BPL_TEST2_Batch_colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/janpeter19/BPL_TEST2_Batch/blob/main/BPL_TEST2_Batch_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": "UFcMzs4Y3HwH"
      },
      "source": [
        "# BPL_TEST2_Batch script with PyFMI\n",
        "\n",
        "The key library PyFMI is installed.\n",
        "\n",
        "After the installation a small application BPL_TEST2_Batch is loaded and run. You can continue with this example if you like."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ID68dwep2zus",
        "outputId": "ef075cd2-620e-4ee3-badd-6a91b632ba50"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No LSB modules are available.\n",
            "Distributor ID:\tUbuntu\n",
            "Description:\tUbuntu 22.04.3 LTS\n",
            "Release:\t22.04\n",
            "Codename:\tjammy\n"
          ]
        }
      ],
      "source": [
        "!lsb_release -a # Actual VM Ubuntu version used by Google"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pVDcSg-j3SSV",
        "outputId": "941f48ee-2d89-4199-dfd7-47154d7460a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: PYTHONPATH=\n"
          ]
        }
      ],
      "source": [
        "%env PYTHONPATH="
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04VeBmLU3UFx",
        "outputId": "4314151f-70a3-4b55-9001-a7f426b05e01"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-03-05 07:11:38--  https://repo.anaconda.com/miniconda/Miniconda3-py310_23.1.0-1-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8303, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 74403966 (71M) [application/x-sh]\n",
            "Saving to: ‘Miniconda3-py310_23.1.0-1-Linux-x86_64.sh’\n",
            "\n",
            "Miniconda3-py310_23 100%[===================>]  70.96M  95.1MB/s    in 0.7s    \n",
            "\n",
            "2024-03-05 07:11:39 (95.1 MB/s) - ‘Miniconda3-py310_23.1.0-1-Linux-x86_64.sh’ saved [74403966/74403966]\n",
            "\n",
            "PREFIX=/usr/local\n",
            "Unpacking payload ...\n",
            "                                                                               \n",
            "Installing base environment...\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "\n",
            "Preparing transaction: - \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "installation finished.\n"
          ]
        }
      ],
      "source": [
        "!wget https://repo.anaconda.com/miniconda/Miniconda3-py310_23.1.0-1-Linux-x86_64.sh\n",
        "!chmod +x Miniconda3-py310_23.1.0-1-Linux-x86_64.sh\n",
        "!bash ./Miniconda3-py310_23.1.0-1-Linux-x86_64.sh -b -f -p /usr/local\n",
        "import sys\n",
        "sys.path.append('/usr/local/lib/python3.10/site-packages/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5iuP4_vl3WFA",
        "outputId": "e6e7edec-e9a9-48e8-d145-89cdfc4577ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - conda\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    archspec-0.2.3             |     pyhd3eb1b0_0          47 KB\n",
            "    boltons-23.0.0             |  py310h06a4308_0         430 KB\n",
            "    brotli-python-1.0.9        |  py310h6a678d5_7         907 KB\n",
            "    bzip2-1.0.8                |       h5eee18b_5         262 KB\n",
            "    c-ares-1.19.1              |       h5eee18b_0         118 KB\n",
            "    ca-certificates-2023.12.12 |       h06a4308_0         126 KB\n",
            "    certifi-2024.2.2           |  py310h06a4308_0         159 KB\n",
            "    cffi-1.16.0                |  py310h5eee18b_0         253 KB\n",
            "    conda-24.1.2               |  py310h06a4308_0         985 KB\n",
            "    conda-libmamba-solver-24.1.0|     pyhd3eb1b0_0          39 KB\n",
            "    conda-package-handling-2.2.0|  py310h06a4308_0         268 KB\n",
            "    conda-package-streaming-0.9.0|  py310h06a4308_0          27 KB\n",
            "    cryptography-42.0.2        |  py310hdda0065_0         2.1 MB\n",
            "    distro-1.8.0               |  py310h06a4308_0          31 KB\n",
            "    fmt-9.1.0                  |       hdb19cb5_0         194 KB\n",
            "    icu-73.1                   |       h6a678d5_0        25.9 MB\n",
            "    jsonpatch-1.32             |     pyhd3eb1b0_0          15 KB\n",
            "    jsonpointer-2.1            |     pyhd3eb1b0_0           9 KB\n",
            "    krb5-1.20.1                |       h143b758_1         1.3 MB\n",
            "    libarchive-3.6.2           |       h6ac8c49_2         900 KB\n",
            "    libcurl-8.5.0              |       h251f7ec_0         416 KB\n",
            "    libedit-3.1.20230828       |       h5eee18b_0         179 KB\n",
            "    libev-4.33                 |       h7f8727e_1         111 KB\n",
            "    libffi-3.4.4               |       h6a678d5_0         142 KB\n",
            "    libmamba-1.5.6             |       haf1ee3a_0         1.8 MB\n",
            "    libmambapy-1.5.6           |  py310h2dafd23_0         315 KB\n",
            "    libnghttp2-1.57.0          |       h2d74bed_0         674 KB\n",
            "    libsolv-0.7.24             |       he621ea3_0         492 KB\n",
            "    libssh2-1.10.0             |       hdbd6064_2         292 KB\n",
            "    libxml2-2.10.4             |       hf1b16e4_1         753 KB\n",
            "    lz4-c-1.9.4                |       h6a678d5_0         154 KB\n",
            "    menuinst-2.0.2             |  py310h06a4308_0         138 KB\n",
            "    openssl-3.0.13             |       h7f8727e_0         5.2 MB\n",
            "    packaging-23.1             |  py310h06a4308_0          78 KB\n",
            "    pcre2-10.42                |       hebb0a14_0         1.5 MB\n",
            "    platformdirs-3.10.0        |  py310h06a4308_0          33 KB\n",
            "    pybind11-abi-4             |       hd3eb1b0_1          14 KB\n",
            "    pycosat-0.6.6              |  py310h5eee18b_0          94 KB\n",
            "    python-3.10.13             |       h955ad1f_0        26.8 MB\n",
            "    reproc-14.2.4              |       h295c915_1          30 KB\n",
            "    reproc-cpp-14.2.4          |       h295c915_1          22 KB\n",
            "    requests-2.31.0            |  py310h06a4308_1          97 KB\n",
            "    sqlite-3.41.2              |       h5eee18b_0         1.2 MB\n",
            "    tqdm-4.65.0                |  py310h2f386ee_0         132 KB\n",
            "    truststore-0.8.0           |  py310h06a4308_0          36 KB\n",
            "    tzdata-2024a               |       h04d1e81_0         116 KB\n",
            "    urllib3-2.1.0              |  py310h06a4308_1         158 KB\n",
            "    xz-5.4.6                   |       h5eee18b_0         651 KB\n",
            "    yaml-cpp-0.8.0             |       h6a678d5_0         607 KB\n",
            "    zstandard-0.19.0           |  py310h5eee18b_0         474 KB\n",
            "    zstd-1.5.5                 |       hc292b87_0         647 KB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        77.3 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  archspec           pkgs/main/noarch::archspec-0.2.3-pyhd3eb1b0_0 \n",
            "  boltons            pkgs/main/linux-64::boltons-23.0.0-py310h06a4308_0 \n",
            "  brotli-python      pkgs/main/linux-64::brotli-python-1.0.9-py310h6a678d5_7 \n",
            "  c-ares             pkgs/main/linux-64::c-ares-1.19.1-h5eee18b_0 \n",
            "  conda-libmamba-so~ pkgs/main/noarch::conda-libmamba-solver-24.1.0-pyhd3eb1b0_0 \n",
            "  distro             pkgs/main/linux-64::distro-1.8.0-py310h06a4308_0 \n",
            "  fmt                pkgs/main/linux-64::fmt-9.1.0-hdb19cb5_0 \n",
            "  icu                pkgs/main/linux-64::icu-73.1-h6a678d5_0 \n",
            "  jsonpatch          pkgs/main/noarch::jsonpatch-1.32-pyhd3eb1b0_0 \n",
            "  jsonpointer        pkgs/main/noarch::jsonpointer-2.1-pyhd3eb1b0_0 \n",
            "  krb5               pkgs/main/linux-64::krb5-1.20.1-h143b758_1 \n",
            "  libarchive         pkgs/main/linux-64::libarchive-3.6.2-h6ac8c49_2 \n",
            "  libcurl            pkgs/main/linux-64::libcurl-8.5.0-h251f7ec_0 \n",
            "  libedit            pkgs/main/linux-64::libedit-3.1.20230828-h5eee18b_0 \n",
            "  libev              pkgs/main/linux-64::libev-4.33-h7f8727e_1 \n",
            "  libmamba           pkgs/main/linux-64::libmamba-1.5.6-haf1ee3a_0 \n",
            "  libmambapy         pkgs/main/linux-64::libmambapy-1.5.6-py310h2dafd23_0 \n",
            "  libnghttp2         pkgs/main/linux-64::libnghttp2-1.57.0-h2d74bed_0 \n",
            "  libsolv            pkgs/main/linux-64::libsolv-0.7.24-he621ea3_0 \n",
            "  libssh2            pkgs/main/linux-64::libssh2-1.10.0-hdbd6064_2 \n",
            "  libxml2            pkgs/main/linux-64::libxml2-2.10.4-hf1b16e4_1 \n",
            "  lz4-c              pkgs/main/linux-64::lz4-c-1.9.4-h6a678d5_0 \n",
            "  menuinst           pkgs/main/linux-64::menuinst-2.0.2-py310h06a4308_0 \n",
            "  packaging          pkgs/main/linux-64::packaging-23.1-py310h06a4308_0 \n",
            "  pcre2              pkgs/main/linux-64::pcre2-10.42-hebb0a14_0 \n",
            "  platformdirs       pkgs/main/linux-64::platformdirs-3.10.0-py310h06a4308_0 \n",
            "  pybind11-abi       pkgs/main/noarch::pybind11-abi-4-hd3eb1b0_1 \n",
            "  reproc             pkgs/main/linux-64::reproc-14.2.4-h295c915_1 \n",
            "  reproc-cpp         pkgs/main/linux-64::reproc-cpp-14.2.4-h295c915_1 \n",
            "  truststore         pkgs/main/linux-64::truststore-0.8.0-py310h06a4308_0 \n",
            "  yaml-cpp           pkgs/main/linux-64::yaml-cpp-0.8.0-h6a678d5_0 \n",
            "  zstd               pkgs/main/linux-64::zstd-1.5.5-hc292b87_0 \n",
            "\n",
            "The following packages will be REMOVED:\n",
            "\n",
            "  brotlipy-0.7.0-py310h7f8727e_1002\n",
            "  pyopenssl-22.0.0-pyhd3eb1b0_0\n",
            "  toolz-0.12.0-py310h06a4308_0\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  bzip2                                    1.0.8-h7b6447c_0 --> 1.0.8-h5eee18b_5 \n",
            "  ca-certificates                     2023.01.10-h06a4308_0 --> 2023.12.12-h06a4308_0 \n",
            "  certifi                         2022.12.7-py310h06a4308_0 --> 2024.2.2-py310h06a4308_0 \n",
            "  cffi                               1.15.1-py310h5eee18b_3 --> 1.16.0-py310h5eee18b_0 \n",
            "  conda                              23.1.0-py310h06a4308_0 --> 24.1.2-py310h06a4308_0 \n",
            "  conda-package-han~                  2.0.2-py310h06a4308_0 --> 2.2.0-py310h06a4308_0 \n",
            "  conda-package-str~                  0.7.0-py310h06a4308_0 --> 0.9.0-py310h06a4308_0 \n",
            "  cryptography                       38.0.4-py310h9ce1e76_0 --> 42.0.2-py310hdda0065_0 \n",
            "  libffi                                   3.4.2-h6a678d5_6 --> 3.4.4-h6a678d5_0 \n",
            "  openssl                                 1.1.1s-h7f8727e_0 --> 3.0.13-h7f8727e_0 \n",
            "  pycosat                             0.6.4-py310h5eee18b_0 --> 0.6.6-py310h5eee18b_0 \n",
            "  python                                  3.10.9-h7a1cb2a_0 --> 3.10.13-h955ad1f_0 \n",
            "  requests                           2.28.1-py310h06a4308_0 --> 2.31.0-py310h06a4308_1 \n",
            "  sqlite                                  3.40.1-h5082296_0 --> 3.41.2-h5eee18b_0 \n",
            "  tqdm                               4.64.1-py310h06a4308_0 --> 4.65.0-py310h2f386ee_0 \n",
            "  tzdata                                   2022g-h04d1e81_0 --> 2024a-h04d1e81_0 \n",
            "  urllib3                           1.26.14-py310h06a4308_0 --> 2.1.0-py310h06a4308_1 \n",
            "  xz                                      5.2.10-h5eee18b_1 --> 5.4.6-h5eee18b_0 \n",
            "  zstandard                          0.18.0-py310h5eee18b_0 --> 0.19.0-py310h5eee18b_0 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "brotli-python-1.0.9  | 907 KB    | :   0% 0/1 [00:00<?, ?it/s]\n",
            "xz-5.4.6             | 651 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "libarchive-3.6.2     | 900 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "libmamba-1.5.6       | 1.8 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "reproc-14.2.4        | 30 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "fmt-9.1.0            | 194 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "zstd-1.5.5           | 647 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libssh2-1.10.0       | 292 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "cryptography-42.0.2  | 2.1 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "tqdm-4.65.0          | 132 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "zstandard-0.19.0     | 474 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "krb5-1.20.1          | 1.3 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "requests-2.31.0      | 97 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.10.13       | 26.8 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-package-handli | 268 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "bzip2-1.0.8          | 262 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pcre2-10.42          | 1.5 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "menuinst-2.0.2       | 138 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-package-stream | 27 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "brotli-python-1.0.9  | 907 KB    | :  49% 0.49416111467180845/1 [00:00<00:00,  4.85it/s]\n",
            "\n",
            "\n",
