diff --git a/a2c_sb3_panda_reach.ipynb b/a2c_sb3_panda_reach.ipynb
index 9a6bbcff98c4de3a269b44bdcf9dee456ff54f4a..d86d8d91595346cdfdb062dc0f25a5fa032a771b 100644
--- a/a2c_sb3_panda_reach.ipynb
+++ b/a2c_sb3_panda_reach.ipynb
@@ -1,45 +1,3318 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "k-Am822Jb3rY",
+        "outputId": "27dae454-cdd8-4f9f-f8a2-619d2fe7400d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: wandb in /usr/local/lib/python3.11/dist-packages (0.19.6)\n",
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+            "Collecting stable-baselines3\n",
+            "  Downloading stable_baselines3-2.5.0-py3-none-any.whl.metadata (4.8 kB)\n",
+            "Requirement already satisfied: gymnasium<1.1.0,>=0.29.1 in /usr/local/lib/python3.11/dist-packages (from stable-baselines3) (1.0.0)\n",
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+            "Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch<3.0,>=2.3->stable-baselines3)\n",
+            "  Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
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+            "  Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch<3.0,>=2.3->stable-baselines3)\n",
+            "  Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch<3.0,>=2.3->stable-baselines3)\n",
+            "  Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cublas-cu12==12.4.5.8 (from torch<3.0,>=2.3->stable-baselines3)\n",
+            "  Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cufft-cu12==11.2.1.3 (from torch<3.0,>=2.3->stable-baselines3)\n",
+            "  Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-curand-cu12==10.3.5.147 (from torch<3.0,>=2.3->stable-baselines3)\n",
+            "  Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch<3.0,>=2.3->stable-baselines3)\n",
+            "  Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch<3.0,>=2.3->stable-baselines3)\n",
+            "  Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
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+            "Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch<3.0,>=2.3->stable-baselines3)\n",
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+            "\u001b[?25hDownloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)\n",
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+            "\u001b[?25hDownloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
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+            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m48.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, stable-baselines3\n",
+            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
+            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
+            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-curand-cu12\n",
+            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
+            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
+            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
+            "  Attempting uninstall: nvidia-cufft-cu12\n",
+            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
+            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
+            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
+            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
+            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
+            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
+            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cublas-cu12\n",
+            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
+            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
+            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
+            "  Attempting uninstall: nvidia-cusparse-cu12\n",
+            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
+            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
+            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
+            "  Attempting uninstall: nvidia-cudnn-cu12\n",
+            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
+            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
+            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
+            "  Attempting uninstall: nvidia-cusolver-cu12\n",
+            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
+            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
+            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
+            "Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 stable-baselines3-2.5.0\n",
+            "Requirement already satisfied: wandb in /usr/local/lib/python3.11/dist-packages (0.19.6)\n",
+            "Requirement already satisfied: tensorboard in /usr/local/lib/python3.11/dist-packages (2.18.0)\n",
+            "Requirement already satisfied: stable-baselines3 in /usr/local/lib/python3.11/dist-packages (2.5.0)\n",
+            "Requirement already satisfied: gymnasium in /usr/local/lib/python3.11/dist-packages (1.0.0)\n",
+            "Collecting shimmy\n",
+            "  Downloading Shimmy-2.0.0-py3-none-any.whl.metadata (3.5 kB)\n",
+            "Requirement already satisfied: click!=8.0.0,>=7.1 in /usr/local/lib/python3.11/dist-packages (from wandb) (8.1.8)\n",
+            "Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from wandb) (0.4.0)\n",
+            "Requirement already satisfied: gitpython!=3.1.29,>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from wandb) (3.1.44)\n",
+            "Requirement already satisfied: platformdirs in /usr/local/lib/python3.11/dist-packages (from wandb) (4.3.6)\n",
+            "Requirement already satisfied: protobuf!=4.21.0,!=5.28.0,<6,>=3.19.0 in /usr/local/lib/python3.11/dist-packages (from wandb) (4.25.6)\n",
+            "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.11/dist-packages (from wandb) (5.9.5)\n",
+            "Requirement already satisfied: pydantic<3,>=2.6 in /usr/local/lib/python3.11/dist-packages (from wandb) (2.10.6)\n",
+            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.11/dist-packages (from wandb) (6.0.2)\n",
+            "Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from wandb) (2.32.3)\n",
+            "Requirement already satisfied: sentry-sdk>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from wandb) (2.22.0)\n",
+            "Requirement already satisfied: setproctitle in /usr/local/lib/python3.11/dist-packages (from wandb) (1.3.4)\n",
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from wandb) (75.1.0)\n",
+            "Requirement already satisfied: typing-extensions<5,>=4.4 in /usr/local/lib/python3.11/dist-packages (from wandb) (4.12.2)\n",
+            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.11/dist-packages (from tensorboard) (1.4.0)\n",
+            "Requirement already satisfied: grpcio>=1.48.2 in /usr/local/lib/python3.11/dist-packages (from tensorboard) (1.70.0)\n",
+            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard) (3.7)\n",
+            "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard) (1.26.4)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorboard) (24.2)\n",
+            "Requirement already satisfied: six>1.9 in /usr/local/lib/python3.11/dist-packages (from tensorboard) (1.17.0)\n",
+            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard) (0.7.2)\n",
+            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard) (3.1.3)\n",
+            "Requirement already satisfied: torch<3.0,>=2.3 in /usr/local/lib/python3.11/dist-packages (from stable-baselines3) (2.5.1+cu124)\n",
+            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.11/dist-packages (from stable-baselines3) (3.1.1)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from stable-baselines3) (2.2.2)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from stable-baselines3) (3.10.0)\n",
+            "Requirement already satisfied: farama-notifications>=0.0.1 in /usr/local/lib/python3.11/dist-packages (from gymnasium) (0.0.4)\n",
+            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.11/dist-packages (from gitpython!=3.1.29,>=1.0.0->wandb) (4.0.12)\n",
+            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.11/dist-packages (from pydantic<3,>=2.6->wandb) (0.7.0)\n",
+            "Requirement already satisfied: pydantic-core==2.27.2 in /usr/local/lib/python3.11/dist-packages (from pydantic<3,>=2.6->wandb) (2.27.2)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.0.0->wandb) (3.4.1)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.0.0->wandb) (3.10)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.0.0->wandb) (2.3.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.0.0->wandb) (2025.1.31)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (3.17.0)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (3.4.2)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (3.1.5)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (2024.10.0)\n",
+            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (12.4.127)\n",
+            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (12.4.127)\n",
+            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (12.4.127)\n",
+            "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (9.1.0.70)\n",
+            "Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (12.4.5.8)\n",
+            "Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (11.2.1.3)\n",
+            "Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (10.3.5.147)\n",
+            "Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (11.6.1.9)\n",
+            "Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (12.3.1.170)\n",
+            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (2.21.5)\n",
+            "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (12.4.127)\n",
+            "Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (12.4.127)\n",
+            "Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (3.1.0)\n",
+            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch<3.0,>=2.3->stable-baselines3) (1.13.1)\n",
+            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch<3.0,>=2.3->stable-baselines3) (1.3.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard) (3.0.2)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->stable-baselines3) (1.3.1)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->stable-baselines3) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->stable-baselines3) (4.56.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->stable-baselines3) (1.4.8)\n",
+            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib->stable-baselines3) (11.1.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->stable-baselines3) (3.2.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib->stable-baselines3) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->stable-baselines3) (2025.1)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->stable-baselines3) (2025.1)\n",
+            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.11/dist-packages (from gitdb<5,>=4.0.1->gitpython!=3.1.29,>=1.0.0->wandb) (5.0.2)\n",
+            "Downloading Shimmy-2.0.0-py3-none-any.whl (30 kB)\n",
+            "Installing collected packages: shimmy\n",
+            "Successfully installed shimmy-2.0.0\n",
+            "Collecting huggingface_sb3\n",
+            "  Downloading huggingface_sb3-3.0-py3-none-any.whl.metadata (6.3 kB)\n",
+            "Requirement already satisfied: huggingface-hub~=0.8 in /usr/local/lib/python3.11/dist-packages (from huggingface_sb3) (0.28.1)\n",
+            "Requirement already satisfied: pyyaml~=6.0 in /usr/local/lib/python3.11/dist-packages (from huggingface_sb3) (6.0.2)\n",
+            "Requirement already satisfied: wasabi in /usr/local/lib/python3.11/dist-packages (from huggingface_sb3) (1.1.3)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from huggingface_sb3) (1.26.4)\n",
+            "Requirement already satisfied: cloudpickle>=1.6 in /usr/local/lib/python3.11/dist-packages (from huggingface_sb3) (3.1.1)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface-hub~=0.8->huggingface_sb3) (3.17.0)\n",
+            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub~=0.8->huggingface_sb3) (2024.10.0)\n",
+            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub~=0.8->huggingface_sb3) (24.2)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from huggingface-hub~=0.8->huggingface_sb3) (2.32.3)\n",
+            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub~=0.8->huggingface_sb3) (4.67.1)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub~=0.8->huggingface_sb3) (4.12.2)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub~=0.8->huggingface_sb3) (3.4.1)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub~=0.8->huggingface_sb3) (3.10)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub~=0.8->huggingface_sb3) (2.3.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub~=0.8->huggingface_sb3) (2025.1.31)\n",
+            "Downloading huggingface_sb3-3.0-py3-none-any.whl (9.7 kB)\n",
+            "Installing collected packages: huggingface_sb3\n",
+            "Successfully installed huggingface_sb3-3.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "! pip install wandb tensorboard\n",
+        "! pip install stable-baselines3\n",
+        "! pip install wandb tensorboard stable-baselines3 gymnasium shimmy\n",
+        "! pip install huggingface_sb3"
+      ]
     },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-rkwVtpujKtz"
+      },
+      "source": [
+        "### Get familiar with Stable-Baselines3"
+      ]
     },
-    "language_info": {
-      "name": "python"
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17,
+          "referenced_widgets": [
+            "a52deb23470f4f9d9acfe28e69f47fb5",
+            "c7294d001a9c44aca712bba2e7141b35",
+            "659607dd3c294904913ccb88a2ecfea5",
+            "ec4b7fdf9f344b5eb5aabfe00395ddea",
+            "4064541754324b308f9e02565edc7fc2",
+            "cc92a18e333e454eaf4d2b27ccad782b",
+            "136b0ee69b6a4012a1c4a92ea45a352e",
+            "02dcba9d3e194f79890227e180c37474",
+            "6376dea1e82f4c159ea5062c1b3b14ef",
+            "1a6532f14ca74a479f01155019a2a30f",
+            "3f9f02c927bb4e0c9e0002703189fbfa",
+            "c8a5fea9ebed4821821592ae85b0af71",
+            "439ac621f2cb4c0d91749ee09729453b",
+            "f116aadb2ef94eb8aa8a6b43c7b4fb5d",
+            "4f814644155549caa91d2d81d9333740",
+            "3675a21548ee4857b98b3bc0a9c206f3",
+            "463f22dcd9da484f98795b42c7406fb4",
+            "632684e2f5e648f3ad8eb6f65acbdd6b",
+            "b5bc5fcb6fea4586839b5dc6e43ce0f9",
+            "decc3d18711a459badab9c4def213303"
+          ]
+        },
+        "id": "dWr7eVP7x5r5",
+        "outputId": "fb6dd84c-a12f-4d1e-b1fc-a3f694704037"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a52deb23470f4f9d9acfe28e69f47fb5"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from huggingface_hub import notebook_login\n",
+        "#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\n",
+        "notebook_login()"
+      ]
     },
-    "widgets": {
-      "application/vnd.jupyter.widget-state+json": {
-        "6d61991108934e0d8e819af335834917": {
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 373
+        },
+        "id": "OEMHjcAijHgB",
+        "outputId": "ada91d96-a577-4ebb-a68d-1ee792c71599"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Using cpu device\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 24.3     |\n",
+            "|    ep_rew_mean        | 24.3     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 420      |\n",
+            "|    iterations         | 100      |\n",
+            "|    time_elapsed       | 1        |\n",
+            "|    total_timesteps    | 500      |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.653   |\n",
+            "|    explained_variance | -0.722   |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 99       |\n",
+            "|    policy_loss        | 2.18     |\n",
+            "|    value_loss         | 17.5     |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 24       |\n",
+            "|    ep_rew_mean        | 24       |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 454      |\n",
+            "|    iterations         | 200      |\n",
+            "|    time_elapsed       | 2        |\n",
+            "|    total_timesteps    | 1000     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.619   |\n",
+            "|    explained_variance | -0.0863  |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 199      |\n",
+            "|    policy_loss        | 1.87     |\n",
+            "|    value_loss         | 8.7      |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 22.2     |\n",
+            "|    ep_rew_mean        | 22.2     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 457      |\n",
+            "|    iterations         | 300      |\n",
+            "|    time_elapsed       | 3        |\n",
+            "|    total_timesteps    | 1500     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.63    |\n",
+            "|    explained_variance | -0.139   |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 299      |\n",
+            "|    policy_loss        | 1.65     |\n",
+            "|    value_loss         | 7.89     |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 22.7     |\n",
+            "|    ep_rew_mean        | 22.7     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 474      |\n",
+            "|    iterations         | 400      |\n",
+            "|    time_elapsed       | 4        |\n",
+            "|    total_timesteps    | 2000     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.672   |\n",
+            "|    explained_variance | 0.0291   |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 399      |\n",
+            "|    policy_loss        | 1.41     |\n",
+            "|    value_loss         | 6.71     |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 23.7     |\n",
+            "|    ep_rew_mean        | 23.7     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 489      |\n",
+            "|    iterations         | 500      |\n",
+            "|    time_elapsed       | 5        |\n",
+            "|    total_timesteps    | 2500     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.508   |\n",
+            "|    explained_variance | 0.0956   |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 499      |\n",
+            "|    policy_loss        | 2.08     |\n",
+            "|    value_loss         | 6.14     |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 26.6     |\n",
+            "|    ep_rew_mean        | 26.6     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 501      |\n",
+            "|    iterations         | 600      |\n",
+            "|    time_elapsed       | 5        |\n",
+            "|    total_timesteps    | 3000     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.662   |\n",
+            "|    explained_variance | -0.00099 |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 599      |\n",
+            "|    policy_loss        | 1.13     |\n",
+            "|    value_loss         | 5.36     |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 28.1     |\n",
+            "|    ep_rew_mean        | 28.1     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 509      |\n",
+            "|    iterations         | 700      |\n",
+            "|    time_elapsed       | 6        |\n",
+            "|    total_timesteps    | 3500     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.627   |\n",
+            "|    explained_variance | 0.024    |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 699      |\n",
+            "|    policy_loss        | -10.7    |\n",
+            "|    value_loss         | 642      |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 30       |\n",
+            "|    ep_rew_mean        | 30       |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 516      |\n",
+            "|    iterations         | 800      |\n",
+            "|    time_elapsed       | 7        |\n",
+            "|    total_timesteps    | 4000     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.663   |\n",
+            "|    explained_variance | 0.00948  |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 799      |\n",
+            "|    policy_loss        | 1.05     |\n",
+            "|    value_loss         | 4.53     |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 33.5     |\n",
+            "|    ep_rew_mean        | 33.5     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 519      |\n",
+            "|    iterations         | 900      |\n",
+            "|    time_elapsed       | 8        |\n",
+            "|    total_timesteps    | 4500     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.648   |\n",
+            "|    explained_variance | -0.00115 |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 899      |\n",
+            "|    policy_loss        | 0.79     |\n",
+            "|    value_loss         | 4.03     |\n",
+            "------------------------------------\n",
+            "-------------------------------------\n",
+            "| rollout/              |           |\n",
+            "|    ep_len_mean        | 36.1      |\n",
+            "|    ep_rew_mean        | 36.1      |\n",
+            "| time/                 |           |\n",
+            "|    fps                | 521       |\n",
+            "|    iterations         | 1000      |\n",
+            "|    time_elapsed       | 9         |\n",
+            "|    total_timesteps    | 5000      |\n",
+            "| train/                |           |\n",
+            "|    entropy_loss       | -0.623    |\n",
+            "|    explained_variance | -0.000646 |\n",
+            "|    learning_rate      | 0.0007    |\n",
+            "|    n_updates          | 999       |\n",
+            "|    policy_loss        | 0.915     |\n",
+            "|    value_loss         | 3.6       |\n",
+            "-------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 39.5     |\n",
+            "|    ep_rew_mean        | 39.5     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 526      |\n",
+            "|    iterations         | 1100     |\n",
+            "|    time_elapsed       | 10       |\n",
+            "|    total_timesteps    | 5500     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.555   |\n",
+            "|    explained_variance | 0.00192  |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 1099     |\n",
+            "|    policy_loss        | 0.82     |\n",
+            "|    value_loss         | 3.08     |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 43.3     |\n",
+            "|    ep_rew_mean        | 43.3     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 530      |\n",
+            "|    iterations         | 1200     |\n",
+            "|    time_elapsed       | 11       |\n",
+            "|    total_timesteps    | 6000     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.479   |\n",
+            "|    explained_variance | 0.000166 |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 1199     |\n",
+            "|    policy_loss        | 1.29     |\n",
+            "|    value_loss         | 2.62     |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 49.3     |\n",
+            "|    ep_rew_mean        | 49.3     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 530      |\n",
+            "|    iterations         | 1300     |\n",
+            "|    time_elapsed       | 12       |\n",
+            "|    total_timesteps    | 6500     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.533   |\n",
+            "|    explained_variance | 0.00133  |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 1299     |\n",
+            "|    policy_loss        | 0.644    |\n",
+            "|    value_loss         | 2.19     |\n",
+            "------------------------------------\n",
+            "-------------------------------------\n",
+            "| rollout/              |           |\n",
+            "|    ep_len_mean        | 52.9      |\n",
+            "|    ep_rew_mean        | 52.9      |\n",
+            "| time/                 |           |\n",
+            "|    fps                | 533       |\n",
+            "|    iterations         | 1400      |\n",
+            "|    time_elapsed       | 13        |\n",
+            "|    total_timesteps    | 7000      |\n",
+            "| train/                |           |\n",
+            "|    entropy_loss       | -0.479    |\n",
+            "|    explained_variance | -0.000509 |\n",
+            "|    learning_rate      | 0.0007    |\n",
+            "|    n_updates          | 1399      |\n",
+            "|    policy_loss        | 0.566     |\n",
+            "|    value_loss         | 1.82      |\n",
+            "-------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 57       |\n",
+            "|    ep_rew_mean        | 57       |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 536      |\n",
+            "|    iterations         | 1500     |\n",
+            "|    time_elapsed       | 13       |\n",
+            "|    total_timesteps    | 7500     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.561   |\n",
+            "|    explained_variance | 4.95e-06 |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 1499     |\n",
+            "|    policy_loss        | 0.387    |\n",
+            "|    value_loss         | 1.46     |\n",
+            "------------------------------------\n",
+            "-------------------------------------\n",
+            "| rollout/              |           |\n",
+            "|    ep_len_mean        | 60.7      |\n",
+            "|    ep_rew_mean        | 60.7      |\n",
+            "| time/                 |           |\n",
+            "|    fps                | 538       |\n",
+            "|    iterations         | 1600      |\n",
+            "|    time_elapsed       | 14        |\n",
+            "|    total_timesteps    | 8000      |\n",
+            "| train/                |           |\n",
+            "|    entropy_loss       | -0.376    |\n",
+            "|    explained_variance | -6.74e-05 |\n",
+            "|    learning_rate      | 0.0007    |\n",
+            "|    n_updates          | 1599      |\n",
+            "|    policy_loss        | 0.585     |\n",
+            "|    value_loss         | 1.13      |\n",
+            "-------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 65.3     |\n",
+            "|    ep_rew_mean        | 65.3     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 541      |\n",
+            "|    iterations         | 1700     |\n",
+            "|    time_elapsed       | 15       |\n",
+            "|    total_timesteps    | 8500     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.268   |\n",
+            "|    explained_variance | 1.09e-05 |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 1699     |\n",
+            "|    policy_loss        | 0.103    |\n",
+            "|    value_loss         | 0.85     |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 68.6     |\n",
+            "|    ep_rew_mean        | 68.6     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 543      |\n",
+            "|    iterations         | 1800     |\n",
+            "|    time_elapsed       | 16       |\n",
+            "|    total_timesteps    | 9000     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.474   |\n",
+            "|    explained_variance | 0.000158 |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 1799     |\n",
+            "|    policy_loss        | 0.164    |\n",
+            "|    value_loss         | 0.603    |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 72.6     |\n",
+            "|    ep_rew_mean        | 72.6     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 545      |\n",
+            "|    iterations         | 1900     |\n",
+            "|    time_elapsed       | 17       |\n",
+            "|    total_timesteps    | 9500     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.486   |\n",
+            "|    explained_variance | 4.12e-05 |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 1899     |\n",
+            "|    policy_loss        | 0.291    |\n",
+            "|    value_loss         | 0.398    |\n",
+            "------------------------------------\n",
+            "-------------------------------------\n",
+            "| rollout/              |           |\n",
+            "|    ep_len_mean        | 76.6      |\n",
+            "|    ep_rew_mean        | 76.6      |\n",
+            "| time/                 |           |\n",
+            "|    fps                | 547       |\n",
+            "|    iterations         | 2000      |\n",
+            "|    time_elapsed       | 18        |\n",
+            "|    total_timesteps    | 10000     |\n",
+            "| train/                |           |\n",
+            "|    entropy_loss       | -0.351    |\n",
+            "|    explained_variance | -0.000275 |\n",
+            "|    learning_rate      | 0.0007    |\n",
+            "|    n_updates          | 1999      |\n",
+            "|    policy_loss        | 0.4       |\n",
+            "|    value_loss         | 0.236     |\n",
+            "-------------------------------------\n"
+          ]
+        },
+        {
+          "data": {
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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import gymnasium as gym\n",
+        "from stable_baselines3 import A2C\n",
+        "from stable_baselines3.common.env_util import make_vec_env\n",
+        "import numpy as np\n",
+        "\n",
+        "# Créer un environnement vectorisé\n",
+        "vec_env = make_vec_env(\"CartPole-v1\", n_envs=1)\n",
+        "\n",
+        "# Initialiser le modèle A2C avec la politique MLP\n",
+        "model = A2C(\"MlpPolicy\", vec_env, verbose=1)\n",
+        "\n",
+        "# Entraîner le modèle\n",
+        "model.learn(total_timesteps=10000)\n",
+        "\n",
+        "# Sauvegarder le modèle\n",
+        "model.save(\"a2c_cartpole\")\n",
+        "\n",
+        "# Charger le modèle après l'entraînement\n",
+        "del model  # Supprimer le modèle pour simuler l'enregistrement et le rechargement\n",
+        "model = A2C.load(\"a2c_cartpole\")\n",
+        "\n",
+        "# Réinitialiser l'environnement\n",
+        "obs = vec_env.reset()\n",
+        "\n",
+        "# Variables pour suivre les récompenses et les épisodes\n",
+        "episode_rewards = []  # Récompenses totales par épisode\n",
+        "current_rewards = [0] * vec_env.num_envs  # Suivre les récompenses de chaque environnement\n",
+        "num_episodes = 0  # Compter le nombre d'épisodes terminés\n",
+        "\n",
+        "# Liste pour stocker les images pour la vidéo\n",
+        "frames = []\n",
+        "\n",
+        "# Exécuter le modèle et capturer des images pour la vidéo\n",
+        "while num_episodes < 500:\n",
+        "    action, _states = model.predict(obs)\n",
+        "    obs, rewards, dones, info = vec_env.step(action)\n",
+        "    # Mettre à jour les récompenses et vérifier la fin de l'épisode\n",
+        "    for i in range(vec_env.num_envs):\n",
+        "        current_rewards[i] += rewards[i]\n",
+        "\n",
+        "        if dones[i]:\n",
+        "            episode_rewards.append(current_rewards[i])\n",
+        "            current_rewards[i] = 0  # Réinitialiser pour le prochain épisode\n",
+        "            num_episodes += 1\n",
+        "\n",
+        "\n",
+        "# Fermer l'environnement après l'évaluation\n",
+        "vec_env.close()\n",
+        "\n",
+        "\n",
+        "# Afficher\n",
+        "import matplotlib.pyplot as plt\n",
+        "plt.plot(episode_rewards)\n",
+        "plt.xlabel(\"Episode\")\n",
+        "plt.ylabel(\"Total Reward\")\n",
+        "plt.title(\"Total Reward per Episode - A2C\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CZOZmiAwyqNE"
+      },
+      "source": [
+        "upload the model\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489,
+          "referenced_widgets": [
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+            "10bbdba263394d43937799fb02a5308e",
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+            "8ab12f359394454ea92d8810ff7a3f21",
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+            "64b05aba20224e9caebc639dc6dd7c8f",
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+            "c0b5a4535a3b4ca2b0e7c86e1b3ab5ec",
+            "53e05aff6c98440e852b7d426197edb1",
+            "666bb151dce54bec81944df29f2a8a3b",
+            "3d9b748f2b5d460cbba3332dd1ee43b7",
+            "360923166a664f24971d958a204eb04c",
+            "331d5ea6d2a743c2a9bea658cda2e80a",
+            "e8db5be66c3342dc8817b3990fd4fd12",
+            "290118356e7444249cf0ea7a6bfe37f6",
+            "bf2f7a273e7a479b8e97cf5dedcaddb8",
+            "e77629a2b480411692154ed4200c12b4",
+            "b2c8bbf1f1024fe59e602e89b3908d1d"
+          ]
+        },
+        "id": "QoXoWAXjyvbM",
+        "outputId": "b360fad3-510a-4624-923b-e2b8f18e92ca"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[38;5;4mℹ This function will save, evaluate, generate a video of your agent,\n",
+            "create a model card and push everything to the hub. It might take up to 1min.\n",
+            "This is a work in progress: if you encounter a bug, please open an issue.\u001b[0m\n",
+            "Saving video to C:\\Users\\BYCInfo\\AppData\\Local\\Temp\\tmpee8exbb8\\-step-0-to-step-1000.mp4\n",
+            "MoviePy - Building video C:\\Users\\BYCInfo\\AppData\\Local\\Temp\\tmpee8exbb8\\-step-0-to-step-1000.mp4.\n",
+            "MoviePy - Writing video C:\\Users\\BYCInfo\\AppData\\Local\\Temp\\tmpee8exbb8\\-step-0-to-step-1000.mp4\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "                                                                          \r"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MoviePy - Done !\n",
+            "MoviePy - video ready C:\\Users\\BYCInfo\\AppData\\Local\\Temp\\tmpee8exbb8\\-step-0-to-step-1000.mp4\n",
+            "\u001b[38;5;1m✘ 'DummyVecEnv' object has no attribute 'video_recorder'\u001b[0m\n",
+            "\u001b[38;5;1m✘ We are unable to generate a replay of your agent, the package_to_hub\n",
+            "process continues\u001b[0m\n",
+            "\u001b[38;5;1m✘ Please open an issue at\n",
+            "https://github.com/huggingface/huggingface_sb3/issues\u001b[0m\n",
+            "\u001b[38;5;4mℹ Pushing repo oussamab2n/a2c-cartpole to the Hugging Face Hub\u001b[0m\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "290118356e7444249cf0ea7a6bfe37f6",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "a2c-cartpole.zip:   0%|          | 0.00/101k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "bf2f7a273e7a479b8e97cf5dedcaddb8",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "policy.optimizer.pth:   0%|          | 0.00/43.4k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e77629a2b480411692154ed4200c12b4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Upload 3 LFS files:   0%|          | 0/3 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b2c8bbf1f1024fe59e602e89b3908d1d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "policy.pth:   0%|          | 0.00/41.1k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[38;5;4mℹ Your model is pushed to the Hub. You can view your model here:\n",
+            "https://huggingface.co/oussamab2n/a2c-cartpole/tree/main/\u001b[0m\n",
+            "Model successfully uploaded to Hugging Face Hub!\n"
+          ]
+        }
+      ],
+      "source": [
+        "import gymnasium as gym\n",
+        "from stable_baselines3 import A2C\n",
+        "from stable_baselines3.common.monitor import Monitor\n",
+        "from huggingface_sb3 import package_to_hub\n",
+        "\n",
+        "# Load your trained model\n",
+        "model = A2C.load(\"a2c_cartpole\")\n",
+        "\n",
+        "# Create an evaluation environment with render_mode=\"rgb_array\" and wrap it with Monitor\n",
+        "eval_env = gym.make(\"CartPole-v1\", render_mode=\"rgb_array\")\n",
+        "eval_env = Monitor(eval_env)\n",
+        "\n",
+        "# Define your Hugging Face repository name\n",
+        "repo_id = \"oussamab2n/a2c-cartpole\"\n",
+        "\n",
+        "# Upload model to Hugging Face\n",
+        "package_to_hub(\n",
+        "    model=model,\n",
+        "    model_name=\"a2c-cartpole\",\n",
+        "    model_architecture=\"A2C\",\n",
+        "    env_id=\"CartPole-v1\",\n",
+        "    eval_env=eval_env,\n",
+        "    repo_id=repo_id,\n",
+        "    commit_message=\"Upload trained A2C model on CartPole-v1\"\n",
+        ")\n",
+        "\n",
+        "print(\"Model successfully uploaded to Hugging Face Hub!\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xko_YulD3c-o"
+      },
+      "source": [
+        "evaluation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "referenced_widgets": [
+            "b5d5c5d5cb464200b7c955c40cab6b01"
+          ]
+        },
+        "id": "21hr5rXB3bFD",
+        "outputId": "e8758121-92ff-42d5-8e52-5f62ba053c4f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b5d5c5d5cb464200b7c955c40cab6b01",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "a2c-cartpole.zip:   0%|          | 0.00/101k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Episode 1: Total Reward = 500.0\n",
+            "Episode 2: Total Reward = 500.0\n",
+            "Episode 3: Total Reward = 500.0\n",
+            "Episode 4: Total Reward = 500.0\n",
+            "Episode 5: Total Reward = 500.0\n",
+            "Episode 6: Total Reward = 500.0\n",
+            "Episode 7: Total Reward = 500.0\n",
+            "Episode 8: Total Reward = 500.0\n",
+            "Episode 9: Total Reward = 500.0\n",
+            "Episode 10: Total Reward = 500.0\n",
+            "Episode 11: Total Reward = 500.0\n",
+            "Episode 12: Total Reward = 500.0\n",
+            "Episode 13: Total Reward = 500.0\n",
+            "Episode 14: Total Reward = 500.0\n",
+            "Episode 15: Total Reward = 500.0\n",
+            "Episode 16: Total Reward = 500.0\n",
+            "Episode 17: Total Reward = 500.0\n",
+            "Episode 18: Total Reward = 500.0\n",
+            "Episode 19: Total Reward = 500.0\n",
+            "Episode 20: Total Reward = 500.0\n",
+            "Episode 21: Total Reward = 500.0\n",
+            "Episode 22: Total Reward = 500.0\n",
+            "Episode 23: Total Reward = 500.0\n",
+            "Episode 24: Total Reward = 500.0\n",
+            "Episode 25: Total Reward = 500.0\n",
+            "Episode 26: Total Reward = 500.0\n",
+            "Episode 27: Total Reward = 500.0\n",
+            "Episode 28: Total Reward = 500.0\n",
+            "Episode 29: Total Reward = 500.0\n",
+            "Episode 30: Total Reward = 500.0\n",
+            "Episode 31: Total Reward = 500.0\n",
+            "Episode 32: Total Reward = 500.0\n",
+            "Episode 33: Total Reward = 500.0\n",
+            "Episode 34: Total Reward = 500.0\n",
+            "Episode 35: Total Reward = 500.0\n",
+            "Episode 36: Total Reward = 500.0\n",
+            "Episode 37: Total Reward = 500.0\n",
+            "Episode 38: Total Reward = 500.0\n",
+            "Episode 39: Total Reward = 500.0\n",
+            "Episode 40: Total Reward = 500.0\n",
+            "Episode 41: Total Reward = 500.0\n",
+            "Episode 42: Total Reward = 500.0\n",
+            "Episode 43: Total Reward = 500.0\n",
+            "Episode 44: Total Reward = 500.0\n",
+            "Episode 45: Total Reward = 500.0\n",
+            "Episode 46: Total Reward = 500.0\n",
+            "Episode 47: Total Reward = 500.0\n",
+            "Episode 48: Total Reward = 500.0\n",
+            "Episode 49: Total Reward = 500.0\n",
+            "Episode 50: Total Reward = 500.0\n",
+            "Episode 51: Total Reward = 500.0\n",
+            "Episode 52: Total Reward = 500.0\n",
+            "Episode 53: Total Reward = 500.0\n",
+            "Episode 54: Total Reward = 500.0\n",
+            "Episode 55: Total Reward = 500.0\n",
+            "Episode 56: Total Reward = 500.0\n",
+            "Episode 57: Total Reward = 500.0\n",
+            "Episode 58: Total Reward = 500.0\n",
+            "Episode 59: Total Reward = 500.0\n",
+            "Episode 60: Total Reward = 500.0\n",
+            "Episode 61: Total Reward = 500.0\n",
+            "Episode 62: Total Reward = 500.0\n",
+            "Episode 63: Total Reward = 500.0\n",
+            "Episode 64: Total Reward = 500.0\n",
+            "Episode 65: Total Reward = 500.0\n",
+            "Episode 66: Total Reward = 500.0\n",
+            "Episode 67: Total Reward = 500.0\n",
+            "Episode 68: Total Reward = 500.0\n",
+            "Episode 69: Total Reward = 500.0\n",
+            "Episode 70: Total Reward = 500.0\n",
+            "Episode 71: Total Reward = 500.0\n",
+            "Episode 72: Total Reward = 500.0\n",
+            "Episode 73: Total Reward = 500.0\n",
+            "Episode 74: Total Reward = 500.0\n",
+            "Episode 75: Total Reward = 500.0\n",
+            "Episode 76: Total Reward = 500.0\n",
+            "Episode 77: Total Reward = 500.0\n",
+            "Episode 78: Total Reward = 500.0\n",
+            "Episode 79: Total Reward = 500.0\n",
+            "Episode 80: Total Reward = 500.0\n",
+            "Episode 81: Total Reward = 500.0\n",
+            "Episode 82: Total Reward = 500.0\n",
+            "Episode 83: Total Reward = 500.0\n",
+            "Episode 84: Total Reward = 500.0\n",
+            "Episode 85: Total Reward = 500.0\n",
+            "Episode 86: Total Reward = 500.0\n",
+            "Episode 87: Total Reward = 500.0\n",
+            "Episode 88: Total Reward = 500.0\n",
+            "Episode 89: Total Reward = 500.0\n",
+            "Episode 90: Total Reward = 500.0\n",
+            "Episode 91: Total Reward = 500.0\n",
+            "Episode 92: Total Reward = 500.0\n",
+            "Episode 93: Total Reward = 500.0\n",
+            "Episode 94: Total Reward = 500.0\n",
+            "Episode 95: Total Reward = 500.0\n",
+            "Episode 96: Total Reward = 500.0\n",
+            "Episode 97: Total Reward = 500.0\n",
+            "Episode 98: Total Reward = 500.0\n",
+            "Episode 99: Total Reward = 500.0\n",
+            "Episode 100: Total Reward = 500.0\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Evaluation Completed!\n",
+            "Number of Perfect Episodes (Reward == 500): 100 / 100\n"
+          ]
+        }
+      ],
+      "source": [
+        "import gymnasium as gym\n",
+        "from stable_baselines3 import A2C\n",
+        "from stable_baselines3.common.monitor import Monitor\n",
+        "from huggingface_sb3 import load_from_hub\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Define your Hugging Face repository and model file\n",
+        "repo_id = \"oussamab2n/a2c-cartpole\"\n",
+        "filename = \"a2c-cartpole.zip\"\n",
+        "\n",
+        "# Load model from Hugging Face Hub\n",
+        "model_path = load_from_hub(repo_id=repo_id, filename=filename)\n",
+        "model = A2C.load(model_path)\n",
+        "\n",
+        "# Create evaluation environment\n",
+        "eval_env = gym.make(\"CartPole-v1\", render_mode=None)\n",
+        "eval_env = Monitor(eval_env)\n",
+        "\n",
+        "# Initialize tracking variables\n",
+        "num_episodes = 100\n",
+        "perfect_episodes = 0\n",
+        "episode_rewards = []  # List to store the reward of each episode\n",
+        "\n",
+        "# Run evaluation for 100 episodes\n",
+        "for episode in range(num_episodes):\n",
+        "    obs, _ = eval_env.reset()  # Reset at the start of each episode\n",
+        "    done = False\n",
+        "    total_reward = 0\n",
+        "\n",
+        "    while not done:\n",
+        "        action, _ = model.predict(obs, deterministic=True)\n",
+        "        obs, reward, terminated, truncated, _ = eval_env.step(action)  # Gymnasium returns terminated & truncated\n",
+        "        done = terminated or truncated  # Handle both termination and truncation cases\n",
+        "        total_reward += reward\n",
+        "\n",
+        "    # Store the total reward for each episode\n",
+        "    episode_rewards.append(total_reward)\n",
+        "\n",
+        "    # Check if the episode reached a total reward of 500\n",
+        "    if total_reward == 500:\n",
+        "        perfect_episodes += 1\n",
+        "\n",
+        "    print(f\"Episode {episode+1}: Total Reward = {total_reward}\")\n",
+        "\n",
+        "# Plot the total reward for each episode\n",
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.plot(range(1, num_episodes + 1), episode_rewards, marker=\"o\", linestyle=\"-\", color=\"b\", label=\"Episode Reward\")\n",
+        "plt.xlabel(\"Episode\")\n",
+        "plt.ylabel(\"Total Reward\")\n",
+        "plt.title(\"Total Reward Per Episode\")\n",
+        "plt.legend()\n",
+        "plt.grid()\n",
+        "plt.show()\n",
+        "\n",
+        "# Final results\n",
+        "print(\"\\nEvaluation Completed!\")\n",
+        "print(f\"Number of Perfect Episodes (Reward == 500): {perfect_episodes} / {num_episodes}\")\n",
+        "\n",
+        "# Close the environment\n",
+        "eval_env.close()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SxieP2wTkr67"
+      },
+      "source": [
+        "### Get familiar with Weights & Biases\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RKDOi7DWvBQE",
+        "outputId": "ca02cbe6-bd40-4f7c-cdb5-67d2543ae872"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.8/20.8 MB\u001b[0m \u001b[31m78.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
+            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
+            "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit: \n",
+            "\u001b[34m\u001b[1mwandb\u001b[0m: No netrc file found, creating one.\n",
+            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n",
+            "\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+          ]
+        }
+      ],
+      "source": [
+        "! pip install wandb -qU\n",
+        "#0b197edd6d50d8cc0ed00564436ada87f46084fa\n",
+        "! wandb login --relogin\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uRSvh1iYzQUH",
+        "outputId": "33790305-1d03-432b-9b07-b1332c659f2a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n",
+            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mbenyahiamohammedoussama\u001b[0m (\u001b[33mbenyahiamohammedoussama-ecole-central-lyon\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ],
+      "source": [
+        "import wandb\n",
+        "\n",
+        "wandb.login()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 125
