940 lines (940 with data), 241.6 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "WNXEyZ23rdz-"
      },
      "outputs": [],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "WYVI3RMhAdap"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "sANL984ird0B"
      },
      "outputs": [],
      "source": [
        "#!pip install pennylane\n",
        "# Some parts of this code are based on the Python script:\n",
        "# https://github.com/pytorch/tutorials/blob/master/beginner_source/transfer_learning_tutorial.py\n",
        "# License: BSD\n",
        "\n",
        "import time\n",
        "import os\n",
        "import copy\n",
        "\n",
        "# PyTorch\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "import torchvision\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "# Pennylane\n",
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "\n",
        "torch.manual_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "# Plotting\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# OpenMP: number of parallel threads.\n",
        "os.environ[\"OMP_NUM_THREADS\"] = \"1\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o7eGTk_Prd0B"
      },
      "source": [
        "Setting of the main hyper-parameters of the model\n",
        "=================================================\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "To reproduce the results of Ref. \\[1\\], `num_epochs` should be set to\n",
        "`30` which may take a long time. We suggest to first try with\n",
        "`num_epochs=1` and, if everything runs smoothly, increase it to a larger\n",
        "value.\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "Wncy61Mdrd0B"
      },
      "outputs": [],
      "source": [
        "n_qubits = 8                # Number of qubits\n",
        "step = 0.0006               # Learning rate\n",
        "batch_size = 6              # Number of samples for each training step\n",
        "num_epochs = 30             # Number of training epochs\n",
        "q_depth = 6                 # Depth of the quantum circuit (number of variational layers)\n",
        "gamma_lr_scheduler = 0.1    # Learning rate reduction applied every 10 epochs.\n",
        "q_delta = 0.01              # Initial spread of random quantum weights\n",
        "start_time = time.time()    # Start of the computation timer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PVqjLo8Rrd0B"
      },
      "source": [
        "We initialize a PennyLane device with a `default.qubit` backend.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "qLOa5trRrd0B"
      },
      "outputs": [],
      "source": [
        "dev = qml.device(\"default.qubit\", wires=n_qubits)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dxlK7Jjtrd0C"
      },
      "source": [
        "We configure PyTorch to use CUDA only if available. Otherwise the CPU is\n",
        "used.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "Br_YGwRDrd0C"
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WFHuYd5xrd0C"
      },
      "source": [
        "Dataset loading\n",
        "===============\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "The dataset containing images of *ants* and *bees* can be downloaded\n",
        "[here](https://download.pytorch.org/tutorial/hymenoptera_data.zip) and\n",
        "should be extracted in the subfolder `../_data/hymenoptera_data`.\n",
        ":::\n",
        "\n",
        "This is a very small dataset (roughly 250 images), too small for\n",
        "training from scratch a classical or quantum model, however it is enough\n",
        "when using *transfer learning* approach.\n",
        "\n",
        "The PyTorch packages `torchvision` and `torch.utils.data` are used for\n",
        "loading the dataset and performing standard preliminary image\n",
        "operations: resize, center, crop, normalize, *etc.*\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#os.system(\"rm -rf /content/data/.ipynb_checkpoints\")\n",
        "#os.system(\"rm -rf /content/data/braintumor_data/.ipynb_checkpoints\")\n",
        "#os.system(\"rm -rf /content/data/braintumor_data/train/.ipynb_checkpoints\")\n",
        "#os.system(\"rm -rf /content/data/braintumor_data/val/.ipynb_checkpoints\")"
      ],
      "metadata": {
        "id": "DM8SDO3Wthcc"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "jQrNNVnUrd0C"
      },
      "outputs": [],
      "source": [
        "data_transforms = {\n",
        "    \"train\": transforms.Compose(\n",
        "        [\n",
        "            # transforms.RandomResizedCrop(224),     # uncomment for data augmentation\n",
        "            # transforms.RandomHorizontalFlip(),     # uncomment for data augmentation\n",
        "            transforms.Resize(256),\n",
        "            transforms.CenterCrop(224),\n",
        "            transforms.ToTensor(),\n",
        "            # Normalize input channels using mean values and standard deviations of ImageNet.\n",
        "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
        "        ]\n",
        "    ),\n",
        "    \"val\": transforms.Compose(\n",
        "        [\n",
        "            transforms.Resize(256),\n",
        "            transforms.CenterCrop(224),\n",
        "            transforms.ToTensor(),\n",
        "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
        "        ]\n",
        "    ),\n",
        "}\n",
        "\n",
        "data_dir = \"/content/drive/MyDrive/Colab Notebooks/data/4_cl_2xbraintumor_data\"\n",
        "image_datasets = {\n",
        "    x if x == \"train\" else \"validation\": datasets.ImageFolder(\n",
        "        os.path.join(data_dir, x), data_transforms[x]\n",
        "    )\n",
        "    for x in [\"train\", \"val\"]\n",
        "}\n",
        "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"validation\"]}\n",
        "class_names = image_datasets[\"train\"].classes\n",
        "\n",
        "# Initialize dataloader\n",
        "dataloaders = {\n",
        "    x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)\n",
        "    for x in [\"train\", \"validation\"]\n",
        "}\n",
        "\n",
        "# function to plot images\n",
        "def imshow(inp, title=None):\n",
        "    \"\"\"Display image from tensor.\"\"\"\n",
        "    inp = inp.numpy().transpose((1, 2, 0))\n",
        "    # Inverse of the initial normalization operation.\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    inp = std * inp + mean\n",
        "    inp = np.clip(inp, 0, 1)\n",
        "    plt.imshow(inp)\n",
        "    if title is not None:\n",
        "        plt.title(title)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "piWk71nkrd0C"
      },
      "source": [
        "Let us show a batch of the test data, just to have an idea of the\n",
        "classification problem.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "u55iZYEOrd0D",
        "outputId": "935e09e3-4f0f-4b15-8cff-ecff93d56693"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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2u1FbW4umpqa0z5pMJgDjrIzf78fY2BiWL1+O7du3H3fbj1c+9KEPpTEMK1euhKZp+MhHPpL2uZUrV6K9vR1jY2MAxl1kqVQKV111FQYGBuT/oqIizJo1a4Kl4XA40pIaTCYTzjjjjLS+OJp8/OMfT/t99erVad996qmnkJOTg5tuukmu6fX6KbEGHM/Pfvazadc/97nPAcAEl9K8efOwatUq+Z3Wz7p161BRUTHhutpO1bodGRnBwMAAzjzzTAB4W8Zbnaecj3a7HVdddZVcr62thdvtTmv3Qw89hNWrVyMvLy9tvM8//3wkk0m89NJLac95//vfj7y8PPl99erVADCl8T7//PNRXV0tvy9cuBAul0u+m0wm8eyzz2Ljxo0oKSmRz9XU1ByTHQXGx/uMM87A2WefLdccDgc+9rGPoaWlBfv370/7/ImuESB9vEOhEAYGBrB27Vo0NTUhFAods61vRabSj8888ww2btyImTNnyueKi4txzTXX4JVXXkE4HD7p7brxxhvTmFv2p6orqUPV+fLkk0/CYDDglltuSbvf5z73OWiahr/+9a9p19euXYt58+ad9PZnkw996ENwOp3y+5VXXoni4uKse8U/Uo5nPs6YMWOCKzA3NxeXXXYZHnzwQXGRJZNJ/OEPf8DGjRtht9tPSbufeuoplJaW4tJLL5VrFoslTd9PVU5k3p/onAXS+zwQCCAUCmH16tWnVN+fMqDV2tqKmpqaCdezXZvKvYDxDSdT5s6di4GBAQwPD6ddVzdYYHxCWiwWoW7V64FAIO3afffdh4ULF8JiscDr9cLn8+GJJ5445Yp4snYDQHl5+YTrqVRK2tTQ0ABN0zBr1iz4fL60/w8cOIC+vr6075eVlU1wi+Xl5U3oi2xisVgmuIQyv9va2ori4mLYbLa0z01l/FtbW6HX6yd8tqioCG63W+YD5Xj6DEBaO/1+Pz7zmc+gsLAQVqsVPp9P3HOneryz9WNubm7Wscmcpw0NDXjqqacmjPX5558PABPGO7OPCLqmMt6Z3+X3+d2+vj7EYrETXu+tra2Trm3+/WjtmeoaAYBXX30V559/vsR5+nw+fOUrXwFw6sf7WP3Y39+PaDQ6aV+kUqkJcUanol1H68/MNV5SUpIGaNhW/l0Vrqu3Q2bNmpX2u06nQ01NzTuuHMbxzMfJ+u9DH/oQ2tra8PLLLwMYD7Pp7e3FBz/4wVPW7tbWVlRXV0/QUyeyv5/IvD/ROQsAf/nLX3DmmWfCYrHA4/GI2/hUrv9/2VQvg8EwpWsA0oLl7r//ftxwww3YuHEjvvCFL6CgoAAGgwHf/va3cfjw4VPW3mO18VhtT6VS0Ol0+Otf/5r1sw6H47judyJtPNky1fioE+0zALjqqquwefNmfOELX8DixYvhcDiQSqVw4YUXpgWVnwp5K+1OpVK44IIL8MUvfjHrZ2fPnn3c9zzedk7lu6dCTrTfDh8+jPPOOw9z5szBD37wA5SXl8NkMuHJJ5/ED3/4w3/YeL9d/ZiZrEI5nv58K239Z82QA8Z1UbZ3n6xPpyLHOx8n67/169ejsLAQ999/P9asWYP7778fRUVFYnT9K8qJztmXX34Zl156KdasWYP/+Z//QXFxMXJycnDPPfdkTTg6WXLKgFZlZSUaGxsnXM92bSr3AsaDATPl4MGDyM/PP2kU6cMPP4yZM2fikUceSdvov/GNb5yU+58qqa6uhqZpmDFjxoRN9h8hlZWV2LRpE6LRaBqrNZXxr6ysRCqVQkNDQ1qAb29vL4LBoMyHtyqBQADPPfcc7rjjjrSA6YaGhpNy/1Mp1dXVGBoaekco04KCAlgslhNe75WVlZOubf79ZMjjjz+OeDyOxx57LM0iPtUZxVMVn88Hm802aV/o9Xqx2I9mhOTl5U0ojplIJNDd3X1S21tZWYlnn30WkUgkjdU62eN2IpK5hjVNQ2Nj41GD24/Vp9nc7Jms3fHc92TNR4PBgGuuuQb33nsvvvvd7+LRRx/FTTfddEoN4srKSuzfvx+apqW934ns78cz79+q/PGPf4TFYsHTTz+dlph3zz33nJT7TyanzHW4fv16vPbaa9i5c6dc8/v9eOCBB477XsXFxVi8eDHuu+++NAWyd+9ePPPMM3jPe95zElo8LpycKgp+/fXX8dprr520Z5wKueKKK2AwGHDHHXdMsLw0TTvl5QoyZf369RgdHcXdd98t11KplJQvOJpwPDMz537wgx8AwEnLDMk21tme+06Uq666Cq+99hqefvrpCX8LBoNpcUmnWgwGA84//3w8+uij6OrqkuuNjY0T4nSyyXve8x688cYbaWtseHgYv/zlL1FVVXXS4nqyjXcoFDrlSnaqYjAY8O53vxt//vOf01xcvb29+N3vfoezzz4bLpcLAMSwzFZtvLq6ekKM3i9/+cu3xL5kk/e85z1IJpP46U9/mnb9hz/8IXQ63ZTi806V/OY3v0EkEpHfH374YXR3dx+1TTQIJ+vTgwcPor+/X67t2rULr7766jHbMtlYncz5+MEPfhCBQAA333wzhoaGTnlR8fXr16OzsxOPPfaYXBsZGUnT91OV45n3b1UMBgN0Ol3aWmhpacGjjz56Uu4/mZwyRuuLX/wi7r//flxwwQX49Kc/LeUdKioq4Pf7jztt/nvf+x42bNiAVatW4cYbb5TyDrm5uSf16J+LL74YjzzyCC6//HJcdNFFaG5uxi9+8QvMmzcPQ0NDx/y+TqfD2rVr3/bjVqqrq/HNb34Tt912G1paWrBx40Y4nU40NzfjT3/6Ez72sY/h85///NvWno0bN+KMM87A5z73OTQ2NmLOnDl47LHH4Pf7ARzdely0aBGuv/56/PKXv0QwGMTatWvxxhtv4L777sPGjRtx7rnnnpQ2ulwuKa0wOjqK0tJSPPPMM2hubp7S9++99158+MMfxj333PO2H0/xhS98AY899hguvvhi3HDDDVi2bBmGh4exZ88ePPzww2hpaZkQj3gq5fbbb8czzzyDd73rXfjEJz4hG/D8+fPTjK1s8uUvfxkPPvggNmzYgFtuuQUejwf33Xcfmpub8cc//vGkVZF/97vfDZPJhEsuuUQ2pLvvvhsFBQVTYnuYan4qXX3f/OY38be//Q1nn302PvnJT8JoNOKuu+5CPB5Pq3O3ePFiGAwGfPe730UoFILZbJZ6TB/96Efx8Y9/HO9973txwQUXYNeuXXj66adP+ny45JJLcO655+KrX/0qWlpasGjRIjzzzDP485//jFtvvTUt8P945a3qUY/Hg7PPPhsf/vCH0dvbix/96Eeoqak5arC21WrFvHnz8Ic//AGzZ8+Gx+PB/PnzMX/+fHzkIx/BD37wA6xfvx433ngj+vr68Itf/AJ1dXXHTFBYtmwZAOCrX/0qPvCBDyAnJweXXHLJW56PqixZsgTz58/HQw89hLlz52Lp0qVT+t6J9vPNN9+Mn/70p7j66qvxmc98BsXFxXjggQekEOnx7u9TnfdvVS666CL84Ac/wIUXXohrrrkGfX19+NnPfoaamhrs3r37pD0nU04Zo1VeXo5NmzZh7ty5+Na3voUf/ehHuP766yUz6Hgrw55//vl46qmn4PV68fWvfx3f//73ceaZZ+LVV189qUGWN9xwA771rW9h165duOWWW/D000/j/vvvx/Lly4/5XQKx4uLik9ae45Evf/nLsjHdcccd+PznP4/HHnsM7373u9OyQ94OMRgMeOKJJ/D+978f9913H7761a+ipKREGK1jjf+vfvUr3HHHHdi6dStuvfVWPP/887jtttvw+9///qS283e/+x3Wr1+Pn/3sZ7jtttuQk5MzJRYG+MeOt81mw4svvogvfOELeOGFF/CZz3wG3/nOd9DQ0IA77rhDgkPfLlm2bBn++te/Ii8vD1/72tfw61//Gv/+7/+O884775hjXVhYiM2bN+OCCy7AnXfeidtuuw0mkwmPP/74hBpab0Vqa2vx8MMPQ6fT4fOf/zx+8Ytf4GMf+xg+85nPTOn7Q0NDKCoqOmntySZ1dXV4+eWXMX/+fHz729/GHXfcIW54tZZQUVERfvGLX6Cvrw833ngjrr76asnOvOmmm/ClL30JL730Ej73uc+hubkZf/vb3056Bpper8djjz2GW2+9FX/5y19w6623Yv/+/fje974n7POJyMlYV1/5yldw0UUX4dvf/jZ+/OMf47zzzsNzzz03ITknU371q1+htLQU//Zv/4arr75aClnOnTsXv/nNbxAKhfDZz34Wjz32GH77299OCdCsWLEC//Ef/4Fdu3bhhhtuwNVXX43+/v63PB8zhbWiphoE/1b62eFw4Pnnn8e6devw4x//GN/85jexevVqfO1rXwNw/Pv7VOf9W5V169bh17/+NXp6enDrrbfiwQcfxHe/+92TqmeyypQLQWjj9TDKy8u1/v5+LRAIHM9XRT7zmc9oFotFGxsbO6Hvv5PliSee0HQ6nbZ79+5/dFPesfKnP/1JA6C98sor/+imvGV53/vep61YseIf3Yx3tFx22WVaTU3NP7oZb1nC4bBmNBq1n/70p//opvzLy1vRo6yj9dBDD52Clr2z5Uc/+pGm0+nSagceTU7FfvXDH/5QA6B1dHSctHu+UyQej2v9/f3anXfeeerraLW3t8Pn86XVvJlMYrFY2u+Dg4P47W9/i7PPPvtty1x7O2XTpk34wAc+gAULFvyjm/KOkMzxTyaTuPPOO+FyuaZMbb9TRdM0vPDCC/jmN7/5j27KO0Yyx7uhoQFPPvnkWzqg950iL730EkpLS0+oTtC0HJ9M69HjF03T8Otf/xpr167NWkYkm7zVfs5c7yMjI7jrrrswa9YslJaWntA938ny5JNPwufz4dOf/vRxf1enaVMPONi/f78EuzocDinsOJksXrwY55xzDubOnYve3l78+te/RldXF5577jk58mZa/nXlox/9KGKxGFatWoV4PI5HHnkEmzdvxre+9S3cdttt/+jmTctJluLiYjk3srW1FT//+c8Rj8exY8eOCXWNpmVaToW88MILOPfcc/HQQw/hyiuv/Ec355TL8PAwHnvsMWzatAl33303/vznP79tYSIbNmxARUUFFi9ejFAohPvvvx/79u3DAw88gGuuueZtacPbKf39/di1a5f8vnLlygk15CaVU8SyaZqmabfddps2a9YszWq1ajabTTv77LO1v/3tb6fykdPyDpIHHnhAW7p0qeZyuTSTyaTNmzcv7biRafnXkhtuuEGrrKzUzGaz5nK5tPXr12vbtm37RzdrWk4jOd1chzxiy+12a1/5ylfe1mf/8Ic/1Orq6jS73a5ZLBZt6dKlaUeHTcsROS5G6+2Un/3sZ/je976Hnp4eLFq0CHfeeSfOOOOMf3SzpmVapmVapmVapmVapiynLOvwrcgf/vAHfPazn8U3vvENbN++HYsWLcL69esnHCsyLdMyLdMyLdMyLdPyTpZ3JKO1cuVKrFixQgrhpVIplJeX49Of/jS+/OUv/4NbNy3TMi3TMi3TMi3TMjV5x511mEgksG3btrRgab1ej/PPPz9rdfZ4PI54PC6/p1Ip+P1+eL3e4y6aNi3TMi3TMi3TMi3/GNE0DZFIBCUlJSetUPE7Qd5xQGtgYADJZBKFhYVp1wsLC+UMLVVY3GxapmVapmVapmVa/vmlvb0dZWVl/+hmnDR5xwGt45XbbrsNn/3sZ+X3UCg05Toier1eWC8t43DMbKJ6WXU6HaB8XAfdpCe8q9czn5Ht81P15k5ozzE+q75r5snw2WTZkjoMDgbR0tY5pfZM1haj0Yjc3FwsXLgQxcXFCAaDiEaj0DQNsVgMgUAAqVQKY2Nj0DQNBoMBBoMBdrsder0eer0eOTk5GB0dxfDwMIaHh+W8KqvVCrfbDZ1Oh1AohGg0Cr1eL/fR6XSw2Wwwm80wmUwYHR2Fy+WCx+PBggULMDY2hkceeQT19fVyPuDJ9qYbDQZcfOE5+NsLWybtd7XPso1ltr/r9XqYTCZ4vV7U1NRg/vz5mDt3Lmpra2Gz2XD48GE88cQT2Lx5M0KhEGbMmIGFCxciLy9P3jGZTMJoNGJ0dBT19fUwmUz4t3/7NzidTtx111148cUX4fV64XQ65bkWiwUGgwGlpaUoLCzEwoULYbVa0d3djWAwiEQigZ6eHuj1eqRSKYTDYRQUFGD+/PmYM2cOrFarXG9oaMC2bduwdetW1NfXIxKJyDlk2c7mm11dgXg8jv2HJh7wezzCtc+5kkqlZGw45zRNk3Wj0+lgNBphtVrhcDiQl5cHu90Oq9UKi8UCTdMwOjqK0dFRGAwGGI1GGI1GGAwGJJNJxGIxjIyMYHh4GOFwGNFoFCMjI0gmkxgZGZFxVfWEwWCQtmXOyamu4aPJZe9Zh2df3ILR0eznYk6mX9gvKuOQ2T61zzweDwoLC5GbmwudTofR0VF0d3ejp6cH8Xg87b3z8/Nx5pln4pJLLkFfXx9efvlltLe3o6CgAKtWrcKMGTOwc+dOPPLII+jt7Z3SWlV1n/qsyXR15t8y50KmVJaXINflwJs79h6zLeozgMnarj5DA/6+t2SKXq9HXl4evF4vDh8+jGQy+RZ0V+b3Mtsw8TrHWH3m+L+z7WlTa8Vl71mHPz/5/NTLJvyTyDsOaOXn58NgMKC3tzftem9vb9bjL8xmc9op3FMVKtls11XJXKTpH+aHjky6yb6fuYAnA0YTFBgnrZb+GQDHTa2yHVNV0EaD4biekfn+3MzKyspw6aWXQq/Xo62tDTqdDlarFR0dHQiFQsjJyYHD4YDNZkNOTg5ycnJkkyL4MplMSKVSCAQCyMvLg81mQzKZhF6vh9VqRU5ODgoLCxGNRjE2NgaTyQS9Xo+xsTEMDw9jYGAAJpMJ+fn5sFgs8Pv9ePbZZzFv3jx8/OMfx2OPPYZXXnkF8Xg8TbGeDNCl0+mQk2Oc0D9HA1SZf8vs15ycHOTl5WH27NlYs2YNFi5ciLKyMphMJhw4cADPPfcctm3bhpGREQwNDQlIbW1tRXNzM1KpFOLxOMbGxpBMJgWUut1uPPnkk3C5XBgZGYHX60Vubi4sFov0s8ViQSKRQCwWQ2trK6xWK5YsWYLVq1dD0zR0d3eju7sbAwMDKC