[Car Racing] Proximal Policy Optimization (PPO).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kuds/rl-car-racing/blob/main/%5BCar%20Racing%5D%20Proximal%20Policy%20Optimization%20(PPO).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Proximal Policy Optimitization (PPO)"
      ],
      "metadata": {
        "id": "Nc5ukRcu_RK0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJfGFgyL4S84",
        "outputId": "99211089-c4ae-47f0-b224-354276631ceb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: swig in /usr/local/lib/python3.10/dist-packages (4.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install swig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74HCvC9D4thW",
        "outputId": "1eb974c5-8eb8-4b0e-93b7-5d2f700f029e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: stable_baselines3 in /usr/local/lib/python3.10/dist-packages (2.4.0)\n",
            "Requirement already satisfied: gymnasium[box2d] in /usr/local/lib/python3.10/dist-packages (1.0.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.20 in /usr/local/lib/python3.10/dist-packages (from stable_baselines3) (1.26.4)\n",
            "Requirement already satisfied: torch>=1.13 in /usr/local/lib/python3.10/dist-packages (from stable_baselines3) (2.5.1+cu121)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from stable_baselines3) (3.1.0)\n",
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            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from stable_baselines3) (3.8.0)\n",
            "Requirement already satisfied: typing-extensions>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (4.12.2)\n",
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            "Requirement already satisfied: pygame>=2.1.3 in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (2.6.1)\n",
            "Requirement already satisfied: swig==4.* in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (4.3.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.13->stable_baselines3) (3.16.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.13->stable_baselines3) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13->stable_baselines3) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.13->stable_baselines3) (2024.10.0)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13->stable_baselines3) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.13->stable_baselines3) (1.3.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (4.55.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (1.4.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (24.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (11.0.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (3.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->stable_baselines3) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->stable_baselines3) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->stable_baselines3) (1.17.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13->stable_baselines3) (3.0.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install stable_baselines3 gymnasium[box2d]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "fApW3L294u-g"
      },
      "outputs": [],
      "source": [
        "import gymnasium\n",
        "from gymnasium.wrappers import RecordVideo\n",
        "from stable_baselines3 import PPO\n",
        "from stable_baselines3.common.env_util import make_vec_env\n",
        "from stable_baselines3.common.atari_wrappers import WarpFrame\n",
        "from stable_baselines3.common.vec_env import VecFrameStack, VecVideoRecorder\n",
        "from stable_baselines3.common.callbacks import BaseCallback, EvalCallback\n",
        "from stable_baselines3.common.evaluation import evaluate_policy\n",
        "from stable_baselines3.common.vec_env import VecTransposeImage\n",
        "import os\n",
        "import torch\n",
        "import numpy\n",
        "import platform\n",
        "import stable_baselines3\n",
        "import matplotlib\n",
        "import matplotlib.pyplot\n",
        "from platform import python_version\n",
        "from importlib.metadata import version"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Python Version: {platform.python_version()}\")\n",
        "print(f\"Torch Version: {version('torch')}\")\n",
        "print(f\"Is Cuda Available: {torch.cuda.is_available()}\")\n",
        "print(f\"Cuda Version: {torch.version.cuda}\")\n",
        "print(f\"Gymnasium Version: {version('gymnasium')}\")\n",
        "print(f\"Numpy Version: {version('numpy')}\")\n",
        "print(f\"Scipy Version: {version('scipy')}\")\n",
        "print(f\"Swig Version: {version('swig')}\")\n",
        "print(f\"Stable Baselines3 Version: {version('stable_baselines3')}\")\n",
        "print(f\"IPython Version: {version('ipython')}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2l9J4mI13mn",
        "outputId": "e8f51904-6e18-4e45-d82b-16e6a966a14d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python Version: 3.10.12\n",
            "Torch Version: 2.5.1+cu121\n",
            "Is Cuda Available: True\n",
            "Cuda Version: 12.1\n",
            "Gymnasium Version: 1.0.0\n",
            "Numpy Version: 1.26.4\n",
            "Scipy Version: 1.13.1\n",
            "Swig Version: 4.3.0\n",
            "Stable Baselines3 Version: 2.4.0\n",
            "IPython Version: 7.34.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "env = gymnasium.make('CarRacing-v3')\n",
        "print(\"Observation Space Size: \", env.observation_space.shape)\n",
        "print(\"Action Space Size: \", env.action_space.shape)\n",
        "env.close()"
      ],
      "metadata": {
        "id": "xL3nYK-TqB-W",
        "outputId": "b4f2c2f2-076b-449c-c887-59f9243089f8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Observation Space Size:  (96, 96, 3)\n",
            "Action Space Size:  (3,)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<frozen importlib._bootstrap>:241: DeprecationWarning: builtin type SwigPyPacked has no __module__ attribute\n",
            "<frozen importlib._bootstrap>:241: DeprecationWarning: builtin type SwigPyObject has no __module__ attribute\n",
            "<frozen importlib._bootstrap>:241: DeprecationWarning: builtin type swigvarlink has no __module__ attribute\n",
            "/usr/local/lib/python3.10/dist-packages/pygame/pkgdata.py:25: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n",
            "  from pkg_resources import resource_stream, resource_exists\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:3154: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:3154: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google.cloud')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:3154: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('sphinxcontrib')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "env_str = \"CarRacing-v3\"\n",
        "log_dir = \"./logs/{}\".format(env_str)\n",
        "gray_scale = True\n",
        "\n",
        "# If gray_scale True, convert obs to gray scale 84 x 84 image\n",
        "wrapper_class = WarpFrame if gray_scale else None"
      ],
      "metadata": {
        "id": "4CNv4MOyogr0",
        "outputId": "e32f6739-9402-4c20-d898-f991b86ba77d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "DLGv9DrP4zCo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "d6ffb0d573374597a2aaa718b6c1c643",
            "57e7cdc331084440b97d395bb03d3e0b"
          ]
        },
        "outputId": "5f57adc2-f36b-4f7b-e236-85cf451b000f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Output()"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d6ffb0d573374597a2aaa718b6c1c643"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=25000, episode_reward=-76.34 +/- 2.13\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=25000, episode_reward=-76.34 +/- 2.13\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 1000.00 +/- 0.00\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 1000.00 +/- 0.00\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "New best mean reward!\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">New best mean reward!\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=50000, episode_reward=-6.85 +/- 33.42\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=50000, episode_reward=-6.85 +/- 33.42\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 962.90 +/- 161.72\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 962.90 +/- 161.72\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "New best mean reward!\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">New best mean reward!\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=75000, episode_reward=57.75 +/- 84.01\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=75000, episode_reward=57.75 +/- 84.01\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 882.20 +/- 280.45\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 882.20 +/- 280.45\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "New best mean reward!\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">New best mean reward!\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=100000, episode_reward=349.67 +/- 123.28\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=100000, episode_reward=349.67 +/- 123.28\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 1000.00 +/- 0.00\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 1000.00 +/- 0.00\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "New best mean reward!\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">New best mean reward!