+        },
+        "id": "DrrkjdbszeGM",
+        "outputId": "75e6a500-66ae-4db0-fb46-bc5aee836327"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Tracking run with wandb version 0.19.7"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Run data is saved locally in <code>/content/wandb/run-20250222_124637-5aqhfh3z</code>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Syncing run <strong><a href='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/wb_sb3/runs/5aqhfh3z' target=\"_blank\">distinctive-wave-6</a></strong> to <a href='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/wb_sb3' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              " View project at <a href='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/wb_sb3' target=\"_blank\">https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/wb_sb3</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              " View run at <a href='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/wb_sb3/runs/5aqhfh3z' target=\"_blank\">https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/wb_sb3/runs/5aqhfh3z</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/wb_sb3/runs/5aqhfh3z?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
+            ],
+            "text/plain": [
+              "<wandb.sdk.wandb_run.Run at 0x7c057798c4d0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "# Initialize a new run\n",
+        "wandb.init(project=\"wb_sb3\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "s6edpedP0dor",
+        "outputId": "47300af2-3b08-4a47-8a18-ea5175a78c56"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Using cpu device\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 58.2     |\n",
+            "|    ep_rew_mean        | 58.2     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 554      |\n",
+            "|    iterations         | 100      |\n",
+            "|    time_elapsed       | 0        |\n",
+            "|    total_timesteps    | 500      |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.544   |\n",
+            "|    explained_variance | -0.208   |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 99       |\n",
+            "|    policy_loss        | 1.11     |\n",
+            "|    value_loss         | 10.2     |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 58.7     |\n",
+            "|    ep_rew_mean        | 58.7     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 553      |\n",
+            "|    iterations         | 200      |\n",
+            "|    time_elapsed       | 1        |\n",
+            "|    total_timesteps    | 1000     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.493   |\n",
+            "|    explained_variance | -0.311   |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 199      |\n",
+            "|    policy_loss        | 1.79     |\n",
+            "|    value_loss         | 8.13     |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 53.3     |\n",
+            "|    ep_rew_mean        | 53.3     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 553      |\n",
+            "|    iterations         | 300      |\n",
+            "|    time_elapsed       | 2        |\n",
+            "|    total_timesteps    | 1500     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.572   |\n",
+            "|    explained_variance | 0.00166  |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 299      |\n",
+            "|    policy_loss        | 1.24     |\n",
+            "|    value_loss         | 5.54     |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 56.3     |\n",
+            "|    ep_rew_mean        | 56.3     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 555      |\n",
+            "|    iterations         | 400      |\n",
+            "|    time_elapsed       | 3        |\n",
+            "|    total_timesteps    | 2000     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.629   |\n",
+            "|    explained_variance | -0.00653 |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 399      |\n",
+            "|    policy_loss        | 0.855    |\n",
+            "|    value_loss         | 5.41     |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 58       |\n",
+            "|    ep_rew_mean        | 58       |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 557      |\n",
+            "|    iterations         | 500      |\n",
+            "|    time_elapsed       | 4        |\n",
+            "|    total_timesteps    | 2500     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.343   |\n",
+            "|    explained_variance | 0.00255  |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 499      |\n",
+            "|    policy_loss        | 0.0657   |\n",
+            "|    value_loss         | 382      |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 63.6     |\n",
+            "|    ep_rew_mean        | 63.6     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 543      |\n",
+            "|    iterations         | 600      |\n",
+            "|    time_elapsed       | 5        |\n",
+            "|    total_timesteps    | 3000     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.382   |\n",
+            "|    explained_variance | 0.00138  |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 599      |\n",
+            "|    policy_loss        | 0.461    |\n",
+            "|    value_loss         | 256      |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 64.6     |\n",
+            "|    ep_rew_mean        | 64.6     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 530      |\n",
+            "|    iterations         | 700      |\n",
+            "|    time_elapsed       | 6        |\n",
+            "|    total_timesteps    | 3500     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.51    |\n",
+            "|    explained_variance | 0.00348  |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 699      |\n",
+            "|    policy_loss        | 0.722    |\n",
+            "|    value_loss         | 3.78     |\n",
+            "------------------------------------\n",
+            "-------------------------------------\n",
+            "| rollout/              |           |\n",
+            "|    ep_len_mean        | 67.8      |\n",
+            "|    ep_rew_mean        | 67.8      |\n",
+            "| time/                 |           |\n",
+            "|    fps                | 512       |\n",
+            "|    iterations         | 800       |\n",
+            "|    time_elapsed       | 7         |\n",
+            "|    total_timesteps    | 4000      |\n",
+            "| train/                |           |\n",
+            "|    entropy_loss       | -0.409    |\n",
+            "|    explained_variance | -0.000384 |\n",
+            "|    learning_rate      | 0.0007    |\n",
+            "|    n_updates          | 799       |\n",
+            "|    policy_loss        | 1.49      |\n",
+            "|    value_loss         | 3.25      |\n",
+            "-------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 72.2     |\n",
+            "|    ep_rew_mean        | 72.2     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 495      |\n",
+            "|    iterations         | 900      |\n",
+            "|    time_elapsed       | 9        |\n",
+            "|    total_timesteps    | 4500     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.463   |\n",
+            "|    explained_variance | -0.00283 |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 899      |\n",
+            "|    policy_loss        | 0.622    |\n",
+            "|    value_loss         | 2.78     |\n",
+            "------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 75.9     |\n",
+            "|    ep_rew_mean        | 75.9     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 501      |\n",
+            "|    iterations         | 1000     |\n",
+            "|    time_elapsed       | 9        |\n",
+            "|    total_timesteps    | 5000     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.306   |\n",
+            "|    explained_variance | 0.00123  |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 999      |\n",
+            "|    policy_loss        | 0.944    |\n",
+            "|    value_loss         | 2.34     |\n",
+            "------------------------------------\n",
+            "-------------------------------------\n",
+            "| rollout/              |           |\n",
+            "|    ep_len_mean        | 82.3      |\n",
+            "|    ep_rew_mean        | 82.3      |\n",
+            "| time/                 |           |\n",
+            "|    fps                | 505       |\n",
+            "|    iterations         | 1100      |\n",
+            "|    time_elapsed       | 10        |\n",
+            "|    total_timesteps    | 5500      |\n",
+            "| train/                |           |\n",
+            "|    entropy_loss       | -0.447    |\n",
+            "|    explained_variance | -0.000973 |\n",
+            "|    learning_rate      | 0.0007    |\n",
+            "|    n_updates          | 1099      |\n",
+            "|    policy_loss        | 0.402     |\n",
+            "|    value_loss         | 1.91      |\n",
+            "-------------------------------------\n",
+            "-------------------------------------\n",
+            "| rollout/              |           |\n",
+            "|    ep_len_mean        | 88.6      |\n",
+            "|    ep_rew_mean        | 88.6      |\n",
+            "| time/                 |           |\n",
+            "|    fps                | 510       |\n",
+            "|    iterations         | 1200      |\n",
+            "|    time_elapsed       | 11        |\n",
+            "|    total_timesteps    | 6000      |\n",
+            "| train/                |           |\n",
+            "|    entropy_loss       | -0.557    |\n",
+            "|    explained_variance | -5.52e-05 |\n",
+            "|    learning_rate      | 0.0007    |\n",
+            "|    n_updates          | 1199      |\n",
+            "|    policy_loss        | 0.328     |\n",
+            "|    value_loss         | 1.52      |\n",
+            "-------------------------------------\n",
+            "-------------------------------------\n",
+            "| rollout/              |           |\n",
+            "|    ep_len_mean        | 94.6      |\n",
+            "|    ep_rew_mean        | 94.6      |\n",
+            "| time/                 |           |\n",
+            "|    fps                | 514       |\n",
+            "|    iterations         | 1300      |\n",
+            "|    time_elapsed       | 12        |\n",
+            "|    total_timesteps    | 6500      |\n",
+            "| train/                |           |\n",
+            "|    entropy_loss       | -0.471    |\n",
+            "|    explained_variance | -0.000189 |\n",
+            "|    learning_rate      | 0.0007    |\n",
+            "|    n_updates          | 1299      |\n",
+            "|    policy_loss        | 0.475     |\n",
+            "|    value_loss         | 1.17      |\n",
+            "-------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 99.8     |\n",
+            "|    ep_rew_mean        | 99.8     |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 517      |\n",
+            "|    iterations         | 1400     |\n",
+            "|    time_elapsed       | 13       |\n",
+            "|    total_timesteps    | 7000     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.512   |\n",
+            "|    explained_variance | 0.000282 |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 1399     |\n",
+            "|    policy_loss        | 0.422    |\n",
+            "|    value_loss         | 0.883    |\n",
+            "------------------------------------\n",
+            "-------------------------------------\n",
+            "| rollout/              |           |\n",
+            "|    ep_len_mean        | 101       |\n",
+            "|    ep_rew_mean        | 101       |\n",
+            "| time/                 |           |\n",
+            "|    fps                | 520       |\n",
+            "|    iterations         | 1500      |\n",
+            "|    time_elapsed       | 14        |\n",
+            "|    total_timesteps    | 7500      |\n",
+            "| train/                |           |\n",
+            "|    entropy_loss       | -0.574    |\n",
+            "|    explained_variance | -1.93e-05 |\n",
+            "|    learning_rate      | 0.0007    |\n",
+            "|    n_updates          | 1499      |\n",
+            "|    policy_loss        | 0.33      |\n",
+            "|    value_loss         | 0.632     |\n",
+            "-------------------------------------\n",
+            "-------------------------------------\n",
+            "| rollout/              |           |\n",
+            "|    ep_len_mean        | 104       |\n",
+            "|    ep_rew_mean        | 104       |\n",
+            "| time/                 |           |\n",
+            "|    fps                | 522       |\n",
+            "|    iterations         | 1600      |\n",
+            "|    time_elapsed       | 15        |\n",
+            "|    total_timesteps    | 8000      |\n",
+            "| train/                |           |\n",
+            "|    entropy_loss       | -0.38     |\n",
+            "|    explained_variance | -0.000158 |\n",
+            "|    learning_rate      | 0.0007    |\n",
+            "|    n_updates          | 1599      |\n",
+            "|    policy_loss        | 0.321     |\n",
+            "|    value_loss         | 0.42      |\n",
+            "-------------------------------------\n",
+            "-------------------------------------\n",
+            "| rollout/              |           |\n",
+            "|    ep_len_mean        | 113       |\n",
+            "|    ep_rew_mean        | 113       |\n",
+            "| time/                 |           |\n",
+            "|    fps                | 523       |\n",
+            "|    iterations         | 1700      |\n",
+            "|    time_elapsed       | 16        |\n",
+            "|    total_timesteps    | 8500      |\n",
+            "| train/                |           |\n",
+            "|    entropy_loss       | -0.472    |\n",
+            "|    explained_variance | -2.01e-05 |\n",
+            "|    learning_rate      | 0.0007    |\n",
+            "|    n_updates          | 1699      |\n",
+            "|    policy_loss        | 0.135     |\n",
+            "|    value_loss         | 0.253     |\n",
+            "-------------------------------------\n",
+            "-------------------------------------\n",
+            "| rollout/              |           |\n",
+            "|    ep_len_mean        | 117       |\n",
+            "|    ep_rew_mean        | 117       |\n",
+            "| time/                 |           |\n",
+            "|    fps                | 526       |\n",
+            "|    iterations         | 1800      |\n",
+            "|    time_elapsed       | 17        |\n",
+            "|    total_timesteps    | 9000      |\n",
+            "| train/                |           |\n",
+            "|    entropy_loss       | -0.465    |\n",
+            "|    explained_variance | -0.000141 |\n",
+            "|    learning_rate      | 0.0007    |\n",
+            "|    n_updates          | 1799      |\n",
+            "|    policy_loss        | 0.159     |\n",
+            "|    value_loss         | 0.133     |\n",
+            "-------------------------------------\n",
+            "------------------------------------\n",
+            "| rollout/              |          |\n",
+            "|    ep_len_mean        | 118      |\n",
+            "|    ep_rew_mean        | 118      |\n",
+            "| time/                 |          |\n",
+            "|    fps                | 529      |\n",
+            "|    iterations         | 1900     |\n",
+            "|    time_elapsed       | 17       |\n",
+            "|    total_timesteps    | 9500     |\n",
+            "| train/                |          |\n",
+            "|    entropy_loss       | -0.54    |\n",
+            "|    explained_variance | 4.63e-05 |\n",
+            "|    learning_rate      | 0.0007   |\n",
+            "|    n_updates          | 1899     |\n",
+            "|    policy_loss        | 0.108    |\n",
+            "|    value_loss         | 0.0489   |\n",
+            "------------------------------------\n",
+            "-------------------------------------\n",
+            "| rollout/              |           |\n",
+            "|    ep_len_mean        | 122       |\n",
+            "|    ep_rew_mean        | 122       |\n",
+            "| time/                 |           |\n",
+            "|    fps                | 531       |\n",
+            "|    iterations         | 2000      |\n",
+            "|    time_elapsed       | 18        |\n",
+            "|    total_timesteps    | 10000     |\n",
+            "| train/                |           |\n",
+            "|    entropy_loss       | -0.439    |\n",
+            "|    explained_variance | -2.03e-06 |\n",
+            "|    learning_rate      | 0.0007    |\n",
+            "|    n_updates          | 1999      |\n",
+            "|    policy_loss        | 0.0152    |\n",
+            "|    value_loss         | 0.00595   |\n",
+            "-------------------------------------\n",
+            "Model saved successfully!\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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Z2G677eB5HkaPHo1+/frh0ksvxfz58zFr1iyMGTMGVVXKGKBQKOCrX/0qvvjiC1x11VXYeeed0aVLFyxatAhnn3126N1qa2tLz7Y2dFl++ctfYu+99xbv6dq1a+CYrta3NXK5HB566CG8+OKLePzxx/Hkk0/i29/+Nn71q1/hxRdfDJU9Deg6+8Y3vmFUQPfcc89U8uIBJTRMDF3cb2OKgOg5+L6ljaOPPhqdO3fGAw88gDFjxuCBBx5AVVUVTjrppNI9H3/8Mb7yla9g5513xi233IKhQ4eiQ4cOqK+vx69//WvruOFal3H7b6XQnr5Nhgz/15ApUhkyZKgott12Wzz11FNYt25dYKX//fffL13XMAkwDz30EM466yz86le/Kp3bvHkz1qxZk1o58/k8brzxRhx22GH43e9+h6uvvrpkWlNTU+PElNTV1WHmzJkYPnw49t57b3Tr1g177bUXevTogf/85z+YM2cOrrvuutL9b731Fj788EP87W9/w5lnnlk6P23aNOdyb7vttigWi/j4448Dq9jaxNAVmnHS8DwP8+bNKwn6mhXp3r27U11UErysAPDhhx+ic+fO6Nevn/XZUaNGYdSoUfjf//1f3HPPPTjjjDNw33334ZxzznFuq651riPtFQqFRHUW59v26tVL7A+tZd6ly/rpp58GGMN58+alnleXLl3w9a9/HQ8++CBuueUW3H///airq8OgQYNK9zz++ONobGzEY489FmBspk+fHpm+ZnR4ffK6jNN/TWMbR79+/dC5c2fxG7///vuoqqrC0KFDndLKkCFD5ZH5SGXIkKGiOPLII1EoFPC73/0ucP7Xv/41crkcjjjiiNK5Ll26iMJgPp8Pra7+9re/Na62J8W4ceNw4IEHYvLkydi8eTP69++PcePG4Y477sCSJUtC9/MQw3V1dfjss89Kgh0AVFVVYcyYMbjlllvQ3Nwc8I/SK8n03TzPw6233upcZl1/NPQ6gJCZUxT+/ve/Y926daXjhx56CEuWLCmlv99++2GHHXbAzTffjPXr14eeb81wy7Nnzw74Fi1YsAD//Oc/cfjhhxtX51evXh1qQ5pZa2xsBODeVl3rPJ/P44QTTsDDDz+Mt99+O1SmqDqL82132GEHNDQ04M033yydW7JkCR555BFrHmlBM7a///3vA+d/+9vfViS/U045BYsXL8af/vQnvPHGGzjllFMC16W+1dDQgDvvvDMybb1oQEPqFwqFkHlmnP6r91CLWvzJ5/M4/PDD8c9//jMQfn3ZsmW45557cMghh6B79+6R75AhQ4bWQcZIZciQoaI4+uijcdhhh+F//ud/8Nlnn2GvvfbC1KlT8c9//hOTJk0K+H/st99+eOqpp3DLLbeUTOUOOuggfP3rX8ddd92FHj16YNddd8Xs2bPx1FNPoU+fPqmX98orr8RJJ52Ev/71rzj//PNx22234ZBDDsEee+yBc889F9tvvz2WLVuG2bNnY+HChXjjjTdKz2ol6YMPPsANN9xQOn/ooYfiiSeeQG1tbSBk884774wddtgB3/ve97Bo0SJ0794dDz/8cCzfhr333hunnXYafv/736OhoQFjxozB008/HZsJ6N27Nw455BB861vfwrJlyzB58mTsuOOOOPfccwEohfBPf/oTjjjiCOy222741re+hcGDB2PRokWYPn06unfvjscffzxWnhyzZs3C5s2bQ+f33HPPgAnc7rvvjgkTJgTCnwMIsH0cf/vb3/D73/8exx9/PHbYYQesW7cOf/zjH9G9e3cceeSRANzbapw6//nPf47p06fjoIMOwrnnnotdd90VX3zxBebMmYOnnnoKX3zxhbHMcfI59dRTcdVVV+H444/HJZdcgo0bN+L222/HTjvtVFZAFlfst99+OOGEEzB58mSsWrWqFP78ww8/BODOyMyZMwd333136PwOO+yA0aNHl471Xmrf+973SgorxeGHH44OHTrg6KOPxn//939j/fr1+OMf/4j+/fuLiyIUu+22G0aNGoUf/OAH+OKLL9C7d2/cd999aGlpCdwXp//q4CiXXHIJJkyYgHw+j1NPPVXM//rrr8e0adNwyCGH4MILL0R1dTXuuOMONDY24he/+IW17BkyZGhltGqMwAwZMnzpIYX5XbdunXfZZZd5gwYN8mpqarwRI0Z4v/zlLwMhez3P895//33v0EMP9Tp16uQBKIVCX716tfetb33L69u3r9e1a1dvwoQJ3vvvv+9tu+22gXDpccOfS6GwC4WCt8MOO3g77LCD19LS4nme53388cfemWee6Q0YMMCrqanxBg8e7H3961/3HnroodDz/fv39wB4y5YtK5177rnnPABeXV1d6P53333XGz9+vNe1a1evb9++3rnnnuu98cYbHgDvzjvvLN131llneV26dBHfZ9OmTd4ll1zi9enTx+vSpYt39NFHewsWLIgV/vzee+/1fvCDH3j9+/f3OnXq5B111FGB8M0ar7/+ujdx4kSvT58+Xm1trbftttt6J598svf000+X7tEhs1esWGHNm5fB9EffAYB30UUXeXfffbc3YsQIr7a21ttnn31C35yHsJ4zZ4532mmnecOGDfNqa2u9/v37e1//+tcDYdQ9z72txqnzZcuWeRdddJE3dOhQr6amxhswYID3la98xfvDH/4QWTdx8pk6daq3++67ex06dPBGjhzp3X333cbw5xdddJGYH0/X9C2lEOEbNmzwLrroIq93795e165dveOOO8774IMPPADez3/+c+t7RoU/l7ZFOOOMM0rhvyU89thj3p577ul17NjR22677bybbrrJ+8tf/hIqNw9/7nmqz48fP96rra31ttlmG++HP/yhN23atND44tp/W1pavIsvvtjr16+fl8vlAt9E+pZz5szxJkyY4HXt2tXr3Lmzd9hhh3kvvPBC4B7TOOY6DmbIkKF85Dwv80bMkCFDhv+rmDFjBg477DA8+OCDOPHEE9u6OJHI5XK46KKLQuZ3Gdon5s6di3322Qd33303zjjjjLYuToYMGTKkisxHKkOGDBkyZMhQNjZt2hQ6N3nyZFRVVeHQQw9tgxJlyJAhQ2WR+UhlyJAhQ4YMGcrGL37xC7z22ms47LDDUF1djSeeeAJPPPEEzjvvvCzSXIYMGb6UyBSpDBkyZMiQIUPZGDNmDKZNm4af/exnWL9+PYYNG4Zrr70W//M//9PWRcuQIUOGiiDzkcqQIUOGDBkyZMiQIUOGmMh8pDJkyJAhQ4YMGTJkyJAhJjJFKkOGDBkyZMiQIUOGDBliIvORAlAsFrF48WJ069bNedPADBkyZMiQIUOGDBkyfPngeR7WrVuHQYMGoarKzDtlihSAxYsXZxGFMmTIkCFDhgwZMmTIUMKCBQswZMgQ4/VMkQLQrVs3AKqyunfv3iZlaG5uxtSpU3H44YejpqamTcqQYetD1m4yxEXWZjLERdZmMsRF1mYyxEV7azNr167F0KFDSzqCCZkiBZTM+bp3796milTnzp3RvXv3dtGAMmwdyNpNhrjI2kyGuMjaTIa4yNpMhrhor20myuUnCzaRIUOGDBkyZMiQIUOGDDGRKVIZMmTIkCFDhgwZMmTIEBOZIpUhQ4YMGTJkyJAhQ4YMMZEpUhkyZMiQIUOGDBkyZMgQE5kilSFDhgwZMmTIkCFDhgwxkSlSGTJkyJAhQ4YMGTJkyBATmSKVIUOGDBkyZMiQIUOGDDGRKVIZMmTIkCFDhgwZMmTIEBOZIpUhQ4YMGTJkyJAhQ4YMMVHd1gXIEEShADz/PLBoEbBsGbBiBbBwITBkCNC7N7BmDVBVBYwbB9TVAS+8ACxZAvTvr55fulQ9068fMGCAOrd8OTBwIDBmDDBrFjBjhjoflUafPsCqVcG0bOnX1anjWbNUevpcPh9+x6h7pPsXLQrnbSqPfhf+2yU/neeMGcAzzwCffw4MGwaMHauek9IcMyZcj1H38bKY6kWXhX63ceP8a2+91Qdr1+YwdKj9Gf2taT0OHmyvj6gymerH9q62NPX5/v39YyDYtngd8HLa2nHU+/Lvb6pz3h51urqMUe0/qu6SlFE/k7TN29IvFtX4M2CAXIe2Pi19m6hyuaZna1e8T+rrxSLQs2dwLOXf1mVscrnXpR1J5Y0znrp+M56+bSyS0uXjjFQ2fS7pGCO1D5pP0nZtm0Nc0jJ9R+l9pbnJVAdSGwLMbcb2TlHjnGncitMOXOrE1p9tY6aWeVatUuds440tfVMakvxD+yLtN3p80Gn076/STDKX2MoojT+u44Wpb0gyEZ0LV6ww5x23P7s8Y6ozqd8vXpzD/Pl9MGECUFPjVq/tAl4b4pprrvEABP5GjhxZur5p0ybvwgsv9Hr37u116dLFmzhxord06dJAGvPnz/eOPPJIr1OnTl6/fv28733ve15zc3OscjQ0NHgAvIaGhlTeKwmampq8q656yRs8uOgBntNfVZXbfbb746Zh++vTR/3Rc0OGeN7DD/vv+fDD6pztHgrp/nL/bPnpPPl7RP3l88nu02Ux1cuVV8pl6dNHXePtxfaM6Vub6iNumVze9corzWnG/c59+pjrrhLfX9e5KS/X9u9Sd2m3UZpeU1OT9+ijj3pNTU2J06fp2fp01LeR3jNueqb249onbd82bt9w+damvHh5XdpTnG/G0zeNRVK6fJyRyiadS1KPrmm69BOXPFzKKJWha9fkfdBUrj59VLpSPUQ9G7ePxm0HLnVCy6nHmfvvb441Zsb9zq7fV5r/dF+MO9/HqSuXMtrKYRovXOsu6i9J+qYx16VMLvP94MFFp3qtNFx1A7RSeURcc8013m677eYtWbKk9LdixYrS9fPPP98bOnSo9/TTT3uvvvqqN2rUKG/MmDGl6y0tLd7uu+/ujR8/3nv99de9+vp6r2/fvt4PfvCDWOVoD4rU/fc3e0Bxy1/5naO9/OVy6k8LQbmc/R4K0/1plonj4Ydbv37KSyOd9sLro1J1317+Wuv78/af5Lk0ykjTi1KkXNPP5dSEaOrTSd7TNka0VTtx7RtJv3W55Umr3drftfxxphJjjK2fxM3D9P5pfz/bXBj1F/dZWx8tp05dyqkXh3O5ysxRabWhSpSrvZVxa/vL5YqR9doa2GoUqb322ku8tmbNGq+mpsZ78MEHS+fee+89D4A3e/Zsz/M8r76+3quqqgqwVLfffrvXvXt3r7Gx0bkcba1ItbToFb8vlxLldwq1+mBbgcjlPG/oUFUXuk7SZqJs+fnfoe3rq62+ka6PStd9e/hrze+v23/ctNMuo05v0yazIhU3/TisT1S5GhvbZ7uL0zeSfuty2kWa7baS40Cl0pb6SdI5hJcx7e/oMhfa/gYPjv9skj5qq1OXOhkyxPPWr2/y+vTZ6KW52NeaMkK5ddWeyrg1/tnqtbXgqhu0uY/URx99hEGDBqFjx44YPXo0brzxRgwbNgyvvfYampubMX78+NK9O++8M4YNG4bZs2dj1KhRmD17NvbYYw9ss802pXsmTJiACy64AO+88w722WcfMc/GxkY0NjaWjteuXQsAaG5uRnNzc4Xe1Ixnn81h0aI2/xQVg+cpP6+oexYsAKZPb8HYsR6efTaHhQsrVyc8P+DL/x1soPUBoKJ13x7Qmt/fpf2bnkuzjDq9GTMKACCOdXHTLxQSFUUs129/W8DChTEduVoBcfpG0m+dtDx6rEyr3VZyHKhU2lI/AZBoDuFlTHs8KLd9LFoU/5kkfdRWpy51snAhcNttHlat6hQ/c8cyVVpGSFouivZSxq0RtnptLbjqA236hQ866CD89a9/xciRI7FkyRJcd911qKurw9tvv42lS5eiQ4cO6NmzZ+CZbbbZBkuXLgUALF26NKBE6ev6mgk33ngjrrvuutD5qVOnonPnzmW+VXzMnDkYwP6tnm97xBNPzMWGDYtarU50fkD2HQBVHwr/N+pha/j+aZdx2rS3ceihwLRp00LX2rIOnnlmPoDt2yRvF7S3vlHJsbKS71qptGk/Acpry+3tW7cVyqnTZ59diEr059aWEVzB6wpov3PK1gSpXlsLGzdudLuxlRgyJ6xevdrr3r2796c//cn7xz/+4XXo0CF0zwEHHOB9//vf9zzP884991zv8MMPD1zfsGGDB8Crr6835rN582avoaGh9LdgwQIPgLdy5Uqvqamp1f+mTWtucxq1vfxNm9bcqnWi88u+g18f/5fqYWv4/mmX8YknNnmPPvqot2HDhnY1Ft18c0ub13XUd2hPbaSSY2Ul37VSadN+Um69tLdv3VZ/5dTpTTc1VrRM7e378Lpqj2XcGv+kem2tv5UrV3pAO/eRkrD//vt7V199tff00097ALzVq1cHrg8bNsy75ZZbPM/zvB//+MchH6tPPvnEA+DNmTPHOc/MR6qyf9Qu3OR0mflItf034v4LX2YH2cxHKj0fqTQCBlAfqfbW7mj5nnrK83r3Tv9bl9MuKu0jldb3qFTaW6uPVJJ3z3ykMh+p/yt/W5OPFFqpPE5Yt26d16tXL+/WW28tBZt46KGHStfff/99DwgHm1i2bFnpnjvuuMPr3r27t3nzZud821qR8rwsal8WtS/pu2ZR+5LUXRa1L92ofbz+Xdu1KWpf0vQq0a70e0YJRF/mqH1pRF7LovaF58K4759F7Uv+fSv1l0Xtq0SdZlH7nHHFFVd4M2bM8D799FPv+eef98aPH+/17dvXW758ued5Kvz5sGHDvGeeecZ79dVXvdGjR3ujR48uPa/Dnx9++OHe3Llzvf/85z9ev379tsrw562xj5TUmSu9j9TQoeGJqbrafg+F6x4Rcf5s+ek8O3WKl2bSfaR0WaT3HDpUTYI9esh1feWVnte1a1F8pmNH929tqo+HHw7nrdN3qR/pXSWB1PaeUe3NVHflfv+09pGS2n/nzumUsWfPZO/suo9Ur15u6al9htzbddR7xk1Pt5/u3d3au+nbSe1ap+0iCEnfuqbGnBc/z/NwaU+83qQ9iEz1wY9t44C0jxSvK9seMqa0pXqgf1272seFqH6iy+8yPtjen7ctXTZpnI3TPqR9eEztM66Cx/uoqQ5c2wGvk9bcRyoNGUHqw7ovSt83yTdN2gb9+Tx8LWnZXP903v36uX8bPS4m+Z4mOYD+DRmS7SPljFNOOcUbOHCg16FDB2/w4MHeKaec4s2bN690XW/I26tXL69z587e8ccf7y1ZsiSQxmeffeYdccQRXqdOnby+fft6V1xxxVa7Ie+jjz7qbdrUVBKQrrjC866+2vO+8Q31/xe/8LyRI9W1iy9Wpia08dGOMGKE551wgvo9frznTZ/ueffeG7z/Rz9SaRx8sDr+9reDjXuPPYL3Dxrk/+ZC3PTpvjmEPjdhgkzL7rxz+DkbWlo8b889/WcGDw6mof923VV1UsDzfvAD//zVV6v/HTu65ed5nnfddeqZvfZSdQmo76DrgKZ/113Bb3H66Z7XoYP/DfX5qVPVfd26qeNf/SpstqUH0h//2L/2yivB97z0Uv/ayScXSudvu80/f/nlwWceekjlvc02/rmdd46uD/qejz/u3/uTn6hz++zj1883v+l5w4ap39ddp/L7+tfV8XHHBU0ytBD2//6ff/5nP1Pnunb1vBNPDJb/K18JHl91VbjudtlFXeMC8SGH+P3mwgvdvv+GDcH8Dj44WH567cAD/Xpct84//9OfynnddJO6vttunrf77ur3sceq9qvL71LGTz8NluOEEzxvwAD/W+jzf/tbMD0XRcrzPG/lSj+Nb39b/e/WTW4zH38st09dX/vso65961u+gvaLX5jfs6HBT+8vf+HlD5ZLX7v++uC4Mn++f3zZZep/376e98Mfqry1Qkvb4MSJwTRcQ7Lfe6/8LocdFrzvj3/079PnqqtVXuedZx9Pu3aNbhd//rO6d4cdPO/II9XvvfdW6T3wgDru0UMdr1jhp33ddfa0N21qKt3bv7+696qr1PGYMcHyUsHP1F48z83sacgQz7v7bv/4m9/0BbWf/MTd7KelxR8DgLDQGDUOPvlk8P4f/EDdf/HF/vi0445++6fz46672utA39e5s+qrgFJutJJG2wwFLY8eOwCVt5RfS0tw3tx+e3Xf88/75554wr1ON20KluHkk83jTEuLP9bp70rbTF2dP4b98Ieed8YZ6njQIHcZQX/fb39bpXHKKep4221VGv/+d7C8117rp/vyy34f0+PDD3/ol2vcOM/baSf/2QEDosvV0qLuuecefyzRc9TZZ/v95He/89N56KFgGW+5xX0M2n9/fy4G1Fx87LHq9377+YtT3/mO34eoDPL00+pc9+7Bb6PHEUAtDNH31nIOH7Oo+fMBBwTLqZ+l7efHP9a/i97PfjbL27TJPje1FraK8Of33Xef9XrHjh1x22234bbbbjPes+2226K+vj7torUZ8nmgpkb9PvtsYPfdg9fnzgU++ADYbjugQ4fwsxp9+wJDh6rfO+4IjBsHPPhg8P6ddlJp9OqljrffHuhEopUOHAi89ZZ/rMsFANWs5YwbF36XHj2CZdLwPPtzHPk80K1bMN1+/YD33w/et802fqjXnXf2z48c6ZfZJT+Kgw5Sz330kaqf2bODaQLA/vsHv8Wuu/rvveOO/vm6OnVfVZU63muvYP3k837djBzpX8vlgmUaMcK/RoNT7ryzf56HvD34YJU3/YZ9+0bXx5adAQAAY8aEv+fo0eq/rp9XXlHHu+6q8hswQB0PGuQ/m8/7dbDvvv75TZvU/6oq1fYoOnYMHtP60WnqNrLLLsH2ucMOQOfOqt9sv73cJjl4nXfvbv4eQ4b49Uijpe63n5xXsaj+H3ggMHgw8Pbbqq8uW6bO77STWxlpP9Ll0O2QBjM98EC39Djoe06aBPzlLyp9qc00NPi/d9gh/G26d1e/hw/3v82ee5rLRd+NthHArz9A1Z++Rr/5uHHBMNPbb+/f/7//q37feiuwcaPqvzoNmva4ccCMGe7hqqV32bw5eCy9c48eKi86HUp1XFsb/R11ve2yC3DiiUB9vWoL48YBU6aoa/obrlnjP7fjjva06bWqquDxsGHB8tJ2UyiYx5hZs6LrduFC4N57g3npMYyPATbk82oM0ODBeaPGQd7X6BgPqPorFIB589Q4TOeD3r3N6dM0Nm0CzjpL/S4WgaYm9dul/w4fDrz7rvpdWyvnl88DXbuGy6XnNEDNE651qsdwjW23NT8rzeG0jH36qP+HHw6cdx7w4ovAP/6hxn1XGUHLLiefDEyYADz7LHD//er8uHHA1KnBZ3bfPTym9+zpjw8AcP31qp2OGKHGlw8/VOe7dbOXa8oU4NJLg+17yBB/Hps4UZVv7drg+MbrdN99VVu69VbghBPC+eRyftvccUc1x2n06KHmXUDNw01NKnz+qaeq+l21Cthnn3DevP3oOVzfQ6/R703PUzlj2219uYA+Q8eJXXYpvRF2331VovmqLVEVfUuG1oaezHmnAlQjBwCyDVYJVIgrFv10dEejQgLgD9T0Pjph8Ptp+lL+HKbIkTxdF1DFwPP8eqCorfXL39Lin9fljpOvTqeqyv8OtE5p+sVisG46dvTv03VM89fvIpVH38/fl4JeW7zYH402bPDP0/JJefPfJlABmd6v08vn/QGRth/+n78rvw747aW5OVifgBr0KaSy29qxvubaBnj6VCDmdUuPtTIImIUKnXZU3UWB11FLi18W2hZc0+OgdaXfxZQW/T5SHdM27/It+FhGQfsU/c3HS/pd9H1U2ZIWHXheS5aYy0ihlWAOPgbS76KhhduoLUuk+YCDti0tzOh60Nf0MX1X2m6joMd+03ek77Fxo3mcca3bf//b//2b3/h1GHd/JNoeeB+O8ywQnlt5/6X9hLZRG3jf0nk89VT0s/Qb2PoVzYO3B3rOBabymkDzMT1LhXkgvBDhkr7u41qx0m2bl48e0/meQhqfAXs9TZmiFjH4IsGiRUrR1ulKizemeXLiRLXwyqEVJUAtVvH5XR/TObWmJvhePG9bPfFrpjlOqlvbPcExOBe+uZ0jU6TaIXTD4yvfgL+iISkydMCWBDPeCaQJ0dZpTIoUZ6c0TIpUEsGOCztJFKk4+dJvQAc8XSdc0KOCSMeO/vNxFCnP89O1CXf63TwvKIxQIY0LZUkVqS++kO+nE5+kaPL/PC9p0I6jSEmCkCSsm8oVBVNfkfI2KVKm9kYnfFvdRYGXg06c69f755MsXPDnpEmfgn4fqV1RYV5ft/VHm3BH2wb9zcdL+pz+fpwpA8wTOhBmRk3o21c+zxUUSZHq0kX9jxLukypSfEyRxp84ihRf7DGNTxqmecC1binWrVN/Uj5RqIQiJc2dfPyxKVIuY/AvfhF9n22+oJAEZ9qHylGkouZXWxm5ImWTc0ygigIQVqRMcxBglrmk8Vkqv0ahoJgoqS7oOc+TF3JsdcotkK6/Hnj6af84nw+PZVSR0u2wpib4XjyvOIqUaUzKFKkMbY6kjFRcRYpPiHzVhXcAaYUXMCtSpsk5DUaKDyqAOicxRlTxcEWUomBTpDp0sCtSfDVTg95rm3T0tTVrgA0b/EFHCxiAeeLnTFoUTAKy/l1V5U8+kjKQhJFqaQkrUlSh4++h0d4YKVN7o8JumopUEkaqUFDma/feq/6blGVpBZMiSpGiQryNkdWwtVPaNuh3sTFSkiKl77f1tRUr3Eyd9ttPPq/btFaW6GKBRpqKFFXS9bhsUqToe1eKkQKCSj1FXZ0yd5IWDF3gyvRoJGVe+LNAeFyj/ZePPzZlYNas6LyXLYu+b2tgpGwKQyUYKa2MuTBSJpnLxEiZ3tXFXBVQZpiSMmOaJ4HwHLj77kGLEa7sxWWkkihSLoyUqa7o+eB4nylSGVKAjZFyVaRs7AC/3yaAUphMT6g9LEV7Me1LokjRb+Bi2kff1fP8++IwUiZFSjLtmzKF2hUrfP/7vh9EWqZ9VECWhFuqaNL2E8VISYM2rUMu2K1eHTyOY9onlSsKPH3a3/g1kyJlyotO+HRCMynYJtgUKRdG6pFHcthuO+Cww4DTT1f/t9vOb0P6Od4HJESZ9pXDSNnGIaowpm3aN2UKcMopbv3EpAzo9qAZK11e6nuoFamofNIy7ZPGH4kpM4GbBnKhiX9XkyKVzyvfj6Smp9xHNgrt0bTP1bzRdl9Njfu4Lgm4tD/FqZdyGKkoRcqVkaILQXohkZv2aWXMpiCYZC5pkVBKS8P1e65eHZ+R0opUz57+NTovSooUlUP0d+7QIfhe9Hl+jh/z8pnGJNuCvJRuUJnPFKkMKSAOI0UFA5PwaPK94IpU1GBhUqRa27SvNX2kTIoUN4fgTEQS0z5XRurNN5UNNvfLWLNGnZ8ypfVM+0ysShQbKg3atL3wtsOfrzQjxe+zMVK0Xuh9cRmpuMpeOT5Ss2cPxKmn5kU7ft2G4jBSpnbCzyVhpGymffQ9baZ9um9Jpn2mvmYy0dGg47PpXbgipds1XUnW5a6Uj5SLaZ9prLZB6lP0u2nfL5MiBSjfj5tuip83EF5ciUJrmfbFUaRczRtt91FLDFomCe2FkTLNB5yRamkxl2nKFAQWgpYvV+dnzlT/tSLV3BxUKqQy0EUjCmmRUCq/huv37NMnHiNVLPrBYXRgjmIxPO66+kjR95LyorCxZGmZ9mWMVIbUEYeRMrFBklDLO4ROw9W0Ly1FShqEbCZG/JmkilRrmfbR+7giJSm4Gq6M1L//bX+XSZPMilScSbNQCAorLmZfcRkpybSP/5ZgU6SkBYG4bI+NkXI17XNhpGxKaBRsPlJUweDlKBSAP/1pD6sdP21DvA9IcDXtc33PtBkpm4+UaXEhykQnSnj1PL8d2xgpSaCV4GICRxWpKNO+pIyUhqRI0W+jV85tihQAjB2r/g8YAPz0p+750wh0LkjTtE+aW5MoUnV10Xlvs439vtraZIqUaVHQFZVgpHQbp1FaJVbKFNAB8C0zaATiTZvcmJZyTftczVX32MONkdL5NDT417Qi5cJImXyk4pj22RRm12ATkpzKy+qfzxSpDCnAhZHSK98mJUYS2E0MiCsjZRo8TMqcq48UX1niJkaAGyNFV+Ykc4XWMu2j78f91myrcvRemyBJhTCp3AsWhE0MJIUmagVxzRp5BZP+jgo2US4jpcNmc8Qx7TMxUi7+QRpbm48UFV55OZ57LodVqzoBkCcs3YZeekkdp+EjJZn2uTJSrooUF15cFSlXIdQG6TkqANoYKclnSUovjo+UzbRPL+iUy0hJ35HWuasipZ/v0gU44gj1m0a0NGHYMOeihspWLiPFxzW+iGSaAzhc/O8mTbLf16GD+7jeXhgpkxLGGSkg7CdlC+igMWlSUC5xVaTKDTahzVWltOgxX0QzpavLpRWmzp39uvG8ICMllVFipOgWLFLeUXJfc7M/b5oW13m6kk/7l0mRatN9pDLIiMNImRQpF0YqKthEsag6XNQAWY6PlF5Z4oOiNjF66CFl/pGGaV+ajBQX9ExMBGekXBUpPVgtWWIOrWyDyTQujmmfNpWQyhdVP6b/GhIjRYVi/bt796Di2KmTqutyTftM+3zcemu4vQGVY6Qq5SNlY6TihvTmAUU4CgXg00/N5dL36P9xGSn+LWg7NDFSnpdO+HNXSM/RPsgZKUmR4swAV5zimPbZgk3o8laakdL7E7oqUnQs6dlTCYl0nxzTc66oBCMlLULGYaQoqquD+eTzqpyasTOBm/aV4yPVWoyUSVjX37+62n9/rkhFBXTQC0HPP6/qpqlJpWFTEEyL13EZKUDNHw89BFxyiZJjNIYMUf8XLAjuxeaiYGqFqXfvYJlcTfuoj1Q5wSYAtV8VfS8JXEmtrQ2PMaYFmK1RkcoYqXYIF0YqSpFKwkhJpn3SShjfHNWkSJki7tByRIUKnTQpPEBoBY+DTrpphj+n0b1MQg8VmiTfDF1uV0Xqhht8hu6yy9zLrcHbhbQKbZs0p0wJbzh4+OHhQARReyFJCpxpQqJ1qAfdHj2CZdDmPElN+4pF+z4f2j+I140rI9WWPlImpoaXI25Ibx5QhEKzye+955/7zW+CbDLg15FtfygK230upn28r7lG7aO/bSY6uVxw02PpXbRSXV3tt2PdxunigK6bKEE/rWAT+jc9TttHKpfz2eQkilRtrRJG6WagQHAj3XJYpXKUMPo8He+k34C7IrXjjv7G0b/8pdrIlOZBQc/V1rYNIxUlcHO4mLDRNm4KrBUnQAcNgd4ajJTGxInAO+/4x3//u1ps0psSmwL4RDFSvXoFn3M17Wts9H+XE/4cMCtRdMzn6UaZ9pWzyNEekClS7RCtxUi57CMlpc9t001lMIEOoi4rS7NmhQdhaSCjwmilTftsPlKuihQvD50w6Io1R9eudgFv6NDgLvJS3vqcBK1ocEZq2bKwopHEtE9aBQPcTPv0e8Ux7aPHLS1uyrukpEhsJC9LGj5SrkIeLwdV4mxR+w45xEOfPpuQy8kdQrchHdLbxEiZFNJ16/x2okFXRjWSMlIupn2FQjIfKfp+JhMdjUmT5Oc0dFvo1MlXAGyMVJTpWVrBJvTvSjBSutw1NW7BJujzuVywnU2cCDz3XPDeo44K5+WKcgS0csKf07HDhupqv53su699E2zaj5IGm5DmsvYStQ/wF2z5gmycAB02RUoqj8kcLw4jxZ8FgP33V9+T5uPKSBUKfvh7Wj+FAvDhh/5xS4tZkaLzUjkb8tqgF72ldKNM+zJGKkPqsDFSPCyoCyNl6iAupn0SI8UVKRMjZULc1cAlS8KDjUkQloRdOlG4KlPSKimdLG2mfTZFyjbhue7lEmXqMXmyvILKBQlTUAAX+3OdftQ+UkkZqShFKsq0j5/X1z7/3E15p/5BGjw4i0baPlJJTfuowGFSXHW+55zzFgCz4DB5sv9bYqRc2wn3/3FlpMoNNsEVKVfTPvpbm+gMHhzMv6pKnacBAKR30W24c+fwPlJRilQajJTNtI8rUi6MVHV18CWleUXnVV0dX5GyMZ8aafo5xYEpeI/kFyXNT1ERGQH17nSBymZOy/ctdJkfaXmB1veRsvXpOIxUVEAHvRBUVxfcS8qmIJhkrrjhzymkBRqTXGFK9/nnFeP/s5+p4zfeAGbPVr8vvhh44QX/3n/9KzgeUkWK9m9T+HMTo+oqr+lFbyndL7uPVKZItUNUipHiA19UsAnJtK+qKhgNx1YGKU/TORsGDnRXpGymfXHyjstIpWHa9+qrbmXr0EEJcpx10ucnTpQVKRuTouFqf67NK6L2kZIYKem357kpUlo4szFS0oSpz9FNi23QbBw1JdJ9rtI+UkkZKdOeK1KbHz16Ce67rxAwTwOUgKLbEJ30+QqmazvRE2tc0740FCmpH8ZhpABVD599BowZ45/r3TvsRye1RxsjFRW1L6ki5RJsQp+Ly0i5KFK0bbsqUrpNScKlafEPKN/PqZxnJebdJIgCbuZ91B+ZLrJI/ZeOM/l8uJ5M85w09ranqH0ujBQN6GDC5MnqPrqXlE2RijLtS6JcSIpCXEbqhhvC46we5/kGvZs2hRdodBno3BAV/pz/jrPwreUCXrfSYrtp/MwUqQypwMZIuYY/t/mraHDTPhdGqro6HOjBxkhJLAstR1So0CFD1MpSOYoU/R2XkaIDnkmRKhTSMe1zDSqxfr0S5M47Tx337au0jpEj1XlANkVxYaRc7c+14BXFqkgCkTRgNzbKq6VckdLHNkZKMmHU16hiZEPv3up/x45++9QTeqWi9pkWPExwVaRME+Hxx3uYNs0/njJF2fHrNmRbPY27kWilTPuo7X9S0z7Tyii9T4ccpuWO8kuRGClX0z6pHHHCn0cFm0hi2ldT4z9AFVWJkUpi2ie1M14PpsimUeCLhHERx7RPystFkdLBFYB4jJRk6m5SiNqSkXJRpGgbt23Kq9liCXfd5Y9hrj5SUYxUEkVKGh+kBVpX/7a44ItJGnwBT8rbZtFgw8CBsmk9ZaQkeTQz7cuQOirFSJkGdzoh8s7EFamamrAiJSlbGtIETfOI8kPYtAn45z/dFCkqjKbFSLma9qXBSJlCfXPoCUbXWd++alalZZBMUVx8pFztz3UbiNpHKkro1L9NpkVJTPtsjNSgQW5mIXvvrY7z+XCfszF7JvM6irR8pPg3NgV3cW3zBx0U7MsSI6XTi7uRqKRIJWWkuFCqxxibaV8SHykKSeCIek5ipDZulH0baBnpOYq0g03QMsc17aMr/OUyUq2hSJXrwF7OPlKAuyJlY4EpaF+XBOY47TgtRSoOI2VSaiTTPtO4NnGi3Ce0EgW0XbAJQGZc6HtKCzlxrXWi8uffUytRtmAT/LzrfKTNKSUlmSpSet7KFKkMFQNthO0xap+kSNk6f5QipVeWNAPA8cUXynGdC6gmRkqaHJJEa4oy7UvqI2WbTEaMcCtbv37B5zt1UoWhdZ3UtM/V/lyK6CYpA66MlKsi5WLaZ2OkgOh9Pqh/UD4fNjGpBCNVSdM+V4XFJIzRb6zLGsdPgeaTNiMF+O3exkhJPlJRUfsopPYbNa5QRUozUkuWKJ+HZ57x7/v0U8UGcuGYo9xgE7xO4zJSVJGigqkkENXU+O9cjiJlU6LjKFLlmPVJz/PytaVpn8SAuShSkt9iWzFSdOFSw8ZIaUjyD7WQcfWRMilS5TBSkjLS1oyUVmjiMFKuZdLmlPx+zkhxSyggU6QypAzasCvFSOnBxSXYBE9fUqR4Z6VpRClSAHDssWG/K54WF1CjTPvo5FCJYBOViNrnOonx79qxo3qQ1rULI2UyY3KxP6f324JNuDJSJkGOhz93YaSk9kj7gFbeOatC/YOoiU0UI5WGj5TUX6NAhVZaPg5betxvhsLGSNk2ntTQEyvNJ20fKcBvO1xIpO/WFowUNe3TjNTChWGfh0JBLRatWRM8x5E02IRuW1xhpMcbN8ZbDTcxUuUGm+DmbGkxUmkrUjbTvjiKFK27pKZ9aTJS7SlqXxQjBcjyD+3jrj5SUv70uFxGSlKkKs1IeV74e+q5ohxGis/JGpoJlNpFlCKV+UhlSBW0cdkYKT24JGGk+EqPNBnoYxdGSlqB0JAEZN7RXBzXOYshDWQmH6mtJdgEvVev5krgjqsdO/qMlPT+Ut68nBRa0eDRGfv1kwMRSA65kqChUQ4jVa5pn/4/cSLw8sv+PVdfLfsH2RgpPTmk7SMVl5EyOWVruCosNkWK79EE+O2kf//gc126+O2Ep+3aF133kQLMilQS0z4X3xJXRYoyUnzvPQl0T5ikpn1SsAl93mba53l2gVXfo9EeTPvisCe0Pl3q0fY8EBaMkzJSvFymcZUjipFyYVbbkpEyKTVxGSnJrYAu7JRr2pcWI2Uz7asUIwWEx0o9Jtg25OXl4GW66ir1f8cdgZ12CudpkweBTJHK0Aool5HSHUUSHk2MlGklTTLtk4JNlMtIuTqu0/SlyYUKo+UGm4ii4LmZTBrhz+mEccIJ/u/zzw/exxUpbdpXLPppSIyUSRiQMHEicNZZwXN/+pMvHNOVUxfTvrR8pMqN2md650GDgm3dhZHS58thpMoJf66/cZQiVQlGSmPiROCJJ4LPnXRSUImi+STxkXI17bMpUq7hzyvFSNHNOU3pmwQLjTiMVHV1UJFqbrYrUrS85jL6jcDFtC+N8Oe2xaakjBSfv+I+T8slLULS37q9mZQB2papWRRn+jkqxUi1lo9UWoyUzT8bSC/YRCUYqShWiJchCaIUKdM72d5VH/fpA/TsGc5Tup++V2bal6HicGWkTIqUvi6xAz6Dof4nDTbBV1fLZaRcHdfp8ybTPmlySMu0z2QumJSRsq1a0vSHDw/ex1e3tGkf4Nd3OYwUfSbqmmuwCdNkEaVIUTOC6mq/7SWN2mcSVmk4anpNYqT0Nd3XTMEm4jBStO22R0aKTuY8PT7RSwKWfiaJj5QrI8W/ratpn4uAVC4jxdtXFNI07QPcFKkoPynOSPFxSOcDqLy1ELt4MTBjRjRL4sJIRQXkMMHE1MV9XkMa36SFED1GmBgprkhJpn1SP+E+wzYlgYIvNvAyVIqR4sK0iyKVlJGiSCvYhCRzxGHg+OKnKfw5T/N73wvvY6frpVev4Pna2nB98HanrSiiwp/bxkTdF4pFeUyS5MFMkcrQqohipKgSJA2weuCWBva4jJRJkbIxUrxMLoyUdlw3QRLUTIpUlGmfq5AqrRzZFKk0gk3Qe20CAx+U8/kiOnRQB2kqUrb76cppawWb6NTJb4+SEKXLZ1OkTJMFDUdNr1FFijNSklKXho9UUkXKBFeFJU6wCek+07GtPyQplylqXyVN+6T268pIdeoEDBsmp2uC1L7jhD+Pa9pHy2tCHEZq/XrglFPU76VLgcMOU0E2pkwJp9uailR1tbzJvOvzGtKCEV+wBOIpUrmcnZ2jSCPYRLmMlG0BlcNmtULTom08CSPFlWQabMLG2sU17ePPS5CUEVtbp+XQOPBAtY+dDkT1858D48er39dfH7z3oIOiTUqTmPbxNHV78Tx3RYqei1KkCoVMkcpQJlwZKUB1Et5obYwUV6SifKQ8zy3YhK3TuShStgAHkvDAV7do3vo8naDK2UeKDni2SIAmATqpImXzBaADmSqjF9rwU78/HaxdovbZrktl507RXGGJEjqjGCmuSOn2mIZpH02DK1KSaR/3kYpSpOIwUrRNu7ZRbmJogm3CT2LaF0dx4setyUhJpn1U8IoTNStKALUxUp07+8KPCXycS4ORot8tDUaK3h8VbOLTT/1NrTUWLVJBNbgyRYXoSkft40xd3Oc1pIUiqcxRipQp3XJN+1wWBPSY0xqMVJTpYaUZqbSDTfDnJbgGm7CNP9q9QitABxxgXgjgCgtgNu2TlDiX30BQ/pDKEsVI6XnUNBdnjFSGsuHqIwWoAYY3Wj34uChSLlH7ymWkZs4Mm3VIA9DEicA224TPDxkC3H9/8Fxb+UiZWK5CIX3TPhdGik7cegKZOTMoRFKG0tWO3pSvtHJpCjZRKUZKv2c5Ufv4OwBh0ysXRkpif9PwkXJlpLiPlAmuCotrsIkoAcom1FUiat/06eExhguXrj5SJkGyXNO+2lo/7zjMEkWcYBM6L7qXlK1+gHQYKZvQq+tw0iS3RRldTopyg01IjFScetWQFoykMSYOI0XH6NZgpPRxa0Tti2Kk/EVB/1wajJTNtE+aiyvFSEmmfS6MlD7W/YpuEO+iPJtM+8oJNhFl2ifVU5RpX6ZIZUgVUYwUDyPJG62LaZ8eXFxM+3gZooJN8E73t7+FzTpMA65mVQAVEWb6dLWyecwxwftspn3SanFaUfts5oKVNO0zDZi6jP/61w5YuVINPuefr+paTz66vdC86WQTZxXRJPzYlAFXRsq0Gt61qz/oR5n2mYSvSjNS9P409pGKy0iZtg7QKJeRyuXMwSak9E0TL+DeF+MEm/jzn9UY8z//E3xGSiNO+PMoIcPVtE+PazriJI9yqN+BIg1GCgjuJcW/dVxGiipSJkbq7bej0gAWLFCRWjVMYwlPGyifkaquDrMWSRQpacFIahd80ZKDzyM2pZIijWAT+tm2YKRMz0qMVFsGm0ibkaL5uDJSgF8HtbXmdiH1B5NpXznhz6n8QetfWkjQ56MUqcxHKkOqiGKkqqqC+8akyUjxwSKJad8jj8jvZTLrMKXTuzcwblzQAZeWSxrETKZ9SXykpAHP1bQvDUXKtvKqjz/+WP3ftCn4kRYt8p+nijUP2y2lLeUjHVPGxhZsolxGigY3aQvTPhcfKXo/dwKXkLaPVJRpX9qMVJRA4Wral5SReust+Rm+D5MkWMTxkYpqs3EYqULBz++aa4L1WV0dNv1LGv6cK1K6v7iY9r30kn08oHVAfU5oOitXRpcRCEZqpWwEZz5tjFQcRUqnIylSLj5TOi8uxNLxrlxFin4TPq5ypBFsQt/XGlH7kpj28cBaEuL4SNnqqBKMlDSuVJKR4gtMQDwfKZPy5MpImeYvLq9lilSGiiOKkQKCAwxvtHGCTfAGLTFSfGCxmfYVCsDll8tlpmYdJrisjkjlpPe1lmkfF/RcTftsAxSdMGymfdpM54UX9JngR6LvSMPh63KloUhJJihSm3NhpAoF4N135TLU1Piriq6mfUkUKZtpnwsjpc+5MFLUXyPNfaRMcBVwkoQ/p/dJafJ0XX2kTKvkhQLw5JPm52g+UhuRTPtcBAhpDIoyCdRjwvvvK5ZYKxkXXhhOm6+6c8ELKI+Rkkz7Zs4MPnvtteaAEKqc0YyUZt2iQCO1mhR2SZGi9ZFWsIk4jBRl+On/KNM+l/DndIxujX2k9H02RqpQUGaz994bNp/dmhipKB+pKEUqCSMljR80HxdGSh9TRkry7QSSKVJJGClTsAmqYPF3yBipDK2KKEYKCAp2JtM+aWDXx3qAam4OrhZIgj5P38ZIzZql2BDbuy1YYL5uGlBsPi+m8+3FtI8PTraJKI5p36xZ0aY4Ok+eN1WkXFkBXg5JyJYG3yhG6uWXlfB2111yGbgi1RamfaYNeZMqUnR1vBzTPlcfKddvbFo1bk+M1KxZwLp15uc0XntNFibjMFJRQqNrsInbb7dvOG5TpGgZ0jTte+YZ4Gc/Cz9vsxzgjJTUp7bf3l6+XA4YOlRFatWwtbM445MNaZn26fq0mfZJipRLsAmaRmuZ9tkYqSlT1Nh82GHA6adHm+jHYaRcFKk0GKm0TPvSYqT0M2kwUiYFh6KS4c+5aZ9JkXIx7ct8pDKkitZkpIDgBCt1Yp6+jZGKu7Euh2lAiaNI6TTKVaSkyT1JsAmepqtpX1SwCde6phFyKmXaZ4t+FqVI/fa3ZiFTCxN6Muzc2WzaZ2svUl/g99nCn/MJXT9H+0FLi0o7TrCJqNDxUWivjJRNsUriI0XTc233y5dHm/ZFRe2zCThRfRmIDt5AQdsN4JedvkOSYBOUwaXlnTxZfp5aDoT7WDQjRX+bVvcnTw5+B5siZRuf2gMjJZn2xVGkTIxUlGlfWsEmTIzUlClKoeZjM1W0K23a15o+Uq3NSMXxkSoW/W9k85GSGClpIRyIZ9rH06AKk7SJvekdNKIVqfB7tHdkilQ7QxxGqlwfKSAYQj2KkgWCm6LS+4D4G+tymDqyNAjHYaQqbdrX2GjeTJeXO4kiJSk0rnW9enU476SKlCTcUuFHejdXfxIJuswujJRtBc1kdkPLtG6dfM3GSNXUBO3VubDkwkjZQsdHwVWRcl3Vr5SPlEkpSsKUubb73r3dTfsqoUgVCmr/F1dwdrnSjNSKFeY0tOUADQgBBN8xah+p0aPDG4kOGQI89JCK0CqlK4XZjzM+8WvUJI1uYF8OI5XUtC+uj5RpnyGNSjFSWuG+9FLzHAsoRds2BnO4MlLSPlKt4SNVaUaKj+1xGCn6/q4+UqZNp12DTdhcK6JM+6R3yEz7MrQqbCt6GtTHyWTa58pIUWXMtpKvYWOk6urCkyeFNuswoVxGiipYNn8mF8RhpGi0MAD417/kNLnNfzmmfXV1wSiHUaCCVNo+UtLALpm+8GsumDHDr/eGBj8v2ySexLTP89QmovyaxEhJZkItLWFWIS4j1RY+UjZFiq6eluMjZWpjSRgp13a/227JTPtcFSnel+m92iRq8eLocmpw9kpSpOJuyAuYg024gLN/LoyUHhe3204pkrocDzygIrByJYo+bzPtk97dxEhJJmknnaSulRtsgvqcAjIjRes5brAJqc9J/SRpsAmpr/IyzJplN0XVivYrr4TPm7C1+0ilZdpH83FlpOj723ykaH+IUqTKCX9O3yWJj5S0j1Rw4SxTpDKUidZgpGpq/A7Q1CRP2vo5F0VK35PPq523JVCzDhNaw0eqpcXsPMvT0uXWdWWKBMjNwrhArZGUkeIRo1paVF0feKCcj4R33gkLA1IZKFx9pCRFMw1GCgBOPhl47z31+8knge98J5weT9NVkeLloN+RtmkTI8UVKaqISeXg500+Uq71k4aPlKtpny29qGOTwOva9uh9+bzalNIFaUbtk95RutdkEhUFF0UqzWATLuDsH99HSjLl4SZ0un+MGmVWWGyKlLT4w/OiMNW/ZuA2bEjHtI/PqXTuofUcx0eK/uZbI1AUCsH3k76t6+IFZ6TimI3zDZfjzCWSwgDIilQ5jJT+XitWKEXeVAaTIlVO+HNp/JDaum1xlTJSuVzQCoLn78JIcR8p0xhnG/uowiQteEvfNmOkMrQqTMILRbk+UnRvHGra52JCF7Uh79FHy2U2mXXw/DSiGClpEHPxkTrgALPzLE8LcFOkXBFHkZLCn+tBUB8PG+ae9xdf+M9xkzQTqMDP76WKhpQWnziSMlJffBE81pHPVq0Kno8SeqMYKSAYuY8ybi6M1L//HRbwf/YzuW3R520RD6OQNiNlUk51GU2CXaUZKf68FvC5sNCzZ/AZV0XKRYBwMe2zmURFgZv2ST5SLowUZTsBs2lf377mNKSAEEB4VdnGSNmig3FQ1skUtc9FkXKp/8WLKxO1zzTGxDHt4/5wUt1pto2yQVFsC4WklPG+5mo+y9tQ2oxUkg15KSM1ZQpwwgnq94oVwN13y3ma8gdah5GyzV2UkdL+UWmY9qWxIS+vE1uwCXouCzaRoeIwrYxQUMHOZNonMVJ01UdPCDZGSlqFiVKkeHn239/fWNemROn8pN9xGCkN0wTFV9tMUaqiGBfb4G4Cr8+4pn16EDSt/NjQo0dwtVpajefQ1/T3jmKkbDbhSRkpExYvjsceuChS06eH+0IUI6Xr8bvfBZYuDabX0CC3LZp2GvtItRYjZRKKkypSSRgpwO/bmpHdbTf17a66KphnpX2keBmjTKJscPGRcgFf/DCZ9p1zjvy8KSAEEGSk3nvPZ3BNjBRgD+GtYWOk9DVJMOTf16X+m5vDdZ0kap+0SCn13zimfSZFSufhwnby8nFI7Zib9tXVqYVPkwyiFe199w2nZUI5pn1JGCldV8uWmZ9tC0YqyrTPxkjp+jApUrr90EAWHGlsyKvbC1eQsqh9GdoNKsFI8f+5nK9IUYXAhfmprrZvyMsHgoED/Y11o2AaUOL4SGnQycE0idF8eJQqiZGy+ZO4IIqRohOGtNLDmSF9T+fOTcjl7MvgI0cGV6ul1TAOExNGf5sUzTg+Ui4r7RwtLUFneJuCZBJynn02eN/FF/sMpcTeSoyUrkeboMjblomR0mgrRipKkXJlpEwrmEnLxb+n7s96z6JevdQYQ9tRoRDtI8UXADzPrMC5MFJxo5ZSpc7FtM+lXbia9h14oLznn81ygAo4zz8PzJsXLjsfq+IwUnFN+/j3da1/3h5dFCk9n7iY9kmMlMs+UvQ39590ZTujxnWpr/K+ls8Dt96qjm2RF3m9tQUjxctQXe1eV1IE0Ur7SEltPQ4jpZ+V7tXv46JIpbUhrwsjxe/LTPsyVBxxGSmTImXzkaKMFHdapeAdAIhmpKJWp21w7chSuXheJtM+CZ4XjlIVxUjFXSnWabqa9lGYGCldB6NGKQkiSiGRfHNs78HNWUxR+2ymfZJwQb/duefaA5TYQAUnVwaTru7a9tF5+WV1LDFStB6jJlOpbVFhNyoEtw2t7SPVmowUDwstXevaVf3X/Ya3gbg+Urb3klaK+TdzNYkaPVr9r631y+Bi2leOIsUZqUJBWQtQnHCC2XLgkUfMg8vatT7ryk374jJSpqh9fM4Bwt/Xtf51AAKNtMOf03qOw0jR35yRcmU7o8Z1ybRPYsUmTlQK9YABwfupoh1HseBzGC9LUkaKl6G62r2uaDAY0wJ2OeHPbfNhJRgpF0VKt984pn28TCbTPi6X0OczRSpDqyIOIyXZRuvORju+zUfKtJEsT0PDFmyC5mVK0wZTOq6MVLkhz6lgHsVIuQq7FFyRspn2UfBVXh5mdPjwtbjvvgK22Sb4XFUVsNde4bzjmvZJjJR+f9d9pEyKwp57quhevXqp4/HjzeXhoIJTlOJNy6NXLCXoOr33XvXfFrWPmuZFgbYtiZFK0rbaKyPVWqZ9Xbqo//q78DEjrmkfX3CJalO8TUeZRGnsuKOfvy6DCyPl0i64ImXaR0pi7AYMkAWwQgG4/HK7SYFmXZMwUtTkvBzTPpf6r60N+/ak7SNF6zWtYBOubJstZLpOi0JipDQmTgReeME//uc/g4q2JDCbwE1O+f3UWkbDhZGS5BPXuqKLF1GmfUkYKWn8kOSKqHGGM1JRC1p0XOGwmfbFZaTS9JHKovZlSBWV9JGSGCmbIiWZ9sX1kaqUIvX55+Hn6+v930lM76hgLq0cJUmTolxGiis0dPI5/ngPL70UfK5/f/+d6ISZho8UNe0rJ/y556my6GdHjjSXhyKfDzrD2xgprki5hPfV+2/Z9pGqrjY79XLQtiX5SMU14aLlkFbrKVwVFqnegDAj1damfSZGio8ZUaZ9vA/YIou5mPa5mETR8lLTUBcfKZd24RpsolBwtx6YNQtYtMgu3GjWtVKMlEuwCVr/JowcGe6zaUfto/UaJ9iEiZGKw3bG9ZHijJRtPhgzJth/kjBSVJGShPW4jBQvb3W1e13RBagoRqoSpn1pM1IaSU37XMz8+LtIypCkSGU+UhlaFS6MFB1gkjJSLj5SxaKbaV9ajJRJyeCrrADw3HPh5+m7xFF6pChVtK4k1oCCMwImwbZcRcpk2geoH3qFXqNDh7CdPRAU4F580RwK3oWRKjfYhP6tV+R79AiWwSSQdu0abA9xFKk4viw0hPOSJaqeqCDM65xDalsSI5XEtI86wNt8EF19F1wZqSjFqbUYKa1ImSbmuFH7bIyUiyIF+CZR0ma0RxyhfutxyqZISaZ9cRipqGATcRQp1/6yZEn5PlL0PzWfdGGkNHr3Dp/TbWXw4PL2kXIx7UtbkfI8d7ZT77GWBiPF05EEZNsxRRLTPv2d1q83b1UiLfS61lW/fuGyxAk2ESXbRJn2lesjZVOkTPKjLfy5qyIVZdonzQ/0PmkfqUyRypAqWstHKqlpX3W1bEokTSJSmibwTil1UD4B2uAantwUpSrKtI9i3LjgMY9mpJFUkYoKNqHfgX8XHsyAKqS6fs46yxwKnjMeUYqUJPRFMVLFoiqLfpYqUjvvHBZItfkiF6xs9cqFHNcVS0DZ0eu9qz79VNXTnXeqY1dGirctyUcqiSmq/oZUIJeQlmmfKyPF+3ylGKkkpn1pMlI2k6jPPvOPH35YtZ2dd1bHkiLV2sEm4ihSrv1l4MDyo/bR/7TfugSb0JHa+NYIgL/Hm9RX4jB9JtO+NBgpHrWP1p2N7aTgi20c0je3MVK2cbVcRkqa7/U7T5kCjB2rfjc2mrcqkRgp17qiMMld5TBSkqKQxEdKjxeckTLl31rBJipl2pcpUhnKhgsjZTPtoxvy8lUQOli5BJtwNe2jzyZlpGyDVBJFyjQ5du8ePDZFqZLs9k0CoU0A4+dtwpkrI8UHLB2xjytSdONlKvgtXw6sWxfOh4eCd/WRihtsgtcBXY2newENGKAE0unTgXvuUf+feUZds9W5jTEpFv0VSxNyOb8cs2f7m3lq6PJ++KHfJq+9FujTJ3hf9+7htkX7VFo+UnQ/KwkuAg4vA33OlZEyTfBpM1K6nyQx7UvqIyUJODYhkwoyhxwSZDapIqXPLVgQfD4tH6k4pn2m8a2uDhg0yK5taNa1nH2kpHbmatpXKLhHteN9xaVeTeHP6X9p0YhuM0JRKCiWhZpj67rTewXxujOxnRRJwp/bFi3iKFJxGSlpzK6q8hVil61KJPkEsNeVDjZiyp+inPDnUt1JC5BRjJTJtM+Uf1IfqbiMFGeayjHtyxSpDKnChZHSneGNN8IrmVTJMfmp0PDnUYyUZNonCW1hczM5TRNsg3QSRcqESy5R/4cPt+9vResqyoaeC2A2wdE2MZlswfkkbmKk8vngRGVipN5+W85Hp6cdx7mPlDThmkz7eNszrboVi34bzueDpnLaZG3cOOC009R/KYIgzYfnxY+LRbsvha7LY46Rr1M88YQ/YY0ZA9xxR/D6pZeG2xYtS1o+UqY+6ZJemoyUaTXc1B9cy8X7SVqmfbzdxlXOXYVMXW+SIqXf5cUXg8/PmRMsm85jxgyzKS693yXYhKvCm88DN95oH8g165o2I+Vq2ucaqW316nQYKcnag5sx0wVLOrbrTXUPOwy4665wHrbgLhMnqj28TCjXRypOH4jD0PA5jD9PF95MCjGfn3T5Keg4yJnhgw9W/yXzx0ozUnxhMQkjxYNNtJWPVBQjJdUTPff552r8MinwmSKVoWxEMVJTpvgC2xNPACtXBq9TRYoL3DRtV0WKd6L33lOR1qRy0bxMaZpgs81OU5HS5evRw76/lWTaZ0JailRSHyk6+FNWijNS+jlbFCTP8x3HXX2kygl/7nm+ItW5c3CilYQnbt7I85OuSWZzEyeqfaM4NEOpJ1sbGhr8/uPqk0fvS8tHKsq0r7UYKdO3MdWNKyNlMu2LYqTimval4SMl3a/rjStSmzap0OES/vIXNabSd5g9WwnfJlNcIKzI2BgpqV5NitpRR5m1jepqf8GAmpsCyRgpKeR0FCPl6sfV1FQeI2WL2qeh640uWOo26rKprm6Xprozma1T4TxKkZIsFWjZpeM0GKmoqH2vvBIdCIhuJ2FipDQoO6OtDHQZXBiptMKfS3N2UkbKxUcqSfhzUzm4nEEVJqndS++gTWsBJUMedlhw/s0UqQypwsZI6UHYNPkC7oqUFFqUd0xpFeZvf1MUO8cZZwQ3MtVwFQpbi5GSJj7bfTbHTY2kilRS077woOwnRBUpzkjFCcBBHccrGf6cMlJxFKk4K6cmdoP7sp1xhs9QrlkTzlsCfb8oQUPfp2HykUqiSKXBSJmU07iMlCvTYSuXyz5S1EdKmtRdw5/r58qN2md6Vtebrh895nKTUY5Jk4JjAmesJVOnckz7Hn/crKjxewcMCIcRB8LMQxxGStdTEtM+Vz+url3DfSUOI8XbOGeh6LVcLugj5Wp+qOvB1N9MC2Em6wAKadHD1UcqSnFyWbCJitoX1Sc0tOLM85TGQV0nXMl3YaTKCX/O2wRfXKkUI1Up0z6+UMbfyWTat2EDsGxZuCxffBF+Vj2fKVIZyoRpZcR1EKbCZ7mMlOmcCZMmhYURSTmTYBNEeEcuByaTAI72xEiZgk1wAQSwK1JxvuXAgWHTPhMjZVMG0makTKHoXRkp231Dh/rpa7YjCrq+W1rcFhFMjFS5wSYq6SPFBTvTe8Y17UuLkdL+Si4+UpUKNpGEkYpa2FiwAHjtNfN1m6lTEtM+OhcAQUWNv3+XLrLi3FZR+1wjtQ0aFBYykzBSNtNl2m8oI+VqfqhhUkJ1++EbC9OFmah3ospE0qh9LgtHPF3JtI8qKXwvRBO04szLa5szuP+eiyKVZrAJbu5bKR+pSpn2SftYuihSVGEyIbhwlilSGcqEqUO7DsIffOD/bk1FSlPufC8jV6HKpnBJq1l77x1OI2o/HZpW1CDY2oqUSfCj6fFgExIjRSdXatpHhRKb2RoN121TpKgfgOs+UpJSlZSR4gxEGooUvTZ0aDhvjl69/FDLXFDVZeTgjJTNLFJCoeCbX+mAIa3lI2US7EwrlqZ045bLxEhRZZcH3qm0aR/tT1IZJUaKK1IuePVV+3Vu6uTKSEmKv5Q2oBQ1aYyT2H3TPlJJFClqTiUxUnQMcNlDSqdTDiNlMu2T2itXpFzND6MWLrRg3alTUE4wBTDQoO+ZhJGKUpziMlLczwxQvqY2hZhvJ+HCSHFFShqnTAvYUlt0VVZtjJSrj1SxWBkfqSglTvrNFzG5ImWSrVxkSLqInClSGcqGqUO7DsLU7M9FkbKZ9pnO2bB8uf15V38JSeClg2T//ur/5ZcD552nfpvCjkvpRr2XxLiYkIZpny1ce5RpH4WLad9BB8n58FDw3KwmipGymfbR8vKVL6pIUYHJpkiZ8uO/bfe5KvmmSf3kk4PKbVxGymQWaRLsqJP66aer0OyAEqKT+kglCTYRxUi5TqSu5TIpUjQwCd8KIokiValgEyZGygWu7KSeH0yMlItpnyn/BQuA2bODnYA/r8tpYqRcTPtswSZMi2T0nXSktl69gvf066f6qi5Xmj5Skmmf/s0VKVfzQ/3tohip2trg3EQF6ChFigrFkjAsHUcpUkl9pOhzNTXRm1rT7SRcGCnO+scx7ZMYKel5CXw8osflRO2LMt+0mfbZfKSiGCmuSFHlkp+PC1sdbA3IFKl2BlOHjrOXhwYXuKmjqbSPVJTg5wJuN5/UzCeKkdLndtkFGDUq/IwJlTDtk0LbSuADD/1tMusDzFH7JKXbJdjEjjvK4b95KHguPEjCrWv4c3qOT2BJTPtM5eG/TWXg5TVdmzBBmQNR6Do+8MDg5BKHkdJMnquPlM1J/eqrowOImOBi2ufKSJlM+8plpHh6uq/QFfmmJjfTPjqGcIEkzWATEiPFfaQkloVi6FBgjz3s92jocd8UbKKlJVyncQSWpUuDx/x5/Zuv+rus3tN5iT9jM+0Dwm1r4kTgxhuD5/7xD2CnnfxyxY3aR8dtXQ6baR/t41SRcjU/jAodT/cVomN/lGkfPae/D/e7s5nmp8FISeHPuYJh29SabydRDiMltV8XRioqoIdGGoyUzUfKlH9SRiot0z7XxR+KYH4ZI5WhTJg6tOsgPGaM/5sL3NTkQHeK998P30/hykhpyn3//e3PuypSUYwUfZeoFRopf9fVJNugpJGGaZ9NkTJNQv6A5Y9cLj5S+by/aqv31aqrC4eCT5uRkpi0YtHfl4n6XfD3pe/E0zPlIyGuIrXTTsBHH/nnH38cOPRQvyzUTCRK0ADM4aFtilShEO0fyaN3UrgyP6b6cA02YTLtqxQj1aGDeXPytBmpuIqUjZHSwuv224fLRzF5MrDrrvZ7uKmTq2lfXEWqX7/gMVdUdVrlBJuIG7WP5mdDLmcPzBJHIDYxUi6mfa7mh1FsHlWkeDh/21womfZxRco2JlaKkeKKFOCHLtcbtP/lL/78RM2b+fYvSRWpOIwUTc+2HUESRcqFkXJRpEyLv+VsyJvURypqITpcjkyRypASeId2HYSp/4vJtO+NN1T0PQB44QX//oaGcHrSgGxS5iZPloNkRKUnnZcYKWk1S9q40AZpBVFCOT5SpnJw4Yu+I58QKKT3pyYZruHP9UBHqX9d1m7dwgojN6sxrbraAiZIAzU3J0jiI8Xzc1Wk4pqx5PPBOj3ooKBQQCcX22IAz8MW4pgLJFH+kZ5nFyjTZqRM71mpfaR4n6F+OFRQTeojxdkUU7687HEZKS7kDRgAbLttuIwAcNxx8v52FDZTJ5dgE9QEzZaHWiALVoCJkUoz2AQdL00+gNI3lr6FNPZpRK2g0zxMPlJSW6DCckODEraPPVaxKrZgNlEMsBasuWlfFCOVRJFKm5FyVaQA9T7a53fffdUxN2/mC0iVMu2jipB+/sQT7dsR8DmJv2cUK6SPKxG1L2rekc5HRe0zLVJ36yaXxYRMkcpQNnQjkoR3kw04BV25MylSf/hDMK6/hhSikk9UP/iBvFv47ber8vGBoFKmfZIilSYjVY4iFZeRmjLF7LdE0+NsjF8HfmXRYBMmRoqat+iJVDINsylSVPixmfZJCg4XwEyKlLQKncS0j8J2n1RW/X66XE1N8uq2q2mfiZEylQNw9480wZX5cfWRijLt4/mVu4+USUmuqQkyUi6KlBT+PCkjZWPNJOGQKwPV1UCfPsFzw4ap/zvsECwbEBYSuakTD75An7ExUjz6mwZV1LiAGWXaVw4jZWLRJeEwinnR6ZTDSNHv7GLaR88dcYT6vW6dL2wDwFlnmfOL8km0MVJxfaTSVKRcFkZcTPso6PjqsgdXJU37+LvwUO18O4JKMVKcdZRCtkf5SEn9Molpn1Qv/B1qa31mkcIkx2aKVIayYVoZ0Zg4UU1sALD77sHVciB4zBUpVzM9Cv7M2LGKcp8+HbjnHl+QOfxw9V8yS7Ida7gItZJZV1xFypWRogNrWoqUtIqtJwfug0AhTUKFQnxGigol+rwuu6RIpbGPVNqMVJTiRs9HKSkuZix6QqLMR5qMlFRGLpC4+keakJSRsvmuUFSCkTLtI0XPc0aKf1sp36RR+6R3jmvax9uz5K+jTeh02jSPPfbwFazvfz9sikvLEEeR0lYMfOGCKmqSclJpRooKaibh0IWRokq1pEhxBZyba9kYKWlu1dc2bw6P61rY/vDDcLk1XINNcB+pqKh99FwlTPvSYKS43KPvb2py2/5FaiNpmPbRejFZj+g09HYEfDziilS5jBRXcDSShj83fWs+F7qa9tE8aLCYLl2U/HjttXIZC4VMkcpQJkwrIxS6M2yzTbjj0/DjXJGy+VFQ0Lx5x66pUR1q3DjgtNPMGxTyMpiONVwYKTo40M7qGo6UptUeGKmWFrfJga/y6jyiFClT1D5pdZdPqPQ9JEWKMzb8ehqMlMnBXNqUV8pHmkxcFSmujFGBXarHSjJSUf6R1BeDltVWDl4e/puWo1wfqXIZKZoeV6TiMlKV8pGytSWTaV8+Hz6n2SFdFm4uq5WeHXcMt2++Rxn9b9tHSreXvff2rQ2uuiqoqEnKiY2R4u27UoyUq2mfxMZr6LJxszHNID36qH8vn++khTkXM9vnnzffE8VIUYbCZNoXxdTZgk1QRTIYltqcnnRMIY3JUaZ99P7XXnPb/uXTT8Pn+KKhTZFyYaRs45bn+dsR8PmQv2e5PlKmea61gk1wlo3P77oMXOGqrlbyowlbIyNl2X0kQ1vA1KEpJOFY42tf839zRYpvuGhL3zRYSMKAlJdGFEPleWpgcFkJi2Kk4ihSUYwU/Q6VUqSWLHGbHPR3MytSfqW7MFKSMMEZKTr4aWFVUlxMDs6uPlJJFKl8XtV5lGmfvo/CpkhJgrHESEmr24VCuJ24MFKScsSf0/6RJ56o7pcEluHD/T3kOnYMCkA2wS5OsAlXRiqqz2u4Kng0Pfpe5Zr2VTJqn8RISaZ9vB/q/iuNU5ShkeqUnovDSNHvptv5iBHywpX0PL3O2fM4jJS+t1xFKsq0j/c5ahnAn120CPjOd8Jl4GMZLYfka8zLZ/OJjVq4oAyFKdiEVN8uitR//hNUHGkU3ijFyYWR0soeVSxsipQuJ99WxQTJZYEzKTbTPhdGygVLlkSb9qXlIyXJZK0R/tzESFGfKm2pQes1ajF7a1SkMkaqncHUoSl0B1y2zL7/kO6AumFHhdvVsEWp48KtaWLRiFqdlgZTno40YFAhr5KmfS6MlKvgyFeldLS6KPAJQJ+T2oqLj5Q00HJFir6Dq2mfNPhGnYtr2qffy1QeXjYOV0aKpxGlSKXJSEnPmUICA8Cddyp2WoMLEzfcEHSCprCtpMdlpOKa9rkyUvQ+Pd7pVdc0TfviMFKSma7pWRMjJSlSmnGSFCk6fkgKhKRIuTBS9LtJTBh/H32v9P6m9h2HkZKi9pVj2scVKZ5OsWi2DOB+RbwPSG1YYvfjIKlpX5xgEyYfKT4PUCuWtBgpXofSooOGbkc2v3AKvUE6BTftkxZ8TKZ9NobPhoEDw+NR2j5S0gKzvp6EkZJ+U2VJMomlz3DTPhMjFSWDZYpUhrIRh5GaN8+eFlekevZ0K4MpQhIQFm75QBOlVJiETZsgYlOkKhX+PI5pnykPDr6Kzf3bokAnoSSMFDVJ4+/EJ1DJL0Aa8KkiSyEpTRJrSRkpl/Dnuvy8jFI+lTbtq5SPlKlt6pDAHMcdJ/cNjYaGoBM0RRLTPtN7xjXtc2WkJNM+3TbSMO3T5a8UI8X3kdKQhPoo0z6bv2scRooqVrR/6/xsfUNflwQvbj6VVtQ+yQxSKqeUF1ekeDotLdFRMXW5uAAqvZdt/nRBmsEmCgXfVG/mzHAZ4yh9UdFpXRkpmzJqUqR22cVt+xdp37Vygk1Ii7S270u3I4gy7XNhpIrFaEYqiWmfKyMlsZj0HpuPlEmRyhipDBWHCyOlG6ht7yEgPFBF+eJomARY6VpcRso0QdtW8/W11gx/TgfWSilSffuG92exge7/xVe4NFz2kZIEOJvTsWv4cwqpPUjnPM9n5tL2kZImPKldScdJGKmoFVta5riMlIY0QdLvbIN2gqaIw0iZVsg5s2GaODnKYaR0XjZGKk0fKZtwbroOBMfx9mraRzfcjqNISX2bm/aV6yNF+2Glgk24zBuAbI4lvZcpCiJNhzrfc3CzX56Hq48U9/k66ij/3iSK1OuvB49dGKlCAXj22RxeeUUdS3UosbcaVBh32f5FsrrR+ZUT/pz2B8r+S/fq7QjSYqSo4kzzMTFSNtM+m8mtiZHSkBgpFx8prnDZFiFUOpkilaFMxGGk4qapG+6kSTJVzqljCVGMVFxFKg4jZQo2kcRHqj0wUo88Eg6hakuPO6j6Akg8H6m4pn1ckaIDqImRkr6rVPeUkSrHtM+mBFGkzUjR7xElfNJ04/hIRaGmBli71n4PdYKmSJORkhY6AHN/qDQjJeWbZvhzF0aKjh0uipQWwpOY9tFz3C8rrmmfi5kqRSUYKWo+Gce0L8pHyma6bkNNjZtpn8RG8mMdfVFClE+ii4/Uq6/aQ4VrP644ihSfq6J8pB55JIfzzjscX/1qNe6+W5177LGwkG5ig4DgWK/Nm22WNdKcYWKk+CKFVAbJ2kXLTlHbEfC+m9RHiu4bpp+l6ccx7Ysb/pyWTQrS4spIRckBFBkjlaFsuDBS5SpSo0YBv/yl+t2li38fdfY15W8KNmFaZXBlpGyr+ZIipc8l3Ucq6t7WUKRsDsdSelyRistIxTHto+Xkpn3c1r4cRiqJIuVq2hdlNseZTpvyniT8eVJGKq4ipZ16XcD3pDIpo7QcroyUFAwGSHcfKZMiVc6GvLqsaZr2uTBSSaL2uTBS1HwqTrAJqnya2kJ1taxRVYqRsi3+0Pyk9OixjZECoqNiAkEGyMZI6TJ37x72adTCtm2TUtdgE7bw5/fdZ69zHd0ujiLF/Y9sjNSUKcCpp+axalXQfn3jxnBQAhdFSj8zcaIfNnvoULMpIEXawSb089tvr/6PGqXCefPtCHh/475gSRkprtxJpn0mmSWuaR8tj8Tk0utxfKS4chVMN1OkMpSJOIyUzQRPSpMOWBIFTgeTqI7Iy9JajBRf4YzrI2UTRKT7bKs7Uc9K523BQUygSiOdFCRzBGpWEidqH1+xlnykpA33okz7olaiPC+oSNHBuhKmfa6KFJ8MkgSbcGGk4pr2cej2H2VOpMH3pGqPjJTnRe8jpb+H/t/YGM4zSpHi40aawSakcTyOj1QUIxWlSPE8uRl4XEZK+sbSdS6sujBSvK7SNu2LYqSo2ZhJkAbC46mUF+DXXadOyqdRt9F77vGFbZtZfjnBJvSza9aY0wd8BSqOIrX77sFj04JKoUCDd5iFYt2XbYvHtkWzbt3C9W/ae1C6R+rfpmATEjOjyzR0qArnLQUxob/psSsjVSiozZwB4M031THvU3zh22ba99JLKg3XYBNxGCkuA5oUKX0uU6QqgJ///OfI5XKYNGlS6dy4ceOQy+UCf+eff37guc8//xxHHXUUOnfujP79++PKK69Ei+vybDtEHEZq0CC3NHUjphOWFLWHruSb8jf5SPFOZFJuTIqUzZREUqSksrqs5LeGaZ8JxaIKpxsXUYyUKdhEnKh9gNwWuF8W/a/LZDNPi8tI0fwqHbVP/47aJ4vew/eRamtGSqczYID9PuoETWFjjuIyUjw8vyld/lzUeZqW3gqgsVE50es8k4Q/b2tGKq4iRQUSW9Q+mqbOU2KcefunPlKmthClSJn2kbK1aZvCHmXaJymUUaZ9kq+HKSrmkCHAjTeq366mffTb5/P+mLzffuZoeRRRCxfUR8pk2ueKOIoUH+dNjNSsWdqk0C4Qv/yy+h+HkQLMCx/0fgrebtIKNsEVQQ6baR8tl20cmTzZN9v+9reVz9v77wfv5TKKbfH3uOOCaZTDSEWZ9tF6lsb0L5MiVWZ8mXTwyiuv4I477sCee+4Zunbuuefipz/9aem4s44PC6BQKOCoo47CgAED8MILL2DJkiU488wzUVNTgxtuuKFVyp42XBgp3UmoWZ5LmnSwsA1QSRgpLiTX1PjCjef5A1JU+PN8PtzJqIBQVRXsmEmDTeiBzaQw0rpynZyiTKyKRfeQ56ayUCVCEtaSbsgLKGGLh1+myoKkhJrqRxI0pMGZM1KAajstLclN+1wVqbQZKRuryvO0+UjFYaSilE4KyQk6CSMV17TPZuoqwVSOKVMAvY62YoVyotdtppzw55xNkcpnYzmk65X2kZLeTdomgSqaFEkZKVPEMl6HNhMi07OtZdpn6qcTJwLHHuu/46GHAs88429yz8dT03vxcVmaa+MoUiZGqrbWHGzCFVGKVO/ewBdfqN9RC0X6OjcfNkHvDeWiSEkLMxKrZ/OR4mmWy0hpRcrUtvl4xPNwYaS47+uiRcCDD/pp6vS03KTTtVnRLFoEPPCAPW8XRYpft5n2cXzZFKk2Z6TWr1+PM844A3/84x/RS4iA0LlzZwwYMKD0171799K1qVOn4t1338Xdd9+NvffeG0cccQR+9rOf4bbbbkNTVEi7doo4jBQdmB9/XJkOTJ8eDqvNhVq66i/dF0eR4qsqcYUqXjap80kUtqRIxTHt43lwJGGkooTZYtEerSkKPNKPNPi7bsgrvRNdtZYEGEmRoiwhhSRoSOafLS2+4/Pbb6vJUaf3wQfyN41ipEy246b7bD5S5QSbSMpIFQp+yOIZM+ztWped9rc+fYL3dO8edIKWysN/A3YBV7ovLdM+Xo5Cwd8wlTu8ayV8zpzW8ZGS2BJ+Lz1Oyki5+kjRtvLCC8H3onlKjJROgzJSJkVNv3+UIpUGI5W2aR9tCybTPg16rUcPdUyfdTHt49+e+qlp2BQYbtrH87CFP9fP9OhhlyH0PGErx/TpwM9/7h+bFCmu8HHzYRP0Zr+2xWPbgq8k4knt0+RH5aJI2cwN4zJSvJ27MFIcEgPEFzNNc7KURpRVRzmKFLVgkJTuL5Mi1eaM1EUXXYSjjjoK48ePx/XXXx+6/o9//AN33303BgwYgKOPPho//vGPS6zU7Nmzsccee2AbEo9ywoQJuOCCC/DOO+9gn332EfNsbGxEIxlB1m5R+5ubm9GcxIklBeh8m5tbAFQjl/O2/A5DNbRqFAoeNH0+enQztI6Zz1eXzqv7VVqFQh5AFYpFna78+auqvC0dMdygPa85MCFUVam8mppa0NzsoblZlU05JavnGxubSx2psTEXyLexUaXX1OQ/p34DTU3NyOWApqYqAHnkckXkcjkAORSLKv1CoWXLgBSsDxNaWorQ6webNzdbTFWqS+mrdKOX/Ok7S2huLqB37xzs6xd6xJHqvoB8vgpADps3N6NYzG9Jyyu1n5oav35zucKWwSqPlpbClm9TBaAFuVxVqBzr1/vfVplQ1SCf9+B5BQDVaGlR7Uh1HVUfhULzljyC9VMoFNHcXCjVI+B/a91GAOCuuzxs2KCun3UW8K1veaWB9Mc/Bv7f//Nwyy0FHH+8PxLr9r15s2pzgN9+VN7qO6hohsF69Dy/XzU3q/rr0EHd19ysykyvAQU0NxdRXa2ON20qoKVFfQPPa95Sj3k0NRW21J0v2bS0+Olp6PZfVaWuqYksWHevvebhsMP8cg8ezOvAv7+6Wr1PLqfLC9x+ewt69QJuuqkKTz1VhQsuKODoo4ulb+uPNc1oafG/j+q//pij3jMPz1N1oOo3h+Zmv97Vc+q+fL5Qev/Nm/22pPsvh1Q/gN/26H2XXJKz+lz8+98ejjnGK9VBU1MRnhfuZ8WiXy7P88dR1a6D5dRjmrpXvXs+76GlRY13ajzNie+ihLwaVFXxcZx+68KW8cwvZ02NGv91erTuPM/b0tdyaGws4IEHPFx+eR6LFgXrRPdTAFvSr0ZjY7AvNDcX0dLiQX03NSbSe1R79iWdpqYCgBrjGNfY2LzFT02/XzPpD1WhNhMsr7pHt7Ncjs4pVaVrVVVVobzpGOC/W/A7qrx1OvLcp+cahZot76S+webNqg7zeQ/FohoLpfFNQ48/eg6vqVH3bNpEx6vwcxq5nEpbtV89dvvfYtMmVV81Nbrf50rPqXeowjHHFHD33VXI5XQ7D2L33Yt49dUqbN7sz4ccBx/cjPfe88fVcL/XY5mHQiFXkjNGjQIGD67G4sVy3mqOy2HPPVUb0fNJuK+gNK41Nvp5637a1CS1xaB8AgBVVf7YqI7VOKX7vXoX3WaCda2Ug+D4rPuLKk+u1BY4aDtsbi6W+pBuF3z8AVCS0Vywbp1+f2+L0uK3gyVLPEhjLseiRX7ZCwVaXlXfehzT6fKyUZmrubmw5T39/qLmybBMo+YHeV4oFv05qq3hWo42VaTuu+8+zJkzB6/oTQYYTj/9dGy77bYYNGgQ3nzzTVx11VX44IMPMGXLzpJLly4NKFEASsdLly415nvjjTfiuuuuC52fOnVqwHSwLfDaa68DGI21axtQX/+seM/bb/cBcAjWrt0EQJV32rQn0amT7hBHgX7aDRs2or7+KaxefSiAXpgz59UtfjWjxfQ3b94Mz6uB1Dyefvo/qK31B5qNGw8D0B0vvPAS1q9fiQ8/7AXgUBSLTQAU/fLvf/vPvPWWKrvG1KnT0LVrM95/Xz2nJrqaLc/Vo6oKePfdEQB2xeLFC+B5QwDksWGDevfXX5+D6uoigFFoavKfNWHZslUA1OZN9fX/QU2NvCyydq16r1deeQkrVzYAONKarkIzACGKxxZ8+OE8LF/eGcDQLWdkoaSqyhNXZebN+xBNTdsC6IyZM1/A6tV7AuiFqipg2rRpAIB33ukNoG5Lfu9g7dpaACPx6afzsWxZZwAD8M47b2HZsn4AhgTSnzp1JoYOXQ8AWLy4C4DxAFrw8ssvAqjD2rUbUF//NNavrwagNiV58sknsGBBNwCHBdJaufIL1Nc/j2LxmNK5Z599DvPnr8UrrwwAcBCAsKkjX6VatAg45ZQ8rrrqFYwevWTLM2MB9MTs2S9j82ZFUbz66jYARgHAlomhGps2rQPQPZDepk2NqK9/EgDw2Wd7A9gWTU0bAHTFsmXLUV//0pb3PwDAILz33tuor/8MK1fuB2AI5s59F42NuwCoxqxZ0/Hxx0MA7IpPP12IVauaAIwo5bVw4SLU188J5P/KK4MAHIC1a1X9rFnTAcARgXv4Siutg1GjlgA4tnStUFDvs2SJehcAePPNV7D//stRW7sHgO3xySfzUF//PjimTZuGtWu/AqArAKChYR3q66eXrs+btxuAHfHpp5+gvv5drF+v6v2ll15Bc/Nyct+uAEZgwYJPAewIAHjyyafQs6d6kU8+2RPA8FD+ixcvRn39a6Hza9fWgPa3BQvWYdGiHqH7gs/k8PbbywGo8f+zz5YAGBy678kn60sC8yef9AAwDhs2bEZ9/VS8//4uAHYq3fvqq3PQoYNqc01NRwDosEWYyOOll17GunV7Q4+/CxYsRH3966VnlyxR/adQaEF9fX3gnMb8+R/jiy86AhgGQPX79957A8B+WLp0JerrZ+P993cCsAsAYP36jVsU/I6YOXM5brlFdoxbvx748Y9fx+jRS/Dee2o8WLvWH48BYNGipVi/vhHAcKxcuQjAUGzapN4NAD788GPU179Xuv/DD3sCGIvm5s2ld6aYPv1ZvPPORgDHbDlW43pDQx2A3njlldeQz8tz8sKF+wIYivfffw/19R9j3TrVzl5++RUsWrQtdD/cuHE7AMF2MHt2sC0CwEcf7QxgZOn4zTffxooVwwD0xhtvvLZFgD0w8IyeaxRU/1q8eBXq61/A66/3AzAGGzc2YM6cDwAchNWr16C+fhYKha+DC4MbNjQC6IjGxk2or5+G5ubxALrg2WdfwLJlqwEA69cfCdNc1dCg0p4/f3cAO2DevOC3WLRoNID+eO+9N7Bhw46lOlm3rgFLlqwDMAz5/Hv4/vc34k9/2gOrVgUj0eRyHqqrlwIYhEWLVgLoL5ajvr4eb765HYC9AAAvvfQympp8SvjVV/sDGF3qE5s3+2PrN74xEDfddADCc5yviD333Ex8+un60lxD+4rGqlUHAhiIuXPfRn39fADA+++r77txY3i+f/HF57B0adAerqHhYAB9S8eff67GqbVr16O+/hkAwCefqDFMj3UaS5d2BvDVQHqrV6s627xZzTNLly5Dff3Lofr77DN/3Fu0aAmefvptABMAeKivr8c772g5zi/HkiXqfV2wbt1mAJ2wceMGeF5tqS5WrlwBVc8RjrMAPv9ctXFe3jffVPW9bp0/Fq9cqdpMsAwbASgfk48/no/6+rcwd+4QAPuV5t9CIbww/8QTUzFv3gjQ8VajWMyV5Jm2xkbX0MpeG+Hzzz/3+vfv773xxhulc2PHjvUuvfRS4zNPP/20B8CbN2+e53med+6553qHH3544J4NGzZ4ALz6+npjOps3b/YaGhpKfwsWLPAAeCtXrvSampra5G/Dhg3eo48+6j388CYP8Lz99isY733mmWYP8LxBg4qeJk7XrPGvd+vmnwc8b9tti15TU5O3774FD/C8f/6z2fv3v5sD99C/YcOKoTT038aNwbLssYe674knmr2mpiZv1iyV7oAB/vNffOHf/5//BPNdulSdnzFDne/Vy39u0yZ17ZprWjzA8845p8Xr2LEYePcHH2z2/vlP9WyXLnKZ6d+hhxbEOuN/O++s0po2rdlbsaIpMl3A8/r3t+d/1VUt3skn+/l36BC+f+TIgldbK6dz7bUt3vDh6trMmc2l7/mjH73gbdiwwWtqavJeeMGv39/+tsX70Y9U3f33f7d4hx+u7v/zn5u9008vhNJ/6SX//d98s6n0PZ59VqW5/faqHS1d6tfHxo1N3iuvhOvn4INV+83l/Hd5+WWV9v33m9ue9JfLFb0hQ4ql9rDffn471uWdMsVPU7eR3XcP12O/fsXSM9/8pkpnl13UfRMm+H3u6KPVtd//vjlw7403tng1Ner+Tz5p8m68UdXvN79Z8C6/XP2uqlLXTzst3IfvukuVc9w4dW3xYre2petg/frg/YMGqfc599yW0rl//UuV+bvfbSm1O2ms2bBhQ6k9AZ63007FwH2XXKKev/JK9fw++6h7H3usOXCffu/LL28pfe/PP/ev07LRvxNPlMe4BQuC77jDDuG2Kv0dfLB/39e/Hn6mqir4frrdbrONOn/FFcFy3nOP/549e6r36ty5WKrjIUP8uuPf+p13VNo9evh5fvhh8L1++MMW7+yz/XLW1hZD7eOHP/TLtO22xdIY07Vr0QNM443fX557rpnc799z9NEF7zvfUXmfeWah1Mb09SuuCLaZ6dPVvDR8eKHUvunfW281eWvW+O+3apV6bswYlfYDDzSL37qpqck75RR1z803t4T697HHqt+33dbi7b13ON+HHw6ne/XVwe/429+2eAccoNJ55JFm76GHwuOPHluamvx3GDVKfQM9v+y3X6E0zhxwgLomjeHbbKPODRumvv2IEer46af9suoxytSOpf6n/3Q7v+++5kCdHHRQofQt//d/1TObNvnvM3KkupbPF72JE9XvQw4pBMYs+tfU1OTdeqtfl7zfP/qoqgv9LnRsbWpq8u65Z3OojXbrViy1xTfeUPe99ZY/1/Bvqcv5m9/4dfD976sydeoULvPcueH2NXZscCz43vfU83S8mzSpRWz3H3wQHp/1PKrngSOPlMexb3/bz/f44wvevHlNpX5OZZ4dd/TLIY1bpj8tA40cWfR69PDr4sgjC95JJ8ljLv/72tf8sp93nv/Mb3/bEpqfTjwxXLZtt/XzPfdc9cxf/qLeS8+/+XzRq64OfqulS8Pjrf7beeeVJXmmrf9WrlzpAfAaGhqs+kybMVKvvfYali9fjn333bd0rlAoYObMmfjd736HxsZG5Jkx80EHqVXsefPmYYcddsCAAQPw8svBlYBly5YBAAZYwljV1taiVnBWqampQY1rTPEKQZnKAVVVVaipkSlef4PUHDlXE9q7Q8PzcqipqYHnqeMOHaqt/jwqQqJ87cUXa3Doob59r+80Xx3wyamu9hOoqqoJOR9r5PM1xuf0NY2amjzxh8qV3sWPuhZtW0vNfWi5TOjQodrZr0mZ1ZmRy+VL3wAA9trL3/H9kkuA3/wG6N+/CgsXyrbrNTV5ElbY77q5nN92u3b17+/YMU/eL19ie2prq0V750Ih/J3y+Rw6dtQmc6od0W7ZsWONWD+ep9ovfV9d3x98EL7fBs/LYeFC1fbGjaO+ONVim9ftIJ/PbTFt8a8Vi7lSH9fn9XfTZabQfUX7FBQK+ZJdeadONaW2Vyz6z1VX57b4epn7cE2NuubatnQdzJ7NzUxyW769f65jR1Vmv63kUVMTNqGoqalBS4vfZvX3Dd+nnvd9WILjh+9Uny/5lNC+ZbKFN9UPHyOqq91MXTp18u9TZiNB6LrS8L+pOs/LSd/T99XJla4F/YiC7+KPi36e3NihtjYfqMcOHXKora3ekl+4borFXKkc69fbxhq/v2hzb2VSStPy066tVb+pGZbnBdtMVZXfp/L5XKiu8vmawHfr3Dk4rvM2I0G3M99HqJr00bz4vJ53guf4sd9nO3bk380vP0+nuTn4DWpqqtChg66r8Pimoa0J9LfXY4TnqbJ6XpSPlErbr4fgt9DPdu1azfzhqkJ9nl4fMqQKH3ygy6XNsaq21EsutK8hHwv4N/RlAD1+BvvXiSc245xzmrFxo2+lceGFOfzpT4o1ra5WdS71Fb8M2JK2Xwe6zrULAIVudxR8rqut1en4+dExjNY1H5+Vn7KqM2XaCPC+L6OqNI7xdkHnJJqP1LaC76HHohwLalOF7baLKM4WbLONX3bab6T2I70jlbn0N+KynGTeWVVVY5QxdX20tSwOhPuACW0WbOIrX/kK3nrrLcydO7f0t//+++OMM87A3LlzQ0oUAMydOxcAMHCLN+Po0aPx1ltvYflyn9qfNm0aunfvjl133bVV3iNt2CLYaEjBJoKdIHi/7pD6P43+JsEWYOG//kuFz9xiXRmKfKXziBtsQtr7h18z7SOVNNiEiwN0nGATtjrVaZrCQg/fYvlki7hjCjahfIEU6H5CcaP2SeHPpWAT9B1o/VPoqIj8HOBHgYoLHQ3KNWqf9O2kIAhxwp9v3hwMqCJFUZScmXm6tn2kbODh87UZIG17fA8f24ScZvhz0yaT5Ubt69zZvmEqoCKY9vWtd5wc0eNE7dPvTJ+xRe2TxvGoYBNUqDSFP49yRqdYskQOdKDTlYKE0OsUUtQyfp3WBw82YWuDpuiQSaP28byigk2YyqfHQ/ps3PDnQPgb0LFbQlR/cwl/rsug9yACUFpko+MiTUtCVORKINyPKGpqPHZsrsO4wSZsgYgoyonaJ0XxCy9Uh/Pk5aPzob9IGb5P32PaF5CWh6bH20GUfGdLT5eX/jfJQS5R+0yygDlIR5vHwIuNNmOkunXrht3ZDm9dunRBnz59sPvuu+Pjjz/GPffcgyOPPBJ9+vTBm2++icsuuwyHHnpoKUz64Ycfjl133RXf/OY38Ytf/AJLly7Fj370I1x00UUi47Q1wNShKSRFiu8jIKVJByxbR4sK+b1okYqg9dBD/n08IpurIsU7LB0QeAQpGt5VUqRchAwXAQ8Ifoc0o/ZJAw/gT7I2RYpek8JtA+5R+0zhzzWke3nUPj7hU0gDpT7u2TN8vwt0NCjXfaT0t4uK7meL2sfDn+t9jPQ1Kfy5PidNsKYNS13Rn7kz6G8mKVIuEdOShD9vblaR4pYsUd9Eih7lso+USQDhQr/nqQ1TTzzR/B777x+sS2nFn7d53q7jKFKeZ4+2RRetNCRFjpapQ4dw2zZF/nLBwIHm9iVF7ePXKSRlmV+n342H92/LqH1UyYuKOkghKVLlRO3T6ejQ0yasWhV83hS1Twp/zpUaujEvZe/5flYmccmljdvCXKvADMEycgXRRZFyGU8Atw15bfMHv1faV8q2OEfBZQ1bO+dpdeyoIpJ26RL0Ix4yBBg7Frj77mB6tn2kamuD4+GQIWrriL//3awo8/ZtkgldFCl+HxAV/lw+357RblW/Dh064KmnnsLhhx+OnXfeGVdccQVOOOEEPP7446V78vk8/vWvfyGfz2P06NH4xje+gTPPPDOw79TWhqSMFL3fxEjRAd51xUKCTm/SpPBgIAkQtkGQl60cRiquIuU6uVeKkZLqxSQwAGZGynUfqajV3bjhz22sirZ4ptDP77yz/H4m8M1kpVVK0+oib8uuihR/Ry1sUvMXvo8UZ1XTZKR0HYxm8WH0N5PCerswUtJKr4Zp4j/3XDURn366+n/nnf51aXU6jX2k9IapvXsHr2mlfJttguklUaRcwp/T90s7/HmHDmG2lYc/1+XQm1ebMHQosHIl8NWvytepIJOWIiUxN0kYKWnMMuWbRJGybf1B4aJISc9xNptu4FooAFdeGX6G4sMP1X2munMJf67LoLeWoOfiMFJxFguk/qwCQfmQ5nCbzCNZH9gUqUozUpz9oc9y8PkkDiOl6+Kkk/xrTz0FfPopsMcewee4csfn94MPVqHs9fY4n34K6C1badltjBTdeoZCkmEkRUp6zqxIxVxdbAdo8/DnFDNmzCj9Hjp0KJ599tnIZ7bddttQpJetGUkZKXq/acUkDiMVZSbnecCCBSjZ4HMhm24SF4eRouWSGClJkXKZrKX802ak4ipSVHBzZaToalqUIsUZKaqsSe9EFSlJsTMxUibTPhfhMgr6Xr2ZrC4TLSNNmyLKDIMLki6mfTZFik8gmrWYNctnb7gpnkvbonXAoQWhpIxUHNM+bZKpV8w11qtAj/jgg3imfab+Ku0jBShlatMm4BvfAHbbDfjd74D33gMuvFDVK1UIJNO+NBmpKEVKWlCSfDf45rmupn0HHADMnAkjTj0VOPlkcx0vXQpoN2IX0z76PlGMFH0nl0UuXld0PE9i2helSMl+TeFzUaZ9USZdkmnfrFnA4sXyczTfWbOiGamOHcNMBO/zlJHiczN9RxMjxZUBOp7Nm6fO2xgpFS6fHocXeKS+Qu8H7As+FEk35DUpcy6mfa6MFJ+vbYyU1Nb/67+C8ohJkeJ9tLoaGDcumJbUtqS5kX4b6ftI47xklcRhY9czRSpD2XBhpCRB0sVHylWRcnF01NBl4J3IVZGK4yNFV0UkRsoFLivl0nu4oBwfKWoe5WLaR1d0VCh7BTqRvPdekG2JMu2L8pHi3zrKtM/ESNFv0KFDUOjVbUZjyBClQNDNZKNM+zRcfaTKYaTo9+BteOFC5U+4cKH/jGZQbIpUdXWwbdA64EoMfYb/jstI2RSpQgH47DNzOgDw5JPxTPviMFIcAwcq4UCXqbEx2PYlRor3T1rWQiH4nfR5XoZKM1Kupn2DByuG7swzw1sIDB2qNui1ffd58wBtWR/HR8o0Pr38st836HuWy0jRepfylcZwyR8jSpGSzulxycRIRTERkiKl/TyjsGSJuf9SFokzEVw4j2KktFJGTZYpaP3Onq2sUHg/0fUk9VPJtC+Jj5Srad/s2cARR8gMPU/ThZFyMe1zZaT4e9oYKemaLhuXgXif5MdSv5H6pTT/RVnmZIyUQrs17fu/ijiMFB0YbYyUNGDZGnkc5UEzINIKhsvqNC+bZFdLr/HAEnEVqSSmfa7pu/hImSYEF0YqyrRvyhRgxx39+3/0I+CGG8J5xzHt4wEVdFq6rDR//q4m4ZIO3trEoFMnZXawcWPYDIEqUbpMQDQjVY4i5cJIRflIvfhiWOjQK8R6VVqquxH+VlQ499xgHZjaLBVe4/inuDJSs2bJLA/F2rXhPsvzoHBlpCSBR38XvZLe1BS8TwublKUyMVItLUrhfeqp4PVXXw3nSxUpW/tz8ZFKYtpHWfqJE4ETTlDHdXXATTf56fB2x9HUBOgYTZJpn60tSGPH+ecDl1+ufm/Y4AcjKtdHKg3TvkIhOtiEKyMlKXmmtCRFSvt5RmHgwGjTvtpaswAtMVLSYp1mmefPl8tB++6vfiW3K52H7CPlseOwsueiSLkyUkcfHQyGJaXbXoJNuDBS0rtKMpDNtE9q75KSHmXaV46PFEemSGWoKFwYqSj6uVxGqqrKbDNN8xg61PdZMDFSNF96nwbvsK4+UhJL5YIkpn2uyloUI8Ud1CVFyuYjRSfAlhY68Hl45JEcTjwxPNHpFcn581vfR8qkSEntoWNHxTB06KD+n3aa+i+VsxzTPilvPwxt+JqJkdJp20z7bHjttaAfhAlDhgTTM7VZbsqhywgkZ6SoMuC6ki6Vs1xGSlr84ApuY2PwPbXSR02WTIpUsSgLiLff7gtkcYNNSIwUH6viRu2jwpiuI308bJjPMNmUFgrdluMwUi6LbJ6nFLwHH0zGSFHlyzRm6e/alj5SSRSpujrlz2dDx47qPkkJLRb9tm1jpCQfKVouzeRGWZ64tiWaJwXf8N7GSElzbFwfKcAPhsUjC2vEMe1zYaRMdRRl2ufiI8UX9OjzrqZ9NkZKUibpefptXE37XOZBe//JFKkMZSIOI+V6LNkiR5n2RTFWgDI34oOSNOHGYaSifKT4ABDHh4k+x3+b7rOZr3GUw0iVY9oHAJdfnrdOiq+9FvRhSNNHKq5pHz1P39sV1Hl7xgxlxvTOO+H74jJSNlMuHrVPl0EKNsGDFkjYuFGxPFGrfFLYagk20z5XRspmzuW6ki6Z4rgyUoWC+p7/+Y9cDl4mwBeoGxujGamohQ4JkyYFhSBT/3NhpIDgGMGVA8pIRflIcb8u2v5cA9bquknDtE/CaacBW7Z1TMxImfK1KVJRpn1SO5DYcl7HafhI5fM+c2fCnnsG5zqaDzVZdQl/Thkp+k2ff95eBn2/bbFRup+jtU37aLq675Zj2lcOI8XHB1s758/wRWjJ4sjUN6RjDomRksoRxUjFNe2Ttgvh2BoVqcxHqp3BhZHiDTSKoUrCSNkUOeqz8Yc/qHM2RspFWHPxkaKDMB1gKs1IAb6fiA0uPlISFQ64m/ZJwSY++6w7Fi2yV8KmTX5QABMjFXcfqaSmfTbmxwW6Dn7yk6CgwCG1DSlvaWKNMu2TFClJGLNhyZL4ipQ0+cyYIZuQRbEBXNm1mXPV1YXD6HL06OHvf2JjoXn6gFo9vvRSmRmSFFz9XfV3aWqSBU7N8kgCFdvLXcSCBUrh5YpUlB+XaZW9ujoYcIQzUtyUibOufLykwr7+3bOnGqMXLTJ/+5oa35ogXtQ+D3oD1igUCsCWrR+dFCnJ5KkSjJTNr0oqZ1JGSt/L95E65BD/naSyDB2q/ksLIVyRiuMjRcdf7lcnYdaseIqUVB+tbdqnoYNhSYtVNkWqvTFSUt2kYdoXl5EyyYSSPBOlSOm5MlOkMlQMSRipKAdJiUKPYqS4YDZtmh95rK7O7yR85aRcH6lKK1IuAh7Nm0/uNsRlpKiQnISR0mVcv16QhARoAS6ujxTPM01GiiqQrtCb0tqUKKCywSZ4+6ert5JgKsG0z49kosbLTHHYYUqJ0XD1kZJM+YpF84rniBHA22/LaQHAMcf4keRcVpD1e06ZokxxTEK/XgDgZQKCjJSkSOm2LilSK1aY34ViyZJwe4hScE2CGWcO6XEcRoorWi0t/u+aGn/fLVPgoAED/PNxo/bF6aum9ChM4wk1n6T+iIAcaZOnR48pw25jpKT3jhtsQoMzUjod3TZHjFDmo0uWqEUE3R55/6X56DFaz+Emky4bIxVnoSeOIiXBFv7chZFKYtpHsWRJuN/bxvsoRqqcYBPl+EhJwcRMVjkupn2V9JHii04UX1ZGKjPta2dwYaRcVk0o4gab4J0mnzf7rXBb3ihGKmofKZo2n9ykQbjcYBPanOjee9X/OOZrHOVE7XPxkaJ1Sn2kunePiAJAntflTOIjpd+BfyuTMmBapS/HtK9QCAYBsME0+HMFPUn4cxsjRZUaE7p2DfpBUJiUbQAw7fRAV55dGSlJSJL8znQ62reDK4qdO6v/e+3ltnhC0y8UlBBpE+7WrAmPL/y78GATUnAB3j/jOP5z4SBKkbIxUvR3uaZ99D/ts3rfrcGD5Xfq2jXYj3k/ScO0j+KDD8zX2oNpn4mR2rw5aBJN5x8bywbIpn1AMOqenld1HwLMCuWMGf5mvrW1YRN8Ot5JPlJUmXTBwIHR70jhGmyCz+GmRQcgOSOlMXCgmyJVjmmfKyPF23kcRkoy7aNzlKkd0HwoTP53/HfUAkpcH6lMkcrQKqiEj5S0uhDFSEkrIBL4Cpi0ShLHtI92PhsjRcsWZ4WU5v/kkyrCD91cVEf8ScJIpaFIxWGkdN0NH74Ggwd71u/UqZNfPklwAsw+UvS9pAkhLiNVjmnfrFnByHk2mNoGb1e2qH0mRsrmI0VX+E3fZOxYP21eRqld6DL97GdyehQ6T2nV0ZSPdM70nffdV/0fMUJFVjz2WP+6tMpq85GaNSs6wlyhoO6TyqTrf/VqtQEtBxWceRvTGzzboDeCjqtIufhI2Uz7JEVKqlPJR0qnOXGiCirANzHWz9uUo7hR+6JgY49tilQapn2FQnTUPpMitWlTfNM+DbpwBYQVKerLxhkF+vzHH/vz1KWXqnObNwM//WlwfOFmc4UC8Mkn/nU6PnXtai87oNo977txFi0BmZFKYtoXx0dKl1P3XRfTPlMZXEz7kkbt47IT/c2VrDimfS6MFP0GeiFZ+zPSckQxUhQupn30nTNFKkPFYFud0YhioMr1kZI6ZlRZkvpIJWWkaP5JTfsuvzwsxOmIPzqoQGsxUuWY9uXzwC23qIRNdbHbbsE6TuIjpc/zwd2kSFWCkYoTPc6kSPF2lcS0z8ZI6XN77x1mBLp1U/933tk/56pIzZqlNlKNgvb9kZQairiMFJ/A+/QJbvRoMue1Re1z/Z7//GewTLmcWvA4+mh1vHq1CjfPQdsvb/OUWTP1G7oRNODXRVQQEBdGivetqPDnUp1KPlJciNJ7l/Hy2pQj2/skUaR0u5dgamdpKlK0b8Yx7Vu3zt94dunS4HwV17RPtxkpoqTEIOjnp02TFxuuuQZ44olgGjqdefOU8vXaa/71OXP8+8aPt5ddl4PWxwUXyAznsGHqvzTOcEWKs3r0OVdGKkqRosGwpLku7WATZoUg+Nu0OBuXkYoy7XPxkdJpPPmkv5A8fXq47GkrUi6MVKGQKVIZyoQLI8UbaNQqSpJgE66KFBec4vpI2TosZw4kpakc0z5pIvT9jvz0dd5RiKtIUVCFwpSOKdhELgccf7wnmvLo1egBA6JXZaN8pPR5F9M+SdDg3xqIr0i5mmPpNG2KlP5vC38umZABbopUv36KERg1Sh0feSTwne+o37ROef2ZFClXpUMzM0kYKYkB4QozF+IlgdzFtM/z3L/n5MlKcdJ5LVmiFjzoKqoE2n55m6fHUjnOPFOxOrT+OLug4cpI2Uz7osKfS0qVpEjx8UMHAKHgihR/Jm3TPrq/HUfapn38W9BvFZeRGjUKuPtu9fvRR332NQ3TPqrIu863HHwzc/3sk0+GlS+qyO2yi1v69B333ju4KfcOO6j/lPGMUi7T8JHibZM/N2SIMmvVe++ZFCmbAmNKOykjZTPtoxEg+dwq1Q2vP8m0L0qRev119Z/O+RTvvRfM38XyJzPty9BuYBtUNFw6O4Vki1wp0740GCm+4kKvuQxsNrjYfNNJspLBJiji7iNFB6JcTlWiNuWhG9r+8pfhvF1M+2yMlItpnyRoSM7OcRWpujo3sxTArGSbGCmpnXJGSoObi1GmTl/zPHVd+0z17u2/O+1/royUq9IxaFAw3bQZKZsiJeVpY6Tq6pTgE4Vczg9nDKhocC5O8zYfKdquX3sN2HVX9Vu3r91288vJnyknap9UNkC1MZtpn6RU0W9h8oGR9gTkzLIrIxVHkXJd4OJCrItpn+6PLj5SVNmIy0hxBlhvYrxhQ3T70+9hUqRMe5zp50yb5NrgUufr14fvO/FE+V6+sELL2b27+k/HLxdFymTaJ5XdxbRv8GD7Ju5xTPsq5SNlY6RoGtyEmJrv0TLwMsYx7SsUgPvuk8us8dRT4bk+qm1xRkrqZ7TuXZi8rQWZItXOkEbUvnJN++IwUpyelkxApJV+DT6B2Rgpk49UUkbKBXz1yIa44c8p4vpI0WAT3E6eBgahA5dJKNGI2kdKn+dsjUlZMQmXUntwFc7yeWWK4II0TPs4I6XhwkjpY12XTU2ysGtTpKgQ6LKZJ6D8rwB3RooK9JIiZTJFictI8YUGrWjeemv0O+lwxh99pI616W0UqI8fb2N87NAKFA/hTusvbR8pU9Q+U/hzDRsjxd8zipGSxoM0FKkhQ4DRo9Vvm4BtY6RMY5ZWDqXxnPdDOq698II5OA59Ngpr1kTvFxfFSEWZ9tFolS5YsQL4/PPo+4rFMFt10EHyvTYLDmluDFshBCs7LiPlYtrH5zwb80zTLBR8/yCtMEuKE0UcRor3V5OPlL5On7EpUlF+4lGmfbNmKVNoG9atU/clMe0rP2rf1qeWbH0l/pIjDUYqyrQvys49jo+UiZFyNfORBlPOSLWlItVajJSLjxRn+fwB3CylUKEkyrTP1UfKhZGSTPskRipJ+HNtliKttFOUo0i5MlJSsAnKSOlrgKpfyfyKl5G2ESqs5fPAFVeE34dDl9WVkaLKRrmMlK3P8zrUz02cqNgmF9AoZC6wmfYBQeVQl4e3hySKVBJGKsq0j4IqUPq/iyKlhfe4PlImMyKOa68NMgN6TySbgmIaTyirncS0T9f1vff61772NRVd0vSMqyJVLCqlzIakPlL6t2Z8XNHY6K588YA9krINyItLGtL4H15QKE+RcjHti1LsbT5S2j/oqafUOb69g0twK1dGyjZvSvITTUMy7dOIa9rnaiJOt32Io0i5mPbZGanMtC9DmdADTxxGytW0j3cKUx5xTPt4hy83/HmlGSmXiZKm2Vo+Ukmi9sVhL+MyUrzOJYFTp2FSVkzCZTmmfTRfLaSZYBr8TVH7JP8gkyK1bp1azaTKRVJGin8/KqRzgV0Hd+Dfr08f/7e+FoeRclGkXBgpm2kfr0NaLu17EgVXs04Nm2kfEFwI4kqu1F71+/H9vUxt3WRWpPPm5mquihRXZqmpnk2RoqH+bRYKSaP27bVXkBngY7mEJIxUnPDnXGmQBEk+Jrggyj+PK1J8H6koH6ntt3cvCwB06eK29QIQVtK4IsX7OhBeHHNhpPixzbTPxkjZZIiouYO31+eeM997553KF9OUtjSnVJKRkhZkopS7KEUqybYPLopUmj5SLmbb7QmZItXO4MJI8QYapViZBiyT4B/HtM/ESLn6SPGyUWXJhZFy6eBSfibo9PVEbWNdOCrtI8WDTfiDrBsjRQVF6X0WL/b30uJCJf2WJqaCgjrQavCBlp5zNReiZdLRtExIg5EymfYtXKhWM7/2NXVsY6T0eVdGyuQjRdMaMiToF/DHP6rztI9EMVKS+aakTPLvnNS0z8RIAdG+UrmcUpyHD1fHnTu7LaDYwp/Tc66MVKWDTfBgMib/Msm0jyrGFJS5NSlSrqZ9UdYMPG8+lkvgwiJ9xqRI6W/0/vvB/f9oepRhp5CEtLiMFKCCydhQrmlf1MIcx+DB9qAeNH2upNGyAPLeenz+khbSwgsK0YxUuftIRc0dPN0oU2Lqi+nCSCUNf+7CSEnyYJS5Ie8r/P66OnlLBAq9z2E5jFSUYuwyJmwtyBSpdobW8pEC7NHhXBUpvnIlTbgupn1t4SN13XXhyVBH/NF101qMVNzw5yYfKekZnbfJ70nj009R2ktLh83lZlFUyYoy7XNhpCTThSh8+GE4HQlpmvbR0LAUelV63Tpg1Sr1u1wfKfpeXBikrBf1C9CCj8R0RU30cRmptE37dLmjBJzJk/3fBx6o/kf1fdrWoxQpEyPFywkkDzZh20eKMlI6jbRN+8pVpKIYKdMiX1JGypTvY4+p/9OnI7D/H00vzqp2XEYql/PboAn6PZIGm9Btp64uyDibwH3uTH2jZ8/wXMX7Jl8g1b/pAkJSRqpc075yGSkdMMSEBQtQ2rcuyowOcDft40qSCyPl4iMlLXzbGKl8HvjmN+Uya9TVqfvoOJaGaR9tVzZlKa4LRlsjU6TaGVwYqSgGyiSYxWGkXE37TIwU7dxxGCmat8RISUpkUkXq4IOBu+7yjx991I/4Y7NnNiENRqpSpn30fhvrBai9tP78Z/9e+t+VkeJmIPoc/S+VMwpTpgD33+92r8l8lbcrKfw5fcdCIXoj3KYmP6SsNh3iPlJNTW6MFAUPT2sSlrnPFhDNBsQ17XNhpKQ+72LaB6h+Z/J70+GMdV7Dh6tzOkKhCVE+UnEZKVcfqSSMFFekqHLH4bqPFCArUjxoTFo+UuUwUlGmfTRtbq6n9/+jIfLjwJWR0u/jwojq65zFdPWR0v+HDlULNpJJVpcu/m86Px58cPh+/d26dAl/QykQDBBWBuj4IM2N4X4QZqSSmPalqUi5QI/hUewP4G7axxdX4jBSUaZ9XBGXGE4KHVzEJLNo5l+Sy0yQ5gP+jDTe2tLaWpApUu0MSRipKIbKRKFXgpEqN/x5azJShUJQIBo1yi+zjYY3odKMFK/TOKZ9fC8VW53RiYHbc1NfoLg+UryNSOW0oVAALr00+j6NuIyU1E7zebU6GWcj4OefD+ZDfSNcfKQoOCNlYvDoKq82dYpiAyQhNU6wCUkQklayXRgpDV3We+8Fbr/dP6/DGVPBYuJE4IMP/Hv0vjYUy5f7yqjNR8rGSFUy2AQ9pqZ9vEwcnJFK20cqLdO+chkp29hPodOfNCmZImWK2sc3M9ZRM6ur3ZWupKZ9VAnN533riV/+0jfpPeOMYBo6nUGD1BYBUtmlcdG0yMEVqUoyUtL3lXykyjXtc4FWQl1cCeIyUkl8pGieUeaGfKFUqh+dxogR/rmddvJ/2+QyEzZsCJrZ8mdM8ouETJHKUBaSMFJSZ6cwMVI25sNVkeLKUrmmfdJASzumi82yDZx1oIIqFSJbW5FKso9UHKWbb9wY5V+k8cUX6r9k22xj6yQfKduqr8uq4axZ4bC9NpRj2kcntDhKlJRPmoyUZDYxZQrwjW+o35s2+aZOb74ZfIZD7yBfadM+fT/3w5CEa90PDj00uLeNaTGIrqh27hxO7803gU8+Ub/5nkA0HVdGSupP9F5e3nIYKer3xFEuI0XfVxpzkjJS/Fo5jBSP2rdokTkNff+CBb7pVhwmgo8JGlddBRxwgPp99dVKUNT3u5gWA8k35OXzoP7m++3nm/TS9k/nbam+beaZSRQpSSEILygEJye6iJdW+PO4jNSAAfb7hw5Vpm0anA1KykjxMYGmy8cam2mftJAex7SPpkG/Lx0/pUUyF0XqsMP8BTAur/GFtkyRylAxuAjHJjt0jSgfKW5ywMFZHlcfHJpX0mAT0mRAJ/xyGSnun2NSpJKY9lV6HykebMKlrehrPIz2unX2smpoQd7mI2Uyn0ubkYqr0EQpUibBGQi+o2uUIw4dqjuKkbK9u4mR0s9PmaIUjhUrgvctWgT85S/qtwsjJfXVKEYqTR8pfUy/Ca0XKS/6n5bLhJdfDkbkAoITuwsj5RpswrTKTgVfrsDEMe3j5pVpB5tIGrXPxEilYdrHzflM0HuM9e3rdj9gZqReecVXmvfeOxhIJk1GShoPeN1J39fERBQK5m8ojYvcxEta9OKKlKS4hxmpsGlfuT5ScRkpfv0nP7HfP3my2WJAqjtXRkoaE0wL0XEZKd5+XBkpk2yWRJHS0LLFRx+Z6y5jpDJUFEkYqajjSgab4AOBtHIZ17TPxEiloUhRpM1IVXofKXotGGwivmlfVNQeDR1uWqLk45r2SYIpL6cNejNWV5gGf96upAhVtM3V1amIWHGhBbBK+EhVVfmmjlJ90nOmCau1wp+bFClebpo33VOJpleOIgUEI3IB8gIBV6zpu0iCHb8HKD9qHy8TBzfti7uPlOfF85HSzEKUBYCJkYpj2kdZLDrWuIb21u+ozfL4uCyNr1Onqv/8vadMAd54Q/2eMyfYvpMqUlE+Uty0T9edfp6H0KdpSEqoBmU3kjJStvGBnuPp0PKaFkptjJTEcGtEzR38+te/br73/PN9M2LpeWlOSRq1j6bN50Y+1kb5SCVlpExMn1SeOJY/QDhAU2bal6HVUImofaYBq7XCn9MOYxJAJNaJm1tIipRk7ucKGyPFB7TWMu1zVaSCPlLmPE2mfbvuai+rhlYgpHyjGCmX8Oe0TDYUCsAf/uBWZo00TPt0nUdFlJPAlZM0fKRoP3E1dTRFqUoabCKpaV8UI0WVfc5I8XFCl4XW3YYNiASNyAXI7TqJj5TNp4giyrSPK4aupn1xfaToO1RVuftItRUjVVUVva9SLqfMsnr1CpZFhwTXpkvSGDxpklKatEIl4Re/8K+7mPbp9zDtI+UabIKb9nFzPg2upNgUKVdGiqbhefEZKZfw5+Wa9sVlpKR5etgw9X+//cLXkjJSJtO+OIyUiyLF5bU4PlImpq8cRkqDW73QuvuymfbF3KkgQ6XhwkhF+USZno0TbCKuaZ/kI8Wv8d+0TJKDoisjFbeDa9gYKS4Iudjbp8FI2Xyk+IqOP2nFY6TyebvSl8v5afOw2nF8pNJkpGbNivaR4DCxlVxI4IIzvabf+4QT1H0urIeGjqhFBShtgvfhhyoPKvxIsEXtczV11KZOHDQtaYUyCSMVx7SPtwOuSElCm+RnoNsr91sygdabCyPVWlH7dL75vF+etBkpkyIVzUj599rmA1PUPpvSYVq04qZ9tvFV5zN5sm/SKi1k2HDppdFt6Lrr/DInYaQKBbVfH6B896QxQGLm6HuY2GzOSPGxKgkjZTPtS8pI8TZh6iv6fsBu2hc1d7goUnzfSArOIEmK1IwZalwZONAPHW4y7ZPYRxMjJT0jLaQnNe1zVaTKsfyRypUxUhkqChdGCrD7WNiER3q/jflIk5GK6yNlY6Skd60kI5VmsAlqTsPhykjF9ZHiipSuX5oH39V+yBDg+OPVb30fFUpcfaTSDH+eJOCDaRWNtytb+HNaT9TX5NRT1X+Xtqfb1dq1/v5cN9zg731je/fGxrBfH6CecfXd4kEeeLlMjJTJOTquaV9SRsrFtI//dgGtNxsjxZU3mlcaPlI8ap+0aBHHR8qkSEk+UvQd4ipStkUlfq1cRor2Q5o2N03W+//REPm6brVyZBPOPE+xu1F7DOlxyIWR4orU8uWqz7/4ojq++WZ5DDCZ9kmKlEmAltqOtFipkcS0L6mPVBLTvnIYKdtihobE/EjPSwu38+erIAunn47AnmYujJRpIToOI1WOaZ/JZDINRormo8tJ5cVMkcpQMbgwUvx6FEMFBBumCyPlKqjwDpmGj1RcRqq9mPaltY+UzeRSNu2LZqS0MKGfp4Pr8cf7+/H85jdqLy1tDsPvp/nyCZ9CGiglwZSX04QkAR/imvZJ7ZQ+T7/vTTcBDz8s+04dd1wwH5OTvN77xsQYaUybpsKBz5gRjO5YV6cEyKj2b/KHo/1KWvmNYh7jMlJRUfvou/Fv56JIubDGPCIXHb9cfKS4cM7Lp+HCSHHlQAuzVJGLYrCpiZ8p2ISJkaJjQlqKVBJGytW0j+b7+9+r/8OHK18Mvf8fzcv0rdJAlCAIhIM6ffxx2BRXjwErV/rnokz7bMEmbKZ9VLnn39B1HymJkaLjTxJGykWRsskQaTBSNkUqipHi5dHflJoaU8VbMu2Lw0jFNe2zsWymek1DkerePbyvIR1vM0UqQ8XgykhFUb0ccRUpV9O+chkpPpjG9ZFK07RPTxJ08E+TkbINHnH3kQoGmzDnyVfQJUWqQwc/JOyOOwbNEmyKlK0NxmWkooRgV6WBIqmPFA3dTstFJ8ZttlGC26ef+ue0M7z2P9MMpElR0nnoEPMmTJjgr3ZecIFfLuq7ZfOTNAmxEiMlTaYm5tFFkaLtwJWR0mWh304yHdZwYT00eEQuaYXUxbQvKtiEiZFyNe3TeZhMSeMwUkl8pHi+ScOfx2Gk+FhL2w4XDnUQnK5dVRhwib00RVhMA3EUKdvcod/v/ff9c3yBSucTN9iEzbQvipHSZbOZ9tH0TApznKh90theCR8pSSmg8y9HFCPFod9r/Xr/XNS8WS4jFde0T+dbSR+pr33NzEjZFol4WbYGZIpUO0MSRorfG8VI8ZUyKe2kjBQdFCX62LRKVo6PVDmMFPVB0YOKZMqTliJlgouPFBdUkyhSkpBaU6NWjwBlfkbLqu+XBkBbG5F8CMphpGxKgwmuipQWIrQCRctNvwVd2dbsChWG+Up4sah8u6L8Q+JMGqtWBf9PnKhMmjgzNmQIcOGFwXJx0G8cJ2ofv24z7aN1psusYWKkuEJhyktDGmckfO1r4Yhc5fpImRSFJD5ScUz7yt1Hir9De2SkaFvk46J+J4ltMilSURuRDxnib3orIZfzmXu62GKCfo/33rPf53nBeahSjJTNR0qypuBjE1fOpLk+vKBQnmmftHBR7j5SUhvWaUYxUq4Lt5ISWg4jJS0cmcokMVQcko9UWqZ92jd4t93C5XY17Yvji9wekClS7QxJGCnbarRGpUz7+MpJuYxUVZWZkaLXaNmSKlKckdKdl3bwNBkp28qo6z5SlTDtq64OK1I8ulwUI8W/gY2RShr+3KQ0mGAa/LnCRIUILoDE+e66juleM0k3843CvHl+OSdOBD77TJk43XOPb+qkNxKNYkHz+XhR+zSiGKkpU3wTUQD4z3+Cz5sYKf09aH6S6bCGJHhrXHEFcPDB6vfIkeHrUru2KVK6TLysrowU95GSGCkX0z7dv+jijxQREgj6SNH8dXuNo0hJQjhFGlH7eB3razRtmyLFFzR0OloJMs0Xt96qovfZcP31/u8oxV3ns2aN/T4OXg+6fqKi9nFB1aYMuzBSPA2baZ+ZkQoeV8K0L66PlM08L4qRSsLMAGZFKoqRSsO0rxxGKm748/p633+Yy2aZaV+GVoMrI2WzgS1XkZI6gAl8oozrI2Vb+bCZ/dGype0jVSnTPpsi5WI6Q4WJuIwUnYxnzAjeIylSLqZ9tjYorTjxyYLCxb8FCCsNv/61+V5T2+BKHlekJEbKtLoLmBWpYjH5Zr5RaGxEKIz3uHHAaaf5pk5RbAD9xvq7v/mmrCAB4brUCqnkxzJ3rvITsEVa5OWSBEUuUMRlpI4+Wplh0rQoJPbHFP6cCgJRilQ5PlIupn06T/rOeiyzMVKUEWjNYBNJGCkeaZSmrZVDF0ZK39O7t3kh5qab1NhC/ec47rgDOOYY/9hVkdLtzxXctE/3s6hgE/TbUp85qVz8O5Vr2ufKSFFWMI4iRedmnkcSRoo/kzYjxWFagDQtRFfatE/ykYpipFzkLL1opZ/hCqAkR0jIFKkMZSENHympo0uKlI35qBQjZbK9l8z3OCNFJwpTWePApEjRDm4ya5IQFWzCxVY/SpGSfKTgEP5cY8MG5Wtz5pn+OZtpn86PTmguq2SAeZU+KSOlQZUGzbpIsJn20TYZxUhNmaIiMVHoyEy6PPpZIMhI0YnFVMYoBdyEKLaL9yMO3d5nz1abjQLA//5vOOqUrQ9wAUHf889/Rps+8cmSKyc0Tz6xS+YxkuDYrZtvUrhokXk128ZISe+nyypFfDSVEwia1vJv36FDUACePdsespsrWto8zBZsgrKPNuUobdO+OIyUtFpOFanqar/ebYoUbxf5fHghRis5Y8fK5dQsFqCCyEgsqQn63gMPjL6PfiNeD1x5cA1/biqfNC4mMe1z85EKHpdr2ie9Uxo+UpLCosEZJJe5yrToJKUHhOuiHEYqqSJl85FylbPoAi9Xvnj7zBSpDBWDKyNlU3TawrRP6nhJTfv4KmalfKRMpn1twUhp2HykeJ26KN0zZ8rnaZSo6molcALp+UjRNDR4G6FIqgxH7YflokjR1Vh+7fHHFbNiirY1ZUq4DFRQtSkT+rvpQB9xEcV2RbEBL72kCsCF9YUL1b5Zq1cH0zGNK5KQ3dAQUXiEg3DYFCkTS0Z/S4rUUUcBzz6rft9/f1AB5umnyUiZxnGdNu9XgCrndtv5it/559sjzvH35ea7GlyR4tepcqXRHhgp+n4033zerkhx0z4NWud6IUZv0svnIQm1tcHvGeXHofsLNa00meHvvbd/jvc3rsC4BpuwKVIuPlJRpn2SyX2YmQ37SHGly8TeAmHTPqnO48hK+tjESElpcSUmKj+TD7rEepkWovm4F8dHKo5pH4VNkXJVIKmCxBWpOIwUZzLbOzJFqp0hDR8pV0YqTdM+aSDgShYvB70mdb62Cn+eVJFqTUbKZCpAUSioKGUueZbrIyWVwRQ1qlxGisKmSJkGfxsjxQf4731PLq8+N2lS+HtRRsombOm9b3r2NN9jQseOdjMkwM4GFArAfffZK12b5dn6gEmRcgGvG0mRinLGpr8lwXHp0uAxVYB5+lE+UmmY9nE/KFpf3/teWGG3gSvAJtM+KshL16XvZo7a57UaI2Uy7YtSpDgzTNPgMFk/aNAFgQ4dgt/TlZHS37xjx7CZnx4Dhg4Nl5POg7QuXINNmMYePi5Klh46X/qOvBwSW1EpHyke7p+/jw38u0vKUDmMFD/W35RDUtaiGKlKm/ZJ5ZPK46pIBX23zYpUxkhlqCi2dkYqjWATfHBpbUYqqWlfGoyUTRil7/r557Sc8nLvrFnRG0wCykSsXB+pqDYH2BmpOAI4RRqMFE2DX4vy8VmwIPw+lNEwCTOPPebvfZNEidxzz+g6s7EB777bB6tXu3Uc7YsVh5FyAX/vchkpF1AFuFCI7yOl6yBpsAkbIxVlCsnBFSmtNH7ySbANU0aqudlNkQLC/jG2ezVMwSbSjNqXz/v1Xq4ixcunn9V1Rrc8iKtI6Xtpe378cfW/Z8/g/ldSezYpRdJCg75fas8cXPCW/KOAZOHPXRgpk/IgjS9pm/aZ5nPXYBMSm9erF3Dooer36af739TkVxuHkaqUaV9rMVImNi/zkcpQUbgyUtLgYDoGgg3TRD3T510FFRMj5apI2ZQlV0YqKZvRWoyUPu8S0tMmqMyaBVx9tfr9+ut+OV97TfZmdo0Yt3Gjr0itW6f+0zovFHwF6/HHgRdfVL9t5gY0DX7cWoxUUtM+2wAvgb+PCyM1YUJ45dkFvXqp/3QF2wST3wIArF7dMXzSgN/+VtWXCyOl7+nePfq9eH+x7ZNjE7rith2tAM+aJfscujBSWoDX5ommBSJeNq5IlQOuSM2dq/7/5jdBE0aqSDU1uStSkmAlCZIU0uo/fV4CryuurPJ8KSMlKQxJGCk+12iTP43q6rClhisjRaMHaoZr8ODg/lfSfE7LZjLtszFSrj5S2jePQzLtiwp/Ho7UGm3aV2lGyqakargyUqY5pU8f9X/wYPWOUnh8ybQvLUYqriIlvSctbxqMFO8vJosaUzpbEzJFqp0hCSNlsrumiKNISZSsCZX0kXJhpHhnjQMXRUqXxWWl3VSfWjAr10fqxz/2/VYobrttbzzySLgSXCPG9expNu17+20llGkh7a67gL/9Tf2ur/eFtTiMVGua9pkmDJtpX9xBnK/o0j4hTfzUWV6X0wX/+AdwzTXBPGzg/YiiV6/N4ZMGLF+ulI44jNRXv6r+x+mbcYJNJGWkKJYs8csrmU7ZhAndDzULtHFj0PfKhZGaMsUPcpAEtkAU1ISR1o+JkZL6kaRIRQlUaYQ/56Z9uh9LihS9TyPKR8pWPv1f74WjofeNo+/u6iOl23Oh4G++rRdEeDkA/z2pckKFcFPb53Ona9S+YjEcTAdQAWNcfKTsjFTwOKkipfOqFCOlIY1XpjrWoItvklWLhmTal4SRkiyQeJpJGCkKyZTaZSx39ZHKTPsyVBSVjtpHBz2bL065jBRNgw6CfFPOtvSRKhblDXlpB4/DSKWhSEWZzphwxRX50OBTV+cWenfECLNp35//bPbb2LRJFtY0+ES+tZn25fPK3t3UvnI5xQx17SqXycRIcSHNVRHYd1+7Y7ZUPl0Ojl13XYUePdxtyZYsMdelpEiNGKH8BGyBNHg7cDHts/lIxcXAgbIiZWOkPvwweI6C+l5F+Ug1Nan7y9lnbONG8zVuwqhRLiMVNT6ZGCkX0z4+1pr8NLkixc37ymGk9LOckZIUqbg+UoBvat27d/BeqT1T5UQKfQ6EBWYXRorP7+vWyWP8N78Z9BHjipSbj5SZkbItjmjQsdSkHEb1/ziKlHQ+ipGiJpA2hc8WOMLESEkLMtJiss1HKuqdJMRlpHR6hYJ57ykX076aGq90fWtCpki1M1TaR4re25ob8uoQ0m+/HXz+hRfUfzqA8IGWll1610oyUluHIpXDwoW5wL5CgErnqqsMT5A669DBzEi5YNIkN0aqEqZ9trpyUaR4m6KKQXW12qRTp8XTBlQwD26iFuUjlVSRam6W/dNMsLEBL7880Kk9agwc6MZI0TwnTvSjRnboAFx4YfDZtPeRcoVWgOvqwuwHYPaRAoBp0+xpa8UlipFaty6+TxTHSy/Zr1MTRo3GxnCbTEuRklat02KkKJqagOefDx5L6fFy2lbm+VzD+6hW3JKY9pWrSNEgDybGT/9O4iNlA/WxtZn2mRgpfvzcc/EYKVpO075qrr6i9HccRoorPuUyUjYfKV4XSU37JNbL9E4SJAsjFzmQ1oVk2RRl2kfZ260JmSLVThGHkTJNXhRxFSlX0z4+EUmK1HvvySGkAeDnP1dKliTY8oE2bR+pYrF19pGKq0glVQyl1e3DD5fv7d/f/y1tyLtihVueWliTJrg4jFTSb1guI8XNaLiSNXGivImnjsw0caLZhMiVkXL93k1N4XLbYGIDHnkkh5tuOsDKaFD06BFUOihMpn18IuzQARg5MnguCSMV10fKpgBTExgXRqpY9H0IJVDFJcpHKg1BgUckNIGOC1OnBrc+0Oek9iQJzTZFSuqLcRgpPtZSRmrKFOCcc9TxmjXA+PH+81yRMpn2xWGkOnYMtp00TPsAXzGJY9rnykjxhQybaZ/LeCvtIxW1IS//zps2BQs8YYK/IXyc8Oc6P6nfJGGkbIsBtnMmRkq/gy0ohs20j481/LxN3qukaV9ck15XHymTaZ++f+7c3FalTGWKVDuDKyNlo27LZaR4p4nDSEk+Uk89ZZ9IJ00KDjImRipt077WZqRcWJ65c4Ebboi+T4LkE8XLPXKkihY1dWqwfHQfKc+zmw1JkL5vWwebMA3+dBDnZkN0ItDn+CaeNNoWvY+XKW1GiipSLs9IbEChAFx+uS6wW8dpaFD+EnF8pPh4IPn+8XbgEv48DiO13XZ2BZimT4VxGyPlgiVLohmpNKAXP6JAxwXJr+qCC4D588Pnaf+VtrXgcGF8JEQpUsWiWojjCqDGY4/J6ZmYYlv56FxDw8ZrRoq2tSRR+5YtU/85I+UabIK/ExegpQUhjjiMFIXNtE9ipB55JIeGhtpQOtqc/pVXgs9EKVJJGalyfaSiGCnJtM+VkTItFOkyS64ePH/eJ/lxGoyUqyIV5SMVZdq3YYN64H/+Jx/a8689I1Ok2hkq7SNVKdM+yTlRX1u/3vw8oFZx9eqqxEhV0kfKNdhEGoyUC267zW0z0yA8DBniifsK8XIPGqSiRVEfA8pIeZ4K+RtX4IsyDQLap49UFCNFyzdunNrEc9y4YHnLZaRcFalNm+KZ9klswKxZwKJFObgqUTqdSZPMyrLJtI/+lwTwOIyUNL7weziGDLErwIAbIxWnzgGluJhW2eO085NP9n9L7XynnezPaxPGgw6KzouaymlIipRNCE+LkdLP6G/S2Gh//rrrZDNEF9M+EyPFFSnNSCUx7aP9QitSNkaKzzl0HIky7XNVpJIsXPGFIRsjFVywkXHffXYzWCBs2pcWIxVHkYpipCTTPlcfqShGylQGXmbeDtoLI8XL7RpsAgjv+deekSlS7QxJfKRM5isUtk4spR3XtM/mI+UCvV8H95GinU1SpKI6uA0mRormmQYjpUMkVxK/+lXBacDUQiJXBGpr/Xf9z398hioKWljjm34Cbb8hr0nJdlWkXNuvSZFKm5E65RTgrbfcyyaxAUmCG2iTtcWLw9eiGCmqlJajSJl8CPhvCs0ymRRgIB4jRRccJFDfK0k4nDIFuPJK8/Mc48b5v6W9fmwMt8731FOBYcPs+XievNglKSe2cT1tRkq3hyhGcOnSoB+YTi/OPlKS4CgxUvR7upr2AX6bdvGR0uWk86BrsAna/2zKl+tCDC2XbUNezki5LNisXq3uc/WRou9kukeCTUm13athYlU0KCMlKVL6eduGvLbxTSoDvxbXtC8JI2V7RlKQJNO+KEaKwhQwpz0iU6TaGb4sjFRcRYraoNPVLdqB2sK0T6McRkoSQJNCEuQuu2wOjj9eljZ4ufU3p+dffx0YPtx/75NOAmbPdi+T9jfhaA/hz6V0abvS122mfXHLQBkpaQJI6iO1cqVaxQXc6ktiA1xD4kugm5NqxDHtkwQQirT3kXr5ZUSah8TxkaqqAo46ypwW4PcFzkhNmaJWV3X4axMkQd3EINjCnw8ZAnzve8DNN5tN4qIQN9hEEkaK+pdwITdOwBu6QBAn/DkvHx0X6P5btcRCjQu/JtBvqfOOw0hJpn1JGCn+jNSeTGMQjUhq8pGSrEhcF2yoGazUxmnaJtO+1mSkpDmFKg+SaR/fCysJI2VTpHifTJORMkXg46CMlClkOpUJXRQpnT8PmNMekSlS7QztwUcqjiJlWlGhnaZLF7uwOHSov6Ed7XzcPEESxiph2hdn1ZvC1RwuqRkbAJx7rvo/aJB/7sADzbOWCyM1eXI4EIheoT/2WCWUSejSxfc3sbU5ja3ZtC9OGSrFSFG4tHmJDairAwYP9gDEDxknsZRRpn1tyUgB0eYhnP3g5idAcHFojz3Med17r282SMfBQgG49FI3X6shQ4Ddd1e/dR+UFpDodY7f/Q6YN0+Vp5zIgGkoUlGMlM2MOm5USV7Wchgpk2kffSaOIqXbtA5W4uIjRctmitpnCzahy8cX+Pgi5+67h30JAeCPfww+a1KkJEbKdcFm4MBomUeX1WTal6aPVFJGyhRsQlpciuMjJZVLkoFsyl4ajFSUOajESPFn4pr2UZSzTURrIFOk2hmSMFJxTPtcFClpJcEEFx+p0aPt6UyeHMy7tRippqZgZ+aMlMuqNz3v6gsVZwDh0NH26Eqhi+mlhqRI2fD888DHHyv/kt/8JnjtzDN9wdHGgvLj1jTtS6JIxWWkTOZiaftIUeiVbRskNiCfB265RX8YNylbm6xJJmKtHWwi7j5SUeYhnJGSlEE6HvC8qF/iMceE862qUquppr3YKH79a+XDpffe0mWSFpAAMyM1apTaVsIlT0C9l8R0x43a5+KDxEHHQhMjFWVW1L9/8DvEUaRsjJRk2kefiWLMbHPDRx8F26Mk+MZlpHiwCVOACt6OBw0K+hLqfI88MqxMu/pIuSzY9OoVNIM19WPK6CRhpKS6jcNImczTNCQfKam/xInaZ1scdzHto+cqHWzCxDRJciRVHOPIQeVYUrQGMkWqnSGJjxS/1ybUVpqRkoSOYcPkENKAEnAmTpQDSnhecNVVcoiOWimxgUemS6JISUJfFJKuEg8Y4K+ISzbYEspVpFauVELZuHHA6acHr9H2Uy4jlfQb2t4jjiJFJ7S0GKlKKlIuURX5KrHG8cd7uOqqV5z94AC12CGNF9wm3qRIlctISRO7RpQPos08hPtIcWGU58m/1c47y+9DV2VdV1O32SaYvy6TSZEwKVK1tfFXcKVtEuJG7UubkdJ9R5vBmca5888PlilJ+PMoH6lyGSk+DnzjG0GzU2m+pcqJS9Q+2j5pqHDpGfpchw7BYDqmhUzOSNF5ktdjcMFGxoknqvtcFSlT+PM0Gako074oHynuG0r7c5KofaYy8Gv8HQsF/765c8P1FjXnxFWkJAWJj1t8fnBRpKjfaXtGpki1M6ThI5UGI+WqSHHBybQhrw4hrYW3vn3V//33V/+lDvv8876ZC6D2oNAb+GpEMVK2spsUKWlgNw3WepIqR6HjML3PD34gh1LP5cyaGS+T5CMVBS2U8YASUW2kNRgpmy+eafC3MVJ0sm6tYBNJGFWX0Nd8lZhi9OglOPdc1dCPOEKtRj/4oGzG+bvf2c03XU37JAGEQhIWTYIGTcvVl0ZSLmyMlG1xSIObPmlQxSOOmZNUpriMVIcO8VZw77orOM5qpBlsIg4jpduZfv+uXeWFOF33PCqhiZGyKXpRUfsoIxVXkZoyRQVW4KBmp9JYSpWTuMEm4pj28UAmNF++OCCZW0qMFKAWbDp2DHdO7Xu2557BZyrFSKVp2ufKSEmmfS4b8qbBSD33nFLSdZoXXhj2Fa0UI2ULNhHXtE8/a/LBbk/IFKl2hiSMlLRCwWEK6SyBKycuiopkU8sHCZofDbdNn6Od7+c/Dwdq4MKDabVWw2b6lZSRkpRRkx9DXFx1lczcAWrVWJrEbfnya3EZKcAXymrZliA2Pz0g3oa85QyUrhtL03KkadrXFoxUVCQ2INqsSl/fdVe1Gn3iiWqxY999g/cdcYS5nOWY9rkwUi7BJni7NEFSLlwYKWnlXYMKoSZGqq5OKaimfspXXaUyxfGRqq2NzpPiuOPkPpRmsAmT0ETP63t1/VNFUtrLbb/91HVeD6b+G9e0zxRsQj/jokhp/zgJ1OxUKmdc0z6+kOFq2mdiuSRGShrPJEZKo7paveTf/uZ/Nx2wRZr7JVBFpFzTvkozUnwxlo4nkgxmGmtswSYkJYue+/GPw2a93Fc0CSNlGkv44pNJ+Ypr2sf3/GvPyBSpdgbPU601agJMGmyCXqskIyUJVYA/OeqJia9cx1VIohipOIoUD7cbpTwBQdOwNBipgw/2BYYf/jB4TVot31JiY3qmFXRXJaFfP1/Aq64OvrtNmQdaJ/y5LpeEpD5SW0OwCZfAJnyS5pDaeT6vTMykdFwYKd7nbcEmXHykTAs1tMxR7JzJPKRQ8H3NFizw07UpUvxbUUXKpnjcequfBi8bEFx1dVWkbIyULU8Ok3IUfJ+c9V7AjfHhcPWRAoLmZ+PG+XOISZGqdLAJl/DnUf5x0tYCvB5o2PEoRkoax5IyUlGmffR+03fW7Wb06PAWBFyRMrXT1jTti2KkbHOKLiP9H2XaR8dL7stqKleUaZ8E7itaSUbKFLWPzw/SvDRlSgsuv/xVTJvWEtrzrz0jU6TaGbY2HymTs6TESAH+QKwHb2lVSkc2ckGUSZ0tAAT3reCrSaZVIGoeR4WfNBgpnea4ccBXvhK8xld0pLJJ6VGYFClT2S++OHgvFS6i2khrhD8H3BcENOgKIhdek4Q/5/elxUhxU8qePX12yKVsUYyUSYAxrVDHYaQkwZSXi7cPSVjk7d2k/JlgMg+ZMkWZuzz+uDqeOtXPxzam2RQpk2kfoAQCyTxNWnVNw7TPlmfnzsFjN0XKvzeKOaCIw0jxdkbfX4J+T65I6brn7diFkTL5SCU17XP1VaNbC3BGymbaZ2OkXH2kJEVLp+Fq2mcaa2yLkrbFEYpKmPbZrHFs6Uf1FZdgE5JcwevaNj9HmfaZQH1FXRkpl/DnJqZJsmwyva9GXZ2HQw9dhLFjvXZvzkeRKVLtDJXykZIGK1eTKBfTPlv4cyoE6d82RSpO2Ns0GSlX0z6qSNGBmX+HHj3MeZsQpSCnxUjR8zfeGBa29DsedljwvEmRsrGgGm1h2rc1MFKm9nv44YqZHDtWHV95pdrvi5bbhiSMFBBeoY7DSJnGg/XrgfPOC+ZbKATt9uOY9rku9EiKit7TSWIKNm4EXnrJnKepP9H76G9aZ5J5mrTqqvOICjZhM+2z5cmDxpiEw7hR+5IEm3BhpOIqUkl8pCSfvnKDTVRVufuqUVZVYqRMZnpcSaBzrs20z8ZI0XxdTftM31mysLEprxKoIpKEkZLGCptSYDtnmlN0vXAfKYmRkhaBOCNlWxxPqkhpLFkSn5Gy5WHzkeLljmKkyllQbUtspcX+8qI1faTSNO1zYaSogsRN++h7u/o86HySKlJRjJSpDqgyYmOkfvIT4Ec/MucvwWayaVKkXBRdDerTpXH44UrY0gLWxIn+vl5vvhnMiwoXUT5SrRFsgpeDwkWR4hOrpGRFoVI+UpqZ1JHhWlrcxwcgPUZKXy/HtG/RImDVqvDz1G4/zj5SNp8B6o/BFRXts2KLnPnnPwfzigo2IbEuJiWVm6dJbSwtRsqUJ/X90dejfKRcova5MD4ccUz7OKIUqSQ+UibTviThz3M531fNds/QoYod1eALF66MVBzTPvqcybSPj9/UxJDfb/rO0ngVV5Fq78EmgPB45xpsIgkjZZMLXDBwYLqmfVwuMflVmRQuntbWiK202F9eVIqRkialSoY/pwOI7jB0EOaMFBVee/Y05xdVVo5KMlJ04pKEnerqsHleFKK+KxcyeDlt6QGyqePrr6v/u+yi/j/5pO87ctFFwYg/cUz74gSbKGcATYORKse0z+S3UK6PlD6v+0pzczwlLykjlaZpnwu7rO3202KkRo0yKyouezqtXBnMyxZsgguvGlF+HzaU6yMVtZ8dNxnlDIVGGsEmymGkyjXtS+Ij5RJswpWR0vWqfdWk60B4awFu2pc02ISrj5Rp4UQav6N8pEyMlNRf45r2UZaNIk0fqSjTPtOcYjLto+OD5Keuy/bee8B998l58mckRkoynebQSntdXfR8W46PlEmRMilcPK2tEZki1c7guuIcxVxwxInaJ60kRJXDhZGig6CeBKUOG8fMK4qRSuIj5Rpsgg4MkrCjVyT1BpsuiGIay2WkamqUUjRypH/unHOUsvT00+qY2usDwYg/5Zj2tXawCd6ONdI27TOtfLsyUqbvp9OhAmMcRSptH6kkpn1vvmkvI7Xbdwk24bIhr+27xdlfyYWRqq6WhciokM42lMNIdegQLYzwCHQmQSxu+HNb1L5ywp+nZdpXDiNVzj5Sxx4bZJw0qNmpTdi3KVL0Oa7U2/aesjFSpvdLy0cqKSP16qvAzJnh62kyUlGmfVGMlItpH01Pj0e/+Q1w1ln++Tlzgunb5lppXJLuAXxf0SSMlE1Bi8tIZaZ9GSoK1wk4qmNxVCrYBB90JZtaybTP5iOlyz9pEkKbhtr2MpJgY6S4wpAk2ISNkdJK4bXX2svIn5F+6/KEV7csNkpCGh9+KPuHLFwIzJghp0Ej/kgChZQPLaPG1uIjVQ4jxVeF02CktCDU1BTPtC8pI2USrKJYR7oIoutXsztRWLIknmlfVD8xIc7+SuUoUm3FSPFvJ0Hqw7St6zHWhZGSmBSKOIyUfkeqQNBjjkr4SNH3TMO0Twc1+ewz/3yvXsB11wXNTqX2TE37TFH7TIwUjXDHn3FlpLjSZBrP+JhHITFSXJGK+s7r16v/l18O/Pa34etp+khJ5+MwUi6mffr5KVPUXpkS+HvaFra5cnfLLdFBbSrJSJkCVHBFKu0F1bbEVlrsLy9cJ+C4K7Jxwp/HUaRMjJQkVNFNL20mOzq//fcHTjhB/T7pJOXzMH58uKz8vShsjFRS0z7qI0UHZpNCq/fNcEFcH6k47QQA6uvt/iEmaOaACm9R5gRtzUglVaTKCTZBv1G5PlL6fHtmpLgipe/RirnLxsGAUnCkFXTTOBHlM2CCy/5K/frJeUrCRHV1uIxAeYyUfibJPlIu/qWSIkXbsL7uEmyCfqtyGCnbfFNJ0z5evjSDTXz2mbxotWaNWlz75z/DadJyupj2mYJNxPGRSsO0z8xImRUpW5/WmDIFmD8/fJ6iNRkp05zCg01EmfZpX01XRC0c0XecMCE6qE0SRso2T0mMFLcWMrkm8LS2Rmylxf7yIgkj5bIiGzfYhKtpHx8kXIJNUAdtyUeKDrTLl6vfEybIPg98FZPDpkjxFV3XYBPUR4r72EgCHi+b3khSQpSCzOs7TjsBgIYG+/1xEKVsm3yk2kP4c5uPVDnBJnj7lYStuXPdwtdz07726CNFBat//xu47DL1++23gcMOA379a3sZqd1+azBS1GfFdN+FFwbzaitGii48SXVfKUZKX28NRsrFVLOSpn0mRiqfD/pIJQl//uKL8ljH9/ShadLftGw2Mz0NV9O+KEbKJOwm8ZGi+4/x9KNM+1yVjag+Vm6wCV5220I1N+2TGKlczs1XkyLKR4qXkQeY4W3flZFyDX/u6iPFxzaOTJHKkAqSMFIS1WtK10WRSsJIxVWkbIMpvbZ0qfqt/YxMrI+pvlw2LtXgG/LSNKWJh5ZVUqRMSt7227sN5ElsoW3ppYGuXeW0bZOLxv810z66kky/03HHBQN4mL6hxEjFMe1rDUaKTohnnw188UXwOj02vae225fMl2yMNy9fVD4apv2VADXO6HDzLsEmKukjlcS0Ly4jxRcS6PW4ilSlGClTHVYiap8LI6WfiVKkuOk4BfUN5GXj/S1JsAnArkhJDBi9Tp/XMJn2mRgp+luaF6MUKVdlg5pNSrApqRzlMlJ8wUfykaqqiuerKZWBgn/3ONYKJkhsoYsiFeUjFcVIJVl4ag9wEjMvv/xy5wRvueWWxIXJ4D4B20zAXBkpW7AJ254GUjmkjufCSNmcGltbkXrmGSXcjhgRTpPWgU2RqqoKTwy8/jp0CN5H4cpIabSWIpXLKZMoKnzalD7g/45pH++LtC5ee0395++sA3g89FC0Uk19pNJlpMKrxUA8HymqSNlMRqurVR/mgtE99/gmJy7BJiShK+5qK6DyPPZY4IILgD/+ERg0CFi8WJki8jxtjBRnATTSZKTiBpuIQhJGyhT+PEqRimqDLqaaaTFScXykXIJNRPlIuUAL1FJ7lhZkXINNAH69SAsj/DmKJKZ9tj5A34Wmb+vTgLuyoX2oTGjLYBMmRiqOryYvryQD2eRBCWma9vHvb3qGltPESG2tipSTqPD6668H/v785z/jjjvuwIwZMzBjxgz84Q9/wJ///GfMnTs3cUF+/vOfI5fLYdKkSaVzmzdvxkUXXYQ+ffqga9euOOGEE7BMx2Xegs8//xxHHXUUOnfujP79++PKK69ESxojXBuhUj5SccOfl2vaRwcQF0aKDjx0otKfO4kixQeYKDQ2KuF22rTgu/HfpvDnvBwmRkorUhJcfaRM93Dw6716xR+s9P2TJwfNXaIG7/YQ/lx6V4l1om1Vt9OVK5WfT9TKM2dQ6Ls88oj8DDXvMUFipNqbj5Tr5tktLcBf/6rs9f/yF//8MceE0yp3HynX9p3P+9EraT+Oo0ht7YyULp/kI+UStS/KtC8JI8XftTV9pFyCTfA2WQ60QC3NNVThcA02Qd/RpEhFMVImRTGuj5RJkeJzv0mRclU2oja+l+Yp2yKy7Zxpcc4lah/1kYraX4wjaryzyYMSou6xLXBLZXNhpCSlMk6Z2jOcij59+vTS39FHH42xY8di4cKFmDNnDubMmYMFCxbgsMMOw1FxvOoJXnnlFdxxxx3Yc889A+cvu+wyPP7443jwwQfx7LPPYvHixZhIPOYKhQKOOuooNDU14YUXXsDf/vY3/PWvf8VPfvKTROVoD9jaovaZTG+SMlJUaFi+3H/2vffUb5MZo2kATNI5f/EL/x14PkAwah8XxKV6i6NI2eo9iWkfv37GGfJ5k9IBBCP+JAl/rifytgh/HsVI8W/03HPAd7+rfr/3HnDYYUEzvKj8+eRh80nT5j2myHaSj1Sapn1p+0hFYflyZa9/8snhMtC0pGAT774L3HuvbyoY1U9cob8dVVpsLJirIlUOI6XTk8pEkZSRkvqwu2mfFyvYhCsjFXdhEIhmpHg7bu1gE1262IVP7RtI06S/XUz7bIyU1J90ukkZKZNpn20xgb4TTT9KkXJVNnbayX7dZU7mZTOdi2KkXE37qK+mC2xzLS9TazNS9FqxaDaFpu1Emje2ZkUqhuGTwq9+9StMnToVvXr1Kp3r1asXrr/+ehx++OG44oorYqW3fv16nHHGGfjjH/+I66+/vnS+oaEBf/7zn3HPPffgv/7rvwAAd955J3bZZRe8+OKLGDVqFKZOnYp3330XTz31FLbZZhvsvffe+NnPfoarrroK1157LToYZpXGxkY0kllo7dq1AIDm5mY0x5EMUoTOt1AoAqhCsVhAc7Nh9gEA5OHrwUU0N/ujuudVbbnuo6mpACCPXM5Dc7MeDXOQmoDn6bR0GsH0KdTAUYNCQaVbKKi8Pa8Az/MAVKNQUM9v2qTyq672tlyrQkuLes9CoRpADsViy5b3qsINN3hbygh89avA4MEehgzxyHsDLS3N8Dwgl6su3atRVeVBhQd376Ge57NgVVV+XdE6zefVN1LpA0CudC8tR7HYguZmr1RHGjU1BeRyVaHyqvpsLg0y/LlCoTl0Tg9Utnaby1WXzLgOOKAF990HXH55HosW+fkPHuzhqqsKuPhivz0MGODhrrsKOOQQr+TD0qGDXw+e57fRXI62R10mVU/V1R6am3OlNtLSEr4XiGrvZuTzUnpAsVjYMqgH+4Lf/qtRVaXapi7/jTfq2d+vm0WLPJx4InDffQUcf3xYK1HfUrcND4VCC+g3isLGjUWx/LpOcjnVbxobi1sEqSoAqm3ZoNtKsUj7vN9WWlpUvvQ7AkBVVXD80G2yWAyPK5s3F0LnTOjXL9wfGhubiUCsvkEu57/bkiXq3M03B9OaPbuAY47RZQ5+f9qHoqC/XVOTGmtyOQ/FYgFAdanempv1OOlf89GCqqo8gBw2b/bzbWnRdRW/XYfLVIRqj8HxYvNmL3SupsY8Vmvk8/64r8ctNT6ocx06qHaxebP/HQqFHHRbUX1K3Vtd7ZchlwvnrYINVKNYlMulpuGawFirn9HQfTT8HqqeNm8OXi8W1RjseS0IfivpW6i2o76zh+ZmlWYuV0BNjUfqya8LPcY3NZn6rcIhhxQwdWoVcjnfjFY9r9K5+eYCikVvi/Dp961iUfc3VTfFoofGxiKAPPL54DvQvsTnB/VMFfL5YB/1vCIKhQL8MSqYpn6/zZuD9ae+Ybgdqj6RC9Sjyr85UDbfB1m9a3OznvvVN+DjEAD86lc5nHJKPpQn4Lc7+m0k6HpU76bbijxnSGMHndvCdafQ0qLK09Ki2nFjo2rDuZyHqipvS93o8VaV4eijgQkTqvDkk+Hx8wc/KODGG4PfTLdxpZD5+at5zm+ruv3YwOUIDj326XHM8wpbZNPwM/QdGxtbtnxPJcupscyXUz2vCKC6NLbxdPTc1FZyOIdrOWIrUmvXrsWKFStC51esWIF169bFTQ4XXXQRjjrqKIwfPz6gSL322mtobm7GeBLveuedd8awYcMwe/ZsjBo1CrNnz8Yee+yBbbbZpnTPhAkTcMEFF+Cdd97BPvvsI+Z544034rrrrgudnzp1Kjp37hz7HdLEsmUrAAzEO++8hfr6+cb7li/fD4Barlm48HPU179RujZ//u4Adgjc/8477wHYHevWNaC+/lkAwOuv9wMwJpT2xx/P2/Jr5Jb0F6C+fq5YjgULugAYjw0bCrjpppfw0UcDAOyITz75BF27rgQwGqtXr0V9/bN4773eAOrQ1LQBS5euBjAU7777LurrP0FDw2EAuuOVV17CBx/sCqBXyPZ50SIEhH8AeOKJelRVAcXiUQg35yLWrPkCQD+x7FHYvHkz6uunAgA++WTnUn00NKwC0A+bN2/CunVNAHpiwwZVr7Qcb775JurrF2DjxmoAPlu7cOEn8LzhQnmBF154DosXK8X+88+7Afiv0rVp06Zi3bpaAH6fUJMYME3bIwrI5Y4uTeRvv/06Dj54MX7zG+Ddd/tg9eqO6NVrM3bdddWWtL9Weq5LlwZs2PAsnnzST2vx4l0BKCey999/B/X1nwIA1qw5BECfQL7z5y8AsC0ApVisWbMO9fXTsXTpgQCCdhvvvmtv7zasWTMG0jf+6KMPtwjnwSXLN998G/m8B2AfrFq1HPX1L6GhgZY/2MZU3Xm46KImVFdPC63gfvzxjgB2AwC0tDRh2rSnQL93FDZuXAFgm9D5BQs+R339m3j33cEA9sfixavQoUMBwIAt48Pn1nQ//bQ7gMOweXMj6uufDF1ftGgxgGH44IP3UV8/r3T+o4+GA/CtA5588gnk8x7efTd4HgDefFONKwrhyVGjpqaAtWv/jfp6oKmpCsDRW9Keis6dlQC9atVYAD0xd+4rqKpajtmzB+LVVw8Q0/vVr6pQXf0aRo9eguXLDwAwqHTtmWeeRq9eAl0j4P33twWwNzZtUgrShg0NePHFNwEcivXrN6K+/im89VYfAIdgw4b1eOON9wH4ZXrjjdfQ3LwXgI6YOfM5LFig+u7HH+8GPQ7W17/rVBaNzz5TfayhYQOArli7dg02bqwB0C1wX0PDRgDBWPrr169Cff0L1vTfeKMvgIMBqPZaX/8fvP7/2TvvOCmKvP9/Zmc2EZYMCyxgAEEUUFBxVZIK6HKIomfCMzw+eqZHjGf4GVBP8e7M9xjuuTOe3nnqrd7pLQoqSQREkiCIikhYlhyWzO5s//5oaqa6prq6qsNMz2y9X6997YSe7uru6qr61uf7/dbCDgBOTuwDaIeFC79By5ZrAQCbNg0EUIrly5dh9+5dAAYDAPbvN8sIABs3VqOqaoHlWEuWdAHQHxs3bkZV1ZyUslRXm/1HPF6PqqoqAMDChaUABia2Ic8oC6mnq1fXoKrq68Tn9fVmG/zVV18mygkAy5Z9k/LMbN5stkXffLMUVVWr8eOP5n1bteonLFhQk/j9okVz0dBgSsf79p0BoBlWr14HoGtKuQix2Gr85jdb8Je/9MHWrUmf6DZt9uHqq5eisLAGh04Zy5aZ9RAAZs6cjp9+2oOVK1sAGIp9+/bj++/XAeiBNWtWoarq28S+Vq9O9hEzZ05Hmzb7AfwCALBu3UYAHbF+/WoARyR+s3FjDSZPng/gnEPHtra9Bw+OAFCMuXPng74P27btQG1tEQDrGGnlyh9RW9sOQGvMmzcfkYgZ2HzgQDRRlilTPkFRkdlXrV3bF8Dh+OGHH1BVtQLr1vUH0AUrVphjAZrCQuDIIwdj5cpWls/z8oxERkCntpAux86d21BVNQtbtqT2QQDwxRczsXatdRy7ffupANoCAKqr1+HLL1cCGGbZxpyojmH/frMez5/fHkA5du/eecjIaIXNm7cDaIPq6nWoqloIAIjF+oC+N4SCgrmgx2YbNybreHV1MwBnJL778cfvsX//bpB2aerUz9C6tbj927bN2tezHDhQh6qqSfjpJ3MsuWrVSsyatQ70eCR57gewZcsOAKVYtGgJdu3qDqA5vvpqDnbsOBqkX92xYwcWL/4RwEnYvr0WAOuT2ZAYx4jGM+lkL7tGjg3KhtR5552Hq666Ck8++SROOukkAMDcuXNx5513WtzuZHj77bexYMECzJs3L+W7DRs2oKCgAC1btrR83qFDB2w4lIFgw4YNFiOKfE++s+Oee+6xJNCora1Fly5dMGLECJTILnziM3V1dZgyZQratjUHhH37HouKimNst//735OjuW7duqCiIpkFYOrU1JmWHj2OBgC0bFmCiooKAEBxMX/Qc9RR3S3vzf13Stnu/fcj+O1vzXIcOBDD/fefhmbNzJmh7t2PwMCBhwMAmjZtgYqKCjRtah6vRYum6NzZbIx79uyNiopeuPtusyoOHDiQmolhy2cOaGlGjapAJALEYtEUV5dYLA/t2lkH9yo0aVKUuFbz5iWvaWlpG3zzDdC8eTFatTL9P1q1Mq9rfn404Wpy3HF9UVHRJ8Ug7NnzCHz2WR7270895pAhp+GYQ7d9+XLrd2edNSLFDSw/37xWw4cPR75NrvdoNJKQ6E866XhUVBwHABg92rodOw/SpUuyrhDmzs3DBx+Yr/v0OQYVFWa9euKJ1Fm10tIuAICiohj27QOaNm2OiooKvPxy6rZO9V3E889HsWRJ6ue9eh2FfftSPz/mmD6JWeF4vD2aNh2FWIzcXzufhwi2bGmCkpJRGDLEWge/+y5ZN4qLC3DWWSMS75s1M7B7N3+fkYiBzp2BPn3aYeHC1O+POKIrKirKsH+/+fuSkjaJrIn9+vVBRcWxqT+iINckP7/Qch9JW1Naaj7TvXv3QkVF0tisrra2H6NGnY28PGDNmtR2pXv3o6nzwaGZUfp8zffHHBPB6NFmGWhXrDPPHAHSxP+//2e2AaecciKGDDFw443i7umtt07EhAn1eOMNa30aPvwMtG8v/GmCDRvMspoqqdk+nnaaOYApKmqCiooKNGlibtO8eTP072+dnDvppAF4/fUotm8HTjnlNJC5u2nTzGvVvfsRqKg4TK4wh/jyS/O3+fmmkdS2bUvk5aXWoWg0ddKvY8c2Kc8sS4sWyX0VFxegoqICsVjys06d2mDJEuDYY832CwBeeMEs07HH9kbfvlFqX02xfr35ulu3zqioKLUca/v2yKFzaMct13ffmf8LC2OJ7031K0mHDu25v12/3tyuTZuOlu9NBQEYPNg6SXj88anPzCuvmNsec0wfVFQck+g7e/Q4AsOGHZbYbvDggTjlFPO5b9bMrJcdO4r9zg4/vBseeaQLJkwAvviiHjU1ZtzPaaflIxo9HkCyLpF6CADDhg1B9+7mMgkAUFhYhG7djjhUrsNRUdEtse0yykY//fQhlmRArVqZY6Hu3ZPbA0CnTh0xatTZiffHHWdte4uLzfPr12+A5XclJS25bVmPHt2xerX5ef/+A1BRYV6n7duTs/lnnTUSZI76k0/Ma3zkkT1QUXFkYizTu7c5FmB55ZUoVq60fnb44ZHEZ8cdJ24L6X62bdvWqKioSNx3lsGDByX6X8LTTye37dq1DIMH84K3zG0iEbMek4nLVq1KEIsBP/wANG/eKrGPigpzH1OmpLapkYiRGFsTOnVK1vEffrBu37PnUejVK9knDR9+BjqkzstZYFINpJ5NNB8VFRWJ8vXocSSGDDmcu21RUSFKS80G99hj++Djj81rccopJ6OqKnl+bdq0xAkn9D/0m9RxdiyWh+HDh2PKlCnC8Uw6Id5qTigbUi+99BLuuOMOXHrppQnZKxaL4eqrr8Yf/vAH6f2sXbsW48ePx5QpU1BEOyOngcLCQhRyonLz8/NDcPNIJxoTroFkzZYUTQyo2e8IpHOJRvOQn28ewy4wORaLWnxbY7HkbwiVlcDFF6fGX5CGduXKKM46NOHR0BBBfn5+YtuCgkii487LM8tOvvvuuxjXwKDOxPKuoCD/0H5St8zLixxyY5EnEgHatzcbmry8SKI+0PeCXAt6/+S60teN3EP2OhcXW68vTWFhfuJY7O8KC/NTPiP7EdVd+toUF9vXK3YOoUWL1PtOLyabn5+sd7z4A9OFA4lrtHNnBLNm5afUGQAoKBDXdxF2MVKxWJT73eLFUbz7rvl60aI8DB+eJ52UZPPm1HJa4yciiToJAIMGRTBpUup+iEvos88CkybxKwN5rskApL4+L2EQFxY6Xy/iMmcYEZu6kWc5DoFtjgsK8g9NVqTuwTCSv3v33QhuuCG59hsAtG4dwbZtQOfOybpkbVuS9Z3EEBQXxzBnjqlA2xPBunXAnDn5KeUqKMiXrkvJeKDIofLkHXJfTbZbyfjHCAoKrAcrKopRcUapx2WvrQxJV8dkmfgxUqn1pqgo9ZlloZ9h0saR6xCJIHH+QLKOme6F5jNPX4P8/Aj1OvXY5PeGwS8XfW1JHWU98nn7BZKJb+rrrd+TZ6SoyHqveG1M8vjW+2Q+d8n3y5fHcNppbAyd+DpHo9FDbWTqQvIsdLmKisx6RK5DQ0MEDQ1mWQoLreWk2x62fzDdsszfWMuVh8LCZNnZ65J8Pq3XzzDyuC5jsViUuo7JfVnj7vJTPo9EUq8571nhPctFRXS9k+87SD9t12fQ5SRYE3NYrx2BtB/19WY9JtcwFkv2LaS+kDJUVgKvvppahkgkgvx8awHpcRv7fMRiUct9J/VHhFMsJWn76GMUFPDbsby8CDURE6PGeTFYY7eS166+PrXtso63wjAWh3QZlMK74vE4vv76azz66KPYunVrIovftm3b8MILL6Bp06bOOznE/PnzsWnTJvTv3x+xWAyxWAzTp0/Hc889h1gshg4dOuDgwYPYsWOH5XcbN25E6aEUbqWlpSlZ/Mh7sk22wVvDiIdqsLWfC/KShfJE6Y4//TT12DLJJrZvt9+nCN45R6NqWfsIJJNaLiSbsCs7j1jM2sDyxFmVZBNkYEwSBKxfbyZvoOsGb1+qqKY///OfAaZZkc7AxcskZbeOFAB0OzQZTK+/BVgTeNjdQ7Kf4NKfW7cjsIMqu+yTQPKZjkSA888HPvrIfN+qlZmhb+LE1N/S50uXjc5MJpv6uKbGn2QTBDZwmv6vkmzCS9a+oJNNiNKfx2KpSYIAuax9fiWb8Jq1z8uCvOScv//ejMslXH99MumMbLIJlXrolGzCLmuf22QTdvugj5/prH125QX4a6HZ4TX9OVt2r8kmIhGzHl1wAcDzHGtoMJMe2ZWBlyhKNdmE0zaq6c/tFuRly81LBa9S7rCipEhFo1GMGDECy5cvx+GHH56SZU+FM844A0sYf5yrrroKvXr1wl133YUuXbogPz8fn332Gc4//3wAwIoVK7BmzRqUl5cDAMrLy/Hoo49i06ZNaH/Il2PKlCkoKSlB7969XZctk7jJ2ieT5chL+nN2fzIL5e3aBXx7yJVbZUHetm3F+2XLyXtNl1vl4YzFgH/8w1xThvye3heBnlljByJe05+rZOfh7dtpn04TLE2aJAcmToaUU/rzNWvM/+wgiqc4ptOQckMkYho/JMuW3fHZVNXkWt54I3DWWUi49wwaxDe+adisfelakJeX6pm3HZB8psl2pH7k55sZ+lasSN0P/ZpnSOXny6c+7tjR3TNB4A1M2cG1m/TnKveJhV1rhZ3YIrAGBODekKIhEx8yC/LyMizSuEl/Lns/VdOfyywYTMrz3nup25K130gmuaANKbpeuVmQl1wX3m/osn//vfme7cNkDSne5AP7mteOyBhS8XiqKxvgXIfZ8rGvZfpftrzktchwq683z0u0jhTgPBH94oviMrBlVjWk/MzaR7eL8bh91j56H3TbRjL9uclwGhaUm/ljjz0WP/30k/OGDjRv3hzHHnus5a9p06Zo06YNjj32WLRo0QJXX301brvtNkydOhXz58/HVVddhfLycpx8shkUO2LECPTu3Ru/+tWvsHjxYnzyySe47777cOONN3Jd97IB2bS59MPsR/pz+rXTDIfsbDFRl2QUKbJN//7WtYpEODUesoYUGRx36WIqBE5r1dCKFGtIyTTaBQX291d1HSlVRUrGkCKoKFK866yyrFwQhpSqIW0HucbPPMPvREXpz0mdLygwDYtLLjH/yyygSD53u46Ukxogo0g53WPWkGIHt+x6XYD1+tilP5dJfUzSR7t5Jgi82XiRIcXuO4j057wFeWUNKTfrSFVWAhddlDzmjBnma7KYNGBvSAWhSLH3c906/npuQShSoiRdpB6Q8Gun5SpV2h5eOnJe+nNRSne2ntgpUmvXmuoa4ZFHrEs8iBQpu/TnPIPZa/rzykqzXIsXpx5TRZGi9+1GkZJJf07DrlNIGxmAGTbgNBHN5nMTPR/seE1ljUE7/FKk7IxQemLf6Z5kA8pF/+1vf4s77rgDH330EWpqalBbW2v585Onn34av/jFL3D++edj8ODBKC0tRSW1oEs0GsVHH32EaDSK8vJyXHbZZbj88svx8MMP+1qOdBKUIqViSPEeABrZ2eJ27azH5hlSbGOan28aUzxEBqOdIiXTqBBhlV3nyM6QIh0Tz5DilYk34y8zI+bUYPL27bRPJ0OK9s716tonjnWz4sYFk+C3IsUa8rQbHg8Z9xqRS6WsIVVXl/4FeVUVKfJb8rndIImdSKF/k58vt84KMWy9GFIiRYrXHogUqa+/Nte6ogf9XhQpJ9c+HqrrSO3da6osnES8eOWV5ODarSHlVAed2loAmDMH3PXceIYUfRz2GZFRzEjiDDtoN7t0KVKidaR4v2PrD9vmz5iROpAnaltlpboiRT8XdooU7/7yBusE4vpmZ3DQrtkyzwY70aliSLFtoNPx6EXdN21KriVIricvAZITorEOq5LJXA8ZY5D+LzKk6O/YBXntrh3db+SCIaWcbIJkDjnnnHMQoe6oYRiIRCKHcuy7Y9q0aZb3RUVFeP755/H888/b/qZbt26JtKm5gOxMptOAm4VeVZtg1yE6KVJktri62r6DLCkBBgywHlvGtS8vz5xp5lFWBhxxBDB9euq5eHHtI/ErosEfz53FiyLlxpDyQ5ESDegBfxUpFYJQpNwaUhdfbAYBDx0KPPig1Q3P6fh2ipToujupk3SMlIrLWCYVKXawyVv00zDEC/KOHQucfjrw+eepx33wwaRhKzORZIdMjJTIkIpGgT17zNc335z8nExIeFGkyHHtFCkeqorUtm1iF6NbbgHGjJEzpGQUHxZefZ41i78tGeyTSQ0nQ8qNIkXupQxBGVLspJyKax/5H4/bG1I8iGvVLbckt2cNqfp6/n10ipGKRAzLWJHdlm2HZGKwSbZHQG5SiayD6DRo533OjjOc2pd//jPZFtBZFYlBpZBKwLYMNOy4QKb9y7QiReqmn2OITKJsSE2dOjWIcmgO4UaR4j1YLKqKlKiCk9niCy5IDohYzjor2dGRY9OdgZ1rH++BPe88s2EaNAi4+urgDClSPqdZUnKt6GQWKopUYaF/ipTq7JOKa1/z5qnf28VIefVvDsKQYhtyGVq1Arp2NV/36mUaUyrHZ+ucn4qUqmufHzFSTs+YnSJ18KB5XDvDLy/P2unS+6Lr6JFHWg2pbt2A1auBIUOs+6LxS5GSGUxMn853dSYDcnrAJwuvvfVTkaINKSdjYO1aMyY2KEWKrR/xOPDCC/xt6cH+mDF8Q4quT6kZFJ3LpxIREIZkEzSzZwMVFamz/rLJzwzDvN8khTprSPFcSUk5RYqUnScJO4lKPpeJwaa9HVT6QHbik8VJkZIZU4wbx6/vJJtpp07OE9Ht21uzn4rGe04T3zxkFSmRWzPv+KwixY6heK59XhLzhAVlQ2oI3YNpfCcoRYo3oLGbTZR5MMeONWcGb77Zmqa4aVNzEHHMMdZZCkBOkeLNvvbtmxzQ2g3seGVkO3073BpSsooUeU3OUTZGymnmibcND7eGlFdFqrCQn1mMRzpc+2Ix57iGiy6yj7GQOb5dsolMuPZlQpGif8t2qjS8ATY78IvHU40UOk23XblUOmTVGCl23088Id7/pEnWQH63ZQpKkZKhpsZ6DYKMkZo5k+9mSCCD/Zkzk22VnSEl49rHlo+4o9sRiZjnWVfn3JaoGFKizHJ2ilRlJXDTTcn355xjDtDJvVJRpGjYiU+CyJASKVJ2z6eda59sDDZBVpGijxGkIiVS0sg+nCai/+d/gPvv55eB15byYuycyiCCeAv4rUixRj5tSHmdjM0krm3AvXv34rvvvsM333xj+dN4Q9Y6d0pKwKKabMJpJhowjamff06+r6w0g+nJb+hZCkAu/TnvgbUzLvxSpIjyQjoOpwBokSElY+iFOUbKT0PqyCOdyyb6vSwqhhQAXHppqtpG4qKOO87ejcYO9llUde1zqgtuXfvcKlJeYqRoRUSkoPGMFTqgngSak3TqBBIbQZeL5zYoC0+RYieARLOyTgtb7txpDvpV4BkAovtNG0YyipTqpEXHjunL2qeS9t4P1z5R+eyUFKJYp8u1j5dsgsQQsderujp5PdwaUnaufXaTYk6KlNMkCrudbAw2QaYt9CtGSnZMIWLDhuRENL2AMqFJk1RPCFE7zJbJD0UKkDek6DaBzdrHlpttW+n9ZrMipVz0zZs34xe/+AWaN2+OY445Bscff7zlT+MNN4qUXYNPw0t/Tm9HdwoqD2YsluywBg60urPRDxcgl2yCN/tqZ0g5DfJkGz1VRUo1Rop97caQImVxM/selCHl5NrXurX5nx3c8WbE02lI9e5tpiMHgJEjzfWOzj7bfE8HCqdLkXJSJ4Ny7VNVpGTaFfq3dXXygym6/fn4Y/tAc+Lu4jThIIvXGCkZvM6wO8VI0c+XjCFFI3LZAZKZEdOVtU8l7b2Ta58bQ4rUw6uuSh3kkqQzZIkOJ0NKpa7w6jP5zyabEMUQ0Z+pGlKRiHm/SQxPUIqUkyFFYrBF9ZJely+dWfvctgE0ZO0oMhE9darpUk7geayIJo15BosTMtuw6pLomtHPuUyMFKHRGlK33HILduzYgblz56K4uBgff/wxXn/9dfTo0QP//ve/gyhjoyJdWfsqK4FDy3EBALZsSb5W9bklHRY7cBIZUnQnwZbPT0XKi2ufkyJFzxST/7KKlF1D5mSIuRk00tukM9kEuacnnmj+Ly42O42BA1O3DcKQYusS2Y5u7Hv3tqYjFwV2yxyfvU9+xkjRRp4frn12Ax27daR45RS59olcEdmy0dnA7r3X2T3GLrWyXTnt8LqOlAyqM+yqhhT9TKq67fHaLRqSGdFtsglVRWrQILF7HRnsDxrkbEix5ZEx9Mj/449PDnL/9jfz/6pV1gW0/XTt4z1ntNcG3SbJxBAByTginiFlN1B/5pnUtougqkjZjWVE3iiANWOn3TUcNiz52k9Fivc52497HfCzRuDQoda+ljeZ7GRI0eeXCUOKHuuJYqR4bbXdJHE2oVz0zz//HE899RROOOEE5OXloVu3brjsssvw+9//HhPJMvYa1wSdtS8vz94tgLB0qZxrH4FOeczzpVdx7eMNGuxmW0R+w2RfbhQp3j3gDcjpQUUQihTvXL0aUkG59onUCnLMWMzsNHiDqnTESNHGEqmT9L0k39mt2SJzfNYtx88YKSA5mJGp106DWHY7ghfXPvqZELkismWjB21OKagBgF7L3YsiJRMjJRpMOBlJLVrwF3EWwZtIEt1vVdc+moYGexej885LZkZMlyIVjQK33cbflh7s0y6Y+/YlU86zKbedYkfsFClynkOHpq79RsqaCde+WExe4bRLNjF8uL3aJjIURYaUiiLFTqLythO5vgHAscem7k8Er3/m4eTaJzOZ4nTfeevjiWLU2TLwvlNVdWS280ORYq+dqL1VeV7ChrIhtWfPHrRv3x4A0KpVK2w+FBnap08fLFiwwN/SNUKCztoHOKcWff995/3RuDGkRI2pSJGSMarobWUaDBIvQ3yCvSSbkFWk7Mpld65eFCn6XqczRoo1VtgZfpogFCnWbYmnOrGGcEODN9c+PxUpNv05kFyDJEhFykuyCfr3Itc+kSIlw9at9uXy4trnpEix+3ZasnDMGPVJAtVkE7QhpapIGYbVxehvfwMuvND8rnv35HYNDZFEWYLM2geYKe950IP9ykrg1FPNz+vrkVhn6l//Sm7Ptv8yySboCT07gjCkRMkm2Kx9sgonKR/b5h9xhL3aRh9XdiUbO0XKrWsfga2Xb72V/M7rgrx2baKTIuWHax+vvKI4W14Z2O9UDSk3ipTIi0ZGkbIzEFXLHkaUi96zZ0+sWLECANCvXz/86U9/QnV1NV566SV0VPVh0KQgq0iJkk3wfkv2u327s1vAjh3ATz/Z75+FdKD0OhNuFSneA+vWtS8SsaYQtYNOPECfg90gknRM9KBLVZHyy7VPpvGhO0QnQ4okXYhEzIUw2c6UNqScysEaUuwCp06/l0XVtc8w7BWpeNx7sglATZFyqgu0yqBiSMkOYtnj27n2yShSgDU5hmgdKbpsqoYU7f7l5pkgeHXtO+ccoEeP1P2SCQmy2LcKQSebsDsmUV8OO8z8jH72ZbL2yRgqLLy2lq1nl15qHeyLEi1cfrn12E6GlOygnvebTLn2ycQQ0bBtPrmHPLWNPq7sM0n32fx1pFKPD5jnNG1aMmGLnacCKeeIEdbP6eM7IasmOilSMpOzzz7LXytKpLyIkn2xZRAZWYZhXRDcDplrRq/zJzIgRYoUO5msY6QOMX78eNQcasEefPBBTJo0CV27dsVzzz2Hxx57zPcCNjb8iJESufbZBYyy1NaK90fDU6ToMpEGk7gGiGKkeIMGt4bUTz8BMms10z7L9fXOihR5vWlTMrYs6Kx9Xlz76EZVNKCvrEz6phuGOTN82GHm5wTakKI7WlGdYxUp3qDKi2uf3W/Zxp8uB6tI0R2Blxgpdn9+xEjRzwSJe5DpdHiD2HgcmD49ghkzOidUHb8VKTpdu6wiRZ9Xu3bO9bp/f/tyeVGk6I5dxpCKxZLJBwhPPGEqUbyyyaAaI+WHIUXDTnwA9h4DdF3xS5Fir9nJJycH+7KJFsixec8/DXuvedlt7X4TdLIJnmtffr44hohXT3iGlAjWUHRqB1UVKfL+r381lcTly833N91k7WtYaOPk+++Tr2X6Dl47z8NuQpZ+7XRfTz89qe5dconpSkkfk/d7FUWK/f3ixcDo0ebrurqkOiu6lm4UKZEhZZe1j7122pA6xGWXXYYrr7wSADBgwACsXr0a8+bNw9q1a3HRRRf5Xb5Ghx9Z+0TqAFEcnGjZUrw/Gp4htXRpMqmAYZgP95/+ZL7nLcjrNWsfr4xOM4YEO0PKbv+/+535f+lS00AEzLVNRL+hX8suyMs7V6+GlJ0iRWZ5yerrhOpq83PSKNOd6sKF4oEH2xGnW5ESGVKskcdz+3MTI2WnSMm4CrHQvyEDZDIZoaJIAeY1JynFhw+P4amnTsD8+eaBv/7a+jv6vOl7papI0ckm7OotKRdJfNPQYK4j5BTXZWfs2ZXTDif1x2lWNhZL/YyOgXDj96+qSH37bfK1qmuf6Ph0++k22YTTwNVp0gqwnpNTogU2CYlbRSrdrn10u8FOmvEmd+xiiMrKktlSCWw7JjtJK2NUAsA334gVKfb3xHAii1YTtm619jU0lZXAUUcl3//1r6nlFSGrSPH2papI1dcnPQfKy+XaJ3aMo+La93//l7oMA9tvs/htSNHXVRQjxTOq7SaJswnlov9E+3wBaNKkCfr374+27LScxhVBK1Lt2zu7BbRqBfTsab9/FnrdCVL+N95Idb3Yvdv8v3Kl2KVCVtZ2UqRkiESsxiXrnkigGzrW2ABM15PKSu+KlFOMlIzRzEJ3GrwBgsws7y23mB03HTfxyCPJmS+vilS6DSk7RcqP9Of0/vxQpIDUwZBKXAAA/POf9inFn33W2uHSx6IH06qKFJ1swi5r3yefmOXasEF8LiyigbdfihTgPJjgGTlbt8q34zx4xxCdE+1l4IcixbpkA+lLNsG+BqwquEoqefb5lzH0/FSk3Bj0vL6OTTZBYGOIiPsj61bmVpEiz7aTi9/776dOjNKv6XOKx83lDUTccov12pJJPjsDesYM8f7oMtATZjz8UKTi8eRY58EHzTaOhlbTCOxknIprHw+63+bVUz8NKbpN8JL+3O0YLgwoN/Pdu3dH165d8atf/Qovv/wyfvzxxyDK1WjxI0ZKpA7IpBb95S/tVSAedPpzGRVo8uTk6yDTn8tQVGTdv51rnwy33GJ9b1c+NzFSZHueXO4Em8mKRWaWd+1as16w25GZr+rq1N+xnT+Z3U9X1j67GWnZrH1ekk2wipSXGCkgdYAsM0Cn93vrrWKVh+5w6WPRnbAbRcop4HziRHG52rUD3nxTPKuuOsigEcVIAanJZ9j98xSprVvl23EedkanDLJ1Vub4fhhSfrj20YqUShg2O3CTKZ+MIsW6vjltJwM70Kc/Y5NN0PBindiye3Xtc1KHd+xIJn+ht+VNJsycaQ0bYCF9DVnEWjTJR3jmGWej1i/XPllFavVq8/X27anff/BBqlLkRZGyg72WLE778UORYq+ddu07xNq1azFx4kQUFxfj97//PY466iiUlZVh3Lhx+Mtf/hJEGRsVhmHWPBVFSsZPmjZUnFKLDhgg7thY6IGTTHKH2trkwJseqAD82VeZrH1uH8LiYnM/vIQZ9D55s0gsa9daU8SyLkIENzFSKveDxamTUV0wlIac1/z59selO3+6caYJSpGi60hQMVKijFt+ZO0DUg0pVUXKKcEM3eHSAy92oW4W3kLfPNc+O+OAdUlh2bzZbKdkDSnVeiSjSKm69m3b5k2R4rn28VRwHt99p348u+PbGVL0cxVEsgmRIaWSaKGyUl2Rkkk2EYQixRtMOilSPOLx1DTlqoaU7PnRkGM6KVKyfQ3ZTmbNrI0bky72dsgaUrz7zt4Tp7b3wAHASV9glSKnGCkvbZzdNXfaj2zWPvqaOClSvEn/RmlIde7cGePGjcP//d//YcWKFVixYgXOPPNMvPPOO/j1r38dRBkbFW5ipGQUKfLQkv2ybgH0ugxshVcxpIhvsBPEP5qkHKePlW5FCrAaUjxFSnYgYzeDT89eigwpO+NQ5nOZMvHwmmzTMJKrtfOOyxpSmYqREilSfsVIsUlH/HLtc2NIqT4TpMOl9+1GkeK59sm0UaJyiVQaL4qUUzySkyHFa69oRcovQ0o2SZBsOyVzfF7WPlY5SYciRbv2yXhUEC64wOqWJhMjlSnXPpEhBdivCUVDYiDZgTNrZDjVSbbtkoG4xzvFSMn2NWQ7WcPrwgvFyRXY6xuka9/Chc7XjlWKnLL2eRnr2F3zoBUp9lo5ufY1KkNq7969mDx5Mu69916ccsop6Nu3LxYvXoybbroJlaKarJEiqBgpXqNGuwW0amX9vcqDSxshdumxWUjKcVahcJqNSachRR+XDeC1wy4Inm5Y7Vz77FJEs/tSnZ1y6vBV0+nKwptFpbP60KTDkKJdDIOKkfJTkeKpPE6/Ud2Ghtfh0hMAblz7nJJNyJZL9PtMKVIkaY7IkHLzTPEmkmTbVT9WIOFl7WMnl8hzQ9fLoNKfswk0iEdFp078fdLQCQ1UFCk/XPtU6iLtWjhtmnnt6d87uQiL4ohuv936XtW1z2nSplUrM/YasN5n3oTkoEHmItWiY3fpklzEWrY+b9smTq7gZ7IJp+sn45UDWI1EVpEStXeybQp7LVlkFCmZ9Od5efzJF54ixTOkyDaNypBq2bIlfvWrX2H//v24++67sX79eixcuBBPP/00xpCcrxrXBK1IySohbhWpNm3E2wJmw9u1q/maXtOHd2xAXZFSiRPgGVK8e9C7t/O+SHpeXpnoTpfnDgSI76OXGXgZ/3GVdLpOsJ2VjGufXzFSonosytpHD6j8SH9OykHO1e8YKVVFqnNn8b2063CdXPuc1pFySjbRvr3YZYSUKyhFSiZGym5Wlq07BK/JJniKlEy7CiQzpTohqo9OWfvobdIdI0UYOxZ4/XX+Pgmst4NM+TKhSFVWAueea77evx+J9NUffZTcRjQhIxNHpFIu9vzoNdR4d1YAAKrISURBVNt4XHBBsj5YFanUMIVoNJmq245nnknuT3WSzy65gp/pz3mKEQ2d8VgEbSQ6xUg5tXF2/TZ9LZ1+w+Im/TmbnEjUH7Of+T2Rm06Um/mKigrE43G8/fbbePvtt/Huu+/ie5kAEo0Ush2wKBmEU4wUD5F8rWJIOZUDAC66yKoO0I2aU4yUzEw0KY9MBivikuDk2ifj5hWPW2cE6TKxM/syhpSdoehl4GiHKJ3uO++IO7NIxJopip3RTqcixdYVO0OKnW3lufb5oUjxtmFhBxq8115jpP7wB/G2dh2uF9c+mWQTd9zBLw87EBAZUiqJcVhUFSl6/2zdIQSRbEL2+SALATshas9ImVetSqoj9H2kn2ESVA+kJ2sfjezMP0GkSGUq/TlRkthzqa4Grroq+V5kSKmkhJcpF+vaR3ur8Ojfn3+f7Z79Pn34+2nXzuyDyBpMgHWSzwlRcgW2XfayIC/7GUuvXs5lZSeunLL22fX/APA//8Pvt9lryWJ3DnbxTqKxI/mONaTYsYp27TvEBx98gC1btuDjjz9GeXk5Jk+ejEGDBiVipzTeyJQiJZKvncrCS39+zTWpDzfpEE86yTpj73eMFGmUTjwxGQN22238ssu69pHXBQXy6gldJrbhdmqwyTY82duLK5MIu3S6v/yls2J18snJz1iZP53JJlh1ijfgFilS8bh6jBQv2QR7ndwYUrzJAd53dtBlGD3a7Fh5s/t3323f4bLPJousa5+dITRsmFkudtabHQi4UWtl8BIjZadIBZFsgt4Pve4d+142/bloPbkHHzRfz52bVEdIOucZMyI47LBkLCxZG5BXbiA4RQpQd2MUlU8l2YSsa59Tv6myuLDItU81WZCqa5/Mgry8+6waH/nmm/x2iEzyybrX866Hl2QTvHGGqH7QyT7srjU7ceVFkTrpJH6/LTKiRGWTSRzB7of8hp5M540jRYpUozKkCH369MGpp56K8vJynHjiidi0aRP+8Y9/+Fm2RklQMVK87Fp2v1FVpOj056T8xx9vPtyEf/4TOO0083V+vnUm0O8YKTqBwNChZgyY3SyRrGsf7dIim81Ixj2ERtTRB61I0WUg12zo0GSZRIrVe+8BRxxh3QegFiPll2ufKPuRbNY+P9aR8tOQisdTk7iouvYZhnkPycK3ANCrl1nRTznFeV/s/giyrn12bVRDg1mut98233fqxB8IyMYPBq1I8Qwptj5v25bMRuiHIsWq9DfcYC33jTcmX8suyEv/nqhORB3ZscO67bp1yfTW99yTZ6t+zJmT+pkfipTdOfFcPmnYGfCwJZtQUZJEhpSqQSk7tpCdUKKfCxlFyu66iI4zdqzpGSED73rIxkg5uc3JDPrphC+8zMgXX5xq5Dhl7RNNbJN6zuu3RagaUna/odtMJ9c+rUgd4qmnnsI555yDNm3aYODAgfj73/+Oo446Cv/85z+xefPmIMrYqHCjSIkMD4KKIuXFtY8uPz2TevLJ1oaZHkipxEjJGFVso8l+TyObtc/NQy66h7z9iT6zux/p9Cu2U6zGjuUbA5lQpGRd+4JeR4o9H9kYKfZcSCaupUutv2EXeeTBGgSA9VkjSobstZdVpGjXPrt2hx14kWvQsiV/IJDOGCl6HzKGFC9j5Vdfmf8XLFArD8Bv/9jj0goebWh89ZXzAL+y0mosDRsGdOsGXHutKM7G+cK++KK98u6kSInaWjvXvmg0meTADjq2TMb10E/XPqfnSkVJItePZ2w4xRHJuP7zvpdtB+kBM28dKdnjO12voUOdz9Mu1pNtl72kP3cqKzGkCgvN/vKyy6zfH3NM6m+8ZO1z23fa/U5kSNkpdrwYKfY87Fz7eJPF2YbyLSCG0xtvvIEtW7bg66+/ThhXrZycaTWOBKVIsenPWdiOzI1rH61I0QoOYJ3pFylSKjFSdp2vV0NKNEuqopyoKlJuDKl0z+LYzXzxjAGeW1fQ6c9FhhQvDsrvGCk/Fal58+wzcV1zjTjdL1sGcs1p1wvVhae9KFJ2ihIbmyIz0cO+9/I88GZN6QGATLKJNWvs9//88873icVJkcrLsxoIdAzJmDGm4W13TKI6sQPJ6uqk6iTGvrJs3pwan+I0cOX1d2w9E6lsZMhBssDS+3jvPevnfitSXl373GRY5LUjomRBBNrlU9aQIs82q1DythcpUrIDf6dnVyYpkl2sp1+KlIwhRa5X06bmcWlvDbtjOClSquM9GZwUqdmzk8q6aFxK33+nGCleO94oFal58+bhiSeewC9+8Qu0EOWx1LhCdnBjp9Lw3gPOAxU/XPt4ag7dcNGGFG+gQrYXKVIyrn0qhhTppHnuibx9FhbKZxESbWMnkdt9li7XPrfwBpi0OyldD/w2pOj6IYqRol2xgo6RUlGk7AypV18VZ+Kyy1DF2y95xuj1iHhZtUTwtuO5DMskm7BTApxmSXll8fo88Ny/ePWVnWElbr50wgUeTvdJVB7ynj1f2pBi146qruanglbN7uYGVmWRVaRE/Y3IkCLPFZ2YATCVzbFjnV37VOshkDwnlclJHqpKEmDfjti5XgPAa69Z2zJZb5e5c83/bC4x3kCed59VXftk2iEnF3O7uCAvWft4dVNGkSKKv6jtYstHyqDq2ucGO6OItOm/+hXwww/m6+uuS13gmv6NbIyUdu2jmDlzJi677DKUl5ejuroaAPDXv/4VX3zxha+Fa4y4UaRkHizVZBMqhhRPkSJlsFOk6IaXzQwmq0jZXQOyPVnxnF2Tg1d22WQTMrNivPKxODWm7D79HDgGgZ0xAKTOWAaZ/pw1aniGVCZipObNk1u7hN6XSCEQZagiOClSqpnlRNvxFClRsglVJUA2RspNZyxyzXRy7Zs501mZcLpPLCL3F/JeFHhPyssacE4xOX7AqixOA1cvWfuAZF2jA/zpfYjaJcBeGZVx7RPtl963HSp9CWHOHPt2hLheV1RYPz/3XLHrJAuJ0LBbBNqpfSeoGlKy7ZDIxdwO3nMt2s6uXCqufSSTLduPOBnIToqUX659duobvfYaYetWc3LGbhLUTpFi6502pA7xz3/+EyNHjkRxcTEWLlyIA4dasJ07d+Kxxx7zvYCNDT+y9rkxpNjGwq1rH1t+O0WKfE679vGUJPa9jGvf+vXm/yVLkMg6NX8+v+xk9twp2QRpQA4cMAcx77zDnxXr0YNfPhaR+sT7zN6QCnB6WQGeIUugG9Cg05+LXAlEMVJ+ufaR4+3fb93mkkvsXa5E184JUZwFT5GiOzrVzHKyEwMyySbslAC78xe1CV4nFniGsKwhJRvnohIP4+TaF4nYD3IJPENbNbubKh06pMan+KFIiZRhcu/YZCxsu8lzK6K/Z2MIVSbBRAuCOyFSWP7+99TtR48Wu25Go9Y+iJRP9hmJx5MqhB3sxEE6FSmCanIFL659fitSTq59vLqaLkXKzsAksEvckOPbKVKi/phsI2Ochh3lov/2t7/FSy+9hD//+c/Ip1q4U089FQvcRNZqLPgRI8X7bZCKlEjNcVKkZDLDqLr2sY1kdTXwv//LLzvPkGJVtcpKgGT237nTNM5uvRV46qnUWbG2bZP7Fl03v1z7wtL4iGZ+6foUZLIJXsPNM1J4xhLdwaoaUmxnUFkJbNmSup2dy5XTrLkIUZxFphQplXWkZGOkgnTt4xlSdF0RGVKycS4q8TA8xUPUPougjSc3MTkq3Hmn/X1yq0gVFIjvKRmCOBlSTqqRiiIl6p9E29lhp7DY/d6uHSGwa4nxDHE7Zs5MVfdYeOtSqShSMmMQv6ENqcrKZDIYFjuVhn2tokipuvbxFCkVVz9ZVH9nGPwJEfr+ixQpO0PKbtyXTShX3RUrVmDw4MEpn7do0QI7nKISNY74kbWP99sg05+LXPtUFCkvhpQIUUwAz7WPLg8JzmYTUlZXmwsLb9tmnRWTLZ9/ipT9MdKJSFWhZ6yCTH/OcyVwUqR4CoRqjBS970jEjEPhYedyZWdItW0rdoOxy1DFbgck6zQv2YTsAMZP1z47RcqrIeVmMObk2idKNiGTOl7mPtHIJJvo1EluX7TxJBOT06aN8wKsdowcyd8n4M2QEmGnSLH9j5PSqRIjxTN0ZbYTwSosAHD77fxt7doRAr04Otm3bJ/hRrXMhCKlCrlH331n9ud2iq6Tx4CKImVnSDm59rETgLzjqbhq2uHX2IHu30VZ++zGMjLXNOwoF720tBQ//vhjyudffPEFjmDTk2iUkR3cqCabUE1/7ta1TxQjRc/00x0sadQaGsyYJha7c6XLRRaMVKWgwLw2xC940SJr5+S0YKJoUKzSGQPiYGi7hjMshhTPYKG/C1KRojtxdjFEO0PKTpFyEyMFJO/dpk3Oa8OwLld21+7665PnwcMuQxUN+e2sWaarEP2cqCpSsvVZZR0p2Rgp2YGEm+dBlGyCVaTo/e/aBRx5pPP+Ze6TXXnIe7ZdOeEE8T54hrZMTM7//Z/5Z79f82I89JCpnvz1r8nvROnFVVz76LK5NaRYA0pWkXLj2udVkeIhs8aUXeydkyIlOjc3qqWdIpUcyxgp2/MIaiAdjyfjTT/8UD2BD1s3aa8FHkRPcOvaly5Fyq/rTffv9ESd3TjSLo65URlS11xzDcaPH4+5c+ciEolg/fr1eOutt3DHHXfgetLza1wTdIyUaDaS3p8bRYqXOtxOkSLHW7062dnX1Zluc888Y92/XSdLv3YK+LZj5UrT53zJEvP9hAnAb35jvt6+Xb0z81uR4rkSeJ2BDwI7VQWwzlj5nf68sjIZZByPm4YMfVxeuXiKlJcYKXpbNjbKDnrm107NGzzYPhPXP/7hvHI9zQUXAJdeak1gEZQixVtHSlaRshuUpsu1Ly/PLDMp9+zZ1raTPtaqVc7JG+64Q+0+Ac7JJiIRcbIJsi3PgJPJekba86OOSv19hw7m4uoPPGCqJ/QCzzJZ8VicFCknVVjWtc/uWVath4D/rn08vMTe0YoUqTuyz8igQeLkHgA/eYJIkXJSV5w+9wJZh2/WLPM9b803Gp5xSpf/m2/M/W3fbr8Pp2QTTpOoqoqUn659on3xykU+ZxUpsi2v3uWiIaUwVDC5++670dDQgDPOOAN79+7F4MGDUVhYiDvuuAP/8z//E0QZGxV+ZO3j/TYd6c9VsvaRfc6Ykbq/2lrre5msfU4zl3b885+pn5FGcsMGuX3QnZnTdeMpTDLb+zlwDAK6HCJFys9kE8Tt0m6Gcc4c4MwzU8tlGP7GSNHbsm41dtAzv3ZGaF6eObAdMwZ49FHgwQeT311wgfMxKivFwcOkww+DIhUW175ly8yBElGoL7/cTKVNyqy6/5NPVi+PkyL17bfAk0/a/76szDSi7Aw4UqdmzjTbro4dzQE0q8a1bGkqbnT666lT69GrV9K6ESnRAH+ATePVkJJNNhGkIuUl2YQdXmLvaEWKl8BJVK5oFOjdW7yQdFmZGdNF749nMGfatc+pf7CDNU7pcr3yivPv3SSbcMraJxrjub1uvN8VFIhj5Jo1M5V4tmysIsUb59hNCvM+zzaUix6JRPD//t//w7Zt27B06VLMmTMHmzdvxiOPPIJ9bGumUSZTWfvsKrdMWdzESKk0bjIxUkEsaXYos78jdGfmdN3YgTuNrOEVRkPKSZGiO1o/0p/LrInzl79Yv09HjFRZmflnB8/lym4wSqtlp56auh8R5PqIIHF/QSpSfq0jZfd79rVXRer111NVJuKqU12t3tm7GRywv2Fds/7619SYTcJDDzmngib7HDqUn/WMnvxiJwbYZ0L03NPfBxUjRcrDKg1s/+MUx5SJ9OciSDybHbx2hEDfMzp2U7ZcJP7O7rzat08ti0iRyoQh5WXNNNY4Vb2PZJyjEiPlpEgF4drH+11+PtCuXern7dqZinVxMX8/rCIlGrfQ57pxY3LiPCxjGTe4rroFBQXo3bs3TjrpJOTn5+Opp57C4Ycf7mfZGiUywa6A/zFSXhQpN+tIrVol3ieNjCElKqPbB/TgQbMBEblDsp2ZF0NKNBDJJkUqHenPZdbE2bIFWLo0tVw89z1ePXWjSOXnJ+NQWOxcrkSKFIGoIuw2dshcn/r6SMpxRKgqUulaR0rUFsoga8QvWqQ+MPOjPKwhZUckYk4eeIU2flhDStTXeFGk7NpyWUWKdalVVaSCSDbhpW2m49ns9msXe+ekSDnVSbJ/koH29NOt37MLJNMeB16y9vnZl7ldM41nnLo18DZtMts1P7L2iYxRt9fNbuKW51Xx9tvm5IydJ42dIcUbq9AxaEuXJvvojRvVyh8mpKvIgQMHcM899+CEE07AKaecgg8++AAA8Oqrr+Lwww/H008/jVtvvTWocjYaWEPEjiAVKbeufaIYKdaQYuVhEXaGg13nSw86AXNmzy4DkhMk7bndjJBoUMy7brwZQtH2dnK46DeZQDSgklGkVM9DNoZg27bUcokUKXoywE2yibw8s8PhzSbTcSg0MkYBrbjKXCuV7FuqdZEHb/Dr5zpS6YqRErFvn+kuqoKb8ji59tkhs0izDPS9YRMXsOWgDaS5c+0D9YOKkZJNNuEUI6Xi2icbI+W1bR47lr8Pu3aEQA+ESdlUBt7sgLdXL+v3rEoYRkXK7ZppPOPUraHy8summ/DChc77c8raJ3rv9rrZxTvx1G7yHNr1C+RzOkaKPUYkYrpb2sWpLVgAvP9+SGaGFZG+BQ888ABefPFFHHbYYfj555/xy1/+Etdeey2efvppPPXUU/j5559x1113BVnWRoGsIiUaPHhVpPx07aOVCNqQUnHFk8naR7+eODF1TQ43sQqAGUvgFJxNo6pIORlFfrr2BaleiVx86Bkru2QTqq59sjEEtJuCTNY+Oi2uG0WKnAddvwsLk/WQN/hxcu1j9yeDl3WL2GOLtuN9J5Nsgh14hcW1zwk6mYkMfihSvBlqEV4X3iVljsdTDSm6HJWV1nZ17NjUxWKdFCleTDB9jKBd++wUqUy79vH23bKluB0h0PeM5wEhO0nLLlpOYBUpdqKMkElDyk32wcJC/nX1ch+rq4E//tF5f3YJGOjP7N776doXiSTjQ3nHszOkSPlFMVKG4exufvvtUdvFksOMdBfy7rvv4o033sA555yDpUuXom/fvqivr8fixYsRCYt/UQ4QlCLltI6UF0VKJkaKHqDm56euvi5CxrWPfh2LJdfi4H0vS3FxMghbFJxNI6tIke9iseS1ERlSfgwcVY0VFZxc++jOme5gZScOWEgMQXW1/SCtXTvguOOS70WKFPnP1lNZRAZykyap9ZHGzgi1U6R4q8uzkOsjcm+JRg3E4xHbjp09jqoi5WeMVLqSTTjBi58QufulU5EieF14l570sFOk7AL5yWKxZJLJqyLlNf25rCEVtmQTvH00by5uRwg8RUqlz2AVKRlDimcw2yXOkp0s8YJs/3D11cDjj5vv7dp7L+WSdWPPRIyUitcB+zzR7R5PkeIZUhs2OLtbrlsXwbJlbTB6tHP5w4R0FVm3bh0GDBgAADj22GNRWFiIW2+9VRtRPuNGkZJpqNKV/txOkaIzwcRiaoN6VUOKd46i7GV2HH+8daA9dCg/OJtGVpEi39kpEAQnRcrp3tCzO5EIfwFHP3By7eMpUl5iW+gYArs6fd111s5RFCNFjs/WU1lYRYouk5NBZnftVAaULKIYCwJZeNVvRUrFtS8MipRsW9SkCXDaacn3/fqlKtVOrnAy8BQPmfOKRNQX/xUdv6GB7+0gCuRn19dzUqT8Sn9u51LoZEiR79evN9dZIxMpmU5/zpYPkG+P/HLtI20kW/9UFSkno4DgpyEl6h+IofLSS8CIEc7Hly2/LE6ufTwFOl2ufSTjoN229AQwfXw7RYoum1PqecL27Q7590OI9C2Ix+MooHrzWCyGZiS/o8Y3ZBUpO+PC7req6c9VGl46/bldjBQ9QKXTn590UjKolcDGOMmkP3caTH33nfAUuMfv2lX+N05lIrC+xvRss2iGyM3AkayhQairS3W98QsVRYo3U+mmM7BbE4cwaBB/IGIY9jFSXg0p3r10GgzaXTuvCuLYsakDexqyXoxTx06QVaRUXPtIe+El2YRXQ4pn+PM48UTrvezY0UwDTbsRX3ih3L5EyChSdm2+6uK/POhBMc9AUVksNl2KFIto4EdD1g/89FNznTXi1jRliv0xeYauqAxeUJmQIdDPvJu2lqcuiO6JnSKV6fTnMmumybQd9DY33GDf38ji5NrHU6RE19BPRcruOZ0+3fobtrwyipTs0iCtWkkuxhgipKuuYRi48sorMXbsWIwdOxb79+/Hddddl3hP/jTeCFqRkpnxdatIiWKkWEOKfN+xI/Dqq+brbt3Mwcj999uXza0iRVIYOzF4cPL4bhooXjnowQgZXMoaUrzvZBp/4nrDDniI643fxhRdJtn0505qnAxjx1oHs7ThyM7syShStGufSsfOGmRuFSmvGehY2NljGtGadfRn06ZZ1QUefilSXpNNuLlmdD286abUgRJxq+zWLdWAY5VqdpDppjxOWft+/Wv5mE030JMe7MAqL09tsVg3ipTKs+OUREKkSFVWmunuedx0k30bKWtI+fH8si7rMtADVtLvulGk6DEDfS5eY6RkVWc/YPsHNsZMpi+lPz/hBOv++vVTL5OTB4CMIuV18kj1d08/be0DWEOKzdpHtqOP0aWLOKU/AJSVGejde6t4oxAiXXWvuOIKtG/fHi1atECLFi1w2WWXoVOnTon35E/jjUxl7WMbWr8MKfJbOj0tbTwYRrLBLS3lu82puvbxytu6tfgcCF278hsBWdjrxqpC69eb78kaVXQnJRrQqszAq7je+IXIkKKlf1qREqmqKtCDWVokZ+uxSoxULKZWJta1T0WRsnPfYeuDGwcA0TnYud5UVlpdPIYNM+vsZ5/Z74t3viqKlKprX1CK1EknmQOls84y319zDXD33cl9i+o57zM/FCl2YDVggHhw6BXepAf9ncpisW4UKRpZ1z4W1oBir6nMOmt2bWTYXftoY54YUirPCGtIsYpoUIqUH9eLh8gtX2biiu3T6f3JjitonDwAVBUptwaoyu82brQqzGx57RQptr10cjd/8sl4oLHcQSHtvPIqkQ00gZKprH3s/ty49tHpz+0UKaJG0QMpMni1S7EpY0g5dRTHHCM+B0LHju4TILDH/vhjc/aYF5BNlCLayBDFpajMwKu43gwdar+dCqKZUztFivbf96sTFU0IkHLF46mB1GQ7up6qwO7HD0WKrQ/NmwO7d6uVS1SHeYqUKImAaHUL3kCLTlYhq0jZlVfk+uJVkWKveTQK9OxpPr9t21onVpzaGbbu+6VI2Q3mgoBWbnmGlFMgfyRifj9oUDKVsooiRePWtc9JkfLSRrJlTVeyCVlDiv6NG0WK96y6UaTsFO90ufbJoKpIOSlFvN+ydd/JtU9GkfLDtU/1dzU1/OeJHsOwzzNbzjFjzG15ExSnnQacd56Bqiq1coWBDFRdjYigFCmVGCm24XSjSLEdGVGkWGPJMKxp0Xnlt5s1UnHtE3VC9Pa0YuJVkXrwQbEqRLb3W5FScb3xC9HMKdvQsoqUnzNQbL3llYvMmtHHZg0plfgoens3ihTP/ZDdBxC8IiWjZNrBO1+Rax+rSKnESPnt5sJTAUtKzP+1tdY2zald9EOR4k0k+TEDrXp8XowUceMRBfIDyVgtup3n1aGgDCnWI4LdzksbKZro45XBC24UKRrec+VUf3jPF/2ZbNa+TMdIySBzXUTPntM9LisDHnjA/piETGTtU/0drTDbxUgReOOWH34wPRvoNqV1a+Coo8zX3bqplSdMaEMqZMiqIaIBl+gBkVWkvBpSbEdG0tMahhlzQc9EE0OKdJqyAyUVQ0p0TcrKkm4xdOZBr4qUTGddXy82pNjryL7mnZeK641f2BkD5DtSZjrugmd0+FkOO0WKt1YUz7VPhXTESDVvrlYm3j5o2HouM0tvt187RcrOtc9OkZKJNxEd26shRY5PG1K0welk0IhiuWRxUqSCcoEi8J5V9juZQH62rDxDikxq/PhjMhaPxqtrn50i5aWNTKch5UaR4qFSf3j9L+/5prcXKVIyHjOiz4NE5rqItrErc7NmSZdbVtF0mujlKVKiNs/tdVP5XWmpNXkT69pn1+7R5/Gvf6X2L9u3A99/r16esJHFRc9N3ChSsg+3aL/sAEFlxkMm/flvf2v+37cPGDYMuOMO871huHfts7sGImUHMNeHYuML+vQxv6MzD/oxmyyDyKDgGVlOjShxvRHdaz/SJPPKCYhjpHiKVLoMKTZFK68MbgypeBzYtct8vWpVamKGIGKkeINOHvZ1wEip5yoKJXtOXhUpFdc+vwcVToYU3aY5tTOyGQBFhEmRsjOkAOdAfnZ71pCqrASee858PWUKErF4dJKHoFz7SBtph6iNFHlMiLZzg0o7IrsfVdc+1pBXVaRk3eHCaki5ce3r0CEZjyWjUoddkbrrLms9kFWknO6pk6dDtqANqZDhR9Y+N4qUaADqVBZe+nOyv+3bzf+1tdbfkM83bFBz7bNrQJwaRFYxGTrUGnwqMgZVUP1NLGbf2QP8mR0ZtVLW9cYvnAypdClSbD3gKWW0IeU1RookE1m0yHz/0kvme1K/ZfZlN+tMX8fKSuCrr5LveYNOHqLnnVWkVBRKGUPKjSLl1ZBy88zy9k3Uv1277NsDmWQTblVttv1NpyHlFCPFbsu2pTR0uel9kVg8MgFBIFlFCW4VKXYij6fyOQW+27WRsoqUH/fJbpLFy378VqTo761Z+yIpxxYdP9OGlIxSJmp7aOglXGTaBLYNcjqOH4aUjKJOGDnSeiy7GCm2TCplI/GU2Yg2pEKGHzFSogZJprHww7UvL88cRP30k/i3y5cn46dIAy2rSLl17RNJ67w4LxXo33Tq5HwfmzTxpkjZ7V/W9cYvRC4orOtHkDFS7ICTNxARKVIqMVKiFPMrViTfe3XtI8fZzyyvIZPK3q5+0NkyyXFklEyCKM037drnV4yUrAIfhGufiouSHzFS7H54ySaChH5W7Ra6Vd0XkLyOKrF4fqU/5203dqyZDIjH66/bt5HsNQhbsgmn/TjdQydDymuMlMwzni5EsZe8z2UVKbLYOXsMu984Ze0TvXd73Xjl6Nw5qTDfc0/qMXjPk6xrnxMk/CMbkXo0//3vf0vv8JxzznFdGI0/Wfu8KlLsDKisax8bIzVzpjUehceBA0kfWTtFyu5cZdQp2e/pc/Di2kf/5rHHgKuuss7+k21oY0Ikg5P9qRpSgDkQGDPGvA81NabiMGiQv4YLr0wi1z5e+nM/O1CRssqLkWKNOWJkOQ1a/BwMigwpp+NEImaaZpINSbRv628jMA7tlJ5lfPZZ0zjj1VnROfEUKZl1pFjXPru6KVKkRN/JoJJsgsZpQOS2POzvwurap7Ivsj9ALRYvKNc+wgknAH/6U+rnY8bYHzOdrn2ZUKR4z6pIkWInygiqySZmzQIqKoLpn+yQuS5eFSkZd1/VrH1eJ4/YfdCfDR1qvqbXIxM9TyLXPpWyyS7YG0akHs1zzz1XameRSARxPxenaYQYhlnznCqgrKsLy48/moMzkdzs1rWPdYuTjbkgblAyipSqUSX7vd05qEKX75xzTPVn/HjrwKGszEyv/OmnzoYUr0FSuTdBpkmm4bnQ0d/xBmfpNqTIa3pxZrtr7zRocRoM0nhJf+41lb2oDvNUIKJk8urs//t/wHXXme+9uvaxM9iZdO1TSTZhd1xeOd2Wh91PmJJNqB6b3p7ca5VYPCdDym2yCV75aESDeVH/JLNvFdwqUnl51nunUn94hqKfipTd8c85x2xnnn3Wf48JO2T6Ur8VKad2g6dIia6h23rG+x19HDom96uvgCOOsDek7M5RpW/v1El+27AhdZoNDQ1Sf9qI8oYoKxaLqAEQPVjPPcePrRC5RDmVhafm5OXJx1wUFVn3I5rxk1GhnBoI3vd256AKWz67gOwjj0weQ+TixmuQ0jmokkU0c0qfYybTn998s/l/K7VwOnkO2HvtZPx4SczAYjdYysvznspexpBit7GrsxUVyW1EhpSMa5+dIiXj9iMypNw8szwjza1rn1+KFGtIpVOREsVIzZgRUVrIm6dIqcTiVVeLk6rIpj9XNXZE1zgbDCnaAJ02zX6fTscEUttR2XWkVF37ADl3ZT9JhyKl6tono0j50R7wfkf2W1lpxj0SLrvMHDeSSUi2vLKKlOi9F8U10wTcJGtUELnSsLh5uAm8xoptUPxw7Rs0KLXRZSksTM5E2GXtU1WhnBQp3vURpXBXgXdfeAHZ9HdeFKkwGlIiRSqT6c9pA4pAngO2zE6NupfEDCx2105lMsJuO9G1ZVehpxHVWUDdtc9PRcpvNxeRIrV3b9LdU8aly49kE+x+VCe2vEIPijdutH43fHhMKskJgadIOcXi0bz+ujipipMiRa6jkwsgi1tFyu+2WfTM2VFZmYz1BIBhw4ClS/n7dDomec+bKCHYKVJ2/ShrnNOQ39xyi1xWUq9kQpFycu3LtCJFYnI3bbJ+V10NrFzJL69MjNSFF/JjtgcPti9PtuCqSd6zZw+qqqrw0ksv4bnnnrP8adxD3PoAtcZO9uFOHsf8TzdWfihSrFtcNAocc4z4t0cemSwDbx0pdnZGxqAQzbSwrwk81z4/FCmn7ehrLSq33f0IelAli8i1z0mRCtKQohNL2HHLLakxSE6GlMpg0Itrn9dU9m4UKTtEs4e8gVY8bm+ssTPYdi6AhHS59pF90Wt2kcxymXTtS6ciRfZ/8KCZDIhFRTXgKVIyGfNkj+c1RsqNIsX+xi7Tph/3SXXGngyC2faMbgczrUjx6hQN7a4cNDJth6hP90uRykSMlN3vZGJ/9+yx7oe9Drt2pSqhxxzD93Q47DDz+7CMZdygLKYtXLgQFRUV2Lt3L/bs2YPWrVtjy5YtaNKkCdq3b4+bif+MRhkVRcptjBR9LDq2QjST77Q/XsY7sr/OnYEFC8y1m+isLG3amApBmzbidaRkZ2JUFClZ1z43DZSsIUV38EFk7Us3dJl4g2xakSIEYUjR+5o6FXj8cfH25Dn49lvr506DFqfEDPR7L+tIySSAEKWyF11bu/TEdtB1jT2n9euTsZf0dyTToN1zLKtIySrwbuoSbyBcUGAOGA8cAHbudD4ub19uy8P+jjWkgn7maUOKh0ySEwJPkQKSsXgXXZQ0tu0QHc+ra59MfXP6jjV67RRMN4jaVRZRYhoap+95yiv9mawiZfdM00tDiFBxn3aLTNY+2fEIjWqMVCay9vF+t3+/XOzvDz9Yy8KeY02NqYQSZZ/ebuhQfjmy2ZBSLvqtt96K0aNHY/v27SguLsacOXOwevVqDBgwAE888UQQZWw0uFWkZB9uHqSxYgfnbl372MaT/D/9dPP/kUeaA9wXXzTfNzQkOx6eIiWazVFxd5N17fMz2YRMZ0wbGaIYqVxx7aMHTUHESNHX4667gG3b5H5HJ6AA5NxoRCnm+/WT35ddfSfXzEsqe5n6IVuH6Hu8e7f1u/ffT7pg0edLXIzsBhN+L8jr5nmw2zcZBJC6IaP8pyPZRLoUKRGyqgFPkSKMHQu0aGG+vuYad8cLKtmEW0PK77ZZRZGSTYBDD4J58J4v+t55VaTatHEuI6DmPu0Wr659MoqUH1n7ZCeUVeD9zo075dq15oLaPOj1Q52etUZlSC1atAi333478vLyEI1GceDAAXTp0gW///3vce+99wZRxkaD2xgp2YebB2ms/HDt4ylSbFrp9u3NGQnyG8MQK1KyhpRTw+LUwfEWFXbzYKu69snGSIVdkZJ17eMZUn42oHYz4E60bm19L2vc2SVmKC1NbuN1HSnRcZyyW8lcWzeKFPGVpyEuWP/5T/IzvxSpdMdIAUlDiihSMu1sLiWbkMFJNRA9j4aRvLZ9+7o7npd1pOjvVeApNrzX6TakZBUccs1ljgmY14geYHvN2nfssc7HF7kr+4lM2+G3IuVkSPFc5USTOG7rmWhCWYW5c4GHH3ZXBroc2WxIKbv25efnI+/QGbdv3x5r1qzB0UcfjRYtWmDt2rW+F7AxkU5FKhIxZ7RJY8U2KCqGFGng6SxPrGsFMaRIg0G+pxUpXtY+kSwuo07Jfh9ksgnRdrSR4caQCkvjI3JBcVKkgjKkZLdv3RqYONH6+Zw5proik4ZX5K4AuHftY+s+7zhOBKVI8SAuWLffnvyM3G8nRUplQV6RIuWmLvFipABnQ0om2YQfipTqxJZX3EzEyeyLVaT27EnWD5LFVPV4sskm/Mysx14fu2fWb9c+p3ZEVsGhB/lOxyTvaUPK6zpSovOQcVf2k6AUqRUrzDXKaNd90W+csvb53eYB/PrZtKk5Lqyulp+I3LvX/HNi9erUz+Lx5ATAunXpSTASBMq34Pjjj8e8efMAAEOGDMEDDzyAt956C7fccguOdZpq0AjxK/250295jRVraLhx7QOS6hKrSJHP2eMZRqprn6yR6Na1T6RIpSvZBN3Ri2ZkyD7C7tonq0jxYqSCSn8ug2GYsXqbN1s/P3DAWxpe+nr4oUi5JShFyg7D4LsXffop/5isIpXpZBP0cUjCiepqa1ntysH+3m4bGcKQbEKErGogUqToVMrDh7tLquIUI0X4+Wcz+J0dqLm5luxv7BQpP+6TiiIlmwCnVy/5YwLmOYkUKXq84GUdKUDOXdlPglKkrrgi6ersR9a+dClSdCIYv8cWJGkPobLSvEbEg+Hdd4Hu3WOYPTsNPp0+o/yoP/bYY+h4aOrj0UcfRatWrXD99ddj8+bN+BNviXCNNLQi5VSJRTNfTr/lNVbsg+nGtQ9IGkysccAqUvRAinXtEw3KZQwKN4aUX8kmZK8brdixs6e8/YXdtS8bFalIxNlf320aXpWZZLs67fdAzMs2gLfyjB9vNUppRZr+L+Pa5/fsLG/flZXmIpQAsGWL+f8vf7FfMoIgEw8hA2tcp/OZdzMR57QtkKpIkaQDLVua181uACc6nshlr7ISeOMN8/306cCwYamp1N1cy3S69onaVRbZQbDKPSNloNtAUftO3+OkZ4eRsj3NO++ouSv7icxz5UaRApKuzpMm2e+P4GUdKbf1zG6cZBeT6xUSDwkks0uyk27r1wO/+92JeP/9kAxsJFHudk444QQMGzYMgOna9/HHH6O2thbz58/Hcccd53f5GhXpUKQmTOA3Vn649gHqipSqa59dA6JiSAXp2qeqSMm69gU52PYDO1WFfJeJGCmn+9exI39tKYKXNLxuFCm2A/VDqUu3IiWCNkrtFCkZ1z7RYMYPRYp08iS+i7B7t/k577j07+3KpkJYYqR47mKqqgFrNBOIIkVczdwkVbF7tjZuNO8VmxSFTaXutyLlt8GrokgB9teQVpGcysUbT9DtNm9Qr6JIse9PO826Xl068eraJ3J/I9/df7/9MQlOipRICfPTtY98RmJyP/00NX6YpUkTObfSI44w/4uySxIx4fbbo1nl5qd8C04//XTsYFNcAaitrcXpJDWbxhV0R6PS2KlI5/368Rsrv1z72NSvrCJFOgN6ICVy7XNjSIkMEnZbgl+ufbJGjqxrX7YoUk7Gb7oUKZVnQRY3aXhVFCn6HmdCkZI9jkryDt5vaaOUHVw7xUily7XPMORSSPPKAQSXtS9Trn3Fxeb/J56I47bbvsaUKfXKqgFrNBPIMILOcKaaVMXOuPj2W/FaOMSo9+p1AIQn2QSBdw1POSX5vaziSG9PD2p5bSxPkZJ17Uu38WR3bDeufUSttsMwkq7BouM4Ze3ze/KI3Qfvs2gUOOMM4M9/5ht3hFNPTY0x5kHO0Tm7ZATr1kXSso6YXyg3ydOmTcNBzgIT+/fvx0zFM3/xxRfRt29flJSUoKSkBOXl5ZhE6aBDhw5FJBKx/F133XWWfaxZswajRo1KrGN15513ot5pYYrQkqypKoqUimufXaPlRZGiB4Ak3bGKIiVy7RM1wjJufk7bEjK1jpSTIsUapOzrsBhSovpiGGJDKqj05888I3ZPYIOm7XCThldFkSKDO8MwB0C8fbhFZtAte5zZs72VBUhdbsHvGCk3RgY9QF20SNzJO3kNBJW1L50qNL1/8rz2729g8OBqDBliKD+vdooUce1jkx+QpCoyKoXds8WqiTS0Ue+HIpWuZBOyhhQph901dCoXz9ChDSmvilSYDCmvipSontnh5NrH+152HOS1HLzPnFz9jjzSXN+NBz1JQvYtOzGZjnXE/EL60fzmm28Sr5ctW4YNGzYk3sfjcXz88cforOhUWVZWhscffxw9evSAYRh4/fXXMWbMGCxcuBDHHHMMAOCaa67Bw1RuxSZNmliOO2rUKJSWluLLL79ETU0NLr/8cuTn5+Oxxx5TKksY8EuRklFCWFhDQ7Xjzs83jSi7GCm3ySbcZO1zaiBkFSkvs5WynRU9SBLFSIXdtY8u3yOPWL/7r/8Cevc2X6fTtW/kSODGG80BU02N6aJw1lnJ70tKxBmKIhFrZksVZBWpykrghhvM1/E48PLLqb/3gp+K1MaN3soCpC63IBsjJarzXgev9O9Frp6iMvH25bY87H5UPQS8Qp8XaZu91EcVRUoVFeOCpaYmqbipIDIEgnTtc5OamrcfN659ToZUtipSMvdL1PZQw1FXxyTw6lAkYj8W8UOhFk3csowdaxpLpC9dvBj43e+S+2Hv4aBBwMMPAwsWJLO4kuPJTkymYx0xv5Buho477riEKsRz4SsuLsYf//hHpYOPHj3a8v7RRx/Fiy++iDlz5iQMqSZNmqCUXpSFYvLkyVi2bBk+/fRTdOjQAccddxweeeQR3HXXXZgwYQIKZKecQ4O8IiWaofVDkVLtuGMx05Bi1RxyPFGyiXTHSPG+Fy0qrALZt2xnRRut2ezaR5eJ9fzdsgWYMcN8ne4YKTplODsjnp9vBmdfcIG106Jxm4ZXZgBEYnF4x21okE+/LsJPRcpLx8YapXaKlIxrn99uLvRgvEMH+d+tXWsOMOmy+aVIsZNJ6Zw8oc/HD0NKVZFSwYsh1bGjdcFQWWRd+/x2zfVyrirPCO/5ou8db7yhokiJrl+6kbkuorGDzdDUsn3nzlaV28m1jx5DyBhSfipSojpL96WkXSD7YX/Xu7e5LaW/JI5Hskvap1g3DvUVIRncSCD9aK5atQqGYeCII47AV199hXbt2iW+KygoQPv27RH18ETE43G8++672LNnD8rLyxOfv/XWW3jzzTdRWlqK0aNH4/7770+oUrNnz0afPn3Qger9Ro4cieuvvx7ffvstjj/+eO6xDhw4gAPEBw1mfBcA1NXVoY6uIWmkrq7O0ljV14vLYc4Q5Se2pYtNfxeJGJZsgIZRj7q61Nrb0JAHIJrYH72PeNy6fx75+THQhmA8To5j7vfgQQNABJFIA+rq4ojHIwBiiMcNHDhgbheJmL9paDC/A4Bo1EBdXT213+R3gLkv87yiIJ6qDQ2p58heE3qf1Fmgrs5APN4AIIqGhjjq6ho424kwzzcvz+4YOFResl3DoUYoDwDveOZ5GUbyXMln5n7M7TNVbwn19fR94WHe//374yD1LBJpAJCHvDz63LwRiSSvDa/e5ufHUFdn1tO8vAaMHh3H229HcNttUVRXJ+tv06YGXnkljtGjDce6zydZjry81PsajwM33xw71JHwOgwD48cDFRX1HgcayXLYIfN8A8DAgQB5hviYz5w5AGDPycATT8TR0GCgoSH5vNbVmdemvp7Uc7tnLtk+sc+v9dlWr0uRSHLf/fvXoXPnGNav550DOUfz87/9DZg+3cBTT8Vx3nnmuZu/ST4HbNssS14e3Z7VWdpnXvvmJ9a+xTzfhgbzertpZ/LyzL7h4EHrtdi2zTyn5s3dtLM0qXWyuNjA/v38exiJGOjcGTj55HpMmsRvt0TnSdruJMk2LS8vWT+SfaB7IpFkvxqJeLlO4v6Rhq5rZHu2TtPXPB6vP9SOxRCPJ58/c9IseqjvaqC2t17zhgZ3z4gfyIwL6PEIe+3o/oZuG8j+ADO+8OKLk+fLqxfm9SPX1LzPeXmxQ8fmHTdZL9y2MdayE+TaT/oeGkYcDQ0NoOtEYaF5DtZ2K1l/n3wygosvjqb0FeZ4Ffj97w+ioSEvZfIl3ci2d9KGVLdu3QDg0AXzjyVLlqC8vBz79+9Hs2bN8P7776P3IT+gSy+9FN26dUOnTp3wzTff4K677sKKFStQeSjlzoYNGyxGFIDEe9r1kGXixIl46KGHUj6fPHmyxXUw/ZipdSIRA1VVVcItd++OARgFAJg5czpWrtyT+G7t2uYATk/si66oX389FwcPpkZI/vhjTwDmAhMzZkxDs2YHE/v/4osZWL16d8pvaAzjrET5AWDWrC+wceNOrFvXB8AR2Lu3HkA+Nm1aj6qq+fjmm7YATkVt7S7s2dMAoCUWL/4KeXmbMX9+BwAnAwD27duDqqrPEvtdvLgzgBMAAJs3b0RVlZmjuLr6OABmHf3qq7nYt896jj/91ALAUADA3r3WfQJATU1TAGdi//56rFq1FsAR+OmnH1FV9Z3wvFl+/vlYAEcCEN9Dcr03b96IvXvzAbTFmjWrUVW1xLLd5s0nAeiI9evXoapqIQBg3bp+AA4DAKxfb051TZkyRamcfvPRR8n6w8esg3Pm1AAoAwBs2lQDoDNqa3eiqmqGL+XYvHkgAHOacPr0afj+e+tKgdHoKNTVmc3erl3bUVX1BQoLgeeeA669dgS2bjV9fQYN+hmFhd/A4TG0ZcOGEwCYrs7Ll3+Dqqo1lu+XLGmD6urTBHuIYN064Ikn5qJPHwV/M4ba2sEAxFP+kyd/gqIi586zri4PAPEisA4aiBF17rk/YubMssR1JNx000IUFq5NXM/1648D0A3fffcdqqp+xOrVxwPoih9/NN+zrFhxJABzncK9e3ejqurzxHebNxcDGAEA2LJlC6qq1IK5li/vCsCcdPvii6m47LKW+N3vTrQ9R5rqauCii6K46655KC+vwaJF7QAkI/unT5+K777bp1QeANi1axCA1gCAyZMnHTp/00tj7twvsXPnduV9ymIOLs2gB3PiI4avv56L7t3dtTOGMQpADJ9/Pg2lpcnncckS855v2sS/5zJs3NgEwPCUz7t124bvvmsN3j00DGDcuHn45JMaS19DI2q7ly7tBuC4xPuffvoewNEAgH37dgMwFyDj9UOq7N49FICZN3rFim9RVbXK1X62bEm2i4sWLUCTJvYBKD//3BtAj8T7hQu/Bn2Npk37HMDIxPvZs788NOY4Hhs3bkJV1VwAwKpV5n7Mfu3bxPbLl7cGkPSXnjLlY+TnZ2bETLdpZj80PWWbJUu6AOgPAJgzZzZ27dqW+K66OtkX5+c3oK4uaYC2abMPV1+9FEVFNYhEzkmMw77++ivU1VkXLtywIVmP165dg6qqbw6Vi/xmHoBNie137RoKUi9mzJiGFSskVsRl2Lz5ZADW8fPOnTtQVeWc62DJkjIAAwAAq1evxiefLAVwTuL76uqVqKpajmXLDgfQFwCwfHmy/hYWAr/5TUf85S99LH0FuWZNmtS47nv9ZK/MSsNQMKRoVq5ciWeeeQbLly8HAPTu3Rvjx4/HkbLLk1P07NkTixYtws6dO/Hee+/hiiuuwPTp09G7d29ce+21ie369OmDjh074owzzsDKlStdHYtwzz334Lbbbku8r62tRZcuXTBixAiUkOXs00xdXR3+8Q9zMBmJABUVFcLtaZeEYcOGWFaG/44a+8diEdC5QU45ZSAGD04dECxYkJyZGDZsqCXl5ZAhgx0X8WvSJIadO5PvBw06FccfD0yZQpQTs6p16dIJFRUd0KSJ2UA0bdqcKttJGDbMQITSnJs3b2q5FrW1ye86dOiQ+O5f/0o2YCefPBBDhljPcdGi5Gt2n0By1W3DiKFrV9Mg69GjOyoqjhCfOMPUqeb5RqMR4T1cuNDcrmPHDol7eeSR3VBR0cWy3SuvmOfVtWsZKipM36r//Cd5r7p06QxgMYYPH458L070HvnsMzk/lmbNOiVel5WZ59O6dQvH+i7L//1fsh4MGzY0kXKV0LRpNBEg3LZtK8txDzssmoiR6dGjKyoqylyX429/S5ajf/8+qKiwLlZO12MR3bqdjIoK97Pajz3mLGedffZIqVgRuh1p0sS6mn1ZGfDkk3Gcd95hiMeBE05owLffJuvEfff1QatWfRLvP/jALFePHr1QUXEU3nvPfH/00eZ7lh9/TO6rpKSZ5b7RbjMdOrRVrktbtiTvxZlnDkOXLkD//nFce23U0qaZsPctgkjEwFtvnYgJE+oT7Rrh9NOH4dAcpBK/+13yvv3iF2dbzv/UU0/BwIHBKVK0u42pHAInn3wStmyZ7KqdicXMfRQXD0NtrelSV15uJM6xVateGDnyKFfKq11ikL59W+Ghh+KHlObk58l6ejyA4y19DU3TpqNw2mn8xBobNlh/c/TRyfraokWzRJl4/ZAqDzyQHKL163cMKiqOdrUful3s37+/sE2ZNcvalp900gmW92ecYQ3tOPXUU3BoKIi2bdsnnr/PD811HH54N1RUJB+C1q2t12/UqLM8uS16gXY1b9WqhNt2bNuWLO8pp5Tj1FOT1+7DD+n+JoI776xHTY1Zx087LR/RqFnPotHksQYOPAmnn269/mT8AQCHHWb2PXl5yeMOHHgiRo5M/ubBB5MXbNiwoTjsMLnzpfnTn1Ird+vWLaXaz507k2U7/PBu+MUvrOOWY445EhUVh2PNmmRdOvZYa/2tqDCX4/nii+Q1GzgQ+PzzmoyPZwi1kr6/ytX3k08+wTnnnIPjjjsOp556KgBg1qxZOOaYY/Dhhx9i+PDU2SERBQUF6N69OwBgwIABmDdvHp599lnu4r4DTd8S/PjjjzjyyCNRWlqKr8iqiYfYeCgi2i6uCgAKCwtRyC7PDSA/Pz/DN4+4HEUcy0EXv6Ag3xKHQYeG0Q+j+bsYN2aDbsgKC/Mt+y8szHcMdGW/J2Uinx88GDm0XR7y8/OoMkYSDUyTJjHLb8xyWa8F/V00au7LfE0fO/Uc2WvCXl8ykKyvjxySvM0BQH6+Wu+eTKYhvodku5qavEQgL+945L7Q52rNEmV+lum627at3HbFxcmGNXnvkufmFfrasM8FYH1uSF0k0LEaBQXq956Gro/Fxan1sYu137GlSxf+8yqLTJwG7zrxoMecY8dGcPXVSHSAgwZFEI2aFz8/H2jf3kxBTWDbkOQyCNbrnJ/Pv+6i55e+p3l56nWJ3jcp54UXAhs2mKnQk/AH3YZhqodz5ljbTXp/qrD1mH6fn++tTqhA3GALCsi9VW9nSPt2xRXJk6AzwT37bBT//GcUzz6rHhNoNwEQjebhwgvzcP75yQB5tp4C9vGLw4fHUFYGbplSFwWOUt8l64gf94l+fouK3LdJdJmdysUaNeTeEwoLrT+27i/5/EUi8UP7y2Oecev+i4ryMxbra41p47cddPvAji3oaxWL5eHMM/ltD21I8a4/XY+jUfM+W+Nsrb+h64Vs+80rE4ts+2m9BlEUFlp31rSpeQ7W65Naf/PzgTPPTL4n7ouZHs8QZMugbEjdfffduPXWW/H444+nfH7XXXcpG1IsDQ0NlvglmkWHZIWOh6Key8vL8eijj2LTpk1o3749ANP1oKSkJOEemE24Tbv91VdAt2784H32YQki/TmQ2jiyabtJpymzIK+o/EEnm6iv95ZsQpQ4glBZCTzxhPl63rzk5ytXyu0vjMkmDuWGceTQYwog+PTnvHtAD3TZ49Irr3udIXVKNuEccGsaW24yBtJsl/AAk63nbOA7CTzmweb5sUuI40fWPj+TTVgHnGr7qalBivrk9vlkM3mmO1MnMXS8tIWA2dbt43g2sottkoVyVRb6BeyfU/r6ieqp6Lx4ZaqsBH7zG+t2JHsZOR7B76x9fiWbcLqXTskh/Mzaxya2SjcySRtE28hmz3NKQmKXtc/uN360B07jJBG85GR0cgxiGKYz22gmUb4Fy5cvx9VXX53y+X/9139h2bJlSvu65557MGPGDPz8889YsmQJ7rnnHkybNg3jxo3DypUr8cgjj2D+/Pn4+eef8e9//xuXX345Bg8ejL59TZ/LESNGoHfv3vjVr36FxYsX45NPPsF9992HG2+8kas4hR3ZhWArK2FxtbvkEuCww5KrtYseQLtBK/sb1QfAzpCyM+ToLD+idaRk0587DaacGjzRosIqOGXtI5naeIrxv/+dvIdsWe0azrCkP5cddNJZiFhj2w+c6i3dLLADEzoNs1dDyin9eTRqznYD9nXFbcZAQjxuLmbqhGzYq0q9Y5tfu+eY1Ad2ooUlXetI0b9XrQMdO4rbKxXYSQY/0h2r4EdmtXicVfTsYRfKlcWu3ZG9RqK6z5aJtN3s5ATt/ul31j4v9ZFGpT93Sk/Oe/7cZu3LlEsfQaYfkp3EkTWknLL28bYLImufKEuwE7zjW9XT1O3CMlYJAuVTa9euXUIZolm0aFFCFZJl06ZNuPzyy9GzZ0+cccYZmDdvHj755BMMHz4cBQUF+PTTTzFixAj06tULt99+O84//3x8+OGHid9Ho1F89NFHiEajKC8vx2WXXYbLL7/csu5UNkGCEUUPBmnM2dWyyexZZWVmFCm7tL92HTIv/TlvHSmn2Syn17z98L6ny8+uhaWCSJEiAws7BQJIHUjwDLMwKlKkHAUFqSmk27cHRpl5SxLuDXQdy5QhJVKkvHoVyKQ/Fy10WFLiPfX5zJnWuCY7Zs2S259Kp+i3IiVrSPmpSMkaD5FIUj0MypBK9+SJkyohw8yZ4sWNWeiFcmWxG4jL1gOnuV9SpmnTnNtuwP+2OSzpz0Xfe1GkMpn6nMBTgGj8VqScxh+8lOfpSn8uuy/e8a3u7Ob/MI5VgkD60Xz44Ydxxx134JprrsG1116Ln376CaecYmYnmjVrFn73u99ZEjjI8DJZfZJDly5dMH36dMd9dOvWzTHDXbbgpEiJBuKGYVbUW25JBnkC8oYU+2DQx5g7F+jaVW2FeVlFiufaJ2poZQwK3vWTde0DkoNPvxUpmYEFGUgQd5Rsce0jZcrPT7qaEt57D/jgA+A//+EbUn52pk7XJiyKFIFd6HDOHDODoJuFQllkV4YXJDhNgbQNfitSmXTtszPS7GaJ6baRHI+oh2y9cft8ihSpdDzzfhhSsvXPy++8KlLbtjlvA5iGlIxRuJtKbuvHfcqEIuXGtU9FkQqjIRWPB9v22KneBPo68EI8RNfQ7cSK0zhJ9re8MYp27bPhoYcewu7du3H//ffjgQcewB//+EcMGTIEQ4YMwf/+7/9iwoQJuO+++4Isa87jpEg5DcTJ7NnXXyc/c+PaN2kS0COZ/TTFdZAlHk/1g7eTy5NB5skys659osGM3YPpp2tfUIaU7ACB3s7JXzosjRN93nTGR8C856TMxJCi3UGCUqR4+yUuB0D6YqSc9kXiOC65JOmy68cAQ3YB3U6dnLch2E2QsASpSIlmzL269okUqUsuSVUPy8qsMTR+K1JOz39Q+HEebhdwVvmdU4yUE7JJcmTheRN4IYyKFO9ZZidG6Nei/YXBkOJNVtCkQ5FyMqRE19BPRcqLax99DjzXvrCMVYJAunk0DLLYYgS33nor1q1bh507d2Lnzp1Yt24dxo8fD7tUoho5nBQp2YH4ZmqJAjeuff/936kGG+06SFNZaRpZ3zHLLX36qfj4PEWKDL5kFSkV1z4V17+gXPtkBwj0dk6KVFj8julyNm2aWkbWkAqDa18YFCm73/lxTQYNSlWGeAweLL9P2fKxx7UbjJFBAxmAZkKRko2R6tMH+PlnYOpUczHeqVPNGDTaBVPUXqnAqrWZdu1zM+AlCVVkoV0kZaEnZNjPZTgUbu1YJuIh4AQ9UeO3IeVX9k6na8OW26lO04pUrrv2idoev1z7eNdNdA3D4trnpEiFZawSBEqnxhpKzZs3R/PmzW221qjipEjJDsTpzO9uFCk710HAGsND4rV4Ktktt6TGa9HHp10BRK59sg2IiiFlJ2knU7Xb78cJkSJFBhai/ZaVWQcSvEYqjIoU3RlFImaMDyESSd73dCpSToZUJrP2Of3Oj2sSjQJHpS7JlHI8lcGMbPloRYoeaLH7YV37Mp1sQqRIkWtF1MOhQ3mpsO3LpkLYkk24OSadUMUJ1kVSBd7zJdsuip5NukxDhzq33YBVjffjPoXRtU9WkWpoiNhuTwiTISUzieOHIuXGtc/tcUU4TdLK/JYuC0+RCuNYJQiUbsFRRx2F1q1bC/807nFSpJwG4mT27NByWwDkXTRkHiA6GFg2cYLd7BY5Hh0Mz0s2IZu1z6lhkXmgSUcVlCIlk6lt3z7gX/9K3V+2uPaRstFGiZMiFVSMFO8eyCpSfq7/4kaR8uuasIk/WFSP48aQEj2PYUg2IRsjJbNvvxSpXEg2AZhqHb0+m93+WBdJFXgGhhsXJVpNYssk03azZclV1z6vilTYXPtUFCnRtfHLtS/dySbc1FneeISXbKKxuPYpPZoPPfQQWtAjJI2vOClSpDG/4AJzG7ugZ1Fgo4wi5URNjXy81po1/OOT49FLhqkmm1AxLmQe6Px805AJSpECkpnarr0W2Lo19ftt26xrl2Sjax9gr0jRLlxakbL/nV/XhN7P3/9uPpfjxxvYvFnc1jjtT8W1T5Ta102yCZHrkZtn1k6RcpM4Ih2KVDoGJH4ZUgDQpIk1ZfiYMcDjjwNHH23Wk48/5mc8lIX3fLmZWR8+HLjtNnrxXmuZSNs9fry172vdOpm0wu9JrkwoUk6GlLwiZb89IRsMKT9c7JyMFfozmWQTftQzso9YTH3MwztvukyNLf250qN58cUXK6c418gjs46UXWNeVmYaUWPHWrNwuYmRcqJjR/l4rT17rO/ZZBM8Q0p2VlhGnXL6HY0frn0yA80xY+zXV6GzL44Zk32KFPmvokhlypDK9RgpwHoNBg82E0vcc0/q8VT3F1ZFyqtrX9gUKS/uN16Q7TdkYMtLL/TbrJl8/JEdvGfVzYCwsNC5LCTL5kknAQsWmJ/97/8Cl15qvvZq1IvKl6kFedlnz6siFVZDyu66+K1IOV3/dLn28RQpv1z7Glv6c+lboBNJBI/MOlKA2ZiLgp5FDZVs+nO7bUgwsGy8FitgsjOstGufF0VKxZAK0rXPSZEC5NW8adOSRvG6dc4B+ZmE7Yzo+04rUmFKf84et1mz5Ovly9UWBmVxq0gFcU3Ycnjp3NwoUiJDyu8Feb2oyAAwY4Z9WWSeO7+y9rH1IN0zu34qUuw9+eYb4IsvzNesO50bvLj20duxmSbtiEaB447j74NOp+63IeXF3dgvRSoa5Q/qVRSpsLn2OWXtk1WkvLj20aimP/dDkVLdF+/49Gc6a58NhigYRuMLMooUQRT0LGqoZF377N6TYGDZeK3u3fnHJ2UkiSaiUbnZVxnjiXf9wqRIyap5F14IfPSR+fqdd5Ip6MM4y0Pq7oEDpgFIGyU8RSodrn28/dopUpWVwPHHJ98/8YQ45b8TbhUpv68JbQzyDCm3ipRK+nORa5+sIiUqs5fzqawEzjkn+X7YsOR998O1z6silQuGFPvbNWuAX//afE3aAy94ce2jr6usIQVY0+Bfe23y9Zw5/H27xS/XPpU+w8mQ4vXJPEXKLv15Y1ek3BhSomvotp7xFCk3hhQ5b7qvaWwxUtLNY0NDg3brCxhZRcoJUacr49r317/KrZfiFHzLxmvRx2d/Q3eGbhQpp45CpiMhZSXuhl5mt0W/lVXz2MUiSQr65cuTn4VBnaqsBC66yHy9c6c5GCUGIBBe1z5St+yyT9ql/FctRyZd++iBKk/xDYsi5WUdKbfnQ+77xo3Wz8l9nzXL/jh2+L2OFC/ZRDoGJH4aUnv32n+3caP7yQqCX8kmZJ/TykrTnY9QW8vfbupUuf2JCMK1z6n+OClIPGNCRZEKqyElmhRmt5X5jsZv1z4/XH3pSTHVCTxefaK9ixpbjFQOn1r2oaJIifCqSJ13nvN6KUAyXos1ugDgxReT2Y54x2cbBruYinS69rGKlJv7IDPQlEmDzoPUj0mTkp9lepaHDEbptcsAa2wcz7UvHYqUkyEVi4mzT/JS/stC7y+TihTPkPJDkXL6nd+KlNNAxGkwxCJz3194gV9mEUFl7Uv3gMSvGKl4nJ9Uh8bN80XjJf25qmsfae/o5Bl2/P733s4L8E+RUqk/omtnGKnPjJ0ilSuGlMgIlTVQVRQpnpIXpGsfbQh7ce1zysCc6bFKkGhDKkSERZEiA18710EaEq81erT184oK8fHZz2UVKbsHU8WQcnLtI+6GQSlSsql0eRiGqfqwx8sEMinwAfN7kSKVzvTndExGNCofrzZzplo5eC51KuzcabpIeh2IObn2hUWRcoqRcrqvqkqezH3ftMn6mRvXPq+KlIy7cxD4pUiRpTJEuHm+aPxSpJwMKdn2jrBxo7fzAsKhSA0fnny9dy/Qo4f1e7otz8YYKS+ufbIGqor7XLqTTQRhSPHGQdqQ0qSFIBQpWUPKS4WPRs24At7+2OORBkVWkZKV0p0GGjLnF/Q6UjR2ap7qUmyZlMudBqOEBQv46c9Jff/5Z3+MBkBdkZKNV5PdjsBTgpyorARuvtl8vXo1LPE6bqHLwVOT0qFIiZ7HTClSqvfT7rgsfilS7CRDuhUpvwypoJ4vGl6f4sZFycmQkm3vaLycF+CfIaXSv7Pf01mAAdP1ld2+sShSoufCb9c+2eP6qUi5eW7YSVIabUhp0k4QipQb1z43nSY7WLQbcHlVpPxw7XNSpIJcR4qGl33xnXfcHS8TyA4StmxJbWwPHAD+9Cfz9cyZ/hgNgJohFY3Kx6vJbkdQVaSIy5BdXJzb68IzTr10brIdrpNrHzuD7SVGiv6d7Pmo3k/ZfadjHalsMqSCer5oyPNFq81uZtadnlM3RpGX8wKs191L1j6Vgbes6xm9vYoiFTZDym5MQvBDGQpzsgk6gYgXRYpHupX0TJHDp5Z9ZDJGyusMB9vI27ltySSbEJXfrSElc37pytpHw7pQDh3qnA2RXu8ok4aU7CChffvUGKnaWmDXLut2Xo0GwPk+s4qUbPbJQYPUykHPzjkNFoKK02LLQUiHIuXk2kf2s3atuVDwjh3i/frt2idz3zt04JdZRFAxUumONfArRmrQIGclxc3zRUP2TzKFAcEoUqpGUWmpt/MCgnHtc7o2btoEkSLF1tewuva5UaTCYEi5HS/yFCm/DSmtSGnSTjYrUmwn5KRIiVz7vCpSXl37vKzXpKJI8RDFT5H3552X+lkmkE2aQRsIJP5MtJ2X4HOnesAqUjLXm6T8V4Euv9P1CSpOiy0Hwcug3I1rH+/affed+f/f/zYXM/3+e/M9nT6axm/XPpn7ftdd/M9FiAY8KuSKIiWj+rp5vmh4ilQQMVKqSYLuuce7oRAG1z6ZffMUqeTEsJGyPcHrwud+4NR2iK6dbFsaxnWk6Lbci2ufNqS0IRUqMhkj5bci5Zdrn2wD4lR+Fdc+0X6cUG2QeNjFT5EU9HaLQaYb2aQZl18O3Hef+ZrO5sfDi9HAlkNGkQKcrzebrVIGlfVxgowj8VuRslOaWUSKVGUl8I9/8H/329/yFUlZQ0rlfJzu+8iR1s/dtAdeDSneeWWTIQWkLspOc8YZ7p4vGp4iJXvdVRQp1SRBbP1xA12+TK0j5YSTIhV21z6ntkN2PCJ6Ruh75/Qs8bL2uTXgRPAMKTeKlOh80j0BlCly+NSyD7sF7FSRVXTsPvfTkPI7/bldA+JUfhVFincsWbwqUgRe/BRJQZ9uNx8RohT4NMR1a/duuf26DdJ2Y0gB4uvtBhVFLcg4Er4iZVCv1fbnRpGityVujCJ4iqRTnVcdCBBE992vNaHcwBqs2apIsb9l29hevdzvl92nV0VKJgbJrr1r186st8OG8fftlkwoUm7aBLEiZb//MBlSMq59IoMmSNc+twaciHS49oVprBIkIRBWNQTi2heUIiVqtIKKkWLPhXQGsopUJpJNiPbjhB+KFIHET9kdAwhH4zR2LDBmjJl578ILUxMm0IgW56RxG6Tt1LHxFuSl3/OutxtUFCniMlRdzY+TikTM793EWzgpUqr1R7Z+211nmcxnRJGk74Xfrn00dvedHbim81nLdIwUezwvx6R/26aNdQFk2vhxC2m3g46RIpD2buZMc8KnY0fz2YxGzVhXgh/3iS5fppJNRCLWdon3XqRIidxdw2RIyShSonPJVIyUV0WKTjahXfvcoRWpEGHX8Khi11CJGi2vSopdjFSQySbsHmbRQIt3bF4ZRNuJ8EuREpFuNx8ZSLyRyIgCnNdgiUS8BZ+7VaT8RsWQCipOC3COkUq3IuXWjTEI1z4n/Eoc4eXYYViQ18+JPXZ5Bz8MKb8UKVlDCrBfZ9HvgaNfipTKM8+Wm53UKitLXReJp0hli2uf3ZiEILp2bhQpp+ufLkMqHYqUNqQ0GSBzipTsAMkOrzFSXpNNqMRIZYNrn4iwKVIEVXc8v40Gdp+8a8MuyBsUqskygojTAjKnSNklm3Drxug0YHEaDLmhMRtSfk7W0GVv1cr6nZ+GlNcYKS+KD29/ftwnv4wOL659Cxemur6yhpRIkcp21z4/DBq/Xfu8KPBsOfyOkbKLBwvLpG8QaNe+EJFJRcrrgxlU+nPRbFauu/Y5HQMIlyGl4o7XoYN5vWk3r7Iy04jyEnzudG3CqEgRRC5DbsmUImWXbIK4MYrc+3iKpN/rSMkQJkMq3QMSP48XpCIVjwPbt5uv6fjLoBUpmf356doXjXrbnxfXvoKCVNdXtj8VKVLZ7trntyIla0iJ7pkfE7Y8RcoP1z67SfEwjVX8RhtSocIfRSoTrn2qipTIkHKjSKkYUrKKVDa49oWpcXKK9aFp1gxYscJfowFQc+0LkyJF8DNOC/B/tXk3ihQ7A33JJcAf/mD/W54imQnXPr8W1/XChg1m7CE905+OZ95P1YAur5+GVGWlmbiEGOWzZye/yxVDiuzD66SPSrnY72Vc5XmKlF2yibAaUl4VKVGd85q1z+4ahkGR0oaUNqRCBRn07Nxpdp5uB5ZeFCm3gwXVGCmRa59sjJSMmx/vM7sH2k5VUyHdilSY5HIS63PBBeY15hlTeXlmZxuJ+G80AM4dTJgVqSAIw4K8tFFZWQk88YT978aN4yuSTmXOJUWKvkaLFwPDhgFt2ya/z+YYKb9c+yorzXbGbsLm22/VyxZGQ4qUz2tbpfLMi1QXAnueKjFSYeu/nPpsGRc70e8BZ0WKbiO3bTPfyxhwXq4fPUZTHbewZWMnDuPxVBU1DPc6KHL41LKL99+P4OmnTwBgzrANGwYcdhh/TRUZeA9aGBQp0iG4VaTsDCKVGKkgXfsasyIF2Mf6tGlj/rfrXP1CxZAKoyLlN0HFSDldO3pQSspAUp+L1MpPP+Vfu8aSbIIYCDt3Wj/fsiX5Ottc+/xWpGTq0UcfyT2DdNn8iJEKSpHyWrZMKFIyySbC0H85TcLIuNix27GIDKnKSnOsR1i82HxPu6ra3RO/Xftk90ef6zffWMu/Y0dy7BrmsYqfaEMqBFRWAhdfHEVtrXVKrLra7FTdGFO8gUWQipRqjJSsIiVqhO0eUr9c+7JBkQpj48Rbm+ell6zbBFVup+uvFan0K1KkDDKpzzdu5C/GnIkYqXSnP5cxEADn7/0gqBgpPxQpmXq0c6fcot5hV6T8cu3zEiPFGzew50nq5J49pjdNPC5nSIVBpXBKVCO6p7LX1W4iiEycsPW5utqavCnMrn0vv8wv/wUXAF98wf9NrhGCaty4sXae1ppGGifeApVO8FzrwqBI2TVamzcnz1GkSMm49jUmRSoMHREPNj0wO0jRilR6cEo2oVpPZQ0p+toSQ8pt6nP2eGF37XNbBhkDAQBmzXK3fxXCHCPlpR6x0GXzw5Dyu20OwrXPqX7KuPbRn/3nP8AvfmG+3rYt6U1DVNRsd+1zM7HLwpsIEk2csJ8F4drHU6TcuPbxIOV/8UX532QzIajGjZtk58mvZYaRXKBSBVVFyuuDqRoj9Z//WD+vquLLwW7Sn8v4dPPQWfuCQ9QB+YmOkbLid9Y+N/WbXAu3qc+BcLj2ye7bbRlkDYQNG9ztX4UwryPlpR6xNBZFSkUFknHto7f5r/8CNm2yfl9dDXz/fYT7+2xz7ZNVpFQNKdmJE96+M61IybQJhmFOkKv8JlvJ4VPLDvycXaNRVaS8zuiqKFKVlWbjy0Lk4EmTrNvz9iv7miBjfOh1pILD7aBUlbAoUrQhRVxdwkI6FCkaci1IVkfRMUtL+YsxyxpSYXDtc1sGPw0ErwQVI+WHa59MPSopkVvUO+wxUplQpFRjpJwUFTpuit1/GAbX6VCkeFn7VMZ0YYuR8uKtk4uEoBo3boLoPOPxZENGzwik07XPLkYKcJazH3wwdT9sOWVf8z4L0rWPsG1bcAPosLlGyJAuRcqpUzSMZJ1ctiyY+1NZaaZ2J3hNHOM3mVKkSFZHwP7+33MPv83IhGuf2zrr9pmUMRDIdkET5hgpmXp01llyEyXZokilM9kEa3Q59aeCowIAvvvOunFYDal0K1IqY7ogXPvoyXa/Xfv8+k22EIJq3LhJdp78COJIhL9ApR0kA8zBg+b7yZOT34keEq+uMbKK1NKlYjnbMID165PvZbP2OTXOMh2JV0WqshK49Vbz9Q8/BDeAzkZFKgyufeTZIMbTPff4f39I8DDr2uclcYxbeClpWVTruBdFCrDP6kgYOZL/uWyyCb8HZioLabJlcXMsJwMhGk3P2jt+xkjR+2rWzNpX0AqxCk71qGdPuf3QasmCBd4nVsLq2udWkbKryyrntmOH+7KkAydDSlaREp0Lrx2RnTgRHTdTipRMGxeJAO3bW9/nKtqQyjB05wlYjSlS8XgLVPKwywBD2LPH/rdeH0zZGKlt29T26yZGKhOKFLn27PkFMYAOW0ckQ6Zd+0TZkfy6PzLBw24Sx7iBTakL+JOS1osiRaCzOnbpwt8/SyZc+9jjBm1IAfYGQrt2qeUJkqBU74ICoGnT5HsvC/LysoMSWHcyHpWVwIknJt9feqn3iRW/r1sQrn0qMVIy7m5OkKUvVPafTpzaND8UKd45iyZOZN9nKkZKtnw335z8LAz3Oihy+NSyh7FjgbffjqNNm/2Wz8vKzE6Vt0Ali0zq3E2b7Adx6VKk6BkKGUSGlIw6Jfs94N6QSvcAOhsNqUy69qXr/jgFD7tNHKOKk9FIJytQed7j8eRkzHffyV8vXuINktWRXmiWfM5D1rXP786aF9vghNcy8AyEt9/2Z9+y+Jlsoq4u+bqw0D9DCkjNDkpwMqTIM0J7PwDeJ1bCqki5de1zY1wkMRvXPn34E8Si/acTL+nPZc/Frs+2mzgpKwO6dbPftx/tHc+QctPG3XADv/zvvWd9JrNlrOKGEFRjDQCcd56B//u/yZgypT7Rea5aJWdEAXIZYOrq7Adx6YqROvFEsZwdiVgfymxINpHuAXQ2xkhlUpFK1/0JKnGMCjJG47ffJi+O7PNOFK6VK833EybIz96LMhjKPnPZpEj5UQbWQCDta7qedz/bmCANKTtEE4pBTqz4bUgFoUipuPbJTGzw9km/txsb8PaTCby49smei+j68yZOVq2yxhMGoUh5ce2jtzvhBH75x471/3kIKwEmANaoEo0CQ4YYrgJLvQ7ignLtYxuXggJTzr7gAvsyPPYYcMUV/N/LGE9OMVJ+u/alewCtFSl7ePU4XfcnDFnXZIzGffuSF0dmIENm79mBJ5m9d1LNRQNSWUPKqc47zSq7Jd2ufXa4iU3zgp8xUrQhlZ+fHkNKVOdUJlboGXUZckGRUo2RevNN4M47repeWZmpClZXi9v+MBlSbtQ3N4oUbzsycaJ6XD9c++hkE24MqUiEX352uzDc66DI4VNrXHgdxPnt2kfgrSM1dqw5c8FC5OBzzrH/vR8xUrKKlGyjku4BtPVcBVOvISKTilS67o9T8HAkopY4xg1ul0mwI2i3SL8VqSBd+zJpSAV1fk7H8+OYtCGVl5ceQ0rk2hfkxEpQhpTXrH0qk28y957+/NxzgcWLk++nTDEViWbN+McL2+DaiyIle7/dTH6KrpMfEys8RcqNa5/f552NhKAaa/xAJgNMQYH9IM5v1z4C+2CSgcl551l/S8vBotlQt659ojLR5ZDZjiXdA+iwuUbIwJZz69Zg0sPzOoR03R+Z4GHZxDFuUTUGnepP0G6RuejaF6Qila7BSFAxUgDQpElyv0Etii0ypIKcWAmra5+K8aKqSEUiVoP4lFPM+kMmWkTucGEYXDsZEXRdmj7d2mf54dpnh6gu+dHekfuzbh2we7f7sonOu7G49mXJMEzjhEzq3COOcPZ5TocixR6nWzdTFua557hJfy4aMIu+Z48lk/mJ/C6dA+iwdUQysPVq5UoEkh6ed4/TeX9EwcOyiWO8IGM0NmliWN6LCNotkh0gek02EaQh5Wa21i/IoKe+Pj0LPAelSAFJRaqoKLj2S9R2Bzmx4vckl1+ufX7HSLH9Ld3/k/tN7kG2uPbxrktlJTB4cPL96adb+yw3BoXsOcsoUm6fn8pK4IknzNdffQUsX26+/vFH9bKJyqANKU3W4bS2hihjntcH024xQ7YB4BlLZWX2v5FthGUaKqcG87bbrJ9deaX8ID+dA+hsNKSmTeN/7nd6eLt6nM77Yxc8HLQRBcgZjQMGJA0pp049aLdIWUUqEhG3UUG5voVBkaqsTCr4e/YEMwHB4meMFFnTkEAbUkEhMqSCnFgJyrUvnYaUqmufnSFlp0iFzZCyi68ksaF0llPA2mdlSpHy4tpHzmvnztTvPvpIrl1x49oXhnsdFDl8ao0TdhB3yy3J70QdA+l49u1zN+OpqkjRMRexmPV4ohkxtzFS9OfsA223BtSWLWqD/HQNoLPNkIrHk7NfLH6nhxcNqNNp4LBZ19K1/g/gbDR27Zr8zKn+BO0WqZIpk6dms7/z+3nIdIwUaZs2bbJ+HvQCz+lSpILCyZsgqImVbHDtU1GkZF378vKSn6koUmHov3h9hmxsKP2934aU6DduJ75llsmR6Yu1ImVFG1I5CD2Io1d4txvMVVaa2wJm7IqbGU/ZNKnRqLnfI45Ifvbpp9bjBRUjxWt8/GpYCOkYQGebITVzJrBxo/33fqaHl3HfzJSBk05ERqPKIDlot0gVQ0pkJOeiIpXJBZ6DNKSKi83/QbopyrhlBzGxEpQi5WeyCaf76UaRApJlJMsd2BlSdvvJFLxJGNnY0BUrkp/5rcyIDFq3E0cyy+TI9MU6RspKCKqxJkic/J3JjOfmzdbPVWc8ZeOOqqrEi4VWVrpTpNwaUmFZRFWFbDOk0pke3quLai5hZzSq1h8/Zu/tBsyyMVL0d2FPNuFXGTLZNgWVbKKyEnj5ZfP1pk3+uinS9WvdOjkDze+JlVxTpGRjpICkIcUqUrIGRqbgTcLI9kW0e1w2uPb51RfLnk/Y3DiDIodPTQOIG8V0zHiyD8+993qTy2Vc+5zcEdw0mEEuoqpKthlS6UwPrw0pZ9zMjqrO3rMDY7sBs0qmTNFaUelw7Ut3solMtk1+xkjRhtQFFwC7dlm/98NNkSwYTfjww+DjyHjkQoyUzOBXpEiluvbZu3uEYXDNaztk+6LWrVP3IzoGexwRovvgtp/zqy+WrefZNlZxSwiqsSZIRA9jOmY82U6YXrTP7nizZtn/3kvWPi8NZpCLqKqSbQGcgwYBpaX233uNs6EJysUrl3Bbf2Rn74nKzcIbMGvXPnsy2TYF5doXxKQdqW8iL4d04XfbTK7NmjXe3CD9TjYho0iRsssqFpkgHk+6na9dm7y+srGhRx+d/EzWxc2Na59fMVIyy+TI9MU6RsqKHm7kOKKZxXTMeLrpTOgsOSLXPhmjivdb+nd+NSzpJNtmeaJR4P77+d/5EWfD2182XJdMQc8Q+32dVFXusCebyKQhlckFnoOMkeLhdtIuk3FkPPwcONJukDNnenODVBnIy2zLO0/yLNfVmdd73z7z/cKFEdvrn8kJL6JiTppkvv/735PXVzY2lFbU0+na53biSGaZHJm+WLZ90IaUJicQufalY8bTzeC4Uyf73/sdI0U3LHZcfHG4khJkmyEFABUV/M/9Tj+uDSlnglQ0VVVuP2Kk4nFg+3bz9cqV/g6aM5m1L5MLPPsZI6WC6qRd2GJc/Ro4EpWNLJRKcKuyuXXtk0kixSpSU6aYBsn27eYX110XszUAM2VIyaiYMrGhQbq4BeHaB4iXybngArm+2M1557KXSA6fmgYQN4rpmPFkHx6Z49EL4AWR/pz9fuxY4I47+L8BzNTd6fa1F5GNhhRbD4YPDyb9uHbtcybI+qOqcrtx7aPLTGaV584137/0kr+xMZleRypTCzz7GSOlguqkXdhiXP0wpIJQ2VTKpeLaR++LGFJ33SXvZpmJ/kvl+jrFhsoaCkElm3B7/djzOvlk8/Pevb2Xzc122Y4ebuQ4IkMqHTOe7G9lj8dzw2N/x3st81DzZrT//nf73wHpdQ9xIhtnedh60LdvMOnHtSLlTJD1R1Xl9uLal47YGDfKjN/XNBMLPAdVR/yetAtbjKsfA8cgVLagkk3Q24oSYtgZgJnov1Svryg2VNZl0s15yhhSXq4ffV7t2vGPY4ebrH253CdnyTBM4xYnmd7PGU/e/ukHLhaTPx4pt6prn4zMzG4TNvcQJ3JBkQpqMU5tSDkTZOemqnKzgy9ReWhDKl2xMZlMf86Ww24wFwRBGtt+TtplMo6Mhx/XLQiVLagYKXrbgwfF++X1o5kwpPy8vm4UKVlEfZnfMaGq+5OtT9qQ0uQEMv7Ofs148mak6GOS1zLHIw+nqiEl81Cz24TNPcSJbDSk2PuoDanMEeQgWVXlptsM2QDnvLz0TX5kMkYqk/gVI8Uasu+846+bYibjyHj4MXAMQmVzm7XPKUaK3pfMIsiAtR/NxLPi5/UNUpESGTd+93OqLvE6RsqKx9UJNGFHxpAi3w0d6u1YsRhw4ID1M7tG2el4doqUXYcgM6Ni1/iEzT3EiWxsnNKlSOkYKWeCNsSJ6jx+vNXYKSszB7X0gFllnSZ6Hal0TX5kOkYqU/gRI1VZadYBmltvBZ56ynQlqqkx29RBg7wZOir1LWj8eJ6IylZdzVdcIxHzexWVzW/XPl5fWlwsVxa6H83Es+Ln9ZW9rl4UKd5v/XDto1FVpLRrnxVtSOU46QwaZhfXZI+pcny7AbFdI68SI8VuE0THFSTZqEhp177wkA5DfOxYYMwYUxESDZjdGFJ5eemb/NCGlLvzIfFrbHtaXQ1cdJFp+Fxyibcy0sjWt6Dxo/0hKtsFF5j7oa+hW5VNZUDr1pCiF6e1+w3bj2biWfHz+qYjRipoRSoeB7ZsMV+vWmW+dzp32frUWAypHGjyNSJkFSk/cIqRctPw+xkjZTeLEzb3ECey0ZDSrn3hIV2dm0xcj4ohRU+upCs2RqV8qtuFGS+GVKbWdkp3HBkPv9ofv7M1unXtU0k2UVBgfwy7fjRT7bRf1zcdMVK83/oVI0Wynk6fbr5/5RW5rKduYqRyoV20I4dPTQOk15CSjZGSIYgYKVHjk6k0w27IRkMq3a592XJdMkGYXEPdKlLpmvzQipT6+WRb8h4/8XMix89sjSoDWpkxA+88ybN8881Ahw7W7e360Uw+K35cXzcGheq+RYqUl+vnJeupG5fGXO6TtWtfjpNpQ4rX0DoRjycDVxcuBAYMsMZH8PatEiNl1/iExT3EiTANhGVJtyKVLdclE4Spc1NJNsG2AemIjQlL1r5043YCDMi+5D1+4neMph+xy4D/MVK8/pa49h9/PHDZZcBJJwFNmx7EBx/kYdiwmKPHSibwen0zlbXPq8HupBpHIqZqPGYM//nXrn1W9HAjx0mnIcWLkaKPK3N8IjXv32++v+46q9Tsh2ufaJswuIc4EaaBsCw6Rio8hMkQd+vaRwh6jaXGmrXPSx3JtuQ9fhLW9sdv1z7ehBXp/+vqgH37zNctWhzEkCGGo7KVrcgaCkElm3B7/byqxtqQsqIVqRwnnT6qolSpMgGMogDlCy4wZ5/p2aMgDKlsIBsNKR0jFR7C5Lfu1rWP/dyPWXvRMQFtSMmSbcl7/CSs7Y/KgNarIlVXB+zebb4uKqoHUGh7rGx/Vty0pdOmyXm6iOqSV+XTq2rsRonL9nstIodPTQNk3rUPsI93opENUKbXquA9pDJ+ytn+QGdj45TuGKlsuS6ZIEyGuB+GVJC4WU8pF+qelzYm25L3+ElYDSmV++k2Roo2pPbsMV8XF9dLlysbkbmulZVm2n/CsGFqCR2CUKS8qsZakbKS5dVY40S2uPbJSs1z5iQ/U1WkciURQZgGwrJo177wECZD3O2CvOnCa4zUtGn+Z6dLB15ipIDsSt7jJ2Ftf1QGtG6z9pFnOVWRskI/DytXZufzQXBSpIiXzbZt1s9VEjoEESPlNeupNqSsZLQbffHFF9G3b1+UlJSgpKQE5eXlmDRpUuL7/fv348Ybb0SbNm3QrFkznH/++di4caNlH2vWrMGoUaPQpEkTtG/fHnfeeSfq68WzII0Jt+nH3eC0Crro+LJSM337tWtf9pxLJGItqzakMkeY6g89+SIykOLx5ODs++/TN/hSjZGqrATmzUu+l519Dht+GNtBx6+FkbC2P34nmxApUvX1tCFlfVBJDDThpZey8/kgiJ4Tr8sAyCSbcPtselWNZeuTNqTSQFlZGR5//HHMnz8fX3/9NU4//XSMGTMG3377LQDg1ltvxYcffoh3330X06dPx/r16zGWaoXj8ThGjRqFgwcP4ssvv8Trr7+O1157DQ888ECmTil0hMG1jxxXlLVPVmouLU2+dmtIZXoW3ithGgirQJdbG1KZI6yKlMg15rDDgGXLzPdPPJG+wZeKIkVmnw8etH4uM/scNvyqI9mQvMdPwupa7HeMlJNrH0+R8pJuO6yIrqtfCR1498AP7xovqrFsbFiY+pogyeipjR49GhUVFejRoweOOuooPProo2jWrBnmzJmDnTt34uWXX8ZTTz2F008/HQMGDMCrr76KL7/8EnMO+XdNnjwZy5Ytw5tvvonjjjsOZ599Nh555BE8//zzOMj2Zo2UMBhSMoqUrNR86qnWz9hjyMRIZfsgO1sbJ/r+6xipzBEmQ9zJkMr04EvWkMrUIrRBka1tTKYJax+jcj9lvFhkk02QGKlcez4IouvqNaGDjCLltZ65VY21a5+V0GTti8fjePfdd7Fnzx6Ul5dj/vz5qKurw5lnnpnYplevXujatStmz56Nk08+GbNnz0afPn3QgVr9beTIkbj++uvx7bff4vjjj+ce68CBAzhw4EDifW1tLQCgrq4OdXV1AZ2hGHJcv48fj0eQvM1x1NU1iDb3RCwWBbHN6fOIRmMAIsjLM1BXZ+92+eSTEVx8cRSRCGAYyacuEjFb2ieeiMMwDABmi93QUI+6OvM7cp6RiP0xIhGzHIYR7HUIGrOzyT/02jzXTNVbNczrDwDRaB2CKHKyvjegri7LeuU0YT5DZISU6euUbJ+iUeuzG48DN98cOzTQsvbC5lonBsaPByoq6gObJMrLywO5VvG4fZ2dPj2Cdevsu1My+zx1aj2GDOGMJkNH8rwjkYbA+qdco6HBvG6ifigTNDQkn7P6evE9NCMjzP7FvPe89sHs6+nzJM/K/v3xQ+nPoygqiqOurg5ffplrz4cJPb6ixyMA0K4dPfayp1076+8IhmFeY964KVnP/Gm/6QnqhgZrUi8eZpucHIPwyg9Y61J9vXOfH7Z2RrYcGTeklixZgvLycuzfvx/NmjXD+++/j969e2PRokUoKChAy5YtLdt36NABGzZsAABs2LDBYkSR78l3dkycOBEPPfRQyueTJ09GkyZNPJ6RN6ZMmeLr/hYsKAUwEADw448rUFX1g6/7p9mx41QAbQEAVVVVic/r6kYCKMLu3TtRVTXd9veFhcBvftMRf/lLH2zdWpz4vE2bfbj66qUoLKzBlClRAL8AACxdugRVVWsAAAsXtgNwCurqDqKq6mPu/vfvPxNAU3z/fbDXIWh2744BGAUAWLBgPvr29b/eBEF9/TmJ19Onf4LiYv8H8N98UwZgAGprd6Kqaobv+88FVq48EsCxAID169ehqmphxsqyeHGyfdq3by+qqj5NfLdkSRtUV59m+1vDiGDdOuCJJ+aiT5+tgZRvzZpjARwJAJg16wvU1NRyt5sxozOAExz3N2nSIuzZU+1jCYNh2bLDAPQDAKxbtwZTpiwGkB3tTCb57jvz2aqvr7f0gZlmwYL2AMoBwLFcq1c3B3A6AGDz5o2oqvoqZZutW8sBtLf0t2vX9gbQA99/vwq7d+cD6IaionpMmTIl554Pwg8/tAQwBAAwd+5s7NqVzCoRjwNt2ozA1q1FYCeCTAy0bbsPtbVTwLslmzadCKATDh48gKqqTyzfff99DwC9sWPHdlRVfeHT2agyBgAwZ85s1NZu425RW5sPoAIA8Pnnn6JFCzlPsbC0M3v37pXaLuOGVM+ePbFo0SLs3LkT7733Hq644gpMn24/2PaDe+65B7fddlvifW1tLbp06YIRI0agpKQk0GPbUVdXhylTpmD48OHIt0t/5wJzJsqkd++eqKjo4du+WZ57LopD4W2oqKhIfN6kSQw7dwKtW5dYPudRUQFMmAB88UU9amrM2KnTTstHNHo8gOMTC/0BQL9+fVBRYQ4ICwrM8ywsLLA9RtOmZnXv1SvY6xA0tdRY7sQTB+DAgU98rzdBQNfFMWNGCmPm3LJzp3mMVq1aONa1xsry5cnZwy5dylBRkbmVUWnluXnzJpZ7Vlsr5wvSrdvJqKgIZhZ7+vSkv86gQaehXz/+dk2bRvDUU877O/vs4zBkiM1OQkR1dfK8DzusC4YPbx9I/5Rr/PCDed0KCmKhan/y881nKRIxHMtFYhEBoLS0A3f7F14w1Uq6v/3yS/Pcu3Y9HDU15vGKiuoxfPhwNG1akFPPB2H+/GQbdeqp5Sgvt7ZDL7wQwcUXA4DB9bJ5/vkCjB7Nvx+vvWZe4+LiwpR7sHSpea1bt26VsXoWiZjndMop5TjlFH77S2crHD78TLRtK95nUONgt9TW8ifOWDJuSBUUFKB79+4AgAEDBmDevHl49tlncdFFF+HgwYPYsWOHRZXauHEjSg9lHCgtLcVXX1lnS0hWv1I6KwFDYWEhCgtTF4nLz8/P+M3zuwz0rgoKosjPDy5Qij4WfQ7EdzgWy0N+vrPDfX4+QHl0WqB9qGOxWOKYBQXkWBHb60d8dPPzg70OQUNX3fz8GA4cCEfdVaG4OJiyEhevbdvyMGtWntTCh42NWCz5EEWjcs9kUNCxcuyz26WL3D66dInZLr3gFdKumK/zbY8zbJjcIrTDhsWyoj7S5xmLRRP3JdvamXRDLo2oH8oEpCiRiHO56Dqfn89vH0gdpvdHnuV4PAoykV9cHEd+fj6GDYvl1PNBsF6r1HbowgvNONDx461xnmVlETzzDDB2rP0QnHeNCWQSMpPtd16eOR4rKLBvf2XbT5awtDOyZQhdGGlDQwMOHDiAAQMGID8/H5999lniuxUrVmDNmjUoLzcl6vLycixZsgSbNm1KbDNlyhSUlJSgd+/eaS97GEln+nOndaT8UCDsghd1+nNNZSVw883m65Urszf1dNCEKZGAaB0pr2ud+IFssolcW4Q2THUkmwhrH6OShEfm3qtm7cu154Mgk73Oa0KHIJNNeEGmDHSs1axZ2ZdMRJaMNpH33HMPZsyYgZ9//hlLlizBPffcg2nTpmHcuHFo0aIFrr76atx2222YOnUq5s+fj6uuugrl5eU4+eSTAQAjRoxA79698atf/QqLFy/GJ598gvvuuw833ngjV3FqjKQza5+XdaRk8cOQyvaBgR7kpOJl4cPGRpgMcVHWvjAMvuh9Oz1rubQIrdcFeRsrYRjg8lBJl62S/pz+3mlB3lx6PgiyfbGbZQBExm8Y6plTGSorgb59k+/HjMndic2MuvZt2rQJl19+OWpqatCiRQv07dsXn3xixnsAwNNPP428vDycf/75OHDgAEaOHIkXXngh8ftoNIqPPvoI119/PcrLy9G0aVNcccUVePjhhzN1SqEjDOnPyXGDNKRkZtz8WHshDIRpIBwGnFLrRiJmat0xY/SgEAiXIe6U/pwMvlJdY3DINSbY8qmsIwWY5RkzxlwbhsR4ZqN7aZjqSDYRhgEuD5VyyRhSsunPaUMKyJ3ngyC7npKXffPuWRiW+RCNp8jEJtsnk4nNbDWc7cioIfXyyy8Lvy8qKsLzzz+P559/3nabbt26hSo7TtgI8kFn8bKOlCx2RgSRkPfuBaZN4zfOYe3kVNGGlBWVhQ+HDk1bsUJLmAbJMgvyZnLwRZdP9lkjs8/ZTJjqSDYR1j5GZRJRJhxA5NpXX0+vI5Xqy5ULzwchyOdE5EEThnpmV77GOLGpm8gcJ1OufdOmJf1hg1KkyANcWQlcdJH5ets2+9iYXHTtC1uHnQm8LnzY2AhT/ZExpAB3rjF+oKpI5QrakHJHGAa4PFT6viAVqVwjyAVnZWKkMvls2pVPZWIzV9BNZI6TLkOqshL44IPke9qYCTJGikjImzdbt+PFxoS1k1MlnSpjNtBRMnu37Ha5Tl6eQb3OYEEgTjYRBhqrIaVjpNwRBpcrHkHFSNHbOsVI5SLpUKTCmmzCrk41xonNkD3uGr9JhyFFjBl27TJizJBG1e+sfYYhlpABU0ImylgYZnH8IBIJR0PqFr/XjwpDdrdsIhsVqUwR9vIFhVak3BHWdlmlXDKufSJFqrY26WofxKLrYSIdMVK8/YYh3tuuTjXGiU3dROY4QRtSTv6wQHLmwe/jf/edmoQchsbHL7L5XOi1JfwgDNndsokwKZphN1QaqyKlDSl3hNWQSociRQyp7duTnxUUaEXKLWF27YvHkxPU8+db05o3xolN3UTmOEGvIyXjD1tX5+/xyQO6c6fc9sSQC2sn54akCwl/RfEwE8Q6e7mYWjcotCIljzakwnlfwkpY+xgVl0O/DKmCAgPLlrXJ2bWDgMwpUvQkNR2Png4qK82wjX37zPfXXGONSW+ME5u6icxxglakVPxc/TakWrWS255IyJmexfETrUil4nbhw8ZGWBWpMHasjdWQ0jFS7girIeXWtc9NsgkysXrwYAT3338auneP5eTaQUCwk1J296yyEnjwQfP14sX2ybWCgIRxsJPnbEx6Y5vYzIEhpUZE0IaUip+rX8cnjVfv3moSclg7OTdkegDshSAUKUKmsrtlE1qRksdN+vNcQCtS7ghrsgmiYNTVOSsYMmMGXl/61VfWYxHWr8/dRdGDnJTiGavEkNmxw7ptOhaelwnjoGPSG9PEZsged43fBG1IyfjDEgXCb0UqFlOTkHPFkIrHkw3XggWRrHOdCEqR0sgRpkEybVRnuiw86DYrjOULijDVkWwijH1MZSUwerT5eu9eZwXDTfrzeBz4y1/42xqGuRE9yM4V0unap2rI+I2btOaNZWJTN5E5TtCGlIw/7BFHmP/9ytZGd1YqEnJYZwtVIP7J+/eb76+/PoZrrx2B998PUc/tgDakMotWpORprK592pByR9gMKaJgbNpk/VykYMjce3aQP3MmsGWLfTlyce0gIL3JJjK9PlNjTGsui24ic5x0pD93Mmbat/f3+GwDIyshh62TU8XOP3nr1iJcfHE0a1wngnTt0zgTpkFy2GOktGtfOO9LWAlTH+NWwXCjSDXWQXY6FalMX+PGmNZcFm1I5TjpMKQAsTFDjhuUIUX27SQhZ3OyCVGnCGSX64RWpDKLVqTkaayKVGN1afRKmAwptwqGmxipxjrITmeyiUxf48aY1lwW3UTmOOmcWbQzZkgZgjSkgvxdGHDuFCOhdp2gDbx9+7LD4MtVwqpIZbosPBqrIRWmOpJNhKmPcatgqLj2kf+DBiU9T+y2z8VBdjoUKfoaZ9KQaYxpzWXRTWSOE4ZUx34rUm5Tf2dzyvBMy/peIHFdhO++S1+6Vk0qYWgTCGE3pMJevqDQhpQ7whSH61bBcOPaF40Cv/kNf9tIxHSjyMVBdjpipOiJ6EwbMo0trbksIXjcNUGSLtc+EaQh8DvZhGrDlc2ufZmW9d0iu+6EJn2EybUv7LE4WpEK530JK2FSpNwqGDKGFO88zzqLv23nzrk7yE6nIgWEw5BpTGnNZfFpaKsJK2EwpNIRI+VEPA7U1pqvv/vOfJ9NAwTSKVZX8+OkIhEDZWWRULlOOAU7RyJmXNeYMdl1L7KdMKkNkYg5wVJfn/my8GishpSOkXJHmAwpomBccIFZHrodFikYMka0aEFewsUXN+CYY77EHXcMRFFRbmYYSqciRRg71uwzZ840PVA6djTHB+nsQ0kYh8ZEN5E5ThgMqUzHSBHXsm+/Nd8/9lj2uZaJZH0gnK4TmU7XquETJkUKSCrVYRywN1ZDKkzGdjYRJkMKcKdguFWkWI+TwYMb0KfP1lD1SX5Dn3/QySZoGsv6TNmCbiJznDAYUplUpHLJtcyuU2zbdh/efjseOmk9m+O6cpmwDZK1IRU+wlZHsoWwGVKAuiuWmxgpIFWRat7cdZGzhiCfk2yO6W5saNe+HCfThlQ8Dmzdar5eu9YflzrZzioXXctYWb9du3rU1k7B6NEVmS5aCtka15XraEVKnsZqUOgYKXeE0ZAC1Fyx3GTtA1INqZISu+U6coeGhuTrL78EKir8nzBuTO1OtqJvUY6TSUOKuNQR163XX/fHpU62s8pV1zJa1h8yxAjtQCfT6Vo1fMJmHBBDKoz1OGxGZ7oIWx3JFsKUtc8tMmMGGUWqpMTfcoWNykrguOOS7885x9+QgbAa5ZpUsvhx18iQqZnFIF3qZDsr7VqWWcKQrlWTStiMg2xRpMJwrdKFTjbhjlwY/LqNkUp17ctdOYqMb9avt37uZ8iAVqSyB32LcpxMKFJOLnWA6VLndlFW2c5Ku5ZlnjCka9VYCZvaEGZDSsdIhfO+hJVcMKRUXPvo79lkE7mqSAU9viHkQl1qLOgmMsfJhCEVtEudbAOjXcvCgV53IlwEmWnKDWE2pBqrIqVjpNxBYmZ27ACmTfM+mM4EOtmEmHSFDGhDKnsIYdel8ZNMGFJBu9TJNjDatSw86HSt4SFsakO2xEiF4Vqli8Z63l6orASuu858vXo1MGxY9i2zAciNGWTSn+eqIZWukAHt2pc96FuU42TCkArapU5lpka7lmk0VsKmsmhFKnzoGCk1SMwMyVBLyMZlNmSMaJ4ixT4fBQX+lisspCtkQCtS2YNuInMcGZneb4J2qVNtYLRrmUaTJGxqA3EJCkNZWBqrIRW2OhJm0hUzky7cJptoLKQrZEArUtmDvkU5TiYUqaBd6tykmNWuZRqNSdgGyVqRCh86RkqeXFtmw22MVGMhXSEDjfkaZxsh7Lo0fpKpDjEol7p4HDh40Hy9aFH2zPJpNGEhbMZBmGOkaJVh5szG096EzdgOM7m2zIbbGCmaoiJ/yxQ20hEy0JhVv2xDN5GNiHQPVPx2qSML/G7ZYr6/+ebsDObVaDJJ2AbJYVWkKiuBiork+7POajztjY6RkicXl9lwcitzGuQXF/tfprARdMiAdu3LHmLOm2iyGZKOFcjMjC9xqfMKCeZl/dBJMK9OHKHRyJGXl3yIwjDbGUZDqrG3N2EztsMMiZmprubHSUUi5vfZtMxGXp6pvrp17WvSJJhyhQ2/xjc8tCKVPegmMsfJtCHlB7kWzKvRhIUwDJLDZkjp9kbHSKmQi8tsaEUq82hFKnvQtyjHyQVDKteCeTWaTKJjpMTo9kYrUqrk2jIbpF2weyZ5ihQ9sVBfn9sTDelAK1LZg24ic5xcMKRyLZhXo8kkYRskh02R0u1N+OpINpBLy2w4ZcZl1RISv0z4+Wege/cYZs/OosCwkKENqexBx0jlOHV1yddffAGcfnr2GVS5GMyr0WSKsCpSYRmw6/ZGJ5twS5AxM+nEya2MVqTs4gnXrwd+97sT0b9/HBdeGFxZcxXt2pc96FuUw1RWAr/4RfL9iBHZmXUqXQvgaTSNgTCpDfE4sH27+Xrt2nC4A+n2RsdINXZkY6QAUTyhudHtt0dD8VxnG1qRyh60IZWjkFmiTZusn5OsU9lkTOViMK9GkynCokgRd6AvvjDfv/lmOCZ6dHsTLmNbk37IPXdaR2rnTnE8IRDBunWRnI4nDAon90pNeNC3KAfJxaxTuRbMq9FkijAMkslEDzsIC8tET2Nvb8JQRzSZQ9a1jw4dEJHL8YRBoRWp7EHHSOUgKlmnssmfe+xYYMwYs9w1NWaMwqBBuT0zrNH4TaYVKaeJnkjEnOgZMyazz3Zjbm90jFTjRta1r7BQbn+5HE8YFNqQyh60IZWD5HLWqVwJ5tVoMkWm1YZsmuhprO2NjpFq3Di5lZHPW7USL0YMGIcWI9bWgCo62UT2oG9RDqKzTmk0GjvoGc5MdNK5PNGTK2Ta2NZkFqd1pOhBvn08oWlZPflkXBvjLtCKVPagm8gcRGed0mg0dmTatU9P9IQfbUg1blTSn9vFE3buDNx11zycdx5XqtI4oBWp7EHfohxEZ53SaDR2ZHqQrCd6wk+m64gms8guyEv+8xYj/uGHepSXa1nZLcRVcs0aYNq07EoO1tjQTWSO0tizTmk0Gj6ZVqT0RE/4aWhIvl6wQA/iGhuyySbo55fEE15yiflfP7/uqawE/vAH8/Xs2cCwYeFYGkLDRxtSOQxvlmjVKm1EaTSNmTCoDXqiJ7yQ9b0IV18NdO8ew+zZ2teyseAUI0W79mn8hSwNUVtr/TwsS0NoUtFZ+3Kcxpp1SqPR8Mm0IkVozOnFwwoZxLEZ2NavB373uxPRv38cF16YmbJp0oesa592+/SXbFkaQmNFPwYajUbTiAiDIkXQ7kDhQTyIM0fOt98e1W5+OU48Dhw8aL5evJjv1qkVqWBQWRpCEx60IaXRaDSNiLAoUppw4TSIAyJYty6iB3E5DHHr3L7dfH/rrfzYHGJsr1+vEyH4iV4aIjvRhpRGo9E0IsKkSGnCgx7ENW6IWydrTLOxOZWVwMMPm68XLtSJEPxELw2RnehuVKPRaBoRtAqlFSkNQQ/iGi9OsTmAGZvz3numUbVjh3UbnQjBH/TSENmJNqQ0Go2mEaEVKQ0Pp0EcYKCszNCDuBxENjbnhhucjS3t5ucevTREdqK7UY1Go2lE6BgpDQ/xIM4cKT/5ZFwP4nIQWXfNzZvtv9OJEPxBLw2Rfej05xqNRtOI0IqUxg4yiBs/3qpQdO4MjBs3D+edd3zmCqcJDD/dNXUMnXf00hDZhTakNBqNphGhDSmNCN4g7uST6/HJJzUAtCGVixC3zupqvuteJAK0bStWpAg6hs4f9Bqg2YPuRjUajaYRoV37NE7o9b0aFzKxOc8/rxMhaDQ8tCIlgWEYqK+vRzzAKMq6ujrEYjHs378/0ONocotcrzfRaBSxWAwRPeL3Da1IaTQaFju3zrIyM8HB2LGmwXXBBabRRCtXOhGCpjGjDSkHDh48iJqaGuzduzfQ4xiGgdLSUqxdu1YPGjXSNIZ606RJE3Ts2BEFBQWZLkpOoBUpjUbDwyk2R8bY0mgaG9qQEtDQ0IBVq1YhGo2iU6dOKCgoCGyw2tDQgN27d6NZs2bI09PEGklyud4YhoGDBw9i8+bNWLVqFXr06JFz55gJ6CZMX06NRkPjFJujEyFoNFa0ISXg4MGDaGhoQJcuXdCkSZNAj9XQ0ICDBw+iqKhIDxY10uR6vSkuLkZ+fj5Wr16dOE+NdyIRA4YR0YqURqNRRidC0GiSZHTkNXHiRJx44olo3rw52rdvj3PPPRcrVqywbDN06FBEIhHL33XXXWfZZs2aNRg1ahSaNGmC9u3b484770R9fb1v5czFAapGky3o589/yLpA+tJqNBqNRuOejCpS06dPx4033ogTTzwR9fX1uPfeezFixAgsW7YMTZs2TWx3zTXX4OGHH068p9WheDyOUaNGobS0FF9++SVqampw+eWXIz8/H4899lhaz0ej0Wiygbw8oKFBx0hpNBqNRuOFjBpSH3/8seX9a6+9hvbt22P+/PkYPHhw4vMmTZqgtLSUu4/Jkydj2bJl+PTTT9GhQwccd9xxeOSRR3DXXXdhwoQJOkBdo9FoGLQipdFoNBqNd0IVI7Vz504AQOvWrS2fv/XWW3jzzTdRWlqK0aNH4/7770+oUrNnz0afPn3QoUOHxPYjR47E9ddfj2+//RbHH5+6gOCBAwdw4MCBxPva2loAZirpurq6xOd1dXUwDAMNDQ1oaGjwdG7xuDg40ziUS5QcLxuJRqP45z//iXPPPTfTRQmE008/Hf369cPTTz+d6aIk8FpvXnvtNdx2223Ytm2b30VLcMQRR2D8+PEYP368q983NDTAMAzU1dUhqiOaPVNXV4dIxJxgisfrUVfHWYFTo6Eg/SLdP2o0InSd0agStjojW47QGFINDQ245ZZbcOqpp+LYY49NfH7ppZeiW7du6NSpE7755hvcddddWLFiBSorKwEAGzZssBhRABLvN2zYwD3WxIkT8dBDD6V8PnnyZIvbYCwWQ2lpKXbv3o2DBw+6PrcPP8zH3XcXY/365PRvp04NePzxfRg92nqjdu3a5fo4hFatWgm/v+uuu3D33Xdzv1uzZg369euHGTNmoE+fPsrH3rdvX8IwZbnhhhvw97//HYB5bTt16oQxY8bg3nvvzYokAvX19Th48KDt+aWbvn37Yu3atSmfP/DAA7j11lul9nH22Wdj0KBBgZ5TQ0MD9u/f7/oYBw8exL59+zBjxgxfYx8bM3l5owAAc+Z8ie3bt2e4NJpsYcqUKZkugibL0HVGo0pY6ozsskehMaRuvPFGLF26FF988YXl82uvvTbxuk+fPujYsSPOOOMMrFy5EkceeaSrY91zzz247bbbEu9ra2vRpUsXjBgxAiUlJYnP9+/fj7Vr16JZs2auB/qVlcAVV0Qsi9cBQE1NBFdc0QTvvGNg7FhTUdi1axeaN2/uOcV6dXV14vU777yDBx98EMuXL0981qxZMzRr1oz7W/J506ZNLddCluLiYtvf5efnY+TIkXjllVdQV1eH+fPn46qrrkJRUREef/xx5WMFgWEYiMfjiMVSH41YLIaCggJX18ULdXV1yM/PT/k8Ly8PEyZMwMUXX4xmzZol6k3z5s0tMYYiSkpKUiYi/CYvLw9FRUWur9v+/ftRXFyMwYMHZ4XBHXZMRcpskE499RScdJJWpDRi6urqMGXKFAwfPpzbFmk0LLrOaFQJW52Rnvw1QsCNN95olJWVGT/99JPjtrt37zYAGB9//LFhGIZx//33G/369bNs89NPPxkAjAULFkgdf+fOnQYAY+fOnZbP9+3bZyxbtszYt29f4rOGBsPYvVvub+dOw+jc2TDMNcBT/yIRwygrM7errY0b69ZtN2pr47b7a2iQOh0Lr776qtGiRYvE+3g8bjz00ENG586djYKCAqNfv37GpEmTEt8DsPwNGTLEMAzD+Oqrr4wzzzzTaNOmjVFSUmIMHjzYmD9/vuVYAIz333/ftixXXHGFMWbMGMtnY8eONY4//nhL+R577DHjsMMOM4qKioy+ffsa7777buL7AQMGGH/4wx8S78eMGWPEYjFj165dhmEYxtq1aw0Axg8//GAYhmG88cYbxoABA4xmzZoZHTp0MC655BJj48aNid9PnTrVAGBUVVUZ/fv3N/Lz842pU6cau3fvNn71q18ZTZs2NUpLS40nnnjCGDJkiDF+/Hjb83vwwQeNfv36GS+99JJRVlZmFBcXG7/85S+NHTt2WLb785//bPTq1csoLCw0evbsaTz//POJ71atWmUAMN5++21j8ODBRmFhofHqq69yj9etWzfjqaeeMrZv327E4/GU78m5ffTRR0afPn2MwsJCY+DAgcaSJUsS27D1Y9GiRcbQoUONZs2aGc2bNzf69+9vzJs3L/H9e++9Z/Tu3dsoKCgwunXrZjzxxBOWY27cuNH4xS9+YRQVFRmHHXaY8eabbxrdunUznn766cQ227dvN66++mqjbdu2RvPmzY1hw4YZixYtsr2uvOdQ4559+w4ahYV1BmAYL75oGPX1mS6RJuwcPHjQ+OCDD4yDBw9muiiaLEHXGY0qYaszdrYBS0ZDjQ3DwE033YT3338fn3/+OQ4//HDH3yxatAgA0LFjRwBAeXk5lixZgk2bNiW2mTJlCkpKStC7d2/fy7x3L9CsmdxfixYAJQ6lYBjm6uAtWgAlJXkoK2uJkpI82/1JqoxCnn32WTz55JN44okn8M0332DkyJE455xz8MMPPwAAvvrqKwDAp59+ipqamoQL5a5du3DFFVfgiy++wJw5c9CjRw9UVFR4ckVcunQpvvzyS0tCkIkTJ+KNN97ASy+9hG+//Ra33norLrvsMkyfPh0AMGTIEEybNg2AWX9mzpyJli1bJpTM6dOno3PnzujevTsAc4bjkUceweLFi/HBBx/g559/xpVXXplSlrvvvhuPP/44li9fjr59++LOO+/E9OnT8a9//QuTJ0/GtGnTsGDBAsdz+vHHH/HOO+/gww8/xMcff4yFCxfihhtuSHz/1ltv4YEHHsCjjz6K5cuX47HHHsP999+P119/PaU848ePx/LlyzFy5Eil68py55134sknn8S8efPQrl07jB492tb3d9y4cSgrK8O8efMwf/583H333YmZofnz5+PCCy/ExRdfjCVLlmDChAm4//778dprryV+f+WVV2Lt2rWYOnUq3nvvPbzwwguWZxMAfvnLX2LTpk2YNGkS5s+fj/79++OMM84INE5LY1JZCXTvHsOBA6biev31wGGHmZ9rNBqNRqNRJC1mnQ3XX3+90aJFC2PatGlGTU1N4m/v3r2GYRjGjz/+aDz88MPG119/baxatcr417/+ZRxxxBHG4MGDE/uor683jj32WGPEiBHGokWLjI8//tho166dcc8990iXQ0WR2r3bXmEK+m/3bvVrzCoOnTp1Mh599FHLNieeeKJxww03GIaRVEQWLlwo3G88HjeaN29ufPjhh4nPIKFIRaNRo2nTpkZhYaEBwMjLyzPee+89wzAMY//+/UaTJk2ML7/80vK7q6++2rjkkksMwzCMf//730aLFi2M+vp6Y9GiRUZpaakxfvx446677jIMwzD++7//27j00kttyzBv3jwDQELBIqrNBx98kNhm165dRkFBgfHOO+8kPtu6datRXFzsqEhFo1Fj3bp1ic8mTZpk5OXlGTU1NYZhGMaRRx5p/O1vf7P87pFHHjHKy8sNw0he/2eeecb2OIRu3boZBQUFRtOmTS1/M2bMsJzb22+/nXIe//jHPwzDSK0fzZs3N1577TXu8S699FJj+PDhls/uvPNOo3fv3oZhGMaKFSsMAMZXX32V+H758uUGgIQiNXPmTKOkpMTYv3+/ZT9HHnmk8ac//Yl7XK1I+cM//2mq4EBDijIeiZjfazQ8wjZTrAk/us5oVAlbnZFVpDIaI/Xiiy8CMBfdpXn11Vdx5ZVXoqCgAJ9++imeeeYZ7NmzB126dMH555+P++67L7FtNBrFRx99hOuvvx7l5eVo2rQprrjiCsu6U37SpAmwe7fctjNmABUVzttVVQGnndaA2tpalJSU2C5ASuXBcEVtbS3Wr1+PU0891fL5qaeeisWLFwt/u3HjRtx3332YNm0aNm3ahHg8jr1792LNmjVKZRg2bBhefPFF7NmzB08//TRisRjOP/98AKaas3fvXgwfPtzym4MHDyayLw4aNAi7du3CwoUL8eWXX2LIkCEYOnRoIsZq+vTpuPPOOxO/nT9/PiZMmIDFixdj+/bticx2a9assSiWJ5xwQuL1ypUrcfDgQQwcODDxWevWrdGzZ0/H8+vatSs6d+6ceF9eXo6GhgasWLECzZs3x8qVK3H11VfjmmuuSWxTX1+PFi1aWPZDl0fEHXfcgfPPPx/NmjVL1Bv6+KQM7HnQMXM0t912G/77v/8bf/3rX3HmmWfil7/8ZSIWcfny5RgzZoxl+1NPPRXPPPMM4vE4li9fjlgshgEDBiS+79WrF1q2bJl4v3jxYuzevRtt2rSx7Gffvn1YuXKl1Dlr1InHgfHjcShW0xqDaRjmelK33AKMGWPNJqrRaDQajcaejBpSBpuBgaFLly4Jly4R3bp1Q1VVlV/FEhKJAJJx/BgxAigrM937eKcaiZjfjxhhvo7HzX2HcW2XK664Alu3bsWzzz6Lbt26obCwEOXl5crZDJs2bZpwu3vllVfQr18/vPzyy7j66qux+5CF+p///CfFGCgsLAQAtGzZEv369cO0adMwe/ZsDB8+HIMHD8ZFF12E77//Hj/88AOGDBkCANizZw9GjhyJkSNH4q233kK7du2wZs0ajBw5MqXcsskZvEDO789//rPFSAOQktZbtjxt27bFEUccITTAVZgwYQIuvfRS/Oc//8GkSZPw4IMP4u2338Z5553ned+AeQ06duyYcM+koQ0ujb/MnGm6EdthGMDateZ2zLyWRqPRaDQaG0I4ZM8dolHg2WfN12wiPvL+mWfSNwNcUlKCTp06YdasWZbPZ82alVBnSLxSPB5P2ebmm29GRUUFjjnmGBQWFmLLli2eypOXl4d7770X9913H/bt24fevXujsLAQa9asQffu3S1/Xbp0SfxuyJAhmDp1KmbMmIGhQ4eidevWOProo/Hoo4+iY8eOOOqoowAA3333HbZu3YrHH38cgwYNQq9evVLidXgceeSRyM/Px9y5cxOfbd++Hd9//73jb9esWYP169cn3s+ZMwd5eXno2bMnOnTogE6dOuGnn35KOT+Z+EC3zJkzJ/GanMfRRx9tu/1RRx2FW2+9FZMnT8bYsWPx6quvAgCOPvpobt056qijEI1G0atXL9TX12P+/PmJ71esWIEdO3Yk3vfv3x8bNmxALBZLuQZt27b16Yw1LDU1/m6n0Wg0Go1GG1KBM3Ys8N57ACOwoKzM/Hzs2PSW584778Tvfvc7/OMf/8CKFStw9913Y9GiRYnFUtu3b4/i4mJ8/PHH2LhxY2KR5B49euCvf/0rli9fjrlz52LcuHEoLi72XJ5f/vKXiEajeP7559G8eXPccccduPXWW/H6669j5cqVWLBgAf74xz9akjEMHToUn3zyCWKxGHr16pX47K233kqoUYDpZldQUIA//vGP+Omnn/Dvf/8bjzzyiGOZmjVrhquvvhp33nknPv/8cyxduhRXXnmllOJTVFSEK664AosXL8bMmTNx880348ILL0RpaSkA4KGHHsLEiRPx3HPP4fvvv8eSJUvw6quv4qmnnlK9dADMJCAbN27Ehg0bEn9sys6HH34Yn332WeI82rZty100ed++fbjpppswbdo0rF69GrNmzcK8efMSRtftt9+Ozz77DI888gi+//57vP766/jf//1f3HHHHQCAnj174qyzzsKvf/1rzJ07F/Pnz8d///d/W+rJmWeeifLycpx77rmYPHkyfv75Z3z55Zf4f//v/+Hrr792dQ00zhzKzePbdhqNRqPRaBCO9OeZRiXZhFvq6w1j6lTD+NvfzP9syuF4PG6bxtoLvPTnEyZMMDp37mzk5+enpD83DDM9d5cuXYy8vLxE+vMFCxYYJ5xwglFUVGT06NHDePfdd1PSWsNF+nPDMIyJEyca7dq1M3bv3m00NDQYzzzzjNGzZ08jPz/faNeunTFy5Ehj+vTpie23bt1qRCIR46KLLkp89v777xsAjJdeesmy77/97W/GYYcdZhQWFhrl5eXGv//9b0syDZKQYfv27Zbf7dq1y7jsssuMJk2aGB06dDB+//vfS6c/f+GFF4xOnToZRUVFxgUXXGBs27bNst1bb71lHHfccUZBQYHRqlUrY/DgwUZlZaVhGPLJPgzDTDYBJl09AOPXv/615dw+/PBD45hjjjEKCgqMk046yVi8eHFiH3T9OHDggHHxxRcbXbp0MQoKCoxOnToZN910k6X+k/Tn+fn5RteuXS2p6A3DMGpqaoxRo0YZhYWFRteuXY033ngjpZ7U1tYa//M//2N06tTJyM/PN7p06WKMGzfOWLNmDfc8dbIJ79TXm0stmMkm+EsxdOmiU6Fr+IQtCFwTfnSd0agStjojm2wiYhgOgUqNgNraWrRo0QI7d+5MWZB31apVOPzwwwNfCLShwTnZhCbcTJgwAR988EEiRX86ENWbadOmYdiwYdi+fXtWxx+l8znMZSorgQsuAAADhpH0NSZuxplQyDXZQV1dHaqqqlBRURGKhTI14UfXGY0qYaszdrYBix6xazQaTSOAuBl36mT9PFNuxhqNRqPRZDsZzdqn0Wg0mvQxdixQUVGPJ56Yi27dTkaXLjEMGqRTnms0Go1G4watSGk0PjFhwoS0uvU5MXToUBiGkdVufRr/iUaBPn224uKLDQwdqo0ojUaj0Wjcog0pjUaj0Wg0Go1Go1FEG1IS6HwcGk3m0M+fRqPRaDSaMKINKQEka8jevXszXBKNpvFCnr8wZPHRaDQajUajIehkEwKi0ShatmyJTZs2AQCaNGmCSCTi8Ct3NDQ04ODBg9i/f79Of66RJpfrjWEY2Lt3LzZt2oSWLVsiqoN5NBqNRqPRhAhtSDlQWloKAAljKigMw8C+fftQXFwcmLGmyT0aQ71p2bJl4jnUaDQajUajCQvakHIgEomgY8eOaN++Perq6gI7Tl1dHWbMmIHBgwdrFyaNNLleb/Lz87USpdFoNBqNJpRoQ0qSaDQa6IAuGo2ivr4eRUVFOTkg1gSDrjcajUaj0Wg0mSG3gio0Go1Go9FoNBqNJg1oQ0qj0Wg0Go1Go9FoFNGGlEaj0Wg0Go1Go9EoomOkkFzws7a2NmNlqKurw969e1FbW6tjXTTS6HqjUUXXGY0qus5oVNF1RqNK2OoMsQmIjWCHNqQA7Nq1CwDQpUuXDJdEo9FoNBqNRqPRhIFdu3ahRYsWtt9HDCdTqxHQ0NCA9evXo3nz5hlbi6e2thZdunTB2rVrUVJSkpEyaLIPXW80qug6o1FF1xmNKrrOaFQJW50xDAO7du1Cp06dkJdnHwmlFSkAeXl5KCsry3QxAAAlJSWhqECa7ELXG40qus5oVNF1RqOKrjMaVcJUZ0RKFEEnm9BoNBqNRqPRaDQaRbQhpdFoNBqNRqPRaDSKaEMqJBQWFuLBBx9EYWFhpouiySJ0vdGoouuMRhVdZzSq6DqjUSVb64xONqHRaDQajUaj0Wg0imhFSqPRaDQajUaj0WgU0YaURqPRaDQajUaj0SiiDSmNRqPRaDQajUajUUQbUhqNRqPRaDQajUajiDakQsLzzz+Pww47DEVFRRg4cCC++uqrTBdJkyFmzJiB0aNHo1OnTohEIvjggw8s3xuGgQceeAAdO3ZEcXExzjzzTPzwww+WbbZt24Zx48ahpKQELVu2xNVXX43du3en8Sw06WTixIk48cQT0bx5c7Rv3x7nnnsuVqxYYdlm//79uPHGG9GmTRs0a9YM559/PjZu3GjZZs2aNRg1ahSaNGmC9u3b484770R9fX06T0WTJl588UX07ds3sfhleXk5Jk2alPhe1xeNE48//jgikQhuueWWxGe63mhoJkyYgEgkYvnr1atX4vtcqC/akAoB//jHP3DbbbfhwQcfxIIFC9CvXz+MHDkSmzZtynTRNBlgz5496NevH55//nnu97///e/x3HPP4aWXXsLcuXPRtGlTjBw5Evv3709sM27cOHz77beYMmUKPvroI8yYMQPXXnttuk5Bk2amT5+OG2+8EXPmzMGUKVNQV1eHESNGYM+ePYltbr31Vnz44Yd49913MX36dKxfvx5jx45NfB+PxzFq1CgcPHgQX375JV5//XW89tpreOCBBzJxSpqAKSsrw+OPP4758+fj66+/xumnn44xY8bg22+/BaDri0bMvHnz8Kc//Ql9+/a1fK7rjYblmGOOQU1NTeLviy++SHyXE/XF0GSck046ybjxxhsT7+PxuNGpUydj4sSJGSyVJgwAMN5///3E+4aGBqO0tNT4wx/+kPhsx44dRmFhofH3v//dMAzDWLZsmQHAmDdvXmKbSZMmGZFIxKiurk5b2TWZY9OmTQYAY/r06YZhmHUkPz/fePfddxPbLF++3ABgzJ492zAMw6iqqjLy8vKMDRs2JLZ58cUXjZKSEuPAgQPpPQFNRmjVqpXxl7/8RdcXjZBdu3YZPXr0MKZMmWIMGTLEGD9+vGEYup3RpPLggw8a/fr1436XK/VFK1IZ5uDBg5g/fz7OPPPMxGd5eXk488wzMXv27AyWTBNGVq1ahQ0bNljqS4sWLTBw4MBEfZk9ezZatmyJE044IbHNmWeeiby8PMydOzftZdakn507dwIAWrduDQCYP38+6urqLPWmV69e6Nq1q6Xe9OnTBx06dEhsM3LkSNTW1iZUCk1uEo/H8fbbb2PPnj0oLy/X9UUj5MYbb8SoUaMs9QPQ7YyGzw8//IBOnTrhiCOOwLhx47BmzRoAuVNfYpkuQGNny5YtiMfjlkoCAB06dMB3332XoVJpwsqGDRsAgFtfyHcbNmxA+/btLd/HYjG0bt06sY0md2loaMAtt9yCU089FcceeywAs04UFBSgZcuWlm3ZesOrV+Q7Te6xZMkSlJeXY//+/WjWrBnef/999O7dG4sWLdL1RcPl7bffxoIFCzBv3ryU73Q7o2EZOHAgXnvtNfTs2RM1NTV46KGHMGjQICxdujRn6os2pDQajSaHuPHGG7F06VKLH7pGw6Nnz55YtGgRdu7ciffeew9XXHEFpk+fnuliaULK2rVrMX78eEyZMgVFRUWZLo4mCzj77LMTr/v27YuBAweiW7dueOedd1BcXJzBkvmHdu3LMG3btkU0Gk3JUrJx40aUlpZmqFSasELqhKi+lJaWpiQqqa+vx7Zt23SdynFuuukmfPTRR5g6dSrKysoSn5eWluLgwYPYsWOHZXu23vDqFflOk3sUFBSge/fuGDBgACZOnIh+/frh2Wef1fVFw2X+/PnYtGkT+vfvj1gshlgshunTp+O5555DLBZDhw4ddL3RCGnZsiWOOuoo/PjjjznTzmhDKsMUFBRgwIAB+OyzzxKfNTQ04LPPPkN5eXkGS6YJI4cffjhKS0st9aW2thZz585N1Jfy8nLs2LED8+fPT2zz+eefo6GhAQMHDkx7mTXBYxgGbrrpJrz//vv4/PPPcfjhh1u+HzBgAPLz8y31ZsWKFVizZo2l3ixZssRihE+ZMgUlJSXo3bt3ek5Ek1EaGhpw4MABXV80XM444wwsWbIEixYtSvydcMIJGDduXOK1rjcaEbt378bKlSvRsWPH3GlnMp3tQmMYb7/9tlFYWGi89tprxrJly4xrr73WaNmypSVLiabxsGvXLmPhwoXGwoULDQDGU089ZSxcuNBYvXq1YRiG8fjjjxstW7Y0/vWvfxnffPONMWbMGOPwww839u3bl9jHWWedZRx//PHG3LlzjS+++MLo0aOHcckll2TqlDQBc/311xstWrQwpk2bZtTU1CT+9u7dm9jmuuuuM7p27Wp8/vnnxtdff22Ul5cb5eXlie/r6+uNY4891hgxYoSxaNEi4+OPPzbatWtn3HPPPZk4JU3A3H333cb06dONVatWGd98841x9913G5FIxJg8ebJhGLq+aOSgs/YZhq43Giu33367MW3aNGPVqlXGrFmzjDPPPNNo27atsWnTJsMwcqO+aEMqJPzxj380unbtahQUFBgnnXSSMWfOnEwXSZMhpk6dagBI+bviiisMwzBToN9///1Ghw4djMLCQuOMM84wVqxYYdnH1q1bjUsuucRo1qyZUVJSYlx11VXGrl27MnA2mnTAqy8AjFdffTWxzb59+4wbbrjBaNWqldGkSRPjvPPOM2pqaiz7+fnnn42zzz7bKC4uNtq2bWvcfvvtRl1dXZrPRpMO/uu//svo1q2bUVBQYLRr184444wzEkaUYej6opGDNaR0vdHQXHTRRUbHjh2NgoICo3PnzsZFF11k/Pjjj4nvc6G+RAzDMDKjhWk0Go1Go9FoNBpNdqJjpDQajUaj0Wg0Go1GEW1IaTQajUaj0Wg0Go0i2pDSaDQajUaj0Wg0GkW0IaXRaDQajUaj0Wg0imhDSqPRaDQajUaj0WgU0YaURqPRaDQajUaj0SiiDSmNRqPRaDQajUajUUQbUhqNRqPRaDQajUajiDakNBqNRpPz/Pzzz4hEIli0aFFgx7jyyitx7rnnBrZ/jUaj0YQLbUhpNBqNJvRceeWViEQiKX9nnXWW1O+7dOmCmpoaHHvssQGXVKPRaDSNhVimC6DRaDQajQxnnXUWXn31VctnhYWFUr+NRqMoLS0NolgajUajaaRoRUqj0Wg0WUFhYSFKS0stf61atQIARCIRvPjiizj77LNRXFyMI444Au+9917it6xr3/bt2zFu3Di0a9cOxcXF6NGjh8VIW7JkCU4//XQUFxejTZs2uPbaa7F79+7E9/F4HLfddhtatmyJNm3a4De/+Q0Mw7CUt6GhARMnTsThhx+O4uJi9OvXz1ImjUaj0WQ32pDSaDQaTU5w//334/zzz8fixYsxbtw4XHzxxVi+fLnttsuWLcOkSZOwfPlyvPjii2jbti0AYM+ePRg5ciRatWqFefPm4d1338Wnn36Km266KfH7J598Eq+99hpeeeUVfPHFF9i2bRvef/99yzEmTpyIN954Ay+99BK+/fZb3Hrrrbjsssswffr04C6CRqPRaNJGxGCn0DQajUajCRlXXnkl3nzzTRQVFVk+v/fee3HvvfciEonguuuuw4svvpj47uSTT0b//v3xwgsv4Oeff8bhhx+OhQsX4rjjjsM555yDtm3b4pVXXkk51p///GfcddddWLt2LZo2bQoAqKqqwujRo7H+/7dz7yCNbHEcx3+G4BMfBEKIYiFEYmIhmoj4KkQU7CIpRQbERkVCUAstfGChhQRrwc4XWGihomgrqKBgpYKIWmwEG1MYiIRwqx1uuLtwA+66uff7gYE5c878kzndj5lzvn2Tw+FQeXm5wuGwJiYmJEnJZFJVVVXy+Xza3d1VIpGQzWbTycmJmpubzdqDg4OKx+Pa2Nj4FdMEAPiNWCMFAMgKHR0daUFJkmw2m3n+98Dyvf2zXfqGhoYUDAZ1dXWl7u5uBQIBtbS0SJJubm5UV1dnhihJam1tVSqV0t3dnfLz8xWNRtXU1GT2W61W+f1+8/O++/t7xeNxdXV1pf3ux8eH6uvrM394AMAfhyAFAMgKRUVFcrlcn1Krp6dHT09POjg40PHxsTo7OzUyMqKlpaVPqf99PdX+/r4qKirS+v7tBhkAgD8ba6QAAP8JZ2dn/2h7PJ6fjrfb7TIMQ2tra1peXtbKyookyePx6Pr6Wu/v7+bY09NTWSwWud1ulZaWyul06vz83OxPJpO6vLw0216vV3l5eXp+fpbL5Uo7KisrP+uRAQBfiDdSAICskEgk9PLyknbNarWam0Rsb2/L7/erra1N6+vruri40Orq6g9rTU9Py+fzqba2VolEQnt7e2bo6uvr08zMjAzD0OzsrF5fXzU6Oqr+/n45HA5JUigU0uLioqqrq1VTU6NIJKK3tzezfnFxscbHxxUOh5VKpdTW1qZYLKbT01OVlJTIMIxfMEMAgN+JIAUAyAqHh4dyOp1p19xut25vbyVJc3Nz2tra0vDwsJxOpzY3N+X1en9YKzc3V5OTk3p8fFRBQYHa29u1tbUlSSosLNTR0ZFCoZAaGxtVWFioYDCoSCRi3j82NqZoNCrDMGSxWDQwMKDe3l7FYjFzzPz8vOx2uxYWFvTw8KCysjI1NDRoamrqs6cGAPAF2LUPAJD1cnJytLOzo0Ag8NV/BQDwP8EaKQAAAADIEEEKAAAAADLEGikAQNbjK3UAwO/GGykAAAAAyBBBCgAAAAAyRJACAAAAgAwRpAAAAAAgQwQpAAAAAMgQQQoAAAAAMkSQAgAAAIAMEaQAAAAAIEN/AdavI4EDsp1/AAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>episode</td><td>▁▁▁▂▂▂▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇▇▇██</td></tr><tr><td>total_reward</td><td>█▅█▇█▆▆██▃█▄▇▅▂▃▆▅█▆▅▃▄▂▆▂▃██▁▆█▆▆██▄█▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>episode</td><td>500</td></tr><tr><td>total_reward</td><td>414</td></tr></table><br/></div></div>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              " View run <strong style=\"color:#cdcd00\">major-oath-3</strong> at: <a href='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/wb_sb3/runs/h0conaa8' target=\"_blank\">https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/wb_sb3/runs/h0conaa8</a><br> View project at: <a href='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/wb_sb3' target=\"_blank\">https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/wb_sb3</a><br>Synced 5 W&B file(s), 1 media file(s), 0 artifact file(s) and 0 other file(s)"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "Find logs at: <code>./wandb/run-20250210_220810-h0conaa8/logs</code>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import gymnasium as gym\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from stable_baselines3 import A2C\n",
+        "from stable_baselines3.common.monitor import Monitor\n",
+        "from stable_baselines3.common.vec_env import DummyVecEnv\n",
+        "import wandb\n",
+        "\n",
+        "# Initialize W&B for experiment tracking\n",
+        "wandb.init(project=\"wb_sb3\", config={\"learning_rate\": 0.001, \"total_timesteps\": 100000}, sync_tensorboard=True)\n",
+        "\n",
+        "# Create and wrap the CartPole environment\n",
+        "env = gym.make(\"CartPole-v1\")\n",
+        "env = Monitor(env)\n",
+        "env = DummyVecEnv([lambda: env])\n",
+        "\n",
+        "# Initialize the A2C model\n",
+        "model = A2C(\"MlpPolicy\", env, verbose=1)\n",
+        "\n",
+        "# Train the model\n",
+        "model.learn(total_timesteps=100000)\n",
+        "\n",
+        "# Save the trained model\n",
+        "model.save(\"a2c_cartpole_WB\")\n",
+        "print(\"Model saved successfully!\")\n",
+        "\n",
+        "num_episodes = 500  # Number of learn episodes\n",
+        "episode_rewards = []\n",
+        "\n",
+        "for episode in range(num_episodes):\n",
+        "    obs = env.reset()\n",
+        "    done = False\n",
+        "    total_reward = 0\n",
+        "\n",
+        "    while not done:\n",
+        "        action, _states = model.predict(obs)\n",
+        "        obs, reward, done, info = env.step(action)\n",
+        "\n",
+        "        total_reward += reward[0]\n",
+        "        done = done[0]\n",
+        "\n",
+        "    episode_rewards.append(total_reward)\n",
+        "\n",
+        "# Log the total rewards of each episode to WB\n",
+        "for i, reward in enumerate(episode_rewards):\n",
+        "    wandb.log({\"episode\": i + 1, \"total_reward\": reward})\n",
+        "\n",
+        "# Plot the episode rewards\n",
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.plot(range(1, num_episodes + 1), episode_rewards, marker=\"o\", linestyle=\"-\", color=\"b\", label=\"Total Reward per Episode\")\n",
+        "plt.xlabel(\"Episode\")\n",
+        "plt.ylabel(\"Total Reward\")\n",
+        "plt.title(\"Total Reward per Episode during Evaluation\")\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.savefig(\"episode_rewards_plot.png\")  # Save the plot as an image\n",
+        "plt.show()\n",
+        "\n",
+        "# Log the plot to WB\n",
+        "wandb.log({\"Episode Rewards Plot\": wandb.Image(\"episode_rewards_plot.png\")})\n",
+        "\n",
+        "# Close the environment\n",
+        "env.close()\n",
+        "\n",
+        "# Finish WB run\n",
+        "wandb.finish()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Dd80KFQO6ncb"
+      },
+      "source": [
+        "upload"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489,
+          "referenced_widgets": [
+            "105a958acd644ed2bcab9791de217aed",
+            "b5ddd303cc494ecd865fab744c7c7017",
+            "477c8ee0c2d94a5f9113b04e7e564aae",
+            "22963ccded02458d8047644dea108c0f",
+            "9cd838bc92c144108ad7fb2717dfa058",
+            "8bf9caee159f4f03b4b9be6500f7118a",
+            "5dfdcec102ef454893d2337010e465b7",
+            "d2b3dfcf04a44fb692b800cf0bb7f802",
+            "a0fffd5ee818454da360030f6e1466ae",
+            "5a73da851a4443208a79bfadb025bec4",
+            "10f2c206b3b6403f871d78f6f665c937",
+            "f48bd46317b54ebc9d55eee77b4eb165",
+            "dac9eb72052f448fbb17389d3acfbbfb",
+            "a7098ce07385468ba0b61a39601155ca",
+            "03743e7926594213aaa8a4ea7f149e47",
+            "e097d0070f30469abbd542994e74bf81",
+            "451f1b9792f64fa68eb0f68c0aefbc0d",
+            "ba48466f16a44983b96b20123c966be9",
+            "a6d98acbf717475dbfd80407d9256f5d",
+            "57b8419adedf4abe8d950d18057aa7e5",
+            "4916f07bcd1c47339946d15b1b44285b",
+            "1c9b00b0286f45a1a38ad992723d0efa",
+            "3a3f1bb3be714c71bc71a7db8fc31189",
+            "28e092db03774e4db332da6f1f0d3dd6",
+            "b899e2f587a34972ae41853ae62236fe",
+            "23f1b8afdbbf4dc3a3ddaaae094a287a",
+            "aa6c1072bcd94715a93b9c0f598981a5",
+            "e64bd642eab44cffa9d8c8e3a64103e7",
+            "053eca19faa24c35833c132c1af7b94c",
+            "536343b057e64933a62fe9aac074da24",
+            "333eaf4f77744246b98456a844fcca2d",
+            "7595680d757448eab257e813cbf506b1",
+            "b99ab3e32f10455398319b8c3e99ed18",
+            "d996b268e9c24f85a73ca1017f8cc5fa",
+            "cdca8fecc2cc412282c4f41ba141017d",
+            "508ba1fe83a24b83a6b26626ff71f22f",
+            "c135ded2efad4a0d8bddea2e51bc6459",
+            "9b4427af69fd454eb54eaf678465ceee",
+            "406df9e39d29497dab95d0890ebc006f",
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+            "8711ae2fa5174670afb295d8c788d2ac",
+            "f183c2895da7440a80edc87f5a130fee",
+            "8eecebc2ee5a42978670c9f2b0e5f592",
+            "d7cfb3fea4444e6ca69e7bdb4f86d40d",
+            "c8df02b3721d4a97b6c6336e896ff179",
+            "c48c8dc4a77f4dfaa2adede6518d373c",
+            "ea68977e2bc642358854ed2313d4c9bc",
+            "13f76670252d4fcbaf43c831b8fe1ee7",
+            "f22b7b1e400a4ed98dc754b073a9a40a",
+            "19a6e413bee74cc083c01d1b5d8dfee6",
+            "0c6aaba7ffb04fe2a5babdcb5c3e955d",
+            "b6d13ee968fa4473baecb877b8f302f8",
+            "7b3c7a0ac0424cb7af4f7e1dfbdc513b",
+            "79a31c28dd1e4bb8b2e2252b5fd1a940"
+          ]
+        },
+        "id": "8OIhM8GAm8gT",
+        "outputId": "5e91e213-44a8-41dc-c203-44f3c2358283"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[38;5;4mℹ This function will save, evaluate, generate a video of your agent,\n",
+            "create a model card and push everything to the hub. It might take up to 1min.\n",
+            "This is a work in progress: if you encounter a bug, please open an issue.\u001b[0m\n",
+            "Saving video to /tmp/tmprujl5nt1/-step-0-to-step-1000.mp4\n",
+            "Moviepy - Building video /tmp/tmprujl5nt1/-step-0-to-step-1000.mp4.\n",
+            "Moviepy - Writing video /tmp/tmprujl5nt1/-step-0-to-step-1000.mp4\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": []
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Moviepy - Done !\n",
+            "Moviepy - video ready /tmp/tmprujl5nt1/-step-0-to-step-1000.mp4\n",
+            "\u001b[38;5;1m✘ 'DummyVecEnv' object has no attribute 'video_recorder'\u001b[0m\n",
+            "\u001b[38;5;1m✘ We are unable to generate a replay of your agent, the package_to_hub\n",
+            "process continues\u001b[0m\n",
+            "\u001b[38;5;1m✘ Please open an issue at\n",
+            "https://github.com/huggingface/huggingface_sb3/issues\u001b[0m\n",
+            "\u001b[38;5;4mℹ Pushing repo oussamab2n/a2c-cartpole-wb to the Hugging Face Hub\u001b[0m\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "105a958acd644ed2bcab9791de217aed",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "policy.pth:   0%|          | 0.00/41.1k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f48bd46317b54ebc9d55eee77b4eb165",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "policy.optimizer.pth:   0%|          | 0.00/43.4k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "3a3f1bb3be714c71bc71a7db8fc31189",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "pytorch_variables.pth:   0%|          | 0.00/864 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d996b268e9c24f85a73ca1017f8cc5fa",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Upload 4 LFS files:   0%|          | 0/4 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d7cfb3fea4444e6ca69e7bdb4f86d40d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "a2c-cartpole-wb.zip:   0%|          | 0.00/101k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[38;5;4mℹ Your model is pushed to the Hub. You can view your model here:\n",
+            "https://huggingface.co/oussamab2n/a2c-cartpole-wb/tree/main/\u001b[0m\n",
+            "✅ Model successfully uploaded to Hugging Face Hub!\n"
+          ]
+        }
+      ],
+      "source": [
+        "from huggingface_sb3 import package_to_hub\n",
+        "\n",
+        "\n",
+        "repo_id = \"oussamab2n/a2c-cartpole-wb\"\n",
+        "\n",
+        "# Create environment\n",
+        "eval_env = gym.make(\"CartPole-v1\",render_mode=\"rgb_array\")\n",
+        "eval_env = Monitor(eval_env)\n",
+        "eval_env = DummyVecEnv([lambda: eval_env])  # Wrap environment\n",
+        "\n",
+        "# Upload model to Hugging Face\n",
+        "package_to_hub(\n",
+        "    model=model,\n",
+        "    model_name=\"a2c-cartpole-wb\",\n",
+        "    model_architecture=\"A2C\",\n",
+        "    env_id=\"CartPole-v1\",\n",
+        "    eval_env=eval_env,\n",
+        "    repo_id=repo_id,\n",
+        "    commit_message=\"Upload A2C model trained on CartPole-v1 with W&B logging\"\n",
+        ")\n",
+        "\n",
+        "print(\"Model successfully uploaded to Hugging Face Hub!\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-IY2wgfc6p8U"
+      },
+      "source": [
+        "evaluate"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "34h0joTT7ooj",
+        "outputId": "4284581c-00c1-49a9-f298-807495fb2fcc"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Tracking run with wandb version 0.19.7"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Run data is saved locally in <code>/content/wandb/run-20250222_130200-si01h5dj</code>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Syncing run <strong><a href='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/cartpole-evaluation/runs/si01h5dj' target=\"_blank\">A2C-CartPole</a></strong> to <a href='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/cartpole-evaluation' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              " View project at <a href='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/cartpole-evaluation' target=\"_blank\">https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/cartpole-evaluation</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              " View run at <a href='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/cartpole-evaluation/runs/si01h5dj' target=\"_blank\">https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/cartpole-evaluation/runs/si01h5dj</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/gymnasium/wrappers/rendering.py:283: UserWarning: \u001b[33mWARN: Overwriting existing videos at /content/videos1 folder (try specifying a different `video_folder` for the `RecordVideo` wrapper if this is not desired)\u001b[0m\n",
+            "  logger.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Episode 1: Total Reward = 500.0\n",
+            "Episode 2: Total Reward = 500.0\n",
+            "Episode 3: Total Reward = 500.0\n",
+            "Episode 4: Total Reward = 500.0\n",
+            "Episode 5: Total Reward = 500.0\n",
+            "Episode 6: Total Reward = 500.0\n",
+            "Episode 7: Total Reward = 500.0\n",
+            "Episode 8: Total Reward = 500.0\n",
+            "Episode 9: Total Reward = 500.0\n",
+            "Episode 10: Total Reward = 500.0\n",
+            "Episode 11: Total Reward = 500.0\n",
+            "Episode 12: Total Reward = 500.0\n",
+            "Episode 13: Total Reward = 500.0\n",
+            "Episode 14: Total Reward = 500.0\n",
+            "Episode 15: Total Reward = 500.0\n",
+            "Episode 16: Total Reward = 500.0\n",
+            "Episode 17: Total Reward = 500.0\n",
+            "Episode 18: Total Reward = 500.0\n",
+            "Episode 19: Total Reward = 500.0\n",
+            "Episode 20: Total Reward = 500.0\n",
+            "Episode 21: Total Reward = 500.0\n",
+            "Episode 22: Total Reward = 500.0\n",
+            "Episode 23: Total Reward = 500.0\n",
+            "Episode 24: Total Reward = 500.0\n",
+            "Episode 25: Total Reward = 500.0\n",
+            "Episode 26: Total Reward = 500.0\n",
+            "Episode 27: Total Reward = 500.0\n",
+            "Episode 28: Total Reward = 500.0\n",
+            "Episode 29: Total Reward = 500.0\n",
+            "Episode 30: Total Reward = 500.0\n",
+            "Episode 31: Total Reward = 500.0\n",
+            "Episode 32: Total Reward = 500.0\n",
+            "Episode 33: Total Reward = 500.0\n",
+            "Episode 34: Total Reward = 500.0\n",
+            "Episode 35: Total Reward = 500.0\n",
+            "Episode 36: Total Reward = 500.0\n",
+            "Episode 37: Total Reward = 500.0\n",
+            "Episode 38: Total Reward = 500.0\n",
+            "Episode 39: Total Reward = 500.0\n",
+            "Episode 40: Total Reward = 500.0\n",
+            "Episode 41: Total Reward = 500.0\n",
+            "Episode 42: Total Reward = 500.0\n",
+            "Episode 43: Total Reward = 500.0\n",
+            "Episode 44: Total Reward = 500.0\n",
+            "Episode 45: Total Reward = 500.0\n",
+            "Episode 46: Total Reward = 500.0\n",
+            "Episode 47: Total Reward = 500.0\n",
+            "Episode 48: Total Reward = 500.0\n",
+            "Episode 49: Total Reward = 500.0\n",
+            "Episode 50: Total Reward = 500.0\n",
+            "Episode 51: Total Reward = 500.0\n",
+            "Episode 52: Total Reward = 500.0\n",
+            "Episode 53: Total Reward = 500.0\n",
+            "Episode 54: Total Reward = 500.0\n",
+            "Episode 55: Total Reward = 500.0\n",
+            "Episode 56: Total Reward = 500.0\n",
+            "Episode 57: Total Reward = 500.0\n",
+            "Episode 58: Total Reward = 500.0\n",
+            "Episode 59: Total Reward = 500.0\n",
+            "Episode 60: Total Reward = 500.0\n",
+            "Episode 61: Total Reward = 500.0\n",
+            "Episode 62: Total Reward = 500.0\n",
+            "Episode 63: Total Reward = 500.0\n",
+            "Episode 64: Total Reward = 500.0\n",
+            "Episode 65: Total Reward = 500.0\n",
+            "Episode 66: Total Reward = 500.0\n",
+            "Episode 67: Total Reward = 500.0\n",
+            "Episode 68: Total Reward = 500.0\n",
+            "Episode 69: Total Reward = 500.0\n",
+            "Episode 70: Total Reward = 500.0\n",
+            "Episode 71: Total Reward = 500.0\n",
+            "Episode 72: Total Reward = 500.0\n",
+            "Episode 73: Total Reward = 500.0\n",
+            "Episode 74: Total Reward = 500.0\n",
+            "Episode 75: Total Reward = 500.0\n",
+            "Episode 76: Total Reward = 500.0\n",
+            "Episode 77: Total Reward = 500.0\n",
+            "Episode 78: Total Reward = 500.0\n",
+            "Episode 79: Total Reward = 500.0\n",
+            "Episode 80: Total Reward = 500.0\n",
+            "Episode 81: Total Reward = 500.0\n",
+            "Episode 82: Total Reward = 500.0\n",
+            "Episode 83: Total Reward = 500.0\n",
+            "Episode 84: Total Reward = 500.0\n",
+            "Episode 85: Total Reward = 500.0\n",
+            "Episode 86: Total Reward = 500.0\n",
+            "Episode 87: Total Reward = 500.0\n",
+            "Episode 88: Total Reward = 500.0\n",
+            "Episode 89: Total Reward = 500.0\n",
+            "Episode 90: Total Reward = 500.0\n",
+            "Episode 91: Total Reward = 500.0\n",
+            "Episode 92: Total Reward = 500.0\n",
+            "Episode 93: Total Reward = 500.0\n",
+            "Episode 94: Total Reward = 500.0\n",
+            "Episode 95: Total Reward = 500.0\n",
+            "Episode 96: Total Reward = 500.0\n",
+            "Episode 97: Total Reward = 500.0\n",
+            "Episode 98: Total Reward = 500.0\n",
+            "Episode 99: Total Reward = 500.0\n",
+            "Episode 100: Total Reward = 500.0\n",
+            "\n",
+            " Evaluation Completed!\n",
+            "Number of Perfect Episodes (Reward == 500): 100 / 100\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": []
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>Episode Reward</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>Episode Reward</td><td>500</td></tr></table><br/></div></div>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              " View run <strong style=\"color:#cdcd00\">A2C-CartPole</strong> at: <a href='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/cartpole-evaluation/runs/si01h5dj' target=\"_blank\">https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/cartpole-evaluation/runs/si01h5dj</a><br> View project at: <a href='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/cartpole-evaluation' target=\"_blank\">https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/cartpole-evaluation</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Find logs at: <code>./wandb/run-20250222_130200-si01h5dj/logs</code>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import gymnasium as gym\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import wandb\n",
+        "from stable_baselines3 import A2C\n",
+        "from stable_baselines3.common.monitor import Monitor\n",
+        "from stable_baselines3.common.vec_env import DummyVecEnv\n",
+        "from huggingface_sb3 import load_from_hub\n",
+        "import os\n",
+        "from gymnasium.wrappers import RecordVideo\n",
+        "from IPython.display import Video, display\n",
+        "\n",
+        "\n",
+        "# Initialize Weights & Biases for evaluation\n",
+        "wandb.init(project=\"cartpole-evaluation\", name=\"A2C-CartPole\", config={\"num_episodes\": 100})\n",
+        "\n",
+        "# Define Hugging Face repository and model filename\n",
+        "repo_id = \"oussamab2n/a2c-cartpole-wb\"\n",
+        "filename = \"a2c-cartpole-wb.zip\"\n",
+        "\n",
+        "# Load model from Hugging Face Hub\n",
+        "model_path = load_from_hub(repo_id=repo_id, filename=filename)\n",
+        "model = A2C.load(model_path)\n",
+        "\n",
+        "# Create video folder\n",
+        "video_dir = \"videos\"\n",
+        "os.makedirs(video_dir, exist_ok=True)\n",
+        "\n",
+        "# Create evaluation environment\n",
+        "env = gym.make(\"CartPole-v1\", render_mode=\"rgb_array\")\n",
+        "env = RecordVideo(env, video_folder=video_dir, episode_trigger=lambda e: e % 10 == 0)  # Record every 10 episodes\n",
+        "env = Monitor(env)\n",
+        "env = DummyVecEnv([lambda: env])\n",
+        "\n",
+        "# Initialize tracking variables\n",
+        "num_episodes = 100\n",
+        "perfect_episodes = 0  # Count episodes with reward == 500\n",
+        "episode_rewards = []\n",
+        "\n",
+        "for episode in range(num_episodes):\n",
+        "    obs = env.reset()\n",
+        "    done = False\n",
+        "    total_reward = 0\n",
+        "\n",
+        "    while not done:\n",
+        "        action, _ = model.predict(obs, deterministic=True)\n",
+        "        obs, reward, done, info = env.step(action)\n",
+        "\n",
+        "        # Correct step call for Gymnasium\n",
+        "        total_reward += reward[0]\n",
+        "        done = done[0]\n",
+        "\n",
+        "    episode_rewards.append(total_reward)\n",
+        "    wandb.log({\"Episode Reward\": total_reward})  # Log reward in Weights & Biases\n",
+        "\n",
+        "    # Count perfect episodes\n",
+        "    if total_reward == 500:\n",
+        "        perfect_episodes += 1\n",
+        "\n",
+        "    print(f\"Episode {episode+1}: Total Reward = {total_reward}\")\n",
+        "\n",
+        "# Print final results\n",
+        "print(\"\\n Evaluation Completed!\")\n",
+        "print(f\"Number of Perfect Episodes (Reward == 500): {perfect_episodes} / {num_episodes}\")\n",
+        "\n",
+        "# Close environment\n",
+        "env.close()\n",
+        "\n",
+        "# Plot rewards\n",
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.plot(range(1, num_episodes + 1), episode_rewards, marker=\"o\", linestyle=\"-\", color=\"b\", label=\"Total Reward per Episode\")\n",
+        "plt.xlabel(\"Episode\")\n",
+        "plt.ylabel(\"Total Reward\")\n",
+        "plt.title(\"Total Reward per Episode during Evaluation\")\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# Finish Weights & Biases logging\n",
+        "wandb.finish()\n"
+      ]
+    }
+  ],
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-      "metadata": {
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-        },
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-          "text": [
-            "Collecting panda-gym==3.0.7\n",
-            "  Downloading panda_gym-3.0.7-py3-none-any.whl.metadata (4.3 kB)\n",
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-            "\u001b[?25hDownloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hDownloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hDownloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m106.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hDownloading pybullet-3.2.7-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (103.2 MB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m103.2/103.2 MB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hInstalling collected packages: pybullet, nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, panda-gym, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, huggingface_sb3, stable-baselines3\n",
-            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
-            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
-            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
-            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
-            "  Attempting uninstall: nvidia-curand-cu12\n",
-            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
-            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
-            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
-            "  Attempting uninstall: nvidia-cufft-cu12\n",
-            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
-            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
-            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
-            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
-            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
-            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
-            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
-            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
-            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
-            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
-            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
-            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
-            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
-            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
-            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
-            "  Attempting uninstall: nvidia-cublas-cu12\n",
-            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
-            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
-            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
-            "  Attempting uninstall: nvidia-cusparse-cu12\n",
-            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
-            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
-            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
-            "  Attempting uninstall: nvidia-cudnn-cu12\n",
-            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
-            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
-            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
-            "  Attempting uninstall: nvidia-cusolver-cu12\n",
-            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
-            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
-            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
-            "Successfully installed huggingface_sb3-3.0 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 panda-gym-3.0.7 pybullet-3.2.7 stable-baselines3-2.5.0\n",
-            "Requirement already satisfied: panda-gym==3.0.7 in /usr/local/lib/python3.11/dist-packages (3.0.7)\n",
-            "Requirement already satisfied: gymnasium>=0.26 in /usr/local/lib/python3.11/dist-packages (from panda-gym==3.0.7) (1.0.0)\n",
-            "Requirement already satisfied: pybullet in /usr/local/lib/python3.11/dist-packages (from panda-gym==3.0.7) (3.2.7)\n",
-            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from panda-gym==3.0.7) (1.26.4)\n",
-            "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from panda-gym==3.0.7) (1.13.1)\n",
-            "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from gymnasium>=0.26->panda-gym==3.0.7) (3.1.1)\n",
-            "Requirement already satisfied: typing-extensions>=4.3.0 in /usr/local/lib/python3.11/dist-packages (from gymnasium>=0.26->panda-gym==3.0.7) (4.12.2)\n",
-            "Requirement already satisfied: farama-notifications>=0.0.1 in /usr/local/lib/python3.11/dist-packages (from gymnasium>=0.26->panda-gym==3.0.7) (0.0.4)\n"
-          ]
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-      "source": [
-        "!pip install panda-gym==3.0.7 stable-baselines3 wandb huggingface_sb3\n",
-        "! pip install --upgrade panda-gym==3.0.7\n"
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-        "!pip install wandb -qU\n",
-        "#0b197edd6d50d8cc0ed00564436ada87f46084fa\n",
-        "! wandb login --relogin"
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-        "id": "NiIjvtLfasLj",
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-      "execution_count": 2,
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-          "text": [
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.8/20.8 MB\u001b[0m \u001b[31m66.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
-            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
-            "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit: \n",
-            "\u001b[34m\u001b[1mwandb\u001b[0m: No netrc file found, creating one.\n",
-            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n",
-            "\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
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-      "source": [
-        "import wandb\n",
-        "wandb.login()"
-      ],
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         },
-        "id": "c2HWR0VVay1J",
-        "outputId": "fa32ba9f-d6c6-4c5a-f2ac-dd5a94d253df"
-      },
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n",
-            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mbenyahiamohammedoussama\u001b[0m (\u001b[33mbenyahiamohammedoussama-ecole-central-lyon\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
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-        "from huggingface_hub import notebook_login\n",
-        "\n",
-        "notebook_login()\n",
-        "#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"
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-      "source": [
-        "import gymnasium as gym\n",
-        "import panda_gym\n",
-        "import wandb\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from stable_baselines3 import A2C\n",
-        "from stable_baselines3.common.monitor import Monitor\n",
-        "from stable_baselines3.common.vec_env import DummyVecEnv\n",
-        "from huggingface_sb3 import package_to_hub\n",
-        "\n",
-        "# Initialize Weights & Biases (panda-gym)\n",
-        "wandb.init(project=\"panda-gym\", config={\"total_timesteps\": 500000})\n",
-        "\n",
-        "# Create and wrap the environment\n",
-        "env = gym.make(\"PandaReachJointsDense-v3\")\n",
-        "env = Monitor(env)\n",
-        "env = DummyVecEnv([lambda: env])\n",
-        "\n",
-        "# Initialize A2C model\n",
-        "model = A2C(\n",
-        "    \"MultiInputPolicy\",\n",
-        "    env,\n",
-        "    learning_rate=0.0003,\n",
-        "    gamma=0.99,\n",
-        "    n_steps=10,\n",
-        "    ent_coef=0.01,\n",
-        "    vf_coef=0.5,\n",
-        "    max_grad_norm=0.5,\n",
-        "    normalize_advantage=True,\n",
-        "    verbose=1\n",
-        ")\n",
-        "\n",
-        "# Training and Logging Rewards\n",
-        "num_episodes = 500\n",
-        "episode_rewards = []\n",
-        "\n",
-        "for episode in range(num_episodes):\n",
-        "    obs = env.reset()\n",
-        "    done = False\n",
-        "    total_reward = 0\n",
-        "\n",
-        "    while not done:\n",
-        "        action, _ = model.predict(obs, deterministic=True)\n",
-        "        obs, reward, done, info = env.step(action)\n",
-        "        total_reward += reward\n",
-        "\n",
-        "    episode_rewards.append(total_reward)\n",
-        "    wandb.log({\"Episode Reward\": total_reward})  # Log reward in panda-gym\n",
-        "    print(f\"Episode {episode+1}: Total Reward = {total_reward}\")\n",
-        "\n",
-        "# Save the model\n",
-        "model.save(\"a2c_panda_reach\")\n",
-        "wandb.log({\"model_saved\": True})\n",
-        "\n",
-        "# Plot Total Reward per Episode\n",
-        "plt.figure(figsize=(10, 5))\n",
-        "plt.plot(episode_rewards, label=\"Episode Reward\")\n",
-        "plt.xlabel(\"Episode\")\n",
-        "plt.ylabel(\"Total Reward\")\n",
-        "plt.title(\"Total Reward per Episode\")\n",
-        "plt.legend()\n",
-        "plt.grid()\n",
-        "plt.show()\n",
-        "\n",
-        "# Upload the model to the Hugging Face Hub\n",
-        "eval_env = DummyVecEnv([lambda: gym.make(\"PandaReachJointsDense-v3\")])\n",
-        "package_to_hub(\n",
-        "    model=model,\n",
-        "    model_name=\"a2c-panda-reach\",\n",
-        "    model_architecture=\"A2C\",\n",
-        "    env_id=\"PandaReachJointsDense-v3\",\n",
-        "    eval_env=eval_env,\n",
-        "    repo_id=\"oussamab2n/a2c-panda-reach\",\n",
-        "    commit_message=\"Initial model upload\"\n",
-        ")\n",
-        "\n",
-        "\n",
-        "wandb.finish()\n",
-        "\n",
-        "print(\"Modèle entraîné, sauvegardé et visualisé avec succès !\")\n"
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-            "Using cpu device\n",
-            "Episode 1: Total Reward = [-5.9671183]\n",
-            "Episode 2: Total Reward = [-8.410566]\n",
-            "Episode 3: Total Reward = [-7.69908]\n",