4uht1uR2dnJ7Zu3YrXXnsNCxYswDnnnIOysjKUlZVhwYIFWLRoEZ599lm89tpr6O7uljmauSnq/w7CT1QIojjG7FvD3+e6ujY4900mE3JzczF79mxUV1ejpqYGFRUVArLMZjPsdjvGxsYQDAbhcDgEdGmahpGREfj9fpSWliIWi6GzsxOtra1obGxEa2sr2tvbMTo6KmBP0zQkk8m0ttCw0yF9Hpzo5so5KffO+FsmqODvmfpJ/Zuq44xGIwoKCjB79myUlZXBaDQKsNTpdJgxYwZCoRB6enowNDSEeDyOgoICXHDBBfD7/dDr9RgYGEBLSwuGhoYQi8WQm5uLZcuW4eyzz0Y8HkdDQwP27duHUCiU1g4aICaTCZqmYWhoKCtozwRd2fS3+tnJfjcY9DAex5w88tXJ9oDM52T/XCqVwuDgoBiqRz6fCcrTAeTRDP9JWpy1bUcDqtlkKvN0sjn5zy7vOKBlMpmwbNkyPPfcc9i4cSOA8Qn13HPP4VOf+tQpe+5UBzbr5/5+KZM1yrZwp6LUKNqEBTP575lKMfPapGDxJIi6aanP0+v1KCoqwkc+8hGYzWbs3LkT/f39iEajCIVCGBsbQ35+PtxuN8xmszABavtzcnKkzalUSpgVk8kkoIj/m0ymtD5nG1wuFywWC3p6etDe3g5N05CTkwOTyYQdO3ZA0zRcfvnlsFgsePbZZzE6Opr2Hm+VOTgeURVhtjEzGAywWCyoqanBqlWr8K53vQt1dXWIx+NoamrCs88+i4aGBpSXl2PJkiV45ZVXkEgkBKgODw8DAJxOJ+x2u7xfTk6OANd9+/bBaDQiGo0CGGckAWBsbAwGgwGjo6Mwm81IpVIwGAzYt28f2tvbcdNNNyE/Px8zZsxAUVERBgcH0dHRgc7OTmHTWlpasGPHDvzv//4vzjnnHNTW1qKwsBBnn302ioqK4HK5sGnTJnR0dAj4UEU3yaYzVdHr9QLUCGjUfub8MxgMcLlcyMvLw6xZs7B06VLU1tbCbDZjdHRU+qStrQ3d3d2IRqNIpVKIRqNwuVwYHByUzT4Wi8Hv9+OCCy5AaWkpzj33XLhcLgwMDGDv3r149dVXsWvXLnR2diIcDkvMafrmeeTdVSZuMsZrqqLDRL0hf8uimyZjVvh9gvXy8nKUlJTA7XZjZGQE0WgUwWAQQ0NDYhQ5nU7MnDlTDCqDwYDOzk7odDrcf//96Ovrg9PphNlshs/nQ1tbG+69914kEgkAwKxZs5CXl4eRkRFhZUdGRoTFttlsMBgMGBgYwOHDh9Hb2yvgNxMwHk1PH9XYxtTZmiN9Ovnfxn8C6WBpnNXKeKr8K5UiiNRl/OR90+f9+HeOzJnxeTbV/e/oe9K/GD46afKOA1oA8NnPfhbXX389li9fjjPOOAM/+tGPMDw8jA9/+MMn7RnHAziyuQSPpYCyL8hjuyezUdeT3fdoyvVoz8lG+Z6oTMbW6fV6eL1eXHvttdDpdNixYweampoQCoUQi8VgNpths9ngdDrhcDiEUeEmRyaLIIr3tVgsGB0dRSKRECaC3wUgTBYVCe/pcrmQTCYRCoWQSCSQSCQQDofF3ZhKpfDe974Xer0ezz77rBwvcTL7KlOONh9Uxc7+NBgM8Hq9WLRoEd797ndj5cqVsFqt2L9/P+6//34MDg5i4cKFuOyyy1BfX489e/bAZrNhzpw5yMnJgdVqndBfwBElbDSOs27JZBLJZBI5OTkCqAg+yRJQaRuNRthsNhQUFMBisQg75nA44HQ6UVJSglgsJptgUVERzjnnHLz55pu4//77UVdXhw0bNsDr9aK2thbXXXcdPB4PnnjiCRw+fDgNbGmaNtX9IGtfqnOJAEUFLQT2DocDBQUFmDlzJmbNmoW5c+fC7Xajq6sLoVAIjY2NaG9vR3d3N8LhsMxLu92O3Nxc2Gw2YQwjkQgSiQQikQhef/112O12VFVVYenSpVi+fDkWLFiA5cuX49ChQ8JC0gUbi8VkHQCQPme7OQ5vlXnNxnBkusoy2R/2p3rNarVixowZqKioECA6NDSEcDicxr4lk0kkEgkkk0lZzwwjiEajAoxMJhPy8vKQTCbh8/mQk5ODLVu2wGazobS0FL29vbBYLMjJyZF5rWkaxsbGEIvFMDQ0JIbWokWL0NXVhZ6eHvT19SGRSEwAsplGTua6z9RzR4DaCYLcjOdm/PVY3570LzQ63W438vLy4PP5UFJSgry8PBiNRiQSCfj9fsTjcQwPDyMQCKC/v1/mHNk/dU4ca34de187+SEZ/0zyjgRa73//+9Hf34+vf/3r6OnpweLFi/HUU09NCJB/q5LJQFGOBqCORTFnCsGA0+lETk4OYrEYhoeHJ7oFlPsf+QXpcWCTsGHZnpn5fidbjmbh6XQ65Obm4n3vex9yc3Oxbds2dHR0CIXv8Xjks+wHlWUgs5D5LJW16uvrQ05ODvLz89M2TTI03ECBcSbGaDQiLy8PZrMZ8XhcwMTAwAB6enpEgW/YsAE6nQ7PPPMM4vH428JmZRvXzDlmsVhQVlaGtWvX4vzzz0dNTQ38fj8eeughbN++HTNmzMBll12GSCSCJ554AgMDA3C5XMjPzwcAAU5Go1Hcs3Qb6nQ6iZmz2WxpY2s2mwU8qe0bHh6WMTIajSgqKhJWUm03mUMAAj76+/uRn5+P2tpatLS04Kc//SkuuugiLFq0CGVlZXjve98Lm82Gp59+Gvv378fQ0NCRTfEE+pZzIZtBwH+bTCZYrVbY7XasWLECa9asQXFxMSKRCIaGhrBnzx7s3r0b3d3dSCQSqKiowLJlyzBz5kyUlJSgsLBQYrb0er0AVAKNcDiM5uZm9Pb2Yu/evfjtb3+LP/7xj5g3bx5Wr16NpUuX4sYbb8SqVavw2GOP4eDBgwgEAhgcHASANMCZufYyAc/xdVB2PZH5DNUtp7o4CdJLS0tRV1cnem54eBjJZBJ9fX2S4MSxMBqNCAaDGBsbQ1FRkfRbMBiEyWRCd3c3cnJyUFlZCaPRKC7u6upq6Zfa2lphC8lCapqGeDyOkZERJBIJDA0NCeg3m83wer1wuVyora3Fvn370NfXN0EPZ+4Jk+nXEwW3KqifrK8pE2Of0j+bCYQdDgdmzpyJhQsXorKyEm63W8aC/RKLxeB2u+HxeGRv0unGQzlGRkbQ19eHvXv34sCBAwgGgxMM1mxsWba2T0u6vCOBFgB86lOfOqWuQlWOa8Fk8xxOMslocZaUlIjFD4xnVHR1dYkld9RJ+nf/uupGVBfdsZirydr1VmUygAWMx81t3LgRs2fPxuuvv47W1lZxM5WUlMBkMslGBIyX9NDr9RJAPMFdlMGakZlgHEsm+6N+T90M1OB4/t3tdqOpqQn9/f14+eWXMTo6inXr1mFwcBBvvPGGMDlvRblOBR1MNlZ6vR5OpxMLFizA+vXrsXr1algsFrz00kt46qmnYLfbcdVVVyE3NxdPPPEE3njjDYlFYzwVlSgtf7vdjuHhYcRiMaRSKej1esTjcdm0CFAZC8axI3tAsEumy2KxwO12C+BVQQ3HjPezWq0oLy+H3W7H5s2bodfrkUgk8OCDD6KxsREXX3wxfD4fLr30Uni9XnmnYDDIjjr+7leAVqabjS5Tu92Ouro6rFu3DgsXLsTIyAiamprQ0dGB5uZmNDc3IxqNYvHixVixYgXKysrQ3d2NxsZGBAIBPPPMMxgaGhJ2z+v1or29HUNDQ/B4PJg5cyZycnKgaRpmzZqFgoIC9Pf3Y/fu3XjzzTcxZ84crF69GosWLcL73vc+7NixA1u2bAEwnomt1+tlLhoMhrQNkON1Ii7EbK7YTEZnsrHU6/UoKSnBqlWrUFxcjHA4jMrKSnR0dCAej4u7kLFZqps2HA6La6+kpAQOhwMLFy7Eli1bJHHA5/NhcHAQRqNRWG8mzTChoqioCHq9HqFQCENDQwgEAhgZGZmgX7j+6SY/99xzsX//fhw8eFAC8vlemUb3ZAB9MvBxzD7Pos8y+z+bGzfz3xyDgoICnHnmmTjrrLNQVlYmzH0gEEBbWxsGBwcRi8Vw+PBhxGIxmEwmAcSlpaVwOBxwu93Izc3FvHnzsGLFCoRCIWzevBkvv/wyBgcHZW6dsIv6NAdi71ig9XbIVAY/22c0aGnxEpOJXq9Hfn4+fD4fxsbGEA6HhZkwGAxoa2sT5TkZTXssa+d4ghpVF9xbcYdNVDRI25yXLl2K+fPno7GxEXv37oXZbMbw8DB8Ph9sNhtMJlOaq4qKWN3k1UWttjdzsTPwnVZtNstcdSuQmeEGZTAYUFFRgdbWVgQCAezatQulpaW49tprEQgEcOjQoTTG7IT67HhJhr+3Ta/Xo7CwECtXrsTGjRsxZ84cdHZ24tFHH0VDQwOWLFmC2bNny6bc1tYGr9cLr9crYJbgnn1LwDE2NoaRkRFomiZKd2xsTFw5BLFq39FqHhsbS8uuAyCAjJtpNqtdHbu8vDxccMEF2L59O15++WUsXboUu3fvRiAQwNVXX43c3FysXr1aAPgbb7yBwcHB44rRUsc4c/6o1nxJSQnWr1+Pyy+/XGLO/H4/Nm/ejMbGRoRCIbjdbpSUlECn02Hr1q14/vnnMTQ0hIqKChQVFcHn8yEvLw8OhwMvv/wyHnroIXz4wx9GUVERxsbGEIlE4Pf7JaEgFArBbDajpKQEAFBfX48DBw5g8eLFmD9/PtauXYs1a9bg97//PV5//XV0d3fLuGUCLQBpTOLxsrCTMaqZf1PnvslkwpIlS3DxxRdLFmFdXR0ikQhisRji8TgGBwfFLa8CQgCSiUlWyWKxwOl0Ij8/H01NTbBYLMjNzcXg4CAMBgNsNhsikQisVivMZjMSiQSWL18OvV6P5uZmxONx+P3+tGdy7thsNrhcLrjdblitVthsNoyOjuJd73oXfD4ftmzZgqGhobT3z6aP1QzV9P6Y+gLPZrBlC0vJNi7qPRiace6552L16tUoKipCJBLB4cOH0dLSgs7OTukLumkBIBqNimfBZDKhoaFBEpFsNhvy8/NRXl6OmpoaXHLJJTjzzDPxpz/9CTt27EA8Hk97/0yZjP1T/z9d3YenLdDKZiFMRulm/lu9lg0MUCwWi4AsnU6HRCKBkZEROBwOVFZWQqfTCdhSLSkqCIvFIsBE0zQJKh0ZGZHYjckU4bGA2YnIRAZtouVbXl6OSy65BHv37sW2bduQSCQQj8clHZ4xVKpLh4Ha7IPM7C9g3HWiMhOapkmQu7qQM9urvj83V3Wz1ev1cDgcKC4uRl9fHwYHB/H000/jkksuwSWXXIL+/v7xDf4UKInJ5hTdKyUlJVi3bh0uvfRSlJaW4vXXX8dDDz0Eu92OBQsWSGbgX/7yF0QiEVgsFng8HgGeOp1O3NS8L0szqLE/Y2NjMJvN0Ol0iEQiMh5qnyUSiQkbLYO2uUmq7l91nDKF9zWbzVi6dClsNhs2bdqEBQsWoLGxEXfffTeuu+46FBQU4JxzzoHdbsfo6CjeeOMN6PRTM45UsKqCBmaiUkpLS3HdddfhoosuwuHDh3HgwAHs3LkTO3bsQDQahd1ux9KlS1FaWiprMScnB8XFxaitrUUwGIROp8Pg4CDC4TB6e3uxcOFC9Pf3Y+XKlXjllVcQDodRVVWFK664Am+++SY6OjpgsVjg9XphsVjQ1NQEk8mE+vp6bN68GT09PRgZGcG73/1ufOELX8Cjjz6Khx56SLJ0CWZVw0l9v+NhHjSkl7BQ9QSvcZPm3+12O84//3xceOGFGBwcRFdXF0wmE1544QU0NzdLjBZd05mgQjXSjEYjqqqqJIvT4XAAAGw2GzRNk8D3nJwc9Pf3AwCqq6vh8XhQWVmJnp4eGAwG9PT0oLe3VxI4AMh3+dPj8cBgMEjsUjAYxOrVq5Gfn49nnnkGgUBgQv+oeihTJx15jyl1dRrImkxv83OTfV+nG48jXLx4MS6++GLU1NQgEolg586d2LdvH4LBIIaHh6FpmmTL8nmpVArt7e1i2ALj3odIJIJwOAyr1Srldg4fPozZs2ejpqYGN954I15++WU89thjMt+ztX2ydp+u4EqV0xZoZZOjsUKZoCrbdfXvBoMBPp9PUr2HhoZkY6Syr6iogM1mw8DAgGxaDGJ0uVzIyckR9w3damNjYwiFQhgcHERvby8ikchRraNs7MJb6Z90Gj39nlarFRdccAHC4TAOHjyIZDIpMVF5eXnyHtwAuZnTfaNuHpNlMpKVstvtsFgsaUxWNpo/s+18hhrvotfr4Xa7BWREIhH89a9/xdq1a3H22Wfjb3/7G2KxWFZG7UT68Fj9azabMWPGDLznPe/BhRdeCJvNht/85jd4/fXXUVpaCrvdjk2bNqGyshJr1qyBw+FANBqF2WxGTk4OAEgcGucV6zmlUikBvKz/xDnG7C2CUX6P7lwCfrpf7Ha7KOx4PC7ZiNn6P5tC5mZQV1eH/Px8vPTSS/D5fOjt7cWdd96Jm266CZWVlVi6dCni8fh4UHlwANG/Z04eq5+zAQ+2XdM0FBYW4pprrsGVV16J3bt34+GHH8b+/fsRi8Wg0+mwYMECrFmzBna7XQwcp9OJiooKJJNJyTgMhUKS9cYEgrPPPltKPwwODiKRSEjQMZMCuOFVVlZi9erV6O/vx5/+9CccPHgQv/3tb7F3717ceOONuPbaa1FYWIj7779fYmfUcgXq+s7MRjyWZHP3cp2pAJ2ftdvtuPjii7F27VoEg0Hs27cP0WhUYp48Hg+ampqwcOFC2O12RCIRAfuZgM5oNKKwsBCVlZXi4g4EAigvL0dXV5fUhmKmp16vh8fjQUtLC2pra8UQLSkpQVdXl5QEUhk0Bt5HIhEMDAzA5/PJHPB4PAgGg1i+fDncbjceeeSRrOfpTmbAHa9Onar3IdNboOpFi8WCtWvX4pJLLoHdbseBAwewb98+BAIBSRBKJpPivqbbMBwOpyW52O12eDwecfHq9XqMjIygra1NYu6SySQ6Ozsxa9YsrFq1CkVFRfj973+Ptra2tLmVuRdM9nrTjNZpLscCI5OxQ5kLTr0Psz2A8c0qFovB6/XCYDBgaGgIoVBIrOOKigrZ7Gw2G8bGxjA0NIRIJCJKg351u92OwsJCFBYWYt68eejr68OBAwcQCoUm0NKZ7VP/lo01OpqoGVtqP6gLZ/bs2Vi4cCGefPJJaJqG0tJSdHZ2ori4WECjeh+V2eL/BAT8O5+hAiq9Xg+73S6sDduSLatMFdWFpL4LwV9ZWRl6e3tRWFiIrq4u1NfXY+nSpTh8+DD27t07QQEen+iy9nnm+5vNZtTV1eHiiy/GeeedB03TcPfdd+PQoUOYOXMmenp6sGvXLmiahuHhYTz99NMoLy9Hfn6+uGAYx5LN8tS08awstS3sX5PJhGQyKUwEDQKTyQSXy5W24Wna+FEZjLPp6OhAUVFRGnjLnCt8X/5UA6qLiorwnve8B5s2bcLo6Cj6+/vxk5/8BB/96EcxZ84cLFu2DDqdDs/89TH0909+uDznB+tiaZqWlkVFV2deXh6uu+46YZnuueceDAwMoLKyEsFgEFVVVairqxN3FBMjyAQODw/D4XBgZGQk7e+lpaUwmUw477zzcOjQIXkeMxUBIDc3F4WFhYjH4wgEAohEIigoKMDSpUthNBrh9/vx8MMP480330RfXx+uv/56XHnllTAYDLjrrrsE3NC1mzkfVSPlWGs8GxOcCbAoZrMZF1xwAdauXYuuri7s2rULBQUFOHToEHp7e7FmzRosX74cv//972E0GlFZWQkAUrdNBVk0RJcsWQKbzYZAIICcnBy8+eabAIAZM2ZIPS2r1SqM6gUXXICXXnopLQ7M6/Wiuroazc3NacH6qj6kkcEj2hobGzFz5kx4PB50d3djyZIlMJvN+MMf/pBWCFXV6ZmM1FSBkzoumbo423cnMzAJst7znvdgdHQUTzzxBBKJBAYHB9Hf34+SkhKUlZVB0zR0dHRg9+7dkkjCWmPMtmR9wWQyiUgkAp1uPIGJ67+5uRmBQADFxcUS8zV37lzcfPPN+N3vfof9+/dnCZQHgMkJiZNh7P+zyr/OYULHKermpk74zNpV2YRZRfRr22w2sch8Ph+qq6sxY8YMaJqG/v5+SW2n5ZhKpdIyb0ZGRuByuVBUVASDwSDZRtFoNC1bhNYhrbiSkhLMmjULF154IWbNmjUhBivbO6s/T6S/MgOdgXHFYDabsWrVKkSjUXR1dcHn8wkd7XA45HuZ9VwYb0ImJTMLiN9hphwZGzXVXQVZ2dxGansJ+FRgR7bQYrFIZmtNTQ0aGxthNBpx7rnnwul0HrdinaQj5X91PHQ6HZxOJ5YuXYprrrkGF110Efr7+/Gd73wH9fX1mD17Npqbm7F//37J3DQajRLrwqrlOp1O5gs3YgYG63TjVbkZa0HA5XA4pF4Zr6tjy6xZMlqMwUkkEhgdHUUsFsNrr72GV199VaqcqwU4AWQdj0wgGAwG4fV6UV5ejqKiIlitVvz85z9HQ0MDXC4XFixYgLlz503SrUfGXHVhsg1qO2w2Gy6//HJce+21OHToEP73f/8X/f39WLx4MaLRKIqKiiQgeGhoCMPDwwI6VdDGvopEIohEImkZeADQ2tqKgYEBeedIJJLm2mJVeIvFApPJhG3btuHw4cNIJBK4/PLLUVtbi1AohJ/85Ce4++67sWzZMnzgAx9AYWGhxHrReFAZ2kyweTRR9/RsLKQ655ctW4a1a9diZGQEBw8eRHFxMTo6OlBfXy+bdF9fH0pLSxEMBlFbW4tly5Zh3rx5wqKaTCYUFBRgwYIFOO+881BXVydB83/7299w8OBB7Nu3D/v370/TlQQIW7duhcViQTgcRkdHB/r6+mA2mzF37lxhxghu1Yxmu90Op9MJi8WCw4cPY/fu3Xj88cdRX1+PoaEhNDc3o7a2Fhs3bpRzciebp+qc+/vVo/bxZH2q/j6ZC05l/5YvX47zzz8fer0eb775JoaGhqTMSF5eHnJzc9HZ2YmDBw+ipaUF7e3t6OzsFJcza7QxNGV4eBgjIyOwWq3weDySqTk0NCRGX1dXF9ra2tDe3o79+/cjJycHH/zgB7Fs2bIJe8F4n018Z3VOnq4yzWj9XcRqmZC+emSx5eTkIDc3F16vF3a7XQKF1UKZZK+YksyJPTY2hkQiAYfDAZfLhZ6eHphMJqGzbTabpN7TFaRS7lScmjZ+1AdjcpxOJ0ZGRnDeeefB5/Nh69atEpOQaYmp78p3mgo7o95DzfKjItTpdPB6vaiqqsKBAweEmRseHkZ5eXlalXZ+l5thZpXvTJdhGghWGC2CL2YfqVaruqlntpl/z/aOzG7q7++H2WxGNBpFc3Mz1qxZg5dffhm7d+9OAyHHKzodxrNIM54LjLMcZ555Jq644gosXLgQDQ0N+J//+R+Mjo6ipKQEu3fvRk9PD8xmM1wulzB/ZEDV9zabzRgbG5OyFszq5HglEgm43W44nc60uaKCYQIKq9UqiQq8Jz9L1otgr76+HjU1NZgzZ46AG/6dMWDZgDQARCIRPPXUU+jt7UVVVRV0Oh1cLhcikQh+/etf4ytf+Qq8Xi/q5tcdpX/TY/iyMQNWqxWrV6/GBz/4QRw4cAD33nsv2tracNZZZ6GjowOlpaWorKyU4qFjY2PiVk0kErKJc1Pq6+uDpmkoKytDcXExvF4vKisrMTQ0JEHIdCcSmJKxYx/RNdvU1AS/3w+n04ni4mJcccUVeOqpp9Df34+7774biUQCH/vYx9DV1YUHHngAfr9/QgV5mWgKcw1MzmyNY/6JzLcqmqahpKQE55xzDgDg9ddfl9Ihf/7znxGNRpFMJrF582a88cYbKCwsRCKRwIIFC2AymbBhwwb09fVJMpDH44HH45Hs35GREQQCAWzbti0thECNIySgTyQS2L59O2KxGAoKClBRUQGdbjyTeM2aNTAYDGhtbZVq8zQSampqkJeXh1AoBL1ej6uvvhqPP/44Nm/ejGXLlklIx5w5c3D++efjL3/5ixT3nQxwHW9IBud7+lgcO1zFYDCgpqYG5557LiwWixR+bmlpERA2MDAAv98v64yASS3FAYyzkhUVFejs7JTirirLb7fbxZgaGRlBbW0tent70djYKJ+dPXs23vve92JsbAw7d+48Kmt6MsIt/hXktARa2disTLZCdbWQbSgrK5MaUMy84iRjeixrZbW3t6O3t1cmrk6nQyAQSKttwvglxnfwLDAAUvuElDnBgsPhgNfrlWc4nU643W6EQiGsXLkSFotFmIXJ3pHvdTyTX+2PzL7U6/WorKyUgFWLxYK2tjapS8TNT918MwN5M++Z+Sz2AX+yOCZBhDqu2dqXKSqgU8dep9NJLR+TyYTm5masW7cO8+bNw549e6bcX+mN4DtMfE9S9mvWrMG1116LmpoavP7667jrrrskvqq/vx8GgwHl5eWiWDn/aHnS4idg1OvHU969Xq8wdgBkTgHj4C6RSMButyMUConRwEry/J7Vak1zwXADI4ij0RGNRrFnzx6Ul5cLiCPQIOgDIEwb104qlcKhQ4fQ0NAAANi9ezfmzp0rYLexsRGPPPIIrr32Wjj/HiydbYwzGZxMlsBgMGDp0qW45ZZb0NPTg/vuuw+NjY2Ix+PYvn07zjjjDMycOVPS2QmuGLcXj8dht9thNpsxMjKCgYEBiY2x2+0SLM8xIPNCMMW5yXgt9rfJZJJAbgKBYDCIgoICnH/++Xj11VdhMpnw+OOPY8GCBfjABz6Aw4cPY9OmTXL/TBdiNvCUbb1nVobPxuTk5OTgjDPOgNfrRWNjI3JycjBjxgxxKVssFjgcDphMJoRCIXR3d2NsbAybNm3CypUr0d3djXnz5skxPPF4HFarVeY33a88w5R9QqDFcg4mkwmtra3ShwcOHIDP55N14Ha7sXbtWhw+fBiHDx8Wpn3OnDlSHuLxxx/HsmXLUFhYiHPOOQePPfYY9u3bh9zcXLS0tMBoNGLBggVobW3F1q1bJ4QgTPCAaFMv73Asfcs/q+BXr9ejvLwcGzZsQGFhIerr69Ha2ipZheoJBpTh4WE4nU5xZ1NPapoGh8OB3NxcpFIp9PX1CUjmfsHkJOry4uJizJw5E01NTRKiQrB26aWXIhwO4/Dhw1nfL9tecbrKaQm0KNlcQZnKisUY8/PzZfOIRqMSq6LT6RCPx6UWi8fjgV6vFxeN1WqVM98CgYAc3+HxeCRtvLKyEuXl5bL5WK1WRKNRcQPRxcPq05qmSVXkQCAgFZZ7enqwZMkSJJNJbNmyRRRZ5nvx3abCaKmLXv0e70mmr6qqSgCg2WxGR0eHZFaqLFYmwMq0CrnYM5kojoXKXh3rPVRrnhabuuGoz1F/N5lMiEajAmC7urowY8YMWK1WqX12XKId6UddxrNtNhsWLlyIK664AtXV1XjllVfwq1/9Cr29vcJuWq1WSWvnuxAQsZaV+i4ul0sAGdtKFxP7hLWGyAjSDcmgdwoBg6Zp4ha0WCyIRCJy/3A4LBviwYMH4fV6sXbtWgHX8XhcXJOqi4s/BwcH8eqrr0pxSqPRiM7OTmzcuBFOpxMDAwN4/vnnsXDhQtgsE8+UU5ks9d4qAGEBzPe///3Q6/V49NFHcfDgQYyNjcHhcODiiy+G1+tFIBAQ9pn9SXAaj8fF3R8KhTA8PCwsFw+MTiQS6OzslGKanHfsa+qK7u5ueL1eAYj9/f0Ih8OiX6LRKHp6eqS+l9/vRzQaxU9+8hN87Wtfwwc+8AG0traivr5e2HO1DIe6jtiGbHW2UtrEOJvMNV5WVoa6ujpo2ngwPI29eDyOiooK2dAJbBwOB1paWnDgwAEpNGoymVBVVQUAcDgcwpjk5OQgGAwiGAxiwYIFcnQR4wHJ3BqNRmEQufZ5OD0PnNc0TWpBLViwQJJsqIt37tyJ1tZWOXqLFev7+vrQ39+P2tpaNDc3o66uDqtWrUJHR4eU1ZgMbOlIU09BMvt1/JqiJuTakbmcl5eH888/H9XV1ejq6sKhQ4fQ3t4ubaDbube3V+Y7jaOysjIxMJqamqTPVbA0OjqKaDQqQfN0j7MET3t7O8bGxuByuTA2NoaWlhYpveFyuXDZZZfhnnvukYxNdS5lM3pPVzktnaaTsSiZ161WK2bOnInS0lK43W6xcunWCwaDonAZtGm325Gfn4+ZM2dKxmFJSQmWLVuGGTNmiPL1er0oKysTNqGvr09S+j0ej8RslZSUoLS0VH4yhsjn82HFihXwer3w+/2IxWLIyclBR0cH5s+fj7q6uglMj7ohZXvfo8lk8UncpOfOnYuenh6JOeMGogZc09WaGatztPgqNe5Gjafi5qG+Y7b7qNeZap4JplWwptbf0ev1UrgyPz8fJSUlWeO+jlf4rJycHNTU1OC6665DXV0dXnrpJfz85z9HZ2cnjEaj1MQiC0rLndY92RMAaX09NjYmiROZtcnYF0ajEfn5+aJQc3NzhcUiKOPxSGTQVGvX4XBI4GwsFpOg+LGxMWzfvh0NDQ1S3JQghf+PjIxIra5AIIAXX3wR/f390LQjJTv0+vFCjMuWLUNubi4MBgMefvhh+Af9af2ojjnfUXWX0L2Zl5eHm2++GbNmzcKDDz6I119/XfrhrLPOgk6nQ0dHBwYHBxEKheSoKB7lQlBLl00gEBDjqqCgQNyLzKyli9lgMCAvL0+MKZvNhng8Lms+Eomgp6dHCkuyCj8ZnUQiAZ/Phzlz5mDevHno6OjA3XffjYKCAlx22WVwOp1p61ONSeN852ablfFFupGjrg91bdPI5GHmev14kVuWD2CWK11XPCWgqakJBw8exO7du6XyO9cVzz4Mh8NSHJb3ZMkB6o1wOIyuri54PB45o9NsNkt71eQA1o/T6cZL6vT392Pz5s146aWXMDIygv3796O5uRnBYFAA2ujoKIqLi2GxWHDo0CG4XC6sWLFC3odgazLPx1TXvfrvydh3XrdYLFi5ciVmzZqFoaEh9Pb2orOzU+YW35P17xgn7HQ607K7AcDr9Up8K5BeQsjlckmJDRryNNJZI62vr0/u39rair1798Lv96OkpARnnXWWsNWZxk7mu56uYOu0ZrSAdOtEReE8Iy43N1cCBZnZx7P21CBuMg7AuBtxaGgI5eXlUkNnbGxMCvDl5eVhaGhIqPFoNIrDhw+jq6sLZ5xxhliNBoMBhYWFMJvNYqXYbDbU19dD08ZPpc/Pz0deXh727NkDp9OJ3Nxc9Pf3o66uDoFAQKqynwzJZqXo9XoUFxejuLgYe/bsgaZpCIVCkv2W+b1jWTmZwIkbj8pS0L2Q7f6q5Z7JWE0m6obN71osFgwODiInJwc9PT2YNWsWFixYgJaWFmF2TiTuQAVGZWVl+MAHPoBly5Zh8+bNuOeee6QGDmNYyCbR3acec6MeU8Q2M46KtafU55IdY9wR62dxg/J6vWn12chsqZuvWkWbNY/I5ACQjNlXXnkFHo9HDopmGxhPFo1GcfDgQbz22mtynBLHKxQKobS0VMAIAVtHRwdefOnFtHfKBBBqTAjnitVqxSWXXILa2lo8/vjjeOWVV5BKpRAMBjFjxgxs2LABTU1NAszIKPGMRsYLqSCKrkCbzZbGOFosFlRWVsJqtaKrq0sANQ9PTyaT6OjoQDQaFQart7dXdIvP50N+fj48Hg+sVqscdbRw4UK8+uqrqK6uRkNDAx5++GFs3LgRr732Gp5//vk09pbzODPeUY29k3mDiVl0vA/nYU1NjVzjUUWpVEp0WCKRkDnAzNPy8nKp6h6NRtHX14ddu3Zh9erVcLvdsFgswsTxXsFgUA7bHh0dhc/nE8PU7/dj+/btcDgcqKioQF5enpxGoNPpEAqFxDBgUV2/349t27Zh//79chIHj5+hUUEGsaKiQk5UoFuOSU0HDhyQ/uPcmoo3INvaz2bkqfdU9cOcOXNwxhlnwGw2o7GxEd3d3QL+yUBz78ksfM32ctycTqd4UzJrGdJTwNhjkggkFcg009BiodmysjK4XC4sXboUBw4cEBfiRNZuYqb36SanJdDK3PQzJ7zFYsG8efOQm5sLt9uNWCyG3NxcUSqccDqdTgJdrVYr8vLyYLFYEAqF0NvbC4fDgaGhIdjtdrS1taVZR8xEtFqtAMYXQyQSwb59+1BQUACj0Qin04mCggKpqTU6OioFAskKkE4fGhrC4OAgLrroIsTjcYTDYSxbtgyBQCCtyNxkzNSxJBsY5UY3Z84c6PV6tLe3ywbu8XjSAqDV7KhsC05lW7ghq5smP5NKpWCz2eB2uzEwMDDB7UWlwY17suBr9bkA0tqk148fe8PaM6xbtGTJErz66qvo6el5S+CVQacXXnghzj33XOzcuRN33303hoeHMTo6KtlRaskFulHICDJomAkGalIBGQ1W09bpxgPLmTnIgHi6+wDI/dQ6WtyI2Je0nMlmqHFRiUQCxcXFGBwchMVigd/vx+OPP453vetdmDdvHgYHB7F3716Ew2GMjo6ivb1d3BAM3uf4mc1mmEwmPP/88wJKgHHjp+PvJRKyzcvMuU0AXVtbiw0bNuDNN9/EM888I4eKm0wmrFmzBl1dXXA6nZg7dy40TcPu3btx8OBBMaiA9LM4+dNoNEpsFsfLYDDg8OHDcDqdMJvNyMvLkzYajUZh7Jjowrgkvp/D4ZDq/KnUeM2zyspKFBUVIRAIoLOzE9FoVIDspZdein379klJApW9VEsqZDK3ky1/FQwwCJsAXI2VisVi0Ov14m5iXBCPu9Hr9aiurkZpaSkikYi45xoaGrBq1SoAEDDLtcsyAmSqaDjw+Cev1yuGiMViQWNjI/R6vWRoc96YzWYEAgFs2bIFb775pqwdMjL0PrCfPB6PxBLm5uYiHA4jGo1Kpmtra+uEWmDpfXV8ujQbSONcpeTn52PVqlVwOBxob2+XUiBk7pLJpMQEc6wy9XNmuASJAFWPqOuHYI0HwqdSKYTDYXELkrE2Go0IhULw+/3wer0wm80444wzJMBeBY7qmskE+aeTnJZAC8gel0VlOHfuXDmugZk9zJTR6XTiMgEgAbCcnKOjozh06BDi8XhaTSO6XrgwuBFyk9Lr9RgeHpYDWHnYqlpNvaenB5s3b0ZHR4ccwkplz42wpaUFy5cvx44dO2AwGDB79mxs27ZNnj9ZH0xFsikHu92OuXPnCqBj0U8GYavuO1URZAYtqxsBK5EPDw+LQlTFbDajqqpKSl5kliNgTI1akkFtc6YVma0vCE542HJbWxvmzZuHmTNnSmHEzJivY4nqMuSxOt3d3bjrrrsE1BFkEwRxA1DPiQPG551aRBU4klmm0+kkjoIbPgEuAQOPzOG/+Tz1rEm6nhgAzwOmuXFZrVYYDAYxGnp6egSAAEBnZyfefPNNOdbmpZdekto9nCOctwSVqVQKHo8HsVgMBw8elPsxm7ek0Aso80Qdd3UdcyPJzc3Ftddei4GBATz33HMIBoPQtPGsqne/+91wOp1oaWkRQ4EMTOYB3HyGmgDDoq8MGWCQeywWg8fjQVdXV9oh3QR4ZMPV46AImsls2e125OTkwOl0oqqqCnv27MHs2bPR2dmJlpYW+P1+/OUvf8Hll1+Oc845B48++mgak5dtTqprDBkuQ3V+8jMOhwOzZs0SNybjREdHRyXoPZFIyOHDLP9BgD579mxUVFRIFvauXbvQ0tKCqqoqYUn8fr8w4DQui4uLceDAAfT390uCUF5eHubMmYOuri5UV1dLtqfD4UBXV5e02Wq1IhwOY+/evWhpacGMGTPgcrnQ0tIiZ1qqhhmLzHKeM7ic+onhGw0NDVl1xHg/T239T2ac0dDgHLNYLDjjjDNQVlYmLuz6+npZ7wCE8VZLl6gua9UFybmWWeNOdYlSl6ksmMFgQHFxsRhsPCdxZGQEJpMJ7e3tKC0thdPpxIwZMzBnzhzs3r17gq7ie52ubkPgNAVa2RQLMD45S0pKUFVVlRZszSy0WbNmCfXMjYeKmYVIaaExTonsksvlkoXORe52uyW7sKioSFwkubm5ACCMFTezrq4uyYZjDAKzIOPxOGw2m6SkV1ZWYv/+/SgvL0djYyMGBwfT3lUNND+WqEo7k/rOz8+H1+tFU1MTzGYzuru74fP5xHrKBHeqq0e1uPlZvV6P7u5uAR7MqOF9DAYDhoeHsWvXLnE9qACNsRyFhYVwOp0So5P5vpnvwZ8qCGN9GZ1Oh97eXuTl5aGmpgbbtm2T6uEE3lMRtrG4uBjve9/7YLfb8dOf/lQOeCYjqo5RJBIRQKL2kWqRA0g7cJiWvfo5xhfSXciNM53lODImqtuVCpgMD+O6qORZkZobV15eHkwmEwYHB7Fnzx7U19cjkUjI8T/c7BgfQ9cRN5lkMiluWwCSnZtMJuFwOqD/u0FEQMdNksJ2GQwGvOtd70JFRQUefPBBtLa2wuFwCPsyf/58iZPimhwZGRHWbWxsTFyDBJOcG5x3ZB/JRrLAKxNXWLuITIrf7xcWi0YBASvdpnyX/Px86PV67Ny5E01NTYhEIpg7dy76+/vx0ksvIScnB3v27MFFF12ErVu3ytE+rPQ9mZtKnTeT6QOdTidJQJxbqrs+FAqhsbER/f398Pv9E04ZcDqd8Hq9kixgs9mwePFibN++HQcOHMCiRYukHxlMn5ubi7lz5yIYDEr5DLvdDmDcJR0MBmGxWLB371709PRIvJ/f70dubq4YCfX19WhsbMS8efOkaGpOTg4aGhqEOaVr0efzyTjGYjHJoGTGrM1mw+zZs9HW1iZxS9kC449H1O/odDoxYAh45syZgyVLlkDTxsNKWltbEQqFJNaRLCrXKceOiQOZ7CV1rtlsFg8N57GaQEH9ogI3AHJeKg0QGsIDAwNobW1FTU0NrFYrli9fjubmZoRCoax7xvH207+SnJZAC5iIsJkZNH/+fGjaePZKT0+PWFKHDx9GX18f6urqUFRUJN8bHh5GIBDA8PCwuDiobIHxTSIWi6GyslICgOnOodVNKyGRSEjGGI/s4Vl7jCkheNDpxuND7HY7gsGgKLexsTE0NDRg/vz5KCwsFNBFqzMzFuBEhe0vLy/H2NiYuDMNBoOAE3WT5nfUDVyNiaJSILigZU+FxyN8qATUdGSOp9/vR29vL8xms7BZ2Vyl2ZiszPgLto0HTvMcO8baUFlNqR/lIxqsVhs2bNiAxYsX44EHHsCOHTvS3sdgMKS9J5kebqB0GzBOgzXXCJoYfE2FyBgjzkcCI1q/apC9mrVE5orMA9ktlnpQA+/5nVgsJrGFBoNBEkgYG0MXJK3jRCIhgJXzwWw2w+/3SwwU1w83cII6fiebkCnKz8/H+vXrsX//fhw6dEjAXiqVwpw5czA0NCQ16QiyWOeK7Ha2bEnO1Wg0ira2NoTDYRknsssOhwOapsnxL+FwGMB4qY7h4WEJeM8M5Ge/ExCw7APb6Pf7sXDhQrz22msYHh7Gnj17sHjxYlx00UW46667JH4wUzJZ4yN/UKapLt1tWFlZKYVIyR5pmiZngtLwKygoEKOH7B7BSm5urryPy+VCbW0tDh06hNLSUqlrRT2WSCSwY8cOeVcCKYKkffv2IScnR4wouprJsJrNZhw6dAjbt2/HrFmzUFxcLK5Er9eLnp4eYRPJvA0MDKCqqkoOsmb2N+PQGIPqcrmEyZ645k/cM8B4RYJrlp1wu91oaWmRwqwmk0mMbjURhQYtx47rXK1xRwDqdrtlXqleBlXfqcCNc5xzx2AwIBKJCDgzm81oaWlBfn4+CgoKUFVVhblz5+L111+flFE9XeW0BFqZrixgXLHMmDFDrPGhoSGYTCb4fD5o2ng21O7du+FyuTB79uw0VsBgMEjMChUHD6T1eDyStTQ6OgqTySR0NUECa+E4HA7odDqJuaK1xZIJ/B6VPmPA+HeXyyVxHB6PBz6fD319faiqqsLBgwcRi8UATDxL6xi9NaHv1JgTZsQwHV11j6rskPp7tvupFpbKgo2NjQnQ9Pl8aZkzAMT1RBeEw+FAUVGRjA83fSoRZsFx7NQNSHXnAJCyFSwGyJi9yspKSaeeUl/+/U905V588cXYs2cPHn74YYyMjEhJEGbv0UXDzCC+J0E2mSm6relGVOteEaRyE2I71QOkyciqzBI3ILp/1ErotKJVgMMxo7uTzAbvqbrgyArRFckSJjQaCLJoSKRSKYnXoXtnNJEQbJDNaqYyZzkBnU6HV199Vc4EjUajGBoaQl5enrjxWJ6B7DX7Sy2foR5LxPFgDEsymRQXLUExvwNAWCxWkc9kGzg3eX1kZERisfgcskKapqG6uhper1ey9jZt2oRrrrkGNTU1OHDggAA21QhR5/n49fTYokx23+VySZYt5y5w5EzL5uZmdHd3i+uILm5mpbLeWH9/vxQrtdvtKCoqgtfrxd69e7F06VIMDg7KgdxkjDiWBAqMT0okEhgZGUEkEoHVakVhYaHEtJlMJgQCAWzevBkFBQUoLS2VeayWJSEjzndk9f6ysjIEAgEUFhZCpxuPvaVh43A4UFBQIEVmM+fciQr7m2vMZrPhzDPPREVFhSQE1NfXw2KxSPgAAaVa0oVrg+uFek/VrWTxGL+qrpfJQkoy2W0aB6FQSBKzQqGQHMXl8Xgwf/587N+/X478ybzniTCA/wpyWqYBqOwJxWw2Y9asWVJfiPVDDh8+jLa2NnR1dSEcDuP555/HCy+8IEUkaWGYzWahZl0ulwQMtrS0yKZD9wJ/p+VHP7xOp5Nq5AMDA1LnpaSkBJqmiTvS5XLB7XZLPZ6WlhZZZEwz37t3r8RN5Ofnizsym/tgKv2lWsL8t9PpRF5eHvr7+yVIVi2cmFlFXQVUk91br9dLzTKCjNHRUQE7KuvCIpkDAwMwGo2orq5GdXU18vLy0jYvAgi2jfdQaXI1holZZBaLBbW1tSgoKJDYomAwiLlz504AfFPoRVgsVjnO58EHH4Tf74fD4YDb7UZpaalkt5K1YZwO6zex0nNmCQG73Y7c3FzJKmSgu06nk8xAuqnoplCVPAEd5wvZBCpZsiyBQEAUu8rGMtNQZc14jf3Kej3RaBR+v1+OpLJYLMjPz5daUslkUqrRj42NYWBgQFwmCxcuRFl5+RF4oNOlFyJS5pTBYMBFF12E9vZ2KYXBexJs0lpnijsz1kpLS6Wki8q2qbFPBIbsKwIoxlTS1UVgQoBA5pnuGODIWZs0Xmw2G1wul7BtBGmFhYXIz89HZWWl1DUCxs8S7OzsxLp16wRkiyH59/+yzcdsBAPnBUMkhoeHJfORa5FAy2azSUC2y+WC1WpFbm4uCgoKEIvFsH//frz00kt44403cOjQIezatQtvvPEGdDodwuEwIpEIKisrMWPGDIk3UnUDC20SHKugn8VdaZxYrVbs2rVL5vvrr7+OXbt2oaenRw5TpuHK+cgY16amJrS3t8sRSTQ2CIJZdkfN8uU80+kyzxLJLkfzInB+zZ49G0uXLgUADAwMoKurKy2xhbGNarhEtj7LZOzVz/L9yRir+jhzT6TOVBON+D0y6wDQ3d0tuqGsrAzV1dVpTK3K2J6OIAs4TRktAGkTT6fTiXUEALFYTIBOYWEh+vv7EQwGxaojhc0T5GkJswQE6e2DBw+ira1Njniw2+2ycDhxuZkyeycSiWB0dFTqJxmNRgF9VBh0/4RCIXR2doorqa2tTVwdw8PD6OnpwezZszE2Noby8vK0w1LZB8eW7NV+NU1DQUGBuOwAYN68eZIho7oN1YB4PjfTbataWB6PBzk5Oejr60tzefb390v9J51Oh/7+fqkzRGBGUJDpIlVjioAjNYKYMepyuaSWFDdBp9OJWbNmoaGhAdu3bxewzNiVzs5OafOxFIhOp0NVVRXOOussbNmyBdu2bZPgXwJHCl0mVKqpVAqxWAwGg0HOxgPGg6vJSnFzMJlMkiFFFxjdkRS+N+eRmqHJjTSVSgloI7NFd40KWumq5CkIVL6sLccsVM4FghxmV3KcU6nxQqt04QFHgDrHsa+vD1Zz+vFK2SSVSqG4uBjz5s3D7373OzQ2NspJDDwImvfle/NZHo9HyrG0trbC7/cLe0DWmi5VJrIwholgi/FmnGvM0mPQv91uFzcl577qInY6ncjPzxcGiy5Zn8+HQCCAhoYG+S43wi1btuC6667Dk08+iaamJgHIR5uPwHh5B5XNUoGk0+mEXq+XkAD+rb+/H4FAAFVVVbDb7SgpKcHY2JgYfe3t7fJ+NFoIJgKBAFKpFEpKSrB//36sWLECLpcLXq8XoVBI5pfZbBZml2fyqcDAZDKhuroaVVVVku3K0w1aW1tFXzY0NIgOpGuX683tdkusEeshEqjQiCC76vV6ZT1MYOr1J+Y6VMGI2+3GqlWrUFBQgIMHDyKRSEg4hlpFX3UPZrK41LUq087Pcfz4f2YyEueL6iJnlqHKhDJGk+vMaDSit7cXxcXFiMfjkr176NAhYQ4z95zTEWydlkArE2Aw1igWi6WdAs/gX6Ys0z3HeJRdu3YBgARQapqGpqYmGAwGFBUVSTE/MgeMD+Gmz0XsdrslAJcHU1dWVsLpdMpREplWCzdJMhTqkUB0e3R0dCA/Px9WqxUzZszAnj17Jo3hOEpv/f0d0o+QMRgMmDt3bhoTR7qYZxACEBaOlhDjcOiWUd0bqsKw2WwoLCxEKpUS5cz4try8PGFFSkpKxIWmglG1LhP7jsCam6TD4YDH40FeXp4AXjXGgWdRst1UeslkEmVlZVInSXUFTSYGgwErV65Efn4+fvGLX6TVvOKcZAwSXRw6nS7tGBe+m6psVaWrAhoCbgZbq20jmGGml1pglt/n89SjfeLxuGSWAZA4HB4fRaaNCph9pWYWqtlNdLsRXDDdn33KNlHpB4NBxHImMtLZ1vOSJUvQ29uLQ4cOIRwOy5pjQG9ZWRmSyST8fr/EoAFH6pQBkFpkdH0SYOh049ltVVVV2LFjh+iI4eFhObGhr69P3KRdXV1S8b6urg6JRALtfy9TwbpUbrdb+j83NxeFhYVShLK4uBilpaXYtWsX2tvbBZipjGxvby+Gh4dx1llnSb2lTPaa/T7et+OZh5msA/ud8WPM3jUajQgGg3A4HHKQs8ViwdDQEDo7OyU+srCwUNhOl8s14YxLnU6HYDAoDE1rayvcbjeqqqoQDocxNDQkcysej6dl2nHuMGN11qxZKCwsRCwWw8svvyzsIhnD0dFRDA8P480335QYQR4VRKa/paUFeXl5aGtrQ0tLCxYtWpQWa8h54XK5pLi06ooFAC11YvX0OD45OTmYN28e5syZg0gkgt7eXvT390vcG8NN1OfS5a+C7cx1oD5DZfepA9R3UJOGMmNq+R3qbiZz8G/xeBw9PT0oLS2FXq9HRUUFSktL0dDQILoj2zo9neS0dB1mTjKbzQafzyfBxSznwNgD+qbV+iPcyM1mM+x2u4AoZr8MDAxISi1PQydlzhgGWmAOh0PYFsaKzZw5E319fXJ8BK0S1UphWQk1YJebLTC+4bS1tUkmWLZSCceSce/MEaXMBWa1WlFXVycbORk9Ho4NII0dYn/TpcT2qdS2uoGS7fF6vQI8CH55D46Rmp6tUtxqTajxdzniBiovL8fixYvl/EqPxyMlPVgaQdM0qevDNhgMBoRCIcycOVOqoU9FrFYLzjzzTLS0tGDPnj2SRp7ZP2QArFar9CMZKW747Ada/bFYTMaeypexXMA4Q6smavBe0WhUqrUzaJv3IrBSATzPOCQQ5rP0ej3cbrdszkxGIGumuhkI/Bkjx804EonIOskWG8I4nPH5kYVlVSoWmM1mrF27FgcOHBBmUK1IrpY6IfsKHIkJYjmCnJwcmRs+nw9ut1vmfiAQQF1dHVavXi19FI1G0d7ejoMHD2JwcBDBYBBNTU1iAMyZMwfr1q0TdpFgtKysDDNmzIDP5xMgwP5obm7Grl278MorrwjTEY1GYTKZZDzI7u7Zs0cOl6dOmEzGl0Q6COMa4XzLNJBCoRDGxsbQ19cn65wu6c7OTjFEqZc4P3kfggIAOHz4sNRWGxgYQF9fn7gC4/G4HElEgMqaZIxhdDqdEov5+uuviyeAf2ffsuwO18ng4CB6enoQiUSg1+slq9Fms2FwcFDWA4C0kw1Y7001Bo+wglOTbMwODe1ly5bB5XLh4MGD0Ol00tecs0ykYswax0QdIwBpxrYaX6cy+9mAT7awDnXPoSGXyZRzTPv7+wUc5ufnY8aMGWntynz/001OO0ZLnWRU6HTRqdVzi4qK5EgMukEAyObd29srG29/fz8qKirg8/lkw2JAImsCAeOunsHBQfh8PthsNvT09KCwsBButxs9PT0Axg/6nT9/PoaHh1FWViYTm5WMSW8nEgmpRs3FoFo4VARDQ0MC6Jihcrz9pTJD7D+fzyfBwFVVVdDr9YhGo3K4qQqYVNZHtRKZtq/GDRAI0Q1I61TNsuHCZ4aXGkegKp5MhcMxMZlMKCkpkX9brVYJNuVmq9Pp5PBgNQbKbDYjHA6jtLRUXECZbpds4nS6UF09E3/606MIhUIoLCxEXl5eGthkIDkBlVp7LdOqZIwQgQJBGlOvOQfImDGmS2WrCMoZQKu6RDjv1KxDvX681hvBIceWYDeVGi/TwBgusq4EWwTEjGXkOLF2HO/B52uaJhsnx884BdOwoKAA1dXVeOyxxwBA5pHdbpf6S8z8c7lc4g7h/OQxMBwHt9uN0dFR9Pf3S4aw0+nEnj174PV6UVdXh6amprSMYrooGRdXUlICn8+HN998U8aaldV1Oh3mz5+PQCCAgYEBaJomMUlM9GD2odPplHXPAso2m01qeOXl5WHWrFkTwgSyretswfCqa44beU5OjrCqDQ0NaG9vR35+vowpAQDnHucp/+dcVDNXWWaDxV0jkUhaRiB1CMEWz+njOohEIujv70dbWxvq6+vhdrtRVlYmca2qO52gkaCF/8diMclgjMViCIVCwhwNDQ0JsGZbCBbVWLFxmTqAUHUoADmGa86cOQgEAnLGKYENk0FYa5FgRw17UfVj5v05ttSxbIMai0VR30sdA4YsqPfn8ywWiyRj9ff3w+fzyXFxDofj77FbbMfpC7ZOO6AFpFsTtMaj0SiKiorQ39+PkZER+Hw+VFdXY8eOHUKBOp1OoaAJbKhYWD+ktrYWPp8Pu3btkoVCRioYDErQPEFCIBCQoyMKCgqg1+vR1NQEAAKmOLEZ8M76PNzUuGhU9kav10vBP9K6Lpcr7VBWtS+Op98MBgNKSkrEyrJYLGhvb5csHZWdU79L5aAqZxWA8TP8NzdiviMZEFpUaqwAgQBZD3WMVeuPDILP55NNUG0nwSr702g0Ijc3F0VFRWhra5P3ZgA7XYtH7zjAbrfBaMzBtm3bJCBaTR7ghkAlx5RvAhAqebo/1EwkNX5CZQ0oKv2vFs9lJW/OI96H/csgbjVRgPdQa1gxWJqZkuw3tlllXvg+ZDvGxsZk0+XfCHzpXuW80jQtLVmOBgBF08YDv0tLS6FpmoAWbuJqf1mtVqxevRqHDh2SbF/GAtFtR/fStm3b0N3dLe0g28jyEKWlpVLnjnOYblZWNJ89ezai0ajUjSIYY/xbY2MjCgsLhY1ijCXXgaZp4l5kVjPBMw2a7du3o66uDsuWLcOrr746Pl5/X+oajmyuaetdm1jXiUYa16XRaERzc7NkXnMdAuMuvu7ubgCQ5AEaSZx3nDfqHPJ4PCgpKZHYOeo5tX06nU7KgKibPNsbDocF/Obm5qbpHbadOoLvxDCPwcFBSU6gTjUYDBgYGMCCBQuEXaXRoNZtm8gITd0lprL3wLhLcvny5fB6vXj88cfFuCMzRV3k9/vlyLXi4uK0wrmRSCRtravjqbLCBFi8L9c6r9M4UkMt1PIZzBLm/AsEAvD5fMIWck8aHR2V7NJgMAgViJ6u7sPT0nVIoVJgQUyj0Yiuri6EQiGEw2FZjIwdUalTisqYBAIBDA4OYvbs2bjqqqvkGBpNG0937+jokNouVOjNzc1yfElhYSH8fj+amprQ1dUl9WQIIOg2IwtXUFAgabZGo1HiCEi102UUCARgNptRVlaWxnZNZdJr2hFXqxofUFFRIYUeGxsb0dnZCZ/PJ3Vb1Hgc9pNKX6uMDUEGGcJMJaFaYDwKiZ9XXYYEJJNR1nQJzpw5U4CBmn1DplCNE6JbtLKyUs5QoxInPZ7J9k3sRMBgMEoJDCpuslMEHWSA1PlFMEImiG5MAFIcl/Fl3KAJprhBMWsLgLit1DIRDHqlpU93NUGB2g7ed2RkJO1IEE3TJGOX78PAYY6jyjQCEECn0+nSmBAyQQyAZkzkuMGS4uIdrxCvT69DRYaNbiDG2hG85efnSzHRM888E1deeaUYI5o2DtLKysqQSCTQ0tKCF154Ae3t7RgcHJQMN4IMtj8Wi6GkpESy55j5q9PpJJaI84PsTG5uLnw+HwyG8SK7PLg3EAigo6MDdrsdK1euxIoVKzBv3jzU1NRgyZIlWL16NZYsWSJJFJy7ev14MdRXXnkFixYtSjsHMJPNUH9Cl73GFkE9x5VHig0MDEidNs7ZwcFBOfhbZY84HjTs1HaEw2FYrVbMmjULZrMZxcXFAnhV/aTT6cQo5btyDPh3t9sNAHI8Dw0v9Ww/eiJoUNAgGxsbk+8xnIPjyrXB+Zx59memq+1owj5R+9hoNKK8vBxz5sxBf3+/JJQEg0EpD6TX6yXzdObMmZg9e7bEVTJsAziSuaj2H4Et+00t76OyV6oOVvUYdYrD4ZDYOyY+6XQ6OXwdOHLGKQt05+bmphVazpxfp5uctkBLtUqY2k0rKRaLoaurCw0NDRI0TGud8Rg825AHcfJAZ6J7FuirqqoSN0J/f79YLKFQSGq/DA8Pw2az4fDhw5LeTmva5/PBYrHIpsgjL1jE0efziRXMDD0qSTU7TdM0Od7l+CQ9foMutYKCAjlfLB6PiyuIC4ugh8BOjd2hglYt5syyELS0VAaAG6RaqI+f5b1Ul4XqkiRD43K5BFioc4FlHehiUsEg3R08FDiVGi+qWlpaOmkG0MT5BlGgVOI6nU7GlSBLTSunq4UKj/F8nAdkfwjOCKyGhoYQiUTkYFg+S81a5akDw8PD4jZTWS8qXG5SmUVN+dNoNIp7J9MS5lpQWQh+TnWBcDNjDSaey8i4R7XdOv2xVRaPYaFrjoyZunGuXLkSIyMjqK+vlyD3xsZGNDc344033sDhw4elRhD7lrGcDORmjBtBcFlZmWTBFRQUwOFwYP78+TLOiURCkjiYVFNQUCDxWDwPMJlMoq+vD5WVlVixYoUUq2UGpNvtRnFxcVqJCPZ7a2srDAYDZs6cOYHxm2xtT2AFNW3CBrxs2TIEg0EJZGcFfRYBJcAEIKBEZVL5DG78iURCshSZnKDGK1IsFgsKCgrkOuujFRYWCqCl25LzjPGKZNVUVof/pt4iG0RA7/f7xTWr6hKuwcw1PlXgkK2fzWYz5s6dC5/Ph4MHD4rrmckpLLocDAZRUVGBefPmwWq1itGhGi7q+qahRr0PIE03U9+pY6KGWbAfqX9olPE4Ik0bL+NAVpfjybYzQ59xz6cruFLltHMdZtLTev149pnX6xV3DScoNzemrasB0bm5uSgpKZH6IcwOa2lpERfC2NgY7HY78vLyEI/H5eBnMlPRaFTKNDD+BYCkFQeDQaxYsQK1tbVoaWlBd3e3HMXAhZGTk4PS0lLk5uamVWdn2Yn8/HwJ/MwMpj+ePlOBKQ/RZVo+qWKVVVID01UrmbS9ammrlpQaOM3v8nN0t+l0OgGXfF8yS2TUKNxoGUTK71OoSAFI/Jdan4qFC4eGhtDV1SUsxODgIIqKiuB0OoUtUin7bMLxBsYtepvNJm2kgksmkwI6+E4sSupwONKOeaGCIxBSWSPVYmW71AKkVLQEqiwJQQaA761mMpKdVRkqbhixWEzinugSYxC6Gl/G37kZmkwmiTsik0h2jfFnBGVmsxlWi2XcE6FY3mqPkz1mKQYyc3q9HpWVlZLIUFZWhlgsBqfTCY/Hg/b2diSTSQQCAQmg5mHioVBI5o6aHEM2xOVyScXsVatWYc+ePaiurhb3Lg82Zk0qhioQ3LOsDBMc+Le9e/ciEomgublZgvp5RM/s2bPxwgsvSHYdgUAsFkN3dzcWLlwoZ5yq82DCpqdNBAsquGfJhYKCAmzfvl3coYODg/D7/eK2czgcExgVuvzV5CPVtczTNMjSzpo1CwD+7m4aj7UrKCiQwsGcv9FoFMXFxRKsrsYhck6q1fVVl6UKxlwuF7q7uzEyMoLi4mJhkxhSwHuSFVXZIUoqlUpPx56ikKnjyRqHDh2SWDjVGOvv7wcwzuS3trbKOmfcII0HtiUbCOQYMDmFY6zGPrJPGINGPUEQzCLaBQUF6O7uRjKZTNNH1A1ctwbDeOY9DcHTXU5roEVlwMObWXRRVQxUJHSL0KfPQ5O58HiYLDOSeL4hmRi6H5uamlBUVCTIPxKJ4NChQ5g5c6YonFQqJeUSAoGABHpygasxJ0wVZ1V7xmBxI3c4HAiHw9KOTIVzLHCQTcheMUONAI9B8bw/QQwX98jICPr6+mSTV11oXPiqBUq3QGFhIbq6uuD1epFKjRe5ZA0cFShxXFUGhNl1BMNkxIAjVh43cxVk8OgOVt/nsUKk9BkQrVZxHm/z5HrX7/dLSQ7GPNDyp5uDbjluwOxH1WVN65sAghsjGSOVJSLbQLcV+0oF63RBqiyaCnA4FnznVCol/UQww2OTCNg493w+n7A37Hcq55ycHAEXAMTdGIvFBHQx0Fd1oWYvwHlECNQ4t3U6ncS1tbS04Nprr8XQ0BC2bduGxsZGeQda+nPnzkVeXh6amprQ2toqmyJwJA2ebCcrnrNG05IlS1BSUoJEIiGV2rmeuTbJrKgbIN2yOt14IeDCwkJ0dnZiYGAgLRmHyRlr1qzBI488IsykwTB+3JLD4UBraysqKipgtVpl7rN/2SfiQlQyD1UGjKydwWCQYPH+/n55V84Vi8Uip2mogEZNxuC4E3hxLqi6J5Uar61VUFCAvr4+eL1eKSvBewHjB5Vv3rwZ4XAYy5cvTzuChvpanUsEInx3viuTn4LBIPx+P7xer7DFgUAANTU1UoFe1ZmZotNlL5qb9XM4Ei9JA56G8PDwsGTjqqVlWNRVPQZLr9cjGAwKOKKBmOmiZf0t6kTqXP6bn+WcVkWv10siysDAgCRnMJ6T65ZJCkwqGRgYEO8Q1z5LYpzOzNZp7TpUF2cqlZIJoQb9kh2ir9pms0lFZJ6BNzg4CABSP4QprqzRNDY2hvb2doyOjqKzs1OqFTPbSt3QYrEYRkdHYbfbUVNTI/SxyrSRKWD8TCKRQHNzM4AjCiYYDGJ0dFQKiNrtdhQUFEiF+OOJL+DnKaxgTmDAw1/ZViC9dAOVHN0QKkAi6FIVIRUGs+cKCgrkFHlaUgxqJlhTFzGBAi00uqE4joxVICvEviD4YJzRyMgIotEoHA4HysrKUFhYiGg0iv7+frnfkiVL0g6CPlpgbF9fr1idKmulvjfHmgyR6gqgQiQYV1k8ug9Vtonvx5R1NfaK7SA4IxvANHaOIceFmYUcR44rC5zm5ORgaGgIAwMD6OjowIEDB7B161a8+eabwnQSKLENZHWHh4cltV89942MperOGh0by9657H3dkYw+viOBXzgcRkFBAWbNmoWXXnoJHR0dwn6QLcnNzcXg4CAGBgbk3VW3CnDkUOHR0VH4/X50dnYikUigtLRUskkHBgZgs9nksHjGs6mJCjQIyJpwTtbU1MBsNiMQCCAYDMoc9ng8WL58ORYvXoxkMomamhoxPLh5syI7k1+OZUixrrnqOgYghgyZHMbjkNWjTmE5B76XWiyYa5NGC5/BdUk2NTc3Fy6XC3r9+IHKK1euRFlZ2YSkGbPZjAULFqCmpgahUAg9PT0Ih8NpJyHwf+oasttqph7nNQtL6/V6OeaLiT1ss5oVrfbP8RqomZ8l+DebzQLAc3NzxRgi4KuoqBDmiO1XjdHR0VHRFZltUzMFmWHLzG4A8izqL/5PxpYsbE9PT9reyHGlnmCcpk43nkEcj8cxPDyM3Nxc5OXlpQHc4zXq/1XktARa6qZO/zQtQ1ryqmXHmiw+nw/l5eXwer3o6urC4OAgAoEAxsbG4HQ6UVZWBrfbjYULF2LGjBmora3FokWLUFVVhcrKSpSUlMBsNqd9z+12Y8mSJaipqUFeXh58Ph9mzJiBmpoaaJqG3bt3Y2BgAACE+qUP32q1yibBitFqNd6+vj50dXXJZl1UVIRVq1ZNcJ9NRVTQVFhYKO3nZsCNXo0FyOY2YIyUuvjUjBgVdGmaJq6LhQsXora2VmLhxCLXjpQiUBlKla0rKCiQkggAxDqkwlUDQcnsxGIxDA0NiSXHw3LJTObm5sLpdMoRPapFmK1vx8uERFFSUgKv1yubGM/8A47UHVOZTSpDq9Uqz+AmQeVGcM7NTK1xRMVKcMNxYsYoFScBCmtqqSCDDBGZODXQWQ2uJxvH+l08ImrXrl2yYRGgMx6FyRpqhhM3AnWTZX0zwxRitFiXi33KzLXm5ma85z3vQTweR29vr2RTGY1GOdjYbrcjFovJ2iHbp4JhGkfsM7rAFixYAL1+PGuYdaHcbjdGRkbg9/vTgCnnLcEz26LT6dDU1IR4PC6FeAEIUB0eHsaLL76IcDiMZcuWIZkcr/mnjmcgEBC3twqosy/s9PXNNcR4RIJ8uj+pL2OxmBh8XL8qS6KGBdATkLkeVMabh0qzziDdUYyX4j2TySSqq6ullIS6+fPvBPYEECpA4nrnWrJarSgqKhKXLBMpGA/n9/tlnNX1/VYYGoIZu90u5wXSBUowHgqF4HK5UFpamhZDxfdi0hPnkXpv7l9qHCr/PTQ0JGyvWk5FHX+OD/+mniLB2FU+i4faDw0NyYkPjOVzOBwTSticrqzWaes65L9VunxoaAjhcFhOjAcgLAAPMR0YGIDJZEJ/fz/0er1kD+Xk5KC5uVlcFDwWpqWlBYlEAlVVVSgpKZFUaFpbJpNJrMLy8nKpPG2321FWVoZgMIhwOIyBgQEUFRVBp9OhtbVVNmCz2SyHVhsMBmG5yKgB4zEPVJ4bNmzArl27pOL8sSa+Clh0uvHEgerqagEHzDxUy15Mdh/GwKhp0qoi5OJWg9kJvMbGxjB//nw4HA6pWUTXAJ+r3k+NW6CLg9XLdTqdAGu10jk3XeBIICk3AqPRCJ/PJ8HAfr8fjY2NqK6uRmlpKVpbW4/SiUA8nsBofBjnnnsuKisr8cQTT0j76SqkAua/CZJoFaoxTHxvFcTymB7V9adu6uxXMljc4Nn/vI96hJK6RgjyqfDZzyrwGhsbQ2VlJc444wzs3LkTu3btkiKMdI+TleRpCNwEVHcHLW0emK0G2KvrmM9VA+sJlsgQ6nTjGVIejwdnnXUW9u7di1gsJgku+fn5UnQ4FovB7/cLW8u4M+oCNZmA4J6MSEtLC2bNmiVH93R1dcFoNIrOIMBkiAEBBRnD4eFhKZJKtzE3fB7tdfDgQRgMBhQWFuLcc8/Fvn375BqBMhkSldHKBELj/agb/08BYpqmiRvt0KFDmDNnDnp6etDR0SEbu3ooMzd21fWemTmsGl4ErXQ705W1Y8cOtLa2SlxqUVGRtIdxcqlUCsFgEKFQSNxajG9TmSrVTUaQoOoxldkCgLy8PGHseL4jwzoIWlwulxhqJ8LKqPop0yCkHmJpIRoklZWVkknMvkwkEnA4HMjNzZUTAOhiV0MHaJxwXdB9zbXG+ZgZlqDX60W3q3MlPz8fACR+jAws683xYGnqX5YfYYgN9QnveboxW6cd0KJQAVMBcfKodYP0er2AruLiYon3IdMBQIJh6XoJBoPo7u4WC4XMDw9dZRozhRsIN0a/3w+n0ymZUB6PBxaLRQJizWYzioqK0NnZmWZt+3w+Cbq32WzIz8+Hy+WCpmmor6+HyWTC4OCgBJg2NzenWULH6iv+pN+dbq1YLAaj0SgxLFQkPL6EbiVeZwalSn+rypFWORUirblQKISmpiZEIhFxgwEQJU6lmbmZMACbViDHhVawykQxw5Hgi7EInCcMwI1Go3J8TG1tLWbNmoXt27fLO0xQIhowMNCPzr4IzGYz3njjDWGoWEONliItVjV9Xo0PVEtbqMG+BIaMAQOOuCL5OSpZ1ULmhqSWWiAjoMZrARCAxs00FovJ2Ho8HsmGveiii9Df3y/KORwOo76+HkajUc7Fo6s5c47RXcMxY4zOkTiy7PNT7XOz2YzDhw/Ls8mIXHHFFdIvbrdbQgG4mdbW1mLPnj3SzyxPQTDAPiPrxxg5Bvxu2bJF4jkNhvFTGdra2uTYH5XFIqjiGuB40l3T3t6OgoICeL1e9PT0ICcnR0536O7uRmNjIyKRCM477zwcPnxY4gY5jxOJhJQHUOcwAaoA1Yy+NBgMWLhwIfR6vTD8ra2tAooIKtVDtwnyVb2purpU44vvynWnaePxrd3d3QKitm7dCqvVipkzZwKAAM/R0VH09PRg69atSKWOnMIBHHHnqht65voGIHoj83OMJWIdqKGhIZSUlGD79u0CYHnGqtp/48+dGmjINGh4yDVjTumuDoVC8Pl8KCoqkphW6kSeIsI4Xb4fXY+sD8jSQAw1YSgEGVjqZbLeKgjk/GFbk8kkSkpK0NfXJ7pG/TzdvpFIRPY5GmQej0f0jdpvp5ucdkBLnSDqJsINjkpVZUYcDgfsdjva2trEOub3NE2TatA6nU7Sj2OxWFoMCN05VOLq2Xz8ydihtrY2OeC4p6cHLpcLJSUlApbKyspgtVrR1NSEUCgkZ/bF43EUFRVJ1XZN0wT0NTc3o6KiQuqhHA/9zc/o9XoUFxcjGo2is7NTgp+tVmtawUBaZDxEG4AsPPZ1ZrwB4wkotLbIAoyNjaG5uTktuFZtG4WxCXRNaJqGzs5OAZ/qM7lBqwG33PBIg/P5Op1OQGp9fT0OHTqE2tpajI6OYsaMGcjNzZV6PNlkvO6UH7m5bvj9flRWVqZZogAkg4xAkn1nNBolJkY9YkftBzUeibFmKsCiwlRdggROBoNBqovrdOOuR859MjucTzy/UHXxMsGgsrISRqMRNTU1MudfeuklieNrbGyEyWSSExTI2nHzY1tU65ysEtszqk9mjYLjuHLTYBykpo0Xf501axZmzpyJ1157TfotGo1KogrLVDBOjcwA1ynnWWYRVwJNVsZ+9tlnsW7dOhQWFuKRRx5Bc3OzsNeqmzwSiQj7rc55FkolQ0BGk8dNsTAy54zJZJLjurgWufmSYVCNj4n9lr6OysrKsGLFCrS2topOUlnMaDQq5W1UN5K6Xjk31BIEfG/VXQUcOVOUcWfFxcWi17Zs2YLR0VF4vV7k5uYiPz9fGF/qR7Ld1GlqzKfK5qj9z/FXx9Vms0l8UU5ODg4dOoRly5bB6XSip6cHjY2Nwtqr5U+O1x3GtjEOirF9nZ2domfoDqanIjMrWafTpe05NFBZwiRzrRO8B4NBKbmglldhCRbOFxWgsh/9fn8aa8zP8OxDl8uFjo4OOBwOYeaCwaC4sNX3Px3ltANawEQLmJPV5/PB7/fD5/MhEAikTW7WDCHap1IBINSr3W5HIBDA6OioKFIGe5JZ4N9yc3MlqJSLhgHHFosFvb29spBolefm5qK0tBShUAilpaXweDzYt28f/H4/wuEw8vPz5WgZnrPGg17NZjM8Hg/0en1aDNPxKAmDwYDi4mKEw2EMDw+jubkZw8PDKC0thdfrxcDAQFrcEMGBGjPBDCn2LQGPWv2Zyoh9y8VOmjyTglbdAOq/1Uw6NZtUPW6FgINZdFRMavyDqqx5Mv3AwAAKCgoQDAZRVlYGr9eL7u7uSWlxTQPKyyswZ84cPP744xKzRAtSp9OlnUdIMMTihNwgqOgZO8Z3pjFAZcu+oyuC76yON+9HFpEgjkCHbC6/y0rtakYj3QQmk0l+RiIRLFmyBDt37hSQQXaKteSY2MA1xA2ShxirmbV02Y23w6j06RGXGOeSwWCQcxfZB2azGWeeeaYUHiWwMxgM6OvrE4sfAIqLi6UMA9dnpuuNwExlpAi6yN5xTaoAlnODIG50dFQ5v/FIxifdnzz6icVbDx06JC4t3ndkZATnnnsuNm3alLZ+ODfUvlF/P8JSH9n8jEYjVq9eDbfbDZfLJdlmnZ2d0v6+vj6JDyIAIMOq6lPVkFIZEz5HbSPrkfn9fuzatQvhcDhtjXd2doobqqSkBGvWrJFYtGAwKAA9kwlXY+L4XBUcUydzLnBMzGYzgsEgWltbUV5eLmy91+uVTE72JftwKqLOAa4LJkoQaI2MjIjRFgqFJOicjDXLfKhrH0Dau5IZZwwo3ZPBYFDOIOU9+D0WHuXzVANA0zQJS2A/pVIpqQ+peg4IzpkRqurVrGz/aSKnHdDiQqTCIXPAuKqDBw/CZrOJe5CgpLW1VdgNLmIqdoPBgLKyMvj9fmFc8vLyRKHQt89q1bTkCd4GBwfFMgOO1INxOp1wOp0IBALYuXMnUqkU5s6di2XLlkkF64ULF+Lpp59Gc3OzuOUCgQDq6+uFyk2lxqs3Hz58WFJxT8QKM5lMcLvdGBwcRHd3N/x+PzRNQ1FRkWRrqXFO3FSAcdBTWFiIVColbhP2HZBOq3NRqlaVysSwj9Qg5Uy3CJlJ9R14b1rbOt2RgFA1OJffpcuFjFIsFkNFRQWcTid6e3sxPDyMSCSCxYsXy1EUmRsZxWDQo7SwFOvWrUNnZye2bt2KyspKAZ5qvI7qZlKtbqabM1NPtTDZF2RXhoaGhDFjnxC0cJOjVQtAygAQeKkuiVQqJS42blQEWXRDsNDr2NgY9u7di9bWVjQ3NwvIU+t7BQIBcQ2qzBkAsfKTyaTcj6718fcDoCjtTPc3AR2LJyYSCRQVFcHj8WBwcFDay/dQY/w4vrt27ZK5Q7ZZNZQymQQVaCSTSTm0mM8n2CDbSFGBEecNWS5mN7tcLrz88svo6uqStnAj5ekQIyMjWLRoEV544QVhtKjbuH4mZbWUza+0tBSVlZV47bXX4PV6EY/HRWeZTCYpcMvaVnx/tcwNDQIyxUyg4FxV1zHdjSzYyjWpJv0QKOt0OglSt9vtKC0tlRM9qM/VuaAyR6rBpYK/TP3BxAXWqGpra8PMmTPhcDiQn58Ph8OB4uJiqY5+BDgcGzxkM+wJ7FpaWqQtnI+sRUU9xLkKHDmRQNU3fHcCJ85pGmvqYdtqcDpdjmTPmFzF+Dc1MYzjx3FWjWOyv7xHV1eXsPaZiRCno5x2QAuYqGyMRiMcDodUW9+2bZvEXqjBgfxJmpULwev1Ii8vT4LaOzs7MTQ0JGeiMcOKQekGg0GyNAyG8eKnVIpMlWVMDtvAOjb79u0TlojxOLW1tRgZGcHBgwfF+mcwPdOgOzs7Je6LVuBUCslpWnpFeJ1OJ4e5csF5vd60GjmMMVKtdwCySagBslzsqisqM1iez1FjPvi98TZq4o7NNs5UYGwDLT01+4b34AZMgKimgjMIubKyUuob9fT0wO/3o6SkJC1NP9ucczodKCkpwcaNG3Ho0CE0NzfD7XanVcTODCRWY090Op1kTKqZRXRPU8GGw2F5F9VlwnuqblW6SvgMsjhU5DqdTo75IDPDzZVAkO4lt9sNj8cjbHBxcTFcLtcEtzHrRhHwsI10mTIOjNmCnAPj1bonNxDIVg4PDyMcDsv4zZw5U9qobmZ0MfO9y8rKUF5eLvNDjf9TXbNWq1XeR3U5c8OhS4XrAICcFpHpPlOTCZhtxs01mRw/3orHA/HsU9bVs1gs2LlzJwBgxowZ2LJli/QrN85jiu6I27W0tBRbtmxBX1+fVAFnYgAAOahaDbhWQRT1IQEh55vKPJPdJshnxiYwXlIiLy9Pil8yMYngeXh4WGoU8gB7njPJecR1zueRqVSTW7iuVBaO9RJZTsNsNkuBaLJBPp8Py5cvR0NDgzCd47ro+JL3yRAxfjcvL0+uZzMeuZbVeagCN2YUM/id32Gh6rGxMXi9XomnVdloYDwJLNOoTSQSiEajMn4qSwkcySgHkNZGGhd8N5VJPV3ZLOA0BFrZ3GVcpDk548d/RKNR2Gw2KZ/Aha5pmlQ1Z6xKSUkJdDodgsEghoaG0N7ejkgkgsLCQgkmpWuQ92fGk9PpTKtBxKwQuhd5wjwzlzhxd+zYgfr6egDjgaIMTGWqMN2D3d3dUk0YAPr6+tDR0SHVe6fiOtS0I0HTvC9BCos0FhUVSWVsKgO6ndQgdTJgXKRqqrxq1aubfKabUQUymayLCsL4v8qY8QwuxgGpKfWJRELSwVV3S6YL02g0orq6Gtu3bxc3HfuAfZON0UomU9ixYyfmzp2HBQsW4Oqrr8avfvUrdHR0wOPxpGXnqOwaFZzaZ+pcppuPGzpZABWEAUdcc2pqNlkKjhOpf7UwKo2FTBcBQRHPRiTIJAMCQGJp1No9BMPcaDlfaFQwoJtt4bs4nc5xF6ruCAhX2UPOY7Jn6uZfWFgooM1ms0n8GOuL0UDgZlxaWordu3dPAEWcT4wr46aeOVfUGDuCTDUmTR0H3o/9kUwm5VgTTdOkCnc8HofFYsH8+fOlTtf+/ftFV/GQZh7hMzAwgM7OTul3tY+U1S1kDNvQ3NwMv98vldhHRkZk/IPBoLhZuR74Xa4R9oUav5XpykwkEmmFaQEIO0cQpNfrpQYh55bdbofX60VfXx/C4bDEytJ7kBl7mWnYqUAm051KkB6LxRAMBiWxqK+vDzabDUuWLEEkEsGCBQvw4osvorm5OY0pPF5hrcNEIgGv1yv6lYA7c6wIJlUWi6xlNBoV/a+6Svl+BFY+n0/WHtc/S2jwxI7+/n4JB6A+Z0auavip7aPxRbY9lUohEAjIPqMCftV4Pp3ktANamUIlyk2NB2EWFBTIhFcnD5ksk8mEJUuWwOv1orm5GS0tLejs7ITBYEBNTQ28Xi/2798vRUlZB4jF5RifQcvI4/FImnlPTw8GBgbkOIjS0lIA42nIeXl5iEQiaG9vl2y09vZ2YclorXd0dKCnpwepVEritgKBgFjHANIsuqkK2TwCR7PZDJfLhXA4LDFm/BxrtpjNZlHkjO8hC6OyBsARi0pVmJMtykxFp7ongCPuRW5gg4OD8Pl8km3GTDaCBzXlPJPqVxVLRUWF0O1MAS8pKYHD4RC3QnqnAYCG/fv34+GHH0Y4HIbH48FFF12EF154AV1dXQgGg8JW5ObmTgCMqqtMBb5kl8gAcINn7A9BEEUFL9zkCXJYMoOMKjevzDgWshAEJmazGW63W2KbOM7sU7rTGSenZsHRAqZRQ9DLDZ5ZiFInzZgjwfCZClvd5BlzxmQWPotZu7xWX18vFvhDDz2EOXPmSMCw6r7lfOMcUTc+PlM1DhhfRgZSBWx0xfLe/BvHwWq1orq6Gk1NTQJIdDqdMG6HDh1Cd3e3FOtk1ldBQYGUTxkeHhbdo7ISKsuLjGKlLS0tGBwclFhPdX2x7Aw3aBVU8fvq+wNHAIwKSNkexnCSiaG+VV2zqnuMwgxwAOIKJgOqjo0KpNTwADWrOb0vjtQp7Ovrg9PpFHcls2sjkQjy8vKwcuVKtLW1HTF6jgM08L1YamZkZET0qerWpC7jOqdkAmaudQBi4KhGFo0NZviGw2GZsw6HQ6rSs/Ya+4b34P/UFZnrTf035wDdx4w/pvGU7Xuni5x2QEvdwIEjx3UwGJDZZkNDQ1JPpri4WAKSI5EIYrEYzjnnHKxcuVIm6CuvvIJQKAS73Y7BwUE5PmdoaEgy3oAj6cUqdRyLxaTuld1ux5w5c5BKpeQQYrPZjMWLF8NqteLgwYNSZZoLlgGIVO70lfNoDNb0IgBj9W0ujqOJTpdesT0ej6OgoEAyxzRtvDAqj2MYHBwUdigvL29C0ObY2JhYR7RsOR5qNks25UKLVN3gqEgmU8zAETaKAa4zZsyQLFC2jdleqjVJIMP/gXGlPXPmTBQVFaGxsVFcQswKy6pEtCMZdYcOHUIgEEB+fj4qKipw9tlni6srEomgp6dHzsokO0M3suouVRkR9iX7Q93Y+Dv7lgVDVatWPVeR8yIej6OwsBC1tbWIxWI4ePCgrBG1qKm6IRkMhrQaPIy14gZIhUsATFaUmwoBBdkQgkiCtfGszonzle3muzCukpsqv0/X3NjY+PmXtbW1SKVSaGxsxPDwsDAsK1asgE535NgdngSgulDUOX1krejS4lHoZkwkEmlFculqYRu5KRoMBlRWVqKurg719fUCtFKplJRr2b17N7q6uiTjt6SkRNYlz1wk8OKcy85mHWkzN/S+vj4xNnNzcxEKhaStsVgMPp9PNmp13fE+fAfVZURApsa88X58b4IOhhBk6gOV7SHDlZeXB7/fL3ORCQpsG8EH+zeTwVJdYXx/AFJah3FpTJ5wOp04ePAg+vr6sHDhQpSVlUntvOPlZqxWK+bMmQO3241wOIyysjIpz8F2qMZ/trlG4TsTVDEzmTpe1bNcX9S/TDrhe5C5UrOn+VP1GKggkJ9RmXCVjWcsqcqGnY5y2gEtKnLVPRIMBmGxWNDd3S0WYXNzM7q7u1FWVgaXywWz2Qyn04lwOCwWcVNTEwYGBrB3714cPnxYNuTh4WEMDQ0hFovB7XbD7XZLHRNaTdFoVALXdbojAcx6vR7V1dXIz89HKpVCc3MzRkdHxVqNRCI466yzsG/fPvT29gI4Uv2arpxgMCibs8vlklpNhYWFks1Fpc8+mIw1Utc3lWVdXR3a29vR0NCAkZERtLW1SQyAWmNpYGAAbrc7zf9PlkUttsm/qRZypvXNuIaRkRHk5eVJe1UrOpP9Ul2AwLiS7+vrw9jYmFTxZ9yZaoWrikH9nYyG1+tFbW2tHMHE7CTG02WL07JYLBJPsnjxYlRVVaGgoACzZ8+WA3mTyfHCjI2NjWhubkZ/f78cUs64J6vVKkdAqe5Wtl11m6hMFl02w8PD0gdVVVUT4qPo3iosLIROp8OhQ4cwODiIYDCYtgEys48smqZpyM/PFzBG5ouMC8dRHS8VxBL8MLOR/aFmXI4r+4kqS9006e4kI6fT6VBZWSmbst/vh9/vRzQaRVdXF4qLizFz5kx0dnamZfcVFhbCZDIhGAwKS6e6YjM3Ps4Ts9kMn8+HgoIC7N27VzZPAh+yDgS5ZBypA5LJJJqbm7Fv3z4YjePniTJLc2xsDN3d3XLkl9PplD5iVjLd+zwiSm1rtjWu4cj8ITAsKioSYGgymdDX14fZs2ejs7Mzbf2qBSpVHaHGZ6ljyevDw8PitiWgVeeyqgPUdazOb7ql6apmTKyqB/hMlQ2mYaKy35yPKtMYDodRWlqKgYEBGYfe3l40NDTAaDSitLQUbW1t4/NuQq9OFPVdXC6XGN69vb2oqKiAx+OR0IpIJCLGQjZRwasKdlWwyHhjAjHG/Ol04wkXZPWLiorQ398vZTsY46h6PairVf2aybDTiGPf0ugwGo2YP38+RkdHceDAgQns1ukipx3Q4kJTZXh4GE6nE16vV2qA+Hw+UcYtLS3Q6cb94YWFhcjPz0dnZyeam5vR3NwsDI7ZbJbz1JgWXFpaiqGhITQ0NCAajUogrc1mk9gsVSG0tbXJZsOsmu7ubsTjcTQ1NYl7KpUaz0BkMPPw8LD4xOk+0uv18Pv9iMViUj/L5/MJ2HK73QL4JhNVN7MeCwvj8Uw4Zp/Q/WQ0GqU4qHqwKRWaWv+JC1etv6MCHwqpf9L3wJGNW2UYqExVdoZKlGCjt7cXIyMjKCkpQX5+flq9ITW2RD1yRNM0YQX1ej3mz5+PF198UZhEUuuqq00VnU6H8847D3V1dbJB5uXlCSPJ1OuSkhJUV1cDgJy1ODAwgBdeeEGySwl8WMxWzRLMdJ9kJhoQMHBzp/uGa4NuvFAoJPOE91GLiI6NjQk7REXc3d0t8V7JZFJq7DBeT20bmS6OOxWz6komM0F34vgYAdnOk+RnuHkDR1x9brdbqqVzzoXDYQSDQXR2dsqZpKOjoxgYGEBbWxuGhoYwa9Yscacw9oVzQi0oy3EnuJk3bx7mzZuHUCiElpYWWYtMVGAb1BpJrAmVk5ODtrY2JBIJqQZvt9vhdrvR29srZ/IxzlA9FoluWR7oTbCissqZ4Fz9aTabMW/ePNTU1KCurg5btmwRo6m0tBT9/f3Sdham5GbL+6huX8bBZbq61E1aPeieY6ay2ioQVUMNjEYj3G63nOlK3cQ+UQuTUi+o64RrnTqE42oymeTUgvLycoyMjCAcDsPn88Hj8ciYMkEE2afjpMIxoBHEkz9KSkpQX18Pg2H8dA/qBa7bTICv9iF/B8b3Mo/Hk5bQoyYncB3Sk6PX6+HxeCYYvBS1jAmfpQJVFZBRr2iaJrUdnU4n8vPzpT9ZC/F0k9MKaFGRqZlMzM4YGxv7e1aTXjYPZhHG43EEAgHJ6ujp6UFvby8GBgbkLCyn04ny8nI4nU4UFBRIOrDBYBCwQ1eJGvuRm5srBR1NJhNGRkbwxhtvYPny5aiurobL5cL+/ftx8OBBqeyel5eHCy64AHq9Hps3b067vypkFTo6OqDT6eS7PN5i5cqVUmpgsmw51YpJJpOSYefz+YRyzsZC5eSMV7KmiyCTdVAXLVkU1VXE52WjrlkwUVXa6jur/85sG5/NGkE8t9Dj8UhxSqvVmuZG1DRNFFdLSwvC4TDsdrsAPgYqpx8unSk6AZzRaBR//etfUVxcjPLyclRWVqKqqkoqeTNehwAwFothw4YNuPDCC7FlyxY8+uijOHjwIMLhMKxWK7xeL5xOZxrI0+v1aewJ33t0dBQ+n0/cYmo8Bt0LqgsXGFfUNptNDAAGMKubFcsB0NWlsmksAMo5ptYxY90ixo4NDg7K+iOw5XodX5+TO2rYt8PDwxIHaDab5SB3Jqfw3ehy7+3tRSQSgdPpTCvtUl9fL22ji5vvpQIr4EiywZIlS6R479q1a2G1WtHY2ChrX80KJRjgvEgkEpLRFggEpP35+fno6OgQ9pvtIHOmxsNws1Mz1LK7bcbLEnDtmM1mnHvuuVi5ciUWLVokAJ5FUP1+v5wfyeODVDBDQyPTTQqklyMAIGczsu6eqgvU+EgCK5URVN+DrmIGdVNfEHipmcgqOMhMbmC7qIcYBsGK/mopCJ1OJ0crZbrXjiYqQGJx5lQqJewq5wwNIRov/K76v8rwqf3B9W8wGFBaWoqWlhYBWXxHm80mDCjvz7VGMKq2me+vAj5VJ1PHRiIRhMNhOS6M5W7I4pIlzs/PR19f3zH7619NTiugBWDCZOHG3d3dLdZsR0eHnHHmdDrhcrnEzz0wMIDW1lZZFNz0yT4VFBTIvZn519DQIBsYJ6xaNNHhcAhFT9dHX18fGhoaxG3Jz/t8PuTn56cVQaTbg4uEWVH8nS6pgoICYdnUSvXHI2TQ1EWZaQVR2EaKqpD5PgQxaryUCpA4VnwfbgD8DBWLarWqfa2Ou2q9c6Pt7OxEIBBAb28v8vLykJOTg/z8fGiaJgwOMB6n09jYiLa2NoyNjaGwsBAOh0NinvR6vdRLyxaLYLVaUFhYKIG8ubm5At7ZNh6jpMYDkd7v7e1FMBjE8uXLsXTpUmzZsgVPPfWUuK+j0SiKi4sFaKlJAalUSrLrVFcAFbBa7oGZS+xPXgMwoU4cx41sEplZbpgcFyZNABAAy02YGbhkJMjwqn1A96HBYIBukvIOfA6NFo/HIxm3Q0NDsknT0FE3EbouA4GAMERc79y81TGlW55zmWuguLgY1dXVkg3Y1taGuro6BINBcddyzXF+ERQQXKlB216vFxaLBT09PZKhRrcZjcCcnBwBLn/9619lPMmUHRMEaOM6aNGiRbj00kvxt7/9DW63GzU1NYhGowgGg0gmk1LqgGPKMSEYVOMk6bal0IBhm3gEmcPhQHd3twAprnWu60xQoTLdHBO32w2fzweDwSAZl3R/qedLql4DtcSI6lIkw8Y+HRgYQGVlpWT26vXjZ9v29/fLWByP8B1isZici8uYN64nglvWC1PBVea9KJkemlgshoKCgrQSPqrOY3iMeoQS5zKLQnN9MJ6L7cgErGxvKBSSGOFkMikMLI96Yw1Al8slIS+nk5x2QEudeACEodq7d6/URert7ZVFOTg4KJWSuYC5UG02m2RclZeXw2g0Yv/+/TJpZ82ahddff10UPelmUuq0/gcGBiQrMRwOSyDmc889B5vNBpPJhLKyMklr5rlgdBHyQFpVKalZftxcWIogEomI1TalWjt/l1RqvHZXT0+PuAdVpcjNnUqJwZeqxUhlqQLFTLchP6sq9UQiITEoPLeQB1oD6a5HsgO0qDILSqrWIePaIpGIVL3myfNkVLq6utDb24tQKCQuhnA4LIwhGR/1+JNMSSZTKCoaB1pmsxmXX345/vjHP2Lnzp0SyFxQUIB58+Zh0aJFKC0tlQB4k8mE0tJSKaPR29uL2bNnw+v1YuvWrXj99deFscnNzRXXJN0QqguRWbPcZAjQ1c2SipfKFoC4GRhHyKQGAGlp+mSF6W4gq8C5SbcSx4UV4jlfeD8V6PF7DocD0NKLb2a6UuLxOLq6ulBWVoadO3emAa3y8nLk5+cjHo9Lf3Gt0EVG4MN1RICvuuhU44GGx9jYGILBIB566CF0d3dLqZWdO3dKFXd1TZC9JeOQSqXSiszyWQSf6lpxuVwSQ0b20GazSekZlkVgEVq1H4+0XSc/DHqDbIB79+6VmDu/34+BgQE5poggnvqJY8ifXE8qs8t1TABEwEP9QUaO65J/JxglmONnCOA4bqWlpWnMKvUBg8JVVpzuSAJ5ugw5pqydptfrUVVVhV27dkkyUjgcRl1dndQr7O7uPqF4I87RpqYmlJeXy7FoZEdpFDHONlucHdeVqu9VMORyuSawgfwbmTSucboQaUio9ec45zPXm8pAJpNJCR8hCGMCVltbm5zTyYxaxsSebnJaAS1a8UB6dd7e3l5s375drA2yGkyTpvXDgFoAKCgoQG5uLubOnSt1bYLBIPx+P2w2G/x+P1588UX09PTA4XCkpd4mEgmJBeju7kZfX58cZ0Clz4J8gUBAlA6DHFn2gae6MyWcDJvqgqPbg0q5v78f3d3dMBgMYrVNNRuEi2z27NniWqHioJJTN2t+B0gPqlTBDhdrpiJRLTkqB5PJBKfTiWg0KrFm/KwaB8Nnq64N4Ajzxg2DDINaXmBkZAR9fX0IBoMAIBXh1RgPAjgqe1q6ZCa5wR3pOCAWiyIQCAr7Mzg4iMbGRjQ1NUkhTavViu3bt6O+vh6XXXYZZs2aJf3CoOfR0VF4PB64XC4pXFtYWIg333xTilv29/fLURsECaw0zo1ZrQPFMeTmzorsPKdMjbFjXSy6/9TxVF0J7ANN08Q44bMITMgcqBliLFDJ/iDbk0qNZ+Ea3A6wtps6R+gWTSaT6OjowOLFi2VsU6kUamtrZaza2trS2A62i8yxWnJBNcjI1HCNMiaRh2kzMJtzw2q1YmhoSAwTuvssFotkIzocDvT09AiDxnsyjIBB4wT4drtdjABmKlNfcB4VFRVJCRmOQTY3FIWb7sGDB6FpGvbs2YPZs2fLemDb1EQXo9EIj8cjmyf7X3XLUgh21JhJMkhWq1XaStaRa0xdQ/we3ep0OXMNjo6OynFFPPqLYIT3UrNZ6Rbk36hjqENDoZAcK+ZyuVBTUwOn0wm3240FCxagqakJzz///N/1xLGBg/oumqZhaGgIgUAAc+bMkbnHWCnuEWS7qb/UeLPMe3NdkRBgXxGUUX8CkOrtZGaNRiOi0ahk1Ov1eqmvpfY/n8++JIuoxuElk0lJ9Nm7dy8CgQCKiopQWFiI6upq8QadbnJaAS1ahKrFCoxPvK6uLsnW8fl8qK2tBTDuLmltbYXf7xflQeszmUyKuyAvLw9btmwRBRoKhcRCV+Nh+LxIJCLgx+v1ynlfjBGiEiA4YVE5Tn4G6QKQDbOnpydNybHMg+oi7OzsREtLC8rKypCTk4Py8nK0trZOKUCRG3J/fz8KCwul3hOLXqquROBI/RoqcvYdPxOPx9Hd3Y3y8nKkUuPp/QQEKggCjpwzVlBQINQ9A+0zxxhAGuVNpZP5GXVMCE75HTJWma5mALLhMq6Ofc009QlUvzYepPrII4+goqICiUQCr732Gnp6eiRehwCQYIoFHBnzwNIEu3fvFldhbm4uVqxYgcrKSulnnpPJmCBmF5E1yXTvEKzzbwQbBCIMbmbAP49visfjcpYfN7NMtxX7Wk0qUEEIz7QjM0y3BRU978NxDoVCSIymV//nM7hhplIpdHZ24sILLxQ3KJNBGhoaJF2fWZxqsgOZF/7kda5DHuUTDAalijhBFt9fBSMEuQx0T6VS6O7uRl5eHvLz81FZWYmCggLs2bMH7e3tklzAzZFsI+cmWW+6OVkihqENZLBZAHOyuMtMcblcmDNnDg4fPgyDwYCOjg4cPnxY1gfdhOwHBuirle/VekvsCxonXHs8xzKzwCkBNQGu6iqkW08VgjLOMY4di+sS5KvsI4EB/ydzyXtw3lJ3Hzx4EOXl5QiFQhgZGcHg4CB6e///9r48vK3ruvMHkCAWggBIggT3VQsla5e1Wptt2bLsyWI7rpM6sdM2TePaGWeZJG26Tadf6kw6M/lmSZxM3Cb9ZpI49dSu4y22bCmSbFELJVGUxEXcdxAEQCwEN5B48wf0O7wAocWxZCcSTj7FJPD43r3n3Xvu7+wjqK+