08: AutoGluon_rapids_ml_titanicdata_colab.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "08: AutoGluon_rapids_ml_titanicdata_colab.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/AutoML/blob/main/08%3A%20AutoGluon_rapids_ml_titanicdata_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "scfLT2i0MLyD"
      },
      "source": [
        "# Environment Sanity Check #\n",
        "\n",
        "Click the _Runtime_ dropdown at the top of the page, then _Change Runtime Type_ and confirm the instance type is _GPU_.\n",
        "\n",
        "Check the output of `!nvidia-smi` to make sure you've been allocated a Tesla T4, P4, or P100."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QfMPUqDvsnNM"
      },
      "source": [
        "Video walkthrough the code : https://www.youtube.com/watch?v=BgANq4MGtr8"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0C8IV5TQnjN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "133e6869-441f-49de-ec0b-3bc76121e005"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Wed Jun 23 06:38:21 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.27       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   45C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CtNdk7PSafKP"
      },
      "source": [
        "#Setup:\n",
        "Set up script installs\n",
        "1. Updates gcc in Colab\n",
        "1. Installs Conda\n",
        "1. Install RAPIDS' current stable version of its libraries, as well as some external libraries including:\n",
        "  1. cuDF\n",
        "  1. cuML\n",
        "  1. cuGraph\n",
        "  1. cuSpatial\n",
        "  1. cuSignal\n",
        "  1. BlazingSQL\n",
        "  1. xgboost\n",
        "1. Copy RAPIDS .so files into current working directory, a neccessary workaround for RAPIDS+Colab integration.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Jeh6EJBaBkv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0e055a0-d035-41d3-943b-e1768b914bfb"
      },
      "source": [
        "# This get the RAPIDS-Colab install files and test check your GPU.  Run this and the next cell only.\n",
        "# Please read the output of this cell.  If your Colab Instance is not RAPIDS compatible, it will warn you and give you remediation steps.\n",
        "!git clone https://github.com/rapidsai/rapidsai-csp-utils.git\n",
        "!python rapidsai-csp-utils/colab/env-check.py"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'rapidsai-csp-utils'...\n",
            "remote: Enumerating objects: 272, done.\u001b[K\n",
            "remote: Counting objects: 100% (101/101), done.\u001b[K\n",
            "remote: Compressing objects: 100% (82/82), done.\u001b[K\n",
            "remote: Total 272 (delta 56), reused 38 (delta 19), pack-reused 171\u001b[K\n",
            "Receiving objects: 100% (272/272), 79.66 KiB | 8.85 MiB/s, done.\n",
            "Resolving deltas: 100% (118/118), done.\n",
            "***********************************************************************\n",
            "Woo! Your instance has the right kind of GPU, a Tesla T4!\n",
            "***********************************************************************\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JI7UTXbhaBon",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8706f3a-c2f5-47f9-a7f4-32c62ba36726"
      },
      "source": [
        "# This will update the Colab environment and restart the kernel.  Don't run the next cell until you see the session crash.\n",
        "!bash rapidsai-csp-utils/colab/update_gcc.sh\n",
        "import os\n",
        "os._exit(00)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updating your Colab environment.  This will restart your kernel.  Don't Panic!\n",
            "Hit:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease\n",
            "Ign:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
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            "Get:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release [697 B]\n",
            "Hit:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Get:6 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release.gpg [836 B]\n",
            "Get:7 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n",
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            "Ign:11 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages\n",
            "Get:11 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages [599 kB]\n",
            "Get:12 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n",
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            "Fetched 9,989 kB in 4s (2,598 kB/s)\n",
            "Reading package lists... Done\n",
            "Added repo\n",
            "Hit:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease\n",
            "Ign:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:3 http://security.ubuntu.com/ubuntu bionic-security InRelease\n",
            "Ign:4 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release\n",
            "Hit:6 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Hit:8 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease\n",
            "Hit:9 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Hit:11 http://archive.ubuntu.com/ubuntu bionic-updates InRelease\n",
            "Hit:12 http://archive.ubuntu.com/ubuntu bionic-backports InRelease\n",
            "Hit:13 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n",
            "Hit:14 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease\n",
            "Hit:15 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n",
            "Hit:16 http://ppa.launchpad.net/ubuntu-toolchain-r/test/ubuntu bionic InRelease\n",
            "Reading package lists... Done\n",
            "Installing libstdc++\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "Selected version '11.1.0-1ubuntu1~18.04.1' (Toolchain test builds:18.04/bionic [amd64]) for 'libstdc++6'\n",
            "The following additional packages will be installed:\n",
            "  gcc-11-base libgcc-s1\n",
            "The following NEW packages will be installed:\n",
            "  gcc-11-base libgcc-s1\n",
            "The following packages will be upgraded:\n",
            "  libstdc++6\n",
            "1 upgraded, 2 newly installed, 0 to remove and 81 not upgraded.\n",
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            "After this operation, 981 kB of additional disk space will be used.\n",
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            "Get:3 http://ppa.launchpad.net/ubuntu-toolchain-r/test/ubuntu bionic/main amd64 libstdc++6 amd64 11.1.0-1ubuntu1~18.04.1 [580 kB]\n",
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            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package gcc-11-base:amd64.\n",
            "(Reading database ... 160772 files and directories currently installed.)\n",
            "Preparing to unpack .../gcc-11-base_11.1.0-1ubuntu1~18.04.1_amd64.deb ...\n",
            "Unpacking gcc-11-base:amd64 (11.1.0-1ubuntu1~18.04.1) ...\n",
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            "Replacing files in old package libgcc1:amd64 (1:8.4.0-1ubuntu1~18.04) ...\n",
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            "Setting up libstdc++6:amd64 (11.1.0-1ubuntu1~18.04.1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.2) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qg2SasWKaBsB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2cac4ad0-309e-4874-b507-025633f11a43"
      },
      "source": [
        "# This will install CondaColab.  This will restart your kernel one last time.  Run this cell by itself and only run the next cell once you see the session crash.\n",
        "import condacolab\n",
        "condacolab.install()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "⏬ Downloading https://github.com/jaimergp/miniforge/releases/latest/download/Mambaforge-colab-Linux-x86_64.sh...\n",
            "📦 Installing...\n",
            "📌 Adjusting configuration...\n",
            "🩹 Patching environment...\n",
            "⏲ Done in 0:00:35\n",
            "🔁 Restarting kernel...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKSMDrN_aB-v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "946d7902-5a6c-4457-f56a-6a261eecac0e"
      },
      "source": [
        "# you can now run the rest of the cells as normal\n",
        "import condacolab\n",
        "condacolab.check()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "✨🍰✨ Everything looks OK!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0jdXBRiDSzj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "916f726b-5b93-4195-da43-a6aba5827b11"
      },
      "source": [
        "# Installing RAPIDS is now 'python rapidsai-csp-utils/colab/install_rapids.py <release> <packages>'\n",
        "# The <release> options are 'stable' and 'nightly'.  Leaving it blank or adding any other words will default to stable.\n",
        "# The <packages> option are default blank or 'core'.  By default, we install RAPIDSAI and BlazingSQL.  The 'core' option will install only RAPIDSAI and not include BlazingSQL, \n",
        "!python rapidsai-csp-utils/colab/install_rapids.py stable"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing RAPIDS Stable 21.06\n",
            "Starting the RAPIDS+BlazingSQL install on Colab.  This will take about 15 minutes.\n",
            "Collecting package metadata (current_repodata.json): ...working... done\n",
            "Solving environment: ...working... failed with initial frozen solve. Retrying with flexible solve.\n",
            "Solving environment: ...working... failed with repodata from current_repodata.json, will retry with next repodata source.\n",
            "Collecting package metadata (repodata.json): ...working... done\n",
            "Solving environment: ...working... done\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - cudatoolkit=11.0\n",
            "    - gcsfs\n",
            "    - llvmlite\n",
            "    - openssl\n",
            "    - python=3.7\n",
            "    - rapids-blazing=21.06\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    abseil-cpp-20210324.1      |       h9c3ff4c_0        1015 KB  conda-forge\n",
            "    aiohttp-3.7.4.post0        |   py37h5e8e339_0         625 KB  conda-forge\n",
            "    anyio-3.2.0                |   py37h89c1867_0         138 KB  conda-forge\n",
            "    appdirs-1.4.4              |     pyh9f0ad1d_0          13 KB  conda-forge\n",
            "    argon2-cffi-20.1.0         |   py37h5e8e339_2          47 KB  conda-forge\n",
            "    arrow-cpp-1.0.1            |py37haa335b2_40_cuda        21.1 MB  conda-forge\n",
            "    arrow-cpp-proc-3.0.0       |             cuda          24 KB  conda-forge\n",
            "    async-timeout-3.0.1        |          py_1000          11 KB  conda-forge\n",
            "    async_generator-1.10       |             py_0          18 KB  conda-forge\n",
            "    attrs-21.2.0               |     pyhd8ed1ab_0          44 KB  conda-forge\n",
            "    aws-c-cal-0.5.11           |       h95a6274_0          37 KB  conda-forge\n",
            "    aws-c-common-0.6.2         |       h7f98852_0         168 KB  conda-forge\n",
            "    aws-c-event-stream-0.2.7   |      h3541f99_13          47 KB  conda-forge\n",
            "    aws-c-io-0.10.5            |       hfb6a706_0         121 KB  conda-forge\n",
            "    aws-checksums-0.1.11       |       ha31a3da_7          50 KB  conda-forge\n",
            "    aws-sdk-cpp-1.8.186        |       hb4091e7_3         4.6 MB  conda-forge\n",
            "    backcall-0.2.0             |     pyh9f0ad1d_0          13 KB  conda-forge\n",
            "    backports-1.0              |             py_2           4 KB  conda-forge\n",
            "    backports.functools_lru_cache-1.6.4|     pyhd8ed1ab_0           9 KB  conda-forge\n",
            "    blazingsql-21.06.00        |cuda_11.0_py37_g95ff589f8_0       190.2 MB  rapidsai\n",
            "    bleach-3.3.0               |     pyh44b312d_0         111 KB  conda-forge\n",
            "    blinker-1.4                |             py_1          13 KB  conda-forge\n",
            "    bokeh-2.2.3                |   py37h89c1867_0         7.0 MB  conda-forge\n",
            "    boost-1.72.0               |   py37h48f8a5e_1         339 KB  conda-forge\n",
            "    boost-cpp-1.72.0           |       h9d3c048_4        16.3 MB  conda-forge\n",
            "    brotli-1.0.9               |       h9c3ff4c_4         389 KB  conda-forge\n",
            "    ca-certificates-2021.5.30  |       ha878542_0         136 KB  conda-forge\n",
            "    cachetools-4.2.2           |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    cairo-1.16.0               |    h6cf1ce9_1008         1.5 MB  conda-forge\n",
            "    certifi-2021.5.30          |   py37h89c1867_0         141 KB  conda-forge\n",
            "    cfitsio-3.470              |       hb418390_7         1.3 MB  conda-forge\n",
            "    click-7.1.2                |     pyh9f0ad1d_0          64 KB  conda-forge\n",
            "    click-plugins-1.1.1        |             py_0           9 KB  conda-forge\n",
            "    cligj-0.7.2                |     pyhd8ed1ab_0          10 KB  conda-forge\n",
            "    cloudpickle-1.6.0          |             py_0          22 KB  conda-forge\n",
            "    colorcet-2.0.6             |     pyhd8ed1ab_0         1.5 MB  conda-forge\n",
            "    conda-4.10.1               |   py37h89c1867_0         3.1 MB  conda-forge\n",
            "    cudatoolkit-11.0.221       |       h6bb024c_0       953.0 MB  nvidia\n",
            "    cudf-21.06.01              |cuda_11.0_py37_g101fc0fda4_2       108.4 MB  rapidsai\n",
            "    cudf_kafka-21.06.01        |py37_g101fc0fda4_2         1.7 MB  rapidsai\n",
            "    cugraph-21.06.00           | py37_gf9ffd2de_0        65.0 MB  rapidsai\n",
            "    cuml-21.06.02              |cuda11.0_py37_g7dfbf8d9e_0        78.9 MB  rapidsai\n",
            "    cupy-9.0.0                 |   py37h4fdb0f7_0        50.3 MB  conda-forge\n",
            "    curl-7.77.0                |       hea6ffbf_0         149 KB  conda-forge\n",
            "    cusignal-21.06.00          |  py38_ga78207b_0         1.0 MB  rapidsai\n",
            "    cuspatial-21.06.00         |  py37_g37798cd_0        15.2 MB  rapidsai\n",
            "    custreamz-21.06.01         |py37_g101fc0fda4_2          32 KB  rapidsai\n",
            "    cuxfilter-21.06.00         |  py37_g9459467_0         136 KB  rapidsai\n",
            "    cycler-0.10.0              |             py_2           9 KB  conda-forge\n",
            "    cyrus-sasl-2.1.27          |       h230043b_2         224 KB  conda-forge\n",
            "    cytoolz-0.11.0             |   py37h5e8e339_3         403 KB  conda-forge\n",
            "    dask-2021.5.0              |     pyhd8ed1ab_0           4 KB  conda-forge\n",
            "    dask-core-2021.5.0         |     pyhd8ed1ab_0         735 KB  conda-forge\n",
            "    dask-cuda-21.06.00         |           py37_0         110 KB  rapidsai\n",
            "    dask-cudf-21.06.01         |py37_g101fc0fda4_2         103 KB  rapidsai\n",
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            "    ucx-proc-1.0.0             |              gpu           9 KB  rapidsai\n",
            "    ucx-py-0.20.0              |  py37_gcd9efd3_0         294 KB  rapidsai\n",
            "    wcwidth-0.2.5              |     pyh9f0ad1d_2          33 KB  conda-forge\n",
            "    webencodings-0.5.1         |             py_1          12 KB  conda-forge\n",
            "    websocket-client-0.57.0    |   py37h89c1867_4          59 KB  conda-forge\n",
            "    websockets-8.1             |   py37h5e8e339_3          90 KB  conda-forge\n",
            "    widgetsnbextension-3.5.1   |   py37h89c1867_4         1.8 MB  conda-forge\n",
            "    xarray-0.18.2              |     pyhd8ed1ab_0         599 KB  conda-forge\n",
            "    xerces-c-3.2.3             |       h9d8b166_2         1.8 MB  conda-forge\n",
            "    xgboost-1.4.2dev.rapidsai21.06|   cuda11.0py37_0          17 KB  rapidsai\n",
            "    xorg-kbproto-1.0.7         |    h7f98852_1002          27 KB  conda-forge\n",
            "    xorg-libice-1.0.10         |       h7f98852_0          58 KB  conda-forge\n",
            "    xorg-libsm-1.2.3           |    hd9c2040_1000          26 KB  conda-forge\n",
            "    xorg-libx11-1.7.2          |       h7f98852_0         941 KB  conda-forge\n",
            "    xorg-libxau-1.0.9          |       h7f98852_0          13 KB  conda-forge\n",
            "    xorg-libxdmcp-1.1.3        |       h7f98852_0          19 KB  conda-forge\n",
            "    xorg-libxext-1.3.4         |       h7f98852_1          54 KB  conda-forge\n",
            "    xorg-libxrender-0.9.10     |    h7f98852_1003          32 KB  conda-forge\n",
            "    xorg-renderproto-0.11.1    |    h7f98852_1002           9 KB  conda-forge\n",
            "    xorg-xextproto-7.3.0       |    h7f98852_1002          28 KB  conda-forge\n",
            "    xorg-xproto-7.0.31         |    h7f98852_1007          73 KB  conda-forge\n",
            "    yarl-1.6.3                 |   py37h5e8e339_1         141 KB  conda-forge\n",
            "    zeromq-4.3.4               |       h9c3ff4c_0         352 KB  conda-forge\n",
            "    zict-2.0.0                 |             py_0          10 KB  conda-forge\n",
            "    zipp-3.4.1                 |     pyhd8ed1ab_0          11 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        2.67 GB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  abseil-cpp         conda-forge/linux-64::abseil-cpp-20210324.1-h9c3ff4c_0\n",
            "  aiohttp            conda-forge/linux-64::aiohttp-3.7.4.post0-py37h5e8e339_0\n",
            "  anyio              conda-forge/linux-64::anyio-3.2.0-py37h89c1867_0\n",
            "  appdirs            conda-forge/noarch::appdirs-1.4.4-pyh9f0ad1d_0\n",
            "  argon2-cffi        conda-forge/linux-64::argon2-cffi-20.1.0-py37h5e8e339_2\n",
            "  arrow-cpp          conda-forge/linux-64::arrow-cpp-1.0.1-py37haa335b2_40_cuda\n",
            "  arrow-cpp-proc     conda-forge/linux-64::arrow-cpp-proc-3.0.0-cuda\n",
            "  async-timeout      conda-forge/noarch::async-timeout-3.0.1-py_1000\n",
            "  async_generator    conda-forge/noarch::async_generator-1.10-py_0\n",
            "  attrs              conda-forge/noarch::attrs-21.2.0-pyhd8ed1ab_0\n",
            "  aws-c-cal          conda-forge/linux-64::aws-c-cal-0.5.11-h95a6274_0\n",
            "  aws-c-common       conda-forge/linux-64::aws-c-common-0.6.2-h7f98852_0\n",
            "  aws-c-event-stream conda-forge/linux-64::aws-c-event-stream-0.2.7-h3541f99_13\n",
            "  aws-c-io           conda-forge/linux-64::aws-c-io-0.10.5-hfb6a706_0\n",
            "  aws-checksums      conda-forge/linux-64::aws-checksums-0.1.11-ha31a3da_7\n",
            "  aws-sdk-cpp        conda-forge/linux-64::aws-sdk-cpp-1.8.186-hb4091e7_3\n",
            "  backcall           conda-forge/noarch::backcall-0.2.0-pyh9f0ad1d_0\n",
            "  backports          conda-forge/noarch::backports-1.0-py_2\n",
            "  backports.functoo~ conda-forge/noarch::backports.functools_lru_cache-1.6.4-pyhd8ed1ab_0\n",
            "  blazingsql         rapidsai/linux-64::blazingsql-21.06.00-cuda_11.0_py37_g95ff589f8_0\n",
            "  bleach             conda-forge/noarch::bleach-3.3.0-pyh44b312d_0\n",
            "  blinker            conda-forge/noarch::blinker-1.4-py_1\n",
            "  bokeh              conda-forge/linux-64::bokeh-2.2.3-py37h89c1867_0\n",
            "  boost              conda-forge/linux-64::boost-1.72.0-py37h48f8a5e_1\n",
            "  boost-cpp          conda-forge/linux-64::boost-cpp-1.72.0-h9d3c048_4\n",
            "  brotli             conda-forge/linux-64::brotli-1.0.9-h9c3ff4c_4\n",
            "  cachetools         conda-forge/noarch::cachetools-4.2.2-pyhd8ed1ab_0\n",
            "  cairo              conda-forge/linux-64::cairo-1.16.0-h6cf1ce9_1008\n",
            "  cfitsio            conda-forge/linux-64::cfitsio-3.470-hb418390_7\n",
            "  click              conda-forge/noarch::click-7.1.2-pyh9f0ad1d_0\n",
            "  click-plugins      conda-forge/noarch::click-plugins-1.1.1-py_0\n",
            "  cligj              conda-forge/noarch::cligj-0.7.2-pyhd8ed1ab_0\n",
            "  cloudpickle        conda-forge/noarch::cloudpickle-1.6.0-py_0\n",
            "  colorcet           conda-forge/noarch::colorcet-2.0.6-pyhd8ed1ab_0\n",
            "  cudatoolkit        nvidia/linux-64::cudatoolkit-11.0.221-h6bb024c_0\n",
            "  cudf               rapidsai/linux-64::cudf-21.06.01-cuda_11.0_py37_g101fc0fda4_2\n",
            "  cudf_kafka         rapidsai/linux-64::cudf_kafka-21.06.01-py37_g101fc0fda4_2\n",
            "  cugraph            rapidsai/linux-64::cugraph-21.06.00-py37_gf9ffd2de_0\n",
            "  cuml               rapidsai/linux-64::cuml-21.06.02-cuda11.0_py37_g7dfbf8d9e_0\n",
            "  cupy               conda-forge/linux-64::cupy-9.0.0-py37h4fdb0f7_0\n",
            "  curl               conda-forge/linux-64::curl-7.77.0-hea6ffbf_0\n",
            "  cusignal           rapidsai/noarch::cusignal-21.06.00-py38_ga78207b_0\n",
            "  cuspatial          rapidsai/linux-64::cuspatial-21.06.00-py37_g37798cd_0\n",
            "  custreamz          rapidsai/linux-64::custreamz-21.06.01-py37_g101fc0fda4_2\n",
            "  cuxfilter          rapidsai/linux-64::cuxfilter-21.06.00-py37_g9459467_0\n",
            "  cycler             conda-forge/noarch::cycler-0.10.0-py_2\n",
            "  cyrus-sasl         conda-forge/linux-64::cyrus-sasl-2.1.27-h230043b_2\n",
            "  cytoolz            conda-forge/linux-64::cytoolz-0.11.0-py37h5e8e339_3\n",
            "  dask               conda-forge/noarch::dask-2021.5.0-pyhd8ed1ab_0\n",
            "  dask-core          conda-forge/noarch::dask-core-2021.5.0-pyhd8ed1ab_0\n",
            "  dask-cuda          rapidsai/linux-64::dask-cuda-21.06.00-py37_0\n",
            "  dask-cudf          rapidsai/linux-64::dask-cudf-21.06.01-py37_g101fc0fda4_2\n",
            "  datashader         conda-forge/noarch::datashader-0.11.1-pyh9f0ad1d_0\n",
            "  datashape          conda-forge/noarch::datashape-0.5.4-py_1\n",
            "  decorator          conda-forge/noarch::decorator-4.4.2-py_0\n",
            "  defusedxml         conda-forge/noarch::defusedxml-0.7.1-pyhd8ed1ab_0\n",
            "  distributed        conda-forge/linux-64::distributed-2021.5.0-py37h89c1867_0\n",
            "  dlpack             conda-forge/linux-64::dlpack-0.5-h9c3ff4c_0\n",
            "  entrypoints        conda-forge/noarch::entrypoints-0.3-pyhd8ed1ab_1003\n",
            "  expat              conda-forge/linux-64::expat-2.4.1-h9c3ff4c_0\n",
            "  faiss-proc         rapidsai/linux-64::faiss-proc-1.0.0-cuda\n",
            "  fastavro           conda-forge/linux-64::fastavro-1.4.1-py37h5e8e339_0\n",
            "  fastrlock          conda-forge/linux-64::fastrlock-0.6-py37hcd2ae1e_0\n",
            "  fiona              conda-forge/linux-64::fiona-1.8.20-py37ha0cc35a_0\n",
            "  fontconfig         conda-forge/linux-64::fontconfig-2.13.1-hba837de_1005\n",
            "  freetype           conda-forge/linux-64::freetype-2.10.4-h0708190_1\n",
            "  freexl             conda-forge/linux-64::freexl-1.0.6-h7f98852_0\n",
            "  fsspec             conda-forge/noarch::fsspec-2021.6.0-pyhd8ed1ab_0\n",
            "  future             conda-forge/linux-64::future-0.18.2-py37h89c1867_3\n",
            "  gcsfs              conda-forge/noarch::gcsfs-2021.6.0-pyhd8ed1ab_0\n",
            "  gdal               conda-forge/linux-64::gdal-3.2.2-py37hb0e9ad2_0\n",
            "  geopandas          conda-forge/noarch::geopandas-0.9.0-pyhd8ed1ab_1\n",
            "  geopandas-base     conda-forge/noarch::geopandas-base-0.9.0-pyhd8ed1ab_1\n",
            "  geos               conda-forge/linux-64::geos-3.9.1-h9c3ff4c_2\n",
            "  geotiff            conda-forge/linux-64::geotiff-1.6.0-hcf90da6_5\n",
            "  gettext            conda-forge/linux-64::gettext-0.19.8.1-h0b5b191_1005\n",
            "  gflags             conda-forge/linux-64::gflags-2.2.2-he1b5a44_1004\n",
            "  giflib             conda-forge/linux-64::giflib-5.2.1-h36c2ea0_2\n",
            "  glog               conda-forge/linux-64::glog-0.5.0-h48cff8f_0\n",
            "  google-auth        conda-forge/noarch::google-auth-1.30.2-pyh6c4a22f_0\n",
            "  google-auth-oauth~ conda-forge/noarch::google-auth-oauthlib-0.4.4-pyhd8ed1ab_0\n",
            "  google-cloud-cpp   conda-forge/linux-64::google-cloud-cpp-1.28.0-hbd34f9f_0\n",
            "  greenlet           conda-forge/linux-64::greenlet-1.1.0-py37hcd2ae1e_0\n",
            "  grpc-cpp           conda-forge/linux-64::grpc-cpp-1.38.0-h2519f57_0\n",
            "  hdf4               conda-forge/linux-64::hdf4-4.2.15-h10796ff_3\n",
            "  hdf5               conda-forge/linux-64::hdf5-1.10.6-nompi_h6a2412b_1114\n",
            "  heapdict           conda-forge/noarch::heapdict-1.0.1-py_0\n",
            "  importlib-metadata conda-forge/linux-64::importlib-metadata-4.5.0-py37h89c1867_0\n",
            "  ipykernel          conda-forge/linux-64::ipykernel-5.5.5-py37h085eea5_0\n",
            "  ipython            conda-forge/linux-64::ipython-7.24.1-py37h085eea5_0\n",
            "  ipython_genutils   conda-forge/noarch::ipython_genutils-0.2.0-py_1\n",
            "  ipywidgets         conda-forge/noarch::ipywidgets-7.6.3-pyhd3deb0d_0\n",
            "  jedi               conda-forge/linux-64::jedi-0.18.0-py37h89c1867_2\n",
            "  jinja2             conda-forge/noarch::jinja2-3.0.1-pyhd8ed1ab_0\n",
            "  joblib             conda-forge/noarch::joblib-1.0.1-pyhd8ed1ab_0\n",
            "  jpeg               conda-forge/linux-64::jpeg-9d-h36c2ea0_0\n",
            "  jpype1             conda-forge/linux-64::jpype1-1.3.0-py37h2527ec5_0\n",
            "  json-c             conda-forge/linux-64::json-c-0.15-h98cffda_0\n",
