[404218]: / Code / All Qiskit, PennyLane QML Nov 23 / 33a1 A100 Light.gpu,qsim 4.10s kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "DpuwMygzRa5x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "6f3c3291-870d-4a9e-ed1e-aad8966def6a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1700593979.996332\n",
            "Tue Nov 21 19:12:59 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 pennylane-lightning-gpu custatevec-cu11 --upgrade\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "# !pip install Pennylane-Cirq\n",
        "# !pip install qsimcirq\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": "markdown",
      "metadata": {
        "id": "-eVitlw3Ra5y"
      },
      "source": [
        "Ensemble classification with Rigetti and Qiskit devices\n",
        "=======================================================\n",
        "\n",
        "::: {.meta}\n",
        ":property=\\\"og:description\\\": We demonstrate how two QPUs can be\n",
        "combined in parallel to help solve a machine learning classification\n",
        "problem, using PyTorch and PennyLane. :property=\\\"og:image\\\":\n",
        "<https://pennylane.ai/qml/_images/ensemble_diagram.png>\n",
        ":::\n",
        "\n",
        "*Author: Tom Bromley --- Posted: 14 February 2020. Last updated: 13\n",
        "December 2021.*\n",
        "\n",
        "This tutorial outlines how two QPUs can be combined in parallel to help\n",
        "solve a machine learning classification problem.\n",
        "\n",
        "We use the `rigetti.qvm` device to simulate one QPU and the `qiskit.aer`\n",
        "device to simulate another. Each QPU makes an independent prediction,\n",
        "and an ensemble model is formed by choosing the prediction of the most\n",
        "confident QPU. The iris dataset is used in this tutorial, consisting of\n",
        "three classes of iris flower. Using a pre-trained model and the PyTorch\n",
        "interface, we\\'ll see that ensembling allows the QPUs to specialize\n",
        "towards different classes.\n",
        "\n",
        "Let\\'s begin by importing the prerequisite libraries:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "_2DspgdXRa5z"
      },
      "outputs": [],
      "source": [
        "import pennylane as qml\n",
        "\n",
        "from collections import Counter\n",
        "\n",
        "import dask\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pennylane as qml\n",
        "import sklearn.datasets\n",
        "import sklearn.decomposition\n",
        "import torch\n",
        "from matplotlib.lines import Line2D\n",
        "from matplotlib.patches import Patch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2DvrUEEbRa5z"
      },
      "source": [
        "This tutorial requires the `pennylane-rigetti` and `pennylane-qiskit`\n",
        "packages, which can be installed by following the instructions\n",
        "[here](https://pennylane.ai/install.html). We also make use of the\n",
        "[PyTorch interface\n",
        "\\<https://pennylane.readthedocs.io/en/stable/introduction\n",
        "/interfaces.html\\>](), which can be installed from\n",
        "[here](https://pytorch.org/get-started/locally/).\n",
        "\n",
        "Load data\n",
        "=========\n",
        "\n",
        "The next step is to load the iris dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "fADxw3MLRa5z"
      },
      "outputs": [],
      "source": [
        "n_features = 2\n",
        "n_classes = 3\n",
        "n_samples = 150\n",
        "\n",
        "data = sklearn.datasets.load_iris()\n",
        "x = data[\"data\"]\n",
        "y = data[\"target\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VvU3dpkBRa5z"
      },
      "source": [
        "We shuffle the data and then embed the four features into a\n",
        "two-dimensional space for ease of plotting later on. The first two\n",
        "principal components of the data are used.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "dxG69NptRa5z"
      },
      "outputs": [],
      "source": [
        "np.random.seed(1967)\n",
        "\n",
        "data_order = np.random.permutation(np.arange(n_samples))\n",
        "x, y = x[data_order], y[data_order]\n",
        "\n",
        "pca = sklearn.decomposition.PCA(n_components=n_features)\n",
        "pca.fit(x)\n",
        "x = pca.transform(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4hn2rxA_Ra5z"
      },
      "source": [
        "We will be encoding these two features into quantum circuits using\n",
        "`~.pennylane.RX`{.interpreted-text role=\"class\"} rotations, and hence\n",
        "renormalize our features to be between $[-\\pi, \\pi]$.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "QpFJIA_TRa5z"
      },
      "outputs": [],
      "source": [
        "x_min = np.min(x, axis=0)\n",
        "x_max = np.max(x, axis=0)\n",
        "\n",
        "x = 2 * np.pi * (x - x_min) / (x_max - x_min) - np.pi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uzJ-pXmARa5z"
      },
      "source": [
        "The data is split between a training and a test set. This tutorial uses\n",
        "a model that is pre-trained on the training set.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "_zk_t0IFRa50"
      },
      "outputs": [],
      "source": [
        "split = 125\n",
        "\n",
        "x_train = x[:split]\n",
        "x_test = x[split:]\n",
        "y_train = y[:split]\n",
        "y_test = y[split:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "joAmbsxdRa50"
      },
      "source": [
        "Finally, let\\'s take a quick look at our data:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "5bTij62oRa50",
        "outputId": "014a1fde-b6cc-46e5-9c92-49a7039a0e9c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "colours = [\"#ec6f86\", \"#4573e7\", \"#ad61ed\"]\n",
        "\n",
        "\n",
        "def plot_points(x_train, y_train, x_test, y_test):\n",
        "    c_train = []\n",
        "    c_test = []\n",
        "\n",
        "    for y in y_train:\n",
        "        c_train.append(colours[y])\n",
        "\n",
        "    for y in y_test:\n",
        "        c_test.append(colours[y])\n",
        "\n",
        "    plt.scatter(x_train[:, 0], x_train[:, 1], c=c_train)\n",
        "    plt.scatter(x_test[:, 0], x_test[:, 1], c=c_test, marker=\"x\")\n",
        "\n",
        "    plt.xlabel(\"Feature 1\", fontsize=16)\n",
        "    plt.ylabel(\"Feature 2\", fontsize=16)\n",
        "\n",
        "    ax = plt.gca()\n",
        "    ax.set_aspect(1)\n",
        "\n",
        "    c_transparent = \"#00000000\"\n",
        "\n",
        "    custom_lines = [\n",
        "        Patch(facecolor=colours[0], edgecolor=c_transparent, label=\"Class 0\"),\n",
        "        Patch(facecolor=colours[1], edgecolor=c_transparent, label=\"Class 1\"),\n",
        "        Patch(facecolor=colours[2], edgecolor=c_transparent, label=\"Class 2\"),\n",
        "        Line2D([0], [0], marker=\"o\", color=c_transparent, label=\"Train\",\n",
        "               markerfacecolor=\"black\", markersize=10),\n",
        "        Line2D([0], [0], marker=\"x\", color=c_transparent, label=\"Test\",\n",
        "               markerfacecolor=\"black\", markersize=10),\n",
        "    ]\n",
        "\n",
        "    ax.legend(handles=custom_lines, bbox_to_anchor=(1.0, 0.75))\n",
        "\n",
        "\n",
        "plot_points(x_train, y_train, x_test, y_test)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C3MY22AMRa50"
      },
      "source": [
        "![](/demonstrations/ensemble_multi_qpu/ensemble_multi_qpu_001.png){.align-center\n",
        "width=\"80.0%\"}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yvi_BMwbRa50"
      },
      "source": [
        "This plot shows us that class 0 points can be nicely separated, but that\n",
        "there is an overlap between points from classes 1 and 2.\n",
        "\n",
        "Define model\n",
        "============\n",
        "\n",
        "Our model is summarized in the figure below. We use two 4-qubit devices:\n",
        "`4q-qvm` from the pennyLane-rigetti plugin and `qiskit.aer` from the\n",
        "PennyLane-Qiskit plugin.\n",
        "\n",
        "Data is input using `~.pennylane.RX`{.interpreted-text role=\"class\"}\n",
        "rotations and then a different circuit is enacted for each device with a\n",
        "unique set of trainable parameters. The output of both circuits is a\n",
        "`~.pennylane.PauliZ`{.interpreted-text role=\"class\"} measurement on\n",
        "three of the qubits. This is then fed through a softmax function,\n",
        "resulting in two 3-dimensional probability vectors corresponding to the\n",
        "3 classes.\n",
        "\n",
        "Finally, the ensemble model chooses the QPU which is most confident\n",
        "about its prediction (i.e., the class with the highest overall\n",
        "probability over all QPUs) and uses that to make a prediction.\n",
        "\n",
        "![](/demonstrations/ensemble_multi_qpu/ensemble_diagram.png){.align-center\n",
        "width=\"80.0%\"}\n",
        "\n",
        "Quantum nodes\n",
        "-------------\n",
        "\n",
        "We begin by defining the two quantum devices and the circuits to be run\n",
        "on them.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "ZC9IBsTLRa50"
      },
      "outputs": [],
      "source": [
        "n_wires = 4\n",
        "\n",
        "dev0 = qml.device(\"lightning.gpu\", wires=4)\n",
        "dev1 = qml.device(\"cirq.qsim\", wires=4)\n",
        "devs = [dev0, dev1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6MreXphRa50"
      },
      "source": [
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "If you have access to Rigetti hardware, you can swap out `rigetti.qvm`\n",
        "for `rigetti.qpu` and specify the hardware device to run on. Users with\n",
        "access to the IBM Q Experience can swap `qiskit.aer` for `qiskit.ibmq`\n",
        "and specify their chosen backend (see\n",
        "[here](https://docs.pennylane.ai/projects/qiskit/en/latest/devices/ibmq.html)).\n",
        ":::\n",
        "\n",
        "::: {.warning}\n",
        "::: {.title}\n",
        "Warning\n",
        ":::\n",
        "\n",
        "Rigetti\\'s QVM and Quil Compiler services must be running for this\n",
        "tutorial to execute. They can be installed by consulting the [Rigetti\n",
        "documentation](http://docs.rigetti.com/qcs/) or, for users with Docker,\n",
        "by running:\n",
        "\n",
        "``` {.bash}\n",
        "docker run -d -p 5555:5555 rigetti/quilc -R -p 5555\n",
        "docker run -d -p 5000:5000 rigetti/qvm -S -p 5000\n",
        "```\n",
        ":::\n",
        "\n",