vRyQSwd13343x8XEcOHAA773AAyQesb+/H8PDw8jJyUFRUZFYkchzJjqpsohzVq2GqnJqMBjQ398v36nhFRkZ8YxhztPhcMiZpSoTaokUNY6OoJT7OBKJyH2pTNTW1koMHmVkaWkpMjMzU9cZvAnopgJadAWoi5RE1G8ymVBSUiJWKo/HI7EpFB48POx2O/x+P86ePSttaNQMjbGxMWRkZCSY/oH5InNc2NRUdTqdWFIoJKkV04rCw5TAKxqNSmFSh8MhvfN4PwI6BvWzMvzo6Kgc9Fcba6BpmlxfXV0t7lK1dhLnSa2cn3HeqqAYHR2VIGQKOroX1IM5FosJcPB6vdJyyGg0wmq1Spo1tVvGhBB8sOhlMmBSBTFdmewJpgo1VZDQbczq/RSOqjBLZRqnpeDZZ5/Fzp070dXVJZo850uwOzw8jBdffBETExPYu3cvqqqqYDQaUVZWhszMTLS0tKClpQUNDQ3Q6XS4++67sWXLFhw7dgyaponwVOOI1EKyBAOs8eb3+xGJRFBUVCTAhvFndKfxsFfdzcw8pDuAQp5AitYdu90urhxaQ5YuXYpDhw5hYmICY2Njki126623Styc6hJiwG4q12zyOw0Gg+J+4/7w+XwYGxuT2Jr8/HxZczw86Dri7wRuer0eNpsNtbW1YjVpbGzE6OhoAjhgQWKv14toNIqlS5ciOztb2rjQEjM+Pi73u+WWWyTLk7xV46s4d7peaA3RNE3aejGri4ezGsOZKrYnvj4vrm3oBBiqa5tlOchDFQClOoi5tlTwo1oLNU1LKOOQLC+4Z6enp8XSwjqETqdTxsV1qFozCew4Tu53AgI1IF7d98mWPmC+V2YwGJTYNwLbtrY2nD17Fpqmwev1Kq71K1u0kt8FrVZutxt9fX2ora1FaWmphGPo9Xq43W6sWrVKGpwDSEjQUcfNPcfCwcFgEJqmieynJVCN96UHp6CgQN4vzw++D5Iaz0keUpGgPDAajSgpKUFpaSmCwWBCzBxLJvl8vqs+b24kuqmAFpA6UBmY17ztdrsU/JuYmBDLBq8B5gV7dnY28vLyMDExAb/fLxottVnGMlCLopDj4UdtkeCPm4QWDlqjVI2FQE+1HJSUlIjGT0tXLBavBcPK7UajEX6/H+Pj4wCAYDAo2YZXo/ny0JmYmEBvby+WLVsm8SQUWAQM1GwAJAjeZCHLQ5euD2bvMdZJtYwBECuWxWJBSUkJIpEIOjs7xbqjBr0CkHgWg8EgwcTJ75zPUIPIVfeKCspp6VAz62iGpzasCsBkwTo9PY2GhgYpPsnnq24L1eo5MDAglZVpTWDG55YtWxAIBKT6dzgclnYtXA9q/InqVuV6JsjnumdJAL1eL+tSPUjpVlFjv/iuKND5N2q2E6uAZ2dnS1mTCxcuiGbNwzMzM1OKwLKAaGZmvLk6rULQkFDcLTluhArO1NSU1EHyer1SboTjYrcH9jvkeyDQ4b5T3S8WiwXr169HKBRCQ0ODAAfO3+FwwGq1Yt++fSgtLcXnP/957N+/X/YpM/I0TUNPTw8KCwuxbt06uFyuhD1GSxDXG79jHAxdeXSdTU9PS1xpbm4uqqqq8NZbbyUcqMl7GYiXyohp8UBln88Hs9ksyiG7UNC6TIDFYGrWsqMCqMZOqVlutHQRPJEXlGGqXON+YnzZ4OCgyNGysrKEv6WVS7VYU74w000tkEsgoe5vNZSBbmsCDYaU8PNQKITOzk4MDAygtLQU7e3t6OvrW2BRvxQlWqMga9Xr9UrbtsLCwoQSQ4FAQILI1VizZIs8ZW5WVhYsFgt6enpkrahAVK+PZweqbndgvlk925OpGezkqcoz1ooLBoPo6+sT+Wy327Fq1SoAkJqUc3Nz8Hg8ovBwTd5sdFMBLXXR8L/UxhmHY7fbUV5eLvV/GFfARc4NnpGRIenOtEbw0OFhRSHEa9RNTlIzA2kSVzNkaBLn86mdMVWdmjQPfwaaM4iT89Pr9RgZGUmIO1EP0SsReRCJRCSTRJ0fBdbk5CRsNpuAB3W+KvCgdYM1gmZnZxEKheDxeMQKQkGoAoNoNIqDBw8iPz8fmzZtksNR1WI5N76D8fHxlL0duR7UmBD2+OLYCUxUgcz36XK54PP55FBk0T+VX8nrLxKJ4Pz58ygrK0Nubq60TUpenwzYdblcAp55T2absfr+8PAw3n77bVEKWFaE1hc19oSWKhXgEYTRKsfPGTNIYKJpmliV6A6Ym5sTF2JZWRmMRiPa29tF+E9MTEhjabbt4cFMfnJM3IeTk5MJYNvpdKKqqgplZWUYD/pwpunMZdfq3Nwczp8/j1tuuQVnz55FKBQStycPTlpkqFAlu1/4jglyaS2ZmZnBa6+9JsknTFbhfaxWK/7Lf/kvcLlc8Hg8cLvdmJ2dFauEaqllsHVFRYVYBwjEOA8G21MRpKJBSy33d39/P2ZnZ1FcXAyHw4GWlpYEy00qKytpcnIS77zzDjIyMuDxeGCz2VBSUoLi4mJUVlbiwoULIgs5NlZip6tQBfXAfD89AAmgSy1bwL1NsER+8l4ul0v2n1q2QpUnnJ/q5uV3DBFhwVzylMR704VMYMt3zf6tXLOqZY5xh3HX/mWXo9C8TJh/H9PT0/D5fGIdt9lssFgsIocGBgZQVlYmfOE+5jrlOrDb7SIL1FptfC4wH7PKeVKZV7Nfaf0igFQVXp1uvhNJUVERysvLJcOWITdOpxNTU1MYHByUM7Szs1PCItTx3Ex0UwEtlWiNWbt2LYxGI+rr6yV7hy6dmZkZ6YmXDJ6YdcXYCR7Sc3NzCTFGwLyfXKfTJVioOAZV02Kqu8fjgV6vF0HOTaHT6RK0KNY1otmdAaaMV5icnEROTo5khqiC92pNuKqJPhKJoLe3Fx6PRwJEw+Ew/H6/VOBmeYVU91cFMV1/0WgUTqcTo6Ojok2x8rmajUSB6vV60dPTgzNnzogWlhx8ywzJoaEhARbkL5DoxiTQoDWBwCcZlKl/MzMzg7q6Opw4cQJlZWXwer0SY7Ng3opcmZ2dRW9vrzTEVYGveo2maRKTlwwASPx7NiTn3LhOCWwIzpO11eTCjrTG8n3TMpSTkyOVt7Ozs5GdnY2xsTGMjo5KHa/Z2Vl0dXWJizoWi0kjaGbH0qKldhBgc3W/3y/xIdS6eeDwHU5OTsKUdXWBx729vdi7d68E7HZ1dUmSCzMvvV6vXK9aNJNJr9cjPz8fubm5OHjwINra2jA7G69kTzeeXq+XmngEmUePHkU4HJZDk/ucVr/JyUk0NzdjZGQEJpNJKr6rz+XYKHN40FOWMGC6r68PGRkZ2LRpk7gUr7S/dbp52eR2u+VzZlm7XC6sWLECHR0dEjTNfWuz2cStr1qw4vedDxWg9U3to6fGaaYCSHwW3d9qaQg+MzlRQbXa8B1wLfK+HCv3gnofgi0qOKqVE4AU4OQ4q6qqUFdXhzfeeAOx2StnHyavK1X+siQJzx+649RQBfZETK5Vl52dDZvNJt4QujRVL4OqQHENqoobvSi0KDI2jLJBlT9M+GG8FTPlgXm3K2UDx8nvrwb036h00wGtZGuWxWJBZWWluGgoeLKysmCz2eDxeOByueD3+yWYk4HfZrNZLBpAvGyDzWYTAaPWqKLmlxwXQPcNFz4BE8EL7wkgQaip96BlgBuJMU+0QphMJni93gS30XvlGZ/J1Pje3l6sX78eb775JkKhkAAtuvRUjVa9j/pfZrawICIzYdRAf1U409JCa4hacVxNVKA2y2BMg8GAiooKaUCrBuPy8CKYTba+qVq5eiCYTCacOXMGc3NzqKysxNjYmACtZIGiI9K6eM/x8XHRiJndSnDIw8Rut2P58uViQaLQ5NqgqzgQCCAWi2Hz5s3o6+uDx+OR96TTzfdL5NhV1yAPJj5bjb+iZYlxcXRt8OCnFZXWIWqsc3NzUm5EXQPq2qXSwMOT74VxePynXj8+Po5Dhw7B5XQsiD1W1yfXSm9vr/Tk7O/vh9frhdPplDmtWLEChw8flv3APQjMA3W+D50uHuu4f/9+DA0NyX4joGTGMC0Bb775ZkLvVLp+WLtuampKMpinpqbg9XrFDajOhYf+7OysxFqq7koCHo/Hg2AwCLvdjk2bNonCQr6rLnJ+lirWjbzMysrC+Pg4TCYTamtrBbwkW+z5/mgJoUKjJr4QKMViMSnAS35TdqnAK/l71T1J+UjLlmoBVv+WYE0FG/w8WQarChQzutWyKswMLS0txeLFi3HixAl4PB54vV5JiLBZTSl5mbw+U4EtAOKyBCCudQBS0mbbtm3o6uqSsjyjo6PSzaSgoAAAJFNajWFTzwSV3+Qv58/9yIzfiYkJWWtcJ0yamZmZgcvlgtvtRktLiygRmZmZEveodjBQ157qgnwviv6NQDcd0AISFzhT4UtLS9Hf349z587BbDbLIcdDOy8vTwq9sZTCzMyM1L2am4sXacvJyZGFR01eFTx0B3LDq0HcqjuDFrVgMJjgZqEgo2aibt6MjAxxJzC4tqioKKFfG7U2dZFfadGr7qzp6Wl4PB6cOXMGW7ZsQW1tLc6ePYuJiQkEg8GEauSX4z+1p0AgIJYTmqTVljhAonuVGV5MZSaveZCrbgimLOfn50vW2djYGBwOhwgjCmJVy1MBoSoUyHMGvwcCAZjNZuTk5KC7uzuhtllKPirzNxqNKCwsREVFhWQ1USgaDAYsWbIEtbW18r4ikQiamprQ1NQEj8cjIG3x4sVS2Z6Ze7SCqnErnCMPPVqPCHTY1JrXGwwGCVTnfNijjm4uZnbS7U7QS+A6NzcHu90uYFcNNufaZzwNrZuhUEj2DtcpYxjjsXnFaGtrTbmmVHDMLOE1a9ZgcHAQp06dwgMPPCDxKB6PRyyvHDd7N1JB4fN5QI+Ojsray8rKQm5urhxGrIxtMpkQDocRDofhdDrFvev3+5Gfn4+CggKMj49LAVAemtwTdBXymTzsOS+DwYDi4uKEwpr79u3D7OwsVqxYgdWrV+OnP/1pAv9S78WFPUFVCwvXMstIBAIBOYiT9xv/hmtAdcepwEg9cIFES5RqvQaQ4B5Tx6muZ95TtUBzTVHOcv1QTjBGLzkkJBab783JshYsb7JmzRpUV1djYGBArLn19fWSCXwloAVgAdgg8bljY2OYnZ1FSUkJDAYDrFarlOwwGo3YuXMnGhsbYTQa4fP5xPoVi8WzbBlzqCqR5G/yWqayRHlH5YNhAOQ/S74A854TKu4jIyMiD/R6vQTis5+oasVK3p/qGrhZ6KYCWslus9nZWVy4cAHT09NYt24dJicnMTg4CL0+Xkto5cqVcDqdsvEYrMiYCwYX03XDezK+SC1+qGpoqquQFhUKfNal4eHKWBCr1SoxDupcVHBBTczr9WJychJWqxU2m00aXqda6Kp2dymeAYlB4n6/H52dneju7saGDRvw+uuvJ9wrOQhTBR68B4Ow1eanVqtVsjfV8dHMz2Bz8pkWAjVOhM8wmUwSG2S1WkX77+jowOzsLBwOhwTyq5YyAAvAgPoZBQ8LSRYVFcFgMCQIl2TSmOGlA3S6+OFZWFiIqqoqqSNVU1Mj68pms2HXrl0oLy/H3Fy8ev0rr7yCAwcOoKenRwRgWVkZlixZgqysLLhcLqxevRrBYBDDw8NSuBSYr2PEuTCmiJYin88nlbQtFgu2bduGvLw8dHR0oKGhQVyQtMQUFRWJtUV105BnaoA7QZtOpxPApPKZ7kNaYOkap/auuscyMzMxdjEr93JEy9KpU6fw4IMPYv/+/WhtbUUgEMDixYsRDoelLY5Op5MSFVwHXLvJZTd4eLHPHxswM8uXoDMnJwcbN26UfopUkPr7+zE5OYmqqioBVLRQMItVTZyhq5WxYmplfu5HxsDo9Xrcf//9MBqNUq5FzRoDkKCgKYtzgYWBBTMJgNkYfnp6GlarVbKbgcQq+fx7NWZL5ae6fzgOWlQpE6gQ8HeuU7XTBQGdqqiq74yuctbco9tNlXV8thouwNAKKktTU1NYtGiRBHjT4j43F29FNm9Jv3pXWCoFl1bNQCCAiooK5ObmwufzSSeSN998Exs3boTZbMbIyAj8fr94Lbq7u0VhSD7f+E7UsjXko7ofGYdGCyVBIcE0eUP5Ozo6iqGhIXi9XuE7u6PQ+EBQpgJL1RNzs9FNBbRUUs3SeXl5UqcJiC+wzs5OVFRUSFuTubk5qQlSWVmJ9vZ2GI1GTExMyD9WLCfKpwaaDGrUQG0KHC5oAFKYk7561cVITQSYj+XhQp6YmJBYGrvdjoqKCni9XrFKqJrglQDWPJ9wceyJlsC+vj40NTVh+/btqK2tFc1crW9DIKq6OzleWgTURrUOh0MObL4j9QCnhk9XqsPhQEVFBQYHBwW0qUDOZrNJHZyuri4Br+ShaoHhekgFQFXTfywWz9LiXGtqamC1WhEMBhO63ie7INV1l5ubi5KSEjgcDpSUlMjPrHtFIDYzM4MjR47gzTffRENDgxzINPV3dXXB4/GgqKgIPp8Pmzdvxic/+UmcOHECZ86ckdgOAlm+C64x1eLKprmPPvooFi1aJG6Lw4cPyzOnp6fR398vYINWpmQeqZZX1bqlxifyXaqAm9cCkHecbF3UYvP1fC5lrSHPx8bGsGrVKqxfvx4HDx7EG2+8gcWLFwsfuSY5t6ysLOTn50Ov14vVkCBnbm5OYn5MJhNKS0sFGMzOzqKmpgbFxcUYGBhIsACOj49LFhjdhqFQCFVVVcjJyZF2Xyz8aLVaZZ9S+WByCQO0Ozs7kZ+fD6vVit7eXoyOjmL58uW499574fF4ZD+ovEvmjXyksI/8oIUzGo3CZrNJD0hmj05NTUlMV1VVVULnBT5Dzejjf8ljguZUQCxZWUu2OHOc3K+q1YbfAfMdINgTle+JYILrTlUkWY5EzYxbsWIFKioqpAwDLfa8f/zvLy9Hky1KybKBxW59Ph+WL1+OW265RRTC0dFRAVE2m02KNIdCISmBolqoOH9aHdW9Q8VBBbmqlTtZeed6VcFvNBrFyMiIJHlkZsZ7QzLEgcoFkzrU53AsNyPYuimBlnqYckF1dXWhp6dHvhsfH8epU6ewdu1aqe7ucDjEPchsH1VLI8jh4uXBQuuWWtaBmyPZYsN0dqvVKk1BGZfB+CJVQyC4YHYiA0/ZwZ1Vp5PN7qomeTWkXhaLxdDf34+WlhZs3boVt912G37+858jEAhI2jcP11T1fChImXXHLJ+cnJwFGjf5xHHSXZebm4uKigpUV1cnvDc1LoBuPY/HI1lf1FRHR0dhMplgtVoTtDjV8sdxphr/zMyMpNJrWjxwnBaZhTydf192uwNVVVXQ6+NtPu666y44nU5JqKBWPj4+jpdeeglvvPEGmpubJR5IBYSMD9q+fTvuvPNOZGdno7OzE3v37oXNZsPx48elWCnfBZ9hMpmQlZUFp9MJl8uF4eFh7N69GzqdDocPH5ZDiYHqaoAwmzCvXr06oWSC6gbi+lIzxZIPAfJKjRVTM9PUA5GWnpmZaSCFsFbXN93cmZmZMJlM2LNnD44cOYL+/n4cOXIEJSUlci1joJxOp7g3WcyUY1Pd7cwKy8/PRzAYxMDAAABI30q6zvx+vxzMOp1OukNwrff29sLlconFinuY1kHGzA0PD4sFtqSkRJQpKgn9/f0wm834kz/5ExQVFeHw4cOSgKC6vlUwEgctF2O0tETXIUE8++2Vl5dj8eLFGB4elvlEIhFxkdJil5ubm3CoqtnI5AkD6VW3ljoudZzcq+QFAZmqvFF20iWoWiTV2k45OTmw2+3ScJwuN1XWMKOPlkuC3KqqKsmIHhkZEc8F19jVK6yXlrPRaFTWS0lJCSoqKmC1WuH1esU9OTAwAIfDIXXtuD8IWFWLtbon+L75XpK9KsmKoert4X5MtgSqRZa5T6qrqzE3F2/HEwqFEuQon3slPtzIdFMBLdkQF981hdnRo0cTgA+vHRoagt1uR2FhoYAon8+HkpISMUtbLBaYTCaJXUlOe1YtPKrfW01TJuBh410ecIwFAuK1Thh0mp2dLYCNzzIYDHA4HOLSoZWFvn8gUdtLjm+4HKnWP9LExAR6enrQ09ODLVu24OWXX0ZmZqYUT1TT0lUNTnUdsNK7z+dDVlaWgFfObcF7w3yJBMaOMPkAiB/gkUgkoRwG29QEAoEEQU7gor7vZMGjxlXwGZqmSQFaxuswEP5SNWIoWrIt2di4cSMWLVqE+vp6HDx4EB0dHWI1YOo0U7qPHTuG06dPJ2RUquOjRekTn/gESkpK4PF4cPDgQZSVleH3f//3UVBQgJdeeknKAhCY8SBiTN/58+fh9/vx7LPPisa/fv163HHHHbjvvvvwwgsvCGCmdWBubg4XLlxAYWEhHA5HwoGnWhGAeRcED8qpqSkBOaobXa1Grz6LSQ+RSAR6LZoyGD55jRCgTU9PY+vWrXA6nRgZGcHJkyfhdDrFTafX61FdXS3ZjSMjI5ienobdbpdUemZ6mUwmAVlerxddXV3Sp5JglDFM1P4JJmidHh8fx8jICMxmc0J2LsERAOn/x/p8zLhctWoV8vLy0NPTg6ysLHR1dUmpkF27dolyyMQSFUAlUyyWKAtVPpLnfr8feXl5qKurQ3t7OyYnJzEwMCDzmpuLN5tW+5DScqKCKO7pZAVKjSFT96Faw0xVdlRgpMoV1R1GUEa3LIt30iKn9g7kHJicQnDHZy5evBg1NTUAgIGBAWmxpM7hNwEOyX+nafECqFx/paWlKCkpwcDAAPT6ePYwK73zb5MVQ/6uKmKUW+pnyeshmfeqrOYaUvnCNcyzKhaLobCwEC6XCzMzMxJCkQzg1J/FOn0TWbZuKqBFYhaYeoCqqFtdkF1dXcjNzYXdbkckEkFGRgba29tRWlqKoaEhRCIRSftl41U1/oL3pKuGwcqpgq95+IyOjuL8+fMJsQrcTNRoCDCotWdkxBsdGwwG5Ofnw+l04tSpU5J+zueogeUq2LsUCTZN2hhM5z9z5gxWrVqFuro69PT0CMhrb29PcMvxHurPRqNRXLYEi7y3ahXh9ep7UvsrUriqoJPWG7PZLC4f9R6qQFWTClQtja5aFYCxsjfbhqxYsQLNzc0Jh1vyoa8h/pzyigrceeedCIfD8Hq9GBgYwLlz5+RgUcHTf/gP/wGLFi0SJSBh/SrvbGxsDD6fDxkZGQgEAli0aBF0Oh1KS0txzz33wOFwYP/+/dLhgGMKBALioh4fH5ckDq5hr9eLiooKlJSUoK2tDQ0NDQmWCLZhYQV4tekylQl13LSi0dJAoU0LViwWE1BDpYefcx11d3ejqMABTVuYvaXGH5E/7OywbNkybNiwAa+99prElxQUFCAUCon7je10jEYjamtrEYlEhBcEh2xFRLCkxnARaNIi5nQ6UVZWhvb2dgFfGRkZElagNmlmYUdabxiEzSLI4+Pj0jMxPz8fJpMJkUgEv/71rzE7O4s9e/bIe/J4PAIik5Wo5EM1/pl+weczMzMYGxuD2+3GypUrkZubi/LycoRCIbGQswRDNBpFfn6+7CvVZUzrHWUNk3ouBZi4H1WFjPdULTGqHFBDImiNUzNWGVtkt9sBQLIguU/Zc5BAn4qyzWZDTU0N8vPzJWCdrXA41mTr+5VIlQ3JQCQcDieszeXLl6O5uVlACxUBp9Mp/FUBl3ov8kQFk+oZkMxDNbSD8k49F3lO0ppFRYvW2MLCQgAQl7PaKSQZvKmf30yUOsf3JiFu3mRLhvr55OQkzpw5g+zsbAlOZvB7RUUFHA4HnE4ncnNzkZeXl5B1qN5HTeFNtqZwI5hMJonhmJycxPj4OMLhsMT/TE1NyUbgYcZyCDR5050VCASk3Q4pWSikKsGQTJoWS9h48c80sRKxxtHGjRvFlbB+/XrU1tYu4DWFgioc1NoxqubF8apCg6CLAtTv98Pn84nAY5aU6opRXa28t6r9JceGUIjw0OcYVIHDchQsH9Db24v+/v4F7Z1UYWIwGLBz5045sCi06TJm7Mv09DSKi4tRWFiICxcuJDxffQfq2AcGBqDT6bBs2TLU1dUhPz8fra2tCIVC2LhxIx5++GF89atfxbZt2+QwYYZRd3e3WEHZkJfjHx8fx8mTJxEOh6UZsqpJU3EgOFItC6rGS7fO+Pi4BMoT4HLetIKo1i+6LyYmJtDR0YFwOAydbn4N8QAm8TPe1+v1SuXq3bt3i1Xj1KlTAgJ8Ph9GR0fh8XikQCytL6xjxHszWYFFdQks9Xo9HA6H1NHiu2HMJgAJ+mcWoabF21kFg0FxOROU+Hw+6PXxQq3FxcXSONrj8WBkZATZ2dloamrC5OQkamtr8elPf1r6Z7IHonqYcr0kH7SahpSgNRaLV9cfHByUGMri4mJYrVZkZ2dLZp7VaoXJZILdbpdMbFp8VFcfAaT6OX9XFSX+znguvn+CXHYLSD6w1TgwFeDTA8AuDOo+J+gdHR0VdyDfdW5uLlwulxTs1TQtoXdtssJ4NVYtzjs+Nv6bl0mzs7MYGRlBV1cX+vv7UVtbizVr1kjCDi2kzKZNtsTzH+Mu+U+1RiU/j3tT3aPkuXouxmIxKdlCGQdAzsTc3FzZb6oCob4j1cJ2s4Es4CYEWqlMmUBq1xEQ3yDhcBhHjx6FxWLBypUrYTabMTQ0hGg0iuLiYpjNZslEslgscDgcCb3BotEoQqGQaBdqzR41DT8cDmNgYEAELwUzq4Dn5ORIwUhaECwWi9RvKioqQlVVFbKzs9HV1SV9Ezk/dd4qmPlN+chU3qmpKSxduhSxWDyJ4PDhw5LZxgNJdSUxCDpZw+NY1JgpjpECgNdqWjz7pa+vTzb23Nyc1H/ivdQWR+rz1HGplr3kNaGa3DmfwsJCuN1u7Nq1C2NjY+jo6JAgcfUZ6sFmsWRj8+ZNUkgylfCncOzq6sI3v/lNHDt2bME9k8cWiURw9OhR4Q2B98jICEZHRzE6Oor29na8+uqrkhaenZ2NgoICif2bnZ2VODbWc8rKysLQ0JAAlYceegg1NTUJ69rn80mwPa1T09PTUupDTZfnfzlPtV+iWlWcfKYlZ2hoCK2trRgZGUFGRgby8vLURZgYPHiRaM0ZHh7GkSNHEAqFsGPHDixbtkyKAQ8PD8PhcEi2IIGEzWaTTgKMdaS1g2BxampKCr/ynbHnJYP8GUvFfc4+hIwJ5LrjOuHPLJ8yMjKCmZkZlJeXY9myZXA6nQIQenp60N3dDZPJhMceewxlZWUYHBzE+fPnMTQ0lNA+RV3TlzroUoGFSCSCcDgMj8eDWCyG6upqWK1WsQwRzNGCTdDFd6q6C5lFqrqx1ADrZBBAUEWwpcYAcv2plmiCVHUf0xrKHpqcJ5UBehh4bzUzb8mSJVizZg327t0rwJLuW94neZ9fieblCxD31/Jv5udOK3dbWxvC4TC2bduGVatWCdijkhcOhwUgqaAoGbQCiW577g11HSTLRfX9qEHyPp9P4mn1ej2sVqsk0NBSyf2hWv3mQeU8uLxant1IdNO5DpMtDskHrLrg1GyJ0dFRHDhwAP/u3/073HXXXTh//jwGBwdht9vFEmW1WiUOgC4P1sIiWGKqNNO5udmZDUfrADMh1WKcFMYcY1ZWFvx+v1y/aNEi5OXloampCb29vRIQq7rg1L+/Ks1CSwyqJJ8olBjgS9Dp8Xhw9uxZuFwuCTROtiixsKHqSiCpwjNZC1JTmPV6Pbxer8yfAFYFa6oVRX3P1F55fXIwabI7g9dZLBZYLBaMjIxAr9dj7dq1OHv2LHp6eqQG1aV4aDabUVxcInVxTp48mRDEr/6tWtZjwa2SBCIPbLvdjmg0CpPJhBUrViAQCODw4cNoa2vD3NwchoaGsGzZMixfvhwVFRUYHx/HP//zP8v8uV4JIkZHR/HKK69ILbaenh4J+M7KyhJhzw4Eakasyk9+xueoaf88uJLnRyDW0dEh4CwrKwvl5eUwW8zqxbjIjAV/zwrzbW1taGpqwo4dO/DAAw+gubkZ0WgUDQ0NWLJkicSs0eKk0+mkiTsPNrZa4rri+uVzNC1e7JIuOx78dHkyvowV12mdVV2lvHdWVpbUVSP/ampqUFpaCp/Ph2AwiIMHD0rdrIceegjj4+N47bXXcP78eQwPDwuf1f2avJfEqovUsTJ0gY6MjGBychL5+flYtWqV9LCjdYkyjsVMGcdGN2qyZUXN9iTvVLcgQyGYAagqK1xbarsxJioQiKnWMz4DgCRUqJ+x7hwD9P1+vyi2dXV1KC0txcTEBPr6+qSzRipeSbzbJUidQ2oZoQGIu6AHBgZgt9vhdDpRU1OD7du3IxKJ4OzZs9L/kHKTZYTUDEj1nfPZnH9ZWRkcDgfa29sTLFO8TpXzKoALBAJiBdfpdFLWxG63o6CgQMIPaKFNpRgumPFNZtW6KYEWgAUCKBl8pbJs9fX14Ze//CX27NmDDRs2oL29HefPn4emacjNzZV2NCz1EIvFpGihpmniImOKOOsTAZBaXXStsBIvq/9mZ2djYmJCqnfzwJudnUV+fj6WLl2KjIwMnDp1CsePH08I/k7UMLDg58vyC4mAlMQNzfT1++67D0899RT+8i//EtPT0xgcHERRUZHwQeUvYyTIF95PjRVQec+DQwWaFNrAfNNpdYPTHcdaL+o7Zd0jAoDk79WYEH5HF47f70dzczMWL16Mubk5tLe3o7u7OyGuLHkdaYjPldqfzWaTvoCq9S2V1UqlS2mBBDHMhJydnUV3dzcKCwvx7rvvIjMzE3/yJ3+CzZs3Q6+P13M6fPiwCN6srCwZ2zvvvIPe3l74fD4pn5GTk4MlS5aINYUWve7ubgENtAoQ3KuFE1WL4PT0tATLqzF5nF8ckBbjrbfeEl75/X6UlpaisLAQy+rq0NrampReP88fNSaFZT3efvttLFu2DB/72Mfw9ttv4+DBg5iZmcGLL76IO+64QyzQNptNyjPQupWXlyfZVAASsoY5PxYPLS4uRllZGQCgoKBADu1YLB53xWK8fX19aGlpkbIrPCA5B8ayscFzd3c36urqYDAY8NZbbwmAefjhh5GdnY3Dhw+jvr5eAtY5vuTwhUSL1sV/Ur5lobwj0BodHYWmaSgpKZF2U7RoxWIx9PT0oKCgQNppEXCqruZIJAK9Xi9ZsPxb1Y1PImBTrS9cZ2rR1GTZzftwXzPzm0oXwe3ExAQcDgcCgQCsVisMBoOEg7DUSl1dHTIy4r0Gm5qaJOhfpfmxp9yWQonA49IXa5omwI5xemVlZdi9ezcMBgPOnTsn9Rtp8WO2bLJbWJW3er0ehYWFGBwcRGtrq4Rp8B0knwv8neEFBFl8fy6XC06nE9XV1ZKNzFImTHRJVfJC0+K8utlAFnATAq1kzSIV+k9eCNxQsVi8rMFrr72WkM104cIF+Hw+5Ofni6bAPnKapiWUEGAcE7N01BpDsVhMtF4KAPY+VKuuU+OLRqOoqKhAXV0dfD4fDh8+jPb2dgmAp8BRgY6qPaaa63uhWCyGYDCIs2fPYuXKlVizZg3Wr1+Pd999F4sWLYLb7UZlZWWCQEx2H6j1d1Trlno4q1WM1bGrTVIpiFUQ5na7peq2Ok+W0CA/VIGvWon4PQ/hzs5OqSHz2GOPQdM0yUa6PE91Eu/D9P1bb70VIyMjOHHixEJzu24+YUMlPkP9F4vN9xKj6zkWi6G8vBzvvvsuXC4XnnzySXkP3d3daG9vl1IlZ8+ehcPhwJIlS5CbmyvtN7xeL9asWYNPfepTaGlpQUtLC3Q6HcrKyhAMBrF69WpxKRQUFMh41NpEdNGo5UQI/KksqE28OaampiaMj48jLy8PIyMjWLJkiXRruPXWW/Hiiy8mxOuprmb12Zqmwe124/jx42hoaMCePXvw8MMPo7m5GV6vV4DLzp078dGPflRcNzabDbm5uaitrUVfX5+48gFIA3N1LdGd4/f7kZ2djerqalRWVsJkMsHtdsPj8UgmLmNxWBaB+1OdA9eC2odydnYW586dk7jNnTt34q677sLw8DDefPNNkUEAxOWjutIWgHhaYbRL1zYKBoMYHBxER0cHXC4XiouLsXXrVgwODkpQPzDvZiwuLhYLP3s30grFIqf8HpgPm1Dj83Q6XUKckLrvVWWLf5dcw4vrjLIk+TvGtYZCIZGTbIfERKIVK1ZIseOOjg709vZiZGQkwSX3/l1fcStWMvCKxWLSrYRdPpxOJ26//Xbk5+ejubkZg4ODiMXmK9kzRk5tI6YqrHNzc+jq6hILLRX9ZCVTXY+MKaZ7