            "libmamba-1.5.6       | 1.8 MB    | :   8% 0.07680760075215777/1 [00:00<00:01,  1.33s/it]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "libarchive-3.6.2     | 900 KB    | :  82% 0.8180778106553835/1 [00:00<00:00,  7.82it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "fmt-9.1.0            | 194 KB    | :   8% 0.08233951985365437/1 [00:00<00:01,  1.35s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "zstd-1.5.5           | 647 KB    | :   2% 0.024726684414823938/1 [00:00<00:04,  4.56s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libssh2-1.10.0       | 292 KB    | :   5% 0.05480478471460301/1 [00:00<00:02,  2.17s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "cryptography-42.0.2  | 2.1 MB    | :   1% 0.00747819165056981/1 [00:00<00:18, 19.01s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "zstandard-0.19.0     | 474 KB    | :   3% 0.033748877371938005/1 [00:00<00:04,  4.65s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "tqdm-4.65.0          | 132 KB    | :  12% 0.12119598183243827/1 [00:00<00:01,  1.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "reproc-14.2.4        | 30 KB     | : 100% 1.0/1 [00:00<00:00,  5.91it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "reproc-14.2.4        | 30 KB     | : 100% 1.0/1 [00:00<00:00,  5.91it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "krb5-1.20.1          | 1.3 MB    | :   1% 0.011704077710961287/1 [00:00<00:15, 15.78s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "requests-2.31.0      | 97 KB     | :  17% 0.16543813235858393/1 [00:00<00:00,  1.17s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "libmamba-1.5.6       | 1.8 MB    | :  70% 0.6998025846307707/1 [00:00<00:00,  3.80it/s] \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.10.13       | 26.8 MB   | :   0% 0.0005833059849315943/1 [00:00<05:37, 338.07s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-package-handli | 268 KB    | :   6% 0.05979081971520535/1 [00:00<00:03,  3.76s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "cryptography-42.0.2  | 2.1 MB    | :  76% 0.7627755483581207/1 [00:00<00:00,  3.78it/s] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "bzip2-1.0.8          | 262 KB    | :   6% 0.061068247046106826/1 [00:00<00:03,  4.25s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pcre2-10.42          | 1.5 MB    | :   1% 0.01009007411129874/1 [00:00<00:26, 26.79s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.10.13       | 26.8 MB   | :   7% 0.07407986008631248/1 [00:00<00:02,  3.22s/it]   \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "menuinst-2.0.2       | 138 KB    | :  12% 0.11595515796625523/1 [00:00<00:02,  2.56s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-package-stream | 27 KB     | :  58% 0.5842247896163172/1 [00:00<00:00,  1.93it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.10.13       | 26.8 MB   | :  12% 0.11782780895618206/1 [00:00<00:02,  2.81s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.10.13       | 26.8 MB   | :  19% 0.18899113911783658/1 [00:00<00:01,  2.10s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "libarchive-3.6.2     | 900 KB    | : 100% 1.0/1 [00:00<00:00,  7.82it/s]               \u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "python-3.10.13       | 26.8 MB   | :  24% 0.24323859571647485/1 [00:00<00:01,  2.01s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "fmt-9.1.0            | 194 KB    | : 100% 1.0/1 [00:00<00:00,  1.60it/s]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "fmt-9.1.0            | 194 KB    | : 100% 1.0/1 [00:00<00:00,  1.60it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "python-3.10.13       | 26.8 MB   | :  30% 0.2974860523151131/1 [00:00<00:01,  2.06s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "python-3.10.13       | 26.8 MB   | :  37% 0.3744824423260836/1 [00:00<00:01,  1.76s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.10.13       | 26.8 MB   | :  47% 0.46606148196034386/1 [00:00<00:00,  1.49s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "xz-5.4.6             | 651 KB    | : 100% 1.0/1 [00:00<00:00,  8.56it/s]               \u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libssh2-1.10.0       | 292 KB    | : 100% 1.0/1 [00:01<00:00,  1.01it/s]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libssh2-1.10.0       | 292 KB    | : 100% 1.0/1 [00:01<00:00,  1.01it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "brotli-python-1.0.9  | 907 KB    | : 100% 1.0/1 [00:01<00:00,  1.26s/it]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.10.13       | 26.8 MB   | :  69% 0.6853845322946234/1 [00:01<00:00,  1.12s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "zstd-1.5.5           | 647 KB    | : 100% 1.0/1 [00:01<00:00,  1.12s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "zstd-1.5.5           | 647 KB    | : 100% 1.0/1 [00:01<00:00,  1.12s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "zstandard-0.19.0     | 474 KB    | : 100% 1.0/1 [00:01<00:00,  1.18s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "zstandard-0.19.0     | 474 KB    | : 100% 1.0/1 [00:01<00:00,  1.18s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "tqdm-4.65.0          | 132 KB    | : 100% 1.0/1 [00:01<00:00,  1.24s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "tqdm-4.65.0          | 132 KB    | : 100% 1.0/1 [00:01<00:00,  1.24s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.10.13       | 26.8 MB   | :  78% 0.776380265943952/1 [00:01<00:00,  1.22s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "requests-2.31.0      | 97 KB     | : 100% 1.0/1 [00:01<00:00,  1.33s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "requests-2.31.0      | 97 KB     | : 100% 1.0/1 [00:01<00:00,  1.33s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.10.13       | 26.8 MB   | :  86% 0.8621262457288964/1 [00:01<00:00,  1.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.10.13       | 26.8 MB   | :  95% 0.946122307559046/1 [00:01<00:00,  1.24s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "conda-package-handli | 268 KB    | : 100% 1.0/1 [00:01<00:00,  1.44s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
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            "conda-package-handli | 268 KB    | : 100% 1.0/1 [00:01<00:00,  1.44s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "libmamba-1.5.6       | 1.8 MB    | : 100% 1.0/1 [00:01<00:00,  3.80it/s]               \u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "krb5-1.20.1          | 1.3 MB    | : 100% 1.0/1 [00:01<00:00,  1.70s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "krb5-1.20.1          | 1.3 MB    | : 100% 1.0/1 [00:01<00:00,  1.70s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "bzip2-1.0.8          | 262 KB    | : 100% 1.0/1 [00:01<00:00,  1.76s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "bzip2-1.0.8          | 262 KB    | : 100% 1.0/1 [00:01<00:00,  1.76s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "menuinst-2.0.2       | 138 KB    | : 100% 1.0/1 [00:01<00:00,  1.84s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "menuinst-2.0.2       | 138 KB    | : 100% 1.0/1 [00:01<00:00,  1.84s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "conda-package-stream | 27 KB     | : 100% 1.0/1 [00:01<00:00,  2.18s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "conda-package-stream | 27 KB     | : 100% 1.0/1 [00:01<00:00,  2.18s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "cryptography-42.0.2  | 2.1 MB    | : 100% 1.0/1 [00:02<00:00,  3.78it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
            "pcre2-10.42          | 1.5 MB    | : 100% 1.0/1 [00:02<00:00,  2.22s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "pcre2-10.42          | 1.5 MB    | : 100% 1.0/1 [00:02<00:00,  2.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "python-3.10.13       | 26.8 MB   | : 100% 1.0/1 [00:05<00:00,  1.24s/it]              \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \n",
            "                                                                        \u001b[A\n",
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            "                                                                        \u001b[A\u001b[A\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\u001b[A\n",
            "\n",
            "\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\u001b[A\n",
            "\n",
            "\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "Preparing transaction: | \b\b/ \b\b- \b\bdone\n",
            "Verifying transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n"
          ]
        }
      ],
      "source": [
        "!conda update -n base -c defaults conda --yes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uoBMBSS63Z58",
        "outputId": "6c0b7dab-7b1e-43ee-8888-3f6bb8991cda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conda 24.1.2\n",
            "Python 3.10.13\n"
          ]
        }
      ],
      "source": [
        "!conda --version\n",
        "!python --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RMuJRdfH3dDq",
        "outputId": "40075dfc-1e42-4041-c67a-6a8c634ee910"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Channels:\n",
            " - conda-forge\n",
            " - defaults\n",
            "Platform: linux-64\n",
            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Solving environment: \\ \b\b| \b\b/ \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - pyfmi\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    _libgcc_mutex-0.1          |      conda_forge           3 KB  conda-forge\n",
            "    _openmp_mutex-4.5          |            2_gnu          23 KB  conda-forge\n",
            "    assimulo-3.4.3             |  py310hd2a66c7_3         2.0 MB  conda-forge\n",
            "    ca-certificates-2024.2.2   |       hbcca054_0         152 KB  conda-forge\n",
            "    certifi-2024.2.2           |     pyhd8ed1ab_0         157 KB  conda-forge\n",
            "    conda-24.1.2               |  py310hff52083_0         947 KB  conda-forge\n",
            "    fmilib-2.4.1               |       h59595ed_0         383 KB  conda-forge\n",
            "    gmp-6.3.0                  |       h59595ed_0         550 KB  conda-forge\n",
            "    libblas-3.9.0              |21_linux64_openblas          14 KB  conda-forge\n",
            "    libcblas-3.9.0             |21_linux64_openblas          14 KB  conda-forge\n",
            "    libgcc-ng-13.2.0           |       h807b86a_5         752 KB  conda-forge\n",
            "    libgfortran-ng-13.2.0      |       h69a702a_5          23 KB  conda-forge\n",
            "    libgfortran5-13.2.0        |       ha4646dd_5         1.4 MB  conda-forge\n",
            "    libgomp-13.2.0             |       h807b86a_5         410 KB  conda-forge\n",
            "    libhwloc-2.9.1             |       hd6dc26d_0         2.5 MB  conda-forge\n",
            "    liblapack-3.9.0            |21_linux64_openblas          14 KB  conda-forge\n",
            "    libopenblas-0.3.26         |pthreads_h413a1c8_0         5.3 MB  conda-forge\n",
            "    libstdcxx-ng-13.2.0        |       h7e041cc_5         3.7 MB  conda-forge\n",
            "    libxslt-1.1.37             |       h873f0b0_0         251 KB  conda-forge\n",
            "    lxml-4.9.1                 |  py310h5764c6d_0         1.4 MB  conda-forge\n",
            "    metis-5.1.0                |    h59595ed_1007         3.7 MB  conda-forge\n",
            "    mpfr-4.2.1                 |       h9458935_0         626 KB  conda-forge\n",
            "    numpy-1.26.4               |  py310hb13e2d6_0         6.7 MB  conda-forge\n",
            "    openssl-3.2.1              |       hd590300_0         2.7 MB  conda-forge\n",
            "    pyfmi-2.11.0               |  py310h278f3c1_0        11.6 MB  conda-forge\n",
            "    python_abi-3.10            |          2_cp310           4 KB  conda-forge\n",
            "    scipy-1.12.0               |  py310hb13e2d6_2        15.7 MB  conda-forge\n",
            "    suitesparse-5.10.1         |       h9e50725_1         2.4 MB  conda-forge\n",
            "    sundials-6.6.2             |       h777d08e_1         920 KB  conda-forge\n",
            "    tbb-2021.9.0               |       hf52228f_0         1.5 MB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        65.7 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  assimulo           conda-forge/linux-64::assimulo-3.4.3-py310hd2a66c7_3 \n",
            "  fmilib             conda-forge/linux-64::fmilib-2.4.1-h59595ed_0 \n",
            "  gmp                conda-forge/linux-64::gmp-6.3.0-h59595ed_0 \n",
            "  libblas            conda-forge/linux-64::libblas-3.9.0-21_linux64_openblas \n",
            "  libcblas           conda-forge/linux-64::libcblas-3.9.0-21_linux64_openblas \n",
            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-13.2.0-h69a702a_5 \n",
            "  libgfortran5       conda-forge/linux-64::libgfortran5-13.2.0-ha4646dd_5 \n",
            "  libhwloc           conda-forge/linux-64::libhwloc-2.9.1-hd6dc26d_0 \n",
            "  liblapack          conda-forge/linux-64::liblapack-3.9.0-21_linux64_openblas \n",
            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.26-pthreads_h413a1c8_0 \n",
            "  libxslt            conda-forge/linux-64::libxslt-1.1.37-h873f0b0_0 \n",
            "  lxml               conda-forge/linux-64::lxml-4.9.1-py310h5764c6d_0 \n",
            "  metis              conda-forge/linux-64::metis-5.1.0-h59595ed_1007 \n",
            "  mpfr               conda-forge/linux-64::mpfr-4.2.1-h9458935_0 \n",
            "  numpy              conda-forge/linux-64::numpy-1.26.4-py310hb13e2d6_0 \n",
            "  pyfmi              conda-forge/linux-64::pyfmi-2.11.0-py310h278f3c1_0 \n",
            "  python_abi         conda-forge/linux-64::python_abi-3.10-2_cp310 \n",
            "  scipy              conda-forge/linux-64::scipy-1.12.0-py310hb13e2d6_2 \n",
            "  suitesparse        conda-forge/linux-64::suitesparse-5.10.1-h9e50725_1 \n",
            "  sundials           conda-forge/linux-64::sundials-6.6.2-h777d08e_1 \n",
            "  tbb                conda-forge/linux-64::tbb-2021.9.0-hf52228f_0 \n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates    pkgs/main::ca-certificates-2023.12.12~ --> conda-forge::ca-certificates-2024.2.2-hbcca054_0 \n",
            "  libgcc-ng          pkgs/main::libgcc-ng-11.2.0-h1234567_1 --> conda-forge::libgcc-ng-13.2.0-h807b86a_5 \n",
            "  libgomp              pkgs/main::libgomp-11.2.0-h1234567_1 --> conda-forge::libgomp-13.2.0-h807b86a_5 \n",
            "  libstdcxx-ng       pkgs/main::libstdcxx-ng-11.2.0-h12345~ --> conda-forge::libstdcxx-ng-13.2.0-h7e041cc_5 \n",
            "  openssl              pkgs/main::openssl-3.0.13-h7f8727e_0 --> conda-forge::openssl-3.2.1-hd590300_0 \n",
            "\n",
            "The following packages will be SUPERSEDED by a higher-priority channel:\n",
            "\n",
            "  _libgcc_mutex           pkgs/main::_libgcc_mutex-0.1-main --> conda-forge::_libgcc_mutex-0.1-conda_forge \n",
            "  _openmp_mutex          pkgs/main::_openmp_mutex-5.1-1_gnu --> conda-forge::_openmp_mutex-4.5-2_gnu \n",
            "  certifi            pkgs/main/linux-64::certifi-2024.2.2-~ --> conda-forge/noarch::certifi-2024.2.2-pyhd8ed1ab_0 \n",
            "  conda              pkgs/main::conda-24.1.2-py310h06a4308~ --> conda-forge::conda-24.1.2-py310hff52083_0 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages:\n",
            "scipy-1.12.0         | 15.7 MB   | :   0% 0/1 [00:00<?, ?it/s]\n",
            "pyfmi-2.11.0         | 11.6 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "numpy-1.26.4         | 6.7 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "libopenblas-0.3.26   | 5.3 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "metis-5.1.0          | 3.7 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libstdcxx-ng-13.2.0  | 3.7 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.2.1        | 2.7 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libhwloc-2.9.1       | 2.5 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "suitesparse-5.10.1   | 2.4 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "assimulo-3.4.3       | 2.0 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "tbb-2021.9.0         | 1.5 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "lxml-4.9.1           | 1.4 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgfortran5-13.2.0  | 1.4 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-24.1.2         | 947 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "sundials-6.6.2       | 920 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-ng-13.2.0     | 752 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mpfr-4.2.1           | 626 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "gmp-6.3.0            | 550 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgomp-13.2.0       | 410 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pyfmi-2.11.0         | 11.6 MB   | :   0% 0.0013413277032063577/1 [00:00<02:04, 124.94s/it]\u001b[A\n",
            "\n",
            "numpy-1.26.4         | 6.7 MB    | :   0% 0.002337542641177788/1 [00:00<01:33, 93.63s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 15.7 MB   | :   0% 0.0009938020815009904/1 [00:00<04:17, 258.03s/it]\n",
            "pyfmi-2.11.0         | 11.6 MB   | :  77% 0.7672394462340366/1 [00:00<00:00,  3.52it/s]    \u001b[A\n",
            "\n",
            "\n",
            "libopenblas-0.3.26   | 5.3 MB    | :   0% 0.002937237172041532/1 [00:00<01:30, 90.72s/it]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "numpy-1.26.4         | 6.7 MB    | :  73% 0.7339883893298255/1 [00:00<00:00,  2.90it/s]  \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 15.7 MB   | :  27% 0.26932036408676846/1 [00:00<00:00,  1.04s/it]   \n",
            "\n",
            "\n",