        "The circuits for both QPUs are shown in the figure below:\n",
        "\n",
        "![](/demonstrations/ensemble_multi_qpu/diagram_circuits.png){.align-center\n",
        "width=\"80.0%\"}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "JTD0RbZpRa50"
      },
      "outputs": [],
      "source": [
        "def circuit0(params, x=None):\n",
        "    for i in range(n_wires):\n",
        "        qml.RX(x[i % n_features], wires=i)\n",
        "        qml.Rot(*params[1, 0, i], wires=i)\n",
        "\n",
        "    qml.CZ(wires=[1, 0])\n",
        "    qml.CZ(wires=[1, 2])\n",
        "    qml.CZ(wires=[3, 0])\n",
        "\n",
        "    for i in range(n_wires):\n",
        "        qml.Rot(*params[1, 1, i], wires=i)\n",
        "    return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)), qml.expval(qml.PauliZ(2))\n",
        "\n",
        "\n",
        "def circuit1(params, x=None):\n",
        "    for i in range(n_wires):\n",
        "        qml.RX(x[i % n_features], wires=i)\n",
        "        qml.Rot(*params[0, 0, i], wires=i)\n",
        "\n",
        "    qml.CZ(wires=[0, 1])\n",
        "    qml.CZ(wires=[1, 2])\n",
        "    qml.CZ(wires=[1, 3])\n",
        "\n",
        "    for i in range(n_wires):\n",
        "        qml.Rot(*params[0, 1, i], wires=i)\n",
        "    return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)), qml.expval(qml.PauliZ(2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7gMHFIhRa50"
      },
      "source": [
        "We finally combine the two devices into a\n",
        "`~.pennylane.QNode`{.interpreted-text role=\"class\"} list that uses the\n",
        "PyTorch interface:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "4FVRVMfsRa50"
      },
      "outputs": [],
      "source": [
        "qnodes = [\n",
        "    qml.QNode(circuit0, dev0, interface=\"torch\"),\n",
        "    qml.QNode(circuit1, dev1, interface=\"torch\"),\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ikfJ1bNURa50"
      },
      "source": [
        "Postprocessing into a prediction\n",
        "================================\n",
        "\n",
        "The `predict_point` function below allows us to find the ensemble\n",
        "prediction, as well as keeping track of the individual predictions from\n",
        "each QPU.\n",
        "\n",
        "We include a `parallel` keyword argument for evaluating the\n",
        "`~.pennylane.QNode`{.interpreted-text role=\"class\"} list in a parallel\n",
        "asynchronous manner. This feature requires the `dask` library, which can\n",
        "be installed using `pip install \"dask[delayed]\"`. When `parallel=True`,\n",
        "we are able to make predictions faster because we do not need to wait\n",
        "for one QPU to output before running on the other.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "f43ELlgORa51"
      },
      "outputs": [],
      "source": [
        "def decision(softmax):\n",
        "    return int(torch.argmax(softmax))\n",
        "\n",
        "\n",
        "def predict_point(params, x_point=None, parallel=True):\n",
        "    if parallel:\n",
        "        results = tuple(dask.delayed(q)(params, x=x_point) for q in qnodes)\n",
        "        results = torch.tensor(dask.compute(*results, scheduler=\"threads\"))\n",
        "    else:\n",
        "        results = tuple(q(params, x=x_point) for q in qnodes)\n",
        "        results = torch.tensor(results)\n",
        "    softmax = torch.nn.functional.softmax(results, dim=1)\n",
        "    choice = torch.where(softmax == torch.max(softmax))[0][0]\n",
        "    chosen_softmax = softmax[choice]\n",
        "    return decision(chosen_softmax), decision(softmax[0]), decision(softmax[1]), int(choice)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l6Pmc_SPRa51"
      },
      "source": [
        "Next, let\\'s define a function to make a predictions over multiple data\n",
        "points.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "H4cinMByRa51"
      },
      "outputs": [],
      "source": [
        "def predict(params, x=None, parallel=True):\n",
        "    predictions_ensemble = []\n",
        "    predictions_0 = []\n",
        "    predictions_1 = []\n",
        "    choices = []\n",
        "\n",
        "    for i, x_point in enumerate(x):\n",
        "        if i % 10 == 0 and i > 0:\n",
        "            print(\"Completed up to iteration {}\".format(i))\n",
        "        results = predict_point(params, x_point=x_point, parallel=parallel)\n",
        "        predictions_ensemble.append(results[0])\n",
        "        predictions_0.append(results[1])\n",
        "        predictions_1.append(results[2])\n",
        "        choices.append(results[3])\n",
        "\n",
        "    return predictions_ensemble, predictions_0, predictions_1, choices"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fiwOjmj6Ra51"
      },
      "source": [
        "Make predictions\n",
        "================\n",
        "\n",
        "To test our model, we first load a pre-trained set of parameters which\n",
        "can also be downloaded by clicking\n",
        "`here <../demonstrations/ensemble_multi_qpu/params.npy>`{.interpreted-text\n",
        "role=\"download\"}.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "G1cPzrPXRa51"
      },
      "outputs": [],
      "source": [
        "params = np.load(\"/content/drive/MyDrive/Colab Notebooks/data/params.npy\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fLL0vpBQRa51"
      },
      "source": [
        "We can then make predictions for the training and test datasets.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "3JCGVAfCRa51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "7f0dc7fa-45eb-4303-b867-ab936cde5300"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicting on training dataset\n",
            "Completed up to iteration 10\n",
            "Completed up to iteration 20\n",
            "Completed up to iteration 30\n",
            "Completed up to iteration 40\n",
            "Completed up to iteration 50\n",
            "Completed up to iteration 60\n",
            "Completed up to iteration 70\n",
            "Completed up to iteration 80\n",
            "Completed up to iteration 90\n",
            "Completed up to iteration 100\n",
            "Completed up to iteration 110\n",
            "Completed up to iteration 120\n",
            "Predicting on test dataset\n",
            "Completed up to iteration 10\n",
            "Completed up to iteration 20\n"
          ]
        }
      ],
      "source": [
        "print(\"Predicting on training dataset\")\n",
        "p_train, p_train_0, p_train_1, choices_train = predict(params, x=x_train)\n",
        "print(\"Predicting on test dataset\")\n",
        "p_test, p_test_0, p_test_1, choices_test = predict(params, x=x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tgN2CzYiRa51"
      },
      "source": [
        "::: {.rst-class}\n",
        "sphx-glr-script-out\n",
        "\n",
        "Out:\n",
        "\n",
        "``` {.none}\n",
        "Predicting on training dataset\n",
        "Completed up to iteration 10\n",
        "Completed up to iteration 20\n",
        "Completed up to iteration 30\n",
        "Completed up to iteration 40\n",
        "Completed up to iteration 50\n",
        "Completed up to iteration 60\n",
        "Completed up to iteration 70\n",
        "Completed up to iteration 80\n",
        "Completed up to iteration 90\n",
        "Completed up to iteration 100\n",
        "Completed up to iteration 110\n",
        "Completed up to iteration 120\n",
        "Predicting on test dataset\n",
        "Completed up to iteration 10\n",
        "Completed up to iteration 20\n",
        "```\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FVdIuxQwRa51"
      },
      "source": [
        "Analyze performance\n",
        "===================\n",
        "\n",
        "The last thing to do is test how well the model performs. We begin by\n",
        "looking at the accuracy.\n",
        "\n",
        "Accuracy\n",
        "--------\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "kczX36uTRa51"
      },
      "outputs": [],
      "source": [
        "def accuracy(predictions, actuals):\n",
        "    count = 0\n",
        "\n",
        "    for i in range(len(predictions)):\n",
        "        if predictions[i] == actuals[i]:\n",
        "            count += 1\n",
        "\n",
        "    accuracy = count / (len(predictions))\n",
        "    return accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "20fSyuPGRa51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a0ece94a-5c8a-4862-a262-eb1cdeaaf8cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy (ensemble): 0.832\n",
            "Training accuracy (QPU0):  0.648\n",
            "Training accuracy (QPU1):  0.288\n"
          ]
        }
      ],
      "source": [
        "print(\"Training accuracy (ensemble): {}\".format(accuracy(p_train, y_train)))\n",
        "print(\"Training accuracy (QPU0):  {}\".format(accuracy(p_train_0, y_train)))\n",
        "print(\"Training accuracy (QPU1):  {}\".format(accuracy(p_train_1, y_train)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "usYgkClaRa51"
      },
      "source": [
        "::: {.rst-class}\n",
        "sphx-glr-script-out\n",
        "\n",
        "Out:\n",
        "\n",
        "``` {.none}\n",
        "Training accuracy (ensemble): 0.824\n",
        "Training accuracy (QPU0):  0.648\n",
        "Training accuracy (QPU1):  0.296\n",
        "```\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "1IKXhuDYRa51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a26852df-5186-4b24-9f49-4399b9259ddf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test accuracy (ensemble): 0.72\n",
            "Test accuracy (QPU0):  0.56\n",
            "Test accuracy (QPU1):  0.24\n"
          ]
        }
      ],
      "source": [
        "print(\"Test accuracy (ensemble): {}\".format(accuracy(p_test, y_test)))\n",
        "print(\"Test accuracy (QPU0):  {}\".format(accuracy(p_test_0, y_test)))\n",
        "print(\"Test accuracy (QPU1):  {}\".format(accuracy(p_test_1, y_test)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nd04PGDFRa51"
      },
      "source": [
        "::: {.rst-class}\n",
        "sphx-glr-script-out\n",
        "\n",
        "Out:\n",
        "\n",
        "``` {.none}\n",
        "Test accuracy (ensemble): 0.72\n",
        "Test accuracy (QPU0):  0.56\n",
        "Test accuracy (QPU1):  0.24\n",
        "```\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3I6d13XxRa51"
      },
      "source": [
        "These numbers tell us a few things:\n",
        "\n",