            "  jsonschema         conda-forge/noarch::jsonschema-3.2.0-pyhd8ed1ab_3\n",
            "  jupyter-server-pr~ conda-forge/noarch::jupyter-server-proxy-3.0.2-pyhd8ed1ab_0\n",
            "  jupyter_client     conda-forge/noarch::jupyter_client-6.1.12-pyhd8ed1ab_0\n",
            "  jupyter_core       conda-forge/linux-64::jupyter_core-4.7.1-py37h89c1867_0\n",
            "  jupyter_server     conda-forge/noarch::jupyter_server-1.8.0-pyhd8ed1ab_0\n",
            "  jupyterlab_pygmen~ conda-forge/noarch::jupyterlab_pygments-0.1.2-pyh9f0ad1d_0\n",
            "  jupyterlab_widgets conda-forge/noarch::jupyterlab_widgets-1.0.0-pyhd8ed1ab_1\n",
            "  kealib             conda-forge/linux-64::kealib-1.4.14-hcc255d8_2\n",
            "  kiwisolver         conda-forge/linux-64::kiwisolver-1.3.1-py37h2527ec5_1\n",
            "  lcms2              conda-forge/linux-64::lcms2-2.12-hddcbb42_0\n",
            "  libblas            conda-forge/linux-64::libblas-3.9.0-9_openblas\n",
            "  libcblas           conda-forge/linux-64::libcblas-3.9.0-9_openblas\n",
            "  libcrc32c          conda-forge/linux-64::libcrc32c-1.1.1-h9c3ff4c_2\n",
            "  libcudf            rapidsai/linux-64::libcudf-21.06.01-cuda11.0_g101fc0fda4_2\n",
            "  libcudf_kafka      rapidsai/linux-64::libcudf_kafka-21.06.01-g101fc0fda4_2\n",
            "  libcugraph         rapidsai/linux-64::libcugraph-21.06.00-cuda11.0_gf9ffd2de_0\n",
            "  libcuml            rapidsai/linux-64::libcuml-21.06.02-cuda11.0_g7dfbf8d9e_0\n",
            "  libcumlprims       nvidia/linux-64::libcumlprims-21.06.00-cuda11.0_gfda2e6c_0\n",
            "  libcuspatial       rapidsai/linux-64::libcuspatial-21.06.00-cuda11.0_g37798cd_0\n",
            "  libdap4            conda-forge/linux-64::libdap4-3.20.6-hd7c4107_2\n",
            "  libevent           conda-forge/linux-64::libevent-2.1.10-hcdb4288_3\n",
            "  libfaiss           conda-forge/linux-64::libfaiss-1.7.0-cuda110h8045045_8_cuda\n",
            "  libgcrypt          conda-forge/linux-64::libgcrypt-1.9.3-h7f98852_1\n",
            "  libgdal            conda-forge/linux-64::libgdal-3.2.2-h804b7da_0\n",
            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-9.3.0-hff62375_19\n",
            "  libgfortran5       conda-forge/linux-64::libgfortran5-9.3.0-hff62375_19\n",
            "  libglib            conda-forge/linux-64::libglib-2.68.3-h3e27bee_0\n",
            "  libgpg-error       conda-forge/linux-64::libgpg-error-1.42-h9c3ff4c_0\n",
            "  libgsasl           conda-forge/linux-64::libgsasl-1.8.0-2\n",
            "  libhwloc           conda-forge/linux-64::libhwloc-2.3.0-h5e5b7d1_1\n",
            "  libkml             conda-forge/linux-64::libkml-1.3.0-hd79254b_1012\n",
            "  liblapack          conda-forge/linux-64::liblapack-3.9.0-9_openblas\n",
            "  libllvm10          conda-forge/linux-64::libllvm10-10.0.1-he513fc3_3\n",
            "  libnetcdf          conda-forge/linux-64::libnetcdf-4.7.4-nompi_h56d31a8_107\n",
            "  libntlm            conda-forge/linux-64::libntlm-1.4-h7f98852_1002\n",
            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.15-pthreads_h8fe5266_1\n",
            "  libpng             conda-forge/linux-64::libpng-1.6.37-h21135ba_2\n",
            "  libpq              conda-forge/linux-64::libpq-13.3-hd57d9b9_0\n",
            "  libprotobuf        conda-forge/linux-64::libprotobuf-3.16.0-h780b84a_0\n",
            "  librdkafka         conda-forge/linux-64::librdkafka-1.5.3-hc49e61c_1\n",
            "  librmm             rapidsai/linux-64::librmm-21.06.00-cuda11.0_gee432a0_0\n",
            "  librttopo          conda-forge/linux-64::librttopo-1.1.0-h1185371_6\n",
            "  libsodium          conda-forge/linux-64::libsodium-1.0.18-h36c2ea0_1\n",
            "  libspatialindex    conda-forge/linux-64::libspatialindex-1.9.3-h9c3ff4c_3\n",
            "  libspatialite      conda-forge/linux-64::libspatialite-5.0.1-h20cb978_4\n",
            "  libthrift          conda-forge/linux-64::libthrift-0.14.1-he6d91bd_2\n",
            "  libtiff            conda-forge/linux-64::libtiff-4.2.0-hbd63e13_2\n",
            "  libutf8proc        conda-forge/linux-64::libutf8proc-2.6.1-h7f98852_0\n",
            "  libuuid            conda-forge/linux-64::libuuid-2.32.1-h7f98852_1000\n",
            "  libuv              conda-forge/linux-64::libuv-1.41.0-h7f98852_0\n",
            "  libwebp            conda-forge/linux-64::libwebp-1.2.0-h3452ae3_0\n",
            "  libwebp-base       conda-forge/linux-64::libwebp-base-1.2.0-h7f98852_2\n",
            "  libxcb             conda-forge/linux-64::libxcb-1.13-h7f98852_1003\n",
            "  libxgboost         rapidsai/linux-64::libxgboost-1.4.2dev.rapidsai21.06-cuda11.0_0\n",
            "  llvmlite           conda-forge/linux-64::llvmlite-0.36.0-py37h9d7f4d0_0\n",
            "  locket             conda-forge/noarch::locket-0.2.0-py_2\n",
            "  mapclassify        conda-forge/noarch::mapclassify-2.4.2-pyhd8ed1ab_0\n",
            "  markdown           conda-forge/noarch::markdown-3.3.4-pyhd8ed1ab_0\n",
            "  markupsafe         conda-forge/linux-64::markupsafe-2.0.1-py37h5e8e339_0\n",
            "  matplotlib-base    conda-forge/linux-64::matplotlib-base-3.4.2-py37hdd32ed1_0\n",
            "  matplotlib-inline  conda-forge/noarch::matplotlib-inline-0.1.2-pyhd8ed1ab_2\n",
            "  mistune            conda-forge/linux-64::mistune-0.8.4-py37h5e8e339_1003\n",
            "  msgpack-python     conda-forge/linux-64::msgpack-python-1.0.2-py37h2527ec5_1\n",
            "  multidict          conda-forge/linux-64::multidict-5.1.0-py37h5e8e339_1\n",
            "  multipledispatch   conda-forge/noarch::multipledispatch-0.6.0-py_0\n",
            "  munch              conda-forge/noarch::munch-2.5.0-py_0\n",
            "  nbclient           conda-forge/noarch::nbclient-0.5.3-pyhd8ed1ab_0\n",
            "  nbconvert          conda-forge/linux-64::nbconvert-6.0.7-py37h89c1867_3\n",
            "  nbformat           conda-forge/noarch::nbformat-5.1.3-pyhd8ed1ab_0\n",
            "  nccl               conda-forge/linux-64::nccl-2.9.9.1-h96e36e3_0\n",
            "  nest-asyncio       conda-forge/noarch::nest-asyncio-1.5.1-pyhd8ed1ab_0\n",
            "  netifaces          conda-forge/linux-64::netifaces-0.10.9-py37h5e8e339_1003\n",
            "  networkx           conda-forge/noarch::networkx-2.5.1-pyhd8ed1ab_0\n",
            "  nlohmann_json      conda-forge/linux-64::nlohmann_json-3.9.1-h9c3ff4c_1\n",
            "  nodejs             conda-forge/linux-64::nodejs-14.15.4-h92b4a50_1\n",
            "  notebook           conda-forge/noarch::notebook-6.4.0-pyha770c72_0\n",
            "  numba              conda-forge/linux-64::numba-0.53.1-py37hb11d6e1_1\n",
            "  numpy              conda-forge/linux-64::numpy-1.21.0-py37h038b26d_0\n",
            "  nvtx               conda-forge/linux-64::nvtx-0.2.3-py37h5e8e339_0\n",
            "  oauthlib           conda-forge/noarch::oauthlib-3.1.1-pyhd8ed1ab_0\n",
            "  olefile            conda-forge/noarch::olefile-0.46-pyh9f0ad1d_1\n",
            "  openjdk            conda-forge/linux-64::openjdk-8.0.282-h7f98852_0\n",
            "  openjpeg           conda-forge/linux-64::openjpeg-2.4.0-hb52868f_1\n",
            "  orc                conda-forge/linux-64::orc-1.6.7-h89a63ab_2\n",
            "  packaging          conda-forge/noarch::packaging-20.9-pyh44b312d_0\n",
            "  pandas             conda-forge/linux-64::pandas-1.2.5-py37h219a48f_0\n",
            "  pandoc             conda-forge/linux-64::pandoc-2.14.0.2-h7f98852_0\n",
            "  pandocfilters      conda-forge/noarch::pandocfilters-1.4.2-py_1\n",
            "  panel              conda-forge/noarch::panel-0.10.3-pyhd8ed1ab_0\n",
            "  param              conda-forge/noarch::param-1.10.1-pyhd3deb0d_0\n",
            "  parquet-cpp        conda-forge/noarch::parquet-cpp-1.5.1-2\n",
            "  parso              conda-forge/noarch::parso-0.8.2-pyhd8ed1ab_0\n",
            "  partd              conda-forge/noarch::partd-1.2.0-pyhd8ed1ab_0\n",
            "  pcre               conda-forge/linux-64::pcre-8.45-h9c3ff4c_0\n",
            "  pexpect            conda-forge/noarch::pexpect-4.8.0-pyh9f0ad1d_2\n",
            "  pickle5            conda-forge/linux-64::pickle5-0.0.11-py37h5e8e339_0\n",
            "  pickleshare        conda-forge/noarch::pickleshare-0.7.5-py_1003\n",
            "  pillow             conda-forge/linux-64::pillow-8.2.0-py37h4600e1f_1\n",
            "  pixman             conda-forge/linux-64::pixman-0.40.0-h36c2ea0_0\n",
            "  poppler            conda-forge/linux-64::poppler-21.03.0-h93df280_0\n",
            "  poppler-data       conda-forge/noarch::poppler-data-0.4.10-0\n",
            "  postgresql         conda-forge/linux-64::postgresql-13.3-h2510834_0\n",
            "  proj               conda-forge/linux-64::proj-8.0.0-h277dcde_0\n",
            "  prometheus_client  conda-forge/noarch::prometheus_client-0.11.0-pyhd8ed1ab_0\n",
            "  prompt-toolkit     conda-forge/noarch::prompt-toolkit-3.0.19-pyha770c72_0\n",
            "  protobuf           conda-forge/linux-64::protobuf-3.16.0-py37hcd2ae1e_0\n",
            "  psutil             conda-forge/linux-64::psutil-5.8.0-py37h5e8e339_1\n",
            "  pthread-stubs      conda-forge/linux-64::pthread-stubs-0.4-h36c2ea0_1001\n",
            "  ptyprocess         conda-forge/noarch::ptyprocess-0.7.0-pyhd3deb0d_0\n",
            "  py-xgboost         rapidsai/linux-64::py-xgboost-1.4.2dev.rapidsai21.06-cuda11.0py37_0\n",
            "  pyarrow            conda-forge/linux-64::pyarrow-1.0.1-py37hb63ea2f_40_cuda\n",
            "  pyasn1             conda-forge/noarch::pyasn1-0.4.8-py_0\n",
            "  pyasn1-modules     conda-forge/noarch::pyasn1-modules-0.2.7-py_0\n",
            "  pyct               conda-forge/noarch::pyct-0.4.6-py_0\n",
            "  pyct-core          conda-forge/noarch::pyct-core-0.4.6-py_0\n",
            "  pydeck             conda-forge/noarch::pydeck-0.5.0-pyh9f0ad1d_0\n",
            "  pyee               conda-forge/noarch::pyee-7.0.4-pyh9f0ad1d_0\n",
            "  pygments           conda-forge/noarch::pygments-2.9.0-pyhd8ed1ab_0\n",
            "  pyhive             conda-forge/noarch::pyhive-0.6.4-pyhd8ed1ab_0\n",
            "  pyjwt              conda-forge/noarch::pyjwt-2.1.0-pyhd8ed1ab_0\n",
            "  pynvml             conda-forge/noarch::pynvml-11.0.0-pyhd8ed1ab_0\n",
            "  pyparsing          conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0\n",
            "  pyppeteer          conda-forge/noarch::pyppeteer-0.2.2-py_1\n",
            "  pyproj             conda-forge/linux-64::pyproj-3.0.1-py37h2bb2a07_1\n",
            "  pyrsistent         conda-forge/linux-64::pyrsistent-0.17.3-py37h5e8e339_2\n",
            "  python-confluent-~ conda-forge/linux-64::python-confluent-kafka-1.5.0-py37h8f50634_0\n",
            "  python-dateutil    conda-forge/noarch::python-dateutil-2.8.1-py_0\n",
            "  pytz               conda-forge/noarch::pytz-2021.1-pyhd8ed1ab_0\n",
            "  pyu2f              conda-forge/noarch::pyu2f-0.1.5-pyhd8ed1ab_0\n",
            "  pyviz_comms        conda-forge/noarch::pyviz_comms-2.0.2-pyhd8ed1ab_0\n",
            "  pyyaml             conda-forge/linux-64::pyyaml-5.4.1-py37h5e8e339_0\n",
            "  pyzmq              conda-forge/linux-64::pyzmq-22.1.0-py37h336d617_0\n",
            "  rapids             rapidsai/linux-64::rapids-21.06.00-cuda11.0_py37_ge3c8282_427\n",
            "  rapids-blazing     rapidsai/linux-64::rapids-blazing-21.06.00-cuda11.0_py37_ge3c8282_427\n",
            "  rapids-xgboost     rapidsai/linux-64::rapids-xgboost-21.06.00-cuda11.0_py37_ge3c8282_427\n",
            "  re2                conda-forge/linux-64::re2-2021.04.01-h9c3ff4c_0\n",
            "  requests-oauthlib  conda-forge/noarch::requests-oauthlib-1.3.0-pyh9f0ad1d_0\n",
            "  rmm                rapidsai/linux-64::rmm-21.06.00-cuda_11.0_py37_gee432a0_0\n",
            "  rsa                conda-forge/noarch::rsa-4.7.2-pyh44b312d_0\n",
            "  rtree              conda-forge/linux-64::rtree-0.9.7-py37h0b55af0_1\n",
            "  s2n                conda-forge/linux-64::s2n-1.0.10-h9b69904_0\n",
            "  sasl               conda-forge/linux-64::sasl-0.3a1-py37hcd2ae1e_0\n",
            "  scikit-learn       conda-forge/linux-64::scikit-learn-0.24.2-py37h18a542f_0\n",
            "  scipy              conda-forge/linux-64::scipy-1.6.3-py37h29e03ee_0\n",
            "  send2trash         conda-forge/noarch::send2trash-1.7.1-pyhd8ed1ab_0\n",
            "  shapely            conda-forge/linux-64::shapely-1.7.1-py37h2d1e849_5\n",
            "  simpervisor        conda-forge/noarch::simpervisor-0.4-pyhd8ed1ab_0\n",
            "  snappy             conda-forge/linux-64::snappy-1.1.8-he1b5a44_3\n",
            "  sniffio            conda-forge/linux-64::sniffio-1.2.0-py37h89c1867_1\n",
            "  sortedcontainers   conda-forge/noarch::sortedcontainers-2.4.0-pyhd8ed1ab_0\n",
            "  spdlog             conda-forge/linux-64::spdlog-1.8.5-h4bd325d_0\n",
            "  sqlalchemy         conda-forge/linux-64::sqlalchemy-1.4.19-py37h5e8e339_0\n",
            "  streamz            conda-forge/noarch::streamz-0.6.2-pyh44b312d_0\n",
            "  tblib              conda-forge/noarch::tblib-1.7.0-pyhd8ed1ab_0\n",
            "  terminado          conda-forge/linux-64::terminado-0.10.1-py37h89c1867_0\n",
            "  testpath           conda-forge/noarch::testpath-0.5.0-pyhd8ed1ab_0\n",
            "  threadpoolctl      conda-forge/noarch::threadpoolctl-2.1.0-pyh5ca1d4c_0\n",
            "  thrift             conda-forge/linux-64::thrift-0.13.0-py37hcd2ae1e_2\n",
            "  thrift_sasl        conda-forge/linux-64::thrift_sasl-0.4.2-py37h8f50634_0\n",
            "  tiledb             conda-forge/linux-64::tiledb-2.2.9-h91fcb0e_0\n",
            "  toolz              conda-forge/noarch::toolz-0.11.1-py_0\n",
            "  tornado            conda-forge/linux-64::tornado-6.1-py37h5e8e339_1\n",
            "  traitlets          conda-forge/noarch::traitlets-5.0.5-py_0\n",
            "  treelite           conda-forge/linux-64::treelite-1.3.0-py37hfdac9b6_0\n",
            "  typing-extensions  conda-forge/noarch::typing-extensions-3.10.0.0-hd8ed1ab_0\n",
            "  typing_extensions  conda-forge/noarch::typing_extensions-3.10.0.0-pyha770c72_0\n",
            "  tzcode             conda-forge/linux-64::tzcode-2021a-h7f98852_1\n",
            "  tzdata             conda-forge/noarch::tzdata-2021a-he74cb21_0\n",
            "  ucx                rapidsai/linux-64::ucx-1.9.0+gcd9efd3-cuda11.0_0\n",
            "  ucx-proc           rapidsai/linux-64::ucx-proc-1.0.0-gpu\n",
            "  ucx-py             rapidsai/linux-64::ucx-py-0.20.0-py37_gcd9efd3_0\n",
            "  wcwidth            conda-forge/noarch::wcwidth-0.2.5-pyh9f0ad1d_2\n",
            "  webencodings       conda-forge/noarch::webencodings-0.5.1-py_1\n",
            "  websocket-client   conda-forge/linux-64::websocket-client-0.57.0-py37h89c1867_4\n",
            "  websockets         conda-forge/linux-64::websockets-8.1-py37h5e8e339_3\n",
            "  widgetsnbextension conda-forge/linux-64::widgetsnbextension-3.5.1-py37h89c1867_4\n",
            "  xarray             conda-forge/noarch::xarray-0.18.2-pyhd8ed1ab_0\n",
            "  xerces-c           conda-forge/linux-64::xerces-c-3.2.3-h9d8b166_2\n",
            "  xgboost            rapidsai/linux-64::xgboost-1.4.2dev.rapidsai21.06-cuda11.0py37_0\n",
            "  xorg-kbproto       conda-forge/linux-64::xorg-kbproto-1.0.7-h7f98852_1002\n",
            "  xorg-libice        conda-forge/linux-64::xorg-libice-1.0.10-h7f98852_0\n",
            "  xorg-libsm         conda-forge/linux-64::xorg-libsm-1.2.3-hd9c2040_1000\n",
            "  xorg-libx11        conda-forge/linux-64::xorg-libx11-1.7.2-h7f98852_0\n",
            "  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.9-h7f98852_0\n",
            "  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.3-h7f98852_0\n",
            "  xorg-libxext       conda-forge/linux-64::xorg-libxext-1.3.4-h7f98852_1\n",
            "  xorg-libxrender    conda-forge/linux-64::xorg-libxrender-0.9.10-h7f98852_1003\n",
            "  xorg-renderproto   conda-forge/linux-64::xorg-renderproto-0.11.1-h7f98852_1002\n",
            "  xorg-xextproto     conda-forge/linux-64::xorg-xextproto-7.3.0-h7f98852_1002\n",
            "  xorg-xproto        conda-forge/linux-64::xorg-xproto-7.0.31-h7f98852_1007\n",
            "  yarl               conda-forge/linux-64::yarl-1.6.3-py37h5e8e339_1\n",
            "  zeromq             conda-forge/linux-64::zeromq-4.3.4-h9c3ff4c_0\n",
            "  zict               conda-forge/noarch::zict-2.0.0-py_0\n",
            "  zipp               conda-forge/noarch::zipp-3.4.1-pyhd8ed1ab_0\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates                      2020.12.5-ha878542_0 --> 2021.5.30-ha878542_0\n",
            "  certifi                          2020.12.5-py37h89c1867_1 --> 2021.5.30-py37h89c1867_0\n",
            "  conda                                4.9.2-py37h89c1867_0 --> 4.10.1-py37h89c1867_0\n",
            "  krb5                                    1.17.2-h926e7f8_0 --> 1.19.1-hcc1bbae_0\n",
            "  libcurl                                 7.75.0-hc4aaa36_0 --> 7.77.0-h2574ce0_0\n",
            "  libxml2                                 2.9.10-h72842e0_3 --> 2.9.12-h72842e0_0\n",
            "  openssl                                 1.1.1j-h7f98852_0 --> 1.1.1k-h7f98852_0\n",
            "  readline                                   8.0-he28a2e2_2 --> 8.1-h46c0cb4_0\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "\n",
            "olefile-0.46         | 32 KB     |            |   0%\n",
            "olefile-0.46         | 32 KB     | ########## | 100%\n",
            "\n",
            "scikit-learn-0.24.2  | 7.5 MB    |            |   0%\n",
            "scikit-learn-0.24.2  | 7.5 MB    | #######1   |  72%\n",
            "scikit-learn-0.24.2  | 7.5 MB    | ########## | 100%\n",
            "scikit-learn-0.24.2  | 7.5 MB    | ########## | 100%\n",
            "\n",
            "psutil-5.8.0         | 342 KB    |            |   0%\n",
            "psutil-5.8.0         | 342 KB    | ########## | 100%\n",
            "\n",
            "partd-1.2.0          | 18 KB     |            |   0%\n",
            "partd-1.2.0          | 18 KB     | ########## | 100%\n",
            "\n",
            "openjdk-8.0.282      | 99.3 MB   |            |   0%\n",
            "openjdk-8.0.282      | 99.3 MB   | 9          |   9%\n",
            "openjdk-8.0.282      | 99.3 MB   | ##2        |  22%\n",
            "openjdk-8.0.282      | 99.3 MB   | ###5       |  35%\n",
            "openjdk-8.0.282      | 99.3 MB   | ####8      |  48%\n",
            "openjdk-8.0.282      | 99.3 MB   | ######     |  61%\n",
            "openjdk-8.0.282      | 99.3 MB   | #######1   |  72%\n",
            "openjdk-8.0.282      | 99.3 MB   | ########2  |  82%\n",
            "openjdk-8.0.282      | 99.3 MB   | #########3 |  93%\n",
            "openjdk-8.0.282      | 99.3 MB   | ########## | 100%\n",
            "\n",
            "pydeck-0.5.0         | 3.6 MB    |            |   0%\n",
            "pydeck-0.5.0         | 3.6 MB    | ########## | 100%\n",
            "pydeck-0.5.0         | 3.6 MB    | ########## | 100%\n",
            "\n",
            "boost-cpp-1.72.0     | 16.3 MB   |            |   0%\n",
            "boost-cpp-1.72.0     | 16.3 MB   | ####4      |  44%\n",
            "boost-cpp-1.72.0     | 16.3 MB   | ########## | 100%\n",
            "boost-cpp-1.72.0     | 16.3 MB   | ########## | 100%\n",
            "\n",
            "xorg-libx11-1.7.2    | 941 KB    |            |   0%\n",
            "xorg-libx11-1.7.2    | 941 KB    | ########## | 100%\n",
            "xorg-libx11-1.7.2    | 941 KB    | ########## | 100%\n",
            "\n",
            "re2-2021.04.01       | 218 KB    |            |   0%\n",
            "re2-2021.04.01       | 218 KB    | ########## | 100%\n",
            "\n",
            "cudatoolkit-11.0.221 | 953.0 MB  |            |   0%\n",
            "cudatoolkit-11.0.221 | 953.0 MB  | ########## | 100%\n",
            "cudatoolkit-11.0.221 | 953.0 MB  | ########## | 100%\n",
            "\n",
            "libkml-1.3.0         | 640 KB    |            |   0%\n",
            "libkml-1.3.0         | 640 KB    | ########## | 100%\n",
            "libkml-1.3.0         | 640 KB    | ########## | 100%\n",
            "\n",
            "matplotlib-inline-0. | 11 KB     |            |   0%\n",
            "matplotlib-inline-0. | 11 KB     | ########## | 100%\n",
            "\n",
            "s2n-1.0.10           | 442 KB    |            |   0%\n",
            "s2n-1.0.10           | 442 KB    | ########## | 100%\n",
            "s2n-1.0.10           | 442 KB    | ########## | 100%\n",
            "\n",
            "libxml2-2.9.12       | 772 KB    |            |   0%\n",
            "libxml2-2.9.12       | 772 KB    | ########## | 100%\n",
            "libxml2-2.9.12       | 772 KB    | ########## | 100%\n",
            "\n",
            "greenlet-1.1.0       | 83 KB     |            |   0%\n",
            "greenlet-1.1.0       | 83 KB     | ########## | 100%\n",
            "\n",
            "grpc-cpp-1.38.0      | 3.6 MB    |            |   0%\n",
            "grpc-cpp-1.38.0      | 3.6 MB    | ########## | 100%\n",
            "grpc-cpp-1.38.0      | 3.6 MB    | ########## | 100%\n",
            "\n",
            "pickle5-0.0.11       | 173 KB    |            |   0%\n",
            "pickle5-0.0.11       | 173 KB    | ########## | 100%\n",
            "\n",
            "pickleshare-0.7.5    | 9 KB      |            |   0%\n",
            "pickleshare-0.7.5    | 9 KB      | ########## | 100%\n",
            "\n",
            "curl-7.77.0          | 149 KB    |            |   0%\n",
            "curl-7.77.0          | 149 KB    | ########## | 100%\n",
            "\n",
            "thrift-0.13.0        | 120 KB    |            |   0%\n",
            "thrift-0.13.0        | 120 KB    | ########## | 100%\n",
            "\n",
            "xorg-kbproto-1.0.7   | 27 KB     |            |   0%\n",
            "xorg-kbproto-1.0.7   | 27 KB     | ########## | 100%\n",
            "\n",
            "aiohttp-3.7.4.post0  | 625 KB    |            |   0%\n",
            "aiohttp-3.7.4.post0  | 625 KB    | ########## | 100%\n",
            "aiohttp-3.7.4.post0  | 625 KB    | ########## | 100%\n",
            "\n",
            "jupyter-server-proxy | 27 KB     |            |   0%\n",
            "jupyter-server-proxy | 27 KB     | ########## | 100%\n",
            "\n",
            "libgcrypt-1.9.3      | 677 KB    |            |   0%\n",
            "libgcrypt-1.9.3      | 677 KB    | ########## | 100%\n",
            "libgcrypt-1.9.3      | 677 KB    | ########## | 100%\n",
            "\n",
            "shapely-1.7.1        | 438 KB    |            |   0%\n",
            "shapely-1.7.1        | 438 KB    | ########## | 100%\n",
            "shapely-1.7.1        | 438 KB    | ########## | 100%\n",
            "\n",
            "rapids-xgboost-21.06 | 4 KB      |            |   0%\n",
            "rapids-xgboost-21.06 | 4 KB      | ########## | 100%\n",
            "rapids-xgboost-21.06 | 4 KB      | ########## | 100%\n",
            "\n",
            "lcms2-2.12           | 443 KB    |            |   0%\n",
            "lcms2-2.12           | 443 KB    | ########## | 100%\n",
            "\n",
            "libdap4-3.20.6       | 11.3 MB   |            |   0%\n",
            "libdap4-3.20.6       | 11.3 MB   | ##4        |  25%\n",
            "libdap4-3.20.6       | 11.3 MB   | ########## | 100%\n",
            "libdap4-3.20.6       | 11.3 MB   | ########## | 100%\n",
            "\n",
            "llvmlite-0.36.0      | 2.7 MB    |            |   0%\n",
            "llvmlite-0.36.0      | 2.7 MB    | ########## | 100%\n",
            "llvmlite-0.36.0      | 2.7 MB    | ########## | 100%\n",
            "\n",
            "geos-3.9.1           | 1.1 MB    |            |   0%\n",
            "geos-3.9.1           | 1.1 MB    | ########## | 100%\n",
            "geos-3.9.1           | 1.1 MB    | ########## | 100%\n",
            "\n",
            "openssl-1.1.1k       | 2.1 MB    |            |   0%\n",
            "openssl-1.1.1k       | 2.1 MB    | ########## | 100%\n",
            "openssl-1.1.1k       | 2.1 MB    | ########## | 100%\n",
            "\n",
            "dask-2021.5.0        | 4 KB      |            |   0%\n",
            "dask-2021.5.0        | 4 KB      | ########## | 100%\n",
            "\n",
            "blinker-1.4          | 13 KB     |            |   0%\n",
            "blinker-1.4          | 13 KB     | ########## | 100%\n",
            "\n",
            "freexl-1.0.6         | 48 KB     |            |   0%\n",
            "freexl-1.0.6         | 48 KB     | ########## | 100%\n",
            "\n",
            "brotli-1.0.9         | 389 KB    |            |   0%\n",
            "brotli-1.0.9         | 389 KB    | ########## | 100%\n",
            "\n",
            "aws-checksums-0.1.11 | 50 KB     |            |   0%\n",
            "aws-checksums-0.1.11 | 50 KB     | ########## | 100%\n",
            "\n",
            "pyasn1-0.4.8         | 53 KB     |            |   0%\n",
            "pyasn1-0.4.8         | 53 KB     | ########## | 100%\n",
            "\n",
            "sniffio-1.2.0        | 15 KB     |            |   0%\n",
            "sniffio-1.2.0        | 15 KB     | ########## | 100%\n",
            "\n",
            "xgboost-1.4.2dev.rap | 17 KB     |            |   0%\n",
            "xgboost-1.4.2dev.rap | 17 KB     | #########5 |  96%\n",
            "xgboost-1.4.2dev.rap | 17 KB     | ########## | 100%\n",
            "\n",
            "cuml-21.06.02        | 78.9 MB   |            |   0%\n",
            "cuml-21.06.02        | 78.9 MB   |            |   0%\n",
            "cuml-21.06.02        | 78.9 MB   |            |   0%\n",
            "cuml-21.06.02        | 78.9 MB   |            |   1%\n",
            "cuml-21.06.02        | 78.9 MB   | 2          |   2%\n",
            "cuml-21.06.02        | 78.9 MB   | 7          |   7%\n",
            "cuml-21.06.02        | 78.9 MB   | #2         |  13%\n",
            "cuml-21.06.02        | 78.9 MB   | #8         |  19%\n",
            "cuml-21.06.02        | 78.9 MB   | ##4        |  24%\n",
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            "\n",
            "pyarrow-1.0.1        | 2.4 MB    |            |   0%\n",
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            "\n",
            "requests-oauthlib-1. | 21 KB     |            |   0%\n",
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            "\n",
            "libthrift-0.14.1     | 4.5 MB    |            |   0%\n",
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            "\n",
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            "\n",
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            "openjpeg-2.4.0       | 444 KB    |            |   0%\n",
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            "\n",
            "networkx-2.5.1       | 1.2 MB    |            |   0%\n",
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            "\n",
            "poppler-21.03.0      | 15.9 MB   |            |   0%\n",
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            "\n",
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            "\n",
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            "ucx-1.9.0+gcd9efd3   | 8.2 MB    | ######2    |  63%\n",
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            "\n",
            "libcblas-3.9.0       | 11 KB     |            |   0%\n",
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            "\n",
            "typing_extensions-3. | 28 KB     |            |   0%\n",
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            "\n",
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            "\n",
            "rmm-21.06.00         | 7.0 MB    |            |   0%\n",
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            "\n",
            "pyct-0.4.6           | 3 KB      |            |   0%\n",
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            "\n",
            "panel-0.10.3         | 6.1 MB    |            |   0%\n",
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            "\n",
            "libcudf_kafka-21.06. | 125 KB    |            |   0%\n",
            "libcudf_kafka-21.06. | 125 KB    | #2         |  13%\n",
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            "\n",
            "fsspec-2021.6.0      | 79 KB     |            |   0%\n",
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            "\n",
            "mapclassify-2.4.2    | 36 KB     |            |   0%\n",
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            "\n",
            "faiss-proc-1.0.0     | 24 KB     |            |   0%\n",
            "faiss-proc-1.0.0     | 24 KB     | ######7    |  68%\n",
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            "\n",
            "attrs-21.2.0         | 44 KB     |            |   0%\n",
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            "\n",
            "libxgboost-1.4.2dev. | 115.3 MB  |            |   0%\n",
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            "\n",
            "async-timeout-3.0.1  | 11 KB     |            |   0%\n",
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            "\n",
            "dask-cudf-21.06.01   | 103 KB    |            |   0%\n",
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            "\n",
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            "future-0.18.2        | 714 KB    |            |   0%\n",
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            "future-0.18.2        | 714 KB    | ########## | 100%\n",
            "\n",
            "numba-0.53.1         | 3.7 MB    |            |   0%\n",
            "numba-0.53.1         | 3.7 MB    | ########## | 100%\n",
            "numba-0.53.1         | 3.7 MB    | ########## | 100%\n",
            "\n",
            "libtiff-4.2.0        | 639 KB    |            |   0%\n",
            "libtiff-4.2.0        | 639 KB    | ########## | 100%\n",
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            "\n",
            "protobuf-3.16.0      | 342 KB    |            |   0%\n",
            "protobuf-3.16.0      | 342 KB    | ########## | 100%\n",
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            "\n",
            "ucx-proc-1.0.0       | 9 KB      |            |   0%\n",
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            "\n",
            "pynvml-11.0.0        | 39 KB     |            |   0%\n",
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            "\n",
            "ptyprocess-0.7.0     | 16 KB     |            |   0%\n",
            "ptyprocess-0.7.0     | 16 KB     | ########## | 100%\n",
            "\n",
            "json-c-0.15          | 274 KB    |            |   0%\n",
            "json-c-0.15          | 274 KB    | ########## | 100%\n",
            "\n",
            "rtree-0.9.7          | 45 KB     |            |   0%\n",
            "rtree-0.9.7          | 45 KB     | ########## | 100%\n",
            "\n",
            "cugraph-21.06.00     | 65.0 MB   |            |   0%\n",
            "cugraph-21.06.00     | 65.0 MB   |            |   0%\n",
            "cugraph-21.06.00     | 65.0 MB   |            |   0%\n",
            "cugraph-21.06.00     | 65.0 MB   | 2          |   2%\n",
            "cugraph-21.06.00     | 65.0 MB   | 8          |   8%\n",
            "cugraph-21.06.00     | 65.0 MB   | #5         |  16%\n",
            "cugraph-21.06.00     | 65.0 MB   | ##2        |  22%\n",
            "cugraph-21.06.00     | 65.0 MB   | ##9        |  29%\n",
            "cugraph-21.06.00     | 65.0 MB   | ###6       |  36%\n",
            "cugraph-21.06.00     | 65.0 MB   | ####3      |  43%\n",
            "cugraph-21.06.00     | 65.0 MB   | #####      |  50%\n",
            "cugraph-21.06.00     | 65.0 MB   | #####6     |  57%\n",
            "cugraph-21.06.00     | 65.0 MB   | ######4    |  64%\n",
            "cugraph-21.06.00     | 65.0 MB   | #######2   |  73%\n",
            "cugraph-21.06.00     | 65.0 MB   | ########1  |  81%\n",
            "cugraph-21.06.00     | 65.0 MB   | ########9  |  89%\n",
            "cugraph-21.06.00     | 65.0 MB   | #########8 |  98%\n",
            "cugraph-21.06.00     | 65.0 MB   | ########## | 100%\n",
            "\n",
            "jpype1-1.3.0         | 482 KB    |            |   0%\n",
            "jpype1-1.3.0         | 482 KB    | ########## | 100%\n",
            "\n",
            "tzcode-2021a         | 68 KB     |            |   0%\n",
            "tzcode-2021a         | 68 KB     | ########## | 100%\n",
            "\n",
            "python-confluent-kaf | 122 KB    |            |   0%\n",
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            "\n",
            "pyee-7.0.4           | 14 KB     |            |   0%\n",
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            "\n",
            "threadpoolctl-2.1.0  | 15 KB     |            |   0%\n",
            "threadpoolctl-2.1.0  | 15 KB     | ########## | 100%\n",
            "\n",
            "expat-2.4.1          | 182 KB    |            |   0%\n",
            "expat-2.4.1          | 182 KB    | ########## | 100%\n",
            "\n",
            "pixman-0.40.0        | 627 KB    |            |   0%\n",
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            "\n",
            "pyrsistent-0.17.3    | 89 KB     |            |   0%\n",
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            "\n",
            "cudf-21.06.01        | 108.4 MB  |            |   0%\n",
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            "cudf-21.06.01        | 108.4 MB  | ##         |  21%\n",
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            "cudf-21.06.01        | 108.4 MB  | ######4    |  64%\n",
            "cudf-21.06.01        | 108.4 MB  | ######9    |  70%\n",
            "cudf-21.06.01        | 108.4 MB  | #######4   |  75%\n",
            "cudf-21.06.01        | 108.4 MB  | ########   |  80%\n",
            "cudf-21.06.01        | 108.4 MB  | ########5  |  86%\n",
            "cudf-21.06.01        | 108.4 MB  | #########  |  91%\n",
            "cudf-21.06.01        | 108.4 MB  | #########6 |  96%\n",
            "cudf-21.06.01        | 108.4 MB  | ########## | 100%\n",
            "\n",
            "heapdict-1.0.1       | 7 KB      |            |   0%\n",
            "heapdict-1.0.1       | 7 KB      | ########## | 100%\n",
            "\n",
            "widgetsnbextension-3 | 1.8 MB    |            |   0%\n",
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            "\n",
            "treelite-1.3.0       | 2.7 MB    |            |   0%\n",
            "treelite-1.3.0       | 2.7 MB    | ########## | 100%\n",
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            "\n",
            "cyrus-sasl-2.1.27    | 224 KB    |            |   0%\n",
            "cyrus-sasl-2.1.27    | 224 KB    | ########## | 100%\n",
            "\n",
            "libllvm10-10.0.1     | 26.4 MB   |            |   0%\n",
            "libllvm10-10.0.1     | 26.4 MB   | ##7        |  28%\n",
            "libllvm10-10.0.1     | 26.4 MB   | #####7     |  58%\n",
            "libllvm10-10.0.1     | 26.4 MB   | ########## | 100%\n",
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            "\n",
            "pyproj-3.0.1         | 484 KB    |            |   0%\n",
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            "\n",
            "blazingsql-21.06.00  | 190.2 MB  |            |   0%\n",
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            "blazingsql-21.06.00  | 190.2 MB  | #1         |  12%\n",
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            "blazingsql-21.06.00  | 190.2 MB  | ######     |  61%\n",
            "blazingsql-21.06.00  | 190.2 MB  | ######2    |  63%\n",
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            "blazingsql-21.06.00  | 190.2 MB  | #######1   |  71%\n",
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            "blazingsql-21.06.00  | 190.2 MB  | ########1  |  82%\n",
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            "\n",
            "glog-0.5.0           | 104 KB    |            |   0%\n",
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            "\n",
            "terminado-0.10.1     | 26 KB     |            |   0%\n",
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            "\n",
            "aws-c-io-0.10.5      | 121 KB    |            |   0%\n",
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            "\n",
            "prometheus_client-0. | 46 KB     |            |   0%\n",
            "prometheus_client-0. | 46 KB     | ########## | 100%\n",
            "\n",
            "locket-0.2.0         | 6 KB      |            |   0%\n",
            "locket-0.2.0         | 6 KB      | ########## | 100%\n",
            "\n",
            "xorg-libsm-1.2.3     | 26 KB     |            |   0%\n",
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            "\n",
            "backports-1.0        | 4 KB      |            |   0%\n",
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            "\n",
            "libcuspatial-21.06.0 | 7.6 MB    |            |   0%\n",
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            "libcuspatial-21.06.0 | 7.6 MB    | ##2        |  23%\n",
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            "\n",
            "nlohmann_json-3.9.1  | 122 KB    |            |   0%\n",
            "nlohmann_json-3.9.1  | 122 KB    | ########## | 100%\n",
            "\n",
            "librmm-21.06.00      | 57 KB     |            |   0%\n",
            "librmm-21.06.00      | 57 KB     | ##8        |  28%\n",
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            "\n",
            "pandas-1.2.5         | 11.8 MB   |            |   0%\n",
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            "pandas-1.2.5         | 11.8 MB   | ########## | 100%\n",
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            "\n",
            "thrift_sasl-0.4.2    | 14 KB     |            |   0%\n",
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            "\n",
            "nbclient-0.5.3       | 67 KB     |            |   0%\n",
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            "\n",
            "geopandas-0.9.0      | 5 KB      |            |   0%\n",
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            "\n",
            "cfitsio-3.470        | 1.3 MB    |            |   0%\n",
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            "\n",
            "libblas-3.9.0        | 11 KB     |            |   0%\n",
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            "\n",
            "backports.functools_ | 9 KB      |            |   0%\n",
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            "\n",
            "bleach-3.3.0         | 111 KB    |            |   0%\n",
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            "\n",
            "dask-core-2021.5.0   | 735 KB    |            |   0%\n",
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            "\n",
            "jupyter_client-6.1.1 | 79 KB     |            |   0%\n",
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            "\n",
            "libutf8proc-2.6.1    | 95 KB     |            |   0%\n",
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            "\n",
            "numpy-1.21.0         | 6.1 MB    |            |   0%\n",
            "numpy-1.21.0         | 6.1 MB    | #########1 |  92%\n",
            "numpy-1.21.0         | 6.1 MB    | ########## | 100%\n",
            "\n",
            "pyct-core-0.4.6      | 13 KB     |            |   0%\n",
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            "\n",
            "aws-c-event-stream-0 | 47 KB     |            |   0%\n",
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            "\n",
            "fontconfig-2.13.1    | 357 KB    |            |   0%\n",
            "fontconfig-2.13.1    | 357 KB    | ########## | 100%\n",
            "\n",
            "liblapack-3.9.0      | 11 KB     |            |   0%\n",
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            "\n",
            "cuspatial-21.06.00   | 15.2 MB   |            |   0%\n",
            "cuspatial-21.06.00   | 15.2 MB   |            |   0%\n",
            "cuspatial-21.06.00   | 15.2 MB   | ###1       |  31%\n",
            "cuspatial-21.06.00   | 15.2 MB   | ######5    |  66%\n",
            "cuspatial-21.06.00   | 15.2 MB   | #########9 | 100%\n",
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            "\n",
            "libwebp-base-1.2.0   | 815 KB    |            |   0%\n",
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            "\n",
            "prompt-toolkit-3.0.1 | 244 KB    |            |   0%\n",
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            "\n",
            "websockets-8.1       | 90 KB     |            |   0%\n",
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            "\n",
            "jsonschema-3.2.0     | 45 KB     |            |   0%\n",
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            "\n",
            "xorg-libxrender-0.9. | 32 KB     |            |   0%\n",
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            "\n",
            "websocket-client-0.5 | 59 KB     |            |   0%\n",
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            "\n",
            "sasl-0.3a1           | 74 KB     |            |   0%\n",
            "sasl-0.3a1           | 74 KB     | ########## | 100%\n",
            "\n",
            "xorg-xextproto-7.3.0 | 28 KB     |            |   0%\n",
            "xorg-xextproto-7.3.0 | 28 KB     | ########## | 100%\n",
            "\n",
            "geotiff-1.6.0        | 296 KB    |            |   0%\n",
            "geotiff-1.6.0        | 296 KB    | ########## | 100%\n",
            "\n",
            "krb5-1.19.1          | 1.4 MB    |            |   0%\n",
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            "\n",
            "ipython_genutils-0.2 | 21 KB     |            |   0%\n",
            "ipython_genutils-0.2 | 21 KB     | ########## | 100%\n",
            "\n",
            "libcugraph-21.06.00  | 213.6 MB  |            |   0%\n",
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            "libcugraph-21.06.00  | 213.6 MB  | 2          |   3%\n",
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            "libcugraph-21.06.00  | 213.6 MB  | #2         |  13%\n",
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            "libcugraph-21.06.00  | 213.6 MB  | ##         |  21%\n",
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            "libcugraph-21.06.00  | 213.6 MB  | ######     |  60%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ######2    |  63%\n",
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            "libcugraph-21.06.00  | 213.6 MB  | ######9    |  69%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #######1   |  71%\n",
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            "libcugraph-21.06.00  | 213.6 MB  | ########   |  80%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ########2  |  82%\n",
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            "libcugraph-21.06.00  | 213.6 MB  | #########1 |  91%\n",
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            "libcugraph-21.06.00  | 213.6 MB  | ########## | 100%\n",
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            "\n",
            "cycler-0.10.0        | 9 KB      |            |   0%\n",
            "cycler-0.10.0        | 9 KB      | ########## | 100%\n",
            "\n",
            "snappy-1.1.8         | 32 KB     |            |   0%\n",
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            "\n",
            "libglib-2.68.3       | 3.1 MB    |            |   0%\n",
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            "\n",
            "libhwloc-2.3.0       | 2.7 MB    |            |   0%\n",
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            "\n",
            "libwebp-1.2.0        | 85 KB     |            |   0%\n",
            "libwebp-1.2.0        | 85 KB     | ########## | 100%\n",
            "\n",
            "arrow-cpp-1.0.1      | 21.1 MB   |            |   0%\n",
            "arrow-cpp-1.0.1      | 21.1 MB   | ##2        |  23%\n",
            "arrow-cpp-1.0.1      | 21.1 MB   | #######6   |  76%\n",
            "arrow-cpp-1.0.1      | 21.1 MB   | ########## | 100%\n",
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            "\n",
            "geopandas-base-0.9.0 | 950 KB    |            |   0%\n",
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            "\n",
            "scipy-1.6.3          | 20.5 MB   |            |   0%\n",
            "scipy-1.6.3          | 20.5 MB   | ####4      |  44%\n",
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            "\n",
            "pthread-stubs-0.4    | 5 KB      |            |   0%\n",
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            "\n",
            "cligj-0.7.2          | 10 KB     |            |   0%\n",
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            "\n",
            "gflags-2.2.2         | 114 KB    |            |   0%\n",
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            "\n",
            "pyzmq-22.1.0         | 500 KB    |            |   0%\n",
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            "\n",
            "google-auth-oauthlib | 19 KB     |            |   0%\n",
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            "\n",
            "readline-8.1         | 295 KB    |            |   0%\n",
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            "\n",
            "postgresql-13.3      | 5.3 MB    |            |   0%\n",
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            "\n",
            "ipython-7.24.1       | 1.1 MB    |            |   0%\n",
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            "\n",
            "freetype-2.10.4      | 890 KB    |            |   0%\n",
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            "\n",
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            "\n",
            "pillow-8.2.0         | 684 KB    |            |   0%\n",
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            "\n",
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            "\n",
            "aws-sdk-cpp-1.8.186  | 4.6 MB    |            |   0%\n",
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            "\n",
            "cupy-9.0.0           | 50.3 MB   |            |   0%\n",
            "cupy-9.0.0           | 50.3 MB   | #5         |  15%\n",
            "cupy-9.0.0           | 50.3 MB   | ###6       |  37%\n",
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            "\n",
            "tzdata-2021a         | 121 KB    |            |   0%\n",
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            "\n",
            "markdown-3.3.4       | 67 KB     |            |   0%\n",
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            "\n",
            "librdkafka-1.5.3     | 11.2 MB   |            |   0%\n",
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            "\n",
            "webencodings-0.5.1   | 12 KB     |            |   0%\n",
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            "\n",
            "gettext-0.19.8.1     | 3.6 MB    |            |   0%\n",
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            "\n",
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            "rsa-4.7.2            | 28 KB     |            |   0%\n",
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            "fastrlock-0.6        | 31 KB     |            |   0%\n",
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            "hdf4-4.2.15          | 950 KB    |            |   0%\n",
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            "\n",
            "async_generator-1.10 | 18 KB     |            |   0%\n",
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            "pyjwt-2.1.0          | 17 KB     |            |   0%\n",
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            "\n",
            "ipykernel-5.5.5      | 167 KB    |            |   0%\n",
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            "libntlm-1.4          | 32 KB     |            |   0%\n",
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            "orc-1.6.7            | 751 KB    |            |   0%\n",
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            "libgdal-3.2.2        | 13.2 MB   |            |   0%\n",
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            "\n",
            "gdal-3.2.2           | 1.5 MB    |            |   0%\n",
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            "\n",
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            "\n",
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            "\n",
            "nvtx-0.2.3           | 55 KB     |            |   0%\n",
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            "python-dateutil-2.8. | 220 KB    |            |   0%\n",
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            "libcudf-21.06.01     | 187.7 MB  |            |   0%\n",
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            "\n",
            "munch-2.5.0          | 12 KB     |            |   0%\n",
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            "py-xgboost-1.4.2dev. | 151 KB    |            |   0%\n",
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            "\n",
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            "pygments-2.9.0       | 754 KB    |            |   0%\n",
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            "\n",
            "bokeh-2.2.3          | 7.0 MB    |            |   0%\n",
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            "\n",
            "importlib-metadata-4 | 31 KB     |            |   0%\n",
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            "\n",
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            "Preparing transaction: ...working... done\n",
            "Verifying transaction: ...working... done\n",
            "Executing transaction: ...working... By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html\n",
            "\n",
            "Enabling notebook extension jupyter-js-widgets/extension...\n",
            "Paths used for configuration of notebook:\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/pydeck.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.json\n",
            "Paths used for configuration of notebook:\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/pydeck.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n",
            "      - Validating: \u001b[32mOK\u001b[0m\n",
            "Paths used for configuration of notebook:\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/pydeck.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.json\n",
            "\n",
            "done\n",
            "RAPIDS conda installation complete.  Updating Colab's libraries...\n",
            "Copying /usr/local/lib/libcudf.so to /usr/lib/libcudf.so\n",
            "Copying /usr/local/lib/libnccl.so to /usr/lib/libnccl.so\n",
            "Copying /usr/local/lib/libcuml.so to /usr/lib/libcuml.so\n",
            "Copying /usr/local/lib/libcugraph.so to /usr/lib/libcugraph.so\n",
            "Copying /usr/local/lib/libxgboost.so to /usr/lib/libxgboost.so\n",
            "Copying /usr/local/lib/libcuspatial.so to /usr/lib/libcuspatial.so\n",
            "Copying /usr/local/lib/libgeos.so to /usr/lib/libgeos.so\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9oOCJ4NYMjY7"
      },
      "source": [
        "# cuDF and cuML Examples #\n",
        "\n",
        "Now you can run code! \n",
        "\n",
        "What follows are basic examples where all processing takes place on the GPU."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V38dg-oUJtEO"