            "scipy-1.12.0         | 15.7 MB   | :  65% 0.6539217696276518/1 [00:00<00:00,  1.94it/s] \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libstdcxx-ng-13.2.0  | 3.7 MB    | :   0% 0.004273188843701285/1 [00:00<01:46, 106.97s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libhwloc-2.9.1       | 2.5 MB    | :   1% 0.006377320653426253/1 [00:00<01:25, 86.00s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "scipy-1.12.0         | 15.7 MB   | :  92% 0.9152917170624123/1 [00:00<00:00,  2.02it/s]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "suitesparse-5.10.1   | 2.4 MB    | :   1% 0.006510694523092818/1 [00:00<01:37, 97.77s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "assimulo-3.4.3       | 2.0 MB    | :   1% 0.007800121876904326/1 [00:00<01:26, 87.31s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "tbb-2021.9.0         | 1.5 MB    | :   1% 0.010723460515163315/1 [00:00<01:07, 68.41s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "lxml-4.9.1           | 1.4 MB    | :   1% 0.011328130671757319/1 [00:00<01:07, 68.09s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgfortran5-13.2.0  | 1.4 MB    | :   1% 0.011355941249084227/1 [00:00<01:11, 72.73s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-24.1.2         | 947 KB    | :   2% 0.016899206096256576/1 [00:00<00:49, 50.61s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-ng-13.2.0     | 752 KB    | :   2% 0.02126394862596787/1 [00:00<00:40, 41.71s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "sundials-6.6.2       | 920 KB    | :   2% 0.017390307535870692/1 [00:00<00:50, 51.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "metis-5.1.0          | 3.7 MB    | : 100% 1.0/1 [00:00<00:00,  3.51it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "mpfr-4.2.1           | 626 KB    | :   3% 0.025539662547232164/1 [00:00<00:35, 36.04s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "gmp-6.3.0            | 550 KB    | :   3% 0.02909488690747847/1 [00:00<00:30, 31.75s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgomp-13.2.0       | 410 KB    | :   4% 0.03903266460353876/1 [00:00<00:23, 24.85s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "libopenblas-0.3.26   | 5.3 MB    | : 100% 1.0/1 [00:01<00:00,  2.82it/s]               \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "pyfmi-2.11.0         | 11.6 MB   | : 100% 1.0/1 [00:01<00:00,  3.52it/s]               \u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libstdcxx-ng-13.2.0  | 3.7 MB    | : 100% 1.0/1 [00:02<00:00,  1.87s/it]                  \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libstdcxx-ng-13.2.0  | 3.7 MB    | : 100% 1.0/1 [00:02<00:00,  1.87s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "numpy-1.26.4         | 6.7 MB    | : 100% 1.0/1 [00:02<00:00,  2.90it/s]               \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libhwloc-2.9.1       | 2.5 MB    | : 100% 1.0/1 [00:02<00:00,  2.24s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "libhwloc-2.9.1       | 2.5 MB    | : 100% 1.0/1 [00:02<00:00,  2.24s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.2.1        | 2.7 MB    | : 100% 1.0/1 [00:02<00:00,  2.39s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.2.1        | 2.7 MB    | : 100% 1.0/1 [00:02<00:00,  2.39s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "assimulo-3.4.3       | 2.0 MB    | : 100% 1.0/1 [00:02<00:00,  2.52s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "assimulo-3.4.3       | 2.0 MB    | : 100% 1.0/1 [00:02<00:00,  2.52s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "tbb-2021.9.0         | 1.5 MB    | : 100% 1.0/1 [00:02<00:00,  2.56s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "tbb-2021.9.0         | 1.5 MB    | : 100% 1.0/1 [00:02<00:00,  2.56s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "lxml-4.9.1           | 1.4 MB    | : 100% 1.0/1 [00:03<00:00,  2.91s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "lxml-4.9.1           | 1.4 MB    | : 100% 1.0/1 [00:03<00:00,  2.91s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "suitesparse-5.10.1   | 2.4 MB    | : 100% 1.0/1 [00:03<00:00,  2.96s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "suitesparse-5.10.1   | 2.4 MB    | : 100% 1.0/1 [00:03<00:00,  2.96s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "libgfortran5-13.2.0  | 1.4 MB    | : 100% 1.0/1 [00:03<00:00,  3.00s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "libgfortran5-13.2.0  | 1.4 MB    | : 100% 1.0/1 [00:03<00:00,  3.00s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
            "libgcc-ng-13.2.0     | 752 KB    | : 100% 1.0/1 [00:03<00:00,  3.08s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
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            "\n",
            "\n",
            "libgcc-ng-13.2.0     | 752 KB    | : 100% 1.0/1 [00:03<00:00,  3.08s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "conda-24.1.2         | 947 KB    | : 100% 1.0/1 [00:03<00:00,  3.30s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "conda-24.1.2         | 947 KB    | : 100% 1.0/1 [00:03<00:00,  3.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "gmp-6.3.0            | 550 KB    | : 100% 1.0/1 [00:03<00:00,  3.34s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "gmp-6.3.0            | 550 KB    | : 100% 1.0/1 [00:03<00:00,  3.34s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "mpfr-4.2.1           | 626 KB    | : 100% 1.0/1 [00:03<00:00,  3.42s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "mpfr-4.2.1           | 626 KB    | : 100% 1.0/1 [00:03<00:00,  3.42s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "libgomp-13.2.0       | 410 KB    | : 100% 1.0/1 [00:03<00:00,  3.49s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "libgomp-13.2.0       | 410 KB    | : 100% 1.0/1 [00:03<00:00,  3.49s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "sundials-6.6.2       | 920 KB    | : 100% 1.0/1 [00:03<00:00,  3.72s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "Preparing transaction: \\ \b\b| \b\bdone\n",
            "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n"
          ]
        }
      ],
      "source": [
        "!conda install -c conda-forge pyfmi --yes # Install the key package"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTETJm3l6heL"
      },
      "source": [
        "# BPL_TEST2_Batch setup\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mxILgQ103nZ6"
      },
      "source": [
        "Now specific installation and the run simulations. Start with connecting to Github. Then upload the two files:\n",
        "\n",
        "* FMU - BPL_TEST2_Batch_linux_om_me.fmu\n",
        "* Setup-file - BPL_TEST2_Batch_explore.me.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RfWPh1B63yBF",
        "outputId": "ce5013f3-aa8e-4b69-d606-be4ee932ac9d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning into 'BPL_TEST2_Batch'...\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "git clone https://github.com/janpeter19/BPL_TEST2_Batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i6uOVB44300a",
        "outputId": "57bfcabc-efc1-42a4-91e3-333891c481ce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/BPL_TEST2_Batch/BPL_TEST2_Batch\n"
          ]
        }
      ],
      "source": [
        "%cd BPL_TEST2_Batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b-y82jZY33cO",
        "outputId": "039a10b6-ad69-4ee9-9571-e40dd784a400",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linux - run FMU pre-comiled OpenModelica 1.21.0\n",
            "\n",
            "Model for bioreactor has been setup. Key commands:\n",
            " - par()       - change of parameters and initial values\n",
            " - init()      - change initial values only\n",
            " - simu()      - simulate and plot\n",
            " - newplot()   - make a new plot\n",
            " - show()      - show plot from previous simulation\n",
            " - disp()      - display parameters and initial values from the last simulation\n",
            " - describe()  - describe culture, broth, parameters, variables with values/units\n",
            "\n",
            "Note that both disp() and describe() takes values from the last simulation\n",
            "and the command process_diagram() brings up the main configuration\n",
            "\n",
            "Brief information about a command by help(), eg help(simu)\n",
            "Key system information is listed with the command system_info()\n"
          ]
        }
      ],
      "source": [
        "run -i BPL_TEST2_Batch_explore.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HUFhuE-H4RCw"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "plt.rcParams['figure.figsize'] = [25/2.54, 20/2.54]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cMO4OlAgJIlb"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dx9oMnP0RJkM"
      },
      "source": [
        "## BPL_TEST2_Batch - demo\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "process_diagram()"
      ],
      "metadata": {
        "id": "UaUymVuae4EO",
        "outputId": "513e88bc-4fd3-4b4b-fe35-4c1d86aeec10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No processDiagram.png file in the FMU, but try the file on disk.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-Y3NHWIj4TZ6",
        "outputId": "5864aa22-191b-4b26-db3c-69cc22430202",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Simplified text book model - only substrate S and cell concentration X\n",
            "\n"
          ]
        }
      ],
      "source": [
        "describe('culture'); print(); #describe('liquidphase')                                  # Pump schedule parameter\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UeMOu19LJT6b",
        "outputId": "f0b10417-3e6b-48c7-8cff-ee10f571a0c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Simulation with default values of the process\n",
        "newplot(plotType='TimeSeries')\n",
        "simu()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mwNxR9h5LR1S",
        "outputId": "e68163c2-d1e2-4b11-e583-c71e59aac82f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 2 Axes>"
            ],
            "image/png": 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1AFAXX3wBpEmjIFs2BXnyAMWL61GoEODvryBTJostUbAiDMeqJY9ZwfqR40SILXKsCLFFjhXbISIiAoqiQK/XW8QswfDTHPvW6/VQFAURERHRUuPicqzajBB6+PAhACBDhgwm4xkyZHh3X0yMHTsWI0aMiDa+detWs+dPGnPnjjtcXCoiNNQRAPD8OfD8uQanTgFLlzJj0d09DDVqBMLPLwg+PsH455/syJAhBBkyvPnvZwhSpgxHMhD+wn9s27bN0ksQbAA5ToTYIseKEFvkWLF+HBwc4O3tjdevX5vdPtvAq1evzLKf8PBwvH37Fnv27EFkZKTJfSEhIbF+HJsRQvFl4MCB6NOnz7vbwcHByJo1K2rWrAkPDw8Lrgxo1y4CY8cex4MHxRASosWFCxoEBmrAoJUGr187Y82ajxtBuLsr8PMD2rTRo3dvKnC9Hrh0CcieHXBxSfrnISQ9ERER2LZtG2rUqAFHR0dLL0ewUuQ4EWKLHCtCbJFjxXYIDQ3FnTt34O7uDhcznwAqioJXr14hZcqUZknNCw0NhaurKypWrBjtuRqyv2KDzQghb29vAMCjR49MrPIePXqEwoULf/DvnJ2d4ezsHG3c0dHRKv6hS5V6hLp1AUdHNay3aBGwahVQpAhw/z5w+jRw/DgQk7h//VqDs2eBOXN0CA/XoVgxIEMGoFgxQKMBsmYFcuY03YoUAXx9zfcchcTDWo5bwbqR40SILXKsCLFFjhXrJyoqChqNBlqt1qzW2QDepcMZ9h9boqKiUKFCBXh7e2P16tXvxoOCgpA/f360bdsWo0ePjvZ3Wq0WGo0mxuMyLsepzQghPz8/eHt7Y8eOHe+ET3BwMA4fPowuXbpYdnGJTOvW3Iz5+29g6FAgY0agRAng1Cng5Eng3j3ef+UKMGSIOl+jARQFCAzk9u+/6n39+gHjx/P3x4+BAQOiiyULB8sEQRAEQRAEO0en02H+/PkoXLgwFi1ahNb/nQD36NEDadKkwbBhw5J0/1YlhF6/fo1r1669u33z5k2cOnUKadKkQbZs2dCrVy+MGjUK/v7+8PPzw5AhQ5ApUyaTXkP2SqNG3BQF72qCnj5lxCc8HPjxR+DCBUaOLl3ivPdxcQGcnYHLl4E1a4CiRYFbt4B586LP9fKiIOrRA/j8c45FRABv34pIEgRBEARBsHoUBYhDvUyC0OvpBKbTAVot4OaG2Bax58qVC+PGjUOPHj1QtWpVHDlyBEuXLsXRo0eT3ArcqoTQsWPHUKVKlXe3DbU97dq1w/z589GvXz+8efMGnTt3xsuXL1G+fHls3rzZ7HmQlsT4mEqXDnj5Ejh/nqLGQMOGwPr1QP36QNq0FEfnzwOhodz+/psbAHh6spbIzY1C5+lT4Nkz4MkTbu3aqY975AhQvjzg7Q3kzs0tVy71dz8/wMGqjihBEARBEIRkSkgI4O5ull1pAXgaD7x+DaRIEeu/79GjB9asWYMvvvgCZ8+exdChQ1GoUKFEXmV0rOq0tXLlyu/s92JCo9Fg5MiRGDlypBlXZd04O5uKIICpb4UKAf/7H1CgAMe2bQNq1gRSpgRatABOnGAfo5cvuRmTOjWQNy+QKRPg6EhB5OUF3LzJ+x8+5LZ7t+nfTZsGdOvG32/fBrZvV8WSl1esLwwIgiAIgiAIyQiNRoMZM2Ygb968KFCgAAYMGGCW/VqVEBISh/LluRmj1QIlSzLdbc4cjoWFUUQ9fw6ULg3cvQucOQO8eAEcOMA5K1fyp68v//7HHylqdDqKnStXmGp35QpFj4E9e4CvvlJve3qqkaPcuSnG/D9uiCcIgiAIgiDEFzc3RmbMgF6vR3BwMDw8PGiWEI8WNXPnzoWbmxtu3ryJu3fvwtcMzl4ihJIJ1aoBhw+b1g5FRFDAREYympM5M8XRnDnAhg0UTzduABcvspbo1i1g+XL+rVYL5MtHcdS1K13q8uVTHztNGkagLl+mWcPLl9z/4cO8v2hRVQht2gTMnAkEBDASFRAA5MljtmiuIAiCIAiC/aHRxCk9LUHo9UBUFPcXD8e6AwcOYPLkydi6dStGjRqFjh07Yvv27UluxS1CKJlhfDy5u9M17sgRiiCAqXYnTlCc9OlDQRQUBBw7BmzcSGF07BijR2fP4j/rbv6tiwsFTokSQKlSwKxZgI8P65KuXaMoMmz586vrOHQIWLeOmzHZslEU/fSTOl+vj9f/lyAIgiAIgmCFhISEoH379ujSpQuqVKkCPz8/FChQADNnzkxyZ2gRQsmc1KmBWrVMx2rVAl69otkCAKRKRdvuSZNY73P7NmuEjh6liDL8DApiSp0hrQ6gsUKZMtzKluVjvu9t0bw5kD49Xe8uXGAE6tEj1fr7l1/UuePGAb/+SoFkHEEKCJA6JEEQBEEQBFtj4MCBUBQF48aNAwD4+vpi4sSJ+P7771GnTp0kTZETISREo0ULbsZcvAg4OTHCo9XSSKFRI/YnKlqU0Z+ICAqiI0cY5Tl5koJpzRpuAM0XihQxFUcFCqimDgaePeM+L1ygG52BCxdUswbj3kgAHfKOHKELHsA5rq4UcoIgCIIgCIJ1sXv3bkyfPh27du2Cm1Fd0ddff43Vq1cneYqcCCEhVjRrRhOFJ0/UsbAwYPZs9hZq1QooWJARo6pVmcJmsO4+eJBRooMHGekxiKWpU/k4mTOrwqhMGQqrtGljNn347TegZ09VJBkiSDduMCKVJYs6d/BgYO5cptgVKMD0OsPPPHmYBigIgiAIgiBYhkqVKiEyMjLG+7Zs2ZLk+xchJMQaNzdGhIyZPRvYv980ojN1KjBhAtC/P1PZDGJGUWi4cPCgKo5Onwbu3aM7ncGhzsmJYsgghMqXpzAC2My1ZEluxrx9S3tv475bjx7xpyHF7p9/1PscHSmcXF15++xZ1vf5+koNkiAIgiAIQnJAhJAQb5ydgdatuRnz6BHFRMGC6tjjx0DnzkCdOvzZqhXH37yh+YJBHB08yKjToUPcJk7kvIAAoEIFiqIKFaILMldXzjFmwwZag587x+3sWfVn2rSqCAKA7t1p+Z0iBd3v8udnL6bChflT0usEQRAEQRDsCxFCQqIzfz7w88+mpghbtwJ//83IzNdfq+N37jAdrlIl3lYU4Pp1Rov27eNmnAY3axbnZc2qiqIKFSiCYorkpEkDVKzIzYCiMM3PGAcHCrs3b9TUPQPe3sCDB+rtgwdpHuHjI+YMgiAIgiAItooIISFJMKSyGShdGhg9mu5wBvR6oEoVICQE2LuXESSNhk1fc+YE2rblvCdPmH63bx/nnThBAbVkCTeA7nflyqnCqFgx0zQ5YzQaCiRjduxgP6Vr11Rb8NOngVOnokeaWrRgOl+qVIwYGaJGhQtzrtQeCYIgCIIgWD8ihASzkDMnMGiQ6djduxRDej2QO7c6vnIlo0LNmwM5ctAWu3FjbgCjNocPUxTt3csIzYsXTIXbsIFzXFzYy6hyZYqtUqWi23a/j4MDTRTy5DF1zQsPV38PCQHSpWOqX1AQsHs3NwMVKjDFzsCBA7T4Tp06li+UIAiCIAiCYBZECAkWI1s2ppzduGEaRfntN2DnTo716sWxyEgKJicn1vFUrcoNoG33qVOqMNq3D3j6VBUpI0bwscqUoSiqXJnCKLaRG+PIkpsb9xUezpS9U6e4GaJHxnVRb94wfU9RaAFerJi6GZzxBEEQBEEQBMsgQkiwKFoto0XGfP45ozcNGqhjO3YA//sf0+V+/dV0vqMjUKIEtz59KDwuX2ZkZtcuiqqHD/n7rl38GxcX9jCqXJlbyZJxS2lzcmI6XKFCQLt2HFMUWoobuH+fLnQ3b6qbwRkPoMibPJm/R0UxqpUuXezXIAiCIAiCIMQfEUKC1dGpEzdjduwAgoNNhQYALFzIdDTjpqsajZri1rkzBcqVKxREBjH06BEbshqasrq6qsKoShWKqg/VGH0IjcY0/c7fn9GuFy9Y13T8uLpdvx69UWzBgoySvR85Mq6rEgRBEARBEBIHEUKCTTBuHGuG3N3VscBARmO0WtbsfCjVTKNhDVLu3MA331AYXbqkRot27aIhw44d3AAKo3LlmH5XvToFiU4Xv7WnTg1Uq8bNwMuXpo5zly+rzykwEFizRr0vSxZg3DiNyXMXBEEQBEEQEoYIIcEm0GqjN1F98YLRG8BUBA0YwLqhLl2ip90BFCB583Lr0oXC6OJF04jR06fA9u3cBg2imDGIourVaeKQEOtsT0/T282bUxydPGkaPbpyhaYSxiJo/Xqgb1/WORmayxYsKG51giAIgiAIcUGEkGCzFCrE1LaoKHUsIgKYOZOObk2aqELo1SumusUkFjQa2l4HBADdutGU4cIFCqMdO/jzxQtg1SpuAHsIGURRtWp0tksoqVKpNUsGXr2iCUPevAr27+fY4cOMIF2+zNRAgM+tcGGKo169gOzZE74eQRAEQRAEc/DkyRMMHToU//zzDx49eoTUqVOjUKFCGDp0KMqVK5dk+xUhJNg876eszZ3LBq5lyqhjv/wCjB8PDBnCaMrH0GqB/Pm59ehBx7pjx9QI0YEDwO3bwJw53ACKEIMwqlCB7nKJQcqUfLyICHWsd2/WMxkavx4+DDx/rt7u1k2du2YNI0yGyFGGDImzLkEQBEEQhMSiWbNmCA8Px4IFC5A9e3Y8evQIO3bswLNnz5J0vyKEBLvC0RFo2pSbMfv3M7pi3M8nJIRpZvXq4aP1Nw4ObAhbujTwww+0xd67VxVGBuvsU6eAiRMZnSlbFqhRA6hVCyhShOIqsUibFqhblxvA1L4bNyiCTpygSYOB5cuBpUvV29myUSCWKcM1JqT2SRAEQRAE60VReK5jDvR6nh/pdDzncXOLfQnBy5cvsXfvXuzatQuVKlUCAPj4+KDk+zURSYAIISFZsGEDcPQokCuXOrZpE9CyJWuFLlyI/WOlSAHUrs0NUB3otm8Htm0D7txRa40GD2baXK1anF+zZuKk0Rmj0bBmKUcOWo8b06gRjR+OHOFzNJgxLFvGD6ugID4fADh/ng51ib0+QRAEQRDMT0jIxy/0Ji5aAJ7vbr1+rZ5ffAp3d3e4u7tj7dq1KF26NJzNWPQsQkhIFmi1rJ8xJjKSNUT16qljisJ0uLJlWWPk6vrpx86QgQLk88/599euURBt20Zx9OQJ8Ndf3DQa2mIbhFSpUow4JRUtW3IDGBE7dgw4eJDpfWFhph9SHTpQLPr7qxGjsmVZOyVRI0EQBEEQkgIHBwfMnz8fnTp1wsyZM1G0aFFUqlQJLVu2REHjTvVJse8kfXRBsGI++4xNWo17E124AEyfDsyeTYFkEEKKErsQr0ZDIeHvD3TtCoSHU3hs3szt1CmKkWPHgFGjaJBQowZFUa1atMpOKlKmpMuewWnPmKgorhUArl7lZjBi8PAAGjcGFixIurUJgiAIgpC4uLkxMmMO9Ho9goOD4eHhAa1WG+da6WbNmqFevXrYu3cvDh06hE2bNmHChAn4448/0L59+yRZMyBCSEjmvN8ENVUqprMFB/N3A59/DoSGAiNG0K0utjg5AZUqcRs7FnjwgEYOmzfz5/PnwMqV3AAgXz41WlShgvkssXU6irQXL2i+cOAAt8OH+Vq8favOVRQ65eXLxzVWqABkzGiedQqCIAiCEDs0mtinpyUUvZ4XVVOkiH9dtIuLC2rUqIEaNWpgyJAh+OqrrzBs2DARQoJgLrJkYaTGmNevgbVrGTkaMUIdDwpixMjJKfaPnzEjm8C2a8cPjOPH1WjR4cOs0zl/Hvj5Z+b11qwJ1K/PqJE5SJ3atP4pKgo4d840GnblCi3Fd+4Epk3jWI4cQMWKFEVVq9JeXBAEQRAEIb4EBARg7dq1SbqPRPSyEgT7JEUK1s789BMblxoYN47CZtas+D2uTkdL66FDGX158oQmBh06AN7eFGCrVwNffglkzeqIvn0rYvRoLU6eZFTGHOh0jIAZP+9MmehG16MH79NogOvXgXnzuNbp09W5ISF01TPu9SQIgiAIgmDg2bNnqFq1Kv766y+cOXMGN2/exIoVKzBhwgQ0atQoSfctESFB+AQaDVCgADdjdu1ialuaNOpYcDAjJsWKxd420kCaNKxZ+t//GGI+eZJudxs2sKbo6tXUGDGCUalMmVjDVL8+09TMFfoGWGvUogU3AHj5kkJu715uxjVIe/cyupQqFVCunBo1Kl48bpE0QRAEQRDsE3d3d5QqVQqTJ0/G9evXERERgaxZs6JTp04YNGhQku5bhJAgxJN9+5geVrasOrZqFaMiDRoA69bF/7G1WoqpYsWAYcOAwMAITJhwDnfuFMKOHVrcv09Dh9mzWUdUtSpFUb165k9L8/Q07WtkzP37TPELCgI2buQGMKWwXDnWTRUvbtblCoIgCIJgRTg7O2Ps2LEYO3as2fctqXGCEE90OqB6dZg4ozx6xJN8Y6tuRQF+/x14/Dj++8qYEahRIxArV0bh2TNgyxampvn6snZp0yagWzfeLliQhg9Hj5ovhe5DdOhAA4Zjx4BJk2hJni4dzRe2bzc1g9iyBZgwgeuOjLTcmgVBEARBSB5IREgQEpEBA4Du3U1rYg4cAL7+Gujfn0IpoSlhzs40UahZE5g6Fbh4UU2h278fOHuW25gxQObMbKrauDGd6yyRjubgoEa3evemOLtwgWlz+fKp8xYuBBYv5u8eHkyjq1IFqFyZtUjSy0gQBEEQhMREhJAgJDLvd3EOD6cpQt68pkJkyhSKg3Ll4m81qdGw4WlAANCvH2uWNm0C/v6bP+/dA377jVuqVEyda9yYdTspU8b3GSYMjYYCyFgEAXTGe/0a2L2bqXQGcQcAadMCd+7ErsGtIAiCIAhCbBAhJAhJTJUqtMY2NCwF2E/ou+9oinDtGu2nE4M0aYDWrbmFhgL//kvr77//Zmre4sXcnJyY1te4MeuZvL0TZ/8JoX17blFR7GlksOjeuxfIls1UBDVpAjg6UjxVrw74+Vlo0YIgCIIg2CwihATBTBhHg8LDedJ/756pCPrlF57gt2zJnj4JwcVFNTGYMYNibO1aYM0aii+DeYFGA5QpQ1HUtGniibL4otOpqXTff896oYcP1ftDQoB//gEiIoAVKziWIwdFUY0aFJ4Jfe0EQRAEwVZRLF0gbAb0en2iPI4IIUGwAD4+wJw5pmYGERHAjz8CT5/S9KBOncTbn05Hd7uyZYHx41lXtHYtt6NHWcd04ADT6woXBpo355Y7d+KtIb44OLDRrQFnZ0aKtm/ndugQ+xhdvw7MnBndsS88XKy6BUEQBPvH0dERGo0GT548gZeXFzRx7eORAPR6PcLDwxEaGgptfPP9Y4GiKAgPD8eTJ0+g1WrhlMAveBFCgmBBjD+jIiKAgQNZ21Ojhjq+ZAlw9qwWvr5u0R8gnvs01BUNGgTcvUvhsHo1eyOdOsXthx+A/PlVURQQEPfeSEmBTse6qnLlaC0eHMy6om3bKIyMX7tbt1iLVKkSU+hq1OBzsobnIQiCIAiJiU6nQ5YsWXD37l3cunXLrPtWFAVv376Fq6urWQSYm5sbsmXLlmDRJUJIEKwENzegTx9uxkyaBBw7psOXX3qjQ4fE32+WLEDXrtyePmU90cqVFBXnznEbPhzIk0cVRQULWo+Y8PBgFKhBA942jpbv2sVUuk2buAFAhgx03Ktdm1E3SaMTBEEQ7AV3d3f4+/sjIiLCrPuNiIjAnj17ULFiRTg6OibpvnQ6HRwcHBJFcIkQEgQrRlGAvn2BefP0qFTpLoA8ABgBWboU6NQJKFo08faXLh3QsSO3Fy8YKVq5Eti6Fbh0CRg1ilvOnKooKlrUekQRYOrA164d17d9OyNGu3fTwvzPP7mtWMHnAFAwOTuLTbcgCIJg2+h0OujM/GWm0+kQGRkJFxeXJBdCiYk0VBUEK0ajAf73P2DduiikSqXazs2axXqYOXOSbt+pU1NIrF9Px7m//qKhgrMzzRbGjQOKFweyZ2f/pFOnLN/A9X00Gkav+vRhROjFCzrpGWqhqldX506cyGhRq1YUSY8eWWzZgiAIgiCYARFCgmCDfP01LbI7dlTHbt4EWrRg9COxSZWK+1uzBnjyhNGo5s1paX3rFg0YihRhHdGIEcDly4m/hsTA2ZmucuPHAydPAp6e6n179wLPnrEmq21bWooXL85aqf37TVPuBEEQBEGwfUQICYINUqkSIzTGaXHz5zONbdy4pN13ypTAZ58xrezJE2D5ctpuOzszfc5QT1SkCAWHmes1483GjRRDgwZx7QBw/DgwejQtyKOi1LmhoZZZoyAIgiAIiYfNCaGoqCgMGTIEfn5+cHV1RY4cOfDjjz8mC890QfgYLVoA3bsDPXuqY2/fAh06MB0sKf5FUqTgfletYvrcwoUUDQ4OTJUbMIDNTsuWZY+kBw8Sfw2JhaMjUL48hc+JE1zr/Pns6fTZZ7wf4OtYsCBQujTrpU6ftr6UQEEQBEEQPo3NmSWMHz8eM2bMwIIFC5AvXz4cO3YMHTp0QKpUqdDT+AxQEJIZ+fMDv/5qOrZiBU/md+5kn52krJ308AC++ILbs2cUR0uX0rnt4EFuvXoBlStTXDRrBqRNm3TrSSje3qyRatfOdPzmTeDqVf5++DAwZAiQNStQvz63qlXZzFYQBEEQBOvG5iJCBw4cQKNGjVCvXj34+vqiefPmqFmzJo4cOWLppQmC1VGiBPDNNxQgBhGkKIzU7NmTdJGMtGmBzp0Zibp3D5g6FShThvvbuZM1Tt7eQMOGFGu2lGqWPTuf0++/c/2ursCdO8CMGUC9esD331t6hYIgCIIgxAabiwiVLVsWv//+O65cuYJcuXLh9OnT2LdvHyZNmhTj/LCwMISFhb27HRwcDIB+5+b2WH8fw/4tvQ7B+onvsZIzJ1PS+Lf8efiwBuPHO2DqVAWBgZEmhgFJQbp0QJcu3G7dAlas0GLZMi3OnNFg/Xq60nl4KGjWTEHr1nqUL68gCZtSJwpeXkD79tzevgV27tRg40YNNm7UombNKEREUGEePKhBnz5a1KunoF49PQoXTlqrcflMEWKLHCtCbJFjRYgN1nScxGUNGsXGimv0ej0GDRqECRMmQKfTISoqCqNHj8bAgQNjnD98+HCMGDEi2vjixYvh5uaW1MsVBKvj/v0UWLMmJxwd9ejc+ey78W3bsiFXrhfw8XlllnXcuZMSu3Zlwe7dWfD0qfq/6OUVgooV76Jy5TvImvW1WdaSWCgKN4OQW7gwAKtX+7+7P23atyhZ8iFKl36AfPmewsHBpj5+BUEQBMHqCQkJQatWrRAUFAQPD4+PzrU5IbR06VL07dsXP/30E/Lly4dTp06hV69emDRpEtq9n8yPmCNCWbNmxdOnTz/54iQ1ERER2LZtG2rUqGFTzacE85PUx8qDB0COHA6IjNTg3LkI5MqV6Lv4IHo9sHevBosXa7FqlQbBwWrIpEgRRok++0yPDBnMt6bE4uFDYNMmDTZs0GLHDg1CQtTn5umpYP/+SPj7f+QB4oh8pgixRY4VIbbIsSLEBms6ToKDg5EuXbpYCSGbS43r27cvBgwYgJYtWwIAChQogNu3b2Ps2LExCiFnZ2c4OztHG3d0dLT4G2XAmtYiWDdJdaxERLBZ6pMnQL586uPv3g3kzs16nqSkenVu06cDGzawoemmTcDJkxqcPKlD//461KhBI4ZGjehWZwtkzcpaqc6dWQe1Ywewdi2wbh2g0WiQJ4/ju+jRzJm0IG/QgOmECUE+U4TYIseKEFvkWBFigzUcJ3HZv5Vn4kcnJCQE2vcKCHQ6HfTS7VAQ4k2OHDQtMG7GGh5O2+hs2YBDh8yzDldX2nGvWwfcvw9MmwaUKsUePps3s6mrtzcbye7fb1u21S4uNFOYPZvP7cABNYVOr2cj2i+/BDJkoLPe1KnA7dsWXbIgCIIg2DU2J4QaNGiA0aNH459//sGtW7ewZs0aTJo0CU2aNLH00gTB5nEwihE/eECHNC8voFgxdfz6dfO4vHl5Ad26UYRdvgwMHcr1vH4NzJ3Lnj958gBjx9LFzZbQ6fhcDISF0UyicGGKot276fTn68umuTNnWmihgiAIgmDH2JwQ+vXXX9G8eXN07doVefPmxffff4+vv/4aP/74o6WXJgh2hY8PoxanTqnNRAFGZbJlozW2uciVixGTa9do+92+PdPjrlwBBg3ieurWZVTLqCTQZnB1pdA7eRK4cQOYPBmoWJERo5Mn+bwNREQAFy5Ybq2CIAiCYC/YnBBKmTIlpkyZgtu3b+Pt27e4fv06Ro0aBScnJ0svTRDsEi8v9fenTxkpCgoC8uVTx8PDzbMWjQaoUAGYN4/rMESG9HrWFP3vf0CmTEDPnhQQtoifH6NBu3fTbGHOHKbMGdi5k699QAAwbBhw9qxtpQgKgiAIgrVgc0JIEATLkS4dU+P27YOJi1unTqxrOXrUfGtJmRLo0AHYu1eNDGXODDx/Dvz6K1PKihRhH6Vnz8y3rsTEy4siKCBAHbt8GXByAi5eBEaOBAoWBPLmBX74ATh9WkSRIAiCIMQWEUKCIMQJBwegRAn19qtXwMqVjGBY6iTc3x8YPZrmAobIkJMT0/q+/RbImJEmDNu2MXpky/ToATx+TGe9hg35PC9f5vMvUcIRgYEpLb1EQRAEQbAJRAgJgpAgUqbkifivvwIlS6rjv/wCfPcdEBhovrXodEDt2sCyZUydM0SGIiIo1mrWBHLmpMHCw4fmW1dikyoV0KYN8PfftDxftIj25/nzK8iWTW2I+8MPwJAhUlMkCIIgCDEhQkgQhASTJQvQvbt6OzwcGDcOmDSJkSJLkCYN13T8OCND3btTQNy8yTS6rFmBZs2ALVtsO0rk4QG0agWsWQMcPRoJzX89W9++pRgdNYo1RYUK8T25dcuiyxUEQRAEq0GEkCAIiY6DA/DHH+xD9Nln6vi+fUxdM3cKXaFCjA7dvw/Mnw+ULQtERgKrVzOClD07BcP9++ZdV2Kj06m/a7XArFls0OroCJw5AwwcSDOGMmWAJUsst05BEARBsAZECAmCkOhotbSzXrqUNSwGBgzg+KRJllmXmxvQrh2bsZ49S3c5T0/WFg0ZQhvuxo2BjRvZxNWWcXYGPv+czWkfPmQj16pV6bx36BBw9ao6NywMePHCcmsVBEEQBEsgQkgQBLMQEQGULg2kTctULgOPH9Nwwdzkzw9Mncoo0MKFtOGOimLdTb16jJyMGgU8emT+tSU2adIAX30F7NjB5rNTprDGyMD69XQBbNSI4vXNG4stVRAEQRDMhgghQRDMgqMjMHEiT8QzZlTHDfU6ixZZZl2ursAXX9CG+/x59vBJnRq4c4dRoqxZGVnZt88+rKkzZqSTXvbs6tj+/RSq69bxuWbIwMjZ9u22HxkTBEEQhA8hQkgQBLPi7Kz+HhnJ3kNBQYzAGLCU4AgIACZPVqNEpUtTICxdykauhQuz7ub1a8usL6mYPBk4dw4YPJgC6c0bPv8aNQAfH0mbEwRBEOwTEUKCIFgMBwfg5Eng339pYGBg6lQ6up0+bZl1ubgwSnTwIF3nOnZk5OjMGeCbb9i4tWdP4NIly6wvKciXj6mA164xQvTNN4yMZcrEnwY2bLCPdEFBEARBECEkCIJF0WqBKlXU25GRTKFbvZrRIktTtCgd8O7do8lDzpxAcDBd6PLmBapVA1at4rrtAY2GonTGDPZiWrpUve/lS6B5cwrBunXpPBcSYrGlCoIgCEKCECEkCIJV4eDA3j7dugFt26rjhw4BW7daLm0udWqgd282j92yhcYCWi2jWc2bA76+wI8/0vzBXnB2Nq0lun+f6YFRUbRBb9UK8PYGvvwS2LnTtvsxCYIgCMkPEUKCIFgd+fIB06aZWm/37QvUqmU5620DWi1Qsyawdi1w4wbNHry8GDEaOpQW3B06sImrvREQQEF6+TKNJHx96fg3bx6tuadPt/QKBUEQBCH2iBASBMHqCQ8HihVjVObzz9Xx4GDLpqT5+ACjR9Nh7q+/gJIl2ZNn/nygSBGgcmVgzRr7c17LlQsYORK4fh3Ys4fW3GnTAi1aqHN27AAWLwbevrXcOgVBEAThY4gQEgTB6nFyYu+be/dYvG+gb18gTx6mqlkSZ2egdWvg8GEaLHz2GaDTAbt3A02bsq5o0iTW2NgTWi3d9GbPZtNWb2/1vjFj+JpkysQ0x+PH7cN+XBAEQbAfRAgJgmAzuLqqv4eG0sHs+nXAzc1ya3qf0qVpMHDrFjBwIJuZ3roFfPcdkCUL0KMHcOWKpVeZ+Dg4qL8rClCpEtMEX74EfvsNKF6c9UVTpwJPn1pqlYIgCIKgIkJIEASbxMWFtSqLFjEqYWDxYuD335lOZ0myZGFU5M4dridfPvbnmTYNyJ0bqFfPsuYPSYlGw3qpmzeBbduYzujsTPvxXr2Ali0tvUJBEARBECEkCIIN4+5O5zIDoaFAv37A118Df/5puXUZ4+YGdOoEnD0LbN8ONGhAobBxI80fChYEFiywvHBLCrRaoHp1itMHD2imUKyYqRvg48c0Xrh923LrFARBEJInIoQEQbAbNBrWDZUsCbRpo47fvw9ERFhuXQDXVq0asG4dU+N69qSQO3cOaN8e8PMDJkywvzoiA6lTA127AseOsVmtgT//ZCNXPz9Gyf7+2356MgmCIAjWjQghQRDsBmdn4NtvafHs7KyOt25Np7P9+y23NmNy5mStzJ07wPjxNBS4fx/o3x/ImhXo08e+IyQajfp7gQIUiIrCKFnjxnTjGzoUCAy02BIFQRCEZIAIIUEQ7A7jE+2HD4GLFyk0smWz3JpiwtOTqXw3b9JyO39+4PVrYPJkIEcOCriTJy29yqSlZk2mDF65wtfCy4vv1Y8/spbq1StLr1AQBEGwV0QICYJg13h7s/Hpxo2MthgYO5ZpWdbQ48fJCWjXjmYCmzYxQhIVxdqaokVZZ7N5s30aKxjw92d07M4duu5VqQI0aQKkTKnO+eMP3i8IgiAIiYEIIUEQ7B43N4oLA/fuAcOHs2h/3z6LLSsaGg1QuzYjJCdOMCKk07E5aZ06qrGCpeudkhJnZ/Zh+vdfPlcDFy7QdMLXF2jYkMJQr7fYMgVBEAQ7QISQIAjJDk9PYORINjutWFEdf/TIeqIuRYoAf/3FaFafPqbGCjlz0oY7JMTSq0xaHB3V39++ZW8ivR5Yv57CMHduphG+eGG5NQqCIAi2iwghQRCSHSlS0Jhg1Sq1nigsDChVCqha1bqK9LNlA37+mSlh48YBGTJwfT16MDoydiwQFGTpVSY9xYoBu3ax3uvbbwEPD+DaNYrELFlokCEIgiAIcUGEkCAIAoAjR2iscOUKC/atDU9Pirdbt4DffqMIevIEGDSIYmngQEa07J08eYApU5jeOHMmXedSpAAKF1bnXL5sn32ZBEEQhMTFIT5/1LRp0zj/zcyZM5E+ffr47E4QBCHJqVABuHoVuH4dcHVVx8ePZ9F+rlyWW5sxLi5Aly6sl1m2jBGh8+cZLZoyxQFVqxZAvnxMn7Nn3N3ZOLdzZ+DuXb4uAFPn6tWj+17nzpyTObNl1yoIgiBYJ/GKCK1duxZOTk5IlSpVrLZ//vkHr1+/Tuy1C4IgJCpZswKVK6u39+wBBgwAChUCnj612LJixMGBZgpnzgBr17KJbGioBhs3ZkfevA5o145pZPaORmPqBnjrFmunHj2iBbePD9CiBbB3r/XUfwmCIAjWQbwiQgDwyy+/xDrCs3LlyvjuRhAEwWJ4eQH16/NEO106dTwqim5u1oBWCzRqRCe1bdsi0a/fc5w+nR4LF9IevHFj4IcfaMOdHMienc1o16wBpk+nmF25klvRoqy3Mha7giAIQvIlXhGhnTt3Ik2aNLGev2nTJmSW3ARBEGyMvHnpUPbLL+rYgwfseTNtmnX0IDKg0QBVqigYMeIgDhyIRJMmjICsWUOjgYYNgWPHLL1K8+DoCPzvf8Du3YyYderE1LkTJ6xHwAqCIAiWJ15CqFKlSnBw+HgwKSQkBAcOHAAAlC9fHs7OzvHZlSAIgsUx/ribPh24eRNYtIjRGGukeHEFq1ezdqh1a65z/XqgRAnWzxw5YukVmo8CBYDff6fr3qxZQPny6n0DBgAdOwJnz1pufYIgCILlSLKv8atXr6JChQpJ9fCCIAgWYdgwYMYMplgZrLcjI4H9+y27rpgICGAvogsXgC++oCDauJE24XXqJC/L6XTpaJ5geM9ev6b73ty5bFRbvTqwYYM0aRUEQUhOWOn1TEEQBOvE0RH45hugbFl1bN48Rhrat7fYsj5K7tzAwoXApUtAu3ZMD9u8GShTBqhVC/gveJ+sSJEC2LKFRgpaLbBjB9CgAe25p02jUBIEQRDsGxFCgiAICeTePYqLIkUsvZKP4+8PzJ9PQdShA9e8dStQrhxQowawb5+lV2g+NBoKweXLgRs3gL59gVSpaKHeowcd5wRBEAT7RoSQIAhCAhk+nFbV33yjjp04wR4/oaEWW9YHyZmTKWFXrrBGxsEB2L6dvZSqVaPVdHLCxweYMIH9iKZNY0pht27q/RcuJA8rcqvi1Cm6Xbx5o45du8bQ5o4dpnNXrgTmzDHtKHzjBjB7NvMdjVm1iuN376pjgYEcW7PGdO66dRy/dUsdu3+fYytWmM7duJHj166pY0+ecGzJEtO5W7dy3PigevGCY3/+aTr33385blzI9uoVx+bONZ27dy/HT55Ux96+5djs2aZzDx7k2NGj6lhkpDo3IuLdsObYMfhs3QrN+7m0hrlv36pjJ09ybM8e07nz5nH81St17OxZjv37r+ncP//k+IsX6tilSxzbutV07pIlHH/8WB27do1jGzeazl2xguP376tjt25x7O+/TeeuXcvxwEB17O5djq1aZTp3wwaO37ihjj16xLFly0znbt7M8StX1LFnzzi2aJHp3O3bOX7+vDoWFMSxBQtM5+7axfHTp9WxN2849scfpnP37+f48ePqWFiY+n4auwAdPsyxw4fVMb1enRsWpo6fOAH3O3dgcyjx5O+///7oNmXKFEWr1cb34ZOMoKAgBYASFBRk6aUo4eHhytq1a5Xw8HBLL0WwcuRYsS30ekWpVElRAEXp0cN8+43vcXLzpqJ06qQoDg5cM6AoNWsqypEjSbNOa0evN71dvz5fk/r1FWX37uj32yJW85ly7ZqiTJqkKEuXmo6nT88X/fRpdWzePI7VqWM6N2dOju/bp44tX86xSpVM5xYqxPEtW9Sxf/7hWPHipnPLluX4mjXq2M6dHMub13RujRoc/+svdezIEY75+JjObdSI47NmqWPnz3MsXTrTua1acXzyZHXs5k2Oubqazu3UieOjRqljjx+r/9TGfPstxwYOVMfevFHnvn79bjiyf39FAZTI9z/MDHMfPVLHRo/m2Fdfmc51c+P4zZvq2OTJHPv8c9O5Xl4cP3dOHfv9d441amQ618eH44cPq2OLFnGsenXTuQEBHN+5Ux1bs4ZjZcqYzi1RguMbNqhjW7dyrFAh07mGD/tly9Sx/fs5ljOn6dy6dTk+b546dvIkxzJmNJ3bogXHp01Tx65c4ViqVKZz27Xj+Pjx6ti9exxzcDCd27Urx4cNU8devFDfz7AwdbxvX459/706Fh6uzn3x4t1w5A8/KCe7dLH8Z4oSt3P9ePcRaty48SfnaAxVqYIgCMmMTp144fH779UxRVGL9a0JX186qw0ezCjWnDm88Lp1K3sUjRxJQ4HkgvF7FBEBODlxbMMGbiVLAv36sUeT2HHHkbdvWWhnsGLcsQPo04fNnT77TJ3n7w+836Yjc2YWtRUrZjpetSqLu1KnVscyZaJnfP78pnMrV2YI0MtLHUufnnNz5jSdW748XTa8vdWxtGk517iLL8A8S1dXIEsWdczTk3Pf77lYsiQ/DHx81DF3d8718DCdW7QoC9ayZ1fHXF0518nJdG6hQhz391fHnJw49j7583M8b151TKdT5xod2EqePHhQsiS88uUzfQzDXON1+PtzvFAh07n16jF64OqqjmXPzrnvv5+1azPykTKlOubjw7klS5rOrVGD0SBPT3Usc+aY11ClCt/jtGnVMW9vzs2Tx3RuxYpAxoxAhgzqmJcX5/r5mc4tV455tZkyqWOpU3Nuxoymc0uV4rFvfPx4eHCu8fELAMWL8zUz3l+KFJybIoXp3CJFGEEzPoZdXDj3/Q+pAgU4nju3OubgoL6fxh+AAQEcDwhQxzQada6RpaqSKxfeGkdEbQSNoiSvXtvBwcFIlSoVgoKC4PH+B46ZiYiIwMaNG1G3bl04OjpadC2CdSPHim2i15tabPfvTxvnceOAbNkSf3+JdZzcuEHx8+efqovaZ58BI0aYfncmJ65coVPgggVqNkjOnMDo0exZZGuY/TNFUYDevamyV6zgyS7AoqxevYCaNYFvv036dQhxRr5/hNhgTcdJXM71410jNHToUBw3zi8UBEEQTDAWQc+esTHrkiXW37cme3aaKpw/r57kL1vGi4IdOrCPUnIjVy72Ibp9G/jhB168vXaN76sQCzQaIDyc0Y1Nm9Rxf3/gn39EBAmCYBHiLYTu3r2LOnXqIEuWLOjSpQs2bdqE8PDwxFybIAiC3ZA2LWtUBwwA6tZVx+/csd7eNXnyUACdOkVrab2eAil3bqBrV7rlJTcyZKCj3J07bK7brp1635IlwHffmdZiJ0v0er5IBQtSORoYPBjYuROYMsViSxMEQTAm3kJo7ty5ePjwIZYsWYKUKVOiV69eSJcuHZo1a4aFCxfi+fPniblOE+7du4c2bdogbdq0cHV1RYECBXDs2LEk258gCEJiULQoa3AMKdihoSxZKFPGuqMshQrRQOvQIabkR0SwqWyOHDzxf/rU0is0PylSUAy6ufF2VBRTBydNYkr/N9+YmkglK7Raqv6zZ4HJk9XxzJl5wFtjoZwgCMmSBNlna7VaVKhQARMmTMDly5dx+PBhlCpVCrNmzUKmTJlQsWJFTJw4EfcS8bLhixcvUK5cOTg6OmLTpk24cOECfv75Z6R+v8hMEATByjl1inW+9+6Z1m5bK6VK0UBh1y7WkYeF8cQ/Rw7Wyhg7HSc3tFoGOsqXZwbYrFlMp/viC1P3W7tkyxbW/BjbV48cyYKqESMsty5BEIRPkGRmCY8fP8b69euxbt06VKhQAd8bWyclgAEDBmD//v3YG8tGF2FhYQgz8jkPDg5G1qxZ8fTpU6swS9i2bRtq1Khh8cIywbqRY8V+efAAuHVLgzJl1I/iDRs0qFVLQVzfanMeJ4oCbN2qwQ8/6HD6NK/we3sr+OEHPTp00Md57fbEvn0ajB+vxZYt6rXGoUOj8MMP1pMDmWjHSng4HPLlg+b2bUT+8QeUtm0Tb5H2iqIAt29Dc/s2cPcuNPfuAffuQXP3LovOIiKgiYhg6DUigv19Yps/G5dTuljOVRQFoaGhcHFxETdg4YMYjhPd8OHQdupk0bUEBwcjXbp0sTJLsDnXuICAANSqVQt3797F7t27kTlzZnTt2hWdPvCiDx8+HCNiuCK1ePFiuBlyGgRBEKyEs2fTYciQcvDxCcLEiXvg6Gg9J88xodcDe/dmxuLFefHoES1dM2V6jdatL6Js2fvJOgvq2rVUWLUqFw4dyogRIw6gYEHmEEZGaqDTKTb52mgiIpBlzx7cL1cOUS4uAICMhw4h7fnzuF6/Pt4a2w0L79CGhSH9qVPIeOgQ0p88CZeXLy29JEFIEs5++SVuxGTZbkZCQkLQqlUr8wihJk2axHiFQKPRwMXFBf7+/mjVqhVy5cqVkN28w+W/D94+ffqgRYsWOHr0KL799lvMnDkT7YyrVv9DIkKCPSDHSvJh7VoNunbVoUULPaZOjZsIsuRxEh4OzJ6txZgxWjx5wu+E4sX1GD1ajypVbOp6W6Jz/Tqd+AxflYMHa7FvnwYDBuhRu7ZlBFG8jhVFgUPx4tCcPYvIVaugNGiQtIu0dYKDodm0Cdq1a6HZvBkao9xRxdER8PWFkiULkDkzlMyZ+dPLC3B2Zq8lw+bgELeGVXE9oD4xPzIy8l3pg4NDvNtPCnaO4Tgp0bQpHN/vs2Vm4hIRSvARnSpVKqxduxaenp4o9l9TrBMnTuDly5eoWbMmli5dinHjxmHHjh0oV65cQncHvV6P4sWLY8yYMQCAIkWK4Ny5cx8UQs7OznB2do427ujoaDUnlNa0FsG6kWPF/mnRAqheHdBodHB05MnP48esOfnuO7U4/2NY4jhxdGQ7mI4d2W9n4kTg2DEtatXSolYt9k4qXNisS7IajHs1hoezdObxY6BRIy2KFweGD6eToCUE0SePlbdvTZtgNmoEPHkCBycnJOv8x4+xezf/AbZu5RtuIGtWoGlToHFjaEqXBlxcYAtBQSUiAi+fP4eudGk4yHsufADDceKYNavFz1Pisv8EmSUAgLe3N1q1aoUbN25g1apVWLVqFa5fv442bdogR44cuHjxItq1a4f+/fsndFcAgIwZMyLAuMMtgLx58yIwMDBRHl8QBMHSpE5t2ih90CBg6FCgVSuLLSnWpEzJE/vr14Hu3XmuvGULG5+3bm3qppwccXKiScb331PUHjsG1K8PlCzJdjpWk6yuKLS7zpgRuHBBHe/fnxaH9etbbm3WyJs3wF9/AQ0b0hlvwwaKoFy5gIEDgaNHefBPmcL7/8tuEQTBsiRYCM2ZMwe9evWC1qhzoFarRY8ePfD7779Do9Gge/fuOHfuXEJ3BQAoV64cLl++bDJ25coV+Pj4JMrjC4IgWBu1awPZsvEc1FbIkAH49Vfg4kXg8885tngxexANGAAEBVl2fZYkY0bgp5+oJ/r1MxVEo0dbenX/odEAly7xjVq4UB13d5eTeGOePuU/ZubMtAhcv56v3TffAOfO8TUcMwYoXlxswwXBCkmwEIqMjMSlS5eijV+6dAlRUVEAkKhOI71798ahQ4cwZswYXLt2DYsXL8bvv/+Obt26JcrjC4IgWBvNmwPXrrHfkIElS4AJE2gqZc3kyEEBdPw4UKUKLbfHjwdy5gR++83615+UpE/P1+LWLQoiT0+eSxsIDjZjhOjyZeDrr00V6vDhwN9/80ReUNHr2Wm4UiUgUyb+IwYFsRDshx/YP2nGDCBfPhE/gmDlJLhG6IsvvkDHjh0xaNAglChRAgBw9OhRjBkzBm3/s9HcvXs38uXLl9BdAQBKlCiBNWvWYODAgRg5ciT8/PwwZcoUtG7dOlEeXxAEwRoxTnl++RL49lvgyRPAw4MXn62dokWBHTuYMdS3L8+7u3Vj1Oinn4B69ZLvOaOXFwXRsGGmNWAdOjCbatgwRouS7PVRFKrtc+cAX1+mcgFAgQLcBBIRAWzbRrFz8qQ6XrQoRWO9emwoJVgPQUHAnTtMXQwJ4RYeTjGrKHzvsmfn3IcPgX//5XhMVyCKFQPy5uXvT54w5/dDFCqk/u+8eMG81w+RLx9zhwHg1StefPgQuXMD/51r4+1bYNWqD8/NkUO9ehYeDixf/uG5Pj5AhQr8Xa/n1asPkSUL0zsNLF4M6PXQREYiRUjIh//OWlESSGRkpDJq1CjF29tb0Wg0ikajUby9vZXRo0crkZGRiqIoyu3bt5U7d+4kdFeJQlBQkAJACQoKsvRSlPDwcGXt2rVKeHi4pZciWDlyrAjG6PWKMn++olSurChhYeq4rRwn4eGKMm2aoqRLZzjjUJSqVRXlxAlLr8x6ePxYUdzd1denaFFFWbeO731iEH70qLJ29Wr1WPnrL0Vp2FBRjh5NnB3YE+fOKcqXXypKypTqG5IypaIMH64oV68m3ptipdjE58qTJ4qyfLmi9OunKGfPquOLFqnvWUzbzJnq3K1bPz7355/VuQcOfHzujz+qc0+f/vjcAQPUuVevfnxujx7q3Pv3Pz63Y0d1blDQx+e2bKnOjYj4+NwGDUxfeyend/ed7NLFKo6TuJzrJzgipNPpMHjwYAwePBjBwcEAEM2qLlu2bAndjSAIgvAfGg3Qrh3Qtq0aJVAUoGVLHby8fFGrlnUbejk6MhrUpg2zrqZO5YXYYsX4nEaPZslFcsbLC7hxgw5806YBJ06wDr9ECWDUKKBGjQREiCZOhEP//vDu3181PWjdmptAFIUhzJ9/BjZvVsfTpAHat2fULF06iy1PABAYyAjdihXA9u3Af+UYyJoVyJ+fv/v68p8pRQqGW93c6Fii1fIfyNtbfby0aYFq1dT73se4Fj1VKqBmzQ+vzRBlAlhX97G5/v7q766uH59rbEHp5PTxucaZWDrdx+cWLKj+rtF8fK4hemWgRg0gIgJ6RUFo2rQf/jsrxeYaqiaU4OBgpEqVKlbe4klNREQENm7ciLp161rcalCwbuRYET7Fhg1AgwaAk1MUzp/XI2dO2zlObt3ieeXSpbzt6kpXtf79ef6S3Hn6lOfjv/7KDB8A+PNPCsl4MXIkMGwYLn32GXL8+ad8phgTHs4D8eefgTNnOKbRAE2a0B++XLlkl/5mdd8/ly/TQebWLdPxAgWAsmXpzlKpkkWWlpyxpuMkLuf68fpvLlq0KF68eBHr+eXLl8e9e/fisytBEAQhFtSpA0yZEoXWrS+aXLi0hUtdvr40fzh0iOcxb98CP/7IdPjFi23jOSQl6dIBY8cyQtSrF1+vpk3V+9++/cgfP35MG+zTp9Wx7t0RuXAhLhvs/ATWcYwbB/j5Mdx65gyjB927A1evshajQoVkJ4KsglevaEBhIFs24P59RjkMIdLLl/mezZwpIkiIE/FKjTt16hROnz6NNGnSxHp+WFhYfHYlCIIgxAKdDujaVY+NG68DyA2AJ84NGgCTJgG1all2fbGhVClg3z6ec/btywu+rVsD06cDv/zC1LnkTPr0wOTJNFZwcuKYXs966Fy5GOgxzpwBwC68f/3Fg2HJEo6lSQOlZUtg40azrt8quXGDvX3mzlXDbRkzAj160EUvluc5QhKwfz/zQtesYbrb1ascd3UF9uwBAgLYuEwQEkC8a4SqVauG2GbVJZZ1tiAIghB7Ro5kL8zx45nybQsfxRoNDczq1aOAGzMGOHCAF36//JK306e39Coti0EEAcDBg7wQfvo0BeSg+mfQaWRWZCuUmhO+/54nkC1bWmax1sqRI7QrXL2aahJgatV33/G1cna27PqSK4GBwIIFwKZNPLgNKArw/LkqTEuVssz6BLsjXkLo5s2bcf6bLFmyxGdXgiAIQjyZNo1pVV9/rYqgqKgP1wJbE66uzOhq3561QosWAXPmsC566FBesDcWBMmVcuWAU6eAIUOASuv6oM+6yRixYQSedBmKwYOBjIUKMedQoODZtIkCaPdudbxmTQqgBDlQCAlm+nSgd2+1uZijI91TunShzbW8N0ISEC8h5GOcgC4IgiBYJe7uwMSJpmOTJ/NccNYsNjW1djJnZmZX165Az55szPr998Ds2XwudepYeoUWJDIS0GhQsKAOf/8NXB1VElFDtPDSP8Tw6cz2OnEihnS55EZUFN0lfvqJIVIAcHAAWrXiwSS9kizD8+fssJwxI28XLkwRVKUK0KIF83rlIrqQxEjVnyAIQjIhJIT14P/+C+zda+nVxI2yZZnNNGcOU+MuXwbq1mUK3ZUrll6dBZg/n7a7Rg0V/Qc0h+7GNeT99zeUKcPz+9y51T9JlqYTFy+y+WOHDhRBKVNS/Ny8yRQsEUHm58wZoFMnipzhw9VxQ3jz338ZBRIRJJgBEUKCIAjJBDc34PBhWlW3b6+OGzJRrB2tlnVCV6/yXNbRkfX++fMDAwYAr19beoVm5NYtbnPnqmMODoCfH6pUYZ35pk1qNlFwMFCyJAMjhpIYu0VRgJ07GVEICKADh7s7rwLcucPIkJxkm5eICOa1VqwIFCoE/PEH7Q7PnTM9IAsVstwahWSJCCFBEIRkRI4cNBwwnCBHRPBCbL9+n7BhtiI8PHgue+4cI0IRETSEyJuXARK7i3xcucJCL2ML7G7dgN9+o6NWDGg0poZn06YBx46x5KJoUWDrVo39vU7h4cyjLFYMqFqVzbU0GqBRI9ov9+/PRpiCeZk+nbbk//sfQ9E6HX/fs4ciVSzJBQsSb9c4QRAEwfbZuBE4epRRlt69aVJgK+TKxXPd9euBb79ltlPz5qx9//VX3m8XDBvGJp8hIQzpAICXF9OHYknv3jzfHDeOeqp+fQcULFgWmTIxUmTTPH3Korfp04EHDzjm6sp0uG+/taMDwYZQFPVqy6NHwL17zGn9+mtumTNbdn2C8B+JKsNDQ0OxYMEC/Pbbb7hq8HsXBEEQrJZGjYB162