[404218]: / Code / PennyLane / Data-Reuploading / Batch Studies / 35 Batch 84.5% kkawchak.ipynb

Download this file

520 lines (520 with data), 195.7 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "f0e87d3e-3738-4e58-d622-f5d9e0bae028"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696871206.0847218\n",
            "Mon Oct  9 17:06:46 2023\n"
          ]
        }
      ],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "%matplotlib inline\n",
        "# !pip install pennylane\n",
        "\n",
        "import time\n",
        "seconds = time.time()\n",
        "print(\"Time in seconds since beginning of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "19caf854-2940-4138-be93-a3934b8dd22d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "from pennylane.optimize import AdamOptimizer, GradientDescentOptimizer\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# Set a random seed\n",
        "np.random.seed(42)\n",
        "\n",
        "\n",
        "# Make a dataset of points inside and outside of a circle\n",
        "def circle(samples, center=[0.0, 0.0], radius=np.sqrt(2 / np.pi)):\n",
        "    \"\"\"\n",
        "    Generates a dataset of points with 1/0 labels inside a given radius.\n",
        "\n",
        "    Args:\n",
        "        samples (int): number of samples to generate\n",
        "        center (tuple): center of the circle\n",
        "        radius (float: radius of the circle\n",
        "\n",
        "    Returns:\n",
        "        Xvals (array[tuple]): coordinates of points\n",
        "        yvals (array[int]): classification labels\n",
        "    \"\"\"\n",
        "    Xvals, yvals = [], []\n",
        "\n",
        "    for i in range(samples):\n",
        "        x = 2 * (np.random.rand(2)) - 1\n",
        "        y = 0\n",
        "        if np.linalg.norm(x - center) < radius:\n",
        "            y = 1\n",
        "        Xvals.append(x)\n",
        "        yvals.append(y)\n",
        "    return np.array(Xvals, requires_grad=False), np.array(yvals, requires_grad=False)\n",
        "\n",
        "\n",
        "def plot_data(x, y, fig=None, ax=None):\n",
        "    \"\"\"\n",
        "    Plot data with red/blue values for a binary classification.\n",
        "\n",
        "    Args:\n",
        "        x (array[tuple]): array of data points as tuples\n",
        "        y (array[int]): array of data points as tuples\n",
        "    \"\"\"\n",
        "    if fig == None:\n",
        "        fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
        "    reds = y == 0\n",
        "    blues = y == 1\n",
        "    ax.scatter(x[reds, 0], x[reds, 1], c=\"red\", s=20, edgecolor=\"k\")\n",
        "    ax.scatter(x[blues, 0], x[blues, 1], c=\"blue\", s=20, edgecolor=\"k\")\n",
        "    ax.set_xlabel(\"$x_1$\")\n",
        "    ax.set_ylabel(\"$x_2$\")\n",
        "\n",
        "\n",
        "Xdata, ydata = circle(500)\n",
        "fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
        "plot_data(Xdata, ydata, fig=fig, ax=ax)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# Define output labels as quantum state vectors\n",
        "def density_matrix(state):\n",
        "    \"\"\"Calculates the density matrix representation of a state.\n",
        "\n",
        "    Args:\n",
        "        state (array[complex]): array representing a quantum state vector\n",
        "\n",
        "    Returns:\n",
        "        dm: (array[complex]): array representing the density matrix\n",
        "    \"\"\"\n",
        "    return state * np.conj(state).T\n",
        "\n",
        "\n",
        "label_0 = [[1], [0]]\n",
        "label_1 = [[0], [1]]\n",
        "state_labels = np.array([label_0, label_1], requires_grad=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqAg65FbTnyx"
      },
      "source": [
        "Simple classifier with data reloading and fidelity loss\n",
        "=======================================================\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "ZyKIWD9bTnyx"
      },
      "outputs": [],
      "source": [
        "dev = qml.device(\"lightning.qubit\", wires=1)\n",
        "# Install any pennylane-plugin to run on some particular backend\n",
        "\n",
        "\n",
        "@qml.qnode(dev, interface=\"autograd\")\n",
        "def qcircuit(params, x, y):\n",
        "    \"\"\"A variational quantum circuit representing the Universal classifier.\n",
        "\n",
        "    Args:\n",
        "        params (array[float]): array of parameters\n",
        "        x (array[float]): single input vector\n",
        "        y (array[float]): single output state density matrix\n",
        "\n",
        "    Returns:\n",
        "        float: fidelity between output state and input\n",
        "    \"\"\"\n",
        "    for p in params:\n",
        "        qml.Rot(*x, wires=0)\n",
        "        qml.Rot(*p, wires=0)\n",
        "    return qml.expval(qml.Hermitian(y, wires=[0]))\n",
        "\n",
        "\n",
        "def cost(params, x, y, state_labels=None):\n",
        "    \"\"\"Cost function to be minimized.\n",
        "\n",
        "    Args:\n",
        "        params (array[float]): array of parameters\n",
        "        x (array[float]): 2-d array of input vectors\n",
        "        y (array[float]): 1-d array of targets\n",
        "        state_labels (array[float]): array of state representations for labels\n",
        "\n",
        "    Returns:\n",
        "        float: loss value to be minimized\n",
        "    \"\"\"\n",
        "    # Compute prediction for each input in data batch\n",
        "    loss = 0.0\n",
        "    dm_labels = [density_matrix(s) for s in state_labels]\n",
        "    for i in range(len(x)):\n",
        "        f = qcircuit(params, x[i], dm_labels[y[i]])\n",
        "        loss = loss + (1 - f) ** 2\n",
        "    return loss / len(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Bz83H0xTnyx"
      },
      "source": [
        "Utility functions for testing and creating batches\n",
        "==================================================\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "i1-TLseuTnyx"
      },
      "outputs": [],
      "source": [
        "def test(params, x, y, state_labels=None):\n",
        "    \"\"\"\n",
        "    Tests on a given set of data.\n",
        "\n",
        "    Args:\n",
        "        params (array[float]): array of parameters\n",
        "        x (array[float]): 2-d array of input vectors\n",
        "        y (array[float]): 1-d array of targets\n",
        "        state_labels (array[float]): 1-d array of state representations for labels\n",
        "\n",
        "    Returns:\n",
        "        predicted (array([int]): predicted labels for test data\n",
        "        output_states (array[float]): output quantum states from the circuit\n",
        "    \"\"\"\n",
        "    fidelity_values = []\n",
        "    dm_labels = [density_matrix(s) for s in state_labels]\n",
        "    predicted = []\n",
        "\n",
        "    for i in range(len(x)):\n",
        "        fidel_function = lambda y: qcircuit(params, x[i], y)\n",
        "        fidelities = [fidel_function(dm) for dm in dm_labels]\n",
        "        best_fidel = np.argmax(fidelities)\n",
        "\n",
        "        predicted.append(best_fidel)\n",
        "        fidelity_values.append(fidelities)\n",
        "\n",
        "    return np.array(predicted), np.array(fidelity_values)\n",
        "\n",
        "\n",
        "def accuracy_score(y_true, y_pred):\n",
        "    \"\"\"Accuracy score.\n",
        "\n",
        "    Args:\n",
        "        y_true (array[float]): 1-d array of targets\n",
        "        y_predicted (array[float]): 1-d array of predictions\n",
        "        state_labels (array[float]): 1-d array of state representations for labels\n",
        "\n",
        "    Returns:\n",
        "        score (float): the fraction of correctly classified samples\n",
        "    \"\"\"\n",
        "    score = y_true == y_pred\n",
        "    return score.sum() / len(y_true)\n",
        "\n",
        "\n",
        "def iterate_minibatches(inputs, targets, batch_size):\n",
        "    \"\"\"\n",
        "    A generator for batches of the input data\n",
        "\n",
        "    Args:\n",
        "        inputs (array[float]): input data\n",
        "        targets (array[float]): targets\n",
        "\n",
        "    Returns:\n",
        "        inputs (array[float]): one batch of input data of length `batch_size`\n",
        "        targets (array[float]): one batch of targets of length `batch_size`\n",
        "    \"\"\"\n",
        "    for start_idx in range(0, inputs.shape[0] - batch_size + 1, batch_size):\n",
        "        idxs = slice(start_idx, start_idx + batch_size)\n",
        "        yield inputs[idxs], targets[idxs]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B5s1nRL2Tnyy"
      },
      "source": [
        "Train a quantum classifier on the circle dataset\n",
        "================================================\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "7171436e-5b12-40cb-afb7-e99710f9b3a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.415535 | Train accuracy: 0.460000 | Test Accuracy: 0.448500\n",
            "Epoch:  1 | Loss: 0.165288 | Train accuracy: 0.830000 | Test accuracy: 0.807500\n",
            "Epoch:  2 | Loss: 0.124680 | Train accuracy: 0.840000 | Test accuracy: 0.816500\n",
            "Epoch:  3 | Loss: 0.138785 | Train accuracy: 0.780000 | Test accuracy: 0.764500\n",
            "Epoch:  4 | Loss: 0.120626 | Train accuracy: 0.815000 | Test accuracy: 0.772000\n",
            "Epoch:  5 | Loss: 0.107903 | Train accuracy: 0.890000 | Test accuracy: 0.846500\n",
            "Epoch:  6 | Loss: 0.104454 | Train accuracy: 0.900000 | Test accuracy: 0.847500\n",
            "Epoch:  7 | Loss: 0.115496 | Train accuracy: 0.840000 | Test accuracy: 0.793000\n",
            "Epoch:  8 | Loss: 0.109129 | Train accuracy: 0.860000 | Test accuracy: 0.808000\n",
            "Epoch:  9 | Loss: 0.102006 | Train accuracy: 0.895000 | Test accuracy: 0.840000\n",
            "Epoch: 10 | Loss: 0.101120 | Train accuracy: 0.890000 | Test accuracy: 0.845000\n"
          ]
        }
      ],
      "source": [
        "# Generate training and test data\n",
        "num_training = 200\n",
        "num_test = 2000\n",
        "\n",
        "Xdata, y_train = circle(num_training)\n",
        "X_train = np.hstack((Xdata, np.zeros((Xdata.shape[0], 1), requires_grad=False)))\n",
        "\n",
        "Xtest, y_test = circle(num_test)\n",
        "X_test = np.hstack((Xtest, np.zeros((Xtest.shape[0], 1), requires_grad=False)))\n",
        "\n",
        "\n",
        "# Train using Adam optimizer and evaluate the classifier\n",
        "num_layers = 3\n",
        "learning_rate = 0.6\n",
        "epochs = 10\n",
        "batch_size = 35\n",
        "\n",
        "opt = AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999)\n",
        "\n",
        "# initialize random weights\n",
        "params = np.random.uniform(size=(num_layers, 3), requires_grad=True)\n",
        "\n",
        "predicted_train, fidel_train = test(params, X_train, y_train, state_labels)\n",
        "accuracy_train = accuracy_score(y_train, predicted_train)\n",
        "\n",
        "predicted_test, fidel_test = test(params, X_test, y_test, state_labels)\n",
        "accuracy_test = accuracy_score(y_test, predicted_test)\n",
        "\n",
        "# save predictions with random weights for comparison\n",
        "initial_predictions = predicted_test\n",
        "\n",
        "loss = cost(params, X_test, y_test, state_labels)\n",
        "\n",
        "print(\n",
        "    \"Epoch: {:2d} | Cost: {:3f} | Train accuracy: {:3f} | Test Accuracy: {:3f}\".format(\n",
        "        0, loss, accuracy_train, accuracy_test\n",
        "    )\n",
        ")\n",
        "\n",
        "for it in range(epochs):\n",
        "    for Xbatch, ybatch in iterate_minibatches(X_train, y_train, batch_size=batch_size):\n",
        "        params, _, _, _ = opt.step(cost, params, Xbatch, ybatch, state_labels)\n",
        "\n",
        "    predicted_train, fidel_train = test(params, X_train, y_train, state_labels)\n",
        "    accuracy_train = accuracy_score(y_train, predicted_train)\n",
        "    loss = cost(params, X_train, y_train, state_labels)\n",
        "\n",
        "    predicted_test, fidel_test = test(params, X_test, y_test, state_labels)\n",
        "    accuracy_test = accuracy_score(y_test, predicted_test)\n",
        "    res = [it + 1, loss, accuracy_train, accuracy_test]\n",
        "    print(\n",
        "        \"Epoch: {:2d} | Loss: {:3f} | Train accuracy: {:3f} | Test accuracy: {:3f}\".format(\n",
        "            *res\n",
        "        )\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWspC-9BTnyy"
      },
      "source": [
        "Results\n",
        "=======\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "70335759-2197-4b95-b9d1-ffa8f0e30386"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.101120 | Train accuracy 0.890000 | Test Accuracy : 0.845000\n",
            "Learned weights\n",
            "Layer 0: [ 0.29974826  1.58610385 -0.28874178]\n",
            "Layer 1: [-0.24279141  0.1350922  -0.42188469]\n",
            "Layer 2: [0.76346681 1.37284626 0.32099704]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 3 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAEiCAYAAADklbFjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9fXxcVZ0//p6bpulDkpkkfZDnh1BK07Qd8lBimXTo2DgSkJeCuui64gPC+gAi20LXdZVAV77SKlVE2mahdFtcshQf2UgKrO3iA01W0f5EyUAr4IrrQ6LICspaPr8/zv3MPffcc+65M5mkpdzP6zWvZO7ce+65557zPp/nT4KICDHFFFNMMcUUU0wxxRRTTDHFFFPFyTncHYgppphiiimmmGKKKaaYYooppqOVYqE7pphiiimmmGKKKaaYYoopppgmiWKhO6aYYooppphiiimmmGKKKaaYJolioTummGKKKaaYYooppphiiimmmCaJYqE7pphiiimmmGKKKaaYYoopppgmiWKhO6aYYooppphiiimmmGKKKaaYJolioTummGKKKaaYYooppphiiimmmCaJYqE7pphiiimmmGKKKaaYYoopppgmiWKhO6aYYooppphiiimmmGKKKaaYJoliofsw08knn4x3v/vdxe979uxBIpHAnj17KnaPRCKB6667rmLtHW566qmnkEgkcOedd0Y+d+PGjZPfsUkkdZ4c7XTOOefgnHPOKfva1tbWynYoplc1xTgdnXbs2IEzzjgD1dXVSKVSh7s7k0Lvfve7cfLJJ5d17XXXXYdEIlHZDsUUU0yvWpoIHsU0tfSqFrrvvPNOJBKJ4mfGjBk4/fTT8eEPfxi/+tWvDnf3SqLBwcGjgmErl17tzx9T6fTss8/iuuuuww9/+MPD3ZWYQijG6VcOPf7443j3u9+N5uZm9Pf3Y+vWrXjhhRdw3XXXVVRBYaN4bccU05FJMpaHfaYSL3T0xS9+MZJhZyL0k5/8BNdddx2eeuqpSb1PGH3qU5/CV7/61cN2/1cbTTvcHTgS6Prrr8cpp5yCP/3pT/j2t7+N2267DYODg/jxj3+MWbNmTWlfVq5ciRdffBHTp08v6brBwUHceuutWobuxRdfxLRpR8+rPumkk/Diiy+iurq6eCzs+WN65dHu3bsn/R7PPvss+vr6cPLJJyOdTk/6/WKaGMU4feTTnj178PLLL+Nzn/scTjvtNADAb3/7W/T19QFA2d4rpdJkr+3+/n68/PLLZV378Y9/HOvWratwj2KK6ZVBO3bs8H3/l3/5FzzwwAOB44sWLZrKbgXoi1/8IubMmTOpHoY/+clP0NfXh3POOeewWao/9alP4S1veQve9KY3HZb7v9rolb3DV4jOPfdcdHR0AAAuvfRSNDU14bOf/Sy+9rWv4e1vf7v2mj/+8Y+YPXt2xfviOA5mzJhR0TYr3d7hJrZ2HS6arHcfk0elCjMxHf0U4/SRT7/+9a8BYErcyiv5bl944YWSFDeywrdUmjZt2iteuRJTTOXSO9/5Tt/3Rx55BA888EDguEqlrtGYYjoS6VXtXm6iXC4HAPjZz34GQMRL1NbW4sCBA+jt7UVdXR3++q//GgDw8ssvY9OmTVi8eDFmzJiB+fPn4/LLL8fvfvc7X5tEhPXr1+P444/HrFmzsGrVKjz22GOBe5tiBfft24fe3l40NDRg9uzZWLp0KT73uc8V+3frrbcC8LvuMOliBR999FGce+65qK+vR21tLV73utfhkUce8Z3Dbp3f+c53cPXVV2Pu3LmYPXs23vzmN+M3v/mN79z/+q//Qj6fx5w5czBz5kyccsopeO973xs6zldffTWamppARMVjV1xxBRKJBD7/+c8Xj/3qV79CIpHAbbfdBiAY0217fqatW7eiubkZNTU16OzsxMjISGj/5DHYu3cvPvjBD2LevHk4/vjjAQBPP/00PvjBD2LhwoWYOXMmmpqa8Na3vjXgKlTKOEadJwBw8OBBvPWtb0VjYyNmzZqFrq4u/Pu//7vvHJ5P//Zv/4a+vj4cd9xxqKurw1ve8hY899xz+POf/4yrrroK8+bNQ21tLd7znvfgz3/+c+iYfP7zn0dVVRV+//vfF4995jOfQSKRwNVXX108dujQIdTV1eHaa68tHou6XnQx3U8//TQuuOACzJ49G/PmzcNHP/pRDA0NGV3RfvKTn2DVqlWYNWsWjjvuONx0002+cens7AQAvOc97ynOGZ5TTzzxBC666CK85jWvwYwZM3D88cfj4osvxnPPPRc6NjFNHcU4LWiycRoAvva1r+G8887Dsccei5qaGjQ3N+OGG27AoUOHiuecfPLJ+OQnPwkAmDt3LhKJBN797ndj7ty5AIC+vr7iM8vP+fjjj+Mtb3kLGhsbMWPGDHR0dODrX/+69hl1OKySbW1zzofvf//7WLlyJWbNmoWPfexjkZ8TCMZQyrlDbPuMLqY7kUjgwx/+ML761a+itbUVNTU1WLx4Me6//37t83V0dGDGjBlobm7Gli1b4jjxmI4qClujptwXurw3v//973HVVVfhhBNOQE1NDU477TR8+tOftnqpnHzyyXjsscewd+/eIn7I/EjUdu+++260t7ejrq4O9fX1WLJkSXE/uPPOO/HWt74VALBq1arIbvWMETNmzEBrayu+8pWvaM/buHEjVqxYgaamJsycORPt7e3YtWuX75xEIoE//vGP2L59e/H+PIZR+duYSqNY3aqhAwcOAACampqKx/7yl78gn88jk8lg48aNRY3b5ZdfjjvvvBPvec97cOWVV+JnP/sZvvCFL+DRRx/Fd77znaJG/BOf+ATWr1+P3t5e9Pb24gc/+AFe//rX46WXXrL254EHHsD555+PY445Bh/5yEfwmte8Bj/96U9x33334SMf+Qguv/xyPPvss1oXHR099thj6O7uRn19Pa655hpUV1djy5YtOOecc7B3716cddZZvvOvuOIKNDQ04JOf/CSeeuopbNq0CR/+8IcxMDAAQFg3Xv/612Pu3LlYt24dUqkUnnrqKXz5y18O7Ud3dzduvvlmPPbYY8XEVw8//DAcx8HDDz+MK6+8sngMEC6dOory/F/60pfw/PPP4/LLL0cikcBNN92ECy+8EAcPHoxktfjgBz+IuXPn4hOf+AT++Mc/AgBGRkbw3e9+FxdffDGOP/54PPXUU7jttttwzjnn4Cc/+UlAK2sbRyD6PPnVr36FFStW4IUXXsCVV16JpqYmbN++HRdccAF27dqFN7/5zb7zb7zxRsycORPr1q3Dk08+iVtuuQXV1dVwHAe/+93vcN111+GRRx7BnXfeiVNOOQWf+MQnjGPR3d2Nl19+Gd/+9rdx/vnnA/C/N6ZHH30U//u//+t7b1HXi0p//OMfkcvl8Mtf/rK4Br70pS/hW9/6lvb83/3ud3jDG96ACy+8EG9729uwa9cuXHvttViyZAnOPfdcLFq0CNdffz0+8YlP4LLLLkN3dzcAYMWKFXjppZeQz+fx5z//GVdccQVe85rX4Be/+AXuu+8+/P73v0cymTSOTUxTRzFOTw1OA4JBrK2txdVXX43a2lr8x3/8Bz7xiU/gD3/4AzZs2AAA2LRpE/7lX/4FX/nKV3DbbbehtrYWS5YsQVdXFz7wgQ/gzW9+My688EIAwNKlS4vPePbZZ+O4447DunXrMHv2bPzbv/0b3vSmN+Hee+8N4JgOh1UKW9tMY2NjOPfcc3HxxRfjne98J+bPnx/5OcNoIvvMt7/9bXz5y1/GBz/4QdTV1eHzn/88LrroIjzzzDPFOf7oo4/iDW94A4455hj09fXh0KFDuP7664uKjZhiOlrItEaj0gsvvIBsNotf/OIXuPzyy3HiiSfiu9/9Lv7+7/8ev/zlL7Fp0ybjtZs2bcIVV1yB2tpa/MM//AMAFO8ftd0HHngAb3/72/G6170On/70pwEAP/3pT/Gd73wHH/nIR7By5UpceeWV+PznP4+PfexjRXf6MLf63bt346KLLkJLSwtuvPFGjI2N4T3veY9WAfm5z30OF1xwAf76r/8aL730Eu6++2689a1vxX333YfzzjsPgHD1v/TSS7F8+XJcdtllAIDm5mYApfO3MUUkehXTtm3bCAA9+OCD9Jvf/IZ+/vOf0913301NTU00c+ZM+u///m8iIrrkkksIAK1bt853/cMPP0wA6K677vIdv//++33Hf/3rX9P06dPpvPPOo5dffrl43sc+9jECQJdccknx2Le+9S0CQN/61reIiOgvf/kLnXLKKXTSSSfR7373O9995LY+9KEPkel1AqBPfvKTxe9vetObaPr06XTgwIHisWeffZbq6upo5cqVgfFZvXq1714f/ehHqaqqin7/+98TEdFXvvIVAkAjIyPa+5vo17/+NQGgL37xi0RE9Pvf/54cx6G3vvWtNH/+/OJ5V155JTU2Nhb78LOf/YwA0LZt26zPz+c2NTXR+Ph48fjXvvY1AkDf+MY3QvvIY5DJZOgvf/mL77cXXnghcP73vvc9AkD/8i//EmjDNo6lzJOrrrqKANDDDz9cPPb888/TKaecQieffDIdOnSIiLz51NraSi+99FLx3Le//e2USCTo3HPP9fX/ta99LZ100kmhY3Lo0CGqr6+na665hojEPGxqaqK3vvWtVFVVRc8//zwREX32s58lx3GK8zbqeiEiymazlM1mi98/85nPEAD66le/Wjz24osv0hlnnOFbL3yt+g7+/Oc/02te8xq66KKLisdGRkYC84iI6NFHHyUAdM8994SOQ0xTQzFOH16cJtJj3eWXX06zZs2iP/3pT8Vjn/zkJwkA/eY3vyke+81vfhN4NqbXve51tGTJEl8bL7/8Mq1YsYIWLFgQeEYdDuvItLaJPHzYvHlz2c95ySWX+HCylH2Gx0gmADR9+nR68skni8d+9KMfEQC65ZZbisfe+MY30qxZs+gXv/hF8dgTTzxB06ZNM86rmGI6kkmHiWFr1IQlJ510kg+jb7jhBpo9ezYVCgXfeevWraOqqip65plnQvu1ePFiHw9Sarsf+chHqL6+PhSv7rnnngD/EkbpdJqOOeaYIqYTEe3evZsABPg2Fcteeuklam1tpVwu5zs+e/Zs37iZrifS87cxlUaxezmA1atXY+7cuTjhhBNw8cUXo7a2Fl/5yldw3HHH+c77wAc+4Pt+zz33IJlMoqenB7/97W+Ln/b2dtTW1hatcA8++CBeeumlous001VXXWXt26OPPoqf/exnuOqqqwJxcuW4kx06dAi7d+/Gm970Jpx66qnF48cccwze8Y534Nvf/jb+8Ic/+K657LLLfPfq7u7GoUOH8PTTTwPw4vfuu+8+/N///V/kvsydOxdnnHEG/vM//xMA8J3vfAdVVVVYu3YtfvWrX+GJJ54AIKyomUxmQu5zf/VXf4WGhgbfMwDCRTsKvf/970dVVZXv2MyZM4v//9///R/GxsZw2mmnIZVK4Qc/+EGgDds4ljJPBgcHsXz5cmQymeKx2tpaXHbZZXjqqafwk5/8xHf+u971Lp+l5ayzzgIRBVxLzzrrLPz85z/HX/7yF+NYOI6DFStWFN/bT3/6U4yNjWHdunUgInzve98DIN5ba2trcX5EXS86uv/++3HcccfhggsuKB6bMWMG3v/+92vPr62t9cWITZ8+HcuXL4/0vtmSPTQ0hBdeeMF6fkxTQzFOHx6cBvxY9/zzz+O3v/0turu78cILL+Dxxx8v9fEAAOPj4/iP//gPvO1tbyu2+dvf/hZjY2PI5/N44okn8Itf/MJ3jQ6Hy6Gamhq85z3vCRyf6HNOZJ9ZvXp10coECG+A+vr64rWHDh3Cgw8+iDe96U049thji+eddtppOPfcc63txxTTK4lMazQq3XPPPeju7kZDQ4MP91evXo1Dhw4V+ZfJajeVSuGPf/wjHnjggbKfQaZf/vKX+OEPf4hLLrnE523X09ODlpaWwPkylv3ud7/Dc889h+7ubi1vqqNS+duYolHsXg7g1ltvxemnn45p06Zh/vz5WLhwIRzHr4+YNm1awIXjiSeewHPPPYd58+Zp2+WkMsz0LFiwwPf73LlzfRu0jtiFslJ1h3/zm9/ghRdewMKFCwO/LVq0CC+//DJ+/vOfY/HixcXjJ554ou887jPHQ2azWVx00UXo6+vDzTffjHPOOQdvetOb8I53vAM1NTWh/enu7sbg4CAAIaR1dHSgo6MDjY2NePjhhzF//nz86Ec/wjve8Y4JPbftGWx0yimnBI69+OKLuPHGG7Ft2zb84he/8MWm62J/bX0oZZ48/fTTAfdSwHNNevrpp31zRr03g/YJJ5wQOP7yyy/jueee87ntqtTd3Y3rrrsOL774Ih5++GEcc8wxaGtrw7Jly/Dwww+jp6cH3/72t/G2t72teE3U9aKjp59+Gs3NzQEBhjMkq3T88ccHzm1oaMD+/fuN92A65ZRTcPXVV+Ozn/0s7rrrLnR3d+OCCy7AO9/5zti1/DBSjNOCDgdOP/bYY/j4xz+O//iP/wgI++XmOXjyySdBRPjHf/xH/OM//qP2nF//+tc+pYoOh8uh4447TpuscaLPOZF9Rr2Wr+drf/3rX+PFF1/UYp4JB2OK6ZVKpjUalZ544gns37/fGHoRxm9Uot0PfvCD+Ld/+zece+65OO644/D6178eb3vb2/CGN7yhrPua9icAWLhwYUAQvu+++7B+/Xr88Ic/9OXpiaoELpW/jSkaxUI3gOXLlxez4pqopqYmwOC9/PLLmDdvHu666y7tNUdLnJXJssCLMJFIYNeuXXjkkUfwjW98A0NDQ3jve9+Lz3zmM3jkkUdQW1trbDuTyaC/vx8HDx7Eww8/jO7ubiQSCWQyGTz88MM49thj8fLLLxctBpP1DDaStX5MV1xxBbZt24arrroKr33ta5FMJpFIJHDxxRdrE3VMtA8TIdO9y+1TJpPB//3f/+F73/te8b0BQhh/+OGH8fjjj+M3v/mN771N5XqZ6Fh/5jOfwbvf/W587Wtfw+7du3HllVfixhtvxCOPPGJM4BTT5FKM0+E0WTj9+9//HtlsFvX19bj++uvR3NyMGTNm4Ac/+AGuvfbasktn8XVr1qxBPp/XnqMKkzocLod07VTiOSeCO4dzf4gppiONSl3rarLDl19+GT09Pbjmmmu0559++ull9Stqu/PmzcMPf/hDDA0N4Zvf/Ca++c1vYtu2bXjXu96F7du3l3XvqPTwww/jggsuwMqVK/HFL34RxxxzDKqrq7Ft2zZ86UtfitRGqfxtTNEoFronQM3NzXjwwQdx9tlnhwLESSedBEBoyGRXwd/85jdWDTi7m/34xz/G6tWrjedF1V7NnTsXs2bNwujoaOC3xx9/HI7jBKyfUamrqwtdXV34p3/6J3zpS1/CX//1X+Puu+/GpZdearyGhbIHHngAIyMjxfqlK1euxG233YZjjz0Ws2fPRnt7e+i9D0fm1l27duGSSy7BZz7zmeKxP/3pT76s3qVQKfPkpJNOMr5Dua3JouXLl2P69Ol4+OGH8fDDD2Pt2rUAxHvr7+/HQw89VPzOFHW96Oikk07CT37yExCR710/+eSTZT+Dbc4sWbIES5Yswcc//nF897vfxdlnn43Nmzdj/fr1Zd8zpqmnGKf9VCpO79mzB2NjY/jyl7/sW8+cNd5GpmfmMa6urg4ds3KonP1gos852TRv3jzMmDFDi3kTwcGYYnolUUNDQ4DHeumll/DLX/7Sd6y5uRn/+7//Wza2mDCklHanT5+ON77xjXjjG9+Il19+GR/84AexZcsW/OM//iNOO+20knBK3p9UUveJe++9FzNmzMDQ0JDPi2nbtm2Ba019qDR/G5OgOKZ7AvS2t70Nhw4dwg033BD47S9/+Utxcq5evRrV1dW45ZZbfFrrsOyJTG1tbTjllFOwadOmwGSX2+J6pbYFUVVVhde//vX42te+5kv9/6tf/Qpf+tKXkMlkUF9fb+2XTL/73e8C2vh0Og0A1vJTp5xyCo477jjcfPPN+L//+z+cffbZAIQwfuDAAezatQtdXV3WuqZRn7+SVFVVFXjuW265JaBxjUqlzJPe3l4MDw8X46cBkeF769atOPnkk7UxPpWkGTNmoLOzE//6r/+KZ555xmfpfvHFF/H5z38ezc3NOOaYY4rXRF0vOsrn8/jFL37hKyX0pz/9Cf39/WU/g2nO/OEPfwjEtC9ZsgSO41jnc0xHHsU4LahcnGYLrHztSy+9hC9+8YuR7stZbtVnnjdvHs455xxs2bIlwDADCJQ7K4XK2Q8m+pyTTVVVVVi9ejW++tWv4tlnny0ef/LJJ/HNb37zMPYsppimjpqbmwPx2Fu3bg3wXW9729vwve99D0NDQ4E2fv/734fmrQEEhujwI2q7Y2Njvt8cxylWbWC8LQWnjjnmGKTTaWzfvt3n3v3AAw8EcvhUVVUhkUj4xuSpp57CV7/61cjPWWn+NiZBsaV7ApTNZnH55ZfjxhtvxA9/+EO8/vWvR3V1NZ544gncc889+NznPoe3vOUtmDt3LtasWYMbb7wR559/Pnp7e/Hoo4/im9/8JubMmRN6D8dxcNttt+GNb3wj0uk03vOe9+CYY47B448/jscee6y48NkSfOWVVyKfz6OqqgoXX3yxts3169fjgQceQCaTwQc/+EFMmzYNW7ZswZ///GdfLeOotH37dnzxi1/Em9/8ZjQ3N+P5559Hf38/6uvr0dvba72+u7sbd999N5YsWVKMgWtra8Ps2bNRKBQixXOX8vyVovPPPx87duxAMplES0sLvve97+HBBx8MjYUOo1Lmybp16/Cv//qvOPfcc3HllVeisbER27dvx89+9jPce++9ARfbyaDu7m78v//3/5BMJrFkyRIAgpFeuHAhRkdHAzUzo64XHV1++eX4whe+gLe//e34yEc+gmOOOQZ33XUXZsyYAaA8y1ZzczNSqRQ2b96Muro6zJ49G2eddRZ+9KMf4cMf/jDe+ta34vTTT8df/vIX7NixA1VVVbjoootKvk9Mh5dinBZULk6vWLECDQ0NuOSSS3DllVcikUhgx44dJYXmtLS0YGBgAKeffjoaGxvR2tqK1tZW3HrrrchkMliyZAne//7349RTT8WvfvUrfO9738N///d/40c/+lHJzwmY13ZYTPhEn3Mq6LrrrsPu3btx9tln4wMf+AAOHTqEL3zhC2htbcUPf/jDw929mGKadLr00kvxt3/7t7jooovQ09ODH/3oRxgaGgpg9Nq1a/H1r38d559/Pt797nejvb0df/zjH/H//X//H3bt2oWnnnoqFNfb29tx2223Yf369TjttNMwb9485HK5yO1eeumlGB8fRy6Xw/HHH4+nn34at9xyC9LpdDH3TjqdRlVVFT796U/jueeeQ01NDXK5nDH/yI033ojzzjsPmUwG733vezE+Po5bbrkFixcvxv/+7/8WzzvvvPPw2c9+Fm94wxvwjne8A7/+9a9x66234rTTTgvktWlvb8eDDz6Iz372szj22GNxyimn4Kyzzqo4fxuTS1OUJf2IJC5DYiuhcskll9Ds2bONv2/dupXa29tp5syZVFdXR0uWLKFrrrmGnn322eI5hw4dor6+PjrmmGNo5syZdM4559CPf/zjQJkDtRQN07e//W3q6emhuro6mj17Ni1dutRXSuQvf/kLXXHFFTR37lxKJBK+EgzQlFj4wQ9+QPl8nmpra2nWrFm0atUq+u53vxtpfNQ+/uAHP6C3v/3tdOKJJ1JNTQ3NmzePzj//fPqv//qvsGEt0q233koA6AMf+IDv+OrVqwkAPfTQQ77jupJhpufnczds2BC4r25cVAqbI7/73e/oPe95D82ZM4dqa2spn8/T448/HninUceRKPo8ISI6cOAAveUtb6FUKkUzZsyg5cuX03333ae9h1r+ytQnXckfE/37v/87AQiUHbv00ksJAN1+++3a66KsF7VkGBHRwYMH6bzzzqOZM2fS3Llz6e/+7u/o3nvvJQD0yCOP+K5dvHhx4L5qmR8iUdKnpaWlWHJn27ZtdPDgQXrve99Lzc3NNGPGDGpsbKRVq1bRgw8+aB2TmCpPMU4ffpz+zne+Q11dXTRz5kw69thj6ZprrqGhoaHAGJjw47vf/S61t7fT9OnTA8954MABete73kWvec1rqLq6mo477jg6//zzadeuXdZnDCPd2iYy40Mpz2kqGRZlnzGVDPvQhz4UuFaH+w899BCdeeaZNH36dGpubqZ//ud/pr/7u7+jGTNmhA9ITDEdgWQqGWZao4cOHaJrr72W5syZQ7NmzaJ8Pk9PPvmkdq08//zz9Pd///d02mmn0fTp02nOnDm0YsUK2rhxo6+Eqo7+53/+h8477zyqq6sjAD5+JEq7u3btote//vU0b948mj59Op144ol0+eWX0y9/+Uvfffr7++nUU0+lqqqqSOXD7r33Xlq0aBHV1NRQS0sLffnLX9byNrfffjstWLCAampq6IwzzqBt27Zpsefxxx+nlStX0syZM32lMaPytzGVRgmiI0iNG1NMMcVUAm3atAkf/ehH8d///d+B0lExxRRTTK8GetOb3oTHHntMG+8ZU0wxxRTTkUFxTHdMMcX0iqAXX3zR9/1Pf/oTtmzZggULFsQCd0wxxfSqIBUHn3jiCQwODuKcc845PB2KKaaYYoopEsUx3THFFNMrgi688EKceOKJSKfTeO6557Bz5048/vjjxlJQMcUUU0xHG5166ql497vfjVNPPRVPP/00brvtNkyfPt1YwiimmGKKKaYjg2KhO6aYYnpFUD6fxz//8z/jrrvuwqFDh9DS0oK7774bf/VXf3W4uxZTTDHFNCX0hje8Af/6r/+K//mf/0FNTQ1e+9rX4lOf+hQWLFhwuLsWU0wxxRRTCMUx3THFFFNMMcUUU0wxxRRTTDHFNEkUx3THFFNMMcUUU0wxxRRTTDHFFNMkUSx0xxRTTDHFFFNMMcUUU0wxxRTTJFEc022hl19+Gc8++yzq6uqQSCQOd3diiimmSSAiwvPPP49jjz0WjhPrIkuhGCNjiunophgfy6cYH2OK6eimUvAxFrot9Oyzz+KEE0443N2IKaaYpoB+/vOf4/jjjz/c3XhFUYyRMcX06qAYH0unGB9jiunVQVHwMRa6LVRXVwdADGZ9ff1h7k1MMcU0GfSHP/wBJ5xwQnG9xxSdYoyMKaajm2J8LJ9ifIwppqObSsHHWOi2ELsD1dfXx4AZU0xHOcXuf6VTjJExxfTqoBgfS6cYH2OK6dVBUfAxDs6JKaaYYooppphiiimmmGKKKaZJoljojimmmGKKKaaYYooppphiiimmSaJXlND9n//5n3jjG9+IY489FolEAl/96let1+zZswdtbW2oqanBaaedhjvvvHPS+xlTTDHFNNUU42NMMcUUk5lijIwpppgOJ72iYrr/+Mc/YtmyZXjve9+LCy+80Hr+z372M5x33nn427/9W9x111146KGHcOmll+KYY45BPp+f1L4WCgUcOHAAp512GhYsWFCxcyebjqS+2Cisr5V4jkq3cfDgQezbtw+vfe1r0dPTU1Z75dJUv1f1fqb7l/IOX0lz83BQjI+TT0dSX6JQOetuom2X28arCR/VexLRhPHxcD3HK4leKRgZ4+PU0JHOQ6oYsXfvXiQSCWSz2Skf38PJQ5rw0davI5KHpFcoAaCvfOUroedcc801tHjxYt+xv/qrv6J8Ph/5Ps899xwBoOeeey7S+WNjY5TP9xKA4ief76Xx8XEiIhodHaXBwUEqFAo0NjZGvfm879zefJ7Gx8d9500FhfXlSKOwvlbiOXRtZDMZXxu29zM2NkYrM5ni9VVSWwBoflMTHTx4cMJjYevLvn37qLOtbdLfK/dheHg4MHbzm5oCY3n77bdTVhofeYx146+2UelnKHWdH+k0VfhIVNrY2dZnjI+VIVN/Dxw4MCn42NHWRiMjI8VzouCj3MbhwsexsbEADk0mPurmtaM8e28+Tw888AB1KLjNY6wb/55cjnpyuUl7jqMNH4mOTB6yUvionjvZdLTg41TxkKXio4oRjrvmKzG+tr4cbh5Sh4/Dw8M0MDBwxPCQpazxo1ro7u7upo985CO+Y3fccQfV19dHvk+pm00+30tVVY0E7CTgGQJ2UlVVI61atTogjM9pmkcpx6GdAD0D0E6AUo4z4cnBk3doaCiwmEwLrDefp8aqKl9fGquqqLdEBnwqKKyvtufQPb96rDefp5TjUFrDCEZhWsfGxnzvsAqgpNsX7lPSbW8iFGXjcDT3Lve96sZO14cagLa490sbnp2BNKUZ455cLvAOk25bkzU3jzamcqrwkai0sTOtz9WrVmk3ycnARyIxl7du3Ur9/f1HHT4Smfs7v6mpZHxUj3PbmwHKKes3qvC3etUqqjmM+Mi/z29qmnJ8lOd1DqAG5f6pRMLHaM7VYKT6DlOJBNVU6Dl0dLThI9GRyUNOFB9N51aKhzza8XGyeci5jY0l46OOh2uA4LMmMr5R8LGSPKRp7oTxkFHw8UjgIWOh26UFCxbQpz71Kd+xf//3fycA9MILL2iv+dOf/kTPPfdc8fPzn/888mCOjo66L34nASR9biLAIcdp8AnjQJKWwJFP1AoppsmhMo46jRRPztWrVhkXO/d7p7/TtMM9L4qmNExbVkmNq62vYb9lu7t9z9+ycCGd3dXlO8aaszRAjQgygnNSKevmks1kiu9wh6VPu3fvLnssTJvDykyGOtraqDaRmPB7JQq3bOn60ABQL0Cjlmdv0cz1Wss1hTKfwUZHG1M5WfhIVD5GmtbuTS5ONSgMJG+SE8FHmXEcGhqigYGBwJp33HU/mfgo9ydMoK0E2fq7MSI+Lm9vFx4pynEHgiHqNWCkTfgbHR0V7xugaw4TPg4ODlJne/uk4ePKTKZoiTExfzZ83KuZ7xsi4mOlMfJow0eiI4+HrAQ+7nDPjSIA6wTrMB7SpOw82vCxFB7yzKVLS+YhS8FH23qfyPoOUy6Mjo5WjIcMw0dZiavykCvLwMfDxUPGQrdL5QDmJz/5Sd/k4E+UwRwcHHTP30PAIAHDBMjWbVUY3+GbBLZNmCfH2NgYrV61yqftcaDXiDW6E7MaoNkQDJe6wLjfzyj3fcZte3Bw0PjME3XTCbPKh42xqa+m33SbVo1mvBochxKW92BiWguFQgDQ+0L6BID6+vq0z2nbaHQbx5j7rtW5W857lSnMssUMuG48+i33143xGlufy3wGGx1tTOVk4SNR+RjJa3eP+x6HIQQ3G6NTDj4y7jBGyn8Dax7C2joZ+Kj2R8bBKF4zpeKjPM6m/m7XHDcx9SYrdFfEd6Z7P1u3bi1eWy4+ymNjUvRONT7uBGi/5h5pgMY142HDx62aMR609XkCzxFGRxs+Eh15PKS8bkcBGgAoK10fZa3tsZ2ruKVH5SFTEMLMWghh50jDR+a9JpuHVMemXB4yKj7a1nvY+JaKjwTQbQi6c08UI234GMZDloqPhMPDQ8ZCt0vluAZNxNK9b98+AhxpQjkEpAhY635/hvzz4BnfJJAX2Kj7vaCZHL35PNXAc7vIAVRvmIC6RdQLwQTwxB4aGrICtY5YG5bUuDjZ3HR0QMv9DHOFsmkpE5oFfJNmbGwMfKlMKwAaGBgIAPo/W+5zxx13+IDRphVk0m0cKyE2RhYcrFaRiB4M3IbJspUzjIcJFMPG2Mo0lPEMUehoYyonCx+JysfIffv2BZi8FAQjF2WTtAocEj42VlUVLQ38V7cedIJYJfFxcHBQa+20uXqXi49837D+Xq8c1+Fj2PEoGKkT/gYGBojIYyqfAeh+yz1UfCSKFlOuw8deCLxiwaHS+Mj3UDGy0T1eKj7qhHKr4qnM57DR0YaPREceD8lzifGIhbyo+EiIIHAMDgbwsVwecjOOTHycKA8ZFQsnwkPq8LGjrY3Gx8d9+BjlHmqIVLn4SBDK1FmoHA8ZBR+TMPOQpeIj4fDwkLHQ7dI111xDra2tvmNvf/vbJy0JRj7fS4lEioRFe487aXYSYHI7F5buPnfi8MJe4hPcve+yJZUnFTMtJmDOQa+57IUeiHe4x3dAAFxnW1ukGIxOgHa5YLIB9gWjBVq4DLLG3Ua29mS7u4N9hedqUuMe499muffcK/UjigaxVEt3NpMpvp/N8Kx4vHnKfaoDaGZ1tW8Me/N5XyyKTiuounRtgNCId6rnQQgOac29kxBuUqbx1Qn3UYFMPrbXcP9GhLsO6cYrCS8eh+emyV2uHBA92pjKqcJHouhj15vPUyqRoJ3KfLIxFio+ms4bGRnxrQvA776sW/NhQpIsLJrwMeu6KtuYnmaARuApU21u1aXgI1Ew1IjHWu4vxwFGwUeCUC6Wi5E6PMhmMsW+yhhZCj6Oj4+Het7oXF5HAboZKFqfZIxcorl3g9snXcymipGqdbIUZm8DvJhF9f5h4Tk6TE1p3mslMfJow0eiI5OH5BwDsrBjm1eLIHiFHQAlHSf0XBaQ5fbL5SF57VUCHxdB8DFDKB8fG9w+ycI5k46HNOFjyn0+/u0m97g6NhPhIXX4mHScomu3DR+TAE2DHvvKwcfJ4iHLwUf5WBdKx8fDwUMetUL3888/T48++ig9+uijBIA++9nP0qOPPkpPP/00ERGtW7eO/uZv/qZ4/sGDB2nWrFm0du1a+ulPf0q33norVVVV0f333x/5nlEHMxjPza7mbN3uJaCRhKD9jPs3RcB0AhLFiT69egYBSVJjv+c0zSMi2YXdv6B0AqFtkjPwFgoFGh8fD2QCNGkOdRbsJPxMDf9fDiBxv+QshqqmdU4qpQWIHcp5Og3teISx0S3aRoDmGI6n3es2bdpE2e5unyeCTnieWV2t1eI6Up+0AoGk6VVjrNLuvXhT7AGoWzMGDsRGGcWaxvPapm1fI42HmujC1E8T8zgdoDOUPs+YNs3/HjUJP0y/V3KdH8l0OPCRKNrYqRpvlWHhua5urtXKPJjf1EQpzXmcVIbx8VZl3us2/aj4yBlR1TnWUF+vnXMmfFTX4UTxkV1F1VAjAHRWR0eodcqGjwS7ksPE2ASEP/jxsVAoUE8uV8RITpgTBR95jzJ53siMlJyMyISRTZox6Hb/Dg4OWjFyeHi42B8bE74dHrMnY6J6/xr3PT0DPUbWQygl5GuakklKL106aRh5NOAj0SuDh9TNJRM+NkMozRLSO53X2Khdl/Obmor4yAo1ed3t0az3KJbWIw0fC9L/YTxklfJdxsesYW2m4eFjOTxkCnp87JWu6+/vp7O7ukLxscpta7LxcaI8ZCn4aOIhS8XHFATfcKp6XVXVEYGPryih+1vf+lZgUQKgSy65hIiILrnkEspms4Fr0uk0TZ8+nU499VTatm1bSfeMOpieMMxCtiqEj5M/vhvkuaKrLunvJKCfgIJ77Y7iYrz99tuL59YCdEXgetDpSNB+2F2Nat0JLQOTag3IuYteTrCgAo3ORXM67GAdxpwAoM62toAbVFGr6cbNbIdfQ8ZtbILQ3AWuc59pB7x4HN+CdbPAbtEs2l6AbteAQC8EUKnjpz47M8s33nhj6NjshQfoa2GwGrnWfpMbI59X7/62122rHp4VR+v+j6A1rTeft2rQVbAaGRnxaQwLhYK2xIO6MScgrE/ysUXK99bFi32uUrZso5Vc50cyHQ58JIo2dqo7m8qwjMMf361utvx/27Jl2vXHscGf+tSnCBDMaBJB9ziZebVZcmvh4VpUjFwpMT1h+MhzeiL4yB5KsoJPZWL3wgtTKhUfGyEYGBNGvhFBLGzTjI8OH9PLlgWevwBvz7KNzR6E4yML9uxZEYaRG+HhY1J6V1Exks+J4o7P+Dg+Pk6FQqGIkfw/J6cMw8iUYS7xZ0lLS1G5wTRRjDwa8JHolcFD6hSEUfGR99cTlHN5Tq9fv54A0Pvctagm61KFexsPyTzSkYSPg9L/YTxkEsKSqsNHjqfX4WoaHhaG8ZBXIIiPPQCdqRxjReczyvEjBR8nykOWio+MkTIPWQo+qsqUTnj8wZGAj68ooftwUPmWbiIhZDeQ37pdTcAsAjaSZ8luICBHwGYCapQJeA4BnDHRUX5zSFjJ6whIK795Wh3TJOdkOB0hwKTTwqmAp9OocXIik8tRWL9k93RbTInJ1ftUy3UAqBUIaPoceDHhstuVmsxpI/xgLbfLG55pU+jr6wv9/cMwu/pE1fTyeWsN59jisFQvCAa6GgjXL1WDrgrZJtLVxc26Mev9/f0i8yqCc9ABaJk6JoomVTsWEd2Ejham8nBQOZZuxg3VdawWYlNPauZAGsLCpzIy86W1Kx9Pu2umByhax/cjyOAZXRjhCYulYGQUfDQJtKXgI6/hycLHtPsuUsp4MUbeL92DsTAKPspCogkDbb81K31S8VFORmTDyEHN73KokO09qIygDiPnpFKBvBw6MtX3HRkZob6+vlB8vBX6cKRKYGSMj+VT1LETOYH0CkIZH2ejNHw8C573hg4jD0BgaBLQuiSHzZ1v4MjDx4Jyr3Iy/kfBR0AYl5LKMcbHUemYzEPq+Eq57WWGcTpc+DhRHjIKPlaKh1y6eHFoKERgXA4DPsZCt4VKGcympvkkXMNZyNYJ0SBTbDdwNulcy0UbaRLu6fJvDSQEb91vSWqGQwsgGCfVLSmL0oBpr2Gx2lxslrS0BCa57GKkc0NsrKqiDrdshc0aNUt5Nm4jCjDJm9CmTZu0CgXejOR7VGuOyQubYyR3wp8QLypYVSOoYbVtXPKzDSrvTXfOFVdcEfo7j/vWrVvpc5/7XMC9m7WJaYjayVE1gjqXphoI0OywlPBRwTTpOLSguTl8LCJmpoyZyvIp6thxvCKvm80IKr1sWV91AnkSXhyebs30aO5TDSH8LYU+Ziyr3LcUjCwFHwNW4Yj4KLvSTwU+dnZ0FBVj8nXl4GMtgjGrKkaGjVlTKmXMU8LnsEUvCkbqPKVuvvlmq3KUMXLdunXU2d4esLCUg5G9eXN93zM13gHyuKieCxsAqnWc4h48EYyM8bF8KiXnBccUmxSE5eJjDfQlrBrdNZzS3CsF0L3wvIbU9azyI4cbH31eOiXwkGswefjY6469vMesca9TFSqsPDvS8HGiPGRfXx99+ctfnnQe0lYCMoEjAx9jodtCpVu6F/kmg7B2jxCwRjqmz2IuPiaBvJTfRpX7gWQreQ6C4eVEaVGBCfC7lOyA0K6HXXfBBRfQtm3bAhosXQwQA5isfbIBeZgrY9h1AOj18JJXyO9QvuYggpaeulmzQuMlNwK0GkFGvwagnlyOiEjPVFdVFWs+mvpdl0hQkxvPHqZhbHAcX3w4f8Ky2ctt9EnnOdBv5DOUa/j9htXbdBBUKLBXRK3rwm6aS6xxHUPQ1S4NUYJK3ZxiS/fkUymWbtUVthciwdga5XgYs1OqVZevKbjzY0i6X59mnabhJSYqByPlRI9RkpFdddVVgQy0NnyU8y1MFj6+xV2TnABNfo983UF4ngal4CMhHCPDEjPZ8FGOlw7DyCSCdY51+JiGvuSXPJdNGDlHuSastJGcvVoVjpLwEifZ1oYOHx2Iua7zPogt3ZNLpeDjFoCWK+8uC+HuLHsAloOPUbCzAJFV/33SOqlH0GV3IjzkZOMjY2RUHnIy8ZE9rVRM4XWu+36k4ONEecgo+FhJHpL7VC4+sndCOTxkLHRXkMqL6c4SMJuADeS5lTeQcC0HhQvPYQJ52G97CBhw7y1PqGUEbCEHSZqpuKeXAkyycCoDnk3zyp/5TU108ODBwLhxrMbu3bsDi2xlJkO1jkOLoNEKSgBjcmVc3t4emvSMx0BOlmACszOXLStaVXmR1kFsJHvhaVodCNCeZwCZnlyOxsbGqCeXC8b75HI0MDAQChzJ2tqiG1dSuX8SfsacM6HLz1IDBOJ4ZA1xgzs+fJ5tXtwi9Y2zmKpzjMeXvSQ2GN7XtZZ7sUbVVHZCHs9qgM5ctiwWuqeASo3pzkK4SG6Q5iUnQ6ufPduKJ2GWR9NmuwfBure8jlshrAuLoVEKlIGROobQho8OxPpXE7eE4SORwMjpCFrrU4nEpOAjkR4jaxMJmj1zZiR8fAaC8dFhZAqgc7q7J4yPOyHCp9hqxP2UcWJ+UxOlHMeKj0koWW/hub7uhL2yw26pfyyg6MZX9lwwuZ5GWRtR8PEMgGa7c6RSazwmPZWT86IJwktFnbt1E8BHG3buA6gDQVxqde/dAU2emyMEH+W431J4SAdTg49Jx6FFZ5xBra5VlYXQDe7Yb4DAlMnGx9pZsyLj40R4yFLwsRwecjLwcRr841kKDxkL3RWk8mK6xwno8b1AL/7aIb8L+g73exSB3PSbGuudJmC/e34jCWu7OJdLMezevbvYd06okEYQmHgRMrjKoLZr1y4CYLyuRZnU85uaIo25rQYjL8QDBw4EXFYZTHmhzmts9F2XhtDctrmLSdWo6TYEGXx4oW5BUAvJDDtn3jYtfrncRTGBj+NQZ1ub1fUcEBrmHuXeDkBt6bQvbnB8fLyYuCRsg7sJ+nHeCXvGyRmQMn7qkrtJyShuvvnm0CR026GPY+M5yK5WYePzDZjLrFVinccUpFJjuln7r87fhPS/uqaTsCv4TJZudc6l4WVo5fIjRa02POaRMZIrBSRhxjoHXlwZr7+RkRFqbWkpauZVV0gHYv1zPzgDexRSMVLHgE0GPhLpMZIF2Cj4uBPhJQMBgYcTwUdT0qlsd3fxHZWDj3JbfJ4NI9e5/XPcsTfho1rHnvFRjsVkF3J1/vHY2gSfGB+nnsrJeaHzIGFrc7n4GPZbi3KvHMQajoKPROXxkIVCocgTTDY+mnjIczSlw1KJRFHA11VeqYdI3sXJXMvhIfdr1iHj5ZGCj0REw8PDRTf9UnnIqPgYlYccGxsLhqvCj4/LDPPvRETDx42Gd2PDyFjoriCVWqe7qkouC7aBEolZNHt2HTlOkoQQvJ+CSc8cAvpIL5CnyB/TLf/W6J7PbXM8NwvasmC+lwB/5lsmLt+iA6bOtjZ68MEHtSDCWrX9hkU8oJnUd9xxhzVZgimuraGurggIo6Oj1NnWpj1vflNTEdxXdHUFyg7VQGgLVYZLXliy1tSWUGfTpk1Fq5LszhmmcQ4DxTluTI6qYW2GtyHqNHWdbW3FMdRtOgsWLPD1y+SmPTuRKJ4XpWRINaDNSiqfVygUKJvJGBPAMOBxUhff+6qqogQ8y37Y2HZoxiZKFsqYqSyfSolZlLXmGwCalUhQ3ezZRSbCJLA50l+VQUsiWFuV10wd9EmH1AytHG8oZ75ljFTxSMXItmXLaIUbFqJiCVuwcso1Oel+6npSXSl1pItrqwZo0cKFkfCR3dPPXLYsoOFnfLSVUGGMtDF6Mj46ENYVG0aakvdMBB+XLl5c7Hu5+NgC4aUhn2fDyBNgr5/MYVumucrnPQg9PvLYcj6RGB+PHCoXH2UPklrJshiGj4tgLlOVhh4jORlY2LwLw0eiifOQlcZHxmzVc0nlIc9cutTowj06OkoDAwOUqqvz/Z6GiEFevWrVhHlIFgLvvfde6s3nJw0fG+AlaCsXHznrtw0jGYej4iPvXTYeMqxiD59zPfQK/aj4OAh7qbWJrHGiWOi2UimDOT4+Tvm8vyxYJrPS/V+1Uq91j1/q/n2GhGCtWq17yJy9PGFomwXtAnku6BcSpEXOQCAnRuOYx93Qx3Crk1BdJHx98R5Sp/ZDr2lUtUcMTksQrEOYhKg/K4OBeu8+zX3SEHGj7EJ0G4IxSgykuoWlun4FFqq7+RQKBVq3bl2gbzqQCQPTpFLnGi4QDEvfw5g3IrPiQr7WlDVULddhsj7PldpUwVh9Llu2zMZksug2n0LQzbcxmaQzpTq05WjzwzbqmKksn6KOnc4CYNtoARE7B/iZBrkNnofqmrFZfuQMre91/+6Wfh8aGgrMW8YZ2WMorNyIzGzxtQWlD6XgI5EX15ZEEB8dwJfZ1YTN2e5u372yEEpS7lsC4TkpZCoFHwcGBqh18WLrWjUl5zkS8ZHP1SmDeJ9ptSTrseFjHTzBSYePc1Ipuv32263JhGJ8nHqaKnzMQSiE1LXBtZbLwUgWtnX4WGkespL4CPhdnGWMPLurK8BDyvfWxSy3wMNHwuTxkCMjI74wxnLxUe1bpfCxwVVA2DDSQTR8LIWHtOHjvMbGIg9Zh2AoRKq21pqMciji+JS7xoliodtK5Ww2soYrWL+bP9vd43vcvzsJGHb/30jAIAmhWU6K9hYChKZv9+7dxeyq5ljvAVKt6tMgYqFV5UAWji9ZTFRmQKdVSyKYoCYNjTZVoz2S49pM7qJJx6G17v/qAs3p7gPPsmXsCzyL69DQkM8ar9NSjsK/qTCpyXDUcVluYYrUDJVqHfJlhueWwTssIQ/XlWR3IFM/EhCJNHZA724zH6DPSN9t80SXGV7u9x133FHcgHX9TkIICoVCoVh7U9Xkcpk128ZWqXUek6BSx06Hj2Eb7R737/XSnNUlRWPmU8XHMK22jsGqAWhuQ4PvWA4oCx8LhYLWxZvdNkvFRyKiG264IRQfZyUSRnx8xn3eBsmzQGf9d2BOVqO6nZeCj0R2jFQZtiMdH5+BPhN/Svp/ovh4puRNEYaPRETZ7m5f32J8PLw0Vfi4BZ5bslrGb410Pie2jYKRH4IeH7OZTEBBUC5Gmiyz5eLjZPOQo/A8CsrlITlhl2wEY6oUPuYRFNA7DM8cFR+TEMqGKBhZK/Vdh4+l8pBh/e5oa6ODBw9G4iF1+Jh0r7G5wpswMha6K0gT3Wz09buJgJuk473kuY7nSCRdu839X56oDjU0zC1q98xts6V7Eamu54lEihob57lu8N5xB0nKwzEyA3vgTzYhg76qdU/BX6bMtjgZcHT19+SsiHzP90HvsmJzYxmGPVbGpE1lTe1tCFraZI1rFLdOjn9SXcB6NGOr9rMvwljKm4563mblGcM284WnnebrfzVAbwToLniuaWnpvqYkdL35PN1///0UtglyPKyp3/Lz6SwCNQBdF3GeTcY6fzXTRMbOFrrBv7FGPQ3BkOnWoQMhLKv4aGp7AzwhS92ca5RjKhMYFR+5rIqKkTXuWiwVHwMumgji41tgxkLbvbLKuOvOWSpZqhn/OF4xCj4ODg5StrvbiJHaOEscefiojkMNQFcCdDWEJ0Qp+Mhz1ZQgKMbHVy5NJT7ugEiQlYIZI+WEZLb2ufyfjIWpRKJoVSwFI1UF2R6pX5XARyKBkWzYCMPItSiPh4yCj2E8ZBhfKL/zMB5Sl9wsCSHEyvHNun4Oa46Vgo98XpfUvgkj1coZcwCaCdCFKJ2HZGE6DB/lcK6wvqt1wwERo19f4lwrd43HQreFKrHZ+GO99xCwhhwnSU1N893jm8kvYDvkxXerdblrKJ/vNbQtJ2ZjV3STQL5Re5wzzTIzoEvu0gOR0VIHMFmAvq05HrY4WbvGFswwCwwA+rT7XXV7XmO5zzJ48TLPwF8egC1ASei1qQyaunMaHCfgshQYFxcUOI5SF2fOTKW84c1ygYYzXDZWVdH8piYtMHW2tfksT2FjcYMFXLgsGX9WQGTTlI+l4blTcfzT2UpsawLQJmvaDz/oytnobXOFiTeIkZERX8xoUpoTvCGxFWgy1/mrlSY6dupGexO8Uiz822Z4DKRjWKu6ZDvaTLLK+gyzGqjHSsVHHRasgOfuGRUfiYiymYzVQh2GjzsQjEdW71ULr1yQCSMDfaiqKpaxiYqPzECqGDk8PPyKwMcVXV0BBfFMpc9R8NGBl41YPp6DW5IJMT6+0mmq8bHSGGlaAyYFuoyRq1etCqyTHvjjbiuFj8xDRsFItRJKVB6yFvBZyEvlIdnrScXHbHe3NTkm4+PAwECAP0tDKFrk+OZrITJyz4K/igQrmlV8zGYyvhwdoWMOuyt2gxIL34PK8ZA6fJSVl1Hmi+xRMjw8HCiRq1OOVmKNx0K3hSqx2YyPj9OqVatJjcleuXIV5XL+LOfNzQvolltucb+bM5nLmRJVV/Hu7qwUW2xyPd9uOC4+DBIrMxmaDQGwqiXI5mIDCICyLU45CVGYVYrdG98HAXLjCCZxCGuD2+HFLl9jyziui+9Uz6lTxiLpONThMno6K1UW/jhK7l8KwWQ5DAjzm5ro0UcfDc3M2ZvPF8c0TKDIQWwiMiAnAZpZXe3LRMybdTeCGumeXM7HMCYgmM/3uW2mNXOkFp4WlLXQ3C9bTGKYNSabydDsRCLQR1WTPFnr/NVKEx072TIXEGCV+QWgGPMWts7DMslyhlabS++g5lgp+LgTwiJgw0hbqbwHH3zQGtsp4+O1bt9HEMTHKBng2dpTKkay23tUfJQZveHh4SMOH5khvwmeUJ+E39rkswC6z1gKPu5EsATPBghGOYEYH48GOhz4yDxkuRjZ2dZmjbENK9HI66wnlyu6cvN8T6Gy+Liiq8s3BmEYmXTHkC2mpfKQ18LDxyUKP386wpMl3n777UWM1f3elEoFMIV5SB0+qnHmMs6a8NGk5FMVoCzU2xTSpfCQjRDKaFWhqsPI2e47svGQWfjxsVAoRErqq6PefJ6SjkMfht+SL+/3lVjjsdBtoUptNp5F2rNcV1U1Uj7fS8PDw9TW1qksfIdEpnO9YCxr93R1XO1u7dcqx4VAz5khw1y9Vdcm3cRmBo0BuxcCbGXtEWtda4DQGES+1zRlIbC2KwkvaYIDfYZOR2p/vmYR27Iayh/TOaaMksZEIvA0sPJ95kOfnCINr5wFkdDUdbS1+TI/y5YnU5bfXoisk0uU53IgMjHbNmu5NJKsaVeBSnXl12W6nA9RHoWf/+abb6aG+vqSNY1EBgHL9TCYqnX+aqRKjR3HWqlzefWqVYHEXw6Et4RprdrwkX/TzXV2Mdutmf+l4KNN2ShjZBg+zm9qolo3+3UYPk1HECPTEExYBzwLdg7mWEFuX1cjNipGvpLxUXaRPQB/XDYAmjFtGj344IOh77WzvT0SPsrJjXZCj49zEOPjK50mGx+5FJdaAz4KRsq5GdQ61zZ8vNYw/xkjTSXwuDxUqfio4+t4zUflIasTCV9/SuUhWdEwR+uJmiTACdx/P5if9+6bl3Ioyf0zurWHZO5Wn7MBZnxMwlOy8fuWS9gWFYiOQzOmTQvFx3J5yGwmQyMjI8W5ZnK7V/GR76liZA+8srNbt26lgYEBPe9rwUgdPna0tRVLw1VqjcdCt4UqAZi22Ovu7mxAIBcLOK09HwB97GMfo5aWVt8E6e7O+jbQrq6zyStBpitVlnaP7ygqAJh683mjm44MEiaA26QslmFoYl0QHYABvbYLENqzb0jnqYtX/m6K+bPFcmyEF4NkOseUUZLjOsO0hvz/HZZ7qNnnbWOmjnkaXmI0dTyT8OIUTe+1v7+/uFHLpYI4O+VOd5zWQtRflNvSZbpMQjDRakIrtd9qDeQwUpmHKBQzleVTJTFSN5cdBF2akwgma5Tn/cc+9rFixmj+dCob6OjoKLUtW1bcnHld6JiyVCIRcMm04SNjTRSMtOGjzdoDBLP1bnDXYB2E1ZvxawvMdVp3ArTPcC8bRu60/P5KwEc5ltSEkYvPOCP0vbIwI3tGyfjI76YWoAUIChbl4KNaAzmMYnycWppsfAREgtZSMXKBkrOlHHxkfkIn1HS2tWmt2DmUjo/j8MdTMz6WouScrVnPpfKQG+Hho4mfVy3ZSzQCepWUQ0l+J2EKA5OFXLV0R8HHbCYTaV5NJj7KPGQafuzT4SNBj5EpCOFf7escxQU/Kg852fgYC90WqgRgesH9e8jLSk7kd+k2uZJvIC9Wu4GAab6JJDRo3QRsIUDEiR84cEByOXekv/6FL8d+n3ba6bRp06ZIzMoG2Be37J63A1598L3QJxwaVM7fA+GykoSnTZO1Xas1iywBkeW1saqKNrj3XAt//BNbgnSAwMy9agGSNy5d6QPWgspjMYYgcHfB7w7Ez74GnrZ2TUj/II3jpk2brJlN17r//73bPzWxRRgzGqalhNLOHvfvZnjMvKO0ZUtQUg2/iyW7Vp25bFnRpVIGzzDrjKy9n8p1/mqlSmLkHvjx4euWebNBWocNCFp6HYiwiC3wrLmrV63yubM50t8kgkwEr51/+Id/KAkfreVH4Fm4ed2b8HE7zPjYqdzLhI+nu3+TLsbthSgFNCuR8MWHssUnKkbK1o8jBR9bFy8uJh6z4WOfNG/UMasERu6B/93oMHKDpU0TPna0tdE999wTsHKGuUPG+Di1NJn4uB/2kBEZIxnPKo2PgFDYyzWwbeunFHyUeUg1IZvKQzL+MEbWA7RcWvMFdx2uQoK8srv8Sfh4yE9DeAglHaeYK+Lk4jX60M1aCR9vKrar5+83QOBjVjMWwpLst5Bn4BQxUsa8HS4+hPG3Mj4mgKK12Xb+ZOOjfL4JH6PwkKbwpYGBgUnnIWOhu4JUCcDct28fBWts95JIoBa+gP2fKgLqKZhcbToB2eJCTqXmkOM0SOddG7rw/R+HFi5cRMwA6hbibHg1Q5kJlEF9DgTzNIJgDHAYwI5Dzyy2KUDSC+FKySUI1PM5PkU9fnaE2HGbG+s4NNl5IVxcZGYzjfANSrZiASKuJWpsOwBa0tJi3dh4Y+KNiN2vrlbGU32/CQQZ5wb3+KxEohi/yn3hdnOQElu4/cq519qY5bDnmA1NyTJNHKI2u7MlFqeS6/zVSpXCyID1DqCTIswb/lRBMFjymmuAwIqsNJ90GcvZdTqMiWA8WLRwYWifGB93QDC0SQQx8mwIJnEXoiV12wgzPvZA4C33ScVHHRbqlHA9uRy9tgyMzMHLDjyZ+Bg1bhMA1ScS9tg+6PFxL+xWuFMR3PtqAaqfPbvozr4Hfnzkd6PDSFuCO9vcjFJTPcbHw0OTiY+t8BL4RcHIGggB24aPqoKnFHwE7HXpp7nXMn9g4iFVfLSt53GIRGzqNfUK/90Ah/wGLP/vjY1zSRXIV65c5cNHEz+tFyrN/D1b7BkTeSx0FnLZUHa60ueeXM5ah5rxsRbCs4GrytgUH+ylcyui8ZA6fExCJFerdZwAD2nDxxTsPKQpfKkplaI6TC4PGQvdFaRSAVOnIcnneymRSJEuE7ltAQO7Cegn4CLLeSDABAimWuG88Ne6/29W+gTKIBFIZiMnrqhSJnJK+c5JE7ZDALuOUZkGTxurY8Y4QZCs7UpAMNjy4pQ1XHNSqYC7VYPjkANzTGNDfT0REQ0PD1OHZDkwAdKsRKLYb10dwjAGmuM1i0ymu7CzmQxNRzC+UwYiBo9sJmPMRptz7ylrB/l/m1UFCDLOc5Xv8kZRKx3foNxvANHKbEQBcJVBVzPuauNCI8Q6lrPOY/KolLEzaZB78/kAo8eKnrB5sxugfoAuijC/5Jha9bwoFlR1jZvuJeNjAkFc0FnjvwFhsVZxqT6RoFRdXdE6zUyq6rLJyk1ef4yPOw3XJBEsi8YZvnn96zCys60tEM8+lfhYKBQoOXu2NlcF46OcwbccfIxiVfkG/C76OoGIkzTVK+9G/ssJ7zot9wubm6wk1e2Z8jqL8fHw0GTiowN7gsPdEGUEJxsfd0LwLamQdmSM5HNsPCTj4yD0PGQdwnnIoPC6gYRAPYtEFR8u06sKtzWk5mDyci/lyCvz61UNamvrpLGxMeruzvqewcS3XyM9R8q1pke5TvTbE8Dl0NKGujqaBb+HgwkfTRjJ56k4GJWHVPFR954ZI89Q2tTxkMul60z3NBkJAb2ckETleMhY6K4gRR3MsbGxQBZxTpIWvnjgLpwGZQE3uMfZFX27e26Y4JwiT2Mnn2er58336JXAJxgDPg1VtKKrq/jMHI+xEX5A1FkvdiKYKVJlVGwMdi1Azco1DoTmVne+KQbmeg0gcF/UpD4mTayj6X9vPk8jIyN02WWXhYLAIPzgxAubrTkJTduyBWg/j4fj0Bboa3FugQAs1WLDSeQYhHxAW1VVrHG5EULRMgjBEM4E6FJ4GUgbpb440rtj9yW1T10wM8BzLJkyw34bGhoKxJfrzrO5CcVMZfkUZezCNMhR4sp0nhcOPGslz7swLXhKmqfqeXss80zV+Bst2Bp83CmtpSz0AnAaE8dHtlrJ12QjPJduvZnivr/whS8cVnzkOaQycDI+yrVqr0Bp+Ci7s+qejRW3vLcUIMKfTPjIln8HnrXwVt3YuPfSzank7Nll4+MNN9wQ4+NhpsnGR8DsdZjF1OEjwS/wmjBSTnTIeQ9sPGTabb9UjBwt/sa87xgBS3zXCJ55i/xYFOSNVZ59Cwl+2d/OF77wBU3S5DR5uZXUsr6etZrf9/DwMJ1wwknu8b1Kv5jfHyRZAK+qaqRcricgg3D7JnxcC3Os/jD0Hj9hPKQOH7cC9BqI/CJylQ+Zh6yCh48mHrIBwohn2m9sXhim3znxn9VzNAQjY6G7ghR1ME3ZyT3NmM19PGf4Puiez1nHwwTnm6Tr1fNMC7/LvceQcl0vCU2e9zyJRIq6ulYUNbFyPIjJMsAxLbXSQtkAwRwm4QfXWjezpGljaNBck4RntVDPN5W0mOX2ZS+8uEhe4OwWxc+hKymRdoFiJ/xlwfbt2xeMrYPneilvUDKDKf/Grol7AbrAPcYlgNSEGbJSoQBPG72iq0trVVJLAakblpp4ohWgM5VzHAhXUXb9vMb9yyVKNsATknRWyzmaex48eNBaK1SdE/uhV0zsN7xzuX7tRNZ5TEGKMnZhGmRbXJluA+bvvH5sSb4KkOPr9Oc50DARCMZMk7sGdC69K7q6tPhIsGMkWz2M+GjJYN6muYaVE6ZrdGXREtI4yBjJ601WdB4ufHwGoI/AE745VlP33qPiIyGY0M6k/IyKj7ulvix0E1eZSm22IKhMqALooYceMuIjezjE+Hjk0lTg437NOnQgrIRR8VEWUnQGizB8LCCIbzqMrIbIu8MCTCk8pGwVrgVodk2Nz1VZ5iH3QGDHpuK9mf+WDUuyVTtH/tvKwq16LEGelXsvAWtItjoHhfhxCiYxzhGwpSgjsNA3MDBAqdQc5dxetw2V3w8K4I6TJJVnBxwrPhI8C/OZy5YZ8dHGQ6r4+FrN7z3wJ8BjHnJJq0gMHcZD6pS5Z7ueZSo+sgKAz5PXzxiCPAXvTca9MgQjY6G7ghRlMG3ZycN+a2/vlH4vkJdoja/dS5xdvKlpvruIZMG5kYAeCmrdaki4i/N59STARj5HjTNncNI9z1gAODKZbHFBDioTW5faX10s8kIehBf7Vo5GXwcoJku3Gssoxw/WJxJaJnWn0ib3uZgNsrtbm2ikwV3cssuO3JfdysJW41LSCGqLGUyMzPeyZT5XNG5DdhFjy9tueJrFWsUdXxf7ym6fMmBxbAzXOQx7T3shQJYzm7OlemRkRFtbl7NXqu2lNX1LwpytNbbkTB7Zxs6mQbZZ4fg3nrMyg8i1QRurqmh+UxOlEomAm1wPglgkZ2FlASaNYIZcFbNU5pD7NIygtSCrxBSXipHyeIxCJD0rFx9tmW/VdtRnSUvtJBFUdB4ufGRhQJcA06SMNeHjDugTfhak45x8rRR8lOOrbXW2FwF0F0AXQiiHs93dRWZcl2/EVI0jxscjh6YKHwkeHvG6i4KPvdALIWlImckhBKhO5RwZrzpgxkg1zAzwMu6Xx0OqCcZEGOQVmt88ITiqx6ftGEhfCQjkuX2rQvwe9/e1UpujJAR2SO7oujjuBrc95vd7lf7sJr/Ha/DZkhXGRxMPGRUfe6X7yTxktrub6l3FiWm+3wWR3G4mBC8/NDSkxUfef3XhF7LXmqzkckLuG1u6p4iiDKaXnVxvzW5r63Ct4J6wzCW6xsfHqalpPgWt0CkfeOTzvXTw4EHK5XqUxd5LQujWlRyTNW/yNdM0i3sDeYCke56g5buqqpHmNM2jVCIR0KRyQgR1wVUDNGP6dAL02tkqiBg4FSBsiWbWSOfzBhPQfLn363XbfC9EdkrOICnHuqhMKgusxj5bgEK3iTkIWnJUl8tvIMj4q9Z43b3k3zj2WvebfN1GeMxylBJu/KmGYCrHx8eLsfBhWnlAuJ6pyo+VmQxt2rSp6O7D9RtT8CsebFp7OZZJrts70XUek55sY2ez1AwODmoteexSPL+pKaB4Sinrojefp4MHD/qSbQFeEi8Vi5LwW27V+TwTwprC18hxZipzaMI7ldHdo8xb3TVJgKZJHj86xlOnMLDho+xpxOuiRjkm42Oj+8zF7L0ICsSywH648JHHlEsd8icBEUZUDj6G9XMI5eHjOW7soC2juvxxADpz6VLfsdaWlmKVEV4zS+DPARLj45FFRzo+sreKLuyF20gpc3MaBObI53PuAh1GmvCxN58vxqtH4SHrABKW5loyhUEKg9MWCsZnr6EgX+vx6eJ3wX8L41YNBT1D0+TxwhsI+DQB73N/U4VhWWCX7z1GfiMZ8+IyD65TCoDkMr+iDw75Xc1Vd/Q9Rdw40vBRTqLGismJ8pCd7e20bt062r17NxGRlodU92G1b+peGcd0TzFVwtI9MjJijPdmK5+aeCGf7/UVkJfv1d/fT8cccxwJ4LG5nR9P5gyIJhd0tc3w5+OJz5YM26YvM1k6prNec55tsfs2EneDUS2nORdADkBsMPJv8wE6KC3sWlUz6FpwmcFRmXE5Q60OKE4wPJOsiZa1bDpmu3bmTN93lflurKqyAtap0FvOEwhqum3AJwNeFcQ8t2nt+yGY9TQ8Leh+BEFdfYeqYBQFlGUwr8Q6j0lPE7XkFAqFoqeE/O643MfIyEhAi83xwSZ8XLBgAdXBjkXHQ28R1Gm8ec6qbdqSbsnrxIHAjygYuRNmwVxeD1HwUZepXGXAGR/DsqTL8YCyovNIwMdFCxdSi1sjVm5nP6Lj49XQ556oQzCZZFg76nhHxcfd8CyVjtvnzdBkhI/x8RVDk4WPbCkeGBig5R0dZeFjVCFEh5Fpzfk6HtCGj7t27QqUJVPxUVjidRWAdAanBvIEYFVoBUWp4pPL9WgMXA4JgXecgNUUNGb1uL8FhXjh9s337iFhVNtJngXcZOySlQInKPdLu383uEY9madXBXvQsmVn0imnNPuOLYFTEj72QeTnSWLi+DgTHm7VuMeixFZXiocM45Hl83RZzktd4zLFQreFSo/pDlqzOSPl7t27aXBwkIaHh7VCuA4kmXSJ2kQJMf4/LGbcBDKqVmw/+a3jSfdcWxI34fKhuhCZJvV2RMu4yW6bzHDomKEkRKKZe++9Vzt2hUIhYBk2WeGzCGdSOeOm3GfW6EbZuLh2+AYIC4OqiZbjUnJun3QMVw7+upp8PNvd7XPX0vWjD0HwWdLSEoihsWWnZE2pvAkvXbyYiEireU9CgKLOEhOmBR8bG/OVepM/trkjuyHH7pOTR6XELOo0yHLG3kKhIFzFlLIrJiaSSZeIqKG+3opFYfNItaruBwLMoepyZ7rHpyGYhRH4hcWwjZ+rAtiYD+5XGD6OjIwU48zl8dPhI6/JFMIxUmVMznIZ/8ONj2lIMeXSO4uCj4x7LUqbVSgNH2sBWojy8FFWYpgULzI+qgqpGB+PLJooPhKRloc0KSnLwcdyMbKgOZ+9ThiP1ljaP+kEIUyG8ZB5OJTQGo/C+Fu2NJv4Ws+CXV/foN1fCoUCtbV1UCJRq9xLFxveQHLpXvnT1DSfVq5cpQjfRH5B2+b+XkvCGr7d/ZssPlc+30u5XI8kg+QoLCkyx5TLbWQzGSs+FiAwV+Uhy8HHeng4zfHa/f39RDQ1PGTYM6phGpXEx1jotlDUwRwfHw8IxatWrQ5oy/yLI5h0TfdyR0dHqa2tM3CNcIGptizUMGFZH/+xa9cuWro0TUFNXriGMA+HxmF3LeEYnzAgZrdC2e1pP4J1v3nxy9oomYlnrVnaXZRscTHFOFa7n40QTPdaCAaoN5+nTZs2Bfosa3F54atA4UBTLkEjRKjabCPDhWDZBxNzWtzApTHohT+xkAmE0tCXkkhL71EdP44/Syttq94LPI42LXg2k9GWSpkGBOLTkjDHKcWJgiaPooydzlKzetWqoLujNH/VDbTTTcil0ujoKHW2tWmvqamqCp1fYRhkqvu5a9cuSi9dGrl+rO/5IKzF11iuGYGncbdhJK/TLfCX/SkHH2+CUBCE9a0aItHbRggheaM71rr65YcTH3XvICo+Evzuj6Xgo4zNavLLqPiYqqsjwK6syGYywXkPBEJyYnw8fFQuPvbm83TgwAHt8VIxMpvJBEqoNlZV0Yxp04r5VcrByEHN+YVCgYaHhwMYWQo+yjxkMAM5f2zu4pwELU2et+c3CGhW7iv43Kam+QGM5PUqrmW38rXusY3KfZkfriZguvv7dpKzi7e0tCp9VgVtvkd4pnNAxIHLSpagDGJOiuzFhis8fL5Xn5QMwdwbKg9ZCXxcv369b0+S+9aomS/l8pAcb3448DEWui1U6mbD1oShoSFatqyNort2e5OfY72D1m3TNQnSZyZPWK6rk665iRynljKZrJKJfa8LMjWGe6SLz5ZAkrIQbtk1CApFMkNjWygXwZ9YTedO2Ay/RePsrq7ABsUZCR+GcCHXAby8uGYhWF6B+3TVVVeFgovO6pKcPZs+9alPFa14W7duLcYsm2j9+vUUheHa6/49Gf6N9LVdXYEswWy51z3z1dBvrLJlTx6z/e7/ffBAk9vq6+srtsUbp6ooaZaeSxcfK7cX9vxq3xyYy8fFlpzJo1LGTsbHtmXLIrt2ywwal1ZRrTema6ZXVWk32CglCvmamyBCTrKZjC/TMCvmdJt4Cppa2HCFO0PeCRkjbet/LTyM3IwgPiYAug/l42MOHlboMFLGEtUtPio+OgC1pdM0MDBQtOJxckXTmo2KjwWpz1xH2IaPWQTx8SKUho+yC36/0lYUfJS/29zyw55fp6CO8XHqqRx8ZIVYJTBypZLQUT2/oa7O6CVjw0g5P0AdUKxUoGJks6Z9xkf2cNkIv3DHPKQn2KnC9R4K52/fQ8Cl7v+bKZhsOEHAR0jmzbu6zjaU3dpPwAEC5iu/yZnFWdif5V4TzDrOPKS/z2w130E6q3R9fQPdfvvtkXnI/v5+abxs1vO97t/FxMa/XK6nOGdUfDTxkJXCx/7+/mKOA8b3D8Oc4LRcHlJnrZ8qfIyFbguVutlEF5RN9ffWFt3SPeGXNWthLuSqVZrdR0zCsqw5U691yF/yYJSAD2vOS0ug4lcc9ORydI7i9paWFus+COtGEkGgV60eOeiTIXHiH3lxJDXaXAf6+HGdRYStGxul33nxXnHFFdqawbo+JyBK2sguLqY6nPLckcHOxnDJWsaCO6anKv3gGq86wCxaXxC+sZpKlsltcvZ3OdPqILwyI+q4V0HEkrOb0IaQe4SBaX9/f3ETYquPyUWvkus8Jo9KGbuogrKphvNa6Z0yYxdFMFE32BzEBmvSeMvWV9vmPArBGJSyiWczGfrBD35A85uatOtzDEJoMjHDjnKfbs06S0JkGy4XH2V81WFkUvqdx7oUfNwi9SEKRqq/v8/y3uV637vdMT1D6ccZCxcaGcOo+Mjz0pQJXsbHO+64IxI+OhAKEXYzLwcfOezhiiuuiPHxMFI5PGQlMdJWarBcjKwKuVbFvmF4ynYZA1oUnnKJ9N3jIU3Go+tIhFia+FuZn+0mfbLhTqk9fdmtYAI1ncVY5n85IVpS+u2Z4loU/WEh+xkSCoEa3zgAi9y/1xblAV2IKRvo5LnT3s7x/RvJHie+Rmqv4I5pwneP+np/1SP2aJXnYqXwcffu3UVLN+/rOej3tob6et/+H3aPsLk/1fgYC90WKhUwowvKetduf7kwPk+toa1es4iAr0vny1kTN5POLYVd9zo6lpPjmOoWBpMxiGQO60Kf7cQTTy5qO5OOqBHYBT8z1gzhUpf2te0J4vICk7Ni6ha1bNEwuYQCZpdyOXu5jmFjJnNoaIgS0NfnbYW/pi0zoyxUdra3F8diL/zMJpEAS5UBr7Y8dy280lutSp9yEEmN1LFkTfJNEBar091nzSLo+snuuUkEN9y00mYNXOuX5PpmKwF35rJllECQIU8DlHKtijYwV+MZA0KMotio1DqPyaNSxi6qoGxaxwUEN3ObULQIoK9L58sbs07j3dnWVswRsbyjI+CWmXTXkM77JgFRtqbf8nx9fX2U7e6mxqoq2gjQMvirNqThuYyr1uEa9zj3xxb7vTvCuIbhYzGZGfwYyUo/+Z1Ewcd6+D2euJ3O9vZA3V0ZI1evWhVoO+y5N8ATDjYhaMlPQ2CsipGNEML5LPfvKITyQoePvfk8NdTXB5NTuu9NxUfGqJS7F4T1/84776S5DQ1l46Muf0eMj1NPpY5dpTGSlVNh+2gOIkdLVIzMdnfTyMgI9ff309LFiwOu7mEY+Rpw/WyHqhRP0CpXYL7yyiupUChI/HSagm7X00mUwk0rmKBmL1fjsVX+ebf73cSb3yS1bWpjA3kCuCrQejy9MEokKChk1xCwgLx4bW7LKyuWyWRdhcBaEka7nUWBnEjwkKISktwuJ5YMixOf4f7fqlybI2AJqd66juvRKvOQlcRH5iFZ4Rg2dxvq67XKos3u/W0YqeZumgp8jIVuC5UymP4s5paM34l6BUBkjZmc+IE/8ykIOjIYsXbLJOS/jwAUU+kH+6tbkFnSawcXaa7zwEHHEHFZCt1vnLTAxIQwU2bafDrgacpk7a+u9qTJNUa1bsiCfK0b001E1JPLBRi/aZp7HFCeV2dNka3DrS0tvhJFOyHcr0zeAGyd4vbqlWuThrG8TeqL2qda5TsDVlo5nobf7ZTH7Hp4ZUxkTb3pvbW2tNB06EuVyLW/1TAFBml2z1Utd9nu7lD31Imu85j8FHXs5KyktvCS+pDQlP2auTsfmk0fngW3wzIX+yEJke68sWVRzUKvga9BEMvUutWl4CNj5HsNY2bDR3kNl4uPOozk8JY18BisKPiYQzR8lO91++23UwJBnAvzVJCf+2ToMzCr4zkGP96pHgUqQ8aJh1SM1HkUfQMeRsnMnXFfa2sTLraafsv4aIpPZ/fcGB8PL5XDQ1YaIx34y8qxezcLPlEwkoV3HQ9ZKkaeWuybXCFnkDwrsfzZScKjUjUAydcXCNiqHOOPLfYbbtub3f/ZC1VndMqR37tTbWOcgq7ba3zCscjzpArd05Tvq0lkOZePVSnfvT4PDQ258eJcSk3m18O8AdJSe/Waa3Xj6RkF1coclcJH5iG5fdOcrIYeH9kAFMZDTsPhwcdY6LZQKYMZrNetS4rQQIATKBHmj/9IUzAWPEXmeBLZMh4eu8EJAUZHR4vxZeVlPuc48ttIAJHcrwQtNyyWvdJ56m+mLMB7YNl84GnK5HPkJGyyFUN1Kb8G+o1LdgVlbZcp4cmuXbsI8Gpds+ZvJ7xMu2o/umFmNuX7ByxxEImWZKY4KiOeg2ep1m2GCQhmjz0huI0ChOVEbXNUem998Gd7lF3Nde/N1PcdUhvj4+OBZFsOQK/VZJJXr52sdR6Tn6KOnVqPVpdYi7OYBkqESesijaAAlYI5Z0MUFzhZyTY4OOjDxzBhNmxuM6OrMhwOQOcb1udk4ONGeBbfcvBxFvyxm3yOz0XQZXBM+DgyMkJbt24t9jMr3duEj5w/IgwjtVY4iESd8juNipFyYjZdv2ohMJL3BHlOs3eDqZb4u+C3Qsqu5hPBR3W8nYjXTsYajylI5fCQlcZIdhNX10kpbsKMTYyPg4ODxfjhcjFSGJh0sdYfIjYU+fnTAnnVdNTfTK7Ue9zjJl52Iwm+vIb8+ZZ07uSmcmTXUNCAFszTRKRPupzP9xZ5SNGfLPmzo+tkgkYSbvOqgS4YSx48p5OAEQrn9aMoK0AL3bJjlcbHQqFQHJOwOWTDOBMPebjwESW1/Cqk8i3dRHrtXA3lcj1E5JUjEC4jXkKzcICYRcAy8ruQ8yLoJH+sSBAAPve5z9EJJ5xEHriF3cu84C677DLy4sIZDL5OwKm+510ixX/YFotaBkC+xoE+EUeP1OaKrq6i1t/GiLJLuc7lcL97XoPjULa7W/uudeV3Vq9aVXQJ53vbtNWqdVtmelVL0/vglQlSxzIKI859sZVz4GztqhZbfhad25gj/W1taaGeXE7/3hIJnzXQtGHLGSMLhQL19/cXE4iozEnYtZVe5zH5qRxLN0Fv3ZXrBhcKBepw41p5/oRhBCAExGXwu0fynOhEMN5YJ2Seftppvj6Fbfim+cfJvlLwmN+vI5hzoRRmwoaPuljq+fArHngsZXxgK7zM8DA+9mjeUQ6e+54pW7IOH4kERrLlmxWUYeOwCHr3bxNGXoPosc8qRsp9sfVLh5HqNTqMrIKXYf7222/XlsZprKqihvp6mmXotwkf5ZJSNmEoxsepo3It3ZOFkTPcOaibF82wZ5yW8dGWaC1sDopPmjzBNsg/2vlT1dvSdL5DwnClWnvnk2y46upa4bqz2/jwveTx16oxLEfA5tCKRPzuVIxctWo1eVZvzo4epZSYKc78GaVv7yOgnzz5IYzX3xNhDIRFvAlVgX1moviYXrrUyEMm4XlQRcU4mYe04ePAwMCkrfFY6LZQ+THd8uKuJRZw1cQH4+PjgbJi+gXAiydBwSRn73V/u5KCbisCABwnRdOmzaBgvcIlGjBKEXB66IIrFAqUyax0z9FlhuREbklaAicQ52Fyi2MmVdXszoU+VrBHWSiq1j8M8FkzzOV7lrT6Y1qixnMQiXiaeY2NvutzsJdGixKXlayr08b11cOLh46iqGDLjq2usOxmq76nNMIt5fz+2HVHF5PqQGjebYxtmLZRBfNS686qFDOV5VM5Md3y+mbtuG7N6bTUYXOXE3TxcTlm+J0IYkganpKtPpHwuUI7EKWxVDxKAnR2hLnLSRF1mcVz8HIuqAJXOfjYA70CMQV/HHaHkrU7q1hA5O+y5ewKpe1S8ZHIjTlsnOtrow32JE9h3gn8f7XjBNzYGSO5/JYNI2VctGXENWEkW8fl/1WMnObetymVopTjBDwh5qRSBNiVo1HxkddBKXVnVYrxsXwqN6Z7KjHyve5vsxGMa2Ulmw4fkxA4lkLQIHK6Zf62t3eSnX8MJgIOJhqWeVdT4uBWCgrHaRK8rheHPTAwoFihw628nlX5isD4q7y+jcbGxqixcZ6m/QFLX+z5oRynmoLu6WkSLuV83KSsUMdTb+3PIFFRfKxGOA95vWV+TQQfs5lM5PdGFAvdFaVSAVPnOqLW02NiF51MRk6QsDNkAYhkCgJobiPhWiJPRocaGubSV7/6VWpr6/D9Vl090wWkBvLiZnaSiBvRJXUQbisiyVoQ8IiIBgYYDHIUtQ4g155kDZbvN4AOIuiOyZqvnfDiv1WrjLzIuKxC2IK8BoLZVu+V7e7WvisbZTMZbdKylRZgMGUgXeMC0Mzq6iLjr4ITCxAJILBJJ93j6gYahZmT3ch07ouzLdezEkNOzFSAP9t6mPtcKpHwZYyU6wrLx0xJmxyARkZGSnp/MVNZPpUydrr51NnRYVxzo6Oj1NHWRrWJBK1151zY3OMMpLdBv1EDwiPmjjvuKOYs4M80aZ3tcY+1GubYCnihGjqhmcjDxxz0Gap7oIl1cxnqAwcOFIUvGz7mILID85rTWXpljPTw0SFHcVlMuLF+s9xr98OfURgAtbV1lry+iIgymSypLpKcOCmqpUwOZ1njYkVjMlkUAFSMZKUkJ61TMXJuQ0NAYIli6Q7DSEdpy/Rc8u+8r8kx/8/AXNtczairYuTo6GhxTNT5UgXQQw89VNK7i/GxfCqHh1Qx0sSX8HvPZjLFRK2lYGS3MjccgM7p7qaHHnoooKDT4eNOCBzTYSSHzzVIVngZI6Pxj8FEwPl8Lx04cMA1Vqlu0z0UjIV2SNTnBnkZvVVL74YiPhKRUp9bJ9BWudfsJzWRWyXxUYzDSktfTJWQ1hCQlIxtuoTJVSTqljsaBUaSgIQmOZs5rp3xq9L4SNDzkOXiI5Hg2esM+JhAaTxkLHRXkCZeMkyf1j9YCzBNwLALCFlS3cRZ2PWEetmtW443qSkKxey+8k//9E/SfXaSF/uyRzpWkMDIE5TV2HP5WTx3ehsg+JlKNbv1dndBsbZpL/yLwOaGXKsIaURE999/fzHxji4eihd/EgozHLFEgEy2ZCJq5nZ2XXcsoNNQXx8ZnNS6s51uXDaR2EBEqQrPQs1/w9zIZIAqFArFGNerLe+Df99u+F1+Hp37XE8up63DzPOHY4VMjPZ09/lLoZipLJ/KwchAPKKmfJ767ldCeI5kNeuJ1y1v8Nq1zfNeWuMsgL7rXe/yzctB+OeqTtmnyw4tP4eMj6b1u9NwvVzqx4aPO2G3zMrJIIkEPnpMqA63mVkN7jOy4jUq2ZJ2BnDacWh5ezsxIzcAYUlRmekVbm4HG0bqvKCy3d00Pj5Oo6Oj1N/fTzfccIPPTV+2ykTBSHZZ3AvQZZb3gQi/74QeH8/u6jKWUeM5xALNDM06SELsLaVQjI/l00RLhkXFxzSE8o2rkZSDkZykVMbHwcFB+uhHP0qAp0hnvNkjzVUdRvI60z1LNP5xp+9a5kFzuR6lStB2ErwrJ2S7yz3OYxReTchxan2YNjo6KiUm0yUvljFy8vFRJC9O+voiDGJOyDVqua8wN32hLFC/63jIsHZqQ/BR5iHfcZjxcXx8nAYGBrTVkngNlMJDxkJ3Bal89/INLhBsJMdpoO7urOYcXqi6On1+Nzy9sGue/Lt37zYI9zkSwr0diNraOojIHJ9HRJJF3eT6IpIxLINwaeIyALI1Qed+ycwsW5PCmKrOjo7QRSYzxmz9eoulzVIs3bb4YrUPvPDP6ujQuq62QmxuDY5jzd4Iqb/qe9KNxXQIdzBdn9iNzKR44HlnGzudpVv+/ayOjkA2yVqAWhYu9I07uyXplCIsNIQx2nF23qmhctwnw8rn8Tn87nUeKXM160nFR9PceJ/7d3h4OLA+chAb+ah0zCbIhuEjW4vUNvZr1l8aohQUu6Wz1cCGj42wx3FyYhv9niAn3vFcFoX3lXmfKQcjTfuEWrqlN5+nBx54gOrrU0pf0ySsSzuLMZNRGDTZC4rflQ4fG+vrabpmnKNgJM+7DfBi90ux5Mi/n93V5cPIDRD5ClZ0dQXWkraCg+TFYbqHnIXaRjE+lk/lupdvgFC4MS8g55dR3/tmBK3NE8FImYcMCC3wPGtsBpEOV3AxYaSZf9xPQSu2cDn34xLzwjoXdYeA+9zj4ViWyWRD8FHuR6/bN5BXamvy8REApVJz/FiU66H6+kbSu38vI5Y9vGvCXeVNPGRwPGrc8Q4qIrpC8FGed/MmiI8qD1kqPlaah4yF7gpSeYnU0sokTRcXNqfQ9y/UXgq6fjQQkCgmjpJJv0DHSM0i3tQ0XxHu5eyLvQTUhYJGFPcK/fPICoAkAU5RA8buIbJ7cQ30sR0OhKDeC30itSRE/JtM2kUGkURJ1sBuUvrBH94sSkk0Y9vAWltafIDGzL7JPbpHs/hNbXOmSB3pNu9UIuFjJE8H6CSlD2wBkmnfvn0+azprCNX3we9ymuZ31iDObWjQP7ebHCbKmEbJmlrKO4yZyvKplLHbt29faPm8kZGRwLuXM0rL2uiEOw9M+CjPDV15rIb6+gBWNMBL0tVkWXtyVQMTMT6qbaRhtsRz/9h9bjpA0xQGdBqcYmwmC+QmjJSZ9aDCV028w7g9QrbsteVgpGmf6Gxv9+Gj2ZtL19fw99TZ3q7tk7xX7IEQIJKOQzOrq33tngxNWUjF8jg6OkoDAwPFkAAHHhaq72Oa+9vM6mrt+5rf1KQtvVYqRp50wgmhGNnX1xf5/cX4WD6Vw0OmlXfP37OZjBZTdJUIKoGR2rKc7v3mwyszapqDNh7SzD+mQ9a+iku95IVOqu7TafISGuvik5MWgxjzzJ0UdEnXZVefHHxsb+8kIvJhpHD71tUp5+RwKkaajXSZTFbbL50R0Ss3xu0ucvvhHdPtjTIPacPHNPTlII9UHjIWuitIpZd7cEgfn+KQ49RKmnleqLzY1pI/I7lZWxZcoKMk6nTLdfY2UPhC20J+90E/EHV1nR15jPL53kDst1oH8HTXNVCNNxpGOGjXuv/fB5FZU90QDh48GBgXU1sbpEVri7UuNaa7J5cLJBNJAtSYTAZisZa3t1OddH+dW5Zc7kYnwDI4zW1o0DL+ts1b/iTcfsrHuGwYa7plV7Q9hrbSUntLWlsD1qssvLJjxud2x8nmPWCL2Y+y4csUM5XlUylj19nWphU2mdnraGvzvXu2OK+FPyN52DrVJUnpgL/Gsy2vAeNU0rD22tPpyOPTm8/74hptVmlIvwt8DMZeO5IycweEx8CHAZ+VFvBCNeRxMe8JG8grndNLtuy1pWDk2NiYyySqe00t1dclfWs1k8lSIlFv6WuBmLltbWkhB8KVWi5tlgRoxrRpofho8iKor631HVMFcRUf+bqkNLe2GNoGQOllywgQ2dnl3+XvE8XIv//7vw+dZ/fee2/k9xfjY/lUKg/pQJ8DwoHwrulUvGfud9+nybPMhpEctpKFX3C3YSQgvPJ0CvgGJZwljIL8oy1zuJr3yGb42eC220eAfx3ncj0l4CNnLE+SEGynBh/r6lIBXmbx4iXKvTk8lPl+liXYmj1N03aSgOnU1DQ/FCNNRkT509Awlzo6zvIda2vr0PKQUfCRMfKVwkPGQncFqZTB9OLkTIv2GmkC7SRhnfbHUXiufuHaMmEJ0Gm5+Hq7u4r4dJAukdrSpWf67qdLRMA0PDwciAcR7i19xK4nnDBnjbuwOKmNLZO2uug6Ozpo3bp1Wte4KG7eHdKi5XI0pkRIUZ6dSWeZUK0j53R3F+O0reWyALoN/vhzFZxWSLErurEI27zXSsdqICzgm6HXdNcnPIUJwR8/KoMe932hUnJJ7vsyw3Mz0K1fv7445mGAWCgUisnrdJpQOS4tCsVMZflUbskw3SYHoJjEZjOEh4r8G2fUtnmk9Obz2szQfL0tBpo/3dB7o5y5dGng2UwYMT4+HkjaZrs3J/56b/GYfk85Q2kz291NmzZtKsFDSt0TOlzcZmY1mGBHjVmMgo+CsU5RMB7SzwB3dCx3v4eHPon97bZAG+K7wKumZNKnlNWNBbvn6/JC8LG0e8yEjynH8Qko6txijORYfBUf5yht6kKK7oeXK4PnvG09DQ0NUUN9vVFhG+Pj1FA5PKQtdIvxURVa5IoDNoxcvWqVtpoDXx8FI3MQmcpVnG2orw/wJiacGB8fd5OIQfmEhSzKuLTdcr6E593ZCeIjSFh2px4f8/le+v73v0+plJwcNwwfScHIIFZ2da0I5SHDjIgCo/lYDSUSKRJu536P2zlN83w8pA0fL7vsskAeABsPWS4+shFgojzkUS90f+ELX6CTTjqJampqaPny5bRv3z7judu2bQssvJqamsj3Kt3SHbYQBDi0tXW4CzVNQZcYdqHZUZwYJqAS5QWShuvDtXave93rpDHRJ1LjuDdTYjjdbzNn+i0EnhJAtFmbSFBPLleMW+SPLbZIzcyqG5OoTD0v4lkIZveW3QajJDRR78sAkoXeZZ7reO+xPPcGeO413MZGCGtO3ezZVi2qbfPe7X6Xs/Tq3HiT8DwMntFco7bNChXVzS3n/l+vXPskgqWO2INBWzapqop6cjlavWoVJaBXRvTAc1eejJicI52mEh+Joo+dTSm2DN6m2ZvPa8NO2LVR3jhN+Mg1kHXX27JTdykJukwadRtGqL/LSsQwt/Xh4WEle7B+T+H4dJmJtOGjLaEOM2SJxGzK5XpKwn9dmZzgfQsklM3+vU8wbWyJ2mPp6wbyygMFk4l2dCwPnYtRktxx8jobPqbhZyRLxUdug7/LLrs6fJyTShWVCSaM5H1Nh5HzAPqMNIejUIyPk4+PRHaMZCNFR1tbKD7K89iEkeydZ7rehpHq+lEz8IfFBZsxRE6WqF/7jpOkrq4VgQo9Ybh2NOBjVVWjW4VoVoRnZqu8ipEbCZhBs2fXReYhzffY7X6X+99LQSE9SSdLpSFt84oVzlF4yMnAx3J4yKNa6L777rtp+vTpdMcdd9Bjjz1G73//+ymVStGvfvUr7fnbtm2j+vp6+uUvf1n8/M///E/k+5UX0x3GKAiXBa/GtRlY1FT9mUy2BFeYAnlxMayFu42CVm0zQzc4OKiNcamqaqRMZiW1tXW4SS0kt0dHxKIDnyagn1Q3Fy54L9+/Cvos4zXwxyNGEYJ1iyzptiUnbkq6i3N8fNyY5CMsCYNM6kYZZbOSmbibIDbTDfBi2dUNTa0jaEuCY9u8B+HXZu+J2Gc+zjFkajI0GxPLjGfSEV4PjdBn153f1KQtncLZ72WGY6/7bmshlB2+55yEmJwjmaYaH4kqZ+lmD5RCoWCMheZzk44TUJjJcWRWTwl4Fkx5Dqc07RrXkKsc0GHEykymWMpH/b0WoDr3r+oOnayrc0vh+Bk8EQ4UxPmk5MYZhcnT17ZNuvvCWhLMmmCUUqk5xWt1GGnaG9TMvUFldBTmVmbgdpAQwteQyCbsKOdxm7J7pZ1ZMiW5kzEyKj4OKb/rStlEwUf+noBwJa6HGR+J9OWl2PqehqckYIxMwl+3PcbHIwcfiaKFyAGgXbt2Wc+ThQuVX4qCj4QgRt6GoOdPGD4S6XHCcZLU1tYhlcCVhbRaEl6c+pBFlS9uaVlCgudMKucLBRzHbB89+Mges+kI+MjtMD56RrWoPKTdmq6rhqTH9TB8TMJTStt4yMnCx3qUx0Me1UL38uXL6UMf+lDx+6FDh+jYY4+lG2+8UXv+tm3bKJlMln2/8rOXB8sMyAvONqHr6lJuXFvaN2E4/sK+INaQ46QUgHIISEkgEB73ra9VOBbokz/7rc7lj+stQsuEphB0xXYQjFeOIgTrFpmqyZITN5kYsyjuzaZzbW5ZHe5i/4z7V+7bHIBuB6jf/b4f+riXdevWafvMwB91U5VjZqP0mUFSly3Vlml9EPqszUZrvLspyBtaVAtVqbH5RwtTOdX4SFTa2OmUYg0IlvKyKY1SdXVUn0gE3BpZWWO7fg30Avbcxsai9WePZZ4xPsq/j0Gf62Bc+l2te8146QDU1XW2hglNkWD6ggyorHSMwuR5JSeD99fhtQ0fo8QzBs+17V0dJPbMzQR0EwKlwtoI2OT+v5/0WYv1zJKMkTbFjmzpjhIOJDOS+zXzYJmljQHok1nZ8JHIw0iek7Z4XNUaWck1fiTTkY6PRAbrHPwYGSWULizZVBSLuk4J6QCh+DgKL1muf7/mtT+mWa9p8vhHmb8M4mQ63abFOOH1qRqUHGpomHsU4uN2tw8pAlo1/TubgAESRi+Q6u7N33WJFHU8ZLhhT1YShIcEyTykDh9l3AvDyKnAx1J5yKNW6P7zn/9MVVVV9JWvfMV3/F3vehddcMEF2mu2bdtGVVVVdOKJJ9Lxxx9PF1xwAf34xz+OfM9SATNs0ebzvTQ8POx7+eHa/jTpXDW6u7ORXGG4zh7XotWfnyZdgoWmpvnFWp/+RaRzH2GXdnIXdFL5XWgcM5ls6ESXP2r2w1KEYCLSuGeKxboF/sRNJi2W1VIsXTc6OkodbW2UdJMl6TYjua/3ArQaXgbHwKYIj9lLQ+/SuLyjo3h/kwcA10NXtYnT4bew1QC+xG5ynznp05UasOsFaAR+C7wNyNLwNJNXIxxgdZuCN4/DBapS660fDUzl4cBHotLGLkwp1pvPB/AxDCtMayPb3R0p1IRr2asbss2jI+n2WWcp1bkgyy6beThufgvJrRpJmoYqKeTG7t4o11AlKo3JIxJMyMDAADlOtdJ2mgQzl4uEj1Ey946OjkpeUWyVCXvOe8ljzKsM/ctK34N7JOD4ntlk5VIx8iaAZkOEAfGxNOxW6vcB9A0EGcksBKNYQISybtJ8myg+ftpyrVq33UYxPk4NPhJFw0hbEqg77rijWHNeddN1oFcYqvgIeJVMZB5Sxcd6gD4E0HJNDHKQh7Txj+rvG0lYb6ut3qGZTNZ3/0wmumfo4cRHIqJMZiU5Ti0JQ5gNH68loaRYTUK2COZkAnpICMMO6SsjOT6h1ISPXj30YAI2L0Edu7GbqiGJxHg6HjIr4aPJq1Kel9kpwsdSecijVuj+xS9+QQDou9/9ru/42rVraflyfQzXd7/7Xdq+fTs9+uijtGfPHjr//POpvr6efv7zn2vP/9Of/kTPPfdc8fPzn/888mDKxIwcxyN7ZVC8ieWV9PInYfAnJTODhH+hiusdp5Hq6xt898lkVtK6desMILCfgpqyNDlOSgNyNkE/XJGwadOm0Il+5ZVXahNcEJUmBBN5GYPD4kF1YMvE8SxhGUHDaoI70Gc95t958zNtigV4SZJsigaTB8CKri5fmS+5f/L/PbkcrejqKvbHpInk81PwssoXNfGuS63chmzJdOAlgeHnuR/hz6duCmpYQhqeBVG+jpkTWzknmY4GpnIq8JGoMhipw0edG5guHkue02FrY2UmQ7Vukqvi9Y5TTGbIn5WZDA0MDND69esDGDOOIKPAyruk4/j6YAsrGSq2EY6PJkYtCj6WUr6mvX056XOCpCPjo2CK9ftTeM1bh/TZdPn3LgJmW/p3auh4RnH1XLasTUpyp/dAAEBNqVQA23Sutg5AKyDKMOowck4qFUzeCT/WVgofHXee6q6NUu5OphgfpxYfiewY6QC+WsU8x3rzebrhhhtC505/f78eH905qsNHtcSS8MpQ13MwDNFbo5XhH00Y19fXF8j/w3Qk4iORqQ62EwEfFxHQTmFCtW08o+Dj0qVnUnd3VtM///+5XA91da2Q+rODhGyR1l472/2UykNOFT6WykPGQncIvfTSS9Tc3Ewf//jHtb9/8pOfVCYJIg9mGOnjWlT3b3ny8jE9SASzhYvJ3NQ0382CuNMw6cOsKH2k1iLs7s5KigFblsgLQ3+PkqLfNNkn4u6tnstZEhc0Nwfe09jYWCDJWxpCCJU3NSJ9IpKkCyZAkFlPQ1hD1sIr7RNmOZntZnyUhYBReMlUBgcHIyePS9XVUV0i4RfwHacYMy9n82VASirPlgKoC0IRoYYDzG9qok996lPa51a/y88zH3rlBMfkMPXm85RS+p+El4CIr+tsayu53BvRq5epVMmGj0STg5E6xZHO/ZsVRLq5RPCUcCZl05xUilKuMk6nWApbS3kEy5Y5iF6J4eriPcLx0YzRAg+iJeMxM1hRzgdAzc0LAu9pbGxMYqT5kyax1/gz9+pr3iZJlK8BBd0e0wR8g4Sb4ixr/zwLjzyeo8T7lJrFNqytVGoOJRJBoaGtrbMo/KhYJrvaykrTNED1iUSgzNicVIr+9m//VhvutF/6Xkl8rFGulTG/FIrxUdDhwkeiIEZuQVDp05PL+YQLExYtXbzYd105+Oh57YSHKXZ3Z90kiWtC8Q+4OvT3Vwo+BoVoPT4SkZu/g/l1GSMTFMTHFvLwUU5YbOrj6w3juYcAoXiJ+qyZTJaWLTtTm8OJY+Y9xQb3O6iIcZCk0+EY8fGqq64iGw85mfjISv1SecijVuguxz1IR295y1vo4osv1v5WKS2lTLZJLWvn/MXo5WvkRDGOgZlhDRkn3VFddRZRUHsma+7kOuEC6AYGBjTlHGzuj2Zg48mvS5qWgzlZGZE5E6F6Lrs0hbmOAME6pWNjYwEmPw3hPiW7d0VJRMLXym2pWRbD+gigKPzvhNAqq7HdXB4orJ3t8Auour6qMeBqUiD1/ASEhn2j234f/Jv/TuizPd9xxx2Bdg9CMK6+cSqx/rrMcJRivZHpaGAqpwIfiSqPkbb3K+MjY4AakyUnGmRBWN1geQ2nISzYqis4X2vSsu81rLElLS2+eWgPnzHjY1hOEP6bSKQol+sJjKPuWl3inrGxMSnzr4kB1uOjqiQW/aonOYRKxkd7+JTcltp2eP/a2zulewRjRbu7szQ8PEx9fX2WttaS2BfTxveiVqjYannXTakUNVZV0UaAbkWwHvdGePiozpHJwkd5/yqVYnz0aKrxkcj+jtkDZjLxcSe8iiocKiHWDscO69fXzTffTH7raPn845GMj0REq1atJr27d1AhYMfIPvKXFFY9cRLWPvrbD2JkJpMN8YL18FGMmxP6bvzPE25lt+GjiYeMgo9y9vIoa2eq8fEVJXQTiUQYH/7wh4vfDx06RMcdd5wxEYZKf/nLX2jhwoX00Y9+NNL55W42ckICm2tLX1+fIXEBJ0tQmRNZsFYX6iwSNbJ1k37cbU/HNKXIS2bhX0xe/7eTiKXzA5fjNBQtAjZgGx4eDk9sBr8wKJMpm7W6SDrb20MXWS3EBpPNZOjmm28ublhc91l1SU/DY/6ZWOtqEnYT8FuK08p3WyKH9evX+zbRNIIbYCqRiJTtUfdd7itbhPj5WSkRphDQxb3uhFCc6GJg5WzkquIklfAnSlLfqRxawMxDAV7sPDMdE6Gjgakkmnp8JCpv7HT4aJpvKj7y3OuFvkZsmNvYte765/rf6rpphT7PQlib7Ha5HSLmrFGZ/2xdHBwcVDyHgvior1nrL7vIClkdPkYpUZPJZN0knWEMX4IymawPH0W/kmRyt5Tx0Z7oM6G0lVa+h1vPFixYSIODg1L2Y2a6pVj5RIqiMfsF5X9/X1V81Hk1MC7thTf28jyVsXsJNFnz3VKak4WPvIbKpRgfBU0VPhJFw0h+x/39/RXBx43u3Gw14ONmsHCuC8XQ86SMjyLzdyfp+Mfu7uwrDh/z+V665557im7tYvzN7t5qtnDPcm/CyGnkWcFzmnZrQ/u4fv16CR93GNpIkie8R8HHvdq+MkaKd5Mku1eDF7o5UXxsrKqiVF0dye9VxkgTPsr87JVXXjll+PiKE7rvvvtuqqmpoTvvvJN+8pOf0GWXXUapVKpYxuFv/uZvfJmd+/r6aGhoiA4cOEDf//736eKLL6YZM2bQY489Ful+pQKmzr0kk2GQCM8C7rmM80QdJ6H9VxmdpLuA5Mm8n4JAqJv0uvNY8E6TDuj8yoBxMlkUBgcHaWRkJPD83d3ZwALYC7+mvygAIigMqmQq8SW7hqdhLkmwDKDr4dfu2oRXtT9RXOVtsZ5pBVx0MYI9uRy91nWnNd2rC0FBtxFe8iZ1fNXreRwHBga05crU8xMQrmcEL66GAXQcQYt+GsJVmIFQVZzUQDABRUWH4r3A80/HPPD/nPClXDpamMqpxkei0sZOlwchK3l08FzTZQHvVBKX8VxLIhjikVPm7n5lvvBHZWL3Q5QwVOdvPfSuaL35fHF+boBICpNVrs92d0fGRyJSlJxBIZDdp02KpjB89MdYyiW52GrE5XquJ38MoY0xCyZPs51vzxXCgrjcvzpKJPxuianUHMu99hraaiQvgROP7WDgehM+AkIIyRtiwZ9BEB8JIlGmKat0jI+TS0c6PhJFw0id59tE8LEYlqO0qVeEBt2GdRUWmIeMwj+yd8zAwEAghlhNGnl48TFJQnC9gtTyXLNnmxKJ7dD2x+4uz7+FYSm/C+5jsCyw3zvJdK8WioaPa7XXyxgZTdEp9pN29+9E8JFLgKk5jRgjTfiYUr6Xa+UmOsqFbiKiW265hU488USaPn06LV++nB555JHib9lsli655JLi96uuuqp47vz586m3t5d+8IMfRL5X+SXD/LFhwaRpzAT44yP8LhxRU/aTpj3ZUiDX6LvJPb5ROqYudE8D6CkR1MW9gRynls46a0WAiczlenxxl7whPPjggz6XafmhZEusydJto958nmrd5Eb7EdyUapXvvAg3Sr+FWXeHhoZ85RTCXFHltkwlxFRBwEFwg5QTR5n6NqB51jT0ScbUpCk65m0jxOarKi3Y6p+E0ISr9+wFaFgaU537JL/TQqHgup1Fi9Of39QUGJsG2EMSotLRwlQSTS0+EpVXMkyd42rSNJ5nag4CB3Zlls6rQ21P9jSRtd/sNhnm/qtu0mNjYzSnaZ7vt9Ph0OxEglacdZYWH/15O4RidmBggIaHh61ZehnbS/XuyOd73cSbcDFcZYB1yljeK+Tr5D55lgsZH4nkREQqI9estGWyigcVxI5T4+6Tun3T1LdB91nT5H8+udSlPLZ611PGx2sBugKgBQBVwaGEVinuBJg8GR9N7pMyPtoUuzE+lkdHMj4SRcNI9ihTzykXH4v5Kdzz9yjXMkZy6Ty7i7ifhxSCX5B/zGSyWkNVV9cKWrbsTN+xtrZOevDBBw8jPoKAOcp3h4D73L7MCMXIG264wYePfsu4jJGMZ9yWzWvIUfrjx6OqqkZavHippY2BSPjoVaAIx0ixZwS9GjzPqBSpXrd5OCXh4+DgIN1zzz3hc9w9X4ePSXg5gSaKkUe90D2VVMpg2rT7mYxfg2cGjVqKlrhsjfv/TYb2WikYX1Id2ua6desMWQ23kJrYwV9SwO/WNw3BmKEqCDeRtAvuN0G46W2AxyCwRbrUZC+ytUlehAWImGN5Q5Fdxx13YdvKuagZPXvzeTqnu1urkZumtCVvfjKDz23/wz/8Ay1obg69f9hvQ26bd7nfl7S0BF0UEbTgqTHQY2NjNLexkZIQFpwu5fxeCEGeN+jApg9PU74HBk8GyRqmK7mkOzdKzfFyFTVMRxNTOdUUdexs7zGrJDAMCw+Jkrhsjfu/aW3rXMmrLW22trTQwMCANfMrkKQ5TfOM+ChwWZ+TQ/yeJs9Nby/5GRbBlJUy1729See23U96t8hG9/ggmfcYsWd51mZvbzh48CBVV8/0HRf9n620Je+bsoJYtH3LLbdQc/MCqQ3TvqnPFCxCrQbdcRQJkFTmUTCAVb6+5nI9AXxsbJhDUa3/tRPEx6gVO2J8PLKpHB4yCkZWCh+Z51Lb7HWPL9F6cuxX5rzgH1taWrX4KJL7+oW6pqb5RZfv6BhZJeFjo4tL20ng2mTiI0hUVAjivBAuicwYeRupykNWSIhEaroY8GlSW6pcIed3Am3bto02bdokYWQU7yIdRgp5Q4ePXi10/3OoGPnkk0+6mJ8kUcvcb3zzBHne3yTlAJLUJnkJ6VzBVe/XKDxkFHz0fZ/kRLyx0G2hUgYzSlmCQqEQIbHLqRRlUw9+1Pa6AxPbY+7sWRz1SoQCcbzG7bffTmHMjqpJBYQF6QCCLsiyQMjxvyaSY53UsX8GXowIC52snV1r6NNu9zy2NKsu6TOrq7Wa55YzzqBFCxf6nsMB6MylS+nsri6f4GuKFU24z6vLVC6DR0dbGzVWVQUUFeo4OgA99NBDRhfFve44JDV1WnvzeZqu6ed2Zdz2IHzTTyjX+2L2fVreYDuj8GLK/S5LIcAKMyhHpZipLJ+ijl0UIULGR9N5pyrzLEqCFF173dC7XjohbZaT5dYsDBYM528mUWdVZXR5P6jRJgpS+yVjpH9vUt0m17q/rTX0abd7nurtJNwtq6tnar272ts76aqrrpLcLsUnlZpDixcvVTy/dApikYDIq8jB/dTvm8JKJTPhDRRMzubQQw89pMkwXENCsbyXgLXkOMlAgiVxTY3bbpps1v+10CtZo+AjkVd2SJ2LrETatm1btHUV4+NhpXJ4yDCMVEt3qecsKwMfGVPlNscBSmpdyZMUlnRQpiA+FkgWGG08pB6PNhJwQLO2WWlWaXy8SbpHWD9NGFlDaoZyUU5N5MxYuHCR8hwOLV16JnV1na3EY/O4B5+5qWk+JRKszNTjUVtbhytMq4pc/zh2dZ3tKgO8Y/6qSGaMFB4NnCNK7qccCmDfM9PK/OTvevkknIeMgo++72VgZCx0V5AqY+kWi5YTKdgZtQJ5mcbTpLposEvHyMiIlGVRbm+UAFu8iB8YdBkfzUqEoNuf3xXlGd9klif01fCSDemY3bD6obpYJ102cc68yeeocXhssZUX3Q7DuQ1uggZbvOniM86gTZs2FUFBjT3RuY4nAWpx//KmZ9ogH3zwwUBm9SqIWFO1TS55UIqLogxg7C3wPkOfbInWZil94uz0spAvl+BphIhnz6nvKZ+n4eFhanWzQ8eWnCOTKmXp5oRdUTTTi+C5hwXCIFw3sUKhQAMDA4HEiqOwZ59OIqh868nlAs+kx8gxCpZ7CeKjP374Gelck0VFYK4u+Q+TzlUzn++l4eFh8vYDm1s591Xu5w7tuXV1DVK78vP7mcP6+ga66qqrintgMKmRibnnWMNFZNvTUik/PgqGtD7QJif9lPHRpoSW8dGzhoUne+ssKknEJ+uOXRR85LnFHlo7oC/htNJNBhrj45FLlbB0s6LFS9gV/r6z8JKo2fBRZz3fB1CL1ejjhWIAyaLlWqaJ8pB+jNzjHvs0BSvzeBg5dfio9pPH5UzlfN0YBq3fZ5yxOMBD+uOxTRi5xP17quFeol9qSJNojwVkf7hOd3e2KLAODUWr9+3VJmdc3Eh6zA43Tpp4ZbUMWBQeMio++r7Hlu7DS+XHdO8gXZ1sBoR8vtd1k1HjHbLKRDUnnmAqFArU1tapdeExW9M7lPOC2kGzciCtWfyN5CVd8Fu6dUJqVEuSTKZYJ2ZW1MyGfRCCqbqAGyEEbznOuQGidrZqOWZtsSxg6kpppBKJANPE42cSXuX7y2CjZj7uzee1z56EuQwYIJg4q5XY1ep5m6PfLU31GtgBL/7d9DwbDcflZCg8Nlvce6hAuxl+i7t2bOCFJMhJrXRJUmwUM5XlUzkx3WHJA3vzeerJ5QKlBRshmElZYFYVbIA+aVS2u5uSbmiLfK7Rs0Q5bzr0QrceI3tJ76rtx0dP8x8UUk0MDls3TWTKKZLP92oqS2wgEW5kwnKZuW4gYDoFrUu6sjV6hripab5h7N4X+sweE8eMon/f9OdMiWaR472Y8THMO43Ij4/+sK9gsqWEy/wnUOvrj+Mej4KP8vjwnJUxUhXAY3w8cqncmG6ToiUMH3vhJe8rBR8LhQJ1trVRyuFcBA6JSjhhPKT8aSbHSQUsnxPlIQVGBktdRRECdVRZfFQxKknAXENf1THMBdpNJFIh8dE2IxpjZNDS7jgNIfho9nrNZIScEcWDl4gkD141LNbkOWC2dEeRD8J4yCj4mIQX0z1RjIyF7gpSqYDp1+AHNVO8wIVmTdWgqQyNPMkLxcmsc3/wtGJ8vz2WiV2QFupukgFLnnRBILItmA3FmG5dUiRbnFHr4sVaLaVVu1soaDMbhl3DpcN4UTYhKEyrAqY1OYnG5f3Tlmfm37+I4AbLmY9D76lpc60LItbEdSGWbtOm7bifFIIg5oQ8pzpvmblQY27H4GVd3QxPe6l6Icjfe3I56sn5rYulZKOMmcryqZSx03mApBKJgCLt7K6uwPtWFWLyPCvAwxYTPsrJVPbY1rH7Ya8OXhMqPhKpilYb7m6Q4hV1STVteTzMlm6bB5Uuc3p4X+XsvA4BTRQUptWyNeF90JcV+7TlmdmtfDsJF3C/G3omk7U8hy7D8VrXxTM8KVO4pVuvFPfv68FkRKZ677p5yxh5jTJfeyDwN8bHI5/K4SEZI3VWPxM+LnHnhQ0fBwYGjPed39QkJZwN9+QQfOMaaY2J42pCxXJ5SA8jc+RXYoaHmSxe3DpF+MiCNq/3uQSsIj9G6sYwvB96l3dbeTHG0C+SqsAVCS3DnkNfBsxxajXZ5839DVq6+XwdRlZR0A3fSyA3UR6S8ZEF8FYNPjYp3yeCkbHQXUEqd7OxuWT4axd2kNCoMbCEM246DUy4xUWd2DkylQTwu6uLRAlqfIeNKczlegKTN6rQWq+xGBN5ALQHXozc/RDu6oC/Dim7xdhin1oXC3eeG264wddPNQ5P1pLZlAa6sjk2S/cJ8Bh9gsfgd7a309jYmD1ZhKbNLLw4wWx3t7a2oS6muwbCQpJ2n3kNBJO4CMItcqPbxhYELZQMalG9GFQBjJ9vpdSO6lXAmeYXueewS7LNC8JGMVNZPpUzdlFCH1hA6YBIcMaJ+/aUOM+I9Eo7ri8fqv12z+N7ellgxYcThgWZtXB8DGJqNKEV2BjIGMsULKVzPwF9BNzlw0gVH8OYVxUf9cnOZOtKuNKgr68v8E7slm4WZEd8x1taWml4eFjas0xjHiwDJjzKNhMAa21gpmBMdyN5MeTXkEh+5Ldu6/bYtSXMWx1GynuoCR+z8PaR3bt3x/h4GGmiPKQNHwfd9817t5pMVr0um8lo78fr0ctQrvfksPGQ0fCxVIyMLrQmEvUR8ZHDMD9dxKZS8BEAdXQsp/e///3ud1PFoDT5Bcw1oe2qCRJFezZL9wnuX1Ywir60t3davXlMZcDE3gGD8U2PkZ7hT8VH9hqYTcJyv5+C3l2ezDARHlKVMWSMLOY0gmfgqgQPGQvdFaRyAdPmkrF+/XppEcnxJAwqDplKCeg2Z/39xikYWyhr4VeSKBXgaSmBOpK1dFzWgeN/2ts7KWzxy5aMkZGRYlbuZ+DVltS5eqgu3+oz7tu3z6epUjNx83dZM2WzjsvufBw3rMaE8Pdm5XgUy/Hg4CBlMxlKuqWOTOXFaiGseNwWC9MDAwPUm89T0i2DFuaeLo9j2m27S2lH7r9Ogzc+Pk49uRwloLeabDHcvx/eRr+8vZ0aHMcq4MtjdMcddxSfb1S65x7LWC847bRoMcAR3IRiprJ8mihGmpRJ/E6HlXkwCoSupzB8lO81rlnv9cr31wK0zGftTWrxkYhoeHiYWlqWRMbHQqFA69atU3B7jITHk8ykqcxtUPkqasxmpb77M3HL39lSHsXyw+QJtup+wt+blePhz89rP5PJusl9zPudYNJSpDL3AwMDbmbkZOg9/bGnjeQxhMt87cj913kTeNmGeT6oY+yQsMTr+uDtsbWJRFGZGQUfC4WCTwDj8AobPs5KJCibycT4eJhpsvGRNBiZhZ7HSoe8c9Uzz0voqHpk2njIjeTFByepra2DCoWCqyDrlK61Y+SmTZsoiI+6ErYyRkbBR90zJXxr34aP9957b7F9f7kxFSO7NfcztyvzkF7ys7UUjpG1JPYNv5wxMDAQocRarabNGmJ8HBwc1OTg0GPkwYMHpTj0KPgY9LbtbG+nWscJLW/L4xOGjzoBnD+MfZ3t7cW2JoKRsdBdQSoXMKNktfUyAqqaLxag9QyO6mIhAMW2sE5wF9cGAhaTiM+T265xQWcnAftIJGfwfu/uzkrlb5hpCdd6jY6O+qxZrHHaAs9SyZ9AcjNJ28fCK7uhzoU+0UJKszh783mtG/Q0CHeSsbGxogs2M/JqkhsHIsP5BogYb9Yoy21yTLcu2dv8pqbidfLxGgg3GFNCBxlIdLHVSYBmG8ZRzpTLzDNrcG0Aku3upgbF5TcJIaDIk4vf1Rp4dUR5HH19UgR80xilHKfozgt4GvcwpgMAnblsWeh5UbJRxkxl+TRRjAyz5NQA9GFlHnDde5OCTPe+9+3bF3qv90FY05MQ2DQbjFHM3MlWjH0kysRUDh893O4lIWCmFXzWJKp0y6Gw8OrF7M0lfcKduSSHOBGx9TZFfqYrScC0Yo6Pffv20SmnnEr6smLM9O0ujlF9fQPpspw3Nc3XJjISTFqC9NnLe9xnDgqvfm8ynUUuSSJRkG4cvX2Yc6RExcdCoUCLFy+lREIdiySJPVqeYmxNWiOV3fEzorrSjTolaU8uR41VVfTeGB9fUTTZ+LgZQQ+8AegTyO43vPOgUAoSnhuMI3tJJOqy8ZCtbjeDPKQ/Aza7jJsx0oyPjSSENzXrtz8BpMxDesIr31vFRxY00wF8DOZeEkpCThy3b98+qaZ4GEZeSgCoo6OTPI8ZKReEG9Otw0hPKWDCSDWURsXI4J4knqOaoCR8FOO4ufhdziEVFSM7Opa741YaPvoTx/Fz+3nIqPi4Ex6vEIaRPbnchDEyFrorSBPZbHK5Hs2CTZHITrtTM8l0LoZcYsFjNq688kqfNtBz/dAtrAYCTifgXvIYOdViIydV6Nb+7jgMHBzHFixpwzX7dIuCBc7N8McIm8p4DQ8PB9pIA3SPtKBIcx3HvrEF9Oabbw5sPml48cdNSv1tY2ZlaQGeuXRpoE1mnHQuKqlEQms9VhUN2+HX6smabl1sda3yPeueJ4PFTIhyY7pSHjrwvOeee0LHgd+VXJYB8IRmn5uj42hd2UxuPHJ2dn5HYX3Z6/5WZzlPTVBU6XX+aqdyx25sbKwYZ61LAMVrRw1bkLXXBQRLMqn4SOQPnfApywBaDpF08UppjVb51tdO8itCw/AzGj7qS1Zd5/6/mfwxcPoyNZmMyiSnCbhH6rNO+epZFIaGhtzMrqoVJl18nuA9TO32EzNQt99+e4B5amqaX3Qz1dfkTWj6kaVgluA1RabY792lixs8mYA25Ziamf19Pm8FJhM+8p4SPhbsuuqVRuIx8IQO8ew6ZYwJH3tyOV+sL3tlxPh4ZFOl8TEF4cWm4qMOIzdK+Ci/czUho25dem7CHKYShYc8kzzruO73tDv9giW/GCNKx0d9qcPh4WFNOzbjFIpr1oyPORJCf5JSqTnKb2FtCzf2gYEBxWNGfHh/ML8LHUaqeLadZAWGh5H7KYiPCQJeoxzLUhR8JNJjZHR8ZDlnTfHeOnzkTOoyRcXHBiAQ680fOelvKpGg6gliZCx0V5AmKnQHNVO8aDxt0u7du6mtrVOJmWDGJ6hhkyfpgw8+KE1wW1KXuRGAYZbld0660Ov2Z617zGMgTJm2HXjp/Xe6/6tMMAuc2oUF0AK3/yaNFMd4d7b5ma1FENpfdfOZgWgJ3vqlazjup7+/vxgPQmTXTJus+zvU45oyaNwWJ3fSWeXZRZ8gskLrLM4HDhzQagkffPDBYux42Dh8CEEL43KlJJP63PL42OJ477jjDlrQ3Ey1EIK3LhShQXpOvtaB3hNAV4qn0uv81U7ljl1vPk8px6ElynxyIDxA5LWxpKXFl5cgrZkXjDHczvymJjp48GAgy6l8L1OYCqDGNu4jr8TKxPDRLHgyVufIS8ajtwjps9E2ErBA6rPcP2ai+khfrmcReW6i3vMkErMoWoK3/uI1vN53795NfX19JZTK3EzAMUq/gpbpcPfPAnmMnK70GrugBkv2dHdn6cCBA1o3ygMHDkjeZPK80I3Fqb7z6usbaNeuXaHPriYjMmU4Hxoaov7+fmo+5RSqifHxFUETxce0Bh+Zf+C8Lf39/YHcLTaMVPExODc3UtBFOAoPOdvye4E8i/VGF1s2VhQfTe14iR9NaxcE3BrABiGgXkHmuuHPkC3BG+eukHMPmXhI89idqPQriI/hGMlGvA2kt8qH46NJaZzL9WhKkpnG4UOkevHakr2p+BjGaw4NDdGpJ59cFKTDwlnla7MajDSVc5zIGo+FbgtN3L18LQlL8yySY13E5BYTzxQzEXS1qCGhYfO0X3V1Kc0EL5BgDtWkLrLQbloQF1l+X0tRituHuUTxbzrrbWdbm9XaGvbbNe4ia5CsrqpASvCEyPe5322xHxtgT6oQFoPFC1/tE2+MDoRFWtWoqSWWdgCRYrxr4GVvVK3JsjJDLc1lG9+E298NEIqKje5YO4bnBvQx5eq5+2HOvqsez8Gz6BO82MaTNdeza3IlY3Ji8lM5Yycn7clCWO3WwrPO8XoNm0Nq3foaCMFaVvTNb2oKlM1j63iDZk3yWoQ0rwTOscBlY6yi4WP4b/Lv49K9ozEo4b/tJiFgJ8mc8EtlFCnCM20wJnhjsuU50VvH2IMrSfX1DQF8NCX4gbXGcDXp6tPqlBmOk6Lq6pnK+wlrm11wPWHCi2XVP7surjyDRBHndPjICqIYH49smig+FiAyk8uJTBkj5ZAaXfUWFSPT7lzS4WNwbprCVCbKQ9qSgpWKj/5109KyJMTaai9XJfJT6J47Z3geTtR4f2jbjpMMxUciG0aG4yPgUFtbRySMtOfB0OOjXpmxmYLGxbC2EyQw0lNIe560ZnzUJd3lD/MJqrHNxEOywUu+9jJ3ncnndcJTbIVhZCx0V5DKAcyxsTElYQTchTEuzRNvkcsvU42ZKBQKUvbYqCAVvvjDf9tp+b2WbNkX5UXBmcD3SmPBv3FiNXmc5kju3tuhL4eVgl6DmwSsbiKqpXsvwhO8JaUFaysfYLJ021xcuiCEBZ1Qr9tI5eR0OuBJWO4nW1E4g7MseNQYxuFEeBu3upGr7cr3y2Yy1FhVRZvhJXdT+5aGXgBqdt/ZEEBXKNfq5o/jttUnvV9mSiq9zmMSVOrYjY2NBTbHNPyCgqxAkjEyDB9N831Ja2vgd7mWre4a/i0Ph5yidQRkLwkWDR+939gNea97/BTp92B92pkz65S+cOiR3H6K9F5STQScYel/Qfm+l8KTF3lMuKmUGVOUPCfm3xZp6wDrlNVtbZ20cCHHe4a9A9O9VHd+ZmplZrfGMBYJCsbjp633zGSyrmul/9omVNF4CD62QOyvd8T4eMRSJfDRgTmRqY2HvP322ylsf25t1SV/nEwe8n0R1mYUfCTS1/CWLbScqVy+hw7HOKab8dWGj/KxYakPZoy04SNRGEbalAVdBGzRKj51GLl4cWuEdxD13XOVpCj4uIj0LvIcQ64PpcpmMpRyHDpDwTj+FL15NB62zRD84w0I8qljCPKzDkAn8bpCtLjuWOiuIJWz2ejdWvSWhLa2Dmt7XtF5dYEwA5ggv4vNhaELasaM2UZgEH3VJ6WZPdtjsGyLUo3d5sksT3q13AkvEjVWWeeGrXMLTcBLLGYSSDluOun2R03wprpOZ7u7aWBgIBC3YkomobNMz7L0iUt+yS4y6j3uueeeorDNH5PAUIfwetnb3e8m6/5m6K3On4Tnpqha7B2IGG41VCAr1QnncU7D78ZjU0qcqvQj6f6Wgz7xXY37CcuGX4l1HpOgUsfOFDqi80SpdRyraxfjo2m+AyK0Q55zF1rW5Mzp04t14lt9iWZM+Mia+mj4GIxLZIbto5r7qBaXU8iW/EafvTxBXviQidm6lcz7wRZS3QI7OpaXhI86q4u416mWfg0Wx8+Ej83NC8j/zGHvIBFyr+3SMRMTvJmCzCO/f93eL6xQOqt8JpN1r09r3/fJCPdsivHxyKZK4GMS5kSmHW1toe1FwcdgPqBwHnL69JlkrrNs5iH9uDRRfCTSY2SKPOFZxkfZDdu0dm34+GHledJKH/aTqjhra+sMWJ9Lx0hbv9jarsfIffv2SZU1JoqPcj9KxccFpLfWi31FZIDX4yPzjSr/GMX7U4ePcgiGDiPnK23Elu4polIB067N91sSoiQx8YrOc5s67V5j5AV17733Und3loILopUEYG2mYOb0KhIu6ztJaB3ZBcdbIAkkqRMOnQp/XV2d62aYxkq+bxrCss2LowteYpDd8GK4r7jiCgLsNSp9i6+urigQy9ovjptWF5opa6KsveSyW/I5NsszW99NLjLzm5oC7umLELRGs+Cis2iT5rgts+OlEAqQavcZmLkLK7+gjg27rn1aulYXVmBnCsSnFX53+LD33IfJqbMYk59KGTtraQ51TnV0WK0DujgvnQa7QTOfTP249957KdvdrZzPjKkOH2tIZPM146NX3/YMEpmBGU9lgZoZlWoK30f4kyMhDLOlpos86/duAq4mAJK31AZLuyzMO+QX0mULhBc33d/fX3wPulg/1brjld1S956vW/pVIE9R7fciE2FYbGHi8dzgvg/1HTSSxxTrrSr+4zaX+FkkFAazyRbLeu+992rHx3OFDX/fMT6+Mmky8FE+ZuMho+HjflLDWGxzOchDpkkkUTPxkNNIYJUJH1mI7SI7PtYQ15EOx0jGR173SRKlsArup584wVlp+Mg8MYedqFgi2lm/fr3vXUTByAMHDmhCS23hMn5vJz9GskKhUvgo98OGj5eS8P6qJrsXQTA5aD7fK4U/CN5Vxz8uWriQomBkK0DTlWNhGBklrLTUNR4L3RYqdbOxx61tp1JcTlhblUo1SQskR0F3DhmQlroTPAhqTU3zi23LbkgCCOpJLfMgPlwOQV4s46Rq9BoUzVYaepfRjbBbf7fDH/cMCMHPWCu6v7/Ynq68VgqewP9pCAuWuvh0cR5y2YnOtjZt1kRekGpt7rXw3PjSmj4lAZoj3VPnIpOGZ/nfCb/LoClORbYSqpZnjuneAS/WbwP0GU6H4CkfGpNJ6ztjixfPqbGxsaKlmz9yvGHBfc9R3OFli6jMLNhA1hYSUO46j8mjUsbOVntW9USJ8g5HR0cpVVdHtQjXYHObSwB6J4R3TFKzJuc3NRXb5vksGMz6AOb5GRuuQTpKIimZWtJmNQWzmsulwGShz2bZ2E5+Dyq+9nrl/B0+fBTXmyz1/GxvJcGEq3tBsGwZC92iLE9nwMNLVwaora2DEgmO6WPrfJh1bD55VipHuUeavORIOymokFatLb3us8Htg9+q4sV083F269RnSAauIQBUVTXd+s4GBgZ8c0qfXTlH/jA0z7oU4+MrkyqJj2sUrHImiI8binNBxbUTyeQqreMhM5msm+xsM4kKODqB0YaPrRQdH5NSuyaMXEtBfExQsFa0LExGwUcQ8EkqFR95HwnDSH95s40kPI9OkN6RDh/nKGMkY2SO/Mk/J4aP/pjuHeR5224gf6gTj+kQCQWt3cuqri4VKN2oK2WXh0Pj8PhHQCRaDcNIOVcM4+OVV15JlcLIWOiuIFXe0s2fZaSLweDFqduQ/clcwuLRBCAlEtW+67kkg478Fgh1IYYngQFqKImE1u1Y5zI6CLtLsSoAAqBWJYOxnO1c1hDrNGFd0v8FiORNqZD+8n3V0mWm/qrC5RKIDMyAV95FZoYALw49B72LDLt/y/VYVZf8jRAu9VnpGhaU1T715vN08OBB3/Oobvppt6/zleMOQNdbxqCzwx8qoXOTkzPrjgLFmopp6JUSLYZ72d4HgEDZqEqu85g8qqQlhz/N8BL+qJpmGSNVzxMnwtyolc6bWe3HSM7oq1I4Pi51/+4nfVkWkLDu6FwhmSmU8XSQ7DF8KnMjrDU697x8vlfZl3RVLrLEzBawyf1uymzr3Te4T+n7qxcwe8hfXkhnHVtCXLc8mPxHTlaqMsxyv+sI6CCvlJew0mcyCjOX7y2WNfOOq+865/YzRWrJI1u8fEvLEp/nlD4MrUEaY6+vCQQrfTS481hn/Yzx8cihycBHALQM+lwwpeJjE6pInzgsEUggaOIhJ4aP06S1FRUfo8SWF0jFR3OIh1xKzIaPIGA5lYeP5v5mMlnlPMZHHlOd0jdJXpI3NUFa5fFxfHxcEyOuhjKl3b6Who+JRH2gPJgOI6uQpDwcInhyBGDmIbOGdWSrpFMKRsZCdwVpYjHdqttGlrwkO36mKbg4VXcQoRFbsOB0aQHpBGAPkBwnSe3tnb6yLTqSYz9EZlwVgOVYHR24JUInrypAc3ICB6D6RILWQGii1FT+hKDWSc3GKWuhevN5apCsuxsgGOysdO8GgFYinCHZAM/i1ZPLUWNVlU/wJU3/apVs6SkIN7+dAHUgaFWvhhDK5QXOJbv4Huz+vUfqV1i/u5R79ObzNDIyoo0dKhQKwnKv9DsJUdtbVaAkIZQDWejd2tPuPZe0tNDIyIiVeVD7uh/6HABfNIw3IOLtU4lEWSUeKrHOYxJUbsyiOoeykMI6NHNGZSLVkIudEJn0j3vNa0LXKt9jIwTD2tneHoqR0fExTfoYbIfsbosF5f9nCHAokagnsWfsJW8f0WUZFx+1bqzsSRXclzaQsBJnlb6kLH3dUHwuzzIcntXdY3bV2EuuyNHo67dw2a4j2UrV0qIm/2Gvsj1Sv8L63RUYmzB81Fnuvfc5X/OuG0lYnZIU3Ps9ZrmtrdNaQkztK+BQt4KPOffvIGJ8PJKpUvjYCyX0rQL4OFqcL/p5WCoP2dGxnBIJVRgNx8f6+gZpXpeCj0KRJRSNG5T1pgrqLt/RdXYgvIUxMjo+2kJ/dPi4k2xlF8Vz6PBxJwmheLqCCdUkhHJPAdHW1iHdY/LwkcjDSMfRYeRMzbtuJA83VXyUcX6Jj4c09VctwWviIQcM+FgoFERZvgpgZCx0V5DK2Wx02QI9DbmeafJv8HtCJ5sdnBho1hQnl4508SXhbc/2LZaECyytrmuHzWVUFqh1daQdiJgLnUt60T3EwCSPjY1RTy4XaLMbwpU86S6kBDx37TC3kjSEQgAQwu5WhAu8pvjpTqU/HQCNKM8FgDZt2kQrpaRjBH+is94I/Z6FYIkwBo77779fWzO31Ezr/6B5b70QgMd9cBBUIMh9lZmBPdL9RiHqoffDUzCEZUTXxc87APXkcpFcJie6zmMSVOrY6TLy56R1mjPMbzm8Q543uvkR9lsxY7N0TIeRpeOj+TdR79rMcAmslveGYJ1UL++GrgqGVwvcxCTrYwVTBHybPFfFerK7tvN+JsdUTiTbsVrpI0v+pEcic7EndKuWbnZNt9XgnUVhbp1bt24t1sydWKZ1s8umUE4kpXN0fQ0q3IEknY5EER8LCCq1KcbHI5IqgY9piD02zDBRDj4OFr+H4VKpPGSYcUbv2h1UqJWLj7zeVJd0Dx/z+V4aGhoKYGQ0fJxGXhx3Kfg4SvZSaaYYahUfOwgYIRUfN23apFjsJw8f+Zj+eWxeWguV5+H3JffDURQIwf4yv8tzncMkd7t/i5VPDPjIa60SGBkL3RWkiWw2IyMj0sThT5pMpcO8RWeqm6gK6DptkQo05lT3QdeNcGvFbAXksu73yy67jGwAr24cNQClEomANbUGQRc63mB0yZHY0i27Mu+FcMmW3UjT8GKgeDFyDUy1v7vd7x+CF2/MizGl9s9So7pWAoa1bh/k+sOstW5taaGk4wQSpHG/w8pt2QR/uQwbICz4XEZE7TfHyKjH98MvbG+EPg6cx7Y2ofd+0An1qxGsFV4D0NyGhmCNcvd5ZEAsFAp01VVX0QUXXEDbtm2b8nX+aqdyx25kZKSonJHXmJq3QXYh47wGHYZ5qjKgYYkGbeVASsXHsIyvXmbtMCEuTUI420HCwpEiVfASx9VYQ8b9MVLdD/WW7o3ENaSDyYkS5FlETHHMu93vbElZRh7Tm/T1z3EaaPFidi81jRtbePaQJ5T2Sr+/lwCOMUyTv1JHmjzXdLbUlMbUnnXWClIZ+GXLeF6GMdbqb/vJCyfYSPoYR9liF5VhDb7XJXCoHsH9MglQQ329L7lWjI+Hl8odu0KhQAMDA4EwsTT0hgnel0vBRw9bTWumFB4y3JIrhKmc9rePfexjln7o8DFJpeEjkU5YD1q6TfiYJqGUZDfpUvGRMTJFKkaG1ai246NQjLS0tLru5YsomP+pfHzs6OgMjFku1xNS3900D/Yr7ZjGT+w/Au/N/WV+dwzBcMg0EBkfiYiGhobob/7mb+hd73pXSWE35azxWOi2UCU2GznZhF9Yvo2CReV7CVgROtlGRkY02kUZkBpJZKI0ayn1Wiq7dp+1SLKmnZnlpGaCt0C4d2SVRaETyHRxSzl4G4yuxJhalkrX3m7lu+razFY2VcBvVM7TZT504E/wxnXJWfi8HnqXF06wpgqbLZoxmGkZ5zDB34EQ/NfC8xZIwhPETUKxqpBIw7NOd7ltbkBQoJGVCY77fuS+1rpx63vgzaMeBOPrU4kErVq50hiTxsqWJ598MhByYIrLnex1/mqliY6dnDhFdq3cj6CSLefOlyTCMWRkZMRoTU8BtEJZpypGlouPYRlfU6k5LoOlJsI5hYR1V137YffhT448JW4wXk8f061rc7f0v+razF5aSfKY5jFNn7spmFzIkf5uIa/urix8Xk/68kDXUtCadTZ55cX4M1O5Lij4hzO11SQY243kZ+AdyztQmca0e90Zbns2l9dTNX1lRnOPNE760nFNjXO1Vhr+vzefp+9///sxPh5mqiQPmc1kfHvqbQjyEPMhyoZGx0eO6VaNOKXykDaMYWWeTtAS8eLCVXmy8JHc735h3V+2Lwwf9UJ7dHzMEfBZCvL8DnleAKXgI1vS1fZOU75XKdep75qxzoSP00jFx0QiFTJmpuSTjI8bSCgi6ik451RlghMwMDKeb4fgH02lh+c1Nlrx8cCBA7Gl+0ijSm42Qbdzx834qMY9OGTKVLh48dJie5lMlhKJ2RTMAjnD9z2TyQZqqZqzrC/TLMpG4gQ3H4ZfqE6737dowN+BYJy5cTnbIGDWwvb39xfLqOhcreVrdkRob1D6ztZqXVbjFngCfhbBeKhG91qdEDsNwRiTKgiNs650WrX7u87az8DQCdDZ7n2vhSiP9hHNODfU12vH5jroXcE3c/sdHZR0nGJMvW4Dz7ntAOG119Va6uzCq5Y/e+1ZZwX6FPZeC4UCrTjrLJqmnF8DAYzzm5q0WarlDNRTvc5fbVRpjGRmUBuT6B7nkItGBBV9nVLd2hVdXYG5o35vSqUqho8Cj1VLS46AneQ4KY37okNCWUokGJRB8krY6JkgGR+jMrz+7OU6xmqQPNdmdU9ipowFZ72A7zFNi0hYtWQhto68hEn8YWZxmaatpHt+UnOcmd5mEkJ4IwkB/WoCPkIqE+rFjKpjo2Og2dXRi2kVTOR2EsxiiryYd5mZvs79P615vyDPZV5m5L8RuP9ZZ71W0yfzey0UCjQ8PEypOn9d4jRAKcehmdXVMT4eZposfGSM1PEQ6Yj42N2dpUQiqZm3fh5SV2taj5G95PdEMQlTU42PUZQCNnzUC+2l42OahBC7ltjt3YyPCRJKUF21omoCqjQyBPenhYA2974b3XF7J3kx6fzhWPHS8TGTyboYucZ9Fp0hMQo+BttmLwu1RGR9vYq/4fxjoVCg9NKlVK1cUwMhmKvrJ47pPsw0GZtNoVAoZs4zA8B9pNNufexjH6PBwUEaGhpSrmdAYtcSPaPCrjRmy8d1hoUmSrvI7tZp6N2c+vv7izWby4m5ZItXDbyMrSa352citJd1+2mLV066Sdj2WM7TuXHrBIQkPAGh1DHYCOEew+OsMlSAP/bZQXCDna7pUyO8BDwnHX+8r90q954mJUAOeo3iiQhavPk5hoaGqL+/n/r7+2l4eDggJNsS1G3dupUcBBUXDbCXGouz804NTcbYMb6FxWXrqhQ4ELFtclzuTgiF3yAEFvBc2g/hpitfb8dHnfXVw0dR2lHHpHjYvnv3bqlslu4e4TFxXonHGvIY3HDXTvt+M2K9bybDnlq2mD3db+H7UulxgRvJi1FUGTn+3k+edc2hoBJb56LqFxBqamYrbc+kcKWEymizlV0WNPge4lnWr19fxEchdMh9Cg9pKJaoRBDnT43x8YigyeYhjdbsiPhYCg8ph6voMfIAeSWqTMKUHR8HBwepvb1T0365+PgMeUmMTYK1DR/DhfZMZirxMUpuCc7NsZn0lvJ/IM+irrOA2/HxjDMWK+1Wkbm+ehg+vo9M+Njf30+7d++mgYEBakg1+u53sjs+RoPb4CCNjo5qeUjmr8P4c1Mug4ms8VjotlClAFPOgEsUpZ73WvIDYR8FmT1ZC0jkLxGgc+HZ6EuOoM+ynqRZqCLB0LD2agdVIVlkUG317XgzSEOvaT0RetdjtfzXFgQ3DaNgrbhdyVm1+a+tzrQaX2o6b7t0bAz2WOu90rFRRLP2D0Jy0UaQoXLcc2SlQ1oZK8Ac521SEqQN59vGnz9FF17HCbg1Nmlc2m0eDDfccEPxd3bdV0MbTGPY19c35ev81UiVGDsTPpre7VrpWAHB/AuA52XBbcgl+AoQ9T6rNK6G4fhYQ8Iy4MdHOUP1+vXr3T7sJX/3dQIwW0RU63g9qUxQ0FV8CwWZqXBGVFhF1HvVkL/etX5PGhgYUDy1wphX/m2MhJuqjXHk89m9MtyaJc4R7od6i5JDnvVeFcblT1hcITOiKgOZDnmOqC6vnyHVIlRXl9K0Ec7oy8qpGB+PTJpsHtLm4WfCR4/feUYz3/TrwnGSvhK3uVyPgimMIwkKhlgIAcuGjywoeWs2yKOKpL7l4GO40GzGxxzZhPapwUd+R9sj3OdC8vBGxcgkiQRt8vmqfHGk4SPvi9J+7Qr0YQY8WTklYyTnUwoz5JlyGUxkjaOcxf9qookCpi67Yz7fS8PDw9Lk1SVc8ddbNSfWSbuLcIC8eoj8UbMCeuXECoUCjY+P05ymeb5rlsCh2zULMA+nmKH6ne98JwFmgY5dGffD7I4MmMt/qZsJW6m6oClX5Qrq4+PjtNy1rhfbg9/CzR+bK4pNgyw/d5Ss4msB2gcvuUmkvkj/m85ZD8/Sne3upsaqKuqDqN+pGwcVUMLurT7DqaecEvqMC5qbffec29AQcFVnt161jVzIe+V3kVPakr/HlpzDSxMZu7GxsUDsdW8+X8RHzkiqJuyrV8p81EDj1VFVVUycOADQUs36CxNSx8fHNe6OIOCKAD7KGaoZH03x3X5Xb11NWNG2WiOXrUxBhS0rZrvIxIgSEd1zzz2Gfm/23TdsTIhI42UVxlxFyZoLtw8rKTjWpnvIMY+mc+4gZpS7u9kKtYGAWwk4UTMO8l45I8L9w5hp/2+bNm1Skqo67rg0K/1QFelEnktr8L3yXIjx8cilSvCQ5WCknHNFh49Jh9c786DDFMyUra4Lf4y3KL+luhOzd0ZOOS6+syJdWDeDAh3Hr4tz9pMJH00J0cz4uJcAJ5BXg9eSGM8wfLTvGUSThY87CdhHInN5VIzcGeEczxuou5tdxT9EwZDVw4GP9Zp+1Lv9uJ+EIfIaAgQPruMficjIQ9qMZfI7reQaj4VuC00UMLXF3asaadWq1RqGLk2Ok6JcrqeMUjW8ictxIn43DbmcmKxN3Agvsdcz8CxCaoZqXZmvJZBKWSiWap7MBQiBU3VdNpX/MpWz2qy5PydDUDelLEAHEBT6G+rrjRZ2mXS1MpPwaljLbui2+tlqLEkOXgy8WiOQ3bMJQjvN70Ru9xmlPU6M05vPay3YcpulxMDL53/+858PfUZWWAwODtI999xDVZp+JKF3tze9V3bzNbmXOxAJ4WoBX633JOKYxamkiYydXHVAxoXVq1YFlHJpCA+KnlwusN5tmyeviw0QnibvK/6mZwL81hbORM3C8h7lODMYuhi4JcQJLsOTmmVJTVZjKv9ldn3fHLh/Pt9LBw4c0OwnHSRcJsfIzxw7AWuPLLgzmbykvBrWDeS5Wdrqwy6nYDxjmoDpGssT72lEgjm0Masu5uZ6pPq8OguNuleeYGl7kILPYWfIC4WCG2/qGPqRpKClSP9eo+DjjGnTYnw8zDTRsSsHI9XjOny8DaBgki0bD+llM/fjUIG8xF57lONqYjAVI7sJ2BwoUeVfTwUSmOW3dCYStdTcvCAiPor7d3dnA2vpwIEDgdhhgckHaHLwsZFKw0ddnPQW8lzAdRgZJVZdfJqa5tPBgwfdfSIKPtraLR8fPU+wlKYfSQruFVXKd49/5PlgwsgqIICPcUz3YaaJAGb44ne0ReWbmuYXJwsLMfYkOO+kcA1dktRyYrI2URa0GZh1STi02lIEM0oT+YXWPRrQH0Mw8ZjpelU4lrMdy+fuhL8013xoYpATiWLiMd19mXS1MlsAOoigIP+MYbyShjFjIVgnbKbhKTFYWA9zW+cNWKfsUM/f4PbFFgu9FkIw2QCPeQP0mdN1Coslbt12U/uyVlKXvI1rzMprSHVJZ9egszr8ml8HwsoeZ+edOip37MLmqwNQo+NoE0CZ8NGkRGIBO63MsyA2j5JsyQlaTNS6pypTZSpjI7BZX75rB/mZVO7LGKlafvP1fuZPxUe/4ncP+cvOcOIj7jMzcvr7MgWTgoKE1XaEhCVCZmyfMYxXyr1Xo2bcGknUJFf3tTR5VTo4IVBY3LenwOD6vOHX1Lr31L0T+bz3kRdawO+YmdVwhlww9+HlcDy33GBiokwmGxkfO84803dtjI9TT5XgIcvBSBs+Zn3C1dc1a03lIbM0MXxkgUmXFMwJ4Ew4Rgazg0fFRyIqASPnU+Xw0SGRiVz1bgrDx+kEzCOzEBxUyHE29aqqRkomGy04469fbq9yUUuOM126V+XxUcyrhKX9Rb75k1AEcVXoltcQu5j3Ich/OxD8cZy9/DDSRADTHLcdvqFHK18jT8ClhkXJGrpTyWNUxALozefpgQce8E1GWXDcDz2TGsVVbWxsjHpyucCEZtDnunpJBC3fLMDphF6dcMxjY8qsrdb8la1frS0txaQiJjKV6tggLdidMCd1ChszzvB9KUC3IqiEYEFBFebl0mZyezbhQ+6Trt0kgpmdZ8ATxPdDeDaozygDlDdXzf1Qa5+HzQNZMTSmjLHu2gbHKUlDyRQzleVTuWNnikncY1k3Jnw0nd8PfW6EaUUm4DZSXSHz+d4iPvpxl4XUzYFrwjBaxUdhdVWZJY5xHCPB7PkxXWZQdEydjvnz1qMpmY65z6edtiAyPmYyajnMHeRZIHZSmBt9+N4GAi4l4RKuuhqCzDGfaW17dgW2zOyZBAe538xMvp/EHhssmZbL9Wjw0VbzXW7fPA9ifDzyqRI85EQw0qSY8a+7zsA88/OQfkNFefgYLkjJfTZj5DNUSXyUx8eMkVu0/W1paaVdu3b5hHeVZOE+qAi4RhqPcvFxr/v3b8hfDxyUyXC4jg4j1frlUfHxBOkeJuwtHx+JiP75n/9Zed8mfPSH5F6LcB5SDXc14WO2u7vkdRoL3RWkybF0hydk0AXvm91VbAXv/Z8sHNriTsr5TU2+DOE6QTubydC6deuK340uyVKfe/N5SjmONuv2OEArEX3DYNAaGhrSgpsc0xawarvHdf1la7jOSq8jnRKAn0kWXjdAJGxrhj3jOlsjdku/sas6JxtRgYKfdVzTnpx0TDeuyySwSWnedVj28n54NRHrIayHn4YIQWCAGxsb8yWjC3P3XQ/Q1gjzQGYY5HqMpTAdUShmKsunSlu6bQlOdPhoCgfJAXS/Ya6I0nmyBt7PwInwHzUD7mZSLR3NzQuk73ZM97B8MwVrYvcSsIKiMqcyU6cmWiKSFb850ifTcYx9Ftlvg1Z6HektOzrGbIPbbrP7PYrw2U8egyVcMb1kTPtJz6zu17Z3xRVXhI6tdz2/H1VwqCHBhPMYNpCwRrWSV1O73n2unQSsLSafGhsbk+IW90Tox99Y50GMj0c+TYalu1SMVPGRQ9fEPI5SSquWysXHtrYO2rRpk3QsGkY6Tor0lQkqh49EUTAyp+2vSObmCZjlYaSjjN8G8hLR7SU7PrIssds9Xige8wRoHUbmyF+/PCo+niFdr3s/OnysIaHUGSThnWDGx+D4hM3Jfl/f+ww4x2uIefXDjY+x0G2hysV0e8KySFZgnlC6BCfmBcuLJExzLzLB9msm1/UICnUJ+MtKeDUP7ZNUN8FlAe4M6T6mDaOvr6/Y3tjYGGUzGV//dK4joX0zHJMTo+ncpHWkumzphGIHInGTLTN3LYJ1rnkMOK5FZ8FPwy90y7H2Whdw9xq5zFZaafNUS1/lZ2tVjvE77WxvLyZn4fevCkGy98OZlnnAm3BvPl9sl/s3GPHaqBQzleVTJWK6ffNEedfqXOzv7w9sijqlGHu62OZK+OZ+PemEunvvvbeoDJTx0cYI+hWxbEmVmTzZoqTHdBkfiYj27dsXiEcMlj4Le0Z9wjcv1nCj1g1QR8GQKJNQPODey8bwJ6TrvGR1/gzwQVdTwQwyUzlGfuYw6OboZZ83lSE7nsL7Kbf9Gc0zJ2jhwkVKmbgolnTzPIjx8ZVBlYrpLgUjVR4yiI+y1dlWSScRMu/t+DgwMCCV/yoFI9MUxMdasq2LrVu3+oTrMHz03y9sfZsyeINUF20bBTFSVezx90Gy4yOHwzA+esnOPIzUWfDLxUfen0rLDO//LCGgRzkm8FHIRjyu/P7D8NELgZCNVyrOrXRliCMBH2Oh20ITBUyTm0su1xOSaEEUhB8ZGfG1NTY2RpmMzFyk3Wtsk11MYl1Wai5/xRnC2eV5YGAgwMROg2cVZ/BPJRI+YdXTGgZjKDbAv/hsAt7qVatoXmNjqPvx2NgYperqQhfQGvgF0F7l90GUruHSJYuTn5HH2hTr7cCL35bjvOV+ZDOZoieC/Py1EEIyJ36Q48Z13gos0HPbrYsXU9Jxikml2DofNobb4SlOatxrdC7xaYB6oLek10AIQfwcdZZ5wN4NIyMjRQs698+m0CglMy9RzFROhCYydqYwkp5cLsBophIJn9Kms63Nh5Gqgi7trqubLHMlnOHc7n6XM+DqysOAvHhFD9MTiZSPEfPwcY+C21wq631Se+EMzKpVqyUXzCTpXC3HxsakUlRhFm1dIh7+3V/1IgqZawAzQyUzsTkKMnlJdzxlqwkzflDcNU3M+anu++J90hyX6TGsNxGQkJi/7eRZ523zhC1jc92/awn4BgUF+PkkrEQ2S3p4gqWJ4KNOcRVGMT6WT5XgIaNiZBKeYluHj/4kYrxubgqdZ5XDxxQFLeNhGDkxfATgWuPN+MjjYsfINRSOj0HlgY2CiejUhHOMkSZ8dMjLcRFMdlYoFFx5gce80vi4w71+DXnZzE3jt1a6N88DriShy5TeQ6VY0mtRFcpDsqdFVHxcv379pOFjLHRbqFKbjZq4QW+5lheQWFT65BDscsJWBJ1mSq6/Kmpsy5OLGdFrDZOOS1DJwl49hAuy3Oezu7p8LjWy1vAb0FvRZYZY3TBapPvZEokVCgWtll89z7dZwbMSy1bvcjRcJpfWtNSHcQSFTwfCw0DX16QmOzM/lxqzx23pnp2F/93StTnDtQDodLfkl81bQJfBPg2/8qBH009T21rLvBv6IF8raypZiZNFUAmURPSQAZliprJ8qsTY6fBRZTRVpQ2/a37PvB7XuufLXii6eZZ054+egWNG9FrluGBovBJUqpXan0m1q+tsAz5eRH4MV9cLM8Qqo9Ui3a+GgDpD/71+2jyrFi82lZqUmT/BPA0MDER+p+aQqLTSD10yIIdMsZSZTFaK+VTjRMc042luS3iBFUKuE/97ljqbJUyXwV5lFBtJCN7yOSZLOjPE/uRDauWTTEYIU7KiuwNizw7zNoqKkTE+lk+TyUOqGCnvxfyu+R2b+Uee6zrBrtEwL8vBx0YSCin/+lBjeu+//37p91LxsUa6Z5psCQtZeWfDSDs+egJmKTykHiPVeGsTPl6v7avjJDUVkI4UfDRhZJr8yoMew3vXtz8bdh4yCj6mpgAfY6HbQpO92XjZVPUufhzv4NeKqVaEEQLaNAtCMCRzmuYVJ5fOEspAzZNVdcXgT1ELBBHPqwKMrp5kDUBXwMu0nYQXO6z2IwVPIB6Vjpusr61SlmzVonwTQLWOQ53t7dTf31+07prKc4UlaTIlyjhw4EBgYddUVWkZnYS7mKdbnqmjra2owV6rnCvH7KmCh6m9rfALqGrSiKWLFxefbX5Tk9Y1XU7alkO0kmQF93NhhPfY2eafu3NSKUopWVkbq6ooWVdnrP3NnyqA7pOuiZo0KGYqy6fJHDu5hIgJj5KOU7Rws1JGPr8A0O0QmKPOlSDDuZ/0cYT7iQUeL0GNicFYTxzSo+Kj3vrDMW5cpqfGPab2I0WeS2ApCbl0rsw3kePUUiaTpdHRUWppWUKOU0te1mzZouMXeFUyYeT4+LhUpos/Ve6z6T28Zs2qJYDrk+ufia1ofgGCz9W57CfJHJf5PvJiDf3XOU4DNTefXvSayed7LSXMiDyrlHx/fdIiEYd5ofv/dsMz7yeVQU2l5rjxrn7LXX19iuqgV/LK+/FCBVejYGSMj+XTVPGQG0PwcWXRA0jHPxYIuJ3UZGmeAlEuk1UJfNxNxZDH/v7ic0wcH6vIEx6j4WNLS6vUVxkj9xCwhhwnSZlM1oCP+nVdKg8ZLB1cQ0LQDuKj+MvZw/XP1NbWIXnSHm581LWtw8jgXiPmJXs2fDr0mTsi8JA1QCR8TCBYGchGsdBdQZpswDRnOJfdWnRZBYPxYI6TpJaWVurv76fdu3cXF/rw8HAg5lcnuAGi3MQdd9xBYUKSyR1bV09SbpvLZPHklid+AkLw53sNSr+ZmO1ZiUSxn3L2cNUSKz+bfDwNT9nA8c7sdqdTIKhaL37ejRDu1xshSpLVTJvmuy4HYaFLwmP8w1yqdcKDzR3GtOHKH9O1XKLr4MGD2jFSFSFh1nDZHV2OPQu7bnh4OBC3L1u0Of5eN3dTAJ0NvWt/KSEDMVNZPk0VRoaFj/C8URVUqva7s729WPeaLUe7du0iP1OjMgR+pmfZMi7DZMJsvTu2vyyNrm2QwPXPUNASkCB/cjCTm7rK4HI/5ey4OquJ7rjHTIu9RiTRYSZLxyTryv4IKxIzzTrXRb4Pjwe7G5rdqr3fw5TR6ljo4jLlj3kM8/leOnjwoEaJkKagIsR2f29v9yxs4a7k/phY+Xn9Lqk6jGwEqBP+PCalYmSMj+XTkYSPYfxjVVUjtbd3BvBxZGRECms8MvGxufl05Z7R8DGRkMNGxglYHWjbjI+qcFxPQKJkfBTPvJGE4u16suMjK0deCfioWtGj9KF0fNTxkBvg8Y8EIXtUHQH4GAvdFppswLSXAxMxMv7EMUS6EgOm7IkMyjsRLvy8BX6tqOm8DQhqgGxleza6k5tdnHkjYGs1IDKK82RnAS8NvRt6FvpYzf+fvXePruuu7sT3PZItPyTdK8mOAjQB7Dz90o0sOYpz5Ysv0U8ddaZvmNLyaAnQmSkJWUycZDqlqRPTNrFLvGBSHGuc4IkZUONAH1SNRVLsAi2WoGndQka32KyatkDXSKuBPqBTsn9/fM++Z5/93d/zPffq6mHn7LXuku655/F9fs5+7zIYQUxuqmLYZqohPSQAZaP43tvTo1pbqc++/sr+8N8GlT5RKZeJiQnkL1ASHnzZStvBHqO2sH2+LOrrcjkc6O+vAcvU1BTu378fr7/mmpjl25sxFWxhv1wq4UilEsuSz2N0x0ZHY8oaaqt0haf5SiPwT8p2pXD3ypjKxmmpMNI192fYOqFztBJ+LlexSPF5woPF+xDgUApXxINWUp10OE8a/0qtzZElhp5PjJGvJm4kJMefWUZjPZKW2Aoa60UeX/vazRhPYAYo3aHjeUn0WEl3nx9m93IlcStbfQqCLqxURlgGcClA3C2OS2afx2UWMHJFdVmZI2sPeQUgGu+L8fHxkNHPszb6nh+P/QQwngPROBatPpMreTTO1NZojcjvi4GRGT42TisJHxvhHxFdcdZyzx5CgH0iWeDi4uPhw4dFniPCkzT4OIZ2PLtmCY7jY1Stgu/BuEC+MHzkAq8LHwetPq00fDTVdEhJUw9G1oePo6NjMR7yUbANb2MA+LsrBB8zodtDS/Gy0eM6CBTisSdaJvTNm69NTBxFm/t2BsJ80V0UCzQHgFtvuEGNgeRJOjhAk1IgrUBGL4I2sJNxVcBopdpAd0PvBcNUnw7b2hHe+7RnU/FEbicgSn5WBtv1Og/x2OzY5gw1wNRfbpHlY+kaizeEz+R9Kg8P4/z8vKXwkMKDq29SiRBAVKPcZ6HmH0q+Mjs7i/fdd58FXr7x7W5pwQ2FQuz8kUoFXzc8bN1rpFLB6enp2D1nw7ZryeOSxnQcGtdSImZM5UJoKcZubHQUuxzhIdxjo7ulBR+GKEFgey6H127enAofI1e2MxhfZhcxvgduRHcMpGG++vsHVXysRyAzn7VoEt5IAcuU5jHufFpCrjEE+F00wnMHRi6TSUxeFW2GT3ctNO+hwHkvjpHmuWSRRYyYuKTxOIxSKBgeNgyYrfSQAkSauExugUnD/JrP1q07asmpTNZ6af1Kus8Za50AmJjWyEIUv1+pVBb3nMWozJAUDMy1qTzU6sTIDB8bp5WCj1GcdeQ+DbDOyz8iygzfPnwENK7ei4+P119/A5qEi/zZRYzc4PPoxkeKy85hLteJ/oRyEh9PhMdIAG4UHy9i3GuFj6drPN6FKxEfd+7chRMTE7GwsCgvQBqMPIj14OPo6JjFQ5ZBt2ZfBYvHQy660P3P//zP+Dd/8zfW8b/8y79s5HYrmuoFTB6/kRTLwekP//APMRAJeIix4pqx8+fP44aeK5TNYf53WXLm5uYst2EtoRj/dK5bZwlJuwDw3WIRSjfsNFqkIrjjyrlL+kilgiOVOFiWAfA8mGRd/DhvK20qEoZJwCcrag6MlTWNoK5lfJ+cnIyVRuPt4AoEn5B7IwCuz+WwXCrV1g7dMw+AbwcTO38QjNCZX7/eStzW3dKCAcSVCONiHDCcb8vSDJE1XIYD8PFcBfFxzoNbGZPkIUAaUJ45V7rGuRQEaTJQt4EpZ8bHZrliujOM1KkRfPzqV7+Khc5OC4+GwSjnaJ5ljgVL252Aj3ZcHTFlSUyJFLh2IMCbLXzUE9poDB1nql6t3L+ChhEyTEmcGaHPAAI8i3ZJFn4vzsjNYiQET9ae39FRCBlHHyOqM+CTk5N49uxZpQ8DCHBnwnhwK3gFAfbHYs+ja/agySh+O5IwGwR57OzsUhTTJJCSEkGGaiHq1rACxhMzRe6u8fXCvQKSlTHmE0+sRu94YsR5aJgdfuZngF0YeSNESux6MTLDx8ZpsfFxbm4ObyoWrSS3GwHwN9gcz8/P49693H3aFmBc+GjHWvvwcbWy95cWHwFAwfVNaHIgfRXtZIa0j5cGH+Ol0Xg7eDbvRvFxDE0SuR9HgI9iEj5GCoLm4WN8fcl5KoQfF0amx0dEtAxgPj5xMXjIRRW6n3rqKXzVq16FfX19uH37dvzCF75Q++2mm26q93YrntIOpg0efkCbnZ3FiYkJbAUjuDwAgNuEWx+/bk+phO1BgPcCYF/4AtWEGwnWY6Oj2C0EoS4wFuWHwZSLWgXGHZonPMu3t1tA3gZGGCbibh1DoNeIlkm2roOUybjC9lerVRzo768lQqso13dBJBxqta0DANwf/v/fwI4pT9J+yc3JE49piRnWAmCPGIsPK89sA8CNXV21+SXwGAI9Lv2DH/ygFWfe29ODOfEsTUCdB9ujgFvDZR87w3EuirHer7StDJEyJgnUNOaBXhTkip9UP9En8FMSDGrHcmUvzzDSJqmcSysQnz17FttaWmpr8Ey4VmI17sNrCR8PhWtXeku48NHEHbtcCh9GI9ytwngynzyuX9+JxhJ9VawvPT29SsWJEwgwhHqVCS3J1mp0J5uJx0NWq1Xs7x9gZVwqyrVdYR+IKdOy0ZaRap4CrBcMq8vi8hOoxQKSZ5bb0rQm7COVDyLLFD+nDQFytXdgJIAOoxZ3uWPHTXjhwgWWyIm/i3Ns7E+zcaB2zyvP59YeOT/t4fkVjFud9yttGwuPuxhpPQETIrcwHkQ3QxzNRw7cFR3aAHCNyDeyHNnLM3y0aSH4WOjoqL0bY/llIJ69fHZ2luFEETXrLCXtlfhox1oTPj4Z7oVWBLgHjYB6KPw9F+7htPhIe6k5+HjXXXchYpT1vVTilv6NyvXcLX7x8RERsbOzC40nksSeNehP8OnCx88igDTOteCWLdtVfDT9aDY+0hrYiLZXznq0MbKMAO9m16bDR8Q4D6kZnBAifp68KJvNQy6q0N3X14ff/OY3ERHxi1/8Im7duhU/+tGPIiJisVis93YN0f/4H/8DX/3qV2NbWxvu2rULz549m3j+b/3Wb+H111+PbW1tuG3bNvz93//91M9KO5g6eOixHFqSLi6oVCGKn33iiSes+rM+4YZ/XPHZWukn+n4bAG4Xv1XACGiFXK4mdMtYognlnkWIkpXl2YJOYxEfYC7ssjyG63piLjR38bbww12uTNxJ8lgOgRHktb4XQc8ozoUCalcebEVBABGYzM7OJrb/1qEhNTv8B8COge7t6XHW8FwHUaiBC5x48hU+NiQUn4F4koo0bvUyLkbbB77Ybdf64uNZbw3aevZ5GlpujFxKfERMN3ZcOZckEBOl9aAB0JOo1IuPtiCklTWh77chwHbxWxEp2Q1nXuP3nlDuqWWqlWWwJENj8nz09w/E8DGdxShIqG1NZSap1CTUkmr6XQuHkHtmxfteRpvxKqBxQeXWEE1REMTw0Zyn1Z3N49DQrYpFjqwmkmkNrEy7QdCFHR0kLPjqzt6N+lgT43sG4y71fpdRLW5Q98JIno/V4lx6dxM+SktRs/Z4Wsrw0aaF4mMS3p08eVLZF2n2NKTIRO7CyII4ngYf59G4ajcHHwHiRqt0GBmEnjGLg4+5XAErlRFERGHpls/KY9ybKi0+0tjLc/OYz3db6yCqhPAYyhj1heHjJLq9AAg7XRh5XL2nCx/lPkjaCzf19VnGr2bwkIsqdG/ZssXq9J49e3D//v1LoqX8+Mc/jqtXr8bHH38cv/zlL+M73/lOLBQK+K1vfUs9//Of/zy2tLTgww8/jF/5ylfwl37pl3DVqlX4F3/xF6mel2Ywk8t52doabpHxJbl61ZVX4kilgl3MUi1LSclr9gGLhQ2zR8tzNWtxd7gA20BPRsaFVe76dhqMAHYq/O0QGAG8LBa35gIu287jzvJhcjFOlMXddb1v010b/iWX88nJSbXeNnkCkMDbAoaZIaE7ildxP+uB8Dm3e86jshk8bsp1bl7xWCAPAa6smZmZSSWgp0m+wsc6jfuOzBrpsnS7st1ThknpRn/Na1+LAICb2DpLGs96qJlM5XJi5FLjI6J/7LhyzpsBP3SrJM8WH9blALC/r2/B+GgzD5q1mITVNrSTkZGVJcL6eMWJWQQgzCA3vhnU3cBf4WhTXOCjcpKc7CoX8lrALVtIYeBiFO+rnTs5Oempt82ZxBbs6tqI8/PzDB+HPM96IPzNfY6Gj65zjRVPMqbcUmay4D799NMeBtTX7jPsWumO6mPCyWpdjf2mMXm6lbENtdrdN9xgkoS+lq1zLZFnho+XNj4iYl085Lq17WIN+coM7qutNVMmy3WuJgh2oy70pcHHSQQ4xdowg0m1of0KsUNWwjZExDvvvDPx+quueo1nD19ba0O9+AiwGiuVEZydncX9+/3eL/XgIyLizp0Diefa+EjtpPOqtfEbHi7Hxr4+fKyiOwHbac+1h1CrzKDho8ZDtoHOP946NISbXvOamBzSLB5yUYXu173udfjnf/7nsWPf+9738Kd+6qewpaWl3tvVTbt27cJf+IVfqH3//ve/j6985Svx137t19Tz3/jGN+IP/dAPxY7dfPPN+PM///OpnpdmMONxV8klwHi2bgATJ500+esVEE5TuomOaa7GC4l74MLq2bNnLctjL8TdN24EY1k9FH4/6Ln/wXDD7ABdWEuTNTzpBUQW6LvZvaUVHUCPeSf3bLKq5TzPGgi/J7lM8w3OM4QmCQxJc86tytJDgl7qkwA4BYCbwR0OkLQWitp1LS142969asmxgqI88c2jywsDAPAHUo5nPdRMpnI5MXKp8RHRP3Y85sq3F2S9zTJb95oAsU5Zo43gY5wBSCM4JTEbBusjwbPIruMugxfD39ox7rre4XlGO5r4SJsZSZMR1y+Y99XOJXy0LWVFjGI6+f1zorxQEtMOaGK8k9+ZGj4mCwyueYnOI3yM2kofLhCX0Y49jLuw6mNdRE0o3rv3NsVqXcQgKFiCQfJcHkFpYbRzvWT4qNGljo8TExOWV4+Ph6wf37g7tMtaWe/xtPgIGAnttH9uRBPicyj8nlw+ymDO1tgxjpHPPPNM4vUHDhzAZIxpr+2/+vHxRnGeFkfeGD4iIt53332e+/nwMTp3YmJiAfjoWwvyPWgwsqtrI9ol09pq3gGcXDzkEbD5R8mXXunZZytC6P72t7+NiIhf//rX8Rvf+IZ6zuc+97m0t2uIvve972FLSwt+8pOfjB1/61vfij/8wz+sXnPVVVfhI488Ejv2y7/8y7hjxw71/O9+97v44osv1j5f//rXvYNZj6W7M5ezsjKT8FaEuCv2AERW0uMQZxbJUi3dhyvKIgrAWK7pXBI4k6zFrt/eHv6dmprCsdFRLIT9OQ2GMe4AwFax4NMIbXm2UQIAPAxxAZITPVe7vifMnO3LYL4GwGlF92V3bw8CKxO69iwI5yyt9jqNpdvVtknlnqQFJMufdEEfBhucKmBAqwtM3oAA7PJmBQAr1n9sdBT7+/pq64Fbrnt7eqy4GF9t0Z8Ak3m60NFhWfcpk3mS58ByuJcvN0YuBT4i1o+RmiXHJUTLuS5APMaK1mc3AO4BwB+HheNj/WVNkn47Wru+Wq2GQlYeDbNCdVhXx/aOmynRrCfkcngYuQDJaXR0zHIN5AnAKNurzyWyVCrH7lutVpl1xj02pvYtMcY+Bhkwsm4lxzqnsXS7MytPWveMW5EfRdtVdgRN/Dg/VkEj9OYRYLNjngoIIjHq6OgY9vX1h/MStzTxGFdOdhI12ac12N8/UMvaT/vGp9jO8PGTseMrER9d3mLlUinm1ePjIcmzz+3JIzGioqw1W0ByK9N85aV8+HgIATqxtZXcl137vehoP8fH6LkSI6Pnxq9ftWptCnw0mLZz52Dsnmnx0Xz2YWTxbQ4+IvoVCn58jM4tlcoN4GMRTRgBxeMXlHHuRi0vx+joGG7b1ofSS4K75HPy8ZD/H5hyuD2FgmUNd/GQpIT3ZfOXtChCd19fnxMol4r+9m//FgEA//iP/zh2fN++fbhr1y71mlWrVuH//t//O3bs0UcfxSuuuEI9//777xeLCFINZtzFpIIyIQRlCKRJngM72RfFGnBBSApFe8C4bmsJrQJwJ8aSJaWSXsq+3/jzPqD0g9r13vCv3BTnlLZT+0jJECtULxiE+fl5K6t5Jex7dxBgb0+PWj6jDJErFgBgdz6PFy5cqN3XZ32lF9shiDTSfaBbi0nAJWG4qJyXh3hSOkSjUNBqWufDMUsSxnm2RfkiD8COE6P4FQDAQkcHanNIL3JQvh84cADHx8fx05/+tFfZIecwrceC65xAjKeWqG6pE6ktN0YuBT4iNoaRY6OjWAgCay3VhGiWhZ/meU5ZezTHvco6XQg+1lf6yfeb+ZRKZYwYEf7b5vDve8O/LgZNxjcGSNl/k9zu5ufnlazmFQR4DIPAuFv29w+izgz11a5pb8/jc889F7t3utqyt2PcOuN6FmH4JBrriEyiZOK0ORmLUpt6blKJHspw7o4nLaM7nABQ1i2PW6zl/JrvHB8LhR5MGjeNyUs31mbdSozcDhk+Slrp+Njd0oIfBpN1XL5vC0FQs3DXy0Paa2gWTdIqiXkBuhNjyXNdsd4+S3cafOTfXRh5ztF+jo+6YIqIeOHCBVy1aq313Fwuj8PD5TC5psSYbjQ4YRQLa9asaxAfqX2EkSRoLhwfEd0KhWR8PIhcVonmJi0+AsM4+U6V8xQllnziiSdwcnIyFUYuJg95DmxeY7HwMbXQ/bM/+7N49dVX4wsvvBA7/vzzz+O/+3f/Lu1tFkRLAZqNWLoRDaMTd/GKL7StW3cgQCSAjoGdgKsLjAWRhKMK+19bFF2dnTgzM1NLiqLFJufDez4GUXboU6Bn8SMhbDXoAqJWXmqj0o98eB/SuLusWlMQaXPpWDsYCywXICXNzs7WXJNc995ZjAN5L8THjl5Ka1etim2scqmkCtFF1q8zEGmkNeZ+DIwgARAJw51gW4dlrXNaRyOVinVPKmWgWZ35uQQU9ZRRIIaP4vRPnTpVK+1VHh7G7pYWPAhGYbEfbOZt7apVNTffJLdh2VfXei1Cutjc68R8Siv7UpcMW26MXCqmshGMnJ+fx96eHiuPBO2pHVu3WnOdhJG0J+n3heBjT62uPMVan8KoFjJnXKj0FGXOlUxNvHyKYdpaUE/IE2BkBd6HWoZbc4yYMzq3HY2V4Ek1XhExjo+ue3/kIx9BdxIkQC5kysobO3fuUvpPSYYCNCVqOPM5rTxrDKNMwGeQ6o7L9kxMTFjryCgU4vfr7OzGeDZ0yWzG+2LXyHUze1u2bMdqtRrDRypd098/GCrUyVK33+qHYe7XsedpCha9wonO/FPm6Cjhkk+xneHjysfHsdFRNeFqPsSyw4cPYyM8ZCsEGEAeTXLIuLFi27YdtbrK7thksh4/gJFrMa8XL9dmi4IP9eBjN9uz92CEzZqwOIU2Pm5HLkBqGBlZhO9FLb/C0NBuHBq6VeCRDA0J1H3rx8e+8FmEO66KB/XjI6JRKMgwFoNBOj52d8dDU0ZHx3BiYgLrxUdErNXnHh8fryVsHB4uh7HkdyPA76FUsoyOjoVtSMZInjiUaKE85CsYPubFXlosfEwtdCMat5qenh787Gc/i7Ozs/iGN7wBgyDAf//v/309t2mYlso9iFPawSStC1lBqxBPbEWfNMkyDinnqAArYmWJseXPuwFMCatVYFwnjkNkTZbuxn3h31eD20pEMcFV8NdOlm7mZNXKh+25GyKhNA92fWipadIyFVYgir3mm2mgvx/LYbKRa0Ev17UtfO6ugYHYGObXr4+dS/Hd1K93iDkphvfh/SnkcrG+kCW+AwBfEx5LcvGjmta/+Iu/WBvjebA12xsKhZhgIdfjCUgXJ+YaX1krXTJvpFjxuTRqSfG0WPoim0/fPjkIRpkBnvPSuFI2K2ZxOTFyOfARsb5kkz6t9ImUc3+P+L1RfCwCYEcuh50d+ZAxeBgj60qcQY2swEW0s5eTlYgngPFZfVrFPbjr8io0TMoZtJlfXUjT6+lWMB5bGDEwRqDLo6ldq5Wt2YjGOt4VY1wfeeQRTGYS3xE+i9d2rSBAp6NPdI8imnIyBSRLiGvvEj4ePnwYd+4kj4DHUEu89PTTTzvx0cxNshv3448/7hxfu1Z6IFzI07rZ60mf5ufnlbhKu0ayK563HaIcGBk+rlx8RKwPI+vhIR8DwNEaduRRCr605vTY5AoaYXs9Gkx6GI1y6Xb2u4aR7eJ4I/go71HEyHW5HQ1uxbFECpvpMJLvJ7Png6AdR0fHsFQqYy7XjiZ5mh1rDLCtAXx8C+srWbCLYR8ifDQ4wu/D5+I6796dmprC/fv34wc/+EFMwsehod01haIdyuPHx76+m3B+fl4d29HRMbxw4QI7rq3BPKbBSC1x6EJ5yDxEiXmXCh/rEroREd///vfjmjVrcNWqVfiDP/iD3nILzaZdu3bhu9/97tr373//+/iqV70qMRGGBPRbbrmlqYnUEP3xBfsgzAqey3ljqo9DXFCqNyZYyx7uEqKnwcSO89/I1Xu7SPom3Z76lH7MgS3Mk8CZh0ijJNty044dODMzE9v8ar1xEZvBs3fzMQEwGbz3lEqJrtUkLPKNRRplGSdK87MGbJcUOb67du5EACOga5b4cqlUm7Ok8i2UWIqPcRXibvKu+q6UAdqnHKG2aONL2j7SYMr78HVKgk9SYjatrdVqFa/dvFndExVIjv/nYROu/aSVmmh0n6eh5cTIpcZHxPqSTabBxyfBn5H3vez3RvBxEgz2lWJMjWSUigjwLJpkNvx4GQE+h9Ll2AipGgPKmZU5tBlV4/odCaF58bupQS3xkfpE3931dLU6twYfo5JARdTL1pRr19AzIyu6q+TL2vCac2gnkZOM+JvR1J6OW5qIwfLho4lflxYS8g4wGca1pDjxmsXJzH+pVK49S44vCcocH+P34QwrV0JwN9J4Vmetr1u2bGNjbjO+7SKcKh9i5BgA3p/hY41WKj4iLg0PmT5vwj40HipciHK5o0+jjZHyezPx0W7HDTdswZmZGUTERB5Sx0h7D5IAODMzE2YzJ6u+22Opfny8iHELdrxfxmIOaJSYtrdSqVSuzVkSRvb301zo+KhhTj34SFidhI+IiKdOuWLTeQ6VJIx042OjPORy4GNqofub3/wm3nnnnbh27Vrs7+/HdevW4cc//vG0lzeNPv7xj2NbWxt+5CMfwa985Sv4rne9CwuFQq3u41ve8ha87777aud//vOfx9bWVjx06BC+8MILeP/99ze9ZBiiX0s5A3ZWvSQt5TPsnKPh/84kWuGi4KDNLT8VsIXOQi6HbaDXma65cDIBrAjuOtO8H2PKs6TgJe+TB2OxJY2kZnF11RvnMc0Pg9HskyC2f/9+bx3uh8K/3E0nbWw3fcqlEk5MTODU1JSaXZRnQqc5O3bsmJ0xXVj20yRWAzBZQAl0tbEjDwIJPGSlB4AaILqec+rUKbzjjjsscOICzzwADoqxob77AIySl8jna5koywB4DOySdMttyVkJGLnU+IjYHEu3ho9J53NLdyP4OAeAPTU3yBMY5eGQQmcBdffHAsY19kW0Y96IKeNMxpjynDhTodVYLRQ2pLDYuBkjw2A9jMZCZMq97N+/n2Uy910bjaM/bnEo1qZSqVxzYa1WqzgxMcHKltGHrE2R9UTGpUurVdSO2xPb8+CDD8bw0R43bgWUzF4ROT4mCS30rokLEXys5tG2NNlWNg0jfcmd5DtnEACfhbhnVIaPKxcfERePhzwIJizMXpv2mouHXZAApOOjscRSzWpNYcfdyRcXHwGCGD5o+9xfc3wfGnwsIyU5Gx8fx/e85z2e6x6KjWG9+Dg6OlbzViQeslQqe3Hi2LFjqmXZxUMmxZnv37+/QXz0VXLw4SNivISYVqs9/m5oBg+5nPiYWuheu3YtFotF/NSnPoWIiH/wB3+AnZ2d+PDDD6e9RdPoQx/6EF599dW4evVq3LVrF37hC1+o/VYul/Ftb3tb7Pzf+q3fwuuuuw5Xr16NW7duxd///d9P/ax6BlOLB+4Co50cg3g6+v6+Pqf2hVyhc6AkPwG7lJXUUnJX34WUByOXNa+LRqhlP+05j8qNucpeJVlcXfXGaUylICw3meu628WziW4dGsJ2EC7jYBLUAEAtXiVN7WlSOsi+0nmnwzGRLtj0AhyCZIGZf3p7erAgspvmQQ8Z2AOAj4b/r1+7NjZOFEZAc8bHV85vkbWP1oAr1j4JwLT4HG5pH+jvx47wuFQk8TbU9l4QYHl4uOn73EUrBSOXEh8R04/d2OhoanycnJzEW4eGYlUXCCNXh9c0io8nALBUs1InVZzwuT9SnGEyk2WsBU+ivz4pWR70si6lUrk2lrZVwVd7V1rlpbUo6doo2zCREYjbMe4uXkCT0fbJ2jymrz0dZ+BKJZ459zQC7LPcCyMB4TQmx+BHfezp6Q1rzUqm/dXKmOxBk7UXcO1aCjmiMSI3WTNnHR0Fdp0v0zLNlR6nmmTVlzG33JJUrVax0NGB7QpG0v8ZPq5cfERsDg9J+Ahg55FJb+nmrr4LLaG4NPjI8UHDmOSa43KcIiyIvExc191u7VuDj3mM3PEPYnPwMf4+SI+RQ5ikVPTj41Y0ZSqlMDyNUcZ6OUaztd+S8fFJjAv1tA70fCSLwUNq+Njd0oKD/f3LJ3R/7GMfs4596Utfwle84hX4X/7Lf0l7m0uO6hnMRx55RE2q9Svh/2SdcdWGrgDgFogswcOgW5crYjFxGhsdjQmovnjepN+Oh9/lPaRAJmvruu5HLlFJJblcFlefi3SHY5y8scYQCa+0uebm5vDWobg2MgATA67VnCbyaaoPhnNGVokjYMdo5wDw6aefjt3vMbATwRXBxDNrye2KjucD6KEHACbG3tWmgI2vBk6FIMC1q1bFzs+DDWC+pBRqvXRm/ZdZ6/k4z4OthAiUezRjn7sow8jksZuenk5MOkgKMNqHrhjsVeH62g5GEVYvPuYDLlxdRHfMmq/8zfHwu7w+LpBF7n3phFt3WReouUzazIuPMe5A28pUCf9f5bm2EwGCGD7aSYYCNAnejjiTFyH6rUBBkGdWniNoW4ZzFj6ae42gHnO5Gm0GsujoJ6ARhCfQWLv4vWiMtDYFaOIsuUUwYm6DoGBlSl61am2iAK2RFnMrLVsXLlyI7RnCSC0fSIaPS0OLyUMm4WMR4l6M2x21keWaM7keuIDqK12X9Nukcv1i4aP53e2VkkaJauNjFHOcDh8RET/96U9jZ2eX6NdGBPiNBeEjwEGRXVzHSHK1j+73GNqJ4Iph24uYDh9pXLXQA8LBx9CECMjfVoe/2fjY0tKNw8OvE/H46daqpEZ5yCR8TIORixrTLelrX/sa3nDDDQu9zYqlegaTYjnOQBQzKCeS1yymDUFWQW5RfkYsCvo8KRbFSKUSWwzz8/O1WOxmW7pdAhnFGmoxv/x+7ZBc9goAanUONcFdE+bynntWwS6fImM6zoXnHj16FCcmJnBDoeDMspy0+XwxWbR5KTNkBcxL8QjYcfD0HBIS3g1RDD0fe1cJJC0WfRMYoUS+jHlIAZUrcwnyGjhRWylxx9NPP53Kdd4VhyTjViWRS+xDADjO+noRjNKiPVy7NU+DFAJ/M2MWJWUYaYj2B+FjFfQcELRWtOSUhEUykVo9+BhXEjbT0q0LZBIfkxMHSXfLuFA4OTmZUL+5gvWXiqmG7XZlHA6QYiwnJyfx7NmzITOZR83FE0DPwi3XQFKG2ihzbgWNNeYIyjhPekZkFfoV5OXO4kygq9+Sad+EUVIjaWkqhGNE5cpcgrztQk5tJXycmpryCtALwUdExLvuugsJI/m7YAgA12X4uKS0WDxkvfh4HgDbRf34np7eWNlURIORUfhHMy3di42Pxs07uVa25iadJGhSaUbXdTY+GiUCnZ/sBi/Jh4+EE3GMLKC0WHd2dtWeESXMfDfaGFnEeKLNJHy8GyOLuYaReXSHG1BuETc+ImKMh0zjOt8oRvKwU9o7GO67HNhGPB9GLqnQjYipa5ldilTPYEpLp5ZRl9dRlgImWZQ5E+oS4B5KWAyzs7MmyzTE3Sg0y6PLHaO3pyd2vAhugYw/35XCPwDAW4eGsHPdusTEWJSAzMVM82zVAICbPeM0CXpd8EEwMVIIpoZp2lhSV9IyKiWTdC2V5pKx2q51ctvevVY9cvlJ6rd8PkDkJl6PEoa8DKbYMfJycCUpovgkCXxazHk99RBdXggjEMWtNRKbs5hMJWKGkYi6J4iaA6KlBfeUSqoCbkLsVdf6vx6S8TFiOMjaqGvge3p6VYukfbyILoFseLhce3ZSWZ5KZQQ7OvLoZu4Ah4fLCfG9R9B2kd6MyUzcZO3/a665Xlw7iAAzSIzY8HCZ/aYzvq560zyxUdL18XPoPB5basaVrB7nz5+3MhbHP0n91oQHHnZQr4DBE8IZK96dd97p3AsaRroyANeDj/KdQfg4neHjstBi8ZBcQKe5TMLHUQiwBfJolIXH0ZUxP2pHgEagc+NjPIFW9FsU791cfDSlASU+FpDjXpSAzLVP7dJbJuljMj7aeSiS8DEZ4+RYp8VHjYd0CcDDw2Wcm5uzcmMsDB8bxUhSYE/hQvARcWEY6cp5BOFea5SHXHKh+3Kmel82I5UKFnK5Wn04OXmagFcEY0Gchah+ts8tesqzGEYqlVi8o3zmQH8/zszMON0xLly4gIPCbTypPeTOMj09bV3X39eHu5mgJOOMbgTA3wSjseUCP5U4OwhGML8xPF/T7CZZurnQSxm9udC/CiLtvy9rMk/iMDc3ZyWwkcoKruDg4ErWttMJ7Q/CazVL+HUJ10E4ZlpMy1ve8pbE/vHf5sDWspOyQlt3aQTqpAzpzzzzTM0S5KKx0VGr3izFvLXWMXcL3ecZRVTP2NH8PwxREkO+hufADhMgfEQwYRF58OOj3PsSH432v4BJ2bVLpbIodxK94C9cuID9/TLhi5sRKZXKOD8/r5aA6u8fxGeffZY9p0Xc80YE+E3kTHJkveAx1RTDTPXG/fGUkSXHjFGpVA5dSw8iZ647OrrC0jmUtExn1iQ+yr6Ojo5hpTKiKjJKpXINHyOX09OJ7Tc1YOU8Ahp3+qR+x/tHGcgJH5Nj4/lvWrblQaSSZxLLfAxjUgZgqr/uigWl/eXCx00ZPi4LNcJDdgDg2x0Yp/GQlMui7MDHtNnLORlhjQuocXzs7x+o8ZBaKb0dO24S+6JZ+CgVi23hfosUCZFSVMZUk6WcMngnJ0hMi4+FQk+Ij6TQ8GPkQvAREYVbvhsfzb1sb6FkTyAdHyNvhCSM5L9pruaN4yNiMkb6eEhXzqViiJON8pCZ0N1Eqhcwz58/H4uzkZNXAT17dxFsAcxVgimAyJLpWgyaML375puxv68vdoyEInLHOHXqlKV12wfGjTdpMW7futV6Hgn2cqE/xhb3jQCxa4rh3+58Pna8BUzNcdmGuXBM8mKcXLGdclzky2uPmAP6EPO+fYspTTE3N4e9PT3WXBaCwIqzkrWuAaBmrSXlzEWI10Dna0HTdufDMZT9zgNgoaMj9iwu+D7zzDOJ/dPWn+Zm3wamzBt/WScJ1IjumPdDYCtient6LLc3niiQuwQ9Ka5diZacy5nqGTuJj3I/u9ZcPfhICqpJSMZH+XK/+ebdVqwyvfC1MjRR7OA+NFbOJEZkPQ4Pv07JqFsWbtIn0FhdNoXn3Bg7n4TLgYFd4niAJj5PtmEOjRUkj3aSsQrKWDltXFpb14hnuZm8LVu21/DRWKDzKJmjSmXEeoa0VkfzwBPE8Rq/nMErou7q2ILGJTVuNYsn9bHdupP6Zz48AZqWbTmPAAG2tLTVlNFEPqFaf/aHUSuTVKmMqJmKM3xcWVTP2GmeCml4yC6IqrZo+BiVGPUry4hcGNnXFzeqSIwkHnLx8PEimnrVpJwsir1hvufz3eI4nS+t2sOoexcNYxp8tJ+TnCV9y5bt+Oyzzy4IH0dHx/DZZ59lx5LwUfcWMs9ebfU7CR8REXfuHMCk/sV/cz03wNbWNbWqFkS+smPEvyY/W+chffh4kF2bWbqXkep92ZDgoVli0sRXc5DV4meLNPnh/e72LAYOhIP9/apQVC6V8JFHHsFtW7fGnjU2OoojlUrNMuVre15kzeZJw+R1d0NoyQXhOhUeXw/2S6UN4vHbcxAlGJPCMxfiNPflarWKg/391surO7ynZObzECW5CwCwp1BIHA/uFuMSRnt7empJnYqi/WSVOM0AQQMKKazycRgMlR6cJicna+OuKXNWt7RgHvyJ69rZs8ZGR53lGuj86enpWM1xrmDYCDYT0Q5GecDX9cTEhKrhP8e+X6X1bZljFi93asTSfQJsIboZ+MgTs52C9PgYWTHiAhRZGI4ePYqHDx+2LBNRtldf/LfJBmys0/b97Wsn0QhakmHpxnjiLjrehcbiE7D7zGGUPEcKbZE1XXPNo3EpFDagHZvYhpHbKWdStyAxVIUCKVbcljV6RqlUVhktM7akfNUZ6ziTx5lOzoQF6v/9/YMWPiIibtvWh3qmXxKG1mG6bMsmBpXG1yfQx7Mk875UlDnoRIDWWEb7DB9XJjULHzElRibho8vtN8m7jPOQ/f2D6l7t7x/Ep556ykqKlh4f78H68BHRePk0gpFFdo85pERo8XFajeRGnYSPxqVc7s1uNLgr3eQpX0SePa9xfBwdHWNl0JLwkXDqIMbrhtPY88oWfnw08eQ07hIjg/B++RRz3l573ujomLcsYrQOCCNt5c2+cP0fBOO92lMoxNqdhI/vDf/2gTsctxl7PBO6PdSMeByavF8QoEifi2IhSEAlAf4eiLL2SnfjcqmkxjRIl18fQwvhvR8LF9pIpVK7PgB3QrIAAO9IuLfsM/3mas+97BhXLgBrwxCYhAdF0f6O8G+SG54v07i0wPeCeYm5BAA5l7Jmo+s55VIJW0AXOgEA3w26KxkBRTsYl7IB5R5J8axyzOj7008/jbsGBrz9o/mgkAAuUGvnk1u/9mw+PppLOylNqJxKzLMg/PDzpSJiqbLzvlypGTHdT0JUvq5RfPwoRF4uG8U1LnxENBi5pxYm4tegG4bmHHLhMGJYpLBGDBYJbHq5G5uR8Gn07xXHH2b3oTYMoXGzlgyZ8YRJwkfEJKuCFjveiyZJjhyvZMuaTxAtlcoIsXrqcWtJ9BzpOsnHuxsNw2sz9Fo8q2H+ZP/Gwn4DbtsmS+e4rHd3I0BUysiXJCmXW8fWl1xzXJESt4IND5czfFzB1Cx8TMtDuvBxBwQY1LDhnLXOXHGxtstvGoysIMBjdeAj/X5Hwr3lvqHf0mDkLEbZzwEj9+nr0I7xXoUARgGWpIzwe8VILyVKJMaxemH4ODMzE1ZGSMLHd6OOZ+fC/9uxHnyM50SR70XAD33oQ/iKV/yAt4/RfBwKFTcDiedHbv3u8dASD+4aGKgLHwOwSxCvqOzllzulHcy5uTlL8JCaxjRJrNrALdgCAG4oFGrZ9YpiYfDM6ESkOeVuzPzZBNakISJr7xhrF2nd3vnOd1oCIAnoeTBCsHZvrc8ktLna8xDopa3k84vgdoHWXKbINZQyYLuev47N1yBEMaVSAHBZoImZ9WU051kU5ZoIwNTclEDRzeb+YB1tkWviIMRj5rnl+oMf/GDiWqVEapMQX78+t/Ve0R+5Ll2JY/Y4PCaKyvh0BQHuGhjwZvhtZJ9nZFOasdPwkTCSBOS0+FjI5eIl63K5GC60gFFGpcFHxHiZRTeDsA/jlox4zdSpqSmcmJhQkpEZ5jOKt3aXA7Mt3UnteSg8x1W6hTNCunufKz6NMPLOO+/0tGENGqvGIEYZcC+KtugWaImPrmfccccdythwxnYzRjW6pVUrwLjV+17