      },
      "source": [
        "#[cuDF](https://github.com/rapidsai/cudf)#\n",
        "\n",
        "Load a dataset into a GPU memory resident DataFrame and perform a basic calculation.\n",
        "\n",
        "Everything from CSV parsing to calculating tip percentage and computing a grouped average is done on the GPU.\n",
        "\n",
        "_Note_: You must import nvstrings and nvcategory before cudf, else you'll get errors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwaJSKuswsNi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01f9c57f-3305-4542-b6bc-dc4587fa9cf3"
      },
      "source": [
        "import cudf\n",
        "import io, requests\n",
        "\n",
        "# download CSV file from GitHub\n",
        "url=\"https://github.com/plotly/datasets/raw/master/tips.csv\"\n",
        "content = requests.get(url).content.decode('utf-8')\n",
        "\n",
        "# read CSV from memory\n",
        "tips_df = cudf.read_csv(io.StringIO(content))\n",
        "tips_df['tip_percentage'] = tips_df['tip']/tips_df['total_bill']*100\n",
        "\n",
        "# display average tip by dining party size\n",
        "print(tips_df.groupby('size').tip_percentage.mean())"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "size\n",
            "6    15.622920\n",
            "1    21.729202\n",
            "4    14.594901\n",
            "3    15.215685\n",
            "2    16.571919\n",
            "5    14.149549\n",
            "Name: tip_percentage, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ul3UZJdUJqlT"
      },
      "source": [
        "#[cuML](https://github.com/rapidsai/cuml)#\n",
        "\n",
        "This snippet loads a \n",
        "\n",
        "As above, all calculations are performed on the GPU."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCE8WhO3HpL_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b704f796-7ecf-412e-a45a-9ac7fbd6148c"
      },
      "source": [
        "import cuml\n",
        "\n",
        "# Create and populate a GPU DataFrame\n",
        "df_float = cudf.DataFrame()\n",
        "df_float['0'] = [1.0, 2.0, 5.0]\n",
        "df_float['1'] = [4.0, 2.0, 1.0]\n",
        "df_float['2'] = [4.0, 2.0, 1.0]\n",
        "\n",
        "# Setup and fit clusters\n",
        "dbscan_float = cuml.DBSCAN(eps=1.0, min_samples=1)\n",
        "dbscan_float.fit(df_float)\n",
        "\n",
        "print(dbscan_float.labels_)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    0\n",
            "1    1\n",
            "2    2\n",
            "dtype: int32\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dlsyk9m9NN2K"
      },
      "source": [
        "# Next Steps #\n",
        "\n",
        "For an overview of how you can access and work with your own datasets in Colab, check out [this guide](https://towardsdatascience.com/3-ways-to-load-csv-files-into-colab-7c14fcbdcb92).\n",
        "\n",
        "For more RAPIDS examples, check out our RAPIDS notebooks repos:\n",
        "1. https://github.com/rapidsai/notebooks\n",
        "2. https://github.com/rapidsai/notebooks-contrib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWLG0GTUf9pH"
      },
      "source": [
        "!pip install \"autogluon.tabular[all]==0.1.1b20210312\"\n",
        "!pip install AutoViz\n",
        "!pip install xlrd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QaBBhCmOmjyt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8287071-3dd8-438a-e5d9-48a32b25af84"
      },
      "source": [
        "import pandas as pd\n",
        "dfe = pd.read_csv('https://raw.githubusercontent.com/gulabpatel/Dataset/master/Data_Machine_Learning/titanic_train.csv')\n",
        "\n",
        "from autoviz.AutoViz_Class import AutoViz_Class\n",
        "\n",
        "#Instantiate the AutoViz class\n",
        "AV = AutoViz_Class()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Imported AutoViz_Class version: 0.0.81. Call using:\n",
            "    from autoviz.AutoViz_Class import AutoViz_Class\n",
            "    AV = AutoViz_Class()\n",
            "    AV.AutoViz(filename, sep=',', depVar='', dfte=None, header=0, verbose=0,\n",
            "                            lowess=False,chart_format='svg',max_rows_analyzed=150000,max_cols_analyzed=30)\n",
            "Note: verbose=0 or 1 generates charts and displays them in your local Jupyter notebook.\n",
            "      verbose=2 saves plots in your local machine under AutoViz_Plots directory and does not display charts.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2nZIiey4DfCO",
        "outputId": "dad9200a-b9b3-4e8e-a05b-4e49285bffad"
      },
      "source": [
        "dfe.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_cEzul0_n90F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6178c8db-96ce-4e7b-a834-ef3b2c35e9a2"
      },
      "source": [
        "dftc = AV.AutoViz(filename='', \n",
        "                  sep ='' , \n",
        "                  depVar ='Survived', \n",
        "                  dfte = dfe, \n",
        "                  header = 0, \n",
        "                  verbose = 0, \n",
        "                  lowess = False, \n",
        "                  chart_format ='png', \n",
        "                  max_rows_analyzed = 100000, \n",
        "                  max_cols_analyzed = 30\n",
        ")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of your Data Set: (891, 12)\n",
            "############## C L A S S I F Y I N G  V A R I A B L E S  ####################\n",
            "Classifying variables in data set...\n",
            "    Number of Numeric Columns =  2\n",
            "    Number of Integer-Categorical Columns =  3\n",
            "    Number of String-Categorical Columns =  1\n",
            "    Number of Factor-Categorical Columns =  0\n",
            "    Number of String-Boolean Columns =  1\n",
            "    Number of Numeric-Boolean Columns =  0\n",
            "    Number of Discrete String Columns =  2\n",
            "    Number of NLP String Columns =  0\n",
            "    Number of Date Time Columns =  0\n",
            "    Number of ID Columns =  2\n",
            "    Number of Columns to Delete =  0\n",
            "    11 Predictors classified...\n",
            "        This does not include the Target column(s)\n",
            "        4 variables removed since they were ID or low-information variables\n",
            "\n",
            "################ Binary_Classification VISUALIZATION Started #####################\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Total Number of Scatter Plots = 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1440 with 7 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PUG60jAGepnzOPlf1AP895Xt9JaUUzCnV6wOquK9m0d8NjfxungKcGBGnUD5bF1J+z1xa7cu2EfFLys2wI4E5dH8ej6EqT1T9HhpH+X4u7jMkSZLUL/ZgliRJS53M/CMlibc5dTVcq96IuwIrUxKwkyk9NLerBvaC0pPzpZR6q7su4XZvpyR4LqT0WHw7JYF5C3BtRHQmlC6ttvsYpfzALygJru58hFKX9TZKz9YTgI9m5s1Vomu3ahtPADMpA6d9NDO71nnu9B5KHeu/Vtt/L/DmzLyrj/s4mzJY4rrAw8CjlF6kb6kGgXuMkpD7POUcfBo4kNKrfGJE7JOZ3we+SOl5PAs4jDKI3ANdt7cEPgIcQanh+zjwFPCNars7ZubcarkDgZuB64FpwNGUAfCuWYJtnUtJVnatl9urqj7ux4HtKOfrcl48MN3xlAH2LqUcn7OBI6pB36jO1SmUmyOzgKO6tPETyr5fSkmGT6IkG3tMVFY3Ui4HduKFwf3oy/lcgt3/L/BH4G7KzZhnePHAhRdSkrm99V4+hJIcfjAi5nX5eXkP+ziZkli9lfJ74RlKwvTdwPsy82vVot8EbqKc339SjuGHKefr4YhYmXJ830U5tqtTvnfnAz+o2r2SchxPq9r8dbXMhZRe6u+q3teXvxjod3MGpXb1TpQbGTdV+3laZi6gDKS5J+Uz81XK5+bbwOcj4rguzZ0J/B+ljvez1bGYjT2YJUnSIGjp6BhwuUJJkiRJIiJeD/wBWK+zZ/PyIiJeWldyg4g4EfhYZnabEJckSVpRWCJDkiRJ0oBFxDqUHrVnL4fJ5TcAf4mIvSg9xTcB9gd6GhRQkiRphWGJDEmSJEkDEhHfAv4F3Al8ucnhNFxVPucgSn3pp4HfUMpofKGZcUmSJC0NLJEhSZIkSZIkSeoXezBLkiRJkiRJkvrFBLMkSZIkSZIkqV9MMEuSJEmSJEmS+sUEsyRJkiRJkiSpX0wwS5IkSZIkSZL6xQSzJEmSJEmSJKlfTDBLkiRJkiRJkvrFBLMkSZIkSZIkqV9MMEuSJEmSJEmS+sUEsyRJkiRJkiSpX0wwS5IkSZIkSZL6xQSzJEmSJEmSJKlfTDBLkiRJkiRJkvrFBLMkSZIkSZIkqV9amx2ApOVfRGwJnAGsTbmxNQ04OjNvaVD7pwEPZea3GtDWPsCBmbljD/O/CswDvg9MAs7KzM92WeZ64JWZuf5A4+lm+6cBCzLziz3Mvwn4bmZe3M/2TwQOBx6vJg0DbgCOysy5i1lvQNsdKhFxPOXc7NfNvA8BRwMjgOHA3cCnMnPKkAYpSZKWW14XN47XxQPT03VxROwIXAc8WE2qAQ8An87MB2mA6tiuk5kHNqI9Sc1nD2ZJgyoiWoBfAV/PzE0yc2PgTOCqiBjRiG1k5nGNuIjuTURsA7wDOLma9CSwe0QMq1vm/wGvGMQwvgS8PyJeN4jbuKw6V5sAWwAvA04YxO01XURsBpwNvK/a740pF9UXNjUwSZK03PC6uOG8Lh48D3fud2ZuBPwe+Emzg5K09LIHs6TBtjrlQuzPnRMy8xcRcVtmzo2I/YB9MvMtAPXvI+IHwHTgLcDlwGHAGpm5oFr2SuA3wNbAv4HRwMqZeWg1f3XgIWAtSi+Rb1axtAEfy8y/VhfB5wLvpvRO+P1i9uWLlD8IFkQEwLPAf4Ad6tb7AKVnw1s6V4qITwBHAi8F/gTsn5nPVhfdPwTWB1YCzsvMr1frTAZOAw4A1gV+kplHZeZzEXEO8AVgjx7ifFVE3Fbt62+Ag4GfAn/JzK9V7W8B3Ai8rPN4dqfa3reBrwDHVsf0+8DmwNPAZzPzuvp1IuLdwCnAS6plDsjMv0fEKsBFwCbV/t4AfKp6/aLpmTm/S7vbAOcDI4GFwGcy8/qIWL86rqcBHwfGAUdm5qURsTLwA8pnZDJwXw+7ujnwRGZOrva7PSK+AKxcbXslyh+Ab6v269uZeWpEvIHy2dwsM5+OiM8Dr83M9/d0TCVJ0grL62Kvi5eF6+LunA+cHhFjgDnAeZTz+hLgFsp5nN/lc3oycDUwkfK5mAecUteze6WIuKSK5wlgj8x8dAlikrQUsQezpMH2FHA7cGNEHBARGwBk5iN9XH9nYKvM/DLlQncHgKqXx06UC+xOlwG71b3fjXJRNge4EvhR1VPkYEpPkVZKwnBXYDPgTcAbuwsiIkZTLpSu6jLrZ8CH695/EPh53Xo7UC6udqoeDZzFCz09jgcmVT0idgZOi4h169p6I7ANsCVwaESsU02/AnjnYnq6vBnYEYhqn94FXNIlzvcCly/uIrrOcMofHwCnA//KzA2BfYFLquRr5/62Uv44+HhmBuV4fa2avS8wMzM3pfQQXkC5IO9pelffBs6sjtfpQH3vnNWBhZn5KsqjjF+ppn8MWJPSe+Z9lHPdnT8C60XELyPivRExLjOfzczp1fzPUT4jr6pi2zMi3pWZt1POx+cjYm3KHwaf6WEbkiRpxeZ1sdfFy8J1cXdaKYns5yjHawdKj+5NKedkr7plOz+nPweOAl6SmRsAuwDnR8Ra1XJvAY6t5k0F9l+CeCQtZUwwSxpUmdlBuZi4gtLT4sGIuCci3tfHJm7IzHnV68soPSqgXADflplT67Z1G9ASEa+pJr2XcqG7CbAGVbmDzPwj5SJmW8rF6q8z8+nMfLZavjuvo9Szm95l+i+Ad0fE8Ih4OaV+b9bN3w24tK6O77coF3RQEpGHVjE9SPlDYYO6dX+Sme3Vuk9QemyQmY9Xy/b0OOBlmTm3qg33a8rF+DXAK6LqYlIdm0t7WP95ETEKOKTaTyiPQl5SxfE3YP3M7LzIprowXyMzO3vm3AxsWL1+EtgmInYFapn5ycz8+2Kmd/U/vHB+6tuFctH7/er1ncB61es3Ar/IzAWZOY3Si+JFqmO8FfAYpefO1Ii4PiJeXS2yG3BBZrZl5jPAj3jhPH4BeH+1/ZMz87HutiFJklZsXhd7XcwycF3czX7XKJ0tflN1wLgceH1mzq8+j7d32X795/QdlB7jnTdS1qk7/zdn5kPV678D6yBpmWWJDEmDLjNnUWqkfal6/G0/4Kd1F7yLU3/hehnlgvwIYHe6vxC8nHJh+29ge2BvSq/TEcC9L1xHMhpYjfLYWP0gbjN6iGMNygVf132bERG3A2+l9PboeiG+KvDe6iIRyo29l1Sv30DpnbEe0E55dK/+xt+sutftlAE2Oj1ZxdSdqXWvZ1Ee95sXEVcAH46I71Xb6umxxz0jYvvq9XOUYz6her86MLNzwcyc0836n4mIfSmP9b0U6KiW/XlEjKP0VNkkIi6mPLLX0/S2Lu3uXbU9qjoWLXXz2qvELyx6rMax6HGcAYzqbqcz837gIICI2BQ4Fri26j2zKjAhIk6tFl8JuK1a7+mI+Bnlc9nXPxAlSdIKyOtir4urZZfq62LKk331JTRuo/SuJiLGA+dFqX29kNIr+uy6Zes/p12P0dN182bXx8yi51TSMsYEs6RBVT2+tn5WI2Nn5hPAVyPiA5THvbpeTIztqa3M/EdEtFcX4G+lXFB3dRlwDnAP8PvMnBMRU4DZ1SNkXePbGhhTN2l8D5tv6WE6lLvye1b7s1eXeVOAH2aXEbUrF1MuUL+VmR0R0aiaY+PqXo/lhYu8S6rtzaL05ljYw/qXZc8jOj9FuVCcDFDVeXs+7ojYFjiG8ljc5IjYBfhO5/zMnAhMrMpJXA58FPhOT9Pr2l27ev+/WerWbQTc38txgHLh3Ov5jYjXAnMzM6s4742IT1MufDv/2PpaZr6op0f1mN+HKcf3S8DRfYhLkiStYLwu9rqYZeC6uPJwd5+RyinAfOBVmdkWET9eTDudx6gz9nVYNAEtaTlhiQxJg21d4MqI2LJzQpSB0dajPE71WJkUL61qp+3ZS3uXAScCf68e7erqT0Bnb5DOXhMPAY9ExJ7V9lePiEsiYmS1/FsjYkS1/Z4GZ3uSni/CrqLUdmuvHumr90vgfdWdfiLiPRFxTDVvDeCO6iJ6X8ogHav0uOeLGs+iPTLqva86niOBt1MemwO4ntI75TP04THAHvyScmyJiM0oj93V36zs7NHycHU89wVGRkRLRHwxIvYHyDKAxySgo6fp3ezvM8B9VT27T1Qx9Ha8/kTpuVOLMhDLO3pYblfgR1VPos5R3veh1NV7inKOD6zaaYmI4yPibdW65wJnUGrc7RUR/9NLTJIkacXkdbHXxcvCdXFv1gDurpLLrwG2o+dz9Uvgo9U+rwn8jbqEs6TlhwlmSYMqM/9Euej5ZkRk9YjeBGCvqubWjcBfKHfdr+XFg4V0dRnlMcBua8JVte2upAwa8au6aR8EPl096vUHSm2wZ6pl/kipD/d7Sk227twBrB8Rq3azzWcoo4H/vJt5dwKnAjdFxL2UUbM79/GLwBUR8Q/KRdlE4DsR8YrFHYCIWIPyKN8dPSxyPeW43lu9/k0VS3sVY63a5/44BlgnymjelwIfrmr0dfoNpXfKf4DrKI/LzaKct4uAj1Sfg/sojxletJjp9e6inJv7KRfHv6Ic88WNbg6ld8cs4EFKvbwreljuDMp5uTEisop/Z14YHOcblD/I7qGMuL0pcEtEvJNSH3Bi9Vjk5ynn0Ef8JEnSIrwu9rqYZeO6uDdnAQdX5/AQykB+B0ZEdzckJlCS7A8BNwGfzcyH+7ldSUuxlo6OrjfDJEndiYjfUAYY+VGT4/gE8I7M3L0f634OWD0zP9f4yCRJkrQi8LpYklTPHsyS1HdfAY5qZu/UiBhOKcVwSj/WHU/pNfOtRsclSZKkFYrXxZKk55lglqQ+qgZkuQ44volhnAhckZm3L8lKEXEQ8Ffgq93Uw5MkSZL6zOtiSVI9S2RIkiRJkiRJkvrFHsySJEmSJEmSpH5pbXYAfTV16hy7Wi/Dxo4dwYwZc5sdhrTC8bsnNYffvWXX+PGjWpodQ394rbzs8veF1Bx+96Tm8Lu3bOvpWtkezBoSra1NG/tBWqH53ZOaw++epL7y94XUHH73pObwu7d8GtQezBGxBXAVMCEzz+8y7y3AqUA7cE1mnjyYsUiSJEmSJEmSGmvQejBHxEjgPOCGHhY5F9gD2A7YNSI2G6xYJEmSJEmSJEmNN5glMtqAdwBTus6IiA2B6Zn538xcCFwD7DyIsUiSJEmSJEmSGmzQSmRk5gJgQUR0N3tNYGrd+yeBVyyuvbFjR1inZRk3fvyoZocgrZD87knN4XdPkiRJ0opgUGswL4FeR+t2hMll2/jxo5g6dU6zw5BWOH73pObwu7fs8saAJEmStGQGs0TG4kyh9GLutDbdlNKQJEmSJEmSJC29mpJgzszJwOiIWD8iWoF3Adc1IxZJkiRJkiRJUv8MWomMiNgSOAtYH5gfEXsCvwQmZeYVwCeBS6rFL83M+wcrFkmSJPWsVuu1WtkSaW/vaGh7kiRJ0vJqebgWH8xB/u4AdlzM/D8A2wzW9iVJktS7Wq2FVcaMoFGXoS3A07PmLvbCdsGCBXzykwfw8pevz/HHf7kh233ssSkcf/wxfO97FzWkPUmSJGmw1WotjBu3SkPbnD796V6TzOeeexb33PNPWlpaOOywo9h0080HtM2lZZA/SZIkNUlH9TNUnnrqKebPn9+w5PKyKiK2AK4CJmTm+V3mvQU4FWgHrsnMk5sQoiRJkpYzf/vbHTzyyH+ZOPH7TJ48idNOO4mJE78/oDZNMEuSJGlInXfeWTz66COceuqXmTv3GebMmUN7ezuHH340r3zlRnzgA+9ht93ey0033cA666xDxKbceOP1rLPOenzpS1/hgQfu5+tf/yqtra0MGzaMk08+fZH277rrb0yc+A1aW1tZY43/xzHHHM/w4cObtLfdi4iRwHnADT0sci7wVuBR4PcRcXlm/muo4pMkSdLy6Y47bmeHHXYEYP31N2DOnNk888zTjBzZ/57UTRnkTy9Wq7Us1z+SJEmdPv3pI1hvvZez1lpr87//uy3nnPNNjjrqWM4/fwIACxcuJGITvvvdH3H33f9gzTXX4jvf+RF33fU35syZw8yZ0zniiKM577yJvOpVr+G6665dpP2zzz6T008/i3PP/Rbjxo3jxpJrfMEAACAASURBVBuvb8Zu9qYNeAcwpeuMiNgQmJ6Z/83MhcA1wM5DHJ8kLdP68jeqf8NKWhFNmzaNVVdd9fn3q646lmnTpg2oTXswLwUaXftwaeN/zZIkqTt33/0PZs6cwW9/ew0AbW3znp+36aab09LSwtix49h44wBg7NhxPPPM04wduxrf/OZ5tLXN46mnprLLLm97fr3p06fxyCP/5fOfPxqAefPmMWbMqixtMnMBsCAiupu9JjC17v2TwCsW197YsSNoba01LkANqfHjRzU7BGm586tf/YrZs2f3a93Ro0ez2267NTgiSZ38f29w9VbTeeWVhzN69MrPn4fW1mGMGzdyQOfFBPNSYqhrH0qSJDXb8OGtHHHE0WyxxatfNK9Wq3X7uqOjg3PO+Rp7770vW2+9LT/5yUU8++zc5+e3tg5n9dXHc/753x7c4IdWr/frZ8yY29siWkqNHz+KqVPnNDsMablSq7Uwe/ZsZs2a1e82+jJIlqQl5/97i2rGIH8jR45h0qRHnj8Pjz32BMOGrdyn89JTEtoSGZIkSSu4lgb/9NVmm23BH/5wEwCTJj3IT396cZ/WmzVrJmuvvQ7PPfccf/7zH1mwYMHz80aPHv18ewCXXfZT/v3vB5YgqqXCFEov5k5r000pDUmSJGlJbbXV1tx0UxkGJPM+Vl99dUaMGDmgNu3BLEmStAJrb+/g6VmN7f3a1x5fe+65F6ecciKf+tSBLFy4kMMP/2yf1ttjj7047rjPsvbaa7PHHnsxYcIZ7LTTLs/PP/bYEzj11C8zfHjpzfzud7+vX/vRLJk5OSJGR8T6wCPAu4C9mxuVJEmSGq29vYPp059ueJuL86pXvYaITTn44P1paWnhyCOPGfA2Wzo6lo1HPqZOnbNsBNoPtVoLI5fzGswr1Yb5CITUBD5+JDWH371l1/jxo4Zk+IiI2BI4C1gfmA88CvwSmJSZV0TEG4GvVotfnplfW1x7y/O18vLO3xdS49VqLVx77VX9LpExZswY3v7291giQxoE/r+3bOvpWtkezJIkSdIQy8w7gB0XM/8PwDZDFpAkSZLUT9ZgliRJkiRJkiT1iwlmSZIkSZIkSVK/mGCWJEmSJEmSJPWLCWZJkiRJkiRJUr84yJ8kSdIKrlbrdjDofmtv72hoe5IkSdLyanm4FjfBLEmStAKr1Vr40JkHMemxhxvS3gYvW49Ljp7Y8AvbU045kR133Jntttuhoe1KkiRJzVKrtTBs2ALa29sb1F4NaO3TtfiDD/6bY489ir32+jB77LHXgLZrglnSCq3RdwolaVk06bGHeeDRSc0OQ5IkSVrhtLe3NyzBDNDS0nu699lnn2XChDPZcsutGrJNE8ySVli1WgurjBnB8vogt6lzSUura675FX//+53MnDmTSZMe5BOf+CTXX/9bJk+exAknfIXf/e46/vWve3juuefYffc92G233Z9ft729nTPOOIUpUx5lwYIFHHjgwWy55RuauDeSJEnSsmX48OF87WvncPHFP2xIeyaYJa3QOqofSdLQ+u9/H+aCC77Lr351JRdf/AMuvPDHXHvtr7jmml+y/vobcuihR9LWNo8PfGD3RRLM//d/v2G11VbnuONOYObMmRx22MH88Ic/beKeSJIkScuW1tZWWlsblxY2wSxJkqQht8kmm9HS0sJqq63OK16xEbVajbFjV2P+/PnMnj2Lgw/en9bWVmbOnLHIev/85z+4666/8Y9//B2AtrY25s+fz/Dhw5uxG5IkSdIKzwSzJEmShlwZgOTFrx97bAqPPvoI55//bVpbW9lll0UH9WttHc5HP7o/u+zytiGLVZIkSVLPTDBLkiSt4DZ42XpLTVv33Xcv22//RlpbW7nllt/T3r6Q+fPnPz9/s8224JZbfs8uu7yNGTOm87OfXcJBBx0y0LAlSZKkpqjvbNGIthYubFhzfWaCWZIkaQXW3t7BJUdPbHib/fX612/FI488zKc//Ql22OFNbLvt9nzta6c9P3+nnd7CnXfezsEH7097ezv77/+JRoQsSZIkDbly3dxKS0tjUrQLF/btWvy+++7l/PMn8Pjjj9Ha2sqNN97AqaeeyejRY/q13ZaOjmVjeKupU+csG4H2Q63WwsgxI5bbgcZagJVqw5g6dU6zQ5EW4XdP0mAZP36U371l1Pjxo1qaHUN/LM/Xyss7f19IjVertXDttVcxa9asfq0/ZswY3v729wzohqmk7vn/3rKtp2vlYUMdiCRJkiRJkiRp+WCCWZIkSZIkSZLULyaYJUmSJEmSJEn9YoJZkiRJkiRJktQvJpglSZIkSZIkSf3S2uwAJEmSJEmSJGlFVKu1NLS99vaOhrbXFyaYJUmSJEmSJGmI1WotPP74w7S1tTWkvZVWWok111yvT0nmCy44h7vu+jvt7e185CP78aY37dTv7ZpgliRJkiRJkqQmaGtrY968eUO6zTvv/CsPPvgfJk78PrNmzeRjH9vbBLMkSZIkSZIkqXevec1r2XTTzQFYZZVRzJs3j/b2dmq1Wr/ac5A/SZIkSZIkSVpB1Go1Vl55ZQCuvvoqttlm234nl8EezJIkSZIkSZK0wrn55pu4+uqrmDDhGwNqxwSzJEmSJEmSJK1A/vKXP/GjH13IWWedxyqrrDKgtkwwS5IkSZIkSVITrLTSSkPe1tNPP80FF5zD2WdfwOjRYwa8XRPMkiRJkiRJkjTE2ts7WHPN9RreZm9uuOE6Zs6cyRe/eOzz044//iTWXHPNfm3TBLMkSZIkSZIkNUFfEsKN9p73vI/3vOd9DWtvWMNakiRJkiRJkiStUEwwS5IkSZIkSZL6xQSzJEmSJEmSJKlfTDBLkiRJkiRJkvrFBLMkSZIkSZIkqV9amx2AJEmSJEmSJK2IarWWhrbX3t7R0Pb6wgSzJEmSJEmSJA2xWq2FP/7xRmbPnt2Q9kaPHs122715sUnmefPmccopJzJjxnTa2trYb78D2W67HQa0XRPMkiRJkiRJktQEs2fPZtasWUO2vT/+8Q9sssmm7L33vjz++GMcfvghJpglSZIkSZIkSb3beeddn3/9xBNPsMYaawy4zUFNMEfEBGBroAM4LDNvr5t3CLAP0A78NTMPH8xYJEmSJEmSJElw8MH78+STT3DGGWcPuK1hDYinWxHxJmCjzNwGOAA4t27eaOBoYIfM3B7YLCK2HqxYJEmSJEmSJEnFt751IV/96tc5+eQv0tExsIEBBy3BDOwMXAmQmfcCY6vEMsBz1c8qEdEKjACmD2IskiRJkiRJkrRCu+++e3niiccB2GijoL29nZkzZwyozcEskbEmcEfd+6nVtNmZOS8ivgw8CDwL/DQz719cY2PHjqC1tTZowTZbW/vCZocw6MaPH9XsEKQX8bsnabD43ZMkSZLUm9GjR/e+UAPbuuuuO3n88cc57LCjmD59GnPnzmXMmFUHtN2hHOSvpfNF1ZP588DGwGzgdxHxmsy8q6eVZ8yYO/gRNkmt1sLIMSOaHcagmzp1TrNDkBbhd0/SYBk/fpTfvWWUNwYkSZI0VNrbO9huuzc3vM3F2X33PTjttJP51KcOpK2tjSOPPIZhwwZW5GIwE8xTKD2WO60FPFa93hR4MDOfAoiIm4EtgR4TzJIkSZIkSZK0POktIdxoK630Uk488ZSGtjmYNZivA/YEiIjXAVMys7Mrz2Rg04hYuXr/euCBQYxFkiRJkiQtI2q1ln7/DBvW0vsGJEkNM2g9mDPz1oi4IyJuBRYCh0TEfsCszLwiIs4EboyIBcCtmXnzYMUiSZIkLU0iYgKwNdABHJaZt9fNOwTYB2gH/pqZhzcnSklqjlqthccff5i2trZ+rT9qlOWOJGkoDWoN5sw8tsuku+rmTQQmDub2JUmSpKVNRLwJ2Cgzt4mITYELgW2qeaOBo4FXZuaCiLguIrbOzD83MWRJGnJtbW3MmzevX+uutNJKDY5GkrQ4g1kiQ5IkSdKL7QxcCZCZ9wJjq8QywHPVzyoR0QqMAKY3JUpJkiSpD0wwS5IkSUNrTWBq3fup1TQycx7wZeBB4CHgL5l5/5BHKEmSJPXRoJbIkCRJktSr50ejqnoyfx7YGJgN/C4iXpOZd/W0MsDYsSNoba0NbpQaNOPHWy9W6uqhh5q7/XHjVmluANJyzP/3lj8mmCVJkqShNYWqx3JlLeCx6vWmwIOZ+RRARNwMbEndWCbdmTFj7iCEqaEwfvwopk6d0+wwpKVKrdbS+0KDbPr0p2lv72h2GNJyx//3FlWrtXDxxRcybdq0hrS32mqrsc8++/fp91db2zw+8pG92G+/A3nHO3brU/s93RwwwSxJkiQNresoZTAmRsTrgCmZ2fmX1mRg04hYOTOfBV4PXNOcMCVJkjTYpk2bxpNPPjnk2/3BD77H6NFjGtKWCWZJkiRpCGXmrRFxR0TcCiwEDomI/YBZmXlFRJwJ3BgRC4BbM/PmZsYrSZKk5ctDD01m8uRJbLPNdg1pzwSzJEmSNMQy89guk+6qmzcRmDi0EUmSJGlFcf75EzjiiM9x7bVXN6S9YQ1pRZIkSZIkSZK0VLv22qvZfPNXsdZaazesTXswS5IkSZIkSdIK4E9/+iNTpjzKrbfewtSpTzJ8+HDGj1+DN7zhf/vdpglmSZIkSZIkSWqC1VZbbUjbOumk055//b3vTeRlL1trQMllMMEsSZIkSZIkSUOuvb2DffbZv+FtDjUTzJIkSZLUg1qtZUDrN+OPPEmStOxo5rXCAQcc1JB2TDBLkiRJUjdqtRYef/xh2tra+rX+SiutxJprrmeSWZIkLddMMEuSJElSD9ra2pg3b16zw5AkSVpqmWCWJEmSJEkNNZDyMsOGDaw0jSRpaJlgliRJkiRJDVOrtTBs2ALa29v7tf6wYcMbHJEkaTCZYJYkSZIkSQ3V3t4+gATzsAZHI0kaTCaYJUmSJEmSJKkJBlJSqDvNGFzYBLMkSZIkSZIkDbFarYUTTjiWKVOmNKS9tdZai5NOOn2xSeY77/wrJ5xwLOuvvyEAr3jFKzniiM8NaLsmmCVJkiRJkiSpCaZMmcLDDz88pNv8n/95HV/5yhkNa8/CRpIkSZIkSZKkfjHBLEmSJEmSJEkriMmTJ3HMMUfwyU8ewO23/3nA7VkiQ5IkSZIkSZJWAOuuux4f+9jH2WmnXZgy5VEOPfQgLr30SoYPH97vNu3BLEmSJEmSJEkrgPHj12DnnXelpaWFtddeh9VWW42pU58cUJv2YJYkSZIkSZKkJlhrrbWGtK3rrruWp556ig9/+CNMm/YU06dPZ/z4NQa0XRPMkiRJkiRJkjTE2ts7OOmk0xve5uJsv/0bOfHE47nllt8zf/58PvvZYwdUHgNMMEuSJEmSJElSU/SWEG60ESNGcsYZExrapjWYJUmSJEmSJEn9YoJZkiRJkiRJktQvJpglSZIkSZIkSf1iglmSJEmSJEmS1C8mmCVJkiRJkiRJ/dLa7AAkSZIkSZIkaUVUq7U0tL329o6GttcXJpglSZIkSZIkaYjVai186EN7MGnSpIa0t8EGG3DJJZf3mmS+7rpr+fGPf0StVuPAAw9m2223H9B2TTBLkiRJWm4NpFfQsGGN7VEkSZLU1aRJk3jggQeGbHuzZs3kwgu/w4UXXsTcuc/yve9NNMEsSZIkSd2p1VoYNmwB7e3t/Vp/2LDhDY5IkiSpuf7619t4/eu3YsSIkYwYMZJjjvnCgNs0wSxJkiRpudXe3j6ABLNjokuSpOXLY49Noa1tHscccwRz5sxh//0/wetfv9WA2jTBLEmSJEmSJEkrgI4OmDVrFqeeeiZPPPE4hx56EJdffjUtLQMoK9bA+CRJkiRJkiRJS6lx48bxqle9mtbWVtZeex1GjBjJzJkzBtSmPZglSZIkSZIkqQk22GCDIW1rq6225pRTTmTvvfdlzpzZPPvsXMaMWXVA2zXBLEmSJEmSJElDrL29g0suubzhbS7O+PFrsOOOO3PQQfsBcMQRRw943AkTzJIkSZIkSZLUBL0lhAfD7rvvwe6779Gw9qzBLEmSJEmSJEnqFxPMkiRJkiRJkqR+McEsSZIkSZIkSeoXE8ySJEmSJEmSpH4xwSxJkiRJkiRJ6pfWwWw8IiYAWwMdwGGZeXvdvHWBS4CXAHdm5sGDGYskSZIkSZIkqbEGrQdzRLwJ2CgztwEOAM7tsshZwFmZuRXQHhHrDVYskiRJkiRJkqTGG8wSGTsDVwJk5r3A2IgYDRARw4AdgF9W8w/JzIcHMRZJkiRJkiRJUoMNZoJ5TWBq3fup1TSA8cAcYEJE3BIRpw1iHJIkSZIkSZKkQTCoNZi7aOnyem3gHGAy8OuIeGdm/rqnlceOHUFra21wI2yitvaFzQ5h0I0fP6rZIUgv4ndP0mDxuydJkiRpRTCYCeYpvNBjGWAt4LHq9VPAQ5n5H4CIuAHYHOgxwTxjxtxBCrP5arUWRo4Z0ewwBt3UqXOaHYK0CL97kgbL+PGj/O4to7wxIEmSJC2ZwUwwXwd8GZgYEa8DpmTmHIDMXBARD0bERpn5ALAlcMkgxiJJkiQtNSJiArA10AEclpm3181bl3Jt/BLgzsw8uDlRLj1qtZbeF+rGsGEttLc3OBhJkiQtYtASzJl5a0TcERG3AguBQyJiP2BWZl4BHA78oBrw727gV4MViyRJkrS0iIg3ARtl5jYRsSlwIbBN3SJnAWdl5hUR8Y2IWG9FHhC7Vmth3LhV+r3+tGnzGhiNJEmSuhrUGsyZeWyXSXfVzfs3sP1gbl+SJElaCu0MXAmQmfdGxNiIGJ2Zs6vOFzsAH6rmH9LEOCVJkqReDeUgf5IkSZLKOCV31L2fWk2bDYwH5gATqjJzN2fmcb01uLwPiL0s60vva2t/a3k0bVpbs0MYkIE8OSFp8fx/b/ljglmSJElqrpYur9cGzgEmA7+OiHdmZo+DYcPyPyD2spzomT79adrbO3qc76CgWh71t2760qS3766k/vH/vWVbTzcHhg1xHJIkSdKKbgqlx3KntYDHqtdPAQ9l5n8ysx24Adh8iOOTJEmS+swEsyRJkjS0rgP2BKjKYEzJzDkAmbkAeDAiNqqW3RLIpkQpSZIk9YElMiRJkqQhlJm3RsQdEXErsBA4JCL2A2Zl5hXA4cAPqgH/7gZ+1bxoJUmSpMUzwSxJkiQNscw8tsuku+rm/RvYfmgjkiRJkvrHEhmSJEmSJEmSpH4xwSxJkiRJkiRJ6hcTzJIkSZIkSZKkfrEGsyRJkjRAETEWWBVo6ZyWmQ82LyJJkiRpaJhgliRJkgYgIs4D9gWe4oUEcwewYdOCkiRJkoaICWZJkiRpYHYExmdmW7MDkSRJkoaaNZglSZKkgbkPeK7ZQUiSJEnNYA9mSZIkqR8i4qTq5dPA7yPiFmBB5/zMPKEpgUmSJElDqE89mCPiU13eD4+IMwYnJEmSJGmZ0F79TAZuANrqprU3LyxJkiRp6PS1B/MbIuK3wMeANYHvAVcPWlSSJEnSUi4zvwwQETVg28y8uXq/G/DrZsYmSZIkDZU+JZgz82MR8SbgVmAu8P7MvGdQI5MkSZKWDd8CngJurt7vCLyP0jlDkiRJWq71tUTGBsAxwG+BB4AjI2LMYAYmSZIkLSM2zszjOt9k5lHABk2MR5IkSRoyfUowA78BJmTmQZn5HuAPwJ8HLyxJkiRpmbFyRIzrfBMRawEvbWI8kiRJ0pDpcw3mzJzd+SYzfxgR1w1STJIkSdKy5CTgnoh4GKgBawEHNDckSZIkaWj0NcG8QUR8D1glMzeJiC8C1wGPDV5okiRJ0jLh18CGwGZAB3BfZs5tbkiSJEnS0Ohrgvl8YH/gnOr9pcD3ge0GIyhJkiRpGfK7zHwzcEezA5EkSZKGWl8TzPMz8x8RAUBm3h8RCwYvLEmStDyr1VqaHYLUSH+PiJOAW4HnOidm5u+aF5IkSZI0NPqaYF4QERtQHvkjIt4O+JehJElaYrVaC6uMGVEuKpZDXiCtkP6n+neHumkdgAlmSZIkLff6mmA+CrgKiIiYBUwG9h2soCRJ0vKto/qRlgdVeYxFRMQezYhFkiRJGmqLTTBHxGjggMycALw6Ik4A9gMewAH+JEmSJCJiPeDTwOrVpJWAnYDLmxaUJEmSNESG9TJ/IrAGQERsDBwOfAK4jhcG/JMkSZJWZBcB04FtKAP9jQc+0tSIJEmSpCHSW4J5w8w8rnq9J/DzzLw+M78NrDm4oUmSJEnLhAWZeTrwRGZ+A3g3cEiTY5IkSZKGRG8J5qfrXu/IogOVLGx4NJIkSdKyZ+WIWAdYGBEbAvOB9ZsbkiRJkjQ0ehvkrzUi1gBGUR752wsgIlYBRg5ybJIkSdKy4AxgZ+BM4O9AO/CTpkYkSZIkDZHeEsynA/8CRgAnZuaMiFgZuAX4zmAHJ0mSJC2tqgGxjwc2AW4GfgyMA0Zl5oxmxiZJkiQNlcWWyMjMa4GXAWtm5hnVtGeBz1X15SRJkqQV1QXVv98GNgW+lJkLTC5LkiRpRdJbD2Yycz6ljlz9tOsGLSJJkiRp2bB+Zu4DEBHXAjc0OR5JkiRpyPU2yJ8kSZKk7j3fCSMz24GOJsYiSZIkNYUJZkmSJKl/uiaUTTBLkiRphdNriQxJkiRJ3do2Ih6ue79G9b4F6MjM9ZoUlyRJkjRkTDBLkqT/z969x8lVl/cD/8zOQrglkEgoBEVQ4Quo9VYUioqKldZqvbZWwYriHVS8ULGCtXhBQYoiVbH1UquiVSsqovLTauutlmJLbYWvWBJAwyUmIQEhkUz298dMYAnZbJid2dmdfb9fr7wyc+ac73lmds7ss898z3OA7pRBBwAAAIOmwAwAAF2otV496BgAAGDQ9GAGAAAAAKArCswAAAAAAHRFgRkAAAAAgK4oMAMAAAAA0BUFZgAAAAAAuqLADAAAAABAVxSYAQAAAADoigIzAAAAAABdUWAGAAAAAKArCswAAAAAAHRltJ+Dl1LOTnJokrEkr6m1XrKFdU5Pclit9XH9jAUAAAAAgN7q2wzmUsoRSfavtR6W5Lgk52xhnYOTPLZfMQAAAAAA0D/9bJFxZJILkqTWenmShaWUBZutc1aSN/cxBgAAAAAA+qSfLTL2THLpuPsrOsvWJkkp5dgk/5Jk2bYMtnDhThkdbfY2whlkfWvjoEPou8WL5w86BLgbxx4MhmOPuU4rOQAAhkVfezBvprHpRillUZIXJnlikr23ZePVq2/tU1iD12w2svOuOw06jL5bseLmQYcAd+HYg8Fw7DGTTccXA+NbyZVSDkry0SSHbbbOplZyt/c9IAAAmIJ+tshYnvaM5U2WJLmuc/sJSRYn+W6SLyZ5eGcWBwAADDut5AAAGBr9LDBfnOTZSVJKeXiS5bXWm5Ok1vr5WuvBtdZDkzwjyY9rra/tYywAADBT7Jl2+7hNNrWSS3LPW8kBAMAg9a1FRq31B6WUS0spP0iyMcnxnWR5Ta31i/3aLwAAzDJTaiWXDP/1SmazRYt2mXQdPdsZRitXrh90CFOyLccu0B2/94ZPX3sw11pP3mzRZVtYZ1mSx/UzDgAAmEG2tZXcvCT3L6WcPdnZfsN+vZLZXOhZteqWtFpjEz6+ePF8PdsZOs1mY/KVZrjJjl2gO37vzW4TfTnQzxYZAADA3WklBwDA0FBgBgCAaVRr/UGSTa3kzkmnlVwp5RkDDg0AAO6xvrbIgPGG4TSpLXHaFABwT2klBwDAsFBgpu8aSY5609FZet01gw6l5/bba5+cf9J5iswAAAAAzEkKzEyLpdddkyt/uXTQYQAAAAAAPaQHMwAAAAAAXTGDGQAAAACGwFSvf6UFKN1QYAYAAACAWa7ZbGRkZENarVaX2zeTjCoyc48pMAMAAAB3MZVZkCMjjXRZ3wKmqNVqdV1gTpJGQ6mQe867BgAAALhDs9nIokW7TGmMlSvX9SgaAGY6F/kDAAAAAKArCswAAAAAAHRFgRkAAAAAgK4oMAMAAAAA0BUFZgAAAAAAujI66AAAAAAAgNmv2WxMaZ1Wa6yX4TBNFJgBAAAAgClpNhv5/ve/nbVr13a1/YIFC3L44Y9XZJ6FFJgBAAAAgClbu3Zt1qxZM+gwmGZ6MAMAAAAA0BUFZgAAAAAAuqLADAAAAABAVxSYAQAAAADoigIzAAAAAABdUWAGAAAAAKArCswAAAAAAHRFgRkAAAAAgK4oMAMAAAAA0BUFZgAAAAAAuqLADAAAAABAVxSYAQAAAADoigIzAAAAAABdUWAGAAAAAKArCswAAAAAAHRFgRkAAAAAgK4oMAMAAAAA0BUFZgAAAAAAujI66AAAAAAAgMEbGWkMZFtmNwVmAAAAAJjjRkZGsnz51Vm/fn1X28+fP7/HETFbKDADAAAAAFm/fn3WrVvX1bbz5s3rcTTMFnowAwAAAADQFQVmAAAAAAC6osAMAAAAAEBXFJgBAAAAAOiKAjMAAAAAAF0ZHXQAAAAAAEDSbDa63nZkpJFWq4fBwDZSYAYAAACAAWs2G1m0aJcpjbFy5boeRQPbTosMAAAAAAC60tcZzKWUs5McmmQsyWtqrZeMe+zxSU5P0kpSk7y41rqxn/EAAAAAANA7fZvBXEo5Isn+tdbDkhyX5JzNVvlwkmfXWg9PMj/J7/crFgAAAAAAeq+fM5iPTHJBktRaLy+lLCylLKi1ru08/ohxt1ckuVcfYwEAgBnDmX4AAAyLfhaY90xy6bj7KzrL1ibJpuJyKWWvJE9KcurWBlu4cKeMjjb7E+kMsL7lb4bZaqoN+BmsuXDsLV48f9AhwN049pjLxp/pV0o5KMlHkxw2bpUPJ3l8rfUXpZTPpX2m30UDCBUAACbV1x7Mm2lsvqCUskeSryR5Za115dY2Xr361n7FNXDNZiM777rToMOgS6tW3ZJWa2zQYdCFuXLsHDDw5wAAIABJREFUrVhx86BDgLtw7DGTTdMXA870AwDog2bzbuXHe0R9pzv9LDAvT3vG8iZLkly36U4pZUGSryV5c6314j7GAQAAM0lPz/RLhv9sv9lsW852c8YDzDzOVIXBmOqxd8IJJ2T58uVdbbtkyZKce+65U9r/XNXPAvPFSf4qyXmllIcnWV5rHT+V56wkZ9dav97HGAAAYKab0pl+yfCf7TebCz2Tne22ePF8Zzww48z2464XnKnKIDj2pnbsNZuNLF++PNdcc81A9j8XTPSleN8KzLXWH5RSLi2l/CDJxiTHl1KOTbImyTeS/FmS/UspL+5s8ula64f7FQ8AAMwQzvQDAGBo9LUHc6315M0WXTbu9rx+7hsAAGYoZ/oBADA0pvMifwAAMOc50w8AgGGiwAwAANPMmX4AAAyLkUEHAAAAAADA7KTADAAAAABAVxSYAQAAAADoih7MAEOu2WwMOoS+aLXGBh0CAAAAzHkKzABDqpHkqDcdnaXXXTPoUHpuv732yfknnafIDAAAAAOmwAwwxJZed02u/OXSQYcBAAAADCk9mAEAAAAA6IoCMwAAAAAAXVFgBgAAAACgK3owAwD0QbPZGHQIfeHimgAAwHgKzAAAPdRIctSbjs7S664ZdCg9t99e++T8k85TZAYAAO6gwAwA0GNLr7smV/5y6aDDAAAA6Ds9mAEAAAAA6IoCMwAAAAAAXVFgBgAAAACgKwrMAAAAAAB0RYEZAAAAAICuKDADAAAAANAVBWYAAAAAALqiwAwAAAAAQFcUmAEAAAAA6IoCMwAAAAAAXVFgBgAAAACgKwrMAAAAAAB0RYEZAAAAAICuKDADAAAAANAVBWYAAAAAALqiwAwAAAAAQFcUmAEAAAAA6IoCMwAAAAAAXVFgBgAAAACgKwrMAAAAAAB0RYEZAAAAAICuKDADAAAAANAVBWYAAAAAALqiwAwAAAAAQFcUmAEAAAAA6IoCMwAAAAAAXVFgBgAAAACgKwrMAAAAAAB0RYEZAAAAAICuKDADAAAAANAVBWYAAAAAALqiwAwAAAAAQFcUmAEAAAAA6MpoPwcvpZyd5NAkY0leU2u9ZNxjT0zyziStJBfVWt/Wz1gAAGCmkCcDADAs+jaDuZRyRJL9a62HJTkuyTmbrXJOkmclOTzJk0opB/crFgAAmCnkyQAADJN+tsg4MskFSVJrvTzJwlLKgiQppdwvyapa67W11o1JLuqsDwAAw06eDADA0Ohni4w9k1w67v6KzrK1nf9XjHvsxiT372MsM15j0AH02X577TPoEPpiWJ/XXOLYm52G9XnNJY692WlYn9cAyJOnUbPZnNK28+bN63r7efPmZWRk8k+8ZnN4PxVbrbFBh8CADPLY23777bNgwYKut1+wYME2HbszmWNv7prLx97ISCNLlizpevslS5ZM+difq8deX3swb2ZrP6FJf3qLF8+f3Z/uc9w3Tv/UoEOAOcmxB4Ph2OMemlKenMiVt2a33Xab0vYHH9z/DiWLFu3S933AdBv0sfeABzxgStvDbDXXj71zzz13oPufq/rZImN52jMwNlmS5LoJHtu7swwAAIadPBkAgKHRzwLzxUmenSSllIcnWV5rvTlJaq3LkiwopexbShlN8pTO+gAAMOzkyQAADI3G2Fj/eoOUUt6V5LFJNiY5PsnDkqyptX6xlPLYJO/urPqFWut7+hYIAADMIPJkAACGRV8LzAAAAAAADK9+tsgAAAAAAGCIKTADAAAAANAVBWYAAAAAALqiwMyUlVKag44B5qJSyl6llP0GHQfMNaWUPUsp9xl0HMDsIFeG6SdPhsGQJ89dLvLHlJRSjkhSkvxTrfVXg44H5opSyh8mOSXJr5NcX2s9ZsAhwZxQSjkqyVuS3JxkWa315QMOCZjB5Mow/eTJMBjy5LnNDGam6tVJnpDkGaWU3QcdDMwFpZR7p33sPb/W+sQkB5ZSThhwWDD0SikPTvIXSV6R5FlJFpRSdhhsVMAMJ1eGaSRPhsGQJ6PAzFTdluTaJAcleZbEGabFb5LskKTVuf/uJKODCwfmjPVJLq+1/neS+yZ5aJJ3llL+ZrBhATOYXBmmlzwZBkOePMcpMDNVf1FrPSnJd5IckOTZpZTFSVJKaQwyMBhiq5P8Va116bhlh2y6UUqRREN/3JDkc53bRyX5TJIzkty/lPLRgUUFzGRyZZhe8mQYDHnyHKcHM1NSShmptW7s3H5WksOT/FuSxUlGa63vG2R8MBeUUp6S5E9qrX9WSnl+kiVJzqi1+oCHaVJKuSjJC2qtKwYdCzBzyJVhsOTJMHjy5LnBt3dMSa11YymlUWsdq7V+oZSyIu2m7ouTPG/A4cFccWOSn5ZSDk3ygiSvljRDf5VStk+yR5INSR6WZJck6wYaFDDjyJVh4OTJMM3kyXOTAjNTVmsd25Q4J1mQ9rfCz6i11gGHBnPFDUlOTfLsJEc79mBazEvyZ2mfdrtTklfUWm8ebEjATCRXhoGSJ8P0kyfPQVpk0DOllGaSP0hSa61XDjoemCtKKSNpz4b6ZK3154OOB+aKUsqCJPOTtGqt1w86HmBmkyvD9JMnw2DIk+ceBWZ6atzsDGAalVJGa60bBh0HADAxuTJMP3kyQP8pMAMAAAAA0JWRQQcAAAAAAMDspMAMAAAAAEBXFJgBAAAAAOjK6KADAGBipZR9k9QkP+ws2i7J1UleWWu9aQvrH5vkibXWY6YrRgAAGAS5MsDMoMAMMPOtqLU+btOdUsqZSU5J8oaBRQQAADODXBlgwBSYAWaff03yslLKo5K8N8lvkqxK8mfjVyqlPCPJnydZl/bn/fNrrctKKa9JckySWzv/jkkyL8mnkjSS7JjkvFrrR6fn6QAAQM/IlQGmmR7MALNIKaWZ5JlJvpvkk0leUms9Ism/JPnDzVbfLclzaq2PT3JRkhM6y09L8pTOdu9NsiTJc5Jc0Zn9cUSSnfr8VAAAoKfkygCDYQYzwMy3uJTync7tkbQT5o8leUOt9X+SpNb63uSOvnKb3JDk70spI0n2zJ296T6S5OullM8n+Vyt9WellNuTvLKU8vEkX01yXl+fEQAA9IZcGWDAzGAGmPlW1Fof1/n32Frrm5O0spXP8FLKdkk+m+SlndkX79/0WK31dUmenvapgheUUv6g1npFkoPTnunxxCTf6duzAQCA3pErAwyYAjPALFRrXZnkV6WUQ5KklPL6Usorx60yP8nGJMtKKTskeVqSeaWUhaWUtya5ttb6wSR/k+SRpZTnJTmk1vrNJK9Msk8pxVkuAADMOnJlgOnlAxFg9np+kvd1Ttm7qXP/mUlSa11VSvl0kkuSXJ3kzCT/kPaMi/lJLimlrE5ye5LjkuyR5EOllPVpX7zk3bXWDdP8fAAAoFfkygDTpDE2NjboGAAAAAAAmIW0yAAAAAAAoCsKzAAAAAAAdEWBGQAAAACArigwAwAAAADQFQVmAAAAAAC6osAMAAAAAEBXFJgBAAAAAOiKAjMAAAAAAF1RYAYAAAAAoCsKzAAAAAAAdEWBGQAAAACArigwAwAAAADQFQVmAAAAAAC6osAMAAAAAEBXFJgBAAAAAOiKAjMAAAAAAF1RYAYAAAAAoCsKzAAAAAAAdEWBGQAAAACArigwAwAAAADQFQVmAAAAAAC6osAMAAAAAEBXFJgBAAAAAOiKAjMAAAAAAF1RYAYAAAAAoCsKzAAAAAAAdEWBGQAAAACArigwAwAAAADQFQVmAAAAAAC6MjroAIDhUkp5RJIzkuyd9pdYK5OcVGv9Xo/GPz3J1bXWD/VgrGOSvLjW+rgJHn93knVJPpZkaZKzaq1v2GydbyZ5QK1136nGs4X9n55kQ6311Ake/06Sv6u1frLL8d+a5MQk13cWjST5VpLX11pv3cp2U9rvdCmlnJL2z+bYLTz23CQnJdkpyXZJfpLklbXW5ePfY6WUsST3qbX+Ygtj/F6Sv0qyKO3fp8uSvLrW+tM+PSUAYIaR+/aO3HdqJsp9SymPS3Jxkqs236bWeuA9GP+tSe5da33xFOOcML/exu23+j4GBsMMZqBnSimNJF9J8te11gNrrQckOTPJl0opO/ViH7XWN/UiwZ5MKeWwJE9O8rbOohuTPL2UMjJund9Kcv8+hvGXSf64lPLwPu7j852f1YFJHpRkryRv6eP+Bq6UcnCS9yZ5Zud5H5B2wv3RZNveY6WU3ZJ8LskJndfvAUm+muQLneMAABhyct+ek/v2zzWbnvf4f4MOChgeZjADvbR72knav21aUGv9p1LKv9daby2lHJvkmFrrE5Nk/P1SyseTrEryxCRfSPKaJHvUWjd01r0gydeTHJrk50kWJNmx1vqqzuO7J7k6yZK0Z5B8sBPL+iQvrLX+RydBPifJH6U9c+FftvJcTk37j4UNpZQkuS3J/yV5zLjt/iTtWQ9P3LRRKeWlSV6XZIckP0zyolrrbZ2E/O+T7JtkXpL311r/urPNsiSnJzkuyX2SfLrW+vpa629KKe9L8uYkz5ogzgeXUv6981y/nuTlST6T5Ee11vd0xn9Qkm8n2WvT67klnf19OMnbk5zceU0/luSBSW5J8oZa68Xjtyml/FGSdyTZvrPOcbXW/yql7JLkH5Ic2Hm+30ryys7tuy2vtd6+2biHJTk3yc5JNqY9M/ibpZR9O6/r6Ulekvbs4dfVWj9bStkxycfTfo8sS3LFBE/1gUluqLUu6zzvVinlzUl27Oz740l+Xmt9e2f955ZS/izJrkneVWv9QJL9k4wluWzcuOck+Uytdawzw2PftI+JByf5RZJn1FpvnCAmAGD2kfvKfWdD7rtVnfflU9J+7zwmSU1yWpJ3p/2Fwqm11g93Vp9fSrmw8xpdneQ5tdYbSvtN85Ek90r77MBTa63nd8YfS/IXSY5NcvBm+35HZ6xndl6jqb6PgQExgxnopV8luSTJt0spx5VS9kuSe3D605FJHllr/au0k4fHJElnBsgT0k6+N/l8kqeOu//UtBO2m5NckOQTnVkkL097Fslokt9P8qS0E5sjkjx2S0GUUhaknTh/abOH/jHJ88bd/9O0Z7Fu2u4xac/6eELntME1uXMWyClJlnZmChyZ5PRSyn3GjfXYJIcleUSSV5VS7t1Z/sUkf7iVWTCPT/K4JKXznJ6S5PzN4nxGki9sLcEeZ7u0E7okeVeSn9Za75fkBUnOL6XMG/d8R9P+w+EltdaS9uv1ns7DL0hyU631oLRnCG9IO3mcaPnmPpzkzM7r9a4k42fu7J5kY631wWmf5ripEPzCJHumnQg/M+2f9ZZ8P8k+pZQvl1KeUUpZVGu9rda6aoL179vZ15OSnFVKWZzkf5OsTfKdUsrzSil71Vpbtdbrxm33zCSvqrXeN+0Z0m+aYHwAYHaS+8p9Z0Puuy2OSrv12/5JDkq7ldxj0v4SYHzLkj9Iu/i9X9oTKE7uLH9Pkgs7z/NFST5SStlu3HaNWmuptbY2LSilPCft9+jRnUVTeh8Dg6XADPRMrXUsye+lnRi+JslVpZT/LaU8cxuH+FatdV3n9ufT/pY6aScV/15rXTFuX/+epFFKeUhn0TPSToIPTLJH7mx38P0kK5L8btrJyFdrrbfUWm/rrL8lD0+7193mBcd/SvJHpZTtSin3Tbt/bx33+FOTfLbWurxz/0NpJ3tJ8uokr+rEdFXaf0TsN27bT3cKlMuT3JD2bI7UWq/vrDvRqYKfr7XeWtt9476adqJ+UZL7d2YSbHptPjvB9ncopcxPcnzneSbt0yTP78Txn0n2rbVuSsDTSdr3qLVumrXz3ST369y+MclhpZQnJWnWWl9Ra/2vrSzf3ENz589n/LhJ++ybj3Vu/zjJPp3bj03yT7XWDbXWlUku3NLz7LzGj0xyXdqzIVaUUr5ZSvntCV6aT3S2uyLtmSGP6LzehyX597ST8eWllB+VUo4Yt923a61LO7f/Ke33IAAwJOS+ct/Mgty3Y59SyhWb/Ttr3OM/rbX+rPN8r0xycacY/JO0Z8lv8r3OzzNpf9lwWOf209JuD5Mk30t7Rvte47bbPLaHp/1lxB/VWn+d3ryPgQHSIgPoqVrrmrT7p/1l59S4Y5N8ZlwyvDXjk9rPp52svzbJ07PlJPELaSe9P0/y6LS//X5w2snv5XfmmFmQ9ulai5IsH7f96gni2CPtZHDz57a6lHJJ2t/wH5y7Jze7JXlGJ4FM2l/ibd+5fUjaMzf2SdJKO+Ea/yXfmnG3W0ma4+7f2IlpS1aMu70m7VMB15VSvpjkeaWUj3T2NdGpZM8upTy6c/s3ab/mZ3fu757kpk0r1lpv3sL2ry6lvCDtU/52SLttRGqtnyulLEo7cTywlPLJtE/nm2j5+s3GPboz9vzOazG+r3Grk4gmd32tFuWur+PqJPO39KRrrT9L8rIkKaUclPbsi69tNrNmk81f44WdMZYneX2S13dOXzw+yUXjxhj/fl69aTsAYHjIfeW+nXVndO6bTg/mCR5L2jPh79hf2u0/Nt0e/3PbYl6c9nvklM6Zfhs78Y/fbvMvLz7U2eem5btl6u9jYIAUmIGe6Zzatm/tXDW71npDkneXUv4k7VPBNk8eJyy41Vr/u5TS6iTnR6WdbG/u80nel3a7gn+ptd5cSlmeZO2WEqhSyqFp99HdZPEEu9/aRdo+k+TZnefznM0eW57k7+tmV9vu+GTayeuHartH7y+3so97YtG42wtzZ5J2fmd/a9Ke6bFxgu0/Xye+EvSv0k60lyVJp4h6R9yllN9N8sa0T+1cVkr5vSR/u+nxWut5Sc4rpeyd9h9Ef5bkbydaPm7cvTv3H1XbPe32T/KzSV6HpJ1sTvrzLaU8LMmttdbaifPyUsoJabe8WLSFTRalfSX1pPMal1IOSLJLrfXHnTGWJTmplPKi3DnjZPfNxpioBQcAMAvJfeW+mQW5b4/d7fXvtML4XJI/qbVe1Gkrctsk4zwv7cke70p79n8v3sfAAGmRAfTSfZJcUEp5xKYFpZRD0j6N65K0WxKUUsoOnb5qz55kvM8neWuS/+qc9rW5HybZNFNk04yKq5P8opTy7M7+dy+lnF9K2bmz/lGllJ06+//jCfZ7YyZOXL6Udt+31rjTwzb5cpJndr65TynlaaWUN3Ye2yPJpZ0E+wVpX8Bjlwmf+V0tzl1nC4z3zM7ruXPaPdG+21n+zbS/8X91tuEUwQl8Oe3XNqWUg9M+JW/8F5ObZrtc03k9X5Bk51JKo5RyaqfYmlrrL9Mu0I5NtHwLz/fXSa4o7b5rL+3EMNnr9cO0Z/U0S/siLU+eYL0nJflEZ5bRpivAH5P2qYG/2sL6z+usd2CSB6T9Xn5Yks+XUu44fbGU8odp99W7vLPo0eNmMz87d/5sAIDhIPeV+86G3LeXHt2ZlZ7cmd/u3Pn3H53lr0l7dvjW4v95khOS/HEp5XHpzfsYGCAFZqBnaq0/TDsh+mAppZb26Xtnp3114avTvprzj9L+Rv5rufuFRDb3+bRPEdxin61O37sL0r4oyVfGLfvTJCeUUq5I8q9p97f7dWed76fdO+5f0u7XtiWXJtm3lLLbFvb567SvFP65LTz24yTvTPvCb5enfUXtTc/x1CRfLKX8d9rJ1nlJ/raUcv+tvQCllD3SPs3v0glW+Wbar+vlndtf78TS6sTY7Dznbrwxyb1L+0rfn03yvE7fs02+nvZsg/9LcnGS96YzayTtq2U/v/M+uCLtJPMftrJ8vMvS/tn8LO2E8itpv+aTXTH6bzv7vyrtXnpfnGC9M9L+uXy7lFI78R+Zu144Z7xlpZT/SvKNtC9qsqrW+tm0Z1x8sfNc/i/tP2h+f9wpjP8vyd+UUq5N+w/Nd08SPwAwi8h95b6ZHblvsuUezFeUUh45yT429+Uk7y+lXJX2BQbPqLXelHZ+/Z+llP9M+/W5IMmFnQLxFnW+RHl52v2ld8nU38fAADXGxjb/8gyAUsrX0774yCcGHMdLkzy51vr0Lrb98yS711r/vPeRsTWllLcmufdWTsEEAJgx5L4ATIUZzABb9va0L97WnHTNPun0MzsxyTu62HZx2jNqPtTruAAAGDpyXwC6psAMsAWdi7VcnOSUAYbx1iRfrLVeck82KqW8LO0eaO/eQq88AAC4C7kvAFOhRQYAAAAAAF0xgxkAAAAAgK4oMAMAAAAA0JXRQQewrVasuFkvj1ls4cKdsnr1rYMOA+Ycxx4MhmNv9lq8eH5j0DF0Q648e/m8gMFw7MFgOPZmt4lyZTOYmRajowO7GDHMaY49GAzHHrCtfF7AYDj2YDAce8NJgRkAAAAAgK4oMAMAAAAA0BUFZgAAAAAAuqLADAAAAABAVxSYAQAAAADoigIzAAAAAABdUWAGAAAAAKAro4MOAACA4dZsNno6Xqs1tk3rnXPOWfnf//2fNBqNvOY1r89BBz2wp3EAAEC3BpEj9ys/7muBuZTyoCRfSnJ2rfXczR57YpJ3JmkluajW+rZ+xgIAwPRrNhtZtGiXno65atUtkybQ//mfl+YXv7g25533sSxbtjSnn35azjvvYz2NY6rkygAAc1Oz2ciiXXZMxrZt4sSkGo2suuW2rebI/cyP+9Yio5Syc5L3J/nWBKuck+RZSQ5P8qRSysH9igUAgLnl0ksvyWMe87gkyb777pebb16bX//6lsEGNY5cGQBgjhsbS8bSo3+TF6r7mR/3swfz+iRPTrJ88wdKKfdLsqrWem2tdWOSi5Ic2cdYAIZSs9nY6r/J1gEYVitXrsxuu+12x/3ddluYlStXDjCiu5Er3wOT/b7r5T8AgGHUz/y4by0yaq0bkmwopWzp4T2TrBh3/8Yk99/aeAsX7pTR0WbvAmTaLV48f9AhwND5yle+krVr13a17YIFC/LUpz61xxEBm/i91z/b0nJjxx23y4IFO97xcxgdHcmiRTvPmJ+LXPke+k2rd6eQbk2jkWw//a/jTHlfwlzj2IPBcOx1rN/Q0+Emy5H7mR/PlIv8TTpVYPXqW6cjDvpk8eL5WbHi5kGHAUOl2Wxk7dq1WbNmTddjbEsfU+Ce83vvToPqwbzzzrtm6dJf3PFzuO66GzIysuOkP5cZ+gfPnM6Vm81GFu28Q/v0z74bm/bfjT4vYDAcezAYjr22O/KbHposh+k2Px5voly5ny0ytmZ52jMzNtk7Wzg9EAAAuvHIRx6a73yn3d641iuy++67Z6eddh5wVNtMrgwAQE/1Mz8eyAzmWuuyUsqCUsq+SX6R5ClJjh5ELAAADJ8HP/ghKeWgvPzlL0qj0cjrXvfGQYe0zeTKAABzQKORnp2i1Zj8OhL9zI/7VmAupTwiyVlJ9k1yeynl2Um+nGRprfWLSV6R5PzO6p+ttf6sX7EAADAYrVa75UCvx9wWr3jFq3q6316SKwMAzF2t1lhW3XJbz8ecTL/y435e5O/SJI/byuP/muSwfu0fAICZQa/3u5MrAwDMbcOUIw+qBzMAAAAAALOcAjMAAAAAAF1RYAYAAAAAoCsKzAAAAAAAdEWBGQAAAACArowOOgAAAIZbs9no6XjbesXtq676eU4++fV5znOel2c96zk9jQEAAKZiEDnytddek3POOSs33bQ6rdbGPPjBv53jjz8x22+//ZT2rcAMAEDfNJuNjIxsSKvV6tF4zSSjkybQt912W84++8w84hGP7Ml+AQCgV5rNRq687pbctm5DT8bbcYfR7L/XLlvNkVutVk455c9z4okn5WEPe0TGxsby3veemY997G/zspcdP6X9KzADANBXrVarZwXmJGk0Jk9ht9tuu7znPe/LJz/59z3bLwAA9Mpt6zbk1z0qMG+LSy75UfbZZ9887GGPSJI0Go288pWvTqMx9Q7KCswAAAyd0dHRjI5KdQEAIEmuuWZZ9t//gLssmzdvh56M7SJ/AAAAAABDrZGNGzf2ZWQFZgAAAACAIXbf++6bn/70f++y7De/+U2uuurnUx5bgRkAAAAAYIgdcsijcsMN1+V73/vXJMnGjRvzwQ++P9/61v+b8tga0wEA0FfNZrOnY23LmX1XXHF5zj337Fx//XUZHR3Nt7/9rbzznWdmwYJdexYLAAB0a8cdeleW3ZaxRkZGctZZ5+aMM96Rj33sb7PddtvlkEMelRe+8CVT3r8CMwAAfdNqjSUZTaPRm7Rz48ZNY27dgQcelHPP/XBP9gkAAL3Uao1l/7126fmYk9l9991zxhln93S/iQIzAAB9ti3JLgAAzCXDlCPrwQwAAAAAQFcUmAEAAAAA6IoCMwAAAAAAXVFgBgAAAACgKwrMAAAAAAB0ZXTQAQAAMNyazUZPx9vWK25/4APvy2WX/VdarVae//xjc8QRT+hpHAAA0K1B5MjLl/8y733ve7Jq1cps3LgxD33ow/Kyl52QefPmTWnfCswAAPRNs9nI9ddfk/Xr1/dkvHnz5mXPPfeZNIH+8Y//I1dd9X8577yPZc2am/LCFx6twAwAwIzQbDZy0jd+ll+sWdeT8e696w4586gDtpojb9y4MW9+80k5/vgT8zu/88gkyfnnfzJnnvnOnHLKX01p/wrMAAD01fr167NuXW+S5231kIc8LAcd9MAkyS67zM+6devSarXSbDanNQ4AANiSX6xZl2Wrbp22/V1yyY9y73vvc0dxOUn+9E+PznOf+6ysXr06Cxcu7HpsPZgBABg6zWYzO+64Y5Lkwgu/lMMO+13FZQAA5qyrr16WAw4od1nWaDRyv/vdP9dee/WUxjaDGQCAofXd734nF174pZx99t8MOhQAABiYsbGNabVaW1g+lrFtu8TJhMxgBgBgKP3oRz/MJz7x0Zx11jnZZZddBh0OAAAMzD777JtaL7/LsrGxsSxbdlX22ee+UxpbgRkAgKFzyy235AMfeF/OOOO9WbBg10GHAwAAA/XIRx6BO7/aAAAgAElEQVSaZcuW5oc//N4dyz772U/lQQ/67Sn1X060yAAAoM/mzZs37WN961sX56abbsqpp558x7JTTjkte+65Z89iAQCAbt171x2mdaxms5mzznp/3v72v8yHPvQ3ScbyoAf9dt7whjdNef8KzAAA9E2rNZY999yn52NO5mlPe2ae9rRn9nS/AADQC63WWM486oCejzmZJUv2zgc+8Hf5yU8uy/vff3Ze//qTMzIy9QYXWmQAANBXrdZYT/8BAMBsN8gc+cEPfkgOPvhBOe64Y/LP//zNKT8XM5gBAAAAAOaQE098Q8/GMoMZAAAAAICuKDADAAAAANAVBWYAAAAAALqiwAwAAAAAQFdc5A8AgL5qNhs9HW9brpK9bt26vOMdb83q1auyfv36HHvsi3P44Y/paRwAANCtQeTIX/jCP+Yb37go22+/fdavX5eXvvT4HHLIo6a8bwVmAAD6ptls5Pvf/3bWrl3bk/EWLFiQww9//KQJ9Pe//6858MCDcvTRL8j111+XE088XoEZAIAZodls5OjT3p+ly2/syXj7Ldkjn3rLq7aaI1933fJ85SsX5O/+7hMZHR3Ntddek3e/++0KzAAAzHxr167NmjVrpnWfRx75pDtu33DDDdljjz2mdf8AALA1S5ffmCuvvX7a9nfLLbfkN79Zn9tvvz2jo6O5z332ybnnfrgnYyswAwAwtF7+8hflxhtvyBlnvHfQoQAAwMDsv/8BOeigB+aP//iPcthhh+fQQw/PEUc8PqOjUy8Pu8gfAABD60Mf+mje/e6/ztvedmrGxibvSwcAAMPq1FNPy7nnfjj7739APv3pT+S1rz2+JzmyAjMAAEPniisuzw03tE853H//klarlZtuWj3gqAAAYDDGxsayfv367LvvfvmTP3lePvzhj2fFihvvyJmnQoEZAIChc9llP85nPvOpJMmqVStz6623ZtdddxtwVAAAMBgXXvilnHHGO+6YsfzrX9+SjRs3ZuHChVMeWw9mAAD6asGCBdM+1tOf/qycfvrb8spXvjjr16/P6173xoyMmFsBAMDMsN+S3l2EelvGevKTn5qrr16Wl770Bdlxx52yYcOGnHjiSZk3b4cp71+BGQCAvmm1xnL44Y/v+ZiTmTdvh7z1re/o6X4BAKAXWq2xfOotr+r5mFvTbDZzwgkn9nSfmygwAwDQV9tSEAYAgLlkmHLkvhaYSylnJzk0yViS19RaLxn32PFJjknSSvIftdb+lNABAGCGkScDADAs+taIrpRyRJL9a62HJTkuyTnjHluQ5KQkj6m1PjrJwaWUQ/sVCwAAzBTyZAAAhkk/r3RyZJILkqTWenmShZ2EOUl+0/m3SyllNMlOSVb1MRYAAJgp5MkAAAyNfhaY90yyYtz9FZ1lqbWuS/JXSa5KcnWSH9Vaf9bHWJhEs9mY0j8AALaZPBkAgKExnRf5u6MK2Zmh8RdJDkiyNsk/l1IeUmu9bKKNFy7cKaOjzf5HOUf99Kc/zfr167vadt68eTn44IMnXW/x4vldjQ/0z6JFuww6BBhafu9xD0wpT07mQK68fsO07WoQvxt9XsBgOPZgMBx7w6efBebl6czE6FiS5LrO7YOSXFVr/VWSlFK+m+QRSSZMnFevvrVPYdJsNrJ+/fqsW7eu6zFWrbplq1e/XLx4flasuLnr8YG768XZA5Mdu0B3/N67q16f7XRPPrfWr1+X5z//OTn22BfnyU9+6qTrT9MfPD3Nk5PhzpWbzUYW7bzDtO1vun83+ryAwXDswWA49ma3iXLlfhaYL0779L7zSikPT7K81rrpHbQsyUGllB1rrbcl+Z0kF/UxFgAABqDZbOSTn/xoVq5c2ZPx7nWve+WYY160zQXAj3/8I1mwYNee7LuH5MkAAAyNvhWYa60/KKVcWkr5QZKNSY4vpRybZE2t9YullDOTfLuUsiHJD2qt3+1XLAAADM7KlStz4403Tvt+r756WZYtW5rDDjt82ve9NfJkAACGSV97MNdaT95s0WXjHjsvyXn93D8AAHPXueeende+9s/zta9dOOhQ7kaeDADAsBgZdAAAANBrX/vahXngAx+cJUv2HnQoAAAw1Po6gxkAAAbhhz/8fpYv/2V+8IPvZcWKG7Pddttl8eI9csghjxp0aAAAMFQUmAEAGDqnnXb6Hbc/8pHzstdeSxSXAQCgDxSYAQDoq3vd614zciwAAGDqFJgBAOibVmssxxzzop6PeU8cd9zLerp/AADgTgrMAAD01T0tCAMAALPHyKADAAAAAABgdlJgBgAAAACgK1pkDIlms9H1tiMj3W8LAAAAAMxdCsxDoNlsZGRkQ1qtVlfbj4xs1+OIAAAAAIC5QIF5SLRarSkUmHVKAQAAAADuOQVmAAD6aiqtvLak1RqbdJ0f//g/8pa3nJx9971fkuT+939AXvvaP+9pHAAAgAIzAAB91Gw28pa3nJzly5f3ZLwlS5bktNPetU1F5oc+9OF5+9vP6Ml+AQCALVNgBgCgr5YvX55rrrlm0GEAAAB9oPkuAABDadmypXnjG1+bV7ziuFxyyb8NOhwAABhKZjADADB07nOfffLCF74kT3jC72X58l/mVa96WT772Quy3XbbDTo0AAAYKmYwAwAwdBYv3iNHHvmkNBqN7L33vXOve90rK1bcOOiwAABg6CgwAwAwdC6++Gv59Kf/IUmycuWvsmrVqixevMeAowIAgOGjRQYAAH21ZMmSaR/r0Y9+bN761lPyve/9S26//fa84Q0na48BAAB9oMAMAEDftFpjOe20d/V8zMnstNPOOeOMs3u6XwAA4O4UmAEA6KttKQgDAACzkx7MAAAAAAB0RYEZAAAAAICuaJEBAADQMTLSGHQIAACzigIzAABAkjSS+subc9u6DdOyux13GM2jFu0yLfsCAOgXBWYAAICO29ZtyK+nqcAMADAMFJgBAOirZrO3LQdarbFJ17n11lvz9rf/ZW6+eW1uv/32vPCFL8mjHnVYT+MAAAAUmAEA6KNms5HnPvdZWbp0aU/G22+//XL++V+YtMj8ta99Jfvsc9+8/OUn5Fe/WpFXv/rl+fSnv9CTGAAAgDspMAMA0FdLly7NlVdeOa373HXX3fLzn/88SbJ27drsuutu07p/AACYKxSYAQAYOk984lG56KIL85znPD0333xzzjjjvYMOCQAAhpICMz0xMjJ5b8Wt9V/cll6KAADb6hvfuCi/9Vt75q//+v258sqf5V3vels+8pF/GHRYAMwgvb5GwNb4mxcYZgrMTNm8efPy3e/+c9auXdvV9gsWLMjhhz/eL1wAoGd+8pPL8qhHHZok2X//A/KrX61Iq9VKs9kccGQADFqz2chx5/84v1izblr2d+9dd8iZRx3gb15gaCkw0xNr167NmjVrBh0GAECSZO+975Of/vR/8rjHHZnrr78uO+64k+IyAHf4xZp1Wbbq1kGHATAUFJgBAOir/fbbb9rHetrTnpnTTz8tJ5zw0rRaG3LSSW/qWQwAAMCdFJgBAOibVmss55//hZ6POZmddtopb3vbu3q6XwAA4O4UmAEA6Cs9JwEAYHiNDDoAAAAAAABmJwVmAAAAAAC6osAMAAAAAEBXFJgBAAAAAOiKAjMAAAAAAF1RYAYAAAAAoCsKzAAAAAAAdEWBGQAAAACArigwAwAAAADQFQVmAAAAAAC6MtrPwUspZyc5NMlYktfUWi8Z99h9kpyfZPskP661vryfsQAAwEwhTwYAYFj0rcBcSjkiyf611sNKKQcl+WiSw8atclaSs2qtXyyl/E0pZZ9a6zX9igcAAHqplPKWrT1eaz1tgu3kyQAADI1+tsg4MskFSVJrvTzJwlLKgiQppYwkeUySL3ceP17SDADALLNd59/BSZ6WZGGS3ZM8K8n9trKdPBkAgKHRzxYZeya5dNz9FZ1la5MsTnJzkrNLKQ9P8t1a65u2NtjChTtldLTZr1hnvZUr1w86hClZtGiXQYcAc5JjD/pn8eL5gw6BPqu1npokpZQvJ3lkrbXVub9dks9uZdOe5snJHMiV128YdAR95fMChp+8G+7k997w6WsP5s00Nru9d5L3JVmW5KullD+stX51oo1Xr761v9HNYs1mY/KVZrhVq25JqzU26DBgVunFse/Yg/5YvHh+Vqy4edBh0IUu/+DZJ3fNdceS3PcebD+lPDkZ7ly52Wxk0c47DDqMvvJ5AdNrEH9Dy7uhTZ48u02UK/ezwLw87ZkYmyxJcl3n9q+SXF1r/b8kKaV8K8kDk2w1cQYAgBnoq0l+Vkq5NMnGJA9PpwXGBOTJAAAMjX72YL44ybOTpHN63/Ja681JUmvdkOSqUsr+nXUfkaT2MRYAAOiLWuubkxyV5Pwk/5jkabXWk7ayiTwZAICh0bcCc631B0kuLaX8IMk5SY4vpRxbSnlGZ5UTk3ys8/iaJF/pVywA/dJsNqb0D4DZr5QyL8mT0u7D/IUk80spE/Z0kCczSFPNXeQ6AMDm+tqDudZ68maLLhv32M+TPLqf+wfop2azkZGRDWm1Wl1u30wyqhcbwOz3gbQLwYd37j88yWuT/OlEG8iTGYRms5GjT3t/li6/cVr2t9+SPfKpt7xKrgMAQ246L/IHMHRarVbXBeYkaTR8DAMMgQNrrYeXUr6dJLXWD5ZSnjvooGBLli6/MVdee/2gwwAAhkg/ezAD8P/Zu/c4Oe+6XuCf3dk2vSUlbVPbcgfLl3I5CBwuPQiFFuEIKgJVBAELRblbbgIHKRZFKiAWEJSqgHgQREBAEbSCgkABS8upIuUnl7aAaWmahN6TNpucP2YCy5I0m8k8M3t5v1+vfWUuz/N7vjs7M/nsd3/zewBYCbYN/t2RJFV1cJIDJ1cOAACMjwYzAADsm/dV1SeS3KGq3pTk/yV594RrAgCAsVjQZ7Or6oQkf5DkuPRnZvx7khe01j7XYW0AALDotdbeXFVfSPLgJFuT/FJr7fzJVgUAAOOx0MU/35DkhUk+m2QqyQPTP5nJPTuqa8XZlzMsT09PZR+WgAUAYB9U1ctba69Kct7g+uFV9XettZ+dcGkAANC5hTaYN7bW/nnO9X+qqv/uoqCVqNebymGHHbJPY2zcuGVE1QAAsJfuUFVvbK2dVlUnJfmTJK+fdFEAADAOC20wf6Gqnp/kH9Nft/nEJF+pqjskSWvtmx3VBwAAi1pr7alVdUZV/VuS/ZM8qrX25UnXBQAA47DQBvMTBv/++rzbfyH9NZnvMLKKAABgCaiqE+dc/dckd0y/wXxkVZ047xOAAACwLC2owdxau33XhQAAwBJz+s3cviOJBjMAAMvezTaYq2pNklNba2cNrj89yTOTfD3Js1tr3+2+RAAAWHxaaw9Jkqq6V2vtgknXAwAAkzC9h/vPTnJkklTVnZKcmeRFSf4pyRu7LQ0AAJaE3590AQAAMCl7WiLjDq21xw8un5zkfa21jyf5eFU9/mb2AwCAleJbVfXJJJ9PcuPOG1trr5hYRQAAMCZ7ajBfO+fyg5O8bc717SOvBgAAlp6LB19z7ZhEIQAAMG57ajDPVNWRSVYnOT7J45Kkqg5JcnDHtQEAwKLXWnvl/Nuq6nWTqAVgqer1psZ2rOnp8R0LYCXYU4P595J8JclBSc5orW2uqgOTfCbJn3ZdHAAALHZV9VNJXp3k8MFNq5JsSvIbEysKYAnp9abytcuuzQ1bto3leGvXrBrLcQBWipttMLfWPlZVRyc5sLV29eC2G6rqxa21c8ZSIQAALG6vSvLcJG9Icmr6n/r79EQrAlhibtiyLdeNqcF84Ko9zbUDYG/s8V21tXZTVW2rqkckuWv668n9R+eVAQDA0nB1a+3zVXVja+0/k7yiqj6W5J8mXRgAAHRteoHbvSvJS9L/2N+6JL9ZVe/orCoAAFg69quqn0yyuap+paruk+T2ky4KAADGYaGfCzm2tXbfnVeqairJ57spCQAAlpSnJzkq/TWX35zkx9JfkxkAAJa9hTaYv1VVB7XWrh9cPyDJNzqqCQAAlozWWkvSqmpdkie01q6cdE0AADAuN9tgrqr/m/6aywcl+XpVfT7J9iT3S/LF7ssDAIDFraoel+SN6efmqaraluS5rbUPTrYyAADo3p5mMH98zuX3zrn8dx3UArDiTE9PTWRfAEbq5Uke0Fr7RpJU1Z2SfCCJBjML0uuN5/902QEA6MKeGszntNYuq6o7jKUagBVkeno669dfmq1btw61/+rVq0dcEQBDWr+zuZwkrbX/qirLybFHB66ayanvuSDfuWrLWI5371sdOpbjAAAry54azK9P8oQkn0j/I3/zaTwD7IOtW7dmy5bhfqlctWrViKsBYG9U1YmDixdV1R8m+af0l5M7KcnXJlYYS8p3rtqSSzZdv+cNR+CWhx4wluMAACvLnhrMz6yq57fWbp8kVfWMJM9MPzA/u+viAABgETt93vW7zbm8q8kZAACw7OypwfzWJJck319L7tVJfiHJHdM/kckvdVkcAAAsVq21h0y6BgAAmLQ9NZjv0Fp7/ODyyUne11r7RJJPVNUTui0NAAAWv6p6aJJnJTk0yffPotZaO3G3OwEAwDKxpwbztXMuPzjJ2+Zc3z7yagAAYOn54ySvSvKdSRcCAADjtqcG80xVHZlkdZLjkzwuSarqkCQHd1wbAAAsBf/VWnvnpIsAAIBJ2FOD+feSfCXJQUnOaK1trqoDk3wmyZ92XRwAACwBf1pVf5bk3CTbdt7YWvuLyZUEAADjcbMN5tbax6rq6CQHttauHtx2Q1W9uLV2zlgqBACAxe1lSa5LsmrObTuSaDADALDs7WkGc1prNyW5ad5tmssAANB3Y2vtIZMuAoDFaXoqmZ6e2vOGIzQ7u2OsxwNWtj02mAEAgJv1t1X1kCSfzQ8vkeGk2ADk6DUH5PFnvCkXr79iLMe7/TFH5i9f8VxNZmBsNJgBAGDfnJ7+CbB3/iY/Nbjcm1hFACwqF6+/Il/79uWTLgOgE9OTLgAAAJaiqnphkrTWVrfWppPcv7XWG1x+52SrAwCA8dBgBgCA4Txy3vXXzLl8uzHWAQAAE6PBDAAAw5l/xqapm7kPAACWJQ1mAAAYjrMnAQCw4mkwAwDAaOzYzWUAAFi2ZiZdAAAALFH/q6q+Nef6kYPrU0mOmFBNAAAwVhrMAAAwnJp0AQAAMGkazAAAMITW2qWTrgEAACbNGswAAAAAAAzFDGZgRev1pobed3p6KrOzIywGAAAAYInRYAZWrF5vKocddsg+jbFx45YRVQMAAACw9HTaYK6qs5LcP8mOJKe11s7bxTZnJjm+tfbgLmsBAIDFQk4GAGC56GwN5qo6IcmxrbXjk5ya5E272OYuSR7UVQ0AALDYyMkAACwnXZ7k76QkH0qS1tpFSdZW1Zp527w+yW92WAMAACw2cjIAAMtGlw3mo5JsmHN9w+C2JElVnZLkU0ku6bAGAABYbORkAACWjXGe5G9q54WqOizJU5I8NMktF7Lz2rUHZWam11FpTNq+nmgNGI7XHnRn3brVky6BpWOfcnKyArLy1m2TroAhyRos2MXfm3QFy4rXHouZnLz8dNlgXp85MzGSHJPkssHlE5OsS/LpJKuS3LGqzmqtPX93g23efH1XdU5crze14t/8N226NrOzOyZdBiuM157XHnRl3brV2bDhmkmXwRDG9AvPSHNysgKy8sEHTLoMhiRrsBC93tSeN2KveO2xWMnJS9vusnKXS2Sck+TkJKmqeyVZ31q7Jklaa+9vrd2ltXb/JI9OcsGeQjMAACwTcjIAAMtGZw3m1tq5Sc6vqnPTPzP2s6vqlKp6dFfHBACAxU5OBgBgOel0DebW2kvn3XThLra5JMmDu6wDAAAWEzkZAIDlYpwn+QMAAAAAFglroDMKGswAAAAAsML0elP52mXX5oYt28ZyvAMPmMn9DjtkrE1tJ7scDw1mAAAAAFiBbtiyLdeNq8G8aianvueCfOeqLWM53q0OPSCve/idNJnHQIMZAAAAAOjcd67akks2XT/pMhix6UkXAAAAAADA0qTBDAAAAADAUDSYAQAAAAAYigYzAAAAAABD0WAGAAAAAGAoGswAAAAAAAxFgxkAAAAAgKFoMAMAAAAAMJSZSRcAADBuvd7UPu0/O7tjRJUAAAAsbRrMAMCK0utN5fLLv5WtW7cOtf+qVaty1FG30WQGAACIBjMAsATtywzk6empbN26NVu2bBlhRQAAACuTBjMAsKT0elOZnt6W2dnZofafnt5vxBUBAACsXBrMAMCSMzs7uw8NZuc4BgAAGBW/YQEAAAAAMBQNZgAAAAAAhqLBDAAAAADAUDSYAQAAAAAYigYzAAAAAABD0WAGAAAAAGAoGswAAAAAAAxFgxkAAAAAgKFoMAMAAAAAMBQNZgAAAAAAhqLBDAAAAADAUDSYAQAAAAAYigYzAAAAAABD0WAGAAAAAGAoGswAAAAAAAxFgxkAAAAAgKFoMAMAAAAAMBQNZgAAAAAAhqLBDAAAAADAUDSYAQAAAAAYigYzAAAAAABDmZl0AQDAytPrTQ297/T0VGZnR1gMAAAAQ9NgBgDGqtebymGHHbJPY2zcuGVE1QAAALAvLJEBAAAAAMBQNJgBAAAAABiKBjMAAAAAAEPRYAYAAAAAYCgazAAAAAAADEWDGQAAAACAocx0OXhVnZXk/kl2JDmttXbenPsekuTMJLNJWpKntda2d1kPAAAsBnIyAADLRWczmKvqhCTHttaOT3JqkjfN2+RPkpzcWntAktVJ/ndXtQAAwGIhJwMAsJx0uUTGSUk+lCSttYuSrK2qNXPuv3dr7TuDyxuSHN5hLQAAsFjIyQAALBtdLpFxVJLz51zfMLjt6iRprV2dJFV1dJKHJTn95gZbu/agzMz0uqmUiTvssEMmXQKsSF57MJyFvHbWrVs9hkpYokaak5MVkJW3bpt0BQxJ1mDBLv7epCtYVrz2WLBl/trzWhiPTtdgnmdq/g1VdWSSv0vyrNbaxpvbefPm67uqa+J6vakV/4TftOnazM7umHQZrDBee157TMZyeO3t6bWzbt3qbNhwzRgrYlQm9IeBfcrJyQrIygcfMOkyGJKswUL0ej/yNsg+8tpjIVbCa89rYbR2l5W7bDCvT38mxk7HJLls55XBxwA/luQ3W2vndFgHAAAsJnIyAEDHpqeS6enxNtFXajO7ywbzOUlemeTsqrpXkvWttblTeV6f5KzW2j90WAMAACw2cjIAQMeOXnNAHn/Gm3Lx+ivGcrzbH3Nk/vIVz12RTebOGsyttXOr6vyqOjfJ9iTPrqpTklyV5B+TPDnJsVX1tMEu726t/UlX9QAAwGIgJwMAjMfF66/I1759+aTLWPY6XYO5tfbSeTddOOfyqi6PDQAAi5WcDADAcjE96QIAAAAAAFiaNJgBAAAAABhKp0tkAADww3q9fTuT9Uo8aQgAALB4aTADAIxJrzeVd73r7dm4ceNQ+x9++OF54hOfqskMAAAsGhrMAABjtHHjxlxxxRWTLgMAAGAkrMEMAAAAAMBQNJgBAAAAABiKBjMAAAAAAEPRYAYAAAAAYCgazAAAAAAADEWDGQAAAACAoWgwAwAAAAAwFA1mAAAAAACGosEMAAAAAMBQNJgBAAAAABiKBjMAAAAAAEPRYAYAAAAAYCgazAAAAAAADEWDGQAAAACAoWgwAwAAAAAwFA1mAAAAAACGosEMAAAAAMBQNJgBAAAAABiKBjMAAAAAAEPRYAYAAAAAYCgzky4AAGCpmZ6e2uM2vd6PbrOQ/QAAAJYSDWYAgL2watWqfPrT/5yrr756r/c9+uijO6gIAABgcjSYAQD20tVXX52rrrpqr/dbvXp1B9UAAABMjjWYAQAAAAAYigYzAAAAAABD0WAGAAAAAGAoGswAAAAAAAxFgxkAAAAAgKFoMAMAAAAAMBQNZgAAAAAAhqLBDAAAAADAUDSYAQAAAAAYigYzAAAAAABD0WAGAAAAAGAoGswAAAAAAAxFgxkAAAAAgKFoMAMAAAAAMBQNZgAAAAAAhqLBDAAAAADAUDSYAQAAAAAYykyXg1fVWUnun2RHktNaa+fNue+hSV6dZDbJR1trv9NlLQAAsFjIyQAALBedzWCuqhOSHNtaOz7JqUneNG+TNyV5bJIHJHlYVd2lq1oAAGCxkJMBAFhOulwi46QkH0qS1tpFSdZW1Zokqao7JNnUWvt2a217ko8OtgcAgOVOTgYAYNnocomMo5KcP+f6hsFtVw/+3TDnviuS3LHDWpa9Xq+3T/uuWrVq6P3333//rFmzZuj916xZk+npqaH3n7TZ2R2TLoEJ8tqbHK+9lW2pvvYOOeSQHH744UMf+/DDD5/469ZrbyTk5L01NZX+aiKdHygHHtDpKoI/5IBVvdzq0APGdryjVq/K7Y85cmzHG+exGL1eb3z/30xPe+2Nktfe0ua1Nzpee+MzvmdRcnOvkD2+etatW710uyBjcItb3GKf9r/LXfbtk5c//uM/vk/7w1LltQeTsZRfeyeeeOI+HZtlaZ9yciIrj9L97nrUWI/3tmPXjfV4+Zm7jvd4sED3u8XBYz2e1x70ee0xCl0ukbE+/RkYOx2T5LLd3HfLwW0AALDcyckAACwbXTaYz0lycpJU1b2SrG+tXZMkrbVLkqypqttV1UySnxlsDwAAy52cDADAsjG1Y0d3a5lV1e8leVCS7UmeneSeSa5qrX2wqh6U5DWDTT/QWvv9zgoBAIBFRE4GAGC56LTBDAAAAADA8tXlEhkAAAAAACxjGswAAAAAAAxFgxkAAAAAgKFoMLPPqqo36RpgJaqqo6vq9pOuA1aaqjqqqm496TqApUFWhvGTk2Ey5OSVy0n+2CdVdUKSSvI3rbUrJ10PrBRV9cgkL09yXZLLW2tPnHBJsCJU1cOTvOXYVL4AACAASURBVCLJNUkuaa09Y8IlAYuYrAzjJyfDZMjJK5sZzOyrX09yYpJHV9URky4GVoKqulX6r70ntdYemuTOVfWcCZcFy15V3T3Jy5I8M8ljk6ypqgMmWxWwyMnKMEZyMkyGnIwGM/vqhiTfTnJckscKzjAWNyY5IMns4PprksxMrhxYMbYmuai19u9JbpvkJ5K8uqreMtmygEVMVobxkpNhMuTkFU6DmX31stbabyT5ZJI7JTm5qtYlSVVNTbIwWMY2J3lla+3iObfdZ+eFqhKioRvfTfK+weWHJ/mrJK9NcseqevvEqgIWM1kZxktOhsmQk1c4azCzT6pqurW2fXD5sUkekOTzSdYlmWmtvXGS9cFKUFU/k+QXW2tPrqonJTkmyWtba97gYUyq6qNJfqW1tmHStQCLh6wMkyUnw+TJySuDv96xT1pr26tqqrW2o7X2garakP6i7uuSPGHC5cFKcUWSr1TV/ZP8SpJfF5qhW1W1f5Ijk2xLcs8khyTZMtGigEVHVoaJk5NhzOTklUmDmX3WWtuxMzgnWZP+X4Uf3VprEy4NVorvJjk9yclJftlrD8ZiVZInp/+x24OSPLO1ds1kSwIWI1kZJkpOhvGTk1cgS2QwMlXVS/LTSVpr7WuTrgdWiqqaTn821Ltaa1+fdD2wUlTVmiSrk8y21i6fdD3A4iYrw/jJyTAZcvLKo8HMSM2ZnQGMUVXNtNa2TboOAGD3ZGUYPzkZoHsazAAAAAAADGV60gUAAAAAALA0aTADAAAAADAUDWYAAAAAAIYyM+kCANi9qrpdkpbkc4Ob9ktyaZJntda+t4vtT0ny0NbaE8dVIwAATIKsDLA4aDADLH4bWmsP3nmlql6X5OVJXjSxigAAYHGQlQEmTIMZYOn51yRPr6r7JXlDkhuTbEry5LkbVdWjk7w4yZb03++f1Fq7pKpOS/LEJNcPvp6YZFWSv0wyleTAJGe31t4+nm8HAABGRlYGGDNrMAMsIVXVS/KYJJ9O8q4kv9paOyHJp5I8ct7mt0jyuNbaQ5J8NMlzBrf/dpKfGez3hiTHJHlckq8OZn+ckOSgjr8VAAAYKVkZYDLMYAZY/NZV1ScHl6fTD8zvSPKi1tqXk6S19obk++vK7fTdJO+squkkR+UHa9O9Lck/VNX7k7yvtfZfVXVTkmdV1Z8n+fskZ3f6HQEAwGjIygATZgYzwOK3obX24MHXg1prv5lkNjfzHl5V+yV5b5JfG8y++MOd97XWXpDk59P/qOCHquqnW2tfTXKX9Gd6PDTJJzv7bgAAYHRkZYAJ02AGWIJaaxuTXFlV90mSqnphVT1rziark2xPcklVHZDkUUlWVdXaqjojybdba3+c5C1J7ltVT0hyn9bax5M8K8ltqsqnXAAAWHJkZYDx8oYIsHQ9KckbBx/Z+97g+mOSpLW2qareneS8JJcmeV2S/5v+jIvVSc6rqs1JbkpyapIjk7y1qramf/KS17TWto35+wEAgFGRlQHGZGrHjh2TrgEAAAAAgCXIEhkAAAAAAAxFgxkAAAAAgKFoMAMAAAAAMBQNZgAAAAAAhqLBDAAAAADAUDSYAQAAAAAYigYzAAAAAABD0WAGAAAAAGAoGswAAAAAAAxFgxkAAAAAgKFoMAMAAAAAMBQNZgAAAAAAhqLBDAAAAADAUDSYAQAAAAAYigYzAAAAAABD0WAGAAAAAGAoGswAAAAAAAxFgxkAAAAAgKFoMAMAAAAAMBQNZgAAAAAAhqLBDAAAAADAUDSYAQAAAAAYigYzAAAAAABD0WAGAAAAAGAoGswAAAAAAAxFgxkAAAAAgKFoMAMAAAAAMBQNZgAAAAAAhqLBDAAAAADAUDSYAQAAAAAYysykCwBWjqq6d5LXJrll+n/g2pjkN1prnxnR+GcmubS19tYRjPXEJE9rrT14N/e/JsmWJO9IcnGS17fWXjRvm48n+fHW2u32tZ5dHP/MJNtaa6fv5v5PJvmz1tq7hhx/Oskrk5ycZCrJfkn+Nv2f17ahih6x3f2Mqup26f9M2uCm6SSXJzmttfalER37lCRPbK09dBTjAQDIyqMjK9/8z6iqfir9+g9Lvy90SZJfb619paqek+THWmunV9Ul6WfeH3kOdv18BZYWM5iBsaiqqSR/l+QPWmt3bq3dKcnrkny4qg4axTFaa/9nFIF5T6rq+CSPSPI7g5uuSPLzg6C5c5sfS3LHDsv4rSS/UFX36mj8X0vyk0nu01q7c5KfSHKfJC+62b0Wj9nB82znc+0tST5UVftPujAAgPlk5ZGTlXejqm6R5H1JnjN4rv14kr9P8oGqmmqtvXl3jfk5Y3T+fAWWFjOYgXE5IsnRST6/84bW2t9U1b+11q6fPyN07vWq+vMkm5I8NMkHkpyW5MidswOq6kNJ/iHJ/ZN8PcmaJAe21p47uP+IJJcmOSb9v7D/8aCWrUme0lr74iDwvinJz6U/2/VTN/O9nJ5+mNpWVUlyQ5JvJHngnP1+McknBjVnUMevJXlBkgOSfC7JU1trNwwC9juT3C7JqiR/2Fr7g8E+lyQ5M8mpSW6d5N2ttRe21m6sqjcm+c0kj91NnXevqn8bfK//kOQZSf4qyRdaa78/GP9uSf4lydHzZlvcPcmXW2vXJklr7ZqqelSS6wf73WrwONZg+9Naax+rqhcmOaG19nOD7c5J8uHW2lvmFlb9B+5tSQ5Pf8bH6a219wzu25HkyYPH6qgkr22tnbWXP6Mf0lp7b1X9YZI7J/n3qjo9yRPT/3/wovSfa9+rqjPSf47cI8m7k7wxyeuTPDrJTUn+tLX2ujnfx5uTPDzJjUke11r78kJrAgCYQ1aWlb+v46x8bJIdSS6cc9ubkvxVa23HIA/fqrX2tMF9Jw5y9BFJ3tlae3kW9nz9xfRnNf+v9J8Dj26tfW03NQFLnBnMwLhcmeS8JP9SVadW1e2TpLX2nQXuf1KS+7bWXpl+YHpgkgz+Qn5i+mF6p/cn+dk51382/QB7TZIPJfmLwV/Zn5H+X9lnkvzvJA9LcpckJyR50K6KqKo16QfhD8+766+TPGHO9V9Kf2bAzv0emP4sjhMHHwO8Kj+Y1fHyJBcPZj+clOTMqrr1nLEelOT4JPdO8txBYE2SDyZ55M3MEnhIkgenH2xPSPIzSd4zr85HJ/nALj7K97EkT6+qN1bVQ6rqgNbaxtbaDYP735nk/w0ex0ckeVdVHZ7kDUluWVUPG4Ts1emH6/l+P8lHWmvHJXlqkrdV1X5z7r9ra+2e6QfkV1dVLwv8Gd2MmSRbBx/ne076s0yOTf8XlefM2e4RSR7RWntDkl9Oct8kd0ryP9N//O872O5+Sd7RWjs2/V88nr+X9QAA7CQry8pzdZmV/zPJ1Uk+WVVPqKqjW2uzrbXLdrP9vdPPwfdO8qyqukcW9nz9qSRvaa3dMf3n1Wt3Mz6wDGgwA2PRWtuRfsj4YPqzKr5ZVf9ZVY9Z4BCfaK1tGVx+f/phKukHqX9rrW2Yc6x/SzI1CD9JPxj+dfqzV49M8vbBdp9NsiH9v6o/KMnft9auHQTDv95NHfdKf+26TfNu/5skP1dV+1XVbZMclB+sAZz0g/t7W2vrB9ffmmTn9/7rSZ47qOmb6f9ScPs5+757EPrWJ/lu+rMz0lq7fLDt7j769/7W2vWttevT/9jb8Uk+muSOg1kROx+b987fsbX2kfTD8K3SD4SbqurPq2ptVR2cfiA/a7Dt15N8OskjW2uzSX41/Vm/v5fkV1tr23dR26PS/xhdknwm/ZkqR8+5//8O/r1gcN+RWfjP6IdU1dRgRsx3knyttXZ+klu31q4e1HZukjvM2eULrbUrB5cfkf7jeFNr7eokx6UfppPkosFYSfKlwWMFALDXZGVZeZ7OsvLg+z0+yb+lvw7z+qr6QlWdsJvH6S8Hj+8V6c+KPn6Bz9evtNZ2znD+QPrPI2CZskQGMDattavSXw/ttwYfdTslyV/NCbc3Z25IfX/6Yeb5SX4+uwh96YeYn6uqr6e/Ptovp/9RtoOSXPSDzJg16X/07LAk6+fsv3k3dRyZ/jpy87+3zVV1XvrLJdwlPxrobpHk0VX1sMH16SQ71wO+T/ozMW6TZDb98Dj3D4BXzbk8m6Q35/oVg5p2ZcOcy1el/9G+LVX1wSRPqKq3DY61y4/PtdY+nuTjgxkRD0h/JsUfJXlh+iczOXfO43hIkn8e7HdBVV2d/jrIu1sy4uFJXl5V65JsH4z3I99za212cIxeFv4zSpJeVX11cHkqyVeSPKq1tn0wi+Wsqnrw4P7D0v+lYqe5z7Ujknxv55XW2nVJMqjp6jnbzf+5AADsFVlZVp6j06w8aMa/MMkLq3+C7Gcn+ei8meE7zX+c1g7G2NPzde5zcvPO/YDlSYMZGIvBR9Vu1wZnFW6tfTfJa6rqF5PcNT8aBncbQFpr/15Vs4Pw8vDsemmC96e/fu5/JvnUYF209UmuHny8bn59909y6Jyb1u3m8FO7qyv9NdtOHnw/j5t33/r01yzb1Yk/3pX+DIe3DtY9+++bOcbeOGzO5bX5Qch7z+B4V6U/c+NHZk1U1U8nObe1dtVgpsW/VtXvpL/G3RXp/7z+58515+bt+8gk25IcUFWPaK19dN79+6X/kchfbK19tKpWpb8u255szsJ+RsngJH+7ue956S+Nce/W2rVV9bvprze4K1em32TeWfuPLbBWAIAFk5Vl5Tn3d5qVq+pOSQ5prV2QJK21S5L8RlU9NT/8qb6dfuRxWsDzNZmToQdjzJ/VDiwjlsgAxuXWST40WP82SVJV90lym/SXHLisf1MdMJhhevIexnt/kjPSX9ts4y7u/1ySnX9J3zlD4tIk36mqkwfHP6Kq3jP4GNvnkjy8qg4aHP8XdnPcK7L7QP3h9Ndxmx18fG+uv03ymMEshFTVo6rqJYP7jkxy/iAw/0qSg9Of5bAQ6/LDswrmeszg8Tw4yU+n/9G8JPl4+jNRfj27ntGSwX2vqaoDBvUekP7P5FODNej+Pv11+TJ4zN5eVbceHOuN6a9p/NwkbxncNtfBg68vDq6flv5J8vb0PS/0Z7QnRyb56qC5fNv0P964u2P/bZLHV9WqwffxmSR3G/K4AAC7IyvLyjt1nZXvmeT9VfX9ZvKcpvdFu9j+l6pquqqOTH9t709nz8/XwU11z8Hlk/ODxxdYhjSYgbForX0uya8l+eOqaoOP452V5HGttUvTP0naF5L8V/onzZh/YpD53p/+R/52t7bYjvTXQ3tokr+bc9svJXnOYPmEf01/vbrrBtt8Nv214D6V/vpru3J+kttV1S12cczr0j+T8vt2cd8FSV6d/sk0Lkr/rM87v8fTk3ywqv49/eB4dpI/rao73twDMAh5Rw9q2pWPp/+4XjS4/A+DWmYHNfYG3/OuPGFw/39UVUt/dsvGJC8e3P/MJCcMHscLknyztfbt9Ndx+0hr7T8G6/t9Ismr5j0W30v/JB9fqqovpX9W8Q8l+cguAvZcC/0Z7clbB7W39Ne/e0GSk6rqebvY9r1J/jHJ19JfZ/ltrbVzhzwuAMAuycqy8pzHotOs3Fp7b/rrP39w8Fz7RvoN8/+9czm4ec5Lf73mLyY5q7X2lQU8X5P+eU6eX1UXp78m+Et2MTawTEzt2LFj0jUALClV9Q/pn0zkLyZcx68leURr7eeH2PfFSY5orb14jxsDAMACycpU1SlJnthae+ikawHGwwxmgL33qvRPiDGxk7oN1mZ7XpLfHWLfdenPOHjrqOsCAGDFk5UBVhgNZoC9NDiZxTlJXj7BMs5I8sHW2nl72nCuqnp6+h9ve80u1r4DAIB9IisDrDyWyAAAAAAAYChmMAMAAAAAMJSZSRewUBs2XGOqNYvS2rUHZfPm6yddBsCS4r2TxWrdutVTk65hGLIyi5X3e4C9432TxWx3WdkMZthHMzMTO3cFwJLlvRNgZfB+D7B3vG+yFGkwAwAAAAAwFA1mAAAAAACGosEMAAAAAMBQNJgBAAAAABjKTJeDV9VZSe6fZEeS01pr582579lJnphkNskXW2vP67IWAABYLORkAACWi85mMFfVCUmOba0dn+TUJG+ac9+aJL+R5IGttZ9Mcpequn9XtQAAwGIhJwMAsJx0uUTGSUk+lCSttYuSrB0E5iS5cfB1SFXNJDkoyaYOawEAgMVCTgYAYNnocomMo5KcP+f6hsFtV7fWtlTVK5N8M8kNSf6qtfZfHdYCAMCE9HpTIx1vdnbHSMebgJHn5LVrD8rMTK+TYmFfrVu3etIlACwp3jdZajpdg3me7/9mMZih8bIkd0pydZJ/rqp7tNYu3N3OQjOLmTd/gL3nvXPluHH2uuzI9pGMNZXp7N87ZEHbvvrVr86FF16YqampvOxlL8v/+B//YyQ1dGCfcnKSbN58fbcVwpDWrVudDRuumXQZAEuG982VpdebyiGHHpRRTZ+YSnLtVdfvcULGm970+vznf345U1NTOe20F+a44+66oPF39ztclw3m9enPxNjpmCSXDS4fl+SbrbUrk6SqPp3k3kl2G5yFZhYrb/4Ae89758rR603l4EN3jKzBnCSbNl27x9D8pS+dn6997Rt585v/LJdccnHOOOO3c/bZ79jj2GP6w8dIczIAAEvXjsHXuHzpS+fnO9/5ds4++x255JKLc+aZC8vJN6fLNZjPSXJyklTVvZKsb63t/E3ykiTHVdWBg+v/M8nXOqwFAIAV5Pzzz8sDH/jgJMntbnf7XHPN1bnuumsnW9QPyMkAAExEFzm5sxnMrbVzq+r8qjo3yfYkz66qU5Jc1Vr7YFW9Lsm/VNW2JOe21j7dVS38qFGvhTjfMlgbEQBYwjZu3JiqO3//+i1usTYbN27MwQcvbHmNLsnJi5ucDAAsZ13k5E7XYG6tvXTeTRfOue/sJGd3eXx2rb++y9RIP6o611Smc+1VwjMAsHjs2LG4comcvDiNeh3E+Ra6LiIAwLiMIieP8yR/LCI7sr2zBnNftzM/AABuzhFHHJGNGzd+//qVV16ZI444YoIVsVSMex1EAIBx6iInd7kGMwAAZCrTI/1aiPve9/755Cc/kSRp7as54ogjctBBB3f5bQIAwF6bGvHXnnSRk81gBgCgM7OzO3LtVcnoPt20Y0HLC9z97vdI1XF5xjOemqmpqbzgBS8Z0fEBAGA0+ln5+pGPeXO6yMkazAAAdGpS680+85nPnchxAQBgoSaRlUedkzWYAQAAloler9tzoThBIQAwnwYznZieFmwBAGCcer2pfPnGK3Pdths7Gf/gmf1zt/2PkMUBgB+iwczITWUq1+23ITdtv6mT8feb3i8HbhVsAQBgvuu23ZhrO2owJ0n2725oAGBp0mCmEzdtvyk3be8u2B7Y2cgAAAAAwEJNT7oAAAAAAACWJjOYAQDo1KhPOmaZLJayLs9V0vV5UACA0VsOWVmDGQCAzvR6U7lh1ZUjOzfD3pyL4Zvf/Hpe+tIX5nGPe0Ie+9jHjeT4sC+mktyw/3S2bd/eyfirer3k2k6GBgA60OtNZesBvZFlg5np6azaMjv2rKzBDPMM85ejvdnHrCsAVppRn5thIediuOGGG3LWWa/Lve9935EdF0Zh2/btuWlHN3lwpqPGNQDQnZFmg+3bs2oBm406K2swwxy93lQ+vPHSbN66ZeE7rV/4pmtXHZBHHX5bTWYA6Nh+++2X3//9N+Zd73rnpEsBAIBFZdRZWYMZ5tm8dUs27k2DGQBYdGZmZjIzI+oCAMB8o87K0yMbCQAAAACAFUWDGQAAAACAofjcIAAAndpver9FORYAAEzazPR0MqIT9c5MTyeZHclYe3XcsR8RAIAVY3Z2Rw7cekQOHPGYe/LVr16UN7/5rFx++WWZmZnJv/zLJ/LqV78ua9YcOsJKAABgeLOzO7Jqy2xWjW7EiWRlDWYAADq1kJA7ane+83F585v/ZOzHBQCAvbEcsrI1mAEAAAAAGIoGMwAAAAAAQ9FgBgAAAABgKBrMAAAAAAAMRYMZAAAAAIChzEy6AAAAlrdeb2qk403iTNsAANCF5ZCVNZgBAOhMrzeVr83+V27YdsNIxjtw5sAc27vTgoLzH/3RG3Phhf8vs7OzedKTTskJJ5w4khpGoarOSnL/JDuSnNZaO29w+y2T/OWcTe+Q5KWttXePv0oAALrU603lyzdemeu23TiS8Q6e2T932/+IsWflThvMgjMAADdsuyHXbbt+dAP29rzJBRd8Md/85jdy9tnvyFVXfS9PecovL5oGc1WdkOTY1trxVXVckrcnOT5JWmv/neTBg+1mknwyyd9OplIAALp23bYbc+2IGsxJkv33vMmos3JnDWbBGQCASbnHPe6Z4467a5LkkENWZ8uWLZmdnU2vt4DudPdOSvKhJGmtXVRVa6tqTWvt6nnbnZLkA621a8ddIAAAy9eos3KXJ/n7oeCcZG1VrdnFdqdEcAYAYIR6vV4OPPDAJMlHPvLhHH/8/1oszeUkOSrJhjnXNwxum+9pSd42looAAFgxRp2Vu1wi46gk58+5vjM4z5+Z8bQkD9vTYGvXHpSZmUXzS8GSt3X2mkmXsE8OO+yQ7gZf393QSce1Aywh69atnnQJjMtlox1ub/4v/fjHP55//MeP5O1vf3tWr160z7kfObNLVR2f5Ku7mNW8S7Ly6Gyd3T7pEha1YbKs93uAveN9c4UZ4UpyyWSy8jhP8rdPwXnz5hE/2itYrzeVgw+ddBX7ZtOmazs5K+aoz9y5K13VDrCUrFu3Ohs2LO0/drIwXfzfutD/S7/whc/lz/7sj/P61/9htmxJtmzZ83NuTL/Qrc8Pz1g+Jj/ahv+ZJB9f6ICy8mj0c/JBky5jUdvbLOv9HmDveN9cWZZLVu6ywTzy4AwAwNJz4MyBYx/r2muvzR/90Rvzhjf8UdasWXR/WT8nySuTnF1V90qyvrU2P9HfJ8lfjb0yAADG6uCZBZyVb8RjjTord9lgFpwBAFa42dkdObZ3p2SEqzcsZEbGJz5xTr73ve/l9NNf+v3bXv7y385RR+1qqePxaq2dW1XnV9W5SbYneXZVnZLkqtbaBwebHZ3kiknVCABA92Znd+Ru+x+RjK7HPJGs3FmDWXAGACBZWMgdtUc96jF51KMeM/bjLlRr7aXzbrpw3v13H2M5AABMyHLIyp2uwSw4AwAAAAAsX9OTLgAAAAAAgKVJgxkAAAAAgKFoMAMAAAAAMBQNZgAAAAAAhtLpSf4AAKDXmxrpeJM40zYAAHRhOWRlDWYAADrT603lwxsvzeatW0Yy3tpVB+RRh992j8F5y5Yt+d3fPSObN2/K1q1bc8opT8sDHvDAkdQAAACj0OtN5S/++wu5cuu1IxnviFWH5Mm3vN/NZuUucrIGMwAAndq8dUs2jqjBvFCf/ey/5s53Pi6//Mu/kssvvyzPe96zNZgBAFh0rtx6ba644ZqxHa+LnKzBDADAsnPSSQ/7/uXvfve7OfLIIydYDQAALA5d5GQNZgAAlq1nPOOpueKK7+a1r33DpEsBAIBFY5Q5eXoE9QAAwKL01re+Pa95zR/kd37n9OzY4eSAAACQjDYnazADALDsfPWrF+W73708SXLssZXZ2dl873ubJ1wVAABMVhc52RIZAAB0au2qA8Y+1oUXXpDLL788p532wmzatDHXX399Dj30FiOrAwAARuGIVYeMdawucrIGMwAAnZmd3ZFHHX7bkY+5Jz//84/NmWf+Tp71rKdl69atecELXpLpaR/eAwBg8Zid3ZEn3/J+Ix/z5nSRkzWYAQDo1EIawqO2atUBOeOM3x37cQEAYG+MOyt3kZNN4wAAAAAAYCgazAAAAAAADEWDGQAAAACAoWgwAwAAAAAwFA1mAAAAAACGMjPpAgAAWN56vamRjjfuM20DAEBXlkNW1mAGAKAzvd5UXnPhZbn8uq0jGe+og1flJfc4esHBeevWLXnSkx6XU055Wh7xiJ8dSQ0AADAKvd5UXnnB27L+2itHMt4xhxyR37rXqQvKyqPMyRrMAAB06vLrtuY714ymwby3/vzP35Y1aw6dyLEBAGBP1l97Zb59zRVjP+4oc7I1mAEAWJYuvfSSXHLJxTn++AdMuhQAAFg0Rp2TNZgBAFiW3vzms/Lc5z5/0mUAAMCiMuqcbIkMlqTp6dEugN71uADAeH3sYx/JXe969xxzzC0nXQoAACwaXeTkThvMVXVWkvsn2ZHktNbaeXPuu3WS9yTZP8kFrbVndFkLy8fM1H5pN7XcsO2GkY+9dtUtRj4mADB+n/vcZ7N+/X/n3HM/kw0brsh+++2XdeuOzH3uc79Jl5ZETgYAYDK6yMmdNZir6oQkx7bWjq+q45K8PcnxczZ5fZLXt9Y+WFVvqarbtNa+1VU9LC83bLsh1227fuTjHtg7YORjAsBKd9TBq8Y+1m//9pnfv/y2t52do48+ZjE1l+VkAACSJMcccsRYx+oiJ3c5g/mkJB9KktbaRVW1tqrWtNaurqrpJA9M8vjB/c/usA4AACZkdnZHXnKPo0c+5hInJwMAkNnZHfmte5068jHHrcsG81FJzp9zfcPgtquTrEtyTZKzqupeST7dWvs/HdYCAMCETLohfOqpT5/o8Xdh5Dl57dqDMjPT66LWFWfr7PZJl7CoHXbYIXu9z7p1qzuoBGD58r7JuLz0pS8ayTjjPMnf1LzLt0zyxiSXJPn7qnpka+3vd7ez0DxaW2evmXQJK9YwoRxgORKc4fv2KScnyebNo186bCXq9aZy8KEHTbqMRW3Tpmv36o9G69atzoYNfvcAWCjvmyxmu/sdrssG8/r0Z2LsdEySywaXr0xyaWvtG0lSVZ9Ictckuw3OQvPo9IPzpKtYufY2lAMsR4Izi9WY/vAx0pwMAACTNN3h2OckOTlJBh/vW99auyZJ1IDG8wAAIABJREFUWmvbknyzqo4dbHvvJK3DWgAAYLGQkwEAWDY6m8HcWju3qs6vqnOTbE/y7Ko6JclVrbUPJnlekj8fnMjkP5L8XVe1AADAYiEnAwCwnHS6BnNr7aXzbrpwzn1fT/KTXR4fAAAWIzkZAIDlYpwn+QMAYAXq9ab2vNFecC4DAACWi+WQlTWYAQDoTK83lae//8v51qbRnLD5NocdlLNPvtseg/MFF3wxr3jFS3O7290hSXLHO/54nv/8F4+kBgAAGIVebypP+eD/yaWb149kvNuuPSbvePSZN5uVu8jJGswAAHTqW5uuzzeuvG7sx/2Jn7hXXvWq1479uAAAsFCXbl6fr1/5rbEec9Q5eXpkIwEAAAAAsKJoMAMAsCxdcsnFeclLnp9nPvPUnHfe5yddDgAALAqjzsmWyAAAYNm59a1vk6c85Vdz4ok/lfXr/zvPfe7T8973fij77bffpEsDAICJ6SInm8EMAMCys27dkTnppIdlamoqt7zlrXL44Ydnw4YrJl0WAABMVBc52QxmAAA6dZvDDhr7WOec87FceeWVecITnpSNG6/Mpk2bsm7dkSOrAwAARuG2a48Z61hd5GQNZgAAOjM7uyNnn3y3kY+5Jz/5kw/KGWe8PJ/5zKdy00035UUveqnlMQAAWFRmZ3fkHY8+c+Rj3pwucrIGMwAAnVpIQ3jUDjro4Lz2tf+fvTsPk6usEj/+ra4OJJ0ESEwji2IE4YgOOoKyuBHFcRsXFBQHnHFBBQcVl1FxxWUUB8UIgws6bqMjLvgDRwVlFHXGQRFxGxWPIiBoogQSspAQku7+/XFva9F0p7ur63Yt/f08Tz+pust7T71VXE6deu97V876cSVJkqTpmO1cuYo82TmYJUmSJEmSJElNscAsSZIkSZIkSWqKBWZJkiRJkiRJUlMsMEuSJEmSJEmSmmKBWZIkSZIkSZLUlP52ByBJkqTeVq/XWtrebN9pW5IkSapKL+TKFpglSZJUmXq9xnPe9SWuX72uJe0t33MJnzztqVNKnC+99BL+4z/+nXq9zgtecDIPfejDWxKDJEmS1Ar1eo2/e/dJXLf6hpa0d+899+H8V5+3w1x58+bN/PM/n87GjRvYtm0bz3veCznssCNmdFwLzJIkSarU9avXcc3v187qMdevv5WPfewjfOxjn2Lz5i189KPnWWCWJElSx7lu9Q385g/XzdrxLrnky+yzz704+eSXcPPNa3jZy07mM5/54ozadA5mSZIk9Zwf/vAHPPjBhzIwsJBly5bx2te+od0hSZIkSW236667sX79egA2bNjArrvuNuM2HcEsSZKknrN69Sq2br2d1772FWzcuJHnP/9FPPjBh7Y7LEmSJKmtHvOYx3HxxV/huOOOZuPGjZx55vtm3KYjmCVJktRzRkZg/fr1vOMd7+YNb3gL73znWxkZ8eaAkiRJmtu+/vWLufvd9+Bzn7uIs8/+ICtXnjnjNi0wS5IkqecsXbqUgw56AP39/ey99z0YGFjIrbe25kaDkiRJUrf6v//7KYcddjgA++9/ADffvIahoaEZtekUGZIkSarU8j2XzHpbhx56OO94x1s44YTnsHHjBrZs2dyS+eUkSZKkVrr3nvvMalt7731PfvnLn7NixVH88Y+rWbBggHq9PqPjWmCWJElSZYaGRvjkaU9teZuTGRzcnRUrjuKkk54LwCte8Wr6+rx4T5IkSZ1jaGiE8199Xsvb3JGnPvXpnHHG23jJS17E0NB2Xv3q1834mBaYJUmSVKmpFISrcPTRx3D00ce05diSJEnSVMx2rjwwMMDb3/6ulrbpMA5JkiRJkiRJUlMqHcEcESuBw4ER4NTMvLJh3fXAjcDoLNInZOYfqoxHkiRJ6gTmyZIkSeoVlRWYI+JIYP/MPCIiDgQ+BhwxZrMnZOamqmKQJEmSOo15siRJknpJlVNkHAVcBJCZVwNLImKXCo8nSZIkdQPzZEmSJPWMKqfI2AO4quH5mnLZhoZlH4qI5cB3gddl5oSzWi9ZMkB/f72KOOekrUMb2x3CnLV06aJ2hyBJHWFwcHG7Q5DapaV5Mpgrt9LWoeF2h9DRmsllPd9L0vR43lS3qXQO5jFqY56/GfgasJZiBMcxwAUT7bxu3ebqIptj6vUaC3dtdxRz19q1m2b9DqGS1GkGBxezZo0/dqrztOkL3YzyZDBXbpUiTx5odxgdbbq5rOd7SZoez5vqZBPlylUWmFdRjMQYtRewevRJZv776OOIuBg4iEkSZ0mSJKkHmCdLkiSpZ1Q5B/OlwLEAEXEwsCozN5bPd42Ir0fETuW2RwI/rzAWSZIkqVOYJ0uSJKlnVDaCOTMvj4irIuJyYBg4JSKeC6zPzAvL0Rjfj4gtwI9xVIYkSZLmAPNkSZIk9ZJK52DOzNPGLPppw7qzgbOrPL4kSZLUicyTJUmS1CuqnCJDkiRJkiRJktTDLDBLkiRJkiRJkppigVmSJEmSJEmS1BQLzJIkSZIkSZKkplhgliRJkiRJkiQ1xQKzJEmSJEmSJKkpFpglSZIkSZIkSU2xwCxJkiRJkiRJaooFZkmSJEmSJElSUywwS5IkSZIkSZKaYoFZkiRJkiRJktQUC8ySJEmSJEmSpKb0T3XDiFgC7AbURpdl5rVVBCVJkiR1g4g4EngvcCAwAvwMeGVmfq+tgUmSJEmzZEoF5oj4V+A5wM38pcA8AuxbUVySJElSN3gf8Crgfyny5EcAHwAe1M6gJEmSpNky1RHMK4DBzNxaYSySJElSt7klMy9reP5fEfGHtkUjSZIkzbKpFph/BdxRZSCSJElSF7oiIl4BfJ3i/iaPBn4ZEfuCU8pJkiSp9+2wwBwRbysfbgK+ExHfBbaPrs/MN1cYmyRJktTpji//fdmY5c/AKeUkSZI0B0w2gnmo/Pf68k+SJElSKTPv3e4YJEmSpHbaYYE5M98KEBF14KGZ+T/l8ycDX60+PEmSJKnzRMQuwImZubJ8fhLwYuAa4JTM/FM745MkSZJmS98Ut/sQ8MSG5yuAj7Y8GkmSJKk7nAfsDhARBwBnAP8E/BdwdhvjkiRJkmbVVAvMB2Tm60afZOarAC8HlCRJ0ly1b0N+fCzwhcz8RmaeB+zRxrgkSZKkWTXVAvOCiFg6+iQi9gLmVxOSJEmS1PE2NTxeAVzW8Hx4dkORJEmS2meym/yNehvwi4i4AagDewEnVhaVJEmS1Nn6I2J3YDFwBHAcQEQsAha2MzBJkiRpNk21wPxVYF/gfsAI8KvM3FxZVJIkSVJnexfwS2AAeEtmrouIBcB3gY+0NTJJkiRpFk21wHxZZj4KuKrKYCRJkqRukJmXRMSewILM3FAu2xIRr8nMS9scniRJkjRrplpg/klEvA24HLhjdGFmXjbxLpIkSVLvysxtEbE9Ip4I3J/iSr//m8q+EbESOLzc59TMvHKcbc4AjsjMFa2LWpIkSWqtqRaY/7r89xENy0a4881M7sLEWZIkST3u08A9gO8BNeANEfGszHzeRDtExJHA/pl5REQcCHyMYh7nxm3uBzwS2FZZ5JIkSVILTKnAXE6PcScRccyO9jFxliRJ0hywf2YeOvokImrA9yfZ5yjgIoDMvDoilkTELqNTbZTOAt4AvKXF8UqSJEktNaUCc0TsA7wEWFYu2hl4NPDFHexm4ixJkqRed0NEDDTcAHs+8NtJ9tmDO9/bZE25bANARDwX+A5wfUsjlSRJkiow1SkyPgVcAjwZOBd4KvD3k+zT0sR5yZIB+vvrUwxXk9k6tLHdIcxZS5cuancIktQRBgcXtzsEqWkR8SmKaeAGgGsi4vvAMHAY8MNpNldraHcp8DzgMcDeU23AXLl1tg4NtzuEjtZMLuv5XpKmx/Omus1UC8zbM/NdEfH4zHx/RHwUOB/4xjSONaPEed26zZNvpCmp12ss3LXdUcxda9duYmhopN1hSFJbDQ4uZs0af+xU55nGF7rGPPhzDY+/PIV9V1EMvBi1F7C6fPxoYBD4H4qrBveLiJWZ+YodNWiu3BpFnjzQ7jA62nRzWc/3kjQ9njfVySbKladaYF4QEfcAhiNiX+B3wPJJ9ml54ixJkiR1iEszc3WZG097X+CtwHkRcTCwKjM3AmTmBcAFABGxHPiEObIkSZI62VQLzGdSzKn8buAnwBDwmUn2MXGWJElSrzoLOB74JsVUGWNNWHjOzMsj4qqIuJxiWo1Tyunj1mfmhVUEK0mSJFVlhwXmiNgFeCNwX4rRxv8BLAUWZ+a6He1r4ixJkqQe9uKIeEVm3hsgIk4GXgz8Bjhlsp0z87Qxi346zjbXAytmHKkkSZJUoclGMH+AYqqLDwNPB07PzDcBOywujzJxliRJUo/6EOXNqiPiAOCdwDOA/YCzgWe1LTJJkiRpFk1WYF6emc8GiIhLKC4BlCRJkua6fTPz78rHxwJfyMxvAt+MiOPbGJckSZI0q/omWb9t9EFmDjH+/HKSJEnSXLOp4fEK4LKG58OzG4okSZLUPpONYB5bULbALEmSJEF/ROwOLAaOAI4DiIhFwMJ2BiZJkiTNpskKzA+NiBsanu9ePq8BI5m5T3WhSZIkSR3rXcAvgQHgLZm5LiIWAN8FPtLWyCRJkqRZNFmBOWYlCkmSJKmLZOYlEbEnsCAzN5TLtkTEazLz0jaHJ0mSJM2aHRaYM/N3sxWIJEmS1E0ycxsN9ywpl1lcVk/r66tNe596fer7DA05K6MkSd1mshHMkiRJkiQx0D+PT9z4fW7eumnyjUddO/VNl+28iH/Y+zCLzJIkdRkLzJIkSZKkKbl56yZu2rKx3WFIkqQO0tfuACRJkiRJkiRJ3ckCsyRJkiRJkiSpKRaYJUmSJEmSJElNscAsSZIkSZIkSWqKBWZJkiRJkiRJUlMsMEuSJEmSJEmSmmKBWZIkSZIkSZLUFAvMkiRJkiRJkqSm9Lc7AEmSJEmSakBfX63SYwwNjVTaviRJc5EFZkmSJElS2y3deSGn//DfWLXp5kra32vRMk4/+ESLzJIktZgFZkmSOkC97ogtSZJWbbqZGzfe1O4wJEnSNFhgliSpzer1Glt2vpltw9sqaX9e3zwWbF1mkVmSJEmS1HIWmCVJ6gDbhrexbfiOytpf6JyWkiRJkqQKWGCWJKnH9dfmkduSLdu3VNL+gv4F7F8/wCKzJEmSJM1BFpglSZoDtmzfwm3bN1d3gHp1TUuSJEmSOldfuwOQJEmSJEmSJHUnRzBLkiRJkiSpY9Xr3k9E6mSVFpgjYiVwODACnJqZVzaseyFwIjAE/BQ4JTP9L1qSJEk9zzxZkqSpqddrbJ1fZ/vwcCXt9/f1sfPtQxaZpRmobIqMiDgS2D8zj6BIkM9pWDcAPAt4RGY+DLgvcERVsUiSJEmdwjxZkqTp2T48zLaRkUr+qipcS3NJlXMwHwVcBJCZVwNLImKX8vnmzDwqM7eVSfSuwB8rjEWSJEnqFObJkiRJ6hlVTpGxB3BVw/M15bINowsi4jTgVOB9mXntjhpbsmSA/n5vUd8qW4c2tjuEOWvp0kXtDkFSB9rQ5aflZs5tg4OLK4hE6gotzZPBXLmVtg45kq2XmYtL3em2TbdV2n6nnRvMk9VtZvMmf3eZkT0z3xURZwMXR8R3M/N/J9p53brNlQY3l9TrNRbu2u4o5q61azc5t5OkO6nXazCv3VHMzHTPbYODi1mzpsur6upJbfpCN6M8GcyVW6XIkwfaHYYqZC4udZ96vQY7VXuTv046N5gnq5NNlCtXOUXGKoqRGKP2AlYDRMTSiHgkQGZuAS4BHlZhLJIkSVKnME+WJElSz6iywHwpcCxARBwMrMrM0Z9g5gGfiIjRaxAOBbLCWCRJkqROYZ4sSZKknlHZFBmZeXlEXBURlwPDwCkR8VxgfWZeGBFvA74VEduBnwL/WVUskiRJUqcwT5YkSVIvqXQO5sw8bcyinzas+wTwiSqPL0mSJHUi82RJkiT1iiqnyJAkSZIkSZIk9TALzJIkSZIkSZKkplhgliRJkiRJkiQ1xQKzJEmSJEmSJKkpFpglSZIkSZIkSU2xwCxJkiRJkiRJaooFZkmSJEmSJElSUywwS5IkSZIkSZKaYoFZkiRJkiRJktQUC8ySJEmSJEmSpKZYYJYkSZIkSZIkNaW/3QFIaq16vVZp+0NDI5W2L0mSJEmSpO5hgVnqIfV6jZMu+Dk3rN1cSfv7LB3gvGP/yiKzJEmSJEmSAAvMUs+5Ye1mfnvzbe0OQ5IkSZIkSXOAczBLkiRJkiRJkppigVmSJEmSJEmS1BQLzJIkSZIkSZKkplhgliRJkiRJkiQ1xQKzJEmSJEmSJKkpFpglSZIkSZIkSU2xwCxJkiRJkiRJaooFZkmSJEmSJElSUywwS5IkSZIkSZKaYoFZkiRJkiRJktQUC8ySJEmSJEmSpKZYYJYkSZIkSZIkNaW/ysYjYiVwODACnJqZVzasexRwBjAEJPCCzByuMh5JkiSpE5gnS5IkqVdUNoI5Io4E9s/MI4ATgXPGbPJh4NjMfBiwGHh8VbFIkiRJncI8WZIkSb2kyikyjgIuAsjMq4ElEbFLw/pDMvP35eM1wN0qjEWSJEnqFObJkiR1kL6+GvV6NX/SXFDlFBl7AFc1PF9TLtsAkJkbACJiT+CxwJt21NiSJQP099eriXQO2jq0sd0hzFlLly5qdwgz0u3xS51qQ5eflps5NwwOLq4gEqkrtDRPBnPlVto65GwkvcxcVupOt226rbK2+2s1fnb7Tdy2/Y6Wt72wfyceucfyae9nnqxuU+kczGPc5WebiNgd+DLwj5l5y452Xrduc1VxzTn1eo2Fu7Y7irlr7dpNDA2NVNL2bPw6WmX80lxVr9dgXrujmJnpnhsGBxezZk2XV9XVk9r0hW5GeTKYK7dKkScPtDsMVchcVuo+9XoNdqr2u+5t2+9gUwUFZjBPVm+ZKFeussC8imIkxqi9gNWjT8rLAC8B3pCZl1YYhyRJktRJzJMlSZLUM6qcg/lS4FiAiDgYWJWZjT/BnAWszMyvVRiDJEmS1GnMkyVJktQzKhvBnJmXR8RVEXE5MAycEhHPBdYDXwf+Adg/Il5Q7vKZzPxwVfFIkiRJncA8WZIkSb2k0jmYM/O0MYt+2vB45yqPLUmSJHUq82RJkiT1iiqnyJAkSZIkSZIk9TALzJIkSZIkSZKkplhgliRJkiRJkiQ1xQKzJEmSJEmSJKkpld7kT5IkSZIkSb2tXq9V1nZfX3VtS2oNC8ySJE2BSbMkSZJ0V/V6jUW7DjBSUfs1YOOWLRW1LqkVLDBLkjSJImmuMcJwJe3X6OPWzZU0LUmSJFVupPyrgkMxpM5ngVmSpCkYYbjCArNpsyRJkiSpO3mTP0mSJEmSJElSUywwS5IkSZIkSZKaYoFZkiRJkiRJktQUC8ySJEmSJEmSpKZYYJYkSZIkSZIkNcUCsyRJkiRJkiSpKRaYJUmSJEmSJElNscAsSZIkSZIkSWqKBWZJkiRJkiRJUlMsMEuSJEmSJEmSmmKBWZIkSZIkSZLUFAvMkiRJkiRJkqSmWGCWJEmSJEmSJDXFArMkSZIkSZIkqSkWmCVJkiRJkiRJTbHALEmSJEmSJElqigVmSZIkSZIkSVJT+qtsPCJWAocDI8CpmXllw7r5wHnA/TPzwVXGIUmSJHUS82RJkiT1ispGMEfEkcD+mXkEcCJwzphN3g38pKrjS5IkSZ3IPFmSJEm9pMopMo4CLgLIzKuBJRGxS8P61wMXVnh8SZIkqROZJ0uSJKlnVFlg3gNY0/B8TbkMgMzcWOGxJUmSpE5lnixJkqSeUekczGPUZrLzkiUD9PfXWxXLnLd1yO8t7bJ06aJ2hzAj3R6/1CzPmzvWzLlhcHBxBZFIXWlGeTKYK7fS1qHhdoegCpnLStXw3Dkx82TNBVUWmFfRMBID2AtY3Wxj69ZtnnFAKtTrNRbu2u4o5q61azcxNDRSSdv1+oy/n06qyvilTuV5c3LTPTcMDi5mzRqL9uo8s/SFrqV5Mpgrt0pxvh9odxiqkLms1HqeO3fMPFm9ZKJcucopMi4FjgWIiIOBVV7uJ0mSJJknS5IkqXdUVmDOzMuBqyLicoo7Y58SEc+NiKcBRMQXgM8WD+PbEXF8VbFIkiRJncI8WZIkSb2k0jmYM/O0MYt+2rDuGVUeW5IkSepU5smSJKkVqp4q02mFNBWzeZM/SZIkSZIkSS1Qr9d4648+yqpNN1fS/l6LlnH6wSdaZNakLDBLkiRJkiRJXWjVppu5ceNN7Q5Dc1yVN/mTJEmSJEmSJPUwC8ySJEmSJEmSpKZYYJYkSZIkSZIkNcUCsyRJkiRJkiSpKRaYJUmSJEmSJElNscAsSZIkSZIkSWqKBWZJkiRJkiRJUlMsMEuSJEmSJEmSmmKBWZIkSZIkSZLUFAvMkiRJkiRJkqSmWGCWJEmSJEmSJDXFArMkSZIkSZIkqSkWmCVJkiRJkiRJTbHALEmSJEmSJElqigVmSZIkSZIkSVJTLDBLkiRJkiRJkppigVmSJEmSJEmS1BQLzJIkSZIkSZKkplhgliRJkiRJkiQ1xQKzJEmSJEmSJKkpFpglSZIkSZIkSU2xwCxJkiRJkiRJaooFZkmSJEmSJElSUywwS5IkSZIkSZKa0l9l4xGxEjgcGAFOzcwrG9Y9BngnMARcnJlvrzIWSZIkqVOYJ0uSJKlXVDaCOSKOBPbPzCOAE4FzxmxyDnAM8DDgsRFxv6pikSRJkjqFebIkSZJ6SZUjmI8CLgLIzKsjYklE7JKZGyJiX2BtZt4IEBEXl9v/ssJ41KBW6ewofczrm1dZ6/P6+lnQv6CStuf3z2fJzpU0DcCSnefT11errP2+vhr7LB2orP19lg5UGj/A0NBIpe1LzfK8ObEF/QuaOjfU69WeT+YKz5tdyTy5g1V9Zurv64Ph4Urarvf1sbB/p0raBhioz2PZzosqa3/JTgPstWhZZe3vtWhZ5bls1Tznq1N57hzfwv6dKs2T+/pqnjcn4XlzdlRZYN4DuKrh+Zpy2Yby3zUN624C9ttRY4ODi7v7Ez3H7L1weaXt333gHpW1/cCllTU9K77wosPbHYKkJnTzebNZS5dWV6SQOlxL82QwV+4mew5U94MfwO4Lllfa/kMGq/3/yXPjoZW2L6k7dfu5c7qmkyef/ZhTK4xEmprZvMnfjpJeE2JJkiTNVebJkiRJ6lpVFphXUYzAGLUXsHqCdXuXyyRJkqReZ54sSZKknlFlgflS4FiAiDgYWJWZGwEy83pgl4hYHhH9wJPK7SVJkqReZ54sSZKknlEbGalusuuIeBfwSGAYOAV4ELA+My+MiEcC/1Ju+sXMfE9lgUiSJEkdxDxZkiRJvaLSArMkSZIkSZIkqXfN5k3+JEmSJEmSJEk9xAKzJEmSJEmSJKkpFpglSZIkSZIkSU2xwCxJkiRJkiRJaooFZmkCEVFrdwyS1G3GO3dGhPmGJPUQ82RJmj7zZPWy2sjISLtjkDpORNQyc6R8vAL4I3BbZt7Y1sAkqYONOXf+NdCXmT8au06S1L3MkyVp+syT1essMEvjiIi+zByOiFOAJwC/AhYBZ2Xmb9obnSR1tog4GXg68FvggcAjMnOovVFJklrBPFmSmmeerF7lUHypQUQ8JCL2KZPm+wBPyMwnATWglpm/iYj5bQ5TkjpKROwbEQeUjx8IPDkzHwv8BFhj0ixJ3c88WZKmzzxZc4UjmKUGEfFG4GiKXxRvBs4HrgDuC7wIeACwKDMva1uQktRBymLCucAfgI8CtwKvAXYHdgFOAA4BHpiZH2lXnJKkmTFPlqTpMU/WXOIIZunOzgD+H/BJiv8+fga8HnhxZt4OPBp4ekTU2xeiJHWGcr6424F3AYPAc4H7A/OA5cDbylEZDwTu5k2hJKmrmSdL0hSZJ2uucQSz5rzxJtSPiFcDjwdOAp4NPAn4MvBE4NnOLydprht77oyIPYDTgV9TjNJ4OMVl0zsDBwEnZuYv2xGrJKk55smSNH3myZqLLDBLpYg4keLylCuAy4BjgKdQXAp4KHA34MeZ+eu2BSlJHSYiXgDcD7ga+DSwEvgFkMAm4EHAJZl5bduClCTNiHmyJE2febLmkv52ByB1gog4ieIu2J8GTgTuBXwKGAa+DTwlM3/ftgAlqQNFxD8AfwecCvwU2Al4OfBuYAnwscy8vH0RSpJmyjxZkqbPPFlzjXMwa05qnN8oIvqAuwNvBpYB8ykm3H86xSiN9+OPMZI09ty5C8VotpeU/34ZeAPwQuAbwB7A5jaEKUmaAfNkSZo+82TNdU6RoTktIu6TmddExAOAA4DnUcwjdwLFCI29gEMzc30bw5SkjhIRLwWWUhQZfgusyMxnRsQjgc8CXwfekpm/a2OYkqQZME+WpOkzT9Zc5QhmzSljflV8AXBRRFwA3AD8CrilnIx/C/AB4DCTZklz3Zhz5/2ApwJ/AkaAw4C7R8R8YBHwduC1Js2S1F3MkyVp+syTpYIjmDUnlSMxnk6RHL8dWEhxycq/AzcBfwUcnZnZtiAlqQM03gW7LDjcC9icmWdExBEUc8s9H/gBsAB4lkmzJHUv82RJmhrzZOkvHMGsOWHMr4onUSTMDwXWZuZJwO3Aq4HnAO8BHmPSLEnQkDQ/Bfh7ips6PSEinpWZ3wM+AfwvsA44waRZkrqLebIkNcc8WfoLRzBrTomII4HHAp8H3gd8PzNfV677HLABODkzh9oXpSR1log4CDgd+FxmfiEijqYY3XZJZp4fEQ8BrsvMm9saqCSpaebJkjR95slSwRHM6mmjIzIioi8i9gK+AOyWmT+luFzlkIh4F0BmHge82aRZku7iTxTzbx4XEQdl5kXAF8vnx2TmlSbNktRdzJMlqSXMkyUsMGvu2CUzVwFHAX8TEX+XmX8EjgceHRFyoXrGAAAgAElEQVRvA8jM1e0MUpI6UWbeBLwXuAI4MSLun5lfAj4CfK+twUmSZso8WZKaZJ4sFZwiQz0pImJ0briIOAV4FMV8SJ+kuDnJ54DXZ+ZnI+JuwMLMvKFtAUtSF4iIZRRzcB4InJWZV7c5JEnSNJknS1LrmSdrrnMEs3pORDwY+Gz5+GnAkRQjMNYBz8jMK4ETgA+Vl6zcYtIsSZMrL+/7FPAzYG2bw5EkTZN5siRVwzxZc50jmNVzIiIoRl58CrgfxWUpy4AHUPyi+GjgcuCvgdWZeU2bQpWkrhQRdefhlKTuY54sSdUyT9ZcZYFZPSki3gucCHwduBnYCzg+MzdHxFuBb2Xmt9sYoiRJkjTrzJMlSVKrOUWGetUHgRdRjMh4KPBr4CkRcSrwRMBL/SRJkjQXmSdLkqSWcgSzelpEHAW8GdhEcTng44G3jt7YRJIkSZqLzJMlSVKrWGBWz4uIFcCHgFOAyzLTD70kSZLmPPNkSZLUChaYNSdExN8A12Tmde2ORZIkSeoU5smSJGmmLDBLkiRJkiRJkpriTf4kSZIkSZIkSU2xwCxJkiRJkiRJaooFZkmSJEmSJElSUywwS5IkSZIkSZKa0t/uACRJzYuIJwCvA4aAhcB1wEmZeWtbA5MkSZLayDxZkmaPI5glqUtFxE7Ap4HjMvNRmXkocD1wYlsDkyRJktrIPFmSZpcjmCWpey2gGI2xcHRBZr4WICIeAJwFzCv/XgJcC1wJPCEzfxsRnwB+mJnnznLckiRJUpXMkyVpFjmCWZK6VGauB04HfhIR34iIN0RElKv/Azg5M1cA/wj8W7n9S4BzI2IFsDfw/tmPXJIkSaqOebIkza7ayMhIu2OQJM1ARNwNeCzwKOCZwErgDcDlDZvtDURmDkfEh4HHAQ/PzBtnO15JkiRpNpgnS9LscIoMSepiETGQmbcA5wPnR8QXgA8DW8tRGePZA9gC7A6YOEuSJKnnmCdL0uxxigxJ6lIR8TjgexGxuGHxvsCPgesj4onldgdExJvLx88BbgGeAXw0Inae5bAlSZKkSpknS9LscooMSepiEfFS4O+BzUAN+BNwKsXoi3OAEYqbl7wS+B3wLeCIzFwbEe8Ads7Mf2pH7JIkSVJVzJMlafZYYJYkSZIkSZIkNcUpMiRJkiRJkiRJTbHALEmSJEmSJElqigVmSZIkSZIkSVJTLDBLkiRJkiRJkppigVmSJEmSJEmS1BQLzJIkSZIkSZKkplhgliRJkiRJkiQ1xQKzJEmSJEmSJKkpFpglSZIkSZIkSU2xwCxJkiRJkiRJaooFZkmSJEmSJElSUywwS5IkSZIkSZKa0t/uACR1t4g4BDgT2JviR6tbgFdn5ndb1P4ZwO8y80MtaOvZwAsyc8UE6/8FuB34OHAdcFZm/tOYbb4B3Cczl880nnGOfwawPTPfNMH6bwP/lpmfbrL9PuCtwLFADZgH/CfF+7W9qaBbbKL3KCKWU7wnOc5uR2XmH6bY/nOBZ2fmY2YY5/VlO019ziPi4cCnq/gcSZKkzmb+3DqzkD+/BXg58MdyUR/wTeBVmbm5mTZbHeM0jzVhDrqjz2VEPA14cmY+f0fxRsR+wEogykWbgbdm5kVVvB5JncMCs6SmRUQN+DLwwsz8arns6cCXIuKerUi6MvN1M21jKiLiCOCJwIOAewA3AUdHxGsyc7jc5u7AfhWGcTrws4i4MDN/VEH7LwIeDjwkMzdFxGLgEuCfgHdVcLxWG8rM+7Y7CEmSpGaZP7dc1fkzwAWZ+QKAiNgJ+DzwZuC0io4366bwubwQuHAKTf0H8KnMfErZxmHAZRFx38y8saLwJXUAC8ySZmIZsCfw/dEFmfn/IuIHmbl57GjRxucR8QlgLfAY4IvAqcDuoyNpI+Ii4GvA4cA1wC7Agsx8abl+GfA7YC+KX9k/WMayFXheZv6wHLF7DvAUilEH39nBa3kT8N7M3B4RAFuA3wKPaNjvmRQjFv48+jUiXgS8EpgPfA94fmZuKZPpTwLLgZ2Bf83M95b7XA+cAZwI3BP4TGa+KjPviIizgTcAx0wQ50ER8YPytX4NOBn4LHBFZr6nbP+vgG8Be44ZmXwQ8PPM3ASQmRsj4qkUIwuIiHuU/Tg64uDUzLwkIl4FHNmQKF4KfCkz398YWBQd91HgbhSjo9+UmeeX60aAfyj7ag/gzMxcOc33aEIRsYKiT68o21oLnEJROD8QOC8zTy83r0fEp4CHAuuBv8vMnMJ79jHgBOBvxhz7BcALgRXla79LH5bbvRE4CbiZYuS4JEmae8yfuyt/vpPyeB8G/hk4bZr5Yz/wCYr+Xwec1FAUv3c5Mnh/4L+BE0aL9A39Nu3+KddNJQed1udyvD7NzCGK7xuNbVwREQcAq8p8/Rzgv4AnATtR5OF/3l5S93IOZkkzcTNwJfCtiDgxIu4NkJm/n+L+RwGHZuZbKRLYRwBExADwaIrEedQFwJMbnj+ZIlndCFwE/HtmHkCRMH4pIvqBxwOPBe4HHAk8crwgImIXiqT3S2NWfR44vuH5s4AvNOz3CODtwKPLy8zWl88B3ghcV464PQo4IyLu2dDWI4EjgEOAl5bFXShGBvxt2QfjeRRFITPK1/Qk4PwxcT4N+OI4yfElwEkRcXZEPCoi5mfmLZm5pVz/SeAnZT8+Efh0RNwNeB+wd0Q8tixIL6b4QjLWe4CvZOaBwPOBj0bEvIb198/MB1F8YXlnRNSZ4ns0RQdTfBb2A4aBc4G/pXhvXx8R88vtHg58IDP3K/tkdPT2ZO/ZPTIzMvOG0QXlZYavBZ5S9uO4fRgR96P4IvXg8u8BM3idkiSpe5k/d1f+PJ55FEX5qcTcmD9+GDg/M+8DvAP4VMN2K4AnlDE+CnjYOMeddv9MIwed7udyvD4FuBi4ICJeFhEHlm38ITNHyvX3A36QmVH2wXjfKSR1IQvMkppWJgp/Q5HUnQpcGxG/KC+nmopvZubt5eMLKAqPUCS2P8jMNQ3H+gFQi4gHloueRpHA3hfYnWJ0AJn5v8AaitGpjwS+mpmbyuLf5yeI42CKeerWjln+/4CnRMS8iLgXMMCd5wB+MvC5zFxVPv8QMPraXwa8tIzpWoovAPdu2PczmTlU7vsnipEGZOYfy20PniDWCzJzc3n55FcpksiLgf3KEcSjffO5sTtm5lcoip73oPhSsTYiPhERSyJiIUWiuLLc9hrgf4C/LUcjvBA4i6IY+8KxIypKTwXeXT7+LsWolD0b1o8m0T8q1+3O1N8jKEYe/2rM32cb1t+amd8uP5e/AL5T9tMvgDowWG73m8z8Xvn48xR9CJO/Z18ZE88+FKNQnp6Zf9pRH5av8zuZ+aeyPyufY0+SJHUe8+fuyp/HimKKuVPK1zmVmL9S7jefIk88v1z+JeCwhu2+mJlbyisNf0ORr4/VTP9MKQdt4nM5Xp8C/D3wfopR2z+PiOsj4uSG/Tbxl8/UF4G/3sEPA5K6iFNkSJqRzFxPMffZ6eVlW88FPtuQyO5IY0J6AUVC8wrgaMZP8L5IkbBeQzEK9QSKy7AGgKv/kh+yC8VUBUuBVQ37r5sgjt0p5owb+9rWRcSVwOMofm0fm2DvBjwtIh5bPu+juNQL4CEUowr2AYYoCq2NP+qtb3g8RFEAHXVTGdN41jQ8Xk9xGd/tEXEhcHxEfLQ81riXM2bmN4BvlKOHH0Yx6vgDwKsobvx3eUM/LgIuK/f7UURsoJgH+ecTxPY44I0RMUgxgrg23mvOzKHyGHWm/h7B5HMwb2zcliKBJTNHImKYv/Tx2D5cUj6e7D0b+wXqfRSvc/SzsysT9+Ei7vye7+h1SpKkHmb+3F35M3BsedUawB0Ufb5yijGPvl9Ly+Wj+fAIZa5a2rCD1zaqmf5ZyhRz0Gl+Lu/Sp2Ubt1N8v3hPROwGPAN4X0RcRzHqe13DaOZby393o5yyT1L3ssAsqWnlZWnLs7zjdWb+CfiXiHgmcH/umhwtuWsrhcz8WUQMlQnM4ygS5bEuAM7mL6NTN0bEKmDDeIXHiDicoug3anDsNqXaRHFRzM92bPl6jhuzbhXwyRxzp+zSpykSzw+VBc4/7OAY07G04fES/pK0nl8ebz3FiIK7jDCOiCcAl2fm+nIEw39HxNsp5mu7ieL9enA5cmLsvn8LbAfmR8QTM/PiMevnUVz++MzMvDgidqaYh28y65jae9RKE/XhdN+z11BcFngexZe6CfswIl7M7L9OSZLUYcyfuyt/Lv35Jn/jmGrMtwAjFEX8m6O4qd5+FHNWT1Uz/TOlXHsKn8ux7tKnUczx/aDM/K+yjVuBj0TE4yl+1Pghxetv3A/uOohDUhdyigxJM3FP4KKIOGR0QUQ8hGLqgCuB1cWimF9e+nTsJO1dALyFYg7bW8ZZ/z1g9Nf00dEQvwN+HxHHlsdfFhHnl9MVfA94XEQMlMd/xgTHvYmJk+cvUcwvNlReitboP4GnlyN2iYinRsRry3W7A1eVyd9zgIUUo1inYpA7jwpo9PSyPxdSzNP2P+Xyb1AkbC9j4sv7XkaRKM4v451P8Z58p5xv7qsUc/BR9tnHIuKe5bHOBl5CcVne+8tljRaWfz8sn59KMcJjstc81feolaLhM3ssf+nD6b5n11CM8rhPRDxnR31I8TofHhGD5ejxZ7f+ZUmSpC5g/txd+fNkphRzZm4FLqV4H6D4QeDihtG8LTvWGFPNQSf7XI41Xp8OUMy//LiGNu5DMRXIaJ8PRMTR5eNjgR82TPkiqYtZYJbUtHIe2xcBH4yILC+9Wwkcl5m/o7gT8xXArylupjb2JiBjXUAxEnTcud7KBOwiihuKfLlh2bOAl0TEryjuuvzNzLyt3OZ/KeZ9+w7FXGvjuQpYXl7GNfaYt1HcCfkL46z7EfBO4NsRcTXFDTRGX+ObgAsj4mcUid95FL/g77ejDoiI3SkuMbtqgk2+QdGvV5ePv1bGMlTGWC9f83iOL9f/X0QkxUiWWyhG4gK8GDiy7McfAddm5o3AWylu3vd/5Vx+36S4c3ZjX9wKnAn8OCJ+TDEa4yLgK+MUoxtN9T2C8edg/lVEPG0H+4znMuBlEfEbipvYnFYun/Z7lpl3AM+huAzwHkzQh5n5E4o5Bn9E8d5+d5oxS5KkHmD+3HX582SmE/MLgCdHxLUUufTx42zTqmMBMNUcdAqfy7Hu0qdZ3MjwKRQ31/51mWt/DnhFZl5R7nc9RcH718DrgX+cTgdI6ly1kZHp/GAmSb0pIr5GcWOMf29zHC8CnpiZR0+68V33fQ2wLDNfM+nGkiRJ0gyYP2s6ImIF8G+ZeZ92xyKp9RzBLEmFfwZeVV461hZRzGP8cuAdTew7SDHq4EOtjkuSJEkah/mzJAmwwCxJAJQ3tLgUeGMbw3gLcGFmjjfP2YQi4iSKuY//ZZx57iRJkqSWM3+WJI1yigxJkiRJkiRJUlMcwSxJkiRJkiRJakp/uwOYqjVrNjrUWh1pyZIB1q3b3O4wJKmreO5UpxocXFxrdwzN6PZc2XNC+9j37WX/t4993172f/vY9+3V7f0/Ua7sCGZphvr723ZPC0nqWp47JTXynNA+9n172f/tY9+3l/3fPvZ9e/Vq/1tgliRJkiRJkiQ1xQKzJEmSJEmSJKkpFpglSZIkSZIkSU2xwCxJkiRJkiRJaooFZkmSJEmSJElSUywwS5IkSZIkSZKaYoFZkiRJkiRJktSU/nYHIEmSpN5Ur9da2t7Q0EhL25MkSZLarRdyZgvMkiRJarl6vcbSRQtgpEUJbq3G2k1bppQwn3POWfziFz+nVqtx6qmv4sAD79+aGCRJkqQWqtdrLNq1xgjDLWmvRh+b1k9eZG51vlxpgTki/gr4ErAyM88ds+4xwDuBIeDizHx7lbFIkiRplo2MQMsGUEytoR//+Cp+//sbOe+8j3P99ddxxhlv47zzPt6qIFrKXFmSJEkjDLeswFzY8YjoKvLlyuZgjoiFwL8C35xgk3OAY4CHAY+NiPtVFYskSZLmhquuupJHPGIFAMuX35uNGzdw222b2hvUOMyVJUmS1A5V5MtV3uRvK/BEYNXYFRGxL7A2M2/MzGHgYuCoCmPRGPV6rSv+JEmSpuOWW25ht912+/Pz3XZbwi233NLGiCY0p3Pl8XI+8z9JjZr9nuj3S0nasSry5cqmyMjM7cD2iBhv9R7AmobnNwH77ai9JUsG6O+vty7Aue6OodbNiViVWg126o73fHBwcbtDkKSu47lzDti6vaXNLV26aNJtFiyYxy67LPjz56u/v4+lSxd23OdtrufKH//NL1i39fa/LCjL7Et2ns/z9nfO7NnWaf99zDX2//juGLpt3EvGa/SxU33i/x9cseZHbNm+5S7LF/Qv4LDBg++0zL5vL/u/fez79hrb/1uHNra0/cly5iry5U65yd+kPyWuW7d5NuKYE+r1GksXzm/hnIhVGWHt2k0df8f4wcHFrFnT2pOBJPU6z52978/5RgtNJS9YuHBXrrvu93/+fK1e/Sf6+hZM+fPWoV+4eipXrtdrrNt6O7c0FpgbdEP+10s8H7eX/T++er3Gwl1HJpyTdKLzRL1eY8v2Ldy2ffxzYuN+9n172f/tY9+319j+L853rT3GZLnUTPLliXLlKqfI2JFVFCMzRu3NOJcHSpIkqYvVakVptCV/U7u0+dBDD+fb3y6mNc78FcuWLWNgYGHrXtPsMFeWJEmaI2r0tfRvMlXky20ZwZyZ10fELhGxHPg98CTghHbEIkmSpNYbGhph7aa7XqI80zYnc9BBDyTiQE4++fnUajVe+crXtjSG2WCuLEmSNDcMDY2waT1M4YK1KRqZNGeuIl+urMAcEYcAZwHLgW0RcSzwn8B1mXkh8GLg/HLzz2Xmr6uKRZIkSbOvXdMcvPjFL23LcafDXFmSJEnQnpy51flylTf5uwpYsYP1/w0cUdXxJUmSpE5lrixJkqRe0a45mCVJkiRJkiRJXc4CsyRJkiRJkiSpKRaYJUmSJEmSJElNscAsSZIkSZIkSWpKZTf5kyRJ0txWr9da2l477rAtSZIkVakXcmYLzJIkSWq5er3Gb1ZvYsvt21vS3oL5/ey/56IpJcw33ngD55xzFrfeuo6hoWEOOugBnHLKy9lpp51aEoskSZLUCvV6jS0738y24W0taW9e3zwWbF02pZz52muv4bTTXsVxxx3PMcccN6PjWmCWJElSJbbcvp3bWlRgnqqhoSHe+MbX8PKXv5oHPegQRkZGeN/73s3HP/4RTjrplFmNRZIkSZrMtuFtbBu+o2XtLZjCNlu2bGHlyndzyCGHtuSYFpglSZLUM6688gr22Wc5D3rQIQDUajX+8R9fRq3mrUckSZIkgHnz5vGe95zNpz/9yZa0Z4FZkiRJPeOGG65n//0PuNOynXee36ZoJEmSpM7T399Pf3/rysIO5ZAkSVIPqTE8PNzuICRJkqQ5wwKzJEmSesa97rWcX/7yF3dadscdd3Dttde0KSJJkiSptzlFhiRJkiqxYH7rUs2ptvWQhxzGBz5wNt/97n/z8Ic/kuHhYT74wX9lYGCAffe9T8vikSRJklphXt+8jmxrOiwwS5IkqeWGhkbYf89FLW9zMn19fZx11rmceeY7+PjHP8K8efN4yEMO43nPe2FLY5EkSZJmamhohAVbl7GgxW1O5le/uppzz13JH/+4mv7+fr71rW/yzne+m1122bWpY1pgliRJUiWmktxWYdmyZZx55sq2HFuSJEmajnbkzPe974Gce+6HW9aeczBLkiRJkiRJkppigVmSJEmSJEmS1BQLzJIkSZL+P3t3Hi9nWd+N/3MyiSFhEzAYFhGx8HUtilWhakGwWm0trfsuiooV3FGx7ktFccEFrVq11fporfVRq5Xqoz7WBWtdftL20V5ugGJAA0S2hJCcnN8fM9FjyDkJkzPnPsv7/XqdV2bue+aab65M7lz5zDXXBQAAQxEwAwAAAAAwFAEzAAAAAABDWdp1AQAALEy93tiMttfFDtsAADBKC2HMLGAGAGDG9XpjOf2L5+fiK9fPSHsH770ybzj+yJ0aMK9Z8/O8+c1vyBVXXJ4tW7bkTne6c0455bQsX758RmoBAICZ0OuN5YfjP8iGzRtmpL0VS1fk8N4ROzVmfsc73pLzz/9uxsfH89jHnpRjjz1+6NcVMAMAMBIXX7k+F667ZlZfc8uWLXnRi56XU099Vn7v9+6WJPnwhz+Y17/+NXnxi18xq7UAAMCObNi8IddunplJGUmS3o4f8p3vfCs/+cmP8653/W2uvPJXecITHi1gBgCAJPnmN7+Rgw8+5NfhcpI84hGPziMf+eCsW7cu++yzT4fVAQBA94488s657W1vnyTZY489c91112V8fDy93k6k09thkz8AABaMiy66MEccUb91bGxsLIcdduv87GcXdVQVAADMHb1eLytWrEiSfPrTn8wxx/z+0OFyYgYzAAALyMTEloyPj2/n+EQm7BEIAAC/9pWvfCmf/vQnc/bZb9+ldsxgBgBgwTjkkEPT2vd/69jExEQuvPAnOeSQW3ZUFQAAzC3f+MbX84EPvC9vfONbs8cee+xSW2YwAwAwEgfvvXLW27rb3Y7OW9/6xnz961/NMcfcM0nykY/8r9zhDr9r/WUAAOacFUtXzHpb11xzTd7xjrfkzW9+R/baa+9dfl0BM3PakiVjXZcAAAxhfHwibzj+yBlvc0d6vV7e+Ma35dWvflne+c63J5nIHe7wuzn99BfOaC0AALCrxscncnjviGT45Y+32+aOfOELn8uvfvWrvOQlZ/z62Itf/MqsXr16qNcUMDN3jSXt51dnw3Wbu65kSit2W5q777trXyMAgIVqZwa3o3DggQflHe94T/7rv87P2952dp773DOyZImV4QAAmHu6GDOfeOKDcuKJD5qx9gTMzGkbrtuca+dwwAwAzF13vOORud3t7pCTT35MHvvYJ+b44+/TdUkAALDgCJgBAFiwnvWs07suAQAAFjTfFQQAAAAAYChmMAMAACwwvd7Um2V3tT46ALAwCZgBAAAWkF5vLD8c/0E2bN5wg3Mrlq7I4b0jhMwAwIwRMAMAMBLTzaAchkAMdt6GzRty7eb12z/Zm91aAICpLYQxs4AZAIAZ1+uN5VFv+MtccOmaGWnvVqsPzIdOf81ODZg/9rF/zGc/+5nc5CY3ycaN1+UpTzk1d73r3WekDgAAmCm93lg+eflFWbfxuhlpb5/lu+XE/W457Zj5uuuuy1/91cuzbt0V2bhxY0466Um5xz3utUuvK2AGAGAkLrh0TX7485/N6mtecsmafOpTn8h73vOBLF26ND/72U/zute9WsAMAMCctG7jdbl8hgLmnfG1r305t7nNbfPoRz8+l156SZ71rFMFzAAAsNU111yT66/fmE2bNmXp0qW5xS0OyTnnvLvrsgAAYE444YT7/vr2L37xi+y///673KaAGQCABePww4/IbW97+zz0oX+aY465R44++h459th7Z+lSw14AANjqqU99Yn75y1/krLPevMttLZmBegAAYM54yUtemXPOeXcOP/yIfOhDH8izn31qJiZsEAgAAFu9853vy+te96a86lUv2eWxsoAZAIAFY2JiIhs3bsyhh94qD3vYo/Lud/9d1q79ZX7xi0u7Lg0AADr3P//z/V+PjQ8/vDI+Pp5f/WrdLrXpu4IAAIzErVYfOOttffrTn8x3v/udvPjFr8jY2FiuvfaabNmyJfvss8+M1QIAADNln+W7zWpb55//nVx66aV55jOfmyuuuDzr16/P3nvfdJdeV8AMAMCMGx+fyIdOf82Mt7kjD3jAA3PRRRfmKU95fFasWJnNmzfnWc96XpbP4MAdAABmwvj4RE7c75Yz3uZ0/uzPHpwzz3xVnva0J2Xjxo15znNekCVLdm2RCwEzAAAjsTOB8Ezr9Xo57bRnzfrrAgDAMGZ7zLx8+W55+cv/akbbtAYzAAAAAABDGekM5qo6O8nRSSaSPLO19s1J505N8pgk40m+1Voz1QQAgEXBOBkAgIViZDOYq+rYJIe31o5JcnKSt046t1eS5yW5V2vtnkluV1VHj6oWAACYK4yTAQBYSEa5RMYJST6RJK217yfZZzBgTpLrBz97VNXSJCuTXDHCWgAAYK4wTgYAYMEY5RIZq5N8e9L9tYNjV7XWrquqVyT5SZINSf6htfaD6RrbZ5+VWbq0N7JiF52Nm7uuYEFZtWrPrksAmHdcO1nEZnScnMzDsfKaqU/tu+8es1fHQnbJ1Ke27WPX427p/+3bOH71lOemvU54788b+r87+r5bC7H/R7oG8zbGtt4YzND4yyRHJLkqyRer6sjW2vlTPXnduvWjr3CR6PXGsu/uu3VdxoKydu3Ugx8AbmjVqj1dO5mTOhrw79I4OZlfY+Veb2za81dccc2s76a+0NyYPnY97pb+375ebyy77z31+amuE97784f+746+79b2+n9H164ba5TjqKnGyqMMmNekPxNjqwPzm88Sb5vkJ621y5Kkqr6S5C5Jph04AwDAAmCcDABAer2xvO78S3LptRtnpL3Vuy/PC448YKdC5o0br8tjH/vwnHTSk/KABzxwl153lAHz55K8Ism7quqoJGtaa1sj+guT3LaqVrTWNiT5vSSfGWEtAAAwVxgnAwCQJLn02o25+OqZCZhvjL/7u/dmr72m+brIjTCygLm1dl5VfbuqzkuyJcmpVXVSkitbax+vqtcn+b9VtTnJea21r4yqFgAAmCuMkwEA6NJFF12YCy+8IMccc48ZaW+kazC31s7Y5tD5k869K8m7Rvn6AAAwFxknAwDQlXPOOTvPfvbzc+65n56R9pbMSCsAAAAAAMxp55776dz+9nfMgQceNGNtjnQGMwAAAAAAc8PXv/61rFnz85x33lezdu0vs0rsWQ4AACAASURBVGzZsqxatX/uete7D92mgBkAAAAAoAOrd18+q2298pVn/vr2e9/7rhxwwIG7FC4nAmYAAAAAgFk3Pj6RFxx5wIy3OdsEzAAAAAAAHegiEN7q5JNPmZF2bPIHAAAAAMBQBMwAAAAAAAzFEhkwA3q9sa5L2KEuv3IBAAAAwMIkYIZdsGL50jzxn76Wi69c33Up0zp475V5w/FHCpkBAAAAmFECZthFF1+5Pheuu6brMgAAAABg1gmYAQAAAAA6MNPLrnbx7XUBMwAAAADALOv1xnLKP/13fnrFzCy9esi+K/Ouh9xh2pD5O9/5Vl760jNy6KGHJUlufevfybOf/fxdel0BMwAAAABAB356xfr8+LJrZ/U173Sno/LqV581Y+0tmbGWAAAAAABYVMxgBgAAgO2Ybl3MLta4BICZcOGFF+QFL3h2rrrqqjzxiU/OXe969C61J2AGAACAbfR6Y9lj77FMZMsNzo1lSa65UsgMwPxzi1sckic84ck5/vg/zJo1P8/Tn35KPvKRT2TZsmVDt2mJDAAAANiOiWyZ8gcA5qNVq/bPCSfcN2NjYznooIOz3377Ze3aX+5Sm2YwAwAAAAB04JB9V85qW5/73Lm57LLL8qhHPTaXX35Zrrjiiqxatf8uva6AGQAAAABglo2PT+RdD7nDjLc5nXve8w/y8pe/OF/96r9l06ZNOf30M3ZpeYxEwAwAAAAA0InZXs9/5crdc9ZZZ89om9ZgBgAAAABgKAJmAAAAAACGImAGAAAAAGAoAmYAAAAAAIZikz9YBJaMJUuWjHVdxk6Z7cXtAQAAABiegBkWgQP2XJlHnPXCXHDpmq5LmdatVh+YD53+GiEzAAAAsCj0ejM7IbCLTEXADIvEBZeuyQ9//rOuywAAAAAg/XD58a/9ZC68ZN2MtHfoAfvk/WecOG3IvH79+rz61S/L1VdflU2bNuUJT3hy7n73Y3bpdQXMAAAAAAAduPCSdfnRxVfM2uude+6ncsght8xTn3paLrtsbZ7xjKfmQx/62C61aZM/AAAAAIBFYO+9b5orr7wySXLVVVdl771vusttmsEMAAAAALAI3Oc+98tnPvPpPPzhf5arr746Z5315l1u0wxmAAAAAIBF4LOf/UxufvPV+chHPpG3vOWvc/bZZ+1ymwJmAAAAAIBF4L/+6/zc/e5HJ0kOP/yIXHbZ2oyPj+9Sm5bIAAAAmKN6vbEpz023QzwAMD8cesA+s9rWQQfdIt/73n/nuONOyKWXXpIVK1am1+vt0usKmAEAAOagXm8se+w9lolsucG5sSzJNVcKmQFgPhsfn8j7zzhxxtuczoknPihnnvnKnHbaUzI+vjnPe94Ld/k1BcwAAABz1ES2bDdg7pt6djMAMD/M9ofFK1euzKte9doZbdMazAAAAAAADEXADAAAAADAUATMAAAAAAAMRcAMAAAAAMBQbPIHAAAAN9KSJdvfZHG2N2sCgK4JmAEAAOBGGMtYrl22Npu2bPqt48uWLMuKjTcTMgOwqAiYAQAA4EbatGVTNm25/gbHV3RQCwB0yRrMAAAAAAAMRcAMAAAAAMBQBMwAAAAAAAxFwAwAAAAAwFBGuslfVZ2d5OgkE0me2Vr75qRzt0jy4SQ3SfKd1tpTR1kLAADMFcbJAAAsFNMGzFX10unOt9ZeOc1zj01yeGvtmKq6bZL3JTlm0kPemOSNrbWPV9Xbq+qQ1tpPb0TtAAAw7xgnAwCwkOxoiYxlg5/bJTkxyT5JbpbkwUkO28FzT0jyiSRprX0/yT5VtVeSVNWSJPdK8s+D86caNAMAsEgYJwMAsGBMO4O5tfaSJKmqf05yt9ba+OD+siQf2UHbq5N8e9L9tYNjVyVZleTqJGdX1VFJvtJae+F0je2zz8osXdrbwUuy0zZu7roC2K59992j6xKAWbJq1Z5dlwBdmdFxcjIPx8prpj5lLPDbNo5fPeW5afvqkqlPbfs81+OpTdf/U7mx72H9v33e+wuf/u+Ovu/WQuz/nV2D+ZAkY5PuTyS55Y18rbFtbh+U5C1JLkzyL1X1x621f5nqyevWrb+RL8dUer2x7Lv7bl2XAdt1xRXXZHx8ousygBFbtWrPrF174//TDqPW0YB/l8bJyfwaK/d6Y9OeNxb4jV5vLLvvvf1zYxnLz6+9MJu2bLrBuRW9FdO2O7mPXY+nNl3/T+fGvIf1//btqO+n6uMbc33R993S/93R992a7/0/1Vh5ZwPmf0nyg6r6dpItSY7K4Gt901iT/kyMrQ7Mbz5LvCzJRa21HydJVX0hye0HrwMAAAuZcTIzYtOWTdm05fobHF86tqyDagCAxWpHazAnSVprL0pyv/R3s/7HJCe21p63g6d9LslDkmTw9b41rbWrB+1tTvKTqjp88Ni7JGk3vnwAAJh3jJMBAFgwdipgrqrlSe6b/jrMH0uyZ1VNu8ZCa+28JN+uqvOSvDXJqVV1UlX9+eAhz0ryt4PzVyb51LC/CQAAmC+MkwEAWEh2domMd6Q/uL3H4P5RSZ6d5BHTPam1dsY2h86fdO5HSe65k68PAAALhnEyAAALxU7NYE5ym9bac5KsT5LW2l+nv1YcAAAAAACL1M4GzJsHv04kSVXtnmT6rYkBAAAAAFjQdjZg/uhgB+vDquqtSb6b5EOjKwsAAAAAgLlup9Zgbq2dU1XfSHJcko1JHtFa+/YoCwMAgLmuqo5N8qYkt03/237/meQ5rbWvd1oYAADMkp0KmKvqxa21Vyf55uD+flX1qdbaA0daHQAAzG1vTvLcJF9LMpbkXulvkH3nLosCAIDZsrNLZBxWVW9Jkqo6Icl/JDl3ZFUBAMD8cHlr7YuttY2ttetaa/8nyc+7LgoAAGbLzi6R8cSqenlV/UeSmyQ5sbX236MtDQAA5rxvVNWzk3w2/ckbxyf5XlUdliSttZ90WRwAAIzatAFzVR0/6e6Xk9w6/YB5/6o6vrX2xVEWBwAAc9yjBr8+Y5vjD01/TebDZrccAACYXTuawfySaY5PJBEwAwCwaLXWbtV1DQAA0KVpA+bW2r2TpKqOaq19Z3ZKAgCAua2q9kpycmvt7MH9U5L8RZIfJTm1tfaLLusDAIDZsrOb/L1hpFUAAMD88q4k+ydJVR2R5Mwkpyf5P0ne0mFdAAAwq3Zqk78kP62qLyX59yTXbz3YWnvpKIoCAIA57rDW2iMHtx+S5KOttc8n+XxVPXKa5wEAwIKyswHzBYOfySZmuBYAAJgvrpl0+7gk7510f8vslgIAAN3ZqYC5tfaKbY9V1etnvhwAAJgXllbV/kn2THJMkocnSVXtkWT3LgsDAIDZtFMBc1X9YZLXJNlvcGh5kiuSPG9EdQEAwFz22iTfS7Iyyctba+uqakWSryb5m04rAwCAWbSzm/y9OsnTk/wyyQPT/wrgc0ZVFAAAzGWttXOTHJBkdWvtrMGxDUme31p7e6fFzbJeb2y7PwAALA47uwbzVa21f6+q61tr/y/JS6vq3PR3yQYAgEWntbapqjZX1QOS3D79PUr+q+OyZlWvN5YNyy/Lpi2bfuv4siXLsmLjzTI+btsWAICFbmcD5mVVdc8k66rq8el/HfBWoysLAADmhQ8mOTjJ15OMJXlRVT2itfaEbsuaPZu2bMqmLdff4PiKDmoBAGD27WzAfEqS1emvuXxOkpunvyYzAAAsZoe31u629U5VjSX59w7rAQCAWbVTAXNrrSVpVbUqyaNaa5eNtiwAAJgXflpVK1tr6wf3d0vy4y4LAgCA2bRTAXNVPTzJW9JfV26sqjYneXpr7eOjLA4AAOaiqvr79MfGK5P8qKr+PcmWJHdP8q0uawO6tWTJ1JtcWpccgIVoZ5fIeHGSe7TWfpwkVXVEko8lETADALAYfX7S7Y9Muv2p2S4EmDuWji1L29SyYfOGG5xbsXRFDu8dIWQGYMHZ2YB5zdZwOUlaaz+oKl/9AwBgsfpca+2Sqjqs60KAuWXD5g25dvP67Z/szW4tADAbpg2Yq+r4wc3vV9Xbkvyf9L/6d0KSH464NgAAmKvemORRSb6Q/lIZ2xI8AwCwKOxoBvNLtrl/h0m3fa8HAIDF6i+q6tmttVslSVU9NclfpD8J49ROKwMAgFk0bcDcWrv3bBUCAADzyDuTXJj8en+S1yR5aJJbp7859iM6qwwAAGbRTq3BXFX3SfK0JHsn+fWWuK2146d8EgAALFyHtdYeObj9kCQfba19IckXqupRHdYFAACzamc3+fvrJK9OcvEIawEAgPnimkm3j0vy3kn3t8xuKQAA0J2dDZh/0Fp7/0grAQCA+WNpVe2fZM8kxyR5eJJU1R5Jdu+yMAAAmE07GzD/TVW9J8l5STZvPdha+8BIqgIAgLnttUm+l2Rlkpe31tZV1YokX03yN51WBgAAs2hnA+a/THJtkuWTjk0kETADALDotNbOraoDkqxorV01OLahqp7fWvtcx+UBAMCs2dmA+frW2r1HWgkAAMwjrbVNSTZtc0y4DADAorKzAfM/V9W9k3wtv71Ehg1MAAAAAAAWqZ0NmF+S/mYlE4P7Y4PbvVEUBQAAAADA3LdkupNV9dwkaa3t2VpbkuTo1lpvcPv9s1EgAAAAAABz07QBc5I/3ub+6ybdPnRmSwEAAAAAYD7ZUcA8Ns39bc8BAAAAALCI7ChgntjBeQAAAAAAFqkdBczbmpjiNgAAAAAAi8zSHZz//ar66aT7+w/ujyW52ejKAgAAAABgrttRwFyzUgUAAAAAAPPOtAFza+2i2SoEAAAAAID55cauwQwAAAAAAEkEzAAAAAAADEnADAAAAADAUHa0yd8uqaqzkxydZCLJM1tr39zOY85Mckxr7bhR1gIAAHOFcTIAAAvFyGYwV9WxSQ5vrR2T5OQkb93OY26X5A9GVQMAAMw1xskAACwko1wi44Qkn0iS1tr3k+xTVXtt85g3JnnRCGsAAIC5xjgZAIAFY5RLZKxO8u1J99cOjl2VJFV1UpJ/S3LhzjS2zz4rs3Rpb2YrXMw2bu66Atiufffdo+sSgFmyatWeXZcAXZnRcXLS7Vj5qqu3f3zaf9PXTH3KWOC3bRyfooN3wbZ97Ho8tZnu/+29v/X/9k3X99NeJy6Z+pT3/tyi/7uj77u1EPt/pGswb2Ns642q2jfJE5LcJ8lBO/PkdevWj6isxafXG8u+u+/WdRmwXVdccU3Gxye6LgMYsVWr9szatTMfmsCu6mjAv0vj5KS7sXKvN5Ys2/65qf5N7/XGtvPoHT9vMer1xrL73jPf7uQ+dj2e2ij6f9v3t/7fvh31/UxcX/R9t/R/d/R9t+Z7/081Vh7lEhlr0p+JsdWB+c1niccnWZXkK0k+nuSowUYnAACw0BknAwCwYIwyYP5ckockSVUdlWRNa+3qJGmt/VNr7XattaOT/HmS77TWnj3CWgAAYK4wTgYAYMEYWcDcWjsvyber6rz0d8Y+tapOqqo/H9VrAgDAXGecDADAQjLSNZhba2dsc+j87TzmwiTHjbIOAACYS4yTAQBYKEa5RAYAAAAAAAuYgBkAAAAAgKEImAEAAAAAGIqAGQAAAACAoQiYAQAAAAAYioAZAAAAAIChCJgBAAAAABiKgBkAAAAAgKEImAEAAAAAGIqAGQAAAACAoQiYAQAAAAAYioAZAAAAAIChLO26AAAAAFjser2xKc+Nj0/MYiVz35Il2++rqY7DQuE6wVwlYAYAAIAO9Xpjed35l+TSazfe4Nzq3ZfnBUceIDwaGMtYrl22Npu2bLrBuRW9FcmGDoqCWdDrjWXD8su2+95ftmRZVmy8mesEnREwAwAAQMcuvXZjLr76hgEzN7Rpy6Zs2nL9DY4vHVvWQTUwe6Z67yfJilmuBSazBjMAAAAAAEMRMAMAAAAAMBQBMwAAAAAAQxEwAwAAAAAwFJv8AcAC0uuNdV0CAAAAi4iAGQAWiF5vLD+85JpsuG5z16VMa8VuS3P3fffougzgRpjqw6slS3yoBQCw2AmYAWAB2XDd5lw7xwNmYH7p9cayx95jmciWG5wby5L8an0HRQEAMGcImAEAgGlNZMsUAbMZzAAAi51N/gAAAAAAGIqAGQAAAACAoVgiAwAAAGbB9jbG7PXGbJgJwLwmYAYAAIARW9HbLR9fe2HWbbzuNwfX9H+55R57dVMUAMwAATMAAADMgnUbr8vlkwPmgZveZHkH1QDAzLAGMwAAAAAAQxEwAwAAAAAwFAEzAAAAAABDETADAAAAADAUATMAAAAAAEMRMAMAAAAAMJSlXRcAAAAsPEuWjN2o4wAAzE8CZgAAYEYtHVuWtqllw+YNNzi3z/KbdlARAACjImAGAABm3IbNG3Lt5vU3OL6it1sH1QAAMCrWYAYAAAAAYCgCZgAAAAAAhiJgBgAAAABgKAJmAAAAAACGImAGAAAAAGAoAmYAAAAAAIYiYAYAAAAAYCgCZgAAAAAAhiJgBgAAAABgKEtH2XhVnZ3k6CQTSZ7ZWvvmpHP3TnJmkvEkLcmTWmtbRlkPAADMBcbJAAAsFCObwVxVxyY5vLV2TJKTk7x1m4e8O8lDWmv3SLJnkj8aVS0AADBXGCcDALCQjHKJjBOSfCJJWmvfT7JPVe016fxdWmsXD26vTbLfCGsBAIC5wjgZAIAFY5RLZKxO8u1J99cOjl2VJK21q5Kkqg5Ict8kL5musX32WZmlS3ujqXQx2ri56wpgu/bdd4+uS4D57YJfdV3BTlu1as+uS4CuzOg4ORn9WHnj+NUja3tbxgK/bRR9v20fux5PbTbf+9NZjH8vvPcXPv1/4101zV+LG3Od0PfdWoj9P9I1mLcxtu2Bqto/yaeSPK21dvl0T163bv2o6lp0er2x7Lv7bl2XAdt1xRXXZHx8ousyYF7q9W7wT+2ctnbt3PhPO0zW0YB/l8bJyWjHyr3eWHbfe2TN34CxwG+Mqu8n9/GqVXu6Hk9htt/701lsfy+89xc+/X/j9XpjybKpz+/sdULfd2u+9/9UY+VRBsxr0p+JsdWBSS7ZemfwNcBzk7yotfa5EdYBAABziXEyAAALxijXYP5ckockSVUdlWRNa21yRP/GJGe31v51hDUAAMBcY5wMAMCCMbIZzK2186rq21V1XpItSU6tqpOSXJnks0kel+TwqnrS4Ckfaq29e1T1AADAXGCcDADAQjLSNZhba2dsc+j8SbeXj/K1AQBgrjJOBgBgoZjNTf4AAOaV+bBx4mLa9AkAAJh7BMwAANvR643lUW/4y1xw6ZquS5nSrVYfmA+d/hohMwAA0BkBMwDAFC64dE1++POfdV0GAADAnLWk6wIAAAAAAJifBMwAAAAAAAxFwAwAAAAAwFAEzAAAAAAADEXADAAAAADAUATMAAAAAAAMRcAMAAAAAMBQBMwAAAAAAAxFwAwAAAAAwFAEzAAAAAAADEXADAAAAADAUATMAAAAAAAMRcAMAAAAAMBQBMwAAAAAAAxFwAwAAAAAwFAEzAAAAAAADEXADAAAAADAUATMAAAAAAAMRcAMAAAAAMBQBMwAAAAAAAxFwAwAAAAAwFAEzAAAAAAADEXADAAAAADAUATMAAAAAAAMRcAMAAAAAMBQlnZdAACwOPV6Y12XMK0lS+Z2fQAAAHOBgBkAmFUrli/NE//pa7n4yvVdlzKtuxy0b9clAAAAzHkCZgBg1l185fpcuO6arsuY1kF7rey6BAAAgDnPGswAAAAAAAxFwAwAAAAAwFAEzAAAAAAADEXADAAAAADAUATMAAAAAAAMRcAMAAAAAMBQBMwAAAAAAAxFwAwAAAAAwFAEzAAAAAAADEXADAAAAADAUATMAAAAAAAMRcAMAAAAAMBQBMwAAAAAAAxFwAwAAAAAwFCWjrLxqjo7ydFJJpI8s7X2zUnn7pPkNUnGk3ymtfaqUdYCAABzhXEyAAALxchmMFfVsUkOb60dk+TkJG/d5iFvTfLgJPdIct+qut2oagEAgLnCOBkAgIVklDOYT0jyiSRprX2/qvapqr1aa1dV1WFJrmit/SxJquozg8d/b4T1MNnYWPoTZuaysazYbaST7HfZbst7OXjvlV2XsUOr99wtt1p9YNdl7NB8qJHFq9cb67qEHVqyZO5fNxPXzpk01+tjSvNunDw25byUJVm2ZNkNji5bsjQrlq7Y7jN2W7pb9lm+/db2Wb5bliyZ2evt+PhcH/NO78b2fTJ9/69YuuIGfTzKf+MWW/8P+97f+ybLs3r37Z9bvfvyof9ezOf+997v1myMffX/9k3VL0uWjE3z3l92o64T+n5q3vvDGeX/Qlcn+fak+2sHx64a/Lp20rlfJrn1dI2tWrXn3P+fPTPu7rdf3XUJO/S+w1d1XcLO+aO7dF0BMAvuftMp/nc6x7h2ssjN6Dg56XasfNDuh273+M1XHjzlc47cd0TFLDJT9X0yff9va99995iBahafmX7vP3j7zbEd3vsLg/6/8W6amRnr6/tuLcT+n81N/qYb9AqPAQBYrIyTAQCYt0YZMK9JfwbGVgcmuWSKcwcNjgEAwEJnnAwAwIIxyoD5c0kekiRVdVSSNa21q5OktXZhkr2q6tCqWprkTwaPBwCAhc44GQCABWNsYmJ0iz9X1WuT/EGSLUlOTXLnJFe21j5eVX+Q5HWDh36stfaGkRUCAABziHEyAAALxUgDZgAAAAAAFq7Z3OQPAAAAAIAFRMAMAAAAAMBQBMwAAAAAAAxFwAwAAIxMVe1TVXt3XcdiVFW9rmtYzKrqgKq6Vdd1LEZVtbqqbtF1HYtVVd2mqm7ddR2LUVX9flX9cdd1LFaD6/7BXdfRBQEzTKGqxrquAWC+2d61s6qMN2CRqqoHJPlEkndW1Vu7rmcxqapjk5xcVTfrupbFaBDw/O8kf1NVH+y6nsWkqu6X5GPp9/07u65nMamqJVV10yT/muS0qrpD1zUtJlV17ySvTvLLrmtZjKrqxCQfT/LyqvqDruuZbf7DB9tRVWOttYnB7eMGn8D6BBxgGttcO+9UVUclSWttiw/tYPGpqkOTPDvJaUlOTnJEVb2tqvbrtLDF4xlJjk/y50Lm2TWYvfaMJI9trd0nyW2q6rSOy1oUquqOSf4yyV8keXCSvapqt26rWjxaa1taa79K8tkk+yY5tqru3HFZi0JVnZDkXUlOa619s6p2r6o9uq5rsaiq3ZM8NskzW2tPaq19uaqWd13XbBIww/aNJUlVnZrk9CRPSvKiqjq806oA5rBJ4fJTk5yV5MlVdV5V9baeAxaV9Uk2J7m+tbY+yQOT7J3klZ1WtXhsSPKzJLdN8mAh86y6PsluScYH91+XZGl35SwqG5N8v7X2n0lumeROSV5TVW/vtqxF53+SbEmyKsnRVfXAqjqy45oWrMFEjsOSXJ5kfVWtSPKPSf6+qt5bVSs7LXBxmEhysyRLq2qvqvrnJB+pqvd0XNesETDDJFV116o6ZDDb7neS3L+19ifpB85jrbUf+gQc4LdV1WFVdcTg9pFJHthau2+S7yZZ21obn7YBYKG6LMm/JblnVd28tbYpyROT3L6q3tRtaYvCX7bWnpfkS0mOSPKQqlqVWApuFqxL8orW2gWTjt11642qEjaPzi+SfHRw+35J/iH9D71vXVXv66yqRWLSteXfkpzXWnt5+u/9v03/OsQIDCZyfDDJOYOfcwf3T0r/g91zOitukRh8kP7XSR6e5C1JPpx+/x9cVe/tsLRZMzYxYUIRbFVVL07yZ0kelP5/ij6c5BtJbpPkKUl+N8kerbUvdlYkwBwy+NDtnCQ/T/LeJL9K8vwk+yfZK8mjk9wlyZGttb/pqk6gG4NNns5I8vkkX26tXVJVN0/yqiRPa61t7rTABayqlrTWtgxuPzjJPZL8e/ozCpe21t7SZX2LSVX9SZKHtdYeV1WPTXJgkrN8u2d2VdVnkjy+tba261oWusHyki9M8un0v7Xy/STfTvL51tp/d1nbQjaYufyoJHdL8qzW2oZB6P/pJI9rrV3eaYEL3OCbQs9MclSSl7TWvjM4/i9JntBaW9BrY5vBDL/tzPQ343h/+n8//jODNbxaa9elv47dg+zIDfDrNZevS/La9AOLk5LcPsmyJIcmeeVg9vKRSfYzYw4Wn9baj5Ocnd+MoY5MckySQ2LJgJGavP59a+1j6W+2+JTBz+e7rG0R+mWS71XV0Uken+RTwuXRq6qbVNXBVbW6qu6fZI8k13Vd1yKxJv0lYl6Z5Fnpr8e/MsmlXRa10LXWNiT5QJIXDcLllUn+KP2+v77T4haB1tplSd6Z5AdJ/riqjhlsdrx7+stWLWhmMLPoTd6UatKx56V/IT4lyWOS/EmSTyV5QJLHtNZ+OOuFAswh2147q2p1kpelP6D6eZJ7pr+80PIkd0xycmvte13UCnSvqm6V5MQk901/jdSXmMU2O7ZerwezaM9K8uettdZ1XYtJVd0yyffSn8X5aP0/O6pqzyRPT3+JhpVJntNa+3/dVrV4VNVtk+zXWvvq4P7y1trGjstaNKrqxCQPS/8bE6cah8+eqto//W8NPTT9tcjPGqwLv6AJmGGgqk5O/2vc30jyxfR3Hf7T9JfMuFuS/ZL8f621H3RWJMAcU1VPSnK79P/T/MH0Zyr+vyQtyTVJ7pzk3NbaTzorEpgzqmrv9Pe1+FXXtSwmg2/f3T9JM1Fi9lXVkiQvTfLB1tqPuq5nMamqvZLsmWS8tWb2bAe2N6GL0Rt8wLIq/Y12L+66nsVosGTJ2GB95gVPwAxJquqU9AfdH0xycpKvJ/n79Hc7PynJn7ooA/y2qnpc+l/1fWaS85M8I/11mF+fZG2S97l2AswNQp5uVdVSa44DsFBZg5lFafI6oIMZBTdPf1bBzZLslv7GVA9Kfzbz22ONQIBtr517pf+tj9MGv34qyYuSPDn9tT1XJ1kUn9YDzAfCzrnG1wAAIABJREFU5W4JlwFYyMxgZlGrqt9prf2oqn43yRFJnpD+esuPTn8m84FJ7tZau7LDMgHmlKp6epJ90/8w7sdJjmutPayq/iDJPyT5bJKXt9Yu6rBMAAAAZoEZzCwq28y+e1KST1TVPyX5aZL/SXL5YHbHhiTvSHJ34TKw2G1z7bxd+ht1/SLJRJK7J7l5Ve2W/u7sr0ryAuEyAADA4mAGM4vSYMbyg9IPkV+VZPf0v9r9gSS/THKHJH9mh2dgsZu8Zufgg7lbJlnfWjuzqo5J8sgkT0zyH0lWJHmEcBkAAGDxEDCzKGwTkJyS5LHprw36gNba5qp6T5Lrkrwh/fWYL26t/byzggHmmKr60yTPTfKlJPdO8o7W2j9U1VFJzkxyTZLntdZ+0l2VAMBiVFWHJmnpb9aeJMuSXJTkaa21X+1Cuy9PsrS19uJdrRFgIbNEBovCpHD52CSHJDk1/UHHqwbnn5RkVfqzmL8lXAb4jaq6Y5LHJTmntfayJG9K8oCqemRr7TtJXpzkFOEyANChta214wY/90jy8/THKACM2NKuC4BR2jpzuaqWJFmd5KNJPtpaO7+qHpnkA1X12tbaGa21h1fVAa218W6rBphzfpH+OvUPr6r/aa19oqomkjyhqq5vrX2s4/oAALb15SSnVNWfJ3l++t9YXZrksa21C6vqS0m+m+TOSY5Pcv8kLxs87gdJThm0c/Bg357bJPlSa+20Wf1dAMwDZjCzWOzVWluT5IQkfziYdXdpkkclOb6qXpkkrbVLuiwSYC5qrf0y/VnL30hyclXdvrX2ySR/k998FRUAYE6oql76e+58JclNkzy8tXbvJJ9JMjkgvqa1dmyS5Unek/4SivdKclmSewwe8ztJHpHk95I8vqr2m53fBcD8YQYzC1JVVeubqKpTk9y7qrYkeX+SRyf5SFVNDNYPvX/6m/wBMIXW2hVV9bdJHp/k2VX1xtbav3RdFwDAwKrBrOSkP5nuK0nOTnKfJO+f9K3WyR+Onzf49XZJftZaW5skrbUXJElV3TvJV1trm5NsrqrL0w+sLx/x7wVgXhEws+BU1e+lP6vuzoOvQx2b/kzltyV5aGvtpKp6dJJzq2rT4KvdBggAO9Bau6yq/j79WTxXdF0PAMAka1trx00+UFXLknwkyVGttR9W1Wnpz0Te6vrBrxOZ+hvem7e5PzYDtQIsKJbIYCG6OslYVT03yZ8k+VyS5yTZM8mTq+p+Sf47yQOTnN9ZlQDz0GC5jLe31n7RdS0AADuwZ5ItSS6sqt2SnJj+chjb+p8kB1XVwUlSVW+qqhNnr0yA+U3AzILTWmtJvpjkpekPKI5KcnSSJ7XWNiX5/SR3aa19pbX2o+4qBZifbIYKAMwHrbUrknwoyTfTn8n8+vT34HnoNo+7NsnJST5WVV9Osl8SS4EB7KSxiYmJrmuAGVdVh6cfLJ+SZN/0ZzF/J8nNkzwm/U0eftJdhQAAAAAw/wmYWdCq6oT0ZzJfk/4n1n+U5BWDWc4AAAAAwC4QMLPgVdVxSd6Z5NQkX2ytedMDAAAAwAwQMLMoVNUfJvlRa+2CrmsBAAAAgIVCwAwAAAAAwFCWdF0AAAAAAADzk4AZAAAAAIChCJgBAAAAABiKgBkAAAAAgKEs7boAAIZXVfdP8sIk40l2T3JBklNaa7/qtDAAAABgUTCDGWCeqqqbJPlgkoe31u7dWrtbkguTnNxpYQAAAMCiYQYzwPy1Iv1Zy7tvPdBae0GSVNXvJnljkmWDn9OS/CTJN5Pcv7X246r6uyTfaq2dM8t1AwAAAAuEGcwA81Rr7cokL0vy3ar6fFW9qKpqcPp/JXlqa+24JE9L8p7B409Lck5VHZfkoCRvn/3KAQAAgIVibGJiousaANgFVbVfkvsmuXeShyU5O8mLkpw36WEHJanW2paqeneS+yW5Z2vtZ7NdLwAAALBwWCIDYB6rqpWttcuTfDjJh6vqo0nenWTjYPby9qxOsiHJ/kkEzAAAAMDQLJEBME9V1f2SfL2q9px0+LAk/1+SC6vqAYPHHVFVLx3cfnySy5M8NMl7q2r5LJcNAAAALCCWyACYx6rq6Ukem2R9krEkv0jyzPRnKb81yUT6m/w9J8lFSf5vkmNaa1dU1V8lWd5aO72L2gEAAID5T8AMAAAAAMBQLJEBAAAAAMBQBMwAAAAAAAxFwAwAAAAAwFAEzAAAAAAADEXADAAAAADAUATMAAAAAAAMRcAMAAAAAMBQBMwAAAAAAAxFwAwAAAAAwFAEzAAAAAAADEXADAAAAADAUATMAAAAAAAMZWnXBQDzQ1XdJclZSQ5K/8Opy5M8r7X21Rlq/8wkF7XW3jkDbT0myZNaa8dNcf51Sa5L8rdJLkjyxtba6ds85vNJfqe1duiu1rOd1z8zyebW2kumOP+lJO9prX1wyPZfnuRZSS4dHFqS5AtJnttaWz9MmzNd4418rXsm+eD2/iyqaiLJj5Ns3ubU41pr/7GT7R+X/u/ld3axzi9l1/7cDk7ys9ba2K7UAQDMPmPlmTMLY+UlSV6R5CFJxpIsS/LP6f95ba6qLyR5XpK9Ms0YsaqekeTJg+ffJMlXkpzWWrt6mLqGZawMzAUCZmCHqmosyaeSPLm19i+DYw9K8smqusVMhJattRfuahs7o6qOSfKAJHdOcnCSXyb5s6p6fmtty+AxN09y6xGW8bIk/1lVH2+tfWdEr/FPrbUnJUlV3STJPyZ5aZIzRvR6XTqutXZx10UAAIuTsfKMG/VY+SlJ7pnkrq21a6pqzyTnJjk9yWtbayckvw5Wt6uq/ijJXyS5V2vtsqpanuTvk7w+yVNHUPOuMFYGRk7ADOyMmyU5IMm/bz3QWvvfVfUfrbX1VXVSkse01u6TJJPvV9XfJbkiyX2SfCzJM5Ps31rbPHjsJ5L8a5Kjk/wo/ZkCK1prTx+cv1mSi5IcmP6MkL8e1LIxyRNaa98azEJ4a5I/TX/W7r9N83t5SZI3DWYnJMmG9D/Vv9ek5z0s/Rm/99n6pKp6SpLnJNktydeTPLG1tmEwwH5/kkOTLE/yttbamwbPuTDJmUlOTnKLJB9qrT23tXZ9Vb0lyYuSPHiKOu9YVf8x+L3+a/oD1X9I8o3W2hsG7d8hyf9NcsDW/tyeweu9O8mrk5yxEzW/L8mjk/xh+v9O/F36/b8uySmTBvq3GsxEODzJl5M8eut/PCb1243un8G5Fyc5Jcll6c8oudGq6tD0/6zOHrzGWJLHpf8euFOSz7bWnjjp8W9I/z20Jf0/3/OqamX6s3fulP7MlI9tncEz+L1/LcmDBu3//+3de5ykZ1kn/F9PDZmQSUJmkkFMIAzhDRdHlSAQxHA+iMqyLLiiyBsUF9QgCSKuK2ERifoCZiMB5SAuG9YFg7ABFBQWlJNBN8YDB8MNEYaETEyGySTkQCakp98/qgY67Ryqa/rpp7vr+/18+jN1eOqpq+6u6f7V1fdzP/Of+4cyfE8+Msm3krw+ycMzHM9XtdbeNtruZzL8EPWNJP9rktcJAPROVl5dWflBST7XWrspSVprN1bV05LcMq+un5r32v5NRhzt4/LW2tdH+9hdVT+bZG70mI8l+ViSH0pyrwzz7M+11mbnvwhZWVaGtcIazMA4vp7kkiR/VVXPq6p7Jcki/hL++CQPa629MsNQe1qSjALJ4zIM03u9O8lT511/aoYB9sYk703y9tbafTIMke+rqvUZBrcnJbl/kkcnedS+iqiqozMMwu9bcNe7kvzkvOvPSvIn8x53WpJXJXnc6NCzG0bXk+TsJF9prd139Dp/u6ruMW9fj0ryiCQPSfKLo0O7kuSiJD8yGoN9eWySxySp0Wv60STvXFDn0zMMcfttLs9zpww/aIxT891ba9VauyLJW5K8c3RI3G9mODNjr8ckecqoxsdmGBAXWvT4VNX9M/yA8v2jr+8Z4/Xtz3FJ/rW1Vkk+k+TCJKeP9vmTVbV39s3WJH83em+dm+T3Rrf/fJKjktw3ySlJnjs6DHGvhyR5wOiDRpKkhp/G3pjkaa21HaP97Rnt4+FJXllVD6yqTRkG6x9qrT0oww+GAMDqIyuvrqz850leUFWvq6rHVtXhrbWdrbVv7uN5tmbfGfEjSZ5UVRdU1VOq6qjW2jcWLI/xlAy/f/cavc4f3cf+ZWVZGdYEDWbgoFprcxnOZr0ow1kVX66qz48O/RvHR1trt44uvzvDv3wnw7D7f0fBYu9z/d8kM1X1vaObnp5hqL1vkrtmOLs2rbW/TrIjyQ9kGLw+0Fq7aRQM37WfOk7JcO266xbc/r+T/LuqulNV3TPJEUnavPufmuTC1tr20fU3ZfiX+CR5UZJfHNX05Qw/FNxr3mPf0VqbHT32mgxnH6S19q+jbU/ZT63vbq3dMjqk8gMZBssPJrn3KJTtHZsL9/P4bxsd9nfG6HWOU/OfjR53eIbh/Z2j29+XYejb6z2ttW+OZn98KcPDKBeaZHweleTjrbVrRrM8DrZO28eq6gvzvj457771+c4HoM8muaS19vXW2s4kV+c7QfXWfOd9864k3zf6sHFuhuF3rrW2K8nnk5w0b/8fXDBr+y4Zfgj8T621y0a3PTXJ61pre0bv9f+d4fvn4Um+NG+7Cw7yOgGAFUhWXl1ZubX2ZxkuA3L3DJvy11XV/xg1NBfaX0b8hwwnV6zLMMPtrKqLqurEeY/943k1/kWG34uFZGVZGdYES2QAY2mt3ZDh4UmvGB3K9dwkfzwv3B7I/JD67gzD94uT/Pvsu0H6ngxD7OUZro/27AwPQzsiyWXfyYw5OsmxSTYn2T7v8bv2U8ddM1xHbuFr21VVlyR5coYzOxaG7mOSPL2qnjS6vi7DQ8CS5KEZzjQ4Mclshofpzf/j3Q3zLs8mGcy7fu2opn3ZMe/yDRke2ndrVV2U4WyCPxw91/4OcXzmvNkDt2U45ueNWfPe79fm0e03JN/+8HTTvO2+cYDXttck47N5we37+37udaB15WbnzUaZXVD//Jp3zgu/e1/Xpqo6Msl/q6r7jra/R4aHAe618APYqzJ8ffPfj8ckeVdV7Z09c+cMg/xiXycAsELJyqsrK7fWPpLkI1U1yLBR/DtJfj/JTyzYdJ8ZMcnVrbW/S/KcGq7BfUqGOfDCDJvdyR2/r7uy7xm4srKsDGuCBjNwUKND1ba20VmwW2vXJHl1Vf3HJA/Ivw2D+/rrf0aP/UxVzY7C9pMzDM8LvTvJ6zL8C/jH23BdtO1JvjE6fGxhfadm+Nfwvbbs5+kPdMbhP87wTNIPSPLjC+7bnuSCtuDs2SN/lGHj9k2ttbmquuoAz7EYm+dd3pTvhLN3jp7vhgxnbuxZ+MCRb5/kbx/GrXlnhuvIHZvk66PwfO8M1+Eb1yTjsyvjfT+X0vz37DGjf6/LcKbEpUn+fWtttqr++iD7OT/D2R5vr6pTR4dkbh89/nPzN6yqp2T5XycAsMRk5dWVlUcZ7OLW2g2jGcCfqKpXZbje8UL7zIijiRxfaa1dNZqEcWlV/ecM1zTe67gF9S5stiaysqwMa4QlMoBx3CPJe6vqIXtvqKqHJjkxw/Xmrh7eVIeP1kl75kH29+4kv57kH0eHXy306SR7Z37snSHx1SRfq6pnjp7/uKp6Z1VtHG3/5Ko6YvT8P7af5702+w8m78twHbfZ0eFp870/yX+oqi2j537aKEAmw1kVl44C4elJNiY5cr+v/I625I6zL+b7D6Px3Jjh+m17D2X7SIYN3xdljOUx9mOsmltru5N8OMPvQzL8kPPBUYhe0uda4NNJfrCqtoxmlfzUQbZfCkdU1dNHl5+Z4eGBuzOs/x9GgfmJGZ7Q8ED1X95ae3OGgftlo9vel9HZxKtqfVWdV1WnJPm74U118mi705f2JQEAy0RWXl1Z+UUZ/gHg8FG9h2f4PdnXbOf9ZcRnJ3ljDdetTg3Xuv6JBft4elVt2EeN88nKsjKsCRrMwEG11j6d5PkZhqg2OhzvvCQ/3lr7aoZnZ/7bJF/M8KQZC08MstC7Mzzkb5/rv40amO/N8CQjfzrvtmcleWFVfSHJJzJcr+7m0TZ/neFacB/PcP21fbk0ydaqOmbhHaP9/E3mnbBk3n1/n+S3Mly/7LIMT6qx9zW+PMlFVfWZDMPUm5P8QX3nhBj7VFV3zfAQuEv3s8lHMhzXy0aX/2JUy+yoxsHoNU9iMTX/bJKnVtWXk5yTO544ZamfK0nSWvvHDNfu+/sMx+dTB3mOhevKfaGqXrjIOr+Q5BGj99aLM1yzOhm+5nOr6nMZnkDmlRmeeGRfJzSc73kZnojllAzH4C5V1TKcaTRI8pnRGnMvyfDwzM/ljmsZAgCrhKy86rLyT47u/+y8fLYzya/sY9v9ZcSzMvx+XjLaxxczbPr/9LzHXjyqcdvo3z/fx/5lZVkZ1oSZubnFTEQDWN2q6i8yPFnG23uu4/lJfri19u8neOyvJDmutbavEAwAABORlZdGVX0syVtbawc7AR/AmmAGMzBtzknyktHhZL2oqjtlOOvhNyd47JYMZ8i8aanrAgBg6snKACyaBjMwVUYnX/lwkrN7LOPXk1zUWrtkMQ+qqhdkuBbZq/ex9h0AABwSWRmASVgiAwAAAACAiZjBDAAAAADARDSYAQAAAACYyPq+CxjXjh03ruq1PDZtOiK7dt3SdxlTy/j3x9j3y/j3x9j3y/j3Z7WP/ZYtR830XcMkZGUmZez7Zfz7Y+z7Zfz7Y+z7tdrHf39Z2QzmZbJ+fW8n4SXGv0/Gvl/Gvz/Gvl/Gvz/Gnkl43/TH2PfL+PfH2PfL+PfH2PdrrY6/BjMAAAAAABPRYAYAAAAAYCIazAAAAAAATESDGQAAAACAiWgwAwAAAAAwEQ1mAAAAAAAmosEMAAAAAMBE1vddAAAAa9NgMLOk+5udnRtru/PPPzef//znMjMzkzPPfEnud78HLGkdAABwqPrIyl3l5E4bzFX1wCTvS3Jea+0NC+57QpLfSjKb5IOttVd1WQsAAMtnMJjJkXc5MnPj9YQPamYmuemGmw4anP/hHy7N1752Zd785rdl27av5Ld/+zfy5je/bWmKWGKyMgDAdOojK3eZkztbIqOqNiZ5fZKP7meT85M8I8kjkzypqu7fVS0AACy/ublkLkv0NWb4vvTSS3LaaY9Jkmzdeq/ceOM3cvPNNy3VS1oysjIAwHRb7qzcZU7ucg3m3Ul+OMn2hXdU1UlJrmutXdla25Pkg0ke32EtBzUYzCzqC6APflYBHNjOnTtzzDHHfPv6Mcdsys6dO3usaL9kZQAAlk2XObmzJTJaa7cnub2q9nX33ZLsmHf92iT3PtD+Nm06IuvXD5auwAVumx1/ZszMTHLYBKVs2XLU4h/EkjH+/TH2S+eTX96Zm3fPjrXtxg2DnLb5SOPfI2PfL+Pfn71jv/v2pd3v5s1HHnSbO9/5Tjn66Dt/u4b169dl8+aNK+79ICv/WyvtezRNjH2/jH9/jH2/jH9/jH2/+srKXebklXKSv4NOc9i165bOnnwwmMnGo4/M2MuezCXXXXfwNQDn27LlqOzYceNE9XHojH9/jP3SGQxmcvPu2dx023gN5r2Mfz+89/tl/Puzd+z35qulNE7+2rjxLvnKV7727e//1Vdfk3Xr7jz2+2GFfuCSlemMse+X8e+Pse+X8e+Pse9Xn1n5UHNysv+s3OUSGQeyPcOZGXudkH0cHggAAIvxsIedmo99bLiscWtfyHHHHZcjjtjYc1WLJisDALCkuszJvcxgbq1tq6qjq2prkq8l+dEkz+6jFgAAujEzk4w/7XWMfY3hQQ/63lTdLz/3cz+TmZmZ/NIv/eelKWAZycoAAGvfcmflLnNyZw3mqnpIknOTbE3yrap6ZpL3J/lKa+2iJD+f5J2jzS9srX2xq1oAAFhes7NzuemGpTkr9fx9juPnf/4Xl/R5uyArAwBMr76yclc5ucuT/F2a5DEHuP8TSR7R1fMDANCvxazBO21kZQCA6baWsnJfazADAAAAALDKaTADAAAAADARDWYAAAAAACaiwQwAAAAAwEQ0mAEAAAAAmMj6vgsAAGBtGgxmlnR/455p+8orr8j555+b66/fldnZPXnQg74nZ5xxVg477LAlrQcAACbVR1buKidrMAMAsOQGg5l8/vrduXn37JLsb+OGQR5wzIaDBufZ2dmcffav5KyzXpoHP/ghmZuby+/+7mvztrf9QV7wgjOWpBYAADgUfWTlLnOyBjMAAJ24efdsbrptaULzuC655G9z4olb8+AHPyRJMjMzk1/4hRdlZsbKcAAArBzLnZW7zMkazAAArBlXXLEtJ598nzvctmHD4T1VAwAAK0OXOdlUDgAA1pCZ7Nmzp+8iAABghekuJ2swAwCwZtzznlvzz//8+Tvcdtttt+XLX768p4oAAKB/XeZkDWYAANaMhz704bnmmqvzqU99IkmyZ8+evPGNr89HP/p/eq4MAAD602VOtgYzAACd2LhhsOz7WrduXc499w15zWt+M2972x/kTne6Ux760Ifnp3/6Py1ZLQAAcKiWOyt3mZM1mAEAWHKzs3N5wDEblnyf4zjuuOPymtect6TPDQAAS6WvrNxVTtZgBgCgE+M2hAEAYNqspaxsDWYAAAAAACaiwQwAAAAAwEQ0mAEAAAAAmIgGMwAAAAAAE9FgBgAAAABgIuv7LgAAgLVpMJhZ0v2Ne6bt7duvyu/+7u/kuut2Zs+ePfm+73twXvCCF2bDhg1LWg8AAEyqj6zcVU7WYAYAYMkNBjN55f/5crZff+uS7O/4Yw7PK5540kGD8549e/Kyl700Z5xxVr7/+x+WJHnnO/8or33tb+Xss1+5JLUAAMCh6CMrd5mTNZgBAOjE9utvzZW7vrmsz3nJJX+bu9/9xG+H5iR51rOenZ/4iWdk165d2bRp07LWAwAA+7LcWbnLnGwNZgAA1oyvfnVb7nOfusNtMzMzOemke+fKK7/aU1UAANCvLnOyBjMAAGvG3NyezM7O7uP2ucyNt4QzAACsOV3mZA1mAADWjBNP3JrWLrvDbXNzc9m27cs58cR79lQVAAD0q8ucrMEMAMCa8bCHnZpt276ST3/6U9++7cIL/1ce+MDvsf4yAABTq8uc7CR/AAB04vhjDl/2fQ0Gg5x77utzzjmvyJve9HtJ5vLAB35PfvmX/8uS1QIAAIdqubNylzlZgxkAgCU3OzuXVzzxpCXf5ziOP/6E/P7vvzWf/ew/5fWvPy8vecmvZt06B+4BALAy9JWVu8rJkjYAAJ2YnZ1b0q/FetCDvjf3v/8D87zn/VT+8i8/0sErBACAyfSZlZc6J5vBDADAmnXWWb/cdwkAALDiLGVONoMZAAAAAICJaDADAAAAADARDWYAAAAAACaiwQwAAAAAwESc5A8AgE4MBjNLur9xz479nve8Kx/60Adz2GGHZffuW/P855+Rhz704UtaCwAAHIo+snJXOVmDGQCAJTcYzOT0sy/Itqt2Lsn+tp5wbC445/SDBuerr96eP/3T9+atb3171q9fnyuvvCKvfvU5GswAAKwYfWTlLnOyBjMAAJ3YdtXOXH7Ftcv6nDfddFNuu213vvWtb2X9+vW5xz1OzBve8JZlrQEAAA5mubNylzlZgxkAgDXj5JPvk/vd7wH5sR/7d3nEIx6ZU099ZB796Mdm/XqxFwCA6dVlTnaSPwAA1pSXv/w38oY3vCUnn3yfvOMdb8+LX3xG5ubGW78Z4FAMBjOL+gKA5dRVTjaVAwCANWNubi633XZbtm69V7ZuvVee8Ywfz7Of/cxcc82/5m53++6+ywPWsMFgJp+/fndu3j071vYbNwxy2uYjO64KAIa6zMlmMAMAsGb82Z+9L695zW9+eybGzTfflD179mTTpk09VwZMg5t3z+am28b7GrcRDQBLocucbAYzAACd2HrCscu+rx/+4afmq1/dluc///Tc+c5H5Pbbb89ZZ700GzYcvmS1AADAoVrurNxlTtZgBgBgyc3OzuWCc05f8n0ezGAwyAtfeNaSPi8AACylPrJylzlZgxkAgE6M0xAGAIBptJaycqcN5qo6L8mpSeaSnNlau2TefWck+akks0n+rrVmqgkAAFNBTgYAYK3o7CR/VfXoJCe31h6R5HlJzp9339FJXprktNbaDya5f1Wd2lUtAACwUsjJAACsJZ01mJM8Psl7k6S1dlmSTaPAnCS3jb6OrKr1SY5Icl2HtQAAwEohJwMAsGZ02WC+W5Id867vGN2W1tqtSV6Z5MtJvprkb1trX+ywFgAAWCnkZAAA1ozlPMnfzN4Loxkav5bkPkm+keQvq+p7W2v/tL8Hb9p0RNavH3RW3O7bF7f95s1HLvo5tmw5atGPYekY//4Y+yV0zS2Lfojx74+x75fx74+xX7RDysmJrMyhMfZLSFZbVYx9v4x/f4x9v9bi+HfZYN6e0UyMkeOTXD26fL8kX26tfT1JquqTSR6SZL/Bedeuxf+iHtdgMJONRy8uBF933U2LOtvjli1HZceOGxdbGkvE+PfH2C+dwWDm4Bvtg/Hvh/d+v4x/f1b72C9T4F/SnJzIykzO2C8dWW118d7vl/Hvj7Hv12of//1l5S6XyPhwkmcmSVWdkmR7a23vCG5Lcr+quvPo+vcn+VKHtQAAwEohJwMAsGZ0NoO5tXZxVV1aVRcn2ZPkjKp6bpIbWmsXVdVrk/xVVd2e5OLW2ie7qgUAAFYKORkAgLWk0zWYW2u/uuCmf5p335uTvLnL5wcAgJVITgYAYK3ocokMAAAAAADWMA1mAAAAAAAmosEMAAAAAMBENJgBAAAAAJiIBjMAAAAAABPRYAYAAAAAYCIazAAAAAAATESDGQAAAACAiWgwAwAAAAAwEQ1mAAAAAAAmosEMAAAAAMBENJgBAAAAAJiIBjMAAACLYIofAAAd30lEQVQAABPRYAYAAAAAYCIazAAAAAAATESDGQAAAACAiWgwAwAAAAAwkfV9FwCsLoPBTN8lAAAAALBCaDADYxsMZvL563fn5t2zY22/ccMgp20+suOqAAAAAOiLBjOwKDfvns1Nt43XYAYAAABgbbMGMwAAAAAAE9FgBgAAAABgIhrMAAAAAABMRIMZAAAAAICJaDADAAAAADARDWYAAAAAACaiwQwAAAAAwETW910AAAAAS2MwmOm7BABgymgwAwAArAGDwUw+f/3u3Lx7dqztN24Y5LTNR3ZcFQCw1mkwAwAArBE3757NTbeN12AGAFgKGswAAADQk8UsazI7O9dhJQAwGQ1mAAAAWGZHHDbImRd+Jtuvv3Ws7Y8/5vC84oknaTIDsOJoMAMAAEAPtl9/a67c9c2+ywCAQ7Ku7wIAAAAAAFidNJgBAAAAAJiIBjMAAAAAABPRYAYAAAAAYCIazAAAAAAATESDGQAAAACAiazvuwCYxGAw03cJAAAAADD1NJhZdQaDmXz++t25effsWNtv3DDIaZuP7LgqAAAAAJg+GsysSjfvns1Nt43XYAYAAAAAumENZgAAAAAAJqLBDAAAAADARDSYAQAAAACYiAYzAAAAAAAT6fQkf1V1XpJTk8wlObO1dsm8++6R5J1JDkvy9621n+uyFgAAWCnkZAAA1ooDNpir6r8e6P7W2m8c4LGPTnJya+0RVXW/JP89ySPmbXJuknNbaxdV1e9V1YmttSsWUTsAAPRm0qwsJwMAsJYcbImMO42+7p/kaUk2JTkuyTOSnHSQxz4+yXuTpLV2WZJNVXV0klTVuiSnJXn/6P4zhGYAAFaZSbOynAwAwJpxwBnMrbWXJ0lVvT/Jw1prs6Prd0py4UH2fbckl867vmN02zeSbElyY5LzquqUJJ9srf2XA+1s06Yjsn794CBPObndty9u+82bj1z0c2zZctSiH8N+XHPLoh9i/JeIse+X8V9VjH2/jH9/pmXsDyErL2lOTmRl5pEV+jXB+C/GJP+32D/v/X4Z//4Y+36txfEfdw3mE5PMzLs+l+Sei3yumQWXT0jyuiTbknygqn6ktfaB/T14167uflEPBjPZePTiflFfd91NmZ2dG3v7LVuOyo4dNy62NPZhMJg5+Eb7YPwPnbHvl/FfXfzc75fx789qH/sJA/+hZuVDysmJrMyQrNCvScd/MRb7f4v983OnX8a/P8a+X6t9/PeXlcdtMH8gyRer6tIke5KcktFhfQewPcOZGHsdn+Tq0eWvJ/lqa+1fkqSqPprkAaPnAQCA1WSxWVlOBgBgzTjYGsxJktbay5I8OcOzWb8rydNaay89yMM+nOSZSTI6vG97a+3G0f5uT/Llqjp5tO1DkrTFlw8AAP2aICvLyQAArBljNZirakOSJ2W4ttx7khxVVYcf6DGttYuTXFpVFyc5P8kZVfXcqnr6aJOzkrxtdP8NSf500hcBAAB9WWxWlpMBAFhLxl0i4/czDLePHF0/JcmLkzzrQA9qrf3qgpv+ad59lyf5wTGfHwAAVqpFZ2U5GQCAtWKsGcxJ7tta+6UktyRJa+2NGa4VBwAA005WBgBgao3bYL599O9cklTVxiR37qQiAABYXWRlAACm1rgN5j8ZncH6pKo6P8k/JnlHd2UBAMCqISsDADC1xlqDubX2hqr62ySPSbI7ybNaa5d2WRgAAKwGsjIAANNsrAZzVZ3dWjsnySWj68dW1Z+21p7aaXXQg8FgZlHbz87OdVQJALAayMoAAEyzsRrMGR7u97rW2plV9fgkb0lybod1QS8Gg5mcfvYF2XbVzrG233rCsbngnNM1mQFgusnKAABMrXGXyPiZqvr1qvq/SQ5L8rTW2ue6LQ36se2qnbn8imv7LgMAWCVkZQAAptkBG8xV9bh5Vz+R5N4Zhua7VtXjWmt/2WVxAACwUsnKAABw8BnMLz/A7XNJhGYAAKaVrAwAwNQ7YIO5tfbYJKmqU1prf788JQEAwMonKwMAQLJuzO1+p9MqAABg9ZKVAQCYWmOd5C/JFVX1sSR/k+S2vTe21v5rF0UBAMAqIisDADC1xm0wf2X0Nd/cEtcCAACrkawMAMDUGqvB3Fp75cLbquq1S18OAACsLrIyAADTbKwGc1U9MclvJTl2dNOGJNcleWlHdQEAwKogKwMAMM3GPcnfOUl+Mcm1SZ6a5A+T/FJXRQEAwCoiKwMAMLXGbTB/o7X2N0lua619fnTCEqEZAABkZQAApti4J/m7U1X9YJJdVXV6kn9Ocq/uylr51q2b6bsEAABWBlkZAICpNW6D+QVJ7pbhOnJvSPJdGa4zN5Vmknz2ultz8+7ZsbbfuGGQ0zYf2W1RAAD0RVYGAGBqjdVgbq21JK2qtiT5ydba17sta+W7efdsbrptvAYzAABrl6wMAMA0G6vBXFU/nuR1SeaSzFTV7Ul+sbV2UZfFAbC8BoPxl/+ZnZ3rsBKA1UNWBgBgmo27RMbZSR7ZWvuXJKmq+yR5TxKhGWCNGAxmcvrZF2TbVTsPuu3WE47NBeecrskMMCQrAwAwtcZtMG/fG5iTpLX2xar6lwM9AIDVZ9tVO3P5Fdf2XQbAaiMrAwAwtQ7YYK6qx40uXlZVr0/yf5LsSfL4JF/quDYAAFixZGWm0WKW00osqQUA0+BgM5hfvuD6A+ddlhQAAJhmsjJTZTHLaSWW1AKAaXHABnNr7bHLVQgAAKwmsjLTyHJaAMBCY63BXFVPSPILSe6S5NvHRLXWHrffBwEAwBSQlQEAmGbjnuTvjUnOSfK1DmsBAIDVSFZeYN26xa3TCwDA6jVug/mLrbULOq0EAABWJ1l5npkkn73u1ty8e3as7TduGOS0zUd2WxQAAJ0Zt8H8B1X11iQXJ7l9742ttbd3UhUAAKwesvICN++ezU23jddgBgBgdRu3wfxrSW5OsmHebXNJpjY0AwDAiKwMAMDUGrfBfJuzZAPLYTBY3JqNs7NzHVUCAGOTlQEAmFrjNpjfX1WPTfLXueNhf3s6qQqYSoPBTE4/+4Jsu2rnWNtvPeHYXHDO6ZrMAPRNVgYAYGqN22B+eZKNGR7qlwzP3TGXZNBFUcD02nbVzlx+xbV9lwEAiyErs6qNewTZunWLO9IMAJgOB2wwV9VLWmvnttaOGl1/aGvtktHl/74cBQIAwEokK7PaHXHYIGde+Jlsv/7Wsbb/3rsf3XFFAMBqdLAZzD+S5Nx511+d5HGjy1u7KAgAAFYJWZlVb/v1t+bKXd8ca9vvvsvhHVcDAKxG6w5y/8JjoGYOcB8AAEwTWRkAgKl3sAazM2cBAMC+ycoAAEy9cU/yt9fcfi4zhnFPnpEks7OGFwBglZGVAQCYOgdrMP9AVV0x7/pdR9dnkhzXXVlry2JPnnH8MYfnFU88SZMZAGBlk5UBAJh6B2sw17JUMQUWc/IMAABWBVkZAICpd8AGc2vtq8tVCAAArCayMgAAHPwkfwAAAAAAsE8azAAAAAAATESDGQAAAACAiRzsJH+HpKrOS3JqkrkkZ7bWLtnHNr+d5BGttcd0WQsAAKwUcjIAAGtFZzOYq+rRSU5urT0iyfOSnL+Pbe6f5FFd1QAAACuNnAwAwFrS5RIZj0/y3iRprV2WZFNVHb1gm3OTvKzDGgAAYKWRkwEAWDO6bDDfLcmOedd3jG5LklTVc5N8PMm2DmsAAICVRk4GAGDN6HQN5gVm9l6oqs1JfjrJE5KcMM6DN206IuvXDzoqLdl9e2e7nsjmzUf2XcLKds0ti37Ili1HdVDIFH6vVtDYJ8Z/HN77/enyvc/BGf/+GPtFO6ScnKyNrOx9s0QmyApdmrq80PH4T914dszPnX4Z//4Y+36txfHvssG8PfNmYiQ5PsnVo8uPS7IlySeTbEhy76o6r7X24v3tbNeu7n5RDwYz2Xj0yvpFfd11N2V2dq7vMlakwWDm4Bvtw44dN3ay72n6XnU59pPu3/gfXFfjP01jP4ktW44ae+xZesa/P6t97Jcp8C9pTk7WRlZeze+blWLSrNClacoLyzH+0zSeXVvtv69WO+PfH2Pfr9U+/vvLyl02mD+c5JVJ3lxVpyTZ3lq7MUlaa+9O8u4kqaqtSf7HwUIzAACsEXLyPiymOafBBgCwcnTWYG6tXVxVl1bVxUn2JDljtJ7cDa21i7p6XgAAWMnk5Ds64rBBzrzwM9l+/a1jbX/8MYfnFU88SZMZAGCF6HQN5tbary646Z/2sc22JI/psg4AAFhJ5OQ72n79rbly1zf7LgMAgAms67sAAAAAAABWJw1mAAAAAAAmosEMAAAAAMBENJgBAAAAAJiIBjMAAAAAABPRYAYAAAAAYCIazAAAAAAATESDGQAAAACAiWgwAwAAAAAwEQ1mAAAAAAAmosEMAAAAAMBENJgBAAAAAJiIBjMAAAAAABPRYAYAAAAAYCIazAAAAAAATESDGQAAAACAiWgwAwAAAAAwkfV9FwAAAAAc2LqZZN26mUU9ZnZ2rqNqAOA7NJgBAABghfuuow/Pc37tf2TbVTvH2n7rCcfmgnNO12QGoHMazAAAALAKbLtqZy6/4tq+ywCAO9BgBgAAVo3FLhNg9iYAQLc0mAEAgFVjMcsEWCIAAKB7GswAAMCqYpkAAICVY13fBQAAAAAAsDppMAMAAAAAMBFLZKxAiz1xSeLkJQAAAADA8tNgXoEWc+KSxMlLAAAAAIB+aDCvUE5cAgAAAACsdNZgBgAAAABgIhrMAAAAAABMRIMZAAAAAICJaDADAAAAADARDWYAAAAAACaiwQwAAAAAwEQ0mAEAAAAAmMj6vgsAAOjbYDCzqO1nZ+c6qgQAWIlkBYD902AGAKbaYDCT08++INuu2jnW9ltPODYXnHO6D44AMCVkBYAD02AGAKbetqt25vIrru27DABghZIVAPbPGswAAAAAAExEgxkAAAAAgIloMAMAAAAAMBENZgAAAAAAJqLBDAAAAADARDSYAQAAAACYyPq+CwAAAACA/RkMZha1/ezsXEeVAPuiwQwAAADAijQYzOT0sy/Itqt2jrX91hOOzQXnnK7JDMuo0wZzVZ2X5NQkc0nObK1dMu++xyb57SSzSVqSn22t7emyHgAAWAnkZAAY37ardubyK67tuwxgPzpbg7mqHp3k5NbaI5I8L8n5CzZ5S5JnttYemeSoJD/UVS0AALBSyMkAAKwlXZ7k7/FJ3pskrbXLkmyqqqPn3f+Q1trXRpd3JDm2w1oAAGClkJMBAFgzulwi425JLp13fcfotm8kSWvtG0lSVd+d5ElJXn6gnW3adETWrx90U2mS3bd3tutlsXnzkX2XsLyuuWXRD9my5agOCjH24+hq7BPjPw7v/f50+d7n4Pzs6Y/3/kEtaU5OZOUDmbr/rxNkhS4Z/35N3fgvkqzQL+PfH1mtX2tx/JfzJH//5pSfVXXXJH+a5BdaawdcrX3Xru5+UQ8GM9l49Or+4XPddTdNzQL2iz177F47dtzYyb6N/cGNM/aT7t/4H1xX4z9NYz+JLVuOGnvsWXqLGX8/e5bWan/v9xT4DyknJ7LygUzT/9dJs0KXjH+/pmn8F0tW6Jfx789qz2qr3Wof//1l5S4bzNsznImx1/FJrt57ZXQY4J8neVlr7cMd1gEAACuJnAwAwJrR5RrMH07yzCSpqlOSbG+tzW/Rn5vkvNbaX3RYAwAArDRyMgAAa0ZnM5hbaxdX1aVVdXGSPUnOqKrnJrkhyYeS/L9JTq6qnx095B2ttbd0VQ8AAKwEcjIAAGtJp2swt9Z+dcFN/zTv8oYunxsAAFYqORkAgLWiyyUyAAAAAABYwzSYAQAAAACYiAYzAAAAAAAT0WAGAAAAAGAiGswAAAAAAExEgxkAAAAAgIloMAMAAAAAMBENZgAAAAAAJqLBDAAAAADARDSYAQAAAACYiAYzAAAAAAAT0WAGAAAAAGAiGswAAAAAAExEgxkAAAAAgIloMAMAAAAAMBENZgAAAAAAJqLBDAAAAADARDSYAQAAAACYiAYzAAAAAAAT0WAGAAAAAGAiGswAAAAAAExEgxkAAAAAgIloMAMAAAAAMBENZgAAAAAAJqLBDAAAAADARDSYAQAAAACYiAYzAAAAAAAT0WAGAAAAAGAiGswAAAAAAExEgxkAAAAAgIloMAMAAAAAMBENZgAAAAAAJqLBDAAAAADARDSYAQAAAACYiAYzAAAAAAAT0WAGAAAAAGAiGswAAAAAAExEgxkAAAAAgIloMAMAAAAAMBENZgAAAAAAJqLBDAAAAADARDSYAQAAAACYiAYzAAAAAAAT0WAGAAAAAGAiGswAAAAAAExkfZc7r6rzkpyaZC7Jma21S+bd94Qkv5VkNskHW2uv6rIWAABYKeRkAADWis5mMFfVo5Oc3Fp7RJLnJTl/wSbnJ3lGkkcmeVJV3b+rWgAAYKWQkwEAWEu6XCLj8UnemySttcuSbKqqo5Okqk5Kcl1r7crW2p4kHxxtDwAAa52cDADAmtHlEhl3S3LpvOs7Rrd9Y/Tvjnn3XZvk3h3WclAzMxkeoDjWxsnGDYOx933EYety/DGHj739XY86LFtPOHbs7Rez7Uo1GMyMve26dTOLGv+9247zHOvWzRj7A+hy7Pfu3/jv30oa/2kb+5X4HLOz4/7SWplW0vhP28+elTT2k1jt7/2RVZWTk9WblVf7/9ek26zgc8rBGf/+rKTfV9OWFRLj36eVNPaTWO1ZzfhPZmZurpsnrqq3JPlAa+19o+ufSvIzrbUvVtUPJHlpa+3po/t+NslJrbVf66QYAABYIeRkAADWki6XyNie4QyMvY5PcvV+7jthdBsAAKx1cjIAAGtGlw3mDyd5ZpJU1SlJtrfWbkyS1tq2JEdX1daqWp/kR0fbAwDAWicnAwCwZnS2REaSVNX/l+RRSfYkOSPJg5Pc0Fq7qKoeleTVo03f01r7nc4KAQCAFUROBgBgrei0wQwAAAAAwNrV5RIZAAAAAACsYRrMAAAAAABMRIMZAAAAAICJaDAvgaraVFV36buOaVVVg75rmFZV9d1Vda++65hWVXW3qrpH33VMo6q6b1Xdu+86plVV/UBV/UjfdUyj0c/9u/ddB6uLrNwfOblfsnJ/5OR+ycr9kZP7VVV3rarj+66jDxrMh6iqfjjJe5O8qarO77ueaVNVj07yvKo6ru9aps3ol9b/TvIHVfVHfdczbarqyUnek+H4v6nveqZFVa2rqmOS/EWSF1bVA/uuadpU1WOTnJPk2r5rmTZV9bQkFyX59ap6VN/1sDrIyv2Rk/slK/dHTu6PrNwvOblfVfWkJO9O8paqekvf9Sw3DeZDUFVbk7w4yQuTPC/Jfarq9VV1bK+FTZcXJXlckqcLz8tnNHvtRUme01p7QpL7VtULey5ralTVg5L8WpKfT/KMJEdX1eH9VjUdWmt7WmvXJ/lQks1JHl1VD+65rKlRVY9P8uYkL2ytXVJVG6vqyL7rmgZVtTHJc5Kc2Vr72dbaJ6pqQ991sbLJyr2Tk3siK/dHTu6XrNwfOblfVfW9SV6W5IzW2o8mOWbajt7SYD40tyS5PcltrbVbkjw1yV2S/EavVU2Xbya5Msn9kjxDeF42tyU5PMns6Pqrk6zvr5ypszvJZa21zyS5Z5LvS/JbVfV7/ZY1Vb6QZE+SLUlOraqnjkIFHamqmSQnJdmZ5JaqunOSdyX5n1X1h1V1RK8Frn1zSY5Lsr6qjq6q9ye5sKre2nNdrGyycr/k5P7Iyv2Rk1cGWXkZyckrwm0Zvu+vGP0h/aFJXjlNR29pMB+aryf5eJIfrKrvaq19K8nPJHlAVf23fkubGr/WWntpko8luU+SZ1bVluTbP2Tpxq4kr2ytfWXebQ/de6GqBOhuXZPkT0aXn5zkj5O8Jsm9q+q/91bVFJj3c+XjSS5urf16hu/9t2X4M4iOtNbmkvxRkjeMvv58dP25GTas3tBbcVNg1Bx8Y5IfT/K6JO/McOzvXlV/2GNprGyycr/k5P7Iyv2Rk3skK/dDTl4R/jXJDRnm5I8m+b0kv5nk4dOyXIYG8yFore3J8JfXqUkeU1Xf3Vq7PcMPX0cKDsvia0nSWnt/kosz/KX12Ko6I8PD0uhAa+1brbW/nHfTNzOaoVFVz0nyEh9cutNau6G19tHR5fNaa7/RWvvX1toPJbnb3g+PLL1ReEuSHUkePFpb9HsyDHEnWGeuW621b2a4nuVFSVqS97bWbkjyY0m+y2H3nftohsH5rklaa+36eT937tpvaaxEsnLv5OSeyMr9kZP7JSv3R07uV2ttV5JXJXllksuTvKu1tiPJo5JMxcmOhbpD1Fr7l6o6L8mZSY6rqk8luVeSEzMc39v7rG+ta63tqaqZ1tpca+09VbUjyX/N8FCcn+y5vGlybZJ/rqpTk5ye5EXzwgUdqqrDMmz23J7kwUmOTHJrr0VNh+0ZflD8jSRnZXg41PMz/Ms1HWqtfbOq3p7kfaPLRyR5dJIjMjw0jY601r4+OlnSLyf5kdEazJuSbMyweQL/hqzcHzl5RZGVeyAn90pW7oGc3K/W2o1VdVOSLyV5VFV9OMMZ/MdkCvLOzNyc32tLoaruleRpSZ6U4bpPL2+tfa7fqqbH3vBcVT+a4SFQT2+ttb7rmhZVdc8k/5zksiTPNvbLp6qOSvKLGf7iOiLJL7XWPt9vVdOhqu6X5NjW2qdG1ze01nb3XNZUqaqnJfmPSY7P8IQa/9xzSVNhNFv5kRnOiNmT5DWjtS5hv2Tl/sjJ/ZOV+yEn90tW7pec3J+qum+Gf1jfnOFkjLOmYfw1mJfYaNr7zOjMqSyjqhokeUqGh+1+qe96pklVrctwRswftdYu77ueaVNVRyc5Kslsa82sgGW294N733VMo9EHxy0ZnkDsa33XM21GJ5CZGa3PDGORlfshJ/dLVu6PnNw/WbkfcnK/qurIJMcm+VZrbXvf9SwHDWbWFL+8+lNV60frKgIAsMLIyf2SlQFYyzSYAQAAAACYyLq+CwAAAAAAYHXSYAYAAAAAYCIazAAAAAAATGR93wUAcHBVtTVJS/Lp0U13SvLVJL/QWrv+EPb760nWt9bOPtQaAQCgD1X1lCT/Jclsko1JvpLkBUnelOQlSZ6Y5AmttZ8a97GHkrEBpo0GM8DqsaO19pi9V6rqtUnOTvLLvVUEAAA9qqrDkvxRkge21q4e3fbqJM9rrT1rdH3Rj01ybvfVA6wNGswAq9cnkrygqp6e5FeS3Jrhz/XntNa2VdXHkvxjkgcneVySpyR5xWi7L2Y4qyNJ7l5V705y3yQfa629cFlfBQAATO7OGc483rj3htbaf06SqtqW5Amjm4+tqvckOTHJl5I850CPnff4dyR5eJLjkpzVWvurrl4IwGplDWaAVaiqBkn+Q5JPJjkmyY+31h6b5INJ5jeIb2qtPTrJhiRvTfLDrbXTknw9ySNH2/w/SZ6V5PuTnF5Vxy7PqwAAgEPTWrshw0kU/1hVH6mql9W+pyw/OMlzkzwsyd2TPGXMx+5srT0+yS/FrGaAfdJgBlg9tlTVx0Yzk/8qyfYk5yW5JskFVfXxDEPzcfMec/Ho3/snubK1tiMZzsxorX18dN+nWmu3t9ZuTbIzw4Y1AACsCq21Vye5Z5I/HP37t1X18ws2+5vW2o2ttbkMz2vygDEf+6HRv3+dYaYGYAFLZACsHndYgzlJqupOSS5Mckpr7UtV9cIMZyLvddvo37ns/4+Kty+4PrMEtQIAwLKoqiNaazuTvDPJO6vqT/JvZxvvmXd5JsN8fKDHvnG07bqFjwHgjsxgBljdjsowLG+rqsOTPC3D5TAW+kKSE6rq7klSVf+tqp62fGUCAMDSq6onJ/l0VR017+aTkly+YNOHV9XGqppJ8ogknx3zsY8b/fuDST6ztNUDrA1mMAOsYq2166rqHUkuSfLVJK9N8j+r6scWbHdzVT0vyXuqaneSryT5QIZr0QEAwKrUWvtQVd0nyUer6pYMZxpfk+SMDJfC2OvvMlwG46QMJ198qLW25wCP3evuVfWBDNdt/oXOXxDAKjQzN+cIDwAAAID5qmpbkie01hbOhgZgHktkAAAAAAAwETOYAQAAAACYiBnMAAAAAABMRIMZAAAAAICJaDADAAAAADARDWYAAAAAACaiwQwAAAAAwEQ0mAEAAAAAmMj/DyWGC2jzJQzIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x1800 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x2160 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Time to run AutoViz (in seconds) = 8.143\n",
            "\n",
            " ###################### VISUALIZATION Completed ########################\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QURvB-emoALK"
      },
      "source": [
        "from autogluon.tabular import TabularDataset, TabularPredictor\n",
        "from autogluon.tabular.models.lr.lr_rapids_model import LinearRapidsModel\n",
        "from autogluon.tabular.models.knn.knn_rapids_model import KNNRapidsModel\n",
        "\n",
        "train_data = TabularDataset('https://raw.githubusercontent.com/gulabpatel/Dataset/master/Data_Machine_Learning/titanic_train.csv')\n",
        "test_data = TabularDataset('https://raw.githubusercontent.com/gulabpatel/Dataset/master/Data_Machine_Learning/titanic_test.csv')\n",
        "\n",
        "label = 'Survived'"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "UOoXvBaaa05s",
        "outputId": "27ecfb9d-53de-4eb7-eef0-4aa42c782fc0"
      },
      "source": [
        "train_data"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0              1         0       3  ...   7.2500   NaN         S\n",
              "1              2         1       1  ...  71.2833   C85         C\n",
              "2              3         1       3  ...   7.9250   NaN         S\n",
              "3              4         1       1  ...  53.1000  C123         S\n",
              "4              5         0       3  ...   8.0500   NaN         S\n",
              "..           ...       ...     ...  ...      ...   ...       ...\n",
              "886          887         0       2  ...  13.0000   NaN         S\n",
              "887          888         1       1  ...  30.0000   B42         S\n",
              "888          889         0       3  ...  23.4500   NaN         S\n",
              "889          890         1       1  ...  30.0000  C148         C\n",
              "890          891         0       3  ...   7.7500   NaN         Q\n",
              "\n",
              "[891 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0dDAEhImoChe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfffd3fa-65cc-44dc-c659-985b34a19ef5"
      },
      "source": [
        "#using integrated RAPIDS models along with some boosting models\n",
        "predictor = TabularPredictor(\n",
        "    label=label,\n",
        "    eval_metric='accuracy',\n",
        "    learner_kwargs={'ignored_columns': ['PassengerId']}\n",
        ").fit(\n",
        "    train_data,\n",
        "    presets='best_quality',\n",
        "    hyperparameters={'XGB': {'ag_args_fit': {'num_gpus': 1}},\n",
        "        'GBM': [{}, {'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, 'GBMLarge'],\n",
        "        'NN': {'ag_args_fit': {'num_gpus': 1}},\n",
        "        'CAT': {'ag_args_fit': {'num_gpus': 1}},\n",
        "        KNNRapidsModel: {},\n",
        "        LinearRapidsModel: {},\n",
        "       \n",
        "    },\n",
        ")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No path specified. Models will be saved in: \"AutogluonModels/ag-20210623_070741/\"\n",
            "Presets specified: ['best_quality']\n",
            "Beginning AutoGluon training ...\n",
            "AutoGluon will save models to \"AutogluonModels/ag-20210623_070741/\"\n",
            "AutoGluon Version:  0.1.1b20210312\n",
            "Train Data Rows:    891\n",
            "Train Data Columns: 11\n",
            "Preprocessing data ...\n",
            "AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).\n",
            "\t2 unique label values:  [0, 1]\n",
            "\tIf 'binary' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
            "Selected class <--> label mapping:  class 1 = 1, class 0 = 0\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Dropping user-specified ignored columns: ['PassengerId']\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    11284.56 MB\n",
            "\tTrain Data (Original)  Memory Usage: 0.31 MB (0.0% of available memory)\n",
            "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
            "\tStage 1 Generators:\n",
            "\t\tFitting AsTypeFeatureGenerator...\n",
            "\tStage 2 Generators:\n",
            "\t\tFitting FillNaFeatureGenerator...\n",
            "\tStage 3 Generators:\n",
            "\t\tFitting IdentityFeatureGenerator...\n",
            "\t\tFitting CategoryFeatureGenerator...\n",
            "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
            "\t\tFitting TextSpecialFeatureGenerator...\n",
            "\t\t\tFitting BinnedFeatureGenerator...\n",
            "\t\t\tFitting DropDuplicatesFeatureGenerator...\n",
            "\t\tFitting TextNgramFeatureGenerator...\n",
            "\t\t\tFitting CountVectorizer for text features: ['Name']\n",
            "\t\t\tCountVectorizer fit with vocabulary size = 8\n",
            "\t\tWarning: Due to memory constraints, ngram feature count is being reduced. Allocate more memory to maximize model quality.\n",
            "\t\tReducing Vectorizer vocab size from 8 to 6 to avoid OOM error\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('float', [])        : 2 | ['Age', 'Fare']\n",
            "\t\t('int', [])          : 3 | ['Pclass', 'SibSp', 'Parch']\n",
            "\t\t('object', [])       : 4 | ['Sex', 'Ticket', 'Cabin', 'Embarked']\n",
            "\t\t('object', ['text']) : 1 | ['Name']\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('category', [])                    :  4 | ['Sex', 'Ticket', 'Cabin', 'Embarked']\n",
            "\t\t('float', [])                       :  2 | ['Age', 'Fare']\n",
            "\t\t('int', [])                         :  3 | ['Pclass', 'SibSp', 'Parch']\n",
            "\t\t('int', ['binned', 'text_special']) : 12 | ['Name.char_count', 'Name.word_count', 'Name.capital_ratio', 'Name.lower_ratio', 'Name.special_ratio', ...]\n",
            "\t\t('int', ['text_ngram'])             :  7 | ['__nlp__.john', '__nlp__.master', '__nlp__.miss', '__nlp__.mr', '__nlp__.mrs', ...]\n",
            "\t0.9s = Fit runtime\n",
            "\t10 features in original data used to generate 28 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 0.07 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.94s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n",
            "\tTo change this, specify the eval_metric argument of fit()\n",
            "Custom Model Type Detected: <class 'autogluon.tabular.models.knn.knn_rapids_model.KNNRapidsModel'>\n",
            "Custom Model Type Detected: <class 'autogluon.tabular.models.lr.lr_rapids_model.LinearRapidsModel'>\n",
            "Fitting model: LightGBM_BAG_L1 ...\n",
            "\t0.8451\t = Validation accuracy score\n",
            "\t2.25s\t = Training runtime\n",
            "\t0.09s\t = Validation runtime\n",
            "Fitting model: LightGBMXT_BAG_L1 ...\n",
            "\t0.8373\t = Validation accuracy score\n",
            "\t2.01s\t = Training runtime\n",
            "\t0.09s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ...\n",
            "\tTraining S1F1 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\tTraining S1F2 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\tTraining S1F3 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\tTraining S1F4 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\tTraining S1F5 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\tTraining S1F6 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\tTraining S1F7 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\tTraining S1F8 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\t0.8462\t = Validation accuracy score\n",
            "\t51.72s\t = Training runtime\n",
            "\t0.08s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L1 ...\n",
            "\t0.8328\t = Validation accuracy score\n",
            "\t11.01s\t = Training runtime\n",
            "\t0.05s\t = Validation runtime\n",
            "Fitting model: NeuralNetMXNet_BAG_L1 ...\n",
            "\tWarning: Exception caused NeuralNetMXNet_BAG_L1 to fail during training (ImportError)... Skipping this model.\n",
            "\t\tUnable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade`\n",
            "Fitting model: LightGBMLarge_BAG_L1 ...\n",
            "\t0.8429\t = Validation accuracy score\n",
            "\t3.54s\t = Training runtime\n",
            "\t0.09s\t = Validation runtime\n",
            "Fitting model: KNNRapidsModel_BAG_L1 ...\n",
            "\t0.6599\t = Validation accuracy score\n",
            "\t1.94s\t = Training runtime\n",
            "\t3.76s\t = Validation runtime\n",
            "Fitting model: LinearRapidsModel_BAG_L1 ...\n",
            "\t0.8451\t = Validation accuracy score\n",
            "\t4.61s\t = Training runtime\n",
            "\t1.31s\t = Validation runtime\n",
            "Fitting model: WeightedEnsemble_L2 ...\n",
            "\t0.8507\t = Validation accuracy score\n",
            "\t0.41s\t = Training runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "Custom Model Type Detected: <class 'autogluon.tabular.models.knn.knn_rapids_model.KNNRapidsModel'>\n",
            "Custom Model Type Detected: <class 'autogluon.tabular.models.lr.lr_rapids_model.LinearRapidsModel'>\n",
            "Fitting model: LightGBM_BAG_L2 ...\n",
            "\t0.8664\t = Validation accuracy score\n",
            "\t2.62s\t = Training runtime\n",
            "\t0.08s\t = Validation runtime\n",
            "Fitting model: LightGBMXT_BAG_L2 ...\n",
            "\t0.8608\t = Validation accuracy score\n",
            "\t2.17s\t = Training runtime\n",
            "\t0.08s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L2 ...\n",
            "\tTraining S1F1 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\tTraining S1F2 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\tTraining S1F3 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\tTraining S1F4 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\tTraining S1F5 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\tTraining S1F6 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\tTraining S1F7 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\tTraining S1F8 with GPU, note that this may negatively impact model quality compared to CPU training.\n",
            "\t0.8687\t = Validation accuracy score\n",
            "\t52.65s\t = Training runtime\n",
            "\t0.09s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L2 ...\n",
            "\t0.8575\t = Validation accuracy score\n",
            "\t7.71s\t = Training runtime\n",
            "\t0.06s\t = Validation runtime\n",
            "Fitting model: NeuralNetMXNet_BAG_L2 ...\n",
            "\tWarning: Exception caused NeuralNetMXNet_BAG_L2 to fail during training (ImportError)... Skipping this model.\n",
            "\t\tUnable to import dependency mxnet. A quick tip is to install via `pip install mxnet --upgrade`, or `pip install mxnet_cu101 --upgrade`\n",
            "Fitting model: LightGBMLarge_BAG_L2 ...\n",
            "\t0.8575\t = Validation accuracy score\n",
            "\t5.05s\t = Training runtime\n",
            "\t0.08s\t = Validation runtime\n",
            "Fitting model: KNNRapidsModel_BAG_L2 ...\n",
            "\t0.7037\t = Validation accuracy score\n",
            "\t0.05s\t = Training runtime\n",
            "\t1.22s\t = Validation runtime\n",
            "Fitting model: LinearRapidsModel_BAG_L2 ...\n",
            "\t0.844\t = Validation accuracy score\n",
            "\t1.93s\t = Training runtime\n",
            "\t0.16s\t = Validation runtime\n",
            "Fitting model: WeightedEnsemble_L3 ...\n",
            "\t0.8687\t = Validation accuracy score\n",
            "\t0.41s\t = Training runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 159.51s ...\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20210623_070741/\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "cImVm4IZMLcx",
        "outputId": "79267a8b-b7fe-46a4-9616-6faee4b91136"
      },
      "source": [
        "test_data"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
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              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
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              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
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              "      <td>Q</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>1305</td>\n",
              "      <td>3</td>\n",
              "      <td>Spector, Mr. Woolf</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A.5. 3236</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1306</td>\n",
              "      <td>1</td>\n",
              "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17758</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>C105</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>1307</td>\n",
              "      <td>3</td>\n",
              "      <td>Saether, Mr. Simon Sivertsen</td>\n",
              "      <td>male</td>\n",
              "      <td>38.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SOTON/O.Q. 3101262</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>1308</td>\n",
              "      <td>3</td>\n",
              "      <td>Ware, Mr. Frederick</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>359309</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>1309</td>\n",
              "      <td>3</td>\n",
              "      <td>Peter, Master. Michael J</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2668</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Pclass  ... Cabin Embarked\n",
              "0            892       3  ...   NaN        Q\n",
              "1            893       3  ...   NaN        S\n",
              "2            894       2  ...   NaN        Q\n",
              "3            895       3  ...   NaN        S\n",
              "4            896       3  ...   NaN        S\n",
              "..           ...     ...  ...   ...      ...\n",
              "413         1305       3  ...   NaN        S\n",
              "414         1306       1  ...  C105        C\n",
              "415         1307       3  ...   NaN        S\n",
              "416         1308       3  ...   NaN        S\n",
              "417         1309       3  ...   NaN        C\n",
              "\n",
              "[418 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-AU_OBVoEJe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "4be5f195-4f0e-4904-9959-098d443efd2f"
      },
      "source": [
        "import pandas as pd\n",
        "submission = test_data[['PassengerId']]\n",
        "test_pred_proba = predictor.predict(test_data)\n",
        "test_pred_proba=pd.DataFrame(test_pred_proba,columns=['Survived'])\n",
        "submission = pd.concat([submission, test_pred_proba], axis=1)\n",
        "submission.to_csv('submission.csv', index=False)\n",
        "submission.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived\n",
              "0          892         0\n",
              "1          893         1\n",
              "2          894         0\n",
              "3          895         0\n",
              "4          896         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WjAtqSnLoFtv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "outputId": "6eb7eb99-3136-451f-e87f-68e4f39ed4b2"
      },
      "source": [
        "predictor.leaderboard(silent=True)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>score_val</th>\n",
              "      <th>pred_time_val</th>\n",
              "      <th>fit_time</th>\n",
              "      <th>pred_time_val_marginal</th>\n",
              "      <th>fit_time_marginal</th>\n",
              "      <th>stack_level</th>\n",
              "      <th>can_infer</th>\n",
              "      <th>fit_order</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CatBoost_BAG_L2</td>\n",
              "      <td>0.868687</td>\n",
              "      <td>5.560091</td>\n",
              "      <td>129.712930</td>\n",
              "      <td>0.092424</td>\n",
              "      <td>52.645744</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>WeightedEnsemble_L3</td>\n",
              "      <td>0.868687</td>\n",
              "      <td>5.561881</td>\n",
              "      <td>130.124581</td>\n",
              "      <td>0.001790</td>\n",
              "      <td>0.411651</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LightGBM_BAG_L2</td>\n",
              "      <td>0.866442</td>\n",
              "      <td>5.547325</td>\n",
              "      <td>79.691172</td>\n",
              "      <td>0.079658</td>\n",
              "      <td>2.623986</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LightGBMXT_BAG_L2</td>\n",
              "      <td>0.860831</td>\n",
              "      <td>5.550713</td>\n",
              "      <td>79.232604</td>\n",
              "      <td>0.083046</td>\n",
              "      <td>2.165417</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XGBoost_BAG_L2</td>\n",
              "      <td>0.857464</td>\n",
              "      <td>5.524891</td>\n",
              "      <td>84.781804</td>\n",
              "      <td>0.057224</td>\n",
              "      <td>7.714617</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>LightGBMLarge_BAG_L2</td>\n",
              "      <td>0.857464</td>\n",
              "      <td>5.551937</td>\n",
              "      <td>82.117039</td>\n",
              "      <td>0.084270</td>\n",
              "      <td>5.049852</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>WeightedEnsemble_L2</td>\n",
              "      <td>0.850730</td>\n",
              "      <td>1.707910</td>\n",
              "      <td>75.532775</td>\n",
              "      <td>0.001760</td>\n",
              "      <td>0.406082</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>CatBoost_BAG_L1</td>\n",
              "      <td>0.846240</td>\n",
              "      <td>0.084143</td>\n",
              "      <td>51.724092</td>\n",
              "      <td>0.084143</td>\n",
              "      <td>51.724092</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>LightGBM_BAG_L1</td>\n",
              "      <td>0.845118</td>\n",
              "      <td>0.086281</td>\n",
              "      <td>2.246378</td>\n",
              "      <td>0.086281</td>\n",
              "      <td>2.246378</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>LinearRapidsModel_BAG_L1</td>\n",
              "      <td>0.845118</td>\n",
              "      <td>1.308981</td>\n",
              "      <td>4.605862</td>\n",
              "      <td>1.308981</td>\n",
              "      <td>4.605862</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>LinearRapidsModel_BAG_L2</td>\n",
              "      <td>0.843996</td>\n",
              "      <td>5.628559</td>\n",
              "      <td>78.998227</td>\n",
              "      <td>0.160892</td>\n",
              "      <td>1.931041</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>LightGBMLarge_BAG_L1</td>\n",
              "      <td>0.842873</td>\n",
              "      <td>0.088473</td>\n",
              "      <td>3.537056</td>\n",
              "      <td>0.088473</td>\n",
              "      <td>3.537056</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>LightGBMXT_BAG_L1</td>\n",
              "      <td>0.837262</td>\n",
              "      <td>0.085796</td>\n",
              "      <td>2.005749</td>\n",
              "      <td>0.085796</td>\n",
              "      <td>2.005749</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>XGBoost_BAG_L1</td>\n",
              "      <td>0.832772</td>\n",
              "      <td>0.052475</td>\n",
              "      <td>11.007555</td>\n",
              "      <td>0.052475</td>\n",
              "      <td>11.007555</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>KNNRapidsModel_BAG_L2</td>\n",
              "      <td>0.703704</td>\n",
              "      <td>6.685460</td>\n",
              "      <td>77.119312</td>\n",
              "      <td>1.217793</td>\n",
              "      <td>0.052125</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>KNNRapidsModel_BAG_L1</td>\n",
              "      <td>0.659933</td>\n",
              "      <td>3.761517</td>\n",
              "      <td>1.940494</td>\n",
              "      <td>3.761517</td>\n",
              "      <td>1.940494</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       model  score_val  ...  can_infer  fit_order\n",
              "0            CatBoost_BAG_L2   0.868687  ...       True         11\n",
              "1        WeightedEnsemble_L3   0.868687  ...       True         16\n",
              "2            LightGBM_BAG_L2   0.866442  ...       True          9\n",
              "3          LightGBMXT_BAG_L2   0.860831  ...       True         10\n",
              "4             XGBoost_BAG_L2   0.857464  ...       True         12\n",
              "5       LightGBMLarge_BAG_L2   0.857464  ...       True         13\n",
              "6        WeightedEnsemble_L2   0.850730  ...       True          8\n",
              "7            CatBoost_BAG_L1   0.846240  ...       True          3\n",
              "8            LightGBM_BAG_L1   0.845118  ...       True          1\n",
              "9   LinearRapidsModel_BAG_L1   0.845118  ...       True          7\n",
              "10  LinearRapidsModel_BAG_L2   0.843996  ...       True         15\n",
              "11      LightGBMLarge_BAG_L1   0.842873  ...       True          5\n",
              "12         LightGBMXT_BAG_L1   0.837262  ...       True          2\n",
              "13            XGBoost_BAG_L1   0.832772  ...       True          4\n",
              "14     KNNRapidsModel_BAG_L2   0.703704  ...       True         14\n",
              "15     KNNRapidsModel_BAG_L1   0.659933  ...       True          6\n",
              "\n",
              "[16 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    }
  ]
}