\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=125000, episode_reward=187.87 +/- 113.74\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=125000, episode_reward=187.87 +/- 113.74\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 990.15 +/- 42.94\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 990.15 +/- 42.94\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=150000, episode_reward=263.07 +/- 122.29\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=150000, episode_reward=263.07 +/- 122.29\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 986.50 +/- 58.85\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 986.50 +/- 58.85\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=175000, episode_reward=241.62 +/- 192.34\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=175000, episode_reward=241.62 +/- 192.34\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 1000.00 +/- 0.00\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 1000.00 +/- 0.00\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=200000, episode_reward=234.28 +/- 174.34\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=200000, episode_reward=234.28 +/- 174.34\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 1000.00 +/- 0.00\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 1000.00 +/- 0.00\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=225000, episode_reward=311.44 +/- 228.22\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=225000, episode_reward=311.44 +/- 228.22\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 975.40 +/- 89.84\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 975.40 +/- 89.84\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=250000, episode_reward=436.83 +/- 243.13\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=250000, episode_reward=436.83 +/- 243.13\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 993.00 +/- 30.51\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 993.00 +/- 30.51\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "New best mean reward!\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">New best mean reward!\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=275000, episode_reward=495.19 +/- 280.76\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=275000, episode_reward=495.19 +/- 280.76\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 1000.00 +/- 0.00\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 1000.00 +/- 0.00\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "New best mean reward!\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">New best mean reward!\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=300000, episode_reward=674.57 +/- 251.61\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=300000, episode_reward=674.57 +/- 251.61\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 995.05 +/- 14.99\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 995.05 +/- 14.99\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "New best mean reward!\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">New best mean reward!\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=325000, episode_reward=638.02 +/- 281.53\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=325000, episode_reward=638.02 +/- 281.53\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 983.45 +/- 52.16\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 983.45 +/- 52.16\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=350000, episode_reward=807.90 +/- 199.40\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=350000, episode_reward=807.90 +/- 199.40\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 988.65 +/- 49.47\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 988.65 +/- 49.47\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "New best mean reward!\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">New best mean reward!\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=375000, episode_reward=810.91 +/- 148.03\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=375000, episode_reward=810.91 +/- 148.03\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 934.15 +/- 90.04\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 934.15 +/- 90.04\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "New best mean reward!\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">New best mean reward!\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=400000, episode_reward=883.90 +/- 43.98\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=400000, episode_reward=883.90 +/- 43.98\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 961.45 +/- 56.67\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 961.45 +/- 56.67\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "New best mean reward!\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">New best mean reward!\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=425000, episode_reward=873.87 +/- 31.53\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=425000, episode_reward=873.87 +/- 31.53\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 981.80 +/- 37.85\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 981.80 +/- 37.85\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=450000, episode_reward=876.24 +/- 72.47\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=450000, episode_reward=876.24 +/- 72.47\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 942.65 +/- 68.49\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 942.65 +/- 68.49\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=475000, episode_reward=845.40 +/- 136.14\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=475000, episode_reward=845.40 +/- 136.14\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 963.45 +/- 52.69\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 963.45 +/- 52.69\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=500000, episode_reward=510.43 +/- 329.19\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=500000, episode_reward=510.43 +/- 329.19\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 713.85 +/- 220.66\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 713.85 +/- 220.66\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=525000, episode_reward=682.63 +/- 353.18\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=525000, episode_reward=682.63 +/- 353.18\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 822.85 +/- 248.67\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 822.85 +/- 248.67\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=550000, episode_reward=800.99 +/- 175.63\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=550000, episode_reward=800.99 +/- 175.63\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 932.15 +/- 94.13\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 932.15 +/- 94.13\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=575000, episode_reward=359.30 +/- 168.00\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=575000, episode_reward=359.30 +/- 168.00\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 984.95 +/- 65.60\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 984.95 +/- 65.60\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=600000, episode_reward=680.51 +/- 219.28\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=600000, episode_reward=680.51 +/- 219.28\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 990.00 +/- 37.28\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 990.00 +/- 37.28\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=625000, episode_reward=812.42 +/- 85.00\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=625000, episode_reward=812.42 +/- 85.00\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 1000.00 +/- 0.00\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 1000.00 +/- 0.00\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=650000, episode_reward=791.34 +/- 109.78\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=650000, episode_reward=791.34 +/- 109.78\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 1000.00 +/- 0.00\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 1000.00 +/- 0.00\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=675000, episode_reward=806.51 +/- 71.06\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=675000, episode_reward=806.51 +/- 71.06\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 1000.00 +/- 0.00\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 1000.00 +/- 0.00\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=700000, episode_reward=822.35 +/- 191.25\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=700000, episode_reward=822.35 +/- 191.25\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 985.30 +/- 45.32\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 985.30 +/- 45.32\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=725000, episode_reward=762.92 +/- 260.81\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=725000, episode_reward=762.92 +/- 260.81\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 986.65 +/- 58.19\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 986.65 +/- 58.19\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eval num_timesteps=750000, episode_reward=870.58 +/- 47.73\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Eval num_timesteps=750000, episode_reward=870.58 +/- 47.73\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Episode length: 969.75 +/- 72.77\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Episode length: 969.75 +/- 72.77\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean reward: 830.99 +/- 194.60\n"
          ]
        }
      ],
      "source": [
        "# Create Training CarRacing environment\n",