        "-   On both training and test datasets, the ensemble model outperforms\n",
        "    the predictions from each QPU. This provides a nice example of how\n",
        "    QPUs can be used in parallel to gain a performance advantage.\n",
        "-   The accuracy of QPU0 is much higher than the accuracy of QPU1. This\n",
        "    does not mean that one device is intrinsically better than the\n",
        "    other. In fact, another set of parameters can lead to QPU1 becoming\n",
        "    more accurate. We will see in the next section that the difference\n",
        "    in accuracy is due to specialization of each QPU, which leads to\n",
        "    overall better performance of the ensemble model.\n",
        "-   The test accuracy is lower than the training accuracy. Here our\n",
        "    focus is on analyzing the performance of the ensemble model, rather\n",
        "    than minimizing the generalization error.\n",
        "\n",
        "Choice of QPU\n",
        "=============\n",
        "\n",
        "Is there a link between the class of a datapoint and the QPU chosen to\n",
        "make the prediction in the ensemble model? Let\\'s investigate.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "-2PFfMi0Ra51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1a0a9119-f2cf-4318-9633-75dbce67fb87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Choices: [0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1 0 0 0 1 0 0 1 0 1 0 0 0\n",
            " 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0\n",
            " 0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0\n",
            " 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0\n",
            " 0 0]\n",
            "Choices counts: Counter({0: 111, 1: 39})\n"
          ]
        }
      ],
      "source": [
        "# Combine choices_train and choices_test to simplify analysis\n",
        "choices = np.append(choices_train, choices_test)\n",
        "print(\"Choices: {}\".format(choices))\n",
        "print(\"Choices counts: {}\".format(Counter(choices)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DRtS5EQJRa51"
      },
      "source": [
        "::: {.rst-class}\n",
        "sphx-glr-script-out\n",
        "\n",
        "Out:\n",
        "\n",
        "``` {.none}\n",
        "Choices: [0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1 0 0 0 1 0 0 1 0 1 0 0 0\n",
        " 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0\n",
        " 0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0\n",
        " 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0\n",
        " 0 0]\n",
        "Choices counts: Counter({0: 110, 1: 40})\n",
        "```\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n9woZMLVRa51"
      },
      "source": [
        "The following lines keep track of choices and corresponding predictions\n",
        "in the ensemble model.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "Xl6Emr7sRa52"
      },
      "outputs": [],
      "source": [
        "predictions = np.append(p_train, p_test)\n",
        "choice_vs_prediction = np.array([(choices[i], predictions[i]) for i in range(n_samples)])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ocSVwH1PRa52"
      },
      "source": [
        "We can hence find the predictions each QPU was responsible for.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "AR-1Q8XDRa52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "f5d84a1f-692c-4201-da8d-f296602bc43e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "When QPU0 was chosen by the ensemble, it made the following distribution of predictions:\n",
            "Counter({2: 56, 0: 55})\n",
            "\n",
            "When QPU1 was chosen by the ensemble, it made the following distribution of predictions:\n",
            "Counter({1: 36, 0: 3})\n",
            "\n",
            "Distribution of classes in iris dataset: Counter({0: 50, 2: 50, 1: 50})\n"
          ]
        }
      ],
      "source": [
        "choices_vs_prediction_0 = choice_vs_prediction[choice_vs_prediction[:, 0] == 0]\n",
        "choices_vs_prediction_1 = choice_vs_prediction[choice_vs_prediction[:, 0] == 1]\n",
        "predictions_0 = choices_vs_prediction_0[:, 1]\n",
        "predictions_1 = choices_vs_prediction_1[:, 1]\n",
        "\n",
        "\n",
        "expl = \"When QPU{} was chosen by the ensemble, it made the following distribution of \" \\\n",
        "       \"predictions:\\n{}\"\n",
        "print(expl.format(\"0\", Counter(predictions_0)))\n",
        "print(\"\\n\" + expl.format(\"1\", Counter(predictions_1)))\n",
        "print(\"\\nDistribution of classes in iris dataset: {}\".format(Counter(y)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A7fuUr6BRa52"
      },
      "source": [
        "::: {.rst-class}\n",
        "sphx-glr-script-out\n",
        "\n",
        "Out:\n",
        "\n",
        "``` {.none}\n",
        "When QPU0 was chosen by the ensemble, it made the following distribution of predictions:\n",
        "Counter({0: 55, 2: 55})\n",
        "\n",
        "When QPU1 was chosen by the ensemble, it made the following distribution of predictions:\n",
        "Counter({1: 37, 0: 3})\n",
        "\n",
        "Distribution of classes in iris dataset: Counter({0: 50, 2: 50, 1: 50})\n",
        "```\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQQxg-9qRa52"
      },
      "source": [
        "These results show us that QPU0 specializes to making predictions on\n",
        "classes 0 and 2, while QPU1 specializes to class 1.\n",
        "\n",
        "Visualization\n",
        "=============\n",
        "\n",
        "We conclude by visualizing the correct and incorrect predictions on the\n",
        "dataset. The following function plots correctly predicted points in\n",
        "green and incorrectly predicted points in red.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "GnYUU0O8Ra52"
      },
      "outputs": [],
      "source": [
        "colours_prediction = {\"correct\": \"#83b5b9\", \"incorrect\": \"#f98d91\"}\n",
        "markers = [\"o\", \"v\", \"d\"]\n",
        "\n",
        "\n",
        "def plot_points_prediction(x, y, p, title):\n",
        "    c = {0: [], 1: [], 2: []}\n",
        "    x_ = {0: [], 1: [], 2: []}\n",
        "\n",
        "    for i in range(n_samples):\n",
        "        x_[y[i]].append(x[i])\n",
        "        if p[i] == y[i]:\n",
        "            c[y[i]].append(colours_prediction[\"correct\"])\n",
        "        else:\n",
        "            c[y[i]].append(colours_prediction[\"incorrect\"])\n",
        "\n",
        "    for i in range(n_classes):\n",
        "        x_class = np.array(x_[i])\n",
        "        plt.scatter(x_class[:, 0], x_class[:, 1], c=c[i], marker=markers[i])\n",
        "\n",
        "    plt.xlabel(\"Feature 1\", fontsize=16)\n",
        "    plt.ylabel(\"Feature 2\", fontsize=16)\n",
        "    plt.title(\"Predictions from {} model\".format(title))\n",
        "\n",
        "    ax = plt.gca()\n",
        "    ax.set_aspect(1)\n",
        "\n",
        "    c_transparent = \"#00000000\"\n",
        "\n",
        "    custom_lines = [\n",
        "        Patch(\n",
        "            facecolor=colours_prediction[\"correct\"],\n",
        "            edgecolor=c_transparent, label=\"Correct\"\n",
        "        ),\n",
        "        Patch(\n",
        "            facecolor=colours_prediction[\"incorrect\"],\n",
        "            edgecolor=c_transparent, label=\"Incorrect\"\n",
        "        ),\n",
        "        Line2D([0], [0], marker=markers[0], color=c_transparent, label=\"Class 0\",\n",
        "               markerfacecolor=\"black\", markersize=10),\n",
        "        Line2D([0], [0], marker=markers[1], color=c_transparent, label=\"Class 1\",\n",
        "               markerfacecolor=\"black\", markersize=10),\n",
        "        Line2D([0], [0], marker=markers[2], color=c_transparent, label=\"Class 2\",\n",
        "               markerfacecolor=\"black\", markersize=10),\n",
        "    ]\n",
        "\n",
        "    ax.legend(handles=custom_lines, bbox_to_anchor=(1.0, 0.75))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7eNf8urQRa52"
      },
      "source": [
        "We can again compare the ensemble model with the individual models from\n",
        "each QPU.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "5oYzHtj6Ra52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "32ad5d14-94c8-49e8-9d08-5cad80ea4a9d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_points_prediction(x, y, predictions, \"ensemble\")  # ensemble\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nuYo5p1XRa52"
      },
      "source": [
        "![](/demonstrations/ensemble_multi_qpu/ensemble_multi_qpu_002.png){.align-center\n",
        "width=\"80.0%\"}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "drMCzSURRa52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "6beb0c9a-217e-426e-8e1f-e3ac369187a5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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jx45hxYoVsLOzw+HDh/Gvf/0LCQkJmlo5A7V8+XIsWrQITz31FE6ePInp06ejvb0dP/74I44cOYLXX39dq+syPT0dd9xxB55//nlUV1cjMjIS//znP1FUVIS///3vFvqJDTP1d7F+/Xp88cUXWLJkCZ588knN1OyQkBBNtxygGhOzceNG/OpXv8LkyZNx9913w8vLCyUlJdi2bRtmzJiBjz/+eMh/LkKsxgjOpCKjiHr6bn9Tig1NH1b79NNPeUpKCrezs+NOTk48KSmJr1+/npeXl2uOUSgU/JVXXuF+fn7czs6Oz507l2dmZvKQkBCjU7PVDh8+zBcuXMidnJy4g4MDT05O5h999JFmv1wu52vWrOFeXl6cMaY1bRV9pvRyzvnZs2f54sWLuaOjI7e3t+fz5s3jR48eNen56dvGs2fP8nvuuYcHBwdzqVTKvb29+Y033shPnz5t7GnlnBufmr169Wq9jxlM29VTemtqakxqhz5KpZJv3ryZz5gxgzs5OXEAminsXV1dOscbm3LdV2dnJ9+wYQOPjY3lUqmUOzg48KlTp/J//etfeo/v6Ojgzz77LPf19eVSqZSnpqbynTt3mnQtQ+0KCQnhN9xwg852fb8TU34XnHN+8eJFPmfOHG5ra8sDAgL4a6+9xv/+979rTc1W279/P1+8eDF3cXHhtra2PCIigj/wwANaf080NZsQzhnnQziKjRAyrshkMixfvhx79+7FTz/9hCVLlox0kwgh4wAFM4QQi2pra8PcuXORm5uLgwcPYvLkySPdJELIGEfBDCGEEEKsGs1mIoQQQohVo2CGEEIIIVaNghlCCCGEWDUKZgghhBBi1ahoHlRVRcvLy+Hk5GTW6suEEEJGDuccLS0t8Pf311k3jowPFMwAKC8vH9Jy54QQQoZeaWkpAgMDR7oZZARQMAPAyckJgOqFMJTr2RBCCLG85uZmBAUFad7LyfhDwQyur7Ls7OxMwQwhhFgpGiYwflHnIiGEEEKsGgUzhBBCCLFqFMwQQgghxKpRMEMIIYQQq0bBDCGEEEKsGgUzhBBCCLFqFMwQQgghxKpRMEMIIUQL7+qC/PBR8Pb2kW4KISahYIYQQogWxdETUJ48DfmhwyPdFEJMQsEMIYQQDWVNLZTnLwAAeHYulNfKR7hFhPSPghlCCCEAVKtPK/bsu76BMcj37ANXKkeuUYSYgIIZQgghAABlzmXwikqAc9UGzoG6eigvXBrZhhHSDwpmCCGEgHd1QXHgkN59isNHwdtoMDAZvSiYGQeqmluQXVGJgppayBWULiaE6FKcOQd0denfKZdDceLk8DaIkAEQj3QDyNCpaGrGtktZqGxu0Wyzk0gwKyocqSHBYIyNYOsIIaMJk9pc717SRyodvsYQMkCUmRmjqptb8Pnxk6jqFcgAQIdMhp+zL+NIQeEItYwQMhoJEycALi6AvpscOzuIUlOGv1GEmIiCmTFq/5U8yJVKGLrPOpRXgPbu7mFtEyFk9GIiEcQZ8/RmZ8Tz54DZ2IxAqwgxjdUHMxs3bkRycjKcnZ3h7OyMadOmYceOHSPdrBHV3t2NvOpaoxljzjmyyiuHr1GEkFFPCAkGi4y4np1hDCwoECwqcmQbRkg/rD6YCQwMxNtvv40zZ87g9OnTmD9/Pm6++WZkZWWNdNNGTFtX/xkXxhhaOg0M9iOEjFviebMB4fpHg3jBXBpfR0Y9qw9mli9fjmXLliEqKgrR0dF444034OjoiOPHj49000aMvY2k32M453CUUtqYEKKNOTlBNC0dACBMmQzm7j7CLSKkf2NqNpNCocB//vMftLW1Ydq0aQaP6+rqQlevKYjNzc3D0bxh4yCVIsLTA1fr6gx2NTHGEO/vO7wNI4RYBSFlEuDoAIG6l4iVsPrMDABcunQJjo6OkEqlWLVqFb7//nvEx8cbPP6tt96Ci4uL5isoKGgYWzs85sVEQWACDCWHZ0SEwZGmWhJC9GAiEUTxcWCS/rO8hIwGjHNjw0StQ3d3N0pKStDU1IRvvvkGf/vb33Dw4EGDAY2+zExQUBCamprg7Ow8XM0ecmUNjdh6KQu1rW2abTZiEWZGhGNaeCj1gxNCxoTm5ma4uLiMufdwYroxEcz0lZGRgYiICPz1r3816fix/ELgnKO8qQn1be2QisUI8/SARCQa6WYRQojFjOX3cGKaMTVmRk2pVGplXsYzxhgCXF0R4Oo60k0hhBBChoTVBzPPP/88li5diuDgYLS0tODLL7/EgQMHsGvXrpFuGiGEEEKGgdUHM9XV1Vi5ciUqKirg4uKC5ORk7Nq1CwsXLhzpphFCCCFkGFh9MPP3v/99pJtACCGEkBE0JqZmE0IIIWT8omCGEEIIIVaNghlCCCGEWDUKZgghhBBi1SiYIYQQMmjK8grwjo6RbgYZpyiYsUIKpRJdMjnGYPFmQogV4nX1kH/1DeQ/bqP3JTIirH5q9nhS3tiEIwWFuFJVDQ7A3kaClOAgTA0PhVRMv0pCyPDjnEO+dz8ADn6tHMrLVyCKjRnpZpFxhjIzViK/ugabj53EleoaqO972rtlOJx/FZ8fO4kumXxE20cIGZ+UV/LAy65B/cak2H8IvLt7ZBtFxh0KZqyATKHA9+cvQcm5TgqXA6huacUv+QUj0zhCyLjFu7uh2H9Ie2NnJxTHToxMg8i4RcGMFcipqEKX3HDmhQM4W1oGuUI5fI0ihIx7iuOngL6DfjmH8ux5KGvrRqZRZFyiYMYKVLe0QGDM6DHdcgWaOzuHqUWEkPGONzRAeeYsYGDAr2LfgeFtEBnXKJixAmKRyKTjJCL6dRJChgdvaTUYyIBz8MbGYW0PGd/o088KRHt7QWlkuiMD4OvsBCdb2+FrFCFkXGNBgWAB/oCBrLFo1oxhbhEZzyiYsQL+ri4I9XAz9J4BDmBmZPiwtokQMr4xxiBaME/fDjB/Pwg0PZsMIwpmrMSKyRMR6OoKABAYAwM0X4vjYxHr6zOCrSOEjEeCpweEyRN1sjOijPlg/YzzI8SSqNKalbCTSLByaiqK6xuQU1GFboUcHg4OmBDoT91LhJARI5qWDmVOLtDeATAGYdIECJ4eI90sMs5QMGNFGGMI9XBHqIc7AKC9uxvt3d0QdXfD3sZmhFtHCBmPmI0NRHNnQ7F9F2ArhWha+kg3iYxDFMxYoarmFhy4koe86lrNtihvT8yLjoK3s9MItowQMh4JMdHgVdUQQoLBpNKRbg4ZhyiYsTLljU34/PgpKJTaBfLya2pRWFuPlVNT4e/qMkKtI4SMR4wxiOfMGulmkHGMBgBbEc45tl7KgkKpRN+J2pyrVtPedilrRNpGCCGEjBQKZqxIZXMLqltadQIZNQ6gqqUVlU3Nw9ksQgghZERRMGNF6lrbTDuuzbTjCCGEkLGAghkrIpWYNsRJKqahUIQQQsYPCmasSKiHe7+BilQs1kzdJoQQQsYDCmasiEQkwszIMKPHzIoMN3lhSkIIIWQsoGDGykwNC8WsyHDNkga9/50dFYH0sJCRbiIhhBAyrGhwhZVhjGFOdCRSQoKQVV6J1q4uOEmlSPD3hQMVqyKEEDIOUTBjpRylUsrCEEIIIaBuJkIIIYRYOQpmCCGEEGLVKJghhBBCiFWjYIYQQgghVo2CGUIIIYRYNQpmCCGEEGLVKJghhBBCiFWjYIYQQgghVo2CGUIIIYRYNQpmCCGEEGLVKJghhBBCiFWjYIYQQgghVo2CGUIIIYRYNQpmCCGEEGLVKJghhBBCiFWz+mDmrbfeQmpqKpycnODt7Y1bbrkFly9fHulmEUIIIWSYWH0wc/DgQaxevRrHjx/H7t27IZPJsGjRIrS1tY1006xKt1yOxvYOdMpkI90UQgghZEAY55yPdCMsqaamBt7e3jh48CBmz55t0mOam5vh4uKCpqYmODs7D3ELR5emjg4cvFKArPIKKHr+FCK9PDEnOhJ+LuPruSCEWKfx/B5OVMQj3QBLa2pqAgC4u7sbPKarqwtdXV2a75ubm4e8XaNRQ3s7Nh09gQ6ZHL1j2oLaOhTW1uHetBSEeBh+HgkhhJDRwOq7mXpTKpVYt24dZsyYgcTERIPHvfXWW3BxcdF8BQUFDWMrR4+fs3N1AhkA4JxDyTl+uJAJ5dhK3BFCCBmDxlQws3r1amRmZmLLli1Gj3v++efR1NSk+SotLR2mFo4ezZ2dyKuu1Qlk1HjPMYW1dcPbMEIIIWSAxkw30xNPPIGtW7fi0KFDCAwMNHqsVCqFVCodppaNTvVt7f0ewwDUtrYhwstz6BtECCGEmMnqgxnOOdasWYPvv/8eBw4cQFhY2Eg3ySrYiET9HsMB2Ij7P44QQggZSVYfzKxevRpffvklfvjhBzg5OaGyshIA4OLiAjs7uxFu3ejl6+IMJ1spWjq7DB7DGEOUt9cwtooQQggZOKufms0Y07t906ZNeOCBB0w6h7VM6+Oco7i+AQXVtWjv7oabgz0mBPrDydbWrPOdL72GrZeyDO6fEhKEJQlx5jaXEEKGhbW8h5OhY/WZGSuPxUzW1NGJr0+fRVVLq9b2A1fyEeLhhuXJiXAdYCZqYlAAOmTd2H85H5xzMMbAoXpOJwYGYGFcjAV/AkIIIWRoWH1mxhJGe1QvVyjw11+OoaHd8KBdO4kED8+cChczutZau7qQea0CTR2dsLORINHfF+4ODoNpMiFkhHGlErzsGlhgAJgwpiau6hjt7+Fk6Fl9ZmY8yK6oMhrIAECHTIYDl/Nx88SkAZ/fUSrF1PBQM1tHCBmNlOcvQnHgEERzZkGUMmmkm0PIkBrb4foYkVNRadJxWRWV6JLJh7g1hJDRjre1Q3HkKABAceQYeCutVUfGNgpmrECn3LQARck5WroMz04ihIwP8kOHAblC9Y1CAfmhX0a2QYQMMQpmrICXoyP0z9nSZSuhnkNCxjNl2TXwnFxAPRySc/DcK1CWlo1swwgZQhTMWIFJwYEwZZR2sJsrHMd5ZWNCxjOuVEK+Zx/Qt2QFY5Dv2Q+uUIxMwwgZYhTMWAE/F2dM62eALgMwJzpyWNpDCBmdlBcuAfUN17MyapwDDQ2q/YSMQdQnYSXmx0TBzd4ee3Iuo7vP3ZWtRIKbkhMQ4uFu0rk45yhraERdWztsxCJEeHpCSt1ThFg93tioysroq7jBGHhD43A3iZBhQZ9gVoIxhsnBgZgUFIDypmbkV9dAYAwejg6I8fGGyMQ6EtcaG/HjhUzU9VpoUiwImBoeijlREQYrKhNCRj/RlBQoL2UB+iYNCAJEaSnD3yhChgEFM1aGMYYAVxcEuLoM+LFVzS344vhpKJRKre1ypRKH86+iWy7HovhYSzWVEDLMmJMjRNOnQnHosM4+0bR0MCenEWgVIUOPxsyMIwfz8qHgSoODiU8WlaCpo2NY20QIsSxh0gTAzfX6IGDGABcXCFQ4j4xhFMyME50yGa5U1ejtSldjADKvVQxbmwghlsdEIogXzNOami3OmAcmEo1swwgZQhTMjBMdMlm/xzDG0NbdPQytIYQMJSE4CCwmCgDAoiIhhATrHMM7u8Dr64e7aYQMCRozM07Y29hAYAxKI6kZzjmcbG2HsVWEkKEinjMbCpEIohnTdfZxziH/74/g1TWQPPircTOWRqFQQGbCjR0ZHSQSCUQmZhQpmBknpGIxYn19kFNZabSrKdHfb/gaRYgeXKkEL6/QP71YzcYGzNuLZt8ZwRwdIF6ySO8+Ze4V1XPMAPnBw5DcuHSYWze8OOeorKxEY2PjSDeFDJCrqyt8fX37fa1TMDOOzI2OREFNLbrlcr2DgGdFRcDJlioIk5GlzL0Mxc7d/R4nefBXgJvbMLRobOFdXVAcONjzDcCv5EFZkgghOGhkGzaE1IGMt7c37O3tKQi2ApxztLe3o7q6GgDg52f8RpuCmXHE3cEeD0xLw/bMbJT2Kp5lJ5FgVlQ4UvX0qxMy3ISwUCjEYv21UtQ8PQBX12Fr01iiOH4S6Oy1IG3PUgeS++8bk4OEFQqFJpDx8PAY6eaQAbCzswMAVFdXw9vb22iXEwUz44yXkyPun5aGutY21LW1wUYsRpCbq8lF9wgZaszODsLkiVCeOmOwq0k8YxrdXZtBWVsH5dnz2s8r50BjI5Rnz0OUOvaK6qnHyNjb249wS4g51L83mUxmNJihT7BxysPRAdE+3gj1cKdAhow6opRJgL43LsYATw+w8LDhb9QYoNi73/C+o8fBW1qHsTXDi4Jf62Tq740+xcYQpVKJxvYONHd0ghsbPEnIKKfOzuis/sw5ZWUGgdfUGh5YrVCANzUNb4MIsRDqZhoD5Aoljl4txOmiErT3pFQ9HOwxPSIMyQH+9MZPrJIoZZKqS0Q9doYxwMOdsjKDIJqeDsWBX3R3MAYW4A8W4D/8jSLEAiiYsXIKpRJfnT6Lorp6rRlKdW3t+OliFurb2jGvp3gWIdZEZ+wMZWUGTZg4AYqLmUBDo06GRrxg7rh7bl/f/vOwXu/3y/RPle9PZWUl3njjDWzbtg3Xrl2Dt7c3Jk6ciHXr1mHBggUWbuXgbN68GevWrRv2afDUzWTlLpSVo7BPINPbkYJCVDW3DGubCLEUrbEzNFZm0JggQJwxXzuQYQzC5ElgNNNnVCoqKkJKSgr27duHd999F5cuXcLOnTsxb948rF692qxzdhuo9G7NBQUpmLFyp4tLjO5njOFsSdkwtYYQy9KMnQHNYLIUITAALDbm+ngkO1uIpqWNbKOIQY8//jgYYzh58iRWrFiB6OhoJCQk4Omnn8bx48cBACUlJbj55pvh6OgIZ2dn3HnnnaiqqtKcY8OGDZg4cSL+9re/ISwsDLY9ld4ZY9i4cSNuuukmODg44I033gAA/PDDD5g8eTJsbW0RHh6OV155BfJepRIaGxvx2GOPwcfHB7a2tkhMTMTWrVtx4MABPPjgg2hqagJjDIwxbNiwYVieJ+pmsnL1be1G93POUds6dmcokLFPlJ4KwdeHsjIWJJ4zE7KCAkAmh2jeHDAbm5FuEtGjvr4eO3fuxBtvvAEHBwed/a6urlAqlZpA5uDBg5DL5Vi9ejXuuusuHDhwQHNsfn4+vv32W3z33XdaU5w3bNiAt99+G++//z7EYjF++eUXrFy5Eh9++CFmzZqFgoICPProowCAl19+GUqlEkuXLkVLSwv+9a9/ISIiAtnZ2RCJRJg+fTref/99vPTSS7h8+TIAwNHRcWifpB4UzFghmUKBkvoGyBQKiAQBcqXS4LEMgFRCv2ZivZhEAhYZMdLNGFOYgwNECzPAr12DEE1j6kar/Px8cM4RGxtr8Ji9e/fi0qVLKCwsRFCQqorz559/joSEBJw6dQqpqakAVF1Ln3/+Oby8vLQef++99+LBBx/UfP/rX/8azz33HO6//34AQHh4OF577TWsX78eL7/8Mvbs2YOTJ08iJycH0dHRmmPUXFxcwBiDr6+vZZ4EE9GnnBXhnONwQSGOXS1Et1xh2mMAxA/zHxUhZPQTxUYDsdEj3QxihCklNnJychAUFKQJZAAgPj4erq6uyMnJ0QQzISEhOoEMAEyZMkXr+wsXLuDIkSOaLidAVUW5s7MT7e3tOH/+PAIDAzWBzGhBwYwV2Z1zGSeLjI+R6Y0xBjd7O8T6+gxhqwghhAyFqKgoMMaQm5s76HPp66bSt721tRWvvPIKbrvtNp1jbW1tNUsMjDY0ANhK1Le1mRzIqIdIejs54n/SpkAsol8zIdZKoVRCrjDclUzGLnd3dyxevBiffPIJ2tradPY3NjYiLi4OpaWlKC0t1WzPzs5GY2Mj4uPjB3zNyZMn4/Lly4iMjNT5EgQBycnJKCsrw5UrV/Q+3sbGBgqFaT0HlkSZGStx8VoFGGMmpR05gJTgQCxJiKPZH4RYMc45vjh+GjKlAg/NmAqBXs/jzieffIIZM2YgLS0Nr776KpKTkyGXy7F7925s3LgR2dnZSEpKwn333Yf3338fcrkcjz/+OObMmaPThWSKl156CTfeeCOCg4Nx++23QxAEXLhwAZmZmXj99dcxZ84czJ49GytWrMCf/vQnREZGIjc3F4wxLFmyBKGhoWhtbcXevXsxYcIE2NvbD8u6WHTLbiVaOjsHdPyZkjJcra0botYQQobDxWvlKGtsRFVzC86WlPb/gGHCGxog+/pb8IbGkW7KmBceHo6zZ89i3rx5eOaZZ5CYmIiFCxdi79692LhxIxhj+OGHH+Dm5obZs2cjIyMD4eHh+Oqrr8y63uLFi7F161b8/PPPSE1NxdSpU/Hee+8hJCREc8y3336L1NRU3HPPPYiPj8f69es12Zjp06dj1apVuOuuu+Dl5YV33nnHIs9DfxinRXzQ3NwMFxcXNDU1wdnZeaSbo9e+3DwcKywyec0lxhhC3d1wX/rAI3NCyMjrlMnw8YFf0ClT1fewEYmweu4sOEhHdho15xzyb74HLy0DCw6CeMUtI54BNvYe3tnZicLCQq36KsR6mPr7o8yMlUgK8BvQ4pGccxTV19OCk4RYqQNX8tElu16oTKZQYN9l/eMUhhPPywcvVRXi5CWl4PkFI9wiQiiYsRpeTo6YGBQwsAdRHEOIVapsbsbp4lKtlzCHavmSshHs2uHd3ZDvO6i1Tb7vALgVl8EnYwMFM1ZkWUIcpoWHQiT0/2tjAPxcnEc8/UsIGRjOObZfytb72mUAtmdmQzlCGVfFiVNAR4f2xvZ21XZCRhAFM1ZEEAQsiI3GUwvm4MakeKMzGziA9LAQg/sJIaNTa1c3ypua9XYRcwDVLa1obO/QfeAQ4/UNUJ4+q7PaNjigPHUGvKFh2NtEiBoFM1bIViLBxKBALE2IA8P1ujIANHdzU0KCEO9HlX8JsTZOtlJE+3jpzcwIjCHMwx1u9sNfuExx5tyg9hMylKjOjBVq7+7GjxcykV9Tq7WdAQh0dcH08DBEentSFxMZs3hbG3hjk9FjmJsr2DDUtxgKi+JjUVBzGAo9vUkjVT+K+fsBlzL17+RctZ+QEULBjJWRKRT44vhp1OqpBskBlDY04uK1cng5OcJ1BO7eCBkO8h+2gldWGT2GBQZAcueKYWqRZbna2WFWZAQOXMnXbGMApoWHwsNRf1n6oSbEx0J54SJ4VbV2VxNjYL4+EOIML4ZIyFCjbiYrk1legZrWVqNTrnMrq/D3I8dR39Y+jC0jZPgIMf0vcmfKMaPZ1LBQuNjZarqRHaRSzIgIG7H2MMYgypivO2YGgChjPmWCyYiiYMbKXCwr7/cYDqBTLsfunMEvTkbIaCQkJwLGCqA52ENIiBu+Bg0BsUjAssR4zfTsJQmxsBGPbDJd8PaCMDEZUAcujEGYmAzBy3NE20UIBTNWprWry6TjOOfIq65F8wCXQSDEGjCJBKL0VIP7RVPTwUb4g9+YC2XX8P9OnYVCaXwByQgvTyQH+CHW1xsxPt7D1DrjRNOnATY9VYilUoimTQUAcIUCyuISKtRJRsTofbUPwKFDh/Duu+/izJkzqKiowPfff49bbrllpJs1JFzs7NDY3mFyPbyG9nY4UwlvMkx4QyMU2bkwVrGR2dlCmDgBzIR6ScYIyYmq+iZ9A/ZRnpVp6ezEjswcyJVKnC4u7beEwk0TksA5HzXdOMxWCtHc2VDs2g3R3FlgtlIAgOL4SShPnIJoySKI4q1j/Ez3nz4c1uvZPL12QMc/8MADaGxsxH//+9+hadAImjt3LiZOnIj333/fIucbE8FMW1sbJkyYgF//+te47bbbRro5Q2pSUACK6upNPl46iu9OydijLC6B8sRJVTeEvg9fpRIQBAiJCdfv7s2kzs4oDv6itX20Z2X25FyBoid7ceBKPuL9fOHUExAYMloCGTUhPhaCny/g5gpAtfCk8tQZAIDiwCEIEWFgUuM/Exn9uru7YdPndapQKMAYgzDImxFLG12tMdPSpUvx+uuv49Zbbx3ppgy5WF8fBPe8gfTH1c4OPk5OQ9sgQnoR4mJUQQrnqsCl7xdjEBLjwQYZyGiu13fszCjPyhTV1SOrolLTFSNXKrE3d+TXWxooxhiYuxsYY6qFJ/ceuD4wuKsLiqMnRrR9Y9HcuXOxdu1arF+/Hu7u7vD19cWGDRu0jmlsbMRjjz0GHx8f2NraIjExEVu3btXs//bbb5GQkACpVIrQ0FD88Y9/1Hp8aGgoXnvtNaxcuRLOzs549NFHsXnzZri6uuLHH39EfHw8pFIpSkpK0NXVhWeffRYBAQFwcHBAeno6Dhw4oHW+I0eOYO7cubC3t4ebmxsWL16MhoYGPPDAAzh48CA++OAD1d8SYygqKhrU8zMmgpmB6urqQnNzs9aXtRAJAu5JS8Hk4MB+j50THTnq7ujI2MakUohSU7QrOfYhSjM81mXA1+szdmY0Z2UUSiW2Z2ZrPTWcc2SWV6C43vRs62jD86+Cl5ReD2Y4h/L8BSj71MEig/fPf/4TDg4OOHHiBN555x28+uqr2L17NwBAqVRi6dKlOHLkCP71r38hOzsbb7/9NkQiEQDgzJkzuPPOO3H33Xfj0qVL2LBhA1588UVs3rxZ6xr/93//hwkTJuDcuXN48cUXAQDt7e34wx/+gL/97W/IysqCt7c3nnjiCRw7dgxbtmzBxYsXcccdd2DJkiXIy8sDAJw/fx4LFixAfHw8jh07hsOHD2P58uVQKBT44IMPMG3aNDzyyCOoqKhARUUFgoKCBvXcjM5X/RB766238Morr4x0M8wmEYmwLDEec6Ii8NPFLOTX1KoqATMGJecQCQwZsTFICui/iJWSc+RX16Cgpg4KroSfizMS/f2oe4qYTZiYDMWpM0B3t/YOxiAkJYA5WzZbqBk7IxJGdVbmVFGJ3nIJDMD2Szl4dNY0k9ZdG024TAb5vgN69yn27AO7+w66obKg5ORkvPzyywCAqKgofPzxx9i7dy8WLlyIPXv24OTJk8jJyUF0tKosQXh4uOaxf/rTn7BgwQJNgBIdHY3s7Gy8++67eOCBBzTHzZ8/H88884zm+19++QUymQx//vOfMWHCBABASUkJNm3ahJKSEvj7+wMAnn32WezcuRObNm3Cm2++iXfeeQdTpkzBn//8Z825EhISNP+3sbGBvb09fH0tU6neul45FvL888+jqalJ81VaWjrSTTKLvY0N4vx84GpnCw5VYCIWBEwJDjJphe3G9g789dARfH3mPM6VluFiWTl2ZObgg70HUUB3VcRMxrIzlszKaK4nkUC84haIb7t51GZlAOBIQaHe7RxAXVubVb7mFCfPAO26BTzBOXhFJZRW2IU2miUnJ2t97+fnh+rqagCqTEhgYKAmkOkrJycHM2bM0No2Y8YM5OXlQaFQaLZNmTJF57E2NjZa17506RIUCgWio6Ph6Oio+Tp48CAKCgo07VmwYIF5P6gZRu8rfwhJpVJIx8DgtIN5BTicf1Vrm1ypxMmiEpQ3NeO+tCkQi/THq3KFEv86eRpNHaqZIL1X4e1WKPD16XN4aOZUeNOYG2IGnezMEGVlNNcbgmnLnHPszMqBl5MjpoQED/p8oR7uyK2q1jt1WSwI8HNxHvQ1hhtvbIAqajUwe62xcRhbM/ZJJBKt7xljUPZM77ezs0zFdwcH3QrTdnZ2Whm21tZWiEQinDlzRtONpebo6GjR9phqXGZmxoKalladQEZNvazB2ZJSlNY3ILuiEsV19VoBS05lpWqKt4GaEBzAicLiIWg5GQ/0ZWeGIiszlPKqa3CmpAw/Z1+2SDXthfExEBnocpkbHQknKyyhIJ6WbninVAph0sRha8t4l5ycjLKyMly5oj8bFhcXhyNHjmhtO3LkCKKjo3UCkv5MmjQJCoUC1dXViIyM1PpSdxslJydj7969Bs9hY2OjlREarDGRmWltbUV+/vU1TAoLC3H+/Hm4u7sjOHjwd1Sj0bnSMs1MAkP25F7RCmCcpFIsjI9BvJ8vLldWG7ufgpJz5FRWYXlyomUbTsaN3tmZ/rIyyiv5kP+8B8bq08DWFpJf3TssU35lCgV2ZOZoXiM7s3JwT+rkQY3/cLa1xZzoCOzNzdNsYwDcHeyRGmqd71PM3R3ClMlQnj6rs8xB7xo0ZOjNmTMHs2fPxooVK/CnP/0JkZGRyM3NBWMMS5YswTPPPIPU1FS89tpruOuuu3Ds2DF8/PHHWmNaTBUdHY377rsPK1euxB//+EdMmjQJNTU12Lt3L5KTk3HDDTfg+eefR1JSEh5//HGsWrUKNjY22L9/P+644w54enoiNDQUJ06cQFFRERwdHeHu7j6o6d4mP1Iul+P1119HdHQ07OzsEB4ejvXr16OhocHgYx588EGIh6EP+/Tp05g0aRImTZoEAHj66acxadIkvPTSS0N+7ZFS19rWb6VNZZ/9LV1d+O7cRWSVV6Bboei38J5cYbw6KSHGMKkUorQpgEjUf1bGVqrqkuqWGf5SKoFhGhNzpKAQrV1d4FB1N12trcOVqppBnzctNATu9vaahBUHsDQx3uoG/vYmSk8FencpqBeejB+9g7HHqm+//Rapqam45557EB8fj/Xr12uyH5MnT8bXX3+NLVu2IDExES+99BJeffVVrcG/A7Fp0yasXLkSzzzzDGJiYnDLLbfg1KlTmgRCdHQ0fv75Z1y4cAFpaWmYNm0afvjhB01M8Oyzz0IkEiE+Ph5eXl4oKSkZ1M/OuIm1p2+66SZs27ZN6wOUMYaAgAB8/fXXmDp1qs5jHnzwQXz++ecWTSUNhebmZri4uKCpqQnOztbRb/3duQvIqagyuRJwb4wx2IhE6JLLjR7n7eSIR2dNN6+BhEAVCKCzE6yf/nPOOeRffQNeUal3IUNAtZihaBgyhfVt7fjLoSNaNwMMgKNUisfnzoSkT0q+Wy6HRCQyOWtTWFuHf59UFZjzcXKEp5MjbpmQZJFZPwU1tXCwsYHvMI+/UV7Jg3zrDs334v+5G4L38C2/YOw9vLOzE4WFhQgLC4OtFXbljXem/v5Muh348ssvsXXrVtjb2+O1117D1q1b8d577yE0NBRlZWVYsGCBZq47GR5xvj5mBTKA6oOjv0AGgEUGPZLxjTHWbyCjPk40farBQAaODsMy7Zpzjl1ZObrboVoXre+MpLauLny4/9CACt+FeXog3tcHYkFAVUsrssorkV1ROdimo76tHV+dPof/d/osuk14fVsSi4oEC1LVvhImJg9rIEMIYGIws3nzZjDGsH37drzwwgtYtmwZnnzySWRmZmLlypXo6OjAzTffjJ07dw51e0mPaB9veDk6DFkNh0gvT0wM9B+ScxOiDwsKBPP307sMgmhqOtgABymao6qlBQW1dTpdtIAqoDlaUKi1OOTe3Dx0yuQ4XliMquYWk69z04REeDs7Qej5WXdlXzbpBsMQdRDGwdHe1Y3D+YVa+4YaYwzijPkQEuJUQSkhw8ykYObcuXNITU3FrFmztLbb2dlh8+bNeOWVV9DZ2Ylbb70VO3bsMHAWYkkiQcCtk5LhaKGy8GrOtlJkxEbjjpSJo27tDTK2GczODFNWBlAtAWIr0T8uhzEGn14BSFlDIy5eK9fs256ZbXLgcLmqBuWNTZqgqaO7G7/kFZjd7rzqGhTU1oFzVdB1rLAIta1tOFNSio2HjqBTJjP73KZibq4QL14IRl05ZASY9GnV1NSkVUmwrxdffBH/93//h66uLtx2220U0AyDquYWfH78FFq7unT22Zh5B/vr6elYM282poaHWvWARGK99GVnhisrAwC2EgkyYmP07uOcY1livKbS9rZL2ZrMKOcc1xqbcKm8ot9rdMnl+Dk7V/vcUJVCqGlp1Tm+pqUVlyurDZ6v98wrNQbgp0tZ2NMzrfzgFfMDJUKsgUmfWM7OzkZnLQGqGUQffPABurq6sGLFCmzfvt0iDSS6FEol/t+ps+iSy/WOm+k2c8C1rURCpcfJiNLJzgxjVkZtQqA//FyctV4LDMDk4EBNYbuzxaWoaW3VycTszr6skwUpa2hEW9f1pR0O519Fe9+lHgCAMezok92RK5TYcvos/nP2vMFurN4zr9SUnONaQyPkPV1ip4pLUGlFa9ARMlAmBTNxcXE4ffp0vynUNWvW4KOPPkJnZydWrFiBkydPWqSRRFtuZbXqzcvIr8PJVgqnAdTjcJRK4Wo/vBUbCdFHk53B8GZlNNdnDMsS47Xe72zEYsyLjgKgGvS773Ke3sd2ymQ41Ku7qLa1Ff88fhLfnD0PzjkUSiWOFxbpvQnhnKOkoREVTdeDjuOFRWjq6FSt36SnG6u5oxNHCwoNTgZQbxcYw/ZLOcMyfoaQkWBSMDNv3jzU19djz549/R67evVq/PnPf0Z3dzdyc3P7PZ4MXEl9vabf3pCWzi48MnMa/id9Cm5KToCTVGo065IeFtLvOQkZDowxiObPAUuIG7GFI/1cnJESHKjpusmIi4adjaqUfFFdvcHsJweQVa6amcQ5x47MHICrKnJnlVdCYAyBrq4GX4v2Egk8esrJN3Z04Jf8As159XVjKbjSpABFyTnKm5o0Y3wIGWtMqkC1dOlSvP7663jnnXewcOHCfo9ftWoVRCIRVq1aNegGjmeccxTXNyC3sgrdcgU8HR2QHOhv+pRsploPBgACXF3x+fFTaO/u1jxeXUE4wc8X6WEhQ/EjkHFEvmcflFeLjB4jREdCPHd2v+cSvL0hLO7/vWYozY2JQlZ5Jdwc7DEx8PrCrVHeXnCQ2mh1HfWmruabU1mF4vrr3fM/5+QiytsLyxLj8ekvR/U+dlF8LKQ9A5B/zs6Fss+LfXf2ZUR7e8G2Z40eN3tV9eBTRSUmvS/szrmMGB9vzePJwHDOUVdXh9bWVjg6OsLDw4O65kcJk4KZqVOnIi8vb0C/tEceeQRpaWlopIXGzNLRLcNXZ86hrKERQk/QwQHsu5wHT0cHvVNHe/NwsIddrzcsD0cHrJo9A+fLypBZXokumQyejo5ICQ5CpLcnvSDJoHGZHGjVHcDam7K4FPKDhyGaMglMz4J2o4mdRIJHZk2DVCzWen3YiMVYEh+Lb89d1DqeAXCxs8PUsFB0y+XYlaWdme7oluFQfgEWxsUgLSxEa+0zxhgCXV2Q4K9a1ya/ukZvxWF1N9ai+FjNttlREbh0rQIdJsxY6pTJkVNZhUk9NWGIaRobG/HPf/4TH330kWZVaACIiIjAmjVrcP/998PV1XXkGkhM62ZijCEiIsLojCZ9JkyYgDlz5pjVsPGMc47/nD2Pa41NAFQp4t6hS21rW7/nSA8L1QlQ7GwkmBYehkdmTsMT82bj7tTJiPLxokCGWIR4qgkLSdbVQXnmLHjP3/Zo52JnpzeLEevrgxAPd63XjmppgjiIRYLeQb4cwMnCYlS3tGB2ZITWrEPOOZYmxmnOtzMrF/pelRzAyaIS1PV6D7CVSLAwXv8MrL7EgoBIL0+TjiUqu3btQmBgIJ566ilcvaq9uO/Vq1fx1FNPITAwELt27RqhFqo+o//73/+O2PVHA5p/OwqVNzWhpL5hwIP11GNeJgcFYlJQQD9HE2JZzM0NLK6fD1XGwHy8NQN8rRVjDEt7jedhjCHGxwsRXp6oa23Dsav6B/mCMezMVBW3670/2tsL3k7XF+KUK5VGu40UXHvdtCR/PwS4umiCIYmB0grWujr3SNm1axduuOEGdHR0qLLjfd6T1ds6Ojpwww03DElAU1lZiTVr1iA8PBxSqRRBQUFYvny50RWphxPnHC+99BL8/PxgZ2eHjIwM5OXpHyA/lCiYGYUuV9YMeDCuWBAQ6e2Je1Ina93hETKcxFPTjB/AOUTTp46Jv09PRwdMCwsFAAgMmq6f5s5Ow7OLOEdDewcOXimAvNcg4vKmZsh6fb/YQKaFMdXU8d6Bj2q7agYWOIetWIylifHa+2Hdq3OPhMbGRqxYsQKccyiVxhfdVSpVA7FXrFhh0aEVRUVFSElJwb59+/Duu+/i0qVL2LlzJ+bNm4fVq1db7DqD8c477+DDDz/EX/7yF5w4cQIODg5YvHgxOjs7h7UdFMyMQjLlwOvEyJVKLEmIQ4QXjX8hI8dodoYxwNsbcHMDb2oy/NXRMbyNHoSZkWHwcnTEgthouPSsQRXq4Y5gdzeDr8OUkCCcKtYesNvW1YWjvdZ9UmdP+p5BIogwPyZa73l9nJ1w04RE3J4yEUkBfgjr1Q3GASyz8tW5h9s///lPtLe39xvIqCmVSrS3t+Pzzz+3WBsef/xxMMZw8uRJrFixAtHR0UhISMDTTz+N48ePG3zc7373O0RHR8Pe3h7h4eF48cUXIes1purChQuYN28enJyc4OzsjJSUFJw+fRoAUFxcjOXLl8PNzQ0ODg5ISEgwWDeOc473338fv//973HzzTcjOTkZn3/+OcrLy4e928ukAcBkeHk7OfY7wFefotp6JNN6SmSEiaemQZZzWXcH5xB8fSD/xz+Nn0AQIFn1sFWUxbcRi/HorGnaBfZ6uqD6zlgSGIO/izOuVFVD6KkirMahKn6XFOAPV3s71ZRuQCfDsyA2Gg5Sw0uYJAVcf/0vTYzDXw4dBQeQ4OermdlI+sc5x0cffWTWYz/88EOsWbNm0DeV9fX12LlzJ9544w046Bksb2zAsZOTEzZv3gx/f39cunQJjzzyCJycnLB+/XoAwH333YdJkyZh48aNEIlEOH/+PCQ9Y8NWr16N7u5uHDp0CA4ODsjOzoajo6Pe6xQWFqKyshIZGRmabS4uLkhPT8exY8dw9913D+IZGBgKZkahcE8PiAVBU73TVOYEQIRYmjo7w3sHNIyBeXuBJScCFy8Zf7yXJzCAgo9qvKMDUBh5zYhEYHaWD5D0fWh5OTkivWfGkvpVyTlHjK839ubqH0/AAezNvYxwT09UtWhX+2U955wUbPosJHcHB8yMDMeJwmJkxOnP5owGXKmEMisHQnQkmBm/96FQV1enNWvJVJxzFBQUoL6+Hh4eHoNqQ35+PjjniI2N7f/gPn7/+99r/h8aGopnn30WW7Zs0QQzJSUl+O1vf6s5d1RUlOb4kpISrFixAklJSQBgdOJPZaWqppKPj4/Wdh8fH82+4ULBzCjS3t2NPTlXkFleYVZgEuDqMgStImTgdLIzPWNlBG8vKCMjwAuu6i4o2cOcMTXK4hLIv/1v/+36n7sheHsP6NzmmtUzZbqtuxsMqvozzv1km2QKJfZevqKzXd1NNNCxdLOjIjA1LAQ24tH7Vq88fwGKA7+AV1VDnDFvpJsDAGjtp8RAf1paWgYdzAymWvNXX32FDz/8EAUFBWhtbYVcLoezs7Nm/9NPP42HH34YX3zxBTIyMnDHHXcgIiICALB27Vr85je/wc8//4yMjAysWLECycnJg/pZhgN1oI4SnTIZ/nnsFC5eKzcrkPFwcICXk/5UICHDjbm5QZiWDvSsZcR8vMFCVYUZRdPS9Qcy6plOoQMv4Mi8PIF+lj3osLfHscZmrYG3Q0kqFmsGBdtJJJgdFYF4P1+ddZ96sxGJIJPrts/N3s7sm5XRHMjw1jYoDh8DACgvXoKyyvCCmsPJULeKqZz6DNA2R1RUFBhjA66kf+zYMdx3331YtmwZtm7dinPnzuGFF15Ad69SARs2bEBWVhZuuOEG7Nu3D/Hx8fj+++8BAA8//DCuXr2KX/3qV7h06RKmTJlisMvN11dVF6mqqkpre1VVlWbfcKFgZpQ4UViM+rb+68foIxGJcE/qJAu3iJDBEU9Lh3jpYkAkgmjmdM0HuODlCRYZobUyNgC9M514Swt4XZ3hr/p6cM7B7O0hTJqoe85e9kdFYX/+VRztp0qxJcX7+WBaeChunpikWci177pPgKobKcnfF7lV1XpnQjW0dyCzfHjT9sNBfugwoA4uGYNiz75RsX6Uh4cHIiIiBpwhVNdkc3cf/Pgkd3d3LF68GJ988gna9Hw2GJo1dfToUYSEhOCFF17AlClTEBUVheLiYp3joqOj8dRTT+Hnn3/Gbbfdhk2bNmn2BQUFYdWqVfjuu+/wzDPP4LPPPtN7rbCwMPj6+mpNE29ubsaJEycwbdq0Af7EgzOokL2goAB//etfcfToUdTU1ODmm2/GO++8AwA4ceIELly4gLvuugsuLtT90Z+zJWUmlSPvPZZGJDBE+3hjSXwsHEZJXzMhvQn+fpA8/ihYn8JzomnpkOf3GpOgHlPTKyvDW1sh+2wT+iPKmA9RciJEUyZBee789Q/HXspcXJDZ1QUAOJJ/FUkBfnCztzfvhxoAxhgWxGqPV/FzccbkoECcK73+mneQSiE1kkFhALLKK5AUYN31eXpTll0Dz9XuiuRV1VBmZkOUlDByDYPq97ZmzRo89dRTA37s2rVrLTaj9JNPPsGMGTOQlpaGV199FcnJyZDL5di9ezc2btyInJwcncdERUWhpKQEW7ZsQWpqKrZt26bJugBAR0cHfvvb3+L2229HWFgYysrKcOrUKaxYsQIAsG7dOixduhTR0dFoaGjA/v37ERenf400xhjWrVuH119/HVFRUQgLC8OLL74If39/3HLLLRZ5DkxldjDzz3/+E6tWrUJXzxsEYwy1tbWa/e3t7fjNb34DGxsbPPDAA4Nu6FimVCrR1q1/nZe+5Eol1i2YA0epFDKFAvVt7WjvlsFOIoFA0y7JKMQkEvDubqCz6/o2qRQICQaKS1QbOIcwNU37Q8DBAfD0AGrrjJycQQgJUv23JzujPHNWqxtLCeDnwEAwpQKcq77flZWLu1MnW/CnVKlpaYWno0O/H2bzYqKQVVGJLrkcALA4PhZeTg44XVyqf0VtAFN6fs6xgCsUkO/Zp8qk9cnEKA4dhhAZMSSDtQfi/vvvxwsvvICOjg6TpmcLggA7OzusXLnSYm0IDw/H2bNn8cYbb+CZZ55BRUUFvLy8kJKSgo0bN+p9zE033YSnnnoKTzzxBLq6unDDDTfgxRdfxIYNGwAAIpEIdXV1WLlyJaqqquDp6YnbbrsNr7zyCgBAoVBg9erVKCsrg7OzM5YsWYL33nvPYBvXr1+PtrY2PProo2hsbMTMmTOxc+dO2A7zbETGzcjpHT9+HLNmzYK9vT1efPFFzJkzB+np6XjggQfwj3/8A4DqA9rT0xPz5s3Dt99+a/GGW1JzczNcXFzQ1NSkNUhquHDO8c7PeyEzNhOjl0dnTsOl8gqcKSlFd0//uqPUBtPCw5AWGkx1Zsio0/23zUBzs9FjWGICJIsWaG1T5hdA/uM2Aw9gYAlxkCy6Pi2Ut7ersjm9sjNnvLywV89gzLumTEKUt5fpP0Q/ciqq8O25C1gUH4M0E8b9nCstw7ZL2Qj1cMd9aSlgjGFv7hUc71M9mDGGSC9P3DVl7HQlKy5chGLvAf07GYMwMRnieaYvhWPsPbyzsxOFhYUICwsb8AesugJwf4XzBEEAYwzbt2/HokWLBnQNYpypvz+zbuXfeecdcM6xbds2PPvss0hN1V2TRRAETJw4EdnZ2eZcYlxhjCHJ31/vWix9SQQB2zOzcfxqkSaQAYDWrm7szrmM7ZnZo6LPmZDemAkLGwp6jmER4arsjIEAXZyu/d7Td+xMm0iEQ566gQwDsCMzx2KDgbvlcuzMVqX891/OR2tXVz+PACYGBmBhXAyWJydqbkBmRobD3ka7jowAVeZmTJHJje7mJiyaORwWL16Mbdu2wc7ODowxnRtF9TY7OzsKZEaYWcHMkSNHkJaWhpkzZxo9ztfXFxUVFWY1bLyZGh4KcT/dRIwx+Lu6oKyxyeD4mnOl11BmJYv4kfFDnJ5qdHAuXFwgxETpbGaMQTx9qu7sp56sDNMzHk80ZRLQ81o64uMDuZ4XC4dq2YFTxaUD+TEMOpxfiPYuVVexXKnA3lzd6dV9McaQHhYCl17dKb1nQKnNjAyHq72dRdo5WggTkgBHR/1/E4IA8bSpw98oAxYvXoyysjK8//77OjVXwsPD8f777+PatWsUyIwws4KZxsZGBAf3v8ZHR0eH1nQwYpi7gz1WTkvTuStTYwDc7OzQ3m38jkVgDOdKyoaghYSYj7m6gMXHGQxoRNOnghkI5g1lZ/pmZTTHq7MzAEQ+xmvKmFK3JbO8AvnVNQb317W24VhhUa/ieMClaxUoqW/QOq5bbjwboRbv54NgdzcAgIudLaaFh5r0OGvCJBKIF8zVO0VfNGMa2CgrM+Hq6oq1a9ciLy8PtbW1KCwsRG1tLfLy8rB27Vqa5DIKmDUA2MPDQ+9Ur77y8/OHfa65NfNzccZTC+bgeGExzpaUoqFdtUaNjUiEiUEBmBUZgY/2HzJ6DiXnWlO8lZyjUyaDRCSCpJ86HISYijc1Qb5rD2DsA1oshnjhAjA3V9W36amQZevOvjCUlVFTZ2c0Y2eMZGXURGlTwGylmJ0Qh8wjJzQDbTXnBOBqb9fvoNra1lb8cP4SxCIRVs+dCcc+swY559iRlQMG9BnnAmzPzMajM6dBEATUtbbhb4ePYV5sVL/jaVTTt+PwrxNnsCwxHuIx+rpl4WFgocHgxaWqoIYx1d/CpAkj3TSDGGPw8PAYdEE8YnlmBTNTp07FTz/9hKysLCQk6J9Cd+TIEWRlZeF//ud/BtXA8YYxhmnhoZgWHooOmQzdcgUcbGwgFqnuWqUSMbqN9PMzALYSCbrlchy7WoQzxaVo7+l/Dvf0wMzIcM1dHyFmU3LwsmsmHHd90KQ6O8Ozc7TuyI1lZTSPVWdnemY2idP0Z2U0x9tKIUqbAgeo1jPanqk9do8DWJpgfOFFzrlqjSTGIFcqsTfnCm6emKR1TG5lNYrq6vU8FqhtbcOZkjJMCQnCjqwcyJRK7MvNQ5yvj2YhSUM8HR3x5PzZY3owP2MM4vlzIdv8L9UTxjnEGfPAxmjwRoaWWd1Mq1evhkKhwIoVK3D+/Hmd/Tk5Ofj1r38Nxhgef/zxwbZx3LKTSOBiZ6sJZAD0O1CYA4jx9cbnx0/hcP5VTSADAIW1dfji+ClkV4y94ltkeDE3V7CYaMPjYBgDi4wA67O4oU7XUD9Zmeun6xk7A6iyMgOohjsxKAA+zk6apjLGEOvjjXAv43fXOZVVKK5vAOccnHNcKtftOrrW2Gjw9cgAlDU0agU8Cs6xJ6f/8TTqdo51zNUVQuoU1f+joyAEj53p52R4mRXMLFiwAE8//TSuXLmClJQUREdHgzGGXbt2ITk5GUlJScjLy8Nvf/tbTJ06egZyjQVTQoMglYj1voEyxuDp6IC6tnZUNbfoDBLmPV8/XshEVz+zCQjpj3hamsH1lcC5atmCPvqOnTElK6N5bEQ4RAvnQzxz+oDaKTCGGxLjNU0VGMPCfmYHdcvl2JWlXUaeQdV11HuKbmpoiMH6ThxAamgQdmVfPw/nHFkVlXqzOeOVKG0KhKlpA5qKTUhfZhfN+7//+z/ExMRgw4YNyM/PBwBUVFSgoqICnp6eePnll7F69WqLNZSoCIwhwc8X50qvaaZgq/vrA1yccdukZHx6+JjRasJypRKZ5RVIGUNFuMjwY+7uYDHR4FfytIMaxsAiwiF4eep9nGbsjIlZmeunZRAlJZrVVn9XF0wMCsD50muYHRWhNYNIn8P5V9HeZ/ICh6rr6HRJqWbci4udLeZERWDfZe2VsBmAKaHBuFJdi7Y+07RV08Kz8eis6Ua7ucYLJhFrsm6jSWlpKWpqDA/8NsTb2xuBgaavbk4sY1DLGTzyyCN4+OGHce7cOVy9ehVKpRJBQUFITU2FeBQvbmatyhoa8eWpM5DJFZpgRR3IJAf446YJiWjp7ERnP1kXgTFUtwxuVVgyvnDOtca/qInSUiDvu8qzgayMGnN1gWjpYlVX1TB+mGfERsPHyQmTg41/0DR3duJYn8J1ve2/nI+JgQGaBRzTw0JwtqQMjR0dmmNsJRIk+fth07GTejOkdW3tOFVUgqljcKbSWNDV1YXU1FSdBRRN4evri6KiIkhpiZlhZVbEMX/+fAQGBuLzzz8HYwyTJ0/G5MmWLw1OruuWy7Hl9FmtQAa4PoPi4rVyRHl7IczTtAXOJCK6IySmU+w7COWFi/0f2E9WRk3UZ72i4WArkSA1tP+SEiImQNRrDbS+xIKgNaVbJAjwd3HWCmYWxsWguGcRTEOyKiopmBmlbGxsEBwcjJqaGpOWMlATBAFBQUGwMVBiY6gwxvD9998P+3pIo4lZn2hHjx6l+jF9KJVKKIew8m5meSU6ZXKDd4sMwPHCIthKJAhyczU6SFjJOWL6qb9BSG/M1Blw/WRlrIGD1AazoyIM7l8UH6M1Xbq2tQ05VdWa772dHJEU4IdEfz+jhTBTQ/oPrMjIYIzhtddeG1AgA6g+B1577TWLDt6urKzEmjVrEB4eDqlUiqCgICxfvlxrpeqR9N1332HRokXw8PAAY0zvpKDhYFZmJjAwULPA5HjGOUdWeSVOFBWjokm17kyQmyumhYci2sLBQkl9g04tC622ALjW2ITq5hY42NgYDnoYQ6CrCwJ7an8QYgohKQGKEyeB9g6jx7HIiH6zMtYgPSwEh/IKtLIzAmPwd3FGov/1lav11ZlR39Q42dpibnQk9vSpBsx6zjOWVsAeixYtWoTU1FScPXsWChOWvRCJRJg8ebJFKwEXFRVhxowZcHV1xbvvvoukpCTIZDLs2rULq1evRm5ubv8nGWJtbW2YOXMm7rzzTjzyyCMj1g6zMjM33ngjfvnlF7T1Ks423nDOsSs7F/+9cAmVTdcX0CtraMTXZ87jcP5Vy17P6JDe6z49fAyX9VQrVafFA1yccWfKpHEx7ZNYDhOLIUpPM7CTgYWHAl5eEM8YfQM5zXHpWoVONxPnHMsS47VeO7mVVSiuq9fKyta2tuFCWTkAIDU0GB4O9lqZUn3nIaOPOjtjSiADqFabtnRW5vHHHwdjDCdPnsSKFSsQHR2NhIQEPP300zh+/LjBx/3ud79DdHQ07O3tER4ejhdffBGyXmU6Lly4gHnz5sHJyQnOzs5ISUnB6dOnAQDFxcVYvnw53Nzc4ODggISEBGzfvt3gtX71q1/hpZdeQkZGhsFjhoNZwczLL78MFxcX3HbbbSZVAh6LCmpqcbpnXRd9Y1gOXMnXZGssIdjNzcRwBnr76W0lEvxPWgrun5YGOxuJxdpFxg8hKQHQt0YQ5xDPngWbX90DNgYqoyqUSuzM0q1UnBoaDG9nJ833qsUl9d8Z78m5jI5uGUSCgKWJ8VoD9lNDg+HT6zxk9FJnZ0T9FPITiURITU21aFamvr4eO3fuxOrVq+Hg4KCz39XV1eBjnZycsHnzZmRnZ+ODDz7AZ599hvfee0+z/7777kNgYCBOnTqFM2fO4LnnnoNEovpcWL16Nbq6unDo0CFcunQJf/jDH+DoOLqWl9