examples/notebooks/U-Net_Train-Model-and-Make-Submission-File.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/open-pack/openpack-torch/blob/main/examples/notebooks/U-Net_Train-Model-and-Make-Submission-File.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "62133d2f-db52-4903-b8da-afb282959973",
      "metadata": {
        "id": "62133d2f-db52-4903-b8da-afb282959973"
      },
      "source": [
        "# U-Net | Train Model and Make Submission File"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dd2922d2-1fca-4cc2-8739-81c76a437ec9",
      "metadata": {
        "id": "dd2922d2-1fca-4cc2-8739-81c76a437ec9"
      },
      "source": [
        "This is a tutorial of [OpenPack Challenge 2022](https://open-pack.github.io/challenge2022/).\n",
        "In this notebook, we will build the U-Net, one of the basic architecture for segmentation, with the acceleration data from the left wrist (atr02). This notebook also shows how to make submission file (`submission.json`) for OpenPack Challenge 2022. In fact, the performance of this U-Net is poor. Please enjoy finding a better architecture and parameters."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d6306579-9f17-49fc-bc1b-e85fa4302b66",
      "metadata": {
        "id": "d6306579-9f17-49fc-bc1b-e85fa4302b66"
      },
      "source": [
        "**NOTE: When you run this notebook in colab, please change runtime to GPU first.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "KR-QP08B4Rnr",
      "metadata": {
        "id": "KR-QP08B4Rnr"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "if not torch.cuda.is_available():\n",
        "    raise ValueError(\"Change notebook runtime to GPU !\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1149019d-005a-4071-81b4-5dde35836302",
      "metadata": {
        "id": "1149019d-005a-4071-81b4-5dde35836302",
        "tags": []
      },
      "source": [
        "## [0] Inital Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bb015bbf-633d-4dd5-9451-59b6f5cc3ecb",
      "metadata": {
        "id": "bb015bbf-633d-4dd5-9451-59b6f5cc3ecb"
      },
      "source": [
        "### 0-1: Download Code and Install `openpack-torch`\n",
        "NOTE: You can also install `openpack-torch` from PyPI with `pip install openpack-torch`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3d848e0e-64c3-4376-b2a9-342e14d82b74",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3d848e0e-64c3-4376-b2a9-342e14d82b74",
        "outputId": "f689e71a-b391-487f-b659-7b7c928dff40"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'openpack-torch'...\n",
            "remote: Enumerating objects: 1249, done.\u001b[K\n",
            "remote: Counting objects: 100% (418/418), done.\u001b[K\n",
            "remote: Compressing objects: 100% (252/252), done.\u001b[K\n",
            "remote: Total 1249 (delta 125), reused 342 (delta 99), pack-reused 831\u001b[K\n",
            "Receiving objects: 100% (1249/1249), 55.48 MiB | 23.45 MiB/s, done.\n",
            "Resolving deltas: 100% (482/482), done.\n"
          ]
        }
      ],
      "source": [
        "! cd /content && git clone https://github.com/open-pack/openpack-torch.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e1a8f425-bb1a-4f8f-a2e7-5fa4fb2b276c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "e1a8f425-bb1a-4f8f-a2e7-5fa4fb2b276c",
        "outputId": "9575cc14-58c9-4adc-b74f-0709d3852c31"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting openpack-torch\n",
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            "\u001b[?25hCollecting openpack-toolkit==1.0.1 (from openpack-torch)\n",
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            "Building wheels for collected packages: antlr4-python3-runtime\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=1c9d40aad1af237b7c44d5a635c60f7bb49d7c3fa9467941d3f2ddb1985a8ba0\n",
            "  Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n",
            "Successfully built antlr4-python3-runtime\n",
            "Installing collected packages: antlr4-python3-runtime, omegaconf, lightning-utilities, hydra-core, torchmetrics, openpack-toolkit, pytorch-lightning, openpack-torch\n",
            "Successfully installed antlr4-python3-runtime-4.9.3 hydra-core-1.3.2 lightning-utilities-0.10.1 omegaconf-2.3.0 openpack-toolkit-1.0.1 openpack-torch-1.0.1 pytorch-lightning-2.1.4 torchmetrics-1.3.0.post0\n"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pydevd_plugins"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "! pip install openpack-torch"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7effb91e-bfb4-4bb3-ba5e-81cf8cf0c92b",
      "metadata": {
        "id": "7effb91e-bfb4-4bb3-ba5e-81cf8cf0c92b"
      },
      "source": [
        "### 0-2: Download Dataset to Your Google Drive\n",
        "Follow the instruction of [Tutorial - Download OpenPack Dataset to Google Drive.ipynb](https://colab.research.google.com/drive/1YOnegl9L6UnlfermwJpevWLQ43anwwGd?usp=drive_link) to download OpenPack Dataset (v1.0.0) to your Google Drive.\n",
        "\n",
        "After you finish downloading the datasets, mount your Google Drive to this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "70b778f2-ee96-44df-b20b-1930d2033c5a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70b778f2-ee96-44df-b20b-1930d2033c5a",
        "outputId": "9c38d9c1-f3b6-4c3c-d9d6-ce56069bebef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8CfE3Nzp5Mb4",
      "metadata": {
        "id": "8CfE3Nzp5Mb4"
      },
      "source": [
        "Create shortcut to the dataset directory with `ln -s`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cb99f5ab-27f3-469b-91ce-4fc0c6a4ad1b",
      "metadata": {
        "id": "cb99f5ab-27f3-469b-91ce-4fc0c6a4ad1b"
      },
      "outputs": [],
      "source": [
        "! ln -s \"/content/drive/MyDrive/Colab Notebooks/openpack/data/\" \"/content/data\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "035bc544-06e5-47b2-a679-bf421d78b074",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "035bc544-06e5-47b2-a679-bf421d78b074",
        "outputId": "36fa9c10-051d-4338-9bf8-2535895fb665"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 92\n",
            "drwx------ 2 root root 4096 Feb  3 09:55 preprocessed\n",
            "drwx------ 2 root root 4096 Feb  3 09:56 U0101\n",
            "drwx------ 2 root root 4096 Feb  3 09:56 U0102\n",
            "drwx------ 2 root root 4096 Feb  3 09:56 U0103\n",
            "drwx------ 2 root root 4096 Feb  3 09:56 U0104\n",
            "drwx------ 2 root root 4096 Feb  3 09:56 U0105\n",
            "drwx------ 2 root root 4096 Feb  3 09:56 U0106\n",
            "drwx------ 2 root root 4096 Feb  3 09:57 U0107\n",
            "drwx------ 2 root root 4096 Feb  3 09:57 U0108\n",
            "drwx------ 2 root root 4096 Feb  3 09:57 U0109\n",
            "drwx------ 2 root root 4096 Feb  3 09:57 U0110\n",
            "drwx------ 2 root root 4096 Feb  3 09:57 U0111\n",
            "drwx------ 2 root root 4096 Feb  3 09:57 U0201\n",
            "drwx------ 2 root root 4096 Feb  3 09:58 U0202\n",
            "drwx------ 2 root root 4096 Feb  3 09:58 U0203\n",
            "drwx------ 2 root root 4096 Feb  3 09:58 U0204\n",
            "drwx------ 2 root root 4096 Feb  3 09:58 U0205\n",
            "drwx------ 2 root root 4096 Feb  3 09:58 U0206\n",
            "drwx------ 2 root root 4096 Feb  3 09:58 U0207\n",
            "drwx------ 2 root root 4096 Feb  3 09:58 U0208\n",
            "drwx------ 2 root root 4096 Feb  3 09:59 U0209\n",
            "drwx------ 2 root root 4096 Feb  3 09:59 U0210\n",
            "drwx------ 2 root root 4096 Feb  3 09:54 zenodo\n"
          ]
        }
      ],
      "source": [
        "! ls -la \"/content/data/datasets/openpack/v1.0.0\""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "13c4a349-8af8-4840-849f-d97b04f143cb",
      "metadata": {
        "id": "13c4a349-8af8-4840-849f-d97b04f143cb"
      },
      "source": [
        "### 0-3: Import Modules"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0fca86ac-b992-47e1-a809-02dc17776d10",
      "metadata": {
        "id": "0fca86ac-b992-47e1-a809-02dc17776d10"
      },
      "outputs": [],
      "source": [
        "import logging\n",
        "from logging import getLogger\n",
        "from pathlib import Path\n",
        "from typing import Dict, Optional\n",
        "\n",
        "import hydra\n",
        "import openpack_toolkit as optk\n",
        "import pytorch_lightning as pl\n",
        "import torch\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from omegaconf import DictConfig, OmegaConf\n",
        "from scipy.special import softmax\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import openpack_torch as optorch\n",
        "from openpack_torch.lightning import EarlyStopError\n",
        "from openpack_torch.utils.test_helper import test_helper\n",
        "\n",
        "from openpack_toolkit import ActSet\n",
        "from openpack_toolkit.configs.datasets.annotations import (\n",
        "    OPENPACK_ACTIONS,\n",
        "    OPENPACK_OPERATIONS,\n",
        ")\n",
        "from openpack_toolkit.codalab.operation_segmentation import (\n",
        "    construct_submission_dict, eval_operation_segmentation_wrapper,\n",
        "    make_submission_zipfile)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "85f8cc4e-42a3-4ff2-a853-32c8992ba6b6",
      "metadata": {
        "id": "85f8cc4e-42a3-4ff2-a853-32c8992ba6b6"
      },
      "source": [
        "### 0-4: Setup Logger"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "88d0bdbf-b9cd-40cf-bfa0-e7f18c23b96f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "88d0bdbf-b9cd-40cf-bfa0-e7f18c23b96f",
        "outputId": "cbdb1e6c-b873-449e-9440-54f1ad353893"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:__main__:info\n",
            "2024-02-04 06:55:00 | INFO | __main__ | info\n",
            "WARNING:__main__:warning\n",
            "2024-02-04 06:55:00 | WARNING | __main__ | warning\n"
          ]
        }
      ],
      "source": [
        "_ = optk.utils.notebook.setup_root_logger()\n",
        "logger = logging.getLogger(__name__)\n",
        "\n",
        "logger.debug(\"debug\")\n",
        "logger.info(\"info\")\n",
        "logger.warning(\"warning\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6f742547-dbd0-4e89-a318-54f05cd99924",
      "metadata": {
        "id": "6f742547-dbd0-4e89-a318-54f05cd99924"
      },
      "source": [
        "### 0-5: Set Random Seed\n",
        "\n",
        "In machine learning, a lot of random variables are generated and it affect to model's performance.\n",
        "By resetting a random seed before each run, you can improve reproducibility."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fb8628b5-baa0-43fe-8135-5d7894d402d7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fb8628b5-baa0-43fe-8135-5d7894d402d7",
        "outputId": "11f1270c-6432-40ea-874c-d347454ab33e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:openpack_torch.utils.random:Reset Seeds: python=0, numpy=0, Pytorch (cpu=0, cuda=0)\n",
            "2024-02-04 06:55:02 | INFO | openpack_torch.utils.random | Reset Seeds: python=0, numpy=0, Pytorch (cpu=0, cuda=0)\n"
          ]
        }
      ],
      "source": [
        "optorch.utils.reset_seed(seed=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aefa1636-e689-4053-bf60-8a324967bbc9",
      "metadata": {
        "id": "aefa1636-e689-4053-bf60-8a324967bbc9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "53707866-bfc4-49dc-9bba-5b7bab6e12da",
      "metadata": {
        "id": "53707866-bfc4-49dc-9bba-5b7bab6e12da"
      },
      "source": [
        "## [1] Load Config File"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "778e85e0-0816-46d0-be35-fa7af4d9dfb7",
      "metadata": {
        "id": "778e85e0-0816-46d0-be35-fa7af4d9dfb7"
      },
      "source": [
        "### 1-1: Overview of Config Files\n",
        "Thare are a lot of parameters that are needed to train deep learning models. Managing these parameters is a laborious task.\n",
        "Here are some useful tools to ease this burden. [hydra](https://hydra.cc/) is a framework for elegantly configurein complex application, and also you can use it to manage parameters.\n",
        "To update default parameters, please update YAML files in [configs](https://github.com/open-pack/openpack-torch/tree/main/examples/unet/configs).\n",
        "We will show you some examples here, but when you want to utilse yaml files, please refere to other tutorials.\n",
        "\n",
        "#### Ex.1: Change Training Parameters\n",
        "\n",
        "For example, when you want to change default `batch_size`, edit [unet.yaml](https://github.com/open-pack/openpack-torch/blob/main/examples/configs/unet.yaml) as follows.\n",
        "\n",
        "```yaml\n",
        "train:\n",
        "  num_workers: 3\n",
        "  batch_size: 128 # Updated (original value = 64)\n",
        "  epochs:\n",
        "    minimum: 300\n",
        "    maximum: 1000\n",
        "```\n",
        "\n",
        "#### Ex.2: Change Input Modality\n",
        "\n",
        "An another example is to add IMU sensors at left&right arm (atr03, atr04).\n",
        "You can change it by editing [atr02-iot.yaml](https://github.com/open-pack/openpack-torch/blob/main/examples/configs/dataset/stream/atr02-iot.yaml)\n",
        "\n",
        "\n",
        "```yaml\n",
        "spec:\n",
        "    imu:\n",
        "        devices:\n",
        "        - atr01\n",
        "        - atr02 # Add\n",
        "        - atr03 # Add\n",
        "        - atr04 # Add\n",
        "        acc: true\n",
        "        gyro: false\n",
        "        quat: false\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d78c9b91-dc07-4105-8de5-b300968083fd",
      "metadata": {
        "id": "d78c9b91-dc07-4105-8de5-b300968083fd"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "d6cc1278-a243-431a-ab2a-5f3c3a90afac",
      "metadata": {
        "id": "d6cc1278-a243-431a-ab2a-5f3c3a90afac"
      },
      "source": [
        "### 1-2: Register Configs to Hydra in `openpack-torch`\n",
        "First, register OpenPaack official configs to hydra."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7700afe9-8e59-4394-aa11-3d4589b92623",
      "metadata": {
        "id": "7700afe9-8e59-4394-aa11-3d4589b92623"
      },
      "outputs": [],
      "source": [
        "optorch.configs.register_configs()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8ed102f9-8875-4259-81c9-e9e1c16cdb21",
      "metadata": {
        "id": "8ed102f9-8875-4259-81c9-e9e1c16cdb21"
      },
      "source": [
        "### 1-3: Load Custom Configs\n",
        "Load configuration files in [/content/openpack-torch/examples/unet/configs](https://github.com/open-pack/openpack-torch/blob/main/examples/unet/configs/operation-segmentation.yaml).\n",
        "In this notebook, we will copy YAML files to the current directory and load default parameters from there.\n",
        "You may want to change some default parameters, please save config files into your Google Drive and edit them.\n",
        "For temporal changes such as learning rate, we can change them by `overrides` param in `hydra.compose()`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7996697c-e4b9-40ae-b95d-88e2a65322e7",
      "metadata": {
        "id": "7996697c-e4b9-40ae-b95d-88e2a65322e7"
      },
      "outputs": [],
      "source": [
        "! cp -r /content/openpack-torch/examples/configs /content/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f8ce8be4-ac7e-4b9c-88d9-66545265b102",
      "metadata": {
        "id": "f8ce8be4-ac7e-4b9c-88d9-66545265b102",
        "tags": []
      },
      "outputs": [],
      "source": [
        "overrides = [\n",
        "    \"mode=train\",\n",
        "    \"metadata.labels.issue=I01-EXP1-V1-nb\", # subdirectry for logging\n",
        "\n",
        "    # -- debug --\n",
        "    # If true, some training parameters (e.g., bach size, epochs) are switched to debug mode setting written in `train.debug.XXX`.\n",
        "    \"debug=true\",\n",
        "\n",
        "    # -- path.logdir.rootdir --\n",
        "    \"path.openpack.version=v1.0.0\",\n",
        "    \"path.openpack.rootdir=/content/data/datasets/openpack/${path.openpack.version}/\",\n",
        "\n",
        "    # -- path.logdir.rootdir --\n",
        "    # path to write training logs and resutls.\n",
        "    # If you want to keep logs, please use Google Drive (i.e., `/content/data/XXX` (symling to Google Drive), `/content/drive/XXX`)\n",
        "    \"path.logdir.rootdir=/content/data/log/${metadata.name}/${metadata.labels.issue}/${metadata.task}/${dataset.split.name}-split/${dataset.stream.name}/${model.name}/${metadata.labels.params}/RUN${run}\",\n",
        "\n",
        "    # \"hydra/job_logging=custom\",\n",
        "    # \"hydra/hydra_logging=disabled\"\n",
        "]\n",
        "\n",
        "with hydra.initialize_config_dir(version_base=None, config_dir=\"/content/configs\"):\n",
        "    cfg = hydra.compose(\n",
        "        config_name=\"unet.yaml\",\n",
        "        overrides=overrides,\n",
        "    )\n",
        "\n",
        "# -- Data Split --\n",
        "# Change Train/Val/Test/Submission set to submit for Development Phase.\n",
        "# Please comment out the following block when you want to train on the main set.\n",
        "cfg.dataset.split = optk.configs.datasets.splits.DEBUG_SPLIT\n",
        "\n",
        "# (default value)\n",
        "# cfg.dataset.split = optk.configs.datasets.splits.OPENPACK_CHALLENGE_2022_SPLIT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6329efb0-9e8d-438d-8b6e-538026ed8bb6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6329efb0-9e8d-438d-8b6e-538026ed8bb6",
        "outputId": "1eef2503-68c7-4a66-d0f3-d49e785c31cc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "run: 0\n",
            "debug: true\n",
            "mode: train\n",
            "path:\n",
            "  openpack:\n",
            "    version: v1.0.0\n",
            "    rootdir: /content/data/datasets/openpack/${path.openpack.version}/\n",
            "  datasets:\n",
            "    openpack: ${path.openpack.rootdir}\n",
            "  logdir:\n",
            "    rootdir: /content/data/log/${metadata.name}/${metadata.labels.issue}/${metadata.task}/${dataset.split.name}-split/${dataset.stream.name}/${model.name}/${metadata.labels.params}/RUN${run}\n",
            "    model: ${path.logdir.rootdir}/model.pth\n",
            "    predict: ${path.logdir.rootdir}/pred/${train.checkpoint}/{user}-{session}\n",
            "    summary:\n",
            "      rootdir: ${path.logdir.rootdir}/summary/${train.checkpoint}\n",
            "      train: ${.rootdir}/train.csv\n",
            "      test: ${.rootdir}/test.csv\n",
            "      test-b2: ${.rootdir}/test-b2.csv\n",
            "      test-b3: ${.rootdir}/test-b3.csv\n",
            "      submission: ${.rootdir}/submission.csv\n",
            "dataset:\n",
            "  annotation:\n",
            "    kind: dataset/annotation/csv/sequence\n",
            "    name: openpack-operations-1hz-annotation\n",
            "    metadata:\n",
            "      labels:\n",
            "        type: annotation/operation\n",
            "        version: v3.5.0\n",
            "        dependency: openpack-operations\n",
            "        resolution: 1Hz\n",
            "    spec:\n",
            "      path:\n",
            "        dir: ${path.openpack.rootdir}/${user.name}/annotation/openpack-operations-1hz\n",
            "        fname: ${session}.csv\n",
            "      classes:\n",
            "      - id: 100\n",
            "        name: Picking\n",
            "        description: Check the order-sheet and go to the back table to pick up the\n",
            "          items.\n",
            "        version: v3.0.0\n",
            "        ignore: false\n",
            "      - id: 200\n",
            "        name: Relocate Item Label\n",
            "        description: Peel off the label from the items and place it on the bottom\n",
            "          margin of the order sheet. Check the item names and quantity on the list\n",
            "          and label with a ballpoint pen. (When picking is done in one round trip,\n",
            "          this action is set as the beginning of the box.)\n",
            "        version: v3.2.2\n",
            "        ignore: false\n",
            "      - id: 300\n",
            "        name: Assemble Box\n",
            "        description: Assemble cardboard boxes that match the size of the items.\n",
            "        version: v3.2.2\n",
            "        ignore: false\n",
            "      - id: 400\n",
            "        name: Insert Items\n",
            "        description: Put the goods into the box. Fill the box with air cushion.\n",
            "        version: v3.2.2\n",
            "        ignore: false\n",
            "      - id: 500\n",
            "        name: Close Box\n",
            "        description: Close the box with craft tape.\n",
            "        version: v3.2.2\n",
            "        ignore: false\n",
            "      - id: 600\n",
            "        name: Attach Box Label\n",
            "        description: Attach the box number label to the side of the box.\n",
            "        version: v3.2.2\n",
            "        ignore: false\n",
            "      - id: 700\n",
            "        name: Scan Label\n",
            "        description: 'Scan barcodes with the handy scanner in the following order:\n",
            "          (1) order sheet, (2) box number sticker, (3) item label. Then press the\n",
            "          \"ESC\" button on the handy scanner twice. Next, scan barcodes with the scanner\n",
            "          of the label printer in the following order: (1) order sheet, (2) code on\n",
            "          the label printer.'\n",
            "        version: v3.2.2\n",
            "        ignore: false\n",
            "      - id: 800\n",
            "        name: Attach Shipping Label\n",
            "        description: Attach the shipping label printed out from the label printer\n",
            "          to the box.\n",
            "        version: v3.2.2\n",
            "        ignore: false\n",
            "      - id: 900\n",
            "        name: Put on Back Table\n",
            "        description: Move the completed packing box under the back table.\n",
            "        version: v3.2.2\n",
            "        ignore: false\n",
            "      - id: 1000\n",
            "        name: Fill out Order\n",
            "        description: Write their signatures in the confirmation column of the order\n",
            "          sheet and insert it into the tray for completed order sheets.\n",
            "        version: v3.2.2\n",
            "        ignore: false\n",
            "      - id: 8100\n",
            "        name: 'Null'\n",
            "        description: For the operation label, ID8200 is translated into this label\n",
            "          (ID8100).\n",
            "        version: v3.2.2\n",
            "        ignore: true\n",
            "  stream:\n",
            "    kind: dataset/stream/multimodal\n",
            "    name: atr02-iot\n",
            "    mdetadata:\n",
            "      labels:\n",
            "        app: openpack-benchmarks\n",
            "        version: 1.0.0\n",
            "        multimodal: true\n",
            "    spec:\n",
            "      imu:\n",
            "        kind: dataset/stream/imu\n",
            "        path:\n",
            "          dir: ${path.openpack.rootdir}/${user.name}/atr/${device}\n",
            "          fname: ${session}.csv\n",
            "          full_path: ${.dir}/${.fname}\n",
            "        devices:\n",
            "        - atr02\n",
            "        acc: true\n",
            "        gyro: false\n",
            "        quat: false\n",
            "      iot:\n",
            "        kind: dataset/stream/iot\n",
            "        spec:\n",
            "          anchor:\n",
            "            dim: 2\n",
            "            linked_class:\n",
            "              0: 6\n",
            "              1: 6\n",
            "          devices:\n",
            "            kind: dataset/stream/multimodal\n",
            "            spec:\n",
            "              ht:\n",
            "                kind: dataset/stream\n",
            "                spec:\n",
            "                  path:\n",
            "                    dir: ${path.openpack.rootdir}/${user.name}/system/ht\n",
            "                    fname: ${session}.csv\n",
            "              printer:\n",
            "                kind: dataset/stream\n",
            "                spec:\n",
            "                  path:\n",
            "                    dir: ${path.openpack.rootdir}/${user.name}/system/printer\n",
            "                    fname: ${session}.csv\n",
            "  split:\n",
            "    name: debug\n",
            "    train:\n",
            "    - - U0102\n",
            "      - S0100\n",
            "    - - U0102\n",
            "      - S0200\n",
            "    - - U0102\n",
            "      - S0300\n",
            "    val:\n",
            "    - - U0102\n",
            "      - S0400\n",
            "    test:\n",
            "    - - U0102\n",
            "      - S0500\n",
            "    submission:\n",
            "    - - U0102\n",
            "      - S0500\n",
            "  classes:\n",
            "  - id: 100\n",
            "    name: Picking\n",
            "    version: v3.0.0\n",
            "    is_ignore: false\n",
            "    category: null\n",
            "    event: null\n",
            "  - id: 200\n",
            "    name: Relocate Item Label\n",
            "    version: v3.2.2\n",
            "    is_ignore: false\n",
            "    category: null\n",
            "    event: null\n",
            "  - id: 300\n",
            "    name: Assemble Box\n",
            "    version: v3.2.2\n",
            "    is_ignore: false\n",
            "    category: null\n",
            "    event: null\n",
            "  - id: 400\n",
            "    name: Insert Items\n",
            "    version: v3.2.2\n",
            "    is_ignore: false\n",
            "    category: null\n",
            "    event: null\n",
            "  - id: 500\n",
            "    name: Close Box\n",
            "    version: v3.2.2\n",
            "    is_ignore: false\n",
            "    category: null\n",
            "    event: null\n",
            "  - id: 600\n",
            "    name: Attach Box Label\n",
            "    version: v3.2.2\n",
            "    is_ignore: false\n",
            "    category: null\n",
            "    event: null\n",
            "  - id: 700\n",
            "    name: Scan Label\n",
            "    version: v3.2.2\n",
            "    is_ignore: false\n",
            "    category: null\n",
            "    event: null\n",
            "  - id: 800\n",
            "    name: Attach Shipping Label\n",
            "    version: v3.2.2\n",
            "    is_ignore: false\n",
            "    category: null\n",
            "    event: null\n",
            "  - id: 900\n",
            "    name: Put on Back Table\n",
            "    version: v3.2.2\n",
            "    is_ignore: false\n",
            "    category: null\n",
            "    event: null\n",
            "  - id: 1000\n",
            "    name: Fill out Order\n",
            "    version: v3.2.2\n",
            "    is_ignore: false\n",
            "    category: null\n",
            "    event: null\n",
            "  - id: 8100\n",
            "    name: 'Null'\n",
            "    version: v3.2.2\n",
            "    is_ignore: true\n",
            "    category: null\n",
            "    event: null\n",
            "optimizer:\n",
            "  name: adam-exp\n",
            "  type: Adam\n",
            "  lr: 0.001\n",
            "  weight_decay: 0.0001\n",
            "  scheduler:\n",
            "    type: ExponentialLR\n",
            "    gamma: 0.99\n",
            "model:\n",
            "  name: UNet\n",
            "  spec:\n",
            "    depth: 5\n",
            "metadata:\n",
            "  name: benchmark1\n",
            "  task: ${dataset.annotation.name}\n",
            "  labels:\n",
            "    app: openpack-benchmarks\n",
            "    issue: I01-EXP1-V1-nb\n",
            "    params: ${optimizer.name}/LR${optimizer.lr}\n",
            "    benchmarkType: benchmark1\n",
            "train:\n",
            "  num_workers: 3\n",
            "  batch_size: 32\n",
            "  epochs:\n",
            "    minimum: 300\n",
            "    maximum: 1000\n",
            "  window: 1800\n",
            "  checkpoint: last\n",
            "  random_crop: true\n",
            "  val_split_siz: 0.2\n",
            "  early_stop:\n",
            "    monitor: train/acc\n",
            "    min_delta: 0.001\n",
            "    patience: 30\n",
            "    verbose: false\n",
            "    mode: max\n",
            "  debug:\n",
            "    batch_size: 8\n",
            "    epochs:\n",
            "      minimum: 5\n",
            "      maximum: 10\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(OmegaConf.to_yaml(cfg))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7d581508-54ab-44b1-8351-d7e3a31efc0a",
      "metadata": {
        "id": "7d581508-54ab-44b1-8351-d7e3a31efc0a"
      },
      "source": [
        "## [2] Define Utilities (Pytorch Lightning Modules)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "40f19069-4df8-49f6-9d10-3290b2709d23",
      "metadata": {
        "id": "40f19069-4df8-49f6-9d10-3290b2709d23"
      },
      "source": [
        "### 2-1: DataModule\n",
        "\n",
        "Define a new DataModule class (e.g., `OpenPackImuDataModule`) and  set [`optorch.data.datasets.OpenPackImu`](https://github.com/open-pack/openpack-torch/blob/main/openpack_torch/data/datasets.py) to `dataset_class`.\n",
        "When you implement custom dataset, set your dataset class to `dataset_class`.\n",
        "\n",
        "Paramters to initialize the dataset class should be prepared in `get_kwargs_form_datasets()` method.\n",
        "The dataset class is initialized with`kwargs`, an output of `get_kwargs_form_datasets()`, as follows.\n",
        "\n",
        "```python\n",
        "# https://github.com/open-pack/openpack-torch/blob/6c6933e24c7c3311e582ebf3aac1a659467be0fb/openpack_torch/data/datamodule.py#L91\n",
        "self.op_train = self.dataset_class(self.cfg, split.train, **kwargs)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "59e2f279-35fa-421d-b9aa-7b0042906759",
      "metadata": {
        "id": "59e2f279-35fa-421d-b9aa-7b0042906759"
      },
      "outputs": [],
      "source": [
        "class OpenPackImuDataModule(optorch.data.OpenPackBaseDataModule):\n",
        "    dataset_class = optorch.data.datasets.OpenPackImu\n",
        "\n",
        "    def get_kwargs_for_datasets(self, stage: Optional[str] = None) -> Dict:\n",
        "        kwargs = {\n",
        "            \"window\": self.cfg.train.window,\n",
        "            \"debug\": self.cfg.debug,\n",
        "        }\n",
        "        return kwargs"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eca2982d-33b3-43bf-a3e8-705eeb097749",
      "metadata": {
        "id": "eca2982d-33b3-43bf-a3e8-705eeb097749"
      },
      "source": [
        "### 2-2: Lightning Module\n",
        "\n",
        "In the `init_model()` method, you can initialize your model.\n",
        "\n",
        "In the `init_criterion()` method, you can register your custom loss function.\n",
        "But make sure that ignore classes are excluded from evaluation process.\n",
        "For `torch.nn.CrossEntropyLoss`, you can use `ignore_class` parameter."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "652fef87-bb07-46b2-994f-ada53675ee7f",
      "metadata": {
        "id": "652fef87-bb07-46b2-994f-ada53675ee7f"
      },
      "outputs": [],
      "source": [
        "class UNetLM(optorch.lightning.BaseLightningModule):\n",
        "    def init_model(self, cfg: DictConfig) -> torch.nn.Module:\n",
        "        dim = 0\n",
        "        if self.cfg.dataset.stream.spec.imu.acc:\n",
        "            dim += 3\n",
        "        if self.cfg.dataset.stream.spec.imu.gyro:\n",
        "            dim += 3\n",
        "        if self.cfg.dataset.stream.spec.imu.quat:\n",
        "            dim += 4\n",
        "\n",
        "        input_dim = len(self.cfg.dataset.stream.spec.imu.devices) * dim\n",
        "        output_dim = len(self.cfg.dataset.annotation.spec.classes)\n",
        "        model = optorch.models.imu.UNet(\n",
        "            input_dim,\n",
        "            output_dim,\n",
        "            depth=cfg.model.spec.depth,\n",
        "        )\n",
        "        return model\n",
        "\n",
        "    def train_val_common_step(self, batch: Dict, batch_idx: int) -> Dict:\n",
        "        x = batch[\"x\"].to(device=self.device, dtype=torch.float)\n",
        "        t = batch[\"t\"].to(device=self.device, dtype=torch.long)\n",
        "        y_hat = self(x).squeeze(3)\n",
        "\n",
        "        loss = self.criterion(y_hat, t)\n",
        "        acc = self.calc_accuracy(y_hat, t)\n",
        "        return {\"loss\": loss, \"acc\": acc}\n",
        "\n",
        "    def test_step(self, batch: Dict, batch_idx: int) -> Dict:\n",
        "        x = batch[\"x\"].to(device=self.device, dtype=torch.float)\n",
        "        t = batch[\"t\"].to(device=self.device, dtype=torch.long)\n",
        "        ts_unix = batch[\"ts\"]\n",
        "\n",
        "        y_hat = self(x).squeeze(3)\n",
        "\n",
        "        outputs = dict(t=t, y=y_hat, unixtime=ts_unix)\n",
        "        self.test_step_outputs.append(outputs)\n",
        "        return outputs\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "47a06407-1935-4b2b-a843-d25ab545dd27",
      "metadata": {
        "id": "47a06407-1935-4b2b-a843-d25ab545dd27"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "8edefca6-e7a6-4316-a1c0-a2661ed3dfac",
      "metadata": {
        "id": "8edefca6-e7a6-4316-a1c0-a2661ed3dfac"
      },
      "source": [
        "## [3] Train U-Net"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e7a61ead-99e5-4072-8fd3-e5abecc5529a",
      "metadata": {
        "id": "e7a61ead-99e5-4072-8fd3-e5abecc5529a"
      },
      "source": [
        "### 3-1: Initialize U-Net Model and DataLoader."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f8d63c1a-20ce-4581-8e79-7826e2afd083",
      "metadata": {
        "id": "f8d63c1a-20ce-4581-8e79-7826e2afd083"
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda\")\n",
        "logdir = Path(cfg.path.logdir.rootdir)\n",
        "logger.debug(f\"logdir = {logdir}\")\n",
        "\n",
        "datamodule = OpenPackImuDataModule(cfg)\n",
        "plmodel = UNetLM(cfg).to(dtype=torch.float, device=device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c44f0a87-ca40-40d8-a6ee-16d9c38e0c1c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c44f0a87-ca40-40d8-a6ee-16d9c38e0c1c",
        "outputId": "2899609e-de75-4e94-e8e8-51178bb3c989"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "UNetLM(\n",
              "  (net): UNet(\n",
              "    (inc): Sequential(\n",
              "      (0): Conv2d(3, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU()\n",
              "    )\n",
              "    (encoder): UNetEncoder(\n",
              "      (0): ModuleList(\n",
              "        (0): DownBlock(\n",
              "          (double_conv): Sequential(\n",
              "            (0): Conv2d(32, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (2): ReLU()\n",
              "            (3): Conv2d(32, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (5): ReLU()\n",
              "          )\n",
              "          (pool): MaxPool2d(kernel_size=(2, 1), stride=(2, 1), padding=0, dilation=1, ceil_mode=False)\n",
              "        )\n",
              "        (1): DownBlock(\n",
              "          (double_conv): Sequential(\n",
              "            (0): Conv2d(32, 64, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (2): ReLU()\n",
              "            (3): Conv2d(64, 64, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (5): ReLU()\n",
              "          )\n",
              "          (pool): MaxPool2d(kernel_size=(2, 1), stride=(2, 1), padding=0, dilation=1, ceil_mode=False)\n",
              "        )\n",
              "        (2): DownBlock(\n",
              "          (double_conv): Sequential(\n",
              "            (0): Conv2d(64, 128, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (2): ReLU()\n",
              "            (3): Conv2d(128, 128, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (5): ReLU()\n",
              "          )\n",
              "          (pool): MaxPool2d(kernel_size=(2, 1), stride=(2, 1), padding=0, dilation=1, ceil_mode=False)\n",
              "        )\n",
              "        (3): DownBlock(\n",
              "          (double_conv): Sequential(\n",
              "            (0): Conv2d(128, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (2): ReLU()\n",
              "            (3): Conv2d(256, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (5): ReLU()\n",
              "          )\n",
              "          (pool): MaxPool2d(kernel_size=(2, 1), stride=(2, 1), padding=0, dilation=1, ceil_mode=False)\n",
              "        )\n",
              "        (4): DownBlock(\n",
              "          (double_conv): Sequential(\n",
              "            (0): Conv2d(256, 512, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (2): ReLU()\n",
              "            (3): Conv2d(512, 512, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (5): ReLU()\n",
              "          )\n",
              "          (pool): MaxPool2d(kernel_size=(2, 1), stride=(2, 1), padding=0, dilation=1, ceil_mode=False)\n",
              "        )\n",
              "      )\n",
              "      (1): Sequential(\n",
              "        (0): Conv2d(512, 1024, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (2): ReLU()\n",
              "        (3): Conv2d(1024, 1024, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "        (4): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (5): ReLU()\n",
              "      )\n",
              "    )\n",
              "    (decoder): UNetDecoder(\n",
              "      (0): ModuleList(\n",
              "        (0): UpBlock(\n",
              "          (up): ConvTranspose2d(1024, 512, kernel_size=(1, 3), stride=(1, 2), padding=(0, 1))\n",