ULD2uWrl2zLwZt9HRMTx58qRj3njbqK6wYS7dpYzo/ENolBec+ZX11213zSDoqlmBMnxcedQsfEyDkTmw+cc4PhI25FArsVepjFhti1x+5VpMg5E+fCwiKTDNuh9y3FvbNyS0udrzXvTjYxJ2pOMh3c9fF47z9WgSrtHvvK71YuPjFjRWeymUd2MkNJMgWx8+mjVxEKOY+QgfI6t1GnycxLhyJQkfOQbGlTdlCFT+sZDL1c0/loeHU1WJqGePE2VCt4fSDiav/8qFH3IHohq0mxVQ5HGImmXz1qEhnJiYwFOnTuEjjzyCAMb9WC6WPACWh4drbeKa0zQJx+SxmmDFtG4E+vsc12jHyqWSmtHcx2CTZV0TqPn4uO5B9VwRbYt/mpcXsGdRohLevg7RjiKY7OquOXD1U75oEZIVFrVxZeclWcMnJydjSdy0eH+6vghRLXKKWZdrNaaQYW2g8AXpljOYkDSOr0v637W25Dh513RKhrKefZ6RTWnGjuMjzfUkGBdwAOPZ0ig+9vb04HPPPYdHjx7FAwcOIIDJ/yBLx0l8RIzHeSW/8KuOY3HLRGQd3ue45pB6/1KJXAiJkfC55nGmsYDJVmD9Hjt3DsaUELZFy5elNsfOISsOb9+1qDH2Q0O7rfH399PF2L4/xbydwyRLj8RHRBO7aWf9DRBgGwZBoZbMLsrILN0sx1Ayl5OTk2p2ZhPSEKDurs7Hh/53ra0MH1ciNQMfDwHg0ZQYKfFxQ6GAzz33XM1F/Bd/8RfDfUuJv+LCJl8X8f2ZJimjLljZlls3Fmr3KZUkPpISzIcdjeMjAMTcqxvDR2rDCEaKSVJeLAU+PuK5voyN4qOMO4/u0cbi0tPgY/QejcIXNHykPtplxwCCWubxZvGPmmv9QvY4USZ0eyjNYNLGOAJRjDF9Nod/KZHF7ymgWIYoDpEE3YfCxdMOgBu7utQySQCRMMgXi8vKqiUeyoMBa77oSIh6e/iXu9jcFCZiS3I7lnEQmpBXBMBhsGMn8mCyiRMT7toQ94C+kXhbBvr7a+3msVIViNerjmmFwViXhyFuVe4GwG0AsYzw1I9zEDH1a1etsqxpe0olbA+CWEZxSvTmK1GQ1L8JiCzieXBbw2WGdZqTwZ07a1nbT4frLc+ue+6556ykVxUwa5WEbwlSWvbyT3/60zUw1PpTAaPA2AzxsaW17VJATDruJ138m7XPM9LJN3Zc8XSbsocCMNULXPg4yPYJ4eM7wGDqITCKrh6ReJHfQyrMOFPJMTIqqSMZuwpfXhgxNEeRrC1pLRPGRS6elZbXnI4zEhvRZljyaNwfD6E/pjiZIQuC9pj7YDyJTQXjNatlG9rQWPC72TnbUGMizXkRwyutaaXSHisjcFQL1tdHHgetzdEkRnGUuqWnVCrH2kzzYY63o2GST4R/87XrZmZmQsFcMqwVNK7oceaSFJ5ynvWkcbwvFTQhAZvFc0jREVkDM3xcedQMfDzH5tPHQ0p8zIPkITUBK1KY8TKgNp7pVkY3Rv54be0T9fXdhPo6p2vuxnT4WEST7EzDyM3hnk3Cjp/0tKOlQXwsoLEuD2M8lnwYbZdsPz66vW7S4KPPGj+BjeKjWbdceXMaDYaZGPn0+Bi1d2ZmJgEfZR+NhX4KDL++SuyJhfKPXH5Y6B7nlAndHkozmARMFXC7OpMlZR8A7lYWSBuYWprcgkixzElCVR5MPC+3OLriiamkhAT0x8Si0+K8SVDzJczSriE6deoU3nfffTgY1uKWzLH23ckwQJTNPKktPAu7tPhriUbIpXtE3Otgwjxowufs7CxOTExgeXhY7d8Yez7VP5fKB19ZsHdAFPvtOqenUFCTuJG7zRFlDMjKT+vol37pl7Cnqyt2ThEMI6AlmZBuOWOjo9gGUY112dYjAGpcu7Sq0/jTOPmSvSXFYzWyzzPSyTd2XLAdAaPYkhhJORQOgsGz9RAlE6RPL0SMJeHjZHitXDuk1KL7D4DtuYMYx8h5MIJ3HMcCdJdVcTEibiZoYGBQvY7acvjwYZZQS2OQ+ffkGLhIIE1O7mbXh+X/a1aFzvAzJu4VoM28dWHEWEXPPnXqFE5MTCi1bal/RYwsQ+RyLRlrrmhJTtDk+r1Q6FGTFEUJio4o/c/F1tGpU6fwjW98I77iFa8S5xnmUouN5BgZWYQ6HW09gnpce1yol67FGT6uDGoGPhYh7gnm4iG3KfjIeZfr1D2aR4MlxirNhW4bz+bRWEfrw0iOc76EWa7rECMecufOQfF8F0Ym4eNRTztMWav68TFAI3SPiPulx8eTJ0/i5OQkTk9POz1u/Pi4AQEe9PTxHYm/u/BxdHSsFhrpwsjDhw/X5kzHxyIaa/qTFkbq+BhXruQgj6MQIILJc5WH5vKPNPfN2OOcMqHbQ/VYupMmkAOkJrwVwoXTDUbbKQUheV9XvW+5UMjC+2Gwk6/1FAq4GnSBr00uYCZY0T1dcecDImu2Vr95Q5j9mz6Fjg7sZPVFfXV4q2CEPhLWXK5Wk5OTsZea1G7NgimHRjWDx8UzSMjXagrL9tC5VLLL5R7fB3HXsceU+Q7YX807gX5vFf2hj0u7J0GlAmbtFcXzWwDwE5/4hGXpzre3xxKtASQnmaC9QX2UwPckQMytTmsnxeeec7RTm/8AMkvOUlFaS45vT/NwDW3vUAWDYWW/JIVgyH0l3cbcGElxj5olpQ1lXO3wcBkRUXUh5i6NpVI5VipFq006NHSrqHNLFgXOsPlqUc+gsZZLhoyEtYsWRppj/H+KN5zCKBaRx+BRXN5DnraQy2hkldWY0FyugJGrNa+jW4yNT/x7BXXrW4ARA+5ivJPazO9t17kdGro1tALFmfz29nzsuytBFd8bRmgZY2PC3Svznna2YysE+Ji1fpPxkQtXC93jGbmpWfjIk7u6eAvy0rOzlmshNFoN5SChzN2HrT0QJR3TMLKIGj7G76ljJNUAJ9Iw0lRWiL6vXduOcYxMTrplMMlVLmwEG8fHcfGMNFij4aOZD4OJUkkyhOnxMUA3PpILfKP4SBhZsNqwfn2nio/btu3AoaHdsWMujLTxMT42n4J0CHLlJwAAnQdJREFUlXbaAOrCRy4/NGOPc8qEbg+lHUxf9uZ14aI4nQJYRyCymJNbuuaSmwcbdHt7emLtItdu1Urb0oJrWltVa7PL+j0zM+N0Fz8HtoCOqNdvzofXJAnYBWVD5MFYvHibZDbWXgD8jfB/l6Vbs/AGADjD5ownCaOY/CTLO9/kWp/mwBYayXUMwQjukxAPMQDlGj7WPkt3Upv5/TQPjTWtrc41duzYMXzzm9+MTzzxBCKiFfNDJEMcZsC4C/Pn+/bOJohCKJCN7b2gC+L0PYtZXBpKG7NIycoWipHXgR8fL4Z7q0tZvzKjvwsjHwPAAHLIy2tFTIxu2SmVynjhwgWHK+Q51LLCarVJDaNFgv1p1JmfIupKgQJG1pNDSvt7EeBCrc1uS46LkeOMJCUzuh2TmbfJ2Di5LdB6TWozdlXU69BqDFklPN7ueA5nHJMYTrrWVW+2FWUiplyugDt37sI77rgDDxw4EKvt7sLHqA0zaBLTRc/3ZfUFyMXe11WIEpV+KgEfM6F7aWg58fEiRGFddq1ufU1Lr4zItVuz1BYwrtziWDHPmhfh4/z8fIK7eHqMjAv2LgG7gLpQ3YtLg48ySVhafHSFD80pz03CR1KGymsIH13eNTwu/ZyjzbdjvO/p8XF0dCwVD2njI/XxDMb74943OYC68LEMzKMus3QvPaUdTJ/b9aHwuy+OYI0AVC3Yv94EKZTc55Dj/ENgBL19ALgul0tsH49zoGysrvtKgVc9xzEm1EcXw3AQTJbB3p4eLORyeC+YOnv3gmG0V4OJZSbi1vkKRC7d0tuAu6MchMiNn6xwrn7QuUF4jtYnLbNiPry/a2yoZMg7xDri52rWY62uubw/uaKlWbf00Tws1q5aFfvOLd+u+SfBeWpqyrtGzoh1QczDfjAlIvaI9rQB4O6hoUXZ5xnZlGbs5ufnnRlE68VIHz4i+LXfaTFye43RPITGkkHueDrzxOOko2zVesIgm5nTmB5iMuQzky0AAJ8K79EWCvCH0DBgt4f9uRGDoD1W5zlufSILuWSiiFml9hnG12+NlbGIrtj3iuO5Rcd9y2iYOoqH1pKM2dZjva65vDe552vMr2SQ+XGX4sD8L0MK9HuH8YoMH5MUB/LdSQrbww58zCl7YKF7PCOdlhMfaT0QjxKto+Q15cJHd/KzQ+Ee/wnHvrbxEXGpMFIKnPT9IC4+Ph5EgzXEI6XFR3T0yaX8c+FjNbyGhOu0+NiNUax30r2512Pj+GjKnkXf04ZrjY+P19ZQWsOTxkNuUjCyHh4yE7qbSPUMZpLbNXdnTlogHevXW4tEJkAjLY3T6hq6RGhu3TzxGrfS0j3qiXPwlcOSrt38nNPhOeOOMeEvFhLMuQs3ANRiw4tiw/DvJABq1vkkdxTu3g0AOFKp4BXd3Ynl3LpYm2WftO8Ub0UvRO7eQnHi8xB3iXGNtRSE94DJ9Axg3Le15HnTKe57XBx3eVgU2HeXpwOPpWkPgphSREs2x5UYvH4376vsdxFM7PhIpbJo+zyjONUzduXhYexS1mM9GOnDxych8gBJg5EjlbjLJGHkbO2YK5u0xmBEcdK+pGq22yI/hwS9cccz01sA9Jg7Wwi0rU9Jfcwjt3CNjo7hnj17QwZWdxmNnnvO0Sd5jFw3D7LncqaQEkDx+XNZkqQQPIYUs5nLxZPbRYmhKPbUVyrpuDjuUhxUMNmKF3cp7+8fqL1rS6VymMU3OqcF8lgK+0UYeVtKfLyiu3vR9nhGcVpufESI3tvba8kiyUPFjU2Iult3tO/4HiBLbXp8RPQnnmweRrq8ZBYbH829KpWRlPjYggCfc4xlI/iI4Vz1JY6zjo/TbJ3IJJeV8HpeGmyh+FiofecYqeGjqes9WFtHLv6xzPbKYvKQmdDdRKpnMDXBbkgA5CyYpD6doLhN9/TgRz7ykdj5CIYBLIqFkQS6PIGVlUQL7MRfPL0+uWXmRfu6IRL+JiYmEDEei+SqDy2tmHzhcyZ3m3imT/jfvnUrTk5O1mKeZR8DMJZ7LgDOzs7i+Ph4rf5skjtKEYxLCkDkhjc/P28lRtsCJvPyITBCN7csc2GANGvnlP5rm567U/OXadKL9gzotc27Ojut55G7zR7Pfbnlz/eyn1LmX+4L2dfb9u61hB46ZwyiUIui+HsCoiz0cu5pr4yPj2d1aJeA6sVImSeAYhXTYCTlS/Dhoy/JIsfIAsslwTFysnY/lzXWHSc9MTHBtPQH0VX/1Nbka3GWY2hiDLnQlVxSbPPma1kd2Qrq1pE+5AwOufdR2bVkt+sAAbbW9hnNre4y+inkWW2jNstMyMTgnVPGQGMKifHn7ptJbuRnMF1t8wCj8IEx9Jcm4pacNFa5uDXRHrd4Xzf0XKEeH4WgloyTcI97cGX4uDJoKXlIypUjce8I8HcvX0fJlm7drZsLdDImOT0+InJPD70MXn0YuU08Nxkjr7761VgPPlJ7iX/04+M1tX1Gc2vjYxsCPIBGMD2EtmV5DCPcp5jxNPhYxLh7f9oSlMexPnxEBNjjuXc9+DhlzX9yeTKw+AnOP0p8rJeHTJtsMhO6m0j1vmwIIHituHIIkJIx5AJIEUx95IH+fjV5VjcYYfC9YITcNlBcisP6yrwdSe7QeXC7NkvhaAyiOIfBnTtxcnISn3rqKUugo364LJ3aIid38E7xzFW5nGpZprJc5PKUJATWEiqJslm+6/h3yZhQ1kaXW31nLodPgjveWHMx3wWGyR9S5pUUB2tXrcI8GIXEcTaHPCRBc2HvbmnB8vAwHj16FMfHx3Fw587aM86BHjvf3dJSe15aD4v9EHfdkUkoSIvP29YGYAk9BTB7BsGAJglQPE4+jba/tnYTEr01us8ziqiesSNcOgSRom4WTLm4DmW/SIxcn8ulwsduMLhinSeUcElrKCpLqGWTdgmBhlnYuXMQJyYmsLOzS5xXrNV5Jopr8isoY+AMQ7gdZTmuXG4VavGKq1atFa556YRA++O65gBqwiNRlNVWdxnN5TrD/8+h7fpJDKPGBO9C484Y7zMxxT09lMX3YTTMI1mcWlEX9uMZebds2Y7j4+OiJjC10XbBNFYrilmUigMXQ34UiXmW+FitVlmd2njft0OAF8N9sw6MoopuzjEyw8eVRwvlIWfB5EHJp8DHQhBgV2dnotHkvSG2tShrWgqYyThA+6sofk+Pjzt37hLnVVDL+O/HSAoX6Y7dz7gt6xgZZVFPh4+lUlm0tTF8jJShvrG9GI6lLMOYhI+TaJKs2fMa4eOTaLwE7kajCG1Dk+mc2mPjYxB0YX//IJ46dQr7+wfCcCK6/xE0OCjHuRF83I9GMLcxslqt4kB/P+YZD1kE3fNyiwMfG+Ehm42PmdDtoXoBk1xhTkPc6hgoi6MrBFNpISYAlcIa/74bjBWQHysPD9cWh3TrJnfmM+z8FohrSx8G45ZJJXhWAeCPA+BHw9/Whp8a0xeeryXa4jFrsnRWGkbgqvBvPnS3p8+GQgEvXLig9pE+NRdS9n+7Q9iTAn0RdAad09GjRxOf+5qrr47PS7gWKIFYUnx7XCMdbXp6Qcganm3sGfk6xlcmn9ugPPP555+3tIhJ998lzuPrURNwfKB3JvyfEszwpFlp4trOgO7u3ox9nlFE9YydCx/TYiR5wPjwsQyAF0DxqmEvUI4fhI8yhCUpHnj9emJY7sHIsrI2/ADqGcfz2NPTa+FjvHSWixGTezGHHR2F2LFCYUMNHxExRRKuSfb/PtbONocrZBE1Bp0T4aPrmVdf/RrRj0E09WKraBjGpDGYQSmok/vnJz7xCbSTIq3FqGwOCeRJ94/GMX6f3QjQHztWqYzg8PDr0BYwtPvbcYzDw+UYM+cTciSfwF0pCSMzfFx51CgP+WgD+JjGaEKfEQCsSO8Jlm8g7tZNrszSPbsFjeDGrcvtCHBj+LcVTZz0GUyLjwBBLKZ3cnJSqd/ss9ia+z/66KOW4pNjZD34GAS8rUXUE7T58TGuDE2yltNnCA3uTWKUZDFJSWArPUZHx/DZZ58Nx1ziVT78W0a/V498N9L/Iwhwa+z3+vCR7h9XwnCMlDykj3/U8LFeHvLQIuBjJnR7qF7APHv2bA3wchAXkpKELT7ZpKk8CJFFswNsS3AFjHVRxsciRgvUlaX7zvD/1ewYP4eXp8gpv/PvPE6c+nTy5EnLTYozBdoi3xf+fwRs4fLazZvx6aefVvuYNK7STZqY6/1Kn2QZMxJ2ZUZF33P5uAEYF+4JMEltkvq/CSIvAVnr2udK/ygAvjrl+JJyZAjiYQGkkJFZbaempnD//v04NTWFvT09qvdBm9Y2BlaagiQN6AFALcFMPVrKsrYeElwpM6aycapn7Dg+BmBbt9NgJDGfHB87ISqfx/HxCJicBgP9/db80z4uiuv49/eAZC4AjSXgV8SxQDkvYOfHXaFPnjxpucxdeeUrw/9djBhn+mwLyMDAoKWR99fErYr/ibneb/VHCqKjo2OJ+Ohn3HLs/wE0jOW7PWPw7rCOdtl6bn//IOq1rCvhtVvRXyLnOJJlx/T/OMbdXu1axtVqFcfHx2tWcj2jfBsay0/cus6Zcm98qwMfAaL8Jhk+rjyqZ+zm5uZqXnmN4iNd2w7GO+0MmASokqdKi4/uRGRUgWA1xvGOPpvFXl8YPm7Zso19d+1fUhza+Lh587UWD1kfPh7CCB9t5V9afERELJV8LtkSH/egUUwe9vR/U82TSuMhzZhrnlQBAjyKAK9OOb7cBT89PlarVWFx5woL8lTQMVLykGn5R4CF8ZDNxsdM6PZQvS8bihE8CFHJmn2exTEpJrczl7MYQXK1llblAOxs0eRGXB4eVrN058EA8BgAvp21U7s3gGFmNZfwCthx4nzBrwZTe1xuAN+LY0x5XndLC+4plSwAcwmBGyFyuQ/AHUt94/XXG0u8cD+/dWjITrDExnmkUolZyh+GyO2Pu0kX6uh/O8S9BDj5XOkBIiHfN76uY2RFTIpjuXDhgmX9bmttTX6uI4u9D/QIpGdnZ/HazZtr9brJZbgCeo35jRCPia/ts4SaixlT2TjVm2yS8BHYfNaLkVvEnlrb2ooFqA8fq9VqDT/kda1g8OJo7RlJ8cAFNIynxswUMR4DyS0ZbQjwAbRr36aJjRuznieTbxG5mZyN7P9hpV8BXn/9jTgxMWG5Vg4N3YqVykjsGLeSVSojiqW8HQE2hWN5grWBP9OX5Ten1nRNJ+gHCLDOc14V3WOOSJZyF0bOz8+H45LeukPzVa+le2pqCicnJ/HUqVO4f//+mpKfMDHDx5VBjSTjXSg+PgDxBKyalTzvwEdaU5OTk6EgKRVZeTSYR7j2dtSFuXx4vICRIqoZ+Jhm/zYPHyMFnGyH+X7ffffVjY/z8/PKc8lLYAgjl/lh1ENwXP1vj3lScfqf//N/eq6tZ3xdx5LxEdHwkKbv0fNaW9sSn6vxkN4cQwwfJycncfuWLTVvVh8P2QtKwukm4WMmdHuokXjFExAxa2k0KmfYZAfsPMrafY/n+pMnT+LExATuHhqyrLdJ192U4hzfs6uO/w9B6MINcaDXYtHz4eJ3bSStvvXY6Cg+9dRTCMpvm9j/ZL2mjaa9dMrDw1bCuUIuh21gC/5kuU1KDjYGURkC6X7v6n8vxIVUST5XempH4Lh/EeJWbbru7WCs5NyzgcY3KY6FW799oQwEVlJR8SRbI1rcrZZ9P4BIuaCNfQDuGvOZJWdxKO3YcXycZHNWL0YWoT58HNy5E48dO1Y3Psb3hDse2F8LmrLLSqtJF5oSM/x+RdQZwAAj64M/sRBZWSJXb8m43cj+bwm/a3GCAQ4Plz11xG2rhC85mGnvtWiE4EPimTIOkOIDi8597LMSx9vgChkYxIiBpOvWoIlD/D1rDDXhn4hbd6IYTt42PW5RV1ZEMd0+fMxn+LjiaLnwEUOMfHtKfIxCXORedeHajWjCLnzn+RRd9eCjCX0xeCAxssL2VjI+Dg+XU+Nj5J6uxZEHODCwq258RDQY6R5zqq6QQ1vp0YZ6/PQYSkUep/3792MSRm7c2Mvaor2DihhPCEoY+XY0VnL+TknGR0Sdh4y/46JqHC4esgjuHEgaPlJYJcdFiZFbQE9knFm6l4gaiVe8CJHQTYKIVtamICacXpa/J8DxuLgXfS6y6wKwa0+TdvQ06AIXQCSguu693/M7j5u+GyKrt+slocUsc0agnvrWATt3GqKYafpcDcZdm+LCZVseZufWZR1mm29DoRDTIGuu8dz9Xus/MfcfpTFVNGo+l/ZcOCaPQVzDrYHKGAD+hnK8CMYjIG2cn2ybK5RhZmYGEQ1gyrFZDaZ8Tax9ocCvZd/Pg0m69dFwveXD0ArygNDK9mUxi4tLacdOxlDTfNeLkQfFvvTh4zowL1sXPlJ79Jhuw4S0JMYD+5LDUNbZuzFu1dHuN482A7hanCcZFFftVs7InUMTsxfvF8Cb2blJFhBXfV494zBRqVTGKMkOtW8/6xNnMufZfTVXVMMgJ+FjkoU8asM5ZYwD0ZbfUNrQhiZrr85EuyjeNl0A4B5UrSIuPSfa4cPHG8HU6b49w8cVQZcCPkaCVpFhCeHWRXTHdAMa75Uk/Ptxz+/14COinqgtwCijdnPxMbJ0Nx8fERHXr89jHB+15GncBd/V/6drY6phZFRrXe+HSXCZBh+TMLIYXp8eHxE5Rurl25J4SBlWloSPZEy7G3QeslwqLTo+ZkK3hxq1dEuhk1LX88VRBMBPAeAvQLKg5iqf9bC4Rp5zFpITahwCf/KtU57fuXWbtzttvEW5VMKRSqW20E+L56XJMHgCHFm7IW4Fp7ZoZcsqENduUTvGlbYToEmX7zkwFuu80g7pfv8OMFbwHtEOLqRKItdcacWmOeZjVIUo2zjNNbWnCwxQFRLaKRUM5Hbm0vaNjY6qoQyFXA7HRkdje4PXXZeuQNLV0hc3v6dUwomJCbU8mQThZu3zjOLUiCUHwS5Xp2FkBYzibz3YOS0Ia3z4eA/EsYJ+J2wpivvS9zMQxYBtjzEXknk8jUnMTGTJkczTccf94kzt6OgYDg3divHMs/Q8n7B5AiNmSGM8y+zcM+z6ObTdKXnbqQ3jVrsJH+3s6SRwEoMvXU2jkkImdjCPxoKjM2CSRkfHPHVw5RjxeeHW9i40ioKCaGMXRgKBzUQnYWSUfdmeB16yLQkjeYmvDB8vHVrp+PiO2jW0H2if+GK6eZhIGgxaHHwslcqXJD4iaoLwHBrrvgsfef/fgQDrMR77bebGxae5Xek1pQLNC8Xup8FI3s70+IiIoWeUHYaQyxUWDR8lRmr4WC6VsuzlS0mNxHSTADkMtutDeziRPMN1ALYARDHTpGlpYfc6B+7SEdJCrN2X4rlpEQagl+Chc4pKP3j7qD51PTWd+QaRC53G40nwa2kHIH3WbvpdE9C7wuOuOuLzStvJXYfa5qt5neSKL4VUjebn56048wpEL03XGF0FcU8HXw10bu2bmJhIxaRRQpKk+aY2alZFqZlN406vJb47f/58xlQuMTUSs0g4VgAbW9ZD3EUWwCizXEo1wkfCjLT4iGDiW/PivvnweXQO4Z+7hNiTGMU0cmaGhCyylOjxwS6GUGJkPA6OBFeflX0S/XVUz4R/97Hf7JhI058R1GvkRlZqiqGLl8a5iOlqXrtcTeMMmEZRPDVvWwUNc5o0Rldh3G3Sl+E87l45MTFhJX2SrpXz8/Pe5Ek0XqdB90zjGJnh46VDzcbHTqgfH8kjz4WP5qMJuZoAmA+Pp8G/Mc/vL1985Dxk1D5fW2TiS95GMzc9Pb3O9aXFU5v65kljtA6N63i9GJkeHxH9Se3q4SHT4GN/Xx/uHhqKtYswkldbSoORmdDdRKr3ZaMJkHzyNorv5FqcJDAO9vfH7hWAOylGWgvxjFiE0jW7CAacCeRbxO+xBQmA28B+OawC47rEU/cnuWrMzMzggOgrfZL6MQA6Q8032O6hISzkcl6BcwiUJG7heBTCGsF0z4EwYyz3bEhqB3e/58cnlXZo2sC5ublaFkZeB94n7Mu5etTTzkl27fYtWywXHW0OfSBHsVPyZU/fXZlTXX0iL42iaFdvT0+q9i50n2cUUT1jp2mSC+x/ue/7APCkZ31v37KltnfT4mMajKT9dS7WLj3erbV1DdqudmSBKKNhbOLXGatsgIapOVg77nLLM4Jbmd2fPy+J+ZnAZKaKGNMOtC1F2j3JDZEzm0VWC1Z7T5Awvc/Tlna0LTqTVhtc+BgJtvswYhDTZArmDPKjnjZOxq7fsmW7FdOpzaEv7vyRRx5J9Eyrp4JGho8rh5qJjwA2D0mlOl1roVwq4bFjx5z840GQe1RautMpn6JyVvSpoHEXzqNe3YE+Lw98DAIbH3fuHGD3SlNKTLrg65jksibrGOnDR821PA1GNhcf6+Eh0+CjK29TIxiZCd1NpEZfNk899ZRdkgoU99sQBF3Cyvj4eKy0DVnKXYuJ6m77LMQy2yWVJbupr8/K5F0eHsZnn30Wd2zdigTactF35nKxrNbyHPqeRqtOpQ6mpqbw6NGjtezq0tJOwqs39X/oHsJfZkkCuus+LQD4AbERKav83SmuJyZKllerKu3QYnLGRkdr9QZPh3NI7v9FsL0V8mBipqUC4QZPOw+C22091u46QC4pU3RvT4+6DtT4Q1Bc4MPvpFDhCglXeyVlTGXj1MjYVatVS8jQhGWqQ5u0Z+llfIKd68PHi2zPpsXI9QBY3LFD1NQ2Mbnnz58XxyWj0oZdXfFM3baAaq7xJaDhpWBOnTqFuhWJu0InM89BkMdKZcSyRrgZqn3qfVpb21hcIDGcbWjcECmZko9hLaMsHxSPi3THLI6OjoW1dKnts0hx4JH7qHSrXI02g3yDp41n2PUu13Wb+fXFVJZLJSzkcrpnmsLwZfh4aVCz8NHFQ5KnoRPHJidr7+dNDnzcbsV0P4l+9+64oGcE4/XY0VGItbm/fxBnZmZwenoat27dgUuJj9VqFbds2Y564sSlxUejhF2NMi9ELrcK41b5JOwhodeFkW58RJQYeRr9+NiCuqu7DyMPYrPxsV4eUsPHLlBKgbGHNYqRmdDdRKoXMCluQb5AeX04VCbzkOM4TfLY6CgWgsCb+Ey6HbmeJzOmE9NL8XKyxh8tYBLutEU/Pz+Pp06dwus2b8auIIi/GIIAy8PD1jglveiJtOQJbWAsALSZnFnBRQkuw6QmC8ausW2HiKHhG7TIrqXx0eKu80FgHS965p2PFwDgtWArNADcJdHucNyfhyvIdtL43uEZDwJ2yhIZsHuehihJhYzpTtNfRN2dPgDAETCKCy4czYGxivJzx+R5WUmcRaF6817wpE6EkachGa98a4fw6d2eNSvxsR6MTMLHKGb3BBoLjx2XVqmMYLVaxaNHj+LWrTss7X8QdOHwcNkaJx8Zl2qZeKcNjZWJGCgt461hijgTS+3zC50as7kODfPGfyP3Sc6kUWIj2ZY25Zi8n27JieLH6XlFMR7nUEvQA3CHo4/EbLriw2mM78AkBnxychLn5uYYw257SlDd8aS1qMWxZ/h4aVAz8LFZPOT6nPG+kfh4rrY2NYG4HuWTybkg8RExOa/BYuKj7Xq+XPi4D+28EOSmzfFKF4DXru3AIJBtTM4zIdeWuf+1yhy78DGpnz6MbB4+yphufjMXD6l5jdSMf7U1HynXF4KRmdDdREo7mFp6+iJEVk1fYrF1YKe953Wp5QJKYk73AeB14C4fxYW2MgBOgF3eiRMtdh/ocwu565zp6em6Y8pk3x8AW8DMhX3mx3bt3BlLIkN9Gezvd2rAktpO/ScNGHfTrobP7wTb/YXcwfr7+mLHWyBueUtyv5+cnKxlqG8P55gneqI2U4IJOn7Gsd6uBjt5XxsAdqxbV7uf1/1WKGQeA5PHQCoFRioVnJiYSFz/LjArl0rYmcvV+ssTvnFNpVrbXZ6XWXIWhdKMnav8W1LVAr4+KDu/CyNnZmZS4+P9APiLEFV98GHkIJiElz58TJO4p1QqszFIc47fsmOX55JMHKBx34wzWv39g3jy5EkrgeHk5CQrEyYZvMDZ7sg9dYb9xt0Qd6BhqI6gnYQowB07bhLHWtC4atoMmKTINbESXkelyE6Lsa6isdBRW10M8tWoKTI6O7swl+MlkDy1tatVoZA5Z83Nhp4r8NixY4nrP8PHS5eagY+YAiPT8JAUnpYcinYcAU4yzPApn8poXLTj5Z04nT17VmDE0uEjouZ6vhz4KOOxJT6Os/GW7WvBY8eOKTkr4sqDpIzhBiOD8Jp2NEoAmeRO4iO1LT1GrlvXwe65cHwECBrmIWdnZ/G6zZtxHcRzJ3UDc0sP77MQjMyE7iZS2sGk+nHahCH4BRgrMQZz1QaIBNNTp06pjCK5oQVg3IfbwQinFXFfErTqcZ8ghsbnst4eBLEyPNo5hY6OxHiJJA1mtVrF/r4+bAeR8h+iZEc0jl0dHbF+37Z3b8wqIAVDspxXwHZlp3mkPgyAUaZIN+1p5b5liGLyyqUS5oMAbwdT0uUBULLWOxQQ5Hoj7++Kp+8GO4aVzzO1W8sCyedPK1Mi54uPgZpFvqWlppBJq6Xk8esHWRv5Ne3hvJ8W8yDvTdb2ZuzzjGxKM3YaPubD/ZYGH7W1r2HkyZMnvfh4MNzDHaCX2GsJ96a25vw1opPj0oxr37tTnKPHv/nwcWJiAguFDWiYqtsR4CE0DFQXGgYpynQrrT++7+b6bWhbYSjGkJixAkbuj9zCPY22hWUAyTpdrVaxVCqH/b8HAX4TAbbEzncx2JHiQ4s/HEE9iVMSg0xtJiaUM8wyMZttuZfWGVdm4HubgI+kIJU4nuHjyqA0Y+cq/1Zh89UMHnJ6etqJj6ti657W9GNoK8hI0OJ5E+KClKT+fopdTnZZN3s/Oe9Do/iIaDCyr68fbXwkC3w7EkY2Hx+5wDqABiM5Prqyxd9YG1fjHp4P2/4rmBYfEbn7tsRILaN5NwLcytpWL0Y2Bx8/Co3xkFKJ5eIhV0HkmbkQjMyE7iZSmsH0uj2E34sK2OUhzkwO9PfjrUNDTsGUGDxN+DoijtHxGYjiF2WJLp+FFTHarGncm55h/2vnJF0vY8mlADo3N2fVdA4getkUIaoznYd4Epk2AJvpDwIc6O/H3UNDMeFXG1uevbwdogR4RTBC5sNglBI3hr/fDZF7KgeMorg3feeZODUiS3cebE2cZl3uBRPPrSkQBsJzXIoRPkdamRKal9nZ2VgG9zSx9b51VwvPGB7G9SJxHc0FuQUVhGLF1Z+B/v4sO+8ikm/s0uIjaZr5+iiAnYU5CSNl0sk0+DgP8RJ7u8N714uP6UrU3INRtljXOXrN11KpHGu7xmDt3Xsb2taHALV4wsiicjH8m0fJzJZKZZycnMShod0Y1dbWBFteYxsQYAjjrpN5NDGLQ2is13cjuafaDJjOdE5NTSWuw87OLqsPUUZhKTiMIcBG1JlNEhBcFp6HxPzNo3TNHB0dw+npaSU7cfxePGllvfhI+T2k0oi+Z/i4MqhZ+Iig85BWgtcF4WOARvCU+FRFijkulcpYqYxYll6XlTVeOjDZ0m2w7xn2f3PxcW5uDru7rxBYQPhI2HMOG8PHNnY/DR95H9qRcM3Gx65wnI4jwMGm4WNk6dYwciPabaawJU1h6cPI5uEj1oGRHB8p3CyJh7xJeJ82ipGZ0N1ESjOYFOPhmrDj4eLoBMArxAJoA2Nt4YJgEgAPhhmzAYxQp5UW2QdxoYxbaScnJxNrdWqaQhL4yCVDWj4J9Icg3rciRBnQu8GfEKldxIFLhqNcKqmJlgJIiGeC9LW+KSnZITAW6naIZ16nZ/04u+5/h8d5v1vE97HRUZyYmKiNoRSaA0iOp0O0a4JrfaC5p348AJGAzRmy/Z7xuFGZ43Ywyo3BgQG17EwRTJhC0vze1NdnxSDSutPc624EXclQpLkNE6XwRFrqGkgR95UxlY2Tb+x8+Hh3+P8RsD0/WgCwIHDBh5GN4CP/PalMngsfAQJsqQlwFZSMisnE2xK7px1XSO6J9Vt4EIm5DdAuZVPAZKvuKUxihCkPRjwD+SAaxjHKKmyYMSncAkZKBvrEx4GY44gpJFdDzhQGiRjptphI1/fb0QgQdP4m0bYKRnHhSYKBFpfejuvXd+Kzzz7rcPeft+7FS95o+FgeHnbi40GAWpJR7Z2Y4ePKIN/Y+Sp/ED6SErJX7K96eUgfPgY1odEhCIU8pKsMlMTIeOgHWZTjeyfKVC7xo4hGCF44PiJiKJhLodNVsqxefDzBzr0XjWAqlXr0rB9nfRzAeJ+bj4+I/kRlOj4+oLQvDUbeaM0xQDuuXdvuwMcb0YWP6MFIVynEE2D4+DwsPg+ZCd1NpHqEbh/IBWDie2fAuBdr15C1JUkw9WWf1tzGa/HILDZlfHy8ZmHVXurkjiRT9UvNEVmb8+FzjkNUsoTOHQPApzztvjdhwRNT5XKLd8UuT4I/FgogSkRG1mRyNef9LIrvLex86Ra2a2Ag9uKRQjO5A9K8pNNSJmviyEXtF5Q50hQDmkW+q7NTjUsfApNYrTOXw41dXWo8+LWe+c0HgTNsgrvXHWe/J1nNiebm5mpZLdNaJxvZ5xnplFbo9uEjYcluiPIlNIKRP+BZO2pYDdh4kwYf5+fna3t7e8zCIa0dFJ/MmaW8OG8XJjMy96rHCWOiBD+626c7fjnZ2hBZIyoYWUq0uOyx8HiICW3rULesGMZTevfYHgNxl8UkjPSVmwFYjz09vaF17sMI0Cfa3qXMVx6NtT6yOEXW9CNoW4c2IUA7dndfEQoRcq6LWLMKQh5HIbDwcaRSUT2+yDuN4+Np3zoPxzbDx+WlhVq6JT6OwMJ4SB8+3lt7njsOlwTrqampGp8TT4hlPqOjY/jUU0+F3x/DyOIp8ZGszeReHtWdjs5dGD5GirnFwsdzYf+SMLIovmvZwQvYbHxE9GNkR0eBeS88qsyRhpHtyL2WgqArPF4IP/z8GxBgHRYKGzAICspYFJAULAHDRwQ/D9kIPhIPqSUCrhcjM6G7iZTWvZw0ztJ1fDVALMlJHozFd8QBjL6FcgiMO2SvskDyEI8BQogL62Ojo07mkS9aEh6521IQfieh+h4wJXRWQSQ4FmMbKPo+FbZlMryPtKKStfe4o+0TExM1t6hamyGeVXAfu24WohfPOESW36SXWjFshxbj2QfGOpyHuMu6r3ybbQ1zZxk/duxY4jr0vZhnZmZqwqumCOgGwM3hue973/uspG4BmIRnzz77bExgfzdECeb4uS7rigpcEE9GEUtoESZ74X3jNc9Pg66Nn5iYqI0NZfaX609mrl/oPs9IpzRMpYaPXRBl0ZcYuYnNY70YOQP14+Pd4Vq8be/e1PhINT1pT+QhUjpSCcZWMFiZzCyOYxQXR5YMLf74uLg+sjzNzc2FpXH4HiC3RmIeqZQNMWz3hMcfT2xfvCyZFuO5CQF+DyNLTiW8l+amajPDRHGLGL9/pdZPF/ks3UNDu/HChQuhUOBysSwjwNsRAPChhx4KBeyoHT09vfjJT34S48zoA2jXJ06aa/PZDkHMC4zjY97j2UH4SPlTXBhJ45Xh4/JSmrEjLz753ixAPHEq4eMAAP4ENB8fg/D7aMxzx+BQEHTh3r23OS3c8YRYZl9FFmzac0+iEdDuRoD1aMoJHkKA30XdLZuUeFO4UHzs75f71IWPhJFUwutA4p6OLN1FdMfB96ER3rnyLdnVvl585DyRRj6MfO6555Qs4hIjNyPxkCY2nrcjCN3sAaNY9UNokuyVxbk0VroSuhVaYtWJknjIRvGRxosnApa8f5oSx2n3OFEmdHuonkRq0voXQDz75Bzoguk8xIFRZVCDIFaLcR50YYg/j+4HEK9VbSXs8LgjnQnv6+ofF6Y112lyj6JNoI0BbSTaNNwKTPEZ8t5jEJXtWgeAH2abRlp6W8GdKVzL6MljPF1z5dMocyaR11qX45QHE7cqibtpOa0VIsEDZQlNUjCQAkUKECOVSk1oXxOeOwTmpU8gdzskv9B/4FWvqtVzrwEX2CVstHbxcbxFmUMelyu119Qemb09jetkPfs8I5vSJlLTvCP43M2BrpDSME1T7uQhEqrrxUd6wWrCtQ8fD4FRpklc4/hojrmssHeH/1MCoWLsPtF37v4YMYV2BlgpSBLzuJ7dSzK4BXRlCteZtSrG68pKRhbZ7243VU5u93hjGdfKhHFvIpPZNy4oAORxaOjW2jVRFuVkoRggsKzVLS3dYfKkAAHWYMTw8nFPTgJ11atehdu3xJMfJSmQEfT3zBgYpY4PIzN8XH5KM3bz8/OWBY/yxjSKj5oRyIeP9Lx5MII3/314mMdyy9jmPZ599XuoZaU2AiqiUVxpQh4JmMdx8fGxHY0XDD1TYqQ7U7jefw0jixjhY7LluV58LJXK1vkcH+fm5kL8ksqUuBt+5JmQhJE2PgYBx8d1rG97wrE9hFEiPff9n376aa+RjfOQzcRHhDjvvxj4mAndHko7mFqctLYQNIGrCHGNzgjY4OrK3Ecvz3vAzYjeOjSEiG5rqVd4hDiTeeedd+KBAwdq11DyNJ+gBxBZtrhFiCxGmja+J0yilMb1qgWikl1ynAsAVhI20mL5ShG80jE+pz39lhuW4vFd55N7kOaNsLGrC1eDDSJXdHfj9PR0DVwHQrBy9cXlksatLA+AcRWX2VDHAPCwZ60AAP7qr/4qEvOoufJOOa7jbRoB21pPGai5ksHndu+Lc6p3n2dkU1qmUq5psmbT3LnwsQ1sC9AIuIXcJHzUGNGuzk6cmZlZMD7y5731rW+18NHHxER/88gT6eRyBezuvoK5RsctHX6G13xyuVVoBOsialaGVavWxs6PxxK6mUOAV4d/pXvm6cR2SXz0xRxyfJTWtr17bwuzttuM8tDQLTV8jLIou/qyz9tuY93uY8/g5/liy/34+FGxzrT3zDyYd1oekjEyw8flp3p4SBlasBB8TCOgc3wEALw+XFd0v4NgPBp3Dw2lyJuQtK8mw+/Gukv4aH5Pc19KdLZ4+Gis7jmMrNFuSyzHR0Ss8ZDu/r9S+d1fTotTmvFPcvMfHn4dmmSYcYzM57tjpXX9GCkTSSbh42YxxmXvWrnzzjtrJRTT8JCXGj5Cqju+jKnel021WsXJycmagEWLRkvmxd2g6UMaHYSodjZtJrJSS4AthotrA+i1l0cqFUR0LzBt0fIFXgWjZZWuF9KS41q41117LXaFAnUBbGa5t6cHL1y4ULPk8k1CGVpd9+ZJkfJgl/GyNiuLQSLyuW6fSLgnWfhjXgm5HO7YutUCzfvuuy+xL/v370dExD2lErYHAR4K52YfmDAFGpsz4bFOsJO25RLaCmDc7ZPasEncrwhRRvhuANyj3F/z4GiB+MubhxFolm7yZngSAH/X04eZmZnUc5dZchaf6hk7wsdyqYTtOcNIHYL0+MgxkitspqamvPi4BewkREUwidp4ZYhG8JH2gcTIIlCCQcVdM2Tirrnm+jAW7gQCDKNW+3R4+HVO12iTQCiJSTqOEdPIS9TYDFMSPrqZrBMJv9vuoEHQhZs3X2fFIPoy2RI+lkp7wj4fwshNczUblzNohOc82nGFuYS2AkZu/knjqSUW4kmAKuirbZyEj9LSXXvPsLX9K541SRiZ4ePyUyM85EB/fw0feXJUOY/k6ZeGh+TvWA0fCwD4OnDzkH7lm29fzaEe45wssLa1rVtCfAzQ5/atVZrxJylzYWRRxYqrr36thY9pxn9ycpKVFaPY+IPhGHArOWEkHTfXl0rlFHM57mmHxMciRhnhuxGgx3P/+nnISwkfIdUdVwjNzc3hT//0T2NHRwfm83l8+9vfjt/5zncSrymXy2IBAP78z/986mc28rKZm5vD1w0Px57ZB5Gw43ITesAx8bSZNGsRWT6pxuIJ0Gsvu1wp+KLNiwXeBVFmX61wfCGXwzaIgD/JGi3LjUk3N4qLkffY57m3pgXTGGdi0GXcS1ISBRnTLn/Pg6mRrSkj6P/dQ0M1LaIvA/nJkyctTTe/F49Z4WN3O8TjvTaCHjc/Bv5M7nkxx3QdP+c6MRZF5bo8GMDkfSmytVBrV+jiPz8/X3Pz982h1D6qwlYdSYIa3ecrjZYDHxHrHzsNHzezOXfh4x3g3u8+fGwDwJ3h/4dAr9npwqAkd/YiO0/DyDxEisDtwsIQ/07Jceh/u/ZpPEsub+I+x3HO8EqmRmeYNHyMM7NaxmFQfzfft6HNaEf9LhR68PHHH4/ho6sfJ0+etMoCmRqzGpPIYzJvx3hipo1ox4Xmw3bStUnjGWfqDSM7xs47gtKa1ApBPMM0uPExHwQWlo1UKjF8JOVqGozM8NHQpYKPiIjnz59Xy6OeC+e5ER5Sq8pA+FgE446bxEO68Yf2hYYBfG+Moe0anUefInDp8ZEsuX6MjFuVG8HITrQVg1H2co6PaSzdxjVcevvI9wxh3CRGCoaHEOBQqNwIMAqbkbHzY1g/PtJ1/JxOx1oJYrysCyMlD0mhkXKvrkR8vKSE7h/8wR/Evr4+/MIXvoCf/exn8ZprrsE3velNideUy2V85zvfid/4xjdqn3rArxHAHBsdVYWQIPy/HjchWmQyhnUfGBcLAsa08cXaAusCU+uZnkWfNjDxD6chWVAj8M6DzZRSfC+5NSdZefnv/KXiunfFca+ktvKs13w8XEkUZmZmcHJyEmdmZmyXb3F+ACb++QToMZ5jo6N4RXe32pfenh4sl0pWrHUXRAqbc6C/aGv3hyhepSzOK0Kk/dbqIdP65OfxceOlbdbncrh21arY/V3jfQ/YYQRyTD796U9bid2S7im1j0kl8BZzn680Wg58RKx/7JYDH9Mo5CYnJ1V8zIOuXKNwlifBj5GEYTysJg+A22r5Dy6iz8JqW4LnMJ4NWDIxnNmJ7pPEMJVK5dhcRbGQdnKg0dGxGD7aZWDi2b1bWlajiSs/gcbyUbTuZ2ro2sJ7T08vlkplR1bwTWxc+JhIppMnZpLCVIBRfCkXEDRrtYvZPFM7r7Vldez+afFRYx5333yzlSfD59HEMTLDR0OXCj4i6uVR8xAp+RaCkSSc3gtx5WMaHjLCA7kviuqejuKgTyfijktpt3XrDra3j7L/FxMfb09sa6lUrs1TPFZcx7RkjJQZwVehwSH9XpXKSIiBUlhtw1JpD27efB3qSkHir86hGx+BPfMB5bwiRh49SfjIz5PKDRrjdWjc+eNrpQK5JechlxofLxmh+ytf+QoCxF1L/+AP/gBzuRz+7d/+rfO6crmM73nPexp+br2A6Utktd6zGPinCJHrIyVFGB83rh11u0CGi0xdYBAJWmQ9XQ+20CifeQ7iQl8gzu/t6cFPf/rTqrVWtk9amfhLRRNgA3AnRdLqTCcx6PUkUTh58iReu3mz2l/eJu2l2N3Sgq8bHrZAY2N3N+7yxHvTepD37AIjDJBVmgSDu8FYv+8G4wVRyOVq43EITGK5WBsA8AMQt2wjxONWueLiwoULMau860WtARm5GE9PTzuzrbeB7VqUB8DB/n5rTmT5klOnTlkuss3e5yuNlgsfEesbu+XCxzQKuWq1quJjESJ85PjwANiCOH+udDfX8PHZZ58Nv0tLjs3s2ZYcqhWtM3xu5kerodpdu94usaMnB3Lh4+bN16LNzNF3Emx52w1z2NLSjcPDrwuT8UTXdXdvxJ07feWC6Df7vvHawKQ4uBspi3IQ5Fk5MRqTIyjdWNd76hfzfj733HMN4yOicTGemJjAcqnkLEupeTTlEzCSSuBl+GhoJeLj3NycmtgVGU75vP80jCyXSrU5bziMJsRHfw16sp6+B20vF7l3zmHcEhyd29PTi9PT0+wZfJ8vJj6SEiEZI92WZ9N/rYTX7OwsHj58GK+//garv2nwsVIZCRNG8uty2NXFFZxJGFm07huvONGNAEHojs4zza9iwv45NB5Msv3DYftdyozJWluGhnZb/ShDEDP4LDUPuVT4eMkI3ceOHcOCyPD8//7f/8OWlhb8xCc+4byuXC7jhg0bsKenB7du3Yr33Xcf/tM//VPq59b7srkp1La4XrLEeLl+3yYym946NGQVg3cBo2YRdrlJEHN6AvR0+hRXVgXAo45nFpWF3h62g2dLp+RmvvaRlcnlrk4KgXshEmrlhlorxrm2WcEI7wCR1b+eJAoyuZmLEaqA34W7LFxrewoFXB/Gb7nast1zT2nN45+RSiW2hqjthyCqqU7CtnTbp+8HwdTo3iZi1ZNccgEAB3fujCXp4DQ2OurMDH1EmcMAjLs+MaZawrmuzk4VpJu9z1caLRc+ItY3dr5Ef4uJj3zt+zCyWq3WLCenwVGSCSKMfLvyXM3dXMPH1lqt1ifRMD9xC0I8S245dAF8GJMYPpMNWLNIUbI0yTCNoWGmbHxMk13XTt5DliVpla6gT7kwPFyOta1Q6MFcbn1iW6Lsx0lM50GU6wTAWJCicmLR8e0Q4O+BeRfmwdSPT7r/7QDYnsvhQH+/ZVVMEpAG+vtjwiBREj7W3iWiL2kwMuYdleHjisLHsdFRbx4b+qTFSKnkT0ro2gg++pOnVZFK8bljmgknDiHAOmxtbautSyNQkgLMtlg3Fx8DjEJWkjFyufBxenrawkhjIb/DMx/bE+/LXfV37oyXV4sL+7ztPH+GdCGXbvsHMZfrwq1bd9TwsVqt4tGjR53rcTl4yMXGx0tG6H7/+9+P1113nXV848aN+Ju/+ZvO6x577DF85pln8Ny5c3jixAl81atehT/2Yz/mPP+73/0uvvjii7XP17/+9dSDGWm+EhaQ5/fp6WkrrrcNIBYPpmlwultacPfQkJ1qf3Q0lt2a6OzZs4np9M+I9g2BKctFsRS+OO6pqalajB5ZnpPq4M3NzeFIpRJrk+ulsiaXw/1gu3cXw3HpD79L9ylp9a8niQIvtXba0/ekZGUBAOZzOcty3+m552HPmEyy/4npk3OeBuAo8cpxiBQZOVCUGGze1FJm4Xy4lD7cDTipXw+BsSzmlfup5e8gXks9bWzOpc5ULhU+IjaOkUuJj9Z6DNfBzMyMhZHlUkl9qfswkseXXwz3SXsuhwdTYATHx+vAxPzGGbK4UHj+/HnGHPDfXQzWv0fDVPL2F9HED1L/78UoLjJijpIt3fZ5iNLN8nTidcmJeALM5fJoW6U6PfecwSiBj2tMTLma/v4BFR8RIyH5EOjzNgxBmADPZtY1jDx//ryKjzEPJQWj0uIj92jKQzqM9D1bowwflw4fZf4buQZ9GOrDxzwArl21So1j1XjIJHxM3pNVduwiAuQwl2tHI/heRLdgHCV1NBiZC/e/24X7/PnzIrY6DT4OCYypYGSpfQ36MHLx8PFAYtv7+wcxCArWOABs8Nz3sGdMJpErDDSM9Mf1cxfy42gnkYzmhvDRlVNpuXjIxcbHZRe67733XrFw7M8LL7zQMGhKeu655xAA8Ktf/ar6+/3336+2Ic1gkkBTAd0KWwy/36gtsJYWLJdKONDfj/kgsBYBd/nVNDjSNY27XmjnjY2OqvHDbeG9D4IR5KYhOY7YtdDJBZs+RdBdNGlD88V/AtK9dIg50gTrVWDHLRdyuZgr6rFjx/DazZuxnblea5pdKZxPePp+laP9pKjYBLqVfIOybggAfEoObul+7rnn1PU5NzfntTTeLsa3DICvVdpLYzQ2OqqWeusN59sVh01a4tMp53pM3M9nQXIpWly0UpnKlYaPiI1jJM35cuMjYtx913VeEkYWIUpoOA16+Eu9+DgDEZYR400uipI5uLd2ra8MziHUmEZjGYlb1HO5Qq0uN+Hj5s3XhoyybllC1IRzX/mcqxxtJyb8BozqunIr0AbUrVNFTMPEk5VrIfh4GOyEeMMQ4GtAx0hXKUzCRxdG1YuPjWJkho8rDx8vgu7JR7zAQYiqmUiM7Ors9OIjzfmGsByrhn1p8bEVWhxKqCJGybqmUa/RTf/rOBGFqdBnDCN3cLOXDxw4gIhSqL2IRlhOg4+AxlJbVc5JxsijR4/iNddci7ncOowUCWnwMU0WeO5qrrV9E+qWcg0jyX3ch4+RpVvDg7m5uRTlxPaj7VFURoB3I4Dh1yU+OnMqwfLzkIuBj8sudP/93/89vvDCC4mf733vew27B0n6x3/8RwQAfOaZZ9TfF2LpJqH7MbAF1RbxXbr8WMkBQHkpg2H0joffDxw4kBh7oGlxultavDFD3fl8rR0uN2qqXe26hwR+cl+m87gLtxRq58AwJXlwM+Y+7dYdyhzkwLijavPBv0sGXbqha2WzeN/bIapHTu3/MJgXpW+Oy+IcLjz0gq1IoBcx/S/rWMv14HNXBDBW9/XheFH9zqTz6TeZkZ4nX5MZx/mcu0IFqO/lcJz42n/DG96QOP+T8run3uJKZSpXGj4iLtySs1T4CKCXdyFy4SMp5pLW/SqI71GJkQWIEsMtBj6a8bNdLSOG9zgmM0d3oJ0oJ4dDQ7eG/7eI36LvvD4touaG7quL2x7eL95240KuuXTOs2vL4ndpzd+ojAnFYpqEQ5whlushDT62AeC1YDy/+sCvECWruYaPLoyqBx/7wChsSAlESut3vetd6MPIDB9XHj6eAINtScYOGTImf0+Lj1qJQKI0+PgYAI6qsclt4rsmIF6RiBOm7JUrC7bBsfHxcUWopQRqSfh4EaOs5vVh5MDALgWnAqRyhH589GX/PhS2sy3W9iAoYGenTLzGFREujORt1fCRhPIoaaVGUSmypLbTpxNN0swcGiE8j9shSMRHBGGQS8BHuV+SMLJT4CN5c6bhIRcDH5dd6E5LlAjji1/8Yu3YqVOnvIkwJH3uc59DAMA///M/T3V+PYM5OzuLAUSCEbl9dYaTLwGO16y1wA0iJowmnlwv6bNj61ZnvIGPaUxabO0hQ3gakpmJHOiCcRKzqVkgpVA7Bno97xvAWJlddc/5fck9nhidM2G7uoIAC6ArEvLr16svHz6W9NyiY5OXWRsGB6J6hRpjrs1xHwDuByO43xgeGwdjTRsBPUaFv2hrrq+iHz6AkgnZ5Ni71ooPtJI0hfRSPwJ6feNzEAkwsr4y9ftyt3SnpeXCR8T6xo6y8nJ8zLP1xoXkheJjAHYJLCIfPvoSsq0Pr/VhZDssDj52g2F4yxbzV0TDhPkYuzPhdyq9cwZNAp0uNC7oNpO8fn0+ER/jyeCKqFuly7U2bNtGmYnNp1DYoDyXGG1ihPvQWFPaY/cyLutV1Mp1adnLF4KPBTBYLC0prrVy3HF8woNRafExD2589GFkho8rFx8vglHQxHgBoUSkmt5JSrylwEfOZ8WF7NMeHMqhu6Z9OousLdRSEjK74oKxEM+E5zWGkUYYljhlFHoDA7ucYxkv1xWgnf2bynVV0RZiTWK5IOhGnyLC9LEDTbgNWZ3H0XgcaAnQ+PeiigfxPtD4upQZPHll9IxzTcZHxGSMrIB5R+ZXGD5eMkI3oin5cNNNN+HZs2fxc5/7HF577bWxkg9/8zd/g9dffz2ePXsWERG/+tWv4gMPPIBf/OIX8Wtf+xr+zu/8Dm7atAn37NmT+pn1vmxGKpWYFQTAaMZHKhX1fAlusgbzePg/CfOa1lEjX5IweiY9jy8w0jxNQjIz8SgoZcZajEXkNICafIhihXnb+RicFRuhCsaVUgqbY2DKnOXBWBp4SQGNqSVrhM9yO7hzp6rIoM1NWrhzoGuiJ1hfScHii6PmY0+JlopglDVUB72Pzf8ZAPyJ8PwzjnGWmjm+HjQt+iDYmnEAwLvuuiux7Um/0Xy41qiWKZqvP/oUQVdYFCDZG0ILFXDRpc5UIi4PPiLWN3bz8/OW1boIURZySQvBxzyYpIUa+fCR79mFYOQmsDEiBwY/+H3T4qOmbJwGwCtrz+BWG5s5Mi6YGkNLrof3YBIjunPnoIqPkXvn3eH151AvTTOBWtygP1aQmMd2jOrIHsE4s2eYv+0Q1EpUAnwU4+710bNd6yEtPpLF5MEHH0zEQVd8+JAHoxaKj0XQ879w76gMHzN81KhRfHy41u60rtR3oKwS0NJC30+rexfgbszlClipjMTGwF0BYhoBXiH2EVmIKyGmUDWDNBiZhFOmpJjEyHipNfJCkgoB+h7FVo+Pj6fERy6ot6OxaHN8PI0AmzFSFlAt8jMYr3mejI/m93m0sX0z6png/Txko/hI+0Vi5D6I51vR8JEwkH5bSny8pITuubk5fNOb3oTt7e3Y2dmJP/dzP4ff+c53ar9/7WtfQwDAz3zmM4iIePHiRdyzZw92d3djW1sbXnPNNbhv375Fq9NNycAkkzVSqeD8/HwtVk6L3ZJCnGbRdJXI0lwofZrK3UND2Cae0QaGISSg9VmTaWHzrOJdQWC5inILbO2YcOEmoZaYVA72ZWXTdIPR/mpuqa8bHraShKwLf3+vcn+E6GWyJpfDQZZ9lki+DOllMw7Ri43GRM6L7wV2N9jZwyGci1b2nc+Dd25SrAfNlYe3i+5TLpWwKwji8WPh+HPPDg5atH592R95qYa3vOUt1jj5+nkVxOffWnsvk+y8iMuDj4jNw0gt4eNy4WO1WsXb9u5tCkYSNnwUIma3IO5b9KxbqfTje2QsvKfNlM2jjKXc0HMFDg+/Tqm1uy48570YMVc2k5zLrcFSqWzN0/z8vCj1Re0gho6E5og55PPiywRsGMkxtC1AcTf47WHZmVl1PCJmsBn4SL+dOnUKB/v7VeatFdwJgtJg1ELx8c3g8Y7K8LH2e4aPETWKjxG/ktaVmguLDyHAodDbRoa3RF4qACabNl+3ttJPKh+70LYQ34ZRRm/6BLVM3W6MTMIpwCBox/7+QQsf7VJrJxDgFBrPnSl1TCQP6X7u3RhZvTlGBhi3ZHNre/346CofGXkF2OORxEN2LhAfqW3j4+N45513Yr34eBgiL5KlwsdLSuheDqq33IMWAzNSqTgTmtFiLkJUg7kCulaywhaNrAFL9+TATO2JLehQczNSqVhJgvJguz5S2Ru5KbT4w3G2mHnChC4wMZBXAeC6XA7LpZI1dvPz87VYc75JkjZNAHryGm28h8K/Pkv3PaBvOJqnQwB4E4DKjA+Did2Wv/li6AGiGCway4fC/hXAHb9eARuwCrkc3jo0pMZp0ZxrlmGtXbsGBmrjqHkaUEI5uQ77aD2Mj6v7ZHZ2Vk3SotUq91kS6XMtGLd8+u4qw9OMfZ5RnBaMkUFgx2wvMz4iYtMwkuMj318cI9vBlDtMwkeOaRo+jkKALYqr5nWQi0oDhvgoGcDBMB7RZ+mOftfnycQjauV4KG7wwygtW6OjY6wer+u5ZTRKBJ6w50ztHj+n4IQ2HmQh0xTg9eJjXqxbDSPfoRwvA+Bv0ppQMHIx8HE7GLwmBdC2LVsyfFwiejnhY0ftfnwfk9Ar8yxIt+hxsed5IsUuNInNrsRcrt3Ky2ALtWmESzvWnJKg2UpEQJPgUesfbzMgTyTG47sjjNyHALstHDTfh1HDyFLJlycDMLLe03g+FPaxgHr8uu0JlYSPlcoIq9WdLhRgYGBXIg95rAF8pLHUMFIql9LiYw4A71sifMyEbg+lHUyfZtCKuWHgxYWytNZlrQZsQSxgLdlGeXi4xuCkiUM8ArYQWQSIFbHnwiMBOSWymgM9s6+2oLXsxpT0Q26a055xIncc7mpCY1YA3S25l23GfWKOuLX6VtBjwoPwI19EPEujfOZmiGLUuVsYCdpJa0LL0rwaDIDIlzOieUlqmulOZSx6CgW8qa8vtm4PhWujDDbDPw2AA+Le5eHhmJZQq4lYhCg2sbulBde2tsbmxpek6AxEip02ANVLoZn7PCObmoWRMrNps/GR9qgPHzkz2yhGaomM6Pg5iDAySormx0dExPHxcQxAx8d5sJMakeU3DT6O1sphFVCPs+xFYuiOi3nyux8Gtb+GcbMZ3bgLJj23gEbglkxmPO7xqLIu5sHONN4KLbixK56MiDCyHnzMAWChowM7FKXMABtTek8dAiP0lsX9OUYuNj4WwXiiuVyKm7HHM7LpcsLHwVCZndxWmcDsCNoCppYAjI6fC/+nOt9zqGU/1zDSxJoHGAmTroSSp8PjurAYuXPL6g9FjGK6pVKxDeP5J/bFMpnHMfJW1JPLGcu0hpE9Pb0KPubRuHZXMcoWf7D2/KiPmvLB9oQCaAvrokfHSHFg6nTbioKuro1quzo7u3D30FBMieTiIdPgI6IfIwu5HLY1gI95MBbvpcDHTOj2UNrB9LkQ73OBYFiaAcC8oMla57rP3eAXOGkhdbe04G1791raIF9ZFC60AdsI+8L/ieHjbnQViF7uAcTBnTSwHNwH+/utMUzKbiz7qrlZ8j7s37/f+n0eIqWA5ZYOgBcg/nLic0Rtu8Ez9km/lYeHY89sBQNAm0VbCmASNfH2u7IzbgM94/mnIP5y5i9JSnpSBcBfAVtwt0qJgJ4JFSGKidkGuvWf5zJQtfjh/WfZnPaI52s1RV1lUJ5++ulF3ecZ2dQsjJxU5nMx8PH3IBkfx0ZHa89Mi5EBAD4ARuDKgxsfuyFSQlahPnxExJhly4WPVQD8cc84ufAxSswm3Tt7EeAC1twPxZjqMYeR++EUAL69di+d0Z2ZmVFcMNvQMO3SYs6zksfH0lIgOuaKCzBlJrj48LGrszP2PQkfySOru6XFi5FLgY+DAwOp3CUb3eMZ2bRU+HgRIuXTGcc96sVHsn4PiNrdPh7STtZVDv9uQnf2bJmAiwRdssjGBdT+/kFrDCNrclE8X2KO5oaOSAIz4aP9+zk0yd+0ZGQjGK+0EE/4FrWNLOZJVmv9t+HhsnhuK5rQm83ieAFN9nDeB1cCtB60+/IAcoG/VKLnnsB4DLhp19atO5R7mP+LoBvpFpOH5J8W0PNaaPi4vU4Ld717HDETur3ULC2lCwQnJyfx7Nmz1ovdtVD5xwfONRfsIMB9wLQ6KcqivAOMO6TcGFqWQM50kGZpCuqPO0bEmEsTZTduA7Dc/jpzucR7yxp8s2A0aTJ+ow2MS3nNfQpst1BKKtHb01OLDU9yV0lidKempvCRRx4xGxx0F/l82A7eflcJkWFwMO1iPGS2Uc3CNtDfj7cKzSQHNd6X94KxyheCAHsKhVppBr7OagoY9sJJWnP0Gezvx6effhr379+Pv/qrv4r33Xcf7mKZ4AHMGuT7g49/2jicRvZ5RjY1CyO1uNnFwMcBdo2Gj2nKKkqMJMtRWnzcDI3hI2KEkQfBWJy1uuY+jNfwcRKMUiOuUGhD41IeJmKDPI6y0i98njb0XIGSoebnR6WK3IxutVrFarWKBw4cQMm82d+LmIPO2v3nwVZGbIRkfJRz2mx8JHf+PaVSIkb6asa68PHxxx/HRx55BHds3Zrh4wqlxcZHlyLO5W1TDz4CmOSy7UEQs7Sn4SF1K3En2gIxt3pTsrLN4ff6Yo8ReXz3QTSW7hstXPKVvtKViLMI8AsKJq1BXqdbc50nHtK4rFNsuDsu3PUbxXgTD2li0m03efO9W/RB80DaiJpCw4QIxcfE32bj/eMrh3k6PPehcL0sBg+5Y+tWPHz4MD744IP4i7/4i3XhYyMYmQndTaRG4nFkDEyQsFAodqYANmNQFPcZGx2tCU9Ji28q/P67oMdRkFuy1lYZO1QJzy8EgWUBBYiUCRqT0gfJ4E4vDB47omUjdNWkDMAWxrnbVblUwg6IXkZSwH0M0rmF8g1P7t+NWLrp09XZWWtL0vk5sDV07WCA7eTJk97n8azIMoO6amFzvEhJUDgJ9ou9t6cHf+mXfsm5zgAA3/KWt6hJLvha2Mfa0d3Sgq8bHrbWYgCAJU+fKW41TcbJRvd5RnFaKEYS1jUDH6vVKt5xxx3efViFZHwEMJ4paTHyN8DGEh8+kodLPfiIqGOktlcIY9Lio8TIc2C7Z/vc1eX5o+x8O7OxxtQZhqfQ3o7tbE9Pgqm1WqrFnptPK5hyMTHLdqGAhw8fToWPfLwbxUe632Gwlbq9PT144cIFfOSRRxLX2s0331wXPo5UKnjb3r3WPX19zvBx6Wmx8RER8YrubmeG5kbx8Rzo+DgPcQ8OHR+li7mpM23KAvJ7UgIuqq3NfysiwG+G/+vC3tGjR1V8lB4zdnx2oLaRu4OXSuXw9w9jlFlcCriPYRrX+bilm9y/67d0Axh37x07iqwtSedrJdk68Prrt6R4lswa7z73DPh548Oge2WOVCp44MCBRHz84R/+4ZoiNhVGBgFu7O6uGx+54r0ejMyE7iZSveUetIRpI5WKMyFFWu2NTJwRgO1K1x0eJ0v3ZtCzfpMlZlC4DPX29GBBaKny4T25y+VxAHy/WLwuN8kkhYPm1knaJcrYyMG0XCrFNK6q0MzuMTExgS1hO3yAsA2SGVTu+qW5MXaFm7xVuU8eIgHhBECNmXQlSCMQOXz4cGL5EJ+Ll6yTTS90V5zL28EvKLSF434xHNP2IMCOtWvVdTYM9ku7CMmuRvxYu7KeWiBKMCfHuBcihoAzJM3e5xnFaaEYSbjTDHxENBYjVeCEeM4JHz5OTEw42+rCSBL2JN40Gx8RbYyUNXvPQXJWdI6PJ8Dtdkp9GQTlnSNiui+Ge1lzs+9uacFVrVo8ZB7bIbCEWxc+UqmuZuCjHO968JEwsgJgteEc68tAfz9ef8011lorgLHEN4qPq0CPy6U5zfBxZdBi4iOi30LeKD4WQcfHMbaHXDzkY8BDVSJhkXjIqGQVCXG6C3lkVU5W1NH9ffjY3z8QWrlPoHEVLzrvYdrKhXOfsLzNwjZ3TLft6h0EXdjdvRGNy7iNkeSCb6z4pHzUEqRFgvL119+IWhx2pTKC/f0DiddGMfWmf6VS2YrdDpgnkxba0Gx8DMCdhV/DyM4G8DF2z5QYmQndTaRGXjanTp2quRIjYi1Ji1w8W264ocakuRgDAKjdh4hcHyVDRd/PgD+BAC0oWSc1ySLPwX0SIsHf9yyedIheGFd0d1sJxwq5nFrPfHZ2tmaplTX4uPu03CDPPPNMrL1J4zwBtoCpZS8/AW5X7xxEAjb/rQWMlYaeSXEnLiZXWo/I0iPjKMna7RqT/RB/OcsM8TQWmnA9BoAjYAsKXeFxX8x9zXUXbNArQhzsKo45ceVBeA/ES6nR2i9AnCGQtSabvc8zMlTv2PEySNVq1YmPm1/zGvzv//2/142PiIi3DA0las59VQz4C7dRjOwK1/dS4OPk5CQeO3ashhf8OSQ0y3Hi+IiQDiMltkllMN1Lw0jylGq1kpsFmIMIw5qNjwcdvz8GcQGmXnykPkrBtxvSYWQRFoaPSWPTKZ6d4ePy0WLi49NPP13jjVz79sCBA9YzFoqPtRKpHh6yyvZz3Np7Ao0Lcx4jl3JdmG1vz2MQaAkW25AL6bx2txxPjo/G7Z0/56CKj1FbqW2+sl0TKK31evbyE+hONkmW6aL4rQUBZmLjYj6nE8cu+s2Ow468JQ9iPAyAru1DgCOxjO7Sg6AMAZ5XsG4x8ZEnSmuUh0zCR359WozMhO4mUr01Fl2lwcZGR+3Yaoji55KYP4p1I6Ka0fkQ/I6HfwtgWxZdQHxTX1+s7d4kHuHik9bSYopnySQcZU+8JPWVj6f2guAltjQ3I94nX/zkGdrAoeaN7sPdO6Xr10EwCc/6APB2cX9KxkMvp3H2TM5MupL/BBBpkWlcKe6SLD1Sy1wB89KkTN4tENWId2V9nAfdCkexNK7xIjBsNLs8Zx5cmsuHQI9juyP862K2OUOwGPs8ozilHbvFwkeXi2FvTw+2g2H6avsb4liSFh8R68fI+1I+azHxkT9HjpPsjw8jKUa5XCrF7kMYqbnjdwDg9eFeXcfycPBkZTXFKTQXHycmJlRLeB4MPgYQuTeeP3++bnykMpSNYKRvrNPgI723tDWV4ePKocXExwAAO9avT1xLDz74YNPxsT30JuGUFh+jmOtfwXh8tC7MTkxMKAkWAzSu3bagyfExuk7GYXMXcLeretwa7Ks5fgYpXry/f0DlIUulcliHnBQIB9EkPOtDgNvF/UlQJgv7eKy90bl6grTOTqrSQONK2c3P1MbVdrsvolFoFNEI/y21muguHtJlnFkKfHRhJMknH3WsxSR8lEmUm7nHETOh20vNqNPtS8qzCmx3hw6I3JAlACMa0JTZsAfCBCuTk5P41FNPJT5z99BQrO3eJB5KzDVZMn1aUa4NrVarXs0s1eYbGx3FQhDUSnxJbdkgGGuuxXA6rC8aA1fI5WLX07UawGzs6rKeRd+3K8AR65MYlwDciZeGwn61B8YCdARsrWALmLgV1c0VWFxqWPNXW5t5ANzkmTsXU0caxLOO60m77RqPayGegIrmRKtzzuPIAAAPeO6tMQTN3OcZxSnt2DUTH7vDNeTa+4g6Rg4ODODExETd+IjYOEZS8sXlwMdquKe0cdJKR8r9KBlxPr4SI3PKc2rXAeDPePYtr2NO75ZG8TEAMEnPFIzshQgf8yFWNIqPSf3ZB8ZKvke5h8+roBF8TNuuDB+XlhYbH9dBxE9IHnKx8LGnULASTdWDj3FhmBKLJSdLI4x88MEHw/PdycYQuXC/HY0gyV3Xu9FY2qtoYrZtV/j5+fkaRtpu8PHa1vx6ujYu9APyson8WdH37Yn9suuYt6HJ/H4Eo5hz+mzBXK4TjeV8P2pW9aGhW8PxkS79vcizsJdKZef6JBmlUXxE0HlIHz5eBZHAzDHyHKRPKOi6992QxXQvKzUr82TSJL8b4iWsSAOfB7AAmMfxTE5O4smTJy2L52B/P+4eGsI20BkoWpRSi+NKBKctPipVcevQUK3NefEs17UyqZccq/Hx8bpKdPE4YzlOvE/qhhwdxZmZGUvDKQGmyObjBjAvukNgrDL7wB+feVDMQXeYTI23hax68nirWAu++HSeQA0gXWZcHzDKa0gYHwvHX66zNGB8BuwaxwEodc7Z+PeCXwtaLpWy7LxLSGnGrpn4CBB59bgwkvBxenraZiwbxEfE+jHy2LFjuCHMzLpc+NgKdmwbPVv2R6s5Xh4exomJCef74gQYwZjcCDk+El61Q5RY0+ttBIA3DwxYVuq0+HgCjMDhexYxWOA5N2lt+p5Bit4ixBW+acK/0uIjT5hVAF3Iz/Bx+Wix8fHucG/IddIoPpZLJSzu2OHER0oW2Ax8nJiYwKGhW9GX1IwT4aNLSOf4mC5pWSuarOrxutj07Eh4fxJdceAaDxlddwKNUNzFnnEVGmXDITQu4vswimFPtqQD5HFo6NawbrYU4CnWWx5vQ+MZQMJ1h2dcqsgt6i4e0mdc8eEjgs5DpsFH4qk5Rmprvl58JLkgy16+TNSsGotpGIDtW7ZgAH6BhSfY0V7C+fD4NjCuH3zzBWBqOAPY8QquRHBJi4/cnu6AyJ0k6dq5ubla+RQJ6F1gXh4Uz8TvlSQUumrunTp1CicmJtSXisZEEskXIBfy6H+XdWU92C+pbuU8ev74+Di+5uqra0nDOPPK57PI+ufTAvIEagB6Td60azOv9IeUCzQWWl31AEBNKtcNSkmlUPHhEzY6APBz4XetjFxXEGB5eDjlzo5TxlQ2TmnGrhn4+JqrrqqtrTRKnWbjI2L9GElM6AMQCZ0LwccAoJYzJC0+Svzg46QpbV2KSE4cIzV8PAF6LHQARgHgsqRzfEREnJqawlddeWVd+IjgZwbp9zPgH8PEdzG43d+5clSLcW+B5uIjgPG4IuWyvHeGj8tDS4WP123e3FR8BDBK7sXER0SDkZ25HL5GCIobeq5Q8bFU2oNGqORu2lTvOxD4SAnbXNZjEnaL4veodNjExIRVH7tUKqfiIY1QK93S6fsR1OO616N0FY8L0UENH6vValgZhmLBNQH/RPidypgh+uqUGzf0SDHh4iFPe9bZjeDGxzQ8pMQwDR9pjVGMeqP42N3SgoMstLQeyoTuJlKzLN27du5UF9AYO4e0Sb6s1u2hy5xvwefBMJVlsUA3h38psYVkrrTM4Wn7LBNnSCImdBvYFpVWZdP5XjhV8T8fJ/7xCdqc5AuQC7n0fwX0bMRtLS1qH8pgEhHJeDoX8+rqK6Y47yBE1hW+rlznD4Gu0W4D2zOgt6cHdw8NYVcQWIwtxWkSE/BmsK1S3PWdEsXQGpS1xOW655/uzk4ruUwj9WeJMqaycWqGJac8PIxdMkMvxPFxamoqNUbug8XDR8R0GNlsfFwNgFd0d8eOBZ4+avjo2lMD/f04MzOTas45Rmr4eBHcGdt78nmrDzdB8/ARU849X1uN4qPm/l4eHsabduzA9jCGna/RKkQxjM3Gx9eIe0lPgQwfl4cuVXw8FD6jR6wrjo/U9mbxkDIXjrw+sh5vQzsr92rs7r5CHLs6/Jtk0ZUlsrjwGX1KpWRBm1M8U7lMwEbfK6hlbG9pkf0KEOAmNInaDlrjQgkxdQFf6y+iPwHbwbBtRfTxkJqCOA8mcWYhCCweckOhgDft2JGKh0zCx6mpqdoam52d9RqXJD7KEshLhY+Z0O2hZtTpJoFvTWtrbJIr4QLKhxNOGzUNGCL4LZ60oPNgMz4bu7vr1khKSps4g4gD7HmIa1EDRzsDsF3z+Asn9jwxTjTGj4E/RoO/ONJYupPmR87xY2DcWraD7d7FGak0Fmw6VgTdCk1uuEWISucgIo5UKrblA+JZJnm7+/v6LKF2+5YtODMzY2mzfe6VeYgna+kK26XFzacd1xYAfO6552oxsJJBPXXqVKqXPlHGVDZO9cYsaq6H09PT2NXZ6cTH3p4eRMTUGEkv78sFHwEAVwcBdikxygWoDx+bjZEaPvpCYOgzFD6b+kGYtVB8JGZQw8iArS3qc734GADgTTt2xI5du3kzPv3004gYrxSR4ePLm5qFj67EqXkAXLtqFSI2Fx+5xxyFihDm9Pb0JCZ+S0v1YKSd/fs2jNypSTjNox2j3IK2VbwbI8uvLJHFhU8Sjh9zurvz9kl8TLZ0Jwm9/FNE4/5ewCAo1NogMVIX8KUSgZcBs13641b1Yu15c3NzuKFQUDFSy0QeAOCxY8es9VEvD1lOwEdEPfngSsfHTOj2UDNqLPLv6yGKTeMLdGZmJsbIuGpBBwyg0mTkBnDH5bbncvFsmCJzN6KuxeS/8edT9lhiuGTsmKx1XYC4JdXVj/UQxS7SZxCiMly8vwQEFYjigbk2WPbD9eKQtdWLEDFwfZD8stoHLIZKPH/3zTdbwixdc8ozDjwuvBPsONdieJy0g/wFOFKp4GpxfgUMQ6m5gZaHh2uJRGR2ZbrvzMwMdnV22h4cYYKjfeEcue6tJebQ3Mbz4XFL2OjosOZOS+aUhhHImMrGKe3Yafg4UqlY+0GuU3pBIqIXI/MQlQ9ZKD7mg6BWHeIQ6PhIbdIwUrNezUIk7PvwsRtsS2qSG58PHzl+NBsjKxBZOyoQube6MPK449ld+XxT8LGQy2EL2N4CbRB//xI2JOEjgsFIGT5VHh7GZ5991omPiIi3Dg05Bf+DYKz7ZeW+GT5ePtRsfJQegQW2b5uJjzw3jKawK5dKdkb1ReQh49ZjRCM0FzAeY+0SYlcJTCmjsRxzS/dBjLuqVzBKujZWO0/DR5ldfXR0DCuVERYLTi7fJOBuEn2RwvE+jCd9M30sFHrw/PnzSjZ3uuaUZxyiuHCAnFW+0ZVQbmx0FNeLpMOSh3ThWCM8ZB4Mz5+Ej4ju3EsrGR8zodtDjbxsyLWmXCrZ2SjDRTQefojxk+VmVLc1RXOuZZuVjMwZBmZVMDENcmFJq4oG9triowy6RXF9AeKWVsQIYAlQi2Fbfa5Q+bAkBn1424sQFzQBjOZNZiuU48zbr2UMHalUrA0phdykl5V8edDzt23ZUnveObDdtzeCHsci54cYR1fZA15zUo777aL9mhuoTLSk/YaoZ0CViqYKRNmGKZu6y53uiLI2A3CXzWkX8WhtkJyAsJn7PCND9Y4ddz1U1xdElQk+quzbJIyUa6URfCTc2azcm2OhVmZKYiS19cNKW9sAYnW35T7ljLMPI7U9w/FRYpeW0XWhGMnb4Cv1dgp0fAQwjHuz8PEEOMqThZYNbdwlPvJ1JMegt6fHi49yjdy2d68VJnAjAK7P5TJ8vAxpMfFximFAGh6yHnx08TQu19/F5iF163ERjVC6L/yuC7Fr13bE7h0XLtvQTjw2glFJMS6w2vgYT5gWJWKrVEYc2cv5J8kNnEp8HYxdUyqV2fPsxG4AG9G2YBdQxoU3io+HAPAoLC4PuXZVXEki8ZG3i2PkPNjvjJWGj5nQ7aFGXzbe8gnyu1Juhj481i5NtllyO+puacEc2FaQgrKw8uFipe+FXM7SDknX6Gq1ivPz8zW3E/lioMUvs4K3B9Hm98XqBeFz+b25lSYf9ucxAPwJMBaWPWAz0HcrbUlT4oJegOTC83sAOABu18Vh0F1t3s2+HwzbVwY95lEyyDSfpEXmSeZcTDiVzXjmmWfwzW9+c+3c2yCqUZuHdFkiXb+RYD87O1tLfke1erUwAf7C9bmVjY+P49GjR/GKDRsSz/sJdsyruU9wFcqYysZpMTFSm7skjGwGPhYhilNz4U6SwLWH1bGen5/HcpgYTWIkJUZz4eNptp59GCndzgl/CR/PgSnt2AbGU0dTMiwEIyme7gxE1gkNIwugJ2fi+Hg7LB0+zs7O4h133FFru8THJyF9uFcafHQJUnkwysoMHy8/Wkk8ZBp8LILBC+JptoFtbWyBxnlIjo/U3rQ8ZCTg3s3a44tlDpTSWNySnUdTrusEAvwEArQjwB40Ai/P4n23Ex+Typ1xfDSC+wQaS7vLvXsY9QRr/PvBsH1l1OLCjUs9P7+CAPsxCNpxy5btteP14iP3UG0WD0nyBMVou3hIjo+Ifozcv38/vvLKKxPPWQ58zIRuDzUKmL4FcZyBGsUrcnIlonBlh/zt3/5tLHTEtXkbu7txTWtr3eWmtO8Ier1XX9waQFwzyGPd+PiorlBB4G2rjNeQ7SPQzgd2bdJ64okoWQUJzE+D0TbLZ28BA0oSMFaD7rri6lt7LmfNJwmtEdC7r3//+99vWZyLAPg6iJQUXAvuGgPfb/IZSVpFTXvqar/UfLrO+yg75o1RU7KtLnSfZ7R4GHk3uPERUcfIheLjCfBn/uW4wwWuOdBLEj7yyCOJ9yMGh9pP+Cit241gZM1NU+zRZmOkjPEmjJTeAgXQBelWMMqPtPh4L9ihWvXg4+HDh/G2vXut510BxjNA4qPvXZ4WHzVvNd4uLqhn+Hh50GLzkIVczlmSKy0+zszM4LFjx7BHJJda09raUMlS7bsLH6kWdhoeMl7jmz7c3TxeQzsI8uE5aRKp0UcKuEUEAAyCvBXTbbu80+eiio+RgmASAZ5GgM3Ks7egXVc8j1GZL9k+vW+5XDt2dMTnc3R0jNUerx8fRyDyNmoGDynvnxYf+ZimwciVhI/gPeNlToulpZQMT9oYGCIJqKTBPARRDOIqZcGlTUYjv2O4wfIgGNSQ6UvacFqWy8GdO2Nt05LV3NTX520r/b8P3OVkWkB3a9IYRWld4/GM0sJSBMCOXK4Ww+RTQOTY+FEG2ySBg0BGWwsbCgVnHc0AAPPt7c7EdLx9vjjJpN+2KOshD1HMmLYW6EXLYxZjrm2KBbEA7qRxvG2ZJWd5aDExcqnxEcFfborjDhe4XC5227ZsSbwfF7qpb4MDA9gZZr9OwkiKlUtizAHAW3LLh5GkbNSsa4SRslYwhN93Dw2ZPgnMl/PNrWc+fORJnrgyj8iHj9ddc02itY6eVwVjDUlqt8vSreGjzMuiYWSGj5cXLTYPGQDEqg40go+Ii89DuvDRh2EaDzk1NcUstiR0zqO0EPf3D4T/JyUW43HUWrmtPAK01OKbtTmKC9ORIE/4aBQFObQzrrfh0NDu2lyYcmhJSgKeLO64p2/GMq/xkD583L5lC65S8It+5+vhmAdXXL8FoHiT1YGPfM0mYaQWkrSc+JgJ3R5ayMtGXRBgrA7StW9ycrLhbJBkhdUWkASztGVX5HfXdT7Xkms3b8bDhw9bmvmRSgU3dnWpm2ETGKuLT4jlArJPeJRJi4hu27tXZRQp1pLmsBjOXSyxEsRdXg4fPmyNtwQMal/aJE+ado2A3lXb0Ccsnwm/UwK1APSahRSPkw9LO1Cium4wLvxp1pF87gl2b03rrs35BYiSxdDniu5uvHVoyEoq1BaeK9eUrz5txlQ2TouBkYOwPPiIkC7zrxS4fPs56bcHH3xQxcjenp6aoo6PTycYy3C5VPIy5jwOz9dGDSPn5uZUjxkea0lzuD3cexwjC7lcDUsnJia8+Jh2POXa4JQGH4MU9+cJJl0YSczdw2GfqVyjDx9dgvohaA4+rl21Cm8eGMjwcQXQYvKQnE9oFB9nZ2fVGvDN5CEXgo9JPKQpEZZHbt3OQTsGEMTwMZ2lOzkJmYuH3Lv3NtSE6UplBBG5S/x2NBbsg2gE5kOYyxVq5yFGPKRbkObt8/VNj0FPg4++nBxnIB0+unjIvOf+afARUffckBh5AeyQprWrVuGtQ0PW3loKfMyEbg8tBDC1BVGEKIFNDJiqVSyXSnZsXkIQvwRZDoyT7JlyYReVDZKHKB6HvreF/58G41biYpi0DZeHOIOTh7hGq5DL4d49e3BXaP2ovSTC8aFx2bZ1K+ZFfUqKraTnVMCvfW1XXCcRTVbvgkimQIyilvhNziXNHSKqLy4+x7J9mrtoN7gzCbvqtcrahl2esbhdPF9zpaREUTIRShCuhQnPM94OERNKa0HOLfWNa919jPmtYFzvx0ZHsVwqWZlcW8BYmPgxGQvU7H3+cqdmY6SW5Gsp8ZG08BLTJO5sZPuXrONnQF+3jWJk37ZtibVKDx8+XEvYKXGkyJ5xERrDSF/8cT0YmcZyp3kOaPgo1wZienzsBsB1nrGYhLhl7pzSt7HRUfzTP/1TSynRC4CPe+6/LpezmNBeWDg+/hgY1/vulhbsyeczfFwBtFT46EtYJUkT0pvJQxYg4iEfgsXBx5sHBtDKuA1BDB+Hh8ssi7jMTk5x1BX0ldty8ZCVygjmcgXk1nESpiOhn5KhFWNtpe+Sh0wuJcbbZ7vUJ2Vb1+pZS3y8Evz4eDukw0cXD3mT5/7tQRBTZNJa4O8+V114F0ZSUtY3QJQIdKNIaElK7sXEx0zo9lAzXjYym7nUBt22dy8OeFzvNNcGAlktxoY0i0WwGZdOAGxriSda0JLbaDF2RdCVBrKcShuY7JoEtq5+EYN0HOKxP1IIDBL+fwz8mlQao3qSBPG2BaC4RoXHedxOALbLDiVMks/SMi1WIF4/FlF/OfqY16Tf14DuoVCFeDIl7SVOtWvTaK7psxEAPwARw+yyUCFiXd4NNNbcHY7AmZ7t0k5LypjKxqlZGDnQ328p2JYDH/MAmO/ocGYEp/8/AMbi1CLO05jiTyl7fTUA3gERE5G0l85A5B2l4aOWWwHAxGTSvevFyDRJ1CimMS1GlkslNfs4jQ23cGj4WIQoyRNhZCP46ItL3ef4na6j2EJVEQR+S7ecq21ghJRm4CN9z/BxZdBi4+PY6Cg+9dRTDePjCdC9exbKQ26HKKFiI/iYlodMg48beq4Q+05m8n4MfZbjJB7SdU2U1PF4+ByZ9KwbAQIR+x2gXVe8i7WZP2sebUG+ggBHYnXF661n7cPH1Y7f0+BjF/jxUeYW4Dwkza0rxrpeHrITIF72DiJL/2LgYyZ0e6iZLxtX+ZANhUJtkp2ad4eLCC0szSrQFi4oCWYFAFwb/v8QRC/qUwD4XnaOlj4/D3FtJglhCHGhTWYEdvXrrrvusjaIqzzLYBg7TczezMwM9vb01Ji4CrjLX2jjyBOVaNl8Sev4kx6AOHDgQA2IRyoV1V19m2OOultacPfQUGKt17T1Wrsh0iBWlGflwYDM5hTz4kv+dAZ0bbeWsTQPJuO7LMnjqmusveTzYF7gvI1J7bvzzjsTY3AkZUxl49SssUtTXmmp8fGjDBc4Pq6GKDO4xEhuEef4yJkS/vFhf1p8LA8Px7LAyuoS9WKkDx8nJydr4+zCSFLuPfHEE7U51tzVOyFKsib3/eDAAE5MTNSS