DHrG6mZ555BgkJCdi6dStiYmIwadIkhIaGws5O942OMYa///3vg27oaHOquBSMMYMD/ATGcKa4FDcm66+QbCrOOa7W1qGwts5oN1N/2ru7NcvUE2IOdXZGsf9gr40MLCba9DE1VuDHi5k6WRkRYzrjaE4UFmsWl+yrSy7HkYKryIiLQaiHO+L9fJFdUQlbiQRzjIzHIaOLOjuzZMkSo8cNRVYmPz8fnHPExg58xfTf//73mv+Hhobi2WefxZYtW7B+/XoAQElJCX77299qzh0Vdb1EQklJCVasWIGkJFW1676La45WZgUzmzdv1vzSuru7ceLECZw4cULvsWM1mKlsajY6U0HJ+aAzMzKFAl+fOa8KZJj+QEZgzKSBxwJjyK+ugbuDPS5dK0d9WztsJRLE+/nCz8V5UO0k44fO2BnOIZ5qoPvJCtW1tiGrXLdCtoJztHZ1wVZyPavZ3yKVvfcvjIvGtcZGLIiN1joHGf36GzszFGNlAP0ZdlN99dVX+PDDD1FQUIDW1lbI5XI4O19/n3/66afx8MMP44svvkBGRgbuuOMORESoguy1a9fiN7/5DX7++WdkZGRgxYoVSE5OHvTPM9TMCmY2bdpk6XZYHVOKXYkHOf15V3YuinrWotH3dy0RBER4eSLW1wf/vXCp3/Nda2zCh/sOgXOuCUaPXS1CpJcnbpuUrKmbQYghWtmZMZCVudbYhONXi7A8OQE2YjG+OnPO4LHfnr2AR2dN17x20sJCcLqkFK2dXTo3GrYSCaZHhGm+d7K1xRNzZ1Fm1Ar1l50ZiqwMoMqWMMYGPMj32LFjuO+++/DKK69g8eLFcHFxwZYtW/DHP/5Rc8yGDRtw7733Ytu2bdixYwdefvllbNmyBbfeeisefvhhLF68GNu2bcPPP/+Mt956C3/84x+xZs0ai/58lmbWp9f9999v6XZYnVhfb5wqLjUYPTNgUNOf27q6cbGs3Gi3kpJz3JCUABuxCLuyc9HRa4CXvmOLe60r07vdBTW1+PFCJm5PmWh2e8n40Ts7Y81ZGYVSiR8vZKKurQ1u9vaI9fVGfVu7weNrWttQ09KqGTcjEYmwJCEO/zlzXufYhfExOhkYCmSsl6HszFBlZQDA3d0dixcvxieffIK1a9fqjJtpbGzUO27m6NGjCAkJwQsvvKDZpm9sa3R0NKKjo/HUU0/hnnvuwaZNm3DrrbcCAIKCgrBq1SqsWrUKzz//PD777LOxGcwQYEpIMM6UlEHJdecZMQBSsRgTgwLMPn9xfX2/3UcKzlHS0IAYH29MCQnC4fyrRuvQGJvWnVtVjbrWNng46g40I6Q3JhZDvHQxeEPjgLMyyit5UPaz0KkQ4A8hcujHlZwuLkVdz4zMY4VFCPFwNXo8Y4Cbvb3WtmhvL0R4eqCwZ0aTZtq1P027HksMZWeGKiuj9sknn2DGjBlIS0vDq6++iuTkZMjlcuzevRsbN25ETo7uQPWoqCiUlJRgy5YtSE1NxbZt2/D9999r9nd0dOC3v/0tbr/9doSFhaGsrAynTp3CihUrAADr1q3D0qVLER0djYaGBuzfvx9xcXEG21hfX4+SkhKUl6tm8F2+fBmAqhKyr6+vJZ8OoyiYMZO7gz3unjIJ/zlzHt0KhWbqJYcqxXxvWgrsTagCyTnH5apqnCkpQ11rK2zEYiT6+5k840ipVIUoMyLCUFzfoLOqr8AYGFSBjzEMqoBmhmOY0eMIAQAhJBgwo+ib4vxF8LJrgKFuWqUSvLFxyIOZls4uHLhyfT0lBuDY1WIk+vki00CwNTMiHBKx9qwWxhgWJ8ThL4eOqDbQtOsxq292ZiizMmrh4eE4e/Ys3njjDTzzzDOoqKiAl5cXUlJSsHHjRr2Puemmm/DUU0/hiSeeQFdXF2644Qa8+OKL2LBhAwBVNqmurg4rV65EVVUVPD09cdttt+GVV14BoArQVq9ejbKyMjg7O2PJkiVaM6H6+vHHH/Hggw9qvr/77rsBqGY9q685HBg3Y5TRr3/9a9MvYAUDgJubm+Hi4oKmpiatQVKm6JTJcOlaBUobGsEYEOrhjkR/P0j6mcoHqKpFfn/+EnIqq7QyJwyqgMhYt5HaE/NmwbVnFplcocS50jKcLi5BfVs7JCIR4v18EeXjif+cuWD0PAJjmB4RhrnRkf1ekxBzKa8WQv7fn4weI75zBYRA87Oapvj+/EVkV1TpdBPflJyI7ZnZOrOZ7CUSPJUx12CQcuBKPg7nX0VaaDAW9bMiN7E8Y+/hnZ2dKCwsRFhYGGxtjS8w2p9du3ZpZWd27tyJxYsXD+qcxDhTf39mz2YyRv2CVw80He3BzGCo13sxZc2Xvo4VFiOnUrWQWd9aNZ1yGcRG1odhjCHC00MTyACqAcf62tIll0MkCFAYKc2t5BxeVlBLgFg3FhYK5uUFXlurO6qdMbAA/yEPZIrr6/XOWAKAvZevICM2Wqd+jBIcnXI57AzMRJoREQZ7GwkmDHHbychSZ2dOnTpl8boyZHAsOptJqVSiuLgY27dvx+nTp7Fu3TpMmDBhUA001SeffIJ3330XlZWVmDBhAj766COkpY3ewYlKpRInCw0XHOQckHMl7G0k6OiWaQU7DICrna3JNWykYjGS/P1w4Vq5wQHLdhIJrddEhhxjDKIZU/VnZziHaPrQVxA+eKXA4Biytq5uyJVKnZIHnTI5DlzOx9JE/WMHJCIR0kJDhqbBZNRgjOHNN9/E2rVr8eabb1J34igyJLOZNmzYgPXr1+Ozzz7D2bNnzWrYQHz11Vd4+umn8Ze//AXp6el4//33sXjxYly+fBne3qPzA7qxoxNt/SzWKTCGRH8/2NvY4HzpNbR1dcHBVopJQYFICQ4cUL2K+bFRKK6vR2NHh9YNMesZU3PzhMRBTyUnxBR6szOMgQX4DXlWBgB8nZ1Q2mdsWW/1be16B9+fKSnFxKAAqss0zmVkZCA7O3ukm0H6GLJPrzfffBNOTk546aWXhuoSGn/605/wyCOP4MEHH0R8fDz+8pe/wN7eHv/4xz+G/NpDTSISYWZkOJ6YNwu/W5KBJ+bOwoyIsAEX3rK3scGD09ORFhoCm55BjAxApJcn7p+WhkhvryFoPSG61NkZraiac4imDc+6TrOjIvS+fhhjiPL2wqWeWRn6bM/MHlQxM0LI0Biy2UxisRiTJ0/Gnj17huoSAFQViM+cOYPnn39es00QBGRkZODYsWN6H9PV1aW16ndzs+XWUDKVq70dnKRStBhZfVzJOUI83C12TXsbGyyMi8H8mCh0yGSwEYmoUB7Ri3OumnVk5O8TjIEFB4GZUdFWKzsDqLIyQYHmNndAbCUSLIyPwY8XMrW2i3qylHKl4WCloqkZF8rKB1V2gRBieUP6SdbR0YGGBsPpXEuora2FQqGAj4+P1nYfHx+DlRPfeustzTS0kSIwhvSwEOzJvaJ3P2MM7vb2COsTzHDOkVddg1NFJahsboFIYIjx8UFaaLDJNWJEggBHqXTQPwMZw1rbIP/Pd/0eJsqYB1Fy0oBP33fszHBlZdSS/P1wprgU5b2WJZkZGY4DV/L7feyenMuI8fU2OBiYjG0//fQT1qxZg48++gjLly8f6eaQHkPWzZSTk4PDhw8jKChoqC5htueffx5NTU2ar9LS0hFpR3pYCJICVMW1+q7z4mBjg7umTNIaYMY5x46sHHx95jyK6urRIZOhtasbZ0vL8OnhoyioqR3W9pOxizk5ggUGqCrFGSISDaoeDAsLBfP1AQsOGrasjObajGFZYrymq8vdwR7TwkMR7e3V76DOTrkchT3LjAyFiqZmFNXVD9n5ifna29vxm9/8BsXFxfjNb36D9nbDFaPJ8DIrM/P5558b3NfS0oKcnBx88cUX6OzsxL333mt240zh6ekJkUiEqqoqre1VVVUGqw9KpVJIR0FmgjGGm5ITkejvh7MlpahpbYNtT9G85EB/nX79zPIKnC0pA9BnKjfnUHDgm7PnsXb+HLpjJBYhmj4V8q+/1b+TMQiTJ4L1qYg7EIwxiO9cYTxgGkI+zk6YEhqMU0UlWJYYD5EgYFF8LAoOHYbCyLAYBxsbRHh5DkmbOmUyfHnyDGQKBR6fMxPOdoOri0Is66233kJFRQUAoKKiAm+//TZeffXVEW4VAcwMZh544AGjdy/qtO3NN9+stRT5ULCxsUFKSgr27t2LW265BYBq2vPevXvxxBNPDOm1LYExhggvT5PeHE8UFhtdlkCmUOJiWTnSw2iKKBk8ITAALDAA/Fq5bk0YQYAoZdKgr8FGeMxWRmw0kvz94O/qAkA1lm1mZAQOGuluWhQfA+kQtftQXgE6ZTKAAbtzLmPF5OEpbUH6l5+fj7fffhvKnnpdSqUSb7/9NlauXInISCo2OtLMekWuXLnSYDBjY2ODgIAAZGRkYPr06YNqnKmefvpp3H///ZgyZQrS0tLw/vvvo62tTavEsrVTco7K5hajxzAAZQ2NFMwQi9GbnbFAVma0EAmCJpBRmxYWgvOlZWjq6NTazhhDkKsL4v2GZr2Z6uYWnCoqUd2scCCnsgpXa+sQ7ukxJNcjpuOc67055pxjzZo12L59+4jWnGGM4fvvv9fc0I9HQ1IBeLjdddddqKmpwUsvvYTKykpMnDgRO3fu1BkUbM0YjC8WqTmOijgRC9KbnbFQVma0EotEWJoQhy2nz2nv4BxLB7nuUlNHB86VXsPMiHCtuk6cc2zPzFZ1ufU8zwzAjswcrJo9HSJDa1mRYfHjjz9i165dOtvlcjl27tyJn376CTfddNOQXLuyshJvvPEGtm3bhmvXrsHb2xsTJ07EunXrsGDBgiG5pqlkMhl+//vfY/v27bh69SpcXFyQkZGBt99+G/7+/sPaljEzL/eJJ56wim4lczHGEOzhjpK6eqOrXxu6i+uWy1HW2ASlUgkfZyc4DXKNEjJ+aGVnDGRleGsbFL8cAZfLDZ6HiUQQzZgGZgVF5yK9vRDn64OqlhY0tLWDQzVg38vJ/CU/OOfYdikbV2vrIBGJMCPi+qKumeUVKGts0j4eQEN7O04UFmN6r2PJ8Gpvb8fq1ashCIKmi6k3QRDw+OOPIyMjA/YWzlYWFRVhxowZcHV1xbvvvoukpCTIZDLs2rULq1evNjhjd7i0t7fj7NmzePHFFzFhwgQ0NDTgySefxE033YTTp08Pa1vMCvdFIhEeeuihfo975JFHIKY6JhYzLSzEYCDDANjbSJDgr50CVyqV2H85D+/tPYgvT57BltPn8OG+Q/jm7Hm0GashQkgPdXZG9Y2BrIysG8qcXPC8fINfytzL4FY0++O2Scl4bNZ0zI2Jgpu9HWZFDW4l7yvVNbjaMwvqUF6BphtLrlDg5+zLBh93KK+AXqsjSD3oV18gA6jeY9WDgS3t8ccfB2MMJ0+exIoVKxAdHY2EhAQ8/fTTOH78uMHH/e53v0N0dDTs7e0RHh6OF198EbJeCxdfuHAB8+bNg5OTE5ydnZGSkqIJPoqLi7F8+XK4ubnBwcEBCQkJ2L59u97ruLi4YPfu3bjzzjsRExODqVOn4uOPP8aZM2dQUlJi2SejH2YFM5xzk6tgUrVMy4n09sKC2GgAulO5bSUS3JuaorVaN+ccP1zMxJGCQsgUiuvbAVyuqsHmY6dUgw0J6Yd6zSRDY2WYmxtYRLjhmUmMgQUGQBii8SZDgTEGkSBgRkQYHp8zc1CDfmUKBXZm5kD97Cg5x+6exSw5YHBBWQBQcA4FvY+OiL6Dfg1RDwbOz++/TpGp6uvrsXPnTqxevRoODro1xFxdXQ0+1snJCZs3b0Z2djY++OADfPbZZ3jvvfc0+++77z4EBgbi1KlTOHPmDJ577jlIembBrl69Gl1dXTh06BAuXbqEP/zhD3AcwCLETU1NYIwZbd9QGNK0SXt7u+YJIpYxLTwUEZ4eOFNShvKmJkgEATG+PkgO9NeZkl3W0GhwdWDOORrb23GquBSzIsOHo+nEigmBARDfuQLM1/A4NNH0dMgLrurfOUyLSA6VwY5FO1JQiNauLk1mlXOO3KpqXK2pQ7iXB+bFROrNzjCoMrLO1C087AwN+u3v+B07dlhk7GJ+fj4454iNjR3wY3vPIg4NDcWzzz6LLVu2YP369QCAkpIS/Pa3v9WcOyoqSnN8SUkJVqxYgaQkVTHM8HDTPx86Ozvxu9/9Dvfccw+cnYe3O3nIgpnGxkYcPnwYfn5+Q3WJccvb2cng6r29XSgr11n9tzcO4FwJBTNjHW9pAe+nyBtzd+93LEt/i0AKXl5gEeHgVwvRZzVTsAD/YVlEcjSqb2vH0YJCnS5iBmBHVjZWzZ6BKcFBOFtShrrWNq3j7KU2mEmvzxGRm5urd9CvIXK5HLt27UJubi7i4vp/f+7PYHo1vvrqK3z44YcoKChAa2sr5HK5VnDx9NNP4+GHH8YXX3yBjIwM3HHHHYiIUHWjrl27Fr/5zW/w888/IyMjAytWrEBycnK/15TJZLjzzjvBOcfGjRvNbru5TO5mCg8P13wBwDfffKO1rfdXcHAwvL29UVZWhqVLlw5Z44lxTZ2dBgMZtdYu4yt3E+sn37kb8u9/NP61bYdFriWanq5bk8bKszKDtf9ynt6xbqoBvh04V1oGQRCwLDFe57jF8bG0ftoIiY2NxeLFi00e9ykWi7FkyRKzMin6REVFgTE24EG+x44dw3333Ydly5Zh69atOHfuHF544QV0d19/r9+wYQOysrJwww03YN++fYiPj8f3338PAHj44Ydx9epV/OpXv8KlS5cwZcoUfPTRR0avqQ5kiouLsXv37mHPygADyMwUFRVp/s8YQ2trK1pbWw0eb2Njg1tuuQVvvvnmoBpIzOcotQFjzGiET9WCxz4hLhaK0jLjx8Rb5g1YJzujJyvDZTLVIpbG2NqqljoYA6UGpBLjb7PqsTjB7m5I9PdDVoWqazjYzRVxRrr1yNBijOHjjz82OcuiPt5Sf7Pu7u5YvHgxPvnkE6xdu1Zn3ExjY6PecSlHjx5FSEgIXnjhBc224uJineOio6MRHR2Np556Cvfccw82bdqEW2+9FQAQFBSEVatWYdWqVXj++efx2WefYc2aNXrbqQ5k8vLysH//fnh4jExdJJODmcLCQgCq1Fd4eDhuv/12vPvuu3qPtbGxgZeXF81kGqAOmQwXy8pR3tgEkSAgwssDsb4+ZteYSPT3w6VrFQb3MwATxmnqfzwR4mKgOHYCaDFQdNHeHkJigsWupzV2Rk9WRnkpC4oDh/o9j2TVw8AYKMw3NzoSWeWVWoPwAdWHn6+zExL9r3fFZ8RF43JVNeRK5aBr2pDBi4yMxHPPPYc333zT6CBgQRDw3HPPabpqLOWTTz7BjBkzkJaWhldffRXJycmQy+XYvXs3Nm7ciJycHJ3HREVFoaSkBFu2bEFqaiq2bdumyboAqgWgf/vb3+L2229HWFgYysrKcOrUKaxYsQIAsG7dOixduhTR0dFoaGjA/v37DQZ0MpkMt99+O86ePYutW7dCoVCgslIVjLu7u8PGxsaiz4cxJkcbISHXq8ref//9mDVrltY2MjhXqqrx3bmLkCuVqhkPDLh4rRzOtnm4Ly3F5BWxewv39ECYhzuK9NSmYYzBXiJBWliwJZpPRjEmEkE0LR2Kn/fo3S+ammrRZQU02ZmCq6oZTH0CZiEqEopDhwFDHw6MgQUFjokKwwDgKJVifkwUdmVrdxdwzrGsT8DiKJXi9skT0CGTwdOM1zyxvOeffx6bNm0yOD1bEAT4+/vjueees/i1w8PDcfbsWbzxxht45plnUFFRAS8vL6SkpBgcl3LTTTfhqaeewhNPPIGuri7ccMMNePHFF7FhwwYAqtIqdXV1WLlyJaqqquDp6YnbbrsNr7zyCgBAoVBg9erVKCsrg7OzM5YsWaI1E6q3a9eu4ccffwQATJw4UWvf/v37MXfuXIs8D6ZgnOZOo7m5GS4uLmhqahqRvr6q5hb8/chxveNbGAMcbaR4fO5MrWnXppIpFNiRmY1L1yq0ApoAVxfcPCEJ7g5j4wODGMcVCsj+8bludkYqBRzsjZaWZlIpxLfcOKDgQllTC/l3/4V4+Q0Q/HUnAcj3HYDywiXd8TU9xHffoXlcp0yGLafOYnpEGKJ9vE1uw2iiVCrx2eFjqG1rU/W+AZgcHIilifEj3bQxwdh7eGdnJwoLCxEWFgZbM2eF/fDDD0aXCvjhhx+GrALweGfq74/6gUaBE4VFBvdxDrR0dSHzWjnsbGxQ1tAIxhhCPdwR7unRbxpaIhLhpglJmBcThcLaeii4En4uzvAdgaCNjBxD2RkhPg7Kc+eNPpaLxYAwsEBa8PKE5NGHDP59ilKnQHkxUzeY6cnK9A6ADuUVoKyxCVsvZWO1h/uQLfI4lNQDfP95/BQA1TiZuTFR/TyKjBY33XQTFi9ejL1790Leq8q1WCxGRkYGli9fPoKtI8Agg5nTp0/jm2++weXLl9Hc3Kx3oCljDHv37h3MZca83Krqfmcd7czOhULJNUW3jl0tgq1EjBuTEhBrwiBBJ1tbJAcO71oZZHTRGTtjbw/RrOng1dXg5RX6sySMQUiZBGYrHfgFZTIojSyOyqIiwa/kaV+3zxibqp7FFwGgo7sbv+QVICMuZuBtGQWC3N2QHOCHi9cqkBEXQ4PvrYihwcCWHvRLzGd2MPPss8/ivffe0wQwfWfNqL+nX3L/FP1Ul1Qdo3pue3/cdMrk+ObsBYR7euD2yRNoCicxqm92Rj1WRjR9KuT/+U7/g0QiiCabt6ikfOsO8CLdWRSGG6idlVEvvqh+L+EAThQWY0JggNYaSWUNjVByjmB3N7PaOZwWxccixMMdyQF0Y2Ft+g4GHqpBv8Q8Zk2T+c9//oM//elPCAgIwF//+lcsWrQIALBr1y58/PHHmDZtGjjneO6557Bv3z6LNngs8nZywmBCvqu1dfjxQqbF2kPGLiEuBnBy0prBJAQFggX46y5FoM7K2Jk3zkAID+33GObrc/26fbIyl8orcK2xSTtryRi2Z2Zrbpzau7vx/06dxf87dRatVrB+ka1EggmBAXSTZ6Wef/55TSHYoRr0S8xjVjDz6aefQiQSYe/evXjkkUc0v9yFCxfi8ccfx5EjR/DCCy/gT3/6E1xcXCza4LGmpbMTrnZ2xsZfmiS3qhqZ18px/GoRThYVo9ZIDSAyfjGRCJLbb4X49lu1ZjCJpk/V7WYaRFYGgCpYMjZoWCKBaNFCTTDDgoO0Bv3u1lPen3OO0oZGZPfUYtl/OQ/dcjnkSiX25V4xu62EmMLe3h4bN25ESEgI/vznP1t8lWxiPrP6Jc6dO4f09HSt9Rz6euWVV/Cvf/0Lr7/+Or755huzGziWHcm/igNX8o2uhD2QIOe/FzI1GR4OIMLLA7dMSIadDfXNk+uYm6tOJlCdndGMnRlkVgaAqgtraioU+w7q2ckgmjIZgqc7hOREKM9fhGj6VOzLvQI/F2e0dXejw8giqEcKCuFmb49zpT3F9zjHxWsVmBQUiCAr6G4iw6+/xSJNtXz5chrwO4xM/b2ZFcy0tLQgOPh6fRJ1YZzW1lbN6pqCICA9PR0HD+p5IyM4V1qG/VeMr7Aa4uGO9u5uVLeYnmXpHfxcra3Hv0+exoPT080uvEfGD62xMyZkZXh9PXg/f5ssKhI4fgpob9feIRZDmDRRdamZ0yFEhCNfJMLRq0WQiES4e8okg8E8AxDh5dkznuZ6QokB2J6ZjUdmToNAf++kh42NDQRBQHl5Oby8vGBjY0PdfFaAc47u7m7U1NRAEIR+C/CZFcx4eXmhsbFR872npycA1ZIHiYmJmu1tbW1obm425xJjmpJz/JJX0O9xN09IRGlDI747d9Gs63DOUdncgitV1Yjz8zXrHGT80GRnrpWblJWRffk10G18bS9hQrJudqYnK6OeIcVsbCAPDMDOg0cAAHKlAhfKriE9LATHC3UHENtLbeAktUVln5lSHEBNaxvOlJQhNdSyxSBzK6twurgUd02ZZFa9JzJyBEFAWFgYKioqUF5ePtLNIQNkb2+P4ODgfm9QzApmQkNDtdZ6mDRpEjjn+PLLLzVrMVVWVuLgwYNUJViPquYWNHf2P1jxSlUNJgUFIMjNFaUNjWZdiwG4VF5JwQwxiWjOLCgOHTZprIwQGwPlxUv9HBMN5uMNRe/sTK+sjNrRgkK0dHYCUGVaLl6rwI1J+pdYWBATrVNNt7d9l/OQGOBnsanPHTIZtl7KQqdMjqMFhZgTHWmR85LhY2Njg+DgYMjlcij6LCtBRi+RSASxWGxSJs2sYGbBggV4/fXXUVRUhNDQUCxduhTu7u74wx/+gLy8PAQHB+Obb75BW1ubZr0Hcl3fNVr0YQAa2zvw11+Ooa6tzexrcahmfBBiCsHXB8Kdpr1mRelToLykp/CdmkQMxd79qv/3fjPy8tSqW9PQ3o4jBYVaXUqMQSdgYQwIdndHlLcntmUa7kdXKJVQKi1X2PzglXx0yVSF0o4UFCIpwJ8qZ1shxhgkEgkkVN9nTDKrY/nuu+/Gr3/9a5SWlgIAHBwcsGnTJtja2uLbb7/Fe++9h9LSUkyePBnPP/+8RRs8Frg72Pc7FZtDNa6mvt14INNfyltgDO72dgNrICEmYE5OEJISdad0q8nk4LV14LV1QK+AvO+yCDuzdLMsnOsG/ZwDSxPiYGdjg5TgQIPtmhkZDgepZRa4q2xqxuniUk2gxaEbZAGWG1xKCDGPWZmZuLg4fPbZZ1rbli9fjry8PPz000+or69HXFwcli9fDhH1L+twlEoR7eONK9U1+