6xTOSxTyGXZ2Njg5VdeYWd/D2sy4b1r7xKPJLh0cY2j8hHUDX7kpRe5du0aiVde\nplkp0W7WyRdnKe/t0en1+c6331b92OYXF9ja/UPS6TSrK4vc397B9PkwDZ0bN2/SH1g0h0PW33sP\nv2mSSsQ5f+4MqVgMWzf4o2/9MTPFPOl0mpl8nm6rTTcaQzdc5+3DUpm9vT2evPwMh6UjWvUGfl+U\nqC+KPxAgNhVe+jttLMuhP3A312A4RDCdZdztkyzOYPl87B4dcWdjnWazyVG9im1o7Bzsk0jEGE5b\nWZxdXqGYzxP0+8kmEwT9ARKxOLOFIqGgu/HopqEAQjQYYDR52NpnMplg6Pq00wG4VRWOO84sW+kA\nPy+HLPLeReo0ZuoH/Run/dv7t2RhjkQiRKNRxWaJgL5QKByzGxBfq2g0SqPRUP5WAhJk0xetibSd\n8fv9SrQvkev58+eJRCJqkS+Xy+pcDg8PGQ6HzMzMcPPmTRYXF+n3+3S7XQW4RLgrwmLRXokr/scf\nf6xSM/fu3VMNnw8PD9F1nXw+z8rKCo7jUKlUKJVK6LrOxYsXVWXgaDTi6OhIWV80m01yuZyyb5CU\njGEYSoAvhQdyraWCrN/vEw6HmUwm1Ot1tXlIalRAr4BX2YiFSSwWi8zMzJBOpxWIg4fVj/BQY3Ma\n4PaOg5Ppucd1POocTq733+v1p7Fij3o/+fnY3uEJPOT9ZDx729t4gyO5lyJaF/ZH2CgRikciEZaW\nlrBtm4WFBUKh0DGAVq1WFWAWHzppji4gRECdBDdSqCFsqqa5gnQJdMRUtFKpKBsTSS2Ktmo0Gqn2\nXUdHRwq4S1swabkjfU3FUsayLOWVJW2IRGTfbDY/k9aTQEY+k2maJJNJ1W5JTHqlr6sw0e+//75a\nH4Uha7Vaqmm99IEULz+5F8Lan8wGnLzvJ8fLDyqgl+Px+nRNNz0NcKTCRNOYTBeIRwEu72Rzfz6O\nQh3HO/Ee/luqGnGmF0+zQfcBOo5uM3EsLMNPpdMilUiQnS1i6BDPpBhOxiyvrLjNoTstdJ9Jvd6k\n3x8Si4bxBUNEYlHiqSTdbpdSuUqukMfWIByNkIoneP+tdzja32dgDTnz1Dkq/Q7VbpcL5y+yt7lF\nMp/mwe4utz79mEA4wquvvkrtqMY777zD8uoKK/OrNJt1DGvM/du36XY7RBJJbl3/wP3byTTDQZ9+\nr8d3371KsVh0G99GY2zuHfLaa6/xwfUP8Qeh1WyiGxpXr99C5E2JSJW7D7ZYnJ1h0O/y5de/yLjf\nYzwe0u326PcHVOp1XnrxZcyMj7Nnz1Ot1bh5/Qb54gzxaIxub8LEsTnc3SUYDhOMhOl0ekQTceYX\nl1VqpdrtEMsW6XUHXL/1iUvBa6D7A4RjUUKdCIlEjHQkwuxskWgkQjFfIDpNoQQNH6Ggn4DpLg6T\naS2sT9PQptqtwWSsmBcA0+9ziy5cYR84Ds5Uh+RMjXcf9yFj3BstnvQb+n43kD9vM/pe7+GNACXi\nNU2TYrGodEiDwUA1oJaASVJrUmkokXGz2VR2EQIyOp0Oe3t7KuUQj8fJ5XJsbGyoTeTdd99VDZsl\n2r5//z69Xo+5uTnArcgSfx/RlkgqyOfzce/ePdrttgJSEin7fD6eeeYZ9vf3lS+X6D8k2j48PGRz\nc1P9ztzcHOl0mk8++YREIqEYg1QqxcrKColEgmazydbWFolEQm02Ug3obcgtvfMMw1ACaLl2N27c\nUNWgmqYpsJvJZEgmk+RyOVZXVxVrIfYEsVhMpW9kvEi6Ss7hUeyWF1h4rUQe93GSlT758/cjkP+L\nCvEfBbjkOsn1EbNTSTOHQiG3cnuqVRRtUjqdJplMKvBVqVTQddeDUMC7bdvcv39fib0FuEjlbj6f\nxzRNZTsi1Xi9Xo9ms0k2m6VQKCimV0To4XBYsVWTyUTpnqTzQzKZpFarsbe3p+xTvJ9FDIMlPW4Y\nBvl8/ljV8Pr6+jFdl7Q36vV6quWV2LR4xeuiGzspfJfCGWGjwmG3t2+tVlOaN9ExSmGDgFqRHoi3\nmaR0xd5C5sPJ++29t49iTn8YAe/jtYzwTAShar2DSZ9aPLgf8iGIenhMf3Z09bpHv17D0WyY2kUA\naI7u9nTUwbYdHAw03U84HqXd74Gu0e0PME2DXqdLIBhUOWZ0jYkzwdamaRjdYTyZEInHGE5GhJww\nnb4bPQzHBh1HIxKLYpoGQTPC3Xv3WF5ZIhiO8Pa3/gSrP+RC0gVtxZk5csUCd+7cwbHg6OiQZrtF\nNp8jkUjQ7bX55J1PXfuIlbMEIhEMn4lVr/Pss8/ya//H/86ZtbPcvPUxhbk5Jp0OV65c4d7WNk8+\n+SRX/+xd9Omg002dQNBHIhrj8KCEoUGgWuPs8hJX332PL7z8AulAGmfsipPb3T6bm5tcuHCB3d1d\nHMctzd/Y2HAHqh5k0nQp5/5oiGPozC0ucFgqM540XV2PbjC2HNrt3rRNRQ9/MEAyEadZd8uH5+bm\nMDWdZDTMTLFIMp6YMlpBsCxCgSDmdCIZhg9LtzH9fjfljJs6NgwNzXB94BzHAV1Hs8F2bNdwd5q+\nRgPNcbA/B1G9HCc3FFmUJZr+fib+X5YZ8zIigIropUIqGo2qRr9SvSeaFV3XVRWTADXRHAm7IyaJ\n4jUkAEn+ViwW486dO2xvb7O6uqoi2/v37xMMBsnlcnQ6He7cucODBw949tlnmUwmbG5uqorEtbU1\nJcQNBoN8+OGHytDSNE0ymQxzc3N85zvfUX3xJA0huhfZNOv1ugIr1WqV8XjM8vKyAo/Cjkm1mgDI\no6Mj1fpFQJ6AU2EuxIhV0kLextcieO50OioFGQqFSKVSzM7OsrKyoqJ3uc6iCxPgJalgua9yX04G\nsSeBzcm0y+M8vhfT9f2e30kQ9YOeh3eD9l5HSbdJhZ+wKZJGlk1fgg4JCL3gQhgab2eBcrlMvV4n\nlUqxsLCgWgcJOOt2u8p6YXZ2lmKxyI0bN6jVahSLRUKhkGLMpNpSqoRl7Mo5ybgTw1Y5N28ANhwO\nj6UNxS5Ffgfc8ba8vKwCin6/r/pUSpNu77omQMw0TcWwef21arWaukYyl+WcJDUZjUbVOJf5IF5n\n8JDx9QYeJ8eR/PxvK+B4rJqu+3ulr5+k9T6zSUw3Ue/P4jKP/K7339rJ1z38bk9fI5owXdPQDR0N\nHctxsB1XZD+ejMA0GNsT/P4AnU4X0+en1miSLRSIxePYgOVY+AN+tne2sbAIhoJ0h13CsQjtfgdN\nt9m4u044GKHb6ZHOZtivVEjlshgBP+g6h7v7JKIJwrEkv/FP/yn+cAw9GOTbb/8pxfkFspkczVaL\nZ559lt/8nd/m6rtX+dIbX2JsWfSHY65eu8YbX/0qH93+lGqzyc1PbjG7uMhbb71NoVCg1elQKM5w\n5sxZtyKs22EytggF3YbhY9tiYtl0ul0M00c4EqJaa7G7V6JYyBEJR/j4o4+IRqIEglIKPeZgKkYO\nBsLs7O5y7tw5NBzWN7dA02h1OkRiMSw0ur0Bpt+HafqxLJtavUlvMGY4HGFNbDLJJPFIlEatRqfZ\nwtAM7PGYleVlziwvsjC/SDKZIplIEo8liCVi09y/W6Ho8/kxQgF00wTdcLui6zqaYcJULO8K5uXe\n626HA30qVtc09Tvn1i49Vv1KqVT6OpxeXfiof8vxKJr8tNd5K3FOfnlL7L2vEfGplGeLpksaQos+\nStO0YxqTyWRCNBpVGpBGo6GMJSWyDoVCapPp9/tK13X9+nW1sZVKJWU6Kq1Vtre32dnZYW1tDU1z\nG0iXSiUuXbqk0nPCKuzs7JBIJJhMJkQiEVW9JQJ82QBkI/Q+JsJeSeE1Gg0VPQtoajabKqpvt9vE\n43F6vR6tVgsRVAt7IBuOMIay0cqmJRuDRO8C8AQsFotFstkskUjkWM86QwUiD81PT7v33pT1ycOb\nVtQ0jdXV1c+NpgtOd5Z/1Jf3OPn4acUmj5pr8pj355PpVxFyCxNz2lySay8Cc+9rRH8lYMHbx7Hd\nbrO7u0uj0eDixYvKzkTmjKSfvX03h8MhDx48cL0T2201L0VLVa/XAZTGUEDLzs6O0j16m1B7q1xl\nngh7KgJ7b2sf6R8pY1SqhOVLwJmwgeK47592CZF5L+BHDGdF+F+dVsT3ej0l1JfnJY0o19FbqesF\nkY8aJ3LPHnX8lRXSC+j68ybNaZHX8e+PniDe7452fOLouLYUDuC45ZA4gOFMEa7jar4C0w1lOBqR\nSKVodzqMJhb1Zp14Io5lTWh3OgRCIUbjIf6Af+qWbtJqtynmi/gMk+5gQLPdJhiNUK5UsC0HzbLJ\nZrJ0B32+++57bGxuEk8k8QeDlMsVLj1xiWazze7BAS+/+gUebG3T6XV44smnSeey9AYDyrUaP/21\nn+Wdf/NvmF1cYuPePXx+H6PRhKXlZYKhMOVqjTNnz/LkpSf4+JNP8PlMyuUjDL8fazrZHdtmPLFw\nbJtIyE8iFuP5K8+SzeVo1WsYhkG73SUcjSjDx4NSiWw2S6VSIRQKkUjniMZiLtDVdYKhMBPbZu+g\n5OrmNAPT52M0dvCbBpPxmLvr67Rbbfd8ppvF8vIKi4tLZNNpctmMm6qKuhVamuMWSogdiKbpboUq\nHgCOpr47jsqgHi+8cB4WXcjXubUnHjvo+l7z4FHHaa951Jzx/nwSnMl3ARveyh1v6snrQC+RpDwv\nKQlZTKVNkCzMIgKX52XBlOjXcRyy2Sy2bfPOO+9wdHQEPLReePrpp5WGI51O0+l0GAwGSssklhFz\nc3McHByo9jzNZhNN06ZtpVxmaWVlhdnZWeCh2FjSlCcXX9G6iB5G0zRVSXh0dKRE761WS/2+6GcE\nEImAWiJ30bQJgJXrsbu7S6fTUeL8bDbL4uIiS0tLzM7Oks/nVepIWDNvVZ13M3/UGDht/Mjh3XA+\nL6DrURqu73V459Cj0pSP+h3vz6fZT8g5eZkRAQiJREKBJgEoYh3hLaqQnofe9Jqcq/yOV/AtxR8y\nnrwskQC5RqNBo9FgaWnJLZCq15VvnbBOonuSNKB8TpnTMl/ElsTLannPT8BSIBBQLvbSiUGqH0Xs\nLmzxeDxWjeK9aXB5b68di7CCwlh5jVHL5bLqTylz0dviSuw95PCC7L/oOJJD7vdfWdB1b/fw66dN\ndjWIBRBNmSn1M2DjPHxc0449h/ZwA/b2bzx+sW00z4aCprnvo+nYkxGGz8947CqF/IEAE8sik8/T\n7Q9xDBPTHyCdydDrD+gNhsRTCUqVMhPbxhcIohkmmq5jmD6sodt+qNxq4pgm/z97b/YjSZad+f2u\nmbm7+e6x5xK5VlZV1l7VXS1uGnaTbA6GIARChEYz0GAAUQO9CHqT/oAW9CDoSXoUSIyIAUUORxIx\nHJAAQXSTVG/sbrKL3dVdVVlVWZWRkbGHR/i+2nb1YH5u3LD0yMzaOrNbuolARPpibm527z3f+c53\nzokU/PzP/TytVpelpWVGQcjKxUvc3dgichzeeu99xlFMpz+kUV/kG9/6Fhub97j2zDMcHh+zsLzC\nN771HXb2Dnjt9S9w8co1/vCP/i2vfv51tra3ufHcs/h5n8FwSKFQRJMWvSOJ2bq3xfPP3eT1z73G\ncfOQWrVG+/g4LaugHXSUAJpEgGeScNQ+5vmbN/GLJX7hF3+R7/3d35HL5SiVq6m35Do4nstB85Bi\nuU40Y9B6wxFhFDEJI1yvAMqh2WrheB4qVhwc7tNtd8h7LuPRELQmn8tRr9V47XOf48aNG6wsL7O0\ntEKpVCGX91HKA8dDuTm06+G4OZSbI3YUOCnTpZULuKDSH42DctL2QjjODHQ5M1bMTeu2KReUw41n\nnn2sBmZ/f/8r8GAW60HOiD0eBYjB/DCNHXISAAYn7I9omaShtL35S/hKwI5d10peEwQBhULBABTH\ncUwRUKXS1Pdarca3v/1twjDk+PjYnKfrunQ6HRzHMVqTra2tWa/RBdbW1uj3+ybbaTqdsrS0ZLQs\nEuaR7K4oirhy5Yr5v/SUFK9YwKEtvr169SqNRoNCocDq6qop72A3wVYqDY+IZy7DrpQtrJeIgQeD\ngSluKRmQnuexvr7O1atXuXz5MisrKwZwnQW2zpob8+bUw+bVtWvXngjQNY+V+CjZpKMtAAAgAElE\nQVTjLGbjQa+f93/bEbHDi7ZAXGQAcD8rJPdemCphlLJlYgSYyRwqlUqGdZVzkc8SdqdcLpt2QWEY\nUqvVTKjOns/iHNl142Qdy/yqVComzJjVQtnfOZ/Pm4xLGVKuQSllAJh9LNFZaa1NmywR00vxZQFO\nkngj4FSupTDCUo2+VCoZRs0GqmfdT5t9fNTxaYCux67pmveFzcXgdLjR/tt+rVJyDHXqd/r6048r\npVCma5A6tRky+4x8sUoUBRT8MkkUMR4N0cqj3U9FegrYP9pjeWGZ8XiC6xYplRYolWKCcMLBYXuW\nnlqhWlkkDkKOO23Onb/ExtY9VhaW+fs336ZS9Lm3v0cpn+O9O3f50j/5Tf7b/+6/Z3GxwlKuSGcw\npD0cowpFnr35PL/7v/8bLl++TLs/4pVXXmFra4u///6PePfdd1GuQ/Gtd3jq6RvsNw+ZBBFPP/sc\nf/M3f8Nv/dZvpV7IoI+jIxYrJX70w3/g9Vdf4u7mDpP+KnGiODxqEQCum0OTcNhs8+4HH1AulqiX\nizQaDd79sw8o1+rcfOFF3nrrLa7deIrt7e20Wvnly+zs7HBhfZ12r8srn3+dVrfHcDwl0jGFfJ5K\nrUrzqIWrHOI4pFwtcdQckPNztLoteuM+hXIJ7bokXpoFGqt08/Fcb1agbXYvmSVjAI4+2QxSEG0D\n6iQVzWuNVtHJY7PnE5Wk/TqfINGwTavD6cwZGwzNq7Ukvx9F82J76XIsu5q0PC9etLBakmUkxiKX\ny9Fut41oVzL1pOkvYABPvV43+o5SqUSz2aRarXJ0dMS5c+doNpum2Olv/dZv8e///b/n8PDQFHKU\n7C3JJpSaVwDvvfeeYZykVMS1a9fI5/NcvXqVVqvF5uYmL7zwAkql4Ugxiv1+n0ajwUsvvcT29rap\n8C6gKEkSUxtLetotLi7SbDbpdDrGKFarVXq9nmE69vb2DBsgdZmCIDBC+el0agTKcs+lcbawBqLl\nqtVqpjq3GKPsHjpvDs0DCvb+m2U1BTj//+P0EKAh1yjbFFrWpISP5ZqK7lFAgwjvZe0IawScYm6k\nv6A0TV9cXDSlT86dO8fa2pppYXV4eIjWmtXVVS5fvmzqauVyOZ577jkjjheWtd1uMxwO0VqztrZm\nmDBxChYWFkxbn8PDQ1qtlmFrJSEGMI6VlHsIw9A4JNJjURgwuxejLdaXELoAN5EE2Mkgdq/LKIqo\n1+uGcX4UFtSe7/be+FG0gQ/7jEcZj5Xpur2z/5VZr5/TrNTsd9bzOstjn2dwHvQ6J1VOA6czH817\nSEhindZZJS3Wqkgp1kkYsLi4yGg8RUeKarVCp9OlVKqk2g43T6/bI58roLUC5TKZTkgSTbXR4LB5\nSK5QIJfPcXTc4trVq4SjCcViiWbzmFdfe5U3fvADur0+Wsd0211++7d/m63tHRaXl9ne3uHS+iWC\nIKRRb9BqHnHtqev4xSLvvvsuSZJw7anr1OtpRsqPf/xj3nrrLRbqVUbDAf1el53tLdYvXebNH77J\n+fPnOb96jt39XeIEpkG64DzPTcOMvs+F9QtUS0WKxRIXL17kzp07fPOb3ySMohT0KMXi0hLj8Zhn\nbr7Ava0trly7ysHREaVyGVyXbq8/YwC8WV2htDdiEAUcHzeZTqaEcUStVsPxcpy/eJGl5WVK+YIx\nLp6bm7FU3PeDSueRMh0J7P8zu89qdrslJH0SnkSltOlTNx5vKGV/P2V/s3N5no4FuI+afxi78Siv\nm2fI7XNI54dnDJCE5ASYSR/AUqlkNnXP84zHK1ql8XhMpVIxG66wZtVq1dQharVarKysMB6PTWq7\n7/vGiNjtUsRTbzabRpDc6XSYTqc0Gg0TyhuNRty6dcvU9RoOh7RaLZMhKCycDGGv5BqIjk0pZbz0\nSqXCxsaG0ZElScLy8jKu65qaQlJlX7x3YT6k4r+wH5PJhIODA9NbrtFosL6+zurqqgknynW3dTbZ\n3/bf9h6avbcP2i+VUly6dOmxromvf/3rX/korz9r/j5szCMB7GPZQNW+rnbYLftjPw7cx/LYJVTk\nMQEmAtrtfo7S/UBaXYlA/+rVqziOw+HhIXt7e3Q6HeNMSCmI/f192u22yRgUZ0V0icJaCUASkBgE\nqc2TMLa999hzR8pT2N9DgKmwssL0SeaisFpSMFnCr9KJQXRmUohV1rQUOs3ui2fd/2xoed59mvf6\n7D2Xx39qw4u3tw6+Yhs++Tv7I2zVCWt19t+nDGnmuLGbVqLXqBkgck6MNGl4TeEwiQHHZTQeoRwF\nSYyXcxlPJjQai2zv7JHPF/ErZQ6aR3T6XeqLC/zo7bfwyyUSpRiMx0yjiIXlZXZbLaaJpj+egOMy\nHE1wvQLlap1mu4/2yxyNpiyfv8jewT7/6W//Np7rEEwn9IY9vv/DN/nlX/kyP377PZ578RXubG9x\n/dnnuLd3gHZy5ItlDo5aLCwtsL2zBTrhww9uc+udt/nca6+ytrqCUoof//jHvPjii2xsbJDEEc/d\nfIa/+7u/p3XcZDSakMvnKfhpTD+KIjSaMAzo9YdEwMbWDhEOP7r1Li+88hpbe/sctjopI9cfMI0T\nutM+1599lu5wiJsv0+mN2N9rUq/WSaKEo/09gsmIIOizt7tDp9VlZ7fJcByyvHqJ6zdf4Kmnb3L9\nmRvkSkXyTgHHy6OcHFGi0colwUlDiI4VapyFmROl0E5afT5RaWJErFTKnDmppkwrF+146W/7x/G4\nce3yYzUwe3t7X4EHOxr2/7MGNhsCeVRH5az328PWa8lrhSGREGGSJKbNRqVSYW9vz/wtLJG0FbHb\nC0k/QgFVkgEmTML6+rrRzHS7XbrdLi+//LLZ+KWIr2hFBMAJyMrn8+zs7NBut03R0dFoRBAEXLx4\n0RR2rFar3Llz51RxU8natJnHfD5vwiFRFLG1tYXv+4Y9kPdKX0m7+rwUoZT/d7tdtNYGWA4GAw4O\nDoA0K2xtbY2XXnqJ5eVlYyxtIXw2JCU/WcOSBQvzwEF2fimlWF9ffyLCi2eBxSxT8TAm4qOGk+Z9\npv3bvuY2W2wzXwJgbCF3tr6XHE9CZxKmlufkmAK8RHcl2saVlRXjOMi6lBIRnU7HVKGXhBYpoSAl\nKQQItVotU9xYQFuSJKeE8dn9RWttSqBIuFF6QkoLL7sJuLBi4qSJJk1q0Il2UgCZ67qmNZfdsssW\nys+7//Pu9Tyg9SjATYbj/BRXpL+9fZK9aI95F2TeOIvhepBxcWbNj81IRCAcEyeplkknETqJ8D0P\ndETByzEeDWep8hMWG4skOuHw6JBiyader3N0dJQK/XyfcqVCfzDg6LiFXyyyee8eSqUZKJVKFa3B\n9TyUVniux+rKKvlcnmAasLyyTKVaYWmpwYd3Nrhw4SILjQYbd+5yfn2dw+Yh/+yf/ed89atf4+bN\nm4RJwhd/5Uvs7u+xvLJCnCTk/VQcv7a2yg9+8AMGgwFHh01+9Vd+lcGgx7PPPsNCo8Hh4SHFos+l\n9Yvs7u3TPD4mjGPCMM1YSRKNchyiJGI6HacNpNH0BwMGwyHHrRaNhQbVWhW/6FNv1BmNJwyHI6I4\nZjwJ8Wbe1O7uDnEcUSz6jMdDbt16i/FwTH84oFJfoFavc/HKFa5eu8a58+epNhZSAaVWp+LzjuMY\nJjRtNjD7zWlPJjtvzGNz5pA9T556zKDLZroeNJ/hbPbrUZnf7HNyzHmMCZykUMvn2uyPFIkUsCOF\nHyV8V6vVaLVajMdjU4VewmYSNlBKmfCEXcVeehBKBW7HcWi1WhwdHXHt2jXu3bsHpCzCysoKvu9T\nLpdNKET0JL7v88EHHxj912Aw4PLly/R6Pa5cuWJ62CmlTG2wVqtlMrjEkGX1OtIWaHd3F6VOwkJ2\nWrxoxERPppQyxSylorgUkhVj5/s+y8vLnDt3jhs3bhhdj60detD9neftn3Xf7fdn59mTArrs7zuP\nkXvY45/kfQJi7ezQeVlwZzlCEk4WplSaodvASspN2H077X1PzkMAvIA5+a11Wg9sdXWVWq0GpKDd\nFqZL6FAcmE6nYxJc5HxlHUu4O5/P0263DTiy2TnbERFBvrByoj2zm4QLgANMeNG+DnJsCVfKPjKd\nTjk4ODDhfq21qU1nA69Huf+P+hrbeclmPn7pS1/62GvisVeknxdbzVKB81D1Wa/L/j51DKVSkMVZ\nm1M6GZI4xFMOURzgak2kA/Kuh0pmGy4J9Xo91XH4RfxS0RRvlB5V+byP1n20coyOYzQasbKyYhqR\njsNxGvYYjyiWS/S7Xbr9AcVCntVzF3jplZe5d3eDJHJYv3KZH/7wRzzzzDP80R/8H3z1698ijCJu\nPv88/9vv/i5f/vKX2b53D60cFDl+7Vd/lT/5kz/hc6+9yve///20fcTt97h8+TJvvPEGL7/8MkdH\nh1y4sM69rW1u3rzJwbe/QzxbjDLRoigmimLCachwOCYMY4rFMjs7e9y8eXMW0kl1YP3+kCtPXWVz\nc4uFxYRYT0gSmEyDNH1+OCCOpoxGAzrHLaZhTN4vkivVUDmPxsIC9cUlcn6BJNEQp2U+9Cz8p9Fp\nb81kTvzecUFro90y6EpbIWStUUlsNoZ5c+hxjyytbc/nbN0Yu7ZddgPOvm8euDpr2OtMfmxG61TB\nWUvXJVqtcrnMwsICx8fHJoVbQgZyzktLS4RhyMHBAaurq0bfJEzXYDBgcXHRgJJqtcqrr77KhQsX\n+OpXv4pSaa/HO3fumMbaSin+7M/+jFKpxEsvvWRS10Vjdf78eV555RVT6fvu3bumfMTOzo7JkqpU\nKmidyglWVlb40Y9+ZK6tCN5brZYpG5HL5VhZWTFgc3l5Gd/3zXeS+9ZsNjk+PjbevFTLF89dajEp\npdIkkpUVLl26xJUrV0z9IbnfosGza29lvX15XO6lGHIJO9lzYl6m6pMy5oHKT+t4j/I4cGot2WBL\nQuq2WB5O1iZg2CLpjyidCoSFlaQPSLMTBbyIkZcfKQMxGo1MwojMm+l0yu7urqm9JetRXvvCCy8Y\n3aKUNpEWXc1mk83NTeMYXL9+3XwPz/MIw9CANymGmgWGMo/F4RDnQvqkyvUQsCd9FcWpksb28pmN\nRsO0AhPWT0CWze7Ou3fZUKD9/IPCjvPu+Vnz4JOMxy6kly9wlnBzHij7KF/aNj7KNtY6BV+JSpkv\nc6OSiLzjEE4nqDjAddNiqqWyTxjE6ChkOmOC1lZX0sa/jfP0Ol0mwZRyucr+/j4ol5XVc6AdqtU6\nSg3w/RL1hSUmQUSUgJsr0Dxuk8vlWFtbg3yRysIS6IT2oM0/+tKv8e/++A+5eHGZra0tXnzxeVqt\nDl/64j/i1S98nnK1wv/8v/yv/Nwv/AJf/+7fsnV3k//6d/4r3n3rHf78z/+cl19+mffff59f+7Vf\nIZxM6Q+65D24+cx1HCK+8NqrbN7bxnU0B/vbXLhwnuNWm044TsNwxKawaN73iJWi1evhui7Xnn6a\nrb09crkcl69fNwUvO50elWqNQiEtpHpwkIaXOu0jxuMhh819JsMBDm6qG1AOg/GI0sIC4zjNePQK\nPoVAp+J2LwY7rThRKDUTzQOOOg2ss4vNBiDp3wJKZq+zfpR6cgwNnA4RzgsX2hu8GN1HcUTmOSbZ\nY9vesIAtOAlxSGhChl1nSx5XSp0qkeA4aRV2MSiSni6vE+Akm6x424VCwYTxFhcXqdfrrK6uGkbs\n+PjYePOvv/46f/3Xf021WqXRaHD37l1eeuklXNdlY2ODa9eu4TiOaTm0N5vDci4SihRwNR6PT1UW\nt/cjm+1oNptcvXrVZD9KKFA0WmKkpHK2Uso8t7W1xWAwADAhJwnXSvKAhChtwJQNS2Xv4Vl7pQ0W\n5L7OY2ns4z/OMW/u/yQ/W661zXjZ4VtbpyRrI+sMybFs8C73wQ5LJpbTK/+XdSXJE8JAiZ7RbgIv\n7JDoswqFAjs7O+zs7PBLv/RLJiuxVCqxu7tr2FTRXUkoUMT5dlshadVlZ+Pae6u8Xua7gEwBmHYL\nMQlDyjWCk+K94sjJ39IeyC4F4XneqYLE9pD7knUk5o2z5tSDwNVPNeiSMe/CZA1A9v/zXnfWa2Vi\nuKmZJuFkEcdxjEo0cRzCbOKoKCIMxuRntZ8KfgESjaPSkga5fB7H8YiVw1Q5DAcDMyFkwkWxNkJY\n2dTtRSfaGFmg7XabaRhSrS+wv7uFoxz2mkdcuXadhWrF0LCLi4t873vf4fbmJl/+J79BqVLmW9/+\nNk9dv47jePzev/59fuU//hJJkrC7u8trr73C3t4eN597hm984xvcvXuXc6treCWXb37zm7z0ystp\nlsq777Hb3LTKAISp5s0FtDY6mSRJGAwGbG9vc+HCBSqVCvv7+5RKpZTt832T0QWzIqUqDa/0B11G\n/VnD3v6Acm0hpaKDgMl0Sm84YDAa4eYKVEpVvLxPFI7xkplYnpl3r9Lm5UrNGpuThhfnzREbYKSv\n++kY2TXxsIUum15W3CrPPcyhyR5Hz+65GAERfNs90yT9XOYmYDzy8Xh8qvq2GC4BE/ZxtNYGBMCJ\ncF0YKQk5iuD36tWrJmwohVS3t7fZ3NxkcXGRX//1X+cv//IvqdVqrK6u8o1vfINf/uVfNmnsCwsL\nplL80tKSYeTsMIWEOESfJnsFnG6rYxtVEeynzkfHZG9JUgFwKktL9DVpweGpyWgDTOPtTqdDPp8/\n1b9S5oNtbLJtTWQ8bE3YmXj2ewREPAns7+M8h+xn2+BLrrmUMsg6MgLkRa9ks872vLFBl83i2A6P\nzTzJ2pHPlIbnciyxM0GQRhguX77M9vY2t2/f5ty5c6ZDxPLysmnk7rouQRCwtLTE/v6+AUoyxLb1\nZk53rVa7D+jbLKCI96VSvXwvCbnbTpe9Z8l3ly4QNggVoCbX