        "env = make_vec_env(env_str, n_envs=1, wrapper_class=wrapper_class)\n",
        "env = VecFrameStack(env, n_stack=4)\n",
        "env = VecTransposeImage(env)\n",
        "\n",
        "# Create Evaluation CarRacing environment\n",
        "env_val = make_vec_env(env_str, n_envs=1, wrapper_class=wrapper_class)\n",
        "env_val = VecFrameStack(env_val, n_stack=4)\n",
        "env_val = VecTransposeImage(env_val)\n",
        "\n",
        "# Create Evaluation Callback\n",
        "# eval_freq - can cause learning instability if set to low\n",
        "eval_callback = EvalCallback(env_val,\n",
        "                             best_model_save_path=log_dir,\n",
        "                             log_path=log_dir,\n",
        "                             eval_freq=25_000,\n",
        "                             render=False,\n",
        "                             n_eval_episodes=20)\n",
        "\n",
        "# Initialize PPO\n",
        "# ent_coef - encourages exploration of other actins\n",
        "model = PPO('CnnPolicy', env, verbose=0, ent_coef=0.005)\n",
        "\n",
        "# Train the model\n",
        "model.learn(total_timesteps=750_000,\n",
        "            progress_bar=True,\n",
        "            callback=eval_callback)\n",
        "\n",
        "# Save the model\n",
        "model.save(os.path.join(log_dir, \"ppo_car_racing\"))\n",
        "\n",
        "mean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=20)\n",
        "print(f\"Mean reward: {mean_reward:.2f} +/- {std_reward:.2f}\")\n",
        "\n",
        "env.close()\n",
        "env_val.close()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Evaluation CarRacing environment\n",
        "env = make_vec_env(env_str, n_envs=1, seed=0, wrapper_class=WarpFrame)\n",
        "env = VecFrameStack(env, n_stack=4)\n",
        "env = VecTransposeImage(env)\n",
        "\n",
        "# Load the best model\n",
        "best_model_path = os.path.join(log_dir, \"best_model.zip\")\n",
        "best_model = PPO.load(best_model_path, env=env)\n",
        "\n",
        "mean_reward, std_reward = evaluate_policy(best_model, env, n_eval_episodes=20)\n",
        "print(f\"Best Model - Mean reward: {mean_reward:.2f} +/- {std_reward:.2f}\")\n",
        "\n",
        "# Record video of the best model playing CarRacing\n",
        "env = VecVideoRecorder(env, \"./videos/\",\n",
        "                       video_length=10000,\n",
        "                       record_video_trigger=lambda x: x == 0,\n",
        "                       name_prefix=\"best_model_car_racing_ppo\")\n",
        "\n",
        "obs = env.reset()\n",
        "for _ in range(10000):\n",
        "    action, _states = best_model.predict(obs)\n",
        "    obs, rewards, dones, info = env.step(action)\n",
        "    env.render()\n",
        "    if dones:\n",
        "        break\n",
        "\n",
        "env.close()"
      ],
      "metadata": {
        "id": "Y749dFcTdYFh",
        "outputId": "975a5ae1-3ecc-4937-ebc9-b20ae128de5b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Model - Mean reward: 887.84 +/- 46.79\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/moviepy/config_defaults.py:1: DeprecationWarning: invalid escape sequence '\\P'\n",
            "  \"\"\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moviepy - Building video /content/videos/best_model_car_racing_ppo-step-0-to-step-10000.mp4.\n",
            "Moviepy - Writing video /content/videos/best_model_car_racing_ppo-step-0-to-step-10000.mp4\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready /content/videos/best_model_car_racing_ppo-step-0-to-step-10000.mp4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the evaluations.npz file\n",
        "data = numpy.load(os.path.join(log_dir, \"evaluations.npz\"))\n",
        "\n",
        "# Extract the relevant data\n",
        "timesteps = data['timesteps']\n",
        "results = data['results']\n",
        "\n",
        "# Calculate the mean and standard deviation of the results\n",
        "mean_results = numpy.mean(results, axis=1)\n",
        "std_results = numpy.std(results, axis=1)\n",
        "\n",
        "# Plot the results\n",
        "matplotlib.pyplot.figure()\n",
        "matplotlib.pyplot.plot(timesteps, mean_results)\n",
        "matplotlib.pyplot.fill_between(timesteps,\n",
        "                               mean_results - std_results,\n",
        "                               mean_results + std_results,\n",
        "                               alpha=0.3)\n",
        "\n",
        "matplotlib.pyplot.xlabel('Timesteps')\n",
        "matplotlib.pyplot.ylabel('Mean Reward')\n",
        "matplotlib.pyplot.title(f\"PPO Performance on {env_str}\")\n",
        "matplotlib.pyplot.show()"
      ],
      "metadata": {
        "id": "YY5Go4hk6GrM",
        "outputId": "95b12e8b-08fb-45ff-bd13-e0e43983839c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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utlgssFgsgX9LJJLAG5HGxka8+uqruPLKKwH4tstKS0sDNYTBGhsbAzVdl19+OU4//XScc8452LBhQ8wAj+QOCojIiHX88cfH1Xtn2rRpWLhwYcTPT5o0Cdu2bUNTU9OQFzo//5P15MmTY95faWlpxPvzZykuvfTSiC/e06dPD/l3PNmhZEW67XBZo2iXBwc9N9xwA5577jncdNNNmDt3bqBx5JIlS8JmaeK5zVj8t/voo49i5syZYa/jr7NKhWTX3NjYiG3btsHlcsU8jm40GnHyySdDq9Xivvvuw9ixY6FUKrFlyxb88pe/HPKzjPY4KSgoCHlMnnTSSTjmmGPw//7f/8OTTz4ZuPzCCy/El19+idtvvx0zZ86ERqOBIAg444wzUtazKBW/b7/f//73+O1vfxv4d0NDQ6B4/6KLLsKDDz6Inp4eFBYW4t1338VPfvKTkDdDkfzoRz/CNddcg71794Z9g0FyEwVEhMRw9tln4+WXX8aLL74YcsLHz2w245133kFjY2PCBdWDlZWVobCwEF6vN2qQluxtq9Vq7NmzZ8jndu/eDZ7nh2R40uH111/H0qVL8Yc//CFwmcPhSPrUztixYwEA3377bcSfv/86Wq02qZ9rQ0MDtm/fDkEQQrJE/m0p/6m+4TrnnHOwfv16vPHGGyHbc+GsXr0avb29ePPNN7FgwYLA5bFOUcZj+vTpuPTSS/HMM8/gtttuQ319Pfr6+vDJJ5/gt7/9Le65557Adf3ZN7+ysjJotVp8++23w15HJA0NDdi1axcYYyEZmv3794dc7/LLL8e8efMC/w4OCi+66CL89re/xRtvvIGKigqYzeaQbeBo7HY7gPAZTZK7qIaIkBh+9KMfYfLkyfjd736Hr7/+OuRzgiDg2muvRV9fH+69995h35dEIsEFF1wQOOo/WLjj5onc9umnn4533nkn5Ih7Z2cnXnrpJcybNw9arTbp209kHYPf7f/pT38aclQ8XqeffjoKCwvx8MMPw+FwhHzOfz+zZ8/G2LFj8fvf/z5kC8Uv1s918eLF6OjoCDnZ5vF48Kc//QkajQYnn3xyUmsf7Oc//zmqqqpw6623Yu/evUM+39XVhQceeADA0UxK8M/S5XLhL3/5S0rWcscdd8DtduOxxx6LeH8A8Pjjj4f8m+d5nHfeeXjvvfeG/L2E+/pkLFq0CK2trXj33XcDlzkcDvztb38Lud6YMWOwcOHCwMdJJ50U+NykSZMwbdo0vPrqq3j11VdRVVUVElgCQ7enAV/d24svvgiVShVXRpjkDsoQERKDXC7H66+/jtNOOw3z5s0L6VT90ksvYcuWLbj11lvjfncZy+9+9zt89tlnmDNnDq666ipMnjwZBoMBW7ZswccffwyDwZD0bT/wwANYtWoV5s2bh+uuuw5SqRTPPPMMnE5n2F4v6XD22Wfjn//8J3Q6HSZPnoz169fj448/jtkOIRKtVos//vGP+NnPfobjjjsOF198MYqKivDNN9/AZrPhhRdeAM/z+Pvf/44zzzwTU6ZMwbJly1BTU4PW1lZ89tln0Gq1UccxXH311XjmmWfw05/+FJs3b8aoUaPw+uuvY926dXj88ccTKoSOpqioCG+99RYWL16MmTNnhnSq3rJlC15++WXMnTsXAHDiiSeiqKgIS5cuxY033giO4/DPf/4zJQEH4Nv+Xbx4Mf7+97/j17/+NUpKSrBgwQI88sgjcLvdqKmpwUcffRQ2I/XQQw/ho48+wsknn4yrr74akyZNQnt7O1577TWsXbs25Ch/Mq655hr8+c9/xk9+8hP84he/QFVVFVauXBmoeYq3rueiiy7CPffcA6VSiSuvvHJIjdg111wDs9mMBQsWoKamBh0dHVi5ciV2796NP/zhDyndZiUikI2jbYSkU6RO1YNF6lQdSVdXF7vlllvYuHHjmEKhYHq9ni1cuDDmUftg4TpVh9PZ2cmWL1/O6urqmEwmY5WVley0005jK1asiGv9kY7dM8bYli1b2KJFi5hGo2FqtZp973vfY19++WXIdaL9DMN1No72vQFgy5cvD/y7r6+PLVu2jJWWljKNRsMWLVrEdu/ezRoaGtjSpUtjriHccW3GGHv33XfZiSeeyFQqFdNqtez4449nL7/8csh1tm7dys4//3xWUlLCFAoFa2hoYBdeeCH75JNPhqx7sM7OzsC65XI5mzZtWkg7Acai/9wBsHvvvTfm/TDma4Vw8803swkTJjClUsnUajWbPXs2e/DBB5nJZApcb926deyEE05gKpWKVVdXszvuuCPQ6mDwcfZwv7NYn1u9enXIultaWtgPf/hDptfrmU6nYz/+8Y9ZW1tb2O/tyJEj7PLLL2dlZWVMoVCwMWPGsOXLlzOn08kYi3zsPtxali5dyhoaGkIuO3jwIDvrrLOYSqViZWVl7NZbb2VvvPEGAzCko3wk+/btCxynX7t27ZDPv/zyy2zhwoWsoqKCSaVSVlRUxBYuXMjeeeeduG6f5BaOsRS9nSCEEEKy6PHHH8fNN9+MlpaWIe01CImFAiJCCCE5x263D+mLNGvWLHi93rD1V4TEQjVEhBBCcs7555+P+vp6zJw5EyaTCf/617+we/durFy5MttLIzmKAiJCCCE5Z9GiRfj73/+OlStXwuv1YvLkyXjllVdoxhhJGm2ZEUIIISTvUR8iQgghhOQ9CogIIYQQkveohigOgiCgra0NhYWFNMiPEEIIyRGMMfT396O6ujrscO9gFBDFoa2tLSMzngghhBCSes3NzaitrY16HQqI4uBvy9/c3JyRWU+EEEIIGT6z2Yy6urq4xutQQBQH/zaZVqulgIgQQgjJMfGUu1BRNSGEEELyHgVEhBBCCMl7FBARQgghJO9RQEQIIYSQvEcBESGEEELyHgVEhBBCCMl7FBARQgghJO9RQEQIIYSQvEcBESGEEELyHgVEhBBCCMl7FBARQgghJO9RQEQIIYSQvEcBESGEEELyHgVEhBBCCMl7FBARQghJOaPNhWaDLdvLICRuFBARQghJOYPVhb2d/ejqd2R7KYTEhQIiQgghKWewusAYsLPVDJPdne3lEBITBUSEEEJSyiswmB3uwP9/02yE3eXN8qpIurg8AnosTlicnmwvZVik2V4AIYSQkaXP5oIgHP23yyNga3MfjhtVDJmE3ofnMofbi36HB/0ON/odHpgdbjjdvl+2Xi3DsaOKs7zC5FFARAghJKX6rK4hl9mcXmxvMWJWXRF4nsvCqkiiHG4vzA43zPajAZDLI0S8vtHmhsHqQnGBPIOrTJ2shuqff/45zjnnHFRXV4PjOLz99tshn2eM4Z577kFVVRVUKhUWLlyIffv2hVzHYDDgkksugVarhV6vx5VXXgmLxRJyne3bt2P+/PlQKpWoq6vDI488ku5vjRBC8lZvmIAIAPqsbuxqN2d4NSRedpcX+7v6saWpD2v2dmPtvh5sbzbhcI8VvRZX1GDI71CPNQMrTY+sBkRWqxUzZszAU089FfbzjzzyCJ588kn89a9/xYYNG1BQUIBFixbB4Th6auGSSy7Bzp07sWrVKvznP//B559/jquvvjrwebPZjNNPPx0NDQ3YvHkzHn30UfzmN7/BihUr0v79EUJIvnF5BFgckWtJOkwO7O+yRPw8yZ4mgw2He2wwWFxwxxH8hNNndcFoCx8Qix3HGGPZXgQAcByHt956C+eddx4AX3aouroat956K2677TYAgMlkQkVFBZ5//nksWbIE3333HSZPnoxNmzbh2GOPBQB8+OGHWLx4MVpaWlBdXY2nn34av/rVr9DR0QG53JfGu/POO/H2229j9+7dca3NbDZDp9PBZDJBq9Wm/psnhETlFRgcbi8KFLTLL3adZgd2tJhiXm9StRY1elUGVkTiIQgMn+/rhsc7/JCgWCPHMfVFKVjV8CXy+i3a6rZDhw6ho6MDCxcuDFym0+kwZ84crF+/HgCwfv166PX6QDAEAAsXLgTP89iwYUPgOgsWLAgEQwCwaNEi7NmzB319fRn6bgghw3G414qvj/TR8e0cYIiwXTbY7nYzeizONK+GxKvb4kxJMAQABosrJ/9WRRsQdXR0AAAqKipCLq+oqAh8rqOjA+Xl5SGfl0qlKC4uDrlOuNsIvo/BnE4nzGZzyAchJDvsLi+O9Frh9gjY0tQXtmCXiEe8vx/GgB2tJvQ7cu+FcyRqM9pTenu5WEsk2oAomx5++GHodLrAR11dXbaXREje2tfVHzjC7fUybG3uQ3c/ZRbEyOH2wpZAvyGvl2FbsxEON/UoyiaH2xt3Zi9ePf3OQC+qXCHagKiyshIA0NnZGXJ5Z2dn4HOVlZXo6uoK+bzH44HBYAi5TrjbCL6Pwe666y6YTKbAR3Nz8/C/IUJIwnotTnSZQ4MfQQB2tBrRYaKREGIT6XRZNE63gG3NRni8yRXxkuHrMDmQjmriwzmWJRJtQDR69GhUVlbik08+CVxmNpuxYcMGzJ07FwAwd+5cGI1GbN68OXCdTz/9FIIgYM6cOYHrfP7553C7j0aqq1atwsSJE1FUFL7oS6FQQKvVhnwQQjKLMYa9neFPIwkCsLPNhJY+Gh4qJsluZ1ocHmxvNUEkZ3zyTqq3y/y6zLnVvTqrAZHFYsG2bduwbds2AL5C6m3btqGpqQkcx+Gmm27CAw88gHfffRc7duzA5Zdfjurq6sBJtEmTJuGMM87AVVddhY0bN2LdunW4/vrrsWTJElRXVwMALr74Ysjlclx55ZXYuXMnXn31VTzxxBO45ZZbsvRdE0Li0WywwxrlyZQxYHd7P4705ta70JFsONsuBouLehRlgdHmSmibM1G5lCXK6hnWr7/+Gt/73vcC//YHKUuXLsXzzz+PO+64A1arFVdffTWMRiPmzZuHDz/8EEqlMvA1K1euxPXXX4/TTjsNPM/jggsuwJNPPhn4vE6nw0cffYTly5dj9uzZKC0txT333BPSq4gQIi5OjxcHe+LrVbOv0wK3l2FcuSbNqyLRWJzRuxjHo93ogEomwZgy+l1mSpsxvVvPnWYHRpcW5ETLDNH0IRIz6kNESGbtajMnnMavLVahsZL+Pv0EgaG5z4b6YjU4Lv2jMpp6bdjb2Z+S25pSo0WVjnoUpZt3oPeQN0XH7SOp1CkxtUaX1vuIZET0ISKE5CeT3Z1UTUOLwY5vqQ4FgK+W56tDvdjXaUGfLTMnfQwp7E78Xbs5Z7sd55KufkfagyHAlyWyucRfS0QBESFEVIaTZegwObC9xQRByM+gyO0V8F27GZuP9MHm9NWFtJvSUzAbjDGGvhQGMIIAdFFrhbRL93aZH2PA4R7xH4CggIgQIhptRjtMw8xodPc7sTUPj3F3mR346mAvWvtCA6Cufie8aQ4QTXZ3yjMNTnd+/f4yze7yZrTJaYfZLvp+UxQQEUJEweMVUjb0s8/qwtZmI9x5EBQ53F5802zE9hZT2CDC62Vpb2SZ6qZ+AODwiPvFM9e1ZSBzGEwQxN+9mgIiQogoHOyxDvuUUjCTzY3NR/rgHKEvrIwxNBtsWH+wN2bAk+5ts1Rul/mJPZuQ67LR2LTdJO4sEQVEhJCsszo9aWmyaHF4sPlwH+xp7LOSDRanB18f6cOejv64tqoMVlfaAkOvwNIyyNPlEfK2FizdDFZXVv4mBAE40iveWiIKiAghWben8+i8slSzubzYeNiQtm68mSQIDAe6Ldh4qDehWivGgE5TerbNjDZXWn53jAHOFGYMyVHZ/FtoM9pFm7WlgIgQklVd/Q4YLOkt7nR7BOxqM+Prw4acna5utPmO0h/qtiYVgKRr2ywd9UN+Yn3hzGVur5DV4chegaFJpFkiCogIIVkjCAz7IswrSwejzY2NhwzY09GfM6fQ/Efpvz589Ch9MvodnrTMlUpnQOSgk2Yp12l2pP3UYSwtffaU1gumCgVEhJCsOdxrzXgtA2NAs8GGLw/0ZqRHz3CY7O6wR+mT1ZHi79flEdI6vFPMBbi5qj0LxdSDeQWGJoP4skQUEBFCssLh9ma1wNLlEbCz1YzNRwyinMjdaXZgy5G+lPbjaTc5UtrJ22hzIZ2NwenofWpZnZ5h9/lKleY+m+jaYlBARAjJin2dlqyn7gGgz+rGhoO92Nspnm20g90W7Ggxpfzn43QLKR3l0Zvmxn60ZZZaYsqIer3iyxJRQEQIyTiD1YVOc/ZT936M+YaTrj/Ym5X+LH6CwPBtqwkHu9PXwC6VL4rp7nRMW2apwxgTxXZZsGaDTTRvQgAKiAghGcYYw56O1ExFTzWnW8C3rSZsPtIHa4a30ZweLzY39aU9IEvVKA+H2wtbmuu/KCBKnR6LS3TjUDxehuYU1celAgVEhJCMaumzZzzYSFSf1YUNh3qxL0PbaP0ONzYd6stIfUeqRnmk83SZn8fLRLGtOhKIabssWJOIskQUEBFCMsblEXCgO3PH7IfD31V3/cFetBrtaeua3N3vxNdH+jKaDUnFi2MmAiKAskSp4PII6LFkr/dQNG6PgFaRNE2VZnsBhJDcYHb4ZoMppRKo5BKoZL4PpZwP/L9UEv091v4uCzwpnoqebk63gO/azNjfZUGNXoXaIhWUMklKbrup14Z9Xf1pPakVjn+Uh0Ka/PeRyYCoQEEvVcPRaXakrRN8KhzptaG2SA0Jz2V1HfQoI4TEpdPkgNfLYPV6Im55SSWcLzgaCJiUQf/v9gqiTdvHw+0RcLjHiiaDFeWFStQVq6FTyZK6LUFg2N3Rn7URCv5RHvUl6qS+3uL0ZKyxnkOEDfxyjVgyMJG4PAJa++xJPx5ThQIiQkhcOs2xU+4eL0O/14N+h7hrhIZDEHyTwjtMDujUMtQXq1FeqADHxffu1u0VsL3FiD5rdvvBtJuSfwFK9+myYLRlNjz9DjcsOfD32GqkgIgQkgOMNhe9MIVhsrmxw2aCQsajrkiNmiIVZFG2Da1OD75pNqb9dFY8/KM8NElsR6W7/1AwsZ2MyjVtRnEdtY8klQ1Dk0VF1YSQmOLJDuUzp1vA/i4L1u7rwXft5rCdrw1WFzYdNogiGPJLZpQHYwx9tgxmiKhbddIEgaFDRP2+xI4yRISQqBhjomqiKGZegaG1z47WPjuKNXLUF6tRqlGgpc+GPR2ZL56OpcPkxLjywoS+xmz3wJvBwnjKTCavx+KEm2qw4kYBESEkqj6bW5STqcXOYHHBYHFBIeNFu+3jcHthsLpQXCCP+2sMGcwOAbRlNhxtIutMLXYUEBFCoqLs0PCI/QW93WRPLCCyZnb71CswuDwC5NLMVXj4e07xaTwGLggMLq8Aj8Dg8QpweQW4vQyCwFBWqBh2aweH24tekfYeEisKiAghETHG0JWCrsZEvLr6nWgUWFw9YLwCg8me+dNxTo83owHRwR4rDvdYwfMAz3GQ8AMfHAephIOE5yEJvnzgQ8pz4HkOHHynCd1eNvDfo//vGfhvtA7c+7r6UaFVoqGkIKmid8B3ElJsW7RiRwERISSiXquLahBGOP8oj0qdMuZ1jTZXVhr82d1eFCqT6/mUDH/dkiAAAljGm4kKAtBudKDd6ECJRo6GkoKEsngA0JbDPb+yhQIiQkhE2Zz8TjKn3WSPKyDK5OmyYJnedhTTScBeiwu9FhcKlVKMKi2Iq+eVyeaGzSme7yFXUEBECAlLEBi6qQYBJrsb7SY7avSqjGYpMineUR69liwFRBk+em9zia+RYb/Dgx0tJqjkEtQXq1GtV0Xc5hR7Z2qxooCIEBJWj9WZ0ePVYmRzefDQ+98FGhEWq+WoL1GjoVgd+K9OJYu7S7VYxTPKw+0VwvZXygRHBjNE/jofsbK7vNjT0Y8D3RbUFqlRV6wKCWS9AkNnP2V2k0EBESEkrE4TZYdWbmhCr9UFmYSD28tgsLlgsLmwrdkYuE6hUhoUIBUM9B6SpyxIYoxlJOCKNcqjz+rKWpFuJnsRiWm7LBqPlwVm61VqVWgoUaNAIUVXvyPv38gkiwIiQsgQXoGhJ8+3yzYc7MWGQwbwHHDb6RNRrVOhuc+GJoMNR3p9/2032dHv8ODbNjO+bTMHvlY9sK1RX6xGXbEaMgkHp1uA0yPA6fHC6RbgGPhv4DKPAIfbO/BvAc6B/1fKJFg8rRKnNpZDyqfvpFW/wze0N9Jk+Uz3HwqWyQyRPUcCIj9BANqMdrQZ7SgrVFAjy2GggIgQMkR3vzPqseCRrtfixL82NAEAzp5ejbFlGgDAhIpCTKg42tnZ5RHQYrShaSBAajLY0NJnh83lxe6Ofuzu6B/2WixOD/79dQvW7u/BxcfXo7FSO+zbjKTd5MC4ck3YzxmyVD8E+GqIMpUps+dwQNGdwy0yVu3qRN3AG4hsoYCIEDJEPjdjFASGv689BLvbi7FlBThrWlXE68qlPMaUajCm9GgQ4REEtBsdOGLwBUotRhsYAxQyHgqpBAopD6VUMvDvgctkvsvkUh7KoOsppDx2tpvx5pZWtBkd+P1He3H8qGL8+NhaFKkTO4Ydj44IAZHD7c3qVhJjCGTL0k2MBdUjXZPBhue+PIyVG5vw1V2nJdxiIFUoICKEhHB7BfRmuBuxmHy4swP7uixQSHn8bN6YuBoWBpPy/NF3uuOGv54F48twTH0R3t7aijV7u7HxsAHftBhx7oxqnDYptdtoDrcXfVYXiga9IBkyON0+Eqc7MwFRrm2ZjQTv72gHAJwxpTJrwRBA0+4JIYN09zuz0nxPDA73WvHOtjYAwMXH16OsUJHlFfloFFJcekID7j5rEsaUFsDpEfDa5hb89r1d+K7dHPsGEtAepveUGAKiTE29z+Uts1zUYXJg85E+AMB13xub1bVQQEQICdGRp9tlTo8Xf//iELyMYXZDEU4cW5LtJQ3RUFKAO89sxE9PHIVCpRTtJgf+sGov/rrmQMqCls5+x5D6sWw1ZAyWiWJhr8BEP3tupHn/23YwALPr9Wmtj4sHBUSEkACXR0CfCLIB2fDvr1vQYXZAr5LhsjkNou0txHMc5o0rxQM/mIpTJ5aD44Cvj/Th1+98iw++bYfHO7wXdK839IShxekRRZCQiZNmVD+UWb0WJzYcNAAAfjCzJsuroYCIEBKkqz8/B0JuazZizd5uAMAVJ42GRin+8soChRQXz6nHr8+ajHFlGjg9At7Y0orfvLcLO9tMw7rt4G0zsQTImcgQ0XZZZn24swNexjCpsjDi6cZMooCIEBKQj6fLTHY3Xlh/GADw/ckVmFyd3bR9ouqL1fjlGRNxxUm+bbQOswN//Hgfnh7GNlqvxRkYlyGG+iEgQwERFVRnjMnuxhf7egAAZ02PfJIzkyggIoQA8L3gGG3ubC8joxhjeP7Lw+h3eFBbpML5s7Kftk8Gx3E4cWwpHjxvKk5r9G2jbT7Sh7vf+RYbDxkSvj3GgC6zE4wxUdQPAb5j9+mWK12qR4JVuzrhERjGlBZgYlBvr2yigIgQAsD/ApjtVWTWZ3u6saPVBCnP4ar5YyCT5PZTolouxU+Or8c9Z0/G+HINXB4B//66Oanbajc5YLZ7RDPXy+URIKS5WShtmWWGxenBZ3u6APiyQ2Kp1xP/RjkhJCPybSBkm9GO1zb7goUfza5FjV4V8bo87ytmlvI8JDwHqYQb+DcHycCHlOfA86GXSXgOHDgc7Lag35G5gt26IjVuOHUcbnxlG4x2N+wuL1TyxHr4mO1uNPfZ0rTC5Dg8Xqjl6XvZoi2zzPh0dxecHgG1RSpMr9FlezkBFBARQmB3eWHKo+0yt1fA3744CLeXYUqVFqc2lke8Ls/7miNKh5E9KimQY29XP1oM9qRvI1FquRQ6lQwmuxsdZgdGlxYkfBsdYXoSZZPDLSANDboB+DqU0xyw9HO4vfj4u04AwOKp4skOAbRlRghB/hVTv72tFc19dmgUUiw7aRT4KE/KOpV8WMEQAPA8h8ZKLabV6iCRZO4FoFKrBCC+wCZZzjQ2Z3R4vHm3ZZwNa/Z2w+byoqJQgWMbirK9nBAUEBFC8qoZ4+4OMz7a6XuHunRuA/QxUg5lmtR1q67QKjFndDEKM3Ssv1I3EBCNkN9vOnsRUUF1+rm9Aj7a5fvbO3NqFfgEx+KkGwVEhOQ5q9MDSwbrW7LJ6vTgH2sPgQGYP64Us+pjv0MtLUztHo1aLsVxo4pRWxy5ZilVKrS+YG7kBETpC1qofij91u7vgcnuRrFajhPGFGd7OUNQDREheS5ftssYY/jXhiPos7lRXqjARcfVxfwatVySliJe/xZasVqOXe3mtJ3kqtL5gq6RsmWW1oAoj+uHei1OfNNiwnftZsyo02PeuNKU34dHEPDhtx0AgDOmVg57GzodKCAiJM+NlOxBLF8dNGDT4T7wHPCz+aPjmpxeksLtsnDKtUoUKmXY3mJMyyk0fw1Rp9kBQWCi26JIFG2ZpQZjDM0GO7Y292FbsxHNfUeL/bc2G6GU8jh2VGozOBsPGdBrdaFQKQ0bcIkhQKKAiJA81u9ww+Yc+S8E3f1OrNx4BABw7oxqjCmNb0xAqSZNR5qCqOQSHDeqGPu6LGg2pPaYe0mBHFKeg0dg6LW6UFaY3gAv3dI58X6kzzHzeAXs6ezHtmYjvmk2wRDUcJPjgHFlGqjlEnzTYsLf1x6CXi1P2TgNgTG8P5AdOn1yBeTSocGPOsG2EOlAAREheazT7Ix9pRzjFRjsbi/sLm/gv29ubYHDLWBsWQHOnBrfmAAJz6EoXWe8B+F5DhMrC1GklqV0C43nOVRolWg12tFhduR8QOT1Mri9QloaaKZqO+7T3V041GPFnNHFmFytjXqCMd1sLg92tJqwrdmIb1vNIduCcimPKVVazKzTY3qtDoVKGQSB4S+rD2BbixF//mw/7jqzERUDWcbh2NpkRIfJAbVcglMmhG9xUaDIfjiS/RVE4fV68Zvf/Ab/+te/0NHRgerqavz0pz/F3XffHehdwBjDvffei7/97W8wGo046aST8PTTT2P8+PGB2zEYDLjhhhvw3nvvged5XHDBBXjiiSeg0WR/mBwh2ZQr9UOtfXbs7jCHBDq2oIAn+L+RRjwoZTx+Nm8MJHFuGxUVyDO+xeTfQtvRaoLZnpq+UJUDAVG7yY5pImqClyynJ/UBkcPthZCC3bg+mwsvb2oCY8D6g70o0yiwYEIp5o0rRaFSNvw7iEOvxYltzUZsazZib6cF3qBeAlqlFDNq9ZhZr8ekSu2QTA3Pc7hq/mg8+tEeHO614clP9uGuMycNa9gxYwz/3dEOADi1sTxig9ACBWWIovq///s/PP3003jhhRcwZcoUfP3111i2bBl0Oh1uvPFGAMAjjzyCJ598Ei+88AJGjx6NX//611i0aBF27doFpdIX2V5yySVob2/HqlWr4Ha7sWzZMlx99dV46aWXsvntEZJVpoEOxmLn9gp45H+7YU1wrXIJD6WMh0ouQaFChnNmVCWUIcnEdlk4KrkExzYUYX+3BU29w99C8x+9HynZQIfbC02Kswmp+jtYt78HjAFFahkcbgHdFife2NKKd7a1YXZDEU6ZUIZx5ZqUNiMUBIaDPVZsbzFie6sJLX2hzT+rdErMrNNjZp0eo0sLYmasFDIJbjh1PB56/zt09jvx58/249bTJyQdhH7bZkaTwQa5lMdpURqgFqSxA3m8sr+CKL788kv84Ac/wFlnnQUAGDVqFF5++WVs3LgRgC/yfPzxx3H33XfjBz/4AQDgxRdfREVFBd5++20sWbIE3333HT788ENs2rQJxx57LADgT3/6ExYvXozf//73qK6uzs43R0iW5Up2aG9nP6wuL9RyCWbXF0Ell/g+ZL7/qmWh/1bJfB/DLdIsTXNBdTQ8z2FCRSH0ahl2tQ1vCy0XmzMKAsMRgw31xeohGb10nDSzpeA2Bcawdr9vevv5s2pxTL0emw73YfXeLhzutWHDIQM2HDKgRq/CyRPKMHdMScLjVPysTg++bTNhe4sJO9vMsDiP1j/564H8QVAyW146lQy/OG08Hv5gN/Z3W/DsukO4av6YpLb/3h/IDp08oSxiloznqYYophNPPBErVqzA3r17MWHCBHzzzTdYu3YtHnvsMQDAoUOH0NHRgYULFwa+RqfTYc6cOVi/fj2WLFmC9evXQ6/XB4IhAFi4cCF4nseGDRvwwx/+cMj9Op1OOJ1H302ZzeY0fpeEZEeuBETfNJsAALPri7D0xFEZuU+NUhrXKbR0Ky9Uwl3B8F1b8s9Budic8Y2tLfjfzk5cdkIDTp5QFvK5dJw0s6egoHp3ez96LC6oZBIc06CHQirBvPGlmDe+FId7rFi9txsbDxnQarTjpY1NeGNLC+aMLsYpE8pRX6KOetuMMbSZHL4sUIsJB7otCJ5zq5ZLMLVah2m1Okyt1qZke65ar8J1p4zF45/sw6bDfSjVtOKCY2oTuo29nf3Y12WBlOewaHJFxOupZFJRjPAQdUB05513wmw2o7GxERKJBF6vFw8++CAuueQSAEBHh69qvaIi9AddUVER+FxHRwfKy0PTdFKpFMXFxYHrDPbwww/jt7/9baq/HUJEo8/qgjONR5hThTGGbS1GAMCMOn3G7jdb22XhVOuUONJrTfo0oD9DZLK7YXN50jocNRVsLg9W7+kGABzusYYJiNKQIUrBltkX+31rPmFMMRTS0GB6VGkBflpagAuPrcX6A71Yvbcb7SYHPt/Xg8/39WBMaQFOnlCG40YVB+p6XB4BuzvM2NHqywT1Wl0ht1mtV2J6ja8gemyZJu7auERMqtJi6dwGPLvuMD74tgOlGsWQ30c0/tqhk8aVRu0IL4b6IUDkAdG///1vrFy5Ei+99BKmTJmCbdu24aabbkJ1dTWWLl2atvu96667cMsttwT+bTabUVcXu4kbIbkiVybbt/TZYbC6IJfwmFRVmLH7zeZ22WAcx2FcuQbbBzJliVLJJSFDXuNtOZAta/f3BArjDYOCACA988yGW0PU73Bja5MRADB/XOSAQS2X4rRJFTi1sRx7Oy1Ys7cbm5v6cLDHioM9Vrz6dTOOH1UMg82F3e39cHmPvmmR8hwaqwoDQVCmHqMnji1Fr8WFd75pw8oNR1BSIMfUOIrzD/dYsbPNDJ4DzphSGfW6YjhhBog8ILr99ttx5513YsmSJQCAadOm4ciRI3j44YexdOlSVFb6fsidnZ2oqjp6lLazsxMzZ84EAFRWVqKrqyvkdj0eDwwGQ+DrB1MoFFAoxPOESEgqMcZypsDWnx2aVFU45F13ukglHHSqzJwIild5oRJFBTb0WZM7eVapVcJkd6PT5BR1QCQIDJ/uPvp83WsLExClIbM53Bqirw4a4BEY6ovVMbe/AF+QO7GyEBMrC2Gyu7Fufw8+39eNHosLq/d2B65XpJZheq0e02p0mFRZCEWWtnHPnl6FbosTXx7oxdNrDuDOMxpRVxz9+/zvt77s0PGji2MeZhBDQTUg8oDIZrOB50MLIyUSCYSB85GjR49GZWUlPvnkk0AAZDabsWHDBlx77bUAgLlz58JoNGLz5s2YPXs2AODTTz+FIAiYM2dO5r4ZQkTCYHXBHeFouth802wEkNntspIChSjqGQYbV1aITVZDUl9bqVNiT2e/6OuItrUY0WNxBZpJGqwuMMZCfh+pbs7o8gjwDqNonTEW2C6bPz7xkRc6lQyLp1XhjCmV2NluxtamPpRoFJheo0NtkUoUj0WO43D5CQ0wWF3Y3dGPJz/1HccvLgi/DdZmtAcyZovj6Pslli2z7PfKjuKcc87Bgw8+iP/+9784fPgw3nrrLTz22GOBQmiO43DTTTfhgQcewLvvvosdO3bg8ssvR3V1Nc477zwAwKRJk3DGGWfgqquuwsaNG7Fu3Tpcf/31WLJkCZ0wI3kpV7JDRpsLhweOnc+o1WfsflM9zDVVdGoZyrXJZa5z5aTZx9/5JqF/b6Kv7tPlEWAdVDslCKndNhvudtnBHivajA7IJTzmjE5+3AXPc5hWo8Plc0fhrGlVqCtWiyIY8pNKeFx3ylhU65Tos7nx5Kf7Iv7s3h/IDh1Tr0e1PvoQY46DaOraRB0Q/elPf8KPfvQjXHfddZg0aRJuu+02XHPNNbj//vsD17njjjtwww034Oqrr8Zxxx0Hi8WCDz/8MNCDCABWrlyJxsZGnHbaaVi8eDHmzZuHFStWZONbIiSrBIGhK0fqh75p8dXMjC4tyNgWFsf5MkRiNa5cAz6JZ+1cOGnWZLBhb6cFPAd8f3IFtAPNAMPVEaXypJnNPbwTZl/s8x21n91QJJoX9nRRy6X4xWnjoVPJ0NJnx18/PwDPoI6W3f1ObDzky2QunhY7O6SUSdJSEJ4MUf/2CgsL8fjjj+Pxxx+PeB2O43Dffffhvvvui3id4uJiasJICIBeqyttk9VT7Rv/6bLazHVX1qpkYecsiYVaLkWNXp3wzLNcGPLqzw7NbihCcYEcxQVymB0eGGyuIXU5TrcXSFGQPJwMkcPtxabDvhf/ZLbLclGJRoEbvjcOj3y0BzvbzHhpQxMuO6EhkM36cGcHBAZMqdZiVElBzNsTS0E1IPIMESEktdqM9thXEgGn24vv2n29d2ZmtH5InNtlwUaXFkAiSSygGTzkVWxMdncgq7Bwkq+Nij9Tl/YM0TACoo2HDXB6BFRoFRifokGouWBUaQGunj8GHAd8vq8HHwwMbu2zubBuoDnlWXFkhwCgQAQNGf0oICIkTxhtLnT350b90K52M9xehlKNHDUxahBSqTQHhp/KpXxc77yD+Ye8AuLcNvt8bzc8AsPo0gKMLfMFFkUFvgxQr3XoYzaVhdX2YZwwWzuwXTZ/XJmo6n0yYWadHj85rh4A8ObWVmw8ZMBHuzrhERjGl2swoSK+NhlqyhARQjJtf5cl20uIm79+aHqtPmMvNHIpD22GBnAOV32xGgpZYk/f/jqidpO4soRur4DP9viO2i+cdLSJrv8EU9heRCLIELX02XCwxwoJx2Hu2JKUrSeXnNpYju8PZPSeXXcIawYaasZTO+SnEVHdFQVEhOSB7n4njLbUTE9PN4GxQP3QzAyeLisRUXfqWCQ8hzFliW3RHK0jEleW8OvDfTA7PNCrZJjdUBS4POqWWYoyRB6vkHQLCn8x9Yw6nej6VmXSj4/1zW3zCAwur4D6YjWmVmvj/nq1SI7cAxQQETLiMcZyKjt0qMeKfocHKpkEEyoyV5dRJqLu1PGo1ikTKkgVY4aIMYZV/qP2jeWQBh2hi5YhStX4jmS3y9xeAesP9gIAFoyPf5TFSMRzHK6cNxpjSn3buOfOqI47qyuX8pANcwhzKoknV0UISYt2kwNW5/CHV2aKvxnjlGrtsCfWx4vnEbHJnFj5R3r4f16xiDFDtL/LgiaDDTIJhwWDTmn5fx9GmxseQQgJllweISWn5ZI9YbalqQ82lxfFajkmV8WfDRmpFFIJbl80ET0WJ6p08df8iemEGUAZIkJGNEFgONhtzfYyEuKvH8rk6TKdSp6x4CuVygoVgeLjWAYPeRWDj7/z1Q6dMLpkyIT2QqUUUp4DA4Zs9zKGkDlfyUq2fsi/XXbSuBJRtjDIBpmETygYAsTTodov954BCCFxa+mzp2U6eLp09zvRarSD5xDXAMlUEdN0+0SNK4/vNI9/yCsgjpNmvRYntjT3ATh61D4Yz3Fp3zZLJiDq6ndgd0c/OADzxuVH76F0EcsMMz8KiAgZodxeAYd6cy07ZATg68isyWA6XUzT7ROlU8kCR+pjCWybmbK/bfbp7i4wBkyqLERNUfjMQvSAaPgZomRqiNYO9NmZXK1FSQ4/bsSAtswIIRlxpNeWM0Nc/fz1MJncLlPJJaJ7Yk7U2PKCuEZ6BAqrzdktrHa4vfhiILBYOHlodsgv3RmiRGuIvALDuv2+Yup86UydTmoRNWUEKCAiZERyuL0Jj3fINpvLg72dvtNwmRzmmkvH7SPxj/SIRSwZovUHemFzeVFeqMC0KFuj/oAoXHft4R69FwSW8JDYHa0mmOxuaBTSjLaEGImkEg5KGQVEhJA0O9RjhVfIjZllft+2muFlDFU6ZdxbQKmQy9tlweIZ6VElggyRwBg+2e0rpj6tsRx8lCPa6dwys7u9YAn+ifg7U584tiQni/DFRIxZWfqNEjLC2FyenJlZFuzoMFd9xu5TwnMoUud+hgiIb6SHP9DsMjshZClg3tlmRofZAZVMgpNiFCWXpHHLLNGCaqPNhe2tRgDiLaaW8JzotqEiEVtBNUABESEjzv4uS8LvfLPNIwjY0eo7bj+jLnOny4oK5JCMoGPTsUZ6iGHIq3+q/bxxpTG3TNJZQ5Ro/dCXB3ohMGBcmQbVGZyvFy+eB6bX6jA+zhli2Sa2I/cABUSEjCgmuxtdImq8F6/9XRbYXF5oFFKMLc1cd+pcmG6fiFgjPYKHvGajY3Wb0Y6dbWZw8M3BiqV4IHtnd3uH9E7yeNmwtoUTOWEmMBYoAhdjMTU30KaiRKNAWaEC2hwYJaKmDBEhJJ1yaURHsG0Dp8um1+oy2uiuLAem2ycq1kgP/0mzbPQi8tcOzazTx/WzV8gkgfYLqc4SJdKccm9nP7r7nVDKeBwbNG8tHJ4HZtTpY9ZzpYo/GCovPFp3N6o0doF9tmWyrUa8KCAiZITosTjRl6VtkOFgjAW6U2eyfqhAIRXdKZdU8I/0iKRqIEPUYcpsQGRxerD+gO/I+mmTYmeH/NK1bZbIltnne33ZoTmjS6CI8ZgpkEtRVqjA7IYiyKXpf4mdVKUdcgihvFCJQqX4Ag4/ngeUUbZ2s0V8KyKEJCVXs0PtJge6+52Q8hymJDAle7hyuTt1LNFGelRkKUP0xb5uuLwC6opUmJhAnUvUgCjJPluMsbiP7VscHmxp8nXUnh9HMbV/BIlWKcNxo4rTWuQ8sbIwYj3T6LLoBfbZpJZL4x4Am0kUEBEyAnSYHLA4xDGfKlH+7bLGqsKMZmxGynH7SCKN9KjMQobIKzB8trsbgG9MRyIvhv46olRmiBxuAUKcsdRXh3rhERjqilRoKIm9FRWcmVHJJTh2VDF06tTX9Iwr16CuOPJ6yguV0Ig0SyTG7TKAAiJCcp4gMBzozs3sEJCd4/ZSCQd9Gl6kxCTSSA9/QGR2eDI25HVrUx8MNhcKlVIcP7o4oa+N1pzRmWQvoni/b8ZYYJDr/PFlcQVy2kFDauVSHsfUF6W0Xm10WQFGlcbOAI2J4zrZINbWABQQEZLjWo32hI8Qi4XZ7sbBbt+8tUwGRMUFclGm7FOtIUxxbTaGvK4aOGp/yoQyyBJsaBh9yyy5x328J8wO9VrRarRDJuEwJ45AjuMQNisj4TlMr9VFnNmWiIYSNcZGOUkYrFwbvcA+WyhDRAhJOY9XwMGe3BrgGmx7qwkMvv45xRk8Aj/St8v8VBG2IP0dqzOxbXaox4oD3VZIeA6nTIy/mNrPP1ollVtm8b6B8Hemnt1QFFdgoZJLIva14jgOk6q0GBul4D2W2mJVwn2GxoiwlkhNAREhJNWOGHJvgGuwo9tlmWvGCIyM+WXxkEn4sENf/VtpmcgQ+RsxHj+qOJCZSoQ/UO6zuYZ0105+yyx2QORwe7HhkAEAMH9cWVy3O3i7LJzRpQWYXK1FognKKr0SjZWJHzooL1RALaImiBwHqEV6upMCIkJylNPjRVOODXAN5vYK2NlmBpDZ6fZalQwKqTifkNMh3PeaqcJqo82Frw/7TmglctQ+mE4pg4TjIDDAaHeHfM4rMLiSeEMQz5bZ14f74PQIKC9UYEJFfFmdeI+6V+tVCfUqqtAqMbkquROYHMdhTAabncaikkky2mssERQQEZKjDvfY4PXm2IyOIN+1m+HyCChSy1Af5bRMquVLdsgvXC+cqgwdvV+9pxtexjC+XBNzzlokPM8FWgiE2zZLdGI9EN+W2Rf7fafi5o8vjbverDCODJFfqSa+XkWlhQpMqdYOq+atQiueLJEYa5r8KCAiJAfZXV60GnM3OwQgpBljJguc86V+yE8R5gU3E0Ne3V4Bq/f6gopks0N+0QqrExnBAfi2wmKN/Gg12nGg2wqeA04cG/+ojkSbIcbqVVSskWN6zfC7t3Mch9EiOXFGAREhJKUOdFvi7qMiRowxfDPQf2hGBrfL5FI+qTqWXBYuA1FSIIdM4hvy2mNNz+y7DQcNsDg9KC6QY1Zd9HEXsUQLiBKtI4qnENtfTD2jVh/340UllyR8gs7/deF6FenVMsyo1adse6lSqxTFcXcxDnX1o4CIkBxjdrgzPnYh1Y4YbDDa3VBIeTRWZm46dyZPsolFuBoinucCs6/S8VhijAWO2p86sTziyat4RWvOmOiWWayCardXwPqDvhEjiQxyHc6ojMG9irQqGWbW6Yf9cwvGcVxcvYvSTYxDXf0oICIkx+TqiI5g/uzQlGptUu+qkzUSh7nGEqlGJZ11RE0GG1qNdsglfEqmw0efZ5ZYhihWQPRNsxEWpwdFahmmVMd/+jGR+qFw/L2KxpZrMKteD2ka/i6qdNnPEhWIIEsVCQVEhOQQk90NgyU7A1xtLk/KGkBmY5grx+Vrhij803w6T5r5f79TqrUpqRk52q166PZeor2IYl1//0DX92PqixLK0KRimKq/1iddbxI4jkNDFrNESpkkLYFeqoh3ZYSQIdpN9qzcr9Pjxd1vf4s73tiOrwa2E5JlsLrQZLCBAzA9g/2H9GpZRrNRYhEpIErnkNftKR7HUlLgy+xlIkPUbvT9PBLtKi3m6fLBqnVKqLKUpRHLSbdI8u/ZgZAcJQgMneb0FMDGsqejH2aHB3a3F39fewh/++Jg0nOw/NtlY8s0w95mSIT/RTXfRNwyS1OGyGhz4XCv7wTktBQFvP4MkdXlHZLhcXq8YCz+k3KxHrftAz8P/5ZiPBQyPmd6W2WzlkisIzv8KCAiJEf0WJ1Z60q9o9W3BVKpVYLngA2HDPjNe7uwt7M/4dsKdKeuy2x36tI8rB8CALmED9sVuSJNQ163DzxWRpcWpOxEn0ouCYwhGZwlYgxwxvl34fYK8ETp3eVwe2Gw+W6/Shd/hiiTgX0qVGmVUGahW3S265dioYCIkBzhT+VnGmMsEBD9aHYtfnlGI8o0ChisLjz60R68ubUFnjh7ADjcXuzu8AVRmawfUsokon93mi4cx4XNEqnkEuj9Q15TmCXa3uyvD0ttwJuKo/extsv8P4dCpTShx0uuPbZ4nsOoMIN/003sPycKiAjJAS6PELagNBM6zU70WFyQ8hwaKwsxtkyDe8+ZjBPHloAx4P0dHfjdB7vRGUctys42MzwCQ3mhIqEtieGqTcGU8Vwmj1A7VZniOiKXR8Cudt84lukpDnhL/AGRLfmp97EOBSSzXQYAWpW4X+jDqdapoJBlNgQQ85F7gAIiQnJCp9mRtUaM21uNAIDxFZpAml0pk+CKk0bj5wvGQC2X4HCvDb/9zy58vq87aj3H0e2yzHWnri9Ri6L/SjYpImyPVKS4jmh3hxkur4BitRx1KQ5Cox+9jzMginE9/6GF6gS2ywCgUJFbW2bAQJYoyXEqyZBJ+ZhjSrJN3KsjhAA4+s41G/zbZdNqhm6BHDuqGL85ZwoaKwvh8gh4cf0R/GXNAVgcQ2tSBIFhe0t6tlMiqdarMKEic40fxSpShijVvYj8x+2n1+pSHvCmohdRrFqptiQyRFIJl7VTW8NVo89clkjM/Yf8KCAiROSsTg/Mg6Z8Z4rD7cW+Tl9fluk1+rDXKS6Q45bvT8CPjqmFhOewtcmIe9/biZ1tppDrHei2wOL0QC2XYFx5+qdvV+qUmFRFwRCAiC96gV5EKQiIGGOB4/bpaKeQkgxRzC0zX4ZoJBdUB8tklkjMM8z8KCAiROSymR3a3dEPj8BQqpGjQhv5lBbPcThjaiV+deYkVOqUMNnd+OPH+/Dqpma4vb5379sGXiyn1egg5dP71FOWggnhI0nE5oy61A15be6zo8/mhlzKY1KVdli3Fc7R5ozhxnfElyGKtmXm8Qro7vfV6VXp488QaXOk/1AkNXpVRrayCkRePwRQQESI6GVzblnwdlk8wUV9iRq/PmsSvjexDACw6rtOPPj+d2g12jPWnbqoQB73evNFpBe84hQOefXXh02uSs84Fn9RdZ/VBWFQnVo8GSKvwKKeRuvsd0JggFLGB07fxSOXM0RA5rJEYh7q6kcBESEiZrC6Eh5NkCrBx+3D1Q9FopBKcMmcBtxw6jgUKqVo6bPj/v/sQofJAQnHYWpN6rMHfnq1byhmqiaEjxSRmgbyHJeywmp/w810dR/XqWXgOMAjMPQPqlFzeYSYGa54C6qrdKqEgulc6VAdTU1R+rNEtGVGCBmWbI3qAHwFpgar77j9xCQm0s+o1eM350zB1BotPAMvVhMqNWk7eluolGJGiieEjxSRtsyA1NQRmezuQHfq6QkEz4mQ8kczN2FnmsU4ep+ODtUSCSf6ZoPxkPAcGkrS15dIIuGy0ggyURQQESJSXoGhqz87vYcA4NuB7FBjZWHSYwl0Khl+cep4XHx8Pap1Spw+uTKVSwxQKySYVV+Ul7PK4hHplBmQmiGv/mLqUSVq6NXpG6BbHNg2G3rIINZJs5gF1cbEA6JChXTEbM3WFqkhS1OWKBfqhwAgN1ZJSB7q6nfAG2XMQLr5t8umDvMdP8dxOLWxHKc2lqdiWUOo5BIcU18k+h4n2cTzHGRSPuzol1Q0Z8xUfVhJgQIHuq1hM0TOmBkiOmEWjYTn0FCsxv4uS8pvO1eyaPQMQohIZfN0md3lxb6BJ8ZE6ocyTSHjcUx9UU6k47Mt4kmzYWaI3N6j3anTHRANpxdRtBoigR0dnJxQhmgE1A8FqylSpWXLWewjO/ziWuWsWbPiTgtu2bJlWAsihPhOzfSFedLPlO86zPAOjNjwF92KjUzqC4ZytSlepkXKoPkzRP4hr4nWeO3u6IfLI6BILUNdceq6U3Ocb3BrsOH0Ioq2ZdZrccHlFSDlOZRq4h8CPNICIpmER7lWkfK5ieocOGEGxBkQnXfeeYH/dzgc+Mtf/oLJkydj7ty5AICvvvoKO3fuxHXXXZeWRRKSbzpMjiEvBpm0oyXx02WZJJVwOKZenxMnV8QiUoZIKfMNeTXa3egwOTCmLLGmmUdPl6V2HItOJYPRFlorlGxAJAgs6uf922UVWmXcGRKez53amETU6tUpD4hGVIbo3nvvDfz/z372M9x44424//77h1ynubk5tasjJE9lc7uMMYZv28QbEEkkHGbVFY2o+o1MiHrSTKf0BUTmxAIiX3fq9IxjKdUoIgZE4ZozRtsyc3i8Ud9gJHPCrEAuHZHtHXRqGTRKadjxO8ngeUCVI1vaCdcQvfbaa7j88suHXH7ppZfijTfeSMmiCMlnZocbVmdqnoyS0WIc6Dgs4UU3B4znfXUqOjUFQ4mKdlIw2Tqilj47DDYX5BIejZWp6y/F874Gm4P5A6J+hweuQQXi0Y7dxy6oTuKE2QgOyGv0qdv6VMly5yRewgGRSqXCunXrhly+bt06KJWprzVobW3FpZdeipKSEqhUKkybNg1ff/114POMMdxzzz2oqqqCSqXCwoULsW/fvpDbMBgMuOSSS6DVaqHX63HllVfCYkl9JT0hqZDqdHWigo/bi+nkFs8D02r0gRdFkphov8tkT5r5u1NPqkrtY6VALg2bVSiQSwKZrj5baJbI62WBMTGDpWeGWW5sAyWjUhf/1mEsudCh2i/h3+hNN92Ea6+9Flu2bMHxxx8PANiwYQOeffZZ/PrXv07p4vr6+nDSSSfhe9/7Hj744AOUlZVh3759KCoqClznkUcewZNPPokXXngBo0ePxq9//WssWrQIu3btCgRol1xyCdrb27Fq1Sq43W4sW7YMV199NV566aWUrpeQ4WKMpWzyeLJSddw+1SZX6VBWGH/BKwkVV3PGBDNE29N03L5AIYVcykPCc/AGdaDmOA7FBXK0DzQNHVzw7/QIYXtRRTthxhg7miFKaIbZyM0QpbK4Opfq/BJe6Z133okxY8bgiSeewL/+9S8AwKRJk/Dcc8/hwgsvTOni/u///g91dXV47rnnApeNHj068P+MMTz++OO4++678YMf/AAA8OKLL6KiogJvv/02lixZgu+++w4ffvghNm3ahGOPPRYA8Kc//QmLFy/G73//e1RXV6d0zYQMR4/FFbZXTKbYXJ5AHxIx1Q/JpHwgi0GSE0+GqKvfCa/A4soOmOxuHOqxAkj9uA5/9kUh42FzhgYzwQHRYA63N2wBb7QtM9/pOi847mhgGAvHAZoRnCECfNtmKQmIcqjwPKEcp8fjwX333YcTTzwR69atg8FggMFgwLp161IeDAHAu+++i2OPPRY//vGPUV5ejlmzZuFvf/tb4POHDh1CR0cHFi5cGLhMp9Nhzpw5WL9+PQBg/fr10Ov1gWAIABYuXAie57Fhw4aw9+t0OmE2m0M+CMmEbI7qAIBd7WYIzPfCIKZszEjensiUaDVEIUNeLfF1R9/RYgID0JCG7tT+oCZcf6mSJE6aRRvb4f+bK9Uo4u50rpZLR/yIGL1anpLsTi5tmSUUEEmlUjzyyCPweDJT8Hnw4EE8/fTTGD9+PP73v//h2muvxY033ogXXngBANDR0QEAqKioCPm6ioqKwOc6OjpQXh7aIVcqlaK4uDhwncEefvhh6HS6wEddXV2qvzVChnB7hbhfjNJFrMftR/L2RKZIeA4SSfgX8eAhr51xbtl+02oEkJ5mjP4XYmWYIK4oiZNmUY/cJzOyI08C9Nqi4RVXc9wIzhABwGmnnYY1a9akYy1DCIKAY445Bg899BBmzZqFq6++GldddRX++te/pvV+77rrLphMpsAHtRMgmdBpdkDI3m7ZwHF7XzZUdAGRKneeVMUsVUNe3V4BuwYeK6neLpMGDQIN13Qz0V5EDrc36t9VMifM8iVAH25xtVImyanWBAk/y5x55pm48847sWPHDsyePRsFBQUhnz/33HNTtriqqipMnjw55LJJkyYFjvdXVvoGRXZ2dqKqqipwnc7OTsycOTNwna6urpDb8Hg8MBgMga8fTKFQQKEQz3YByQ/Z7D0EAM0GO0x2NxRSHuMrEmvOl2758gKUbgrp0Jocv8BJszgeh3s6+uH0CNCrZGgoTu2U9OAaIKVsaAAXbcss3DyzWCfM2uiEWUTDLa7OpYJqIImAyN+N+rHHHhvyOY7j4PVGf/Al4qSTTsKePXtCLtu7dy8aGhoA+AqsKysr8cknnwQCILPZjA0bNuDaa68FAMydOxdGoxGbN2/G7NmzAQCffvopBEHAnDlzUrZWQobD5vLAZBs6wTuTdgw0Y5xUqRXV1Hi5lKdZZSniqyMK/zhLJEPkP24/vVaX8h4zwcXK4bbMgjNEjLGQ+3eG2TKzxRjp4X8jUp1AhmikF1QHG05xdUGOjdVJ+LcqZDCnf/PNN+PEE0/EQw89hAsvvBAbN27EihUrsGLFCgC+AOymm27CAw88gPHjxweO3VdXVwfGjUyaNAlnnHFGYKvN7Xbj+uuvx5IlS+iEGRGNbGeHgKP1Q1NrUtdgLxW0KsoOpUo8J81iPRYZY4Hp9tPTUD8UmiEKU0M0UMDt8gqwOD0hDRLDNWe0Rymotrk8MNl9AWK8pxhVcomo3jCkm7+4OplmseocyxCJ+rd63HHH4a233sLLL7+MqVOn4v7778fjjz+OSy65JHCdO+64AzfccAOuvvpqHHfccbBYLPjwww9DmkSuXLkSjY2NOO2007B48WLMmzcvEFQRIgbJThpPFYvTgwM94jtuDwDaPHo3nm7x1BD1Dwx5jaTVaIfB6oJMwmFSVeo7mQcHRAopj8EJKJmEh24gSB68bSYIQ7fN7K7Ib+L9wZ9eJYt7qG2+bJcFS7a4WpNDBdVAEhkiALBarVizZg2amprgcoU+IG+88caULMzv7LPPxtlnnx3x8xzH4b777sN9990X8TrFxcXUhJGIVp/VFbPOId12tZnBGFCtV6IkgWnfmUAZotSJdvQ+3iGv/uzQpCpt1NtLVnDdCc9zkEv5IVthxQVymOxuGKwuNJSE1rE63ELIuqIfuaeRHfGo1Cmxv8sS0iQzHrky5d4v4YBo69atWLx4MWw2G6xWK4qLi9HT0wO1Wo3y8vKUB0SEjHSi2C4b6E49rVpc2SEgP9+Rp0us