qsmA7ARi9EllxudzSQwBld7O9QYGYSp5BwTgwy/8RMyGKK0nuzMAIhnXK8lU97YhIKaWpMfq1AqwTlHSX2Dzj4GVaXraSbUt+mrWy5Hc2cnPB2vF+PrW7RPva2gphZXqqo160Rda2zElyfP4rZJyQhxd4OY3vMIGXYWLRnr7++Pxx57zJKnHLMWx8fiWmMT2rq7dSonCwywlaiCGWOUnBsNZAAgKcAPQW6ulmgyITqYsxOEpAQoL2UZ7m7SHMzAoiLBPDw0mxxtpRAJgklVsNWzlSYGBugM/gVUWZOlCXFmDdD974VLyK+uxarZMzRdSBevlaO8SXcCA4MqmxTm6QGRIGDbpWx0yeX47txFMACr5syAo9SM5R8IIWaj+vcjxNnOFg/NSMcv+VdxsawccqUSDEC0txdmRYVjy6lzgzq/nUSC9LAQTI8Io2mI4xTnHLLP/gG0Gu+qFM2eCdGUyWZfR5SWqgpmAFXA4usD3lPUrk+DIJ6WprXJ2dYWc6IisO9yXr/X4QCuNTYZLFUQ6eWJKB+vgTYf+dU1uFJVAwDYmZWDe1IngzGGw/lXDbajubMTOZVVkMkVmvaobz725V7BTROSBtwOQoj5BlWM4ZdffsGdd96JwMBASKVSPPTQQ5p9u3fvxv/+7/+islLPmxoBoEqJL0uMx7ML5+PJ+bPx20XzcdukZHg7OsLDhHE1hiyMi8GT8+dgZmQ4BApkxjXm5NT/MYNcQV2dnQGgWpJg9kywuBjtsTSMgUVHaWVl1NLDQuBmb2fS37vAGOR6BtAzAIsTYgfcdrlCiR1ZOZprX62tw5VqVWAT6uFusE0CY/Cwt8fey7pVhy9eq0Cpnm4wQsjQMTuYef311zF37lx88803KC8vh0wm0+oucXFxwR/+8Ad8952BBeyIhkhgKKlvwBcnTuOtnXvw5s49aO7oNGuJA8aAwto6iEVUNGy8Y4xprXWk5wDA3Q0savAL5YnSUq8vFBngD/HUNJ3VsPtmZTSPFQQsTYw36e9d2bPgZF9SsRgO/RTV0ud4YRGaer3WGICdmTmQKRSYFxOld/FWBmBaeCjOlpZBJtc/M3F7ZrbOoOCzJWU4W1I24DYSQvpn1ifejh078NJLLyEgIABff/01qqqqdI5JS0uDl5cXtm7dOuhGjmWcc+zOuYzvz19CZa/++YaODjPPB1RSoULSgwUHgfn56p9xxDnE06dZpBuSOTtBfMdtEC9brPreze16dsZIVkYt3NMDsb7emkxIhJenVlaEMQYbschgpqRTLr++tIGJGjs68Eu+dvFKDqC1qwtHCgphb2ODBbHR2j8nAAepFGGeHrhQVm4wAFMX79N839KKHZnZ2JGZ3e84N0LIwJkVzHzwwQeQSqXYsWMHbr/9dnh56e+nnjBhAvLy+u8LH88K6+pxsqgEwMDWYTJGLNBsCqKiyc70HZxrwayMmhAYAObgoPlek50xkpXpbWFcLESCAF9nJ9w2MQkOvQbRcs4RZeB9Rs19gIv+7c7O1TtmmUNVxK++rR2TggLg6+x0fWFvAEsSYpFbWdVvt9i+y3lo7eoC5xw7MrM1gd2OXqt+E0Isw6wBwKdOnUJaWhoSEvRX6FTz8vLC0aNHzWrYeHG6uAQCY1Ba6M2NMYZYX2+LnIsMrY5uGQ7m5aPbQFcFoOqCnB4RBrdBrM6rzs7wyqrrQY0FszIGr+vmBiFtCqBQGM3KqLnY2eLB6elwlNpAKpFgSUIsvjl7AQCQEhyIeTFRuFpbp7MApcAYwjw9EOntOaD2lTc1G3zdKTlHbWsr3B3ssSwxHv84egKAKoMU4+MNJ1spTheXGj2/TKFAQU0txIKAkl4VvEt6Vv1O6FkhnBAyeGZlZtra2uDr2395/KamJiom1Y9KI2+oA8UAiBjDlBDLrktDhkanXIbTxaW4eK0cmeUVOl+XrpXjXOk1NLab1+WoppOdGYKsjCHimdMhnjPL5ON9nJ00GZkYH2+EeXrAViLB3Jgo2EokyIiL1vu4xfGxAw7M5kTpX5aAAfB2ckSktyoT5O/qgslBgRAYw5IE1XUCXF0xMTDA6PltJWKEebhjV/ZlnX27si/3W3qBEGI6szIzPj4+yM83vuIzAFy+fBlBQUHmXGLcsORq1jZiEe5MmQRXqvhrFdzs7RHbUzxRX0DLAPg4OSHUw92k8/HOLsi++g/Qs8aR9k4OCAKgVA5LVsYSGGO4K2UiOuVyzTpLyQH+OFtShvKmJk1sNiMizKzlBSYE+uNMSSkqm1u0un04gGWJ8VozAZckxGJGZDhcei2+OT82CtkVleg2sDzJwrgYnCwqQYee5UQ6urvxS14BMuJiBtxuQogusz5JZ86cifPnz+PIkSMGj9m6dSvy8/Mxb948sxs3HsT6+hisBm8MA+Dr7ISkAD8k+vthaUIc1s6fgxATP/jI6DA7KsJgZo4DmBsdaXrQIRapFnNs0/PV3qEKZADAyWlYsjKWIBaJtArQMcawNCFOE8hIRAKmR4SZdW7GGJYlxmsXrYQqyAnsU2hSEAStQAaA3gHC6vP6uzgjwtMTJwqL9Y6F4wBOFBajrav/BWcJIf0zK5h55plnwBjDbbfdhv/+97+Q90mX7ty5Ew8//DAkEgnWrFljkYaOVSkhQRCbkZ3hABRKjpsnJOGWiUlICQmCVM80UjK6eTs7IdbHW6cekDorM5BxIEwshig91fhBjg4QLVs86rMyxvReeFWmUKJKTzVgU/m5OCMlOFAzmFciFmF+jP6uLH0mBQfC28lRaxvnHMuS4iGViGFnY3hRQzsbiVnVigkhuswKZiZPnow//vGPqK2txYoVK+Dq6grGGL799lu4urrihhtuQHV1Nf74xz8iPj7e0m0eU1zt7HB36mRIxQN/UzP2Rkmsh77szICzMj2EpCSgTwZBi60dlEeOQfb1tzpfigGusTQSuuRynTEo2zOzBzXubG5MlOZGYEFM9IAWqRQYww1J8bARiTQB6ZSQIPg6O0MiEmFRvOFCfoviY/XWsSGEDJzZAzaefPJJbN++Hampqejo6ADnHC0tLWhubkZSUhJ+/PFHPPHEE5Zs65gV4u6OtfPnYEl8LDx7TW3tTyLNhhgT+mZnzMnKqDFJP9mZ2lrwsmv6v+rqzfwJhs8veQU6Y1CqW1pxbhDF6OwkEtw0IRFJAX6YZGQ1bkMCXF3x7MJ5WBgXA1c7O8yNvj6wOMHPF0FurlpBKWMMQW6uSPDrfxIFIcQ0jFug4EFdXR0KCwuhVCoRFBQEPz/r+pBtbm6Gi4sLmpqa4DzI0u4WaU9HJ86UlOFEYRHkemaDMcbgameLR2ZOozu7MaK6uQWfHj6m+f6ulEn9rjPEOzoApe7Ll8vlkP97i/6BwIYIAiQPPQDWp8tkNKlpacWnvxzVOwbFRiTC6rmzBpRVGQqcc51sWt92MwCPzpoOr1H8XFub0fYeToafSZ+EH374IeLj45GRkaF3v4eHBzxMqCNBTONsZ4t5MZGI9PbAN2cuoK27W9Onz6Ea+HvH5IkUyIwh6uxMblW1SVkZZcFVyH8wsbo2Y4CPt+qPp7pabwE9ITlpVAcyAAwu/AioarqcLCrGvJioYWyRLn3dgl5OjkgLC8HJwmIAQFpYCAUyhFiYSd1M69atw5dffql33/z58/HOO+9YtFHjHeccB67k4/NjpzSBjPrjZ3JQIB6clgZnY+MiiFWaHR0BqViM+bFR/Y6VYd5eqqnWpuBcVe9lhp5KwADAGESpKWa0eOjw+gYoLl/R+vLsMrxeGQd0BuKOJrMjI2BnI4GdjQSzI61jJhkh1mTQt/YHDhxAaGioBZpC1I4WFGrdhfJe/54tLYOnkwPSQkNGpG1k6Hg7OeHpjLkm1R5iTk4QkhKhvHhJf4ACAM7OQHMzYG8HpXp1Z0dHoK1Nq4DeaMzKyPcdAC/RrrCbwhjOhYWhRSIB7zMGJdDVBfGjeAyKVCLGyqlpADikEsqoEmJptLTyMGru6MTxq0XYdzkPZ0pK0dmnLDsAdMvlOFxQaPQ8v+RdhVxBlZXHooEUURSlpehfQFKttWfKckcnlFk5UF7KAmQy7eBnFGZlAEBI1F0qRcw5FlVVaQUyAADOsSwxftRPN/d0dICn4+gKGgkZK+gWYRgoOceenMs41bOgJOtZi+nn7MvIiI1Gauj15QcK6+ohM1BRVK1DJkNJfQPCvWic0njWb3ZGPTi4Z7FHAICLE9AMoFNVrK2/rAxvaQWM/T2KREOS1RGiI6E46go0NmptD29rQ2RLCwocHcEZAwOQTmNQCBn3KJgZBgcu52lWxgagqTiqUCqxKzsXthIxkgL8AQBdMtPWa6F1XQigys4oL2Ua7mrqq7pW+/FGsjLKsmuQf/1tv6cU33U7hJ6/X0thggDR9KlQbN+ps29BVRUKnZyggKrW0qw+1YzVr6/RnqkhhFgOdTMNsfbubpzomcVgyIEr+Zo3YFPXmDFnLRoyMhRKJSqbm41+NXUMYBp1L+rsjFZ3k4kf4iws1GhWhXl5Ajb9THWW2qiOGwJCdCTg6qqz3UVqg5mR4QBUhed6V77mnOO7cxfx3/OXhqRNhJDRyeTMTH5+Pj7//PMB7wOAlStXDrxlJnrjjTewbds2nD9/HjY2Nmjsk5YeaXnVNVD0c9fc1NGJiqZm+Lu6IMDVBZ6ODqhrbdM7c4MB8HVxho+z05C0l1je0atFOHjF+MKsDMDvFi+A2Izy9lrZGXt7CDFRUJ6/aDxbY28P8fJlxtsklUKUmgLFkWOGD7K3h3zrDoO7hegoiBLNqwJuKDsjmpaOGZHhCPXyRKCri9a+K1U1yKmsAqAqKtlfrR5CyNhgcjBz5MgRvQtLMsYM7lPvH8pgpru7G3fccQemTZuGv//970N2HXN1yeRaU6sNHtfTbcQYw41JCfjixGkoOddeBI8xiAUBN5j54UBGRpSXp9FghgEI9/QwK5ABeo2duXARoqmpEKIiobyYaXSsi2hqKpgJdYqEiclQnDoD6Fn5GYwBDY3gDY0GH89dXQCY//eqM3bGwR5CQjyYICCoz2KQMoUCO7NyNDWZdmTlINTTndY/ImQcMCmYCQ4OHrX9z6+88goAYPPmzSPbEAPcHez7DWQAwK1Xt1GgmysenJ6G/ZfzUVBzfYxDlJcn5sVE0WBHK+Pr4oxoby/k1dRCX8FtDmB2rxL45hBNTwdzdICQmAAmFkOYmAzl2fP6szP29npnC+ljLDsjxMdCmZVj5MEMoimTTfwJDJyiT3ZGNC0dzEBwcqSgEK1dXZrXW3NnJ45dLcJsK1khnBBiPpOCmaKioiFuxvDq6upCV1eX5vvm5uYhu1a4lyccpVK09rpeb4wxhLq7wdXOTmu7r7Mz7kmdjLaubrR3d8NBagP7/sYvkFFrdlQErlTX6GwXGEOYhzsC+nSXDBSzs9Nak0k0ZbKqq0mhUBXX67UshqlZGU0b9WVnpDYQ5s4Gr60Dr67RX1U4MQHMAqXlNdkZWTeEBP1Znvq2dhwtKNS5cTiSfxVJAX5ws6cxZoSMZeNyAPBbb70FFxcXzVdQUNCQXUtgDMuTEyD0TCPtjTEGG5EISxLiDD7eQWoDLydHCmSsnDo70zfDqeR80FkZNcXFTMj37Id8z34ojp0EPNwBiUT1r5rUxuSsjJo6O9ObKHUKBKkUoukGqgoDEKVPGfDPoPf6ggDJHbdCctcderMynHPsNJAhUgLYlZVrkXYQQkavURnMPPfcc2CMGf3KzTX/Der5559HU1OT5qu0tLT/Bw1ChJcnfjU1FcHubpptDECsjzcempEOD0fTV8om1mt2VIRWN5PAGMI9PQadlVFTZmZBefGS6t/MLKCmFpDLgdq66we5uQ0oK6Np68Tk6zObpDaq7wGw0BAwH2+d2VSWyspoTunkBGbgeWrq6MTV2joo9XXhcY78mlqzZ4sRQqzDqKwz88wzz+CBBx4wekx4eLjZ55dKpZBKpWY/3hxBbq741dRUtHR2oUPWDSepLexsJMPaBjKy+o6dUXKOOdGWG88hSk2B/KftWl1KOsfMn2vSuXhbO+T//Qm8u1f3qLo6sSBA9u8tgCBAvHABRNOnQv79j9rXsVBWRk15tRC8plbvPgfO4SMSoVqh0OlmYozB19kJzrbD+3onhAyvURnMeHl5wctrbE6pdLKVwoneWMet3mNnVFkZV4udm0VGqLqU6hv0jmFBeBhEvj6mnUwkgNfVqTI7fXV0qr7Up+7JzvCen8vSWRkAUBw7CV5VZXBxzUU2NvgiJESnxg63kqUOCCGDMyq7mQaipKQE58+fR0lJCRQKBc6fP4/z58+jtbV1pJtGiA51dgaARbMyQM/UfUNjWDhX7TP1XLa2ECZPNFyAjzGwoEAI/n5gjGmNnbF0VgaAqi2AKuuk58uvsxMT3d20xqUxACnBQfBzsWxgRQgZfaw+mHnppZcwadIkvPzyy2htbcWkSZMwadIknD59eqSbRoheSxPjcOvEZItmZdQ02Zk+Y1hYZASEAVbqFaVMAgzVaOFcFcCoLxEaAhYSDCE1xeJZGQAQYqIAFwNjixgDC/DH/JSJsOk1HkgqFmNujGUGVxNCRjerD2Y2b94M3lNcrvfX3LlzR7pphOjlZGuLBH/fITm33uwM5xBNSx/4uezs9Gdn1FmZXusxMcYgvu1miGdON7Pl/bSlp96MXj2Blb2NDRbERms2Z8TFwE5C49IIGQ+sPpghhGjTys6YmZVR05ud6ZOV0Vx3iMel6M3O9GRlhKBAAMDEoAD4uzgj0NUFEwItu/glIWT0omCGkDFGKztjZlZGc66+2Rk9WZnhojc70yewEhjDyqlp+NXUVBr0S8g4QsEMIWMQi4wA8/YGi402OyujppWdMZCVGS5a2Zk+WRk1sUiAyMCsJ0LI2DQqp2YTQgaHMQbxPXcYnMpsiLKyCryiUvd8Af7gxSWAq6vx1biHmGatph27LBZYcc4hUyi0Bg8TQqwLvXqHUV1rG04WFSOnsgoyhRLeTo5IDQlGgr8vpcSJxRlakNEYxbHj4IXFhg9obITi0GEI9941iJYNjhATBcXxE2AODjpZGXP8dDELhbV1WDV7BqQSekskxBrRK3eYFNbWYcvpc5rKrwBQ3tiE/zZewuWqatw6KRkCBTRkhIkmJENuLJgBNEsZjBQmCJDcc5fhaeMDUFxfj4vXygEAv+QXICMuZtDnJIQMPwpmhkG3XI5vzp6HUqnUKreu/n9OZRWCi0uRGho8Es0jY5z80GHwIuMBCouMgHj6VLCwUDAvL/DaWv3dSc5OEGJH/gOfWaCKtlKpxI7MHDCoXosnCosxITAAXk6Ogz43IWR4UTAzCN1yOc6UlOJsSRmaO7tgJ5FgQqA/UkOD4dhr7afM8kp0yRVGz3WyqBhTQoKou4lYHG9uAe+92KQ+PcscMMYgmjEV8v/+pPcw0fSpYGNkcO3p4lLUtrZd38AYtmdmYyXNhCLE6oyNd6UR0CGTYdPRk9iXm4eG9g4olEq0dnXhaEEhPvvlGOrb2jXHXmts6rcLqaG9A1361sEhZJBEU9OMH8AYxGnXlyBQZ2d0iuWNkqzMQCgMLLrZ2tWF/VfytbZxzlHa0IgsPQOgCSGjGwUzZvo5Oxe1bW06q/RyAO0yGb4/d1GzzdSxMDRmhgwFwdMDLCpS/zpLjIHFx4L1WlpBnZ3p281kbVmZLpkcnxw4jH25V3T27cm5ArmBQOfn7Fy6sSDEyljPO9Mo0t7djazySnADU1Q556hobkZ5YxMA1erISiPTWRmAAFcXmhpKhoxoWrrBKdXi9FSdbTrZGSvMyhzKL0BzZyeOXS1CVXOL1r6cyiqDr9/2bhnKGhqHoYWEEEuhT08zVDW3GA1O1MqbmuDv6oJoHy+42NmiubNT/4LGAKaFh1q8nWR84jI5oNQeo8WcHIHwcODq1V4bGVicdlbm+i7tsTMDycrIjxyD8tx5o8cwHx9I7rjNpPOZo7qlBSd7ZmWxnrEwD0xL04yFSQ0JwonCYp3MKgPgam+PUA/3IWsbIcTyKJgxgyCY2m2kevMXCQLuTU3Bv06cRktXV6/9DErOMS86ErE9AzAJGQxeXw/Z518CBrpQ+tKXlVFjYaFg3l7gnV0DysowiQTolhk/aAgXgOScq2YpMaYphXCtsQmZ5RVI6lmGYVZUBC5eq0B7d7f2YwEsS4yjCsKEWBkKZszg7+ICG7EI3f3MUAr39ND838PRAb+ZMwOZ5ZXIrayCTKGAj7MTJgcHwtvJaaibTMYLJyfAxgbo7Oz3UBYXC+bmang/YxDffhugVAxorIwwMRmKU6eBrm6Dx4imm79elJpcLsd/zl7AnJhI+PdagDKrvBKlerqJfs6+jChvL9hKJJCKxVgUH4P/nr+k2c8YQ6yPN8J6vW4JIdaBbj/MIBGJkBYaYnA/AxDn6wNXezut7TZiMSYHB+LetBTcPy0NSxLiKJAhFsUkEoiMZFt6LxhpLCujOdxWCmZvP7A22NhAlDrFwE4GFh4Gwdt7QOfU5/sLmSiorcOWU2c127pkcvyck6v3+E6ZDIfyCjTfJ/j5IsjNVdP1JGIMC6loHiFWiYIZM82ODEeivx+A67OQ1J1PAW6uuDEpwaTzKDlHQU0tThQW41xpGVp7dUMRYg4hORGwtTW4nyXGQ0ibYjQrM+g2TEwGpDa6Ozi3SFamqrkFl6uqAagG7B68rJpmXVRXj3YDXVwcwIWycs33jDEsS4zXDIyeEx0BZzvDzxshZPSibiYzCYKAmyckIiU4EOfLrqGxvQMOUhsk+fsjwtvTpGnWxXX1+OFCJpo7OzVVSBljSAkOxMK4GOq3JxryM+fA8/KNHsMC/CGeNUOTnVEc/KXPAQxCUiLEGfOGsKU9l+rJzigOH9W6PgsLtUhW5j9nzml9f7jgKlLDghHm6Q4HqQ3a9HRxMQCTggK0tnk5OWJ2VATyqmuMZlsJIaMbBTODwBhDkLsbgtzdBvzYiqZmfHnqDJRK1V2helYF5xyni0shUyiwPDnRgq0lVq2+Aby8wvgxNtczIUJyIhQnTumMnRGlG+j+GQI6Y2dMzMrUtrbhcmUVpkeE6a3Ee6qoBI0d2j8XB/Dt2Qv41dRULI6PxXe96jyp2dvYYFZUhM72WVERmBkZTlV/CbFidOs/Qg7lFUDJoTM1VO1CWTnqepdaJ+OaKDVFf9G73sf0qvSrM3amJyvDhnGMltbYGRPHyig5x/fnL2L/lXxc0hO8yeVy7Mm9rPexxfUNKKqrR5yvD0Lc3XSCk0XxMZAaqOVEgQwh1o2CmRHQJZMjr7rGYNEuQPXmmtnfnTgZN5irC1hCnOEqvsFBEHrGcKn1HTsznFkZTRvUY2f+f3t3HxVlnf8N/H1dM8zwODynoDyTiIqI+IiYmJb1Kx/Xfp37dApd261ubbetbbXOMbazddzK7eH2lFLu4rk7uepPcsnfyRZ/JrTdKplKDyIW+ACCJIgwSAo4873/YBkZmIERxrnmmnm/zpmTcz3N58qY3nyu7/e6HOzKlNfWWW5wt7/iNK53WY9/+fRkJUxm+z83ReXfQZIk3D9h3M0aJAmxoSEYFzVyiGdBRO6OYUYB128Mcg8OdF/fv9Y1+HbkPbTT7Mw+EgKarBn9Fvfuzri6K2OpQaeD9p55kKdPHbQr83NnJw70evRA39lHAKAZ5B5PPfeAiggMwMyE7jEwQgjcP2Ecuy9EHoxhRgH+Ot2gX8pCCAT7+Q24DXkXm90ZO12ZHvLENMjTp3Y/zkAh8pg7oZ01c9DtDp7+EZ29nokk0D0+pvejCBaMT4V2gIHxyyalW/6cnZyIUH8/ZCcnIjIocGjFE5EqMMwowEejwYToqAFnPEmShLRRtv8HRd6rX3fGTlemh+SjhXbWTEgBt3avGFera2nFidq6/mPI/v0ogp5LslpZxv3jU20eIykiHKNCb948T6fV4qk52ZgzJvk2VU1E7oJhRiFz7kyGn4+P3TGdOWOSEajXu7YocntW3ZlBujJq8j+nTtv8WRD/fhTBDz81Wpalx4xCeJ9wJksSfjE5ve/ufBI9kZdgmFGIwc8XK7OmIzky0mp5kK8eD6SNQ1ZSgkKVkbuzdGcG6cqoiU6rgQT7wcNHo7F6/5+ZGVbvc8Yk86nzRF6MP/0KCvH3w8NTMmC8fh3N7T9Dp9FgZLCBv03SgKSQYMhTMyHarnpEVwYA7kkdi/x/Heq3XJYkJEaEIzHS+nlJ4YEBmBAdhe/rLyJIr2f4J/JyDDNuwODrC8MAt58n6kubnaV0CU7VM/vo8JlzVuNmJAALxo+1uc+iieNhFgKzkxNdUiMRuS+GGSIvZDr9A8ynbN98rocUHgbt7Fkuqqh79tE3dfVWjyKYlZyIUDsPupRlGcsyJrqqPCJyYwwzRAoxnaoEek07tkVKTIAcGWF3vbhyBWKwY9wRCanPNH9xqRHizNkB9xOtrYALw4xOq7V6FIHB1xdZifEu+3wiUi+GGSKFmA5+0f3sJFv3HBIAhIB8vQPynGy7x+jaWQj8/POAnyOPHwftgvlWyzSTJ8F87ARgNtvdT6vAvWlSR45AXHgYzl9uxv0TUqHtM/CXiMgWzmYiUohm8qTuP5hF/5cQgCRBk5424DHk1JRBP8fWNlJAQPejBuw8HgGhoZDudP39WSRJwpL0CViSnoY774gcfAciIjDMEClGzkgHfHxsr5QkSKNHQTQ1wVxVbf2qPgPR1X2nXM2UyYC97oUkQYoaCSlmtM3Vdh9eKQS0s2Yodvv/IF9fTOANI4noFvAyE5FCJL0emqmZMB060n+lEBC1F3Cj9oLNfTX3zoNmwnhLh8V8