g+YKhZBlhfbis0aACcP083OWdnYOtWln4MHkztYFNERjKF6OlTdez77/kkv/463g/r6sr0wevXgT59ACcnBWfOeKFUKUe0aWNb7/c7zp1jrUnWrMAPP1AEZczIBpt37lAYiQgyHxERFOxly5r2qPrmGwr4wEB6+osIEqyIeAuhPn36oEePHu9uh4eHo0yZMujUqRMGDRqEIkWK4KCxB7wgCIJglTRowEiKgePHWVqxa5fFlhRnXF2BESN4blynDrOkxoxhutzatTaULte8OdOGZs1Sx4oVo1otXz7BD582LfDzz8C5c5GoVOkOAGYq6XQJfmjzoNcD//xDq+sCBVhrEhoKFC9Oj/Vbt6iA06a19EqTD0+eAKNHA76+wOefs//P9Onq/ZkyAW3aSG8mwSqJtxDaunUratSo8e72okWLcPv2bVy9ehUvXrxAixYtMGrUqERZpCAIgmA+hg1jWcrMmZZeSdzJmZPnyWvWANmy8SJ0kyZA/fqMhlgdN24wCmSgeXM2f0qRIkl36+sL9O59AocPR2D0aHX8+nVg6lQrNNB4/Zo1UXnz8s3cvp25fs2bs87kyBHaYUtzKfNx9y7QsaMakbt/H8iQgU5wxiYegmDFxFsIBQYGIiAg4N3trVu3onnz5vDx8YFGo8G3336LkydPJsoiBUEQBPOxeDFrSn79VR2zmYgKWJrQuDHdkgcNUt3l8uWjyLMaU4hOnajc1q5Vxz77jOqtVy+zLKFIEVMDtf79uev8+RmEsvj7HhhIJ4+sWWkQceUKDQ++/54icsUKun1Is03zoigMvc6dy15AJUrQqCIwkP9k3t6WXqEgxIp4CyGtVgvF6BPy0KFDKF269Lvbnp6eePHiRcJWJwiCIJgdDw9g0iTW4xvo14/n7UFBlltXXEmRghk7584xkyosjCUKBQsyoGB2jCM/AOtZFIVuFQYcHS3mWKEoQK1arGm/coX1Y9Wrs7WL2Rdy4ABTBLNnp0Xgy5cUjb/+qlpgS3N383LyJGA4r9No6LxRqhS92g8fBlq3loicYHPEWwjlzZsX69evBwCcP38egYGBqFKlyrv7b9++jQwZMiR8hYIgCIJFCQwEJk9mOca+fZZeTdzJlQvYsgVYvpzlCteuURh98QXLG8zChAk8cT9zRh3r0YNhq/HjzbSIj6PRUOxevcpeU87O7G1ZtCgzoAyGbElGWBiL6kuVYpRnxQq6OFStSmvAy5eB7t3ZFFUwD2Fh0CxejAr9+8OxVCl2NDZQrx4Fa9myEpETbJZ4C6F+/fph4MCBqFatGqpVq4a6devCz8/v3f0bN25ESZv35BQEQRCyZWN/yiFDeO5ji2g0QIsWwMWL1B8aDTN58uRhdk+Sp4AdP84aij/+UMe8vFjzYmV4eNBo4vJloGVLvjZz55qeAycqd+7Q4CBrVjY6OnqUKuzLL+n1vWMH64Kk34z5uHuX//DZssGhfXukuXwZiqNj9CsH8p4INk68j+AmTZpg48aNKFiwIHr37o1ly5aZ3O/m5oauXbsmeIGCIAiC5alQgWllBl6/BurWBQ4dstya4oOHB/DLL1x34cLA8+eMdlSuTJGUYPR6hp6qVweePVPHBw5ktGPixETYiXnw8QGWLOFF/yZNaLtt4NEjPtV4oyhU182a0blhzBieZGfJwnzGwEAqr4IFE/o0hLigKExx8/WlDfnjx1AyZ8bFzz9H5PXrVhO9FITEIkENVQ3RoJgYNmxYQh5aEARBsGJGjwY2baJ4uHKFpS22RMmSDDxMncr+Q3v2AIUKsdfOoEGAi0s8H1ijAcaOZWHN3LlA374cL1yYmw1SpgywerV6OyoKqF2bpUy//kp371jz+jUF4bRpTAs0UKUK094aNgQcpNe7WXn9GnB35+8aDRVuVBSvDnTrhsi6dXFl2zbkFAMEwQ6RmKYgCIIQZ/r1Y43NH3/Ynggy4OAAfPcdm7HWrUvL6B9/ZBDi339j8QCKwp4pPXqoHUk1GiqpoUN5Zd0OuXCBdUQHD9IsrHPnWNRaXbkCfPstm2l27coHSZEC6NKFbhb//gs0bSoiyFwoCi3HO3SgO4ZxOHTYMODsWUbsmje33X9wQYgFIoQEQRCEOJM6NbBwIWCcFLBtGzNnDJrAVvD1BTZsYG1+xow8ya9WDWjfHnj69CN/GBbGbrTTpvEBDLRowe6umTIl8cotQ4ECrB9q1Yrn07NnA/7+TDk0McaLiuLrUrs2kDs3JwQHc/LUqcC9e+wNlC+fxZ5LsuPNG169KF6cphTz59NP3jjklycP/dMFIRkgQkgQBEFIMK9e8eLygAHAlCmWXk3c0Wh48fviRQYsNBpgwQIgIABYtuw/M4Xbt4Hff1f/yMWF6Vzt2/PkPhmROTOwaBGwdy8z/oKCGPApXBh4ePYJ0wNz5qRQ3LKFL2j9+sDmzcClS0DPnuwHJJiHFy/4mmfOTGvAEydoSNGmDe2vBw2y9AoFwSJIDFoQBEFIMO7urK2eNg345htLryb+pEoFTJ/OtL9OnZi11bIl8Pf8F1j0by5owsOB8uWpkABg+HCLrtfSlC8PHDsG/DFbweD+EfB4eBPpixQAoiI4IXVqulF06cKeQIJlcHOj80VQEJAjB/9J27cH0qWz9MoEwaJIREgQBEFIMBoNz6uOHGHph4HffgMeP7bYsuJNaZ8HODFyA4YNY4nEks2psUFfD/fzVIUSGmbp5VkPz55BN+VnfD05D64Ee+PPZ3WhjYoASpfGqxl/YXzvh3g78icRQebk1i3akZcrp1r7OTuzGdiWLazX+v57EUGCgHhGhIoUKQJNLJtnnThxIj67EARBEGwQ47Yiq1YB3boxUnTpEq2rbYKLF4GCBeHo5ITh9++jWbNU6NgRaHZ0KSIuOaFaP2bIJdtze0Whp/bMmSysCqMwTOPujjRf1AK+/hooVAhjBgLjxgEz5jBdslEj6buZZISHA3//zfqfbdvUxlg7drB7MMA0OEEQTIiXEGrcuPG730NDQ/Hbb78hICAAZcqUAQAcOnQI58+flz5CgiAIyZhcuVhzXb++lYugJ0+AmzfpqQ2wWDxXLqZ1PXyIAgVS4cABYMoUJwwZwnPLAgVoId6jB6DTWXb5ZiMoiNbXM2fSas9AkSJMtfr8cyBlynfDxYuzR+rt2+xDVK8e/RKSrYBMCgID6WG+YIGpdV/16nxPKle22NIEwRaIlxAy7hH01VdfoWfPnvjxxx+jzblz507CVicIgiDYLAUKsFePcZTo2TPg/n3eZxXs3ElXsyxZaBen1TJscfCgiXpzcGA2UePGwFdfAbt3A717s3fqnDlA3ryWewpJiqLwTfz9d9aYhIRw3NWVwuebb6h4Ygj1NGvGl3b0aPaR/ecfisjBg9leydnZzM/FHrlzR23SmzEjHUs6dhS1KQixJME1QitWrEDbtm2jjbdp0warVq1K6MMLgiAINoyLC+DkxN8VhQYExYsDf/1loQU9e8YaCQMlS/KkPk0a4NEjdfwDIaycOdnyZuZMBj8OHqRT2ujR7ENkNzx7RovrggVpszxnDkVQvnyMQNy/z7ESJT6a75YiBTBmDHD6NHumhoYCQ4YA/fub8bnYC2fOMARp7PBWtizF6Nq1jA6NHi0iSBDiQIKFkKurK/bv3x9tfP/+/XCJd2tuQRAEwd4IDaVYUBTVdM2sLFvG3j49eqhjKVKwuefRo7yiHgu0WpbBGBqxhocDP/xAvXD2bBKt3Rzo9awvadmSr1OvXrTNc3Fhc9i9e/kEu3cHPD3j9NB58zIatGgRncb79k2SZ2B/vH7Nup9SpYBChWjL+NtvamROowFmzGABljSjFYQ4k+D/ml69eqFLly44ceIESv6XX3348GHMnTsXQ4YMSfACBUEQBPvA1RVYt47RgcKF1fHHj9ncPtEJDKRqyZKFt0uUoGp5+pQF/obcrHg2Ps2alf1CFy9mi5aTJ4FixdhLtW9fGzovvXMHmDcPmDuXBT0GihRhmlWrVqyXSiAaDR+qZUvTdMnOnSmMu3e3odcsqTl6lGHH5csphgC+OI0bM6wqF5oFIVFIcERowIABWLBgAY4fP46ePXuiZ8+eOHHiBObNm4cBAwYkxhoFQRAEO0GjMRVBd+/Sm+Cbb9jgPtEYMwbw9aVtmYHs2YHLl4HjxxOtQEWjYbDk/Hn2Do2IYOZSuXJ0yrNawsOR8cAB6Bo0AHx8gGHDKIJSpWJH2ePH2XSzW7dEEUHGGIugAweA2bNZb1WsGG8LYKrb3LkUQf7+wIQJwL17dOmrWdP0RRQEId4kyrWX//3vf/jf//6XGA8lCIIgJCM2b2bT++PHE+i+du4ckDmzetJevDhz8B48MJ2XK1cCdvJhvL3pXrxwIfDtt+ynVKQISza+/daKnOUuXADmzIHDwoUo+fSpOl65Ml0gmjZl6M5MlC5NH4b+/VkCU64cI0Tjx8c5+842efuWomf+fB4odetyvH171mF16ABUqCC+44KQRCTaJYXw8HDcvXsXgYGBJpsgCIIgfIivvmJZytKlpqYKhjYoseLLL2lD9+ef6lj16sC1a2xmZCY0GqBdO2qyWrVYE/Xdd9QY16+bbRnRef6cdSSlS9PsYNIkaJ4+RWjq1Ijq149ueTt3MrRlRhEEMLDRqRMDdV9+ybHff2dN0YoVcTwObAVFoVLu0oV1aa1aAVu30nzCgL8/0xUrVhQRJAhJSIKF0NWrV1GhQgW4urrCx8cHfn5+8PPzg6+vL/z8/BJjjYIgCIIdU706kCOHenvuXPZ+fPUqhslRUcD27UBkpDpWrBjrJ+7dU8e0WtMHNSNZsgCbNvGE3t0d2LeP5mvTp9OPwCxERLCAqUULnmx37QocPszQVKNGiFyzBlv/+AP6UaNohWdhvLyoA3bvBnLnBh4+pFeDwRPALoiKAn76ic21SpViDVBQEJAtG630Jkyw9AoFIdmR4NS49u3bw8HBARs2bEDGjBmhkSsXgiAIQjx5+ZL1Iq9eMU3KpC+3ojDl7dQpYONGoE4djrdtCzRvDmTIYIEVx4xGw0hHjRqMdOzcSTOA1asp9Hx8kmjHp0+zueaiRXShMFCwIMNVrVoB3t5QIiKgbNyYRIuIPxUr8imMHcvUwhQpOK4oFJFWk2IYW6Ki1EXrdHxfLlyg2UGzZkx9q1JFan4EwUIkWAidOnUKx48fR548eRJjPYIgCEIyxtOT0ZS5c4Fvmj8Flv8LGGpQNRqeKd++zZCBgZQpuVkhvr4MYP32G+tg/v2XWXyTJ1MgJcq1w0ePaF23YAFVhAEvL6a7tWtn6lBh5Tg7A8OHm44tWMCI2uzZNvBU9Hpajf/5J7vIXr6s9qX64QemKn72GY0pBEGwKAkWQgEBAXhqXHApCIIgCAmgXDmgXP4gwDsrEBoKfeGiGLU0J7p3B9IMG8YUokRyfTMHWi2jQbVrswZ+/37WRm3YwPQ5L694PGhYGLB+PRXCpk2MPAAstGrQgOKndm3A0TExn4pFiIoCfvwRuHGDAcHvvqPJnZubpVf2HhcvUvwsWkTrdgMbNjASBzByKQiC1ZDgWOz48ePRr18/7Nq1C8+ePUNwcLDJJgiCIAgfRVGAY8d4AmkgVSq6DBQtivGTnTBsGAVSpEcamxJBxuTMyRqYCROoT9auZXRo06ZYPoBeD+zZQ6/xjBlZ/7NhA5VCyZIMmTx4AKxcSTFkByIIYEbZvn18ulFRfP3y56e/gFVw7Bjr1AICmNMXGMgIUMeOwK5dbJwkCIJVkmAhVL16dRw6dAjVqlVD+vTpkTp1aqROnRqenp5Inci9B95n3Lhx0Gg06NWrV5LuRxAEQUhCjh9ns9NvvgHevFHHV64Ejh9HrU7ZkDMnU8tsveGmTsdmq0eO0MDt0SM6Jnfr9hFjgDNngAEDAD8/oFIlYNYseo5nzszxCxdohNC1K5AmjVmfj7nImJG9RdetYyPbmzfpzNe2LTPNzEpICHDrlnrb25vddB0cKECXL2fq5h9/8P2S+h9BsFoS/JWyc+fOxFhHnDl69ChmzZqFggULWmT/giAIQjx4/BhYtowRn7ZtOWa4ml6oEN0SDBXy//0sWpRawMVFfZjAQN6dNq15l59YFC4MHD0KDBwITJ3KGqIdOxgUK1YMrINasoQD586pf5gyJYvsW7dmkb3NuQckjAYNGCgcMgT45Rfgr7/Yd6h8+STecUQEC7wWL6bjRblybIIF0CZw5UrWr6VLl8QLEQQhMUmwEKpUqVJirCNOvH79Gq1bt8bs2bMxatSoj84NCwtDWFjYu9uGdL2IiAhEREQk6To/hWH/ll6HYP3IsSLEBls4TjTr18OhZ08ouXMjsmVL1S3AuKNqDOt3cFAds0NDgUaNHPD8ObByZaT1F89/AAcHuinXqqXBV1/pcPmyBqVLRmF4lj8wILArdKDXtuLkBKVOHehbtoRSt67a60evj7cfty0cKx/CxYWvW/PmGuzbp0GpUvp3h0xERCJmBOr10OzfD82yZdCuXg2NUT20cuMGIl+/VtM0GzRQF2Bn2PKxIpgPazpO4rIGjaIkTruykJAQBAYGIjw83GQ8KSI27dq1Q5o0aTB58mRUrlwZhQsXxpQpU2KcO3z4cIwYMSLa+OLFi+FmdZWWgiAI9oPX6dPw2bIF9ypWxIPSpQEADiEhKDVqFB6ULYubdepAiUdE49EjNwwfXgZv3jhi0qRdSJcuNLGXbjZ0YWHwPnIEbjtOYeipb7AKLKYvh32YkmsUdDVy4kGZMohwd7fwSq2fBw9SYOjQsmjb9gLKl7+XYEe+4hMmIPOBA+9uh6VKhftly+JuxYp4niePNDoVBCslJCQErVq1QlBQEDwMjo0fIMFC6MmTJ+jQoQM2faDaM8rgZJNILF26FKNHj8bRo0fh4uLySSEUU0Qoa9asePr06SdfnKQmIiIC27ZtQ40aNeBoJ0WtQtIgx4oQGyx+nOj1PDn87wRRO2QIdOPHQ1+/PqJWr07UXQUFAdevM23OePc2UY4RFgbN1q3QrlgBzYYN0Lx+DQBQACzMOhg9Hg/BqzBnpEypYPLkKHzxhZLo59wWP1aSgG7dtJg9m8K6QQM9fv01CpkyxfKPz5+Hdvly6Lt2fdePSjt7NrSDBkFp3Bj6zz6DUrmy7RepxQN7PFaExMeajpPg4GCkS5cuVkIowf/RvXr1wsuXL3H48GFUrlwZa9aswaNHjzBq1Cj8/PPPCX14E+7cuYNvv/0W27Ztg4txsvhHcHZ2hnMMDkOOjo4Wf6MMWNNaBOtGjhUhNljkOBkwAFi4kI1ODblq7dsDkZHQtm4NbSKvJ10603KMI0dYK7J4McuNrI7wcGDbNhbSr10LGLuq+vkBrVpB06oV2gUEoOJN4IsvgP37NfjqKwds3kyb7aTwH7Knz5Rp02ik8OOPwPr1Wuzdq8XkyXQSj1FIXr/OerUlS97VYekyZaLXOcBmpx07QuPsnHBnKTvAno4VIemwhuMkLvtP8P/2v//+i0mTJqF48eLQarXw8fFBmzZtMGHCBIwdOzahD2/C8ePH8fjxYxQtWhQODg5wcHDA7t278csvv8DBwSHRo0+CIAhCDOj17JlizI0btG5etkwdy5MHmDgRKFIkSZejKECfPuwlOnp0ku4qbkREAFu20EbZ2xuoX59iMTiYjm+9ewMHD/KEfNSodwrOz48222PGMACxciW15f79ln061o6TE00UTpxgv6GXL6ll6tQxauvz8iULjEqWpJ/54MEUQY6OQMOGPGYNuLrarFW7IAixI8ERoTdv3iB9+vQAgNSpU+PJkyfIlSsXChQogBMnTiR4gcZUq1YNZ8+eNRnr0KED8uTJg/79+0OXzNxzBEEQzM7z5zwrf/CA3s8Gu+bvv+el9xo1zL4kjYZGXoMGAYmciBB3IiOpYpYt46KePVPv8/ZmM5z//Q8oW/ajeXw6HR3lqlcHPv+cWqliRWD4cD5P+br7MPnzU19OmgQMHUotumABRRIUheInIoKvf9Wq7PPTtGnShNwEQbBqEiyEcufOjcuXL8PX1xeFChXCrFmz4Ovri5kzZyJjxoyJscZ3pEyZEvnz5zcZS5EiBdKmTRttXBAEQUgghkanDx7wajlA4ePpySvrZ87QyxjgFXYLkj4927YYM2sWe834+ibxzqOi2PFz2TJg1SpahBvw8gKaN6f4qVAhzgqmRAlGOLp2pZP20KG02f7rL7o2CzHjcOMK+kWuRCO/I5jw7Ev07//f8Zs6NZTvvocmaxaKH29vyy5UEASLkmAh9O233+LBgwcAgGHDhqF27dpYtGgRnJycMH/+/IQ+vCAIgmApNm9mt88sWZjWZYhgrFrFYoxY1mpagn/+YX/W1KmB8+fZkDNRCQ8Hdu5k1GftWlPxkyYNe/38738UigkssPfwoPCpVQvo0oUBp0KFgLlzgUaNEvTQ9sXFi8wjXLEC+C97JDeAOQ7/AEH3AS8vhIcDdY6MwZf5gVYZAPF9E4TkTYKFUJs2bd79XqxYMdy+fRuXLl1CtmzZkM4MjcV27dqV5PsQBEGwe06cYP5QqVJAq1Ycq1KFCqJcOda1eHpy3N/fYsuMLQUK8KmULJmIIujtW+ZZrV4NrF/PqJgBT0+gSROKn2rVErGZjcoXXwClSzOT68QJoHFj1vX/9JNVa1Lz8O237LBqwMGB70Pz5nyh/jsfmTOHfVH//Zdv48yZDNoJgpA8SXQfSDc3NxQ19jMVBEEQrI+wMJ4sGlK1/v2XJ5LVqqlCyMWFVeY2aBmcLRsjJ8ZlOG/fsoQnZco4PFBwMMNLq1fTES8kRL0vfXqKn2bNGPkxg1OSvz/rXwz1UNOmqSVJefMm+e4tj17PdM21a+mEYBDlpUsDM2awRq15c4bKDPVrRnTqxDK34cP5lu7dS0e+xo3N+SQEQbAWbO/bTRAEQUgYPXsy+rNmDYvFAdZLnDvHYn5jbFAEGTA2/FIU4OuvgaNHeQ6dO/dH/vDZM2DdOqYAbtvGNDgD2bLxtWrWDChTxiKuBU5ONOOrXp3+FGfPAsWKUcd27GiHfT7Dw6n21qwB/v4buH+f4x4etG0HqGQePfqk4YGDA70S6tUD2rbla9ekCaNtv/yiBj0FQUge2O43nCAIgvBpXryg73L9+upYSAgjHZs3q0Ioe3bAjus6Hz1i0OvhQ55HRxNC9+7xJHvVKp50G7djyJWLwqdpUyoOK1EatWvTMrxtW+q1Tp348/ffgVSpLL26RODhQ+C77xiRCwpSx93dWbtWrJg65urKLZYULkxRPHw4MGEC8Oef/LdYuTLRVi8Igg0gQkgQBMFeefmSrljh4cCdO6rNWJ8+DB2UKmXR5ZkTb2/g+HFgzx6WPkFR6Hq3bh0F0PHjpn9QuDCFT9Om7O9jJeLnfby9qWd//pnpcsuXs35oxQq1r63N8OgRj9PixXk7VSq+N2/eABkyMN2tcWOK90To7+PsDIwdS0PErl35uyAIyQsRQoIgCPbA7dvQ/vEHcl+9yqvlAPN8SpTg1fR791Qh9F/jzuRGhtThaJF6D9Djb2DdOjwJDMEgjMFPuAZPjYZ1Jk2bMlcqRw5LLzfWaLVA377sM/S//wHXrvGpTJ0KdO5stRqOXL1KsbN2LXDgABuaXrjA+1xdgd9+Y+PTUqWSLA2xTBmKR+PXafJktnpKRtcKBCFZEm8hNGTIEAwbNgwOH8gfDwwMRMeOHbFt27Z4L04QBEH4AKGhjPR4ePD2nTvQjRqF7O7udAQwFO5v3sxUouTKy5c0OVi3Dti0iSmB/9Feuwkb9bXxoGAtbNjqzKiDDVOqFHDyJOuGNmygffiePeynZFWHwOHDTEHcsIGW18akSMH3yHBct21rliUZi6A9e5iRp9Wyb9OgQTZdKicIwkf4cFvrT7BgwQKUKFEC586di3bfrFmzkD9//g+KJEEQBCEBjB1LO+Bp09SxMmWgb9UK5zp0MK1vsaozYDNx8ybDIdWq0Ru5dWvaqgUHU+x89RWwbh1+3FMJ+fMDPy3NZvMiyECaNAywTJjAAMrixcw0+6+tjmV4/pxubwZmz6bn98WLVBjVq/NYDgxk4Y5BBFmIAgWAzz7jv9GwYYy03bhh0SUJgpBExFsInTt3DgUKFEDx4sUxduxY6PV6BAYGonr16ujXrx8mTpyITZs2JeZaBUEQkheKwjSh8eNZJ2EgTRrePnxYHdPpEDV/Pu5Uq5Yo9RM2RWQkDSEGDwYKFqTxQ69edEeIjATy5QMGDqTv9P37PBFv0ABFy7ni9GlT2+lLl0zP2W0RQ6rc7t1A5szA5cuMFs2bZ6YFKApFzk8/AZUq0Wb82DH1/hYtgDZtgKVLgSdP6PDQrRub9FoBqVMDS5awia2HBw+bQoXoJaIoll6dIAiJSbxDNh4eHli4cCGaNWuGr7/+GsuWLcPNmzdRsmRJnDlzBj4+Pom5TkEQhOSBXm/a/KZ+fUY4cudWm500b85OoTZXDZ+IPHnC5qYbNzL978UL9T6dDqhQgVXwDRt+tN7H+KW+dImCoUoVYNEiZmnZMuXKAadO0Rp682bgyy8pjqZPpwV3ohIWxpyyDRu4vR9COXCAxywA1KrFzcpp3Zqv4RdfAPv2sW3R3r1syioIgn2Q4Ny10qVLo0CBAtixYwdSpEiBH374QUSQIAhCXLl5E+jeHbh7l57IAAsXWrSgu5mxH3LatNySE3o9K9o3buR25Ijp5fnUqeknXbcutxiaaX6KCxdYevXsmVl6o5qFdOnoPj1+PPDDD2wfdfQoU+YSTFSUamBw9ChQs6Z6n5MT3d3q12fTHl/fRNih+fH1BXbt4us3bBj1tSAI9kOChNCSJUvQvXt3FC5cGBcvXsScOXNQs2ZNdO3aFWPHjoWLi0tirVMQBMF+MFw9d3enZRVAYbNtGxARQduvnDk5Pn685dZpaV6+5GuycSONDh49Mr2/cGGKnnr1GG1IYF1q06Z8W/z8kiBiYkG0WmYGli0LfP45BV/Zsg7o3DnLO4PBWBEezhTEzZv5fpQrB8yYwftKlwby5+fP+vVZn2Un9Wk6HQ0TWrRQ/y0BRhD9/JJfJqog2BPx/tZo1qwZtmzZgrFjx6JHjx4AgAkTJqBx48bo0KEDNm7ciPnz56OM4UteEARBIBMn8vJ848bAmjUc8/BgEUKBAjZl3ZyoKApw7hxPsv/5hyfd7xs/1KhB4VO7NgtgEpn37ZJnzWJgrnPnRN+V2alUia5ybdoA27drMGVKMYSGRmHq1I8Iv5s3KXw2bwZ27DCtVXv9Wv3dwcHCjgxJj7+/+vvz5/R4SJeOaZT58lluXYIgxJ94C6GHDx/i5MmT8Df+ZABQtmxZnDp1CgMGDEClSpUQHh6e4EUKgiDYLGPGMA9p1ixeQQd4Ej99OpAtm+ncVq3Mvz5L8+QJsH076322bgUePDC9P29eNd2tfHmzhmrOnGENf1QUr/zXqGG2XScZGTJQ0wwfHoXRo7WYOVOHU6fYgDVLFtBcwjiyVrMmI5QG0qfn8Vu7tmkqXDLj6lUGdk+fpivf1KlAp05W3rNJEIRoxFsI7d27F1ptzKZzrq6umDp1Kpo1axbvhQmCINgUhmiGoTrdwNmzwPnzaioRABQtyganyfGsKTychfNbt1L8nDhher+rK1C5MqM+derQAc5CFCgAjBhB17Xq1S22jERHpwOGDtUDOIJpv5bCi0NXMDPPZvQO2Iy0t08Cd+6ogrN+feD4cb4XtWvTPu0D3/3JiVKl+K/drh0P5a+/pp7//Xf2MRYEwTaItxD6kAgypmLFivF9eEEQBOvHuFj8/n1aN2s0PIk3FOt37coUOONwQnISQIoCXLnCs8WtW4GdO03TqwCeXNeqxQhDuXKAldSXajR05FYU9S2LjGS7Gwvqs4Tz+DE0mzej04EFGOp6BY5Bd4E3AI7ybuXQYWgq/ucKMGlS8jpe44C3N69v/Pwza4hWrKCHx5IlaumfIAjWjXQ8FQRBiCsbNgBDhgAlSvASMMB6lRIlaHrw7JkqhJKjzdSLF6wnMYif27dN70+fnqKnVi2GWry9LbPOWGKsA/r1A/74gy1w4mQ0YEnevGEUx9WVt+fMgcOgQTAkZirOzjifpgLmPqiDzaiNgKl5Ma8IkDIlRAR9AkPPpkqVaERx4wbw668ihATBVhAhJAiC8DHOnePJfJMmLBQBWENx6hRP+I3DBYcPJ88Tx5AQGhvs2MEmpsePm3YldXKiIDSInwIFbDK9KiyMT+3VKz5lqyUqigvdto35WgcO0De7ZUveX6MGlGXLcM3PD36dOsGhcmXkc3VDrlnAtJ7AxdXA+QvA6tWmzWaFD1OyJLM8hw0Dhg+39GoEQYgtIoQEQRAMKAqL9TNlUse++45CyMEB6NmTYxUr0iqqenVT4ZNcRFBEBHOADMLn4EHW/hgTEEDhU7MmL5e7uVlmrYmIszO1xdatLJ2xKl6+ZE7W9u18T16+NL3/6FFVCBUvjsijR3Fh40b41qgBODpCA+Cbb4AiRYBmzWgNXbIkMHcubaOFT5MqFTBlinpbUdTMWBvoHysIyRIRQoIgCADw8CFT2549Y6TH0Bykfn1GL4wbRbu5JS+HN72e9lj//kvxs2dP9DqfLFnYO6ZaNaBKlf8syOwPJydTEfTqFeuIfvzRtOdtknPrFndeoABvv3nDs24DqVKxoWmNGhTsxg1wPkKpUoxstGzJcq7//Y/XAsaNS3CbpmTH0qXAzJnc+vYFRo2yr/5UgmAPyMeaIAjJj6NHgTlzgFy5gD59OJYhAyMdUVHsOFmkCMd79OCWnDAYHBiEz86dbJxiTLp0FDzVqvGEO2fO5BMRM+Krr4Dly2kMuGNHEu7ozh2+Dzt3Art2UQjVrEnnPYA1au3asdlN9epAsWLxVi7p0zPqNXgwMGECzQCOHePzTJ8+0Z6R3dO4MbXpb78BP/3Et23JkuTbJkwQrBERQoIg2DfPnwP79gFly/LkHWATkFmzaGNtEEIaDdOKsme3izSuOKEo7BWzZw+wezcF0L17pnPc3ZniZhA+Nlrnk9j0789ynDFjkmgH337L5rLXr5uOOzjwfTOuUZs/P9F26+AAjB/P9Lj27XlYlCgBrF2rXiMQPo6rK9uFVa8OfPklr78ULcp0Q+kuIgjWgQghQRDsi9evedJuoE4d1rP89RfQujXHqlYFevXiSb0x+fObbZkWRVFYBLJ7N7c9e2j/bYyTE62sq1bl61S8OODoaJn1WjFFiwIXL5q+NMHBgIdHHB/o4UOGDC5cAEaOVMcvXqQI0ukY5alShVu5cqbHeRLRrBnLvRo14vWDcuV4Im8oNxI+TZMmfOs+/5y+Fc2b8y0eMsTSKxMEQYSQIAj2wc2b9DN+9gx49Ei9Sl6pEs9Mo6LUud7ewOTJllmnJdDr6X5nLHyePDGd4+TEApFKlbiVK6faLQsfxVgEBQYCpUsD3bsDAwd+JFvw9m1g716+F3v3Upga6NpVtRQfMICivXz5eKirxCFvXl5L+PxzYPNm/jx1Chg9Wm2jJXycbNmocwcP5kdPpUqWXpEgCIAIIUEQbJHdu4GFC3ni3rkzxzJn5snl27e8gm4oDh87loUOyYmoKLa9NwifvXuj1/i4urLZScWKPCsrVUqETyKwciWNB5cupX5xc4NqJW5IJezbF5g40fQPNRo2lq1SxVS0V61qjmV/Ek9Pts8aPJgpc+PHA2fOAIsX8z7h0zg68qPom29MG/I+esQSRUEQzI8IIUEQrJtLl1jj89ln/3V4BE/y584F7t5VhZCTEyu8AwLUZqZA8rhkHRoKHD0K7Z49KLVmDRzatmUUzJgUKRjlMUR8SpQQC6skoE8fwCNFJGpmPAe337ZThO7bB2zaxIIbAChYkEU4xYqxv1KFCoz4GB+3VohOR/e4woVZ82J4Sn//Lf2G4oKxCLp4ka9h9+50HhRnPkEwL/IvJwiC9RAaSnFjbPXboAEL+TNnZr0PQLes/v1ZhWxM+fLmW6slefKEDUwN27FjQEQEdAC8DXM8PHiCbRA+RYvKWVZScvMmo5R79+KrgwdNOq5uRB3kWnMWOQ1CqFkzoGlTilMbpGVLIHduuqJdvcpg4qJF/FcV4saGDSxrHDeO/8pLl5q2MRMEIWmRb0VBECyHsePVgQNMC/L1BS5fVudUrw5kzGga2cmVi2cOyQFF4ethLHyuXIk+L2NG6MuUwXlPT+T5+ms4FiuWPKJhluDuXR6vOXIwqmMYGz5cnZM6NVC+PI75NkezWW3g/ocGB76ku7U9uBIWKUL93bw5y5waNaIBwODBydJFPd707cuPvI4dGTwsXJjphu9f4xEEIWkQISQIgvmZPBmYMQPo3Rvo0oVjefMC4eFM6TJ2fpsxw3LrtARhYTzDNBY+z55Fn5cvHyNg5cpx8/NDVGQkbmzciDxFiogISiwiIugMcOAAt4MH2dMHALp1U4VQiRJ0JSxXjpG4gABAq0WWh0C+A/Q+8POz2LNIEry86DjfuzdtoocM4Us1f75ZDO3shhYtWB7WogXrrmrWpKYePFj+jQUhqREhJAhC0hEWBkyZAhw+DCxbptprBQczp2bfPlUIpU5Ns4OsWZPXJeW7d4FDh9Tt2DG+bsa4uLCQwCB8ypTh6yUkPuHhau3U8+dAliw04DBGp2Odj4+POubiQov29/D2plsYYJ+ZiY6OwLRpjGR07QqsWsWA5bp1jHQIsSNXLv779+wJ/PEHMGwYA+GdOll6ZYJg39jhx7IgCBbh3j2aFbi789ImwBPKCRN4QnnypFos3qoV+9KUKWP6GNmymXfN5iYkhN03DaLn8OHojUsBIH16Ch6D8ClSRIwNkoLISOD8eUZ5DBGf3LnZwBSgeUH69MCrVzxWy5RhY94SJeIU8nh/6vjxwIsXbMJqLz1pv/qKQbBmzehlUrIksGYND18hdri6ArNnM6C4ZAnQoYOlVyQI9o8IIUEQ4s6zZzyJL1kSSJeOYzt30kqqTBlVCGk0wPffA87ONDsw4O//X7GEHaMojHodPqwKn9OnTa2RATW6ULo0q87LlWPtSXKKipmbESOAf/+lKH3zxvS+Fy9Ma9cOHaK3cSK9H+fPs7+QotDDwuD/YQ+ULQscPQo0bMjrHlWr8sS+bVtLr8y2aNsW+OIL9ZALD6c2b9LEsusSBHtEhJAgCB8nPJw1ETlyqGO1azOFa+lS2loDpj1pjBk40HxrtSQvXvAs0Dja837vHoD5LmXKUPiULs0aEzsonrc6Xr7k+3HkCLuczpql3mdoKgvQkr1UKTXaU6qUqejx9kZiki8fMG8ebZPtSQQZyJKFRf9t2wKrVwPt2vG5jh5tP9Evc2B8CH7/PfDrrzRUmDaNWZiCICQOIoQEQVCJiqLwMTTWPH+eKWweHsDDh+q3c5kyTBeKjFT/NkcOnmAmB16/5iXvo0cpCI8epcX3+zg7U+gYRE/p0jxTlGhP4mNoIHvkCEXo+856Y8eqfXp69uSZesmSTIUzc0V6u3amt8PDgadP7cc2OUUKYMUKYOhQCqBx49gO7M8/xUQhrigKtbhGA8yZw6DyqlX2n0UsCOZChJAgJFeM038AdvObMIH9eX74gWM5clDsREQAjx+r7c+nTEk+l3dDQ3n2YRA8x47xErdeH31u9uym0Z6CBaW2J7GJiqKd+PHjjEYaXt9ffmGVuTE5clDslCplerw2bmy25X4KRWFB/I4dwMaNPGTsAa0WGDWKZpAdOwJr17Lkbd06OYmPCxoNMGgQr0e1asWPn6JFGYwXi21BSDgihAQhuREeDtSvD5w4AVy/DqRKxXF3d0Y6Tp1S57q4MNKRLZupaLJXERQRwSiYseg5c8Y08mUgc2YWzRcvzp/FigFp05p/zfZMZKQqegzbqVNqXU9AgGpfXbUqjSdKlaL4KVnSJt6PFy94mD18yOXbixAy0Lo1rw80bszrCSVLUhSVLm3pldkWNWvy8G/WjD9r1WK0rX9/CTALQkIQISQI9szOncDPPzP95+efOebkxCL+Z88ohqpU4fjnnwM1agB58pg+hrFFsD0RGcnIzsmTqvA5dYoRoPdJl85U9BQvzlofIfGIjGT+VJYsgKcnxyZPBvr1iz7XzY1Oesbv1eefc7Mx0qShi/z+/fZZMwQwSHrkCE0UzpwBKldmnZQNvl0WxceHx0r37kyTGzuWQjNrVkuvTBBsFxFCgmAvTJjAhiXjxqmXlV+/pt3QrVuqEAKA33/n1fL8+dUxb+9ELwy3Gt6+ZQ3JiRMUPidP8ozs/X49AOuhjAVPiRLRI2JCwoiMBC5cUKM8J05QhL59S9/gli05r2hRRiqLFGHkp1gxjlmgricpSZ2aQVoDz58zavLllxZbUqLj40Ox17o10+NateJ1iOHD7TfAnBS4uDADtHRpNrQVESQICUOEkCDYGjdvAlOn8mRy2jR1fNs2tnlv0kQVQmXK0G6oRAnTx6hRw3zrNTcvX/Kk2iB4TpxgpOF922qAjmFFivDk2iB8cuaUM7PE5M0bHquGFMx//wXq1Ys58ubuzkilgcqV+X7akej5FOHhQIMGbGn06JF9mS66u9NJbtAgXrf58UeKoQULxDgxrnz1lent3buBJ0+A5s0tsx5BsFVECAmCNbN2LbBxIzTGDSRCQymE3Nz403CS+M03QNOmrJUwkC4d8yjslYcPTQXPyZPAjRsxz02fXhU9RYpwy55dRE9ioSh8P06dYjHIqVPcrlwBRo5UDTiyZ+cxnDIl3wtDpKdYMfaWMn4/kpEAMuDoCDRqRO3esKGlV5P46HRsKJs3L9C5M7ByJd3N161TvViEuHHvHgXQ06f8NxsxQj7WBCG22JwQGjt2LFavXo1Lly7B1dUVZcuWxfjx45E7d25LL00Q4s+rV8Bvv9GY4Pff1TSsLVuA2bOh8fBgu3EAyJUL6N0bKFSIUQ7DyWKzZpZZuzkID+el4zNnTLeHD2Oe7+NjKniKFmVNj6S3JQ6RkUy7NNTy3L7NKvjHj2OebyxOfXxogCCRtxjRaFgW9eWXaq9ie6R9e5r6NW7M+qHSpZnFGxBg6ZXZHhkysAHr5Ml06jt9GvjrL2b5CoLwcWxOCO3evRvdunVDiRIlEBkZiUGDBqFmzZq4cOECUqRIYenlCcKn2bOHjSCKF+e3FwA4ODBfRK/n5TxDQ5FGjYDUqaFUr06xBFD4TJpkmbUnNYoCPHgQXfBcvBizc5tGQ3MHY8FTuLDaL0ZIOC9esL7q7Fk10nP2LK2r58/nnMyZgaAgCpvcufkeFC5MsV6okGntmUZDMS98FGMRdOkSxdH8+fZ1aFeoABw8yEzJa9fYz3b1atOgtvBpHBz4lVC4MKNs69fTPPHvv+VfTRA+hc0Joc2bN5vcnj9/PtKnT4/jx4+jYsWK0eaHhYUhzKggOjg4GAAQERGBiIiIpF3sJzDs39LrEJIIRYF2zBhozp5F1MyZ766eaw8fhu6XX6Bv0gRRhqJwBwdoe/QA0qWDHqCNMwBUqwZUq8ZjZNs2+zpW3r4FLl6E5uxZbmfO8KdxjYgRiqcnlAIF3m0oUABKvnzs3vg+9vQ6xYEEfaaEhjK3JksW3o6MhEOePNAEBsY4XX/xIqKM93PwINPeYir2SKbvR2KgKMBnnzngzBkNevbUY968GGrd4oG1fP/4+fHaUPPmOhw4oEWtWgpmzoxC27aKRddli3z+OeDvr0GLFjpcuqRByZIK/vwzCrVrJ+y1tJZjRbBurOk4icsaNIqi2PSnzbVr1+Dv74+zZ88iv7ED1n8MHz4cI0aMiDa+ePFiuEl1ppBIeF67hmzbtyM0bVpcadHi3XiNTp3g9uQJ9o4ejef58gEAUl27hix79uBZQAAeJodmGlFRSPH4MVLevg2PO3fgcesWPG7fhvv9+9DE0JRU0WrxOlMmBPn6ItjHB8G+vgj29cXbdOkktS0xiIpCikeP4BEYyPfkv839wQO88PfH3vHj302t1rUr3O/fR4iX17v3IsjPD0F+fnjj7S2pbWYiMDAl/vijAL7//hg8PMItvZwkITxci19/LYK9eynEW7S4jFatLsm/fDx48cIZEyaUwMWLaVG58h306nXC0ksSBLMSEhKCVq1aISgoCB6fyBG1aSGk1+vRsGFDvHz5Evv27YtxTkwRoaxZs+Lp06effHGSmoiICGzbtg01atSAo6OjRdcixB7tqFHQ7N+PqLFjmYsAQLNuHRyaN4dSqBAijx5V506dCuj10DdrlqB26jZxrOj1wK1b0Fy4YLLh0iVoYnIIA6CkTQulYEE10lOwIFPdXF3NvHj7wOQ4cXCg7dj9+0wZ/A+H/PmhuXIlxr9XfH0RefmyKjivXGFamxQbWB0vX6olWvHBGj9T9Hpg2DAtxo9n3WPLlnrMnh0FZ2cLL8wGCQ8HJk3S4ttv9Qn+OLXGY0WwPqzpOAkODka6dOliJYRsLjXOmG7duuHcuXMfFEEA4OzsDOcYPkUdHR0t/kYZsKa1JHsURT0JPH2aifmurnRvM7B/P7BjB7Tnzqm21GXKAAMGQFO0qOl7+f33AIDE8r6yimNFUWjzdP686XbhAhASEvPfuLhQ4OTLR2vvggWBQoWg8faGRi75Jpz794Fz56A9dw6FtmyBy08/QXvxIhvSpE9PQWQgZ06+f/nysY/Uf2mGKFAAGm9vOBq/H/9FMQXrYssWlmgtWZLwJqxW8ZlixLhxNA/85htg6VIt7t3TYs0atj0TYo+jIzBkCGD49tHracXepQvg6xvfx7SuY0WwTqzhOInL/m1WCHXv3h0bNmzAnj17kMWQ0y4IsSU0lCfnBvr2BZYuZXMLQ7tzBwdg61ba/BoLpG7dOKdyZfXvM2Vim297QlGAu3cpcAxi59w53n79Oua/cXJSBY/xlj17srRCTlT0erqzXbjABrnduqn3tW0L7NgBHQBf47/Rahk2ePWKxzEA/Pkne/rI+2GzzJ5Nb4rFixMuhKyRjh1pLtisGbB3L68zbdxIDS/Ej59+4tfb3LnAihWmX1+CkJyxOSGkKAp69OiBNWvWYNeuXfDz87P0kgRrRVF4pdzRkVfFAdr4Vq7MQv3Hj1VxExzMk/5z59S/9/enlXW+fKZCyLinjz0QGgpcvUprKuPt8mU2w4wJR0faERmETv78/JkjBwWkkHD27WMV+cWLFD8XL/K4NfDFF2rKWsGCwL170OfJg6uOjshRvz4cChakg9v7eTH2ZDuWTFm8GPjlF6BnT0uvJOmoXp1NZevW5cdT6dJ0QStXztIrs01atQKWL2e7terVefx07WrpVQmC5bG5M5Zu3bph8eLF+Pvvv5EyZUo8/K+PSKpUqeAqdQXJk6gonrhfv27agbBLF2DWLNpRDx3KMW9vCh5FYRtug0Dq0YNX1Y0NN5ycgE6dzPc8kpqnT6OLnUuXgJs3GW2ICQcHCsL3Izz+/hRDQvx58YJi07BduQIsWKC64P31F49fY5ycKG4CAhiVMwihn38GJk1CVEQELm3ciOx168r7Y8c4Ob3Lun3H7t1AxYr25SeSLx9w+DDQoAFw7BhNNP/8EzDyoxFiSdasvLbSqROwaBEDyhcuAFOmyLUrIXljc4f/jBkzAACV34vrzps3D+3btzf/ggTzcvkyP839/NRmEyEhqoB5/hxInZq/G9KxXrxQ/97Njd37smc3vTIeg+OgTRIVxbQpg8i5eFH9/QO21ACYKpU3L9PajLfs2eWEOiFERPAYNLirLVgA/PEHj+MnT6LP/+GHdwYcqFKFEbmAAHXz84v5rMWezn6FODN1KtCrF/ss//yzfR0O3t7Arl1A69aMCH32GXDvHp+vEDdcXSkk8+dn27rp0/lRtHy5+rUpCMkNmxNCNmxyJ8QFvR4YP55d9iZPVq98r1zJk8W2bVUhlDIlTxLd3U2FULdu/LZ0cjJ97OLFzfY0kgRDyt/Vq9yuXFF/v34dMHJJjIaPjypyjIVP+vT2dfZkThSFaZbG0R1DhOfGDeaiFCjAuY8eUcgbyJyZEZ5cufgzQwb1vs8+4yYIn8Dwr5s6tX3+G6dIwR7U337Lk/feven3MXGiOLjHFY0GGDCAH/tt2jD79upVoGRJS69MECyDzQkhwQ4IDWWUwVCs/c8//EYrUYLVnAC/3aZM4Qlm165AsWIcL14cqFlTvWpu4Pz56PuJqdGmrWBI3ftP4GgvXkTxffvgMHQoxeGH3NkAmkDkzh09upMrV8zNLoXY8fw5X/tr1yjCvb05PnEi3QU/xJUrqhBq2JA5Kgbx4+6e9OsW7J6ePWkoYOvXeD6GTgf8+iv/fQYM4PWxe/cYZDX2vRFiR+PGNEAVESQkd0QICUlDRARrT0JDWchtoFgx4ORJboUKcezVK+Y+vN8J2OCKlS6dOlarFjd74cWLmCM7V67QwOE/dAAyG/+dTsc0KX9/dcuViz+zZRNHsIRy9iyjj9eu8f24ds00xXLtWqBRI/6eIwcvs/r4UOC8v2U2eucMolQQEhmDkz/ADNkJE1j6aE9aW6MB+vcHsmQBOnRgStfDh/x3lNSuuFOokPo1DACnTtGgQkwUhOSECCEh4WzZwohMixa8XAfwG6pNG1bv7t6tznVyYrTj6lX1E7h8eWDhwug9SwwGB7aMXs80tuvXmSZl/PP69Y/X7Wg0FDX+/ojKkQMXIiKQt2FDOOTNSxEktTtxR1GYnmaI7BgLnbFjGW0EWFs1cmT0v8+UiR6+xpeg69VjLY+YtQhWQq9ewLRp/GjeudP+0uVat2ZAtmlTpnaVLw9s2pSgntXJnhcvGLC+c4fmqVOnyleMkDwQISR8GEXhibwhunDmDJsReHoyR8HA0KE0IPDzU4WQvz/TsN7PWVi4kIX5Xl7qWJYstAK2Vd6+ZfQrJrFz8+bHa3YAnlzHFNnJkePd66ePiMCNjRuRR9zAPk1oKPvt3LzJiIzBYn/jRuB///uwJfj586oQKlyY9kr+/hQ+OXPSOCKmdEtpey9YGa1bA8uWMWXO3kSQgWrV2GOoTh26nxl6DRlHOITY4+kJdO/OtMMZM5iUsHy5uO0L9o8IoeROeDhP2F++5DeJgZYtgXXr2LCicWOOvXlDS99s2UyFUM2abFVtnMJWvDjtfd//Fvb3T6InkoQY6nViEjo3bjDi8zEcHJg2lSMHT6YNP7Nn5wm2PeWumJtbt1gkcOMGhY/h/TCYqkydqjZb8fLiMazVvou0vRM5OXOqdWgAxejvv5v96QhCYlC6ND+eDD107ZWCBYGDB1UxVKECsGYNRZIQNzQaljrmycOeQzt2AKVKARs28KtKEOwVEULJiQ0bGLlp2FCtqj16lHkFPj48qTTm7Vt+mxrImxcYNy66mPnxx+j7siUrH0VhIfytWx/eXr/++GN4eFDgvC92cuRglEwaNcSNoCBV2Lz/s3dv4OuvOe/xY2D48Oh/nyIFX39jc4iCBWkj7usrURzB7jEWQUFBDLqPH8+PcXsiWzYaMTZpwizsOnWA+fN5Mi/EnYYNWSfUoAEzhkuVAhYvttOwoiBAhJDtExZGH1FPTzXd7No1VpJGRvJymYE//1Rj3QYhlCMHvzHTpWManEHAjBoFjB5tmnTt6clKVVtDUViLYyxsbt+Om9DRaJjC977IMfyeJo395qAkNob34/ZtHru3bwNFi7KeDKBYL1Xqw39/6ZL6e86cwFdfMf0te3b1Z7p00d8PZ2emyglCMuO774D163ld6+xZ27pOFRtSp2Y9VNu2/Ipr3Zq1Lv36ycdyfChYkNdImzals9z06Vq76i0uCMaIELIVXr7kJ/zLl6ZWvV98AaxYQavpb7/lmJsbL5HpdBRDhmhErVo8YTduHpohAy8Xvv9tkTNnEj6ZRMaQuhYY+OGIzofqQozx9ma04P3Nx4c/xaM1dkRFMT3NwQHImJFjt24BXbpQ9Ny+Hd3+u3dvVQj5+vJn+vTRBY6fn+kl7TRpgNmzk/oZCYJNM348Px4nTLA/EWTA2RlYsoTXqyZNYq3LnTvMjhUTzbiTPj3T4378EejVKwr791t6RYKQNIgQsiTh4Uh14wY027cznm/gp5+ARYt44mhIAXrzhr/rdECfPqq48fWl8DE+0ff2BpYu5X3GAufLL7kZYwuXy1694jdaYGDMP+/c+bQhAcCT8g+JnGzZxPUrrgQHU4QbxI1hu3uXArxPH7a5BygiN282/Xtvb77+Pj5AkSLquJcX33OpnRKERCFtWmDrVtOx0FDLrCUp0Wr5kZM1Kz9+pk9nr6HFi+XjPT44OzM5xNDZQlFYHtymjdiVC/aDCCFL8vw5KvfpA0Wr5Ym8Qdw8fgycPk3bFgMZMzJpN1s2foMZThJHjeLlPmNBo9XaTkf68HB+U31I5AQGMmIVG2ISOoYtWzaJ6MSGqChGb+7e5et/967p7w0aqDU5ISFMS4sJBwfTqE/69MAff6jCJ2vWD78fGo2IIEFIQtgT2AGffZYJdetaejWJT69ebN/1xRfsMVSjBr1/xAEtYfz2mxa9e1Ngrl9vm95HgvA+IoQsiZcX3qZNC5ds2Xh13fAp3a4dUKUKEBCgztVq+Un+Pk5O5llrfAgPZ4rUvXvqZojgGITOw4eqw9fH8PTkyXO2bPxp/Hu2bPzWs+bXwhoIDeV78L7IKVpUjRQ+ffrxtEhj+6D06YH69fnaGwSOYcuY0TQfRasFOnZMmuclCEKcmDkTuHNHg9Wr/TFypH068rdowczvhg1Z51K+PIPS0mso/pQrp0fWrDpcvswyztWrgcqVLb0qQUgYIoQsiU6HrXPmoG7dunA0/ibKn9+0jsfaUBR2XzMWODFtT57E7vGcnaMLm/fFjr37wCYERWEq2f37qvDMmBGoXp33P39OT9QPvR/NmqlCyMuLfZ7Sp2eyfdas/Gn4PVcu9e+0Wl4WFATBphg/HnBzi0KOHIfg4GC/XtMVK7Jctk4d9kguU4aNVwsWtPTKbJPChell06gRf9aowZ5DH0oMEARbQISQYEp4OPDgwadFTmwTzJ2cGDEwbO8LnKxZefJtC7VKliAkBLh/H5rAQKS6dk0df/MGqFdPFT/vm0E0a6YKIU9PNb3Q1TW6wDE4CAIUNy9eyPshCHaMTgcMGaLHxo1qbeWlSzRVtLd//fz5aQddpw57JleowHS5KlUsvTLbxNsb2LWL186WLmXf6YsXacQhphSCLSJCKLnw9i3T0B484Gb8u2G7f5/1SbElbVpTkRPTljat/X2zJgZhYXzNFYVOaADFZefOqri5f/+dgHEAkKt0abU5qJsbrdHDw9XH9PQEMmViNMi4vbpWC5w8yW+w1Kk//X7I+yUIyYodO3hd5Ztv6Lhmb85yWbMCe/eyN/iePUDt2sDChbZTSmttuLrSgCJPHpaMTpnCVMTSpS29MkGIOyKEbBlFoZ22sZj5kNiJreEAwChOpkwfFziZMon5wPvo9UxDi4hQbaMjIoDBg/leGLYHD1iLA7AL4OrV/N3ZGVi2zFTcAICbG5RMmRDm6amOaTS0U0+dmu9FpkymzUPfx7jeTBAEwYirV9WWdMbt5OwJQ6+hL74AVq4EWrbktabevS29MttEowGGDVOzrkUECbaKCCFrwyBunjwx3Qwn0O8LndjYRhtwduYJumHz9jb93SByYmpGmVwx1N+EhakNayMieBns4UPg0SNV4Dx6RNvoxo2BNWs418EB+OWXmN8nJydTowiNBpg8mfVQBnGTKRPg4YHIyEic2bgRWYz/vlGjpHnOgiAkK775huaaVaqo5qX2iIsL07l696YNdJ8+9Iv56Sf7FH/m4P2o2vXrTCwpU8Yy6xGEuGLHH3lWzpMn0A4bhuJnzkA3ZQojBIYtMjJuj5U6dXRRE5PgSZVKBA6gis3wcNoKAXzNR42KLmwePmRaYaNGTCwHeKYwadKH66SM63U0GmDQIH4De3tzfwaBkyZN9Peja9fEfraCIAifpHZt09vLl9MU8mOBZltEp2OT1SxZgP79+VH+4AEwbx6vFQrx5+VLdli4fh2YOxdo3drSKxKETyNCyFJERUE3YwYyf+j+lCkZgTBs3t4xix1vb0lRM0RtDK5oOXLwZ1QU8P33vDz1+DHvN/yMjOQntsGSXKdjtefbtzHvwzi1UKMB+vblt2aGDOp7kyED3dbe/zYdOjRxn68gCEISMmsWo0QVKgDbt9tfZwKNBujXj1+hX34JLFnC615r1gAeHpZene3i4EDDjYsX2XT14kVg5EiJtgnWjQghS5EuHaIGDsSFx4+Rt2JFOGTMqIqedOmS96UpRaFbmiEt0NlZ9TuNiuI31/vixpB6Vr++aums0wG//27a2NOY16/V3zUaduFzcooubjJkAFKkMP3bkSMT9SkLgiBYC/nyURBUqmR/IsiYL77gx3uzZsC//9Jue+NGBuyFuOPuDqxaxSSI8eOB0aOBy5eBBQvsL7Io2A8ihCyFgwP0I0bgxsaNyFO3rn12tAMoal6/Bp49U1P/nj2j4KtZk3OiooBatUzvN047q1cP2LCBv+t0/KR93y4aoFh5P8H9hx/4Te7lxWhN+vSq4Hw/kjZmTOI9b0EQBBulfHng7Fm6rdk7NWsCu3cDdesCp08DZcuy8WqePJZemW2i1QLjxvH169yZxhQ3bwJ//80SZEGwNkQICbFHUZgiZhA17//09wfat+fciAhW3z59Gt0FDeC3jkEI6XTAoUPRxY2zM4VL2rSm4xMnUsS8L25iuuQ0cGBCn7UgCEKyI1s29Xe9nvU0XboA2bNbbk1JRdGi7DVUuzYd9MqVY2JB2bKWXpnt0r49s9SbNAGOHwe6d1c9hATBmhAhlBxRFLVIPzIS2LaNTTSfP4/+s3hxtcYlIoLGDB+ibl1VCDk6AsHBqghycWHKX7p0FDZFipj+7Z9/UsikTavOcXeP2dzhm28S9PQFQRCE2DNyJK8/rVjBxqv2WJaaPTuwfz+zq48cAapVo8OcmHPGnwoV+Fp26wbMmGHp1QhCzIgQslUUhREUg2hxc2NEBmDB/8iRqph5X+A0aEDhYXicunU/vB9jBzsnJ4oTRTEVLIafxk08ATb89PDg/Z9KEG7SJO6vgSAIgpDkfP01TTMHDrRPEWTAy4u1Qi1bMhu7aVPgt9/4/IX4kT07sGmT6dju3azHEhNbwRoQIWRJoqLg9ugRcOoUC/qDgky3gABVILx6xToaY3FjLFJatQIWLeLvDg5M0v0Qz56pvzs6Mg/A2ZnRnjRpTH/mzGn6t0+fxt7IIX/+2M0TBEEQrJaMGYFjx0xLMI0TC+yJFCmYwvXNN8CcOfx59y6vLdrj8zU3ixbRUa51a+CPP+xbWAu2gQghS/LiBWp87FJTq1aqEHJxYYTlfZycKFrc3dUxR0faRqdMGbO4MTQGNbBvX+zXnJzd7ARBEJIpxiLo1SsmFvTvD9SpY7k1JRUODsDs2ew1NGIEW8zdvw/MnGm/vkbm4u1blgUvWgTcuEHRaWjnJwiWQISQJUmVCpFOTtClSQNNqlRseJoqFdPJUqUybc3s6MjcBE9PU1Hj5hbzZaqffjLXsxAEQRCSERMnMr3pyhXg2jX7tEbWaIDhw2ml3aULG4Q+fMhGs+93UxBiz1dfAX5+QPPmvLZbsiTTEAsUsPTKhOSKCCFL4uiIf5YvR926deEYm8tMUrUpCIIgWJgffmCE5Ouv7VMEGdO5M1vKtWzJHkNVqgD//BM9sUKIPdWq0Si2QQO69JUty6a29etbemVCckT6/QqCIAiCEGscHZk6Vry4OvbiheXWk9Q0bAjs2EFPoKNHeeJ+/bqlV2Xb5M5NMVSlClsNNmrE5quCYG5ECAmCIAiCEG9u3mRq04gRNFGwR8qUob22jw/TAcuWpYGEEH/SpAG2bAE6daIjYe7cll6RkBwRISQIgiAIQrzZtAm4d4/1M+/3xbYncudmXUvhwsDjx0DlyjyRF+KPoyMwaxbw44/q2KNHpua2gpCUiBASBEEQBCHedO1Kq+lt20wNTO2RjBlpFFGtGkVf/frA/PmWXpVto9Gonk9v3zJNrnRpNu8VhKRGhJAgCIIgCAniyy/psGZgzx7WftgjHh40TmjViu38OnQA+vUDoqIsvTLb58EDRoSuXaMY2r7d0isS7B0RQoIgCIIgJBqHDrH/d8WK9pvi5OQE/PknMHgwb//0EyMZwcGWXZetkz07cPgwa7CCgoDatdm/SRCSChFCgiAIgiAkGlot+3lnzszWd/aKVstmq4sWsef5P//QVEEc5RJG+vR06WvThlG2Ll1o1f72raVXJtgjIoQEQRAEQUg0SpYEjhxhbxidztKrSXpatWIqYKZMwIULQIkSFEVC/HFxARYuBEaPZv3Q779TDAlCYiNCSBAEQRCERMXX19Q4YcQIYPhwQK+31IqSlhIl2GOoZEn2VKpfHxg0iDVEQvzQaPgabt0K5MrF40cQEhsRQoIgCIIgJBlnz/IkdsQI4N9/Lb2apCNTJkaGunfn7bFjgapVgRs3LLsuW6d6dUbasmdXx+bOBR4+tNyaBPtBhJAgCIIgCElGgQK0mO7blye19oyzM/Drr8DSpYyI7d0LFCzI1C57bTZrDoxTLPfsATp2BAICgHnz5HUVEoYIIUEQBEEQkpR27YD/t3fn4U1V6R/AvzfpRulG6QZtoeyLQAstlLKJyDLghgsiMlIrMjNKGZiOozCjLDOOoCKigqLOD1FnEBQHdATBWjaRfamAyCp7d5butCHJ74/DbboBKSQ5N8n38zz3SXp7kry5OZT75j33nNdes/xcUgJMneq63+qPHg3s3y9mzistFde3DBgghs/R7WnSBIiPF0MQn3oK6NtXJEdEt8JpE6GFCxciJiYGPj4+SExMxM6dO2WHRERERFZISwNefRV46CHZkdhPq1bAhg3AG28AjRoBW7aIa4jGjgUOHpQdnfPq2lVM0f7qq+K4btsG3HknMGIEsHat616HRvbhlInQ8uXLkZaWhhkzZmDv3r2IjY3FsGHDkJeXJzs0IiIiuolx44DEROCllyz7LlwAvvoKyMqSF5et6XQi6Tt6VLxnAFi6VJzM9+0LzJ0L/PgjUFYmN05n4+EhFrE9flxU2/R64NtvgcmTxSQLqosX5cVIzsFDdgC3Yt68eZgwYQJSUlIAAIsWLcLq1auxePFiTJ06tUbbiooKVFRUVP1cdG21M4PBAIPB4Lig66G+vuw4SPvYV8ga7CdkLdl9JTHRMpxJDWHlSgUTJnjg7rtN+PZbY1XbiRN1KC9X8MorRkREiH0//KBgxQoF3bub8eSTlotEZs3SobgY+NOfTIiMFPt27lTw+ecKOnY04+mnLW1nz9bhwgVg4kQTWrUS+/btA/7zHx3atAGeecZSWnjjDR2ys4EJE0zo0EHsO3QIWLxYh+hoYPJkS9t33tHh9GkgOdmErl3FvqIiIChIh4kTgexsBatWKdi6VcHWreL3Op0ZLVsCoaFm+PkBZ88q8PExo3t3wNMT8PQ048gRBRcvAm3bmtGsmXhcaSmwd68CLy8gMdHy3o4cUZCfD7Ruba46DuXlwO7dCnQ6oG9fS9tjxxTk5AAxMWa0aCH2VVYC27eLjKJvX+DMmS5ITwdOnTIiK0tBdLS56phdvQps3aq2NVddz3PypHgfkZFmtGkj9pnN4rMDgN69zfDyEvtPnwZOn1YQEQG0b2+JbcsWBSYT0KuXGT4+Yt+5c8CvvyoICwM6dhT7x44F1q3ToawM+N3vTGjcGKioABYt0kGvF9NxBweLhXD1ejMuXgQaN1YwaJAJAQHAlStARoaCy5cVeHubERJSFQLy8gBvbwV33mlCUJDorxkZCi5eVODlZUZYWM22Hh4K+vc3oWlTsQ5SRoaCggIFnp5mhIdb2ubnAzqdgn79TAgNFfvS0xXk5yvw8DBX9XUAKCgAAAV9+piq9m/YoCA7W4FeDzRrZjlmFy4AJpOCpCQTmjcX+zZvVnDunPjsmze3tL14Ebh6VUFiognR0WLf1q0KTp1SoChAZKSl7aVLQEWFaNuypdi3Y4eCEyfE59msmQ4BAfHw8TFi0CBI1ZC/a4rZ7FyXmVVWVsLX1xcrVqzAyJEjq/YnJyfj8uXL+Oqrr2q0nzlzJmbNmlXneZYuXQpfX197h0tERERW+P77Fli1qi0SEnLw5JOHqvY/9tg9uHLFA++99z2aNSsFAKxe3QofftgNffqcx/PP765qm5IyDJcu+eDNNzegVSvxxWdGRgu88053xMfn4KWXdlS1feaZu5Gd7Yc5czajY8dLAIAtW5pj7tye6NIlHy+/vLWq7ZQpA3HqVCBmztyKuLh8AMDu3eF4+eXeaNfuEl5/3XKRygsv9MeRI8GYNm0HEhPFRVAHDoTgpZf6Ijq6CO+8swEXL/rgxx+b44sv2qOoyNvWh5JImqeeOoD775c7VWJZWRkef/xxFBYWIiAg4IZtna4iVFBQAKPRiPDqaTWA8PBwHD58uE77adOmIS0trernoqIiREdHY+jQoTc9OPZmMBiQnp6OIUOGwNPTU2ospG3sK2QN9hOylhb7yogRwLx5gNkcA0WJASAqCHPmKCgvN+Lhh+9EUJBoGxKiICTEiM6dwzFixIiq50hL06G42IiHHupXVTUJD1fg42NEhw4hNdpOnqzDhQtGPPxwUtU33FFRgKIY0bp1kxptT5zQISfHiFGjeqJdO7GvdWugosKIqKiAGm3PntXhzBkjRo/ugc6dxb4OHYCiIiPCwhpXtf3tb4FevRQcP27EwIEm+PsryMsDjhwB1q/XwdtbVFgMBlGFyMxUcOGCqJio3/SXlgK7dinw9LRUecxm4NAhURFq08aMqCjRtrxcVMd0OqB/f0vbo0dFlSomxlx1HCorgW3bxDf9/fsbcfLkScTEtMKvv+rqVISMRuDHH2+vInTmDHDqlIKICDPat7f0iRtVhEJDgU6dLN/lb9umwGAA4uPNaNzY8ryHDilo1AgICRHH0WgUsXl7AwMHmuHvLypCmzaJyo2Pj6VyYzYD2dmikjRwoBlBQeLYrF8v2np716zy5OQAnp4KBgyoWRHKyxPVo+pVnrw8QFFE2+oVodxcUT1S+y8gqkdms6geqc+xfr2CrCwFer2l6geI6pHRqKBv35oVoTNnFOh0lv4AiOqRwSCqR2pF6McfFZw8KSpC0dE1q0cVFQp6965ZETp2THyeUVEm+Pmdx5gx7XDnnR0hkzr6yxpOlwg1lLe3N7y9637b4unpqZk//lqKhbSNfYWswX5C1nKGvjJ5snrPMody375iq+3FF+u27d1bbLW98ELdtvHxYqvN8n2qpW3XrtVnwrPsV9cRqr6vY8f62/7ud3X3AWIhUa0wGIxYs+YXjBjRCp6e+ps/wOkpN29y07b1HSdna3u99vW3NRhMWLNmH+68c4T0vykNeX2nmywhJCQEer0eubm5Nfbn5uYionqqTUREREREdB1Olwh5eXkhPj4eGRkZVftMJhMyMjKQlJQkMTIiIiIiInIWTjk0Li0tDcnJyUhISECvXr0wf/58lJaWVs0iR0REREREdCNOmQiNHj0a+fn5mD59OnJychAXF4e1a9fWmUCBiIiIiIioPk6ZCAFAamoqUi1XJBIREREREVnN6a4RIiIiIiIiul1MhIiIiIiIyO0wESIiIiIiIrfDRIiIiIiIiNyO006WcKvMZjMAoKioSHIkgMFgQFlZGYqKiqSvwkvaxr5C1mA/IWuxr5C12FfIGlrqJ+o5vnrOfyNulwgVFxcDAKKjoyVHQkRERERE9lBcXIzAwMAbtlHM1qRLLsRkMiErKwv+/v5QFEVqLEVFRYiOjsbZs2cREBAgNRbSNvYVsgb7CVmLfYWsxb5C1tBSPzGbzSguLkbz5s2h0934KiC3qwjpdDpERUXJDqOGgIAA6Z2GnAP7ClmD/YSsxb5C1mJfIWtopZ/crBKk4mQJRERERETkdpgIERERERGR22EiJJG3tzdmzJgBb29v2aGQxrGvkDXYT8ha7CtkLfYVsoaz9hO3myyBiIiIiIiIFSEiIiIiInI7TISIiIiIiMjtMBEiIiIiIiK3w0SIiIiIiIjcDhMhIiIiIiJyO0yEJFm4cCFiYmLg4+ODxMRE7Ny5U3ZIpEGbN2/Gfffdh+bNm0NRFKxatUp2SKRBs2fPRs+ePeHv74+wsDCMHDkSR44ckR0WadB7772Hbt26Va3+npSUhG+//VZ2WKRxc+bMgaIomDJliuxQSGNmzpwJRVFqbB07dpQdltWYCEmwfPlypKWlYcaMGdi7dy9iY2MxbNgw5OXlyQ6NNKa0tBSxsbFYuHCh7FBIwzZt2oSJEydi+/btSE9Ph8FgwNChQ1FaWio7NNKYqKgozJkzB3v27MHu3bsxaNAgPPDAA/j5559lh0YatWvXLrz//vvo1q2b7FBIo+644w5kZ2dXbVu2bJEdktW4jpAEiYmJ6NmzJxYsWAAAMJlMiI6OxqRJkzB16lTJ0ZFWKYqClStXYuTIkbJDIY3Lz89HWFgYNm3ahAEDBsgOhzQuODgYr7/+OsaPHy87FNKYkpIS9OjRA++++y5efvllxMXFYf78+bLDIg2ZOXMmVq1ahczMTNmh3BJWhByssrISe/bsweDBg6v26XQ6DB48GNu2bZMYGRG5isLCQgDiBJfoeoxGI5YtW4bS0lIkJSXJDoc0aOLEibjnnntqnLMQ1Xbs2DE0b94crVu3xtixY3HmzBnZIVnNQ3YA7qagoABGoxHh4eE19oeHh+Pw4cOSoiIiV2EymTBlyhT07dsXXbp0kR0OadCBAweQlJSEK1euwM/PDytXrkTnzp1lh0Uas2zZMuzduxe7du2SHQppWGJiIpYsWYIOHTogOzsbs2bNQv/+/XHw4EH4+/vLDu+mmAgREbmQiRMn4uDBg041Rpscq0OHDsjMzERhYSFWrFiB5ORkbNq0ickQVTl79iwmT56M9PR0+Pj4yA6HNGz48OFV97t164bExES0bNkSn3/+uVMMt2Ui5GAhISHQ6/XIzc2tsT83NxcRERGSoiIiV5CamopvvvkGmzdvRlRUlOxwSKO8vLzQtm1bAEB8fDx27dqFt956C++//77kyEgr9uzZg7y8PPTo0aNqn9FoxObNm7FgwQJUVFRAr9dLjJC0KigoCO3bt8fx48dlh2IVXiPkYF5eXoiPj0dGRkbVPpPJhIyMDI7RJqJbYjabkZqaipUrV2L9+vVo1aqV7JDIiZhMJlRUVMgOgzTk7rvvxoEDB5CZmVm1JSQkYOzYscjMzGQSRNdVUlKCEydOoFmzZrJDsQorQhKkpaUhOTkZCQkJ6NWrF+bPn4/S0lKkpKTIDo00pqSkpMa3KidPnkRmZiaCg4PRokULiZGRlkycOBFLly7FV199BX9/f+Tk5AAAAgMD0ahRI8nRkZZMmzYNw4cPR4sWLVBcXIylS5di48aNWLdunezQSEP8/f3rXGPYuHFjNG3alNceUg3PPfcc7rvvPrRs2RJZWVmYMWMG9Ho9xowZIzs0qzARkmD06NHIz8/H9OnTkZOTg7i4OKxdu7bOBApEu3fvxl133VX1c1paGgAgOTkZS5YskRQVac17770HABg4cGCN/R999BGefPJJxwdEmpWXl4dx48YhOzsbgYGB6NatG9atW4chQ4bIDo2InNC5c+cwZswYXLhwAaGhoejXrx+2b9+O0NBQ2aFZhesIERERERGR2+E1QkRERERE5HaYCBERERERkdthIkRERERERG6HiRAREREREbkdJkJEREREROR2mAgREREREZHbYSJERERERERuh4kQERERERG5HSZCRERERETkdpgIERERERGR22EiREREREREboeJEBERERERuR0mQkRERERE5HaYCBERERERkdthIkRERERERG6HiRAREREREbkdJkJEREREROR2mAgREREREZHbYSJERERERERuh4kQERERERG5HSZCRERERETkdpgIERERERGR22EiREREREREboeJEBERERERuR0mQkRERERE5HaYCBERERERkdthIkRERERERG6HiRAREREREbkdJkJEREREROR2mAgREREREZHbYSJERERERERux0N2AI5mMpmQlZUFf39/KIoiOxwiIiIiIrIRs9mM4uJiNG/eHDrdjWs+bpcIZWVlITo6WnYYRERERERkJ2fPnkVUVNQN27hdIuTv7w9AHJyAgACpsRgMBnz33XcYOnQoPD09pcZC2sa+QtZgPyFrsa+QtdhXyBpa6idFRUWIjo6uOue/EbdLhNThcAEBAZpIhHx9fREQECC905C2sa+QNdhPyFrsK2Qt9hWyhhb7iTWXwHCyBCIiIiIicjtMhIiIiIiIyO0wESIiIiIiIrfDRIiIiIiIiNyOJhKhhQsXIiYmBj4+PkhMTMTOnTuv23bJkiVQFKXG5uPj48BoiYiIiIjI2UlPhJYvX460tDTMmDEDe/fuRWxsLIYNG4a8vLzrPiYgIADZ2dlV2+nTpx0YMREREREROTvp02fPmzcPEyZMQEpKCgBg0aJFWL16NRYvXoypU6fW+xhFURAREWHV81dUVKCioqLq56KiIgBimj+DwXCb0d8e9fVlx0Hax75C1mA/IWuxr5C12FfIGlrqJw2JQTGbzWY7xnJDlZWV8PX1xYoVKzBy5Miq/cnJybh8+TK++uqrOo9ZsmQJnn76aURGRsJkMqFHjx545ZVXcMcdd9T7GjNnzsSsWbPq7F+6dCl8fX1t9l6IiIiIiEiusrIyPP744ygsLLzpmqFSK0IFBQUwGo0IDw+vsT88PByHDx+u9zEdOnTA4sWL0a1bNxQWFmLu3Lno06cPfv75Z0RFRdVpP23aNKSlpVX9rK42O3ToULkLqpaVwbRkCYoXLUJQkyZQ2rUDdDpAUYCCAiiXL8MUEwPcdZdobzZD9+9/i7utWwMeHqLtpUvA5cswR0YCd98t9gFQPvsMMJmAFi0Ab2/xHIWFwKVLMEdEAIMHW9p++SVgMACRkYCPj9hfXAxcvAhzaKilraJA+d//gCtXgGbNxPMqClBWJp43KAgYMkS8lqIA6elQSkuBsDDxvABQUSHi9fOrES82b4ZSUgIEB1tiqKyEubAQ8PWFeeBAy/Pu3AmlqAgIDLTEcPWqiNnLC+YBAyxtDxyAcvky0LixJQaTCSgvh1mvB3r3tsRw7Jg4Rl5eYlMUwGwWx8bDA+jaFdDrxf6zZ4GiIvGzuk93baSpTgdU/4zy80Xb6m2uHU8oiviMvLzE/kuXxPtQ21x77qtGI3748Uf0Gz0ano0bi98VFtZsW/s2JARQFzUrLQVKSq7fNjDQ0vbKFaC8vG6c6ubjI94bABiN4tjX107dyGEMBgPS09MxZMgQzSxoR9rEvkLWYl8ha2ipn6ijv6whfWhcQyUlJSEpKanq5z59+qBTp054//338Y9//KNOe29vb3iriUA1np6ecj+o8nJgyhRURbZtW50muh9+AD79tO5jN2yo/zkXL7b+9Rctsr7tW29Z3/a116xvW0+ljurnCWAoADzzjPUP8vYWyY2iiP529er12zZpAjRqJBK1S5dE4nQ94eGivU4H5OYCFy5cv22LFkBEhGh7/rxIIIG6CZLZDLRrB7RsKdqeOweoX4bo9TXbmc1Ax45iUxQgOxvYuVPc96j2J81kEm27dAG6dRO/z8sT/36qt1UU0RYA4uJEcqzTiff13/9a2qqJndksbhMSgIEDRduiIvHvT6ezfKFRfevZExg+XPyurAyYP79mu+r3ExKAhx8W+65eBf7+d8vv1Vu9XtyPiwOeeKJqv/6vf0V8bi68ly2D3sPDkqjr9eI4PP20eC96PfDSS+J9qzFXf41WrYBx4yzH8p13xJcYtdvpdOLzffhhS9vPPxfvsXob9X5QEPCb31jarl8v+lrt59TpAF9foE8fS9uffhLPW19bT0+g+qiArCygslL8rvox0OvF+6/+JZjJ5NZJu/T/C8lpsK+QNbTQTxry+k43NK4+o0aNgoeHBz777LObti0qKkJgYKBV5TK7unQJpkcfhXnjRuh0OigBAZb/iMvLxUlHkybiRMdsFv9Zb9okfu/vb3meigrxH76fHxAbazlR3LpV/F49uQVEu8pKsa9LF7HPbAb27BG3aiUGECdglZXiZLpjR0vbgwdFLF5elue9elVsHh5A27aiHSAqLEajOEnR6Szv4+pV8XNMjCXeM2fE79QTPLWtepLSvLmlbW6u5QROjVdtDwBNm1r2XbpkOXFVVe/yfn6Wn2908g/UPMm+UVJB5Ow8PUUlV02aTp2y/Puqzd9fJG9q240bRVWxPk2aAKNHWxKSTz4R/0brExIC/POfIhZPT5G4nTpVf9vgYPG6atsxY4Bdu+pv6+dnqaQCIkFdu9aSYNZOmi5etLT9/e9F29rt1G37dksF/p//BL77rv52Op147+r/QUuWiPg9PET81W89PIC//EUcOwDYvBnYt+/6bYcNszzvr7+KLx9qt/H0hMFsxtpDh/CbBx4QJw1Xroi/+dXauGtySDUZDAasWbMGI0aMkH6CS9qlpX7SkHN9qRUhLy8vxMfHIyMjoyoRMplMyMjIQGpqqlXPYTQaceDAAYwYMcKOkdpBkyYwrlmjmU7jVtSkyWyumdwYDJZkTN1vMolvoQFxAqUqLRXtfXwsQ+6MRlFFMJlE1URN3C5eFBWDgADxjbjZLBKpEydE23btLJWG8+eBggKRzDVrVtX26vbt2LljB3pOmABPb2+x/9QpkUCGh4uheOp7Wr9e3E9KEgmr2Sxe6+hRkVB27mxJMr/9VsRy553iG3izWbTbv1+8fvfu4n2ZTMCaNeJYDBkiTsrMZuCXX4AdO0QMvXpZju2aNWIo3rBhomJgNosKz8aN4gS3Tx9L27VrgcuXRaVATY4PHxaxNWkihoeq723dOnF8fvMbkaCbTCLhXrlSHN+hQy3Pm5EhkubBg8WXBCaTODH8/HMxVHLYMLHPaAS2bAFycsQx69FD7D93TlSEvL1F5Uc9Zj/9JCpLaqXJZBIxff+96Dvq400m8RldugRER4vP2WQSJ+F79og+07q1Jd4LF8QxCwoSn5PJJL7oOHlStFWPudksPgeDQXy+fn6ircFQlcybdTooattbYTCIvmiN4uLrV6lru3TJ+mp0QYFIPKxx8aL4LKxRUiKOmZo0qUMo1H+X1b/kUBSRuHl7i3/n338v/s1dz4oVlmG4GzaIpOV6qr/O1q3Axx9fv+2zz1oSoZUrRUXxeg4ftiRCixeLhKwengD833jDsuPNN4G//rVWI0/x3r28gG++Ef8+AOCzz4C5cy2/q307darl89i1S8RcXztvb/Hvu0UL0TY/X/ytatSo/k398o2IyEakD41LS0tDcnIyEhIS0KtXL8yfPx+lpaVVs8iNGzcOkZGRmD17NgDg73//O3r37o22bdvi8uXLeP3113H69Gk8/fTTMt8GORP1m9/a6ktGdbqaCZCqceO6+/R68S169dcBgNBQsVXn5SWuOaqtXTux1WIeMgT5BoNIONQ4w8JE8lHbk0/W3de2rTjxr62+GHr0AB57rO5+9Xq16gYOrH+43qOP1t13//3A88/X3T9xYt19APDyy3X3TZ9ef9sbnRjW9uGH1retj9EoNp3OkjBfvSpOxo1GkUCqzp0TCU5EhPjsAPHN+48/iuRFvaYOECeMR4+KBCs21tJ20SLx/GlplhPBNWvESXb//sA994h9BgNMv/89zp06hWarVsFTPRn+6CMxxPaee4Df/U7EaDCIk9rKSuCrr0TydfUq8P77wLx5wAMPAC++aHmvd98tKtWffCL63dWrwKpVwL/+JZLaSZPEPqNR3C8uFifVERFi/7ZtwBdfAB06AI8/bmn77rsiCR41SiTIBoM4Bps3i5/79hX7DAZxfMrLxbA9Hx+xr6hIHHedTpwoq22vXLlx1fZm1V+V2SwSZ2v99rfWtVMUMQy0cWPxBYTJJJJltRKjfhmj9rEFC0TVq3FjkQT371/3mkM1oa7+t6lpU/GFgcEgjod6e/UqzAYDTDercqvHU30NVVYWsHfv9d/fU09Z7u/dC1z7/7teq1ZZEqHvvrvxMfz3v4GxY8X9jAzgj3+smSj5+lrup6SI4wSIf4fr1om/5dU3f39xq16bSkRuR3oiNHr0aOTn52P69OnIyclBXFwc1q5dWzWBwpkzZ6Cr9i3QpUuXMGHCBOTk5KBJkyaIj4/H1q1b0blzZ1lvgYjciTq8qToPj5pJsCoqSmzV+fiIxKK2nj3FVrvtlCl1244YIbbqPD1hfP997FuzBs0aNbLsT0kRW23Hj9fdN3eu2NShp6qjR0XSFB1tScTj4sRJadOmdRPqoiLxu+Bgy3sLDhZJXvVq/+HDosIyaxbQqZPYt2wZsHs3kJgoTpJV7duL6t+HHwL9+ol9X34JPPKI+PmHHyxtExJE1e3rr0WyaTAA6eniWqaOHYHVqy0n+f/8p6jcPf64+BKiokJUEnfuFCfKrVuLfVeuWHdbe195uUhe1CoeIJKWkhLLBCY3o1YQb8bTU3wuAQGWrU0by/0mTcTn1bQpjIGB8DxyRFR1IyKAF14QX1SoCZPBYHk/lZUicVM9/LCoLFdWWn5f/bZDB0vbLl2AyZOv37b6Uhg+PiLRLS+3bJWVNX+vKigADh26/rEYMMCSCP30k7g+7nreflsk8ICocD/+uPjs/f3FMQsKsmzDh1sqY6Wlok+qbfz9WbUicjLSEyEASE1Nve5QuI0bN9b4+c0338Sbb77pgKiIiNxU7ZO5embkRGSk2GpLTq67r18/S/JS3Zdf1t332GNiqz2s7+uvRfWo+qQIXbsCb7xhqbZVf73QUHHyrg5fVSsaISEiuVEdOyYSjRdftEzkkJEhhuXdcYe4LlJ1zz2i7fvvi6oZIKojn3wiEo5Ro+q+n+oMBktSVFpa89aafaWlotpWVCS2wkJxq17zZDCICuSNJjC5xgNAf6DmcLjAwKpEqWqLiBBVztq3v/mNddcQ9e0rNms8/HDNiTcAUTlUk6Lq1fm77hKfU/WkqfqWkGBpGxwM3HefSDyLiy1JaHGx2Ko/78WLYgjt9TRtakmEMjNr9mudThzD4GDR/yZOtFS4Ll4UldywMPE79VadMZSIpNBEIkRERFRD7ZNsddKW6tq3F0MGa6tvqOSDD1qGzVU3Z464HiouzrKvcWPxzb86ZEuVnS2qRdWH0R46BEybJpKm6onQI4+Ia+3mz7dU78rKxEQHbdqI4aq2YjKJE/vaCVL17dryCWqiZCooQPnZs/C9cgVKYaF4nsJCsd0oEVB5e4ukKCJCJMQtWohr/Fq2tGxNmtz+hAt6vWUoW3VhYcCgQdY9R1KSSKTrU/tauqQkcc2WmnBevly1TAUuXRLXTaoMBvH+L18W/cpkEm0uXRLXOlUfYnz4sJjhsbagIHH8/vQnYPx4sa+oSFy3GBkpvoQIDubEFUR2wkSIiIjcg4dH3RPqwYPrtuvdW3x7X9vq1SIRatXKsq9pU1EFqz7ECxDDCY8dqzkhy65dYqhex45iSJrqvffEifTIkTWf21o6nWX4m5WMBgO+VyfrUdekU6tJ6pafL95vTo5IAtXby5fFsLbTp8V2PX5+IiGKiRHDDjt0sGzNmmnj5L721OlBQZaKz80MHCiOByA+PzVZUo+dOjsrICo/d98tJlrJzxeb0Sgec/myZe02QFQh1Wv/AFHRbNlSJNBt2ogkW10vj4huCxMhIiIiazRrVnMyDEBUCJYsqdv2q6/E1NXVr5+qrBQJQe3q1sKFwM8/i/1qIrR/v5hMom/f+isJtuThUf+kLtdz5YpIkLKzxXb+vCUpUrfcXFGl+vlnsdXm5ycqeh07islB4uLEbe1hjs7Cx8dSIatPQoKYdVBlMokESD1+1a+rMhrF8Th3TlwLdeUKcOSI2ABx3NREaNcukRi1aycSry5dRJ/r3LnmUhtEVC8mQkRERLbWqlXd6o46yUXt658ee0wMmas+Bfi2beJapNOnayZCr74qqlAPPmhZM83R1ApF9QkUaisvF4ng6dNiqN3Ro5aT+ZMnRZK0d6/Yli61PC4iQiQBCQliRsLevS1Th7sSnU4MeQsOrnndGyAmedi3T9y/ckUkSidPiuF2J07UXGj42DEx4ciZM+KaqepiYsTkJ+p1V7XX1CMiJkJEREQOVftk9MUX67aJjxfXHrVvb9lnMIgZ9srLxcmymgidOiWuP+rUSTsnuo0aidirx6+qrLSsbfbzz2JWt8xMcVKfkyPWFlu71tK+c2dx8t+3rxheFh3tsLchnY+PZUhcfcM4771XTMd/5Ahw4IAYVnfggDiOp06JKcVVq1YBf/6zWHYhMVFUleLi6l9OgshNMBEiIiLSmoSEmjOfAaI6MHmyONGtvt7YggVi9rzUVOCddxwb563w8hJJW6dOltn3ADEr3oEDohqyY4c4wT9+XExIceiQWLcKEI8bOlSsjXbXXe69BlBAgEgSq1eJADGk7uefa04CsmOHqCydPAksXy72BQWJBbUHDRKVyfqWASByYUyEiIiInIG/f/2Lk165IpIBdd0cQFx/8qc/iembBw3STqXoRho3FkPheve2LNSclyeGCW7dCmzaJK6J+eUXsb31ljgm990nrpP5zW9EJYrENPF33llz37RpYrKOHTvEMd28WfSTr74S25AhlkSooEAkSR48TSTXxh5ORETkzBYsAF57reYQpy++EJM47NolqizOKixMVI3UytHFi8D69cB334mZ/c6fF9cYLV0qEqmHHwZ+9ztRIXGG5M+RAgPF0EJ1QeerV8U1Whs2WCbrUE2cCGzcKBaXHTOm7nVtRC6CiRAREZGzq34tCCCuA/n978VMbGpCYDaL4XMPP1z/ArfOIDhYVH8eeUTMvLZzJ7BihdhOnxaL237yibiu6He/A8aNc83JFmzBw0P0k169au43m0UCnZcHzJ8Pz/nzcVeLFtAdPgxMmCBvkg4iO9DdvAkRERE5ldhYYNEiyxAzQFRR3n1XDCW7cEFebLai04lhdHPniutetm0DnnpKJIWHDgFTpoiFXv/yF8t6P3RziiImX/jf/4BHH4XZ2xsBZ85AP22aWOD1pZdkR0hkM0yEiIiI3EH79mLI09Sp4hoSVU6OvJhsRVFEUvR//wdkZYmEr2tXMU333LliKvM//ck1EkBH8PQUM9ItX46rZ88i89lnYY6NFdej1V6UmMiJMREiIiJyB61aieuJqn+jf+yY2P+734lprV1BYKCohP30E7B6tbheqKICmD8faNtWzLDnKu/VEYKCcHroUFzduRP44Qfg6actv/vqK3Ed0dmz8uIjug1MhIiIiNyUbs0a8S3/2bOiCuBKFEUsYLtlC7BunViw9vJl4LnnxNTku3fLjtC5KIq4tky9RshsBv7xD+Czz4AOHYCZM8UaV0ROhIkQERGRmzJNniymUX7nHcukCkajWKDVVSiKWHdo714xdC4kRMykl5gohgmyOnRrFAX48EMxbXt5uVjst0cPMYEFkZNgIkREROTO+vcXQ8ZUr70mkoTTp+XFZA96vZhM4dAhMSW0yQS8+qpYlDUrS3Z0zql7d7G+0/LlQEQEcPiwGIr44otiem4ijWMiREREREJ5OfDee8DBg2KdHlcUGirWHfrvf8X1RFu3ikrGpk2yI3NOigI8+qjoM2PGiIriP/8pji+RxjERIiIiIqFRI5EYvP56zam3XdGDD4rrhLp1A3JzgSFDePJ+O5o2FQnmsmXAH/4AjBolOyKim2IiRERERBZRUWJCAZXRCOzZIy8ee2rbVqw/9MgjgMEgTt4/+UR2VM5t9GhRVVSvOSsvB/btkxsT0XUwESIiIqL6Xb0KjBsnrvvYuFF2NPbh6yuqGE89Ja4bSk4G3n9fdlSuoaICeOghcR2aq/YfcmpMhIiIiKh+ZrP4Rt9sdo2FV69HrxczoP3xj+LnZ54BvvhCbkyuwGQSW2mpmMr8++9lR0RUAxMhIiIiqp+nJ/Cf/4iJBB57THY09qXTiUVXn3lGJH6//a0YNke3rlEjsejqiBEioR45UkxjTqQRTISIiIjo+ho1ApKSLD8bjSJRcEWKItZUGjlSrC/00EPA+fOyo3JuPj5iEorBg0Vl6J57gHPnZEdFBICJEBEREVnr1Clg4EBxMbyr0uuBTz8FunQRwwEff1wkf3TrvL2BFSuArl3FMX3kEXH9EJFkTISIiIjIOv/7H7BlCzB9OlBcLDsa+/HzA1auFLebNwOzZ8uOyPkFBgKrVgFNmgA//8whcqQJTISIiIjIOhMnAi++COzcCfj7y47Gvtq2BRYuFPdnzQL275cbjyto3VokQ5mZNYdbEknCRIiIiIiso9MB//iHOKF1B088IRZevXoVePppDpGzhQEDgDZtZEdBBICJEBEREd2qs2eBsjLZUdiPogALFgABAcCuXcAHH8iOyLVs2MBhhyQVEyEiIiJquHnzgPbtxSxrrqx5c+Dll8X9GTOAwkK58biKw4eBQYOAv/1NDLUkkoCJEBERETVcaChw5Yr4Vt9Vp9NW/eEPQIcOQH4+MGeO7GhcQ8eOwLhxou/8+c+u34dIk5gIERERUcM9/jjwww/At9+KIWSuzNMTeO01cf/tt0VCRLfvn/8U61Rt2SImUSByMCZCRERE1HB6PdCvn+snQar77gPi48U1UW++KTsa1xAVBaSlifsvvgiYTHLjIbfDRIiIiIhuj9EoJk5wZYoCvPSSuL9gAXD5stRwXMZf/iLWGDp0iFUhcjgmQkRERHTrdu4EWrUC7rnH9a/zuP9+4I47xGKyH30kOxrXEBgITJok7r/8suv3IdIUJkJERER069q1E9fMZGUB587Jjsa+FAX44x/F/QULuK6QrUyZAvTtC6SmcngcORQTISIiIrp1TZoA69eLJCg6WnY09jd2LBAUBPz6q5gogm5f06ZiwoSnnhLXnhE5CBMhIiIiuj1JSYCPj+woHKNxY3HCDgCLF8uNhYhuCxMhIiIisp2rV2VHYH8pKeL2m2+ACxfkxuJKiorEkMNFi2RHQm6CiRARERHdvp07gYEDgVGjZEdif126AHFxgMEALF8uOxrXsXq1mDjh1Vd5rRA5BBMhIiIiun2+vsCmTeK6mZIS2dHY37hx4vaTT+TG4UoeeADw9wdOnQJ+/FF2NOQGNJEILVy4EDExMfDx8UFiYiJ27txp1eOWLVsGRVEwcuRI+wZIREREN9alC/DBB8DRo4Cfn+xo7O+xx8Qscjt2uP5seY7i6ws88oi4/+mncmMhtyA9EVq+fDnS0tIwY8YM7N27F7GxsRg2bBjy8vJu+LhTp07hueeeQ//+/R0UKREREd3QhAlAixayo3CMZs3EJBEA8PXXcmNxJU88IW5XrBBDD4nsSHoiNG/ePEyYMAEpKSno3LkzFi1aBF9fXyy+wUwsRqMRY8eOxaxZs9C6dWsHRktERER0jToiZdUqmVG4lgEDgNBQ4NIlYPNm2dGQi/OQ+eKVlZXYs2cPpk2bVrVPp9Nh8ODB2LZt23Uf9/e//x1hYWEYP348fvjhhxu+RkVFBSoqKqp+LioqAgAYDAYYJH/ToL6+7DhI+9hXyBrsJ2Qte/YVZf16KCtXwvzkkzDHx9v8+TXlnnvg+fzzMG/YgKv5+WJ9IRcj4++K/t57ofvoIxj/+1+YBgxw2OvSrdPS/z8NiUFqIlRQUACj0Yjw8PAa+8PDw3H48OF6H7Nlyxb83//9HzIzM616jdmzZ2PWrFl19n/33Xfw9fVtcMz2kJ6eLjsEchLsK2QN9hOylj36SvwbbyDqhx9wtKAAv6jDnFzYXdHRCDh7FpmvvYasfv1kh2M3jvy7Et68OXp5eODckSPIXLPGYa9Lt08L//+UlZVZ3VZqItRQxcXFeOKJJ/Dhhx8iJCTEqsdMmzYNaWlpVT8XFRUhOjoaQ4cORUBAgL1CtYrBYEB6ejqGDBkCT09PqbGQtrGvkDXYT8ha9uwritkMY5cuaD1yJFrddZdNn1uLdBs2AG+9hR6XLyNuxAjZ4diclL8rgwfDOGUKmgcGorljXpFuk5b+/1FHf1lDaiIUEhICvV6P3NzcGvtzc3MRERFRp/2JEydw6tQp3HfffVX7TNfmmffw8MCRI0fQpk2bGo/x9vaGt7d3nefy9PSU/kGptBQLaRv7ClmD/YSsZZe+MnKk5doZdzB0KPDWW9CvXw+9C/+7c+jfFU9PoHFjx7wW2ZQW/v9pyOtLnSzBy8sL8fHxyMjIqNpnMpmQkZGBJHUmlmo6duyIAwcOIDMzs2q7//77cddddyEzMxPR0dGODJ+IiIjc3YABgIcH8OuvYiPbKi6WHQG5MOlD49LS0pCcnIyEhAT06tUL8+fPR2lpKVJSUgAA48aNQ2RkJGbPng0fHx906dKlxuODrl2YWHs/ERERSWI2AydPApcvAz16yI7Gvvz8gN69gS1bgIwMgLPZ2sbFi8DgwcDhw8CFC0CjRrIjIhckffrs0aNHY+7cuZg+fTri4uKQmZmJtWvXVk2gcObMGWRnZ0uOkoiIiKz2n/8AbdoAkyfLjsQxBg8Wt+vXy43DlTRpAuTnA+XlIskksgPpFSEASE1NRWpqar2/27hx4w0fu2TJEtsHRERERLeud2/Ay0vcN5kAnfTvXe1LXdx961a5cbgSRREJ5pIlQHo6MGSI7IjIBbn4XyYiIiJyuDZtxLUdP/zg+kkQAPTsKd7nmTNAVpbsaFzHoEHilhUhshM3+OtEREREDqUoloqQO/D3B9RrlW+wIDw1UJ8+4nbPHqCiQm4s5JKYCBERERHdLnW2WyZCttO6NRAaClRWAnv3yo6GXBATISIiIrK9I0eAe+91n2s71ERo+3a5cbgSRbFUhXj9FdmBJiZLICIiIhfj5wesXg3o9UBZGeDrKzsi+1ITod27RQXDnYYG2tP99wMBAUDXrrIjIRfERIiIiIhsr3lzYNEiIDbWPZKCdu2AwECgsFCsfdOtm+yIXMNTT4mNyA44NI6IiIhsT1GA3/9eTKXt4QbfuyqKJfn56Se5sRCRVZgIEREREdlCbKy4ZSJkWwYDcPAgcO6c7EjIxTARIiIiIvsoLgbWrgWWLZMdiWMwEbKPp58W1wh9/LHsSMjFuEGtmoiIiKQ4fhwYPhxo2hQYPVoMH3Nl1RMhs9n136+jqEMOMzOlhkGuhxUhIiIiso+OHcVCo4MGuceCmF26ADodkJ8P5OTIjsZ1xMWJ2337pIZBrocVISIiIrKPRo2AAwdkR+E4jRoB7duLWeMyM4FmzWRH5BrUStuJE0BRkZhOm8gGWBEiIiIishX1pP3gQblxuJKQECAqStzfv19uLORSmAgRERGR/ZlMsiNwjI4dxe2RI3LjcDWciILsgIkQERER2c/GjWK42ODBsiNxjA4dxC0TIdu64w5xe/iw3DjIpfAaISIiIrIfX1/g2DGgpER2JI7BRMg+7rkHCAwE+veXHQm5ECZCREREZD9duoiqUJs2siNxjPbtxW1+PnDpEtCkidx4XMWAAWIjsiEOjSMiIiL78fUF7rzTcrG7q/PzAyIjxX1WhYg0jYkQERERkS2pVaGjR+XG4WpOnAC+/hrIypIdCbkIJkJERERkX/v2AQsWAJs2yY7EMXidkH089RTwwAPAhg2yIyEXwUSIiIiI7Ovzz4FJk4AVK2RH4hhMhOxDnZqcM8eRjTARIiIiIvvq1QsYORKIi5MdiWOoiRCHxtkWEyGyMc4aR0RERPb14INicxetW4vbU6cAsxlQFKnhuAwmQmRjrAgRERER2VKLFuK2uFhMoU22oSaYJ0+KBJPoNjERIiIiIscwGsXm6ho1AsLDxf1Tp6SG4lJathS3paVAQYHcWMglMBEiIiIi+xs6VCQIP/4oOxLHiIkRt6dPSw3Dpfj4WNZoOnlSbizkEniNEBEREdmfwSC2c+dkR+IYMTHAjh2sCNnaK68Anp6WYXJEt4GJEBEREdnfBx8AXl5A8+ayI3EMtSLERMi2xo2THQG5ECZCREREZH/t2smOwLGYCBFpHhMhIiIiIltTL+xnImRbFy8CW7cCV6+KtamIbgMTISIiIrK/7Gzgyy/FtMeTJsmOxv6qV4S4lpDtHDgA3Hcf0KYNEyG6bZw1joiIiOwvO1skQLNny47EMdSKUFERcPmy1FBcSqtW4vbMGfeYip3siokQERER2V/LlsBDDwGPPeYei2H6+gJhYeI+h8fZTmSkmDXOYADOn5cdDTk5JkJERERkf02biqFx8+a5zzCxqChxm5UlNw5XotcD0dHiPtdootvERIiIiIjIHtSpwpkI2ZaaYLIiRLeJiRARERE5jskEVFbKjsIxmAjZR2SkuGUiRLeJiRARERE5xpQpQKNGwNtvy47EMdREiCfstqVWhM6dkxsHOT1On01ERESO4e0tqkHucgKrVi5YEbKt0aOB7t2B2FjZkZCTYyJEREREjpGWBkycCISHy47EMTg0zj7i48VGdJuYCBEREZFjuEsCpGIiRKRpmrhGaOHChYiJiYGPjw8SExOxc+fO67b973//i4SEBAQFBaFx48aIi4vDp59+6sBoiYiIiKygJkJ5eWLdG7INgwH4+mvgvffE5BtEt0h6IrR8+XKkpaVhxowZ2Lt3L2JjYzFs2DDk5eXV2z44OBh/+9vfsG3bNuzfvx8pKSlISUnBunXrHBw5ERERNUhREfDWW8A//iE7EscICQE8PMQCsrm5sqNxHYoCPPgg8OyzIskkukXSh8bNmzcPEyZMQEpKCgBg0aJFWL16NRYvXoypU6fWaT9w4MAaP0+ePBkff/wxtmzZgmHDhtVpX1FRgYqKiqqfi4qKAAAGgwEGyd/OqK8vOw7SPvYVsgb7CVlLWl8pKYHnlCnitf/8Z8DT07GvL4FHs2ZQzp7F1TNnYHbCoYFa/bviEREBJSsLV0+dgrlpU9nhuD0t9ZOGxGBVIvTQQw81OIhFixYhLCzshm0qKyuxZ88eTJs2rWqfTqfD4MGDsW3btpu+htlsxvr163HkyBG8+uqr9baZPXs2Zs2aVWf/d999B19f35u+hiOkp6fLDoGcBPsKWYP9hKzl8L5iMiG+Xz9UBgbi0P/+B6OPj2NfX4L+jRohGMDe//0P2fn5ssO5ZVr7uzKgcWM0AbDn66+Rw2qbZmihn5SVlVnd1qpEaNWqVXj00UfRqFEjq5506dKlKCkpuWkiVFBQAKPRiPBa35CEh4fj8OHD131cYWEhIiMjUVFRAb1ej3fffRdDhgypt+20adOQlpZW9XNRURGio6MxdOhQBAQEWPV+7MVgMCA9PR1DhgyBpxt8K0a3jn2FrMF+QtaS2lfuvRcAEO3YV5VGv2QJcPQo4ps1g2nECNnhNJhW/67oP/oIOHYMCRERTnlcXY2W+ok6+ssaVg+Ne/vtt2+a2KhWrFhhdQC3wt/fH5mZmSgpKUFGRgbS0tLQunXrOsPmAMDb2xve3t519nt6ekr/oFRaioW0jX2FrMF+QtZiX3GAa4t/6nNzoXfiY625vhItUml9To5TH1dXo4V+0pDXtyoR2rBhA4KDg61+0m+//RaR6iJiNxASEgK9Xo/cWiXN3NxcREREXPdxOp0Obdu2BQDExcXhl19+wezZs+tNhIiIiEhjzGbg6lW3uEYIzZqJ2+xsuXG4Gk5NTjZg1axxd955Jzw8rJ9XoV+/fvVWYWrz8vJCfHw8MjIyqvaZTCZkZGQgKSnJ6tczmUw1JkQgIiIijZo8GfD2Bt58U3YkjqGOpnHi64M0Sb2sgrPG0W24pVnjTCYTjh8/jry8PJhqzd8+YMCABj1XWloakpOTkZCQgF69emH+/PkoLS2tmkVu3LhxiIyMxOzZswGIyQ8SEhLQpk0bVFRUYM2aNfj000/x3nvv3cpbISIiIkfy8hLrwLjLBe5qIsQTdtu66y5g2TKgTRvZkZATa3AitH37djz++OM4ffo0zGZzjd8pigKj0dig5xs9ejTy8/Mxffp05OTkIC4uDmvXrq2aQOHMmTPQ6SyFq9LSUjz77LM4d+4cGjVqhI4dO+Lf//43Ro8e3dC3QkRERI723HPAH/9oSRBcXWiouGUiZFutWomN6DY0OBH6wx/+gISEBKxevRrNmjWDoii3HURqaipSU1Pr/d3GjRtr/Pzyyy/j5Zdfvu3XJCIiIgmccC2d28KhcUSa1eBE6NixY1ixYkXVZAVEREREdB1qRai0VGyNG8uNx5WsWgXk5ABPPMHjSrfEqskSqktMTMTx48ftEQsRERG5usuXgfnzAXcZ3eHvLyaHAFgVsrWUFOCZZ4AzZ2RHQk7KqorQ/v37q+5PmjQJf/7zn5GTk4OuXbvWmau7W7duto2QiIiIXEdZGfCnPwE6HfDXv4pbV6Yooip07pxIhGJiZEfkOsLDRWKdmwt06iQ7GnJCViVCcXFxUBSlxuQITz31VNV99Xe3MlkCERERuZGmTYExY8StwWCplriysDCRCHHCBNsKCwOOHHGfGQjJ5qxKhE6ePGnvOIiIiMgdeHsDS5fKjsKxOGGCfagTbzARoltkVSLUsmVLTJ8+HQ888ADi4+PtHRMRERGR6+AU2vbBRVXpNlk9MPfcuXMYPnw4oqKi8Mwzz+Dbb79FZWWlPWMjIiIiV2U2A+4ynJ6LqtoHK0J0m6xOhBYvXoycnBx89tln8Pf3x5QpUxASEoKHH34Yn3zyCS5evGjPOImIiMhVjB7tXkPk1IoQh8bZFhMhuk0NmqpFp9Ohf//+eO2113DkyBHs2LEDiYmJeP/999G8eXMMGDAAc+fOxfnz5+0VLxERETk7RRETJVy4IDsSx2BFyD4GDwaWLwdmzpQdCTmpBi+oWl2nTp3QqVMnPP/888jPz8fXX3+Nr7/+GgDw3HPP2SRAIiIicjFvvAHMnWuplLg6TpZgH61bi43oFt1WIlRdaGgoxo8fj/Hjx9vqKYmIiMgVRUbKjsCxOFkCkSbZbBWzEydOYNCgQbZ6OiIiIiLXUH1oXLU1Gek2mUzAl18C778PXLkiOxpyQjarCJWUlGDTpk22ejoiIiJyVb/+CqxaBQQFAdUWaHdZTZuK24oKoKwMaNxYbjyuQlGAxx8HKiuB4cOBFi1kR0ROxupE6O23377h7zlBAhEREVnl6FHgz38G4uLcIxHy8wM8PcUEERcvMhGyFUURSWZ2NlBQwESIGszqRGjKlClo1qwZvLy86v091xQiIiIiq7RqBYwZA7RvLzsSx1AUIDhYTPN88SIQHS07ItcREiISIXeZgZBsyupEqGXLlnj11Vfx6KOP1vv7zMxMxMfH2ywwIiIiclEdOrjPGkIqNRHiCbtthYSI24ICuXGQU7J6soT4+Hjs2bPnur9XFAVmXgBIREREVJd6nRAXoLct9bgyEaJbYHVF6O9//zvKysqu+/vOnTvj5MmTNgmKiIiI3ID6BaqiyI3DEYKDxS0TIdtSK0KstNEtsLoi1LlzZyQkJFz3956enmjZsqVNgiIiIiIX16UL4OUFHDggOxLHUCsXPGG3LQ6No9tgs+mziYiIiKxmMgFXr7pPYsCKkH2MGgV06wZ06iQ7EnJCVlWEevTogUuXLln9pP369eN02kRERHR9X38NnD0L9OsnOxLHUBMhd0n8HKVbN5EMdekiOxJyQlZVhDIzM/HTTz8hWP1HbEX7ioqK2wqMiIiIXFjbtrIjcCxOlkCkOVYPjbv77rutnhVOcYeLHomIiIisxaFx9lFcDKxbB5SXA088ITsacjJWJUK3MhtcVFRUgx9DREREbmL7dmDrViA2Frj7btnR2B8nS7CPggIxNK5RIyZC1GBWJUKcDY6IiIhsavVq4OWXgYkT3SMRYkXIPtRZ48rLgbIywNdXbjzkVKyePpuIiIjIZnr0AMaOBXr2lB2JY1SvCHEBetvx8wM8PcV9VtuogTh9NhERETnegw+KzV2oFSGDASgtFSfwdPsURVSFsrPFMLnoaNkRkRNhRYiIiIjI3nx9xQKyAIfH2ZpabeOiqtRATISIiIiI7E1ROGGCvajXCTERogZiIkRERESOd+AAEBYGtGkjOxLH4YQJ9qEe10uX5MZBTqfB1wjpdLobrhNkNBpvKyAiIiJyA40aAfn5wJUrsiNxHFaE7GPyZOCxx4D4eNmRkJNpcCK0cuXKGj8bDAbs27cPH3/8MWbNmmWzwIiIiMiFtWghqkJBQbIjcZwmTcQtKxe2NWCA7AjISTU4EXrggQfq7HvkkUdwxx13YPny5Rg/frxNAiMiIiIX5uUFdOkiOwrHYiJEpCk2u0aod+/eyMjIsNXTEREREbkWtfp1+bLMKFzPuXPAF18A69bJjoScjE0SofLycrz99tuIjIy0xdMRERGRO1i+HJg3D8jKkh2JY7AiZB8//gg8+ijwyiuyIyEn0+ChcU2aNKkxWYLZbEZxcTF8fX3x73//26bBERERkQv7+9+BQ4eA7t2B5s1lR2N/rAjZB2eNo1vU4ERo/vz5NX7W6XQIDQ1FYmIimqjfdBARERHdzPDhQFycpVLi6lgRsg8eV7pFDU6EkpOT7REHERERuZu5c2VH4FisCNkHEyG6RQ1OhADgypUr2L9/P