0gZo8lo7C9R2JOY5sPP2VpvYeVTM\n8VHHY9V0vX9vz3z4PE8ty1hkH8s+l70Y2f87zMpQ6LTUgE4UoEnimDCYoqOI6XTE8KhJEgZ4DuQd\nh1KxgKcc/EKBnOfi5VzCIN2Mq5Uyk/GIfKGA0orhoM/qyipxEuE6LqViEcdVJhbuFwqUKxWzwdqo\nfWlljaPjQ1ZWV3Bmk6xWrZDzciwtLTIejVm/tE4yndBYWuB73/ser3zuNV557VX+9m+/Q85LtSzH\nRy2uXFxlb28Xz0vDOlv3Nnj99c/zwnPP0Wwesr5+EWcGQLvdDhcvXmYSRRwcNIkSUK5CJzGuxMod\nhyCMCMKIWr1BLl+g0+0xDUI8L8doPCGMYopFn+3t3Zmh6NE6bhFMx+xsb9FuHdEf9AjDgCRKSFAk\nKBw/zzSMSRwFeEyDKTk3RxJDSASuS6LUrCaXkxaynWW94syYSgSAqRQszpIn7MdSyKVPQpXK1oWl\nP9cunX+s+pXd3d2vyN8PWxPynM3AnFVaInvMeSMbShQditTzmU6nRiQrIEmcDdlYxcMWz9Q2Tnb9\nLtnkZTOVzVj6MAp7J2LcrFERQCNs13A4BODWrVsolfYv/PDDD815Srsi6c0m2rNr164ZBknAnfyI\nuPjg4OCUfk02ezGIsrYllV2MiPSiE+2M9MmTZt7Sj1KyPW1QVywWDRiFtNimDY4EMMn1nRcFeBjr\nNY/xnDfnzp0790RouuDxsV6yNsSBthknKZlgG/nsWhQwIvfPBtBwGnjZ98rWEdk/cu/l/5JBK2J2\nAWWSBXzx4kU2NzdNcopSaTsdCWfbLapkbYiT5bquCe2J42TPTRtECvixnQNJjIHTXR6E6ZMwu93V\nQtaSva/Yui2b9bKvQzbSISN7/eSx7PMPmwMyfmo1XfMW/FnhjyxjcZaXfhbLdfJ4Qowi7deocbQm\njmKSIGA47DMeDshPUy/93OWL5JzUI/XyHsF0nHrxkwkumlw+j+s6KL/AYDxhoV5B6QTP0eQdBS74\nniLUeQYa8rPeVqurq3SUIokiYn2ik1FJiCKh3+mCTkOY4vHrOPX093Z2U3yQJPzyL/0if/x//d9c\nvv4U/9Frr/Gdv/07otVzOMtrbG5t88//+X/BX/zFn6c1U0pFbt16x9T4kZT2Dzc36A1G9CZpGOOZ\nZ57h1u0PCKchBd9nOp4AsLy6AEil4g79vuLpp5+m3W7j+3mj69nem1Cv19ne3qZarbLYqPPmm2+m\nHkkc0O500oy0IEZPQtzRiBCHUi2iUqtz4O7S6/XIeXlCnZDzC8S45PIeJV+h3LzUiACtcFVaRNe1\n7rk97psjzEImMrfUbEOd/Xvc40He11n/t4fNvnxUw2R/thRLFLZJQhlSBuEUe5jZ0Owq03aauM0O\n2ZqwJElO9Qycdy725/i+z3A4NABPQNXq6irj8Zi33nqLq1ev8tRTT7GxsWGyoF577TXu3r3LdDo1\n/d8ODw9NeroApv39fVMbKZfLmQxEMX6ysUsWl2Q6Li0tEQQBvV6PfD5vkgfq9TqO43B0dEStVjMh\nHDEqo9HoVG0mrbXpgScM18HBgWEcbVYly0bY++OjhAazerCszONJYLrs8WmdT5a5OGvfyK4jmYP2\n9bLb/shj9jwRoJ4FxjbTmD2+/ZPVL9l9SmVOSBhe1pNdaHRnZ4coilhdXTX17tbW1lheXmZhYYGD\ngwMDIrXWRogveioBStLzUMq/yPe2v4t0igiCwPR4lCxiea29ZuU6iZMmxxBnRr6nXSRV7oucrzwv\nThBwivF60D21H7P/n50j2ec+yXjsoOthQOph74f7AdlZx5JCmqjTFGOcpHHoyWhMEoT02i1WV1eZ\nDEeMtSbnOejEMeG1/MxAuE6KsmMnJu+mTJhfyKWATCmU6+I6iihMcEk1ZTpJUPr0ZidGajoeEk0D\nPL+AUg5Jki6warXKj37wQ4r5HIuLiyxeusQ0CvFzOa5dvsR4NOTO+7f5p//Zb/OHf/TviMKElhPw\nl1/9KtVqjSAKUwHiLByzfmE9DbPUFzl3YZ2dN96giMO5cxfY2z9IY+2ux2QyIpdziZOTYn4XLlxg\ne3vbXPd6vU6322V1dRWA/mhs0uX39vYY9PoUCgX29/dnWWoarWPQmjiK0EoxGachl+5xGzfnEyUx\nR51jtCNNwj1KpOxKPjqpTZQ4aSMg4KRZtb2BysYnE0ApyBT+zM6/xz3kHGzP+WGvtw2AXWDTDlnY\nr5/nrMiQ94qWyO4duLq6anouiscvQnE7c0n+H8dpw2VpXSLeuWy68lvCWLJxinjXfkzmlDBlQRCw\nvb1t0ucXFhbodrtcunQJgO3tbaIo4ktf+hJbW1u89957HB8f89xzz6FUqsPxfZ+DgwNarRaFQoHl\n5WVTo+vevXtMJhMjjBdNi1wrMWrlcpnV1VXTbkWqeEtGo+d5NJtNw+qJgZOejxKakvsgRkrYsePj\nY+r1OnEcmyzOpaUlVldXzXeV+yD7yDxPPjuye+68uZ/VCP6sjHlr4KxrJaAnq7uS0LCAbwEKtk2S\ntSDvEWByFojLrvlsIkT2XAWESJhd1oLoukRTKGUjcrkcy8vLjEYj3n//fXZ3d7lx4wbXr183FeyD\nIDC15Gq1mmGdms0mhUKBRqNBo9HAcRyTEGBLZ0qlEtVq1TgfYr8ERLmua7IahTWTsKFkNM9LGBI9\nqL3viOYTTvpe9no9E760w7JZJ2Te3w+zA58W6Hqs4cV3N3e/8nG9qnlU4cOOpUhISJPf9KwTdjAe\nE4zHqDBk2OuyfXeDn3/tZcJgCjpJo1izG+66LkkU4iiF57oox5t5qJpCPkc4nVAs5FBJjI5DHBJy\nDuDmmEzG5DyXyWREvVal2+3iOg6Oo0jiCHRCMOzjuc6scnyCTmLKvk80meIqB4Vmf3+f3vER0/GE\nUX+A73os1mr4hQJHzRa/+Z/8Jn/9jW8QjEbk/UJambvok/NcxpMxjuuws73D2ur5tI/jeMrzz7/A\nV//qbwiShM3tba5cvYYCer0BDppC3gMFURQSRxGXLq2zsrzM/t4ek/GYXM4jjiPQmkkYpWLiXhc/\nlxrco2aThcYSw9GYKIxIEp3q6mYLIQ4TdJwQhhHj6ZTeoM9wOqEzGtDrpKHbOE6L7LmOi+N6OEqh\n0DjKQam0eflZc8N+TMOs3VMaTpQWUsx+X19fe6yhlJ2dna+kpzO/cN9Zc3zeOngYnZ4dsjHJ5iua\nIvGSpeyCFDIUz9TWksn/BTzJ37b3L8ZL2B0BQJNJyqpmDY3oCMWIyWYtKejSPDcIglM93SScd+nS\nJQaDAYeHh2lh49HINBcWDeZoNKJSqaBUWj/J9302NjZMpqJosuTcbM9dAKQUZLWNrxgzpZSpLSRp\n+gI6pZRAltmXzxNmS4CcgLRqtXpfKrsY4rMA+7z5ZD82D2itrT3eNfFRey8+zLGYd50fdjz7tcPh\n8FQJEAkXyzoQR8EOM9ohsHnnk3UAbXF5NoQmDo6wP6IRFsdAkpok41UyCuEkM1CptJyL9CF1XdfU\n1rLPWcTu4mQolWYNSwNsYbaS5KQ6vwAx3/fp9XqnulZINrMcVwCbHb63sxwftMcJOy5rRP6W/cYW\n0ttjHptoP/6g18j44he/+NMZXsyOeaHFsxbFw8KL85ivxPz3tGjRdV2OOh2mozFf+MLr3Lr1No1G\nw6R++75PHEbEnIRuUtTu4DoQzLJLxJsPwxDXcdAO6CRCqUKqI5stSgkRAMTRiSeURDHlaomD/V2o\nVHAdmMYR+XyearWKTiKGvT7L9TLBdJyyTJ0Ouzu7VGpV3v5wg1gnXLp0kdbODj988x2++Ms/x9tv\nv8Nzz16n3TqiXq8z7PXZPzzg6aefZmN7h06/x3F7xPKFPCsrK9y6dYtKpcL6+jkO9vbRcUSsMBkt\nvV6P9fV1FhcXmUwmprBkyvp55GZx+mEnZUjQDp1ZWHGKg6McEieCWAx9TBQGjAZ9IsfFGQ5JcjkC\nEtA5ut00xFItl1io1068Fu1a93O+x/4oLNbD5trjHA9icB/lnM9iuR7m3c4LqaMAACAASURBVCml\njBDXcdKK88J02gyXtAOSayhslr1xCgtjn4f9A5j1IN66MAWSQt/v981GLwBHwnadTodms2nOQ4pG\n+r5Pq9UiSRJefvll/vRP/5QPPviA559/nnv37pnaQVKAcXt7m0ajwWQyoVKp0Gg0uHPnjgmlpK29\nagbw2WGN8XjMlStXGI1GphDk0dHRfSzGcDg0IEqSDebdQwFiYtSEIRCdmed5nDt3zhgz+3o+yjw+\ni92a97onZXyUc3nYd3vQsbLXUO6fGHGt03IijuMYsG+Hnm2GKxsazJ6DvB5OskVt3Z4NpO1zsB0Q\nYaVc16XX65n9+OrVqyRJYooAV6tV85mNRoPBYECr1eL8+fNmLct8EmdIbI8UNJYm7BKedxznVA0u\nuVayL9hsmTBhcl2kwLBkPYp9FE2onOtZAFXqdYmTY/+d1ZJmr/lZTue8+ZHdbz8p+/tYQZdK4lNZ\nZ1mUmaj5feWyf9vD3nh0+oB5bV45BIkm1OnkCOMQV2m0o1hu1OkmEe+88UOeeeYSCwsLFMs+eb+A\nTlsm4+DhiieCIopDlE7IO4okiVNwEiqm01RYKcJdNxhSdjRKxygSGA0oJCHxdEoUhqmHEIXktcNa\ntUYTcAZdwnDKFE2+VGRxZYFio0qxscDff+NreF6OcaK5dvNZPtjZpbV/wI3raR/E/+Z3fof/4X/8\nn8jn0r5vV65cYePeAUEU8v7dfV5++WXe3dzlg51D/Noim5ub/Ivf+Zf82z/+P2dMRcpyKaVYWl1j\nOg1RRAyHo5l37XLnzsZMQzOhWmmws7Mzm/BnT+ZpmPbgS5IEEo+06XhCMp0wDSYE4YDhqINb8IEA\n30nYSRzypQLjaEK1VqbdbtOoVXFzeZSXkHPzkKTlHhJ1EkIWgJ32VFRAGorUKsmc10xzoU8vzsc1\n7LD3g5yPeWviYcecN+zNXDxaCff5vm+qQNfrder1uslatD3vrJ5Fwgg2wyVD2C9be2aHUaQBtTBm\nYRgaUbkA+16vR6lUMnW/XNfl9u3bVKtVEybc3Nw0upKDgwPOnTtHoVDg8PCQpaUl8vk8d+/eNb0f\nL1++TLvdNj3fWq0Wi4uL3Lt37xRrBhiZgYQJhf3rdDpGlO95nhH3A8Yg2R69fN95Q9g6acNi67X6\n/b4pNGun6GfB+YPu/zyQnTVwT9qYB2DsNfuo5/5xHCz5bNvhkFIednYunPQuza5d+/pmE17kJ/t9\nZC0Jq2VXupffnueZRs8C1AeDAbu7u6azQ7/fB6BWqxkm9/Lly2xsbBgRvr3u7OKoohcTFuzo6OiU\nJlPKWdjJYXJvlpaWjCZU9Mk2MyvP2d87m4GddcxsNiwLUrNgV/a07Os+zvgoe+6DxhPFdH2SMY8u\nzk7OSCdoDe6s2KYD6Chk0O0StFtMBn0WFxaolMrGQ3XVSVqqiIOUUrMkOMuLBRPnlnCFmQSxNhuu\n55xkrORcj8lkQmcwSFuIdLq4TsLi4iLvvfsjoiAkV/AY7k3xS2XcQlqO4dq1a7z55o9YX18nn8+z\ntFCnXi1zeLhPrVLiD/7g3/Av/8U/5etf/zrHx8eEYcjm5h6/8Euvc/v2bd544w1Wzq1xb3ObGzef\np1wu8/u//wdcunKBo6MjyuWiadkg2VaKtEBlu9021b43NzcJg5OKyqkW53TNlEcdSpNq3sKQWEO3\n3SHnFXBih269huc4HB6l96Y7HKOKCq3zOCoh5yq0TutKaEA7mTmAxeacsV6eFGPzWZyHvTbOCrPI\nsDVd0nJDwooSypCwGHAKcMl8t+l4Ww8jnrPom7Kbl4QLxCt2XdeI2QFTlFE0LIuLiyilWFhY4Ny5\ncyZTS9jpTqfDZDKhUCjw5ptvcuXKFfb29jg4OODixYscHx/juq6pJF8ul+l0OqysrLC3t8fR0RHP\nPPMMP/zhD00vOjEIAgQltCHZndLSxw6V2l74oww7vCrAczQaGQMn31PWphhWWYPZ2k9yrLMY0weN\nJ2VdwNlO0Uc5xwc562e9RthacTiktY3MY1s7OS9Uddaay4IAmStZVssO30Ea2hYwLs6RZPoKQGq3\n27RaLWq1mpkPYgtEPyVV6/f29lhdXTXrWillsn0BU/5BKWUq5kuvVRHZi87T/ltAqF1eRvqvPuj6\nZ0uVyN9hGCKtiiRkmmUY7SzKs8DsvM+cd9+zjsinxfr+TIAuR58wGw9iB9KLp4AErdOJPer30UlC\nEEwYDQbkK6n3XPKLZpJns6fM31jId2YkgiDAU859Xq2nHCIdnVpgJu04ioiCgOFkiNMKuXjhfNoO\npJvSuF6xwPa9TW6++CLNgz3K5QqvvPIKt2/f5rnnnmNlZeVULL04GvHWW29x/fp1Pvjgg1nGZINm\ns8nLL7/Mmz9+m9EkoNMZcnCwz7PPPgvAb/zGb/B7v/evWVlZQhPT6/WoNerpggmnJmNsMBikoR7l\nEcfBbEElwPwCt/PHzLBwgoO01pBEoFziacBkPKaSJMRByGSSZngFocbzEkKtcWfXUTtOCrrSm4G9\nluxjPzmBkocPO6Ps4wx7oxGgkN1k7LViG/her8d0OjXV0yW8IJlyUhvrrIwsOaZ46ZIVKGUfbG8/\nm/klGYmS0XdwcJCym420gfvx8bFpB3Tnzh3q9TqlUskwVaPRiHK5zM/93M/RbDaNqHY4HNJsNnnx\nxReJ45iNjQ1qtRr7+/uUy2Xee+89c96DwYALFy6wsbFhsnClqr14zsLYiZcuyQI2G3EWi/VRhuwh\nEpaU9HqlFNvb2wb0xXFswqQyd7KA+KxQMpyuSG/PjydhnOU0fNTxoL1p3nMCHmxdlZQTAcxzUkzX\n1hbJeZ9l2LOsl22zsmyY1trUd5Nwdj6fJ5fLGUBmM3ErKyumQbUUVi2Xy+zu7pqG13IsaQT/wQcf\n0Gg0qNVqppuCrH8B8q1WyySXyPnIZ0r5BmFnbe2l7C1ngR+5LvPY2SzwETsnEh1hAe3WROJwyPfM\nJkHYYPdhzod9Xh+HIZ03Hjvo+jRQpNZp+C9Bz528J69L0DhEcYzWIZPhkOlwSKfZJOy1KShFrVQ0\nnoZQ94LuFZZxkX9KkQBqNsnEsJT8IqNJKrZMVIxS4Dgpg+A6kHcdHJ1QcBwatWqa6aRga+seOU8x\n7KdZGEfHTQp5n0I+x4e3blGuVrl+9TJ/9dWvcfOZZ9neuseNp66zt7fHysKzFItFtre3iVWOZrPJ\nb/7mb/K9732Po9YxW1s7bGxuUa2n6cCurxgMBvzDP/wDr7zyPP/hP/wp/+pf/Zf81V/9tYnVd7tt\nU2JiYWGBZrNJEkMwHeN5DlGU4LrpRmOHiT7WkLfGMQQBejSmtb9LXrkMazUq5UXyxXqaPeMWCLWD\n9iKivEdOzeaQq1CJbfxjIAYFOjkpC/EoIZefpXHWhmHrp2S+C2MjzJTNWokXGkWR8WyzrJWwWtnP\nk6xEuJ+qt48h1bFF8zQajUz9LvG6JZTSbDap1WosLS2xs7NDpVJhOBwabZZ4xHZlecnC3NvbMyBG\nNC6i15pMJly7do39/X1WVlaMmF42cmHjhA2Wx7IZbHLtP+kQQyOgWJiLbFVvyfAShzEr4M4ammz4\nNzuepDXxSc/l4xhN24iLA2GXNpDreRbAtufAR2UWBWzYRUK1TtvtCHskAvdKpQKctBwS/aForVzX\npdFomPOUpAxZSxIulWNIBq4wXFJqaDAYMBwOKZVKpyQJjuOYHqryGXbGov3dPsp9zIZns7/tY9kg\nOY5joyWTLEsbcGWPNe/efFZz/7GDrgcNZxYNsn/g/sc0J8BLJEXGA8f6/+y3jtOLHwchg16fYDom\nmU4IHUUYTCgXiymijmIc1/IYM+FFnTEcsjgF5Ut7oSwQdJWDN9PFJFK12AlxXMxklwU9HgzJ1z1G\nwyloRc512Pjwjkkt7/f7rK+v0z5qksvlGI/HXLt2jb1mG601xUKBF154gW9865usrq6yu79PLpej\nXKsyGk5M3P7mzZts7WyzsbHB/v4ei4tLprhjEASMh1Ny3pRkFiHJ5z2CQGqJnabLH32cfr3cSxKN\njiKicEoyHpMEUyajEZ1Oh3qvh5vLUZtl5uQdl5xWRMQ4yjGV6gHT9skssJ8mqutTGA8KKWU3dxt0\nDYdDEz4RJ0J6o8nctml9e8wz7vJbmLFsGMbObJRK8wLspLioMArCtJZKJcMCSQ9EpVLxf6VSoVqt\ncnx8bD5jbW2NwWBgdGFPP/20SQ4Qsf7CwoIRRbdaLa5du8bVq1dRSrE/Wzdy/hImEdAjGYrzWpJ8\nkmGzhkopIzAuFAo0m03znICsWq12nyGZF2b8tEMmn+XIztn5EYyz3/dxR1braIer4KR+VPYcHsbI\nZUNn9pqYx74odTpTWNrf2IBHwIYdapP3yONSN0tAkO2USFeUbMkGcSSUUjQaDZaWlhgOh6eylQUY\n2i1+hAG2Iz6Pen0+yrDnhoBNO7QpgHie9i/L/mbP66z580nP/bHX6XqQUXCcR/vSWksMWOHMLGuS\nRvywRTxxkuB6eRQQBgHT4YBgNGTU6ZCLpoTjMZdffpFer0e5XDZZTEEQUCz4YHv0s0rotsco1LJM\nNNPdPZmik4RCziUKklmlewWOIkajFZT9Akm1SjHvEQch58+fZ9jvM+r1SFtYO7R7HXJuWmTxypUr\nvPPOO7z+hc/TbR2zsrLC7u4u1VoZL+dw69bbvPTSS/zgzTd5+umnuXHjBne3d3j11VfZOTjk+Wdu\n0ul16XW6JEnCX/zFX/Brv/5lvvOd7/C5z32e7373uywuLjIcjRiPpwD0ej0qlQq93sAArU9znNzW\nGCIYttrkF11aB7t4fhGNS5BoBoMLeI7DqFzEXVlM6WUSfD+PUrMNhxkDqdSpZA1bY5N+ptWD8QkY\n81gSeyN+0EYgAEaAvx2eyL7OHvJ6CZ2IRkk8VkkIkcw5uyaUnGv2fG0DYhsD2aTtEIps6vIjzksu\nlzOFSz3PY2lpyWRc2X0Ni8WiaZfTbDbNmpWMKwFFUnBUxPQXL15kZ2eHRqPBeDw29YWGwyFxHLO1\ntcX169cZjUYsLi5yeHhodC9ikEVTJRlXZ93Pjzvs99vtVAaDAcfHx+Y8fN83rVqElZGwmG2cs8Zc\nPiMb+pkXGvtpGZ8WkBT2UkLJkq0o9eeEIRL93kdhteY5QPPeo9Tp0gjijNhZf0opU0RX1gqcsJxS\nNFXmgVLq1PnbPR1brZZhqySTETBsW7VapVQqmSQCwGjKBHTJ/Js3hz4K2M9ig+xzArRsmYOALNlH\nRENqa03naco+TSD4sPHEMF0fdaLaj817ja3zkuF6eeMV5pVLznFwooBoMuHlZ64x6Ha5d+c2S5eu\nMEw0C0upUNexFkestclg1AoTrpoXLvEkzm+ds+e4aR9HFCQadwYMHeXgezkKjiLKRVy9dJVW+4hB\nr0+tVj0pM5Fo8p5Hu92mWq3yxhtvcOXKlbTsRbvFfvOQ9fV1br7wPO9/+AGFfJH3br/P4dEx586d\n4+CgydXLl0324fLyCnfv3sV1Pb7//TfI59MGqV/84hf5q699HcdTVCoVpuN04vZ6AwMmT4atPXpU\ntsva4Oc8q2bPB902cTjFyRdxnRylsk8SDim4qYC6kPcIo4RGwQFH4TkurnLI5WbGXINWpzMB7bli\nvEyerFAKPJqua96ayBrPeZ61veHbAlTApIBXKhWazSaO47C2tkYURWnpEstIZ1mTLOiCE1AngEE8\n9uzrssd1XRff9w2bVC6XjeHI5XKGmRMjIQbwBz/4AVeuXDE9EO/du0ej0aBSqdDv9ymXy7RarVlZ\nlHXa7TaXLl1ib28PpdJ5tbGxwWQy4ejoCNdNW2mtrKzw4YcfGqMrgFGMzzyv/tMacl0lvCr1mDqd\njilie3R0RC6XY2lpiVKpZOqOybW0S1fYx7TDLvZ9kL+flHGWfcga53lr/WHHmDdslkju88LCAvl8\n3nQhEAM+D8xmh5zPvIhA9nyza1/+zuqTJIQmdd8klCZ1t+S4doajrCvP8+h2uyZRQ9a3ADXb+ZE+\nwgLwpW1Xp9MxjkcW8NjM9se5/g+ae7bjY2u8bIG9fL4ATRHi2+yifS8e9dw+Kah/7KBrHtsF94cn\n5LF5QwzmfcfJGBjRZyVBSBSngvfjZpNaqcitd96he9zk+WeeNoUSPc8jiSLcGcgwN0s+QzRk1mnJ\nZ7kqLbx5yrAkJ4UhlUor1KskLVegE42HIog0cZCmyauWMytkl8bHG/V6WsE6SWiUy3y4sUEUpxkh\nBd8nCGM6nS7VWgNvJqh96ukbvPvuu1y+fJn6whJ3796jXKpQr9dN13jf92m323R6fc6tnWN3d4da\ntQFAEqf1hFzlmQl6olv5+ELvh430eicoBToKiZKEUb9Fr11HKeh2jvA8h8l4Fb+QEHqKXKRxvNl7\nZm2eZqbjvmNnN4MnxbjYxs5mrs4CN/ZrsyMLpuZ9jmiz5J5KhfRqtcr7779v2J8gCIxmwy56ah/L\nPhfbMNjZi/IaAWDyW15r/8imKEBGQgeiJ4nj2DTU7fV6JhQptYQWFhZMuryEAkW3YmdURVFEu902\nZSFEsC4i5Xq9bsBXrVaj0+nMDZ18Ij3jI4ys0ZaK6JJAI0ZUCrfm8yftuWxwZWsvRRdkH99eH0/K\nunjUcRaT93EMpcxjuQ7lctkkeQgjPA90fZxx1jnK2rfZI/s+ynuE8bFLsgg7NZ1OCcOQRqOB1tpk\n4pbLZVOoVKrqS+cHcXbsPWc6nRqHTOacMEliS+R62eD+LMbq07pmNgMoGZ2iQ7YTeOQzRZcpzkgW\n7H7W44kIL2Z/nzx3Ng17Fjg79VyS1uCS//u+TxgExGFINA3otI9Zv3CBH//D31PRCSqOiIMJfi5P\ntZROwlKpRH84NOm1wCnQJedgGzeHlMc5yR5T6BhinZDLeZDEeE46SaIwMILDaBLgKkXOzeM5OUrF\nCvm8T5ptCWEYsLCwQNJpp7qUp57ib7/7XSbTgMbSIuMwZjgN+eE7t8jn04yO73zvu3iexziM+HBj\nE8/z+NrXvkalUqHb7XPzhee5d2+bJAHHVezu7uM4DrduvcfKygrH7Vb6nKON3iz9v4vJGDRDvvnD\nh9FvZR+05wcawgDiCI1mpGJaOQgGLVwHxsM+br7ANS+PE6YbY8UvolVCQNrgPAaUY8R4oO9vC6F1\nGl58kg3MPJCVdUbOYr1E3yCvsd9jZxgJ4JI2OMIeSThDNFU22zTvfOT4WSYr27NRhm1MxJBJ+MT2\nTOUzJbQoP5K95DgOy8vLdDodU4xXqbRYqqTPA+zv7xuP+Pj42DwvBurixYunMp/k83u9nsnaFKAi\nAOZhgvRPc8i1kHCm67pG37W7u3uK7cnn84aFkO9jG5Z5gMv+nCdhPMgxf9B7HvSa7HfL2h84cS4F\nhJRKJaN1lLVhz++HjYe9bh47Z6/feSAmC65thsdmf4UJA8x3sMsr+L5PvV4nSRKj2bLPYTqdMh6P\nzWeaIuCzEKSdVWuvB3vv+SyGzQBrrQ0gBAzjJWBZ7Jc4Vrao/icBtmQ8dtCV3ajP8uYfZcxjvAR4\nASbmHOtZTN5xubt5j5zj0jo6ZLFSYXlh0WQ6+eU0g0rqldwHAOcwXI7jpPW/LI/fPp/czMA4pJMz\nnrXNiaKI6WiCk5sJJqNU/9KoL3J0fDgLYWiGo1FazmK20J56+ga9wYhpEPHsc8/z3nvv8eaPf0yt\n4rO2tkYQBHT6PTw3j9aK4+Nj4iih2+4RAwf7TZ5/7kVu3bpFNDNglUplpuGp0mwe4+VyBMHU1GuK\n43nZOp+Q9VKZv/XJn+l9jdHDPt1D6B4fMUXRancZxw65QoWgAMlKTG7BIed6KK3Je7MFbxkUR93P\ndD1poMs+t8/SkIvxttlXyYqT1HLRtEg4QYCR7QXLseYZjaxRsv+fDUdmgZeAL1lDwirYTbSz6fuV\nSoXz58/T7Xbp9/ssLi5ydHTE0dGRKTUhzJaEOUWfIiBGGLRWq4XW2pRK6Xa7phq+6L7s5r0/yY1b\njJv0ypPrtbW1ZRIRJBEGTpoMC1C2jyMjy3TZjz3OkV0Dj7ImHhbuO+ux7Pe1NUODwcBoHbXWxgkR\nIJvtujDv8+YxPfOekyEssVLK6K7ssJl8brYqPnCqfMl4PDZdHeS7CNslAETCiqJVtJ0euRb2Odmf\nIwyTPH/WdT6L9fokw2aA7bph8pyEVeU1st/MY9rPInY+zfF4K9JbP8y9AfGpVymlZsZYnTpCrEiz\n1uYcwZmF8dK/PcJwip/LEUQhOTdBRVPyjiYqFQk8ly4JlSRkeanBaNAl75dQnmNNtLRSfhzHeHmr\n6vap09donZAkszBcEFFwPQIxTHHCdNhDRzGlJGA07KLCKUEwot+f4leraNdhEkyYTvtE4YQ4CsnN\nwjuTYZ9kqKmUa5RnhRHz+RLHBwdcvXyF27dvE+FycNxJjcYoYP/wgKtX1xkFASqXGp44gYPmYfo9\nVAIKojgiTiYkOuDu5h18P89kcmKMToBn1sB8NINz393W8//WgE4s4eNwSK5aY9Jq4YQR3UqF3Xtl\nhueuQEOjgiklL6aiFWHi4nsuOdLSBrFWxGqW5j07XBxHD9VN/SSHyXy1FrpsFFnP+kEbuT2ym5sN\nsuwCh3aoSQr8Sh2sfr+favumU4qz7N7s58u5n8Ui2K+1wZZ8NxsMS4hPyjEIw5TtQyjgQjx5cRqm\n06l5zdLSEnt7e/T7fYrFomlW3Ov1WFpaMhlb8tmtVotqtWp0YxJKUUoZINfv943h+yhsx6c17Gsl\n597tdtnf3zf3VWvNwsKCMS5ieGytUvZe2V7/kwC4HmVk1+8nAb+2oZV7KpqnMAwZj8fk83nTf9B+\nn83YngW6HgQGZR1kX2/31RQHSICeACb5LUDMDrHl83nTcgcwYfjxeEyj0SCfzxOGoakFBxg2T0a5\nXKZUKp0qvyDfVSrSy9/2vpC9FzbA+TSHfKZdXsOuXSnnaANUYcrn3S97fFTy52HjiWK6Pso4FVLR\nnNJvmQ2d0xdMaw2JJlGzixwnrF+4yPGh4rlnb9Dvpy1P4knAdj7P6toacTjFi3IknoQKAZxTC8Qc\nG1A6MQZdqVTHJVkinufhKgiiEM9VxFFMEIwIJ0OiIGDU79LstnALBVQu1Q4c7DcJggitYGwyVXoU\nyyXanR5RovFLFdrtNs12WiZiGsYMup20RkquMGt8Cjs7O9RqDY5bbUDhug5ax7RaLXzfZzga43nM\nMr9KxHHC1SvX2dzcZDoNTeYOnLANP/GhE8JhnzBMBcURDg6KUnuACqY4ly/SqJTxqjWcKMClgHZO\nakZpZQlUn1CmK1uR+axhe2lwv6cuxzgJc58uwijHsIGOFFf0PI+VlRUODw9pNFJ938HBgemlZoer\nxGvMgsKzNlYxULIuABMeEXH8eDxmPB5zcHBg6lJJuYj9/f1TTa3FKObzeQMIpV/j1taWEZffunXL\n9JcT5u7g4IBisWgAnFJpf0QJnYiGR1oKpckkPVNKJVs4+Sc9hwT4jcdjk1Swv7/P1tYW58+fZzqd\nzhJmlqlUKqYRsd1EGTiVYn/fnvmEj0/TgGfF61lt4erqqtnLfd9HKWW6j8iwS0nAyT2SkWV75q1Z\nWzAvwy7MKhmzwt7aIMPORrb1fKLlErar3W6bOl3ynl6vd1+jesC8TtaNXaNManENBgPz3W2Hyv5+\nn2WiiRxfCrSKFhUwrbok1CjhUpFUZPdS+e6fBXv9Uwe6zjIsWcCV3TgEACmliKOIOEjbIYyJWVlZ\nodfu8Pa7b/OP//GX6TTbBOMROknI5fM4ZOu0pJPYVVboUEAXNgADlMLxFHGYpKyb0mnPyThGhwFx\nGKDiGFdr4umEUacDXo5Qa/rDAd12j/E0INaKwXRMnCTgKFqzshaD0ZhiNa3Ns7WTZl8trSzT2xwS\nxhGjyXD2/SEMNaPxGK3B80TLM8u6SkJ832MyiYiihHw+XXS7u7tcuXKF27c/PCXSfCybsZ5d4AQI\nA7RSjLtdeseHhHi063Xa1RIq0RRyeTwUnpPD9xSuSpMbEmV5kvb8e0JAl8xVAbR2yMCmzG2GyJ7n\n81i7eSDINrLCFslwHId6vc67777L+vo6w+GQwWDAysqKCTfKXJD32uGVeQbcZhCytL69wYlhsTUa\ng8HAzLlOp8Px8bHZLGWDleyyWq1mZASQ1vAKgoDV1dVTXrwYNQF5AkCyxkbO0xaeS6KBtBHK6qIe\nx5BrbQOAJElYWVkx1zMIAtOoWYBpVgJhg/BP4hT/rAybJZSODFJ4VBxPqS0nxhs+HjtiOyx2WNEG\nL1rrU2H+bMKKzZLbIUdIgcd4PDYtpYTNFocny9xm2Tnp7iCdV7L2NVuq4nEPccgk81IAoQBVecxm\n7+D0Os5qID+N8dhBl+0dZ4GSXX/DjsNmj8GcG2wLtc2xdQxJRBLFoGOSaUi9UmXn3iYL1TJfePVz\nfOdb32a1voCroVGrkyvkaRTykKSfG1ugS1gTz3FJtEaTnAqNmc1v9h1cBwgjdDhFxQEEE5LREKYD\ndByjhwPCfhfteoyiiOloyqTfoz8a0x+NiVCMgymh46abq+uiXA91cIST84gTTW/YBy/PcDhG6pxN\nJlOiOGXpRqOJ6RuXTq6E4XAMCkqlPNVqSl1Pp1N8P2UAPvjgA8LwRHiYNdKf/bAE+jotJ6GjEKKY\ncXTA/mRMsnfAtN9m0GmzvHaOq1euU/J9VhYTGn6BvKMp5D00J0DGBsjMocIfx7DBCTC3onN2ZJ2N\n7GMPGrLZ2EA6l8vR7XZZWlpie3vb9GlzHMcY9ew6lM+Tjdk+33nnbW/o9ueLjkzS8afTKZPJxAAr\n8VDteloCpiRLS4oEC3Mm+hUbTIlxEZGwXclazlmMkGRliWERpkzqYj0pwERrbWoSBUHAeDymVquZ\n0FgQBKayuGh3hG3Msi+2kf//6rBZQBGh+75vQrbC9khYXa6lAKKHIPPJrgAAIABJREFUXb95ocys\nkT8LDMtjsj60Pum7aQ87fFatVk85GvJ8lt20dZ5ZIXw2CUPmid1X1S7W+7jnj6wFmzmUxt3SocJm\n5myZgbz/0x6PHXSdRcmf9Xj2sbns1gyI2Y87GpROC5omYUASpu14hsM+ly5c4P/5q7/EceCofQzD\nMdE0oFj2yfslQg1uwcfNeXj5NG0dxyWvvJPNiQTi5KRMhJ5lzWlNMCtPoXVCHAboJCKZTIinY5Jw\ngkeC68BkOqTXaTOcBsSui++XmI5GxHHEdDpmnETguPT7M8CVQLFcoN3t09rtUVuoEceao+Nj1tcv\nsrm5g+sqptOYQt5jGpx4+jK5XAdcB0oln9Foguumnlwhl6dRq8+qk4954YUXePvtt41h+iw8gIcN\nMQs5BVGicRxNEk4I2xHRdMIBMUEY0er2CGKHCxcukPd98o4i8Rw877RgHE6DridhZEEXnACvLKCy\n33MWuzuPFZZj2touWzQrzNLGxgbdbte8HjDlEsTDt5thy8Y/Lzxlf7acVzZ7yGat5H1SIV+0XcI0\nCQATwCXhjyAITGhR9DfSaNf3fXq9HnBSKkOGDTpl05U5LiBdahZJGKZer5vwxZOigxIjI9qW3d1d\nA4TlOqdlaE6cWGHr5mW5/iyPebbEHnJNlDopM7C8vHxfgofpoZskp8LeNnCf99nzHKK5kpUZ820n\nkmSPNZ1ODbMjnymaM2E5hd0UICb3W1gy+SyZJw/a421QJUDT7goh6+dx6WXl+sp6EGAoe4bNBtss\n5U/C0XisoMtmTuZNwnmPZb2y2QFOGDHuFy065homOOrkQsdRxPm1c3z43nvk83n293dRXlrtWTkO\nraNjqosxuuWwsHqeBE25WpsZHB/XTfVEmhPNjExCnZzcRNd10xBlEOKimISRqS1EGOLPKM44jomS\nmERHDGf95rxCHj0MgVQQ6XguR90hKoqYhiEra6sMRhMqZR/idIIpx8FxPCqVEoPBCNedVSImJeLE\nuJ7ocMQIYzy6NLV2SLFYJIoS9vf3uXLlCnfv3v1Y1PmnNRwgTmbFTJM4/T4kMHWYDAZEYUAwnrWy\nGY+Ikpg4SYgSTaRdk1QBEgqeMa3Jk2FkbA/LnsdZxmueLuRBTNeDNvjsBiVhlG63y3g8NvqIJElO\nVawWjZAAm6yIVobtQdqfZzNLYgiy4Eo8eRH5SjsiG7R5nmc2U6lib9dXEiO4vLx8Kl1cAJN9DeWc\nbFAqn5MkiQGaouuSav2PwwmxR5ZVFFAqGZetVsvUOLMLSc4rR2DPh5/VcZZDPw94yXOi47Mz5CRk\nJQ6DhHIlE9aeV9nPnje/4LSgXj7fbscjgC7bx9QWkMt5iujecRw6nY6pTyf1q+xMYPlOAu7s/cG+\nNgKwZO7I2s3W/HvcIUZ7HktI1r4fAhJFaG+L8D/L8cSArrPCitkxjwGTNi8n4SJ1ypCaTLUoAZ2k\nPQ5zDl4hx4cffsBg0GM8HhKGAX6+RBhOGU+GdHsdAh3TWF5hNB7QWFgy8W/PcUHHaAUKJy1wqjWO\nSpsqJzbDptKsyxMK2cPxXNx8gSSOUHmHnFJM4phpFDKJUgPb7/cp+QVanTY5z0XlFIuLC1Rri9y9\nu0kh5+HqhMVKiVqpyGA4plops7t7wLDX5fzqCh8ONvFzaaNhr+ASRTGOo0gSPdt0U2YiXYzOTFDf\nJ63DlV651dVl7mxsmcX52ET0nORIOqd+J8RRSDTo0WsepB5dsQQqplEpUc6vgsrhawdPR6fmjzEy\nPBnevQ2Is2H3rGZKRpb1kjGXBZ7zefI+0Xn0ej1arZbp7ykAqNvt0mw2ieMY3/dNQURIDYjt/WYB\nIJxmk+RxO6wi30tCNfKd7YxKyZ60weHCwgKVSiUthzIzAMvLyywuLjKdTllcXKTdbhNFEWtra6bK\nvpyjeMKyCRcKhVQOMDNO8rn25t1oNOh2u1QqFVP53r7mj2vYnx3HMZ1OxwACSRBYWFhgeXkZOMlg\nzepwfhLG56OMTzvr7WGAYB7r0ev1TD/PbMsdu/CvrZezzz372dnvYjsK9lqRGljy2GAwMO8XR0CY\nNvl8cZTsULgkXNj7eK1WYzwen2L17N+2c5N1nCTs6vs+xWLR1PHyfd+E+oVt+6zXxDxnT/5vJ9xM\np1PK5bK5P/I9ZU+x9aSflYTmsYcX53kC89iseWGTuQDMekzr+xeXVpaYVynq9RpJkDb+LJfLTMIJ\nvpumlhZ8H8dR5Ap5irOeU7E+SeONkxMKmQzDoHVabf604XEAhXIdwEE5LtpxwXVROcj7RfKFIhGK\nXN6fedsexUIe5Sl0LkepUKBSLtKuVtNJkcQ4JBRnk9v3fbrtYybjMYsLC3gOeLOWEDLxgiCgODNY\n2jnpURWGAQU/h3IqJubf6/WMh9zpdFhbW+Po6OiT3/yPMURHD/cXqMi5DmESE07GROMxwXTIdDRk\nPB4yDQJcV5EPI8oquQ/Up/fryQBdWYbL/lsMow2+7KxBuB+Aiad3FvCS94s37LoulUrFiNI9zzPg\nSpisyqzZeKFQMKLUrHg+a7CyxiRrcOT87FCerEnpvyglHKR2nrxGdEoA4/HYiMVFsyEg8ejoiHq9\nbrRphULBbMTS3FsMjV0TSdgt0UaF4f/b3pdtyXEkV173iMjItRYABEGyW1KrdfQDM+foR/TVetDb\nPOj0tDTNbjaW2nKNPdznweN6WnpFFgoEWJUg4/KAVZUZe7i7mV3ban8+lpDY7XZeOJ4K6FJh6QyW\nydhsNt4lDLgYF/ncARx9T8+NL1ke4hjC8SsF9m63g1LKz4ckSXw8ILCvBXnsekNIZVe6JIE940VG\ni4qAbH0FuPfnPBL7+nbScCH7xbFLZY2lUqIoOihuynEvszC9J0kokjwur1s2p2dccK9n6gujz0PW\n971k16mkkvk7lAX3164vOc6eVemSNKR8qTLegODLkwKz74V6mtQ6IS0HsI4UbJetZxWASCNJR/jm\n29eoy3/GerdBlm0RGcc2lFWON7//AVm2xeU3r1HXJSaTOQxcDMko3Reqg7WwtoXCfjCa2lkUkRZB\nw9DQUQwVjzGaLlzj7CSGTlp88+13WO0yRKwJtFpht8nw/eUr5E2Fi9evkE4n2GYldq8undZ+fg5j\nFabzGVrrYmD+4ft/w9/e32K73eLf/vf/8mnA693WB0n7CdalD2utEEVzKG39JL6+ukWWuYmaF42n\nnp8ypkvBiNwE3dOnsSs5UJcAYlTbNTbWQKcJqmKH6STFZDzC5dk5tI6RRHtrzWWTdkcxv7wv/zHo\nU7qOuQplplHoXuJ2Dxkp4XzjgpskCc7OzvD99997QcOFajwe+2PM5/ODa6fA6VuwZIbjQ0YVkzy4\nGL548cIbZUoprNdrX22+rmtMp1Ocn59jtVr5a6SLkcdiTBOZsTdv3iDPc2itfRAyBRAAH7dFpU5r\njel06lmGpmmw3W69y3W1WmE6nR64ME4BdPnkee5j2ZIkwYcPH7xbmIJVdgv40qzSl8BTXoucU7Ik\niLXWj5eyLH39K3ZJkPNHdms4du0hm83tCZ6fbnAqCizkSxcxgINMXgDeQOK75DtWSh1kCF9fX/t5\nIcH5J+9FGn4yNozXx16PAHzXiKdQukIDuu/7vp+8J8mwSwZcrmVfUt6djHtRBjY/5F58DNOljL3X\nD5EDWCkLREA0SqDrESbn50BTY5z+C/I8w+3yBrquscsy/MM//xGttXjz5g0A9+BZrT5K9rSpY7T2\nlKQxBqbeB+eNurITVrkIpChKMOqsyypNAeUmzPff/w7jibPqV8sl6stvUOYFiipHZVvosVsk7/IS\nWimsNxusdxlmsykiBfzTP/wjRuMUu90Ob374A6x1ZSfiOMb19TX+5fyfsV6vsX156a2QsnXPr2lc\nP8qiKD0l7NgxChf3LG9vb/Hy5UusVqsnWwhlJurBhwDoYIwB1NYAVYYaFrfX71DWFabzRSe4FZLx\nBIvxPmA00hqAY1dgT0Pp6lsYgIdLpYSLTh8rHB6L31NxA/btePj+f//73/tq7bSuX7165RUdggt4\nn3uRiy4VAColIQMgWTYqPE3T4M2bN55ZY62s6XTqY1zYfFdeO1mrxWKB2WzmLfmLi4sDgcNSEbvd\nDovFwqeOk03kfZA5oCuFzBG35f6nMH4kJFOSZRkA+LVgNpt51ygZG2DfFuqXFpSniFDmSK8FAN86\nSsZb7XY7r5BTCTvGbkmBLg0dnhs4DB0gC5V0vX85R3gcMrAA/HrO8zAkANgbEWQ0uQ0AP6/5ORNh\neE2yLphUuGS2bzjGeH4mtjwFJLnSR8Twe65D0siUayRZOqnAfulkgOdluoxjMbTWsEYUVvSLtgF8\nNXpaX3vr3nR1t6xSvm69tqKOPQd25xJUOoI1LVTjKsirkUZrIhjVIsIE4yjGrGqRZzVe/fADKuss\n5rwCEtVAxRajNEWjLVpTYtTGiKDQ6n1aro6A1rSoTAGrLJRWMG0JA/d5oypAGVSo0aCGHmko02WR\nTCY4V9plVrYKRVIgj3NE1RgTWKTzzppWOcyFQZqMXP2vFkhHCvPRCONRCpsXiKBQ1w3mLy6QZRkm\nb75xLhcVQbUWi3SCqqmxyYuOCYjQVECMBNZoRHoMpVtUdQulIpcp2C0Cq9XqYFL/kjgUY+bYFzCI\nEQGwbQsUOcxNhaZukL24xN1yjUpFmL1+g7u8wYuzKZTViKCgrYHuFqvniVI7hHRPSEOkbzGRig0N\nlbDMSog+NyPdDbLOjtau2Tpr+TArioqRfP8yC4qCntctXTQyK1H+5PZcqOU9X15e+qKkRVF4VoGs\nEt18FBx3d3fYbDb+mubzOdKuawOZLwoJCgRmOjL4WJaokFljXKTJOMjrJHP2tKVUHgdr96Uk+IzO\nz88PetIx4FoKr98ipBEjmR0+j/Pzcz82qMBTaSVzCOyzgyWT0sdg85xK7etcEdJVJ929s9nsIFaK\nY1SuG1JhrLui2uPx2O/L+CXZsYDHkXOY18n4NZmxyQQakgvSYAH2pXmeEsfWvNDQBA5du3x/wH49\nDBMKvhQD/OwxXfLn/c/vf9Znufs+fXYfs8V96GY8hO5cgQC6SVHm+wyt8/NzRKME5+fniEcJlLV+\nwFlrYZtuYTXKBczrfQxXVbigXGPb/aXF/QUroygCWosoShBFCYqmhp6MoVKDs7MF5sY1H013O7Sw\nUF3s1byqkd7cYLPZIE5SZFmG1WaLfLdD1Amq2/UWsBq3H97DWovpbIHrm2vMzxa+andeFj693loL\nBY2yzLC+vUFdt1A+iHjfDuKUXA6HMHAqunu3MApVXeDu5hY//uXP+Kb5Pb79/gfEtsUoioCJKyUx\nigC0xu1vn1/QyMUrdPf0MSlcmKWrsQ999Hvo2gAOY7yka5DtdV68eOFdC6PRyC/QPD+LpvJckv09\ncPXrfaNobkeljsojY7jG47EPjjfGlWtgijywL/q42Wxwe+vc6tfX18iyzLf+efXqFW5vb5Hnuc+E\nTJIEu90O4/HYV6nebrfepUr3zd3dnT8fn5lskcL76F2bnhFyfaWisF6v8ec//xlZlnnlMssyRFHk\nmZNQ+P9WELrvJSNLg2A6nR4keZC9DZNbQkUrdLsDOGB+P6acyGubzWZ+zLEGFZUwqXCxcjwD6Bn/\nyEBysrVUGtmjkHOawfAMK5DJLlRIyPzRiOG98PpkgP9T4KHzyPWRTDeThciI013MLGq5PhGfy3w9\n++wKhYC8wT6lS27nf6Jb7HoWPfmXZbC0oneq89O2FlARWmuw3Kzx3XiB7WqNi4sLFEWBRToCjHXu\nQWZyWAvbxRgpxSzJvRDRCr4fo78m4xQzY61riq0UDPbB7IhidwxlEE2niAFUSiEGEFkLC2A8nQJ5\n7iwUFcMdSaOqnIW+WCwwHo0Qa4UoSZBEsRNIa5fFtF6uMJ3PsM0yl47fOEFydXWF8WTqA4Kbxnb3\ndtijTQrUUwTHAYxBWxYodltURY7t8g6r2xtM53PUxqAxLTQUYh3BWtO5MJ//nvoMESpBFOzyX1+s\nwcfc9aECJpUeeR0ULAwcp5Dh4iTLPUh2ILweyf7w3FyUpeIl4z+k1S7dLFprH6NFC5vuRSoLzJ5y\nZU8yX7OITZ+bpsFms/EK1na79b3jAFeLjE2xGVIgsyh5XeFzO+k50YFFZTebjY/nYUV1XzQ4YGJ+\nqwjHKp8jq7FTQaVLLpwDfS4vOWZChUsyL3LNlWwwDSzOD1n2ATiMS6JhL2t3sY4dxzvjNVmQeDqd\nejaMjDKzMXm9ZLl5XD6jcB70rQXPiWPPmqwWP2NiWVmWB++S4+BzcVIxXaG1SPdhHy3Lh6GUAtrO\nraEOs7iiLgybx9TQMNZS+0JrLeLRCIhatE2FOElx+eKVs47nM1xdXeHsoqOTu1o88WifqVIFwkQp\n27lGLbTdDzjfHqJu0dQ1lOms/8a57pSOAKWgVYomMlARgGQCYy2iicLIRrBNC9saKBtjOhshiscY\nTxZIJ1MnbIwLBr67usHv3nyPRDk3IM7OUY4n+Nv7t4AxuFuv8OH6CkmaIt9t8Zf3H6CU6x+2XG1g\nu96S1lIFOcymOVUB467W7hV1o4FGoSp2uPnwFoDBT//zX0h++CdMp2MAFqNYo7EJEq2ckqye/57k\nIvaxjF65fZ8iJT/nZyEbEx6T52UgLV1yDNzd7XYHcQ90W8h5Gl47sI8XkyUqZHC9bNgrWYNQgNCq\nV0p5Zlop5d2SbOC72Ww8W7XZbHBxcYGzszOfbs/r3G63Psh8NBqhKApsNhsvVGRWl7wmKWT7nusp\nIVSmi6LAer3GYrHA9fU15vO57ynId8D3/1tHqPjIWB8qJrLDB7cj6xMeR/7OOdKn4Mq/KR/lOeQx\n5ByRWYdsZC0zFl2nEedmZNkXyifGSZKdIusDwHei4HlluEDfGnTK8wE4dBlKBZgGHvtayoxQycR/\nDp7dvdgnxP1nwbbH0Lfw6c7VeHA+iAlkAW0toJ07JB5PABUhKyqMJq5AapTEyN7lWOwWWJy79PnU\n7lPkW2vQgm6RbmK0zn+uLNA2zhJI4hG0BUzbAE0N0xqYqitQOEpgoWGMRWQjaBW5yvfxqHPrWCS6\nRt0a2MpgvVphejFHVdbQSmE6nkC/fIX5dIb//M//RJnv8NNf/4LvvnmNy8UcMAbKGvzDdz/g//zf\nP8E0LYq6xnK9RpTEvoHxeruD6SLWpTYvmRRaOKc4oaynFbt/MIBp0WQbVOs7bCLgbpKgbQyiRKOu\nX2EySnE+m2GcJtCqRXICSpdEqLz0PfNQ8IfbhYti3wIZWtJkkchmcbEhK1TXtc9wlbEeXPy5wMu/\nQ+teKmGyKKGsFyQFi2SyaHVT+OV57pv40oJnYPzV1RWWyyXG4zF+97vf+efC+/rw4QMAF4jMQGDG\nvISCVD43Xpd83qc2L8Lr4b2wUr8sB/Ptt996RZPZbKfEUjwXQgaLzDHBZyQLpMqxDBwqVxJhvFjI\nPMvtpIITMpE0OvgZ+0RS+eN7JatFpozFfVkeiPfjC3V3awAVsmPGWngPp86QhkwclUwy6DTwuM4Q\nX2p+P6vSpcQ/yJvh74GVfgwygFcpdRDHJQWSMcYfWykFFcVIIoVoFKPKCmgVYbaocfXTW5RViSbb\nOYthnGIiWgfwpRjbwBqLqq4RQyNCBMtJiH3TYlsXiGINNDU0DOqqgGkqKGsBOKq3ahuMRwqbPAO0\ncu7E7ro13PmqxgX7Vnd3GI9T1M0+jiZJEkzHI2w2JX788Ue8fvES8+kUV1c3GMUxbpY3ngZnyruO\nE9xunOWfpinyvEDbHjz2A5eKLAB4aui9pNYAbYtstwEAbGYTVE2El5eXOJsvMBqNkNUlWlhMRxGS\n5Pkte7nwPrR4hbT+MXeQHP9y/EpWuc8VwhgRKlS0lql0SXaJgojKGgUP54k8J4/DLDmyZQzs5UIn\nK14zforbhpW/l8ulL5rKODCWs6jrGu/evcOf/vQnH69JgUIlK8sy3wiYsU7WWp+txqKPfF4PGYqn\nhL5roivp7u4OVVVhtVrh+voacRzju+++84wgi8+eUnxX3/380gJeKluhodK2LVarFe7u7jzLFBog\ncn/OE8mWcD8ZoC63p5J84MHpDJBwPAJ7JTCKIt+gngVRWcCULveLiwv88Y9/9JmHcp5xTWDM2Gw2\n86EGcn4ToUIoPz8l9M1Z3heNPVmjj78zvk3Gcv5cnAzT9ZDr5Ni+99LOGc4DEb9ysA/uSeeW3kYd\nARbQoxEW52dIO9dFkiQuWytJEHUxJV6AdEyWRYuyboG6RtS1lTHWoty5RT1SGqapYMvS9Q2sK9RN\ngzhOoJMYdVOiNYC1LYoqh7Y1VOIYpqaukRU7tHWDGg02xRbv/+dveP36tQ9i1FojUhavX78G4GJS\n/uM//gMvXrxAlI6RFQWWmzVWmzXyosJyvYZVGn/96QPayIWpxbFCFCeAatG2BlCywr9oOH2qEHUl\n3HV3n9cVzGqJbb6DbQq00yVGIxfrdnZ2gR+++x6mtYgwhjXPn7/4sTFPfGy+yGB4+V3ICvcyzIH7\nYjQaYT6f+yB5ulUky0UhJBdsfkbBEgbbUgFjOQjZXkUyBQxi53nKsvTuTv799u1bXFxc+MKoAHy9\nseVyiaIo8OOPP/rMy7IssdlsfKsj1ukigyYTCCTbe2ps1s+B7A/I5sdv3749sPLpYj4l1uKpryWc\nb2FyCRUQyiKyQhzffI7yWCFTFSopUpmRx+I1APtq+NLlSMg5yPnErDyywLwHGht0N9K9KAPjeY+8\nf3mdIQvfx7SfOqTixVg9JheE2dwyfOJzcDJKV5/AkNuFqfDSWldKufitwLJQSnnaRinl/I3WAkbB\nwv1dty1i7bo+W1ik4ylGr7Sv4eM13mhfdZuLsjYtLFqYtkVtWnTqnnMtti3K7Q7WWozHMawxaDLH\nKrVNhapqoGdTmDpBqTSgNdqmwHZ94wZ45azruq6RbzbuXuMIebHG8u4KSewmXzqaQEWOYZjP57hb\nr9DCYrndYZMXqJoGKorx9voa725vsd5todMpWmswP5vhZrODS6K0mHUBtUUha6tw0filRsEXhEhe\ncPyggm0tLBqgbbAzLdBq/PX//Q8m6QSXlxlGyRiL+dzV7DqR7EX5U86FUEHqW7ylchBS48D9zJtj\nrI1kryh8ZS0fzgupcHExDwPkeU72UmTMEBklsldkV2RBSlagl/O/KArkeX6gvK3Xa2jt5i3bopCt\nYp2j7Xbr2S1rrW93tNvt/P2PRiMftyYVz4fe1deGMBlGa43b21tMp1PPbtH99FvGMSVPPjcyTnRH\nhQyJVJhCpeRYJfTwvCHbyEB4WRePyhLnJOAKeEsFWl4jy6XQ9U7FTN475V+YfSzXpmMM+6kjnLuU\n6VyHRqPRQVkOqQt8Lk5S6eqzJqV1HqIvfgvYkx9eCWOWo3X9Ea21MMqiMQaxjqCtgk0iWNsgQQpt\n4bM/TGRhsBcuTdMgaWsYBRRV2b0YoIECTAtUDZrcNT01iULTZdK12hVvresKKo5gk5ELmI8SNHmG\nzd0tlLGI6wa2dQO9KQvUbYNknMJUJSIAZZYjTkeOOejuN53OvHsFnSK32qyhowRZWSArC1S1hTW5\nq/Mex9AaaFsgjnEg7OQzhzmFvL7HQov/W6dcwynfyljYIodpqi5jbYyqapAXBerpBKMvMKE+Fw8Z\nIcfcKw+5uah89Vncch8qTMC+QCYXcC7wUvGRgpuMlS8MbPatS6TbUQamM2WbtD5dfrSuKbh2ux3u\n7u4AwMeeSMaMza7rusZ2u/XuQC6UUvjRPb7b7XzsClkwmS0pnzndDWQ0wvv/mtDn9qGLls+ZsXrM\n3qIL+beIY14YyfpIJUsqR7KVjnQhSgVJPtswppKf9REQMmtSXpMs+imZYp6f71UpdcCG7XY770aU\nWbpSOZTlUhgD1WfUnTr61kc+L2koErKYrAx5+BychNIVvrC+B/Og2+Ujn+/PcV+RM9ZlrSmlYLVC\nrCIYE+9pWAVkZYFFN/A4aeqyQlM6IbDcrBFFXRyMtWirCm1eoi0rREqjbnYwbYN8dYcRujgcBTSm\nRd62QJIiSUew6w3Wbz/Ati3UJndlD6wFtEJZVxjPZ2iyHO/f/R0XLy5RFjXOLi9wc+tiCibzhcvs\n0hq3t0tXRThKsN5tMZ5OEK9HQFSjagx0ojEejZHVNbQGrFHIMseuab2Pw7GWLOGjXukzQvdfI5kW\nZWGaGmhqbFZL7LYbWKswm/4dL1++xCQZQU9HPQc4PTxE5fd9JxWvh4wXeQzvtu6Ehxz7NDq4mDOO\ni3FWbD1DhS2KIuR5flBbqyxLX4pBuklkdiTr5bBpM5UuGaicpql3N9JlMh6PXQ27LiaL2Ujr9doL\nHLJsPB4D+K3dp8RTeZQLLZ+ddDl+jZCCnZXVN5sN1uu1T5C4vLx85qt8XvQpErKsBpUPqVDJuedj\njLvvOWYkM8bPQve/LDzN88n9ORd5Ls49YP9Ow2vhtZJNprHRtq3P6uWxQgVDPgvJeoeKaV/CySkh\ndNUSoWIrFVapWMsCzz8Xzxwl6VLNrDXoCChhRShoy954rjAonxO9hvzXy3J1cVX8yh2mgbIGCjUU\nHDOljHUteloDrZQLhh8lSDvr1jQtppMZlOoGvIVjftoWtamRZTtk6zUuzhco1wUSBWS7HXabFZxG\nZzFrpyjzAtlyiW8uL7BdbzAdj2GaHFHZACpCPJtitd1gvVk6gTWKUFUNzs7OfJNVVZYwdYNSJ1iX\nLeqmxfbqxhe/e6EjXF1fO6EBx5BNZlNM1QybXYbz+QzQFrfrDG1jkBdbJBEnCxVQgOPPLQauJhm5\no+6bLz4SPhu2Y166P9uDr4y/5EleIJ7k2K6vUKHCLD9HfVdjPksxSS6e9JL7ICl7+U8qQsBh5lOf\nAia/kwKW4P6S4ZLXEG5Hy5qxVGx3QoHBGJcsy/D+/XtEUYSrcYKFAAAgAElEQVTZbOYD0//+978f\nVKqOogg3Nzc+NZv3dn197RmAOI7x4cMHXF1doSgKX8Dw4uLiIJtqt9vh3bt33jXIezBmX7FeKoUs\ncZFlma94z/gWFk1l/BoZBBnLGT6jrwXHGFHWJKsqxwDf3t7i9evXvpjmbxF9c0Z+J5VuKaQ5Tsj0\nsuisMQZ5nsMY147n/PzcZwdTkLNEA7MHpXteCn5jjGeMmRzF89HdGcabsSsD67Ex6WS5XPp7kPdT\nFIU3VsioyVi2MM6JOFVli3jM9UmFleEJcr1lYs3PxUkwXfydQsB/1rNt30Pj5x/1t1Kz62K76Hxq\nrUWne8DaFjrSUF1PPmUBGxkRVO7QWou762tkux2UMWjnUzRNg//6038h32Wwbe0FU/Zjjrc//RWv\nLl64TCGlkTDWBBqNAcbTCd5+eI/1eo08z3Gz2WA8HuNlXaLIywPBtlxvUDUtlsuln5hpmmL7t588\nOzBZzDuhESEaAXGdAFWNs/kCZWuQFQVaY9Fap2QlSYw47iaWkfVgPuMFnyAatKi3W+Tv3iFarWEb\nhZcvX2Eep0hOdL3oY6xCtwO/72OyHpo3j4W0EKUFLOcvXXZFUWA+n/sF6u3bt7i6uvIV3umyf//+\nvSvm25UoUEr5PnZUft69e+dZsru7O78t3ZCAczuyqC97RTJtXhY4DF0yvBYWgSRbRtaLyp+sJ/a1\nxnE9BCoEvE/i22+//SqVyy8Bed+hMUNIw+WhcBhmynL8s63Vdrv1TC9ZM7rKub+MnaSLX7bdoYLA\nIP6Q1ZZufKk0cTt2lwD2fRflnJaFj2UQPjOMpTLyNcyNx1yjfG9UgLl2sPXR5+BklC5p4fvfcT9o\nngMgtPLDgMW+z2EtlDXQsDDWABbdOcSkUa7kBARLoOPIFSaF6vQ1t33ZtEAc4Wx+gdUuw82HK/zl\np78DxiJNIpQ3dzC2wc1mhaurK1TGYjYaI8+2SCI3ScaTKVabtXPLwHprqK5rpOkEf7u+OYivsNZi\n1zRY390hiiK8ff/eBUJWlaM/u/YF2+Ut4miEUd109PEOUTyC1QqTtLP08xKjaeonUxw7IVOU9YH1\n9GtCgwa23EFtFWxVYhslKDcb2LZFa5//fvvmQ8hkSbaFC6VktaSSFs6Lh87JnzL+K9xH9lnkceVC\n3LatbwxtrcVyucT79+99diAX+KIo8OHDBy9omDnEmBMqQKvV6qBAIY/LOSLdhYBTkLIs83NFrhlV\nVXnWQSptXEzJikmXEAVfmE7/tQiZx8Ba6wOr2S4GAD58+PCrucfH4JjL+Ni7Dl1ufcoZDREGtTNR\noa5rLJdLz0KxkbpP0hKKGLAP5gbgswz5L5yT0rXJuC1g7xql/OI2MkuV85hsGVkt+YwkC8jr+hIB\n5qcEhknItYDM4ufGOZ6M0tX7/c843kNQ6PILhZCx2mW6WeW+b2ERBcdRnR+TgfiGBVGTGFESIxmn\nWC2XuFuvvGBobYuyrrBer3G1XWGXFVjtds5qz13zXaU0dk2Nu7uVu55I++yqqqqgyxILa3EWxdgV\nJaLaUcPL1dpb6cYCddPC2ApR03YLpoLWERrTAlUFrV0hV9s0sHpvFdVVCy2seWvhrZ/9ZP11Lbq2\nqydhywKmbpFFK4zbFsvVLW6Xz+9eDBUgCS6GwP0Yir4Mx/CYfceSwkIqdWGMQ4hj8WFaa89esQfi\n7e2tV5ZY8b0oCqxWKz+Oq6o6aC/EWkIURAB8dXnAKUqskq2Uwmq1OugbJ6tr817CLDC6YBj7RdYg\njFdhtqUUWL82ZYQB9RQ2ZCJlnb5fO46xen1zsi82SCr4BJ+rbF3FTglZlnkjg8KdrAr3pWJDI8Ja\n6+cGcBjgHRpfck2QRYd5bP5jRqPcDsABo8bvwxg2aYAcS/b52sB7oNIl7x34fBfq8xZHNRYKFmGM\nltad++8T7026IEPL3loL3Q2O1nZxXNYFkLtq5oe1UuTxvDASJQkMLM5fvnIxIukIGN3BRhEuXr+G\nssBus8LVnXP/3W5c4bm/vb/GZDyCqRuM66ZrO1Lh/ZUrXLo4n6ON3MS7222hdYxt3eL9auUztiaT\nCZadJT8ej9FGEZarHawFFouxDxxOtfIBxRpAEqfIqxK2UUjHY6RnZ9BRglWVe4YBXUFXHSUiBub5\na1d9UXT5FCNjYJsKzfoOu7pCBYP4BDK1HlK6Qsarz90ovzt2LHmcY+cJP38su8PGuFzwGezuGqeX\nKIrCFyEtyxK3t7c+OzCOY9+aZL1ewxjjK80zbpHzO45jH6OSpqnvn8g074xtu7pjSuucmVoAfLxW\nHMc+toyuTTINRVH4WBnJ1v2aQKHC1krW2t+c0kU8xHj1Cdw+xYvPkAwtxxuNjqqqMJvNAOzLFRAy\niJ2fUwFgsohUfpRSPvlBsuAy41YyWOEcpjtTzlkmwfAe+npMysLHH1sbjoU4nDLk85PK6Od6gE6q\n96L8/J7C1JMdEipXoQuSx/bHYmNqAwD7bA4LC2Oc4qW19vFbbdu6oPlOYVNKwyqnJKbTCWzTuSNg\nMEpTvHr9LfLdFm1V4+7uDnGSuv6HAPKiwmKxQNXUKMoC6XSCurXI6wYGFnXbwMYa2XaLOBrBWIVR\nkmCUpthmO4zHE2RFgVoUNeQ9J4lCVVlsNq5r/GKxQF3XB9V0m9pgMpuiqpwyNUlHSEYRxnrsLR6t\nu6ytep/6/xAOJxEF/CcNgadHlyxgTQtAwdY1bFSiLHJk+faju//SkEHbwOE4B/rd6GRo6G4k+oQE\n/+Z861POZCApfzJzJ6zZI2l31nmia2I+n+MPf/gD3rx5g59++slb6BTuDMrntcznc2y3W1+Hq+pc\n5hRKtPgZv8VU9izLfOVtxyArn52otfZuRaVchvF8PvcVuKfTqV9cqRwy/kYqZ30xT30I163w+Z8q\nEyCvk2zM+/fvD1rE/FYQvuNQPknFqg/yWTJmkNuzPAdlVVmWfp0GXA0uehro3pOucpnxyMQQKmJk\nY2UWo2TC+C7DGnuyRRC3JbsVxm3Jc4SGSCi3+57Jx9y0pwTqAJS3si7a5+Bk3IvyJwdDnyIWCiF5\nrNDlEW5jlAWMhbKuCruCQl030AqAVlDaIjJArAVr1u3bWuMD8T2bwAHY1JienUPFCdLxGOvVCpff\nfIMPV9c4e/ECyzLHixcvkRcV4kghncwwPTtHmblqwEnqXDG7bY44coF607mruaWiCFmWo2laQGuU\ndY3ReOqEU9WgrhsAGjp2QqmoGpjNDmfTCZrG4PLypV9E43gEQAOxo0snkwmKLhPDCSbHCtzcLrtF\nYv+sH5I1bhu+h9Ntig2ACbP+DwUDW2SAbXE7Sp/xwvaQSs1DjFUIpdSBEnPM5dhnlUohwrkUxlnK\n6+J24XFkHNR4PPbZgZvNBnVdu2byk4kPduc5GQdGgSHbCZHeZ1FIuljkuWWGlWT9aKnTtcOYmsVi\ngbJ0dfSkxU4hJavSc1+pdH2sZIR088jneqoI10syfL+GWJ0wE/GhzMRjCLcNMxjD76QCIucyWSP5\nfJktC+BeiRLg0M1orfUueBlHyfEaxhvJjGGpLMpnwQr39G5INzq34VgOSQ758yHl6SFW/RQRupKZ\nUPCrqNPVF6cSIrT0+6z1PoXsHkNg9yUfAEAb62gv7QpoUnkwRnRPt/vJAhwG3KtkBCgDWIPZ+QWm\nizNMJhOMZ3Mszs7w93fvYUyDbyrnhizKHGyMHSsNPZkAKHGxOEOiI6g4OgjUm0wmPgNru90i8WxD\njTRNoBR980AUAVGkYK1Bmia+9sqrV69wNptjNHJum9l8jqKu0FqDvCy8cCqKomP7pFsV4p7vL77y\nPbDWmTH9yvTJoNWwAGrWkLAtlI5dCZDl+lkvDdgLPwr7PoaLP/vGOhUk4H5s1rH9eN4+5eAhpSt0\nw3NhptJGV3ee53j16hWUUj5FnS4Nuk2YmcV5TAYqTE2n0kRmS94HLW9pdcs1ggH3s9nMGTtdIWHO\nb1q2vF9+F8Z0hM8ofL7A/WbG4fM6JfTNVfm8ThmfohSG24Z/982XUKniOD/2uTx2qIxIdzeVLp6D\n2YWcO7JUiyweXJYlptOpZ2BkP1x2dKBrvI+Zlm5GGZaQJPuQkvAZ9DFesmAw8RCbJZ9TuP0pQpI3\nAO6tCZ+DZ1e6+l6UHCAf054/dtzgm47pEGnuVvk4LcAFWtvWHPytLAXLYXNh911nBUQRbPczTVNU\neYbxZIKyLPHq8gXqpsJmo73SNZvNusmgMEknSEcj5E3eFZNMoLT2E+hsPgdMl02oLOLuHC4Ys0CS\n7OuJ0Je/WJy5QVLUSOMUi+nMp96PpmPvOqmLshtMBlEUUsSPFw5SMJ6qcAG6pAkYGcIHaxug0UBd\nHd/xiSDHPnAoGCTzxG3DvyXbGwoHyXyFLJhUlPvmXV9sj7SaJVsUCo6maTCbzVAUBc7OznzwLmsH\nUbGZTqc+vX06nfr0bFnqgUqXtRZ3d3eegWIvSAoiCivZP41By1TmFosFbm9vobu5Jou7MoaSwc08\nxqeM7T438SnOiT5IBfSU8Sls1Zc43mOeh1TeQqVdBq2TQZV9DmUza24ns3fJTDHJRCZ/8FicW8y6\n5TiWwe8AvKuQbkOGBvAcvH4qWcChgRXiobF96uOoD73esi8w3p5d6ZJat7Qi5M32CZdwu1DgHDnj\nvmCqUVCdP9t0GYyJVdAaB9mLfUwA0C3A1qI1rhaWUgambaHiBIm1SMYT/Ou//qtTrIoC2+0GNzc3\n0HFniTfOT/zi4iWiKMbbt28RbRVevnwJRBpnZxdQyqW5ny0WWK5WWC6XyPMCH5ZLXHTCa9oxYcz8\n4nVqo5F0JSDKssR4PHYWVsdqWQVknaXlKnUDWeaUjijmggGQATw21qQbRbKNpwoNhRa6U7qMdzfG\nkUbTPr9ADK1QqXyF7IlkrcJnHs4nHvuhLEeeTwaLywrchHTbhcpEXywgFSq6tKuq8gVTfeFfpbyV\nzQDjt2/f4u7uDovFwrsfaWy8fv0a6/Ua2+0W2+0WNzc3ODs7QxzHvngqhQnjungtFHby+ZBFYIHU\nPM/99fSxG8cUqD7WSLqGvibwHf9a0Mf69kGu9VJxlt+FjE3ovpMtebiNdElK5Z2Zu2maHsxxFiYN\n5yKVKmYE8/hsXs4EEyaQMCYp7B3IHoNUvsbj8UG/Td6nUvsSLn1rj7yGhxCywAC82+7UELoRv6TR\n9OyB9DJjQgrwUNHqi/MCuoehu0Wxq0quoLxb0B+jcytqrYGOsarbFhotGgOgtmhNhCSOHRPij2/Q\nmtq5IXkspdBaC1W3UBRAaP05tY4xGU3wzcvXLshxdQutFWAamKZG2zihExng1eUMcTSCshXU2wqJ\ngmO+Ost8VdVIkhF+9+33SJMUWZZhNpkiSmK8ePUS7z5co6ydlTSbzZAmrt1Jkrj2KHlVorYGtdZY\nZzmyIsf8/BzGGKw3GaJ0ijiZoii3wrKSQvO4sOhzL536It26gcBsCqgusN7Y5iQK7YfMU8hY9blT\npLIrlZ8+IyR0eUhjRQoTKXBlOjqPIY/TJ5Tk39yXTBMX+fl87oWQVIJocZ+fn/v2JGQImBiitcbl\n5aW36ClkFosFNpuNV55kA2cqX1TAmAEpg5VZNiEUKH3upI+9P+mqPEXB8hC+JlbusfgYS9EX6/WY\nYHm5nSQS+pQROY/JaMlAec6DsKk13e2MA2PhbYJzgF0jrLUH8WGcfzwXy6XweuhVkYwcAM/+SiNL\nnvexrkRegzT65BpyymNNzumvnukC+l9anz9VfnbvJQVWxzGtlFmJEFanKx+xf6CtMYjU/clitUIk\nfFJKKVhZuR1BkL2wfmKlMBmlaCdTZLsNWuyVTJZoYPxW23YpusJf39QtGmMP6N+y3tchSnW6jw+A\nxXx+5tmK9WYDVRSY6wgGFk3T4urqCnHHADCWAPi4OzdEOPnC533KEymEEm7n50bfs5QKT8gqHhvv\noYIVKkd9v8vzSyVaGkTymsJjh9cuFQ66LYwxPttRa+0VJLYooRI0Ho8xGo0OYimUUr5FCd2O8/nc\nswFJknjXPQPl2c6G2ZIsWcGClJvNxl+TzAzm/fzcOdHnivya5sTXdK1fAo8RqKHLmPt5ORAoWg+N\nHZl0ItneUOniusw+pswcpMEBwGfXpWnqXZUc/7xWZucqpXxcJLN6Oc45/xgywDnB72SYwWPX+2PK\n52MZsqdG+H6/NJ6d6epzl/B3+VnoRjn4HR9PqwfIaqguRssAxri+fAAaY6F1F8gYdy4eFgqz2tX4\nUgbKCDdKu78Pi33zUQMLqxRUpBEphbqqABhEGqgqR//e3rpK89P5DEVnXbugY1e/aL3dQkUu6HKz\ncwVTR+MUWZHDwmI2P0NjLC5fvsBf/vIXqCjCh/fXuLy8xGg0QlY6C95qhW2eYZllUHGEtjWo6hqm\nLNA0BnVTHgjtTxlocp/HZLCcAhTMvUg1awGlDD65MNwvgHDhD8cwt/mY8hPuAxwmnkiFQCp0fe+S\nQf1kpaQSxWPIcRAuzJKx43GozMm09aIofByXMcYzUsy+ZcYjP5NV6BkMHEURzs7OfMD+drv1wcuy\n4XZRFF4oMRaGAo9C53Ms8D4B/DXha1e4+pTfh4LuP1XhCvcJx4r00Bw7H8e+3IeKlPwnz0kmi6Ul\n6D6XbC7Lnex2u4P5TaZZBvPzH92hbLPlEqvMgUIo2d9PGdPHnlnfczsFfOr9fSqePabrUz5/4ED3\n3JH92+1/SsbLNXRWcBl6LYxWzh2oghgNq7xrsjut/852BzdCAPHzOI5hGreoN2WFKi+8W2O83UJp\nFmp01rlB1wPRuoKsTcs2PZ11kzgrxWqF1roaLfFohKptsN5tMTdT1KZB1dQwcMkC690Gs/nCDfpI\nwzSNU77Mz19gP4VaPlXwzbs/nv+aD8bbke/63I19CwU/owJBi7XPPSAVaKkw8He6OfpiHeT2VGr6\nWIAwRg2Aj+/K89wXTLXWHmRssSgqjzEajXwRTwocYF9kUmvtq35TcWOgPFksKm10qfB8slHwQ+/i\n5+BrmRMSX+M1A/1K1Oe6hkJjhwgNi085p2RV6B6UITcAPEMFwHdoCMNy2MIKOCyfItllGS9K9yTj\nv7g95wuPTYY57Obw2HHRtyZ9DTjmPfgSCtmzM119gzhkwI7BP5gjgff3tgOguwRG3/TaulgwCwvb\nWgARrO2uIYpcwI+1sJZlJND9s+5YQrniuay1aK1BbVqgNZhOp8h3Wy+8OEHqusbV1RXqrgJ2ms6Q\nd1W7oSMg0mhag8a41F9YDaOw78mlXdxL1Rhss7VPEc6KHEXlrKHWKmR5hiiJcXXrCrWmY1e5ezyN\nsVntC4LK7KzHvr9j7+W0ceQe7fMHdYUZY30WppwbYV2uvn1lbJcUEHKbUFmiEAhLNoRCArjfHPiY\n4iYta3ZL4Fyn8rNer3F7e+szHLXW2G63yLt6ckmS+EKlEhy7aZpiNpuhaRpst27OMQaMWY7S9ci2\nQsxgnM1mvqAx7/dTxnPfnPjS86FvXTz9OffL4bFlIx67tkn5Ez7XvmM8RlZ9DDwPiwbLOZqmqXct\nAntliMki1h5mC9MVSNaKho9LmFLeLS/bDTF2i+sPs385b9g4/lPv9di4/CVceH1M48/FsXn/JRiw\nZ1e6CPlC/SRSxrl+gv8A+GbY/P5jsNZCIYLVGqZ1WUmM8XKKVwurFGxj0eoYWivncuq2MQ1/3y/2\nRrgaZfC9ES/LagWlI+hkhGQ0RjKeoLEGk8kUrTXYZRmqqoG1Cq116fMGCnVTI1EJoBU2mx3G4yl2\nRXYwyaIowmq5weXlJbbbLe5WS8eolTmsilAZV7clnYzRGIuzsxh126CpDcq68jEDoYvwc3DqAkbD\n4F7xA8UkjOcvBCkZovDewwXvIStSLmrh56Hbi+9eBhKHLsM+Zu1YzFKf0hVuKy1uMlK08umipHtF\nVr7mcyErQEFBtornSFOXSMKsKzJcNHxYXmUymXh3ogwU7ru33xJO0e1zDMeM95+LvjVHftbnuvwS\n55QlJELjh4YDx75keaWyRbmglDpwEXJbyWRJZY2B+DwfYxzZWitUBD8Xv8TY+lrc+CcRSH9UeNj+\nQOGPLQh9WnSf+8RY5QPgwc+F6wZNF7AM0zXKBqwVWV/yeHQvdtl+bVeewv+uFNLxFEWRoWlbTKZz\nGGNQlDWiyPqSD8YABgptnsFi3zBVRS7TquyyFCOlUJUloGPsitwHJRdVidYqRAlQyVISbRejAg2t\nHHtGt034jH67eH6m62Pomwt98SFEGIMR/h4eO1SUuD0zGPu2P8YGPMSAWeuChafTKQB0teUWXoGi\nQsbkEWvtQTFUWSl+t9v5VHcG5LOXorX2IPZLFpCULCAVPSnwBnw9+FLKD3AovEPZFJZtCUuwSKMp\n3O+ha+YY55yTHRFkmyAqUTQyZNyV3IbH6WOu6KKMouigbhi3kckzvCeeq2++9z2nrx2/5Brw7DFd\nDwl6ug1lXEq4n/zZV2ri4Gd3GtP1XrRdIL1tDWzTAsqdo0Dn8tDR4fUp46vZdxe4j+Hq+JO2bWHN\nvheVcwNqROkYY6XQWIPpvMZo7OoVtRaItMvUMspgm+XQcYTaGkRRDBtpZFWNum0xnk6h6gplXeFi\ncg6tY+R53gXbb3H58gXW2wyNccJFpv3WrXO96MhVBW8a41kuoo9S/9ULoBNbKx563jLAXZZQeSjG\nK5wX3D/8nfMrDK7l8WXNqo9dc6h08XhyTpCNkou1zI6ydp+tG0WRLwTJ+2NRVFrwjPWSz8IYg9ls\nBmvtQUYW9+c+zNjqqzt2zM30q58Xv3GE5SNkEkofwjEiYx+PkQUhY8bvuC/HvLXWM7bAYVN5Oa/I\nCnO7NE39/mSOyWrxMzJaMpMS2JeK4HX3ESPysy/Jgp0qvhQpcVJMV+j2kJ/1IVS8jm3rH9aBpS2s\nebPPYoRq0WoNpQy0vc8UyLgfZV0mnDEGrXBF8l/bsWhKx7CqBSKXYWXjGHPriuKdn116CrdqG1il\nUZsW46qEjmOkkwk2uwwjAFVTYzKb4ubmBugUQpZ9iOMYsF0lYe0+zzKX9RjHMYoqR1FUmE6nODs7\nw3a7RRsUAz3GEH7sPXztsNYVzD3FJePYuziW/SSFRGh9PwR5PCpcXLBDq14inHuhG1Ba735edNY7\nBUQcx5jNXK9RFmxksci6rn3za+nyoMBg7Fee594dSQWLbkn+o5uE98JAfQoxNv0Nn1ffYvtrZ4VP\n6d6eQ6CHTJWcH8fGQ7htiGMsmPxOlojgZywBQSODrnhJNkhmi3MO2NfYo3udSpdUxrgdFS7GHJdl\neVC4NbzXX7uS9UvhJJiuvt+7LXr362NjHrMIGljAOHZKWX0gDBrblYNQBo1iXFnrg+V9vAiMbxtE\npcta6wuzwtouY7ATNrCItUJVGSRxgjgZAdYgVnOMU4MkTpEkXXsS22I8nSAZp7BwJSegNRbn52ia\nBmdd1mKUjHz5C1r3eekysWaRRt6Vi2BtorKouuBhlyE2n89xNj9H3TY+uFI+21+7QFE4NrKeHw8J\n9zDOK0w4kb/3tXDp206eV7o2+LfMfJSxTlKZCeculSH+La1q6caQVeOZmcsFn0G+RVH4Mbter/1x\nJpMJjDFe4WIR1bZtPUNGFwpjWVhAmEHIdE0C8B0bABwUjuyLret7T0+JX/Pc7MMx4f6piT+PQeg+\nBHAwfvu25zbcT7JN4XEBHMTRcl9uI3stUulhJi4VLI5tsl8slyLnGvtmsqyEjH8E9mw5r0X2UWRS\nyXQ69fGQWZYdxJD14TnlxlPM0S91vGdVupR2So5F99CU6oShC5DXltXkLUhFdEt7dwApcEC/ofuD\nL6E7plIKkWo942Ua+hpdZiJgoLQ7d9spZq1Rvtp9Y/aDtdsRse1YLuMWaW1dKLY1BqYsoJvWtZ2p\nckQAru6u0VqDdDJBOnGulelshki55qEaNaY6QpROMJ6edxkjEarKCbGzs7kTGKXBertBMk5xs7wD\nAEzjxDdC1XcWpXUTIJnO3OTMW5RlDa2BvMxgkSFNUx9Hk2WZd1edkivlS5/bB9EHhz0VMXZMwPS5\n1Pu26VsQZe9AeZ5Qwf7Ys+aC3ueulExX2GiXn8n6V2WXpesY19YXB6aAIBNFRQhwihHbnFAxmkwm\nmEwmvowE4BS1tm3vtVFhrEqSJN7dQlDJYpwL4BgGKUx/a8rOqeAY0/WlFS4eM1wDHzqP/I4uP+C+\nt6bPxUgFTfYJZVFUAMjz3Nfa4rwjk0tGmVXnOWalYha6CNmKSxoUPDfnKjN7pYLFTEjJog2GyM/H\n8ypddu/SUYAPnPevU91nwPhoj7m9Hl4c9y4OZi76b7pj39vfswyHSpdSFsYcWkHWdNfXDexss3XN\ns60TQh+uPqC1BqPxGK9fv3bBvqMJTOsmzyjd1xyCYsuTGovFAtvtFovFAnmeYzKOUcN42ldmlrAn\nV9W4isSjydgvImma7ONW6JYUsUHHBPmA50MfG/yQi+Njbg/gePDvY5Rt6bqQi690QVLpkq462duQ\nytd6vcZyuUTbtpjP53j16hXm8/mBQKEgovuRwmU2c8YEY7Ymk4lXxkajEe7u7g5iYJRSyLLMH5PP\najQa+eB5CiTZkJj3PMyD50OoqPzSeKwyFxo4ocfg2Hf8mwoS13EmPbGGFhNJaLBQmeI27EMqxyrH\nLplcKpFkvtnpgRnDkt0ieEzOYanUyTgw4Hi82oDjeHb3InA/NqtvOw5SEwiecP/QKj0UCodxVwAA\nI2JOxHf+WN5Sr4PjWVjshUNT18496TQxFFmG29tbZ323DaqqwF/++iPK2ilCy+USL1++xHevv0NV\nuKKNedEC0N5iGo/HsCqCWrvJudlt8e233yLbVfjHizPXhyuK8Le//+TrHmmtMZlNsc53mJ0tfC0k\nd49uUlZNA2Vd9phslSLp5wHPg2NK1mO3e2hOEKF1HunWgPMAAAXKSURBVAoZ6Wrk3zymhHQ1ynMy\nHkxaxixyut1ukWUZjDG4vb3Fhw8f0DQN5vM58jzHH/7wB2+l0w0or5W1uygk6toZJZPJBLPZDHme\nI0kSrFYrGGN8ux8yCdwndJNSgWSMJM+nlPJuUPl8BjwtfkllSwbNh4rUxxSwPvf6p5zX2n0fRFm0\nlLGLbJclG05LpUkmmFBpY39SX88Re2OI8yBNU6Sp6+VLQ4PzQJZekW76h9yKAx6PZw+klzhmyR8M\n6CNj+nGDXgiRTrnSOGTReF5vPRxlAQxMp3RJAaNbg0b0vVLKuSu5cNd1jcYa3N3dIY5jXJ5dwjTO\nqijzHbR2ky2K3eRbrrdOEDQNmq3rSTednPnA4rZtfTFICkr68pk6n5cF0tEYq+0WcRwhirRgKayf\n+OFzH4TL8yN0rXyKZXnMmJGWMRfl0AUiz3XsWo4pXZLpohLPYHiZCk9mij/LssTl5aW/Fn5HYSIz\nr3jM7dbND5aeoBJFd6O11sejyH/WutiyLMsOKnb31SobFK7nxS/NcknF6lNclo/Zlop7KNtCA4Xb\nsrwJlS663YG9sRDGcMnYLI59Br5TLsl/TD7hnJGuRWlsKaUOlLEwBkximBePx0koXQ8xXvfdfY8/\n5v1Juh94bHJNdkteB0tVGGN8X8f717WnWlkbyFqLJi+ggAMXRl2z67treaKTGO/ev8V6vcY4GWM8\nmjhhZCvkeYk0TXF+8cJPyO1uh+l0ih9/+hFV02CxaPDixQvkXa86wAmgoijQWOe20XGXtVJ1TYRN\ni4uLc1dsdb2DgkJZ1jDNoevkIXZlwNMgVIAemhOhe0/+fEhISdegHP/S6JAKWXgtkh2SxzKdgcE5\nEda+4nYMEqbFXpalryD/ww8/uNpynZuEpR0WiwWsdZW18zxHHMfY7Xb47//+b3z//fc+5ovzki2G\nZNA8hQqVr5cvX8IYg91ud5AlJt9FiJ/Dagz4PHwphSs0NuRnfcp16JL/FKVMMkNSgefc6muZxSB6\nzomqqlxdxo5poryhcsUivxy37ClKd71kndnVAYBX7tI0PWCj+R3vkzGWnOcyRix8VgMeh2dXuh4S\n+OGLVUrBmr56QwY4SPrnosh/xwdF07aw1i3SqpsYkWR7RPrugVsSBqrdWwh107jSE7CullcwODmg\n4zhG0y3yRV7i/fv3SOIYChGMrWAM2TGgNi022wxWK+RViTiOcX19jeVqh/Vm4ycn6V+jgLqqkaYp\nVmvnVkSkoW1XiyWOkMQp7NwxbpvNFoDqFb7EIGCeFw/NCSJ0i4Quj2PHPea2lCyPvAa5TXgcKTik\n0ibjv+R+0vXBgoxVVWGz2WC1WnljgvOGMS1Upng9cRxjs9ng9vbWV5eX7kOem8eQSqO8Fip5UjAN\nOA38+7//O3744QcAD8dNSYTzQeJjBgnRN85lnGLfHAvng2RtJctLJYfjld0Tbm9vsdvtDupyXVxc\n4Pz8/IDxYnwW58l2u/XuRjZ2v7i48EpZVVVYr9dekaOSxnhgAN71OJ/PPZslmTg+u761Y5ATj4ca\nHtKAAQMGDBgwYMAvj+dvNjdgwIABAwYMGPAbwKB0DRgwYMCAAQMGPAEGpWvAgAEDBgwYMOAJMChd\nAwYMGDBgwIABT4BB6RowYMCAAQMGDHgCDErXgAEDBgwYMGDAE2BQugYMGDBgwIABA54Ag9I1YMCA\nAQMGDBjwBBiUrgEDBgwYMGDAgCfAoHQNGDBgwIABAwY8AQala8CAAQMGDBgw4AkwKF0DBgwYMGDA\ngAFPgEHpGjBgwIABAwYMeAIMSteAAQMGDBgwYMATYFC6BgwYMGDAgAEDngCD0jVgwIABAwYMGPAE\nGJSuAQMGDBgwYMCAJ8CgdA0YMGDAgAEDBjwBBqVrwIABAwYMGDDgCTAoXQMGDBgwYMCAAU+AQeka\nMGDAgAEDBgx4AgxK14ABAwYMGDBgwBNgULoGDBgwYMCAAQOeAP8fUnZelJFm9TgAAAAASUVORK5C\nYII=\n",
            "text/plain": [
              "<Figure size 720x576 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "u4XonbrpNNTr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "42858ba7-69aa-4373-b4f0-94b2a5f44f1b"