vLy7m9PnGJpZM+2GEsu+J765ua8kASEhinRliIiGip0ZIgUN2J2xR6OBFBcHceMGhMnU3Z3p+7yiQboylkP17c4o3JUhIhoKhhkiBfV0Z26JyYQbH/wNXf/nPXRt2gzR3t5//MsgXRnL5/cdO6PQWBkiouFgmCFSmFV3RpIgjUuF5OidfTUaSIZg6+6Mg10ZyyF6dWfYlSEiNWKYIVJY3+6MdsZUx565JEnQTJkMyVd/s8MCONyVsRwmIACaGdMgRUexK0NEqsQwQ+QG5Ix0QK+HND4VUkgIpJjRg3dntFrIGZMsb3tmNt1KV8ay74xp0D683G27MsfO1+K/jpXD3HeQMxEROJuJvIxovoKu//4UuGGyv5FWA58H74cUFuayuiS9Hj4rHwX+/YwuSZKgyZqBG7v32NnhZlfGsiggANqHlkEKChpSKHHXINPy8zUUV1TCJATKa+swOfbWghoReT6GGfIusgxcbu4/jbk3SQJk1995VurzDKKe7oyov9h/4z5dmR6e8hTt3oorKtFzn+L9FZUor72A/zUtE363OguMiDwWLzORV5FCgiGlpti+WRzQPXg2NQVSSLBrC7NZinRz7MzIETdrttGV8VRVlxrxw6VGiH+Hzy6zGfWtRpT+UKVwZUTkThhmyOtop08b1npXkmJGQ/vQMmgfuP9mmLHTlfE0N0wm7Dt5CrZi59fna9FgNLq8JiJyTwwz5HWk0BDb3ZmerkxoiCJ12SJJEuSY0ZCDDZbZSt7SlTl89jxar12HvQuCn35XYenYEJF3U3WYOXfuHFatWoWEhAT4+fkhKSkJeXl56OzsVLo0cnP2ui/u1JXpSzN9KuQpkyH3PKDSwx2qPjPg+vpWI76tq3dRNUTkzlQdZiorK2E2m5Gfn4+TJ0/irbfewpYtW/Diiy8qXRq5uX7dGTfsyvQl+flBe1c2JL3nd2UAIC4sbNAZVvtPncb1ri4XVURE7koSHtanfeONN7B582acOTPwb3W9GY1GBAcHo7W1FQaD4TZWR+5EXGlB17YPu2c2SRJ8Vjzq1mHG21z5+WdsKf1/MA3yFXVvagqmJcS5qCpyR/wOJ1V3ZmxpbW1F2CD3B+no6IDRaLR6kfexdGcAt+/KeKNQf3/MSk4cdLvYsFAXVENE7syj7jNTVVWFTZs2YePGjQNut2HDBrz88ssuqorcmXbGdNwwtkE7Y7rSpQyJ6OqC6fNSiOvX7W8kSdBMmwJ55AjXFeYkMxPjcaLmAto6OvqtkwBkxsVgZDB/Eyfydm55mWndunV47bXXBtzm1KlTGDt2rOV9XV0d5syZg5ycHGzdunXAfTs6OtDR68vRaDQiJiaGLUpSHdHRga4tWwHTAHc0BqB54D5oUsa4qCrn+vGnRuw8dqLfcl8fLdbkzIYvb57n9XiZidyyM/Pcc89hxYoVA26TmHiz/VxfX4+5c+ciKysL77///qDH1+v10HvJIErybJJeD3nyJJi/Pm7/rsYGA2QVP0DyzhGRSIoIx5mmy4AkWaZj35OawiBDRADcNMxERkYiMjLSoW3r6uowd+5cZGZmoqCgALLsccOAiAakycyA+Xi53e6MJms6JJX/XDyUmYH61lZ8WPY1ACA62ICJo6IVroqI3IWqv+Hq6uqQk5OD2NhYbNy4EY2NjWhoaEBDQ4PSpRG5jOTv333vGVvTmA0GyGNTXF6Ts2k1MmLDQpGVGA8JwH9MGOe2D8YkItdzy86Mo/bv34+qqipUVVVh9GjrJ+m64VAgotvGXnfGE7oyveWMScbk2BgE+/kqXQoRuRFVf8utWLECQgibLyJvYrM74yFdmd4kSWKQIaJ+VB1miOgmTWYG0KsL42ldGSIie/hNR+QhLN0ZwCO7MkRE9jDMEHkQTWYGYAjqfoYTuzJE5CVUPQCYiKxJ/v7wWbWCM32IyKvwVzciD8MgQ0TehmGGiIiIVI1hhoiIiFSNY2bchBACPxnbcLWjA4F6PUYYgni5gIiIyAEMM27gTONlFJ+qRNPVdsuyiMAA3JOagqTICAUrIyIicn+8zKSw6sYm/P3oMVzuFWQAoOlqO3YcPY7qxiaFKiMiIlIHhhkFCSGw7/tTEABsPYBBAN3r+XgGIiIiuxhmFFR7pQUt164NuE3LtWuovdLimoKIiIhUiGFGQcbr1526HRERkTdimFGQv07n0HYBDm5HRETkjRhmFBQfFjpoUAnQ6RAXFuqiioiIiNSHU7MVJMsy5qeOQdE339vdZl7qGMh8YKDLiM5OoK1t4I0CAyHp9a4piIiIBsUwo7C0UdEwC4H9p07jetcNy3JfHy3uSU3BxFHRClbnfW789z6Ic+cH3igsFNp75kEKDIQUbHBNYUREZBfDjBtIHz0K46OiUN3UhKvXOxDoq0dSRAS0GnZkXE1OiIdpsDDTfAU3du4G9HroVj/hmsKIiMguhhk3odXISBlxh9JleD05bTxMR74CBpkyDwDymDtdUBEREQ2Gv/oT9SJptdDMmObAhhI006fc/oKIiGhQDDNEfchp4wE/P/sbSBLkCeMhGThehojIHTDMEPXhSHeGXRkiIvfBMENkg93uDLsyRERuh2GGyIaBujPsyhARuReGGSI7LN0ZjebmMnZliIjcDqdmE9khabXQLn4QoqsL5kNHIBp+YleGiMgNMcwQDUCOjur+Z2goxJUr7MoQEbkhhhkiB0iGIEiGIKXLICIiGzhmhoiIiFSNYYaIiIhUjWGGiIiIVI1hhoiIiFSNYYaIiIhUjWGGiIiIVI1hhoiIiFSNYYaIiIhUjWGGiIiIVI1hhoiIiFSNYYaILExmM/7v4a9wqPqs0qUQETmMYYaILI6eq0HNlRYc/KEKl6+2K10OEZFDGGaICADQdv06Sn+ssrzfd/IUhBAKVkRE5BiGGSICAPzPqR9ww9wdXoQQOHe5GZUNlxSuiohocKoPM4sWLUJsbCx8fX0RFRWFRx99FPX19UqXRaQq5y834+TFhn6dmH9WVKLzxg2FqiIicozqw8zcuXOxa9cunD59GoWFhaiursby5cuVLotINUxmMz79vgKSjXXtHR34koOBicjNScLDLop/8sknWLJkCTo6OuDj4+PQPkajEcHBwWhtbYXBYLjNFRK5l+rGJvz96HG767WyjHX3zXdhRUS3ht/hpFW6AGdqbm7GRx99hKysrAGDTEdHBzo6OizvjUajK8ojckujQoLh5+ODa11d/dZJkoQxIyIVqIqIyHGqv8wEAGvXrkVAQADCw8NRU1ODoqKiAbffsGEDgoODLa+YmBgXVUrkfnx9fHBPaorNdRpJsruOiMhduGWYWbduHSRJGvBVWVlp2f7555/HiRMnUFxcDI1Gg8cee2zAKaUvvPACWltbLa/a2lpXnBaR20obFYVRIcGQJeuRMzljkhHk66tQVUREjnHLMTONjY24fPnygNskJiZCp9P1W37hwgXExMTg0KFDmDlzpkOfx+utRMBPxjZ88OVhAIAEIDTAH0/MzoJGdsvfeYgs+B1ObjlmJjIyEpGRQ7tObzabAcBqTAzR7SBajej6+y7AxlgTC1mGdtEDkGNGu66wIRphCMLUuFgcPV8DAeA/JoxjkCEiVXDLMOOosrIyHD16FNnZ2QgNDUV1dTXWr1+PpKQkh7syREPmq+8OMgOFGQCSv5+LChq+OWOScPJiAxIiwhAfHqZ0OUREDlH1r13+/v74+OOPMW/ePKSkpGDVqlWYOHEiSktLodfrlS6PPJyk10MzNROQbN2hBYAkQRpzJ6TwcNcWNgy+Pj7433NmYXF6mtKlEBE5TNWdmbS0NHz++edKl0FeTM5Ih+noMdvdGSGgnTnN9UUNk6+D92ciInIXqu7MECnNbndGhV0ZIiK1YpghGiY5Ix3Q9mlyqrQrQ0SkRgwzRMPUrzvDrgwRkUsxzBA5gVV3hl0ZIiKXYpghcgJLdwZgV4aIyMVUPZuJyJ3IGekwt7RAO4NdGSIiV2KYIXISSa+Hz333Kl0GEZHX4WUmIiIiUjWGGSIiIlI1hhkiIiJSNYYZIiIiUjWGGSIiIlI1hhkiIiJSNYYZIiIiUjWGGSIiIlI1hhkiIiJSNYYZIiIiUjU+zgCAEAIAYDQaFa6EiIhuVc93d893OXkfhhkAbW1tAICYmBiFKyEioqFqa2tDcHCw0mWQAiTBKAuz2Yz6+noEBQVBkiSnH99oNCImJga1tbUwGAxOP75SPPG8PPGcAM88L088J8Azz+t2n5MQAm1tbYiOjoYsc/SEN2JnBoAsyxg9evRt/xyDweAxX069eeJ5eeI5AZ55Xp54ToBnntftPCd2ZLwbIywRERGpGsMMERERqRrDjAvo9Xrk5eVBr9crXYpTeeJ5eeI5AZ55Xp54ToBnnpcnnhO5Fw4AJiIiIlVjZ4aIiIhUjWGGiIiIVI1hhoiIiFSNYYaIiIhUjWHGxRYtWoTY2Fj4+voiKioKjz76KOrr65Uua1jOnTuHVatWISEhAX5+fkhKSkJeXh46OzuVLm1YXn31VWRlZcHf3x8hISFKlzNk7777LuLj4+Hr64vp06fjq6++UrqkYfniiy+wcOFCREdHQ5Ik/OMf/1C6pGHbsGEDpk6diqCgINxxxx1YsmQJTp8+rXRZw7Z582ZMnDjRcrO8mTNnYt++fUqXRR6IYcbF5s6di127duH06dMoLCxEdXU1li9frnRZw1JZWQmz2Yz8/HycPHkSb731FrZs2YIXX3xR6dKGpbOzEw899BCeeuoppUsZsp07d+LZZ59FXl4ejh8/jvT0dCxYsACXLl1SurQha29vR3p6Ot59912lS3Ga0tJSrF69GkeOHMH+/fvR1dWFe++9F+3t7UqXNiyjR4/Gn//8Zxw7dgxff/017r77bixevBgnT55UujTyNIIUVVRUJCRJEp2dnUqX4lSvv/66SEhIULoMpygoKBDBwcFKlzEk06ZNE6tXr7a8N5lMIjo6WmzYsEHBqpwHgNizZ4/SZTjdpUuXBABRWlqqdClOFxoaKrZu3ap0GeRh2JlRUHNzMz766CNkZWXBx8dH6XKcqrW1FWFhYUqX4dU6Oztx7NgxzJ8/37JMlmXMnz8fhw8fVrAyGkxraysAeNTPkMlkwo4dO9De3o6ZM2cqXQ55GIYZBaxduxYBAQEIDw9HTU0NioqKlC7JqaqqqrBp0yY88cQTSpfi1ZqammAymTBixAir5SNGjEBDQ4NCVdFgzGYznnnmGcyaNQsTJkxQupxh++677xAYGAi9Xo8nn3wSe/bswbhx45QuizwMw4wTrFu3DpIkDfiqrKy0bP/888/jxIkTKC4uhkajwWOPPQbhhjdivtXzAoC6ujrcd999eOihh/CrX/1KocrtG8o5EbnS6tWr8f3332PHjh1Kl+IUKSkpKC8vR1lZGZ566ink5uaioqJC6bLIw/BxBk7Q2NiIy5cvD7hNYmIidDpdv+UXLlxATEwMDh065Hat11s9r/r6euTk5GDGjBnYtm0bZNn9svJQ/q62bduGZ555Bi0tLbe5Oufq7OyEv78/du/ejSVLlliW5+bmoqWlxSM6gpIkYc+ePVbnp2Zr1qxBUVERvvjiCyQkJChdzm0xf/58JCUlIT8/X+lSyINolS7AE0RGRiIyMnJI+5rNZgBAR0eHM0tyils5r7q6OsydOxeZmZkoKChwyyADDO/vSm10Oh0yMzNx4MABy//szWYzDhw4gDVr1ihbHFkRQuDpp5/Gnj17UFJS4rFBBuj+b9Adv+9I3RhmXKisrAxHjx5FdnY2QkNDUV1djfXr1yMpKcntujK3oq6uDjk5OYiLi8PGjRvR2NhoWTdy5EgFKxuempoaNDc3o6amBiaTCeXl5QCA5ORkBAYGKlucg5599lnk5uZiypQpmDZtGt5++220t7dj5cqVSpc2ZFevXkVVVZXl/dmzZ1FeXo6wsDDExsYqWNnQrV69Gtu3b0dRURGCgoIsY5qCg4Ph5+encHVD98ILL+D+++9HbGws2trasH37dpSUlOCf//yn0qWRp1F2MpV3+fbbb8XcuXNFWFiY0Ov1Ij4+Xjz55JPiwoULSpc2LAUFBQKAzZea5ebm2jyngwcPKl3aLdm0aZOIjY0VOp1OTJs2TRw5ckTpkobl4MGDNv9ecnNzlS5tyOz9/BQUFChd2rD88pe/FHFxcUKn04nIyEgxb948UVxcrHRZ5IE4ZoaIiIhUzT0HNhARERE5iGGGiIiIVI1hhoiIiFSNYYaIiIhUjWGGiIiIVI1hhoiIiFSNYYaIiIhUjWGGaADx8fGDPpjy7bffVrpMj1BbW4v8/Hz8+te/RmZmJvR6PSRJwuOPP650aUTk5vg4AyIHzJo1C8nJyTbXjRs3zsXVAOfOnUNCQgLi4uJw7tw5l3/+7VBYWIjf/e53SpdBRCrEMEPkgMcffxwrVqxQugyPlpCQgKeffhqTJ0/G5MmTsWvXLrz66qtKl0VEKsAwQ0RuYfHixVi8eLHl/ccff6xgNUSkJhwzQ+Rkx44dwyOPPILY2Fjo9XqEhYVhwYIF+PTTT21uX1FRgby8PMyaNQujRo2CTqdDeHg45s+fj127dvXbfsWKFUhISAAAnD9/vt8Ynt7bSZKEbdu22fzcbdu2QZKkfh2n3subm5vxzDPPICkpCXq9Hjk5OVbbHjhwAMuWLUNUVBR0Oh3uuOMOLF26FIcPH3b8XxgR0TCxM0PkRO+88w6effZZmM1mTJo0CdOnT0dDQwNKSkpQXFyMl19+GS+99JLVPm+++Sb++te/YuzYsUhLS0NISAhqampw8OBBHDhwAEeOHMGbb75p2T47OxtXr15FYWEhAgICsHz58ttyLk1NTZgyZQpaWlowe/ZsZGZmQqfTWdb//ve/x1/+8hfIsowpU6Zg9uzZqKmpQVFREfbu3YsPPvgAK1euvC21ERFZUfqx3UTuLC4uTgAQBQUFg2772WefCUmSREREhCgtLbVa9+2334rRo0cLAKKkpMRqXUlJiaiuru53vMrKSss+ZWVlVuvOnj0rAIi4uDi79eTm5g5Ye0FBgQAgcnNzbS4HIObNmydaW1v77fv+++8LACI5OVl88803VutKS0tFUFCQ0Ol04ocffrBb32Dy8vIEALFq1aohH4OIvAMvMxE5YOXKlTanZfe+7JKXlwchBLZs2YK77rrLav+0tDRLd2XTpk1W6+bMmYPExMR+n5mSkoL169cDAHbv3u3kMxqcj48P3n//fRgMBqvlZrMZf/zjHwEAO3bswMSJE63W33XXXVi/fj06OzuRn5/vqnKJyIvxMhORA+xNzR47diyA7ksyX331Ffz8/LBw4UKbx+gJPocOHeq37urVq9i3bx9OnDiBpqYmdHZ2AgAuXrwIADh9+rQzTuOWZGRk2AxZJ06cQH19PZKSkpCZmWlz34HOlYjI2RhmiBww2NTss2fPQgiBa9euQa/XD3isxsZGq/d79+7FypUrcfnyZbv7GI3GW6rXGeLj420uP3PmDACgurraasCxLX3PlYjodmCYIXICs9kMAAgMDMQvfvELh/erq6vDww8/jGvXruEPf/gDHnnkEcTHxyMwMBCyLKO4uBgLFiyAEOK21WyPn5/fgPuNHDkSCxYsGPAYERERQyuOiOgWMMwQOUFMTAwAQJIk/O1vf4MsOzYcbe/evbh27RqWLl2K1157rd/6H3/8ccg19cw8amtrs7n+/PnzQzpuz7mGh4fbnfZNRORKHABM5ATR0dGYOHEi2tra8Nlnnzm8X3NzMwAgLi6u3zohBLZv325zv56gcuPGDbvHHjVqFADg1KlTNo+9b98+h+vsberUqYiIiEBFRQVOnjw5pGMQETkTwwyRk7zyyisAumc+7d27t996IQTKyspQXFxsWZaamgqge7ZSz2BfADCZTHjppZfsDqCNjIyETqdDQ0ODJRD1NX/+fADAhx9+iIqKCsvyrq4urF27FkePHr3FM+zm4+Njmbm1dOlSfPnll/22MZlM+Pzzz3HkyJEhfQYR0a3gZSYiJ1m4cCHeeecdPPfcc1i0aBGSk5ORkpKC4OBgNDY24ptvvsGlS5ewdu1a3HvvvZZ9MjMzcezYMYwZMwZz5sxBQEAAysrKUF9fj7Vr19q8/OTj44NFixZh9+7dmDRpErKzs+Hv7w8A2Lp1K4DuGViLFy9GUVERpkyZguzsbPj5+eH48eMwGo347W9/i3feeWdI57pmzRrU1NTgjTfewOzZszF+/HgkJyfDz88PDQ0NKC8vR0tLCzZv3owZM2Y4dMyLFy9i6dKllvcXLlwAAHzyySdWx3jvvfcwefLkIdVNRJ6JYYbIiX7zm9/g7rvvxqZNmyx38JVlGSNHjkRGRgYeeOABqwHCWq0WJSUl2LBhAwoLC3HgwAEYDAZkZWWhsLAQbW1tNsMMAOTn5yM8PBz79u3D7t270dXVBeBmmAGAnTt34pVXXsH27dtRUlKC0NBQzJs3D3/605/wr3/9a1jn+vrrr2PJkiV477338OWXX+Kzzz6DTqdDVFQUcnJy8OCDD2LZsmUOH6+jowNlZWX9ljc2NlrNilJiZhcRuTdJ3I5pEkREREQuwjEzREREpGoMM0RERKRqDDNERESkagwzREREpGoMM0RERKRqDDNERESkagwzREREpGoMM0RERKRqDDNERESkagwzREREpGoMM0RERKRqDDNERESkagwzREREpGr/H6Y7Wd/r1bIRAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_points_prediction(x, y, np.append(p_train_0, p_test_0), \"QPU0\")  # QPU 0\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SPDOMYApRa52"
      },
      "source": [
        "![](/demonstrations/ensemble_multi_qpu/ensemble_multi_qpu_003.png){.align-center\n",
        "width=\"80.0%\"}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "AzWCUcp1Ra52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "586ddad6-a9a3-41bf-a960-c272120a5dfd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_points_prediction(x, y, np.append(p_train_1, p_test_1), \"QPU1\")  # QPU 1\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1KFPjO5QRa52"
      },
      "source": [
        "![](/demonstrations/ensemble_multi_qpu/ensemble_multi_qpu_004.png){.align-center\n",
        "width=\"80.0%\"}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YZNL9oSxRa52"
      },
      "source": [
        "These plots reinforce the specialization of the two QPUs. QPU1\n",
        "concentrates on doing a good job at predicting class 1, while QPU0 is\n",
        "focused on classes 0 and 2. By combining together, the resultant\n",
        "ensemble performs better.\n",
        "\n",
        "This tutorial shows how QPUs can work in parallel to realize a\n",
        "performance advantage. Check out our `vqe_parallel`{.interpreted-text\n",
        "role=\"doc\"} tutorial to see how multiple QPUs can be evaluated\n",
        "asynchronously to speed up calculating the potential energy surface of\n",
        "molecular hydrogen!\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "407TS0JdRa53"
      },
      "source": [
        "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": "WCB5ZxK5QHfb",
        "outputId": "27eea90c-d38b-4b49-f383-25a1b2e95e2f"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1700593984.0918827\n",
            "Tue Nov 21 19:13:04 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": "3.9.17"
    },
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "A100"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}