              "          (double_conv): Sequential(\n",
              "            (0): Conv2d(1024, 512, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (2): ReLU()\n",
              "            (3): Conv2d(512, 512, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (5): ReLU()\n",
              "          )\n",
              "        )\n",
              "        (1): UpBlock(\n",
              "          (up): ConvTranspose2d(512, 256, kernel_size=(1, 3), stride=(1, 2), padding=(0, 1))\n",
              "          (double_conv): Sequential(\n",
              "            (0): Conv2d(512, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (2): ReLU()\n",
              "            (3): Conv2d(256, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (5): ReLU()\n",
              "          )\n",
              "        )\n",
              "        (2): UpBlock(\n",
              "          (up): ConvTranspose2d(256, 128, kernel_size=(1, 3), stride=(1, 2), padding=(0, 1))\n",
              "          (double_conv): Sequential(\n",
              "            (0): Conv2d(256, 128, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (2): ReLU()\n",
              "            (3): Conv2d(128, 128, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (5): ReLU()\n",
              "          )\n",
              "        )\n",
              "        (3): UpBlock(\n",
              "          (up): ConvTranspose2d(128, 64, kernel_size=(1, 3), stride=(1, 2), padding=(0, 1))\n",
              "          (double_conv): Sequential(\n",
              "            (0): Conv2d(128, 64, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (2): ReLU()\n",
              "            (3): Conv2d(64, 64, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (5): ReLU()\n",
              "          )\n",
              "        )\n",
              "        (4): UpBlock(\n",
              "          (up): ConvTranspose2d(64, 32, kernel_size=(1, 3), stride=(1, 2), padding=(0, 1))\n",
              "          (double_conv): Sequential(\n",
              "            (0): Conv2d(64, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (2): ReLU()\n",
              "            (3): Conv2d(32, 32, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
              "            (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (5): ReLU()\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (dense_clf): Conv2d(32, 11, kernel_size=(1, 1), stride=(1, 1))\n",
              "  )\n",
              "  (criterion): CrossEntropyLoss()\n",
              ")"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "plmodel"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fc4bfa59-1e6e-4f6e-ba66-2f8b89c6cd61",
      "metadata": {
        "id": "fc4bfa59-1e6e-4f6e-ba66-2f8b89c6cd61"
      },
      "source": [
        "### 3-2: Initialize Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "04df6672-0398-4daa-b38e-5b04e428db63",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04df6672-0398-4daa-b38e-5b04e428db63",
        "outputId": "742db471-c8d9-4dcc-9d0c-0c34c6a8749b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "2024-02-04 06:55:22 | INFO | pytorch_lightning.utilities.rank_zero | GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "2024-02-04 06:55:22 | INFO | pytorch_lightning.utilities.rank_zero | TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "2024-02-04 06:55:22 | INFO | pytorch_lightning.utilities.rank_zero | IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "2024-02-04 06:55:22 | INFO | pytorch_lightning.utilities.rank_zero | HPU available: False, using: 0 HPUs\n"
          ]
        }
      ],
      "source": [
        "max_epoch = (\n",
        "    cfg.train.debug.epochs.maximum if cfg.debug else cfg.train.epochs.maximum\n",
        ")\n",
        "\n",
        "checkpoint_callback = pl.callbacks.ModelCheckpoint(\n",
        "    save_top_k=1,\n",
        "    save_last=True,\n",
        "    mode=cfg.train.early_stop.mode,\n",
        "    monitor=cfg.train.early_stop.monitor,\n",
        "    filename=\"{epoch:02d}-{train/loss:.2f}-{val/loss:.2f}\",\n",
        "    verbose=False,\n",
        ")\n",
        "\n",
        "early_stop_callback = pl.callbacks.EarlyStopping(\n",
        "    **cfg.train.early_stop,\n",
        ")\n",
        "\n",
        "pl_logger = pl.loggers.CSVLogger(logdir)\n",
        "trainer = pl.Trainer(\n",
        "    accelerator=\"gpu\",\n",
        "    devices=1,\n",
        "    min_epochs=1,\n",
        "    max_epochs=max_epoch,\n",
        "    logger=pl_logger,\n",
        "    default_root_dir=logdir,\n",
        "    enable_progress_bar=True,\n",
        "    enable_checkpointing=True,\n",
        "    callbacks=[checkpoint_callback, early_stop_callback],\n",
        "    log_every_n_steps=4,\n",
        ")\n",
        "logger.debug(f\"logdir = {logdir}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "996d74a9-b366-40c7-b344-d71a932e3482",
      "metadata": {
        "id": "996d74a9-b366-40c7-b344-d71a932e3482"
      },
      "source": [
        "### 3-3: Start training!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ccc3b111-16b1-41a1-b8a0-60112611968a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:__main__:Start training for 10 epochs.\n",
            "2024-02-04 06:55:24 | INFO | __main__ | Start training for 10 epochs.\n",
            "WARNING:lightning_fabric.loggers.csv_logs:Missing logger folder: /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0/lightning_logs\n",
            "2024-02-04 06:55:24 | WARNING | lightning_fabric.loggers.csv_logs | Missing logger folder: /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0/lightning_logs\n",
            "INFO:openpack_torch.data.datamodule:dataset[train]: OpenPackImu(index=129, num_sequence=3, submission=False, random_crop=True)\n",
            "2024-02-04 06:55:28 | INFO | openpack_torch.data.datamodule | dataset[train]: OpenPackImu(index=129, num_sequence=3, submission=False, random_crop=True)\n",
            "INFO:openpack_torch.data.datamodule:dataset[val]: {'U0102-S0400': <openpack_torch.data.datasets.OpenPackImu object at 0x7c5ae179ce20>}\n",
            "2024-02-04 06:55:28 | INFO | openpack_torch.data.datamodule | dataset[val]: {'U0102-S0400': <openpack_torch.data.datasets.OpenPackImu object at 0x7c5ae179ce20>}\n",
            "INFO:openpack_torch.data.datamodule:dataset[test]: None\n",
            "2024-02-04 06:55:28 | INFO | openpack_torch.data.datamodule | dataset[test]: None\n",
            "INFO:openpack_torch.data.datamodule:dataset[submission]: None\n",
            "2024-02-04 06:55:28 | INFO | openpack_torch.data.datamodule | dataset[submission]: None\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "2024-02-04 06:55:28 | INFO | pytorch_lightning.accelerators.cuda | LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:openpack_torch.lightning:Adam optimizer is selected! (lr=0.001)\n",
            "2024-02-04 06:55:28 | INFO | openpack_torch.lightning | Adam optimizer is selected! (lr=0.001)\n",
            "INFO:openpack_torch.lightning:StepLR scheduler is selected.\n",
            "2024-02-04 06:55:28 | INFO | openpack_torch.lightning | StepLR scheduler is selected.\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name      | Type             | Params\n",
            "-----------------------------------------------\n",
            "0 | net       | UNet             | 11.5 M\n",
            "1 | criterion | CrossEntropyLoss | 0     \n",
            "-----------------------------------------------\n",
            "11.5 M    Trainable params\n",
            "0         Non-trainable params\n",
            "11.5 M    Total params\n",
            "46.196    Total estimated model params size (MB)\n",
            "2024-02-04 06:55:28 | INFO | pytorch_lightning.callbacks.model_summary | \n",
            "  | Name      | Type             | Params\n",
            "-----------------------------------------------\n",
            "0 | net       | UNet             | 11.5 M\n",
            "1 | criterion | CrossEntropyLoss | 0     \n",
            "-----------------------------------------------\n",
            "11.5 M    Trainable params\n",
            "0         Non-trainable params\n",
            "11.5 M    Total params\n",
            "46.196    Total estimated model params size (MB)\n"
          ]
        },
        {
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              "model_id": "76b624acb5294d62b55c6d17033172ba",
              "version_major": 2,
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            },
            "text/plain": [
              "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 3 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        },
        {
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              "Training: |          | 0/? [00:00<?, ?it/s]"
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              "Validation: |          | 0/? [00:00<?, ?it/s]"
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              "Validation: |          | 0/? [00:00<?, ?it/s]"
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n",
            "2024-02-04 06:55:50 | INFO | pytorch_lightning.utilities.rank_zero | `Trainer.fit` stopped: `max_epochs=10` reached.\n",
            "INFO:__main__:Finish training! (logdir = /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0)\n",
            "2024-02-04 06:55:50 | INFO | __main__ | Finish training! (logdir = /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0)\n"
          ]
        }
      ],
      "source": [
        "logger.info(f\"Start training for {max_epoch} epochs.\")\n",
        "try:\n",
        "    trainer.fit(plmodel, datamodule)\n",
        "except EarlyStopError as e:\n",
        "    logger.warning(e)\n",
        "logger.info(f\"Finish training! (logdir = {logdir})\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "80ad09e2-9b02-4582-b012-3ce9fdfc3e0e",
      "metadata": {
        "id": "80ad09e2-9b02-4582-b012-3ce9fdfc3e0e"
      },
      "source": [
        "### 3-4: Plot Training Curve"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aa0a6811-11fb-4937-a300-735ca1170b0f",
      "metadata": {
        "id": "aa0a6811-11fb-4937-a300-735ca1170b0f"
      },
      "source": [
        "Training logs are stored in `plmodel.log` (dict). Let's visualize training curves for training and validation set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "65f6ac03-5491-4cb2-b5c0-7d37b71af199",
      "metadata": {
        "id": "65f6ac03-5491-4cb2-b5c0-7d37b71af199"
      },
      "outputs": [],
      "source": [
        "pl_logdir = Path(cfg.path.logdir.rootdir, \"lightning_logs/\")\n",
        "latest_logdir = list(sorted(pl_logdir.iterdir()))[-1]\n",
        "\n",
        "df = pd.read_csv(Path(latest_logdir, \"metrics.csv\"))\n",
        "df[\"phase\"] = df[\"train/loss\"].apply(lambda x: \"val\" if np.isnan(x) else \"train\")\n",
        "df = df.set_index([\"epoch\", \"phase\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "65b6eb10-9584-483b-8a44-deb539528f90",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "65b6eb10-9584-483b-8a44-deb539528f90",
        "outputId": "5f95b67e-92cd-456b-fa82-7902b5fa5875"
      },
      "outputs": [
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-02480661-5721-42f6-917b-18e29fb769a6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-02480661-5721-42f6-917b-18e29fb769a6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-05c202ce-5164-4787-926d-a829a998f506\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-05c202ce-5164-4787-926d-a829a998f506')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-05c202ce-5164-4787-926d-a829a998f506 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "              val/acc  val/loss  step  train/loss  train/acc\n",
              "epoch phase                                                 \n",
              "0     val    0.082268  2.387441    16         NaN        NaN\n",
              "      train       NaN       NaN    16    2.211711   0.269642\n",
              "1     val    0.181134  2.343647    33         NaN        NaN\n",
              "      train       NaN       NaN    33    2.002056   0.341471\n",
              "2     val    0.245270  2.128481    50         NaN        NaN"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "61b0b6ad-9a6b-4264-8abd-8db8dcf477e1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "61b0b6ad-9a6b-4264-8abd-8db8dcf477e1",
        "outputId": "9f45485d-9b45-4978-eef2-cd469fc6d529"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.xs(\"train\", level=\"phase\")[[\"train/loss\"]].plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8db64e85-bdb1-481a-9071-907f09fd98a8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "8db64e85-bdb1-481a-9071-907f09fd98a8",
        "outputId": "49f5324c-efa9-4752-f88f-503e58588f9e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.xs(\"val\", level=\"phase\")[[\"val/loss\"]].plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3b1a002a-2ff5-4bf6-a1b2-763d95bbaf08",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "3b1a002a-2ff5-4bf6-a1b2-763d95bbaf08",
        "outputId": "4f4e85c1-2c65-4629-fa40-7f4806db2e33"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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iIhMhCAJW7j6HVzako0orYERPb3w9JRLOdq17DIipcLSxxuJxvbBobAjs5TKkXCjAQ0v2YcfxbLFLazSjx9RERUWhd+/eWLZsGQBAp9PBz88P06dPx6xZs+o9RqvVYsCAAZg8eTL27duHwsJC/PTTT7e9xuHDhxEZGYlLly7B398fQE1LzYwZMzBjxgxjytXjmBoiItOh1Ql49+cT+DrpEgBgyv2BeGt4V0ilrf9pHVN0Mb8Ur2z4C0euFAEAxkf6Y87D3VrFWljNNqamsrISqampiI6OvnUCqRTR0dFISkq67XHz5s2Dp6cnpkyZ0qDrFBUVQSKRwMXFxWD7ggUL4O7ujl69euGjjz5CdXX1bc+h0WigVqsNXkRE1PpVVGnxwrpUfJ10CRIJ8K8RXfH2w90YaJpRgIc9vn++L54f2B4SCbA+JQsjl+3HyWumde80apXu/Px8aLVaKJVKg+1KpRKnTp2q95j9+/dj9erVSE9Pb9A1Kioq8MYbb2D8+PEGiezll19GWFgY3NzccPDgQcyePRvZ2dlYtGhRvedJSEjAu+++27APRkRErUJBaSWe+eow0rIKIbeS4v/GhmJEMBesbQlyKylmPdQF/Tt6YObGdJzNLcHo5Qcwe3gXPN3XNBbGbNann4qLi/HUU0/hs88+g4eHx133r6qqwtixY2v6UVeuNHgvPj4egwYNQnBwMJ5//nksXLgQH3/8MTQaTb3nmj17NoqKivSvy5cvN8lnIiKi5pF1vQxjVh5EWlYhnG2tsW5KFAONCPp18MAvr/THkC6eqNTq8O7PJzHlqz9xvaT++21rYlRLjYeHB2QyGVQqlcF2lUoFLy+vOvufO3cOFy9exMiRI/XbdDpdzYWtrJCZmYn27dsDuBVoLl26hN9///2u/WZRUVGorq7GxYsX0blz5zrvKxQKKBSmuyopEZElOXqlEJPXHEZ+SSV8XWzx1eTeFrH4ZGvl7qDA55Mi8HXSJby/PQO/n8rFsCX7sGhsCPp3bL0LYxrVUiOXyxEeHo7ExET9Np1Oh8TERPTp06fO/l26dMGxY8eQnp6uf40aNQqDBw9Geno6/Pz8ANwKNGfOnMGuXbvg7u5+11rS09MhlUrh6elpzEcgIqJW5o9TuYj95BDySyrRzdsJm17sy0DTCkgkEkzqG4DNL/VDR08H5BVr8NTqFCRsr1n9uzUyqqUGqOkGmjRpEiIiIhAZGYnFixejtLQUcXFxAICJEyfC19cXCQkJsLGxQY8ePQyOrx38W7u9qqoKjz/+ONLS0rB161ZotVrk5OQAANzc3CCXy5GUlITk5GQMHjwYjo6OSEpKwsyZM/Hkk0/C1dX1Xj4/ERGJaENKFt766Ti0OgH9O3pgxYQwONrwke3WpKu3E7ZMux/vbTuJb5Kz8Mne80g6fx1LxvVqdctTGB1qYmNjkZeXhzlz5iAnJwehoaHYsWOHfvBwVlYWpNKGNwBdvXoVW7ZsAQCEhoYavPfHH39g0KBBUCgU2LBhA9555x1oNBoEBgZi5syZiI+PN7Z8IiJqBQRBwP/tOoOliWcAAGPC2mLBmJ6wlnGi+9bIVi7D+4/2RP+ObTBr01EcvVKEEUv3Yd4jPTAmzLfVDCLm2k9ERNSiqrQ6vLnpGL5PvQIAmP5AB8QP7dRqbox0Z9lF5ZixIR3JFwoAACNDfPD+oz3g1EwtbFz7iYiIWqUSTTWmfPUnvk+9AqkE+ODRnnj1wc4MNCbE29kW3z57H157sBNkUgl+PnINw5fsQ1rWDbFLY6ghIqKWkauuQOwnSdh7Og+21jJ8NjEC/4jyF7ssagSZVIJpD3TEd8/1QVtXW1y5UY4nViVh+R9nIWYHEEMNERE1u7O5JXh0xUGcuKaGu70c66fehyFdlXc/kFq18Hau2P5Kf4wKqVkYM79EI2qrm9EDhYmIiIxx+GIBnvnqTxSVVyHA3Q5fTY5EO/fW9dQMNZ6TjTWWjAvF8J5eGNxF3GlWGGqIiKjZ/HIsG69sTEdltQ6hfi5YPSkC7g6cGNXcSCQSDOsh/uzPDDVERNQsvjxwAfO2noQgANFdlfh4fK9WseozmS+GGiIialI6nYCEXzLw2b4LAIAn7/PHOyO7w4pz0FAzY6ghIqImo6nW4rXvj+LnI9cAAK8P64wXBrbnI9vUIhhqiIioSRSVV2Hq138i+UIBrGUSfPh4MB7t1VbsssiCMNQQEdE9u1ZYjqe/TMFpVQkcFFb45Klw9OvgIXZZZGEYaoiI6J5kZKvx9JcpUKk1UDop8OXTkejmw+VoqOUx1BARUaMdOJuP59amokRTjY6eDlgzORK+LrZil0UWiqGGiIga5ce/ruD1/x5FlVZAVKAbPn0qAs52zbOoIVFDMNQQEZFRBEHAyj3n8OGOTADAw8HeWDg2BAorzkFD4mKoISKiBtPqBMzdchzrDmUBAKYOCMKsYV0glfKRbRIfQw0RETVIeaUW09f/hV0ZKkgkwJyHuyGuX6DYZRHpMdQQEdFdXS/RYMpXfyL9ciHkVlIsiQ3FQz3FX+uH6O8YaoiI6I4uXS/FpC9ScPF6GZxtrbF6UgQiAtzELouoDoYaIiK6rSOXCzF5zWFcL62Er4stvpociQ6eDmKXRVQvhhoiIqpXYoYK0779C+VVWnT3ccKXcb3h6WgjdllEt8VQQ0REdXybnIV//XQMOgEY0KkNVkwIg4OCtwxq3fh/KBER6QmCgEU7T+Pj388CAJ4Ib4sPHusJa5lU5MqI7o6hhoiIAABVWh1m/XAMP6RdAQC8MqQjZkR3hETCOWjINDDUEBERSjTVeGFdKvadyYdMKsH7o3tgXKS/2GURGYWhhojIwuWqK/D0l4dxMlsNW2sZVkwIw+AunmKXRWQ0hhoiIgt2NrcYk744jKuF5fBwkOOLp3sjuK2L2GURNQpDDRGRhUq5UIBnv/4TReVVCPSwx1dxkfB3txO7LKJGY6ghIrJA249lY8bGdFRW6xDm74LPJ/WGm71c7LKI7glDDRGRhVm9/wLe23YSggA82E2JJeN6wVYuE7ssonvGUENEZEaqtTrkl1RCpa5AbrEGucUVyFXf+m92UQVOZqsBABP7tMPckd0hk/KRbTIPDDVERCZAU61FXrGmJqio/yesFGugUmuQV1yB66WVEIS7n2/WQ13w3IAgzkFDZoWhhohIROWVWn0wqQ0pqpv/zfvbthtlVQ0+p0wqQRsHBZROCrRxtIGnkwKejgp4OtpA6aRAB08HtHO3b8ZPRSQOhhoiomZQoqlGrrq2FaXib60st0JLbrEGxRXVDT6nXCZFG0eFPqQonWz0YaWNkwLKmwHGzU4OKbuUyAIx1BARNZAgCFCXV99qWaltVflbSKkNMmWV2gaf18ZaahhQ/h5YnGq2eToq4GJnze4iojtgqCEiugNBELDvTD4+23ceKRcKoKnWNfhYB4UVPB0V9YeUv/3XUWHFsELUBBhqiIjqUaXVYevRa/h07wVk3HxaqJazrXX9AcXx5tiVmwHGXsG/YolaEv/EERH9TYmmGhtSsvDF/gu4VlQBALCTyzCutz/+EeWHtq52sLHmnC5ErRFDDRERgNziCqw5cBHrDl2C+ubgXQ8HBeL6BeDJqHZwtrMWuUIiuhuGGiKyaGdzS/D5vvPYlHYVldqa8TJBHvZ4dkAQHu3ly1YZIhMibcxBy5cvR0BAAGxsbBAVFYWUlJQGHbdhwwZIJBKMHj3aYLsgCJgzZw68vb1ha2uL6OhonDlzxmCfgoICTJgwAU5OTnBxccGUKVNQUlLSmPKJiPDnxZrFHKMX7cGGw5dRqdUhvJ0rPnkqHLviB2J8pD8DDZGJMTrUbNy4EfHx8Zg7dy7S0tIQEhKCmJgY5Obm3vG4ixcv4rXXXkP//v3rvPfhhx9i6dKlWLVqFZKTk2Fvb4+YmBhUVFTo95kwYQJOnDiBnTt3YuvWrdi7dy+mTp1qbPlEZMF0OgG/nsjBYysO4PFVSdh5UgWJpGb9ox9e6IMfXuiLmO5enOOFyERJBKEhE2rfEhUVhd69e2PZsmUAAJ1OBz8/P0yfPh2zZs2q9xitVosBAwZg8uTJ2LdvHwoLC/HTTz8BqGml8fHxwauvvorXXnsNAFBUVASlUok1a9Zg3LhxyMjIQLdu3XD48GFEREQAAHbs2IHhw4fjypUr8PHxqXNNjUYDjUaj/1mtVsPPzw9FRUVwcnIy5iMTkYmrqNLix7+u4rO953E+vxRAzUR2Y8J98Uz/ILRv4yByhUR0O2q1Gs7Ozg26fxvVUlNZWYnU1FRER0ffOoFUiujoaCQlJd32uHnz5sHT0xNTpkyp896FCxeQk5NjcE5nZ2dERUXpz5mUlAQXFxd9oAGA6OhoSKVSJCcn13vNhIQEODs7619+fn7GfFQiMgOFZZVY9vsZ3P/vPzB70zGczy+Fk40VXhrcHvtnDUbCY8EMNERmxKiBwvn5+dBqtVAqlQbblUolTp06Ve8x+/fvx+rVq5Genl7v+zk5Ofpz/O85a9/LycmBp6enYeFWVnBzc9Pv879mz56N+Ph4/c+1LTVEZP6u3CjD6v0XsPHwZf3Mvj7ONpjSPwixvf3gwPljiMxSs/7JLi4uxlNPPYXPPvsMHh4ezXmpOhQKBRQKRYtek4jEdeJaET7dex5bj2ZDq6vpWe/i5YjnB7bHiGBvWMsa9WwEEZkIo0KNh4cHZDIZVCqVwXaVSgUvL686+587dw4XL17EyJEj9dt0uppHJq2srJCZmak/TqVSwdvb2+CcoaGhAAAvL686A5Grq6tRUFBQ73WJyHIIgoD9Z/Px6d7z2HcmX7/9/g4emDogCP07enAJAiILYVSokcvlCA8PR2Jiov6xbJ1Oh8TEREybNq3O/l26dMGxY8cMtv3rX/9CcXExlixZAj8/P1hbW8PLywuJiYn6EKNWq5GcnIwXXngBANCnTx8UFhYiNTUV4eHhAIDff/8dOp0OUVFRxn5mIjID1Vodth3Lxid7zuPkzWUMZFIJRvT0xtQBQejh6yxyhUTU0ozufoqPj8ekSZMQERGByMhILF68GKWlpYiLiwMATJw4Eb6+vkhISICNjQ169OhhcLyLiwsAGGyfMWMG3nvvPXTs2BGBgYF4++234ePjow9OXbt2xbBhw/Dss89i1apVqKqqwrRp0zBu3Lh6n3wiIvNVqqnGxsOXsXr/BVwtLAcA2FrLENvbD1PuD4Sfm53IFRKRWIwONbGxscjLy8OcOXOQk5OD0NBQ7NixQz/QNysrC1Kpcf3Wr7/+OkpLSzF16lQUFhbi/vvvx44dO2BjY6Pf55tvvsG0adMwZMgQSKVSjBkzBkuXLjW2fCIyUXnFGnx18CLWHrqEovIqAIC7vRxP9w3Ak/e1g6u9XOQKiUhsRs9TY6qMec6diFqPc3k1yxj8kHYVldU1Y/ICPezxTP9AjAlry1l/icycMfdvPtdIRK1S6qUb+GTPOezMUKH2n169/F3w3IAgDO3mBRln/SWi/8FQQ0Sthk4nYFeGCp/uPY8/L93Qb4/u6onnBrZHRDtXPslERLfFUENEoquo0uKnv67i033ncT7v1jIGo3v5YOqAIHTwdBS5QiIyBQw1RCSaorIqrEu+hDUHLyKvuGatNkcbK0yIaoe4fgFQOtnc5QxERLcw1BBRi7taWI4v9l/AhpQslN5cxsDb2QZT7g9EbG8/ONpYi1whEZkihhoiajEZ2Wp8uvc8fj5yDdU3lzHorHTEcwOD8HCwD+RWXMaAiBqPoYaImpUgCDh47jpW7TlnsIxBnyB3PDcwCAM7teHgXyJqEgw1RNRsLl0vxavfHdE/ySSVAA/19MZzA4IQ3NZF3OKIyOww1BBRkxMEAf9NvYJ3tpxAaaUWNtZSjI3wwzP3B8HfncsYEFHzYKghoiZVWFaJt348jm3HsgEAkYFu+L/YUPi62IpcGRGZO4YaImoyB8/l49XvjiC7qAJWUglmDu2E5we25+y/RNQiGGqI6J5VVuuwcGcmPt17HoJQszbTknGhHDdDRC2KoYaI7snZ3BLM2PgXjl9VAwDGR/rhXyO6wV7Bv16IqGXxbx0iahRBEPBNchbe23YSFVU6uNhZY8FjwRjWw0vs0ojIQjHUEJHRrpdo8MYPR7ErIxcAcH8HDywcG8JlDYhIVAw1RGSUPafz8Nr3R5BXrIFcJsXrwzpjcr9ASDkYmIhExlBDRA1SUaXFv3ecwpcHLgIAOno6YMm4Xujm4yRuYURENzHUENFdncpR45X16chUFQMAJvVph9nDu8LGWiZyZUREtzDUENFtCYKANQcvIuGXU6is1sHDQY6PHg/B4C6eYpdGRFQHQw0R1Su3uAL//P4o9pzOAwAM7twGHz4egjaOCpErIyKqH0MNEdWx86QKb/xwFAWllVBYSfHWiK546r52XE2biFo1hhoi0iuv1OK9bSfxTXIWAKCrtxOWjgtFR6WjyJUREd0dQw0RAQCOXy3Cyxv+wvm8UgDAs/0D8VpMZyisOBiYiEwDQw2RhdPpBHy27zz+81smqrQCPB0VWDQ2FPd39BC7NCIiozDUEFmw7KJyvPrdERw8dx0AENNdiQWPBcPVXi5yZURExmOoIbJQ249lY/amYygqr4KttQxzR3ZDbG8/DgYmIpPFUENkYUo01Xh3ywl8n3oFABDc1hmLY0MR1MZB5MqIiO4NQw2RBfkr6wZmbEzHpetlkEiAFwe1x4zoTrCWScUujYjonjHUEFkArU7Aij/OYnHiGWh1AnycbbAoNhT3BbmLXRoRUZNhqCEyc5cLyjBzYzr+vHQDAPBwsDfef7QnnG2tRa6MiKhpMdQQmbGf/rqKt386jmJNNRwUVpj3SHc82suXg4GJyCwx1BCZoaLyKszZfByb068BAMLbuWJxbCj83OxEroyIqPkw1BCZmZQLBZi5MR1XC8shk0rw8gMd8dLg9rDiYGAiMnMMNURmokqrw9LEM1j+x1noBMDfzQ7/FxuK8HauYpdGRNQiGGqIzMCF/FLM2JiOI5cLAQBjwtri3Ue6w0HBP+JEZDn4Nx6RCRMEAd//eQXv/HwCZZVaONlY4YPHeuLhYB+xSyMianEMNUQmqrCsErM3HcMvx3MAAFGBbvi/2FD4uNiKXBkRkTgYaohM0MGz+Yj/7ghy1BWwkkrw6oOdMXVAEGRSPqpNRJarUY9DLF++HAEBAbCxsUFUVBRSUlJuu++mTZsQEREBFxcX2NvbIzQ0FGvXrjXYRyKR1Pv66KOP9PsEBATUeX/BggWNKZ/IZGmqtUjYnoEJq5ORo65AkIc9fnyxH14Y1J6BhogsntEtNRs3bkR8fDxWrVqFqKgoLF68GDExMcjMzISnp2ed/d3c3PDWW2+hS5cukMvl2Lp1K+Li4uDp6YmYmBgAQHZ2tsExv/zyC6ZMmYIxY8YYbJ83bx6effZZ/c+Ojo7Glk9kss7mFuPl9ek4ma0GAPwjyh//GtEVdnI2uBIRAYBEEATBmAOioqLQu3dvLFu2DACg0+ng5+eH6dOnY9asWQ06R1hYGEaMGIH58+fX+/7o0aNRXFyMxMRE/baAgADMmDEDM2bMMKZcPbVaDWdnZxQVFcHJyalR5yASgyAIWJechfe2noSmWgdXO2v8e0wwHuzuJXZpRETNzpj7t1HdT5WVlUhNTUV0dPStE0iliI6ORlJS0l2PFwQBiYmJyMzMxIABA+rdR6VSYdu2bZgyZUqd9xYsWAB3d3f06tULH330Eaqrq297LY1GA7VabfAiMjX5JRo889WfePun49BU69C/owd2zBjAQENEVA+j2q3z8/Oh1WqhVCoNtiuVSpw6deq2xxUVFcHX1xcajQYymQwrVqzA0KFD6933q6++gqOjIx577DGD7S+//DLCwsLg5uaGgwcPYvbs2cjOzsaiRYvqPU9CQgLeffddYz4eUatRWa1DYoYKb28+gfwSDeQyKd54qAvi+gZAyrEzRET1apHOeEdHR6Snp6OkpASJiYmIj49HUFAQBg0aVGffL774AhMmTICNjY3B9vj4eP2vg4ODIZfL8dxzzyEhIQEKhaLOeWbPnm1wjFqthp+fX9N9KKImVlRehd2Zudh5UoU9mXko1tS0RHZSOmDJuF7o6s1uUyKiOzEq1Hh4eEAmk0GlUhlsV6lU8PK6fXO4VCpFhw4dAAChoaHIyMhAQkJCnVCzb98+ZGZmYuPGjXetJSoqCtXV1bh48SI6d+5c532FQlFv2CFqTa4WlmPXSRV2nlTh0PnrqNbdGuLm4SDHmPC2mBndCTbWMhGrJCIyDUaFGrlcjvDwcCQmJmL06NEAagYKJyYmYtq0aQ0+j06ng0ajqbN99erVCA8PR0hIyF3PkZ6eDqlUWu8TV0StlSAIOHFNjZ0nVdiVocKJa4Zjvdq3scfQbl4Y2k2JXn4u7GoiIjKC0d1P8fHxmDRpEiIiIhAZGYnFixejtLQUcXFxAICJEyfC19cXCQkJAGrGtkRERKB9+/bQaDTYvn071q5di5UrVxqcV61W4/vvv8fChQvrXDMpKQnJyckYPHgwHB0dkZSUhJkzZ+LJJ5+EqysX66PWrUqrQ/L5Auw8mYNdGbm4Wliuf08iAcL9XTG0mxJDuykR1MZBxEqJiEyb0aEmNjYWeXl5mDNnDnJychAaGoodO3boBw9nZWVBKr31UFVpaSlefPFFXLlyBba2tujSpQvWrVuH2NhYg/Nu2LABgiBg/Pjxda6pUCiwYcMGvPPOO9BoNAgMDMTMmTMNxswQtSbFFVXYnZmHnSdV+CMzF8UVt57Us7GWon/HNhjaTYkhXTzh7sBuUiKipmD0PDWmivPUUHO7VliOXRm3xsdUaW/90XK3l2NIV08M7eaF+zt4wFbOMTJERA1hzP2bU5ESNZIgCMjILsbOkyrszMjB8auG42OC2thjaDclHuymRKifK5cxICJqZgw1REao0uqQcqGgJsicVNUZHxP2t/Ex7Tk+hoioRTHUEN1FcUUV9py+OT7mVC7Ufxsfo7CqHR/jiQe6KNHGkeNjiIjEwlBDVI+cogrsrB0fc+46KrU6/Xtu9nIM6eKJod2U6N+xDcfHEBG1Egw1RKgZH3Mqp7hmIrwMFY5eKTJ4P9DDXt+tFObP8TFERK0RQw1ZrGqtDikXC/QT4V0uMBwf08vPRT8RXgdPjo8hImrtGGrIopRoqrH35viY30/loqi8Sv+ewkqK+zt41Mwf05XjY4iITA1DDZk9lbpC3xpz8Gzd8TEP6MfHeMBOzj8SRESmin+Dk9kRBAGnVSXYeTIHO0+qcOR/xscEuNvdHB/jhfB2HB9DRGQuGGrIrGRdL8PUtX/iVE6xwfZe/i41QaZrzfgYiYRBhojI3DDUkNm4dL0U4z49hOyiCsgNxsd4wtPRRuzyiIiomTHUkFm4mF+K8Z/VBJqgNvZY/+x9UDoxyBARWRKGGjJ5F/JLMf7TQ8hRV6B9G3usn3ofW2aIiCwQQw2ZtPN5JRj/2SGo1Bp09HTAt8/ex0exiYgsFEMNmaxzeSUY/+kh5BYz0BAREUMNmaizuSX4x2c1gaaTsibQeDgw0BARWTKGGjI5Z3OLMf6zZOQVa9DFyxHfPBMFdwYaIiKLx1BDJuWMqibQ5Jcw0BARkSGGGjIZp1XF+Mdnh5BfUomu3k745pkouNnLxS6LiIhaCYYaMgmZOTWB5nppJbrdDDSuDDRERPQ3UrELILqbUzlqfaDp7sNAQ0RE9WNLDbVqGdlqTPg8GQWllejh64R1U6LgYsdAQ0REdTHUUKt18poaEz4/hBtlVejp64x1U6LgbGctdllERNRKMdRQq3TiWhEmfJ6MwrIqBLd1xtopUXC2ZaAhIqLb45gaanWOX70VaEIYaIiIqIHYUkOtSm2gKSqvQqifC76eEgknGwYaIiK6O4YaajWOXSnChM8PQV1RjV7+LvhqMgMNERE1HLufqFU4eqVQH2jC/F3wNQMNEREZiS01JLojlwvx5OpkFFdUI7ydK9bE9YYjAw0RERmJoYZE9VfWDUxcnYJiTTUi2rlizeRIOCj4vyURERmP3U8kmrS/BZrIADcGGiIiuie8g5AoUi/dwKQvUlCiqUZkoBu+fLo37BloiIjoHrClhlpc6qUCfaCJYqAhIqImwjsJtag/L9YEmtJKLe4LcsMXT/eGnZz/GxIR0b3j3YRaTMqFAjz9ZQrKKrXo294dqyf1hq1cJnZZRERkJhhqqEUkn7+OuDWHUVapRb8O7vh8IgMNERE1LYYaanaHzl9H3JeHUV6lxf0dPPDZxAgGGiIianIMNdSsks5dx+Q1NYGmf8eaQGNjzUBDRERNj6GGms3Bs/mY/NVhVFTpMKBTG3z6VDgDDRERNZtGPdK9fPlyBAQEwMbGBlFRUUhJSbntvps2bUJERARcXFxgb2+P0NBQrF271mCfp59+GhKJxOA1bNgwg30KCgowYcIEODk5wcXFBVOmTEFJSUljyqcWcOBvgWYgAw0REbUAo0PNxo0bER8fj7lz5yItLQ0hISGIiYlBbm5uvfu7ubnhrbfeQlJSEo4ePYq4uDjExcXh119/Ndhv2LBhyM7O1r/Wr19v8P6ECRNw4sQJ7Ny5E1u3bsXevXsxdepUY8unFrD/TD4mr6kJNIM6t8EnDDRERNQCJIIgCMYcEBUVhd69e2PZsmUAAJ1OBz8/P0yfPh2zZs1q0DnCwsIwYsQIzJ8/H0BNS01hYSF++umnevfPyMhAt27dcPjwYURERAAAduzYgeHDh+PKlSvw8fG56zXVajWcnZ1RVFQEJyenBtVJxtt3Jg/PfPUnNNU6PNDFEyufDIPCioGGiIgax5j7t1EtNZWVlUhNTUV0dPStE0iliI6ORlJS0l2PFwQBiYmJyMzMxIABAwze2717Nzw9PdG5c2e88MILuH79uv69pKQkuLi46AMNAERHR0MqlSI5Obnea2k0GqjVaoMXNa89p/Mw5WagGcJAQ0RELcyogcL5+fnQarVQKpUG25VKJU6dOnXb44qKiuDr6wuNRgOZTIYVK1Zg6NCh+veHDRuGxx57DIGBgTh37hzefPNNPPTQQ0hKSoJMJkNOTg48PT0NC7eygpubG3Jycuq9ZkJCAt59911jPh7dg92ZuZi6NhWV1TpEd1Vi+YReDDRERNSiWuTpJ0dHR6Snp6OkpASJiYmIj49HUFAQBg0aBAAYN26cft+ePXsiODgY7du3x+7duzFkyJBGXXP27NmIj4/X/6xWq+Hn53dPn4Pq90dmLp77OhWVWh2GdlNi+T/CILfismJERNSyjAo1Hh4ekMlkUKlUBttVKhW8vLxue5xUKkWHDh0AAKGhocjIyEBCQoI+1PyvoKAgeHh44OzZsxgyZAi8vLzqDESurq5GQUHBba+rUCigUCiM+HTUGH+cysVza2sCzYPdlFjGQENERCIx6u4jl8sRHh6OxMRE/TadTofExET06dOnwefR6XTQaDS3ff/KlSu4fv06vL29AQB9+vRBYWEhUlNT9fv8/vvv0Ol0iIqKMuYjUBNKzFDpA82w7l5YPoGBhoiIxGN091N8fDwmTZqEiIgIREZGYvHixSgtLUVcXBwAYOLEifD19UVCQgKAmrEtERERaN++PTQaDbZv3461a9di5cqVAICSkhK8++67GDNmDLy8vHDu3Dm8/vrr6NChA2JiYgAAXbt2xbBhw/Dss89i1apVqKqqwrRp0zBu3LgGPflETW/XSRVe+CYVVVoBD/XwwtLxvWAtY6AhIiLxGB1qYmNjkZeXhzlz5iAnJwehoaHYsWOHfvBwVlYWpNJbN7fS0lK8+OKLuHLlCmxtbdGlSxesW7cOsbGxAACZTIajR4/iq6++QmFhIXx8fPDggw9i/vz5Bt1H33zzDaZNm4YhQ4ZAKpVizJgxWLp06b1+fmqE307k4KVv01ClFTCipzcWjwtloCEiItEZPU+NqeI8NU3j1xM5eOmbNFTrBIwI9sbiWAYaIiJqPsbcv7n2EzXYjuM5mPZtTaAZGeKD/xsbAisGGiIiaiV4R6IG+eVYtj7QjGKgISKiVoh3JbqrbUezMW39X6jWCXgk1AeLGGiIiKgVYvcT3dHWo9fwyoZ0aHUCHu3li/88EQKZVCJ2WURERHXwn9t0Wz8fuRVoHmOgISKiVo4tNVSvzelXMXNjOnQCMCasLT58PJiBhoiIWjW21FAdfw80T4Qz0BARkWlgSw0Z+PGvK3j1uyPQCcDYiLZY8FgwpAw0RERkAthSQ3qb0m4FmnG9/RhoiIjIpLClhgAAx68W4dXvj0AQgPGRfnh/dE8GGiIiMilsqSEAwHd/XoYgANFdPRloiIjIJDHUEKq0Omw7mg0AePK+dgw0RERkkhhqCAfO5uN6aSXc7eXo18FD7HKIiIgahaGGsOXINQDAiGBvrrhNREQmi3cwC1dRpcWvx3MAAKNCfESuhoiIqPEYaizc76dyUVqpha+LLcL8XcUuh4iIqNEYaizc5vSrAIBRoT4cIExERCaNocaCFZVX4Y9TeQDY9URERKaPocaC/XoiB5VaHTopHdDFy1HscoiIiO4JQ40F25Je89TTI6G+kEjY9URERKaNocZC5RZX4OC5fADAyGB2PRERkeljqLFQ245mQycAvfxd4O9uJ3Y5RERE94yhxkJtru164gBhIiIyEww1FujS9VKkXy6EVAKMYNcTERGZCYYaC/TzzWUR+nXwQBtHhcjVEBERNQ2GGgsjCAJ+utn1xLlpiIjInDDUWJiM7GKczS2B3EqKmB5eYpdDRETUZBhqLEztitwPdPaEk421yNUQERE1HYYaC6LTCfrxNI+EsuuJiIjMC0ONBUnNuoGrheVwUFhhcBdPscshIiJqUgw1FqR2WYSY7l6wsZaJXA0REVHTYqixEFVaHbYdywbAriciIjJPDDUWYv/ZfBSUVsLDQY6+7d3FLoeIiKjJMdRYiJ9vdj2N6OkNKxm/diIiMj+8u1mA8kotfj2RAwAYFeorcjVERETNg6HGAiSeUqG0Uou2rrYI83cRuxwiIqJmwVBjAbb8bVkEiUQicjVERETNg6HGzBWVVWF3Zh4A4BF2PRERkRljqDFzO05ko1KrQ2elIzp7OYpdDhERUbNpVKhZvnw5AgICYGNjg6ioKKSkpNx2302bNiEiIgIuLi6wt7dHaGgo1q5dq3+/qqoKb7zxBnr27Al7e3v4+Phg4sSJuHbtmsF5AgICIJFIDF4LFixoTPkWpXatp1Gcm4aIiMyc0aFm48aNiI+Px9y5c5GWloaQkBDExMQgNze33v3d3Nzw1ltvISkpCUePHkVcXBzi4uLw66+/AgDKysqQlpaGt99+G2lpadi0aRMyMzMxatSoOueaN28esrOz9a/p06cbW75FyVVX4OC56wBqxtMQERGZM4kgCIIxB0RFRaF3795YtmwZAECn08HPzw/Tp0/HrFmzGnSOsLAwjBgxAvPnz6/3/cOHDyMyMhKXLl2Cv78/gJqWmhkzZmDGjBnGlKunVqvh7OyMoqIiODk5Neocpmb1/guYv/UkwvxdsOnFfmKXQ0REZDRj7t9GtdRUVlYiNTUV0dHRt04glSI6OhpJSUl3PV4QBCQmJiIzMxMDBgy47X5FRUWQSCRwcXEx2L5gwQK4u7ujV69e+Oijj1BdXX3bc2g0GqjVaoOXpdmiX5GbA4SJiMj8WRmzc35+PrRaLZRKpcF2pVKJU6dO3fa4oqIi+Pr6QqPRQCaTYcWKFRg6dGi9+1ZUVOCNN97A+PHjDRLZyy+/jLCwMLi5ueHgwYOYPXs2srOzsWjRonrPk5CQgHfffdeYj2dWLuaX4sjlQsikEgzv6S12OURERM3OqFDTWI6OjkhPT0dJSQkSExMRHx+PoKAgDBo0yGC/qqoqjB07FoIgYOXKlQbvxcfH638dHBwMuVyO5557DgkJCVAoFHWuOXv2bINj1Go1/Pz8mvaDtWK1rTR927ujjWPd3x8iIiJzY1So8fDwgEwmg0qlMtiuUqng5eV12+OkUik6dOgAAAgNDUVGRgYSEhIMQk1toLl06RJ+//33u/abRUVFobq6GhcvXkTnzp3rvK9QKOoNO5ZAEARsTr8KgF1PRERkOYwaUyOXyxEeHo7ExET9Np1Oh8TERPTp06fB59HpdNBoNPqfawPNmTNnsGvXLri7330V6fT0dEilUnh6ehrzESzCyWw1zuWVQm4lRUx35d0PICIiMgNGdz/Fx8dj0qRJiIiIQGRkJBYvXozS0lLExcUBACZOnAhfX18kJCQAqBnbEhERgfbt20Oj0WD79u1Yu3atvnupqqoKjz/+ONLS0rB161ZotVrk5NQsvujm5ga5XI6kpCQkJydj8ODBcHR0RFJSEmbOnIknn3wSrq6uTfV7YTZql0UY0sUTjjbWIldDRETUMowONbGxscjLy8OcOXOQk5OD0NBQ7NixQz94OCsrC1LprQag0tJSvPjii7hy5QpsbW3RpUsXrFu3DrGxsQCAq1evYsuWLQBquqb+7o8//sCgQYOgUCiwYcMGvPPOO9BoNAgMDMTMmTMNxsxQDZ1OwM/6p544Nw0REVkOo+epMVWWMk9NyoUCjP0kCY4KKxz+VzRsrGVil0RERNRozTZPDbV+tQOEY3p4MdAQEZFFYagxI1VaHbYfywbAriciIrI8DDVmZP+ZfNwoq4KHgwJ9gu7+BBkREZE5YagxI7VdTw8He8NKxq+WiIgsC+98ZqK8UovfTtZMijiKXU9ERGSBGGrMxK4MFcoqtfBzs0UvPxexyyEiImpxDDVmYvPNCfdGhfhAIpGIXA0REVHLY6gxA0VlVdhzOhcA13oiIiLLxVBjBn45no0qrYAuXo7opHQUuxwiIiJRMNSYAX3XEwcIExGRBWOoMXEqdQUOXbgOABgZzFBDRESWi6HGxP185BoEAYho5wo/NzuxyyEiIhINQ42J23KEXU9EREQAQ41Ju5BfiqNXiiCTSjC8p7fY5RAREYmKocaEbbk5QPj+Dh7wcFCIXA0REZG4GGpMlCAI2HykZq2nUSHseiIiImKoMVEnrqlxPq8UCispHuyuFLscIiIi0THUmKjaAcLRXZVwtLEWuRoiIiLxMdSYIJ1OwM83Q81Idj0REREBYKgxSYcvFiC7qAKONlYY1LmN2OUQERG1Cgw1JmjzzVaah3p4wcZaJnI1RERErQNDjYmprNZh+7FsAMCoEK7ITUREVIuhxsTsP5uHwrIqeDgo0Ke9u9jlEBERtRoMNSamdkXukSHekEklIldDRETUejDUmJCyymr8dkIFgBPuERER/S+GGhOyKyMX5VVa+LvZIdTPRexyiIiIWhWGGhOyJb1mWYRHQn0gkbDriYiI6O8YakxEYVkl9pzOA8CuJyIiovow1JiIX47noEoroKu3EzoqHcUuh4iIqNVhqDERm//W9URERER1MdSYgJyiCiRfKADAtZ6IiIhuh6HGBGw9eg2CAPQOcIWvi63Y5RAREbVKDDUmoHbCvVGhXBaBiIjodhhqWrnzeSU4drUIMqkEw3t4iV0OERFRq8VQ08ptubkid/+OHnB3UIhcDRERUevFUNOKCYKALTe7nvjUExER0Z0x1LRix6+qcT6/FAorKYZ2Y9cTERHRnTDUtGJbjtTMTRPdTQkHhZXI1RAREbVuDDWtlFYn6MfTPMK5aYiIiO6qUaFm+fLlCAgIgI2NDaKiopCSknLbfTdt2oSIiAi4uLjA3t4eoaGhWLt2rcE+giBgzpw58Pb2hq2tLaKjo3HmzBmDfQoKCjBhwgQ4OTnBxcUFU6ZMQUlJSWPKNwkpFwqgUmvgZGOFgZ3biF0OERFRq2d0qNm4cSPi4+Mxd+5cpKWlISQkBDExMcjNza13fzc3N7z11ltISkrC0aNHERcXh7i4OPz666/6fT788EMsXboUq1atQnJyMuzt7RETE4OKigr9PhMmTMCJEyewc+dObN26FXv37sXUqVMb8ZFNQ20rzUM9vKGwkolcDRERUesnEQRBMOaAqKgo9O7dG8uWLQMA6HQ6+Pn5Yfr06Zg1a1aDzhEWFoYRI0Zg/vz5EAQBPj4+ePXVV/Haa68BAIqKiqBUKrFmzRqMGzcOGRkZ6NatGw4fPoyIiAgAwI4dOzB8+HBcuXIFPj51u2c0Gg00Go3+Z7VaDT8/PxQVFcHJycmYj9ziKqt16P3+LhSVV+HbZ6LQt4OH2CURERGJQq1Ww9nZuUH3b6NaaiorK5Gamoro6OhbJ5BKER0djaSkpLseLwgCEhMTkZmZiQEDBgAALly4gJycHINzOjs7IyoqSn/OpKQkuLi46AMNAERHR0MqlSI5ObneayUkJMDZ2Vn/8vPzM+ajimrv6TwUlVfB01GBqCB3scshIiIyCUaFmvz8fGi1WiiVSoPtSqUSOTk5tz2uqKgIDg4OkMvlGDFiBD7++GMMHToUAPTH3emcOTk58PT0NHjfysoKbm5ut73u7NmzUVRUpH9dvnzZmI8qqtqup4eDfSCTSkSuhoiIyDS0yHPCjo6OSE9PR0lJCRITExEfH4+goCAMGjSo2a6pUCigUJjeDLylmmrsPKkCwAn3iIiIjGFUqPHw8IBMJoNKpTLYrlKp4OV1+8nhpFIpOnToAAAIDQ1FRkYGEhISMGjQIP1xKpUK3t7eBucMDQ0FAHh5edUZiFxdXY2CgoI7XtcU7cpQobxKi3budghu6yx2OURERCbDqO4nuVyO8PBwJCYm6rfpdDokJiaiT58+DT6PTqfTD+INDAyEl5eXwTnVajWSk5P15+zTpw8KCwuRmpqq3+f333+HTqdDVFSUMR+h1dMvixDiA4mEXU9EREQNZXT3U3x8PCZNmoSIiAhERkZi8eLFKC0tRVxcHABg4sSJ8PX1RUJCAoCaAbsRERFo3749NBoNtm/fjrVr12LlypUAAIlEghkzZuC9995Dx44dERgYiLfffhs+Pj4YPXo0AKBr164YNmwYnn32WaxatQpVVVWYNm0axo0bV++TT6bqRmkl9pzOAwCMYtcTERGRUYwONbGxscjLy8OcOXOQk5OD0NBQ7NixQz/QNysrC1LprQag0tJSvPjii7hy5QpsbW3RpUsXrFu3DrGxsfp9Xn/9dZSWlmLq1KkoLCzE/fffjx07dsDGxka/zzfffINp06ZhyJAhkEqlGDNmDJYuXXovn73V2X48G9U6Ad28ndDB01HscoiIiEyK0fPUmCpjnnMXS+wnSUi+UIDZD3XBcwPbi10OERGR6JptnhpqPtcKy5FysQAAMJJrPRERERmNoaaV2Hr0GgQBiAxwg4+LrdjlEBERmRyGmlaidsI9DhAmIiJqHIaaVuBsbgmOX1XDSirB8J7edz+AiIiI6mCoaQVqW2n6d/SAm71c5GqIiIhME0ONyARBwM83Q80job4iV0NERGS6GGpEduxqES7kl8LGWoqh3ZR3P4CIiIjqxVAjss03l0WI7qqEvaJF1hclIiIySww1ItLqBGw9yq4nIiKipsBQI6LkC9ehUmvgbGuNgZ3aiF0OERGRSWOoEVHtitwP9fCC3IpfBRER0b3gnVQkmmotfjmeA4AT7hERETUFhhqR7D2dj6LyKiidFIgKdBe7HCIiIpPHUCOSzelXAQAPB/tAJpWIXA0REZHpY6gRQammGrsyVACAR9j1RERE1CQYakSw86QKFVU6BHrYo6evs9jlEBERmQWGGhHUdj2NDPGBRMKuJyIioqbAUNPCCkorse9MPgBgVAi7noiIiJoKQ00L234sG9U6AT18ndDB00HscoiIiMwGQ00Lq51wj600RERETYuhpgVdKyxHysUCSCQ142mIiIio6TDUtKCfj9S00kQGuMHb2VbkaoiIiMwLQ00L2nIz1HBZBCIioqbHUNNCzuYW48Q1NaykEgzv4S12OURERGaHoaaF1A4QHtipDVzt5SJXQ0REZH4YalqAIAjseiIiImpmDDUt4OiVIly8XgZbaxmiuyrFLoeIiMgsMdS0gM03u56GdlPCXmElcjVERETmiaGmmWl1ArYe5YR7REREzY2hppkln7+O3GINnG2tMaBTG7HLISIiMlsMNc2stutpeE9vyK34201ERNRceJdtRppqLX45ng2AXU9ERETNjaGmGe3JzIO6ohpeTjaIDHQTuxwiIiKzxlDTjDbfnJtmZIg3ZFKJyNUQERGZN4aaZlKiqUZihgoAMCrEV+RqiIiIzB9DTTPZeTIHFVU6BHnYo4evk9jlEBERmT2GmmZS+9TTqFAfSCTseiIiImpuDDXN4HqJBvvO5APgU09EREQthaGmGWw/ngOtTkBPX2cEtXEQuxwiIiKL0KhQs3z5cgQEBMDGxgZRUVFISUm57b6fffYZ+vfvD1dXV7i6uiI6OrrO/hKJpN7XRx99pN8nICCgzvsLFixoTPnNbkv6VQDAI1yRm4iIqMUYHWo2btyI+Ph4zJ07F2lpaQgJCUFMTAxyc3Pr3X/37t0YP348/vjjDyQlJcHPzw8PPvggrl69qt8nOzvb4PXFF19AIpFgzJgxBueaN2+ewX7Tp083tvxmd7WwHIcv3oBEAjwczFBDRETUUoxeMnrRokV49tlnERcXBwBYtWoVtm3bhi+++AKzZs2qs/8333xj8PPnn3+OH374AYmJiZg4cSIAwMvLy2CfzZs3Y/DgwQgKCjLY7ujoWGff29FoNNBoNPqf1Wp1g467Vz/fnJsmKtANXs42LXJNIiIiMrKlprKyEqmpqYiOjr51AqkU0dHRSEpKatA5ysrKUFVVBTe3+mfYValU2LZtG6ZMmVLnvQULFsDd3R29evXCRx99hOrq6tteJyEhAc7OzvqXn59fg+q7V7VPPT0SyrlpiIiIWpJRLTX5+fnQarVQKpUG25VKJU6dOtWgc7zxxhvw8fExCEZ/99VXX8HR0RGPPfaYwfaXX34ZYWFhcHNzw8GDBzF79mxkZ2dj0aJF9Z5n9uzZiI+P1/+sVqubPdicURUjI1sNa5kED/VoWIsSERERNQ2ju5/uxYIFC7Bhwwbs3r0bNjb1d8188cUXmDBhQp33/x5QgoODIZfL8dxzzyEhIQEKhaLOeRQKRb3bm9OWm11PAzu1gYudvEWvTUREZOmM6n7y8PCATCaDSqUy2K5Sqe461uU///kPFixYgN9++w3BwcH17rNv3z5kZmbimWeeuWstUVFRqK6uxsWLFxtcf3MSBOFvE+6x64mIiKilGRVq5HI5wsPDkZiYqN+m0+mQmJiIPn363Pa4Dz/8EPPnz8eOHTsQERFx2/1Wr16N8PBwhISE3LWW9PR0SKVSeHp6GvMRms2RK0XIKiiDrbUM0V1bR01ERESWxOjup/j4eEyaNAkRERGIjIzE4sWLUVpaqn8aauLEifD19UVCQgIA4N///jfmzJmDb7/9FgEBAcjJyQEAODg4wMHh1sR0arUa33//PRYuXFjnmklJSUhOTsbgwYPh6OiIpKQkzJw5E08++SRcXV0b9cGb2uabc9M82F0JO3mL9uoRERERGhFqYmNjkZeXhzlz5iAnJwehoaHYsWOHfvBwVlYWpNJbDUArV65EZWUlHn/8cYPzzJ07F++8847+5w0bNkAQBIwfP77ONRUKBTZs2IB33nkHGo0GgYGBmDlzpsE4GzFpdQK2Hs0GwAn3iIiIxCIRBEEQu4iWoFar4ezsjKKiIjg5Ne2q2QfO5mPC58lwsbNGypvRkFtx9QkiIqKmYMz9m3ffJlDb9TS8pzcDDRERkUh4B75HmmotfjleM07oEa7ITUREJBqGmnu0OzMPxRXV8Ha2Qe+A+mdJJiIioubHUHOPctUVcFBYYWSID6RSidjlEBERWSw+e3yPnuoTgCci/FBRpRW7FCIiIovGUNMEbKxlsLGWiV0GERGRRWP3ExEREZkFhhoiIiIyCww1REREZBYYaoiIiMgsMNQQERGRWWCoISIiIrPAUENERERmgaGGiIiIzAJDDREREZkFhhoiIiIyCww1REREZBYYaoiIiMgsMNQQERGRWbCYVboFQQAAqNVqkSshIiKihqq9b9fex+/EYkJNcXExAMDPz0/kSoiIiMhYxcXFcHZ2vuM+EqEh0ccM6HQ6XLt2DY6OjpBIJE16brVaDT8/P1y+fBlOTk5Nem4yHr+P1oXfR+vC76N14fdxd4IgoLi4GD4+PpBK7zxqxmJaaqRSKdq2bdus13BycuL/lK0Iv4/Whd9H68Lvo3Xh93Fnd2uhqcWBwkRERGQWGGqIiIjILDDUNAGFQoG5c+dCoVCIXQqB30drw++jdeH30brw+2haFjNQmIiIiMwbW2qIiIjILDDUEBERkVlgqCEiIiKzwFBDREREZoGh5h4tX74cAQEBsLGxQVRUFFJSUsQuySIlJCSgd+/ecHR0hKenJ0aPHo3MzEyxy6KbFixYAIlEghkzZohdikW7evUqnnzySbi7u8PW1hY9e/bEn3/+KXZZFkmr1eLtt99GYGAgbG1t0b59e8yfP79B6xvR7THU3IONGzciPj4ec+fORVpaGkJCQhATE4Pc3FyxS7M4e/bswUsvvYRDhw5h586dqKqqwoMPPojS0lKxS7N4hw8fxieffILg4GCxS7FoN27cQL9+/WBtbY1ffvkFJ0+exMKFC+Hq6ip2aRbp3//+N1auXIlly5YhIyMD//73v/Hhhx/i448/Frs0k8ZHuu9BVFQUevfujWXLlgGoWV/Kz88P06dPx6xZs0SuzrLl5eXB09MTe/bswYABA8Qux2KVlJQgLCwMK1aswHvvvYfQ0FAsXrxY7LIs0qxZs3DgwAHs27dP7FIIwMMPPwylUonVq1frt40ZMwa2trZYt26diJWZNrbUNFJlZSVSU1MRHR2t3yaVShEdHY2kpCQRKyMAKCoqAgC4ubmJXIlle+mllzBixAiDPyckji1btiAiIgJPPPEEPD090atXL3z22Wdil2Wx+vbti8TERJw+fRoAcOTIEezfvx8PPfSQyJWZNotZ0LKp5efnQ6vVQqlUGmxXKpU4deqUSFURUNNiNmPGDPTr1w89evQQuxyLtWHDBqSlpeHw4cNil0IAzp8/j5UrVyI+Ph5vvvkmDh8+jJdffhlyuRyTJk0SuzyLM2vWLKjVanTp0gUymQxarRbvv/8+JkyYIHZpJo2hhszOSy+9hOPHj2P//v1il2KxLl++jFdeeQU7d+6EjY2N2OUQasJ+REQEPvjgAwBAr169cPz4caxatYqhRgTfffcdvvnmG3z77bfo3r070tPTMWPGDPj4+PD7uAcMNY3k4eEBmUwGlUplsF2lUsHLy0ukqmjatGnYunUr9u7di7Zt24pdjsVKTU1Fbm4uwsLC9Nu0Wi327t2LZcuWQaPRQCaTiVih5fH29ka3bt0MtnXt2hU//PCDSBVZtn/+85+YNWsWxo0bBwDo2bMnLl26hISEBIaae8AxNY0kl8sRHh6OxMRE/TadTofExET06dNHxMoskyAImDZtGn788Uf8/vvvCAwMFLskizZkyBAcO3YM6enp+ldERAQmTJiA9PR0BhoR9OvXr840B6dPn0a7du1EqsiylZWVQSo1vAXLZDLodDqRKjIPbKm5B/Hx8Zg0aRIiIiIQGRmJxYsXo7S0FHFxcWKXZnFeeuklfPvtt9i8eTMcHR2Rk5MDAHB2doatra3I1VkeR0fHOuOZ7O3t4e7uznFOIpk5cyb69u2LDz74AGPHjkVKSgo+/fRTfPrpp2KXZpFGjhyJ999/H/7+/ujevTv++usvLFq0CJMnTxa7NNMm0D35+OOPBX9/f0EulwuRkZHCoUOHxC7JIgGo9/Xll1+KXRrdNHDgQOGVV14RuwyL9vPPPws9evQQFAqF0KVLF+HTTz8VuySLpVarhVdeeUXw9/cXbGxshKCgIOGtt94SNBqN2KWZNM5TQ0RERGaBY2qIiIjILDDUEBERkVlgqCEiIiKzwFBDREREZoGhhoiIiMwCQw0RERGZBYYaIiIiMgsMNURERGQWGGqIyGLt3r0bEokEhYWFYpdCRE2AoYaIiIjMAkMNERERmQWGGiISjU6nQ0JCAgIDA2Fra4uQkBD897//BXCra2jbtm0IDg6GjY0N7rvvPhw/ftzgHD/88AO6d+8OhUKBgIAALFy40OB9jUaDN954A35+flAoFOjQoQNWr15tsE9qaioiIiJgZ2eHvn37IjMzs3k/OBE1C4YaIhJNQkICvv76a6xatQonTpzAzJkz8eSTT2LPnj36ff75z39i4cKFOHz4MNq0aYORI0eiqqoKQE0YGTt2LMaNG4djx47hnXfewdtvv401a9boj584cSLWr1+PpUuXIiMjA5988gkcHBwM6njrrbewcOFC/Pnnn7CyssLkyZNb5PMTUdPiKt1EJAqNRgM3Nzfs2rULffr00W9/5plnUFZWhqlTp2Lw4MHYsGEDYmNjAQAFBQVo27Yt1qxZg7Fjx2LChAnIy8vDb7/9pj/+9ddfx7Zt23DixAmcPn0anTt3xs6dOxEdHV2nht27d2Pw4MHYtWsXhgwZAgDYvn07RowYgfLyctjY2DTz7wIRNSW21BCRKM6ePYuysjIMHToUDg4O+tfXX3+Nc+fO6ff7e+Bxc3ND586dkZGRAQDIyMhAv379DM7br18/nDlzBlqtFunp6ZDJZBg4cOAdawkODtb/2tvbGwCQm5t7z5+RiFqWldgFEJFlKikpAQBs27YNvr6+Bu8pFAqDYNNYtra2DdrP2tpa/2uJRAKgZrwPEZkWttQQkSi6desGhUKBrKwsdOjQweDl5+en3+/QoUP6X9+4cQOnT59G165dAQBdu3bFgQMHDM574MABdOrUCTKZDD179oROpzMYo0NE5ostNUQkCkdHR7z22muYOXMmdDod7r//fhQVFeHAgQNwcnJCu3btAADz5s2Du7s7lEol3nrrLXh4eGD06NEAgFdffRW9e/fG/PnzERsbi6SkJCxbtgwrVqwAAAQEBGDSpEmYPHkyli5dipCQEFy6dAm5ubkYO3asWB+diJoJQw0RiWb+/Plo06YNEhIScP78ebi4uCAsLAxvvvmmvvtnwYIFeOWVV3DmzBmEhobi559/hlwuBwCEhYXhu+++w5w5czB//nx4e3tj3rx5ePrpp/XXWLlyJd588028+OKLuH79Ovz9/fHmm2+K8XGJqJnx6SciapVqn0y6ceMGXFxcxC6HiEwAx9QQERGRWWCoISIiIrPA7iciIiIyC2ypISIiIrPAUENERERmgaGGiIiIzAJDDREREZkFhhoiIiIyCww1REREZBYYaoiIiMgsMNQQERGRWfh/lhIEipIOQYoAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.xs(\"train\", level=\"phase\")[[\"train/acc\"]].plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2c92629a-743f-4097-bb41-86f39a4f3053",
      "metadata": {
        "id": "2c92629a-743f-4097-bb41-86f39a4f3053"
      },
      "source": [
        "## [4] Test"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1924d48c-4cda-488a-9ac4-4c439277db45",
      "metadata": {
        "id": "1924d48c-4cda-488a-9ac4-4c439277db45"
      },
      "source": [
        "Evaluation function is defined in `optk.codalab.eval_operation_segmentation_wrapper()`. This function takes dict as the 2nd argument and prediction (`y`), groundtruth (`t_idx`; activity class index) and corresponding unixtime (`unixtime`) must be included in the dict object. Each sequence should be stored in the different entries with a key `{user}-{session}` (e.g., `U0107-S0100`).\n",
        "\n",
        "```python\n",
        "outputs = {\n",
        "    \"U0102-S0100\": {\n",
        "        \"t_idx\": [0, 0, ...], # groundtruth (class index)\n",
        "        \"y\": [\n",
        "            [\n",
        "                [0., 0., ...],\n",
        "                [0., 0., ...],\n",
        "                [1., 1., ...],\n",
        "            ],\n",
        "        ], # prediction. shape=(B,N_CLASSES,T), dtype=float\n",
        "        \"unixtiume\": [634885794000, 1634885794200, ...], # unixtime\n",
        "    },\n",
        "    \"U0102-S0200\": {\n",
        "        \"t_idx\": [...],\n",
        "        \"y\": [...],\n",
        "        \"unixtiume\": [...],\n",
        "    },\n",
        "    ...\n",
        "}\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e73192e1-876e-44b7-9c8e-ae7b5079da4d",
      "metadata": {
        "id": "e73192e1-876e-44b7-9c8e-ae7b5079da4d"
      },
      "source": [
        "### 4-1: Get prediction results for each sequences and build result dict."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cf3b1f4e-ad31-41aa-8624-da375d531228",
      "metadata": {
        "id": "cf3b1f4e-ad31-41aa-8624-da375d531228"
      },
      "source": [
        "NOTE: In `DEBUG_SPLIT`, U0102-S0500 is set to both the test set and the submission set for debugging purpose."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6ec859ea-fc23-4f48-a611-8253523ad245",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605,
          "referenced_widgets": [