      },
      "cell_type": "code",
      "source": [
        "x = np.random.randn(4, 3, 5, 5)\n",
        "w = np.random.randn(2, 3, 3, 3)\n",
        "b = np.random.randn(2,)\n",
        "dout = np.random.randn(4, 2, 5, 5)\n",
        "conv_param = {'stride': 1, 'pad': 1}\n",
        " \n",
        "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
        "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
        "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
        "\n",
        "out, cache = conv_forward_naive(x, w, b, conv_param)\n",
        "dx, dw, db = conv_backward_naive(dout, cache)\n",
        "\n",
        "# Your errors should be around 1e-9'\n",
        "\n",
        "print 'Testing conv_backward_naive function'\n",
        "print 'dx error: ', rel_error(dx, dx_num)\n",
        "print 'dw error: ', rel_error(dw, dw_num)\n",
        "print 'db error: ', rel_error(db, db_num)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing conv_backward_naive function\n",
            "dx error:  5.1380819701569406e-08\n",
            "dw error:  3.1208232877473233e-09\n",
            "db error:  1.5104148409795e-11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2u0Kvz0bNRaz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "0eb37a28-f435-40e8-acbb-ed9ee746744a"
      },
      "cell_type": "code",
      "source": [
        "x_shape = (2, 3, 4, 4)\n",
        "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
        "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
        "\n",
        "out, _ = max_pool_forward_naive(x, pool_param)\n",
        "\n",
        "correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
        "                          [-0.20421053, -0.18947368]],\n",
        "                         [[-0.14526316, -0.13052632],\n",
        "                          [-0.08631579, -0.07157895]],\n",
        "                         [[-0.02736842, -0.01263158],\n",
        "                          [ 0.03157895,  0.04631579]]],\n",
        "                        [[[ 0.09052632,  0.10526316],\n",
        "                          [ 0.14947368,  0.16421053]],\n",
        "                         [[ 0.20842105,  0.22315789],\n",
        "                          [ 0.26736842,  0.28210526]],\n",
        "                         [[ 0.32631579,  0.34105263],\n",
        "                          [ 0.38526316,  0.4       ]]]])\n",
        "\n",
        "#print out\n",
        "# Compare your output with ours. Difference should be around 1e-8.\n",
        "print 'Testing max_pool_forward_naive function:'\n",
        "print 'difference: ', rel_error(out, correct_out)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing max_pool_forward_naive function:\n",
            "difference:  4.1666665157267834e-08\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RW2s5v5UNVpm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "aaadcc13-6cff-4904-e998-a809e5d99c81"
      },
      "cell_type": "code",
      "source": [
        "x = np.random.randn(3, 2, 8, 8)\n",
        "dout = np.random.randn(3, 2, 4, 4)\n",
        "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
        "\n",
        "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
        "\n",
        "out, cache = max_pool_forward_naive(x, pool_param)\n",
        "dx = max_pool_backward_naive(dout, cache)\n",
        "\n",
        "# Your error should be around 1e-12\n",
        "print 'Testing max_pool_backward_naive function:'\n",
        "print 'dx error: ', rel_error(dx, dx_num)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing max_pool_backward_naive function:\n",
            "dx error:  3.2756504013812898e-12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JmsMTNE6NYVK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "c78a12b3-58a6-4823-9b9c-60d1c3c4edef"
      },
      "cell_type": "code",
      "source": [
        "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
        "from time import time\n",
        "\n",
        "x = np.random.randn(100, 3, 31, 31)\n",
        "w = np.random.randn(25, 3, 3, 3)\n",
        "b = np.random.randn(25,)\n",
        "dout = np.random.randn(100, 25, 16, 16)\n",
        "conv_param = {'stride': 2, 'pad': 1}\n",
        "\n",
        "t0 = time()\n",
        "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
        "t1 = time()\n",
        "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
        "t2 = time()\n",
        "\n",
        "print 'Testing conv_forward_fast:'\n",
        "print 'Naive: %fs' % (t1 - t0)\n",
        "print 'Fast: %fs' % (t2 - t1)\n",
        "print 'Speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
        "print 'Difference: ', rel_error(out_naive, out_fast)\n",
        "\n",
        "t0 = time()\n",
        "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
        "t1 = time()\n",
        "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
        "t2 = time()\n",
        "\n",
        "print '\\nTesting conv_backward_fast:'\n",
        "print 'Naive: %fs' % (t1 - t0)\n",
        "print 'Fast: %fs' % (t2 - t1)\n",
        "print 'Speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
        "print 'dx difference: ', rel_error(dx_naive, dx_fast)\n",
        "print 'dw difference: ', rel_error(dw_naive, dw_fast)\n",
        "print 'db difference: ', rel_error(db_naive, db_fast)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing conv_forward_fast:\n",
            "Naive: 0.088410s\n",
            "Fast: 0.012393s\n",
            "Speedup: 7.133878x\n",
            "Difference:  2.0823753049308718e-11\n",
            "\n",
            "Testing conv_backward_fast:\n",
            "Naive: 15.840531s\n",
            "Fast: 0.014397s\n",
            "Speedup: 1100.273280x\n",
            "dx difference:  3.853390789178144e-11\n",
            "dw difference:  5.515450594726364e-15\n",
            "db difference:  0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "oBdL-hnRNmOW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "46c1bcf8-5c70-4a2e-f767-646c5bdf5bb5"
      },
      "cell_type": "code",
      "source": [
        "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
        "\n",
        "x = np.random.randn(100, 3, 32, 32)\n",
        "dout = np.random.randn(100, 3, 16, 16)\n",
        "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
        "\n",
        "t0 = time()\n",
        "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
        "t1 = time()\n",
        "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
        "t2 = time()\n",
        "\n",
        "print 'Testing pool_forward_fast:'\n",
        "print 'Naive: %fs' % (t1 - t0)\n",
        "print 'fast: %fs' % (t2 - t1)\n",
        "print 'speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
        "print 'difference: ', rel_error(out_naive, out_fast)\n",
        "\n",
        "t0 = time()\n",
        "dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
        "t1 = time()\n",
        "dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
        "t2 = time()\n",
        "\n",
        "print '\\nTesting pool_backward_fast:'\n",
        "print 'Naive: %fs' % (t1 - t0)\n",
        "print 'speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
        "print 'dx difference: ', rel_error(dx_naive, dx_fast)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing pool_forward_fast:\n",
            "Naive: 0.292031s\n",
            "fast: 0.003775s\n",
            "speedup: 77.356701x\n",
            "difference:  0.0\n",
            "\n",
            "Testing pool_backward_fast:\n",
            "Naive: 0.623541s\n",
            "speedup: 48.543340x\n",
            "dx difference:  0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "PVGe0v5oIHRy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "8c9b20ff-c386-464c-d1f7-680976f58ebc"
      },
      "cell_type": "code",
      "source": [
        "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
        "\n",
        "x = np.random.randn(2, 3, 16, 16)\n",
        "w = np.random.randn(3, 3, 3, 3)\n",
        "b = np.random.randn(3,)\n",
        "dout = np.random.randn(2, 3, 8, 8)\n",
        "conv_param = {'stride': 1, 'pad': 1}\n",
        "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
        "\n",
        "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
        "dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
        "\n",
        "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
        "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
        "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
        "\n",
        "print 'Testing conv_relu_pool'\n",
        "print 'dx error: ', rel_error(dx_num, dx)\n",
        "print 'dw error: ', rel_error(dw_num, dw)\n",
        "print 'db error: ', rel_error(db_num, db)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing conv_relu_pool\n",
            "dx error:  1.5848762185476805e-08\n",
            "dw error:  8.827274722099247e-09\n",
            "db error:  7.975236334021455e-12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yZmAdnlQN1jb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "d38d64c0-7233-4f77-ada3-275cd8d9e188"
      },
      "cell_type": "code",
      "source": [
        "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
        "\n",
        "x = np.random.randn(2, 3, 8, 8)\n",
        "w = np.random.randn(3, 3, 3, 3)\n",
        "b = np.random.randn(3,)\n",
        "dout = np.random.randn(2, 3, 8, 8)\n",
        "conv_param = {'stride': 1, 'pad': 1}\n",
        "\n",
        "out, cache = conv_relu_forward(x, w, b, conv_param)\n",
        "dx, dw, db = conv_relu_backward(dout, cache)\n",
        "\n",
        "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
        "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
        "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
        "\n",
        "print 'Testing conv_relu:'\n",
        "print 'dx error: ', rel_error(dx_num, dx)\n",
        "print 'dw error: ', rel_error(dw_num, dw)\n",
        "print 'db error: ', rel_error(db_num, db)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing conv_relu:\n",
            "dx error:  2.708435356614735e-08\n",
            "dw error:  3.6402067218363743e-10\n",
            "db error:  6.127087914151002e-11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "H9f-0jXBN6yq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "4bc06edb-0637-47ec-cb3d-6f909197bd5e"
      },
      "cell_type": "code",
      "source": [
        "model = ThreeLayerConvNet()\n",
        "\n",
        "N = 50\n",
        "X = np.random.randn(N, 3, 32, 32)\n",
        "y = np.random.randint(10, size=N)\n",
        "\n",
        "loss, grads = model.loss(X, y)\n",
        "print 'Initial loss (no regularization): ', loss\n",
        "\n",
        "model.reg = 0.5\n",
        "loss, grads = model.loss(X, y)\n",
        "print 'Initial loss (with regularization): ', loss"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Initial loss (no regularization):  2.302586595028302\n",
            "Initial loss (with regularization):  2.508726234048268\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "kwrS2BiHOBTP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "259a211b-15e0-4a4f-805f-31816e24f8aa"
      },
      "cell_type": "code",
      "source": [
        "num_inputs = 2\n",
        "input_dim = (3, 16, 16)\n",
        "reg = 0.0\n",
        "num_classes = 10\n",
        "X = np.random.randn(num_inputs, *input_dim)\n",
        "y = np.random.randint(num_classes, size=num_inputs)\n",
        "\n",
        "model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n",
        "                          input_dim=input_dim, hidden_dim=7,\n",
        "                          dtype=np.float64)\n",
        "loss, grads = model.loss(X, y)\n",
        "for param_name in sorted(grads):\n",
        "    f = lambda _: model.loss(X, y)[0]\n",
        "    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
        "    e = rel_error(param_grad_num, grads[param_name])\n",
        "    print '%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name]))"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W1 max relative error: 2.453549e-04\n",
            "W2 max relative error: 1.614795e-02\n",
            "W3 max relative error: 1.862328e-05\n",
            "b1 max relative error: 5.925452e-06\n",
            "b2 max relative error: 2.253219e-07\n",
            "b3 max relative error: 8.203459e-10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MV_Ob77kOMnE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1054
        },
        "outputId": "eb6314ff-b0f7-4754-be90-967cdc308f1f"
      },
      "cell_type": "code",
      "source": [
        "num_train = 100\n",
        "small_data = {\n",
        "  'X_train': data['X_train'][:num_train],\n",
        "  'y_train': data['y_train'][:num_train],\n",
        "  'X_val': data['X_val'],\n",
        "  'y_val': data['y_val'],\n",
        "}\n",
        "\n",
        "model = ThreeLayerConvNet(weight_scale=1e-2)\n",
        "\n",
        "solver = Solver(model, small_data,\n",
        "                num_epochs=20, batch_size=50,\n",
        "                update_rule='adam',\n",
        "                optim_config={\n",
        "                  'learning_rate': 1e-3,\n",
        "                },\n",
        "                verbose=True, print_every=1)\n",
        "solver.train()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(Iteration 1 / 40) loss: 2.468011\n",
            "(Epoch 0 / 20) train acc: 0.180000; val_acc: 0.123000\n",
            "(Iteration 2 / 40) loss: 4.027249\n",
            "(Epoch 1 / 20) train acc: 0.110000; val_acc: 0.107000\n",
            "(Iteration 3 / 40) loss: 3.350242\n",
            "(Iteration 4 / 40) loss: 2.337226\n",
            "(Epoch 2 / 20) train acc: 0.250000; val_acc: 0.136000\n",
            "(Iteration 5 / 40) loss: 2.407492\n",
            "(Iteration 6 / 40) loss: 2.207492\n",
            "(Epoch 3 / 20) train acc: 0.290000; val_acc: 0.145000\n",
            "(Iteration 7 / 40) loss: 2.164090\n",
            "(Iteration 8 / 40) loss: 1.805462\n",
            "(Epoch 4 / 20) train acc: 0.420000; val_acc: 0.175000\n",
            "(Iteration 9 / 40) loss: 2.150717\n",
            "(Iteration 10 / 40) loss: 1.679258\n",
            "(Epoch 5 / 20) train acc: 0.440000; val_acc: 0.212000\n",
            "(Iteration 11 / 40) loss: 1.642329\n",
            "(Iteration 12 / 40) loss: 1.436673\n",
            "(Epoch 6 / 20) train acc: 0.550000; val_acc: 0.206000\n",
            "(Iteration 13 / 40) loss: 1.398531\n",
            "(Iteration 14 / 40) loss: 1.311352\n",
            "(Epoch 7 / 20) train acc: 0.670000; val_acc: 0.189000\n",
            "(Iteration 15 / 40) loss: 1.215615\n",
            "(Iteration 16 / 40) loss: 1.090855\n",
            "(Epoch 8 / 20) train acc: 0.680000; val_acc: 0.210000\n",
            "(Iteration 17 / 40) loss: 1.135631\n",
            "(Iteration 18 / 40) loss: 0.907204\n",
            "(Epoch 9 / 20) train acc: 0.780000; val_acc: 0.229000\n",
            "(Iteration 19 / 40) loss: 0.945290\n",
            "(Iteration 20 / 40) loss: 0.904577\n",
            "(Epoch 10 / 20) train acc: 0.890000; val_acc: 0.228000\n",
            "(Iteration 21 / 40) loss: 0.530464\n",
            "(Iteration 22 / 40) loss: 0.496924\n",
            "(Epoch 11 / 20) train acc: 0.890000; val_acc: 0.225000\n",
            "(Iteration 23 / 40) loss: 0.468042\n",
            "(Iteration 24 / 40) loss: 0.338214\n",
            "(Epoch 12 / 20) train acc: 0.960000; val_acc: 0.229000\n",
            "(Iteration 25 / 40) loss: 0.245689\n",
            "(Iteration 26 / 40) loss: 0.190223\n",
            "(Epoch 13 / 20) train acc: 0.960000; val_acc: 0.232000\n",
            "(Iteration 27 / 40) loss: 0.111985\n",
            "(Iteration 28 / 40) loss: 0.162562\n",
            "(Epoch 14 / 20) train acc: 0.970000; val_acc: 0.246000\n",
            "(Iteration 29 / 40) loss: 0.164252\n",
            "(Iteration 30 / 40) loss: 0.087008\n",
            "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.243000\n",
            "(Iteration 31 / 40) loss: 0.068706\n",
            "(Iteration 32 / 40) loss: 0.033487\n",
            "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.217000\n",
            "(Iteration 33 / 40) loss: 0.037379\n",
            "(Iteration 34 / 40) loss: 0.041387\n",
            "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.218000\n",
            "(Iteration 35 / 40) loss: 0.030532\n",
            "(Iteration 36 / 40) loss: 0.010735\n",
            "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.228000\n",
            "(Iteration 37 / 40) loss: 0.020938\n",
            "(Iteration 38 / 40) loss: 0.018681\n",
            "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.222000\n",
            "(Iteration 39 / 40) loss: 0.006168\n",
            "(Iteration 40 / 40) loss: 0.006448\n",
            "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.227000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uHC3cjHDOlHV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "e88541aa-f886-4f3b-9289-0a133f708a83"
      },
      "cell_type": "code",
      "source": [
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(solver.loss_history, 'o')\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('loss')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(solver.train_acc_history, '-o')\n",
        "plt.plot(solver.val_acc_history, '-o')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('accuracy')\n",
        "plt.show()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl8AAAHgCAYAAACbywggAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5h9n6o66OoTN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 741
        },
        "outputId": "50271ca1-d1ab-459d-bb46-dbfc2e8c5f17"
      },
      "cell_type": "code",
      "source": [
        "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n",
        "\n",
        "solver = Solver(model, data,\n",
        "                num_epochs=1, batch_size=50,\n",
        "                update_rule='adam',\n",
        "                optim_config={\n",
        "                  'learning_rate': 1e-3,\n",
        "                },\n",
        "                verbose=True, print_every=20)\n",
        "solver.train()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(Iteration 1 / 980) loss: 2.304641\n",
            "(Epoch 0 / 1) train acc: 0.136000; val_acc: 0.136000\n",
            "(Iteration 21 / 980) loss: 2.083599\n",
            "(Iteration 41 / 980) loss: 2.027519\n",
            "(Iteration 61 / 980) loss: 1.980401\n",
            "(Iteration 81 / 980) loss: 1.806757\n",
            "(Iteration 101 / 980) loss: 1.741621\n",
            "(Iteration 121 / 980) loss: 1.826143\n",
            "(Iteration 141 / 980) loss: 1.871321\n",
            "(Iteration 161 / 980) loss: 1.614698\n",
            "(Iteration 181 / 980) loss: 1.953457\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
            "\u001b[0;32m<ipython-input-42-49e9b0ed4fe9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m                 },\n\u001b[1;32m      9\u001b[0m                 verbose=True, print_every=20)\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/content/assignment2/cs231n/solver.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_iterations\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m       \u001b[0;31m# Maybe print training loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/assignment2/cs231n/solver.py\u001b[0m in \u001b[0;36m_step\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    170\u001b[0m       \u001b[0mdw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrads\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m       \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptim_configs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m       \u001b[0mnext_w\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnext_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_rule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    173\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext_w\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptim_configs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext_config\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/assignment2/cs231n/optim.py\u001b[0m in \u001b[0;36madam\u001b[0;34m(x, dx, config)\u001b[0m\n\u001b[1;32m    143\u001b[0m   \u001b[0;31m# pass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m   \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m't'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m   \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'm'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'beta1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'm'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'beta1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdx\u001b[0m \u001b[0;31m# update first moment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m   \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'v'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'beta2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'v'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'beta2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdx\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# update second moment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m   \u001b[0mmb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'm'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'beta1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m't'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# correct bias\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "1DyA-_YlOqQD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_1LfjHWhPJ6v",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ymndp0SzPJ1K",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = MultiLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001, dropout = 0.5)\n",
        "\n",
        "solver = Solver(model, data,\n",
        "                num_epochs=5, batch_size=50,\n",
        "                update_rule='adam',\n",
        "                optim_config={\n",
        "                  'learning_rate': 1e-3,\n",
        "                },\n",
        "                verbose=True, print_every=20)\n",
        "solver.train()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VhS08_zIvnTR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 8534
        },
        "outputId": "7f4def87-fd8c-4cfa-d332-b73574655c3b"
      },
      "cell_type": "code",
      "source": [
        "model = MultiLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001, dropout = 0.5)\n",
        "\n",
        "solver = Solver(model, data,\n",
        "                num_epochs=10, batch_size=50,\n",
        "                update_rule='adam',\n",
        "                optim_config={\n",
        "                  'learning_rate': 1e-3,\n",
        "                },\n",
        "                verbose=True, print_every=20)\n",
        "solver.train()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(Iteration 1 / 9800) loss: 2.301808\n",
            "(Epoch 0 / 10) train acc: 0.094000; val_acc: 0.078000\n",
            "(Iteration 21 / 9800) loss: 2.308905\n",
            "(Iteration 41 / 9800) loss: 2.166078\n",
            "(Iteration 61 / 9800) loss: 2.206949\n",
            "(Iteration 81 / 9800) loss: 2.115482\n",
            "(Iteration 101 / 9800) loss: 2.111055\n",
            "(Iteration 121 / 9800) loss: 2.167971\n",
            "(Iteration 141 / 9800) loss: 2.194879\n",
            "(Iteration 161 / 9800) loss: 2.036517\n",
            "(Iteration 181 / 9800) loss: 2.067466\n",
            "(Iteration 201 / 9800) loss: 2.193193\n",
            "(Iteration 221 / 9800) loss: 2.056926\n",
            "(Iteration 241 / 9800) loss: 2.122484\n",
            "(Iteration 261 / 9800) loss: 2.378369\n",
            "(Iteration 281 / 9800) loss: 1.987427\n",
            "(Iteration 301 / 9800) loss: 2.094105\n",
            "(Iteration 321 / 9800) loss: 2.151069\n",
            "(Iteration 341 / 9800) loss: 2.194262\n",
            "(Iteration 361 / 9800) loss: 2.235325\n",
            "(Iteration 381 / 9800) loss: 2.071418\n",
            "(Iteration 401 / 9800) loss: 2.086211\n",
            "(Iteration 421 / 9800) loss: 2.266798\n",
            "(Iteration 441 / 9800) loss: 2.119746\n",
            "(Iteration 461 / 9800) loss: 2.197246\n",
            "(Iteration 481 / 9800) loss: 2.158908\n",
            "(Iteration 501 / 9800) loss: 2.161009\n",
            "(Iteration 521 / 9800) loss: 2.399129\n",
            "(Iteration 541 / 9800) loss: 2.066485\n",
            "(Iteration 561 / 9800) loss: 2.184267\n",
            "(Iteration 581 / 9800) loss: 2.051450\n",
            "(Iteration 601 / 9800) loss: 2.175824\n",
            "(Iteration 621 / 9800) loss: 2.314196\n",
            "(Iteration 641 / 9800) loss: 2.179730\n",
            "(Iteration 661 / 9800) loss: 2.301074\n",
            "(Iteration 681 / 9800) loss: 2.481190\n",
            "(Iteration 701 / 9800) loss: 2.274025\n",
            "(Iteration 721 / 9800) loss: 2.395016\n",
            "(Iteration 741 / 9800) loss: 2.281713\n",
            "(Iteration 761 / 9800) loss: 2.385696\n",
            "(Iteration 781 / 9800) loss: 2.237977\n",
            "(Iteration 801 / 9800) loss: 2.145200\n",
            "(Iteration 821 / 9800) loss: 2.304516\n",
            "(Iteration 841 / 9800) loss: 2.462901\n",
            "(Iteration 861 / 9800) loss: 2.277797\n",
            "(Iteration 881 / 9800) loss: 2.117450\n",
            "(Iteration 901 / 9800) loss: 2.202906\n",
            "(Iteration 921 / 9800) loss: 2.158087\n",
            "(Iteration 941 / 9800) loss: 2.246345\n",
            "(Iteration 961 / 9800) loss: 2.201236\n",
            "(Epoch 1 / 10) train acc: 0.242000; val_acc: 0.232000\n",
            "(Iteration 981 / 9800) loss: 2.374062\n",
            "(Iteration 1001 / 9800) loss: 2.265088\n",
            "(Iteration 1021 / 9800) loss: 2.250589\n",
            "(Iteration 1041 / 9800) loss: 2.207921\n",
            "(Iteration 1061 / 9800) loss: 2.374623\n",
            "(Iteration 1081 / 9800) loss: 2.275875\n",
            "(Iteration 1101 / 9800) loss: 2.309943\n",
            "(Iteration 1121 / 9800) loss: 2.448726\n",
            "(Iteration 1141 / 9800) loss: 2.208390\n",