8Rr+Ia/tUYa8+qfbp+O4vULGD9mOUsokYQOiQz3W8IXVbi8QFERHqyGikR3xkUl4lBUqEspkh/tdil3Cq7355ptxzjnnoK+vDyqVCl999RWOHDmC2bNn4/e//3061kjIiCUIDJ392Q2IhODp9ik+Qj1cSpkkLVmIfBVtyww4um3WGeGFz5zG7tQAwnaZDjfTLPrR+6PBk8sjwOuNnNVILkOUfwERkPi2WbxbkGKScEC0bds23HrrreB5HhKJBE6nE3V1dXjkkUfw//7f/0vHGgkZsbr6nVGfsDOhqdeGfocHCimPcRGyAtlC/YdSK54MEQC0RzhptqPV1526vlgdKG5OpXDBRrij98XqyM0Zg2uIYg91TWyGmULG522Anmjn6lzqUO2XcEAkk8nA874vKy8vR1NTEwDfyAxqYEhIYrI9qgM4ul02uVoLqchS3PlYr5FOMgkPPsqvOFaGyH/cfkaaMonhXnDDBSDxZohs7sj1Q8Gd4ePdMsv3x2MiWaJc6lDtl/CKZ82ahU2bNmH8+PE4+eSTcc8996Cnpwf//Oc/MXXq1HSskZARyenxhn1Cz7RA/ZDITpcBdMIsHRRSScTMSbQhr26vgJ0D3anTUT8EhN8yS3ieWZwZog6zA4wBarkk7sdZvm6X+SVSXJ1rTRmBJDJEDz30UKAr9IMPPoiioiJce+216O7upqPshCSgw+R7Qs6mfsfRmpCpIiyophNmqRetjijakNe9nb7u1DqVDPUlqe1ODUSeexV2y2wgIDLZ3fB4Qwuug8d3RJtyHzzDLN7mkvkeEPmLq+ORi1tmCf92g6fGl5eX48MPP0zpggjJF2I4XbarzQwGXyrc/yIjFuo8a4CXKdHqiPxDXlv67Og0OwIDXwHgm+aBZow1OvAp7k4N+Boehpt7FS5DVKiUQsr7Arc+mzvkRdrlESAIDDzPwZ7iE2Y0Qsb3XBHrtJlUwuVkrVXCzzbPPvssDh06lI61EJI3+h1uWByJd35Nte0D22WUHcofsV6o/HVEwQE7Ywzb/dPt6/RpWVehIvzvWybhIZGEBkocx0XcNmPM18UaiL5llugJMxmNkAEQX3F1Lm6XAUkERA8//DDGjRuH+vp6XHbZZfj73/+O/fv3p2NthIxYYsgOCQIL1ISk4wj1cOX79kS6xHvSrDPopFmb0YEeiwtSnsOkytR3pwaib7FEnWlmCz/13uMV4PLE7lIdb0BEj8ejYhVX52JBNZBEQLRv3z40NTXh4Ycfhlqtxu9//3tMnDgRtbW1uPTSS9OxRkJGFMZY1kd1AMDhXissTg9UMgnGlBXE/oIMo+2J9IjVi6gqTIbIf7psUpUWijRlSaINTFWFGRIa66RZtO0yr8ACAV/cJ8xyNOuRDpU6ZUjB/WC5WD8EJDnLrKamBpdccgn++Mc/4oknnsBll12Gzs5OvPLKK6leHyEjTq/VFfWda6aEHLePdhY7CziO3pGnS6wMUYVu6NT7dB+3B8KfMPOLVlgdPiDyRt0u67E44REY5BIeJZr4aufy/ch9sFjF1bnYlBFIoqj6o48+wurVq7F69Wps3boVkyZNwsknn4zXX38dCxYsSMcaCRlRxJAdAsR93F4ll4iuJ9JIEW+36n6HB1anBwJjONjt706tT8uaJDwXtiO1X7Qts16rc8jnwk29D+bPflVoFXEXiFOAHipacXW04FbMEl71GWecgbKyMtx66614//33odfr07AsQkYml0dAV5ZHdQC+EQyHe20AgKnV2iyvZijaLkufWBkipUyCIrUMfTY3OswOX78eAHVpPIlYoJBGPfoerpg51paZECUJm+gJM4mEy9lC4XTxF1dbnaGHQyQ8FzajlwsSXvVjjz2Gk046CY888gimTJmCiy++GCtWrMDevXvTsT5CRpQOkyPqE3Wm+Iup64vV0KdhBMNw6eiEWdoopBLESor4j9t3mB3YPjDdPl2ny4DYNSfhXmBLCnxbNr0WF9ighl4OtzfGkfvERnZQ/VB4NfqhAaVKLom7r5PYJBwQ3XTTTXjzzTfR09ODDz/8ECeeeCI+/PBDTJ06FbW1telYIyEjRqsx+6M6AHFvlwGUIUq3WFki/8mrlj47dg4M/k3nScRIR+79wmWIigp8X+P0DC2gjlVDlPgJM3o8hlOlVw4ZBZOr22VAkkXVjDFs2bIFq1atwv/+9z989tlnEAQBZWVlqV4fISOG0eYakl7OBq9wdLr91BrxbZdxXPQTR2T4YvUi8meIvjrYC4fb1516VEn6TiLGyhAppPyQrJZCKgm8+A4e8urxspCO1cEYYwlvmVH9UHgyCY/ywtCgUh3mRGCuSDggOuecc1BSUoLjjz8eK1euxIQJE/DCCy+gp6cHW7duTccaCRkRxJIdOtRjhc3lhVouwZhScU23B3z1JNGO9JLhi9mLKKiwGvBlEtPRndovVn0Ox3EJ1xFF0mdzw+EWwHNARZxjKCggimxwT6JczhAlvPLGxkZcc801mD9/PnQ6cabbCREbj1dAl3noaZhs8G+XTanWijLwoO2y9IvZi2jQVlI6j9vH2wFaKeOHbIOVFMjRZLAlFBD5s0Plhcq4TjLyfG6/yKfb4OJqdQ7/rBJe+aOPPhr4f4fDAaUyvj1YQvJZh9kR14ToTBB9/ZAqd59Qc0WsDFFRgRxyCQ+XV/B1p65K39aqJs4mfr5tPnfIZclkiBKtHyqQRz8BR3zF1Xs7+8FxgDqHx5skvGUmCALuv/9+1NTUQKPR4ODBgwCAX//61/jHP/6R8gUSMhK09olju8xoc6HJ4D9uL9aAiDJE6RYrQ+Qb8urbTmqsLEzrDC9NjIJqv1RtmVFBder5i6sjDejNFQkHRA888ACef/55PPLII5DLjx7XnTp1Kv7+97+ndHGEjARmhztQi5Ft3w4ctx9VohZl4MHzgCZHu9zmklgZIgAYU+arLztuVHFa1xLvmIdEu1VHkmhBNWUsY/MXV+fqDDO/hAOiF198EStWrMAll1wCieToA3nGjBnYvXt3ShdHyEjQJpJiagD4VuTbZRqFLKffYeaKWKfMAOCCY2pw6/cn4MSxJWldS7z1OeE6WR/tVp3GDFGcGax8V1ukyvnmlQmvvrW1FePGjRtyuSAIcLvdYb6CkPzlFcQxyBXwrcXfkHGqSAMiejeeGbG2zADfPKp01g75xRsQhdsyKxkIiIw2F7wCi3lIwOL0BLK1lXEERNQCIn56tTzna60SzhBNnjwZX3zxxZDLX3/9dcyaNSsliyJkpOg0O+DxiqOY+kC3BXa3FxqFFKPT2FNmOOiEWWbIRTInTimLf2ZduIBIq5JBwnMQGGCyx35D7t8uK1bL46qLUsupBUQicr3DfMKh7z333IOlS5eitbUVgiDgzTffxJ49e/Diiy/iP//5TzrWSEjOEst2GWMMa/f3APAdtxfrthT1e8kMnucgk/Jwe7I7RyaR7IskzJp5jkORWoYeiwu9VmfMWWvtxkQLqunxmE8Sfpvwgx/8AO+99x4+/vhjFBQU4J577sF3332H9957D9///vfTsUZCcpLF6YHRJo5t5I92deLLA70AgJPGlmZ5NeFJeI76vWRQPNtm6RbvkXs/ZZg1J1JYnegMM8pY5peknn3mz5+PVatWDbn866+/xrHHHjvsRREyEoglO7TxkAGvbW4BAFx4bC0mi3C6PeB7N57rNQi5JJ6TZukW75F7P6VMMuTEpm/IqyXOgIhGdpDIEv6LsFgssNtDn+i3bduGc845B3PmzEnZwgjJZYLAAu9Gs2l3hxnPrjsEAFg4qRzfn1SR5RVFJsY2ACOZGDJE8R659xtuL6LEexBRQJRP4v6LaG5uxty5c6HT6aDT6XDLLbfAZrPh8ssvx5w5c1BQUIAvv/wynWslJGd0W5xZr89oNdrx1GcH4BEYZtcX4cLZdaLOwND2RGbFc/Q+nTgOCfetiXb0PlZA5HR7A8fz4wmI1PL4C77JyBD3o/H222+Hw+HAE088gTfffBNPPPEEvvjiC8yZMwcHDhxAbW1tOtdJSE5pyXJn6j6bC098vA92txfjyjS4ct5o0RZS+9G78czKdoZILZcm/JgcTnPGDrMvO6RRSOPqPk0dqvNP3M9An3/+Od58802ccMIJuPDCC1FZWYlLLrkEN910UxqXR0jusbu86EugUVw67v+JT/bBYHOhUqvE9d8bJ4p6kWgkEi7nm7rlmmwHRMkU0CuG0Zwx0e0y6j+Uf+L+i+js7MTo0aMBAOXl5VCr1TjzzDPTtjAifoJIhpWKTWsWi6k9XgF/Wb0fLX126FQy3LRwfE48sdN2WeZlO0hO5nEZNkOk9gVENpcXDrc34tcmGhBpc+DvhqRWQn8RPM+H/H/wLDOSf9rN2S8aFhvGWOAkSzbu+/n1h/FdRz8UUh43njoOpRpFVtaSKHrxybxs1xAlWlAN+NbMD3rVUsklUMt9txVt2yzxE2YUpOebuJ+FGGOYMGFCoCjTYrFg1qxZIUESABgMhtSukIhWd78TRWoZ1Dk+0C+Vui1OON3ZKaZ+a1srvjpoAM8B1548Fg0i7UYdDp0wy7ysZ4iS3CJVSiWwuUIzQcUFcthcdhisLlTrwwc8iWSIFDI+6z8fknlxPyKfe+65dK6D5CCb04PuficaSigg8mszZidrtnpPF97f0QEAuHzuKNHOKouEtswyT8JzkEq4rIyWkfBc0m+kFLLwAVFLnz1ihsgjCOgyOwHEFxBRdig/xf2IXLp0aTrXQXKMIDDY3V70WJw5lYlIJ4fbi16LM+P3u63ZiJUbmwAAP5hRjXnjxNmJOhKZlIdKnt3tm3wll/LweCPX3aTLcArow9URlcQ4adZldsLLGBRSPuZ4D4BOPOYrygmSpNjcXjAGGG1uuL3Z7bcjFm1GO1iG32wf7LFgxecHwRgwb1wpzp5eldkFpAC9+GRPtuqIhjOiJVovokgnzfzbZZU6ZVy9uOgxmZ8oICJJsTl97fMZA3ot2TtiLhaMsYxvl3WaHfjTp/vh8gqYWqPFpSfUi7rxYiS0XZY92Tp6P5yAKJlu1UcLqmmGGYmMAiKSFGvQHn5PFraJxMZgdUU98ptq/Q43nvhkH/odHjSUqPHzBWMhHXz8JkdoVfRuPFuyFRAlc8LML2xApI4VEPnerFTHccJMKuHC3gcZ+XLzGZRkndV5dMBij8UJlum9IpHJZO8hp8eLJz/dj65+J0o1ctx46vicfgKnd+PZk62TVMPpjRW1W7XNBSHMc1EiJ8yooDp/UUBEkhJ8ysPjZeizubO4muxyeYSMZcm8AsOKzw/iUI8VBXIJbjptAnQ5fGRdIeNzOpjLddmoIZJJ+WHdrzLM1+rVcnCc7+/DbA99LhIYC4ztiKcHEfXEyl8J/+a9Xi+ef/55fPLJJ+jq6oIghBbUfvrppylbHBEvm8sT8u8eizOu0xsjUbvJDiFDdeUvb2zCNy0myCQcbjh1PCrjrIkQK3o3nl3ZyBANp34IAHieg1zKwxU0PFnCcyhSyWGwuWCwuqBXH30u6rO64PIIkPAcygpjNyqlx2T+SviR+Ytf/ALPP/88zjrrLEydOjUnizjJ8Djc3iG9S3r6nZhQUZilFWVXprbL9ndZsHpvNzgAP5s3BuPKNRm533Sid+PZlY0aouEGRICvjig4IAJ822b+gGhM2dHL/dtlFYUKSOIYJpsLo25IeiT8m3/llVfw73//G4sXL07HekgOGNwUzX+Z1enJiQGdbq+ADpMDVTolpJLhvSD0WV2wOTNTTP3Zni4AwNyxJZjdUJSR+0w36lCdXVnJEKUg4FDJJEO2xooL5EC3r44oWFsCIzskPIcC6omVtxJ+ZMrlcowbNy4dayE5IrigOliPxSn6gMjp8WLLESOsTg8O9lhRX6xGXZEq6cAoU9mhfocbm4/0AQBOnViekfvMBCqozi6ZhAfPI2NbvgCgScGon6iF1YNOmrUb4y+o1iiltOuRxxJ+Fbj11lvxxBNP5P2ponxmj3C8vLtf3Mfv7S4vNh/uCwR0bo+AA10WrN3fgwPdloQbTLq9Qsa+57X7e+ARGEaVqDGqdGR0BlfKJDQvSgQyXVg9nCP3fuEK8UsiNGdM5IRZKrbzSO5K+Le/du1afPbZZ/jggw8wZcoUyGSh7/DefPPNlC2OiJMlQobIZPd1rZYNcxsqHaxOD7Y09YUdvOrxMhzqtqLJYENdkRr1xeq4Xqg7TA54hfS/MRAYw5q93QCAUyZkLzvEcb6PVGUTqP+QOCikPOxhtsHTQSWXDHubGvCdThysKEyGiDGW0JR76lCd3xL+7ev1evzwhz9Mx1pIjohUM8OYb9ssnieeTDI73NjaZITbE/2V3OtlONxjRXOfDXVFKtQXF0QNjDK1XbazzYweiwtquQTHjc5e7VChUoYqnRJ7OvpTcnu0XSYOmczSpWpLPVqGKDgg6nd4YHV5wQGo0NEJMxJdwo9Omnqf37wCi9qRuaffJaqAyGhzYVuzMaGJ3r7AyIZmgx01RSo0lKiHbCuY7G5YHOEzZam2eqCY+sSxJVmbPQUARWoZ6orVMNnd6DANf0wJvRsXh0w+plK1JRVtnlm/wwOXR4Bcyge2y0o08pjfJ8fRllm+E9/eRhS/+93vwHEcbrrppsBlDocDy5cvR0lJCTQaDS644AJ0dnaGfF1TUxPOOussqNVqlJeX4/bbb4fHk5kXs5FmcP+hwXqsTggZ2EaKR6/Fia1NiQVDwbwCQ1OvDev292BPR39IINjal5nsUK/Fie2tJgDZ3S4DEOjtMqlKm5KTQnTCTBwymSFKVcAhk/CQSEKLn9VySaCNgP+kWSLbZWq5NK5j+WTkSurR+frrr+Pf//43mpqa4HKFFrBt2bIlJQsbbNOmTXjmmWcwffr0kMtvvvlm/Pe//8Vrr70GnU6H66+/Hueffz7WrVsHwNdI8qyzzkJlZSW+/PJLtLe34/LLL4dMJsNDDz2UlrWOZNYYR8y9XoY+mwslmtjp6XTq6nfg21ZTSupdBAFoNtjQarShWq9CbZEanf2ZGeT6+b4eMAY0VhZmtQkjxwF6tS+AkfAcptfqsPGQIelgUy2XiLLWLB9lshdRKnv8KKQ8bN6jz0ccx6GkQI42kwMGiwuVWmWCIzsoO5TvEv5LePLJJ7Fs2TJUVFRg69atOP7441FSUoKDBw/izDPPTMcaYbFYcMkll+Bvf/sbioqO1lCYTCb84x//wGOPPYZTTz0Vs2fPxnPPPYcvv/wSX331FQDgo48+wq5du/Cvf/0LM2fOxJlnnon7778fTz311JBgjsRmjZEhAoAeS3Z/ru0mO3a0pCYYCiYIQIvBjq8O9MKbZCCQCI9XwOf7fMXU38vyUXuNQhoSwKjlUkyu1iZ9e5QdEo9MZYh4HlCncExLtG0zfx1RIgER1bSRhP8S/vKXv2DFihX405/+BLlcjjvuuAOrVq3CjTfeCJPJlI41Yvny5TjrrLOwcOHCkMs3b94Mt9sdcnljYyPq6+uxfv16AMD69esxbdo0VFRUBK6zaNEimM1m7Ny5My3rHcniOY2Sqble4TQbbNjZasZI6AqxpcmIfocHOpUMM+p0WV1LUZixLOWFSowqVSd1e/TiIx6ZyhCpZFLwKdySCjv1PmjIK5DYlhl1qCYJPwKamppw4oknAgBUKhX6+30nTi677DKccMIJ+POf/5zSBb7yyivYsmULNm3aNORzHR0dkMvl0Ov1IZdXVFSgo6MjcJ3gYMj/ef/nwnE6nXA6j76om83m4XwLI0qkI/fB7C4vLE5PxgsUD/VYcaDLktH7TKfVe33F1AvGl0LKZ3d7yb9dNtjYMg1Mdg/6rIllBWl7QjwylSFK9e88akBkdcHu8gaGTtOWGYlHwn8JlZWVMBgMAID6+vrA1tShQ4dS3qyxubkZv/jFL7By5UoolZmrn3j44Yeh0+kCH3V1dRm7b7GLt19JT4abNO7v6h9RwVCb0Y69nRbwHDB/fFnsL0gjjgOK1OEH93Ich6k12rB9YaLdHr34iIdCKkEmmjOnuot9uG7VJQW+2sVeqxPtZl92SKuUxrxvFdW0ESQREJ166ql49913AQDLli3DzTffjO9///u46KKLUt6faPPmzejq6sIxxxwDqVQKqVSKNWvW4Mknn4RUKkVFRQVcLheMRmPI13V2dqKyshKAL4AbfOrM/2//dQa76667YDKZAh/Nzc0p/b5ylcPtjbsRYSa3zXZ3mHG4x5ax+8uE1Xt8tUMz6vSBd73ZUjCofmgwhVSCaTU6xJvEUsulKWnOR1InE1miVGeMlWGO0RcV+DKZBqsrqH4oju0yOm5PkMSW2YoVKyAMVKv6j7t/+eWXOPfcc3HNNdekdHGnnXYaduzYEXLZsmXL0NjYiF/+8peoq6uDTCbDJ598ggsuuAAAsGfPHjQ1NWHu3LkAgLlz5+LBBx9EV1cXyst9hamrVq2CVqvF5MmTw96vQqGAQpHdU1JiFGmGWTgmuzvQCyRdGGPY2WZOSU8cMXG4vVh/sBcAcMqE7GaHAMQVkOnVcowrK8TezthNGyk7JD4KqSRsF/dUSnXQoQozhNWfITJYXQnNMKPHJAGSCIh4ngcf9FZwyZIlWLJkSUoX5VdYWIipU6eGXFZQUICSkpLA5VdeeSVuueUWFBcXQ6vV4oYbbsDcuXNxwgknAABOP/10TJ48GZdddhkeeeQRdHR04O6778by5csp6ElQrCP3wfxdq6v16WnSKAgMO1pNop+floyNhwywu70oL1RgUlXyJ7lSJVL90GD1JWqYHbGbNurohJnopDtDJJFwYQOY4VBIeXAcQg5Q6NUycADcXoZ9Xb7gPL6AiB6TJMnGjF988QUuvfRSzJ07F62trQCAf/7zn1i7dm1KFxePP/7xjzj77LNxwQUXYMGCBaisrAyZpyaRSPCf//wHEokEc+fOxaWXXorLL78c9913X8bXmuviOXIfLJ3bZnu7+kdkMMQYw2cDnalPnlAGXgSTt/Wq+LfsJlVpY9Zr0Akz8Un3SbN0bElxHDek+7RMwgdaOhzssQKgGWYkfgk/Ct544w1cdtlluOSSS7B169bAaSyTyYSHHnoI77//fsoXGWz16tUh/1YqlXjqqafw1FNPRfyahoaGtK8rH9gSHADZa3VBEFhKj9oCvu24THWKzrSDPVY099khk3A4aWxptpcDjVKaUPYg0LTxsCFsryYqqBandGeICuTp+Z0rZfyQUUIlBXKY7O5A5qhaHz1DJJPyYU+skfyT8F/BAw88gL/+9a/429/+FjLp/qSTTkpbl2oiDrHGdgzm71qdSowx7G4fGX2GwvEXUx83qlgUfVEinS6LpkAhxZQIW30FitT2oiGpke4MUbqC4GhH7wFf88ZYW7QUoBO/hP8K9uzZgwULFgy5XKfTDTntRUYOj1dIqugy1V2rW/rs6M/QUNVMszg82HTY19JCDMXUgG+gazLKtUrUlwxt2kjbZeKU9gxRmk5xhTt6HxwQVemU4GJsO2spICIDkupDtH///iGXr127FmPGjEnJooj4WBPcLvNLZZ2Pw+3F/u6R02tosHUHeuARGOqL1RhdWpDt5QA4OtA1GePLNUMKsrUqevERo3RPvE/XsfZw6w4OiOKZ/6dRUJBOfBIOiK666ir84he/wIYNG8BxHNra2rBy5UrcdtttuPbaa9OxRiICiW6X+TncXvQ73ClZw75OS0ZmiGWDwBhW7/Vtl50ysSzmu9pMKFAkVj80mK9poy7kNmiGmTilc8tMLuXTloEKf/Q+NEMUC22ZEb+EHwl33nknBEHAaaedBpvNhgULFkChUOC2227DDTfckI41EhFI5Mj9YD0W17CPtfZanOg0j6x+Q8G+azeju98JlUyCOaOKs70cAEeb3A2HUuZr2rilqQ8cB2jSVFxLhkeexkaZ6douA8LXEBWFBETRT5hJeA7qFLcDILkr4Ucqx3H41a9+hdtvvx379++HxWLB5MmTodFo0rE+IhKJNGUcrMfiHNYWkCAw7OmI3fAvl/mLqeeOLYFCJCdekimoDns7BXKMK9eg0+ykgmqR4nkOMikPtyf1zRnTmYFRhsk8JZIhKlRKRZGNJeKQ9CNVLpdH7PRMRp5Ej9wHM9vdcHq8SdcpHOq1Duv+xc5gdWFbixGAeIqpgfgbMsajoaQgrZkCMnyKNAVE6fy9SyU8pBIOnqCtdI1Cill1eri9AsoKozffFcNJTiIecT8arrjiiriu9+yzzya9GCJOjDHY3UMzRAarC3q1LGbzQF/XahdqkuhabXN5cKTXmvDX5ZLP93WDMWBiRWHaOnsnSq2QpLzQtlRDneHFLF11PumeE6aUSWDxHn1+4jgOy783Lq6vpQ7VJFjcj9Tnn38eDQ0NmDVrVsqn2hNxs7u9EAa9cdzWbMSfP9uPs6dX4byZNTFvo6ffmVRA9F17/5D7Hkk8goAv9vUA8BVTi0WqtstI7khXYXVGAqIkW3FQQTUJFvej4dprr8XLL7+MQ4cOYdmyZbj00ktRXCyO4k+SXuEKqne0mgAAX+zrwbkzqmNmiQxJdK3uMDnQZ01tHyOx2dZshMnuhlbpS/OLBQVE+ScdR+9Vcgkkaa4bC9eLKB5U5E8Gi/uR9NRTT6G9vR133HEH3nvvPdTV1eHCCy/E//73P8oYjXDhjtz7R2eY7G4c6om9peUVGAwJdK12e4W4JqfnOn8x9YLxZZCm8aRPolJZP0RyQzoyROnODgG+btTJoK7pZLCE/gIUCgV+8pOfYNWqVdi1axemTJmC6667DqNGjYLFMnIb5uW7wRkixhhajUdniW1p6ovrdhIZ9nqg2wJXGgo8xaTdZMfujn5wHLBARMXUarmEZjvloXQERJkopE/2sUrbZWSwpP8CeJ4Hx3FgjMHrHbkngMjQDJHB6oI9aKDiliZjXFnCnv74MkQjeXhrsDUDjRhn1OhDuutm23C6U5PclY6i6kwEHcokt/oKqUM1GSShvwCn04mXX34Z3//+9zFhwgTs2LEDf/7zn9HU1ER9iEawwWM7WgayQ2UaBaQ8h+5+Z0jGKJJ4ulaP9OGtfk63F+v29wIQVzE1kJqGjCT3pKOGKBMZIkWSNUSUISKDxf2IuO666/DKK6+grq4OV1xxBV5++WWUlpamc21EBFweYUhvEn/2ZkxZARxuJb5pMWFLkxG1RUOHeQ7W3e+MetR1JA9vDbbxsAF2txdlGgUmV4efDJ8tVFCdn1KdIeJ5oCADXaCVMgl4HgmfRqWAiAwW9yPir3/9K+rr6zFmzBisWbMGa9asCXu9N998M2WLI9kXrqC6ZSAgqtGroFPJ8E2LCVub+nDujOqYt9djcWFMhITISB/eGsw/t+zkCWUxT+hlkorqh/KWhOeGNDkcjiK1PGNdoBVSCewJNG9VyyWiOsRAxCHugOjyyy+nFud5KNyUe//2WG2RCmNKNeA5oLnPjq5+B8oLo7fKN9vdcLi9YV90R/Lw1mCHeqw40muDlOdw0riSbC8nBJ0uy29yKQ9PimpC45k0nypKGZ9QQEQdqkk4CTVmJPnHNmiGmccroMPkG7Jao1dBo5RiQkUhdnf0Y2uTEYumVMa8zV7r0K7VI314a7DP9nQBAI4dVSS6Trm0XZbfFFIJbMMY5OzH874aw0zx1T9Fr08MJra/OyIOlDMkUQ3OELWbHfAyBpVMEjgZdUx9EYAEjt/3hx6/z4fhrX4WpwebDhsAAN+bWJ7l1QwlptNuJPNSdfS+VKPI6JaUKsFaJaofIuFQQESiGpwh8hdU1xapAluos+r1AIAD3VYY42i+6O9a7TfSh7cG+/JAD9xehroiFcaUFmR7OSGUMqofynepCogqtZnbLgMS70WUiYaRJPdQQEQiEgQW0m8ICC2o9itSywMv7tuajTFv1ysw9A6M5LA6R/7wVj+T3Y3/bm8HAJwysVx0NXlUP0RScdJMIuEyPshXmcC65VKeAn8SFgVEJCKb2zukH5C/oLqmKLQGyJ8l2tJkjOu2/V2rd3eM7OGtfowx/POrI7C6vKgvVouumBqg7TKSml5EZRpFxkdiJBLg0HYZiYQCIhLR4O0yIGjLTD84IPLVEe3p6Ic1zNcN1mNxot1kH/HDW/02HDJgW7MREp7DFSeNgpQX358eFVSTVGSIMnm6zC+xgIgyoSQ88T0rE9EYXFBtdXoCA1oHZ4gqtUrU6FXwMobtLaaYt+10C9jdnh+F1EabCy9tbAIAnDO9Kq4GlpmmkPEJF6aSkWe4NUQyKY+SLGQaJTwHWZxrpwwRiYQCIhLR4EyPf7usuEAOtXzok8rRbbP4Tpt5hZHfc4gxhn991QTbwFbZGVNjtyXIBsoOEWD4GaIKrSJrtXHx1hFRQEQioYCIRDT45Fek7TI///H7nW1mON35cWoslq8OGbCtRdxbZQBQRPVDBIBMwkMyjPqfTJ8uCxZPhlMi4cK+mSMEoICIRGEdNLajJUJBtV9dkQqlGjlcXgHftpnTvj6xM9pceFnkW2V+RXTCjAxINkuklEmgz2KmMZ46okI6bk+ioICIhOVwe4eM0WjpswGInCHiOC5QXL21Ob5ts5HKf6rM5vKioUSNM6dWZXtJESlkPL1rJgHJ1hFVaDN71H4wZRwn5KigmkRDAREJa/B2GWMMbUbfaI1omY5jBuqIvmk2wePNg/P0Eaw/2ItvWkyQ8hyuOHH0sLYh0o3qh0iwZDNEFVk4XRZMKYu9bqofItFQQETCGlxQbbC6YHd7IeE5VOgivxMcW6aBVimF3e3F7jwZxzGY0ebCK5uaAQDnzKiOuMUoFtSQkQRLpheRWiGBNsvZF0U8W2YUEJEoKCAiYQ3OEPnrhyq1yqiFwXzItpkxbesTK8YYXlzv2yobVaLGGXEMu802yhCRYMlkiLJZTO2nihEQ8TxQQFvDJAoKiEhYgwuqg2eYxTKrTg8A2NrUFzKzLB98ebAX21t9W2XLThL3Vhnge/EroEJTEiSZGqJsNGMcTC6NfkKuQC7NeAdtklsoICJh2ZyxZ5hF0lhZCJVMArPDgwM9lrSsT4z6bC68stG3VXbujOq4flbZRtkhMliiAVGhUiqaonxFlDoiKqgmsVBARIbwCgyOwUNdjQMnzOLIEEklPKbX6gDEP9ssUW6vADZ40FoW+bfK7G7fVtmiHNgqA6h+iAyV6JaZGLJDftGO3lP9EImFAiIyxODtMo9XQKfJN4w13l46/iaNW5v6Uh647Gwz4YaXt+Lut7/Fql2dcc1OS7cvD/Rix8BW2RU5sFXmRw0ZyWCJFlVXiKB+yC/a0XsKiEgsFBCRIQZvl7WbHfAyBpVMEncDv6nVWsglPHosLjQPbLelgsXpwXPrDsMjMHT2O/Hq1824/fXteP7Lw2jqtaXsfhJhsB49VfaDmdWozoGtMsA3d0pD9UNkELmUR7wN1fVqWUKDVdMt0tF7jgM91klMFBCRIaIVVMc7p0ghk2BKtRZA/LPN4vHShiYY7W5UapW4dE49avQquLwC1u7vwX3/3YWH3v8O6w/2wp2hHki+rbLDsLu9GF1agNMn58ZWGUDdqUlkMkl8Lw1iyg4BkbfMVDIJpHF+TyR/UchMhhhOQXWwY+qLsLXZiK1NRpw3s2bY69p02ICNhw3gOeCKeaMwplSDkyeUYX+XBZ/t6cbmpj4c7LHi4NpDeHVTM+aPL8XJE8pQqklfB911+3vxbZvZd6rsxFE5s1UGUEE1iUwhlcDpjv6mguPEFxBFOnpPBdUkHhQQkSGGzjCLv6A62PRaHSQch1ajHZ1mx7CePI02F/711REAwOJpVRhTqgHgGxcyvqIQ4ysKYbK78cW+bny+twcGmwsffNuBD7/twLRaHU6dWI7J1VrwKZzEbbC68OrXubdV5kcF1SSSeE6aFRfIk+5qnS6RMkRUP0TiQY8SEoIxBnuEKfeJdlwuUEgxsbIQu9rN2NLUl/Q8L/8JLqvLi/piNc6eFv52dCoZzp5ejTOnVmF7ixGf7enGrnYztreYsL3FhLJCBU6ZUIaTxpUOu56AMYYXBrbKxpQWYFEObZUBvvohetdMIokn0BHT6TI/hZQHxwGDz3FQQETiQY8SEsLhFuANaqZodXrQZ3MDSHzLDPDNNtvVbsbWJmPSAdEX+3sCzQ6vnDc6Zi2AhPd1y55VX4QOkwOr93Zh3f5edPc78drmFry9rRWz6opQW6RCeaEC5VolygsVCRWHrt3fg53+rbKTRuVcwze9ioIhElmsDJGE51CWxq3oZPE8B7mUH7LdR8E/iQcFRCTEkILqgZEdxQXypJqvzazTY+WGJhzssaLP5kq4bqW734lXB05w/XBWTcJBWaVOiSXH1eOHM2uw4bABn+3uQnOfHRsPG7DxcOh1dSqZL0AaCJIqChUoL1SiXBsaLBmsLvz76xYAwHkza1Cly62tMoDqh0h0sTJEJRq5aIuUlbLQ+ieFjBfd1h4RJwqISIhIBdWJ1g/56dVyjCkrwIFuK7Y2GXFqY3ncXyswhue+PASnR8D4cg2+P6kiqTUAvlNvC8aXYf64UhzssWJXmxld/U50mh3o6nfC4vTAZHfDZHdjX9fQ7tpapTQQHLWbHIGtstMnJ7+mbCoqoHfMJLJYvYjEMLssEqVUAhPcgX9TdojEiwIiEiJShqh2GAXDx9QX4UC3FVua+hIKiD7+rhN7Oy1QSHlccdLolGxLcRyHsWUajC3ThFxuc3nQZXb6gqR+x8D/+4KlfocHZocHZocF+7t9wVKubpUBgFTCUU8WElW0jIpEwqX15OZwDe5FRI91Ei96pJAQtsEnzPp8J8wSLagONqtej9c2t2BvZz8sDg80cRQ4thrteHNLKwDgomPrUFaY3idgtVyKUaVSjCotGPI5m8uDrn5nIEjqsbgwo1aXk1tlgG+7LN5+UiQ/RashKi9UiPqNwOBaQC0VVJM40SOFhLAGbZkxxoIyRPGN7AinvFCJuiIVmvvs+KbFiJPGlUa9vkcQ8I+1h+ARGKbV6DB/fPTrp5taLsWoEilGlQwNlnIR1Q+RWCKd1gLEvV0GDA2IaMuMxIsqzUiA2yvA5TlajNhrdcHhFiDhOVTohmZoijXxv7DOGphtFk/X6v9ub0eTwYYCuQRL5zZQNiPF9FQ/RGLgOC5st2qZlEexyOffBW+ZSSUcVHLxjBYh4kYBEQmIVFBdpVNCGma4UX2xGooIs4MGO6ZeDwDY2WaGw+2NeL1DPVb8d0c7AODSExqgp2xGSkklHAqppoLEIVwdUYVWIfo3KMEZIuo/RBJBAREJiFRQHemou1Ypi7v7dI1ehbJCBTwCw7etprDXcXl8W2UCA44fVYzjRhUnsHoSDz3VD5E4hasjEvt2GeCbwyaR+B7jtF1GEkEBEQkYXFDdGuXIvVImgVzKo6IwvidIjuMCWaItTcaw13lzaws6zA7oVDJcPKc+/oWTuEglXE68oBFxGJwhUsokOZOxVQ60DaAMEUmEqAOihx9+GMcddxwKCwtRXl6O8847D3v27Am5jsPhwPLly1FSUgKNRoMLLrgAnZ2dIddpamrCWWedBbVajfLyctx+++3weEJf/EloQTVwdIZZuAyRVuV7otGpZXF3eD5moI5oe6txyDT63R1mfPxdFwBg2Ymj6KhsCunUMkyu1mL++DJRjlsg4jS4F1FlmDpCsfLXEVGGiCRC1AHRmjVrsHz5cnz11VdYtWoV3G43Tj/9dFit1sB1br75Zrz33nt47bXXsGbNGrS1teH8888PfN7r9eKss86Cy+XCl19+iRdeeAHPP/887rnnnmx8S6IWvGXm9groMDkAALVFQ0+YaYOeaCq08T1Rji4tgE4lg8MtYHdHf+Bym8uDZ9cdBgCcPKEMU2t0ySyfBJFIONQWqzBnjG/rsVqvgkTER6WJ+AzeMhPbZPtolDIJeB4ooIJqkgBRvw3/8MMPQ/79/PPPo7y8HJs3b8aCBQtgMpnwj3/8Ay+99BJOPfVUAMBzzz2HSZMm4auvvsIJJ5yAjz76CLt27cLHH3+MiooKzJw5E/fffz9++ctf4je/+Q3k8txIAacbYyyk2LnD5IDAALVcgqIwU9GDU9HlWiWO9Npi3gc/sG322Z5ubDnSh2kDgc+rm5phsLpQplHgx7NrU/Dd5C+tSoaaIhUqtUoKgMiwBAdEaoUkp7ItKpkEGoWM6uVIQkSdIRrMZPIV4xYX+4ptN2/eDLfbjYULFwau09jYiPr6eqxfvx4AsH79ekybNg0VFUdHLCxatAhmsxk7d+4Mez9OpxNmsznkY6Szu70QgnaxWoIKqsM9qWiDhoPqVDKo43wnNqvOt222rcUIQWDY1mzEugO94ABccdKohAasEh+JhENNkQrHjynG8aOLUUPZIJICwTVEuVZ7ppRJaNudJCxnAiJBEHDTTTfhpJNOwtSpUwEAHR0dkMvl0Ov1IdetqKhAR0dH4DrBwZD/8/7PhfPwww9Dp9MFPurq6lL83YiPxRl/QbVaLhnSo6Q8zm2zCZUaqOUS9Ds82NpsxAvrDwMAFk2pxPiKwiRWnr8KlVI0VhVi/rhSTKrShmxjEjJcwTVEuVZ7ppTxVFBNEpYzAdHy5cvx7bff4pVXXkn7fd11110wmUyBj+bm5rTfZ7YN6UEUtaB66AtvvPUFUp7HjFo9AODvaw+i3+FBjV6FH8ysTnDF+YnngWq9CseNLsacMSWoLVKLduo4yW3+LbNCpRRqeW4FF0qZhN4gkITlxDPp9ddfj//85z/47LPPUFt7tMaksrISLpcLRqMx5PqdnZ2orKwMXGfwqTP/v/3XGUyhUECr1YZ8jHRDehAFMkRDC6rDvfMqVMa/beY/fu/2Mkh4DlfOGx22Ky4ZqlqvwuRqLXRhglJCUonnOV+rhhzLDgG+YC6emYmEBBP1qxBjDNdffz3eeustfPrppxg9enTI52fPng2ZTIZPPvkkcNmePXvQ1NSEuXPnAgDmzp2LHTt2oKurK3CdVatWQavVYvLkyZn5RnKAzXU0Q2R1etBncwOIkCGK8M6rPM4s0ZRqXeDd57kzqlFfnPyctHzCcUBD8ciYp0Zyg0IqyanTZX4cx1EdHUmYqEPo5cuX46WXXsI777yDwsLCQM2PTqeDSqWCTqfDlVdeiVtuuQXFxcXQarW44YYbMHfuXJxwwgkAgNNPPx2TJ0/GZZddhkceeQQdHR24++67sXz5cigUudNXI92sQTVE/g7VJQXyIXOAOC5ys7NKnRKHe6xhPxdMLuXxs3mj0Wq044wp4bN0ZKgKrZLmMpGMqtAq6KADyRuiDoiefvppAMApp5wScvlzzz2Hn/70pwCAP/7xj+B5HhdccAGcTicWLVqEv/zlL4HrSiQS/Oc//8G1116LuXPnoqCgAEuXLsV9992XqW9D9JweLzzeo2Ot/TPMasIUVKvkkog1KxqFFAUKaUhwFcms+qLAwFcSn4YSyqSRzAq3ZU7ISCXqgIgxFvM6SqUSTz31FJ566qmI12loaMD777+fyqWNKHbX4KGuvoLq2gS2y/wqtAoc7KYu4KlWopHnVB8YMjKEG/BKyEhFj3YC66CAyL9lFu7dYaxi3lysN8gFo0qodogQQtKJAiISssXFGDs65T7MllmsDFGBQkqnO1JMp5ahqIA6qhNCSDpRQERCAqJeqwsOtwAJzw2ZUcZxiCvYoSxRalHtECGEpB8FRCTkyL2/oLpKp4SUD314FCikcR1ljXfYK4lNrZCgvJACTEIISTcKiPKcIIQOdfUXVCfSf2gwtVxKbfNTpIFqhwghJCMoIMpzVpcHwYf5jhZUhxvZEX+QQ9tmw6eQ8aiinyMhhGQEBUR5bvCR++gjO+I/9k0B0fDVF6vBU7ddQgjJCAqI8lzwkXu3V0CH2QFg6JYZzwOFivgzRCq5JOwQWBIfqYQLu21JCCEkPSggynPBJ8w6TA4IDFDLJShShwYzBXJpwtmKSsoSJY2m2BNCSGbRM26eCw6IWvqO1g9xXGjwk0y2p5xOmyVFwnM08JYQQjKMAqI8Jggs9Mi9McoJsyQCIqVMAr06Pdtm/Ah+5FbplTQygRBCMoyedfNYu9kBr3D0iFm0gmptksfo01FczfPA7PriETmFm+OAhmI6ak8IIZlGAVEeO9JrDfl3YGTHoAyRhOegSaCgOlhZoQJcig9KNVZqoVPLMLZ85AUOFVolVPKRF+gRQojYUUCUp3osTticR7fLLE4P+mxuAEMDIo1SOqSmKF6p3jar1qtQPbC+Kp1qxJ1kozEdhBCSHRQQ5akjvbaQf/u3y0o18iEZing7VEeSqtEThUopGisLQy6bUKFJyW2LQYlGnlCvJ0IIIalDAVEe6ne40Wd1hVwWabsMSKxDdTjl2uFvm0klHKbX6occ/der5SPmNNsoGtNBCCFZQwFRHhqcHQKCZpiFGdkx3KyFQiqBXi0f1m1MqdZFrK0ZX16Y86fOdGoZigqG9zMihBCSvBx/GSGJcri96Op3DLk8MMNMH1rDIpFwKEhBkW/FMLI4o0oLUFYY+etVcgnqwpyMyyVUO0QIIdlFAVGeaemzQRBCLxMYCzRlHJwh0g6joDpYeaEyqW2zYo0cY8tibyWNKi2ALEd796gVkpTVWRFCCElObr6CkKR4haOBT7BeiwtOjwApzw3J5KSqyFcu5RPeElLKJJharYsrIJNJeIwpzc0aHKodIoSQ7KOAKI+0Ge3weNmQy/3bZVU6JaSDinGGe8IsWCKzzXgemFajS6hjc22RCgVJ9kvKFoWMp5lvhBAiAhQQ5QnGGJoNQ4upgegF1cM9YRasrFARd/Hz+PJC6BLsX8RxHMbn2DH8huKChIfmEkIIST0KiPJEd78zZG5ZsEhH7qUSDmp56gIimYRHcUHs4upKnRJ1SQ43LdUoUKzJjdNaUgmHaj1lhwghRAwoIMoTTRGyQ8DRKfeDT2qlo0lgrNNmBQopJlVph3UfEyoKUz4uJB1qi9SQSuhPkBBCxICejfOAyeaGcWAsx2Bur4BOs+8Y/uAtM10Kt8v8yjSRt80kEg4z6nSQDHMLSaOQBsZ7iJWE51CfZBaMEEJI6lFAlAeiZYfaTQ4IDFDLJdAPmguWyoJqP6mER0mEbbMpVdqUbdGNKSuARCLeNFGVXplQwTghhJD0omfkEc7uCt+I0c8/w6y2SDXkeHu6BqdWhDlVVV+iRnkKT1sppBKMFulxdo7zFVMTQggRDwqIRrjmPhvY0JP2AS3GgRNmg7aYZFIeStnwO1SHU6qRh2yL6dUyjC9P/emw+mJ1xHEf2VShVYpyXYQQks8oIBrB3F4hcIIskpZAhii0nkWrTF8/H6mER8nASTC5lMfUmviaLyaK5zmMLRPPMXyJhENDiTrnWgMQQkg+yK0udiQhbUY7vGEaMQYL3jILlo4TZsEqtEp09zsxrUaXtkwU4DvC39xngylCUXkmKGUS1BerUa1X0qkyQggRKQqIRihfI8bo2SGL0wOj3RcoDN4yS2VDxnBKNQpMqCjMyIT3CeWF2HTYkPb7GaxQKUVDSQEqtIq0ZMAIIYSkDgVEI1Sn2QmHO3wjRj9/dqhUIx+SpUnHCbNgEp5LuvlionRqGSp1SnSYIheXp1JpoQINxeqMBHuEEEJSgwKiEepIrzXmdSJ1qFbI0ldQnS3jyjXo6ndAENJz+zwPVGpVaChR59w8NUIIIRQQjUh9Vhf6HZ6Y1/PPMBtcUJ3u+qFs8NfxHO6J3JMpGTIpj9oiFWqLVFBIR1YQSQgh+YQCohEoWiPGYP4TZkPqh9J4wiybRpUUoM3ogMsz/DSRWi5BXbEa1XrVsDtrE0IIyb6R+cqXx2wuD7r7nTGv12dzocUY/oRZuhoyZptUwmNMWQF2t/cn/LUquQRapQxalRQ6lQw6lYwKpQkhZAShgGiEiSc75PYK+MvqA3B5BNQVqVCpC+0Qne6C6myq0avQ0meHJcqWokLGDwQ/MmiVUhQqZTRmgxBCRjgKiEYQl0dAuzH6SSrGGFZuaMKhHivUcgmuO2Uc+KBMh1ImGdEv/hzHYXy5BlubjAAAqYQbCHx82R+tUjbiCsoJIYTERgHRCNJqtMMrRG/EuGZvN9bu7wHHAdcsGIOywtBBq+nuPyQGJRoFZtbroZZLUjZMlhBCSG6jV4MRQhAYmmNsl+3r6sfLm5oBAOfPqsGUat2Q64zk7bJgpRpF7CsRQgjJGyN3byTPdJijn54y2lz465qD8AoMxzYU4YwplWGvVzhCT5gRQggh0VBANEJEK6b2F1Gb7G7U6FX46YmjIp6QGqknzAghhJBoKCAaAXotzqinpl7e2ISDA0XUy783NmLRsFougYyGjxJCCMlD9Oo3AhyJkh1as7cbn+/rAQfg6vljUF6ojHhdyg4RQgjJVxQQ5TiL0wODxRX2cwe6LXhpYxMA4IezajC1ZmgRdTCqHyKEEJKvKCDKcYd7wg9xNdpc+MvqA/AKDLPri3Dm1PBF1MHy5YQZIYQQMhgFRDnM6vSg0zy0EaPHK+DpNb4i6mq9EstOilxE7cdxlCEihBCSvyggymEHu61gYfowvrypGQe6rVDJJFh+yri4Oi+r5BJIqaCaEEJInqJXwBzV73CHzQ59vq8ba/Z2gwNw1fzRqNBGLqIORttlhBBC8lleBURPPfUURo0aBaVSiTlz5mDjxo3ZXlLSDnYPrR060G3BSxt8RdQ/mFmN6bX6uG9PRyfMCCGE5LG8CYheffVV3HLLLbj33nuxZcsWzJgxA4sWLUJXV1e2l5Yws8ON7n5nyGUmuxtPrz4Aj8Awq16PxdOqErpNyhARQgjJZ3kTED322GO46qqrsGzZMkyePBl//etfoVar8eyzz2Z7aQkbnB3yeAU8vfoAjHY3qnRKXHnS6JAJ9rFwHKChgmpCCCF5LC8CIpfLhc2bN2PhwoWBy3iex8KFC7F+/foh13c6nTCbzSEfYmGyudEzKDv06tfN2N9tSaiIOliBQgoJH38ARQghhIw0eREQ9fT0wOv1oqKiIuTyiooKdHR0DLn+ww8/DJ1OF/ioq6vL1FJjOtBjCfn32n09+GxPNwDgZ/NHo1IXXxF1MNouI4QQku/yIiBK1F133QWTyRT4aG5uzvaSAPiaLQZ3pT7UY8W/NhwBAPxgRjVmJFBEHYz6DxFCCMl3efFKWFpaColEgs7OzpDLOzs7UVk5tIOzQqGAQqHI1PLidqD7aHbI5vLgmc99RdQza/U4a3piRdTBaIYZIYSQfJcXGSK5XI7Zs2fjk08+CVwmCAI++eQTzJ07N4sri5/B6kKf1Q0AYIzhhS+PoMfiQqlGjivmjUqoiDoYzwOFiryIiwkhhJCI8uaV8JZbbsHSpUtx7LHH4vjjj8fjjz8Oq9WKZcuWZXtpcTkYlB1avacbm5v6IOE5XLNgLNTy5H+NBXIpeCqoJoQQkufyJiC66KKL0N3djXvuuQcdHR2YOXMmPvzwwyGF1mLUY3HCaPNlh5oMNrz6ta+m6YJjajC6tGBYt03bZYQQQkgeBUQAcP311+P666/P9jIS5u875HB78cwaX93Q9Fodvj9p+MEcBUSEEEJIntQQ5bKufgfMdjcYY/jnV0fQ2e9EkVqGK04cHXOCfTy0dMKMEEIIya8MUS7yZ4fW7e/FhkMG8Bxw9fwxw+osrZJLUKSWo1Qjh4YKqgkhhBAKiMSsy+yAxeFBq9GOlzb6hraeN7MG4ysKE7odiYRDsVqO4gI5SjTyYRVhE0IIISMRvTKKFGMMB7qtcHp8dUMur4DJVVqcMXVo36TBOM5XG1RcIEdJgRw6lSwl22uEEELISEUBkUh1mp2wOj14eWMz2kwO6FQyXDkv8tBWtVyCYo0vC1SklkMmofIwQgghJF4UEIkQYwwHuy346mAv1u7vAQfgZ/NGQzfoRJiE5zCmrADlhUqo5IkNdCWEEELIURQQiVC7yYGDPVb88yvfnLKzp1dhUpU25DpqhQTTanQopMGshBBCyLBRQCQygsCwp6Mfz6w5AKdHwIQKDc6ZXh1ynSq9Eo2VWkiowzQhhBCSEhQQiUybyY4X1x9Gc58dGoUUV80fExitIZFwaKwsRJVOleVVEkIIISMLBUQiIggM/97Ugs/2dAMArpw3GkVqOQBAo5Rieq2OjswTQgghaUCvriKy6bABf/viIADgjCmVmFajAwDUFqswobyQhrASQgghaUIBkUjYXV7c9dYO2N1ejCktwHmzqiGVcJhcpUW5Vpnt5RFCCCEjGgVEIvHb93biYLcVarkE1ywYg+ICBabV6Og4PSGEEJIBFBCJwMe7OvHKpmYAwLITR+GYhiKMLdPQFhkhhBCSIRQQZVm7yY5bXtsGAPj+5Ar89KTRKCtUZHdRhBBCSJ6hgCiLPF4BN768FWa7B2NKC/CHH8+AVkWNFgkhhJBMo4FXWbTxkAFfH+mDWi7BP5YeS8EQIYQQkiWUIcqiE8eVYuWVc2B2eDC6TJPt5RBCCCF5iwKiLDtxXGm2l0AIIYTkPdoyI4QQQkjeo4CIEEIIIXmPAiJCCCGE5D0KiAghhBCS9yggIoQQQkjeo4CIEEIIIXmPAiJCCCGE5D0KiAghhBCS9yggIoQQQkjeo4CIEEIIIXmPAiJCCCGE5D0KiAghhBCS9yggIoQQQkjeo4CIEEIIIXlPmu0F5ALGGADAbDZneSWEEEIIiZf/ddv/Oh4NBURx6O/vBwDU1dVleSWEEEIISVR/fz90Ol3U63AsnrApzwmCgLa2NhQWFoLjuJDPmc1m1NXVobm5GVqtNksrzI58/d7p+6bvOx/k6/cN5O/3PhK/b8YY+vv7UV1dDZ6PXiVEGaI48DyP2traqNfRarUj5gGUqHz93un7zi/0feeffP3eR9r3HSsz5EdF1YQQQgjJexQQEUIIISTvUUA0TAqFAvfeey8UCkW2l5Jx+fq90/dN33c+yNfvG8jf7z1fv28/KqomhBBCSN6jDBEhhBBC8h4FRIQQQgjJexQQEUIIISTvUUBECCGEkLxHAdEwPfXUUxg1ahSUSiXmzJmDjRs3ZntJAZ9//jnOOeccVFdXg+M4vP322yGfZ4zhnnvuQVVVFVQqFRYuXIh9+/aFXMdgMOCSSy6BVquFXq/HlVdeCYvFEnKd7du3Y/78+VAqlairq8MjjzwyZC2vvfYaGhsboVQqMW3aNLz//vsJryVeDz/8MI477jgUFhaivLwc5513Hvbs2RNyHYfDgeXLl6OkpAQajQYXXHABOjs7Q67T1NSEs846C2q1GuXl5bj99tvh8XhCrrN69Wocc8wxUCgUGDduHJ5//vkh64n1GIlnLfF4+umnMX369EBTtblz5+KDDz4Y0d9zOL/73e/AcRxuuummEf29/+Y3vwHHcSEfjY2NI/p79mttbcWll16KkpISqFQqTJs2DV9//XXg8yP1uW3UqFFDfuccx2H58uUARvbvPCMYSdorr7zC5HI5e/bZZ9nOnTvZVVddxfR6Pevs7Mz20hhjjL3//vvsV7/6FXvzzTcZAPbWW2+FfP53v/vd/2/vzmOiON84gH+XYym4LofAclQOC+KFFCFSwKophKOmpTUVQwgB7YUFxdS22BNJU+2dWtLS1jZoqoFa49WqIEVApYiCgKwQVAShDYjWLpeUa5/fH4YJU1BW/MnW2eeTbMLM+zDv82Qmkyc7++6SpaUl7d+/n6qrq+npp58md3d36u3tFWIiIiLIx8eHTp06RSdOnCAPDw+KiYkRxjs6OkilUlFsbCyp1WrKzs4mc3Nz+vbbb4WYkpISMjY2po8//phqa2vpnXfeIVNTU6qpqbmrXHQVHh5OWVlZpFarqaqqip588klycXGh7u5uISYxMZGmT59OBQUFVF5eTo899hgFBQUJ44ODgzRv3jwKDQ2lyspKOnz4MNna2tKbb74pxFy+fJksLCzo1VdfpdraWsrIyCBjY2PKzc0VYnS5RsbLRVcHDx6kQ4cO0YULF6i+vp7eeustMjU1JbVaLdma/+306dPk5uZG8+fPp5SUFJ3nexBrT0tLo7lz51Jra6vwunbtmqRrJiK6ceMGubq6UkJCApWVldHly5cpLy+PLl26JMRI9d7W3t4uOt/5+fkEgAoLC4lIuud8snBDdA8WLlxISUlJwvbQ0BA5OTnRli1b9JjV2P7dEGm1WnJwcKBPPvlE2KfRaMjMzIyys7OJiKi2tpYA0JkzZ4SYI0eOkEwmoz///JOIiL7++muytramvr4+ISY1NZW8vLyE7ejoaFq2bJkon4CAAHr55Zd1zuVetLe3EwAqLi4Wjm1qako///yzEFNXV0cAqLS0lIhuNZNGRkbU1tYmxGRmZpJSqRRqfeONN2ju3LmiuVauXEnh4eHC9njXiC653Atra2v6/vvvDaLmrq4u8vT0pPz8fFqyZInQEEm19rS0NPLx8RlzTKo1E926vyxatOi244Z0b0tJSaFHHnmEtFqtpM/5ZOFHZhPU39+PiooKhIaGCvuMjIwQGhqK0tJSPWamm8bGRrS1tYnyt7S0REBAgJB/aWkprKys4O/vL8SEhobCyMgIZWVlQszixYshl8uFmPDwcNTX1+Pvv/8WYkbOMxwzPI8uudyLjo4OAICNjQ0AoKKiAgMDA6L5Zs2aBRcXF1Ht3t7eUKlUopw7Oztx/vx5nerS5RrRJZeJGBoaQk5ODnp6ehAYGGgQNSclJWHZsmWj8pNy7RcvXoSTkxNmzJiB2NhYNDc3S77mgwcPwt/fHytWrIC9vT18fX2xbds2YdxQ7m39/f3YuXMnVq9eDZlMJulzPlm4IZqg69evY2hoSHRhAYBKpUJbW5uestLdcI53yr+trQ329vaicRMTE9jY2IhixjrGyDluFzNyfLxcJkqr1WL9+vUIDg7GvHnzhPnkcjmsrKzumNNE6+rs7ERvb69O14guudyNmpoaKBQKmJmZITExEfv27cOcOXMkXTMA5OTk4OzZs9iyZcuoManWHhAQgO3btyM3NxeZmZlobGzE448/jq6uLsnWDACXL19GZmYmPD09kZeXhzVr1mDdunXYsWOHKHep39v2798PjUaDhIQEYS6pnvPJwr92zyQtKSkJarUaJ0+e1Hcqk8LLywtVVVXo6OjAnj17EB8fj+LiYn2ndV+1tLQgJSUF+fn5eOihh/SdzqSJjIwU/p4/fz4CAgLg6uqK3bt3w9zcXI+Z3V9arRb+/v7YvHkzAMDX1xdqtRrffPMN4uPj9Zzd5Pnhhx8QGRkJJycnfaciGfwO0QTZ2trC2Nh41Kfmr169CgcHBz1lpbvhHO+Uv4ODA9rb20Xjg4ODuHHjhihmrGOMnON2MSPHx8tlIpKTk/Hrr7+isLAQDz/8sLDfwcEB/f390Gg0d8xponUplUqYm5vrdI3oksvdkMvl8PDwgJ+fH7Zs2QIfHx9s3bpV0jVXVFSgvb0dCxYsgImJCUxMTFBcXIwvv/wSJiYmUKlUkq19JCsrK8ycOROXLl2S9Pl2dHTEnDlzRPtmz54tPC40hHvblStX8Ntvv+GFF14Q9kn5nE8WbogmSC6Xw8/PDwUFBcI+rVaLgoICBAYG6jEz3bi7u8PBwUGUf2dnJ8rKyoT8AwMDodFoUFFRIcQcO3YMWq0WAQEBQszx48cxMDAgxOTn58PLywvW1tZCzMh5hmOG59Ell7tBREhOTsa+fftw7NgxuLu7i8b9/Pxgamoqmq++vh7Nzc2i2mtqakQ3zfz8fCiVSuFmPF5dulwjuuRyL7RaLfr6+iRdc0hICGpqalBVVSW8/P39ERsbK/wt1dpH6u7uRkNDAxwdHSV9voODg0d9jcaFCxfg6uoKQNr3tmFZWVmwt7fHsmXLhH1SPueTRt+f6n6Q5eTkkJmZGW3fvp1qa2vppZdeIisrK9En+PWpq6uLKisrqbKykgDQ559/TpWVlXTlyhUiurUc1MrKig4cOEDnzp2jqKioMZem+vr6UllZGZ08eZI8PT1FS1M1Gg2pVCqKi4sjtVpNOTk5ZGFhMWppqomJCX366adUV1dHaWlpYy5NHS8XXa1Zs4YsLS2pqKhItET15s2bQkxiYiK5uLjQsWPHqLy8nAIDAykwMFAYH16eGhYWRlVVVZSbm0t2dnZjLk99/fXXqa6ujr766qsxl6eOd42Ml4uuNm7cSMXFxdTY2Ejnzp2jjRs3kkwmo6NHj0q25tsZucpMqrVv2LCBioqKqLGxkUpKSig0NJRsbW2pvb1dsjUT3fpqBRMTE/rggw/o4sWLtGvXLrKwsKCdO3cKMVK9txHdWtHl4uJCqampo8akes4nCzdE9ygjI4NcXFxILpfTwoUL6dSpU/pOSVBYWEgARr3i4+OJ6NaS0HfffZdUKhWZmZlRSEgI1dfXi47x119/UUxMDCkUClIqlbRq1Srq6uoSxVRXV9OiRYvIzMyMnJ2d6cMPPxyVy+7du2nmzJkkl8tp7ty5dOjQIdG4LrnoaqyaAVBWVpYQ09vbS6+88gpZW1uThYUFPfvss9Ta2io6TlNTE0VGRpK5uTnZ2trShg0baGBgQBRTWFhIjz76KMnlcpoxY4ZojmHjXSO65KKL1atXk6urK8nlcrKzs6OQkBChGZJqzbfz74ZIirWvXLmSHB0dSS6Xk7OzM61cuVL0XTxSrHnYL7/8QvPmzSMzMzOaNWsWfffdd6Jxqd7biIjy8vIIwJjHkPI5nwwyIiK9vDXFGGOMMfYfwZ8hYowxxpjB44aIMcYYYwaPGyLGGGOMGTxuiBhjjDFm8LghYowxxpjB44aIMcYYYwaPGyLGGGOMGTxuiBhj/1kJCQl45pln9J0GY8wA8K/dM8b0QiaT3XE8LS0NW7duhb6/OzYhIQEajQb79+/Xax6MsfuLGyLGmF60trYKf//000947733RD/aqVAooFAo9JEaY8wA8SMzxpheODg4CC9LS0vIZDLRPoVCMeqR2dKlS7F27VqsX78e1tbWUKlU2LZtG3p6erBq1SpMnToVHh4eOHLkiGgutVqNyMhIKBQKqFQqxMXF4fr168L4nj174O3tDXNzc0ybNg2hoaHo6enBpk2bsGPHDhw4cAAymQwymQxFRUUAgJaWFkRHR8PKygo2NjaIiopCU1OTcMzh3NPT02FnZwelUonExET09/ePOy9jbPJxQ8QYe6Ds2LEDtra2OH36NNauXYs1a9ZgxYoVCAoKwtmzZxEWFoa4uDjcvHkTAKDRaPDEE0/A19cX5eXlyM3NxdWrVxEdHQ3g1jtVMTExWL16Nerq6lBUVITly5eDiPDaa68hOjoaERERaG1tRWtrK4KCgjAwMIDw8HBMnToVJ06cQElJCRQKBSIiIkQNT0FBgXDM7Oxs7N27F+np6ePOyxjTA73+tCxjjBFRVlYWWVpajtofHx9PUVFRwvaSJUto0aJFwvbg4CBNmTKF4uLihH2tra0EgEpLS4mI6P3336ewsDDRcVtaWoRfDK+oqCAA1NTUNGZu/86BiOjHH38kLy8v0mq1wr6+vj4yNzenvLw84f9sbGyop6dHiMnMzCSFQkFDQ0PjzssYm1z8GSLG2ANl/vz5wt/GxsaYNm0avL29hX0qlQoA0N7eDgCorq5GYWHhmJ9HamhoQFhYGEJCQuDt7Y3w8HCEhYXhueeeg7W19W1zqK6uxqVLlzB16lTR/n/++QcNDQ3Cto+PDywsLITtwMBAdHd3o6WlBT4+Pnc9L2Ps/uGGiDH2QDE1NRVty2Qy0b7h1WtarRYA0N3djaeeegofffTRqGM5OjrC2NgY+fn5+P3333H06FFkZGTg7bffRllZGdzd3cfMobu7G35+fti1a9eoMTs7O53qmMi8jLH7hz9DxBiTtAULFuD8+fNwc3ODh4eH6DVlyhQAt5qo4OBgpKeno7KyEnK5HPv27QMAyOVyDA0NjTrmxYsXYW9vP+qYlpaWQlx1dTV6e3uF7VOnTkGhUGD69OnjzssYm1zcEDHGJC0pKQk3btxATEwMzpw5g4aGBuTl5WHVqlUYGhpCWVkZNm/ejPLycjQ3N2Pv3r24du0aZs+eDQBwc3PDuXPnUF9fj+vXr2NgYACxsbGwtbVFVFQUTpw4gcbGRhQVFWHdunX4448/hLn7+/vx/PPPo7a2FocPH0ZaWhqSk5NhZGQ07ryMscnFj8wYY5Lm5OSEkpISpKamIiwsDH19fXB1dUVERASMjIygVCpx/PhxfPHFF+js7ISrqys+++wzREZGAgBefPFFFBUVwd/fH93d3SgsLMTSpUtx/PhxpKamYvny5ejq6oKzszNCQkKgVCqFuUNCQuDp6YnFixejr68PMTEx2LRpEwCMOy9jbHLJiHiNJ2OM/b/xN1wz9mDhR2aMMcYYM3jcEDHGGGPM4PEjM8YYY4wZPH6HiDHGGGMGjxsixhhjjBk8bogYY4wxZvC4IWKMMcaYweOGiDHGGGMGjxsixhhjjBk8bogYY4wxZvC4IWKMMcaYweOGiDHGGGMG73+8olTRcAuXOwAAAABJRU5ErkJggg==\n"
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DT2YA3QWSC_O"
      },
      "execution_count": 9,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyO/N+oYUP24sx1rKzU346dK",
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    },
    "kernelspec": {
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    },
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    },
    "accelerator": "GPU",
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            "_view_name": "OutputView",
            "layout": "IPY_MODEL_57e7cdc331084440b97d395bb03d3e0b",
            "msg_id": "",
            "outputs": [
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