nOPIyclBeXk58vLyRFFmKRCv16vEZ8Xri2mayqubD2CRbjqgxUNfDYJWP1cPMGD+IFY1peHhYezfvx8GgwHbt2+X4GO32y21eCjsGKdBLY1Chw2Dg8GgfM9DmKZhxgWoQdaFhYXQ6/UIBoOoq6tDbW0tzp8/j+PHj6OzszPBXZg8v+vBx+HhYTQ3N+NXv/oVHn74YezcuRNvvvmmHNiTk5MitMhPYKHViN+pgEXV0FQgpAZNs8Al50shy/Y7qeZNF6AaOJvsvkx2WTQ3N2NychItLS2wWCxYsmQJvF4v3G53UlmHRLN5/B6AXj8fe8SA0aVLl2JwcBBdXV2J6fgX/5eQeq9kyhIMqnyIRqNobGwUa8Pg4CDGx8dx7733orS0VEC+pmnIz89Hb28vZmZmsHz5cokfMhqNePDBB7Ft2zZ897vfRXNzM9ra2lBUVISmpib4/X6sXLkSkUgEjY2NsFqtAOJudQpermNVaPOQVMEWY7PIq4qKCgwNDaG+vh4WiwUFBQUYHh7GsmXLUFNTA71ej127diHHMt8/9FKkgouZmRmx4K1duxZ33nknDh8+jF/84hcIBAIoKirCgQMH4HQ6sXLlSlkbpaWlApIIhLme1aLBk5OTEj9GWTEwMID29naYzWY0NzfL+yIw0Ol0wnMejiqg4NiZBep0OtHa2orDhw8jHA7DZDLhoYcegsViwZtvvokzZ84IECDfVYChyrNk0nDpwq/T09MYHh5GV1eXZMIVFRVh0aJFGB8flxjQcDgMABIPxTnabDaJ3aMVmT/TGk+LLve1zWYTa//o6Chyc3MBICGGivNMdvED8wBM/RtN08TFSdfs7Gy8swSbH8/MzCAvLw/Lli3D6tWrAcSzQXt6etDX1yfZf8kK69XS1VyvWh9HRkZERsVi8SSnNWvWSCuxwcFBDA8PS1V5KuVsX0ae0jKYl5eHqqoqAEBHR4d0GeBzyTtgvtWWGpaQl5cnzasZ25iRkYHx8XGMjo5KYexE70Bc9l1Kyb+Z6KYDWqQE185lNoBqiVGF4NDQEA4dOgSz2YwNGzZg9erVokHTMkDLCt19zIIDIBtX1TIIGiicKDQodBkcH4vFmwjn5OTg2LFjqK+vh9vtThA+BB3JlCxwLyWAL8UH9XdqM42NjcjPz8epU6ewfPlyrFq1Co2NjaitrUUwGJTKynQVRaNR4UOq5yRnk/FzYH7TEtDS1aJaNniQpZpbZmamuMlIqYCpOhav14upqSkMDw+jp6cHO3bsQGFhIU6ePJnQo1AdZ+Lg559DmpqaQnFxMZYsWYJgMIiRkZEF80wYC7SEd8t72e12lJeXo7GxEadPn5asM8bMjY2NifWBByIbw5rNZnELsW6UXq+XhIxIJILnn38ey5Ytw8TEBAwGA0pLS0WgHzx4UBSJYDCImZkZWCwWAVvcAzwYuT75HLp2rFYrWlpa0NzcjNzcXOh0OgwNDWH58uWorKyEy+XCtm3bUFtbi4HezpTrQv1dXUO0EDQ1NaGhoQF79+7F5z73OQwMDODYsWMIhUIoLCzEL37xC1mrDBMgaGBMFoEl50EASYt0RkYGcnJyEI1G0d3djdWrV6O2thYTExOi7bOfY0ZGBoqKipCZmSkdJ8i3yclJzM3NSQkNnU6HgwcPStzWF77wBezZswc9PT04ePAgent7pbab6r5W19GlwFYq9xs/1+ni7bbcbjcGBgZQU1MDu92ODRs2YGBgAJOTk2KpY80qJv4wLpV7joom37+6Z9TuD6wtFgqFxI3c398v1lICSLWwbXL5Aq4vylfygYrV3Nyc1L3LzMwUq6vT6cTWrVuRnZ0Nr9eLs2fPSkPy9wsQrkbOqu9ufHxcamfpdDpp11VdXQ2XywW/3y911DweT0KiBvnN84RWRMZPmUwm6bkZCoVEVqoZi8wGLywsRF1dnXgMsrOzkZubC70+3pg+GAzC7/eLhZZKFvmfDOI4z5uNbjqgRaRNy4MqkOe/X/g3yS5GCuP9+/djbGwMlZWVWLZsGcrLy9He3i7uBpfLBU3TxOSf3HRYDYxn8VMKR1Xr1+vjlc6tVisqKiowPT2NAwcOyOF6qUV9qTn9JkQrSrKG4vP50NjYiI0bN2L79u1Yu3YtTp48iQsXLmDx4sUAIEVEGYOmxlWpfFULW6rPVK+jMIhEIpI+z6r6quVRfVe8T25uLqxWa0qXsWpZ4NhYZ0ev16OtrQ12ux333nuvFGv0+/0pA1BVEK+B2t384ZyVlQWTyYSysjLJDkyOl1BJtbSyUXFOTo4kQLz11lsJ8XuaFq+VxUOY/AyHw3C73ZKByEKbtLDMzMygu7tbLKOhUAhNTU2oqamREg+LFy/G8uXLsW7dOhw5cgSHDh3C2NgYvF4vcnJykJ+fL7XiVFei6srQ6eKJFCyvQAvD3Fy8sfg999yDQCCA4eFh3HbbbSgtLZXOAZejVHs1Go2ivb0dr7/+Ompra7F8+XL89V//NX7wgx/g9ddfx9DQEAoLC/HGG2/gzjvvREFBgQTq87Bm+QadTgePx4PCwkLYbDYpxRAOhyWGJzc3F3Nzc3C5XHA4HDh+/LiUA2FyTF5eHoqLi+H3+yXOhkkl7O1oNpsxNTWFl156CX19fcjMzMSjjz6KL3zhC9A0DceOHcPZs2fh9XrFHZvKjZpspY+7py9+h0SrkLp2ua/cbjeGhobQ29uLW265BaWlpVi1apWAKoIbxpDm5OQsAHwAEsbF/chK7zzcqRQYDAasXr0aoVBIALx6DwJ1daxULAmgKKvoIeB3ExMTYl0lYDObzVi/fj0WL14MTYtnALa2torSoa4nFcC9F5oHtfJJ0hWJYFiV69FoFFVVVRL3mpOTg9LSUmRnZ6O/v18s2FTSNS1elJm1z1SgKWsAiZmodIPznMnPzxeLItswEZROTk4KCPd4PPD5fAlZ4an+u9CVePPQNQNaTz/9NF544QW0trbCbDZj69at+M//+T9j6dKlcs3U1BS++tWv4rnnnsP09DT27NmD73//+3C5XHJNX18fHn/8cRw4cABWqxWPPfYYnn766UtaQH5ToqzhIZnKopF4faIFTKeLF8drb2/H0NAQrFYrKisrsXnzZqxfv15M3wwmVBuVUgtm8Tymk9ONwANT0+KxXrm5uViyZAnKy8sRi8XQ0tKCU6dOSUNRlS7nJrg2fFNdfQCFw9jYmGjqK1euxIoVK/Duu+9K89Hq6mopB6CCn+RDkYKUVgLVPaBeT7chg42BeeE7MzMj1hR1zNnZ2VJckZ+r4Ed13/AgYHPj8+fPIxQK4a677sKePXvQ2NiIjo6OhBIaV+AcYrF4A1qW4wiHw1i5ciUmJyfR1NSU4EJJ9Q7pjsnPz0dxcbGk/Dc2NqKrqws1NTUoKSkBAGkC3d3djddeew2bNm0CEHfzsbbQ2NiYpOWHQiGxrnZ1dSESiaCwsBAWiwWDg4PIycnB3XffDYfDIdctWbIENTU1uO2223D8+HE8//zzcLvdGBsbk9pPRqNRGqXTAjkxMYGxsTEpoJqTkyMWtrKyMuzduxcnTpyQEgwcp6Zp8Pt9KdekGvsXZxbkHOP6bGhowP/7f/8PjzzyCKqrq/HUU0/B6XTiX/7lXwQ8HThwADt37kRpaamULjGbzcjLy4PD4YDb7RarCFspsbYe1yYPwWg0it7eXpkn3dkAxOVDixA7GBiNRlgsFtjtdvT09GD//v3w+XwwmUz40z/9Uzz++OPQ6/U4ffq0WLnYleJSVmDVup0s3y516Kmfj42NwePxoKurC+Xl5RLH1NLSAqfTid7eXtmvrDFms9kSQJFadJnrnH+jggnWcrNYLOjs7EQoFJKG0GoAvAqoeTZwjuxZq+5trhG6upitx5ZlNTU1uOOOO6DXx1tMtba2Ynh4GCMjIwus6Qv5nJKFC/h5ZYVX/X5erqqWutnZWZSUlCA3Nxf5+fno6elBZmYmCgoKMDQ0JFZPKupUbFliRX0flJ8MVyFPLBaLtJoi4GKdtFgsJvGIfr8fHo9H+h4m8uTSIStpi9b7oIMHD+KJJ57Ahg0bMDs7i29+85u4++670dzcLFaHL3/5y3j11Vfx/PPPw26348knn8QDDzyAd999F0B8o9x3330oKirCkSNHMDw8jEcffRQGgwF///d/f62GCp2Oh29ikF6y5p1sbUnlpmB9IBb4a29vF6BRVFQEl8sl5RsoWBngbjQa4fP5MDw8jCVLlsDpdEoNGYPBAKvVKr5xZvfQxcLCdeq4gcSKycl0LRa4qvWq95yamsLAwAD8fj9uueUW3HPPPfD7/Whra8OJEyek5tfExIRUglf5TJDDzcx50crF61Shp1r/VBcCMw75NwS1BQUFknGTDPKSD2sCAjZWHR4eRlVVFe69914MDAzg6NGjaG1tXVCz6NKkg6YBMzPxmkKhUAjDw8MYHh5GJBJBRUWF1CyqqqpCe3u7BLlzPBkZGSguLobL5YLBYMCpU6cSMlz1er24D8bGxuTJTU1NsFqtKCwsRG1tLbxeL7q7u1FeXi6ar16vR0FBAYLBIDweD+bm4v3r6NYJBAKor6/H0qVLsWvXroT4l8rKSrS2tsJgMKCwsFAK2er1enHNsJo5rbSZmZnIz89HYWEhsrKyYDabsW7dOqxatQoNDQ04e/YszGYz7rjjDmzbtk2Cq9+5KCvU95a8X3U6XTymTTkPZ2Zm0NfXh1deeQUejwef+MQnsGjRInz5y1/GqlWr8Oyzz+L8+fPIzs7Gyy+/jNWrV+OWW24RTb6srEySHiwWi5RgiEQiUtmddeVoITQajfB4PGJViUajAhoYO8OkFq5zur2PHDmCvr4+jI+Po6ysDF/84hfxwAMPYGJiAvX19Xj11Vdx8uRJOeSSrc0qILmcpVRT6jglW7ZUhXJwcBDFxcUYHx+XQqorV66UDDiuXb1eLyUm1FZhybJKVaRkh+jma2/p9XrJVFSvB+bjMYH5RBjuD1q5uMfpsta0eJhDb2+vdPjg/UpKSnD77bejvLxcAuC7urowPDy8oOWOOof5z95LrNbVXsn3ArGasxTQ9PQ0ysvLpWAo25iZzWZZeyy/oiYcEAyxa4maxc1K/w6HQ5res18ugSpd4JFIBIFAAH6/XwpWL5yfbsFcGLOl8vJmoWsGtH71q18l/P6Tn/xE4lh27NiBYDCIf/zHf8TPfvYz3HHHHQCAH//4x1i2bBmOHj2KzZs3480330RzczPeeustuFwurFmzBn/3d3+Hb3zjG/iP//E/is/5ehCFkhp0nfx98u/JFi4GMQYCAbS2tqKwsFAWbVlZGaqrq5GVlSXF8iYmJlBcXIza2lopnMh2P9PT0+K26uvrQ1tbGzo6OjA6OrqgBoo6/uSxXo8FnQxAgXhQcFdXFw4fPoyPfexjePjhh2E0GvGTn/wEra2tGBsbg8vlQkFBAWw2W8r7JWuKtDLR969qw9TKGLBJ1wkPNPWgURMQ1OeooFF1c1ALZlLD2bNnYTKZ8Oijj2Lt2rV455130NjYiM7OTgXQxs3itJQm8OiidjoxEUEgEE9+8Pv96OnpkXpLrGg/OjqKkZGRhAB7jjcjIwMVFRUypomJCSmoyLpRdN9MTU2JhdTtdsPtdqO2tlZcNFu3bpXrWI3c6XSiublZXLLkl8vlQm5uLmw2G4qKihK025GRERw9ehSvv/46qqqq8Hu/93uSMOLz+XD69Gl0d3dLHFJ+fj5KSkrEEpmZmQm73S5xJPv27UNbWxuys7PxwAMPYOfOnVLR/Re/+AVOnjy5YD2qyQvJPCMxQaK3t1fAzT333IPdu3fj/vvvx+rVq/Gd73wHb7zxBiYnJ3Hw4EG0tbVh0aJFWLp0qcS4sJ8oASrXn16vF6ud3+8Xiyo7ANBykJGRITWoWAfKbDZj1apVOHXqFM6ePYuWlhbpJ7dhwwZ87Wtfw4oVK9DW1oazZ89i//79aGlpQX9/v1gsOP9koHW11u1U1jD1Xn6/HyMjI2hubobRaERBQQE2b96MoqKii2t7QvoFGo1GuN1uFBYWwul0SnyqCia5f9X3o8ZYEnTRyqIqXwASLGHqXlfXhPr91NSUvPvZ2XjzaLvdjsrKSmzbtg3r1q2TciAs2TI8PJzS6q7K/Ph3v7kr7FLiWXUvxmLxpvVU6qPRKCKRCMrLy1FQUACz2SzlT2pra+F2uxEKhdDb2yvWc8YYqokEVIJYmJcdTuj6pSWMbkKua1qkh4aGBIguBJA3F5C6El23GK1gMAgAUsn55MmTiEaj2L17t1xTV1eHiooK1NfXY/Pmzaivr8fKlSsTXIl79uzB448/jvPnz2Pt2rULnkNTM4lWgCtRfMMs/FzVDBPBy8LFlArQMPZkcHBQtPrs7Gy4XK6EjcHMF5fLJYuXDZl1Op1YBZhd4vP55PBNBXQ+LOLzo9GoWK/Kysqwfft23HvvvXA6nfinf/ontLS0YGhoCJqmSdZKsik+GXCR/8mFSlVBl3yY0C0LzMeq0TKojlmN/Up2WapNUeky3Lt3Lz760Y+is7MT9fX1aGhokNYdiWB2Pg5B3o0OmJycwDvvvAu9Xi/V63ngVldXo7i4WAqfqocnx0tB2d3dLSUrqqurkZmZKa2WGHcVb1WjE/dNNBpFR0cHiouLsXv3blRXV4ulAoAU6FRbq7AKuM/ng9VqFQDR0dEhMU2ZmZk4fvw4zpw5g8rKSnzkIx/BunXrxHUbiUSwZs2aBIsK+ztOTk6itLRU3G6sTu/xeJCTk4NPfOITuP3226Wx+oEDB/Cv//qvsJpTZx1eCqzzO66VmZkZSWTxeDzwer244447UFVVhb/927/FsmXL8P3vfx8+n0/cph0dHTh06BAqKyuxd+9edHd3Y3p6GhUVFbDb7ejr6xNFgu+U76WxsRHr16/H1q1bJXOVgKO/v18UrNbWVpw4cQJutxsTExPIycnBQw89hD/+4z+GxWLB66+/jvr6erS2tqK7uztB4VLBiWp5vxol60ryg98zds9qtSIrKwtr165Ffn6+JHNQuaQLlYV9dTqdxEQmr2e6twiw1L3I79RYKxWMqWEByfG1BGa8LhaLSTICXb2ZmZlwOp1Yvnw51q5dKzWzzp07h5aWFqmNp475vfA1ma6FwhuJRNDV1YVwOCyKuNfrlQrtRqNRwO3s7CxWrlyJ4eFh6YHLuVMxy87OltqONGLQ+sWG5uFwWJJrJicnEQwGMTo6KsHv5M3VAav5WNWrc6XeOHRdgFYsFsOXvvQl3HbbbVixYgUAwO12IysrCw6HI+Fal8sFt9st16ggi9/zu1T09NNP42//9m/f13iTQZQa8xP/Pm4ennc1Jl6vCvlkLSsWi0kWTmdnp2QyGQwGlJSUSIB8f38/pqamkJOTg7m5OQQCAcnkupxWevVZg+/FbH0191uYheN2u3Hq1CkUFxfD4XBg1apVuP3221FQUIA33ngDBw8elHnSVUqLgAqkqHXRvE8BSdcihS4zvQBIBhIrShuNRqkIb7fbJauLAofWCAAJzyTP8/Ly0NjYiEAggD179uBzn/scJiYm8Otf/xpHjhyBx+NJAbJSZAtqGqDFwczRo0fhdDpRUFCAoqIitLS0ICMjA9FoFKOjo7IWWPWZrkAAkunHwolGoxGVlZUoKiqCzWbDhQsXEAgEpL8eC95aLBZxFYRCIXR2dqK2tlaCZtmkOBaLwefzSR0onS5eI2tyclIsUbReMAtzYGAAg4OD0k+Rf8f5AMCiRYvQ29uLvr4+nD9/Hr29vdDpdKitrUUgEJC4PqbaMy5z06ZNYrVoa2vDD37wAwwPD6O2qvSya5LvINVa5TuOxWJwu91SAb+jowMPP/wwli5dis997nMoLi7Gd7/7Xfh8PixevFjS2Ds7O/GjH/0IeXl5KC0tlftlZWVJkorb7RYLayAQwNjYGJqamlBSUiJWLJvNJvdk42vGb2VmZqK8vBx/+Id/iE9+8pPo6+vDyy+/jHfffRfd3d1Sw4ruRgITFaC8N0q07qp8VEEcAGk3xbW4evVqZGdnS8X4yclJdHd3IxgMSsPygYEBVFVVyb5TAZdaB48ASnVnsXk1LX+qYqXGVAKJvQTV90xrbSAQSNhP5eXlWLt2LXbu3ImSkhKMjo7i3LlzOHPmDDo6OhJ6xCaDrQ9SueXrVN24Q0NDCAaDkn04OjqK/Px8Sdhg6QWLxYLq6mrhhco//peufNXSFwwGpR8rDRn83Ov1KoaNmwcovV+6LkDriSeewLlz5/DOO+9cj9sn0J//+Z/jK1/5ivweCoVQXl7+vu5JQXO5QPlEeaaTzxa4jZSDV/WT02rV0dGRkFrL56c6sJO1VtWykzz21PPimN47T1Lfb2Gvwo6ODhw5cgQA4PF4sG3bNtTV1UlM2r59+zA4OIjTp0+jtrZWsrcYyE7inCicqYWRR8m/qzzKyspCUVGRWM0cDkdCTBGvI1hjfA1LNdhsNjQ3N8Pj8WDPnj347Gc/i+LiYjz33HM4duwYOjs7E0pnXE7watrFqlgX1waFYU9PD0KhELKzs6UEAwsoEggyRqi1tRW5ubmorKzEoUOHJBYlNzcXv//7vw+fz4f/+l//K1paWiTVnm4sarkOh0MOaMZk+P1+jI2NITs7Wwo42mw2aXxNiwL/funSpZIW3tzcjOnpadjtdqxevRrV1dVSciMYDKKvrw/Hjx9He3s7xsbGJBjXYDDAYrHA7XaLe9NgMMDtdsNut+OTn/wkNmzYIBp2b28vvv/976OjoyOeTXUVa1JdC+r+UDPyqMywJ6Hb7cbu3buxc+dOfOxjH0NNTQ2eeeYZHDhwAAaDQbKKafHs6+uT7GK6EgcGBmA0GjE4OIiZmRkBvn6/H++++66EEjDZZXp6WjLqYrF4GYry8nJ8/etfx7p169DQ0IB9+/ahoaEBvb298k5UOZAMjtR9eTWgKxabWyBD1HWdrDi63W5YLBbJTlu+fDlMJhPWrVsHn88nWa2BQABmsxk5OTmiZLPGU/y5MXHp8fPk8jTqz6zDxnc3MTEhCRL8XiXGZqnxY4wRdDgcuO2223DvvfciOzsbgUAA7e3tuHDhAjo7OyVhQVX8ksd1PShReV/4vbq2Wcl+eHgYTqdT9ih74bJ6PpVVTYsnE1Bm0kKldiMhvyYmJjA1NYVQKJQAuNSQlVQsUMd8aRYx6//momsOtJ588km88sorOHTokLSjAICioiIRPqpVa2RkBEVFRXLN8ePHE+7H+kK8Jpl4qFwvUsFWon/+6rJNgEuDH6bJpvo+laVEtbD9Nppddbp4S56GhgbRkjIyMrBlyxasXr0aFosF+fn5ePHFF9Hb24uuri5xialxGMmHpdrPjJ+ptZgoEFX+mEwmSZPn2FTNXwVdak/E2dlZNDc3Y2ZmBg8//DA+/elPw2w2480338SRI0dw6tSplO9MFcIchxrfYTAYUFxRiYqKChHa0WhUAqqzs7OlMCbH5HQ6ce+99+L1118XlxMtfHNz8fY/IyMjKC4ulvpTAKR1i9lsllYuah0jIF5bJycnB83NzRgdHZWeezwQGcMCxONhZmZmcO7cOZSXl8NkMiEcDksD5qKiImhaPNi4ubkZLpcL9fX1OHnypGjEOp1OXBQEXXze6OgozGYzHn74Ydx+++0SgD8yMoJnnnkGhw4dmu8zdxXLPnn9qGBE1eZ1uniGb19fHyKRCC5cuICjR4/ivvvuw65du/AP//AP2L9/P5599lm0t7dL4U620mEZDQa208qSn5+PzMxMCRhm/0haQel+40E5OTkJs9mM+++/H3/wB38Ap9OJQ4cO4Ve/+pUUIw0EAgkgi2tOPfTVcgfvRz4ku+bUZ83OzqKvrw9Wq1Wa27MX43333Qej0YgTJ05gZGREqsWz4n5xcbEAAO4TKgS0WhEUEOSrlqpkAKaGeTCDm3KCvCcR1FqtVtx666246667YLfbMTk5ifb2djQ2NqKtrS2hpAKfwfmr/32vdK3lNdfwxMQE+vv74Xa74XA4kJeXh5ycHFitVrEUko/kLxVX8kT9LxvC012YrMgClwZRiby5nML/23l+XU+6ZkBL0zR88YtfxIsvvohf//rXqK6uTvh+/fr1MBgMePvtt/Hggw8CANra2tDX14ctW7YAALZs2YJvfetbkmoNAPv27YPNZsPy5cuv1VAvMX71N13CZ2ocQKKJfqGFKxnVp1pQ78U/nXxQJFuxUt0r0ZU5P5b3M45LUSp3TSgUwsmTJ8XtOT09jRUrVqCmpgaLFi1Cbm4unn32WQwODsLr9cJsNiM3N1eau/J+LIdB9wEPWwIstoBQ30/y2FLxhgKagoUBpqyoTsvKpz/9aUxNTeHVV1+VfnIs+rfQkpA6OYHXZWTokZcXT5NmlXW6AgncVMEWi8UwNDSEV199FefPn5eWNMB84LDb7cYLL7yA5cuXw2KxIC8vT+q3MZOQIJbFRtmug1axwsJCeL1esQTSssbm6JFIBE6nEzabDT09PXjxxReRmZmJZcuWIRqNYvHixViyZAkmJiZw8uRJ9Pb2AoBUv6dwp0WRgdHMPGT9uIceegi7du0SN2Y4HMY//uM/4tVXX5VYuKul5L2qWrT4vWqpmJmZgcfjQSAQgNfrxYULF3D+/Hnccccd2LNnD7Zu3SoWejYDp1taPczovgaQ0DhdteL09fUlZF4WFRVh8+bNuP/++7Fs2TIMDg7i7bffxpEjR3DmzBlpZqyCLNWllywbkgHSlSjZSk5SY6Z4bz5zamoKnZ2dCdnDtbW1cLlcuOuuu2Cz2fDWW29JGQUqEUNDQ1LHjuA0vjcy0NXVJUozXfz5+flicTWbzQlWZCYWUE7wnc7OzoprjY3WmSDicDiwc+dOfOQjH0Fubq640hsbGxNiSFNZCVWlSf3+aoHXlUDKpf9uoaWLv6sKxfT0NDweD0ZHRyV0gtZirlH+TsDMOFEmxJCP6n3VMSeP/WosWKmuudlAFnANgdYTTzyBn/3sZ3jppZfEXAxABLvdbscf/dEf4Stf+YqULPjiF7+ILVu2YPPmzQCAu+++G8uXL8dnPvMZfOc734Hb7cZf/uVf4oknnrjmVqvEl3251Z8IuqC0rEh0RSXed34zKndSrkkWbMkbNpUmfqnxx39OPQf1tqliMN4vLRxHnCYmJnDmzBkxO4fDYSxbtgxVVVXYu3cvotEofvKTn6C/v19iBfLy8iSWCpgv5kqzt1rYcMWKFQgGgxgbGxMtmDEZasxVKk2Y2vL09DTGxsYwOzsrvbqKi4vxyCOPYPv27ZiYmMAvf/lLHD58GMeOHRNtl7xMFMrzPEgFPuMHQrw5MZsK00yvVrnmPefm5tDd3Y2uri4JME52G/t8Phw9ehR+vx/bt2+HTqdDQ0MDpqenpVcaMF/g1W63o6ioSDL+WNxUbWXEvppZWVmSgMGCojabDeFwGJWVlZKRqLr5IpGIuLfU6tB8BuPnGHRPsPyRj3wEe/bsEaAdDofx/PPP45VXXkEkEklYr+9lzSavzeRCnvw82cIYDocxMjKC+vp6bNy4EatWrUJdXR02bdokcXwej0fKOuh08cQDZtPShcoYsIGBAeELi2OWl5ejrKwMVqsVwWAQvb29+Nd//Vc0NTWhu7tb6m+pCTCq3FGts+o6v9qYTVmXSJZjC9tgpbLehkIhXLhwQcoNhMNhlJWVYWZmBtu3b4fFYsHLL78srkYqArSw2mw2yYqdmppCIBCQ7he0POl0OrFKc15qD0N1vJQhrCuWm5srZQ4Yc7lp0ybcfffdyMvLw9jYGM6dO4fe3l5JMGDowqXkcjLAer9Wrqt6P7rUvydvA/VsYUsh9d0lj/dSSqh6v/lnL7RUJT8//vvl9+ZNiK+ErhnQeuaZZwAAu3btSvj8xz/+MT772c8CAL773e9Cr9fjwQcfTChYSsrIyMArr7yCxx9/HFu2bEF2djYee+wx/Kf/9J+u1TCFEoXte//7Sx2oC59z5TFcajOrKcpXS7rLuDRTHVLXSrtIZTljb8BQKITz58+jvLwcq1atwp49e3DfffchOzsb/+t//S8JkI9EIrDb7TAajeJeJuhQf66ursYdd9yBc+fO4a233koo+0GgRleYasECIC41tjnJz8+XQn8EWQ888ABGR0fxwgsv4MCBA2hoaEAgEEjQ8C4FuJJrgM1bOKJobDwDnU4n2ViM1eHf0dIDQNx3tAARGNH9x0O2oKAAK1euxPj4OKqrqyUgORKJwGQyyYEViUTQ2dmJpUuXSvp2dnY2otEo7HZ7Qu0dm80mQcQAxMLA7MZ169bh1KlT8Hq9sNvtmJubQ0VFhVRCt1qtCUkswWBQ5sJ3wur4GzZswN13343s7GzRzN966y38n//zf6TieYLV5j0G4CZbSJMVGF6j7gO6Ufx+P5qbm5GXl4clS5Zg7dq1WLJkCZYuXYqamhrhH+MF+a7UVll6vV7mnpWVJXEv4+PjGBwcRE9PD9rb2zEwMCClPZjplVwEUn3v6pxUS/d7Jl0ij1LxLxlg8DnsE0hrcCAQQE1NDaxWK1avXo1IJIKGhga0tbVJZikzq1mjzmg0igU1IyNDCgTr9XqpDUW3OPeBqlgxiJulcDRNk2K8rD9VVlaGHTt2YPv27XA6nfB4PDhz5gx6enrQ09ODzs7OBb1YU1m1kvf5+wFYqV7Ve3l9qR59OeCUvD4WKoo3MQr6AOiaug6vRCaTCd/73vfwve9975LXVFZW4rXXXrtWw7rmlEqTuJx78L2Ymy+ljV6ZtwutYVdL18J9GKd5q5o657m5OQwMDGB4eFiqPUciEdx1113YsWMHsrOz8aMf/QjNzc24cOGC1GyKxWIJrVwo0AmgWNPI4XDA5/MhJydnQbIB286ovemAeAFAZu6NjY1hYGAA5eXl2LVrF3bs2IGGhgYcOnQIR48exdmzZ6UZ86X4pIKq1Ly/mIk3FsJcLCZFE9nag1Y2ZpxmZWWhsLBwQeuM6elpFBQUiEUjLy8Ps7OzaGxsRF1dHaqqqrB8+XIJtmegNl2sbJmigsTZ2VmMjY2JdYIZsUyHB+JxkHNzc2hra0NhYSF0Op00Q/d4PHjllVdgMBhw2223oaysDBkZGVizZg3a2tqgaZpkKfKAZNbSLbfcgocffljCBGKxGA4dOoQf/vCHCTWM3s/65IGsvh/V7XYp5YO8CofDkpl49OhRlJWVYfHixZLUwFpDBFp00bD8hclkwtjYGMbHx+WeExMTCIVC6O/vl+9oHSMfVOWAY1LHTautaoX5TfiUylqlfp68nlXLFy1bLS0tCIfDsqYnJydRU1OD3bt3Y+nSpXj55Zdx7tw56YlnNBphMpmk2Ons7Kw0nNbpdMjJyREet7W1obe3V0BZTk6OFMCORqOS0EALjvpZVlYWNm3ahDvvvBMVFRWwWCzo7++XEg49PT2SuEC60ppLxZf3yvfUXo7Ea5LFyLXGQam8Gpc6Nq7umvfmPrzZ6KbrdUi6msV+hTsscBup91MXcrKp9nIbM3nB/q4pGgsF0bxLLxqNSjVzn8+HXbt2Yf369fj6178ucSltbW3w+/3w+Xzw+/1wuVwJsViZmZkCIgoKCvDxj38cHo8HTU1NGBsbS4gzINjiwcRkDI5lYGAAAwMDKCoqwmc+8xls2LABx44dw/Hjx3HixAm0tbUJsLvCrC+OLzXI1SEuwKdnphGJTEiDZqPRKH3XODer1Sqp1AwUZsA/+bh06VI4HA6Mj48jGAzKwQNAyj2YTCbJjlTdLexJaDabpaTC4cOHMTk5iby8PGRnZycEDjOw3mazYXp6Gk6nE7W1tRgeHsbo6CgqKioQiUTg8/mwb98+WCwWLFu2DIWFhZicnMTQ0BAikciCCuEVFRX4+Mc/jvLyckl2OH36NJ599ln09PTInN+XtUZZk+rfE8So8VvyrhSLhQpmZmdnMTw8LDFcdGXz/iqAY20itjlhnTzGLGqaJjEy7Ld3KXdUstubz2TB2PfqKlzInMR5p7JyXIpH/J5WVNZOo6W2qqoKixcvxuc//3k0NDTg9ddfR19fn9QQJH/UODe2O6KLlqVZWI2cMX3cw2zCzeBtZtouWbIEt912G3bs2CHKWldXF06fPo0LFy6go6ND5EUqBem9eBKuB3i4muWuei+uBNSu9Az1Xpc7f96PJe5mBVnATQy0Ur3097YQVHff5a9MrTVqKRdo8r3e7+KcF9QAoFsgPK90gL2Xx/MZqe7A501NTeLs2bMIheJxLoODg9i1axcee+wx7Nq1C6+++ireffdd9PT0XHSluOFwxKsV8x/dYZOTk+jv70dRURGKi4vFhcFAcAZhz83NSRxIJBJBOBzG8PAwsrIM2LZtG3bv3o3y8nLs378fR44cQWNjowQtJ8c4pHIfzs85dbyaBkDTYohG5wFTlsGAsBL4zvgSNeYnJydHeinSTRQIBJCXlycV8NX4KAYOE4xFo9GEPmQ5OTkIBoNobW3F4sWLodPpsG/fPpw4cQJOpxMTExPIy8uTfpR0saogdnJyEnV1S2G329HU1ISTJ0+KVUKtbeb1enHmzBkMDQ1hdjYKgyFLDtecnBzs2LEDq1auFJA5PDyMH/7wBzh37lxSYd7E9ar+/29CycAtuUI5gYwkJmga5hQwQ3eV2qUgGSRRW0oGTmrcGveKHPTxixLGmpwdq9froQMwp8Qf/qYkj9ItlDEJlnhcKfImTmyyTqA1Pj6O3t5eLFmyBKWlpdi+fTtWrVqFw4cP48CBA2LlZEYqQaTBYMDQ0FB8LVss0F10vfJ9sIcnFYi52VlEL4LOzMxMLFq0CLt378Zdd90Fk8kk2Ye0mLe1taG/v1/qzMV5sTB0Iz6mOAcuFbM1rzjjqpbk/L1TfZsYinCpv134OZT1dHWUeCZc6TmXHxfHcKnnxPfCwvvfbK5KnXaDzZgupfvvuxMGQwocmRIM3MykXVKS5uRYEY1GMTU1nfqCS9HVCB19BixmCxx2O6w5VuTYbDBe1P7H/H74/GOYnp4CoEPmxcy1DEMGDJnzTVAlHRxAdHoas3MxxGJziM3F4o5MHaDFNMS0GDRomI3OYjYaz/jSoMGakwNXYSHMZjNC4RBCwTAmIhGMT0SgxWK4uiNGoUtdrtfBVeDETHQOsbk5TE3PIDMjAzPRGczOzYHW0bhguijUoEGv089nWl3kaaY+EyZz3O0CHRCbi0GfoYchw4AsoxE6XfxSS3Y2piYnEYlMYDY2C0NmFozGLMTmYjAYDbDn2KDP0GNocBgTExEYjFkwZZmQmZmB2dk5zERnMBeLITY7i6npaZiMRkxNTcOcbUZ1ZRViWrz3mc/vw2x0FlxHOp0OmVkGZOjjWaLRmagc3DEtBh10yDJmobSkFNYcqyyYYDAg2XwSi5WCnxazGVoshshFt+v7Ih3igIjM1QE60Pocm39+KrRxtQhErucfpD7IEm88/6MOOoYfJ/7J+xXbOsCZnwv/WAixBffS5LkXVcLUN5DB6BI+NWQaZI0aDAaYjEaYTGYYsgzQYvEkjrHA2HzJDp1u/g58FXwvSY/RNO3iUtPkc/5syjKhoLAQtovtrDKzMhGbnUMgGMD4eDwpJ3LRnZ7M93k+q3MiH5SwCLli/mqz0Xgxg3b8UtxOZNmlSGWlOryrOa6udjmo97rca72acV3ts5L10cvcx2634Uf//DwCgYCUl7kR6IazaDGA98VX3/6QR5KmNKUpTWlKU5reK4XD4TTQ+m0m1hrq6+u7oV7UtSZW0O/v71/Q5DlNcUrz6MqU5tGVKc2jK1OaR1emm4FHmqYhHA6jpKTkwx7KNaUbDmgxxsJut9+wi/Faklr/J02pKc2jK1OaR1emNI+uTGkeXZludB7diAYS/ZUvSVOa0pSmNKUpTWlK029CaaCVpjSlKU1pSlOa0nSd6IYDWkajEX/zN39zXRtN3wiU5tOVKc2jK1OaR1emNI+uTGkeXZnSPPrdpRuuvEOa0pSmNKUpTWlK028L3XAWrTSlKU1pSlOa0pSm3xZKA600pSlNaUpTmtKUputEaaCVpjSlKU1pSlOa0nSdKA200pSmNKUpTWlKU5quE91wQOt73/seqqqqYDKZsGnTJhw/fvzDHtIHQk8//TQ2bNiAnJwcFBYW4uMf/zja2toSrpmamsITTzyB/Px8WK1WPPjggxgZGUm4pq+vD/fddx8sFgsKCwvxta99TZoS32j07W9/GzqdDl/60pfkszSPgMHBQXz6059Gfn4+zGYzVq5ciYaGBvle0zT89V//NYqLi2E2m7F79260t7cn3MPv9+ORRx6BzWaDw+HAH/3RH2F8/Aq94H6HaG5uDn/1V3+F6upqmM1m1NbW4u/+7u8Sm4nfZHw6dOgQPvKRj6CkpAQ6nQ7/9m//lvD9teJHU1MTtm/fDpPJhPLycnznO9+53lO7ZnQ5HkWjUXzjG9/AypUrkZ2djZKSEjz66KMYGhpKuMeNzqMbkrQbiJ577jktKytL+6d/+ift/Pnz2h//8R9rDodDGxkZ+bCHdt1pz5492o9//GPt3LlzWmNjo3bvvfdqFRUV2vj4uFzzhS98QSsvL9fefvttraGhQdu8ebO2detW+X52dlZbsWKFtnv3bu306dPaa6+9pjmdTu3P//zPP4wpXVc6fvy4VlVVpa1atUp76qmn5PObnUd+v1+rrKzUPvvZz2rHjh3Turq6tDfeeEPr6OiQa7797W9rdrtd+7d/+zftzJkz2kc/+lGturpam5yclGvuuecebfXq1drRo0e1w4cPa4sWLdI+9alPfRhTui70rW99S8vPz9deeeUVrbu7W3v++ec1q9Wq/ff//t/lmpuNT6+99pr2F3/xF9oLL7ygAdBefPHFhO+vBT+CwaDmcrm0Rx55RDt37pz285//XDObzdoPf/jDD2qa74sux6NAIKDt3r1b+8UvfqG1trZq9fX12saNG7X169cn3ONG59GNSDcU0Nq4caP2xBNPyO9zc3NaSUmJ9vTTT3+Io/pwyOPxaAC0gwcPapoW38QGg0F7/vnn5ZqWlhYNgFZfX69pWlwI6PV6ze12yzXPPPOMZrPZtOnp6Q92AteRwuGwtnjxYm3fvn3azp07BWileaRp3/jGN7Rt27Zd8vtYLKYVFRVp//AP/yCfBQIBzWg0aj//+c81TdO05uZmDYB24sQJueb111/XdDqdNjg4eP0G/wHSfffdp/3hH/5hwmcPPPCA9sgjj2ialuZTMoi4Vvz4/ve/r+Xm5ibstW984xva0qVLr/OMrj2lAqPJdPz4cQ2A1tvbq2nazcejG4VuGNfhzMwMTp48id27d8tner0eu3fvRn19/Yc4sg+HgsEggPkm2ydPnkQ0Gk3gT11dHSoqKoQ/9fX1WLlyJVwul1yzZ88ehEIhnD9//gMc/fWlJ554Avfdd18CL4A0jwDgl7/8JW699VY89NBDKCwsxNq1a/GjH/1Ivu/u7obb7U7gkd1ux6ZNmxJ45HA4cOutt8o1u3fvhl6vx7Fjxz64yVxH2rp1K95++21cuHABAHDmzBm888472Lt3L4A0n5LpWvGjvr4eO3bsQFZWllyzZ88etLW1YWxs7AOazQdHwWAQOp0ODocDQJpHv6t0wzSV9nq9mJubSzgAAcDlcqG1tfVDGtWHQ7FYDF/60pdw2223YcWKFQAAt9uNrKws2bAkl8sFt9st16TiH7+7Eei5557DqVOncOLEiQXfpXkEdHV14ZlnnsFXvvIVfPOb38SJEyfw7//9v0dWVhYee+wxmWMqHqg8KiwsTPg+MzMTeXl5NwSPAODP/uzPEAqFUFdXh4yMDMzNzeFb3/oWHnnkEQBI8ymJrhU/3G43qqurF9yD3+Xm5l6X8X8YNDU1hW984xv41Kc+JU2k0zz63aQbBmilaZ6eeOIJnDt3Du+8886HPZTfKurv78dTTz2Fffv2wWQyfdjD+a2kWCyGW2+9FX//938PAFi7di3OnTuHH/zgB3jsscc+5NH99tC//Mu/4Kc//Sl+9rOf4ZZbbkFjYyO+9KUvoaSkJM2nNL1vikaj+L3f+z1omoZnnnnmwx5Omt4n3TCuQ6fTiYyMjAUZYiMjIygqKvqQRvXB05NPPolXXnkFBw4cQFlZmXxeVFSEmZkZBAKBhOtV/hQVFaXkH7/7XaeTJ0/C4/Fg3bp1yMzMRGZmJg4ePIj/8T/+BzIzM+FyuW56HhUXF2P58uUJny1btgx9fX0A5ud4uX1WVFQEj8eT8P3s7Cz8fv8NwSMA+NrXvoY/+7M/wyc/+UmsXLkSn/nMZ/DlL38ZTz/9NIA0n5LpWvHjRt9/brJ3wAAABAVJREFUwDzI6u3txb59+8SaBaR59LtKNwzQysrKwvr16/H222/LZ7FYDG+//Ta2bNnyIY7sgyFN0/Dkk0/ixRdfxP79+xeYjtevXw+DwZDAn7a2NvT19Ql/tmzZgrNnzyZsZG705MP3d5HuvPNOnD17Fo2NjfLv1ltvxSOPPCI/3+w8uu222xaUBblw4QIqKysBANXV1SgqKkrgUSgUwrFjxxJ4FAgEcPLkSblm//79iMVi2LRp0wcwi+tPExMT0OsTxWdGRgZisRiANJ+S6VrxY8uWLTh06BCi0ahcs2/fPixduvSGcIkRZLW3t+Ott95Cfn5+wvdpHv2O0ocdjX8t6bnnntOMRqP2k5/8RGtubtY+//nPaw6HIyFD7Ealxx9/XLPb7dqvf/1rbXh4WP5NTEzINV/4whe0iooKbf/+/VpDQ4O2ZcsWbcuWLfI9SxfcfffdWmNjo/arX/1KKygouGFKF6QiNetQ09I8On78uJaZmal961vf0trb27Wf/vSnmsVi0f7v//2/cs23v/1tzeFwaC+99JLW1NSkfexjH0uZpr927Vrt2LFj2jvvvKMtXrz4d7ZsQSp67LHHtNLSUinv8MILL2hOp1P7+te/LtfcbHwKh8Pa6dOntdOnT2sAtP/23/6bdvr0acmYuxb8CAQCmsvl0j7zmc9o586d05577jnNYrH8zpQuuByPZmZmtI9+9KNaWVmZ1tjYmCDH1QzCG51HNyLdUEBL0zTtf/7P/6lVVFRoWVlZ2saNG7WjR49+2EP6QAhAyn8//vGP5ZrJyUntT//0T7Xc3FzNYrFo999/vzY8PJxwn56eHm3v3r2a2WzWnE6n9tWvflWLRqMf8Gw+OEoGWmkeadrLL7+srVixQjMajVpdXZ32v//3/074PhaLaX/1V3+luVwuzWg0anfeeafW1taWcI3P59M+9alPaVarVbPZbNof/MEfaOFw+IOcxnWlUCikPfXUU1pFRYVmMpm0mpoa7S/+4i8SDsSbjU8HDhxIKYMee+wxTdOuHT/OnDmjbdu2TTMajVppaan27W9/+4Oa4vumy/Gou7v7knL8wIEDco8bnUc3Iuk0TSllnKY0pSlNaUpTmtKUpmtGN0yMVprSlKY0pSlNaUrTbxulgVaa0pSmNKUpTWlK03WiNNBKU5rSlKY0pSlNabpOlAZaaUpTmtKUpjSlKU3XidJAK01pSlOa0pSmNKXpOlEaaKUpTWlKU5rSlKY0XSdKA600pSlNaUpTmtKUputEaaCVpjSlKU1pSlOa0nSdKA200pSmNKUpTWlKU5quE6WBVprSlKY0pSlNaUrTdaI00EpTmtKUpjSlKU1puk6UBlppSlOa0pSmNKUpTdeJ/j8kVRQNowntegAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Get a batch of training data\n",
        "inputs, classes = next(iter(dataloaders[\"validation\"]))\n",
        "\n",
        "# Make a grid from batch\n",
        "out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "imshow(out, title=[class_names[x] for x in classes])\n",
        "\n",
        "dataloaders = {\n",
        "    x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)\n",
        "    for x in [\"train\", \"validation\"]\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ds09ighxrd0D"
      },
      "source": [
        "Variational quantum circuit\n",
        "===========================\n",
        "\n",
        "We first define some quantum layers that will compose the quantum\n",
        "circuit.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "OIbOa-KBrd0D"
      },
      "outputs": [],
      "source": [
        "def H_layer(nqubits):\n",
        "    \"\"\"Layer of single-qubit Hadamard gates.\n",
        "    \"\"\"\n",
        "    for idx in range(nqubits):\n",
        "        qml.Hadamard(wires=idx)\n",
        "\n",
        "\n",
        "def RY_layer(w):\n",
        "    \"\"\"Layer of parametrized qubit rotations around the y axis.\n",
        "    \"\"\"\n",
        "    for idx, element in enumerate(w):\n",
        "        qml.RY(element, wires=idx)\n",
        "\n",
        "\n",
        "def entangling_layer(nqubits):\n",
        "    \"\"\"Layer of CNOTs followed by another shifted layer of CNOT.\n",
        "    \"\"\"\n",
        "    # In other words it should apply something like :\n",
        "    # CNOT  CNOT  CNOT  CNOT...  CNOT\n",
        "    #   CNOT  CNOT  CNOT...  CNOT\n",
        "    for i in range(0, nqubits - 1, 2):  # Loop over even indices: i=0,2,...N-2\n",
        "        qml.CNOT(wires=[i, i + 1])\n",
        "    for i in range(1, nqubits - 1, 2):  # Loop over odd indices:  i=1,3,...N-3\n",
        "        qml.CNOT(wires=[i, i + 1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ue__y4sQrd0D"
      },
      "source": [
        "Now we define the quantum circuit through the PennyLane\n",
        "[qnode]{.title-ref} decorator .\n",
        "\n",
        "The structure is that of a typical variational quantum circuit:\n",
        "\n",
        "-   **Embedding layer:** All qubits are first initialized in a balanced\n",
        "    superposition of *up* and *down* states, then they are rotated\n",
        "    according to the input parameters (local embedding).\n",
        "-   **Variational layers:** A sequence of trainable rotation layers and\n",
        "    constant entangling layers is applied.\n",
        "-   **Measurement layer:** For each qubit, the local expectation value\n",
        "    of the $Z$ operator is measured. This produces a classical output\n",
        "    vector, suitable for additional post-processing.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "C-O_cK6jrd0D"
      },
      "outputs": [],
      "source": [
        "@qml.qnode(dev, interface=\"torch\")\n",
        "def quantum_net(q_input_features, q_weights_flat):\n",
        "    \"\"\"\n",
        "    The variational quantum circuit.\n",
        "    \"\"\"\n",
        "\n",
        "    # Reshape weights\n",
        "    q_weights = q_weights_flat.reshape(q_depth, n_qubits)\n",
        "\n",
        "    # Start from state |+> , unbiased w.r.t. |0> and |1>\n",
        "    #H_layer(n_qubits)\n",
        "\n",
        "    # Embed features in the quantum node\n",
        "    RY_layer(q_input_features)\n",
        "\n",
        "    # Sequence of trainable variational layers\n",
        "    #for k in range(q_depth):\n",
        "        #entangling_layer(n_qubits)\n",
        "        #RY_layer(q_weights[k])\n",
        "\n",
        "    # Expectation values in the Z basis\n",
        "    exp_vals = [qml.expval(qml.PauliZ(position)) for position in range(n_qubits)]\n",
        "    return tuple(exp_vals)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FNHREgVrd0D"
      },
      "source": [
        "Dressed quantum circuit\n",
        "=======================\n",
        "\n",
        "We can now define a custom `torch.nn.Module` representing a *dressed*\n",
        "quantum circuit.\n",
        "\n",
        "This is a concatenation of:\n",
        "\n",
        "-   A classical pre-processing layer (`nn.Linear`).\n",
        "-   A classical activation function (`torch.tanh`).\n",
        "-   A constant `np.pi/2.0` scaling.\n",
        "-   The previously defined quantum circuit (`quantum_net`).\n",
        "-   A classical post-processing layer (`nn.Linear`).\n",
        "\n",
        "The input of the module is a batch of vectors with 512 real parameters\n",
        "(features) and the output is a batch of vectors with two real outputs\n",
        "(associated with the two classes of images: *ants* and *bees*).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "C96kWCjWrd0D"
      },
      "outputs": [],
      "source": [
        "class DressedQuantumNet(nn.Module):\n",
        "    \"\"\"\n",
        "    Torch module implementing the *dressed* quantum net.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        \"\"\"\n",
        "        Definition of the *dressed* layout.\n",
        "        \"\"\"\n",
        "\n",
        "        super().__init__()\n",
        "        self.pre_net = nn.Linear(512, n_qubits)\n",
        "        self.q_params = nn.Parameter(q_delta * torch.randn(q_depth * n_qubits))\n",
        "        self.post_net = nn.Linear(n_qubits, 4)\n",
        "\n",
        "    def forward(self, input_features):\n",
        "        \"\"\"\n",
        "        Defining how tensors are supposed to move through the *dressed* quantum\n",
        "        net.\n",
        "        \"\"\"\n",
        "\n",
        "        # obtain the input features for the quantum circuit\n",
        "        # by reducing the feature dimension from 512 to 4\n",
        "        pre_out = self.pre_net(input_features)\n",
        "        q_in = torch.tanh(pre_out) * np.pi / 2.0\n",
        "\n",
        "        # Apply the quantum circuit to each element of the batch and append to q_out\n",
        "        q_out = torch.Tensor(0, n_qubits)\n",
        "        q_out = q_out.to(device)\n",
        "        for elem in q_in:\n",
        "            q_out_elem = torch.hstack(quantum_net(elem, self.q_params)).float().unsqueeze(0)\n",
        "            q_out = torch.cat((q_out, q_out_elem))\n",
        "\n",
        "        # return the two-dimensional prediction from the postprocessing layer\n",
        "        return self.post_net(q_out)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7hQlpDQdrd0D"
      },
      "source": [
        "Hybrid classical-quantum model\n",
        "==============================\n",
        "\n",
        "We are finally ready to build our full hybrid classical-quantum network.\n",
        "We follow the *transfer learning* approach:\n",
        "\n",
        "1.  First load the classical pre-trained network *ResNet18* from the\n",
        "    `torchvision.models` zoo.\n",
        "2.  Freeze all the weights since they should not be trained.\n",
        "3.  Replace the last fully connected layer with our trainable dressed\n",
        "    quantum circuit (`DressedQuantumNet`).\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "The *ResNet18* model is automatically downloaded by PyTorch and it may\n",
        "take several minutes (only the first time).\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "MAh4FqBYrd0D"
      },
      "outputs": [],
      "source": [
        "model_hybrid = torchvision.models.resnet18(pretrained=True)\n",
        "\n",
        "for param in model_hybrid.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "\n",
        "# Notice that model_hybrid.fc is the last layer of ResNet18\n",
        "model_hybrid.fc = DressedQuantumNet()\n",
        "\n",
        "# Use CUDA or CPU according to the \"device\" object.\n",
        "model_hybrid = model_hybrid.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ovX-Tkb0rd0E"
      },
      "source": [
        "Training and results\n",
        "====================\n",
        "\n",
        "Before training the network we need to specify the *loss* function.\n",
        "\n",
        "We use, as usual in classification problem, the *cross-entropy* which is\n",
        "directly available within `torch.nn`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "gkmFIK4Brd0E"
      },
      "outputs": [],
      "source": [
        "criterion = nn.CrossEntropyLoss()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qhFORuvard0E"
      },
      "source": [
        "We also initialize the *Adam optimizer* which is called at each training\n",