            "(Iteration 1161 / 9800) loss: 2.103387\n",
            "(Iteration 1181 / 9800) loss: 2.081227\n",
            "(Iteration 1201 / 9800) loss: 2.100127\n",
            "(Iteration 1221 / 9800) loss: 2.200757\n",
            "(Iteration 1241 / 9800) loss: 2.443249\n",
            "(Iteration 1261 / 9800) loss: 2.360680\n",
            "(Iteration 1281 / 9800) loss: 2.261033\n",
            "(Iteration 1301 / 9800) loss: 2.152226\n",
            "(Iteration 1321 / 9800) loss: 2.314282\n",
            "(Iteration 1341 / 9800) loss: 2.476322\n",
            "(Iteration 1361 / 9800) loss: 2.590973\n",
            "(Iteration 1381 / 9800) loss: 2.386890\n",
            "(Iteration 1401 / 9800) loss: 2.234518\n",
            "(Iteration 1421 / 9800) loss: 2.234730\n",
            "(Iteration 1441 / 9800) loss: 2.301935\n",
            "(Iteration 1461 / 9800) loss: 2.357989\n",
            "(Iteration 1481 / 9800) loss: 2.353789\n",
            "(Iteration 1501 / 9800) loss: 2.280770\n",
            "(Iteration 1521 / 9800) loss: 2.362211\n",
            "(Iteration 1541 / 9800) loss: 2.378865\n",
            "(Iteration 1561 / 9800) loss: 2.123295\n",
            "(Iteration 1581 / 9800) loss: 2.211365\n",
            "(Iteration 1601 / 9800) loss: 2.204660\n",
            "(Iteration 1621 / 9800) loss: 2.247793\n",
            "(Iteration 1641 / 9800) loss: 2.260981\n",
            "(Iteration 1661 / 9800) loss: 2.268079\n",
            "(Iteration 1681 / 9800) loss: 2.165455\n",
            "(Iteration 1701 / 9800) loss: 2.346184\n",
            "(Iteration 1721 / 9800) loss: 2.194718\n",
            "(Iteration 1741 / 9800) loss: 2.060744\n",
            "(Iteration 1761 / 9800) loss: 2.378864\n",
            "(Iteration 1781 / 9800) loss: 2.197215\n",
            "(Iteration 1801 / 9800) loss: 2.034426\n",
            "(Iteration 1821 / 9800) loss: 2.085656\n",
            "(Iteration 1841 / 9800) loss: 2.271580\n",
            "(Iteration 1861 / 9800) loss: 2.273584\n",
            "(Iteration 1881 / 9800) loss: 2.181096\n",
            "(Iteration 1901 / 9800) loss: 2.215395\n",
            "(Iteration 1921 / 9800) loss: 2.170796\n",
            "(Iteration 1941 / 9800) loss: 2.390265\n",
            "(Epoch 2 / 10) train acc: 0.231000; val_acc: 0.256000\n",
            "(Iteration 1961 / 9800) loss: 2.479107\n",
            "(Iteration 1981 / 9800) loss: 2.288509\n",
            "(Iteration 2001 / 9800) loss: 2.332084\n",
            "(Iteration 2021 / 9800) loss: 2.210556\n",
            "(Iteration 2041 / 9800) loss: 2.188211\n",
            "(Iteration 2061 / 9800) loss: 2.307799\n",
            "(Iteration 2081 / 9800) loss: 2.416980\n",
            "(Iteration 2101 / 9800) loss: 2.273653\n",
            "(Iteration 2121 / 9800) loss: 2.268517\n",
            "(Iteration 2141 / 9800) loss: 2.044255\n",
            "(Iteration 2161 / 9800) loss: 2.234889\n",
            "(Iteration 2181 / 9800) loss: 2.127279\n",
            "(Iteration 2201 / 9800) loss: 2.225844\n",
            "(Iteration 2221 / 9800) loss: 2.269883\n",
            "(Iteration 2241 / 9800) loss: 2.279499\n",
            "(Iteration 2261 / 9800) loss: 2.422853\n",
            "(Iteration 2281 / 9800) loss: 2.215239\n",
            "(Iteration 2301 / 9800) loss: 2.251155\n",
            "(Iteration 2321 / 9800) loss: 2.263035\n",
            "(Iteration 2341 / 9800) loss: 2.318565\n",
            "(Iteration 2361 / 9800) loss: 2.248602\n",
            "(Iteration 2381 / 9800) loss: 2.330059\n",
            "(Iteration 2401 / 9800) loss: 2.301430\n",
            "(Iteration 2421 / 9800) loss: 2.316619\n",
            "(Iteration 2441 / 9800) loss: 2.274942\n",
            "(Iteration 2461 / 9800) loss: 2.033162\n",
            "(Iteration 2481 / 9800) loss: 2.189938\n",
            "(Iteration 2501 / 9800) loss: 2.307418\n",
            "(Iteration 2521 / 9800) loss: 2.185821\n",
            "(Iteration 2541 / 9800) loss: 2.136962\n",
            "(Iteration 2561 / 9800) loss: 2.212532\n",
            "(Iteration 2581 / 9800) loss: 2.170780\n",
            "(Iteration 2601 / 9800) loss: 2.053485\n",
            "(Iteration 2621 / 9800) loss: 2.335154\n",
            "(Iteration 2641 / 9800) loss: 2.187099\n",
            "(Iteration 2661 / 9800) loss: 2.191462\n",
            "(Iteration 2681 / 9800) loss: 2.304653\n",
            "(Iteration 2701 / 9800) loss: 2.262369\n",
            "(Iteration 2721 / 9800) loss: 2.353858\n",
            "(Iteration 2741 / 9800) loss: 2.324962\n",
            "(Iteration 2761 / 9800) loss: 2.288665\n",
            "(Iteration 2781 / 9800) loss: 2.240716\n",
            "(Iteration 2801 / 9800) loss: 2.183136\n",
            "(Iteration 2821 / 9800) loss: 2.167708\n",
            "(Iteration 2841 / 9800) loss: 2.178422\n",
            "(Iteration 2861 / 9800) loss: 2.216499\n",
            "(Iteration 2881 / 9800) loss: 2.524866\n",
            "(Iteration 2901 / 9800) loss: 2.207503\n",
            "(Iteration 2921 / 9800) loss: 2.229832\n",
            "(Epoch 3 / 10) train acc: 0.254000; val_acc: 0.259000\n",
            "(Iteration 2941 / 9800) loss: 2.166257\n",
            "(Iteration 2961 / 9800) loss: 2.370940\n",
            "(Iteration 2981 / 9800) loss: 2.152104\n",
            "(Iteration 3001 / 9800) loss: 2.248659\n",
            "(Iteration 3021 / 9800) loss: 2.434415\n",
            "(Iteration 3041 / 9800) loss: 2.133959\n",
            "(Iteration 3061 / 9800) loss: 2.229175\n",
            "(Iteration 3081 / 9800) loss: 2.533069\n",
            "(Iteration 3101 / 9800) loss: 2.248994\n",
            "(Iteration 3121 / 9800) loss: 2.315802\n",
            "(Iteration 3141 / 9800) loss: 2.130677\n",
            "(Iteration 3161 / 9800) loss: 2.239732\n",
            "(Iteration 3181 / 9800) loss: 2.377673\n",
            "(Iteration 3201 / 9800) loss: 2.288628\n",
            "(Iteration 3221 / 9800) loss: 2.369080\n",
            "(Iteration 3241 / 9800) loss: 2.296184\n",
            "(Iteration 3261 / 9800) loss: 2.404029\n",
            "(Iteration 3281 / 9800) loss: 2.329529\n",
            "(Iteration 3301 / 9800) loss: 2.112444\n",
            "(Iteration 3321 / 9800) loss: 2.251818\n",
            "(Iteration 3341 / 9800) loss: 2.155000\n",
            "(Iteration 3361 / 9800) loss: 2.327588\n",
            "(Iteration 3381 / 9800) loss: 2.156805\n",
            "(Iteration 3401 / 9800) loss: 2.277097\n",
            "(Iteration 3421 / 9800) loss: 2.161236\n",
            "(Iteration 3441 / 9800) loss: 2.006215\n",
            "(Iteration 3461 / 9800) loss: 2.217947\n",
            "(Iteration 3481 / 9800) loss: 2.250353\n",
            "(Iteration 3501 / 9800) loss: 2.400288\n",
            "(Iteration 3521 / 9800) loss: 2.229727\n",
            "(Iteration 3541 / 9800) loss: 2.373581\n",
            "(Iteration 3561 / 9800) loss: 2.401309\n",
            "(Iteration 3581 / 9800) loss: 1.989177\n",
            "(Iteration 3601 / 9800) loss: 2.346584\n",
            "(Iteration 3621 / 9800) loss: 2.310502\n",
            "(Iteration 3641 / 9800) loss: 2.233011\n",
            "(Iteration 3661 / 9800) loss: 2.049632\n",
            "(Iteration 3681 / 9800) loss: 2.124825\n",
            "(Iteration 3701 / 9800) loss: 2.186880\n",
            "(Iteration 3721 / 9800) loss: 2.279129\n",
            "(Iteration 3741 / 9800) loss: 2.257346\n",
            "(Iteration 3761 / 9800) loss: 2.401398\n",
            "(Iteration 3781 / 9800) loss: 2.052290\n",
            "(Iteration 3801 / 9800) loss: 1.967297\n",
            "(Iteration 3821 / 9800) loss: 2.149394\n",
            "(Iteration 3841 / 9800) loss: 2.230726\n",
            "(Iteration 3861 / 9800) loss: 2.386332\n",
            "(Iteration 3881 / 9800) loss: 2.286509\n",
            "(Iteration 3901 / 9800) loss: 2.441395\n",
            "(Epoch 4 / 10) train acc: 0.261000; val_acc: 0.252000\n",
            "(Iteration 3921 / 9800) loss: 2.408958\n",
            "(Iteration 3941 / 9800) loss: 2.135640\n",
            "(Iteration 3961 / 9800) loss: 2.216903\n",
            "(Iteration 3981 / 9800) loss: 2.262886\n",
            "(Iteration 4001 / 9800) loss: 2.321328\n",
            "(Iteration 4021 / 9800) loss: 2.180801\n",
            "(Iteration 4041 / 9800) loss: 2.392176\n",
            "(Iteration 4061 / 9800) loss: 2.283507\n",
            "(Iteration 4081 / 9800) loss: 1.974479\n",
            "(Iteration 4101 / 9800) loss: 2.104938\n",
            "(Iteration 4121 / 9800) loss: 2.105945\n",
            "(Iteration 4141 / 9800) loss: 2.217356\n",
            "(Iteration 4161 / 9800) loss: 2.252039\n",
            "(Iteration 4181 / 9800) loss: 2.611492\n",
            "(Iteration 4201 / 9800) loss: 2.144208\n",
            "(Iteration 4221 / 9800) loss: 2.256894\n",
            "(Iteration 4241 / 9800) loss: 2.254335\n",
            "(Iteration 4261 / 9800) loss: 2.439410\n",
            "(Iteration 4281 / 9800) loss: 2.159867\n",
            "(Iteration 4301 / 9800) loss: 2.164699\n",
            "(Iteration 4321 / 9800) loss: 2.405530\n",
            "(Iteration 4341 / 9800) loss: 2.193669\n",
            "(Iteration 4361 / 9800) loss: 2.216420\n",
            "(Iteration 4381 / 9800) loss: 2.275708\n",
            "(Iteration 4401 / 9800) loss: 2.235939\n",
            "(Iteration 4421 / 9800) loss: 2.314216\n",
            "(Iteration 4441 / 9800) loss: 2.448051\n",
            "(Iteration 4461 / 9800) loss: 2.109542\n",
            "(Iteration 4481 / 9800) loss: 2.340011\n",
            "(Iteration 4501 / 9800) loss: 2.197241\n",
            "(Iteration 4521 / 9800) loss: 2.303061\n",
            "(Iteration 4541 / 9800) loss: 2.384625\n",
            "(Iteration 4561 / 9800) loss: 2.288117\n",
            "(Iteration 4581 / 9800) loss: 2.260314\n",
            "(Iteration 4601 / 9800) loss: 2.329700\n",
            "(Iteration 4621 / 9800) loss: 2.269702\n",
            "(Iteration 4641 / 9800) loss: 2.463024\n",
            "(Iteration 4661 / 9800) loss: 2.301211\n",
            "(Iteration 4681 / 9800) loss: 2.384157\n",
            "(Iteration 4701 / 9800) loss: 2.350918\n",
            "(Iteration 4721 / 9800) loss: 2.196455\n",
            "(Iteration 4741 / 9800) loss: 2.381478\n",
            "(Iteration 4761 / 9800) loss: 2.279925\n",
            "(Iteration 4781 / 9800) loss: 2.170164\n",
            "(Iteration 4801 / 9800) loss: 2.348572\n",
            "(Iteration 4821 / 9800) loss: 2.456769\n",
            "(Iteration 4841 / 9800) loss: 2.677686\n",
            "(Iteration 4861 / 9800) loss: 2.339062\n",
            "(Iteration 4881 / 9800) loss: 2.295455\n",
            "(Epoch 5 / 10) train acc: 0.223000; val_acc: 0.228000\n",
            "(Iteration 4901 / 9800) loss: 2.326184\n",
            "(Iteration 4921 / 9800) loss: 2.433058\n",
            "(Iteration 4941 / 9800) loss: 2.308342\n",
            "(Iteration 4961 / 9800) loss: 2.432162\n",
            "(Iteration 4981 / 9800) loss: 2.283958\n",
            "(Iteration 5001 / 9800) loss: 2.181525\n",
            "(Iteration 5021 / 9800) loss: 2.221846\n",
            "(Iteration 5041 / 9800) loss: 2.177904\n",
            "(Iteration 5061 / 9800) loss: 2.176782\n",
            "(Iteration 5081 / 9800) loss: 2.059547\n",
            "(Iteration 5101 / 9800) loss: 2.162691\n",
            "(Iteration 5121 / 9800) loss: 2.335763\n",
            "(Iteration 5141 / 9800) loss: 2.103982\n",
            "(Iteration 5161 / 9800) loss: 2.407878\n",
            "(Iteration 5181 / 9800) loss: 2.173476\n",
            "(Iteration 5201 / 9800) loss: 2.170105\n",
            "(Iteration 5221 / 9800) loss: 2.452734\n",
            "(Iteration 5241 / 9800) loss: 2.222848\n",
            "(Iteration 5261 / 9800) loss: 2.300552\n",
            "(Iteration 5281 / 9800) loss: 2.290429\n",
            "(Iteration 5301 / 9800) loss: 2.277948\n",
            "(Iteration 5321 / 9800) loss: 2.198892\n",
            "(Iteration 5341 / 9800) loss: 2.165661\n",
            "(Iteration 5361 / 9800) loss: 2.482655\n",
            "(Iteration 5381 / 9800) loss: 2.390823\n",
            "(Iteration 5401 / 9800) loss: 2.206562\n",
            "(Iteration 5421 / 9800) loss: 2.240263\n",
            "(Iteration 5441 / 9800) loss: 2.275175\n",
            "(Iteration 5461 / 9800) loss: 2.324451\n",
            "(Iteration 5481 / 9800) loss: 2.163889\n",
            "(Iteration 5501 / 9800) loss: 2.239073\n",
            "(Iteration 5521 / 9800) loss: 2.265352\n",
            "(Iteration 5541 / 9800) loss: 2.277959\n",
            "(Iteration 5561 / 9800) loss: 2.272418\n",
            "(Iteration 5581 / 9800) loss: 2.298555\n",
            "(Iteration 5601 / 9800) loss: 2.344145\n",
            "(Iteration 5621 / 9800) loss: 2.185382\n",
            "(Iteration 5641 / 9800) loss: 2.284767\n",
            "(Iteration 5661 / 9800) loss: 1.960212\n",
            "(Iteration 5681 / 9800) loss: 2.289633\n",
            "(Iteration 5701 / 9800) loss: 2.069714\n",
            "(Iteration 5721 / 9800) loss: 2.409461\n",
            "(Iteration 5741 / 9800) loss: 2.463490\n",
            "(Iteration 5761 / 9800) loss: 2.106840\n",
            "(Iteration 5781 / 9800) loss: 2.386081\n",
            "(Iteration 5801 / 9800) loss: 2.292169\n",
            "(Iteration 5821 / 9800) loss: 2.214368\n",