-            "Episode 4: Total Reward = [-6.0059395]\n",
-            "Episode 5: Total Reward = [-9.254221]\n",
-            "Episode 6: Total Reward = [-7.5025005]\n",
-            "Episode 7: Total Reward = [-8.524258]\n",
-            "Episode 8: Total Reward = [-11.886195]\n",
-            "Episode 9: Total Reward = [-10.59598]\n",
-            "Episode 10: Total Reward = [-6.130044]\n",
-            "Episode 11: Total Reward = [-6.687639]\n",
-            "Episode 12: Total Reward = [-6.3373575]\n",
-            "Episode 13: Total Reward = [-6.9856863]\n",
-            "Episode 14: Total Reward = [-10.955867]\n",
-            "Episode 15: Total Reward = [-9.1398735]\n",
-            "Episode 16: Total Reward = [-0.04263674]\n",
-            "Episode 17: Total Reward = [-11.842399]\n",
-            "Episode 18: Total Reward = [-6.888912]\n",
-            "Episode 19: Total Reward = [-11.743179]\n",
-            "Episode 20: Total Reward = [-11.637946]\n",
-            "Episode 21: Total Reward = [-7.9327655]\n",
-            "Episode 22: Total Reward = [-9.340415]\n",
-            "Episode 23: Total Reward = [-8.821059]\n",
-            "Episode 24: Total Reward = [-9.734911]\n",
-            "Episode 25: Total Reward = [-10.058818]\n",
-            "Episode 26: Total Reward = [-7.415555]\n",
-            "Episode 27: Total Reward = [-8.002658]\n",
-            "Episode 28: Total Reward = [-8.890517]\n",
-            "Episode 29: Total Reward = [-4.6750913]\n",
-            "Episode 30: Total Reward = [-9.393371]\n",
-            "Episode 31: Total Reward = [-9.889639]\n",
-            "Episode 32: Total Reward = [-3.5552294]\n",
-            "Episode 33: Total Reward = [-8.432571]\n",
-            "Episode 34: Total Reward = [-7.811293]\n",
-            "Episode 35: Total Reward = [-7.216642]\n",
-            "Episode 36: Total Reward = [-10.083422]\n",
-            "Episode 37: Total Reward = [-8.601984]\n",
-            "Episode 38: Total Reward = [-10.444173]\n",
-            "Episode 39: Total Reward = [-8.461686]\n",
-            "Episode 40: Total Reward = [-10.118482]\n",
-            "Episode 41: Total Reward = [-11.790754]\n",
-            "Episode 42: Total Reward = [-3.167772]\n",
-            "Episode 43: Total Reward = [-5.935916]\n",
-            "Episode 44: Total Reward = [-4.86994]\n",
-            "Episode 45: Total Reward = [-6.2489777]\n",
-            "Episode 46: Total Reward = [-12.358703]\n",
-            "Episode 47: Total Reward = [-10.038326]\n",
-            "Episode 48: Total Reward = [-9.355382]\n",
-            "Episode 49: Total Reward = [-6.459985]\n",
-            "Episode 50: Total Reward = [-5.8644485]\n",
-            "Episode 51: Total Reward = [-11.213013]\n",
-            "Episode 52: Total Reward = [-13.141105]\n",
-            "Episode 53: Total Reward = [-6.2076764]\n",
-            "Episode 54: Total Reward = [-6.7242165]\n",
-            "Episode 55: Total Reward = [-8.260598]\n",
-            "Episode 56: Total Reward = [-10.427072]\n",
-            "Episode 57: Total Reward = [-4.2750735]\n",
-            "Episode 58: Total Reward = [-6.5299673]\n",
-            "Episode 59: Total Reward = [-8.192318]\n",
-            "Episode 60: Total Reward = [-10.826809]\n",
-            "Episode 61: Total Reward = [-4.6873603]\n",
-            "Episode 62: Total Reward = [-11.678388]\n",
-            "Episode 63: Total Reward = [-8.007297]\n",
-            "Episode 64: Total Reward = [-7.8474684]\n",
-            "Episode 65: Total Reward = [-4.7966886]\n",
-            "Episode 66: Total Reward = [-5.0496535]\n",
-            "Episode 67: Total Reward = [-3.7868679]\n",
-            "Episode 68: Total Reward = [-10.254663]\n",
-            "Episode 69: Total Reward = [-7.1501255]\n",
-            "Episode 70: Total Reward = [-0.03696994]\n",
-            "Episode 71: Total Reward = [-11.576838]\n",
-            "Episode 72: Total Reward = [-5.6762643]\n",
-            "Episode 73: Total Reward = [-10.3013]\n",
-            "Episode 74: Total Reward = [-6.570519]\n",
-            "Episode 75: Total Reward = [-7.640373]\n",
-            "Episode 76: Total Reward = [-8.844466]\n",
-            "Episode 77: Total Reward = [-8.597115]\n",
-            "Episode 78: Total Reward = [-3.4072177]\n",
-            "Episode 79: Total Reward = [-8.521702]\n",
-            "Episode 80: Total Reward = [-11.437904]\n",
-            "Episode 81: Total Reward = [-7.5225515]\n",
-            "Episode 82: Total Reward = [-3.3836095]\n",
-            "Episode 83: Total Reward = [-7.4247594]\n",
-            "Episode 84: Total Reward = [-8.038719]\n",
-            "Episode 85: Total Reward = [-6.676204]\n",
-            "Episode 86: Total Reward = [-10.868242]\n",
-            "Episode 87: Total Reward = [-11.089296]\n",
-            "Episode 88: Total Reward = [-6.012974]\n",
-            "Episode 89: Total Reward = [-6.984603]\n",
-            "Episode 90: Total Reward = [-7.612347]\n",
-            "Episode 91: Total Reward = [-8.0490055]\n",
-            "Episode 92: Total Reward = [-6.654027]\n",
-            "Episode 93: Total Reward = [-8.738589]\n",
-            "Episode 94: Total Reward = [-9.093354]\n",
-            "Episode 95: Total Reward = [-10.832252]\n",
-            "Episode 96: Total Reward = [-9.461725]\n",
-            "Episode 97: Total Reward = [-7.4711823]\n",
-            "Episode 98: Total Reward = [-7.3934474]\n",
-            "Episode 99: Total Reward = [-7.3053355]\n",
-            "Episode 100: Total Reward = [-7.153119]\n",
-            "Episode 101: Total Reward = [-4.6575537]\n",
-            "Episode 102: Total Reward = [-7.6773477]\n",
-            "Episode 103: Total Reward = [-8.446212]\n",
-            "Episode 104: Total Reward = [-9.364282]\n",
-            "Episode 105: Total Reward = [-6.0935087]\n",
-            "Episode 106: Total Reward = [-7.773395]\n",
-            "Episode 107: Total Reward = [-9.339773]\n",
-            "Episode 108: Total Reward = [-5.421175]\n",
-            "Episode 109: Total Reward = [-7.056646]\n",
-            "Episode 110: Total Reward = [-4.7394485]\n",
-            "Episode 111: Total Reward = [-7.5590076]\n",
-            "Episode 112: Total Reward = [-3.9078624]\n",
-            "Episode 113: Total Reward = [-6.9006386]\n",
-            "Episode 114: Total Reward = [-6.1587925]\n",
-            "Episode 115: Total Reward = [-3.5906382]\n",
-            "Episode 116: Total Reward = [-9.04726]\n",
-            "Episode 117: Total Reward = [-11.3741665]\n",
-            "Episode 118: Total Reward = [-8.395387]\n",
-            "Episode 119: Total Reward = [-10.353529]\n",
-            "Episode 120: Total Reward = [-5.9687853]\n",
-            "Episode 121: Total Reward = [-9.134439]\n",
-            "Episode 122: Total Reward = [-2.623698]\n",
-            "Episode 123: Total Reward = [-10.515835]\n",
-            "Episode 124: Total Reward = [-10.613643]\n",
-            "Episode 125: Total Reward = [-4.6210694]\n",
-            "Episode 126: Total Reward = [-8.604385]\n",
-            "Episode 127: Total Reward = [-11.2408285]\n",
-            "Episode 128: Total Reward = [-12.888561]\n",
-            "Episode 129: Total Reward = [-9.98479]\n",
-            "Episode 130: Total Reward = [-9.820236]\n",
-            "Episode 131: Total Reward = [-11.059117]\n",
-            "Episode 132: Total Reward = [-7.6503367]\n",
-            "Episode 133: Total Reward = [-9.88062]\n",
-            "Episode 134: Total Reward = [-11.45039]\n",
-            "Episode 135: Total Reward = [-3.19086]\n",
-            "Episode 136: Total Reward = [-7.4213686]\n",
-            "Episode 137: Total Reward = [-12.6502075]\n",
-            "Episode 138: Total Reward = [-9.471887]\n",
-            "Episode 139: Total Reward = [-8.305141]\n",
-            "Episode 140: Total Reward = [-5.4048815]\n",
-            "Episode 141: Total Reward = [-8.7401905]\n",
-            "Episode 142: Total Reward = [-0.04611371]\n",
-            "Episode 143: Total Reward = [-7.7613034]\n",
-            "Episode 144: Total Reward = [-7.8480377]\n",
-            "Episode 145: Total Reward = [-8.127039]\n",
-            "Episode 146: Total Reward = [-4.532159]\n",
-            "Episode 147: Total Reward = [-7.052411]\n",
-            "Episode 148: Total Reward = [-10.428407]\n",
-            "Episode 149: Total Reward = [-9.080467]\n",
-            "Episode 150: Total Reward = [-8.01737]\n",
-            "Episode 151: Total Reward = [-7.7718005]\n",
-            "Episode 152: Total Reward = [-9.660548]\n",
-            "Episode 153: Total Reward = [-8.932415]\n",
-            "Episode 154: Total Reward = [-8.174668]\n",
-            "Episode 155: Total Reward = [-7.8583484]\n",
-            "Episode 156: Total Reward = [-6.357939]\n",
-            "Episode 157: Total Reward = [-9.811417]\n",
-            "Episode 158: Total Reward = [-5.4961247]\n",
-            "Episode 159: Total Reward = [-6.849422]\n",
-            "Episode 160: Total Reward = [-11.61618]\n",
-            "Episode 161: Total Reward = [-10.0345335]\n",
-            "Episode 162: Total Reward = [-4.7807317]\n",
-            "Episode 163: Total Reward = [-5.8183403]\n",
-            "Episode 164: Total Reward = [-12.357819]\n",
-            "Episode 165: Total Reward = [-8.143105]\n",
-            "Episode 166: Total Reward = [-9.1994]\n",
-            "Episode 167: Total Reward = [-6.5571346]\n",
-            "Episode 168: Total Reward = [-8.939854]\n",
-            "Episode 169: Total Reward = [-8.4599695]\n",
-            "Episode 170: Total Reward = [-5.911991]\n",
-            "Episode 171: Total Reward = [-10.29421]\n",
-            "Episode 172: Total Reward = [-5.7541685]\n",
-            "Episode 173: Total Reward = [-6.6648917]\n",
-            "Episode 174: Total Reward = [-2.7550805]\n",
-            "Episode 175: Total Reward = [-8.4382715]\n",
-            "Episode 176: Total Reward = [-8.604507]\n",
-            "Episode 177: Total Reward = [-5.792222]\n",
-            "Episode 178: Total Reward = [-6.958585]\n",
-            "Episode 179: Total Reward = [-12.8616085]\n",
-            "Episode 180: Total Reward = [-5.225053]\n",
-            "Episode 181: Total Reward = [-3.038085]\n",
-            "Episode 182: Total Reward = [-7.518964]\n",
-            "Episode 183: Total Reward = [-7.544255]\n",
-            "Episode 184: Total Reward = [-8.09302]\n",
-            "Episode 185: Total Reward = [-7.0538483]\n",
-            "Episode 186: Total Reward = [-12.256677]\n",
-            "Episode 187: Total Reward = [-5.670684]\n",
-            "Episode 188: Total Reward = [-0.04658706]\n",
-            "Episode 189: Total Reward = [-11.454335]\n",
-            "Episode 190: Total Reward = [-10.762045]\n",
-            "Episode 191: Total Reward = [-8.032895]\n",
-            "Episode 192: Total Reward = [-3.8488445]\n",
-            "Episode 193: Total Reward = [-7.120258]\n",
-            "Episode 194: Total Reward = [-4.3575344]\n",
-            "Episode 195: Total Reward = [-5.856792]\n",
-            "Episode 196: Total Reward = [-10.232478]\n",
-            "Episode 197: Total Reward = [-4.93827]\n",
-            "Episode 198: Total Reward = [-10.689916]\n",
-            "Episode 199: Total Reward = [-0.03666218]\n",
-            "Episode 200: Total Reward = [-9.7943325]\n",
-            "Episode 201: Total Reward = [-5.8606367]\n",
-            "Episode 202: Total Reward = [-8.912671]\n",
-            "Episode 203: Total Reward = [-9.757352]\n",
-            "Episode 204: Total Reward = [-6.1759953]\n",
-            "Episode 205: Total Reward = [-2.9874816]\n",
-            "Episode 206: Total Reward = [-6.896487]\n",
-            "Episode 207: Total Reward = [-8.581869]\n",
-            "Episode 208: Total Reward = [-9.268516]\n",
-            "Episode 209: Total Reward = [-10.243534]\n",
-            "Episode 210: Total Reward = [-10.984198]\n",
-            "Episode 211: Total Reward = [-10.802228]\n",
-            "Episode 212: Total Reward = [-4.930272]\n",
-            "Episode 213: Total Reward = [-4.2587147]\n",
-            "Episode 214: Total Reward = [-6.475476]\n",
-            "Episode 215: Total Reward = [-8.626359]\n",
-            "Episode 216: Total Reward = [-6.2319064]\n",
-            "Episode 217: Total Reward = [-6.4460926]\n",
-            "Episode 218: Total Reward = [-9.595624]\n",
-            "Episode 219: Total Reward = [-9.319967]\n",
-            "Episode 220: Total Reward = [-4.282688]\n",
-            "Episode 221: Total Reward = [-3.5191536]\n",
-            "Episode 222: Total Reward = [-7.1235905]\n",
-            "Episode 223: Total Reward = [-11.018953]\n",
-            "Episode 224: Total Reward = [-7.2461386]\n",
-            "Episode 225: Total Reward = [-6.4262705]\n",
-            "Episode 226: Total Reward = [-9.852872]\n",
-            "Episode 227: Total Reward = [-4.86384]\n",
-            "Episode 228: Total Reward = [-6.1907005]\n",
-            "Episode 229: Total Reward = [-5.7535667]\n",
-            "Episode 230: Total Reward = [-8.796441]\n",
-            "Episode 231: Total Reward = [-8.755129]\n",
-            "Episode 232: Total Reward = [-1.3025322]\n",
-            "Episode 233: Total Reward = [-9.825844]\n",
-            "Episode 234: Total Reward = [-11.589789]\n",
-            "Episode 235: Total Reward = [-6.8748145]\n",
-            "Episode 236: Total Reward = [-7.5225573]\n",
-            "Episode 237: Total Reward = [-5.30235]\n",
-            "Episode 238: Total Reward = [-5.1248894]\n",
-            "Episode 239: Total Reward = [-11.32144]\n",
-            "Episode 240: Total Reward = [-12.80694]\n",
-            "Episode 241: Total Reward = [-9.918229]\n",
-            "Episode 242: Total Reward = [-7.565697]\n",
-            "Episode 243: Total Reward = [-9.477199]\n",
-            "Episode 244: Total Reward = [-12.023693]\n",
-            "Episode 245: Total Reward = [-6.059112]\n",
-            "Episode 246: Total Reward = [-7.4803815]\n",
-            "Episode 247: Total Reward = [-10.162849]\n",
-            "Episode 248: Total Reward = [-7.156686]\n",
-            "Episode 249: Total Reward = [-6.5280404]\n",
-            "Episode 250: Total Reward = [-10.396179]\n",
-            "Episode 251: Total Reward = [-8.298218]\n",
-            "Episode 252: Total Reward = [-10.647128]\n",
-            "Episode 253: Total Reward = [-4.182121]\n",
-            "Episode 254: Total Reward = [-10.029212]\n",
-            "Episode 255: Total Reward = [-11.044879]\n",
-            "Episode 256: Total Reward = [-6.536899]\n",
-            "Episode 257: Total Reward = [-8.121197]\n",
-            "Episode 258: Total Reward = [-6.621592]\n",
-            "Episode 259: Total Reward = [-6.4405923]\n",
-            "Episode 260: Total Reward = [-9.301907]\n",
-            "Episode 261: Total Reward = [-5.442333]\n",
-            "Episode 262: Total Reward = [-6.8829575]\n",
-            "Episode 263: Total Reward = [-5.2317867]\n",
-            "Episode 264: Total Reward = [-6.2464848]\n",
-            "Episode 265: Total Reward = [-5.9521976]\n",
-            "Episode 266: Total Reward = [-7.93829]\n",
-            "Episode 267: Total Reward = [-8.984676]\n",
-            "Episode 268: Total Reward = [-9.047547]\n",
-            "Episode 269: Total Reward = [-5.792929]\n",
-            "Episode 270: Total Reward = [-8.414955]\n",
-            "Episode 271: Total Reward = [-8.654358]\n",
-            "Episode 272: Total Reward = [-2.9784145]\n",
-            "Episode 273: Total Reward = [-4.9111633]\n",
-            "Episode 274: Total Reward = [-8.120749]\n",
-            "Episode 275: Total Reward = [-10.178794]\n",
-            "Episode 276: Total Reward = [-5.5463705]\n",
-            "Episode 277: Total Reward = [-9.921348]\n",
-            "Episode 278: Total Reward = [-7.6656346]\n",
-            "Episode 279: Total Reward = [-7.9819555]\n",
-            "Episode 280: Total Reward = [-8.410989]\n",
-            "Episode 281: Total Reward = [-6.312636]\n",
-            "Episode 282: Total Reward = [-6.642037]\n",
-            "Episode 283: Total Reward = [-4.355426]\n",
-            "Episode 284: Total Reward = [-6.9416146]\n",
-            "Episode 285: Total Reward = [-6.124326]\n",
-            "Episode 286: Total Reward = [-4.0506287]\n",
-            "Episode 287: Total Reward = [-6.198772]\n",
-            "Episode 288: Total Reward = [-7.056612]\n",
-            "Episode 289: Total Reward = [-6.828329]\n",
-            "Episode 290: Total Reward = [-7.910471]\n",
-            "Episode 291: Total Reward = [-4.3945837]\n",
-            "Episode 292: Total Reward = [-9.272408]\n",
-            "Episode 293: Total Reward = [-10.480657]\n",
-            "Episode 294: Total Reward = [-8.969076]\n",
-            "Episode 295: Total Reward = [-4.732938]\n",
-            "Episode 296: Total Reward = [-9.976276]\n",
-            "Episode 297: Total Reward = [-9.09023]\n",
-            "Episode 298: Total Reward = [-5.763799]\n",
-            "Episode 299: Total Reward = [-11.143803]\n",
-            "Episode 300: Total Reward = [-9.756708]\n",
-            "Episode 301: Total Reward = [-7.345696]\n",
-            "Episode 302: Total Reward = [-5.7863007]\n",
-            "Episode 303: Total Reward = [-0.03332504]\n",
-            "Episode 304: Total Reward = [-8.679352]\n",
-            "Episode 305: Total Reward = [-8.910865]\n",
-            "Episode 306: Total Reward = [-3.4091823]\n",
-            "Episode 307: Total Reward = [-2.8295734]\n",
-            "Episode 308: Total Reward = [-7.2285814]\n",
-            "Episode 309: Total Reward = [-3.4981174]\n",
-            "Episode 310: Total Reward = [-5.907518]\n",
-            "Episode 311: Total Reward = [-7.3263717]\n",
-            "Episode 312: Total Reward = [-11.255321]\n",
-            "Episode 313: Total Reward = [-9.373063]\n",
-            "Episode 314: Total Reward = [-8.654061]\n",
-            "Episode 315: Total Reward = [-7.2846136]\n",
-            "Episode 316: Total Reward = [-8.636514]\n",
-            "Episode 317: Total Reward = [-6.8962955]\n",
-            "Episode 318: Total Reward = [-13.158796]\n",
-            "Episode 319: Total Reward = [-9.286875]\n",
-            "Episode 320: Total Reward = [-5.8470283]\n",
-            "Episode 321: Total Reward = [-8.625118]\n",
-            "Episode 322: Total Reward = [-0.04864819]\n",
-            "Episode 323: Total Reward = [-11.293492]\n",
-            "Episode 324: Total Reward = [-0.02976922]\n",
-            "Episode 325: Total Reward = [-8.765668]\n",
-            "Episode 326: Total Reward = [-8.243297]\n",
-            "Episode 327: Total Reward = [-5.169654]\n",
-            "Episode 328: Total Reward = [-0.04114949]\n",
-            "Episode 329: Total Reward = [-5.676384]\n",
-            "Episode 330: Total Reward = [-4.514897]\n",
-            "Episode 331: Total Reward = [-8.140277]\n",
-            "Episode 332: Total Reward = [-7.2189317]\n",
-            "Episode 333: Total Reward = [-8.427456]\n",
-            "Episode 334: Total Reward = [-3.2052464]\n",
-            "Episode 335: Total Reward = [-7.5342183]\n",
-            "Episode 336: Total Reward = [-2.8846667]\n",
-            "Episode 337: Total Reward = [-4.853927]\n",
-            "Episode 338: Total Reward = [-8.300977]\n",
-            "Episode 339: Total Reward = [-8.978411]\n",
-            "Episode 340: Total Reward = [-8.597188]\n",
-            "Episode 341: Total Reward = [-10.555589]\n",
-            "Episode 342: Total Reward = [-3.7850003]\n",
-            "Episode 343: Total Reward = [-10.121953]\n",
-            "Episode 344: Total Reward = [-3.5275638]\n",
-            "Episode 345: Total Reward = [-6.5529757]\n",
-            "Episode 346: Total Reward = [-11.735918]\n",
-            "Episode 347: Total Reward = [-12.06421]\n",
-            "Episode 348: Total Reward = [-9.325599]\n",
-            "Episode 349: Total Reward = [-4.7304945]\n",
-            "Episode 350: Total Reward = [-9.17161]\n",
-            "Episode 351: Total Reward = [-6.6971755]\n",
-            "Episode 352: Total Reward = [-8.564734]\n",
-            "Episode 353: Total Reward = [-8.58038]\n",
-            "Episode 354: Total Reward = [-11.143603]\n",
-            "Episode 355: Total Reward = [-11.0648155]\n",
-            "Episode 356: Total Reward = [-0.0163045]\n",
-            "Episode 357: Total Reward = [-11.745212]\n",
-            "Episode 358: Total Reward = [-4.416221]\n",
-            "Episode 359: Total Reward = [-4.2968345]\n",
-            "Episode 360: Total Reward = [-8.882763]\n",
-            "Episode 361: Total Reward = [-8.215191]\n",
-            "Episode 362: Total Reward = [-7.5643597]\n",
-            "Episode 363: Total Reward = [-5.1558523]\n",
-            "Episode 364: Total Reward = [-4.1805887]\n",
-            "Episode 365: Total Reward = [-7.843344]\n",
-            "Episode 366: Total Reward = [-9.884493]\n",
-            "Episode 367: Total Reward = [-3.072082]\n",
-            "Episode 368: Total Reward = [-6.24222]\n",
-            "Episode 369: Total Reward = [-7.44074]\n",
-            "Episode 370: Total Reward = [-5.2467575]\n",
-            "Episode 371: Total Reward = [-7.416548]\n",
-            "Episode 372: Total Reward = [-5.223847]\n",
-            "Episode 373: Total Reward = [-7.905354]\n",
-            "Episode 374: Total Reward = [-9.210388]\n",
-            "Episode 375: Total Reward = [-9.408889]\n",
-            "Episode 376: Total Reward = [-9.074169]\n",
-            "Episode 377: Total Reward = [-8.783485]\n",
-            "Episode 378: Total Reward = [-7.724216]\n",
-            "Episode 379: Total Reward = [-5.47056]\n",
-            "Episode 380: Total Reward = [-7.046235]\n",
-            "Episode 381: Total Reward = [-9.0465975]\n",
-            "Episode 382: Total Reward = [-5.971547]\n",
-            "Episode 383: Total Reward = [-7.713044]\n",
-            "Episode 384: Total Reward = [-9.371549]\n",
-            "Episode 385: Total Reward = [-6.0029483]\n",
-            "Episode 386: Total Reward = [-8.0601635]\n",
-            "Episode 387: Total Reward = [-6.660711]\n",
-            "Episode 388: Total Reward = [-3.7914395]\n",
-            "Episode 389: Total Reward = [-4.9596314]\n",
-            "Episode 390: Total Reward = [-7.1678224]\n",
-            "Episode 391: Total Reward = [-10.379615]\n",
-            "Episode 392: Total Reward = [-5.841095]\n",
-            "Episode 393: Total Reward = [-5.739059]\n",
-            "Episode 394: Total Reward = [-5.476198]\n",
-            "Episode 395: Total Reward = [-8.004061]\n",
-            "Episode 396: Total Reward = [-6.8199887]\n",
-            "Episode 397: Total Reward = [-3.4413033]\n",
-            "Episode 398: Total Reward = [-11.560875]\n",
-            "Episode 399: Total Reward = [-9.080534]\n",
-            "Episode 400: Total Reward = [-5.6159267]\n",
-            "Episode 401: Total Reward = [-6.838743]\n",
-            "Episode 402: Total Reward = [-11.0941925]\n",
-            "Episode 403: Total Reward = [-12.347287]\n",
-            "Episode 404: Total Reward = [-11.718087]\n",
-            "Episode 405: Total Reward = [-9.642539]\n",
-            "Episode 406: Total Reward = [-12.126976]\n",
-            "Episode 407: Total Reward = [-5.515474]\n",
-            "Episode 408: Total Reward = [-5.155887]\n",
-            "Episode 409: Total Reward = [-3.496639]\n",
-            "Episode 410: Total Reward = [-7.53995]\n",
-            "Episode 411: Total Reward = [-6.83739]\n",
-            "Episode 412: Total Reward = [-4.778639]\n",
-            "Episode 413: Total Reward = [-6.748523]\n",
-            "Episode 414: Total Reward = [-9.194431]\n",
-            "Episode 415: Total Reward = [-11.172936]\n",
-            "Episode 416: Total Reward = [-5.6614985]\n",
-            "Episode 417: Total Reward = [-3.2513633]\n",
-            "Episode 418: Total Reward = [-5.4374866]\n",
-            "Episode 419: Total Reward = [-8.547086]\n",
-            "Episode 420: Total Reward = [-8.322181]\n",
-            "Episode 421: Total Reward = [-5.3579884]\n",
-            "Episode 422: Total Reward = [-5.861393]\n",
-            "Episode 423: Total Reward = [-8.108382]\n",
-            "Episode 424: Total Reward = [-12.110879]\n",
-            "Episode 425: Total Reward = [-2.9235196]\n",
-            "Episode 426: Total Reward = [-10.7372265]\n",
-            "Episode 427: Total Reward = [-6.941126]\n",
-            "Episode 428: Total Reward = [-8.082399]\n",
-            "Episode 429: Total Reward = [-7.0238442]\n",
-            "Episode 430: Total Reward = [-6.042348]\n",
-            "Episode 431: Total Reward = [-7.733967]\n",
-            "Episode 432: Total Reward = [-9.479974]\n",
-            "Episode 433: Total Reward = [-4.0262756]\n",
-            "Episode 434: Total Reward = [-12.965476]\n",
-            "Episode 435: Total Reward = [-5.1862836]\n",
-            "Episode 436: Total Reward = [-7.853198]\n",
-            "Episode 437: Total Reward = [-8.906016]\n",
-            "Episode 438: Total Reward = [-6.2571216]\n",
-            "Episode 439: Total Reward = [-6.821951]\n",
-            "Episode 440: Total Reward = [-7.966923]\n",
-            "Episode 441: Total Reward = [-9.2721615]\n",
-            "Episode 442: Total Reward = [-3.765447]\n",
-            "Episode 443: Total Reward = [-7.6465573]\n",
-            "Episode 444: Total Reward = [-7.636]\n",
-            "Episode 445: Total Reward = [-5.110538]\n",
-            "Episode 446: Total Reward = [-7.254657]\n",
-            "Episode 447: Total Reward = [-4.0453153]\n",
-            "Episode 448: Total Reward = [-2.5903356]\n",
-            "Episode 449: Total Reward = [-2.9406614]\n",
-            "Episode 450: Total Reward = [-9.305373]\n",
-            "Episode 451: Total Reward = [-6.3455725]\n",
-            "Episode 452: Total Reward = [-11.124517]\n",
-            "Episode 453: Total Reward = [-5.0577407]\n",
-            "Episode 454: Total Reward = [-10.3903675]\n",
-            "Episode 455: Total Reward = [-8.421566]\n",
-            "Episode 456: Total Reward = [-10.779638]\n",
-            "Episode 457: Total Reward = [-6.7059927]\n",
-            "Episode 458: Total Reward = [-8.873179]\n",
-            "Episode 459: Total Reward = [-3.0017097]\n",
-            "Episode 460: Total Reward = [-8.580726]\n",
-            "Episode 461: Total Reward = [-5.850458]\n",
-            "Episode 462: Total Reward = [-3.450256]\n",
-            "Episode 463: Total Reward = [-7.691778]\n",
-            "Episode 464: Total Reward = [-10.074162]\n",
-            "Episode 465: Total Reward = [-4.1968994]\n",
-            "Episode 466: Total Reward = [-8.857825]\n",
-            "Episode 467: Total Reward = [-11.293989]\n",
-            "Episode 468: Total Reward = [-8.641296]\n",
-            "Episode 469: Total Reward = [-6.25516]\n",
-            "Episode 470: Total Reward = [-2.967138]\n",
-            "Episode 471: Total Reward = [-7.7442904]\n",
-            "Episode 472: Total Reward = [-6.9092755]\n",
-            "Episode 473: Total Reward = [-4.3945494]\n",
-            "Episode 474: Total Reward = [-8.815357]\n",
-            "Episode 475: Total Reward = [-7.0304337]\n",
-            "Episode 476: Total Reward = [-8.374066]\n",
-            "Episode 477: Total Reward = [-8.132146]\n",
-            "Episode 478: Total Reward = [-4.598558]\n",
-            "Episode 479: Total Reward = [-8.011614]\n",
-            "Episode 480: Total Reward = [-7.5608096]\n",
-            "Episode 481: Total Reward = [-8.954275]\n",
-            "Episode 482: Total Reward = [-9.885136]\n",
-            "Episode 483: Total Reward = [-8.592543]\n",
-            "Episode 484: Total Reward = [-8.243979]\n",
-            "Episode 485: Total Reward = [-9.713594]\n",
-            "Episode 486: Total Reward = [-8.735621]\n",
-            "Episode 487: Total Reward = [-9.34907]\n",
-            "Episode 488: Total Reward = [-10.153732]\n",
-            "Episode 489: Total Reward = [-4.795839]\n",
-            "Episode 490: Total Reward = [-5.6104407]\n",
-            "Episode 491: Total Reward = [-10.9914]\n",
-            "Episode 492: Total Reward = [-5.15489]\n",
-            "Episode 493: Total Reward = [-8.5694475]\n",
-            "Episode 494: Total Reward = [-9.659996]\n",
-            "Episode 495: Total Reward = [-3.6822321]\n",
-            "Episode 496: Total Reward = [-7.2618623]\n",
-            "Episode 497: Total Reward = [-7.784929]\n",
-            "Episode 498: Total Reward = [-5.147265]\n",
-            "Episode 499: Total Reward = [-3.2987652]\n",
-            "Episode 500: Total Reward = [-10.168329]\n"
-          ]
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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[38;5;4mℹ This function will save, evaluate, generate a video of your agent,\n",
-            "create a model card and push everything to the hub. It might take up to 1min.\n",
-            "This is a work in progress: if you encounter a bug, please open an issue.\u001b[0m\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.11/dist-packages/stable_baselines3/common/evaluation.py:67: UserWarning: Evaluation environment is not wrapped with a ``Monitor`` wrapper. This may result in reporting modified episode lengths and rewards, if other wrappers happen to modify these. Consider wrapping environment first with ``Monitor`` wrapper.\n",
-            "  warnings.warn(\n"
-          ]
+            "layout": "IPY_MODEL_e097d0070f30469abbd542994e74bf81"
+          }
         },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Saving video to /tmp/tmpbf1n3199/-step-0-to-step-1000.mp4\n",
-            "Moviepy - Building video /tmp/tmpbf1n3199/-step-0-to-step-1000.mp4.\n",
-            "Moviepy - Writing video /tmp/tmpbf1n3199/-step-0-to-step-1000.mp4\n",
-            "\n"
-          ]
+        "f4987c77de034d6a9a5fbe63f5db9b9e": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "HTMLModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_adc0827744e1479b8833205a44c83e1b",
+            "placeholder": "​",
+            "style": "IPY_MODEL_9564480530bc447da27e15749428bdfd",
+            "value": "policy.optimizer.pth: 100%"
+          }
         },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": []
+        "f4c0efc7782e41e5966d2ae9eba94a6a": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "ProgressStyleModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
         },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Moviepy - Done !\n",
-            "Moviepy - video ready /tmp/tmpbf1n3199/-step-0-to-step-1000.mp4\n",
-            "\u001b[38;5;1m✘ 'DummyVecEnv' object has no attribute 'video_recorder'\u001b[0m\n",
-            "\u001b[38;5;1m✘ We are unable to generate a replay of your agent, the package_to_hub\n",
-            "process continues\u001b[0m\n",
-            "\u001b[38;5;1m✘ Please open an issue at\n",
-            "https://github.com/huggingface/huggingface_sb3/issues\u001b[0m\n",
-            "\u001b[38;5;4mℹ Pushing repo oussamab2n/a2c-panda-reach to the Hugging Face Hub\u001b[0m\n"
-          ]
+        "f824d4ff15ec435f94fd0ffd1950064a": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
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-              " View run <strong style=\"color:#cdcd00\">chocolate-pond-14</strong> at: <a href='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/panda-gym/runs/w0uqcle5' target=\"_blank\">https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/panda-gym/runs/w0uqcle5</a><br> View project at: <a href='https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/panda-gym' target=\"_blank\">https://wandb.ai/benyahiamohammedoussama-ecole-central-lyon/panda-gym</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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-      "source": [
-        "### evalute"
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-        "import gymnasium as gym\n",
-        "import panda_gym\n",
-        "import wandb\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from stable_baselines3 import A2C\n",
-        "from huggingface_sb3 import load_from_hub\n",
-        "from gymnasium.wrappers import RecordVideo\n",
-        "import os\n",
-        "from IPython.display import Video, display\n",
-        "\n",
-        "# Initialize Weights & Biases for evaluation\n",
-        "wandb.init(project=\"panda-gym\", name=\"evaluation\", config={\"num_episodes\": 100})\n",
-        "\n",
-        "# Load the model from Hugging Face Hub\n",
-        "repo_id = \"oussamab2n/a2c-panda-reach\"\n",
-        "filename = \"a2c-panda-reach.zip\"\n",
-        "\n",
-        "model_path = load_from_hub(repo_id=repo_id, filename=filename)\n",
-        "model = A2C.load(model_path)\n",
-        "\n",
-        "# Create video folder\n",
-        "video_dir = \"videos\"\n",
-        "os.makedirs(video_dir, exist_ok=True)\n",
-        "\n",
-        "# Create environment with video recording\n",
-        "env = gym.make(\"PandaReachJointsDense-v3\", render_mode=\"rgb_array\")\n",
-        "env = RecordVideo(env, video_folder=video_dir, episode_trigger=lambda e: e % 10 == 0)  # Record every 10 episodes\n",
-        "\n",
-        "# Run evaluation\n",
-        "num_episodes = 100\n",
-        "success_count = 0\n",
-        "episode_rewards = []\n",
-        "truncation_rewards = []  # Store reward at truncation\n",
-        "\n",
-        "for episode in range(num_episodes):\n",
-        "    obs, _ = env.reset()\n",
-        "    done = False\n",
-        "    total_reward = 0\n",
-        "    truncation_reward = None  # Initialize reward at truncation\n",
-        "\n",
-        "    while not done:\n",
-        "        action, _ = model.predict(obs, deterministic=True)\n",
-        "        obs, reward, terminated, truncated, _ = env.step(action)\n",
-        "\n",
-        "        total_reward += reward\n",
-        "\n",
-        "        # Capture the last reward before truncation\n",
-        "        if truncated:\n",
-        "            truncation_reward = total_reward  # Store the reward at truncation\n",
-        "            success_count += 1\n",
-        "\n",
-        "        done = terminated or truncated\n",
-        "\n",
-        "    episode_rewards.append(total_reward)\n",
-        "\n",
-        "    # Store the threshold reward if truncation happened\n",
-        "    if truncation_reward is not None:\n",
-        "        truncation_rewards.append(truncation_reward)\n",
-        "\n",
-        "    wandb.log({\"Episode Reward\": total_reward, \"Truncation Reward\": truncation_reward})\n",
-        "\n",
-        "    print(f\"Episode {episode+1}: Total Reward = {total_reward}, Truncation Reward = {truncation_reward}\")\n",
-        "\n",
-        "# Compute the average truncation reward (if any truncations occurred)\n",
-        "if truncation_rewards:\n",
-        "    avg_truncation_reward = np.mean(truncation_rewards)\n",
-        "else:\n",
-        "    avg_truncation_reward = None\n",
-        "\n",
-        "print(f\"\\nTotal episodes with truncation: {success_count}/{num_episodes}\")\n",
-        "print(f\"Average reward at truncation: {avg_truncation_reward}\")\n",
-        "\n",
-        "# Close the environment safely\n",
-        "try:\n",
-        "    env.close()\n",
-        "except Exception as e:\n",
-        "    print(f\"Warning: Unable to close environment properly: {e}\")\n",
-        "\n",
-        "# Plot Total Reward per Episode\n",
-        "plt.figure(figsize=(10, 5))\n",
-        "plt.plot(range(1, num_episodes + 1), episode_rewards, marker=\"o\", linestyle=\"-\", label=\"Episode Reward\")\n",
-        "if truncation_rewards:\n",
-        "    plt.axhline(y=avg_truncation_reward, color=\"r\", linestyle=\"--\", label=f\"Avg Truncation Reward ({avg_truncation_reward:.2f})\")\n",
-        "plt.xlabel(\"Episode\")\n",
-        "plt.ylabel(\"Total Reward\")\n",
-        "plt.title(\"Total Reward per Episode (Evaluation)\")\n",
-        "plt.legend()\n",
-        "plt.grid()\n",
-        "plt.show()\n",
-        "\n",
-        "wandb.finish()\n"
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-            "Episode 1: Total Reward = -12.330416902899742, Truncation Reward = -12.330416902899742\n",
-            "Episode 2: Total Reward = -7.438601955771446, Truncation Reward = -7.438601955771446\n",
-            "Episode 3: Total Reward = -7.99461267888546, Truncation Reward = -7.99461267888546\n",
-            "Episode 4: Total Reward = -5.936902545392513, Truncation Reward = -5.936902545392513\n",
-            "Episode 5: Total Reward = -9.3701601177454, Truncation Reward = -9.3701601177454\n",
-            "Episode 6: Total Reward = -7.898426979780197, Truncation Reward = -7.898426979780197\n",
-            "Episode 7: Total Reward = -11.39137826859951, Truncation Reward = -11.39137826859951\n",
-            "Episode 8: Total Reward = -9.334654226899147, Truncation Reward = -9.334654226899147\n",
-            "Episode 9: Total Reward = -7.807370632886887, Truncation Reward = -7.807370632886887\n",
-            "Episode 10: Total Reward = -0.045403968542814255, Truncation Reward = None\n",
-            "Episode 11: Total Reward = -9.252317532896996, Truncation Reward = -9.252317532896996\n",
-            "Episode 12: Total Reward = -5.457004480063915, Truncation Reward = -5.457004480063915\n",
-            "Episode 13: Total Reward = -10.861534595489502, Truncation Reward = -10.861534595489502\n",
-            "Episode 14: Total Reward = -5.093386605381966, Truncation Reward = -5.093386605381966\n",
-            "Episode 15: Total Reward = -3.9243303686380386, Truncation Reward = -3.9243303686380386\n",
-            "Episode 16: Total Reward = -3.915019504725933, Truncation Reward = -3.915019504725933\n",
-            "Episode 17: Total Reward = -3.334887094795704, Truncation Reward = -3.334887094795704\n",
-            "Episode 18: Total Reward = -7.133811637759209, Truncation Reward = -7.133811637759209\n",
-            "Episode 19: Total Reward = -10.472936138510704, Truncation Reward = -10.472936138510704\n",
-            "Episode 20: Total Reward = -3.7241543605923653, Truncation Reward = -3.7241543605923653\n",
-            "Episode 21: Total Reward = -10.306911543011665, Truncation Reward = -10.306911543011665\n",
-            "Episode 22: Total Reward = -8.239186570048332, Truncation Reward = -8.239186570048332\n",
-            "Episode 23: Total Reward = -4.929756365716457, Truncation Reward = -4.929756365716457\n",
-            "Episode 24: Total Reward = -11.338424906134605, Truncation Reward = -11.338424906134605\n",
-            "Episode 25: Total Reward = -4.740497775375843, Truncation Reward = -4.740497775375843\n",
-            "Episode 26: Total Reward = -10.669795244932175, Truncation Reward = -10.669795244932175\n",
-            "Episode 27: Total Reward = -9.724496230483055, Truncation Reward = -9.724496230483055\n",
-            "Episode 28: Total Reward = -8.491846114397049, Truncation Reward = -8.491846114397049\n",
-            "Episode 29: Total Reward = -4.465587489306927, Truncation Reward = -4.465587489306927\n",
-            "Episode 30: Total Reward = -11.19448609650135, Truncation Reward = -11.19448609650135\n",
-            "Episode 31: Total Reward = -4.325973592698574, Truncation Reward = -4.325973592698574\n",
-            "Episode 32: Total Reward = -4.037351161241531, Truncation Reward = -4.037351161241531\n",
-            "Episode 33: Total Reward = -6.567140996456146, Truncation Reward = -6.567140996456146\n",
-            "Episode 34: Total Reward = -5.43791987746954, Truncation Reward = -5.43791987746954\n",
-            "Episode 35: Total Reward = -9.746163427829742, Truncation Reward = -9.746163427829742\n",
-            "Episode 36: Total Reward = -5.587778821587563, Truncation Reward = -5.587778821587563\n",
-            "Episode 37: Total Reward = -11.029212862253189, Truncation Reward = -11.029212862253189\n",
-            "Episode 38: Total Reward = -9.79325357079506, Truncation Reward = -9.79325357079506\n",
-            "Episode 39: Total Reward = -10.255291074514389, Truncation Reward = -10.255291074514389\n",
-            "Episode 40: Total Reward = -10.17457777261734, Truncation Reward = -10.17457777261734\n",
-            "Episode 41: Total Reward = -9.76257972419262, Truncation Reward = -9.76257972419262\n",
-            "Episode 42: Total Reward = -7.560746073722839, Truncation Reward = -7.560746073722839\n",
-            "Episode 43: Total Reward = -7.421035185456276, Truncation Reward = -7.421035185456276\n",
-            "Episode 44: Total Reward = -4.329387791454792, Truncation Reward = -4.329387791454792\n",
-            "Episode 45: Total Reward = -8.956344172358513, Truncation Reward = -8.956344172358513\n",
-            "Episode 46: Total Reward = -7.634533554315567, Truncation Reward = -7.634533554315567\n",
-            "Episode 47: Total Reward = -4.648341238498688, Truncation Reward = -4.648341238498688\n",
-            "Episode 48: Total Reward = -5.358693726360798, Truncation Reward = -5.358693726360798\n",
-            "Episode 49: Total Reward = -5.256292395293713, Truncation Reward = -5.256292395293713\n",
-            "Episode 50: Total Reward = -10.259695574641228, Truncation Reward = -10.259695574641228\n",
-            "Episode 51: Total Reward = -8.46430104970932, Truncation Reward = -8.46430104970932\n",
-            "Episode 52: Total Reward = -12.68241798877716, Truncation Reward = -12.68241798877716\n",
-            "Episode 53: Total Reward = -10.03714995086193, Truncation Reward = -10.03714995086193\n",
-            "Episode 54: Total Reward = -9.390622049570084, Truncation Reward = -9.390622049570084\n",
-            "Episode 55: Total Reward = -3.6870924681425095, Truncation Reward = -3.6870924681425095\n",
-            "Episode 56: Total Reward = -2.590249042958021, Truncation Reward = -2.590249042958021\n",
-            "Episode 57: Total Reward = -10.292054265737534, Truncation Reward = -10.292054265737534\n",
-            "Episode 58: Total Reward = -3.809460587799549, Truncation Reward = -3.809460587799549\n",
-            "Episode 59: Total Reward = -6.417629435658455, Truncation Reward = -6.417629435658455\n",
-            "Episode 60: Total Reward = -9.903368830680847, Truncation Reward = -9.903368830680847\n",
-            "Episode 61: Total Reward = -9.433162242174149, Truncation Reward = -9.433162242174149\n",
-            "Episode 62: Total Reward = -12.234330296516418, Truncation Reward = -12.234330296516418\n",
-            "Episode 63: Total Reward = -6.230593271553516, Truncation Reward = -6.230593271553516\n",
-            "Episode 64: Total Reward = -7.767891377210617, Truncation Reward = -7.767891377210617\n",
-            "Episode 65: Total Reward = -5.734282039105892, Truncation Reward = -5.734282039105892\n",
-            "Episode 66: Total Reward = -6.636941626667976, Truncation Reward = -6.636941626667976\n",
-            "Episode 67: Total Reward = -5.25225392729044, Truncation Reward = -5.25225392729044\n",
-            "Episode 68: Total Reward = -6.527664542198181, Truncation Reward = -6.527664542198181\n",
-            "Episode 69: Total Reward = -9.186537191271782, Truncation Reward = -9.186537191271782\n",
-            "Episode 70: Total Reward = -8.276378333568573, Truncation Reward = -8.276378333568573\n",
-            "Episode 71: Total Reward = -5.645369186997414, Truncation Reward = -5.645369186997414\n",
-            "Episode 72: Total Reward = -7.278434291481972, Truncation Reward = -7.278434291481972\n",
-            "Episode 73: Total Reward = -5.579865328967571, Truncation Reward = -5.579865328967571\n",
-            "Episode 74: Total Reward = -6.590580552816391, Truncation Reward = -6.590580552816391\n",
-            "Episode 75: Total Reward = -8.604996785521507, Truncation Reward = -8.604996785521507\n",
-            "Episode 76: Total Reward = -5.933413483202457, Truncation Reward = -5.933413483202457\n",
-            "Episode 77: Total Reward = -9.150287717580795, Truncation Reward = -9.150287717580795\n",
-            "Episode 78: Total Reward = -7.877498239278793, Truncation Reward = -7.877498239278793\n",
-            "Episode 79: Total Reward = -9.337234199047089, Truncation Reward = -9.337234199047089\n",
-            "Episode 80: Total Reward = -8.02486552298069, Truncation Reward = -8.02486552298069\n",
-            "Episode 81: Total Reward = -9.83842833340168, Truncation Reward = -9.83842833340168\n",
-            "Episode 82: Total Reward = -9.059891402721405, Truncation Reward = -9.059891402721405\n",
-            "Episode 83: Total Reward = -9.86920890212059, Truncation Reward = -9.86920890212059\n",
-            "Episode 84: Total Reward = -8.623764723539352, Truncation Reward = -8.623764723539352\n",
-            "Episode 85: Total Reward = -7.960148394107819, Truncation Reward = -7.960148394107819\n",
-            "Episode 86: Total Reward = -8.71947793662548, Truncation Reward = -8.71947793662548\n",
-            "Episode 87: Total Reward = -6.112355023622513, Truncation Reward = -6.112355023622513\n",
-            "Episode 88: Total Reward = -7.338854789733887, Truncation Reward = -7.338854789733887\n",
-            "Episode 89: Total Reward = -6.079448983073235, Truncation Reward = -6.079448983073235\n",
-            "Episode 90: Total Reward = -5.677617602050304, Truncation Reward = -5.677617602050304\n",
-            "Episode 91: Total Reward = -6.817344158887863, Truncation Reward = -6.817344158887863\n",
-            "Episode 92: Total Reward = -4.7121462225914, Truncation Reward = -4.7121462225914\n",
-            "Episode 93: Total Reward = -8.303785011172295, Truncation Reward = -8.303785011172295\n",
-            "Episode 94: Total Reward = -9.318224221467972, Truncation Reward = -9.318224221467972\n",
-            "Episode 95: Total Reward = -5.489169955253601, Truncation Reward = -5.489169955253601\n",
-            "Episode 96: Total Reward = -5.069115057587624, Truncation Reward = -5.069115057587624\n",
-            "Episode 97: Total Reward = -9.525949344038963, Truncation Reward = -9.525949344038963\n",
-            "Episode 98: Total Reward = -8.347278237342834, Truncation Reward = -8.347278237342834\n",
-            "Episode 99: Total Reward = -10.47582121193409, Truncation Reward = -10.47582121193409\n",
-            "Episode 100: Total Reward = -9.687124326825142, Truncation Reward = -9.687124326825142\n",
-            "\n",
-            "Total episodes with truncation: 99/100\n",
-            "Average reward at truncation: -7.6819928002101605\n"