        "step in order to update the weights of the model.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "_K9M-VPMrd0E"
      },
      "outputs": [],
      "source": [
        "optimizer_hybrid = optim.Adam(model_hybrid.fc.parameters(), lr=step)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGG8pksyrd0E"
      },
      "source": [
        "We schedule to reduce the learning rate by a factor of\n",
        "`gamma_lr_scheduler` every 10 epochs.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "nco3IrE6rd0E"
      },
      "outputs": [],
      "source": [
        "exp_lr_scheduler = lr_scheduler.StepLR(\n",
        "    optimizer_hybrid, step_size=31, gamma=gamma_lr_scheduler\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJvpPNCRrd0E"
      },
      "source": [
        "What follows is a training function that will be called later. This\n",
        "function should return a trained model that can be used to make\n",
        "predictions (classifications).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "8uO5BrOPrd0E"
      },
      "outputs": [],
      "source": [
        "def train_model(model, criterion, optimizer, scheduler, num_epochs):\n",
        "    since = time.time()\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "    best_loss = 10000.0  # Large arbitrary number\n",
        "    best_acc_train = 0.0\n",
        "    best_loss_train = 10000.0  # Large arbitrary number\n",
        "    print(\"Training started:\")\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "\n",
        "        # Each epoch has a training and validation phase\n",
        "        for phase in [\"train\", \"validation\"]:\n",
        "            if phase == \"train\":\n",
        "                # Set model to training mode\n",
        "                model.train()\n",
        "            else:\n",
        "                # Set model to evaluate mode\n",
        "                model.eval()\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "\n",
        "            # Iterate over data.\n",
        "            n_batches = dataset_sizes[phase] // batch_size\n",
        "            it = 0\n",
        "            for inputs, labels in dataloaders[phase]:\n",
        "                since_batch = time.time()\n",
        "                batch_size_ = len(inputs)\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device)\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                # Track/compute gradient and make an optimization step only when training\n",
        "                with torch.set_grad_enabled(phase == \"train\"):\n",
        "                    outputs = model(inputs)\n",
        "                    _, preds = torch.max(outputs, 1)\n",
        "                    loss = criterion(outputs, labels)\n",
        "                    if phase == \"train\":\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                # Print iteration results\n",
        "                running_loss += loss.item() * batch_size_\n",
        "                batch_corrects = torch.sum(preds == labels.data).item()\n",
        "                running_corrects += batch_corrects\n",
        "                print(\n",
        "                    \"Phase: {} Epoch: {}/{} Iter: {}/{} Batch time: {:.4f}\".format(\n",
        "                        phase,\n",
        "                        epoch + 1,\n",
        "                        num_epochs,\n",
        "                        it + 1,\n",
        "                        n_batches + 1,\n",
        "                        time.time() - since_batch,\n",
        "                    ),\n",
        "                    end=\"\\r\",\n",
        "                    flush=True,\n",
        "                )\n",
        "                it += 1\n",
        "\n",
        "            # Print epoch results\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects / dataset_sizes[phase]\n",
        "            print(\n",
        "                \"Phase: {} Epoch: {}/{} Loss: {:.4f} Acc: {:.4f}        \".format(\n",
        "                    \"train\" if phase == \"train\" else \"validation  \",\n",
        "                    epoch + 1,\n",
        "                    num_epochs,\n",
        "                    epoch_loss,\n",
        "                    epoch_acc,\n",
        "                )\n",
        "            )\n",
        "\n",
        "            # Check if this is the best model wrt previous epochs\n",
        "            if phase == \"validation\" and epoch_acc > best_acc:\n",
        "                best_acc = epoch_acc\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "            if phase == \"validation\" and epoch_loss < best_loss:\n",
        "                best_loss = epoch_loss\n",
        "            if phase == \"train\" and epoch_acc > best_acc_train:\n",
        "                best_acc_train = epoch_acc\n",
        "            if phase == \"train\" and epoch_loss < best_loss_train:\n",
        "                best_loss_train = epoch_loss\n",
        "      \n",
        "            # Update learning rate\n",
        "            if phase == \"train\":\n",
        "                scheduler.step()\n",
        "\n",
        "    # Print final results\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    time_elapsed = time.time() - since\n",
        "    print(\n",
        "        \"Training completed in {:.0f}m {:.0f}s\".format(time_elapsed // 60, time_elapsed % 60)\n",
        "    )\n",
        "    print(\"Best test loss: {:.4f} | Best test accuracy: {:.4f}\".format(best_loss, best_acc))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CmAl9fIQrd0E"
      },
      "source": [
        "We are ready to perform the actual training process.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "rAQBdDA_rd0E",
        "outputId": "3a885f52-be03-4673-d13f-9a0ab26cdd5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/30 Loss: 1.2268 Acc: 0.5345        \n",
            "Phase: validation   Epoch: 1/30 Loss: 1.1617 Acc: 0.6109        \n",
            "Phase: train Epoch: 2/30 Loss: 1.0966 Acc: 0.6876        \n",
            "Phase: validation   Epoch: 2/30 Loss: 1.0816 Acc: 0.7167        \n",
            "Phase: train Epoch: 3/30 Loss: 1.0927 Acc: 0.6673        \n",
            "Phase: validation   Epoch: 3/30 Loss: 1.0298 Acc: 0.7509        \n",
            "Phase: train Epoch: 4/30 Loss: 0.9962 Acc: 0.7596        \n",
            "Phase: validation   Epoch: 4/30 Loss: 1.0071 Acc: 0.6638        \n",
            "Phase: train Epoch: 5/30 Loss: 0.9651 Acc: 0.7606        \n",
            "Phase: validation   Epoch: 5/30 Loss: 0.9331 Acc: 0.7850        \n",
            "Phase: train Epoch: 6/30 Loss: 0.9180 Acc: 0.7708        \n",
            "Phase: validation   Epoch: 6/30 Loss: 0.9050 Acc: 0.7696        \n",
            "Phase: train Epoch: 7/30 Loss: 0.8829 Acc: 0.7596        \n",
            "Phase: validation   Epoch: 7/30 Loss: 0.8232 Acc: 0.8157        \n",
            "Phase: train Epoch: 8/30 Loss: 0.8274 Acc: 0.7890        \n",
            "Phase: validation   Epoch: 8/30 Loss: 0.8024 Acc: 0.8123        \n",
            "Phase: train Epoch: 9/30 Loss: 0.7407 Acc: 0.8529        \n",
            "Phase: validation   Epoch: 9/30 Loss: 0.7456 Acc: 0.8311        \n",
            "Phase: train Epoch: 10/30 Loss: 0.7541 Acc: 0.8215        \n",
            "Phase: validation   Epoch: 10/30 Loss: 0.7418 Acc: 0.8379        \n",
            "Phase: train Epoch: 11/30 Loss: 0.6864 Acc: 0.8418        \n",
            "Phase: validation   Epoch: 11/30 Loss: 0.7380 Acc: 0.8072        \n",
            "Phase: train Epoch: 12/30 Loss: 0.6835 Acc: 0.8367        \n",
            "Phase: validation   Epoch: 12/30 Loss: 0.6350 Acc: 0.8567        \n",
            "Phase: train Epoch: 13/30 Loss: 0.6608 Acc: 0.8479        \n",
            "Phase: validation   Epoch: 13/30 Loss: 0.6545 Acc: 0.8532        \n",
            "Phase: train Epoch: 14/30 Loss: 0.6444 Acc: 0.8458        \n",
            "Phase: validation   Epoch: 14/30 Loss: 0.6425 Acc: 0.8464        \n",
            "Phase: train Epoch: 15/30 Loss: 0.6164 Acc: 0.8408        \n",
            "Phase: validation   Epoch: 15/30 Loss: 0.6280 Acc: 0.8515        \n",
            "Phase: train Epoch: 16/30 Loss: 0.6199 Acc: 0.8266        \n",
            "Phase: validation   Epoch: 16/30 Loss: 0.6609 Acc: 0.8089        \n",
            "Phase: train Epoch: 17/30 Loss: 0.5893 Acc: 0.8509        \n",
            "Phase: validation   Epoch: 17/30 Loss: 0.5387 Acc: 0.8771        \n",
            "Phase: train Epoch: 18/30 Loss: 0.5676 Acc: 0.8499        \n",
            "Phase: validation   Epoch: 18/30 Loss: 0.6120 Acc: 0.8430        \n",
            "Phase: train Epoch: 19/30 Loss: 0.5392 Acc: 0.8631        \n",
            "Phase: validation   Epoch: 19/30 Loss: 0.5429 Acc: 0.8549        \n",
            "Phase: train Epoch: 20/30 Loss: 0.5342 Acc: 0.8580        \n",
            "Phase: validation   Epoch: 20/30 Loss: 0.5141 Acc: 0.8669        \n",
            "Phase: train Epoch: 21/30 Loss: 0.5289 Acc: 0.8702        \n",
            "Phase: validation   Epoch: 21/30 Loss: 0.4947 Acc: 0.8686        \n",
            "Phase: train Epoch: 22/30 Loss: 0.5123 Acc: 0.8813        \n",
            "Phase: validation   Epoch: 22/30 Loss: 0.5056 Acc: 0.8737        \n",
            "Phase: train Epoch: 23/30 Loss: 0.5172 Acc: 0.8580        \n",
            "Phase: validation   Epoch: 23/30 Loss: 0.5083 Acc: 0.8635        \n",
            "Phase: train Epoch: 24/30 Loss: 0.4853 Acc: 0.8600        \n",
            "Phase: validation   Epoch: 24/30 Loss: 0.5252 Acc: 0.8635        \n",
            "Phase: train Epoch: 25/30 Loss: 0.4808 Acc: 0.8621        \n",
            "Phase: validation   Epoch: 25/30 Loss: 0.5130 Acc: 0.8601        \n",
            "Phase: train Epoch: 26/30 Loss: 0.4561 Acc: 0.8803        \n",
            "Phase: validation   Epoch: 26/30 Loss: 0.4579 Acc: 0.8703        \n",
            "Phase: train Epoch: 27/30 Loss: 0.5020 Acc: 0.8428        \n",
            "Phase: validation   Epoch: 27/30 Loss: 0.4970 Acc: 0.8584        \n",
            "Phase: train Epoch: 28/30 Loss: 0.4142 Acc: 0.8763        \n",
            "Phase: validation   Epoch: 28/30 Loss: 0.4918 Acc: 0.8635        \n",
            "Phase: train Epoch: 29/30 Loss: 0.4193 Acc: 0.8824        \n",
            "Phase: validation   Epoch: 29/30 Loss: 0.4595 Acc: 0.8788        \n",
            "Phase: train Epoch: 30/30 Loss: 0.4629 Acc: 0.8793        \n",
            "Phase: validation   Epoch: 30/30 Loss: 0.4379 Acc: 0.8805        \n",
            "Training completed in 22m 46s\n",
            "Best test loss: 0.4379 | Best test accuracy: 0.8805\n"
          ]
        }
      ],
      "source": [
        "model_hybrid = train_model(\n",
        "    model_hybrid, criterion, optimizer_hybrid, exp_lr_scheduler, num_epochs=num_epochs\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4cEc4mird0E"
      },
      "source": [
        "Visualizing the model predictions\n",
        "=================================\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ARvjv_lbrd0E"
      },
      "source": [
        "We first define a visualization function for a batch of test data.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "CtUY4xU_rd0E"
      },
      "outputs": [],
      "source": [
        "def visualize_model(model, num_images=6, fig_name=\"Predictions\"):\n",
        "    images_so_far = 0\n",
        "    _fig = plt.figure(fig_name)\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for _i, (inputs, labels) in enumerate(dataloaders[\"validation\"]):\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(inputs)\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "            for j in range(inputs.size()[0]):\n",
        "                images_so_far += 1\n",
        "                ax = plt.subplot(num_images // 2, 2, images_so_far)\n",
        "                ax.axis(\"off\")\n",
        "                ax.set_title(\"[{}]\".format(class_names[preds[j]]))\n",
        "                imshow(inputs.cpu().data[j])\n",
        "                if images_so_far == num_images:\n",
        "                    return"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F6I5Rk92rd0E"
      },
      "source": [
        "Finally, we can run the previous function to see a batch of images with\n",
        "the corresponding predictions.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "-RoYcZQXrd0E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "8b99ce58-7511-4ed4-8688-a094c604e725"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visualize_model(model_hybrid, num_images=6)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IBXZTnzjrd0E"
      },
      "source": [
        "References\n",
        "==========\n",
        "\n",
        "\\[1\\] Andrea Mari, Thomas R. Bromley, Josh Izaac, Maria Schuld, and\n",
        "Nathan Killoran. *Transfer learning in hybrid classical-quantum neural\n",
        "networks*. arXiv:1912.08278 (2019).\n",
        "\n",
        "\\[2\\] Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and\n",
        "Andrew Y Ng. *Self-taught learning: transfer learning from unlabeled\n",
        "data*. Proceedings of the 24th International Conference on Machine\n",
        "Learning\\*, 759--766 (2007).\n",
        "\n",
        "\\[3\\] Kaiming He, Xiangyu Zhang, Shaoqing ren and Jian Sun. *Deep\n",
        "residual learning for image recognition*. Proceedings of the IEEE\n",
        "Conference on Computer Vision and Pattern Recognition, 770-778 (2016).\n",
        "\n",
        "\\[4\\] Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin,\n",
        "Carsten Blank, Keri McKiernan, and Nathan Killoran. *PennyLane:\n",
        "Automatic differentiation of hybrid quantum-classical computations*.\n",
        "arXiv:1811.04968 (2018).\n",
        "\n",
        "About the author\n",
        "================\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "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": "3.9.16"
    },
    "colab": {
      "provenance": [],
      "gpuType": "V100"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}