            "39e7c884324b4b0eb360353d31d2a660",
            "b089e9e1654d4243bb66a193d4ef5785",
            "fa342da14c4845fc95e4973478fe4e24",
            "5a9297f0f4eb4b219be8643bb5778ba4",
            "99fd49ed8dda4fec8edfe86d60ee9817",
            "1ffab74ded5740a5a7544a0ec3f04fe9",
            "bc4db8434026452398cfe33ff018e2fe",
            "37bc9ac120a34e5aa4bceeee7724c5fe",
            "4b2d81c3df594737a6f487d63bb54467",
            "38ec090a3a1c4d6c8c6fa4ad92f12a96",
            "789810969a3a4beabfd4245dd9653e81"
          ]
        },
        "id": "6ec859ea-fc23-4f48-a611-8253523ad245",
        "outputId": "3e1be74b-59e3-4de9-8abd-39a8240e6504"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:openpack_torch.data.datamodule:dataset[train]: None\n",
            "2024-02-04 06:56:21 | INFO | openpack_torch.data.datamodule | dataset[train]: None\n",
            "INFO:openpack_torch.data.datamodule:dataset[val]: None\n",
            "2024-02-04 06:56:21 | INFO | openpack_torch.data.datamodule | dataset[val]: None\n",
            "INFO:openpack_torch.data.datamodule:dataset[test]: {'U0102-S0500': <openpack_torch.data.datasets.OpenPackImu object at 0x7c5bf166d2d0>}\n",
            "2024-02-04 06:56:21 | INFO | openpack_torch.data.datamodule | dataset[test]: {'U0102-S0500': <openpack_torch.data.datasets.OpenPackImu object at 0x7c5bf166d2d0>}\n",
            "INFO:openpack_torch.data.datamodule:dataset[submission]: None\n",
            "2024-02-04 06:56:21 | INFO | openpack_torch.data.datamodule | dataset[submission]: None\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 3 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "INFO:__main__:test on U0102-S0500\n",
            "2024-02-04 06:56:21 | INFO | __main__ | test on U0102-S0500\n",
            "INFO:openpack_torch.data.datamodule:dataset[train]: None\n",
            "2024-02-04 06:56:21 | INFO | openpack_torch.data.datamodule | dataset[train]: None\n",
            "INFO:openpack_torch.data.datamodule:dataset[val]: None\n",
            "2024-02-04 06:56:21 | INFO | openpack_torch.data.datamodule | dataset[val]: None\n",
            "INFO:openpack_torch.data.datamodule:dataset[test]: {'U0102-S0500': <openpack_torch.data.datasets.OpenPackImu object at 0x7c5bf1689750>}\n",
            "2024-02-04 06:56:21 | INFO | openpack_torch.data.datamodule | dataset[test]: {'U0102-S0500': <openpack_torch.data.datasets.OpenPackImu object at 0x7c5bf1689750>}\n",
            "INFO:openpack_torch.data.datamodule:dataset[submission]: None\n",
            "2024-02-04 06:56:21 | INFO | openpack_torch.data.datamodule | dataset[submission]: None\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "2024-02-04 06:56:21 | INFO | pytorch_lightning.accelerators.cuda | LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 3 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "39e7c884324b4b0eb360353d31d2a660",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Testing: |          | 0/? [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:__main__:save t[shape=(31, 1800)] to /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0/pred/last/U0102-S0500/t.npy\n",
            "2024-02-04 06:56:22 | INFO | __main__ | save t[shape=(31, 1800)] to /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0/pred/last/U0102-S0500/t.npy\n",
            "INFO:__main__:save y[shape=(31, 11, 1800)] to /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0/pred/last/U0102-S0500/y.npy\n",
            "2024-02-04 06:56:22 | INFO | __main__ | save y[shape=(31, 11, 1800)] to /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0/pred/last/U0102-S0500/y.npy\n",
            "INFO:__main__:save unixtime[shape=(31, 1800)] to /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0/pred/last/U0102-S0500/unixtime.npy\n",
            "2024-02-04 06:56:22 | INFO | __main__ | save unixtime[shape=(31, 1800)] to /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0/pred/last/U0102-S0500/unixtime.npy\n"
          ]
        }
      ],
      "source": [
        "datamodule.setup(\"test\")\n",
        "dataloaders = datamodule.test_dataloader()\n",
        "split = cfg.dataset.split.test\n",
        "\n",
        "\n",
        "outputs = dict()\n",
        "for i, dataloader in enumerate(dataloaders):\n",
        "    user, session = split[i]\n",
        "    logger.info(f\"test on {user}-{session}\")\n",
        "\n",
        "    trainer.test(plmodel, dataloader)\n",
        "\n",
        "    # save model outputs\n",
        "    pred_dir = Path(\n",
        "        cfg.path.logdir.predict.format(user=user, session=session)\n",
        "    )\n",
        "    pred_dir.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "    for key, arr in plmodel.test_results.items():\n",
        "        path = Path(pred_dir, f\"{key}.npy\")\n",
        "        np.save(path, arr)\n",
        "        logger.info(f\"save {key}[shape={arr.shape}] to {path}\")\n",
        "\n",
        "    outputs[f\"{user}-{session}\"] = {\n",
        "        \"t_idx\": plmodel.test_results.get(\"t\"),\n",
        "        \"y\": plmodel.test_results.get(\"y\"),\n",
        "        \"unixtime\": plmodel.test_results.get(\"unixtime\"),\n",
        "    }\n",
        "    plmodel.clear_test_outputs()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "605b50b5-b099-4319-84b6-ad120760dd92",
      "metadata": {
        "id": "605b50b5-b099-4319-84b6-ad120760dd92"
      },
      "source": [
        "### 4-2: Evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c61ad2c2-d92c-4161-9a0c-62677379ec8b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c61ad2c2-d92c-4161-9a0c-62677379ec8b",
        "outputId": "5b4891af-027f-4135-d739-2737779567b6"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:openpack_toolkit.codalab.operation_segmentation.utils:1 elements are missing from prediction.: [1634887609000]\n",
            "2024-02-04 06:56:24 | WARNING | openpack_toolkit.codalab.operation_segmentation.utils | 1 elements are missing from prediction.: [1634887609000]\n",
            "WARNING:openpack_toolkit.codalab.operation_segmentation.utils:fill missing element at ts=1634887609000 (ind=1822) with 100.\n",
            "2024-02-04 06:56:24 | WARNING | openpack_toolkit.codalab.operation_segmentation.utils | fill missing element at ts=1634887609000 (ind=1822) with 100.\n"
          ]
        }
      ],
      "source": [
        "# compute performance summary\n",
        "df_summary = eval_operation_segmentation_wrapper(\n",
        "    cfg, outputs, ActSet(OPENPACK_OPERATIONS),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "72a70472-4511-4c56-bda2-72fe3a71dea4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "72a70472-4511-4c56-bda2-72fe3a71dea4",
        "outputId": "54dc08fc-b1bd-49c3-ca59-4e4353c59303"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>id</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1</th>\n",
              "      <th>support</th>\n",
              "      <th>key</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>avg/macro</td>\n",
              "      <td>-1</td>\n",
              "      <td>0.347146</td>\n",
              "      <td>0.334402</td>\n",
              "      <td>0.313849</td>\n",
              "      <td>None</td>\n",
              "      <td>all</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>avg/weighted</td>\n",
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              "      <th>14</th>\n",
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              "      <th>16</th>\n",
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              "      <td>0.466346</td>\n",
              "      <td>0.375969</td>\n",
              "      <td>0.416309</td>\n",
              "      <td>258</td>\n",
              "      <td>all</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Insert Items</td>\n",
              "      <td>400</td>\n",
              "      <td>0.392157</td>\n",
              "      <td>0.140845</td>\n",
              "      <td>0.207254</td>\n",
              "      <td>142</td>\n",
              "      <td>all</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Close Box</td>\n",
              "      <td>500</td>\n",
              "      <td>0.341346</td>\n",
              "      <td>0.401130</td>\n",
              "      <td>0.368831</td>\n",
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              "      <td>all</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Attach Box Label</td>\n",
              "      <td>600</td>\n",
              "      <td>0.266667</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0.170213</td>\n",
              "      <td>64</td>\n",
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              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Scan Label</td>\n",
              "      <td>700</td>\n",
              "      <td>0.442105</td>\n",
              "      <td>0.758123</td>\n",
              "      <td>0.558511</td>\n",
              "      <td>277</td>\n",
              "      <td>all</td>\n",
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              "    <tr>\n",
              "      <th>21</th>\n",
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              "      <td>0.370370</td>\n",
              "      <td>0.101010</td>\n",
              "      <td>0.158730</td>\n",
              "      <td>99</td>\n",
              "      <td>all</td>\n",
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              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Put on Back Table</td>\n",
              "      <td>900</td>\n",
              "      <td>0.310924</td>\n",
              "      <td>0.468354</td>\n",
              "      <td>0.373737</td>\n",
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              "      <th>23</th>\n",
              "      <td>Fill out Order</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.276119</td>\n",
              "      <td>0.216374</td>\n",
              "      <td>0.242623</td>\n",
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              "        document.querySelector('#df-fdfdc406-6069-47d9-949f-b570289ea828 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fdfdc406-6069-47d9-949f-b570289ea828');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4115753f-4009-4103-b5d0-0f0e08d69634 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                     name    id  precision    recall        f1 support  key\n",
              "12              avg/macro    -1   0.347146  0.334402  0.313849    None  all\n",
              "13           avg/weighted    -1   0.358323  0.364139  0.338915    None  all\n",
              "14                Picking   100   0.304348  0.538462  0.388889     156  all\n",
              "15    Relocate Item Label   200   0.301075  0.218750  0.253394     384  all\n",
              "16           Assemble Box   300   0.466346  0.375969  0.416309     258  all\n",
              "17           Insert Items   400   0.392157  0.140845  0.207254     142  all\n",
              "18              Close Box   500   0.341346  0.401130  0.368831     177  all\n",
              "19       Attach Box Label   600   0.266667  0.125000  0.170213      64  all\n",
              "20             Scan Label   700   0.442105  0.758123  0.558511     277  all\n",
              "21  Attach Shipping Label   800   0.370370  0.101010  0.158730      99  all\n",
              "22      Put on Back Table   900   0.310924  0.468354  0.373737      79  all\n",
              "23         Fill out Order  1000   0.276119  0.216374  0.242623     171  all"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_summary[df_summary[\"key\"]==\"all\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "858ce911-5a7d-4eee-bbdd-592ea387acad",
      "metadata": {
        "id": "858ce911-5a7d-4eee-bbdd-592ea387acad"
      },
      "source": [
        "### 4-3: Visualize Model Output\n",
        "\n",
        "The model's outputs can be transformed into probability by applying `softmax()` function.\n",
        "Here, let's visualize confidence scores as heatmap.\n",
        "\n",
        "NOTE: Evaluation is done in 1 Hz, but we visualize the output sample by sample."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1f3eb760-ce65-4103-aa85-b9d0227779aa",
      "metadata": {
        "id": "1f3eb760-ce65-4103-aa85-b9d0227779aa"
      },
      "outputs": [],
      "source": [
        "def plot_timeline(samples, t_idx, y_softmax, title_prefix=\"\"):\n",
        "    fig, ax0 = plt.subplots(1, 1, figsize=(20, 4))\n",
        "\n",
        "    prob = y_softmax[slice(*samples)].transpose(1, 0, 2).reshape(11, -1)\n",
        "    gt = t_idx[slice(*samples)].ravel()\n",
        "    pred = prob.argmax(axis=0)\n",
        "\n",
        "    print(f\"prob={prob.shape} pred={pred.shape}, gt={gt.shape}\")\n",
        "    seq_len = prob.shape[1]\n",
        "\n",
        "    # -- Prob --\n",
        "    sns.heatmap(\n",
        "        prob,\n",
        "        vmin=0, vmax=1.0, cmap=\"viridis\", cbar=False,\n",
        "        ax=ax0\n",
        "    )\n",
        "\n",
        "    # -- Ground Truth --\n",
        "    x = np.arange(seq_len)\n",
        "    ax0.plot(\n",
        "        x, gt + 0.5, label=\"ground truth\",\n",
        "        linewidth=5, color=\"C3\",\n",
        "    )\n",
        "    # ax0.plot(\n",
        "    #     x, pred + 0.5, label=\"pred\",\n",
        "    #     linewidth=1, color=\"C1\", alpha=0.5,\n",
        "    # )\n",
        "\n",
        "    # -- Style --\n",
        "    ax0.invert_yaxis()\n",
        "    xticks = np.arange(0, seq_len, 30 * 30)\n",
        "    xticks_minor = np.arange(0, seq_len, 30 * 10)\n",
        "    ax0.set_xticks(xticks)\n",
        "    ax0.set_xticklabels(xticks // 30 + samples[0] * 60, rotation=0)\n",
        "    ax0.set_xticks(xticks_minor, minor=True)\n",
        "\n",
        "    ax0.set_yticks(np.arange(11) + 0.5)\n",
        "    ax0.set_yticklabels(np.arange(11), rotation=0)\n",
        "\n",
        "\n",
        "    ax0.set_xlabel(\"Time [s]\", fontsize=\"large\", fontweight=\"bold\")\n",
        "    ax0.set_ylabel(\"Class Index\", fontsize=\"large\", fontweight=\"bold\")\n",
        "    ax0.set_title(f\"{title_prefix} | {samples[0]}min ~ {samples[1]}min\", fontsize=\"xx-large\", fontweight=\"bold\")\n",
        "    ax0.grid(True, which=\"minor\", linestyle=\":\")\n",
        "    ax0.legend(loc=\"upper right\")\n",
        "\n",
        "    fig.tight_layout()\n",
        "    return fig\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d3b55dc4-a8c6-4eb8-9c8b-27e6b30a7df0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d3b55dc4-a8c6-4eb8-9c8b-27e6b30a7df0",
        "outputId": "2b077870-ce56-40f8-8e93-165ddadacf36"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "unixtimes=(31, 1800), t_idx=(31, 1800), y=(31, 11, 1800), y_softmax=(31, 11, 1800)\n"
          ]
        }
      ],
      "source": [
        "key = \"U0102-S0500\"\n",
        "\n",
        "unixtimes = outputs[key][\"unixtime\"]\n",
        "t_idx = outputs[key][\"t_idx\"]\n",
        "y = outputs[key][\"y\"]\n",
        "y_softmax = softmax(y, axis=1)\n",
        "print(f\"unixtimes={unixtimes.shape}, t_idx={t_idx.shape}, y={y.shape}, y_softmax={y_softmax.shape}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7cc7195c-1785-4a90-94f2-23328c735f0e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "7cc7195c-1785-4a90-94f2-23328c735f0e",
        "outputId": "918e4dfc-5bf0-4c51-a6f2-51f99296f230"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "prob=(11, 18000) pred=(18000,), gt=(18000,)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "samples = (0, 10)\n",
        "\n",
        "fig = plot_timeline(samples, t_idx, y_softmax, title_prefix=f\"{cfg.model.name} | {user} {session}\")\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dd5ca495-55ea-42a8-9d58-0b80225ed8f5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "dd5ca495-55ea-42a8-9d58-0b80225ed8f5",
        "outputId": "2688203a-82ef-450d-fb30-54a782998d83"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "prob=(11, 18000) pred=(18000,), gt=(18000,)\n"
          ]
        },
        {
          "data": {
            "image/png": 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jxoxJ2ahu164dxo0b1+R2HnLIIbj++uvr/byiogLTp0/HLbfcgh49euC+++6rtamarr5yzjFy5Eg8+eST+Oabb7B06VJ8+umnuOyyy2qVDr///vuxffv2JrWhrveTv9ec1yyT2N0xUt93k49/9913ceihh2L48OH4/e9/X+uYcePG4eqrr8Z+++2H+++/H+3atUv5/KeffmpMFxrkzjvvxLhx47DffvvhiiuuwFVXXbXHMiorK/G73/0OJ510Ej7++GOUlJSgtLQUX331Fa699lp06dIF1157LT7++GMsXrwYU6dOxemnn44LLrgAVVVVe9wnl8aO/XSMh7o477zzcOedd2LAgAEYO3ZsrY1vQP4Y4Oijj8bBBx+Me+65J+Wz1atXo6KiokEZu0LTNEydOhXHH388Bg8ejJdeegmFhYUpx6TDjhQKhUKhUCgUCj9R+2e0CoVCoVAoFAqFQpFGxowZg0cffdS7s++uu+6q9Uzfmqxfv77WnYAnnnjiLmV9/fXXu9vMvYK6yiq39LPX09UG0zRx6aWX4o033vDeC4fDmDJlSp13/TaGZ555BgMGDMD999/f4HO/o9Eo7r33XhiGgdtvv73e43a3r19//TU6deqU8l7fvn0xatQodO7cGQ888ID3fmVlJf73v//hkksuaXQbGtuOho5tabtpKdLR1xNOOCFlg7Jbt261jkl+xremaejRowe2bdvmvZeOZ1DX3Ajv27dvyuvdkfHOO+/g3//+d72fl5aWYvz48Rg/fnytz/b0efAuezL20zEeANR6Rnu3bt0wd+5c7/WgQYMwYMAA73Xv3r1rnaOkpKTWj7OawnHHHZfy4y1d19GzZ8+UMvDpsCOFQqFQKBQKhcJPqDvGFQqFQqFQKBQKH5NcHra5vmsYRkoJ9W+//RZTp05t8DsbN27crTZt3rx5t77XkqRL5/n5+bU+r6v8cyQSSXld1/f2hJrna0wbdtWOiooKnHLKKXj99de997KysvCf//wHRxxxxB60Frj++uuxbt06fPTRR7j11ltxxBFHIBQK1Xns3/72N8RisXrbvLt9rbkpnsytt95aa4Mx+a7QxrShrnYkf685rlkmsrtjpL7vutSsNJGVlVXrmJqb5TXvWN7TZ0Pn5uaiQ4cOaZfhbiAPHjwYr7zyCn7++Wd8+eWXuO+++xqssDFw4MAmPSqjPpo69tMxHupiV9d4V9cX2PNr3KdPn1rvpduOFAqFQqFQKBQKv6E2xhUKhUKhUCgUCh+Tm5ub8rquDSKg7s2GvLy8Rsu59NJLUxb577777ma5I7Wh5wJnCunSeV2bVFu3bq31Xs07o+u6u3VPqNmOxrShoXZs3boVI0aMwKeffuq9V1RUhOnTp++y0kBjMQwDJ554Ih555BF8/fXXKCkpwdSpU7HffvulHFdeXo7Fixd7r9Pd17rIzc1FmzZtUt5LLvtcsw3FxcV1bo41dN1boh+ZQNeuXWv9yKAxfW3Xrl29JbmB2puqdd0pXVBQ0ISWNp2ioqJa72matsfnZYzhjjvuwNy5c3HJJZdg//33x29+8xvcfffdWLFiBd5//32cddZZ6NixI8LhMIYMGYKnn34ac+fORc+ePfdI9u6M/XSMh7rY1TVu7usLNN81VigUCoVCoVAo/Iwqpa5QKBQKhUKhUPiYmneurlq1qs7jVq5cWeu9fffdt9FyNE3Dvffei4suuggAMH/+fASDwXqP79ixY633Zs6cucvNDF3P/ClOp06dsHDhQu91Xbqt7/3k69WvXz9kZ2ejsrLSe2/RokU45phjUr6zaNGilNd1PSt3Txg6dGhKieGa8up6Lzs7G/3796913MqVK3HCCSdg+fLl3nudO3fGtGnTam1ap5NgMIhTTjkFhYWFte5KTX5u8tChQ/H22297r3/55RcIIVI2zerqf1N0Xl5envJMcSB1E67muRzHweLFi3HggQd675WWlmLDhg31tiGd1yyTycnJQb9+/bBkyRLvvcb0Nd1jpDVxzjnnIDs7u87POOc444wzcMYZZ6Rd7u6O/XSMB4VCoVAoFAqFQuEf1B3jCoVCoVAoFAqFjxk+fHjK661bt+KLL76odVzyZiAgy7kOGjSoSbLOP/98HHDAAd7raDRa77Fdu3attQn+v//9D927d6/3b+XKlSllrwF5Z3AymXBHeU2d//DDD3Vugr/zzjspr3v16pXyfF1N03D66aenHFPzOi1fvhwLFixIee/ss8/enWbXy1lnnZXyev78+SmbW0Dtvpxxxhm1SsrPnz8fw4cPT/nugAEDMGvWrLRsik+aNAkPPfQQiouL6z0m+UcGLsk6P/PMM1M+27FjB2bMmJHyXs2+Dh8+PKXk9aOPPopXX30Vtm3X2YZHHnmk1ntDhgzx/n3IIYegc+fOKZ/XvO7vvvtuSkWGcDiME0880XudrmvWGqjZ1/fffx+maXqvS0pKMH369JRj0j1GWhP1bYo3J3sy9tMxHhQKhUKhUCgUCoV/aH2zWoVCoVAoFAqFQpE2zjjjjFol0c8++2xMmDABCxcuxFdffYVrrrkGr7zySsoxF154Ya1N513BOcd9993X6OOvvvrqlNePPvooLr/8ckyfPh1Lly7FwoUL8f777+Mvf/kL+vbti2OPPRZr165N+U67du1SXn/++eeYPn06Vq5cidWrV9e6M7cluOiii1LK4dq2jREjRuD111/Hzz//jM8++wyjR4/GzJkzU7532WWX1TrX73//+5S7lb/66ivceOONWLRoEb744guce+65KRtC+++/P4499tha51m9enXKX002b96c8nly+fdRo0albF4JIXDOOedg5syZWLRoEcaNG4evvvrK+5wxht///vcp558xYwaOPvrolHLHvXv3xmuvvQbbtmu1b/Xq1U1+tu62bdtwxx13YJ999sHZZ5+NZ599Ft988w2WLVuGBQsWYOLEibj88stTvrPvvvuiV69e3ut+/frV2lC74oor8L///Q9LlizBQw89hNdeey3l8z/84Q8przdu3Og9WuC+++7DF198gaVLl+Kzzz7D5Zdfjoceeijl+KKiIpx88snea845brjhhpRj/v73v2P8+PFYunQp3nzzTdxyyy0pn1966aUppaHTcc1aikgkknLdN2/eXOuYhuz3qquuSnmG/MqVK3HxxRdjwYIF+O6773DOOeegvLzc+7x9+/Y4//zzm60/ilT2dOynYzwoFAqFQqFQKBQK/5D5dQYVCoVCoVAoFApFs5GXl4eHH34Y119/vffezp07cdVVV9X7nXbt2uHee+/dLXlnnXUWhgwZgh9++GGXx44bNw7vvPNOyrEvv/wyXn755UbLO+yww1Jeb9++Hccdd5z3+qKLLqq1kdnc9OzZEzfddBP+/ve/e++tW7cOF154Yb3f6du3L2688cZa7x9++OG4/vrr8cwzz3jvPf3003j66adrHRsIBDBhwoQ67/rt0aNHg22+4IILUl7PmDEDI0aMACA3piZMmICRI0d6d+z/+OOPGDlyZJ3nuv7662tdl1deeSVlcxKQd7s3VO541apVdT5nfVdEo1FMmTIFU6ZM2eWxf/nLX2q998wzz2Do0KHec7/XrFmDU045pc7vn3rqqfjtb39b52erVq1q1Dh6/PHHa/145aabbsKbb76J+fPnA5Cbx9dee22d3+/SpUutzfZ0XLOW4ttvv623XS417Tf5xyDdu3fHAw88gD/96U/ee2+++SbefPPNWudhjOHZZ59Fbm7uHrZa0VjSMfb3dDwoFAqFQqFQKBQK/6DuGFcoFAqFQqFQKHzOddddh7/97W+Nej53jx49MG3atFqlaxsLYwz3339/o44Nh8P4+OOPUzayGyInJ6fWXYAnnXRSyrNmM4WHH364zo3uuhg8eDA+/vjjejfrnnzySYwZM6bBc+Tl5eHdd9/F4Ycf3uS2Nobhw4fj3XffrbWBW5MxY8bgqaeeapY27IqCgoKUu+sbgjGGm266qdadqIAsaf/JJ5+gY8eODZ7jlFNOwVtvvVXr/cZuuobDYbzwwgu49NJLa31mGAY++eQTDBs2rMFz9OnTBzNmzEBhYWGtz1rDNUsXN998M+65554GS8EHg0H861//wjnnnNOCLVOkg3SMB4VCoVAoFAqFQuEP1Ma4QqFQKBQKhUKhwJ///GcsW7YMt912G4YPH4527drBMAxkZWWha9euOOOMMzBx4kT88ssvGDx48B7JOvnkk2s9Z7s+2rVrh08++QTTpk3DJZdcgj59+iAnJwe6rqOwsBBDhgzBmDFj8Oabb2Lz5s212mYYBmbMmIEbbrgBPXv2RCAQ2KO2pwtN0/Dkk09i4cKFuPHGGzF06FAUFRVB13Xk5OSgZ8+eOO+88/DWW29h7ty5Dd7RrWkaJkyYgBkzZuCiiy5C165dEQwGkZ+fj4EDB+K2227D0qVLceqppzZrn0499VTPhgYOHIj8/HwEg0F07doVF154IWbMmFHvHestwTXXXIP169dj0qRJuOKKKzBs2DB06NABwWAQmqahoKAAgwcPxvXXX4+5c+fiH//4R73nGjZsGJYuXYoHH3wQw4YNQ2FhIQKBADp16oQzzzwTU6ZMwdSpUxEOh2t99/7778cPP/yA++67D8cffzy6deuGcDgMXdfRpk0bHHHEEbjnnnvw66+/YuzYsfW2oW3btpg9ezZefvllnHDCCejQoQMMw0Dbtm1x1FFH4amnnsJPP/2UUgq+Jpl+zdLJvffei3nz5mHs2LHo1asXwuEwcnJy0L9/f/zhD3/AokWLGtS3IrNJx3hQKBQKhUKhUCgUez9MJNcYUygUCoVCoVAoFApFvaxevdrbpL7nnnt2u6S8QuEH1HhRKBQKhUKhUCgUCkUm0fp/9q1QKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKRQOojXGFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQ7NWojXGFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQ7NWojXGFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQ7NWojXGFQqFQKBQKhUKhUCgUCoVCoVAoFAqFQqFQ7NUwIYSgboRCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFM2FumNcoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFHs1OnUDKDg+cCGYpskXGgeEAGwbAMACATjV1QBL/GaAaRqEZYLphnxDOPJzjQOWBRYMQtg2IASEaYFxBui6lKFpELEYnOoIGGdgmgYjKwAz5shzWVbiXDW+DyHAdB3CsuT7bpsBCNsGM3TAEYBwIBwBFjDADAMiFvP6AscBOAccByJmAsKR8qM2hGl5/QNnYOEQRGU1oHGImAlh2+ChIMBYXKiQ8gCAMwjTAg+HEp8BAGOwy8sBxsEDAbCAIfvu6lDTZN8AsFAQIhIF4xzCccBCQXl+S7bLbZ+wbbCAkdCHbYNxLnUbiXrt8doJALYDZsSP1TTvXNA4NFgwq5P6Hr+e7r9F3BZSSLIHCEeeM65773P338nfcW3FtRsARlj35O8KFgzGT8UBXYeojsi+xnXt9lP2TZN27NqJLa8xM3TZf+HIzxzh6YUFDIi4vt3vilgMLBCQdpTUL6YbiXPYdt16aqgfQgCMyf6XR7y2pPQHAGxH2mMwCBGNX1/3uHjbXX0yTZNt5Ey2P/7vlLYzLscUAOg6jJAGM2J7ctzzu+f1bELT5HWOn0tYpncuFghAmFbcFllC/65NxI93dSbi42aXOqtpL/F/uzKEI+S/431zz+u1Iek6sUAAwnE8nTrRSML/VMVq2TDTNDixWOI6Mx73aw6YboBxlhhPNeXG/ZVnQ2bq+HJMCzwQADiDEdQ8+/d8gnsOy4ITi8ljXT1oXNp5wIBdUQkA4IaeOC7+Xac6Il8LxztXQinyXE4sBm7oqe+7ek7260LAqa4GD4cT/kOT5xDRqGcHKdfD9eGxmGeXwjKlHcdMzz8bAQ6z2pTticcaZuie/bvHipiZcq3BuHcNk+V7YznuY91+ufbn2Y5tgwUCCf1rHCwUhFNaVlsXrv25agoG5XiJ+0cWCMBxfW8S3NDhmAm9u21sLHUeH2+Te92EO87r87fJr+PndMcmCwWh6wJWTHgxLnls1dWeFP/injceI2vqSESjMu7adsI2kBi3whEwQhy2Fo+bjuP5RRGNer7bjXM1ZewJ7rjl2VmyTZGo13+WlQVRVSV9hm2nxPlknwbA+45I0keKntzvxn1osi8Qto1AThCximgif4mf0/PngNRDPL5518j9jDE5NpLyH+h64r2kXE64MTBpHBk5QcTKIl5cdNvl5l3u+HPb7FRVefFLxGJSh6FgynldXaTEAyDFTybbV33joq7vpnyWnKck+e5axyblNZ5e43mXrgnYWhCwLAjHgYhGU2zVvaYp8SVJR8m5gatv9z0WDEJYUqdOJJrwkYYOJxIFD4dghHVYUUeeI94m2DL/c3NXN7ZCOOBZWV5sEbbt5eHJObAXC5J8PwuHU3QiTNPzYWAcLBwChIBdVg5u6GDhsMwn3XzatbckPXp6cmN98ueQOa17Puh6qv269pcbQqy0OuX83hwiHqd5MCTjZiwGHg7LsRD3EwBkbHPzPbd/SbphASMxPi1L+uR4HPfyz6QYzzQNjDPPd7ttc+cGAOT11LREPgl4cUtEowkbiX/XtR0eCnp5pTt+6sybk3Ns1D1GvHMn9duz2xrnqnmMcES9uXfNuKVlZ3k5c0quU6ON8mCeyFNr4sbpeFuMsA4zEm9jcuyI666hc7h5hOdja+TNjPPEuEn+upvv2Lbn31KoEV/qyind93k45NlezXkVhEi8V0dsdn2/WRVLnYcEjMQ8OCl+uPM3ALVikBPPsWr6bk8Hllmnfw1kGynXv9a1ded2STmGSB7nSf1J/DNhp6598rhvceNPsq6T1xFYICDHT3z9gOk6YOgQFZW7jA8puk2am6T48mS/pWkwQhyxSrNWu1Nib/x7LGDIPK+83Ivjck2D17Ihzz7d3NjQ5dzDzcHj+jKyDVgmEnmym2+48TPp2st8pcY6iXsNku0s3lbPt9fQjbc2YFlJ895AYnzH41dyG3goWGfukJL7oYbdJOV/KbEvyW8YYR2WhZTPXbvx5lbxuJd8/Xg47PlO77yaBpYdhqiOyHwx+ZrUY6tGtiFzX9OS8dY04a6/yEN5Ys0gKZ4jyWcnz315KOhdDzhSl05VFXhWVspalPtfz7bcPCIpV4PG5fqGq4+kHMObzyfnlxoHMwzYZeWoj5rjxfX/9eXmu6LO8Zd6QOLfdcQ4d+5NRl3zpjR8313r9NZR6pHRlLWv+mQ1dj5Z17yuSfKbCW8em7yWFYdpGngo6K0L1FqTQsJe68y36lkTEo6Alp0FPcAS/Xdlx+MoNA2iujqxlpOUc+4qZ3DnJV573DW4GiTrn2mazLlrHpe0juf1Pb726cYVd/0XTo12CCE/r7FWnpw7M540Rt18Or5GnryODc7jfifpezXXWOPyneoIeHaWnD/E4407j/TW/oNBGAZg2Rwp65NJ67We7pPn3/Hr4MWGZPvXuNe2mj5cWCZ4VhacqirPNoIFOYhVyFxVy8/z8lZhWol8Mr52wbOzZMyJx8DknMiLl27sBWScsG0574rPK914Keea8ZgVCsKpqEjNjevIq13b54Zea+6cTHLeDsRzd9uubVeMwwhxmBGnXrnJ852U+OPK2MO1GM/+k/PHpDWLZJ9Xa04e/5wlr18mj4G4XYtYLHENQ0Fvjs8CgVQbSl7TArw5fHJOwjxf5Xg5WHKu6bYztYn1x8iG/G9D19g7JllPScem5EUNrK3oQQ1WTNQad8K25RpPfG7j5rpeHisSOqm57unqGIBn58l2wt11CE1L5FuxWGKvRTdqxYGUeUcd/Uh64bUree23tuIcz/4tm8vrH19Xd/Xq5vawHbl2HAiA6brnP9y1dncvDowl+qBp0saysmQOF1+H8fxS/JxGVkDqX+OJ9Xq3fW4ci+uEhYIJ+aYl1y3r8PdOJJrYH4zvebp7Wsz1p/Fx8nHpi3XqtCbqjvEWxgjrGPvSmTDCNL9JoJZPjRHWcfW/zyPV/1WTz/W1/sdOPJ3W/ied4Wv9X/XqaOV/iDBCOsa8cAqMkNI/BUYo7n8I9X/V5HPJ5FNDbX/K/8f9P6X/IRx/1GRC/kMZ/ylRuT8tmeD7fa//l8+mj71+9v0vnUkbe32uf8rx7+fYC2SG/injPzVGiHbuQ339qfHiL5n/N/y99pMB/kfZP23+qeKvyn8agy+fMa7uGFd3jKs7xtUd4+qOcXXHeMoYVXeM1+mvAKg7xtUd454+1R3j6o5xdce4umMcgLpjHFB3jMd1oO4YT9inumNc3TGu7hhXd4x776k7xgGoO8YBdce4umNc3TGu7hhXd4zXRN0xnqKMeuOcumM8uV/qjvG9AwYEwgawi/x6r5VPDQMCWUr/ZFD3n1o+NdT9p5ZPDQMCYV3pnwrq/lPLp4a6/9TyqaHuP7V8aqj7Ty2fEpX700Ldf2r51FD3n1o+NdT9p5ZPDXX/qeVTQ91/6vhPTSboX9m/0j8V1P2nlk8Ndf+p5VND3X9q+U1AbYy3MEZIx6XPEZbSJZZPjRHScfmEs0j1f9kLdOXUqDFCOi7950m09v/Myb7W/2X/Iiwl7Xf/E9RxyVMnwAgq/VNgBOP+h1D/l71wJpl8aqjtT/n/uP+n9D+E44+aTMh/KOM/JSr3pyUTfL/v9f/8afSx18++/7lTaGOvz/VPOf79HHuBzNA/ZfynxgjSzn2orz81Xvwl8/+Gv9d+MsD/KPunzT9V/FX5T2NQpdQJSql73wVISqm7ZWr8WkrdiZfr8vrewqXUm8LeWEodQKIUKEEpda8dPi2lnlIykqCUenIpKD+WUk8u2UVRSj2lFKEPS6kL04yPa5pS6hAOeE6O/JIPS6l7/yUqpe6VyvJpKXURM8FzsslKqcvTc9+WUvdsjKiUepKBebbrl1Lq9dFSpdRT+unDUurJuTVFKfW6+uS974NS6jXtuaVLqYMzr7yhH0upu32jKqUOtwSzT0upJ9RDU0q9znlLS0JcSj0dsvaklHomQFlKPUUeQSn1mn31Wyl1WFbttdUWLKXOgyHPBvxYSt3LF4hKqae0xf2nj0qpN0RLlFJPzoP9WErdG7tEpdTdOJHppdQzf+t+L4MxhoLOeSjZXOHFYBL5m8qRWeliy8AYUNQlH8UbykgSZsYZCjvlonhjecYl7C0BY0BBp1yUrC0hsT9P/qYKf9o/ZyjcNw/FG0pp5DNI/7OhjMT/UcMYULBvLln/U/Tf8uLJcfVfuqWKxv9whsJ98lBa6vjT/jlDYec8FK8roZHPGAr2zUXxWhr51DAGFHbOQ2mpTRp/SzdX+tT/xO1vTTGN/OT8s/G/a9wrSOQ+xLn/pro3Pfd2XN9fsr6ULPYWdc7z99xr3zwUryeyfzf32lrtz9wnrv/SrVWE8ulyX2qo116S596+9D8Zo38a/0eNt/awsZxm7UGtfaKgcx5Kt0Xo1h7ic29f6j++9lK8qZJOvmv//lO/N/ct2VCOPbkfN5BlIK9tnrdBnPyb8eT/1pLPGfLaZaFse7XcYE3eGHfiG8buxrgVv/lS1+TJ3BPyOn4U6m4UA3JjXCD1R8/BpB8PWpZsoGkmNso1PbGx7tLUjXHOIewaP0KtcT7Guex/cRSwa2yMez82im+M25a8iUvTICIhuTFuxH/Qa+gpG+MwTfmjjFgMCIeB+M0F0PXExbAdQNjIa58j9c+5vFHCSLrZ2N0Yj+sEwfgPOSJBeT3Cobp/CBUzITQN5dsrYKZpTUNtjLcwekjD6L+OxCt/+AhmtbXrLzSX/N//D7FYy8unRg/pOOfh4/HS2Ck0+g9qGP3Q8Xj56vdJ5FOjh3SMvm8EXrn2Q5ixls8OpPz4+Kto+BeteyN6UMPoB0fh5avfh0Wl/7+OxCvX/9ef9h/Ucfbdv8Grf5gGM0Lh/5P0HyO8c4EIPSj9z6s3foJY1Nzl8emXL/3/qzdNJ7n+1OhBmX+8fM1/Ugo6tJj8kIbR99HJp0YP6Rj94Ci8evNnME0i/3PfCLz6h2mIRX0Yf0MaRt83Ai9f/R+a+OvG/2v+A7PKXzvjelDLjNz/mv/4NPeJzz2vI8r91dwLo+8/lsz+3LnXq+M+gVnd8rkXNW7u/eqN02BX11Nhornl3yfl+3LtJ2n8x6yWn/skz7196X+I/S91/KdGzn3l2q9a+2x53Pj76o00cx89qHtzb1/qPyn/tqItP/dJsf+ID9feQhpG33+szP93w/4YA0ZcdhgOPu0gGEkVNsEAiDr+W/P7HMguykLlzqraG+fJ1diQdJ66qPndmseJGgd57axjx97byd+VkPpI1oFIfa/G+RhjyG6Thcqd1bV/mFCzv54+WFK1G/eAJEV7cuPv1ferhPgRUn5c/zV1XlM2Y/HTCymO16On+HumaeH7aUvwxWtz9/iHRxlVSv3LL7/E3//+d3z//ffYtGkTpkyZgjPPPNP7XAiBe+65BxMmTEBJSQmOOOIIPP/88+jTp0+T5KhS6qqUevK19cqruKXmVCl1VUpdlVKHV8pLlVJXpdRVKXVVSl2VUlel1Lkqpa5KqatS6imoUuqqlLoqpa5KqatS6qqUuiqlrkqp7wJVSj3RV1VKXZVSV6XUVSn1muep6xwjLz0UR5w3FEUFReBpuafXlSHifzz+3+Sd9bo3mes+T/JnycfEr3XKprErx6lfRn1hi9X3YldtdvtVVz+Tz5ekD8YAYccPT55TJK1FeLHCSbHf2m1L3n13j08+xv13XCeMx1UU/26dd8K75wUc2CguLcbX78zHjFe+lWfczVLqdUiio7KyEgMHDsSzzz5b5+ePPvoonn76aYwfPx7fffcdsrOzccIJJyASidR5fCbCOEOHPkW77Txau3xqGGfo2Lctrf77tvG1/jv0VvZPBbX9Kf0zdOhdqPRPREb4H7/7f5X/kEFtf9Tjjxrq/lNff0pU7k8Lte9V+mfo0Efl/lRQ959aPjXU459aPjXU/aeO/9RQj3/q60+Nir+0UNsftXxq9sT+g1kBDD3lQBQVFMFAANru/I9pCIaC0JjuvdZY6ucp/00+htVxfF3H1CO3znPUd3wz/i8YDDYsv66+N6Y/u9IdNGjQE/pv6Lwpr/XUc9crV4fBgigsKMLBJ+2HQNjYtVE1QEZtjJ900kl44IEHcNZZZ9X6TAiBJ598EnfeeSfOOOMMHHTQQXj11VexceNGvP/++y3f2N1ED2g4Ydxh0AParg/eC+VTowc0nHjLkaT6P/FmOvnU6AENJ9x4KK39/4FOPjV6QMOJNw1X/ocIPcBx/A2HKP0ToQc4uf+R/j+jUq8Wg9r+lP93/T+N/VHHf2qo+08d/ylRuT8tmeD7fa//Px6uYi8RmWD/ftc/5fj3c+wFMkT/hPGfGurxT339qcmI+OvrtZ8M8D/K/ner/zltsmAENHDsvu4YY8jvkF1n9W4/wBhDfsccuh+GMDS7/jk06AENuW2y9+g8GVVKPRnGWEop9ZUrV6JXr16YP38+Bg0a5B139NFHY9CgQXjqqacafW5VSl2VUk++tl55FVVKXZVSR5JNqFLqqpS6KqUuP1Kl1FUpdVVKXZVS56qUerI/SfkcUKXUVSn1VFQpda8PqpQ6UsegKqWuSqmrUureOVUp9RZAlVInR5VST/RVlVJXpdRVKXVVSr3meWqeo13XQoz552/RoW0HaNCAeh8A3gSS97XcUt3Jfi/5mJSG1ognKc8mr6OUunutk8+bXH68pUup19XP5L4k64MxwKlRSp3XU0o9aa/RO5cnvkYpdff45GO888V1wnniOCE8+07tlkhpiw0HW7ZtwsRxb2Pb2uK9o5R6Q2zevBkA0KFDh5T3O3To4H3WGmCcoctBHXbbebR2+dQwztB10D60+h/Y0df673Jge2X/RFDbn9I/Q5cD2yn9E5ER/sfv/l/lP2RQ2x/1+KOGuv/U158SlfvTQu17lf4ZugxUsZcK6v5Ty6eGevxTy6eGuv/U8Z8a6vFPff2pUfGXFmr7o5ZPDbX9g0GW2PbrLePAHpcY3zNYq9F/q9kY312i0SjKyspS/hzR+Dtd041maDji4oOgGTSqp5ZPjWZwHHHZYNJyKkdeNsS35VQ0g+OI3x0EzaDpf0K+P+1fD2g48tJBZPanGdz3/mf4BQcQ+n+lf8rx7/p/v+pfD8j8g87/aDjid3TyqdEMjiMvHUTrf3wcf6ntjzr+U6IHNJX7E+L6fqrc3+/6l7kfce7va99Pm3v7Xf/U49/PsRfIDP1Txn9qqMc/9fWnxo2/lPqnnHtTQ21/1PKpkXtPg8jyf8YYctqGW8O+bLMg+5/VqP6/9+G7OPjogWmWjybr/y9334zrbro6re1oDK0mQ+7YsSMAYMuWLSnvb9myxfusLh5++GHk5+en/K1yfmnWtjaEFbXwxi2fworSbM5Ty6fGitp4fdz/YEasXR/cDJgRC6//kU4+NVbUxht/ng4rStN/K2rjjVv9a/9mxMLrN31MZn9W1Pa9/3nz9hmE/l/pX/ofmv67/t+v+jcjMv+g8z8W3riVTj41VtTG6zd9TOt/CMcfNVbUwht/nk6bfxLGf0rMiKVyf0Jc30+V+/td/1bUxht/mkab+/t47kWde/td/9Tj38+xF8gQ/RPGf2qoxz/19afGjb+U+qece1NDbX/U8qmxolbc/mn6LxyBnevKMu7RGi2FEAI715XWqp6+O6zfuB79hvTA4qWN30uV8uvW//qN69FvaM8mna85aTUb4z169EDHjh3x2Wefee+VlZXhu+++w+GHH17v92677TaUlpam/PXg+7VEk+uEawy9Dt0XXKP52Qq1fGq4xtDr8C60+ieUTw3XGHoNU/ZPBdcYeh3WWemfCK4x9Dykk9I/ERnhf/zu/1X+Q0ZG+H/C8UcNdf+prz8l1L6XWj411L5X6T8DfL/fYy+1/ftd/9T+36exF8gQ/fvd/1P7H7/rX8VfMqjtj1o+NdT2DwYEswOtopR3zIw1y3mD2YFmOW99pPaDtRr969QNSKaiogLLly/3Xq9atQoLFixAUVERunbtinHjxuGBBx5Anz590KNHD9x1113o1KkTzjzzzHrPGQwGEQwGU97jjK6UBdc5Bp7SF2t+2grHbvlfjnnyF26BH383zHWOwacPwJofNpLpf9Cp/cnkU8N1joEn98aa79fDIfjhmJTfB2t+3AIn5k/9DzqtH9b8sAE2lf5P6Ys1Czb70/41joEn9cLaH2n6n6J/y3+/nOSa9D9rf9pGEv9c/79u6Rx/2n/c/lZ/vwG2QyT/5D5YPXc9iXxqXP+/btlcUHTfjf9rf9rq2/xz4Mm9sXruOrL4O+i0flgzfxMcy19XIGNy//k+zX3c3GP+RrLc3/dzr1P6YvX3dPY/8OQ+WPvTdjiW/4Kvq/+1i7bTyT+5D1nuS03y+LfNlre/5Lm3X/0Ppf+ljv/UeGtfRGsv1NefmoT/30G29uDOvf2qfzf/JvP/rv3HfLj25uX/m/bY/uwrzwEss8nfq2IMabll2kU3oL34boOHVFRW4J6H7sRnMz9FTnYOxlx6FT6bOR39+w3AHTffCQA45rSjMPqMc7Fm7WpM/2I6jh95PP52z6OY9vnHeHr8k1izfg3at22P3513Ma64eIx37n4H98Kzjz2PUSOO9947eMQg3H7znTj7tHOwfuN6HHv60fjno8/itTdfxcJFC9G1Szfcd9v9GDxwqPed9z58B0+PfxLFJcU48vDfYOigxGd1cexpRwEAzrzgFADAsKGHYvK//g9/uedPKCsrxYH7HYR/v/0aAoEAPv/wS6+d5140GrFqE0IAB48c7LXz2NOPlue78FR5viGHYvLENzx5kyZPwEv/ngTTNHHy8afi9j/dDcNovuelZ9TG+Lx58zBy5Ejv9U033QQAuPTSS/Hyyy/j1ltvRWVlJa666iqUlJTgyCOPxMcff4xQKETV5CZjRW28d/cMQKO5Wd+TzzP/VxvNgRW18c5tn5DKf/eOT8nkU2NFbbx37xeAZQNay/9AxYraeO+ema3iV0vNgbS/zwDhgFHp/+4ZLS43U7BiNqb89av0JmdNkZ+sf6IYRIkVk/6HwvaBhP/nOTkk8qlx7U/YNpjR8umn6/+FSSOfGtf/85xsMvnv3fsFGPef7wES/Rcxmyz+vnvHZ7s+cC8kY3J/5mPbv3sGIByy3N/3c6+7PicrJenGXqb7L+4CCfunyjs8/RPlvtRQj//kubcfyQT9U8Z/ary1L0L5Kv5+DhZo2bs2U+TH595+JDn/ZgT7Hyn278M5gGv/6TmZCVitoyT93x5/EPN//B7PPzEBbYra4OnxT2DR0kXo329AynEvTp6I68f+Hjdc9QcAAj8v/hnjbvsDbhj7B5x83CmYv/AH3PfIPSgoKMTZp41uUhueeO5x/HncbejWpRueeO5x3HzHOHzy/gzomoYff16AO+6/DTdd/yeMGnk8vpr1Bf75r6caPN/br07BuZechZeffw29e/aBkeTTZs+djZzsHLz03Ku1vle8oVz+o8b+y9uvTMG5l56Fl5+fjN49esMwEuf7bt5stGvbDq/863WsXbcaf/zL7zGg33447+wLmqSDppBRo3PEiBEQQtT6e/nllwHIh8f/9a9/xebNmxGJRDB9+nT07duXttFNhGscA0Z2JyznQSufGq4x7DeqF7hOY/pc5xhwbE8y+dRwjWHAiO7gRJtyCfk+tX+dY8AxdPbHNeZ7/zPg6K6k5Zx8r3/C8e/5f7/qX4/nH2T+h8vr7+f4ewyd/VGPP2qo7Y86/lPCda5yf0I830+V+/td/xrDgGN60Ob+vvb9tLm37/VPPP79HHuBzNA/Zfynhnr8U19/arz4S6l/wrk3NdT2Ry2fGq7xuP37p/8VlRV4f+p7uHXcbTj80CPQt3c/PHzPo3XeMX/YIYfjit+NQdfO3dC1cze89O8Xcfghh+P6MTegR7ceOPu00bjovIsxafKEJrfjit9diRG/GYkBB/THH64Zhw2bNmDNujUAgFdffxm/OfwojL30avTo1hOXnH8ZjjzsNw2er6iwDQCgoKAQ7dq2Q0F+gfdZVjiMB+56GH169UWfXqn7s6HcYJ03JRYVFsnz5dc+X35ePu6+9V706tELI486Fkf/ZiRmz5nVZB00Bf9YaIbA9fhzFsicM618arjO0ftw2o0pSvnUcJ3L59zodMmhr+1fY+h9OOFzjnyvf46ew/YlTc79rn/5nC26xWHp//2qf+LnrOk+f86jzuP+n9D/HErn/6jhOv1zHinjPyXUuTe1fGo830+V+/td/zpHr8MIn7Hp5p5+zX2Ic29q+dRQj38/x14gU/Tvd/9P6H+U/mX8JdS/r+e+yv+QItdeupDl/xSs37AOpmXioP0Heu/l5uahR7eetY49YMCBKa9Xrl6OIQNTS5oPGTgEa9augd3Eqg/9+vQHAxDMCaB9u/YAgJ3FOwAAK1atwEEHDEo5ftBBg5t0/mT69uqHgFF3VYxgjtHkYr29e/aBllTlqF3b9tixc8dut68x+LOmFSFW1MbUh78mLaU+9eGvfV1K/T/305VytqI2PnxgJpl8aqyojamPfENaSn3q3772dSn1Dx/8krSU+tSHv25xuZmCFbPx37/PJi2l7unfhwuUVkz6H8pS6h8+MNPXpdSnPvw1aSn1qX/72tel1D988EvSUupTH/nG16XUpz7yDWkp9Q8f/LLF5WYCGZP7+7CMIpCUexCWUvf93OuhL0lLqU/929e+LqU+9eGvSUupT/3b174upU45/pPn3n4kE/RPGf+p8da+COWr+PslaSl1d+7tR5Lzb6pS6p79+3AO4Nq/om7C4awmf4cxBlFjLdmqo8S8oesQAijdVA5A2r7jNE8eEg6H621n6aYK940621kXup76LHEGBtHMOZT/RicxXOcYeHIf0l8NU8qnhuscg07rT6v/U/v5Wv8DT+qt7J8IaX99lf6J4DrHQSfSlnP1u/7J/Y/f/b/Kf8jICP9POP6ooe4/9fWnROX+tFD7XqV/joGnqNyfCur+U8unhnr8+zn2Apmhf8r4Tw31+Ke+/tSo+EsLtf1Ry6eG2v4p6LxvFxi6gZ9+Wei9V15ehtVrV+3yuz2798YPP36f8t4PP/6A7l27e3dQFxUWYev2bd7nq9euRnWkuu4TMiCcH3L3xT169eiFhT8vSHnvx59SX9fEMORmdWPvXHfbKeUzrF67KqWd3vmczPjRjn8sNEPgnKFD3zbgRHdsU8unhnOGjv3akuqfUj41nDN06FNEa/+E8qnhnKFjX1r7973/6V2o9E8E9fhX/j8D8h/l/0F1w7bSfwb4H8L4Twm176WWT00m+H6lfxV7qVD6p4V6/Ps59gIZon+/+3/q3NPv+qf2/z5f+1H+h4602p9uALre9D9jN79X75/RYDNzsnNw5qln49Gn/oZv587GryuW4Y77/wLGOdguqtZe8bsrMHvubDw78RmsWrMKU6a+h3+/NRlXXDzGO+awgw/Hv9+ajF+WLMJPv/yEex6+C0Y9bWIAjJBec18cF59/Kb6a/SUmTZ6A1WtX4bU3X8VXsxu+s79NYRuEQiF8NesLbN+xDeXlZQ0e77ZzyfLF+HnxQtzz0J0p7WxT2AahYAhfzfoS23dsR3lFeYPna278WdOKECtm45Mnv6Urpe7K96lztmI2Pn6MsJxQzMa0f3xDJp8aK2bjk6fn0JVSj9n45Knv/FtKPWZj2uOz6Eqpu/7Hp1gxG588M4+ulHqy/n1aSv2Tp+fQlVKP+3/fllKP2x9ZKfW4//dtKfW4/ycrpe6OP7+WUo/3n6yUuhv/fUjG5P4+LKMIJOUeVKXU1dwLnzwxm66Uejz2+raUetz+yUqpu/r3ayl14vGfPPf2I5mgf8r4T4239kUoX8Xf2XSl1JPm3n4kOf8mKaWebP8+nAO49p8OtEnv7PlJ3D0AIQAIeU2EkO+767N17RM0ce32LzfdgXseuhPX3HglcrJzMObSq7Bp8yYEA8EGv7d//wPw5MNP4+nxT+L5Sc+iXdt2+MPV43D2aaO9Y/78x9tx+31/xkVjz0f7du1x+813YdHin+s8nxBA2ZaKWs0fdOBg3H/HQ/jnC0/i6fFP4vBhR+DaK6/DcxOfrbdtuq7jzlvuwbMT/omnxz+Bgwcfgsn/+r96j5ftvBVnnn26bOef7k5pZ+J8T8vzDToEkye+0aB+mhP/jU5iuM5xyDn7kZbzoJRPDdc5hv32QFr9n3eAr/V/yNkDaPU/mk4+NVznOOTc/ZX/IYLrHAefRVvOye/6J/c/fvf/1PmP8v+0+iccf9RQ95/6+lOicn9aMsL3+17/xL7f77GX2v79rn/C8e/n2Atkhv4p4z811OOf+vpTkxHx1+9rP9T+X9k/Xf8ZkF0YbvGb4nKyc/CPB5/Eglm/4OtPvsN5Z1+AVWtWomuXbt4xn3/4JS678PJa3z3hmBPx37c+xs+zF2PGh1/iyqS7xQGgQ7sOmPTMy5j/1U+Y9t50HH3E0Zg3cwHOPu0cAEDnTp2xdN4KDOi3n9f/vLw8LJ23AocefJh3nnPOOBdf/Pcb/DjrF4x/4gVc8bsxmPfFjw3269yzzsfM/32DxXOXY/IEuYn9t/sew3P/+FetY2U7X8Gyn5dj2pTPcfQRIzBvxnyvnfJ8v5Xnm7MMk1+Qm+x/++s/8Nzjqee745a7PXnNhT9/uksIYwzZRWGyG1ap5VPDGJDdJkzyizEAYJwhpyiLTD41jCFufwwguHEhIb/lZWcCjDPktJH2Jwh+OOp7/TMgp5DS/yfpn+bGIVKo7c/z/361fx7PPzgjKZrg5T9E8qlhDNL/Z4L/8SEp9kcRf5Piv99gnKncnxDP91Pl/n7XP/Xc1/e+nzj39Lv+ice/n2MvkBn6p/R/1FCPf+rrT40Xfwn17849/Eiy/VFUzUmxf9t/iw+Mxf0v0QBgYOA6R0svff6yZBFWrl6Bgw4YhPLyMjw74Z8AgGOPPq4FWyGh6L8HI5bfBNTGeAtjmzZmvvA9WRlbT75Pg6NtOpjx3Bw6+TEbM8bTyafGNh3MnPgDWSl123Qwc8IPvi2lLu1vLlkpddt0pP/xKbbpYOaLC8hKqafo34el1F3/Q1VO0vX/fi2lbsdk/kFVSt02bcyc8INvS6nbpoMZ4+eSlVL3xp9PS6nbpo2ZE38gK6XuxX8fYsfszMj9fVhGEUj4fqpS6mru5WDmv+aRlVJ3515+LaXu5t5UeYenf5+WUqce/8lzbz+SEfonjP/UeGtfVPJV/MXMf80jK6WePPf2I8n5N8WPA1Ls34dzANu0MfNf88jkCyFQvq2SRPaLkydi1ZqVMAwD+w84AP+e+CaKCotaNhcQkP2nsr1k/Wf4/osvZyjswD5gqzYCnEFEomCBAEQsBtjxzbokxykcARYw5EZSvx4wC0LQK2JgqzcCjoCwLLBQEKKiEtawAQj8vAZOdbVceLQdiPZtwLfvhFMdAdM0aCEDh/72AMz5YDkcG3CqqsA0DSwcgoiZgG2DBQJg+3aU39+2A6xXV/AtO4BwCE7bfPD1W+FUVIKHwxC2DRGNggc0IGBAdNsHWLIKwrTAwiGw/Fw4m7cCmgZh29AMjsMvGYzv3l0Kq7wK4MwL1E6fLtBWbQIYg1NSJnXVqyuwcStYMOl5CMEgxPYd8vO8XCAWk4aelQU4NlBRCaZp4G0KUXx0N+S/t8CbDDJDB8vOkucJhwHE5TgCrKgQ0DXAtADGwMorAMuGEA5YVhYQi0G0LwLfulPqPRyW3w0YAOdAKAiYFkRVtdRjKCh/nZSXC1ZaBhgGNNvCIRfsj+/e+Am2BbDsMJhhwOq5D8q7hRHaYSG0dDOcLdsBjYPF9SbPxaW+YzF5zYSAcKRjY5zXnXA4IuVHCJrBcehvD8B3b/4M23Tk54A8psaxYAy8Syc4OSFg2ZrEZpptgwWDEDETLGCAdWwvdZOXjUinXISXboXIDqK6Sz7MHA25qyrgBHXoS9fJtruJmcalrhgDz84CwiEgGouPCQNOJas1id5+zgFo+8ESIGbG7/wQDSdajHn2jVAQms4x7KSumPPhKlj5uWDF5dgxsiuKFhQDG7YAjkD5sf2RN3sNEI0BwSBgWUDYAMvKgqioRPmo/sj/ZjUqDumKnB83SXsRAjB0oDoCp7IKPDcXIhaT/XKE7HduDrSwgWGn9sSczzbCLquCqKiUuoxGwUMheQ7TkrI1DtG+COtPaoPszQLFAxiMMsCoANr+FEFgxRaps4pK6Ttyc+Q4YAyojsj3gkE5Pixp0yIak/ZvWYDjJHQXtwMRi0l/EonGbTsgr4HGIaoj0o8YurRL05K2wJn0W3G7QI/OYDvLgFgMTNchyivAwmE40Qj0kIFDzz8Q3/57ARxH/orRMUX8Drak68i4DNrJgVvXwQCwnGww24n3Jyrtp7AAoqxc6iNmJmyZMQBc6pQzaAENh51/IL59faG0f8blH2fguTlwyiukvnRd2qllgWWF4VRUevav5eXCqaxKtBMAMwxonINlZ0FUVgG6DmE7YKEaz3GJxaTfSHqOjDBNKYsn7F2YFphpyffdTbyAAdhO/Fpy6Xfi15UZhtxoth0ZSzRNxpUKU24CaRp4USFYhwIcemwnzPloLexVG+JdYGC6LscIY7C37/C+g/hnyddG2oMm5QohYxNnYIYOYZmybZqW+uMHIXWt9++OAy7thZ8fmAW7Q1uIoAH2czxZidsUDwWlnzMt7xy8XRuInSUQfbqCLVkl/WEslmorjIPlZINXVHqLr8zQwWxbxtEakxH3Pe9YTZN9iMv2YrAtN5F4Qb6MkbEYmGHIMZJ0TjdGieoIAMgYHDCw5fSeaPt9OdgvK6AZHMPOOxBz3l0CJ5AN0bEN2PYSaTO2Le023hepEy7jrGXJ17YN1qYQoqRM2mY4x7vu0DiYYXgLv97mbyAAmCag6+CRKhx67n749t8/wmEytrjPNXJMC5xxYL+eYOUR2G1zoVXFwMqqpJ0B8lj3z7Jk3NN1OWYAaddxX5Dy44usMJyCXGgbt8Lu2Qna5mIgPwd2XhBWlo7wqmKUHNwfhV+uBSoqUXzafij6bFXcT0n/KSxLXpcD+4CXVsEpzAEsByxmge0skdcjHjMRjzGisgosGJRjRdOgBQ0MO6Ez5nyxFdbmHWD5uUAoCLFtB2A7YFlh2X4nPs6EADiHME35eU42YBgwOxfCWF8MsWOn7HZWGOsv6I328yMIbC6D2T4XxqqtYAED1X3aIbxiB6Br0MwoDr5gP8yb8D3MTu2w/sQCFC61EdphwvhpFQCg8vgDkbtkJ1BWAbtzW/BfVsmxGQ7B2b4TzHbA27UBqiMQbYtQ3S0fgdIY9J2VQEWV9NV52RCaBm3jVnlN8nKBeKyF48i4UFkp7VnjgCPiepO5jKiulrboXndNAyvIg9hZApafB3AGZ9sOeZ0Zl/91x4EbDxwh/Z/Gsf7yAeg86RdozMGwC/bHd28ugm1K/+jEx4tn88KR8d99XziAYN4xTNdlnIr7SxHPm1lOtvRj8TxQi8bk9bMdIBiA2LodRpsCDDuzD+a8uwS2nSVjYCQKVpAPmGYiD0/CzVdENCp1Eh9vjLN4HiTbInMoLnOx+Fh18zMIAfTvAbZqI1hWGCISkXINA1p2ljxPXC4Lh4FIVOqOMS+XdDq3x5YjCpC/xkLWmnIIxsArI0BllYyFRXlgEROaIzf+RJWMQ+41YgEDnAscdv6BmP3qDxC6kfATWWEwxwE4h7MlApYdhqioTPgxN55mybaJyiqwtm3gbNnqxQNRUZHqg+J+U1gWECsFAOhZARx63v747o2fYUVMqTfB5H916aulzmWs8zZxNA0sFgMzdPB99wHKygFDth+xGBz3WNMCCxiyT65PzM0B8nPBYiacTVtkLoDkvDI1x3RjAs/J9uxfxPMHpusy90/2+1lZUgd6/Dzxa+6UlqXEHS3AcNiFB+Hb/1sIJ37Hhvs5z82V85aA7BMrKgDyc4A1G6VO3Im8pkmdxvMixjgQMOCUlqEWIj6mGQMsC1pAk7p/S4695OfYMU0Dc+3U/brjSFt2HJnHJv+YJJ53M01L2DhjYNlh8Lw8IGjImJIVAK+MwskKwMoPIfDrZmlXSWNMOE7i2XqAzCk85Sb0zDUNomcX2LkBGIvXwamqSowZjcvcITdH5s7u2EvSgeYAh1440Os/M+K+o193mUOWVgHVEdhbtyfsrm8P8JJy6ZssS85zojHwuC1A08AsS8Z+XYNoX4QdhxSi3WfrgYoqONXVXt80g+Ow8w/EnA9XwiwplzlNeYW8htlhOdesrALbpz2woxjIzgaqqyEiUemX3DEY90FM06SPCAalHyuvkL7ZND2/xziT59E4nE1b5JwtOyzHRCCAql5FYAII7IhAW7MJKMiHkxUEW70BTOOoPLIPcuatQ/HR3RAstsEEEC3QwBwgUsChmQJtv90OsWGzbEs8VifbKnQN0HVoQR3Djpex195aLHN1zuQcSNMQ268rtg0Koe3CKPQqE6wyCgHA2r8rAmu2Y8NZXRDaKdDmo18huhVJf1UdAQwDsc6FCKzaChYOwQkFwHaUgmnx3N6xAdOCFtAx7LQemPOflbAdACWlYO3bgpVXoOyo3qhuwxEol/OK8FYTeoUJfXOJ9PdCSLuKROX4j8cgYdtgnMPZvyf4r+uw/Yz+CO90YGYxCM4Q2mkha8E62dfKKi8fQTgElJR6uVPk6P0R/noJSk7dH1mbTQSXbpTjUdfkj7iFAGLx+NR5H2DNepmjMwYYOphhoOKwHsj+ahlYXh5Ym1wIxqCt2ihlaRyaoWHYyd0w592lsABvzYflZMtrYNveHMu9hu48gLVrA2zZFp+LGIBpyvlSp7bga7fINRJNk98TAiyYBVEd8fIuFjCgMWDY6H6Y8/6vsMqrwLKzwExT5mqV8bWggnywikqgMB8IGmDbioGiPDg5IUTbhqCXm9ArTWDxSjDhSF1yBtGhDVhZJVh1RK5pBQJA133A1myUtmjbcqy7c5N4vEHcx6FzBzBbgFVUATlZQGm51IcQQF4uyoZ0ADcFtKgDx2DgNhBeVw62aRt43FeIeA7l5jwyBjrxeZsNLRSQ9vf+ctimncjF3LmgOwcXjlzrqblo7Qg5dw8GvPUBWLa0EUfIHCdkgMXi7XbnBXE5mrAx7IzemPPxWthRK3WOnJTneXMQy/Z8PTiXba2Z19eINzV9tjfHjY+blOenNhFvblzHeg/r2QVsewmsXp2g/bQiMV9KotbaV13UXAOrC7P2ub12JM8n3fMw4cl343+y/ORcouactDlw81kWMCCS58qcyVwmGABzY7vry3XdW3dw1yNYICDz1LgfZ5BzXzeHS94AYQD03CwMO70Xvnv7Fxn+43knwiHPJpiuy1gYiXr5nd25LdaPzEFoJ1C4pBpGSUT6BdeeAHlsMCBf65r8vmtnXK5FaMLBsLP6YM7UlbBtJm08EgULBuSc3dDBGIdTVSXXkyIRxAb3RHDxRtid2kJbt0WeLxoD69BWros6iesoDA3MtOV/I3JtzMkOQmgcPGpBcA6+dDVYx/ZwsoIAB5hpe+OLmTaExsAsB8LQ5