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++Wbs2LEDgwYNwqeffooLLrjAJxh78skn8cgjj+CWW27B448/jszMTKjVatxzzz0Rv2MqlSpo4OevGq5fvx6XXXYZxowZg1deeQV5eXnQ6XRYsmQJPvroo6jeo1hCOQcGGz87763JXZEgWiIUVBEE0SLo3LkzfvrpJ9TW1vooAPv27eMfZ4RaEf78888xffp0PPvss/w2i8US4O4VDRqNBgsXLsT48ePx0ksv4aGHHkK3bt0AuJ3AxCgoo0ePxrp169C1a1cMGjQIKSkpGDhwINLS0rBy5Ups27bNx4lt165dOHDgAN577z3cdNNN/PZVq1aJHnfnzp3hcrlQWFjos1rP0hDFwpQoBsdxOHToED/pZ6pMamqqqHMRT/zHCgAHDhyA2WxGdnZ22Oeec845OOecc/DEE0/go48+wrRp07Bs2TLceuutoq9VseecOQM6nU5Z50yr1aJ79+4oKiqS/Fwhl19+Of72t7/xKYAHDhzA3Llzffb5/PPPMX78eLz99ts+26uqqiIGFRkZGUHT5/xVoi+++AJGoxE//PCDjyIsTFdkiFWH2Geyf/9+/vsKADabDUVFRVFdq0VFRVCr1ejVq5fsYxAE0fRQTRVBEC2CKVOmwOl08tbNjOeeew4qlcrHcSspKSlooKTRaAJWkl988cWQ9TNyGTduHIYPH47nn38eFosFOTk5GDduHF5//XWUlJQE7F9eXu7z9+jRo3HkyBF88sknfDqgWq3GqFGjsHjxYtjtdp96KrZqLnxvHMfhhRdeED1mdv6Edu4A8Pzzz4s+BuBu2lxbW8v//fnnn6OkpIQ//pAhQ9C9e3csWrQIdXV1Ac/3PxfxZOPGjT41OMePH8dXX32FCy+8MKQSUVlZGXANMcWNpaGJvVbFnnONRoOrrroKX3zxBXbv3h0wJjHnbOTIkfj9998j7heO9PR0TJo0CZ9++imWLVsGvV4f0FQ32Hfss88+42sGw9G9e3fs27fP5/3s3LkzwGpfo9FApVL5fG+PHDmC5cuXBxwz1L3AnwkTJkCv1+O///2vz/jffvttVFdXS3bBFPLHH3+gf//+SEtLk30MgiCaHlKqCIJoEVx66aUYP348Hn74YRw5cgQDBw7Ejz/+iK+++gr33HOPT13KkCFD8NNPP2Hx4sV8Ot2IESNwySWX4IMPPkBaWhr69euHjRs34qeffopprQfjgQcewNSpU/Huu+/ijjvuwMsvv4zzzjsPAwYMwG233YZu3bqhtLQUGzduxIkTJ3z6+LCAaf/+/XjyySf57WPGjMH3338Pg8GAYcOG8dv79OmD7t274/7770dxcTFSU1PxxRdfSEr3GjRoEK677jq88sorqK6uxqhRo/Dzzz/j0KFDkt53ZmYmzjvvPNx8880oLS3F888/jx49euC2224D4A4O33rrLVx00UXo378/br75ZrRv3x7FxcVYvXo1UlNT8c0330h6TX/Wr18Pi8USsP2ss87ySZPLz8/HpEmTfCzVAQTtx8R477338Morr+CKK65A9+7dUVtbizfffBOpqamYMmUKAPHXqpRz/p///AerV6/GiBEjcNttt6Ffv36oqKjAtm3b8NNPPwW1RBfyl7/8BR988AEOHDgQVDH5/PPPg6YuTpw4EW3btuX/vuaaa3DDDTfglVdewaRJk3jjDsYll1yCf//737j55psxatQo7Nq1Cx9++KGP+hOKW265BYsXL8akSZMwc+ZMlJWV4bXXXkP//v1RU1PD73fxxRdj8eLFmDx5Mq6//nqUlZXh5ZdfRo8ePQJqmkLdC/zJzs7G3LlzMX/+fEyePBmXXXYZ9u/fj1deeQXDhg2T3YTcbrdj7dq1+Pvf/y7r+QRBKIiEeg0SBEHECH9LdY7juNraWm7OnDlcu3btOJ1Ox/Xs2ZN75plnfKy5OY7j9u3bx40ZM4YzmUwcAN5SubKykrv55pu5Nm3acMnJydykSZO4ffv2cZ07d/axXZZqqR7MXtvpdHLdu3fnunfvzjkcDo7jOK6wsJC76aabuNzcXE6n03Ht27fnLrnkEu7zzz8PeH5OTg4HgCstLeW3bdiwgQPAjR49OmD/goICbsKECVxycjLXpk0b7rbbbuN27twZYFs9ffp0LikpKej7aWxs5O6++24uKyuLS0pK4i699FLu+PHjkizVP/74Y27u3LlcTk4OZzKZuIsvvtjH0p2xfft27sorr+SysrI4g8HAde7cmfvrX//K/fzzz/w+zO67vLw87Gv7jyHUP+F7AMDdeeed3NKlS7mePXtyBoOBGzx4cMBn7m+9vW3bNu66667jOnXqxBkMBi4nJ4e75JJLfKzZOU78tSrlnJeWlnJ33nkn17FjR06n03G5ubncBRdcwL3xxhsRz43VauXatGnDPf744z7bw1mqB/sO1NTU8N+rpUuXBryOxWLh7rvvPi4vL48zmUzcueeey23cuDHALj2YpTrHcdzSpUu5bt26cXq9nhs0aBD3ww8/BLVUf/vtt/nPrU+fPtySJUv49yIk1L3A/3NlvPTSS1yfPn04nU7HtW3blps1axZXWVnps8/YsWO5/v37B7z3YOP8/vvvOQDcwYMHA/YnCKJ5oeI4EZ0SCYIgCCIK1qxZg/Hjx+Ozzz7D1Vdf3dTDiYhKpcKdd94ZkKLXknn88cexZMkSHDx4MGR6IxFbLr/8cqhUKnz55ZdNPRSCIKKEaqoIgiAIgsCcOXNQV1eHZcuWNfVQWgV79+7Ft99+y1vQEwTRvKGaKoIgCIIgkJycLKqfFREb+vbtC4fD0dTDIAgiRpBSRRAEQRAEQRAEEQVUU0UQBEEQBEEQBBEFpFQRBEEQBEEQBEFEAQVVBEEQBEEQBEEQUUBGFX64XC6cPHkSKSkpUKlUTT0cgiAIgiAIgiCaCI7jUFtbi3bt2kGtDq1HUVDlx8mTJ9GxY8emHgZBEARBEARBEArh+PHj6NChQ8jHKajyIyUlBYD7xKWmpsb99ex2O3788UdceOGF0Ol0cX89omVA1w0hB7puCLnQtUPIga4bQg5Ku25qamrQsWNHPkYIBQVVfrCUv9TU1IQFVWazGampqYq4cIjmAV03hBzouiHkQtcOIQe6bgg5KPW6iVQWREYVBEEQBEEQBEEQUUBBFUEQBEEQBEEQRBRQUEUQBEEQBEEQBBEFVFNFEARBEESrhOM4OBwOOJ3Oph5Ki8Rut0Or1cJisdA5JkST6OtGo9FAq9VG3UqJgiqCIAiCIFodNpsNJSUlaGhoaOqhtFg4jkNubi6OHz9OvT8J0TTFdWM2m5GXlwe9Xi/7GBRUEQRBEATRqnC5XCgqKoJGo0G7du2g1+tp0h8HXC4X6urqkJycHLZpKkEISeR1w3EcbDYbysvLUVRUhJ49e8p+TQqqCIIgCIJoVdhsNrhcLnTs2BFms7mph9NicblcsNlsMBqNFFQRokn0dWMymaDT6XD06FH+deVAVzhBEARBEK0SmugTBAHE5l5AdxOCIAiCIAiCIIgoaJFB1csvv4wuXbrAaDRixIgR2LJlS1MPiZCI08VhY+EZfLWjGBsLz8Dp4pp6SARBEARBEAQRlBYXVH3yySe49957MW/ePGzbtg0DBw7EpEmTUFZW1tRDI0SycncJznvqF1z35ibMXrYD1725Cec99QtW7i5p6qERBEEQhA/NcRHwyJEjUKlU2LFjR9xeY8aMGbjiiividvzmQJcuXfD888839TCIBNHigqrFixfjtttuw80334x+/frhtddeg9lsxjvvvNPUQyNEsHJ3CWYt3YaSaovP9lPVFsxauo0CK4IgCEIxNMUi4IwZM6BSqQL+TZ48WfQxOnbsiJKSEuTn58dtnLFg3Lhx/PszGo3o1asXFi5cCI5TfuBKtD5alPufzWbDH3/8gblz5/Lb1Go1JkyYgI0bNwZ9jtVqhdVq5f+uqakB4G48Zrfb4ztgz+sI/78143RxeOzrPQh2q+QAqADM/2YPxvXMgkbduq1v6boh5EDXDSGXlnbt2O12cBwHl8sFl8sl6xgrd5/CnR9tD/jNYouAL18/GJPzc6MfrB8cx2HSpEkBi8UGg0H0e1GpVMjJyQEA2e8/EsLAh51rOdx6662YP38+rFYrfvnlF9xxxx1ITU3FrFmzYjXUqHA6nVCpVCGNDqJ5760Vdu0k8ty5XC5wHAe73Q6NRuPzmNj7XosKqk6fPg2n04m2bdv6bG/bti327dsX9DkLFy7E/PnzA7b/+OOPCbVZXbVqVcJeS6kcrFbhVI0m5OMcgJJqK176ZCV6ptEqFUDXDSEPum4IubSUa0er1SI3Nxd1dXWw2WwA3BM4i13cBC7SIiAAPPbNHpyVoxe1CGjUqUX3yWKTvmBzFLYwnJGRgUWLFuH777/Hr7/+irZt22L+/Pn4y1/+AgA4duwYBg4ciHXr1mHAgAGoqqrCAw88gNWrV6O+vh7t2rXDvffei2nTpgEA9uzZg7lz52Lr1q0wmUy47LLLsGDBAiQnJ7vPh9OJRx99FEuXLoVGo8ENN9wAm80Gh8MBAKitrYXL5cLzzz+P9957D2VlZejevTseeOABfkzBcDgc0Gq1MJvNMJvNuOqqq/Diiy9i5cqV/NisVisWLFiAL774AtXV1ejbty8ee+wxnHfeeeA4Dj179sSzzz7Lv87o0aNRXl7Ozws3btyIyy+/HEVFRTCbzXj55Zfx4Ycf4ujRo0hPT8fkyZMxf/58/r1+9NFHmDt3Ll599VX8+9//xqFDh7Bt2zaYTCb84x//wNq1a5GTk4OHH34YLpcLFouF/1wIadTW1ibstWw2GxobG7Fu3Tr+umWIbRDeooIqOcydOxf33nsv/3dNTQ06duyICy+8EKmpqXF/fbvdjlWrVmHixInQ6XRxfz0l882fJUDBroj7des/CFPOykvAiJQLXTeEHOi6IeTS0q4di8WC48ePIzk5me9J02BzYPBTsQsay2ptOO/5zaL23f3YRJj14qZkOp0OWq024hxl4cKFePLJJ/HSSy9h6dKlmDlzJoYOHYq+ffvyAUJSUhJSU1Px8MMP49ChQ1ixYgXatGmDQ4cOobGxEampqaivr8fUqVNxzjnnYPPmzSgrK8Ptt9+Ohx9+GEuWLAEAPPPMM/j444/x9ttvo2/fvli8eDG+++47jB8/HgCQkpKChQsX4rPPPsNrr72Gnj17Yt26dfjb3/6GTp06YezYsUHfg1arhV6vR2pqKjiOw4YNG3Dw4EH07t2bf/+333479u7di48//hjt2rXD8uXLcfXVV2Pnzp3o2bMnxowZgy1btuDGG29EZWUlDhw4AJPJhJMnT6JPnz74448/MGzYMOTmulVFs9mMF198EV27dsXhw4dx11134YknnsDLL78MADAajWhsbMTLL7+Mt956C1lZWejYsSOmTp2KU6dO4eeff4ZOp8M999yD06dPw2g0JmQ+2ZLgOA61tbVISUlJWFNui8UCk8mEMWPGBPSpEhsUt6igqk2bNtBoNCgtLfXZXlpayn9Z/DEYDDAYDAHbdTpdQn84Ev16SiQvPUn0fq39XDHouiHkQNcNIZeWcu0IU7ZY2lZT9qwSjiMSKpUK3333XcBE/V//+hf+9a9/8X9PnToVt99+OwBgwYIF+Omnn/Dyyy/jlVde8XnParUax48fx+DBgzF8+HAAQLdu3fjjLFu2DBaLBR988AGSkty/0y+99BIuvfRSPP3002jbti1eeOEFzJ07F1dffTUA4PXXX8ePP/7IH8Nms2HhwoX46aefMHLkSABAjx498Ntvv+HNN9/kg69gvPrqq3j77bdhs9lgt9thNBoxe/ZsqNVqHDt2DO+++y6OHTuGdu3aAQAeeOAB/PDDD3jvvffw5JNPYvz48Xj99dehVquxYcMGDB48GLm5uVi3bh369euHtWvXYuzYsfw5mTNnDv/a3bp1w4IFC3DHHXfg1Vdf5c+Z3W7HK6+8goEDBwIADhw4gJUrV2LLli0YNmwYAPABZrjUQCI4LOUvkedOrXarxcHucWLveS0qqNLr9RgyZAh+/vlnXH755QDcH8zPP/+Mu+66q2kHR0RkeNdM5KUZcaraEjSlQgUgN82I4V0zEz00giAIooVj0mlQ8O9JovbdUlSBGUu2Rtzv3ZuHifrNMulCp74HY/z48fwkn5GZ6fs6LHgR/h3K7W/WrFm46qqrsG3bNlx44YW4/PLLMWrUKADA3r17MXDgQD6gAoBzzz0XLpcL+/fvh9FoRElJCUaMGME/rtVqMXToUH5yfOjQITQ0NGDixIk+r2uz2TB48OCw73XatGl4+OGHUVlZiXnz5mHUqFH82Hbt2gWn04levXr5PMdqtSIrKwsAMHbsWMyePRvl5eVYu3Ytxo0bh9zcXKxZswYzZ87Eb7/9hn/+85/8c3/66ScsXLgQ+/btQ01NDRwOBywWCxoaGviUS71ej7POOot/zt69e6HVajFkyBB+W58+fZCenh72vREtixYVVAHAvffei+nTp2Po0KEYPnw4nn/+edTX1+Pmm29u6qEREdCoVZh3aT/MWrot4DEm/s67tF+rN6kgCIIgYo9KpRKdgje6Z7aoRcDRPbPj8puVlJSEHj16xOx4F110EY4ePYoVK1Zg1apVuOCCC3DnnXdi0aJFMTl+XV0dAOC7775D+/btfR4Lli0kJC0tjX+vn376KXr06IFzzjkHEyZMQF1dHTQaDf74448AcwGW4jhgwABkZmZi7dq1WLt2LZ544gnk5ubiqaeewtatW2G32/kg7ciRI7jkkkswa9YsPPHEE8jMzMSGDRswc+ZM2Gw2PqgymUwJS0sjmg8tTo+85pprsGjRIjz66KMYNGgQduzYgZUrVwaYVxDKZHJ+Hl694WykmXyl1tw0I1694WxMzm/dtVQEQRBE08MWAQHvoh9DKYuAmzZtCvi7b9++IffPzs7G9OnTsXTpUjz//PN44403AAB9+/bFzp07UV9fz+/766+/Qq1Wo3fv3khLS0NeXh42b/bWjzkcDvzxxx/83/369YPBYMCxY8fQo0cPn38dO3YU/Z6Sk5Mxe/Zs3H///eA4DoMHD4bT6URZWVnAcVnZh0qlwujRo/HVV19hz549OO+883DWWWfBarXi9ddfx9ChQ3kV7o8//oDL5cKzzz6Lc845B7169cLJkycjjqtPnz4B73n//v2oqqoS/d6I5k+LC6oA4K677sLRo0dhtVqxefNmH0maUD6T8/MwZ2JP/u+PbxuBDQ+eTwEVQRAEoRjYImBumm9ReyIWAa1WK06dOuXz7/Tp0z77fPbZZ3jnnXdw4MABzJs3D1u2bAlZCvHoo4/iq6++wqFDh7Bnzx58++23fAA2bdo0GI1GTJ8+Hbt378bq1avxj3/8AzfeeCO/YD179mz85z//wfLly7Fv3z78/e9/9wkoUlJScP/992POnDl47733UFhYiG3btuHFF1/Ee++9J+m9/+1vf8OBAwfwxRdfoFevXpg2bRpuuukm/O9//0NRURG2bNmChQsX4rvvvuOfM27cOHz88ccYNGgQkpOToVarMWbMGHz44Yc+Jhk9evSA3W7Hiy++iMOHD+ODDz7Aa6+9FnFMvXv3xuTJk/G3v/0Nmzdvxh9//IFbb70VJpNJ0nsjmjctLv2PaBnYHd6EiiGdMynljyAIglAck/PzMLFfLrYUVaCs1oKcFHfdb7x/s1auXIm8PN+grXfv3j7tY+bPn49ly5bh73//O/Ly8vDxxx+jX79+QY+n1+sxd+5cHDlyBCaTCaNHj8ayZcsAuN3wfvjhB8yePRvDhg3jrc0XL17MP/++++5DSUkJpk+fDrVajVtuuQVXXHGFT2D1+OOPIzs7GwsXLsThw4eRnp6Os88+28dcQwyZmZm46aab8Nhjj+HKK6/EkiVLsGDBAtx3330oLi5GmzZtcM455+CSSy7hnzN27Fg4nU6MGzeO3zZu3Dh89dVXPtsGDhyIxYsX46mnnsLcuXMxZswYLFy4EDfddFPEcS1ZsgS33norxo4di7Zt22LBggV45JFHJL03onmj4qgttQ81NTVIS0tDdXV1wizVV6xYgSlTprQIR6VY8eLPB/HsqgMAgN3zJyHZQPG/ELpuCDnQdUPIpaVdOxaLBUVFRejatWuAfXJLQKVS4csvv+RNu5oKl8uFmpoapKamkgMeIZqmuG7C3RPExgZ0hROKxOrwNmC0OagTOUEQBEEQBKFcKKgiFInF7uT/m4IqgiAIgiAIQslQThWhSIRKldXhDLMnQRAEQRBCqLKDIBIPKVWEIiGliiAIgiAIgmguUFBFKBJfpYqCKoIgCIIgCEK5UFBFKBIfpcpJQRVBEARBEAShXCioIhQJuf8RBEEQBEEQzQUKqghFIjSnoKCKIAiCIAiCUDIUVBGKxGInpYogCIIgCIJoHlBQRSgSMqogCIIgCCJeqFQqLF++vKmHoWgee+wxDBo0KOJ+jzzyCG6//fb4D0gGNpsNXbp0we+//x7316KgilAkVh+jCupTRRAEQRBCNm7cCI1Gg4svvjjurzVu3DioVKqQ/8aNGxf3McglVGBQUlKCiy66KK6v/e677/LnSK1WIy8vD9dccw2OHTsW19dNJKdOncILL7yAhx9+OOx+oa6dZ555JuzziouLccMNNyArKwsmkwkDBgzwCZDq6upw1113oUOHDjCZTOjXrx9ee+01/nG9Xo/7778fDz74YHRvVAQUVBGKhIwqCIIgCCI0b7/9Nv7xj39g3bp1OHnyZFxf63//+x9KSkpQUlKCLVu2AAB++uknftv//vc/n/3tdntcxxMLcnNzYTAY4v46qampKCkpQXFxMb744gvs378fU6dOjfvrSiGaz+utt97CqFGj0Llz57D7sWuF/XvnnXegUqlw1VVXhXxOZWUlzj33XOh0Onz//fcoKCjAs88+i4yMDH6fe++9FytXrsTSpUuxd+9e3HPPPbjrrrvw9ddf8/tMmzYNGzZswJ49e2S/TzFQUEUoEmr+SxAEQTQJ9fWh/1ks4vdtbBS3rwzq6urwySefYNasWbj44ovx7rvv8o9df/31uOaaa3z2t9vtaNOmDd5//30AQG1tLaZNm4akpCTk5eXhueeew7hx43DPPfcEfb3MzEzk5uYiNzcX2dnZAICsrCx+W1ZWFl599VVcdtllSEpKwhNPPIF3330XmZmZPsdZvnw5VCoV/zdTkT744AN06dIFaWlpuPbaa1FbW8vv43K58PTTT6NHjx4wGAzo1KkTnnjiCf7xBx98EL169YLZbEa3bt3wyCOP8EHCu+++i/nz52Pnzp28MsLOlX/6365du3D++efDZDIhKysLt99+O+rq6vjHZ8yYgcsvvxyLFi1CXl4esrKycOedd0YMSFQqFXJzc5GXl4dRo0Zh5syZ2LJlC2pqavh9vvrqK5x99tkwGo3o1q0b5s+fD4fDAQC4//77cckll/D7Pv/881CpVFi5ciW/rUePHnjrrbcAAFu3bsXEiRPRpk0bpKWlYezYsdi2bVvAmPw/LwD4z3/+g7Zt2yIlJQUzZ86Exf96D8KyZctw6aWXRtyPXSvs31dffYXx48ejW7duIZ/z9NNPo2PHjliyZAmGDx+Orl274sILL0T37t35fX777TdMnz4d48aNQ5cuXXD77bdj4MCBfPAPABkZGTj33HOxbNmyiOOMBgqqCEVCNVUEQRBEk5CcHPqf/6p6Tk7off1Ty7p0Cb6fDD799FP06dMHvXv3xg033IB33nkHHMcBcK/Kf/PNNz4BwQ8//ICGhgZcccUVANyr+7/++iu+/vprrFq1CuvXrw+YeEvlsccewxVXXIFdu3bhlltuEf28wsJCLF++HN9++y2+/fZbrF27Fv/5z3/4x+fOnYv//Oc/eOSRR1BQUICPPvoIbdu25R9PSUnBu+++i4KCArzwwgt488038dxzzwEArrnmGtx3333o378/r5D4B5wAUF9fj0mTJiEjIwNbt27FZ599hp9++gl33XWXz36rV69GYWEhVq9ejffeew/vvvuuT0AbibKyMnz55ZfQaDTQaDQAgPXr1+Omm27C7NmzUVBQgNdffx3vvvsuH+iMHTsWGzZsgNNTCrF27Vq0adMGa9asAeBOjyssLORTMGtrazF9+nRs2LABmzZtQs+ePTFlyhSfQBUI/Lw+/fRTPPbYY3jyySfx+++/Iy8vD6+88krY91NRUYGCggIMHTpU9DkAgNLSUnz33XeYOXNm2P2++eYbDB06FFOnTkVOTg4GDx6MN99802efUaNG4euvv0ZxcTE4jsPq1atx4MABXHjhhT77DR8+HOvXr5c0TslwhA/V1dUcAK66ujohr2ez2bjly5dzNpstIa/XXOg+9zuu84Pfcp0f/JZ7efXBph6O4qDrhpADXTeEXFratdPY2MgVFBRwjY2NgQ8Cof9NmeK7r9kcet+xY333bdMm+H4yGDVqFPf8889zHMdxdruda9OmDbd69Wqfv99//31+/+uuu4675pprOI7juJqaGk6n03GfffYZ/3hVVRVnNpu52bNnR3ztoqIiDgC3fft2fhsA7p577vHZb8mSJVxaWhpXWVnJOZ1OjuM47ssvv+SEU8958+ZxZrOZq6mp4bc98MAD3IgRI/ixGgwG7s033xRxVtw888wz3JAhQ3xeY+DAgQH7AeC+/PJLjuM47o033uAyMjK4uro6/vHvvvuOU6vV3KlTpziO47jp06dznTt35hwOB7/P1KlT+fMajCVLlnAAuKSkJM5sNnMAOADc3Xffze9zwQUXcE8++aTP8z744AMuLy+P4ziOq6ys5NRqNbd161bO5XJxmZmZ3MKFC/lztHTpUq59+/Yhx+B0OrmUlBTum2++8Xnv/p/XyJEjub///e8+20aMGBH03DG2b9/OAeCOHTsWcp9gPPXUU1xGRkbw759nzJWVlZzBYOAMBgM3d+5cbtu2bdzrr7/OGY1G7t133+X3tVgs3E033cQB4LRaLafX67n33nsv4JgvvPAC16VLl5BjCndPEBsbaOMbshGEdBxOFxwujv+b0v8IgiCIhCFQeALwqAs8ZWWh91X7JQMdOSJ7SEL279+PLVu24MsvvwQAaLVaXHPNNXj77bcxbtw4aLVa/PWvf8WHH36IG2+8EfX19fjqq6/41KfDhw/Dbrdj+PDh/DHT0tLQu3fvqMYlVa1gdOnSBSkpKfzfeXl5KPOc171798JqteKCCy4I+fxPPvkE//3vf1FYWIi6ujo4HA6kpqZKGsPevXsxcOBAJCUl8dvOPfdcuFwu7N+/n1fG+vfvzytMbKy7du0Ke+yUlBRs27YNdrsd33//PT788EOf9MWdO3fi119/9dnmdDphsVjQ0NCA9PR0DBw4EGvWrIFer4der8ftt9+OefPmoa6uDmvXrsXYsWP555aWluL//u//sGbNGpSVlcHpdKKhoSHAHMP/89q7dy/uuOMOn20jR47E6tWrQ763Rk+Kq9Fo5Ld9+OGH+Nvf/sb//f3332P06NE+z3vnnXcwbdo0n+cFw+VyYejQoXjyyScBAIMHD8bu3bvx2muvYfr06QCAF198EZs2bcLXX3+Nzp07Y926dbjzzjvRrl07TJgwgT+WyWRCQ0ND2NeLFgqqCMXhn+5H6X8EQRBEwhBMrJts3zC8/fbbcDgcaNeuHb+N4zgYDAa89NJLSEtLw7Rp0zB27FiUlZVh1apVMJlMmDx5ckxePxRJfu9PrVbzKYmMYPVHOp3O52+VSgWXy/27bzKZwr7mxo0bMW3aNMyfPx+TJk1CWloali1bhmeffVbOW4hIuLGGQq1Wo0ePHgCAvn37orCwELNmzcIHH3wAwF0fN3/+fFx55ZUBz2VBx7hx47BmzRoYDAaMHTsWmZmZ6Nu3LzZs2IC1a9fivvvu458zffp0nDlzBi+88AI6d+4Mg8GAkSNHwmaz+Rzb//OSQ5s2bQC4DSVYrd1ll12GESNG8Pu0b9/e5znr16/H/v378cknn0Q8fl5eHvr16+ezrW/fvvjiiy8AuIO6f/3rX/jyyy95F8yzzjoLO3bswKJFi3yCqoqKCn6M8YJqqgjF4R9EkVJFEARBEIDD4cD777+PZ599Fjt27OD/7dy5E+3atcPHH38MwF1n0rFjR3zyySf48MMPMXXqVD4g6NatG3Q6HbZu3coft7q6GgcOHIjpWLOzs1FbW4t6gRnHjh07JB2jZ8+eMJlM+Pnnn4M+/ttvv6Fz5854+OGHMXToUPTs2RNHjx712Uev1/P1SKHo27cvdu7c6TPWX3/9FWq1OmoFz5+HHnoIn3zyCV/DdvbZZ2P//v3o0aNHwD+1R+1kdVU///wzXzs1btw4fPzxxzhw4ICPpf2vv/6Ku+++G1OmTEH//v1hMBhw+vTpiOPq27cvNm/e7LNt06ZNYZ/TvXt3pKamoqCggN+WkpLi8x78A+O3334bQ4YMwcCBAyOOadSoUdi/f7/PtgMHDvBOg3a7HXa7nT9PDI1GExDs7t69G4MHD474mtFAQRWhOITOfwAFVQRBEAQBAN9++y0qKysxc+ZM5Ofn+/y76qqr8Pbbb/P7Xn/99XjttdewatUqTJs2jd+ekpKC6dOn44EHHsDq1auxZ88ezJw5E2q12seZL1pGjBgBs9mMxx9/HIWFhfjoo48kmToAbqXmwQcfxD//+U+8//77KCwsxKZNm/j32bNnTxw7dgzLli1DYWEh/vvf//JpkYwuXbqgqKgIO3bswOnTp2G1WgNeh6WiTZ8+Hbt378bq1avxj3/8AzfeeKOPKUYs6NixI6644go8+uijAIBHH30U77//PubPn489e/Zg7969WLZsGf7v//6Pf86YMWNQW1uLb7/91ieo+vDDD5GXl4devXrx+/bs2RMffPAB9u7di82bN2PatGkRFT8AmD17Nt555x0sWbIEBw4cwLx58yJakKvVakyYMAEbNmwQ9d5ramrw2Wef4dZbbw36+AUXXICXXnqJ//uee+7Bpk2b8OSTT+LQoUP46KOP8MYbb+DOO+8E4LarHzt2LB544AGsWbMGRUVFePfdd/H+++/zpiyM9evXB5hXxBoKqgjFQUoVQRAEQQTy9ttvY8KECUhLSwt47KqrrsLvv/+OP//8E4A7UCgoKED79u1x7rnn+uy7ePFijBw5EpdccgkmTJiAc889F3379o1Y4yKFzMxMvP/++1i1ahUGDhyIjz/+GI899pjk4zzyyCO477778Oijj6Jv37645ppr+Jqryy67DHPmzMFdd92FQYMG4bfffsMjjzzi8/yrrroKkydPxvjx45Gdnc2reULMZjN++OEHVFRUYNiwYbj66qsDJvixZM6cOfjuu++wZcsWTJo0Cd9++y1+/PFHDBs2DOeccw6ee+45n75PGRkZGDBgALKzs9GnTx8A7kDL5XL51FMB7muksrISZ599Nm688UbcfffdyMnJiTima665Bo888gj++c9/YsiQITh69ChmzZoV8Xm33norli1bFjENEnDbr3Mch+uuuy7o44WFhT6q2rBhw/Dll1/i448/Rn5+Ph5//HE8//zzPosEy5Ytw7BhwzBt2jT069cP//nPf/DEE0/41Idt3LgR1dXVuPrqqyOOMRpUnH/CayunpqYGaWlpqK6ullzoKAe73Y4VK1ZgypQpAbm6rZW9JTW46AWv7eUVg9vjuWsGNd2AFAhdN4Qc6Loh5NLSrh2LxYKioiJ07do1poFEc6W+vh7t27fHs88+G9HmWgoulws1NTVITU0NSNEiWgYcx2HEiBGYM2dOyGBJKrG+bq655hoMHDgQ//rXv0LuE+6eIDY2oCucUBykVBEEQRBE/Ni+fTs+/vhjFBYWYtu2bfzK/1/+8pcmHhnR3FCpVHjjjTf4ZsVKw2azYcCAAZgzZ07cX4vc/wjF4V9TRe5/BEEQBBFbFi1ahP3790Ov12PIkCFYv3497+ZGEFIYNGgQBg0a1NTDCIper/epT4snFFQRiiPQUj28aw9BEARBEOIZPHgw/vjjj6YeBkG0KCj9j1Ac5P5HEARBEARBNCcoqCIUR0BNlZOCKoIgCCL2kFcXQRBAbO4FFFQRisPqUarUnnYZpFQRBEEQsYQ5GDY0NDTxSAiCUALsXhCNuynVVBGKw+IJopINWtRYHBRUEQRBEDFFo9EgPT2d73dkNptj2viWcONyuWCz2WCxWMhSnRBNIq8bjuPQ0NCAsrIypKenQ6PRyD4WBVWE4mBKVapJ5w6qKP2PIAiCiDG5ubkAwAdWROzhOA6NjY0wmUwUtBKiaYrrJj09nb8nyIWCKkJxsJqqFKMOQCMpVQRBEETMUalUyMvLQ05ODux2e1MPp0Vit9uxbt06jBkzpkU0jSYSQ6KvG51OF5VCxaCgilAcvFJldF+e1KeKIAiCiBcajSYmEyoiEI1GA4fDAaPRSEEVIZrmet1QgiuhOFhNVarJ/UUipYogCIIgCIJQMhRUEYrDq1RRUEUQBEEQBEEoHwqqCMVhsbOaKnf6n83pol4iBEEQBEEQhGKhoIpQHFaHb00VQA2ACYIgCIIgCOVCQRWhOJhSxWqqAEoBJAiCIAiCIJQLBVWE4mBKVYpQqaKgiiAIgiAIglAoFFQRioMpVUadBlq1u+kb2aoTBEEQBEEQSoWCKkJxMKXKqNPAoHVfoqRUEQRBEARBEEqFgipCcTClyqBVQ8+CKjKqIAiCIAiCIBQKBVWE4hAqVXpSqgiCIAiCIAiFQ0EVoTiCKVVUU0UQBEEQBEEolRYTVB05cgQzZ85E165dYTKZ0L17d8ybNw82m62ph0ZIhAVQRp0Geg0pVQRBEARBEISy0UbepXmwb98+uFwuvP766+jRowd2796N2267DfX19Vi0aFFTD4+QgNXuTv9zK1UaAFRTRRAEQRAEQSiXFhNUTZ48GZMnT+b/7tatG/bv349XX32VgqpmhlCpYu5/LNAiCIIgCIIgCKXRYoKqYFRXVyMzMzPsPlarFVarlf+7pqYGAGC322G32+M6PvY6wv9v7bhcHK9KqeGCTuPuU9VoTczn0Vyg64aQA103hFzo2iHkQNcNIQelXTdix6HiOI6L81iahEOHDmHIkCFYtGgRbrvttpD7PfbYY5g/f37A9o8++ghmszmeQySCYHMCD2xxx/pPD3fg7f1q7K9W44YeTgzLbpGXKkEQBEEQBKFQGhoacP3116O6uhqpqakh91N8UPXQQw/hqaeeCrvP3r170adPH/7v4uJijB07FuPGjcNbb70V9rnBlKqOHTvi9OnTYU9crLDb7Vi1ahUmTpwInU4X99dTOlUNdgxbuBoAsPexCfj7xzuwev9pPPGXfvjr0A5NPDrlQNcNIQe6bgi50LVDyIGuG0IOSrtuampq0KZNm4hBleLT/+677z7MmDEj7D7dunXj//vkyZMYP348Ro0ahTfeeCPi8Q0GAwwGQ8B2nU6X0A8y0a+nVJxw105p1SqYjAYYdVrPdhWdnyDQdUPIga4bQi507RByoOuGkINSrhuxY1B8UJWdnY3s7GxR+xYXF2P8+PEYMmQIlixZArW6xTjGtxpY419mUEHNfwmCIAiCIAilo/igSizFxcUYN24cOnfujEWLFqG8vJx/LDc3twlHRkiBNf416txW6qxPFTX/JQiCIAiCIJRKiwmqVq1ahUOHDuHQoUPo0MG39kbhZWOEAH+lyqCjoIogCIIgCIJQNi0mP27GjBngOC7oP6L5EKhUeZr/UlBFEARBEARBKJQWE1QRLQOmVOmppoogCIIgCIJoJlBQRSgKplQZmFLFgiqns8nGRBAEQRAEQRDhoKCKUBRMqTKymipSqgiCIAiCIAiFQ0EVoSgClCoNBVUEQRAEQRCEsqGgilAU/kqVN/2PgiqCIAiCIAhCmbQYS3WiZeCvVLH0P6udgiqCIAini8OWogqU1VqQk2LE8K6Z0KhVTT0sgiCIVg8FVYSiIKWKIAgiOCt3l2D+NwUoqbbw2/LSjJh3aT9Mzs9rwpERBEEQlP5HKAqvUuUbVFHzX4IgWjMrd5dg1tJtPgEVAJyqtmDW0m1YubukiUZGEARBABRUEQrDq1SRUQVBEATgTvmb/00BgrWyZ9vmf1MAp4ua3RMEQTQVFFQRisIaQqmioIogiNbKlqKKAIVKCAegpNqCLUUViRsUQRAE4QMFVYSiCFCqqKaKIIhWTllt6IBKzn4EQRBE7KGgilAU/koVNf8lCKK1k5NijOl+BEEQROyhoIpQFBamVPGW6u7/ZwoWQRBEa2N410zkpRkRyjhdBbcL4PCumYkcFkEQBCGAgipCUfBKlb+lOilVBEG0UjRqFeZd2i/oYyzQmndpP+pXRRAE0YRQUEUoCn+litz/CIIggMn5eXj+2kEB23PTjHj1hrOpTxVBEEQTQ81/CUURUqkiowqCIFo52ckGn79vObcLHr6YFCqCIAglQEoVoSiYUmXQ+br/2Z0cXNSDhSCIVszWI5U+f7fPMFNARRAEoRAoqCIURSilCiC1iiCI1s3vR919qLSeQMpiJwMfgiAIpUBBFaEoeKVK61tTBVBQRRBE68XhdGHbUbdSdXanDACAlWpNCYIgFAMFVYSiYEqV0a9PlfAxgiCI1sa+U7WotzmRYtQiv30aAMBKShVBEIRioKCKUBQsnYUpVSqVyusASEoVQRCtlK1H3Kl/QztnwKxn/fvonkgQBKEUyP2PUBRsksCUKsBdV2VzushWnSCIVsvvHpOKoV0ywXFu0x6qqSIIglAOpFQRioHjOD6oYkoVQA2ACYJo3XAc56NUsfsjKVUEQRDKgYIqQjEIJwg+ShU1ACYIohVzvKIRZbVW6DQqDOyYzt8fSakiCIJQDhRUEYpBaEQRVKly0gSCIIjWB1OpBrRPg1GnIaWKIAhCgVBNFaEYrB47dbUK0Gm8DS1ZUNUcJxBOF4ctRRUoq7UgJ8WI4V0zqVknQRCSYP2phnXJBAAYSKkiCIJQHBRUEYrBYvfWU6lU3sDD0EyDqpW7SzD/mwKUVFv4bXlpRsy7tB8m5+c14cgIgmhObBWYVAAgpYogCEKBUPofoRiYUiWspwKap1HFyt0lmLV0m09ABQCnqi2YtXQbVu4uaaKREQTRnKiot+FQWR0AYEhnd9NfqqkiCIJQHhRUEYpBqFQJaW5GFU4Xh/nfFIAL8hjbNv+bAjhdwfYgCILw8sdRt0rVIycZmUl6AN57JAVVBEEQyoGCKkIxtBSlaktRRYBCJYQDUFJtwZaiisQNiiCIZsnvR1g9VQa/jd0jKf2PIAhCOVBQRSiGYD2q3H8z97/mMYEoqw0dUMnZjyCI1ou3P1Umv82rVDWPeyJBEERrgIIqQjGwVJbmrlTlpBhjuh9BEK0Ti92JXcXVALzOf4BQqaL0P4IgCKVAQRWhGEIpVaymqrlMIIZ3zUReWuiASQW3C+Dwrpkh9yEIgth5vAp2J4ecFAM6Zpr47Uadx/2PlCqCIAjFQEEVoRiYUmXwU6pYkNVclCqNWoV/TekT9DFmFD/v0n7Ur4ogiLD87jGpGNYlM2ibCZvTBRcZ3hAEQSgCCqoIxRBSqWpm6X8AYHUEn+jkphnx6g1nU58qgiAiwtdTCUwqAK9SBZBZBUEQhFKg5r+EYgilVLGgytpMjCo4jsPbG4oAALeO7oq31rv/e+nM4RjZvQ0pVARBhMXp4rDp8BlsOnwGAHB2J9+giilVgDst2qT3XYgiCIIgEg8pVYRiYCuuxmauVG08fAZ7S2pg0mkwa2x3fnvfvNSYBFROF4fNRRX447QKm4sqqN8VQbQgVu4uwXlP/YJpb23m3f3+9sEfPg3DtRo1tJ57CTkAEgRBKANSqgjFEFKpambNf9/2KFNXDWmPrGQDUgxa1FodqG60IyvZENWxV+4uwfxvCjx9sDR4/+DvyEszYt6l/SilkCCaOSt3l2DW0m0BjcNLayyYtXSbT+qwQauGw+ZsNgY+BEEQLR1SqgjF0BKUqsPldfh5XxkA4JZzuwIA0sw6AEBVoz2qY7MJl39j4VPV7gmXcCWbIIjmhdPFYf43BQEBFQB+2/xvCnhlmtVViVWqnC4OGwvP4KsdxdhYeIYUboIgiBhDShWhGEK7/zFLdeUHVUt+PQIAuKBPDrplJwMA0kw6nKhsRHUUQVWkCZcK7gnXxH65VLNFEM2QLUUVAQsmQjgAJdUWbCmqwMjuWYL7YmSlylfhdkMKN0EQRGwhpYpQDKGUKkMzUaqqGmz4/I8TAICZo7vy29NMbqWqJoqgSsqEiyCI5kdZbejvd7D9xCpVpHATBEEkBgqqCMUQyf3PplD3P5ZW83/Ld6PR7kSf3BSM7JbFP57O0v8a5AdVUidcBEE0L3JSQjcMD7Yfuy+y+2YwpKYUEgRBEPKh9D9CMXiVqhBBVRMpVU4Xhy1FFSirtSAnxYjhXTP5FLtgaTUl1Rb8sOcUn1bDlKpo0v+kTrgIgmheDO+aibw0I05VW4IGQSq4+9wN75oJwKtUhUuLlppSSBAEQciHgipCMVh5pcrPqELj/rspgqpwtQgAgjp11TTafZy6UmMQVEmdcBEE0bzQqFWYd2k/zFq6LeAxViU579J+/IKOQYRSRQo3QRBE4qD0P0Ix8EqVQpr/hqtFuGPpNjz0v12i0mpioVSxCVcwgk24CIJofkzOz8OrN5yNNsl6n+25aUYfO3VAnFJFCjdBEETiaJFBldVqxaBBg6BSqbBjx46mHg4hEr6mSgGW6mJqEcLVSAnTatJN+oj7i4FNuDKTIk+4CGmQ3TShFCbn5+Gl684GAGSnGPDxbedgw4PnB3y/2eJTOKWKKdyhllpUcCvvpHATBEFET4tM//vnP/+Jdu3aYefOnU09FEICIZUqjXjr4FgRqRZBLGW1lpi4/zEm5+dBr1Hjlvd+BwDMu7gPbjq3GylUUUB204TSsHjudTkphpC1TmzxKZxSJUwpVAE+i0SkcBMEQcSWFqdUff/99/jxxx+xaNGiph4KIZFQShVzA0ykUhWrGoOcFGNM0v+E2JzeqVG37CSaEEUB2U0TSqTB5r4XmvWakPuIUaoAr8Kdm+ab4kcKN0EQRGxpUUpVaWkpbrvtNixfvhxms1nUc6xWK6xWK/93TU0NAMBut8Nuj80kOBzsNRLxWkqHTQ60Ks7nfKg5dzBlc7gSdp6yzNF9NdzGEQYM7pCCfadqAbj7WMVi/HUWm/e/G2NzzNaI08Xhsa/3RGiovAfjema1mMCV7jfNg9pG92+SSacO+VnpPNdkozXyb9UFvdtgXM/RGP/sepTUWGDUqbH63tHQqFWirwW6dgg50HVDyEFp143YcbSYoIrjOMyYMQN33HEHhg4diiNHjoh63sKFCzF//vyA7T/++KPowCwWrFq1KmGvpVSq6zQAVNi66Tec2u3dfqoBALSoa7RgxYoVCRmLiwPS9RpU2QAErUjgYNYCDQ72t8rnMQ7ARW0b8MPK73HGAgBaVNTHZvxbS1UA3CvYW7btgOPY9qiP2Ro5WK3CqZrQSoC7Ls6Klz5ZiZ5pLavGiu43ymbrKfd3vPpMech7xsnjagBqFBw4hBXWA6KOa7G477EWuwtff/c9DKEv/5DQtUPIga4bQg5KuW4aGhpE7af4oOqhhx7CU089FXafvXv34scff0RtbS3mzp0r6fhz587Fvffey/9dU1ODjh074sILL0RqaqqsMUvBbrdj1apVmDhxInQ6XdxfT8nM27EagB0XjBuDHjnJ/PajFQ1YuHMDoNZiypRJCRuPrksp/rFsZ4CSofL879NXDwQALFixD6dqvGpnXpoRD1/UB5P6twXgrqX69/bVsLtUuGDipADLeKmUbTwKHN4PAOjZtz+mDOsU1fFaK9/8WQIU7Iq4X7f+gzDlrJaRIkX3m+bBifVFQNFBdOvcAVOm5AfdZ99PB7G6pAjtO3XBlCl9RB33sZ2rAZt7xfXsUePQOUv8wiFdO4Qc6Loh5KC064ZlsUVC8UHVfffdhxkzZoTdp1u3bvjll1+wceNGGAwGn8eGDh2KadOm4b333gv6XIPBEPAcANDpdAn9IBP9ekqEFVwnmww+5yLJ6Ha7szlcCT1HlwzqAK1Wg7s/3u5Tx5TrZ2Jw0VntQzYHBoAMjRZqlVv9anAAyebo3oPd5fvfrf26kUteepLo/VraOab7jbKxOtz3m2RD6M8pyeDebndyoj9Li+DmUdHoRA8Z1wBdO4Qc6Loh5KCU60bsGBQfVGVnZyM7Ozvifv/973+xYMEC/u+TJ09i0qRJ+OSTTzBixIh4DpGIARzH8Y5XrKklg7n/OVwcnC4uofUtE/q2db+ek8Pci/rgrA7pAUGTRq0K6dAFAGq1CqkmHaoa7KhutCMnNbqeMBabtzC9MUKROhGa4V0zkWLUotbiCPo4NVQmmop6ZlQRJj9PjPufEJeL87lfUMNfgiCI2KL4oEosnTr5pkAlJ7vTx7p3744OHTo0xZAICdidHDiPGOSfHqcXBFk2hwumMI5YseZAaR0a7S6kGLS4bXQ3qGUGdGmCoCpahBMjiy2xDZGbM04X56MoltZYwgZUANlNR4v/OfdfkCCCw7v/6UL/RIt1/2P4B19lgpRlgiAIInpaTFBFNG8sgh5U/kqV0GI90UHVtmOVAIBBndJlB1QAkG7S4SiibwAM+KbwkFIljmC9qBjje2dj76lanBI85p/iSUiH+n/Jp8HmDvaTRChVYoMq/3tFKSlVBEEQMaXFBlVdunQBx7Usx66WjFUQKPgHVTqNN5ixOp0AEpdfu/1YFQBgcMf0qI6TGsNeVT5KFQVVEWG9qELdDaYO6YhJ+bn4cc8pzPrQ3SR17QPjfRRSQhqhzjnr/0X9kcLDlKpwC0isf5/Y9D//oKqclCqCIIiYQrMGQhF4G/+qoVL5KkIqlYqf4CayATAAbD/uVqoGd8qI6jixbAAsnBw12in9LxxOF4f53xSEDKhUAB7/rgAAcGH/XKhVbiv1qgZbiGcQkQh3ztm2+d8UwOmiRa9Q8EqVPlz6n0Slyua7X1ktBVUEQRCxhIIqQhGw1VZjCLtxgybxQVVVgw2Hy+sBAIOiVKrSPY5/VTEIqlq7UYXTxWFj4Rl8taMYGwvPhJ2cbymqCJryx3D3orJgS1EFNGoVslPcTqCltIovGynnnAgOX1MVTqnSSlOq/IMvMqogCIKILS02/Y9oXgiVqmDotWrACticiQuqth+vAgB0a5OEjCR9VMdiSlVNLIIqR+tN/5NapyN24sj2a5tqRGmNFaU1FgxAWmwG3cqQes6JQBqsLKiKnVLFAjXW3oEWDgiCIGILKVWEIoikVDVF+h+rpxrUKT3qY8U0/a+VKlWsTsdfBWF1Oit3lwQ8JydFnH0924/9PxXxy0fqOScCabC70//CW6rLq6nKSzMBcN+LWtuiDEEQRDyhoIpQBFYxShXETyBiwfZjsamnArxBVSxqdYR1VJZWUlMlt05neNdM5KUZEcq3UQW30sV6UbVNpfS/aJF6zolAvEpV6KDKq1SJDKo8izG5aUb+flpOdVUEQRAxg4IqQhFErKlKsFLlcnHY4Un/i9b5DwDSTO70wVgoVcLVZf/i85aK3DodjVqFeZf2A4CASX6wXlRtPY2Zy2pIqZKL8Jz7Q/2/xMFS9cIZVXiVKnH3AHbfMOs1yE52Lx6QWQVBEETsoKCKUASiaqqQuKCqsLwOtRYHTDoN+uSmRH28WKb/WVqhpXo0dTqT8/Pw6g1nIzfNN90sN80YYO3tVaooqIoGds5Zg1pGsHMuFSlGJc0Rl4vjU/XCWaqzBSirWKXKc0yjTsNf57R4QBAEETvIqIJQBBFrqjSJTf9j9VRndUiDVhP92kP8LNVbR1AVbZ3O5Pw8nN+nLXr93/cAgNdvOBsT+uUGqCU5HqWK0v+iZ3J+Hgb+WoTNRe402v9eNwgXD2gXlULVGhoKC7/TYpQqm9MFl4uL2JycqdomnYb/npBSRRDNC6eLw5aiCpTVWpCT4k6jJtVfOVBQRSgC0UpVgtz/YtWfisEs1asb7eA4LqAXlxSEKX9KqKlKxE2e1emcqrYEratSwa2ChKvTcbi852p0r+ygY2zLTzZpBT8WVNR7FxF65qREHVC1hobC9Z4eVSoVApQ+IcIFKKvDFVbVArzBmkmn4e9HdJ0TRPOhNSwqNXcoqCIUQWT3P/f2RKX/bTtaBQAYHAPnP8CrVNmd7tSecFbJ4XC5OB+1rqmVqkTd5Fmdzqyl2wIeE1unIwxGjdrg1xlLizpdZ4Pd6YIuBipla6ai3mvMUmd1yD5OJKMSFdxGJRODqI/NDXadmnWasIsvwgUoi90ZOaiyeVMK2yS7azzLSJEliGZBa1lUau7QjIFQBKzYOqRSlcDmv7UWOw6U1QKIXVBl1mug9Uz2okkB9E9/tDrcqT9NgRyL82hgdTpslZ0htk6HBaB6rTpkqlSGWQ+dxv0YOaNFh9PFoVLgdllnkR9UtaaGwvVWFvyEX3jRatT8PUVMWrSwTouluVL6H0EoH7nut0TioaCKUAQsjc0Qwf1PrNNVNPx5ohocB3TIMMWsl45KpeKDgaoG+UFVMGXKkoBz4k9T3eQn5+fh/gt7838/OLk3Njx4vqgVOnaNGUME7gCgVqu8vaqoiD8qqhpsEH78tVEoVa2poXCDJ/0vKUyPKga7L4oxrBGm/+WkkCELQTQXWtOiUnOHgipCEURSqhJpqb7taGzrqRipMTCrYBMjpqYATWOr3pQ3+VqB4tElK0l0updFhKMaAORQr6qYIEz9A6JTqlpTQ+EGgaFEJHgHQBH3RUsQowpSYwlC+bSmRaXmDgVVhCLwKlVNb6m+3dOf6uwYpf4xYuEAyNdb6DXQqdwyQFPUVTXlTb7G4j1/Ut67xS5uskpmFbHhjF9QVWuRf923pobCXqUqct2ltwGweKXKqNfwCwdn6t21gwRBKJfWtKjU3KGgilAETKkKZSCQKPc/juOw/Vh8lKp0FlRFkf7HJk9GrQYsNohFryqpvX+a8iZf0ygvqBL26QkH9aqKDQFKVRTpf+EaCjNaSkPhBsHCSSS8adESaqp0GmSa9Xw91uk6UqsIQsm0pkWl5g4FVYQiiKhUJcio4uiZBlQ22KHXqtEvLzWmx46FUmURBAZ6z6lqtEV3TlbuLsF5T/2C697chNnLduC6NzfhvKd+CWs00ZQ3eeH5k2Ipz9dURQiqqFdVbDjjN1mvjSL9DxAYlZh8jUpUAF64dlCLcb6qlxJUSVGqBOl/arUK2SmU5koQzQHhopL/b65Y91siMYjydb733ntFH3Dx4sWyB0O0XnhL9QhKVbyb/27zqFT57VL514wVMUn/41eb1WhU+26Tg1yb1lhYnMulRjA5l6LSeZWq8J9r21QyqogF/ul/0ShVjMn5eThZ1Yh/f7sXZ3VIw/EK7yJIS6GRpf+JaLsgS6nyrMbkpBhQUm1BGV3nBKF42KLS/y3fjdN13ntrLvWpUhSigqrt27f7/L1t2zY4HA707u124Tpw4AA0Gg2GDBkS+xESrQK++W+kmqo4p/9tP1YFADg7xql/AJBmdveGqWq0RdgzNGy12ajXQBdlUBVt7x92k7/nkx0+ilG8b/I+6X8STDosIg0AWPof9fCJDpb+l2HWobLBHlVNlZBKT/rsoI7pGNk9C6+vPYz/bStuMZMKr6W6GKMKCe5//PXv/tnPTjECqCZbdYJoJkzOz4MaKty+9A8AQK+2yfh+9pi4K1ROF4ctRRUoq7UgJ8WdgUKqWHBEBVWrV6/m/3vx4sVISUnBe++9h4wM98SzsrISN998M0aPHh2fURItnkhKlcGz3Soh3UsK7Kaxen8ZAGBgh/SYv4ZXqZK/Ym9xeG3B2ZxLrvufFAe/kd2zgu4zOT8P5/5+HD/vK0deqhGLrxkU9xuu3JoqZj0fuabKo1SRUUVUMKWqU1YSKhuqYqJUAeBXabOSDJicn4vX1x7G6v1lqKy3ISNJH5PXaErYNS3GqIK/L0rsUwV4XS4pqCKI5kOJQFl2uri4Bzcrd5dg/jcFPnOFPFLHQiI5Z+LZZ5/FwoUL+YAKADIyMrBgwQI8++yzMR0c0XpoSqVKWFN0orIRAPD4dwUxb14bk5oqmzcw0KndGpNco4pYOfixQE+rVWFk96y43+SF7n+S0v/EKlUec42qBntMTEBaK6ymqkuWGUB0lurBjpuVrEfv3BT0zUuF3cnhu12x/b42FfWe4FOcpbp4pcrf/ZJ3uaT0P4JoNhRXNfL/HU3PSzGw8gD/xVdWHhDrOVJLQHJQVVNTg/Ly8oDt5eXlqK2tjcmgiNaH2JoqW4wb3Ya6aZTXWmN+0/C6/0WR/ieYGDGjCrkT/1g5+LFgJRH9sjiOQ41A6ZNmqe65xiKkVaWatHytCvXxkQ9L/+uc6Q6qomn+K4QpYG2S3arUlYPbAwC+3F4ck+M3Nex7JK75rwSlym9RgZQqgmh+nKhs4P+7qtEOjgvv1CuXSOUBgLs8IJJTcGtDclB1xRVX4Oabb8b//vc/nDhxAidOnMAXX3yBmTNn4sorr4zHGIlWgDWCUmWIg/tfom8aaebYGVUY9QL3P5lBVawc/Bo9wUpDAoIqi93lo1bKMqoIEbgzVCoVmVXEgApB+h8QD6XKHRRcNqgd1Crgj6OVOHqmPiav0ZTUe4wqTCKMKsQqVRzHCe4dXqMKgPqxEURzorjSq1Q5XVzM0qr9kVIeQHiRHFS99tpruOiii3D99dejc+fO6Ny5M66//npMnjwZr7zySjzGSLQCeKUqRMpLPNL/En3TiKmlulYdtVFFrGxa2Zga7c64rZoxavzMDiQZVfi5n4XD26uKVvHl4HJxfFDF0v+itVRnnOFrqtxKVdtUI87t0QYAsHz7yZi8RlPCFieSRPWpYrWm4b8HVocLbG2IV6r49L/WcY1L7cVHEEpEmP4HxC8FMFblAa0NUUYVDKfTid9//x1PPPEEnnnmGRQWFgIAunfvjqSkpLgMkGgd8DVVIayRvel/sQuqEn3TEAZVLhcHtYzaI5/0P9b8NwqFiDn4PfrVHp80ICkOfiyw4Ti3kiTGtUwu/gGptD5V4pQqQNirin4w5FDVaOcn8Z086X+NdiccThe0Gvn25xa7k08jZEoVAFwxuD3WHzyNL7efwN0X9IBKFfjdai4OVlKa/zKlKlL6n1DJ8k//O11nTUjBe1NCxfZES8Bid/JGPWa9Bg02J6oa7OgYh56/sSoPaG1ICqo0Gg0uvPBC7N27F127dsVZZ50Vr3ERrYyISlUc0v8SfdNgQZWLA+psDqQadRGeEYjQqCLa9D/G5Pw85KWZ8JeXfwUAjO+djbemDxM9yRK+foPNEdegqsYvqJLy3v3dz8LBivjJAVAeFfXuAD3VqEW62evIV291Is0sP6hi6pdOo0Kq0fvzNal/Lky63ThypgHbj1cFtERoTpNqb1AlJv1PXPNfdu3rNWo+qM1K0kOtct+PztRZ+YWElobcXnwEoTSYSpWk16Bjphn7TtVG1aIlHMO7ZiI3zYhTIbJ5VHAvvkYqD2htSP51y8/Px+HDh+MxFqIVE0mpMohckZVCrGqKxGLUafj3Vy1Tshc2/2Xuf9EGVYBvWl2qSSdp1do3qIpvXZV/+p+Umiqvw6SIoIp6VUUFn6KXbIBeq+av+1prdKkqZwR26kI1KsmgxaT+bQEAr60p9Enxam4OVg2emioxSpXY5r98fztBzapWo+bVvpZqVkHF9kRLgrkTt88wId1To10Zp/Q/jVqFvwxqF/QxKeUBrQ3JQdWCBQtw//3349tvv0VJSQlqamp8/hGEHJpCqYpVTZEUoq2rYuluBqFSZYv+nDAFwH088YGK08X5fCaxCPDCwc4bmxxKU6rc4xRjVU1GFdHBHPpY3VOKR1WKtq7qdL3XTt2fjp40wx8LSjF72Q5c9+YmnPufn/HQ/3Y1q0k1a/4bD6XKX6Vt6WYVVGxPtCSYSUX7dBPSTe57YDRuwuFosDnwzQ53jaq/E2lumpEU3hBIDqqmTJmCnTt34rLLLkOHDh2QkZGBjIwMpKen+/SuIgix2J0uflITqaYqlkoV4K0pyk3zTX2J100jPUoHwKA1VTEIZCqFQZUM9YcRd6XKY6fOgh5ZRhUigqoc3qiiZU424w0LqjL5oMp93UfrVCVUwISs3F2Cl345FLD/qRpr2EJuJU6qG+OgVIW69vmgqoUqslRsT7QkiqvcduodMsz8XCJeRhUv/HwQJ6st6JBhwua5E/iFsaeuGoAND55PAVUIJNVUAcDq1avjMQ6iFSOcEETr/ienGH1yfh7G9c5Bn0dWAgDevGkIzu/TNi6ydvRKlTD9z70tFupQheDGLCVQ8X/thjjZuzJYTVXbVCOOnmmQlf5nDGHbL4QFbWImm83FACGRVPDBjzuoSja4f2qitVU/7bFTbyNQqsKleIlFKZNqjuPQ4LlOzWL6VIlVqmzBMwG8imzLDKqo2J5oSRQL0v9YMBWP9L8DpbV4e30RAGD+Zf2RbNQiM0mPWosD3bOTW/3vWzgkB1Vjx46NxziIVozQDlgfwhnMIML9L5pidGFgN6ZXdtxuGiyokru61BjMqCIG6pBcpcr/teOtVFULgipAmvuff/PTcLDj11odqLc6kGQIfqtsTgYIieSMJ02PKVUsqIq2AfAZPqjyKlWRUrzEoJRJtcXuAutKICb9T6xS1cD3vmpd6X+sbvZUtSVo0E3F9kRz4oQg/Y8RK6MKfnGwxoJX1xbC4eJwYb+2uKCvu1Y1VincLR3JQRWjoaEBx44dg83m+4GSIyAhFYtnQqDXqEPajOs17slAqKAqWocnNuHWqlUhA7tYkMbyoOUqVQ6v2hIr9z8AqGiIUfpfnGuqmFFFW89k0OZ0ibbpZudOjFFFskGLJL0G9TYnymqt6BokqCJXsdB4a6rcn1OyMTZKlX+PKiC6gEBpk2rW+BcQF/xLrqnyO2Y2U2RbqFEFq5udtXRbyH2o2J5oLjD3v/YZJtR7Fqjkml4JCbY4CABje2fz/80WxvzNoghfJM8ey8vLcckllyAlJQX9+/fH4MGDff4RhFSsvCtb6MsxXPpfLBye6gUrucF63MSKaNP/hEqVLoY1VVWCoEpK3yv/AKzRFu/0P9+aKsAblEeCpUCJmawKXyNYXRW5ioXHP/0vhSlVUf4gn64PrKmSqzIp0cFKqKaKGZPUmir/Oi2vUtUygyrAWzfL7r2MDLOuVS98EM0Lu9PF/xZ1yDDxrSqqZM4lGKHcUQHg/77czbujsrpYUqrCIzmouueee1BVVYXNmzfDZDJh5cqVeO+999CzZ098/fXX8Rgj0cLhHe3CNGVlkweni4PDL7CKhcMTm8wkiUi5iYZYuf+ZdBroY2ipXlHvHY8Utamp0v+yU7yTarFBpTWEA1oowplVxOKac7o4bCw842P/3VKoCDCq8ChVMUr/E7r/iWmNkG7WITc1MWY00VAvwaQCkKBUCRZjhHiNKlpm+h9jcn4erjq7vc+2q4d2UNRnL6Ql3xsIeZyqtsDFuReY2yQZBJbq8tP/xNSjssXBWN3DWzqSZ5C//PILvvrqKwwdOhRqtRqdO3fGxIkTkZqaioULF+Liiy+OxziJFoxVkNIWCr3AFdDml+4VC4cnJqWLnczIxev+J+9G6OP+F6+aqmiMKhLUpyrNrINRp4bF7hI93kYJRhVAeLOKaK+5ll6LFVBTFaN8fJb+1ybJG1QLU7xUgM8EgQVa/7lyACb2y8VTK/fijXVFGNwxHZ/PGqUYhYrBN/4VYVIBSOhTFaKdALvGy2utcLm4kOnXLYH9pbUAgMGd0rH9WBUKTsa/BYwcE5uWfm8g5HG80u381z7dBLVa5Z1LRJH+J2VxMJVXqij9LxySlar6+nrk5OQAADIyMlBeXg4AGDBgALZtC523TBCh8CpVIoMqvwlELByeGiSqGHKJlfufIYbufxzH+dRUWR0uuESujPqvkMciwAsHH1SZdPwEUYxSZXe64PC8p1ik/0VzzTW3ZrRScbk43pGKGUokG6K3VOc4jg/W/PtUiWmNoFGrcFaHdACATqtWXEAFAA2sR5VO3HqnV6mKFFQFv7+xz8fh4qJa8VY6HMfxQdQ1QzsCAHadqAbHxU8BWrm7BOc99Quue3MT3zftvKd+Cfv9bun3BsKLVDWy2M+kIkOQ/if3OpayOMibDVH6X1gkK1W9e/fG/v370aVLFwwcOBCvv/46unTpgtdeew15ebSKQkjHq1SFnuxq1SqoVADHBQZVsXB4ag7pfxzHBVWqoq2parA5A86pxeEU5T6WaKWKrcqlGnWe68UuKqgUnqNw15kQlhpVGqTehF1zoVb5Ql1zkWqxVHCnW0zsl6vISb8Yqhvt/ASB/fDHwqiixuKA3ek+bmZSYPPfyfl5mNgvN6wywFwc6xWawsJc+sQqVexatkb4DoTqU6XXqpGZpEdFvQ1ltdaA/l8thdIaKyob7NCoVbhkYDs8+tUe1FgcOFbRgM5ZSTF/PTkmNq3h3kC4kaNGMpOKDhnuoIrNJZwuDrVWB68kSUHK4uApz1gpqAqPZKVq9uzZKClxr5bMmzcP33//PTp16oT//ve/ePLJJ2M+QKLlI0apUqm8rnz+qS4s/Sfo8zz/H6kYnU2y4q5URdGwz+rw2i0bBc1/7U4O9gj9u8LB6l+ErodigyNm/sD/bY/fDdfl+fEAgFSTlp8gilHHhIFXuOtMSDilSu41F4taLKXDnP9SjFpeYU5l6X9W+akjrJ4qxaANGRhr1CqM7J6Fvwxqj5HdswLOf7Lig6rghhKhkGqpHuy8NTezCjn1RgUl1QCA7tlJSDZo0TcvBQDw54nquIxPjolNa7g3EPLVSH+lyqjT8KnsclMAxdSj5nkWB8moQhySg6obbrgBM2bMAAAMGTIER48exdatW3H8+HFcc801sR4f0QoQo1QB4R0AWfpPhtl3tUZsMTqbdCeJXCGWSzRKla/a4rVU939MKiztJytZz0/SpNYpMeKpVNXZHHxQ6VWqxLn/We2s+alatLujt6Yq+ERncn4eRvdsE7A93DUXi/o/pROsl1Qsmv/yNu3JgSqVWJgSrdRia29QJS39z+Z0hQ0ueOfLIMFaTpjFA6UhJ6UOAPaWuOup+uWlAgAGdEgDAOwqjn1QJTc4ag33htZONK6xJwSNfxl8CqDMoErK4qC3TxXVVIVDclB1+PBhn7/NZjPOPvtstGkTOLkgCDFYRShVwsdD9aqanJ+Hhy7qw/999wU9sOHB80UV99ZbWXpMYtL/ai0OyY5OTNHTqlXQadTQqAC2EB9NXRVTqjLMen6FXGyQ5r8fO4/xgK3GGbRqGHUafoIoRakSW08FAG159z9ryJz1cr/V/Y9uHRH2motF/Z/S8Xf+A2LT/Nfr/Cc/RY0P7hQbVEkzzDGEqTUVEspSHfAqVf7XstKIpt6I1VP1ZUFVe09QFQelSm5w1BruDa2daNRIvkeVoPEvm09EUw/JFqRNfgZO/ouDsTIbaulIDqp69OiBTp064cYbb8Tbb7+NQ4cOxWNcRCtCrFLFLNfDpboIv/Ad0s2ic88bJU5m5CLslVIjUa3yDwxUKu9/W2zy0//YKldGktf8QazixCZr7H3FM/2PmVSkel6LpT6ICQBD1ZSEg01eGu3OoMFArcXOO4ox+rdPC3vNSUm3aK6cCRZUxaCm6nSQxr9SYUq0xe4KaM2gBKQqVcKgKtz3wOt8GSb9T8FKVbR94faWuIOqfu1YUJUOANhdXC3alEcscoOj1nBvaO3IDbhdLg4l1Z6aqkwzv505AEbbq2pyfh5GdssCAFw7rCM+vu2cgMXBWKRwtwYkB1XHjx/HwoULYTKZ8PTTT6NXr17o0KEDpk2bhrfeeiseYyRaOGJqqgBB+l+YoEoYqNRLaEQr1cpYLjqNGkmewE1qCiDfa0YQ+LFJUqyUKnZsscdjY2IT3Xim/7HGvyyAM0l476H69ITDpNfwPyTBJpw7jleB44COmSb+2oyUGhGL+j+lcyZI8JNiiD4fnz9uNEqV0Rus1MfZVEUOUvtUaTVqaD3XSrjFJmFTYX+aQ01VNCv8DTYHis7UA/AqVT3bJkOvVaPW6sDRioaYjlVucBTu3sBo7veG1o7cgLus1gq7k4NGrUJbQY9Glv5XHQPnzhrPvXlsr+yg9ahUUyUOyUFV+/btMW3aNLzxxhvYv38/9u/fjwkTJuDTTz/F3/72t3iMkYgRSm0oKLqmShM5qBKu2EiZ4LMJllgr42iQW1cVLIWNSfbRBFUsdSAzSS8pUBHux1SJeFqqs/PFAh2xjU8Bb92VlKAKEJpVBE44fz9SCQAY0inDu4on4geHpVuwVDSGEpvRyqEiiO05y8dvtDtlK0TMTr1NFDVVBq0GOo17sqBEswpmqZ4kQTEX8z0Il/7aHGqqoqk32neqFhznDh5ZnZ9Oo+brq/48URWzcQLRLZywe4N/L71kgzYm9walzgFaCyzgDkWogPuEp0dVbqrRp0entwFw9OoR+30VZtMISRFkG8SzFUFzR/IMsqGhARs2bMCaNWuwZs0abN++HX369MFdd92FcePGxWGIRCxQckNByUqVM/TkQRioSKmbYOl/8TaqANzpayerLZIleyufwuM9T0YJDnihCFpTJdGoIjMRSpVf+p88pUraOlLbVCMOltUFnXBuO+YJqjpnYOeJapyus4lexZucn4ffCs/g/Y1HAQDP/nUgLh/UvkWsQnvT/7wrqkmCALLe6kSaWfJ6XlAFTA5JBi2qGuzKDKqYoiShtYNBq0adVaRSFSRYY7WDSlaqoqk38q+nYpzVIQ07jldh14lq/GVQ++gHKYAFR/d9utNHEc0V8Zs7OT8PXX46iH2napHfLhW7T9agX15K1L/TSp4DtBY0ahVuPKcznv5hf8Bj4QJuvp5KYFIBAGmm6IwqhPBBlTl4UMUWAR0uDha7K+5Oyc0Vyb9s6enpuPHGG2GxWPDQQw/h5MmT2L59O5577jn85S9/iccYiShRekNBye5/YSYPwqCqQcKkqT7MpCPW8J3QY6FUSTSWCIZQqTLKrKliqkR80/+8PaoAwXsX8Zp8TZXEzzdHYFYhxOnisP1YFQDg7M5epUpKnZww6O+Vk9IiAirAG6QLgx+9Vs0vmsjNyT8dA6MKQNkOgA0yFnekKFXBa6o8Lpe1oQ1Zmppo6o3866kY+e3j5wAIsL5pbfm/u7dJEmWc5HJxOOJJV7z3wl4AgB3Hq6O6xyt9DtBasDlc+GrHSQAIMIZok2wIqUYy578OfkGVt6Yq+vS/SEpVkl4LZpxLDoChkRxUTZkyBU6nE8uWLcOyZcvw2Wef4cCBA/EYmyy+++47jBgxAiaTCRkZGbj88subekhNSrQFvolAtFIVok+VEOGKjZSaiUaJ/WGiIdr0P+HEKKY1VXLS/2z+6X9xNKrwu+lLee9sQmLUyk3/852MHCitRZ3VgSS9Bn1yU7355hICBlYjBkir/1M63tonX0WJTx+RGczEwlIdULYDoFSjCkBcr6pw6X/ZnhoNm8Plc00qiWjqjQpKQitVALDnZE3MzSoY5XXexZjSWivErJucqrHAYndBq1ZhdM9stE01wOZ0YdvRSlljaA5zgNbC62sLsb+0FplJeqz75/n4+LZz0DMnGQBwz8SekRv/pvsGVRlR9L0UYrE7+ftHqKBKrVbx984aqqsKieSgavny5Th9+jRWrlyJkSNH4scff8To0aP5Wqum5IsvvsCNN96Im2++GTt37sSvv/6K66+/vknH1NQ0h4aCTKkyxECpqvGpqZKiVLEC8QTWVEksLmXBp1Bt4WuqolCIKuvd5yxThqU6m6xleVK9GuzOuK12sxt5qsm/pipyjQ4fkEoMmtvyRfy+36HfPROcwZ0y/Hp4iL/mhKt9Uq5VpRPM/Q+IvtA5WP8rOTAVSJnpf9JdSA0ilCpLmEUjo85ryFKq4B5ILKUu1eh7jzbp1CFX+J0uDvtP+faoYvTIToZRp0ad1WtkEWvKBAp3ndUhKsXycLl7LJ2yzNBp1Lwr28bDZ2SNoTnMAVoqwhq2z/84gf/+fBCAewEgO8WAkd2zcGF/t5rJMh+CURykRxUgTP+LTqliC7waQeAUDJYlosQFKaUgewY5YMAAOBwO2Gw2WCwW/PDDD/jkk0/w4YcfxnJ8onE4HJg9ezaeeeYZzJw5k9/er1/41a2WTnNoKChWqRKzIitUf6T0TEqkUpXOHHvkKlXaGCtVnhtyulknqfeT+3XdnwVTDzjO/flINYQQQ0D6nySlyuXzHLGEMqpgq8Znd84AAFlBlXC1L579vRKJy8V5m0kn+QY/0TQAdjhdfDF2tDVVyfzEQHnnvEHGfUiSUhXiuG1Tjaix1KGsxopebVNEv3aimZyfh02HK/Dub0fQKdOMYxUNMOo0uLBfbtD9j56pR4PNCaNOja5tknwe03rMKrYdc9dVdc9Ojvl4WRCl06hgd3I4VFbH31NCcfh0HQCgWxv3eEZ2z8LyHSexsVBeUNUc5gBKx+nisKWoAmW1FuSkuNNMI6VrB6thA4D+7VJx2cB2/N9DPL8hrEY3GMyoon262Wd7rCzVhSZQKlXo90UNgCMjOahavHgx1qxZgw0bNqC2thYDBw7EmDFjcPvtt2P06NHxGKMotm3bhuLiYqjVagwePBinTp3CoEGD8MwzzyA/Pz/k86xWK6xW74SppsadKmC322G3x//CYa8Rr9fKMov7iLPM2oS832CwlDGtOvx50HluYo224J8Nx3E+N5d6q/jPkK1aGyKMIRYk692ToMp6m6TXqre4J6sGrYp/noE5mVmkHYvBcRy/ypVqUEPvOV6dRdy5a/SctxSDNyCurrdAE+XENxiVDe7vabJeDbvdDjY/bBDxObNzp9dI+3zZ96e0utHneb8fca/qDmqfArvdjmTPYCrrraKPXyPIg69tFP+8aIj3/aaywcanESXrVT6vk+S57qvqLZJfnzWmVauAJJ0qqvGbPepuTUNizrkU2H1IL+E+ZNCGvwfYnS44PJ+JFq6g+2Qn63GwDCipqofdnhb0deJ97Yil2DPBvGFER/z3l0JUNtix7egZDOwQOO7dJ9wT1V5tk+FyOuDyi6P756Vg27Eq7DxeiYvzc2I6TqvdyU9Wz2qfhj+OVeHAqWoM7xz8/DIOeXrfdckywW63Y2gn9/47T1Shur5RcjZFU88BlHLdyOWHPaVYsGIfTgkW1nJTDfi/KX0wqX/bkM/5x7KdQVMu95yswXc7i/nn5ue5g+fD5fUoraoPUPg5juPT/3JTdD7nMUXv/u5XNcj7/WecqXUfP9WoC3sc5kpaWSf9Hi4VpV03YschOaj6+OOPMXbsWD6ISksLf4NIFIcPHwYAPPbYY1i8eDG6dOmCZ599FuPGjcOBAweQmRm8Yd7ChQsxf/78gO0//vgjzGZzkGfEh1WrVsXluC4OSNdrUGUDELTMl0O6Higv2IQVe+MyhIicKFEDUONAwW6sOL0r5H5lpe79du0uwIrKPQGPW5yA0+W9pEvKK7FixQpRY6is0wBQ4ffNv6Fkt8Q3IJHjp1QANNhfdBwrVhwV/bw/T7ifV15SjFWrjgMAzpSWAFBj5569WFFdIHksFgdgd7rP2eZ1v6C42H2O9x4sxArHwYjPP1PtPm9//rEFOpUGdk6FFT/+hMzoMrSCcvSk+7UO7d2FFWV/4mCp+3wcLS7BihXFYZ9bcMT9vkqOH8OKFUdEv2aFFQC0KKluxHffrYBKBdTYgOOVWqjAoWzvFqw4CJQcd4+l4MBhrHCKa4heUet+PwCwdccuJJX+KXpc0RKv+01pIwBoYdJw+PnHlT6P1Ve5P4NNf+yA+sR2Scctrncf16zl8MPK76MaY2WZexzbdu1BVkWcv+wSqaxxXxPbt25Eucivc02l+/1s+WM7VMcDp3HuMin3d3zNz6sQLCHAVuM+xvqtO6E/uSPs68Xr2hFLwVH3OTpdVIDuSSrstKrx1ne/4aKOge/922Pu95Vkqwr6W+A67f7ertt1BIO4wpiO84wFALTQqjikOSoAqPHL7wXIPBP+mtuy1z3mupOFWLHiEDgOyNBrUGkDXvt8FfqkS0uvVsocoKmvGznsPKPCOwfYF8Z77k7VWHDXsh24pZcLA7N8Pw8XB8zfpvEEVMHP9//9bwfsR5x8jV2OUYMyiwpvLf8Z+Rm+x6uzAxa7+/u7c+Ma7BF8f6ttAKBFVb2N/32Sw+4K9/cAtvqwc6ZGz33it63bwR1LTA2eUq6bhgZx/ewkB1Vbt26VPJhoeOihh/DUU0+F3Wfv3r1wudypDw8//DCuuuoqAMCSJUvQoUMHfPbZZyF7aM2dOxf33nsv/3dNTQ06duyICy+8EKmpqUGfE0vsdjtWrVqFiRMnQqcLXiAYLbouwVdNVJ7/XXDlwJArLongo1NbgapKDDt7EKacFdoZ6bev9mBreTG69uiFKeO7Bzx+sqoR2LKe/1tjNGPKFHHq6dw/fgbgxKQLxqFTZnyDaW7XKXxW9CdMaVmYMmWY6OftXXUQOF6EXt27YOLE7li1ahV6dO2E38pOoFPXHpgysafksRyraAC2boBJp8bll07ByTWH8VPxIeS274gpU/pHfP6CXWsAqw3njxmN9w7/jsoGO0aMGoOebWOfSvNy4W9AbR3GjhqOc7tnwbGzBMsO70JqRhtMmTI07HO3frsXKDmOvr16YMqEHqJf0+pwYf62n+DkVBg1fgIyzHr8WFAK/LETvdqm4KrLRgEAyjYexfcn9iMjpx2mTDkr4nFdLg73bPL+WHTu3htTxnUTPS65xPt+s/VIJbBjK3LSkjBlynk+j61u2IVdlSXo3LMPppzXVdJxfy08A/z5B9plpGDKlFFRjfGP7/Zhc/kxdOgi7zsTTx7e9gsAByaOHxuQrhaKb6t2YG9VGXr3y8eU4R0DHi+tsQBb10GjVuHSiy8Kmt6z64cD+H3DEWS074opU/oEfZ1E/FaJ4f+2u8/R5RNGo8uJKuxcXoASZGLKlBEB+375wTYApzFpeD9MGdEp4PGepXX48KXfUGLVYtLkC2PqwLn9WBWwfQvappkwcUR3/PLlHjiTIt+rntm7DoAFl40/B8O6uFPD1lp348vtJ+Fq0wNTLpR+zTblHEAp141UnC4OC59dByBYHZwKKgDfl5rxz2ljfK6bzUUVqNr0e5gjq1BlA7L7nYMRHrfKddbd+GLbSWhzAu9Ju4qrgd83IyfFgMsuudDnMavdiUf/+BkuqDDmgol83apULNuLgf170Cm3DaZMGRJyv1V1f6Kg6hS69e6HKaM6y3otsSjtumFZbJGQVVO1fv16vP766ygsLMTnn3+O9u3b44MPPkDXrl1x3nnnRT6ABO677z7MmDEj7D7dunVDSYnbElRYQ2UwGNCtWzccO3Ys5HMNBgMMhsBldZ1Ol9APMp6vd8mgDtBqNXjg8z99aj7E9MxIBDan+1afZNSHPQdGT2NeJ6cKul+d3XclocHmEnVOXS6Or2VINRvj/rlnJrtz6mssDkmvZfWcJ7PBe60kGXT8Y3LGXWtzHzMzyQCdTocko7TjsVqlFLMBZr0WlQ122EJ8PtHCapCykk3Q6XRI9hTpWh2RP2erg11j0r5nOp3bcKGi3oaKRidy0nTYccJ9cx3SJZM/VrrZfQ+pszlFHb+60Q6hn4fFIe/zk0u87jc1Vq/Fvv/x0zy1hI126e+12uI+bpsUQ9TjTjWxcYi7PyQKjuP42qe0JPH3IbOnVs3uQtDnODh3mqlJp4FeHzwtN89Tq3G63h7xdWN17cipU6lutPO/YV1yUpCZYgKWF+DP4mrU2riA1Kl9p9z1SfkdMoKOuXe7dJh0GjTYnDhRbUWPnNjVk1V4nBRzUo3olevO6Dlc3hD23FnsThR7anB65aXx+57bIxtfbj+JzUcqZZ17NgeYtXSbT2CVyDlAoudU0fJ74RmflD9/3AYfVmw/UYuR3bP47WcaxNWMnmnw/v4P65KFL7adxPYT1QHn6FStO+2sfYYp4DGdTgeTToNGuxP1diAzRd75rfPMA9KTwt9f2T28XuTcKhYo5boROwbJQRVz2Js2bRq2b9/O1yNVV1fjySefFJ1uJZbs7GxkZ2dH3G/IkCEwGAzYv38/H9jZ7XYcOXIEnTvHN6JuDkzOz8O6g+X4aLM7bezSge3w/DWDFNEbhzeqEOv+5wxekM3y1w1aNawOl2hHNYvDm2ifSEt1KT2NAEGvpSCW6nJ7mFTyduq+vZ/E9psSWjV7nxsfZyBv818t/5ruMUR2/7N4ivjlGGjkpBhQUW9DaY0VfXKBPzwmFUM6ZfD7sIbENSILeP0LfVuK+9/pusDGv4xkGWYe/seNtkcV4G1EXKswByurw8XXo0nppxbJqCJcjypGjsflsjzMJDKWyG1Ee7zCvXCWlaSHWa+FWa9Fn9wU7DtVi/UHy32a+FbU23DK0wqhT17wrBONWoX+7VLx+9FK/HmiOqZBFTOpyEkxoIfHBONUjQV1VkdIh7WjZxrAcW5DAKEhC5u07yquRq3FLkuRGN0zmw+odBoV3r9lhKhAtrUi1+BDTqNqZni083g1HE4XtBpvjl8x36MqeAZNulmHxmonKhts6Cgzy8bboyp8SBDNPby1INlSfcGCBXjttdfw5ptv+kRu5557LrZt2xbTwUkhNTUVd9xxB+bNm4cff/wR+/fvx6xZswAAU6dObbJxKYmTVd4vf5Jeo5ibKd/8N1KfqgiW6ixIaefp5dBgc4rqPyIMIKS6w8lBrmNPY5AGxVLd+vzhe1R5VqBMEoI0YQG8SafhA9Jo7N3DvRavJvo3/xUxVv7cyfh8hb2qLHYndhe7laqhXbxBlVT3P/9+QC3F/Y9dT22C9JJKNsi342V26tE6/7nHoUxLdeH3xizhOmXBkjXE94AFVeEWjHJCtA6IB9E0og3WBHVcb7fBxJr95T77sqa/nbPMYW2i49UEmNmp56QYkWbW8a0ADpfXhXwOe6xbdrJPmmb7dBM6ZZrhdHHYekSe9bnQzt3u5DCoY7pi5gBKRE5wBMhrVN0jOxkpRi0a7U7s87QAYDCTivZ+PaoYbJE2ml5V/j0gQ5HKt8VQhnmEEpEcVO3fvx9jxowJ2J6WloaqqqpYjEk2zzzzDK699lrceOONGDZsGI4ePYpffvkFGRkZkZ/cCmA/SIB38qMErCKVKoPHStzqCD55YDeVvDTvTU6M3XaD1TvhVifgR4bduBpsTthDqG7BYIqeb/NfT58quUpVg29PISmBkfA1jXo1H7CIVbmkIFT1WADDrOXFjJVdMya95Fse2qZ6Jpw1Fuw5WQ2b04U2yXqf2jupPzb+ilZLUaoqQvSoAryrnHIs1VlD4WDBmlTYOJQWyLJeeXqt2melOhKRlCqLiAWFHM/CQVmtNW595oDoG9Eya+kOgu/e2F7uTJZ1B8p9FtEKTrqDKv/+VP6wJsC7TsQ4qPIEqCxg7Z7trpErDBdUnXb3qOoepJ6O71cl11rdr4H5mfrEqJLNFTnBERC+UTU7ln+jarVahbM9mQ9/+DV5PhGiRxWDLYhGY6teLTKoiraBe2tA8gwjNzcXhw4Fultt2LAB3brFv9A6HDqdDosWLUJpaSlqamqwatUq9O8fudi+NcBxHP+DBETfgTuW8EqVTpxSFWrywG4MbVONvKuOmNXoBrt7H9YUNN4IUzek9KoKlsYjJQUuGP5KlZS+V2yyplYBeo06rkoVq6dKNmj5CScLkCwhgmwhbEzCHl9iYUrVqRoLfj/i6U/VKcNnJZkFVf4KVCj8Uz/r43DOmoJQjX8B8E1ba63S7z1sAhiT9D+9MicG7BpNkpiCHCkFWEzjazbxb7A543peom1EG0ypGtolA8kGLc7U23zUJqZU9Y0QVA3wKFV7TtaEDObkwKf/eRZluue4UwAPlYVTqtxBVbfswKBqVI/omgCX1/kGUazpOxEcOcERgzWq9n8oN80YslE161cVGFR5FhJCKFV85ksUDYDZPCTdFH7RSk4/xtaG5KDqtttuw+zZs7F582aoVCqcPHkSH374Ie6//34+3Y5QHmfqbbzSAXgVCiXgbf4boaZKEz79T7jawiZOYiarbMVaSh1DNGjUKn6CKSW4bQxXUyVzUs4aqvqn/0lRqkw6DVQqFd8/JR6qS42gOSHDKGOs4SaWocgRNADm66k6+6rf7Mem0S5Ofazx+1FqUNgEXy4sTa9NkOAnmua/fE1VTNL/mFKlrHNezzf+lVbqLLamyhRm0SrJoOWDuWVbjmNj4ZmYBhiMaBvR8hNMQX2JTqPGuZ6AY+0BbwpgQYk4papbdjLMenexfzgVSSrC9D8AfHPhwrL6kM9hjX+7tgl0T2VK1Z6TNaiWsSha5lcvR0pVZFhwZPArTQgXHDEGd8qAi3MHYM9cfRY+vu0cbHjw/JDPCaVUsfS/DiGUKm9QJT9I5ntVRlCqWAo3pf+FRnJQ9dBDD+H666/HBRdcgLq6OowZMwa33nor/va3v+Ef//hHPMZIxABh6h/gnUwrAalKVaigisnf6WYdzBLqJtik3KyTZYYpizTPjVCKUsUbVQhS2EzRpv/xyoJ7PGxCJ+Z4/GTNMxljSlWDzLGEg+/4LrjpswDQ6nBFrJ1j506WUuVZxS+tsfBd74X1VIA3pQwQFzSwHyUWJLYUpSps+l8UBhExVaoUGlSxwFqqWU4kpapBRPrfyt0lvJnLEyv24ro3N+G8p34JW98kB7l1Kgz2O9bRb4LprasqA+D+TWGKUN924YMqjVqF/HZuterPGKYAMqUq23P/6OFRqkIFbhzHhVWqclKN6JadBI4DNhdJV6uENVWAshZWlczk/Dz0zvUamJh0aqx7YHxEx0R2LfVqm4KpQztiZPessDVsAzumQa1yB1GlnlTNGovX7TJU+l86S/+LYk4nNf2PlKrQSA6qVCoVHn74YVRUVGD37t3YtGkTysvL8fjjj6OxsTHyAYgmga3wsWLHqgZbXHPnxeJ0cbB7rMIjKlUi3f+ESpWY+h6mrJgTlP4HyHMADBYYSEnXC0ZFA3P/8yhVevFBGp9Sp/MNquKT/hckqBJMPiOlADI1VI4aydL/9pXU4nSdDXqNGv3b+TY912m8NWVifnBYmmBeGjNVaRk/UuHS/5RSU8WCKqWl/zXwSpW0azRiTZU9vBLPjCP8lSkxxhFSkVunAriDDub+5++ENq63u65qx/EqVDXYcLC0Dg4XhzSTDu3SIgdyzKxid4zMKhxOF78QwKf/eQKlI2fq4QjyG1ZRb+N/w0L1KOPrqmSkAJb7BVXsO0VE5rTg3DXaXThaEbkR7K4TVQCAAR3Swu/oIcWoQ+9c9wLANo9axZz/Msy6kAp2uikW6X/ue6HooEph904lIb1q24Ner0e/fv0wfPhw6HQ6LF68GF27SmvoSCQOtsKX3979pXW4OEVMKoSmE5GUKoNI9780k0CpEjFZlTuZiQbesadR/I0wWAqblHS9YPBKlX9NlcT0PwAwSQhkpcKCkFRBPZowuLREqCnjA9II11gwWFDFgvn89qlB7amZ1bsYW3W2T65nwqc00wQ5uFwcr1RlBQl+UmS6/zXYHPw1FQulKlkQVClhYYnB7lWS0/8i1VT5LX4IidY4QirR1KlUNdh5Rdc/FSovzYTebVPg4oB1B08L6qlSgjY79oeZVfx6qBxf7SiOOv3xTL0NHOeuN83ytBdol2aCSaeB3cm5m677UeQxqWifbgppf8+s1eWYVbCUyhTP9U9KlTg4juPr0dii9M7jVRGf96cnQB8oMqgCgLM7pQPwpgBGMqkA5LsJMziO886dzOT+Fy2iZxhWqxVz587F0KFDMWrUKCxfvhwAsGTJEnTt2hXPPfcc5syZE69xElHCVjx65qTwwYkSClWFk+Foa6qY/J0uWNVpEDFZbZBZyxANrCBUSm58o82jtgQxqpDdp8pfqRKk1EWaVPivgJvj2KcqWHqCWq3i1ctIylqwejSxZPj90Az2/PD5wwxIRAVVjb5OlS1Bqaqx2PlrJphSxVY5G2zOoCv1oWAr6gatWrKJQzCYIY2LixyMJxLeqEKiYm4UfGeDHjeMpXq0xhFyYHUqbCGHEalOhU0ws1MMQYOOsR61as3+MkE9lbgJLbsPHiyrx+xlO6JOf2T1S22SDXyAqFar+LS+wvLAuqpwqX+MczxK1b5TtZIdfJlS1cuTylahgN//5kBlg53PprmgrzvN9E+PChUKjuP49L8BHdJFvxar1WVp5sW8SUXo/lNpJpb+Jy9Itthd/IJhesSaKi3/HCnOxa0J0UHVo48+ildffRVdunTBkSNHMHXqVNx+++147rnnsHjxYhw5cgQPPvhgPMdKRIG3wNfEmxIoYaWKKVU6jSpizwyDTpz7nzv9T3xNFZ/+l0CliqWxVYt0iwNCNP+VkK7nj8vF8bV1Xkt1b2AZKVBjQZ5/+l9clCq/xr8MMUodx3FBz50YVu4uwbhFa3y2fbGtOOhkS0q+ub9SJbanmpJhqX8pBm3QBZIkQa8gKcrcaYH5hRjVIRJJgmtcCWo9g6kwJplGFZHc/4Jd+9EaR8hlcn4eFlye77Pth3vGhG/8K/gNC8Y4gbX6npNepSoSK3eX4N/fFARsjyb9kbdTT/VVVnmziiB1VYUek4puIVL/APd3oGeO+/GXfjkoSVFjQVVvPqgiowoxsPqmDLMOQ7u401J3RKi9K65qREW9DVq1Cn1yI1+DDBZU7S6ugcXu9PaoCqNUZURpVMGyZbRqVcQ5kLB2mOqqgiM6qPrss8/w/vvv4/PPP8ePP/4Ip9MJh8OBnTt34tprr4VGk7gJKSGdE4Ku3EyVUEJQJdb5DwD0nmtMlPsfK0ZvQel/3hS2YJbqTsmpTLUWB/+DzFIIhC5HUtUfUzyDKt79z3clTYxSZ3O6wOYdkXqhCQnVpLSqwR50spXCp0aIr6lql+b9sYyHwUci4U0qQtQ96bVq/vqSYqvOlKpgKYVyUKtVkhZdEkWj514l31Jdep+qaI0josHq9L3ej5wJ7YoHeBcGO2YEX7Uf2iUTZp0ap+ts2OpR1npHmNDGK/2Rt1P3O28sqApmq+5VqgKd/xgrd5eguMp9P3rn1yOiFTW708UvevT1nBMlZKo0B9hn2TbVyKfy7T1ZE3IeAnh7nvXOTQmZyhmMTplmtEnWw+Z0Yc/J6oiNfwGBUYXM9D/hvCnSopWwdlhObWxrQHRQdeLECQwZMgQAkJ+fD4PBgDlz5sRk5ZCIL+4eVV5bzmhXNmKJWOc/ILxRhcvF8av/aSa9RKOKJkj/k+j+Z3e64PD8sAezVOe40ApeKJhJRZJewwe1arXK21A4wrnzD6riaVQRyp1ITPNji817XsQqVeEmWwz/yRZz8hNjPsKCiuwUA9/LpLnbqjM79WCpfww5zSNZwX8wm3a5KNGswqtUyTWqCO/+F6ydQDTGEdHif28+UBrezjxYjyohv+wrhSdLi//e3v7+72EDjnilP3rt1H2v2XAOgKymKpRJBVvk8T9vYhQ1tjChUav4wI4s1cXBmiZnpxjQKdOMdLMONqcL+07VhHwOq6c6S0LqH+A2ghvssVbfdrRKWk1Vg01WtgMrQYhkUsFg93Axae6tEdFBldPphF7v/bHUarVITg69okIoh4p6GxrtTqhUQF66UVHpf5KUqjBGFbUWB5hQ42NUodD0P6nuf8KgwSiwVDcKlCWpdVX+9VQMofoVDovfJNCki2OfKs+qmH8fjUh20oDXGVCtcqeZikHOZEuOUpVq0krqqaZk2Eo4K8wPhpRzxIhljyqGEntVeZv/SlvciaRUhUv/i8Y4Ilr8F18OltaG3T+U8x/gDTj8F5ZKa6xhA454pT/y6X9+QVV3T+peYVmdT2aBw+nC0TOha6qiVdTYeNok63mzFyW1VVEyQtVRpVLxgVI4swpWc3WWBJMKhrAJcHGEhQTAO5dwcUCdjN/eYO1KwpFMtuphEX335jgOM2bMgMHg/kJaLBbccccdSEryvQH873//i+0Iiahhqx1tU4wwaDX8yoYSbqpWzw++f3O9YDCjimCKDLsxmHQa6LVqxStVfPqfyM+ABTBqlfs8ODznQKtRQ69Rw+Z0odHuRLqEMVSGsL8267WobLCLVqoSUlMVpPkv4A3owo21UZD+JFZZlzPZSuV/bMS7/6Ua3QsAtVaHoib4cqgQEfzIaQDsTf+LvVIlJj04UbDPX75SFcFSPYRKy4wj/m/5bj6ABdz1fvMu7RexH49c2H1Cp1HB7uRwIEJQxfeoyvSdYEYKOFRwBxwT++UGBIfxSn/ke1Sl+j6vS1YSVCr3ItHpOhvfw+pEZSPsTg4GrdonJZghZZGHOQT6jEfQiDgjif3+2+B0cXEJmFsSTKli9XGDOqRh3YFy7DxRjRuD7O9jUtFeflC1uegMP0cLZ1Rh1Glg0rmbV1fV2wNS5CMhtkcVI4UcAMMiehY5ffp0n79vuOGGmA+GiA/+aRMZ5ujcYmIJazgpptbFq1QFTqBZbRILGJlSJSa9pymUqnSTtPQ/tgptDBIYGHWeoEpiMMNqYDL8XLjEpNQJHw9I/4tDbVBNiBu/GFXN4vAN/sQgZ7Il1qhCaGGbyvdUs8YlGE0kZyLUVAHyGgB70/9ip1Qxhz0lrbaymjq5NVXWSEYVYY47OT8PZ3VIx6j//AIVgA9vHYER3cI3K40Wdr33yU3FruLqsOl/vinsvhPMaAIOlv54qtoSNChTwR1cSk1/9KobvgsBRp0GHTPMOFbRgENldXxQJUz9Uwc559EqaswSPDvFwN/vOc79+xMuXZcQ1FR5PquBHdMBhFaqjp5pQK3FAb1WjV5txZtUMAa0T4NWreIDqmSDNsCgyZ90sw6N1U5UNdrQCaEDsGCwOUh6BDt1RiopVWERHVQtWbIknuMg4sgJP9ck9uWRaskaD6QoVYYwNVX+qy1epUq8UYWcxrBySZUYVIVL4THpNaixOCQHMyz9z/9HVYz6I3ycNQw2x7NPVZDmv4A4o4pwfXpCIWeyxcYWKde83ubkjTOYUuXe3rx/pLzpf2GCKhkNgGNtVAEI0/+UE8iymjrJfaoiKFWNYYwqhLD7AAegf7u0uCsYzJjjrA5p2FXsLsqvtzp8XCIZZwQp7O3SfRc8ogk4WPrjrKXbAh6LJv2xvCZ4+h/grqs6VtGAwvI6PshjNVah7NSjVdSENV46jRqpRi1qLA5U1FspqIoAHyB7VEeW/neovA51Vgd/L2Gweqq+ean8QrAUjDoN+rVL5dWuDLMOLg4Il7mebtajpNoiq05eulKlvHpUJSG7+S/RfPBf4WM3UWUYVTAFRnxQZXW4Apzu/POCkyRMmhpk1jJEg9f9T1pQFSwwkNurivUp8VeqRNdUJciowq3sRKqpCm3SwR6TEjQLa038f8tCTbbEKlUsbULrMQWR0lNNyTCL5nDBTwpvECH+3sMs1cPVaklFiTVVfBqyzD5VNmfw3nKNIq9/lkYEJKbelr3fdukm3oQkmCseEJjCLiTagIOlP+b62Z9H6psVCmGz2JzUwNfszveq8r7Xwx6lqlub4HXq0RqKlNf5BnkslZZ6VUXGvz4uO8WA9ukmcJzX5U/ILk89lZSmv0JW7i7x+R4cr2yM6PDIMl/kfG+lBlV8tgGl/wWFgqpWgL9SpSyjCqZUiU//4zjwTngMvvEvr1SJb0TbJOl/HrXQ5nCJCoYaw6hpbFLVaJPm/uetqfJTfzyTfPk1VQ7J9u7hEDYnDHT/E5H+x49T2u2On2yl+U6MQk22Ugzics2FAaJKJbD3bu5KVR27nsIZVUhPHeEVsJim/ylvtVVuawehyh/MxEdKjzbeGVamPbMUWLqjWa9Br7buYCJUXZX/b5iQWDgYTs7Pw68PXcB/F5+5+ixsePB8WfVkwmax2UHqAL29qrwW8oc9AVYo5z85izxCmFLF0g0z+GwVcgAMB8dxPvVojIEd3QHTziBNgHdGUU8l1+FRqpuwEPk1Vcq5dyoJCqpaAf5KVbqiLNXFK1X6MJMH/xuDWYZSlcigKtmg5X8AxdwILWGs500ya5mYpXp6gFIlsqbK3/3P8/8uGfbu4WDpdGpVYL0JSz0Ma1Qhs/Ev4J5sbXjwfHx82zl44dpB+Pi2c0JOtsQGDF6TCvf+7Fpt9pbqEtL/xP4gu1wcn6YcS0t1ZSpV0aX/AcHVanZcMemv6QlccLPYhEGVu/bkYAil6nhFaBe0aAMO4XHaefoB5aWZZKc/MmUjw6wLmv7F26oL3iurqQqV/gdIX+TxHRMLqtzPZQsfpFSFp6bRwf+WCRs5sxTAP/2CKqeLwx6ZdurRODzyvaqiSP8T6/7ntVRXzr1TSVBQ1cLx71EFNGOlShM6qPI3MpCkVFkT7/6nUqn4SbWYG2G4Bp5i0/X8CeX+xx8vglLFTEa86X/e8xfLuirhTd/fpENM6mOwpslS0KhVGNk9C38Z1B4ju4cu3hdbU1Xjn6rKK1XNN/2P4zj+egqnKCV71DyxClF1o52fRPinqUaDEt3/5C7uaDVqaD3XZLDFjHAqtz/MGS4RJkYNglrHnh6lKpStOt/4NzN4EX40AYcQNnGWaqEuJJiyIYQpVcVVjWiwOVBndaDU85xwjX8B7yLP+7cM47d9dee5Ed9fOV8X5H5/mUny08VaE+w6SDVqfX4/BvK26r7pf0Wn61Bvc8Kk0/BpnmKJpmea19E5/ul/TKlSksqvJBI3iySaBP8eVYB3ctJgc8Jid8qebMYCKUqVVqOGWuVWQvzNKvj0P+b+p2fpPWKUKnlWxtGSbtajssEuSqkSVVMlcVLO96nyV6pY+p/YPlWe19eoVdBr1bA5XGiwOWJWAB3K+Q8Ql/4X7tzFErEreEI7dUBo8NF8f6RqGh18Sm64z12qUQVz/kszBV/1l0uSQXoaYrypl2lUAbiv7TqrI+jiAl9TKEWpSoCC4U3/0yLPYyMeygEwUuNfwB1wTOyXiy1FFSirtSAnxZ3yJ0VxYoEQU3bkUOYXwPiTkaRHZpIeFfU2HC6v5/srtknWi5rYatQqjOmVg7apBpTWWHGy2hK0dovBcRwfVLF0RKZUnamjoCoc/iYVjAEd0qBSuQPj8lorn1bJzCXy26dCq5F2v4rGcIV3E5ajVPmVTkQiRULrkNaIqLv3119/LfqAl112mezBELGH/RjlpBh4NSjFqOWDk6oGO3LTmjCokqBUAe4UQIvdFTH9j6X3iJmoskl3ksQC8WiR4gAYLoXNKDP9j1m2hlSqRFqqGwXBqFmvgc0h3d49HP5BiBBRzX8lTCqjga3gsTq5UEGcsPEvoEwnOqmw4CfZoA37XebteEUaVZyOg/MfACRLaA6eKBrt8pQqwL0oVWcNVKocTm89oqSaqgQoGI2CWlZWUxXKAfA4X1MV3i6aqcpyYZPj8qiCKovPsYLRPTsJFfU2H7OKUPVUoeiYYUZpjRUnKhswyGPzHYzqRjt/DbAxkVIljlBNnJMNWvTITsbBsjr8eaIKF/RtCwCC/lTpkl8rGsMVvk1ONDVVIi3VUxS4IKUkRAVVl19+uaiDqVQqOJ3Nd2LQEgnW20OtViHDrMeZehsqG2wBKROJRIpSBbhTAC12V8DkwT8vmDloNdiccLm4oL0/APcEmBUVm3WJFW7TJARVwj5V/shJ/3O6OH7ilBFgVBG5Tkn4esLJmlmnQRXscUr/C/x8vO89nPufPKMKqaQYtFCp3EYqtRZHyKCKrfAxYwuzAif4UjkTIpXUH6nNf9lKepsYOv+5x+E+90oJZIX3ITkupCyQ9V9csAjuk6LS//jU8AQoVYK0xHSzHtkpBpTXWnGwrM4nSOA4DsWs8W+EoCpa2OQ5KqUqQvof4K6r2nqkEoVldXxKcyjnv1B0zDTj96OVfL1ZKFiAKExhY5/zGQW0VVEyLC2zbRAlcGDHdBwsq8PO48KgqgqAu02AVKLpmZYmM/2P4zhq/htjRM0yXC6XqH8UUCmPUK5J0eTgxhIpNVWAt0mw1a8BcBXfwM79YyGcmIRNDRNM/hOd/sfbqov4DMI5eImtgRJS02jneyWFtFQX2adKGDyY9N5gNlYwZSfYTV9MTy0p7mfRoFarkKyPnBrB0gNZkJgUx/5eiUJsLympzX/PiLBpl0OShObgiSDa+5DBs2DgH1Sx46pU4noBJtKootGvhiyUA2B5nRVWhwtqFeK+AMiUnLIa+TVV5SEa/woROgAeFmFSEQz2m85UvFAES2Fj36dKCqrCIuzv5Q/fBNijTjmcLuw5WQPAnR4olWgMV+Sm/zXYnHzatuQ+VaRUBYWMKlo4oXLRM6Jwi4klcpQqILJRhVGnBvM0CFeMzh7TaVQxrdkQA0uF2lpUgY2FZ4K6+jDCFZuzbVL6VDHnvxSjFjq/3G/RNVXBlCr+ubG74fLGDkHS/+LV/FcuYhwA/d+PuQVYqleIcP4DpNdUxS/9T1lGFdHeh9iilL+CL/yO+pu8BCNdgnoeLbwxhydDoGeO2wHQv1cVU2JyU41xv0czdSkW6X+haqoAYVBVF9FOPRRMtWO/8aEIFuSx3/8KCqrCEi6Vk/Wh2nmiChzH4WBZHawOF1IMWnTNkvZZMuQarqTLTP9j33OdRiV60VFOW4zWhKx8p/r6eqxduxbHjh2Dzeb7pbz77rtjMjAiNpwIkYueyBXJcEhWqrTBgyp/Cdvd/0eLOqvD7e6XEvx4DWFc9eLJyt0l+GpHMQDgh4JS/FBQirw0I+Zd2i/ojTOc2YIYswZ/Qjn/AdJrqkxxVqrCWb4aQ6zQBxtnYoIqHVBtCR9UWXzfTxJvqd6clSr3xC1S+l+KRPe/M3Fo/AsIm4MrY2Lgdf6Tl4Ic6nsg9f6WkaBaG5eL894/PPeMniGUKv43LITzXyyJTU2VuPQ/wN30lzk3RnL+86dDpnuh9ERFJKUqMDDI4i3VKagKRyijCgDok5sKvUaNqgY7jlU08Kl/+e3TQpYbiEGO4YqwFjJcuYM/bFE9LYizbij4hTGbQ9JrtRYk38G3b9+OKVOmoKGhAfX19cjMzMTp06dhNpuRk5NDQZXCCK1UKaNXlWSligVVAvc/u9PFT9KEDjZmvdsRK9wEjqkY/oXR8YQ1+PPXpViDv2ArUuHqguSk/7EfU/8eVYC3pipcoMJxnMCowjumpHik/1mic/8LV48Wa1hKXzhbdX+jipagVHkb9IYPftgqZ4PNCYfTFdEhi6+pipNSpZT0v2gbkLPFJn+lSuqCQqLc/yyC9G1v+p+nV5WfA6AY579YwdSlWqsDjTan5FRM32axob8L7dJNMGjVsDpcsMGd+tVJYtAoVKrCTW6DjYcFz412p6z32VoIl8qp16rRr10qdhyvws4T1bxJhZx6Kn+kGq6wBToX5752xabySa2nArwZFhznDqyCZZC0ZiRr6XPmzMGll16KyspKmEwmbNq0CUePHsWQIUOwaNGieIyRkIlvjyrfG3ZGkjLkf6lKlT6IUlUjkLyFaobXATD0hLs+wXbqchv8hXX/E9msVwgLpjODOP6w1wh33qwOF28FHDT9Lw41VSxdUogYVc2bAhX/9E4xRbz+RhVJIq5TpSM2/U+4eCHGJMJbUxUfpcpid8HhjF2jarlE24A8lAumlB5VgDAtPL6/C8JrnX2He3nS/4qrGn2C3VDZFvEgxaDl76dyelXVWR38vShc+p9GrfJJ9+uUaZac2piXZoRGrYLN6UJ5XWhljT0mVM6SDVo+lb6CHABDUuqprQtmVAEIUgCPV2GXp+mvnHqqaDHqNPz3SEpdlZygyqBVQ6dxB/BUVxWI5FnGjh07cN9990GtVkOj0cBqtaJjx454+umn8a9//SseYyRkwnpUAUC7dN+bglIaALOVVUMUNVXsxpBi0PpI5LyrWhgFgFeqEtT4V26DP4s99ORITF2RPxW8818wpSpyYCR8rXgbVYRL/+PrycQYVSQgcBZVU8UbVfjVVClENZFDhUj3P71WzasqYmzVeQOMGPU8YwjbJyjBAdCrVMm7D4VSqqSatLAMhnqbMyDFOpZ46xzVvMKSZtbxioCwCfAJ3vkv/kqVSqWKqq6KpYslG7QRP0thc9h0ky5sTW0wtBo18jy1N8fDpAAypUqY/qdSqXi1qoJ6VQWlzurgf8dCqY7MrOL3IxXYW+I2qThLhp16LOCzjxrFf57hekCGQqVSCRYPm+9vVryQHFTpdDqo1e6n5eTk4NixYwCAtLQ0HD9+PLajI6KC/Ri1TTUEKEFKSf+TrVQJVperQvRZ4Juqhpk0JVqpktvgL5zZAu+AJ6emKlj6nxjzB89jOo3Kx+jCzAdVMTSqsISrqfKMNcwEMLE1VZEbANf4WcS3BPe/0yJrqgCBe5SIIJIdN9ZKlUGr4Rdo6hSQdhk3pUrigkKqUQe2LhVPtSpUDRmrqzooMKtgAUMilCpA4AAoJ6gSkfoHuFPA1x44zf+9/XgVznvqF6zcXSLp9cQ4AIbqtcQaAJNSFRzmAJmk14QsDzirQzoAtwOg3ckhyaAJWMBOFGky2iHIUaoAgYsr2aoHIDmoGjx4MLZu3QoAGDt2LB599FF8+OGHuOeee5Cfnx/zARLyCZX6ByjHqEJqTRW/ImsPVKr8bwxJImpVop3MSEVug79wdUHGKGqqgipVIlLqQgV5cbFUD9P8V0w9WSKDKjbGmhAuTBzHBbwfoaLKcdJWq5UCu57aiAh+2CpnpNQRm8PFB6exrqkCvGqVEhRCtvAT85oqiUYVarWKv4/Gs1cVW3TxHxdzAGRKlcvFobgqcTVVgKBXlQxbdTGNf1lNrf+iAquplRJY8XVVYXpVMcXNf0x8A2AyqwhKOJMKxoFTtT725/VWJ0Y/vVpycBwL0iW0aGHIDarIATA0koOqJ598Enl57iL6J554AhkZGZg1axbKy8vx+uuvx3yAhHxC9agCmr9SZXUG1lT53xjMvKuactL/WIO/UH45Krhz5f0b/IWrqRLTANcfFkwHdf/zGE+EC4xCjYfZI8c0/Y93KAr8jIRGFaECkkQaVURKi7DYvU1eefc/z7XHcd6xNic4jgt7PfnjXeUM/4PMAjWtWhWXYugkBZlV8Ol/Mg1z2LVt9Xf/k9GjLRGp4f49qhjMrOKAx6yirNYKu5ODRq3iU93iTTQNgMsjTMTl1tSGoqPH3CKUUmWxO/mFCf+FOmoAHB5WTxVKdVy5uwR3fhTacCrRgRVL55TSDoGlCqYFyVgJBx9UKeDeqTQk38GHDh3K/3dOTg5WrlwZ0wERkXG6OFF2m+Fck5hCoRSlSnRNlSf4Eub7s8Aw3RxKqVJO+h9r8Ddr6TaoAJ8bcrgGf2Gb/8rpU8WUqqDuf5H7VIWqU2KTpMYYpVS5XBx/4w5XUwW4r6VggVOimv8CwhW84D9sTKVSq7zXp3Bc9TZHs3Piqmyw84HiwbJatE01hrX/FdsAWJhSGA/b3mQF2aqze5RZ5jXKB1X+NVUSjSoAbxp1PBfcQmUIsAbATKliwUJemjGiU2SsYAFRNDVVoSbiUmpqxbi/8el/IZQq9h70WjWfbsxgdYqkVAUnXIAcKThWwR0cT+yXG/ZeGEvSTNKdO6s9JlDSlarIhkytFcl3qfPPPx9VVVUB22tqanD++efHYkxEGFbuLsF5T/2C697chNnLduC6NzeFzMX2pk0ES//zrmpILZCNJbyKIFapCmNUEZD+J2LS5FWqEjeRldPgz1sbERtLdZbakxHG/c/mcIW8NhptLp99+efGOP2vzubgXQaDqRVGgWNWqPcfzo4+1kRKi+Cd/4zeviBqtcprRa8A0wQprNxdgknPreX/nv7O1oi1IWIbAIu1aZeLknpVMaVKbmsHlv4XqqZKikqbCAfAhhCLMiz972S1BbUWe9hsi3iRnRxNTVV4dUNuTW0omFJ1oiq4UsXeQ3ayIaAPEVtYJaUqOOECZLmGU/EkXYZRhez0P5HZBq0RybOMNWvWBDT8BQCLxYL169fHZFBEcFgutv+XOZTcHD79z31D5ThpcnGssXr6lYhXqkIHVf5KhhgDAOb8ZUpQ+h9jcn4eNjx4PnI9q2DzLu2HDQ+eH7Jjerg0STnNf8O5tQkDpVDqV6jJmlmGaUY4WOqfXqsOOjHUarz2rsLeN8HGmgilil2DofpUVfv1qGKYFZSKJhZ2Pyr3cw+LlP6Swr/X8Pcd1vg3HvVUgDeAUcLEoEGGoiTEa1QRvE+VlGufTc7iWVPVGMLtUOgAeKisjq8V6pggkwoAyE6NIqji1Y3gQZXcmtpQsPNyssoStDVAOTOpCDIeUqrCEy5AjnVwHAvYAmm8LdWByBkZrRnRM8k///yT/++CggKcOnWK/9vpdGLlypVo3759bEdH8EiVm8P1qAIAnUaNFIMWtVYHKhtsomoh4oHXqELcj76Bd//zTqD59D+T73swiyhEb7RH13QzGjRqFdqlG3GqxoK8NFPYNIFw/Wakuv85nC5+0h/MqEKo6DTYnEFXz0PWVMVYqQrX+Jdh1GlgdzpEKFWJMKoIP1EPZbqRpNegHLF1TYwl/inHQzpnyE5/EVvkHC87dUayAo0q5CrmXqOKUH2qxK+fJkSpCnM/69U2BWW1VhwsrePT/xLl/Ad4J9HlMibEXnUjeFDEampPVVuCfndUcGcs+NfUhhurXqOGzenCqRpLwHkK17xWKb0qlQr7LIP1qIp1cBwL2PxHSkmHHEt1IHLtcGtGdFA1aNAgqFQqqFSqoGl+JpMJL774YkwHR3iRmotd2WDnf7hCWXymJ+lQa3XEvdFjKFwujlecDCIbH4ZTqgLd/yIrVYl2//MnU2RtG1uBDmdUwdL1IuVwVzfa+ZS69CA3U5VKBZNOg0a7M6RSFapWwxRje/BwjX/519RpUGtxBA0qXS6uiYwqQtRUNQYPqtiKfbj6v6Zi5e4SzP+mwOf+k5mkQ0WY3P1wtSHJIoOq03Fq/MuPw6Ccc+5Nh5OZ/hdCqWLfXyn9rzJ4pSoBlupBvpM92yZjw6HTOFBaG7YuOF6wifCZehscTpekWq5I6X9ya2pDoVar0D7DhKLT9The0RgQVJWFcP4DvL89ZKkenHBGFbEOjmMBXwspxajC89nLVaqo+W8gou8WRUVFKCwsBMdx2LJlC4qKivh/xcXFqKmpwS233BLPsbZqpMrNLPUvWI8qBu/yJKGwMZYIe01JVaqEBdkh3f9EWKrXW6VPOmIJs7YPt1rodHH8uQoXVAHizCoqBTfSUBOGSOpXKKUqKcZGFeEa/zLCGXUIr5NEN/8N5kbobfzre70xe+9wTpVNQaiU43ABlZBg961kg8dSPcx7dbo4vplmvdURl7pPRbn/ecYQc6VKhkqbLqPfjVRCuf8BAgfAsjpv49/MxClVmUl6qFXu1Hgp9UbhnPaEyKmpDUe4XlXevlmB48kkpSos4VI5WXAMIMDJV05wHAvYfE5s+p+7vYf7evU3+YpEsoh+jK0V0TPJzp07AwBcruZn+dsSkCo3h0v9YzR1ryrhJFi0UhXEqIIVZga4/4kxqrCzAvGmVarCqYXC8xRsciQ8d4324Ol6QtiEOFzKJwuWQilOoSZrsTaqEJX+pw2+Su/eJjh3Iq+xaGAKlMPFodHuDAjWhUYVQpSoVIVLORZLsPtWJKMKf2Vs2dbjWHugHPMu7Sd5whkOJbn/8cpNlJbq/t8BPs1OaUYVttDKXM8ctwPg/lM1fApoIpUqjVqFNskGlNVaUVZjDZr+FYxwTnv+TM7Pw8R+uaJcfCPBfuPZb77PmOrCKFWCz1lMhkNrwmJ38kp6doi5FwuO/VX83DRjzO9VYkiXqDDXCRaryP0vdsi6gxcWFuL555/H3r17AQD9+vXD7Nmz0b1795gOjvAiVW4W45rU1L2qmIqgUaugE5liISn9j580iTCqSEBqWDAyeKUq9GfQGCH4VKtVMOrUsNhdohwAvXbqkdWfUMcLVathjnn6X+jGvwxjmLGyc6fTqBJiyWzWa6BRq+B0cai1OAKCKm86o/+1yoLRpp/gMyKlHIcjXPoLS+UMphAxZSxU7xc5K/mhUJRSxYwbZN6HQilV3tYHUmqq4v+7EK6WtadHqSr1qCw6jUp0YBMrclI9QVWtBUCaqOcwVTYnJdBpLxgatUqUbXokOma6f+NPVARRqmpDp7CxmioX577PBquvba0whc+oU4dNPY9lcBwtLJW/utEOl4uL2IaCzZtCmUCFg5r/hkbyLOOHH35Av379sGXLFpx11lk466yzsHnzZvTv3x+rVq2KxxgJ+MrNoRDKzWJy0fkJfRMrVWJVKiB489/QNVWRJ6q8pbrMFeJoEVO/wMZo1KlD3ihNutApcP6IadQa6Xihej+ZBec8VDNeKfBBVZiVX5PHWCNYqiJvUiHSsj9aVCqVoLlt4KSUN6rwd//TR14ASDRynasipb+EOj+xbowaCWVZqjOlKsbufzKUqrSEuP+FTv9LM+nQVpBy1S49vIlPPGAKq5ReVd5Uu/jUAIaCOQAGS/8rD2OcodOo+ckx2ar7UsoHo8aIATILjv8yqD1Gds9qMsWPfW9dnLimvHKd/4DwC2OtHclB1UMPPYQ5c+Zg8+bNWLx4MRYvXozNmzfjnnvuwYMPPhiPMRIemNwcbOVkweX5Piu4YtL/EpHmEQ6pzn9AoFJlsTv5iUSaWXpKVYM9sc1//RHThJmtPoebGPG9qkQEVUypSg/TRT3S8ULVVLHz6OICG5HKgeVsh7vxhxsrn6aYwM+XBUzMPl1IKOVNzAJAohGbcuwfnEeqDQnV/DfRvV+Y+58SJgZew5zo+lTFoqZK+LsQi4WRYESykGcpgIDbgj/RvRTl9KqK5PwXL9jCqX/6n9PF4bQnfTJY+h8gsFUnswofmipAjgaDVsMvUoiZ00UTVFH6X2gk38H37t2LTz/9NGD7Lbfcgueffz4WY1IG9fWAJsgNX6MBjEbf/UKhVgMmU/h97XZoLBagsRHQCS7uhgYgyA/a5K6pODqiHRauPYbhXTLRYHPg0NEyHDlWBgxow+93+tQZmGwWdDT4HaOxEfDUxWWr7TDZLKivqPGOLSkp6L5BEe5rsQDOMBP6IPs2VlbDZLNAr3Fi865jGNrFI5ubzQBbHbJaAYd30mOyWWCyWdzjra9HjcM9mVCrgGTOCQhW3JId7n25Wrt7f5PJ/Zng/9s78zA3qivtv6Vd3a3eV+82GBvbGAxmMZiwG9NAQsgy8EFissCEkBkCJDFZHU+GGJJJvsxkIcskIflIIGQlOMbGYGIwGK/Y0N6x21u7903drV1V3x+lWypJVVJJVZJK3ef3PH6gpavSVdWtW/fcc857AIRCQDgMfmQU7lAY5ZFA4vVRaKuKyxUfK9m0DYdRz4nXwD84nDo+nE7AZoM/xMMWjaBaEOJt2LgZGwPsdpTFDukPRcXzFVRfDAwPi8eoLXeotq0UQmK/fLJFbjQqXjsAEe8o3KEAKqLBeJ/sdpTZxSnFwkfhH/TCpeYNs9sBR+w9nhfHmgKBQS/s0XDcCFFoW8WLfY16R8Tf4ow9CAUBoeERuEMB1Ahc6vm12RLawqdcQBNAVvd9rYXHKcgeOLK2AdYfxMZkbI5guTSh4RH1YyfPJypzBADx/ikrS22bNG4U28bu+0sanZjpFtA9HEzwHvkd4nngAEwv4/DSv1+Gt08Oonc0iIYKZ/w+HhtTvO89fBDuUAARL5/wW+WeMWckBIvK3NPfMwDMqlWdI1JQmU8qI+K4CXtH4/3I5r43cI7gR8V5qCIcm0dlcwQU6kJKxOYIl90KWzQizYsMYVS8T8vDAfF322KP/DRzRI0QhS0aQQQ2jAYj8Ng4sa3S2AHE+5j9LZsjFInd9/5wFBY+iopIMGW8b9zXhQPvdcEeBcJWO9rOeHHlmpex+oZZuGF+c9rjAkg7nwDQdN9PckThDgXQP+CNv5hhjujvHwYgEzZItzYwcB0x1SnAHQpguC+A4MgonB7RIB0YC8Ee9MMJoJ4LA2NJz2eOQ025A8f7fWLuWjbzSTZrA79fedwotc1hHaFKmnVEprYDPQNwhwKY4uBTr02R5gjVtrI5otkaQWcoCG/fEMCGV2yOSG472ieuvxotrvhvlLdNM0d4IkHYohGMBMTyPVw0mnbNkcscIbVVGzfJbTPd93rniHT3qBwhS6ZMmSI899xzKa//4Q9/EKZOnZrt4UzH8PCwAEAYFk9l6r/W1sQPlJUptwME4aqrEtvW16u2jV50UWLb6dNV2/ZOO1uYvnKtsOr5NuG1wz3Cobppqm1DU6clHnfxYvX+1tcntr3qKvW2ZWWJbVtb1dsmD7MPfzh929HReNsVK9K2Pdp2TJi+cq1w/uoNgvDZz6Y/bnt7/Lhf+EL6tm1t8barVqVvu317vO13vpO+7auvxtv+6Efp265dKwiCIGw71i880vr5tG2/fc9qYfrKtcKrB7sF4bnn0rZ95n6x7U9efU/8jjRt33rkP+L9ffXV9P39zncEQRCE2V9ZJ9z68e+nb7tqVfy4bW1p2/70ktuF3287IbZtb09/3M9+Nn7cnp70bVesiLcdHU3f9sMfThzDadruWrBEmL5yrfD3PR1iWw1zxA9fOSxMX7lWGKmsUW+7eHFiH9LMEcK8eYlt581Tbzt9emLbNHNEn7tSmL5yrTAj9q9/8RL142Y5R7z5Xp8wfeVaYfrKtcLaOVekvx5ZzBFCT0+87TidI/afGc44Rwjy53aGOWLlLQ8J01euFU72j2WcI4Qf/Sh+XI1zxO0/eSPjHPF/r7hTGg83fPLH6Y/7hS/E+2DgHPHG0pvjbTPMEXsuu16YvnKt8MNXDovt0/UhT+uIwAUXSs32dQwLpyob1Y87b57wqae2C9NXrhXnV4PmiOR1RPR971Nva5J1xESYIwRBEIRf/zp92yzmiEdaPy9MX7lW8AUjeZkjQqGQ8M/vfjd92yzWEXrniGFAACAMDw8L6dAc/vcf//Ef8Pl8uPfee3HffffhiSeewOuvv47XX38djz/+OP71X/8V9957r9bDETrgBQGAmNy+9Ox6OO3ql9FmQkWfrgzJ7xv3daV9X85wgBX+zd6FXSpoCeljYZFacqpYOGRteeZzFolmH8KXj1DKXEIUioUttjOZTRIvy+8RYve22WEhfkYWDWdiPOabscyP0TXY2HjMV1hYtkI2xborlBRF1QjG2hY6/E+OXMBJS05kjYaSHoB4/rce7cfzezqw9Wh/0a4HYT5GghQCKIcTND7FrVYrOjs70dDQgB/84Af43ve+hzNnzgAAJk2ahC9+8Yv493//d02qN2bG6/WiqqoKw2fOoLKyMrWBweF/4XAYGzZswI033QS7/PvSuOK//Nd38UxbP760fA4+e/XZ+MdbR/GF5/agptyOVx6+Gu/1juCjP30LDR4HXlt5narbft+ZYXz4ya1iuy/FCjorhP9FeQE7jw+khvfk4LaP8gKu/c/16BlSDqPgAFTVV2HLo9eJ35Hktt98qAefeXo35k3y4M/3X4GXj4/g0/9vF86fUoXn7704oa0gCJi/agMEAdj8pavR2FiT4IofG/Vj8X++DADY/fUbEo2BArntI/4AFq5+CYIAvL7yGtTLC53GXPEb9nXhgae24ZLJFfj9vZfFPhobNzfeCLvdjhW/24PNx4bwf//lfHzwvOa0rvgP/XIndp0Zxc8/dhGWzalXbPuN59vwx52n8cCN5+JzN8YEUmRu+08+tR1bjw7gux9ZiFsWThLfj7nXl6x5Bd2DY3j+0xfhvMnVyp3Q6Ip//w+34EC/H7++bymWzq5XbPv4iwfwmzdP4NNXzsQjrfMT3PYvbjuKh5/bi8UzavD/PnVp4sHzFP73pb+24bm2Pjx601x85qqzEtpe+Z1N6BsJ4c+fXYJ5LVXSHPHcjlP40p/fwU0zPXjy7ouUD2xA+F/yuFFsmxTa0znkx7Xf2wyrBVhz+0LUN9XGFa5yCBEORXicv/olAMBbX74ungtZXi6p/zmSwv/YU+W/77hADAHTEdrD2h7rHcXN/7MFHpcN2796vfh+EUJ7vIEwLn3sFQDA29+4AS5Pedbhf53Dflz5ny+hHDz2fnOZ9PYFq19CMMLj5Yffh8nNNZpCewDg5p9uw75eP37zyUtw1awaIBhUHjtATqE9V3/3VZzsHcEf71mEi6aLCpHbjvXjnl/vkJpGrFaEreJxOYGHKyyeh6c+cTEunVWneFwAhoT/vX1yEP/nF9vQVFuOf35tedq2jPc/uRXv9AXx609cjGvmNBYs/A8APvP0Lmw+1ItvfuA8/MtV5wAAntt5Ct94ZjuWnl2P//344tTjchzWbD6Bn20+hk8tnYmvXztDcT7ZuK8Lj714EMdlp3RGGYevLp+jHo4pu+/DXi82vPhi6rhRaGuW8L9P/3Ir3nivH9++/Tx8cNHkxLYmDv/7/LNvY8O+bnzt5nNx12XTxfdVwv/+78ZD+Plr7bj7smn46s3zUttmmCMWPf5PDIaBVx65CmfVuAwP/wuHw1j3wgtovfZa5XEjawsg7+F/Xq8XVZMmYXh4WNk2YB9VP2oizPbiOA4PPfQQHnroIYyMjAAAPB6P1sOUDuXliTdwunbZHDOZcBhRlytx0gQSFzlJDEEcYJ7YbuKNF8/E46+dwKkBP/50cAB1FU74HS7UN1WnHkf2PdUNFvgdLnRGOAhlZakGsdudUjMGAFqkOgyy3+PStju3vX0AJ3w84FBv7/MGsb19QJSbdTrjgxuArdIDv8OFEasTKC/HcGAQQKw4bFJbDoClogKjwQh8Nld8cgMAhwM+uwC/wwWOA1zVnvikmozDEb8ZM5FNW7sdNrsdjioPhnxhDMKOeoUxEghHEbHawFXIxiQbN+XlgN0Oh0v83f4QL04INvVbuy8oLlZryx2qbe2x8zzKy86Z1Sp9/xDngN/hgr3SkzKu3Q4reIsVYzaXtvvDYlFt18PbELba42p5Cm1tHtmYkF1/cBxGbeK9YKmoSN8XjtN/L8coq6wA0BfPqZK17YnaEHRY4KmtBsrj9yZTfRuAXXs/0swRqm2Txo0iSXNRR28QfocLU2vduPXy2WnbpiU2RzgA8GVlCEZ4jNgcqJKdBybG88Dv304QJ2Bzzg1K4hdJ931aZG3Lo1b4HS4EBSjPf/m675Pa+qMB+B0uWC0cnFVJ85Ddrn6dZDhtVkSsNgwDiLrLYLVw4HkBQxYH4ABc1ZWJ93mGOaLSUwb0+sWEd9ZWy9iRzRHp8IWi4C1WOKsqpfZd0SEpXy8ZgbNI73VFM3xHmvkkBZX7vr6Jg9/hwhm/AEEQxLGRYY44ExDHqyRuYNB8oqVtY3Mt/O0jOO6Pb0T0jgQRsLtQVVetevxauadKYT5Z39aJ+/9yMMUzdcIn4L6/HMSTZWWZSxy43ZnHDUPjOiLrtrH7PsoLmeXPnU6cCorjra6xJv21KdAckRbZHOGuqYLfMYw+QeU5ImvbJ9jhd7hQVlOl3DbDHOEuc2FwOCBGZGRom4DGOSKhrYY5MKv7Ppc5Ip0BLyMroYrkh864NKZKAKZWxUI0bFYL7r1yFr7x/D78/LVjuOJsUbDCZbemLerH5LzDUQFjoaikysXIR80YrTLNau2S1f+GYgo2akp2ZQ4rRoMRjCmoqjGlNbfdWlQPa02ZA0O+sGoIhhZZ5FzU/9LVJckkqe6Phboo9aksQ42rbNBU/NeuHvoYkBQm81+jiqFWwyMQjkqKiJUp8v/G1vcyko4hcUdvUpVxBVg9LhuCoyFF5b3Lz66XDKpvf3ABZtZX5KX2C5s/eQGKhZoLBZN0L9MxD8nHdzAi/paATAkw25DcmvL81qpSklTPtsB9PmFqeaEoD68/kqIsm0w4ykuy5MUI/5uqUABYklOvVN9wYM8ApWdPphIHHMQSBzfMay6JwsHpN4gT1zFSfa80586MSDXm/JnDdod0qP8BMQXA4QApACaR1UrjnHPOQW1tbdp/RP5hCxG5EfSRi6ai3GHFqUE/nt1xCgDw5tF+LH1iE9a3dSoex223SkbKYNKkmq+aMXofnA5rolEVlwVVXhCxhZPSYlWvjLFR1GSoC6NFFllrnapwlJcW+7XpJNUzGEbxoqJKRpUxBkI4ykvHSFv8N41BGcihTo9e1Iwq9jfHxb3MDLa4VDL+i82ZIXGBMbnaOKOKzV2jCnln+8+IimuTqlz4P5dOz1vtF3mh3WLKquutUQWInioGy+2R37vZ1mmrcudPalsQBPjCqXNvppw6DuIiWKmgtNG47FZpsallI7BvNAhBEGsW1RWhiC4rAHxKVgA4XeFfRl0ao8qIEgdRXsC29gHs6uOwrX2g4NL4DLZBnPx72AaxfI0UjESlZ3FTEfPjcqE6i8LdXmlDOlejSn0On8hktZpcvXo1qqqq8tUX3Rw+fBhf/OIX8cYbbyAUCmHhwoX41re+hWuuuabYXTMUNojlRtXmwz2K9ZjSeZU4jkNNmR3d3iCGfGFMlT2rsplQs6kKzx6cXcMBRYONg5gEr/bglDxVMQEFb4bdFrZYVVo0MU+VUgHKQlKboVZIII1XiJHJCGKw7+C4VG+JnHSGivx70nmq9NZcYtcWiE/gSqT77emMv3zBDEB5/4G4xHqFw5ZSxFky/k1U/JdxhnmqDDSq4nVOUsfIvphRNX9yfp81FotYqHk0GBGLLhcp8MKIzR2rhYPdyiEcFSQPlV9WXF2taLgaNVkszrIlFOWlxbX8vmQF7u9/ejc4JIpTZCoonQ8aPE4M+8PoGQlidlP6wcHqGtVXOLI+10YwRfJUxY0q5qlSq1EFpPdU6Y0qSfQMWfHbIztVPUP5JFuPGztvDqslZ4OjWFS7tdce1VOnClDfPJzoZDWL33HHHWhsbMxXX3Rzyy23YPbs2di0aRPcbjd+8IMf4JZbbsHRo0fR3KySUFmCsHCRitigZpOGEpnc9DVlDnR7gykLer0TqhryB2cyWh6c8SKXsfC/WL/ZZJKMFFalsFj1KYSgFIPqDApMWjxVmYwgxuBYXC0x3eIkbhipeKoi6n3K9FmteGWbBzarulNdS/FfZ5Y79XpQMxjY71EyZs3tqRKNqpZq43Zt1QoAA8C+DrHez4JJ+d/AK3fGwoOL6KkaM2hzx2mzIhyNSJ6qQDj3+Y2pwuXDUyXf/EjuG8upSw7Tai7CYrzR48R7PaOannHFKvzLYOF/faMh+ENRuB1WTX1KV/xXT1RJPlIHciXbDeIemTFaasJrkqfKn3kzRK9RVcE2Dyn8LwHNRpXZB1dfXx+OHDmCX/7yl1i4cCEA4PHHH8dPfvITtLW1jSujaiQpp0qPV6laCj1LnFTzGd++fEELHr/9PKz8y7sJr2t5cDqSjKpME0O5U32xahajinmq1HaXtHhbtOZUseucLp9KfjzVnKqQep/cdmPC/5inpzKNl0r8PrEPQQX5Y38RPFVsBy/5YcN+j5LXTR6mKiXGm4QzsbnFSE9VRZrQkbYzMaNqsrrCklGI5z1Y1PA/di+V6wxDdtktGA3GNzx8OkJfqzOEJOuB9ctu5WBX2CxZvqAFN8xrziwokGdY2BzzXKRDS6hdPqkqs8PjsmEkEMHpQR9mN3niOVUaPFW+UBSBcDRhkyzXqBKz5WJlu0HMvI7pPHxmhW3QavEwk6cqP2St/mdW6urqMGfOHPz2t7/FhRdeCKfTiZ/97GdobGzERRepSBQDCAaDCMqkIL1eMfQkHA4jnE7S0iDYd2j9LkEQpF1Vl1X8XOeQtkrPnUNjCIcTFyqsvlPfSCChD4umeNBc6US3N5hmQnVi0RRPTuepqdIR+68TK288B40eJxZPr4HVwqU9nkUQF86hCI9QKCQZIuUO5c+5YwncI/5QyvsjPvG6u+yWglxrNSpjhl/faFCxH2OxxbnDkjpe2H+ZzeALpB+3vV7R61DjtqdtZ7eIV90XiqS043lBMmpt4FPed9nEB+VoIPWcZ0P/qNhXj8uW9ji2NH31xe4Vu0X7PaYXlt43knQtBkfFh7bS77Fz4m+I8gLG/EE485QDlu18AwBnYuUPmsrTj5lsKI/dl8O+xDHvD0XxXs8oAOCcxrK8X7Py2I2T3I9C4pXmofRzXyZYvulYbK4b8ec+v3mcLNc2qDrn5Ar7vW67Ne2xFk+rBCA+r/hoBHyBI2PrYmIdXUP+jL+5K3aP1FcYd49ky+RqNw52jaC9dwT15TbJeK12qV9/l0WQwkZ7hn1oqUrcJP3qTXPwb8/uTfkcJ3s/+dps07jJu/W9HlxagPy4ujJty9y6MnFe7oqtpxoqHEVdF+RChV28MkO+9M9enhcko6osx3mHzeFehbWVERg13xiF1n5oNqr4dLVITADHcXj55Zdx2223wePxwGKxoLGxEevXr0dNTY3q59asWYPVq1envP7SSy+hLBvJYp1s3LhRU7tgFOAF8bK98c9X4LQCx4Y5AJkXYcf27cG6028nvObtswCwYMfe/agfaEt4r7WZw6+8bCdRvqMkQABwU5MPG9a/qKnfyfyzU+xzo9UP6+m30Q9gw4HMn/NFADZsX/jHi+jotQLgcHDvLkSOp7bv7xZ/39vv7kv5fdu7xT6MDPRh3bp1Of0OIzgd68eh9tNYt+5kyvvvtYu/4cSxI1gXPJzwHhs3R7vEY7Sf6sC6dadUv+uN2HeFRgbS/uYDQ2K77r7BlHZiJKV4DV7b9DKSnUCdp8X+7jv0Xkp/s+HtPrEPEd9I2r4eiY3/3sHhlHZHj8fO3dHDWOc/lHNfsqHbDwA2DIz4E/rzZuzc+4f7U/opppiI5/T5dRtQkedQfq3zTSAKDPvFfr277TUcMUjThd2Xe9oOYp03Hrp8fESc3yrsAna9vkm1yoFRBEbEfryxbScCR4uzcbgzdu96+3t1zUORoDgX/nPLGzhdCRwYjN3r/rGsj9s+AgA2nOlLvae0jh01To2Kx+b4cFHn3Uz0nRHP355D7VgnHE3bdtdRcRwNdZ3CunUnCtK/ZOxBsQ8vvbETJ9oEADY4LQI2v/JS2s+5LVaEoxz+vmETplakvn/LNA4vnEyc5KscAm6fwSN6YheSf+6uPm3rkZde34b+A/m/53gBqHZYMRQCoCiDIqDaAfTufwvrDgBvnhTPo3+gy9TjUwlvCABsGPKFsPYf66DmCPRFACG2jty6+RXkIo575rR4nfcfyXx/6EHvfGMUvnR1LGUUV/ZMA48++iieeOKJtG0OHDiAOXPm4IEHHkBjYyNef/11uN1u/O///i9uvfVW7NixAy0tyiFlX/7yl/Hwww9Lf3u9XkydOhXLli1LW+DLKMLhMDZu3IgbbrhBvcCZjN6RILB9MzgOuO2Wm8BxHKK8gD9977WMXqXP/cv7UtztBzcewZvd7WiYPB2trecmvNcK4MJ93fjSX9oSQrlaqlz46k1zceP8phx+scibz+8Djndg6cKz0Hr97MwfiOEPRfHlHWKhzGtvWIZvt20BEMKya67E3ObUZOJd/ziIbb0nMXXG2Wi9IfF7Ot84Dhw7jJlTJ6O19bycf4te7Pt78OyxPXB4atDaemnK+y/94R2gtwvnL5iH1iViQb/kcePf3YE/te9DdV0jWlsvVPyeKC/g9ef3ATiD2oZG3Lh8kWr4ReOJQfz0wA7Y3eVobV2a8F7/WAjY/k8AwPtvviklMfvopqPYdOYomqdMQ2vrvCzPRhzvjtPAkf2YMbkRra2LVNvtPT2MH+3fBpvTjdbW9yW8t+6ZPUBfDxadNx+tl07LuS/Z0DsSxLf3bEaA57B8efz8nH69HTh2BGdPUx5vX971MgJhHpe/7xpMqTEu1E5OtvPNkZ5RYPubqHTZcPv7l2Vsr5WDG4/g9a52NE2dgdbWudLrv9t+Cmg7gAtn1OPmm9UjDIzi74Nv44i3F2efuwCtF0/N+/cpceq1dqD9CGZOn4LW1gU5H+fJ9q3o6RrBosWX4Mqz62Hd1w0c3Iumuhq0tl6S1bGO9Y7hB21vIMTZ0dp6I4Dsx44a248PAO/uRK0ndW4xE5G9nXj+xLuwV9ahtfXitG2ff/ptoKcXly+aj9ZLijOO9nCH8O6bJ1A1aRbmzW0A9uxEc3Xmc/zksTfh7R7FvAvFcZPM4PZTeOFkfMdzXosHf/nMZarPjrr2Afz2yM6M/V125aUF8VQBgH1GNz6n6nHj8J+3ny+tZ17/6z6gowOLF5yD1qtnFaR/RhGM8Pj6rpchgMOV196gGtp3atAH7NgCl92CD9zSmtN39b91Ev84dRDVDc1obb1AR6+VMWq+MQoWxZYJ0xtVjzzyCO655560bWbNmoVNmzZh7dq1GBwclIyhn/zkJ9i4cSN+85vf4NFHH1X8rNPphFOhcKTdbi/ohdT6fUFeDHercNjgiBWHswP45vvnZ1BNmg+XMzWPpi6WEzUciCp+/y0XTMEzO07jjaP9AIDptWXY9IWrdcdCv9crWv1zW6qyOs8Wa3zICpxVylup87gVj+OJTSr+iJDyPkujqHAX9lon0xCr/zPkCyv2IxhTOqxwOVLeZ+OmIibUEYjwisdIrtGx+XAfrvn+66o5bB63eE8EwqnHiwjiOXfZLXAqjKmK2DkPKpzzbBgNib+7qiz1d2vtazAq3g1lCucuX9R6xG0/QQBCAgdP7Ht9sZyv6nKnYl8qnDYEwiGEeC7vfdU63/SMitd6UrXy/ZUrlWXiNfMlXbODXWLo33lTqgtyvZioSEDnWNVDKDZGPS598xDLG4zExk849iAoc9qyPm5DlRilMRKIgLNYE4Ri9D4bQzyXc78KSYtM/CFdP6O8gOP94vPMG4jCYrUVpW7TtDqxmOmZ4SAGfOImaFOlK+M5rq1wAt2j8AaUnx37O0cAAO87pwGvHe5FtzeouJZgLDm7UVMu1pKzGwt2nuZNrlZ8vanSiW++f37CM7AvJhjVYvCcVwjsdjFH3BeKYiwsoL5Suf8s5apKx9qnulycw8dCyuPGKAq9Fk/XDy0UriJmjjQ0NGDu3Llp/zkcDsk1Z7Ek/iSLxWL60MVsYInd5Ul1bphqUnNSTHRzlSut0k61BpWnQ92j0v+Ho7zuiVAQBBzuFifqczJI1SZjtXDS9w/5QgjHFiRq0qdMplhJ3csXjhfdLCY1GtX/tAhVKAlLZFOjg5G29lM4fQK8W6pTpVNSXUPhXyB98V8thZONxmmzwG4Vx6hXlsTr9cfU/1SEN6SxaiIFwHzUqALUhSokkYoCKP8l9KOIUvZjaURfsoHVomL5jv4QK3yd/XHlY1SLklg2KBX+NSNMhKknjVDF+rZOLH1iE471iXk439t4OG1tyHzCFABPDfriCnYaitemk1UHgHdOi/fkhy6cDI4TIxXSiXcwhV8liiGNDwDP7TwNALj6nHo8c++lknjHI8vmpBb+9bKiyaVVowoQDXy2nnn9SJ9qXTC9IhWATOW2iCI/ZsT0RpVWlixZgpqaGqxYsQJ79+6Vala1t7fj5ptvLnb3DGM0SU5dzvIFLdiy8lo8c+9l+O87LsAz916GLSuvTauml6keSf9oEH2j8QnUCDWobm8QI4EIrBYOsxrKs/48k1VnDw67lVNdNFekK/4bNMfDnV0DbyCCSDR1A4DVqdJS/DfZCMq1iLNU+0nRUElfN4tN6sap/6Wf+OW/PVlQJxDJfWGZKxzHSX2WV5tnRqJafTBJit5EtaryUaMKiBc/lqvuhSI8DnWJmy3zC2RUsc2pYkqq+wxS/3MmbS74dUiq26wWybDSUvMmGyRVwiIXXc8EU38bCUQM26zKJ1NrY0bVgD9eo6ois1GVrgBwIBwVQ4AhqgFOj30Hu0/VWL6gBT+568KUDKZMm7z5IBzl8efdolF1xyXTseSsetx5iRgK/mJbV0r7Hg2qiWaEGfjM0/a1v7WpGvjGGFVM/U99TRjlBWw92o/n93Rg69H+ohV/LiTjxqiqr6/H+vXrMTo6imuvvRaLFy/Gli1b8Pzzz+P8888vdvcMYzSo7KliWC0clpxVhw9cMBlLzqrLuBuUaZfqUMyjxGS//eGoqsy2VpiXanpdWU71gxxJRlWV264qQZ2u/o8kqa5yLguF2H/x/5V2hdnObto6VSpGUDZy+3KYYRSK8CkToVQ3S2WxVpamGG82sIk/XZFieT94IV4UmhEogqcKUJabzWQklisYGsUmHzWqAOUH8pGeEYSjAjwuG6bW5ienLJmKNJ7sQmFUEfJUT5V43FzHfo1Uw8hoT5U5IgQyUemySRt4yZ6ZXDer8gnLwxz2h3GsVzSEGrV4qlikhILxvL/TiygvoL7CieZKF+bE8pYPdmXOL1kwuSrh/JzTWJ5xkzcfvHqwB70jQdRXOHDduWKd1VsWin14/UgvhmXjOxLl0T9W3JpjuZCtgc820fUYVVKtQRVJdWbk3fmLt/Dgs3tw5y/eKpoXt5CMG6MKABYvXowNGzagv78fXq8XW7duxU033VTsbhkKe/h7DDIEaqS6BipGVWxH6sJpNZKBNqwzHEQK/WvMLvSPwaSDe2VGlRpS/R/F4r/GLGb0YrNapN8wqLJbCKRfHEnemlCiUZFrEWd5KFKyoSYZVSoGcVka72A2sNC5TBO//LwEkn5/vEhxYac6j6KnihX/VQv/Yx4+ExlVw6JRZXj4n0Lx330d4kJtwaSqgtXpMoMhG6+Xlx9PVa5eWik0XGXDLVfMUh8wExzHSUZJ8vyY62ZVPil32qTNz90nhwBoMwzqKmJG1WjqdX43Fvq3cIp4T85pFvPVM3mqAKAtVsSblTXpHwsXJdfsuZ2iGu7tF06R6qLNbvJgTpMH4aiADfvi3qq+0RAEQdycrstQy9Es5GLga92wTIdSNAbDbF7cQjKujKqJQLzwrzEPJBZ6NhaKIhRJDT1jBtDcZo9U0ypd/pUWjsRytM5pUtBv1YDDpt2o0uSpMkEYSrq8Kn8WRlWyFzHXIs5Om0XyniV7nNIV/gWMMw60Fv+1Wy2wxR7WzIhK7mshw/+AuOHE8qiA+MPHo+apknKqzBT+Z3zhX0A5p6qQRX+lfmgM/8tnGEv+PFWxMN0cj5spNDxXfAblkBUCFj7H8mwYuW5W5ZupMW8VC9nXUsA2nafq3Q52T4rhuExhl0WwpIPdz1ef0wBAzMVKFyqWD3q8Abx6qBcA8NHFiaqMzFu19t34Ap9dr4YKZ4qqrVnJxcBnRlW1O3fDkUUbBMI8wrIIETN6cQsJGVUlBnv4VziNUUOpdNmlWgZK3qqDsR2pOc0eVJUxb4pOT1WPeMzZWYpUMOLhf+JEosVTpShUYRJPFRBfwCiF2kieKof67SrPgZLnFV0ysxYtVS7F6hyAmDjcUuXCJUnSthzHybxfiYv8jEIVBuRURXkB3bEHxalBX8YJ2JWprwW+xh6ngqdKEqpQyalyspwqc3iqeF5A53C+cqrEcyD3ELGd7ULlUwHaPFX5DmMxynPDPFXBJE9VzuF/LIrBb6ynSk+uV6FRE6vIdbMq30ypSaytqSUviHlklDySkqcqZlSx8L/D3SMZ5+S2mOf50lm1qLCJbU/0a6v1YxR/2n0aUV7ARdNrcHZj4ibuzTGj6o33+qTNzLhIRenkU+Vi4BuRUyXP65dvjpnRi1tIyKgqMdjgrTDIU2WxcPHQs6QFPc8LONwV91RlChXUgiAIeC/mqZqjUFdKC8nhfyxMRYl0u/9m2jGtLVdXYWSLkHT5Z8yoiPKCpIgI6FNiUhO/yBRWpDenii1iO73ixPyttQcyLmLV1Aq1iHzkA7aLl6D+JwlVKHvezOap6hsNIhwVYOGAJoOTttn58YWiiPICoryAAzHp5kJ6qpjHX039rxBhLHHBHH0eczbGmTiLlrDhdFSn2ejRg79EhCqA+OI6Oacq182qfDMlKRdRk6dKJa/aF4rgSGwD9LwpolE1o64cTpsFgTCPkwPqBpIgCPFNkhYPGmLdKqRRJQgC/hhT/fuXJC8VAMxqqMC8lkpE+XgIYCmKVORi4Hsloyr3e9ButUhh9fK8KrN6cQsFGVUlRiahilyoUZFV7xjyYywUhcNqwYz68rTeFK10DgcwEozAZuEwoy575T8AKcnD6T1V6rv/foNUt4ygWiX8j+cFyTDQIqkOpBoWTG4/+TxlUmJSM1Qyh//FFswKanyZyHURy7x48r5GeUESrii8UAXzVInjLhzlJSO+VDxVHTGRiuZKV0KdIiOQz1+jgQja+0bhD0fhtlsxsz63sOBcYMadkie7UGEsLDS5TOdGGZsXJU8VC33NOfxP/yaaEqWSUwXEF9fJC0C2WaVWhwkovGw4EJdVBwCbhUNtmg1HRp1sQ4+XjeUDnV7wgngOmmLy4lYLh9mxsP1DacQqurwB9I+FYLVwmNvsQb1LPO7x/rHsf1SObG8fQHvfGModVskrlcwt58dCAN85AwDojm3kNZSQSEUuBj7zPleplKLRCnvOeWURGWb14hYKMqpKjLE0kuq5wnaqkh+eLBl1VkM57FaLpppWmWCx2DPry6UwvmxhHhtmVKVLtpQv8Pmkxc+YicL/alWuQVCW55bOMLBb4/W7lNQZly9owd2XiTKyV5xVp0lu363icYqHFSlfP/Y5uVGjBT2LWKWcMvn/F1qoQsqpij1s5Dt5HpV712yeqnzlUwFiCC8zAkaCYSlUaN6kyoIuRNOFBxcqjMWouk2Spyq2CeNjYXZ6PVU6w72T8YfNM+9mokEyqlLrMi1f0IKbz0udP4shG86YJKtT6XHZFOfSZNhznRcSRajekYlUyJnTJHqSD6YRq2D38+zGCrjsVjQwo6qvcEbVH2ICFbcsnKS6CX3LeZMAAFuP9qN3JChd56YSCv+TR6OozZzJBr4R4X+AssqtWb24hYKMqhJDqlNlqKdKvLEGkh6eh2QiFfJ2etT/juRY9FcOM8b6Y16dag2eKkFIFTEw045pXKgi8dwmGgbq/UyXA8Vgi+Slsxs0ye1LYXzhxAVnprAi+fnMJgRQzyJWyaiSe63UlArzRbKnioVblDmsql4fs6n/5atGFcPjiuczxfOpChf6B8QNWaWcqkKFsbDNHd11qpinKjbPBXSGNxuxiaaEFHZtckl1QJZT5U01qoC4N/df3zdLc23IfLG+rRMr//yO9PegL6wp989hs0hqwnKxCpZPxUQqGJJYRVqjKjE/sj5m6+U7/I8Jyjy74yRe2Ct6n/7lktTQP8a0ujIsnFIFXgDW7+tCb+xeLjUvCotGaa5K7LfTZlE08I0zqlJzY81Y/LmQFD/uiciKfBhVag/PuEhFZWI7HRK7h2P5VLNzVP4DkOLhSjcxuGxWcJxoVI0Fo5LnShAEU6n/1ZYrKysyw8BhtWSchFx2K0aDEcWCvQBwelB8oLF6JpmIiz8kepsyhRXZrRbYrRzCUfEcV5cpNktBzyLWqdBXZmA5bJaCKzkl12FixlW6QsZxr4lJPFV5EqlgVDht6BsNYTQQwb4zcTn1QsLm0WCERyTKJxi8hQhjicrCe/ULVSR6qvQLVZD6H/NU9Y6mGlUDYyHsPT0EAPjEFTNTFrSFhIVNJ3umWNh0Js9ZbYUDI8EIBsZCOEsU65OU/1I8VRqMqn1JSp4NBQj/W9/WidUv7E/YmLNZOEnwSI1bFrbgndPDWLv3jHTPlFJOFWP5ghbcMK8Z29sHsPvkIL674RCsHHDduU0pbYelOlX6ZOMrVQoAL1/Qgh//nwvxwO8Tx2RzlQurbp1XlE2HQkGeqhIjPzlV7OGZuKA/LBlVogFkROKyIZ4qq3ajymLhpPAXeYhPKBovamuGh7taTlVcFCLzraqUVySnYzBWc0ijUZVJqEKLxHs2CoB6FrFKfdWbqK+HyqSwiEwiFYCspprpPFX5WSzK4/GZ/PL8AopUAInzaLIxW4gwFvl41S1UkeSp0lunSi3XVi9GhTsWAiZU0T8aTAk7fv1ILwRB9NwU06AyIvcvuaTHWDCC92IFhNU8Vcf7xxRDzYF4+B/7LPNU9YwE81JoWy0XN8IL+Ozv0gvKtMZCOLe1D0glZLq9gZKU/LZaOCw5qw73X3UWqtx2+MK85DVk8LwglebR66lKVwB4dlNFwpic01RRNC9uISGjqsQwuvgvIPdUxY2lUITH0V6m0icudPQmLvO8gCM9+mpUAamequoMyZaSB0C2WJWHpZnh4a6WU5VJFEKOFAKnYMiEIjy6Ykm4Wj1VeowqtkDMJvxPzyJW2agqjkgFEPdIsbC/eM2tNJ4qqaaaSTxVLKeqKn+eKgA40DmCkUAEDqsFs3MsCJ4rDptF2qQZCSZuFhUijIUZ0BynP+8vxVOlO/wv7qnKVnAmHew3u+3FjxDIRF25ExZOzDfqT/JWbT4s1j+6ak5DMbomYUTuX12SAuC+M14IgihSk7yJ1eBxoqbMDl6I15yU0zsSRJc3AI4Dzm0R1w5ltvjmrdEhgOmMSkY6o3JKTRlm1ovhFOze+erf2gwtm1BoLBYOl8aek1uP9Se8NxKIgN3OxuVUpW60vx0rQO2RGV7jNeRPDhlVJQaTVM+H+p98QX+sbxQRXoDHZZOSX+OeqtyMqo4hP3yhKOxWDtNzVP4Dsgv/A+QegPhilS1cHVaLVGW9mKgV/2W7zloMAzUjCBDDQHhBjLFmBS0zEZdGV8mpSrNYk5TssvC66JJ/j/UlqJBTVWiRCkAhp0ryVGkQVTGJ+l++c6qY2M5bsYf+nGZPzuI1RvRDKexy+YIWfO+j56e8bpQYgSSnbreC4/QtOJI9VXo9tWxOCkV5Ve93LpSSp8pq4VBXkSpWwfMCXmNG1TnFNaqMyP1LllVnoX/nTUkNx+U4TgoBPKigAMhC/2bWlyekKUyrFQ2XEwaHAOo1Kte3daK9L9XQM7JsQjFYclYdAFGEQw5T/nPbrbrnW+k5p/DMevvUIADghvli+GGXN5BQJHi8UvzVJJEVo3lQ/4vn88R3G1i89Jwmj/SwjxtfuYX/sZoXs+ordBky2RpV7OEtDzvwGyRjbBRsF88biCAim3hYjpCWEB41CXQgnk81ucatefHmkowqlZyqtJ6q3AoAL1/Qgu9+eGHK65nl3y0JfdPaz3yRvIMXL/ybLvwvdZwWi0A4KgnBTM6XUEVswbXzuPjwLbRIBSNeq0r5vMu9Zy67xVAxAimv04BNMqNzqsocVsmLZ2StKkmVsASMKiCeXyOvVbW/04u+0RDKHFYsnl5cFTMjcv+SPVXvxnLFFk5WznGcG4teUcqrUsuPnFEnGlXtBhtVeoxK5uVSwsiyCcWAGVU7jw8iJFMRNkqkAlBW/2MwT9WyeU1wWC3gBdFQHe+QUVVCCIKQX6EKmZeETZbnyAr0SkaVP7dwECNEKoC4yhUj3e4/EFfVSvBUyXaIzUCV2w5m6wzJ1BWzyYtQk0AHgNOxfKopNRpVI6Az/M+ees610hCridJc6dKsqKVkUAZ05pToQap/FIoiEuUl48qTJvyvzESS6sxLVe6wps0D0wM7R+yazVdZwOUbScpexaiSLwKDER6Xzqw1LIzFZ2BZB7mnShCE+H2a47E5jpPJqhuXV1VKQhWAcq0qFvp3+Vn1RfGuyjEi9495qth1fifmqVqg4KkCZAqA3alGFcvhOS/pfp7OPFUKXiE96DEqC1U2oRic0+hBbbkD/nAU78SMZCBuVGVKm9CCWk7VaDAi5addOK1GystlapnjGTKqSohAmAfbMMl38V9mVM2VGVXsJozKEh2z4bABIhVAoqfKZbdkXDSXKexEm+3BbrNapJ0j+QImm91mJVlxxukhZlRp9zqoHc+vIVfJrUMefO+pIQDApbNq8YELJmuSf1cyAPXu1OtBbjyNBiPwMvW/tEIV5pFUl9eo0huWpkayt31BkTxVFWlqVQHACVltHUFQDnXJFSMVSOWeqmCEl/Im9Mxx1QYrAEZ5Qdo1N4PqqhakWlUyWfXNh0Sj6uoi51MB6esUac39Y0WCB3whjATCONYrjvlkw4gRD/9LNapY6GCy6My0mKfKaAVAPUZlocomFIOEvCpZCCAzqjJtRmuhUgpzT5wf3jk9BF4QoxwaK12SONYZMqoIM8ESqTnOWA+LvP4UK5DLdqDmNMlDX6xSmNVQDgUhWVKrHpEKAHDKQge1uLDLFXJVWO0lI41TvSjlVWnJX2Kky6mSwv+yCOVSM4y01L+J17jK3uvCdtUWTqnW/Bl3UugTAATDLHSy8NOcw2aRvnckENEkVMEWmeGokBCuUQzyLacOABXO+LmwWjgpqb3QsDlALfwvOVxJKSk7Vwz1VNnjniq5t9qlw5NidK0q+VxSOuF/sVpVsfC/YX8Yu06KIavFzqdiqNUp0pr7VysL/2Phe5Or3ahXyb9lG6O9I8EEAY8hX0iKipivEv5ntFCFnlzcQpRNKCZSXtWxVKPKyPC/0SRPFQv9u2BaNYD4uoMpEI9nzLOiJDLCQtbKHTZD6+7IK6p7A2HYrBZpYpzTnOhVqilzoHM4gEFfSNp50gLPC3ivh4X/Geep0mRUsVwVhfA/MxWgrCmzox2J+QuBLMQW1HKgAHn4X/aeKn84KadKQ1idO8ecKkEQsOeUuNN5wVTt4WDs+5SK/xbLG+lx2REIB+ENhDUKVcT76QtF4LDpqyGih3yLVACJ+YwtVc6iCcZk8lQd70s0qrz+CFBjzHcbWYDcaWNiLXFhCYfVolpsWgtq5TZyhRl7HJcaxm1WmKw6y6l6870+RHkBsxrKMbVW+zMw38jrFPWMBNDoEb0zWkJV5UIV8aK/6psc5U4bptWW4eSAD4e6RnD52eI5YgbZtNqylGczC//r8gbgD0UNnZeZUfnQc3sTNhQy1UViXq6u4YCieiAXO4aesgnFZMks0ajadWIQwUgUTptV8jobY1QlCjIxmFG1aGo1AGByrFglhf8RpmIsD/lUgGikMDnnQV9YCv1rqnRKBhcj153L04N++MNROKwWaXLV01+pPxqK10mqagqS6mbyVLHdQvm5zUZsIZ2nqiOXnCqVHC1tkuq5GVWdwwH0jQZhtXCY16LdqIoX/zVHThUQF6Xw+iMyoQr1B5ndapHGdrHzqiSjKk/1d9a3deL/vnRY+vv0YKBoEsaZhCrYzjqLgjTSUzVmoFHFNl4Ckahhypc1CuU29CAZkQaoHRaK5JyqzSZR/VOC1SnSGjbNkAtVvCMV/a1O+xmlEECWT6VkkFWX2aWF/IkB44sAL1/Qgpmxjd5PXjFDUy6uEaGTZubsxgrUVzgRjPDYEzN0vAZ6qioUJNXFjVHRk7tomrj7RDlVhCkZkeTUjV8k1sgW9Cz3idWnSmgnCxXMBnbMsxordO2cAonFf7XEBcdV1eSS6rFaKSYKQVEK/zMipyoc5dEZC+eamoWnqkzB+wNoDf9jdaqyyz9hoX9zmjxZXZt0OVXFMqo8snhzryRUkd6IZ5sbxZZVl+dUGQ0r1DmUNIcUS8KYhSGOKkiqewNhSQXx7IaK2GvGXRt2f5QbkVMV81SFo4K0Aad3fqvWqfiaTDyX1TybWZmQcqpGghAEQTKqrp7TWMxuGQp7/vtCUew6LooyqOVTMSSxCrlRFfNUJYf+MVgI4HGDxSoA8TnFxLA+deUszUal3tBJM8NxHC6blVivKt/qf6cH/egbDcFu5SRFV5ZTReF/hKmQPFVpdrtzpabMgdODfgyOhRRFKuTtgOzVoA73MJEKfflUAOCwxRcKWiYGJU8Ve7iXm8moKk89t9kUsFXzLLEaVQ6bRTVGXglmjCTnVGkx9Nz23DxVe2OhJ+dnEfon/z7F8L+iGVXxB86IJFSRSf7fhkFf2DyeKoONqnSFOgWIu8OrX9iPG+Y1F2x3uCKNlD0L/auvcKK5yoUjPaPSTq8RSGHIBnqqgLgRpHfsGx7+FzYuh6xQsHya3pEgDnePonM4AKfNIokAjAcqXTbYLBwivIAzMTW8TEaV5KmSKQDukzxVKkZVfTn2nh7OWKsqygtZhzHu7/Qiwguor3Bk7WHXEzppdpacVYe173Ri69F+fP56Y9X/pJyqUAQ8L8Bi4fB2TGhqXkultIaYIgv/EwShZLzUuUBGVQkRl1M3/oEUL+wblgr6Kan0VcnaZcNhJtGuM58KSIzF1zIxMMNJ7qnyG6i6ZRRKoTbZhLCp1ali+VSTq91Z5eIp5VSFozwiMTETLeF/SvLu6WDKf9mIVACA2xELfZL99mIKVQCJykhxoYr0442F9hbTUyUIghSmYXSNqmwkjFmSdb4pT5NTdTwW+jezvkxV6UoPbMPCiDBkp2yziYUQ6/UIKSnD6sHIHLJCwTxVwQiPF/aeAQBcNquuaB7wfMBxHGrKHVLe2JQat7TJpwbbdD3SPQKeFzAWiuBYbBNCrebc9LpyAOkVANe3dWL1C/sT5omWDLlRAPBO7Nlx3uSqnBbtLHRyvMHyqt4+OYRAOGqop4rNiYIgRv94XHa8fTIx9A8QvX4cJ95DfaMh6Z4aj1D4XwmRjxpVjHhh30yeqtx2LqUaVY1GeKqyE6pghTXHZB4XM4b/xYswp+ZU6VH/Y8p/2YhUyL9TyfsDAC6H+vSRS04VzwtSkvT5WRpVLptC+F+ouJ4qJp8+6AtLMtwZPVUKoiqFZmAshGCEB8cBTVXGPvzMKGGcTv2Peapm1JVL19PI8D+fgWPUauFgt4qLybinSt8jvjrHTTQ1zFbKQgsuu1XaDPnz7tMAzJlPpZda2Qbl5GpXxoK3M+rK4bBZ4AtFcWrQhwOd4rqhpcqlGhGRKfyPhQYnb7xoCQ1+57S2XLCJxsz6cjR6nAhFeew+MWiopLrTZoEttlHLojEkkYqY8h8grtmaPBMjr4qMqhKCPfTzIa7AjKXD3SMY9IVh4cQkx9R22ScuR3kBR3uZnLp+T1W2RlV89z91wW2m8L/qNDlV2or/pnprgNyU/wCZkSYXf4j9v4VLzG1LRgq5zEJSvb1/DCPBCFx2S9ZhopLyobz4b8QcOVUsn018LVNOVWqoaqFhC5qGCmeC98MIzChhXKGw6cJgO+oz6svz5KligjnGnGe2uSAZVYblVBmr/ldKnioAqK8QzwO7N66cXV/M7hjO+rZOtMsMnW3tgxmFY2xWi5RneLBrRCZSoR42yDxVSuF/mUKDATE0WM3YYwIb2YaOj3c4jkuQVjdS/Y/juIQw92Akiv2xvLpFUxMlUidKXhUZVSVEvtT/gPjDc1uscviMunLFxWgu6n8nB3wIRng4bRZDJGgdWYb/sQd4oqfKfAnTTP1PvoAJ5CBUkRxydzoH5T9AuU6VPMcrXYhFWQ6CCyz0b/6kqqzFTOK/PR6qmI1yYj7wxO5TJvrgslsyGinsvKkp0RWCjjzKqesp1JkvJE+VggdK7qnyyNQcjSJep8qYecgZ80xJ4X8G5VQZ7qmym2fezcT6tk6cHkz0nHzsV9uLolSZD5h3KBRNLJ2hxTskF6uQjCoVkQpA9JoAwJnhQMrmXzahwcmMBiPSxi15qlJhIYBvHes3VP0PSBRk2n/Gi1CUR125A1NrE58fUq2qIeNFSswEGVUlBHOv5sOoYgt6Jh+cXJ+KkYv6H1P+O7uxwpDET7tsMX9myJ8xTIEtmuShaHHVLfPsmKZT/9NUp8ro8D+Fgrpaaz/lUqfqnRxD/4B4X4MJnirtIh/5gC3CmZHi0SAwU67gVS00Z/KUTwWYU8I4naQ6y6maUV8mhcuwIuxGYHSOETPa2fysd0OBbaJ5A+GM86wWjCx2XAjUDI7uIilVGo1e79AcuVF1JnN9q5oyuzQvnhxIXFzrCQ1+9/QwBCF9weKJDPNU7Tk1JIWiG2dUxTxVwUhC6F/ypivzVLFNxvEKGVUlxFgew/+SPT5qRlU8xl67p+pIt3EiFevbOvHgH/ZIfz+x/lDGMAXJUyVbNJkxtp8ZrN5ABJHYQzwnT1WSUcUW9bkaVaEoL/VHaziiJKmeRfjfnpinKpfwDbdS+F+RPVVsEc7CHTKJVADKXtVCw4yqljzVqDKbhLEnJqk+lmTIDvvD0gbH9Lx5qowVzDHaU8XmeyFWGF4vpRT+p9fgKAX0eIeA+Dphz6khvNcjeorShf9xHIcZTKwiqai2ntBgVoojk2LhRGVabRkmVbkQjsbHqlFGFdvkHwlEJOW/C2JFf+WwTbrT4zz8r3R88IS00MqUl5ELNUlFfpVEKgBZjP1YNp6qmEiFTjl1tmuY/AhjYQpqCzKWpyI3qsZMqP5X5baD48QFzJA/jPoKp+QlcmkRqnAwb018VzUS5aWHJqtqrhW5wekPR+GxWjSLP5QphA6mIxThsb9TjMXOJXyD5ZJEeAHhKA+71SLzqhVn74h5ptgut7aaaqle1UKTzxpVDDNJGJerSKqzvI8GjxMVTpuUU2WEccFg32mUkcHug0GDcqrsVgs8ThtGghFDalVp9XSbATMqVRqNXuGYubFalmzjrr7CKRVLVmNGfTne7RiWomIYLDS4azigaMhyEDdelEKDJZEKyqdShOM4XHZWHf6yuwMA4LRy2Hl80JA5Vx7+p6T8x4iH/41vo4o8VSWEVPw3D4ZAZVJo0lkNygYQM75GghGEk0IilIjygnSj8byQ866enl1DpqjmC0fBx943Y/ifzWqRdo9Yrapciv/KvTVd3gCivAC7lcv4sEvGabOAefDZMQN5Cv873D2CUIRHpcsmKURlg1yJMLmvLoPFFrSSvPmRfI8poeRVLTT5zKmSwySMP3DBZM2FOvOBXKhCEOLzR7uUTyWORyn8z8jiv2FjhSqYp2pIklTXf9zqcqb4qt+oKiVJdTMqVRqNXuGYpkpnggd+co0LmR7x7H5qTxKrYKHB6T6uFhr8TscQgNxCxycK8rSRYFTAnb94K2OUjxbY9W/vHcPpQT84Dlg4JdW4jQtVUE4VYRLixX+NNarWt3Xi3v+3M+G1j/1SORGXeVOAzA/Z9W2duOLxTTgVc/f+10uHc76J9YQpsMlEEOKKcEYW3TSS2qS8qmzEFlwKQhW51qgCxN0tKa8qlG34X3Z1quKhf6mx2FpwWC1gP48ZU1Jfi3SNU4wqLZ4qhxk8VfnLqTIjzDvIC4kbEmwnnYUrxcP/jPFURXkBgzGP/5HuUUPCyCRP1Zgx4X+ATPHVgN9txvqAaphRqdJo9ArHbNjXlSA4sffUcMZnfDoFwOULWnDHxVNTXndYOdVIlIGxEE4NiHNWutDDicz6tk78duuJlNe1iJFkgs2LW97rAwCc0+hRzB9mzxNvIGKogqrZIKOqhMhHnSoWUseK/jG6vco3m9XCSTvu6WR22XG7vNnXm1BCz66hy2aVDEFmTMV3iM31cE/OWcsqp0qWV8R23HNV/mOUJeUqaQ7/i6l7RXgBoUhmjyaLiVfa4dKCkgEoVyosBsmeKS1hu2UqoWiFIhTh0TsqzgWTqkt3sZgNZY74/CAXq5CU/2KKZXFJ9USPVi6sb+vE0ic2SaGEj/7lXUN2jZmnitXSMmLsZ5JVj/ICth7tx/N7OrD1aH9a49CXxXxWbMyoVGk0eoRj4iIeidc70zM+U60q5iG++9Jp+Ob7xb6FogIumq58ntmzY1Z9uWF5QuMJFuWjhBG5gWyT/2Csvqm8PpWccqdNWt+M5xBAMqpKCGYQGGVU5RpSJxUAVtm5zEeCr55dQ4uFQ5k9MceH/ddsD3emwshyInIRqgDEyuVA7sp/DFfSedMajpiQj6XB66JH+Y+RrH4Y0OhVyxfJRpWW8L9ie6q6vQEIghj6ycbieIfjOOm8y2XVpRpVSZ6qUJSX7q9c0FPgNBPJoa6GhP+51cP/mHF45y/ewoPP7skYUuQvIfU/MypV5oNchGP0PONnSLLqfgQjifNc70gQ24+L0Safufos3HP5TEl84uUD3Yr9Z8+O83LckBvv6BUjyUSyV0pJpIIh5VWNY7EKMqpKCOYyNcq7kuvNVsXCQcaUdy7zcRPr3TUsY3kTwSiivCB5Mcz2cJfLqguCIAth0y6pDsQNmY5BfaFcyXlaWnOqHLJK675weq+LLxSRZPfPTzMhZyLZqMomHy0fJIfpVroz37flaQrRFoIOWehfLmGYpUpcrCK+yJPLqQOiwcvWz7mGAOZbUc6ZVHrBiA0FaRMtyajKxTg0o+pqOsymVJkvli9owZaV1+KZey/Df99xAZ659zJsWXmt6u/T84yvK3egwmmDIACnkmTV1+/rgiAA50+pkqIrli9oBiCGGioRj3KozvArJyb5zg1MjsBQEqlgTASxCnPFPhGqCIIgKdYZ5anK9WZTe8jqPW462K7h/U/vBgckLEq07BqWO6zohbhYledNmC38r6Y8brCGoryU9KtlcWS1cHDYLAhFePjDUdRAFv5Xm6NR5WC1qhLD/7T0x+2wYiQQyeh1aevwghfEpOemytxDztyyPK5wlJcWplpqfOUDq4VDucMq3bfaPFWsaHJxPFVnCiRSYTYqnDZ0IyiF/8nl1JmnymLh4HHZMewPwxuIoFG9HI8q+VaUS/FUGRj+N+gPSyuGTMYhB9E4vGFec8Kc7NMYPmwmzKRUmU+YcIwWsnvGJ94oHMdhel0Z9p3x4nifD2c3xpWGX3xXNMRbz4sbczfOb8J3NxzCm+/1wxsIp8yj8SgH8lQpke/cQLlwmstukQo8KxEXqxi/RhV5qkqEQDi+SDRKqCLXm01KXFaJsc/XTaxn17BMJqvuiy2cOE4MczIT8XMblnKDAO2LkGTP0ukhFv6XW06VdLwkoQot/ZFC2TIYCGynUa9yk5RTFYkmGM7FCv8DEsUptAhVlBXZU5XvGlVmRVIAjM0Ncjl1+caLJFaRY6J1vneNkzcQjBGqiBV8l22i5eqpKCWhCjlmUao0C3qf8VKtKplYRd9oEG8d6weQaFSd3ejBrIZyhKI8Xj3Yk3CcruEAekaCsFo4zJ9ERpUS+cwNXN/WidUv7JP+DoR5XPXdV1XDf6VaVePYU2WuFSWhijyBusygRWKuN1tcTEF5YZHPmzjbMAVGhaz+D9stLXfYTBfiVFseF6pgSoU2Cwe7VdutGjeComKNqljNoVxzqtxJ9aayqf2ktVaVXPlPD3GhiqjkWSu24SwPjdBS/Lc8Syl6o+koQI0qM5IcdsmS5WfWJe66ysUqciHfu8bOpGeDEWF2zHsuz6HN1ThkocClEv5HKKP3Gc9CauVG1Uv7usELYgHfqbWJm4A3zm+W2sjZG9uQm91YQWNKhXzlBrLw3+R1YLrwX7YOOUNGFVFsxmTKf9lKY6uR683GvCnDfmVPlfy4yRiR4JvLrqFcVc3Mcf3ynCqtSnty5OF63SNBRKQaVbkt0iRDJTmnKgs1Ql84k6cqVrhRZ/gGyyfxh6OSl09Ufiye4SxP4lWSmU2GeapGi6T+N9Hk1BnlSeedKZOxxR9Dr6w6W4yqoVdRzpW0gWCIUEVZongOkLtx6C+hOlWEOnoX6nFZ9XhO1bpY6N9N5zWntF8eM6pePdSTIOGuVzV2omB0bmCuuaGTSKiCMAvsYW9UkUhGLjcbCwdhNVbUjvvt289Leb1YCb5yVTWfiRWopJwqX0jyCiXvPqdDLtbAJq5J1e6cDdjkcMJscqq01KoaHAvhZCxZeeHk6pz6yJD3lXn5im04y71TVVqEKmL9DUV4TcW1jaZzeOLmVAGp4X/Tkz1VsRDOXMP/8r3hlOKpMlSoIr6JlqtxWErFf4n06FmoJ4f/DYyFsJWF/il8buGUKrRUueALRbHlSJ/0enxDrlrXb5kI5Brlo0Su4b9ss65nJJii/CgnygvY1j6AXX0ctrUPGFLDr1CUVmDzBCYfNaoY2SbiVmXIqWLMbqwAANRXOPD1W+YVNcGXPcTHQhHZg918w79GpqyYTagdw828NaGoVHtMj9chLv6QXfFf8bPp5cGjvIBnd5wEADRXunTnCsa9dHzc+CtyzpzcO6VFqEI+Jn2hKKrcheu/IAgyQ3xi5VSxzSomqd4eW+wlJ13rDf8DgKvnNKLMYU25L5qrXFh16zxdG07Joa5GFv+Vh/9ZLRzuvmwavrvhcEp7NeNQrmZa7M0OwhhyFfFgHuCOQT9CER4v7etClBcwr6VSklyXw3Ecls1rwm+2nsCGfV24fl4TBEEwpBTHRCIbMZJ05Br+W1vugMtuQSAspiYoXWsxT2t/zGiz4rdHdqLFgLmxUJhvVUkowh72+TCqgOxutkzqfwymPHd2YwU+cMFkfR3USblsJ9rMnipWG8gbiEjXPJfwP384Kiv8q8OoYnWqwiynSntB3TIpvyt1AZo4cQJd3gCWPrFJ18QpD1WMS9EX9xrLPcsHuryoq3CmXXA4bBbYrRzCUQG+UKRgxSyjvIBXD/VISoV6VBhLkQqneJ5HY6IqLCxpep2x4X8AsL6tC75QFC2VTvzXRy9A32jQsA2nfORUVcXm+0CYB7MDBUHApoO94nfYrQnCMGrGYSDMg9VMNuOGFpEbuSzUGyqc0sbCqUEf1rWJcuk3L1Sf+2+c34zfbD2Blw90IxLl0THkx7A/DIfVgjnNHtXPEcaTa/gvx3GYXO3G0d4xdAz5U4wqlqeV7JdieVqlUMaAwv9KBJZAbZTynx4yqf8x4oVnc1OeMxLJUxWMmjoEpcptB0sB6ooZHFkZVTLDwojzL3l/YucskEU+WpmK6EK+ip+6ZCIdUuFfW/Gu8fq2Tvx9zxnp7xW/2pG2MCojrlSp7uHberQfz+/pwNaj/bpDI1gB10//Zqf02vXf36yrCG2pUSHLuVSSU2ew8D89nqpntove2TsumY4rzq43VFEu2TNrhPKlx2mL15yL/ewN+7qx68QgXHYLXnnkKkyLCQusXD5HNaRILlhTSpLqhPGIsurivbX31BDefE8M6btpQWo+FeOSmbWoLrNj0BfGjuOD2BvzUp07qRIOk6n4jnf0CJVMrol7KeXku4ZfoaCRWCKwh3i5CXb4qmWeKkFQH+BGeEqMolxS/4uY2qiyWjjJO3Emlt+SU05VyBhPlZ6Cum4FoyqfE6e8r1qLFOcLZjiOJRmUWgzHuAKgsodv6RObcOcv3sKDz+7Bnb94S5OhlqmfRhu4pYYkVBGK4HhM+a8xSU4diOfI5ZpTdbR3FNvaB2DhgI9ePEVHj5XJR04Vx3HSnD8WAcJRHt9ZfxAAcO+VszCp2i159Bo8LlXjkM0DTptlwkuSE8D0WO3E/3rpMCK8gDlNFZjVUKHa3ma14PpzmwCIhYDfianGLpxMIhWFRo9QiZqsup6C0maCjKoSYSyPOVXZwjxVoSifEPaRTHxRX3xPVbmUUyUXqij+uVSiNnZ+mRJbLp4qf5g35PxLYhPh7HOqyhyJBhmQ34lT7qULxPpbjMK/eg1HqVZVML8evvGyM2gE8vBgljyf7KUC4jlVuYb/PRvzUl0zpxEtVcZvNsk9VVYLB7vVGOOFKQD6Ihye29WBY31jqCt34L73zQIg1vMCIOVxKsHmATNuZhGFZX1bJ16PeafYc65jKJBxDmPS6i/s7cCmWM2qBZNzqMJN6CZXoZIpKgWA813Dr1CYc1VJpCAJVZgg/K/MYZXyPgZ9YVXjJB5+VnxPFVuo+oLm9lQBcU9gZy7hf454GBNTcptsQE4Vy4vyZxH+FxeqiHtc8jlxMkEPvyynqhhhRtkYjkq5CEqeqkwGEAfRALphXrNmL4Defo4n5Op/anLqAFAZU3DMJfwvGIniz7s7AAB3XjIt166mRe6pKrMbV06gOva7d/dxaDt2BADw4PWzJSGWhgrRqOobVTeqzCwQRBQOtbyZ0WAkY96MPxQBB6B/LIz+mPrwdzccQpXbbvpcm/FILkIlzFPVMeRLeD3fNfwKBXmqSoS4pHrxH0hiOEhcpU4JnhfQMWSi8D9ZnorZjSomViF5qrLoJzMiTgz4EI4KsFk4NMV2kXPBneRtyqZOlVJOVT4nzkRPlXaPmtHoNRzjhWjz6+EbLzuDRhCvUxWNe6oUlKmYEZFL+N9L+7oxMBZCc6ULV89p0NFbdeSeKqNEWta3deLdDi8A4M0eC7yBCKwWTpqnAG2eKrZJQMp/E5d0m0MMNe/4+rZOPPjsnpTP9o+GJlSostnItm7oZKkAcOJzRW9BabNARlWJkG/1v2zJpADYMxJEOCrAauHQbAIlMan4byhi+vC/Gin8T5x0sglhY0bEke4RAEBLtQs2a+63uVuWowVkl1MleVxkYWz5nDidsr5mE6ZoNHoNRzYufcH8evjGy86gETChitFgWFP4Xy6eKiZQ8dHFU3Tdk+mQe6qM8NIyr0IwklgzLcoL+Lffvy0tZOs1eKqo8C+R6+YQhSqPH1gNxM5hP3jZ9cp3Db9CQUZViSCp/5nEqKrOoADIQv9aqvQt6o2iQhKqKB1PVS6GAdsFPhZLtp9SrS+fzS3LqZLXmXFpqJ0lhf/JcqrYxKn06NM7cSYU/w0VL/xPr+FYLm0A5NfDl2sB1/EIk1QfC0YloQoloypXSfXjfWN482g/OA746MVTdfZWHfkGjN6xn41XQZunqnj3JGEOct0cGi8iBgTQ5BHLioSjAnqS5ovlC1rw1ZvPTfmMloLSZqH4q12NPPbYY7j88stRVlaG6upqxTYnT57EzTffjLKyMjQ2NuKLX/wiIpHcpW/NBKufYhajKu6pUjOqzBP6B8gl1SOS58SsRhUzWBm5CFWEYjvLes+/PKcqGInXmckm/C+5TtXyBS348EWpymd6J854+B+PQKR4QhV6lJEAZU9VPjx8VguHr99c+juDRsAM2UFfCIMx77tyTlXM+ApFEYnyKe8nw+Tvv/WP/QCA982uz6twj7yEgN7wv2wWsuSpIrSQ6+YQhSqPH2xWixS9lJxXBYg1OgHgwqlV+PjsKJ7+5GLVMg1mxBwrdA2EQiF85CMfwZIlS/DLX/4y5f1oNIqbb74Zzc3NePPNN9HZ2YmPf/zjsNvt+Pa3v12EHhvLaCyG3ww5VUA8RE0t/M9MNaqAeE6VLxSVPCdmDf+rLU8s+JqLUcXQe/6VZMrlr6fti0qdKiC++Lrr0qm4ZGadIcVPpZpa4WhcUKNIu+JMGUle4BhQL4wqh4VNjsqMUWao3f/0bsXPCMjNABqOzSsckOCR0NLP8QTbrGKbBo0ep+L84JEJBY0GIykbIHKSC1wDwN7Tw1jf1pm38+pM8FTp21DIZiHLiq8O+sIIR3nYFaITzB52TeQftjnUNRxQjVZoVtgcolDl8cXkGjc6hvw4PejHRdMT33vxXTGk+M5LpsJxph+XGlAUvZCUzOy2evVqAMBTTz2l+P5LL72E/fv34+WXX0ZTUxMuuOACfOtb38LKlSvxzW9+Ew6H+sOvFGDyyh4TqP8BQFXMUzWoalSZzFMly6li8vRm3TGtSVqoZRP+l7w7rUf5D5B7m+J5SnYrp7hoSvlsUj4WIxiJYtsxMUzj7stm4NwWYyRxmVdKbgBmU+PLaHJRRgLkSpWJ540Zap/93W4kpw4snl6T9UK9dySINesOAAC+cvO5WDCpKqt+jieSN6uURCoAwG61wG23wh+OwutXN6rUFM6GfeGMCmd6kHuq9G4oZLOQrXbbYbVwiPIC+kdDKTLLQLwsAwlVTFzkm0PJGznpvOO5GmOEOZlS7cZ2QBIzY7zXM4ojPaOwWzlcO6cBW84Up396MMcK3QC2bt2K8847D01NTdJrN954I+6//37s27cPixYtUvxcMBhEMBgPWfB6RZWjcDiMcDi3WiTZwL4j03eNxHaUndbMbQtBZcxIGRgNKPbn5ICYl9BS6TBFfx2cOBULAjAQ85KY5Vwm43EmGix2hX6qjRv2OxnNHruu32iLHc8fjmLEJ543l92q6Zgs7WosFElov/3YAPzhKOorHDirzmXYNZD6GopKoXMOE1zjxdMqAYiGIx+NgFcv7QYAYPsmo4FQSt/Pm+QBL4iLiDUfnIcIL+Brzx/AzhOD2HGsFxdMrU57bPm4+dYLB+ENRDB/kgd3Xzw5lvuovZ/jDYfNIoXNTq91q46bSpcN/nAUA6N+tFTaU96P8gK++fd9GeTv9+Hq2ZmVsrLFApk32WbRNfYXTfGgudKJbm8wzULWiUVTPIhGI6gvd6B7JIjOwTHUlaUaTqOBUKxfXNHvSaJwJD+rrptTjx/ecT7+c91BdHnja6/mKie+etNcXDenXnF8fPWmOfi3Z/eqGmNfvWnOhJy3SpHmSjFc+FT/WMK1/sdeseTE5bPqEKviYJq5Qms/xo1R1dXVlWBQAZD+7urqUv3cmjVrJC+YnJdeegllZYULXdu4cWPa94dGrQA47HrrDXSYIKLuVDcHwIrDJzqwbt2plPcPd4j9PX1wL9Z17i14/5IRd/bF4d45NAaAw55d2zFypJi9UqbbD8hvzSP727Cu713Ftsnj5tCQeF2kz+55C/0Hcu+LWArEhnBUwPpNmwHYwEXDWLduXcbPst/hHQsktH/hpAWABTNcAbz44ou5dy6Jntj3jfgCONFxBoAF7x3cj3WD+wz7jkLQ3iVew6MnU++tdwfE95rdAtxd7wAALmmwYHuvBSuf2YZ/nx+FWmkiXgCOejl4wxxeeepl/P2kFRwELK8bxEsb1uf3R5UAdlgRii3R/L0nsW7dCeWGEXFue3nzGzhRlWpuHBnm0OVV93C2iVkAACz/SURBVMaIuUhB/OgP6zFb4fN6kM9z/T2dWLeuQ9fxWps5/MrLNnnkA0uAAOCmJh82rBfvYTsvnpcX//kGTtak/q59x8X7vvPUCaxb166rX0TpkfysWjmPzUdApR04q3IM0RO7oHbbAcAnzuHwl+MWDIXiY7HKIeD2GXzGzxLmYSC2ftxz5CTWrTsuvf7cXnEOaYl2Y+NGcd2eaW1cKHy+1PwvJYpqVD366KN44okn0rY5cOAA5s6dm7c+fPnLX8bDDz8s/e31ejF16lQsW7YMlZX5r9QdDoexceNG3HDDDbDbU3c9AUAQBDy87WUAAlqXXWsKiXL7/h48e2wP7BU1aG29NOE9nhfwhe1ifz900zVSsbdi87Xdr2AsFEUgKk7I175vKeZPMl819oGxEL6955/S35cuXoSbFjQntFEbN80nh/CTA9sBiKEWd7x/uS71xWA4iq/sfAUAMH/RJcC7u1HtKUNr65UZP9s5HMC397yGCCxobb1Rev0XT74FwIt/uWohWi+YlHPflL7vsdj3VdbWAIMDuHjR+YZ+RyEI7TmDP7a3obK2Aa2tFyW8d/DlI8Chdlx+7mS0ti4AACwaDmDZf2/BsREethkX4cb5TSnH3LCvG2uSdoYB4H3nNOAzH70wfz+mhPjuwdcxFgtbvvHyC7Fc4TwCwFOnt6H71DDOPf9CLJuX2uaFdzqB/cqbIHJmzb8ArQuNDwH80o6NCEcFnD1zGlpblYVItNIK4MJ93SlehZYqF75609yEsfaXvt04faQPM+YuROtFk1OOtfXv+4HO05g/dzZarzlLV7+I0kHLGkcrrQC+xAvYeWIQPSNBNHqcWDy9ZkKFKo8HKt/rx7PHdiFs96C19QoAYm3Njq1bYLVweOij18Hj4AwbN0bAotgyUVSj6pFHHsE999yTts2sWbM0Hau5uRnbt29PeK27u1t6Tw2n0wmnM7U4qt1uL+iFTPd9gXBUqr9QXe4yxQCrrxQNJW8gktKfruGAVKNqSm2FKSTVATFXRS5TXWWSc5lMfaUNHBdPmi93OVT7mTxuKtzxHI+WKhfcrtwL/wKAzWaDhRN3wL3BuMCHlvNWWSb+gHBUACxW2K0WDIyFsK9TnJyumtNk6PmXf58vJIZxVbjVz51Z8bjFa+YP8yl9bzsj1h9bNK1Wem9avR33XjkLP9z0Hr770mFUlTkx4AtJuVEb93fh357dqxjC9drhPrxyqG/CCFKoEeUFWGQuvhn1HtVxUxXLo/KFBcU2LdXK+VhK7fIxNl02K8LRCMqdxjzDbrlgCm5aOBlb3+vBS69vw7IrL8WSsxtTFrINsc2+QX/qMwEAghFxBHrSzGfE+MWoNZUdwNJzlDc8iNJgen0FAODMcAA2mw0cx+Hlg30AgMtm1aKpulwKtyv0WlwNrX0oqlHV0NCAhgZjKssvWbIEjz32GHp6etDY2AhAdBtWVlZi3jx9u3XFRl5ostwkyknVklBFqqS62WpUMcodVvTK/jarUIXVwqHKbZeUFbNJOHfIzrfHaUOUF3Tt4nEcB7fdirFQFP2jsZwIjf2RJ6T7QlFUuS14470+CAIwt9mDRoM9rvJ+Man/YgpV5IpUpyqYKEUvCALe7RgGAJw/pTrhvX+96iw89eZxnBjw42O/im8uNVc6EYjwGWsN3TCvecLu9iqp9H36tzuw+v3zFY1NT6wAsFelAHAxk+qjvCCFf/aPBnXf/wyrhcOlM2vRf0BQVePKVKuKqf+RUAVBTGxYAWBfKIphfxjVZQ682CaG+5X6Bp95VrwZOHnyJPbs2YOTJ08iGo1iz5492LNnD0ZHRwEAy5Ytw7x58/Cxj30Me/fuxYYNG/C1r30NDzzwgKInqpRgi6tyhxUWkyx8mFE17A+nVDE3m/IfI1nK16xGFQDUylTFtNabWd/Wibv+d5v094GuESx9YhPWt3Xq6gtbBDEDWquR57BapMUXW1C9fkQ0a5eeXa+rT0o4bfHpbDAHg9QslMsKVcs5OeDDkC8Mh9UiSVgzthzpTdh8YXR5g6plDwAqmslU+pLrMfV4g7j/6d2K905lTEmEiQclI69Tlkw+63+tb+vE0ic2ScbeX94+Y8j9r5VMtarMXnSdIIjC4LJbUV8hrnFOD/rRMeTH3lND4Dgohq+XEiVjVH3jG9/AokWLsGrVKoyOjmLRokVYtGgRdu7cCQCwWq1Yu3YtrFYrlixZgrvvvhsf//jH8R//8R9F7rl+RmNGVYVJ5NQBoDoWZiYIgNefuLgwW40qBvMAMMxcL6WmPG5UaTEM2OIwuUJ513BAdXGolbhRFU74OxMcx0kLKF8oCkEQsOWI6OK/8hxjPNTJ38fO1XBsTGYjR28W4jXVEo2kPaeGAADnTqqEQ2ZARnkBq1/Yr+s7J2LRTHbe1FT6ANGLl7xpJHmq/OqF5Zn8vcuW+IjVW+BaDTXj0Ij7XyuZPFXFrh1HEIR5YLn2HUN+rI95qS6eXlvytcbMu6pM4qmnnlKtUcWYPn26JlWyUoMZVWYp/AuI8sMVThtGgxEM+kIJRkApeKocNoupw51qyuLxu5kMg0yLQ1HCOfcQL7YIGhzLzlMFiLvSI4EI/KEojvaO4cxwAA6bBZfMyE89EbfDKtXTAkpzAccM0bGkOlXvnGahf1UJr29vH0hZTGdLqT/IciHTeZN78ZacVSe9XhnT+vWqeKoYyxe0YN6kY9h9cgifuGIGls1rzkv9r3zf/1phO8+ZPFUU/kcQxOQaN/aeHkbHoF/a9Fm+QF3/oFQoGU/VRIaF/3lMZFQB8ryqZE8VM6rM5amqkJ2/cpM/2OUFgDMZBtksDnOBfX//WHY5VUDckPWFotgSC/27eEZN3hZWyeeqFI0qtnnilwnUAMA7p4cApOZT6fEycRBzHydi0Uyt5y25XWXMU6UW/ienN2Zg3HxeC5acZXxdKiD/979WGjN5qsJxoRuCICY2zFP19qkh7DwxCICMKqJAmNFTBcQX/sP+RLGKePif2TxVVtn/m+tcJlMt81S1dQynhCDJyXVxqBVXsqfKoX3aYEaNLxTB6yz0b7bxoX8Mpz2xby576U1x8nHKFqKRKI+2DlE18fypiZ6qXL1M+czvKQW0nrfkdp5YGHa68D9AFBbpicmQs9C4fJDv+18rLKfKG4ggGEmtwMrCWSmniiCIlipxXv3HO2cgCGIExiSTlN/RQ+mtOCYgZjWqJE/VWHzHlucFdAyZM/xPfv7M/GBf39aJZ7fHi75++rc70yac57o41EpZjkIV8s96AxG8dawfQH5EKhjJfdMq8mEmnLLQVOalPtIzCn84igqnDbNicrQMpjanZhZxEO/V5Pp2+crvKRW0nDclL16lO+apCqb3VInGhSjtn8/wynzf/1qpcttht4pns280VRWWwv8IggDENc4PN70HgBUsB471jRVMVCefkFFVAowGzBr+J3qq5LLqPSNBqUaVGYoUy0n0VJnzwc4SzkeS5LTTJZznujjUSopQRRZGFfvsG0f6MBaKoq7cgXkt+Su4nGJU2cx5ndMhF/hgRhUL/VswuTJFAVSuNpc8Btjfj99+Ht549Fo8/cnF+PjsKJ7+5GJsWXnthDWoAG3nTcmLV6nRU9Ub8wx5nLa8GhL5vv+1wnFcXAFQIQTQT+p/BDHhYWuc5LSRkUCkYKI6+YSMqhJgzKSeKiamIJdsNmuNKiDZU2WucwnkrkaW6+JQKyz8j31vNt4ftoB6+YBYiHvp7Pq8lgWQL16tFk7aOS81yh2Jsup7mUjF1GrF9kxtrrlK3RvFag1dVK9ea2iioeW8JVMp1alK76mSQv8q81vSI9/3fzYwoyo5ryoU4RGJzR9ldvPNvQRB5J90axyG0hqnlKDZrQQYMaGkOqDsqTKr8h9gfk9VNgnni6clenvY4jC5iGlzlQurbp2nyyOhR/yBGa9M5CKfoX8A4JR5ptx2KziuNA2HMqeypypZpELO8gUtuGFeM7a3D6BnJIBGjysvanPjjWzPmxT+F4hAEATVMcZEKhrzmE/FyOf9nw0sdyxZAdAvq7lG4X8EMTHRs8YpFcy1SicUYQurihLyVJlN+Q9I9FSZ8cGeXcJ56oSTr0V1sgGajVGVLBxx+Vn5Nark17UURSoYck9VIBzFwc4RAMDCJDn1ZKwWLkH+m9BGNueNCVVEeQG+UFQ1goB5qgolV28Go5rJqid7qnxh8Rlms3AJNdYIgpg46F3jlALmWqUTirB6NeYzqsQH6JC/NDxV5Q6b4v+bBSMSzvOxqE7xVGk0SNe3deKFPWcSXvvwT9/M6865W2ZIlWLhX4aUUxWKYH+nFxFeQF25Q5KhJYqH226FzcIhwgsYCUTUjarYAqIQnipGsY1qNU8ViVQQBGEWUZ18QltGJcCISXOqqhTU/8xaowqIh1QB5ny4myXhPJnkHCotxgpLRh0LJUorpxPcMAK5AVjKRhW7133BKN45NQRA9FKVajjjeILjuLisepq8qp6Yt6YxzzlVZkLKqVIJ/zNj2DVBEIXBrGscIyGjqgQwb/hfzFOVkFNlzhpVQJKnymm+h7uZEs7lZJtTlavghhHIDcBSLPzLYEbVWCiCdzKIVBCFh+VVef1pjKoCh/+ZAclTNZIoqe4LUeFfgpjomHWNYyRkVJUATFLdfEZVzFMVy6nieQFnhsSQFzMaVaVQ/DcXNbJ8k5JTlWG3OZtkVKORS6iXdk4VK5ocxV4NIhVEYWEKgCMBdVl1Fv6Xz8K/ZkPNU8UK/5byRgdBEPox4xrHSMy5siQSiBf/NdcDian/+cNiMv2wP4xQlDdljSogSajCxA93MyScy0kOo8t07rJLRjWWRKEK817jTDCjv2s4gKO9YwAyi1QQhSOr8L8JZFTFPVUU/kcQhDJmW+MYCRlVJQAzqjwmk1SvdNlgtXCI8gKGfGF0DJm3RhWQ6Lno8voR5QXT3sTFTjiXk1JQN4OxUsxkVHlfzWw4Z4JtoGxr7wcATK52o65i4izOzU68VpWypyoQjkperIkU/sc8VSPBCALhqDRXkFAFQRByzLTGMRLzrXyJBARBMG3xX47jUBXLLRjyh0yt/Le+rRO3/fgN6e+fv9aOpU9sKvnq3YUgOVQy08KomMmo40Wogp3zw92jAIALKJ/KVEieKpWcKiYp7rBZUOk217ydTypdNkkyXS6r7guTp4ogiPEPGVUmJyirRG+2nCoAqJYpAJpV+Y8p0XV7E0NS8q1EN15wOxKniUweoGImo8rrYo0HTxWDQv/MhSRUoRL+J5dTn0iKjRzHoUEhrypAQhUEQUwAyKgyOSz0DzBnbSW5AqAZlf+KqUQ3Xkj2+GgRgChWMmqip6p0p7fkxedCEqkwFZmEKuLKfxMvZLNeIa+Kwv8IgpgImG+VTiTAlP/KHVZYTJj/I1cANKOnKhsluvEY32sEKTlVNm0Lo2IkoyYIVZTwAs5tSzQI500qzery45VM4X9xkYqJk0/FaKgQN9rknipfWHyOlZWw95ggCCITZFSZnFGT5lMxmALgoM+cOVXFVKIbL8i9Jk6bJSvjvtDJqAmeKo3Gn9lY39aJrz2/L+G15T94DatunVfycrPjBRb+p+qpYuF/E6jwL0OpVhWp/xEEMREo3fiYCYJU+Ndkyn8MyVM1FkKHCY2qYirRjRcSFPVMvihylVBflWD5f4O+xOKplP9nLjJJqrPwv4YJqNjIFAD7RpXC/8z5HCMIgjACMqpMDvNUmVGkAoh7qg73jJqyRlUxlejGCy5H6Yg/uEpYUp3y/0oHSVJdTf0vZlBMZE+VXP2PPFUEQUwEyKgyOVL4n0l3+Jj6X1vHMADz1agqphLdeMFhtYCdHrMbKonFf80zDrWQTf4fUVyYTHpmoQrzbDAVCmVPlXieStF7TBAEoZXSWnVMQEZNH/4neqoGxsRwJTOF/jGKpUQ3XuA4TsqrMnvtJ4fMoD814Csprw7l/5UO8eK/6YUqGiag+p/kqVII/yNPFUEQ4xlzrtQJiTHTh//ZE/42k/KfnGIo0Y0nXHYrRoMRU+80r2/rxDf/Hhd4+NGrR/Hn3R0lI/BA+X+lAzOqAmEeoQgvFbwFgEiUR//YxA3/kzxV8vC/WPFfs3u6CYIg9ECeKpPDJNXNalQxTxXDjJ4qBlOi+8AFk7HkrDoyqLKAFQA266KICTx0lXCBZ8r/Kx3kkQMjSd6q/rEQBAGwcEBd+cQzqpinaiwUlcL+qE4VQRATATKqTM5oUHwYmVVSPdWoMqenitAHM6bMGP43XgQeKP+vdLBaOGmjy5uUV8XyqeornBPyWpU7rFI+I5NVjwtVmPM5RhAEYQRkVJmc0aC4C+oxaU5VavifeT1VRG5EeUEySMZCEdMZJ+NJ4IHy/0qHShcTq0j0VE3kGlWAmIMZz6sSzwXzWFFOFUEQ4xlzrtQJiTHmqTLpw8hutcBu5RCOigvtlirK9xhPrG/rxOoX9ktGy9aj/Vj6xCZT5SmNN4EHyv8rDTwuOzAcgNef6KliUuITsUYVo77CiVMDfvTGPFVS+J8JPd0EQRBGQZ4qkxNX/7NnaFl41rd1YukTmySDCgD+5WdvlUT+CpEZlqeU7AUyW57SeBR4oPw/8xOXVU/2VE1cOXUGMyh7R4OI8gKCER4AeaoIghjfkFFlcuLFf831MFJbcHd7zbXgJnKjlPKUSOCBKAYeFVn1iR7+BwD1nrgCIFP+AyiniiCI8Q0ZVSaHSaqbSaiilBbcRG6UUp4SCTwQxYDlVCWH/8UL/05co0ruqWL5VBxXegW5CYIgsoFmOJMzYkJJ9VJacBO5UWp5SiTwQBSaSrfoqVIL/2uYwOF/CZ4qWT4Vx9HGBkEQ4xfzrNQJRcZC5jOqSm3BTWRPKeYpkcADUUiYImuypDoTqpjI4X+Jniomp26uEHaCIAijMc9KnUhBEIR48V8TSaqX4oKbyA6Wp9Q1HFAM8+QgeoHMlqfEBB4IIt9UKuRUCYIQN6omcvifR6xf2CczqqjwL0EQ4x0K/zMxwQiPSCwvyUw5VSQMMP6hPCWCSI8kVCHLqRr2hxGKikp3DRPZqKoQN9R6ZeF/ZXbzPMMIgiDyARlVJoYp/wFAuYlUk2jBPTGgPCWCUIdJqss9VSyfqspth9M2cT0z9TFPVSDMSwWAyVNFEMR4xzwrdSIFpvxX5rCazkBhC255YVhAXHCbqTAsoQ/KUyIIZVj434gsp4qU/0TKHDaUOazwhaI42e+PvUZGFUEQ4xsyqkxMvEaVOS8TLbgnBpSnRBCpSEIVfrmnimpUMRo8Tpzo9+HkgA8AGVUEQYx/zLlaJwAgLlJhUqMKoAU3QRATEyVJ9R5JpIJEeuorRKPqVMyocpsohJ0gCCIfUE6ViWFy6mYSqSAIgiDinqqRYAR8TFCIwv/iMFn1EwNjAIAyO3mqCIIY35BRZWLMWPiXIAiCiOdUCQIwGtsAY+F/E1n5j8HEKrpjhiYJVRAEMd4ho8rEjAVFKVryVBEEQZgLl90Kh018hLINsHjhXwr/Y7LqDMqpIghivENGlYkZDYqx+h4TFf4lCIIgRCqTxCqo8G8c5qlikFFFEMR4h4wqEzMqearoYUQQBGE2kmXVmVAFhf/Fc6oYJFRBEMR4h4wqExNX/7MXuScEQRBEMnJZdV8oIpXBIE8VUJ90DshTRRDEeKdkjKrHHnsMl19+OcrKylBdXZ3y/t69e3HnnXdi6tSpcLvdOPfcc/Hf//3fhe+ogYxJdaroYUQQBGE2mKy6NxCWlP/cdiuJCyHVU0VGFUEQ452SmflDoRA+8pGPYMmSJfjlL3+Z8v6uXbvQ2NiIp59+GlOnTsWbb76J++67D1arFZ/73OeK0GP9mL34L0EQxERGklUPROI1qiqd4DgqgJ4cAukmSXWCIMY5JbNaX716NQDgqaeeUnz/k5/8ZMLfs2bNwtatW/GXv/yl5I0qUv8jCIIwHyynyusPS3LqFPon4rJb4XHaMBJ7jpGkOkEQ451xvVofHh5GbW1t2jbBYBDBYFD62+v1AgDC4TDC4XBe+8e+R/5fOaMB8TWXjStIX4jSId24IQg1aNwYS7lDjKAf8gXRNSR6p+rLHePy/OYyduoqHJJR5bDQuJuI0JxD5ILZxo3Wfoxbo+rNN9/EH/7wB/zjH/9I227NmjWSF0zOSy+9hLKysnx1L4WNGzemvHamzwqAw/69u8GfEArWF6J0UBo3BJEJGjfG0HmaA2DF/iPtOGEDAAvG+juxbl1HkXuWP7IZO9aQ+AwDgF3b3kRXW546RZgemnOIXDDLuPH5fJraFdWoevTRR/HEE0+kbXPgwAHMnTs3q+O2tbXhAx/4AFatWoVly5albfvlL38ZDz/8sPS31+vF1KlTsWzZMlRWVmb1vbkQDoexceNG3HDDDbDbE1X+vnvgNcAXwDVXLsGiqdV57wtROqQbNwShBo0bY+l/6yTWnTqIqoYWMbztzBlcct4ctL5vZrG7Zji5jJ0XvXtxdF83AGDZtVdjel3hNioJc0BzDpELZhs3LIotE0U1qh555BHcc889advMmjUrq2Pu378f1113He677z587Wtfy9je6XTC6UyNgbfb7QW9kErfNxoS61TVlLtMMagI81HocUqMD2jcGEN1ufjsGAtFMRabr5ury8b1uc1m7MjFKo70+jCzsRJWC4l4TERoziFywSzjRmsfimpUNTQ0oKGhwbDj7du3D9deey1WrFiBxx57zLDjFgNBECRJdRKqIAiCMB9yoYpghAdAhX8Z69s68de3z0h/3/+73WipcmHVrfOwfEFLEXtGEASRH0qmTtXJkyexZ88enDx5EtFoFHv27MGePXswOjoKQAz5u+aaa7Bs2TI8/PDD6OrqQldXF3p7e4vc89zwhaIIR8U8qgOdXkR5yqkiCIIwE6xOVYKkOhlVWN/Wifuf3i0p2DK6hgO4/+ndWN/WWaSeEQRB5I+SMaq+8Y1vYNGiRVi1ahVGR0exaNEiLFq0CDt37gQA/OlPf0Jvby+efvpptLS0SP8uvvjiIvc8e9a3deKa//qn9PenfrMTS5/YRA8igiAIE8HqVPWPhTAwFgJARlWUF7D6hf1Q2gZkr61+YT9tFBIEMe4oGaPqqaeegiAIKf+uvvpqAMA3v/lNxfePHz9e1H5nC9vhY7ueDNrhIwiCMBfMUzXsF+V2bRYONWWOYnap6GxvH0DncED1fQFA53AA29sHCtcpgiCIAlAyRtVEgHb4CIIgSgfmqWI0eJywTHAhBlYE2ah2BEEQpQIZVSaCdvgIgiBKhwqHDZzMhprooX8A0OhxGdqOIAiiVCCjykTQDh9BEETpYLFw8MjUWRvIUMAlM2vRUuWCmr+OA9BS5cIlM2sL2S2CIIi8Q0aViaAdPoIgiNLC44rXL2msJE+V1cJh1a3zACDFsGJ/r7p1HtWrIghi3EFGlYmgHT6CIIjSgolVAEBDBRlVALB8QQuevPtCNFclbgA2V7nw5N0XUp0qgiDGJVRV1kSwHb77n94NDkgQrKAdPoIgCPMhF6sgT1Wc5QtacMO8ZmxvH0DPSACNHnFDkJ5fBEGMV8ioMhlsh2/1C/sTRCuaqRI9QRCE6aiUh/9RaHYCVguHJWfVFbsbBEEQBYGMKhNCO3wEQRClgcdllf6/2xtAlBdoriYIgpiAkFFlUmiHjyAIwtysb+vEhn3d0t9f+1sbfvzqexRVQBAEMQEhoQqCIAiCyJL1bZ24/+nd8IWiCa93DQdw/9O7sb6ts0g9IwiCIIoBGVUEQRAEkQVRXsDqF/YniAkx2GurX9iPKK/UgiAIghiPkFFFEARBEFmwvX0gQUgoGQFA53AA29sHCtcpgiAIoqiQUUUQBEEQWdAzom5Q5dKOIAiCKH3IqCIIgiCILNAqnU4S6wRBEBMHMqoIgiAIIgsumVmLlioX1ITTOQAtVWIpDIIgCGJiQEYVQRAEQWSB1cJh1a3zACDFsGJ/r7p1HtWrIgiCmECQUUUQBEEQWbJ8QQuevPtCNFclhvg1V7nw5N0XUp0qgiCICQYV/yUIgiCIHFi+oAU3zGvG9vYB9IwE0OgRQ/7IQ0UQBDHxIKOKIAiCIHLEauGw5Ky6YneDIAiCKDIU/kcQBEEQBEEQBKEDMqoIgiAIgiAIgiB0QEYVQRAEQRAEQRCEDsioIgiCIAiCIAiC0AEZVQRBEARBEARBEDogo4ogCIIgCIIgCEIHZFQRBEEQBEEQBEHogIwqgiAIgiAIgiAIHZBRRRAEQRAEQRAEoQMyqgiCIAiCIAiCIHRgK3YHzIYgCAAAr9dbkO8Lh8Pw+Xzwer2w2+0F+U6i9KFxQ+QCjRsiV2jsELlA44bIBbONG2YTMBtBDTKqkhgZGQEATJ06tcg9IQiCIAiCIAjCDIyMjKCqqkr1fU7IZHZNMHiex5kzZ+DxeMBxXN6/z+v1YurUqTh16hQqKyvz/n3E+IDGDZELNG6IXKGxQ+QCjRsiF8w2bgRBwMjICCZNmgSLRT1zijxVSVgsFkyZMqXg31tZWWmKgUOUFjRuiFygcUPkCo0dIhdo3BC5YKZxk85DxSChCoIgCIIgCIIgCB2QUUUQBEEQBEEQBKEDMqqKjNPpxKpVq+B0OovdFaKEoHFD5AKNGyJXaOwQuUDjhsiFUh03JFRBEARBEARBEAShA/JUEQRBEARBEARB6ICMKoIgCIIgCIIgCB2QUUUQBEEQBEEQBKEDMqoIgiAIgiAIgiB0QEZVEfnxj3+MGTNmwOVy4dJLL8X27duL3SXCRKxZswYXX3wxPB4PGhsbcdttt+HQoUMJbQKBAB544AHU1dWhoqICH/rQh9Dd3V2kHhNm5PHHHwfHcfj85z8vvUbjhlCjo6MDd999N+rq6uB2u3Heeedh586d0vuCIOAb3/gGWlpa4Ha7cf311+PIkSNF7DFRbKLRKL7+9a9j5syZcLvdOOuss/Ctb30Lch00GjcEALz22mu49dZbMWnSJHAch7/97W8J72sZJwMDA7jrrrtQWVmJ6upqfOpTn8Lo6GgBf4U6ZFQViT/84Q94+OGHsWrVKuzevRvnn38+brzxRvT09BS7a4RJ2Lx5Mx544AG89dZb2LhxI8LhMJYtW4axsTGpzUMPPYQXXngBf/zjH7F582acOXMGt99+exF7TZiJHTt24Gc/+xkWLlyY8DqNG0KJwcFBXHHFFbDb7XjxxRexf/9+fO9730NNTY3U5jvf+Q7+53/+Bz/96U+xbds2lJeX48Ybb0QgEChiz4li8sQTT+DJJ5/Ej370Ixw4cABPPPEEvvOd7+CHP/yh1IbGDQEAY2NjOP/88/HjH/9Y8X0t4+Suu+7Cvn37sHHjRqxduxavvfYa7rvvvkL9hPQIRFG45JJLhAceeED6OxqNCpMmTRLWrFlTxF4RZqanp0cAIGzevFkQBEEYGhoS7Ha78Mc//lFqc+DAAQGAsHXr1mJ1kzAJIyMjwuzZs4WNGzcKV111lfDggw8KgkDjhlBn5cqVwtKlS1Xf53leaG5uFr773e9Krw0NDQlOp1N45plnCtFFwoTcfPPNwic/+cmE126//XbhrrvuEgSBxg2hDADhr3/9q/S3lnGyf/9+AYCwY8cOqc2LL74ocBwndHR0FKzvapCnqgiEQiHs2rUL119/vfSaxWLB9ddfj61btxaxZ4SZGR4eBgDU1tYCAHbt2oVwOJwwjubOnYtp06bROCLwwAMP4Oabb04YHwCNG0Kdv//971i8eDE+8pGPoLGxEYsWLcIvfvEL6f329nZ0dXUljJ2qqipceumlNHYmMJdffjleeeUVHD58GACwd+9ebNmyBTfddBMAGjeENrSMk61bt6K6uhqLFy+W2lx//fWwWCzYtm1bwfucjK3YHZiI9PX1IRqNoqmpKeH1pqYmHDx4sEi9IswMz/P4/Oc/jyuuuAILFiwAAHR1dcHhcKC6ujqhbVNTE7q6uorQS8IsPPvss9i9ezd27NiR8h6NG0KNY8eO4cknn8TDDz+Mr3zlK9ixYwf+/d//HQ6HAytWrJDGh9Kzi8bOxOXRRx+F1+vF3LlzYbVaEY1G8dhjj+Guu+4CABo3hCa0jJOuri40NjYmvG+z2VBbW2uKsURGFUGUAA888ADa2tqwZcuWYneFMDmnTp3Cgw8+iI0bN8LlchW7O0QJwfM8Fi9ejG9/+9sAgEWLFqGtrQ0//elPsWLFiiL3jjArzz33HH73u9/h97//PebPn489e/bg85//PCZNmkTjhphQUPhfEaivr4fVak1R2+ru7kZzc3ORekWYlc997nNYu3YtXn31VUyZMkV6vbm5GaFQCENDQwntaRxNbHbt2oWenh5ceOGFsNlssNls2Lx5M/7nf/4HNpsNTU1NNG4IRVpaWjBv3ryE184991ycPHkSAKTxQc8uQs4Xv/hFPProo7jjjjtw3nnn4WMf+xgeeughrFm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-          "data": {
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-            "text/html": [
-              "Find logs at: <code>./wandb/run-20250222_123531-s26hyj9k/logs</code>"
-            ]
-          },
-          "metadata": {}
+        "decc3d18711a459badab9c4def213303": {
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+            "_view_module": "@jupyter-widgets/base",
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         }
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-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "JDD8rSI4pGvD"
-      },
-      "execution_count": null,
-      "outputs": []
+      }
     }
-  ]
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
\ No newline at end of file