            "(Iteration 5841 / 9800) loss: 2.378117\n",
            "(Iteration 5861 / 9800) loss: 2.381179\n",
            "(Epoch 6 / 10) train acc: 0.250000; val_acc: 0.237000\n",
            "(Iteration 5881 / 9800) loss: 2.002265\n",
            "(Iteration 5901 / 9800) loss: 2.164496\n",
            "(Iteration 5921 / 9800) loss: 2.320847\n",
            "(Iteration 5941 / 9800) loss: 2.129598\n",
            "(Iteration 5961 / 9800) loss: 2.291294\n",
            "(Iteration 5981 / 9800) loss: 2.347593\n",
            "(Iteration 6001 / 9800) loss: 2.254425\n",
            "(Iteration 6021 / 9800) loss: 2.273811\n",
            "(Iteration 6041 / 9800) loss: 2.329331\n",
            "(Iteration 6061 / 9800) loss: 2.184142\n",
            "(Iteration 6081 / 9800) loss: 2.309294\n",
            "(Iteration 6101 / 9800) loss: 2.407137\n",
            "(Iteration 6121 / 9800) loss: 2.353617\n",
            "(Iteration 6141 / 9800) loss: 2.226676\n",
            "(Iteration 6161 / 9800) loss: 2.481215\n",
            "(Iteration 6181 / 9800) loss: 2.014220\n",
            "(Iteration 6201 / 9800) loss: 2.249131\n",
            "(Iteration 6221 / 9800) loss: 2.326283\n",
            "(Iteration 6241 / 9800) loss: 2.243395\n",
            "(Iteration 6261 / 9800) loss: 2.348936\n",
            "(Iteration 6281 / 9800) loss: 1.900101\n",
            "(Iteration 6301 / 9800) loss: 2.275650\n",
            "(Iteration 6321 / 9800) loss: 2.674232\n",
            "(Iteration 6341 / 9800) loss: 2.288550\n",
            "(Iteration 6361 / 9800) loss: 2.300283\n",
            "(Iteration 6381 / 9800) loss: 2.208863\n",
            "(Iteration 6401 / 9800) loss: 2.201523\n",
            "(Iteration 6421 / 9800) loss: 2.296985\n",
            "(Iteration 6441 / 9800) loss: 2.090451\n",
            "(Iteration 6461 / 9800) loss: 2.266818\n",
            "(Iteration 6481 / 9800) loss: 2.254313\n",
            "(Iteration 6501 / 9800) loss: 2.493154\n",
            "(Iteration 6521 / 9800) loss: 2.414132\n",
            "(Iteration 6541 / 9800) loss: 2.275513\n",
            "(Iteration 6561 / 9800) loss: 2.348189\n",
            "(Iteration 6581 / 9800) loss: 2.365042\n",
            "(Iteration 6601 / 9800) loss: 2.339372\n",
            "(Iteration 6621 / 9800) loss: 2.203895\n",
            "(Iteration 6641 / 9800) loss: 2.608317\n",
            "(Iteration 6661 / 9800) loss: 2.392591\n",
            "(Iteration 6681 / 9800) loss: 2.352823\n",
            "(Iteration 6701 / 9800) loss: 2.118620\n",
            "(Iteration 6721 / 9800) loss: 2.377626\n",
            "(Iteration 6741 / 9800) loss: 2.260776\n",
            "(Iteration 6761 / 9800) loss: 2.622559\n",
            "(Iteration 6781 / 9800) loss: 2.026426\n",
            "(Iteration 6801 / 9800) loss: 2.255903\n",
            "(Iteration 6821 / 9800) loss: 2.243440\n",
            "(Iteration 6841 / 9800) loss: 2.213511\n",
            "(Epoch 7 / 10) train acc: 0.260000; val_acc: 0.261000\n",
            "(Iteration 6861 / 9800) loss: 2.414059\n",
            "(Iteration 6881 / 9800) loss: 2.326614\n",
            "(Iteration 6901 / 9800) loss: 2.350101\n",
            "(Iteration 6921 / 9800) loss: 2.218836\n",
            "(Iteration 6941 / 9800) loss: 2.438460\n",
            "(Iteration 6961 / 9800) loss: 2.268206\n",
            "(Iteration 6981 / 9800) loss: 2.165793\n",
            "(Iteration 7001 / 9800) loss: 2.302983\n",
            "(Iteration 7021 / 9800) loss: 2.348982\n",
            "(Iteration 7041 / 9800) loss: 2.375197\n",
            "(Iteration 7061 / 9800) loss: 2.392299\n",
            "(Iteration 7081 / 9800) loss: 2.203251\n",
            "(Iteration 7101 / 9800) loss: 2.301721\n",
            "(Iteration 7121 / 9800) loss: 2.292988\n",
            "(Iteration 7141 / 9800) loss: 2.266431\n",
            "(Iteration 7161 / 9800) loss: 2.161036\n",
            "(Iteration 7181 / 9800) loss: 2.183051\n",
            "(Iteration 7201 / 9800) loss: 2.516525\n",
            "(Iteration 7221 / 9800) loss: 2.406381\n",
            "(Iteration 7241 / 9800) loss: 2.384343\n",
            "(Iteration 7261 / 9800) loss: 2.341987\n",
            "(Iteration 7281 / 9800) loss: 2.023709\n",
            "(Iteration 7301 / 9800) loss: 2.370101\n",
            "(Iteration 7321 / 9800) loss: 2.146066\n",
            "(Iteration 7341 / 9800) loss: 2.116014\n",
            "(Iteration 7361 / 9800) loss: 2.192420\n",
            "(Iteration 7381 / 9800) loss: 2.234565\n",
            "(Iteration 7401 / 9800) loss: 2.310289\n",
            "(Iteration 7421 / 9800) loss: 2.334395\n",
            "(Iteration 7441 / 9800) loss: 2.284603\n",
            "(Iteration 7461 / 9800) loss: 2.359655\n",
            "(Iteration 7481 / 9800) loss: 2.218158\n",
            "(Iteration 7501 / 9800) loss: 2.315203\n",
            "(Iteration 7521 / 9800) loss: 2.349726\n",
            "(Iteration 7541 / 9800) loss: 2.668228\n",
            "(Iteration 7561 / 9800) loss: 2.219176\n",
            "(Iteration 7581 / 9800) loss: 2.337919\n",
            "(Iteration 7601 / 9800) loss: 2.363908\n",
            "(Iteration 7621 / 9800) loss: 2.247483\n",
            "(Iteration 7641 / 9800) loss: 2.101234\n",
            "(Iteration 7661 / 9800) loss: 2.429253\n",
            "(Iteration 7681 / 9800) loss: 2.181207\n",
            "(Iteration 7701 / 9800) loss: 2.408865\n",
            "(Iteration 7721 / 9800) loss: 2.444945\n",
            "(Iteration 7741 / 9800) loss: 2.006974\n",
            "(Iteration 7761 / 9800) loss: 2.020149\n",
            "(Iteration 7781 / 9800) loss: 2.265214\n",
            "(Iteration 7801 / 9800) loss: 2.154588\n",
            "(Iteration 7821 / 9800) loss: 2.258102\n",
            "(Epoch 8 / 10) train acc: 0.253000; val_acc: 0.270000\n",
            "(Iteration 7841 / 9800) loss: 2.241932\n",
            "(Iteration 7861 / 9800) loss: 2.345471\n",
            "(Iteration 7881 / 9800) loss: 2.180817\n",
            "(Iteration 7901 / 9800) loss: 2.384994\n",
            "(Iteration 7921 / 9800) loss: 2.153413\n",
            "(Iteration 7941 / 9800) loss: 2.451752\n",
            "(Iteration 7961 / 9800) loss: 2.188779\n",
            "(Iteration 7981 / 9800) loss: 2.238429\n",
            "(Iteration 8001 / 9800) loss: 2.224132\n",
            "(Iteration 8021 / 9800) loss: 2.243296\n",
            "(Iteration 8041 / 9800) loss: 2.318251\n",
            "(Iteration 8061 / 9800) loss: 2.309315\n",
            "(Iteration 8081 / 9800) loss: 2.229589\n",
            "(Iteration 8101 / 9800) loss: 2.236056\n",
            "(Iteration 8121 / 9800) loss: 2.434312\n",
            "(Iteration 8141 / 9800) loss: 2.243162\n",
            "(Iteration 8161 / 9800) loss: 2.122459\n",
            "(Iteration 8181 / 9800) loss: 2.476484\n",
            "(Iteration 8201 / 9800) loss: 2.222022\n",
            "(Iteration 8221 / 9800) loss: 2.156617\n",
            "(Iteration 8241 / 9800) loss: 2.279440\n",
            "(Iteration 8261 / 9800) loss: 2.465593\n",
            "(Iteration 8281 / 9800) loss: 2.170170\n",
            "(Iteration 8301 / 9800) loss: 2.347988\n",
            "(Iteration 8321 / 9800) loss: 2.160326\n",
            "(Iteration 8341 / 9800) loss: 2.107519\n",
            "(Iteration 8361 / 9800) loss: 2.305398\n",
            "(Iteration 8381 / 9800) loss: 2.440368\n",
            "(Iteration 8401 / 9800) loss: 2.296935\n",
            "(Iteration 8421 / 9800) loss: 2.113942\n",
            "(Iteration 8441 / 9800) loss: 2.147699\n",
            "(Iteration 8461 / 9800) loss: 2.388502\n",
            "(Iteration 8481 / 9800) loss: 2.150293\n",
            "(Iteration 8501 / 9800) loss: 2.385398\n",
            "(Iteration 8521 / 9800) loss: 2.208526\n",
            "(Iteration 8541 / 9800) loss: 2.433168\n",
            "(Iteration 8561 / 9800) loss: 2.498924\n",
            "(Iteration 8581 / 9800) loss: 1.820798\n",
            "(Iteration 8601 / 9800) loss: 2.501055\n",
            "(Iteration 8621 / 9800) loss: 2.420097\n",
            "(Iteration 8641 / 9800) loss: 2.385976\n",
            "(Iteration 8661 / 9800) loss: 2.052081\n",
            "(Iteration 8681 / 9800) loss: 2.318072\n",
            "(Iteration 8701 / 9800) loss: 2.285258\n",
            "(Iteration 8721 / 9800) loss: 2.438903\n",
            "(Iteration 8741 / 9800) loss: 2.116257\n",
            "(Iteration 8761 / 9800) loss: 2.181000\n",
            "(Iteration 8781 / 9800) loss: 2.132504\n",
            "(Iteration 8801 / 9800) loss: 2.560414\n",
            "(Epoch 9 / 10) train acc: 0.265000; val_acc: 0.272000\n",
            "(Iteration 8821 / 9800) loss: 2.217034\n",
            "(Iteration 8841 / 9800) loss: 2.212861\n",
            "(Iteration 8861 / 9800) loss: 2.407193\n",
            "(Iteration 8881 / 9800) loss: 2.378892\n",
            "(Iteration 8901 / 9800) loss: 2.231341\n",
            "(Iteration 8921 / 9800) loss: 2.227029\n",
            "(Iteration 8941 / 9800) loss: 2.106842\n",
            "(Iteration 8961 / 9800) loss: 2.383163\n",
            "(Iteration 8981 / 9800) loss: 2.320195\n",
            "(Iteration 9001 / 9800) loss: 2.476375\n",
            "(Iteration 9021 / 9800) loss: 2.232956\n",
            "(Iteration 9041 / 9800) loss: 2.361706\n",
            "(Iteration 9061 / 9800) loss: 2.312597\n",
            "(Iteration 9081 / 9800) loss: 2.160060\n",
            "(Iteration 9101 / 9800) loss: 2.333013\n",
            "(Iteration 9121 / 9800) loss: 2.342502\n",
            "(Iteration 9141 / 9800) loss: 2.309558\n",
            "(Iteration 9161 / 9800) loss: 2.523433\n",
            "(Iteration 9181 / 9800) loss: 2.315473\n",
            "(Iteration 9201 / 9800) loss: 2.227260\n",
            "(Iteration 9221 / 9800) loss: 2.487725\n",
            "(Iteration 9241 / 9800) loss: 2.235336\n",
            "(Iteration 9261 / 9800) loss: 2.219881\n",
            "(Iteration 9281 / 9800) loss: 2.128643\n",
            "(Iteration 9301 / 9800) loss: 2.328287\n",
            "(Iteration 9321 / 9800) loss: 2.132836\n",
            "(Iteration 9341 / 9800) loss: 2.117709\n",
            "(Iteration 9361 / 9800) loss: 2.110111\n",
            "(Iteration 9381 / 9800) loss: 2.351525\n",
            "(Iteration 9401 / 9800) loss: 2.204478\n",
            "(Iteration 9421 / 9800) loss: 2.397959\n",
            "(Iteration 9441 / 9800) loss: 2.237912\n",
            "(Iteration 9461 / 9800) loss: 2.213839\n",
            "(Iteration 9481 / 9800) loss: 2.455029\n",
            "(Iteration 9501 / 9800) loss: 2.234944\n",
            "(Iteration 9521 / 9800) loss: 2.229463\n",
            "(Iteration 9541 / 9800) loss: 2.193515\n",
            "(Iteration 9561 / 9800) loss: 2.119824\n",
            "(Iteration 9581 / 9800) loss: 2.223586\n",
            "(Iteration 9601 / 9800) loss: 2.201964\n",
            "(Iteration 9621 / 9800) loss: 2.221058\n",
            "(Iteration 9641 / 9800) loss: 2.346815\n",
            "(Iteration 9661 / 9800) loss: 2.329364\n",
            "(Iteration 9681 / 9800) loss: 2.345765\n",
            "(Iteration 9701 / 9800) loss: 2.300513\n",
            "(Iteration 9721 / 9800) loss: 2.406646\n",
            "(Iteration 9741 / 9800) loss: 2.194154\n",
            "(Iteration 9761 / 9800) loss: 2.059231\n",
            "(Iteration 9781 / 9800) loss: 2.199825\n",
            "(Epoch 10 / 10) train acc: 0.262000; val_acc: 0.261000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ldWbhItsPJwi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vc9YbQsUQad-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "1ce888ad-2c60-476b-fd19-84239878edba"
      },
      "cell_type": "code",
      "source": [
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(solver.loss_history, 'o')\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('loss')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(solver.train_acc_history, '-o')\n",
        "plt.plot(solver.val_acc_history, '-o')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('accuracy')\n",
        "plt.show()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmoAAAHgCAYAAAAVEUFcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFmqSJEkDZaEmSZI0UP8C++TtjxDi+LcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "1lEQeBCUQvEa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "okMDZAcoQu4L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "ccfb638e-4fd6-4648-dcc8-e0edbf6e7d74"
      },
      "cell_type": "code",
      "source": [
        "from cs231n.vis_utils import visualize_grid\n",
        "\n",
        "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n",
        "plt.imshow(grid.astype('uint8'))\n",
        "plt.axis('off')\n",
        "plt.gcf().set_size_inches(5, 5)\n",
        "plt.show()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATAAAAEvCAYAAADCe529AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "f58nVi7Adx2v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "47e6c265-cb39-4137-9fae-ffaadd987938"
      },
      "cell_type": "code",
      "source": [
        "solver.check_accuracy(data['X_test'], data['y_test'])"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.292"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "metadata": {
        "id": "Qj_AirDWd-pV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}