NrI8nWJtSZNrk3zvBygTSHMdjnQi6vAiqWvYqEgnMoqMM2QeZu7dmLoEI6AnhXCsDN6Yc6HK2GZlrfOx3Qd5cftB6Ex5C0pkXncjlKp02gUMHSI+Jo5r6oGdB3mPvmo3DeE/GlLZK4ajQF5OWCWDScrCL5+C+ySErAO7cDWrgeYAGfA8MsG49vXF8JxGFCQB5EXRlXXXFhhBm4Cub/sBHQOtmo9zEG9UN0+gLyPfpb2584HIde2Vo/bH1o10OWjnYAQ4Ft2yP7k58prXlwCYZrg2VkQ7YvAOMfW4UVoP2snNNOGYAwlg9ug8PttsAqzYYd12CEObglw00HgpzUQwoHTuzPssAErR0dVWw2OLte99AgAIVD4hVwbF9GozLlCwfi6WMKnu2Z46LkDMOe9ZbDDWRA5YZm3GRqcoIFYoRyXWsSBXh6TsRuA1acz9F/XA5D7Nd6YcnN7yHZ4eTSQuq7vai3J/4ncfMBxsOG3PbHPV2XgW4rlukFpucx93XUdQOaTgIwlABAOIda7A/TyGMyCIIziCPh2mcs4BdlgMUvu2QAQ3fYBi1jgW3dIe3TXVUNBed6YKXOPQAAoKpDrOPG8iudky7i+swzVgzojPPtXsFAIIhIBC4VQOrwbrCwGKyQrQAkN0GICgjOweP95vOmOAeiCYeR+Rfh8yU7YjgBzAMdgEAzgtoCjMTB3KiMEBGMAA7SogN4+C052AGbbLFgBA8yRbl1wQK8WsA0GJuS1cwwGoSXHQPl/DAy5WRrKqm1AAHq5Gc/nHXkMA6zcAJgjYAcYjEoHgjNwS+7TCJ0n/EXMTJyfMTg5Idn/qCPHonvjnxAYMGQo3nvrIzkHMDi46cDM04Dt1YAWSKw7ptDAfdWcJ/nWeF7AWcI+XJt0Y72QnWOcIbswC5VlMQgeXzMSiPdfeO31dMZ5Qj8svr9i2bJp8TV7CGn70HUII64fVzduO4MGYNqAbSGnKAuVxdUQYKltTZp/ymvB4nk/5Dhw8yk9/p6bV1m2bJ8RPwYOmM7BigrBNld6e4MimrTv1Qj897MVhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUPgKtTHewtimg1n//gm2RVNOyTYdzHrjFzL51NiWg1mTf6z/F6vNLd90MOu1hWTyqbEtB7PfXkpn/5bA7A9WwbYy/SkXzYNtZoD9v/GLj+1fYNa0DWT2Z1sC037d6V//78ZfZf8k2Jabf9DZ/9dfbPG1/X/zygI6+7cczJ6y3Nf6pxz/Mv77M/8kt32/5/7E/VdzX+rY62D2u7/6WP8Cs99fQTj3dTD77SU+1j/12oOD2R+u9vfaA7H/p4z/1Hj+nzL39/Pam7v2TL3272P7J/X/xGtP1NiOwPRFO2ATPcpHCIHSqD8fIwDIG6ordlRRPcUTEIJWfhNQG+MtjGZwjBgzBJpBo3otoGHE5QeRyadGMzhGXH0wNIPmOVuawTFiLN31p0YzOI6+eH86+zc4jv5tH2h6Zj/jornQDE3af4DQ/v3sf3SGEad3JbM/TWc4fUBb/+qfOv763f7d/ut0+j/muE6+1v/Iaw6hjb8X9PO1/kdcfhBt/L1qqC/1rwU0afuUuvd17q+R9l/FXvrYe/RFA3yt/6PPp4t91HNvaqj7rxkcR5/Ti2z8UUMd/6jjPzXU8c9be/Ox/5Frz2rtn4JM8P+Ua0/U6JzhlEHtoO/G890dISC8suC7B2MMBUEd/lz5l5XHc9tl0z3em7EWkC/kExScPfvxiS+fMU6JEEDlzmqyX00IR6CiONIqfrXRHAgBVO6olk6WSj7h9adGOAKVxRE6/TsClSVR/+pfCGn/TvyhKi0uH8r/lJl0/l8AZVHb3/p3/S9Bgphi/z7M0IVw8w86/19RQTf+qKH+1bAQbvz15wXwxr8jaj5au8Xk+zX/FE78F+tkdyz4V/dAfOxTzn292OvPCyAcgYqdhLE3xff7L/lJzD0J/Y+vY28GrD2Uxnys/wxY+ySM/9Qk5l508itLomRrT9Qk1p6J5Ku1f1r/7/f8HwLl1RZEfc/OboAd5VGUVkaRXbIT4fwCMKbJp3BzwLEFHCafMc4cB47NIERN/yKfah2NObAtede4LSzAYYBwvMd5O7Z8PrpjMTiOAyGY9FeOkMc6DuIPuU46tYCwTXkKRwDClp+7z992TCnD4RC2PJ9jObBF/HnlIunZ9DXaXLcik34g4H5XMADxzWCv7/FnjLvnEQyxaBS2sCAcLtvpthHxc9Z6zeJ9jf9bOInzi6RnjDtI6EfYSW2Lvy9sQNiIRaOwhC3bBjup38k/enD1Em+P2w+Hyz/3uejMPa+UL4SFyupylJVUoXR7Vd26ayRqY7yFcSwHc99bDBYKgrGW/+WQYzmY9/4y0P1shBbHcjD3nUXyBZH+5767uMXlZgqOLTBv6gpAp3E9ji0wb9paIBQgkU+Na//CdsC0ll+Zd/2P8Gk5P8cWmDtzE5hNkx07tsDMlcXo6Ff9u/FX08AIXECy/TMf3rngWALz3l8G1q4NjXxbYM7sbTD8bP9vLyLx/VK+wLyPVgOW8GUO6o5/x3LADRr5c9/5peUFZwCu7QMA2427FtIi/93FvrR7gL7/7thjQZ/m/rbAvA8IY68lMO+/K+X1Jxh/1Di2wLyPV8vYRyHfcjDvw+U+9j/xtQcq/2MLzPt0nY/1T7v2lRz//Yg794LjAJxi7TO+9ib8m/vPfWcRWE42mXxv7YFo/keJYznS/xPtTLtrTxDCl6WabQf4cmkxau1ZN+q7As9+/DNOPao7BnSsgg4uf1rDAG4CjgYwAXBLQGgMgqP2vrKQG+ks/r4WsSEYA/M2jxnskAYm5Pm0mIBgSKzVcgZmy81eYduJ8zMGEdIBxuSxppXwsUIAAQPMdiA4g9DkMXYZh1ZhgnEO4dS3MV6jD95+N/OOZ4zJH3okvZc4vo73tgGM88Rn7p347rmAlHODMQhb/hiAaZpsK2Nx3bsb5AJC0wCNgVmO1I3XNgCGJvtv28B2lmibu7md3EEG731WM0ZxBhHXKRMCAgzMcWT7OYdt2VixeBumv73nj6pWG+MtjB7QcMy1B2PGyz/BNmnkj7p+KD6fuABmdcvLp0YPcIz44+H4/Nk5sMyWD5Du9f/8+XmwYv573oVucIy8ZCBmTF4MAvOX8i/qjxnvrYZFIJ8aPaDhmOuH4bOnZ8MmMD89oOHY64bis2fn+NT+GY45sztmvLWCxP50g+O8A9tjVmCJb+1/5LWDMWPCAlBYnx7QcMzVQ/DZP78jkU+NbnCMvH4oZkxZS+b/jz21M75Yvh4xAvnU6AENI68bTpZ/6AbHyMv2x4xJP8Ly4bMG3fg3/clZoPhphh7QcMw1B+Pz5+bCrPaXB9IDGo694VB89sx3JM8ZlLofis/Hf+/P3Ceg4ZgbDiXrvzv2ZryyyJ++3+AYed0QzHh/HVnuOfLi/THj1V9g+c/8Zf8v6IcZLy6kyX0CHCOvHIgZL/3kz7Ufg2PkFQMx4+Wf6XKf3/WVcz+KyTcx1GtfyfHfn/GXY+TYofj8Xz+Q5N66wTHy0v6Y8e8lsHy4+KAHOEZefThmvPIzzEoK+RqOu16uvfnQ/UAPcBwzZiBmvLSQJv9J8n9+/Fm+rjGcPqQ9PliwFdZu3BhUUhnDxB+WIVhgoMDSoZmAFQYKfhWo6sCgxYC8lVFE2gcQzWNg8YvMAMAWMBjDiIPa4LNfdsKxBYrmbocI6GBVUe/HOjuHd4BRJVDRkaHtT1HYQQ2B0ihY1IaVH4SxvQqIxiC27ZCbxABYMIDokJ4wczVkbYyAr94Ep7QcPDcHiMVg798D+vYKiOwQIu3DCG2txpbDctDhvZVguTkQ5RVALCkjdO+WtlMre3p7yKEQ3AHMAgGISAQIBYGqiKto+X1dB0xT3sUOQAtqOOz8AzDnv2tgBYJARSVg2RCxGGDZYFlh2SfTBCwLLDcHCATgbN8JCAe8XVuIsnK50Q8mZdryeNGmAGbbbAS2lENs3io3toNBCNMEuu0Dtq0UrLwEh188GN++uQi24HAqKsFDQdlm2wEMHeAcoqoKLBiU8oWQ/TFtICcLdn4YzHLAq2IQGsBKKyGqI0BhAarLIqiyBWDu+ehSG+MtjOMIbPl1J/awBP4eyd+8vJhMPjWOA2xZtgMOUTmlxPX336IwEO//yhI4VL/acwS2rC73t/49+ye4a8rzP37VP7BlfSWp/1lXGvWx/0/yvwQ/202xfx/+bNgRtOPfcQQ2b6z2tf1vXradTv9CYMuqMrL4T03K+Ce4aSM1/vsLcttXuT9p/72xp3wPjXwhsGVlaVz/Prxj0BHYspou9jkOsGUF3dybGml/xGsPa32+9kDt/wnjPzUZ4f+9tTc/+n937ZlKfvL196n+V5ao/J8IIQQ27GEpewGg3DQRqTTB5X4uYjsdVIQ5tCgQ3RZBlW4hInhiY1zIu751AEu2BbC5tAqOBTgbyyFCBlhFRJb9ZhxbS/IQqHBQls0htkVghTQEd0bAIxbMmI3A5nIgEoOzqdi7M5qHQ6juWoWYqSFnSzW0dSWwi0uh5VsQ0Sjsdu1hbC6Hk2ehWhMIb6nEpnIN2roSsAIBUVKasjHu3UFu2ymP/XCrnLFwOLExHgzCqaoCC4cgKuWvHZmhQzgOmGHITe/4OXhAw6rvN2DrhlI4wTBQVi43xqNRCNMCz4lB2O5GuQWebwPBIOzNcmNciwVkWwOGrHYdjm/Qx0yImI6YYAhuLIezvhhgDDwchojFIIK54JtLIXbulPLXFEMwDXZZOXg4JDtnO2ABA+AcTkUFeDgMlu8kbYxbQJ4NK+qAmQ60ioi8+764HKKyCiyqS32FA2nxbK1uaba8vBzjxo1Dt27dEA6HMXz4cMydO5e6WY3GsRz8+NFyOESlPB3LwcJpK8nkU+NYDn787zJS/f/4v1/9q39bYOH0NXCoysnZAgu/2ACHqJQ1NZlg/772P7bAj7O3ktmfYwvMXlvqX/2r+EuKYwnZf0L7X/DDDh/r38GPUyn9v8DCGevI4j811OPfz/knve37V/cAff8TY8+nvscWWPjJKrrYawks/Hytv/U/cz3d3NdysHD6av/q38qAtYcvN/l77YHY/1PGf2q8uRdl7q/W3jIg//Gr/Tu0/p947Yka2wG+W1EKm6j7tgPMWk8nn5pE/CW0/1Yy/211G+NjxozBp59+ismTJ+Onn37C8ccfj1GjRmHDhg3UTWsUelDDqX8+AjrR80X1oIZTbj6UTD41ekDDqbcfBT1Ip/9T/3IkmXxq9ADHKX8YCj1A43r0AMcpVx8A3Wh1ri8t6EF6+z/l5kP9a/8Gx6m/601mf7rBcfHgjv72/5TxN+Bz+w9w2X8q+w9wnH52N//af1DDaXcQ+v8AxynXDfS1/inHvx7UcOpt/sw/yW3f77k/cf/V3JfjlJuHkcbeU24YTDb3o0YPcJxyzUG0ueeNB/vb/v8wlFD/HKeMGeDvtQdi/08Z/6nx5p6U9n/1AT72//G1N+q1fz/bP6X/d/d+fKp/Q2O44PB9YGg01QoMjeGSgzqSyafGm/tT7b20orWHVlVKvbq6Gu+++y4++OADHHXUUQCAe++9Fx9++CGef/55PPDAA8Qt3DWO5WDFdxvg2A5IShnbAivnborL9x+O7WDFt3R3LDmWgxXfrm8Vv5pxyWYmRuZsRO9AKULfLwIvjCHl4RcNqTJu4oxx+ZAMBhhvfImBug0R5WAhB/YCA5UxA0vCOZhZ2alZ++LYAisWbPNtORvHEtL+baJS6pYj/U8rsv904jgCK36hLSW9aEulv/2/G3+1lk/QHDvJ/rXUBLVToBpHFW1Ej6plMJgFUcIBIcAMAeh12ItbkpQBCLmfx18LIMUxcw4R4WB5FkSpDhZwIKIM2MEgdjJwOLB/1qCFTdhBYOnKbZjP87AGOWnufzz/oLJ/W2D5slL/2r/lYPlsuvzDsQVWzN/qa/3L/JvoUQ6tMP9MF44tpO2T3THrX90Dbu6Z/v53NipxRPZqdDfKoUUBZ54GLWABBQ5EvpBhMB4WjTd/wqCQDdHRAdM0iEJHZsGMAwEBERJgjgbk2QDngCYgskXqfAdImtcw7+F/IuSAcQ4E3YPi3+FyoIuONiAEqhDActYeM9EXVWnVRMNkQuxd8cMWsrkHNd7ckyj2ObaDFd9v9m/stQVp/x1bYMVCfz7GBKCPf9Txnxpv7km49iz9n1/9f3ztmdD/JNbe/PfjBOr45+39tKL8v6ezDYcFlqHL6u+gFUbBGIMICzDGYC/5DmCAcIcyk2XLU0halgKA4AKGIe7eS9wNuN6gXq+Q9IGjQZquI2UJDmgxwFkhj9NiAs5OBsETspPPGfiR4+D4M6g1ZkHEmFxfi2PP0QEBOEsBRBlWhtvjO9Ed61DQBK1lJl78tQVgEMi3k+Z/PLP9T6vaGLcsC7ZtIxQKpbwfDofx9ddfE7WqaTi2wOKZq8FCQTmpbmn5loPFX671JvN+w7EFFn++Sr5gLT843evfWshiJq5t8wv2NeJLOBHs+bMxY0jkZRxAVD4bo2f2DvQySvGE028PBdSPYwss+W4LEAo0m4xMxrEdLP58FYQtFwZbXr7A4i/XQvh2ciqw+IcdYISlpH/YWI6Oftb/zNVgmgZG8YzfJPtPlt/ZqMR1+6xElmYnJgLu/IkhPesIAtJ3u+siAoCdJCMK6Y850GH7MhyWxTHRORDLYwVpEC5x+8/atUnbOZsq/5efS2D42f4/X0ni+135S2ZvAmzhyxzUtX/HcsCJJqeLZ6xuecEZgGM5WPz5SgCJ57W1qHw39/eh3QPx3DPN/e+ml+Gagl8Q4nbizQhkPKvLxZlRL8YBdRxT8/2muMldTSe91ZYYuqECA8R2/MM+BVU81NC30ob0PetIY++SWRvl9ScYf9Q4tsCSb+Oxj0r+1+t97H8Elnyzgaz/ji2wZM5WX+ufcu0rOf77ETf3hCNI/K+39iZ8nPt/vgosJ5tGfnztv+bag1/w/P8ePON6T+UvnrkaEKJV/Cyhv74DV5g/QzccwETtXDhWTdGsuokl/duu9yg5N3BhNf6b/HlU/mef8lIcjJWYEPgNlqDrnraSFHfuz9sU0si3HLn2oHGKrbcmkeHNSyU3NxeHH3447r//fmzcuBG2beO1117D7NmzsWnTJurmNQo9qOHse48mLedx1p10pWSp0QMazr7/GNJyTmffN6JVlJMAgIPD2xOb4i1Av0AJ9q9ovsci6AGOM28c6OtyZtT2f9ad/i0npBscZ1/Zl7SU+piDO/nb/1PG30Dd9n9c3ka5KZ5BBJiDE0Or03pOPcBl/wnLuZ5zfg//2n9Qw+gHjyUtpX7mTUN8rX/K+KcHNZz915G+jL/ktt/Kcv900xz9Pz57XeqmeCuiEyvDoRUrWkyeHuA46w7a2Hvmnw72cSldjjPHDSbNPc/882H+jb0BjjP/TFtK+swbDvT32sN9dPGPOv5T4809Ke3/xoE+9v/xtTfqtX8f2z+p/3f3flqJ/k8KrYKO1nN3e3MRhI0TzUXUzdhjvLk/YSn11rL20Ooi1OTJkyGEwL777otgMIinn34aF1xwAXg9t+ZHo1GUlZWl/DmO1cKtTiAfQL+ctJzHj9NW+ricloMf/7uMrpyT5eDH//3aasqp9A6WtrjMfpXN9yMXxxJYOHODf8tpWRlg/9NWthr7TzeO7eDH2VvpyrnaArPW+riUtE0cf+267b9PsIykPbuim16W1smRY4l4/kFVzlhgwfc7/Gv/loMFHy4l9P8CCz9f51//Txz/qOM/JfS237py/3ST7v4zCPQOtPz8JJ30rd7cYrIyIfYu/Gwt2WPMqJFzT8pS0g4WTl/lY/8jsPDT1bS5z5cb/b32QBj/qOM/Nd7ck2ztOb725lf/7649U6/9+9j+F366mlb//1veKvQfYhb21cqpm5ExdBfboYnW+QNcl8Tcn+5RYq1l7aHVbYz36tULX3zxBSoqKrBu3TrMmTMHpmmiZ8+edR7/8MMPIz8/P+Vv5Wa6suuOLbBiDt3GXOIZ435NTkTiOQtU8r9rPRuzIdbywSBom812bscRWPnjdv8+5ysD7N/X/scBVvxSQvqM8V+2VvpX/xkaf4MEfrYxaExAZ2ncGHeIn3PqCCz/tczf9k/p/x2BlQu2+Tr+Usa/1pZ/phNy2/ex7oH0959BVjVpzQRFy/1I33EEVs6jjb0r52/1r+8njn2OLbDy+y3+1v8PdP13HIGVfn7GOHH8o47/1JDnnmrtjTz/9PXamx33/5T+h3DtqSkEmOPHp93Uiw4BTbTuuYYXfwnzz9Yy/211G+Mu2dnZ2GeffVBcXIxp06bhjDPOqPO42267DaWlpSl/PTse2cKtTaAHNZz/yCiycgJGSMdvH/R3Ob/zHzsBelDf9cHNJf/R41qN/ve22KgHOM778xD/lnMK6jj/sRNghOjs39f+J8Bx/nUD6MrZBDiuP6yzf/VPHH/rs/+9zc/Whx6I95/Q/i+8pJev7f+Cx08ktf/zbh/ma/3/9sERpPH3/L+3nvwznRghHRc8fiKt7ltR7p9u9KCe1v4zZP4CSyahBzT89oGjCWOvhvPuPMy/9h/gOO8vh9DG3nt9XEo3qOG8ewgfYxLQcN7Ng3y89kAb/6jjPzXUay96UPP52pu79qzW/ikg9//Ea09NgfllQcpHUM/9jZDeatYeWl2GMG3aNAgh0K9fPyxfvhy33HIL+vfvj8svv7zO44PBIILBYMp7nNN12zYdfPPaQtimA4rlcCtmY9bri+Ly/YdtOvhm8gLYpg0K/ade/8xnb1t8si0Hs95fCbsVlPNoDmzTxjeTF8CK2QBr+QmKbTr+9j+Wg2+mrSezP9ty8PGyHf7Vf7L/1Vs+QUuxf90df8I3v861rXj/Ce3/6y82+9r+v35lAVn/bdPBrPeW+1r/s15fBCtmgxH8OFPmv60n/0wnVszG16/Ecx8CWlvun25s005r/30SMtOGbTmY9cYvpLF31rvLyNY+qJFzT7rYZ5sOZr211Mf+x8Gst5fQ6d9yMOvD1T5ee6CNf9Txn5rUtReatc/E2pv/NscTa8909u9df4K1P2pS/H+ARr7r/zI9++F72bq/osbcn2AL1IrZCflaZvufVrcxXlpaittuuw3r169HUVERRo8ejQcffBCGYVA3rVEIR2DdT1vBQkEwgp/lCEdg3c/bfPuTIOEIrPtxi3xBpf+FW1pc7u7SkIqe3bEfNpthCLuBiQZjYJoGFgoCwXg2Eo3hgOBOnBdanN7GNgLhAOuXlgAhgswoA3DtXzgCjOCHW67/ET4tpyUcYN2KclBVABUOsGJnNTr6Vv/x+KtpJJOTZPt35TfUjgh0PBQbAVjxkqtCJP5rWQDngKYBZvzxE4zJ17adOBYAwiE4+Tngm7fD7tYR2tYSiLxsWLlB2GENd6z9APkskube1sbtP2vXptll1S0fWLumEoaf7f/HzWAaza92hSOwfslOwBG+zEHrGv8tLr8V5Z/pxLV9AGAEv0TydO9DuwfS3/+GfrRbIQw8WnwwnEhExsG4TKbrYMEAnMoq8DaFEBWVYIwDWSHAdiAqq8A6tAOKS4CsLCASgYhEIUxT+izO5H81LnOIYAAIBAAh5Llyc2RcFgKwHYAzdM42cZUxNy193hPoY6/A+sU75bXwyy8BkxAOsH5JsbQfEvkC63/Z7mv/s/6XHWT9F47A+mUlvtY/Ze6RHP/9iLf268YxAvnrl5YAAn78XZS39sZysunku3OPzL9pM+14/l/Qxd91P20FBM3cryk01L7NVhjPRYeh7JDOsMIMdpABAhAaoMUEBGdg8RyHuctWOgB3zdM9uQCEziAYwG0BR2PeuigTAoIxgAE8JuAY8kvV7QArC9CrAG4CdhgoXOqgsiOHFgPyf42gukMA0QLuyWYCYLYABBDLY9CrAeYAbWdvhQjqYJVRmZwxjpuyv0GBWZ1OVWYMbvzlbQpJ5cu5E0kTGk1mb9vXwXnnnYcVK1YgGo1i06ZNeOaZZ5Cfn0/drEZjhHRc8s+TYIToyhlc/PgoMvnUGEENlzx3Klk5pcT1bx2/SWlw8ckxGv8nAqhAENFgFs58+DiYev0b082ZNBhBDb+7ZxgMn5ZzMkI6uf1L/9M67D/dGAGOS246gMz+jADHTUd29a//D2mk/rcu+2/I39lgqEAw9U8EUv/qeq+uY1hI/puHEq95COVaGC31G2YjqMn+B4nsP6jh8rF9YbSCck7NgRHScen40+jsP6jhd3893Nf6p4x/RkjHJc+e7Mv4S277rSz3Tzfp7n9D+0s2uIx1SXOQaCCMs/5xMqKBsHwvOW4m/5sFa7+3i3mNd0wdsbiqwduTWm6R1ghquPgfx5LG3t89eKR/fX9Qw+/uo4t9RlDD7x4Z4W/9/+1oWv3fMZRs/FFDHf+o4z813tyLMve/Z5h//U/IXXumXvv3qf2HiP1/K8r/G1r3t+K5dbkeRrkRRlmg/r9yI3FMNJyFy0/tj0g4y/usoe8mn8N7HQqjNCz/WxYMozQURrku/10WCKNCq/+80XAWrhrRz5PvrYElra05DUwqMv3HDLvCm/tT2n8rWXvwZ4ZGiBWzMe2p72DF6MqpfPLs92TyqbFMB9OemE1WTsmK2Zj29HetppxTQ8Fgd5Z0LNPBpy8sgGPT/GrPMm18+vJiWD4tZ2bFbHL7l/6nddh/urEsB9PeWkVmf5bl4K2ftvjX/8ecePzNHPtvaBKS+b8tbhqW6cj+U5WTM218NHUdmXxqrJiNjx+fRWf/poNPX1zka/1Txj8rZmPak9/6Mv6S234ry/3TTbr733DcrFs+ZeyhxjIdfPIcZex18OnEn3ysfxufvkQX+yzTwaf/WuBj/cu1B1L9v7bUv/onjn/U8Z8ab+5Fmfu/vBiW6VP9x9y1Z+q1f//qn9T/e3s/ma//hn50urtrUqYt8O7czTCJKuaYtsCbi7aQyafGm/tT2n8rWXtQG+MtjHAEtizfSVZKWDgCW1YU+7iUscCWX3fQ6v9XuuvfVBpcfBJND5DCEdiyshSCrJwNsGVNOYQ/56aZYf++9j/AlvWVZPYnHGB9adTH+s+8+Nvwj4/2ro3xRP+p5AObN1X72/6XEfv/1WX+1j9h/Gtt+Wc6yQjb96nugfT3v6lxk3rsUSP7X0IYewW2rCr1sf5BGvvk3LvEx/qPrz1Q6n9Nhc/XHojnXoTxnxrq+CftX629ZdLag5/ICP9PuPbUFBp6xvjuLt0LAWwojlJVsocQwLoyOvnUZET8bSXzX7Ux3sIYYR1jJhKW8wvruPL5E1tFOYPmwAjpGPPSWTDCdPofM/F0MvlNpSEHsTv5rRHUcMWTx0Iz6Mr5XfGwj0u5hunt/8rnT2w19p9ujCDHmNsGEpaz5LhtRHd/+3/K+Buqbf+M+eeOcSOkyf4TlrO8+vr+vrb/sa+cTef/QxquePQ3/n2UA3H8M8I6xrx4hi/jrxEmtv1Wlvunm3T3v6HFu7oeDZKY+/rU9wQ1XPncCYSlFDVc8Y8Rvtb/FY8cSVpK94qnfPwYvZBceyDTf1DDFff7uJQ0cfyjjv/UeHMvsrmvz9fe3LVnsrV/w99rbyGd1v+7ez+tQP/NcbNGQGe45eQeCOg0a1oBneGOI7uTyafGm/tT2n8rWXtQG+MtjBWx8O49M2FFLRr5URvv3f81mXxqrKiFd+/6DFaEqJxQxMK798yAFWkd+k93SRUrZmPKI9/CbqCcR0N3qe8pVszGlKd+bBXlPJoDK2JL+4/S2f9793/dauw/3VgxB+9OXEpYTsvBxLkb/O3/SeNvbftvyIu2lJU05HHT6Y+9/IOwnOLbb6zyt/3fMZ3M/1pRG1Me/54s/lDjxT/C+Pvu3a0n/0wnVtSWtk+p+1aU+6cbK2Kntf9NfcwT1dhrMLa24M0TVszGew98Qxd7ozamPDbXv74/ZmPKEz/Q+Z+ohSl/+9a/+o/KtQcy/cdsTHnmJx+vPdDGP+r4Tw312q8V9fnam7v2nEFrD37Cilq0/t/d+2kF+m/qY4oag2kJvPTVepgWUSl1S2DCDxt2Ib+hZ4xn/p3ODeHN/cnyT7vVrD2ojfEWRgigeEM5XTkJR6B4Y4V/y0kIoHh9GV0pb+Lr31TSHSCFAIo3VZKFGCGA4s1VrUb/6UYIIe2fqpyKgPI/2yKk5YS2VZr+1j9l/K3D/n1VSp14/AsB7Nzh43JaAti5vozW/n0df+P2Txl/Ca8/JcIR0vYpdd+Kcv90I4RIa/8b/NFuHY95oo491FD335v7+Vn/hLFP6t/n9k9of0IAxVuq/a1/4rVPyvhPTUb4f7/n/pRzL7X2T+//W0n+3xxrUgLA9nKTbu0fwNYqOvnUZIT/aSVrD2pjvIUxwjqu+/fZpKXUr32ZrpQsNUZIx3Vvnkdazum6/xvdKspJAOkPkEZQwzX/OgE6YSn1a574jX/LOYXp7f/al1tHOaHmwAhyXHfvENJS6veN6ulv/08Zf0O17b/B5zntZRvjRkiT/Scspf77m/b3tf1f//ZvSUupX/PPkf4t50oc/4ywjuveOMeX8dcIE9t+K8v90026+9/wj3brLqUu574+9T1BDde+dCppKfVrnvNxKe+ghmueoislb4R0XPOCjx8lEJJrD5Sl1K/5+3B/rz0Qxj/q+E+NN/ciLKXu67U3d+2ZsJS6r9feQjqt/3f3flqB/huuFLt7BHSGO8/oRVpK/f4RPX1dSv26N86htf9WsvagNsZbGDNi4ZXffwSTqJyKGbHw6h8/JZNPjRm18Mq1H8IkKudgRiy8csP/yOQ3lQYXn3YjQpoxG5P/PBNWA6XUmxMzZmPyvd/B9Gk5JzNCb/+v/vHTVmP/6caMOXjl8Z9gEpVSN2MO/vHVGn/7f8r4G61t/w1OQvayHN6M2rL/RP7XjNl48YWlvrb/l6/+D53/j9qYfNcsmD4tZ0kd/8yIhVeu+68v468ZIbb9Vpb7p5t097+ppdQTc1+f+p6YjVdvmk4Xe6M2Jt/+la/1P/nu2WT9N6MWJt86w7/6j8q1BzL9x2xMfmCev9ceCOMfdfynxpt7kc19fb725q49k639m/5ee4tatP7f3ftpBfpvaN1/d1cuY5bAU9NWI0ZUSj1mCfx99hoy+dR4c39K+28law9qY7ylEUCs2tz9n92kRb5FJ5+ajNA/ofwm0tTFp10igFjEotvxEUAsYrca/acdavtT/gexKKH9CSBqOf7Wf4bZf1PvfGtp0toC6vEvgFhM2T+p/iM+9//U9t+K8s+0Qt13avnUpLn/TY6bRGOvYXEt2JhM8D1+9/2U/aeWTw11/9Xag4q/lGSE/1f2n0lrD74iE/x/K/E/Da7778HafdSiuSGosfJbwaXZfajtj1p+E2CC6mHLhJyQfQl4TjaEaUFUVYGFw4DjQFgWWCAAEYsBtg1oGoRpgYeCELYN3r4tEDMBxwF0HaK0TH6+TweIHTvB8nJh9uwA7btFYFlZELEYWCgIOAJ2aRl4OAQjpGHspDMwYcx/YMUEhG0DQoAFAgAAp7IKPBQEenRGtEMOglsr4YQNbD04BwXLTQSKI6jeJwtZH80HNA2wbdm2/DzAEWBtiwDGIDZvBcvPA0JBoKoayMuB2LwNuuZg7MTTMXHsVERLKsE4AwsGZRvy84BYDKI6AicShbBtaDnZUi+9usLOC0NfvgEIh+Bs2gJh22CDBoBv3A4EDDhFuYADOAuXAAB4IAAWMOBUR8AMHU4kCm7oCXm5OUDMTOggO0u2vaIS1kE9UdkpCDvAYAcZ2n5fAr5hG2A7AGdSdjAorwdnQI3nBgnhgDHu/XvnKf0RKraRWxLFVTcfiEk3fArTdCBiZvx6O4DGwQIBMEOHiJnyRIzFr48B6HpCXptCoLgUIhKVhwUMebwj5OeAPN62gXBI/jsagxHkuPKxozHp5pkwI7b8HAAsG8JK+iWNbUvZnIMVFsApygGvjAKWDZgmwBjsdgVgi5aDZ2XJ/mqa1KUj5L8tC2AMLCsMGDp2HNUZBYsrEGsTQmlPA4VLohAag1EWRXXHMOwgh1Fho6KTgaxtFiJFGh6d8yY6WmV1jqN7tg9DmRNI9LcuHAHYtrTvUBB6p0KMuX0wPntpOq5dMr3Or3xb0RYvDT0d2Yu2QmzdDlaQ7+lTZAfBthXL62pZYMEgnPZFiOyTjWiRjuBOC1mLN0OUlIG1KYQoKYXdtyu2DclGu/mVCDDgqlsOxL+e+gVVAY7gjgis3ACExlDaI4i2c0tgFYZQ1SGArE1RBFZsgSgrl/KiUYBxQOPgXfcFTAtVfdqiup2OYKmN7NXlYCXlQDQmfUdlFaBJG2S6DhGNSVurB6ZpcOL2JAU6YLq0KxYwpK3aNpimyTFgWoBwZJvixzDOgR6dIZatlr6HMemHOIddUYFAThBXTT4XL1z8NsyIA8YZHFPaHTd0OLGYPL/XBDmuGGfSN5oWtHZtvM/tbTvADB08NweiOgIWDsmxpGvS5hmDKCn1+m3kBKX/u/IDmBEbIhpN+INAALBt2JVV4OGQbIcQYMGgvNbZWbC3bEXJRcNQ+Mb3cGIx8EAAYBzM0MFyc+B0LAKWrJL6jpmJcRkPc051NXhWVqrihZDjJBxCrH9nBH7dBFEdgaiOgOdke/btji2nQvpN6Lr8rhP3D4YOdGgH59dV8lpomuf7AIDn5cLQGa585jhM+sOniO4oBywL0HXwdm0gssJgZRVwtu8Ez8uBU1Ep2xXXg3AcONURz1ZYcrsA8HAYTnV13Ji4tBUuvy8cAQgHoX3b4cqnRuGFS96FDR0sPw/2ps1xGzUgLNOLRcm2ygIBef177gu2coOMO6bl+UwRlXbLiwplnwAZJw1D+itHyLGQfHu043j+1f3c84GuPxcCwjTBOJd+IBgAbAeirAzMMOQ53GM5S/hzQNqVZck42XUfwHKgtQnjqpsPxPjnlsCM2DB+WQvoGoTbRiHkOXn8N4MaBwuH4bQtQKxdFkIL1wA52UBlFewdxag8YyjKO3MESwX0iEBVW46s7Q6iuRzgALMBvVrADgB6FGi7ohxj/zIIk+78DjHGwbbuRLZZifvbzUFdbDODeGjb4NofuHpsYvrGCvKBaBTIzobZuRDGuh2AZePO8Jco4pE6v3NnyRGoQkDaeyAAlFfI8e3EJxmGAbtTWzghDVaWjmiRDmYDjsHAYwKVHTm0KBAqdtB2/gYZ/275EqYpIPJzYBaEsHP/MKKFQP4KB4IDwWIbWT9vlNe/skr6v2gMLCsMu0s7sKiNWPssBHZGwLeVQJSWy7FSVCDVsnU7nP16wAnqMFZuQcXBXaBFHOT9uglX/mMEJt36FcySSjilZdI+HIHi3w5G/rIq6Cs2ILZ/V8ARCGwqA0pKpb5zsoHyCs/PiZjpxWk51rjnn90xuesLwhLXkLGEvdp2iu+F65NtW+arkLmi+1lyLGCGLv2fbcsxAkBEomABA0ZQw5gJp2LSX2bJ/CcYgL1kuedLvGbF+5D8PEb3GK1jB9ibt0ifoOsQlgXeoT0QiyXGjTuOXBwHTkkZgm3zPf9n2hwQAk5JqfQbWWGgOiL7Xh2BU1EJYZnQ8vNlfh6NQjhC5uOOA9iOtAs7HkfifguIj/192oPFTOl/TAuiIA+srAKlw7sif+4mwLHhtCvA2pPykLdaoOjz1XC27/DinBs7hG0nfGv8+jgD+4IJgWhREKHNlYh0zEbW8p1AdQTLr+2GosUCRbM2QewsgVNeLtvGGAJZAYyZcKr0v0JeU+E44Pt0gLNuA3jXzrCWr4RWUAARjcKJRKHl53k5rnddolHwnGw4ZRXedXdiMehti4C83LitOPKaCAEr7uODBTkY+9KZeOGSdxGriCaue9xuXDvydBC3JXAOu6xczk0c4dm3tm9HoDoCe2eJjHO2DZaTLfP6mMyDeG6O9OuOkDKq6/Yz3hgApB1pXPaBMdl/05I5TTgEVFZ78wbvuLidud9noSDAubSNUAiGLnDlU6Ok7cWEzLmFAxGJghfkyzwvLl/07gIrNwhuyh/RCEPaqpVtQDCAOQKCM5i5GnJXVoAtX5fazkBAjr+AER/XNvQgx5hnT8CkG6fDrLYScUuT5xaRqOx3vA0sGJCxLxwGcrLgZAdh5Qa9fIXHbNghHdyU1yJWGEB4YxX4ui3efAa2A6tPZ2iVUYjlayD27w2+fJ2cawXj46W8IiHbcbzze68NXcZc05TjNBaTcT03G8LQIDiHCOnQtpUBlVUQVdWeD/DyVcagG8DYSWdI31tlwikuATMMOWetqATCYXnuvBw5XrNDsPKCsLLlOIy00VE4czWQnYWl17dD77lVeGru/9VpRhvNMB7dfEA895E24c69J171X8Qqowk/lWR7LEn/bs7l5Sd1wVnChzPm+SCWFQZCQZQf1AED5v6Em7Lrju+/mgV4bvsAoEdnsJ1l8jyhIEQ4AFYRgSgplecyTTmuI9GEPNMEDANO+wJsOqoAegTQqwSiBQwQQKBctpnbgBYRyI4J3HB5Hzn3rLaA/Fw4YQM7D8pHpJCh8ztr4GzbIdsfCsr5uRAQFZVgug6nvBzQtERe5dpIPAay/DyI8gppd53aI7JvHgLFEVR2yQZzBAI6x40X9sKkm2Ykcv+CfKC8Qua6ti3nMsKRMjQN9oBu4BETvLgSFQd1QKRIgxYVKJy/A6iokm3LzpL5CABn6/YU/TJNA7p0ggjoKN0vD1pUIFBuI7ipHGxHqZe3AgAzDDjFJQDj0rd26whm2tg5uBBaVIDZQP7cDRCl5Z4fZgHD820QAiwUhCgqANtZAlFUgI3Ht0XORgda1EGIc9xwaW9MuuFTxCqjsm16fJ4IePMcz4+4/j4Wk/OquB0K25b5R0WllJe0ViFiMRmX49+DoYPF5wJGSMeY50/ExOunwYIOEYlI35IVBiqr5Lw1PxcIGBAaAysuh8jPBTMt6QeiMpYgKwxn3UYIywTv20uKjsZgtc+HXlwJa+lyaDk5YAX5cHYWx3N4K2U+AsbBC/IgorHE+kBRASr7t0FoawQQAnaWgcC6YmkfpeXeXNq7XvH1JVFVDZadJXP4uJyUnCr+byOsJ+ae1bt511JD5Z2AhO+s+W9X/sTTvblvc8A49/xdyr81TdpCbo5cD0xep3JxBISoY+PA9X3xNVGvX8m+07al3VRHUvIwKVzK8ezv2o/lXWOcpazRMcblOI6PC8RzV3e9C7aTmDMylvh3TVk1jxFyDcrIDuDKx0di0rjPZPwHUnNUFzeHreuzZIRI5B21PorrvUb/hHDAsrKkLZuW9KNJdiJipvT3QgABQ673AVIX7lqP62ML84HKajjtC2Szq2LyeMsGhAORE0Z0nzwwIRBcuhkGt738J7q9zFt7ZMEgnAHdoW0tg9i+Q/pel/g8zymVz2Z3155q6p1xLq9/JCr/rcXn1El49j/mPzCrLc8m3TVCuOtaFZVybIdD0p7i7RHt2wCbtkp/EQqmnDt5DcCz7SR7TlnTsm2Z09Zlx/HveNcqHJY6MC3ZVk2DiOe6yedxKqvkWpEbM7duh1NdDRYMynEYiyFQlCft/5qPECuJ+27TlHO0uO/m++4Dq00OqvcJgdkCgTILjsER2BmJ25tAdZccZK0uBSupgCivAMsKw+lYBDs7AK0yBl4VQ0W/IoT+MwdaQQHsUrl+a4Q0XP1/v/XW/rTCfG+NTFRHAE2DdWAPCM6gf7sIvLAATkmZ57cd0wLjDMIR8ruV1Z4teHNeywJv306u5ewskefPDsvzu3ltzy5yDdcwvPjA9KTyyvHx5677eOts7py6xjXz5tzx8cpzsmH23RfG2u1AMAiha2DlldD2LcTYvwzChEcXoipkQOgMzBHQK0ywiAVeXiWvb3xvw52vgLGEnbo+s65xn7xulPyaMTn3Dmm48qlRmHjVf2FWS58molFgQG/wrTtl/3KypU9wx4ShA1yTY1/X5WvLhti+M3WdId4ulpsDu10BSvvnwMxi6PDuUohO7bBpZBFy1tvIXl8FrSwCxExYHQtQ2iuM3HVRRIoMhLdGUdI3jKxtNvovW4k/ipm1+whgWTQPz+/cP5F3JMcMVv+9tkZYx9iXzsSEy9/f/fgrHOmH6ttziK/loI44ZoR1jH35bEy47D2YEQc8LwcoyIcIGWDl1TA7F+Kvm95CoVb3Gv3tzkmIWByiTI4nputAKAirUxvoW0vlHMbN0az42mhQzletDvnQN5V4vtspLYd9SH/oP61C8an7IVBuI7RN5vZaeQTYsEX6nOSxoGnSXnTdGxcsYMj1aMOAqK6W77nrokn7g0zToHNb9v/y92FGbblvWVIKUVGZGNO2DRGJQkSj0Nq2AXQd9pZtUrWdOsh1NiTtdwUCcn0uJ9tbV3S2bAUcAV5U4OVmVYO7IHf2Ypl/XfYeLC0Iu7gUWk627J9leb7UrqyClp0l5wbhEFAdwdYTuqH9jI3S97oxSNflWMnPgwgHpB+pjq9BlZbBKa8ALyyQ+W0sBlFWgY93TmiUmak7xlsYs9raM8eQBvlyYpr55QyaAzPmyE1xsnJOdmJTvBXAGth3qW+tqCHMqNS/tTtfTgNmzJGbYkSlrKkxqy2ZGBP6n4nXfEQmnxozYsmFeaLxb0Zsf+s/5uCFv/8k7xomkv/3/6xMKWfU8POc6O8YTydmxMakO74ltf9JN/i4nG7EwguXvEtaTkvmX/7Vv7swSCKfeP5BiRmxiWOvFd8U8KntV1tp9b1NfcyTWW1h4tipvp37xkwHE++bR2Z/MdPBM68s9639x0wHz7z0K2kp6YnXtI5Srs2BWZ0BsZdQPjWJ+Ee49jbuM9/6H/L8R9m/nHtR2X+1uWc/CmrlmDEHEx/8ge4xhhGb1P81heaoYkg99zSrLbkp7lf7J+4/9fVvCmpjvKVhQCBspLk+aRPlh3Q6+RlAIEzY/1amf57uAMni+ickEPCx28sE/0M5/qihHv9+1z+AQFDb9UHNSNDgKfpveBKyl8GAQEhT9k+F8v+0ZIT+CeVTomIvLWnuP2ugWkqdcxO/6x/x3Iew/4EauY/fCAQI++93+6eOfdTyqaG2P+r4Tw11/5X9K/9DDOnaD7X/awJN/dFpI0+q7J8S6v5Ty28CPt4hosEI6bj0uVNghGg2B42Qjosf+g2MIO3mJBVGgOPiv48k678R1HDxg0fCIN6caSzp3rRx9a83VH69GTECHFeM6QvDp5vjRkjHZS+cSep/LnniODL51BhBHRc/OpJs/BtBzd/6D3Bc9of9yH4cYwQ4/nBSdxiBxPVvyBM6rSGLbAJGUMPFdx1Cav8X/31kiv79hBHUcdm/TifrvxHU4/mXf/V/6T9PIo2/lPMPSoygRhx7dVzy+Cj/2n5IT6vvbfhHu3XLv+TpE3079w0YHJf8ZTCZ/QUMjqsu7Olb+w8YHFdd1Itw7SE+9/Kp/RuhDIi9hPKpScQ/wrW3vx3tW/9Dnv8o+5dzLyr7Dxm49JmT/av/AMclfxpItvZrBDVS/9cUGlp12t19ceq5pxHScenzp/nX/on7T339m0Lmt3Avw6y28Nz578jnD2gtn6CY1RbGXzcdohWU82gOzJiD8WM+ki+0lg+QZsTG+Os/a3G5u0u6S6qYUan/HofX73oakrmnmDEHTz/5i3x2pA83x81qC8+e87p80cDzYJpT/vOXfQgRs3b9vLa9EDNiYfxVHxPKt/H8ZR/CqbbAiSs3UGDGHDz74I+wsw2SLWcz5uDB91agTdQGQnL8ZcId4y0lx4zYGP+nb4CoTRd/x3wEZIdbXHYmYEYsPHvum9A6d6r9jMkWkj9+zEdStuHD+Bux8NxF70HELPAQTf7/3PnvJJ5N5yPMiJ2IvZzC91h4/vKpieez+Qyz2kqr723qHeNmtYXnL34/o/LOlmxJzHTw/O1zwAhLqT8+cRmyfFrKOGY6ePyFpcgiLKX+/GUfyrWnVrA4mW7Mahl7d/+WtzTKzyAf1FK48Y9Ovo3x13wiXxDEf2pS8h8K+a79+xR37sWzs2jkV5t47sJ342tvPrT/mIPn754HEdCBEIH8iC39XyuYezVXKfXnzn9nd5u0x5jVFp777VvyRX32v9eVaEzg9Z9xmrUf9/oLBywn2OLym4L/vCMxjAGFnfPI8mLGgMJ9sv2YlwOI979TjtJ/I0n3L8dc/VPBGFBUFGw1+k83jDMUdc4DI7pjn3r8USPHP7H/6ZRDdv2pYQwoaks3/hkD2uYaKfIb9LF7WaLOGFDYIUxv//40f2n/1Pmnz/VfuG8ubfwljP+UZEzs9Z/qAaS//02dm3hjz8/6b08be9sUBHyt/zaFdP1X/od2/FPLp4ba/lrb2lu6yYj8x+f2Tzr34ox07kENY0Bhu5CKv42goY3B3a6kTr73xeLyW8EFaAYS/aeSj1ajf7Ux3sLoIR2j/zoSOtEvdvWQjrP+dAj0VlDOoznQDY6zbjucrP96UMNZNx8MvZWUc2r4roymowfi+idKznSD47zfdofuw7vVAGl/ox86nsz+9JCOs+86ksz/UaMHdZz1l8Po9B/UpP5bif9JN7rBMfqyPjCIxr9ucFw2ojP0pHKyDU9CMj+JbAp6UMNZvz+I1P7Puu3wFP37CT2oY/SDo8j6rwf1eP7lY/3fN4I0/o7+60hf6l8PasSxV8fZdx7hS90D8blnGn1vg3OTOj7SQzrOvuco3859DZ3j7Gv2I7M/Q+c4//QuvrV/Q+c4/4yuhGsP8bmXT+1fD8VjL+HaG6V8ahLxj3Dt7dZDfet/yPMfZf9y7kXo/0ff58/cH5BrL2ePHUC29qsHNVL/1xQaumPcEbu3JkW/96Vh9P3HQieo1JYJJPpPmP/8dWSr0H+r2h2ybRt33XUXevTogXA4jF69euH++++HaEW3VZnVFiZe8QHMaqJyWtUWXrx5Jkwfl1J/8fefkvXfjNh48U9fwGwl5eQaLKmyGwHSjEr9Ww5RObOYg/HPL4UZc0jkU2NWW5hwyTuk/mfStR+TyafGjFh48cbpZOPfjNj+1n/MwYTHfkaMaPybMQePfbgKZjRx/RmjL6XeUpgRGy/e+R2p/b/4+09T9O8nzIiFCZe+R9Z/M2LF8y//6n/imA9J4y/l/IMSM2ITx14Lk66b5l/br7bS6nsbesa4U08p9UlX/de3c9+Y6WDSX78ns7+Y6eDZV1f41v5jpoNnX15OuPYQn3v51P7N6gyIvYTyqUnEP8K1tz9+7lv/Q57/KPuXcy8q+682MXHMf/yr/5iDSQ/NJ1v7NSM2qf9rCs3xeD/quadZbWHi5VP8a//E/ae+/k2hVW2MP/LII3j++efxzDPPYPHixXjkkUfw6KOP4p///Cd10xoN4wwd+hTRlVLkDB165Pu6nEqHngXE+m89pSwbWnzarVLqXOqfqp4HY0DHfejKCVLDOEOHvm1o7b9XYaux/3TDOKP3P37WPwM67JtFWk5o36JgyiOOmuN5Tk2nfjkNta/JUjhDh26EpaSJxx81Cf9PKN/v+u9NnP8Tzj8oobY9GXt9bvtp1D9vYjUrOfZaPvdpOHq23E/fGAM6dKF7jA5jwD7tQ/61f+L++z73z4TYSyifGur45/u1z0zIf/xu/9Rrbz7N/QE3/6F7lAK1/2sKDT+maPfaT21/Un7D46+hbDyd62AUNKb/zS+/dfifVrUxPmvWLJxxxhk45ZRT0L17d5xzzjk4/vjjMWfOHOqmNRo9oOGEcYfRlbIMaDhuzIH+LSVqcBx3zWC6cioGx3FXHtiC8kXiTzT9r8GSKrsRIHVd6l8j8jy6wXHyyZ2h663K9aUNPaDhxJuPJPU/x18/1N/+5+pBpP7H7/o/8exuTRj/NXwnavw10Z/qOsPZwzpA15l3vuZ4nlOmohscx13cjzb+EsZ/avSAhhNvGg7dIPL/Sv844cZDSeMv5fyDEvLYG+A4/rqhvrb9hsd+UkytL7YmHdPwj3Zrz030gIbjfz/Ml7YPALrOcPyFvensX2M49dh9/Gv/OsOpozrR+p/rh0IP+FT/mRB7CeVT48U/IvvTDY7jrhroX/9Dnv8o+z/xpuF09h/QcMIffKx/g+P483rRrr1dR7T2Vlce3cA6VbpviAPo5556QMMJf/TxY/SI+099/ZtC5j/sIInhw4fjhRdewLJly9C3b1/8+OOP+Prrr/H4449TN63RmBELr17/P0DjYFrLG4gZsfDaHV9DtIJyHs2BGXPw2q0z5AuC3VkzauO1O79pdjmHBjfhsLyN6KJXwPuBjvv7kXIAq2t8oTTp3ysbJ2N3AqSr/+6HG7vx7T3HjDl4cdKv4KYDBP03QTIjFl65+gP5guC2QTNiYfJN0yFiFlnVAErMqI3X/jyTVP7km6bDiVjg4VYV/tOCGXPwyj8Xw842GvxZz5DQNhwZ3oSuRjk0BiACYB2AgvgB+fG/nxcAPzexEfe9iYPdfxc0cBzqflZqa8aM2njtgXlA1KaLv7fOALLDLS47EzAjFl655kNonTsBBL/c9fTPGeDDBUozYuHV338EEbPACZ615c4/hO2/cqIpsZdT5D42Jt/8GViAJvelxoxYdfhegd+wlTgkZw06axVAAHJioQGIAtgW/wPknCUn/u/nG5ZVV9w0IxYm3zjNl3knAJimwORHFoBRPUbDEpjw+ipk+fUxJqbAhP9bSdZ/MyJzf2gcLOQ/H+TGXqqkOkW+D32QG//I5EdtvHbbl/IFQfynhnztwbV/n2JGbLxyzYfg2VlE8hNzD7KSYYSYMQeT/7EQIqADIQL50bj/a8G515GhDTgk9iP2nVaSegOGDmBH/A8ANsX/uw27ZLdLqbt7X0SYEQuvXjdVvvCj/bv9Z5xm7ce9/sIBywm2uPym0Kqs4y9/+QvOP/989O/fH4ZhYPDgwRg3bhwuuugi6qY1GsYZuhzUgbScQecBraOcQXPAGNB5/7Z7tf6PDG/Eb3OWoZtR0az+b3dKqjAe1z9hKeOu3ejK6VDDOEOXgR1J7b/LAe386384Q+f9aP2Pr/XPgC49cxsc/4eEt+Li/GXoEYhvihOyO1U5MhnGGTr3pS3nRxn/qUn4fzr5vtf/ge1p/T/h/IMSFXtpqWvsn5C9DmfxRXJTPI3UNTehHnvUMAZ06UNXSpgxoNu+Wb7Wf7fOdP1X/icDYq+f/Q+x/cn4T1dKlpqMyH/8bv/Ua28+zf2BeP7TO4+0lHpL+r/T18zH2dnL0UWUpHWjT4jdL6VOvffVZaCP7Z+4/9TXvym0qo3xt956C//+97/xf//3f/jhhx/wyiuv4LHHHsMrr7xS73ei0SjKyspS/hxB94tlzeA44uKDoBHdLaMZHMNH9yWTT42ucww/rz+d/nWO4aP7NKN8gWOyNjTTuWtKajqaq38i36jrHL85qoOvS6kfedkQsnImmsEx/IL9fet/NINj+Hn9aP3/Bfu3inI2zYGucxw5qpMsZV4PI7M3tmCLGmYvu2Fc+v/Te0Aj8r9y/PUnk0+NZnAceekgev372P8f8buDSOPvERfTyackI2Lv+fv52vaTfS+Hg6OzmifW1hU3NYNj+IUH+Fb/usYw/OQuZP3XNYajD2vna/0ffVh78tzfr/p3Yy+l/inlU0Md/zSDY/g5dPGfmkzIf/xu/0deOogu/gY00rkHNZrOMfzELrRz3xbyf0w4OGXdwmY59+6uSdHvfWk44uJB0IgeI0dNov+E/v/ig1qF/ltVhLrlllu8u8YPPPBAXHzxxfjjH/+Ihx9+uN7vPPzww8jPz0/5W2k2tfZp+rCiNt645VNYROW0rKiNtx74lkw+Nabp4K17vqbTf8zGWw9812zyC3gMhVq0Wc6djCk4sBt3M1qxuP6d+o9pzj1z03Tw78krYZoNNGAvxoxYeP2P/4NJ9CgFK2rjzTtm+tb/WFEbb937Dan/f/OOmWTXnxrTdPD6C0thmnWn9yFmYR+juoVbVT/SzzY/LbUBb8VsvPXYfFgxwvznnq/J5FNjRW28ftPHsGI08c/Tv4/9/xt/nk4af9+45VNf+v+MiL13fuFr20/2vfsY1Qjz5tFFrI64aUVtvHnb5xml/5b8fbBpCbz51M9k/TctgVffXZNR+m9JTEvg1XdWk+f+ftW/G3sp9U8pnxrq+GdFbbz111m+1j91/uN3+3/9po/p4m/Ewhu3+jP3BwDLdPDmM4tgEa39tqT/61RdghyrefYBYmL3Njbp974svPGnabCiu2f/mX+fc8Mk+k/o/2/5dLf135K0qo3xqqoq8BrPhtE0DY5Tv6O77bbbUFpamvLX0ziguZtaL1xj6HXovuBEt8xyjaHn4PZk8qnhHOg5tCOd/nlc/81UTsJAyzi9FaJot77n6p+qnA3nQO8+uX58xBSAuP85vAut/zlkH//6H42h59AOtP7Hz/rnQK/++fWOf4Nl1g9mlkfzqJuQVjhn6HlQm2aLf42SP7QjmXxquMbQ67DOdPrXGGn+RQ3XGHoNo83/KecflMixTxh7NYaeB+/ja9+T7HubM9auiObWLX9YJ1/aPiAfK9jzgEI638+APj1y/Gv/bv/V3IuEjIi9hPKp8eIf5dx3SAf/+p8MyH/8bv+9Duuscn8iOAd67l9ItvbLecv5v4DTfPsAK2K1c+vGQG1/1OOPGur+U1//ptCqtodOO+00PPjgg/jvf/+L1atXY8qUKXj88cdx1lln1fudYDCIvLy8lD/O6G7l5zrHwFP6ghOV8+A6x0HHdiWTTw3XOA46vju4RqV/hoOO6QreQCnfPaElNpxj4Pjc6rlb3/X0T7QzzjWOwUPatArn3BxwnWPQqf1J/c/AE3r62/+M6kHof5T+Bx3art7FEZZBxct3WgHMrWpL3Yy0wnWGg47q1Gzxr1Hyj+9OJp8arnMMOq0fnf6J8y9quM4x8OTetPGXcP5BCdeJY6/m89ir8xTf21weaJsVxPdVbeqUP/DE3v71PRrDwCP3IfP9msYw9IBC/8ZejWHoQUX0/sev9p8JsZdQPjXU9sd1hoNGdfOv/qnzH2X/cu5Fqv8+vtb/wOEd6NfeWkB+c61jrTez8XOkcLe+Sz33pJZPDXX/qeU3BZ26AU3hn//8J+666y5cd9112Lp1Kzp16oSrr74ad999N3XTGo0VtfHe3TMAjYNpLb9Bb0VtvP/YPAi/lrMxHbz/8LfyBUGAtP6fvfMOk6wq8//nnHtvhc5hQk8eZoYZ4oAkJQiIArIohl3Durq6gtld17Drqqu7aw6LuqsuGDCAq+BvBQyIgEqSIEFggGFyzrFzVd1wzu+PU1XdPcw0M8NUvzV95/s8/cx0dVW957z3Pd83nHvfExpuvvKRmn3/aA7RoCjig1ZYDSopv9fibmcvwypQtvyvsVitQIGJYFWxkXtbFrKq1AD0HfD4KvqffWZwwJ89FIgjw/+7YQ06spAVGYIo4lLCzz9xh/tFCdh/KeGmz96HDZOxuYujzhCHCTd/6UFR+Td99j5MKUHnZdagJOLI8PMfrSBpDPZamB/NIg1QskMhkzUWAg+rAes+W+XOypfZ6j8oHOcaT6Fj4+zfWvczbC0M6izLmcBt29voSTISy7RmiEPDzd98EkIj53+/8CA05sdcdj3A8f/v8aZPHeHzx0x+mDj9awUpPGswLiXc+O93Y8MEnRv79KuSf9gkffH/CN8r8NhIHCbc9Ln7UJn0+V0o557DuHc09kmsIlQ+VjHkGxWo8hlMSVajEtCJxfgKLBRijxVhK7/dPYU+E7DnASFxKeGmT9+TyrgTII4tN129GCV0jEYUW67/1XoahORLI44t1/9iHQ1Sx8iUY388jcqlj4Mqvhcrc/PrCPkp5KCK/5OTb7j5yw+5X1LYMlC89lCx/5QiDl3upRsbZOSXEm78t7vKtbcU2n9kuOm7S7AZHwTS/yr/jUHupUbxcQmK0HpYrbBaoYyt1qKsVig7VPdXxoIx9JkMy+J2bu2bQcmqg7qrtbr3JYS4lHDjJ//gfkmj/Vfmr7RM7ady/a1B1Xn4eVhtjDc3N/P1r3+dr3/969JDOWhoT7HgvFksvW+9yLNp2lMcc9ZUlv5uOWlMT7VWHPPi6Sy7f6PI/LWnmH/GFJb9aTMmOfQWMBrdrWyayDf1Swk7c/TMCWhfUsJ6iqC3RKErT5LVBP0J/VMDGrbHFDs8mteG9MzJYjLQdftmzM6tqLaJKAYOanzaUyx48XSKydaDm+DzhNaKY45vY+mi3SLypaF9zYLzZrP07jXUsNvOvuV7imPOncmSO1cyygkY4xbaU8w/ezrLHhDkn3Nn8swdywSky0NrxYKF7Sxe2YfdywUYjT+3BG385/YXQFMjDAyS7NzNwKtOpW+6Jttj8YuWwQmahh2GUrMGDSoBv2BJMuCXoH1pLzP+Yhobf7aSJPBR23Zhw8htlvg+5HOYlkbUYIhNNtRMDweCQxlCO/83mWX3b5Cz/xdPZ9kTu9IZ/3iKBRfMYcXKEkYgANWeYv5Z01j24KbU6n/+ubNZ8oeVYvIXnDuLJXetJknZ5rj2FPNfNM35XiHbX3DOdJY9vBUT109nkrGC9sq51xO7MMnoN/E+wySuyZ1L3JIlbnRVlGKnT/tda6CxgaVXTKT9Sc3Eh3ax9ewOgkFL590bYLCANUXYC7toT3FMOfceS9O3dXI6odZw7GkTWXZXv4j9aQ0nLGhhzROqJrlvvUNrOO7oVtY8qsbU/qryPcWCs2aw9MGNddQXaezgfN9slt6zRsT+XO1jlpOfwuBHe4oFZ09n6X0bxPQ//8xy7JnCBTAi/hGSv+DFs1h679p08n8591r+yNYxOvByD/m+5tjzZ7PkDytEan/S0FpxzCkTWLK4R0a+p5h/7gyW3rO25vHXaLH1Mibx3eR04olt9MzN07y+RLEjIL+tRPf8PA3bExqX72Zwdhv5jX2oTdux1qA8D2tj4ODOiK7knkvvkVn/2tNu7+3utfv0v/USq9cC1fnfs04u/z1vFkvvXlP38Wf6bpsQhva16/Mv2M5g7imTD4t2BrWA9hRzT+0SbGemmFvDM95H+9Z6IH2ty/oXa6WumDe/BS13moIotKeYd+ZMuXNGfO3OuUst/2hh/tGpP2dw3jFtB9VK/VDwp/YUx0xL8TmbnmLuSXJHWQz5/5Tq39fMO1PynClZ/pOG9rXsOWuV/COF9i9te0O+N8W2P4x7R0sBalE40b4unzGeUv1rxZwTOsTWvqcV849qTiX3gNP//DnNR/hHCFXfK1l7E5QvDWn7055ytc+02r90/HPE/su5l6D9pzT2Bzf/OSe0i+a+Y8V/etQ6lgzk974Uc180I71H+VTnL8j/L5p+WOg/nR5KEHEp4ddf+COxUCvzuJRwyzcfE5MvjTgy3PL1R4jF2pkZbvnW48Q1aic36sZOHbTvqug/EbplN44Mv7hpHXFU7/cs1QZxKeFXn71Lln+u/FN6+SdMuOW/JPkn5fqPDL+6fhXxPu7YHW2/+lAwdhwZrr9vM3GUwkdGKPu/7y6umf/bL/lff0RMvjTiUsKvPnePoP4T0fhLGnEp4ddfuk/U/0rmH5KoD9/7UKptfzj31vomtL3K/8oD6dV/bLnlh0vFuD+KLTf+dmN6fW9sufHWDfKxf1rtvx58r6B8aUj7vzg03PKNP6fX/qXjnyP2X869BPX/xXTG/lCuPV+7XKz2Mpb8p0Ypb0s9ICede8alhF9//p702r/w/KWv/4HgyMb4GEP7mpP+4mjRuzYWXjAzxXftKRZeOFv0rqWFF9TurqXRZmXq4Ynxiv6FzE97ihe8QO6pCWloX3PSKxbI8s/Fc9LLP75m4ctmi901p32Vbv17ipPOmIC3j/U/Fk+MnzGvNcX8o1h47lRZ+79wdor1rznpFfNFn1h28VeK9X/JPFH/K5l/SEL7Stj3ahZedFSqbX849451dyvtaxa+fG4qbR/cE9sLz+4Ssz9PK045oS219u9pxSknth/JvYRQF75XUL40hvyfYO3tpbNSyz/1EP+k3f5PesX8I7G/ELSnWHjWZMHcV40Z/41ex5KBtP1pX3PSpXLrTxrS85e+/geC+h/hOIPWisnzO8VaqWqtmDynNb2tXBVMntsmtzGrFJOPaq1ZK3E1yq1itg4uudZO/6h9X4BaDlMr6JrSMOqToeMZWiu6FkwQ5Z+uee3p5R9d4R8h/auU619B1/TGfbZxHbVYfwg4WyuY3pGrO/2PVbKklWLyrGa5ozSUEl1/0tBa0TVfkv9l+U8aWismH90hG/8L5h+SkF77WuN8bx10bpKA1iP1P2q7xxo4JK0VXYJrb28Yy5EoBV0zm8TsTymYOimfWvtXCqZOzsvVHir8k9KqX1343jrjn7GEtP1pVa59ppR/5OOfI/Yvm3ulXP/l2o/U9Mey9qZGCaClnhiXzj2fr/zRbjY4HHC4638skdIQWQ5xmHD71x8Ubad1x/eeTG87odhyx9W1a2X+nPIjwx3XPFWzdi71fsZ4HDn9i7VSjy23/mbDPlspj3fEYcJtV94nyj+3f+vR9PJPaLjj27L8k2r9x5bbbly7z/Vf6yfG49hy40Nb09tKPTLccd1SuXZmkXH+P636DxNu++r9gu3kjGj8JY04TLj9vyXbicrmH5KII2HfGxpu/58/p5p7hnPv6GeM16CVephw+zceTqXtA8SJ5fafrpDj/sTy6z+k+BiZxPLr322S5Z9vPXrE90r6XkH50qj6P8na23cXpZd/xOOfI/Z/21fvl+Wf/0rxURqx5faf7fsYvZrLjyr8V3v9j37GuFAr9TrY+7r9ayk+Skl4/tLX/0BwZGN8jKF9zel/dZxoO4PTLk1xOy1Pcdplku20FKf9Re3aKY71uX0Hiqr+pe7a8xQvfNHEVLfSPf31J8jyz6vT3c7mtFfOE20lnWr9e4rTXzx5lFbq+8ahSKe0p3jxse0p5h/FaRfV7iiR/ZJ/2bwU619z+uuOF22l7uKvFOv/tceK+l/J/EMS2lfCvldz2qvmp9r2h3OvRCv1015zTCptH1wr79NeOk20lfqZp3Sm1v49rTjz1M4juZcQ6sL3CsqXxpD/E6y9vWJuavmnHuKftNv/6a87XpZ//jLF+vcUp71kqmgr9bHiv9GeGJe6LUg693Ty5dafNKTnL339DwT1P8JxBqWgsSM/6t3yNZfflhWTLw2loLE9J6h/VdZ/rTbG9w1TBxe9qn+hTXqloKnJT6/9a0VTRwNK6M4EpaBJcP1JY4h/pfSvnP4Pg3Y2tYBS0NQS7PvvNb6xSCloyfti118aSikaW2XtvzHt9t8py/8u/kqv/hs78rL6F5QviVrH3s8tH5o6Um77w7h3rM9BHNJ/Db78MEAl9hG1/8Y0xz6V+QvKT3vuJV17E5QvDWn+lfb/0pCe/xH7L+deUvavVWpjfxge/0jJV2PGf/XYOVZ6/SulaOzMp5v/O+sh/ql//R/ZGB9jJJHhru88SiLUzieJDHf/7zNi8qWRxJa7f/SUrP5/sqRm8uv9ifGq/mtxiOB+yv/97zaTpLSVehIm3Hn1QyRC7UySyHDXDxall38iw93XPS3KP3f9YJHY9ZdGElvuvGUDSbKPVuqjUOShuLEoiS23/Hk7SZxi+/9/K2T9r6D/l0YSGe68+uEj+hdCEhnu+t6fZf3vdx5NJf/Xje9Nse0PX/uj3sRbg1wliQx3XfN4avUfJ5a7blojNv84sdxx79ZU6/+Oe+Tmf4R/yr5XUv+C8qUhbX9JZLj7x4tTrX/x+Cfl9i+ae4UJd31XLveQRhJb7vrFWrHa71jyXz22Upff+0q469uPkEQptf/q/AX5/zuPHhb6P7IxPsbwAs1Zb1mIF8io3gs0Z/7l0WLypeF5ijNffwyeUDsHz9ec+dqjayZ/1DvF5PfF8Xynfz3qJlPtAhfPU7z43H23Uh7v8ALN2W99gSj/nPXG49LLP77mzNfJ8k+q9e8pzn7Z1FFaqdc2ofA8xctO7MSrs3Z+o81tNJ0cKDxfc+YrZ8v6X0H/Lw3H/yeL2d8R/WvO+psTZf2vYP4hibrxvSm2/eFrf6yfGPcCzVlvOmHMbX/UuaixK9J6nuKsv5gpZ/9acd4LJ6bX/j3FeS+Sm/8R/qkD3ysoXxrS9uf5mjP/an567V86/jli/y73kuSfN6cz9ody/PPy6WK137GsvY3WSl3qkTDp3NPJP2lU+eP5cbn9mX/t5R8e/ONLD0ACyvOwSYLKZlCeh5k+Gd1fcGRSLGGjCJXNolqasf0D7jGyJIFCERIDWkGxBL6PAkxnM2rnLiiW8B9fgfV9VFMjDCr3bz6HDkNUJoPSFuV56FweixoiMN+HfA4VRU7exq1k12/BzJuBfnoVk580KO0MqnGZD5kMZAJssYTyfQgjyAQwMEgycxJeTw5KJTAG09XB4IwmmnZ1QykGY8Hz0LksNknA89wY4hga8ihjUcZACGiNymRg0za89ca9f7DgXoti9JZdTjf9EaqnD+V5EPhYY1GZwH0+8FG+j86411Qu68aQCcAYVFNDWbcJBAF4mmDxOlqfNk7fxqAyGezUiaitO6G9FVUsQS5L3N6I3z2IVQoVJ9iMR9zegIotOozRvQVUGNLxy8VgDF5LIyxsxEYhNsHpzvOqhGjD0OkBRjw+aKMYothd+8iidu4e+fckcXOytvq6Mta9HkZD7/M1xDF2sIAd7c6Z8jVRmQDb3QPtjQzM70DFlqjJI7+1RNzok8/nIFduj2QtapS74fwt/eiedeRWKLL3Ontk2iQAmu7bhCkU0I0N5AtFVDZLY+LGN/GR8jhzWSdjR/eQjqyFxI581HKYDrAWtMZag4oi1MbtQBvND62Dln0MVCka7lsO2Swqm6Vw3BSyOwroHT2oQojp60d3dmD7+lHZLHrzdvJrNpJPEsgEmKOmYSe2oYxBhxH+8g10LUnAWrzGHHp7E94za8gV3ZrJlPlg4iMGkoQAaMtmwffAGGzJrXWbJKAsyhrsxi2opkZydz9FPpMBT6OUmyfGrXGMAU+Dsdgwcv/fy1xH/KoVqPL7rHL2ZpwO8bTbvvI0Kii3gzbueqvAd99lLazf4tZcazNkMqie3vLYNSqbBc9DZTLVs6ZUZa3aMjfl887etXI2b936U5mMk9naXL22uq+/0iPQcemUTnRfkWhyMybr4Q3E+GHo3p8J0O3N0JBHT+zE6gC7bQdq0gToH4DAJ57aiX5iGXpqV9m2DIQhCohnTkLv2En7/3sclc+jkgQ9ZTJkMtiduzDdPahC0fGo0qiGPOTzzlajyM07ilHNTW7M+Rw28Ena8virNmMHBgmeWgPZrJtekkAui+nqQA2GqMECDBbQOadDjBm6Li0tbp1u3Y43fSpY43h/2w7nb4olN5YkcnrN51ClCFuwKK2JZk7A7yliJrWhevucDWUyqImd2IYsaqCIiiLspq2OQ7VGtbZAGGIGBt3v07pQazY4XVfalQY+yvPcGMo6sMagc1kSq51us1n3nqZGt6Ya8tDSjN26HazFlvkXgMWrHG9nAmcbiXGcaayz7zB0tl7B8P/H+1jvxrhrXfnOCrR2PqGCQhEKRee7PQ9bCqt2SHldgPPvVR34PnZwELV6A8yYii6578+s20nSN+jWeD5PtGAaUWvAgNoFS57Y6zBVKcHMmIze3o0thSjPo+nWRTQlbp3rxgZaBgbd2iyP2xrntyv/qlxAdnkThaM6oJAQaI0qRYRdLWw8N0frKkv7L55012DY9wwNorzG7R6cO5xv93xtOApF928Y4j+xElPh8amjpAStLUCO4ux2+qYHTPzlMvB9zC7nA5XnoVcU8JoaCcKQXKE4ZDe5LM2Fonuf1thcxsUHg4Mks6eiV2wgWBUx+c9uvakyz+p83sU3pZLTQeKBUthSCW/lJkz/ANlVLpawUYzKBJie3hF68TbsQA8MYoolGu7oRgUBNihzev8ANjFDevIUHb9e4jisf4DgsZWobAYbOxtX2Qz0DzheHK5X3392vKDU0HXa0y/ucR0rMV3VTnzf/asUKhm6s1eV163KZp2OrKlyttOtAi9T9QdoPRRbmvITmtO6YNs2lO8xuGASpZymYXMRXYnXKj4kjt16D0NUxsNGseO88jgpltANDS62jWOIPEpHTSCzcxBVCN0azrn4EN/DVsbS3QtZF7cSBJDNYXOB4+yGPKYpy8AJEwibNR03PeXWsTXOj/k+bN+JshZ79EwwoLfvhsCH1iZYvsb5jMRgd+4CrTCNWTRQOHoCUZNH653LsdbQ8tunsZ6HmtCB3t3PzCtXujhw8kTnVxoboL0ZFRsoRdDbiw1DZz+JQXke3jNrIEnIA2QC8itiyOcAmPPpP6ObGtnxyvl0/imDThLnc+IY1dmKymbx2ttIWlrceBuysLPHxbhbt7trNaEDFYbYjZtRLU3la+pBoeBs0VrnQ8LIXd/ARyVJWY958BRxc5bM8s2O0z0Payy6pRkVBOjGRrQqutfjGOX7ztfs3I0tFNDtbc6fBgHm6Jl4/UV0sTTkB6LI2WlPH6prInqw4PKlYhFz9Ax0IULv7HF+t70V1dvvbKVsnxV7r66TYWsCpdycTDnfYIjTbZKgotity4rfqKwxT494zRZLVfu3puBi7yTBRhE2LnNoZRylcMRY9IZtBIUiqqXZjbuc9wWVMZRjvXyZG6oruzK/xMWbFd26+EtVX7OJGVqrZX82gieUcv7NWmyhgCqW0AM5MjvK3OL72HwGf1uIzTt7CDb3uHlUclRrsWHobNU4u9WbdmC1qn5ndaxVn6mrfDH0eznG1Nr531IJe/REilObCJs0Oob81iLbL55K1y/XoKZOxjTlsErh9Rcx2QBlDLYhAw054qkdRL7GW7qBfSFpypHkMvhPrMQvX5McrnCllOLojz2BbmkGa+n6+Q7Hyy0urqvGBxXeK/vf4VzsuMUOvcfzqrFc9Tpad7uYNQY8NZK/9wKldfl7yrFlQ5bm+1ZiTQka9/EhY50PW70BuiZBGEKhiIpjFzvFMSQJprcfevtRgT8s1yyPvVBgytWrHH+Du5bDbNlGESQGvyEDs0/GDhagpcXF3lFMx9I1YG21I44KfKeHQhFyWVcPyZTzDaVcTjo8LqnotfKasajNO8ht2oYtlmh6ClQQ4AUaf/KxI+dfKLq5qHLeoxUY7a4F4C/bAFphw4jGP+yksTI3z3O5VqVmE8cjxjjiWm3djgLaVq5zfwt8F6MZ49ZxWcd43pDucBxgBwu0r1zn3q8UZIKhawBOrudhSVDKgzhB7erG9PZBXz9TvruhynNeoPGnHFeNzQGIkxHzUb5f/V7AzVFrZ6tR7K6NLfvoXBYaG7EzmtAbt2M72lAalyM3N6C7+4indLhaDmAbAuczOtogwfmLMpLpE/BWbnL/b8rhbdoBpRC1c3c1BqLCYcViORcNiCc04g1GqF3d+Bsidz2reaZ2cVx5PjZ0HKvzeWwck8yYjLd5p7Nxz4MwpPH+lU6n2QweQEdbtSakKtenzK2qsaFqpxUbr6zDEbZZtgXlaVQ5XlVmWO3GmBGfGc4bz8KetnUAUHq4/IN8auu55Jfj7Gf9v8zjtq9/mN95Dll7k6OVq/fs+TfPQynt4rQ9x1j9f+VzFd8LtjIIa7HKjsx3KrWP5wOtqnZhTQJx4vzX8Kfmnm8nsOHj3GPMVg/5+er/SyVn00qV4xhb/ZzyPIgixy+l0pDvzQRDeXV5vahd3S42L5X9eKVmU6lD9vWT3by9PHdLcuxMbFuTy2Fjg+kfqNZrvdWbXT07m8VMnwy+htigB0vQ0+vG4HlDOfaea6NSr6zwpFaoZOR7qvavtStxeZ7ju8Rgp7g6pBosQOjq79VrU67DsWmr85Wl0NW/jRnKvYZxqrUGpXzwFMqqcs42dE2U75ftzzq/XpEDkPGHaqlJQjKlky3nNKMjyPRZst0JjXcP5b0V/gcgl2XTJVOY+st1zk8Yg25uwvT2uVpO4KN8DxUEqIwbt57a5Womu7rdGti2A2/LNpqexo0hn3O2kJjqusovj7CArfy9UERv2oE2BhvFWGtp3LId4weoTICuxOw2RmmF8gOUskO+vFhyvgXwn1zteNr3h/x+XM7xy9dZGYs5egbqsSVgNCqfdTUpa7GFoqu3lGuMVGKiTMbF3kmC2rrT2V7lOjAULw9dpGFrsvy56jWrQI/k8Kpd+j7B+p3Y/gFUoehiuCTB2xRDfytqzSaC/sJQnFz+TmONWwNaV8emstmheOu5+Ghv/sIZJDaMsEZX6+H4w/zSqvXQ3joUfySJi+2SZCgu0dqN19qhfMH3UP6wvCGXhf5BdE8fzet2Qts+hlOKoL8Pb1c37Y+H6HyeoJynTHgydmvc88hvcdxBJoBC6K59uVZejZWtdfHaPnVSvkbPh8eH6d3u7XsqOlPlfFLjfJvaY1yV3z0Xp5HPwa5uZ1/W4u/uho59D8Nu2wH5VlRzE6azhVJHniSnCXoj7PadTkQ+B+0t9J48idYH10NjHrt9J153j4vxynsJWEOwfBMW6PjtMsdxlXo35Q36Si5WsStrXWyl3fWp1AEdnwTV+Kyab2oFVlcfeVHZrFuPuSyqFLt6dRS7mhJAcxNmQrP7jseWOB0FLt7FWMdR5fi5Uqur7BnYwUFXJ1ODw+SXa/BxTMODK51/09rVWJTbf2TWVFQpdrFoGEEYunpJQ97xYmsj7Bxkwk8fh/a2YbVdDWHk4uOt211MX7m+WkFri6srxTH09TvbzWb2y9zgyBPjY44kMtx//WKxVqpJZHjg/9WulXe9I4nL+pdqJxHX9vqP+hx2HdwOlcSG+294BiPVzkb4+ksjiQz3/++TgvZveeDXcu2MpJHEhgd+vlyO/2PD/T9OcTvF2HLf7zbt0/50je+0TaKy/vfRyn28I4kN99+2Qdb+BeMvaSSR4f6fPC1mf0lseODmlenVf2x54EZZ/n/gZ0tSqX/p2E8695NGklgeuGVdlXskWqnf/5N0H+Mgyj2Ry/1Sa//StZ/E8sCv1qQ39xJe/9LypVG1f+naW1r1n1j+eOdmudg/7fYfl3MvSf/7E7lW+tKo1J7Faw9joP+x7sa0P0giw/3XyR6lcf91T6TX/qVr/4cR/x/ZGB9jeIHm/L+TbSdx3luOPyzaGdQC9aD/Wsof/WwReXiB5vy3nlh9WllEvuD1l4YXaM6/4hRZ/vmrOanW/3l/c6ws/7xD7vpLw/MVL7l0+j7nX/NW6hX9p/goh/MvmzVu/W+9wws057/9ZHypdnKB5rw3zE+v/n3NeW86Rtb/vnXs20nXA6TXvrR8aXi+4rzXHlU9xkGilfr5l8u1MpWGF8hzz/lvTXErXWn+8RXnvW5eeltJC69/afnSELf/I/6Xl7x8mtwxSkfsn/PfLsg/GU9UvjSk44+x5J/RmmBInTHuBZrz33mqrP7fdVq67V+49n+48H/9j3CcwVro310Ue3rXWhjoLtXF08MSsNaK679/V+3kj35yt/xmzP7Yfy1Haa2tqf7rHdbCwK6CnP0by0BPuPeWOCmAtdbxr9D8rbWi118a1kJ/b7RP/Y/+xPjzZ6bK+quLu5RGYGx8g9N/KOt/Bf2/NNz8BfnfWgZ6StiUXoAq/wvN31rLQErtX3rtS8uXhrW42K/SdbbGN6HtTX69xf5jmZFJ5/5D9l9HF2AMIT1/t/7S7Htl17+0fGlI+z9p+dKwFgb6oiP2LwRXey7I8b+xormfNOqi9j9G8kffB5CBeO3ZwsDO0eWPNjT53ZPnh3rQ/+HC/0c2xscYJjY8cvMyjFA7DxMbHvnVCjH50jCxldf/L2onvx5bqAyHiQ2P/HI5Rqidk4ltTfVf7zCx4eEbn5Gz/8TyyB0bxK6/NExseeSWVaL2//DP5a6/NExiefjerfvUf63508SGh3/+DElKbwwxseGRuzbL+l9B/y8NExseuWmpmP2Z2PLIb9eKHaUiDZNYHvnNarH5m9jyyC/TGf9Lr31p+dIwieWR32+s+t5Ri3fP99zXvcmPDY/ctCS9+o9NmXsE7f+Xy9PL/dX1L8T9ieWR29enOPeSXf/S8qUh7f+k5UvDJJaH7tsmWHtIu/1bHrlpqWDsb8ryU6z/Xy5PBf/UuvPhwcDEhof/b7Go/h/+v6dTbP/Ctf/DiP+PbIyPMfyMx0XvOxU/I6N6P+Nx4btOxs94IvKl4Qe6rH+Z+fsZzUXvPaVm13+0b63FuX0HCj+jueg9p4i1c/ODiv5Tav8Zj4v+4Qw5+w80F755Pv5h0E6lFvADzYVXnCg2fz/QXPSBF6bX/gPFxa+dtU/917rjhp/xnP6F2tlJw89oLnr9HMH4R4vGX9LwMx4Xvf80MfvzA82Fbzsu3fx/+Qmi/H/hu09Opf0PrX2p2F8295OG7ysufNO8KveM9U28fsbjor8/Pb2xT8Zz3CNYe7joPaekl/ulaz++4sK3LEi3/gXXv7R8aQzZv2DtTVC+NHxf8fLLZsrF/qm3f+1yL0n/+/7T0qv/wNWe5WsPtdd/PT4x7mc8LvrHF8n63w+emV77l679H0b8n84IWRDGWLas2I0RumnCGMvWld2YtD6xZiv6F7prz1DT66/UKMWmOrjkxsCWlbvF2glJX39pGGPZunyXoP1btq7tS6/+rWXrqh45/VvZ6y8NY2DLhoF9zl+NwkuHgrIr68/W/02TNYExsGV9v2D8U1v/W++oxJ9S9mesZeuaXkw9BCMCMMaydXWP2PyNrcT/IuJFMbT25WKfVHOPha3r+qmof6zPQTTGsiXVsU+ZewRrD1tW7k4194vyjyXduZfw+peWLw1x+xf2/9IwFrZsGkRq+kfsX9r+U177tOX4Q7z2UHv9j3bTqdQDcsZYti7bKVt7FpQvjXqo/R8u/H/YbYzPnj0bpdSzft73vvdJD22/YGLDottWibYzWPS7NYdFO4NawMRWXv+3166dXb2fMV6dv2Ar6Vrqv95hYsMTt8q1UjWJZdG9m1Pczs+y6A/rRO3/id/ItZOShkksTzy0Y5/6r/W5pyY2PPGb5alupb7ogW2y/lfQ/0vDxIZFv10p2kp90V0b0ttON7Es+sN60Vbqi+5IZ/wvvfal5UvDJJZFf9wyrJX62BbvKtyXWv3Hpsw90rlvSrm/uv7lWqkvumdTinMv2fUvLV8a0v5PWr40TGJ5/JF95741l596+7fl+R/RvwSka79jyT96tJtOpW6MKde+JPX/xC0pPkpDuvZ/GPHPYbcx/vDDD7N58+bqzx133AHA6173OuGR7R/8jMelH5ZrZetnPS79QIrbqUjrP+Nx6Ydr186i3s8Y9zMel37wDPRordRruH9fa/3XO/ysxys+ejZ+Vq6d2aWXH5vedqIZzaXvf4FoO6dX/Ms5YtdfGn6geeUb993Ke7Qnxg9VK/VX/Ms5dddKfTTfMJpPOVD4GY9L3zJP2P+m+CiBjMel/3SmYDtFzaXvPjG9/B9oLn2f3FFGfsbj0n9MZ/wvvfal5UvDDzSX/t0C/KDSSn3fqMVNvH62zH1jrP96eUDaz3ry3PPBM9LL/dL84ysufUeKjzGprH+p3FdYvjTE7f+I/+Wy182WO8bniP2LxB9V+Uf0X44/xj//1OMZ437W4xUfk6s9+lmPV3z83Ochv04C+YOEnynX/qX55zDwv770AA4UEydOHPH7F7/4RebOnct5550nNKIDg0kMqx7ejEkMqLEPUExsWPnoFic/hRihfwEHMVL+s5FVMS06rP6udID1YjC9FMMEFVuioiYXhyShT9YbRClT7ktoadOlfcuuhyfGE8OqRzbjST2x9hz6H+8wsWHlnzaW79oS4J/EsvLJne6u5fr3j4ccJrGs/PNWubu2E8vKBze466/G3wXIqIRWr+B+8SJQFvwieAnYPnTssf3Pq+lM+jC6/D4SotgjDgM6k/59fvcheWI8MU7/trEO2HjsYRLDqqd3i/Fv6vk/Max6aCPGNAjJt6x8fLvjv0BkCKIwxrLysW2i9p/W+F967UvLPzhY2nUJnwQwKJ1gvRBML6UwISoqVAK5uERmIGSCGgCbYE2IRaFtCWt818c1LLLjoYgJpQJxoGkdJVexNXCOJjasemjTYab/QwcTG3HuWfVIirtFSfOPgZVPpLiVaGX9Cz4xJSlfGuL2f1j630MHk1hWLO0RfWI89fb/0EZB+7dOfpr1/4gs/6x5aAOd9GM83B2Tfgk8g1JeeTsiAhWBMqBj90Glyz8K8EEloIug96jfqgh0EUsyYg9hT0h5fxObodqjmHy5jknSMEm59i9l/8PzL6++b8487DbGhyMMQ3784x/zoQ99CDXagWV1BJNYnrlnHcr3RbRvEsuSP27ApjY5dfrH2NH7jdRU/vpnvd6ui/xN63JmB714extWAVi9l9fb9192PTy5YBLLM/eu53gttzH4zD3r60MZAjCJ5Zm71oC1IvuiJrEseWgbpHhjfMn9myCQcb2V66887zD3/iPRokP+pmU5c4MePLWPtW1wPPoIzvaah/1tR/lnFBySM8bL+jcvPj6N5u/m/+edbv1Lyb9nXd0H5rWCSSzP3L0Oc/YxYvKXPLhF7PpLo8r/gva/5N50xv/VtQ+jH3Bda/mHCfec17CRlzRspNWLnv3HwhOwdo/XHgQq99sMDHu9sv8dAU8Oe32Ue3NqcsZ4Ynnm7rUi174eUOEem8g8M1TJ/VQ2KyBdHtXaT0bmjjBjLEse2ursP4VroLr+UypfGiP8bwrlS8MYy+JFu9HGirSKPWL/LvdSORn/Z2LDM3evc7G/QO1bGpX4QwIBCa9vXMGJS+8j2/4claRM+ee53nMgrw+D2BnjieWZO9eIyK7K/8Nq8NJYeRtW+w2E4s8K/3te3T+TdXhk6PvAzTffTHd3N29729ukh7Lf8DMer/lX2XYGr/7oiw6Ldga1gLj+Mx6v+cRI+TkV8/6Op5ib2cem+CFCPZwx7mc8XvPxs0ZvpV5r+Z+Qu/7S8LMer/3380Rbqb/6fSekuJ2i5tUfOU20lfpr/+P8cdVOy8fwvvYnmZ/p3vem+CHAoWql/tr/OL/uWqmPFfyMx2uuWCDrfwX9vzT8jMdrPvVi0Vbqr/5HuaMkpOEHmld/WK6VuZ/xePXH0hn/S699afkHgrMatvDq5jV73xQfA9SqlfprPnXuYaH/WsDPeuLc85qPn5Ve7pfmH1/x6vefmOpW6q/51PNppXp4y5eGuP0fRv63FvADzV