7EiMTIuPeQDcysbaVce4nT3DNScTExNe5u5GaAwf74E44yrXtA8fEfwhEhk+Lh0tJj7uvvnmmCWxUXzkuJIGIzsgyt/ADSYcI/MQ8QiN4KN85oLxkWVN5zxkT08vRi7qsp72k9gCeRyFIJGHdFnHI8v1PeFfXTgnfEQ0lnPNXR1gG2qW7ZaWbhwa2s3i182Hx6CnrWftw8cCGB7ySs+cpMHHIWW9aTkrDoLB5DK7VsNHvq6bwUMeOHAg9d7MhO4m0kIAUya00LRNlNCg07MAeKIMRDuz5TSYFze/90ilgrcODcVBGiJX8dp5yrEdnvbQpwJxyzctZkrc86TnPm9961sxF55/EPzMgkyaI2v8ubSqmsaXb1DJBAKAxeTxvp4FU3pHMoEXLlyw5ri3pwcLgXF5kh4BXLjWkpvIlyMxv/uVvm6CSOh2xevkwjnyxcD7kj/Rp6uz03oGZS+fE22w5kbEm9GaPudo+4SyBl3t48mp0lDGVDZOC0kUlAYf8wB4g2e9LjU+doOx5Pgw0oWP68LrTrNzXfd55ZVX4jpIh4+nTp1SMxY3gpE+fJyenrbmi/p7FuzqDrTfqYyaxMjOXM56lnTtkxipCQ+zYEqQyX7mAPDVbPy0xHQtYPAnTY6QNElENwiLTRI+0vNdGJkWHxH8yQAzfFw6ahY+IhqMlHs5DT5Ki52WGX0abJ5Gw8gNCh6cV9ZkwdMmAMMPafiYg8hrxsdDXrFhQ2p8PHnypIqPw8Nl1nbbXd3FQ549ezY8zx0Hbu63LjzPHavN8VFmaO/p6cUgKCDAETSKAdY+Jlyn5SEnwDaMpcHH1eGa841zWnyUGM2zl2v42C2+rxQeMhO6m0iNAKYrocVIpWJpcOj3ExDVwJOuyW3h9ZxoIx1xLK6nn34aEaNFSNpILYNkPnwGP0bafrkgT4tnuTYdJe4h4dCy8EKURVZufN9G1TYbBxva8AchrkGjMeQvM7KUW+PR0qK6Vt8G8eRjUptKz+DtmZ+fV8sv7CwWE11XePzN74ItNARgSoMNCit5ga2jM2AYsHaINNNJ1r88QCzO3jW/O7ZujT1zoL/fSiTH1xpZ2Fzjxdc0PZNijWif1BK5hfWVk9pXjxUHMWMqF0L1jl29+MjjhVcKPnYD4LXgx8g0+AiQ7JnSCD42CyO1+rl5MMo2Zz1V8OOjbI8LI0cqFSdGcnw8DVF9cH79jddfj/1hFQwNH2mdrQM7U7MPH30umLsG4pYnqciUa63oGTMfPvK59Ja9y/BxyahZ+Ci9Ok6zeSd81PisorL3+FrShJsdW7fWFJkcI8ug42NBOZ6Gh9znWJ/UtjQ85HLiY3l4GFuhBQOl3nZXwVhsHwPAQciFz9WF80PhGFLSSdme+fn50ALO+5DD3UNDifjIc1w8CjY+AgBe89rXpsLHHEQKF5JXFoKPkocsl0qxeOx68VGuaw0jeQ6NTZ72LZYnUCZ0e6iRl40roUUgJvgoW3CnAfARBURciQPoOavAgNsh9ixet1GWxUpcZOyYjPnVgHlDoYCdYtMVws1ZEecWxPdi+Dcv4j18Fn+ZAVsjVy3K8+fP1xX7p2VQDFK00Rk/Jfqah7glh0BSWpByor0VMNaSPADm16+31xrY2mjS8hFwPQl68ovenp4a8GnuP3kwDLe2vrk7EV9r9ZRi0LK1FnK52L7Ir19f61NeaV9/X1/qvUqUMZWNU71jVy8+Uvmo+2Hl4KNkDl0Y2Ra2z4eP28C2pgcQeQxR2zs8beT9bBZGJlmq5G9FaFzgG6lU1GRJnBGdnZ01rubM2ifxsQgG206AHiLVLHy8CIaJtXJ4BIHlInkC4jXV5VpLi5FJJXJkfzJ8XBnULHyUNYwp0Rnh428C4C6xXpM8/Sir9CZtjyjKHp8QJfmlNDzkaojjYxdE8cJpeUiOecuBj4+BsYbH+crAascwBJgTwjm5rqfFxw6Ixx+nwUf5zmwUH/87RBifD+dNztFC8JHzkHKtNZuHJI8iF0byMU1DmdDdRKoXMH3JZ/aBceNJcrvlSSEQ9Licubk5S4PvAlhahHeHx11awElxjC9I1VIZBJY7XBn0+nt5MO4sMhshMSC8RjZdLy1aPPYnyZ2SyFWrl9pFyXJc43FcHD/N5jBxHFPET/E+7B4aigE5jfsRMAkkJGDmw3HWkqHx+9KH3IjuAcD1uRy2hcmVJNNKLmhnz561SuvIc53WnZ07sSssD0Fj5MuavH///tocaS+7wf5+fPrpp3FiYgJ7mOXtdyHSVvKxk67Gi7HPM4qonrHz7YVDYFsspRvcSsFHAFO6KwkjCefS4GMR4p4p9eAjXa+NqVbHmigJI321suVvxBjVi49p1gVXRPrwsQDGtd3nIt4oPkq3e4mRSfXIAwALHxHSY6QLH2dmZnB6ehoLHR01RU2GjyuDmomPxJdMQ5SMSuIjncOVhHLvzc3NWVmlXVnREQ0u+NyFJb+UhoeU+DgGgL8C6XnIIuj4SM9Mg4+N8pASH2UWcDlW8wA46HBd5+OouTU3go8nAHAYQM0xdBPUj49nwPCc63M5XC2S6OXAePYsFB8BjAVcrrVm8JAf+chHcKC/v5ah3IWR5eHhVMnTGt3jmdDtoXpfNlqsDF8cAEbLUggnfSNbAKc9m4DHPmpu0d0QT4jjelG77n8qXNynIIoJ40llXNfxZENps1oTQyw1ZfR9s3Kcx/6cFr8DJNekdMX+JbVVam5pTNLOUz1rgiz+/N6DYNx6YtpZiMDsekgGouMQaX+lcmQLGM34vWDKMwzu3Fl7uYyNjsbKqB0K18EqgFrCt6S+cJBNY0HU5m96etpKLkfxkS6X4fVguxkv1j7PKKJ6xi4tPhK2rUR8rEKk8FoPkedL0rXjUB8+br3hhrrwMQBjveD3lBjpq9mrxf752sp/I8aoXnxEjErluNbFti1brEz0PnykT7PxERGt9XUobEtPoeBd4xIf0441n8MMHy8dajY+BuGnAHYiR581mid8tUqlgvG24c/jGOlLjEUhM5KHpDCcNPjIccu3F3JghEoNH6WglwYffRi5UHxENiY0Xtp1hw8ftp7dCD4+E45RJ8Tfg7yaThp8zEO05iRG3g+A7wDDv+5hieoWgo80twvBR0SdhyQFhAsjb2rAC6jePZ4J3R5qtqX7HWzDPaMsJC2DZB7ibsg+8JMZcmkhjo+Pq7ExeQC1nmcSaM1CpOnbBJFA7NNIvfnNb8apqSkc3Lkzps08DcZC0gmRW+V+iATLJBelChgXnyR3IddGr4CtFe0GE5eojRO1RcazPAwGVLo6OupeEwAmo/ckmJcPgJJwDeJWOgB32QbpDlsE3YWoDHpCjQCiZD/ynidPnkx8JjHA+yESnriWW9M+19rE5s9VHziAKP5V/tbV2Vm3hrLRfZ5RRM205CwnPlarVbxu82Y74zTY+JgDP2PHMZISuPjw8Y477sDx8XHcvnWrFx8PgonTI019EkYWAbDgqDNLpNU3VSsuhMfLEMdO7nlTDz4iIj744INejDwIUQx8Gnz8/5T1Uw8+joFJCCcVHNLNV9731KlTib+fARsfL0Lctd2HkRk+XjrUbE8gMoRQlnt5rmsdEUZ6SwGKfcQxcsfWrSq/1AO2RbUeHnIAIh7S53H05je/2cJHUji0g8FmSs77CxAlh1sMHjIJH13vKsJjeV07mPfM4MCA9WwSutPio1TWal4MJcf6kfjIvQeawUOmwccTEFUIaQQfEXUesgDJGNlI6E29ezwTuj20kJhuybhptbDlJp5XNgxnmKK6gG5QWudYiK5stB0Q14ZpTEg+7IOWpXI1mJhEyXhRu+aUZ1LfXRon+tsBkTbX6eYe3kOzpFCctGujHwGbeR4DwAugZN8Fownugng8i7yeu6dQUhQCLQnOclzofgVHH8+J57nuS674Pg0hd7Xhc10BfW1NTk6q65vAj7+M6JicV76utZrvPlAGMAIItzxKV1ot82uz93lGhhqNWVxp+IhotONynV4BtrXAVU7RhZGd4d51hYR8WNkfAQD+hnIvfl4ubAvHARdG0l7U8LFararKCq1WNjFwE0qbe6ExfHRhGVlleNz2cuNjVxBgAMnu89oa5yFSGj4C2LWPXRjpw8eDYHIikAVR4qOc+2bv8YwiagY+dkOUxFViwTmxDuQekBjps5reDm5hRsPHMTCKKb5Pisq+ScLH1WCs1k+CHpft4iEfA12IJsxYbB4yCR/nwVbYEQ8p974PI8ulUl346OqfrGHuw0cEv+dBs3hIGUK6DWxFjlR+u/AxDQ95O8Q9LOjdTKXIFgsfM6HbQ428bLSYArKcam4eaVxuaEEcOHDA+9K90bEQyXWY3FCOQxR/l9aNoyOhH9SuFjDx3lJDlUYrT5kSX8E2VQHiAJWkISPGQssAKi0LxOwH4UYb6O/HPGs3jfk+Ng8SSAm0JFNGAsBgf38tg6UmLHRCPDMyz0TL41b53PLnPQY2MHEg8lnWksZTSxzliiukl/8sRAmw6L7TYGdez4HNLFCbKCOxbLPGTNB3GdvJz/O51Ta6zzMyVO/YrWR8RIwSDN0d3kPeqxGMJLzT8PFJiOpKy2s2go6PV4FxFyYGS+JLErOxf/9+tdxXAIbR6QbbmjA4MODEx0MQYVWj+HgC9MRlbQDY3dlphRisBHx0uYe6MLISPld6cGj4eO3mzd52uX6TGJkDY4nkx2SpRx9GZvjYODUDH0lY03Cl6FiH94o9Qvv1rrvu8mIE3yNybXN8PAO2InGhPGQAgLcODcWEMhcPWYF4luvTEHkEkfV1sXnIrnCPbdu6NYaPT0KUPLERHpLqilOVCI2HdOHjCbBj3QGixGZp8RHB73nQLB6S8FHykFXQK1M0i4fMgVGs82Oy1GMz8TETuj3UiHs5aUdI23KvZ2HKTUxggqBr8WhjaMyRTwOvLXa+OBfChNwJEWjJMlZJAH/C0c9AXLvN07a3i02jud+5hLPB/n61xjYxtXycKYbz9hR9k79XIR7XkwPAB9jvmvWILDi+sXwIopIeT4IB4Ld7rkkaz7tZn7VyGVNTUzENMH+p8fvyl2IaZsHFFGgv3m6w49q4kFMEv1ttI/s8o4jqdZ9cyfiIqDO9/JqFYORHQcfHpGsOOfr5SuU6H0Zy3NGE/GHQs6jftGPHkuAjQtz9n5LukIDbTHy8CLbSuR58bBcMtgsjybIovbqS8FHL5i/b5aquIbHQ5ZURQDq32nr3eEZxapSHfPzxx2vz7BNkeWkr6Ynh4q20ZLW0t5aLh6T9ODExUVcVhSSPSX5tM3hIqcAKwODpsWPHrDbvCS3Ui8VDbg/LbhEW0NhL4TytQnYI7LCgNSmuWwgP6cJHH0bmoTk8ZBI+Ei/h4yEzobuJlHYwXbUVdw8N1TRJroUpS54EEMVDaIutLdy0RXEdaa+dWieWaIiXFKCFRVqmRjfYuHjW9PR0ao29a1PRy2OWne8DKDmG8rxBsMsIaTW2qb6hBLC+cM7ac7nEvv2EY7w0bRu5JJVBFyz5XLuety+8x23i/sSISQv/rXWMZ3l4WC2XQRpAnjjjNLtvWmbhw6Bn2aRyIi63XNdxun87G7skN6GMqWyc0oxdUm3udeE+OuNY183CR9Jep8FH2VbCR0yxpn1MCMdHnujFdc1xRz8LEFci1IuRvE/8HMIgXppmKfGRfpd1XMcAcDcsHB+JsQvYX4mPefDjI8V28/XswsiRSgXbwDDfPNmRby1xy5mGkUUw75MnQS/V5rt/O7jdauvd4xnptBAesrenBztyOfxxz9rmn+1hPXiacw07VitrqQ2iBLdJGKm1k2PyQvDxdoivw7Q8ZEXpYx7iSoRm8pDtEMfHPBgeCTGe8ZziwBvFyPc6xsvFQ06D7l6eRqECYX8lPgLoFv5uiKzPacYzLT4uJQ/pu/e94A7NamSPI2ImdPso7WDKoP0jygJwMTmHwDCJOTBuznQvl0ChxSH3hAwlZyBOg2Hy8kJL40rCUgy/3wj6BrvR0R6++GUiA16b1LUp09R/5Bo8rokjBkm6aK8Do7VzgYtLeKN4DsR4UpNpsGO8uzo6EtvtKslQBHdSiqT7rfP83h7+X4E46GqxMW1gLFpqIpCWFhypVCym0ldbkbeLkoiQkOFjFgLQLW9yncv7pClrRPfQShXVu88zsinN2KlJTYIAr+jujr+AwU740gx83D00hCOVSlPw8Umwk7ykxUj+LB7qk3SNK2GSdHNOi5GnwTCMeYjH0K0UfCSLrYU1kBxu4CtnSPjI33EnwF1Wx4WPeTB1XF3Mo4aRu4eGYm2j+/rwkStlfBi5SblXGqtjAICfgmSMzPCxcVoKHvJeiJKI8TDCejCSar0TRj4MUXkub4KqXA7blP21EHykZ6XhIX2/n4D68bEd3HHJ+8RxDR8RVx4PmfQbxWY/CYZ3LoTnVkDPP9ELkRJT4yFpPTWCj4SRi81DpsFH3u9m4GMmdHsozWBq2SBpwWiMW43Zg4jp4ZpE6b7jWhDj4+Mm5qNUisXFbVMW2u6hoVSZK+nTIr4XIXLjlWDKFzZZPvlzXIxLED6HhElXP9dBBGpcE8c3myuhRhlsJp4/S7uGx2/QC6YIugvghkIBu2QcD0RgLfvus8wmjcP6XA5vBNtVqbulBfv7+mLeC3R/rsmrgh3jMwM2mJaHhy1w9AkGst0y4ZXrOu5e5GL0fzzhPr7xfACitZZZuheHfGPnwhyNcegC88JvJj5yJvQxMELWQvExB41hpIaPGk48CYaBSIuPpERIwsgPOI7PKH1cLnx8EsDLZLs8Im7q66tZS+TzJD7S/dPgozZmu8OY09j7PYVwQOM6L+6bZFmsVqsqRs5CPFTptHIvnyXnRjAKrc3seY3s8Yzc1Gwe8gZgwjA0n4f8LBgc42u+0NGBzz33XGp8bJSH5NiSlockZZurj1yJsBB81DDIh4+IjWEkn9dm8pCtoOPjYH8/zszMWKUzJX4QRhI/SHk8JA+5oVDAQlgGtxF8JIxsFg9JvzWCjwWI+IXM0r0ElGYwZSkB10RqGWpHwsVFm+LOO++sTeyxY8dii18yBNVqFZ955hkEiMd3jYFDM9XTo5aF4ZsSwMQdTkC8RjeB5irQrQI5iGdc5BkyJYMB4X2ofuMNnoVPoErg+SQY0Lsb4gypywWzAhETe6vD4iCBkLStaepTSmaOlAAEHFrMadL4O58TtkOuIQ7y5PJJ95/wPI+Emx4w5YcAAN/61rda7VhInNaOrVvtbKxsjOX+ccWgaUmeuiGemZSOd4FZl2lcJ9Pu84x08o2dVmpFw0gtQ21afKR7xhiCMIu9fCGPgZ1Ypx58PANRcpd6MLKblWySY6LhRCukw0eOBxpGUkxeBXQXzD6ImC/pgr8c+LgtdI91zYG0MtUE5JmZmjdDbA1VKrVxr1arsSQ7Plw7DpHrdgEAf4/dV8ZUp7HIyDE6GI6HxXSLGGu+Xlz4SMJEAeJYSDGLEjeL7C+9Xxrd4xm5qVk8pIaPvWAqrfB1duDAAUQ0QisvxZeWh6Q9zysQ0Ic8L1xrPAemvv0ULAwfEevjIZPwByAqr9oIPm5m+yYPcW8bHz4iNoaRRYiMRZpQ2ygvtntoKBEfEeM8ZFp8zEOch2wWPjaThyyC4ScaxcfBnTsXtMeJMqHbQ41oKV0LtQK6SwgXDOhDzA+VhJEAdevQkJ20AQAPswWmAQglpkkScE+Ei3Y32PGUAIZRGAfAB8O/xOTmgwBv27vX0pbRM3gCiBshAuSLEGVVjAlOQYDXX3NNbQNrGQwD1iYf8LZB5BaY5HolXYVcNb4JLHj8DreqPQl6Rl7f+FsAoMRTUhIzKUjKtbgnxbiUwZTxkO0cgvRxrF1hBk2pQaXaoK5YcESsvfSJMRgAPbHFaqWNNP9Sqz4GhmkAiJL7Ze7li0ONWLo1jFQZGEjGxwBMNly5Lta2tuLrBCNTAcCnIMKfheAjgh8jHwSTXHKKXV8ulXBubs7JhPEkYmnwcQdLpDMJumV2Ffs/CQeKYFz+KYxkKfDxIuixeElt5W7imoBarVZxfHw8Vh7LtR7TxJ8mYWQZIow87bnXKmUOC7mc01WdW/y44mgP2DlJ8hDFLw6LNuYAsDuft/BR1jWfmJhoeI9n5KZm8ZBJ+CjXLM++XA8PSe7PhxzP81krKYfKQvGxXh6SBGbJf2x69atr9yBFaVm0JS0+0rht7OqqCx8R68NI8jpoFB/T8JArER9Xgy0QF3K5GhY2g4fcDsuPj5nQ7aF643G4m189bgwEVhchikm+EYz2SFs8a1pbE0utBBDPis2ftWvnTqdmByBKQkSAewaMdWEtxJPPyOe9O/ybDwLcB0Zo0xhFTVDWNJnl4WHV8lQFY50hK3cR4gyqBmo8yZt0ozkN5iU3rbWhVMLp6enEuXv88cdjAjAHCNKs8vlrAxtcCBRnZmaszMa+cgU8I3StDE8Q1MCf5lK+kFZBlISiCLrwQnE7CJE7sGw3F4D4tXQvGhfOeCPaiWNaxfVafC//tIJxKSXN7BmIl0ihub9btGMh+zwjm+qJ6Xa5yDWKj+fAnX20lR3T4iO3gB4fubNYTMRHLXcCx0iqD8ufxV/0gzt3YnsQ1AR/uZ8oEVKj+IgQKZro+gHw4yMfA57P4WI4PxrDmgYfp6amYhglGah6MPLWoaG68RHRxsj+vr6ay2kA7kzO6yA9Rj4JUSy6xNqk9+bMzAwi+vERIC4guJhD+lwVrkcqPQRgLFOEj3z+M3xcPGoGD5kmmVQXGEw6AQajNoU4kFf2Vx4a5yEDUDwzIK4cPQiN42N5eLhuHtLlMn748OGm4ON+EKW3UuLjQOi27XPL5zzkQvCxWTzkUuJjdxDUjCZJ+IjYXB5yufDxkhG6Dxw4gLfccguuXbsW8/l8qmteeuklfN/73odXXnklrlmzBl//+tenKnTOKe1gzs/P40ilYm0UWlw+14pNYDRFmiuya6P+ZLhAVI1k2IYxMMLkJERAc99993nLrgDY7iGUHKPoeN52ZdN0ie8SrGS8ykEwGR3J1U0DK3oBUXmJc+C36FbZWJPF86mnnrLauzrsRww0enpqMc6SmbIsrAzcqCSSbJOWxESCogQXjSTg8Hvy/88p6yoAWwPtmtehsL9kBePX9OTz2MmuOQTmhV9WxlvS2OgoFoLAAukKhGVsINLic+FAusi5xpl+l8lZFrLPVzKtZIyU+Ajh3iHGwZcQz4WPtFddc38AIozU4iN7wcbHTa95TSI+FsLkNy6MbHM8K1D2vYWPw8P4yCOPLAgfEeKxbBfDPvrwkY93UinAgviehI9anwnrXLHKaTAyDT4ipsfIingWfZdMnAsjb4QoAaUUlNvA4Bk/fwD8HjgufCyGa5FbOmnebge9frkPI5Ncy9Pu8ZVOKxkfEZN5yDQJQ8dg6XjI68KM4vzZPAyoUXyU+xIAcIP4nsRDkst4Z2ghXQ583Aa2NZUnEUvLQy4EHxGby0O68FG2oRF8LHR0qDzkEBtrFzXCQ5ICZbnw8ZIRun/5l38ZP/CBD+B73/ve1ID567/+65jP5/G3f/u38c///M/xh3/4h/G1r30t/su//Evq59YzmJRpcR+YbKByISRN6hmwgY+EyiSg9d1XasYDAPzQhz5UA0T+gtZizglMSdD1uXG3KxuOGK/Dhw8jIsZcK10WHC0RhVRgnBZtKYOukdMSjdB9ZbZD0iDKPmiuWAXlXO7Gw2OStPk7cOBAKsYxab2Rppprlakt68X4yCQY2ifJjYhKPsis5knadz7enOhlqIE0ASV/4cmXHx/HyclJa41wpj+NJexyYCpXOkZyfDwD5qUorc/14CNf4wvBSImP9L/Ex9oLWLitS4xsBB/72FrmjGIj+PgkmBrSsi1SgH8SDH4RPmoYyUsBkjWDf0/CxyIY5lPi0krCSF5mi/CR4009GFkuldSSOIuFj9q9XEnmkjCSQoCSKMPHlcFDutbSR8UeXyoech/E38lpeMikZ5FrtMSX7vD38fFxRKyPh1xKfHTxkE6B03Huywkffe9mV9+WiodsNj5eMkI30RNPPJEKMF966SW88sor8eDBg7Vj//AP/4BtbW34sY99LPXz0g6mS6NGL0Ne6kZzldGEWZ9LEbnsJG1KjcnbvmWL6jKiuWnS4qUYI5/G1ZXkph2iOoa8rnPMghMEqkZJi+egcSlDpA0+B/ZLilxQufuNa758412tVmsaMV8ZnyQtJZ1DL5BGKG3cDdeW0zx3Q1xz+RD73zWv1FYO0g95rmlPsDCTO49vfQNEyd6KCWOtrRHKyJmGLgemkmglYqTPvW58fBzLw8N14SOCP5NrGozcBxHeFSBSEmoKvBbQS1lxjGwEHzshnmF/pFLBQi4Xw8f1YBLhSNLWPjGlvHSPho+rIapj6sPIevDxUMrzVwpGWhUhIBIezgDg0RRzyxm3pcDHqrhX4BlrbZ1IBY6LMnw0tJw85K6BASc+utb4SuQhk56V1FYqh4ZYHw+5VPhYL0a+3HhIiY8XIarL7rpm29atzvYvBQ+5GPgIdc3SCqC0gHn+/HkEAHz++edjx/fs2YN33nmn87rvfve7+OKLL9Y+X//611MNZppECa5kALuHhpxlYSrKgufWW98ml+49XIPEN41v8RIw+yzdLk07Wac5aFoWI49FUkvGcwQAN4o+3Agm1qSLuYHK+2vz5cvUODk5GYv98J07OzubGA/TqHZStt/Xbk3zfATic+51zw9jffg5Rz3XbN+yxTmfdK+kdt/N2tjb04MFT4ZfuUbqoZcjU7mUGLlY+HgRdKagHoysKsc0fBzy3GcfxPfUQvExKXGMRnLtk+tdQfSD8LG/ry/xGXLO6sHHNLi0kjBysxijojKXPoyULoqLjY+T7F7lUkl1X20WRmb4GNFy8ZATExNOfMyDO4RxpfGQSeck9Z/jI2L9PGQ9+Lg+l8MtN9yQeP+Mh6yPh9RcuH0YSd6xGq0kHjITuhHx85//PAIA/t3f/V3s+Bve8AZ84xvf6Lzu/vvvt0AkzWD6LDl8AuWkzs/PY48o1UIfLXajAvEEQC5QDRIWIz2LXEbGPYsXwJRWyIOemKvLo2l3xa81KiRJplTGJ/O4mDRZvhHSaSnpOp/ygVustMyhI5VKXf2VVE+GySEwlrS7w3ngYCOVF3IdJZVlmAXdSlSAdC8EXxmNfBDgYH+/UwuZxm08Lb0cmcqlxMjFwke6fk1ra90YmYeoXrQPH3nW3CSMbIUoZlE+aynxkcaN9owLH5Oe0Yilux5cWkkY2QkG02v1jwU+kiWsoKwjOnep8fEgmPcu9yBbLIzM8DGi5eYhNXzkuVZWOg/pwseyZ71r+KiNR1pqNj7Wi5GXEg9JmeIXwkO6SocuRKmwUnjIS0bovvfee1Vw4p8XXnghds1iA2ajWkpEPXZE06RoND8/jxsKBRv4wpfq1NQU3nHHHalB1WeVkWUJfG6aUwD4GwDOLIOuRBHdkL5WciPEAbFe8NXmS63ZJ+aQriuCnn2Yn7uYG523v6IAVyGXcyYnoTZomTJdbdVeMiOglyNJ80LQXMS4gKKN00KEkCRaqUzl5YSRi4mPiFg3RgYQ1Yv24eOT4C+XcycY5k3LzjtSqSwLPiL6ywwmkZyzYh346MKllYSRhDVp8REgXo6Jn7vU+Ogap8XAyAwfI1qJPOT8/DyWSyXdFTi8fqXwkNp+27tnT60PVvvBroTSTGomPl5uPCTHGplEt14e0qVU2raIGLkS8XFZhe6///u/xxdeeCHx873vfS92zWK7BkmqZzAXujnSuMskgfLMzAwOsFIBSQCstZXqs0qNU0UB7/379+MTTzzhLJVFn0oI6GmZ66Ukrb0jlQreOjSUOAeyJFia+V6MjZ4G7GZmZlIpJdIqL+T600oxLXTN1xOL3SxaqUzl5YSRS4GPiG6MHKlUYkkAA4BY3LQPH5MUi0V2LdVBPXDgQKwW6qWGj4h6m11Cp+ualYyRVHatHnxMautS4CNv81JRho8RrWQeMk25qJXCQ95555144MABy2VcCndFMJVTLhV83D00hP19fZcFDymxZqE8pLb2tKo4lxoPeckI3Y1QvUkwDh06VDv24osvLloSDE4L3RxJ16cBZbpe1qNOOtflfhE0sBEkcNe7iZaatPFOM4cL0ZA2kxZi7a+XXOuPC/eN0GK320crlalshFY6Ri4mPiL6MbJRfHTdO8nqqNGlho+I6YVO1zXLvb+Xqi0ZPq58Wun4iPjy5iHJ4n0p46PrmOu6lwsPmbT2FpsvWEyqZ4/nEBHhEqCLFy/C/Pw8/O7v/i4cPHgQPvvZzwIAwDXXXAPt7e0AAHDDDTfAr/3ar8GP/diPAQDAQw89BL/+678Ox48fh9e+9rXwvve9D86dOwdf+cpXYM2aName++1vfxvy+Ty8+OKL0NnZuTida4D+6q/+Cr761a/CNddcA9dee23TzpXnA0Bd1y7kuRldOnS5ze1K3ef1UIaRcUq7RhtZy3RNa2sr/Nu//VuGjxnF6HKb25W6x+uhDB/jtNJ5yMttD2UU0eU2t3Xt8UVXATSJ3va2t8W0I/T5zGc+UzsHAPCJJ56ofX/ppZfwfe97H/b29mJbWxu+/vWvx9nZ2bqeezlpeDPKKCOdLod9nmFkRhlltBh0OezxDB8zyiijxaDL0tK9XLRStZQZZZRR8yjb541TNnYZZXR5U7bHG6ds7DLK6PKmevZ4sERtyiijjDLKKKOMMsooo4wyyiijlx21LncDVjqRI8C3v/3tZW5JRhlltFhE+ztz/KmfMozMKKPLmzJ8bJwyfMwoo8ub6sHHTOj20He+8x0AALjqqquWuSUZZZTRYtN3vvMdyOfzy92MS4oyjMwoo5cHZfhYP2X4mFFGLw9Kg49ZTLeHXnrpJfi7v/s76OjogFwu5z3/29/+Nlx11VXw9a9//bKM37nc+weQ9fFyoHr7h4jwne98B175yldCEGRRN/VQPRh5ua87gMu/j5d7/wCyPkrK8LFxyvAxTlkfL3263PsHsHj4mFm6PRQEAfzAD/xA3dd1dnZetosR4PLvH0DWx8uB6ulfZsFpjBrByMt93QFc/n283PsHkPWRU4aPjVGGjzplfbz06XLvH0Dz8TFTWWaUUUYZZZRRRhlllFFGGWWU0SJRJnRnlFFGGWWUUUYZZZRRRhlllNEiUSZ0N5na2trg/vvvh7a2tuVuyqLQ5d4/gKyPlwNd7v27VOnlMC+Xex8v9/4BZH3MaHno5TAnWR8vfbrc+weweH3MEqlllFFGGWWUUUYZZZRRRhlllNEiUWbpziijjDLKKKOMMsooo4wyyiijRaJM6M4oo4wyyiijjDLKKKOMMsooo0WiTOjOKKOMMsooo4wyyiijjDLKKKNFokzoziijjDLKKKOMMsooo4wyyiijRaJM6F4gvf/974fdu3fDunXroFAopLoGEeGXf/mX4RWveAWsXbsWbrvtNvirv/qrxW3oAmh+fh5+5md+Bjo7O6FQKMDtt98O//iP/5h4zete9zrI5XKxz3/6T/9piVrsp0cffRRe85rXwJo1a+Dmm2+G6enpxPOfeuopuOGGG2DNmjWwfft2mJycXKKWNk719PEjH/mINV9r1qxZwtbWR3/0R38E/+E//Ad45StfCblcDn77t3/be83p06ehv78f2tra4JprroGPfOQji97OjC5/jMzwMcPHlUYZPl46dLnjI0CGkQCXHkZezvgIsHwYmQndC6R//dd/hTe84Q3wn//zf059zcMPPwwf/OAH4ciRI3D27FlYv349jI6Owne/+91FbGnj9DM/8zPw5S9/GT796U/Dpz71KfijP/ojeNe73uW97p3vfCd84xvfqH0efvjhJWitnyYmJuC9730v3H///fCnf/qn0NfXB6Ojo/D3f//36vl//Md/DG9605vg9ttvh+effx5+9Ed/FH70R38U/vIv/3KJW56e6u0jAEBnZ2dsvv76r/96CVtcH/3TP/0T9PX1waOPPprq/K997WvwQz/0Q7B37174sz/7M7jrrrvgHe94B5w6dWqRW5rR5Y6RGT5m+LjSKMPHS4cud3wEyDDyUsPIyx0fAZYRIzGjptATTzyB+Xzee95LL72EV155JR48eLB27B/+4R+wra0NP/axjy1iCxujr3zlKwgAODMzUzv2B3/wB5jL5fBv//ZvndeVy2V8z3veswQtrJ927dqFv/ALv1D7/v3vfx9f+cpX4q/92q+p57/xjW/EH/qhH4odu/nmm/Hnf/7nF7WdC6F6+5h2/a5EAgD85Cc/mXjOPffcg1u3bo0d+4//8T/i6OjoIrYsI06XI0Zm+Jjh40qnDB8vDboc8RExw0jESw8jX074iLi0GJlZupeYvva1r8E3v/lNuO2222rH8vk83HzzzfAnf/Iny9gynf7kT/4ECoUCDAwM1I7ddtttEAQBnD17NvHaj370o7BhwwbYtm0b/Lf/9t/gn//5nxe7uV7613/9V/jSl74UG/8gCOC2225zjv+f/MmfxM4HABgdHV2R8wXQWB8BAP7xH/8RXv3qV8NVV10FP/IjPwJf/vKXl6K5S0KX2hy+nOlSwsgMHy+9vZXho02X2hy+nOlSwkeADCMBLq39leGjTs2aw9ZmNiojP33zm98EAIDe3t7Y8d7e3tpvK4m++c1vwhVXXBE71traCt3d3Ynt/emf/ml49atfDa985Svh3LlzcO+998Ls7Cx84hOfWOwmJ9L//b//F77//e+r4/9//s//Ua/55je/ecnMF0Bjfbz++uvh8ccfhx07dsCLL74Ihw4dgt27d8OXv/xl+IEf+IGlaPaikmsOv/3tb8O//Mu/wNq1a5epZRlJupQwMsPHDB8zfMxoKelSwkeADCMBLi2MzPBRp2ZhZGbpVui+++6zkgLIj2vxXSq02H1817veBaOjo7B9+3b4mZ/5Gfhf/+t/wSc/+Uk4f/58E3uRUbPolltugbe+9a1QLBahXC7DJz7xCdi4cSM89thjy920jFYgXe4YmeFjRpwyfMyoHrrc8REgw8iMIsrwMT1llm6F/ut//a/wsz/7s4nnbNq0qaF7X3nllQAA8K1vfQte8YpX1I5/61vfgmKx2NA9G6G0fbzyyiut5An/9m//BvPz87W+pKGbb74ZAAC++tWvwubNm+tub7Now4YN0NLSAt/61rdix7/1rW85+3PllVfWdf5yUyN9lLRq1Sq46aab4Ktf/epiNHHJyTWHnZ2dmRWnAbrcMTLDxwwfkyjDx4yS6HLHR4AMIy9XjMzwUadmYWQmdCu0ceNG2Lhx46Lc+7WvfS1ceeWV8Nxzz9UA8tvf/jacPXu2ruyVC6W0fbzlllvgH/7hH+BLX/oS7Ny5EwAA/vAP/xBeeumlGgimoT/7sz8DAIi9JJaDVq9eDTt37oTnnnsOfvRHfxQAAF566SV47rnn4N3vfrd6zS233ALPPfcc3HXXXbVjn/70p+GWW25ZghbXT430UdL3v/99+Iu/+AsYGxtbxJYuHd1yyy1WiY6VPIcrnS53jMzwMcPHJMrwMaMkutzxESDDyMsVIzN81KlpGFlvlreM4vTXf/3X+Pzzz+P+/fuxvb0dn3/+eXz++efxO9/5Tu2c66+/Hj/xiU/Uvv/6r/86FgoF/J3f+R08d+4c/siP/Ai+9rWvxX/5l39Zji546Qd/8AfxpptuwrNnz+LnPvc5vPbaa/FNb3pT7fe/+Zu/weuvvx7Pnj2LiIhf/epX8YEHHsAvfvGL+LWvfQ1/53d+Bzdt2oR79uxZri7E6OMf/zi2tbXhRz7yEfzKV76C73rXu7BQKOA3v/lNRER8y1vegvfdd1/t/M9//vPY2tqKhw4dwhdeeAHvv/9+XLVqFf7FX/zFcnXBS/X2cf/+/Xjq1Ck8f/48fulLX8Kf+qmfwjVr1uCXv/zl5epCIn3nO9+p7TUAwA984AP4/PPP41//9V8jIuJ9992Hb3nLW2rnX7hwAdetW4f79u3DF154AR999FFsaWnBZ555Zrm68LKhyx0jM3zM8HGlUYaPlw5d7viImGHkpYaRlzs+Ii4fRmZC9wLpbW97GwKA9fnMZz5TOwcA8Iknnqh9f+mll/B973sf9vb2YltbG77+9a/H2dnZpW98Spqbm8M3velN2N7ejp2dnfhzP/dzsRfC1772tVifL168iHv27MHu7m5sa2vDa665Bvft24cvvvjiMvXApg996EN49dVX4+rVq3HXrl34hS98ofZbuVzGt73tbbHzf+u3fguvu+46XL16NW7duhV///d/f4lbXD/V08e77rqrdm5vby+OjY3hn/7pny5Dq9PRZz7zGXXfUZ/e9ra3Yblctq4pFou4evVq3LRpU2xPZrR4dLljZIaPGT6uNMrw8dKhyx0fETOMRLz0MPJyxkfE5cPIHCJifbbxjDLKKKOMMsooo4wyyiijjDLKKA1l2cszyiijjDLKKKOMMsooo4wyymiRKBO6M8ooo4wyyiijjDLKKKOMMspokSgTujPKKKOMMsooo4wyyiijjDLKaJEoE7ozyiijjDLKKKOMMsooo4wyymiRKBO6M8ooo4wyyiijjDLKKKOMMspokSgTujPKKKOMMsooo4wyyiijjDLKaJEoE7ozyiijjDLKKKOMMsooo4wyymiRKBO6M8ooo4wyyiijjDLKKKOMMspokSgTujPKKKOMMsooo4wyyiijjDLKaJEoE7ozelnTxz72MVi7di184xvfqB37uZ/7OdixYwe8+OKLy9iyjDLKKKPlpQwfM8ooo4zclGFkRvVQDhFxuRuRUUbLRYgIxWIR9uzZAx/60Ifg/vvvh8cffxy+8IUvwKte9arlbl5GGWWU0bJRho8ZZZRRRm7KMDKjeqh1uRuQUUbLSblcDt7//vfDT/7kT8KVV14JH/rQh+Czn/1sDSx/7Md+DE6fPg2vf/3r4eTJk8vc2owyyiijpaMMHzPKKKOM3JRhZEb1UGbpzigjAOjv74cvf/nLMDU1BeVyuXb89OnT/387d4iqQBiGYfTbgGWK3SVYZJqgQTHYBHciAy7FYDYLrmLANtEVWMQoWO/lMhcVfn5hzolavvTAyzATt9st9vu9YAKdpI8A7TSSV3inm847nU7RNE08Ho/o9/u//huPx9Hr9TJdBpCXPgK000heZXTTaXVdx2q1it1uF5PJJLbbbe6TAL6CPgK000je4Z1uOutyucRisYiqqmK9XsdgMIiyLKOu6xgOh7nPA8hGHwHaaSTv8qSbTrperzGbzWK5XMZms4mIiNFoFPP5PKqqynwdQD76CNBOI/mEJ910UlEU0TTNn9+Px2OGawC+hz4CtNNIPuHr5fCP6XQa5/M57vd7FEURh8MhyrLMfRZAdvoI0E4j+cnoBgAAgES80w0AAACJGN0AAACQiNENAAAAiRjdAAAAkIjRDQAAAIkY3QAAAJCI0Q0AAACJGN0AAACQiNENAAAAiRjdAAAAkIjRDQAAAIkY3QAAAJDIE1w3KOcyEkCEAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "print(\n",
        "    \"Cost: {:3f} | Train accuracy {:3f} | Test Accuracy : {:3f}\".format(\n",
        "        loss, accuracy_train, accuracy_test\n",
        "    )\n",
        ")\n",
        "\n",
        "print(\"Learned weights\")\n",
        "for i in range(num_layers):\n",
        "    print(\"Layer {}: {}\".format(i, params[i]))\n",
        "\n",
        "\n",
        "fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n",
        "plot_data(X_test, initial_predictions, fig, axes[0])\n",
        "plot_data(X_test, predicted_test, fig, axes[1])\n",
        "plot_data(X_test, y_test, fig, axes[2])\n",
        "axes[0].set_title(\"Predictions with random weights\")\n",
        "axes[1].set_title(\"Predictions after training\")\n",
        "axes[2].set_title(\"True test data\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYQWz6IdTnyy"
      },
      "source": [
        "References\n",
        "==========\n",
        "\n",
        "\\[1\\] Pérez-Salinas, Adrián, et al. \\\"Data re-uploading for a universal\n",
        "quantum classifier.\\\" arXiv preprint arXiv:1907.02085 (2019).\n",
        "\n",
        "\\[2\\] Kingma, Diederik P., and Ba, J. \\\"Adam: A method for stochastic\n",
        "optimization.\\\" arXiv preprint arXiv:1412.6980 (2014).\n",
        "\n",
        "\\[3\\] Liu, Dong C., and Nocedal, J. \\\"On the limited memory BFGS method\n",
        "for large scale optimization.\\\" Mathematical programming 45.1-3 (1989):\n",
        "503-528.\n",
        "\n",
        "About the author\n",
        "================\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seconds = time.time()\n",
        "print(\"Time in seconds since end of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "8FGVwF_QUYza",
        "outputId": "5c3823ae-3795-45b7-91d1-0166ea0754f2"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696871479.7649212\n",
            "Mon Oct  9 17:11:19 2023\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.18"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}