/Ly8mAymWr87v7777dJYEREREQuhSfs9qEOjSstBSorxYyERFZocCK0du1aPPHEE7hQz2JgiqLc0oKqCxcuxOuvv46cnBzExsbinXfeQa9evept++GHH+KTTz7BwYMHAQDx8fF45ZVXrtueiIiISBNYEbKPwEBAUQCzWSSZ4eGyIyIn0eBZ4yZNmoRHH30U2dnZMJlMNbZbSYKWL1+OtLQ0zJgxA3v37kVsbCyGDRuGvLy8ettv3LgRY8aMwYYNG7Bt2zZER0dj6NChOH/+fINfm4iIiCR64w0gLAz4y19kR+IY1StCZrPcWFyJTieSIYDVNmqQBidCubm5SEtLQ7iNsu158+ZhwoQJSElJQefOnbFo0SL4+vpi8eLF9bb/z3/+g2effRZxcXHo2LEj/vWvf8FkMnENIyIiImdjNAL5+WJzB2oiZDSKYVxkO+++K9YSatZMdiTkRBo8NO6RRx7Bxo0b0aZNm9t+8crKSuzZswfTpk2r2qfT6TB48GBs27bNqucoKyuDwWBAsDo+tJaKigpUVFRU/VxUVAQAMBgMMBgMtxH97VNfX3YcpH3sK2QN9hOylmb6ypgxwODBYiiT7FgcwcMDHp6eUAwGGPLyAG9v2RHdlGb6ys088ojlvtZjdUFa6icNiUExmxtWmy0rK8OoUaMQGhqKrl27wtPTs8bv//jHP1r9XFlZWYiMjMTWrVuRlJRUtf/555/Hpk2bsGPHjps+x7PPPot169bh559/ho+PT53fz5w5E7Nmzaqzf+nSpfD19bU6ViIiIqLbNSw5GT6FhVg/fz6KY2Jkh0PkcsrKyvD444+jsLAQAQEBN2zb4IrQZ599hu+++w4+Pj7YuHFjjcVVFUVpUCJ0u+bMmYNly5Zh48aN9SZBADBt2jSkpaVV/VxUVFR1XdHNDo69GQwGpKenY8iQIXUSSqLq2FfIGuwnZC32FXk8wsKAwkIM6NoV5v79ZYdzU07TV/bvh3LkCMydOgFdusiOxu1oqZ+oo7+s0eBE6G9/+xtmzZqFqVOnQqdr8CVGNYSEhECv1yM3N7fG/tzcXERERNzwsXPnzsWcOXPw/fffo1u3btdt5+3tDe96Ss+enp7SPyiVlmIhbWNfIWuwn5C1pPeVixeBVavENTMTJsiLw5GuDeX3KCkBnOjfqfS+cjMffCC2WbOA7t1lR+O2tNBPGvL6Dc5kKisrMXr06NtOggDAy8sL8fHxNSY6UCc+qD5UrrbXXnsN//jHP7B27VokJCTcdhxEREQkQX4+MH68+8waB1im0ObsZralTkRx8aLcOMipNDibSU5OxvLly20WQFpaGj788EN8/PHH+OWXX/DMM8+gtLQUKSkpAIBx48bVmEzh1VdfxUsvvYTFixcjJiYGOTk5yMnJQUlJic1iIiIiIgcICQHuuQe47z73mU5aPWHnWkK2pU6axQSTGqDBQ+OMRiNee+01rFu3Dt26datTfpo3b16Dnm/06NHIz8/H9OnTkZOTg7i4OKxdu7Zqeu4zZ87UqD699957qKysxCPVZwcBMGPGDMycObOhb4eIiIhkadoU+OYb2VE4FitC9lF9jSYiKzU4ETpw4AC6Xxt7efDgwRq/qz5xQkOkpqYiNTW13t9t3Lixxs+nTp26pdcgIiIiko4VIftgIkS3oMGJ0IYNG+wRBxEREZHrY0XIPtShcbxGiBrg9mc8ICIiIrpVv/kNEBYGbNkiOxLHYOXCPnhc6RY0uCJEREREZDOXLonZ49zlm3y1IsShcbbVqpWYPjs0VHYk5ESYCBEREZE8//d/Ysa4Vq1kR+IYrFzYR1CQ+6xFRTbDRIiIiIjk6dJFdgSOxYoQkWbwGiEiIiIiR2FFyH5++AH44gvgwgXZkZCTYCJERERE8hw8CCxeDGzaJDsSx1ATodJSwGCQG4urGT8eePRR4NAh2ZGQk2AiRERERPJ8/bU4gf3kE9mROEZgoOU+h8fZFqtt1EBMhIiIiEieTp2AESOArl1lR+IYej0QECDuMxGyLTURcpcZCOm2cbIEIiIikufBB8XmToKCgKIiVi5sTV1UlceVrMSKEBEREZEjqZULVoRsi0PjqIGYCBERERE5kjqFNk/YbYuJEDUQEyEiIiKS59dfgQ4dxLVC7oIVIftQh8bxGiGyEq8RIiIiInm8vYGjR8UkAmYzoCiyI7I/VoTsY8gQ4IMP3CupptvCRIiIiIjkCQsDNm+2JAfugBUh++ja1X1mHySbYCJERERE8nh6Av37y47CsVgRItIEXiNERERE5EisCNlHeTnw/ffAV1/JjoScBCtCREREJNd33wHnzwNDhwKRkbKjsT9WhOzjwgVxnZCHB1BZ6R7Xm9FtYUWIiIiI5PrrX4GnngIyM2VH4hisCNmHelyvXgVKS+XGQk6BFSEiIiKSq29fIDzcfSZM4Ho39uHrK645MxjEsfXzkx0RaRwTISIiIpLrrbdkR+BYasLHipBtKYpIMvPyxFpC0dGyIyKN49A4IiIiIkeqXhEym+XG4mrURVVZbSMrMBEiIiIiciS1ImQ08loWW+OwQ2oAJkJEREQk1//9H9C+PfCXv8iOxDEaNQK8vMR9nrDbFhMhagAmQkRERCRXaSlw7Bhw5ozsSBxDUTiFtr088wzwwQdiAg6im+BkCURERCTXgw8C3bu7xxpCKvWifk6YYFv33is7AnIiTISIiIhIruho95vhixUhIumYCBERERE5GhdVtY/cXGD/fsDfH+jdW3Y0pHG8RoiIiIjkKikBVq4EPvtMdiSOw4qQfaxdCwwdCsyYITsScgKsCBEREZFcFy4ADz0E+PgAY8bIjsYxWBGyD3UdoYsX5cZBToGJEBEREcnVpAnQp4+okly9Cni4wekJK0L2wemzqQHc4C8NERERaVpAAPDjj7KjcCxWhOyDiRA1AK8RIiIiInI0VoTso3oiZDLJjYU0j4kQERERkaOxImQf6nE1m4GiIrmxkOYxESIiIiL5fvtboEMHYNMm2ZE4Bodw2UejRmLSDYDHlm6K1wgRERGRfGfPAkePinVg3IE6NI4VIdubOxfw8rIkm0TXwUSIiIiI5Hv9deDKFaBzZ9mROAYrQvYzcaLsCMhJMBEiIiIi+Xr1kh2BY6kVodJSwGAAPD2lhkPkjniNEBEREZGjBQZa7nN4nG0dPw6kpwO//io7EtI4JkJEREQk36lTwMqV7rOekF4v1k8CmAjZ2ssvA0OHAl98ITsS0jjpidDChQsRExMDHx8fJCYmYufOnddt+/PPP+Phhx9GTEwMFEXB/PnzHRcoERER2c///gc89BDw1luyI3EcXidkH8HB4vbiRblxkOZJTYSWL1+OtLQ0zJgxA3v37kVsbCyGDRuGvLy8etuXlZWhdevWmDNnDiIiIhwcLREREdlNq1ZAUhLQrp3sSByHM8fZR0iIuM3PlxsHaZ7URGjevHmYMGECUlJS0LlzZyxatAi+vr5YvHhxve179uyJ119/HY899hi8vb0dHC0RERHZzb33Alu3Av/8p+xIHIcVIftQE6GCArlxkOZJmzWusrISe/bswbRp06r26XQ6DB48GNu2bbPZ61RUVKCioqLq56JrqwwbDAYYDAabvc6tUF9fdhykfewrZA32E7IW+4o26AMCoANgvHABJo1+Fs7YV5QmTeABwJSXB6MTxe3MtNRPGhKDtESooKAARqMR4eHhNfaHh4fj8OHDNnud2bNnY9asWXX2f/fdd/D19bXZ69yO9PR02SGQk2BfIWuwn5C12FfkiistRUsAR7Zvx7HISNnh3JAz9ZXg48fRH0DZ6dPIWLNGdjhuRQv9pKyszOq2Lr+O0LRp05CWllb1c1FREaKjozF06FAEqLO1SGIwGJCeno4hQ4bAk+sH0A2wr5A12E/IWprsK6Wl0A8bBuXiRVzduxfw8ZEdkd3pNm4EMjLQITQU7UaMkB1OvTTZV26mdWvgr39F4/JyjNDocXU1Wuon6ugva0hLhEJCQqDX65Gbm1tjf25urk0nQvD29q73eiJPT0/pH5RKS7GQtrGvkDXYT8hamuorAQHA7t2AyQTPkhLA3192RPYXFgYA0F+6BL1WPofr0FRfuZlWrYA5c6CEhsLTwwNQFNkRuQ0t9JOGvL60yRK8vLwQHx+PjIyMqn0mkwkZGRlISkqSFRYRERHJoNMBX38NbN5smUTA1YWGilvObmZbfn7ACy8ATz3FJIhuSOrQuLS0NCQnJyMhIQG9evXC/PnzUVpaipSUFADAuHHjEBkZidmzZwMQEywcOnSo6v758+eRmZkJPz8/tG3bVtr7ICIiIhu45x7ZETgWEyEiqaQmQqNHj0Z+fj6mT5+OnJwcxMXFYe3atVUTKJw5cwY6naVolZWVhe7du1f9PHfuXMydOxd33nknNm7c6OjwiYiIiG4dEyH7+eUX4OxZoFs3gGtP0nVInywhNTUVqamp9f6udnITExMDs9nsgKiIiIjI4Q4cAI4fBzp3Bjp0kB2N/amJ0HUWkqfb8MwzwKZNwLJlwOjRsqMhjZK6oCoRERFRlddfBx56SFwr5A7URKikBLhyRW4srkZdVJXVNroBJkJERESkDZ07A0lJlgTB1QUGAuoMVzxhty21DxUUyI2DNI2JEBEREWnD1KnA1q3Ak0/KjsQxFIWVC3vh9VdkBSZCRERERLLwhN0+mGCSFZgIEREREcnCRMg+eFzJCkyEiIiISBv27QMSE4F775UdiePwhN0+1IoQrxGiG5A+fTYRERERAMBsBnbuBJo3lx2J4zARso/OnYE5c4CWLWVHQhrGRIiIiIi0oV074MsvgchI2ZE4DhMh+4iMBF54QXYUpHFMhIiIiEgb/P3FOkLuhIkQkTS8RoiIiIhIFiZC9nP4MPDtt0BuruxISKOYCBEREZF2ZGYCK1YAp0/LjsQxmAjZz/jxwIgRwA8/yI6ENIqJEBEREWnH1KnAqFHA+vWyI3GMsDBxm5MjNw5XpF5rdv683DhIs3iNEBEREWlHXBxQXAwEBsqOxDGiosRtcTFQVAQEBMiNx5WoidC5c3LjIM1iIkRERETaMWeO7Agcy88PaNIEuHQJOHsWuOMO2RG5DlaE6CY4NI6IiIhIpuhocXv2rNw4XA0TIboJJkJEREREMjERsg8mQnQTTISIiIhIOw4fBnr3Bu68U3YkjsNEyD6qJ0Jms9xYSJN4jRARERFph5cXsGMH0KiROHlVFNkR2R8TIfuIjARmzxa3JhOg18uOiDSGiRARERFpR2QksHIlEBEhOxLHYSJkHz4+Yjp2outgIkRERETa4e0NjBwpOwrHYiJEJAWvESIiIiKSqXoixGtZbCsrC1i9Gti3T3YkpEFMhIiIiEhbjh0Dli93n5NXdVHV8nLg4kW5sbiad94B7r0X+Ne/ZEdCGsREiIiIiLRlwQLgsceAZctkR+IY3t5AWJi4z+FxttWhg7g9fFhuHKRJTISIiIhIW3r0APr0sVRK3EGLFuL21CmpYbgcNRE6ckRuHKRJTISIiIhIW5KTgR9/BCZNkh2J43TsKG4PHZIbh6tRE6Hz54GSErmxkOYwESIiIiKSrUsXcXvwoNw4XE1wMBASIu4fPSo3FtIcJkJERESkXe4yixoTIfvh8Di6DiZCREREpD1PPikWVU1Plx2JY6iJ0OHDgMEgNxZXw0SIroOJEBEREWnP5ctAbq77DGdq0QLw8xNJ0LFjsqNxLSkpwL//Dfz2t7IjIY1hIkRERETaM3MmsGuXqAy5A0Xh8Dh76dcPGDsWaNtWdiSkMUyEiIiISHvi4oCEBFElcRdMhIgciokQERERkRaoidC+fXLjcEUHDwJz5wKbNsmOhDSEiRARERFp05YtwEsvuU9i0L+/uN24EaislBqKy/noI+AvfwE++0x2JKQhTISIiIhIm955B3j5ZeCbb2RH4hhxcUBYmFj4c+tW2dG4FjXJ/OEHuXGQpjARIiIiIm164AHgiSfEtULuQKcDhg0T99eulRuLq+nbV9weOgScOyc3FtIMJkJERESkTY8/DnzyCTB8uOxIHOc3vxG3TIRsKzQUGDBA3F+yRGoopB1MhIiIiIi0YsgQMZX2Tz8Bv/wiOxrXMn68uF28GDCZ5MZCmsBEiIiIiLStoAD4178As1l2JPYXGgrcf7+4P2eO3FhczSOPAAEBwMmTQEaG7GhIA5gIERERkXaVl4uFMCdMAH78UXY0jvG3v4nb//xHnLSTbfj6AuPGiWSoqEh2NKQBTISIiIhIuxo1Et/kd+0qZlRzBz17iiFyRiMwdixw5YrsiFzHzJnAzz8DDz8sOxLSAE0kQgsXLkRMTAx8fHyQmJiInTt33rD9F198gY4dO8LHxwddu3bFmjVrHBQpEREROdzcucD27UD79rIjcZwFC4CgIGDbNpEI5ufLjsg1NG0KREVZfv7pJ6CsTF48JJX0RGj58uVIS0vDjBkzsHfvXsTGxmLYsGHIy8urt/3WrVsxZswYjB8/Hvv27cPIkSMxcuRIHDx40MGRExERkUMEBYlhTar33gOmTAHOnJEVkf21bw+sWAF4egKrV4ufU1OBVauAw4eBS5fc45ope9q1C+jTB2jdGvjTn4D//hfIzAQKC3ls3YRiNsv9pBMTE9GzZ08sWLAAAGAymRAdHY1JkyZh6tSpddqPHj0apaWl+Kba4mq9e/dGXFwcFi1aVKd9RUUFKioqqn4uKipCdHQ0CgoKEBAQYId3ZD2DwYD09HQMGTIEnp6eUmMhbWNfIWuwn5C1nLqv/PwzPBIToVRWwrBzp1iEFIDyySfQ/+UvMD38MEzvvlvVXN+nD5TLl3H166/FtUYAlOXLoZ81C6bBg2F6+21L27vvhpKdjauffw506SLafvUV9NOmwdy3L4wffmhpe++9UH79FcZPPoH52jpHSno69FOmwNyjB4yffmppO2oUlEOHYFy0COZrC3sqW7ZA//vfw9ypE4wrVljajhsHZc8eGN98E+ahQ4F9++Dx+ONQTpyocyjMiiJmmGvcWCSKHh7iBD4/H9Dra1Y+8vNF5SM4GPD3F/sMBiArS6xfFB1taVtQAJSWAk2awOzvj7Lycvh6eUE5f168XosWlrYXLogFYAMDRcIKiCF96lo9LVqIxwAieSsqEm2bNLn2JszA6dOWtjqdpW1hoYi1aVPL6506JW6josT7BUS7S5cAPz8gJMTS9swZMTtcZKRIKAHx+hcvWo5ZXh6Uq1frHltAxFlWJoZn+vkB6pf0RqOIW68X701RxO9LSgAfH/G43FzR9upVS1v1valtvb3FccjJuXlbLy9xHLKzr9+2cWPxuXl4AOHhwPnzlnhNpvrb6vVAs2aWz6u+tr6+4jjodOJYnj1bs61OB+j1qPDzgzJ7NnRPPVXneDpSUVERQkJCUFhYeNNzfQ8HxVSvyspK7NmzB9OmTavap9PpMHjwYGzbtq3ex2zbtg1paWk19g0bNgyrVq2qt/3s2bMxa9asOvu/++47+Fb/dkmi9PR02SGQk2BfIWuwn5C1nLKvGI0Ie/55NN+2DT+dOQNzVhYAoP2GDeh06RKyjh/H3mpD5of/8gu8SkuxOSMDJUePAgBabt2KuOPHkRsSgl3V2g755Rf4FhTgx/XrUXit2hS1ZQvijx9Hvp8ftlVrO+jQIfifO4ftGzbgwrUT5GbbtqHXsWO46OGBLdXa3nngAIJ+/RW7Nm5EXnExACB03z70OXYMhUYjNlVr23f/foQcO4a9mzcj+9oJevCTT6L/Sy/B4OuLsrAw+OblwbOsDIrZLE6Gi4vFVp3BABw/Xvf45eZaTtKrq69tXh6UvDz4WdO2oEBstdWTwDWo7cWLYqutvkkkLl0SW21q8lTd5ctiuw4FEAkWII5lfZMrGI01X1ttW/uzUNuq7au3LSmxvm1p6Y3bqu/HYKj/PV+vbX3Hsnpb9TgAwK+/1m1rMgEmE3wuXcKBnTvxa0RE3TYOVNaAoY5SK0JZWVmIjIzE1q1bkZSUVLX/+eefx6ZNm7Bjx446j/Hy8sLHH3+MMWPGVO179913MWvWLOTW8w+bFSFyBewrZA32E7KWS/YV9eTa3198a32NsmMHYDTCHBdnGV6XkwPl119hbtIE6NTJ0nb3bqCyEuZu3cQ38YBIBo4fhzkwELjjDsvr7d0Lpbwc5i5dRAXgWgzKkSMw+/sD3bpZ2u7bB6WsDOZOnURFBgAuXYJy6BDMvr5A9+6Wtj/9BKW4GOaOHS3VjcJCKAcOAI0awRwfL/ZduQJs2QKloADmqCgxE5rRCKWoSKw/5OkJdO5sed5Tp8QJbWSk5XmvXAGOHBHf/ld/b2fOiBPliAhcDQ7Grl270DM2Fh5qAqS+N7NZVB0uXBATWagnwFevAocOiftdu1oqQllZojIVGiqqEIA4iVYvb7jjDhHLtc8IeXmiClLt88T+/eK2UydLlScvT7Rv0qRmZevgQfH8HTqI6su1zwhZWeIza9nS0vbQIRF3u3bi5/JycXzUPtW0qXicWsEyGIDmzUUFCBB9q7hYVFrCwsTxNptF9aSyUrxftf+pVR5fX3HM1OTi3DmgokLsa9xY7PP3F8/r4yPe27FjlmNZXi5eS63wVa8excQA1xJ/ZGeLik5oqOgn1dt6eIhK6eHDYn9urtjftKmlwqe21emAjh0tn21enogtOBjGgADsy8tD7NNPw6P6cZXAaSpCjuDt7Q1vtfNX4+npqZk//lqKhbSNfYWswX5C1nKpvtKsmeXkurp+/erui46uecKsqvalbJXIyJon4qrEROtj6NWr7r6wsPpnwbs2zK6GkBDgrrtq7vP0BIYPr9sWAAYPrrtvwID62w4dWv/+a8wGAy6WlEA/eDA8rvd61sZwPXffbX3b2sdBRltn0ZDPa9gw69sOGVJnl9lgQO6aNfBo2VL635SGvL7UyRJCQkKg1+vrVHJyc3MRcZ2yWkRERIPaExERERER1SY1EfLy8kJ8fDwyqq3uazKZkJGRUWOoXHVJSUk12gNijPP12hMREREREdUmfWhcWloakpOTkZCQgF69emH+/PkoLS1FSkoKAGDcuHGIjIzE7NmzAQCTJ0/GnXfeiTfeeAP33HMPli1bht27d+ODDz6Q+TaIiIiIiMiJSE+ERo8ejfz8fEyfPh05OTmIi4vD2rVrER4eDgA4c+YMdDpL4apPnz5YunQpXnzxRfz1r39Fu3btsGrVKnS5Ns0lERERERHRzUhPhAAgNTUVqamp9f5u48aNdfaNGjUKo0aNsnNURERERETkqqReI0RERERERCQDEyEiIiIiInI7TISIiIiIiMjtMBEiIiIiIiK3o4nJEhzJbDYDAIqKiiRHAhgMBpSVlaGoqEj6KrykbewrZA32E7IW+wpZi32FrKGlfqKe46vn/DfidolQcXExACA6OlpyJEREREREZA/FxcUIDAy8YRvFbE265EJMJhOysrLg7+8PRVGkxlJUVITo6GicPXsWAQEBUmMhbWNfIWuwn5C12FfIWuwrZA0t9ROz2Yzi4mI0b968xlqk9XG7ipBOp0NUVJTsMGoICAiQ3mnIObCvkDXYT8ha7CtkLfYVsoZW+snNKkEqTpZARERERERuh4kQERERERG5HSZCEnl7e2PGjBnw9vaWHQppHPsKWYP9hKzFvkLWYl8hazhrP3G7yRKIiIiIiIhYESIiIiIiIrfDRIiIiIiIiNwOEyEiIiIiInI7TISIiIiIiMjtMBGSZOHChYiJiYGPjw8SExOxc+dO2SGRBm3evBn33XcfmjdvDkVRsGrVKtkhkQbNnj0bPXv2hL+/P8LCwjBy5EgcOXJEdlikQe+99x66detWtehhUlISvv32W9lhkcbNmTMHiqJgypQpskMhjZk5cyYURamxdezYUXZYVmMiJMHy5cuRlpaGGTNmYO/evYiNjcWwYcOQl5cnOzTSmNLSUsTGxmLhwoWyQyEN27RpEyZOnIjt27cjPT0dBoMBQ4cORWlpqezQSGOioqIwZ84c7NmzB7t378agQYPwwAMP4Oeff5YdGmnUrl278P7776Nbt26yQyGNuuOOO5CdnV21bdmyRXZIVuP02RIkJiaiZ8+eWLBgAQDAZDIhOjoakyZNwtSpUyVHR1qlKApWrlyJkSNHyg6FNC4/Px9hYWHYtGkTBgwYIDsc0rjg4GC8/vrrGD9+vOxQSGNKSkrQo0cPvPvuu3j55ZcRFxeH+fPnyw6LNGTmzJlYtWoVMjMzZYdyS1gRcrDKykrs2bMHgwcPrtqn0+kwePBgbNu2TWJkROQqCgsLAYgTXKLrMRqNWLZsGUpLS5GUlCQ7HNKgiRMn4p577qlxzkJU27Fjx9C8eXO0bt0aY8eOxZkzZ2SHZDUP2QG4m4KCAhiNRoSHh9fYHx4ejsOHD0uKiohchclkwpQpU9C3b1906dJFdjikQQcOHEBSUhKuXLkCPz8/rFy5Ep07d5YdFmnMsmXLsHfvXuzatUt2KKRhiYmJWLJkCTp06IDs7GzMmjUL/fv3x8GDB+Hv7y87vJtiIkRE5EImTpyIgwcPOtUYbXKsDh06IDMzE4WFhVixYgWSk5OxadMmJkNU5ezZs5g8eTLS09Ph4+MjOxzSsOHDh1fd79atGxITE9GyZUt8/vnnTjHclomQg4WEhECv1yM3N7fG/tzcXEREREiKiohcQWpqKr755hts3rwZUVFRssMhjfLy8kLbtm0BAPHx8di1axfeeustvP/++5IjI63Ys2cP8vLy0KNHj6p9RqMRmzdvxoIFC1BRUQG9Xi8xQtKqoKAgtG/fHsePH5cdilV4jZCDeXl5IT4+HhkZGVX7TCYTMjIyOEabiG6J2WxGamoqVq5cifXr16NVq1ayQyInYjKZUFFRITsM0pC7774bBw4cQGZmZtWWkJCAsWPHIjMzk0kQXVdJSQlOnDiBZs2ayQ7FKqwISZCWlobk5GQkJCSgV69emD9/PkpLS5GSkiI7NNKYkpKSGt+qnDx5EpmZmQgODkaLFi0kRkZaMnHiRCxduhRfffUV/P39kZOTAwAIDAxEo0aNJEdHWjJt2jQMHz4cLVq0QHFxMZYuXYqNGzdi3bp1skMjDfH3969zjWHjxo3RtGlTXntINTz33HO477770LJlS2RlZWHGjBnQ6/UYM2aM7NCswkRIgtGjRyM/Px/Tp09HTk4O4uLisHbt2joTKBDt3r0bd911V9XPaWlpAIDk5GQsWbJEUlSkNe+99x4AYODAgTX2f/TRR3jyyScdHxBpVl5eHsaNG4fs7GwEBgaiW7duWLduHYYMGSI7NCJyQufOncOYMWNw4cIFhIaGol+/fti+fTtCQ0Nlh2YVriNERERERERuh9cIERERERGR22EiREREREREboeJEBERERERuR0mQkRERERE5HaYCBERERERkdthIkRERERERG6HiRAREREREbkdJkJEREREROR2mAgREZHmPPnkkxg5cqTDX3fJkiVQFAWKomDKlClV+2NiYjB//vwbPlZ9XFBQkF1jJCIi2/CQHQAREbkXRVFu+PsZM2bgrbfegtlsdlBENQUEBODIkSNo3Lhxgx6XnZ2N5cuXY8aMGXaKjIiIbImJEBEROVR2dnbV/eXLl2P69Ok4cuRI1T4/Pz/4+fnJCA2ASNQiIiIa/LiIiAgEaUVxTwAAAvJJREFUBgbaISIiIrIHDo0jIiKHioiIqNoCAwOrEg918/PzqzM0buDAgZg0aRKmTJmCJk2aIDw8HB9++CFKS0uRkpICf39/tG3bFt9++22N1zp48CCGDx8OPz8/hIeH44knnkBBQcEtxV1WVoannnoK/v7+aNGiBT744IPbOQxERCQZEyEiInIKH3/8MUJCQrBz505MmjQJzzzzDEaNGoU+ffpg7969GDp0KJ544gmUlZUBAC5fvoxBgwahe/fu2L17N9auXYvc3Fw8+uijt/T6b7zxBhISErBv3z48++yzeOaZZ2pUsoiIyLkwESIiIqcQGxuLF198Ee3atcO0adPg4+ODkJAQTJgwAe3atcP06dNx4cIF7N+/HwCwYMECdO/eHa+88go6duyI7t27Y/HixdiwYQOOHj3a4NcfMWIEnn32WbRt2xYvvPACQkJCsGHDBlu/TSIichBeI0RERE6hW7duVff1ej2aNm2Krl27Vu0LDw8HAOTl5QEAfvrpJ2zYsKHe641OnDiB9u3b3/Lrq8P51NciIiLnw0SIiIicgqenZ42fFUWpsU+djc5kMgEASkpKcN999+HVV1+t81zNmjWzyeurr0VERM6HiRAREbmkHj164Msvv0RMTAw8PPjfHRER1cRrhIiIyCVNnDgRFy9exJgxY7Br1y6cOHEC69atQ0pKCoxGo+zwiIhIMiZCRETkkpo3b44ff/wRRqMRQ4cORdeuXTFlyhQEBQVBp+N/f0RE7k4xy1q6m4iISGOWLFmCKVOm4PLly1IeT0REjsOvxIiIiKopLCyEn58fXnjhhQY9zs/PD3/4wx/sFBUREdkaK0JERETXFBcXIzc3FwAQFBSEkJAQqx97/PhxAGJq71atWtklPiIish0mQkRERERE5HY4NI6IiIiIiNwOEyEiIiIiInI7TISIiIiIiMjtMBEiIiIiIiK3w0SIiIiIiIjcDhMhIiIiIiJyO0yEiIiIiIjI7TARIiIiIiIit/P/ZAKZ8STAvyEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Simulation were initial value of biomass VX_0 is varied\n",
        "newplot(plotType='TimeSeries')\n",
        "for value in [1.0, 0.7, 1.3]: init(VX_start=value); simu(5)\n",
        "\n",
        "# Restore default value of VX_start\n",
        "init(VX_start=1.0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mtWJ3rfbJaVN",
        "outputId": "d9b4f774-39fe-4c2f-a2a5-9b6aff964f61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Simulation were initial value of substrate VS_0 is varied\n",
        "newplot(plotType='TimeSeries')\n",
        "for value in [10, 7, 13]: init(VS_start=value); simu(5)\n",
        "\n",
        "# Restore default value of VS_start\n",
        "init(VS_start=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GHTk1-sCLR1T",
        "outputId": "c58a0bb3-81e0-420b-fc87-add121094706",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 984.252x787.402 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Simulation where metabolism is changed after 3 hours\n",
        "newplot(plotType='TimeSeries')\n",
        "simu(5)\n",
        "\n",
        "simu(3)\n",
        "par(Y=0.4, qSmax=1.0/(0.4/0.5)); simu(2, 'cont')\n",
        "\n",
        "# Restore default value of Y and qSmax\n",
        "par(Y=0.5, qSmax=1.0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IOLRVcsFJ5NX",
        "outputId": "d38ce440-e568-43d3-b861-a678aa399419",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Y : 0.4\n",
            "qSmax : 1.25\n",
            "Ks : 0.1\n"
          ]
        }
      ],
      "source": [
        "disp('culture')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yjcgKrvpJ96A",
        "outputId": "d5585694-c498-4b37-880b-da5b766f6237",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cell specific growth rate variable : 0.0 [ 1/h ]\n"
          ]
        }
      ],
      "source": [
        "describe('mu')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sLF6NdXvKBY4",
        "outputId": "f1203fb3-127b-46d6-c677-194803efd5fa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['bioreactor', 'bioreactor.culture']\n"
          ]
        }
      ],
      "source": [
        "describe('parts')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cZKKtkydKHH-",
        "outputId": "6c8f97ce-488b-4f0e-d91e-344dea3e1b19",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSL: 3.2.3 - used components: none\n"
          ]
        }
      ],
      "source": [
        "describe('MSL')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D7lCWmdiKNLm",
        "outputId": "398b585e-7dc9-42ba-d078-2d6ab6bad586",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "System information\n",
            " -OS: Linux\n",
            " -Python: 3.10.12\n",
            " -Scipy: not installed in the notebook\n",
            " -PyFMI: 2.11.0\n",
            " -FMU by: OpenModelica Compiler OpenModelica 1.21.0\n",
            " -FMI: 2.0\n",
            " -Type: FMUModelME2\n",
            " -Name: BPL_TEST2.Batch\n",
            " -Generated: 2024-03-05T08:05:44Z\n",
            " -MSL: 3.2.3\n",
            " -Description: Bioprocess Library version 2.1.1\n",
            " -Interaction: FMU-explore version 0.9.9\n"
          ]
        }
      ],
      "source": [
        "system_info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vkthWnnrLR1U"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
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