/9zVzRVuqpt/9PvVi09v+aT7043fr/+Fki+v/b1qWclttOVsk/rSx1S7Sf9Xjtp18i6n9f+5kLUm3/r/3384T55/DIvw7rZ8auueYaLrnkEqZOnbrP95RKJUqlkS3bjE3QQn0cTWJ44rZVoq3UF/1udXrbCcXD9C/USn1IvsOCTDcd3r7bCh4q1MMzQiYxPHH7KvKjtHM7lGfaPkt+/Gz9pwkmNjzxmxVyrdRjy6J7NmFie5h7n4ODiS2Lfr/OzV9I/hO/WT6uWqnPzfQwyS/WXM6haqX+xG+WY3JtdXCb0v7hUI7TJIYn7t8q2s4s1fyfGJ64dSUm1yIjP7YsunO94780tlJPLIv+sE60ndyi29ek0v6l1760/APBi/LbROXX5Inx2PDEb1cI6H/fcxnLGMDExnGPpP3fvkos9pWG9Po3ppx7pbBbCAxb/4K+V1K+NMTt/zDyv7WASQyPPbJdtJVu6u3/1pWy+r91ZXr1H5fjjzHWf7MOOSG7a0xljgapB+RMbHjilmWy/vc55I8WGR0u9bp9wdUeJfKfsvzh+Vedd02r79GNgrVr1/K73/2OK664YtT3feELX6C1tXXEz6pw0RiN8tkwiS2fcyPXSnHVo3Jn3ErDmDrQ/x7nrM0I9n2u7aFEwchvhLn5b8EKxWbGPFv/aYJJLCsf2ihn/8ay6sld6T3nzlhWPbZNbP7G2PI5M+NH/2PGn4dgJ88kZf2nMzd1/L+4W9b/Cvp/abj5bxKzP2Msq57YcYT/Bfl/1aNbUmn/0mtfWv7+QmGZFgw89xtriEG9Hz0hDxDujM0UbwwmZe4RzX23pJf7pfnHwKpFKT5jXHj9S8uXhrj9Hyb+t1YwBlYu7ZWL/Y/Yv8u9RO0/xfo35fhjjOc/wx+b+tT+omDljnGUrD2axLozztNq/9K1/8OI/w/bjfEf/OAHTJo0iUsvvXTU933sYx+jp6dnxM+czMIxGuWz4Wc93vA5uVa2ftbj9f9+dnrbSdSB/t/w2ZGtrL0xepZ7edg6JnJGg5/1eMOnz8UTbOe0p/7TBD/r8cYvvUy0lfrrP3RSetspZj1e/69yrWz9rMcbv3zhuLL/seLPFWri8/6Oiv6DtLZSz3q84X3HyfpfQf8vDT/r8YbPv0TM/vysx+s/elp69Z/RvP5fXygb///HOanUv/Tal5a/v9BY8SMol2anHPLv9LMeb/hCilspZj1x7nnDp1PcSld4/QeB5vUfSfExJsLrX1q+NKTtX1q+NIJA89dvP1q0lXrq7f/zcq2kg5wvKl8aUvGHruHxfgeDFVGbiFw/6/HGr8jVHv2sxxv/8+JU279o7f8w4v+D8pDvec97GBjY9x3la9as4YILLjjoQT0XjDH84Ac/4K1vfSu+P/rdL9lslpaWlhE/WrCFbBIZ7v/p0ySRzG17SWS4/2dLxeRLoy70f/3iEfJr2Tq8gicKHSwvybRPHY4kMtx/w2Kxdip703+akESG+368SM7+Y8P9v15DktJ2TklkuP/ny8TmL339a4Gx4M/FwRSeput5f09F//FhcNdkLZBEhvt/u0HW/wr6f2m4+T8lZn9JZLj/FyvTq//YcP/PlwvH/0tSqX/ptS8tf38hfcvWXRMXsCHoOOTfm0SG+3/yVN3rv1Zwsacw99yQ7txLcv3HseH+X65Od+4luP6l5UtD2v6l5Usjjg33/WGzaO3hiP3LzT8OE1H50pCKP6Tj6eF4KOxiXSKzD5BEhvuuE6w9R4b7rns81fYvWvs/jPj/oHoqfPvb3+aOO+7g+9//Pueee+6Iv1111VV89KMfHXXj/Pnid7/7HevWrePtb397zWTUCtZY1j+1HeX7Is/rW2PZsHgHNqXttCr6x1gkHouoyh8GNcowdiY5BowP2QCT8cCC1aAji/UUur8IepghGTP0u7X0RB7L1GTu392KqYMGEdZY1j+9g+NGGUotr0pV/zbF9v/kNrBW5Ihpa2DDsh6of99YE1hj2fDMLghk2hlZY1m/aCvK8w7jfjEjMRpf7E6y9MWeI9lM4P5VCuKEak85rbEZH+sr7HCdWOgnx4pSB/c2HQc9pec91or+7YsnPO/vOhxhjWX9yl7nf6XkP7W97s84qhUc/2/Hnt0pJn/Dkt1i118a1uD4X9D+Nzydzvh/ROw9WtBda/l1zj2j3WiWWMXGqAFyGUzgYT3Ago4Mcd4j6C65+VVyEGOGdK0UGIP1NGiFKoQj8pXdSYYlTObWo86icWN0yOdVjX0Frv2+MXbrsBJ7WiNzymQl91PZrIB0eVRrP5nnfyTPQcm3sGFZdzkGFhmCKKrrP6XypbG32lea5EvDWli3ph9tZZb/Eft3uZfKyfi/au4nVPuWRiX+GGvoUWK8/sRjV5ID39X20dot1MoPjIxXtXLv29t5CFq714fXtpUCa9kd+Sy1k/hTPA2LZSzjzgoqtS8pWGNZ/8RW8A6u8H24r5gK/6pAKP6s8L/n1b0uD7o6v3r1ai644AL+/u//ni984Qts3bqVyy+/nDvvvBNrLaqGyedFF12EPUw3toKczxu/eAE3fPJeokRG/l9/6Xyu/9gfiAYEBiCMIOvxhs+/jOv/5Q9E4djvzgU5jzd+7nyu/8RdRIUYGL0QdVtxNg8PTMAeNZXC9CZUbImaPPJbS8SNPvk/rUDl81UHaAsF9ztAFGH6B9CtLVjq45yTIOvxhs+cz+P/+n9y8r9wLtd//M502n/O56+/ciE//cjtxNHYc2iQ9XjDx07hhi88jIB4cQRZjzd86kxu+MLDhAJLMsh6vOEblzj+i8defi0wWqjxh8Fp3LuzDZXJwIypBI0Bb/z747nh848Q9ZUvQD5POLWDqDUgatRY7bjUCyHoi8lt7CVR3sEHS8MQ5DzeeOUl/PCmDRgB/7MvjNVSDHIeb/zQiVz/8e08/9sMDlL+587n+k/eTVRMJ/+/8YsX8IOb1yOx/B3/vYgbPv0Ah37rq/4RZDVv+NjZ3PBvfxSZf5D1eMO/n8f1n7iTMBQYgCCqa//jdxKVhGL/z7+k7rlnNH/abwO+tuNE7NEzKU5tImzS6BjyW4vsPLGBrl+ugaYGTGMOqxRefxGTDVDG4DVnedM7F/C/P1hOSSkyT67FhiEqn3O5S5Kg8nlsjWoHldj3+n/5A1Fx7NivXsLcIOfzxs+ezfUfv1OG+8v2f8NnHiQq1a/91wrV2s+//ZFQIPgOAs2b//U0bvjPx4jCerHKsUOQ83njl17K9R/9/Ziu/3qRL42q//v4nSL+b4T/r2P/WysEGc2b3nkMP73qGSRmn3r7z3q84YsXOf4vCsjP+fzN1y/ipx+6jShMof1Xar+fvHtM9T9aNPvkYBs/65+Pam91N0vncxCF7sGNqJwhep7b9K483JEk0D/oNtOHI5eF/kFsUr621qJ8HxvH2DBENzWWb4iQ8f1Bzuevr7yIn374djH/+9dXXsRP/+kOosH0VR9c/HmR83+DEvmvz19/9WKXf9RP6XOvOKhb17/4xS+Sy+UwxvDf//3fnHDCCZx44onVTfEpU6Zw0003HeqxjgvEYcLt33qUWLCdyh3ffpy4joryY4k4Mk7/QoFBHBpu/59HR+h/NMc53tLXODLcfvWfSWIh/UfP1n+aEIcJt/3Xn+TsP0q448fLiCXuCqoDxJHhju89Kcf/keG2/5a7/rXAaHfk7vlsVBwbfnvjWjH7i8Oy/us9MqwR4tBw+89WifFvHFb8f1r1n3D7Nx8mjmUiizgy3PHDxWL8J404MtxxjSz/33H1Y6m0f+m1Ly1/fzHajbrGHvymdRwZ7vjJctHc9/ZvPDSuYp8DQRwmjnsk7f/qP6eX+6VrP7HhjuuWplv/gutfWr40pP2ftHxpxJHht79Yd8T/CiGOjMu9JPX/zYfTrf+r/zzm63+0eHq81fdHQxwm3Pb1B0X9721feyC99l+t/Uvzf/3734PaGP/nf/5nFi1axMte9jKstaxatYr+fvf01RVXXMHixYu57LLLDulAxwussWxduVuslaE1lq2rulPZShHqRP8rR+p/1I2d51GIqkdU5i8VEexN/2mCNZatK3YJ2j9sXdePrX/fWBNYY9m6ukeWf5bLXf9aYNQbi/aYpjWwdeOgmP1V9T9+1H9AsMaydcOAsP+V8//ScPy/W8z+rLFsXdObYv3D1tVy809z/C+99qXl7y9qdaOuteXYT5J7VtS//msFF3sKc0/acy9J/rGwdW1funMvwfUvLV8a4vZ/mPjfWsFa2LJp8Ij/FYL0/KXlS0Mq/hitAZLMoTYykK49Ovk7023/orX/w4d/Dvqws8bGRlpbWwFQSqGUwvM82trayKb0DKn9QZDzufyqlxPkZM6YDfI+b/+vl4nJl0aQ82T1n/O5/H8uHiF/NNc43nLYIOdz+TcvwgsEDrimfP330H+aEOR9rvjeKwnyQvaf9Xj7p08nyMpcf2kEOY+3X3m+2PyDnMcV37tsXNn/6HfkjmTXIKt5x0dOENS/zxXfu4xMUN/nzNYKQc7n8o+dLOt/Bf2/NIKcz+Xf/gsx+wtyHm//4tkEuZTyf9bj7f95ntj8g5zH279xYSrtX3rtS8vfXxxIB5YDQZDRvP3fTiPICHFP3ufy71xa9/qvFYKcX+YeQfv/5kXp5f7q+hfi/ozm7Z99YYpzL9n1Ly1fGtL+T1q+NIKM5p0fOE7O/6be/j0u//ZfyPF/3uV+UrU/aQQ5rxx/jO38R61P1f8e4SFDkPe54vuvkqs9532u+MFrUmz/5dq/4N7j4cL/B+Uh/+d//odjjz2WG2+8EYCpU6eSy+WI45grr7ySE088kd///veHdKDjBXEp5sbP/JG4JHPGSlyMuemLD4rJl0ZcSmT1X4q58bP3jZCvVG0KUfWIuBRz4+fvJ5FqZVxKnqX/NCEuxvz83+4iFjrjKQ4TbvrWU+ltZ1NKuOk/5dpZxaWEn//bnePK/g/kxqI4NPz8h8sF9R/z83+7k6juWqnvW4uH0gPFpZgbv7dE1v8K+n9pxKWYGz99r5j9xaWEm77+GHEKz5iFsv+78hGx+celhJu+8EAq7V967UvL31/U6onxODLcdNXTcq1EizE3/sc9Y67/0XRWo+PU94q4FJe5R9D+P39/erm/uv7ljhG76RuLUpx7yaz/epEvDWn/Jy1fGnFk+L8fr5Tzv6m3/4QbP32vaOx/46fvFav9SSMuJeX4Y2znP3o8Pb7q+6MhLsb8/FN3ytWeizE//+TvR5U/nq+Hqz3eJbr3eLjw/0FtjL///e+nt7cXay2XX345ixcv5vHHH+ess87CWsvKlSu5+OKLD/VYxwWshd2bBNvJWdi9WU6+NOpC/3vIT9MZ49X5S8sfb4rdT1gLuzf2ydr/1kK69b95QFb/gte/FjiQM5yshV07Skf0LwRrYff2Yl353zRB2v6shd1b5No5SqMu+D+l9i89d2n5+4tatX60FnZvk4v9pLlPGke4RxbS8z+Se9VB7JN2/pG2/5Tzz66dR3JfKUjP3xp7RP8C6//IGeMO1sLuDb2y/CMoXxri/HMY8f9B91SZOnUqt956K9/97ndpbm7m6KOP5t577+UrX/kKuVwOezjMXgBBzuc9P5RtZ/Du76S4nVDOk9V/zuc9P3jFfrdSH293MAU5n/dccym+YCv1PfWfJgR5n/f+72tFW6m/+8tnpridn8e7/+dloq3U3/uTvxxX9n8ghfwgq3nfJ04SbaX+3p/8Zapbqb/nP06V9b+C/l8aQc7nPde+SrSV+ru/LtdKXBpB1uPd33qpaCv1d3/vklTav/Tal5a/v6hVIS/IaN79hReKtlJ/z3Wvrnv91wpBzi9zj6D9X3Nperm/uv7lWqm/+z/PTnHuJbv+peVLQ9r/ScuXRpDRvP+fTxRtpZ5u+/d4z7WvEm2l/p5r5VpZSyPIeeX4Y6xbqe8badolC/I+773+r0Rbqb/3hten2P7LtX/BvcfDhf8PykP+3d/9HU8//fSzngpXSvHhD3+Yxx57jBe+8IWHZIDjDVEp5toP3kEk1E4gKsZc9893ismXRlRKZPVfirn2Q78bIT9Nd5RFpZhrP/J7YqFW6lEpeZb+04SoGPOjv7+VSKidTRQmXPe5R4lS2s4vKiVc9/F7xeYflRJ+9P7fjCv7PxD+jELDD/97saD+Y370/t8QCrWzk0ZUirn2ykWy/lfQ/0sjKsVc+4HbxOwvKiVc928PEKW0nW4UJlz3iT+KzT8qJVz3T+mM/6XXvrT8/cWoZ4zbg79RNwoN133hMaJQiHuKMdf+w2/rXv+1QlSKy9wjaP8f+X16ub+6/oW4PzRc95mHU5x7ya5/afnSkPZ/0vKlEYWGH/zPM3L+N/X2n3DtB26T4/+iy/2kan/SiEpJOf4Y2/nr0Y5KfR7x9OGGqBjzo/feIld7Lsb86D2/SrH9l2v/gnuPhwv/H9TW/TXXXDPq3xcsWMB99913UAMa97AQFmK5HU8LYVFQvjSG61/CJ+3l+qfpifHK/Ec9d28M5Kfb/iNh/knSrf9iDFIBsYWwKHj9a4BR+XNPPVsIS4L2V1l/dYbR1XEIlVUX+k87/wsmJkf4Xzb+TrP912HsX4+o5RMuYSmBBqEnBg4T/dcMdcM96XxieWj+cjl91feOs7LCfkF6/UvLl4b0/KXl1wFCoU1x4Ij+q7mXkP+Tjn+lIWR/o6k6VY9H1EPt+XnIP+yXTHX+grXnw4T/n1dPlYcffpgPfOADXHTRRZxzzjkUi0WuvfZarr32Wvr7+w/VGMcVgpzPFVfLtTIM8j6X//eFh0U7g1ogyHmy+s/5XHHVyFb2aXpiPMj5XPGti0Vbqe+p/zQhyPu843uXibZSv/wzZ6S4nZ/H5V99iWgr9Xdc86pxZf+jPuG2x+9BVvPOfzpRtJX6O66Ra2UtjSDnc8XHXyDrfwX9vzSCnM8V37lUtJX65V86J73tdLMel195vmgr9cu/mc74X3rtS8vfX9TqjPEgo7n8308TbaV+xXdTfIxSzi9zj6D9f+vi9HJ/df3LtVK//HMvSnHuJbv+peVLQ9r/ScuXRpDRvOsfjxdtpZ5u+/e44jtyR4kEeZf7pbeVtFeOP8a6lXp66vujIcj7vOMHrxZtpf6OH8odIyqNIFeu/QvuPR4u/K/sQR4G/rGPfYwvf/nLAFhrUUqRJAkLFy7k6aef5pprruFtb3vboRzrIcPpb/8qnTc/A4DpH8DraMP09IE14PuYQhGlFcrzsMaitMJEMXrhMeApTC5AP7IYlMaEIeqME+CRp1EnLIAlq7BxhG5qwkYRuqMdCkXiXbur3+lnPeIYV4GIY/A8VCYDxmAKRQB0PodqaSaZ1om3rRfb3YNqa4VSCTIBZst28PTQ530f1ZB3E2xvxSqFshZ27obOdkgMg/M6sL4imeTT9NPHsaUSyvPA91Faoxry2DiGMMKGITZJ8Do7MH396KZG9z2Aaciglq3FFEt4rS2gFbQ0QynE7Nrt9Od56MYGVFsLycYtTg6Ap93/tUY1NmD7yjdQWAuZANXQAGGIDSNUJhiq0hgD+RwUitgJHbB1O6qp0X20pRFb3mhVUYIqRhAnkMtgAw+TD/A27HDfZQyrPnUUxSDG7/ZpXK8odUDLKkvHk730zWlm+ymaJG9REehIYTVkesAEgAarYdKjMWGzx46T3PCa1inCVkjKl8ArQNAPcQO0rLW0rBwgaslgMho/pykZi1UQtmje//TvOaNn1V5t9erm81nW3Uw8sQV/Sze2MYsqRiTtTehiiF22Gj1xgrOFKIZMgO3pQzU2QFMD9PZDSxOmMYteuwVTKKDmzYLVGzALZqMHSiTtDfjberFbtqGamzA9veipXW4AvX3uWgQ+vS+eS8uTO7BZn54T2tl1rMYEYHyLjtXQjUjaglGg3RxVopj3xacZfPECGu5diu9bjs708c7mJ/c65z8HM/nxjnnYwQI2jrHHzyNuzRA1+fiDCbk1u+k7YQINmwpYpdBRglq6BmsMetJE8DS9J03CBIqeozQTn4xpeGgVyVFTUKWE7I6drp2Kp6FivwC7e4jnTsV7ahVmwWy87d3E0zrxt/cRT2zGX7Gxaqs2SWDKROzqDei2VuJZkzBZDyx0z8thAihMgvZlhmK7ptQO/iD0LgzxdgQkzQaUhcTJtlmDv8snaTA0r/IYmGEJehWlWSG62yfo0TRsgbAF4jx0PGOIGhQdT/ez6cXNTL23j50nNtO9wHLUr4r4PUVYvQHV1Ijp7nFLqFBA+QFB3icuJW4OSrt/AaUVNknQmUz1terf/GDo760tkLh7HW2xhI0jvLY2kp5ex6X9A5AYVOCDUphCERtH7jsyAdnOZko7+9znSyV0Pu++t6EBfI946za81lZUJsCGEXga09OLymQwxRI6l63yk87mULmsG0eSOM4AlNaYQsGN1VhIEtCapKcHr7nZcWbgg9ZD84gidGMDtsx/7osUKgiwUYTKZLCFovMTAJ7nOMVadEMDpn8AlQkwhSI6l0Xlc8Q7djl9lnVENkPQlCUqJSQbt1T167W2oDrbMW2N2MefQXkeJorxGhtQ2aH5msFBdCbjhpbJYMs+Q2mFzudJBgbdEgx855v8YMTa8iZ2EjQERL0FUMqtr/4BULrqT1RDA2iF7R/ARDFKK7wpXZjJ7aiVG9j0tuPo+mMfWz4ZE8Ueg7sc6WW2BTRugr5ZlqTRgAWvqEnaY/AMNtaokgbPYjUoz4UeKkjwswnJpgZnpzmDaowwoYeXj2lrHaR/MEv2wSaSLGDdQ0fZbnfz49+tuoeLVyzZK5d8/QUXsO6+wPkPpbAdLXitWaKiIWkMQEHm6XXQ3ASB72yl/F6UKuskgb5+93oQQOBjtmx39pfLlteRQjc3Ybp70RM7sX39KN/HDA667yzHFiSGTFsjMR60t8L2XdgwROVz2Dh2Pr6vHzupAwxse/EEwhYwGap3etuyD6o8eKUj55uscn9XCdXbWyuvZXdB4xZD221LUfm8m0smoHj0ZDLbB/lo/y1M8Qb2qsNP734hu0MfjEHlcwyccRQoGJjkAly/ZGnYEuH3R/i7B6BvAFssQhSjJk/Ebtnm1lFDnm2vnMekVUV2TcnStL6IDTTZpVuw/QMuHslkqu8ln2P7uV0MTlHkdsCknz7p1rjWqJZmbF8/8UlzCVZvI545EX/tNpKpExiY1YhV0PrwRncdJnZiN21FZTJse80CJmyM2bTAp2N5TMOS7YTT2uiblaPzt8uhrdX5vFKIymbA91n57tnMvWYjAydMZssZPuHMkLk/MGx7QY7cbkvj5oj+6QFRg6JvriW3VeGV3DXqnx+jSprsTk3YYlEWTGDJdGtKnQk6VhgPbD5BFTXZHR5+ATCgE4gaoTAzQiWKhrU+U+4roGPLrmMbmPjgDnac0clgl8L4MHFRwuazHJ9ldygGZiVkd3pErYb5394FW7dj+wfwMwqTbcJag/I84u073PoHdC5b9RM2jtzr1lT/rrRC+YHjljKHmmIJf8pkt756+1D5nFs7uSyEkePexBDv2o3X2kKmOUtpRy+6uQkbx5jefryJnY5bkwQ8D9PXN4wbW1G+T9LT68bY0gRRPBQjeh5JTy86n3NxZJJgjXUxq7VV/2AWzEQ/s6a6zm0YojIZVOA7u2poINmyzX0ujh3vWuPiKqD0ogVkN/dR6momt2I7hCGUQgZfNJeGZTud3O4ebKGIntjp4uHuXvfdgVsryvMIcj6l7gEXO4OTAVUeNqUi/qSJmO4eTBjiT5zgOMTT2ELR+cH+AXRbC6av3+UA5bxFl+eD52GjCBKDTRJ3LQGdzxNkFOFA5MbleY7HAh+Vz2MGBp2vam11PjBJXJxtLKavD5XNoubNotjVCEqRvfdpdHsbZtdud93DCNU1Cfr63djA+eDAJ57WyYYLGsmfuZOM565tXzFL4CcEOiEyHqXQr3b5iFY1kUwqd9dQFu0Z8k/mmfTnkNVvVKggIdMQYZc0k+Qs/oAiyUKSN2S6NWGbwWYs5GOya7KUJiRkM4pSYtFFTdKYoHMxTS1FvDva6HlhEWscYS74r0H07gFMZzNqMERFMRRL2I4WrFLga5dr7OyBXJZl7+5CRwplIGox5LZqwnZLbpti6r0DrLu4EeuD1wj5JVBsd5xtAvBClysMTLNYD7yi+x6VuL9neqBthaFpdR8m46OjBOtrrFYoU07frcXbtIvCcVPonxbgReCVLGGTYuIDO6BQZPDYLhqe3MjWS2cDkGRBx1Bqh1K7xR9URM2WiaUefviDn+3VF2z02vjszNeQ6S7h7RpwfkRrbNZHdfc7vs0EI+KqSkxmiyX8jCIajFx8UyyBNXhdkwmP7iJsde9remobpimPShLU5h1ufYRRNZY0Z55AsHTDUPylFLZQgGmTnV48jzWvaqY02a1blSnH2lbRvCFLMmCd7jMQToyxChrW+ujI6TzbAwPTwO+HsA3CzoTspEGyQUwY+WSCmGIpIA497M4sQY8myVmS1oTWJ31MANNv2ggmcTbzyol87d6963Nd0sLX/Qtgd4/z/0FZd5nAxRqexm7dgcpmsIMFVGdHlXcAl+91tGCXr3F8lSTOh2qFUrqqO2sMKpclmD2ZXbNb8UuW3OYBVCnCrt7gvuvo2ShjKMxswXjuOlAKGTx5uvv96e2EM9rJLF7P1lcdTZJzPk4l0LY6Jr+qm23nTCDba2j9zdOoTIZ4/nS8pevcvHyfoK2RqBhjG7KEExrJ7BiAjVuJj5+NDhOi5gwoyC0v1zeshTBi13kzab/lGWdLLc3Q08u2V82nZx50LLZ0PrSDpCmHDTQm0OjIoIsRYWcD2QeXwIypsGmr47KGPCQJprmBnuNb2b1AMfuXvahiSDipmcLkgNbfLiE8dS5JziN/zzOopkZsRwthVzNWK/pmBBQ6FRMXRWS6SxQn5WhcvJ1dL+qi46619Jw1k5Yl3aitO90cynUM38ZExZiNV5xI28qY3lk+1oNpN65jw2tnMvnRAruOy9M7x6IjRX6by/lm/992ADZcMhEdQ7bHku1OaFq8AwYLmN3dqHzOrav+AfTULsz6jegpk5187YFWBEmJqJhgBwbd+vE0Kpt1tafuXqJzjifYVUBv3O7y/QfWQj6HaXO1llJnjvzizSRbtrm1O3mS89lxAh1tmHUbMaUiOp9HT+zE7Njl1n4YVfOmSi1It7VW6256Qqd732AB1dEGYUTS1UHf3EZan95N0pQr+wFQxqKiBJML8JdvYNl/T8df1kBxWuTUPOihTJlHE+XiY99xXZBRlKzFehaVKKw3VP6sxNMmKOclppyba1uOuy0NGz1U7LhDh+V/y00QvAL4RSfLakc5RoM2YDxXC2oeVBStxQLWL8fquM8oOxS7W12uN6mhNWbKqVwlF6jIqPzdalDGfaYSPybZymcsKlLM+/EOVr9ugptfUp6zcp9LMlTzBhNYsE4HJmtRsftbdoeiONFW5aLd9054Ajoe2ELS2cTGlzRT7HS+jMrYyt+bw/lfFKhY4YXub9Z3eUzjBvd/v2DJ9hiUgbBZoyOL9RTFNkVhEnilobmpZEiXXhGSsqm0rrSELYr+GZBkLV5RkclAcfh2lAW/oFCx02XY5uZjPTd+lZR1kDOupqWo+hKrLbYpgXikHSkFeOULYZyOUECsIdR03atpXj3I7mMb2H0cJA0uV1axIr9ZM/Oqp1n14ePxB6E4yaISyG9RRM2Q6YXBLjeX5nVQ7IDmDRYdWQoTNGETRM2Q5C0tKxWtqyPiBs2OhR5HXb+NQFui7gFXEzIWPX0KNheg+gadryqFqPa2oZpYJe7MBESTmhiYlqXUqhiY6uyxYq9N66Hj6QLdR+dp2hiR3VFk40uasZ67rih3XRpLioJ29tS8wdD622ccf2QCx0NJgunuITr7RLxSgr+9D7t9p6tNDxbcJSsU0RM6nH+MoiGlV3L1ck2GOIasq5Xg+xBGmJ270G2t7jWtXfxSvWi43CAx1fnb3v5q7I7vYwtFl6dms67GWiw5NRUK7nuTBNPb53KBJBmRR+mmRry4QBypaj2M8p6AammGlibshs2o5iY3rjjBdnWiuvvdtbHGxf+V9yuF2bEL1Ziv1tRpasTmM9iV67BhiG5uhjjGlOt0uQlthAMlbBTjtbdCNuviiXK+Zrt73RyKJbzJEzGT2mDJavSs6Zh1G1FaY6PY5WDZrNNTKazq0YZRtVbWe+ExtN63Fqyl70Wz6J/iMeW2zdA/wLZXzCMzYGh7bAer3zCJ5nUWv2DJ9BuMr0gCxaaXGXTBY+Kjjj/9kqVxQxF/9yA242PyGTCWtZc2kmQtSUsCRjH/+4PoUsTm8zqq9ZCwBdqXG3JKEZUSGu5ZWs3vaWzAZn1sxkdv63axl9asenMX+W3Qujpi2ykBJlOus3jWcWKk3Lq1Zc73y34jcTxhAkN2p8fFq57iPQ/cz97wezWfW0pH0/3CKSgDfsGQZF1dPckodGTRcdl/RZDpjcmvHBZTJMatgWIJfA8aXQ2t4pNtW5P7fe0m1LQuSAy9J03CamhZ2uNqxOUajepog4EC5HP0nzqdxruXunU3MIidPQ3TmMFq2PDSBoIemPzIIH53AdOYdXl24ji6sp6sUljf5TKUtxmDQA8d5aBAh4nLpzyFig3WL9+0k1iUdd9nPV19ny5Fbn8HnO0ZS2l6G9nNvdjAo+e4NprWFfB6C249lNe1DTysUmRNRBS5fSJ6+qAhj2nJU5rYQP6pjXy04X4m673Xwf6jcB7dvbHTSankdO9pVEsLducuVHMTtrPVffeubic3jh2HTpvk9rwGio7PJk8k6WxizSubmPu1pRRfcBR9MwIy/ZZih6Zha8LWMzziRgPtITowxAMBesAj6NF4ofPX2d3QtNmgQ0u2J2bLC7MEvS4O6Z/hfGChU9E/2zDnxpCBE/K037waM7kdvbPX7Z2tWFtd08rzsMUSSf+AqyXkc0N1l0zGxXCe5+oCmUy1No3nEZ00h+DJ1ZjefrCmvKdpMS9YgLdsHcnuHjKNAVHRDO0lNDRU91MqcWEyMFitR9jZ01Ar17l6fUPe8TM43VuL6e5xcmZOIWnK4i1a4XyJ0m4/ot1xvekfAGO5rXDdXq/tnjioW8f+93//ly996UtYa9lzX/2yyy7DWsuNN954MF89/qEgkw9E+zJkfT0O+kIcJBTklBad/p5Pa41+R9k4u1DS9q8gk/dTbf/i+s+lXP8574j+DyH0KPf22T0ff1OQkXxiJu38A2SEnlgYLj+16j/C/7KQXv/S118QCtnYXwE5Xf+dQkb1p89He8K2V9F/Ck3foR5iH8nYVxr1wP0p9r3S61/ham8pVX9d6D/1/C/IvwrIeinXv3Tulfbag0CntNHi6VS1UgcyWeHaTzbF8SfCe3/S/HcAOCgr/cY3vgHAMcccw6c//ekRfzv22GMBWLx48fMc2vhEkPN527fl2hlkAs0HXj77sGhnUAsEWY9PTz6KrJIh6Eygefcb5oxw0KNtjJvDgUUOAEHW563fuARfqpVrRvOWr7yEIJtS+xfmnyDn85YvvyTV/POW/ziTICPUTivr8ZbPnj2u2ikeCH8GGc3b3n+caDu5v/3aheNK/weCjK94+7uOIePL+LWMr7jibfPIeOPLr+4vgpzP277zajH/F2TTzv8+f/vfcke5BDmft171ylTqP6M0n54wh4xU7K8V/z5zdt1zj6rRxngl9hfLfdH867GzyOj61n+tEGR9/vafXyB3jEbW4y3/eppY7CuNIOvxt/91sZzvzXi85YvnpTf21IpPzpdb/xmt+MhZs+qe/2uFjFL82+zZovr/1JzZZEY7K2QcI8h4vOVTcsfYZbTin1+YXvsPsuXcSyz2D0RzD2lkAsW7X3/UmMc/apSezOPuwbdREGQ0f/feY+VqbxnN3/7zC8TkSyOT0Xzo/Fmix0hK8t+B4KBG+NRTT6GU4nOf+xyTJk0a8bcpU6YAsHnz5uc/unGIqBDzrdfdUG0zPNYII8Nnb15JRyEec9n1gKiY8IHNy7EZg39w94U8L4SR4T9/sMy1HKl0HTmQJx4Pc0TFmP/5mxs5rtVAdu/vGW2j63nLLxmuvuJW94uXPgdZ5R+h5DQqxFz9zt9iUsw/V//j3bLy3/8HCBMR/1MLjJp47DHHqGT41heecLfqBmMfIEaFmKve9itULgv5MRd/UDiUVhLGlm9c+RThrDyZQ/i9ByL/699aQumEDI0C8qURFWK+9Vc/dcc6SMgvDvG/bhYZgiiiYsxVb7kZU4zxBBZAVIj5nzf8DGtqF2PVK0rW8IFty8AqtEDsXzKWD65eSVtS3353tHzEPI+hV2J/rB06WmsMUcLwT0+uwjfAGIqvl5UWFWOu+teHCBe0H1zh53nLT7j6n+53bTUb6r84dqgRFROu+ttfyMkvJVz97tvF5EujZCwfWbzKBbQCteGSsXzqzlWuBXkKUbKWD61c6XIvARdYMpYPL1uZWv1HpYSrP/xHKCYi8kvG8q/3rnIt2EVGIIuo6HKvPY+3GzP5hYir3nJz9YiqtCGMLP/5w+UoY8c0/tFp6gg7CqLQ8M0v7f341LGSf9W/PuRamu/jPfUSq9cCYWj499tW0VVMoHXs5VdqTygttv+wv3heIYK3l+R2wwZ3XlUQyJB/vUMpRcf0FrE9CaVgQnMwXvZEDhhKQZefkesmoaCzLTNC/6MtwvFG1EpB+7RmuXZOCtqnNqXX/nWFf2QUoBS0T0mx/hW0dzWI8n97V+O40v+BtFJXCjomZGX1n2b+ATo6s6LtFDs65Py/NOoh/kw9/09rFksMlYL26S11n5jWAgro8gRjf6ArCOqee0bPRw5+9FXbF2zlOjlb//qvFZSC9kl5We6fLCdfGuL2r6B9yviK/Q8E0utfARMbUsw/wGRB/6eAyZkU619B+2TB2gMwKc32r5DNvbQS9T/S2FvtfUzk1upoosMMdVF7E4x/paEUTGyU2/ur7j0cBrWHg9oYP+aYYwD40pe+xJYtW6qvr127li9/+csopaot1Y9gJPycx19+7mX4Uu1UfM3fnTtdTL40/KzHBzuni7VSD3zNmy6dQeAPa6WeIsfpZ33+8j/OxxN4WhPAz2he87Ez8VPaSt3PVvhHSP85n9f8y4tSzT+v+ccX4Au1k/SzHq/58Kn446id4qhPuO3xu5/R/OXfHi12lIOf83ntJ88ZV/o/EAS+4nV/PYdAqJV64Cve8Jfpbefn5zz+8vMXifk/P5t2/vd57b+dK7b+/ZzPX37mpankn4zSfLB9hmgr9Q9MnU5Q59xTq3ykEvtLrf0MmvfPnZbaVup+1ue17zxuRO45tvI9XvP+E8ViX2n4WY/XfurFcr434/Gaf35hKrkfHP/+/Wy59Z/RinecOi21sWdGKT4wfbqo/v9hxvTUtlL3Mx6v+YeFYus/oxXvPCm99u9nfZd7CcU/1dwjpblX4Cve9BfTxzz+OZD61HiGH2j+6s1z5Wpvgea17zwOP6Wt1INAc8WLpsnVHrKV2lP9x58HZSFvetObsNby4IMP8vrXv7769OGcOXN45plnAHjzm9986EY5jhAVYr771huJhFoJh5HhK7esFpMvjaiY8NGtqyhaGZcURoZv/O9KwmhIfprOGI+KMd+74lckoUw7n6hk+P7f30FUTKn9C/NPVIj5/gd+l2r++f6/3EdUErL/YsL3/+keIqF2arXAaPy5t1bq3/3qU0ShDP9HhZhr3vPbcaX/A0EYW779zWcIY5leKGFsueq7yykl460Xy/4hKsR892//T8z/RcW083/MNe+8RdT/fu/vbkql/kvW8NEdKykJxf4lY/nY2tWEdc49o3ZgeR75SCX2l7K9EoZPLl5DKYXHCECZez776Ijcc2zlJ3z/kw+Jxb7SiIoJ17zrN3K+t5Tw/Q/+IbWxZ8lY/nWp3PovGcvn712T2tizZC0fX71aVP+fWLma0ij+bTwjKiV8/xMPiq3/krF89oH02n9ULOdeYrF/JJp7SCOMLN/4yaoxj39qFU8fbohCw3e+vliu9hYarvnso0SlNN2OMIQwNHzh92vE+L9aezoM+OegNsb/4R/+gQsuuABrLXbYoq/8/tKXvpT3vOc9h2yQw7Fx40be/OY309nZST6f58QTT+SRRx6piaxaQGnF5Pmdoq0Up7VnD4t2BrWA0orZQU7ghMGyfAVTJuZGtLMYdWNnDMY0llBaMXleB0jZv4bJc9pSbf+i/KPVEf3PbkHooTUn/6jDo53N/uJAEg+lYfI0wXZyWjF5bnvd6X+sEjSloGuKbDvXKZPl/L80jvC/LFz8I7f+lVZMPlru+ktCA7N9ubWvgVlZuVaC+4vRnxh/Ht9bjv2lbE8DsxqyY3/9R5nuaLnfIR+GVkyeIXeMhdKKybOaxGJfachzP0w+qjWV3A/l9Z8XWP/D5M9okZMvjYr/E/W/ufTqX2mYPEvuGB8NzGhOs/7rIPcS9D/S2FvtfUzkjvK3NG2MKwVdU2WPkZw847niz30Pbixj9VpAKZjeKrf3J81/B4KD8lG+7/Pb3/6WL3/5y5x00knkcjlyuRwnnXQSX/7yl7nlllvQ+tC7v927d3P22WcTBAG33norixcv5sorr6S9vf2Qy6oV/IzHyz90llwrXU/xl2d0pbedWaD5u/YupE668X3FZS+Zgj+snZAahW/Hm+P0M5qLP/BCPE+onYqvufDdLxBr5yINcf7JeFz4rpNTzT8Xvu04fKmjBALNhW8/YVzZ/2j8afaIwn1f8/JXz5Jr55TxuOh9p44r/R8IAq245JUzxdoJB1rxFy+fNsL/pgl+xuPlHz5H0P51uvk/43HR358h6n8v/uCZqdR/oBR/1zqFQKgyE2jF2yZ1EdR5YaBWT7j4GY+LP/BCMdsL0Lx55mT8Otd/reBnNBe9cZ6Y7/UDzYVvXiAW+0rDDzQXvf90Oe4PPC5850mpjj3fMn2yGP/6WvH64yenNvYMlOKtXV2i/vdvp8jJl4bvay7822PkYn+teOOx6fa/L//wOaKxv2TuIY3AU1x2fteY8+/o8XR64Aeal796pmgr9YveOA9f6Cghafi+5vUnTxatfUry34HgoA+b8H2fj3zkI3zkIx85lOMZFV/60peYMWMGP/jBD6qvHXXUUWMm/1AgKsb86N2/QgU+ErfORLHlv29bS8c4byXtYZhhd5JPYmzko71ep+/YcMuD93CSb/EGNLkdimgQGnZbmuIChd48PesVJmNRCahYgQZ/AKwPKLAKWvsT4kTTu87Jy29XRANgsu53XQK/CEkWGrotDWGJeNDHhor7v7+WY7W7/yiOFa1xYZ/zGG8b41Ex4dq/v5VjWw3kBOSHhh//853uF6HNeUlU+UcoOYmKMT/+6F2Ycc4/+0JUSvjxvz8oK/+T90OYjOp/NJbp2QEaMiWUUmitMfEgKrOLrq1raY8KTF3sOLDU70jP7/bI7YKCspisC/l1qDA7DWiDTTQqUuBZrKJ656byDTpIMDvd99iMReUSTKTR2YSmhhKFUkCwNYcJAAtWQzAAKOgsDOxzHs9qpR4afvStZ9zhTgIF2qgYc92HfofKZaFhzMWLI0wsP/jOUsJZeTJC8q/50UrCEySkyyMqxvzoXb/Aa2sTkp9U+V8HIkMQRVSMue4Dt2GKMV5WRv617/01dgzamTbqiJmZATyvBNojCQtM3pQj+3Qfvnbt7ApRgOcl+MoQW00UeVTu2o+35DA95bZvyqK0Jbs5Q+tgzMQVCuUb/GyM3ZDHBBavqDABmKzF71fE3RbrW8gkBNsCoj7DLWvXcJJVqFBhcgadMeTyIXp7D4PLI1yXdcW0uIRWRUzci7IxigR0hE36yz5FoRKD8gaBgNa1JVSiUBaSvCXoVsS9lkyPoiMsMmljDuvBb7au5vitirCcTxgPdAx+AYqxxXrOZyoLKgHjOz/X2GvJxQUsGpUYMBqrQVW7Alq07qM0GFPc5aFj971xCK22F3REaSAi6+1k6g6nXxO4z0cFiHotXkkRN1imDPTs85ru6U8PBFEx5tq/vxWsRXlj73tLGD63ZB2+Aeq/NnPIERUTrvvK44QL2g++8PN85JcSfvy5R8FaaEjfOadRKeG6f7xdVP6PP3aPmHxplIzls8vXOfcisP5DY7nygXXY9JUdANdK/dNr17rdIIHyQ8lYPrN6bWr1H4WGH3/mYRA6yiI0lq88tM7FHSIjkEVUTPjRu36B8mUSn0ruYZOxu/4tqsg03UexT1HwNR3sBr/I1J0BQcnSaHvp3Fggv8viheAXDMZTGF8xfZVBlTStu8F64EWQLYV4poSNPWzog7FM3JDDBBbTYMAopkUlVBIzZXufG4SBeACadlnu//4ajrGWbGYXyvfB84As1njY2EPrflCu0N+xsUSmGxoHEro3eS5e9wFtnQ2XS5jKuji+6lPKMbn1LUGvZnp39z71M97q+6MhCg0//J8lovKv+8rjoHWKtD6EKDJ89e51dIkd4+lqTyhd90+NH1bZyS9/+UsuvvhiXve613H33Xczbdo03vve9/KOd7xDemj7DaUV00+czMYlO5E46kYpmDMpT48+3BtD7Bunsp6/ChaRtQnEuJ/8sDf8cpQPryn/7C+WH8B7971/s088n0JUPcLZ/yTUhnVC8mH68RPY+MzOcWv/o6HKP09vwwr4R6UV04+bwPpH149Jcb7eoLRi2jHtbFy2W07+sR1sfHLrPv3PiZkd/HXLcvJ6DwMpAG3Ao4vd7z+g7rHnFJWGGUc1s2FVn8h4lFbMOGEiG1f1pZN/FMyc1cRiJROcKwWzZjSyi0hEvjSUVkw/oYvN60uy/P/I4NgLrwMorZh+/ETWPbpBTv4Jk1n/xNaaXX+fhDc3PMnC1u0j/9AH3Ff+eb74+SH4jj3x+B6/BzifB663Wg6eRRuVmzufa7/r7uc1sv1DDthW/tkTAbC1/J6nDl6EeR4lJbf2J7Hhqb0NsPbQwPymPGsKBdJ4yqDSiunzWnlGsJX6tPltbFy+O52xj1ZMP3EiG57eISQfph3XycYlu1Kpfw3Ma8yzYrCARPSpgXkdeVZ2y8iXhgbm5/MsH5DhXw3Ma8izrJhW/odpC9rYuGiriHwNzGvLs2KgkEr+UVox46QuNj4jw79KK2acOIn1i7bUXH5WxXxo5W84vnGTe2Fd+ccDGoHFi4befO8+vmTNKAISoFT+/537eM9jo3x+zybDlX2C4Q9rDY/Zt4zyXc8DadoYVwpmzG5i/Zp+sb2vGfNa2bBGpvYnDaVgbmeeQcFW6jNO6mLDU9uf+83C2K975+bMmXPAP3Pnzj3kg121ahVXXXUVRx99NLfddhvvec97+Id/+Ad+9KMfHXJZtYIXeJzz1pPxpNoZeIoLT5ggJr/WmGO289fqMbJChfdDjfEWQHqB5uw3L0QLPa3t+ZqzXn/MuLX/54KfqfCPzCMzXqA56/ULUqt/L9Cc9aq5eELtfLxAc9Zr5+1T/9Mzg7y1demzN8UPU+zZSt3zNee8dKpYOyUv0Jz118en1v59rTjn/C6xdnq+Vpx7zqTUtvPzAo9z3naKmP0d4X/NWW86QaydmBdozn5LbVvZ/6VaxMKg/pPfIzhwPJ98pBL7i+W+KF45pTPF3K8565IZYq2cPV9z1itni8W+0nCx3wlyvtfXnPVX6fW9vlZcNrkTX+hhA18rXj4vvfzjK8WrJkyQ079SXDZRTr40PF9z1mVHyflfrbhkTnrt3wu0aO7lZzzOetPY+J+3tCzl+P5NNZdzuGO81fdHg+9rzr5giljtzfdd/JvW+NP3FRcf0yk2fz/jak/jppX6mjVrUHsEE7Z8y8f+vn4oYIzhtNNO4/Of/zwAL3jBC3jqqae4+uqreetb37rXz5RKJUql0ojXTCLXxjcuxfz0Q78VbaX+7T+sp0OonUKtcZLZyHiKu8bbHWVxKeH6j/6OY0ZppV7LGceh4Wf/9kf3S0pbqf/0Q78Va2USlxJ+9u/3YcYp/zwX4lLCz770iKz8zz20z1bqCxu68UY7tPsww578GYeGn35vqWt3JRCgxaWEGz5x12HVSv1Q9paJEstPfrSCaFYegU7SRInlup+uTm0r9bgU89MP/kaslfpw/tcpvARxKeGGj/1BrJV6XEq4/iO31axbi8awkM01+e4jkMfzyUcqsb9UK/UQy5XLN4x5K/V6yeHiUsIN33iKSKiVehwm/OzKx10r9cOqV+GhQVxKuOHj+3q8bQzkh4afffp+MfnSCI3lP1dtEOvjHBrLNx/akNpW3qG1fHn9erFW6qG1fGXt+tTqPw4NP/vKY6Kt1L/x5w2pbaUelxJ++sHfiLZSv+Fjf6h5K/WcijkmI9MR8XBDvcSGY4EoMvz0mgNpsXvo5d/wjadGbaU+fiqfz0YUWf7nvg10hXKt1H/6wd8cFq3U9ztEsNaO+NnX67XYEK9gypQpHHfccSNeO/bYY1m3bt9tmb/whS/Q2to64mfzot/XbIzPBe0p5r5oOlrorm2t4NipjWLya42pdt/n4x2O6FMCB3HXENpTzD1jGlqIGLWGOad2jVv7fy6I84+nmHPq5PTqXyvmnDRB0P4Vc14wcZ/6n5op7PX1wxU92fyI37VWzD2mFS1UHNGeYs7pU8SuvzS0gnnzW8RuXtMKjp7bPK5unjsQaE8xElPXHgABAABJREFU98wZR/hfCNpTzDljqqj+a+n/272QvJK78fgIaos+ffD5SDX2l7J9YGFLY6q5f87x7XK+VyvmLOxMb+yjFXNOF+R+rZhzyuT06h9Y2Ny4/0XPQy1fwfETU8w/wEmNgvoHFjbJyZeGNP9q4PgJKbb/esi9xiD3mBwUSWl6d8DoE3k8QAZaw9wFLXK1N42Lf1NKQFrDcZMb5fhfmP8OBPtlosaYET/bt29n4cKFzJkzhzvuuIOenh56e3u5/fbbmTt3LvPnz2fjxo2HfLBnn302S5cuHfHasmXLmDVr1j4/87GPfYyenp4RP1MWvvSQj21/oX3Nya9cgBZqZ6A9xQvntYnJrzXqf8ntP9a2t9HjHSaPFe4ntKc56S/mocSCQ83Ci2aLtXKXhjj/+JqFLztq3PLPc0H7ioUvmYH2hezfVyy8YOY+7f9QPh0sje0NjWxsahvxmvYUJ58xUWz9a19z0sVzUmv/nlacfOoEPKHg3NOKU07uwEtpO0Xta05+xTFy9u+lnf81J718nqj/PenS+TWTP578xxE8G0uCKQf92UrsL2X7PopzJ7aml/s9zUlnT8GTyr18xcJzp4rFvtLQvuakS+YKxp6KhS+blVrf6ynFuZ2tYq20PaU4a0Z6+cdXivPa2kRbqZ/XLidfGtpTLDxvmhj/elpx9rT02r/2yrmXZOw/BrnHkRxg/2CAZWqS9DDGDNpTvOD0fT+UU3v5Lv49HDZmawGtFWfObhWsPcvy34HgoEb4oQ99iCeffJIvf/nLvPSlL6W5uZmmpiZe9rKX8fnPf55ly5bxoQ996FCPlQ9+8IM8+OCDfP7zn2fFihX85Cc/4Tvf+Q7ve9/79vmZbDZLS0vLiB/tyfXxiksJP//E74mF2tnEseWH92wUk19rjBen3JfN8NULzpcexiFHHCbc+O93k0RGRn5kuPkLDxILtRORhjj/lBJu/tKD45Z/ngtxaLj5648Rh0L2HxpuvvLRfdr/eAkZC77P1194wbPaxceR4efXriCW4p9Swk2fvS+1/BMllv/76SqiRMZPR4nlhp+vJapRK+l6h+P/O8TsLw5Tzv+lhJs+fY+o/73xk3+omfzx4j+O4Nm4c/4c/pydfdCfr8T+UrYfYvnmyk3p5f4w4abvLCaKZeYfh4abv/mkWOwrjThMuOnT9wr6XsPNX34ovbGntXxzzSZCKxR7Gst3/5xe/gmt5b83bhTTf2gt31gvJ18acWS4+RuLxPg3MpbvPJFe+4/Dcu4lGPuPRe5xJAd4bhjg2yeey4BKzxPjcWT5vx+vJI6E4s/IcNN3FovV/qQRx5Zr/rRJrvZckuW/A8FB7RD/8pe/BKC/v/9ZfxsYGADg1ltvfR7D2jtOP/10brrpJj72sY/x6U9/mqOOOoqvf/3r/M3f/M0hl1UraE8z//xZLLt/PUbAPrWGk2c1s8FT1L95HjhG2xhfHE+giI+/sI2dNkRFCn9AkWQh6LdkuiPipoBih8J6FmUAo0CBjsAqnNdXkNtlML6i1O6+2x9QmAzY8tl1KnGfMT4EA5agP8b4HnjQ2hLQ3R9jAROAjiHOK+IGi0Wxsn0i9x07k21NLcxlsMYaG1toTzH/7NnwxNJ9vqeWNzdoT3HMi6ez7P6NpNE9al+z4NzZLLt3LUmNzxraq3xPccw501nyu+UYoc0xSWhPMf9FXSx7eKsI/zr5U1j2x3V79T+j3cy9xJvMwIChMKeV3LaI4okWYxRx6EhPFzV+AeJGiy1HFioBm7GgLNYolFHu/2qYLGVRnsUWyuTpAZ5x7/cNmSAhjjV6R1DlVxTo0P03KXd3VQbivGWTmch9C2axtaEVfw/61J5iwUkdLH1it5j+jzl3Jsse3VZX/DNWK1ErOO7Edh7uK46RxGfLP/7YVv6oxteRAfuLSvy54vHdJAIdr7WnWHDOdJbcsWzshdcB3Pxn8szvV8jJP+8olty1pib+fzT/MaAyPDpjJt6EElo5xokTjVIWrSzGKozRVS6y/T42V/0NlMXv9snuMgzMBpRF+wZ6AxevxwqrwXoWHSlMYKu+RPd72Jylw/fZlcTYBKxvUdriBwlqS4aos6IPRfOSGBXG2GyASgwYA4mBTFDOAxTKWAhD0B59R+fAKpQFE1i8osJk3L/5bTEDU3yUhvacT9+umDhTzie081suByj7ReO+BwtWgxdC0GfxByKXwBlbvqVdUWVuC6oQkrTmifPu8ypx+Ud2RwmShKQ1j9c9SHFqk/uI52QnGTAZi0oUxrdD528nTsSAyvHY3Ck8OG8Gc38aHbRtaE8x/8WzWXrPGpHblz3gjPZmnij2janvHW2uY1lE1p5iwakTeWRQ5qgD7SnmnzGJZY9sG5e1h+eC9hTzz5vJ0j+ul5N/9jSWPbgplfr3FJzW1syjPX1IrABPwSlTmnl8Wx9C96aIwgNOb27mkZ4+EfvzgFNbmnm4X0a+NLSnmH/KZJbdu04k9/QUnDq5mT/vHlv/Wy/QnmLBS+ew7I8bZHIvX3PsebNYcvdqkoMP454To9Vve2yOFVErxWnN6NiS2V2if1aOYMCiEvBii1Uuji90WUgUuV0uVlYJ+IMJOkywWoGvwcLAVB/rWWxgwSqaV8YoYyhMylZDZBNAts/S1hjQ0xfibe0Fz3MJi++Dp7BaoYqRKxIoRf/sPF4JMv2GYod2uYXG1a/KcXsF1b0BnDzKsb0uqWpuooH2rM+itjxLwlksmTqJaYt31Ooy1B20VhxzfBtLnurGCNwco7Xi2FMnsnTRLuxB2P/hfsOH1nDKtGa2eEqEf7WvOeYlR7H0nnXYOr856qA2xitnjH/kIx+hUChw2mmnAfDII4/wqU996tCNbi94xStewSte8YqayqgltK+Yd+Z0Vjy0ESNw54bWimOnNrHJ1+MyOByNvH4ZzmeX18TZb3wh1/RvxHR7NK5XlDqgZZWl48le+mY2s/0UTZK3qAh05Jx0psc5V8rOcdKjMWGzx46T3Hc3rVOErZCUj7T1ChD0Q9wALWstLSsHiFoy6AaPV144lV/cvZkwsYQtmqDf0j9NMzDNoqyTGTWbEY53vEB7mrkvnMbqJ2UK41or5p7axYo/bU7txuy8M6ez4oF1JKGAfF8z99Qult21EiOwMS8N7SnmnjyRFX/eJif/lEmseGADZi8746MlNbdmT2Td5iKbTj6Orj/2seVNMVHsMbjLkV5mW0DjJuibZUkaDVjwipqkPXYb3bFGlTR45cTCc7JUkOBnE5JN7tgIkzOoxggTenj5mLbWQfoHs2QfbCLJ4jYMPMh2u4SkMIkyb0KhK6FtsUehxW02PGv+WjHvmDaWP9X9fFV5UNC+Zs7pU1jx+I5UFgc8rZg3v4U/P1YSkz9/XjP3r5XZmJeGiz9nsurJHpJY4saoMv/fuSKV9q99zZwzprL0rpUim4Pa18x90Qx3Y1wtvn+UWW3zWvj6Cy8gf+ZOMp6T3lfMEvgJgU6IjEcp9LHWRfHRqiaSSeUKhrJoz5B/Ms+kP4esfqVCBQmZhgi7pJkkZ6s3uSZ5Q6ZbE7YZd1NWPia7JoudaHh75xS+372ZuKBIGhN0LqappYh3Rxs9LyxijatwLVgziC4MYPLNqMEQlcQQlrBNLVjlinIqSlB9PZDLsuyCLnSkUAaiFkNuqyZst+S2KabeO8C6sxvxA8Xfzp7Mjfdtpa/FYgKXU3ihyxUGplmsB17RfY9K3N8zPdC2wtC0ug+T8dFRgvW1K+RVCgzW4vXsojBxCv3TArwIvJIlbFJM3LUDSkUGJ3XRsHUjW4+bDUCSdT6y1A6ldos/qIiaLSbnfLcuaaxnyezWhO0Gq5+fxVZi/+X3rxMpDHsoFrY28uSWfsbuVrD6gfY0c07o4LFHtmMF5q89xdyFnax4bPu4rD08F7SnmXPGNJY/cOiPGdw/+Yq5p0xmxcNb0ql/FCe1NPJ4bz+xhP0rxQmTGlm0vR9S+NSypxQnNTXxWG8/iYD+PaU4qbmJPw/0k6RQ/1or5p40gRX3C92YoxQnTGzk8e5+TEr977wzZ7LiwU0kAulv5YzxZffVJvavyhmlCL+eVv63cDzbjplHZsDQ1r2D1WdOonmdxS9YMv3ugbMkUGx6mUEXPCY+CsYDv2Rp3FDE3z2IzfiYfAaMZe2LG0mylqQlAaOY//1BdCli88IOKG+ohy0waZXl1ed28cvfbyKzfAkqm3Gb4n4D1vexGR/d2w2ZALRm1dld5LdB6+qIbacEmIyLx61nMVmLilTlfl2M7260BVCJe5jOBIbsTq+amwRa8ZY5k7mhdzPNjx3u26wHDu3BvGNaWfZMt8xDoZ5izgkdLH9K5qEYaWitOK6rkW1irexd7Wn5fetJxuPG+GWXXcaPf/xjdu7cyXvf+94Rf7PWopTila985SEZ4HhDXEr41efuQQX+6I9X1Ep+bPnpA5vpOAzaGRwMRtvYsSji0HD1rk3YjD0443+eiGPLz2/fiPXU4X8L0kEgDhN+/aX7OKZVrpX6LV9/xP2SwnPGq/wjdMZvXEq45b8ewYxT/nkuxKHhlquflJX/rScgTPbqf0azCjsOCCuODL+6YZXrZZXxnvP9h1x+KeGWK/+EymWhcczFiyNKLL/8+VqiWXkkmohFieWmX20gOiEjIF0ecSnhV5+9C6+tTUZ+OMT/OoWXIC4l3PKVBzClBC8nI//Xn7+nZndsj+4/ZBFay9U9G8EqtIAvi6zl21s305Yc/n70YFCJ/bEW5Y297w2xfG/NFnzD0FPxKUIcJtzyo6VEC9plct/QcMv3nnGbgnKn2YkhDh33y8k33PKNP4vJl0ZkLd9dt0Ws7hIZy7VPbHFPHaYQobV8Z/PmoScqJeRv3Jxa/ceR4ZbvPA2CrdR/9NQWTJDK0idx6HIv5Qcy8su5h63xAyn1mgNEieXnd2xCGYtE6hkZyzWrtxB2CBGgMOLI8sufrRGUb7jlR0tBS2R/8ohjy48f3UKXYCv1X332LlBabP9hf3FQIcLXvvY1Tj75ZKy1z/oBWLhwIV/72tcO6UDHC7SvOekV88UOoPe04oVzW8Xk1xqjPbFicU9Mnd/Yhi9EjZ5WnHp8G16dE0OtoH3NSZfMQwnetbTwwtnj1v6fC9L8o33NwpelWf+KhedPR0vZv69YeMEMtL93+aPz5+HPWdpTnHT6BEH9axZePGef+h/v8DScfGqn2D1JnoYXnNSO0OUXh+P/BUf4Xwja1yx8+VxR/Z90ae38/3PdmCoJD8X5+TY8qdhfwXktrenmnkvmyeW+KM6dkG79LzyrSyz31L5i4blTxGIvaWhfyXK/p1j40lnpjT0VnNsht/49BWfNSC//eMB5rbL6P7etNY33RAHl9X/uVLH17yk4a1p67b8ucq8x8D/1mgN4Gk49Tq727il48YR088/JkrU3T7HwrK7Uxp+epzhzVqsY/0vz34HgoEbY2dnJn/70J6666iouueQSFixYwIIFC7jkkku46qqreOihh+js7DzUYx0X0FrRNX8CWoiclYJpHTkx+bXGczllrRSzg5yYe1YKpk7MSzQLqAtorZh8dAdKSAFaw+S5bej65+aaQJp/tFZl/adzAWilmDy7RU7/SjF5dus+5Y+2LKWf+DsU0FrRNa1x1HZfNZc/rx1dZw5grM5BVSi6pjSI8b9CMaUrX3f6HytorehaMEHM/w353xTr/+gOWf87v7Nm8kf7WumNca1gdpA/uKT3UMhHMTuXE+M+aVRifzHbB2Y2ZAW4vz6eodIaumY2ieWeWikmz2xOL/ercuwnyf1zWtMb+6CY1ZAV6RYC7vpPb5Hgn/qAVopZuZyc/lHMzufSq39drj1I1d5QzGxOsf1ryrmXHP+PRe5Rrw9XKKWYOiknF/9U/E9a7V9B19QGudqbcvHvaPY/Hmqc+4JSML1Nzv6Gak/1b/8H3dAqCALe9a538a53vetQjmfcIw4Tbvvq/XKt1BPLjQ9vpSMcn62Mn6sEEUeGH3ZvkWulnlh+ddfmVLdSv/2/H2KBWCt1yx1XP+5+SWMr9Qr/SLVSDxPu+PbjmHHKP8+FODLc8cPFsvK//5Rrp7bXVur1mdQcKsSR4bab17pW6gK9vOMw4fZvPepaqacQsbH89tfriWflxeT/5rZN6W2lHibcduV9gq3UTZX/dQqXQBwm3P6NhzFhgiewBOIw4favPVDDVur16z8ia/lh72ZArpX6j7Ztpc0c/n70YFCJ/aVaqUdYfrxuW4pbqRtuv34FsVQr9chwx4+XuVbqMt1kRRFHhtu/+Yio/Du+u0hMvjQia7luwzbRVuo/e3pbalt5R9Zy7datYq3UI2u5dvPW1Oo/jgx3XLsEIqFW6tZy/ZJtKW6lbrjtyvvkWqmXc4+at1If7eZYwZ1HV3vfItdK3br4M0prK/XY8ttfrBOVf/v1K1LdSv3/PbGNLiH+r9Sexm0r9eHo6+tj/fr1rFu37lk/R/BsaF9z+uuOF22lfu4x7YdFO4ODwXM+Me4rXt7UIdpK/ayTO1PdSv301x4r2k7ltMvk2jlKQ5p/tK857ZVp1r/itJfPEm2lftpfHLXPdjqjjcqMg3BSe4rTz5ks2kr9tFfPT287Sw1nnDlJtJX6i06fkOp2fqe//oQj/C8E7WtOe80xovo//a9q5/9H9x+y8FC8vLFTtJX6xW3pPsbh9NceK9pK/cJJ6db/aRdME22lftqFM1LbylL7itNeI9hK11Oc9oq56Y09FVw0UW79ewpeMju9/OMBF7fL6v+izvY03hMFlNf/RTNFW6lfMDO99l8XudcY5B6jP5wm20r9rJM7RFupXzg53fxzxtmTZGv/F0xLbfzpeYrz57aLtlKX5L8DwUHdOByGIZ/97Gf59re/zY4dO/b6HqUUcRw/r8GNRyilaOpsEGvnoRQ05/xx28r7udq4KKVo89y2uMTNa0pBU2NZ/+O5b8c+oBQ0duQZkGqlq6CxPZde/esK/wjqvy07bvnnuaCUorE1K3bHnFKqrP+9L4DRQhbpJ/4OBZSCpuZA1P82tae3na5C0dTso7bItVJvavJRpZTqXymaOmTjzwr/p9D9uvXfkRPkf2jszNdM/qg3pgpzngbatI8GJPrVKBRtvr9P3zveUYn9lZJ5ckgDrYGHEsv+ZKEUNLUEqC0FIfmKxtZMemMfpWhqlztGbWTsnz4oFK2+3PpXKFqyntO/5KOTQtBK0er7aBSJkP7bfB+tFEkK9e9ibzn+rdp/mv2vZO6llcs9aix/tBxA8uEKpRRNDXJ7HwpFa+Cho5T6XwWN0rW3liC1tQeAlpwnx/+6XHvSqmYd6w4VDmrr/gMf+ACf+9zn2LFjB9baff4cwbORRAl3Xv0wiVQ7g8Ryy+PbxeTXGmoUs7NAEhmu79lGJESNcWK5/b6txEk610cSGe763p9JYhn7S2LL3T96atza/3MhCSv8I9PKPIkMd1/3dHr1HxnuvmGZ2PyTyHD3T5bsU74ahUDHA2MlseXOWzeQxDKzSSLDXT9YlFr7j43lD7dvIhYKjGNj+d2dW8TkS8PFnw/J8k/K+f+uax4nETpKJIkMd337kZrJr9enRcC10r6+b6tc7G8tN+zYnmLuKcf+Uq1csfzfxh3EKa1NJJHhrpvXiOWeSWS4+/9WiuV+0kgiw13ff1zO98aGu3+8OLW+N7aW/7dZbv3H1vKLpTtSy/+Rtfxs+3YiQf3/bKucfGkkseXun62Qqz1by83L0+1/RXOvMHG5R43lP9dxplKIE8vt928Ti39ia/m/DTvE8g9pJLHlzt9ulKu9xZa7bl4jJl8aSWL55dM7RPnnzqsfEqt9HAgOamP8Zz/7WXXz+5hjjuHss8/mvPPOG/Fz7rnnHuqxjgt4gebst56MFwi1k/MULzuhU0x+rfFcd6t5vubVzRPkWql7ivNPn5jaVupeoDnrb05Ej9JLt5aa8XzFma8/Bu8waOdRC4jzT6A583XHjFv+eS54vubMV8/Fk2pn5mvOfO28fdr/qEnNOHjSxPMVZ790Kp5QOyUv0Jz1xuPqkH/2rY9DqSlPK845r0uunZlWnHv2JPxxYMsHA8f/LxCzP89POf8HmrPedIKo/z3rLSfVTP5oN1ZJH8Xho3h100Sx2N9Xild1pPcYpUrsL2X7PopXTukYc+63o4gby5F4vuasS2bK+V5fc+YrZ4vFvtLwfM1Zbzpejvt9xZl/Nb8OY8+xga8Ur5w89ut/uPyXz5OTLw1fKS7r7BTV/2UT5eRLw/MVZ152lNj695XikqPSa/+eX869JGP/Mcg9nus4Uyl4WnH+6RPEaj++UrxiitwxrtLwPMU5F0yRq715ysW/o8afo9XBDu8Ndc9TXLygQ672E8jy34HgoFupK6X4zGc+w8c//vFDPaYjOIKDxqhO2TK2lYgjOIIjOILDCPXaBusIjuAIjuAI6hv1+rTIERzBERzBERzBERzBERzBEdQGo933IN016giO4AiO4Lmg7EH0PL/sssu45ZZb+PnPf86rX/3qGgyrtri46a3oaVNIWhvwtuwinjEBr6+IKoTge5h1G9FNjdDYCIOD2FKIGRhENza4LzAGlc9BYjD9A6jGPLZYQnleVYadPwu9owfb3IDN+PDMKnRDA/geBAF4GnJZGCiAVtiWRvfB9VtQmQAbxRDHWGNQWmPDEMrfr3wf1dSI7WhB9QxAqYSNYlRDHtvXjxkcRGUybjyZANPbX+3rr7Ryf5vQCX39bhxB4HaNlcK2N0NiUQOD2F3dqIa883Rx4sZgLfjufgrledgkcbrQGjswCFqR7O5BBz564gTswKAbRzYDpdD9W9EjgLXYwEf1DUCSQDbrXt6xE2tM9Swm3dwELc0QhpDPuc9GMfHkVlRiQClUlPCvA79mgh3Y63X/xKYXMOg3YvoHwBpQGp3LVuUoz8NGMTZxrR5GPYexcq3L70Vp953DobTTzyjfU7km+zpzQbc0gbHYUsldnzCqjl1phZ7Qie1sxTRk0L1F1K5ubEcbeArVX8Tu3OXsYrDg5mUt7CHLJom7RtYM2ZjnYYolVCaAOHbz87TTUybjbMHYZ895H1BNjVC2UZqbmLttMe/rWrHX9y4qtPOD3uPA06iGBsy27SN0bIfLVbq67irXbW/XAUDnc26uQVAdv8o427fGDOm2PH9VtsXKerJhWNU7ZVvBGpQfDMnyNCRD8lUmGLInz3PXcY9xVcesNDaOhl4vvwagAt/Jr8xZq6H5qpF3YFX0oY6ehc0G6E07oKWJeNkq/LmzYWAQO1hwPJbJYPr60G2tmJ4+bBzhzZgOA24NJbt2O3mZwOmtWHJjKfOSaml2azwTYMMIG4ZOV4XyOYae5+YdhuhJE7FtTSTNObyB0K2dzdtRnR1gLUlHIybr4z30DMkZx6IiAwr83YOYdRvddQpDlB+gAh9TKKDnz8XmA/Sufmx3j5M5fTIqNjBYIJ4+ARUmqIqbW7wSjp2DyfgkeR80qMigY4u/chO2FDpbNQbb1+/WTsnpXU3shP4BpztPO/vJ5zBTOombs2RWb8P29KGaGjGT291rT62Fpgb3ej4HZXsjTqAUYgYHIUlQ2Sw2jt1lD0N0awsYi5k3AzTovhKqFJKsXY/O51ENeeJ50wDwl29wn5syEbtiLSqTAWsx/f14He2OL40FrZzM7l53PZubypybgXyOcEoL/7LhZqbrXvaGz+0+g+09jgtULkvS21c2v5G+pbIu9mab+4Q1Q+u4sr6HrXellVtnw34f+ujQ71UeraxLraocbZOkOj4V+JAkWGPxpk7GtLeAr6t+kNiAr7GewtuyG7N9Z9U/qFwWPI3tHxixtqtzHzanql8o86cNo6rOVCbj7MFYSBKS/oHy9wROhrHYQnH/9PdcKOtH+b6T29zkeLW8Lj4y9Rmme/17/ejnd5/BDhowvf3YOHKfhyE/UtaxNc6HotWQb9hj/pRtHM8b+Xlwus0EkJiqjgkjp1NPu/irsRG7c5fjZD+o8nclztG5bPUaqMb8s64RSlfHW+V0z6tm8zqfJ961G53LujUZhtgwwmttwRQKLiZLEnRzs7s2ZZ9YRWWdDbPD51wDe66Vij6TZIj/taqOXXleNWap+IThcUT1vcP9h+fB8XNRqza6mNXzhnxNee2pfB7V3ITZuQs9aQJm81ZUvhzb5rJuHQ2zV9XeCr5Psm5j9TpUfCbWYqOo+n/d3ITp7kFPnwphBFGE6et33xvFVR3aMKzakY3d5/Xx87FKoTdvxxaKmAWz0QMlVKGE6WzGBB76saWo+bOdT9uyk6Sn163xYVUapbWLoZIE3daK7euHwHd2k89V7To6biYqsRhfoSOD111A9fZje/vc+h0ezw2LpVQ+h2ppItm4GeUH2DhCNzc73m9thmKIbWlADRQhMYQzO9z1TCzBkvXuu5OEpL8fe87JBFt6Sdash1OOQcUGqzXe9m6SyW1u/omF3vIc2pphySqnwyRxPjqKXR5jbZWredGJeP0l1GAJu2MXKptx8UpjntLRk8kuWovpH6B07gnkn94E2QxJexMqStBbdmC6JqDWbARTjpMa8thJHVhfU5rUyDHdG/jA7jv2auZPxRP43sbZ2Dh6FofvDc7OR8aqKvCrdjL8feULPDT/Skw9fG0Njwv3+F3n8259V2LFxgZswcXMlTEM90HD17TK57CDg9XfR8xtWPxT/Vsl1h4OzxvKJfY2Xt938Vc5rhqul6qs4Z+p8JyxLo7WGmsMurHB5T8wgqP2locM55OqD87nHTdV5jM8tk0SvGlTiKdPwJSfytCRwWQ8lLHoQoxesb7q01RjHlsoohsasKVSNaasXIdqPFHRi7HoliZskrgcM5d1uh9w8ZieMhm7bUc1jmLYtRuOveVbVc6EZ/H3CDsa+aGRNqw0qjHvPjttEqq7HzswSEfUwyenPfnszwMbowb+c/tCF9fm845HjSXetXvEta3aYMXWh41HeR4mikfMYc+1VfUl5bjIDre1YTkP4HjQ993vvleNF02hiAp8dEODu0Z7lI50Q4OLZ5VCt7Y43xvHLk4enjM05F3tZHDQ8ZNSziaH50353ND3KzVUeyifz6yyWZdLleelAt/lZBVeqPhhpVzcYCymHAtU46uy37FhWJ1vxXZ0exumu8eNaY+8WGWzQ3F7JjM0puYmTE+v83W9feU4Mh5ai5Tjr9YWzLbtI2L+amxZyRfjeIgrAN3Z7vxPORaq5OGqsQHT1YHuHsC0NqASi9q8nXjuNPxtPZjmBlQcQ9n3mbzz097qzdDWCt09rrY1sdPFWoOFYbGHcnWXIMDs2g1xXJVJ7N5TuS6Va6+bGkl29+DNmg59/diJ7Y4Dt+50PiMIIPBdbtXZAd097rXGBrd2w9DJbWvFVnzWrKnY1RuGbNRa953ZDHS0kSxfNXINDl/LB4O9xWz7k89UeGPP+KDy+/DYcM9/D2Z8++CkEeMf9p7nqjU9F0bUPfZ83ffR+byziXI98qBljIJ9cvHev6zyoYMaS62gm5rcfyo55HCfmwkcf+zurtYPYQ9fXOG8Sv11X/WvPWzVmzuLpDWP11vE+hq1eYf7fFMjdnAQ1dFOPKkFr8/9HQMqjlGlGLNuw7PykxEo55WV+o8qx9WjQWUC1LQurKcwTTlMoFGJxV+zdajGAqi2VpczhCG2sx3V21/9G77vuM3aIX+RJJDJlOsd7nOU8xfb04fqmui4H1w9XinH89rVuavfozV2xy5MX7nOUeHiCZ3uc4lxY5rUAes2o1pbSDZvQWUy6KZGx6szprr9hOYmaG7C7tyNam2GcgxkpkxEGQNrNmLDqOonK2NQ5Vp7hfNIkqG1ZY1bd40N2GLJ/X3YnoEpFPCOmoXZtMXFCIUi3sRObP+A81uNechkMBs3O52VZdlSCa9rsvuuYqk6DhuGEPhOd7OmYpetdvqYMhl8H9uYQw0UOSlay1tzi/Z6zR/sn8D13XOqc31WnWCEgew9Hnu2Ie0Ry0/oxA4MYvr7R/59j/Wwt9q88oNqrcGU7WzPuu3wfGD49z+LV4fn33vGcZW6RaUOkSSu3vMc+wVANU+nMQ/FkrN1T2OyAXrTNmyhWLUh3dbqfh8cdD4zk3F5ecXGh+UH4Gow1Vglk3H59OSJ2C3b3DoJfBdvl2Orve0n1BJuD2No/wMYyp0qNaZKzLYnKvsxwbD9q0rdH5db/tOkJ5mW3XvN7dMbjme3bsYUCiP8UKX+X+Ho4WOsyFGBX15DUTVfVA0N7rr4/sjrsedc95jDiDi/vC9QrWVV9DFsfJWxVvJp3dTo6g5B4Hg28FEtLWASbGuT4/6la5z9dHZgtu8Ea1xMX9nDiWNnd9Y6PvF9bKnkYkNrMT19LlcLo+o1sYUCqqHBfT6fw2zY7GpShQhlDKYph4oS7Iq16K5JzrajyMWtPX3oznbHu76L9SmVQHuu7p8koLUbhzUopaGpwdlCpZbf0catq7+6X3Z2UNHjV7/6VTo6OvjEJz7Bk0/uPeE7gr3DCzTn/fUCwXYqHudfLtdKudZ4ridWvEDzknefjpfxRnln7eAFHue/6zS8QEa+NLxAc95fzcXzpfSvOf/vFo5b+38ueL7mvL85VrSd30tePi297RQDzXlvlOR/zfnvPHWf8vWobbAOf1T5X0r/Ga8sP638X/F/gvb/jlNSrH9h+/dl419peL7ivNfNE/W/579qVs3aqdVrG0WocO8ZorG/5NqThucr0dg79b63Yv+Svncc1x6eC16gOf/tgsdY+YqXXDI91bnXaLnPmMh/xynptf8K/0r5X2H/Lw3p9S+9/qTh+bK1Ry/jjUntb9QavGARSZx/Aln50pBe/9K1D2lI559e4B02ey8H1Ur9oosuIo5jnnnmGU4++WRaWlpob28f8R6lFCtXrjwkgxxPsNYy0F1yd3EI8KO1lv5dxYO+a7TeMXordYW10L9zUGz+1loGdhY4iEYN4wLWWgZ6Qswo86/lWR7WQv/uomiAJokq/wjN31oY6IvSq39T0b8g/+wq7FP/o99YdPgX1Kr8L2X/xpbl29GVPcYY/doeOmUN+b9D9pUHKJ+y/deX/scK4vZvZflPGtbCQE9JMP6E/t6oZvqvZ/9hjeX/s3fmYXZUZf7/nFNVd+l9SWffAyEQCKsim4AKioAsOqA/BZVFAZ3R0ZlRdMYdcUMdN3AXcAFHQAU0gEpAdsKSAEnIRvaks/TefZeqOuf3R93bnUD6Zuvut29Xf5/nPkl333vfqrfe833f856q7+lqka39JceeNKwlmnsOh9w7lHaH1Fr/6I1/wdwbXf/h4pGhRTT3lJv7WwtdhblXDEufnWq/eNqXRi//SuVf4fwvjZ3HvwQBxD7+hXvvfb0nBvX6l745Vg7i/GN3sj/81wYHHNLjf296H6XmqOVeM0nPP3v5rwwSwH4tjK9ZswalFEoprLW0t7fT0dEnv2qtRZXaaCLGMIFl4V/XFCTMJewbFt61LJJSH4HYU1I2geHp/3tp93JZQwATGJ7+w0tDbne4wASWhQ+sZ1YoQ44mMCz843JsMLzktYYKJrQsvHe12Pmb0PLUo1txhK6/NExoWTh/DQRS8W95+g9LIhmz3fx9uE5qBgpF/i/KKYnZ1wqVEDkEUfTmP0fmrmkTGJ6+Y2kk1x5X///fS7ts+zOk9sNC/SvEf9IwoWXh/etBqv4JLQsf3BTJsA8ChnP+MIHh6d+/GP0gVfsLjj1pmNCy8E9ytffOuTeOKMb/Lls/DbH9hXcti6QUY4jo/F9G1dbI2A8tTz/SHEmpixyBLKLac4ms/TuWvlZuPSbo7b1J2i/m/xiiOP6d0ILAU+PS408axfpH1VbL2A9Mb+9vMOe+w/Xm2GHBPwX7KoZPjUuPf+nenzSK51+UUpewv/BPy6P6X2YKstfY7+6AtXaXlf/iz+VwN4AkXE9zxmVzcYXkBNyEw5n/+jox+4ONPSVlN6F56ydOxBVKTG7C4cx/P0HMvjRcT3PG+2YPmpTnHu0nHM78yLHx9v8VR8jFv6d52zum4nrxawxAwf8fOEyO/z3NmR9/Q7/XX6nhK4U7EHATjjj/S9qXhnT+cxMOZ37s+Bj7X7j+Ea5/peF6ijMuOUSO/13FmRfNHDT7e1JskoSbcHjrJ08a5X4huK7izGuOGfW/EIZD/I/k3sOe4CYczvzocYK1v+Kt50/FjamUuptwSs59hsR+rGvPYTD3EuQ/abie5q3nT5PtPQuOP2m4no7qH0H/D0Xvr1QPSfJxpGHBPzGvP6Xzb9z9H52/IP8L8t++YL9unTAmnk9bDgSMtTS/0oERklI3xrJlRUtkfwRij0+MG9iyfLvY+RtjaV6+Y8T6f08w1tK8rrOklPqg2jeWLStb4+t/Y2le3S4a/1s29RDXFGKMpXlNh1z829L8U6plNhKGjDFWnP977Q//+nDAIZ3/jLE0F+ufWPp/GNQ/r8jxnzSMgea1nXL+t7BlQ/eg2R+aDRn2D8ZYtrw8TLg/hjAW0do79v4fDvG/oiXG3F+Ye4rNfWHLxh5MTKXUh1XtGUNI8680/0nDGMuWjYXaT0DRVXr8SUO697hL728Q577D9ebYYcE/o72f4eH/GKLv/AXtC9a/+4IYDk9ZmMCy+MH1GEEp6cXzV4nZH2zoPSRlExgW3bMcIyjnt+heOfvSMIFl8cObsZLxf9/q+Po/tCz+xzq5+A8tzy/cPmL5Z08woWXxgg0YQSn1RX9Z0e/1L31jUfm304YF/wval4Z0/jOBKRn/Ix3S8WfCQv0bYyn1xQ9vkqv/Q8vix5oHzb4uMemVzh9R7L88yv1CMKFl8f2vjPpfCMMh/hfPXxVf7hc+fxNaFj0d47mXcO0nbV8a0vwrzX/SkB7/sY//Yv0j2fscgt7fcL05dnjwT8zrT+n8G3f/C8d/NP8b/vXnXi+Mr1u3bp9fA40vfOELvXubF19z5swZcDuDCTfhcPY1R8rJGSQdzv7PE8TsDzZKPvFI5P9zP/tG3KSQnEfS4ZzPyNmXhptwOPuKQ9FSckJJh7M/eXx8/e9pzv7o0aJSuu/4l+llIacyGHATmrOvmicqZ3POtSf3L6Ve4rPDv5zZM9ykPP9L2u8XJS7uQC5nSec/N+lwzqdPHn7+HyKI1z8JXah/Y+p/T3P2lYfJ8b+nOPvSgwfN/nDeY9xNOpz736eNcr8QXE9z9idfHz//D1Fu3ROGQ/xHvYeYxr9w78X1NOdeNCO+c69kYe4zWnuKQDr/SfOfNFxPc+7Fg7eNzh7tC48/abiJQv0j2fsfgt5fyYfTBtVyaQwL/olz/T8M8m/c/X/uZ98o1/tPOqL8ty/Yayn16dOno/ZBfkUpRRAE+3VQpTB37lz+9re/9f7suvulBi8GExpWPbc1umtMID5MYFn9lNwTK4MNVfKJlcj/Kx/fIHjXnmXVE3F+YsqwavEO0SfGVz+9Ob53jRnLqmebMaHQXWPGsvJlOSl3aZjQsur5bYJPDBpWPbGh3+sfhyfGVz6+QfSJ2ci+zFYq0ujLf3J3ra56Isb+D+XjP6p/Y5x/F+0Q5H9Y/WLroNkvlT+M9BPjoWXl4+tEa3/JsScNE1rR2nuX3BtD9Ma/YO6Neg8xjf/AsPqpjXL8Yywrl7VjwpFQye87+mo/6dozpvFf5F9BtRzJ/C+NaPy3yUmpj8Z/VP8Ixn9v788ZvMlv6YcrBKXUpfkn2Mn+8F8bHHBIj3/p3p80+uJPyv878d8w773t86qy3YM+vFJqj+85ELiuy/jx4wft+wcbJrQse3wzOBo8CfuGpQ+tRY1pHHrj+4kanWe2t4VqtoITgC04ThlMJkVx0ywVWjzCfr/HorChZek/VoM1oIY+O5nQsPQfrwy53eECE1qWPbWVmVL7jISWpQ+vE1uYl4YJLcse2yR3Y0JoWbK4FSfO/n9iMwzh+VeR42BnBzU6BzbELnyZcSkH5SXBKUySrEfYlSKN3+/3jIQrZgr8rzyZG+pMYCL7WqGGeXE4GOjNf4M4MS9t37J0wRoITUz9X4h/Qf8ve3xo+W84Iap/msXO3xjL0me3o/bBvrKWyaaFmYkNaH8HVLRDYY6nPA8bbAerCNoTTM7u6Pd7pK+4CQxL/746+kGs9pcbe9IwxrL04fWiN8UWc28cUYx/5Qo0HijknofWoirSIvalEZ3/OlRtjZz9xS1g7XDvSw4KTGhZ+uAaWfsL1kT9qhhyUJF/Re3/Xc6+NExoWbqoJeq9uAIL48LjTxpR73E9qrZaxn5gent//c19K1TAQZVtNLoZ0Aaro4cbldZYLwStIWEg7IKaluhvTg8ojQ08lPKZ5pSYAwhOAsT5J+yzr2KomiM9/qV7f9Ioxp+uqhSpP0xoIv4rg/p/n7oDe7PgPZiL4gArVqxg4sSJzJw5k/e+972DItk+mHATDud/4hhROZULPvfGspAzADihcitfmrSI91Uu5Tz9Eud5L3MeL0YvZwkX9DzLBdnnuCDzHOfnnydRcmE88v87r3uzqJzHhV9+U3zlPBIO53/0CBxBOacL/vuk+Prf05z/H8eJSqm/672z4ivnl9Cc//Gjh4x/j/Ga+ULyQS5JLo64M72S8xs2cH7NWs5LLue89MpeTr2wYyEVqn+Vl5HwnImblOd/SfvSkM5/btLhwi/GWE5Ruv5J6EL9G1P/e5rzP3qEqJT6BVfO2Wv7CQKu6fkH/5H7Gxeml3N+sIjzq9dwfs3aKIekV3J+uJjz84t4V8vTvCHbf+NJOn9E3HvGKPcLwfU0F3xWrvaOvf+HQfxHvYf4+v+Cz50iKqX+zktiPPdKOlz4pdNHa08hSPOvNP9Jw/U077z0IFEpdcnxJw03Uah/BHv/pXp/h6ba+NLE57hsfF9v6PzqNZxfvYbzKldHNX/VK9G/4Quc37CR8xs2cp67jPP0Es4PFnGeXsJRbnO/xyA5Bxge/BPv+lM6/8bd/++87s2iUuqS/Lcv2OsjNMbs0ysM+1+g3F8cf/zx/OpXv2L+/PnceOONvPLKK5xyyil0dnYOuK3BggkMi/8hJ6VtAsOi+SvLQk5uWqKLixvWDuA3KkxoeP7ul0XlPBbduzzGch6GxQ+XlvIfzNLJBIZF962Or/9Dy+K/r5OTUwktzy3cFmM5RcviBUMj5zney/L/KpcO2M2Bknf7DhRMIM//kvb7w1BdWun8ZwLDor+sGHb+HyrI1z+2UP/G1/+LH94kV/+HlkWPNe+1/XeoJcwOtw6Iben0YQLD8/csG+V+IZhQtvaW8n/puB+6UTEc4j/qPcQ0/gPDor+uEuR+w/NPbY/x3Gu09pSEdP6T5j9pmNDy/FOC27jFvvdpC/WPnP/76/3VOHkub1yBqwb32CTnAMOCf+Jc/w+D/Lsn/5eKz1LbhJUDes9fTErdiPLfvqCsNAXOOuus3v/PmzeP448/nmnTpvH73/+eyy+/fLefyeVy5HK5XX5nbCi2xYMxltXPb4uk1CXsh9Ee4+UgpX5Yqm3Avquo3G1Cy6onNkSrPBL77BTtxxTGWFYv3iEqpb766c3xlVI3ltXPbZWTszSWVS934MSzNuzj/yGI/8MqOwZUMUf6ib+BQJF/xaTUi/ZjK6VeyH+CUt6rntwIJeTkRjJ640/K/0PIf8MRxsDqxTvEzt8YWP1SK2qv7Fvmqi0DZls6f5jQsurx9dEPgrV/fKXUYfVCudp759wbRxTjX1JKffVTm8pCSnEwEM09N8lJqRtY9XI7DP8tHgcFvbWftP24SqkL9752yf8xhDGWVcvacYTiT3r8ScMYy+qFm+Wk1MO+3t+r575z0p2DvigOINn2Gxb8U7Af296DdP4V7P1Jo3j+clLqBf4rg/p/+D/TXgJ1dXXMnj2blStX9vue66+/ntra2l1eq/0XhvAod4WbdLjoM68XlFNxufj6N5WFnEGTmx2w72oJk0Dk//d8+214KRlydJMu7/7WW3GT8SRnN+lw0SePwvHk5Dwuvi7GcmYJzUX//QZRKd33XHZwWfDPYMBNaC769OuGxP9NXm7Pb9pLdBoPv7zLBaCP/6Xi30u5Bftx5f9i/pPj/3d/44wY+182/t2EjurfOOff/xi6rTReDc/TXPzRuXtlP0FIrRq4HNJiZCfEXsrlPd95u2jtLzn2pOF5mou/cupo7hVCMf4lc+/F18d4G7Gkw8VfPV1OyjKhec/ls/FiLKX+7m/KbiUQ1Z7xjP8i/0rlX+n8Lw03oXnPFYeISnlLjj9puIlC/SNV+6fcfnt/Ywaw114KLUFySOzsDtL8U6z/Y8s/wyD/xnn+JT3/dJOu6PxvX1DWFXJXVxerVq1iwoQJ/b7n2muvpb29fZfXTO+IITzKXRH6hsfuXEkoJCcR+iGP/fZFMfv7goG8p2VJphaI/P/Izc8T5Ade6n9vEPohj976PKEvY18aoW947O41gvFveOx3LxH6wz/+BwNhYHjsjuVi5x8Ghkf/sbks+GcwEAaGx/64ckj8P5D8udRvEH/ibyBQ5H+p+A/yYcF+XPm/mP/k+P/RXy+Osf9l4z8MCvVvnPPvn18Ry39BYHhs/vq9sj/QbL8slFWpCvIhj/zqWdHaX3LsSSMIDI/dtmQ09wqhGP+Sufex374Y2/iP5p5yvZcwMDzyj00EcZ17+YZHb108DGrPePq/yL9S+Vc6/0sjDAyP/H2TaO9NcvxJIyzWP1K1fz7st/c3FAtBoYVlGRm1FJDnn2L9H1v+GQb5N87zL+n5Z+iHovO/fUFZLYz/x3/8Bw899BBr1qzhscce44ILLsBxHN7znvf0+5lkMklNTc0uLy2oY2GNZcOyFqxQbFhjWf/CVjH7+4KB2tNhRdjAPe2TgcL5L9qCFZKyjOw3i9mXhjWWDcvbZP3/4rYY+x82LG0R9D+sW9NVFvwzGLAGNixrHRL/DxR/rg1r+WPPwQPyXdIYHvwvZ39/MJALZNL5zxrL+sXxzr+y8U+h/o2r/ynUP0L2Laxf2bFX9gdyT7X/8+ey0cg1xWA4xH75cf9AwlpEa28p/5fet3DoIB1/fb2HmMa/sax/QTL+Yf0rXdh4ul+89pO2L41hwT9xzr8G1r/SKdt7jnX8F+sfKfu2397fYO8sFFq4dccs2kK5J8alx7+0fWlIj/9R/w+D+C+TtZeyWhjfsGED73nPezjkkEO46KKLaGxs5IknnqCpqUn60PYaXtLhfV86AU9QTu6S/32rmP19Qalk/TRTuM8czF/TR/DX5OH8NXU48725zE/MZb4+lPmJudydmMf3vNP4sX8ceRudr5dyeP9N54rKOV36o3NiK6fiJR3e99ljxeTkvJTLJd9+S4z9r3nfdSfjpYT8n9B84Oo5eDGVUveSDu/74tDwf6m5zqL8GO7Lz+S+7HTuC2ZxH4fwl6p53BcezD21R3JvzZHca+bww8yx/CBzHFlGxnjxUq44/0val4Z0/vNSLpd+/6wY+1+4/hGuf6XhJTTv++/j5Or/hOaST87bK/ul8kfGOMzvnMx92enMdw5lvncY99QdxV8q50Wvqnn8peIIfld9PF+qOpfHzdSBO4n9hJdyef+PzxvlfiF4Cc0lN7x51P9CGA7xXy69h8GAl3K55LtninL/+6+J8dwr5XLpD+WktEdrz2Ew9xLkP2l4Cc37P3Ko2PiXHn/S8JJOVP8I9v6j3t9r/V/qJtiXcvXc1zON+Z2Tmd8zNfpXH8r8tvHMbxvPfcFB3GdmM985jPvM7Kiv5M9ifsdE5ndO5nfZw/jK9uN4PtMwmKe3RwwL/ol5/Smdf+Pu/+j8BddeBPlvX1BWEXLbbbdJH8IBI/AND/ziJQI/hMTQuz/Ih9z//acI/OE/OdIlkvXTTGWlqSeoqEWFBpRC+WH0Lz426YG1KD9E0dX7uSBvmP/txwpyJkMvDRzkQ+77zuOxlVMJfMMDv36ZSkE5oft/+Ey8/f+zFwjyclKu8/+0LrZyfoEf8sAvXyIYCin1EvT2TE8jL7pTIQyBBHgp8jU1JFp2kKlvQoeWZFsrhJnCJ0bGeAny4U78L2zfHf45eKAhnf+CfMh933syxv43svHvh4X6N6b8HxgeuPXlqP6XsO8b7v/9qr2yX6pZ1mNc7uuagqpIY2sbsK4mV1+JkwkKH1a9dbnOBDi0DdAZ7D+CfMj8Gx4Rrf0lx540At9w/4/kau/Y+3+X+JexH/UeYsr9+ZD7f/C02PkHgWH+H9cR+Ka8nogZIAT5kPu++8TwqD1jCGn+leY/aUTjf23Ue9FDzwDS408agR9G9Y9U7Z8PC72/1859S1XDL+QaeMqfjM3nwXPBD6BxIrbtFQB0ehwoF+ulUPks+DlQCtPRinIcVHUV1vQA+cE7ub3AsOCf3t5PWS29DQikx7/09ZdG3/kLrr386Jmo/pUTjtgrxLE+FoU1luY1eydlOGj2V7aWhZRxqWS9v2IM1lial+8QlZNoXiFnXxrWWJrXdsn6f9XQSFkPR1gDza+0i8r5bdnUUxb8MxiwhgL/D77/S91YZGLr/2HA/4L2pSGd/yL7cZbylo7/oeO/4QhroHmtoJylheb13XsppV7iewbsiIYO8rEfc+630LxKdhulWPt/OMT/yjjPvWTP3xpo3tQTayl1ydpP2r40hgX/xJr/oXmjXO9lNP6L9Y+Ufdtv769krW+H/ibSwYD0+Je2Lw3p8T/q/2EQ/4Lzv33Bfi2Mv/jii/ziF7/gN7/5DQD5fJ5rrrmG8ePHU19fz0c+8hHCMJ53ZewJXsrhsm+cIienkna5/Cdnl4WcQaknVux+PvHhpVyuvPlCvLSQnEfa5YpfXiBmXxpeyuGyL78eVzL+b3xbfP2fdLjshtMEpXQ1H/rYYXjJeN6T5SUdLvv60EjZl2JIR2grA2l4aXn+l7QvDen856VdrvjZO+Lrf+n6J1mof4XkvKThJTWXfeV4UTndyz9z9F5KqQ98/S0JL+1y5S3vGuV+IXgJzeU/euuo/4UwHOL/8p+cHV/uT7tc/uO3y0lZJjVX/vvc+Eqpp12u+MV5o7WnEKT5V5r/pOElHa78xOGivWfJ8ScNL+lE9Y+g//vr/SlVqtYfGRgW/BPz+lM6/8bd/1fefKFc7z/tRvxXBvX/flXI3/72t7nyyiv53e9+B8D3vvc9brrpJrZu3Up7ezs33XQT3/zmNwf0QEcKglzIXd8WlJPLhtz5xYfLQk6i9BMr+9eYC3IBd3z2bwQ5Of/f8T9/J8gOf/8PBoJcyF0/KC3lXaohe8D2swF3fvkRgmwwaDaGM4J8yF3fepogJ3P+Qd7wh1+vEpNzkUaQD7nrO88OCf+UYsjY+j9b4H+h8R/kwoL94cX/JfNpiUnzvqIv/wn5Pxtwx+cfHHb+Hyr01j9S/s8X6l+h+ksaQd5w1/cXC0rZG+786dK9sj/SnhgPciF3fOZ+2dpfcOxJI/ANd37l0Rjm3v5H0lDeXtIb/4K5984vPhxf7s8G3Pmlf8rxT95wx60r4ytlnw2443MPDoPaM6b8X+RfqfgXzv/SCPIhd9yyQrD3LDv+pBHkw6j+kfJ/rv/e32D02ocbxPknK2tfGtLjf296f6XmteU+CvriT8r/Bf4rg/jfr4XxJ598EoC3v/3tANx5550opZgxYwZVVVVYa7n99tsH7ihHEKyF1i1yclbWWlo3dpaFnFapPXL3W0rdQssGOSlPay2tGzqw5XABBgHWQmtzRuz8rYXWTV1lEf+DAWuhdXO3IP9Ay45cvP0/RPxf6i7geLbGduJ/qfg3tmA/ngOgL/9J2adQ/8TV/8LxL1z/SqOv/hG0vy27V/Z1yadIyq9N0Mu9grW/5NiThnTtHfvca2Tjry/3ytiXhvT5Wwst22M+9xqNfzGI519h+9KQHv/S408aw6H+6a/3V1odamRAevz31v8x5h/p/Bvv+Zd0/W/LZu1lvxbGN23aBMCMGTOw1rJo0SIgWjD/7ne/C8CKFSsG5ghHGLyUw1XfP11UTuXqW8+PtZT6R/7vYlE5j2tuvyi+ch4ph6u+eaKolPrVvzo3vv5POlz1o7eISql/9L+OiLWU+lX/e5q4lLr2Yhr/aXn+l7QvDen856VdrvntO+Prf+n6J1mof8tATmsw4CU1V33rJFEp9au/dNyBS6mXweT21fDSLh/5w3tGuV8IXkJz9S/PGfW/EIZD/F996/nx5f60y9W3nCcqpf6RT8+LtZT6NbfJbiUQ69pzOMy9BPlPGl7S4SPXHinae5Ycf9Lwkk5U/wj6v7/eX6mMUI43we4Ow4J/Yl5/SuffuPv/I/93saiU+tW/PKcs6v/9qpC7u7sBcF2XNWvWkMlkmDp1KmPGjGHmzJkABEE85VL2BD8Xcuv/PIYvJKfiZwNu+bf5Yvb3BYMh7+LnAn714T/jC8l5+NmAm6++W8y+NPxcyK1fWSgmJ+RnA2759wfi6/98yK2f+Se+kJyKnzf88kdL8WMq5e3nQ2793OP40lLq/vDn/8GAn5Xnf0n70pDOf3424OaP/iW+/peuf/KF+rcM5LQGA37ecOuXn5ar//OGW761aK/sl8of5Zi9/WzArz70x1HuF4KfN9zyib+N+l8IwyH+b/m3+fHl/mzALR+7T+z8/bzhVz+M8dwrG3DzNfeO1p5CkOZfaf6Thp8P+dUPloj2niXHnzT8fBjVP4L+76/3V1KdtQxvgt0dhgX/xLz+lM6/cff/rz78Z7nefzaI+K8M6v/9unWgqamJLVu28K1vfYsJEyYAcNhhhwHQ3NwMwJgxYwboEEcYLOSzgZw+iYV8JoAqIfv7gFJPrOz31M5CPuNH/pe4EW5n+3GEhXw2BCt0F2Ix/mPtf2H+yZtR/0tLqUuNP2lI86+0/f3AgEaK9PlL25eG9PlL5x9p9NY/coeQz+2d/cFQbBLFaO0vDtHaW8j/w+Zy956/8NxLezL2pVE8/2RCzn4ZNCUHDdL8K51/pDGc/B9H2L2v/QbNftz9nwkgKZT/Ssy9RlytvztIx5+0fWlIn//e2C9RG5f9KCiev5MUnP+WR/2/X0+Mn3rqqVhr+dvf/satt96KUoozzjgDgCVLlgAwe/bsgTvKEQQv5XD5N98oKqdyxU/l5Fz2BbrkHuP7L6X+oVvk5Ky8tMuVv7owvnIeKYfLv3K8qJT6FTedFV//Jx0u//bpolLqH/743FhLqV/+jVPEpdSdxPDn/8GAl5bnf0n70pDOf17a5cqfnxdf/0vXP8lC/VsGclqDAS+pufy6N4hKqV/x2WP2Ukq9f5Rjb8dLu3zo1n8Z5X4heAnNFTe+bdT/QhgO8X/FT8tDSnEw4KVdrvjJ2aJS6h/6xOGxllK/8pfnj9aeQpDmX2n+k4aXdPjQJ48Q7T1Ljj9peEknqn8E/d9f72+k1fq7w7Dgn5jXn9L5N+7+/9At7xSVUr/ixreVRf2vrN13oYy1a9dy5pln9u4jfsopp/DXv/6ViooKDj30UF5++WW+8IUv8LnPfW7AD3ggcFbTVfiHTsFbt53OYydRtaINf0wl1lEo39B+UAosVG4O6BnnYlwFCppuWwyADUP8NxxG96QEuRrF+N8uofXsQ6n/0wswZSKr/98Ypvw9S9ekJPWLWwmrU2yfV4GbtWQbFElHYVstbtZiNWAh2R7iZA1bjk+gfZh220ZsaxvW9yMtE6UgNCjPRdVUs/b9M0hvg+4JULPWUrU+j/ZD3OdXYrI5nInjIAghDMFabE8GXBdVVYmnAja/aSZdUxQ6D1WbLBXNPqn17dDeSccJ08g0OrQcHTLtbkP3eJemBzdhW9siBxrDjgsPZ/uxljk3bMRWpFE9GWxbB2p8EysvHUtYaajYoJnyuzWs+Nfp1K6EjpmQ3AEV2yzGgYYXuuiaUUmyLSCocKjY0I3e0kL7CVOp+cfLfLR2EbO8jt1ew+smnMuOV/zIL5lsnxaM40AqCVpDPg+uC2GI2bodjIU5M0lsacbvyYPnQmjARM+fq4o0NpfH5nKodArlOFg/QHkuNu9H73HdaMW+shIc3esTVVMDuRwEQWTH0eB54DrY6gpMZTI6PmvxXI2fNyhrITDYlIvuyqLau3uPhVwea030vZ1dmJ4eVCKB6e6J7GkFStP2L0dT/9wOVC7A1Fag27rpOXgM6U1dqJwPnd0QBJj2TnA0evxYzIZN4EbnpBJe9K/jgFboieOhJwN5H6oqCMfV4XRko99pB9vWjmpqjGKytR1rC75TfZNta03vz8W/d542m5pHX4HKCtz6NFNeWcK/jnlpt9d2SdDIz7rmYZvqyY+vxvvnC1hjUVphTYGuCt+L0iitUMkkWIvJ5nb9e/E9ngvGolJJElUpcq2dEBr0+CbI5kBrbCaL6ezsfb8eOwa6ejCdnahEAusH4GiUjs7NZHOR7XQ6um5aoxIeprsHpTXWGHQ6jc3nsWEYfUcm+9oT1io6tuK18FysMZGPC+erqyqj91WkIZPFjm1A9eTY/sYJjHlgLcG0sbgt3dgt2yAMUY0NBBPrcTdsJxxfj7OlldzB43C7fJxKFzZ1kh9fjVWKxJZOslNqcLIh245OM2FBG/6YNMo3uN0+rXOrcXssjm9Jbc3iLF0b8YrjEM6Zxo4jKxn3wCb8iXW4i1ahxo/F7miBvI+qSNNz7HRMQmM1VK7pJJnNkUunsK5m+5HVNL7Ug1/tkdrYgcoF9MxqIL2hA9WTwyYTqLxP+9HjqF20jR1vGEvj/avoOGkG+WpNzStZdN4QVHmYpCa9ug2VyYFWdB0xjqrnN0VjvDD2Wk6bRsOCtdHPXuHONdeJuDKVgK4e0Iq2EybjdRsqX2yOeEQpcDTh+Ho2nlZN5RZL1yRF7WpD3TNbIQjJT2/EeBqvI4/VCndrB92HNeF1BGyfl2LivZtAa7wwh58N6ThhGh3THGrXhFQ/uQ7b2YUNArKnHo7X5ROmXZxsiNOVw29Ik692aZvl4FeDk4XxT+dwMiFbj6sg0WFpWNzB+rfW4nWDk4F/W/dXjn9l/W7H2PWHnMPGjga8rV3YlItNuFilUMagu3NY1yWsSWISmkyTh5OzVC9qxmzYhK6tIX/YVLxnloNSUVyHIaqqMvKVtdiw8GRKaCAMo3iG3rFjjekdI8X3qYS3q26XUtH3at3L7zYMo7EA2Kb66G3t3dHYyGSx2Rxq0njo6ALfB60xHZ3RMYQhKpnE86I/EQRYY9GVFZFtpaL8Ucwl1oIx2FwelU5FeaSzC5VI0HX6HLrHO4y9+bnIB05U7KmGOsyWbdEYDkOU66Iq0r38YmdOwlu9ltBLRbmlIh3FX3eGsL0j+h5HoysrsOPHwLrNfTmhPxTrg1LQGhxN16mzqX5hG3R2Ybt7sPk8KM3Hp69mum3Z7Ue/3nYczVRjgwDbnenl1l4u3gnFvIRTyAdBgEokomuZ97FhiJd28TNBL58rx0HXVmN7Mqh0CtPVja6uwkxqQq9rxubzqFSSljMPJrUjoGLpFsKNW1AJDz1+LJ1HjqNyXTcsWdVriyBAjWvCbNoSfd71ohzgungVHkHORDEahqhUMoqbijSk04TrN6CrqlCN9dgdreBo/CNmkFiyLjrvbA7V1Bj9DXrjW2ndVy8U49/Yvjv8dvZX4XfKcaL3Ft4X1Re69xwgivni9c2dfBjpldshCNhx6hRqV2Vwt3VCVzfhlCacVZui70940ee1xmZz0TEePJWeKVWE41xq/m9JdO2tRU+eyPaTxpKrVTQszbPh9ATT5mfZcXiKMYsz+DUeiR05lLX0TEwD4HVF5+d2B2TGJql6eDk2m0NPmgBt7eC6mPaO3vHOlImsO28M0+5oxlMhbbPHUvHPl1Hjm2g5ron6vy5DVVREYziXI3fCHHrGeXROUUz7vy2QzZGfOZb2WSkan20HDX5dGrc7j+7Osf34Rhqf78AkXDpmVVC9Lou3sTXyZWs7pJLYjk4yJ80h/dRK7JRI6Up3ZzGbtqC0RtXV4k9vwtvQimmsRrdFtROeR25aPcm1rdDeEV2PvN+bo60fxbJKJGDGZOyqdSjPi+Lu4OlYV5NrqqB7vEvVRp+kq7DN3eiuXDR23Yg7Nr6tiTEv5Mg2eNQ+vZlgYj2bTqpg6u3routvLVSksa7Geg56SwtmbAPdM6tJb8my+sIKpt+dxXt2Baq2BtvRiZ09DZN0yTYlUR9tRn27CS/t0NKkcTNRHZ6rUzQs9Ulv6qJtbi3tMzXTf99M5UyXL79yx245Yb3TwFenvIPU5i5UZyaqpbXCao1q64DaasKaFCbp4rZ0Y6pS6I4sansLpqNr13Gwm3ERDQ71Gh1HXVGBHdsIGzZHvjcmGkPFrbsKn+nlgcL4VNVVkMvjJjV+NkAp3Tv+bRBE9WMQ9F5TXVeLaWnDBn4vzykd8fzOvKe0Qk+djG1pjWpeHfFcNJfIoRyH4KBJuKs2gbW4niJIVUY1ZxhG3K5UxI/F2rXAncVjVAkPQoPJZHrnITtDFX3murv8vZdfPTfKQ+kUpFNRrQOFJEiU8wocY32/r14q+K9YlxIEkPD6ckDx+IqcD+gxDdETsYV8aNMJVD6IrlXSI6Es/uYdAHQfPwO3KyT54lpMV3dvTVkcTyiNSqeiceQ4oDWZE2dTsWIHdnsLqqqS/MyxJNZsg1y+z2ZnFF/F66ocp3dO4iY1fo+PSiYwhfftHC+qsR46OqNaf6d4AHaN01fH66v4W41piLinrZ3qru18ccqLr/0ssCWs4Btdb4jqCscBx0F5LmFLa++cB6V7/V281jvHZDGH9h5Lb9ybvngqjA83ofCzZpf5kXK9aC6b99HpVBS72Vzki2wO09Ye1SOZLLguudcdRPKZVdH37zRGVV0Ntq0j8uP4JtjWEs2nHKdQPxm8lEt+/Dh0zscfU4n3ylZaT51G/d9WQmii+aKxMGU8qqM7mndWVmI2bUYlEtH3aE3usCkkl2+OxlFtNXbbDuzMKai1myI/hCaaqylNfu5UvGdXRPOxSROifkHCI2yqwdnRFV3vTJb862eTXLwWG4Z0nzKbivmLesefKs4VJo9DdeeiuUJPBltbRVCbwtvUFnGgH+Vcs25DYewkdrkmiYYqWo6eSvVzm6L5R2cXNpuj+7Q5VD23EX/qGLw127A9PVEspVPQkyE4eDL5+gQVL26Ozi2dimxZg2moBgN6RztmXD1+XZLk4rV0v2EmlU+s7qtrAa8qSa6mGpN0yTUm0DlDsrkHZS2dB9dS/belUbyEJsrnM6ZANo9p3to7PnV9HW1vnMb2IzQH/WB179zX9mQID53Oiqtdxt+XoP7vq+h6wwx0ztB6iIeTB+2Dm7H4FYpxD2yk45jxhClN/bPbYUdrVDe0tKHHjcFs24EqxI71g13HG1EvJpwxAfXCSnRDPbbAIXbnbRxflUO8Cg+/57Ucurco9hl2GWewaw2+u5xmbNT7qUxE9l/9nv7w6vasUtF10Hr384Li74t1V/E9BV7JHD2d9Eub+o7V7MT1ptALs3YXuzYM+3phyUSUI4oo1rfGknnDwdG8vaWjL7e86hy9lNsnJa11b1xibRRDdVWowGC1Blf3HVfhfFTOj8Z8MWfBLvxYjF2swdRVo/IBqrunlw+8hMbPvEpK1nV681fvXL/og0JuLtbSr0Ex1l51nta+6toU84PSUQ5tqEFta43yatHXhbzvz5uBt/iV3s8V54RA1NuEyAfFnws9peL1wdrofCvTkEhE18IPoLICT4XkqippnVvDjiOgeq2i6bmeqD6pr436fsUepla9vrY9mb5jLMzpe1HoAZip49DNrVCRJj+5Du/51a+Zm3opt1fKWFVWkJk7kfTK7VHvslAPbz5zPOP/2YLyw6juqYrOWffkMZVJth1Xy7i/rO2Li8I8HaUiDi72zYpjLOEVxp+h59DxVLywMXqvflWeLMZ/cQylkuAHmMIcUGmNqkiz7eyDqF/WE9W1lUlUPmDz6Y1M/OsWzLoN2HmH4Ncl8ascrAbjKdyMoXJVG+2nNJFcnaPt4ATWgabnMuTrPCqf3RDFXm0NtnlbX05NJqPaxfOicTdpHLyyobcu6TjvSCo3ZPG2dEA2h+3qxubz6PFjWfqf45h+V8i6szycjCKclmHMUxVsnxFdu4N+3Y3O5FCtnVzsLub4VPNr4xv46pvOYGXbNJpfr0hvVmTGWw65aSt2czMoReaNh6ICi84b2mclaVzcie6O4mb7sXVUbg2peH4Dtr2vl28ymWhOXph77/YpuOKYKc6Rd4ed+b3w/mIuMF1dr3l7sffATv3qXu6AqO/b2BDNA1c3R9yQyfbaV44DZqf+1qt7L8UxXkQyEY2nYv71wO/2o9/n8r294d65/h5yQnGeqSaNj+p1VxPUpQkqPbyOPO7ardiOzkLd7KOaGjEbN/d9vsDjvXA0auYUVGtn9LmKdNRbzvpR72t7C3bWZPSGrZjJY6PvWLMpOkfXier63cxJ+sPO43+vsVOuLPYs1M59umIu2Dkn7i5HE92c4hvVO+ZtNocNCms7iQSfGLeEqYnu3R7GVzcfzjaqMTvHg1ZRj98PormfH+zyN+irGYo1sJowFrthM6qmmnDrdpxxTZDPR9crmYg4b+o49Kbt0TXI+328VKjBVDIR/V4rGNcEQHZKDellW6JzTiWhpS26loGB1na6Xz+D2s2dZNJJco1JKtZ0ore10nbKNKrX9KBfXgdTJ2A9h5XvrmbsQkvdoh1sP34M3RMU027byJazJuF1W9yMxclbUs1ZuqalqX2xBbt6PXrqJLa/oYm22YoZf+4i25Si4qGlqIY6Np8zBS+lyIWWupUBnZNdxjzfTZh2UaHBJB2SGzrY8PYxpLdbatbm8J5fTfb4g0k9tgyVSkbrbkHQy8/+xDqczjxq1bqoNzxubFSb531aTphA9doMbnsWqzXZCZU89Jf/2quQ269bB6ZNm8aSJUtYtmwZnudx8MEHowoB+Le//Q2AhoaG/fnqEQ8FJB1NFiFJKwUJoTtG9hUl72Lb00JAiS9NpF38TH7/Pj8ASCSd2O7zhYrOX0yXpHD9c21C9ocBEglNXlAXJpFyyMmZl0WBf4dinxVd4p63ESOPta9QkEh7+MH+N8cOFIm0RyauipZF/0vt81Tg/yAvl/+lkXS1bP4tgzuGBxOJhBbNfwlv754YLLnHeDmmj2LtL7THWi/3yFiXR6H29xUyjyH1zv3kcr8oirk3lxP1v+TcQxTSvZeC/VjPvdJeNP5H+WfoUTx/X0jOuzj3jutWPkq497Jz/RVT/yddObUORWRfEb7G/bqUlPr+9tqHG4ZD7yHlHtCNWWUN6fFftN8T1+ZbNP/KCNkurn3m+7vJZRhhv1nacRzmzp3L7NmzexfFM5kMlZWVTJo0iXQ6PWAHOZKQcBT/dfw0Eq5MsvGSDpd843Qx+/uCwdj3xEtoLv3OGXhJITmJhOYD/3ZYfOXMEg6X/udRYlLOXtLlkm/KSYlLw0toLvvwHDwp/yc0l3zu9bGO/0u+esqQyGmpEgvjThnw/2DAS7m8/8Zz5eSEEpr3/+jsstjKZDDgpVw+8ON3CG7l4HLpt94slv+lkfA0Hz9zumD943DJF0/Y68XZkYaEp7n8gweL5b+Ep/nwu2eScPbM/6XyRzneWCU99r2kE9mP6zYmCc0l1x4tJyUac+73Ui4f+Mn5srn3O2fEt/ZPulzytVPFcp+XdLjkSyfE1/8pl/d//6zR2lMIvb0XsW1kHC758onxnXsVam+p8e8lXS799lviG/8Jzb+/eZoY/yfQ/MfJ00jsZqF7xN0EuxvI1z8Ol3z9tPjGv/D495Iul37vbbH1f8LTXPqfR8n1HhzFf54wba96D9LYLw/985//5HOf+xxf//rXAejo6ODtb3871dXVNDY2cvbZZ9PdvXs5grgjF1r++5+ryQcyt8z52ZCbPjRfzP6+oGSy3s/GnJ8z3PiBu8XuGvPzhh9etyi2T4z7uZAb/+dpghJPzA4mbfrZgJuu+Otr5bRiAj9v+P4NLw7JE8v92b/pk4/EOv5vuuZv+NkheGK8xI1F+eF/096gwM8E/Oji34uNfz9v+NG7/zAk13/fMDTFqp8J+OG/3C7n/2zAjZffK3fXuDDyvuHLf14lV//kQm76+EPk/Xjyf943/O8Plorlv7xvuOEXy8mHe67/B+PGVElIj30/G0b2hWovafg5w03XPimW+6Sv/+4wlKPIzwT88F2/k829H7gbPxdP7vezATdddb9Y7vOzITd9bEF8516ZgB+9987R2lMIvb0XKf7Phdz0rw8Ow7nX0KBYe0uNfz8bcOMH74lv/OcMX7p3tRj/5zB87u+rye3mhteRVuvvDvL1T8hNH74vvvEvPP79bMCNl/yxpP3hvyq2/8j7hhv/52kx/s+Flv95aDW5veg9SGO/FsZvvPFGrrvuOp555hkAbrjhBubPn48xBmst8+fP5ytf+cqAHuhIgQLGVnhySpYK6idUlUWqK52s9/M7FdRPrNrjlqyDBaWgYUxSzL40lIL6sSlR/0tef2koBQ2NcvGnFNSPq4i1/+snVA7J+Zd64k9OS1kWSkH95BrZ+Be0Lw2lFQ2Ta/r2xR1q+6P8T1O1Jxv/4+PN/w0NCVH/N9Yl9or9R1qzTHrsS9uXRlT7p0f9LwSlRnOvJIay9u/Xfsxzb/2k6lH+EYL0+Y/Gv+z5S19/aSgFTVWCcy+gqXL3aw+ljsmMkAs2LHoPgvlfGtLjXzr/S0N87QXZtc99wX4tjBcXxM844wwA7r77bpRSnHjiiUyYMAFrLX/6058G7ihHEBKO4kNHTsITkrJ1kw4XfPoNYvb3BaWCc3/3PXETmgv/52RcITkN19O88wMH48ZUStRNOFx45WE4noyclZt0ueDaE3BjKqXuepp/ec9MXCE5T9fTXPBv82Id/xf8x+twh0RKvf+/OYJ7XUnCTbm888tvFhv/rqd555dOH5LrPxzhJh3eed1bxM7fTbpc+JkTxfK/NDxX88GTJ8vVPwmHCz5+NF5M+cdzNRe9a7pY/vNczXvOmYK3F3JmuqSUevlBeuy7SSeyH1MpdTehueDquaPcLwQ35fDOr54p6///ORk3plLebtLlgv86Xiz3uUmHC/79mPjOvVIu7/ziaXK1f9z5p9h7keKfhMMFnzg2vnOvQu0tNf7dpMuF/31SfOM/obnsxEli/J9Ac+Vxk/qRUh/5e4y7Sen6x+GCT70hvvEvPP7dpMuFn39jbP3vuZoLrzxMjP8TjuLKoyeNXCn1zZs3AzBt2jTCMOSll15Ca838+fO54YYbAFizZs2AHeRIQi60fOXxNaJS6r/42N/KQ0q9xMrO/j6x4ucMP796vqiU+k+/9WJ85cxyIT+/7lkCIS1nPxvwi399INZS6j/+wVJRKfVffPaJWMf/Lz65YGik1EssbMRZSv1nH7xLVEr9Z5f9Kb5yfpmAn75fVs7y5x+9P7ZyZnnf8I2/viIqpf6LTz8aayn1m36yXFRK/Qe/XrWXUur9oxyfGJce+342jOzHWEr9F19cKCqlHmfu9zMBP730D7K59+r5sZZS/8W//0NUSv0Xn4rxNlaZgJ9dcfdo7SmE3t6LoJT6L/7rn/GdexVqb0kp9Z9fE2Mp6Zzh6/evEZVS/+pDa3YrpV5q272RsjAuX/+E/OLjf49v/AuPfz8b8PMPxXcrk7xv+Pl1z4pKqV/36JqRK6Wey+UA8H2fFStW4Ps+M2bMoKqqinHjxg3oAY40aGBKtaCUsVaMm1lXFnISg9GYUxrGzaoXlVMZNynGck4axk2pBCn/F+NfyL40lILxE9IooYcGlIJx02IsZ6Nh3IzaIYm/UlLqNq7xrxXjDm6U5f+DG4Yd/9gShzOQR6q0YtxsQf9rxbhZ8eb/SfVJQf/DuOkx3kpAwfjxsnLSE5r2Tk5tpD1FIj32++yLmBeH0jBuatUw8P/Q2h8ubaDhkXvrYxz/Kqr9JXs/cc69WjHuILnaWzr/SEO699Ib/7H1v2ztPRr/MLlOrvevgSk1yd0u+pQ6pJFyG9WwqH9mDk3vbzhCevxH+X//137K/aopFa29iPGP6p9/hhv26xgnTJgAwOc//3k+/vGPAzB37lwANm3aBEBTU9MAHF5pfO1rX0Mp1XsM5QDXUbz70HF4QuTgepozPnzUXkkpSqNUY25/k7Xras78yLGiUtJvu3BafOXMPIczLz5ITMrZTWjOuOro+MpZepqzzp2KK+V/T3PGpXNiHP+aM644YkjOv9QT405Mi3M34fDWfz9BlP/f+vE3xDf+Ew5v+4ScnLCb0Jx5VYzlRB3Fu44bLyZn63qaMz5wGG4ZbOUzGHBdzdvfNklOztJVnPumCXs1/yh1Y5UpwzaB6xXGvmDsR9wT09rT1Zzx/+S2kZK+/tJwEw5v++TJsrn3I8eKzT2k4SYczvjQkbhCvRfX05zxwbnxrX0SDm/92PHytWdc+ccr9F4E+f+My2Ic/8XaW8r/Cc2Z1xwb3/h3Ne86ZpxY/vPQXHTEOLzdSqn3j3K8CXZ3EK9/PM0ZHzoqvvwjPP7dhMOZ//r6GPOP4syLD5Lr/WvFxYeNE6t/9wX75aG3ve1tWGt57rnneOCBB1BKcfbZZwOwaNEioG+hfLDw9NNP8+Mf/5h58+YNqp2BRj60fPOpdXslZTgY8HMhv/7UgvKQUi/xt/1N1n7ecOsn/iYqpX7z95fGV84sF3LrtxYRikmph/z6vx6MrZyKnzf88icvy8lp5Q2//vLTMY5/w68/+8iQyKmW2mM8pu7Hzwbccs09ovx/y0f+El853WzAzVfdLSpnfOt//iO+/g8s//vAWkE5S8Ovv/AEvj/868/BgO8bfvGrlXL517f85PZX9lJKfWTtMe7nCmNfUMo11tyTN/z6a8/JbeMjfP2l4WcDbv7wn2Rz7yf+Ft/aPxvw62sfxpfaRi8X8uvPPx5r/9/yr3+Vrz3jyj+5Qu9FkP9//bnH45t/i7W3mJR6yK2f/Ht84z9v+O4/1uELSqnf8Oi63Uqpl3wIbYQsjIvXP7mQX3/6ofjyj/D497MBt37svvjyj2+59VuLxPgnH1q+9YTc2ue+YL92of/qV7/KihUrWLBgAUop3vOe9/DBD34QgD/84Q84jsOb3vSmAT3QndHV1cV73/tefvrTn/KVr3xl0OwMBjQwsy7N+s6MiH2lFZMOG8OOV+W6CflWjrSraEy24m/aQLBDkWuxVG2zBN2alNMN1fnozdaSXd1GttNS53aDccENoNoHu5GOx9MYz+J1KSoTnbQ/tp5EO/jN4GTBzYDVkMz4+BtcnJzFtCu8bACJLPkN60hUtlGr8/2ex4FIqU85vIkNL24Vae4pBZNnVrPhlc7y7C4eIJSGyQfVwBY5OZXJc8ew/tkN2BjmR6Vg6rQqtrR0i9mffEgd67f6ZR/+B+9o5vjN6xmrdoBjMW0prFbofAhKoVSeYEsa7RuySxwqdA9KQ+V4l+62HPlNG8h3KBKdFi/ZBcoHawi2daADg+3RqNCi/RDT4hB2afLditADHUJFS4gKLZmXXHQeUtk8Xc8m0AGoACa0d/R/8CNksrOvUFox+chxbFjaIsf/88axaV1P2cf//kBpxeQjxrHhhWY5+3PHsHFZa2z5f2ZTmk5dqhUyiPY1TJpTz/J40g91YTdvTG2lKrcBRRjdvWSi+5O7X0yRbgkJejQJpwfblaR7kUuV2xXdJWqJau2A6DPJLORT+Js83J6QjkdcqttDdG0bJBJQ40N2G9ZXBIGD+l0GtS1FVbVHy/IQ7Ud1eJiAZKvB9X1yaxPkWxXVZKhoKbUwXn4XUGnF5Dlj2LB0h0jtXbS/cXP51z77A6Vh8sG1bHy6HWuG3gO91/+l7UNuezhAacXkw8ez4cVtIrlPacXkw8awvmfobQ8HROffyDJBKeNJc+pZX4LXRzKi+B8b9X6k+GduxD9xvAJKKyYd2sjGl1vE/D9pdr2YfWkUa+/122UmPkX+37Bk+4j3v2NDDuncxOzMZlL4qAC8MEdifRWZHXlygQYFqY4Qk9W4iS5wDSgParL0FqiOA9aCdiARgr8RarsKf7bk13XidodolQfPQJUPxoBupu3vK6neYel8SKNDsC+ENLYn2bExqj9rMwEqDCHpM1H33w8cKU+M79x7KCGGNaj2Jx3SyMYY87/k+I/sN7FhRVtsez9TDqphzQ5fxL5WMLM+zepWmbXPfcF+LYw3NDTw97//na6uLlzXJZVK9f5t9erVA3Zw/eEjH/kIZ599Nm95y1vKbmHc1YqzZjbyi40bsQL06LiaEy86hJee2db7uxO3r+CKzQ+hsZAAWgp/2Fz4twNwgIqdvqi5GZoLv7ev+vvynd7nActe9X07Y8erfvYK9tOlz2N/pRwdV3Pie+Zyxxe2EwjcOOO6mpPfMpE//HIFgdBT05JwXM2JZ03lmSee6fc9g1mGOZ7mxIvm8IcXNhH48fO/62pOPm08d72wbc9vHgQ4rubEd8zgD79aSTnPjc7f8AwXbHg2+kER8d+rG34aaC/8v4c+fZZu+niuyLVe4QXQuZtrExQ+17qbg1m/0/9f2bvjVzqeckKOpznpkqP4w+cXEPR/39Xg2Xc1J713Hnd+4ylMbujt7w8GcgnVTTic/P6j+MNn/iaS/xxPc+LFh3HnVx/HyMwPROE6ijPnjuGPnsZkh96+42pOPG8Wq+/fNPTGhTGjaxuf2vwXKkyBeF5d6Kwr/NtNlE+ywBpeO0szhVcCCOnLCUsL/1YW/vWI7kb1C9/1SOH3Je6XYnvhpYCu/t9WjlLqxbF/x3WPEuYF5l5F7vnBC8RRr8hxNSeePZU7n99AIPDUTO/1//IjDJfKfyhHkeM5nPyBY/jDZ/6GnxHy/3vm8oebV42YfUv3BY6nOfFdh7DywS1y9s8/iD/csmrYxP9QwvE0J71vHn/4n3+I808g9NSWJIq9lzu/9oSM/13NiRcexJ3fXBjf3tt5s/jDzStl7HuaE99diP8R/NSsY0Mu3/4I8zLrX/vHYn+mc6ffZejr/UBf/b47mFf9fXuhV6TYtYcEsLjw75I9HLBX+s/leBPs7uAmCvXPtffj54aefx1Pc+K/zOHO5Y/Fs/4XHv+Opznx/x3OHV99LJ69f0dx4llT2HDbXjaJB9q+Vpw1q5EfP7tRxP6+4IC641VVVbssig8FbrvtNp599lmuv/76IbU7UMgby/ef3YAvJCcQ5EN+/4VHe+3XZnu4YvWCaFG8jLC/d7EFecPtn10gVpj5vuF3P3lZTM5CGkHecPv3XyQQknMKciG///zILsxLwfcNv715pZz/fcPvv/lcWTcGZrc09y2KlynK2P0HhCAXctt/3Cc2/gPfcNt/PjDs+Geosr+fDfjdJ+aLyZkFuZDbP/fwsPP/UMEPLDctWC8X/3nD77++UExOVgzWcs3yv/ctipc5yrFZJj32e+3HVMo4yBt+/90X5P0/5PaHx1gJcgG/+/e/yObezy6Ib/znQn7/pcfEcl+QC/n9154u67nXgSDIhdz2qb/FkH+GB6R7L0E+5PdffSqWi+LQV3tLjf8gF3L7fz804uP/jfnlu18UL1OY4VG+HDD8rHz98/svPjri478/SI//IBdy+7Wlb4qTUBIYKviB5fbvvygqpf79hRvIl8ETcfu9ML59+3a+9rWv8a53vYu3vOUtvOlNb9rl9eY3v3kgjxOA9evX87GPfYzf/OY3e70gn8vl6Ojo2OVlBHUUtIK5YyrRQslGa8XMY8f12p+3beOB3R1RZtAaZr5uAtqRuQBaw6w5tcT0gc0o/ubWi52/dhQzjx0vdv2loTUcNLsGLURAWsPMeY1lHf9HNZf/pGeEqGPtM7SjmPWGybL8f/yk+PKPtP8dxczjYsz/Cg6dUCkY/4qZR44Rq3+l0BR0Mi7Xuec3jmLQID32e+3HLfgL0BpmHt4gV3vGnfsdxawTpsjG/+smlHXtfyDQjmLmMeNkez9HNcXa/7NeL1d7j/KPbO+lL/5j6v9i7S3ZeztOrvc6VJgTyCiCDBZGipS6eP2jC/l/hMd/f5Ae/9pRzHz9xPj6XyG79iK89rkv2C8XrV27lnnz5vHZz36Wu+66iwcffJCHHnqo97VgwQIWLFgwwIcKzzzzDFu3buWYY47BdV1c1+Whhx7ie9/7Hq7rEoavXfC+/vrrqa2t3eW1que5AT+2vYWjFCdNqsWRag64inlvmdFrvzEjs9fwgaCTJF16/5QKtKM58q0z0Y4MO2hHc9TxTWL2paFdxZEnjkcJ+n/emdPRbkz97yiOOnaMYHGimXfqpLKO/4ZseW+SGLoebalq6cMQgXY1R549W2z8a6dgv4zj/0CgXc1R5x4i6/8z5fK/NBxH8YZZdYL1j2Le6XLNCSnUBeWdM16NzW6d9CHsM6THfq99N16xX4R2NPNOGT/K/ULQruaoc+bI+l9w7i0N7WjmvWUajtTcy1XMO21yfP3vao58+0Gj/COE3t6LVP51FfPeNCXG+bdYe4/y/2Ci1o6cWt9Y2FBbJ30YAwLx+sdVzDtDjv+kIT3+taM58m0Hxdj/0dqL1I1hjlKcOKUWpwxutNmvCPniF7/Ili1bsNa+5jWYePOb38wLL7zA888/3/s67rjjeO9738vzzz+P4ziv+cy1115Le3v7Lq9ZFUcP6nGWgm8sP1m0SVBK3fDHrz/Ra38g9w8dKrygJ+6/lLpvuOsrj4rJKQW+4Y6bV8ZXzixvuOunSwml5JzyIX+8XmaPq+GAwLf84Xer5eS0fMMfv7+4rONflbnezqKuWrI2nsVhkAu5U2iPQYji/87PPRhfOb9cyB2f/buonOJdX30stv73A8svH9koWv/88bvPEcRMSr0c6+z+YIAXEpOlD2OfIT32++yXb+1zIAh8wx9vXDIM/B9P7o9yr9w2LkE+jObeZVz7HwiCfMgfv/GUnJR6vpB74+r/XMidXxCUco07/xR7L2L8b/jjt5+Nd/4VHP9BPuSu6+R6r0OF4b/ss/dYYRrpTialD2NAIF//GP749SdHfPz3B+nxH+RD7vrSw/H1f2C566dLxXovvrH89LlN+CNVSv0f//gHSik++clPAqCU4rbbbuO3v/0tTU1NnHzyySxdunRADxSgurqaww8/fJdXZWUljY2NHH744bv9TDKZpKamZpeXVq9dQB8qOAqOHVctJ6flKOacPLnXfrkt8ryoJ3KXe9R+f147ikPfOFVUTuXQo+TkBKWhHcWhx45BlTz/wYtJ7SjmnCIn5SsNrRWHHVEvGv9zjh9X1vFfzoscy/N1PD/v9LKQsxkMaEdx6JtmyPL/6dOHIf8MzfFoV3Pom2YKPrWjOPSU+D2xXITWcNTUalE5szlvGB87Oddyzhk7I0Dzm6oTWOM1SR/KPkN67Evbl4Z2FHOOa4qd/0uP/KHjBe1oDn2zcO4VnHtLQzuKOSdNEpUynvOGCWU99zoQaEdx6Glytfco/8v2XrSjmHPCyJfy7g99tbdg/L9x5Mf/SKn114Y13JqbJ30YAwbtytc/c06K9zZ6kuNfO4pDT5223/bL/appDYceO0bwiXE4dnw15RD+7v58aPPmzQCcccYZ3HDDDQBMmjSJk046iUwmwxVXXMGPf/zj3r+Nog9aKQ5vqmTZ6i6sQALVjmLWseN5cnVH9HOJQ1gweyar2yaTq3WoXtqK7S7IrhtL1+um0DnDMvEvLdhEApXPY3syqNoamk+pwSQtiRZF42Pb2fLWMVRsgcxYcLsg2QlWQeWGLNmmJF53SJjUJFtyqLZueg5uJL0kijGCELTCpCtYl0mzY/xUlNn/Ox61jvY5W/HYeiRuXNGO4qA5dax4qY3dKP+PeGhHMXNuA8vmy0l5zzp2PMsfWo2J4Z1j2on2GH/lkU1C9hWzjhzD8tU9CIlmHDB0iZuJ/l59GBvDWtAK3ZEhN6kGJxPSOcWj/vlWHNfhiJPH88I/1tE1o4HMGE3FNkNq1Q5sJgvG4M+ZjJMLMZ5G+wadCwgrEwRpTfdYTZgC7UPd6gCdN7TPTOBmoGpdDzvmVeDkQOehe4ZhzDMWv8pB5Sxb2qvp3NDBW4+fwhPLu4bQY8MH2tXMesMUVjy5CZMTsF/YY3vVC63Ej30K+e+Eyax4dC2hQALWjmbmcRNY+dSWWPrf0YrDJlaxxdEi56+1YtZRTSx6coeAdTmUapato56H1Sxa51VTvT4gX+VQsboNU1dJ6+wEjU/siGblFkgmsFqBq1FtXdiaKnJjUyTafba+LsmYZ/M4a5pR6TQ2k4EJY7CuQ77GRZ3RjnN/LcccUc/jGzuwOYtV4FdC1cYQry1Lz6QKMmMVY55sJzexGrc7QPf4mAqPnsBjyaQpqJbyfOKqOPZXPLlJlnte7qI8PXhg0Fox64gGVi7QGIHJT+/1f3xjLLlfu4qDTpjKikfXY3wB+45m5usmsHzzpnjGv6OZdcw4nn++Tch+lHuXr9sgYl8a2tXMOn4SKx5bJ84/JojfCCj2XlY+vUXI/4pZR49l5cItmHJtPhwAirX38jXrZewX+H/FE5tErv9QQZeo9V9681ksfLGbziYHq6FmTZ6g0iX5SguEAaTT2PaO3vcr18ViUY6DDUOor4VtLVDogWeOnUqyxcdpy4DvY7M5CAJUXQ2bzq5lzELDjiNddB70GJ/T8008mGsjwDLu0Rwq76O6s1jfR6GwWGw2x8rXHUvXSx2gRs4dzFHvYSorHlkrMv6Lay8rn4hv/SM5/rWjmfn6iax4phkTwxmA1tHay9K1Mls9aKWY21TJ4q3Dv/e8XwvjyWSSIAhIp9Ok02my2Sxr1qzhpJNOoqGhAWstv/nNb4ZkYXww9jIfTPjGcvOLW0iE++n8A0SQN9z7vwvx334wFFJhf1gxbgzPhLPoHuvS9NImbLYt+oMx7Gg6iO2HWObcsxGr0yiTwWY7UHVNrJw5lrDSULFBMyX0WDFrOrUWOmZCcgdUbLMYBxqau+iqrySpAoIKh4rObnTYQnv9VGqKcid5HxwHlawGkz3w8/cN997wZFRYeEN/BQLfcPdtq4HyvwNpfxDkDffespxpgnJO9353ISbGUup/vmMtnqCU+r0/eQlTVwllus97KZWN5anxLAvGY7XCtR101zThEbB9YoqJz22CUPP0X3qAcXTUTaNjkkOtH1IdJLDZLmwQkK2ehad8wrSLkw1xwhx+ZZp8tUvbZAe/GpwsjG/O4eiQrRMrSHRYGjZ3sH5qLV43OBloOTRg2nJLttHF6zJUdPag863ce8NT+G8/lMTQuWzYIMiF3PPVh8GVyL5R/N9z/SPomioR+9IIciF3X/cwwB5UQwbJfj7k3u88hRpBE/59gR9YfvvEZsYISqnfe9MLBEdMiBX/qBJ9mBYqeFZNYeOkJsa05MjWe9SazQSpejZNqWDqY0lQCqwFlcY6Gus66KAF4zbQXVdNOptl9UEVTF+axcuEqEQNNtuJ9aZhki7Z2iTq9c2oR5p4fK2ia+JY3ExUh+fqFA0dPunOLtoaa2mfppn+ZDPdNY0kwjxuLkuQSqGzIXntkUTgjqIBQHHsAyLjv5d76mqH3PZwQOAb7v3lyyAopXjvd54CY1FJOdU4KQS5kLu/skCs2R3kQ+694Uns9En7J1VY5gjyIfd+/1n8E2aQErFvuPemxZimOkjG7woEuZB7vv5olEcl7O/EP3GU7Cr2XqTOPcgb7v3RIrHrL41i7W0aa2Ts50PuveEpEdtDiVK1/g+6x0B6HG0TElgHmpoz5Gs8KsMk5POQrsFmt/XGqEomsdagPA/r++CMg4zGGgPW0tFwEJU9WTw6IMxhc93YfB5dO5alh45j+rKQdbM9nIwinJbhycfTdM5uBOCgp7rRQQ4VdGJzWZTSWGuw3d1kErVU0jkU7hoy9NY/IFIDBXnDvd97BkIDcaw/hcd/kA+595uPQ8ITOwZJBIHl3luWE9SlRez7xnLLC1tEbO8r9osdxowZA0BnZydTp07FWsunPvUp/uu//otPfOITAOTz+YE7yhEER8GJk2pxpOS0XMW8t0zvtV9qkcfs5z7ewxnaUcx7q6ycypGvHxNrOZV5J46Tk1NxNfPOmC52/aWhHcVRxzaKytnMe+PEso7/UpFj93C7i3YU8940Fe0Kxv+ZM8TyjzS0qzny7Nmy/P/2g8WuvzS0qznyHEH/F+I/rvzvaMXxM2tF43/eaZNxYtYYLvUUyVC2aR2tOHZuXVnImQ00pMe+dlVkP47OpzD2Tx4vXvvElfuj3HuIbO5968z4xr+rmffmaWK5T7tR7o2z/48866DR2lMIfb0Xwd7D6VPiO/dyZMd/XOK/1MNmx06rFeN/B8WJU2qJ35JsBPn6RzHvLdNGfPz3B+nxr13NvLfNiq3/HS279uIoOHFybVn0HvYrQor7eW/atImzzz4biOTVb7jhBtasWYNSitNOO23ADnIkQSvF1OokSuh5Ya0U42bVoQqL3qVkgW0ZBPC+QmsYf1C93D47CsZProzjDcNAJOcxfkpVyacFB9M1WkfxH9t91hSMn1AhGv/jpteUdfyXupnI7uFmIq0V42bWooVuOirynxqBNz3tDbRWjJvdKBv/gvb7Q6nFuYE8Uq0V42fL7XOkNYyfVR+7Pa6LUAomN6Tk9jnVinHTa4gb/ZRqlu3pZqoBPQ4FE8amY8n/0mNfa1WwHz/fQ+T/cVOrZGsfgetf6gHFofSE1orxh0jmXlWYe4uYF0ex9peiXq1U2c+9DgRaK8Yd3CAb/zGuPbVGtPeitWLcjBqx/CONYu0tNf77eq8y9ocKpWr9CfVJMXVyDUyuTcY6/kXrH6UYN7NuxMd/f5Ae/1orxh/cUNJ+ybl4KSmIMoBSMH5KlRz/K8WUmvLgn/0K0csvv5wPfehDjBkzhs9+9rMcddRRWGt7X0cccQQ/+MEPBvpYRwR8Y7lt2VYCiQ2uieR0Hvjx8wSFPTZKNuzKIID3FYFvuf+HzxBIyfkFlvvuXEsQlDfJ7i8C33D/7asIBaXUH7jpebHrL40gsMy/Zz2BlP8DywO3LCvr+C+9yFEagW944GcvyPk/b7j/R8/28n/cEORD7v/O46L8f/93nyDIx3GXqcj/9337MTn/5w333/hsfP0fWu5Y2Cx2/oFveOBXS2LHP8NlYTwILfc8uFls/iEJ6bHfa18o90sj8C0P/HalbO0TZ+7Ph9x3w6OCuTeM5t5+/LgHCnPPny4Wy329ubeM514HgiAfcv/3npKN/1jzjyn0XgRrz1+8FOP8Kzv+e3sPIzz+S1Xzf1y8Q8z/Ppbfv7gVP65bCUjXP77hgZ8sGvHx3x+kx3+QD7n/+0/H1/+h5f7bV8nxj7HcvmQrfhn0HvZrYfy8887jxhtv5Pzzz6e+vp6FCxfy0EMP8dvf/pZHH32UZ599lokTJw70sY4IOAreNLVeVEr9uHMP2klKvf/3jlQp9ePOl5XSfd0pcnIW0tCO4rjTJ6KEBoB2Nce9Q05OTRraUbz+hLGiUurHnTm1rOO/pMrGXkipH3f2TFE50ePOmx1rKfXXvWuuLP+/67D4yvm5mtf9i6D/Xc1x7zg4tvzvaMUbD6kXjf/j3iYnJyuFklsWDeFxOFpxwtGNZSFnNtCQHvvaVZH9ODqfwth/8yTZ2ifG3K9dzesuOlw2954/O77x72qOO2eWqJT6cW+bHmv/v+7CQ0drTyH09V4Eew9nyUm5S6NYe0tKqR93nlzvdahQ6ibYk2bViUqpnz6jPtZS6rL1j+K4c+Ir5S09/rWrOe6CObH1v6OjtRdJKfXTp9WXRe/BHYgv0VpzyimnDMRXjXgopahJOgUp9aG/c0IpRWVdEtXqA3va+7AMIngfoRRU1afE5MyUgqoaD6Ukrr48lFJU1STYIej/SsHrLw2loKraFY3/yrpEWfu/9CJH6RPr5V8hBygFVQ0p1LZAxL40lILKxrRs/DfEU8oYQGlFVWMFSiuswJ2j0vlfGkpBTUqS/xWVtUnUFl/mAIRQyt1DLaVeXeEW+CdeFejOY1/izJVSBfvxJB+loLI2IRZ70tdfGkopqhoqSm5jNbj2i/7vian/iWr/tqyQ/eLcQ8S8OPpqbzn7ca89K3vznwD/653n3vFjoN7aWzL+G0Z+/JdaGK9Mu0O7f8pO0EBN0kGHijCGT40rvVP9I6DaopQa7T0Ljn9p+9KI1p7keu+9a59lcAH2amH8S1/60n59+ec+97n9+txIRmAsf1yxnYQZoLsS9hGhb3jo1pcI3n4woEov8pRBAO8rwsCy4JeLwRjwhv7OoTCwPHjvBkCsPhJFGBgW/GkN0wIZOZPQNzx084uYmMpphYHlH/dvwhOSUwkDy0O/X4mpq4QyvXP7QBY5wsDw0G+WDuwB7QNC37Dgl4sJ3n4oSbGjkEPoGxb8eCG4Etm3kH9+8gy6pkrEvjTCfMiDNz0NINKgD33DgptfQElt9CaMILTcs2gbY4TyXxgYHrp9OcERE0iIHIEMSt+AOnQIQsv9jzYTTHRF5h+SKI59QGT893JPXe2Q2x4OCAPLQ3e+AlLcU7z+xqJiWPyEfsiDNz2F1CanxdrTTp+0f1KFZY7QNzz06yUEJ8yQs3/by5imOuL42GDoGxb87FkQWhTamX/iuNF7sfcide6hb3jody+LXX9pFGtv01gjY7/A/yMdpWr9v7zUQkpoKw0fy5+Wbcc/KKbxny/UPyBSAxXXXggNcWy+SY//0Dcs+PnzkPDEjkESQWhZ8Kc1hHVpGfvG8qfl20Vs7yv2ih2+8IUv8MUvfnGfX6N4LVylOGtGg5yciqs54V/m9NovtTBuR2Dt7riKE999GI7AojiA4yhOestEnHLQkxgEOK7ixLdNEZMzcTzNCRfNEbv+0nAcxcmnjscRWpR2HMUJ75hR1vFfUkp9D6fluIoT3nkwjlT8uzrivxg2ZiAa/ydecqQo/594yTyx6y8Nx9Oc9P6j5Pzvak68+ND4+t9RnDG3UZB/FCecP6us+X9/MFz2GHccxamvb4ol/0uP/T778fM9FMb+2VOHgf+HD/cPZSREufdoudzrFWrPuMa/pznhXbNlez8xzL1FOJ7mxPceIRv/w4x/hhKOW+i9CPL/CRccFGP/y9bevb2HEe7/Ut5986ENYv53Ubzt4AbcWD6SNQzqH1dzwr8cMuLjvz9Ij3/H05z4/w4vab/ULSPlPmocJ1p7EeMfrXjbrAbcMnjgdq8fGrD7eJddOTwuP4rSd7eNxCfGRzGKUYziQFDyZqKyL59GMYpRjGIUA4nRnDGKUYxiFKMYxShGMYpRjEwMl5tgRzGKUYxiFPsOZfdixfvmm2/ery9///vfv1+fG2y8tfJSut86D68rJNGSQXdmIQxBKUxdJXrzDmxDDaori61MojJ5bFs7SmlsGEIYoiaNj74sm4fubmiowzZvj2TyTAieB54Lnd3g6OjnXB4cja1Io/wAgjB6byIRbQCQy0EyCT0Z8P3o95lsJP0QhuA42FwOlU5j66rAgE249EyvJlunaXixE71hK6ajk/zJc3G7fNztXZDJYtvaCY46iKDCJb1kM8HUJtwdXZybXsbpPct266ffeq9j4dZKzOSxqNCienLRcfZkyB80Hq81g+roIT+9Ea+5C9XdEx1/ewdmQhO6vRu7owVz6HSs1qAVOpNHt/dEclJB0Cdr4bnRuRobnTuALtzZo1Tf/1NJ6OiEqsro80pF0iQ7h3FQ2D+3KJfr++A4vd9j2zv6vl8pbC7X618K+57qqkpsEESS647TZ8uJNMhUMgHGYPM+GINKJKLYsLZgR6EK7yWZjOSjQhP9bedjNYXf+X70+eK+q4XvUsk+zRUbhtGxGguORmmNnT0NvbUt8kt3DzaTRVVVgrXYTAabzaFra6K4qa7C1lahtmzvOyfXxXZ1o6qrovhMJaN4hSg+lYJ8PnpfQw125Tr0+CbI5rC5fHScusQdYCa6NqqxHtvZBROa8JuqOGjZi3y85tndfuTlTDU/NidgG2oIalM4z7wc+bxwDW0+8pXSCpRGpZLROQcBNpfHZrLYwEc5DjYM0ek0qrIiuvaF8UMQRD5qrEe1dUBlBbS2Y3p6UMlkZCvhYXM5TEcXynOx+XxkTyt0dTUmk0ElEqiEB6Hpu/5hGF2vQlxEP9i+eIn+2HfCSkc/u24UI8UnCozte5/rRrYa6qC9EyrT0TVJJiIuKVwv29Ye+TvhYcePQfkhflMVuYYEVS+3sPWkJowHYRLqVwbkqzWJthDrKpLbczidGba/vhHjKdLbQ6pXtKOyPh3zmki2+HRPTNDwj1d6x0X7SdOoebkd1Z0laKrBWbYWO2MietN2bDYXXZeqCmjrIJw2LjpdP/KP7slDzgdHY2orILTozh5sSxt2xkSs44AGk3TxNrdDNge5HHZCEyqTh5Y27IQxoDVhVRK/yiW9to2Ow8egfUPlKx2ElUmsp/GrXHRgSa3aTnbWGHQuxO3MobIBKpcnN6OR7vEJ6pZ2EFQnSWzpYMVlTUy9P09iazcoRVCbwiq4ZusDHGa27DZ2v58/nleCOmxDTXQdenLkpjeQXLYZKtLRWIIoXhpqUC0d0XjzA8jlsfk8qqYaM64elY+uq+rJ9XFCTwYzcSy4Gutq8nVJUhs7sEkPlc2jMnly0xrJ17lUPbSc7efPoXZlFpNwaJ+RYNxf12K27YC5s9Db28FYmt8+DSdnqV2dpe2gFNXrfZJbu+meUUOyJU/PuCQ1y1oJ6tK4S9aiPA9qqiP/h2HEdZ4HyShfWT/YlRfCMBoLr+KJ4u9skSOc1+o7Fv+G6uNTlUhggwBVkY74IFvYs9EPsEGArq+LfucXxng+j6qowOZyEa9nc6iGOmxnF7arGz15YsR9+Xx0PsVhqTS4DnhexJGeG/FrZQVUVxW24yjkLdeNOCXo+zxaR7nHdaP3ah3FgLXQ3QNBgJk4FpP2cLe2E67biPJcdFVlZLO7J+KcvgPq45Kd///qv736/TtBV0cS7rapHrWjHduTAWP46NilzHLaXvN+gG+ZU9mYbkI178C0d75GClE5TnSO1qJSyajWMSY6d8+LfFjIa6alNfpMMRZcN+LPov8cJ/psMhH9rrsH292DSqcIZ07Eac9A3sdu3R7lrKJP83nM5uboOufzEBp0Qx02l8N2ZyKfuk7kU2OiY3YcTFd39H9r0HW10FBHuHINzrim6Hi0xnZ2oepqsT090bj1fXQ6jenp6cvnxfph5397HVTg9+K/O/9eqz6eL8q7FWoHW6hJCU3v7/XYMdEYy+UJ5s2MeLEnEx1ndSVqR2tvDrE7xT9hiJo1nbA2jbulLao3OjoB8I85GKsVTiagbU4FYx7cwPbTJ9O4sIWgLo23vZugoRJ33Tbys8aRWLMtuq7WRty2thVa26NxNn5sdDzGRLFV8I+qqsR2dBLOnYm7oys6n5Y2SCWxNVXRcScS2J6eqGZpqMf29KCqKslPb8Lt9vFrkiQ3tUdjLIzGnqlJo7tzsK0FO2ksGBMd85ptkY2EB51dUZwEIWbaePTGbeQOm0JyfRSLZHPRcaQTBHVpjKtJbO3Eui4m7ZEdl8LrCkg8+TImm0M5DqoykiNTnofN5VHpVK89296Jqq3GbNuBOmgamYlVnLh+CZf6T+52fD2mZ/AHe2R0TkGI3dGCnTkFnfNhWwvhrImRLT8EAziK7mlVVL+wjfajx5LoiGItuaUbvXlbxFEVaWxPBnPIdEzaxVu+EZvNYeZMi74riHjVuhqrNUGVh9eWQ/khelsruUMmElZEfFuxbBs2nWD92xsZ/0SGxNrt0bH6PsHMCbgt3Ww9ZSx1K7P0jE9Q99gGqEiTHxtxTWJDazSWMtmodsvm+sbB7iQNi/xSHFs7Sc8WazOTyezKfcX3FeHovnxibaG2Cl/DndYYCA2qMo0tzj8AXVuNDYLoWEPTW2+rilfJ0HnR/MV2dO6S73r50HV760hC0zdXsGaX41UJLxovxfPROqoLQhN9h1bR+Oju6ZtL7GQHpVDpFLYnqvl39qOuqQZrMZ1d6AnjeueyaBXlzyAocJDdNVcX/VrgMxuGUZz7QaH+1hHHaBWdUzZH+Lo5ON15dFcu8m/KQ/kh1tWo7hymvhLdHI27cOIYnK3R/21LW3QOqSSmoyvyt+dF+cRxeueEdkJTxGVbW6JjrkxDdyYa556LbW3fpcburb9748Lp9Zvt6dmVm4vn4fuvjaedeXtnvCp+VWE+q5oao7rAcaho28pXJj63249vC1Nct3le37kWagXT0gY2mlMo14v+dZzXzg0K8x+UQnmF+W4xDovjw/OwmQzKdTGdXdF1Lcz9do5p09mF9QOU50Ycl06B52F2tKLSqd5cqcc3RTUk7FK/6cYGTEtr9J0NdRFP+350PFr3zaVdp2/Obot1QdsuPlaJKP+rQh1QrAF6x24qGc11CmMTIi42rW27zOPRCl1fF9kv5uRiLZZMQk80Z8ZaVHUVZkcLKI1uaozqwVy+LwbDMKpLivGW96OfwzCKmQJPqapKbEtbNHd39K61WaFOtm3tfTk+7/fWF6qmBjq7+uq+gl1VVRnFd2EuuktsppLkZk8gsS3Kc2p7W1SXFufvRT9bi22sjeZOPZno/As9ApVKQnUVtqU1Op5kMporT55Q6B91RPnddVG1NeRnjcNty6LWb474qaY66svU10b8UJlCNe8gnD4e6zq42zrpmdVAoj2P05GJjgEKNZYT9cGK8wbHgXQqstuTQdXXYTY39/JVMe5wXZgwFrtmfeTL4lxhZ98MNMpJaWWnXlOx3lVNjdE1LmJnWfdd+O5VPjSFsfnq2raYT61FVVb0jtNeDi6OteLnd54DFsdFoQYu9lx6P/NqHoNorOV2mhO9GsV6mgIXW9t3LLtDoVfX9/17uc1H8Xz6aZ9b89rvKc55VE111KvK+301SeFYVCIBtdVRPtQ71f7FuE+nen2gXDfil8Lnbd7vrRmA6O8Q1Rqh6e2Bkc9jm+qjeNjRHo3BygpsOkFuYg2plzb2HbTnQt4nLPL9znP0nfzW2y/L5qI+VTqFaWsv6ULlujB9ErnxVaTWR+81FQnCygTeknURH+1oITx8Ju7qzeQPnRz1bbZ2FGrlqPZAF/JisY4Jw4g/PDfi6kw28ovjRL51NaozmssV6waCsPdcd5nTKR3NKz038lfCi75bO5CJ5jlm4liCuhReSw/25dVcP20xaRXs9pw/yXmE1YU5S1dP9F3WYrdsxfoBura6r14tjoNCf7i3n71T7KjJE6CjK1oryOV6+5jUF7brCUJMfWXUH6vxSG3oQlmL6sr09rTNth29eVRXVWIzWdrOmUv9gjWYtnb0tMlYR2GqUoRpFycT4DS3E27YBBDNlY3BNtVjUi5ORxZTmURv2g41VRGPt7QV5qMGXVuDae+Ickze33XuuzvsPEcuwBrbtxXbqz6rG+qi69LW0RejxVqZPi4snn9vzyc0qFQS6/uFnkAft/XWxMX438343mVuUUTxe4o99WLdXvhek83h1Nb0zWdevT6w889KRbVVdVU0x0smorh1dNS3D01ff9Hz+nr3xf97XpRnuzNR/yxVWFso1kI795rCwhpWLkc4YwJ6ySu99Zfp6u6r/a3B7jyPGeT8aF/dVynYLB5DsZdjd1MDFN/Xu25QmMOT96OxV1fDR5NPMjPVvVvb39g2jy0V4zHbtu9yHDqd7ju2wjzJFvKZ0iqas6ZTEBpMJoNOpwnbO3DGjsFs34FubOhd1+mt8Y3t5e3efFucFxbmYcX+jqqtjmqn2upobaBw7WxXN6q+LuL77p5oC6PuLDadgI3NqMYGrOvgN1WSr/OoWrI9igGlIl5qbYe6mqi3vK2V/CETSazZEdXvxbXLQp1GLrKhGuqw1RWotq6o7w+wvTWap2Vz0bqd42CTLmF1CndTS+/cy1amyE6sJr10c8Sv1kTnVZxDpJLRz26hPuzqwTbW0j29mqqn10VjsthHdF3CyWPQ3Tms46AK43X+oi/vVZztlabB+9///v16jeK1cDzNaRdMl5OTcBSnvueQXjmz0nsfltEkYC/heJrTLhOUcvU0p/6/GEt5u4o3v3kCWkrKO+b+d7XiHXPH4IrJ+SlOO2dqWcspHsjTf46nOfXi2aL8c9qHji1r/x8IxPnf1Zz6rlmx5R/H05x2xTGy8S9oXxqOqzj14oMF419x6rtmxk5OrhTbDuWOy46rOP2sybHkf8fTnHa5bO0vmXuk4Xg6qv0k/f/BefH2/5WyuTfOc6/e8xeUEj3tPLnekzR65z6S/CM4/qQhzb/S9qXheDrq/Y7G/6Ci1BPjb3znTLHa23UUp72jvHtvBwLpuf8o/zic9uHjcLzXPgAzVPZPv+p1YvalEfVeZslto+ppTr2oPLYy2Wsp9SAIWLJkCQBTp06lrq7uNe9pbW1l/fr1ABx22GG47l5/fWxgjaWrw99nafqBOwDobstFNydRumEndISDCmstXa2Z/m70HBL7kf9Honf3DGuhqysQ9D/x9j+Wjmwodv7WQnenL3b9BwKlbyYqDWss3e1y8WetpbtFjv+k0cv/g/lUyR7sd7fny4p/BrKMthbR+JO2Lw3p/GctZRf/A4HhcgOqtdBV5vl3f2EtdLVkRblHcu4hDWtsofaT456u1qG//qXMDWWLSrr26537CtVe0pCe+1tr6eqIX+4tQrr2k7YvDSn+HS72pSHPP/GI/1I5vbtDrva2Vta+NKTjb5R/LN07MrL1z46eeNc/7Xnh+r886s+9Xrq//fbbOfrooznttNMIgt3LhARBwKmnnsrRRx/N7bffPmAHOZJgQsvCv2/EhDLBERrLwr+u6bUft/1QTGBZeNfLmGAon9N5lf2/vIIJhj85DAZMaHnyyW1Yofg3gYm1/0MDC1a1IuR+TGh5+qHNYvw3ECh9M1FpzjShZeH8tWLxZwLL039YUtb+PxD08r8U/4SWhQ+sjy3/mMDw9J1LBfOvrH1pmNCy8L51cvwTWhY+sCF2/DNc6mwTWp5+pDl2/odC7XfXMlHukZx7SKO39hOrfQwL/7g8vv4PLE/fIZl7C3PfGHIPyJ+/CSwLH9wUY/+baO4jWXsKjj9pSPOvtH1pmKDQ+xXMv3GI//5qfYNi4d/k5j6hsTy9oLx7bwcC6bn/KP8Ynv7DS7L59//k7Eujt/coWX/ev64s+GevF8Zvu+02rLV88IMfZMyYMbt9T1NTEx/4wAew1vKb3/xmwA5yJMH1NGe+5yBcITkN11Wccdlc3IKcwnBp2A0VXE9z5kePw03IyGm4nuaMyw4Xu/7ScD3NWW+fLCYn4yZi7n+t+Jcjx+IJSam7ruLMd83o5Z9yRCnONHvgTNfTnPGBw+T439Oc+fE3lLX/DwRuosD/gv4/432z48s/CYcz/+31cvlX2L40XFdxxvvnyNafMYz/0nX20MF1FW89f2os+d9NOJz5r6+T5Z6PHoebiFfsF+F6Oqr9xGp/hzM/cmx8ud/TnPmx42XnvpfHeO4lPPd3Pc2ZF8+Kr/8TTjT3keR/wfEnDWn+lbYvjYh/5srm3xjEf3+VtQXO+H8Hi9XerqM4819m4Hrxq/1Bfu7vJnS8+SfhcOa/nyDK/2/9xInx9b8n23txPc0Zl8r1nvYFe32EL730EkopTj311JLvO/300wF6ZddHsSuMsWxZ14WRknI10PxKB0XzpfbL3dMiTznCWMuWla1i/jfW0rymHVMGchKDAWMsWzb3yPnfEGv/Wywb2nJi528sNG/oppzVFA9kkcMYS/OaDkH/W5qX7yhr/x8IjBHmf2NpXtcZW/4xxtK8okXW/4L2pWEsNK+Riz9joXltZ+z8fyAqIwMJY2HLxp5Y8r8xli3C3BPlHhHz4jDGFmo/af/HMPgp1H6S8W8tza+0x9v/a+TO31jLlvVyvSdpGFOY+4zWniKQ5l9p+9KI+Few9xCT+O/3iXGlC3P/IT6gAmyx9xbn+lO0/ife/DMM8u+W5dtj7H8i/hFd++ooC//v9cL45s2bAaiuri75vuLfm5ubD+CwRi5MaFn86BZROZXFD67fSUq9f9iRty6OCSyL568SlZNb/I8YS+mGlueeaxGVUo+z/0MDj69tF5VSX/TE1rKQU+kPByqlvnjBBlEp9UV/WVHW/j8Q9PK/oJT64ofl5GSlYQLDor+uFJXTkrQvDRNaFj+0UVRKffE/4yfnN1yUmUxoWfT09tj5Hwq1n2jtL2tfGr21n6CU6+L7VsfX/8XaT3ruG0PuAfnzN4Fl8WPx3EYDCrWfaPzL2peGNP9K25eGCQq9X8H8G4f4L/XE+OJHZHv/ix4v797bgUB67j/KP4ZF98pupbHonhhL2Rd7j5L158PlsZXPXi+MV1ZWAvDiiy+WfN8LL7wAQFVV1QEc1siFm9Cc/YFDRKUsz77myF45FT1MGnZDBTehOfs/JeU8NGd/5MhYyymed/5UtNe//wcz6tyEU/B/POVUPEfxvmPHy0mpe5pz3iu3lcRA4ECeGHcTmrOvOkJs/LsJzTnXnlzW/j8QuAmnwP9yckJnX3HosOP/0rl+4ApZN+lwzqdOwk0K5d+i/Zjyv+spzv7w4YLxrzj78kNjxz/DRkrdU5x7kZyctST6uF9Oyk/SvjTcRKH2k+KepMPZnzxeLPfsDkNZhbsJh3M+fbJc7k04nP2Ro4Zd7TNU6Jt7ytX+Z186O77+TzrR3Eey9hQcf9JwEwX+lcy/gval4SZ01PuV8n9M4r+/Wt+iOPsy2W2sznlfeffeDgTSc/9e/hnh8d8f3KTDOZ95o2j+Pfezpe2XmouX+2pYb+9RcCuNsz8kt5XHvmCvj3Du3LlYa/nGN77Bli1bdvueLVu28K1vfQulFIcddtiAHWQRN954I/PmzaOmpoaamhpOOOEE/vrXvw64ncGECS2rX5SV81v13FZMGP08XJ5kGSqY0LL6qY2YUOiupdCy6tkY37VnLCtXdGDF/G8K/o/nXWOhsSzZIihnGVpWLWkt6/g/EM40oWXV89tEn1he9cSGsvb/gcCEpsD/gvl38Y74+j8wrHpyo+hdw6uelMv/0jAhwvwDq16Qk1OTwrCRUg9h5bL2WPJPxP2bBGt/Izr3kIZ07WcCw+qnN8f4iRET1X5SuTc0hd5D/LgHdp57ytX+q19qia//A+H4F7YvDRMW+Fcy/wral4YJi73f0fgfPFj6e+bEoKK5j2TvYUmMpbyF5/69/DOi479/mMCy6glJxQrLysfl1Dql0dt7lFp7DA2rFpWHlP1eL4yff/75AGzatInDDjuMr3zlKyxYsIDly5ezYMECrrvuOubOncuGDRsAuPDCCwf8YCdPnszXvvY1nnnmGRYuXMib3vQmzjvvPF566aUBtzVYMKFl6ULBxqSBZY9v7g3OUi25kUjfJrQsfWid6OR02ePxkxItwoSWl15qEyTnmPvfwrMbO+Wk1I1l6XPlvTByIM/WmtCy7Ak5OS0TWpY+uKas/X8gGBb8/1Scm8OWpQvWyMa/oH1pGGNZ9qScnKox8Yz/UjdTDWWdbYxl6eKRv8/j7hBx/1pZ7hHMPdIwYaH2k/T/wzH3v3DuXfZYeUgpDgak554mtCx9Jp7baMBOc5/R2lME0vwrbV8aw4J/Rnj8l96aVLHs6W2ie1wvfTbGN+UPg/on3vxjWPqPV0RvTFj6j9WxvjFKsvdiQtne077A3ds3fvjDH+YHP/gBa9asoa2tjc9//vOveY8tPIU4c+ZMPvShDw3cURZw7rnn7vLzddddx4033sgTTzzB3LlzB9zeYMBNaM656jDuuelFgiDc4/unuR0cmdzBRNUZZd2kAceBwIlWYXKKcK2D2ajwegKo9CEZYJo3oAKDIow+0xhgW9dAp2LswQm2bVsENmB8rqtf2yPxiXE3oTnnc6dw99ceIxC4c8hNaM756FHc8/3nCPLxI2jX05x/0XQWXb9Uxn7C4dxPHss931lIkAtEjkESnqN43/ET+OXizSL2XU9z3iWz+POtKw9oQaCKLK9LrWem/zKeMth2je1SaOsTrk6AtTj5ABtq0ArbocCC1nlMcwJlLCow0Z0C2mC3eoRtGjcTYPMaZQMyf0mQajGofAhKYVuj+8jG245+j2tPnOkmNOdccyT3fP85/AM4//2Fm9Cc86XT+fOdG0bkjU97gptwOOe/TuGen7wk439Pc86HDuOe7zxNED/6wU06vOPak7n7648R5PZc/wyG/XM/dQp3f/1x9qL8GnFwPcX5HzuSe773rFD8K87/yOHc85MlDH34W47x1zInvYba7UtQqnAESQNKQ15hWx1QoIIQQoX1FSar0aFFNfZE3SUUOIV7irWGtIn+Lf5cF4Drwhgf8mswmxwags4SRzV0dbbrKc5790z+/OtVBPl4DQA34XDup06MuEfg3N2Ew7n/dRL3fHshfi5+2ddNaM65bDb33PC0yNzHTTqc++mTuPtrj8aT+xMO53zxNP58/SMEAsWHm3A45+PHcc+NiwgycYx/h3M+caxc7ZnQnHPlodxz0wsEPQIHIAw36fCOz76RP1/3sFjt+Y7PnMKfv/pPEfvScBMO5376RO7+xhOC+fcNYval4SY05/zbMdzz7adl+Gc/478h7OL45CtM3bEMNwxROgRlwSn0b1WhfvYKP1sb1eFWRbW9Z/reZ1zwiX7n2uh3VkWPCBoF2hR+Z/seGxybJ3oMXEX/ah29p9JE78slsDs0yg9hXKbf87DA+VfP5c+3vSLSe3FdxYWXz+bum1cQv+jfufcgM/ePes8nRPVnHHv/SYd3/Pep/PkrD8nl36L9/PBfnB1ouJ7mHR89grtvWSHQeynUv/86j3t+unSP+SdBwOv1eg5KbSeVzEd8q3XE+QqwGlIG8i7hOgcnnY0+WORupaDTg9BAUPjdvhzr3r6xoqKCu+++m7POOov169f3LoJHx6J6f546dSp333036XR6nw5kXxGGIf/3f/9Hd3c3J5xwwqDaGkiYwLLokb27a+8Ip5lL04tw1G7eW/yVAXae5LiFV7bQiFOAU3j5HVFRsBHq2enz/WAkLoybwLLor6sKdw0N/fmZwLL4H+viK+cRWp57dkdEWP1hEC+LCU3B//ErTCCSUn98TTuhmJS6YdHjBybnVWMzfIRHGFvZHfGgJeKxYrbt3unNr64ANJDdze9yhRfQO2vY+d4BC+T3fGx7lFIPLIsflJUTWnTvckyYErEvDROaiP+l/B9aFj+8afjxf4nDGUg6NoFh0V9WispZLvrLygL/DP+9jgYaJrQsXrBRPv6H/K5hy4WVKzk5twkSRFxfDGyn9y27cnwxrxRzSHIvTSUK/6YA0/HafPOaIxtKKXXL809tj+Vd8yY0LJq/UvSJhb65R/xgAhvVfmK1j2HRfauj3KOGbp/D0mc7dL6Ict8K0dwb67mv8PmbwLLosS2x9v+ie5cL155y408aJizwr2T+FbQvjaj3uL6s4n+c28M17fdRk8r29WcKa9R7hWLve2dYXjv1K36fftXPAP0tZRTfa+ibN5RY9rAoFj8iuZWAPeDeWzlDeu7fyz9x5f9hkH+fv/vlgv3+CGTkrXkVYUIj1Hsp2A8Mix8qbGNZYvglCbjSfYaZuvW1f9z5cw5965+7W8ku9m3243T3iR0OO+wwnn/+eT796U8zc+bM3t9ba5k5cybXXnstzz33HHPmzNn3I9lLvPDCC1RVVZFMJrnqqqu46667BmU/88GCMZbVL+55nw+F5V8SL+1+UXyIMBKnT8ZYVj8tSA7Gsvo5OTkdaRhjWbmyU1RKPdb+t7CkuRup0zcGVi1tix6820+8JVjGWNW95zcKYE/3GxhjWS24z4oxNtpnKZ61eTT+n94k6v/Vgvv8SMOEllWSe7wL25eGMQjzD6x+YeilvKc6HZyc2jSkNvcWQ+kJY2DVy+2x5H8T2sIe44K1p+DcQxrG2ELtJ+n/GG+jVKz9ROe+W+Nb+wjP/Y2xrH4pxnvMhsLxL2xfGtL8K21fGsZYVj8vyD/7Ef/nVq+lxu7hztIygUUV5j4y9o2FVUsOrPdWzpCe+8eef0LLqic2yOZfQfvSMAbR3uPe9j6PdzbsflF8CLHPt83U19fz1a9+lZUrV9LZ2cmGDRvo7Oxk5cqVXHfdddTX1+/5Sw4AhxxyCM8//zxPPvkkV199Ne9///tZsmRJv+/P5XJ0dHTs8jJWTkjETWgu/tjhuInSrp/odFGlJARv+mBG4N0zbsLh4q+ejpscuicGXm3/os++HjchY18abkLz3vfNFDt/N1nwv9D1l4bnKK45aTIJR2Zse57m3Vcdiuvt/x2bB5vmATyigcWeONNNaC761HFy8Z9wePc3zzgg/5cz3GSB//eQfwfNfkJz0SePii3/uEmHd3/9LXL5V9i+NDxPc9GnjhHjH8/TXPSJI4d8/B3syk60SmEo62zP07zn8tmx5H836XDx9W8S5R7JuYc03ESh9pP0/3Wnxdf/SYd3f+MMUf9f9N/Hx3fuW5x7StWeSYeL/3XPvaeRCjdZmPtI1p6C408a0vwrbV8abtLhos/I9b72Nf4VloMS/W9bV24wSnHRv88Tq709V/Huaw6NN/8L9x7izT8u7/7WW3GTey1UPeD23/Ptt4nZl0Zv71Gw/rzoP4/eo/2D9Y4hOqL+cUAeqqysZOLEiVRWVg7U8ewRiUSCgw46iGOPPZbrr7+eI488kv/93//t9/3XX389tbW1u7xW+y8O2fG+GmFgeOwv6wn3ICdRIb4oDhv14N7kIIEwMDz2uxcJfZnb5sLA8NidK/d4/UcqwsDwz4ebxc4/9Av+F7r+0giM5b5lOwiE7hoLAsOjD2w4oOtfafdC01wAXSpJayktLQrj/0+r5OI/MDx66+L48o9f4H9B/z9295rY8k/oGx799WK5/CtsXxpBYHjsj6vF4j8IDI/ds2bI7VcK19OlsEENXZ0dBIZH/rEplvwf+obHfitY+/uycw9phMXaT9T/L8XX/8Mg9z52x4pYcg/sNPeUnPv+dV2s/f/oraO1pxSk+VfavjSke1/7Gv8uhqQeOddqXbKRx+5ZKzf3Ci2P3ndgvbdyhjT/jvJPyKO3Pk/oyzyYGvohj9wsZ18avb1Hyfrzz6/s0X6lku/vl/2tQ8YYcrlcv3+/9tpraW9v3+U10zt8CI9wV1gD61e0Y/cQm9LPar+Unky32ttNFcsH1ljWv7ANK7QwaI1lw9IWMfvSsAbWresuGf+DGfux97+FVTsyYlLq1sL6VZ17lBwvBTVMN3l4qmLmHveLtQY2LGsV5Z/1i5sPyP/ljD7+l7IPG5a3DTv+Gaqjify/VTb+Be1Lw1rY8LJc/FkLG5bvuf4daEjX0/2hRVWwSo0ZMnvWwvpXumLJ/9JjX3ruIQ1rCrWfpP9fFPC/7Z99hpKXemu/0bmvCPrOX87++pUdYvalMRziX9K+NMT4d5jYl4Y1lg3L5Ph3X+N/uPZ59hdPVs5iw4p2sdq7t/cWZ/6Xrv9jzj/rFwnn30VbStovdWTDtYewt+jrPUrZt4XeU+n3DQc/l9XC+LXXXsvDDz/MmjVreOGFF7j22mtZsGAB733ve/v9TDKZpKamZpeXVnJSFl7S4ZJPHYWXLO16LVQUBCgWMZGfjTlNxP5gw0s6XPLdM/FSMnIaXtLhfV8+ES+mcipeQnPZZQfLyQml4u3/hKP4xKlT5aTUE5pLP3443gHIuQyHxLkzctrl4eTB3FV97B7f6yUd3veFN4jFn5d0uPSHbz8g/5czevlfzP+a93322Njyj5dyufT7Z8nlX2H70vASmvd9/vVy8Z/QvO8zx+yx/h1oDMcm21rVwA8q3kReDV0segnN+6+ZE0v+91Iul/zvWwW5Rzb3SMNLOlHtJ8U9KZdLvv2W+HJ/0pHNvUmH933lpFjHv+Tc00s6XPIfR8bX/yk3mvuM1p4i6ONfKf53RO1Lw0s6vO9LJ4jm3zjGfxdJbplyEovrZ/G+a48Wq709T3HpJw6s91bOkI6/Pv6JV/wX4aVcLv3ROaL59/03nRtf/ycKvUcp/kk6vO9/XrfH3s9w6NWUVYRs3bqVSy+9lM2bN1NbW8u8efO47777OOOMM6QPba8R+CH3/3YlwR7kNEot/rwcNnBvYh5YsJ5LdkIF+WpNzapu9NZWTHc3wZGzcDIBTlsP5PLYzi7C2ZMxaZfxmQzbrYduy+A3VqIDQ77WI7W0meb6ieRDja9dEshLGgw0At9w/w+eJsiHSCyxBb7hgV+8uMfrP1IRBIa//GUDlYHQjR/5ePvfN5bfP98sJ6XuG+77wysE/v7vrFoqcX6XN9I1qx4VGNKbujHpBNZVBGkHHUJiQxv5yXUoP8Tp9lH5AJUPyE+sJdvoUbWmi7Aigbejm/XvqGfcEz5eawaUIqxKYBW4HTmUH0ImS8/s8axtaCC9LodVey44Aj/kgV8tEYu/wDfc990nCJL1w+4Gg6FAL//7cvH/wK9fji3/BPmQ+/73yUL+Fba/F+N1pCHwDQ/8aqko/zzw6+VDbr8U193nz2KJMxGb9AirkxhH47V0Y10Xm3TJNSRwe0K8l9Zi83nQDiodqSkp18XmfVQyEX2Z52K7elBVFZjWdtSUCeTGpEnsyKJ7cigbUDe9kZWdHl15D7RGDWGdHfiG+X9cF0v+CfIh93//KUHuKeae+PkeotqnWPuJ2M+H3P/DZ6Lrr+O3OBL4hvu+J5h7fcMDP38hxvFfnHvK+f/+21eK2ZdGkA+juY9k7Sk4/qTRx79S/G9E7Usj4p+XRPPvvsR/qZq9yyb4ae4YUAq0jv4Nw+ix6DCEZBJcB7K56OXo6H1VlVhHoXqyYCy4bvS+0ET/+kH0XdaCieaIZnsLynWiWt1zo+/WGnI5MAbT1EBYlcDtyGLXbkQ3NhCOrUUZSz4PW1UN7U21pNpCHvjNigPqfR0IgsBy3+9fIYiplLr03L+Pf+LL//d953HR/Dv/24+Jrf1IIwgKvUeprRx8wwO3LIvyT4kHI0tdmV9l5tGqKyGZgEwWW1tFZkIFFUu2RJztOBH/Ow5mbB0qk8c6DspE5/zTvTzWsloY//nPfy59CAcMa6B5fRdqj3IC/Tfuu22C9boBDFjt0pOuJlulaXBS6DDE5BX55Bhc38clCSaL9SHwxhAkXJpXbiaYOgZXdZFP1KCVIZdKUGkyWOUS7TA+MmGNpXlla/SDHnpytMbS/ErHkNsdLrAGtmzJMFNIT6jX/3GVs7GwoT2HEdolwVpo3tAN7H9pUoob11FPR0UT2jdU6iShm8R6Gj/logNLCsgmx6AJcZ0cSgUo8uSSjXRXJahzkgSJJAmVYEVTE7lknoTuBqUIvBRWgaczKBWA6aEn1Uig977Itgaa18jFnzWW5hUtMDeeC+NF/lcNdUL2oXltV3z5x1iaV7ag9mHMDIp9x4H4rY1E/Lu2U45/LDSv6xpyu6VyxnZTwXqnHqsTBF4a42oSOonVLsb1yKZTeGFAwt+ByeVQjoPy0tH3Kg8b5FFeKvoy62EDB2WrMX6I0g1kUlWkdDeaDIQBa7enMSkXxdA3CKyF5k09qBjST2/tr2QyX2/uSSRE7EvDmqj205K1z6rWiPvid09UX+0nGf+vdEQLEDFE7/knZca/NZbm9d177D2NVPTGf0ztS6OXf2NqXxrW2OHRe9j7T/T7lxDFelMb9XCtU1jMDqJzMyGQAuWCyUCYJUr4DqhqrNYo2wPGAF70PsLCvz5RZ8qCDQGNyedQ1gXHQWkPbCqyGWaihXHdQJBI4ekebL4NbWsJvDGo0KCCoO9sCnMfW5US6b0Ue2/KxnFZUH7uv0v9KVSDSSIa/ztk7S/fgdIqpv6Peo82LVh/ru2MFq9LoFSvZqOpZoeqBpUC04PVtXSnq6kyERejXAgDUC6h24jWOax2UPu4phnD6aEsvKTD5Z/bs5RqqWG7p31sSyHhaS77xikxllNxuPzHknJaDpd9840xlnPSXHXVIbhSch4pN9b+T7iKa988naSglPoVnzpyUKTUDQz7gsdLOlz2tZNE5eSu+MV5o/wvKGd52ZdfH1v+8dIuV/xMTs7KS8nal4aX0Fx2/Qly/JPQXPal1w35+CtdTw8dvKTD5dceJSdnltBc+e9zY8n/Xsrl8p+cLco9knMPaXhJJ6r9xGofl8tvfFt8/Z9yuOJn78BLC859v3VqjKW8C3N/MSljh8s/e0x8/Z92o7mPVPynXdHxJw1p/pW2Lw0v5US9X6n8u4/xP1g9cCkkPM1lXzhOrPZOeJorrj1yyLexGi4YDr2HWPNP2uWKX14gmn+vvPnC+ObfpI56j0Lj30s5XHbdnrcRLXV0Q8X78WRIQQT5kDtvWrJHOQlV4pGOA7nhzw8Md337mfjKmeVC7vzSPwlywZ7fPEj277rhGYJcXOVUDL///ZqS8TeYe0wEuSDW/vcDy8+e2Eg+lJOSvuMXByYl3V98lMNkKciH3PXd58TiL8iF3PG5B0f5X1DO6a4fvDDs+KfU2BlIPg6yAXd8foFg/pW1L43AN9z1v4vk+Mc33PXDF4d8/OkSMTyUwoZBPuTOny0TlbK/49Y9b+U0EhHkAu784sOi3CM595BGkA+j2k+s9gm488uPDLn/h4s4Q5ALuePzDxJkJee+C+MrJVqc+0vVnrmQO3+6597TSEWQDaK5j1T8ZwPR8ScNKf4dLvalEeTCqPcrlX8HMP6HS07dF/iB4a4fyUnZ+4Hhjp+9HN+tBIZB7yHW/JMNueN//k6QleKfkDs++7f9tj8c9r4+EAR5E/UepbYyyYXc9b3Fe177LOHnoboCowvjQwxroXVrhj0pSQ/W3XLWQuuWnj3aH6mwFlo3doqdf+T/7lj7v6UlN+p/IVhgW7cvluKthdZt2QPyf3+LHOWwMC7Nv9ZC64aO+Mb/cOD/5j3n/5GKYeF/QfvSGBb8IxD/pW40Hcq8MRD570Dtt2yXq78kIT32pe1LYzjEfuumrnj7Xzr+N8d47iU894x6T3LjTxrScx/p8ScNaf6Vti+NYVH770P8D/9uzr5hb3v/g2pfsP6ShjT/jvKPLeRfqW3cLC2C9qUh3XuM7O85/5ReGB99YnxEwks6XP3V1++FlPrg3DWR8DRXff/0WEopQiTncPUt54lKqV/1gzfFV0o3qfm3jx0mKqUeZ/8nXMUX3jpTVEr9ms8dc4BS6v0tjA9/eEmHq757qqiU+jW3vWuU/wWl1K/65omx5R8v7XLNby4UlTOTtC8NL6G56juCcooJzVXfOCHWUupXf/FYUSn1j3x6Xiz530u5XH3r+aLcIzn3kIaXdKLaT1BK/epfxXgbjZTDNb99p6iU+lU/fHN8pbyLc39BKfWrvzz025gMF3hpN5r7CEq5So4/aUjzr7R9aXgpJ+r9Ckqp70v8D4cFkoFEwtNc9bU3iEqpX/OFY2ItpS7de4g1/6Rdrrn9ItH8+5H/uzi++Tepo96joJT6VTecfIDbSA8N4smQgvDzIbd87Tn8PcoJ9I8DKQryvuHW/3kMP6ZyKn4u5JaP3YcvJGfi50Ju/e9H8YeZlO5Qwc8bfvHz5XJyQrkg1v7PB5ZvL1grJqXu5w03f+eFA+Kf/tjPDvP9xSHi/1s//7hY/Pm5kJuvuXeU/4XkJP18yK1fWRhb/vGzATf/618F86+sfWn4ecOtX3hSjn/yhluve2bIx99wabL5+ZBbblgsxr9+3vCrHy6NJf/7uYBb/m2+KPdIzj2k4efDqPYTq30Cbvn3B+Lr/1zIzR/9C76QlLOfC7n1s4+I1V7S6J37S9WeuZBbvvl8fP2fDaK5j1T8ZwPR8ScNaf6Vti8NPxdGvV+p/Bvz+M/7hlu/+qxY7Z33DTd/+8B6b+WM4dB7iDX/ZANuvvpu0fz7qw//Obb84+dN1HuU6j3kQm790lN7sfYpv+3d6ML4UMNCPhfu8daHwdTZz8eUGIDI/5lA7vFSW/B/OTzeOhiwkM+bUf8LIhcY0dPPH+DErD9uHMq9YvcbFvLZPfP/oNrP+ELGhwGGBf8LXv/9wICOqmL8Scd/Gfl/oCEdfxL2h8NdyEVjB5r/DhTS9sUwHLhf0r409nLuO6j2h5n/h7Rilc59cZ97SZ+/9PiTxnCI/zjXntL8K21fGsOBf/Yh/odNzT6AkK69R/lfmv9jzj/i/i9tf0SrrEv3HvfSfqlF6dEnxkcovKTDFZ8/bo9yAqWDY/+n0wlPc/k33xhLKUWI5Byu+MnZolLql39LTkpZGl5Sc9XVc3ATcnKKcfZ/wlVc+5YZolLqV376qAPgH9svN5aDvJaXdLj86yeLSqlf+cvzR/lfUEr98q8cH1v+8dIuV/7sHaJyZpL2peElNJd/TU7K30toLv/ynrcSGmjoYfLEuJd0uOIzR4tKqX/oE4fHkv+9lMsVPz1HlHsk5x7S8JJOVPsJSqlfcdNZ8fV/yuHKn58nKqV++Q2nxVfKuzj3F5RSv+K/j42v/9NuNPcRlHKVHH/SkOZfafvS8FJO1PsVlFLft/gfWatUCU9z+RdfJyqlfuW1R8VaSl269xBr/km7XPmrC0Xz74duifFWJkkd9R4FpdQv/+qet9Er+VCwHX1ifETCz4X87It7llIdLDmBvG/4+X8+HGM5lZCffUhSTivk5//xEH42nk/t+DnDTTcuI5CSk8sGsfZ/PrBc/7dXyAlKqf/0a8/vN/+U+13Efi7k5596RCz+/GzITz/4x1H+F5Sy//l/Pxlb/vEzAT+9Qk7Oys/K2peGnzf8/NOPyfFP3vDz/3lqyMdfybwxhInDz4X87KvPiUqp/+TbL8aS//1swM+uvEeUeyTnHtLwc2FU+4nVPgE/u+qv8fV/NuSnl/8JPyM49/3kghhvI1OY+4tJGYf87CvPxNf/mSCa+0jFfyYQHX/SkOZfafvS8LNh1PuVyr/7GP+DtZ2oFPK+4eeff1pUSv2n1z+Pn4tf7Q/Do/cQa/7JBPz0A3eK5t+fXHpHfPNvzkS9R6Hx72dDfv6ZPW8jqpT8QwyxvHVCpVNUL92BqUyy48ga6lYmaJ2dxq+C+uU+G981g8rNECRBB1CxzbJj7kQO/s4KlFZYrWg7qhEnZ+mY5jDpjxtpfuNYxt3ZSvcR41C+JdmSBaVwrMUfW83as9IkWqF+ZUgdinxrHoylbXaaRKehclMOv8YjV6Op2Oqj1m/v9/it5+KPqcLtyqO7c6S2ZklvNFGHz3VRrkuuzsXJBJDL0f6GKdQ+GuJXeyTafRJJh3x3jvZ5Y6ha14NVivTmHlAKlfWxKQ+dCwnqUiRaUwS1KRJbOiGfjw4gm0MZC2GIqa/E7cyj/AC6M9i6atZedDBNiwMql2RQqSQAQbVHYnsPJulBbQUY0D058hNrCZMa62kqXt4e2aiqhJ4MOA5kspBMgBuFqk26MH4MfkOaxLoWwoZqnO0dBccY8DxMbQVh2sXd0YPKR7LBNpMBP8DOmkIySBK0ZcH1IJeLPhsEvTbQCirT0TkaA1pHvlUKHA2OQ9hQjfJDdGvBdkUaZS34BdLVOjpWx8FvSNM9MYmTs6jQUlXp0t3poyw4WUOY1CS353DaM9HxKhX5wdjIXk8muq5Vdai29ujv1kIiQffkSqrae7Cei6qqRBmDratCZf2IQkITvT802PZOqK7AdHWjUklsNoeurgLHwXZ2AWAOmgTW4mztIJzSQLYphdcV4LZnUaFFeS5hUx3KWvSWluhcXQfUTvfYWBP9bAsxGRrC8fU4joO1kCwce7/QGtwEfmMFxtN4NdVY34/OBVAJD8LomijHAWOw3T2odAqCAJXwovc4DioIsMZALg9hiKpMk6hJk2/rgpyPqqiAZBLruTBhDKxYi83lUDXV2LoaVE8G1Z1BJRIQhtF5aQVaRbbDEBKVkEygfD+6ZrlcFEu5HCqZjGILousAYC3WGJTWu/xOpVOY7p4+W4XfoxSqIg3pdPT7dApbXYHK5DHVSZy1bZjJY8FatO9H40Up8mMqcbvztB2UomZdnq03KFI/D6lJugQpy445LtUbDWsvtlQu88iOcVBBJY0vWtysoXOKQ/Zyw/ZNDYxfoDDjPHK1KhqXWoPrECYUrfPqqH9mB13T09StS9M9pYp0wsXZ0UXYWMXmk6ronFfHmIc9vB7L+OY8zXOSGE/RMw6SbeBkYcxz7eRmNNL5fY+KH1SS2tJNtqmS9Jp2Np85nlSbZeubc/D1fnhRKXqm1+PkDF6Hz5Y3NjDukTZUV4jOJVB+yLKPjWf2LzqicZJwMdVJwqZKnO6AmldCdEcGLzBkp9Rx8E+2kJ9cT1gVcZjbnqXjkFo6p6fINCrGPpMBY0l0hCg/hJRLz6HjWXuOZuJDiqq13az8QD0THzHsuGA6E/6+HZU1JByNbw3bj6sj01RPttFy0G0d6O3tKNdhyRcm0fS4S/tBinFPhzhZQ7bRxWrYdqxl/GOQaA9x/Gh8bT6tEYD0DkPPWE1lsyG9zaf54kOpWeMTVHv0jHHweiwEPsm6SnxjIZ0CPyDZbnDyljDp4PiQaM2SHV9J93iHrcekGbPYoDJ5vKyPzfuQSmETDkorlJuAVBKCEDwPEgmUUlGcGhvFvjERN+48Buib+Kow7OWz3hzjulHc+37f54r8XF2J8gP8CbWgFO727uj7gxDV2YVtrEX5VRFvhgbb2haNfccBrUhWVxD4EVerdDrihupKTLIOm3SwOuIy7YeonI9q6SA4fHqUT7buANela24TO+Y6TL1pKcp1sUF39G9TPazbDJ4b5YICLxMayGTITa2jYsU6Og+fRuqVFvJNFWTHuFRbi5PJRnmjqxtVkUZVV0FPD6rIrfpVRWExDxTzRJEvjI3euxO3FH+XnTeV1OJ1qGzBr6bA0XtaOQwMdlwj2nGwPRlQCpvLRf8aA/k8Suvob2Si84cof7suKuFhc3lQkKxO8//ZO+8wSY7y/n+qqrsnbA53ezkrooCEBOiQhAgnkZGELbBBIkhgYYlgMGBsTLDB2D+yCZJBkrEkYxAoEERQFso5Sydd0OW7vbB5d2Y6VP3+qJndvdPd3N7d3tbu9n6fZ54NM9PVXf3W+37ft6q/FUcDCD/AlEqYJEG0TYOeXvA9e/11tUQzG/DXG3tva2tIMgqR8ZEDBUyS2Ps7q41oWg3+jn7bTi6LqK/F9PRhWhthzUZ7CUky+F621qNf+zBQRISh9bdxbMdDnAzGFOpqoVBEtDQRzW3B39IDUWRjdG0NMvAhjOy5JIn1ixUeUYk7YI+3u9lXIWw/Jdq2B9avlu+lSMora2UyxH8aahG+jyiW8Lf2YnLl8RbFmHyAGMjaz+VziJIdS6JYwvT0UppTT7HZo+ct9eTvKpB9oR2UYtPHQ8RD9eS2ZahfG9J++lyalxdYc3YLxZkJsl5gtmdoWFRHdFeWulmz2fyGhPwan2lPxGz4wAwW/a+H6OohacijohiURMSxvR5Atzay5iMLWPiL7QQ5SX9LLcFAgWR2Cx1H1TLtpn5MUx001CBLMc99YjrURXibMyz+VQ9iIGT7KfVEr5lO65MxA9M8BmbArLuLhHNqWP/xZlofkhgpqNsQseozc5n/+4SwXtHw1A6SfB769mDecQLFXnTzDDadnGPun7rpPrqZ2rUF5ECEN+Djd5UQdbUIrRFBgFDK3vc4sfe6tw9Rk7ecIxNAFCObm+g6soHOQyW16xvIdtUR5yQNnmTtoYa5fyogw4RwVj06kPg9EdqTxD8R5D4L6982jf65LRz24w5rW0mCiK2deAN5a6uJQYYaVYzpPaSe7jMamfe/aywH7PYJmzP0zg2I/r6FxsvqUAWNmZ9loDOi83BFpgO8oiHxBU0rNJuX1oFuZNbdA1CEOKcoLmxBlRIaV2mEKfuDMLRjXwlMxqdxZRHVF1GzQWPqa4haaggbbb6hG/LI7gHLkevrEF09GDNsbGiDMdr6uV18nIliy7W88jipzVM8tI3ss5vKfqk83krh4DEBy7syGXu8YgnTVIcohmRqfeKe0NplHCPiBFMoIHI5hO9DnNjjNNTDlq1DJ5JojDEwUBjibtbBwfxZdgxW4lOpZHOYUgmkIpzXTLCpG5KETK1HJHxEmAyN+7IfF0rZ3yvHLvsRanLWznp6y3zaDL0P1l+U8zG0RvjezlyzFCJ8D5nP23uhFCbrQ9mWRJwM8c1SiKicUyXvyWZszheG4HmYxLYhslk7xsu+TLQ0IbqLGF8RzajHCCi1BGQ6I2So6TqhhekDgsQIRCmh64hatn4gzxHf3AKBP5g7yYa6wXMHMMWibSKXI2zJ43cVoLUZlCCpy6L6Q8RAycag+jrbT+U+FNmM/V1rG3taaoi9IviBtQ1tbN97nu3HulpEoWj7o2ILydA9ss5MDx1z0FCNHaNKWh5Qk8U01SLbO3fvdIZBZDO2jTgZ5D0oCcbmGBXeMjheAEMCwrN2WInhvocpFBGZjD3vqOx/w8j6qDgmqA+IoxLIAKO19WGl0OZLgMxmhuyqbEPC9ywfKZUQnkc0rxW/b2DQ9kSZX5i6GmSFv2UyoIs2n/F96xsFBFmPUAYgJEldFm9bL0lDHjlQKOdX5bGQz9nfMwGmLg89vfbaGhsg4xO21eFnAsRAAd1Sj1iziWR+mz1OJS8rx1I9oxm5YSumFBIfswjVU0IYQ2l6DdkN3YgwwuzopHTkbIIH+kAp4rmtqOfWYsLQ2oaU4PvEs1rw1rZjWhtt/lWfw3gS1Tlg7a6pAZ3xER1dNo+sqx0W2wVBfY6in0GVx6goj+vCkTPJre1C12aRnrK8VylMJkD0Wm5TWthKsDUPhRDdWIPs6IMoIpndgjAGnQ0ozsjRcXoTc6/fTPub5tJ2x1Z7bzIeJvAIMoIo57P91Bm0PtxJ8dBpBB0lUIKwMUMuDDENtbCjG4xG5wNkFNtzrfSD59E/KyAXSAJvNkZrZE8BfJ/CgiZKTYradUW8Hd4gl9528nT8AUP/rDxa1YCEaQ/30HF0HV7B0HjXWhtP2lpg3Waby4eR5c5RZO2zwo0qYzbwiRtyeHW1mBmtyO4+6+vLub61z2Hcy2gyjXliina8VfysHvZ75W/Y+X+VsRr4lkuqYU896SFuNxjHdvndfhmyjVnizgGEVDt9b9AXDcfwWFh5T8mXcsnKexXOn+ih382wWJHL0n9EKzUrFKYytocfKyn7kcqrkkfEMUhl41kmAzoZateUc4/+AnpWK3LDVqipQdRVfKseqg0JyNQHxH1lOdthXBdtMJ4iabR1Nx0odCDRSqBKGu1LEKCKCbIUY6S0cQp2uk6jpK3bGUPYmEEVE7z+aPAe+7UeYU/ZR2lAgvGH7qUsxhglQNu+MUKAxOb3w3LXwVta3M22ZMbsnO9W+rNsT8b3iFtr8Dd22j7S5WMLCWFI3NaAN/x+V2qJxmCUQgyUEOV+MxkfEdq4KcIIkwns+SYGnQ/QGevLVa+1eW96DT2LffzeyPqsbAZim7eIAmQ29di8KUrscfpsrifK+ZzIZAbv+U62HfiW3/VZX0U2g4zjXWwTsvUZkq6ivf+5LEZr/J6QwvxGgs4iItYYKaC5EZ0zUHxp91b6GK3tPTRlDqK1He9aQ7FkX6Zsw+X/7zixlZbb1g4dI4xsjioEFBiq85bfNy0NyLZW6xO1pn9RI7lNA8gdZY4hJbK/CA0ZG8+mtbLx7HlkOg3dhwhm3xHSfkJA4yqN3x2y403TCR4rUDisjuyOmL5ZHjWbY3LbO6xPyWVtXhtbn0tgOSnlHNU0N9oaRPn+9x7RTN1jIfGsJlTnACKycV7ECd0nzKRuRY+1hUCSW9dNpiFDXEqIpzfgbe6wtlquBYsgGPSXNZssRxaZDCbwEB09qMQg+wTG80jaGhBbt9l+8hTkaiBKUGFs65DdA5jWRqKWPF53gCyWBmuwg7Zetm8hvd2OreGciijeuW66a/20woOktD5KSRuPhbB1CUAoSZDziYrD+ne4/yv7TBEEg3yIfB76+23fVO5Hpa5VGdeVcV75qeRQ/TubGawDISCTV8Q7+i33NsbWIY22uUfFhodfP+ycb1TywExg67wZQCpMbdYeb1un9eP5XHk8SEzPUOIrlMKUObspc6Xo2EV4vSXk5h2Qy1Jc3IoqxKiBCLm+HdVdsPd8/kxUVz+yEvv6+u0xq60sH16DkoJMQ5ZYF+z4H/7e7r63qw+FobrisPqWkHLQrgb7qRL/K98Vohx/siQ9oW1f25yr0ropliBTpRDmKZtj5PODdUqhFKK5EdPVY/mRMYgksbx6uG0mGgLf5lC5LKJQgPpaRE+vnWeojI3A1g6StgbU9t5yXhBDhQ8OFCw3DgLri6Ww/Ldo6z8yTsBTGE8iPIVuqEHoHCKXxShBEPhEfZG1a60RWpBkFX2zPGofKRIubqPUHIAAr7+B3KodrDlnOjJuou2hkM6TZtLwfC8666P6ShghiGfWE6zZYXPwmjwdr2gh395AodVDJobG27rRTfWsPbuRtu0KvbaETGD7yzwaV+WIs4Jii6DxhQhV0iR5ZX3xbqCTBJJwaC4ojAm6ynN8pTJX932oyRHVBXhlP9k/O0t2+8i3EJ16YnyMEfiSD5+3hGAvcirV9xjf/1UTfiA596uvSaWUIpSv/1+XOpMTC3zJR/9iIYGf3v7/4N8egedKSjSjOPeLr8TPpHJNEIEvufCchQSemxW3gSf4wMeP3O/2ZRUSNhFWEfsZxbn/dopTKe/zvrMsvf4/ozj3G6/Dd7SVQ+BLzvveGen1/1nP2p8j/+tnFOf+x2nO7r9rBJ7gH4+e78z+/EBy7hdO2Cv/HW1Ul1IfOwSe4G/PWkiw6yKXsWrfl1z8tgWp9D9+oDj3y69yNvb9QHHuv5yU2tgbeIKP/PUid9sIZJTNfdMqJZ3xeP/33+w29n7t5NTa/2Du6XAbjfM+cyDbWE1s+BnX3NOzuUdKaw/u46/k/Z9M5zYyYPv//Z88yh33r+TeI7T/g1UDdwU/kHz2JLe513mfPia99l/hP66k1APFuf98YmprD37G47zvneEu/mY93v+Dt6Q4/krO/cfj3dU+peBTr9+7/ztY20jvC9LpIR0ijDTfuWQ54V7kVA6WjEwUai69+Lb0yqmUNJd+7HZnckJhpPnGlSsIo/T2/w/+31NV7e9gur6omHDpp+9OrZxNGGm++dMVhLEb4fEwNvzwa0/sd/vVVidOhGQpKiZc+re3OJVSv+QDv02v/y8mXHrBH5zJSYaR5pLzfj3u/P9YjZ2oEFv7c7iVyaV/86fUyomGseFzj652Zn9RSXPpZ+7dK/8dbVSz7gPZmmhfEcaG//jZSkq7Ph02Vu1Hmq//ctW48z9jgaiUcOnf3eV0G41LP3FHamNvGBu+fdkL7rYRKCY29x1j7jVetviJijE/eu91bmPvRbem1v4Hc0+H22hc8s8Ppbj/XXPP2OYeKa09OI+/oeZH/+puGxvXiEoJP/rXx9xx/0ruPUL7n+jb5u2KKNR84U63udclX3ok1f7/R+91KOVdSrj07+9Jbe0hKsZcct6v3cXfQsyP/vraqu1X8yu7EzWYSIhCzaX/cL8z+ytpw1d+v3f/V73GPzaYmhgfYwgBLU3BXgdZ9dVyB9Z+04yaCT/I9xf2+vPOrl8IaGnY+/2frBACmlsyTvu/qc3d/XcNIaClMXA2hSwENLdm9rt9WcX5TQS6LwQ0zXTnf4WAplm1qbZ/l9cvBDTNrpvqf5f973D8uYYQ0Jb1Hcff3Ji3Px6SLbDFvtYGt/G3td7d/XeJ8cD9XbbvGoIy93Ta/+n2/S65x1TsdZt7CgFN07Pp7n/X3DPtuZfr+Dst5fbv8Pqn7B+m5x3nXmm3f9f8x0HuO14wLvo/7bW36e7sTwDTavfu/8bDw29TE+NjDN+TvPvs+fh7kRM4WE+Me77krE+/wpmUtWt4geSsT70Cz5WUsSd575vn4Hvp7f+/eN9ilCM5Dy+jOOvjx+ClVU7Fk/z1W+bgO5JS95XgXR84ZL/brxo0JwDj8TKKs/7+RGf+x8sozv7nk9Pr/zOKsz5/Ep4rOT9PcvYXT0mv/8961v4c+V8vozjrc692dv9dw1eCvz1stjP78wLJWRcfvVf+O9oYL7KMvid43+lznEmp+57kvNenk396geKsTxzrbOx7geKsvzs+tbHX9wTvecdcPEdSol5G2dw3pVLqXsbjXV85zW3s/fQJqbX/wdzTlf37krM/fGSK+9819/Rs7pHS2oPz+OtL3vXBQ52NP9fwAsW7PnioO+5fyb1TKqXu+ZKPHOc29zr7gsNT7f/f9ZXT8BxJqXuB4qyPHZPa2oOX8Tj7i6e4i79Zj3d95XXpjb++5Ky/fZkz+wuk4PyT9u7/xsO2d+n0kA4RRpofXb5iBFLqB4cURKHmis/8Ob1yKiXNFZ+9y6mU+n/+3J2cjmtEJc2Pv/cssSs5rWLCFf90f2rlzMJI8/2frXYqpf6Tbz693+1P9D3Go2LCFZ++w6mU+uUf/WN6/X8x4YqP3exUSv3yv/l9ev1/Ibb251DO9YpP3ppaObMwNnzpiTVO5fyu+OcHHUipu0+2wPb/d3+52qmU+rdveDGV/icqJVzx+fucSqlf8bm7Uxt7w9jwwytXOZVSv+Izf3bGvVwjKsZcdoHbbUyu+Ps7U2v/g7mnQyn1y7/2aIr73zX3jG3ukdLag/P4G2ou+39PplpK/bL/96RTKfXRsv+JKqX+1Xvc5l6Xf/3xVPv/yy74rVMp9Su+8EBqaw9RMebyv/m9Uyn1yy74TXrjb6i54ssPO5VS//eb9+7/pp4YTyGEgJlte5czOZhS6m0L6xEpvfNCQtuCeoSjJ3aEgFmtKZazkTBjVh6q9P/B7BohBW3z65zdf9cQAmY6ltNqm73/cmrjIWgeCIQUtC1scOd/pKBtcVOK/b+gbVGjU//ftqRpQvn/0TzVIftzaP+LGtJr/wLm1TjcykRC2/yxl1MUVUizMWN3MhX+58r8hIDZze7uv0tY26tzNvaHcg837buGEDDToZSz5V5jn/tV46XV8vzRhpCCtiXNbmNvqmsP5dzTZe41tybd/e+ce7rLPVzDefw9wNrDRIeQbq9/X+2/GmefiBAC5tY7zr3mpNz/O+U/pLv2LIWtfbmMv4fs7f5P3nsjBLTNq3U2/iUwp3Hv/q9qrWZUz2jPSKmLdAdPCd56+my8vcoJ7Bn6QKTUPcmyDx211/YnK+z1v8ydnJkSvOO1M/DU5HXA1eB5kje9c54z+/N8ybLz0isn5CvBO06bge9KylUJ3nT2fPz9tP/xEDQPBJ4vWXbB0U7lFE+/6BXp9f++ZNmFx7nrf09y+sUnprf/A2Xtz6Gc4rKPvBzPT6ecma8E71vY5i7+epJl7ztszNsfD/JcYPv/zJPdxV/Pk5x50oz9jr8TGZ6vWPaBI5yNfc9XLPvgy1Lr+30leNsbZjrlPss+dFSqpXTP+MSr3Mbe849Orf0P5p4uuf+7l6S3/we5p0P/c+Fxqa09uI+/kjP+YmF6/b9Xvn5H2+jte+49sR+C2BWeL3nPkW5zr9PPWZRq/++U/3iSZecell7/Eyhb+3K4ldUZH3d3/13D8yXL3nuIs/HvS8FfHte217mv6rWaqSfGX4Kvf/3rnHjiidTV1TF9+nTOPPNMnn/+edentU+IYsNlV60i2pucwEEq5EWR5up/vje9ckKh5uovOpRzig2XXruGyJGUtWtEoeanlywnDt3JWV79rw+lVk4xjA3/9cs1hIk7KfX/+f5z6ZVSLyVc/U93O5VzvepTt6TX/5cSrv7s7e76P9Jc9cmb9hr/JyuiYmztz5WcVinh6n+4M7VyZmFs+Len1zmzvyjUXP21R8a8/fGyX2EYG350wxpnUupRpPnhjWudbaXiElEp4eqvPOg09l79pftSG3vD2PCT/3vRnZR0KbG5b0p9f1SMufJjf3Abe79wT2rtfzD3dCilftU3n0hv/w9yT4f+/7O3p7b24Dz+hporv/dMeu2/cv2RI+65j7n3+K/m7BuiUPON+93mXld9+6n02r9r/hNqrv6qOylr14iKsa19OdzKxOX9d40o1Fz99cecjf+SNnzn9nV7nfuqrgo7NphQE+N33nknF110Effffz8333wzURRx+umn09/f7/rURgwhYP7cmgOUUt9/yiAEzDmiOcVyKjDncIdyKgIWzEq3nNO8BbVVpdQPbvuCOYelWM7Msf0JAXMX7b+cYLWgqSfAoBJSlP2vO/ufe9S0FPt/wZyXtTr1/3OPnpZi/1+xP4f+/8iW9Nq/gEPrck7l/OYc2jj2Uurj5IlxIWDhzLxTKfWFbe7uv0sISZn7OWz/8BRvYyJgvmMpV5fcyzWEFMw9errb2Jvq2kM593SZey1Jt5S9c+7pMPdwDefx9wBrDxMdQrq9/n21/2qfmojLOoWAJU1uc6+5i1Pu/53yn3Lum1r/L2zty2Xt85i29Pa/gDmHuNtGUAKLW/fu//ZU47fPEozNvfPGpJVRwh//+Med/v7pT3/K9OnTeeSRRzj11FNHfJwvNj0IRkK/IHlIIGOD7hIgQMaQXAMiASOsdK/QoJ8Br7680sSAfupR++sqUH5Ccr9CNcaYVQ+DAVF5IsQYzDZB8ksBGlQMNVmPgYEYYwy6UyA0iMTADmHb1JCjtMfzP5CJcc+TLD17CddesWK/jzGRocrXf903HsbFQzOeErzuhFb+d/MAsaOnhlxCeZLXvH4m9z3wjJv2fcnSdyzk2sc3EBednMKo40R/M8cOPMdM3Q05PRg7zBaF0Mb6uAT0VzSyX1Kz1qOvFFtfk0CyEkRkXaIAZAQYMAq4T6MTiSqV/ydBZcsrHgXoJ6wsjUw0+imJzCeYlcr6M2GgS5DcItB/BlkCaaAmo+gLEwzlNo09tgoNpiAwX9GIPolIDGatQCSa5C6JMGCWj48n//YXypMsfdehXPfNh92070uW/tXL+NX/rCKN64aVL1l6zuFc993HnVy/5wmW/tVRrP39Zget7xnVI9HoxamK/V375e24EK1QvmTpXx7OdT94ism4bjhLxBvUag5NOqhV5QCXB7Ias+Ep2Az5lR6FgRgC68fjf5cQWh4qY9DtICND8hthY4AwoAXS05iStBz5BRs7VAjJevASDTUaehX45ZHVMOwGFxXx9QIv1uTbfQYGEsgl0C1JHhI2ppTK/ltA9N8SpG3DGzBgDMlt0safEPSL5VhUMrBNEG+w8aXC4eP/Aa9gPyO1prYQ7rHPxjJueFLw+uNaeGLzpjFrc6f2PcEbjm3l6o0DTtp3CeVJlp65iOu+8zixg4c2bPtLuPby50lS+NSIJwWvffU0rnumk5ixv37lu839doexLJIrX/Ka9x3DtV++k9iB/SlfsvRdh3DtT5YTT8bguxdUcs9NV6/eJ+45TfTzWrWORe3dZOIQlIF+aeOsB3RLTK+tG5kBgV4PSiYk9yqUKbcUApEgv95jIIxIHpKoUGPWy8FalekRiEw5DucN5Az0K5AaWspJGoCU6CeVrVtVahhSQw7MOoXZYOtZIq8xXbYKqx8SNn8bVpRVoUE/LMCAzJftsSSgNQEpodEMDZBspccqA8a+Z7oUoj6BooTAgG/QtbA+rOGhwnSejVt37v+/ehnX/stdxA4CQCX3uPZrO0jSaP+V+Pu9J4gdPLWmPMlrTp/Nry7rI06hYlfl+tf+er2b9iu597/dN8L4M06C9CjB8yRvXtzCr+7vcdK+8iRL3zTHxl8nZ+AWFf7zqy/eTpy48T9L37GQ61Z0EEfp4/82/h7llH/ujf9W8zjjv7pcHcqTvPa1TRRW3c2ibDs5EYMRmPUSvRlULsFslZjtdi5UaBAyIf6tKtfnwWwGGRvMQJn7CYFpB+GXOWIi0Y9KO3+6Ecvt6mMoKpLrJbWeYqAQg4Fkg63VIMp1msieZ24P5GhM6zRj1tJBQHd3NwDNzc379L0mVZ50NtiEAdhpHnp39yVk5+fro/IXIuyIKQEKiHczoa2B4Q+1R5Cp/F7a5XMjwIFKqV/ztQcxLQ0HcJSJizjUXPNvD4Ix4GCvjyg2/Pdv1tnEccxbd4841PzfFSuYG2kn3icuJVzzjccwk6Qw+abiU7w5/xyDdcbhJl35X8XHdZV/Dvc/5b93iwjY3eKB4W0Mn2+Iyu9V/KfAOqviLscJd2l/OJJh58mwY1X85J7nNyZEGhWHCdd89f6xrYgOb7+U8It/ugOzYPbEkosZJcSlhGu+dDfU5Jy0H0WGX/zj7USvPow07nRUsT+0RjjY6ykuJVzzlXugrnbM2z7YyJiIC/2HmCctLx4kGALrl5PQ+tcQssPf797lQJW4UW3udHjMqPhoiXXCw9utwAw7Xm/B+v/K50vsHFME0LebNofHkOEcXQO9u3y28v3KtVQJDmNZIokSw+W/X094rBvvG0WGy25ajxovM4NjiDjUXPMfdkEzauz7Pw411/z7Q5iG2lRy/ygxXHntWjKRdnL9cSnhmq+Vc78U7jMYlxJ+/rlbENKN74lLCdd89QFMU52d+EwZKrln0lo3Yu433fRycfZB6kT40rxKDPu9Eufi8qtSk6p8nvLnesu5VyWPGp4GJ8M+K4d9R8BLTnjXnLFyLgk756KV4LqnvC0c9tkK1C4/q0EP+27lHICWXIljsh38b8+hPFqcBgzjno5QyT2M0QiVQv9Tib+OntiLI83PL11u/X8KEYf2+qNpeXwX7Vdy7xGi+hPjE49BRZHm+w9voNUR945DzS9++Kz9Y0LP/OwfKvzHWfuh5ppvPgYpnBSHcvz9x9ud+d+4lPDzz96cytgLkC/1M/8nP6GV/pfOEyQM8bXhBRHBznWgXespFe45nCPuyi0rdZ7ycbKVY+3j6pyxtJoJm51orfnkJz/Ja17zGo466ijXpzOmOBBSICUsOm5aGvNSoHz9L5+GdETOpYBD59e6yg2cQ0rB4sPq3fW/FCw6pgWpJv4NyBBzetHNk/fjERMhWZJSsOi46W7t/8SZKfb/gkWvmOGw/2HRibPS2/+qbH+O/K+UgkXHtzm7/wcTx8QbhibFpzBijGXckAIOm1vjLPGSEg6fU5NK/imlYNGxrW5jr8PcwzWkgEMW1jqLfZZ7pbj/lWDxK2e7jb0Oua9rDOae+2D/p5qVdlJ8CvsMKeAttWuplFSdc09Vzj0mQe1hf+A+/sLiIxrTm3tJ4fT6R9P+J+LSBinhZa01bvnPkSm2//HAf45pSS//UcLWvhzG38Wvcnf/XeOkZLWdFJ+gGNM6zZi1NMq46KKLePrpp/n5z39e9XOlUomenp6dXhMdyQHcNqkEx7x+HtLBExPjAVJJjnn9XKTnxjkqJTjxyEZUSp2zVILjTpiGcLRoS3qSY147e1LY/wLRgZqQKcLBQezKqPYB0hMc84Z5zvyP9CTHnrFoUtj//kB6kmNOX+Cu/6Xg2DcvTm9y5Nj+pCc4Zpm7+38wsSTe6voUJiRiM3a2qKTgxCOaUI4UQ6QUvPLQdPJP6QmOOW22w9grOOa0OaktzCgpeMVRTQ59v7S57yT0/SOBVJJj37IE6Tnu/5Ta//7knkvM9oN4RpMfLapEk7SPzkvlmHuqcu6R2tzLcfxVkmNfPT29/a+EvX5XCxP20f7FJKttSSV4zdwGh/0vOHZpW3rt33PMf5TgmFNnpZt/vnmxU/557FsOcda+ayxJtrk+hQNCMjUxXh0XX3wxv/vd77j99tuZM2dO1c9+/etfp6GhYafXRMeL7Jt0/HDEkeGGbz2Syj12wMop3fDtR53scQRWSv1//7CBKIVSlmD7/1f/u4okciUnlHDD958kDie+nE3GwT6N4xmrM9Ndn8JeEYeaG775sDP/E4cJ13/1nvT6/zDhhq/f767/Y8P1/3IXcVr9f6lsf478bxxqbviPB5zd/4OJIJU7xx0YBkTAVp0fs/aixHD1TRuIXMnJxYYrb9uYSv4Zh5obHO1vOtj+dx9LbeyNEsPPf7ve2fXHYWJz30no+0eCOEy4ztH+jpX2b/jWw6m1/8Hccx+ufyqmHzgywvb3YO7jjHtWco905u3O42+kue6/X0iv/6lcvzMp79Gz/4mgDrgr4sjw48c2uev/SHP9Zc+n1/5LjvlPpLnhB0+lmn9e/y93uev/UsJ1X7rDWfuuEeg97Zk6MbAmaRyztibUxLgxhosvvpjrr7+e2267jYULF+71O5///Ofp7u7e6TWRsTaqYx1N+/19KQWHn+ROTso1pHJ7/VLC0UvqUy1nc+QxTYgq/X8wV4pKJTj8VW2Twv4n24raA0FReNxbu8T1aewVUgkOX+pWTuiIU1P81I4SHH6Ku6f2pIQjXjtvQvn/0ewp1/YnleDw10xOOa2peLDvuLPm8DGXUj9mcT2uzE9KOHZh3YTyP6MF19xPKsHhr05x7iXgKJfbKDnK/caLV5ZKcMRpC9zav0Pu6xqD/mcf7H8qph84hBiSUnfOPR3mHq7hPP5KwREvT7eUsb1+d+3vi/1X/dQEdItSCl4xwx33lkpwxPGTYxvJ/cG44D+vnBy15/2BVMLWvlzWPg/o/k9ApzMME9nqEiO4O547Zu1NqPLIRRddxNVXX83PfvYz6urq2LJlC1u2bKFQKOzxO5lMhvr6+p1eExF92ufhgVb+q/cYtDgQKXVYfHy69/lafNx0Z85ZScFhC2pRKe7/JYe5lBOSLD62dVLICaXTgnZGLCTLa2fwvflnsD5odX06e4VUgsXHuyzOy/Ie4+m0Hqkkix3u8yelYNErZ6e3/72y/TmTsxRO7//BhJzgidtYYquq49q2E7ix9tgxbVdJweHzapGOoreUgsPnpJN/SiVY/PJpbve4S/Ee40oKDl1Y55T7LD7eXe7nGlJJu8eiMylRaXPvlNr/UO458uufiukHjkpvD+Y+DqXULfec+LWH/cG4iL9HNqbX/8vy9busve2D/VdbFDQRvaJUcNS0Gnf9LwWLXtaU3vjrOeY/UrD42DQvTJC29uVQSn3xq+ekVkpdionnNbWBdbKJK0vH8Gwydoqw3pi1NAq45JJLADjttNN2+v9///d/84EPfGDEx/n8jpMQLU2YmgwdR9XRsLpE15IsUQ00rozYvNQn3w5xBmQM+e2GjsMFiy5ZBcZgEk3P65agQkPvXMXMGzez7XUzmfbblQy8cgEiMmQ6SyAEsnuAeFod69+Yxe+GxtUJXn+CigxoQ/eSLH6vpmZLSFTnUaqT5LfHBBu7oRRCojF9/YRBDl0oIYIDu2VxZLjxh09gWia+pPz+II40N/7oCTAG/LHfkziKDb+6ZRMimYhiQAeOONL85pdrmBtpJ94nDhNu/PEzmEkgZ1YtcXhQz+Ga2afg9Yd0HlFD3YaInot6yVzViEyg2CQpNglqN2na3xCTfyGg1GIQsaD5OeubCtMk/ut3sGNzPW13e4jEUGyStP1hvR0/nqLr1bNAQMPjHfQc1UTDvevpP242ua0lZGc/uqmGLa+uoe9lIS33+ngDhroXB+g6vAbtCwamQaYbVAlanuwhaszSd1EPuR83kN06QHF6nuy6Hrae0kKm27D1dRGH/FeBtW9vIFlQJPNQDapb4hUMft/4v6dxqLnxB4+Boz1m4zDhxm89gFkwe2KtihslxGHCjd99GGpybtqPDTd+4z7iVx82scjXKCEuWftDa0Qw9vE3DjU3/ucjUFc75m0fbFTzKJftOIznXn48xUaPriWSGQ+HZFZuA6VY9y81iEfryO2AuvUhPfMDml4osvGULKW2BFkfYXYE1C/oJrq3idqNmvbXJuTW+bQ+HbP1BI/51+xAdPeiZ7Uid/SCktDbC+VFnKathXVvb2DetTsQcUI0swF/dTt6ZjOdR9bQctsGTGOttYsw4YULW6E2xtsSsPA3vTAQsuUNLUQ10PJMTKHVY2A6zLy/RNjgsfH10PKYxEhB7caI9acr5t6UENYr6p/poLCwkdyTGxl4+WxyT2ymb+EsCtOzZLeHY3NzyogSwzW3byI61o33jWPDL+7ajEqplPqN//W0/cPB5EQcam689ElMQ20quX+UGK7740YykXZy/XGYcOMPy7lf4OAEHCMOE373H/cgHD2yZvv/cUxTHWmUrKjknklrHSNlPtXGyZfCNxBFGj2zmSTvo/ojitOzdB7qMft37Wx7bRvT7t4GBkygIPAgjMEYOl7RTPNjXZTaagi6QpCCsCEg+0I7pr4G0dGDKZVg3gxETwHT0QVJAlIiGhvoOrGNXEdM0FmyMbu3CMUSxSNmUmpU1G4oodZtJZkzDaSg65As3oAhrhFoJUBA6+O9dB5ZiyoYGu5bD4BprocN7YjmRujpg4wdqKa3z9qMkrZaagwi45PMn4lau5kPNz7HQrNjD31ohvr/Ww+MsOdHH5XcwxiNUGPPfV1jMP46mpiLI83vfmZruGlE5frjaXl8F+1Xcu+UIo4M//PUFlodSqnfeNVK+4cLA3CMuGT5j7P2I82NP3kW0iplHybc+I37nPnfuJTwu3+/O5WxF6ja758/8WzEHZ1EC6dTavJBgNefkF3Tydq/aEPGMP2xkEKLR8PKPnTGR/bbOc64MYu/rgPT3QNtrXQd30xua0yxxc4ZNNz1IqatlXVvbyC/CfJbY2QCOw73aFijSTKCYrOgYWWECg3tJwY0L0/Ir+xCD5QotTYjezvHsKMm2BPjxpjdvvZlUhygaDyKIqAgAga8DAVpfw74GQoqQ3/G/j748uz/isYvvzwKnv3sgJ+hiM+Al7H/V/b/BRlQkIFtRwaDxwz9LEccN4PQH2q3UD6Hghr6vSgCivj2ZbxRk3uUSnDM6+emeNWS4JjXzUV67p4YP+GIxlQ+sQO2/19+QivCUf9LT3DMqbMmxaqxahPjEWrQBw36uFxA6Gc58ug2SkHZ33kZ+rMZBgLr4/ozmUGfNOBnGMgF9v1h/xv0S2W/N+BlrJ/z7HsV/1fEt+36mcFjhEGGQ181y7bv2XYrPrZQ9pUDubIvFPZnUQQ7nWtBBPQHGfpzAZGaWNOL0hMc8/p5zvyP9ATHnLEovf7fExyzzJ2clpKCY960OL3+35PW/pytGhYc88b5k9L+RZXEp2gUBWn97yvmTCf0skM+PBdYP+xlKMjMULwIrL/tz5b5a/lzw2NGQWXoDzKDfLWwE28dihMVnx16GQ49dR5hOY5U4lMR3x6j/P3+zFC7hfL/B/yhNiuxo8KbK/FlwBvi8BUuXqzEkXJsKjlckqIknHh4ozMpdSUFJx7SkEr/I5XgmNe620ZBKsExp6VXSldJOP4od0/sWe7lLvdzDelJjn3zErexN821h0ruuQ/XXy3HG2AoxyrIYDBfGqxJ+UNxuSgCSl6GQ18zl3B4zqaG8q5KjBz8jvGG4rnxhl745TpV+bvDYv5ONaxh5zaYP1Zefmao/uYNyylFMNjG4M9d2x982eMXjU9cRUVx8Ilx59xT2txjEtQe9gfjIf4e+yp3T6y7RuX6XXG/wdx7hPZf7Swn4mNFUgmWznHHvaUSHHNSihVzXPMfte/xfzJBepJj3rTYafw99i2HpDf+VnlvwBvG19Sw2gm+rQ0FZb43rE5fmUOtcL3KvGqlBjPI7Uy5LpTJcMwh0wn9rH1/eC3HH5o3rfy/KAKiES8hHV2k00IcQgiYOSPn6oFBpIC2BQ1pXLAN2EXHbQvrkY5ugBAwa1rW2f13DSkFM2blEdWI7UHsGykEbQsmxx7v+5M4CAGzpmcRzuxfMGNOjbP2XUMKQduiBmf+RwrBjCVNk8L+9wdSCNoWu5OTExJmLGniAHZDOSgYq0KDlBX7c2f/bYvc3f+DiZFIDwohmFeTcWZ/Ugra5tWllv8IIZjVmnUmpS4kzG5JJ/+UUtC2oM6plKXlninsfMq2Pz3n6oHBMvd3x712h7E8EykFbYc0u429C91to+Uag7nnPlz+aMoJSwEz5tZOyv6vxl8rfeice0rKuYeT5p3DefwV0Danxln8cQ0pBW1zatxy/32y/8n1ZL8UMK/ebe5l/b+b9l3DOf+Rgrb57vyfa7iPv3u//9U8zkS/a9W55MG/OolgTmNmQtQeUuoi3SFODDfetInYlZxKbLj5iqeJo8lFOkaKODLcfMUzxI7kTOLE8Js/byFO0tr/mj/+Zh1J7Kj/I83NVy4nDie+nM3+BLo4Mfz2Dnf2FyeGP123NtX2f/NlT7nzP5Hmph8+kmL/r7n50sfd9X9suOkHDzuL/64Rh4m1P0dbWcSR5uYfP+Hs/h9MVCPzlXgQJ4b/fXGrO/4ZaW6++oX02n9i+PXdW4hcycnFhhvub09l/I0jzc3/s9xp7L35p89OSt8zEsSJ4Xe3bXbre654Or39Hybc9J8Puo29l6e4/yu55z7Y/0hi+ojbjw03/WLVpOz/6hPjFs65Z1jOPSZB7WF/4Dz+xoabrl2TXu4ZaafXv6/2X/3Bj4mHODb8/Fm3uddN16xOb+1nPPCfq56flPF3JIjDxNa+nMXfhJu+94Cz9t1jz+Nej8FsdWQMv3ps64SoPUxNjI8xlBS8+oRWlEM5oRPesjC9ch5KcMKbFziVUl96bHMqpSzB9v8rXzPdqZT0CafPmxRyKvuTOCgpWPryZhdbbJbbhxNPaXPWvmtIT3DCWxe5tf8zD02v//cEJ7xjiVMp9RPOOiy9/t+T1v4cyrme8LbFk9L+qy2U0uW3lIQ3zmxyKud3wrK5zvivaygJrzm62amU+slHurv/LiGV4IQz5jmVcj3hTe628XANJeGk41ucSqmf8JaFqZZSP/HsI9zG3jTXHiq55yhJqe/zE+NKcMLrJqeUa7WpBiHKT4w7557S5h6ToPawPxgP8ffEU2dMSvsfCSrX71JKfbTsf6JKqb9+vuPc67SZ6bV/1/xHCU44Pc1byUhOOOswp/H3xHe5u/+uUe2qx2JiXAl47SETo/aQTgtxCCGgttbdHodCQM0EkTM4GBi6fndS6nV5L939X+fjSphECEFNYzAp+l9WmwipIqVem/eqS9kfRAghqK33nbXvGtb+XPofQW1TOqV0odz/TVmEK3ImoLYpN/F1mfYTQuDU/gbv/yQcACOVUm/wlTP7EwJqGgI3jY8DCAR1OeUu/gmoy6WTfw7GXmfbaIh0514Iamvc2Z5r7uUaQkBNs7tt3Ib63037rrE/ueeeYrouv7tv7UNt/eTIfXdFtYmySpHTPfekzD3dtO8aQ/HPXe2tpt5Pb/+Xr98d9xf7ZP/V8pmJCCGgPuM295qs/n8kcM9/oKbBXf7hGqJc+3La/w7vv2u4llIXCOqzakL0/9TE+BgjTgy33LGFxJGcQBIb7vzZcpKUygklseHO/3uexKGU+p/umxhyEgcDSWy4/Y8b0Y6k1JNIc+c1K53d/9HE/jxNECeGm+7dSqwdyTklhttv3OCsfddIIs2d//ucM/tLIs0d//1kev1/pLnzf5521/+J4Y4rHncW/11j0P4c2v+dVz3jbCuPg4mRJD5xYrh23Xa3/PNXq1Jr/7E2/PHBbcSOpNSTxPCHR7alkn8msebOX6xw63t+/nxqY2+sDTff1e7s+pNI29x3EnD//UESae647FG39p/m2kMl99yH699TDXF/CplJbLjj12smZf+PZI/xccE9HeYerpFE5fjrqvYTG+743fpJaf8jweD1u+L+o2j/E/EOJrHhhhfc5l53/GZteu3fNf+JDXf+cnLUnvcHSaRt7ctl7fMn7u6/a4gqNQczBrPVsTH89qntE6L2MDUxPsZQUnDq0unOpCSVEpx09hJUSuXklCc46awlKEdyGkoKXudQSt81lCc4+XUzq8qZHMyeUZ7kpHcsdHb/RxPVpdR3/66SgtNObHUmZ6Kk4DVvnDUh5FQOBpQnOeldh7jzP55k6XuOTLH/l5x0zuHu+l8Jlv71yyaU/x/NlfvKL9uf787+T/rLwyal/Y9kaw0lBW+b3eyOf3qCk96+YELZ/2hCScHrj2/Fc7RsWinBG45tSWX8VZ7gpHcudDb2lSc46czFk9L3jARKCl77qmkOfY8s575jG3uqR8+xKxIpX7L0vUe7jb1nH5Je+6/knqMgpb4/E+NKCZa+ae6k7P9qo6hyteOCezrMPVzDefxVgqXLZqeXe3pur39f7X+y3SWlBG9e7Db3WnrGnEnp/0cC9/ynnPum1f/70ta+XPW/L1n6vmOcte8alS1ldoexeGLcE4LTj3Dn//YF6bSQKUxhClOY4HAtjTKFKUxhClMYH6gaD8xUPJjCFKYwhSlMYaJgzxPjUxiOkTwxPoUpTGHiYKq+NYUpTGEKo4Mq8+LoKXe6E6YmxscYiTb8+d6t7uRUEsN91+2bnNdkQhIb7rt+pTs5J224/WF3cjqukcSGu2/f7E5OMdbc95sXJ4WU7kieENwViTbc8dB2EkdS5ok23HPLJmftu0YSa+671qWcnObenz+bYv+vue+a5e76PzHc+7Nn0uv/o7L9OZMz09z3y8kpZzySrTUSbfjdxg6ncn73/XZNeu1fG257dLtTKfVbn9iRyvibxIb7fv2iQ+5puO+GVZPS94wEiTbc+cA2h75Hl3Pfic/99wdJpLn3f59yG3uvW5Fe+6/knvtg/6MqpZ4Y7v3j5JSSrjoxXn5rXHBPh7mHaziPv4nh3ps3ppd7xm6vP/X2nxj+sMpt7nXvnzZMSv8/ErjnP+XcN632H2lb+3IopX7v1e62UnEN1wuNYmO46Tl3/m9fIIxxVKFxiKXv/hb1f3gakc9hSiEAJgxBCGQuhx4YsB8sF49kQx0mjDCLZoMQiChBbN4GUmKiCJHPQ5KQzJ2G7C8huvogjqEmb4/R20fS3YMIfPymOk7+y0O5+5cvkIQJJozsMZXCGA1hhGhtoecVM8ltLeE/v4HisfNRxYQ471Fs8Wh8ugs2bAFAeB74PrqzC3PUYmQhgo3tiLpaCALoH7CZQbEEjfWI5jynvGUe9/zkEaKuPoRSoBRCKfT8Gcj2Tvv5UglTLFF4zeHkn9pIMrOFJO/RcWSO3A5NwwMboFDEzGhF7OgmmdOKSAxiIES/sAoRBMiZbQwcNo3cXc+B54EU9nyVAikhn7PnF0UQBESL2vA6BzBKUZhbi98bI8MEEWmSGp9g5Rb0jGZkXxF6+yGbwXR0IfI5e85KQqLt8YyxbTQ1kDTk0IFCJAY/ijjlzXO56w/riYUkbMxgpKDUpBAGvIIh6IqQpQQE6IxClhLiWh+vN0LEGpRAhDFJbQYRa4wSZbuwDldojZESEWtkMUT09Nv+9zyUL+39/1V5r0NP2XPNZTGexGR8+1NJjBIUWzMkWUHj7asxUWSvL0kg8Aftkzlt1uayGYoLmsm+sAUyGUzWZ8crmmh+opvStDyZ7QVEGIOUJDUBciBCbtlu7bi1kb7FDdQ9vQ2T8TAZH7m1y56bp8D3IU5oXzab6Q90YoQgqc/Y660WaKQgySi8gYiwIaCmvZdT3jSXZ+57kX/c8uvdfmVlzXT+M7MMb1sP9PZhwggR+FBfB2FIMqMJtaWTaP40/K29tr88D5P1kIUIsWV7+bw98D10Sz0iSohaa6DW4w2vnMYtj25HdEaoYkyc9/G7i2AMOuujA4XXUwQNhbm1xFmJV9T0zfLQPhgF+XZNwzOddB/VRM3GEiLWhM0ZwjqJDA1+v8Z4giQQGAHZHRFJRqJCTVTnIUsamRi7t4cAoQ3ak/TP9Cg2CnI7DANtgmlPhJQaPXLbQjoPzVLTnthujQ0nl57jfevu220f/urw47j3nnoIfEyphFCKeHsHfl2OUz5wHHdf/RRxMUJIiQlDTJIga2tJenpRtTWYOAYhMKUSRhtkNoPwPEwc2zFcCRvlqoPIZTGFoh1zWmNKJRASlERIiS6WEIGPl89wynnHcNd/P0YSaUwUgxSgDbKxHtM/QNI/gKrJIzIZ214+h+nrL/vpCDmtBdPVjS4UEJ5v+6O+Ft3Th1w4F3Z0gpTonl5kPm+PX/bnulBA5nIgBSZJ7BiKY3v+SYKor4cwhJo88coX8Vqb7eei8mfCCDMwAEoh58xCr12PyGTA96wvraslWbvethH49pzqayHRiHwO1VzHye9cyN2/XEG8rXPw3iAEIpsB38f09CLyOXR375DPNAaEIOnrRwaBvSYhwBhMFJf9uBz8nFAKXSja+5bPofv6wRj8RXM48UOHc8/vbILs9UeI59fYaywfTwSB9TVxbPtcG8TLDiGclieuUWhPkN9cRPWVyv2qiRtzyEhb/6KHfCBKQIUISQbtYxDDfjW+IqnxkWGC6g8xnhyMt0YIdN5HDkT22IGH3N4NicbU1YAEXZMBDVFjBmEMqpAQ1fn0LPCpWxuRf3QNKutz8l8cwt3XrybRWBuNY2tfSWL7oQwhpY2NjfXQN2DHUl+/jTc1ecy2Hfa+5/O23+tq7P3I+OishxwIiVpq8Lf3YbIBcW1AbnMXJ799Hndd+SS6si6x7MeT7h5kNkPvGUeR3RFSaA0wHsjQIIdOy94jA0KDkaCVQEX2GEbY/wldXiFaPnacVyQBND7dRWFePfnntxPObiSq80gykk+u/ANHFTbu1pd8v+flrC7W2jENiMXzEIWQeHo9Sd5D9ceoviKidwCCgKQhT9SUIclI8i92gRDomgxJziMzEHLqqdP4851bMWu3Yma00r+wnpoXe9A5nzjvk+QVmW0FZCEiaskT5xSqqMk8sw7q64in11OYnkEmhtymAeT2bqJ5rXQvzpLfEmM8QalR0fRkF2FLnrhWDfpbkVW89rQ27rlxA8Wsx0Cbh4qs/61dV0T1Fulf1IDfFyMSQ7ElINMR4XcUiJuyyFKCSDSFGTmSQCIMaB8wIGPwBhK0L4hqJDKG7PYIowRRnb0GowQiNuiMxBtIqDzILRODSAwDMwJkBIVmSakZvH6o25gQdMUUW30yXTED031qtoQE2waIyzG4OC3D51b9liXFrbu9h/98xNn03dGO12T9z11XPkFcigf9hLV3gaitwfQXEL6HiWJ0aPmx9D3rH5VCtragt+8AIRE1OUyxZH1Xosv8rmzXibbjKUkQuSy6tw9/WhMnv/tw7v7ZM8T9JevflBocd6a/YM/F9zBhiI5ivNZmy1H6BzBhaP2T1tZv5/N2/A77v2yox/T2IWryECegpOX582chNm+jcNwCck+sg7payxWFxNTnEX0F9JatNgfo60fkstavSgGeR/fJC4hzApGAVzTEWUHQm1CzqoukIY/qLRI3ZAHw23sxm9sHzxMhEbks0sSc+sHj+PNlj6CVb31uOcbR1IDOB+jHnsWb1oru60cXS3jTW63fhMGfplCwHD+KMcUiQkgbD4NgyH8Fvo3JxqCLJUySEDTWcvK5R3PXFY/Z+F/uexH41hYGBhCeb/9uboQ+2+cYYzlCfR39r1xAEkj8voTc8i32HPrLOVOFm0axjd1K2fjd1kI4vQZZSvC6CoN+DCmHuMSw68MYSm21qGKM0CCLEUZKkryH11/m94lBGEPUZGMPxqAzqvx98LqKiDDGBJ6NBwpOecs87vr9OlscLHMXIwRxUxY1EA39T0miOp/cc5vL56UsDxYC4ymE1vb3wCPJBxhPIMpxznjS/q6Nte/EnpvMKE594yz+fMsmdCnBKInQBlOO5TqwOUPlHGQpRgcKNRARNufIvrgDSqG1x0qeU+EGWtvfMwGmJgvlPEXnA8KmLKUmG7eb71jHwNGzCDpDZGjju854iEijswojBTI2gzwCbDypXFdU52EE1D683o7HyHLICseQDfU2TkTx0D0WAqQcsv3LHyVJjOVNSYKYMR2TCzBCIIshet1GRCaD8D2Kxy4AAQPTfbI7YvIvdkJ/wV57fR0mUIhSjK7PAdB9eD3egMbvTxCxwRuIkIUIIwQqI1l65gJufXg7esDmd6qoEcYQNlgemekIiXMKmRiiWg8jQRU0QY+1OaMEQoP2BEZJkrwiztr7nemK7D0XgmB9B8QxurUBzzN8s+ua3frlbi/HZw75a8I6SfMd6zANNejaLGpLJ3ge4Zwmghe30nv8bPpnKOo2xMQ5SZyzeUWSETSuKBFs6cUEHjrn423tttwzH6AzHkYJ4hqf7EDEaa9p5c+3t2Pae0im1xPXBARbezGeR3FmDcVWDxkZgj6NVrZ/eucGZLs0fTMldRsTcluKDMzK2XxfG7QniHOS3LbI5jaRxivosu0IjAQZGWRW8oZXTePO2zYTG5ClhCTv4XcW6T68vjwWsflQYv1rzdaEYEcJHUiSrCLoKCGiBFkMCdvqBm21e0me+hcL9M/J2pxL2mN5RUPdmgI6sLbtdxVIaqyPVL0hUXMOkWj65mVpWNFP78I8RgoanulGxDFJXY6oIUAVbdz3ekqE0/KofssztK8QiabUEpDfOEBcFxBsG6BvUT2qpPF7I8LGAATIjGTZy1u595crKDbWEOcUfl9EoS1Lrr1o60+lmLguKNcfBN9rv2q3T6+U8PjYEe8nt3kA9eJmm5+XQhsDfN/mamFo+ann2ev1BCf/9cu4+5rnifsKiJZmO/5rM8hN223u0VyPGChh8hl0zkd19GMCRWlmPdoXeP0xXl+IWL3RxkZP2fjf2gy+B4UiyYZNqNYW4kUz8da02/hcKJZrbPZqRC6LyARDPqIc+4xSxA22D42vkN0D4Cm2v7oVVTKokkEHgsQXNKwq4K1pJ5nVygf77uLlesNux9i3572J5TWzUZ7gjSdO49YHtpLEBiOGOLRMGKqn7KYiKow9zySn0Mp+vsKzjbTfMRJ0IBCx/bwRO//0gde/ehq3PLwdHepB7ocQNg7sUpsWeqhtU64R2PPYzUWKoc8PP45IbAzy+mJUMUZt7kA31SPKvqyS8w2uHhDC1iSGG135vSRja2Ri+MSOsGOtf06W/KYShekZ8u0lRKRfsqep8gSnLpvNn2/eSBLpwXiJsWNYJMbmj8ZY3pZoMNrWC4flZZTz5Z1qEJVr2N3Pytd8xclnLeLu61btPDm1u21tdqlvvKSt4d/d9X/Dsct5mqxPUpeFcj8KYyDWg3wirs+gBiKb+2qQxXCn+yMKoY2VGR+0RvaVMFl/8LtedxERlbm0EIhyHDa5AK+nl5PPXszdv1hOXIwA0EvmENcFeN0hqqMXCtYPkSSWiwU+urGOrqMaBi9JxgZ/QA/avtAQZwTegCbOS4SG3FZrC4hd7H/pdG59YBtR2aYbHtwA2li+Xs4dTBzTf8qhLNq2hX/YfuNuu3WlbuaHmdNIGvKIMLE1Al+hcx5xTpHZUUQMhCSNOQozsvh9tk+yz2yE+lri5hq0L60vK/MgEcbla9e2PpTxYd3mci1L2HqAEDB/lvUFdTn6FuRoeKoD1m1GzJiO3rDJ8hrADAwg8vkyB9f4s6Zz8jsXcPe1K0kKEWQz6I5ORBCw48wjCesEuR0aVTIEvQlxXhF0RWhfMjDDH4zLcVYS1gkw0HrrOvqOn03Nii5M1kOXbUF1DmDWrEfOmmHjwrYdCBNx2t+9jrt//hxxdz/JkQuQxYjtxzVQbIXsDqhpj+md7dH282eJj1pAqSVD7RNbCOc1E6zeCpkAenpBGzredCgyNtS/0GPnHYyBKCZprUN1F4im1eJ1F9E5n96FNQCEtZK6DUNcqmuxT92GhDgn0J5Ae9ZWRAJ160toTxLVWr9jJJb7ZLH5dgKJL8h0a7yiRhXLnEMJMlv67diq2LExKAEnv2sxd/1+Hb0za0gCy3WFtraofYFR0DdTUrtJk+lKwBjiGoXXn+D3Rghtyudu6/OmXFsU2h7H+jK7n7OIEjuOAcql/pPfOo+7/rie3uk5hDYMTPPIdmnqH2+33y2F9oNK2TwKrC+s1MySBJPPEc6qI9g2YP+fGEQphDBi4KhZ6Iwkv64XEWuKs+rwBmJkKUZt78XkAptLexKkpDg9R25dNybjU5yeJ7ehB9HVh2msBSnpPaSB+ie20vnKNrI74kF+pn1BWKdIMvZe7TSvWnF3w2Ifxvr/Nx3Typ+e2E5UrmENRyWOVu718GMZaf8vI/szCazfUSXLE5OMPQGvYMq2NPSEtBHWVjwhePORLdz8yHZKHkx7YAelmXV2XscYovqAj/Tfwsu3755HfPnl76T7WQ/R1Yfu6Bz0y7Kpka5T5uEVDLktBdTGHeit2xC11uZ7TzsUv18T5yQDbQoRgwoNTc/0WI4zEJbnfMq5qxKEDYG9b5FGDoTovOVKfXPzqMjWNr1CgijnKSrU1t8n2tqoEMgwIckoVClBxJov9/2aRlPY7bV9bsPxRA3N1teFEUZrxJL5iO4+9NbtAMimRogiTFsLxleIMEb0lzD1efoX1uP3xXh9EXLFekRjA/T2gedhmutBg2pv55Rzj+Hua1eQhAnJjg5bny/XFoRStk7U04esrUHPa0Nu6yZcNB2EwO8s2Fy/lOB1DVCY30iwo4h8fg0Drz2C7LYihRk2L6l58EXrq3M5myuGIQQBf1j/vd1e/66YemJ8jGGMoa+ziKv1CMZAX2/krH3XMAb6e6I98tiD376hv6uU7v7vKu0+GSnjYK5dMgb6+tzd/9FE1SfG99C/xkB/R8Gh/UN/Z3FS9P/+wBjo7w4xjp7YMwZ6C8nePzhJ4dr/GmPGpf1XXTE6ig654v9d+p/egWTc9f9oQI5gRbDlny79/xT/6XM4/l3Hf5cwBvp6Qrd93zs5uOf+wBjo63Br+339car7v7/Lrf339aW7//cp9zRmjwW6/enCyRx7q+0UU+FFxkCvw/E/xD0nX/+PBEO1R1ftG/q7J6f9jwTjofa8L7nXZFP3NcaU+Y+j/teT1/+PBO79z+SpPe8PDIaeQoxxtLWJwdBTTKq3X60MNsFv3P4ozI4mbO2jMCH8z9TE+BhDx4aHb3gB7UjORCeGh+5ud9a+a+jE8NCft6AdyTno2PDw719Mdf8//Kd17uSEtOGB+7dNCilRafb8pP6e3tGx5qHrnkM7kvPRsebh36x01r5r6MTw8K0b3fmfxHD3MxNDzuZgQMeGh3+70mn8ffh3q9Jt/zeudmb/iTbc81SHs/YPJkYipa5jw8PXP+/Q/6ec/8S6zP/dxd+HrnUX/11CJ4aHb9/kNPY+eM/WSel7RgIdax6+frk7208M9z2Szm0EoGz/N61zGnsfeGBbau3fXv/2EV9/tUKmrrKwfI/ficvcaxLG3qpS6uVCbKIN9z3mbvxXuGeSvtALDKs9Ooy/D9+0PrX+R8eGh3+zwtn4T7Thnic7Rjz+RpLPTCTY3Gu5u9pDrHn4T+smpf8fCcaD/3ng/jTzH7hreaez+JdouHOFu/Zdw7U/tfnf8xPC/0xNjI8xPF9y+kWvwPPddL3nCc44cz5ekM5b73mC09+1AM9zsx7R8yXLzj/K2f13Dc+XLHv/4SjlqP+V4M1vnYPnqP3RxP48Me4FitM//kq8QB2ck9oLvECx7MKXO2vfNTxfsuyvl7jz/77kzJNmOPN/ruH5kmV/83Kn/b/sI8emOv4uu+Bod/HXE7zz5Mlp/yOJB54vOf3iE9z5f7cuR/QAAHeRSURBVF+y7EMp5j+BsvzfYfw9/ROvSmX89XzJ6e9e7DT3etM75k1K3zMSeIHi9I+d6M72PcHb3jhrUnD//YHnCZadd7i72KsEb37LnPTavxK85c2zR+x/RntPSC+QlntNwthbrT8qT4x7SvC21890Nv49VeaeKfY/Z5zpLv55vmTZuYdNSvsfCTxfcvpHj3eWe3pK8I5TRsf+x2JP3NGG50vLf1zxz0Cx7P2Hp9r+nfofr1x7TjH/OfuVbe7irxT8xXHTUxt/qyoKVpPcGSV4gbK1pwngf8b/GU4yaGPYsrIT7UhOQBvYsrEfndZV8wbaN/Tj6vK1MbS/2O3s/ruG1ob2Nb3O2jcGtmyeHFKi1Qone3qiQGtD+4oOZ+Nfa0P7qq70+h9taF/X57T/N+4Yf1LeYwVtyvbnKv5qQ/vqrp22WU8TtIH21d3O4q/RsGl70Vn7BxPVpL50uZA0yD9d+R9jaF+Tbv7jtP8dx3+X0NqwZb3D2Gtgy6aBSel7RgKtDVtcck8Dm9onB/ffH2gD7Wt63MVeA1s2p9f+jYHNWwojtv/Rnhi33HNyxt5qVySGSalv3up2G7FN291JKbuGrT26G/9aG9rX9qaS+0CZ+69yxz2H7H9kn59s01falPmPy9rDmt5J6f9HAq2NW/9Trj2n1P1gjGGDy60EMGxI8VYC1VjSmDwxrt3Ofe4LpibGxxg6Njz5J3dyVjoxPPHQ9gkhZ3AwoBPDEw7l3HRsePK29anu/yfv3LgXKeeD1zeJNjz26OSQU5RVAsyeCic61jzxB3dS5jrWPHnzmlRKuULZ/u92uJVDYnjoha5US6k/ecsap/H3yVvWTij7H80ChU4MT97mVs71oeVdk1LOrGohvbxQSseGJ//oTso/9fwn1mX+7y7+PvH7FRPK/4wWdGJ48l63UoqPPzxyKeXJBh1rt74nMTz6VOe44v7VfPZoQyeGJ//sbiuBRBsee2xybmMyEuzr9Y/2npA29k5OKd2RSqk/8nSXUyn1h5Z3pVbKdbD26DD+uvR/rqFjw5M3udvGKNGGh5/bl/E3+aTULf9xJ6X+5J0bJ6X/HwnGg/957NEdqfU/iYYHV3Y7lVK//0V37buGqGJ2Y6HAMZT/jX/7n5oYH2N4geKtn3YnZej5kre/e1EqpRTBXv/b/tqhnGIgeetFL0+vlK4veevfHIVyKaV71vxJIWczkomQXeFlFG/73GucSrm+9ZMn4GXS63/e+sHD3MmZ+ZJ3nzprUtj//sALJG/9xAnu+j+QvPXj7qSUXcPzJW+9+DincsbnvG5y2n9VBZFy4uMFkrd+5iRn/tfyn2PTa/+ZMv931f8Zxdv+4eRUxl/Pl7z1vEMdbqMheMdfLsDzJ5/vGQm8jHLre3zJ2W+Zgz/Gvn+8yL56vuCtH3mZ09j7TodSpq7he4Iz3zl3VKTU9X5JqSvLvSZh7aHqxHj5p+8J3nXG7DEf/xX4Ze7pp1TK1fMFbz9nobP45wWSt374yElp/yOBF0je+nfuttHzPcFfvn7WiMffZBslg7mXq9pbRvHWvzkqvfbvy7L/cVd7e+dZ8yeElPTBgK8E71k601n885XgvSfOqNr+eOHqBwNVpdTHoH0vUE79375gwo3QP//5z7z97W9n1qxZCCG44YYbXJ/SPkEnmtUPbUY7WraiE8PK5V3O2ncNnRhWPevuiTGdGFY9tjW1q8a0Nqx6fJtTKdGVK7onhZxWVencPcR3HWtWPbDRof/RrHpkSyqfWIOy/T/l7qkZrQ3L1/elV8o7Mdb+XPr/R7akO/4+6vCpTW1Yvm5y2r+stiK48sR4Ylj94EanT22uenRreu0/LvN/h0+Mr7p/Qyrjr9aG1c84lPJOYOXz3ejESfPOoWPN6gc3OfU9L6zuHVdPjI8ldAKrnnD4xJQ2rFzRMylj70iQaMOKlSOXcq6W3+2XlHqinXKvg4mRSKkn2vDCi+7Gf1LmnskEkBI9GNAJrFzuLv7pxLDqifQ+sakTw+qH3dWeE214fl3fiMdfdcWMiTeBZXOvTU5r/6sed6eW6hpam7L/cVf7WbnCXfuukWjD8o0jH/8Ho/1nN/enlv9X35rn4EMn2taeJkDtZ8JNjPf393Psscfywx/+0PWp7Bd0Ynjuz+6kRLU2PPdEeuXMtDY89/gOh8Uxw/J7UyznlBiWP9DurDiiNTzz9OTY43d/EgedGJ67Y41Tcrj8rg3ptv+HHW7lkBieeLFnUiwM2R/oxLD8bnf2pxPD8ns2ptf+dTn+OVsYBU+umpz2P5I9SXVieO5Oh/wzMSy/b3N67d81/3cc/11CJ4bnHnE7Mfjsk+72+HQN63vWOu3/p5d3Twruvz/QupJ7uYu9zzzTlV77r1z/CO2/6hM+e1AEq9r+JK49VHuCvrLlmNbw1AvuFmYMcU837buG1obnnnS5MM2w/MHJuTBkJNCJ4bm71juMv/DkypHbf7WFQRMRzvlPrG38T7P9u/Q/2pRrzyntfwOPr+11usf7Yxvcte8ae2JItj/GQErdce1pXzDhJsbf/OY389WvfpWzzjrL9ansF7xAcdYXHEoZ+5J3nbdkQsgZHAx4vuTsDzqUUwwkZ37anZSva3i+5MxPHOtMTi8rNee8ay5ZmeCZEbx0PPjyk6GXp+Oh93fz2Ze8hn92+P9MXH7Z//uVdso/PTP02cF2h31/T6gmpX72l1/rVEr9zM+/OpVSrlC2/4+6k3PzfMl5r5+TWjlLL5Cc+blXO5VSP/Nz7rZScQ3Pl5z59ydUj7/GvNQHmwSP8mtXH1r+nz/cV+/OL5uErEw47/VtZJUe+txO/ng3Pn0vcWGvft/s6b09tD08Drwk1uxybcO+J82eK06VVMQLJGd98RSnUupnfjrFWwlkyvzfoZT62V85LZXx1/MlZ334CKdS6n/x3sWpllI/64unOpVSf8+Z85xJKbuG5wvO/Ngxo27/L4mjldhmdv47KxPOOXuOjb17iKf+sGP5u4uNe8jT/GEx0t/lOCPJ84Z//yXfGckxdvedXY6ZkwnvPms2Wc+8NB/c3TlVy+/25z4FynKvSVh7qPYEqV/u45xMeO+bZ5KT+8DfRvGVK3PPnHDQfsWu2Isd7+9rL7bsmYSsp/mLv5pP1tvN+N/1xei/sr7hrAuPIOubg3L8Eb0OtJ93Pcbejjms3WxgOPuzryQbgIe2r5H0edk/7uRTR2Jvu7H/c9/QtvP4G2ynfD6V38vv7Xm8TzzY3OtUp1LqZ37i2Enp/0cCz5e861yH26j6kr98tzspd9fwleD9r53tVEr9Qye528rE08lQbX9vnHJP7+8upo+Q4+7pwYmxUt/wAmVrTxOg9uO5PoG0QceaJ/602qGcnObxB7dNCDmDgwGdaJ64vyzlqcZ+gOrY8ORt69DxRKR2Bw6dGJ68Y2PVVaOj7qaN4Q3J87xi63pmxt2wAk4d/n7XsN8Hdvnuyj38XsHjw37vOKCztFhb5b3NIz+M3sPEuI41T/x+pVMp9SdvWpNKKVco2/9dW5yNf50YHnwhxU+txYYnb3nR9v9+PHUzKu3fvMaOPzl+COJI9mgcDehE8+St63brf6TRnN31EMe2r6M17rP/HO7zGss/d5RfFWSAreXXrti0m/99F15b+X3Nbt5/ftjvm/fwe/duvjcaqObjK9eybtj/tu3ycw/QQiCw9vfEH1Y5lPKu8J+U+v/h/N931P7vV6Sy/3VieOJet9toPOZQLcY1dKx54o8rnea+jzzZQTLG/V9d5nnsoBPDk3eOjpShMIZ3bn2EE7tX0xb1jPyLu+ZelZzp0f04ifbdHGcvcRDYOcerxNTKsR7f5bOd5ddwbKhyLhVesHzYe8PP6any9Rf28P7wY1TB/kqpP3nr5Kw9VLuiD236Mx/iz/aPp3exv7HG08O4pwtkgeJBOG7Fhturfgp+BKeM5Hiq/KpgNCrVGvjJ72z7rirfEdXrRHvzX6p8jKj8twDCPXzXMPToWwgEwBW3cEoNUFP+fzdD/sYD6nbTZhF4ZC/ntTvs2M3/nt3F/rPln/ldPvf8PdWPbSbe4jYdG7f8J9a29joJ/f9IoBPD4w9ud1r7fOzRHamde0m04YEVXU6l1O9/sdu2v4fJ8dHm6gLDO9of5VVdqyxPXr6HD4a7/N21h/d3/T/s3s/uA8bqbuhEO6097QumJsbHGFobVj+0GZEJHLUPq5Z3o9JanNGw6rku6w0czEtobVj92FYnkzLjAVobVj+xncY5Y1cVfmv0FMuSPUWkyYlqUuqrHtyIcLAopNL+6ke2YFLrfwyrn+7AlZ6P1oblG/qpGf/c5KBAa8PqR9ohk9kjOT7o7T/ajkkMIoULh7WG1Y9tRc9ofolc0Xn993JctG6335vCgcFQnhjXhtUPbQJHMoWW/2xz5v9cQyeW/5vEIFxMjCeGVQ9sTGX/2z3GO51xb61h1fN2EjGN7L+yx6ZJDEgXsRdWrO5DZ+TEk8obBWgNq5/cgW6qPeDrf++mezitI1051XjC/k2Ml2sPkxATcc/hKUxhCvuHichetbb8R9TsugpgjNpPbO01jdwfbP+ver4b4XArjZUrelBprb0ZWL6p3/7hIFxrA89u6ccbQ/M/q2YVp2zbhyfaHGCsuJOtfWxC5HNj0t6BYNLnh6VSiZ6enp1eOomdnY+XUbz7a+6kDD1f8lcXHJZKKUUoy+ldeLg7OZWM4pwvpFhKN5Cc87nj8cZoSY5Cc2q0u0e9Jzf2tKDWyyje8x9vdCrles5XTk6v/wkk53zyaKdS3hecMRc/pXJOXkZxzpcdbmUSSM75kjspZdfwfMk5X3j1S+JvoxmYmhQ/iKhsreEFinf/2+vc+v9/emV67d81/88o3vP/lqWy/71A8u6PHeUs9vq+5K8+dEiqY++7v/56p7nveX+5ILVS6r4vOeczxx1w7pvVIad0PL/3D07hoKHantp7gq09TM5trKYmxqcwhfRgIk7teoFb/uNnPVt7nYT+fyTwAslfne9wG1Vf8t7zFqeW//tK8JE3zHUqpf7RU+aMWfsZEbM0u2VM2joQjJUv9TJua0/7gkk/Qr/+9a/T0NCw02vjc7c6O58k0tz7f8+QRG6WDSWx5u5bNzlr3zWSWHPPTRtJHMk5JJHm3mtXOGvfNZJYc+8Nq0n2vH3QqGKm6CWDu4UwrlD0dv84WhJp7rn6SXf+J9Lce83yVPufe29c587/xJpbH99BnFb/E2nuveZ5p/1/7y9Tbv/XvvCS/l+otzs6o8kPDUTCJiNJrLn3/5526/+vW5le+3fN/x3Hf5dIYs29f3AXe+NYc89tm9Mde3/m0PfEmjvv20qcUrWiONbc+5sXD9j+58c7UBNyamLyIBT7vrLc1h5emJS+v7Qf/TGFKUxhYiJ0Ifd5gEhit/wnDhNbe52E/n8kSGLN3bdtclr7uevOLanl/7E23PLUdmJHigWxNtz03I4xa3+e14sS458nl8ZIONzWPtz5v33BpJ8Y//znP093d/dOr9lHvMHZ+RhtWP/0Nowj52AMrH+x11n7rmEMrF/d60pJFKMNG57rSG//a9jwfNde1HxGr298xr8TPhh4ZtrM3f7faMP6p7a68z/asOGZ7em2/xXdGEdmaTS82D7gzP+5htGGDc+6sz+jYcOzO8af/VddRDt652oMNv7tckjfjNFKqRRivWqhpOxCKev/HfLP1PMfx/xfG9Y/2Z7K/jca1q/scRd7Daxb05fq2OuUexpYu2G8cZ+xOxljbO51oNfvMxWrXWOlN32fvzOZY+8qOc31KUxhClMYI6zWza5PYZ/hnP9oY+P/JPT/I4HRsP5Fd/zbGFi3tn+c8c+xgzGwemvBaf+v2r7/7Yt95Or+BFk8usq0jEk7rmtP+4IJNzHe19fH448/zuOPPw7Aiy++yOOPP866dbuX4cxkMtTX1+/0ksrd6lI/ozj322/EdyWnEkjef9ERztp3DT+QnPeJl+G7klPMKN731dekuv/f96VXjpmczL4Gs8mAe2uXsLWmfrfv+VmP877/ZvysGx/oZxXv+3+vc9a+a/iB5H3/cJxT/3/R2xakV84po3jff5zmrv8zivf9+2vxsyn2/187+SXxd0oI8+AgQfCn3FGDf/sZxbnfPd2d/88o3vevS9PLf7Ke5f/O4q/b+O8SfiA59++Pdcf9A8kHPnq4s/Zdw896nPu9M9zZfiD58HsXpVdKPZC8759PPGD7S2NONZ7QI7PclT10n7/nZ5TlXpMw9q6Q01kxNTk+hSlMemzRtTymd//gx3iGn1GW/ziUUn/fl145Kf3/SOAHkvf/rTv+7QeSD11waGr5f+AJPvam+QSO+HegBH/3unlj1r6YAE+LF5TPHXrRmLTlZz1be5oA/mfCVUcefvhhXve61w3+/alPfQqA97///fz0pz91dFYjRxxpbvrhI8Su5FQizR9vWOusfdeII82ffvWivX5v7AdoHGluvvyp9PZ/rLn5p88R67G5/mrBKUZSED4IYZeTGewMjRBUHmk3w/YjMRU+I0BoELHBeAJRlmY0UtjvGxBm532+h5+GETv/PRxG2pfQ9qeMh/7Wyv4cOiiIxAye4zZZx1PBXG5qOWr3B8fKKf3pew8Qh26e+ohDzc2XPuasfdeIY83NP1tBHDnq/1hzw30plnOKNDf/1+PW/4qxJ+hxlHDzjx8nDjWo8U8QRxtxpLn5sqeII7XTZHi1YnsoFCXhQ5zYeyaF9bWA0Mb6aimsH9SUfbj1z0jxEn8rlMDE2u67PSxHFYmNAUaJwc8PfrfcRmW5caX98smXP8ywGFJ+Sw8dZ/C98s/dxQAj7ftGDn1W6HI8GRYXhC6fk6h8b9j5GNBCska2cE/2EJ73h4pIcaS56QcPufP/kebmK55OL/8JE8v/wwQCB/wzTPjTf7qL/y4Rx5qbfrHSWeyLI80ff70u3bb//Qed+p7f3bKJOEnnjsRxpLn5yuXE0YFdf7V6XyQUBenvnO9QDlM2dKN1+T/GDOZeZnf7PgpAl2N85QCVPI1KPC63ocROudDg9005rle43rCYicEeA4ORO39fxGboGJXvVtoexgPsewYjRPk8be5olCjngWan2KwE6AovYeh9MfwpGjHs+MP+LuGz0p/OLXVHsc3U7fkm7AFD3Gvy+Z9YKH4SnMIylnN4soXGZGCn/LsCJQXJnp5YqmLXgyazt4FT4Xd7gJQCPWjDY4DhXNOUubJ46RgZkUOocm1GWRseHAd7gJSiPP53c57DfzlI/SOVQLvcSkOIl/b18NOp9h4M+czKvTDD7ufg/al8aRc/ZbTt/+HXr+SQv9GGlzxOKQQIgdl1Mml3XTjMjoTe2e9VsOv9FxUutku7xldDOU+kQQr6E4+VuoWb4sUMiGA3JzC+EUfa8h+HUuo3//S5Sen/R4I41vzxBnf8O440v79xfWr7P0oM1z24hciR/4204ZePtdv2d+ObRhvVWgiFGpIwH+auh748zHcP86uDda+K7698dhDmpfFk2N9SCRIDMYqVwXR+v/hYeh6PgfaRX9h+Ig4TW3uaAPY/4SbGTzvtNMwE1qIw2tC+qhORcRPYjYH2jQOoCSBncDBQuX5Xi96NNrS/2ONkUmY8wGhoX9tLw5zd74E92qjWy89kZnNF/hTivI/fXQRj0FkfHSi8niJoKMytJc5KvKKmb5aH9sEoyLdrGp7ppPuoJmo2lhCxJmzOENZJZGjw+zXGEySBnZjJ7ohIMhIVaqI6D1nSyMQmknai3aA9Sf9Mj2KjILfDMNAmmPZESKnRI7ctpPPQLDXttqgoY0OxSVG3pkD/7CzaE9RuLNnijtjzikSjDe0rOxCOJuWMNrSvTrecU/u6Plwp/BsNG3cUqUln9w/aH5mMrVKOefvQvrobow0iffPiNv6+2I2Z0TziifE76o/g17WvIPPMOqivI26ppzA9g0wMuU0DyO3dRPNa6V6cJb8lxniCUqOi6ckuwpY8ca0a9LeJL4lqFfktRaJan4E2DxVZ/1u7rojqLdK/qAG/L0YkhmJLQKYjwu8oEDdlkaUEkWgKM3IkgUQY0D62Th+DN5CgfUFUI5ExZLdHGCWI6hSqqG0BMTbojMQbSAYLrTIxiMQwMCNARlBolpSaweuHuo0JQVdMsdUn0xUzMN2nZktIsG2AuD4DQHFaBhnbYpQMNTIy+D0ljJI7cR3r/ztfWgAbIwzyn9T6f8v/jXYzOWe0oX1FRyr732hoX9/vjHsbA1s2DQDpVMio+B6jx6Yw9ZL2DWxuL2IyMp39b2zuZZpqD2xivEqsfqBxMVe2nYxXsATXSGEX+EYG7Qu8QjI4AS1LCUnew+8s0n24VbgylUVtiSHOCmq2JgQ7SuhAkmQVQUcJESXIYkg4vQ4Z28mc7iV56l8s0D8na3MuaY/lFQ11awroQGGkwO8qkNTY2ovqDYmac4hE0zcvS8OKfnoX5jFS0PBMNyKOSepyRA0BqmjjvtdTIpyWR/VbnqF9hUg0pZaA/MYB4rqAYNsAfYvqUSWN3xsRNgYgQHuCoCsm2NpL1FpLnFP4fRGFtiy59iIIgSzFxHUBspQM+qkkq/B7QkgMSNCeZyeL9hE29nbv8/cmCkLh8dvccVxf62OUQPt2gWNS/mkEZY40tHB9cAF6wlBM3I15VwrRSU6hlf388IWKlcWMOhCIeNjxh/0UiUGXJxht7l85eGVSeZc29VDbplwjEJXFly85wV0WYVb+XZ4E8PpiVDFGbe5AN9XbBRlKDBXgBydXxUsWjA7aYcZOVorhC8uEHWv9c7LkN5UoTM+Qby8hIo2oxjGNGXygwC44sX+LMLZ/Jwkk2gZtpezfFSi188TB8GvY3c/h16HZ/eTvbs5vp/d2bWv4d3f9367HHvZdk/FJ6rJ20Uxc7qNYI7RdqB3XZ1ADEcaToEEWw53ujyiEmFyAyfigNbKvZH8vf9frLiLKi+6NEIgotr9nA8SObkyxiFAKU+5PvWQOcV2A1x2iOnqhYP0QSQJSQuCjG+voOqph8JJkbPAH9E6LdOOMwBvQxHmJ0JDbam0BsYv9+3Jw0twIaHhwg11I1NuH8DxQEhPH9J9yKEF3xMCMgMaHtxK31OOt2+aEN4wWKvxH1OTdtb+2N5XcH8r8f9NA1YV9B7V9A1s2F1Dp7H6MgY0dJfuHg2FsDGzoKuGNUf9X48kPtiziV8XjMb5CDoQYT2IqNXklCBsCvIEYGWnkQIjOW87aNzePigyqkAxy6ajWQ4X2QQ+R6MEFmjJMSDIKVUpszNaauCFLqckn6IoRxtBbm6GFzWPRHUP+L58bk/YOBOnUdHAIP6s4/5I3OZNS9QPJhz91VKqlXC/47DHu5FSyig99M8VSuhnJh75+0riQUk/jcyN+zuOCy97uVMr1Q99flkopV7ByWh/60glOpbw/ddYigpTKOflZxYe+90Z38Tej+NB335Be+w8kH/rWafskpb7Xp3T2AYEv+eQ5i1IrZ+ZnFef/11ucbqXxoW+cml7+k/Ms/8+5i78XXPYOZ+27hJ+RnP9Px+Nn3EkpfuQTR6bX9+Q8zv/xW93ZfiC56INLCNK6jUwg+dDXXn1QpdSr1RwDX3LhhYel1v7t9R/qzv9kleVeKY29gS+5+NzFzsZ/hXu6kpJ1DT+QfPjv3G5j+KGvvmpCSLkeDPhZxfk/ON3Z+A98ySfOWZTe+JtVlv+4qj3kfFt7Tan/9zOO/U8gudChlLtrBJ7g79+20J2Uuif4h2ULxk5Kvcp7LuYe/MDyb1f+18965drT+Pc/6RyhDhGXEq7717uJS+7k5K69coWz9l0jjjTX/vcL7uRUSgnXf+vh9PZ/qLn+e09UlbMczRV9YzXhMlEQF2Ou/dIdxKXYTfulmOu/fp+z9l0jDhOuv+QZh1L2CVfeuoFoAsjZHAzEpYTr//1+d/E3TLj+P+4fd/ZfzeWOppuMI83133ypnNJYLWCKYs3Vf9owIeScDgbiUsJ1/3KXQ/+fcP23Hkkv/ynGlv8X3cXfa790u7P2XSIONdf95Fm7jYWL9iPNr65elV7fU4y57it/dmf7kebnN6wjGmMp/WrxayyfXoojzfX/+eQB21+1WK2rXGsUa665Zk1q7X/w+l35n1JiuVdKY28Ua/7vt+vHfPwPb//qP21wJiXrGnGkufaqlU6lpK///pOp3EYGytz/3+51Nv4H7T+t27iVEst/XNUeSrGtvabU/8ehY/8Taa75xYsp5j+Gn965gSh2E/+i2HD5fRurtm+qqYnt42lX5ckO5h7iyPJPV/43LsXl2tP49z9TE+NjDGOgc1OfKyVLjIGO7SVn7buGMdC5rei0/zs396e7/7cMjNn1y6knxneCMdC5sdet/Tv0f65hDHRuLTjt/+09Ybr7f7Pb+Jt6/7+b6x8rP20MbO9Ouf279v9bUm7/jvm/y/vvEjb2uuX+HTtSnns59j07OlPu+9sPPPeqvth4z+8aAx0d6bb/jg539jfFPWFHl9v+Tzv3dFl7tP7PXe7tGuOBe+5Iuf075T/ajGntdbxhPPifVPN/YHtv5GoXWwywrW/s2h9vT4y75t+u/d++YGpifIzhZxUf/enbnUqpX/T5YyeEnMHBgB9I/vafj3MqpX7hD9+Q3v7PSC78zilj1v/jLTi5hp/z+Nv/PduplPqFl705vVLSGcWFX3cn5+ZnFJ8/Z0mqpdQv/LHDrUwyigv/64z02n8gufBHb9wnKXU9insCB77kc+9dklo5Mz+r+OiV73QqpX7hD16fXv6T8yz/dyil/rc/e1dqpdQ/+q8nOpVSv/izR6fX9+Q8PnrVmU6l1D/1N+6kBF3DDyQXfuvkA7a//V3EFviSj6d4K4HAl3zi40c4lVK/8EfuthFyjcCXfPr8Q51KqX/uvUtSLaV+0T843MYwo7jwm69JtZT6Ry93J+Ud+JLPvi/FW5lkleU/DqXUL/zOKan1/37Gsf8JJB93KOXuGoEn+KezFjuVUv/SW8ZuK5OqKoijWNMaKfzA8m+XUuq29jT+/U86R6hDRKWEK//uZiJXcjah5qc/eNZZ+64RhZr/+e7TRI7kzKJSwlX/dHeq+/+qLz8wZv2/v/vhTVZExZj/+dgfiBxJ6UalmKs+c7uz9l0jChOu+vpjRI7k3KIw4Qe/fZHQkf9zjaiUcNVnb3cYfxOu+twdKbZ/zVX/eNdL/P9YSamHkeZH173oLP67RlRKuPITf3Lo/xOu+sI96eU/xdjyf0dy0lEp5n8u/r2z9l0iCjVXfuNxd9w/1Pz3j55Lr+8pxlz58T+6s/1Q8+OrVxGmVcoy1Fz1Lw8esP3tb6wOI80Vl7+QWvsPI83lV6xwW3v4x7tSG3vDSPNf/7fa2fivcM/QkZSsa0Sh5qc/dBf/ojDhqn99yFnu7RpRKeHKv7/V2fgftP+0xt9SYvmPq9pDMbK115T6f/f+R3PFT55PL/+JDf/5hzXO4l8YG75929oxa7+6KvvYT4xHoeXfrvxvVIrLtafx73+mJsbHGgbCQux0Vi4sJemcFSwjdDkwDYRFt/ffKQyExaTqiqnRDBlVn0RM4RPj1v9E7uxvHPg/pzCO/a+BUkoTU8C9/3Xd/h4wlkQ93M3EyFguYEq9/bv0v+PU/scM46H/XcZ/l3AdeyG1C9IA97ZPyvsfm3sdKKrti272QiNS3/+hnoq9DuF6Ui7V3JPxUHtLce1zPMTfNNu/6/6fsn+3/ocp/lNytL/1aLRfrUa1O1RTVnI19+DU/lz7v33A1MT4GMPPKi649M1OpdQ/8umjJ4ScwcGAH0g+/LljnUqpn/+t09Lb/xnJ+f++FN9Pp5yJa/g5jw9f9g6nUurn/2BZeqWkM4rzv3yCUyn1T5+9ONVS6uf/5zKnUurnf++N6bX/QHL+t1+3T1LqozlpH/iSv3v34tTKmflZxQU/fqtTKfXzv/na9PKfnGf5v0Mp9Q9f/s7USqlf8IVXOJVS/5tPpldK0c95XPCTtzmVUr/4Q4ekV8o1kJz/bycdsP1Vy6mqFfwCX3LhRw9Prf0HvuSjFx7mVEr9/G+/LrWxN/AlHzvPnZRzhXumWUr9I586yqmU+vlfe3WqpdQv+OEZTqXUP/nuxemNv1ll+Y9DKfXz/31pav2/n3HsfwLJhRcdkV7+4wk+8/axkzLfXfufP33hGEqp7xl7W0B6MOAHln+7lFK3tafx73/SOUIdIiomXHbhH4hGYeX2frUfan78raecte8aUaj5yX884U5OpZhw+afvSG//lzSX/8O9RNEYyZmMkUTvREFUiPnJBb9xJ2dZjLn8YndSsq4RlRIu//LD7uS0Sgnfum5ValeuRsWEyz9+s7v4W0q4/BO3pNf+Q83ln7p936TUR3EBUxhpvvOLVamVM4uKCZd95EaH/j/h8r+/M738pxBb/l9wF39/cv6vnbXvElFJc9lXHyEquZNS/K/vPpNe31OIuezDv3Nn+6HmB1esSO1Ta1Goufwf73MqpX7pJctTa/9hpLnk0ufd+Z9iYrlXSmNvGGm+f+VKp1Lq3/nFqlRLqf/42263Mbz8n+6fEFKuBwNRMeGyi/7kbPyHkea7v0jxVibFxPIfV7WHQmRrryn1/1HJsf8JNZc6lHJ3jTA2fOO3q51KqX/9prHbymS8zT1EoeXfzqTUi3G59jT+/c/UxPhYQ0CQ80ZXL3ofEWSU0/ZdI3C5YlRAkHV7/51CQDCGK4aqP4mYQggIcr47+xsH/s8phGP/KyCT0hXbgHv/67r9cYBgN08r7+9TaPuD1Nu/S/+bdvsfD/3vMv67hOvYC6lVagHc2z4p739GJ/eq/iRM9Zub+v4P5FTsdQjXT6ummnsyHmpvKa59jof4m2b7d93/U/bv1v8wxX8ynuP4O4btHwhPPlhwan+u/d8+IH16ekDDbStgZhsm48GajYiGeugDkgSUhCRBBAFGaDAGUwoxhQLFmTWoYkKpwaN+exemWMQUisRHzsd7YhVJbhaqvQvd2YVsbMB0dSNqa+wxjYZEo+IS533nHVz20T8SJQkmDBGeB76H0BKdJLB9B/X3R5jGWgrHLUAVYmQhRglofKoXUYwwiUYEPiaOIYyQs2bAmi30v2ohW89uZuGPV4OQdJ0yn8bHt9N56jya7lxL0N/LB774Ji7/VDvhQBERBJAJIJdF9hYpvGwWuWc2YcIIE0bkH12DCSNkfy2yGDLjpk50Qx76C5hSCRHGmOZ6ZG+JgcWN5NclIMqDr7uX/FMRRimEUpgwxGiDCIAwAiEwxSJCSEz/AP6qLei2JuTGbeSNQYQxcVMeESX4HRH9x8+h5uF1oBREEcncaShtSFrrkcUQEgMSRH8J4hhyWYgT1PZezPR6woaAhm2dvP+Lx5dX7sXkGuohjslLab+jbYHelEogBEopMAbPs0PFGA1RDL6Hl2jQ1kbYnaOT1pZM+buUQvysx7lfe8PQU1um3N6w4wis7xBSUpfLQn0dfa9agDeg6Tg8wCiQEdRuTqi7exUMlMDzoFAk+8IWe37aIApFWm/pJZnVjN8XU2rNIQyUGj1yW0MGZuWQ82sptChyOxIyHSFojQgTRFgeC1EMiQalYWCAtl+vsG0NFPDK546uvgJJAfgeOc/DmzeNc//peG74yu17/nxPEa84YPs0l2XrOw8BAbkdmlKDwCtC/2vqqNug6V6aI7fNICPIb43pOaaW3oVNZLdCttNQWp/Aht23E2UVsr9Epr0bpACl2H58I3ENzLi7hEhiah7fCNkMhCE1DxTtOXkeeloTG85ooeWZCK+jH9E7gLc2IW+MtSMpIbZ9aOIY4fv2PkcRgRgWnKQYsrkkIVMq2bGiNU2JRmQzZLQG32PaA0X7HSEQUpLV1qfUPyqHbMf38RvrmXv3DqirhdoaRBhBcyNs206mqZ73f38Zl3/8ZsLOfkRdLWZHB8LzMcUSotwPomK7ldOsq4VsBhEnrP6bhSRZgywJFv9gBSaO6TtpEbXPd7D6r6Yx59YC7a/MEddA7XrDtN+tRES2D4KaLOd+43VcfvHNhH1FCEOEl7GNZDO23f4Be16tjYgd3eiZLcgNGtPWgnnmBZLZLYit26wvUdL2rTaIwxZCdx9kMvZY/QPoeW0kNQFeTwmd8+GR54iPPQQMeJ399h6VwrIPDEjyAcZTqOVr8WbPJJ7bStgQUGr2qFtTwNuw3foGwGxuR9bVQeBjmuuhvwSlEKEUBD6ipRnR149QCt1foLD0UGp7Q8799NF8/6qVeLd22T4Wgr43HU1YZ4tmzf/3GMIYUBKRzZR9Z7TTOBOeh8hlwRh0Tx8oiaytQXd1g+dZv14oImrymMo5KUX/idP49NsXctlHbiQWHjQ1YAIfKTLgKWsDTY2IQgHTP4CJYmRNBrF5G9ltHl2nzKfhT8tBCkx/oTxgJSrRg+dgBgrWn4G9N0kyaLc7wQxNhhqtkb5vV+ppjUkShJQYrW2MUwqvsQGkjRVSqUEfTVc3CIGUEpQkKNhxInJZlDbkltfRd+xMnv/cEuY/BBe+ezGXfv9ZOtsC+tskfj80PV/A39aPLJRAl8/X9zFZn57Dm8nuCIlrPNa/QdHwgmDGtS9g4phN5x9N7WZNlBfUrwkJdgwwML8Ory8hmZbD642Im2uQYYIsJbCghXMvPoIff+spCr6HKsSoviKF+Q30zfBQkaF+dQF/UxfBU7023uwOethE8jAfsicIpQY/m+/pR7c24PWFeD1FjFKonsIe2WDHaXPYVjObJLB/57dq4ryg/sUiXndI3BDQu6iZKN+CCg35LRF+b4TXB32HNKFKmqArBAPJ/DrOPe84fnjZC2w7roFMlya3LURUiSHe+h1kimvAU9ZfJBqhDaqgkVs6bHx+Zg2ta2vsF4olaoQA32fgqEZUySA0eB0Fardu4/zvvZHLPvpHfOmTK/MPlISmBtCGsE6S2xKhtnQSrPcw7dsQ+RzBNg/T24dJEmpX5WxbgfXrJIm1xbLPH27bAFkhhv6nzWBcACz/MRrhefiPRwilqAtD+37Z1+N5ZMo2n/E86xPiGBWGANRoM2QHUthzMQZZHveNq3zIZvBNwnnfO4NLf/gc7YdkSDIw4wcPWb/vefZcANE2Db12g20nSTAV+1IKSqEdnxkPkc1Couledhg7jpLMuiukd26AVzJ0Hi4wErRvmHtLTHDr43D4LM792JFc/oUHeP4vpzHj/ojcPcvZ8JGjaX4hpn+6hw5g+k8fK/sAgZnRStSUw3/CclqRy0IUY4xB1OQhitCFAsLz7PWW74WZ1gRA3yGN1C7vJMkHeL5PdmMP1NbAQAGCgGhOE0YKPCUQ2xRmRgtis0Y0NqDr8shey0Uablthz0kpzPQW8BU669F7eDPeQEIyPUPdY5swdXmitjq6l06n+akexAtr7f1IEoLaHOd9Zxk/+eANJA314PtQLNkYsqkd2dSABmhpgv4Bay4zWhHGYDxJ1JQl2NSLAIqHtpF9Yq29B0EAvb2IxgZWXrSQpuWG/hmC2bf3orZ0YDZuRkhB0FjD+/9zGZd9+HeEvUWQAiE9hJSI+jpEGGGSBNnUCn0DkAkQ2Yy13f4BMIaae1cBlq/Q2oypyyM8BVJBHGMaahBdfYhiyY7ZbAbR2UtmR3c5pkR75sxY3guQWe8NxQ4sJ5a+B4m2x8AubvSzGdsHuay9p2H5vXKcRmsMEOQDzvvJ2/jJBb8hLunBGAUQ1OQxhaK1byEQDXXoBdNYf848iq2WbwM0LTcIbQb3WZYR1D+wHtPd89ILMcZy5fI1+FmPD/74rbtXbPA9G0N3QYUpZjcEDLxiAQPTPYy0+zwbCSoEocu/Fw06EHgFjZECkRgynRFBV4ncmgHMth2YOTPI3fvCUN9LYdtNkpeukq/4rGE/g6YGyGXZ9K6FBL2GxBfIxBD02g7JtZfwOwYQXX32GKWStU1P4ZcKnPefb+Lrt7xIGBrabnwRvaOTcE4jYYOHjG2/9p42Ha3AKxqan+wjrgtoXN6LiBJrk8ZATd6e4tYOqKlBlCJEYmi6Y4093zDERLG1x/J99nMeH/7nd1jbHwitXSfaXlc5PpowxC/zqowQ1geX+8doPchJlJS2HaWsv8X6cepqIYrsuM5lh/puD5CxpmZ1N9naDGvPnU/zcwn5TQO8cPEckpym9VHJ9g/OYPZvBU0rSkQ1ivyWEtqT+D0lZFc/fUdNp3txK/ltCXFOoGZkUUVNXKPwihoSQ9igSGbWcPE7j+Y7v1jFjtfWo4rWflicoX5tiA4EzX94wcavfH4wjuSeKYHW1JVzE6W2Q/3ur8df30ntqjWWT+dzhG11CG3wOvoJPMmHvnwCl3/yVqK+EFMq4dXVYgYKNK31rD+pmJ5S9jyyGRCCZFqjtdXego2xff34W7YN+vqWbdMw2ztoeHbIpwzG1WwWPbOFsDmL3No1xDELRYK1QBzTvKYR09lN48rAcq5EU3zVISRZSXZrCdVfsnWXHR1ktmUwff32vpbtxlcSkcsh+/oRjfXUrtRELTV4nQN4XYXyEBec/6UTuOyiP8GaTfjlukDds8LybqVASgLYiTN70uYBpt/GoUqMq12VsXldJgNKksxpRb24Gd0/QHzMIrzHV1I6fjFGQtBVImM05/7jcVY1QEjo6YU4Qep6aKyHLuvDksYaepfUULu2YGthiUSGCV6/RkTajkMp0IUCySsOo9Tk4/cmGE+QfaFo6z9aI8PE5sJe+d6Wr1dIiQlDmDcTXtxgOX1tlrg2wN/eB3UBohQRNWbxyIMxtP5+pR1Tvm/HlxDolgZ7fs+uGvRPSkpU4Nu+3MV/+Xmf87/7hrJi026eWhrOz+Sw2FTJz4dz8eF8a3AwV6/4+oHigkveVH5qdwSqGXvh9Dud9/Br3fW9Csq1KNHRBey5Pr2n/0shbF2jUqsahsbns5gwIvA8e2934/P8rMcHfvK2wdrbbi+l0gYMXcvwY5Vzwz2h4p+h7EOMGayt+Xmfc7/xDquYs8v9r3AOs5da1tCJ7v9jFV4+b3lk4FvOMexYfi5rDx9G1v+VuUyFR5hyHVX4vvUJpdByYCHw2yNbq06SIe7hl8deGOHPbeO8fzvJ2n8Bm9s/v45MPoee3ozJBZDPIKIEk/EhSoim1eB1l2j6w3J7HtoM5gQ79V/lf1LYOAjUPpvsNCYG7f+jfyQKE/u5bAbyOUQY2njpeYiuHmrufB6hFMEzAjO9GW9TJ3p6I3J7N6avn+KJSwi6Sqiegu0TX9k6cW8BHzAZH12fRfUUqGvvLp+AB5HNx1VPAS/WGF/ZupAG3ZhDexLtC1RJ43UV6XnT4Wgl8PttzpnfOIBcsxkzowVvey+Na9stF8lloVCk8y9fTsufVkFsa2fC94AsSIFfl+O8f3s9l3/6DkpbOhBhaOs0StJ66zrMwACisQFTk0VnfIKtCbKvAL5HdmUI2QDCiGRaPXV9RUSsIQypfWSD5dhhhIgSCnPqCcgjXgihrx/d04vwPTK5Ws798qss/00M3obtEARM/1OX9alSome1ktmuEPkc3o5yTb67h6C3z+ZY5fssFs2l+aYV9rqj2PoEz/avAgh8vKdexEQRMpuhcU25uCAE0SGzMEoStPeSf7Tbfnc4T9rl96zn7ewLKj4I7Hlrs5NvFvX1mPo8fYvq8QoakRiMEphWj4/+xWK+97NVxMqQ6UwwEowSeEWN1x+heos03rrDHrOSf+eydpxKYa91uJ+t+KpdfOKgD6LsW4TAz3p86Atv5ScX/Aa5UoE21ADMaaPrhDaMhLBOIiODTEBGlg/HGTGYb2gFfsHQeO+GoXqDMeW5K0V+VQc6n0EOlDCeIvfkesJDZpLkfcJDphHnpK3zS0FU55HZXkJs70I01pNbG2Gynq09bmhHtDRRtwJQkqbbX8QM2Bzb1uMkteVxXumnvcHPekPjfzQU64bxbmP0UB3HGGujYueMprKN5+Ufv5koNGx8zxKano/oeFkNRkHtppjsyn7wd99c49PdPHfKEXQc00T9vHr6+rLozoC5Nxly2yJUf8yOo2vZ8f4c5GZyxIqn4Fe7P1ZmbTcy6oD6WnsNO7otVxICtCZTzv0xxvqRMt+oe0pYn1G+3yLwUQMFUAoTRdbWlLL2qJT1q5XcLR/woS++bbD/RU2eludi+71Mhs1nL2Lmr9eCF2L6+hHtOwiPmIO3dbuNPa2NsHYTbNqKFNLm9vV1iI1bqe3uI5nRhOrox0QxenM7srEBtKY0s57shm6UsLWnn1zwGxIZ2PhczucF2FpaJoOJOzFxjNzRQzy3leC59eg504kbsgRbejAZn7gxT27FNttns2dQs3w74exGapfvgFJE1+sW03jLCigUIJezcWagMHLTGvEnpzAqiAoxl5z3a6dSxpd+9r4JIWdwMBCVEi459wan/e/y/rtGVNJc+nd3EYV7TixGc0HR1B7jOyMqxlz6kT+6k3Mqxlzyjw+m1/5DzX9+5xl3cn6x4dKLbk1t/4eR4WvXu5PSjkLNF29dPQ7ltKr4wlGU1ggjzZduWf0S+x8r2amomHDphTel1v6jYswl597gbCuFKNR886crUixnGXPJBx3KSReT1PLPqBjzo/de51TG3mXu4RpRMeY/v/sspcSNVlNUcNP/40WZKowNn3n6wKUsq6VN1WJ1VEq49B/uT3HtQXPJF9zlPlFJ86N/fSzFsTfh0r9xJyUdFWMuOd/dNjau4Tr+RYWYH/31tancRgbK/veT7rYxSr39lxJ+9J5fOZRSj7n0AnfbOLlGGBv+39XuttIY9H8p7f/B3Nfl3NcF1bcxHk2uLqssbHMipV7Jf13GX4f5975gamJ8jCEENM2uG8kCm4PXflvOWfuuMS7632H7riEENM3Ij9n1yyore9O4x7gQ0DSz1q39T0+3/2luybjt/5k1qe7/1jrfaf9Pq3HXvmvs6fqrEdHRTFam7N89/2lpDNLd/7Mcx9+U8k/X1+66fdcQApqbM+6URNPe/0Bb5sC5h6y2iK1KsJ7i/m6vXwhompZNd/875H6uY79ruPa/rtt3jbGuve22/bTb/5z6qf53BCGgpcFd7jnlf8ZB7juG7YsqXNjFYlnX9ieEmDD2PzUxPsbwsh5nf/EUvIwbFXsv43HWRUfhOd5rwxW8QHH2l0512v8u779reIHkrE8cixqjvYamnhjfGV7G46x/eLWz8e9lPM6+8Mj02r8vOefdC/Ed7bXjK8FZnz4htf3ve4IPvHYOnqO9zjxf8uETZjtr3zV8T3LBibNfYv9V/bQZPT/tZRRnffZVqbV/L+Nx9pdOxXdo/3/9ljl4QUr5Z8bj7C+8Bi/rin+q1PJPL+Pxrq+c5rDvPae5h2t4GY9z3r2AQLnh3V423f3ve4KLF83GP8D+r/btaouNvUBx1t++LMW1B8nZH3GX+3iB5F0fPDTFsVdx1ucc577/uDS1/sd1/POyHu/6yuucxX/X8ALFWZ88bsr+HcELFO/6l9e56/+sx1mfPym19u8rwfvOmOOs9jbo/1La/4O5r8u5r8+fNGbjT1R7KG8Ua1ojxWD+6yz+Kqf5974gndVZh4gKMZf/ze+dyklc8cWHUixnlnD57vb4G6v2i27vv2tEJc0Vn7+PeIykXKuFH53CJ8ajYswVe9pjbYzav/xfHkmv/YeaS3+03KmU+hV/f2dq+z+MDN/83YtOpdT/7c4141BKfWwQRpqv37FmH6XURw9RMeGKv7sttfYfFWMu/8iNTqXUv/+z1SmWc425/G//5FRKPa38MyrGXHbBb51KqbvMPVwjKsZcesnzTqXU09z/YWz45+fWHLiU+n7G6qiUcMWXH05x7UFz+Vfd5T5RSXPZ/3syxbE34YpP3uo297043dv4OK29FWIuu+A3E0LK9WAgKiVc8Q/3TNm/I0SlhMs+9NL97ces/ULMFR+7ObX2H8aG713z0m3cxgqD/i+l/T+Y+7qc+/rYzWM2/qqp1bqwwMH812n8dZd/7wumJsbHGEIK2pY0IaSbSTkhBW3zap217xrjov8dtu8aQkLb/Lox8zxVV22NzSmMKwgpaFvU6Nb+56bY/wiYMdOtnGLbwvpU9//sZrdS9nPr3bW/J1TzhdUK4fsKIWBOw0uvv/pTaKMHIQVtCxvSa/8V/uHQ/mdOyyJSmnkIKWhb7Dj+ppR/2mtvnup7RxBSMGNmDleX767/99zeaMbWvZ6FgPm5A+ce+/vEuJCkvPaA09xHSGibnU937F3kjvu5jv2u4Tr+CSloO8Rd/HcNIaFtgbvcf8r+cWp/rmt/riEEzGp1t5WIa//nGq7H/8jsvxpX38f2qkqpj30fjI/8d2LE35RSZHfwAsXpF5+IFziS8wgky953aGqlXD1fcvrHXulMTswLZPn+p1TOzJMs+8ARKDU29ld1P7zxNjs1BvB8ybK/eblTKenT/3pJqv3PW946F8+RnKivBMvOPzq1/sdXgrNfOQPPlZyWJznn6Lb02r8n7PXvg5T6aCp7eL5k2UeOdca/XGOQ/zi0/3ecNiO99h9ITv/bV7jjn356+acXKM74xKsccn/lNPdwDS+QvOUtc/AcFUbS3v++ErxvXtsoSKnv32Jjz5Mse+8h6fX9nuT09yxxV/vxJGf8xUI8P532b7mfw9w3kJx+4fHp5Z6O/a8XKM74uLv47xqer1j2gSOn7N8RPF9xxidf7bD/FcsuPC619u8rwTtPmeGs9uba/7nGUO7rrva87MLjxmz8jbeH8obyX3f+x2X+vS+YkBHqhz/8IQsWLCCbzfKqV72KBx980PUpjRhRMeaqT97kUM4m4ep/ezS9clqlhKs+4VLOIynf//EvJ3EwEIWaq7/yIPEYydlUC06plFIvJVz9uTucjf+olHDVfzyeXv8Taq647AWiA5Sz3F+EseHqL9yTWv8Txobv/3EtkSs5rUjzrXvWpVZKPYoM37573Uv6f3+fQtvn9ksJV3/+zymWcy3zH4f2/1+/XENUSqn9FxOu+vStDuV008s/o2LMlR/7g1MpP5e5h2tExYQrLl9B6EpKPeX9H8aGrz2/7oCl1Kuta6iWU0Wh5uqvP5Zq7n/VNx53V/sJNVd+75n09n8p4ep/uNNd7ltMuOozt6WXezr2v67jv2tEpYSrv3z/lP07QlRKuPKi3zvs/5irP3t7au0/jA2XXL/GWe3Ntf9zjaHc113t+erP3j5m46+alLqLJ8aH4p87/zNR4u+Emxj/xS9+wac+9Sm+9KUv8eijj3LsscdyxhlnsHXrVtenNiIIKZh79DSncgZzDk23lOjco6c77X+X9981hIQ5hzWOmZzbWE24TBQIKZhzZKtb+z8kxf5HwLz5NU6ljOccMTHkbA4GhIBF091K2S9pdte+awgBi3dz/VWfQhvFPNb6v5b02n+F/zi0/wWz0i3nOvcot/w/rfxzfHB/d+27hpCCefNrnEqpp7r/BRxae+DcY3+fGBcS5qSZ+0uYu8SllDfMXVSX6tjrkvsJKZj7Mne5t2u49r9CCuYe05bi/oc5h7uVsk+3/ePU/oQUzElz/wtYMDPvVEo91fxzHOS+Y2n/1aTUXTwW4Nr+XLe/L5hwFPnb3/42H/7wh/ngBz/IkUceyaWXXko+n+eKK65wfWojgvIlS//qKJQjORXlS5a+bYGz9l1DeZKlf+24/x3ef9dQnmTpmYuQVaTUA5Ew03Qzkx5mil7m9nUwt6+D2cUO5vR3MGegg7k9HcwudDCve+h/s0L7ufmd9v05/R00R317bCeNUurKlyw95zC39v+Wuam1f8+TnPJah3JOSrD0XYekt/+V4I1HtzqVkn7TIS3O2t8fTC/0Mqe/Y8gPF6y/nRl1MjPpYlbYyZyB4X64k5mxfc0udjCr1MHMuJNZUSfzok7OboiZG3cyp9/69JlRJ3WmtMf2R1PZQ/mSpX/hzv+5hvIt/3Fp/6ed2IqaQPY/mlC+ZOl7jpzinw6gfMlr3neM2753mHu4hvIlp5za5kxK3VX/V5sslhhm6m5mxp3M6y7Hw6SL+R0dLNhuY+qCreU4GnYOxstZ5dg7k57BvGh20cZm+9mOwdg7K7T/n1/o5N0iZl7YydweG8vn9NvX4LFVHzNlHzNFj82/yjnYTNnHTNXPTNlHvQz3fK1mz/dWeZKlb5ufXvv3JEvf7C73UZ7kNafPTnfs/cvD3fr/d7uL/a7hOv65jv+uoTzJ0ncunrJ/R1Ce5DXnOuaf57jzf67hKcHrX9HirPbm2v+5xrjIffdi/9W4eovpZ26f5eVzN3czv72TBds7Brn5zHJNa8F2y9mbegpV2hl7G3Qd/5SvJkz89VyfwL4gDEMeeeQRPv/5zw/+T0rJG9/4Ru677z6HZzZyxKWEX/zj7aBUdU2yg9j+Nd9+AlIqpxWHCb/4/G3u2q/c/5QiDjXX/MejNOf3HIKmqwKfMbfZZTsB8EiVA76wy98b9/L5YUijlHpcSrjmy/c4bf8X33sakVL/E0Wa/71yFVGDj++i/dhwzVcfwJQSRM7FGbhFFBt+fOt66h1KSf/ggQ20Rdr6tgmADz93V/UP9APte3ive5e/twM/huMB1o+s/dEUPotLCdf8y73W/vOjeOAJgrhk+U90/CFO2o8izU9/vY7alG4lEJcSfvGFOzGlxMmq5EH+OZoyDBMEcSnh55+7BQAhx773K2MvrXIhcSnhf69aTXhE1gnzHuz/cYSMSPhsdLONk7cMe+PaYb8/VuUACnix/BoJnoQTAJ7bw/uNu/lfJU7W7P3w1bxKHGqu+c6TKa49aH7x/aedXX8can5+6XJkimPvNV9xnPt+8c/O2ncN1/43LiX8/LM32z9SGINt7e1hd/6nYv/aOKl9u0Ycan7+mZuRLU1u2i8lXPOlu9GlBKHG/z6/o40oNlzxu/WIxIDnZu7lF5+/DaM1Ijuhpt5GBZXc12X713zpbut79iP/e495lPc88mj1+YVHGNH8g4vsu5L/isBN4TEuxYP5txzn5j/+p+6HYfv27SRJQltb207/b2trY8uWLY7Oat8glWDRibOQjlYtSSVYdHQzMoXEBEBKwaJXOu5/h/ffNaQULDq2dVzIuVV5uGHSQirBole0ubX/o5pSbP+w5JB6Z3mhFLDouOnp7X8Bh8+q2R9ePDrtS3jZdHftT0SM5upaKQWLjnfn/1xDqjL/cWj/h86vTS//VIJFJ8yc4p8OIJVg8Stnu+17h7mHa0glWHJInTvuk/L+HwtUi9Xy/7d37+FRVHcfwL8zs8nmAklIQhJAkkC4S8BAFAMoKBShqFQsWkW8oVWLorYvtdQqvvp4QWvxrkARQbSAlIuCIEHuL4hAEyGAXCKBIgQQCOGW7M7Mef8IWV2SkHt+IfP9PE8ekp1hzsx3z545Z2b3rM5rD60vlxv76DqQ1DHC2fl3ixU7/qJzf5xj2x/p9lc3NCT1kDv/Syu+9sb6L0PXz9c/yfy7Ozh/DWgfL/dVPtLtn7R6MfatJ/X/YjMr1Zb6MP6VLL8yGvzl2cLCQuTn5/v92EruHcu6S0fXQUkXnUq6Vss3dHS5tjl0gXdM1Qe6S0PXgW2gC00nphuyz7803dDQpa9c5/CXnPgd47qho0v/VqLtT9fezRxc/3WkdIsS6xwYuoYu18c7Nn9D19CjrdzFQV3X0DM+3LH5V0VNvrtWd+no0j/Bsfnrhl7U/xGs/6mXR1wSg6PaoBs6ut7QWr7/6cDpdHWXjq6/Fu77D2zj6LYnpVsUDKm2x6WLjv2c4KLTxhsauvRu5tj8dUND115yYx/d0NH16hgHX/vR0eVXibLt/4CLf41cQybd/had/9s6t/1xaehyXUvWfyG6S0PXwe3E2l/dpaPLALn2T5qha0jt2ASG2I1BZ/c/fWNfyXtfAxLrRfsjce/BN/6Vyl94/F0Z9X8PfyE6OhqGYeDwYf95Ow8fPoy4uLhS/8/LL7+M8PBwv5/ssxebm6x2mYUW5j2/BqZHajotC/PfzYLp1Om0PDbmPb8apth0ZrLPvzTTa2P+m9/htCk/l8ZZwy29C3XO9FiYP/4b0fZn3gfbHV3/P5u1F6YpM5Wt11KY//omx+bvtRSmrfpRLH/TVJi86SBMoancy3JW1d9p9c+pmjtXmB4L81/91rH1v6j/sVq0/n/65YF6V//riumxMO/F/5PvfzpwOmOz0MLc51YJZ7/a0W3PZ7Ny4LWE2p5CS2zsd1a/RL43pZou1o8wvTbmv7/NufXfa2PeJLmxj+m1MXfqLgdf+7Ewf/wG2bHvS+ucW/8F29/i8ueOW+nIvg9QdO1z/hsZrP9CTI+Nuc+uEGt/zUIL81/+xrH132spfPLVAXilxr4e2fZPmm/sK3nv6+WLX/s+Z9fNfYmavKZVUb7xr1T+wuPvyrikbowHBgaie/fu+Prrr32P2baNr7/+GmlpaaX+n7Fjx+LkyZN+P0khKXW1yyXohoaOfeJFpzPocJWDp9I1NHTskyCav+TzL003NHToEYsClxsH9AjRfdneqLlo+RJ0Q0OH3pfJ1v/Ups6t/7qGyztHiE5l3KGng6dz0oErEhqLfmK2W3O58suyx4qU3oVSWQrI9obX2PZ0Q0OHXpfGdE61wdf/Eaz/yW3DnJ3/tS3Z/xSgGxo69k0Uzl5u7CFNNzRcfnkEpA5fLH9Nw87gZnVbppDd3ogyl+mGhg5XOrjvb2jo2F3u+HVDQ8cr5Garkibd99MNDR2vkTv3S5M+/0mf/6XphoYOV8tNJcz6r6HjdbL9zw7XyF37k6brQJc2YXLX3tj/Fx/7llf/d5tN6mRf6qqcX5I+/+mGfsmcfy+pG+MA8Mc//hGTJ0/GtGnTsGPHDjzyyCM4c+YM7rvvvlLXd7vdCAsL8/vRNaOO9/pnuktH66taiE4nkZTs7MFR66uai04nJPn8S9N1DUlXNIWua5jn7oZCyLwW14S2x4Gg+nkzqDbpho6k7nGi7U/rzpHOrf+GhjZtw8VuTBmahqSUGMfmb2gaOrRoJPrGhM4xofXu/LvFisF2T9131svzxbkkFNTgu2t1Q0dSt1jH1v+i/ofwd4wnOvk7xnW0vlJuOmFf//MSmM6spukuveg7HkWzb+7otqdNuzC5N+W4dLGx3+dR3XAiOKjOy61LSwsSkWeXfYy6rp2/9uDQ+q9romMfXdeQ1MnZX2MiPvZNdfDXiAm2v8XlJ119mSP7PsD573i9oinrvxDd0Irqn9SNKdf59s+h9d/QNLSPbyT3VT6GbPsnzTf2lbz3Vc75f4u3Kb43o2p1P9ILE3FcBddqGaXxjX/FplLXRMfflSE/n3El3X777Th69CieffZZ5Obm4oorrsCSJUsQGxsrvWsVYhZaWPTaesAwAIEG2vRYWDRlB+DY6bTsovzFyrdEy5dmem0smpgFFepGdlAM3gq8Hqknd6ClK9//FnmAC1AKUAreiKIpz3VTwTY0aDZgBQJGoYIVpEH3ApoCdK+C5dZgBQGaF9DNov8DDdBsAJZCnunG9sAW+Da0LaA57wKB6bGw6M1NsuV/tBOaU6fT8tpYMG8fzPAASEyu6bUUFr2bCeWxoAXX3+mza4vXUpi57hDCBKeSnp6Zi1ivDZEKUAYTBqbmd0Ja+Am0VUfQSPcAAQFQhgZvqAFNAep8c6l7FaBrMAptwFZQhgY7QIcyitpZ3augWUX9CztAh6YAzbShNA3K0GC5NbgKFKxADbqlitY3LShdh2bbULqOI2Yotp4Nxw5PJICaaytMj4VFb/+nqP5fcr3f6ivuf5jd2sqUbyr8O/0gGjl4KvVFr38LVWiJvCvZ1/9UMu2fJLPQwsLx/wcA0ATeGeLL3oH9TqDo+BfM2w9vxyCBb9j7xdhbwCF3E/zptpsw6L/bcPnGEwjOOws72A290AvoOpShwRNuwHVWQffYKIg2oHSFgNMavOEK7uOAZhada3VTQWlFf2uWBSskAHaABt2roPSic7Bmw3c+hgJsFwBNgxkMuM4UjZM0u2jcBBSNpZQOGPkFgAZAN+B7kmx1fiNFY6mif39+Bo9awdhuRmGLFQug7Hbd9NpY9OH3gIP7/oum7RQ7ftNrY+Gn2dAde+3HwqK3NsuWP+FbsfKlSba/xeUvfGVt0R8OPAebHhuLPtgq1/4U139biVz7lmZ6bCx8eS30KJk3oJuFFha9sQl2oQXNkPtwoBSvpfDZ8oPQLAUIfM97cf9f2Ta0IOddfCge+4qW/8amoranjPGfFwamFnRFD20/2gbmoZFhFp0rlAJ0Hd6wAHhDFVyBFixbhzI1uPMAzVLQbAUzxIA3VAE64HJZsJWGwCM6NI+FYx43ttsx+M4TBaDux9/F418tUObC4y/H33o9r/71fPdK9+ijj+LRRx+V3o0q0V06km9IQtaK/bDtun9x6C4dydc0Q1Z6dg1ebr506IaGywcmIWvZXthm3Q8QdZeOzte3Eitfmm5oSO7TAlszjsECcFCPwBdnk6CUDU3Tff+iaSRgWoDXi8MpLQENCD5mozBcg6sAOBOrofEBGydb6wg+qqB7gZAjJvLjA3CqFRB0BAg6oeDOt6E0wHXOhuusBfexM+jcuxn0HfnSUYgoqn8JyFq5T+T1r7t0XN4rDtu+OAHLFNgBYbqhoUu3KGzee0qkfEMHulzfElsX7RTomskzdCClTTh27SsQKV83NPRsGY69m0+LlH8xJnSstROw9mwMoGlQcdGwQgNxtFsojMKiNyMBQMgRG2aIhrC9BdDPmTDDA3E2NhDeEA2GRyEk14uAU14AwLnYIBiFNgLzPLADDdjhAejQKxK7VvyEk81dcOfZCD7qgev4GdihbuhnCmE3CoJx/AxgnsLFLrRXhe7S0LlvArYuzXZk/dddOi6/LgmZx+SmM+veKQK7v/9JpHxpuktH536J2Lpop1D55/u/6T/A8orsgpii7Ftja/oPUAJd7+LnPuvrHGf2/V06UlIisbLwbA236hUvP3lA0djPErg5eCgiDDM7JqPx6TBEfr0XBa0uQ9APP0EFBcFq5MaPfUMRucNCyMGzyL65EaxgG9H/0fHTtV60+MIFd54X3lAD7hNe2C4dAfmF0PPO4HRiDM42NRBy1IIZrMEoUDAKbJihBlwFNmApeMINaG4Nna6OwM6VJ5B3mQ6jADA8RfsWts8DM1hHo//LLuokhYQUXUDUNKCgELBtqMJCaIYB5TV9F9Z94zVdK/dmh25o6Hx1LLKWnILt0L5/57RYZC04BtsjVH6Ppti2TOb1J013aejcJx5ZK/fDFnhjrO7S0bl/IrKW73Ns+9+5v9y1L92lI/lXrbF1aTZsy3m9f93Q0Pmay5CVng3bK1j/l+WIXPuWphsakn/dFts2HxPr/3T5VSK2fLnTie+LhaED3TtGIHN7nti1z879Eh197aHz9Qli5z/dpaPzdfHIWrUf9kWK98LA2oIWWGvGA+5AIDAQ8HqhQoJxJCUax7vYCIs/idOng2CfCETLpQoBZywYZ0wc7xyKY90sINhCWOQZFHpdiJsShKDcM9BPnzzfRzYhcWO8ePybteqA3Pl3UBtsTf+hzsuurPr/mfYGRtc1xLVpIjqVZWxCY+gOfMckcD7/tpGi37FZ9Pw7N//YRNnv+I29LNSJb1gFcP71nxQhmD8QFy83lbU0XQPimgWLvWFd0zTEtpKbyl2apmm4LDJI7PWva8Bl4W7Htj+aBrQMd0OT6v9oGmJbhzu3/dGBuLaRYu2PrgHNY4Ic2/5I9/+ky5ek6xpipfv+guVL03UgrlmI2NjT6flrmob4EDc0wfxj4x1+7UFw7KPrKBr7OrT+F/X9IgTbHyAuSe7anzTp9lf6/C+t6NpbGOu/EF3XENsuSrT/I3ntT5qmaWgeHSQ39nV4/1PXIX/vy8H1/+fzn3T59T9/h56i5JgeC0vf2QRTaDor02MjfcYumE6dytJrY+nbG2EKTidU9Pw78fP6RfmnT/terP6ZXhtL/50DU2gqZWmmx0b6xEzR9mfpv/aIlS/NNBUWLzoAU+gd66alkD4ly7ntj6Uw99vDYq9/01SYnXXEue2PpTB7q9zxm14b6ZO3OLf98Zzv/wjW/y9W5jq3/+mxsPS9/wj2P23H9j9Nj4Wlb30r2/cXHHtIMz02Fn95AF6hT4s5Pn9LYcZ/j8j1Pb020j/d7dy232tj6Uy5sY/pVUVjXwfnnz7pO7lrDx4bS9//j4P7nrLtr+mxsPTNDc5t/7020j/azvovxPTaWPrGN4L5W0j/INO59d9S+HxNrlz/x+n9T499fuwreO/rA7lr39J+Hv/KtT+S4+/K4I3xOqa7dKTe0l7sC+h1l47UX10GXeA7NuoD3dCQeksH2fwFn39puqEh9YZ46IbcVK5XXhsnVr403aUj9aY2Yq9/3aUjtV8LR9f/Hlc3hSH0rjlDB1J/3cqx+Rs6cE2HJqLtz3Wt5MqXZuga+rZuIlb/dZeG1BuTHFv/i/ofHWAI1v+eV0Q6tv7rLh2pQ9oJ9j81x/Y/dZeOK4d2FO77y409pOkuvajvI/WJHYfnb+jAr2KawJD6xIihIbW/w689XC839uHYt7jvJzj2vbmtY9sf6fZXd+m48la587803dCQOjCB9V+Ibmi48redhPNv49j8DR3o1SVS8NqDs/uf8mPf4vrv1P6P/PhXsvzKqP972MBoGtCoieRUukBoeKDYdGrSNF1Do0i56VSkn39pmgaERrhl639YAPMXCkDTgEYOz79RI7nj1zRN9PUnTdM0hAW7RNufMLfh4PyLjh+s/yI0DWgUGSRbfojLuf3P8/lLtv9O7X9qGhAaKTv2knzupRX1feRe+47PHxrCXYZo/o6+9iA89vl57OvU/DWENgmSbX+aOLj9Ee/7yJ7/pWmahtBw4Ws/jq7/2vn6J3j+dXj+jUPkrr1It3/SpI//5/rvzCdA+vz3c/snU35l8MZ4HbO8NlZ+mAlLaDoVy2tj1ZwfxMqXZnltrJwim7/k8y/NMhVWzdoNS2gqV8tUWLnwv2LlS7O8NlZ9vE22/s/LcXT9/3rZQdGp1Fd9+r1j8zcthUUZR0XbnwXf/+TY9se0FD7f8RMsofpveW2smrHdsfXf1/+Ryt9UWLruCCzTwflP3cL+pwDLa2PlP/8jm73g2EOa5bXx9bJDMIWmUnd6/qatMOfgT2J9T8tUWPVvB197MBVWzpcb+/w89nVo/vVh7Dttq3Prf3249jZZ7vwvzTJtrJq1i/VfiGXaWDlps+y1/2lZjs3ftBSWfHNUrv/j8P5nfRj7Orn+y49/LdHyK4M3xuuYEaCj552XwxCaTsBw6Ui7MUGsfGmGS0fPOzvDCBDKv/j5FypfmuHSkDakFQyh6UwMl4aev2ohNpWsNMOlI21YB9H2p+ev453b/hgarrk2Vqz+GbqGtKFtndv+6Br6J0fJ5W9oGNg20rntj6HhBsHjN1w60n7bzrntT3H/R7D+970yWuz8L80I0NHzd53k+p8u5/Y/jQAdPYcnC/f95cYe0gyXjmuujYVL6mtknJ6/ruGmOLmpRA2XhrTBTr72oKHnILmxj2/s69Rzr0tH2rD2smPf2zs6t/4Lt79GgI6ed3Vxbvvv0pD2myTWfyGGS0PPEV3k8g/QkXZbB+fWf13Ddd2j5fo/Tu9/us6PfSXvfd0md+1bmm/8K9j+SI6/K6P+7yEREREREREREREREREREVE18MZ4HbO8NtZ9uk1sOivLtLF+4T7nTqdl2lj3qdx0Gr7n/xKYTqI2WKbC+gV7RacyXpf+o9hUstIs08b6z74XbX/Wfbnfue2PpbBm9WG5qYxthfVzdzu3/bEVlm09Jpe/pbBk93Hntj+WwleCx2+ZNtbP2eXc9qe4/yNY/1dudO5XCVheG+tmyk3lX/T8O7P/aXltrPtEbirPor6/g6fyM22sWX1YdCp1R+dvK3yRexyWVP6mwvpFTr72oLBusdzYxzf2deq517Sx/rOdsmPfWTucW/+F21/La2PdDLmpfKVZpsL6+dms/0IsU2Hdx1vk8vfaWD/buV+jZ9kKKzb/JNf/cXr/0zw/9pW89zVb7tq3NN/4V7D9kRx/VwZvjNcxI0BH3/uvEJ1OqM9vW18S0xnUBiNAR9+RsvlLPv/SDJeGPre3FZ1Kve+NLZ07nVyAjj4j5KZSNQJ09L0l0dH1v1//5nAJTWXsMjT0udO502m5DA2DU5qKtj9DOjh3KmmXoeHmjtFyU3kH6Ohzl9xU1tJ8/R+xqew1DOgZ4+jpzPreJzedp5P7n0aAjr4PdJPNXnDsIc0I0NGvfzPRqdSdnL9L1/Db5tFifU/DpaHPrQ6+9uDS0Pc3cmOfn8e+Ds2/Pox977k0phKtDdLtrxGgo++Dcud/aYZLR5/b27H+CzFcOvr+vrvstf97nDuVt8vQMPDqpnL9H4f3P+vD2NfJ9V9+/GuIll8pymEKCgrUuHHjVEFBgdg+vPPOO2JlS5fP/Jk/82f+Upg/82f+zF8K82f+Ts2f2bPuM3/mL4X5M3/mz/ylMH/mz/yZvxTmz/ydnH9laEopR82rlJ+fj/DwcJw8eRJhYWEi+9CpUyds375dpGzp8pk/82f+zJ/5M3/mX/eYP/Nn/sxfonxmz7rP/Jk/82f+zL/uMX/mz/yZP/Nn/sy/7jF/+fIr6hL4TDsREREREREREREREREREVHV8ca4gFGjRjm6fGnSxy9dvjTp45cuX5r08UuXL036+KXLlyZ9/NLlS5M+funypUkfv3T50qSPX7p8SdLHLl2+NOnjly5fmvTxS5cvTfr4pcuXJn380uVLkz5+6fKlSR+/dPnSpI9funxp0scvXb406eOXLl+a9PFLl19RnEqd6hTzl8X8ZTF/WcxfFvOXxfxlMX9ZzF8Os5fF/GUxf1nMXxbzl8X8ZTF/WcxfFvOXxfxlMf9Lh+M+Me52uzFu3Di43W7pXXEk5i+L+cti/rKYvyzmL4v5y2L+spi/HGYvi/nLYv6ymL8s5i+L+cti/rKYvyzmL4v5y2L+lw7HfWKciIiIiIiIiIiIiIiIiIicxXGfGCciIiIiIiIiIiIiIiIiImfhjXEiIiIiIiIiIiIiIiIiImrQeGOciIiIiIiIiIiIiIiIiIgatAZzY/zHH3/EXXfdhaioKAQHByM5ORmbNm3yLVdK4dlnn0WzZs0QHByM/v37Y/fu3X7bOH78OIYPH46wsDBERERg5MiROH36dF0fyiWpvPyfe+45dOjQAaGhoWjSpAn69++PDRs2+G2D+VddefkDwI4dO3DzzTcjPDwcoaGhuPLKK7F//37f8oKCAowaNQpRUVFo1KgRbr31Vhw+fLiuD+WSVF7+hw8fxr333ovmzZsjJCQEAwcOLNH+MP+qSUxMhKZpJX5GjRqF48eP47HHHkP79u0RHByM+Ph4jB49GidPnvTbxv79+zF48GCEhIQgJiYGY8aMgWmaQkd0ablY/gDQt2/fEssefvhhv20w/6orL//c3FyMGDECcXFxCA0NRbdu3fDvf//bbxs891aNZVl45pln0KpVKwQHByMpKQkvvPAClFK+debOnYsBAwYgKioKmqYhMzOzxHbY9ldNRfL/pYcffhiapuGNN97we5z1v2oqkn9pbZOmaXjttdd86zD/qjt16hSeeOIJJCQkIDg4GD179sTGjRt9yzn2rV3l5c+xb+0qL3+AY9/aVF7+HPvWnNWrV+Omm25C8+bNoWka5s+f77e8vLY+JycHI0eO9Dtfjxs3Dh6Px287W7ZswTXXXIOgoCC0bNkSr776al0cXr1X3fyB0sdrr7zyit86zL90NZH/rl27MGTIEERHRyMsLAy9e/fGihUr/Nbh9YjSlZd/Rca6kyZNQt++fREWFgZN05CXl1diHfaHSlcT+RdTSmHQoEGlbof1v3TVzT8nJ6fM8fBnn33mW4/5y2oQN8ZPnDiBXr16ISAgAIsXL8b27dvx+uuvo0mTJr51Xn31Vbz11lv44IMPsGHDBoSGhuKGG25AQUGBb53hw4dj27ZtSE9Px8KFC7F69Wr8/ve/lzikS0pF8m/Xrh3eeecdbN26FWvXrkViYiIGDBiAo0eP+tZh/lVTkfyzs7PRu3dvdOjQAStXrsSWLVvwzDPPICgoyLfOk08+iS+++AKfffYZVq1ahYMHD2Lo0KESh3RJKS9/pRR+85vf4IcffsCCBQuQkZGBhIQE9O/fH2fOnPFth/lXzcaNG3Ho0CHfT3p6OgBg2LBhOHjwIA4ePIi///3vyMrKwkcffYQlS5Zg5MiRvv9vWRYGDx4Mj8eDdevWYdq0afjoo4/w7LPPSh3SJeVi+Rd78MEH/db55UCf+VdPefnffffd2LlzJz7//HNs3boVQ4cOxW233YaMjAzfNnjurZrx48fj/fffxzvvvIMdO3Zg/PjxePXVV/H222/71jlz5gx69+6N8ePHl7kdtv1VU5H8i82bNw/ffPMNmjdvXmIZ63/VVCT/X7ZNhw4dwocffghN03Drrbf61mH+VffAAw8gPT0dH3/8MbZu3YoBAwagf//++PHHHwFw7FvbysufY9/aVV7+HPvWrovlz7FvzTpz5gy6du2Kd999t9Tl5bX133//PWzbxsSJE7Ft2zZMmDABH3zwAf7617/6tpGfn48BAwYgISEBmzdvxmuvvYbnnnsOkyZNqpNjrM+qm3+x559/3q9P9Nhjj/mWMf+y1UT+N954I0zTxPLly7F582Z07doVN954I3JzcwHwesTFlJd/Rca6Z8+excCBA/3anAuxP1S6msi/2BtvvAFN00o8zvpfturm37JlyxLj4f/93/9Fo0aNMGjQIADMv15QDcBTTz2levfuXeZy27ZVXFyceu2113yP5eXlKbfbrf71r38ppZTavn27AqA2btzoW2fx4sVK0zT1448/1t7ONwDl5V+akydPKgBq2bJlSinmXx0Vyf/2229Xd911V5nL8/LyVEBAgPrss898j+3YsUMBUOvXr6+xfW2Iyst/586dCoDKysryPWZZlmratKmaPHmyUor516THH39cJSUlKdu2S10+e/ZsFRgYqLxer1JKqS+//FLpuq5yc3N967z//vsqLCxMFRYW1sk+NyQX5t+nTx/1+OOPl7k+869ZF+YfGhqqpk+f7rdOZGSkr+3hubfqBg8erO6//36/x4YOHaqGDx9eYt29e/cqACojI8Pvcbb9VVfR/A8cOKBatGihsrKyVEJCgpowYYJvGet/1VWm/hcbMmSIuv76631/M/+qO3v2rDIMQy1cuNDv8W7duqmnn36aY99aVl7+peHYt+ZUJH+OfWtPeflz7Ft7AKh58+b5/q5IW1+aV199VbVq1cr393vvvaeaNGniN/Z66qmnVPv27Wv2AC5xVc3/wv7nhZh/xVQl/6NHjyoAavXq1b518vPzFQCVnp6ulOL1iIq6MP9fKmus+0srVqxQANSJEyf8Hmd/qGKqk39GRoZq0aKFOnToUIntsP5XTHXrf7ErrrjCbwzN/OU1iE+Mf/7550hNTcWwYcMQExODlJQUTJ482bd87969yM3NRf/+/X2PhYeHo0ePHli/fj0AYP369YiIiEBqaqpvnf79+0PX9RLTnpG/8vK/kMfjwaRJkxAeHo6uXbsCYP7VUV7+tm1j0aJFaNeuHW644QbExMSgR48eftOAbN68GV6v1+810qFDB8THx/teI1S68vIvLCwEAL9PKOi6DrfbjbVr1wJg/jXF4/FgxowZuP/++0t9NyQAnDx5EmFhYXC5XACK2p7k5GTExsb61rnhhhuQn5+Pbdu21cl+NxRl5f/JJ58gOjoanTt3xtixY3H27FnfMuZfc0rLv2fPnpg1axaOHz8O27Yxc+ZMFBQUoG/fvgB47q2Onj174uuvv8auXbsAAN999x3Wrl3re/dvRbDtr7qK5G/bNkaMGIExY8bg8ssvL7EN1v+qq2z9P3z4MBYtWuQ3YwvzrzrTNGFZll/fEgCCg4Oxdu1ajn1rWXn5X4hj35pVXv4c+9au8vLn2LfuVKStL83JkycRGRnp+3v9+vW49tprERgY6HvshhtuwM6dO3HixIna2fkGoDL5v/LKK4iKikJKSgpee+01v2lymX/VVCT/qKgotG/fHtOnT8eZM2dgmiYmTpyImJgYdO/eHQCvR0hjf6h2nT17FnfeeSfeffddxMXFlVjO+l93Nm/ejMzMzBLjYeYvq0HcGP/hhx/w/vvvo23btvjqq6/wyCOPYPTo0Zg2bRoA+KZI+WVFK/67eFlubi5iYmL8lrtcLkRGRvrWodKVl3+xhQsXolGjRggKCsKECROQnp6O6OhoAMy/OsrL/8iRIzh9+jReeeUVDBw4EEuXLsUtt9yCoUOHYtWqVQCK8g8MDERERITftn/5GqHSlZd/8SB/7NixOHHiBDweD8aPH48DBw7g0KFDAJh/TZk/fz7y8vJw7733lrr8p59+wgsvvOA3LVNubm6p54biZVRxpeV/5513YsaMGVixYgXGjh2Ljz/+GHfddZdvOfOvOaXlP3v2bHi9XkRFRcHtduOhhx7CvHnz0KZNGwA891bHX/7yF/zud79Dhw4dEBAQgJSUFDzxxBMYPnx4hbfBtr/qKpL/+PHj4XK5MHr06FK3wfpfdZWt/9OmTUPjxo39psll/lXXuHFjpKWl4YUXXsDBgwdhWRZmzJiB9evX49ChQxz71rLy8i/GsW/tKC9/jn1rV3n5c+xbdyrS1l9oz549ePvtt/HQQw/5bYfjscqraP6jR4/GzJkzsWLFCjz00EN46aWX8Oc//9lvO8y/8iqSv6ZpWLZsGTIyMtC4cWMEBQXhH//4B5YsWeL76kPmL4v9odr15JNPomfPnhgyZEipy1n/686UKVPQsWNH9OzZ0/cY85fnkt6BmmDbNlJTU/HSSy8BAFJSUpCVlYUPPvgA99xzj/DeNXwVzf+6665DZmYmfvrpJ0yePBm33XYbNmzYUOIkSJVTXv62bQMAhgwZgieffBIAcMUVV2DdunX44IMP0KdPH7F9bwjKyz8gIABz587FyJEjERkZCcMw0L9/fwwaNAhKKeG9b1imTJmCQYMGlfo9svn5+Rg8eDA6deqE5557ru53zgFKy/+Xb0JITk5Gs2bN0K9fP2RnZyMpKUliNxus0vJ/5plnkJeXh2XLliE6Ohrz58/HbbfdhjVr1iA5OVlwby99s2fPxieffIJPP/0Ul19+OTIzM/HEE0+gefPm7HvWgfLy37x5M95880385z//KXMGEaq6ytb/Dz/8EMOHDy/xCUOquo8//hj3338/WrRoAcMw0K1bN9xxxx3YvHmz9K45QkXy59i39lwsf459a9/F8ufYt/768ccfMXDgQAwbNgwPPvig9O44xh//+Eff7126dEFgYCAeeughvPzyy3C73YJ71vAppTBq1CjExMRgzZo1CA4Oxj//+U/cdNNN2LhxI5o1aya9i0S15vPPP8fy5cuRkZEhvSuOd+7cOXz66ad45plnpHeFLtAgPjHerFkzdOrUye+xjh07Yv/+/QDgmy7i8OHDfuscPnzYtywuLg5HjhzxW26aJo4fP17qdBP0s/LyLxYaGoo2bdrg6quvxpQpU+ByuTBlyhQAzL86yss/OjoaLper3NeIx+NBXl6e3zq/fI1Q6SpS/7t3747MzEzk5eXh0KFDWLJkCY4dO4bWrVsDYP41Yd++fVi2bBkeeOCBEstOnTqFgQMHonHjxpg3bx4CAgJ8y+Li4ko9NxQvo4q5WP6/1KNHDwBFn1YAmH9NKS3/7OxsvPPOO/jwww/Rr18/dO3aFePGjUNqaireffddADz3VseYMWN8n5pNTk7GiBEj8OSTT+Lll1+u8DbY9lddefmvWbMGR44cQXx8PFwuF1wuF/bt24c//elPSExMBMD6Xx2Vqf9r1qzBzp07S5wfmH/1JCUlYdWqVTh9+jT++9//4ttvv4XX60Xr1q059q0DF8u/GMe+tedi+XPsW/vKq/8c+9aNirT1xQ4ePIjrrrsOPXv2xKRJk0psh+OxyqtM/r/Uo0cPmKaJnJwc33aYf+VVJP/ly5dj4cKFmDlzJnr16oVu3brhvffeQ3BwsG+GR+Yvi/2h2rN8+XJkZ2cjIiLCNx4GgFtvvdX31Xqs/3Vjzpw5OHv2LO6++26/x5m/vAZxY7xXr17YuXOn32O7du1CQkICAKBVq1aIi4vD119/7Vuen5+PDRs2IC0tDQCQlpaGvLw8v3d5L1++HLZt+y7kU+nKy78stm37voOK+VddefkHBgbiyiuvvOg63bt3R0BAgN9rZOfOndi/f7/vNUKlq0z9Dw8PR9OmTbF7925s2rTJN50N86++qVOnIiYmBoMHD/Z7PD8/HwMGDEBgYCA+//zzEp9WS0tLw9atW/064+np6QgLCytxQY3KVlb+F8rMzAQA37uzmX/NKC3/4u9y13X/rp5hGL5PU/HcW3Vnz569aLYVwba/6srLf8SIEdiyZQsyMzN9P82bN8eYMWPw1VdfAWD9r47K1P8pU6age/fuvu9WLsb8a0ZoaCiaNWuGEydO4KuvvsKQIUM49q1DpeVfFo59a15p+XPsW3fKq/8c+9auirT1QNEnxfv27Yvu3btj6tSpJc7faWlpWL16Nbxer++x9PR0tG/f3jfdNJVU0fwvlJmZCV3XfbOHMP+qqUj+ZY2HdV33Gw/zeoQc9odqz1/+8pcS42EAmDBhAqZOnQqA9b+uTJkyBTfffDOaNm3q9zjzrwdUA/Dtt98ql8ulXnzxRbV79271ySefqJCQEDVjxgzfOq+88oqKiIhQCxYsUFu2bFFDhgxRrVq1UufOnfOtM3DgQJWSkqI2bNig1q5dq9q2bavuuOMOiUO6pJSX/+nTp9XYsWPV+vXrVU5Ojtq0aZO67777lNvtVllZWb7tMP+qqUj9nzt3rgoICFCTJk1Su3fvVm+//bYyDEOtWbPGt87DDz+s4uPj1fLly9WmTZtUWlqaSktLkzikS0pF8p89e7ZasWKFys7OVvPnz1cJCQlq6NChftth/lVnWZaKj49XTz31lN/jJ0+eVD169FDJyclqz5496tChQ74f0zSVUkqZpqk6d+6sBgwYoDIzM9WSJUtU06ZN1dixYyUO5ZJUVv579uxRzz//vNq0aZPau3evWrBggWrdurW69tprfesw/+orK3+Px6PatGmjrrnmGrVhwwa1Z88e9fe//11pmqYWLVrkW4/n3qq55557VIsWLdTChQvV3r171dy5c1V0dLT685//7Fvn2LFjKiMjQy1atEgBUDNnzlQZGRnq0KFDvnXY9ldNRfK/UEJCgpowYYLfY6z/VVPR/E+ePKlCQkLU+++/X+p2mH/VLVmyRC1evFj98MMPaunSpapr166qR48eyuPxKKU49q1tF8ufY9/aV17959i3dpWXP8e+NefUqVMqIyNDZWRkKADqH//4h8rIyFD79u1TSpXf1h84cEC1adNG9evXTx04cMBvPFwsLy9PxcbGqhEjRqisrCw1c+ZMFRISoiZOnChyzPVJdfNft26dmjBhgsrMzFTZ2dlqxowZqmnTpuruu+/2lcH8y1bd/I8ePaqioqLU0KFDVWZmptq5c6f6n//5HxUQEKAyMzOVUrwecTHl5V+Rse6hQ4dURkaGmjx5sgKgVq9erTIyMtSxY8d867A/VLqayP9CANS8efN8f7P+l62m8t+9e7fSNE0tXry4RBnMX16DuDGulFJffPGF6ty5s3K73apDhw5q0qRJfstt21bPPPOMio2NVW63W/Xr10/t3LnTb51jx46pO+64QzVq1EiFhYWp++67T506daouD+OSdbH8z507p2655RbVvHlzFRgYqJo1a6Zuvvlm9e233/ptg/lXXXn1XymlpkyZotq0aaOCgoJU165d1fz58/2Wnzt3Tv3hD39QTZo0USEhIeqWW2656AmVflZe/m+++aa67LLLVEBAgIqPj1d/+9vfVGFhod86zL/qvvrqKwWgRJu+YsUKBaDUn7179/rWy8nJUYMGDVLBwcEqOjpa/elPf1Jer7eOj+LSVVb++/fvV9dee62KjIxUbrdbtWnTRo0ZM0adPHnSbz3mXz1l5a+UUrt27VJDhw5VMTExKiQkRHXp0kVNnz7dbx2ee6smPz9fPf744yo+Pl4FBQWp1q1bq6efftqvbZ86dWqp7c+4ceN867Dtr5qK5H+h0m6Ms/5XTUXznzhxogoODlZ5eXmlbof5V92sWbNU69atVWBgoIqLi1OjRo3yy5lj39p1sfw59q195dV/pTj2rU3l5c+xb80pazx7zz33KKXKb+vL6ote+Bmp7777TvXu3Vu53W7VokUL9corr9TlYdZb1c1/8+bNqkePHio8PFwFBQWpjh07qpdeekkVFBT4lcP8S1fd/JVSauPGjWrAgAEqMjJSNW7cWF199dXqyy+/9FuH1yNKV17+FRnrjhs3rtR1pk6d6luH/aHS1UT+F7rwxrhSrP9lqan8x44dq1q2bKksyyq1HOYvS1NKqUp+yJyIiIiIiIiIiIiIiIiIiOiS0SC+Y5yIiIiIiIiIiIiIiIiIiKgsvDFOREREREREREREREREREQNGm+MExERERERERERERERERFRg8Yb40RERERERERERERERERE1KDxxjgRERERERERERERERERETVovDFOREREREREREREREREREQNGm+MExERERERERERERERERFRg8Yb40RERERERERERERERERE1KDxxjgRERERERFRHVq5ciU0TYOmabj33nuld6eEnJwc3/4V/0RERFR6O4mJiSW2k5OTU+P7S0RERERERFQRvDFOREREREREVA2l3QAu62flypXSu0tERERERETkSC7pHSAiIiIiIiJykpSUFKxZswYAEBsbK7w3Fzdo0CD89a9/hctV+csHc+bMQUFBAV566SUsXry4FvaOiIiIiIiIqOJ4Y5yIiIiIiIioGopvABcbNmwYcnNzAQBvvfUWUlJSfMuSk5MRHh6O3r171/l+VkVMTEyV9zU1NdW3DSIiIiIiIiJpnEqdiIiIiIiIqBpSU1PRu3dv34/b7fYtS05O9lsWHh5e5neM33vvvb7HFy9ejNGjRyMqKgqRkZF49NFHUVhYiP379+Pmm29Go0aNEBcXh7/97W+wbdtvf5RSmDp1Knr16oWwsDAEBweja9euePPNN0usWxXnzp3DmDFj0LZtW7jdboSGhqJVq1YYOnQo5s2bV+3tExEREREREdUGfmKciIiIiIiIqJ557LHHkJ2d7fv73XffRX5+PtauXYu9e/cCAM6cOYMXX3wRiYmJeOCBB3zr3nvvvZg+fbrf9rZs2YInnngC69evx8yZM6u1b48++ig+/PBD398ejwc5OTnIyclBSEgIbrnllmptn4iIiIiIiKg28BPjRERERERERPVMbm4uJk2ahH/+85/Q9aKh+8cff4xz585h5syZeO6553zrTpw40ff7nDlzfDfF27dvj3/961/44osvcPXVVwMAZs2ahVmzZlVr3xYsWAAASEhIwJw5c7B06VJMmTIFd999N5o0aVKtbRMRERERERHVFn5inIiIiIiIiKieefzxx/Hggw8CACZMmIBt27YBAF588UXcfvvtUErh9ddfx6lTp7Bnzx7f/5sxY4bv91GjRuGyyy4DAIwcORLffPONb53bb7+9yvsWEBAAAIiIiEBSUhI6duwIt9uN+++/v8rbJCIiIiIiIqptvDFOREREREREVM9cddVVvt8jIyN9v6empgIANE1DZGQkTp06hby8PN/yXbt2+X4fPXp0qdvesWNHtfZt5MiRePHFF/Hdd98hJSUFhmGgXbt2GDhwIMaMGYNmzZpVa/tEREREREREtYE3xomIiIiIiIjqmfDwcN/vxVOpA0BYWFi1t33mzJlq/f8XXngBnTt3xty5c7FlyxZkZ2djx44d2LFjB9LT05GRkQGXi5cbiIiIiIiIqH7hd4wTERERERERNRDt2rXz/b5ixQoopUr8ZGdnV7uc3/3ud5g9eza+//57nDp1Cr/97W8BAFlZWX6fWiciIiIiIiKqL/gWbiIiIiIiIqIGYvjw4ViwYAEAYMSIEXj66afRtm1bHD16FLt378aiRYswaNAgjBs3rspl9OrVCykpKbjqqqvQokULnDp1Ctu3b/ctLywsrPZxEBEREREREdU03hgnIiIiIiIiaiCGDRuGu+++G9OnT8eBAwfwyCOPlFhn4MCB1SrjyJEjeO+99/Dee++VWNapUyd06dKlWtsnIiIiIiIiqg2cSp2IiIiIiIioAZk2bRqmT5+OPn36IDw8HIGBgYiPj0e/fv3w1ltv4Q9/+EO1tj927FgMGTIECQkJCAkJQUBAABITE/Hwww9j+fLlMAyjho6EiIiIiIiIqOZoSiklvRNEREREREREVD/k5OSgVatWfo+Fh4cjLy+vUttJTEzEvn37/B7bu3cvEhMTq7mHRERERERERJXHT4wTEREREREREREREREREVGDxu8YJyIiIiIiIiKfZs2aYc2aNX6PuVyVv3wwZ84cFBQUlNg2ERERERERkQROpU5ERERERERERERERERERA0ap1InIiIiIiIiIiIiIiIiIqIGjTfGiYiIiIiIiIiIiIiIiIioQeONcSIiIiIiIiIiIiIiIiIiatB4Y5yIiIiIiIiIiIiIiIiIiBo03hgnIiIiIiIiIiIiIiIiIqIGjTfGiYiIiIiIiIiIiIiIiIioQeONcSIiIiIiIiIiIiIiIiIiatB4Y5yIiIiIiIiIiIiIiIiIiBo03hgnIiIiIiIiIiIiIiIiIqIGjTfGiYiIiIiIiIiIiIiIiIioQft/rg/R2fNB5pgAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 2000x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "samples = (10, 20)\n",
        "\n",
        "fig = plot_timeline(samples, t_idx, y_softmax, title_prefix=f\"{cfg.model.name} | {user} {session}\")\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3a3a7841-3c29-4265-8dbf-c94681bf6112",
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      "source": [
        "## [5] Submission"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f001c120-584d-418f-a2d3-81ddbd31b2cb",
      "metadata": {
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      "source": [
        "`optk.codalab.construct_submission_dict()` can be used to make submission files to codalab.\n",
        "The parameter is almost the same as `optk.codalab.eval_operation_segmentation_wrapper()` (used for evaluation), but groundtruth field (`t_idx`) is exclided."
      ]
    },
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      "source": [
        "### 5-1: Get prediction for test data."
      ]
    },
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          "text": [
            "INFO:openpack_torch.data.datamodule:dataset[train]: None\n",
            "2024-02-04 06:59:48 | INFO | openpack_torch.data.datamodule | dataset[train]: None\n",
            "INFO:openpack_torch.data.datamodule:dataset[val]: None\n",
            "2024-02-04 06:59:48 | INFO | openpack_torch.data.datamodule | dataset[val]: None\n",
            "INFO:openpack_torch.data.datamodule:dataset[test]: None\n",
            "2024-02-04 06:59:48 | INFO | openpack_torch.data.datamodule | dataset[test]: None\n",
            "INFO:openpack_torch.data.datamodule:dataset[submission]: {'U0102-S0500': <openpack_torch.data.datasets.OpenPackImu object at 0x7c5bf15881f0>}\n",
            "2024-02-04 06:59:48 | INFO | openpack_torch.data.datamodule | dataset[submission]: {'U0102-S0500': <openpack_torch.data.datasets.OpenPackImu object at 0x7c5bf15881f0>}\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 3 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "INFO:__main__:test on U0102-S0500\n",
            "2024-02-04 06:59:48 | INFO | __main__ | test on U0102-S0500\n",
            "INFO:openpack_torch.data.datamodule:dataset[train]: None\n",
            "2024-02-04 06:59:48 | INFO | openpack_torch.data.datamodule | dataset[train]: None\n",
            "INFO:openpack_torch.data.datamodule:dataset[val]: None\n",
            "2024-02-04 06:59:48 | INFO | openpack_torch.data.datamodule | dataset[val]: None\n",
            "INFO:openpack_torch.data.datamodule:dataset[test]: {'U0102-S0500': <openpack_torch.data.datasets.OpenPackImu object at 0x7c5bf146ed70>}\n",
            "2024-02-04 06:59:48 | INFO | openpack_torch.data.datamodule | dataset[test]: {'U0102-S0500': <openpack_torch.data.datasets.OpenPackImu object at 0x7c5bf146ed70>}\n",
            "INFO:openpack_torch.data.datamodule:dataset[submission]: None\n",
            "2024-02-04 06:59:48 | INFO | openpack_torch.data.datamodule | dataset[submission]: None\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "2024-02-04 06:59:48 | INFO | pytorch_lightning.accelerators.cuda | LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 3 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
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              "Testing: |          | 0/? [00:00<?, ?it/s]"
            ]
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          "text": [
            "INFO:__main__:save t[shape=(93, 1800)] to /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0/pred/last/U0102-S0500/t.npy\n",
            "2024-02-04 06:59:49 | INFO | __main__ | save t[shape=(93, 1800)] to /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0/pred/last/U0102-S0500/t.npy\n",
            "INFO:__main__:save y[shape=(93, 11, 1800)] to /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0/pred/last/U0102-S0500/y.npy\n",
            "2024-02-04 06:59:49 | INFO | __main__ | save y[shape=(93, 11, 1800)] to /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0/pred/last/U0102-S0500/y.npy\n",
            "INFO:__main__:save unixtime[shape=(93, 1800)] to /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0/pred/last/U0102-S0500/unixtime.npy\n",
            "2024-02-04 06:59:49 | INFO | __main__ | save unixtime[shape=(93, 1800)] to /content/data/log/benchmark1/I01-EXP1-V1-nb/openpack-operations-1hz-annotation/debug-split/atr02-iot/UNet/adam-exp/LR0.001/RUN0/pred/last/U0102-S0500/unixtime.npy\n"
          ]
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      ],
      "source": [
        "cfg.mode = \"submission\"\n",
        "datamodule.setup(\"submission\")\n",
        "dataloaders = datamodule.submission_dataloader()\n",
        "split = cfg.dataset.split.submission\n",
        "\n",
        "outputs = dict()\n",
        "for i, dataloader in enumerate(dataloaders):\n",
        "    user, session = split[i]\n",
        "    logger.info(f\"test on {user}-{session}\")\n",
        "\n",
        "    trainer.test(plmodel, dataloader)\n",
        "\n",
        "    # save model outputs\n",
        "    pred_dir = Path(\n",
        "        cfg.path.logdir.predict.format(user=user, session=session)\n",
        "    )\n",
        "    pred_dir.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "    for key, arr in plmodel.test_results.items():\n",
        "        path = Path(pred_dir, f\"{key}.npy\")\n",
        "        np.save(path, arr)\n",
        "        logger.info(f\"save {key}[shape={arr.shape}] to {path}\")\n",
        "\n",
        "    outputs[f\"{user}-{session}\"] = {\n",
        "        \"y\": plmodel.test_results.get(\"y\"),\n",
        "        \"unixtime\": plmodel.test_results.get(\"unixtime\"),\n",
        "    }\n",
        "    plmodel.clear_test_outputs()"
      ]
    },
    {
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      "metadata": {
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      },
      "source": [
        "### 5-2: Make submission zip file\n",
        "\n",
        "In the `construct_submission_dict()`, following processing will be done.\n",
        "\n",
        "- Convert a predcition tesor `y` (3D array of logits) into a sequence of predicted class ids.\n",
        "- Resample sequence into 1 Hz.\n",
        "- Exclude the segments that is out of range for the evaluation protocol (i.e., unlabeled or null segments).\n",
        "\n",
        "In the `make_submission_zipfile()`, following processing will be done.\n",
        "\n",
        "- extract `prediction` and `unixtime` field\n",
        "- generate `submission.json` and `submission.zip` to `output_dir`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0e13c946-8bd0-424b-8343-955481ec7dba",
      "metadata": {
        "id": "0e13c946-8bd0-424b-8343-955481ec7dba"
      },
      "outputs": [],
      "source": [
        "output_dir = str(cfg.path.logdir.rootdir)\n",
        "\n",
        "submission_dict = construct_submission_dict(outputs, ActSet(OPENPACK_OPERATIONS))\n",
        "make_submission_zipfile(submission_dict, output_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "55666a84-fd2b-4547-af30-8f3e04ff8977",
      "metadata": {
        "id": "55666a84-fd2b-4547-af30-8f3e04ff8977"
      },
      "source": [
        "### 5-3: Submit to codalab and get results!"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "15fb7d89-9b10-4478-835b-2d96ce98ced5",
      "metadata": {
        "id": "15fb7d89-9b10-4478-835b-2d96ce98ced5"
      },
      "source": [
        "Now, you got a submission zip file (`./submission.zip`).\n",
        "Let's upload it in the competition site (codalab) and get your score!\n",
        "\n",
        "NOTE: If you are useing `optk.configs.datasets.splits.DEBUG_SPLIT` (default of this notebook), please use a submission form for \"Development Phase\"."
      ]
    },
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